From c4985252573353e058eda3aab1d80bc5e02e8aa9 Mon Sep 17 00:00:00 2001 From: hebamuh68 Date: Thu, 26 Jan 2023 17:15:45 +0200 Subject: [PATCH 1/4] update pubmed_util.py --- .idea/.gitignore | 3 + .idea/FoodMine.iml | 14 + .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 4 + .idea/modules.xml | 8 + .idea/vcs.xml | 6 + install_additional_packages.sh | 0 notebooks/Data_Statistics.ipynb | 4 +- notebooks/Paper_Screening.ipynb | 1824 ++++++++++++++++- src/.idea/.gitignore | 3 + .../inspectionProfiles/profiles_settings.xml | 6 + src/.idea/misc.xml | 4 + src/.idea/modules.xml | 8 + src/.idea/src.iml | 12 + src/.idea/vcs.xml | 6 + src/collected_data_handling.py | 76 +- src/data_loader.py | 141 +- src/filter.py | 431 ++-- src/plot_utils.py | 20 +- src/pubmed_util.py | 443 ++-- src/tools/chemidr/id_map.py | 465 +++-- src/try.py | 16 + 22 files changed, 2726 insertions(+), 774 deletions(-) create mode 100644 .idea/.gitignore create mode 100644 .idea/FoodMine.iml create mode 100644 .idea/inspectionProfiles/profiles_settings.xml create mode 100644 .idea/misc.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/vcs.xml mode change 100644 => 100755 install_additional_packages.sh create mode 100644 src/.idea/.gitignore create mode 100644 src/.idea/inspectionProfiles/profiles_settings.xml create mode 100644 src/.idea/misc.xml create mode 100644 src/.idea/modules.xml create mode 100644 src/.idea/src.iml create mode 100644 src/.idea/vcs.xml create mode 100644 src/try.py diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..26d3352 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,3 @@ +# Default ignored files +/shelf/ +/workspace.xml diff --git a/.idea/FoodMine.iml b/.idea/FoodMine.iml new file mode 100644 index 0000000..d58acf0 --- /dev/null +++ b/.idea/FoodMine.iml @@ -0,0 +1,14 @@ + + + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..dc9ea49 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..7913fee --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..35eb1dd --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/install_additional_packages.sh b/install_additional_packages.sh old mode 100644 new mode 100755 diff --git a/notebooks/Data_Statistics.ipynb b/notebooks/Data_Statistics.ipynb index a05daea..2c7af46 100644 --- a/notebooks/Data_Statistics.ipynb +++ b/notebooks/Data_Statistics.ipynb @@ -1258,7 +1258,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1272,7 +1272,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/notebooks/Paper_Screening.ipynb b/notebooks/Paper_Screening.ipynb index 8dcdd51..410d447 100644 --- a/notebooks/Paper_Screening.ipynb +++ b/notebooks/Paper_Screening.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -28,9 +28,1805 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=fresh%20garlic&retmax=1000000\n", + "ids 378\n", + "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=36673488,36661532,36558083,36374623,36296587,36241263,36235435,36231461,36122930,36091965,35940117,35822789,35798823,35729146,35637890,35628468,35627064,35507844,35480459,35480455,35436379,35355410,35293213,35211974,35172234,35168162,35125657,35062505,35057517,35011342,34893232,34828984,34819925,34795722,34713965,34527070,34521004,34443625,34395025,34164770,33929534,33785394,33740130,33681348,33638666,33605386,33571752,33549282,33415587,33389861,33325167,33317599,33295000,33242796,33221098,33177859,33142731,33123325,33088979,32963075,32900002,32884733,32328266,32291787,32184427,32178294,32156160,32142159,32107396,32010343,32010338,31991938,31901831,31743742,31721940,31588747,31577841,31516325,31509980,31387036,31382578,31338077,31250635,31031431,30931955,30929787,30923456,30744360,30704623,30603687,30570073,30497454,30457057,30412324,30374433&retmode=xml\n", + "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=30366271,30356168,30320007,30311619,30263743,30169119,30105074,29753990,29621132,29579944,29433241,29391611,29331459,29161814,29024904,28934070,28911676,28911544,28873605,28719747,28560773,28425131,28196294,28078257,28051097,28017989,27976376,27904380,27784931,27778523,27753097,27592824,27584700,27313155,27300762,27296605,27263111,27182249,27043510,27011724,27008423,26969520,26954136,26889365,26786785,26776039,26690030,28433269,26471590,26440842,26212875,26139864,26060559,26019632,26017222,26003845,25941212,25838894,25745260,25745247,25631559,25573280,25532343,25493198,25371585,25329784,25284945,25141133,25133543,25124136,24991105,24598083,26761498,24006751,23755406,23700562,23679240,23623137,23600691,23583806,23578652,23527659,23387242,23292331,26770698,25050258,23259687,23244152,23050048,24471087,22668601,22625420,22610968,22507958,22473701&retmode=xml\n", + "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=22353229,22284504,23983369,21812650,21794123,21729078,21726146,21721585,21631395,21615122,21553301,21538625,21535547,21325240,21290188,21269249,21184771,21170253,21118053,21086547,20951625,20924970,20739164,20633941,20538890,20202327,20192846,19929845,19895494,19878318,19827749,19768983,19735176,19653315,19601391,19382351,19174616,19120662,19053859,19019552,19019099,18952220,20416582,18844255,18588510,18489116,18334029,18205306,17966138,17767872,17918162,19070102,17523869,17472490,17472489,17396504,17330154,17269787,17219900,17189767,17146719,17123005,17075725,17017158,16910057,16715809,16584547,16500553,16484578,16484574,16413559,16405290,16380980,16366855,16298867,16287614,16277408,16230689,16223688,16121720,16076102,16041728,15916949,15796617,15769123,15749368,15718030,15616341,15615431,15380914,15216390,15161196,15065784,15056375&retmode=xml\n", + "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=15031746,14969516,14959768,14727763,12847923,12738093,12706595,12701369,12697379,12570662,12396965,12174037,12098946,12042427,11933151,11906464,11833727,11802218,11767087,11486375,11466175,11454685,11434986,11385050,11365438,11271766,11238807,11238803,11238797,11049697,10882191,10737231,21214446,10552475,10588342,10524347,10354821,10235193,10234740,10193205,10098897,10072338,9726786,9637953,9625398,9246703,8739190,8729671,9772707,8870956,8603796,8560468,8607564,7480084,7604070,7666832,7517069,8170288,8183725,8302920,8439494,1470664,1285693,1531110,1665257,1742542,2065395,1831097,1855874,2083173,17221429,2686739,2793233,17262437,17262412,30991489,28310369,3207435,30965409,3702985,3924535,24318347,6471131,6877039,6870217,30913616,6154673,28223570,973445,1183776,4796677,4738589,5102504,14377534&retmode=xml\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['fresh garlic'] Document Info (378 entries) Retrieved in 0.313481072584788 min\n", + "Creating features...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "1it [00:00, 3.79it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "3it [00:00, 8.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "5it [00:00, 7.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "6it [00:00, 7.65it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "8it [00:00, 9.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "10it [00:01, 10.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "12it [00:01, 7.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "14it [00:01, 8.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "16it [00:01, 10.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "18it [00:02, 10.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "20it [00:02, 9.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "22it [00:02, 5.73it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "24it [00:03, 6.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "25it [00:03, 5.60it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "26it [00:03, 4.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "27it [00:04, 4.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "29it [00:04, 5.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "30it [00:04, 6.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "31it [00:04, 5.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "32it [00:04, 5.01it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "33it [00:04, 5.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "35it [00:05, 6.78it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "36it [00:05, 6.27it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "37it [00:05, 5.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "39it [00:05, 6.98it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "40it [00:06, 5.51it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "41it [00:06, 4.33it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "42it [00:06, 3.49it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "44it [00:07, 4.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "45it [00:07, 4.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "46it [00:07, 4.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "47it [00:07, 4.45it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "48it [00:07, 5.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "49it [00:08, 5.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "50it [00:08, 4.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "51it [00:08, 4.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "53it [00:08, 4.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "54it [00:09, 5.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "55it [00:09, 4.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "57it [00:09, 4.89it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "59it [00:09, 6.78it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "61it [00:10, 6.36it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "62it [00:10, 5.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "64it [00:10, 5.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "65it [00:11, 5.61it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "67it [00:11, 7.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "68it [00:11, 5.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "69it [00:11, 5.07it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "70it [00:12, 4.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "72it [00:12, 6.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "73it [00:12, 6.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "74it [00:12, 6.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "76it [00:12, 7.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "77it [00:12, 6.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "79it [00:13, 8.49it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "80it [00:13, 6.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "82it [00:13, 8.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "83it [00:13, 7.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "85it [00:13, 7.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "86it [00:14, 5.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "88it [00:14, 7.50it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "89it [00:14, 6.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "91it [00:14, 8.41it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "92it [00:14, 7.51it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "94it [00:15, 8.49it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "95it [00:15, 8.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "96it [00:15, 7.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "97it [00:15, 6.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "99it [00:16, 5.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "101it [00:16, 5.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "102it [00:16, 4.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "103it [00:17, 4.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "104it [00:17, 4.06it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "105it [00:17, 3.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "106it [00:17, 3.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "107it [00:18, 4.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "108it [00:18, 3.50it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "109it [00:18, 3.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "110it [00:18, 3.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "112it [00:19, 5.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "113it [00:19, 5.09it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "114it [00:19, 5.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "115it [00:19, 4.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "116it [00:20, 5.01it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "118it [00:20, 5.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "119it [00:20, 5.33it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "120it [00:20, 4.82it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "121it [00:20, 5.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "122it [00:21, 6.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "123it [00:21, 6.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "125it [00:21, 6.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "127it [00:21, 7.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "128it [00:21, 6.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "129it [00:22, 6.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "130it [00:22, 5.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "131it [00:22, 4.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "132it [00:22, 4.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "134it [00:23, 4.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "136it [00:23, 4.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "137it [00:23, 4.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "138it [00:24, 4.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "139it [00:24, 4.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "140it [00:24, 4.78it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "141it [00:24, 5.22it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "142it [00:25, 4.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "143it [00:25, 4.76it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "144it [00:25, 4.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "146it [00:25, 6.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "147it [00:25, 5.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "149it [00:26, 6.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "150it [00:26, 4.97it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "151it [00:26, 4.53it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "152it [00:26, 4.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "153it [00:27, 4.33it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "154it [00:27, 4.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "155it [00:27, 4.22it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "156it [00:27, 4.31it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "157it [00:28, 3.80it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "158it [00:28, 3.58it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "159it [00:28, 3.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "161it [00:29, 3.95it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "163it [00:29, 5.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "164it [00:29, 4.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "165it [00:30, 4.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "167it [00:30, 4.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "168it [00:30, 4.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "169it [00:31, 3.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "170it [00:31, 3.25it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "172it [00:31, 4.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "174it [00:32, 4.89it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "175it [00:32, 4.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "176it [00:32, 4.58it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "177it [00:33, 4.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "178it [00:33, 4.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "179it [00:33, 4.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "181it [00:33, 6.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "182it [00:33, 5.28it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "184it [00:34, 6.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "185it [00:34, 5.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "186it [00:34, 5.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "187it [00:34, 4.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "188it [00:35, 4.37it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "190it [00:35, 4.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "192it [00:36, 4.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "193it [00:36, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "194it [00:36, 3.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "195it [00:36, 4.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "196it [00:37, 3.76it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "197it [00:37, 3.56it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "198it [00:37, 3.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "199it [00:38, 3.58it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "200it [00:38, 2.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "201it [00:39, 2.92it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "202it [00:39, 3.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "203it [00:39, 3.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "204it [00:39, 3.28it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "205it [00:40, 3.29it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "206it [00:40, 3.50it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "207it [00:40, 3.39it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "208it [00:41, 3.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "209it [00:41, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "210it [00:41, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "211it [00:41, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "212it [00:41, 3.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "213it [00:42, 2.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "214it [00:42, 3.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "215it [00:43, 3.29it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "216it [00:43, 3.25it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "217it [00:43, 3.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "218it [00:43, 3.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "219it [00:44, 3.50it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "220it [00:44, 3.68it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "221it [00:44, 4.06it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "222it [00:44, 3.93it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "223it [00:45, 3.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "224it [00:45, 3.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "225it [00:45, 3.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "226it [00:45, 4.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "227it [00:46, 5.00it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "228it [00:46, 5.88it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "229it [00:46, 4.36it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "230it [00:46, 3.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "231it [00:47, 3.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "232it [00:47, 3.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "233it [00:47, 3.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "234it [00:47, 3.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "235it [00:48, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "236it [00:48, 4.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "237it [00:48, 4.68it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "238it [00:48, 3.79it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "239it [00:49, 3.68it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "241it [00:49, 4.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "242it [00:49, 4.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "243it [00:49, 4.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "244it [00:50, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "245it [00:50, 3.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "246it [00:50, 3.68it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "247it [00:51, 3.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "248it [00:51, 3.67it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "249it [00:51, 3.61it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "250it [00:52, 3.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "251it [00:52, 3.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "253it [00:52, 4.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "254it [00:52, 4.06it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "255it [00:53, 3.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "256it [00:53, 2.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "258it [00:54, 4.31it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "259it [00:54, 4.22it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "260it [00:54, 3.76it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "261it [00:54, 3.73it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "262it [00:55, 3.95it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "263it [00:55, 3.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "264it [00:55, 3.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "265it [00:56, 2.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "266it [00:56, 3.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "267it [00:56, 3.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "268it [00:57, 3.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "269it [00:57, 3.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "270it [00:57, 3.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "271it [00:57, 3.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "272it [00:58, 3.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "273it [00:58, 3.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "274it [00:58, 3.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "275it [00:59, 3.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "276it [00:59, 3.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "277it [00:59, 3.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "279it [00:59, 4.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "280it [00:59, 5.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "281it [01:00, 5.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "282it [01:00, 4.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "283it [01:00, 4.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "284it [01:00, 4.01it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "285it [01:01, 4.00it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "286it [01:01, 3.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "287it [01:01, 3.67it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "288it [01:02, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "289it [01:02, 3.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "290it [01:02, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "291it [01:03, 3.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "292it [01:03, 3.07it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "293it [01:03, 2.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "294it [01:04, 3.05it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "295it [01:04, 3.80it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "297it [01:04, 4.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "298it [01:04, 4.45it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "299it [01:04, 4.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "300it [01:05, 3.97it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "301it [01:05, 3.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "302it [01:05, 3.53it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "304it [01:06, 4.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "305it [01:06, 4.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "306it [01:06, 3.93it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "307it [01:07, 3.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "308it [01:07, 3.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "309it [01:07, 3.39it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "310it [01:08, 2.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "311it [01:08, 2.25it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "312it [01:09, 2.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "313it [01:09, 2.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "314it [01:10, 2.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "315it [01:10, 2.19it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "316it [01:11, 2.37it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "317it [01:11, 2.49it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "318it [01:11, 2.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "319it [01:12, 2.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "320it [01:12, 2.09it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "321it [01:13, 2.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "322it [01:13, 2.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "323it [01:13, 2.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "324it [01:14, 2.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "325it [01:14, 2.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "326it [01:14, 3.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "327it [01:15, 3.21it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "328it [01:15, 2.37it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "329it [01:15, 2.88it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "331it [01:16, 3.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "332it [01:16, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "333it [01:16, 3.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "334it [01:17, 2.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "335it [01:17, 2.67it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "336it [01:18, 2.50it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "337it [01:18, 2.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "338it [01:19, 2.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "339it [01:19, 3.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "340it [01:19, 3.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "341it [01:20, 2.60it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "342it [01:20, 2.65it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "343it [01:20, 3.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "344it [01:20, 3.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "345it [01:21, 3.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "346it [01:21, 2.89it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "347it [01:22, 3.04it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "348it [01:22, 3.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "349it [01:22, 3.12it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "350it [01:23, 2.61it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "351it [01:23, 2.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "352it [01:23, 3.05it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "353it [01:23, 3.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "354it [01:24, 3.56it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "355it [01:24, 3.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "356it [01:24, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "357it [01:25, 3.09it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "358it [01:25, 3.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "359it [01:25, 3.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "360it [01:25, 3.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "361it [01:26, 3.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "362it [01:26, 3.79it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "363it [01:26, 4.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "364it [01:26, 4.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "366it [01:27, 5.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "367it [01:27, 5.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "368it [01:27, 4.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "369it [01:27, 5.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "370it [01:27, 4.92it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "371it [01:28, 5.29it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "372it [01:28, 5.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "373it [01:28, 5.98it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "374it [01:28, 5.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "376it [01:28, 8.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "378it [01:28, 4.26it/s]\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data Converted in 1.5174651225407918 min\n", + "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=34893232,31588747,31387036,31250635,30105074,29433241,29161814,28911676,28719747,28560773,27592824,27313155,27300762,27296605,25371585,25329784,23259687,22610968,22284504,21535547,19768983,19053859,18952220,17269787,17017158,16413559,16277408,15161196,15065784,14969516,11767087,11486375,11238797,10737231,10588342,10234740,10193205,8870956,7604070,7517069,17262412&retmode=xml\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n" + ] + } + ], "source": [ "search_terms = ['fresh garlic']\n", "output = filter_results(search_terms)" @@ -38,9 +1834,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'output' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_31499/2415424161.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'output' is not defined" + ] + } + ], "source": [ "output" ] @@ -55,7 +1863,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -69,7 +1877,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/src/.idea/.gitignore b/src/.idea/.gitignore new file mode 100644 index 0000000..26d3352 --- /dev/null +++ b/src/.idea/.gitignore @@ -0,0 +1,3 @@ +# Default ignored files +/shelf/ +/workspace.xml diff --git a/src/.idea/inspectionProfiles/profiles_settings.xml b/src/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/src/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/src/.idea/misc.xml b/src/.idea/misc.xml new file mode 100644 index 0000000..dc9ea49 --- /dev/null +++ b/src/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/src/.idea/modules.xml b/src/.idea/modules.xml new file mode 100644 index 0000000..f669a0e --- /dev/null +++ b/src/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/src/.idea/src.iml b/src/.idea/src.iml new file mode 100644 index 0000000..039314d --- /dev/null +++ b/src/.idea/src.iml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/src/.idea/vcs.xml b/src/.idea/vcs.xml new file mode 100644 index 0000000..6c0b863 --- /dev/null +++ b/src/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/src/collected_data_handling.py b/src/collected_data_handling.py index 9b602bf..181d9b2 100644 --- a/src/collected_data_handling.py +++ b/src/collected_data_handling.py @@ -22,36 +22,36 @@ def build_data_dict(df): data_df : pd.DataFrame Dataframe containing compounds and corresponding metadata """ - + data_dict = {} # Dict that holds all foods and chemicals, and their calculations df['chemical'] = df['chemical'].str.strip() df['chemical'] = df['chemical'].apply(greek_letter_converter, convert_letter = True) - + # Iterate over each unique food, and each unique chemical per food to create food, chem dict for food in df['food'].drop_duplicates().tolist(): - + chem_dict = {} # Holds chemical calculations per food - + # need to do one loop for things with a chem id for chem_id in df[(df['food'] == food) & (df['chem_id'].notnull())].chem_id.drop_duplicates().tolist(): f_chem_df = df[(df['food'] == food) & (df['chem_id'] == chem_id)] # Filters DataFrame for rows with specific food-chemical combination - + new_entry = __build_chem_subdict__(f_chem_df) # Reformats and calculates values from separate resources as dict chem_dict[new_entry['chemical']] = new_entry - + for chem in df[(df['food'] == food) & (df['chem_id'].isnull())].chemical.drop_duplicates().tolist(): f_chem_df = df[(df['food'] == food) & (df['chemical'] == chem)] # Filters DataFrame for rows with specific food-chemical combination - + new_entry = __build_chem_subdict__(f_chem_df) # Reformats and calculates values from separate resources as dict - + chem_dict[new_entry['chemical']] = new_entry data_dict[food] = chem_dict - + data_df = dict_to_df(data_dict) return data_df @@ -74,11 +74,11 @@ def dict_to_df(data_dict): """ df = pd.DataFrame() - + # Creates dataframe for each food and compound for food, food_dict in data_dict.items(): for chemical, chem_dict in food_dict.items(): - + chem_dict['food'] = food chem_dict['chemical'] = chemical @@ -102,12 +102,12 @@ def __extract_digits__(string): digits : int Digits at start of string """ - + i = 0 - + while string[i].isdigit(): i += 1 - + return int(string[0:i]) @@ -129,7 +129,7 @@ def __splice_unit__(unit): units : string The units used for a measurement without scale, i.e. mg """ - + unit = unit.strip() if unit[0].isdigit(): @@ -221,7 +221,7 @@ def __unit_handler__(value, unit, target_unit): if unit.count('/') is not target_unit.count('/'): print("Unit conversion error, one is not fraction") return - + if unit.count('/') > 0: # Check if the first character of digit is string, assumed conversion value if it is @@ -238,7 +238,7 @@ def __unit_handler__(value, unit, target_unit): else: scale_conversion = 1 / (denom_scale / target_denom_scale) else: - scale_conversion = num_scale / target_num_scal + scale_conversion = num_scale / target_num_scale # Receives numerical scales from converter. For __converter__('mg', 'g'), returns 1000 @@ -250,7 +250,7 @@ def __unit_handler__(value, unit, target_unit): value_conversion = value * scale_conversion * unit_conversion return value_conversion - + def __quant_handler__(df): """ @@ -267,26 +267,26 @@ def __quant_handler__(df): quant_pack : dict Dictionary of select quantitative information """ - + target_unit = 'mg/100g' absolute_min = None absolute_max = None - + means =[] - + # Calculate avg mean for paper_id in df.PMID.drop_duplicates().tolist(): - + paper_values = [] - + for _, row in df[df['PMID'] == paper_id].iterrows(): - + conversion = __unit_handler__(row['amount'], row['units'], target_unit) # Sets the units for the whole output - + # If there is an error or issue in __unit_handler__, will return some sort of string if type(conversion) is not str: - + # Keeps track of the global min and max for a chemical if absolute_min is None: absolute_min = conversion @@ -298,24 +298,24 @@ def __quant_handler__(df): absolute_max = conversion paper_values.append(conversion) - + # Calculates average of means from papers if len(paper_values) > 0: means.append(sum(paper_values) / len(paper_values)) - + if len(means) is not 0: avg_mean = np.mean(means) min_mean = np.min(means) max_mean = np.max(means) mean_var = np.var(means) - median = np.median(means) + median = np.median(means) else: avg_mean = None min_mean = None max_mean = None mean_var = None - median = None - + median = None + quant_pack = { 'average_mean' : avg_mean, 'absolute_min' : absolute_min, @@ -345,29 +345,29 @@ def __build_chem_subdict__(df): chem_subdict : dict Dictionary with a chemical, amount, and metadata """ - + # Just take first chemical for general proxy chemical = df.chemical.tolist()[0] # Number of unique papers num_papers = len(df.PMID.drop_duplicates()) papers = df.PMID.drop_duplicates().tolist() - + # Number of terms quantified num_terms_quant = df.amount.notnull().sum() - + # avg mean, total min, total max, min mean, max mean, mean variance quant_pack = __quant_handler__(df[df['amount'].notnull()]) - + # total # samples total_num_samples = df.num_samples.notnull().sum() - + # Number without info on total # samples num_without_sample_info = df.num_samples.isnull().sum() - + # Number without quantity or sample info num_lacking_info = len(df[(df['num_samples'].isnull()) & (df['amount'].isnull())]) - + if len(df[df.pubchem_name.notnull()]) > 0: compound_name = df['pubchem_name'].tolist()[0] else: diff --git a/src/data_loader.py b/src/data_loader.py index d22a30b..53f285d 100644 --- a/src/data_loader.py +++ b/src/data_loader.py @@ -4,28 +4,29 @@ import time import config -import src.tools.chemidr.labeler as lbr -import src.tools.chemidr.id_map as id_map -import src.collected_data_handling as cdh +import tools.chemidr.labeler as lbr +import tools.chemidr.id_map as id_map +import collected_data_handling as cdh report = False + # Function to quickly record given statistics def report_stat(text, filename, varname=None): if not os.path.exists('stats'): os.mkdir('stats') - + if varname is not None: text = text + '\n\tVar: ' + varname - + text = text + '\t' + time.strftime("%m/%d/%Y", time.localtime()) - + with open(config.mfp('stats/' + filename), 'w') as f: f.write(text) # Problem inputing letters into csv, so created system to convert them here -def greek_letter_converter(chem, convert_letter = True): +def greek_letter_converter(chem, convert_letter=True): if convert_letter: chem = chem.replace('*alpha*', 'α') chem = chem.replace('*beta*', 'β') @@ -40,39 +41,41 @@ def greek_letter_converter(chem, convert_letter = True): chem = chem.replace('*delta*', 'delta') return chem + # Clean terms for various file applications -def clean_term(term, convert_letter = True, w_space = True, is_url=True): +def clean_term(term, convert_letter=True, w_space=True, is_url=True): term = term.lower().strip() - + if convert_letter: term = greek_letter_converter(term) else: term = greek_letter_converter(term, convert_letter=False) - + if w_space: if is_url: - term = term.replace(' ', '%20') # To replace ' ' in request + term = term.replace(' ', '%20') # To replace ' ' in request else: pass else: term = term.replace(' ', '') return term + def id_loader(df, chem_key, load, file, fdb=True, pubchem=True): - if load: df = pd.read_pickle(config.mfp(f'data/{file}')) else: df = lbr.id_searcher(df, chem_key, fdb=fdb, pubchem=pubchem) df.to_pickle(config.mfp(f'misc_save/{file}')) - - df.rename(columns={'pubchem_id' : 'chem_id_p', 'foodb_id' : 'chem_id_f'}, inplace=True) - + + df.rename(columns={'pubchem_id': 'chem_id_p', 'foodb_id': 'chem_id_f'}, inplace=True) + return df + def load_raw_data(food, load): food_data = pd.read_csv(config.mfp(f'data/{food}_data.csv'), encoding='latin1') - + food_scoring = pd.read_csv(config.mfp(f'data/{food}_scoring.csv'), encoding='latin1') # Need to remove phenol explorer ids that were manually put into data (for garlic only) @@ -81,22 +84,25 @@ def load_raw_data(food, load): food_data.chemical = food_data.chemical.str.lower() food_data.amount = food_data.amount.str.replace(',', '') - food_data = food_data.merge(food_scoring[['PMID','is_useful']], how = 'left', on = 'PMID') - + food_data = food_data.merge(food_scoring[['PMID', 'is_useful']], how='left', on='PMID') + if report: report_stat(f'Number of papers in search {food}: ' + str(len(food_scoring)), f'num_papers_srch_{food}.txt') - report_stat(f'Number of papers reviewed {food}: ' + str(len(food_scoring[food_scoring.is_useful.notnull()])), f'num_reviewed_papers_{food}.txt') - report_stat(f'Number of unique papers {food}: ' + str(len(food_data['PMID'].drop_duplicates())), f'num_unique_papers_{food}.txt') + report_stat(f'Number of papers reviewed {food}: ' + str(len(food_scoring[food_scoring.is_useful.notnull()])), + f'num_reviewed_papers_{food}.txt') + report_stat(f'Number of unique papers {food}: ' + str(len(food_data['PMID'].drop_duplicates())), + f'num_unique_papers_{food}.txt') report_stat(f'Total number of records {food}: ' + str(len(food_data)), f'num_records_{food}.txt') return food_data, food_scoring + def append_keys_raw_data(food_data, food, load): if load: food_data = pd.read_pickle(config.mfp(f'data/{food}_food_data.pkl')) else: food_data = id_loader(food_data, 'chemical', load, config.mfp(f'{food}_food_data.pkl')) - + return food_data @@ -128,19 +134,19 @@ def unit_clean(df): df.amount = df.amount.replace('n.q.', '0') df.amount = df.amount.replace('no analyzed', '0') df.amount = df.amount.replace('-', '0') - + return df + def clean_raw_data_strings(food_data): food_data.chemical = food_data.chemical.apply(clean_term, is_url=False) food_data = unit_clean(food_data) - + return food_data # Partition data into quantified and unquantified def partition_raw_data(food_data, food_scoring): - for idx, row in food_data.iterrows(): try: row['units'].count('g') @@ -158,14 +164,16 @@ def partition_raw_data(food_data, food_scoring): # Have a separate dataframe for all chemicals that we would put in the category of 'detected but not quantified' food_data_dnq = food_data[food_data['is_quant'] == 0].reset_index(drop=True) - # The quantified dataframe for values that are both quantified and unquantified - unq_chems = list(set( food_data_dnq['chemical'].tolist() )) - food_data_both = food_data_q.iloc[[idx for idx, row in food_data_q.fillna('placeholder').iterrows() if row['chemical'] in unq_chems]] + # The quantified dataframe for values that are both quantified and unquantified + unq_chems = list(set(food_data_dnq['chemical'].tolist())) + food_data_both = food_data_q.iloc[ + [idx for idx, row in food_data_q.fillna('placeholder').iterrows() if row['chemical'] in unq_chems]] # Remove occurrences of overlaping chemicals from the unquantified garlic data - q_chems = list(set( food_data_q['chemical'].tolist() )) - food_data_dnq = food_data_dnq.iloc[[idx for idx, row in food_data_dnq.fillna('placeholder').iterrows() if row['chemical'] not in q_chems]] - + q_chems = list(set(food_data_q['chemical'].tolist())) + food_data_dnq = food_data_dnq.iloc[ + [idx for idx, row in food_data_dnq.fillna('placeholder').iterrows() if row['chemical'] not in q_chems]] + return food_data_q, food_data_dnq @@ -176,11 +184,11 @@ def build_food_mine(food_data, food_data_q, food_data_dnq): quant_food_mine = cdh.build_data_dict(food_data_q) unquant_food_mine = cdh.build_data_dict(food_data_dnq) - + # Need to re-compare quantified chems and unquantified chems with synonym key to do one last removal - q_chems = list(set( quant_food_mine['chem_id'].dropna().tolist() )) + q_chems = list(set(quant_food_mine['chem_id'].dropna().tolist())) unquant_food_mine = unquant_food_mine[~unquant_food_mine.chem_id.isin(q_chems)].reset_index() - + if report: report_stat(f'FM size {food}: ' + str(len(food_mine)), f'fm_size_{food}.txt') report_stat(f'QFM size {food}: ' + str(len(quant_food_mine)), f'qfm_size_{food}.txt') @@ -192,7 +200,7 @@ def build_food_mine(food_data, food_data_q, food_data_dnq): # Loads data from FooDB def load_foodb_data(food, load): # Dataframe with contents of foodb - + if not load: foodb = pd.read_csv(config.mfp('data/contentssql.csv')) foodb = foodb[(foodb.source_type != 'Nutrient') & (foodb.source_id != 0) & (foodb.standard_content != 0)] @@ -207,7 +215,7 @@ def load_foodb_data(food, load): if food == 'cocoa': # Cocoa - ["cocoa bean", "cocoa butter", "Cocoa powder", "Cocoa Liquor"] - target_foodb_food_id = [182, 706, 707,708] + target_foodb_food_id = [182, 706, 707, 708] # Gets the subset of the database pertaining to food foodb_food = foodb[foodb.food_id.isin(target_foodb_food_id)].reset_index(drop=True) @@ -216,31 +224,35 @@ def load_foodb_data(food, load): foodb_food.name = foodb_food.name.str.lower() foodb_food = foodb_food.rename(index=str, columns={"source_id": "foodb_id"}) - + if load: foodb_food = pd.read_pickle(config.mfp(f'data/{food}_foodb_food.pkl')) - foodb_food.rename(columns={'orig_source_name' : 'name'}, inplace=True) - - foodb_food = id_loader(foodb_food, 'name', load, f'{food}_foodb_food.pkl',fdb=False) + foodb_food.rename(columns={'orig_source_name': 'name'}, inplace=True) + + foodb_food = id_loader(foodb_food, 'name', load, f'{food}_foodb_food.pkl', fdb=False) # Creates a list of the unique chemicals in garlic from foodb - foodb_food_lower = list(set( foodb_food.chem_id.tolist() )) + foodb_food_lower = list(set(foodb_food.chem_id.tolist())) # Creates a separate dataframe that holds chemicals for garlic in foodb with a real quantification - quant_foodb_food = foodb_food[foodb_food.standard_content.notnull()][['chem_id', 'chem_id_f', 'orig_source_id','name', 'standard_content']].drop_duplicates() + quant_foodb_food = foodb_food[foodb_food.standard_content.notnull()][ + ['chem_id', 'chem_id_f', 'orig_source_id', 'name', 'standard_content']].drop_duplicates() # Creates a separate dataframe that holds chemicals for garlic in foodb without a real quantification - unquant_foodb_food = foodb_food[foodb_food.standard_content.isnull()][['chem_id', 'chem_id_f', 'orig_source_id','name', 'standard_content']].reset_index() - - q_ids = list(set( quant_foodb_food.chem_id.tolist() )) - q_names = list(set( quant_foodb_food.chem_id_f.tolist() )) + unquant_foodb_food = foodb_food[foodb_food.standard_content.isnull()][ + ['chem_id', 'chem_id_f', 'orig_source_id', 'name', 'standard_content']].reset_index() + + q_ids = list(set(quant_foodb_food.chem_id.tolist())) + q_names = list(set(quant_foodb_food.chem_id_f.tolist())) unquant_foodb_food = unquant_foodb_food[(~unquant_foodb_food.chem_id.fillna('-').isin(q_ids)) - & (~unquant_foodb_food.chem_id_f.fillna('-').isin(q_names))] - + & (~unquant_foodb_food.chem_id_f.fillna('-').isin(q_names))] + if report: report_stat(f'FDB size {food}: ' + str(len(foodb_food.chem_id.drop_duplicates())), f'fdb_size_{food}.txt') - report_stat(f'QFDB size {food}: ' + str(len(quant_foodb_food.chem_id.drop_duplicates())), f'qfdb_size_{food}.txt') - report_stat(f'UQFDB size {food}: ' + str(len(unquant_foodb_food.chem_id.drop_duplicates())), f'uqfdb_size_{food}.txt') + report_stat(f'QFDB size {food}: ' + str(len(quant_foodb_food.chem_id.drop_duplicates())), + f'qfdb_size_{food}.txt') + report_stat(f'UQFDB size {food}: ' + str(len(unquant_foodb_food.chem_id.drop_duplicates())), + f'uqfdb_size_{food}.txt') return foodb_food, quant_foodb_food, unquant_foodb_food @@ -259,10 +271,10 @@ def load_usda_data(food, load): NDB_id = [4501, 19171, 19165, 19166, 19860] # Reads in USDA database - usda = pd.read_csv(config.mfp('data/SR28_plus_flav.csv'), encoding = 'latin1') + usda = pd.read_csv(config.mfp('data/SR28_plus_flav.csv'), encoding='latin1') # Filters out rows not apart of NDB_id - usda = usda[usda.NDB_No.isin(NDB_id)][['NDB_No','food_name', 'Nutr_No_new', 'nut_desc', 'Nutr_Val', 'unit']] + usda = usda[usda.NDB_No.isin(NDB_id)][['NDB_No', 'food_name', 'Nutr_No_new', 'nut_desc', 'Nutr_Val', 'unit']] usda['num_measures'] = 1 # Average chemicals that appear in multiple USDA food categories @@ -271,7 +283,7 @@ def load_usda_data(food, load): if len(temp) > 1: if len(temp.unit.drop_duplicates()) > 1: print(nutr, 'has different units for same nutrient') - new_row = temp.copy().reset_index(drop=True).loc[0,:] + new_row = temp.copy().reset_index(drop=True).loc[0, :] new_row['Nutr_Val'] = temp.Nutr_Val.mean() new_row['num_measures'] = len(temp) @@ -285,22 +297,23 @@ def load_usda_data(food, load): usda = pd.read_pickle(config.mfp(f'data/{food}_usda.pkl')) else: usda = id_loader(usda, 'nut_desc', load, f'{food}_usda.pkl').reset_index(drop=True) - - usda.rename(columns={'foodb_id' : 'chem_id_f'}, inplace=True) - + + usda.rename(columns={'foodb_id': 'chem_id_f'}, inplace=True) + usda = usda[~usda.unit.isin(['IU', 'kcal', 'kJ'])].reset_index(drop=True) if report: report_stat(f'USDA size {food}: ' + str(len(usda)), f'usda_size_{food}.txt') - + return usda def load_ctd(): - skip = list(range(26)) # First few lines are empty / not useful info + skip = list(range(26)) # First few lines are empty / not useful info hdata = pd.read_csv('data/CTD_chemicals_diseases.csv', skiprows=skip).reset_index() - hdata.columns = ['ChemicalName', 'ChemicalID', 'CasRN', 'DiseaseName', 'DiseaseID', 'DirectEvidence', 'InferenceGeneSymbol', 'InferenceScore', 'OmimIDs', 'PubMedIDs'] - hdata = hdata.drop([0,1], axis = 0).reset_index(drop=True) + hdata.columns = ['ChemicalName', 'ChemicalID', 'CasRN', 'DiseaseName', 'DiseaseID', 'DirectEvidence', + 'InferenceGeneSymbol', 'InferenceScore', 'OmimIDs', 'PubMedIDs'] + hdata = hdata.drop([0, 1], axis=0).reset_index(drop=True) health_pubchem_ids = pd.read_pickle('misc_save/health_chem_pubchem_ids.pickle').drop('pubchem_name', axis=1) @@ -309,14 +322,14 @@ def load_ctd(): return hdata - def load_health(): hdata = load_ctd() # Count the number of 'Direct Evidence' listings per chemical with a pubchem id de_health = pd.DataFrame( - hdata[hdata.pubchem_id.notnull() & hdata.DirectEvidence.notnull()][['pubchem_id', 'DirectEvidence', 'ChemicalName']] - .groupby(['pubchem_id','DirectEvidence']).count()).reset_index() - + hdata[hdata.pubchem_id.notnull() & hdata.DirectEvidence.notnull()][ + ['pubchem_id', 'DirectEvidence', 'ChemicalName']] + .groupby(['pubchem_id', 'DirectEvidence']).count()).reset_index() + # The 'ChemicalName' now holds the count of 'Direct Evidence' listings - return de_health[['pubchem_id', 'ChemicalName']].groupby('pubchem_id').sum() \ No newline at end of file + return de_health[['pubchem_id', 'ChemicalName']].groupby('pubchem_id').sum() diff --git a/src/filter.py b/src/filter.py index b2a9a67..9cf5fc6 100644 --- a/src/filter.py +++ b/src/filter.py @@ -5,8 +5,8 @@ import numpy as np import pickle import spacy +import spacy from spacy.matcher import Matcher -import en_core_web_sm import re from time import time, sleep from lxml import etree @@ -14,307 +14,290 @@ class Filter(): + """ + A class that calculates features for pubmed entry filtration + """ - """ - A class that calculates features for pubmed entry filtration - """ - - - def __init__(self): - - # Loads spacy language model - self.nlp = en_core_web_sm.load() - - # Loads vocabulary from predefined dictionaries of words - dicts = pickle.load(open('data/dicts.pkl', 'rb')) - - food_dict = list(set( dicts['food'] )) - chem_dict = list(set( dicts['chem'] )) - sci_dict = list(set( dicts['sci_name'] )) - - # Sets a dictionary to capture broad, possibly relevant concepts - gen_dict = ['food', 'meat', 'vegetable', 'database'] - - # Creates specialized matchers for different dictionaries - self.gen_matcher = self.__matcher_from_list__(gen_dict) - self.food_matcher = self.__matcher_from_list__(food_dict) - self.chem_matcher = self.__matcher_from_list__(chem_dict) - self.sci_matcher = self.__matcher_from_list__(sci_dict) - - self.eval_sci_matches = True - - # Specifies measurement methods to search for and creates matchers for those methods - measurement_methods = ['spectrometry', 'chromatography', 'spectrophotometry'] - self.measurement_matchers = {m : self.__matcher_from_list__([m]) for m in measurement_methods} + def __init__(self): + # Loads spacy language model + self.nlp = spacy.load("en_core_web_sm") - def filter(self, data): - """ - Takes in search from PubMed with the specified features from pubmed_util and filters results based one - pre-specified criteria. + # Loads vocabulary from predefined dictionaries of words + dicts = pickle.load(open('/home/heba/Graduation project/FoodMine/data/dicts.pkl', 'rb')) - Parameters - ----------------- - data : pd.DataFrame - search results data from PubMed + food_dict = list(set(dicts['food'])) + chem_dict = list(set(dicts['chem'])) + sci_dict = list(set(dicts['sci_name'])) + # Sets a dictionary to capture broad, possibly relevant concepts + gen_dict = ['food', 'meat', 'vegetable', 'database'] - Returns - ----------------- - selected_articles: pd.DataFrame - PubMed entries that met the specified criteria and were not filtered out. - """ + # Creates specialized matchers for different dictionaries + self.gen_matcher = self.__matcher_from_list__(gen_dict) + self.food_matcher = self.__matcher_from_list__(food_dict) + self.chem_matcher = self.__matcher_from_list__(chem_dict) + self.sci_matcher = self.__matcher_from_list__(sci_dict) - self.input_data = data + self.eval_sci_matches = True - # Creates word counts for filtration - self.build_features() + # Specifies measurement methods to search for and creates matchers for those methods + measurement_methods = ['spectrometry', 'chromatography', 'spectrophotometry'] + self.measurement_matchers = {m: self.__matcher_from_list__([m]) for m in measurement_methods} - selected_articles = pd.DataFrame(columns=['PMID']) + def filter(self, data): + """ + Takes in search from PubMed with the specified features from pubmed_util and filters results based one + pre-specified criteria. - # Iterates over each PubMed entry and filters out results that don't meed criteria - for PMID, row in self.data.iterrows(): + Parameters + ----------------- + data : pd.DataFrame + search results data from PubMed - if self.__eval_conditionals__(row): - selected_articles = selected_articles.append({'PMID' : PMID}, ignore_index = True) + Returns + ----------------- + selected_articles: pd.DataFrame + PubMed entries that met the specified criteria and were not filtered out. + """ - selected_articles['PMID'] = selected_articles['PMID'].astype('int32') + self.input_data = data - return selected_articles + # Creates word counts for filtration + self.build_features() + selected_articles = pd.DataFrame(columns=['PMID']) - def build_features(self, input_data=None, is_traindata=False): - """ - Creates features. In this instance word count frequencies. + # Iterates over each PubMed entry and filters out results that don't meed criteria + for PMID, row in self.data.iterrows(): - Parameters - ----------------- - None + if self.__eval_conditionals__(row): + selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True) + selected_articles['PMID'] = selected_articles['PMID'].astype('int32') - Returns - ----------------- - None - """ - - if input_data is not None: - self.input_data = input_data + return selected_articles - self.data = pd.DataFrame() - - start = time() - print('Creating features...') + def build_features(self, input_data=None, is_traindata=False): + """ + Creates features. In this instance word count frequencies. - # Builds features for each row of data - for idx, row in tqdm(self.input_data.iterrows()): + Parameters + ----------------- + None - if row['abstract'] == None: - abstract = '' - else: - abstract = row['abstract'] - - mesh_terms = ' '.join(row['mesh_terms']) - # Combine all info into a single string - text = ' '.join([abstract, mesh_terms]) + Returns + ----------------- + None + """ + if input_data is not None: + self.input_data = input_data - # Returns 1 per method if the measurement method is in the abstract or mesh terms, and 0 otherwise - measurement_detection = { - method : self.__detect_word_presence__(text, matcher) - for method, matcher in self.measurement_matchers.items() - } + self.data = pd.DataFrame() - # Retrieves features from pubmed - pubmed_features = self.__get_pubmed_features__(text) + start = time() + print('Creating features...') - data_row = { - 'PMID' : row['PMID'] - } + # Builds features for each row of data + for idx, row in tqdm(self.input_data.iterrows()): - data_row.update(pubmed_features) + if row['abstract'] == None: + abstract = '' + else: + abstract = row['abstract'] - # Use variable classes if data is training data, otherwise just mark as useful for search - if is_traindata: - data_row['class'] = row['is_useful'] - else: - data_row['class'] = 1 + mesh_terms = ' '.join(row['mesh_terms']) - # Add the existence of measurement methods as different features - for method in self.measurement_matchers.keys(): - data_row[method] = measurement_detection[method] + # Combine all info into a single string + text = ' '.join([abstract, mesh_terms]) - # Updates data with new information - self.data = self.data.append(data_row, ignore_index = True) + # Returns 1 per method if the measurement method is in the abstract or mesh terms, and 0 otherwise + measurement_detection = { + method: self.__detect_word_presence__(text, matcher) + for method, matcher in self.measurement_matchers.items() + } - self.data['PMID'] = self.data['PMID'].astype('int32') + # Retrieves features from pubmed + pubmed_features = self.__get_pubmed_features__(text) - self.data = self.data.fillna(0).set_index('PMID', drop = True) + data_row = { + 'PMID': row['PMID'] + } - for col in self.data.columns: - self.data[col] = pd.to_numeric(self.data[col]) + data_row.update(pubmed_features) - # Specifies criteria for filtration - dictionary_condition = '((int(row["gen_term_count"]) > 0) + (int(row["food_term_count"]) > 0) + (int(row["chem_term_count"]) > 0) + (int(row["sci_term_count"]) > 0) > 1)' - - # Dynamically builds the measurement conditions based on different measurement methods - measurement_condition = '' - for method in self.measurement_matchers.keys(): - - if measurement_condition == '': - measurement_condition += '((int(row["' + method + '"]) == 1)' - else: - measurement_condition += ' | (int(row["' + method + '"]) == 1)' + # Use variable classes if data is training data, otherwise just mark as useful for search + if is_traindata: + data_row['class'] = row['is_useful'] + else: + data_row['class'] = 1 - measurement_condition += ')' + # Add the existence of measurement methods as different features + for method in self.measurement_matchers.keys(): + data_row[method] = measurement_detection[method] - # Creates the logical pattern to be used as a filter function in the Model class - self.extract_pattern = { - dictionary_condition + ' & ' + measurement_condition : 'True' - } + # Updates data with new information + self.data = self.data.append(data_row, ignore_index=True) + self.data['PMID'] = self.data['PMID'].astype('int32') - def __get_pubmed_features__(self, text): - """ - Calculates count frequency features from PubMed entry abstract and mesh terms. + self.data = self.data.fillna(0).set_index('PMID', drop=True) - Parameters - ----------------- - row : pd.Series - Single row containing information on PubMed entry + for col in self.data.columns: + self.data[col] = pd.to_numeric(self.data[col]) + # Specifies criteria for filtration + dictionary_condition = '((int(row["gen_term_count"]) > 0) + (int(row["food_term_count"]) > 0) + (int(row["chem_term_count"]) > 0) + (int(row["sci_term_count"]) > 0) > 1)' - Returns - ----------------- - pubmed_features : dict - Dictionary containing the count frequencies for general, food, scientific name, and chemical terms. - """ - - # Count the terms that occur in each dictionary - gen_term_count = self.__matches__(self.gen_matcher, text) - food_term_count = self.__matches__(self.food_matcher, text) - chem_term_count = self.__matches__(self.chem_matcher, text) + # Dynamically builds the measurement conditions based on different measurement methods + measurement_condition = '' + for method in self.measurement_matchers.keys(): - if self.eval_sci_matches == True: - sci_term_count = self.__matches__(self.sci_matcher, text) + if measurement_condition == '': + measurement_condition += '((int(row["' + method + '"]) == 1)' + else: + measurement_condition += ' | (int(row["' + method + '"]) == 1)' - pubmed_features = { - 'gen_term_count' : gen_term_count, - 'food_term_count' : food_term_count, - 'sci_term_count' : sci_term_count, - 'chem_term_count' : chem_term_count - } + measurement_condition += ')' - return pubmed_features + # Creates the logical pattern to be used as a filter function in the Model class + self.extract_pattern = { + dictionary_condition + ' & ' + measurement_condition: 'True' + } + def __get_pubmed_features__(self, text): + """ + Calculates count frequency features from PubMed entry abstract and mesh terms. - # Determines if a word is present in paper abstract or mesh terms - def __detect_word_presence__(self, text, matcher): - """ - Calculates count frequency features from PubMed entry abstract and mesh terms. + Parameters + ----------------- + row : pd.Series + Single row containing information on PubMed entry - Parameters - ----------------- - text : string - Text to search for specified matcher pattern - - matcher : spacy Matcher object - Specific matcher with defined rules to use with the text input - Returns - ----------------- - presence of pattern : int - Returns 1 if there is any presence of the matcher pattern, and 0 otherwise. - """ + Returns + ----------------- + pubmed_features : dict + Dictionary containing the count frequencies for general, food, scientific name, and chemical terms. + """ - # Retrieves the matches from text - match = matcher(self.nlp(text)) + # Count the terms that occur in each dictionary + gen_term_count = self.__matches__(self.gen_matcher, text) + food_term_count = self.__matches__(self.food_matcher, text) + chem_term_count = self.__matches__(self.chem_matcher, text) - # Returns 1 if there are any matches, else 0 - if len(match) > 0: - return 1 + if self.eval_sci_matches == True: + sci_term_count = self.__matches__(self.sci_matcher, text) - else: - return 0 + pubmed_features = { + 'gen_term_count': gen_term_count, + 'food_term_count': food_term_count, + 'sci_term_count': sci_term_count, + 'chem_term_count': chem_term_count + } + return pubmed_features - def __matches__(self, matcher, text): - """ - Calculates count frequency features from PubMed entry abstract and mesh terms. + # Determines if a word is present in paper abstract or mesh terms + def __detect_word_presence__(self, text, matcher): + """ + Calculates count frequency features from PubMed entry abstract and mesh terms. - Parameters - ----------------- - matcher : spacy Matcher object - Specific matcher with defined rules to use with the text input + Parameters + ----------------- + text : string + Text to search for specified matcher pattern - text : string - Text to search for specified matcher pattern + matcher : spacy Matcher object + Specific matcher with defined rules to use with the text input - Returns - ----------------- - match_count : int - Frequency of matches between the text input and matcher pattern - """ + Returns + ----------------- + presence of pattern : int + Returns 1 if there is any presence of the matcher pattern, and 0 otherwise. + """ - text = self.nlp(text) + # Retrieves the matches from text + match = matcher(self.nlp(text)) - matches = matcher(text) + # Returns 1 if there are any matches, else 0 + if len(match) > 0: + return 1 - match_count = len(matches) + else: + return 0 - return match_count + def __matches__(self, matcher, text): + """ + Calculates count frequency features from PubMed entry abstract and mesh terms. + Parameters + ----------------- + matcher : spacy Matcher object + Specific matcher with defined rules to use with the text input - def __matcher_from_list__(self, dictionary): - """ - Creates the spacy matcher object using an input dictionary of terms + text : string + Text to search for specified matcher pattern - Parameters - ----------------- - dictionary : list - List of terms to be included as matching patterns + Returns + ----------------- + match_count : int + Frequency of matches between the text input and matcher pattern + """ + text = self.nlp(text) - Returns - ----------------- - matcher : spacy Matcher object - Frequency of matches between the text input and matcher pattern - """ + matches = matcher(text) - matcher = Matcher(self.nlp.vocab) + match_count = len(matches) - for term in dictionary: + return match_count - #pattern = [{'LOWER' : term, 'OP' : '?'}, {'LOWER' : term + 's', 'OP' : '?'}] - pattern = [{'LOWER' : term}] + def __matcher_from_list__(self, dictionary): + """ + Creates the spacy matcher object using an input dictionary of terms - matcher.add(term, None, pattern) + Parameters + ----------------- + dictionary : list + List of terms to be included as matching patterns - return matcher + Returns + ----------------- + matcher : spacy Matcher object + Frequency of matches between the text input and matcher pattern + """ - def __eval_conditionals__(self, row): - """ - Creates the spacy matcher object using an input dictionary of terms + matcher = Matcher(self.nlp.vocab) - Parameters - ----------------- - row : pd.Series - Feature row with which to evaluate conditionals + for term in dictionary: + # pattern = [{'LOWER' : term, 'OP' : '?'}, {'LOWER' : term + 's', 'OP' : '?'}] + pattern = [{'LOWER': term}] - Returns - ----------------- - eval(action) : - Evaluation of action when an extraction pattern condition is met - """ - - for condition, action in self.extract_pattern.items(): + matcher.add(term, [pattern]) - if eval(condition): - return eval(action) + return matcher + def __eval_conditionals__(self, row): + """ + Creates the spacy matcher object using an input dictionary of terms + Parameters + ----------------- + row : pd.Series + Feature row with which to evaluate conditionals + Returns + ----------------- + eval(action) : + Evaluation of action when an extraction pattern condition is met + """ + for condition, action in self.extract_pattern.items(): + if eval(condition): + return eval(action) diff --git a/src/plot_utils.py b/src/plot_utils.py index d10b631..a92629b 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -2,7 +2,8 @@ import matplotlib as mpl # For higher resolution production graphs -mpl.rcParams['figure.dpi']= 150 +mpl.rcParams['figure.dpi'] = 150 + def clean_plot(leg=True, grid=None, font=None): ax = plt.gca() @@ -10,21 +11,20 @@ def clean_plot(leg=True, grid=None, font=None): ax.spines['top'].set_visible(False) ax.yaxis.set_ticks_position('left') ax.xaxis.set_ticks_position('bottom') - + axis_color = 'lightgrey' ax.spines['bottom'].set_color(axis_color) ax.spines['left'].set_color(axis_color) ax.tick_params(axis='both', color=axis_color) - + if leg: - ax.legend(frameon = False, loc='upper left', bbox_to_anchor=(1, 1)) - + ax.legend(frameon=False, loc='upper left', bbox_to_anchor=(1, 1)) + if grid is not None: - plt.grid(color='lightgrey', axis = grid, linestyle='-', linewidth=.5) - + plt.grid(color='lightgrey', axis=grid, linestyle='-', linewidth=.5) + if font is not None: for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + - ax.get_xticklabels() + ax.get_yticklabels()): - + ax.get_xticklabels() + ax.get_yticklabels()): item.set_fontfamily(font['family']) - item.set_color(font['color']) \ No newline at end of file + item.set_color(font['color']) diff --git a/src/pubmed_util.py b/src/pubmed_util.py index 7e29bad..5477ce5 100644 --- a/src/pubmed_util.py +++ b/src/pubmed_util.py @@ -1,6 +1,6 @@ # Author: Forrest Hooton - +import spacy import requests from lxml import etree import pandas as pd @@ -11,317 +11,317 @@ # Imports from directory from .filter import Filter - def filter_results(search_terms): - """ - Receives search terms to classify usefulness of pubmed documents from search results. + """ + Receives search terms to classify usefulness of pubmed documents from search results. - Parameters - ----------------- - search_terms : list - List of terms to include in PubMed query + Parameters + ----------------- + search_terms : list + List of terms to include in PubMed query - Returns - ----------------- - output_info: pd.DataFrame - PubMed entries that met the specified criteria with metadata attached - """ + Returns + ----------------- + output_info: pd.DataFrame + PubMed entries that met the specified criteria with metadata attached + """ - # Retrieves doc_ids of search terms - doc_ids = search_pubmed(search_terms) + # Retrieves doc_ids of search terms + doc_ids = search_pubmed(search_terms) - print('ids', len(doc_ids)) + print('ids', len(doc_ids)) - start = time.time() + start = time.time() - # pandas dataframe of document info - doc_info = retrieve_doc_info(doc_ids) - print(search_terms, "Document Info", "(" + str(len(doc_info)) + " entries)", "Retrieved in", (time.time() - start) / 60, "min") + # pandas dataframe of document info + doc_info = retrieve_doc_info(doc_ids) + print(search_terms, "Document Info", "(" + str(len(doc_info)) + " entries)", "Retrieved in", + (time.time() - start) / 60, "min") - # Filters out results deemed irrelevant - start = time.time() - filt = Filter() - output = filt.filter(doc_info) - print("Data Converted in", (time.time() - start) / 60, "min") + # Filters out results deemed irrelevant + start = time.time() + filt = Filter() + output = filt.filter(doc_info) + print("Data Converted in", (time.time() - start) / 60, "min") - # Reincorporate metadata - output_info = retrieve_doc_info(output['PMID'].tolist()) + # Reincorporate metadata + output_info = retrieve_doc_info(output['PMID'].tolist()) - return output_info + return output_info def search_pubmed(search_terms): - """ - Receives search terms to classify usefulness of pubmed documents from search results. + """ + Receives search terms to classify usefulness of pubmed documents from search results. - Parameters - ----------------- - search_terms : list - List of terms to include in PubMed query + Parameters + ----------------- + search_terms : list + List of terms to include in PubMed query - Returns - ----------------- - output_info: pd.DataFrame - PubMed entries that met the specified criteria with metadata attached - """ + Returns + ----------------- + output_info: pd.DataFrame + PubMed entries that met the specified criteria with metadata attached + """ - # Gets url to retrieve information from search - url = construct_url(search_terms, 'search') + # Gets url to retrieve information from search + url = construct_url(search_terms, 'search') - xml = __safe_urlopen__(url) + xml = __safe_urlopen__(url) - root = etree.fromstring(xml) + root = etree.fromstring(xml) - # Recursively gets all objects where the tag is Id - elements = root.findall('.//Id') + # Recursively gets all objects where the tag is Id + elements = root.findall('.//Id') - # Converts all lxml objects to their text values - ids = [i.text for i in elements] + # Converts all lxml objects to their text values + ids = [i.text for i in elements] - return ids + return ids def retrieve_doc_info(ids): - """ - Retrieves document (paper) info using pubmed paper ids. + """ + Retrieves document (paper) info using pubmed paper ids. + + Parameters + ----------------- + ids : list + List of PubMed ids to retrieve PubMed entry information - Parameters - ----------------- - ids : list - List of PubMed ids to retrieve PubMed entry information + Returns + ----------------- + info: pd.DataFrame + PubMed ids with metadata attached + """ - Returns - ----------------- - info: pd.DataFrame - PubMed ids with metadata attached - """ + # Can't query too much in a single query, so divides larger id lists into separate queries + num_loops = int(math.ceil(len(ids) / 100)) - # Can't query too much in a single query, so divides larger id lists into separate queries - num_loops = int(math.ceil(len(ids) / 100)) + # Have to split requests larger than 100 documents to keep it within url size + ids = divide_list(ids, num_loops) - # Have to split requests larger than 100 documents to keep it within url size - ids = divide_list(ids, num_loops) + documents = [] - documents = [] + # Retrieves xml data from pubmed + for i in ids: + url = construct_url(i, 'document') - # Retrieves xml data from pubmed - for i in ids: - url = construct_url(i, 'document') + xml = __safe_urlopen__(url) - xml = __safe_urlopen__(url) + root = etree.fromstring(xml) - root = etree.fromstring(xml) + documents = documents + root.findall('PubmedArticle') - documents = documents + root.findall('PubmedArticle') + info = pd.DataFrame() - info = pd.DataFrame() + for document in documents: - for document in documents: + doc_id = int(document.find('.//PMID').text) - doc_id = int(document.find('.//PMID').text) - - paper = document.find('.//ArticleTitle').text + paper = document.find('.//ArticleTitle').text - journal = document.find('.//Title').text + journal = document.find('.//Title').text - year = document.find('.//Year').text + year = document.find('.//Year').text - if document.find('.//AbstractText') is not None: - abstract = document.find('.//AbstractText').text - else: - abstract = None + if document.find('.//AbstractText') is not None: + abstract = document.find('.//AbstractText').text + else: + abstract = None - mesh_terms = [] - mesh_UIds = [] - qual_terms = [] - qual_UIds = [] + mesh_terms = [] + mesh_UIds = [] + qual_terms = [] + qual_UIds = [] - for mesh_section in document.findall('.//MeshHeading'): - mesh_terms.append(mesh_section.find('.//DescriptorName').text) - mesh_UIds.append(mesh_section.find('.//DescriptorName').attrib['UI']) + for mesh_section in document.findall('.//MeshHeading'): + mesh_terms.append(mesh_section.find('.//DescriptorName').text) + mesh_UIds.append(mesh_section.find('.//DescriptorName').attrib['UI']) - if mesh_section.find('.//QualifierName') is not None: - qual_terms.append(mesh_section.find('.//QualifierName').text) - qual_UIds.append(mesh_section.find('.//QualifierName').attrib['UI']) - else: - qual_terms.append(None) - qual_UIds.append(None) + if mesh_section.find('.//QualifierName') is not None: + qual_terms.append(mesh_section.find('.//QualifierName').text) + qual_UIds.append(mesh_section.find('.//QualifierName').attrib['UI']) + else: + qual_terms.append(None) + qual_UIds.append(None) - new_row = { - 'PMID' : doc_id, - 'paper' : paper, - 'journal' : journal, - 'year' : year, - 'abstract' : abstract, - 'mesh_terms' : mesh_terms, - 'mesh_UIds' : mesh_UIds, - 'qual_terms' : qual_terms, - 'qual_UIds' : qual_UIds, - 'webpage' : 'https://www.ncbi.nlm.nih.gov/pubmed/' + str(doc_id) - } - - info = info.append(new_row, ignore_index = True) + new_row = { + 'PMID': doc_id, + 'paper': paper, + 'journal': journal, + 'year': year, + 'abstract': abstract, + 'mesh_terms': mesh_terms, + 'mesh_UIds': mesh_UIds, + 'qual_terms': qual_terms, + 'qual_UIds': qual_UIds, + 'webpage': 'https://www.ncbi.nlm.nih.gov/pubmed/' + str(doc_id) + } - info['PMID'] = info['PMID'].astype('int32') + info = info.append(new_row, ignore_index=True) - return info.reset_index(drop = True) + info['PMID'] = info['PMID'].astype('int32') + + return info.reset_index(drop=True) def pubchem_synonym_info(chem_name): - """ - Retrieves compound id and first compound synonym name from PubChem based on a queried chemical. + """ + Retrieves compound id and first compound synonym name from PubChem based on a queried chemical. - Parameters - ----------------- - chem_name : string - Name of chemical + Parameters + ----------------- + chem_name : string + Name of chemical - Returns - ----------------- - compound_id : int - PubChem ID of queried chemical + Returns + ----------------- + compound_id : int + PubChem ID of queried chemical - compound_name : string - First name of compound listed in PubChem synonyms list - """ + compound_name : string + First name of compound listed in PubChem synonyms list + """ + + # Creates synonym query url + url = construct_url(chem_name, 'synonym') - # Creates synonym query url - url = construct_url(chem_name, 'synonym') - - xml = __safe_urlopen__(url) + xml = __safe_urlopen__(url) - root = etree.fromstring(xml) + root = etree.fromstring(xml) - # Extracts the compound ID and first synonym name - compound_id = float(root.findall(".//{http://pubchem.ncbi.nlm.nih.gov/pug_rest}CID")[0].xpath('.//text()')[0]) - compound_name = str(root.findall(".//{http://pubchem.ncbi.nlm.nih.gov/pug_rest}Synonym")[0].xpath('.//text()')[0]) + # Extracts the compound ID and first synonym name + compound_id = float(root.findall(".//{http://pubchem.ncbi.nlm.nih.gov/pug_rest}CID")[0].xpath('.//text()')[0]) + compound_name = str(root.findall(".//{http://pubchem.ncbi.nlm.nih.gov/pug_rest}Synonym")[0].xpath('.//text()')[0]) - return compound_id, compound_name + return compound_id, compound_name def pubchem_SMILE(chem_id): - """ - Retrieves compound SMILE using pubchem ID. + """ + Retrieves compound SMILE using pubchem ID. - Parameters - ----------------- - chem_id : int - Compound pubchem ID + Parameters + ----------------- + chem_id : int + Compound pubchem ID - Returns - ----------------- - SMILE : string - SMILE corresponding to input compound ID - """ - url = construct_url(chem_id, 'SMILE') + Returns + ----------------- + SMILE : string + SMILE corresponding to input compound ID + """ + url = construct_url(chem_id, 'SMILE') - xml = __safe_urlopen__(url) + xml = __safe_urlopen__(url) - root = root = etree.fromstring(xml) + root = root = etree.fromstring(xml) - # Extracts the compound SMILE - SMILE = root.findall(".//{http://pubchem.ncbi.nlm.nih.gov/pug_rest}CanonicalSMILES")[0].xpath('.//text()')[0] + # Extracts the compound SMILE + SMILE = root.findall(".//{http://pubchem.ncbi.nlm.nih.gov/pug_rest}CanonicalSMILES")[0].xpath('.//text()')[0] - return SMILE + return SMILE # Constructs appropriate url for pubmed api from search terms -def construct_url(url_input, query_type, num_results = 1000000): - """ - Constructs the url for various pubchem queries +def construct_url(url_input, query_type, num_results=1000000): + """ + Constructs the url for various pubchem queries + + Parameters + ----------------- + url_input : string or list depending on query_type + The input for a pubchem query such as pubchem id, pubmed id, or list of search terms - Parameters - ----------------- - url_input : string or list depending on query_type - The input for a pubchem query such as pubchem id, pubmed id, or list of search terms + query_type : string + Specifies which search method should be used... can be "search", "document", "synonym", or "SMILE" - query_type : string - Specifies which search method should be used... can be "search", "document", "synonym", or "SMILE" + num_results : int + Top number of results to keep from a search query - num_results : int - Top number of results to keep from a search query + Returns + ----------------- + url : string + Url to pass to request + """ - Returns - ----------------- - url : string - Url to pass to request - """ - - # Constructs url for search query from list of search terms - if query_type == 'search': - base_url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=' + # Constructs url for search query from list of search terms + if query_type == 'search': + base_url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=' - # Replace spaces with something that works with as a url - adjusted_terms = [s.replace(" ", "%20") for s in url_input] + # Replace spaces with something that works with as a url + adjusted_terms = [s.replace(" ", "%20") for s in url_input] - # Join separate search queries - term_url = '%20AND%20'.join(adjusted_terms) + # Join separate search queries + term_url = '%20AND%20'.join(adjusted_terms) - # Cap the number of results - results_num_url = '&retmax=' + str(num_results) + # Cap the number of results + results_num_url = '&retmax=' + str(num_results) - return base_url + term_url + results_num_url + return base_url + term_url + results_num_url - # Constructs url for document query from list of pubmed document ids - elif query_type == 'document': - base_url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=' + # Constructs url for document query from list of pubmed document ids + elif query_type == 'document': + base_url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=' - # Handles either string or integer pubmed ids - doc_urls = "" - for i in url_input: - if isinstance(i, str): - doc_urls = doc_urls + "," + i - else: - doc_urls = doc_urls + "," + str(i) + # Handles either string or integer pubmed ids + doc_urls = "" + for i in url_input: + if isinstance(i, str): + doc_urls = doc_urls + "," + i + else: + doc_urls = doc_urls + "," + str(i) - url = base_url + doc_urls.lstrip(",") + '&retmode=xml' + url = base_url + doc_urls.lstrip(",") + '&retmode=xml' - return url - - # Constructs url for synonym search from a chemical string - elif query_type == 'synonym': - url = "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/" + url_input + "/synonyms/XML" - return url + return url - # Constructs url for SMILE retrival from a pubchem id string - elif query_type == 'SMILE': - url = "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/" + url_input + "/property/CanonicalSMILES/XML" - return url + # Constructs url for synonym search from a chemical string + elif query_type == 'synonym': + url = "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/" + url_input + "/synonyms/XML" + return url - else: - print('Please enter a valid query type ("search", "document", "synonym", or "SMILE")') + # Constructs url for SMILE retrival from a pubchem id string + elif query_type == 'SMILE': + url = "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/" + url_input + "/property/CanonicalSMILES/XML" + return url + + else: + print('Please enter a valid query type ("search", "document", "synonym", or "SMILE")') # Divides doc ids for larger paper queries in retrieve_doc_info() def divide_list(ids, num_divisions): - """ - Splits a single list of ids into num_divisions numbers of separate lists + """ + Splits a single list of ids into num_divisions numbers of separate lists - Parameters - ----------------- - ids : list - list of pubmed ids + Parameters + ----------------- + ids : list + list of pubmed ids - num_divisions : int - Number of divisions in which to partition lists + num_divisions : int + Number of divisions in which to partition lists - Returns - ----------------- - split_ids : 2d list - list of lists of pubchem ids - """ + Returns + ----------------- + split_ids : 2d list + list of lists of pubchem ids + """ - split_ids = np.array_split(np.asarray(ids), num_divisions) - split_ids = [np.ndarray.tolist(split_ids[i]) for i in range(len(split_ids))] + split_ids = np.array_split(np.asarray(ids), num_divisions) + split_ids = [np.ndarray.tolist(split_ids[i]) for i in range(len(split_ids))] - return split_ids + return split_ids def __safe_urlopen__(url): @@ -339,22 +339,25 @@ def __safe_urlopen__(url): response.content : str (maybe bytes) or None Returns the response of a url query if it exists, else None """ +#Heba ======================================> + print(url) + try: response = requests.get(url) except TimeoutError: time.sleep(.5) return __safe_urlopen__(url) - if response.status_code == 200: # Successful + if response.status_code == 200: # Successful return response.content - elif response.status_code == 429: # Too many requests + elif response.status_code == 429: # Too many requests time.sleep(.5) return __safe_urlopen__(url) - elif response.status_code == 502: # Bad Gateway + elif response.status_code == 502: # Bad Gateway time.sleep(1) return __safe_urlopen__(url) - elif response.status_code == 404: # PUGREST.NotFound (aka doesn't exist) - return None \ No newline at end of file + elif response.status_code == 404: # PUGREST.NotFound (aka doesn't exist) + return None diff --git a/src/tools/chemidr/id_map.py b/src/tools/chemidr/id_map.py index 1cad2a3..9ce0819 100644 --- a/src/tools/chemidr/id_map.py +++ b/src/tools/chemidr/id_map.py @@ -1,5 +1,3 @@ -# Author: Forrest Hooton - import numpy as np import math import urllib.request as request @@ -10,198 +8,218 @@ def cid2prop(cid, prop): - # Create url for InChI query - url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{str(int(cid))}/property/{prop}/JSON" + # Create url for InChI query + url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{str(int(cid))}/property/{prop}/JSON" + + r = __safe_urlopen__(url) + + if r is None: + return np.nan - r = __safe_urlopen__(url) + prop_value = __safe_object_access__(json.loads(r)['PropertyTable']['Properties'][0], prop) - if r is None: - return np.nan - - prop_value = __safe_object_access__(json.loads(r)['PropertyTable']['Properties'][0], prop) - - return prop_value + return prop_value def cids2props(cids, prop, as_dict=False): - """ - Retrieves properties from PubChem using Pubchem CIDS - See property section of https://pubchemdocs.ncbi.nlm.nih.gov/pug-rest$_Toc494865567 + """ + Retrieves properties from PubChem using Pubchem CIDS + See property section of https://pubchemdocs.ncbi.nlm.nih.gov/pug-rest$_Toc494865567 - Input - ---------------------------------------------------------------- - cids : list - list of pubchem cid's for properties (needs to be ints, but also included int typecast) - as_dict : bool (default False) - returns dictionary of info if true, list otherwise + Input + ---------------------------------------------------------------- + cids : list + list of pubchem cid's for properties (needs to be ints, but also included int typecast) + as_dict : bool (default False) + returns dictionary of info if true, list otherwise - Returns - ---------------------------------------------------------------- - props : dict or list - dictionary with CID's as keys and properties as values if as_dict is True, otherwise list - of properties to preserve order - """ - cids = __divide_list__([str(int(i)) for i in cids]) + Returns + ---------------------------------------------------------------- + props : dict or list + dictionary with CID's as keys and properties as values if as_dict is True, otherwise list + of properties to preserve order + """ + cids = __divide_list__([str(int(i)) for i in cids]) - if as_dict: props = {} - else: props = [] + if as_dict: + props = {} + else: + props = [] - # Loop over divisions of ids to avoid overloading query - for ids in cids: + # Loop over divisions of ids to avoid overloading query + for ids in cids: - # Create url for InChI query - url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{','.join(ids)}/property/{prop}/JSON" + # Create url for InChI query + url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{','.join(ids)}/property/{prop}/JSON" - r = __safe_urlopen__(url) + r = __safe_urlopen__(url) - if r is None: - new_props = batch_error_handler(ids, prop, as_dict=as_dict) + if r is None: + new_props = batch_error_handler(ids, prop, as_dict=as_dict) - if as_dict: props.update(new_props) - else: props += new_props + if as_dict: + props.update(new_props) + else: + props += new_props - continue + continue - # option to return InChIKey's as list or as dict (dict has certainty in case some cids aren't - # retrieved, list preserves order) - if as_dict: - new_dict = { - p['CID'] : __safe_object_access__(p, prop) for p in json.loads(r)['PropertyTable']['Properties'] - } - props.update(new_dict) + # option to return InChIKey's as list or as dict (dict has certainty in case some cids aren't + # retrieved, list preserves order) + if as_dict: + new_dict = { + p['CID']: __safe_object_access__(p, prop) for p in json.loads(r)['PropertyTable']['Properties'] + } + props.update(new_dict) - else: - new_list = [ - __safe_object_access__(p, prop) for p in json.loads(r)['PropertyTable']['Properties'] - ] - props += new_list + else: + new_list = [ + __safe_object_access__(p, prop) for p in json.loads(r)['PropertyTable']['Properties'] + ] + props += new_list - return props + return props def cids2names(cids, as_dict=False): - """ - Retrieves properties from PubChem using Pubchem CIDS - See property section of https://pubchemdocs.ncbi.nlm.nih.gov/pug-rest$_Toc494865567 + """ + Retrieves properties from PubChem using Pubchem CIDS + See property section of https://pubchemdocs.ncbi.nlm.nih.gov/pug-rest$_Toc494865567 - Input - ---------------------------------------------------------------- - cids : list - list of pubchem cid's for properties (needs to be ints, but also included int typecast) - as_dict : bool (default False) - returns dictionary of info if true, list otherwise + Input + ---------------------------------------------------------------- + cids : list + list of pubchem cid's for properties (needs to be ints, but also included int typecast) + as_dict : bool (default False) + returns dictionary of info if true, list otherwise - Returns - ---------------------------------------------------------------- - names : dict or list - dictionary with CID's as keys and chemical names as values if as_dict is True, otherwise list - of chemical names to preserve order - """ - cids = __divide_list__([str(int(i)) for i in cids]) + Returns + ---------------------------------------------------------------- + names : dict or list + dictionary with CID's as keys and chemical names as values if as_dict is True, otherwise list + of chemical names to preserve order + """ + cids = __divide_list__([str(int(i)) for i in cids]) + + if as_dict: + names = {} + else: + names = [] + + # Loop over divisions of ids to avoid overloading query + for ids in cids: + + # Create url for InChI query + url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{','.join(ids)}/synonyms/JSON" - if as_dict: names = {} - else: names = [] + r = __safe_urlopen__(url) - # Loop over divisions of ids to avoid overloading query - for ids in cids: + # if r is None: + # new_names = batch_error_handler(ids, prop, as_dict=as_dict) - # Create url for InChI query - url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{','.join(ids)}/synonyms/JSON" + # if as_dict: names.update(new_names) + # else: names += new_names - r = __safe_urlopen__(url) + # continue - # option to return InChIKey's as list or as dict (dict has certainty in case some cids aren't - # retrieved, list preserves order) - if as_dict: - new_dict = { - p['CID'] : __safe_object_access__(p, 'Synonym') for p in json.loads(r)['InformationList']['Information'] - } - names.update(new_dict) + # option to return InChIKey's as list or as dict (dict has certainty in case some cids aren't + # retrieved, list preserves order) + if as_dict: + new_dict = { + p['CID']: __safe_object_access__(p, 'Synonym') for p in json.loads(r)['InformationList']['Information'] + } + names.update(new_dict) - else: - new_list = [ - __safe_object_access__(p, 'Synonym') for p in json.loads(r)['InformationList']['Information'] - ] - names += new_list + else: + new_list = [ + __safe_object_access__(p, 'Synonym') for p in json.loads(r)['InformationList']['Information'] + ] + names += new_list - return names + return names def batch_error_handler(cids, prop, as_dict=False): - if as_dict: props = {} - else: props = [] - - for cid in cids: - val = cid2prop(cid, prop) - - if as_dict: props.update({cid : val}) - else: props += [val] - - return props - - -def cids2inchis(cids, as_dict=False, use_prefix=False, keys = True): - """ - Retrieves InChIKeys from PubChem using Pubchem CIDS - - Input - ---------------------------------------------------------------- - cids : list - list of pubchem cid's for InChIKeys (needs to be ints, but also included int typecast) - use_prefix : bool (default False) - only return the prefix of inchikeys (before the first -), which contains the structural information - (to find out more see https://www.inchi-trust.org/technical-faq-2/) - keys : bool (default True) - return inchikeys rather than the full inchi code - - Returns - ---------------------------------------------------------------- - inchikeys : dict or list - dictionary with CID's as keys and InChIKeys as values if as_dict is True, otherwise list - of InChIKeys to preserve order - """ - if keys: - # Create url for InChIKey query - query_type = 'InChIKey' - else: - # Create url for InChI quer - query_type = 'InChI' - - inchikeys = cids2props(cids, query_type, as_dict=as_dict) - - if use_prefix: - if isinstance(inchikeys, dict): inchikeys = {cid : ikey.split('-')[0] for cid, ikey in inchikeys.items()} - else: inchikeys = [ikey.split('-')[0] for ikey in inchikeys] - - return inchikeys + if as_dict: + props = {} + else: + props = [] + + for cid in cids: + val = cid2prop(cid, prop) + + if as_dict: + props.update({cid: val}) + else: + props += [val] + + return props + + +def cids2inchis(cids, as_dict=False, use_prefix=False, keys=True): + """ + Retrieves InChIKeys from PubChem using Pubchem CIDS + + Input + ---------------------------------------------------------------- + cids : list + list of pubchem cid's for InChIKeys (needs to be ints, but also included int typecast) + use_prefix : bool (default False) + only return the prefix of inchikeys (before the first -), which contains the structural information + (to find out more see https://www.inchi-trust.org/technical-faq-2/) + keys : bool (default True) + return inchikeys rather than the full inchi code + + Returns + ---------------------------------------------------------------- + inchikeys : dict or list + dictionary with CID's as keys and InChIKeys as values if as_dict is True, otherwise list + of InChIKeys to preserve order + """ + if keys: + # Create url for InChIKey query + query_type = 'InChIKey' + else: + # Create url for InChI quer + query_type = 'InChI' + + inchikeys = cids2props(cids, query_type, as_dict=as_dict) + + if use_prefix: + if isinstance(inchikeys, dict): + inchikeys = {cid: ikey.split('-')[0] for cid, ikey in inchikeys.items()} + else: + inchikeys = [ikey.split('-')[0] for ikey in inchikeys] + + return inchikeys def __safe_object_access__(obj, key): - if key in obj: - return obj[key] - else: - return np.nan + if key in obj: + return obj[key] + else: + return np.nan def cids2upacs(cids, as_dict=False): - upacs = cids2props(cids, 'IUPACName', as_dict=as_dict) + upacs = cids2props(cids, 'IUPACName', as_dict=as_dict) - return upacs + return upacs def cids2smiles(cids, as_dict=False): - SMILES = cids2props(cids, 'CanonicalSMILES', as_dict=as_dict) - return SMILES + SMILES = cids2props(cids, 'CanonicalSMILES', as_dict=as_dict) + return SMILES # Divides list into even divisions with a maximum of 100 elements def __divide_list__(ids): - num_divisions = int(math.ceil(len(ids) / 100)) + num_divisions = int(math.ceil(len(ids) / 100)) - split_ids = np.array_split(np.asarray(ids), num_divisions) - split_ids = [np.ndarray.tolist(split_ids[i]) for i in range(len(split_ids))] + split_ids = np.array_split(np.asarray(ids), num_divisions) + split_ids = [np.ndarray.tolist(split_ids[i]) for i in range(len(split_ids))] - return split_ids + return split_ids def __safe_urlopen__(url): @@ -225,18 +243,20 @@ def __safe_urlopen__(url): time.sleep(.5) return __safe_urlopen__(url) - if response.status_code == 200: # Successful + if response.status_code == 200: # Successful return response.content - elif response.status_code == 429: # Too many requests + elif response.status_code == 429: # Too many requests + # print('Retrying...') time.sleep(.5) return __safe_urlopen__(url) - elif response.status_code == 503: # PUGREST.ServerBusy + elif response.status_code == 503: # PUGREST.ServerBusy + # print('Retrying...') time.sleep(1) return __safe_urlopen__(url) - elif response.status_code == 404: # PUGREST.NotFound (aka doesn't exist) + elif response.status_code == 404: # PUGREST.NotFound (aka doesn't exist) return None @@ -256,100 +276,129 @@ def cid2smile(cid): Returns the chemical SMILE corresponding to the cid if it exists, else None """ url = "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/" + cid + "/property/CanonicalSMILES/XML" - + xml = __safe_urlopen__(url) if xml is None: return np.nan - + root = etree.fromstring(xml) SMILE = root.findall(".//{http://pubchem.ncbi.nlm.nih.gov/pug_rest}CanonicalSMILES")[0].xpath('.//text()')[0] - + return SMILE def mesh2pid(mesh): - """ - Retrieves pubchem id's (both cid and sid) from Pubchem by searching the substances + """ + Retrieves pubchem id's (both cid and sid) from Pubchem by searching the substances - Input - ---------------------------------------------------------------- - mesh : str - mesh for which to retrieve the Pubchem id's + Input + ---------------------------------------------------------------- + mesh : str + mesh for which to retrieve the Pubchem id's - Returns - ---------------------------------------------------------------- - _ : dict (of dicts) - dictionary with mesh id as keys, and dictionaries of mesh ids with corresponding cids and sids as values - """ - url = f'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pcsubstance&term={mesh}&retmode=json' + Returns + ---------------------------------------------------------------- + _ : dict (of dicts) + dictionary with mesh id as keys, and dictionaries of mesh ids with corresponding cids and sids as values + """ + url = f'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pcsubstance&term={mesh}&retmode=json' + + r = __safe_urlopen__(url) + + if r is not None: + j = json.loads(r) + + # No results from searching mesh id + if j['esearchresult']['count'] != 0: + + sid = j['esearchresult']['idlist'][0] # get first sid result + + url = f'https://pubchem.ncbi.nlm.nih.gov/rest/pug/substance/sid/{sid}/xml' + + xml = __safe_urlopen__(url) - r = __safe_urlopen__(url) + if xml is None: + return {mesh: {'mesh': mesh, 'sid': sid, 'cid': cid}} - if r is not None: - j = json.loads(r) + root = etree.fromstring(xml) - # No results from searching mesh id - if j['esearchresult']['count'] != 0: + cids = root.findall(".//{http://www.ncbi.nlm.nih.gov}PC-CompoundType_id_cid") - sid = j['esearchresult']['idlist'][0] # get first sid result + if len(cids) > 0: + cid = cids[0].xpath('./text()')[0] + else: + cid = np.nan # No cids - url = f'https://pubchem.ncbi.nlm.nih.gov/rest/pug/substance/sid/{sid}/xml' + return {mesh: {'mesh': mesh, 'sid': sid, 'cid': cid}} - xml = __safe_urlopen__(url) + else: + return {mesh: {'mesh': mesh, 'sid': np.nan, 'cid': np.nan}} - if xml is None: - return {mesh : {'mesh' : mesh, 'sid' : sid, 'cid' : cid}} + else: + return {mesh: {'mesh': mesh, 'sid': np.nan, 'cid': np.nan}} - root = etree.fromstring(xml) - cids = root.findall(".//{http://www.ncbi.nlm.nih.gov}PC-CompoundType_id_cid") +# url = f'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pccompound&term={mesh}&retmode=json' - if len(cids) > 0: - cid = cids[0].xpath('./text()')[0] - else: - cid = np.nan # No cids +# r = __safe_urlopen__(url) - return {mesh : {'mesh' : mesh, 'sid' : sid, 'cid' : cid}} +# if r is not None: +# j = json.reads(r) - else: - return {mesh : {'mesh' : mesh, 'sid' : np.nan, 'cid' : np.nan}} +# if j['esearchresult']['count'] != 0: - else: - return {mesh : {'mesh' : mesh, 'sid' : np.nan, 'cid' : np.nan}} +# sid = j['esearchresult']['idlist'][0] # get first sid result + +# url = f'https://pubchem.ncbi.nlm.nih.gov/rest/pug/substance/sid/{sid}/xml' + +# xml = __safe_urlopen__(url) + +# root = etree.from_string(xml) + +# cids = root.findall(".//{http://www.ncbi.nlm.nih.gov}PC-CompoundType_id_cid") + +# if len(cids) > 0: +# cid = cids[0].xpath('./text()')[0] +# else: +# cid = np.nan + +# return {'mesh' : mesh, 'sid' : sid, 'cid' : cid} def cid2tax(cid, taxonomy='ChEBI'): - url = f'https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/classification/JSON' + url = f'https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/classification/JSON' - r = __safe_urlopen__(url) + r = __safe_urlopen__(url) - if r is None: return np.nan + if r is None: return np.nan - all_taxonomies = json.loads(r)['Hierarchies']['Hierarchy'] + all_taxonomies = json.loads(r)['Hierarchies']['Hierarchy'] - # I think should only have one occurrence of taxonomy source name - raw_tax = [all_taxonomies[t] for t in range(len(all_taxonomies)) if all_taxonomies[t]['SourceName'] == taxonomy] + # I think should only have one occurance of taxonomy source name + raw_tax = [all_taxonomies[t] for t in range(len(all_taxonomies)) if all_taxonomies[t]['SourceName'] == taxonomy] - if len(raw_tax) == 0: return np.nan - else: raw_tax = raw_tax[0] + if len(raw_tax) == 0: + return np.nan + else: + raw_tax = raw_tax[0] - nodes = raw_tax['Node'] - tax = [] + nodes = raw_tax['Node'] + tax = [] - chebi_name = lambda x: x['Information']['Name'] - chebi_id = lambda x: int(x['Information']['URL'].lstrip('http://www.ebi.ac.uk/chebi/searchId.do?chebiId=CHEBI:')) + chebi_name = lambda x: x['Information']['Name'] + chebi_id = lambda x: int(x['Information']['URL'].lstrip('http://www.ebi.ac.uk/chebi/searchId.do?chebiId=CHEBI:')) - last_node = nodes[0]['NodeID'] - tax.append( (chebi_name(nodes[0]), chebi_id(nodes[0])) ) - n=1 + last_node = nodes[0]['NodeID'] + tax.append((chebi_name(nodes[0]), chebi_id(nodes[0]))) + n = 1 - while int(last_node.lstrip('node_')) >= int(nodes[n]['NodeID'].lstrip('node_')): - tax.append( (chebi_name(nodes[n]), chebi_id(nodes[n])) ) - last_node = nodes[n]['NodeID'] - n += 1 + while int(last_node.lstrip('node_')) >= int(nodes[n]['NodeID'].lstrip('node_')): + tax.append((chebi_name(nodes[n]), chebi_id(nodes[n]))) + last_node = nodes[n]['NodeID'] + n += 1 - if n == len(nodes): - break + if n == len(nodes): + break - return tax \ No newline at end of file + return tax \ No newline at end of file diff --git a/src/try.py b/src/try.py new file mode 100644 index 0000000..90df465 --- /dev/null +++ b/src/try.py @@ -0,0 +1,16 @@ +"""import os + +os.system("jupyter notebook") + +docker run -v D:\m:/MyProjectFiles/in -v D:\o:/MyProjectFiles/out brain-docker + +os.system("some_command < input_file | another_command > output_file") + +os.system("docker run -v < D:\m:/MyProjectFiles/in | -v D:\o:/MyProjectFiles/out > D:\o:/MyProjectFiles/out ") +""" + +import subprocess + +with open("/tmp/output.log", "a") as output: + subprocess.call("docker run -v D:\m:/MyProjectFiles/in -v D:\o:/MyProjectFiles/out brain-docker", shell=True, + stdout=output, stderr=output) From 17b7bbd05eeade80ba6af52dfe0bff49f1891bc7 Mon Sep 17 00:00:00 2001 From: hebamuh68 Date: Sun, 29 Jan 2023 02:08:19 +0200 Subject: [PATCH 2/4] update data statstics and Molcule_Embedding --- .gitignore | 4 + FoodMine_Notable_Files/fm_cocoa.pkl | Bin 74891 -> 74576 bytes .../Perspective_Analysis.ipynb | 34 +- data/cocoa_scoring.csv | 950 +++--- data/garlic_scoring.csv | 832 ++--- notebooks/Data_Statistics.ipynb | 2726 ++++++++++++++++- notebooks/Molecule_Embedding.ipynb | 654 +++- notebooks/Paper_Citations.ipynb | 134 +- notebooks/Paper_Screening.ipynb | 936 +++++- src/data_loader.py | 6 +- src/file_downloader.py | 19 +- stats/fm_usda_overlap_perc_cocoa.txt | 1 + stats/fm_usda_r2_cocoa.txt | 1 + stats/fm_usda_r2_r_cocoa.txt | 1 + stats/unique_chems_cocoa.txt | 1 + 15 files changed, 5179 insertions(+), 1120 deletions(-) create mode 100644 stats/fm_usda_overlap_perc_cocoa.txt create mode 100644 stats/fm_usda_r2_cocoa.txt create mode 100644 stats/fm_usda_r2_r_cocoa.txt create mode 100644 stats/unique_chems_cocoa.txt diff --git a/.gitignore b/.gitignore index 894a44c..8ed8cfa 100644 --- a/.gitignore +++ b/.gitignore @@ -102,3 +102,7 @@ venv.bak/ # mypy .mypy_cache/ + +#data +data +misc_save diff --git a/FoodMine_Notable_Files/fm_cocoa.pkl b/FoodMine_Notable_Files/fm_cocoa.pkl index 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a/Nature_Food_Perspective/Perspective_Analysis.ipynb +++ b/Nature_Food_Perspective/Perspective_Analysis.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -35,9 +35,29 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'data/CTD_chemicals_diseases.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m hdata \u001b[38;5;241m=\u001b[39m \u001b[43mload_ctd\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Graduation project/FoodMine/src/data_loader.py:313\u001b[0m, in \u001b[0;36mload_ctd\u001b[0;34m()\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_ctd\u001b[39m():\n\u001b[1;32m 312\u001b[0m skip \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m26\u001b[39m)) \u001b[38;5;66;03m# First few lines are empty / not useful info\u001b[39;00m\n\u001b[0;32m--> 313\u001b[0m hdata \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdata/CTD_chemicals_diseases.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskiprows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[1;32m 314\u001b[0m hdata\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mChemicalName\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mChemicalID\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCasRN\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDiseaseName\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDiseaseID\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDirectEvidence\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 315\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInferenceGeneSymbol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInferenceScore\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOmimIDs\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPubMedIDs\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 316\u001b[0m hdata \u001b[38;5;241m=\u001b[39m hdata\u001b[38;5;241m.\u001b[39mdrop([\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mreset_index(drop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.._deprecate_kwarg..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 210\u001b[0m kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 326\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 327\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m 328\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 329\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m 330\u001b[0m )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 936\u001b[0m dialect,\n\u001b[1;32m 937\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 946\u001b[0m defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 947\u001b[0m )\n\u001b[1;32m 948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 608\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1439\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1733\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m 1734\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles 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\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 851\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 852\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 853\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 854\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 855\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 857\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 858\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 859\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m 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-pred,PMID,abstract,journal,mesh_UIds,mesh_terms,paper,qual_UIds,qual_terms,webpage,year,is_useful,usefulness_tier,info_location,comment -0.91,10917927,"Procyanidins are a subclass of flavonoids found in commonly consumed foods that have attracted increasing attention due to their potential health benefits. However, little is known regarding their dietary intake levels because detailed quantitative information on the procyanidin profiles present in many food products is lacking. Therefore, the procyanidin content of red wine, chocolate, cranberry juice and four varieties of apples has been determined. On average, chocolate and apples contained the largest procyanidin content per serving (164.7 and 147.1 mg, respectively) compared with red wine and cranberry juice (22.0 and 31.9 mg, respectively). However, the procyanidin content varied greatly between apple samples (12.3-252.4 mg/serving) with the highest amounts on average observed for the Red Delicious (207.7 mg/serving) and Granny Smith (183.3 mg/serving) varieties and the lowest amounts in the Golden Delicious (92.5 mg/serving) and McIntosh (105.0 mg/serving) varieties. The compositional data reported herein are important for the initial understanding of which foods contribute most to the dietary intake of procyanidins and may be used to compile a database necessary to infer epidemiological relationships to health and disease.",The Journal of nutrition,"['D044946', 'D002099', 'D002392', 'D002851', 'D004032', 'D005504', 'D005638', 'D044945', 'D014920']","['Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Diet', 'Food Analysis', 'Fruit', 'Proanthocyanidins', 'Wine']",Procyanidin content and variation in some commonly consumed foods.,"[None, 'Q000737', 'Q000008', 'Q000379', None, None, 'Q000737', None, 'Q000032']","[None, 'chemistry', 'administration & dosage', 'methods', None, None, 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/10917927,2000,0,0,,no cocoa -0.9,26759032,"Children are vulnerable to heavy metal contamination through consumption of candies and chocolates. Considering this representative samples (69) of candies and chocolates based on cocoa, milk and sugar were analyzed for selected heavy metals by means of flame atomic absorption spectrometry. The average concentration of Zn, Pb, Ni, and Cd was found to be 2.52 _± 2.49, 2.0 _± 1.20, 0.84 _± 1.35, and 0.17 _± 0.22 __g/g respectively. Results indicate that cocoa-based candies have higher metal content than milk- or sugar-based candies. The daily dietary intake of metals for children eating candies and chocolates was also calculated, and results indicated highest intake of Pb and Zn followed by Ni, Cd, and Cu. Comparison of the current study results with other studies around the globe shows that the heavy metal content in candies and chocolates is lower in India than reported elsewhere. However, to reduce the further dietary exposure of heavy metals through candies and chocolates, their content should be monitored regularly and particularly for Pb as children are highly susceptible to its toxicity.",Environmental monitoring and assessment,"['D002182', 'D002648', 'D004032', 'D004781', 'D004784', 'D005506', 'D006801', 'D007194', 'D019216', 'D013054']","['Candy', 'Child', 'Diet', 'Environmental Exposure', 'Environmental Monitoring', 'Food Contamination', 'Humans', 'India', 'Metals, Heavy', 'Spectrophotometry, Atomic']",Heavy metal content in various types of candies and their daily dietary intake by children.,"['Q000032', None, 'Q000706', 'Q000032', 'Q000379', 'Q000032', None, None, 'Q000032', None]","['analysis', None, 'statistics & numerical data', 'analysis', 'methods', 'analysis', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/26759032,2016,1,1,table 3 ,International standars were given by an agency and the paper is referenced in this paper -0.89,11513631,"At present, the commonly used HPLC method for the analysis of caffeine and theobromine contents in aqueous cocoa extracts employs direct application of the extracts on the column. This practice gradually reduces the efficiency of the column and shortens its life. Also, this method gives inflated values due to interfering substances and difficulty in achieving baseline resolution. In the improved method, the interfering cocoa pigments are effectively removed by passing the aqueous extract through a Sep-pak C(18) cartridge. Subsequent injection on a C(18) reverse-phase column employing acetonitrile and water (20:80) as the mobile phase reduces the analysis time without affecting either resolution of the peak or the accuracy of caffeine and theobromine determination or achieving baseline resolution. Therefore, this method is ideally suited for rapid routine analysis of cocoa and its products.",Journal of agricultural and food chemistry,"['D002099', 'D002110', 'D002851', 'D012680', 'D013805', 'D013997']","['Cacao', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Sensitivity and Specificity', 'Theobromine', 'Time Factors']",Improved high-performance liquid chromatography method to determine theobromine and caffeine in cocoa and cocoa products.,"['Q000737', 'Q000032', 'Q000379', None, 'Q000032', None]","['chemistry', 'analysis', 'methods', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/11513631,2001,2,1,table 1 and 2, -0.89,22394229,"This study has examined the occurrence of aflatoxins in 168 samples of different fractions obtained during the processing of cocoa in manufacturing plants (shell, nibs, mass, butter, cake and powder) using an optimised methodology for cocoa by-products. The method validation was based on selectivity, linearity, limit of detection and recovery. The method was shown to be adequate for use in quantifying the contamination of cocoa by aflatoxins B(1), B(2), G(1) and G(2). Furthermore, the method was easier to use than other methods available in the literature. For aflatoxin extraction from cocoa samples, a methanol-water solution was used, and then immunoaffinity columns were employed for clean-up before the determination by high-performance liquid chromatography. A survey demonstrated a widespread occurrence of aflatoxins in cocoa by-products, although in general the levels of aflatoxins present in the fractions from industrial processing of cocoa were low. A maximum aflatoxin contamination of 13.3 ng g(-1) was found in a nib sample. The lowest contamination levels were found in cocoa butter. Continued monitoring of aflatoxins in cocoa by-products is nevertheless necessary because these toxins have a high toxicity to humans and cocoa is widely consumed by children through cocoa-containing products, like candies.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000348', 'D002099', 'D002846', 'D002851', 'D057230']","['Aflatoxins', 'Cacao', 'Chromatography, Affinity', 'Chromatography, High Pressure Liquid', 'Limit of Detection']",Determination of aflatoxins in by-products of industrial processing of cocoa beans.,"['Q000032', 'Q000737', 'Q000379', 'Q000379', None]","['analysis', 'chemistry', 'methods', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/22394229,2012,1,2,table 3 ,the occurrence of aflatoxin in all cocoa by-products -0.88,10868592,"A method was developed for determining fructan inulin in various foods (yogurts, honey cakes, chocolates). Warm water was applied for extraction of samples, and mono- and dissacharides were determined by a thin-layer chromatographic densitometric method. A portion of the test solution was hydrolyzed 30 min with 1% oxalic acid in a boiling water bath. Fructose was determined in the hydrolysate. The amount of inulin in a sample was calculated as the difference between the amount of fructose in the sample before and after hydrolysis. The fructose from sucrose formed during the hydrolysis was also considered. The mean recovery from yogurt fortified with 4% inulin was 95.5 +/- 4.5% (mean +/- standard deviation); from honey cakes extract fortified with 10% inulin, 97.3 +/- 5.5%; and from chocolate extract fortified with 30% inulin, 98.6 +/- 6.6% (6 replicates in all cases). Determination of glucose is not necessary for analyzing fructans with the composition expressed shortened to GFn-1 (G, glucose; F, fructosyl) with the average degree of polymerization 8 < or = n < or = 15.",Journal of AOAC International,"['D000818', 'D002099', 'D002855', 'D005504', 'D005632', 'D006722', 'D006358', 'D006868', 'D007444', 'D008892', 'D015014']","['Animals', 'Cacao', 'Chromatography, Thin Layer', 'Food Analysis', 'Fructose', 'Honey', 'Hot Temperature', 'Hydrolysis', 'Inulin', 'Milk', 'Yogurt']",Determination of inulin in foods.,"[None, 'Q000737', None, 'Q000379', 'Q000032', None, None, None, 'Q000032', 'Q000737', 'Q000032']","[None, 'chemistry', None, 'methods', 'analysis', None, None, None, 'analysis', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/10868592,2000,,,, -0.87,23017398,"This work reports an investigation carried out to assess the natural occurrence of ochratoxin A in 168 samples from different fractions obtained during the technological processing of cocoa (shell, nibs, liquor, butter, cake and cocoa powder) and the reduction of ochratoxin A during chocolate manufacture. Ochratoxin A analyses were performed with immunoaffinity columns and detection by high performance liquid chromatography. Concerning the natural ochratoxin A contamination in cocoa by-products, the highest levels of ochratoxin A were found in the shell, cocoa powder and cocoa cake. The cocoa butter was the least contaminated, showing that ochratoxin A seems to remain in the defatted cocoa solids. Under the technological conditions applied during the manufacture of chocolate in this study and the level of contamination present in the cocoa beans, this experiment demonstrated that 93.6% of ochratoxin A present in the beans was reduced during the chocolate producing.",Food chemistry,"['D002099', 'D002851', 'D003059', 'D005506', 'D005511', 'D009793']","['Cacao', 'Chromatography, High Pressure Liquid', 'Cocos', 'Food Contamination', 'Food Handling', 'Ochratoxins']",Occurrence of ochratoxin A in cocoa by-products and determination of its reduction during chocolate manufacture.,"['Q000737', None, 'Q000737', 'Q000032', None, 'Q000032']","['chemistry', None, 'chemistry', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/23017398,2013,1,1,table 1 and 2, -0.87,15891868,"Chocolate is a complex sample with a high content of organic compounds and its analysis generally involves digestion procedures that might include the risk of losses and/or contamination. The determination of copper in chocolate is important because copper compounds are extensively used as fungicides in the farming of cocoa. In this paper, a slurry-sampling flame atomic-absorption spectrometric method is proposed for determination of copper in powdered chocolate samples. Optimization was carried out using univariate methodology involving the variables nature and concentration of the acid solution for slurry preparation, sonication time, and sample mass. The recommended conditions include a sample mass of 0.2 g, 2.0 mol L(-1) hydrochloric acid solution, and a sonication time of 15 min. The calibration curve was prepared using aqueous copper standards in 2.0 mol L(-1) hydrochloric acid. This method allowed determination of copper in chocolate with a detection limit of 0.4 microg g(-1) and precision, expressed as relative standard deviation (RSD), of 2.5% (n = 10) for a copper content of approximately 30 microg g(-1), using a chocolate mass of 0.2 g. The accuracy was confirmed by analyzing the certified reference materials NIST SRM 1568a rice flour and NIES CRM 10-b rice flour. The proposed method was used for determination of copper in three powdered chocolate samples, the copper content of which varied between 26.6 and 31.5 microg g(-1). The results showed no significant differences with those obtained after complete digestion, using a t-test for comparison.",Analytical and bioanalytical chemistry,"['D002099', 'D003300', 'D011208', 'D012680', 'D013054']","['Cacao', 'Copper', 'Powders', 'Sensitivity and Specificity', 'Spectrophotometry, Atomic']",Determination of copper in powdered chocolate samples by slurry-sampling flame atomic-absorption spectrometry.,"['Q000737', 'Q000032', 'Q000737', None, 'Q000295']","['chemistry', 'analysis', 'chemistry', None, 'instrumentation']",https://www.ncbi.nlm.nih.gov/pubmed/15891868,2007,0,0,,no cocoa -0.86,23828209,"Ultrathin-layer chromatography (UTLC) potentially offers faster analysis, reduced solvent and sample volumes, and lower costs. One novel technique for producing UTLC plates has been glancing angle deposition (GLAD), a physical vapor deposition technique capable of aligning macropores to produce interesting separation properties. To date, however, GLAD-UTLC plates have been restricted to model dye systems, rather than realistic analytes. This study demonstrates the transfer of high-performance thin-layer chromatography (HPTLC) sugar analysis methods to GLAD-UTLC plates using the office chromatography framework. A consumer inkjet printer was used to apply very sharp low volume (3-30__nL) bands of water-soluble analytes (lactose, sucrose, and fructose). Analytic performance measurements extrapolated the limits of detection to be 3-5__ng/zone, which was experimentally proven down to 60-70__ng/band, depending on the sugar. This qualitative analysis of sugars in a commercially available chocolate sample is the first reported application of GLAD-UTLC to food samples. The potential utility of GLAD-UTLC is further exemplified by successful coupling with electrospray ionization mass spectrometry for the first time to characterize underivatized sugars. ",Analytical and bioanalytical chemistry,"['D002099', 'D002855', 'D005504', 'D005632', 'D007281', 'D007785', 'D057230', 'D011327', 'D021241', 'D013395']","['Cacao', 'Chromatography, Thin Layer', 'Food Analysis', 'Fructose', 'Ink', 'Lactose', 'Limit of Detection', 'Printing', 'Spectrometry, Mass, Electrospray Ionization', 'Sucrose']","Inkjet application, chromatography, and mass spectrometry of sugars on nanostructured thin films.","['Q000737', 'Q000379', None, 'Q000032', None, 'Q000032', None, None, None, 'Q000032']","['chemistry', 'methods', None, 'analysis', None, 'analysis', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/23828209,2014,0,0,,no cocoa -0.85,21140662,"A fast and simple chromatographic method to determine biotin in foods is presented. Biotin is extracted using papain (60 degrees C, 1 h). After pH adjustment and filtration, biotin is determined by LC with fluorescence detection using postcolumn reagent avidin-FITC (avidin labeled with fluorescein isothiocyanate). The method has been validated in a large range of products: milk- and soy-based infant formulas, cereals, cocoa-malt beverages, and clinical nutrition products. The method showed recovery rates of 98.1 +/- 5.7% (average +/- SD) in a large range of concentrations. Biotin concentrations determined in infant formula standard reference materials 1846 and 1849 were in agreement with reference values. RSD of repeatability (RSDr) varied from 2.0 to 4.5%, and intermediate reproducibility (RSD(iR)) from 5.8 to 9.4%. LOD and LOQ were 3.0 and 5.0 microg/100 g, respectively. The proposed method is suitable for routine analysis of biotin in fortified foods (infant formulas, infant cereals, cocoa-malt beverages, and clinical nutrition products). It can be used as a faster, more selective, and precise alternative to the classical microbiological determination, and is easily transferable among laboratories.",Journal of AOAC International,"['D001628', 'D001710', 'D002099', 'D002851', 'D002523', 'D005453', 'D006801', 'D007223', 'D041943', 'D015203']","['Beverages', 'Biotin', 'Cacao', 'Chromatography, High Pressure Liquid', 'Edible Grain', 'Fluorescence', 'Humans', 'Infant', 'Infant Formula', 'Reproducibility of Results']","Optimization and validation of an LC-fLD method for biotin in infant formula, infant cereals, cocoa-malt beverages, and clinical nutrition products.","['Q000032', 'Q000032', 'Q000737', 'Q000379', 'Q000737', None, None, None, 'Q000737', None]","['analysis', 'analysis', 'chemistry', 'methods', 'chemistry', None, None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21140662,2010,,,, -0.84,16001548,"A method using normal phase high performance liquid chromatography (NP-HPLC) with UV detection was developed for the analysis of acrylamide and methacrylamide. The method relies on the chromatographic separation of these analytes on a polar HPLC column designed for the separation of organic acids. Identification of acrylamide and methacrylamide is approached dually, that is directly in their protonated forms and as their hydrolysis products acrylic and methacrylic acid respectively, for confirmation. Detection and quantification is performed at 200 nm. The method is simple allowing for clear resolution of the target peaks from any interfering substances. Detection limits of 10 microg L(-1) were obtained for both analytes with the inter- and intra-day RSD for standard analysis lying below 1.0%. Use of acetonitrile in the elution solvent lowers detection limits and retention times, without impairing resolution of peaks. The method was applied for the determination of acrylamide and methacrylamide in spiked food samples without native acrylamide yielding recoveries between 95 and 103%. Finally, commercial samples of french and roasted fries, cookies, cocoa and coffee were analyzed to assess applicability of the method towards acrylamide, giving results similar with those reported in the literature.",Journal of chromatography. A,"['D020106', 'D000178', 'D002851', 'D013056']","['Acrylamide', 'Acrylamides', 'Chromatography, High Pressure Liquid', 'Spectrophotometry, Ultraviolet']",Determination of acrylamide and methacrylamide by normal phase high performance liquid chromatography and UV detection.,"['Q000032', 'Q000032', 'Q000379', None]","['analysis', 'analysis', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/16001548,2005,1,1,table 3 ,only the concentration -0.83,11087482,"An HPLC method, using detection after postcolumn derivatization with p-dimethylaminocynnamaldehyde (DMACA), was developed for the quantitative analysis of individual flavanols in food. This method was applied to flavanol determination in 56 different kinds of Spanish food products, including fruit, vegetables, legumes, beverages (cider, coffee, beer, tea, and wine), and chocolate. The determined compounds corresponded to the catechins and proanthocyanidin dimers and trimers usually present in food and, therefore, they were representative of the flavanols of low degree of polymerization consumed with the diet. The data generated could be used for calculation of the dietary intake of either individual or total flavanols, which would allow the further establishment of epidemiological correlations with the incidence of chronic diseases. Similar flavanol profiles were found in the different samples of a similar type of product, even though important variations could exist in the concentrations of total and individual flavanols among them. This was attributed to factors such as sample origin, stage of ripeness, post-harvesting conservation, and processing. Total flavanol contents varied from nondetectable in most of the vegetables to 184 mg/100 g found in a sample of broad bean. Substantial amounts were also found in some fruits, such as plum and apple, as well as in tea and red wine. Epicatechin was the most abundant flavanol, followed by catechin and procyanidin B2. In general, catechins were found in all the flavanol-containing products, but the presence of gallocatechins was only relevant in pomegranate, broad bean, lentil, grape, wine, beer, and tea, and most of the berries. Galloyled flavanols were only detected in strawberry, medlar, grape, and tea.",Journal of agricultural and food chemistry,"['D001628', 'D044946', 'D002099', 'D002392', 'D002851', 'D007887', 'D005419', 'D005504', 'D005638', 'D007202', 'D010946', 'D044945', 'D013030']","['Beverages', 'Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Fabaceae', 'Flavonoids', 'Food Analysis', 'Fruit', 'Indicators and Reagents', 'Plants, Medicinal', 'Proanthocyanidins', 'Spain']",Quantitative analysis of flavan-3-ols in Spanish foodstuffs and beverages.,"['Q000032', None, 'Q000737', None, 'Q000379', 'Q000737', 'Q000032', None, 'Q000737', None, None, None, None]","['analysis', None, 'chemistry', None, 'methods', 'chemistry', 'analysis', None, 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11087482,2001,1,1,table 1,soluble cacao -0.83,11552746,"Samples of chocolate, cocoa, tea infusions, soft drinks and fruit juice have been examined by, electrothermal atomic absorption spectrometry (ETA-AAS) for the presence of aluminium (Al). Fruit juices and chocolate were analysed after an adequate sample preparation; the other products were evaluated directly. Sampling was performed in duplicate for 248 independent samples. The mean Al concentration in chocolate was 9.2 +/- 7.5 mg kg(-1), and individual values were correlated with the per cent of cocoa in samples (Y = 0.63 + 0.27X, r = 0.78, p < 0.0001). Al concentration in commercial tea infusions ranged from 0.9 to 3.3 mg l(-1) (mean = 1.80 +/- 65 mg l(-1), whereas in laboratory-prepared samples it was 2.7 +/- 0.93 mg l(-1). In soft drinks, the concentrations of Al were lower, ranging from 9.1 to 179 microg l(-1); the highest values were observed in samples of orange squash (mean = 114 +/- 56 microg l(-1)). Apricot juice showed the highest Al level (mean = 602 +/- 190 microg l(-1)), being statistically, different from that of pear (mean = 259 +/- 102 microg l(-1)), but not different from that of peach juice (mean = 486 +/- 269 microg kg(-1)). Toxicologically, the amount of Al deriving from the consumption of these products is far below the acceptable daily intake of 1 mg kg(-1) body weight indicated by the FAO/WHO, and it is a verv low percentage of the normal Al dietary intake.",Food additives and contaminants,"['D000535', 'D001628', 'D002099', 'D002253', 'D002957', 'D005506', 'D006801', 'D013054', 'D013662']","['Aluminum', 'Beverages', 'Cacao', 'Carbonated Beverages', 'Citrus', 'Food Contamination', 'Humans', 'Spectrophotometry, Atomic', 'Tea']",Evaluation of aluminium concentrations in samples of chocolate and beverages by electrothermal atomic absorption spectrometry.,"['Q000032', 'Q000032', 'Q000737', 'Q000032', 'Q000737', None, None, None, 'Q000737']","['analysis', 'analysis', 'chemistry', 'analysis', 'chemistry', None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11552746,2001,,,, -0.82,25174984,"Phenol-specific extracts of 12 Belgian special beers were analyzed by gas chromatography hyphenated to olfactometry (AEDA procedure) and mass spectrometry (single ion monitoring mode). As guaiacol and 4-methylphenol were revealed to be more concentrated in brown beers (>3.5 and >1.1 __g/L, respectively), they are proposed as specific markers of the utilization of dark malts. Analysis of five differently colored malts (5, 50, 500, 900, and 1500 _EBC) allowed confirmation of high levels of guaiacol (>180 __g/L; values given in wort, for 100% specialty malt) and 4-methylphenol (>7 __g/L) for chocolate and black malts only (versus respectively <3 __g/L and undetected in all other worts). Monitoring of beer aging highlighted major differences between phenols. Guaiacol and 4-methylphenol appeared even more concentrated in dark beers after 14 months of aging, reaching levels not far from their sensory thresholds. 4-Vinylphenols and 4-ethylphenols, on the contrary, proved to be gradually degraded in POF(+)-yeast-derived beers. Vanillin exhibited an interesting pattern: in beers initially containing <25 __g/L, the vanillin concentration increased over a 14 month aging period to levels exceeding its sensory threshold (up to 160 __g/L). Beers initially showing an above-threshold level of vanillin displayed a decrease during aging. ",Journal of agricultural and food chemistry,"['D001515', 'D015415', 'D003408', 'D002523', 'D005285', 'D005511', 'D008401', 'D006139', 'D010636', 'D014835']","['Beer', 'Biomarkers', 'Cresols', 'Edible Grain', 'Fermentation', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Guaiacol', 'Phenols', 'Volatilization']",Guaiacol and 4-methylphenol as specific markers of torrefied malts. Fate of volatile phenols in special beers through aging.,"['Q000032', 'Q000737', 'Q000737', 'Q000737', None, None, None, 'Q000737', 'Q000737', None]","['analysis', 'chemistry', 'chemistry', 'chemistry', None, None, None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/25174984,2015,0,0,,no cocoa -0.81,11312867,"The detection threshold of acetaldehyde was determined on whole, lowfat, and nonfat milks, chocolate-flavored milk, and spring water. Knowledge of the acetaldehyde threshold is important because acetaldehyde forms in milk during storage as a result of light oxidation. It is also a degradation product of poly(ethylene terephthalate) during melt processing, a relatively new packaging choice for milk and water. There was no significant difference in the acetaldehyde threshold in milk of various fat contents, with thresholds ranging from 3939 to 4040 ppb. Chocolate-flavored milk and spring water showed thresholds of 10048 and 167 ppb, respectively, which compares favorably with previous studies. Solid phase microextraction (SPME) was verified as an effective method for the recovery of acetaldehyde in all media with detection levels as low as 200 and 20 ppb in milk and water, respectively, when using a polydimethyl siloxane/Carboxen SPME fiber in static headspace at 45 degrees C for 15 min.",Journal of agricultural and food chemistry,"['D000079', 'D000818', 'D020355', 'D002849', 'D005519', 'D006801', 'D008027', 'D008892', 'D010084', 'D013652', 'D014867']","['Acetaldehyde', 'Animals', 'Cholates', 'Chromatography, Gas', 'Food Preservation', 'Humans', 'Light', 'Milk', 'Oxidation-Reduction', 'Taste Threshold', 'Water']","Flavor threshold for acetaldehyde in milk, chocolate milk, and spring water using solid phase microextraction gas chromatography for quantification.","['Q000032', None, 'Q000737', 'Q000379', None, None, None, 'Q000737', None, None, 'Q000032']","['analysis', None, 'chemistry', 'methods', None, None, None, 'chemistry', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11312867,2001,0,0,,no cocoa -0.81,11871386,"This work relates the development of an analytical methodology to simultaneously determine three methylxanthines (caffeine, theobromine, and theophylline) in beverages and urine samples based on reversed-phase high-performance liquid chromatography. Separation is made with a Bondesil C18 column using methanol-water-acetic acid or ethanol-water-acetic acid (20:75:5, v/v/v) as the mobile phase at 0.7 mL/min. Identification is made by absorbance detection at 273 nm. Under optimized conditions, the detection limit of the HPLC method is 0.1 pg/mL for all three methylxanthines. This method is applied to urine and to 25 different beverage samples, which included coffee, tea, chocolate, and coconut water. The concentration ranges determined in the beverages and urine are: < 0.1 pg/mL to 350 microg/mL and 3.21 microg/mL to 71.2 microg/mL for caffeine; < 0.1 pg/mL to 32 microg mL and < 0.1 pg/mL to 13.2 microg/mL for theobromine; < 0.1 pg/mL to 47 microg/mL and < 0.1 pg/mL to 66.3 microg/mL for theophylline. The method proposed in this study is rapid and suitable for the simultaneous quantitation of methylxanthines in beverages and human urine samples and requires no extraction step or derivatization.",Journal of chromatographic science,"['D001628', 'D002110', 'D002138', 'D002851', 'D012680', 'D013056', 'D013805', 'D013806']","['Beverages', 'Caffeine', 'Calibration', 'Chromatography, High Pressure Liquid', 'Sensitivity and Specificity', 'Spectrophotometry, Ultraviolet', 'Theobromine', 'Theophylline']","Simultaneous determination of caffeine, theobromine, and theophylline by high-performance liquid chromatography.","['Q000032', 'Q000032', None, 'Q000379', None, None, 'Q000032', 'Q000032']","['analysis', 'analysis', None, 'methods', None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11871386,2002,,,, -0.81,19754154,"Cocoa-phytochemicals have been related to the health-benefits of cocoa consumption. Metabolomics has been proposed as a powerful tool to characterize both the intake and the effects on the metabolism of dietary components. Human urine metabolome modifications after single cocoa intake were explored in a randomized, crossed, and controlled trial. After overnight fasting, 10 subjects consumed randomly either a single dose of cocoa powder with milk or water, or milk without cocoa. Urine samples were collected before the ingestion and at 0-6, 6-12, and 12-24-h after test-meals consumption. Samples were analyzed by HPLC-q-ToF, followed by multivariate data analysis. Results revealed an important effect on urinary metabolome during the 24 h after cocoa powder intake. These changes were not influenced by matrix as no global differences were found between cocoa powder consumption with milk or with water. Overall, 27 metabolites related to cocoa-phytochemicals, including alkaloid derivatives, polyphenol metabolites (both host and microbial metabolites) and processing-derived products such as diketopiperazines, were identified as the main contributors to the urinary modifications after cocoa powder intake. These results confirm that metabolomics will contribute to better characterization of the urinary metabolome in order to further explore the metabolism of phytochemicals and its relation with human health.",Journal of proteome research,"['D000293', 'D000328', 'D000818', 'D015415', 'D002099', 'D002853', 'D004032', 'D005260', 'D005419', 'D006801', 'D008297', 'D013058', 'D055432', 'D008875', 'D008892', 'D010636', 'D014556', 'D055815']","['Adolescent', 'Adult', 'Animals', 'Biomarkers', 'Cacao', 'Chromatography, Liquid', 'Diet', 'Female', 'Flavonoids', 'Humans', 'Male', 'Mass Spectrometry', 'Metabolomics', 'Middle Aged', 'Milk', 'Phenols', 'Urine', 'Young Adult']",An LC-MS-based metabolomics approach for exploring urinary metabolome modifications after cocoa consumption.,"[None, None, None, 'Q000378', 'Q000737', 'Q000379', None, None, 'Q000737', None, None, 'Q000379', 'Q000379', None, 'Q000737', 'Q000737', 'Q000737', None]","[None, None, None, 'metabolism', 'chemistry', 'methods', None, None, 'chemistry', None, None, 'methods', 'methods', None, 'chemistry', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/19754154,2010,1,1,text,under the experimental section: cocoa powder composition -0.81,24913883,"In this work, a new procedure was developed for the separation and preconcentration of lead(II) and cobalt(II) in several water and foods samples. Complexes of metal ions with 8-hydroxyquinolein (8-HQ) were formed in aqueous solution. The proposed methodology is based on the preconcentration/separation of Pb(II) by solid-phase extraction using paper filter, followed by spectrofluorimetric determination of both metals, on the solid support and the filtered aqueous solution, respectively. The solid surface fluorescence determination was carried out at __em=455 nm (__ex=385 nm) for Pb(II)-8-HQ complex and the fluorescence of Co(II)-8-HQ was determined in aqueous solution using __em=355 nm (__ex=225 nm). The calibration graphs are linear in the range 0.14-8.03_10(4) __g L(-1) and 7.3_10(-2)-4.12_10(3) __g L(-1), for Pb(II) and Co(II), respectively, with a detection limit of 4.3_10(-2) and 2.19_10(-2) __g L(-1) (S/N=3). The developed methodology showed good sensitivity and adequate selectivity and it was successfully applied to the determination of trace amounts of lead and cobalt in tap waters belonging of different regions of Argentina and foods samples (milk powder, express coffee, cocoa powder) with satisfactory results. The new methodology was validated by electrothermal atomic absorption spectroscopy with adequate agreement. The proposed methodology represents a novel application of fluorescence to Pb(II) and Co(II) quantification with sensitivity and accuracy similar to atomic spectroscopies.",Talanta,"['D000818', 'D001118', 'D002099', 'D003035', 'D003069', 'D060766', 'D004784', 'D005453', 'D005506', 'D007854', 'D008892', 'D015125', 'D013054', 'D014874']","['Animals', 'Argentina', 'Cacao', 'Cobalt', 'Coffee', 'Drinking Water', 'Environmental Monitoring', 'Fluorescence', 'Food Contamination', 'Lead', 'Milk', 'Oxyquinoline', 'Spectrophotometry, Atomic', 'Water Pollutants, Chemical']",Sequential determination of lead and cobalt in tap water and foods samples by fluorescence.,"[None, None, 'Q000737', 'Q000032', 'Q000737', 'Q000032', None, None, 'Q000032', 'Q000032', 'Q000737', 'Q000737', None, 'Q000032']","[None, None, 'chemistry', 'analysis', 'chemistry', 'analysis', None, None, 'analysis', 'analysis', 'chemistry', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24913883,2015,1,1,table 3 and 4,Only number 8 which is the cocoa powder -0.81,12537373,"An in vitro model simulating enzymatic activity in the gastrointestinal tract was developed for the assessment of the potential bioaccessibility of Cd and Pb in cocoa powder and liquor. The model was based on the sequential extraction with simulated gastric and intestinal juices; the residue after the latter extraction was further investigated by using, in parallel, solutions of phytase and cellulase. The solubility of Cd and Pb in the corresponding enzymatic extracts was measured by ICP MS. The bioaccessibility of Cd in cocoa varied from 10 to 50% in gastrointestinal conditions. An additional 20 or 30% of Cd could be recovered by phytase and cellulase, respectively. The bioaccessibility of Pb in gastrointestinal conditions did not exceed 5-10%. Only a few percent more of this metal could be recovered by extraction with phytase and cellulase.",The Analyst,"['D001682', 'D002099', 'D002104', 'D004063', 'D005506', 'D006801', 'D007854', 'D013058']","['Biological Availability', 'Cacao', 'Cadmium', 'Digestion', 'Food Contamination', 'Humans', 'Lead', 'Mass Spectrometry']",Development of a sequential enzymolysis approach for the evaluation of the bioaccessibility of Cd and Pb from cocoa.,"[None, 'Q000737', 'Q000032', None, 'Q000032', None, 'Q000032', 'Q000379']","[None, 'chemistry', 'analysis', None, 'analysis', None, 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/12537373,2003,,,, -0.8,27040819,"Ochratoxin A (OTA) is a mycotoxin produced mostly by several species of Aspergillus and Penicillium. OTA is nephrotoxic in all animal species in which it has been tested and is cancerogenic in rodents. It is associated with Balkan endemic nephropathy. It is naturally present in many crop products such as cereals (barley, wheat, maize) and dried fruits, spices, coffee, wine, olives, and cocoa. The aim of this study was to assess the contamination of three Ivoirian spices with OTA (ginger, chili, and pepper) widely consumed by the population. A total of 90 spice samples (ginger: n___=___30; chili: n___=___30; pepper n___=___30) was taken from various sales outlets of Abidjan. OTA was quantified using an HPLC apparatus coupled with a fluorimetric detector. The chili and ginger samples were contaminated with OTA at a mean concentration of 57.48____±___174 and 0.12____±___0.15____g/kg, respectively. No contamination of the pepper samples was detected. Eight (26.67__%) of the chili samples exceeded the maximum limit of 15____g/kg established by European regulation. These results should serve as an alert on the risk to the consumer population of these products that are highly contaminated with OTA. ",Mycotoxin research,"['D002212', 'D002851', 'D007560', 'D005470', 'D020939', 'D009793', 'D029222']","['Capsicum', 'Chromatography, High Pressure Liquid', ""Cote d'Ivoire"", 'Fluorometry', 'Ginger', 'Ochratoxins', 'Piper nigrum']",Occurrence of ochratoxin A in spices commercialized in Abidjan (C_te d'Ivoire).,"['Q000737', None, None, None, 'Q000737', 'Q000032', 'Q000737']","['chemistry', None, None, None, 'chemistry', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27040819,2017,0,0,, -0.8,16608201,"Since recent reports on the role of N-phenylpropenoyl-L-amino acids as powerful antioxidants and key contributors to the astringent taste of cocoa nibs, there is an increasing interest in the concentrations of these phytochemicals in plant-derived foods. A versatile analytical method for the accurate quantitative analysis of N-phenylpropenoyl-L-amino acids in plant-derived foods by means of HPLC-MS/MS and synthetic stable isotope labeled N-phenylpropenoyl-L-amino acids as internal standards was developed. By means of the developed stable isotope dilution assay (SIDA), showing recovery rates of 95-102%, 14 N-phenylpropenoyl-L-amino acids were quantified for the first time in cocoa and coffee samples. On the basis of the results of LC-MS/MS experiments as well as cochromatography with the synthetic reference compounds N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-tryptophan, N-[4'-hydroxy-(E)-cinnamoyl]-L-tryptophan, and N-[4'-hydroxy-3'-methoxy-(E)-cinnamoyl]-L-tyrosine, respectively, were detected for the first time in cocoa powder, and (-)-N-[4'-hydroxy-(E)-cinnamoyl]-L-tyrosine, (-)-N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-tyrosine, N-[4'-hydroxy-3'-methoxy-(E)-cinnamoyl]-L-tyrosine, (+)-N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-aspartic acid, (+)-N-[4'-hydroxy-(E)-cinnamoyl]-L-aspartic acid, N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-tryptophan, N-[4'-hydroxy-(E)-cinnamoyl]-L-tryptophan, and N-[4'-hydroxy-3'-methoxy-(E)-cinnamoyl]-L-tryptophan, respectively, were detected for the first time in coffee beverages.",Journal of agricultural and food chemistry,"['D000596', 'D002099', 'D040503', 'D003903', 'D006358', 'D007201', 'D008279', 'D010666', 'D012639', 'D021241']","['Amino Acids', 'Cacao', 'Coffea', 'Deuterium', 'Hot Temperature', 'Indicator Dilution Techniques', 'Magnetic Resonance Imaging', 'Phenylpropionates', 'Seeds', 'Spectrometry, Mass, Electrospray Ionization']",Quantitative analysis of N-phenylpropenoyl-L-amino acids in roasted coffee and cocoa powder by means of a stable isotope dilution assay.,"['Q000737', 'Q000737', 'Q000737', None, None, None, None, 'Q000737', 'Q000737', None]","['chemistry', 'chemistry', 'chemistry', None, None, None, None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/16608201,2006,1,2,table 2,only the cocoa nibs -0.8,24779628,"Aflatoxins (AFB1, AFB2, AFG1 and AFG2) are immunosuppressant, mutagenic, teratogenic and carcinogenic agents with a widespread presence in foodstuffs. Since human exposure to aflatoxins occurs primarily by contaminated food intake, and given the greater susceptibility of infants to their adverse effects, the quantification of these mycotoxins in infant food based on cereals is of relevance. Aflatoxin levels were determined in 91 Spanish infant cereals classified in terms of non- and organically produced and several types from 10 different manufacturers, using a extraction procedure followed by inmunoaffinity column clean-up step and HPLC with fluorescence detection (FLD) and post-column derivatisation (Kobra Cell system). Daily aflatoxin intake was also assessed. Preliminary analysis showed a valuable incidence of detected infant cereal samples at an upper concentration level than the detection limit for total aflatoxin (66%), corresponding to a 46, 40, 34 and 11% for AFB1, AFB2, AFG1 and AFG2, respectively. Lower aflatoxin values (median, Q1, Q3) in conventional infant cereal (n = 74, AFB1: 0.99], recovery [71-118%], precision [(RSDr and RSDiR)<33%], and trueness [78-117%] were all compliant with the analytical requirements stipulated in the CEN/TR/16059 document. Method ruggedness was proved by a verification process conducted by another laboratory. ",Journal of chromatography. A,"['D002099', 'D002247', 'D002851', 'D003069', 'D006801', 'D007201', 'D007223', 'D007225', 'D007231', 'D059021', 'D009183', 'D011786', 'D012680', 'D017365', 'D053719']","['Cacao', 'Carbon Isotopes', 'Chromatography, High Pressure Liquid', 'Coffee', 'Humans', 'Indicator Dilution Techniques', 'Infant', 'Infant Food', 'Infant, Newborn', 'Laboratory Proficiency Testing', 'Mycotoxins', 'Quality Control', 'Sensitivity and Specificity', 'Spices', 'Tandem Mass Spectrometry']","Combining the quick, easy, cheap, effective, rugged and safe approach and clean-up by immunoaffinity column for the analysis of 15 mycotoxins by isotope dilution liquid chromatography tandem mass spectrometry.","['Q000737', None, 'Q000379', 'Q000737', None, None, None, None, None, None, 'Q000032', None, None, 'Q000032', 'Q000379']","['chemistry', None, 'methods', 'chemistry', None, None, None, None, None, None, 'analysis', None, None, 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/24636559,2014,0,0,, -0.74,19426987,"The quantitative parameters and method performance for a normal-phase HPLC separation of flavanols and procyanidins in chocolate and cocoa-containing food products were optimized and assessed. Single laboratory method performance was examined over three months using three separate secondary standards. RSD(r) ranged from 1.9%, 4.5% to 9.0% for cocoa powder, liquor and chocolate samples containing 74.39, 15.47 and 1.87 mg/g flavanols and procyanidins, respectively. Accuracy was determined by comparison to the NIST Standard Reference Material 2384. Inter-lab assessment indicated that variability was quite low for seven different cocoa-containing samples, with a RSD(R) of less than 10% for the range of samples analyzed.",Journal of chromatography. A,"['D000975', 'D002099', 'D002182', 'D002851', 'D044948', 'D005453', 'D005504', 'D044945']","['Antioxidants', 'Cacao', 'Candy', 'Chromatography, High Pressure Liquid', 'Flavonols', 'Fluorescence', 'Food Analysis', 'Proanthocyanidins']",Method performance and multi-laboratory assessment of a normal phase high pressure liquid chromatography-fluorescence detection method for the quantitation of flavanols and procyanidins in cocoa and chocolate containing samples.,"['Q000032', 'Q000737', 'Q000032', 'Q000295', 'Q000032', None, None, 'Q000032']","['analysis', 'chemistry', 'analysis', 'instrumentation', 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19426987,2009,0,0,,content was given in multiple groups -0.73,23692768,"In this research 12 different varieties of Capsicum cultivars belonging to three species (Capsicum chinense, Capsicum annuum, Capsicum frutescens) and of various colour, shape, and dimension have been characterised by their carotenoids and capsaicinoids content. The berries were cultivated in the region Emilia-Romagna, in Northern Italy. The native carotenoid composition was directly investigated by an HPLC-DAD-APCI-MS methodology, for the first time. In total, 52 carotenoids have been identified and considerable variation in carotenoid composition was observed among the various cultivars investigated. Among the cultivars with red colour, some Habanero, Naga morich and Sinpezon showed an high __-carotene content, whereas Serrano, Tabasco and Jalapeno showed an high capsanthin content and the absence of __-carotene. Habanero golden and Scotch Bonnet showed a high lutein, _±-carotene and __-carotene amounts, and Habanero orange was rich in antheraxanthin, capsanthin and zeaxanthin. Cis-cryptocapsin was present in high amount in Habanero chocolate. The qualitative and quantitative determination of the capsaicinoids, alkaloids responsible for the pungency level, has also been estimated by a validated chromatographic procedure (HPLC-DAD) after a preliminary drying step and an opportune extraction procedure. Results have also been expressed in Scoville units. Dry matter and water activity have also been established on the fresh berries. The dried peppers of each variety were then submitted to the evaluation of the total nitrogen content, measured by a Dumas system, permitting to provide information on the protein content that was found to be in the range between 7 and 16%.",Food chemistry,"['D002212', 'D002338', 'D002851', 'D005638', 'D013058', 'D015394', 'D009812', 'D010936']","['Capsicum', 'Carotenoids', 'Chromatography, High Pressure Liquid', 'Fruit', 'Mass Spectrometry', 'Molecular Structure', 'Odorants', 'Plant Extracts']",Characterization of 12 Capsicum varieties by evaluation of their carotenoid profile and pungency determination.,"['Q000737', 'Q000737', None, 'Q000737', None, None, 'Q000032', 'Q000737']","['chemistry', 'chemistry', None, 'chemistry', None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23692768,2013,0,0,,no cocoa -0.73,22953909,"N-Phenylpropenoyl-l-amino acids (NPA) are among the key contributors to the astringent taste of cocoa. Two fast and easy to use methods (CE and UPLC_‰, both with PDA detection) for routine determination of the main NPA were developed. Crude extracts of defatted seeds were analysed by means of capillary electrophoresis leading to separation in less than 30min. Separation by means of UPLC_‰ was much faster (<4min), however, a preceding SPE clean-up abolishes this benefit in time saving. Thus, the CE- and UPLC_‰-methods are comparable concerning time consumption and provide similar results. Analysis of 18 samples of raw and roasted beans from the global cocoa market originated from 12 countries and 4 continents showed a great variability of NPA content (0.7-3.6mg/g) and qualitative composition of different NPA. Anyway, all samples from cocoa beans showed a comparable NPA pattern. N-[3',4'-dihydroxy-(E)-cinnamoyl]-l-aspartic acid was the most abundant metabolite, followed by N-[4'-hydroxy-(E)-cinnamoyl]-l-aspartic acid and N-[3',4'-dihydroxy-(E)-cinnamoyl]-3-hydroxy-l-tyrosine (clovamide). The analysis of other plant organs (flowers, leaves, fruits) revealed an entirely different situation. NPA were detected in all parts of the fruit, with husk and pulp being clearly dominated by clovamide. In flowers and leaves no NPA were detected; 2-O-caffeoyltartaric acid was shown to be the major caffeic acid metabolite in leaves.",Food chemistry,"['D000596', 'D002099', 'D002851', 'D019075', 'D005843', 'D006801', 'D012639', 'D013649']","['Amino Acids', 'Cacao', 'Chromatography, High Pressure Liquid', 'Electrophoresis, Capillary', 'Geography', 'Humans', 'Seeds', 'Taste']",Fast determination of N-phenylpropenoyl-l-amino acids (NPA) in cocoa samples from different origins by ultra-performance liquid chromatography and capillary electrophoresis.,"['Q000032', 'Q000737', 'Q000379', 'Q000379', None, None, 'Q000737', None]","['analysis', 'chemistry', 'methods', 'methods', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/22953909,2013,1,1,table 1 and Fig. 3,Recorded all the samples that had the NPA detected and quantified -0.73,1141149,"Rapid confirmation of the presence of aflatoxins B-1 and G-1 in foods is provided by reaction with trifluoroacetic acid at the origin of a thin layer chromatographic plate. The procedure has been used successfully with various nuts, grains, coffee and cocoa beans, and other foods.",Journal - Association of Official Analytical Chemists,"['D000348', 'D002855', 'D005504']","['Aflatoxins', 'Chromatography, Thin Layer', 'Food Analysis']",Formation of aflatoxin derivatives on thin layer chromatographic plates.,"['Q000032', 'Q000379', None]","['analysis', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/1141149,1975,,,, -0.73,18781757,"N-Acetylglutamate (NAG) and N-acetylaspartate (NAA) are amino acid derivatives with reported activities in a number of biological processes. However, there is no published information on the presence of either substance in foodstuffs. We developed a method for extracting and quantifying NAG and NAA from soybean seeds and maize grain using ultra performance liquid chromatography-electrospray ionization tandem mass spectrometry (UPLC-ESI-MS/MS). The lower limit of quantification for both NAG and NAA was 1 ng/mL. The method was then utilized to quantify NAG and NAA in other foodstuffs (fruits, vegetables, meats, grains, milk, coffee, tea, cocoa, and others). Both NAG and NAA were present in all of the materials analyzed. The highest concentration of NAG was found in cocoa powder. The highest concentration of NAA was found in roasted coffee beans. Both NAG and NAA were found at quantifiable concentrations in all foods tested indicating that these two acetylated amino acids are common components of the human diet.",Journal of agricultural and food chemistry,"['D001224', 'D002099', 'D002851', 'D040503', 'D005504', 'D005971', 'D012639', 'D013025', 'D021241', 'D053719', 'D003313']","['Aspartic Acid', 'Cacao', 'Chromatography, High Pressure Liquid', 'Coffea', 'Food Analysis', 'Glutamates', 'Seeds', 'Soybeans', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry', 'Zea mays']","N-acetylglutamate and N-acetylaspartate in soybeans (Glycine max L.), maize (Zea mays L.), [corrected] and other foodstuffs.","['Q000031', 'Q000737', None, 'Q000737', None, 'Q000032', 'Q000737', 'Q000737', None, None, 'Q000737']","['analogs & derivatives', 'chemistry', None, 'chemistry', None, 'analysis', 'chemistry', 'chemistry', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/18781757,2008,1,1,table 1,only cocoa powder -0.73,21819158,"Skins from different hazelnut samples were characterized for total polyphenol content, total antioxidant capacity (TAC), and their content in specific polyphenolic compounds. The main polyphenolic subclass, identified and quantified by means of HPLC-MS/MS, comprised monomeric and oligomeric flavan-3-ols, which accounted for more than 95% of total polyphenols. Flavonols and dihydrochalcones were 3.5% while phenolic acids were less than 1% of the total identified phenolics. The TAC values of the skin samples ranged between 0.6 and 2.2 mol of reduced iron/kg of sample, which is about 3 times the TAC of whole walnuts, 7-8 times that of dark chocolate, 10 times that of espresso coffee, and 25 times that of blackberries. By describing the profile of polyphenols present in hazelnut skins, this study provides the basis to further investigate the potential health effects of hazelnut byproduct.",Journal of agricultural and food chemistry,"['D000975', 'D002851', 'D031211', 'D005419', 'D059808', 'D012639', 'D053719']","['Antioxidants', 'Chromatography, High Pressure Liquid', 'Corylus', 'Flavonoids', 'Polyphenols', 'Seeds', 'Tandem Mass Spectrometry']",Polyphenolic composition of hazelnut skin.,"['Q000032', None, 'Q000737', 'Q000032', 'Q000032', 'Q000737', None]","['analysis', None, 'chemistry', 'analysis', 'analysis', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21819158,2012,0,0,,no cocoa -0.72,7430044,"Four duplicate samples of cocoa-containing materials, a practice sample, and standards were submitted to the collaborators for theobromine and caffeine analysis by HPLC. In the method the samples are defatted with petroleum ether, and dried. The fat-free residue is then extracted with water and an aliquot is injected into the chromatograph. Compounds are quantitated by comparison with internal or external standards, either by peak height or peak area. Results for all the analyses showed that few of the values were more than 2 standard deviations from the mean. The method has been adopted as official first action.",Journal - Association of Official Analytical Chemists,"['D002099', 'D002110', 'D002851', 'D011786', 'D013805']","['Cacao', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Quality Control', 'Theobromine']",High pressure liquid chromatographic determination of theobromine and caffeine in cocoa and chocolate products: collaborative study.,"['Q000032', 'Q000032', None, None, 'Q000032']","['analysis', 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/7430044,1981,,,, -0.72,27795344,"Candida sepsis is a life-threatening condition with increasing prevalence. In this study, direct blood culturing on solid medium using a lysis-centrifugation procedure enabled successful Candida species identification by matrix-assisted laser desorption-ionization time of flight mass spectrometry on average 3.8 h (Sabouraud agar) or 7.4 h (chocolate agar) before the positivity signal for control samples in Bactec mycosis-IC/F or Bactec Plus aerobic/F bottles, respectively. Direct culturing on solid medium accelerated candidemia diagnostics compared to that with automated broth-based systems.",Journal of clinical microbiology,"['D000071997', 'D002175', 'D058387', 'D002498', 'D003470', 'D006801', 'D019032', 'D013997']","['Blood Culture', 'Candida', 'Candidemia', 'Centrifugation', 'Culture Media', 'Humans', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Time Factors']",Rapid Detection and Identification of Candidemia by Direct Blood Culturing on Solid Medium by Use of Lysis-Centrifugation Method Combined with Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS).,"['Q000379', 'Q000737', 'Q000175', 'Q000379', 'Q000737', None, 'Q000379', None]","['methods', 'chemistry', 'diagnosis', 'methods', 'chemistry', None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/27795344,2017,0,0,,no cocoa -0.72,23041474,"This study determined exposure of pregnant women to ochratoxin A (OTA). Forty samples of first-void urine samples from Croatian women in the third trimester of pregnancy were analyzed for OTA and its major metabolite ochratoxin alpha (OT_±). The subjects filled a short food frequency questionnaire (FFQ). Analysis was performed by HPLC-FLD following liquid-liquid extraction. All samples were subjected in parallel to enzymatic treatment (__-glucuronidase/aryl sulfatase) to release OTA and OT_± from the conjugates. The median urinary levels of OTA and OT_± before treatment were 0.02 (range: nd-1.07) ng/mL and 0.16 (nd-1.86) ng/mL; the concentrations after enzyme hydrolysis were 0.02 (nd-1.11) ng/mL and 1.18 (0.11-7.57) ng/mL. While OT_± levels increased significantly following enzymatic treatment, evidence for OTA conjugation was weak. The ratio of urinary OT_± medians after and before hydrolysis was 1.5 times higher than previously reported for nonpregnant female subjects, possibly indicating upregulated metabolism and/or elimination of the mycotoxin and metabolites in pregnancy. The mean daily dietary OTA intake calculated from FFQs (1.08_±0.57 ng/kg body weight) was well below the provisional tolerable daily intake and the greatest contributors to intake were cereal products, fruit juices, chocolate and coffee.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000328', 'D000818', 'D001628', 'D002099', 'D002851', 'D003069', 'D003404', 'D004032', 'D002523', 'D005260', 'D005506', 'D005511', 'D005516', 'D005638', 'D006801', 'D008461', 'D009793', 'D011247', 'D011795', 'D013552', 'D027843', 'D014920']","['Adult', 'Animals', 'Beverages', 'Cacao', 'Chromatography, High Pressure Liquid', 'Coffee', 'Creatinine', 'Diet', 'Edible Grain', 'Female', 'Food Contamination', 'Food Handling', 'Food Microbiology', 'Fruit', 'Humans', 'Meat Products', 'Ochratoxins', 'Pregnancy', 'Surveys and Questionnaires', 'Swine', 'Vitis', 'Wine']",Urinary ochratoxin A and ochratoxin alpha in pregnant women.,"[None, None, 'Q000032', 'Q000737', None, 'Q000737', 'Q000652', None, 'Q000737', None, 'Q000032', None, None, 'Q000737', None, None, 'Q000652', None, None, None, 'Q000737', 'Q000032']","[None, None, 'analysis', 'chemistry', None, 'chemistry', 'urine', None, 'chemistry', None, 'analysis', None, None, 'chemistry', None, None, 'urine', None, None, None, 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/23041474,2013,0,0,,intake content -0.71,1830779,"In order to detect the presence of aflatoxin B1 (AFB1), the use of the enzyme-linked immunosorbent assay (ELISA) and recovery test was evaluated. The detection limit of ELISA for AFB1 was 1 pg/assay and the recovery from maize spiked with AFB1 exceeded 80%. AFB1 was detected by ELISA in seven out of twelve samples of imported food products including peanut, almond, red pepper, cocoa bean, black pepper, buckwheat, walnut, adlay, soybean, popcorn, and pistachio nut, and by high performance liquid chromatography (HPLC) in four of the samples. However, the content of AFB1 in these samples was less than 10 ng/g of the minimum value authorized by the Japanese sanitation law. These results demonstrate that ELISA is more sensitive than HPLC and imported food products are broadly contaminated with AFB1.",The Journal of veterinary medical science,"['D016604', 'D000348', 'D002273', 'D002851', 'D004797', 'D005506', 'D011237']","['Aflatoxin B1', 'Aflatoxins', 'Carcinogens', 'Chromatography, High Pressure Liquid', 'Enzyme-Linked Immunosorbent Assay', 'Food Contamination', 'Predictive Value of Tests']",Detection of aflatoxin B1 in imported food products into Japan by enzyme-linked immunosorbent assay and high performance liquid chromatography.,"[None, 'Q000032', 'Q000032', None, None, 'Q000032', None]","[None, 'analysis', 'analysis', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/1830779,1991,,,, -0.71,27176001,"Vanillin (VA), vanillic acid (VAI) and syringaldehyde (SIA) are important food additives as flavor enhancers. The current study for the first time is devote to the application of partial least square (PLS-1), partial robust M-regression (PRM) and feed forward neural networks (FFNNs) as linear and nonlinear chemometric methods for the simultaneous detection of binary and ternary mixtures of VA, VAI and SIA using data extracted directly from UV-spectra with overlapped peaks of individual analytes. Under the optimum experimental conditions, for each compound a linear calibration was obtained in the concentration range of 0.61-20.99 [LOD=0.12], 0.67-23.19 [LOD=0.13] and 0.73-25.12 [LOD=0.15] __gmL(-1) for VA, VAI and SIA, respectively. Four calibration sets of standard samples were designed by combination of a full and fractional factorial designs with the use of the seven and three levels for each factor for binary and ternary mixtures, respectively. The results of this study reveal that both the methods of PLS-1 and PRM are similar in terms of predict ability each binary mixtures. The resolution of ternary mixture has been accomplished by FFNNs. Multivariate curve resolution-alternating least squares (MCR-ALS) was applied for the description of spectra from the acid-base titration systems each individual compound, i.e. the resolution of the complex overlapping spectra as well as to interpret the extracted spectral and concentration profiles of any pure chemical species identified. Evolving factor analysis (EFA) and singular value decomposition (SVD) were used to distinguish the number of chemical species. Subsequently, their corresponding dissociation constants were derived. Finally, FFNNs has been used to detection active compounds in real and spiked water samples.","Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy","['D001547', 'D002138', 'D000069956', 'D005421', 'D005504', 'D016018', 'D015999', 'D016571', 'D010636', 'D013053', 'D014641', 'D014867']","['Benzaldehydes', 'Calibration', 'Chocolate', 'Flavoring Agents', 'Food Analysis', 'Least-Squares Analysis', 'Multivariate Analysis', 'Neural Networks (Computer)', 'Phenols', 'Spectrophotometry', 'Vanillic Acid', 'Water']",Investigating the discrimination potential of linear and nonlinear spectral multivariate calibrations for analysis of phenolic compounds in their binary and ternary mixtures and calculation pKa values.,"['Q000032', None, 'Q000032', 'Q000032', 'Q000379', None, None, None, 'Q000032', 'Q000379', 'Q000032', 'Q000032']","['analysis', None, 'analysis', 'analysis', 'methods', None, None, None, 'analysis', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27176001,2017,0,0,,no cocoa -0.71,28415017,"An accelerated solvent extraction (ASE) procedure for use with gas chromatography-mass spectrometry (GC-MS) was optimized for the determination of eight polycyclic aromatic hydrocarbons (PAHs) in cocoa beans. Plackett-Burman and rotatable central composite design (RCCD) indicated that three variables affected the recoveries of PAHs during the extraction and purification steps: agitation time in the second liquid-liquid partition, weight of silica gel in the column, and volume of hexane for PAH elution from the column. After obtaining the optimal conditions, a single laboratory method validation was performed. Linearity was demonstrated for benzo[a]pyrene in the concentration range from 0.5 to 8.0mgkg","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D002099', 'D004785', 'D005506', 'D008401', 'D057230', 'D011084', 'D012639']","['Cacao', 'Environmental Pollutants', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Limit of Detection', 'Polycyclic Aromatic Hydrocarbons', 'Seeds']",Accelerated solvent extraction method for the quantification of polycyclic aromatic hydrocarbons in cocoa beans by gas chromatography-mass spectrometry.,"['Q000737', 'Q000032', 'Q000032', 'Q000379', None, 'Q000032', 'Q000737']","['chemistry', 'analysis', 'analysis', 'methods', None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28415017,2017,0,0,,all useful data was from spiked samples -0.71,27484307,"The isotopic profile (__(13) C, __(15) N, __(18) O, __(2) H, __(34) S) was used to characterise a wide selection of cocoa beans from different renowned production areas (Africa, Asia, Central and South America). The factors most influencing the isotopic signatures of cocoa beans were climate and altitude for __(13) C and the isotopic composition of precipitation water for __(18) O and __(2) H, whereas __(15) N and __(34) S were primarily affected by geology and fertilisation practises. Multi-isotopic analysis was shown to be sufficiently effective in determining the geographical origin of cocoa beans, and combining it with Canonical Discriminant Analysis led to more than 80% of samples being correctly reclassified. Copyright _© 2016 John Wiley & Sons, Ltd. ",Journal of mass spectrometry : JMS,"['D002099', 'D002247', 'D002980', 'D005843', 'D013058', 'D010103', 'D012639']","['Cacao', 'Carbon Isotopes', 'Climate', 'Geography', 'Mass Spectrometry', 'Oxygen Isotopes', 'Seeds']",Stable isotope composition of cocoa beans of different geographical origin.,"['Q000737', 'Q000032', None, None, None, 'Q000032', 'Q000737']","['chemistry', 'analysis', None, None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27484307,2018,,,, -0.7,22970585,"An international collaborative study was conducted on an HPLC method with fluorescent detection (FLD) for the determination of flavanols and procyanidins in materials containing chocolate and cocoa. The sum of the oligomeric fractions with degree of polymerization 1-10 was the determined content value. Sample materials included dark and milk chocolates, cocoa powder, cocoa liquors, and cocoa extracts. The content ranged from approximately 2 to 500 mg/g (defatted basis). Thirteen laboratories representing commercial, industrial, and academic institutions in six countries participated in the study. Fourteen samples were sent as blind duplicates to the collaborators. Results from 12 laboratories yielded repeatability relative standard deviation (RSDr) values that were below 10% for all materials analyzed, ranging from 4.17 to 9.61%. The reproducibility relative standard deviation (RSD(R)) values ranged from 5.03 to 12.9% for samples containing 8.07 to 484.7 mg/g. In one sample containing a low content of flavanols and procyanidins (approximately 2 mg/g), the RSD(R) was 17.68%. Based on these results, the method is recommended for Official First Action for the determination of flavanols and procyanidins in chocolate, cocoa liquors, powder(s), and cocoa extracts.",Journal of AOAC International,"['D044946', 'D002099', 'D002392', 'D002623', 'D002851', 'D044950', 'D005504', 'D007391', 'D007753', 'D008055', 'D008956', 'D058105', 'D011208', 'D044945', 'D012015', 'D015203']","['Biflavonoids', 'Cacao', 'Catechin', 'Chemistry Techniques, Analytical', 'Chromatography, High Pressure Liquid', 'Flavanones', 'Food Analysis', 'International Cooperation', 'Laboratories', 'Lipids', 'Models, Chemical', 'Polymerization', 'Powders', 'Proanthocyanidins', 'Reference Standards', 'Reproducibility of Results']","Determination of flavanol and procyanidin (by degree of polymerization 1-10) content of chocolate, cocoa liquors, powder(s), and cocoa flavanol extracts by normal phase high-performance liquid chromatography: collaborative study.","['Q000032', 'Q000378', 'Q000032', 'Q000379', 'Q000379', 'Q000032', 'Q000379', None, None, 'Q000032', None, None, 'Q000032', 'Q000032', None, None]","['analysis', 'metabolism', 'analysis', 'methods', 'methods', 'analysis', 'methods', None, None, 'analysis', None, None, 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22970585,2012,,,, -0.7,26768597,"This study investigated the effects of storage and temperature duration on the stability of acrylamide (AA) and 5-hydroxymethylfurfural (HMF) in selected foods with long shelf-life. Products were analysed fresh and stored at temperatures of 4 and 25 _C after 6 and 12 months (with the exception of soft bread samples, which were analysed after 15 and 30 days). The AA and HMF contents were determined with RP-HPLC coupled to a diode array detector (DAD). AA and HMF were not stable in many processed plant products with a long shelf-life. The highest AA reduction and the largest increase in HMF content were observed in the samples stored at a higher temperature (25 _C) for 12 months. It was found that an initial water activity of 0.4 is favourable to HMF formation and that AA reduction may be considerably greater in stored products with a low initial water activity. The kind of product and its composition may also have a significant impact on acrylamide content in stored food. In the final period of storage at 25 _C, acrylamide content in 100% cocoa powder, instant baby foods, 20% cocoa powder and instant coffee was 51, 39, 35 and 33% lower than in products before storage, respectively. It was observed that a large quantity of _µ-NH2 and SH groups of amino acids in some products can be assumed as the reason for the significant AA degradation.","Plant foods for human nutrition (Dordrecht, Netherlands)","['D020106', 'D001939', 'D002099', 'D002851', 'D003069', 'D061353', 'D005662', 'D011208', 'D013696', 'D014867']","['Acrylamide', 'Bread', 'Cacao', 'Chromatography, High Pressure Liquid', 'Coffee', 'Food Storage', 'Furaldehyde', 'Powders', 'Temperature', 'Water']",Effect of Storage on Acrylamide and 5-hydroxymethylfurfural Contents in Selected Processed Plant Products with Long Shelf-life.,"['Q000032', 'Q000032', 'Q000737', None, 'Q000737', None, 'Q000031', 'Q000737', None, 'Q000032']","['analysis', 'analysis', 'chemistry', None, 'chemistry', None, 'analogs & derivatives', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26768597,2017,1,1,table 2,100% cocoa only -0.7,16517524,"Isotope dilution liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-MS/MS) was applied to the quantification of acrylamide in chocolate matrixes (dark chocolate, milk chocolate, chocolate with nuts, chocolate with almonds, and chocolate with wheat best element). The method included defatting with petroleum ether, extracting with aqueous solution of 2 mol l(-1) sodium chloride and clean-up by solid-phase (SPE) with OASIS HLB 6 cm3 cartridges. Acrylamide was detected with an Atlantis dC18 5 microm 210 x 1.5 mm column using 10% methanol/0.1% formic acid in water as the mobile phase. The analytical method was in-house validated and good results were obtained with respect to repeatability (RSD < 3.5%) and recovery (86-93%), which fulfilled the requirements defined by European Union legislation. The acrylamide levels in chocolate were 23-537 microg kg(-1). Therefore, the method was successfully used for the quantitative analysis of acrlyamide in various chocolate products.",Food additives and contaminants,"['D020106', 'D002099', 'D002138', 'D002182', 'D002853', 'D005506', 'D007201', 'D009754', 'D027861', 'D015203', 'D021241', 'D014908']","['Acrylamide', 'Cacao', 'Calibration', 'Candy', 'Chromatography, Liquid', 'Food Contamination', 'Indicator Dilution Techniques', 'Nuts', 'Prunus', 'Reproducibility of Results', 'Spectrometry, Mass, Electrospray Ionization', 'Triticum']",Sensitive isotope dilution liquid chromatography/electrospray ionization tandem mass spectrometry method for the determination of acrylamide in chocolate.,"['Q000032', 'Q000737', None, 'Q000032', 'Q000379', 'Q000032', None, 'Q000737', None, None, 'Q000379', None]","['analysis', 'chemistry', None, 'analysis', 'methods', 'analysis', None, 'chemistry', None, None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/16517524,2006,,,, -0.7,24786625,"Sorbic acid (SA) and benzoic acid (BA) were determined in yoghurt, tomato and pepper paste, fruit juices, chocolates, soups and chips in Turkey by using high-pressure liquid chromatography (HPLC). Levels were compared with Turkish Food Codex limits. SA was detected only in 2 of 21 yoghurt samples, contrary to BA, which was found in all yoghurt samples but one, ranging from 10.5 to 159.9___mg/kg. Both SA and BA were detected also in 3 and 6 of 23 paste samples in a range of 18.1-526.4 and 21.7-1933.5___mg/kg, respectively. Only 1 of 23 fruit juices contained BA. SA was not detected in any chips, fruit juice, soup, or chocolate sample. Although 16.51% of the samples was not compliant with the Turkish Food Codex limits, estimated daily intake of BA or SA was below the acceptable daily intake. ","Food additives & contaminants. Part B, Surveillance","['D019817', 'D001628', 'D002099', 'D002212', 'D002851', 'D005504', 'D005519', 'D005520', 'D005638', 'D007881', 'D018551', 'D008452', 'D013011', 'D014421', 'D015014']","['Benzoic Acid', 'Beverages', 'Cacao', 'Capsicum', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Food Preservation', 'Food Preservatives', 'Fruit', 'Legislation, Food', 'Lycopersicon esculentum', 'Maximum Allowable Concentration', 'Sorbic Acid', 'Turkey', 'Yogurt']",Sorbic and benzoic acid in non-preservative-added food products in Turkey.,"['Q000032', 'Q000032', 'Q000737', 'Q000737', None, None, 'Q000331', 'Q000032', None, None, 'Q000737', None, 'Q000032', None, 'Q000032']","['analysis', 'analysis', 'chemistry', 'chemistry', None, None, 'legislation & jurisprudence', 'analysis', None, None, 'chemistry', None, 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24786625,2014,0,0,,no cocoa -0.7,2808584,"An interface has been developed which permits the on-line coupling of size-exclusion chromatography in tetrahydrofuran with aqueous reversed-phase high-performance liquid chromatography. The interface isolates the required size exclusion chromatography fraction and dilutes it with water to ensure reconcentration of analytes on the reversed-phase column prior to gradient elution. Operational parameters and the influence of analyte polarity have been examined in detail. A predictive system is presented for determining the applicability of the system to any analyte, based on solute retention times on an ODS phase eluted with a methanol-water gradient. The method is illustrated with examples of direct analyses of crude lipid extracts from a snack product for 2,6-di-tert.-4-methylphenol and from chocolate for dibutyl phthalate. Detection limits of ca. 0.5 mg/kg have been achieved.",Journal of chromatography,"['D002850', 'D002851', 'D005503', 'D005506', 'D008970', 'D013056']","['Chromatography, Gel', 'Chromatography, High Pressure Liquid', 'Food Additives', 'Food Contamination', 'Molecular Weight', 'Spectrophotometry, Ultraviolet']",Non-aqueous size-exclusion chromatography coupled on-line to reversed-phase high-performance liquid chromatography. Interface development and applications to the analysis of low-molecular-weight contaminants and additives in foods.,"['Q000379', None, 'Q000032', 'Q000032', None, None]","['methods', None, 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/2808584,1989,,,, -0.7,24830163,"Single-laboratory validation data previously published in the Journal of AOAC INTERNATIONAL 95(2), 500-507 (2012) was reviewed by the Stakeholder Panel on Strategic Food Analytical Methods Expert Review Panel (ERP) at the AOAC INTERNATIONAL Mid-Year Meeting held on March 12-14, 2013 in Rockville, MD. The ERP determined the data presented met the established standard method performance requirement and approved the method as AOAC Official First Action on March 14, 2013. Using high-performance liquid chromatography (HPLC), flavanol enantiomers, (+)- and (-)-epicatechin and (+)- and (-)-catechin, are eluted isocratically using ammonium acetate and methanol mobile phase. The mobile phase is applied to a modified beta-cyclodextrin chiral stationary phase and the flavanols detected by fluorescence. Using several cocoa-based matrices, recoveries for the four enantiomers ranged from 82.2-102.1% at a 50% spike level, and 80.4-101.1% at a 100% spike level. Precision was determined to be 1.46-3.22% for (-)-epicatechin, 3.66-6.90% for (+)-catechin, 1.69-6.89% for (-)-catechin. (+)-Epicatechin was not detected in any of the samples used for this work, so precision could not be determined for this molecule.",Journal of AOAC International,"['D002099', 'D002392', 'D002851', 'D005504', 'D015203', 'D012680', 'D013237']","['Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Stereoisomerism']",Method for the determination of catechin and epicatechin enantiomers in cocoa-based ingredients and products by high-performance liquid chromatography: First Action 2013.04.,"['Q000737', 'Q000737', 'Q000379', 'Q000379', None, None, None]","['chemistry', 'chemistry', 'methods', 'methods', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24830163,2014,,,,no pdf access -0.69,15764339,"A validated high-performance liquid chromatography (HPLC) method with fluorescence detection for the quantitative analysis of ochratoxin A (OTA) in cocoa beans is described. OTA was extracted with methanol-3% sodium hydrogen carbonate solution and then purified with immunoaffinity columns before its analysis by HPLC. The validation of the analytical method was based on the following criteria: selectivity, linearity, limit of detection and quantification, precision (within- and between-day variability) and recovery, robustness and uncertainty. Detection and quantification limits were 0.04 and 0.1 mug kg(-1), respectively. Recovery was 88.9% (relative standard deviation = 4.0%). This method was successfully applied to the measurement of 46 cocoa bean samples of different origins. A total of 63% of cocoa bean samples was contaminated with a level greater than the limit of detection. The means and medians obtained for cocoa bean were 1.71 and 1.12 mug kg(-1), respectively. Surveillance controls should be set up in both crops and factories involved in transformation processes to avoid this mycotoxin in final products.",Food additives and contaminants,"['D002099', 'D002851', 'D005504', 'D005506', 'D006801', 'D009183', 'D009793', 'D015203']","['Cacao', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Food Contamination', 'Humans', 'Mycotoxins', 'Ochratoxins', 'Reproducibility of Results']",Validation of a high-performance liquid chromatography analytical method for ochratoxin A quantification in cocoa beans.,"['Q000737', 'Q000379', 'Q000379', 'Q000032', None, 'Q000032', 'Q000032', None]","['chemistry', 'methods', 'methods', 'analysis', None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15764339,2005,,,, -0.69,12537419,"Catechins are polyphenolic plant compounds (flavonoids) that may offer significant health benefits to humans. These benefits stem largely from their anticarcinogenic, antioxidant, and antimutagenic properties. Recent epidemiological studies suggest that the consumption of flavonoid-containing foods is associated with reduced risk of cardiovascular disease. Chocolate is a natural cocoa bean-based product that reportedly contains high levels of monomeric, oligomeric, and polymeric catechins. We have applied solid-liquid extraction and liquid chromatography coupled with atmospheric pressure chemical ionization-mass spectrometry to the identification and determination of the predominant monomeric catechins, (+)-catechin and (-)-epicatechin, in a baking chocolate Standard Reference Material (NIST Standard Reference Material 2384). (+)-Catechin and (-)-epicatechin are detected and quantified in chocolate extracts on the basis of selected-ion monitoring of their protonated [M + H](+) molecular ions. Tryptophan methyl ester is used as an internal standard. The developed method has the capacity to accurately quantify as little as 0.1 microg/mL (0.01 mg of catechin/g of chocolate) of either catechin in chocolate extracts, and the method has additionally been used to certify (+)-catechin and (-)-epicatechin levels in the baking chocolate Standard Reference Material. This is the first reported use of liquid chromatography/mass spectrometry for the quantitative determination of monomeric catechins in chocolate and the only report certifying monomeric catechin levels in a food-based Standard Reference Material.",Journal of agricultural and food chemistry,"['D001274', 'D002099', 'D002182', 'D002392', 'D002853', 'D013058', 'D012015']","['Atmospheric Pressure', 'Cacao', 'Candy', 'Catechin', 'Chromatography, Liquid', 'Mass Spectrometry', 'Reference Standards']",Quantification of the predominant monomeric catechins in baking chocolate standard reference material by LC/APCI-MS.,"[None, 'Q000737', 'Q000032', 'Q000032', 'Q000379', 'Q000379', None]","[None, 'chemistry', 'analysis', 'analysis', 'methods', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/12537419,2003,0,0,, -0.69,24962135,"Chinese mitten crab (Eriocheir sinensis) from Yangcheng Lake in Jiangsu Province is a popular species due to its unique pleasant aroma and intensive umami taste. In this study, odorants in steamed male E. sinensis were investigated using the headspace-monolithic material sorptive extraction technique coupled with gas chromatography-mass spectrometry-olfactometry (GC-MS-O). A total of 74 volatile compounds were found, and the results of the GC-MS-O analysis, combined with odor activity values, showed that trimethylamine (fishy, ammonia-like odor), (Z)-4-heptenal (mushroom-like odor), and benzaldehyde (paint-like odor) were the important odorants (IOs) in all 4 of the edible parts of steamed male E. sinensis. Furthermore, heptanal (mushroom-like odor) was common to the abdomen, claw, and leg meat but was not found as the IO in the gonad. The abdomen meat also contained 3-methylbutanal (vegetable-like, grassy odor), while 2 additional IOs were found in claw meat (2-methylbutanal, which has a mushroom odor and 3-ethyl-2,5-dimethylpyrazine, which has a chocolate-like, musty odor). Another IO (2-nonanone, chocolate-like odor) was also found in leg meat, while (E)-2-nonenal (green, fruity odor) was the IO found exclusively in the gonad. ",Journal of food science,"['D000818', 'D003386', 'D003296', 'D008401', 'D008297', 'D009812', 'D012903', 'D013649', 'D055549']","['Animals', 'Brachyura', 'Cooking', 'Gas Chromatography-Mass Spectrometry', 'Male', 'Odorants', 'Smell', 'Taste', 'Volatile Organic Compounds']",Characterization of important odorants in steamed male Chinese mitten crab (Eriocheir sinensis) using gas chromatography-mass spectrometry-olfactometry.,"[None, 'Q000737', None, 'Q000379', None, 'Q000032', None, None, 'Q000737']","[None, 'chemistry', None, 'methods', None, 'analysis', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24962135,2015,0,0,,no cocoa -0.69,22849827,"In this work multivariate experiments were conducted to optimize the operating conditions for inductively coupled plasma optical emission spectrometry (ICP OES) for multielemental determinations in chocolate drink powder. The operating conditions were investigated using a 2(3) central composite design, where the variables studied were radio frequency power, nebulization flow rate, and auxiliary argon flow rate. The effects of these parameters on plasma robustness and on signal to background ratio (SBR) were considered in parallel, allowing the evaluation of robustness and detectability using few and fast experiments to select the best conditions for the determination of the analytes. In this case, the proposed experiments were applied to the optimization of a method aimed at the determination of Al, Ba, Cd, Co, Cr, Cu, Fe, Mg, Mn, Mo, Ni, P, Pb, V, and Zn in chocolate drink powder. The compromise conditions that allowed obtaining a robust and sensitive analytical method were radio frequency power of 1200 W, nebulization flow rate of 0.6 L/min, and auxiliary argon flow rate of 0.3 L/min. Using these conditions, recoveries between 95 and 105% and relative standard deviations lower than 5% were obtained for the majority of the analytes. The proposed method was successfully applied to the analysis of 15 samples of chocolate drink powder. The highest concentrations of metallic species were found in diet and light products.",Journal of agricultural and food chemistry,"['D001628', 'D002099', 'D005504', 'D005511', 'D015999', 'D011208', 'D013053', 'D014131']","['Beverages', 'Cacao', 'Food Analysis', 'Food Handling', 'Multivariate Analysis', 'Powders', 'Spectrophotometry', 'Trace Elements']",Multielemental determinations in chocolate drink powder using multivariate optimization and ICP OES.,"['Q000032', 'Q000737', 'Q000379', None, None, 'Q000032', 'Q000379', 'Q000032']","['analysis', 'chemistry', 'methods', None, None, 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/22849827,2013,0,0,,no cocoa -0.69,24401377,"The concentrations of eight trace elements: lead (Pb), cadmium (Cd), chromium (Cr), manganese (Mn), cobalt (Co), arsenic (As), bismuth (Bi) and molybdenum (Mo), in chocolate, cocoa beans and products were studied by ICPMS. The study examined chocolate samples from different brands and countries with different concentrations of cocoa solids from each brand. The samples were digested and filtered to remove lipids and indium was used as an internal standard to correct matrix effects. A linear correlation was found between the level of several trace elements in chocolate and the cocoa solids content. Significant levels of Bi and As were found in the cocoa bean shells but not in the cocoa bean and chocolate. This may be attributed to environmental contamination. The presence of other elements was attributed to the manufacturing processes of cocoa and chocolate products. Children, who are big consumers of chocolates, may be at risk of exceeding the daily limit of lead; whereas one 10 g cube of dark chocolate may contain as much as 20% of the daily lead oral limit. Moreover chocolate may not be the only source of lead in their nutrition. For adults there is almost no risk of exceeding daily limits for trace metals ingestion because their digestive absorption of metals is very poor.",Talanta,"['D002099', 'D013058', 'D012015', 'D014131']","['Cacao', 'Mass Spectrometry', 'Reference Standards', 'Trace Elements']",Trace elements in cocoa solids and chocolate: an ICPMS study.,"['Q000737', 'Q000379', None, 'Q000032']","['chemistry', 'methods', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24401377,2014,1,2,table 3 ,all -0.69,14760852,"Multidimensional analysis of denatured milk proteins is reported using high-performance liquid chromatography (HPLC) combined with dynamic surface tension detection (DSTD). A hydrophobic interaction chromatography (HIC) column (a TSK-Gel Phenyl-5PW column, TosoBiosep), in the presence of 3.0 M guanidine hydrochloride (GdmHCl) as denaturing agent is employed as the mobile phase. Dynamic surface tension is measured through the differential pressure across the liquid-air interface of repeatedly growing and detaching drops. Continuous surface tension measurement throughout the entire drop growth (50 ms to 4 s) is achieved, for each eluting drop of 4 s length, providing insight into both the kinetic and thermodynamic behavior of molecular orientation processes at the liquid-air interface. An automated calibration procedure and data analysis method is applied with the DSTD system, which allows two unique solvents to be used, the HIC mobile phase for the sample and a second solvent (water for example) for the standard, permitting real-time dynamic surface tension data to be obtained. Three-dimensional data is obtained, with surface tension as a function of drop time first converted to surface pressure, which is plotted as a function of the chromatographic elution time axis. Experiments were initially performed using flow injection analysis (FIA) with the DSTD system for investigating commercial single standard milk proteins (alpha-lactalbumin, beta-lactoglobulin, alpha-, beta-, kappa-casein and a casein mixture) denatured by GdmHCl. These FIA-DSTD experiments allowed the separation and detection conditions to be optimized for the HIC-DSTD experiments. Thus, the HIC-DSTD system has been optimized and successfully applied to the selective analysis of surface-active casein fractions (alpha s1- and beta-casein) in a commercial casein mixture, raw milk samples (cow's, ewe's and goat's milk) and other diary products (yogurt, stracchino, mozzarella, parmesan cheese and chocolate cream). The different samples were readily distinguished based upon the selectivity provided by the HIC-DSTD method. The selectivity advantage of using DSTD relative to absorbance detection is also demonstrated.",Journal of chromatography. A,"['D002138', 'D002853', 'D019791', 'D008894', 'D011489', 'D013500']","['Calibration', 'Chromatography, Liquid', 'Guanidine', 'Milk Proteins', 'Protein Denaturation', 'Surface Tension']",Multidimensional analysis of denatured milk proteins by hydrophobic interaction chromatography coupled to a dynamic surface tension detector.,"[None, 'Q000379', 'Q000737', 'Q000737', None, None]","[None, 'methods', 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/14760852,2004,0,0,,no cocoa -0.69,16637672,"The determination of the occurrence and level of cocoa shells in cocoa products and chocolate is an important analytical issue. The recent European Union directive on cocoa and chocolate products (2000/36/EC) has not retained the former limit of a maximum amount of 5% of cocoa shells in cocoa nibs (based on fat-free dry matter), previously authorized for the elaboration of cocoa products such as cocoa mass. In the present study, we report a reliable gas-liquid chromatography procedure suitable for the determination of the occurrence of cocoa shells in cocoa products by detection of fatty acid tryptamides (FATs). The precision of the method was evaluated by analyzing nine different samples (cocoa liquors with different ranges of shells) six times (replicate repeatability). The variations of the robust coefficient of variation of the repeatability demonstrated that FAT(C22), FAT(C24), and total FATs are good markers for the detection of shells in cocoa products. The trueness of the method was evaluated by determining the FAT content in two spiked matrices (cocoa liquors and cocoa shells) at different levels (from 1 to 50 mg/100 g). A good relation was found between the results obtained and the spiking (recovery varied between 90 and 130%), and the linearity range was established between 1 and 50 mg/100 g in cocoa products. For total FAT contents of cocoa liquor containing 5% shells, the measurement uncertainty allows us to conclude that FAT is equal to 4.01 +/- 0.8 mg/100 g. This validated method is perfectly suitable to determine shell contents in cocoa products using FAT(C22), FAT(C24), and total FATs as markers. The results also confirmed that cocoa shells contain FAT(C24) and FAT(C22) in a constant ratio of nearly 2:1.",Journal of agricultural and food chemistry,"['D002099', 'D002849', 'D005227', 'D009536', 'D015203', 'D012639', 'D012680', 'D014363']","['Cacao', 'Chromatography, Gas', 'Fatty Acids', 'Niacinamide', 'Reproducibility of Results', 'Seeds', 'Sensitivity and Specificity', 'Tryptamines']",Development of a gas-liquid chromatographic method for the analysis of fatty acid tryptamides in cocoa products.,"['Q000737', 'Q000379', 'Q000032', 'Q000031', None, 'Q000737', None, 'Q000032']","['chemistry', 'methods', 'analysis', 'analogs & derivatives', None, 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16637672,2006,1,1,table 1 and 4,"the content in cocoa shells and butter, and the cocoa nibs and shells conly." -0.68,12480305,"Indian-made bidi cigarettes sold in the United States are available in a variety of exotic (e.g. clove, mango) and candy-like (e.g. chocolate, raspberry) flavors. Because certain tobacco flavorings contain alkenylbenzenes and other toxic or carcinogenic chemicals, we measured the concentration of flavor-related compounds in bidi tobacco using a previously developed method. Twenty-three brands of bidis were sampled using automated headspace solid-phase microextraction and subsequently analyzed for 12 compounds by gas chromatography-mass spectrometry. Two alkenylbenzene compounds, trans-anethole and eugenol, were found in greater than 90% of the brands analyzed. Methyleugenol, pulegone and estragole were each detected in 30% or more of the brands, whereas safrole and elemicin were not detected in any of the brands. The flavor-related compounds with the highest tobacco concentrations were eugenol (12,000 microg/g tobacco) and trans-anethole (2200 microg/g tobacco). The highest eugenol and trans-anethole concentrations found in bidi tobacco were about 70,000 and 7500 times greater, respectively, than the highest levels previously found in US cigarette brands. Measurement of these compounds is crucial to evaluation of potential risks associated with inhaling highly concentrated flavor-related compounds from bidis or other tobacco products.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000840', 'D001547', 'D052117', 'D003374', 'D005054', 'D005421', 'D008401', 'D007194', 'D039821', 'D014026']","['Anisoles', 'Benzaldehydes', 'Benzodioxoles', 'Coumarins', 'Eugenol', 'Flavoring Agents', 'Gas Chromatography-Mass Spectrometry', 'India', 'Monoterpenes', 'Tobacco']","Concentrations of nine alkenylbenzenes, coumarin, piperonal and pulegone in Indian bidi cigarette tobacco.","['Q000032', 'Q000032', None, 'Q000032', 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000737']","['analysis', 'analysis', None, 'analysis', 'analysis', 'analysis', None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12480305,2003,0,0,,no cocoa -0.68,25833003,"Cocoa contains many compounds such as biogenic amines (BAs), known to influence consumer health. Spermidine, spermidine, putrescine, histamine, tyramine, __-phenylethylamine, cadaverine and serotonine have been found in several cocoa-based products using HPLC with UV detection after derivatisation with dansyl-chloride. Once optimised in terms of linearity, percentage recovery, LOD, LOQ and repeatability, this method was applied to real samples. Total concentrations of BAs ranged from 5.7 to 79.0 _µg g(-)(1) with wide variations depending on the type of sample. BAs present in all samples were in decreasing order: histamine (1.9-38.1 _µg g(-)(1)) and tyramine (1.7-31.7 _µg g(-)(1)), while putrescine (0.9-32.7 _µg g(-)(1)), spermidine (1.0-9.7 _µg g(-)(1)) and spermidine (0.6-9.3 _µg g(-)(1)) were present in most of the samples. Cadaverine, serotonine and __-phenylethylamine were present in a few samples at much lower concentrations. Organic samples always contained much lower levels of BAs than their conventional counterparts and, generally speaking, the highest amounts of BAs were found in the most processed products.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D001679', 'D002099', 'D002851', 'D005504', 'D058871', 'D015203', 'D012680']","['Biogenic Amines', 'Cacao', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Organic Agriculture', 'Reproducibility of Results', 'Sensitivity and Specificity']",Determination of biogenic amine profiles in conventional and organic cocoa-based products.,"['Q000737', 'Q000737', None, 'Q000379', None, None, None]","['chemistry', 'chemistry', None, 'methods', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25833003,2016,1,2,table 3,only th samples 1-3 which are the cocoa powder -0.68,15053518,"A new European legislation (2000/36/CE) has allowed the use of vegetable fats other than cocoa butter (CB) in chocolate up to a maximum value of 5% in the product. The vegetable fats used in chocolate are designated as cocoa butter replacements and are called cocoa butter equivalents (CBE). The feasibility of CBE quantification in chocolate using triacylglycerol (TAG) profiles was conducted by analyzing 55 samples of CBs and 31 samples of CBEs using a liquid chromatograph equipped with an evaporative light scattering detector (HPLC-ELSD). Statistical evaluation of the data obtained has been performed, and a simulation study has been carried out to assess the viability to use this method for quantifying the amount of CBE in real mixtures and in chocolates. The TAGs POP, POS, PLS, and the ratios POP/PLS, POS/PLP (P, palmityl; O, oleyl; S, stearyl; L, linoleyl) are particularly significant to discriminate between CB and CBE. Analysis of 50 mixtures between 5 different CBEs and 10 different CBs at 2 different concentration levels is presented. The data are visualized and interpreted. A mathematical model has been developed to assess the amount of CBE in real mixtures. This predictive model has been successfully applied and validated on dark chocolates including authorized CBE. The results are affected by +/-2.1% absolute average error. In particular, estimations between 10 and 20% of CBE show a very good match. On the other hand, values equal to or smaller than 5% show a larger prediction error (detection limit of the method). For the main purpose of this method (i.e., quantification of CBE at 5% max in chocolate, which represents about 15% of the total fat) this model shows very good results. For milk chocolate, the mathematical model can also be used if TAG are integrated from partition number (PN) 46 to 54. Consequently, the model proposed provides sufficient information to verify the real application of the European legislation.",Journal of agricultural and food chemistry,"['D002099', 'D002851', 'D004041', 'D005060', 'D008433', 'D015203', 'D014280']","['Cacao', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Europe', 'Mathematics', 'Reproducibility of Results', 'Triglycerides']",Triacylglycerol analysis for the quantification of cocoa butter equivalents (CBE) in chocolate: feasibility study and validation.,"['Q000737', None, 'Q000032', None, None, None, 'Q000032']","['chemistry', None, 'analysis', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15053518,2004,0,0,,no quantification nor useful data -0.68,17555636,"Theobromine, theophylline, and caffeine are determined simultaneously by a rapid and selective reversed-phase high-performance liquid chromatography (HPLC) method with UV detection in by-products of cupuacu and cacao seeds. The determination is carried out in the raw and roasted ground cupuacu seeds and in the corresponding powders obtained after pressure treatment. The by-products of both cupuacu seeds and cacao seeds are obtained under the same technological conditions. The HPLC method uses isocratic elution with a mobile phase of methanol-water-acetic acid (80:19:1) (v/v) at a flow rate of 1 mL/min and UV absorbance detection at 275 nm. Total elution time for these analytes is less than 10 min, and the detection limit for all analytes is 0.1 mg/g. The amounts of theobromine and caffeine found in all the cupuacu samples are one or more orders of magnitude lower than those from cacao. Theophylline is found in all cacao samples except for the roasted ground paste, and it is only found in the roasted ground paste in the cupuacu samples.",Journal of chromatographic science,"['D002099', 'D002110', 'D002851', 'D012015', 'D012639', 'D013056', 'D013805', 'D013806']","['Cacao', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Reference Standards', 'Seeds', 'Spectrophotometry, Ultraviolet', 'Theobromine', 'Theophylline']","Determination of theobromine, theophylline, and caffeine in by-products of cupuacu and cacao seeds by high-performance liquid chromatography.","['Q000196', 'Q000032', 'Q000379', None, 'Q000737', None, 'Q000032', 'Q000032']","['embryology', 'analysis', 'methods', None, 'chemistry', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17555636,2007,,,, -0.68,25053037,"Nutritional composition and fatty acids (FA) profile were determined in cocoa and chocolates of different geographical origin and subject to different processing conditions. Cocoa butter was the major nutrient in cocoa beans and carbohydrates were the most important in chocolates. Cocoa composition and FA profile varied depending on geographical origin whilst in chocolates only carbohydrates and fat content varied significantly due to the effect of origin and no significant effect was observed for processing conditions. Both for cocoa and chocolates differences in FA profile were mainly explained as an effect of the geographical origin, and were not due to processing conditions in chocolate. For cocoa, differences in FA profile were found in C12:0, C14:0, C16:0, C16:1, C17:0, C17:1 and C18:0 whilst for chocolates only differences were found in C16:0, C18:0, C18:1 and C18:2. For all samples, C16:0, C18:0, C18:1 and C18:2 were quantitatively the most important FA. Ecuadorian chocolate showed a healthier FA profile having higher amounts of unsaturated FA and lower amounts of saturated FA than Ghanaian chocolate. ",Food chemistry,"['D002099', 'D005227', 'D013058', 'D009753']","['Cacao', 'Fatty Acids', 'Mass Spectrometry', 'Nutritive Value']",Nutritional composition and fatty acids profile in cocoa beans and chocolates with different geographical origin and processing conditions.,"['Q000737', 'Q000737', None, None]","['chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25053037,2015,1,2,table 1 and 4,it has quantified and unquantified data -0.68,11893788,"Maillard reactions are among the most important of the chemical and oxidative changes occurring in food and biological samples that contribute to food deterioration and to the pathophysiology of human disease. Although the association of lipid glycation with this process has recently been shown, the number of lipid glycation products in food and biological materials has not been clear. In this study, we synthesized the Amadori products derived from the glycation of phosphatidylethanolamine (PE), i.e., Amadori-PEs. Dioleoyl PE was incubated with glucose and lactose for 15 days, and the resultant Amadori-PEs were purified and isolated using solid phase extraction followed by HPLC. With this procedure, essentially pure (>98% purity) Amadori-PEs glycated with glucose (Glc-PE) and with lactose (Lac-PE) were obtained and used as standards in the subsequent studies. To determine the presence of Amadori-PEs in food and biological samples, the carbonyl group of Amadori-PEs was ultraviolet (UV)-labeled with 3-methyl-2-benzothiazolinone hydrazone, and the labeled Amadori-PEs were analyzed with normal phase HPLC-UV (318 nm). The detection limit was 4.5 ng (5 pmol) for Glc-PE and 5.3 ng (5 pmol) for Lac-PE. Among the several food samples examined, infant formula and chocolate contained a high amount of both Glc-PE and Lac-PE over wide concentration ranges, such as 1.5-112 microg/g. Testing biological materials showed Amadori-PE (Glc-PE) was detectable in rat plasma.",Journal of lipid research,"['D000818', 'D052160', 'D001769', 'D001774', 'D002851', 'D005260', 'D005504', 'D005947', 'D006031', 'D006801', 'D006835', 'D007785', 'D015416', 'D008297', 'D013058', 'D008895', 'D010714', 'D051381', 'D017207', 'D013844', 'D014466']","['Animals', 'Benzothiazoles', 'Blood', 'Blood Chemical Analysis', 'Chromatography, High Pressure Liquid', 'Female', 'Food Analysis', 'Glucose', 'Glycosylation', 'Humans', 'Hydrazones', 'Lactose', 'Maillard Reaction', 'Male', 'Mass Spectrometry', 'Milk, Human', 'Phosphatidylethanolamines', 'Rats', 'Rats, Sprague-Dawley', 'Thiazoles', 'Ultraviolet Rays']",UV analysis of Amadori-glycated phosphatidylethanolamine in foods and biological samples.,"[None, None, None, 'Q000379', 'Q000379', None, 'Q000379', 'Q000737', None, None, None, 'Q000737', None, None, 'Q000379', 'Q000737', 'Q000032', None, None, 'Q000737', None]","[None, None, None, 'methods', 'methods', None, 'methods', 'chemistry', None, None, None, 'chemistry', None, None, 'methods', 'chemistry', 'analysis', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/11893788,2002,0,0,,no cocoa -0.68,26923226,"Proanthocyanidins (PACs) are naturally occurring flavonoids possessing health beneficial bioactivities. Their quantification often utilizes the 4-dimethylaminocinnamaldehyde (DMAC) spectrophotometric assay with the assumption that molar absorption coefficients (MACs) are similar across the various PAC species. To assess the validity of this assumption, individual PAC monomers and oligomers were examined for their absorbance response with DMAC. Our results have shown that PAC dimers and trimers with interflavan linkage variations exhibited differential absorbance response. Absence of A-type linkage between the terminal and second units in PAC molecule not only impacts absorbance intensity at 640 nm but also elicits a prominent secondary 440 nm absorbance peak. Cranberry (A-type) and cocoa (B-type) oligomeric PACs exhibited differential absorbance (MACs) relationship with degree-of-polymerization. Thus, PAC structural variations have considerable impact on the resulting MAC. The use of DMAC assay in PAC quantification, especially in comparing across specific oligomers and compositions, should not assume MACs are similar. ",Journal of agricultural and food chemistry,"['D002099', 'D002934', 'D019281', 'D005638', 'D015394', 'D010936', 'D058105', 'D044945', 'D012997', 'D013053', 'D029799']","['Cacao', 'Cinnamates', 'Dimerization', 'Fruit', 'Molecular Structure', 'Plant Extracts', 'Polymerization', 'Proanthocyanidins', 'Solvents', 'Spectrophotometry', 'Vaccinium macrocarpon']",Influence of Degree-of-Polymerization and Linkage on the Quantification of Proanthocyanidins using 4-Dimethylaminocinnamaldehyde (DMAC) Assay.,"[None, None, None, 'Q000737', None, 'Q000737', None, 'Q000032', None, 'Q000379', None]","[None, None, None, 'chemistry', None, 'chemistry', None, 'analysis', None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/26923226,2016,0,0,,no real cocoa -0.67,18371766,"The aroma profile of cocoa products was investigated by headspace solid-phase micro-extraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS). SPME fibers coated with 100 microm polydimethylsiloxane coating (PDMS), 65 microm polydimethylsiloxane/divinylbenzene coating (PDMS-DVB), 75 microm carboxen/polydimethylsiloxane coating (CAR-PDMS) and 50/30 microm divinylbenzene/carboxen on polydimethylsiloxane on a StableFlex fiber (DVB/CAR-PDMS) were evaluated. Several extraction times and temperature conditions were also tested to achieve optimum recovery. Suspensions of the samples in distilled water or in brine (25% NaCl in distilled water) were investigated to examine their effect on the composition of the headspace. The SPME fiber coated with 50/30 microm DVB/CAR-PDMS afforded the highest extraction efficiency, particularly when the samples were extracted at 60 degrees C for 15 min under dry conditions with toluene as an internal standard. Forty-five compounds were extracted and tentatively identified, most of which have previously been reported as odor-active compounds. The method developed allows sensitive and representative analysis of cocoa products with high reproducibility. Further research is ongoing to study chocolate making processes using this method for the quantitative analysis of volatile compounds contributing to the flavor/odor profile.",Talanta,"['D002099', 'D005511', 'D008401', 'D009930', 'D015203', 'D052617', 'D014835']","['Cacao', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Organic Chemicals', 'Reproducibility of Results', 'Solid Phase Microextraction', 'Volatilization']",Evaluation of solid-phase micro-extraction coupled to gas chromatography-mass spectrometry for the headspace analysis of volatile compounds in cocoa products.,"['Q000737', None, 'Q000379', 'Q000032', None, 'Q000379', None]","['chemistry', None, 'methods', 'analysis', None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/18371766,2008,2,3,table 1,only the dvb-pdms and DVB/car-pdms fibers and only fot the NCP -0.67,17454113,"A rapid antibody-based assay for the detection of ochratoxin A in cocoa powder is described, involving sequential clean-up and visual detection of the toxin (""clean-up tandem assay column""). The screening test was developed to have a cut-off level of 2 microg kg(-1) and was shown to have false positive and false negative rates of 10 and 2%, respectively. Analysis of six samples can be carried out in the field in approximately 30 min by untrained workers. Using the proposed rapid screening test, 10 retail cocoa powders were found to contain no detectable levels of ochratoxin A (<2 microg kg(-1)). These samples were also found to be negative (<2 microg kg(-1)) when analysed using an LC-MS/MS method.",Food additives and contaminants,"['D001628', 'D002099', 'D002273', 'D002851', 'D005188', 'D005189', 'D005506', 'D009183', 'D009793', 'D053719']","['Beverages', 'Cacao', 'Carcinogens', 'Chromatography, High Pressure Liquid', 'False Negative Reactions', 'False Positive Reactions', 'Food Contamination', 'Mycotoxins', 'Ochratoxins', 'Tandem Mass Spectrometry']",Application and validation of a clean-up tandem assay column for screening ochratoxin A in cocoa powder.,"['Q000032', 'Q000737', 'Q000032', 'Q000379', None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000379']","['analysis', 'chemistry', 'analysis', 'methods', None, None, 'analysis', 'analysis', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/17454113,2007,,,, -0.67,15161179,"A rapid and selective isocratic reversed-phase liquid chromatographic method has been developed at the National Institute of Standards and Technology to simultaneously measure caffeine, theobromine, and theophylline in a food-matrix standard reference material (SRM) 2384, Baking Chocolate. The method uses isocratic elution with a mobile phase composition (volume fractions) of 10% acetronitrile/90% water (pH adjusted to 2.5 using acetic acid) at a flow rate of 1.5 mL/min with ultraviolet absorbance detection (274 nm). Total elution time for these analytes is less than 15 min. Concentration levels of caffeine, theobromine, and theophylline were measured in single 1-g samples taken from each of eight bars of chocolate over an eight-day period. Samples were defatted with hexane, and beta-hydroxyethyltheophylline was added as the internal standard. The repeatability for the caffeine, theobromine, and theophylline measurements was 5.1, 2.3, and 1.9%, respectively. The limit of quantitation for all analytes was <100 ng/mL. The measurements from this method were used in the value-assignment of caffeine, theobromine, and theophylline in SRM 2384.",Journal of agricultural and food chemistry,"['D002099', 'D002110', 'D002851', 'D012015', 'D013805', 'D013806']","['Cacao', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Reference Standards', 'Theobromine', 'Theophylline']","Determination of caffeine, theobromine, and theophylline in standard reference material 2384, baking chocolate, using reversed-phase liquid chromatography.","['Q000737', 'Q000032', None, None, 'Q000032', 'Q000032']","['chemistry', 'analysis', None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15161179,2004,1,1,table 1 and 2,only the 1-6 and NIST from tb 1 and only the caff. And theob. From tb 2 -0.67,22790716,"In order to investigate cadmium contents in foods sold in Japan, cadmium levels in 40 seafood samples and 30 chocolate samples were measured by means of atomic absorption spectrometry and ICP-OES. We first confirmed the validity of the method according to the guidelines of the Ministry of Health, Labour and Welfare. Among 40 seafood samples investigated, cadmium was detected in 31 samples, in which the concentration exceeded half the LOQ (0.025 mg/kg), and the level was ranged from 0.03 to 0.38 mg/kg. We could not find any sample containing cadmium in excess of 2 mg/kg, which the Codex Alimentarius sets as the maximum standard value. Among 30 chocolate samples, cadmium was detected in 21 samples, and the level ranged from 0.025 to 0.54 mg/kg.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D000818', 'D049872', 'D002099', 'D019187', 'D049832', 'D005504', 'D007564', 'D008452', 'D049831', 'D017747', 'D013054']","['Animals', 'Bivalvia', 'Cacao', 'Cadmium Compounds', 'Decapodiformes', 'Food Analysis', 'Japan', 'Maximum Allowable Concentration', 'Octopodiformes', 'Seafood', 'Spectrophotometry, Atomic']","[Surveillance of cadmium level in octopus, squid, clam, short-necked clam and chocolate].","[None, 'Q000737', 'Q000737', 'Q000032', 'Q000737', 'Q000379', None, None, 'Q000737', 'Q000032', 'Q000379']","[None, 'chemistry', 'chemistry', 'analysis', 'chemistry', 'methods', None, None, 'chemistry', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/22790716,2013,,,,The paper wa sin japanese and could be translated with google -0.66,12526004,"Liquid chromatography coupled with ionspray mass spectrometry in the tandem mode (LC/MS/MS) with negative ion detection was used for the identification of a variety of phenolic compounds in a cocoa sample. Gradient elution with water and acetonitrile, both containing 0.1% HCOOH, was used. Standard solutions of 31 phenolic compounds, including benzoic and cinnamic acids and flavonoid compounds, were studied in the negative ion mode using MS/MS product ion scans. At low collisional activation, the deprotonated molecule [M - H](-) was observed for all the compounds studied. For cinnamic and benzoic acids, losses of CO(2) or formation of [M - CH(3)](-*) in the case of methoxylated compounds were observed. However, for flavonol and flavone glycosides, the spectra present both the deprotonated molecule [M - H](-) of the glycoside and the ion corresponding to the deprotonated aglycone [A - H](-). The latter ion is formed by loss of the rhamnose, glucose, galactose or arabinose residue from the glycosides. Different fragmentation patterns were observed in MS/MS experiments for flavone-C-glycosides which showed fragmentation in the sugar part. Fragmentation of aglycones provided characteristic ions for each family of flavonoids. The optimum LC/MS/MS conditions were applied to the characterization of a cocoa sample that had been subjected to an extraction/clean-up procedure which involved chromatography on Sephadex LH20 and thin-layer chromatographic monitoring. In addition to compounds described in the literature, such as epicatechin and catechin, quercetin, isoquercitrin (quercetin-3-O-glucoside) and quercetin-3-O-arabinose, other compounds were identified for the first time in cocoa samples, such as hyperoside (quercetin-3-O-galactoside), naringenin, luteolin, apigenin and some O-glucosides and C-glucosides of these compounds.",Journal of mass spectrometry : JMS,"['D002099', 'D002853', 'D005419', 'D010636', 'D021241']","['Cacao', 'Chromatography, Liquid', 'Flavonoids', 'Phenols', 'Spectrometry, Mass, Electrospray Ionization']",Liquid chromatographic/electrospray ionization tandem mass spectrometric study of the phenolic composition of cocoa (Theobroma cacao).,"['Q000737', None, 'Q000032', 'Q000032', None]","['chemistry', None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/12526004,2003,,,, -0.66,11675670,"Quantitative analyses of fatty acids from five triacylglycerol products, coconut oil, palm kernel oil, palm oil, lard and cocoa butter, were carried out using two analytical methods: matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) and gas chromatography (GC), in an effort to validate the application of MALDI-TOFMS in quantitative fatty acid analysis. For the GC analysis, transmethylated products were used, whereas, for the MALDI-TOF analysis, saponified products were used. Under MALDI-TOF conditions, the acids were detected as sodiated sodium carboxylates [RCOONa + Na](+) consistent with the mode of ionization that was previously reported. Thus, the MALDI-TOF mass spectrum of saponified coconut oil showed the presence of sodiated sodium salts of caprylic acid (7.5 +/- 0.67, m/z 189), capric acid (6.9 +/- 0.83, m/z 217), lauric acid (47.8 +/- 0.67, m/z 245), myristic acid (20.4 +/- 0.51, m/z 273), palmitic acid (9.8 +/- 0.47, m/z 301), linoleic acid (0.9 +/- 0.07, m/z 325), oleic acid (4.8 +/- 0.42, m/z 327) and stearic acid (2.0 +/- 0.13, m/z 329). Saponified palm kernel oil had a fatty acid profile that included caprylic acid (3.5 +/- 0.59), capric acid (4.7 +/- 0.82), lauric acid (58.6 +/- 2.3), myristic acid (20.9 +/- 1.5), palmitic acid (7.2 +/- 1.1), oleic acid (3.8 +/- 0.62) and stearic acid (1.2 +/- 0.15). Saponified palm oil gave myristic acid (0.83 +/- 0.18), palmitic acid (55.8 +/- 1.7), linoleic acid (4.2 +/- 0.51), oleic acid (34.5 +/- 1.5), stearic acid (3.8 +/- 0.26) and arachidic acid (0.80 +/- 0.22). Saponified lard showed the presence of myristic acid (1.5 +/- 0.24), palmitic acid (28.9 +/- 1.3), linoleic acid (13.7 +/- 0.67), oleic acid (38.7 +/- 1.4), stearic acid (12.8 +/- 0.64) and arachidic acid (2.4 +/- 0.35). Finally, for saponified cocoa butter, the fatty acid distribution was: palmitic acid (32.3 +/- 1.0), linoleic acid (2.6 +/- 0.35), oleic acid (34.9 +/- 1.7) and stearic acid (30.3 +/- 1.6). Quantitative gas chromatographic analysis of the corresponding methyl esters from these triacylglycerol products yielded data that were mostly in agreement with the MALDI-TOFMS data. The MALDI-TOF experiment, however, proved to be superior to the GC experiment, particularly with regard to baseline resolution of unsaturated acids. Furthermore, the ability of MALDI-TOFMS to detect low concentrations of fatty acids rendered it more sensitive than the GC methodology.",Rapid communications in mass spectrometry : RCM,"['D000818', 'D002417', 'D002849', 'D004041', 'D005227', 'D010938', 'D019032', 'D014280']","['Animals', 'Cattle', 'Chromatography, Gas', 'Dietary Fats', 'Fatty Acids', 'Plant Oils', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Triglycerides']",Comparative quantitative fatty acid analysis of triacylglycerols using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and gas chromatography.,"[None, None, None, 'Q000032', 'Q000032', 'Q000032', None, 'Q000032']","[None, None, None, 'analysis', 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11675670,2001,,,, -0.66,16719534,"Cocoa and chocolate products from major brands were analyzed blind for total antioxidant capacity (AOC) (lipophilic and hydrophilic ORAC(FL)), catechins, and procyanidins (monomer through polymers). Accuracy of analyses was ascertained by comparing analyses on a NIST standard reference chocolate with NIST certified values. Procyanidin (PC) content was related to the nonfat cocoa solid (NFCS) content. The natural cocoa powders (average 87% of NFCS) contained the highest levels of AOC (826 +/- 103 micromol of TE/g) and PCs (40.8 +/- 8.3 mg/g). Alkalized cocoa (Dutched powders, average 80% NFCS) contained lower AOC (402 +/- 6 micromol of TE /g) and PCs (8.9 +/- 2.7 mg/g). Unsweetened chocolates or chocolate liquor (50% NFCS) contained 496 +/- 40 micromol of TE /g of AOC and 22.3 +/- 2.9 mg/g of PCs. Milk chocolates, which contain the least amount of NFCS (7.1%), had the lowest concentrations of AOC (80 +/- 10 micromol of TE /g) and PCs (2.7 +/- 0.5 mg/g). One serving of cocoa (5 g) or chocolate (15 or 40 g, depending upon the type of chocolate) provides 2000-9100 micromol of TE of AOC and 45-517 mg of PCs, amounts that exceed the amount in a serving of the majority of foods consumed in America. The monomers through trimers, which are thought to be directly bioavailable, contributed 30% of the total PCs in chocolates. Hydrophilic antioxidant capacity contributed >90% of AOC in all products. The correlation coefficient between AOC and PCs in chocolates was 0.92, suggesting that PCs are the dominant antioxidants in cocoa and chocolates. These results indicate that NFCS is correlated with AOC and PC in cocoa and chocolate products. Alkalizing dramatically decreased both the procyanidin content and antioxidant capacity, although not to the same extent.",Journal of agricultural and food chemistry,"['D000975', 'D044946', 'D002099', 'D002392', 'D002851', 'D044945', 'D021241']","['Antioxidants', 'Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Proanthocyanidins', 'Spectrometry, Mass, Electrospray Ionization']",Procyanidin and catechin contents and antioxidant capacity of cocoa and chocolate products.,"['Q000032', 'Q000032', 'Q000737', 'Q000032', None, 'Q000032', None]","['analysis', 'analysis', 'chemistry', 'analysis', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/16719534,2006,1,2,table 3 ,only the powders -0.66,21094947,"The applicability of comprehensive two-dimensional gas chromatography (GC_GC) for flavonoids analysis was investigated by separation and identification of flavonoids in standards, and a complex matrix natural sample. The modulation temperature was optimized to achieve the best separation and signal enhancement. The separation pattern of trimethylsilyl (TMS) derivatives of flavonoids was compared on two complementary column sets. Whilst the BPX5/BPX50 (NP/P) column set offers better overall separation, BPX50/BPX5 (P/NP) provides better peak shape and sensitivity. Comparison of the identification power of GC_GC-TOFMS against both the NIST05 MS library and a laboratory (created in-house) TOFMS library was carried out on a flavonoid mixture. The basic retention index information on high-performance capillary columns with a non-polar stationary phase was established and database of mass spectra of trimethylsilyl derivatives of flavonoids was compiled. TOFMS coupled to GC_GC enabled satisfactory identification of flavonoids in complex matrix samples at their LOD over a range of 0.5-10 __g/mL. Detection of all compounds was based on full-scan mass spectra and for each compound a characteristic ion was chosen for further quantification. This study shows that GC_GC-TOFMS yields high specificity for flavonoids derived from real natural samples, dark chocolate, propolis, and chrysanthemum.",Journal of chromatography. A,"['D047188', 'D005419', 'D005504', 'D008401']","['Chalcones', 'Flavonoids', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry']","Comprehensive two-dimensional gas chromatography, retention indices and time-of-flight mass spectra of flavonoids and chalcones.","['Q000032', 'Q000032', None, 'Q000379']","['analysis', 'analysis', None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/21094947,2011,0,0,,no cocoa -0.66,2745605,"This paper details a high-performance liquid chromatography (HPLC) method for the separation of triacylglycerols, using a 3-micron, 15 cm x 4.6 mm I.D. Spherisorb ODS column and gradient elution with dichloromethane and acetonitrile. The triacylglycerols are detected using a light scattering detector (mass detector). Separations of a number of different edible oils and fats are reported. The procedure offers a possible method for determining cocoa butter equivalents and the adulteration of edible oils and fats by other non-generic fats and oils.",Journal of chromatography,"['D002851', 'D005506', 'D010938', 'D012031', 'D014280']","['Chromatography, High Pressure Liquid', 'Food Contamination', 'Plant Oils', 'Refractometry', 'Triglycerides']",Rapid analysis of triacylglycerols using high-performance liquid chromatography with light scattering detection.,"[None, 'Q000032', 'Q000032', None, 'Q000032']","[None, 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/2745605,1989,,,, -0.66,26651573,"There are few studies about different types of chocolate and their chemical characterization by Fourier transform (FT)-Raman spectroscopy and capillary zone electrophoresis (CZE). The aim of this study was to evaluate the lipid profile of different types of Brazilian chocolate through characterization by FT-Raman spectroscopy and identification and quantification of major fatty acids (FAs) by CZE to confirm FT-Raman spectrometry results. It was found that the main spectroscopic profile difference of the chocolate samples analyzed was related to the presence of saturated or unsaturated FAs. Well defined bands at approximately 1660, 1267, and 1274 cm(-1) corresponding to vibrational modes of unsaturated FAs (UnFAs) were found only in the spectra of samples with cocoa butter in their composition according to label specifications, mainly in dark chocolate samples. The FA identification and quantification by CZE found the presence of stearic (18:0) and palmitic (16:0) acids as the major saturated FAs in all chocolate samples. Dark chocolate samples showed the highest levels of oleic (cis-9 18:1) and linoleic (cis, cis -9,12 18:2) UnFAs monitored and the lowest levels of 14:0 in their chemical composition. Samples coded as 02 (with not only cocoa butter in their composition according to label) had the highest levels of 14:0 (FA not present in cocoa butter composition) corresponding to label information and inferring the presence of other fat sources, called cocoa butter substitutes, mainly for milk and white chocolate samples. This study suggests FT-Raman spectroscopy is a powerful technique that can be used to chemically characterize the chocolate lipid fraction, and CZE is a tool able to confirm Raman spectroscopy results and identify and quantify the major FAs in chocolate samples. ",Journal of AOAC International,"['D002099', 'D019075', 'D005227', 'D013059']","['Cacao', 'Electrophoresis, Capillary', 'Fatty Acids', 'Spectrum Analysis, Raman']","Lipid Characterization of White, Dark, and Milk Chocolates by FT-Raman Spectroscopy and Capillary Zone Electrophoresis.","['Q000737', 'Q000379', 'Q000032', 'Q000379']","['chemistry', 'methods', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/26651573,2016,,,,no pdf access -0.65,16314166,"A rapid and selective cation exchange chromatographic method coupled to integrated pulsed amperometric detection (PAD) has been developed to quantify biogenic amines in chocolate. The method is based on gradient elution of aqueous methanesulfonic acid with post column addition of strong base to obtain suitable conditions for amperometric detection. A potential waveform able to keep long time performance of the Au disposable electrode was set up. Total analysis time is less than 20min. Concentration levels of dopamine, serotonin, tyramine, histamine and 2-phenylethylamine were measured, after extraction with perchloric acid from 2g samples previously defatted twice with petroleum ether. The method was used to determine the analytes in chocolate real matrices and their quantification was made with standard addition method. Only dopamine, histamine and serotonin were found in the analysed real samples. Repeatabilities of their signals, computed on their amounts in the real samples, were 5% for all of them. Repeatabilities of tyramine and phenethylamine were relative to standard additions to real samples (close to 1mg/l in the extract) and were 7 and 3%, respectively. Detection limits were computed with the 3s of the baseline noise combined with the calibration plot regression parameters. They were satisfactorily low for all amines: 3mg/kg for dopamine, 2mg/kg for tyramine, 1mg/kg for histamine, 2mg/kg for serotonin, 3mg/kg for 2-phenylethylamine.",Journal of chromatography. A,"['D001679', 'D002099', 'D002852', 'D004298', 'D004563', 'D004566', 'D005504', 'D006046', 'D006632', 'D008698', 'D010472', 'D010627', 'D015203', 'D012680', 'D012701', 'D014439']","['Biogenic Amines', 'Cacao', 'Chromatography, Ion Exchange', 'Dopamine', 'Electrochemistry', 'Electrodes', 'Food Analysis', 'Gold', 'Histamine', 'Mesylates', 'Perchlorates', 'Phenethylamines', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Serotonin', 'Tyramine']",Determination of biogenic amines in chocolate by ion chromatographic separation and pulsed integrated amperometric detection with implemented wave-form at Au disposable electrode.,"['Q000032', 'Q000737', 'Q000379', 'Q000032', 'Q000379', None, 'Q000379', 'Q000737', 'Q000032', 'Q000737', 'Q000737', 'Q000032', None, None, 'Q000032', 'Q000032']","['analysis', 'chemistry', 'methods', 'analysis', 'methods', None, 'methods', 'chemistry', 'analysis', 'chemistry', 'chemistry', 'analysis', None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16314166,2006,0,0,,chocolate as sample -0.65,18193748,"A simple and rapid method based on ultrasound energy is described for the determination of aluminum (AI) in complex matrixes of chocolate and candy samples by electrothermal atomic absorption spectrometry. The optimization strategy was carried out using multivariate methodologies. Five variables (temperature of the ultrasonic bath; exposure time to ultrasound energy; volumes of 2 acid mixtures, HNO3-H2SO4-H2O2 (1 + 1 + 1) and HNO3-H2O2 (1 + 1); and sample mass) were considered as factors in the optimization process. Interactions between analytical factors and their optimal levels were investigated using fractional factorial and Doehlert matrix designs. Validation of the ultrasonic-assisted acid digestion procedure was performed against standard reference materials, milk powder (SRM 8435) and wheat flour (SRM 1567a). The proposed procedure allowed Al determination with a detection limit of 2.3 microg/L (signal-to-noise = 3) and a precision, calculated as relative standard deviation, of 2.2% for a set of 10 measurements of certified samples. The recovery of Al by the proposed procedure was close to 100%, and no significant difference at the 95% confidence level was found between determined and certified values of Al. The proposed procedure was applied to the determination of Al in chocolate and candy samples. The results indicated that cocoa-based chocolates have higher contents of Al than milk- and sugar-based chocolates and candies.",Journal of AOAC International,"['D000143', 'D000465', 'D000535', 'D002099', 'D002182', 'D003627', 'D006868', 'D007202', 'D013054', 'D014465']","['Acids', 'Algorithms', 'Aluminum', 'Cacao', 'Candy', 'Data Interpretation, Statistical', 'Hydrolysis', 'Indicators and Reagents', 'Spectrophotometry, Atomic', 'Ultrasonics']",Application of fractional factorial design and Doehlert matrix in the optimization of experimental variables associated with the ultrasonic-assisted acid digestion of chocolate samples for aluminum determination by atomic absorption spectrometry.,"['Q000737', None, 'Q000032', 'Q000737', 'Q000032', None, None, None, None, None]","['chemistry', None, 'analysis', 'chemistry', 'analysis', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18193748,2008,,,, -0.65,21409610,"Novel saccharide-based stationary phases were developed by applying non-enzymatic browning (Maillard Reaction) on aminopropyl silica material. During this process, the reducing sugars glucose, lactose, maltose, and cellobiose served as ""ligand primers"". The reaction cascade using cellobiose resulted in an efficient chromatographic material which further served as our model Chocolate HILIC column. (Chocolate refers to the fact that these phases are brownish.) In this way, an amine backbone was introduced to facilitate convenient manipulation of selectivity by additional attractive or repulsive ionic solute-ligand interactions in addition to the typical HILIC retention mechanism. In total, six different test sets and five different mobile phase compositions were investigated, allowing a comprehensive evaluation of the new polar column. It became evident that, besides the so-called HILIC retention mechanism based on partition phenomena, additional adsorption mechanisms, including ionic interactions, take place. Thus, the new column is another example of a HILIC-type column characterized by mixed-modal retention increments. The glucose-modified materials exhibited the relative highest overall hydrophobicity of all grafted Chocolate HILIC columns which enabled retention of lipophilic analytes with high water content mobile phases.",Analytical and bioanalytical chemistry,"['D000327', 'D002099', 'D002853', 'D004187', 'D005947', 'D057927', 'D008024', 'D012822', 'D013499']","['Adsorption', 'Cacao', 'Chromatography, Liquid', 'Disaccharides', 'Glucose', 'Hydrophobic and Hydrophilic Interactions', 'Ligands', 'Silicon Dioxide', 'Surface Properties']",Chocolate HILIC phases: development and characterization of novel saccharide-based stationary phases by applying non-enzymatic browning (Maillard reaction) on amino-modified silica surfaces.,"[None, 'Q000737', None, 'Q000737', 'Q000737', None, None, 'Q000737', None]","[None, 'chemistry', None, 'chemistry', 'chemistry', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21409610,2011,0,0,,no cocoa -0.65,2312513,"A liquid chromatographic (LC) method has been developed to determine the content of polydextrose, a water-soluble 1 calorie/g bulking agent, in food matrixes such as cookies, cakes, fruit spreads, and chocolate toppings. This analysis, which requires use of a blank matrix, provides a feasible means to control the manufacture of foods containing this additive and provides a component for the accurate determination of the caloric value of a particular food product. The method involves aqueous extraction of the polydextrose from the food matrix followed by separation on a carbohydrate analysis column. The LC system uses a mobile phase of 0.005M CaSO4.2H2O and a refractive index detector for quantitation. Polydextrose recoveries from the food matrixes varied from 91.5 to 100.9% with assay precision, expressed as coefficient of variation, ranging from 0.7 to 4.3%. Each error estimate was derived from 5 parallel determinations. The present methodology is precise and selective in contrast to the modified classical phenol-sulfuric acid colorimetric method for assaying carbohydrates, which had been used for polydextrose determination in food matrixes in the past. Because the coefficient of variation frequently exceeded 10%, replicate analyses were necessary to achieve quantitation.",Journal - Association of Official Analytical Chemists,"['D002099', 'D002853', 'D005504', 'D005638', 'D005936', 'D012997']","['Cacao', 'Chromatography, Liquid', 'Food Analysis', 'Fruit', 'Glucans', 'Solvents']",Liquid chromatographic determination of polydextrose in food matrixes.,"['Q000032', None, None, 'Q000032', 'Q000032', None]","['analysis', None, None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/2312513,1990,,,, -0.65,28779575,"A fast separation based on cation-exchange liquid chromatography coupled with high-resolution mass spectrometry is proposed for simultaneous determination of chlormequat, difenzoquat, diquat, mepiquat and paraquat in several food and beverage commodities. Solid samples were extracted using a mixture of water/methanol/formic acid (69.6:30:0.4, v/v/v), while liquid samples were ten times diluted with the same solution. Separation was carried out on an experimental length-modified IonPac CS17 column (2_____15__mm",Journal of separation science,"['D064751', 'D001628', 'D002412', 'D002852', 'D005506', 'D007554', 'D013058', 'D010575', 'D053719']","['Ammonium Compounds', 'Beverages', 'Cations', 'Chromatography, Ion Exchange', 'Food Contamination', 'Isotopes', 'Mass Spectrometry', 'Pesticides', 'Tandem Mass Spectrometry']",Fast analysis of quaternary ammonium pesticides in food and beverages using cation-exchange chromatography coupled with isotope-dilution high-resolution mass spectrometry.,"['Q000032', 'Q000032', None, None, 'Q000032', None, None, 'Q000032', None]","['analysis', 'analysis', None, None, 'analysis', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/28779575,2018,1,1,table 3, -0.65,7698108,"Extracts of several grain-based coffee-substitute blends and instant coffees were mutagenic in the Ames/Salmonella test using TA98, YG1024, and YG1029 with metabolic activation. The beverage powders induced 150 to 500 TA98 and 1,150 to 4,050 YG1024 revertant colonies/g, respectively. Increased sensitivity was achieved using strain YG1024. No mutagenic activity was found in instant hot cocoa products. The mutagenic activity in the beverage powders was shown to be stable to heat and the products varied in resistance to acid nitrite treatment. Differential bacterial strain specificity, and a requirement for metabolic activation suggest that aromatic amines are present. Characterization of the mutagenic activity, using HPLC and the Ames test of the collected fractions, showed the coffee-substitute blends and instant coffees contain several mutagenic compounds. Known heterocyclic amines are not responsible for the major part of the mutagenic activity. The main mutagenic activity in grain-based coffee-substitute blends and instant coffees is due to several unidentified compounds, which are most likely aromatic amines.",Environmental and molecular mutagenesis,"['D000588', 'D001628', 'D002099', 'D018651', 'D002851', 'D003069', 'D002523', 'D005504', 'D005526', 'D006571', 'D006358', 'D006898', 'D009152', 'D009153', 'D011208', 'D012486']","['Amines', 'Beverages', 'Cacao', 'Chicory', 'Chromatography, High Pressure Liquid', 'Coffee', 'Edible Grain', 'Food Analysis', 'Food, Formulated', 'Heterocyclic Compounds', 'Hot Temperature', 'Hydroxylamines', 'Mutagenicity Tests', 'Mutagens', 'Powders', 'Salmonella typhimurium']",Characterization of mutagenic activity in instant hot beverage powders.,"['Q000302', 'Q000032', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', None, 'Q000032', 'Q000302', None, 'Q000302', None, 'Q000302', 'Q000737', 'Q000187']","['isolation & purification', 'analysis', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', None, 'analysis', 'isolation & purification', None, 'isolation & purification', None, 'isolation & purification', 'chemistry', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/7698108,1995,,,, -0.65,28419110,"This work evaluated the effect of cocoa pulp as a malt adjunct on the parameters of fermentation for beer production on a pilot scale. For this purpose, yeast isolated from the spontaneous fermentation of cacha_a (SC52), belonging to the strain bank of the State University of Feira de Santana-Ba (Brazil), and a commercial strain of ale yeast (Safale S-04 Belgium) were used. The beer produced was subjected to acceptance and purchase intention tests for sensorial analysis. At the beginning of fermentation, 30% cocoa pulp (adjunct) was added to the wort at 12_P concentration. The production of beer on a pilot scale was carried out in a bioreactor with a 100-liter capacity, a usable volume of 60 liters, a temperature of 22_C and a fermentation time of 96 hours. The fermentation parameters evaluated were consumption of fermentable sugars and production of ethanol, glycerol and esters. The beer produced using the adjunct and yeast SC52 showed better fermentation performance and better acceptance according to sensorial analysis.",PloS one,"['D001515', 'D019149', 'D002099', 'D002241', 'D002851', 'D004952', 'D000431', 'D005285', 'D005990', 'D006863', 'D010865', 'D012441', 'D052617', 'D013696', 'D013997']","['Beer', 'Bioreactors', 'Cacao', 'Carbohydrates', 'Chromatography, High Pressure Liquid', 'Esters', 'Ethanol', 'Fermentation', 'Glycerol', 'Hydrogen-Ion Concentration', 'Pilot Projects', 'Saccharomyces cerevisiae', 'Solid Phase Microextraction', 'Temperature', 'Time Factors']",Cocoa pulp in beer production: Applicability and fermentative process performance.,"['Q000032', 'Q000382', 'Q000378', 'Q000032', None, 'Q000032', 'Q000032', None, 'Q000032', None, None, 'Q000378', None, None, None]","['analysis', 'microbiology', 'metabolism', 'analysis', None, 'analysis', 'analysis', None, 'analysis', None, None, 'metabolism', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28419110,2017,0,0,,beer containing cocoa pulp -0.64,16881674,"A straightforward stable isotope dilution analysis (SIDA) for the quantitative determination of the di- and trihydroxybenzenes catechol (1), pyrogallol (2), 3-methylcatechol (3), 4-methylcatechol (4), and 4-ethylcatechol (5) in foods by means of liquid chromatography-tandem mass spectrometry was developed. With or without sample preparation involving phenylboronyl solid phase extraction, the method allowed the quantification of the target compounds in complex matrices such as coffee beverages with quantification limits of 9 nmol/L for 4-ethylcatechol, 24 nmol/L for catechol, 3-methyl-, and 4-methylcatechol, and 31 nmol/L for pyrogallol. Recovery rates for the analytes ranged from 97 to 103%. Application of the developed SIDA to various commercial food samples showed that quantitative analysis of the target compounds is possible within 30 min and gave first quantitative data on the amounts of di- and trihydroxybenzenes in coffee beverage, coffee powder, coffee surrogate, beer, malt, roasted cocoa powder, bread crust, potato crisps, fruits, and cigarette smoke and human urine. Model precursor studies revealed the carbohydrate/amino acid systems as well as the plant polyphenols catechin and epicatechin as precursors of catechol and 5-O-caffeoylquinic acid, caffeic acid as a precursor of catechol and 4-ethylcatechol, and gallocatechin, epigallocatechin, and gallic acid as precursors of pyrogallol.",Journal of agricultural and food chemistry,"['D002396', 'D002853', 'D003069', 'D003903', 'D005504', 'D005638', 'D006801', 'D007201', 'D013058', 'D011748', 'D012906', 'D014026']","['Catechols', 'Chromatography, Liquid', 'Coffee', 'Deuterium', 'Food Analysis', 'Fruit', 'Humans', 'Indicator Dilution Techniques', 'Mass Spectrometry', 'Pyrogallol', 'Smoke', 'Tobacco']",Development of a stable isotope dilution analysis with liquid chromatography-tandem mass spectrometry detection for the quantitative analysis of di- and trihydroxybenzenes in foods and model systems.,"['Q000032', 'Q000379', 'Q000737', None, 'Q000379', 'Q000737', None, None, 'Q000379', 'Q000032', 'Q000032', None]","['analysis', 'methods', 'chemistry', None, 'methods', 'chemistry', None, None, 'methods', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/16881674,2006,1,1,table 3,cocoa powder only -0.64,12166966,"New experimental data on the extraction of caffeine from guaran seeds and mat© tea leaves, and theobromine from cocoa beans, with supercritical CO2 were obtained using a high-pressure extraction apparatus. The effect of the addition of ethanol to carbon dioxide on the extraction efficiency was also investigated. Caffeine extraction yields of 98% of the initial caffeine content in both wet ground guaran seeds and mat© tea leaves were obtained. Extractions of caffeine from guaran seeds and mat© tea leaves also exhibited a retrograde behavior for the two temperatures considered in this work. In the removal of theobromine from cocoa beans, a much smaller extraction yield was obtained with longer extraction periods and consequently larger solvent requirements. The results of this study confirm the higher selectivity of CO2 for caffeine in comparison with that for theobromine, and also the influence of other components in each particular natural product on the extraction of methylxanthines. The effect of the addition of ethanol to carbon dioxide on the extraction of methylxanthines was significant, particularly in the extraction of theobromine from cocoa beans. In general, the use of ethanol results in lower solvent and energy requirements and thereby improved extraction efficiency.",Journal of agricultural and food chemistry,"['D002099', 'D002110', 'D002245', 'D025924', 'D000431', 'D030019', 'D018515', 'D029631', 'D012639', 'D013805', 'D014970']","['Cacao', 'Caffeine', 'Carbon Dioxide', 'Chromatography, Supercritical Fluid', 'Ethanol', 'Ilex paraguariensis', 'Plant Leaves', 'Sapindaceae', 'Seeds', 'Theobromine', 'Xanthines']","Extraction of methylxanthines from guaran seeds, mat© leaves, and cocoa beans using supercritical carbon dioxide and ethanol.","['Q000737', 'Q000302', None, None, None, 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000302', 'Q000302']","['chemistry', 'isolation & purification', None, None, None, 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'isolation & purification', 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/12166966,2002,1,1,figure 4,total -0.64,23349790,"The sensory quality and the contents of quality-determining chemical compounds in unfermented and fermented cocoa from 100 cacao trees (individual genotypes) representing groups of nine genotype spectra (GG), grown at smallholder plantings in the municipality of Waslala, Nicaragua, were evaluated for two successive harvest periods. Cocoa samples were fermented using a technique mimicking recommended on-farm practices. The sensory cocoa quality was assessed by experienced tasters, and seven major chemical taste compounds were quantified by near infrared spectrometry (NIRS). The association of the nine, partially admixed, genotype spectra with the analytical and sensory quality parameters was tested. The individual parameters were analyzed as a function of the factors GG and harvest (including the date of fermentation), individual trees within a single GG were used as replications. In fermented cocoa, significant GG-specific differences were observed for methylxanthines, theobromine-to-caffeine (T/C) ratio, total fat, procyanidin B5 and epicatechin, as well as the sensory attributes global score, astringency, and dry fruit aroma, but differences related to harvest were also apparent. The potential cocoa yield was also highly determined by the individual GG, although there was significant tree-to-tree variation within every single GG. Non-fermented samples showed large harvest-to-harvest variation of their chemical composition, while differences between GG were insignificant. These results suggest that selection by the genetic background, represented here by groups of partially admixed genotype spectra, would be a useful strategy toward enhancing quality and yield of cocoa in Nicaragua. Selection by the GG within the local, genetically segregating populations of seed-propagated cacao, followed by clonal propagation of best-performing individuals of the selected GG could be a viable alternative to traditional propagation of cacao by seed from open pollination. Fast and gentle air-drying of the fermented beans and their permanent dry storage were an efficient and comparatively easy precondition for high cocoa quality.",PloS one,"['D044946', 'D044822', 'D018533', 'D002099', 'D002110', 'D002392', 'D005285', 'D005511', 'D005638', 'D014644', 'D005838', 'D009527', 'D044945', 'D011786', 'D012639', 'D019265', 'D013649', 'D013805', 'D014197', 'D014970']","['Biflavonoids', 'Biodiversity', 'Biomass', 'Cacao', 'Caffeine', 'Catechin', 'Fermentation', 'Food Handling', 'Fruit', 'Genetic Variation', 'Genotype', 'Nicaragua', 'Proanthocyanidins', 'Quality Control', 'Seeds', 'Spectroscopy, Near-Infrared', 'Taste', 'Theobromine', 'Trees', 'Xanthines']","Diversity of cacao trees in Waslala, Nicaragua: associations between genotype spectra, product quality and yield potential.","['Q000032', None, None, 'Q000737', 'Q000032', 'Q000032', None, 'Q000379', 'Q000737', None, None, None, 'Q000032', None, 'Q000737', None, None, 'Q000032', 'Q000737', 'Q000032']","['analysis', None, None, 'chemistry', 'analysis', 'analysis', None, 'methods', 'chemistry', None, None, None, 'analysis', None, 'chemistry', None, None, 'analysis', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/23349790,2013,1,1,table 4 , -0.64,28318272,"The odor-active constituents of cocoa pulp have been analyzed by aroma extract dilution analysis (AEDA) for the first time. Pulps of three different cocoa varieties have been investigated. The variety CCN51 showed low flavor intensities, in terms of flavor dilution (FD) factors, in comparison to varieties FSV41 and UF564, for which floral and fruity notes were detected in higher intensities. To gain first insights on a molecular level of how the cocoa pulp odorants affected the odor quality of cocoa beans during fermentation, quantitative measurements of selected aroma compounds were conducted in pulp and bean at different time points of the fermentation. The results showed significantly higher concentrations of 2-phenylethanol and 3-methylbutyl acetate in pulp than in the bean during the different time steps of the fermentation, whereas the reverse could be observed for the odorants linalool and 2-methoxyphenol. The findings of this study constitute a basis for further investigations on the aroma formation of cocoa during fermentation.",Journal of agricultural and food chemistry,"['D002099', 'D005285', 'D005421', 'D008401', 'D009812', 'D012441', 'D012639']","['Cacao', 'Fermentation', 'Flavoring Agents', 'Gas Chromatography-Mass Spectrometry', 'Odorants', 'Saccharomyces cerevisiae', 'Seeds']",Investigations on the Aroma of Cocoa Pulp ( Theobroma cacao L.) and Its Influence on the Odor of Fermented Cocoa Beans.,"['Q000737', None, 'Q000032', None, 'Q000032', 'Q000378', 'Q000737']","['chemistry', None, 'analysis', None, 'analysis', 'metabolism', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28318272,2018,0,0,,if this can be used as unquantified then add them -0.63,19191673,"Procyanidins (PCs) are highly abundant phenolic compounds in the human diet and might be responsible for the health effects of chocolate and wine. Due to low absorption of intact PCs, microbial metabolism might play an important role. So far, only a few studies, with crude extracts rich in PCs but also containing a multitude of other phenolic compounds, have been performed to reveal human microbial PC metabolites. Therefore, the origin of the metabolites remains questionable. This study included in vitro fermentation of purified PC dimers with human microbiota. The main metabolites identified were 2-(3,4-dihydroxyphenyl)acetic acid and 5-(3,4-dihydroxyphenyl)-gamma-valerolactone. Other metabolites detected were 3-hydroxyphenylacetic acid, 4-hydroxyphenylacetic acid, 3-hydroxyphenylpropionic acid, phenylvaleric acids, monohydroxylated phenylvalerolactone, and 1-(3',4'-dihydroxyphenyl)-3-(2'',4'',6''-trihydroxyphenyl)propan-2-ol. Metabolites that could be quantified accounted for at least 12 mol % of the dimers, assuming 1 mol of dimers is converted into 2 mol of metabolite. A degradation pathway, partly different from that of monomeric flavan-3-ols, is proposed.",Journal of agricultural and food chemistry,"['D015102', 'D002851', 'D019281', 'D005243', 'D005285', 'D005707', 'D056604', 'D006801', 'D007783', 'D013058', 'D010936', 'D044945']","['3,4-Dihydroxyphenylacetic Acid', 'Chromatography, High Pressure Liquid', 'Dimerization', 'Feces', 'Fermentation', 'Gallic Acid', 'Grape Seed Extract', 'Humans', 'Lactones', 'Mass Spectrometry', 'Plant Extracts', 'Proanthocyanidins']","Procyanidin dimers are metabolized by human microbiota with 2-(3,4-dihydroxyphenyl)acetic acid and 5-(3,4-dihydroxyphenyl)-gamma-valerolactone as the major metabolites.","['Q000032', None, None, 'Q000382', None, 'Q000302', None, None, 'Q000032', None, 'Q000737', 'Q000737']","['analysis', None, None, 'microbiology', None, 'isolation & purification', None, None, 'analysis', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19191673,2009,0,0,,no cocoa -0.63,17674523,"In the article results of comparative analysis of grated cocoa and cocoa butter samples are presented. The investigation was done by modern instrumental methods such as HPLC, GC, UV- VIS-spectroscopy, and also with application of titrimetric and grarimetric methods. In the analyzed samples contents of total phenolics changes in an interval 1,0-3,2%, including monomeric proantocyanidins 0,6-1,35%; pyrroloquinoline quinine (PQQ) 0,34-0,76 microg/g; phenyl ethylamine from 2,79 to 14,97 microg/g, tyramine from 9,56 to 71,68 microg/g, dopamine from 5,3 to 25,85 microg/g; theobromine from 3,3 to 8%, caffeine from 0,49 to 0,70%; among the amino acids at the greatest quantities were presented glutaminic and asparaginic acids, arginin and leucin; three main fatty acids were determined - palmitinic (31+/-2% rel.), oleinic (35+/-2% rel.) and stearinic (35+/-2% rel.); the main phytosterins were sytosterin (up to 192 mg%) and obtusifoliol (up to 198,5 mg%).",Voprosy pitaniia,"['D000470', 'D000596', 'D001679', 'D001688', 'D002099', 'D002849', 'D002851', 'D004041', 'D005419', 'D010636', 'D010840', 'D059808', 'D013056']","['Alkaloids', 'Amino Acids', 'Biogenic Amines', 'Biological Products', 'Cacao', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Flavonoids', 'Phenols', 'Phytosterols', 'Polyphenols', 'Spectrophotometry, Ultraviolet']",[Biologically active substances in grated cocoa and cocoa butter].,"['Q000032', 'Q000032', 'Q000032', 'Q000302', 'Q000737', None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, None]","['analysis', 'analysis', 'analysis', 'isolation & purification', 'chemistry', None, None, 'analysis', 'analysis', 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/17674523,2007,,,, -0.63,27784867,"Discriminating vegetable oils and animal and milk fats by infrared absorption spectroscopy is difficult due to similarities in their spectral patterns. Therefore, a rapid and simple method for analyzing vegetable oils, animal fats, and milk fats using TOF/MS with an APCI direct probe ion source was developed. This method enabled discrimination of these oils and fats based on mass spectra and detailed analyses of the ions derived from sterols, even in samples consisting of only a few milligrams. Analyses of the mass spectra of processed foods containing oils and milk fats, such as butter, cheese, and chocolate, enabled confirmation of the raw material origin based on specific ions derived from the oils and fats used to produce the final product.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D005223', 'D005504', 'D005511', 'D013058', 'D010938']","['Fats', 'Food Analysis', 'Food Handling', 'Mass Spectrometry', 'Plant Oils']",Analysis of Processed Foods Containing Oils and Fats by Time of Flight Mass Spectrometry with an APCI Direct Probe.,"['Q000032', 'Q000379', None, 'Q000295', 'Q000032']","['analysis', 'methods', None, 'instrumentation', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27784867,2017,,,,Access to pdf in japanese only -0.63,25062492,"Theobroma cacao is a woody and recalcitrant plant with a very high level of interfering compounds. Standard protocols for protein extraction were proposed for various types of samples, but the presence of interfering compounds in many samples prevented the isolation of proteins suitable for two-dimensional gel electrophoresis (2-DE). An efficient method to extract root proteins for 2-DE was established to overcome these problems. The main features of this protocol are: i) precipitation with trichloroacetic acid/acetone overnight to prepare the acetone dry powder (ADP), ii) several additional steps of sonication in the ADP preparation and extractions with dense sodium dodecyl sulfate and phenol, and iii) adding two stages of phenol extractions. Proteins were extracted from roots using this new protocol (Method B) and a protocol described in the literature for T. cacao leaves and meristems (Method A). Using these methods, we obtained a protein yield of about 0.7 and 2.5 mg per 1.0 g lyophilized root, and a total of 60 and 400 spots could be separated, respectively. Through Method B, it was possible to isolate high-quality protein and a high yield of roots from T. cacao for high-quality 2-DE gels. To demonstrate the quality of the extracted proteins from roots of T. cacao using Method B, several protein spots were cut from the 2-DE gels, analyzed by tandem mass spectrometry, and identified. Method B was further tested on Citrus roots, with a protein yield of about 2.7 mg per 1.0 g lyophilized root and 800 detected spots. ",Genetics and molecular research : GMR,"['D000096', 'D002099', 'D015180', 'D059625', 'D013058', 'D018519', 'D019800', 'D018515', 'D010940', 'D018517', 'D012967', 'D012997', 'D013010', 'D014238']","['Acetone', 'Cacao', 'Electrophoresis, Gel, Two-Dimensional', 'Liquid-Liquid Extraction', 'Mass Spectrometry', 'Meristem', 'Phenol', 'Plant Leaves', 'Plant Proteins', 'Plant Roots', 'Sodium Dodecyl Sulfate', 'Solvents', 'Sonication', 'Trichloroacetic Acid']",Efficient method of protein extraction from Theobroma cacao L. roots for two-dimensional gel electrophoresis and mass spectrometry analyses.,"[None, 'Q000737', None, 'Q000379', None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', None, None, None, None]","[None, 'chemistry', None, 'methods', None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25062492,2015,0,0,,roots of the tree -0.63,9246735,"Oral carbohydrate clearance and acid production were monitored over a two hour time period following the ingestion of six foods (chocolate bar, potato chip, oreo cookie, sugar cube, raisin and jelly bean). Each food was evaluated intra-orally in eight volunteers. Oral fluid samples were obtained from each volunteer at 30 min intervals at five different tooth sites using absorbent paper points. The oral fluid samples were analyzed qualitatively and quantitatively for carbohydrates and organic acids using high performance liquid chromatography. Data obtained for each food were averaged and subjected to statistical analysis. The quantity of lactic acid produced 30 min after ingestion was found to be in the following order: (highest) raisin > chocolate bar > sugar cube > jelly bean > oreo cookie > potato chip (least). Two hours after food intake the order had changed significantly: potato chip > jelly bean > sugar cube > chocolate bar > oreo cookie > raisin. A direct linear relationship existed between lactic acid production and the presence of glucose. In foods containing cooked starch prolonged clearance occurs via the intermediate metabolites maltotriose, maltose and glucose. Results indicated that the term 'stickiness', when used to label certain foods such as jelly bean and chocolate bar, should be used cautiously. Foods containing only cooked starch or cooked starch and sugars can be considered as 'sticky', since glucose arising from their intra-oral degradation contributed to acid production over prolonged periods of time.",Zeitschrift fur Ernahrungswissenschaft,"['D000328', 'D002099', 'D002851', 'D004040', 'D019422', 'D005638', 'D006801', 'D007700', 'D007773', 'D012463', 'D011198', 'D013997']","['Adult', 'Cacao', 'Chromatography, High Pressure Liquid', 'Dietary Carbohydrates', 'Dietary Sucrose', 'Fruit', 'Humans', 'Kinetics', 'Lactates', 'Saliva', 'Solanum tuberosum', 'Time Factors']",Intra-oral lactic acid production during clearance of different foods containing various carbohydrates.,"[None, None, None, 'Q000378', 'Q000378', None, None, None, 'Q000378', 'Q000737', None, None]","[None, None, None, 'metabolism', 'metabolism', None, None, None, 'metabolism', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9246735,1997,,,, -0.63,12621878,"The content of fat and fatty acids in 13 selected snack products (nuts and seeds) purchased on the marked in Warsaw region in 2000 have been investigated. The content of fat in examined products varied from 41% to 68%. The fat of nuts and seeds was rich in unsaturated fatty acids, except cocoa product.",Roczniki Panstwowego Zakladu Higieny,"['D002849', 'D004042', 'D005231', 'D006801', 'D009754', 'D011044', 'D012639']","['Chromatography, Gas', 'Dietary Fats, Unsaturated', 'Fatty Acids, Unsaturated', 'Humans', 'Nuts', 'Poland', 'Seeds']",[The content of fat and fatty acids in selected snack products (nuts and seeds)].,"[None, 'Q000032', 'Q000032', None, 'Q000737', None, 'Q000737']","[None, 'analysis', 'analysis', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12621878,2003,,,, -0.63,16019834,"The aim of this study was to investigate the influence of the shelling process on the presence of ochratoxin A (OTA) in cocoa samples. Twenty-two cocoa samples were analysed for the determination of OTA before (cocoa bean) and after undergoing manual shelling process (cocoa nib). In order to determine OTA contamination in cocoa samples, a validated high-performance liquid chromatography (HPLC) method with fluorescence detection was used for the quantitative analysis of ochratoxin A (OTA). In both types of samples, OTA was extracted with methanol-3% sodium hydrogen carbonate solution and then purified using immunoaffinity columns prior to HPLC analysis. Due to the fact that different recovery values were obtained for OTA from both types of samples, a revalidation of the method in the case of cocoa nibs was needed. Revalidation was based on the following criteria: Selectivity, limits of detection and quantification (0.03 and 0.1 microg kg(-1), respectively), precision (within-day and between-day variability) and recovery 84.2% (RSD = 7.1%), and uncertainty (30%). Fourteen of the twenty-two cocoa bean samples (64%) suffered a loss of OTA of more than 95% due to shelling, six samples suffered a loss of OTA in the range 65-95%, and only one sample presented a reduction of less than 50%. The principal conclusion derived from this study is that OTA contamination in cocoa beans is concentrated in the shell; therefore, improvements of the industrial shelling process could prevent OTA occurrence in cocoa final products.",Food additives and contaminants,"['D002099', 'D002851', 'D005504', 'D005506', 'D005511', 'D009793']","['Cacao', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Food Contamination', 'Food Handling', 'Ochratoxins']",Occurrence of ochratoxin A in cocoa beans: effect of shelling.,"['Q000737', 'Q000379', 'Q000379', 'Q000032', 'Q000379', 'Q000032']","['chemistry', 'methods', 'methods', 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16019834,2005,,,, -0.63,15264891,"An improved sample preparation (extraction and cleanup) is presented that enables the quantification of low levels of acrylamide in difficult matrixes, including soluble chocolate powder, cocoa, coffee, and coffee surrogate. Final analysis is done by isotope-dilution liquid chromatography-electrospray ionization tandem mass spectrometry (LC-MS/MS) using d3-acrylamide as internal standard. Sample pretreatment essentially encompasses (a) protein precipitation with Carrez I and II solutions, (b) extraction of the analyte into ethyl acetate, and (c) solid-phase extraction on a Multimode cartridge. The stability of acrylamide in final extracts and in certain commercial foods and beverages is also reported. This approach provided good performance in terms of linearity, accuracy and precision. Full validation was conducted in soluble chocolate powder, achieving a decision limit (CCalpha) and detection capability (CCbeta) of 9.2 and 12.5 microg/kg, respectively. The method was extended to the analysis of acrylamide in various foodstuffs such as mashed potatoes, crisp bread, and butter biscuit and cookies. Furthermore, the accuracy of the method is demonstrated by the results obtained in three inter-laboratory proficiency tests.",Journal of agricultural and food chemistry,"['D020106', 'D002099', 'D002853', 'D003069', 'D004355', 'D013058']","['Acrylamide', 'Cacao', 'Chromatography, Liquid', 'Coffee', 'Drug Stability', 'Mass Spectrometry']","Improved sample preparation to determine acrylamide in difficult matrixes such as chocolate powder, cocoa, and coffee by liquid chromatography tandem mass spectroscopy.","['Q000032', 'Q000737', 'Q000379', 'Q000737', None, 'Q000379']","['analysis', 'chemistry', 'methods', 'chemistry', None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/15264891,2004,1,1,table 3 and 5,only cocoa -0.63,1479781,"The qualitative and quantitative analytical methods were proposed for the simple and rapid determination of triacetin (TAc) in commercial gummy candies and other foodstuffs by gas chromatography (GC), thin layer chromatography (TLC) and infrared spectroscopy (IR). Each extract from the samples was obtained by pretreatment of the foodstuffs as follows: (A) Gummy candy was dissolved in warm water and the solution was extracted with chloroform. The organic (chloroform) layer was separated. (B) Samples (such as ice cream) containing substantial water were mixed with anhydrous Na2SO4 and stirred to sandy appearance and dried. The residue was homogenized with ether, followed by centrifuging, and the organic (ether) layer was separated. (C) Dried samples (such as chocolate and cookie) were smashed, homogenized with ether, and followed by centrifuging, and the organic (ether) layer was separated. (D) Candy was dissolved in warm water and the solution was extracted with ether. The organic (ether) layer was separated. Each organic layer from (A)-(D) was washed with 10% NaHCO3 and evaporated. The residue containing TAc was dissolved in dichloromethane. The extract obtained was subjected to column chromatography on silica gel. The fractions containing TAc were employed in GC with 25% PEG-20M column, TLC, and IR analyses. Recovery of TAc from gummy candy was 99.1 +/- 3.0% and those from other foodstuffs ranged from was 82.1 to 99.4% by GC. Detection limit by this method was 10 ppm. TAc was found to contain at a level as high as 550 ppm in one domestic gummy candy. On the other hand, one imported gummy candy contained no more than 20 ppm of TAc gummy candy.",The Kitasato archives of experimental medicine,"['D001628', 'D002099', 'D002182', 'D002849', 'D002855', 'D005503', 'D005504', 'D007054', 'D012997', 'D013055', 'D014215']","['Beverages', 'Cacao', 'Candy', 'Chromatography, Gas', 'Chromatography, Thin Layer', 'Food Additives', 'Food Analysis', 'Ice Cream', 'Solvents', 'Spectrophotometry, Infrared', 'Triacetin']",Triacetin as food additive in gummy candy and other foodstuffs on the market.,"['Q000032', 'Q000737', 'Q000032', None, None, 'Q000032', None, 'Q000032', None, None, 'Q000032']","['analysis', 'chemistry', 'analysis', None, None, 'analysis', None, 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/1479781,1993,,,, -0.63,19007497,"A reverse-phase liquid chromatography analysis is used to access the quantity of theobromine, (+)-catechin, caffeine, and (-)-epicatechin in Standard Reference Material 2384 Baking Chocolate, cocoa, cocoa beans, and cocoa butter using water or a portion of the mobile phase as the extract. The procedure requires minimal sample preparation. Theobromine, (+)-catechin, caffeine, and (-)-epicatechin are detected by UV absorption at 273 nm after separation using a 0.3% acetic acid-methanol gradient (volume fractions) and quantified using external standards. The limit of detection for theobromine, (+)-catechin, caffeine, and (-)-epicatechin averages 0.08, 0.06, 0.06, and 0.06 microg/mL, respectively. The method when applied to Standard Reference Material 2384 Baking Chocolate; baking chocolate reference material yields results that compare to two different, separate procedures. Theobromine ranges from 26000 mg/kg in cocoa to 140 mg/kg in cocoa butter; (+)-catechin from 1800 mg/kg in cocoa to below detection limits of < 32 mg/kg in cocoa butter; caffeine from 2400 mg/kg in cocoa to 400 mg/kg in cocoa butter, and (-)-epicatechin from 3200 mg/kg in cocoa to BDL, < 27 mg/kg, in cocoa butter. The mean recoveries from cocoa are 102.4 +/- 0.6% for theobromine, 100.0 +/- 0.6 for (+)-catechin, 96.2 +/- 2.1 for caffeine, and 106.2 +/- 1.7 for (-)-epicatechin.",Journal of chromatographic science,"['D002099', 'D002110', 'D002392', 'D002851', 'D004041', 'D015203', 'D013805']","['Cacao', 'Caffeine', 'Catechin', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Reproducibility of Results', 'Theobromine']","Simultaneous determination of theobromine, (+)-catechin, caffeine, and (-)-epicatechin in standard reference material baking chocolate 2384, cocoa, cocoa beans, and cocoa butter.","['Q000737', 'Q000032', 'Q000032', None, 'Q000032', None, 'Q000032']","['chemistry', 'analysis', 'analysis', None, 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19007497,2008,,,, -0.63,3391947,"A method for the quantitative determination of monoethylene glycol (MEG) and diethylene glycol (DEG) in chocolate is described. The procedure involves dissolving the chocolate in hot water, defatting with hexane, removing sugars by precipitation, and analyzing as trimethylsilyl (TMS) ether derivatives by capillary gas chromatography. The use of butan-1,4-diol as an internal standard corrects for recovery, which is between 50 and 60%, to give a relative standard deviation of 10-11% for the determination of both glycols at the level of 50 mg/kg. The presence of MEG and DEG in chocolate is confirmed by full scanning gas chromatography/mass spectrometry of the TMS derivatives.",Journal - Association of Official Analytical Chemists,"['D002099', 'D002482', 'D002849', 'D019855', 'D005026', 'D005511', 'D005519', 'D010945', 'D012997']","['Cacao', 'Cellulose', 'Chromatography, Gas', 'Ethylene Glycol', 'Ethylene Glycols', 'Food Handling', 'Food Preservation', 'Plants, Edible', 'Solvents']",Gas chromatographic determination of monoethylene glycol and diethylene glycol in chocolate packaged in regenerated cellulose film.,"['Q000032', 'Q000032', None, None, 'Q000032', None, None, 'Q000032', None]","['analysis', 'analysis', None, None, 'analysis', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/3391947,1988,,,, -0.63,730638,"A method for determining aflatoxins by high pressure liquid chromatography (HPLC) with fluorescence detection after CB extraction and cleanup has been applied to various foods. Recoveries at 1--15 ppb levels from green coffee and peanut butter was 72--85 and 74--104%, respectively. Precision of the method has been tested for peanut butter. Other products to which the method has been successfully applied include tree nuts, seeds, grains, chocolate-covered peanut butter candy, and roasted, salted-in-shell peanuts. High levels of aflatoxins found in several samples of nuts by this method have been verified by the official thin layer chromatographic (TLC) method. The advantages of this HPLC method are speed, precision, sensitivity, selectivity, and immediate chemical confirmation of aflatoxins B1 and G1. None of the products analyzed required special cleanup procedures. Preparative-scale HPLC was used to isolate purified B1 for toxicity testing.",Journal - Association of Official Analytical Chemists,"['D000348', 'D010367', 'D002851', 'D005504']","['Aflatoxins', 'Arachis', 'Chromatography, High Pressure Liquid', 'Food Analysis']",Reverse phase high pressure liquid chromatographic determination of aflatoxins in foods.,"['Q000032', 'Q000032', 'Q000379', None]","['analysis', 'analysis', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/730638,1979,,,, -0.63,27418182,"Fast methods for the extraction and analysis of various secondary metabolites from cocoa products were developed and optimized regarding speed and separation efficiency. Extraction by pressurized liquid extraction is automated and the extracts are analyzed by rapid reversed-phase ultra high-performance liquid chromatography and normal-phase high-performance liquid chromatography methods. After extraction, no further sample treatment is required before chromatographic analysis. The analytes comprise monomeric and oligomeric flavanols, flavonols, methylxanthins, N-phenylpropenoyl amino acids, and phenolic acids. Polyphenols and N-phenylpropenoyl amino acids are separated in a single run of 33 min, procyanidins are analyzed by normal-phase high-performance liquid chromatography within 16 min, and methylxanthins require only 6 min total run time. A fourth method is suitable for phenolic acids, but only protocatechuic acid was found in relevant quantities. The optimized methods were validated and applied to 27 dark chocolates, one milk chocolate, two cocoa powders and two food supplements based on cocoa extract. ",Journal of separation science,"['D002099', 'D005591', 'D002851', 'D010936', 'D059808', 'D064210']","['Cacao', 'Chemical Fractionation', 'Chromatography, High Pressure Liquid', 'Plant Extracts', 'Polyphenols', 'Secondary Metabolism']",Fast and comprehensive analysis of secondary metabolites in cocoa products using ultra high-performance liquid chromatography directly after pressurized liquid extraction.,"['Q000737', 'Q000379', 'Q000379', 'Q000032', 'Q000737', None]","['chemistry', 'methods', 'methods', 'analysis', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/27418182,2018,1,3,"table S2.1, S2.1 and S3.1",all the contents are in the supplement material for the paper -0.62,8896285,"A high intake of trans fatty acids in children may be disadvantageous because of untoward effects on lipoprotein metabolism and a possible impairment of arachidonic acid synthesis. We measured the trans fatty acid content of different brands of spreads and cold cuts typically consumed by German children because these foods may contribute a considerable portion of total trans fatty acid intake. The highest trans fatty acid contents were found in regular margarines (4.5, 0.0-10.6; median %-wt/wt of fatty acids, minimal-maximal), chocolate spreads (5.5, 0.7-11.1), butter (4.7, 3.7-5.2) and cheese (3.6, 1.8-4.0), while lower values were present in diet margarines (0.2, 0.0-0.4), vegetarian spreads (0.2, 0.1-0.4), peanut butter (0.0, 0.0-0.3) and sausages (1.7, 0.6-6.4). Calculations of typical dietary plans for young children show that food selection and variations in trans fatty acid contents may lead to marked differences in daily trans intake of > 100% (3.1 g/d vs. 1.5 g/d). We propose that trans fatty acid content should be declared on labels of fatty food products to enable the consumer to choose, and further attempts should be made to lower trans fatty acid formation during technical hydrogenation.",Zeitschrift fur Ernahrungswissenschaft,"['D000818', 'D010367', 'D002079', 'D002099', 'D002611', 'D002648', 'D002849', 'D003611', 'D004041', 'D004951', 'D005227', 'D006801', 'D008383', 'D008461']","['Animals', 'Arachis', 'Butter', 'Cacao', 'Cheese', 'Child', 'Chromatography, Gas', 'Dairy Products', 'Dietary Fats', 'Esterification', 'Fatty Acids', 'Humans', 'Margarine', 'Meat Products']",Trans fatty acid contents in spreads and cold cuts usually consumed by children.,"[None, 'Q000737', 'Q000032', 'Q000737', 'Q000032', None, None, 'Q000032', 'Q000032', None, 'Q000032', None, 'Q000032', 'Q000032']","[None, 'chemistry', 'analysis', 'chemistry', 'analysis', None, None, 'analysis', 'analysis', None, 'analysis', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/8896285,1997,,,, -0.62,28207258,"Cocoa is known as an important source of flavan-3-ols, but their fate ""from the bean to the bar"" is not yet clear. Here, procyanidin A2 found in native cocoa beans (9-13 mg/kg) appeared partially epimerized into A2",Journal of agricultural and food chemistry,"['D002099', 'D002392', 'D003296', 'D005285', 'D013058', 'D015394', 'D010936', 'D044945', 'D012639']","['Cacao', 'Catechin', 'Cooking', 'Fermentation', 'Mass Spectrometry', 'Molecular Structure', 'Plant Extracts', 'Proanthocyanidins', 'Seeds']","Procyanidin A2 and Its Degradation Products in Raw, Fermented, and Roasted Cocoa.","['Q000737', 'Q000737', None, None, None, None, 'Q000737', 'Q000737', 'Q000737']","['chemistry', 'chemistry', None, None, None, None, 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28207258,2017,1,1,text under results and discussion first sub section , -0.62,19753497,"We report the migration potential of newly patented low-migration offset printing inks from cardboard food packaging and estimate the potential risk of their migration into food. The complete printing formulation was available and, due to the fact that the solvent compounds in these inks differ from those used in conventional printing inks, the investigation focused on these solvents. Instead of containing mineral and vegetable oils, the low-migration printing inks are based on a novel fatty acid ester. The migration of this alternative solvent was investigated according to DIN EN 14338 in Tenax simulant and in different types of food. For specific detection of the fatty acid ester, LC-MS/MS (APCI) was chosen due to its higher sensitivity and selectivity than GC/MS. Printed packaging materials from three different commercially available food products (meat, chocolate and sweets) were tested. Migration of the fatty acid ester from the packaging into simulants was analysed. For food samples, a clean-up method based on solid-phase extraction was developed and migration of the fatty acid ester into meat, chocolate and sweets was also demonstrated. Levels of contamination of these foods were between 5 and 80 microg fatty acid ester/kg, but levels in food were lower than those in simulants.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D004058', 'D005504', 'D005506', 'D018857', 'D006801', 'D007281', 'D018570', 'D053719']","['Diffusion', 'Food Analysis', 'Food Contamination', 'Food Packaging', 'Humans', 'Ink', 'Risk Assessment', 'Tandem Mass Spectrometry']",Migration of novel offset printing inks from cardboard packaging into food.,"[None, 'Q000379', 'Q000032', 'Q000592', None, None, 'Q000379', 'Q000379']","[None, 'methods', 'analysis', 'standards', None, None, 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/19753497,2010,0,0,,no cocoa -0.62,16843047,"Highly sensitive and interference-free sensitized spectrophotometric method for the determination of Ni(II) ions is described. The method is based on the reaction between Ni(II) ion and benzyl dioxime in micellar media in the presence of sodium dodecyl sulfate (SDS). The absorbance is linear from 0.1 up to 25.0 microg mL-1 in aqueous solution with repeatability (RSD) of 1.0% at a concentration of 1 microg mL-1 and a detection limit of 0.12 ng mL-1 and molar absorption coefficient of 68,600L mol-1 cm-1. The influence of reaction variables including type and amount of surfactant, pH, and amount of ligand and complexation time and the effect of interfering ions are investigated. The proposed procedure was applied to the determination of trace amounts of Ni(II) ion in tap water, river water, chocolate and vegetable without separation or organic solvent extraction.","Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy","['D002413', 'D005504', 'D008024', 'D009532', 'D010091', 'D012680', 'D012967', 'D013053', 'D013501']","['Cations, Divalent', 'Food Analysis', 'Ligands', 'Nickel', 'Oximes', 'Sensitivity and Specificity', 'Sodium Dodecyl Sulfate', 'Spectrophotometry', 'Surface-Active Agents']",Selective and sensitized spectrophotometric determination of trace amounts of Ni(II) ion using alpha-benzyl dioxime in surfactant media.,"['Q000032', 'Q000379', None, 'Q000032', 'Q000737', None, 'Q000737', None, 'Q000737']","['analysis', 'methods', None, 'analysis', 'chemistry', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16843047,2007,0,0,,no cocoa -0.62,24446916,"The occurrence of the bioactive components caffeine (xanthine alkaloid), myosmine and nicotine (pyridine alkaloids) in different edibles and plants is well known, but the content of myosmine and nicotine is still ambiguous in milk/dark chocolate. Therefore, a sensitive method for determination of these components was established, a simple separation of the dissolved analytes from the matrix, followed by headspace solid-phase microextraction coupled with gas chromatography-tandem mass spectrometry (HS-SPME-GC-MS/MS). This is the first approach for simultaneous determination of caffeine, myosmine, and nicotine with a convenient SPME technique. Calibration curves were linear for the xanthine alkaloid (250 to 3000 mg/kg) and the pyridine alkaloids (0.000125 to 0.003000 mg/kg). Residuals of the calibration curves were lower than 15%, hence the limits of detection were set as the lowest points of the calibration curves. The limits of detection calculated from linearity data were for caffeine 216 mg/kg, for myosmine 0.000110 mg/kg, and for nicotine 0.000120 mg/kg. Thirty samples of 5 chocolate brands with varying cocoa contents (30% to 99%) were analyzed in triplicate. Caffeine and nicotine were detected in all samples of chocolate, whereas myosmine was not present in any sample. The caffeine content ranged from 420 to 2780 mg/kg (relative standard deviation 0.1 to 11.5%) and nicotine from 0.000230 to 0.001590 mg/kg (RSD 2.0 to 22.1%). ",Journal of food science,"['D000470', 'D001628', 'D002099', 'D002110', 'D002138', 'D002182', 'D003611', 'D005506', 'D005513', 'D057141', 'D008401', 'D005858', 'D057230', 'D009538', 'D010858', 'D052617', 'D053719', 'D014835']","['Alkaloids', 'Beverages', 'Cacao', 'Caffeine', 'Calibration', 'Candy', 'Dairy Products', 'Food Contamination', 'Food Inspection', 'Food, Preserved', 'Gas Chromatography-Mass Spectrometry', 'Germany', 'Limit of Detection', 'Nicotine', 'Pigmentation', 'Solid Phase Microextraction', 'Tandem Mass Spectrometry', 'Volatilization']","Determination of caffeine, myosmine, and nicotine in chocolate by headspace solid-phase microextraction coupled with gas chromatography-tandem mass spectrometry.","['Q000032', 'Q000032', 'Q000737', 'Q000032', None, 'Q000032', 'Q000032', None, 'Q000379', 'Q000032', None, None, None, 'Q000032', None, None, None, None]","['analysis', 'analysis', 'chemistry', 'analysis', None, 'analysis', 'analysis', None, 'methods', 'analysis', None, None, None, 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24446916,2014,0,0,,chocolates with different content of cocoa -0.62,12926907,Sorghum procyanidins were characterized and quantified from two brown sorghum varieties and their processed products by normal phase HPLC with fluorescence detection. The DP of the procyanidins was determined by thiolysis. Quantification was done by using purified oligomeric and polymeric cocoa procyanidins as external standards. Sorghum procyanidins were composed mostly of high MW (DP > 10) polymers. Significant differences were observed in levels as well as distribution of the different MW procyanidins between the sorghums. Processing of the sorghum brans into cookies and bread significantly reduced the levels of procyanidins; this effect was more pronounced in the higher MW polymers. Cookies had a higher retention of procyanidins (42-84%) than bread (13-69%). Extrusion of sorghum grain resulted in an increase in the levels of procyanidin oligomers with DP /= 6. This suggests a possible breakdown of the high MW polymers to the lower MW constituents during extrusion. Processing changes not only the content of procyanidins in sorghum products but also the relative ratio of the different molecular weights.,Journal of agricultural and food chemistry,"['D044946', 'D001939', 'D002392', 'D002851', 'D005511', 'D006358', 'D008970', 'D006109', 'D011108', 'D044945']","['Biflavonoids', 'Bread', 'Catechin', 'Chromatography, High Pressure Liquid', 'Food Handling', 'Hot Temperature', 'Molecular Weight', 'Poaceae', 'Polymers', 'Proanthocyanidins']",Processing of sorghum (Sorghum bicolor) and sorghum products alters procyanidin oligomer and polymer distribution and content.,"[None, 'Q000032', 'Q000032', None, None, None, None, 'Q000737', 'Q000032', None]","[None, 'analysis', 'analysis', None, None, None, None, 'chemistry', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/12926907,2003,1,1,table 1 ,the rest of the info could be found in the cited paper -0.62,25494681,"Acrylamide (AA) levels in conventional (n = 112) and traditional (n = 43) Colombian foods were analysed by gas chromatography with mass spectrometry (GC/MS) detection. Samples included: infant powdered formula, coffee and chocolate powders, corn snacks, bakery products and tuber-, meat- and vegetable-based foods. There was a wide variability in AA levels among different foods and within different brands of the same food, especially for coffee powder, breakfast cereals biscuits and French fries samples. Among the conventional foods tested, the highest mean AA value was found in bakery products, such as biscuit (1104 _µg kg(-1)) and wafer (1449 _µg kg(-1)), followed by potato chips (916 _µg kg(-1)). On the other hand, among the traditional foods, higher AA amounts were detected in fried platano (2813 _µg kg(-1)) and yuca (3755 _µg kg(-1)) compared to other products. Interestingly, the arepa, a traditional Colombian bakery product made with corn flour, showed a lower AA content (< 75 _µg kg(-1)) when compared with similar bakery products tested, such as soft bread (102-594 _µg kg(-1)), which is a made with wheat flour.","Food additives & contaminants. Part B, Surveillance","['D020106', 'D003069', 'D003105', 'D005506', 'D008401', 'D006801', 'D007223', 'D041943']","['Acrylamide', 'Coffee', 'Colombia', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Infant', 'Infant Formula']",Acrylamide levels in selected Colombian foods.,"['Q000032', 'Q000737', None, 'Q000032', 'Q000379', None, None, 'Q000737']","['analysis', 'chemistry', None, 'analysis', 'methods', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25494681,2016,0,0,,no cocoa -0.62,26138682,"With the revision of the European Tobacco Products Directive (2014/40/EU), characterizing flavors such as strawberry, candy, vanillin or chocolate will be prohibited in cigarettes and fine-cut tobacco. Product surveillance will therefore require analytical means to define and subsequently detect selected characterizing flavors that are formed by supplemented flavors within the complex matrix tobacco. We have analyzed strawberry-flavored tobacco products as an example for characterizing fruit-like aroma. Using this approach, we looked into aroma components to find indicative patterns or features that can be used to satisfy obligatory product information as requested by the European Directive. Accordingly, a headspace solid-phase microextraction (HS-SPME) technique was developed and coupled to subsequent gas chromatography-mass spectrometry (GC/MS) to characterize different strawberry-flavored tobacco products (cigarettes, fine-cut tobacco, liquids for electronic cigarettes, snus, shisha tobacco) for their volatile additives. The results were compared with non-flavored, blend characteristic flavored and other fruity-flavored cigarettes, as well as fresh and dried strawberries. Besides different esters and aldehydes, the terpenes linalool, _±-terpineol, nerolidol and limonene as well as the lactones __-decalactone, __-dodecalactone and __-undecalactone could be verified as compounds sufficient to convey some sort of strawberry flavor to tobacco. Selected flavors, i.e., limonene, linalool, _±-terpineol, citronellol, carvone and __-decalactone, were analyzed further with respect to their stereoisomeric composition by using enantioselective HS-SPME-GC/MS. These experiments confirmed that individual enantiomers that differ in taste or physiological properties can be distinguished within the tobacco matrix. By comparing the enantiomeric composition of these compounds in the tobacco with that of fresh and dried strawberries, it can be concluded that non-natural strawberry aroma is usually used to produce strawberry-flavored tobacco products. Such authenticity control can become of interest particularly when manufacturers claim that natural sources were used for flavoring of products. Although the definition of characterizing flavors by analytical means remains challenging, specific compounds or features are required to be defined for routine screening of reported information. Clarifications by sensory testing might still be necessary, but could be limited to a few preselected samples. ",Archives of toxicology,"['D005062', 'D005421', 'D031985', 'D008401', 'D033161', 'D040541', 'D052617', 'D013237', 'D014026', 'D062789', 'D055549']","['European Union', 'Flavoring Agents', 'Fragaria', 'Gas Chromatography-Mass Spectrometry', 'Government Regulation', 'Marketing', 'Solid Phase Microextraction', 'Stereoisomerism', 'Tobacco', 'Tobacco Products', 'Volatile Organic Compounds']",Toward the stereochemical identification of prohibited characterizing flavors in tobacco products: the case of strawberry flavor.,"[None, 'Q000032', 'Q000737', None, None, 'Q000331', None, None, 'Q000737', 'Q000032', 'Q000032']","[None, 'analysis', 'chemistry', None, None, 'legislation & jurisprudence', None, None, 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26138682,2016,0,0,,no cocoa -0.61,16009604,"A high-performance liquid chromatography (HPLC) method to determine malondialdehyde (MDA) as the 2,4-dinitrophenylhydrazine (DNPH) derivative was applied to biological samples (serum and liver homogenates). Since MDA is considered a presumptive biomarker for lipid peroxidation in live organisms, a model for nutritionally induced oxidative stress (hypercholesterolemic rats) was studied in comparison with normocholesterolemic animals. The effect of diet supplementation with fruits rich in antioxidant polyphenols was assessed. The proposed method showed to be precise and reproducible, as well as sensitive enough to reflect differences in the oxidative status in vivo. A significant decrease of serum and liver MDA concentrations in animals fed diets containing 0.3% of polyphenols from strawberry, cocoa or plum was observed in the normocholesterolemic groups. This reduction was especially noteworthy in the hypercholesterolemic animals, with increased MDA levels indicating enhanced lipid peroxidation in the controls, yet with values parallel to the normocholesterolemic groups in animals fed the polyphenol-rich diets. These results point out the beneficial effects of phenolic antioxidants from fruits in preventing oxidative damage in vivo.","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000818', 'D000975', 'D015415', 'D002851', 'D004032', 'D004195', 'D005638', 'D006937', 'D008099', 'D008297', 'D008315', 'D018384', 'D010636', 'D051381', 'D017208']","['Animals', 'Antioxidants', 'Biomarkers', 'Chromatography, High Pressure Liquid', 'Diet', 'Disease Models, Animal', 'Fruit', 'Hypercholesterolemia', 'Liver', 'Male', 'Malondialdehyde', 'Oxidative Stress', 'Phenols', 'Rats', 'Rats, Wistar']",Determination of malondialdehyde (MDA) by high-performance liquid chromatography in serum and liver as a biomarker for oxidative stress. Application to a rat model for hypercholesterolemia and evaluation of the effect of diets rich in phenolic antioxidants from fruits.,"[None, 'Q000008', 'Q000032', 'Q000379', None, None, 'Q000737', 'Q000097', 'Q000737', None, 'Q000032', 'Q000502', 'Q000008', None, None]","[None, 'administration & dosage', 'analysis', 'methods', None, None, 'chemistry', 'blood', 'chemistry', None, 'analysis', 'physiology', 'administration & dosage', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16009604,2006,0,0,,cocoa in diet -0.61,11324613,"Eight collaborating laboratories assayed 7 blind duplicate pairs of foods for polydextrose content. The 7 test sample pairs ranged from low (2%) to high (95%) levels. The following foods were prepared with polydextrose mixed into the other ingredients and then baked, cooked, or otherwise prepared: milk chocolate candy, iced tea, sugar cookie, grape jelly, soft jellied candy, and powdered drink mix. Collaborators received a polydextrose standard to develop a calibration curve. The method determined polydextrose by ion chromatography, after removal of interfering food components (high molecular weight solubles). Repeatability standard deviations (RSDr) ranged from 3.93 to 9.04%; reproducibility standard deviations (RSDR) ranged from 4.48 to 14.06%. The average recovery was 94%.",Journal of AOAC International,"['D000465', 'D001628', 'D002099', 'D002182', 'D002852', 'D005504', 'D005936', 'D007202', 'D012015', 'D013662', 'D014461']","['Algorithms', 'Beverages', 'Cacao', 'Candy', 'Chromatography, Ion Exchange', 'Food Analysis', 'Glucans', 'Indicators and Reagents', 'Reference Standards', 'Tea', 'Ultracentrifugation']",Determination of polydextrose in foods by ion chromatography: collaborative study.,"[None, 'Q000032', 'Q000737', 'Q000032', None, None, 'Q000032', None, None, 'Q000737', None]","[None, 'analysis', 'chemistry', 'analysis', None, None, 'analysis', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/11324613,2001,,,, -0.61,26041233,"Multi-element stable isotope ratios have been assessed as a means to distinguish between fermented cocoa beans from different geographical and varietal origins. Isotope ratios and percentage composition for C and N were measured in different tissues (cotyledons, shells) and extracts (pure theobromine, defatted cocoa solids, protein, lipids) obtained from fermented cocoa bean samples. Sixty-one samples from 24 different geographical origins covering all four continental areas producing cocoa were analyzed. Treatment of the data with unsupervised (Principal Component Analysis) and supervised (Partial Least Squares Discriminant Analysis) multiparametric statistical methods allowed the cocoa beans from different origins to be distinguished. The most discriminant variables identified as responsible for geographical and varietal differences were the __(15)N and __(13)C values of cocoa beans and some extracts and tissues. It can be shown that the isotope ratios are correlated with the altitude and precipitation conditions found in the different cocoa-growing regions. ",Food chemistry,"['D002099', 'D005285', 'D005843', 'D007554', 'D013058']","['Cacao', 'Fermentation', 'Geography', 'Isotopes', 'Mass Spectrometry']","Multi-element, multi-compound isotope profiling as a means to distinguish the geographical and varietal origin of fermented cocoa (Theobroma cacao L.) beans.","['Q000737', None, None, 'Q000737', 'Q000379']","['chemistry', None, None, 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/26041233,2016,2,3,"table S1, S3 and S5", -0.61,2086709,"A simple method is described for the determination of molecular species of enantiomeric sn-1,2- and sn-2,3-diacylglycerols derived from natural triacylglycerols by Grignard degradation. The method is based on a preparative separation of the enantiomeric diacylglycerols as 3,5-dinitrophenylurethane (DNPU) derivatives by high performance liquid chromatography (HPLC) on a chiral column (25 cm x 4.6 mm ID) containing R-(+)-1-(1-naphthyl)ethylamine as a stationary phase. This is followed by polar capillary gas-liquid chromatography (GLC) of the trimethylsilyl (TMS) ether derivatives of the enantiomeric diacylglycerols derived from the DNPU derivatives using trichlorosilane, which does not cause acyl migration and racemization during the reaction. The cleavage is better than 94% complete. The method was standardized with synthetic sn-1,2- and sn-2,3-dipalmitoyl- and rac-1,2-dioleoylglycerols and was applied to the identification and quantitation of individual molecular species of enantiomeric diacylglycerols generated by Grignard degradation of the triacylglycerols from corn oil, cocoa butter, and lard.",Journal of lipid research,"['D002849', 'D002851', 'D004075', 'D007700', 'D013237', 'D014280']","['Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Diglycerides', 'Kinetics', 'Stereoisomerism', 'Triglycerides']",Determination of molecular species of enantiomeric diacylglycerols by chiral phase high performance liquid chromatography and polar capillary gas-liquid chromatography.,"['Q000379', 'Q000379', 'Q000032', None, None, 'Q000032']","['methods', 'methods', 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/2086709,1991,0,0,,no recordable data -0.61,2613804,"The enantiomers of salsolinol were completely separated as diastereoisomeric derivatives, after reaction with S-1-(1-naphthyl)ethyl isothiocyanate, by reversed-phase high-performance liquid chromatography and quantified by electrochemical detection. Good calibration curves were obtained for the quantification and determination of the enantiomeric composition of salsolinol in human urine. The sensitivity and specificity to the assay also permit the determination of the enantiomeric composition of salsolinol in food such as dried bananas and chocolate.",Journal of chromatography,"['D002099', 'D002851', 'D004563', 'D005638', 'D006801', 'D007546', 'D013237']","['Cacao', 'Chromatography, High Pressure Liquid', 'Electrochemistry', 'Fruit', 'Humans', 'Isoquinolines', 'Stereoisomerism']",Determination of the enantiomeric composition of salsolinol in biological samples by high-performance liquid chromatography with electrochemical detection.,"['Q000032', None, None, 'Q000032', None, 'Q000032', None]","['analysis', None, None, 'analysis', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/2613804,1990,,,, -0.61,10367386,"An immunoaffinity column was prepared from rabbit polyclonal antiserum for the determination of peanut protein from food matrixes. The anti-peanut immunoglobulin G was isolated from antiserum by affinity chromatography on a column coupled with peanut protein and then attached to an AminoLink gel. The column was applied to the determination of peanut protein in chocolate after extraction, immunoaffinity chromatography, and enzyme-linked immunosorbent assay (ELISA). Overall recoveries from chocolate spiked with 0.2-3.2 micrograms/g of peanut protein averaged 77% (range, 72-84%), and the minimum detection limit was 0.1 microgram/g. Chromatography of extracts with the column improved detection limit and eliminated the matrix effect experienced with direct ELISA of chocolate extracts.",Journal of AOAC International,"['D000818', 'D010367', 'D002099', 'D002846', 'D004797', 'D007118', 'D007074', 'D010940', 'D011817', 'D015203']","['Animals', 'Arachis', 'Cacao', 'Chromatography, Affinity', 'Enzyme-Linked Immunosorbent Assay', 'Immunoassay', 'Immunoglobulin G', 'Plant Proteins', 'Rabbits', 'Reproducibility of Results']",An immunoaffinity column for the determination of peanut protein in chocolate.,"[None, 'Q000276', 'Q000737', 'Q000379', None, 'Q000379', None, 'Q000032', None, None]","[None, 'immunology', 'chemistry', 'methods', None, 'methods', None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10367386,1999,,,, -0.61,10995130,"Polydextrose (Litesse) provides physiological effects consistent with dietary fiber. However, AOAC methods for measuring total dietary fiber (TDF) in foods include an ethanol precipitation step in which polydextrose and similar carbohydrates are discarded and therefore not quantitated. This study describes a method developed to quantitate polydextrose in foods. The new method includes water extraction, centrifugal ultrafiltration, multienzyme hydrolysis, and anion exchange chromatography with electrochemical detection. Six foods were prepared with 4 levels of polydextrose to test the ruggedness of the method. Internal validation demonstrated the ruggedness of the method with recoveries ranging from 83 to 104% with an average of 95% (n = 24) and relative standard deviation of recoveries ranging from 0.7 to 13% with an average of 3.3% (n = 24). The value is added to that obtained for dietary fiber content of foods using the AOAC methods, to determine the TDF content of the food.",Journal of AOAC International,"['D000818', 'D000838', 'D001426', 'D001628', 'D002099', 'D002182', 'D002852', 'D004043', 'D000431', 'D005504', 'D005087', 'D005936', 'D006026', 'D006801', 'D006868', 'D007517', 'D013662', 'D014462']","['Animals', 'Anions', 'Bacterial Proteins', 'Beverages', 'Cacao', 'Candy', 'Chromatography, Ion Exchange', 'Dietary Fiber', 'Ethanol', 'Food Analysis', 'Glucan 1,4-alpha-Glucosidase', 'Glucans', 'Glycoside Hydrolases', 'Humans', 'Hydrolysis', 'Isoamylase', 'Tea', 'Ultrafiltration']",Determination of polydextrose as dietary fiber in foods.,"[None, None, None, 'Q000032', 'Q000737', 'Q000032', None, 'Q000032', None, None, 'Q000378', 'Q000032', 'Q000378', None, None, 'Q000378', 'Q000737', None]","[None, None, None, 'analysis', 'chemistry', 'analysis', None, 'analysis', None, None, 'metabolism', 'analysis', 'metabolism', None, None, 'metabolism', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/10995130,2001,,,, -0.61,3398705,"This is the first report confirming the presence of 1,2,3,4-tetrahydroisoquinoline (TIQ) and 1-methyl-1,2,3,4-tetrahydroisoquinoline(1MeTIQ) in a number of foods with a high 2-phenylethylamine content. These compounds were determined by gas chromatography-mass spectrometry. This study also confirmed that 1MeTIQ and TIQ can cross the blood-brain barrier in rat. Thus, these compounds, suspected to have relation to parkinson's disease, may accumulate in the brain from food sources.",Life sciences,"['D000818', 'D001812', 'D001921', 'D002099', 'D002611', 'D055598', 'D002621', 'D005504', 'D008401', 'D007546', 'D008297', 'D010300', 'D010627', 'D051381', 'D011919', 'D044005', 'D014920']","['Animals', 'Blood-Brain Barrier', 'Brain', 'Cacao', 'Cheese', 'Chemical Phenomena', 'Chemistry', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Isoquinolines', 'Male', 'Parkinson Disease', 'Phenethylamines', 'Rats', 'Rats, Inbred Strains', 'Tetrahydroisoquinolines', 'Wine']",Presence of tetrahydroisoquinoline and 1-methyl-tetrahydro-isoquinoline in foods: compounds related to Parkinson's disease.,"[None, None, 'Q000378', 'Q000032', 'Q000032', None, None, None, None, 'Q000032', None, 'Q000209', 'Q000032', None, None, None, 'Q000032']","[None, None, 'metabolism', 'analysis', 'analysis', None, None, None, None, 'analysis', None, 'etiology', 'analysis', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/3398705,1988,,,,no pdf access -0.61,23931630,"Acyl migration is a serious problem in enzymatic modification of fats and oils, particularly in production of cocoa butter equivalent (CBE) through enzymatic acidolysis reaction, which leads to the formation of non-symmetrical triacylglycerols (TAGs) from symmetrical TAGs. Non-symmetrical TAGs may affect the physical properties of final products and are therefore often undesired. Consequently, an accurate method is needed to determine positional isomer TAGs during the production of CBE. A bidimentional high-performance liquid chromatography (HPLC) method with combination of non-aqueous reversed-phase HPLC and silver ion HPLC joining with an evaporative light scattering detector was successfully developed for the analysis of stereospecific TAGs. The best separation of positional isomer standards was obtained with a heptane/acetone mobile-phase gradient at 25 _C and 1 mL/min. The developed method was then used in multidimensional determination of the TAG positional isomers in fat and oil blends and successfully identified the TAGs and possible isomers in enzymatically acidolyzed CBE. ",Journal of agricultural and food chemistry,"['D002851', 'D004041', 'D007536', 'D014280']","['Chromatography, High Pressure Liquid', 'Dietary Fats', 'Isomerism', 'Triglycerides']",Development of an offline bidimensional high-performance liquid chromatography method for analysis of stereospecific triacylglycerols in cocoa butter equivalents.,"['Q000295', 'Q000032', None, 'Q000737']","['instrumentation', 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23931630,2014,0,0,,no cocoa tested -0.61,20543060,"Pectinolytic enzymes play an important role in cocoa fermentation. In this study, we characterized three extracellular pectate lyases (Pels) produced by bacilli isolated from fermenting cocoa beans. These enzymes, named Pel-22, Pel-66, and Pel-90, were synthesized by Bacillus pumilus BS22, Bacillus subtilis BS66, and Bacillus fusiformis BS90, respectively. The three Pels were produced under their natural conditions and purified from the supernatants using a one-step chromatography method. The purified enzymes exhibited optimum activity at 60 degrees C, and the half-time of thermoinactivation at this temperature was approximately 30 min. Pel-22 had a low specific activity compared with the other two enzymes. However, it displayed high affinity for the substrate, about 2.5-fold higher than those of Pel-66 and Pel-90. The optimum pHs were 7.5 for Pel-22 and 8.0 for Pel-66 and Pel-90. The three enzymes trans-eliminated polygalacturonate in a random manner to generate two long oligogalacturonides, as well as trimers and dimers. A synergistic effect was observed between Pel-22 and Pel-66 and between Pel-22 and Pel-90, but not between Pel-90 and Pel-66. The Pels were also strongly active on highly methylated pectins (up to 60% for Pel-66 and Pel-90 and up to 75% for Pel-22). Fe(2+) was found to be a better cofactor than Ca(2+) for Pel-22 activity, while Ca(2+) was the best cofactor for Pel-66 and Pel-90. The amino acid sequences deduced from the cloned genes showed the characteristics of Pels belonging to Family 1. The pel-66 and pel-90 genes appear to be very similar, but they are different from the pel-22 gene. The characterized enzymes form two groups, Pel-66/Pel-90 and Pel-22; members of the different groups might cooperate to depolymerize pectin during the fermentation of cocoa beans.",Applied and environmental microbiology,"['D001407', 'D002099', 'D002118', 'D002413', 'D002845', 'D003001', 'D003067', 'D004269', 'D004795', 'D006358', 'D006863', 'D007501', 'D008969', 'D010368', 'D011133', 'D055550', 'D011994', 'D012639', 'D017422']","['Bacillus', 'Cacao', 'Calcium', 'Cations, Divalent', 'Chromatography', 'Cloning, Molecular', 'Coenzymes', 'DNA, Bacterial', 'Enzyme Stability', 'Hot Temperature', 'Hydrogen-Ion Concentration', 'Iron', 'Molecular Sequence Data', 'Pectins', 'Polysaccharide-Lyases', 'Protein Stability', 'Recombinant Proteins', 'Seeds', 'Sequence Analysis, DNA']",Biochemical properties of pectate lyases produced by three different Bacillus strains isolated from fermenting cocoa beans and characterization of their cloned genes.,"['Q000201', 'Q000382', 'Q000378', 'Q000378', 'Q000379', None, 'Q000378', 'Q000737', None, None, None, 'Q000378', None, 'Q000378', 'Q000737', None, 'Q000235', 'Q000382', None]","['enzymology', 'microbiology', 'metabolism', 'metabolism', 'methods', None, 'metabolism', 'chemistry', None, None, None, 'metabolism', None, 'metabolism', 'chemistry', None, 'genetics', 'microbiology', None]",https://www.ncbi.nlm.nih.gov/pubmed/20543060,2010,0,0,, -0.61,1173811,"A method is described for the determination of total cholesterol in multicomponent foods and also other products such as nonfat dry milk, dried whole egg solids, and certain candy bars. The lipid is extracted from the sample by a mixed solvent and saponified. The unsaponifiable fraction which contains the cholesterol and other sterols is extracted with benzene. An aliquot is evaporated to dryness and the residue is dissolved in dimethylformamide. The sterols are derivatized to form trimethylsilyl (TMS) ethers. The TMS-cholesterol derivative is quantitatively determined by gas-liquid chromatography, using 5alpha-cholestane as an internal standard. Nine laboratories participated in a collaborative study of the determination of total cholesterol in deviled ham sandwich spread, vegetable beef stew, corned beef hash, frozen chicken pot pie, pizza pepperoni, fish sticks, breaded shrimp, chocolate-covered candy bars, dried whole egg solids, and nonfat dry milk and the results are reported here. The coefficient of variation ranged from 5.64 to 23.2%, with an average coefficient of variation of 14.8%.",Journal - Association of Official Analytical Chemists,"['D000818', 'D002182', 'D002784', 'D002849', 'D004531', 'D005504', 'D005525', 'D008460', 'D008722', 'D008892', 'D014675']","['Animals', 'Candy', 'Cholesterol', 'Chromatography, Gas', 'Eggs', 'Food Analysis', 'Food-Processing Industry', 'Meat', 'Methods', 'Milk', 'Vegetables']",Gas-liquid chromatographic determination of total cholesterol in multicomponent foods.,"[None, 'Q000032', 'Q000032', None, 'Q000032', None, None, 'Q000032', None, 'Q000032', 'Q000032']","[None, 'analysis', 'analysis', None, 'analysis', None, None, 'analysis', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/1173811,1975,,,, -0.61,25772568,"A method has been developed for the specific and sensitive determination of Cr(VI) in foods. First, the interactions between Cr(VI) and the matrices were investigated by size-exclusion HPLC-ICP-MS (SEC-ICP-MS). Evidence was found for the complexation of Cr(VI) potentially present with the ligands. For__quantification of Cr(VI), the method was based on an alkaline extraction (NH4OH solution at pH__11.5) followed by Cr(VI) determination by anion-exchange HPLC-ICP-MS. Analytical performances of the method were satisfactory in terms of linearity, specificity, accuracy, repeatability, and intermediate precision. Detection limits ranged from 1 to 10____g/kg, depending on the matrices investigated. The method was then applied for the determination of Cr(VI) in several products (dairy products, flour, chocolate, vegetables, fruits, meat, fish, eggs, and beverages) from different brands and origins. Cr(VI) was found in none of the samples investigated. To further investigate the reason for this absence, a stability study of spiked Cr(VI) was therefore conducted. A semi-skimmed cow milk was selected for this study. Cr(VI) was shown to be unstable in this matrix with a degradation rate increasing with the temperature. ",Analytical and bioanalytical chemistry,"['D002850', 'D002851', 'D002852', 'D002857', 'D005504', 'D005506', 'D015203', 'D012680', 'D021241']","['Chromatography, Gel', 'Chromatography, High Pressure Liquid', 'Chromatography, Ion Exchange', 'Chromium', 'Food Analysis', 'Food Contamination', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Spectrometry, Mass, Electrospray Ionization']",Cr(VI) speciation in foods by HPLC-ICP-MS: investigation of Cr(VI)/food interactions by size exclusion and Cr(VI) determination and stability by ion-exchange on-line separations.,"[None, 'Q000379', 'Q000379', 'Q000032', 'Q000379', 'Q000032', None, None, 'Q000379']","[None, 'methods', 'methods', 'analysis', 'methods', 'analysis', None, None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/25772568,2016,0,0,,no cocoa -0.61,26829387,"Cocoa is an important ingredient for the chocolate industry and for many food products. However, it is prone to contamination by ochratoxin A (OTA), which is highly toxic and potentially carcinogenic to humans. In this work, four different extraction methods were tested and compared based on their recoveries. The best protocol was established which involves an organic solvent-free extraction method for the detection of OTA in cocoa beans using 1% sodium hydrogen carbonate (NaHCO3) in water within 30 min. The extraction method is rapid (as compared with existing methods), simple, reliable and practical to perform without complex experimental set-ups. The cocoa samples were freshly extracted and cleaned-up using immunoaffinity column (IAC) for HPLC analysis using a fluorescence detector. Under the optimised condition, the limit of detection (LOD) and limit of quantification (LOQ) for OTA were 0.62 and 1.25 ng ml(-1) respectively in standard solutions. The method could successfully quantify OTA in naturally contaminated samples. Moreover, good recoveries of OTA were obtained up to 86.5% in artificially spiked cocoa samples, with a maximum relative standard deviation (RSD) of 2.7%. The proposed extraction method could determine OTA at the level 1.5 _µg kg(-)(1), which surpassed the standards set by the European Union for cocoa (2 _µg kg(-1)). In addition, an efficiency comparison of IAC and molecular imprinted polymer (MIP) column was also performed and evaluated.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D002099', 'D002851', 'D005506', 'D005511', 'D009793']","['Cacao', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Food Handling', 'Ochratoxins']",Evaluation of extraction methods for ochratoxin A detection in cocoa beans employing HPLC.,"['Q000737', None, 'Q000032', None, 'Q000032']","['chemistry', None, 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26829387,2016,1,1,table 1a,only batch 1 and 3 -0.6,26675864,"A multi-residue method based on two different extraction procedures was developed and compared with liquid chromatography electrospray ionization tandem mass spectrometry analysis of eighteen water-soluble artificial colours including Tartrazine (E102), Chrysoine (E103), Quinoline Yellow (E104), Yellow 2G (E107), Sunset Yellow (E110), Azorubine (E122), Amaranth (E123), Ponceau 4R (E124), Erythrosine (E127), Red 2G (E128), Allura Red (E129), Patent Blue V (E131), Indigo Carmine (E132), Brilliant Blue (E133), Green S (E142), Fast Green (E143), Brilliant Black (E151), and Black 7984 (E152) in sugar and gummy confectionary, ice-cream, and chocolate sweets. Sample preparation included SPE clean-up and liquid-liquid extraction for ice-cream and chocolate sweets. Accuracy was evaluated by recovery experiments. Correlation between response and concentration was obtained with R(2)>0.98 for all but six colours. Limits of quantification were within the 10-50 __g/kg range for E129; 20-200 __g/kg for E152; 10-250 __g/kg for E103; 10-500 __g/kg for E102, E104, E107, E110, E122, E123, E124, E127, E128, E131, E133; 20-800 __g/kg for E132, 142, 151; and 10-1000 __g/kg for E143. CV for repeatability ranged from 4.0% to 51.0%, while the CV for intermediate reproducibility ranged from 5.8% to 41.4%. Finally, recoveries varied from 84.3% to 166.0%. Together, these demonstrate that the method has been validated for complex matrices and is, thus, fit-for-purpose.",Food chemistry,"['D002182', 'D002851', 'D005505', 'D057230', 'D059625', 'D015203', 'D021241', 'D053719']","['Candy', 'Chromatography, High Pressure Liquid', 'Food Coloring Agents', 'Limit of Detection', 'Liquid-Liquid Extraction', 'Reproducibility of Results', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry']",Determination of 18 water-soluble artificial dyes by LC-MS in selected matrices.,"['Q000032', 'Q000379', 'Q000032', None, None, None, None, None]","['analysis', 'methods', 'analysis', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/26675864,2016,0,0,,no cocoa -0.6,27591614,"The (13)C/(12)C carbon isotope ratio is a chemical parameter with many important applications in several scientific area and the technique of choice currently used for the __(13)C determination is the isotope ratio mass spectrometry (IRMS). This latter is highly accurate (0.1__) and sensitive (up to 0.01__), but at the same time expensive and complex. The objective of this work was to assess the reliability of FTIR and NDIRS techniques for the measurement of carbon stable isotope ratio of food sample, in comparison to IRMS. IRMS, NDIRS and FTIR were used to analyze samples of food, such as oil, durum, cocoa, pasta and sugar, in order to determine the natural abundance isotopic ratio of carbon in a parallel way. The results were comparable, showing a close relationship among the three techniques. The main advantage in using FTIR and NDIRS is related to their cheapness and easy-to-operate in comparison to IRMS. ",Talanta,"['D002247', 'D000069956', 'D019422', 'D005433', 'D005504', 'D013058', 'D010938', 'D013055', 'D013213']","['Carbon Isotopes', 'Chocolate', 'Dietary Sucrose', 'Flour', 'Food Analysis', 'Mass Spectrometry', 'Plant Oils', 'Spectrophotometry, Infrared', 'Starch']",FTIR and NDIR spectroscopies as valuable alternatives to IRMS spectrometry for the __(13)C analysis of food.,"['Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000379', 'Q000379', 'Q000032', 'Q000379', 'Q000032']","['analysis', 'analysis', 'analysis', 'analysis', 'methods', 'methods', 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27591614,2018,2,1,table 2, -0.6,19897953,"Pollution levels of toxic heavy metals (Pb, Cd, Hg) and arsenic in existing food additives used as food colors (40 samples of 15 kinds) were investigated. Heavy metals were detected in 8 samples; Pb in 1 sample (2.8 microg/g), Hg in 8 samples (0.1-3.4 microg/g) and arsenic in 2 samples (1.7, 2.6 microg/g). The Pb level in 1 sample of lac color (2.8 microg/g) exceeded the limit of 2 microg/g proposed by JECFA and Hg levels in 3 samples of cacao color (1.2-3.4 microg/g) exceeded the limit of 1 microg/g in the EU specification.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D001151', 'D002104', 'D005504', 'D005505', 'D005506', 'D007854', 'D008628', 'D019216', 'D013054']","['Arsenic', 'Cadmium', 'Food Analysis', 'Food Coloring Agents', 'Food Contamination', 'Lead', 'Mercury', 'Metals, Heavy', 'Spectrophotometry, Atomic']",[Survey of toxic heavy metals and arsenic in existing food additives (natural colors)].,"['Q000032', 'Q000032', None, 'Q000737', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000379']","['analysis', 'analysis', None, 'chemistry', 'analysis', 'analysis', 'analysis', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/19897953,2010,,,,japanese paper -0.6,19877640,"Organochlorine and organophosphate pesticides in corn muffin mix and cocoa beans were analyzed using disposable pipette extraction (DPX) for rapid cleanup followed by gas chromatography-mass spectrometry (GC-MS). The DPX method in this study used weak anion exchange (WAX) mechanisms to remove the major sample matrix interferences, fatty acids, from the chromatographic analyses. The limits of detection (LOD) were determined to be <10 ppb for all studied pesticides in corn muffin. DPX-WAX exhibited average recoveries reaching 100% for most targeted pesticides, with relative standard deviations below 10%. These results indicate that DPX with weak anion exchange sorbent is effective at eliminating fatty acid interferences in foods of high fat content prior to multiresidue pesticide analysis. Furthermore, the DPX cleanup method takes approximately 2 min to perform. In addition, removal of fatty acids from cocoa beans demonstrates the high capacity of this extraction method for samples containing up to 50% fat.",Journal of agricultural and food chemistry,"['D000097', 'D000327', 'D000837', 'D002099', 'D004041', 'D004209', 'D005227', 'D005504', 'D008401', 'D006843', 'D010755', 'D010573', 'D015203', 'D012639', 'D003313']","['Acetonitriles', 'Adsorption', 'Anion Exchange Resins', 'Cacao', 'Dietary Fats', 'Disposable Equipment', 'Fatty Acids', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Hydrocarbons, Chlorinated', 'Organophosphates', 'Pesticide Residues', 'Reproducibility of Results', 'Seeds', 'Zea mays']",New approach to multiresidue pesticide determination in foods with high fat content using disposable pipette extraction (DPX) and gas chromatography-mass spectrometry (GC-MS).,"[None, None, None, 'Q000737', 'Q000032', None, 'Q000302', 'Q000295', 'Q000379', 'Q000032', 'Q000032', 'Q000032', None, 'Q000737', 'Q000737']","[None, None, None, 'chemistry', 'analysis', None, 'isolation & purification', 'instrumentation', 'methods', 'analysis', 'analysis', 'analysis', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19877640,2010,0,0,, -0.59,18680942,"Ochratoxin A (OTA) is a mycotoxin produced by Aspergillus and Penicillium species, which contaminates cocoa among other food commodities. It has been previously demonstrated that the toxin is concentrated in cocoa shells. The aim of this study was to assay a simple chemical method for ochratoxin A reduction from naturally contaminated cocoa shells. In order to determine the efficiency of the method, a high-performance liquid chromatography method with fluorescence detection was set up beforehand and validated. Ochratoxin A was extracted from cocoa shells with methanol-3% sodium bicarbonate solution and then purified with immunoaffinity columns. The recovery attained was 88.7% (relative standard deviation = 6.36%) and the limits of detection and quantification were 0.06 and 0.2 kg/kg, respectively. For decontamination experiments, the solvent extractor ASE 200 was used. First, aqueous solutions of 2% sodium bicarbonate and potassium carbonate were compared under the same conditions (1,500 lb/in2 at 40 degrees C for 10 min). Higher ochratoxin A reduction was obtained with potassium carbonate (83 versus 27%). Then, this salt was used under different conditions of pressure, temperature, and time. The greatest ochratoxin A reduction was achieved with an aqueous potassium carbonate solution (2%), at 1,000 lb/in2 at 90 degrees C for 10 min. This method could probably be applicable to the cocoa industry because it is fast and relatively economic. From the point of view of human health, the use of potassium carbonate, partially eliminated by rinsing the sample with water, does not likely represent a risk for human health.",Journal of food protection,"['D002099', 'D002254', 'D002851', 'D004305', 'D005453', 'D005504', 'D005506', 'D005511', 'D006801', 'D006874', 'D009793', 'D011188', 'D013696', 'D013997']","['Cacao', 'Carbonates', 'Chromatography, High Pressure Liquid', 'Dose-Response Relationship, Drug', 'Fluorescence', 'Food Analysis', 'Food Contamination', 'Food Handling', 'Humans', 'Hydrostatic Pressure', 'Ochratoxins', 'Potassium', 'Temperature', 'Time Factors']",A simple chemical method reduces ochratoxin A in contaminated cocoa shells.,"['Q000737', 'Q000494', 'Q000379', None, None, None, 'Q000032', 'Q000379', None, None, 'Q000302', 'Q000494', None, None]","['chemistry', 'pharmacology', 'methods', None, None, None, 'analysis', 'methods', None, None, 'isolation & purification', 'pharmacology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18680942,2008,,,, -0.59,16478236,"A method for the separation, isolation, and identification of phytosterols was developed. A commercial phytosterols mixture, Generol 95S, was fractionated first by adsorption silica gel column chromatography and then separated by means of a semipreparative reverse phase high-performance liquid chromatography fitted with a Polaris C8-A column (250 mm x 10 mm i.d., 5 microm) using isocratic acetonitrile:2-propanol:water (2:1:1, v/v/v) as the mobile phase. Milligram scales of six individual phytosterols, including citrostadienol, campesterol, beta-sitosterol, Delta7-avenasterol, Delta7-campesterol, and Delta7-sitosterol, were obtained. Purities of these isolated sterols were 85-98%. Relative response factors (RRF) of these phytosterols were calculated against cholestanol as an authentic commercial standard. These RRF values were used to quantify by gas chromatography-mass spectrometry (GC-MS) the phytosterols content in a reference material, oils, and chocolates.",Journal of agricultural and food chemistry,"['D000327', 'D002099', 'D002845', 'D002851', 'D002855', 'D005504', 'D008401', 'D010840', 'D010938']","['Adsorption', 'Cacao', 'Chromatography', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Phytosterols', 'Plant Oils']",Separation of Delta5- and Delta7-phytosterols by adsorption chromatography and semipreparative reversed phase high-performance liquid chromatography for quantitative analysis of phytosterols in foods.,"[None, 'Q000737', 'Q000379', 'Q000379', None, 'Q000379', None, 'Q000032', 'Q000737']","[None, 'chemistry', 'methods', 'methods', None, 'methods', None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16478236,2006,0,0,,no cocoa -0.59,25051638,"A method was developed and validated for the determination of ochratoxin A (OTA), a fungal metabolite, in cocoa beans of high fat content. The sample was extracted by blending with a 1% sodium bicarbonate solution (pH 10) followed by ultrasonication, and the sample was defatted by treatment with a flocculant. The defatted sample was purified using immunoaffinity column chromatography, and OTA was detected using HPLC with fluorescence detection. The method was fully optimized, validated, and quality controlled using spike recovery analyses, with recoveries of 89-105% over spiking ranges of 320-2.5 ng/g with CV of analyses generally <10% over 4 consecutive years and an LOQ of 0.66 ng/g in cocoa bean samples. This method overcomes the problems posed by the high fat contents of cocoa and chocolate samples with a high degree of reliability.",Journal of AOAC International,"['D002099', 'D002138', 'D002846', 'D002851', 'D009793']","['Cacao', 'Calibration', 'Chromatography, Affinity', 'Chromatography, High Pressure Liquid', 'Ochratoxins']",Determination of ochratoxin A in cocoa beans using immunoaffinity column cleanup with high-performance liquid chromatography.,"['Q000382', None, 'Q000295', 'Q000379', 'Q000032']","['microbiology', None, 'instrumentation', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/25051638,2014,,,,no pdf access -0.59,28946331,"A novel, rapid, simultaneous analysis method for five sugars (fructose, glucose, sucrose, maltose, and lactose) and eight sugar alcohols (erythritol, xylitol, sorbitol, mannitol, inositol, maltitol, lactitol, and isomalt) was developed using UPLC-ELSD, without derivatization. The analysis conditions, including the gradient conditions, modifier concentration and column length, were optimized. Thirteen sugars and sugar alcohols were separated well and the resolution of their peaks was above 1.0. Their optimum analysis condition can be analyzed within 15min. Standard curves for sugars and sugar alcohols with concentrations of 5.0-0.1% and 2.0-0.05% are presented herein, and their correlation coefficients are found to be above 0.999 and the limit of detection (LOD) was around 0.006-0.018%. This novel analysis system can be used for foodstuffs such as candy, chewing gum, jelly, chocolate, processed chocolate products, and snacks containing 0.21-46.41% of sugars and sugar alcohols.",Food chemistry,"['D002241', 'D002851', 'D013402']","['Carbohydrates', 'Chromatography, High Pressure Liquid', 'Sugar Alcohols']",A rapid method for simultaneous quantification of 13 sugars and sugar alcohols in food products by UPLC-ELSD.,"['Q000032', None, 'Q000032']","['analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28946331,2017,0,0,,no cocoa -0.59,17061837,"Bidi cigarettes, small hand-rolled cigarettes produced primarily in India, are sold in the United States in a wide variety of candy-like flavors (e.g. dewberry, chocolate, clove) and are popular with adolescents. Many flavored bidis contain high concentrations of compounds such as eugenol, anethole, methyleugenol, pulegone, and estragole; several of these compounds have known toxic or carcinogenic properties. Clove cigarettes, or kreteks, are another highly flavored tobacco product with high levels of eugenol due to clove buds present in the tobacco filler. In this study, compounds in the burnable portion-the filler and wrapper material actually consumed during the smoking of bidis, kreteks, and U.S. cigarettes-were analyzed. Flavor-related compounds were solvent extracted from the burnable portion of each cigarette with methanol. An aliquot of the methanol extract was heated, and the sample headspace was sampled with a solid-phase microextraction fiber and introduced into a gas chromatograph-mass spectrometer for analysis in selected-ion monitoring mode. High levels of eugenol were detected in five clove-flavored bidi brands ranging from 78.6 to 7130 microg/cigarette (microg/cig), whereas diphenyl ether (128-3550 microg/cig) and methyl anthranilate (154-2360 microg/cig) were found in one grape-flavored bidi brand. A nontobacco herbal bidi brand contained the greatest variety of compounds, including anethole (489-665 microg/cig), eugenol (1670-2470 microg/cig), methyleugenol (27.7-36.6 microg/cig), safrole (32.4-34.4 microg/cig), myristicin (170-247 microg/cig), and elemicin (101-109 microg/cig). Filler from kreteks was found to contain high levels of eugenol, anethole, and coumarin. Flavored bidis and clove cigarettes contain a number of compounds that are present at levels far exceeding those reported in U.S. cigarette tobacco. Research is underway to determine the levels of these compounds delivered in smoke. It is not known what effect inhalation of these compounds has on smokers.",Journal of agricultural and food chemistry,"['D005374', 'D005390', 'D005421', 'D013058', 'D015203', 'D027842', 'D014026']","['Filtration', 'Fires', 'Flavoring Agents', 'Mass Spectrometry', 'Reproducibility of Results', 'Syzygium', 'Tobacco']",Quantification of flavor-related compounds in the unburned contents of bidi and clove cigarettes.,"[None, None, 'Q000032', None, None, 'Q000737', 'Q000737']","[None, None, 'analysis', None, None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/17061837,2007,0,0,,no cocoa -0.59,25722166,"The main procyanidins, including dimeric B2 and B5, trimeric C1, tetrameric and pentameric procyanidins, were isolated from unroasted cocoa beans (Theobroma cacao L.) using various techniques of countercurrent chromatography, such as high-speed countercurrent chromatography (HSCCC), low-speed rotary countercurrent chromatography (LSRCCC) and spiral-coil LSRCCC. Furthermore, dimeric procyanidins B1 and B7, which are not present naturally in the analysed cocoa beans, were obtained after semisynthesis of cocoa bean polymers with (+)-catechin as nucleophile and separated by countercurrent chromatography. In this way, the isolation of dimeric procyanidin B1 in considerable amounts (500mg, purity>97%) was possible in a single run. This is the first report concerning the isolation and semisynthesis of dimeric to pentameric procyanidins from T. cacao by countercurrent chromatography. Additionally, the chemical structures of tetrameric (cinnamtannin A2) and pentameric procyanidins (cinnamtannin A3) were elucidated on the basis of (1)H NMR spectroscopy. Interflavanoid linkage was determined by NOE-correlations, for the first time. ",Food chemistry,"['D044946', 'D002099', 'D002392', 'D002845', 'D009682', 'D015394', 'D010936', 'D011108', 'D044945']","['Biflavonoids', 'Cacao', 'Catechin', 'Chromatography', 'Magnetic Resonance Spectroscopy', 'Molecular Structure', 'Plant Extracts', 'Polymers', 'Proanthocyanidins']","Isolation of dimeric, trimeric, tetrameric and pentameric procyanidins from unroasted cocoa beans (Theobroma cacao L.) using countercurrent chromatography.","['Q000737', 'Q000737', 'Q000737', 'Q000379', None, None, 'Q000737', 'Q000032', 'Q000737']","['chemistry', 'chemistry', 'chemistry', 'methods', None, None, 'chemistry', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25722166,2015,0,0,,no quantif or unquantif data -0.59,10772173,"An AOAC collaborative study was conducted to evaluate the accuracy and reliability of an enzyme assay kit procedure for measuring oligofructans and fructan polysaccharide (inulins) in mixed materials and food products. The sample is extracted with hot water, and an aliquot is treated with a mixture of sucrase (a specific sucrose-degrading enzyme), alpha-amylase, pullulanase, and maltase to hydrolyze sucrose to glucose and fructose, and starch to glucose. These reducing sugars are then reduced to sugar alcohols by treatment with alkaline borohydride solution. The solution is neutralized, and excess borohydride is removed with dilute acetic acid. The fructan is hydrolyzed to fructose and glucose using a mixture of purified exo- and endo-inulinanases (fructanase mixture). The reducing sugars produced (fructose and glucose) are measured with a spectrophotometer after reaction with para-hydroxybenzoic acid hydrazide. The samples analyzed included pure fructan, chocolate, low-fat spread, milk powder, vitamin tablets, onion powder, Jerusalem artichoke flour, wheat stalks, and a sucrose/cellulose control flour. Repeatability relative standard deviations ranged from 2.3 to 7.3%; reproducibility relative standard deviations ranged from 5.0 to 10.8%.",Journal of AOAC International,"['D001894', 'D004798', 'D005504', 'D005630', 'D006026', 'D006868', 'D007202', 'D007444', 'D011786', 'D012996', 'D013053', 'D013393', 'D000516', 'D000520']","['Borohydrides', 'Enzymes', 'Food Analysis', 'Fructans', 'Glycoside Hydrolases', 'Hydrolysis', 'Indicators and Reagents', 'Inulin', 'Quality Control', 'Solutions', 'Spectrophotometry', 'Sucrase', 'alpha-Amylases', 'alpha-Glucosidases']",Measurement of total fructan in foods by enzymatic/spectrophotometric method: collaborative study.,"[None, None, 'Q000379', 'Q000032', 'Q000378', None, None, 'Q000032', None, None, 'Q000379', 'Q000378', 'Q000378', 'Q000378']","[None, None, 'methods', 'analysis', 'metabolism', None, None, 'analysis', None, None, 'methods', 'metabolism', 'metabolism', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/10772173,2000,,,, -0.59,27507506,Cocoa beans are a well-known source of antioxidant polyphenols. Especially individual oligomeric proanthocyanidins demonstrated a significant contribution to the total antioxidant activity of cocoa compared to monomeric compounds. An NP-HPLC-online-DPPH assay was developed for separating the homologous series of oligomeric proanthocyanidins and the simultaneous assessment of their antioxidant capacity in relation to the degree of polymerization (DP). The present study describes the influence of the different stages of a lab-scale chocolate manufacturing process on the content of oligomeric proanthocyanidins and their antioxidant capacity. The sum of the total proanthocyanidin content (___ DP1-DP13) decreased from 30mg epicatechin equivalents per gram non-fat dry matter in raw fresh cocoa beans to 6mg epicatechin equivalents per gram in the final chocolate. The antioxidant capacity decreased accordingly from 25mg epicatechin equivalents per gram non-fat dry matter in raw fresh cocoa beans to 4mg/g in the final chocolate product. ,Food chemistry,"['D000975', 'D001331', 'D002099', 'D000069956', 'D002851', 'D005511', 'D058105', 'D059808', 'D044945']","['Antioxidants', 'Automation', 'Cacao', 'Chocolate', 'Chromatography, High Pressure Liquid', 'Food Handling', 'Polymerization', 'Polyphenols', 'Proanthocyanidins']",Determination of oligomeric proanthocyanidins and their antioxidant capacity from different chocolate manufacturing stages using the NP-HPLC-online-DPPH methodology.,"['Q000737', None, 'Q000737', 'Q000032', 'Q000379', None, None, 'Q000737', 'Q000737']","['chemistry', None, 'chemistry', 'analysis', 'methods', None, None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27507506,2017,1,2,table 1 and 2 , -0.59,14661763,"A confirmatory method for the determination of low levels of acrylamide in different food products is presented. The method entails extraction of acrylamide with water, precipitation of matrix constituents with acetonitrile, and two clean-up steps consecutively over Isolute Multimode and cation-exchange cartridges. The final extract is analyzed by liquid chromatography (LC) coupled to positive electrospray ionization tandem mass spectrometry employing [13C3]-acrylamide as internal standard. For the chromatographic step, a LC column based on a polymethacrylate gel is employed which shows good retention of acrylamide under isocratic flow conditions (k' = 1.2). Mass spectral acquisition is done by selected reaction monitoring, choosing the characteristic transitions m/z 72-->55, 72-->54 and 72-->27. In-house validation data for breakfast cereals and crackers show good precision of the method, with intra- and interassay variation below 10%. The limits of detection for crackers and breakfast cereals, respectively are estimated at 15 and 20 microg/kg, and recoveries of fortified samples ranged between 58 and 76%. Furthermore, the method is applicable to a number of different food products, including biscuits, crisp bread, wafers, confectionery cocoa liquor, and nuts. Finally, the good results obtained in several small-scale interlaboratory tests provided additional confidence in the performance of the method.",Journal of chromatography. A,"['D020106', 'D002851', 'D005504', 'D007554', 'D015203', 'D021241']","['Acrylamide', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Isotopes', 'Reproducibility of Results', 'Spectrometry, Mass, Electrospray Ionization']",Analysis of acrylamide in food by isotope-dilution liquid chromatography coupled with electrospray ionization tandem mass spectrometry.,"['Q000032', 'Q000379', None, None, None, 'Q000379']","['analysis', 'methods', None, None, None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/14661763,2004,0,0,, -0.58,20565928,"Experimental evidences demonstrate that vegetable derived extracts inhibit cholesterol absorption in the gastrointestinal tract. To further explore the mechanisms behind, we modeled duodenal contents with several vegetable extracts.",Lipids in health and disease,"['D002784', 'D002789', 'D005504', 'D007408', 'D013058', 'D010936', 'D014675']","['Cholesterol', 'Cholesterol Oxidase', 'Food Analysis', 'Intestinal Absorption', 'Mass Spectrometry', 'Plant Extracts', 'Vegetables']",When cholesterol is not cholesterol: a note on the enzymatic determination of its concentration in model systems containing vegetable extracts.,"['Q000032', 'Q000378', 'Q000379', None, None, 'Q000737', 'Q000737']","['analysis', 'metabolism', 'methods', None, None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/20565928,2010,2,1,bar plot,the cholesterol bar plot -0.58,422499,"A high pressure liquid chromatographic (HPLC) method has been developed which is fast, simple, specific, and reliable over a wide range of sugar concentrations in a variety of food matrices. With few exceptions, sample preparation is simple, requiring only a water-ethanol extraction, followed by a rapid mini-column cleanup before injection into the HPLC system. The majority of samples can be prepared for analysis within 1--1 1/2 hr, and the following sugars are separated in less than 45 min: fructose, glucose, sucrose, maltose, lactose, melibioals, chocolate products, chocolate sirups, cookies, health food products, molasses, preserves, processed fruits, and soy protein products.",Journal - Association of Official Analytical Chemists,"['D002241', 'D002851', 'D005504']","['Carbohydrates', 'Chromatography, High Pressure Liquid', 'Food Analysis']",High pressure liquid chromatographic determination of sugars in various food products.,"['Q000032', None, None]","['analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/422499,1979,,,, -0.58,21493169,"Rapid, selective and sensitive methods were developed and validated to determine procyanidins, anthocyanins and alkaloids in different biological tissues, such as liver, brain, the aorta vein and adipose tissue. For this purpose, standards of procyanidins (catechin, epicatechin, and dimer B(2)), anthocyanins (cyanidin-3-glucoside and malvidin-3-glucoside) and alkaloids (theobromine, caffeine and theophylline) were used. The methods included the extraction of homogenized tissues by off-line liquid-solid extraction, and then solid-phase extraction to analyze alkaloids, or microelution solid-phase extraction plate for the analysis of procyanidins and anthocyanins. The eluted extracts were then analyzed by ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry, using a triple quadrupole as the analyzer. The optimum extraction solution was water/methanol/phosphoric acid 4% (94/4.5/1.5, v/v/v). The extraction recoveries were higher than 81% for all the studied compounds in all the tissues, except the anthocyanins, which were between 50 and 65% in the liver and brain. In order to show the applicability of the developed methods, different rat tissues were analyzed to determine the procyanidins, anthocyanins and alkaloids and their generated metabolites. The rats had previously consumed 1g of a grape pomace extract (to analyze procyanidins and anthocyanins) or a cocoa extract (to analyze alkaloids) per kilogram of body weight. Different tissues were extracted 4h after administration of the respective extracts. The analysis of the metabolites revealed a hepatic metabolism of procyanidins. The liver was the tissue which produced a greater accumulation of these metabolites.","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000818', 'D000872', 'D002110', 'D002851', 'D008297', 'D044945', 'D051381', 'D017208', 'D015203', 'D012680', 'D053719', 'D013805', 'D014018']","['Animals', 'Anthocyanins', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Male', 'Proanthocyanidins', 'Rats', 'Rats, Wistar', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Tandem Mass Spectrometry', 'Theobromine', 'Tissue Distribution']","Rapid methods to determine procyanidins, anthocyanins, theobromine and caffeine in rat tissues by liquid chromatography-tandem mass spectrometry.","[None, 'Q000032', 'Q000032', 'Q000379', None, 'Q000032', None, None, None, None, 'Q000379', 'Q000032', None]","[None, 'analysis', 'analysis', 'methods', None, 'analysis', None, None, None, None, 'methods', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/21493169,2011,1,1,table 1 ,from suplementary data in the word doc only for cacao -0.58,28432760,"A method validation study for the determination of ochratoxin A in black and white pepper (Piper spp.), nutmeg (Myristica fragrans), spice mix (blend of ginger, turmeric, pepper, nutmeg, and chili), cocoa powder, and drinking chocolate was conducted according to the International Harmonized Protocol of the International Union of Pure and Applied Chemistry. The method is based on the extraction of samples with aqueous methanol, followed by a cleanup of the extract with an immunoaffinity column. The determination is carried out by reversed-phase LC coupled with a fluorescence detector. The study involved 25 participants representing a cross-section of research, private, and official control laboratories from 12 European Union (EU) Member States, together with Turkey and Macedonia. Mean recoveries ranged from 71 to 85% for spices and from 85 to 88% for cocoa and drinking chocolate. The RSDr values ranged from 5.6 to 16.7% for spices and from 4.5 to 18.7% for cocoa and drinking chocolate. The RSDR values ranged from 9.5 to 22.6% for spices and from 13.7 to 30.7% for cocoa and drinking chocolate. The resulting Horwitz ratios ranged from 0.4 to 1 for spices and from 0.6 to 1.4 for cocoa and drinking chocolate according to the Horwitz function modified by Thompson. The method showed acceptable within-laboratory and between-laboratory precision for each matrix, and it conforms to requirements set by current EU legislation.",Journal of AOAC International,"['D000069956', 'D002851', 'D005506', 'D026323', 'D009793', 'D029222', 'D017365']","['Chocolate', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Myristica fragrans', 'Ochratoxins', 'Piper nigrum', 'Spices']","Determination of Ochratoxin A in Black and White Pepper, Nutmeg, Spice Mix, Cocoa, and Drinking Chocolate by High-Performance Liquid Chromatography Coupled with Fluorescence Detection: Collaborative Study.","['Q000032', None, None, 'Q000737', 'Q000032', 'Q000737', 'Q000032']","['analysis', None, None, 'chemistry', 'analysis', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28432760,2017,1,1,table 4 and 5, -0.58,25902989,"Single-laboratory validation data were reviewed by the Expert Review Panel (ERP) of the Stakeholder Panel on Strategic Food Analytical Methods at the AOAC Mid-Year Meeting, March 12-14, 2013, in Rockville, MD. The ERP determined that the data presented met established standard method performance requirements and adopted a method for determination of flavanols and procyanidins (DP 1-10) in cocoa-based ingredients and products by ultra-HPLC as AOAC Official First Action Method 2013.03 on March 14, 2013. The flavanols and procyanidins (DP 1-10) are eluted using a binary gradient (solvents A and B) consisting of 98 + 2 (v/v) acetonitrile-glacial acetic acid (A) and 95 + 3 + 2 (v/v/v) methanol-water-glacial acetic acid (B). The mobile phase is applied to a diol stationary phase. Detection occurs using fluorescence detection. Recovery of flavanols and procyanidins (DP 1-10) from both high- and low-fat matrixes was 98.4-99.8%. Precision was determined for seven different sample types (cocoa extract, cocoa nib, natural cocoa powder, cocoa liquor, alkalized cocoa powder, dark chocolate, and milk chocolate).",Journal of AOAC International,"['D002099', 'D002851', 'D005419', 'D044945']","['Cacao', 'Chromatography, High Pressure Liquid', 'Flavonoids', 'Proanthocyanidins']",Determination of Flavanols and Procyanidins (DP 1-10) in Cocoa-Based Ingredients and Products by UHPLC: First Action 2013.03.,"['Q000737', 'Q000379', 'Q000032', 'Q000032']","['chemistry', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/25902989,2016,,,,no pdf access -0.58,25307999,"As a consequence of the PAH4 (sum of four different polycyclic aromatic hydrocarbons, named benzo[a]anthracene, chrysene, benzo[b]fluoranthene, and benzo[a]pyrene) maximum levels permitted in cocoa beans and derived products as of 2013, an high-performance liquid chromatography with fluorescence detection method (HPLC-FD) was developed and adapted to the complex cocoa butter matrix to enable a simultaneous determination of PAH4. The resulting analysis method was subsequently successfully validated. This method meets the requirements of Regulation (EU) No. 836/2011 regarding analysis methods criteria for determining PAH4 and is hence most suitable for monitoring the observance of the maximum levels applicable under Regulation (EU) No. 835/2011. Within the scope of this work, a total of 218 samples of raw cocoa, cocoa masses, and cocoa butter from several sample years (1999-2012), of various origins and treatments, as well as cocoa and chocolate products were analyzed for the occurrence of PAH4. In summary, it is noted that the current PAH contamination level of cocoa products can be deemed very slight overall. ",Journal of agricultural and food chemistry,"['D002099', 'D002851', 'D005506', 'D011084']","['Cacao', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Polycyclic Aromatic Hydrocarbons']",Quantitation of polycyclic aromatic hydrocarbons (PAH4) in cocoa and chocolate samples by an HPLC-FD method.,"['Q000737', 'Q000379', 'Q000032', 'Q000032']","['chemistry', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/25307999,2015,1,1,"table 2,3 and 4", -0.58,28039812,"The distribution of fatty acid species at the sn-1/3 position or the sn-2 position of triacylglycerols (TAGs) in natural fats and oils affects their physical and nutritional properties. In fats and oils, determining the presence of one or two regioisomers and the identification of structure, where they do have one, as well as their separation, became a problem of fundamental importance to solve. A variety of instrumental technics has been proposed, such as MS, chromatography-MS or pure chromatography. A number of studies deal with the optimization of the separation, but very often, they are expensive in time. In the present study, in order to decrease the analysis time while maintaining good chromatographic separation, we tested different monomeric and polymeric stationary phases and different chromatographic conditions (mobile phase composition and analysis temperature) using Non-Aqueous Reversed Phase Liquid Chromatography (NARP-LC). It was demonstrated that mixed polymeric stationary bonded silica with accessible terminal hydroxyl groups leads to very good separation for the pairs of TAGs regioisomers constituted by two saturated and one unsaturated fatty acid (with double bond number: from 1 to 6). A Nucleodur C18 ISIS percolated by isocratic mobile phase (acetonitrile/2-propanol) at 18_C leads to their separations in less than 15min. The difference of retention times between two regioisomers XYX and XXY are large enough to confirm, as application, the presence of POP, SOP, SOS and PLP and no PPO, SPO, SSO and PPL in Theobroma cacao butter. In the same way, this study respectively shows the presence of SOS, SOP and no SSO, PSO in Butyrospermum parkii butter, POP, SOP, SOS and no PPO, PSO and SSO in Carapa oil and finally POP and no PPO in Pistacia Lentiscus oil.","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D002851', 'D056148', 'D005223', 'D010938', 'D013237', 'D014280']","['Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Fats', 'Plant Oils', 'Stereoisomerism', 'Triglycerides']",Fast non-aqueous reversed-phase liquid chromatography separation of triacylglycerol regioisomers with isocratic mobile phase. Application to different oils and fats.,"['Q000379', 'Q000379', 'Q000737', 'Q000737', None, 'Q000032']","['methods', 'methods', 'chemistry', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28039812,2017,0,0,, -0.58,11559132,"Key aroma components of cooked tail meat of American lobster (Homarus americanus) were studied by gas chromatography-olfactometry (GCO) techniques. Components of low and intermediate volatility were evaluated by aroma extract dilution analysis of solvent extracts prepared by direct solvent extraction-high vacuum distillation and vacuum steam distillation-solvent extraction, whereas headspace volatile components were assessed by GCO of decreasing headspace (static and dynamic modes) samples. Forty-seven odorants were detected by all techniques. 3-Methylbutanal (chocolate, malty), 2,3-butanedione (buttery), 3-(methylthio)propanal (cooked potato), 1-octen-3-one (mushroom), 2-acetyl-1-pyrroline (popcorn), and (E,Z)-2,6-nonadienal (cucumber), were identified as predominant odorants by all four isolation methods. The highly volatile compounds methanethiol (rotten, sulfurous) and dimethyl sulfide (canned corn) were detected by headspace methods only. These eight odorants along with three unknown compounds with crabby, amine, fishy odors were found to predominate in the overall aroma of cooked lobster tail meat.",Journal of agricultural and food chemistry,"['D000818', 'D002849', 'D003296', 'D008121', 'D009812', 'D017747', 'D014835']","['Animals', 'Chromatography, Gas', 'Cooking', 'Nephropidae', 'Odorants', 'Seafood', 'Volatilization']",Aroma components of cooked tail meat of American lobster (Homarus americanus).,"[None, 'Q000379', None, 'Q000737', 'Q000032', None, None]","[None, 'methods', None, 'chemistry', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11559132,2001,0,0,,no cocoa -0.58,11559122,"The fatty acids from cocoa butters of different origins, varieties, and suppliers and a number of cocoa butter equivalents (Illexao 30-61, Illexao 30-71, Illexao 30-96, Choclin, Coberine, Chocosine-Illip©, Chocosine-Shea, Shokao, Akomax, Akonord, and Ertina) were investigated by bulk stable carbon isotope analysis and compound specific isotope analysis. The interpretation is based on principal component analysis combining the fatty acid concentrations and the bulk and molecular isotopic data. The scatterplot of the two first principal components allowed detection of the addition of vegetable fats to cocoa butters. Enrichment in heavy carbon isotope ((13)C) of the bulk cocoa butter and of the individual fatty acids is related to mixing with other vegetable fats and possibly to thermally or oxidatively induced degradation during processing (e.g., drying and roasting of the cocoa beans or deodorization of the pressed fat) or storage. The feasibility of the analytical approach for authenticity assessment is discussed.",Journal of agricultural and food chemistry,"['D002247', 'D002849', 'D004041', 'D005227', 'D005511', 'D013058', 'D014675']","['Carbon Isotopes', 'Chromatography, Gas', 'Dietary Fats', 'Fatty Acids', 'Food Handling', 'Mass Spectrometry', 'Vegetables']",Characterization of cocoa butter and cocoa butter equivalents by bulk and molecular carbon isotope analyses: implications for vegetable fat quantification in chocolate.,"[None, None, 'Q000032', None, 'Q000379', None, 'Q000737']","[None, None, 'analysis', None, 'methods', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11559122,2001,2,1,table 1,exclude the CBEs -0.58,24360428,"Several HPLC and UHPLC developed methods were compared to analyse the natural antioxidants catechins and quercetin used in active packaging and functional foods. Photodiode array detector coupled with a fluorescence detector and compared with LTQ-Orbitrap-MS was used. UHPLC was investigated as quick alternative without compromising the separation, analysis time shortened up to 6-fold. The feasibility of the four developed methods was compared. Linearity up to 0.9995, low detection limits (between 0.02 and 0.7 for HPLC-PDA, 2 to 7-fold lower for HPLC- LTQ-Orbitrap-MS and from 0.2 to 2mgL(-)(1) for UHPLC-PDA) and good precision parameters (RSD lower than 0.06%) were obtained. All methods were successfully applied to natural samples. LTQ-Orbitrap-MS allowed to identify other analytes of interest too. Good feasibility of the methods was also concluded from the analysis of catechin and quercetin release from new active packaging materials based on polypropylene added with catechins and green tea. ",Food chemistry,"['D000975', 'D002099', 'D028241', 'D002392', 'D002851', 'D005419', 'D018857', 'D007700', 'D010936', 'D010969', 'D011794']","['Antioxidants', 'Cacao', 'Camellia sinensis', 'Catechin', 'Chromatography, High Pressure Liquid', 'Flavonoids', 'Food Packaging', 'Kinetics', 'Plant Extracts', 'Plastics', 'Quercetin']",Analytical determination of flavonoids aimed to analysis of natural samples and active packaging applications.,"['Q000032', 'Q000737', 'Q000737', 'Q000032', None, 'Q000032', 'Q000295', None, 'Q000032', 'Q000032', 'Q000032']","['analysis', 'chemistry', 'chemistry', 'analysis', None, 'analysis', 'instrumentation', None, 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24360428,2014,1,1,fig 2,only for cocoa -0.58,26042917,"Flavan-3-ols and proanthocyanidins play a key role in the health beneficial effects of cocoa. Here, we developed a new reversed phased high-performance liquid chromatography-electrochemical detection (HPLC-ECD) method for the analysis of flavan-3-ols and proanthocyanidins of degree of polymerization (DP) 2-7. We used this method to examine the effect of alkalization on polyphenol composition of cocoa powder. Treatment of cocoa powder with NaOH (final pH 8.0) at 92 _C for up to 1 h increased catechin content by 40%, but reduced epicatechin and proanthocyanidins by 23-66%. Proanthocyanidin loss could be modeled using a two-phase exponential decay model (R(2) > 0.7 for epicatchin and proanthocyanidins of odd DP). Alkalization resulted in a significant color change and 20% loss of total polyphenols. The present work demonstrates the first use of HPLC-ECD for the detection of proanthocyanidins up to DP 7 and provides an initial predictive model for the effect of alkali treatment on cocoa polyphenols. ",Journal of agricultural and food chemistry,"['D000468', 'D002099', 'D002851', 'D056148', 'D005511', 'D006358', 'D010936', 'D044945']","['Alkalies', 'Cacao', 'Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Food Handling', 'Hot Temperature', 'Plant Extracts', 'Proanthocyanidins']",Analysis of Cocoa Proanthocyanidins Using Reversed Phase High-Performance Liquid Chromatography and Electrochemical Detection: Application to Studies on the Effect of Alkaline Processing.,"['Q000737', 'Q000737', 'Q000295', 'Q000295', 'Q000379', None, 'Q000032', 'Q000032']","['chemistry', 'chemistry', 'instrumentation', 'instrumentation', 'methods', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26042917,2015,1,1,fig 5,0 time only -0.58,9708288,"Chemical reactions occurring during industrial treatments or storage foods can lead to the formation of epsilon-deoxyketosyl compounds, the Amadori products. Food protein value can be adversely affected by these reactions, and in particular lysine, an essential amino acid having on its side chain a free amino group, can be converted to nonbioavailable N-substituted lysine or blocked lysine. by acid hydrolysis of epsilon-deoxyketosyl compounds, furosine is formed. In this paper furosine prepared from milk-based commercial products has been evaluated by use of a recently developed HPLC method using a microbore column and phosphate buffer as the mobile phase at controlled temperature. Furosine levels have been used, together with protein, total amino acids, and lysine content, as an estimate of protein quality of a few different products such as cooked-cream dessert, yogurt mousse, white chocolate, milk chocolate, milk chocolate with a soft nougat and caramel center, milk chocolate with a whipped white center, chocolate spread, part-skim milk tablets, milk-based dietetic meals, and baby foods. The protein content of the analyzed products ranged from 34.3 gxkg(-1) (milk nougat) to 188.4 g x kg(-1) (milk tablets). The Maillard reaction caused a loss in available lysine that varied from 2.5% (cooked cream) to 36.2% (condensed milk). The contribution to the lysine average daily requirement is heavily affected by this reaction and varied from 13% (milk tablets and soft nougat) to 61% (dietetic meal). Variable results were also obtained for the other essential amino acids.",Journal of food protection,"['D002851', 'D003611', 'D005511', 'D005519', 'D008239', 'D015416', 'D008894', 'D011786']","['Chromatography, High Pressure Liquid', 'Dairy Products', 'Food Handling', 'Food Preservation', 'Lysine', 'Maillard Reaction', 'Milk Proteins', 'Quality Control']",Maillard reaction in mild-based foods: nutritional consequences.,"[None, 'Q000032', 'Q000592', 'Q000592', 'Q000031', None, 'Q000032', None]","[None, 'analysis', 'standards', 'standards', 'analogs & derivatives', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/9708288,1998,,,, -0.57,16910727,"The characteristic aroma-active compounds in raw and cooked pine-mushrooms (Tricholoma matsutake Sing.) were investigated by gas chromatography-olfactometry using aroma extract dilution analysis. 1-Octen-3-one (mushroom-like) was the major aroma-active compound in raw pine-mushrooms; this compound had the highest flavor dilution factor, followed by ethyl 2-methylbutyrate (floral and sweet), linalool (citrus-like), methional (boiled potato-like), 3-octanol (mushroom-like and buttery), 1-octen-3-ol (mushroom-like), (E)-2-octen-1-ol (mushroom-like), and 3-octanone (mushroom-like and buttery). By contrast, methional, 2-acetylthiazole (roasted), an unknown compound (chocolate-like), 3-hydroxy-2-butanone (buttery), and phenylacetaldehyde (floral and sweet), which could be formed by diverse thermal reactions during the cooking process, together with C8 compounds, were identified as the major aroma-active compounds in cooked pine-mushrooms.",Journal of agricultural and food chemistry,"['D000363', 'D002849', 'D008401', 'D006358', 'D006801', 'D007659', 'D009812', 'D012903']","['Agaricales', 'Chromatography, Gas', 'Gas Chromatography-Mass Spectrometry', 'Hot Temperature', 'Humans', 'Ketones', 'Odorants', 'Smell']",Characterization of aroma-active compounds in raw and cooked pine-mushrooms (Tricholoma matsutake Sing.).,"['Q000737', None, None, None, None, 'Q000032', 'Q000032', None]","['chemistry', None, None, None, None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/16910727,2006,0,0,,no cocoa -0.57,27318471,"Cocoa beans contain secondary metabolites ranging from simple alkaloids to complex polyphenols with most of them believed to possess significant health benefits. The increasing interest in these health effects has prompted the need to develop techniques for their extraction, fractionation, separation, and analysis. This work provides an update on analytical procedures with a focus on establishing a gentle extraction technique. Cocoa beans were finely ground to an average particle size of <100____m, defatted at 20___C using n-hexane, and extracted three times with 50__% aqueous acetone at 50___C. Determination of the total phenolic content was done using the Folin-Ciocalteu assay, the concentration of individual polyphenols was analyzed by electrospray ionization high performance liquid chromatography-mass spectrometry (ESI-HPLC/MS). Fractions of bioactive compounds were separated by combining sequential centrifugal partition chromatography (SCPC) and gel permeation column chromatography using Sephadex LH-20. For SCPC, a two-phase solvent system consisting of ethyl acetate/n-butanol/water (4:1:5, v/v/v) was successfully applied for the separation of theobromine, caffeine, and representatives of the two main phenolic compound classes flavan-3-ols and flavonols. Gel permeation chromatography on Sephadex LH-20 using a stepwise elution sequence with aqueous acetone has been shown for effectively separating individual flavan-3-ols. Separation was obtained for (-)-epicatechin, proanthocyanidin dimer B2, trimer C1, and tetramer cinnamtannin A2. The purity of alkaloids and phenolic compounds was determined by HPLC analysis and their chemical identity was confirmed by mass spectrometry. ",Analytical and bioanalytical chemistry,"['D002099', 'D002498', 'D005591', 'D002850', 'D002851', 'D044948', 'D059808', 'D044945', 'D012997', 'D021241']","['Cacao', 'Centrifugation', 'Chemical Fractionation', 'Chromatography, Gel', 'Chromatography, High Pressure Liquid', 'Flavonols', 'Polyphenols', 'Proanthocyanidins', 'Solvents', 'Spectrometry, Mass, Electrospray Ionization']",Extraction of cocoa proanthocyanidins and their fractionation by sequential centrifugal partition chromatography and gel permeation chromatography.,"['Q000737', 'Q000379', 'Q000379', 'Q000379', 'Q000379', 'Q000032', 'Q000032', 'Q000032', None, 'Q000379']","['chemistry', 'methods', 'methods', 'methods', 'methods', 'analysis', 'analysis', 'analysis', None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/27318471,2018,1,1,Fig 3, -0.57,17613050,"A selective and sensitive procedure has been developed and validated for the determination of acrylamide in difficult matrices, such as coffee and chocolate. The proposed method includes pressurised fluid extraction (PFE) with acetonitrile, florisil clean-up purification inside the PFE extraction cell and detection by liquid chromatography (LC) coupled to atmospheric pressure ionisation in positive mode tandem mass spectrometry (APCI-MS-MS). Comparison of ionisation sources (atmospheric pressure chemical ionisation (APCI), atmospheric pressure photoionization (APPI) and the combined APCI/APPI) and clean-up procedures were carried out to improve the analytical signal. The main parameters affecting the performance of the different ionisation sources were previously optimised using statistical design of experiments (DOE). PFE parameters were also optimised by DOE. For quantitation, an isotope dilution approach was used. The limit of quantification (LOQ) of the method was 1 microg kg(-1) for coffee and 0.6 microg kg(-1) for chocolate. Recoveries ranged between 81-105% in coffee and 87-102% in chocolate. The accuracy was evaluated using a coffee reference test material FAPAS T3008. Using the optimised method, 20 coffee and 15 chocolate samples collected from Valencian (Spain) supermarkets, were investigated for acrylamide, yielding median levels of 146 microg kg(-1) in coffee and 102 microg kg(-1) in chocolate.",Food additives and contaminants,"['D020106', 'D002099', 'D002853', 'D003069', 'D013030', 'D053719']","['Acrylamide', 'Cacao', 'Chromatography, Liquid', 'Coffee', 'Spain', 'Tandem Mass Spectrometry']",Determination of acrylamide in coffee and chocolate by pressurised fluid extraction and liquid chromatography-tandem mass spectrometry.,"['Q000032', 'Q000737', 'Q000379', 'Q000737', None, 'Q000379']","['analysis', 'chemistry', 'methods', 'chemistry', None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/17613050,2007,,,, -0.57,20213173,"Cocoa is well-known to be rich in flavan-3-ols. Previous analyses have established that alkaline treatment of cocoa beans results in epimerization of (-)-epicatechin to (-)-catechin and (+)-catechin to (+)-epicatechin. Now, the question is whether both epimers can be absorbed by the human organism. This paper describes sample preparation and an HPLC method for chiral determination of (+)/(-)-catechin from sulfated and glucuronidated metabolites in human plasma. The sample preparation includes enzymatic hydrolysis of the catechin metabolites, and solid-phase extraction (SPE). A PM-gamma-cyclodextrin column is used with a coulometric electrode-array detection (CEAD) system. The recovery of catechin ranges from 89.9 to 96.8%. The limit of detection is 5.9 ng mL(-1) for (-)-catechin and 6.8 ng mL(-1) for (+)-catechin, and the limit of quantification is 12.8 ng mL(-1) for (-)-catechin and 16.9 ng mL(-1) for (+)-catechin. The relative standard deviation of the method ranges from 0.9 to 1.5%. This method was successfully applied to human plasma after consumption of a cocoa drink. In one human self-experiment, (+)-catechin and (-)-catechin were found in human plasma, but metabolism of the two enantiomers differed.",Analytical and bioanalytical chemistry,"['D000328', 'D001628', 'D002099', 'D002392', 'D002851', 'D005260', 'D006801', 'D057230', 'D052616', 'D013237']","['Adult', 'Beverages', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Female', 'Humans', 'Limit of Detection', 'Solid Phase Extraction', 'Stereoisomerism']",Chiral separation of (+)/(-)-catechin from sulfated and glucuronidated metabolites in human plasma after cocoa consumption.,"[None, None, 'Q000378', 'Q000097', 'Q000379', None, None, None, 'Q000379', None]","[None, None, 'metabolism', 'blood', 'methods', None, None, None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/20213173,2010,0,0,, -0.57,10545668,"The presence of carbohydrates and organic acids was monitored in the oral cavity over a 3-hour period following the ingestion of six foods containing cooked starch (popcorn, potato chips, corn flakes, bread stick, hard pretzel and wheat cracker) and compared to a food containing sugar (chocolate-covered candy bar). Oral fluid samples were collected at 30-min intervals from five different tooth sites from 7 volunteers using absorbent paper points. Samples were analyzed for carbohydrates and organic acids using high-performance liquid chromatography. Analytical data for each food were pooled and compared to the results of the sugar food. The amount of lactic acid produced 30 min after ingestion was highest with the potato chips and lowest with the corn flakes. Potato starch contributed more readily to oral lactic acid production than wheat or corn starch. A direct linear relationship existed between lactic acid production and the presence of oral glucose produced from starch, which occurred via the metabolites maltotriose and maltose. Oral clearance of foods containing cooked starch proceeded significantly slower than that of the sugar food, thus contributing to a prolonged period of lactic acid production.",Annals of nutrition & metabolism,"['D019342', 'D002099', 'D002182', 'D002851', 'D004040', 'D019422', 'D005502', 'D005561', 'D005947', 'D006801', 'D007700', 'D019344', 'D008320', 'D009055', 'D012463', 'D013213', 'D014312', 'D014908', 'D003313']","['Acetic Acid', 'Cacao', 'Candy', 'Chromatography, High Pressure Liquid', 'Dietary Carbohydrates', 'Dietary Sucrose', 'Food', 'Formates', 'Glucose', 'Humans', 'Kinetics', 'Lactic Acid', 'Maltose', 'Mouth', 'Saliva', 'Starch', 'Trisaccharides', 'Triticum', 'Zea mays']",Clearance and metabolism of starch foods in the oral cavity.,"['Q000032', None, None, None, 'Q000378', 'Q000378', None, 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000032', 'Q000378', 'Q000737', 'Q000378', 'Q000032', None, None]","['analysis', None, None, None, 'metabolism', 'metabolism', None, 'analysis', 'analysis', None, None, 'analysis', 'analysis', 'metabolism', 'chemistry', 'metabolism', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10545668,1999,,,,no pdf access -0.57,3597580,"A method is described for simultaneous extraction and quantitation of the amines 2-phenylethylamine, tele-methylhistamine, histamine, tryptamine, m- and p-tyramine, 3-methoxytyramine, 5-hydroxytryptamine, cadaverine, putrescine, spermidine and spermine. This method is based on extractive derivatization of the amines with a perfluoroacylating agent, pentafluorobenzoyl chloride, under basic aqueous conditions. Analysis was done on a gas chromatograph equipped with an electron-capture detector and a capillary column system. The procedure is relatively rapid and provides derivatives with good chromatographic properties. Its application to analysis of the above amines in cheese and chocolate products is described.",Journal of chromatography,"['D000588', 'D002099', 'D002611', 'D002849', 'D007202', 'D010945']","['Amines', 'Cacao', 'Cheese', 'Chromatography, Gas', 'Indicators and Reagents', 'Plants, Edible']",Simultaneous extraction and quantitation of several bioactive amines in cheese and chocolate.,"['Q000032', 'Q000032', 'Q000032', None, None, 'Q000032']","['analysis', 'analysis', 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/3597580,1987,,,, -0.57,19924052,"This report describes the characterization of a series of commercially available procyanidin standards ranging from dimers DP = 2 to decamers DP = 10 for the determination of procyanidins from cocoa and chocolate. Using a combination of HPLC with fluorescence detection and MALDI-TOF mass spectrometry, the purity of each standard was determined and these data were used to determine relative response factors. These response factors were compared with other response factors obtained from published methods. Data comparing the procyanidin analysis of a commercially available US dark chocolate calculated using each of the calibration methods indicates divergent results and demonstrate that previous methods may significantly underreport the procyanidins in cocoa-containing products. These results have far reaching implications because the previous calibration methods have been used to develop data for a variety of scientific reports, including food databases and clinical studies.","Molecules (Basel, Switzerland)","['D044946', 'D002099', 'D002392', 'D002851', 'D044945', 'D012015', 'D019032']","['Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Proanthocyanidins', 'Reference Standards', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization']",Characterization of primary standards for use in the HPLC analysis of the procyanidin content of cocoa and chocolate containing products.,"['Q000032', 'Q000737', 'Q000032', 'Q000592', 'Q000032', None, None]","['analysis', 'chemistry', 'analysis', 'standards', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19924052,2010,0,0,, -0.56,15969528,"Application of chromatographic separation and taste dilution analyses recently revealed besides procyanidins a series of N-phenylpropenoyl amino acids as the key contributors to the astringent taste of nonfermented cocoa beans as well as roasted cocoa nibs. Because these amides have as yet not been reported as key taste compounds, this paper presents the isolation, structure determination, and sensory activity of these amino acid amides. Besides the previously reported (-)-N-[3',4'-dihydroxy-(E)-cinnamoyl]-3-hydroxy-L-tyrosine (clovamide), (-)-N-[4'-hydroxy-(E)-cinnamoyl]-L-tyrosine (deoxyclovamide), and (-)-N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-tyrosine, seven additional amides, namely, (+)-N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-aspartic acid, (+)-N-[4'-hydroxy-(E)-cinnamoyl]-L-aspartic acid, (-)-N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-glutamic acid, (-)-N-[4'-hydroxy-(E)-cinnamoyl]-L-glutamic acid, (-)-N-[4'-hydroxy-(E)-cinnamoyl]-3-hydroxy-L-tyrosine, (+)-N-[4'-hydroxy-3'-methoxy-(E)-cinnamoyl]-L-aspartic acid, and (+)-N-[(E)-cinnamoyl]-L-aspartic acid, were identified for the first time in cocoa products by means of LC-MS/MS, 1D/2D-NMR, UV-vis, CD spectroscopy, and polarimetry, as well as independent enantiopure synthesis. Using the recently developed half-tongue test, human recognition thresholds for the astringent and mouth-drying oral sensation were determined to be between 26 and 220 micromol/L (water) depending on the amino acid moiety. In addition, exposure to light rapidly converted these [E]-configured N-phenylpropenoyl amino acids into the corresponding [Z]-isomers, thus indicating that analysis of these compounds in food and plant materials needs to be performed very carefully in the absence of light to prevent artifact formation.",Journal of agricultural and food chemistry,"['D000577', 'D000596', 'D001224', 'D002099', 'D002851', 'D002934', 'D018698', 'D006801', 'D007536', 'D015394', 'D012639', 'D013649', 'D014443']","['Amides', 'Amino Acids', 'Aspartic Acid', 'Cacao', 'Chromatography, High Pressure Liquid', 'Cinnamates', 'Glutamic Acid', 'Humans', 'Isomerism', 'Molecular Structure', 'Seeds', 'Taste', 'Tyrosine']","Isolation, structure determination, synthesis, and sensory activity of N-phenylpropenoyl-L-amino acids from cocoa (Theobroma cacao).","['Q000032', 'Q000032', 'Q000031', 'Q000737', None, 'Q000032', 'Q000031', None, None, None, 'Q000737', None, 'Q000031']","['analysis', 'analysis', 'analogs & derivatives', 'chemistry', None, 'analysis', 'analogs & derivatives', None, None, None, 'chemistry', None, 'analogs & derivatives']",https://www.ncbi.nlm.nih.gov/pubmed/15969528,2005,0,0,, -0.56,26744789,"Gallocatechin gallate (GCG) possesses multiple potential biological activities. However, the content of GCG in traditional green tea is too low which limits its in-depth pharmacological research and application. In the present study, a simple, efficient and environment-friendly chromatographic separation method was developed for preparative enrichment and separation of GCG from cocoa tea (Camellia ptilophylla) which contains high content of GCG. In the first step, the adsorption properties of selected resins were evaluated, and XAD-7HP resin was chosen by its adsorption and desorption properties for GCG. In order to maximize column efficiency for GCG collection, the operating parameters (e.g., flow rate, ethanol concentration, and bed height) were optimized. We found that the best combination was the feed concentration at 20mg/mL, flow rate at 0.75 BV/h and the ratio of diameter to bed heights as 1:12. Under these conditions, the purity of GCG was 45% with a recovery of 89%. In order to obtain pure target, a second step was established using column chromatography with sephadex LH-20 gel and 55% ethanol-water solution as eluent. After this step, the purity of the GCG was 91% with a recovery of 68% finally. ","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D028244', 'D002392', 'D002851', 'D003911', 'D010936', 'D012117']","['Camellia', 'Catechin', 'Chromatography, High Pressure Liquid', 'Dextrans', 'Plant Extracts', 'Resins, Synthetic']",Preparative separation of gallocatechin gallate from Camellia ptilophylla using macroporous resins followed by sephadex LH-20 column chromatography.,"['Q000737', 'Q000031', 'Q000379', 'Q000737', 'Q000737', 'Q000737']","['chemistry', 'analogs & derivatives', 'methods', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/26744789,2016,0,0,,cocoa tea -0.56,26637047,"Direct analysis of microbial cocultures grown on agar media by desorption electrospray ionization mass spectrometry (DESI-MS) is quite challenging. Due to the high gas pressure upon impact with the surface, the desorption mechanism does not allow direct imaging of soft or irregular surfaces. The divots in the agar, created by the high-pressure gas and spray, dramatically change the geometry of the system decreasing the intensity of the signal. In order to overcome this limitation, an imprinting step, in which the chemicals are initially transferred to flat hard surfaces, was coupled to DESI-MS and applied for the first time to fungal cocultures. Note that fungal cocultures are often disadvantageous in direct imaging mass spectrometry. Agar plates of fungi present a complex topography due to the simultaneous presence of dynamic mycelia and spores. One of the most devastating diseases of cocoa trees is caused by fungal phytopathogen Moniliophthora roreri. Strategies for pest management include the application of endophytic fungi, such as Trichoderma harzianum, that act as biocontrol agents by antagonizing M. roreri. However, the complex chemical communication underlying the basis for this phytopathogen-dependent biocontrol is still unknown. In this study, we investigated the metabolic exchange that takes place during the antagonistic interaction between M. roreri and T. harzianum. Using imprint-DESI-MS imaging we annotated the secondary metabolites released when T. harzianum and M. roreri were cultured in isolation and compared these to those produced after 3 weeks of coculture. We identified and localized four phytopathogen-dependent secondary metabolites, including T39 butenolide, harzianolide, and sorbicillinol. In order to verify the reliability of the imprint-DESI-MS imaging data and evaluate the capability of tape imprints to extract fungal metabolites while maintaining their localization, six representative plugs along the entire M. roreri/T. harzianum coculture plate were removed, weighed, extracted, and analyzed by liquid chromatography-high-resolution mass spectrometry (LC-HRMS). Our results not only provide a better understanding of M. roreri-dependent metabolic induction in T. harzianum, but may seed novel directions for the advancement of phytopathogen-dependent biocontrol, including the generation of optimized Trichoderma strains against M. roreri, new biopesticides, and biofertilizers. ",Analytical chemistry,"['D015107', 'D000363', 'D001688', 'D002073', 'D018920', 'D003512', 'D007783', 'D064210', 'D021241', 'D014242']","['4-Butyrolactone', 'Agaricales', 'Biological Products', 'Butanes', 'Coculture Techniques', 'Cyclohexanones', 'Lactones', 'Secondary Metabolism', 'Spectrometry, Mass, Electrospray Ionization', 'Trichoderma']",Imprint Desorption Electrospray Ionization Mass Spectrometry Imaging for Monitoring Secondary Metabolites Production during Antagonistic Interaction of Fungi.,"['Q000031', 'Q000254', 'Q000032', 'Q000737', None, 'Q000737', 'Q000737', None, None, 'Q000254']","['analogs & derivatives', 'growth & development', 'analysis', 'chemistry', None, 'chemistry', 'chemistry', None, None, 'growth & development']",https://www.ncbi.nlm.nih.gov/pubmed/26637047,2016,0,0,, -0.56,15453694,"The flavor of eight cocoa liquors of different origins (Africa, America, and Asia) and different varieties (Fine grades: criollo, trinitario, and nacional. Bulk-basic grade: forastero.) was analyzed by headspace solid-phase microextraction mass spectrometry (HS-SPME-MS). Their procyanidin contents were quantified by HPLC-UV (280 nm). Fine varieties with short fermentation processes proved to contain more procyanidins, while criollo from New Guinea and forastero beans showed the highest aroma levels. The levels of cocoa aroma compounds formed during roasting are shown to vary directly with bean fermentation time and inversely with residual procyanidin content in cocoa liquor. Measurement of antioxidant activity in cocoa liquor proved to be a useful tool for assessing residual polyphenols.",Journal of agricultural and food chemistry,"['D000349', 'D000434', 'D000569', 'D000975', 'D001208', 'D044946', 'D002099', 'D002392', 'D002851', 'D005285', 'D008401', 'D013058', 'D009812', 'D044945', 'D013649']","['Africa', 'Alcoholic Beverages', 'Americas', 'Antioxidants', 'Asia', 'Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Fermentation', 'Gas Chromatography-Mass Spectrometry', 'Mass Spectrometry', 'Odorants', 'Proanthocyanidins', 'Taste']",Relationship between procyanidin and flavor contents of cocoa liquors from different origins.,"[None, 'Q000032', None, 'Q000032', None, 'Q000032', 'Q000737', 'Q000032', None, None, None, 'Q000379', 'Q000032', 'Q000032', None]","[None, 'analysis', None, 'analysis', None, 'analysis', 'chemistry', 'analysis', None, None, None, 'methods', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15453694,2004,1,3,table 1 and Fig. 3 and 6 q,uantified and unquantified -0.56,12881135,"Benzophenone may be present in cartonboard food-packaging materials as a residue from UV-cured inks and lacquers used to print on the packaging. It may also be present if the cartonboard is made from recycled fibres recovered from printed materials. A method has been devised to test for benzophenone in cartonboard packaging materials and to test for migration levels in foodstuffs. Packaging is extracted with solvent containing d10-benzophenone as the internal standard. Foods are extracted with solvent containing d10-benzophenone and the extract defatted using hexane. The extracts are analysed by GC-MS. For analysis of food, the limit of detection was 0.01 mg x kg(-1) and the limit of quantification was 0.05 mg x kg(-1). The calibration was linear from 0.05 to 20 mg x kg(-1). The method for food analysis was validated in-house and it also returned satisfactory results in a blind check-sample exercise organized by an independent laboratory. The methods were applied to the analysis of 350 retail samples that used printed cartonboard packaging. A total of 207 (59%) packaging samples had no significant benzophenone (<0.05 mg x dm(-2)). Seven (2%) were in the range 0.05- 0.2 mg x dm(-2), 60 (17%) were from 0.2 to 0.8 mg x dm(-2) and 76 (22%) were from 0.8 to 3.3 mg x dm(-2). A total of 71 samples were then selected at random from the 143 packaging samples that contained benzophenone, and the food itself was analysed. Benzophenone was detected in 51 (72%) of the foods. Two food samples (3%) were in the range 0.01-0.05 mg kg(-1). A total of 29 (41%) were from 0.05 to 0.5 mg kg(-1), 17 (24%) were from 0.5 to 5 mg x kg(-1) and three (4%) food samples exceeded 5 mg x kg(-1). The highest level of benzophenone in food was 7.3 mg x kg(-1) for a high-fat chocolate confectionery product packaged in direct contact with cartonboard, with room temperature storage conditions and with a high contact area:food mass ratio. When the mass fraction of benzophenone migration was calculated for the different contact and storage regimes involved, the attenuation effects of indirect contact and of low temperature storage were cumulative. Thus, there was a sixfold reduction in migration for indirect contact compared with direct contact, a sixfold reduction for chilled/frozen storage compared with ambient storage, and 40-fold reduction for the two contact conditions combined.",Food additives and contaminants,"['D001577', 'D005506', 'D018857', 'D005519', 'D008401', 'D006801', 'D007281', 'D011786', 'D013696']","['Benzophenones', 'Food Contamination', 'Food Packaging', 'Food Preservation', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Ink', 'Quality Control', 'Temperature']",Benzophenone in cartonboard packaging materials and the factors that influence its migration into food.,"['Q000032', 'Q000032', None, 'Q000379', 'Q000379', None, None, None, None]","['analysis', 'analysis', None, 'methods', 'methods', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12881135,2003,,,, -0.56,29053889,"The interest towards ""substances of emerging concerns"" referred to objects intended to come into contact with food is recently growing. Such substances can be found in traces in simulants and in food products put in contact with plastic materials. In this context, it is important to set up analytical systems characterized by high sensitivity and to improve detection parameters to enhance signals. This work was aimed at optimizing a method based on UHPLC coupled to high resolution mass spectrometry to quantify the most common plastic additives, and able to detect the presence of polymers degradation products and coloring agents migrating from plastic re-usable containers. The optimization of mass spectrometric parameter settings for quantitative analysis of additives has been achieved by a chemometric approach, using a full factorial and d-optimal experimental designs, allowing to evaluate possible interactions between the investigated parameters. Results showed that the optimized method was characterized by improved features in terms of sensitivity respect to existing methods and was successfully applied to the analysis of a complex model food system such as chocolate put in contact with 14 polycarbonate tableware samples. A new procedure for sample pre-treatment was carried out and validated, showing high reliability. Results reported, for the first time, the presence of several molecules migrating to chocolate, in particular belonging to plastic additives, such Cyasorb UV5411, Tinuvin 234, Uvitex OB, and oligomers, whose amount was found to be correlated to age and degree of damage of the containers.",Journal of mass spectrometry : JMS,"['D002138', 'D000069956', 'D002851', 'D005506', 'D018857', 'D006794', 'D006801', 'D057230', 'D010969', 'D011091', 'D015203', 'D053719']","['Calibration', 'Chocolate', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Food Packaging', 'Household Articles', 'Humans', 'Limit of Detection', 'Plastics', 'Polyesters', 'Reproducibility of Results', 'Tandem Mass Spectrometry']","Optimization of mass spectrometry acquisition parameters for determination of polycarbonate additives, degradation products, and colorants migrating from food contact materials to chocolate.","[None, 'Q000032', 'Q000379', 'Q000032', None, None, None, None, 'Q000737', 'Q000737', None, 'Q000379']","[None, 'analysis', 'methods', 'analysis', None, None, None, None, 'chemistry', 'chemistry', None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/29053889,2018,,,, -0.56,19348344,"This study has examined the effects of type of dairy product (whole milk, skim milk, heavy cream) and chocolate matrix (baking, dark, dairy milk, white) on the oral absorption of the chocolate flavanols (+)-catechin and (-)-epicatechin in a small animal model. In the study, each flavanol compound, as a solution in water or a dairy product or as a chocolate dispersion in water, was administered intragastrically to male Sprague-Dawley rats in an amount equal to or equivalent to 350 mg/kg. In each instance, blood samples were collected over a 5 h period, and used to measure plasma total catechin concentrations by HPLC after enzymatic hydrolysis of flavanol conjugates. Pharmacokinetic data were evaluated using a one compartment approach. Whole milk and heavy cream, and to a much lesser extent skim milk, lowered the oral absorption of both (+)-catechin and (-)-epicatechin and altered the AUC, C(max), k(a), k(e) and t1/2 values in direct proportion to their fat, but not to their protein, content. In addition, the t(max) for solutions of (-)-epicatechin in water and skim milk occurred 2 h earlier than from solutions in whole milk and heavy cream. Similarly, dispersions of baking chocolate in water and in whole milk yielded plasma levels of monomeric catechins that were, respectively, about equal to and much lower than those from aqueous solutions of authentic flavanols. A determining role for a chocolate matrix (dark, dairy milk or white chocolate) on the oral absorption of its constitutive monomeric flavanols was suggested by the apparent variability in plasma total catechins levels that existed among them both before and after their spiking with equal amounts of exogenous (+)-catechin and (-)-epicatechin. Such a variability could reflect differences among different chocolates in terms of their physical properties, matrix components, and matrix characteristics imposed by the manufacturing process used for each type of chocolate. In all the experiments, (+)-catechin demonstrated a higher oral absorption than (-)-epicatechin.",Die Pharmazie,"['D000818', 'D019540', 'D002099', 'D002392', 'D002417', 'D002851', 'D003611', 'D044948', 'D005470', 'D008297', 'D008892', 'D008894', 'D051381', 'D017207']","['Animals', 'Area Under Curve', 'Cacao', 'Catechin', 'Cattle', 'Chromatography, High Pressure Liquid', 'Dairy Products', 'Flavonols', 'Fluorometry', 'Male', 'Milk', 'Milk Proteins', 'Rats', 'Rats, Sprague-Dawley']",Assessment of the effect of type of dairy product and of chocolate matrix on the oral absorption of monomeric chocolate flavanols in a small animal model.,"[None, None, 'Q000737', 'Q000097', None, None, 'Q000032', 'Q000737', None, None, 'Q000737', 'Q000032', None, None]","[None, None, 'chemistry', 'blood', None, None, 'analysis', 'chemistry', None, None, 'chemistry', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19348344,2009,,,, -0.56,15453712,"In this work, the occurrence of ochratoxin A (OTA) in 170 samples of cocoa products of different geographical origins was studied. An immunoaffinity column with HPLC separation was developed to quantify low levels of OTA in cocoa bean, cocoa cake, cocoa mass, cocoa nib, cocoa powder, cocoa shell, cocoa butter, chocolate, and chocolate cream with >80% recoveries. The method was validated by performing replicate analyses of uncontaminated cocoa material spiked at three different levels of OTA (1, 2, and 5 microg/kg). The data obtained were related on the acceptable safe daily exposure for OTA. The highest levels of OTA were detected in roasted cocoa shell and cocoa cake (0.1-23.1 microg/kg) and only at minor levels in the other cocoa products. Twenty-six cocoa and chocolate samples were free from detectable OTA (<0.10 microg/kg). In roasted cocoa powder 38.7% of the samples analyzed contained OTA at levels ranging from 0.1 to 2 microg/kg, and 54.8% was contaminated at >2 microg/kg (and 12 samples at >3 microg/kg). Ochratoxin A was detected in cocoa bean at levels from 0.1 to 3.5 microg/kg, the mean concentration being 0.45 microg/kg; only one sample exceeded 2 microg/kg (4.7%). In contrast, 51.2% of cocoa cake samples contained OTA at levels > or =2 microg/kg, among which 16 exceeded 5 microg/kg (range of 5-9 microg/kg). These results indicate that roasted cocoa powder is not a major source of OTA in the diet.",Journal of agricultural and food chemistry,"['D000818', 'D002099', 'D002851', 'D005506', 'D009183', 'D009793', 'D013552']","['Animals', 'Cacao', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Mycotoxins', 'Ochratoxins', 'Swine']",Occurrence of ochratoxin A in cocoa products and chocolate.,"[None, 'Q000737', 'Q000379', 'Q000032', 'Q000032', 'Q000032', None]","[None, 'chemistry', 'methods', 'analysis', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15453712,2004,1,1,table 2,all but the cake -0.56,25466021,"Hazelnut is one of the most appreciated nuts being virtually found in a wide range of processed foods. The simple presence of trace amounts of hazelnut in foods can represent a potential risk for eliciting allergic reactions in sensitised individuals. The correct labelling of processed foods is mandatory to avoid adverse reactions. Therefore, adequate methodology evaluating the presence of offending foods is of great importance. Thus, the aim of this study was to develop a highly specific and sensitive sandwich enzyme-linked immunosorbent assay (ELISA) for the detection and quantification of hazelnut in complex food matrices. Using in-house produced antibodies, an ELISA system was developed capable to detect hazelnut down to 1 mg kg(-1) and quantify this nut down to 50 mg kg(-1) in chocolates spiked with known amounts of hazelnut. These results highlight and reinforce the value of ELISA as rapid and reliable tool for the detection of allergens in foods.",Food chemistry,"['D002099', 'D002138', 'D002182', 'D002853', 'D031211', 'D004591', 'D004797', 'D005504', 'D057230', 'D009754', 'D010940', 'D053719']","['Cacao', 'Calibration', 'Candy', 'Chromatography, Liquid', 'Corylus', 'Electrophoresis, Polyacrylamide Gel', 'Enzyme-Linked Immunosorbent Assay', 'Food Analysis', 'Limit of Detection', 'Nuts', 'Plant Proteins', 'Tandem Mass Spectrometry']",Development of a sandwich ELISA-type system for the detection and quantification of hazelnut in model chocolates.,"['Q000737', None, None, None, 'Q000737', None, 'Q000379', 'Q000379', None, 'Q000737', 'Q000032', None]","['chemistry', None, None, None, 'chemistry', None, 'methods', 'methods', None, 'chemistry', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/25466021,2015,0,0,,no cocoa -0.56,11929301,"After vacuum distillation and liquid-liquid extraction, the volatile fractions of dark chocolates were analyzed by gas chromatography-olfactometry and gas chromatography-mass spectrometry. Aroma extract dilution analysis revealed the presence of 33 potent odorants in the neutral/basic fraction. Three of these had a strong chocolate flavor: 2-methylpropanal, 2-methylbutanal, and 3-methylbutanal. Many others were characterized by cocoa/praline-flavored/nutty/coffee notes: 2,3-dimethylpyrazine, trimethylpyrazine, tetramethylpyrazine, 3(or 2),5-dimethyl-2(or 3)-ethylpyrazine, 3,5(or 6)-diethyl-2-methylpyrazine, and furfurylpyrrole. Comparisons carried out before and after conching indicate that although no new key odorant is synthesized during the heating process, levels of 2-phenyl-5-methyl-2-hexenal, Furaneol, and branched pyrazines are significantly increased while most Strecker aldehydes are lost by evaporation.",Journal of agricultural and food chemistry,"['D000447', 'D002099', 'D002849', 'D005663', 'D008401', 'D006591', 'D006358', 'D009812', 'D011719', 'D012903', 'D013649']","['Aldehydes', 'Cacao', 'Chromatography, Gas', 'Furans', 'Gas Chromatography-Mass Spectrometry', 'Hexobarbital', 'Hot Temperature', 'Odorants', 'Pyrazines', 'Smell', 'Taste']",Use of gas chromatography-olfactometry to identify key odorant compounds in dark chocolate. Comparison of samples before and after conching.,"['Q000032', 'Q000737', None, 'Q000032', None, None, None, None, 'Q000032', None, None]","['analysis', 'chemistry', None, 'analysis', None, None, None, None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11929301,2002,0,0,, -0.55,14643988,"This paper deals with the physicochemical characterization, including thermal behaviour, by differential scanning calorimetry of mango seed almond fat (MAF), alone and in mixtures with cocoa butter (CB). Results showed that mango almond seeds contain about 5.28-11.26% (dw) of fat. The refraction index is 1.466, the saponification index 189.0 and the iodine index 41.76. Fatty acids found in MAF are oleic, stearic, and palmitic acids (40.81%, 39.07% and 9.29% (w/w), respectively) as well as smaller amounts of linoleic, with arachidic, behenic, lignoceric, and linolenic acids, among others. Calorimetric analysis showed that MAF crystallizes between 14.6 and -24.27 degrees C with a DeltaHc of 56.06 J/g and melts between -17.1 and 53.8 degrees C, with fusion maxima at 18.54 degrees C and 40.0 degrees C for the alpha and beta polymorphic forms. Their fusion enthalpies are 70.12 and 115.7 J/g. The MAF solids content profile is very similar to that of CB, both in stabilized and non-stabilized samples. The mixing compatibility was analyzed using isosolids curves of mixtures of different compositions.",Bioresource technology,"['D002152', 'D004041', 'D005223', 'D008401', 'D031022', 'D008800', 'D012639', 'D013696']","['Calorimetry, Differential Scanning', 'Dietary Fats', 'Fats', 'Gas Chromatography-Mass Spectrometry', 'Mangifera', 'Mexico', 'Seeds', 'Temperature']",Mango seed uses: thermal behaviour of mango seed almond fat and its mixtures with cocoa butter.,"[None, 'Q000032', 'Q000737', None, 'Q000737', None, 'Q000737', None]","[None, 'analysis', 'chemistry', None, 'chemistry', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/14643988,2004,2,1,table 2,no units were given in table 2 for fatty profile -0.55,10395610,"The fructans, inulin and oligofructose, were known to possess many of the physiologic properties of dietary fiber (DF) but were not listed as DF on the labels of foods that contained them because they did not precipitate in 78% ethanol as prescribed in the AOAC International methods for DF. In the latter part of 1995, the Food and Drug Administration (FDA) agreed to consider fructans as DF if an AOAC-accepted analytical method could be successfully developed for fructans. Six blind duplicate pairs of foods, containing from 4 to 40% of inulin or oligofructose, were sent to nine collaborators in five countries for assay. These foods included a low fat spread, cheese spread, chocolate, wine gum, dry ice mix powder and biscuits. In the proposed method, the samples were treated with amyloglucosidase and inulinase, and the sugars released were determined by ion-exchange chromatography. The concentration of the fructan was calculated by the difference in sugars present in the two enzymic treatments and the initial sample. The repeatability standard deviations (RSDr) for the inulin and oligofructose ranged from 2.9 to 5.8% and the reproducibility standard deviations (RSDR) for these fructans ranged from 4.7 to 11.1%. The method was accepted by the AOAC as an official first action.",The Journal of nutrition,"['D002852', 'D004043', 'D005504', 'D005630', 'D007444', 'D009844', 'D015203']","['Chromatography, Ion Exchange', 'Dietary Fiber', 'Food Analysis', 'Fructans', 'Inulin', 'Oligosaccharides', 'Reproducibility of Results']",Methods to determine food inulin and oligofructose.,"['Q000379', 'Q000032', 'Q000379', 'Q000032', 'Q000032', 'Q000032', None]","['methods', 'analysis', 'methods', 'analysis', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/10395610,1999,0,0,,no cocoa -0.55,546878,"The quantitative analysis of benzoic and sorbic acid, methyl, ethyl and propyl esters of p-hydroxybenzoic acid and saccharin in foodstuffs is described. These compounds are quantitatively extracted with disposable clean-up columns packed with Extrelut and simultaneously determined by high-performance liquid chromatography on reversed-phase columns. Complicated matrices such as cheese, cake, ketchup and chocolate were tested and recoveries were generally better than 95% in the concentration ranges normally used in the food industry.",Journal of chromatography,"['D001565', 'D002851', 'D005504', 'D005520', 'D062385', 'D012439', 'D013011']","['Benzoates', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Food Preservatives', 'Hydroxybenzoates', 'Saccharin', 'Sorbic Acid']",Determination of food preservatives and saccharin by high-performance liquid chromatography.,"['Q000032', None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000032']","['analysis', None, None, 'analysis', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/546878,1980,,,, -0.55,20557115,"The potential of analytical chemistry to predict sensory qualities of food materials is a major current theme. Standard practice is cross-validation (CV), where a set of chemical and associated sensory data is partitioned so chemometric models can be developed on training subsets, and validated on held-out subsets. CV demonstrates prediction, but is an unlikely scenario for industrial operations, where concomitant data acquisition for model development and test materials would be unwieldy. We evaluated cocoa materials of diverse provenance, and analyzed on different dates to those used in model development. Liquor extracts were analyzed by flow-injection electrospray-mass spectrometry (FIE-MS), a novel method for sensory quality prediction. FIE-MS enabled prediction of sensory qualities described by trained human panelists. Optimal models came from the Weka data-mining algorithm SimpleLinearRegression, which learns a model for the attribute giving minimal training error, which was (-)-epicatechin. This flavonoid likewise dominated partial least-squares (PLS)-regression models. Refinements of PLS (orthogonal-PLS or orthogonal signal correction) gave poorer generalization to different test sets, as did support vector machines, whose hyperparameters could not be optimized in training to avoid overfitting. In conclusion, if chemometric overfitting is avoided, chemical analysis can predict sensory qualities of food materials under operationally realistic conditions.",Analytical chemistry,"['D000465', 'D002099', 'D002392', 'D006801', 'D016018', 'D025341', 'D012684', 'D021241']","['Algorithms', 'Cacao', 'Catechin', 'Humans', 'Least-Squares Analysis', 'Principal Component Analysis', 'Sensory Thresholds', 'Spectrometry, Mass, Electrospray Ionization']",Operationally realistic validation for prediction of cocoa sensory qualities by high-throughput mass spectrometry.,"[None, 'Q000737', 'Q000737', None, None, None, None, 'Q000379']","[None, 'chemistry', 'chemistry', None, None, None, None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/20557115,2010,0,0,, -0.55,8900578,"A method for the determination of aspartame (N-L-alpha-aspartyl-L-phenylalanine methyl ester) and its metabolites, applicable on a routine quality assurance basis, is described. Liquid samples (diet Coke, 7-Up, Pepsi, etc.) were injected directly onto a mini-cartridge reversed-phase column on a high-performance liquid chromatographic system, whereas solid samples (Equal, hot chocolate powder, pudding, etc.) were extracted with water. Optimising chromatographic conditions resulted in resolved components of interest within 12 min. The by-products were confirmed by mass spectrometry. Although the method was developed on a two-pump HPLC system fitted with a diode-array detector, it is straightforward and can be transformed to the simplest HPLC configuration. Using a single-piston pump (with damper), a fixed-wavelength detector and a recorder/integrator, the degradation of products can be monitored as they decompose. The results obtained were in harmony with previously reported tedious methods. The method is simple, rapid, quantitative and does not involve complex, hazardous or toxic chemistry.",Journal of chromatography. A,"['D001218', 'D002851', 'D002852', 'D005504', 'D013058', 'D013056']","['Aspartame', 'Chromatography, High Pressure Liquid', 'Chromatography, Ion Exchange', 'Food Analysis', 'Mass Spectrometry', 'Spectrophotometry, Ultraviolet']",Simple and rapid high-performance liquid chromatographic method for the determination of aspartame and its metabolites in foods.,"['Q000378', 'Q000379', 'Q000379', None, 'Q000379', None]","['metabolism', 'methods', 'methods', None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/8900578,1996,0,0,,no cocoa -0.55,730647,"A method was developed for determining theobromine and caeffine in cocoa and chocolate products by high pressure liquid chromatography. After a simple hot water extraction, both theobromine and caffeine were separated by using a reverse phase C18 column and a mobile phase of methanol-water-acetic acid (20 + 79 + 1). Theobromine and caffeine were quantitated at 280 nm; average recoveries were 98.7 and 95.0%; and coefficients of variation were 2.31 and 3.91%, respectively.",Journal - Association of Official Analytical Chemists,"['D001628', 'D002099', 'D002110', 'D002851', 'D012995', 'D013805']","['Beverages', 'Cacao', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Solubility', 'Theobromine']",High pressure liquid chromatographic determination of theobromine and caffeine in cocoa and chocolate products.,"['Q000032', 'Q000032', 'Q000032', 'Q000379', None, 'Q000032']","['analysis', 'analysis', 'analysis', 'methods', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/730647,1979,,,, -0.55,18503248,"Ochratoxin A (OTA), ochratoxin B (OTB) and citrinin (CIT) in commercial foods were simultaneously determined and confirmed with high-performance liquid chromatography (HPLC) and liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS). The samples examined were made up of cereal, fruit, coffee, and cacao products. The limits of quantification (S/N> or =10) of OTA, OTB and CIT were 0.1 microg/kg or less. Aflatoxins (AF), deoxynivalenol (DON) and fumonisins were also surveyed. Of 157 samples examined, 44 were contaminated with OTA at levels of 0.11 to 4.0 microg/kg. At least 2 positive samples were labeled as domestics. In most positive samples, the OTA level was low, less than 1 microg/kg. The highest incidence of OTA was observed in cacao powder (10/12), followed by instant coffee (5/7), cocoa (5/8) and raisin (6/13). OTB was found in fruit and cacao products containing relatively high levels of OTA. Co-occurrence of OTA, CIT and DON was found in cereal products, and co-occurrence of OTA and AF was found in cacao products. Approximately 30% of naturally contaminated OTA in roasted coffee bean moved into the extract solution when brewed with paper filter.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D000348', 'D002099', 'D002851', 'D002853', 'D002953', 'D003069', 'D002523', 'D005504', 'D005506', 'D005638', 'D037341', 'D009793', 'D053719', 'D014255']","['Aflatoxins', 'Cacao', 'Chromatography, High Pressure Liquid', 'Chromatography, Liquid', 'Citrinin', 'Coffee', 'Edible Grain', 'Food Analysis', 'Food Contamination', 'Fruit', 'Fumonisins', 'Ochratoxins', 'Tandem Mass Spectrometry', 'Trichothecenes']","[Investigation of ochratoxin a, B and citrinin contamination in various commercial foods].","['Q000032', 'Q000737', None, None, 'Q000032', 'Q000737', 'Q000737', 'Q000379', 'Q000032', 'Q000737', 'Q000032', 'Q000032', None, 'Q000032']","['analysis', 'chemistry', None, None, 'analysis', 'chemistry', 'chemistry', 'methods', 'analysis', 'chemistry', 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18503248,2008,,,,japanese paper -0.55,5796351,"Six normal men were fed formula diets containing either highly saturated fat (cocoa butter, iodine value 32) or polyunsaturated fat (corn oil, iodine value 125). The sterol balance technique was used to compare the changes in serum cholesterol concentration with the excretion of fecal steroids. The method used for the analysis of fecal steroids was chemical, with a final identification and quantification by gas-liquid chromatography. It was confirmed that the chemical method for fecal steroid analysis was accurate and reproducible. The three dietary periods were each 3 wk in length. In sequence, cocoa butter (period I), corn oil, and cocoa butter (period III) were fed at 40% of the total calories. All diets were cholesterol free, contained similar amounts of plant sterols, and were identical in other nutrients. Corn oil had a hypocholesterolemic effect. Mean serum cholesterol concentrations were 222 mg/100 ml (cocoa butter, period I), 177 during corn oil, and 225 after the return to cocoa butter. Individual fecal steroids were determined from stools pooled for 7 days. Both neutral steroids and bile acids were altered significantly by dietary polyunsaturated fat. The change in bile acid excretion was considerably greater than the change in neutral steroids. Corn oil caused a greater fecal excretion of both deoxycholic and lithocholic acids. The total mean excretion (milligrams per day) of fecal steroids was 709 for cocoa butter (period I), 915 for corn oil, and 629 for the second cocoa butter period. The enhanced total fecal steroid excretion by the polyunsaturated fat of corn oil created a negative cholesterol balance vis-_-vis the saturated fat of cocoa butter. The hypocholesterolemic effect of polyunsaturated fat was associated with total fecal sterol excretion twice greater than the amount of cholesterol calculated to leave the plasma. This finding suggested possible loss of cholesterol from the tissues as well.",The Journal of clinical investigation,"['D000328', 'D001647', 'D002099', 'D002784', 'D002845', 'D004041', 'D005224', 'D005243', 'D006801', 'D008055', 'D008297', 'D009821', 'D010743', 'D013261', 'D014280', 'D003313']","['Adult', 'Bile Acids and Salts', 'Cacao', 'Cholesterol', 'Chromatography', 'Dietary Fats', 'Fats, Unsaturated', 'Feces', 'Humans', 'Lipids', 'Male', 'Oils', 'Phospholipids', 'Sterols', 'Triglycerides', 'Zea mays']",Cholesterol balance and fecal neutral steroid and bile acid excretion in normal men fed dietary fats of different fatty acid composition.,"[None, 'Q000032', None, 'Q000097', None, 'Q000378', 'Q000378', 'Q000032', None, 'Q000097', None, None, 'Q000097', 'Q000032', 'Q000097', None]","[None, 'analysis', None, 'blood', None, 'metabolism', 'metabolism', 'analysis', None, 'blood', None, None, 'blood', 'analysis', 'blood', None]",https://www.ncbi.nlm.nih.gov/pubmed/5796351,1969,2,1,table 1 part A,only the fatty acids -0.55,22175758,"Procyanidins, as important secondary plant metabolites in fruits, berries, and beverages such as cacao and tea, are supposed to have positive health impacts, although their bioavailability is yet not clear. One important aspect for bioavailability is intestinal metabolism. The investigation of the microbial catabolism of A-type procyanidins is of great importance due to their more complex structure in comparison to B-type procyanidins. A-type procyanidins exhibit an additional ether linkage between the flavan-3-ol monomers. In this study two A-type procyanidins, procyanidin A2 and cinnamtannin B1, were incubated in the pig cecum model to mimic the degradation caused by the microbiota. Both A-type procyanidins were degraded by the microbiota. Procyanidin A2 as a dimer was degraded by about 80% and cinnamtannin B1 as a trimer by about 40% within 8 h of incubation. Hydroxylated phenolic compounds were quantified as degradation products. In addition, two yet unknown catabolites were identified, and the structures were elucidated by Fourier transform mass spectrometry.",Journal of agricultural and food chemistry,"['D000818', 'D002432', 'D066298', 'D007422', 'D013058', 'D008954', 'D015394', 'D044945', 'D013552']","['Animals', 'Cecum', 'In Vitro Techniques', 'Intestines', 'Mass Spectrometry', 'Models, Biological', 'Molecular Structure', 'Proanthocyanidins', 'Swine']",Intestinal metabolism of two A-type procyanidins using the pig cecum model: detailed structure elucidation of unknown catabolites with Fourier transform mass spectrometry (FTMS).,"[None, 'Q000737', None, 'Q000737', None, None, None, 'Q000737', None]","[None, 'chemistry', None, 'chemistry', None, None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/22175758,2012,0,0,, -0.55,21623500,"In order to determine the levels of ochratoxin A (OTA) in cocoa and cocoa products available in Canada, a previously published analytical method, with minor modifications to the extraction and immunoaffinity clean-up and inclusion of an evaporation step, was initially used (Method I). To improve the low method recoveries (46-61%), 40% methanol was then included in the aqueous sodium bicarbonate extraction solvent (pH 7.8) (Method II). Clean-up was on an Ochratest__¢ immunoaffinity column and OTA was determined by liquid chromatography (LC) with fluorescence detection. Recoveries of OTA from spiked cocoa powder (0.5 and 5 ng g(-1)) were 75-84%; while recoveries from chocolate were 93-94%. The optimized method was sensitive (limit of quantification (LOQ) = 0.07-0.08 ng g(-1)), accurate (recovery = 75-94%) and precise (coefficient of variation (CV) < 5%). It is applicable to cocoa and chocolate. Analysis of 32 samples of cocoa powder (16 alkalized and 16 natural) for OTA showed an incidence of 100%, with concentrations ranging from 0.25 to 7.8 ng g(-1); in six samples the OTA level exceeded 2 ng g(-1), the previously considered European Union limit for cocoa. The frequency of detection of OTA in 28 chocolate samples (21 dark or baking chocolate and seven milk chocolate) was also 100% with concentrations ranging from 0.05 to 1.4 ng g(-1); one sample had a level higher than the previously considered European Union limit for chocolate (1 ng g(-1)).","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D002099', 'D002182', 'D002846', 'D002851', 'D005506', 'D005511', 'D006863', 'D057230', 'D009793', 'D015203', 'D012639', 'D013050']","['Cacao', 'Candy', 'Chromatography, Affinity', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Food Handling', 'Hydrogen-Ion Concentration', 'Limit of Detection', 'Ochratoxins', 'Reproducibility of Results', 'Seeds', 'Spectrometry, Fluorescence']",Ochratoxin A in cocoa and chocolate sampled in Canada.,"['Q000737', 'Q000032', None, None, None, None, None, None, 'Q000032', None, 'Q000737', None]","['chemistry', 'analysis', None, None, None, None, None, None, 'analysis', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21623500,2011,1,1,table 3, -0.55,21623503,"For the analysis of blue-green algal food supplements for cylindrospermopsin (CYN), a C18 solid-phase extraction column and a polygraphitized carbon solid-phase extraction column in series was an effective procedure for the clean-up of extracts. Determination of CYN was by liquid chromatography with ultraviolet light detection. At extract spiking levels of CYN equivalent to 25-500 _µg g(-1), blue-green algal supplement recoveries were in the range 70-90%. CYN was not detected in ten samples of food supplements and one chocolate product, all containing blue-green algae. The limit of detection for the method was 16 _µg g(-1), and the limit of quantification was 52 _µg g(-1).","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D001427', 'D002099', 'D002182', 'D002273', 'D002851', 'D000458', 'D019587', 'D057140', 'D005506', 'D057230', 'D052616', 'D013056', 'D014498']","['Bacterial Toxins', 'Cacao', 'Candy', 'Carcinogens', 'Chromatography, High Pressure Liquid', 'Cyanobacteria', 'Dietary Supplements', 'Fast Foods', 'Food Contamination', 'Limit of Detection', 'Solid Phase Extraction', 'Spectrophotometry, Ultraviolet', 'Uracil']",Determination of the cyanobacterial toxin cylindrospermopsin in algal food supplements.,"['Q000032', 'Q000737', 'Q000032', 'Q000032', None, 'Q000378', 'Q000032', 'Q000032', None, None, None, None, 'Q000031']","['analysis', 'chemistry', 'analysis', 'analysis', None, 'metabolism', 'analysis', 'analysis', None, None, None, None, 'analogs & derivatives']",https://www.ncbi.nlm.nih.gov/pubmed/21623503,2011,0,0,,no cocoa -0.54,19722709,"The contents of extractable and unextractable proanthocyanidins were determined in a large number of commercial food products of plant origin available in Finland. Proanthocyanidins were extracted with aqueous acetone-methanol and quantified by normal phase high-performance liquid chromatography (HPLC) according to their degree of polymerization. Unextractable proanthocyanidins were analyzed from the extraction residue by reversed phase HPLC after acid-catalyzed depolymerization as free flavan-3-ols (terminal units) and benzylthioethers (extension units). Proanthocyanidins were detected in 49 of 99 selected food items. The highest contents per fresh weight were determined in chokeberries, rose hips, and cocoa products. Berries and fruits were generally the best sources of proanthocyanidins, whereas most of the vegetables, roots, and cereals lacked them completely. Many of the samples contained a significant proportion of insoluble proanthocyanidins, which need to be quantified as well if total proanthocyanidins are studied. Considerable variation was observed in proanthocyanidin contents in berries, which requires further research.",Journal of agricultural and food chemistry,"['D002851', 'D002523', 'D005387', 'D005638', 'D018517', 'D010945', 'D044945', 'D014675']","['Chromatography, High Pressure Liquid', 'Edible Grain', 'Finland', 'Fruit', 'Plant Roots', 'Plants, Edible', 'Proanthocyanidins', 'Vegetables']",Proanthocyanidins in common food products of plant origin.,"[None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000032', 'Q000737']","[None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19722709,2009,1,1,TABLE 2 CONTINUED, -0.54,15472952,"Modified micellar electrokinetic chromatography (MEKC) analysis of monomeric flavanols (catechin and epicatechin) and methylxanthines (caffeine and theobromine) in chocolate and cocoa was performed by using sodium dodecyl sulfate (SDS) as a principal component of the running buffer. Because of the reported poor stability of catechins in alkaline solutions, acidic conditions (pH 2.5) were chosen and consequently the electroosmotic flow (EOF) was significantly suppressed; this resulted in a fast anodic migration of the analytes partitioned into the SDS micelles. Under these conditions, variations of either pH value in acidic range or SDS concentration, showed to be not suitable to modulate the selectivity. To overcome this limit, use of additives to the SDS-based running buffer was successfully applied and three different systems were optimized for the separation of (+)-catechin, (-)-epicatechin, caffeine, and theobromine in chocolate and cocoa powder samples. In particular, two mixed micelle systems were applied; the first consisted of a mixture of SDS and 3-[(3-cholamidopropyl)dimethylammonio]-1-propansulfonate (CHAPS) with a composition of 90 mM and 10 mM, respectively; the second was SDS and taurodeoxycholic acid sodium salt (TDC) with a composition of 70 mM and 30 mM, respectively. A further MEKC approach was developed by addition of 10 mM hydroxypropyl-beta-cyclodextrin (HP-beta-CD) to the SDS solution (90 mM); it provided a useful cyclodextrin(CD)-modified MEKC. By applying the optimized conditions, different separation profiles of the flavanols and methylxanthines were obtained showing interesting potential of these combined systems; their integrated application showed to be useful for the identification of the low level of (+)-catechin in certain real samples. The CD-MEKC approach was validated and applied to the determination of catechins and methylxanthines in aqueous extracts from four different commercial chocolate types (black and milk) and two cocoa powders.",Electrophoresis,"['D002099', 'D002138', 'D002392', 'D002793', 'D020374', 'D008823', 'D012967', 'D013501', 'D014970']","['Cacao', 'Calibration', 'Catechin', 'Cholic Acids', 'Chromatography, Micellar Electrokinetic Capillary', 'Micelles', 'Sodium Dodecyl Sulfate', 'Surface-Active Agents', 'Xanthines']",Modified micellar electrokinetic chromatography in the analysis of catechins and xanthines in chocolate.,"['Q000737', None, 'Q000032', 'Q000737', 'Q000379', None, 'Q000737', 'Q000737', 'Q000032']","['chemistry', None, 'analysis', 'chemistry', 'methods', None, 'chemistry', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15472952,2005,,,, -0.54,16410875,"This study aimed to evaluate the co-occurrence of caffeine and the extent of its influence as compared to other traditional water quality parameters (microbiological and physico-chemical) in order to characterize it as an efficient indicator of anthropic pollution of urban aquatic environments. Caffeine is an ingredient in a variety of beverages (coffee, tea, and caffeinated soft drinks) and numerous food products (chocolate, pastries, and dairy desserts). Although the human body metabolizes this stimulant efficiently, between 0.5 and 10.0% is excreted, mostly in the urine. Analysis of water samples from the Leopoldina Basin and Guanabara Bay revealed a significant difference between areas not commonly affected by nutrient enrichment or sewage inputs and areas chronically influenced by sewage discharges and elevated eutrophication. Monitoring caffeine will be fundamental in stressed urban aquatic environments where frequent accidental ruptures of sewer lines and discharges of untreated effluents impede effective water quality evaluation with traditional indicators.",Cadernos de saude publica,"['D001938', 'D002110', 'D055598', 'D002627', 'D002851', 'D002947', 'D017753', 'D004784', 'D005618', 'D006801', 'D015999', 'D014865', 'D014874']","['Brazil', 'Caffeine', 'Chemical Phenomena', 'Chemistry, Physical', 'Chromatography, High Pressure Liquid', 'Cities', 'Ecosystem', 'Environmental Monitoring', 'Fresh Water', 'Humans', 'Multivariate Analysis', 'Waste Disposal, Fluid', 'Water Pollutants, Chemical']",Caffeine as an environmental indicator for assessing urban aquatic ecosystems.,"[None, 'Q000032', None, None, None, None, None, 'Q000379', 'Q000737', None, None, None, 'Q000032']","[None, 'analysis', None, None, None, None, None, 'methods', 'chemistry', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16410875,2006,0,0,,no cocoa -0.54,10457651,"The paper describes a simple gas chromatographic method for quantification of ethanol in distillates of chocolate shell pralines and fillings. The samples were prepared in two steps. The first step consisted of ethanol distillation from the product and the second involved capillary gas chromatography of 10% v/v distillate with expected ethanol content between 0.06% and 2.5% w/w. Quantification was carried out using iso-propanol as internal standard. The range of linear method response was 0.05-3.16% w/w of ethanol, which corresponded to products with ethanol content between 0.5 and 31.6% w/w. The detection limit was 0.0158% w/w and the quantification limit was 0.058% w/w of ethanol with the relative standard deviation of 2.5%.",Arhiv za higijenu rada i toksikologiju,"['D002099', 'D002182', 'D002849', 'D000431', 'D005524']","['Cacao', 'Candy', 'Chromatography, Gas', 'Ethanol', 'Food Technology']",Determination of ethanol in chocolate shell pralines and filled chocolates by capillary gas chromatography.,"[None, 'Q000032', 'Q000379', 'Q000032', None]","[None, 'analysis', 'methods', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/10457651,1999,,,, -0.54,11407581,"On-line liquid chromatography-gas chromatography (LC-GC) has been applied to the analysis of steryl esters in cocoa butter. Separation of the steryl esters was achieved after on-line transfer to capillary GC. HPLC removes the large amount of triglycerides and pre-separates the components of interest, thus avoiding time-consuming sample preparation prior to GC analysis. The identities of the compounds were confirmed by GC-MS investigation of the collected HPLC fraction and by comparison of the mass spectra (chemical ionization using ammonia as ionization gas) to those of synthesized reference compounds. Using cholesteryl laurate as internal standard, steryl esters were quantified in commercial cocoa butter samples, the detection limit being 3 mg/kg and the quantification limit 10 mg/kg, respectively. Only slight differences in percentage distributions of steryl esters depending on the geographical origin of the material were observed. The patterns were shown to remain unchanged after deodorization. The method described might be a valuable tool for authenticity assessment of cocoa butter.",Journal of chromatography. A,"['D002849', 'D002851', 'D004952', 'D005069', 'D010938', 'D015203', 'D013229']","['Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Esters', 'Evaluation Studies as Topic', 'Plant Oils', 'Reproducibility of Results', 'Stearic Acids']",Analysis of steryl esters in cocoa butter by on-line liquid chromatography-gas chromatography.,"['Q000379', 'Q000379', 'Q000032', None, 'Q000737', None, 'Q000032']","['methods', 'methods', 'analysis', None, 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11407581,2001,1,1,"table 1, 2 and 3", -0.54,28142090,"The outer portion of the cocoa bean, also known as cocoa husk or cocoa shell (CS), is an agrowaste material from the cocoa industry. Even though raw CS is used as food additive, garden mulch, and soil conditioner or even burnt for fuel, this biomass material has hardly ever been investigated for further modification. This article proposes a strategy of chemical modification of cocoa shell to add value to this natural material. The study investigates the grafting of aryl diazonium salt on cocoa shell. Different diazonium salts were grafted on the shell surface and characterized by infrared spectroscopy and scanning electronic microscopy imaging. Strategies were developed to demonstrate the spontaneous grafting of aryl diazonium salt on cocoa shell and to elucidate that lignin is mainly involved in immobilizing the phenyl layer.",Journal of colloid and interface science,"['D002099', 'D003979', 'D008031', 'D008855', 'D013055']","['Cacao', 'Diazonium Compounds', 'Lignin', 'Microscopy, Electron, Scanning', 'Spectrophotometry, Infrared']",Chemical modification of the cocoa shell surface using diazonium salts.,"['Q000033', 'Q000737', 'Q000737', None, None]","['anatomy & histology', 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28142090,2018,1,1,in text (contains key word), -0.54,23289516,"Proanthocyanidins and ellagitannins, referred to as ""tannins"", exist in many plant sources. These compounds interact with proteins due to their numerous hydroxyl groups, which are suitable for hydrophobic associations. It was hypothesized that tannins could bind to the digestive enzymes _±-amylase and glucoamylase, thereby inhibiting starch hydrolysis. Slowed starch digestion can theoretically increase satiety by modulating glucose ""spiking"" and depletion that occurs after carbohydrate-rich meals. Tannins were isolated from extracts of pomegranate, cranberry, grape, and cocoa and these isolates tested for effectiveness to inhibit the activity of _±-amylase and glucoamylase in vitro. The compositions of the isolates were confirmed by NMR and LC/MS analysis, and tannin-protein interactions were investigated using relevant enzyme assays and differential scanning calorimetry (DSC). The results demonstrated inhibition of each enzyme by each tannin, but with variation in magnitude. In general, larger and more complex tannins, such as those in pomegranate and cranberry, more effectively inhibited the enzymes than did less polymerized cocoa tannins. Interaction of the tannins with the enzymes was confirmed through calorimetric measurements of changes in enzyme thermal stability.",Journal of agricultural and food chemistry,"['D002099', 'D002152', 'D005087', 'D006868', 'D047348', 'D009682', 'D044945', 'D031826', 'D013213', 'D053719', 'D029799', 'D027843', 'D000516']","['Cacao', 'Calorimetry, Differential Scanning', 'Glucan 1,4-alpha-Glucosidase', 'Hydrolysis', 'Hydrolyzable Tannins', 'Magnetic Resonance Spectroscopy', 'Proanthocyanidins', 'Punicaceae', 'Starch', 'Tandem Mass Spectrometry', 'Vaccinium macrocarpon', 'Vitis', 'alpha-Amylases']","Inhibition of _±-amylase and glucoamylase by tannins extracted from cocoa, pomegranates, cranberries, and grapes.","['Q000737', None, 'Q000037', None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000037']","['chemistry', None, 'antagonists & inhibitors', None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'antagonists & inhibitors']",https://www.ncbi.nlm.nih.gov/pubmed/23289516,2014,0,0,, -0.54,17517419,"The retention behaviour of several triacylglycerols (TAGs) and fats on Hypercarb, a porous graphitic carbon column (PGC), was investigated in liquid chromatography (LC) under isocratic elution mode with an evaporative light scattering detector (ELSD). Mixtures of chloroform/isopropanol were selected as mobile phase for a suitable retention time to study the influence of temperature. The retention was different between PGC and non-aqueous reversed phase liquid chromatography (NARP-LC) on octadecyl phase. The retention of TAGs was investigated in the interval 30-70 degrees C. Retention was greatly affected by temperature: it decreases as the column temperature increases. Selectivity of TAGs was also slightly influenced by the temperature. Moreover, this chromatographic method is compatible with a mass spectrometer (MS) detector by using atmospheric pressure chemical ionisation (APCI): same fingerprints of cocoa butter and shea butter were obtained with LC-ELSD and LC-APCI-MS. These preliminary results showed that the PGC column could be suitable to separate quickly triacylglycerols in high temperature conditions coupled with ELSD or MS detector.",Journal of chromatography. A,"['D002853', 'D006108', 'D006358', 'D014280']","['Chromatography, Liquid', 'Graphite', 'Hot Temperature', 'Triglycerides']",Analysis of triacylglycerols on porous graphitic carbon by high temperature liquid chromatography.,"['Q000379', 'Q000737', None, 'Q000032']","['methods', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17517419,2007,2,1,text under 3.4 appication for cocoa butter,maybe there comething in the text -0.54,24577577,"Hazelnut (Corylus avellana L.) is responsible for a significant part of the allergies related to nuts. Still, it is a very much appreciated nut and as consequence is widely used in all types of processed foods, such as chocolates. Correct food labelling is currently the most effective means of preventing the consumption of allergenic ingredients, namely hazelnut, by the sensitised/allergic individuals. Thus, to verify labelling compliance and to ensure allergic patient protection, the development of highly sensitive methodologies is of extreme importance. In this study, three major methodologies, namely enzyme-linked immunosorbent assays (ELISA), liquid chromatography coupled with mass spectrometry and real-time polymerase chain reaction, were evaluated for their performance regarding the detection of hazelnut allergens in model chocolates. The sandwich ELISA and respective antibodies were in-house developed and produced. With sensitivity levels of approximately 1 mg kg(-1) and limits of quantification of 50-100 mg kg(-1), all the performed methods were considered appropriate for the identification of hazelnut in complex foods such as chocolates. To our knowledge, this was the first successful attempt to develop and compare three independent approaches for the detection of allergens in foods.",Analytical and bioanalytical chemistry,"['D000485', 'D002099', 'D031211', 'D018744', 'D004797', 'D005504', 'D009754', 'D010940', 'D060888', 'D053719']","['Allergens', 'Cacao', 'Corylus', 'DNA, Plant', 'Enzyme-Linked Immunosorbent Assay', 'Food Analysis', 'Nuts', 'Plant Proteins', 'Real-Time Polymerase Chain Reaction', 'Tandem Mass Spectrometry']","Assessing hazelnut allergens by protein- and DNA-based approaches: LC-MS/MS, ELISA and real-time PCR.","['Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000379', None, 'Q000737', 'Q000737', 'Q000379', 'Q000379']","['analysis', 'chemistry', 'chemistry', 'chemistry', 'methods', None, 'chemistry', 'chemistry', 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/24577577,2014,0,0,,no cocoa -0.54,26067163,"The natural xanthines caffeine, theobromine, and theophylline are of major commercial importance as flavor constituents in coffee, cocoa, tea, and a number of other beverages. However, their exploitation for authenticity, a requirement in these commodities that have a large origin-based price-range, by the standard method of isotope ratio monitoring by mass spectrometry (irm-MS) is limited. We have now developed a methodology that overcomes this deficit that exploits the power of isotopic quantitative (13)C nuclear magnetic resonance (NMR) spectrometry combined with chemical modification of the xanthines to enable the determination of positional intramolecular (13)C/(12)C ratios (__(13)Ci) with high precision. However, only caffeine is amenable to analysis: theobromine and theophylline present substantial difficulties due to their poor solubility. However, their N-methylation to caffeine makes spectral acquisition feasible. The method is confirmed as robust, with good repeatability of the __(13)Ci values in caffeine appropriate for isotope fractionation measurements at natural abundance. It is shown that there is negligible isotope fractionation during the chemical N-methylation procedure. Thus, the method preserves the original positional __(13)Ci values. The method has been applied to measure the position-specific variation of the (13)C/(12)C distribution in caffeine. Not only is a clear difference between caffeine isolated from different sources observed, but theobromine from cocoa is found to show a (13)C pattern distinct from that of caffeine. ",Analytical chemistry,"['D066241', 'D008745', 'D014970']","['Carbon-13 Magnetic Resonance Spectroscopy', 'Methylation', 'Xanthines']",Position-Specific Isotope Analysis of Xanthines: A (13)C Nuclear Magnetic Resonance Method to Determine the (13)C Intramolecular Composition at Natural Abundance.,"['Q000379', None, 'Q000737']","['methods', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/26067163,2015,2,1,table 5 , -0.54,12166981,"Myosmine has been regarded as a specific tobacco alkaloid until investigations pointed out that nuts and nut products constitute a significant source of myosmine. In the present study it is shown that the occurrence of myosmine is widespread throughout a large number of plant families. Using a method for extraction practicable for all examined foods, quantitative analysis through internal standard addition showed nanograms per gram amounts. Positively tested edibles were staple foods such as maize, rice, wheat flour, millet, potato, and milk and also cocoa, popcorn, tomato, carrot, pineapple, kiwi, and apples. No myosmine was detectable in other vegetables and fruits such as lettuce, spinach, cucumber, onion, banana, tangerines, and grapes. Myosmine is easily nitrosated giving rise to a DNA adduct identical to the esophageal tobacco carcinogen N-nitrosonornicotine. Therefore, the role of dietary myosmine in esophageal adenocarcinoma should be further investigated.",Journal of agricultural and food chemistry,"['D000230', 'D000470', 'D000818', 'D002523', 'D004938', 'D005638', 'D008401', 'D008892', 'D014675']","['Adenocarcinoma', 'Alkaloids', 'Animals', 'Edible Grain', 'Esophageal Neoplasms', 'Fruit', 'Gas Chromatography-Mass Spectrometry', 'Milk', 'Vegetables']","New sources of dietary myosmine uptake from cereals, fruits, vegetables, and milk.","['Q000139', 'Q000008', None, 'Q000737', 'Q000139', 'Q000737', None, 'Q000737', 'Q000737']","['chemically induced', 'administration & dosage', None, 'chemistry', 'chemically induced', 'chemistry', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12166981,2002,1,1,table 1, -0.54,18052039,"Chocolate is often labeled with percent cocoa solids content. It is assumed that higher cocoa solids contents are indicative of higher polyphenol concentrations, which have potential health benefits. However, cocoa solids include polyphenol-free cocoa butter and polyphenol-rich nonfat cocoa solids (NFCS). In this study the strength of the relationship between NFCS content (estimated by theobromine as a proxy) and polyphenol content was tested in chocolate samples with labeled cocoa solids contents in the range of 20-100%, grouped as dark (n = 46), milk (n = 8), and those chocolates containing inclusions such as wafers or nuts (n = 15). The relationship was calculated with regard to both total polyphenol content and individual polyphenols. In dark chocolates, NFCS is linearly related to total polyphenols (r2 = 0.73). Total polyphenol content appears to be systematically slightly higher for milk chocolates than estimated by the dark chocolate model, whereas for chocolates containing other ingredients, the estimates fall close to or slightly below the model results. This shows that extra components such as milk, wafers, or nuts might influence the measurements of both theobromine and polyphenol contents. For each of the six main polyphenols (as well as their sum), the relationship with the estimated NFCS was much lower than for total polyphenols (r2 < 0.40), but these relationships were independent of the nature of the chocolate type, indicating that they might still have some predictive capabilities.",Journal of agricultural and food chemistry,"['D002099', 'D002392', 'D002845', 'D005419', 'D010636', 'D059808', 'D013805']","['Cacao', 'Catechin', 'Chromatography', 'Flavonoids', 'Phenols', 'Polyphenols', 'Theobromine']",Predictive relationship between polyphenol and nonfat cocoa solids content of chocolate.,"['Q000737', 'Q000032', None, 'Q000032', 'Q000032', None, 'Q000032']","['chemistry', 'analysis', None, 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18052039,2008,0,0,,chocolate analysis -0.53,24001847,"New products available for food creations include a wide variety of ""supposed"" food grade aerosol sprays. However, the gas propellants used cannot be considered as safe. The different legislations available did not rule any maximum residue limits, even though these compounds have some limits when used for other food purposes. This study shows a preliminary monitoring of propane, butane and dimethyl ether residues, in cakes and chocolate after spraying, when these gases are used as propellants in food aerosol sprays. Release kinetics of propane, butane and dimethyl ether were measured over one day with sprayed food, left at room temperature or in the fridge after spraying. The alkanes and dimethyl ether analyses were performed by headspace-gas chromatography-mass spectrometry/thermal conductivity detection, using monodeuterated propane and butane generated in situ as internal standards. According to the obtained results and regardingthe extrapolations of the maximum residue limits existing for these substances, different delays should be respected according to the storage conditions and the gas propellant to consume safely the sprayed food. ",Food chemistry,"['D000336', 'D002073', 'D003296', 'D005503', 'D005506', 'D008401', 'D007700', 'D008738', 'D011407']","['Aerosols', 'Butanes', 'Cooking', 'Food Additives', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Kinetics', 'Methyl Ethers', 'Propane']",New trends in the kitchen: propellants assessment of edible food aerosol sprays used on food.,"['Q000032', 'Q000737', 'Q000295', 'Q000737', 'Q000032', None, None, 'Q000737', 'Q000737']","['analysis', 'chemistry', 'instrumentation', 'chemistry', 'analysis', None, None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24001847,2014,0,0,,no cocoa -0.53,28190808,"In the present study, the resolution parameters and correction factors (CFs) of triacylglycerol (TAG) standards were estimated by gas chromatography-flame ionization detector (GC-FID) to achieve the precise quantification of the TAG composition in edible fats and oils. Forty seven TAG standards comprising capric acid, lauric acid, myristic acid, pentadecanoic acid, palmitic acid, palmitoleic acid, stearic acid, oleic acid, linoleic acid, and/or linolenic acid were analyzed, and the CFs of these TAGs were obtained against tripentadecanoyl glycerol as the internal standard. The capillary column was Ultra ALLOY",Journal of oleo science,"['D000074262', 'D002849', 'D004041', 'D005410', 'D057230', 'D000073878', 'D010938', 'D014280']","['Canola Oil', 'Chromatography, Gas', 'Dietary Fats', 'Flame Ionization', 'Limit of Detection', 'Palm Oil', 'Plant Oils', 'Triglycerides']",Quantification of Triacylglycerol Molecular Species in Edible Fats and Oils by Gas Chromatography-Flame Ionization Detector Using Correction Factors.,"[None, None, 'Q000032', None, None, None, 'Q000032', 'Q000032']","[None, None, 'analysis', None, None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28190808,2017,2,2,table 4 , -0.53,17711134,"The objective of present work was to comparison of fat and chosen fatty acid in chocolates with, approachable on national market. In the investigations on fat and fatty acids content in the milk chocolates, there were used 14 chocolates, divided into 3 groups either without, with supplements and stuffing. Crude fat content in the chocolates was determined on Soxhlet automatic apparatus. The saturated ad nsaturated acids content was determined using gas chromatographic method. Content of fat and fatty cids in chocolates were differentiation. The highest crude fat content was finding in chocolates with tuffing (31.8%) and without supplements (28.9%). The sum of saturated fatty acids content in fat above 62%) was highest and low differentiation in the chocolates without supplements. Among of saturated and unsaturated fatty acids depended from kind of chocolates dominated, palmitic, stearic, oleic and, linoleic acids. Supplements of nut in chocolates had on influence of high oleic and linoleic level",Roczniki Panstwowego Zakladu Higieny,"['D002099', 'D002182', 'D002849', 'D004041', 'D005227', 'D005228', 'D005231', 'D005504', 'D008041', 'D010169', 'D010938', 'D011044', 'D013229']","['Cacao', 'Candy', 'Chromatography, Gas', 'Dietary Fats', 'Fatty Acids', 'Fatty Acids, Essential', 'Fatty Acids, Unsaturated', 'Food Analysis', 'Linoleic Acids', 'Palmitic Acids', 'Plant Oils', 'Poland', 'Stearic Acids']",[Fat and fatty acids chosen in chocolates content].,"['Q000737', 'Q000032', None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, 'Q000032', 'Q000032', 'Q000032', None, 'Q000032']","['chemistry', 'analysis', None, 'analysis', 'analysis', 'analysis', 'analysis', None, 'analysis', 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17711134,2008,0,0,,no cocoa -0.53,10208658,"Selenium content of 1028 milk and milk products of Turkey are presented in this study. The selenium content of human milk (colostrum, transitional, and mature milk), various kinds of milk [cow, sheep, goat, buffalo, paper boxes (3%, 1.5%, 0.012% fat), bottled milk, condensed milk (10% fat), mineral added milk (1.6%), and banana, strawberry, and chocolate milk] and milk products (kefir, yogurt, Ayran, various cheese, coffee cream, ice cream, butter, margarine, milk powder, and fruit yogurt) in Turkey were determined by a spectrofluorometric method. The selenium levels of cow milks collected from 57 cities in Turkey were also determined. Selenium levels in cow milk varied with geographical location in Turkey and were found to be lowest for Van and highest for Aksaray. The results [milk (cow, sheep, goat, buffalo and human) and milks products] were compared with literature data from different countries.",Biological trace element research,"['D000328', 'D000818', 'D002079', 'D002611', 'D005260', 'D006801', 'D007054', 'D007774', 'D008892', 'D008895', 'D012643', 'D013050', 'D013997', 'D014421']","['Adult', 'Animals', 'Butter', 'Cheese', 'Female', 'Humans', 'Ice Cream', 'Lactation', 'Milk', 'Milk, Human', 'Selenium', 'Spectrometry, Fluorescence', 'Time Factors', 'Turkey']",Selenium content of milk and milk products of Turkey. II.,"[None, None, 'Q000032', 'Q000032', None, None, 'Q000032', None, 'Q000737', 'Q000737', 'Q000032', None, None, None]","[None, None, 'analysis', 'analysis', None, None, 'analysis', None, 'chemistry', 'chemistry', 'analysis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10208658,1999,0,0,,no cocoa -0.53,9892779,"The aetiology of dental caries is in part related to the retention time of dietary carbohydrates in the oral cavity and their subsequent metabolism by the oral bacteria. Salivary clearance of fermentable carbohydrates from three different foodstuffs was examined in 5 subjects and analyses performed by high-performance anion-exchange chromatography with pulsed amperometric detection. The clearance of glucose, fructose, sucrose, maltose and sorbitol rinses was studied as well as that of chocolate bars, white bread and bananas. Of the sugar rinses studied, sucrose was removed from saliva most rapidly whilst appreciable levels of sorbitol remained even after 1 h. Clearance of residual carbohydrates from bananas and chocolate bars seemed marginally faster than in the case of bread, but sucrose levels still tended to fall more quickly than other carbohydrates studied. Surprisingly, carbohydrate residues from the three foods studied were still present in the mouth even 1 h after ingestion, which is longer than has hitherto been reported.",Caries research,"['D001939', 'D002099', 'D002241', 'D005260', 'D005632', 'D005947', 'D006801', 'D008297', 'D008320', 'D008657', 'D012463', 'D013012', 'D013395', 'D019862']","['Bread', 'Cacao', 'Carbohydrates', 'Female', 'Fructose', 'Glucose', 'Humans', 'Male', 'Maltose', 'Metabolic Clearance Rate', 'Saliva', 'Sorbitol', 'Sucrose', 'Zingiberales']",Human salivary sugar clearance after sugar rinses and intake of foodstuffs.,"['Q000032', None, 'Q000032', None, 'Q000032', 'Q000032', None, None, 'Q000032', None, 'Q000737', 'Q000032', 'Q000032', None]","['analysis', None, 'analysis', None, 'analysis', 'analysis', None, None, 'analysis', None, 'chemistry', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/9892779,1999,,,,no pdf access -0.53,22663977,"(-)-Epicatechin, an abundant dietary polyphenol found mainly in cocoa and tea, is known to extensively undergo metabolism after ingestion giving rise to a complex series of conjugated metabolites including numerous isomers. In the present study, the combination of fractionation, chemical derivatization and various mass spectrometric approaches is described to determine the exact position of sulphate group in methylated epicatechin metabolites. Four O-methyl-(-)-epicatechin-O-sulphate metabolites isolated from human urine samples were derivatized under mild condition using trimethylsilyldiazomethane (TMSD) in the presence of methanol. The resulting methylated reaction products were then analyzed by high resolution and multistage mass spectrometry for the subsequent identification of the sulphate positional isomers. Results show that O-methylation affects the charge delocalization in negatively charged ions and hereby the fragmentation pattern of the sulphate isomers allowing the identification of diagnostic ions. In addition, this study demonstrates that methoxy derivatives of polyphenol metabolites can be prepared using TMSD. Subsequently, the localization of the sulphate group in the polyphenol metabolites can be achieved by analyzing the methoxy derivatives by multistage mass spectrometry. Using an enzymatic reaction for identification of the O-methyl position, and a chemical O-methylation with TMSD follow by high resolution and multistage tandem MS for the identification of the sulphate group position, we were able to identify the previously unknown O-methyl-(-)-epicatechin-O-sulphate. Accordingly, we identified 3'-O-methyl-(-)-epicatechin-5-O-sulphate and 3'-O-methyl-(-)-epicatechin-7-O-sulphate as the main O-methyl-(-)-epicatechin-sulfates(-)-epicatechin metabolites in humans.",Journal of chromatography. A,"['D002392', 'D003978', 'D006801', 'D013058', 'D013463', 'D014297']","['Catechin', 'Diazomethane', 'Humans', 'Mass Spectrometry', 'Sulfuric Acid Esters', 'Trimethylsilyl Compounds']",Identification of O-methyl-(-)-epicatechin-O-sulphate metabolites by mass-spectrometry after O-methylation with trimethylsilyldiazomethane.,"['Q000031', 'Q000031', None, 'Q000379', 'Q000032', 'Q000737']","['analogs & derivatives', 'analogs & derivatives', None, 'methods', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22663977,2012,0,0,, -0.53,25940929,"This note reports an interesting way to rapidly identify bacteria grown from blood culture bottles. Chocolate agar plates were inoculated with 1 drop of the positive blood bottle medium. After a 3-hour incubation, the growth veil was submitted to MALDI-TOF mass spectrometry: 77% of the bacteria present have been correctly identified. ",Journal of microbiological methods,"['D001419', 'D015373', 'D001769', 'D003470', 'D006801', 'D019032', 'D053719']","['Bacteria', 'Bacterial Typing Techniques', 'Blood', 'Culture Media', 'Humans', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Tandem Mass Spectrometry']",MALDI-TOF mass spectrometry for early identification of bacteria grown in blood culture bottles.,"['Q000145', 'Q000379', 'Q000382', 'Q000378', None, 'Q000379', 'Q000379']","['classification', 'methods', 'microbiology', 'metabolism', None, 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/25940929,2016,0,0,,no cocoa -0.53,20299763,"The amount and characterization of phytosterol and other minor components present in three Indian minor seed oils, mahua (Madhuca latifolia), sal (Shorea robusta) and mango kernel (Mangifera indica), have been done. Theses oils have shown commercial importance as cocoa-butter substitutes because of their high symmetrical triglycerides content. The conventional thin layer chromatography (TLC), gas chromatography (GC) & gas chromatography-mass spectroscopy (GC-MS) techniques were used to characterize the components and the high performance thin layer chromatography (HPTLC) technique was used to quantify the each group of components. The experimental data showed that the all the three oils are rich in sterol content and among all the sterols, beta-sitosterol occupies the highest amount. Sal oil contains appreciable amount of cardenolides, gitoxigenin. Tocopherol is present only in mahua oil and oleyl alcohol is present in mango kernel oil. Hydrocarbon, squalene, is present in all the three oils. The characterization of these minor components will help to detect the presence of the particular oil in specific formulations and to assess its stability as well as nutritional quality of the specific oil.",Journal of oleo science,"['D002298', 'D002855', 'D005233', 'D005504', 'D008401', 'D006838', 'D010840', 'D010938', 'D012639', 'D012855', 'D013185', 'D024505']","['Cardenolides', 'Chromatography, Thin Layer', 'Fatty Alcohols', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Hydrocarbons', 'Phytosterols', 'Plant Oils', 'Seeds', 'Sitosterols', 'Squalene', 'Tocopherols']","Analysis of sterol and other components present in unsaponifiable matters of mahua, sal and mango kernel oil.","['Q000032', None, 'Q000032', 'Q000379', None, 'Q000032', 'Q000032', 'Q000737', 'Q000737', 'Q000032', 'Q000032', 'Q000032']","['analysis', None, 'analysis', 'methods', None, 'analysis', 'analysis', 'chemistry', 'chemistry', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20299763,2010,0,0,, -0.53,26833256,"Cocoa tea (Camellia ptilophylla) is a naturally decaffeinated tea plant. Previously we found that cocoa tea demonstrated a beneficial effect against high-fat diet induced obesity, hepatic steatosis, and hyperlipidemia in mice. The present study aimed to investigate the anti-adipogenic effect of cocoa tea in vitro using preadipocytes 3T3-L1. Adipogenic differentiation was confirmed by Oil Red O stain, qPCR and Western blot. Our results demonstrated that cocoa tea significantly inhibited triglyceride accumulation in mature adipocytes in a dose-dependent manner. Cocoa tea was shown to suppress the expressions of key adipogenic transcription factors, including peroxisome proliferator-activated receptor gamma (PPAR __) and CCAAT/enhancer binding protein (C/EBP _±). The tea extract was subsequently found to reduce the expressions of adipocyte-specific genes such as sterol regulatory element binding transcription factor 1c (SREBP-1c), fatty acid synthase (FAS), Acetyl-CoA carboxylase (ACC), fatty acid translocase (FAT) and stearoylcoenzyme A desaturase-1 (SCD-1). In addition, JNK, ERK and p38 phosphorylation were inhibited during cocoa tea inhibition of 3T3-L1 adipogenic differentiation. Taken together, this is the first study that demonstrates cocoa tea has the capacity to suppress adipogenesis in pre-adipocyte 3T3-L1 similar to traditional green tea. ",Scientific reports,"['D041721', 'D017667', 'D050156', 'D000818', 'D028244', 'D002454', 'D002470', 'D002851', 'D005786', 'D051379', 'D020928', 'D010766', 'D010936', 'D013662', 'D014157', 'D014280', 'D014867']","['3T3-L1 Cells', 'Adipocytes', 'Adipogenesis', 'Animals', 'Camellia', 'Cell Differentiation', 'Cell Survival', 'Chromatography, High Pressure Liquid', 'Gene Expression Regulation', 'Mice', 'Mitogen-Activated Protein Kinases', 'Phosphorylation', 'Plant Extracts', 'Tea', 'Transcription Factors', 'Triglycerides', 'Water']",Cocoa tea (Camellia ptilophylla) water extract inhibits adipocyte differentiation in mouse 3T3-L1 preadipocytes.,"[None, 'Q000166', 'Q000187', None, 'Q000737', 'Q000187', 'Q000187', None, 'Q000187', None, 'Q000378', 'Q000187', 'Q000494', None, 'Q000378', 'Q000378', 'Q000737']","[None, 'cytology', 'drug effects', None, 'chemistry', 'drug effects', 'drug effects', None, 'drug effects', None, 'metabolism', 'drug effects', 'pharmacology', None, 'metabolism', 'metabolism', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/26833256,2017,0,0,,cocoa tea -0.53,15080647,"Normal-phase liquid chromatography/mass spectrometry (LC/MS) was used to determine the levels and fate of procyanidins in frozen and canned Ross clingstone peaches as well as in the syrup used in the canning over a 3 month period. Procyanidin oligomers, monomers through undecamers, were identified in Ross clingstone peaches. Optimized methods allowed for the quantitation of oligomers through octamers. The profile of procyanidins in peaches is similar to profiles found in grapes, chocolate, and beverages linked to health benefits such as tea and wine. The monomer content in frozen peeled peaches was found to be 19.59 mg/kg. Dimers (39.59 mg/kg) and trimers (38.81 mg/kg) constituted the largest percent composition of oligomers in the peaches. Tetramers through octamers were present in levels of 17.81, 12.43, 10.62, 3.94 and 1.75 mg/kg, respectively. Thermal processing resulted in an 11% reduction in monomers, a 9% reduction in dimers, a 12% reduction in trimers, a 6% reduction in tetramers, and a 5% reduction in pentamers. Hexamers and heptamers demonstrated an approximate 30% loss, and octamers were no longer detected. Analysis of the syrup after thermal processing indicates that there is a migration of procyanidin monomers through hexamers into the syrup that can account for the losses observed during the canning process. Storage of canned peaches for 3 months demonstrated a time-related loss in higher oligomers and that by 3 months oligomers larger than tetramers are not observed. At 3 months postcanning, levels of monomers had decreased by 10%, dimers by 16%, trimers by 45%, and tetramers by 80%. A similar trend was observed in the canning syrup.",Journal of agricultural and food chemistry,"['D044946', 'D002392', 'D002851', 'D006358', 'D013058', 'D044945', 'D027861']","['Biflavonoids', 'Catechin', 'Chromatography, High Pressure Liquid', 'Hot Temperature', 'Mass Spectrometry', 'Proanthocyanidins', 'Prunus']",Liquid chromatography/mass spectrometry investigation of the impact of thermal processing and storage on peach procyanidins.,"[None, 'Q000032', None, None, None, None, 'Q000737']","[None, 'analysis', None, None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15080647,2004,0,0,, -0.53,25727461,"The scarce availability of nongenetically modified soybeans on the world market represents a growing problem for food manufacturers. Hence, in this study the effects of substituting soybean with sunflower lecithin were investigated with regard to chocolate production. The glycerophospholipid pattern of the different lecithin samples was investigated by high-performance thin-layer chromatography fluorescence detection (HPTLC-FLD) and by HPTLC-positive ion electrospray ionization mass spectrometry (ESI(+)-MS) via the TLC-MS Interface and by scanning HPTLC-matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOFMS). Especially, the contents of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) were of interest due to the influencing effects of these two glycerophospholipids on the rheological parameters of chocolate production. The lecithin substitution led to only slight differences in the rheological parameters of milk and dark chocolate. Limits of detection (LODs) and limits of quantification (LOQs) of seven glycerophospholipids were studied for three detection modes. Mean LODs ranged from 8 to 40 mg/kg for HPTLC-FLD and, using a single-quadrupole MS, from 10 to 280 mg/kg for HPTLC-ESI(+)-MS as well as from 15 to 310 mg/kg for HPTLC-FLD-ESI(+)-MS recorded after derivatization with the primuline reagent. ",Journal of agricultural and food chemistry,"['D000818', 'D002099', 'D002855', 'D005503', 'D006368', 'D054709', 'D008892', 'D013025', 'D021241']","['Animals', 'Cacao', 'Chromatography, Thin Layer', 'Food Additives', 'Helianthus', 'Lecithins', 'Milk', 'Soybeans', 'Spectrometry, Mass, Electrospray Ionization']",Comparison and characterization of soybean and sunflower lecithins used for chocolate production by high-performance thin-layer chromatography with fluorescence detection and electrospray mass spectrometry.,"[None, 'Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000379']","[None, 'chemistry', 'methods', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/25727461,2015,0,0,, -0.53,27132838,"Assessment of the flavanol composition of 41 commercial chocolates was by HPLC-DAD. Among individual flavonols ranged from 0.095 to 3.264mgg(-1), epicatechin was the predominant flavanol accounting for 32.9%. Contrary to catechin, epicatechin was a reliable predictive value of the polyphenol content. Conversely the percentage of theobromine used as a proxy measure for nonfat cocoa solids (NFCS) was not a good predictor of epicatechin or flavanol content. In a further chiral analysis, the naturally occurring forms of cocoa flavanols, (-)-epicatechin and (+)-catechin, was determined joint the occurrence of (+)-epicatechin and (-)-catechin due to the epimerization reactions produced in chocolate manufacture. (-)-Epicatechin, the most bioactive compound and predominant form accounted of 93%. However, no positive correlation was found with% cocoa solids, the most significant quality parameter. ",Food chemistry,"['D044946', 'D002099', 'D002110', 'D002392', 'D000069956', 'D002851', 'D005419', 'D005504', 'D059808', 'D044945', 'D013237', 'D013805', 'D014970']","['Biflavonoids', 'Cacao', 'Caffeine', 'Catechin', 'Chocolate', 'Chromatography, High Pressure Liquid', 'Flavonoids', 'Food Analysis', 'Polyphenols', 'Proanthocyanidins', 'Stereoisomerism', 'Theobromine', 'Xanthines']",Assessment of flavanol stereoisomers and caffeine and theobromine content in commercial chocolates.,"['Q000032', 'Q000737', 'Q000032', 'Q000032', 'Q000032', None, 'Q000032', None, 'Q000032', 'Q000032', None, 'Q000032', 'Q000032']","['analysis', 'chemistry', 'analysis', 'analysis', 'analysis', None, 'analysis', None, 'analysis', 'analysis', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27132838,2017,0,0,,chocolate samples -0.52,28317738,"The presence of 4-methylimidazole (4-MEI), 2-methylimidazole (2-MEI) and 2-acetyl-4-tetrahydroxybutylimidazole (THI) in some foods may result from the usage of caramel colorants E150c and E150d as food additives. This study demonstrates that alkylimidazoles are also byproducts formed from natural constituents in foods during thermal processes. A range of heat-processed foods that are known not to contain caramel colorants were analyzed by isotope dilution LC-MS/MS to determine the contamination levels. Highest 4-MEI concentrations (up to 466_µg/kg) were observed in roasted barley, roasted malt and cocoa powders, with the concomitant presence of 2-MEI and/or THI in some cases, albeit at significantly lower levels. Low amounts of 4-MEI (<20_µg/kg) were also detected in cereal-based foods such as breakfast cereals and bread toasted to a brown color (medium toasted). The occurrence of 4-MEI in certain processed foods is therefore not a reliable indicator of the presence of the additives E150c or E150d.",Food chemistry,"['D002853', 'D005503', 'D005511', 'D007093', 'D013058']","['Chromatography, Liquid', 'Food Additives', 'Food Handling', 'Imidazoles', 'Mass Spectrometry']",Process-induced formation of imidazoles in selected foods.,"['Q000379', 'Q000737', 'Q000379', 'Q000737', 'Q000379']","['methods', 'chemistry', 'methods', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/28317738,2017,1,1,table 2, only cocoa products -0.52,19348428,"Hazelnut is one of the most important items in high-quality food products from Piedmont, Italy. The 'Tonda Gentile delle Langhe' (TGL) variety is acknowledged all over the world as the best one, and it is particularly appreciated when used to provide flavor in chocolate products. Authentication and/or traceability studies must therefore be developed to safeguard this variety against fraud, which can occur when the product is partially or totally substituted with hazelnuts of lower quality. In this work, a classification of hazelnuts from different countries is presented, showing the possibility to discriminate the TGL from other productions on the basis of the distribution of trace elements as determined by means of inductively coupled plasma-mass spectrometry (ICP-MS), with particular reference to lanthanides. Accuracy of the sample treatment procedure was tested by analysis of biological certified materials. Data from elemental analysis were chemometrically treated with an unsupervised method, such as principal component analysis (PCA), allowing for a good discrimination among groups.",Journal of agricultural and food chemistry,"['D031211', 'D005607', 'D007558', 'D028581', 'D013058', 'D009754', 'D012987', 'D014131']","['Corylus', 'Fraud', 'Italy', 'Lanthanoid Series Elements', 'Mass Spectrometry', 'Nuts', 'Soil', 'Trace Elements']","Authentication and traceability study of hazelnuts from piedmont, Italy.","['Q000737', 'Q000517', None, 'Q000032', 'Q000379', 'Q000737', 'Q000032', 'Q000032']","['chemistry', 'prevention & control', None, 'analysis', 'methods', 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19348428,2009,0,0,,no cocoa -0.52,16934398,"A simple, reliable and rapid method for preconcentration and determination of lead using octadecyl bonded silica membrane disk impregnated with Cyanex302 and flame atomic absorption spectrometry is presented. The influence of aqueous phase pH, type of eluent, flow rates of sample solution and eluent, volume of eluent and amount of extractant has been investigated. The break through volume is greater than 4.0 dm(3) with an enrichment factor of more than 400 and a detection limit of 1.0microg dm(-3). The method developed for determination of lead is good as six replicate determinations using 100cm(3) solution containing lead in the range 1-4900microg provides a relative standard deviation (R.S.D.) of 0.4%. The selectivity of the proposed method was confirmed from the interference studies. The developed procedure was successfully applied for the determination of lead in spiked sea water, USGS standard soil sample, sludge and industrial effluents, medicinal formulation, plant, some food products and wine.",Journal of hazardous materials,"['D000327', 'D002099', 'D028241', 'D004785', 'D005504', 'D007220', 'D007854', 'D010721', 'D029222', 'D010936', 'D010946', 'D012623', 'D012822', 'D052616', 'D013054', 'D014920']","['Adsorption', 'Cacao', 'Camellia sinensis', 'Environmental Pollutants', 'Food Analysis', 'Industrial Waste', 'Lead', 'Phosphinic Acids', 'Piper nigrum', 'Plant Extracts', 'Plants, Medicinal', 'Seawater', 'Silicon Dioxide', 'Solid Phase Extraction', 'Spectrophotometry, Atomic', 'Wine']",Solid phase extraction of lead on octadecyl bonded silica membrane disk modified with Cyanex302 and determination by flame atomic absorption spectrometry.,"[None, 'Q000737', 'Q000737', 'Q000032', None, 'Q000032', 'Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000032', 'Q000737', None, None, 'Q000032']","[None, 'chemistry', 'chemistry', 'analysis', None, 'analysis', 'analysis', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'analysis', 'chemistry', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16934398,2007,1,1,table 6,only cocoa powder -0.52,19424684,"A new micro-solid phase extraction (micro-SPE) procedure based on titanium dioxide microcolumns was developed for the selective extraction of phospholipids (PLs) from dairy products before matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) analysis. All the extraction steps (loading, washing, and elution) have been optimized using a synthetic mixture of PLs standard and the procedure was subsequently applied to food samples such as milk, chocolate milk and butter. The whole method demonstrated to be simpler than traditional approaches and it appears very promising for a rapid PLs screening and characterization also in biological matrices.",Analytical and bioanalytical chemistry,"['D003611', 'D010743', 'D052617', 'D019032', 'D053719', 'D014025']","['Dairy Products', 'Phospholipids', 'Solid Phase Microextraction', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Tandem Mass Spectrometry', 'Titanium']",Selective extraction of phospholipids from dairy products by micro-solid phase extraction based on titanium dioxide microcolumns followed by MALDI-TOF-MS analysis.,"['Q000032', 'Q000032', 'Q000295', 'Q000379', 'Q000379', 'Q000737']","['analysis', 'analysis', 'instrumentation', 'methods', 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19424684,2009,0,0,,no cocoa -0.52,10563922,"Monomeric and oligomeric procyanidins present in cocoa and chocolate were separated and identified using a modified normal-phase high-performance liquid chromatography (HPLC) method coupled with on-line mass spectrometry (MS) analysis using an atmospheric pressure ionization electrospray chamber. The chromatographic separation was achieved using a silica stationary phase in combination with a gradient ascending in polarity. This qualitative report confirms the presence of a complex series of procyanidins in raw cocoa and certain chocolates using HPLC/MS techniques. Although both cocoa and chocolate contained monomeric and oligomeric procyanidin units 2-10, only use of negative mode provided MS data for the higher oligomers (i.e., >pentamer). Application of this method for qualitative analysis of proanthocyanidins in other food products and confirmation of this method as a reliable quantitative tool for determining levels of procyanidins in cocoa, chocolate, and other food products are currently being investigated.",Journal of agricultural and food chemistry,"['D044946', 'D002099', 'D002392', 'D002851', 'D013058', 'D044945']","['Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Mass Spectrometry', 'Proanthocyanidins']",Identification of procyanidins in cocoa (Theobroma cacao) and chocolate using high-performance liquid chromatography/mass spectrometry.,"[None, 'Q000737', 'Q000737', None, None, None]","[None, 'chemistry', 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10563922,2000,0,0,, -0.52,25529702,"The extraction capabilities of a Diamond Hydride__¢ phase, as well as silica hydride phases modified with bidentate octadecyl (BDC(18)), phenyl or cholesteryl groups, were evaluated for the analysis of fatty acids, amino acids, sugars and sterols in a dark chocolate extract. These batch adsorption performances were investigated using either methanol or aqueous methanol as the solvent. The compositions of the extracted fractions were assessed by gas chromatography interfaced with quadrupole mass spectrometry (GC-qMS). The batch binding propensities of the various compound classes with silica hydride particles modified with immobilised phenyl groups or larger ligands followed trends predicted from linear solvation energy relationships. Both prediction and experiment revealed that better extraction results could be obtained with the phenyl, BDC(18) and cholesteryl hydride particles for the major chocolate components. Based on these results, separations in micro-pipette tip format with these three types of stationary phase particles have been undertaken.",Food chemistry,"['D000327', 'D002099', 'D002241', 'D005227', 'D005504', 'D008401', 'D000432', 'D017640', 'D052616', 'D013261', 'D014867']","['Adsorption', 'Cacao', 'Carbohydrates', 'Fatty Acids', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Methanol', 'Silicates', 'Solid Phase Extraction', 'Sterols', 'Water']",Comparison of the performance of different silica hydride particles for the solid-phase extraction of non-volatile analytes from dark chocolate with analysis by gas chromatography-quadrupole mass spectrometry.,"[None, 'Q000737', 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000737', None, 'Q000032', 'Q000737']","[None, 'chemistry', 'analysis', 'analysis', None, None, 'analysis', 'chemistry', None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25529702,2015,0,0,,no cocoa tested -0.52,20371968,"This work has been carried out to investigate the conditions which lead to removal of the biogenic amines through the model system. Also, the main goal of this research work is trying to remove biogenic amines; histamine and tyramine, from some Egyptian foods such as tomato, strawberry, banana and mango to prevent their allergy effect. Histamine and tyramine have been affected by pyrogallol, catechol, starch, ascorbic and chlorogenic acids at different levels with different conditions. Some natural additives like glucose, spices, milk, vanillin, starch, orange juice, ascorbic and citric acids, showed an effective effect on disappearance of histamine and tyramine. By studying the effect of some additives on biogenic amines, it was found that tomato showed a decrease in histamine and tyramine concentrations by adding spices. Strawberry and banana showed a clear decrease in histamine and tyramine concentrations by treating them with ascorbic acid. Treating mango by milk led to increase of histamine level while milk with chocolate increases both histamine and tyramine concentrations.",The Journal of toxicological sciences,"['D002851', 'D002855', 'D004534', 'D005504', 'D005511', 'D006632', 'D013053', 'D014439']","['Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Egypt', 'Food Analysis', 'Food Handling', 'Histamine', 'Spectrophotometry', 'Tyramine']","High performance liquid chromatography, thin layer chromatography and spectrophotometric studies on the removal of biogenic amines from some Egyptian foods using organic, inorganic and natural compounds.","['Q000379', 'Q000379', None, 'Q000379', None, 'Q000032', 'Q000379', 'Q000032']","['methods', 'methods', None, 'methods', None, 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20371968,2010,0,0,,no cocoa -0.51,9214759,"The amino acid sequence of 6.5k-arginine/glutamate rich polypeptide (6.5k-AGRP) from the seeds of sponge gourd (Luffa cylindrica) has been determined. The 6.5k-AGRP consists of a 47-residue polypeptide chain containing two disulfide bonds, and a molecular mass calculated to be 5695 Da, which fully coincides with a value of [M+H]+ = m/zeta 5693.39 obtained by matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS). The mass spectrometric evidence indicated that 6.5k-AGRP is also present partially truncated at the C-terminus. In our preparations, approximately half of the polypeptide molecules have the C-terminal sequence Arg-Arg-Glu-Val-Asp; the other half lack Val-Asp and end with the glutamic acid, making a total of 45 residues in the polypeptide chain. The two disulfide bonds connect Cys12 to Cys33 and Cys16 to Cys29. Comparison of the amino acid sequence of 6.5k-AGRP with those of the other known proteins included in the PIR protein sequence database showed that it is related to the amino acid sequence of the N-terminal region encoded by the first exon of the cocoa (Theobroma cacao) and cotton seeds vicilin genes, sharing a characteristic two Cys-Xaa-Xaa-Xaa-Cys motif.","Bioscience, biotechnology, and biochemistry","['D000595', 'D000818', 'D001120', 'D002851', 'D018698', 'D008969', 'D008970', 'D010455', 'D010940', 'D012639', 'D017386', 'D012697', 'D019032', 'D014675']","['Amino Acid Sequence', 'Animals', 'Arginine', 'Chromatography, High Pressure Liquid', 'Glutamic Acid', 'Molecular Sequence Data', 'Molecular Weight', 'Peptides', 'Plant Proteins', 'Seeds', 'Sequence Homology, Amino Acid', 'Serine Endopeptidases', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Vegetables']",Primary structure of 6.5k-arginine/glutamate-rich polypeptide from the seeds of sponge gourd (Luffa cylindrica).,"[None, None, 'Q000737', None, 'Q000737', None, None, 'Q000737', 'Q000737', 'Q000737', None, 'Q000302', None, 'Q000737']","[None, None, 'chemistry', None, 'chemistry', None, None, 'chemistry', 'chemistry', 'chemistry', None, 'isolation & purification', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/9214759,1997,0,0,, -0.51,12607924,"A simple method for the determination of sucralose in various foods using liquid chromatography-electrospray ionization tandem mass spectrometry (LC/MS/MS) was developed. Sucralose was extracted with water or methanol, and the extract was cleaned up on a C18 cartridge, and diluted with water for injection into the LC/MS/MS. The LC separation was performed with a reversed-phase gradient on an ODS column, and the mass spectral acquisition was done in the negative ion mode by applying selected reaction monitoring (SRM). The recoveries of sucralose from various kinds of foods fortified at 100 micrograms/g and 5 micrograms/g were 88.1-96.7% and 92.7-98.5%, respectively. The lower limits of quantification were 0.5 microgram/g in beverage, low-malt beer, yogurt and chocolate and 2.5 micrograms/g in other foods. Forty-three commercial foods containing sucralose were analyzed by this method. Sucralose was detected in all samples at levels of 3.8-481 micrograms/g.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D002853', 'D005504', 'D013058', 'D013395']","['Chromatography, Liquid', 'Food Analysis', 'Mass Spectrometry', 'Sucrose']",[Determination of sucralose in foods by liquid chromatography/tandem mass spectrometry].,"['Q000379', 'Q000379', 'Q000379', 'Q000031']","['methods', 'methods', 'methods', 'analogs & derivatives']",https://www.ncbi.nlm.nih.gov/pubmed/12607924,2003,,,, -0.51,21329356,"Key odorants in roasted pistachio nuts have been determined for the first time. Two different pistachio varieties (Fandooghi and Kerman) have been analyzed by means of headspace solid-phase microextraction (HS-SPME) and gas chromatography-olfactometry (GCO). The aroma extract dilution analyses (AEDA) applied have revealed 46 and 41 odor-active regions with a flavor dilution (FD) factor___64 for the Fandooghi and the Kerman varieties, respectively, and 39 of them were related to precisely identified compounds. These included esters, pyrazines, aldehydes, acids, furans, and phenols. The results show that the Fandooghi variety presents, not only more odor-active regions but also higher FD factors than the Kerman variety that can lead to the conclusion that the first variety has a richer aromatic profile than the second one. The descriptive sensory analysis (DSA) showed that the roasted, chocolate/coffee, and nutty attributes were rated significantly higher in the Fandooghi variety, whereas the green attribute was significantly higher in the Kerman one.",Journal of agricultural and food chemistry,"['D002849', 'D005511', 'D009812', 'D027927', 'D010936', 'D052617', 'D014835']","['Chromatography, Gas', 'Food Handling', 'Odorants', 'Pistacia', 'Plant Extracts', 'Solid Phase Microextraction', 'Volatilization']",Determination of roasted pistachio (Pistacia vera L.) key odorants by headspace solid-phase microextraction and gas chromatography-olfactometry.,"['Q000379', None, 'Q000032', 'Q000737', 'Q000737', 'Q000379', None]","['methods', None, 'analysis', 'chemistry', 'chemistry', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/21329356,2011,0,0,,no cocoa -0.51,18020409,"Ochratoxin A is an important mycotoxin that can enter the human food chain in cereals, wine, coffee, spices, beer, cocoa, dried fruits, and pork meats. Coffee is one of the most common beverages and, consequently, it has a potential risk factor for human health related to ochratoxin A exposure. In this study, coffee and corresponding byproducts from seven different geographic regions were investigated for ochratoxin A natural occurrence by HPLC-FLD, nutritional characterization, and antioxidant activities by spectrophotometric assay. The research focused on composition changes in coffee during the processing step ""from field to cup"". Costa Rica and Indian green coffees were the most contaminated samples, with 13 and 11 microg/kg, respectively, while the Ethiopian coffee was the least contaminated, with 3.8 microg/kg of ochratoxin A. The reduction of ochratoxin A contamination during the roasting step was comparable for any samples that were considered under the recommended level of 4 microg/kg. Total dietary fibers ranged from 58.7% for Vietnam and 48.6% for Ivory Coast in green coffees and ranged from 58.6% for Costa Rica to 61.2% for India in roasted coffee. Coffee silverskin byproduct obtained from Ivory Coast was the highest, with 69.2 and 64.2% of insoluble dietary fibers, respectively.",Journal of agricultural and food chemistry,"['D000975', 'D002273', 'D002851', 'D040503', 'D003069', 'D005506', 'D006358', 'D009183', 'D009793', 'D012639']","['Antioxidants', 'Carcinogens', 'Chromatography, High Pressure Liquid', 'Coffea', 'Coffee', 'Food Contamination', 'Hot Temperature', 'Mycotoxins', 'Ochratoxins', 'Seeds']",Natural occurrence of ochratoxin A and antioxidant activities of green and roasted coffees and corresponding byproducts.,"['Q000032', 'Q000032', None, 'Q000737', 'Q000737', 'Q000032', None, None, 'Q000032', 'Q000737']","['analysis', 'analysis', None, 'chemistry', 'chemistry', 'analysis', None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/18020409,2008,0,0,, -0.51,16859691,"Here, we report on the optimisation and validation of a liquid chromatographic method for the determination of 12 biologically active amines from vegetal food products in a single 40-min run. The suitability of the method was checked in five vegetal products of distinct matrix: spinach (leaves), hazelnut (high protein and fat content), banana, potato (high starch content), and milk chocolate (processed). Sample preparation consisted of a 0.6 M perchloric acid extraction from a minced homogeneous aliquot. For samples with high starch content, a previous mild hydrolytic treatment was required to prevent gel formation. The range of linearity was from 0.1 to 10 mg/l, except for serotonin and spermine (from 0.5 to 10 mg/l), and the correlation coefficient was higher than 0.997 (P < 0.001) for all standard curves. The detection limits and the determination limit were below 0.07 and 0.2 mg/l, respectively, except for spermine, which was 0.14 and 0.4 mg/l. The precision of the method was satisfactory; the relative standard deviation obtained for each amine in each product was acceptable according to Horwitz. Recovery was between 77 and 110% for all amines, irrespective of the product.",Journal of chromatography. A,"['D001679', 'D002851', 'D005504', 'D011073', 'D015203', 'D014675']","['Biogenic Amines', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Polyamines', 'Reproducibility of Results', 'Vegetables']",Improved method for the determination of biogenic amines and polyamines in vegetable products by ion-pair high-performance liquid chromatography.,"['Q000032', 'Q000379', 'Q000379', 'Q000032', None, 'Q000737']","['analysis', 'methods', 'methods', 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16859691,2006,0,0,,no cocoa -0.51,24817360,"The on-line combination of comprehensive two-dimensional liquid chromatography (LC_______LC) with the 2,2'-azino-bis(3-ethylbenzothiazoline)-6 sulphonic acid (ABTS) radical scavenging assay was investigated as a powerful method to determine the free radical scavenging activities of individual phenolics in natural products. The combination of hydrophilic interaction chromatography (HILIC) separation according to polarity and reversed-phase liquid chromatography (RP-LC) separation according to hydrophobicity is shown to provide much higher resolving power than one-dimensional separations, which, combined with on-line ABTS detection, allows the detailed characterisation of antioxidants in complex samples. Careful optimisation of the ABTS reaction conditions was required to maintain the chromatographic separation in the antioxidant detection process. Both on-line and off-line HILIC_______RP-LC-ABTS methods were developed, with the former offering higher throughput and the latter higher resolution. Even for the fast analyses used in the second dimension of on-line HILIC_______RP-LC, good performance for the ABTS assay was obtained. The combination of LC_______LC separation with an on-line radical scavenging assay increases the likelihood of identifying individual radical scavenging species compared to conventional LC-ABTS assays. The applicability of the approach was demonstrated for cocoa, red grape seed and green tea phenolics.",Analytical and bioanalytical chemistry,"['D000975', 'D052160', 'D002099', 'D002623', 'D002851', 'D056148', 'D005609', 'D010636', 'D010936', 'D013451', 'D013662', 'D027843']","['Antioxidants', 'Benzothiazoles', 'Cacao', 'Chemistry Techniques, Analytical', 'Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Free Radicals', 'Phenols', 'Plant Extracts', 'Sulfonic Acids', 'Tea', 'Vitis']",Comprehensive two-dimensional liquid chromatography coupled to the ABTS radical scavenging assay: a powerful method for the analysis of phenolic antioxidants.,"['Q000032', 'Q000032', 'Q000737', 'Q000379', 'Q000379', 'Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000737', 'Q000737']","['analysis', 'analysis', 'chemistry', 'methods', 'methods', 'methods', 'analysis', 'analysis', 'analysis', 'analysis', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24817360,2015,0,0,, -0.51,3356285,"A UK survey of plasticizer levels in retail foods (73 samples) wrapped in plasticized films or materials with plasticized coatings has been carried out. A wide range of different food-types packaged in vinylidene chloride copolymers (PVDC), nitrocellulose-coated regenerated cellulose film (RCF) and cellulose acetate were purchased from retail and 'take-away' outlets. Plasticizers found in these films were dibutyl sebacate (DBS) and acetyl tributyl citrate (ATBC) in PVDC, dibutyl phthalate (DBP), dicyclohexyl phthalate (DCHP), butylbenzyl phthalate (BBP), and diphenyl 2-ethylhexyl phosphate (DPOP) in RCF coatings, and diethyl phthlate (DEP) in cellulose acetate. Foodstuffs analysed included cheese, pate, chocolate and confectionery products, meat pies, cake, quiches and sandwiches. Analysis was by stable isotope dilution GC/MS for DBP, DCHP and DEP, GC/MS (selected ion monitoring) for BBP and DPOP, and GC with flame ionization detection for DBS and ATBC, but with mass spectrometric confirmation. Levels of plasticizers found in foods were in the following ranges: ATBC in cheese, 2-8 mg/kg; DBS in processed cheese and cooked meats, 76-137 mg/kg; 76-137 mg/kg; DBP, DCHP, BBP, and DPOP found individually or in combination in confectionery, meat pies, cake and sandwiches, total levels from 0.5 to 53 mg/kg; and DEP in quiches, 2-4 mg/kg.",Food additives and contaminants,"['D002849', 'D002850', 'D002951', 'D003998', 'D005506', 'D005511', 'D008401', 'D010755', 'D010795', 'D010968', 'D006113']","['Chromatography, Gas', 'Chromatography, Gel', 'Citrates', 'Dicarboxylic Acids', 'Food Contamination', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Organophosphates', 'Phthalic Acids', 'Plasticizers', 'United Kingdom']","Migration from plasticized films into foods. 3. Migration of phthalate, sebacate, citrate and phosphate esters from films used for retail food packaging.","[None, None, 'Q000032', 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000032', 'Q000032', None]","[None, None, 'analysis', 'analysis', 'analysis', None, None, 'analysis', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/3356285,1988,,,, -0.51,24699984,"In the 1980s, a novel tea species, Cocoa tea (Camellia ptilophylla Chang), was discovered in Southern China with surprisingly low caffeine content (0.2% by dry weight). Although its health promoting characteristics have been known for a while, a very limited amount of scientific research has been focused on Cocoa tea. Herein, a systematic study on Cocoa tea and its chemical components, interactions and bioactivities was performed. YD tea (Yunnan Daye tea, Camellia sinensis), a tea species with a high caffeine content (5.8% by dry weight), was used as a control. By UV-Vis spectrometry, High Performance Liquid Chromatography (HPLC), and Flame Atomic Absorption Spectrometry (FAAS) for chemical composition analysis, C-2 epimeric isomers of tea catechins and theobromine were found to be the major catechins and methylxanthine in Cocoa tea, respectively. More gallated catechins, methylxanthines, and proteins were detected in Cocoa tea compared with YD tea. Moreover, the tendency of major components in Cocoa tea for precipitation was significantly higher than that in YD tea. Catechins, methylxanthines, proteins, iron, calcium, and copper were presumed to be the origins of molecular interactions in Cocoa tea and YD tea. The interactions between catechins and methylxanthines were highly related to the galloyl moiety in catechins and methyl groups in methylxanthines. In vitro anti-inflammatory activity assays revealed that Cocoa tea was a more potent inhibitor of nitric oxide (NO) in lipopolysaccharide (LPS)-stimulated macrophage cells (RAW 264.7) than YD tea. This study constructs a solid phytochemical foundation for further research on the mechanisms of molecular interactions and the integrated functions of Cocoa tea. ",Food & function,"['D000818', 'D000893', 'D000975', 'D002110', 'D028244', 'D028241', 'D002392', 'D045744', 'D002681', 'D002851', 'D008070', 'D051379', 'D009569', 'D064209', 'D010936', 'D018515', 'D059808', 'D013662', 'D014970']","['Animals', 'Anti-Inflammatory Agents', 'Antioxidants', 'Caffeine', 'Camellia', 'Camellia sinensis', 'Catechin', 'Cell Line, Tumor', 'China', 'Chromatography, High Pressure Liquid', 'Lipopolysaccharides', 'Mice', 'Nitric Oxide', 'Phytochemicals', 'Plant Extracts', 'Plant Leaves', 'Polyphenols', 'Tea', 'Xanthines']","Interactions among chemical components of Cocoa tea (Camellia ptilophylla Chang), a naturally low caffeine-containing tea species.","[None, None, 'Q000737', 'Q000032', 'Q000737', 'Q000737', 'Q000031', None, None, None, 'Q000009', None, 'Q000009', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737']","[None, None, 'chemistry', 'analysis', 'chemistry', 'chemistry', 'analogs & derivatives', None, None, None, 'adverse effects', None, 'adverse effects', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24699984,2015,0,0,,cocoa tea -0.51,16848541,"Isolation of the volatile fraction from cocoa powder (50 g; 20% fat content) by a careful extraction/distillation process followed by application of an aroma extract dilution analysis revealed 35 odor-active constituents in the flavor dilution (FD) factor range of 8-4096. Among them, 4-hydroxy-2,5-dimethyl-3(2H)-furanone (caramel-like), 2- and 3-methylbutanoic acid (sweaty, rancid), dimethyl trisulfide (cooked cabbage), 2-ethyl-3,5-dimethylpyrazine (potato-chip-like), and phenylacetaldehyde (honey-like) showed the highest FD factors. Quantitation of 31 key odorants by means of stable isotope dilution assays, followed by a calculation of their odor activity values (OAVs) (ratio of concentration to odor threshold) revealed OAVs>100 for the five odorants acetic acid (sour), 3-methylbutanal (malty), 3-methylbutanoic acid, phenylacetaldehyde, and 2-methylbutanal (malty). In addition, another 19 aroma compounds showed OAVs>1. To establish their contribution to the overall aroma of the cocoa powder, these 24 compounds were added to a reconstructed cocoa matrix in exactly the same concentrations as they occurred in the cocoa powder. The matrix was prepared from deodorized cocoa powder, which was adjusted to 20% fat content using deodorized cocoa butter. The overall sensory evaluation of this aroma recombinate versus the cocoa powder clearly indicated that the 24 compounds represented the typical sweet, cocoa-like odor of the real sample.",Journal of agricultural and food chemistry,"['D002099', 'D002849', 'D003903', 'D005519', 'D008401', 'D006801', 'D007201', 'D009812', 'D012639', 'D012903', 'D013649']","['Cacao', 'Chromatography, Gas', 'Deuterium', 'Food Preservation', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Indicator Dilution Techniques', 'Odorants', 'Seeds', 'Smell', 'Taste']",Identification of the key aroma compounds in cocoa powder based on molecular sensory correlations.,"['Q000737', 'Q000379', None, None, None, None, None, 'Q000032', 'Q000737', None, None]","['chemistry', 'methods', None, None, None, None, None, 'analysis', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16848541,2006,1,3,table 3, -0.51,28465162,"Caffeine and caffeic acid are two bioactive compounds that are present in plant foods and are major constituent of coffee, cocoa, tea, cola drinks and chocolate. Although not structurally related, caffeine and caffeic acid has been reported to elicit neuroprotective properties. However, their different proportional distribution in food sources and possible effect of such interactions are not often taken into consideration. Therefore, in this study, we investigated the effect of caffeine, caffeic acid and their various combinations on activities of some enzymes [acetylcholinesterase (AChE), monoamine oxidase (MAO) ecto-nucleoside triphosphate diphosphohydrolase (E-NTPase), ecto-5",Neurotoxicology,"['D000110', 'D000251', 'D000818', 'D001921', 'D002109', 'D002110', 'D000697', 'D003300', 'D004305', 'D004338', 'D007501', 'D008995', 'D051381', 'D017208', 'D013053']","['Acetylcholinesterase', 'Adenosine Triphosphatases', 'Animals', 'Brain', 'Caffeic Acids', 'Caffeine', 'Central Nervous System Stimulants', 'Copper', 'Dose-Response Relationship, Drug', 'Drug Combinations', 'Iron', 'Monoamine Oxidase', 'Rats', 'Rats, Wistar', 'Spectrophotometry']","Effect of caffeine, caffeic acid and their various combinations on enzymes of cholinergic, monoaminergic and purinergic systems critical to neurodegeneration in rat brain-In vitro.","['Q000378', 'Q000378', None, 'Q000187', 'Q000494', 'Q000494', 'Q000493', 'Q000378', None, None, 'Q000378', 'Q000378', None, None, None]","['metabolism', 'metabolism', None, 'drug effects', 'pharmacology', 'pharmacology', 'pharmacokinetics', 'metabolism', None, None, 'metabolism', 'metabolism', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28465162,2018,0,0,, -0.51,7926169,"Beverages of different kinds have been investigated for their content of lead, cadmium, nickel, chromium, arsenic and mercury. About a ten times higher lead concentration was found in wine than in most other beverages. Cocoa was high in cadmium and nickel and some vegetable juices contained high levels of nickel. The daily intake of trace elements from beverages was estimated. Wine was still the most significant source of lead even if the bottles did not have lead capsules. By consumption of half a bottle per day the daily intake of lead would be doubled and it would contribute 12% of Provisional Tolerable Weekly Intake. Cocoa is an important source of cadmium and nickel, and consumption of tea as well as vegetable juices could increase the nickel intake significantly. The data are compared to Danish maximum limits on lead and cadmium.",Food additives and contaminants,"['D001628', 'D002104', 'D005506', 'D006801', 'D007854', 'D009532', 'D013054', 'D014131']","['Beverages', 'Cadmium', 'Food Contamination', 'Humans', 'Lead', 'Nickel', 'Spectrophotometry, Atomic', 'Trace Elements']",Beverages as a source of toxic trace element intake.,"['Q000032', 'Q000008', 'Q000032', None, 'Q000008', 'Q000008', None, 'Q000008']","['analysis', 'administration & dosage', 'analysis', None, 'administration & dosage', 'administration & dosage', None, 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/7926169,1994,,,, -0.5,25032782,"Oligomeric proanthocyanidins were successfully identified by UHPLC-PDA-HRMS(n) in a selection of plant-derived materials (jujube fruit, Fuji apple, fruit pericarps of litchi and mangosteen, dark chocolate, and grape seed and cranberry extracts). The identities of 247 proanthocyanidins were theoretically predicted by computing high-accuracy masses based on the degree of polymerization, flavan-3-ol components, and the number of A type linkages and galloyls. MS(n) fragments allowed characterization on flavan-3-ol based on the monomer, connectivity, and location of A-type bonds. Identification of doubly or triply charged ions of 50 PAs was made on the basis of theoretical calculations. A single catechin standard and molar relative response factors (MRRFs) were used to quantify the well-separated PAs. The ratios of the SIM peak counts were used to quantify each of the unseparated isomers. This is the first report of direct determination of each of the proanthocyanidins in plant-derived foods and proanthocyanidins containing an epifisetinidol unit in grape seeds. ",Journal of agricultural and food chemistry,"['D002851', 'D005638', 'D010936', 'D010944', 'D044945', 'D012639', 'D021241']","['Chromatography, High Pressure Liquid', 'Fruit', 'Plant Extracts', 'Plants', 'Proanthocyanidins', 'Seeds', 'Spectrometry, Mass, Electrospray Ionization']",UHPLC-PDA-ESI/HRMSn profiling method to identify and quantify oligomeric proanthocyanidins in plant products.,"['Q000379', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000379']","['methods', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/25032782,2015,0,0,,no cocoa -0.5,11486386,"An analytical method using GC/MS was developed for bisphenol A (BPA) in foods and BPA was determined in canned foods and fresh foods such as vegetables, fruit and meat. BPA was extracted with acetone from the samples and the extract was concentrated at under 40 degrees C in vacuo to afford an aqueous solution, which was washed with hexane after alkalization and extracted with 50% diethyl ether-hexane after acidification. Extracts were cleaned up on a PSA and/or a C18 cartridge column, and BPA was derivatized with heptafluorobutyric anhydride and determined by GC/MS (SIM). This method was applicable to the detection and determination of BPA residues in food samples at the level of 1 ng/g. Among canned foods, BPA was found in 6 corned beef, 1 chicken, 9 sweet corn and 3 bean samples at the levels of 17-602 ng/g, 212 ng/g, 2.3-75 ng/g and 3.5-26 ng/g, respectively. BPA was also detected in 1 retort soup and 1 retort pack product at the levels of 11 ng/g and 86 ng/g, respectively. As for dairy products, BPA was not detected in butter and milk. Among fresh foods, BPA was detected in 2 fish and 3 liver samples at the levels of trace (tr)-6.2 ng/g and tr-2.2 ng/g, respectively. In vegetables, fruits and chocolates, a trace level of BPA was detected in only 1 chocolate. Traces of BPA were also detected in 3 samples of 6 boxed lunches.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D000818', 'D001559', 'D005396', 'D005504', 'D005519', 'D005638', 'D008401', 'D008460', 'D008461', 'D010636', 'D014675']","['Animals', 'Benzhydryl Compounds', 'Fish Products', 'Food Analysis', 'Food Preservation', 'Fruit', 'Gas Chromatography-Mass Spectrometry', 'Meat', 'Meat Products', 'Phenols', 'Vegetables']",[Determination of bisphenol A in foods using GC/MS].,"[None, None, 'Q000032', 'Q000379', None, 'Q000737', None, 'Q000032', 'Q000032', 'Q000032', 'Q000737']","[None, None, 'analysis', 'methods', None, 'chemistry', None, 'analysis', 'analysis', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11486386,2001,,,, -0.5,17357118,"Fermented cocoa beans (Theobroma cacao L., Sterculiaceae) from different countries of origin (Ecuador, Ghana, Trinidad) and cocoa beans roasted under defined conditions (industrial roasting; 150-220 degrees C for 20 min, dry roasting in conventional oven) were analyzed for their contents of certain chiral hydroxy acids, catechins, and amino acids. Cocoa beans are fermented, dried, and industrially transformed by roasting for the production of chocolate, cocoa powders, and other cocoa-related products. Fermentation and roasting conditions influence the contents of chiral compounds such as hydroxy acids, amino acids, and polyphenols, depending on technological procedures as well as some technical parameters. The aim of this work was to check if the content and nature of the named chiral compounds present both in fermented and roasted cocoa beans could be related to the traditional parameters used to classify the variety of seeds and the degree of fermentation. The extent of racemization of amino acids in fermented cocoa beans was low while it slowly increased during roasting, depending on the temperature applied. L-lactic acid was always higher than the D-form while citric acid was generally the most abundant hydroxy acid detected in beans. A correlation was found between polyphenol content and degree of fermentation, while epimerization of (-)-epicatechin to (+)-catechin was observed during roasting. On the whole, results showed that several chiral compounds could be considered as good quality markers for cocoa seeds and cocoa-related products of different quality and geographic origin.",Chirality,"['D000596', 'D002099', 'D002392', 'D005285', 'D005419', 'D005504', 'D005511', 'D005524', 'D008401', 'D005843', 'D006880', 'D019344', 'D008956', 'D010636', 'D059808', 'D013237']","['Amino Acids', 'Cacao', 'Catechin', 'Fermentation', 'Flavonoids', 'Food Analysis', 'Food Handling', 'Food Technology', 'Gas Chromatography-Mass Spectrometry', 'Geography', 'Hydroxy Acids', 'Lactic Acid', 'Models, Chemical', 'Phenols', 'Polyphenols', 'Stereoisomerism']",GC-MS detection of chiral markers in cocoa beans of different quality and geographic origin.,"['Q000737', 'Q000378', 'Q000737', None, None, None, None, 'Q000379', 'Q000379', None, 'Q000737', 'Q000737', None, None, None, None]","['chemistry', 'metabolism', 'chemistry', None, None, None, None, 'methods', 'methods', None, 'chemistry', 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/17357118,2007,,,, -0.5,27664658,"Nickel is a metal that can be present in products containing hardened edible oils, possibly as leftover catalyst from the vegetable oil hardening process. Nickel may cause toxic effects including the promotion of cancer and contact allergy. In this work, nickel content was determined in hydrogenated vegetable fats and confectionery products, made with these fats, available on the Czech market using newly developed method combining microwave digestion and graphite furnace AAS. While concentrations of 0.086_±0.014mg.kg(-1) or less were found in hydrogenated vegetable fats, the Ni content in confectionery products was significantly higher, varying between 0.742_±0.066 and 3.141_±0.217mg.kg(-1). Based on an average consumer basket, daily intake of nickel from vegetable fats is at least twice as low as intake from confectionery products. Based on results, the levels of nickel in neither vegetable fats nor confectionery products, do not represent a significant health risk. ",Food chemistry,"['D000069956', 'D018153', 'D005223', 'D005506', 'D006801', 'D006865', 'D009532', 'D010938', 'D013054']","['Chocolate', 'Czech Republic', 'Fats', 'Food Contamination', 'Humans', 'Hydrogenation', 'Nickel', 'Plant Oils', 'Spectrophotometry, Atomic']",Determination of nickel in hydrogenated fats and selected chocolate bars in Czech Republic.,"['Q000032', None, 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000032', 'Q000379']","['analysis', None, 'analysis', 'analysis', None, None, 'analysis', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/27664658,2017,0,0,, -0.5,18278822,"A comparison of two methods for the identification and determination of peanut allergens based on europium (Eu)-tagged inductively coupled plasma mass spectrometry (ICP-MS) immunoassay and on liquid chromatography/electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS) with a triple quadrupole mass analyzer was carried out on a complex food matrix like a chocolate rice crispy-based snack. The LC/MS/MS method was based on the determination of four different peptide biomarkers selective for the Ara h2 and Ara h3/4 peanut proteins. The performance of this method was compared with that of a non-competitive sandwich enzyme-linked immunosorbent assay (ELISA) method with ICP-MS detection of the metal used to tag the antibody for the quantitative peanut protein analysis in food. The limit of detection (LOD) and quantitation of the ICP-MS immunoassay were 2.2 and 5 microg peanuts g(-1) matrix, respectively, the recovery ranged from 86 +/- 18% to 110 +/- 4% and linearity was proved in the 5-50 microg g(-1) range. The LC/MS/MS method allowed us to obtain LODs of 1 and 5 microg protein g(-1) matrix for Ara h3/4 and Ara h2, respectively, thus obtaining significantly higher values with respect to the ELISA ICP-MS method, taking into account the different expression for concentrations. Linearity was established in the 10-200 microg g(-1) range of peanut proteins in the food matrix investigated and good precision (RSD <10%) was demonstrated. Both the two approaches, used for screening or confirmative purposes, showed the power of mass spectrometry when used as a very selective detector in difficult matrices even if some limitations still exist, i.e. matrix suppression in the LC/ESI-MS/MS procedure and the change of the Ag/Ab binding with matrix in the ICP-MS method.",Rapid communications in mass spectrometry : RCM,"['D000485', 'D010367', 'D002099', 'D002851', 'D002523', 'D005504', 'D006358', 'D007118', 'D008670', 'D015203', 'D012680', 'D021241', 'D013194']","['Allergens', 'Arachis', 'Cacao', 'Chromatography, High Pressure Liquid', 'Edible Grain', 'Food Analysis', 'Hot Temperature', 'Immunoassay', 'Metals', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Spectrometry, Mass, Electrospray Ionization', 'Staining and Labeling']",Determination of peanut allergens in cereal-chocolate-based snacks: metal-tag inductively coupled plasma mass spectrometry immunoassay versus liquid chromatography/electrospray ionization tandem mass spectrometry.,"['Q000032', 'Q000737', 'Q000737', 'Q000379', 'Q000737', 'Q000379', None, 'Q000379', None, None, None, 'Q000379', 'Q000379']","['analysis', 'chemistry', 'chemistry', 'methods', 'chemistry', 'methods', None, 'methods', None, None, None, 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/18278822,2008,,,, -0.5,25912451,"Development of authenticity screening for Asian palm civet coffee, the world-renowned priciest coffee, was previously reported using metabolite profiling through gas chromatography/mass spectrometry (GC/MS). However, a major drawback of this approach is the high cost of the instrument and maintenance. Therefore, an alternative method is needed for quality and authenticity evaluation of civet coffee. A rapid, reliable and cost-effective analysis employing a universal detector, GC coupled with flame ionization detector (FID), and metabolite fingerprinting has been established for discrimination analysis of 37 commercial and non-commercial coffee beans extracts. gas chromatography/flame ionization detector (GC/FID) provided higher sensitivity over a similar range of detected compounds than GC/MS. In combination with multivariate analysis, GC/FID could successfully reproduce quality prediction from GC/MS for differentiation of commercial civet coffee, regular coffee and coffee blend with 50__wt % civet coffee content without prior metabolite details. Our study demonstrated that GC/FID-based metabolite fingerprinting can be effectively actualized as an alternative method for coffee authenticity screening in industries. ",Journal of bioscience and bioengineering,"['D000818', 'D003069', 'D016002', 'D005410', 'D019649', 'D008401', 'D055442', 'D015999', 'D012015', 'D045949']","['Animals', 'Coffee', 'Discriminant Analysis', 'Flame Ionization', 'Food Industry', 'Gas Chromatography-Mass Spectrometry', 'Metabolome', 'Multivariate Analysis', 'Reference Standards', 'Viverridae']",Application of gas chromatography/flame ionization detector-based metabolite fingerprinting for authentication of Asian palm civet coffee (Kopi Luwak).,"[None, 'Q000737', None, 'Q000379', 'Q000379', 'Q000191', None, None, None, None]","[None, 'chemistry', None, 'methods', 'methods', 'economics', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25912451,2016,0,0,,no cocoa tested -0.5,17909484,"Cocoa contains high levels of different flavonoids. In the present study, the enantioseparation of catechin and epicatechin in cocoa and cocoa products by chiral capillary electrophoresis (CCE) was performed. A baseline separation of the catechin and epicatechin enantiomers was achieved by using 0.1 mol x L(-1) borate buffer (pH 8.5) with 12 mmol x L(-1) (2-hydroxypropyl)-gamma-cyclodextrin as chiral selector, a fused-silica capillary with 50 cm effective length (75 microm I.D.), +18 kV applied voltage, a temperature of 20 degrees C and direct UV detection at 280 nm. To avoid comigration or coelution of other similar substances, the flavan-3-ols were isolated and purified using polyamide-solid-phase-extraction and LC-MS analysis. As expected, we found (-)-epicatechin and (+)-catechin in unfermented, dried, unroasted cocoa beans. In contrast, roasted cocoa beans and cocoa products additionally contained the atypical flavan-3-ol (-)-catechin. This is generally formed during the manufacturing process by an epimerization which converts (-)-epicatechin to its epimer (-)-catechin. High temperatures during the cocoa bean roasting process and particularly the alkalization of the cocoa powder are the main factors inducing the epimerization reaction. In addition to the analysis of cocoa and cocoa products, peak ratios were calculated for a better differentiation of the cocoa products.","Molecules (Basel, Switzerland)","['D000468', 'D002099', 'D002392', 'D002853', 'D019075', 'D005419', 'D013058', 'D013237', 'D013696']","['Alkalies', 'Cacao', 'Catechin', 'Chromatography, Liquid', 'Electrophoresis, Capillary', 'Flavonoids', 'Mass Spectrometry', 'Stereoisomerism', 'Temperature']",(-)-Catechin in cocoa and chocolate: occurrence and analysis of an atypical flavan-3-ol enantiomer.,"[None, 'Q000737', 'Q000032', None, None, 'Q000032', None, None, None]","[None, 'chemistry', 'analysis', None, None, 'analysis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/17909484,2007,0,0,, -0.5,2808244,"Methyl bromide (MB, bromomethane) is determined in a variety of foods by headspace capillary gas chromatography with electron capture detection. The comminuted food sample as an aqueous sodium sulfate slurry is equilibrated with stirring for 1 h at room temperature before a 1 mL headspace aliquot is removed and injected using a modified on-column syringe needle. Methyl bromide is cryogenically focussed at -60 degrees C and then eluted by temperature programming. The procedure requires blending of soft samples, e.g. raisins, prunes, or oranges, and ultrasonic homogenization of hard samples, e.g. wheat, cocoa beans, corn, or nuts, with portions of water and ice so the final temperature of the food-water slurry is less than 1 degree C. A 20 g aliquot (4 g food) is then added to a cold headspace vial containing 4 g sodium sulfate. Losses of MB during a 3.5 min ultrasonic homogenization of wheat were 11% at 0.95 ppb and 4.4% at 4.8 ppb. For flour, cocoa, and finely divided spices, which do not require blending, 4 g is added to the cold headspace vial containing 16 mL cold water and 4 g sodium sulfate. Studies show that comminution of wheat or peanuts must be carried out to release MB trapped within the food so the headspace equilibrium can be attained in 1 h as well as to obtain homogeneous samples and representative sampling. No interferences were noted with the above foods or with many grain-based baking mixes analyzed.(ABSTRACT TRUNCATED AT 250 WORDS)",Journal - Association of Official Analytical Chemists,"['D002611', 'D002849', 'D004041', 'D002523', 'D004563', 'D005433', 'D005504', 'D005638', 'D008401', 'D006842', 'D007202', 'D010316', 'D012987']","['Cheese', 'Chromatography, Gas', 'Dietary Fats', 'Edible Grain', 'Electrochemistry', 'Flour', 'Food Analysis', 'Fruit', 'Gas Chromatography-Mass Spectrometry', 'Hydrocarbons, Brominated', 'Indicators and Reagents', 'Particle Size', 'Soil']",Determination of methyl bromide in foods by headspace capillary gas chromatography with electron capture detection.,"['Q000032', None, 'Q000032', 'Q000032', None, 'Q000032', None, 'Q000032', None, None, None, None, 'Q000032']","['analysis', None, 'analysis', 'analysis', None, 'analysis', None, 'analysis', None, None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/2808244,1989,,,, -0.5,8471852,"A liquid chromatographic method was evaluated for the determination of the intense sweetener acesulfam-K in tabletop sweetener, candy, soft drink, fruit juice, fruit nectar, yogurt, cream, custard, chocolate, and biscuit commercial preparations. Samples are extracted or simply diluted with water and filtered. Complex matrixes need a clarification step with Carrez solutions. An aliquot of the extract is analyzed on a reversed-phase mu Bondapak C18 column using 0.0125M KH2PO4 (pH 3.5)-acetonitrile (90 + 10) as mobile phase. Detection is performed by UV absorbance at 220 nm. Recoveries ranged from 95.2 to 106.8%. With one exception, all analyzed values were within +/- 15% of the declared levels. The repeatabilities and the repeatability coefficients of variation were, respectively, 0.37 mg/100 g and 0.98% for products containing less than 40 mg/100 g acesulfam-K and 2.43 mg/100 g and 1.29% for other products. The same procedure also allowed detection of many food additives or natural constituents, such as other intense sweeteners, organic acids, and alkaloids, in a single run without interfering with acesulfam-K. The method is simple, rapid, precise, and sensitive; therefore, it is suitable for routine analyses.",Journal of AOAC International,"['D001628', 'D002182', 'D002855', 'D005503', 'D005504', 'D006863', 'D012680', 'D013549', 'D013843']","['Beverages', 'Candy', 'Chromatography, Thin Layer', 'Food Additives', 'Food Analysis', 'Hydrogen-Ion Concentration', 'Sensitivity and Specificity', 'Sweetening Agents', 'Thiazines']",Determination of acesulfam-K in foods.,"['Q000032', 'Q000032', None, 'Q000032', 'Q000379', None, None, 'Q000032', 'Q000032']","['analysis', 'analysis', None, 'analysis', 'methods', None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/8471852,1993,,,, -0.5,21427887,"Atole is a Mexican pre-hispanic drink prepared traditionally with corn; however, cereals as wheat, rice and amaranth have also been used. The aim of this study was to determine the physicochemical and sensory properties of an amaranth flour to prepare a drink (atole) mentioned above, in order to determine its nutritive value. Proximate analysis of the amaranth, corn and rice drink flours was determined by means of official techniques of AOAC. Mineral content was carried out by atomic absorption spectrometry. Viscosity was measured in a reometer from 25 to 90 degrees C. The quantitative descriptive profile (QDA) of the amaranth drink was studied by a trained panel of 10 judges. Results showed that the amaranth drink flour presented the highest protein and fat content compared to corn and rice drink flours. Sodium and potassium were the most abundant minerals in all flours studied. Corn and rice drink flours showed a constant viscosity from 20 to 84 degrees C, to 85 degrees C an important increase in this parameter was observed. This increase was detected in the amaranth drink flour to 75 degrees C. Descriptors defined by trained judges for the QDA of the amaranth drink flours were: starch, almond/cherry, caramel, vanilla, strawberry, walnut and chocolate. The amaranth drink flour, compared to corn and rice drink flours, presented the best nutritional profile; it is important to emphasize its protein content.",Archivos latinoamericanos de nutricion,"['D027721', 'D001628', 'D005433', 'D009753', 'D013054', 'D013649', 'D014783']","['Amaranthus', 'Beverages', 'Flour', 'Nutritive Value', 'Spectrophotometry, Atomic', 'Taste', 'Viscosity']","[Physicochemical and sensory properties of flours ready to prepare an amaranth ""atole""].","['Q000737', 'Q000032', 'Q000032', None, None, None, None]","['chemistry', 'analysis', 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/21427887,2011,,,, -0.49,8112343,"Salivary proline-rich proteins have a repetitive primary structure particularly rich in the amino acids proline, glutamine and glycine. One of the biological roles of these proteins is to bind and precipitate polyphenols (vegetable tannins) present in the diet (e.g. tea, coffee, fruit, chocolate) neutralising their harmful actions which include nutritional loss, inhibition of gut enzymes and oesophageal cancer. Two peptides overlapping in sequence, corresponding to the mouse salivary proline-rich protein MP5 repeat sequence: QGPPPQGGPQQRPPQPGNQ and GPQQRPPQPGNQQGPPPQGGPQ have been synthesised and studied in H2O/(2H6)dimethyl sulphoxide (9:1, by vol.) using 1H-NMR spectroscopy. Low-temperature far-ultraviolet CD spectroscopy and NMR conformational parameters indicate that the peptides adopt an extended random coil conformation in solution. There is no evidence for a defined polyproline type II helix in the peptides, despite the high proline content. NMR data show that the trans-proline isomer predominates to at least 90%.",European journal of biochemistry,"['D000595', 'D000818', 'D002851', 'D002942', 'D005973', 'D005998', 'D009682', 'D051379', 'D008969', 'D010446', 'D010455', 'D055232', 'D011487', 'D012471']","['Amino Acid Sequence', 'Animals', 'Chromatography, High Pressure Liquid', 'Circular Dichroism', 'Glutamine', 'Glycine', 'Magnetic Resonance Spectroscopy', 'Mice', 'Molecular Sequence Data', 'Peptide Fragments', 'Peptides', 'Proline-Rich Protein Domains', 'Protein Conformation', 'Salivary Proteins and Peptides']",Conformational study of a salivary proline-rich protein repeat sequence.,"[None, None, None, None, 'Q000737', 'Q000737', None, None, None, 'Q000737', 'Q000737', None, None, 'Q000737']","[None, None, None, None, 'chemistry', 'chemistry', None, None, None, 'chemistry', 'chemistry', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/8112343,1994,0,0,,no cocoa -0.49,27105753,"An ease-of-use protocol for the identification of resistance against third-generation cephalosporins in Enterobacteriaceae isolated from blood culture bottles was evaluated using matrix-assisted laser desorption ionization-time-of-flight mass spectrometry. A cefotaxime hydrolysis assay from chocolate agar subcultures using antibiotic discs and without inoculum standardization was developed for routine work flow, with minimal hands-on time. This assay showed good performance in distinguishing between cefotaxime-susceptible and cefotaxime-resistant strains, with excellent results for Escherichia coli (sensitivity 94.7%, specificity 100%). However, cefotaxime resistance was not detected reliably in Enterobacteriaceae expressing AmpC genes or carbapenemase-producing Klebsiella pneumoniae. ",The Journal of hospital infection,"['D000900', 'D000071997', 'D002439', 'D018550', 'D004755', 'D006868', 'D008826', 'D012680', 'D019032', 'D013997']","['Anti-Bacterial Agents', 'Blood Culture', 'Cefotaxime', 'Cephalosporin Resistance', 'Enterobacteriaceae', 'Hydrolysis', 'Microbial Sensitivity Tests', 'Sensitivity and Specificity', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Time Factors']",Ease-of-use protocol for the rapid detection of third-generation cephalosporin resistance in Enterobacteriaceae isolated from blood cultures using matrix-assisted laser desorption ionization-time-of-flight mass spectrometry.,"['Q000378', None, 'Q000378', None, 'Q000187', None, 'Q000379', None, 'Q000379', None]","['metabolism', None, 'metabolism', None, 'drug effects', None, 'methods', None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/27105753,2017,0,0,,no cocoa -0.49,11210120,"The antioxidant polyphenols in cacao liquor, a major ingredient of chocolate and cocoa, have been characterized as flavan-3-ols and proanthocyanidin oligomers. In this study, various cacao products were analyzed by normal-phase HPLC, and the profiles and quantities of the polyphenols present, grouped by molecular size (monomers to approximately oligomers), were compared. Individual cacao polyphenols, flavan-3-ols (catechin and epicatechin), and dimeric (procyanidin B2), trimeric (procyanidin C1), and tetrameric (cinnamtannin A2) proanthocyanidins, and galactopyranosyl-ent-(-)-epicatechin (2alpha-->7, 4alpha-->8)-(-)-epicatechin (Gal-EC-EC), were analyzed by reversed-phase HPLC and/or HPLC/MS. The profile of monomers (catechins) and proanthocyanidin in dark chocolate was similar to that of cacao liquor, while the ratio of flavan-3-ols to the total amount of monomeric and oligomeric polyphenols in the case of pure cocoa powder was higher than that in the case of cacao liquor or chocolate.","Bioscience, biotechnology, and biochemistry","['D000872', 'D044946', 'D002099', 'D002392', 'D002851', 'D002853', 'D005419', 'D005511', 'D005690', 'D013058', 'D010636', 'D011108', 'D044945', 'D015203']","['Anthocyanins', 'Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Chromatography, Liquid', 'Flavonoids', 'Food Handling', 'Galactose', 'Mass Spectrometry', 'Phenols', 'Polymers', 'Proanthocyanidins', 'Reproducibility of Results']","Analyses of polyphenols in cacao liquor, cocoa, and chocolate by normal-phase and reversed-phase HPLC.","['Q000032', None, 'Q000737', 'Q000031', 'Q000379', None, None, 'Q000379', 'Q000031', 'Q000379', 'Q000032', 'Q000032', None, None]","['analysis', None, 'chemistry', 'analogs & derivatives', 'methods', None, None, 'methods', 'analogs & derivatives', 'methods', 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11210120,2001,2,1,table 4 and 6, -0.49,20480905,"Staphylococcal enterotoxin B (SEB) is an extracellular pyrotoxin produced by Staphylococcus aureus, a known etiologic agent of food poisoning in humans. Lateral flow immunochromatographic devices (LFDs) designed for the environmental detection of SEB were adapted for use in this study to detect SEB in milk containing 2% fat, chocolate-flavored milk, and milk-derived products such as yogurt, infant formula, and ice cream. The advantage of using LFDs in these particular food products was its ease and speed of use with no additional extraction methods needed. No false positives were observed with any of the products used in this study. Dilution of the samples overcame the Hook effect and permitted capillary flow into the membrane. Thus, semisolid products such as ice cream and some yogurts, and products containing thickeners needed to be diluted using a phosphate-buffered saline-based buffer, pH 7.2. SEB was easily detected at concentrations of 5 microg/mL and 500 ng/mL when the LFDs were used. SEB was also reliably detected at concentrations below 5 and 0.25 ng/mL, which may induce serious disease.",Journal of AOAC International,"['D000818', 'D002417', 'D002845', 'D004768', 'D004867', 'D005189', 'D005504', 'D005506', 'D005516', 'D006863', 'D007054', 'D007158', 'D008892', 'D015203', 'D013997', 'D015014']","['Animals', 'Cattle', 'Chromatography', 'Enterotoxins', 'Equipment Design', 'False Positive Reactions', 'Food Analysis', 'Food Contamination', 'Food Microbiology', 'Hydrogen-Ion Concentration', 'Ice Cream', 'Immunologic Techniques', 'Milk', 'Reproducibility of Results', 'Time Factors', 'Yogurt']",Detection of staphylococcal enterotoxin B in milk and milk products using immunodiagnostic lateral flow devices.,"[None, None, 'Q000379', 'Q000032', None, None, 'Q000295', None, None, None, None, None, 'Q000378', None, None, None]","[None, None, 'methods', 'analysis', None, None, 'instrumentation', None, None, None, None, None, 'metabolism', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/20480905,2010,,,, -0.49,22133078,"Chocolate is a key ingredient in many foods such as milk shakes, candies, bars, cookies, and cereals. Chocolate candies are often consumed by mankind of all age groups. The presence of polycyclic aromatic hydrocarbons (PAHs) in chocolate candies may result in health risk to people. A rapid, precise, and economic extraction method was optimized and validated for the simultaneous determination of polycyclic aromatic hydrocarbons in chocolate candy by high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GS-MS) as a confirmatory technique. The method was optimized by using different solvents for liquid-liquid extraction, varying volume of de-emulsifying agent, and quantity of silica gel used for purification. The HPLC separation of 16 PAHs was carried out by C-18 column with mobile phase composed of acetonitrile : water (70 : 30) in isocratic mode with runtime of 20 min. Limit of detection, limit of quantification (LOQ), and correlation coefficients were found in the range of 0.3 to 4 ng g____, 0.9 to 12 ng g____, and 0.9109 to 0.9952, respectively. The exploration of 25 local chocolate candy samples for the presence of PAHs showed the mean content of benzo[a]pyrene as 1.62 ng g____, which representing the need to evaluate effective measures to prevent more severe PAHs contamination in chocolate candies in future.",Journal of food science,"['D002099', 'D002138', 'D002182', 'D002851', 'D056148', 'D004785', 'D005506', 'D008401', 'D007194', 'D057230', 'D059625', 'D008970', 'D011084']","['Cacao', 'Calibration', 'Candy', 'Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Environmental Pollutants', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'India', 'Limit of Detection', 'Liquid-Liquid Extraction', 'Molecular Weight', 'Polycyclic Aromatic Hydrocarbons']",Optimization and validation of an extraction method for the analysis of polycyclic aromatic hydrocarbons in chocolate candies.,"['Q000009', None, 'Q000009', None, None, 'Q000032', None, None, None, None, None, None, 'Q000032']","['adverse effects', None, 'adverse effects', None, None, 'analysis', None, None, None, None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/22133078,2012,0,0,, -0.49,25230186,"Triacylglycerols are responsible for chocolate's peculiar melting behavior: the type and position of fatty acids on the glycerol molecule strongly affect the melting range of cocoa butter. For this reason, the characterization of triglyceride composition in cocoa products is particularly important. In this work, triacylglycerols extracted from cocoa liquor samples were analyzed by matrix-assisted laser desorption/ionization time-of-flight (TOF) and electrospray ionization tandem mass spectrometry (MS/MS) coupled to liquid chromatography. Extracted samples were initially analyzed by direct injection in MS to obtain information on triglyceride molecular weights; relevant MS parameters were optimized, and the possible formation of the adducts [M___+___Na](+) and [M___+___NH(4)](+) was studied. Tandem mass experiments (both with triple quadrupole and TOF/TOF) were performed to study the fragmentation pathways (in particular, the loss of palmitic, stearic and oleic acid) and identify the triacylglycerols in cocoa liquors. Some signals of the spectra obtained with both MS techniques could indicate the presence of diacylglycerols in the cocoa extract, but different experimental evidences demonstrated that they were generated by the in-source fragmentation of triglycerides. A nonaqueous reversed-phase chromatographic separation was also developed and used to support the identification of the analytes; nine triacylglycerols were recognized in the cocoa liquor extracts. The three different batches of Ecuador cocoa liquor did not show significant differences in the triacylglycerol profile.",Journal of mass spectrometry : JMS,[],[],Triacylglycerol profile in cocoa liquors using MALDI-TOF and LC-ESI tandem mass spectrometry.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/25230186,2015,,,, -0.49,22641023,"Cocoa procyanidins (CPs)-gelatin-chitosan nanoparticles were fabricated based on the procyanidin-protein and electrostatic interactions, with an objective to enhance the stability and bioactivity of CPs. The CPs were purified using chromatographic methods and analyzed using HPLC equipped with a fluorescence detector (FLD) and mass spectrometer (MS). The purified CPs had a purity of 53.1% (w/w) and contained procyanidin oligomers (from monomer to decamers) and polymers, with polymers being the predominant component (26.4%, w/w). Different CPs-gelatin-chitosan mass ratios were tested to investigate the effects of formulation on the nanoparticle fabrication. Using CPs-gelatin-chitosan mass ratio of 0.75:1:0.5, the resultant nanoparticles had a particle size of 344.7 nm, zeta-potential of +29.8 mV, particle yield of 51.4%, loading efficiency of 50.1%, and loading capacity of 20.5%. The CPs-gelatin-chitosan nanoparticles were spherical as observed by scanning electron microscopy (SEM). Fourier transform infrared spectroscopy (FTIR) suggested that the primary interaction between the CPs and gelatin was hydrogen bond and hydrophobic interaction, while electrostatic interaction was the main binding force between chitosan and CPs-gelatin nanoparticles. Nanoencapsulation of the CPs significantly improved the stability of the CPs at 60_C. The CPs-gelatin-chitosan nanoparticles showed the same apoptotic effects at lower concentrations in human acute monocytic leukemia THP-1 cells compared with the CPs in solution.",European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V,"['D017209', 'D002099', 'D045744', 'D048271', 'D002851', 'D004337', 'D004355', 'D005780', 'D006801', 'D006860', 'D057927', 'D007948', 'D013058', 'D008855', 'D053758', 'D010316', 'D011108', 'D044945', 'D017550', 'D055672', 'D013696']","['Apoptosis', 'Cacao', 'Cell Line, Tumor', 'Chitosan', 'Chromatography, High Pressure Liquid', 'Drug Carriers', 'Drug Stability', 'Gelatin', 'Humans', 'Hydrogen Bonding', 'Hydrophobic and Hydrophilic Interactions', 'Leukemia, Monocytic, Acute', 'Mass Spectrometry', 'Microscopy, Electron, Scanning', 'Nanoparticles', 'Particle Size', 'Polymers', 'Proanthocyanidins', 'Spectroscopy, Fourier Transform Infrared', 'Static Electricity', 'Temperature']","Preparation, characterization, and induction of cell apoptosis of cocoa procyanidins-gelatin-chitosan nanoparticles.","['Q000187', 'Q000737', None, 'Q000737', None, 'Q000737', None, 'Q000737', None, None, None, 'Q000378', None, None, None, None, 'Q000737', 'Q000008', None, None, None]","['drug effects', 'chemistry', None, 'chemistry', None, 'chemistry', None, 'chemistry', None, None, None, 'metabolism', None, None, None, None, 'chemistry', 'administration & dosage', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22641023,2013,0,0,, -0.49,15712517,"A method for the determination of six kava lactones, methysticin, dihydromethysticin, kawain, dihydrokawain, yangonin and desmethoxyyangonin, in solid foods and beverages has been developed. Solid samples were prepared using methanol extraction, while beverages were extracted using a separate solid phase extraction (SPE) method. After sample preparation, the extracts were analysed using LC-UV or atmospheric pressure photoionization (APPI) LC-MS in the positive mode. Using the method, 10 beverage products, two chocolate products, three unbrewed tea products, three dietary supplements and a drink mix product were analysed. The results obtained using the LC-UV were comparable to those obtained using APPI-LC-MS for most products. Using the SPE method in conjunction with LC-MS, individual kava lactones were detected in drink products at ppb concentrations. Concentrations of total kava lactones ranged between 135-0.035 mg per serving in the food and beverage products tested and between 40-61 mg per serving for the dietary supplement products tested. Results of these analyses as well as extraction efficiency and reproducibility data are reported.",Food additives and contaminants,"['D001628', 'D002853', 'D019587', 'D005504', 'D008401', 'D006801', 'D020901', 'D007783', 'D010936', 'D018517', 'D012997', 'D013056']","['Beverages', 'Chromatography, Liquid', 'Dietary Supplements', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Kava', 'Lactones', 'Plant Extracts', 'Plant Roots', 'Solvents', 'Spectrophotometry, Ultraviolet']",LC-UV and LC-MS analysis of food and drink products containing kava.,"['Q000032', 'Q000379', 'Q000032', 'Q000379', 'Q000379', None, 'Q000737', 'Q000032', 'Q000032', 'Q000737', None, 'Q000379']","['analysis', 'methods', 'analysis', 'methods', 'methods', None, 'chemistry', 'analysis', 'analysis', 'chemistry', None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/15712517,2005,,,, -0.48,9547407,"The objective of this study was to determine the urinary excretion of methylxanthines in horses following ingestion of chocolate over eight days. The study was performed in response to gas chromatography-mass spectrometry (GC-MS) confirmation of the presence of caffeine in a positive urine test in a racehorse. The trainer of the horse alleged that he often administered chocolate-coated peanuts as treats to his horses, and he believed that the ingestion of chocolate was responsible for the positive urine test. The urinary excretion of theobromine and caffeine after the ingestion of chocolate-coated peanuts was investigated in three horses. Enzyme-linked immunoassay (ELISA), high-performance liquid chromatography (HPLC), and GC-MS assays were performed on all urine specimens. Theobromine (HPLC) was detected for 72 h and caffeine (GC-MS) for 48 h after chronic ingestion of chocolate-coated peanuts. Methylxanthines were detected by ELISA for 120 h after administration of chocolate.",Journal of analytical toxicology,"['D000821', 'D000818', 'D010367', 'D002099', 'D002110', 'D002851', 'D004300', 'D004797', 'D005260', 'D008401', 'D006736', 'D013805']","['Animal Feed', 'Animals', 'Arachis', 'Cacao', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Doping in Sports', 'Enzyme-Linked Immunosorbent Assay', 'Female', 'Gas Chromatography-Mass Spectrometry', 'Horses', 'Theobromine']",Detection and determination of theobromine and caffeine in urine after administration of chocolate-coated peanuts to horses.,"[None, None, None, 'Q000378', 'Q000652', 'Q000379', 'Q000379', 'Q000379', None, 'Q000379', 'Q000652', 'Q000652']","[None, None, None, 'metabolism', 'urine', 'methods', 'methods', 'methods', None, 'methods', 'urine', 'urine']",https://www.ncbi.nlm.nih.gov/pubmed/9547407,1998,,,, -0.48,24088516,"Triacylglycerol (TAG) molecular species were quantified through high-performance liquid chromatography (HPLC) equipped with a nano quantity analyte detector (NQAD). TAG standard compounds, i.e., 1,3-dipalmitoyl-2-oleoylglycerol (__-POP), 1-palmitoyl-2-oleoyl-3-stearoyl-rac-glycerol (__-POS), and 1,3-distearoyl-2-oleoylglycerol (__-SOS), and natural cocoa butter were used for analyses. NQAD gave the first order equation passing through the origin for all TAG standard compounds. TAG molecular species in cocoa butter were quantified using the calibration curves and the obtained values were almost the same as the reported ones of conventional cocoa butter. Furthermore, a recovery test was also carried out and the values were almost 100. Therefore, HPLC-NQAD can be successfully used for the quantification of TAG molecular species in natural fats and oils. ",Journal of oleo science,"['D002138', 'D002851', 'D004041', 'D005504', 'D053758', 'D036103', 'D014280']","['Calibration', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Food Analysis', 'Nanoparticles', 'Nanotechnology', 'Triglycerides']",Quantification of triacylglycerol molecular species in cocoa butter using high-performance liquid chromatography equipped with nano quantity analyte detector.,"[None, 'Q000295', 'Q000032', 'Q000295', None, 'Q000295', 'Q000032']","[None, 'instrumentation', 'analysis', 'instrumentation', None, 'instrumentation', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24088516,2014,2,1,table 1, -0.48,20953776,"Piceid (3,4',5-trihydroxystilbene-3-__-D: -glucoside) is a stilbene which occurs naturally in various families of plants and has been shown to protect lipoproteins from oxidative damage and to have cancer chemopreventive activity. This paper deals with the determination of piceid in cocoa-containing products by using photo-induced fluorescence and the aid of a multicommutated continuous-flow assembly which was provided with an on-line photoreactor. A strongly fluorescent photoproduct is generated from piceid when it is irradiated under UV light for 30__s, which is retained on Sephadex QAE A-25 and directly monitored on this active solid support at 257/382__nm (__ (exc)/__ (em), respectively). The pre-concentration of the photoproduct of piceid on the solid support greatly improves both sensitivity and selectivity. The influence of different experimental parameters, both chemical (pH, ionic strength) and hydrodynamic (irradiation time, flow rate, photoreactor length, sampling time), was tested. The sample pre-treatment included delipidation with toluene and cyclohexane, stilbene extraction with ethanol/water (80:20, v/v) and clean-up by solid-phase extraction on C(18) cartridges and methanol/water (40:20, v/v) as eluting solution. This procedure allowed the elimination of the aglycon of piceid, resveratrol and other potential interfering species and a recovery of about a 90% piceid. The method was applied to the analysis of piceid in cocoa powder, dark chocolate and milk chocolate. The quantification limits were 1.4, 1.1 and 0.09__mg__kg(-1), respectively. Relative standard deviations ranged from 1.8% to 3.1%. This is the first reported non-chromatographic method for determination of piceid in these foods.",Analytical and bioanalytical chemistry,"['D002099', 'D004867', 'D017022', 'D005960', 'D057230', 'D055668', 'D010946', 'D011827', 'D013050', 'D013267', 'D014466']","['Cacao', 'Equipment Design', 'Flow Injection Analysis', 'Glucosides', 'Limit of Detection', 'Photochemical Processes', 'Plants, Medicinal', 'Radiation', 'Spectrometry, Fluorescence', 'Stilbenes', 'Ultraviolet Rays']",Automatic optosensing device based on photo-induced fluorescence for determination of piceid in cocoa-containing products.,"['Q000737', None, 'Q000295', 'Q000032', None, None, 'Q000737', None, 'Q000295', 'Q000032', None]","['chemistry', None, 'instrumentation', 'analysis', None, None, 'chemistry', None, 'instrumentation', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/20953776,2011,1,1,table 2, -0.48,25240144,"The studied area is located in Western Anatolia and situated on the NE-SW directed U_ak-G_re cross-graben that developed under a crustal extensional regime during the Late Miocene-Pliocene. Silica occurrences have been mostly found as mushroom-shaped big caps. They also show sedimentary structures such as stratification. Silica occurrences are milky white, yellowish white, yellow to chocolate brown and rarely pale blue, bluish gray in color and have no crystal forms in hand specimen. Some of the silica samples show conchoidal fracture. Silica minerals are mostly chalcedony, low-quartz (_±-quartz) and sporadically opal-CT in spectras, according to confocal Raman spectrometry. The silica samples have enrichment of Fe (1000-24,600 ppm), Ca (100-10,200 ppm), P (4-3950 ppm) and Mn (8-3020 ppm). Other striking elements in fewer amounts are Ba (0.9-609.6 ppm), Ni (15.7-182.3 ppm) and Co (18.6-343.1 ppm). In chondrite-normalized spider diagram, silica samples display partial enrichment in LIL elements (Rb, Ba, Th). The __(18)O (__ V-SMOW) values for silica samples vary from 18.4__ to 22.8__ and are similar to low temperature hydrothermal silica. Confocal Raman spectrometry and oxygen isotope indicate that the silica minerals may precipitate from host fluid which is relatively has low temperatures hydrothermal solutions derived from the residual melt of basaltic magma.","Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy","['D001464', 'D002118', 'D019015', 'D005844', 'D007501', 'D008903', 'D010103', 'D010758', 'D011791', 'D012822', 'D013052', 'D013059', 'D014421', 'D014961']","['Barium', 'Calcium', 'Geologic Sediments', 'Geology', 'Iron', 'Minerals', 'Oxygen Isotopes', 'Phosphorus', 'Quartz', 'Silicon Dioxide', 'Spectrometry, X-Ray Emission', 'Spectrum Analysis, Raman', 'Turkey', 'X-Ray Diffraction']",The origin and determination of silica types in the silica occurrences from Altinta_ region (U_ak-Western Anatolia) using multianalytical techniques.,"['Q000032', 'Q000032', 'Q000737', 'Q000379', 'Q000032', 'Q000032', None, 'Q000032', None, 'Q000032', None, None, None, None]","['analysis', 'analysis', 'chemistry', 'methods', 'analysis', 'analysis', None, 'analysis', None, 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25240144,2015,0,0,,no cocoa -0.48,3930303,"A preliminary survey in 1982 of aflatoxin levels in peanut butters indicated that 31 out of 32 samples of major national brand-named products examined contained less than 10 micrograms/kg aflatoxin B1 and that 59% of these were below the limit of detection (2 micrograms/kg). In contrast, of 25 peanut butters from specialist 'Health Food' outlets, 64% contained less than 10 micrograms/kg aflatoxin B1, the remainder ranging from 16 to 318 micrograms/kg, with one sample having a total aflatoxin concentration of 345 micrograms/kg. Subsequent surveys in 1983 and 1984 of 'Health Food' products confirmed that these manufacturers were still experiencing some difficulty in complying with the 30 micrograms/kg total aflatoxin voluntary guideline limit. A further survey in 1984 was carried out of 228 retail samples of nuts and nut confectionery products comprising peanuts (shelled, unshelled, roasted and salted), mixed nuts, almonds (both unblanched and ground), brazils (in shell), hazelnuts (in shell), chocolate-coated peanuts, peanut brittle and coconut ice. The results showed that 74% of the samples contained less than 0.5 microgram/kg of aflatoxin B1 with 3.1% exceeding the guideline tolerance of 30 micrograms/kg total aflatoxins, these being predominantly peanuts and brazils. The highest total levels of aflatoxins observed were in unshelled peanuts containing 4920 micrograms/kg and in a composite sample of visibly moulded brazils containing 17 926 micrograms/kg.",Food additives and contaminants,"['D016604', 'D000348', 'D010367', 'D002851', 'D005511', 'D005516', 'D009754', 'D013050', 'D006113']","['Aflatoxin B1', 'Aflatoxins', 'Arachis', 'Chromatography, High Pressure Liquid', 'Food Handling', 'Food Microbiology', 'Nuts', 'Spectrometry, Fluorescence', 'United Kingdom']","A survey of aflatoxins in peanut butters, nuts and nut confectionery products by HPLC with fluorescence detection.","[None, 'Q000032', 'Q000032', None, None, None, 'Q000032', None, None]","[None, 'analysis', 'analysis', None, None, None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/3930303,1985,,,, -0.48,19251438,"Triacylglycerols were analyzed as cationized species (Li(+), Na(+), K(+)) by high-energy CID at 20 keV collisions utilizing MALDI-TOF/RTOF mass spectrometry. Precursor ions, based on [M + Li](+)-adduct ions exhibited incomplete fragmentation in the high and low m/z region whereas [M + K](+)-adducts did not show useful fragmentation. Only sodiated precursor ions yielded product ion spectra with structurally diagnostic product ions across the whole m/z range. The high m/z region of the CID spectra is dominated by abundant charge-remote fragmentation of the fatty acid substituents. In favorable cases also positions of double bonds or of hydroxy groups of the fatty acid alkyl chains could be determined. A-type product ions represent the end products of these charge-remote fragmentations. B- and C-type product ions yield the fatty acid composition of individual triacylglycerol species based on loss of either one neutral fatty acid or one sodium carboxylate residue, respectively. Product ions allowing fatty acid substituent positional determination were present in the low m/z range enabling identification of either the sn-1/sn-3 substituents (E-, F-, and G-type ions) or the sn-2 substituent (J-type ion). These findings were demonstrated with synthetic triacylglycerols and plant oils such as cocoa butter, olive oil, and castor bean oil. Typical features of 20 keV CID spectra of sodiated triacylglycerols obtained by MALDI-TOF/RTOF MS were an even distribution of product ions over the entire m/z range and a mass accuracy of +/-0.1 to 0.2 u. One limitation of the application of this technique is mainly the insufficient precursor ion gating after MS1 (gating window at 4 u) of species separated by 2 u.",Journal of the American Society for Mass Spectrometry,[],[],The renaissance of high-energy CID for structural elucidation of complex lipids: MALDI-TOF/RTOF-MS of alkali cationized triacylglycerols.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19251438,2009,2,1,text under resutls,key word components of cocoa -0.48,19843177,"A platform based on hydrophilic interaction chromatography in combination with Fourier transform mass spectrometry was developed in order to carry out metabonomics of Drosophila melanogaster strains. The method was able to detect approximately 230 metabolites, mainly in the positive ion mode, after checking to eliminate false positives caused by isotope peaks, adducts and fragment ions. Two wild-type strains, Canton S and Oregon R, were studied, plus two mutant strains, Maroon Like and Chocolate. In order to observe the differential expression of metabolites, liquid chromatography-mass spectrometry analyses of the different strains were compared using sieve 1.2 software to extract metabolic differences. The output from sieve was searched against a metabolite database using an Excel-based macro written in-house. Metabolic differences were observed between the wild-type strains, and also between both Chocolate and Maroon Like compared with Oregon R. It was established that a metabonomic approach could produce results leading to the generation of new hypotheses. In addition, the structure of a new class of lipid with a histidine head group, found in all of the strains of flies, but lower in Maroon Like, was elucidated.",The FEBS journal,"['D000818', 'D001708', 'D002853', 'D004331', 'D005583', 'D006639', 'D008055', 'D013058', 'D055432', 'D011621']","['Animals', 'Biopterin', 'Chromatography, Liquid', 'Drosophila melanogaster', 'Fourier Analysis', 'Histidine', 'Lipids', 'Mass Spectrometry', 'Metabolomics', 'Pteridines']",Towards a platform for the metabonomic profiling of different strains of Drosophila melanogaster using liquid chromatography-Fourier transform mass spectrometry.,"[None, 'Q000737', None, 'Q000378', None, 'Q000737', 'Q000737', None, 'Q000379', 'Q000737']","[None, 'chemistry', None, 'metabolism', None, 'chemistry', 'chemistry', None, 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19843177,2010,0,0,,no cocoa -0.48,16848542,"Sensory-guided decomposition of roasted cocoa nibs revealed that, besides theobromine and caffeine, a series of bitter-tasting 2,5-diketopiperazines and flavan-3-ols were the key inducers of the bitter taste as well as the astringent mouthfeel imparted upon consumption of roasted cocoa. In addition, a number of polyphenol glycopyranosides as well as a series of N-phenylpropenoyl-l-amino acids have been identified as key astringent compounds of roasted cocoa. In the present investigation, a total of 84 putative taste compounds were quantified in roasted cocoa beans and then rated for the taste contribution on the basis of dose-over-threshold (DoT) factors to bridge the gap between pure structural chemistry and human taste perception. To verify these quantitative results, an aqueous taste reconstitute was prepared by blending aqueous solutions of the individual taste compounds in their ""natural"" concentrations. Sensory analyses revealed that the taste profile of this artificial cocktail was very close to the taste profile of an aqueous suspension of roasted cocoa nibs. To further narrow down the number of key taste compounds, finally, taste omission experiments and human dose/response functions were performed, demonstrating that the bitter-tasting alkaloids theobromine and caffeine, seven bitter-tasting diketopiperazines, seven bitter- and astringent-tasting flavan-3-ols, six puckering astringent N-phenylpropenoyl-l-amino acids, four velvety astringent flavonol glycosides, gamma-aminobutyric acid, beta-aminoisobutyric acid, and six organic acids are the key organoleptics of the roasted cocoa nibs.",Journal of agricultural and food chemistry,"['D002099', 'D002851', 'D006358', 'D006801', 'D013058', 'D010936', 'D012639', 'D012677', 'D013649', 'D014867']","['Cacao', 'Chromatography, High Pressure Liquid', 'Hot Temperature', 'Humans', 'Mass Spectrometry', 'Plant Extracts', 'Seeds', 'Sensation', 'Taste', 'Water']",Molecular definition of the taste of roasted cocoa nibs (Theobroma cacao) by means of quantitative studies and sensory experiments.,"['Q000737', None, None, None, None, 'Q000737', 'Q000737', None, None, None]","['chemistry', None, None, None, None, 'chemistry', 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16848542,2006,1,3,table 1,conversion necessary -0.48,21945577,"Two kinds of monoclonal antibodies (MoAbs), OCA-10A and OCA-1B, were prepared based on their specificity to ochratoxin A (OTA) and ochratoxin B (OTB) and on their tolerance to 40% methanol. In an indirect competitive enzyme-linked immunosorbent assay, the half maximal inhibitory concentration (IC(50)) value of OCA-10A was 27ng/mL for OTA and 17ng/mL for OTB, and that of OCA-1B was 28ng/mL for OTA and 13ng/mL for OTB. Immuno-affinity columns (IACs) using these MoAbs were prepared with agarose gel beads. The IAC with OCA-1B showed a NaCl-dependent binding ability to OTA and OTB, while interestingly, the IAC with OCA-10A bound to them without NaCl. The IAC with OCA-10A showed a high methanol tolerance when compared with existing IACs, as expected from the high methanol tolerance of OCA-10A itself. Such tolerance was maintained for the application of the cocoa extract with 70% methanol and the wheat extract with 60% acetonitrile, while the tolerance was slightly altered by interference from the cocoa extract. Examinations with organic solvents at higher concentrations than the allowable level in existing IACs showed that OTA and OTB spiked with wheat, cocoa and red wine could be purified with high recovery. The newly developed IAC is expected to show sufficient clean-up ability for food analyses.","Methods (San Diego, Calif.)","['D000097', 'D000818', 'D000911', 'D000918', 'D000937', 'D002099', 'D002846', 'D004797', 'D005260', 'D006433', 'D007117', 'D007118', 'D020128', 'D000432', 'D051379', 'D008807', 'D009793', 'D055601', 'D012965', 'D012997']","['Acetonitriles', 'Animals', 'Antibodies, Monoclonal', 'Antibody Specificity', 'Antigen-Antibody Reactions', 'Cacao', 'Chromatography, Affinity', 'Enzyme-Linked Immunosorbent Assay', 'Female', 'Hemocyanins', 'Immunization, Secondary', 'Immunoassay', 'Inhibitory Concentration 50', 'Methanol', 'Mice', 'Mice, Inbred BALB C', 'Ochratoxins', 'Organic Chemistry Phenomena', 'Sodium Chloride', 'Solvents']",Development of an immuno-affinity column for ochratoxin analysis using an organic solvent-tolerant monoclonal antibody.,"['Q000737', None, 'Q000737', None, None, 'Q000737', 'Q000295', 'Q000379', None, 'Q000008', None, 'Q000295', None, 'Q000737', None, None, 'Q000008', None, 'Q000737', None]","['chemistry', None, 'chemistry', None, None, 'chemistry', 'instrumentation', 'methods', None, 'administration & dosage', None, 'instrumentation', None, 'chemistry', None, None, 'administration & dosage', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21945577,2012,0,0,, -0.47,20492142,"Cocoa beans were alkalized before or after roasting and made into cocoa liquor before analyzing by SIFT-MS. In both alkalized-before-roasting and alkalized-after-roasting samples, there were significantly higher concentrations of alkylpyrazines for the samples with pH above 7 than pH below 7. At pH 8, the concentrations of 2,3-, 2,5-, and 2,6-dimethylpyrazine (DMP), 2,3,5-trimethylpyrazine (TrMP), 2,3,5,6-tetramethylpyrazine (TMP), and 2,3-diethyl-5-methylpyrazine (EMP) in the samples alkalized-before-roasting were higher than those in the samples alkalized-after-roasting. Volatiles increased under conditions that promoted the Maillard reaction. The partition coefficient was not significantly affected by pH from 5.2 to 8. The ratios of TrMP/DMP and DMP/TMP increased while the ratio of TMP/TrMP decreased as the pH increased. The concentrations of Strecker aldehydes and other volatiles followed a similar pattern as that of the alkylpyrazines. High pH favors the production of alkylpyrazines and Strecker aldehydes.",Journal of food science,"['D000079', 'D000434', 'D000447', 'D002099', 'D005511', 'D005663', 'D006863', 'D013058', 'D011719', 'D055549']","['Acetaldehyde', 'Alcoholic Beverages', 'Aldehydes', 'Cacao', 'Food Handling', 'Furans', 'Hydrogen-Ion Concentration', 'Mass Spectrometry', 'Pyrazines', 'Volatile Organic Compounds']","Alkylpyrazines and other volatiles in cocoa liquors at pH 5 to 8, by Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS).","['Q000031', 'Q000032', 'Q000032', 'Q000737', 'Q000379', 'Q000032', None, 'Q000379', 'Q000032', 'Q000032']","['analogs & derivatives', 'analysis', 'analysis', 'chemistry', 'methods', 'analysis', None, 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20492142,2010,1,2,table 3, -0.47,14745773,"Positional distribution of fatty acyl chains of triacylglycerols (TGs) in vegetable oils and fats (palm oil, cocoa butter) and animal fats (beef, pork and chicken fats) was examined by reversed-phase high-performance liquid chromatography (RP-HPLC) coupled to atmospheric pressure chemical ionization using a quadrupole mass spectrometer. Quantification of regioisomers was achieved for TGs containing two different fatty acyl chains (palmitic (P), stearic (S), oleic (O), and/or linoleic (L)). For seven pairs of 'AAB/ABA'-type TGs, namely PPS/PSP, PPO/POP, SSO/SOS, POO/OPO, SOO/OSO, PPL/PLP and LLS/LSL, calibration curves were established on the basis of the difference in relative abundances of the fragment ions produced by preferred losses of the fatty acid from the 1/3-position compared to the 2-position. In practice the positional isomers AAB and ABA yield mass spectra showing a significant difference in relative abundance ratios of the ions AA(+) to AB(+). Statistical analysis of the validation data obtained from analysis of TG standards and spiked oils showed that, under repeatability conditions, least-squares regression can be used to establish calibration curves for all pairs. The regression models show linear behavior that allow the determination of the proportion of each regioisomer in an AAB/ABA pair, within a working range from 10 to 1000 microg/mL and a 95% confidence interval of +/-3% for three replicates.",Rapid communications in mass spectrometry : RCM,"['D000818', 'D001274', 'D002138', 'D002851', 'D005223', 'D007536', 'D013058', 'D008460', 'D009821', 'D012015', 'D014280']","['Animals', 'Atmospheric Pressure', 'Calibration', 'Chromatography, High Pressure Liquid', 'Fats', 'Isomerism', 'Mass Spectrometry', 'Meat', 'Oils', 'Reference Standards', 'Triglycerides']",Quantitative analysis of triacylglycerol regioisomers in fats and oils using reversed-phase high-performance liquid chromatography and atmospheric pressure chemical ionization mass spectrometry.,"[None, None, None, None, 'Q000737', None, 'Q000379', None, 'Q000737', None, 'Q000032']","[None, None, None, None, 'chemistry', None, 'methods', None, 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/14745773,2004,,,, -0.47,19631941,"The development of an off-line comprehensive 2-dimensional liquid chromatography (2-D-LC) method for the analysis of procyanidins is reported. In the first dimension, oligomeric procyanidins were separated according to molecular weight by hydrophilic interaction chromatography (HILIC), while reversed phase LC was employed in the second dimension to separate oligomers based on hydrophobicity. Fluorescence, UV and electrospray ionisation mass spectrometry (ESI-MS) were employed for identification purposes. The combination of these orthogonal separation methods is shown to represent a significant improvement compared to 1-dimensional methods for the analysis of complex high molecular weight procyanidin fractions, by simultaneously providing isomeric and molecular weight information. The low correlation (r(2)<0.2100) between the two LC modes afforded a practical peak capacity in excess of 2300 for the optimal off-line method. The applicability of the method is demonstrated for the analysis of phenolic extracts of apple and cocoa.",Journal of chromatography. A,"['D001704', 'D002099', 'D002851', 'D005638', 'D027845', 'D010636', 'D010936', 'D044945', 'D012639', 'D021241']","['Biopolymers', 'Cacao', 'Chromatography, High Pressure Liquid', 'Fruit', 'Malus', 'Phenols', 'Plant Extracts', 'Proanthocyanidins', 'Seeds', 'Spectrometry, Mass, Electrospray Ionization']",Off-line comprehensive 2-dimensional hydrophilic interaction x reversed phase liquid chromatography analysis of procyanidins.,"['Q000032', 'Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000032', 'Q000737', 'Q000032', 'Q000737', None]","['analysis', 'chemistry', 'methods', 'chemistry', 'chemistry', 'analysis', 'chemistry', 'analysis', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/19631941,2009,2,1,table 2, -0.47,25167469,"Despite the key role of flavan-3-ols in many foods, very little is yet known concerning the modification of their chemical structures through food processes. Degradation of model media containing (-)-epicatechin and procyanidin B2, either separately or together, was monitored by RP-HPLC-DAD-ESI(-)-MS/MS. Medium composition (aqueous or lipidic) and temperature (60 and 90 _C) were studied. In aqueous medium at 60 _C, (-)-epicatechin was mainly epimerized to (-)-catechin, but it was also oxidized to ""chemical"" dimers, a ""chemical"" trimer, and dehydrodi(epi)catechin A. Unlike oxidation, epimerization was enhanced at 90 _C. In lipidic medium, epimerization proved slow but degradation was faster. Procyanidin B2 likewise proved able to epimerize, especially at 90 _C and in aqueous medium. At high temperature only, the interflavan linkage was cleaved, yielding the same compounds as those found in the monomer-containing model medium. Oxidation to procyanidin A2 was also evidenced. With little epimerization and slow oxidation even at 90 _C, procyanidin B2 proved more stable in lipidic medium. Synergy was also observed: in the presence of the monomer, the dimer degradation rate increased 2-fold at 60 _C. This work states for the first time the presence of newly formed flavan-3-ol oligomers in processed cocoa. ",Journal of agricultural and food chemistry,"['D044946', 'D002099', 'D002392', 'D002851', 'D019281', 'D005419', 'D005511', 'D006358', 'D059808', 'D044945', 'D012996', 'D021241', 'D053719', 'D014867']","['Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Dimerization', 'Flavonoids', 'Food Handling', 'Hot Temperature', 'Polyphenols', 'Proanthocyanidins', 'Solutions', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry', 'Water']","Degradation of (-)-epicatechin and procyanidin B2 in aqueous and lipidic model systems. first evidence of ""chemical"" flavan-3-ol oligomers in processed cocoa.","['Q000032', 'Q000737', 'Q000032', None, None, 'Q000032', 'Q000379', None, 'Q000032', 'Q000032', None, None, None, None]","['analysis', 'chemistry', 'analysis', None, None, 'analysis', 'methods', None, 'analysis', 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25167469,2015,0,0,, -0.47,22468361,"The performance of Gluten-Tec (EuroProxima, Arnhem, The Netherlands) was tested through an interlaboratory study in accordance with AOAC guidelines. Gluten-Tec is a competitive ELISA that detects an immunostimulatory epitope of a-gliadin in dietary food for celiacs. Fifteen laboratories, representing 14 different countries, announced their interest in taking part in this study. Of the 12 laboratories that sent the results within the established timeframe, two submitted inappropriate standard curves and were excluded from the statistical analysis. Four different food matrixes (rice-based baby food, maize bread, chocolate cake mix, and beer) were selected for preparing the test samples. Two gliadin extraction procedures were used: the conventional 60% ethanol, and a new method based on the reducing reagent dithiothreitol. The 38 samples (19 blind duplicates) tested in this study were prepared by diluting the different extracts in order to cover a wide range of gliadin levels. Both sample extraction and dilution were performed by EuroProxima; the present interlaboratory study was focused only on testing the ELISA part of the Gluten-Tec kit protocol. Repeatability values (within-laboratory variance), expressed as RSD(r) ranged from 6.2 to 25.7%, while reproducibility values (interlaboratory variance), expressed as RSD(R), ranged from 10.6 to 45.9%. Both statistical parameters were in the acceptable range of ELISAs under these conditions, and the method will be presented to the Codex Alimentarius as a preferred method for gluten analysis.",Journal of AOAC International,"['D000485', 'D001515', 'D002446', 'D002851', 'D004044', 'D004797', 'D005504', 'D005512', 'D005903', 'D005983', 'D006801', 'D007202', 'D007223', 'D007225', 'D057230', 'D010455', 'D011933', 'D015203']","['Allergens', 'Beer', 'Celiac Disease', 'Chromatography, High Pressure Liquid', 'Dietary Proteins', 'Enzyme-Linked Immunosorbent Assay', 'Food Analysis', 'Food Hypersensitivity', 'Gliadin', 'Glutens', 'Humans', 'Indicators and Reagents', 'Infant', 'Infant Food', 'Limit of Detection', 'Peptides', 'Reagent Kits, Diagnostic', 'Reproducibility of Results']",Validation of a new enzyme-linked immunosorbent assay to detect the triggering proteins and peptides for celiac disease: interlaboratory study.,"['Q000032', 'Q000032', 'Q000139', None, 'Q000032', 'Q000379', None, 'Q000276', 'Q000032', 'Q000032', None, None, None, None, None, 'Q000032', None, None]","['analysis', 'analysis', 'chemically induced', None, 'analysis', 'methods', None, 'immunology', 'analysis', 'analysis', None, None, None, None, None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22468361,2012,,,, -0.47,15759751,"Acrylamide levels in a variety of food samples were analyzed before and after 3 months of storage at 10 degrees-12 degrees C. The analysis was performed by liquid chromatography tandem mass spectrometry (LC/MS/MS) using deuterium-labeled acrylamide as internal standard. Acrylamide was stable in most matrixes (cookies, cornflakes, crispbread, raw sugar, potato crisps, peanuts) over time. However, slight decreases were determined for dietary biscuits (83-89%) and for licorice confection (82%). For coffee and cacao powder, a significant decrease occurred during storage for 3 or 6 months, respectively. Acrylamide concentrations dropped from 305 to 210 microg/kg in coffee and from 265 to 180 microg/kg in cacao powder. On the contrary, acrylamide remained stable in soluble coffee as well as in coffee substitutes. Reactions of acrylamide with SH group-containing substances were assumed as the cause for acrylamide degradation in coffee and cacao. Spiking experiments with acrylamide revealed that acrylamide concentrations remained stable in baby food, cola, and beer; however, recovery levels dropped in milk powder (71%), sulfurized apricot (53%), and cacao powder (17%). These observations suggest that variations in the acrylamide content of food, especially in coffee and cacao, can vary depending on the storage time because special food constituents and/or reaction products can affect the levels.",Journal of AOAC International,"['D020106', 'D001939', 'D002099', 'D050260', 'D002853', 'D003069', 'D003903', 'D002523', 'D005502', 'D005504', 'D005506', 'D005511', 'D008401', 'D013058', 'D013696', 'D013997']","['Acrylamide', 'Bread', 'Cacao', 'Carbohydrate Metabolism', 'Chromatography, Liquid', 'Coffee', 'Deuterium', 'Edible Grain', 'Food', 'Food Analysis', 'Food Contamination', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Mass Spectrometry', 'Temperature', 'Time Factors']",Studies on the stability of acrylamide in food during storage.,"['Q000737', None, None, None, None, None, 'Q000737', None, None, None, None, None, None, None, None, None]","['chemistry', None, None, None, None, None, 'chemistry', None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/15759751,2005,,,, -0.47,17394333,"The development and in-house testing of a method for the quantification of milk fat in chocolate fats is described. A database consisting of the triacylglycerol profiles of 310 genuine milk fat samples from 21 European countries and 947 mixtures thereof with chocolate fats was created under a strict quality control scheme using 26 triacylglycerol reference standards for calibration purposes. Out of the individual triacylglycerol fractions obtained, 1-palmitoyl-2-stearoyl-3-butyroyl-glycerol (PSB) was selected as suitable marker compound for the determination of the proportion of milk fat in chocolate fats. By using PSB values from the standardized database, a calibration function using simple linear regression analysis was calculated to be used for future estimations of the milk fat content. A comparison with the widely used butyric acid method, which is currently used to determine the milk fat content in nonmilk fat mixtures, showed that both methods were equivalent in terms of accuracy. The advantage of the presented approach is that for further applications, i.e., determination of foreign fats in chocolate fats, just a single analysis is necessary, whereas for the same purpose, the C4 method requires two different analytical methods.",Journal of agricultural and food chemistry,"['D000818', 'D002099', 'D002849', 'D005223', 'D008892', 'D011786', 'D014280']","['Animals', 'Cacao', 'Chromatography, Gas', 'Fats', 'Milk', 'Quality Control', 'Triglycerides']",Quantification of milk fat in chocolate fats by triacylglycerol analysis using gas-liquid chromatography.,"[None, 'Q000737', None, 'Q000032', 'Q000737', None, 'Q000032']","[None, 'chemistry', None, 'analysis', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17394333,2007,0,0,,0 -0.46,8197829,"Direct injection of oil or fat into a moderately heated injector enables performance of a kind of headspace technique in the injector: oil or fat is diluted 1:1 with acetone and injected into a vaporizing chamber at 200 degrees C. Components, for example organophosphorus insecticides, evaporate from the oil film on the insert wall and are transferred into the column in the splitless mode; the oil slowly flows along the wall to the bottom of the insert and is retained there in a kind of a bag. Using a flame photometric detector, detection limits are below 10 micrograms/kg.",Zeitschrift fur Lebensmittel-Untersuchung und -Forschung,"['D002849', 'D004041', 'D004042', 'D005506', 'D007306', 'D000069463', 'D009943', 'D010938']","['Chromatography, Gas', 'Dietary Fats', 'Dietary Fats, Unsaturated', 'Food Contamination', 'Insecticides', 'Olive Oil', 'Organophosphorus Compounds', 'Plant Oils']",Determination of organophosphorus insecticides in edible oils and fats by splitless injection of the oil into a gas chromatograph (injector-internal headspace analysis).,"['Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, None, 'Q000032']","['methods', 'analysis', 'analysis', 'analysis', 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/8197829,1994,,,, -0.46,8471853,"A liquid chromatographic procedure already evaluated in a preceding study for the analysis of acesulfam-K is also suitable for the determination of the intense sweetener aspartame in tabletop sweetener, candy, fruit beverage, fruit pulp, soft drink, yogurt, cream, cheese, and chocolate preparations. The method also allows the determination of aspartame's major decomposition products: diketopiperazine, aspartyl-phenylalanine, and phenylalanine. Samples are extracted or diluted with water and filtered. Complex matrixes are centrifuged or clarified with Carrez solutions. An aliquot of the extract is analyzed on a reversed-phase muBondapak C18 column using 0.0125M KH2PO4 (pH 3.5)-acetonitrile ([85 + 15] or [98 + 2]) as mobile phase. Detection is performed by UV absorbance at 214 nm. Recoveries ranged from 96.1 to 105.0%. Decomposition of the sweetener was observed in most food samples. However, the total aspartame values (measured aspartame + breakdown products) were within -10% and +5% of the declared levels. The repeatabilities and the repeatability coefficients of variation were, respectively, 1.00 mg/100 g and 1.34% for products containing less than 45 mg/100 g aspartame and 4.11 mg/100 g and 0.91% for other products. The technique is precise and sensitive. It enables the detection of many food additives or natural constituents, such as other intense sweeteners, organic acids, and alkaloids, in the same run without interfering with aspartame or its decomposition products. The method is consequently suitable for quality control or monitoring.",Journal of AOAC International,"['D000818', 'D001218', 'D001628', 'D002182', 'D002855', 'D004355', 'D005504', 'D005638', 'D008892', 'D013549']","['Animals', 'Aspartame', 'Beverages', 'Candy', 'Chromatography, Thin Layer', 'Drug Stability', 'Food Analysis', 'Fruit', 'Milk', 'Sweetening Agents']",Determination of aspartame and its major decomposition products in foods.,"[None, 'Q000032', 'Q000032', 'Q000032', None, None, 'Q000379', 'Q000737', 'Q000737', 'Q000737']","[None, 'analysis', 'analysis', 'analysis', None, None, 'methods', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/8471853,1993,,,, -0.46,17031994,"Detection of peptides from the peanut allergen Ara h 1 by liquid chromatography-mass spectrometry (LC-MS) was used to identify and estimate total peanut protein levels in dark chocolate. A comparison of enzymatic digestion subsequent to and following extraction of Ara h 1 from the food matrix revealed better limits of detection (LOD) for the pre-extraction digestion (20 ppm) than for the postextraction digestion (50 ppm). Evaluation of LC-MS instruments and scan modes showed the LOD could be further reduced to 10 ppm via a triple-quadrupole and multiple-reaction monitoring. Improvements in extraction techniques combined with an increase in the amount of chocolate extracted (1 g) improved the LOD to 2 ppm of peanut protein. This method provides an unambiguous means of confirming the presence of the peanut protein in foods using peptide markers from a major allergen, Ara h 1, and can easily be modified to detect other food allergens.",Journal of agricultural and food chemistry,"['D000485', 'D000595', 'D052179', 'D002099', 'D002853', 'D005506', 'D006023', 'D013058', 'D008969', 'D010446', 'D010447', 'D010940', 'D012680']","['Allergens', 'Amino Acid Sequence', 'Antigens, Plant', 'Cacao', 'Chromatography, Liquid', 'Food Contamination', 'Glycoproteins', 'Mass Spectrometry', 'Molecular Sequence Data', 'Peptide Fragments', 'Peptide Hydrolases', 'Plant Proteins', 'Sensitivity and Specificity']",Confirmation of peanut protein using peptide markers in dark chocolate using liquid chromatography-tandem mass spectrometry (LC-MS/MS).,"['Q000032', None, None, 'Q000737', None, 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000378', 'Q000032', None]","['analysis', None, None, 'chemistry', None, 'analysis', 'analysis', None, None, 'analysis', 'metabolism', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/17031994,2006,0,0,,no cocoa -0.46,19280160,"The antioxidant potential of commercial beverages against peroxyl radical was determined using the Total Oxyradical Scavenging Capacity (TOSC) assay. Peroxyl radicals generated from thermal homolysis of 2,2'-azobis-amidinopropane oxidize alpha-keto-gamma-methiolbutyric acid to ethylene, which is monitored by gas chromatography. The TOSC of each beverage is quantified from its ability to inhibit ethylene generation relative to a control reaction. Nine different beverages (green tea, jasmine tea, black tea, instant coffee, brewed coffee, cocoa mix, oolong tea, prune juice, and grape juice) were selected for this study. Their antioxidant capacities per a cup-serving (125 mL) were measured and compared to peroxyl radical scavenging capacity provided by a recommended daily dose of ascorbic acid (90 mg) dissolved in the same volume of water. The greatest antioxidant capacity was found in brewed coffee, which was followed, in decreasing order, by prune juice, instant coffee, green tea, cocoa mix, grape juice, jasmine tea, black tea, oolong tea, and ascorbic acid. There was an almost 7-fold difference in the TOSC between brewed coffee and ascorbic acid. The data suggest a potential role for commonly consumed beverages in lowering the risk of pathophysiologies associated with peroxyl radical-mediated events.",Archives of pharmacal research,"['D001628', 'D016166', 'D016014', 'D010084', 'D010545']","['Beverages', 'Free Radical Scavengers', 'Linear Models', 'Oxidation-Reduction', 'Peroxides']",Comparison of peroxyl radical scavenging capacity of commonly consumed beverages.,"['Q000032', 'Q000737', None, None, 'Q000737']","['analysis', 'chemistry', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19280160,2009,0,0,,cocoa mix (hot chocolate) -0.46,28110526,"Jackfruit seeds are an underutilized waste in many tropical countries. This work demonstrates the potential of roasted jackfruit seeds to develop chocolate aroma. Twenty-seven different roasted jackfruit seed flours were produced from local jackfruit by acidifying or fermenting the seeds prior to drying and then roasting under different time/temperature combinations. The chocolate aroma of groups of four flours were ranked by a sensory panel (n = 162), and response surface methodology was used to identify optimum conditions. The results indicated a significant and positive influence of fermentation and acidification on the production of chocolate aroma. SPME/GC-MS of the flours showed that important aroma compounds such as 2,3-diethyl-5-methylpyrazine and 2-phenylethyl acetate were substantially higher in the fermented product and that the more severe roasting conditions produced 2-3 times more 2,3-diethyl-5-methylpyrazine, but less 3-methylbutanal. Moisture, a",Journal of agricultural and food chemistry,"['D000085', 'D000293', 'D000328', 'D031622', 'D000069956', 'D005260', 'D005285', 'D005433', 'D008401', 'D006801', 'D007220', 'D008297', 'D008875', 'D009812', 'D010626', 'D012639', 'D055549', 'D018505', 'D055815']","['Acetates', 'Adolescent', 'Adult', 'Artocarpus', 'Chocolate', 'Female', 'Fermentation', 'Flour', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Industrial Waste', 'Male', 'Middle Aged', 'Odorants', 'Phenylethyl Alcohol', 'Seeds', 'Volatile Organic Compounds', 'Waste Management', 'Young Adult']",Optimization of Postharvest Conditions To Produce Chocolate Aroma from Jackfruit Seeds.,"['Q000032', None, None, 'Q000737', None, None, None, None, 'Q000379', None, None, None, None, 'Q000032', 'Q000031', 'Q000737', 'Q000032', None, None]","['analysis', None, None, 'chemistry', None, None, None, None, 'methods', None, None, None, None, 'analysis', 'analogs & derivatives', 'chemistry', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28110526,2017,0,0,, -0.46,11128210,"Supercritical carbon dioxide can be used to carry out a selective and fast extraction (30 min) of volatile hydrocarbons and 2-alkylcyclobutanones contained in irradiated foods. After elimination of the traces of triglycerides still contained in the extracts on a silica column, the compounds were analysed by gas chromatography-mass spectroscopy (2-alkylcyclobutanones) and gas chromatography-flame ionization detection (volatile hydrocarbons). The present method was applied successfully to freeze-dried samples (1 g or less) of cheese, chicken, avocados and to various ingredients (chocolate, liquid whole eggs) included in non-irradiated cookies. It was faster (4-5 h) than the reference methods EN 1784 (volatile hydrocarbons) and EN 1785 (2-alkylcyclobutanones), which take 1.5 days each. The minimal dose detectable by this method is, in addition, slightly lower than those of the reference methods.",Journal of chromatography. A,"['D003503', 'D005504', 'D005514', 'D008401', 'D006838', 'D012015']","['Cyclobutanes', 'Food Analysis', 'Food Irradiation', 'Gas Chromatography-Mass Spectrometry', 'Hydrocarbons', 'Reference Standards']",Supercritical fluid extraction of hydrocarbons and 2-alkylcyclobutanones for the detection of irradiated foodstuffs.,"['Q000032', None, None, None, 'Q000032', None]","['analysis', None, None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/11128210,2001,0,0,,no cocoa tested -0.46,22705559,"The detection and quantification of polyphenols in biological samples is mainly performed by liquid chromatography in tandem with mass spectrometry (HPLC-MS/MS). This technique requires the use of organic solvents and needs control and maintenance of several MS/MS parameters, which makes the method expensive and time consuming. The main objective of this study was to evaluate, for the first time, the potential of using attenuated total reflection infrared microspectroscopy (ATR-IRMS) coupled with multivariate analysis to detect and quantify phenolic compounds excreted in human urine. Samples were collected from 5 healthy volunteers before and 6, 12 and 24 h after ingestion of 40 g cocoa powder with 250 mL of water or whole milk, and stored at -80 _C. Each sample was centrifuged at 5000 rpm for 10 min and at 4 _C and applied onto grids of a hydrophobic membrane. Spectra were collected in the attenuated total reflection (ATR) mode in the mid-infrared region (4000-800 cm(-1)) and were analyzed by a multivariate analysis technique, soft independent modeling of class analogy (SIMCA). Spectral models showed that IR bands responsible for chemical differences among samples were related to aromatic rings. Therefore, ATR-IRMS could be an interesting and straightforward technique for the detection of phenolic compounds excreted in urine. Moreover, it could be a valuable tool in studies aimed to identify biomarkers of consumption of polyphenol-rich diets.",The Analyst,"['D002099', 'D006801', 'D015999', 'D059808', 'D012016', 'D017550']","['Cacao', 'Humans', 'Multivariate Analysis', 'Polyphenols', 'Reference Values', 'Spectroscopy, Fourier Transform Infrared']",Attenuated total reflection infrared microspectroscopy combined with multivariate analysis: a novel tool to study the presence of cocoa polyphenol metabolites in urine samples.,"['Q000737', None, None, 'Q000378', None, None]","['chemistry', None, None, 'metabolism', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22705559,2012,0,0,, -0.46,17604905,"A cloud point extraction procedure was presented for the preconcentration of copper, nickel and cobalt ions in various samples. After complexation with methyl-2-pyridylketone oxime (MPKO) in basic medium, analyte ions are quantitatively extracted to the phase rich in Triton X-114 following centrifugation. 1.0 mol L(-1) HNO(3) nitric acid in methanol was added to the surfactant-rich phase prior to its analysis by flame atomic absorption spectrometry (FAAS). The adopted concentrations for MPKO, Triton X-114 and HNO(3), bath temperature, centrifuge rate and time were optimized. Detection limits (3 SDb/m) of 1.6, 2.1 and 1.9 ng mL(-1) for Cu(2+), Co(2+) and Ni(2+) along with preconcentration factors of 30 and for these ions and enrichment factor of 65, 58 and 67 for Cu(2+), Ni(2+) and Co(2+), respectively. The high efficiency of cloud point extraction to carry out the determination of analytes in complex matrices was demonstrated. The proposed procedure was applied to the analysis of biological, natural and wastewater, soil and blood samples.",Journal of hazardous materials,"['D000818', 'D002099', 'D002182', 'D002417', 'D002498', 'D003035', 'D003300', 'D004784', 'D004785', 'D005618', 'D006863', 'D008099', 'D000432', 'D009532', 'D010091', 'D011092', 'D011720', 'D012965', 'D012987', 'D013054', 'D018724', 'D013501', 'D013696', 'D014881']","['Animals', 'Cacao', 'Candy', 'Cattle', 'Centrifugation', 'Cobalt', 'Copper', 'Environmental Monitoring', 'Environmental Pollutants', 'Fresh Water', 'Hydrogen-Ion Concentration', 'Liver', 'Methanol', 'Nickel', 'Oximes', 'Polyethylene Glycols', 'Pyrazoles', 'Sodium Chloride', 'Soil', 'Spectrophotometry, Atomic', 'Spinacia oleracea', 'Surface-Active Agents', 'Temperature', 'Water Supply']","Cloud point extraction for the determination of copper, nickel and cobalt ions in environmental samples by flame atomic absorption spectrometry.","[None, None, 'Q000032', 'Q000097', None, 'Q000032', 'Q000032', None, 'Q000032', 'Q000032', None, 'Q000737', 'Q000737', 'Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000032', None, 'Q000737', 'Q000737', None, 'Q000032']","[None, None, 'analysis', 'blood', None, 'analysis', 'analysis', None, 'analysis', 'analysis', None, 'chemistry', 'chemistry', 'analysis', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'analysis', None, 'chemistry', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17604905,2008,0,0,,no cocoa -0.46,27503535,"Complicated urinary tract infections, such as pyelonephritis, may lead to sepsis. Rapid diagnosis is needed to identify the causative urinary pathogen and to verify the appropriate empirical antimicrobial therapy. We describe here a rapid identification method for urinary pathogens: urine is incubated on chocolate agar for 3h at 35_C with 5% CO2 and subjected to MALDI-TOF MS analysis by VITEK MS. Overall 207 screened clinical urine samples were tested in parallel with conventional urine culture. The method, called U-si-MALDI-TOF (urine short incubation MALDI-TOF), showed correct identification for 86% of Gram-negative urinary tract pathogens (Escherichia coli, Klebsiella pneumoniae, and other Enterobacteriaceae), when present at >10(5)cfu/ml in culture (n=107), compared with conventional culture method. However, Gram-positive bacteria (n=28) were not successfully identified by U-si-MALDI-TOF. This method is especially suitable for rapid identification of E. coli, the most common cause of urinary tract infections and urosepsis. Turnaround time for identification using U-si-MALDI-TOF compared with conventional urine culture was improved from 24h to 4-6h.",Journal of microbiological methods,"['D001431', 'D004926', 'D006090', 'D016905', 'D006801', 'D018805', 'D019032', 'D013997', 'D014552', 'D014556']","['Bacteriological Techniques', 'Escherichia coli', 'Gram-Negative Bacteria', 'Gram-Negative Bacterial Infections', 'Humans', 'Sepsis', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Time Factors', 'Urinary Tract Infections', 'Urine']",Identification of urinary tract pathogens after 3-hours urine culture by MALDI-TOF mass spectrometry.,"['Q000295', 'Q000302', 'Q000737', 'Q000175', None, None, 'Q000379', None, 'Q000175', 'Q000382']","['instrumentation', 'isolation & purification', 'chemistry', 'diagnosis', None, None, 'methods', None, 'diagnosis', 'microbiology']",https://www.ncbi.nlm.nih.gov/pubmed/27503535,2017,0,0,,no cocoa -0.46,11962690,"Residual levels of 12 solvents in 87 natural food additives (66 samples of food colours, 19 samples of natural antioxidants and two natural preservatives) collected between 1997 and 1999 were determined by automated head-space GC using FID, with a porous-polymer (PLOT) column. Calibration curves were prepared by the method of standard addition. Confirmation was by manually injected head-space GC using mass spectrometric detection. 1,2-Dichloroethane was found in turmeric colour (natural food colour) collected in 1997 at the concentrations of 8.6 microg g(-1), but was not found in samples collected in 1998 and 1999. Hexane was found in three samples of dunaliella carotene (11, 72 and 75 microg g(-1)), and in chlorophyll at 93 microg g(-1) (both natural food colours). Acetone was found in turmeric colour, annatto colour, dunaliella carotene, kaoliang colour, cacao colour at a concentration between 8.7 and 42 microg g(-1) (all natural food colours).",Food additives and contaminants,"['D000975', 'D002849', 'D005503', 'D005505', 'D005506', 'D005511', 'D005520', 'D008401', 'D006801', 'D012997']","['Antioxidants', 'Chromatography, Gas', 'Food Additives', 'Food Coloring Agents', 'Food Contamination', 'Food Handling', 'Food Preservatives', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Solvents']",Survey of residual solvents in natural food additives by standard addition head-space GC.,"['Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000032', None, 'Q000737', 'Q000379', None, 'Q000032']","['chemistry', 'methods', 'chemistry', 'chemistry', 'analysis', None, 'chemistry', 'methods', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11962690,2002,,,, -0.46,28208630,"Phenolic compounds, which are secondary plant metabolites, are considered an integral part of the human diet. Physiological properties of dietary polyphenols have come to the attention in recent years. Especially, proanthocyanidins (ranging from dimers to decamers) have demonstrated potential interactions with biological systems, such as antiviral, antibacterial, molluscicidal, enzyme-inhibiting, antioxidant, and radical-scavenging properties. Agroindustry produces a considerable amount of phenolic-rich sources, and the ability of polyphenolic structures to interacts with other molecules in living organisms confers their beneficial properties. Cocoa wastes and grape seeds and skin byproducts are a source of several phenolic compounds, particularly mono-, oligo-, and polymeric proanthocyanidins. The aim of this work is to compare the phenolic composition of Theobroma cacao and Vitis vinifera grape seed extracts by high pressure liquid chromatography coupled to a quadrupole time-of-flight mass spectrometer and equipped with an electrospray ionization interface (HPLC-ESI-QTOF-MS) and its phenolic quantitation in order to evaluate the proanthocyanidin profile. The antioxidant capacity was measured by different methods, including electron transfer and hydrogen atom transfer-based mechanisms, and total phenolic and flavan-3-ol contents were carried out by Folin-Ciocalteu and Vanillin assays. In addition, to assess the anti-inflammatory capacity, the expression of MCP-1 in human umbilical vein endothelial cells was measured.",International journal of molecular sciences,"['D000893', 'D000975', 'D002099', 'D002851', 'D004847', 'D005419', 'D056604', 'D006801', 'D062385', 'D010636', 'D010936', 'D044945', 'D012639', 'D021241', 'D019032', 'D027843']","['Anti-Inflammatory Agents', 'Antioxidants', 'Cacao', 'Chromatography, High Pressure Liquid', 'Epithelial Cells', 'Flavonoids', 'Grape Seed Extract', 'Humans', 'Hydroxybenzoates', 'Phenols', 'Plant Extracts', 'Proanthocyanidins', 'Seeds', 'Spectrometry, Mass, Electrospray Ionization', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Vitis']",Cocoa and Grape Seed Byproducts as a Source of Antioxidant and Anti-Inflammatory Proanthocyanidins.,"['Q000737', 'Q000737', 'Q000737', None, 'Q000187', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', None, None, 'Q000737']","['chemistry', 'chemistry', 'chemistry', None, 'drug effects', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28208630,2017,1,2,"fig 3, table 3", -0.46,21107975,"The aim of this work was the determination of peptides, which can function as markers for identification of milk allergens in food samples. Emphasis was placed on two casein proteins (_±- and __-casein) and two whey proteins (_±-lactalbumin and __-lactoglobulin). In silico tryptic digestion provided preliminary information about the expected peptides. After tryptic digestion of four milk allergens, the analytical data obtained by combination of reversed-phase high performance liquid chromatography and quadrupole tandem mass spectrometry (LC-MS/MS) led to the identification of 26 peptides. Seven of these peptides were synthesized and used for calibration of the LC-MS/MS system. Species specificity of the selected peptides was sought by BLAST search. Among the selected peptides, only LIVTQTMK from __-lactoglobulin (m/z 467.6, charge 2+) was found to be cow milk specific and could function as a marker. Two other peptides, FFVAPFPEVFGK from _±-casein (m/z 693.3, charge 2+) and GPFPIIV from __-casein (m/z 742.5, charge 1+), occur in water buffalo milk too. The other four peptides appear in the milk of other species also and can be used as markers for ruminant species milk. Using these seven peptides, a multianalyte MS-based method was developed. For the establishment of the method, it was applied at first to different dairy samples, and then to chocolate and blank samples, and the peptides could be determined down to 1 ng/mL in food samples. At the end, spiked samples were measured, where the target peptides could be detected with a high recovery (over 50%).",Analytical and bioanalytical chemistry,"['D000818', 'D015415', 'D002364', 'D002851', 'D003611', 'D005504', 'D007768', 'D007782', 'D008892', 'D010455', 'D053719']","['Animals', 'Biomarkers', 'Caseins', 'Chromatography, High Pressure Liquid', 'Dairy Products', 'Food Analysis', 'Lactalbumin', 'Lactoglobulins', 'Milk', 'Peptides', 'Tandem Mass Spectrometry']",Selection of possible marker peptides for the detection of major ruminant milk proteins in food by liquid chromatography-tandem mass spectrometry.,"[None, 'Q000737', 'Q000032', None, 'Q000032', None, 'Q000032', 'Q000032', 'Q000737', 'Q000737', None]","[None, 'chemistry', 'analysis', None, 'analysis', None, 'analysis', 'analysis', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21107975,2011,0,0,,no cocoa -0.46,14582980,"Of three different solvents (acetone, ethanol, and methanol) mixed with water and acetic acid, the acetone/water/acetic acid mixture (70:28:2, v/v) proved to be best for extracting dark-chocolate procyanidins. High-performance liquid chromatography coupled with electrospray ionization mass spectrometry (HPLC-MS-ESI) was further used to identify oligomers found in the extract. After HPLC fraction collection, the reduction power of flavanoid fractions was measured in the AAPH [2,2'-azobis(2-amidinopropane)dihydrochloride] assay, where oxidation of linoleic acid is induced in an aqueous dispersion. Even expressed in relative monomeric efficiency units, the oxidation-inhibiting power of polymerized oligomers is much stronger than that of monomers. A comparison with 10 usual antioxidants indicated that oligomers with three or more (epi)catechin units are by far the most efficient.",Journal of agricultural and food chemistry,"['D019342', 'D000096', 'D000578', 'D000975', 'D044946', 'D002099', 'D002392', 'D002851', 'D000431', 'D044948', 'D019787', 'D000432', 'D010936', 'D044945', 'D012997', 'D021241', 'D014867']","['Acetic Acid', 'Acetone', 'Amidines', 'Antioxidants', 'Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Ethanol', 'Flavonols', 'Linoleic Acid', 'Methanol', 'Plant Extracts', 'Proanthocyanidins', 'Solvents', 'Spectrometry, Mass, Electrospray Ionization', 'Water']",Effect of the number of flavanol units on the antioxidant activity of procyanidin fractions isolated from chocolate.,"[None, None, 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', None, None, 'Q000032', 'Q000737', None, 'Q000737', None, None, None, None]","[None, None, 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', None, None, 'analysis', 'chemistry', None, 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/14582980,2004,0,0,, -0.45,28924329,"A computational tool was developed to facilitate proanthocyanidin analysis using data collected by ultra-high-performance liquid chromatography-diode array detection-high resolution accurate mass-mass spectrometry (UHPLC-DAD-HRAM-MS). Both identification and semi-quantitation of proanthocyanidins can be achieved by the developed computational tool. It can extract proanthocyanidin chromatographic peaks, deconvolute the isotopic patterns of A-type, B-type, and multi-charged proanthocyanidins ions, and predict proanthocyanidin structures. Proanthocyanidins were quantified by an external calibration curve of catechin and molar relative response factors (MRRFs) of proanthocyanidins. Quantitation results including concentrations of total proanthocyanidins, individual proanthocyanidins, and proanthocyanidins with different degrees of polymerization and different types of linkage were calculated by the program and exported into an Excel spreadsheet automatically. The program was applied to the analysis of seven plant materials including apple, cranberry, dark chocolate, grape seed extract, jujube, litchi, and mangosteen. The identification results were compared with the results obtained by manual processing. The program can greatly save the time needed for the data analysis of proanthocyanidins.","Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems",[],[],A Computational Tool for Accelerated Analysis of Oligomeric Proanthocyanidins in Plants.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/28924329,2018,0,0,,no cocoa -0.45,29169644,"Food allergy is a considerable heath problem, as undesirable contaminations by allergens during food production are still widespread and may be dangerous for human health. To protect the population, laboratories need to develop reliable analytical methods in order to detect allergens in various food products. Currently, a large majority of allergen-related food recalls concern bakery products. It is therefore essential to detect allergens in unprocessed and processed foodstuffs. In this study, we developed a method for detecting ten allergens in complex (chocolate, ice cream) and processed (cookie, sauce) foodstuffs, based on ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS). Using a single protocol and considering a signal-to-noise ratio higher than 10 for the most abundant multiple reaction monitoring (MRM) transition, we were able to detect target allergens at 0.5mg/kg for milk proteins, 2.5mg/kg for peanut, hazelnut, pistachio, and cashew proteins, 3mg/kg for egg proteins, and 5mg/kg for soy, almond, walnut, and pecan proteins. The ability of the method to detect 10 allergens with a single protocol in complex and incurred food products makes it an attractive alternative to the ELISA method for routine laboratories.",Journal of chromatography. A,"['D000485', 'D000069956', 'D002851', 'D004527', 'D004797', 'D005504', 'D005512', 'D007054', 'D008894', 'D009754', 'D059629', 'D053719']","['Allergens', 'Chocolate', 'Chromatography, High Pressure Liquid', 'Egg Proteins', 'Enzyme-Linked Immunosorbent Assay', 'Food Analysis', 'Food Hypersensitivity', 'Ice Cream', 'Milk Proteins', 'Nuts', 'Signal-To-Noise Ratio', 'Tandem Mass Spectrometry']",Liquid chromatography coupled to tandem mass spectrometry for detecting ten allergens in complex and incurred foodstuffs.,"['Q000032', 'Q000032', 'Q000379', 'Q000032', None, 'Q000379', None, 'Q000032', 'Q000032', 'Q000737', None, None]","['analysis', 'analysis', 'methods', 'analysis', None, 'methods', None, 'analysis', 'analysis', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/29169644,2018,0,0,,no cocoa -0.45,9691293,"A HPLC method is described for the analysis of ochratoxin A at low-ppb levels in samples of artificially contaminated cocoa beans. The samples are extracted in a mixture of methanol-water containing ascorbic acid, adjusted to pH and evaporated to dryness. Samples in this state are then placed onto a Benchmate sample preparation workstation where C18 solid-phase extraction operations are performed. The resulting materials are evaporated to dryness and analyzed by reversed-phase HPLC with fluorescence detection. The method was evaluated for accuracy and precision with R.S.D.s for multiple injections of sample and standard calculated to 1.1% and 2.5% for sample and standard, respectively. Recoveries of ochratoxin A added to cocoa beans ranged from 87-106% over the range of the assay.",Journal of chromatography. A,"['D001322', 'D002099', 'D002851', 'D006863', 'D007202', 'D009183', 'D009793', 'D013050']","['Autoanalysis', 'Cacao', 'Chromatography, High Pressure Liquid', 'Hydrogen-Ion Concentration', 'Indicators and Reagents', 'Mycotoxins', 'Ochratoxins', 'Spectrometry, Fluorescence']",High-performance liquid chromatographic determination of ochratoxin A in artificially contaminated cocoa beans using automated sample clean-up.,"[None, 'Q000737', None, None, None, 'Q000032', 'Q000032', None]","[None, 'chemistry', None, None, None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/9691293,1998,0,0,,artificially contaminated -0.45,27554027,"Sensitive detection of food allergens is affected by food processing and foodstuff complexity. It is therefore a challenge to detect cross-contamination in food production that could endanger an allergic customer's life. Here we used ultra-high performance liquid chromatography coupled to tandem mass spectrometry for simultaneous detection of traces of milk (casein, whey protein), egg (yolk, white), soybean, and peanut allergens in different complex and/or heat-processed foodstuffs. The method is based on a single protocol (extraction, trypsin digestion, and purification) applicable to the different tested foodstuffs: chocolate, ice cream, tomato sauce, and processed cookies. The determined limits of quantitation, expressed in total milk, egg, peanut, or soy proteins (and not soluble proteins) per kilogram of food, are: 0.5mg/kg for milk (detection of caseins), 5mg/kg for milk (detection of whey), 2.5mg/kg for peanut, 5mg/kg for soy, 3.4mg/kg for egg (detection of egg white), and 30.8mg/kg for egg (detection of egg yolk). The main advantage is the ability of the method to detect four major food allergens simultaneously in processed and complex matrices with very high sensitivity and specificity. ",Journal of chromatography. A,"['D000485', 'D000818', 'D010367', 'D002645', 'D002851', 'D004531', 'D005504', 'D005506', 'D005511', 'D008892', 'D030262', 'D053719']","['Allergens', 'Animals', 'Arachis', 'Chickens', 'Chromatography, High Pressure Liquid', 'Eggs', 'Food Analysis', 'Food Contamination', 'Food Handling', 'Milk', 'Soybean Proteins', 'Tandem Mass Spectrometry']",Advances in ultra-high performance liquid chromatography coupled to tandem mass spectrometry for sensitive detection of several food allergens in complex and processed foodstuffs.,"['Q000737', None, 'Q000737', None, 'Q000379', None, 'Q000379', 'Q000032', None, 'Q000737', 'Q000737', 'Q000379']","['chemistry', None, 'chemistry', None, 'methods', None, 'methods', 'analysis', None, 'chemistry', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/27554027,2016,0,0,,no cocoa tested -0.45,29146359,"This study aimed to develop an analytical method for the determination of tryptophan and its derivatives in kynurenine pathway using tandem mass spectrometry in various fermented food products (bread, beer, red wine, white cheese, yoghurt, kefir and cocoa powder). The method entails an aqueous extraction and reversed phase chromatographic separation using pentafluorophenyl (PFP) column. It allowed quantitation of low ppb levels of tryptophan and its derivatives in different fermented food matrices. It was found that beer samples were found to contain kynurenine within the range of 28.7_±0.7__g/L and 86.3_±0.5__g/L. Moreover, dairy products (yoghurt, white cheese and kefir) contained kynurenine ranging from 30.3 to 763.8__g/kg d.w. Though bread samples analyzed did not contain kynurenic acid, beer and red wine samples as yeast-fermented foods were found to contain kynurenic acid. Among foods analyzed, cacao powder had the highest amounts of kynurenic acid (4486.2_±165.6__g/kgd.w), which is a neuroprotective compound.",Food chemistry,"['D001515', 'D002611', 'D002851', 'D056148', 'D043302', 'D000074421', 'D007736', 'D007737', 'D053719', 'D014364', 'D014920']","['Beer', 'Cheese', 'Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Cultured Milk Products', 'Fermented Foods', 'Kynurenic Acid', 'Kynurenine', 'Tandem Mass Spectrometry', 'Tryptophan', 'Wine']",Determination of tryptophan derivatives in kynurenine pathway in fermented foods using liquid chromatography tandem mass spectrometry.,"['Q000032', 'Q000032', 'Q000379', 'Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000379', 'Q000032', 'Q000032']","['analysis', 'analysis', 'methods', 'methods', 'analysis', 'analysis', 'analysis', 'analysis', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/29146359,2018,1,1,table 4 ,cocoa powder tryptophan concentration table -0.45,8069126,"The IUPAC Commission on Oils, Fats, and Derivatives undertook development of a method and collaborative study for the determination of lead in oils and fats by direct graphite furnace-atomic absorption spectrophotometric method. Various types of graphite furnaces were used with or without platform. Twenty-three collaborators from 12 countries participated in the study. The materials tested were edible oils (soybean oil) and fats (cocoa butter) containing lead at 3 concentration levels (low, medium, and high). Each level was represented by 2 batches provided in duplicate (blind coded), so that each collaborator received a total of 24 test samples. Collaborators were instructed to analyze each in duplicate and report both results. Twenty collaborators returned the results of the study. After data from laboratories that did not follow the instructions were excluded, only 16 sets of data were evaluated statistically. The method for determination of lead in oils and fats by direct graphite furnace-atomic absorption spectrophotometry has been adopted first action by AOAC INTERNATIONAL as an IUPAC-AOCS-AOAC method.",Journal of AOAC International,"['D005223', 'D007854', 'D010938', 'D015203', 'D013024', 'D013054']","['Fats', 'Lead', 'Plant Oils', 'Reproducibility of Results', 'Soybean Oil', 'Spectrophotometry, Atomic']",Direct graphite furnace-atomic absorption method for determination of lead in edible oils and fats: summary of collaborative study.,"['Q000737', 'Q000032', 'Q000737', None, 'Q000737', 'Q000379']","['chemistry', 'analysis', 'chemistry', None, 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/8069126,1994,,,, -0.44,16154731,"Our investigations deal with the identification and synthesis of volatile, odoriferous compounds contained in the exhaust gas of food factories and on the biodegradation of alkylpyrazines. Collection of odour emissions samples was performed with a gas sampler equipped with filter tubes containing the styrene-polymer SuperQ. After elution with solvents of different polarity, the extracts were analysed by GC/MS and chemical microreactions. Proposed structures were verified by comparison of analytical data with those of synthetic reference samples. Major components in the exhaust gas of a fat finishing factory were found to be aliphatic aldehydes, strongly dominated by hexanal. The identification of 1,2,3,3-tetramethylcyclohexene shows that for structural proof of target compounds the use of authentic reference samples is indispensable. In the exhaust gas from a chocolate factory, several carbonyl compounds and alkylated pyrazines could be identified. Biodegradation of the latter starts with hydrogenation at the nucleus.","Waste management (New York, N.Y.)","['D002099', 'D002623', 'D004784', 'D019649', 'D008401', 'D009812', 'D009930', 'D014835', 'D014866']","['Cacao', 'Chemistry Techniques, Analytical', 'Environmental Monitoring', 'Food Industry', 'Gas Chromatography-Mass Spectrometry', 'Odorants', 'Organic Chemicals', 'Volatilization', 'Waste Products']","Identification, structure elucidation, and synthesis of volatile compounds in the exhaust gas of food factories.","['Q000737', 'Q000379', 'Q000379', None, None, 'Q000032', 'Q000032', None, 'Q000032']","['chemistry', 'methods', 'methods', None, None, 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16154731,2006,0,0,, -0.44,9675711,"The antioxidative substances contained in cacao liquor, which is one of the major ingredients of chocolate, were separated by column chromatography and high-performance liquid chromatography. Three major compounds were purified and two of them were identified by 1H, 13C NMR and mass spectra as (-)-epicatechin (EC) and (+)-catechin (CA). Their antioxidative activity was measured by monitoring the peroxide value of linoleic acid and the thiobarbituric acid-reactive substance values of erythrocyte ghost membranes and microsomes. EC and CA had strong antioxidative effects in all three methods, but one unidentified peak was found to be less effective. Additionally, we analyzed the polyphenol concentration of cacao liquor extractions produced in several countries. The total polyphenol concentration was 7.0 to 13.0%, catechin concentration was 0.31 to 0.49%, and epicatechin concentration was 0.35 to 1.68% in the extractions. It is believed that chocolate is stable against oxidative deterioration on account of the presence of these polyphenolic compounds, and it is also expected to have a protective role against lipid peroxidation in living systems.",Journal of nutritional science and vitaminology,"['D000434', 'D000818', 'D000975', 'D002099', 'D002392', 'D002851', 'D004910', 'D005419', 'D019787', 'D015227', 'D009682', 'D013058', 'D008862', 'D010084', 'D010636', 'D011108', 'D051381', 'D017392']","['Alcoholic Beverages', 'Animals', 'Antioxidants', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Erythrocyte Membrane', 'Flavonoids', 'Linoleic Acid', 'Lipid Peroxidation', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Microsomes, Liver', 'Oxidation-Reduction', 'Phenols', 'Polymers', 'Rats', 'Thiobarbituric Acid Reactive Substances']",The antioxidative substances in cacao liquor.,"['Q000032', None, 'Q000302', None, 'Q000737', None, 'Q000378', None, 'Q000378', 'Q000187', None, None, 'Q000378', None, 'Q000032', 'Q000032', None, 'Q000378']","['analysis', None, 'isolation & purification', None, 'chemistry', None, 'metabolism', None, 'metabolism', 'drug effects', None, None, 'metabolism', None, 'analysis', 'analysis', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/9675711,1998,,,, -0.44,21698686,"The water-soluble protein profile of the seeds of green, red, and yellow Theobroma cacao L. fruits has been determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF-MS). The seeds were powdered under liquid nitrogen and defatted. The residues were dialyzed and lyophilized. The obtained samples were suspended in the matrix solution of sinapinic acid. The obtained MALDI mass spectra showed the presence of a wide number of proteins with molecular weight ranging from 8000 to 13,000 Da and a cluster of peaks centered at 21,000 Da that were attributed to albumin. The abundance of this peak was found to depend on the different portion of the seed (husk, apical and cortical parts); however, the MALDI mass spectra obtained from the different varieties of cocoa were practically superimposable. Changes in the protein profiles were also observed after the cocoa seeds were treated by fermentation and roasting, which are processes usually employed for the commercial production of cocoa.",Rapid communications in mass spectrometry : RCM,"['D002099', 'D003373', 'D006358', 'D010936', 'D010940', 'D012639', 'D019032']","['Cacao', 'Coumaric Acids', 'Hot Temperature', 'Plant Extracts', 'Plant Proteins', 'Seeds', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization']",The protein profile of Theobroma cacao L. seeds as obtained by matrix-assisted laser desorption/ionization mass spectrometry.,"['Q000737', 'Q000737', None, 'Q000737', 'Q000032', 'Q000737', 'Q000379']","['chemistry', 'chemistry', None, 'chemistry', 'analysis', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/21698686,2011,,,, -0.44,19188605,"Chemical analyses of organic residues in fragments of ceramic vessels from Pueblo Bonito in Chaco Canyon, New Mexico, reveal theobromine, a biomarker for cacao. With an estimated 800 rooms, Pueblo Bonito is the largest archaeological site in Chaco Canyon and was the center of a large number of interconnected towns and villages spread over northwestern New Mexico. The cacao residues come from pieces of vessels that are likely cylinder jars, special containers occurring almost solely at Pueblo Bonito and deposited in caches at the site. This first known use of cacao drinks north of the Mexican border indicates exchange with cacao cultivators in Mesoamerica in a time frame of about A.D. 1000-1125. The association of cylinder jars and cacao beverages suggests that the Chacoan ritual involving the drinking of cacao was tied to Mesoamerican rituals incorporating cylindrical vases and cacao. The importance of Pueblo Bonito within the Chacoan world likely lies in part with the integration of Mesoamerican ritual, including critical culinary ingredients.",Proceedings of the National Academy of Sciences of the United States of America,"['D001106', 'D001628', 'D002099', 'D003466', 'D005843', 'D049691', 'D006801', 'D007198', 'D013058', 'D009516', 'D010164', 'D012931']","['Archaeology', 'Beverages', 'Cacao', 'Cultural Characteristics', 'Geography', 'History, Medieval', 'Humans', 'Indians, North American', 'Mass Spectrometry', 'New Mexico', 'Paleopathology', 'Social Environment']",Evidence of cacao use in the Prehispanic American Southwest.,"[None, None, 'Q000737', None, None, None, None, None, None, None, None, None]","[None, None, 'chemistry', None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19188605,2009,0,0,,no cocoa -0.44,9829045,"The level of styrene migration from polystyrene cups was monitored in different food systems including: water, milk (0.5, 1.55 and 3.6% fat), cold beverages (apple juice, orange juice, carbonated water, cola, beer and chocolate drink), hot beverages (tea, coffee, chocolate and soup (0.0, 0.5, 1, 2, and 3.6% fat), take away foods (yogurt, jelly, pudding and ice-cream), as well as aqueous food simulants (3% acetic acid, 15, 50, and 100% ethanol) and olive oil. Styrene migration was found to be strongly dependent upon the fat content and storage temperature. Drinking water gave migration values considerably lower than all of the fatty foods. Ethanol at 15% showed a migration level equivalent to milk or soup containing 3.6% fat. Maximum observed migration for cold or hot beverages and take-away foods was 0.025% of the total styrene in the cup. Food simulants were responsible for higher migration (0.37% in 100% ethanol). A total of 60 food samples (yogurt, rice with milk, fromage, biogardes, and cheese) packed in polystyrene containers were collected from retail markets in Belgium, Germany, and the Netherlands. The level of styrene detected in the foods was always fat dependent.",Food additives and contaminants,"['D001628', 'D002851', 'D003297', 'D004041', 'D005502', 'D005503', 'D005506', 'D018857', 'D006801', 'D011137', 'D013343', 'D013696']","['Beverages', 'Chromatography, High Pressure Liquid', 'Cooking and Eating Utensils', 'Dietary Fats', 'Food', 'Food Additives', 'Food Contamination', 'Food Packaging', 'Humans', 'Polystyrenes', 'Styrenes', 'Temperature']",Polystyrene cups and containers: styrene migration.,"[None, None, None, None, None, None, 'Q000032', None, None, None, 'Q000032', None]","[None, None, None, None, None, None, 'analysis', None, None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/9829045,1998,,,, -0.44,28070080,"The temperature thawing, as called tempering, of triacylglycerols (TAGs) is an important processing method in food productions, such as chocolates, cream, confections, and spreads. Especially, melt-mediation by temperature thawing is famous in chocolate production for controlling the polymorphic crystalline forms and accelerating crystallization. In the present study, we investigated the _±-melt structure of 1,3-dipalmitoyl-2-oleoyl-sn-glycerol (POP), one of the major continuants of cacao butter, under a phase transition from its melt to __-crystal with in-situ attenuated total reflection-infrared (ATR-IR) spectroscopy. The differential IR spectrum between _±-melt via temperature thawing (_±-melt mediation) and melt via simple cooling revealed that crystal-like local ordered structures remained in part in the _±-melt, and that they acted as nuclei for a rapid phase transition to the __-crystal. The changes to the __-crystal occur in the local ordered structures at first from the glycerol moiety to the acyl chains in the crystallization, providing an important suggestion concerning the mechanism for the acceleration of crystallization to the __-form via _±-melt mediation.",Analytical sciences : the international journal of the Japan Society for Analytical Chemistry,"['D013055', 'D044366', 'D014280']","['Spectrophotometry, Infrared', 'Transition Temperature', 'Triglycerides']","_±-Melt Structure of 1,3-Dipalmitoyl-2-oleoyl-sn-glycerol (POP) under a Thermal Thawing Process Studied by Infrared Spectroscopy.","[None, None, 'Q000737']","[None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28070080,2018,0,0,, -0.44,27613945,"Most electronic cigarettes (e-cigarettes) contain a solution of propylene glycol/glycerin and nicotine, as well as flavors. E-cigarettes and their associated e-liquids are available in numerous flavor varieties. A subset of the flavor varieties include coffee, tea, chocolate, and energy drink, which, in beverage form, are commonly recognized sources of caffeine. Recently, some manufacturers have begun marketing e-liquid products as energy enhancers that contain caffeine as an additive.",Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco,"['D002110', 'D000069956', 'D003069', 'D066300', 'D061215', 'D005421', 'D008401']","['Caffeine', 'Chocolate', 'Coffee', 'Electronic Nicotine Delivery Systems', 'Energy Drinks', 'Flavoring Agents', 'Gas Chromatography-Mass Spectrometry']","Caffeine Concentrations in Coffee, Tea, Chocolate, and Energy Drink Flavored E-liquids.","['Q000032', 'Q000032', 'Q000737', None, 'Q000032', 'Q000032', 'Q000379']","['analysis', 'analysis', 'chemistry', None, 'analysis', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/27613945,2017,0,0,,no cocoa tested -0.43,7228254,"Average-sized portions of a variety of food products were reacted with nitrite under realistically simulated gastric conditions. The aqueous incubation medium contained sodium nitrite (10 mg/l) and potassium thiocyanate to mimic the incoming flux of saliva, as well as pepsin, sodium chloride and hydrochloric acid, reflecting the composition of gastric juice. After incubation for 2 hr at 37 degrees C, volatile N-nitrosamines and N-nitrosamino acids were determined in the reaction mixtures. Nitrosodimethylamine (NDMA) was present in the incubation mixtures of smoked mackerel (8.5 micrograms per portion), canned herring (0.66 micrograms per portion) and beer (0.70 micrograms per 'portion'). Smaller amounts per portion, sometimes of other nitrosamines as well, were observed with canned salmon and anchovy, mustard, yoghurt and coffee brew. Negative results were obtained for canned tuna, soya sauce, ketchup, white bread, 'nasi goreng', tea brew and cocoa milk. Nitrosamino acids were detected in the reaction mixtures of smoked mackerel (58 micrograms per portion), soya sauce (24 micrograms per portion) and canned salmon (6.9 micrograms per portion) and in smaller amounts in those of canned herring, anchovy and cocoa milk. In order to reduce the number of analyses to be performed, most products have been studied only after incubation, so that the nitrosamines and nitrosamino acids found may already have been present -- wholly or partly -- in the original products, before incubation. Such is the case for part of the NDMA in the reaction mixture of smoked mackerel and for all the NDMA in beer. The toxicological implications of these findings remain to be established.",IARC scientific publications,"['D000596', 'D000818', 'D055598', 'D002621', 'D005502', 'D005511', 'D008401', 'D005750', 'D006801', 'D008954', 'D009573', 'D009602']","['Amino Acids', 'Animals', 'Chemical Phenomena', 'Chemistry', 'Food', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Gastric Juice', 'Humans', 'Models, Biological', 'Nitrites', 'Nitrosamines']",Formation of N-nitrosamine and N-nitrosamino acids from food products and nitrite under simulated gastric conditions.,"[None, None, None, None, None, None, None, 'Q000378', None, None, None, None]","[None, None, None, None, None, None, None, 'metabolism', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/7228254,1981,,,, -0.43,2954991,"A high-performance liquid chromatographic (HPLC) method has been developed to allow the determination of patulin, penicillic acid, sterigmatocystin and zearalenone in samples of cocoa beans. When this method is combined with a method that was reported earlier for the determination of ochratoxin A [W. J. Hurst and R. A. Martin, Jr., J. Chromatogr., 265 (1983) 353], it allows for the determination of five mycotoxins. Samples were extracted with an acidic acetonitrile solution, partitioned with hexane to remove fat interferences and then partitioned with chloroform to remove the toxin containing fraction. Interferences were removed by the use of a bonded phase column followed by the final HPLC determination step, which uses a cyano column with a hexane-1-propanol-acetic acid mobile phase with dual channel UV detection at 245 and 280 nm. The method exhibits good linearity, accuracy and precision.",Journal of chromatography,"['D002099', 'D002851', 'D005506', 'D009183', 'D010365', 'D010398', 'D010945', 'D013056', 'D013241', 'D015025']","['Cacao', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Mycotoxins', 'Patulin', 'Penicillic Acid', 'Plants, Edible', 'Spectrophotometry, Ultraviolet', 'Sterigmatocystin', 'Zearalenone']","High-performance liquid chromatographic determination of the mycotoxins patulin, penicillic acid, zearalenone and sterigmatocystin in artificially contaminated cocoa beans.","['Q000032', None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, 'Q000032', 'Q000032']","['analysis', None, 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/2954991,1987,,,, -0.43,20627308,"An analytical method for the determination of US EPA priority pollutant 16 polycyclic aromatic hydrocarbons (PAHs) in edible oil was developed by an isotope dilution gas chromatography-mass spectrometry (GC-MS). Extraction was performed with ultrasonication mode using acetonitrile as solvent, and subsequent clean-up was applied using narrow gel permeation chromatographic column. Three deuterated PAHs surrogate standards were used as internal standards for quantification and analytical quality control. The limits of quantification (LOQs) were globally below 0.5 ng/g, the recoveries were in the range of 81-96%, and the relative standard deviations (RSDs) were lower than 20%. Further trueness assessment of the method was also verified through participation in international cocoa butter proficiency test (T0638) organised by the FAPAS with excellent results in 2008. The results obtained with the described method were satisfying (z ___ 2). The method has been applied to determine PAH in real edible oil samples.",Journal of chromatography. A,"['D000097', 'D002850', 'D004042', 'D005224', 'D005504', 'D008401', 'D007554', 'D011084', 'D052616', 'D014465']","['Acetonitriles', 'Chromatography, Gel', 'Dietary Fats, Unsaturated', 'Fats, Unsaturated', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Isotopes', 'Polycyclic Aromatic Hydrocarbons', 'Solid Phase Extraction', 'Ultrasonics']",Ultrasonication extraction and gel permeation chromatography clean-up for the determination of polycyclic aromatic hydrocarbons in edible oil by an isotope dilution gas chromatography___mass spectrometry.,"[None, 'Q000379', 'Q000032', 'Q000737', 'Q000379', 'Q000379', None, 'Q000032', 'Q000379', 'Q000379']","[None, 'methods', 'analysis', 'chemistry', 'methods', 'methods', None, 'analysis', 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/20627308,2011,2,1,table 4, -0.43,25794741,"An improved micellar electrokinetic capillary chromatography method (MEKC) for the simultaneous determination of ten preservatives in ten different kinds of food samples was reported. An uncoated fused-silica capillary with 50 __m i.d. and 70 cm total length was used. Under the optimized conditions, the linear response was observed in the range of 1.2-200mg/L for the analytes. The limits of detection (LOD, S/N=3) and limits of quantitation (LOQ, S/N=10) ranging from 0.4 to 0.5mg/L and 1.2 to 1.5mg/L, respectively were obtained. The method was used for the determination of sorbic and benzoic acids in two FAPAS_‰ (Food Analysis Performance Assessment Scheme) proficiency test samples (jam and chocolate cake). The results showed that the current method with simple sample pretreatment and small reagent consumption could meet the needs for routine analysis of the ten preservatives in ten types of food products.",Food chemistry,"['D020374', 'D005504', 'D011310']","['Chromatography, Micellar Electrokinetic Capillary', 'Food Analysis', 'Preservatives, Pharmaceutical']",Simultaneous determination of ten preservatives in ten kinds of foods by micellar electrokinetic chromatography.,"['Q000379', 'Q000379', 'Q000494']","['methods', 'methods', 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/25794741,2015,0,0,,no cocoa -0.43,17852509,"Fatty acid compositions of frequently consumed foods in Turkey were analyzed by capillary gas chromatography with particular emphasis on trans fatty acids. The survey was carried out on 134 samples that were categorized as meat products, chocolates, bakery products and others. The meat products except chicken-based foods have trans fatty acids, arising as a result of ruminant activity, with an average content of 1.45 g/100 g fatty acids. The conjugated linoleic acid content of meat and chicken doner kebabs were found higher than other meat products. Chocolate samples contained trans fatty acids less than 0.17 g/100 g fatty acids, with the exceptional national product of chocolate bars and hazelnut cocoa cream (2.03 and 3.68 g/100 g fatty acids, respectively). Bakery products have the highest trans fatty acid contents and ranged from 0.99 to 17.77 g/100 g fatty acids. The average trans fatty acid contents of infant formula and ice-cream, which are milk-based products, were 0.79 and 1.50 g/100 g fatty acids, respectively. Among the analyzed foods, it was found that coffee whitener and powdered whipped topping had the highest saturated fatty acid contents, with an average content of 98.71 g/100 g fatty acids.",International journal of food sciences and nutrition,"['D000818', 'D001939', 'D002099', 'D002611', 'D002849', 'D003611', 'D005227', 'D005247', 'D006801', 'D007223', 'D007225', 'D008460', 'D008461', 'D008892', 'D044242', 'D014421']","['Animals', 'Bread', 'Cacao', 'Cheese', 'Chromatography, Gas', 'Dairy Products', 'Fatty Acids', 'Feeding Behavior', 'Humans', 'Infant', 'Infant Food', 'Meat', 'Meat Products', 'Milk', 'Trans Fatty Acids', 'Turkey']",Fatty acid composition of frequently consumed foods in Turkey with special emphasis on trans fatty acids.,"[None, 'Q000032', 'Q000737', 'Q000032', None, 'Q000032', 'Q000032', None, None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000737', 'Q000032', None]","[None, 'analysis', 'chemistry', 'analysis', None, 'analysis', 'analysis', None, None, None, 'analysis', 'analysis', 'analysis', 'chemistry', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/17852509,2008,0,0,, -0.43,29243642,"In 2006, the French Food Safety Agency (AFSSA) conducted the Second French Total Diet Study (TDS) to estimate dietary exposures to the main minerals and trace elements from 1319 samples of foods typically consumed by the French population. The foodstuffs were analysed by inductively coupled plasma-mass spectrometry (ICP-MS) after microwave-assisted digestion. Occurrence data for lithium, chromium, manganese, cobalt, nickel, copper, zinc, selenium and molybdenum were reported and compared with results from the previous French TDS. The results indicate that the food groups presenting the highest levels of these essential trace elements were ""tofu"" (for Li, Mn, Ni, Cu, Zn and Mo),""fish and fish products"" particularly ""shellfish"" (for Li, Co, Cu, Zn, Se and Mo), ""sweeteners, honey and confectionery"" particularly dark chocolate (for Cr, Mn, Co, Ni and Cu), ""cereals and cereal products"" (for Mn, Ni and Mo) and ""ice cream"" (for Cr, Co and Ni).",Food chemistry,[],[],"Li, Cr, Mn, Co, Ni, Cu, Zn, Se and Mo levels in foodstuffs from the Second French TDS.",[],[],https://www.ncbi.nlm.nih.gov/pubmed/29243642,2017,0,0,,no cocoa -0.43,18458064,"Otitis media caused by nontypeable Haemophilus influenzae (NTHi) is a common and recurrent bacterial infection of childhood. The structural variability and diversity of H. influenzae lipopolysaccharide (LPS) glycoforms are known to play a significant role in the commensal and disease-causing behavior of this pathogen. In this study, we determined LPS glycoform populations from NTHi strain 1003 during the course of experimental otitis media in the chinchilla model of infection by mass spectrometric techniques. Building on an established structural model of the major LPS glycoforms expressed by this NTHi strain in vitro (M. M_nsson, W. Hood, J. Li, J. C. Richards, E. R. Moxon, and E. K. Schweda, Eur. J. Biochem. 269:808-818, 2002), minor isomeric glycoform populations were determined by liquid chromatography multiple-step tandem electrospray mass spectrometry (LC-ESI-MS(n)). Using capillary electrophoresis ESI-MS (CE-ESI-MS), we determined glycoform profiles for bacteria from direct middle ear fluid (MEF) samples. The LPS glycan profiles were essentially the same when the MEF samples of 7 of 10 animals were passaged on solid medium (chocolate agar). LC-ESI-MS(n) provided a sensitive method for determining the isomeric distribution of LPS glycoforms in MEF and passaged specimens. To investigate changes in LPS glycoform distribution during the course of infection, MEF samples were analyzed at 2, 5, and 9 days postinfection by CE-ESI-MS following minimal passage on chocolate agar. As previously observed, sialic acid-containing glycoforms were detected during the early stages of infection, but a trend toward more-truncated and less-complex LPS glycoforms that lacked sialic acid was found as disease progressed.",Infection and immunity,"['D000818', 'D002682', 'D002853', 'D004195', 'D019075', 'D006192', 'D006193', 'D006801', 'D007536', 'D008070', 'D010033', 'D010034', 'D021241']","['Animals', 'Chinchilla', 'Chromatography, Liquid', 'Disease Models, Animal', 'Electrophoresis, Capillary', 'Haemophilus Infections', 'Haemophilus influenzae', 'Humans', 'Isomerism', 'Lipopolysaccharides', 'Otitis Media', 'Otitis Media with Effusion', 'Spectrometry, Mass, Electrospray Ionization']",Application of capillary electrophoresis mass spectrometry and liquid chromatography multiple-step tandem electrospray mass spectrometry to profile glycoform expression during Haemophilus influenzae pathogenesis in the chinchilla model of experimental otitis media.,"[None, None, 'Q000379', None, 'Q000379', 'Q000382', 'Q000378', None, None, 'Q000737', 'Q000382', 'Q000382', 'Q000379']","[None, None, 'methods', None, 'methods', 'microbiology', 'metabolism', None, None, 'chemistry', 'microbiology', 'microbiology', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/18458064,2008,0,0,,no cocoa -0.43,19489609,"Oxidative stress enhances pathological processes contributing to cancer, cardiovascular disease, and neurodegenerative diseases, and dietary antioxidants may counteract these deleterious processes. Because rapid methods to evaluate and compare food products for antioxidant benefits are needed, a new assay based on liquid chromatography-mass spectrometry (LC-MS) was developed for the identification and quantitative analysis of antioxidants in complex natural product samples such as food extracts. This assay is based on the comparison of electrospray LC-MS profiles of sample extracts before and after treatment with reactive oxygen species such as hydrogen peroxide or 2,2-diphenyl-1-picrylhydrazyl radical (DPPH). Using this assay, methanolic extracts of cocoa powder were analyzed, and procyanidins were found to be the most potent antioxidant species. These species were identified using LC-MS, LC-MS/MS, accurate mass measurement, and comparison with reference standards. Furthermore, LC-MS was used to determine the levels of these species in cocoa samples. Catechin and epicatechin were the most abundant antioxidants followed by their dimers and trimers. The most potent antioxidants in cocoa were trimers and dimers of catechin and epicatechin, such as procyanidin B2, followed by catechin and epicatechin. This new LC-MS assay facilitates the rapid identification and then the determination of the relative antioxidant activities of individual antioxidant species in complex natural product samples and food products such as cocoa.",Journal of agricultural and food chemistry,"['D000975', 'D001713', 'D002099', 'D002392', 'D002853', 'D006861', 'D013058', 'D010084', 'D010851', 'D044945', 'D021241']","['Antioxidants', 'Biphenyl Compounds', 'Cacao', 'Catechin', 'Chromatography, Liquid', 'Hydrogen Peroxide', 'Mass Spectrometry', 'Oxidation-Reduction', 'Picrates', 'Proanthocyanidins', 'Spectrometry, Mass, Electrospray Ionization']",Screening antioxidants using LC-MS: case study with cocoa.,"['Q000032', 'Q000737', 'Q000737', 'Q000032', None, 'Q000737', None, None, 'Q000737', 'Q000032', None]","['analysis', 'chemistry', 'chemistry', 'analysis', None, 'chemistry', None, None, 'chemistry', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/19489609,2009,2,1,Fig 3, -0.43,17955976,"A collaborative trial was conducted to validate an analytical approach comprising method procedures for determination of milk fat and the detection and quantification of cocoa butter equivalents (CBEs) in milk chocolate. The whole approach is based on (1) comprehensive databases covering the triacylglycerol composition of a wide range of authentic milk fat, cocoa butter, and CBE samples and 947 gravimetrically prepared mixtures thereof; (2) the availability of a certified cocoa butter reference material for calibration; (3) an evaluation algorithm, which allows reliable quantitation of the milk fat content in chocolate; (4) a subsequent correction to take account of the triacylglycerols derived from milk fat; (5) mathematical expressions to detect the presence of CBEs in milk chocolate; and (6) a multivariate statistical formula to quantitate the amount of CBEs in milk chocolate. Twelve laboratories participated in the validation study. CBE admixtures were detected down to a level of 0.5 g CBE/100 g milk chocolate, without false-positive or -negative results. The applied quantitation model performed well at the statutory limit of 5% CBE addition to milk chocolate, with a prediction error of 0.7%, and HorRat values ranging from 0.8 to 1.5. The relative standard deviation for reproducibility (RSDR) values for quantitation of CBEs in analyses of chocolate fat solutions ranged from 2.2 to 3.8% and for analyses of real chocolate samples, from 4.1 to 4.7%, demonstrating that the whole approach, based solely on chocolate fat blends, is applicable to real milk chocolate samples.",Journal of AOAC International,"['D000818', 'D002099', 'D002138', 'D002623', 'D002849', 'D002853', 'D004041', 'D005223', 'D005504', 'D006112', 'D008892', 'D008962', 'D015203', 'D014280']","['Animals', 'Cacao', 'Calibration', 'Chemistry Techniques, Analytical', 'Chromatography, Gas', 'Chromatography, Liquid', 'Dietary Fats', 'Fats', 'Food Analysis', 'Gravitation', 'Milk', 'Models, Theoretical', 'Reproducibility of Results', 'Triglycerides']",Gas-liquid chromatographic determination of milk fat and cocoa butter equivalents in milk chocolate: interlaboratory study.,"[None, 'Q000737', None, 'Q000379', 'Q000379', 'Q000379', 'Q000378', 'Q000378', 'Q000379', None, 'Q000378', None, None, 'Q000032']","[None, 'chemistry', None, 'methods', 'methods', 'methods', 'metabolism', 'metabolism', 'methods', None, 'metabolism', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17955976,2007,,,, -0.43,17899033,"A liquid chromatography-electrospray-tandem mass spectrometry (LC-ESI-MS-MS) method based on the detection of biomarker peptides from allergenic proteins was devised for confirming and quantifying peanut allergens in foods. Peptides obtained from tryptic digestion of Ara h 2 and Ara h 3/4 proteins were identified and characterized by LC-MS and LC-MS-MS with a quadrupole-time of flight mass analyzer. Four peptides were chosen and investigated as biomarkers taking into account their selectivity, the absence of missed cleavages, the uniform distribution in the Ara h 2 and Ara h 3/4 protein isoforms together with their spectral features under ESI-MS-MS conditions, and good repeatability of LC retention time. Because of the different expression levels, the selection of two different allergenic proteins was proved to be useful in the identification and univocal confirmation of the presence of peanuts in foodstuffs. Using rice crisp and chocolate-based snacks as model food matrix, an LC-MS-MS method with triple quadrupole mass analyzer allowed good detection limits to be obtained for Ara h 2 (5 microg protein g(-1) matrix) and Ara h 3/4 (1 microg protein g(-1) matrix). Linearity of the method was established in the 10-200 microg g(-1) range of peanut proteins in the food matrix investigated. Method selectivity was demonstrated by analyzing tree nuts (almonds, pecan nuts, hazelnuts, walnuts) and food ingredients such as milk, soy beans, chocolate, cornflakes, and rice crisp.",Analytical and bioanalytical chemistry,"['D055516', 'D000485', 'D052179', 'D015415', 'D002853', 'D005504', 'D006023', 'D010446', 'D010940', 'D020033', 'D055314', 'D053719', 'D013997']","['2S Albumins, Plant', 'Allergens', 'Antigens, Plant', 'Biomarkers', 'Chromatography, Liquid', 'Food Analysis', 'Glycoproteins', 'Peptide Fragments', 'Plant Proteins', 'Protein Isoforms', 'Seed Storage Proteins', 'Tandem Mass Spectrometry', 'Time Factors']",Use of specific peptide biomarkers for quantitative confirmation of hidden allergenic peanut proteins Ara h 2 and Ara h 3/4 for food control by liquid chromatography-tandem mass spectrometry.,"[None, 'Q000032', None, 'Q000032', 'Q000379', 'Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, 'Q000379', None]","[None, 'analysis', None, 'analysis', 'methods', 'methods', 'analysis', 'analysis', 'analysis', 'analysis', None, 'methods', None]",,2008,0,0,, -0.43,24574140,"Although proanthocyanidins (PACs) modify dentin, the effectiveness of different PAC sources and the correlation with their specific chemical composition are still unknown. This study describes the chemical profiling of natural PAC-rich extracts from 7 plants using ultra high pressure/performance liquid chromatography (UHPLC) to determine the overall composition of these extracts and, in parallel, comprehensively evaluate their effect on dentin properties. The total polyphenol content of the extracts was determined (as gallic acid equivalents) using Folin-Ciocalteau assays. Dentin biomodification was assessed by the modulus of elasticity, mass change, and resistance to enzymatic biodegradation. Extracts with a high polyphenol and PAC content from Vitis vinifera, Theobroma cacao, Camellia sinensis, and Pinus massoniana induced a significant increase in modulus of elasticity and mass. The UHPLC analysis showed the presence of multiple types of polyphenols, ranging from simple phenolic acids to oligomeric PACs and highly condensed tannins. Protective effect against enzymatic degradation was observed for all experimental groups; however, statistically significant differences were observed between plant extracts. The findings provide clear evidence that the dentin bioactivities of PACs are source dependent, resulting from a combination of concentration and specific chemical constitution of the complex PAC mixtures. ",Journal of dental research,"['D000975', 'D028023', 'D002099', 'D028241', 'D002851', 'D032904', 'D002935', 'D017364', 'D003804', 'D055119', 'D005707', 'D056604', 'D006801', 'D028223', 'D024301', 'D010936', 'D059808', 'D044945', 'D020011', 'D012639', 'D013662', 'D027843']","['Antioxidants', 'Arecaceae', 'Cacao', 'Camellia sinensis', 'Chromatography, High Pressure Liquid', 'Cinnamomum aromaticum', 'Cinnamomum zeylanicum', 'Collagenases', 'Dentin', 'Elastic Modulus', 'Gallic Acid', 'Grape Seed Extract', 'Humans', 'Pinus', 'Plant Bark', 'Plant Extracts', 'Polyphenols', 'Proanthocyanidins', 'Protective Agents', 'Seeds', 'Tea', 'Vitis']",Dentin biomodification potential depends on polyphenol source.,"['Q000494', 'Q000737', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000494', 'Q000033', None, 'Q000032', 'Q000494', None, 'Q000737', 'Q000737', 'Q000032', 'Q000032', 'Q000032', 'Q000494', 'Q000737', 'Q000737', 'Q000737']","['pharmacology', 'chemistry', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'pharmacology', 'anatomy & histology', None, 'analysis', 'pharmacology', None, 'chemistry', 'chemistry', 'analysis', 'analysis', 'analysis', 'pharmacology', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24574140,2014,0,0,, -0.43,1874696,"Jute fibers are treated with about 5-7% of a high boiling mineral oil fraction (""batching oil"") to render them flexible for making fabrics. Foods transported in jute bags are contaminated by this batching oil. A method involving automated on-line LC-GC is described for determining these hydrocarbons in various foods. Complete transfer of the LC fraction to GC is presupposed for obtaining the required sensitivity. Results are given for nuts, coffee, cocoa products, and rice. Contamination ranged between about 5 and 500 ppm.",Journal - Association of Official Analytical Chemists,"['D002099', 'D002849', 'D002853', 'D003069', 'D005506', 'D008899', 'D009754', 'D012275']","['Cacao', 'Chromatography, Gas', 'Chromatography, Liquid', 'Coffee', 'Food Contamination', 'Mineral Oil', 'Nuts', 'Oryza']",Determination of food contamination by mineral oil from jute sacks using coupled LC-GC.,"['Q000032', None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032']","['analysis', None, None, 'analysis', 'analysis', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/1874696,1991,,,, -0.43,23022488,"The hemibiotrophic basidiomycete fungus Moniliophthora perniciosa, the causal agent of Witches' broom disease (WBD) in cacao, is able to grow on methanol as the sole carbon source. In plants, one of the main sources of methanol is the pectin present in the structure of cell walls. Pectin is composed of highly methylesterified chains of galacturonic acid. The hydrolysis between the methyl radicals and galacturonic acid in esterified pectin, mediated by a pectin methylesterase (PME), releases methanol, which may be decomposed by a methanol oxidase (MOX). The analysis of the M. pernciosa genome revealed putative mox and pme genes. Real-time quantitative RT-PCR performed with RNA from mycelia grown in the presence of methanol or pectin as the sole carbon source and with RNA from infected cacao seedlings in different stages of the progression of WBD indicate that the two genes are coregulated, suggesting that the fungus may be metabolizing the methanol released from pectin. Moreover, immunolocalization of homogalacturonan, the main pectic domain that constitutes the primary cell wall matrix, shows a reduction in the level of pectin methyl esterification in infected cacao seedlings. Although MOX has been classically classified as a peroxisomal enzyme, M. perniciosa presents an extracellular methanol oxidase. Its activity was detected in the fungus culture supernatants, and mass spectrometry analysis indicated the presence of this enzyme in the fungus secretome. Because M. pernciosa possesses all genes classically related to methanol metabolism, we propose a peroxisome-independent model for the utilization of methanol by this fungus, which begins with the extracellular oxidation of methanol derived from the demethylation of pectin and finishes in the cytosol.",Fungal genetics and biology : FG & B,"['D000363', 'D000429', 'D000595', 'D002099', 'D005110', 'D005656', 'D015966', 'D000432', 'D008969', 'D010368', 'D010935', 'D021381', 'D016415']","['Agaricales', 'Alcohol Oxidoreductases', 'Amino Acid Sequence', 'Cacao', 'Extracellular Space', 'Fungal Proteins', 'Gene Expression Regulation, Fungal', 'Methanol', 'Molecular Sequence Data', 'Pectins', 'Plant Diseases', 'Protein Transport', 'Sequence Alignment']",A potential role for an extracellular methanol oxidase secreted by Moniliophthora perniciosa in Witches' broom disease in cacao.,"['Q000201', 'Q000737', None, 'Q000382', 'Q000201', 'Q000737', None, 'Q000378', None, 'Q000378', 'Q000382', None, None]","['enzymology', 'chemistry', None, 'microbiology', 'enzymology', 'chemistry', None, 'metabolism', None, 'metabolism', 'microbiology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/23022488,2013,0,0,,gene -0.43,19750020,"In this study, 162 samples were analysed for monomer styrene content with using high performance liquid chromatography (HPLC) method in hot tea, milk, cocoa milk. The monomer styrene content, expressed in microg/l of drink and the level of migration of styrene monomer were varied from 0.61 to 8.15 for hot tea, from 0.65 to 8.30 for hot milk, from 0.71 to 8.65 for hot cocoa milk in GPPS (general purpose polystyrene), from 0.48 to 6.85 for hot tea, from 0.61 to 7.65 for hot milk, from 0.72 to 7.78 for hot cocoa milk in HIPS (high performance polystyrene) cups in different temperatures and times. The estimated limit of detection of (HPLC) method for all samples was 0.001 mg/kg. There is linear regression for styrene monomer from 1 to 10 ng/ml. Several samples spiked with a known amount of styrene monomer. The results of the recovery in study for styrene monomer were determinate to be mean, 96.1 +/- 1.92 to 99.7 +/- 1.15%. The results of this study indicate that styrene monomer from polystyrene disposable into hot and fat drinks was migrated and this migration was highly dependent on fat content and temperature of drinks. The derived concentration of styrene monomer in this study was above the EPA (Environmental protection agency) recommended level, especially in MCLG (Maximum contaminant level goal) standard. More study is needed to further elucidate this finding.",Toxicology mechanisms and methods,"['D001628', 'D002849', 'D002851', 'D004864', 'D005506', 'D006358', 'D011137', 'D013056']","['Beverages', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Equipment and Supplies', 'Food Contamination', 'Hot Temperature', 'Polystyrenes', 'Spectrophotometry, Ultraviolet']",Determination of migration monomer styrene from GPPS (general purpose polystyrene) and HIPS (high impact polystyrene) cups to hot drinks.,"['Q000032', None, None, None, None, None, 'Q000032', None]","['analysis', None, None, None, None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/19750020,2010,,,, -0.42,3680113,"Vitamin D in different fortified foods is determined by using liquid chromatography (LC). Sample preparation is described for fortified skim milk, infant formulas, chocolate drink powder, and diet food. The procedure involves 2 main steps: saponification of the sample followed by extraction, and quantitation by LC analysis. Depending on the sample matrix, additional steps are necessary, i.e., enzymatic digestion for hydrolyzing the starch in the sample and cartridge purification before LC injection. An isocratic system consisting of 0.5% water in methanol (v/v) on two 5 microns ODS Hypersil, 12 X 0.4 cm id columns is used. Recovery of vitamin D added to unfortified skim milk is 98%. The results of vitamin D determination in homogenized skim milk, fortified milk powder, fortified milk powder with soybean, chocolate drink powder, and sports diet food are given.",Journal - Association of Official Analytical Chemists,"['D000818', 'D002099', 'D002853', 'D002523', 'D005504', 'D005526', 'D007202', 'D007225', 'D008892', 'D013056', 'D014807']","['Animals', 'Cacao', 'Chromatography, Liquid', 'Edible Grain', 'Food Analysis', 'Food, Formulated', 'Indicators and Reagents', 'Infant Food', 'Milk', 'Spectrophotometry, Ultraviolet', 'Vitamin D']",Sample preparation and liquid chromatographic determination of vitamin D in food products.,"[None, 'Q000032', None, 'Q000032', None, 'Q000032', None, 'Q000032', 'Q000032', None, 'Q000032']","[None, 'analysis', None, 'analysis', None, 'analysis', None, 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/3680113,1987,,,, -0.42,12124611,"The Maya archaeological site at Colha in northern Belize, Central America, has yielded several spouted ceramic vessels that contain residues from the preparation of food and beverages. Here we analyse dry residue samples by using high-performance liquid chromatography coupled to atmospheric-pressure chemical-ionization mass spectrometry, and show that chocolate (Theobroma cacao) was consumed by the Preclassic Maya as early as 600 bc, pushing back the earliest chemical evidence of cacao use by some 1,000 years. Our application of this new and highly sensitive analytical technique could be extended to the identification of other ancient foods and beverages.",Nature,"['D001106', 'D001531', 'D001628', 'D002099', 'D002516', 'D002851', 'D049690', 'D013058', 'D013805']","['Archaeology', 'Belize', 'Beverages', 'Cacao', 'Ceramics', 'Chromatography, High Pressure Liquid', 'History, Ancient', 'Mass Spectrometry', 'Theobromine']",Cacao usage by the earliest Maya civilization.,"['Q000379', None, 'Q000266', 'Q000737', 'Q000266', None, None, None, 'Q000032']","['methods', None, 'history', 'chemistry', 'history', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12124611,2002,,,, -0.42,9410091,"The quality of three vegetable fats (cocoa butter and two commercial fats) and three roasted nut oils (almond, hazelnut and peanut) used as raw material in the chocolate products manufacturing was studied. The hydroperoxide content, oxidative stability and fatty acid composition were determined and its health repercussion by atherogenicity and thrombogenicity indexes. Two commercial fats and cocoa butter showed higher oxidative stability, atherogenic and thrombogenic properties than oils because of its different fatty acid profiles. Peroxide value was a low reliability parameter of raw material shelf live. Rancimat presented a good correlation with the unsaturation index of different fats and oils, it was a better index than peroxide value. In the chocolate products manufacturing it would be advisable a good raw material selection and formulation in order to get a balance between technological properties, organoleptic qualities and the influence on the health. Those raw material with less primary oxidation and higher oxidative stability were also those of higher atherogenicity and thrombogenicity indexes.",Nutricion hospitalaria,"['D000704', 'D002182', 'D002845', 'D004041', 'D004042', 'D005224', 'D005227', 'D008962', 'D010545', 'D010938']","['Analysis of Variance', 'Candy', 'Chromatography', 'Dietary Fats', 'Dietary Fats, Unsaturated', 'Fats, Unsaturated', 'Fatty Acids', 'Models, Theoretical', 'Peroxides', 'Plant Oils']",[Physico-chemical characteristics of different types of vegetable fats and oils used in the manufacture of candies].,"[None, 'Q000032', None, 'Q000009', 'Q000009', 'Q000009', 'Q000009', None, 'Q000032', 'Q000009']","[None, 'analysis', None, 'adverse effects', 'adverse effects', 'adverse effects', 'adverse effects', None, 'analysis', 'adverse effects']",https://www.ncbi.nlm.nih.gov/pubmed/9410091,1997,,,, -0.42,29389646,"Chocolate is a popular food bearing a number of different classifications that are differentiated by proportions of cocoa solids, milk and cocoa butter. Literature brings evidence that chocolates with a high percentage of cocoa solids contribute to good health maintenance due to the presence of phenolic compounds. On the other hand, it is known that the productive process, including pre-processing, may influence the level of these substances in the finished product. Thus, accurate strategies to measure the levels of this class of molecules that can be highly adaptable throughout the manufacturing process are important to ensure high-quality products. Mass spectrometry is an analytical tool of high sensitivity and specificity that is leading the research in food analysis towards new directions. By using mass spectrometry imaging in direct food analysis, this contribution developed an effective methodology for comparatively establishing the levels of catechin/epicatechin as phenolics content markers for cocoa content in a series of commercial chocolates from a single manufacturer, rendering a versatile tool that can be applied in fast screening of cocoa content in finished products and during manufacturing.","Food research international (Ottawa, Ont.)",[],[],A fast semi-quantitative screening for cocoa content in chocolates using MALDI-MSI.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29389646,2018,0,0,, -0.42,15493674,"A European interlaboratory study was conducted to validate an analytical procedure for the detection and quantification of cocoa butter equivalents in cocoa butter and plain chocolate. In principle, the fat obtained from plain chocolate according to the Soxhlet principle is separated by high-resolution capillary gas chromatography into triacylglycerol fractions according to their acyl-C-numbers, and within a given number, also according to unsaturation. The presence of cocoa butter equivalents is detected by linear regression analysis applied to the relative proportions of the 3 main triacylglycerol fractions of the fat analyzed. The amount of the cocoa butter equivalent admixture is estimated by partial least-squares regression analysis applied to the relative proportions of the 5 main triacylglycerols. Cocoa butter equivalent admixtures were detected down to a level of 2% related to the fat phase, corresponding to 0.6% in chocolate (assumed fat content of chocolate, 30%), without false-positive or -negative results. By using a quantification model based on partial least-squares regression analysis, the predicted cocoa butter equivalent amounts were in close agreement with the actual values. The applied model performed well at the level of the statutory limit of 5% cocoa butter equivalent addition to chocolate with a prediction error of 0.6%, assuming a chocolate fat content of 30%.",Journal of AOAC International,"['D002099', 'D002849', 'D004041', 'D014280']","['Cacao', 'Chromatography, Gas', 'Dietary Fats', 'Triglycerides']",Method validation for detection and quantification of cocoa butter equivalents in cocoa butter and plain chocolate.,"['Q000737', None, 'Q000032', 'Q000032']","['chemistry', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15493674,2005,,,, -0.42,27622657,"According to European legislation, tobacco additives may not increase the toxicity or the addictive potency of the product, but there is an ongoing debate on how to reliably characterize and measure such properties. Further, too little is known on pyrolysis patterns of tobacco additives to assume that no additional toxicological risks need to be suspected. An on-line pyrolysis technique was used and coupled to gas chromatography-mass spectrometry (GC/MS) to identify the pattern of chemical species formed upon thermal decomposition of 19 different tobacco additives like raw cane sugar, licorice or cocoa. To simulate the combustion of a cigarette it was necessary to perform pyrolysis at inert conditions as well as under oxygen supply. All individual additives were pyrolyzed under inert or oxidative conditions at 350, 700 and 1000_C, respectively, and the formation of different toxicants was monitored. We observed the generation of vinyl acrylate, fumaronitrile, methacrylic anhydride, isobutyric anhydride and 3-buten-2-ol exclusively during pyrolysis of tobacco additives. According to the literature, these toxicants so far remained undetectable in tobacco or tobacco smoke. Further, the formation of 20 selected polycyclic aromatic hydrocarbons (PAHs) with molecular weights of up to 278Da was monitored during pyrolysis of cocoa in a semi-quantitative approach. It was shown that the adding of cocoa to tobacco had no influence on the relative amounts of the PAHs formed.",International journal of hygiene and environmental health,"['D031002', 'D000069956', 'D003069', 'D003257', 'D005421', 'D000067030', 'D008401', 'D006035', 'D006722', 'D006358', 'D009930', 'D010936', 'D053149', 'D018517', 'D000068242', 'D031786', 'D013213', 'D014026']","['Acer', 'Chocolate', 'Coffee', 'Consumer Product Safety', 'Flavoring Agents', 'Fruit and Vegetable Juices', 'Gas Chromatography-Mass Spectrometry', 'Glycyrrhiza', 'Honey', 'Hot Temperature', 'Organic Chemicals', 'Plant Extracts', 'Plant Gums', 'Plant Roots', 'Prunus domestica', 'Saccharum', 'Starch', 'Tobacco']",Oxidative and inert pyrolysis on-line coupled to gas chromatography with mass spectrometric detection: On the pyrolysis products of tobacco additives.,"[None, None, None, None, None, None, None, None, None, None, 'Q000032', None, None, None, None, None, None, None]","[None, None, None, None, None, None, None, None, None, None, 'analysis', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/27622657,2017,0,0,,tabacco -0.42,28064480,"Milk powder is an important ingredient in the confectionery industry, but its variable nature has consequences for the quality of the final confectionary product. This paper demonstrates that skim milk powders (SMP) produced using different (but typical) manufacturing processes, when used as ingredients in the manufacture of model white chocolates, had a significant impact on the sensory and volatile profiles of the chocolate. SMP was produced from raw bovine milk using either low or high heat treatment, and a model white chocolate was prepared from each SMP. A directional discrimination test with nave panelists showed that the chocolate prepared from the high heat SMP had more caramel/fudge character (p < 0.0001), and sensory profiling with an expert panel showed an increase in both fudge (p < 0.05) and condensed milk (p < 0.05) flavor. Gas chromatography (GC)-mass spectrometry and GC-olfactometry of both the SMPs and the model chocolates showed a concomitant increase in Maillard-derived volatiles which are likely to account for this change in flavor.",Journal of agricultural and food chemistry,"['D000818', 'D000069956', 'D005504', 'D005511', 'D008401', 'D006801', 'D008892', 'D009812', 'D064367', 'D011208', 'D013649', 'D055549']","['Animals', 'Chocolate', 'Food Analysis', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Milk', 'Odorants', 'Olfactometry', 'Powders', 'Taste', 'Volatile Organic Compounds']",Impact of the Skim Milk Powder Manufacturing Process on the Flavor of Model White Chocolate.,"[None, 'Q000032', 'Q000379', 'Q000379', None, None, 'Q000737', 'Q000032', 'Q000379', 'Q000737', None, 'Q000032']","[None, 'analysis', 'methods', 'methods', None, None, 'chemistry', 'analysis', 'methods', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28064480,2017,0,0,,chocolate -0.42,24444418,"There is a growing interest in studying the nutritional effects of complex diets. For such studies, measurement of dietary compliance is a challenge because the currently available compliance markers cover only limited aspects of a diet. In the present study, an untargeted metabolomics approach was used to develop a compliance measure in urine to distinguish between two dietary patterns. A parallel intervention study was carried out in which 181 participants were randomized to follow either a New Nordic Diet (NND) or an Average Danish Diet (ADD) for 6 months. Dietary intakes were closely monitored over the whole study period, and 24 h urine samples as well as weighed dietary records were collected several times during the study. The urine samples were analyzed by UPLC-qTOF-MS, and a partial least-squares discriminant analysis with feature selection was applied to develop a compliance model based on data from 214 urine samples. The optimized model included 52 metabolites and had a misclassification rate of 19% in a validation set containing 139 samples. The metabolites identified in the model were markers of individual foods such as citrus, cocoa-containing products, and fish as well as more general dietary traits such as high fruit and vegetable intake or high intake of heat-treated foods. It was easier to classify the ADD diet than the NND diet probably due to seasonal variation in the food composition of NND and indications of lower compliance among the NND subjects. In conclusion, untargeted metabolomics is a promising approach to develop compliance measures that cover the most important discriminant metabolites of complex diets. ",Journal of proteome research,"['D000293', 'D000328', 'D000368', 'D002957', 'D003299', 'D004032', 'D005247', 'D005260', 'D005396', 'D005638', 'D006801', 'D008297', 'D055432', 'D008875', 'D019032', 'D016482', 'D014675']","['Adolescent', 'Adult', 'Aged', 'Citrus', 'Cooperative Behavior', 'Diet', 'Feeding Behavior', 'Female', 'Fish Products', 'Fruit', 'Humans', 'Male', 'Metabolomics', 'Middle Aged', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Urinalysis', 'Vegetables']",Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern.,"[None, None, None, 'Q000737', None, 'Q000379', 'Q000523', None, 'Q000656', 'Q000737', None, None, 'Q000295', None, 'Q000379', None, 'Q000737']","[None, None, None, 'chemistry', None, 'methods', 'psychology', None, 'utilization', 'chemistry', None, None, 'instrumentation', None, 'methods', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24444418,2014,0,0,, -0.42,10554262,"The antioxidant components of cacao liquor, which is a major ingredient of chocolate, were isolated with column chromatography and high-performance liquid chromatography. Quercetin and its glucoside were identified by spectrometric methods. Clovamide and deoxyclovamide were characterized by (1)H and (13)C NMR and MS spectrometry. Their antioxidative activity was measured by peroxide value of linoleic acid and thiobarbituric acid reactive-substance value of erythrocyte ghost membranes and microsomes. In the bulk oil system, clovamide had the strongest antioxidative activity but was less active in the other experiments. In the case of the two hydrophilic systems, flavans such as quercetin and epicatechin were more potently effective than the glucosides. It is considered that chocolate is stable against oxidative deterioration due to the presence of these polyphenolic compounds.",Journal of agricultural and food chemistry,[],[],Antioxidative Polyphenols Isolated from Theobroma cacao.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/10554262,1999,0,0,, -0.42,16365716,"Volatiles from chocolate mediate upwind flight behavior in Ephestia cautella and Plodia interpunctella. We used gas chromatography with electroantennographic detection and found 12 active compounds derived from three different chocolate types, i.e., plain, nut-containing, and rum-flavored. Eight of the compounds were identified with mass spectrometry, and the activity of three compounds, ethyl vanillin, nonanal, and phenylacetaldehyde (PAA), was subsequently confirmed in both electrophysiological and behavioral assays. In the electroantennogram experiment, PAA and nonanal were consistently eliciting responses in both species and sexes. Ethyl vanillin was active in males of both species, and also in P. interpunctella females. E. cautella females showed no antennal activity in response to ethyl vanillin. All three volatiles were attractive to E. cautella males and P. interpunctella females in a flight tunnel. E. cautella females were significantly attracted only to ethyl vanillin. P. interpunctella males were attracted to PAA. Ethyl vanillin is a novel insect attractant, whereas both nonanal and phenylacetaldehyde mediate behavior in many insect species. A final experiment revealed that a blend of the three volatiles was required to induce landing in the flight tunnel bioassay, and that the landing rate was dependent on dose. The three-component blend attracted both sexes of P. interpunctella and females of E. cautella, whereas E. cautella males were not attracted.",Journal of chemical ecology,"['D000818', 'D001522', 'D002099', 'D002849', 'D004594', 'D005260', 'D008297', 'D009036', 'D014835']","['Animals', 'Behavior, Animal', 'Cacao', 'Chromatography, Gas', 'Electrophysiology', 'Female', 'Male', 'Moths', 'Volatilization']",Electrophysiological and behavioral responses to chocolate volatiles in both sexes of the pyralid moths Ephestia cautella and Plodia interpunctella.,"[None, None, None, None, None, None, None, 'Q000502', None]","[None, None, None, None, None, None, None, 'physiology', None]",https://www.ncbi.nlm.nih.gov/pubmed/16365716,2006,0,0,,no cocoa -0.42,10725135,"The present work analyzes the lipid fraction from seeds of wild Ecuadorian Theobroma subincanum and selected commercial varieties of Theobroma cacao from Mexico (var. Criollo) and Ecuador (var. Arriba). The lipid fraction was obtained from the seeds through supercritical fluid extraction and analysis performed by preparatory thin-layer chromatography followed by gas chromatography. The results revealed that in T. subincanum the triglycerides contain fatty acids with longer chains. The melting point and peroxide and saponifiable numbers were determined for each Theobroma sample. The results lead to the conclusion that T. subincanum would produce a poorer quality butter than T. cacao. Nevertheless, the results do point toward a significant commercial use of T. subincanum for low-profile products.",Journal of agricultural and food chemistry,"['D002099', 'D004484', 'D006801', 'D008055', 'D019660', 'D008800', 'D010936', 'D012639']","['Cacao', 'Ecuador', 'Humans', 'Lipids', 'Malvaceae', 'Mexico', 'Plant Extracts', 'Seeds']",Lipid composition of wild ecuadorian Theobroma subincanum Mart. seeds and comparison with two varieties of Theobroma cacao L.,"['Q000737', None, None, 'Q000032', 'Q000737', None, 'Q000032', 'Q000737']","['chemistry', None, None, 'analysis', 'chemistry', None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/10725135,2000,2,1,"table 2, 4, 5", -0.41,6521612,"A method for the quantitative analysis of triglyceride species composition of vegetable oils by reversed-phase high performance liquid chromatography (RP-HPLC) via a flame ionization detector (FID) is described. Triglycerides are separated into molecular species via Zorbax chemically bonded octadecylsilane (ODS) columns using gradient elution with methylene chloride in acetonitrile. Identification of species is made by matching the retention times of the peaks in the chromatogram with the order of elution of all of the species that could be present in the sample on the basis of a random distribution of the fatty acids and comparison of experimental and calculated theoretical carbon numbers (TCN). Quantitative analysis is based on a direct proportionality of peak areas. Differences in the response of individual species were small and did not dictate the use of response factors. The method is applied to cocoa butter before and after randomization, soybean oil and pure olive oil.",Lipids,"['D002851', 'D009821', 'D014280', 'D014675']","['Chromatography, High Pressure Liquid', 'Oils', 'Triglycerides', 'Vegetables']",Quantitative analysis of triglyceride species of vegetable oils by high performance liquid chromatography via a flame ionization detector.,"['Q000379', 'Q000032', 'Q000032', 'Q000032']","['methods', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/6521612,1985,,,,no pdf access -0.41,10820090,"Catechins, compounds that belong to the flavonoid class, are potentially beneficial to human health. To enable an epidemiological evaluation of catechins, data on their contents in foods are required. HPLC with UV and fluorescence detection was used to determine the levels of (+)-catechin, (-)-epicatechin, (+)-gallocatechin (GC), (-)-epigallocatechin (EGC), (-)-epicatechin gallate (ECg), and (-)-epigallocatechin gallate (EGCg) in 8 types of black tea, 18 types of red and white wines, apple juice, grape juice, iced tea, beer, chocolate milk, and coffee. Tea infusions contained high levels of catechins (102-418 mg of total catechins/L), and tea was the only beverage that contained GC, EGC, ECg, and EGCg in addition to (+)-catechin and (-)-epicatechin. Catechin concentrations were still substantial in red wine (27-96 mg/L), but low to negligible amounts were found in white wine, commercially available fruit juices, iced tea, and chocolate milk. Catechins were absent from beer and coffee. The data reported here provide a base for the epidemiological evaluation of the effect of catechins on the risk for chronic diseases.",Journal of agricultural and food chemistry,"['D001628', 'D002392', 'D002851', 'D005504', 'D006801', 'D009426', 'D013050', 'D013056']","['Beverages', 'Catechin', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Humans', 'Netherlands', 'Spectrometry, Fluorescence', 'Spectrophotometry, Ultraviolet']","Catechin contents of foods commonly consumed in The Netherlands. 2. Tea, wine, fruit juices, and chocolate milk.","['Q000032', 'Q000032', None, None, None, None, None, None]","['analysis', 'analysis', None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10820090,2000,0,0,, -0.41,24390407,"While metabolomics is increasingly used to investigate the food metabolome and identify new markers of food exposure, limited attention has been given to the validation of such markers. The main objectives of the present study were to (1) discover potential food exposure markers (PEMs) for a range of plant foods in a study setting with a mixed dietary background and (2) validate PEMs found in a previous meal study. Three-day weighed dietary records and 24-h urine samples were collected three times during a 6-month parallel intervention study from 107 subjects randomized to two distinct dietary patterns. An untargeted UPLC-qTOF-MS metabolomics analysis was performed on the urine samples, and all features detected underwent strict data analyses, including an iterative paired t test and sensitivity and specificity analyses for foods. A total of 22 unique PEMs were identified that covered 7 out of 40 investigated food groups (strawberry, cabbages, beetroot, walnut, citrus, green beans and chocolate). The PEMs reflected foods with a distinct composition rather than foods eaten more frequently or in larger amounts. We found that 23__% of the PEMs found in a previous meal study were also valid in the present intervention study. The study demonstrates that it is possible to discover and validate PEMs for several foods and food classes in an intervention study with a mixed dietary background, despite the large variability in such a dataset. Final validation of PEMs for intake of foods should be performed by quantitative analysis. ",Analytical and bioanalytical chemistry,"['D000293', 'D000328', 'D000368', 'D015415', 'D002853', 'D004032', 'D015930', 'D005247', 'D005260', 'D006801', 'D008297', 'D013058', 'D055432', 'D008875', 'D010945', 'D015203', 'D012680', 'D055815']","['Adolescent', 'Adult', 'Aged', 'Biomarkers', 'Chromatography, Liquid', 'Diet', 'Diet Records', 'Feeding Behavior', 'Female', 'Humans', 'Male', 'Mass Spectrometry', 'Metabolomics', 'Middle Aged', 'Plants, Edible', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Young Adult']",Discovery and validation of urinary exposure markers for different plant foods by untargeted metabolomics.,"[None, None, None, 'Q000652', None, 'Q000145', None, None, None, None, None, None, 'Q000379', None, 'Q000145', None, None, None]","[None, None, None, 'urine', None, 'classification', None, None, None, None, None, None, 'methods', None, 'classification', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24390407,2014,0,0,, -0.41,11210035,"Selected volatile compounds of chocolate ice creams containing 0.6, 4.0, 6.0, or 9.0% milk fat or containing 2.5% milk fat, cocoa butter, or one of three fat replacers (Simplesse, Dairy Lo, or Oatrim) were analyzed by gas chromatography and gas chromatography-mass spectrometry using headspace solid-phase microextraction. The headspace concentration of most of the selected volatile compounds increased with decreasing milk fat concentration. Fat replacers generally increased the concentration of volatiles found in the headspace compared with milk fat or cocoa butter. Few differences in flavor volatiles were found between the ice cream containing milk fat and the ice cream containing cocoa butter. Among the selected volatiles, the concentration of 2,5-dimethyl-3(2-methyl propyl) pyrazine was the most highly correlated (negatively) with the concentration of milk fat, and it best discriminated among ice creams containing milk fat, cocoa butter, or one of the fat replacers.",Journal of dairy science,"['D000818', 'D055598', 'D002627', 'D002849', 'D003258', 'D004041', 'D019358', 'D005524', 'D008401', 'D007054', 'D008892', 'D013649']","['Animals', 'Chemical Phenomena', 'Chemistry, Physical', 'Chromatography, Gas', 'Consumer Behavior', 'Dietary Fats', 'Fat Substitutes', 'Food Technology', 'Gas Chromatography-Mass Spectrometry', 'Ice Cream', 'Milk', 'Taste']","Effects of milk fat, cocoa butter, or selected fat replacers on flavor volatiles of chocolate ice cream.","[None, None, None, None, None, 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000737', None]","[None, None, None, None, None, 'analysis', 'analysis', None, None, 'analysis', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/11210035,2001,,,,no pdf access -0.41,11601468,"A simple and rapid gas chromatographic (GC) method was developed for the detection of cocoa butter equivalents (CBEs) in cocoa buffer (CB). It is based on the use of a 5 m nonpolar capillary column for the separation of the main triglycerides of CB according to their acyl/carbon numbers. The GC procedure was optimized to avoid thermal degradation of the triglycerides. By computing the ratio C54/C50 and (C54/C50) x C52 and by 2-dimensional plotting of these values, authentic CB samples were clearly distinguished from samples containing various CBEs. The detection of little as 1% CBE in CB (corresponding to about 0.3% CBE in chocolate) in a model system was shown to be possible. Under real conditions, for a wide range of CBs, about 2.5% CBEs in CB were detected. With this method, quantitation was possible at a concentration of 5% CBEs in CB mixtures, which corresponds to around 1% in chocolate; this value is far below the maximum level of 5% CBEs allowed to be added to chocolate.",Journal of AOAC International,"['D002849', 'D004041', 'D007202', 'D015203', 'D012996', 'D013696', 'D014280']","['Chromatography, Gas', 'Dietary Fats', 'Indicators and Reagents', 'Reproducibility of Results', 'Solutions', 'Temperature', 'Triglycerides']",Development of a rapid method for the detection of cocoa butter equivalents in mixtures with cocoa butter.,"[None, 'Q000032', None, None, None, None, 'Q000032']","[None, 'analysis', None, None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11601468,2002,,,, -0.41,16438304,"Recent concerns surrounding the presence of acrylamide in many types of thermally processed food have brought about the need for the development of analytical methods suitable for determination of acrylamide in diverse matrices with the goals of improving overall confidence in analytical results and better understanding of method capabilities. Consequently, the results are presented of acrylamide testing in commercially available food products--potato fries, potato chips, crispbread, instant coffee, coffee beans, cocoa, chocolate and peanut butter, obtained by using the same sample extract. The results obtained by using LC-MS/MS, GC/MS (El), GC/HRMS (El)--with or without derivatization--and the use of different analytical columns, are discussed and compared with respect to matrix borne interferences, detection limits and method complexities.",Advances in experimental medicine and biology,"['D020106', 'D001966', 'D002849', 'D002851', 'D002853', 'D003069', 'D003296', 'D005502', 'D005504', 'D005506', 'D005511', 'D008401', 'D015233', 'D015203', 'D021241', 'D013997']","['Acrylamide', 'Bromine', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Chromatography, Liquid', 'Coffee', 'Cooking', 'Food', 'Food Analysis', 'Food Contamination', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Models, Statistical', 'Reproducibility of Results', 'Spectrometry, Mass, Electrospray Ionization', 'Time Factors']",Determination of acrylamide in various food matrices: evaluation of LC and GC mass spectrometric methods.,"['Q000032', 'Q000737', 'Q000379', None, 'Q000379', None, None, None, 'Q000379', None, None, None, None, None, None, None]","['analysis', 'chemistry', 'methods', None, 'methods', None, None, None, 'methods', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16438304,2006,,,,no pdf access -0.41,21535747,"Selected ion flow tube-mass spectrometry (SIFT-MS) was used to measure the real-time concentrations of cocoa volatiles in the headspace during roasting. Alkalized and unalkalized Don Homero and Arriba cocoa beans were roasted at 120, 150, and 170 _C in a rotary roaster. The concentrations of total alcohols, acids, aldehydes, esters, ketones, and alkylpyrazines increased, peaked, and decreased within the timeframe used for typical roasting. The concentrations of alkylpyrazines and Strecker aldehydes increased as the roasting temperature increased from 120 to 170 _C. For most of the volatile compounds, there was no significant difference between Arriba and Don Homero beans, but Arriba beans showed higher concentrations of 2-heptanone, acetone, ethyl acetate, methylbutanal, phenylacetaldehyde, and trimethylpyrazine. For unalkalized Don Homero beans (pH 5.7), the time to peak concentration decreased from 13.5 to 7.4 min for pyrazines, and from 12.7 to 7.4 min for aldehydes as the roasting temperature increased from 120 to 170 _C. Also, at 150 _C roasting, the time to peak concentration was shortened from 9 to 5.1 min for pyrazines, and from 9.1 to 5 min for aldehydes as the pH increased from 5.7 to 8.7.",Journal of food science,"['D000079', 'D000085', 'D000096', 'D000447', 'D002099', 'D005511', 'D006863', 'D007659', 'D013058', 'D011719', 'D055549']","['Acetaldehyde', 'Acetates', 'Acetone', 'Aldehydes', 'Cacao', 'Food Handling', 'Hydrogen-Ion Concentration', 'Ketones', 'Mass Spectrometry', 'Pyrazines', 'Volatile Organic Compounds']",Monitoring of cocoa volatiles produced during roasting by selected ion flow tube-mass spectrometry (SIFT-MS).,"['Q000031', 'Q000032', 'Q000032', 'Q000032', 'Q000737', 'Q000379', None, 'Q000032', 'Q000379', 'Q000032', 'Q000378']","['analogs & derivatives', 'analysis', 'analysis', 'analysis', 'chemistry', 'methods', None, 'analysis', 'methods', 'analysis', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/21535747,2011,1,3,table 2, -0.41,16638661,"The objective of this study was to determine the effect of high stearic acid (SA) diets versus high polyunsaturated fatty acid (PUFA) diets on several measures of lipid peroxidation in vivo. Sprague-Dawley rats were fed diets that differed only in the fat source (8% by weight) for 19 weeks. High SA fats were beef tallow (BT) and cocoa butter (CB), high PUFA fats were soybean oil (SO) and menhaden oil (MO). Urine was analyzed for lipophilic aldehydes, the secondary products of lipid peroxidation, by HPLC. Decreases (P<0.05) were found for 4 nonpolar lipophilic aldehydes and related carbonyl compounds (NPC) and 4 polar lipophilic aldehydes and related carbonyl compounds (PC) when the BT-fed group was compared to the SO-fed group. Decreases were also found to be significant for total NPC (P<0.01) and total PC (P<0.05) between BT and SO-fed groups. Serum increase in resistance to oxidation (P<0.01) was found in the BT group when compared to the SO group. The differences in urine and serum measurements in the present experiment indicate lower level of lipid peroxidation in vivo due to the consumption of high SA containing BT diet compared to high PUFA containing SO diet without raising serum triglycerides and cholesterol levels significantly for the BT-fed groups.",International journal of food sciences and nutrition,"['D000447', 'D000818', 'D001835', 'D002851', 'D004041', 'D004042', 'D004435', 'D005223', 'D005227', 'D005260', 'D015227', 'D010084', 'D051381', 'D017207', 'D013229']","['Aldehydes', 'Animals', 'Body Weight', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Dietary Fats, Unsaturated', 'Eating', 'Fats', 'Fatty Acids', 'Female', 'Lipid Peroxidation', 'Oxidation-Reduction', 'Rats', 'Rats, Sprague-Dawley', 'Stearic Acids']",Effect of high stearic acid containing fat on markers for in vivo lipid peroxidation.,"['Q000652', None, 'Q000187', 'Q000379', 'Q000008', 'Q000008', 'Q000187', 'Q000737', 'Q000032', None, 'Q000187', 'Q000187', None, None, 'Q000008']","['urine', None, 'drug effects', 'methods', 'administration & dosage', 'administration & dosage', 'drug effects', 'chemistry', 'analysis', None, 'drug effects', 'drug effects', None, None, 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/16638661,2006,2,1,table 1, -0.41,11027026,"A method for identifying refined vegetable fats added to chocolate (cocoa butter equivalents, CBEs) was combined with established quantitative methods for determining the level of vegetable fat added to cocoa butter with the aim of providing improved precision. The identification of fats was based on the analysis of sterol and triterpene alcohol degradation products formed during the processing of the fat. The procedure was able to successfully discriminate between 95% of pairs of fats from a set (33) of CBE-type vegetable fats. Subsequent analysis of 80 mixtures of four CBEs with chocolate successfully identified, on cross-validation, 94% of the samples. Combining the qualitative procedure with established quantitative methodology, based on the analysis of triacylglycerols, improved the method precision from +/- 2.1% to +/- 0.3% (5% addition of CBE at 95% confidence). Identifying the fat analytically permits the use of quantitative methods for determining the level of added fat in chocolate that have improved precision in comparison with the measurement of an unidentified fat. This may obviate the need to use factory inspection as a means to identify the ingredients of a product and monitor compliance with proposed legislation.",Food additives and contaminants,"['D002099', 'D002182', 'D005504', 'D008401', 'D010938', 'D013261', 'D014280', 'D014315']","['Cacao', 'Candy', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Plant Oils', 'Sterols', 'Triglycerides', 'Triterpenes']",An improved method for the measurement of added vegetable fats in chocolate.,"['Q000737', 'Q000032', 'Q000379', 'Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000032']","['chemistry', 'analysis', 'methods', 'methods', 'analysis', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11027026,2000,,,, -0.4,12613847,"The effects of added conjugated linoleic acid (CLA) on the sensory, chemical, and physical characteristics of 2% total fat (wt/wt) fluid milk were studied. Milks with 2% (wt/wt) total fat (2% CLA, 1% CLA 1% milkfat, 2% milkfat) were made by the addition of cream or CLA triglyceride oil into skim milk followed by HTST pasteurization and homogenization. The effects of adding vitamin E (200 ppm) and rosemary extract (0.1% wt/wt based on fat content) were investigated to prevent lipid oxidation. HTST pasteurization resulted in a significant decrease of the cis-9/trans-11 isomer and other minor CLA isomers. The cis-9/trans-11 isomer concentration remained stable through 2 wk of refrigerated storage. A significant loss of both the cis-9/trans-11 and the cis-10/trans-12 isomers occurred after 3 wk of refrigerated storage. The loss was attributed to lipase activity from excessive microbial growth. No differences were found in hexanal or other common indicators of lipid oxidation between milks with or without added CLA (P > 0.05). Descriptive sensory analysis revealed that milks with 1 or 2% CLA exhibited low intensities of a ""grassy/vegetable oil"" flavor, not present in control milks. The antioxidant treatments were deemed to be ineffective, under the storage conditions of this study, and did not produce significant differences from the control samples (P > 0.05). CLA-Fortified milk had significantly lower L* and b* values compared with 2% milkfat milk. No significant differences existed in viscosity. Consumer acceptability scores (n = 100) were lower (P < 0.05) for CLA-fortified milks compared to control milks, but the addition of chocolate flavor increased acceptability (P < 0.05).",Journal of dairy science,"['D000293', 'D000328', 'D000818', 'D000975', 'D002417', 'D002849', 'D003258', 'D003612', 'D005227', 'D005260', 'D005511', 'D005527', 'D006801', 'D007774', 'D019787', 'D008049', 'D050356', 'D008297', 'D008892', 'D010084', 'D013237', 'D013649', 'D013997']","['Adolescent', 'Adult', 'Animals', 'Antioxidants', 'Cattle', 'Chromatography, Gas', 'Consumer Behavior', 'Dairying', 'Fatty Acids', 'Female', 'Food Handling', 'Food, Fortified', 'Humans', 'Lactation', 'Linoleic Acid', 'Lipase', 'Lipid Metabolism', 'Male', 'Milk', 'Oxidation-Reduction', 'Stereoisomerism', 'Taste', 'Time Factors']",The impact of fortification with conjugated linoleic acid (CLA) on the quality of fluid milk.,"[None, None, None, 'Q000494', 'Q000378', None, None, 'Q000379', 'Q000032', None, 'Q000379', None, None, 'Q000378', 'Q000008', 'Q000378', None, None, 'Q000737', None, None, None, None]","[None, None, None, 'pharmacology', 'metabolism', None, None, 'methods', 'analysis', None, 'methods', None, None, 'metabolism', 'administration & dosage', 'metabolism', None, None, 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12613847,2003,,,,no pdf access -0.4,26174671,"The major constituents of agarwood oils are sesquiterpenes that are obtained from isoprenoid precursors through the plastidial methylerythritol phosphate (MEP) pathway and the cytosolic mevalonate pathway. In this study, a novel full-length cDNA of 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR), which was the second key enzyme in the plastid MEP pathway of sesquiterpenes biosynthesis was isolated from the stem of Aquilaria sinensis (Lour.) Gilg by the methods of reverse transcription polymerase chain reaction (RT-PCR) and rapid amplification of cDNA ends (RACE) technique for the first time, and named as AsDXR. The full-length cDNA of AsDXR was 1768 bp, containing a 1437 bp open reading frame (ORF) encoding a polypeptide of 478 amino acids with a molecular weight of 51.859 kD and the theoretical isoelectric point of 6.29. Comparative and bioinformatic analysis of the deduced AsDXR protein showed extensive homology with DXRs from other plant species, especially Theobroma cacao and Gossypium barbadense, and contained a conserved transit peptide for plastids, and extended pro-rich region and a highly conserved NADPH-binding motif owned by all plant DXRs. Southern blot analysis indicated that AsDXR belonged to a small gene family. Tissue expression pattern analysis revealed that AsDXR expressed strongly in root and stem, but weakly in leaf. Additionally, AsDXR expression was found to be activated by exogenous elicitor of MeJA (methyl jasmonate). The contents of three sesquiterpenes (_±-guaiene, _±-humulene and _”-guaiene) were significantly induced by MeJA. This study enables us to further elucidate the role of AsDXR in the biosynthesis of agarwood sesquiterpenes in A. sinensis at the molecular level. ",Journal of genetics,"['D000085', 'D019747', 'D000595', 'D001483', 'D003001', 'D003517', 'D018076', 'D008401', 'D018628', 'D020869', 'D018506', 'D017343', 'D008969', 'D054883', 'D010802', 'D017433', 'D017434', 'D012333', 'D016415', 'D012717', 'D029645']","['Acetates', 'Aldose-Ketose Isomerases', 'Amino Acid Sequence', 'Base Sequence', 'Cloning, Molecular', 'Cyclopentanes', 'DNA, Complementary', 'Gas Chromatography-Mass Spectrometry', 'Gene Dosage', 'Gene Expression Profiling', 'Gene Expression Regulation, Plant', 'Genes, Plant', 'Molecular Sequence Data', 'Oxylipins', 'Phylogeny', 'Protein Structure, Secondary', 'Protein Structure, Tertiary', 'RNA, Messenger', 'Sequence Alignment', 'Sesquiterpenes', 'Thymelaeaceae']","Molecular cloning, characterization and expression analysis of the gene encoding 1-deoxy-D-xylulose 5-phosphate reductoisomerase from Aquilaria sinensis (Lour.) Gilg.","['Q000494', 'Q000737', None, None, None, 'Q000494', 'Q000235', None, None, None, 'Q000187', None, None, 'Q000494', None, None, None, 'Q000235', None, 'Q000737', 'Q000187']","['pharmacology', 'chemistry', None, None, None, 'pharmacology', 'genetics', None, None, None, 'drug effects', None, None, 'pharmacology', None, None, None, 'genetics', None, 'chemistry', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/26174671,2016,0,0,, -0.4,18484765,"Unbalanced diets generate oxidative stress commonly associated with the development of diabetes, atherosclerosis, obesity and cancer. Dietary flavonoids have antioxidant properties and may limit this stress and reduce the risk of these diseases. We used a metabolomic approach to study the influence of catechin, a common flavonoid naturally occurring in various fruits, wine or chocolate, on the metabolic changes induced by hyperlipidemic diets. Male Wistar rats ( n = 8/group) were fed during 6 weeks normolipidemic (5% w/w) or hyperlipidemic (15 and 25%) diets with or without catechin supplementation (0.2% w/w). Urines were collected at days 17 and 38 and analyzed by reverse-phase liquid chromatography-mass spectrometry (LC-QTOF). Hyperlipidic diets led to a significant increase of oxidative stress in liver and aorta, upon which catechin had no effect. Multivariate analyses (PCA and PLS-DA) of the urine fingerprints allowed discrimination of the different diets. Variables were then classified according to their dependence on lipid and catechin intake (ANOVA). Nine variables were identified as catechin metabolites of tissular or microbial origin. Around 1000 variables were significantly affected by the lipid content of the diet, and 76 were fully reversed by catechin supplementation. Four variables showing an increase in urinary excretion in rats fed the high-fat diets were identified as deoxycytidine, nicotinic acid, dihydroxyquinoline and pipecolinic acid. After catechin supplementation, the excretion of nicotinic acid was fully restored to the level found in the rats fed the low-fat diet. The physiological significance of these metabolic changes is discussed.",Journal of proteome research,"['D000818', 'D000975', 'D001011', 'D001835', 'D002392', 'D002784', 'D002851', 'D003841', 'D004041', 'D004435', 'D005978', 'D005979', 'D005982', 'D008099', 'D008297', 'D008315', 'D013058', 'D015999', 'D009525', 'D010875', 'D011804', 'D051381', 'D017208', 'D014280']","['Animals', 'Antioxidants', 'Aorta', 'Body Weight', 'Catechin', 'Cholesterol', 'Chromatography, High Pressure Liquid', 'Deoxycytidine', 'Dietary Fats', 'Eating', 'Glutathione', 'Glutathione Peroxidase', 'Glutathione Transferase', 'Liver', 'Male', 'Malondialdehyde', 'Mass Spectrometry', 'Multivariate Analysis', 'Niacin', 'Pipecolic Acids', 'Quinolines', 'Rats', 'Rats, Wistar', 'Triglycerides']",A liquid chromatography-quadrupole time-of-flight (LC-QTOF)-based metabolomic approach reveals new metabolic effects of catechin in rats fed high-fat diets.,"[None, 'Q000378', 'Q000187', 'Q000187', 'Q000378', 'Q000097', 'Q000379', 'Q000378', 'Q000494', 'Q000187', 'Q000378', 'Q000378', 'Q000378', 'Q000187', None, 'Q000378', 'Q000379', None, 'Q000378', 'Q000378', 'Q000378', None, None, 'Q000097']","[None, 'metabolism', 'drug effects', 'drug effects', 'metabolism', 'blood', 'methods', 'metabolism', 'pharmacology', 'drug effects', 'metabolism', 'metabolism', 'metabolism', 'drug effects', None, 'metabolism', 'methods', None, 'metabolism', 'metabolism', 'metabolism', None, None, 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/18484765,2008,0,0,, -0.4,14558132,"The triacylglycerol (TAG) composition study of cocoa butter (CB) and cocoa butter equivalents (CBEs) has been performed by gas chromatography (GC) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS). These two techniques provided comparable results. The advantage of the MALDI technique was the detection of each compound comprising the triacylglycerol classes (Cn). Moreover, comparison of the data obtained by these two techniques indicated that TAG relative percentages could be obtained quantitatively with the MALDI technique. These techniques have been applied for the composition determination of CB + CBE mixtures. Encouraging results showed that it is possible to quantify an admixture containing as little as 4% of CBE.",Rapid communications in mass spectrometry : RCM,"['D000349', 'D002099', 'D002849', 'D004041', 'D012996', 'D013020', 'D019032', 'D014280']","['Africa', 'Cacao', 'Chromatography, Gas', 'Dietary Fats', 'Solutions', 'South America', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Triglycerides']",Comparative study of matrix-assisted laser desorption/ionization and gas chromatography for quantitative determination of cocoa butter and cocoa butter equivalent triacylglycerol composition.,"[None, 'Q000737', None, 'Q000032', None, None, None, 'Q000032']","[None, 'chemistry', None, 'analysis', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/14558132,2004,,,, -0.4,28784516,"A comprehensive analysis of cocoa polyphenols from unfermented and fermented cocoa beans from a wide range of geographic origins was carried out to catalogue systematic differences based on their origin as well as fermentation status. This study identifies previously unknown compounds with the goal to ascertain, which of these are responsible for the largest differences between bean types. UHPLC coupled with ultra-high resolution time-of-flight mass spectrometry was employed to identify and relatively quantify various oligomeric proanthocyanidins and their glycosides amongst several other unreported compounds. A series of biomarkers allowing a clear distinction between unfermented and fermented cocoa beans and for beans of different origins were identified. The large sample set employed allowed comparison of statistically significant variations of key cocoa constituents.","Food research international (Ottawa, Ont.)",[],[],Origin-based polyphenolic fingerprinting of Theobroma cacao in unfermented and fermented beans.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/28784516,2017,2,3,table 2, -0.4,1806392,"We have found that many foods are contaminated with mineral oil products used as lubricating oils/greases or as release agents. The mineral oil base of such products usually consists of branched alkanes ranging between C17 and C35. It forms a broad 'hump' of unresolved compounds in the gas chromatogram. Examples of such products are described; contamination is shown for a sample of bread, bonbon, and chocolate, respectively. The results suggest that contamination of foodstuffs with mineral oils does not always receive the required attention. However, there is also a lack of guidelines.",Food additives and contaminants,"['D001939', 'D002099', 'D002182', 'D002849', 'D002851', 'D005506', 'D006838', 'D008899', 'D010577']","['Bread', 'Cacao', 'Candy', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Hydrocarbons', 'Mineral Oil', 'Petrolatum']",Food contamination by hydrocarbons from lubricating oils and release agents: determination by coupled LC-GC.,"['Q000032', 'Q000737', 'Q000032', None, None, 'Q000032', 'Q000032', 'Q000737', 'Q000737']","['analysis', 'chemistry', 'analysis', None, None, 'analysis', 'analysis', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/1806392,1992,,,, -0.4,16152941,"A simple and inexpensive liquid chromatography/mass spectrometry (LC/MS) method was developed for the quantitation of acrylamide in various food products. The method involved spiking the isotope-substituted internal standard (1-C13 acrylamide) onto 6.00 g of the food product, adding 40 mL distilled/deionized water, and heating at 65 degrees C for 30 min. Afterwards, 10 mL ethylene dichloride was added and the mixture was homogenized for 30 s and centrifuged at 2700 x g for 30 min, and then 8 g supernatant was extracted with 10, 5, and 5 mL portions of ethyl acetate. The extracts were combined, dried with sodium sulfate, and concentrated to 100-200 microL. Acrylamide was determined by analysis of the final extract on a single quadrupole, bench-top mass spectrometer with electrospray ionization, using a 2 mm id C18 column and monitoring m/z = 72 (acrylamide) and m/z = 73 (internal standard). For difficult food matrixes, such as coffee and cocoa, a solid-phase extraction cleanup step was incorporated to improve both chromatography and column lifetime. The method had a limit of quantitation of 10 ppb, and coefficients of determination (r2) for calibration curves were typically better than 0.998. Acceptable spike recovery results were achieved in 11 different food matrixes. Precision in potato chip analyses was 5-8% (relative standard deviation). This method provides an LC/MS alternative to the current LC/MS/MS methods and derivatization gas chromatography/mass spectrometry methods, and is applicable to difficult food products such as coffee, cocoa, and high-salt foods.",Journal of AOAC International,"['D000085', 'D020106', 'D002099', 'D002623', 'D002845', 'D002853', 'D003069', 'D005504', 'D005506', 'D008401', 'D013058', 'D011208', 'D012015', 'D015203', 'D011198', 'D013431', 'D013696']","['Acetates', 'Acrylamide', 'Cacao', 'Chemistry Techniques, Analytical', 'Chromatography', 'Chromatography, Liquid', 'Coffee', 'Food Analysis', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Mass Spectrometry', 'Powders', 'Reference Standards', 'Reproducibility of Results', 'Solanum tuberosum', 'Sulfates', 'Temperature']",Quantitation of acrylamide in food products by liquid chromatography/mass spectrometry.,"['Q000032', 'Q000032', None, 'Q000295', None, 'Q000379', None, 'Q000379', None, 'Q000379', 'Q000379', None, None, None, None, 'Q000494', None]","['analysis', 'analysis', None, 'instrumentation', None, 'methods', None, 'methods', None, 'methods', 'methods', None, None, None, None, 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/16152941,2005,,,, -0.39,28566081,"Streptococcus uberis is a gram-positive bacterium that is mostly responsible for mastitis in cattle. The bacterium rarely has been associated with human infections. Conventional phenotyphic methods can be inadequate for the identification of S.uberis; and in microbiology laboratories S.uberis is confused with the other streptococci and enterococci isolates. Recently, molecular methods are recommended for the accurate identification of S.uberis isolates. The aim of this report is to present a lower respiratory tract infection case caused by S.uberis and the microbiological methods for identification of this bacterium. A 66-year-old male patient with squamous cell lung cancer who received radiotherapy was admitted in our hospital for the control. According to the chest X-Ray, patient was hospitalized with the prediagnosis of ''cavitary tumor, pulmonary abscess''. In the first day of the hospitalization, blood and sputum cultures were drawn. Blood culture was negative, however, Candida albicans was isolated in the sputum culture and it was estimated to be due to oral lesions. After two weeks from the hospitalization, sputum sample was taken from the patient since he had abnormal respiratory sounds and cough complaint. In the Gram stained smear of the sputum there were abundant leucocytes and gram-positive cocci, and S.uberis was isolated in both 5% sheep blood and chocolate agar media. Bacterial identification and antibiotic susceptibility tests were performed by VITEK 2 (Biomerieux, France) and also, the bacterium was identified by matrix assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) based VITEK MS system as S.uberis. The isolate was determined susceptible to ampicillin, erythromycin, clindamycin, levofloxacin, linezolid, penicillin, cefotaxime, ceftriaxone, tetracycline and vancomycin. 16S, 23S ribosomal RNA and 16S-23S intergenic spacer gene regions were amplified with specific primers and partial DNA sequence analysis of 16S rRNA polymerase chain reaction (PCR) products were performed by 3500xL Genetic Analyzer (Applied Biosystems, USA). According to the partial 16S rRNA gene sequencing results, bacterium was confirmed as S.uberis. This report makes a significant contribution to the number of case reports of human infections caused by S.uberis as the identification was performed by current microbiological methods in our case. In conclusion, S.uberis should be evaluated as an opportunistic pathogen among the immunosuppressed patients and in addition to phenotypic bacteriological methods, the other recent microbiological methods should also be utilized for the identification.",Mikrobiyoloji bulteni,"['D000368', 'D002176', 'D002180', 'D002294', 'D006801', 'D008175', 'D008297', 'D008826', 'D009894', 'D016133', 'D019032', 'D013183', 'D013290', 'D013291']","['Aged', 'Candida albicans', 'Candidiasis, Oral', 'Carcinoma, Squamous Cell', 'Humans', 'Lung Neoplasms', 'Male', 'Microbial Sensitivity Tests', 'Opportunistic Infections', 'Polymerase Chain Reaction', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Sputum', 'Streptococcal Infections', 'Streptococcus']",[A rarely isolated bacterium in microbiology laboratories: Streptococcus uberis].,"[None, 'Q000302', 'Q000150', 'Q000150', None, 'Q000150', None, None, 'Q000150', None, None, 'Q000382', 'Q000150', 'Q000145']","[None, 'isolation & purification', 'complications', 'complications', None, 'complications', None, None, 'complications', None, None, 'microbiology', 'complications', 'classification']",https://www.ncbi.nlm.nih.gov/pubmed/28566081,2017,0,0,,no cocoa -0.39,7362697,"The effects of dietary stearic and other saturated fatty acids on the fluidity of the plasma lipoproteins were assessed with fluorescence polarization techniques. Rabbits were maintained on diets containing either cocoa butter, milkfat, coconut oil, or corn oil as the only source of fat. Microviscosities eta, of the lipid regions of plasma very low density lipoproteins (VLDL), low density lipoproteins (LDL), and high density lipoproteins (HDL) were determined by measuring the anisotropy of fluorescence from the probe 1,6-diphenyl-1,3,5-hexatriene. The microviscosity values followed the sequence eta HDL greater than eta LDL greater than eta VLDL when the lipoproteins were isolated from the plasma of rabbits fed cocoa butter, milkfat, or corn oil, HDL and LDL consist of an invariant phase in the temperature range 0--50 degrees C regardless of diet. VLDL from rabbits fed milkfat, corn oil, or cocoa butter displayed monophasic behavior in the same range, while VLDL, from rabbits fed coconut oil showed a phase transition at 31.9 +/- 3.7 degrees C. Lipoproteins were less fluid in fasted than in non-fasted rabbits and VLDL and LDL from fasted milkfat-fed rabbits showed phase transitions. Despite the fatty acid compositions of the dietary fats, VLDL and LDL were more fluid from rabbits fed cocoa butter than from rabbits fed corn oil; apparently metabolism influences microviscosity.",Atherosclerosis,"['D000818', 'D004041', 'D006838', 'D008074', 'D008075', 'D008077', 'D008079', 'D008297', 'D011817', 'D013050', 'D014783']","['Animals', 'Dietary Fats', 'Hydrocarbons', 'Lipoproteins', 'Lipoproteins, HDL', 'Lipoproteins, LDL', 'Lipoproteins, VLDL', 'Male', 'Rabbits', 'Spectrometry, Fluorescence', 'Viscosity']",Influence of dietary fats on the fluidity of the lipid domains of rabbit plasma lipoproteins.,"[None, 'Q000494', None, 'Q000097', 'Q000097', 'Q000097', 'Q000097', None, None, None, None]","[None, 'pharmacology', None, 'blood', 'blood', 'blood', 'blood', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/7362697,1980,0,0,, -0.39,19486828,"The daily dietary intake of nickel (Ni) and zinc (Zn) by 42 young children, 21 boys and 21 girls, from 4 to 7 years of age, living in urban and rural areas of Germany and having different food consumption behaviour, was determined by the duplicate method with a 7-day sampling period. Dietary records were also kept by the children's parents for the 7-day sampling period. Individual reported food items were identified, assigned to food groups and, together with known Ni and Zn concentrations of foodstuffs, daily intake rates were calculated. The same method was used for calculations of the energy, fat, protein and carbohydrate intake rates. The levels in the food duplicates, determined by atomic absorption spectrometry, were in the range of 69-2000 microg Ni/kg(dry weight) (geometric mean (GM): 348) and 7.1-43 mg Zn/kg(dry weight) (GM: 17.5). Daily intake rates based on the 294 individual food duplicate analyses were 12-560 microgNi/d (GM: 92.3) and 1.5-11 mgZn/d (GM: 4.63). The results from the dietary records were 35-1050 microg Ni/d (GM: 123) and 1.7-15 mg Zn/d (GM: 5.35). The results of the daily intake rates from both methods showed a correlation with regard to Zn (r=0.56), but no correlation was found between either the Ni intake rates determined with both methods or between the Ni intake rates measured by the duplicate method and calculated intake rates from the dietary records of energy, fat, protein, carbohydrates or drinking water. In the case of nickel, the discrepancies between the methods lead one to suppose that the main factors influencing Ni intake by food are not directly caused by easily assessable food ingredients themselves. It is possible that other factors, such as contaminated drinking water or the transition of Ni from kettles or other household utensils made from stainless steel into the food, may be more relevant. In addition there are some foodstuffs with great variations in concentrations, often influenced by the growing area and environmental factors. Further, some food groups naturally high in Nickel like nuts, cocoa or teas might not have been kept sufficient within the records. In summary, the dietary record method gave sufficient results for Zn, but is insufficient for Ni. Based on the food duplicate analysis, children living in urban areas with consumption of food products from a family-owned garden or the surrounding area and/or products from domestic animals of the surrounding area had about one-third higher Ni levels in their food than children either living in an urban area or children consuming products exclusively from the supermarket. Only slight differences were found with regard to Zn. Compared to the recommendations of the German Society of Nutrition (DGE) (25-30 microgNi/d and 5.0 mgZn/d), the participants of the study had a clearly increased Ni and, in view of the geometric mean value, a nearly adequate Zn intake. Health risks are especially given with regard to the influence of nickel intake by food on dermatitis for nickel-sensitive individuals.",Journal of trace elements in medicine and biology : organ of the Society for Minerals and Trace Elements (GMS),"['D002648', 'D002675', 'D015930', 'D005260', 'D005858', 'D006801', 'D008297', 'D009532', 'D013054', 'D015032']","['Child', 'Child, Preschool', 'Diet Records', 'Female', 'Germany', 'Humans', 'Male', 'Nickel', 'Spectrophotometry, Atomic', 'Zinc']",Dietary intake of nickel and zinc by young children--results from food duplicate portion measurements in comparison to data calculated from dietary records and available data on levels in food groups.,"[None, None, None, None, None, None, None, 'Q000008', 'Q000379', 'Q000008']","[None, None, None, None, None, None, None, 'administration & dosage', 'methods', 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/19486828,2009,0,0,, -0.39,21045839,"The diversity of the chemical structures of dietary polyphenols makes it difficult to estimate their total content in foods, and also to understand the role of polyphenols in health and the prevention of diseases. Global redox colorimetric assays have commonly been used to estimate the total polyphenol content in foods. However, these assays lack specificity. Contents of individual polyphenols have been determined by chromatography. These data, scattered in several hundred publications, have been compiled in the Phenol-Explorer database. The aim of this paper is to identify the 100 richest dietary sources of polyphenols using this database.",European journal of clinical nutrition,"['D000975', 'D002099', 'D016208', 'D002523', 'D005419', 'D005504', 'D005638', 'D009754', 'D010636', 'D010936', 'D059808', 'D017365', 'D027842', 'D014675', 'D014920']","['Antioxidants', 'Cacao', 'Databases, Factual', 'Edible Grain', 'Flavonoids', 'Food Analysis', 'Fruit', 'Nuts', 'Phenols', 'Plant Extracts', 'Polyphenols', 'Spices', 'Syzygium', 'Vegetables', 'Wine']",Identification of the 100 richest dietary sources of polyphenols: an application of the Phenol-Explorer database.,"['Q000032', 'Q000737', None, 'Q000737', 'Q000032', 'Q000706', 'Q000737', None, 'Q000032', 'Q000032', None, None, 'Q000737', 'Q000737', 'Q000032']","['analysis', 'chemistry', None, 'chemistry', 'analysis', 'statistics & numerical data', 'chemistry', None, 'analysis', 'analysis', None, None, 'chemistry', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/21045839,2011,1,1,table 1, -0.39,29146253,"Strategies for achieving global food security include identification of alternative feedstock for use as animal feed, to contribute towards efforts at increasing livestock farming. The presence of theobromine in cocoa pod husks, a major agro-waste in cocoa-producing countries, hinders its utilisation for this purpose. Cheap treatment of cocoa pod husks to remove theobromine would allow largescale beneficial use of the millions of metric tonnes generated annually. The aim of this study was to isolate theobromine-degrading filamentous fungi that could serve as bioremediation agents for detheobromination of cocoa pod husks. Filamentous fungi were screened for ability to degrade theobromine. The most promising isolates were characterized with respect to optimal environmental conditions for theobromine degradation. Secretion of theobromine-degrading enzymes by the isolates was investigated. Theobromine degradation was monitored by HPLC. Of fourteen theobromine-degrading isolates collected and identified by rDNA 5.8S and ITS sequences, seven belonged to Aspergillus spp. and six were Talaromyces spp. Based on the extent of theobromine utilization, four isolates; Aspergillus niger, Talaromyces verruculosus and two Talaromyces marneffei, showed the best potential for use as bioagents for detheobromination. First-time evidence was found of the use of xanthine oxidase and theobromine oxidase in degradation of a methylxanthine by fungal isolates. Metabolism of theobromine involved initial demethylation at position 7 to form 3-methylxanthine, or initial oxidation at position 8 to form 3,7-dimethyuric acid. All four isolates degraded theobromine beyond uric acid. The data suggest that the four isolates can be applied to substrates, such as cocoa pod husks, for elimination of theobromine.",Microbiological research,"['D000821', 'D001234', 'D001673', 'D002099', 'D002851', 'D004271', 'D004275', 'D005658', 'D006863', 'D009584', 'D010084', 'D032901', 'D013696', 'D013805', 'D014969']","['Animal Feed', 'Aspergillus niger', 'Biodegradation, Environmental', 'Cacao', 'Chromatography, High Pressure Liquid', 'DNA, Fungal', 'DNA, Ribosomal', 'Fungi', 'Hydrogen-Ion Concentration', 'Nitrogen', 'Oxidation-Reduction', 'Talaromyces', 'Temperature', 'Theobromine', 'Xanthine Oxidase']",Isolation and characterisation of theobromine-degrading filamentous fungi.,"[None, 'Q000254', None, 'Q000737', 'Q000379', None, 'Q000032', 'Q000145', None, 'Q000378', None, 'Q000254', None, 'Q000737', None]","[None, 'growth & development', None, 'chemistry', 'methods', None, 'analysis', 'classification', None, 'metabolism', None, 'growth & development', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/29146253,2018,0,0,, -0.39,10335542,"When an infant presents severe cyanosis which is not associated with respiratory distress, methaemoglobinemia should always be suspected. In children its main inducers are contaminated water or vegetable broths with high nitrate levels (especially spinach and carrots) used to prepare powdered formula or soups. Children affected with methaemoglobinemia have a peculiar lavender colour. Blood from the heel sticks is chocolate-brown and does not become pink when exposed to room air. Diagnosis can be confirmed by excluding other causes of cyanosis and by spectrophotometric analysis of blood for methaemoglobin. When methaemoglobin's levels reach 60% or more, the patient will collapse and become comatose and may die. Therapy with methylene blue results in prompt relief. In this article we report a case of methaemoglobinemia due to the administration of powdered formula mixed with vegetable broths to a newborn aged 16 days. Furthermore we will present a short review of literature regarding methaemoglobinemia caused by toxic agents over the last 10 years.",La Pediatria medica e chirurgica : Medical and surgical pediatrics,"['D004791', 'D005260', 'D006801', 'D007225', 'D007231', 'D008706', 'D008708', 'D008751', 'D013053']","['Enzyme Inhibitors', 'Female', 'Humans', 'Infant Food', 'Infant, Newborn', 'Methemoglobin', 'Methemoglobinemia', 'Methylene Blue', 'Spectrophotometry']",[Acquired methemoglobinemia: a case report].,"['Q000627', None, None, 'Q000009', None, 'Q000032', 'Q000175', 'Q000627', None]","['therapeutic use', None, None, 'adverse effects', None, 'analysis', 'diagnosis', 'therapeutic use', None]",https://www.ncbi.nlm.nih.gov/pubmed/10335542,1999,,,, -0.39,22970581,"A simple and rapid method using an octadecyl-bonded silica membrane disk impregnated with Cyanex302 is described for the pre-concentration and determination of iron. The influence of various parameters on sorption and elution of Fe(III) were systematically investigated. The sorption of Fe(III) at pH 3.2 was quantitative (99.3 +/- 1.1%). It was completely recovered using 20 mL 5.0 M HCI and determined by flame atomic absorption spectrometry. Breakthrough volume of the modified disk for Fe(III) was >2000 mL, pre-concentration factor was >100, and reusability up to 28 cycles. The LOD and LOQ for Fe(III) were 0.45 microg/L and 1.51 microg/L, respectively, while precision for its determination in terms of RSD was < or =2.1%. This method was applied for Fe(III) determination in milk, fortified flour, cocoa powder, tea, and black pepper. To validate the procedure, EPA Method Standard (QC standard 21) was analyzed for Fe(III).",Journal of AOAC International,"['D000327', 'D000818', 'D002099', 'D002623', 'D005433', 'D005504', 'D006863', 'D007477', 'D007501', 'D008892', 'D009946', 'D010721', 'D029222', 'D012015', 'D015203', 'D012822', 'D013054', 'D013662', 'D013997']","['Adsorption', 'Animals', 'Cacao', 'Chemistry Techniques, Analytical', 'Flour', 'Food Analysis', 'Hydrogen-Ion Concentration', 'Ions', 'Iron', 'Milk', 'Organothiophosphorus Compounds', 'Phosphinic Acids', 'Piper nigrum', 'Reference Standards', 'Reproducibility of Results', 'Silicon Dioxide', 'Spectrophotometry, Atomic', 'Tea', 'Time Factors']",Octadecyl-bonded silica membrane disk modified with Cyanex302 for pre-concentration and determination of iron in food products.,"[None, None, None, 'Q000379', None, 'Q000379', None, None, 'Q000032', None, 'Q000737', 'Q000737', None, None, None, 'Q000737', 'Q000379', None, None]","[None, None, None, 'methods', None, 'methods', None, None, 'analysis', None, 'chemistry', 'chemistry', None, None, None, 'chemistry', 'methods', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22970581,2012,,,, -0.38,29391642,"The effect of the partial replacement of cocoa butter (CB) by cocoa butter equivalent (CBE) in the release of volatile compounds in dark chocolate was studied. The fatty acid profile, triacylglyceride composition, solid fat content (SFC) and melting point were determined in CB and CBE. Chocolate with CB (F1) and with different content of CBE (5 and 10%-F2 and F3, respectively) were prepared. Plastic viscosity and Casson flow limit, particle size distribution and release of volatile compounds using a solid phase microextraction with gas chromatography (SMPE-GC) were determined in the chocolate samples. The melting point was similar for the studied samples but SFC indicated different melting behavior. CBE showed a higher saturated fatty acid content when compared to CB. The samples showed similar SOS triglyceride content (21 and 23.7% for CB and CBE, respectively). Higher levels of POS and lower POP were observed for CB when compared to CBE (44.8 and 19.7 and 19 and 41.1%, respectively). The flow limit and plastic viscosity were similar for the studied chocolates samples, as well as the particle size distribution. Among the 27 volatile compounds identified in the samples studied, 12 were detected in significantly higher concentrations in sample F1 (phenylacetaldehyde, methylpyrazine, 2,6-dimethylpyrazine, 2-ethyl-5-methylpyrazine, 2-ethyl-3,5-dimethylpyrazine, tetramethylpyrazine, trimethylpyrazine, 3-ethyl-2,5-dimethylpyrazine, phenethyl alcohol, 2-acetylpyrrole, acetophenone and isovaleric acid). The highest changes were observed in the pyrazines group, which presented a decrease of more than half in the formulations where part of the CB was replaced by the CBE.",Journal of food science and technology,[],[],Impact of the addition of cocoa butter equivalent on the volatile compounds profile of dark chocolate.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29391642,2018,,,, -0.38,29548447,"The nutritional value of cocoa butter is mainly determined by the composition of triacylglycerols (TAGs). In this paper we have developed a non-aqueous reversed-phase liquid chromatographic method, using ethanol as the mobile phase, coupled to electrospray ionization (ESI) tandem mass spectrometry to identify TAGs in raw cocoa beans from six different origins. Tandem mass spectrometry was adopted to facilitate the identification of TAGs using unique diacylglycerol product ions and neutral losses. Additionally, two-dimensional m/z retention time maps aided the identification of entire homologous series of TAGs. A total of 83 different TAGs were identified in unfermented cocoa beans, 58 of which were not previously reported in cocoa. Thirty-one of these compounds represent a new class of TAGs characterized by the presence of one to three hydroxyl groups on the unsaturated fatty acid chain. To date, this represents the largest number of TAGs identified in cocoa.",Food chemistry,[],[],Characterization of triacylglycerols in unfermented cocoa beans by HPLC-ESI mass spectrometry.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29548447,2018,2,3,table 1 ,extract the different cocoa samples and the lipid amount in % -0.38,30027968,"Recrystallisation occurs frequently in confectionery. More information on sucrose re-crystallisation will aid our understanding of popular foods like chocolate. However, progress has been limited due the lack of a robust method for the production of amorphous sucrose, with known purity. Poor control has led to the glass transition temperatures (Tg's) for amorphous sucrose varying between 48-78 _C in the literature. Our objective was to investigate the recrystallization of sucrose in the presence of lactose, NaCl and water. The purity of sucrose was confirmed by ion chromatography, polarimetry and differential scanning calorimetry. Amorphous sucrose was prepared by freeze-drying 10% w/v aqueous solutions. Fisher (99.7%) and Silver Spoon (98.4%) sucrose samples melted at 186 _± 0.6 _C & 189 _± 0.3 _C respectively. For the Fisher sample the absence of invert sugars and low mineral content allowed the observation of a small endotherm (___ 150 _C). The Tg of amorphous sucrose was 58.3 _± 1.1 _C with a recrystallization enthalpy (_”Hcrys) of 72.8 _± 6.0 J g-1. NaCl reduced both the Tg (54.8 _± 1.8 _C) and the _”Hcrys (35.7 _± 3.8 J g-1) without affecting the onset temperature of sucrose's re-crystallization (Tcrys, 129.5 _± 6.9 _C), suggesting that a proportion of the sample remained amorphous. The presence of water (1.6 _± 0.07%) inside the hermetically sealed pans caused an earlier onset of Tg (52.3 _± 1.3 _C) and Tcrys (85.1 _± 4.0 _C), as well as lowering _”Hcrys (45.2 _± 2.4 J g-1) compared to samples contained in pin-holed pans (where evaporation was possible). The presence of lactose inhibited the crystallization of sucrose completely. On the basis of this study, it is apparent that sucrose crystallization is highly dependent on the presence of other common food ingredients within the matrix.",Food & function,[],[],Crystallisation of freeze-dried sucrose in model mixtures that represent the amorphous sugar matrices present in confectionery.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/30027968,2018,0,0,, -0.38,23122117,"The definition of fat differs in different countries; thus whether fat is listed on food labels depends on the country. Some countries list crude fat content in the 'Fat' section on the food label, whereas other countries list total fat. In this study, three methods were used for determining fat classes and content in bakery products: the Folch method, the automated Soxhlet method, and the AOAC 996.06 method. The results using these methods were compared. Fat (crude) extracted by the Folch and Soxhlet methods was gravimetrically determined and assessed by fat class using capillary gas chromatography (GC). In most samples, fat (total) content determined by the AOAC 996.06 method was lower than the fat (crude) content determined by the Folch or automated Soxhlet methods. Furthermore, monounsaturated fat or saturated fat content determined by the AOAC 996.06 method was lowest. Almost no difference was observed between fat (crude) content determined by the Folch method and that determined by the automated Soxhlet method for nearly all samples. In three samples (wheat biscuits, butter cookies-1, and chocolate chip cookies), monounsaturated fat, saturated fat, and trans fat content obtained by the automated Soxhlet method was higher than that obtained by the Folch method. The polyunsaturated fat content obtained by the automated Soxhlet method was not higher than that obtained by the Folch method in any sample.",Food chemistry,"['D001939', 'D002623', 'D005223', 'D005504', 'D005515']","['Bread', 'Chemistry Techniques, Analytical', 'Fats', 'Food Analysis', 'Food Labeling']",Comparison of different methods to quantify fat classes in bakery products.,"['Q000032', 'Q000379', 'Q000737', 'Q000379', None]","['analysis', 'methods', 'chemistry', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/23122117,2013,0,0,, -0.38,25829576,"In the present study, refined dark chocolate mix was conched with the addition of finely powdered cinnamon in a laboratory-style conching machine to evaluate its aroma profile both analytically and sensorially. The analytical determinations were carried out by a combination of solid phase micro extraction (SPME)-gas chromatography (GC)-mass spectroscopy (MS) and-olfactometry(O), while the sensory evaluation was made with trained panelists. The optimum conditions for the SPME were found to be CAR/PDMS as the fiber, 60___C as the temperature, and 60__min as the time. SPME analyses were carried out at 60___C for 60__min with toluene as an internal standard. 26 compounds were monitored before and after conching. The unconched sample had a significantly higher fruity odor value than the conched sample. This new product was highly acceptable according to the overall inclination test. However some of textural properties, such as coarseness, and hardness were below the general preference. ",Journal of food science and technology,[],[],Effect of cinnamon powder addition during conching on the flavor of dark chocolate mass.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/25829576,2015,0,0,, -0.38,23521141,"The dietary intakes of nine synthetic food colours--amaranth, erythrosine, Allura Red, Ponceau 4R, tartrazine, Sunset Yellow FCF, Fast Green FCF, Brilliant Blue FCF and indigo carmine--permitted in Korea were estimated based on food consumption data for consumers and their concentrations in processed foods. The estimated daily intakes (EDIs) by Korean consumers were compared with the acceptable daily intakes (ADIs) of the colours. Among 704 foods sampled, 471 contained synthetic colours. The most highly consumed synthetic colours were Allura Red and tartrazine; the highest EDI/ADI ratios were found for amaranth, erythrosine and Allura Red. The EDIs of infants and children were higher than those of adults. The main food categories containing colours were beverages and liquor for adults, and beverages, chocolate and ice cream for infants and children. For average Korean consumers, the EDIs were not greater than 2.5% of their corresponding ADIs, although the EDI of a conservative consumer in the upper 95th percentile reached 37% of the ADI.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D002648', 'D002675', 'D002851', 'D004781', 'D005260', 'D005505', 'D006801', 'D007223', 'D008297', 'D056910', 'D013056']","['Child', 'Child, Preschool', 'Chromatography, High Pressure Liquid', 'Environmental Exposure', 'Female', 'Food Coloring Agents', 'Humans', 'Infant', 'Male', 'Republic of Korea', 'Spectrophotometry, Ultraviolet']",Exposure assessment of synthetic colours approved in Korea.,"[None, None, None, None, None, None, None, None, None, None, None]","[None, None, None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/23521141,2013,0,0,, -0.37,19559444,"A simple and direct approach was developed for thermochemolytic analysis of a wide range of biomolecules present in plant materials using an injection port of a gas chromatograph/mass spectrometer (GC/MS) and a novel solids injector consisting of a coiled stainless steel wire placed inside a modified needle syringe. Optimum thermochemolysis (or Thermally Assisted Hydrolysis/Methylation) was achieved by using a suitable methanolic solution of trimethylsulfonium hydroxide (TMSH) or tetramethylammonium hydroxide (TMAH) with an injection port temperature of 350 degrees C. Intact, methylated flavonoids, saccharides, phenolic and fatty acids, lignin dimers and diterpene resin acids were identified. Samples include tea leaves, hemicelluloses, lignin isolates and herbal medicines. Unexpected chromatographic results using TMAH reagent revealed the presence of intact methylated trisaccharides (658 Da) and structurally informative dimer lignin markers.",Journal of chromatography. A,"['D002099', 'D002392', 'D004867', 'D008401', 'D020902', 'D028221', 'D008031', 'D028223', 'D010936', 'D018515', 'D011134', 'D000644', 'D015203', 'D013452', 'D013662', 'D013696']","['Cacao', 'Catechin', 'Equipment Design', 'Gas Chromatography-Mass Spectrometry', 'Hypericum', 'Larix', 'Lignin', 'Pinus', 'Plant Extracts', 'Plant Leaves', 'Polysaccharides', 'Quaternary Ammonium Compounds', 'Reproducibility of Results', 'Sulfonium Compounds', 'Tea', 'Temperature']",Use of an injection port for thermochemolysis-gas chromatography/mass spectrometry: rapid profiling of biomaterials.,"['Q000737', 'Q000032', None, 'Q000295', 'Q000737', 'Q000737', 'Q000032', 'Q000737', 'Q000032', 'Q000737', 'Q000032', 'Q000737', None, 'Q000737', 'Q000737', None]","['chemistry', 'analysis', None, 'instrumentation', 'chemistry', 'chemistry', 'analysis', 'chemistry', 'analysis', 'chemistry', 'analysis', 'chemistry', None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/19559444,2009,2,1,text under section 3.2 , -0.37,27730643,"Rapid and early identification of micro-organisms in blood has a key role in the diagnosis of a febrile patient, in particular, in guiding the clinician to define the correct antibiotic therapy. This study presents a simple and very fast method with high performances for identifying bacteria by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) after only 4__h of incubation. We used early bacterial growth on PolyViteX chocolate agar plates inoculated with five drops of blood-broth medium deposited in the same point and spread with a sterile loop, followed by a direct transfer procedure on MALDI-TOF MS target slides without additional modification. Ninety-nine percentage of aerobic bacteria were correctly identified from 600 monomicrobial-positive blood cultures. This procedure allowed obtaining the correct identification of fastidious pathogens, such as Streptococcus pneumoniae, Neisseria meningitidis and Haemophilus influenzae that need complex nutritional and environmental requirements in order to grow. Compared to the traditional pathogen identification from blood cultures that takes over 24__h, the reliability of results, rapid performance and suitability of this protocol allowed a more rapid administration of optimal antimicrobial treatment in the patients.",Letters in applied microbiology,"['D001420', 'D001431', 'D001769', 'D000071997', 'D006801', 'D012680', 'D019032', 'D013997']","['Bacteria, Aerobic', 'Bacteriological Techniques', 'Blood', 'Blood Culture', 'Humans', 'Sensitivity and Specificity', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Time Factors']",Reducing time to identification of aerobic bacteria and fastidious micro-organisms in positive blood cultures.,"['Q000737', 'Q000379', 'Q000382', None, None, None, 'Q000379', None]","['chemistry', 'methods', 'microbiology', None, None, None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/27730643,2017,0,0,,no cocoa -0.37,24780068,"It has been suggested that vitamin D___ is not very prevalent in the human food chain. However, data from a number of recent intervention studies suggest that the majority of subjects had measurable serum 25-hydroxyvitamin D___ (25(OH)D___) concentrations. Serum 25(OH)D___, unlike 25(OH)D___, is not directly influenced by exposure of skin to sun and thus has dietary origins; however, quantifying dietary vitamin D___ is difficult due to the limitations of food composition data. Therefore, the present study aimed to characterise serum 25(OH)D___ concentrations in the participants of the National Adult Nutrition Survey (NANS) in Ireland, and to use these serum concentrations to estimate the intake of vitamin D___ using a mathematical modelling approach. Serum 25(OH)D___ concentration was measured by a liquid chromatography-tandem MS method, and information on diet as well as subject characteristics was obtained from the NANS. Of these participants, 78.7 % (n 884) had serum 25(OH)D___ concentrations above the limit of quantification, and the mean, maximum, 10th, 50th (median) and 90th percentile values of serum 25(OH)D___ concentrations were 3.69, 27.6, 1.71, 2.96 and 6.36 nmol/l, respectively. To approximate the intake of vitamin D___ from these serum 25(OH)D___ concentrations, we used recently published data on the relationship between vitamin D intake and the responses of serum 25(OH)D concentrations. The projected 5th to 95th percentile intakes of vitamin D___ for adults were in the range of 0.9-1.2 and 5-6 __g/d, respectively, and the median intake ranged from 1.7 to 2.3 __g/d. In conclusion, the present data demonstrate that 25(OH)D___ concentrations are present in the sera of adults from this nationally representative sample. Vitamin D___ may have an impact on nutritional adequacy at a population level and thus warrants further investigation.",The British journal of nutrition,"['D015652', 'D000328', 'D000363', 'D002099', 'D016208', 'D004032', 'D019587', 'D004872', 'D005260', 'D005527', 'D055951', 'D006801', 'D007494', 'D008297', 'D008954', 'D009749', 'D009752', 'D009753', 'D014808']","['25-Hydroxyvitamin D 2', 'Adult', 'Agaricales', 'Cacao', 'Databases, Factual', 'Diet', 'Dietary Supplements', 'Ergocalciferols', 'Female', 'Food, Fortified', 'Functional Food', 'Humans', 'Ireland', 'Male', 'Models, Biological', 'Nutrition Surveys', 'Nutritional Status', 'Nutritive Value', 'Vitamin D Deficiency']",Dietary vitamin D___--a potentially underestimated contributor to vitamin D nutritional status of adults?,"['Q000097', None, 'Q000737', 'Q000737', None, 'Q000009', 'Q000032', 'Q000008', None, 'Q000032', 'Q000032', None, None, None, None, None, None, None, 'Q000097']","['blood', None, 'chemistry', 'chemistry', None, 'adverse effects', 'analysis', 'administration & dosage', None, 'analysis', 'analysis', None, None, None, None, None, None, None, 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/24780068,2014,0,0,, -0.37,2433870,"The nickel content of food items consumed in Denmark was estimated on the basis of analysis by atomic absorption spectrophotometry. The highest concentrations (1-10 mg nickel/kg fresh weight) were found in cocoa, licorice, lucerne seeds, dried beans, peanuts, hazel nuts, sunflower seeds, oat meal and wheat bran. A diet instruction sheet is proposed as an aid to reduce the amount of nickel ingested. The nickel intake of 8 normal volunteers for 24-hour periods was measured when they ingested their usual diet, reduced nickel intake by adherence to the diet instruction sheet, and when they made a conscious effort to increase nickel intake. It is concluded that it is possible to reduce daily nickel intake in food items.",Acta dermato-venereologica,"['D003718', 'D004032', 'D005260', 'D005504', 'D006801', 'D008297', 'D009532', 'D013054']","['Denmark', 'Diet', 'Female', 'Food Analysis', 'Humans', 'Male', 'Nickel', 'Spectrophotometry, Atomic']",Nickel in Danish food.,"[None, None, None, None, None, None, 'Q000032', None]","[None, None, None, None, None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/2433870,1987,,,, -0.37,27285570,"Polyphenols play an important role in human health. To address their accessibility to a breastfed infant, we planned to evaluate whether breast milk (BM) (colostrum, transitional, and mature) epicatechin metabolites could be related to the dietary habits of mothers. The polyphenol consumption of breastfeeding mothers was estimated using a food frequency questionnaire and 24 h recalls. Solid-phase extraction-ultra performance liquid chromatography-tandem mass spectrometry (SPE-UPLC-MS/MS) was applied for direct epicatechin metabolite analysis. Their bioavailability in BM as a result of dietary ingestion was confirmed in a preliminary experiment with a single dose of dark chocolate. Several host and microbial phase II metabolites of epicatechin were detected in BM among free-living lactating mothers. Interestingly, a modest correlation between dihydroxyvalerolactone sulfate and the intake of cocoa products was observed. Although a very low percentage of dietary polyphenols is excreted in BM, they are definitely in the diet of breastfed infants. Therefore, evaluation of their role in infant health could be further promoted. ",Journal of agricultural and food chemistry,"['D000328', 'D001942', 'D002099', 'D002392', 'D005260', 'D006801', 'D007223', 'D007774', 'D008297', 'D008895', 'D053719']","['Adult', 'Breast Feeding', 'Cacao', 'Catechin', 'Female', 'Humans', 'Infant', 'Lactation', 'Male', 'Milk, Human', 'Tandem Mass Spectrometry']",Dietary Epicatechin Is Available to Breastfed Infants through Human Breast Milk in the Form of Host and Microbial Metabolites.,"[None, None, 'Q000378', 'Q000032', None, None, None, None, None, 'Q000737', None]","[None, None, 'metabolism', 'analysis', None, None, None, None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/27285570,2017,0,0,, -0.37,22972787,"Flavor is one of the most important characteristics of chocolate products and is due to a complex volatile fraction, depending both on the cocoa bean genotype and the several processes occurring during chocolate production (fermentation, drying, roasting and conching). Alkylpyrazines are among the most studied volatiles, being one of the main classes of odorant compounds in cocoa products. In this work, a mass spectrometric approach was used for the comparison of cocoa liquors from different countries. A headspace solid-phase microextraction gas chromatography-mass spectrometry method was developed for the qualitative study of the volatile fraction; the standard addition method was then used for the quantitative determination of five pyrazines (2-methylpyrazine, 2,3-dimethylpyrazine, 2,5-dimethylpyrazine, 2,3,5-trimethylpyrazine and tetramethylpyrazine). Satisfactory figures of merit were obtained: Limits of quantitation were in the range 0.1-2.7___ng/g; repeatability and reproducibility varied between 3% and 7% and between 8% and 14%, respectively. The total content of the pyrazines was remarkably different in the considered samples, ranging from 99 to 708___ng/g. Tetramethylpyrazine showed the highest concentration in all samples, with a maximum value of 585___ng/g. A preliminary study was also performed on the nonvolatile fraction using LC-MS/MS, identifying some flavanols such as catechin, epicatechin and procyanidins.",Journal of mass spectrometry : JMS,[],[],Characterization of cocoa liquors by GC-MS and LC-MS/MS: focus on alkylpyrazines and flavanols.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/22972787,2013,,,, -0.37,4086433,"A headspace gas chromatographic (GC) method, which can be automated, has been developed for determination of methyl bromide. This method has been applied to wheat, flour, cocoa, and peanuts. Samples to be analyzed are placed in headspace sample vials, water is added, and the vials are sealed with Teflon-lined septa. After an appropriate equilibration time at 32 degrees C, the samples are analyzed within 10 h. A sample of the headspace is withdrawn and analyzed on a gas chromatograph equipped with an electron capture detector (ECD). Methyl bromide levels were quantitated by comparison of peak area with a standard. The standard was generated by adding a known amount of methyl bromide to a portion of the matrix being analyzed and which was known to be methyl bromide free. The detection limit of the method was 0.4 ppb. The coefficient of variation (CV) was 6.5% for wheat, 8.3% for flour, 3.3% for cocoa, and 11.6% for peanuts.",Journal - Association of Official Analytical Chemists,"['D010367', 'D002099', 'D002849', 'D005433', 'D005506', 'D006842', 'D014908', 'D014874', 'D014881']","['Arachis', 'Cacao', 'Chromatography, Gas', 'Flour', 'Food Contamination', 'Hydrocarbons, Brominated', 'Triticum', 'Water Pollutants, Chemical', 'Water Supply']",Headspace gas chromatographic method for determination of methyl bromide in food ingredients.,"['Q000032', 'Q000032', None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032']","['analysis', 'analysis', None, 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/4086433,1986,,,, -0.36,25530151,"Dietary intervention studies have shown that flavanols and inorganic nitrate can improve vascular function, suggesting that these two bioactives may be responsible for beneficial health effects of diets rich in fruits and vegetables. We aimed to study interactions between cocoa flavanols (CF) and nitrate, focusing on absorption, bioavailability, excretion, and efficacy to increase endothelial function. In a double-blind randomized, dose-response crossover study, flow-mediated dilation (FMD) was measured in 15 healthy subjects before and at 1, 2, 3, and 4 h after consumption of CF (1.4-10.9 mg/kg bw) or nitrate (0.1-10 mg/kg bw). To study flavanol-nitrate interactions, an additional intervention trial was performed with nitrate and CF taken in sequence at low and high amounts. FMD was measured before (0 h) and at 1h after ingestion of nitrate (3 or 8.5 mg/kg bw) or water. Then subjects received a CF drink (2.7 or 10.9 mg/kg bw) or a micro- and macronutrient-matched CF-free drink. FMD was measured at 1, 2, and 4 h thereafter. Blood and urine samples were collected and assessed for CF and nitric oxide (NO) metabolites with HPLC and gas-phase reductive chemiluminescence. Finally, intragastric formation of NO after CF and nitrate consumption was investigated. Both CF and nitrate induced similar intake-dependent increases in FMD. Maximal values were achieved at 1 h postingestion and gradually decreased to reach baseline values at 4 h. These effects were additive at low intake levels, whereas CF did not further increase FMD after high nitrate intake. Nitrate did not affect flavanol absorption, bioavailability, or excretion, but CF enhanced nitrate-related gastric NO formation and attenuated the increase in plasma nitrite after nitrate intake. Both flavanols and inorganic nitrate can improve endothelial function in healthy subjects at intake amounts that are achievable with a normal diet. Even low dietary intake of these bioactives may exert relevant effects on endothelial function when ingested together.",Free radical biology & medicine,"['D000328', 'D001794', 'D001916', 'D002099', 'D002851', 'D018592', 'D019587', 'D004305', 'D005419', 'D006801', 'D008297', 'D009566', 'D009569', 'D013270', 'D014664']","['Adult', 'Blood Pressure', 'Brachial Artery', 'Cacao', 'Chromatography, High Pressure Liquid', 'Cross-Over Studies', 'Dietary Supplements', 'Dose-Response Relationship, Drug', 'Flavonoids', 'Humans', 'Male', 'Nitrates', 'Nitric Oxide', 'Stomach', 'Vasodilation']",Interactions between cocoa flavanols and inorganic nitrate: additive effects on endothelial function at achievable dietary amounts.,"[None, 'Q000187', 'Q000187', 'Q000737', None, None, None, None, 'Q000008', None, None, 'Q000008', 'Q000032', 'Q000378', 'Q000187']","[None, 'drug effects', 'drug effects', 'chemistry', None, None, None, None, 'administration & dosage', None, None, 'administration & dosage', 'analysis', 'metabolism', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/25530151,2015,0,0,, -0.36,10956135,"A technique based on solid-phase microextraction, mass spectrometry, and multivariate analysis (SPME-MS-MVA) was used to predict the shelf life of pasteurized and homogenized reduced-fat milk and whole-fat chocolate milk sampled over a 7 month period. Using SPME-MS-MVA, which is essentially a mass spectrometry-based electronic-nose instrument, volatile bacterial metabolites were extracted from milk with SPME (Carboxen-PDMS) and injected into a GC capillary column at elevated temperature. Mass fragmentation profiles from the unresolved milk volatile components were normalized to the intensity of a chlorobenzene internal standard mass peak (m/z 112) and subjected to MVA. Prediction models based on partial least-squares regression of mass intensity lists were able to predict the shelf life of samples to approximately +/-1 day, with correlation coefficients greater than 0.98 for the two types of milk samples. Using principal component analysis techniques, the procedure was also useful for classifying samples that were rendered unpalatable by nonmicrobial sources (contamination by copper and sanitizer) as well as by bacteria.",Journal of agricultural and food chemistry,"['D000818', 'D005511', 'D013058', 'D008892', 'D015999']","['Animals', 'Food Handling', 'Mass Spectrometry', 'Milk', 'Multivariate Analysis']","Shelf-life prediction of processed milk by solid-phase microextraction, mass spectrometry, and multivariate analysis.","[None, None, None, None, None]","[None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10956135,2000,0,0,,no cocoa -0.36,28041933,"Ochratoxin A (OTA) is a mycotoxin (fungal toxin) found in multiple foodstuffs. Because OTA has been shown to cause kidney disease in multiple animal models, several governmental bodies around the world have set maximum allowable levels of OTA in different foods and beverages. In this study, we conducted the first exposure and risk assessment study of OTA for the United States' population. A variety of commodities from grocery stores across the US were sampled for OTA over a 2-year period. OTA exposure was calculated from the OTA concentrations in foodstuffs and consumption data for different age ranges. We calculated the margin of safety (MOS) for individual age groups across all commodities of interest. Most food and beverage samples were found to have non-detectable OTA; however, some samples of dried fruits, breakfast cereals, infant cereals, and cocoa had detectable OTA. The lifetime MOS in the US population within the upper 95% of consumers of all possible commodities was >1, indicating negligible risk. In the US, OTA exposure is highest in infants and young children who consume large amounts of oat-based cereals. Even without OTA standards in the US, exposures would not be associated with significant risk of adverse effects.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000293', 'D000328', 'D000368', 'D000369', 'D002648', 'D002675', 'D002853', 'D004032', 'D005260', 'D005506', 'D006801', 'D007223', 'D007231', 'D008297', 'D008875', 'D009183', 'D009793', 'D018570', 'D053719', 'D014481', 'D055815']","['Adolescent', 'Adult', 'Aged', 'Aged, 80 and over', 'Child', 'Child, Preschool', 'Chromatography, Liquid', 'Diet', 'Female', 'Food Contamination', 'Humans', 'Infant', 'Infant, Newborn', 'Male', 'Middle Aged', 'Mycotoxins', 'Ochratoxins', 'Risk Assessment', 'Tandem Mass Spectrometry', 'United States', 'Young Adult']",A risk assessment of dietary Ochratoxin a in the United States.,"[None, None, None, None, None, None, None, None, None, 'Q000032', None, None, None, None, None, 'Q000032', 'Q000032', None, None, None, None]","[None, None, None, None, None, None, None, None, None, 'analysis', None, None, None, None, None, 'analysis', 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28041933,2017,1,1,table 1, -0.36,30011739,"Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) is an ultra-high resolution mass spectrometry technique used mainly in analysis of unresolved complex mixtures comprising tens of thousands of analytes. For the first time, it was used to analyze samples of raw fermented cocoa beans originating from Cameroon and Ivory Coast. The direct infusion mass spectra of the raw fermented cocoa bean extracts showed 10091 and 10911 peaks, resp., rating cocoa among the most complex organic mixtures ever analyzed. Automated molecular formula calculations could assign 2995 and 2968 of the peaks, resp. to formulae containing only C, H, O, N___3 and S___1 atoms. The formulae were separated into four groups depending on their heteroatom content and the intensities of the groups were compared in class plots, showing the highest population in the CHON species, but the highest abundance in the CHO species. Elemental ratios obtained from the molecular formulae were plotted in an intensity coded three-dimensional modification of the van Krevelen diagram. For the CHO species, the van Krevelen diagram showed that most of the intensity belongs to the lipid, polyphenol and carbohydrate regions of the plot. The biggest difference was observed in the CHON group, assigned as peptide degradation products, where the Ivorian beans showed greater variety and molecular diversity and higher total intensity of the nitrogen containing compounds, in accordance with the fact that the Ivorian beans show generally higher nitrogen content than the Cameroon beans. FTICR-MS proves capable not only for high-throughput comparison of major classes of metabolites from cocoa samples from different origins, but also can give insight into the different molecular formulae comprising these compound classes.","Food research international (Ottawa, Ont.)",[],[],Fourier transform ion cyclotron resonance mass spectrometrical analysis of raw fermented cocoa beans of Cameroon and Ivory Coast origin.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/30011739,2018,0,0,, -0.36,24236083,"The genus Capsicum is New World in origin and represents a complex of a wide variety of both wild and domesticated taxa. Peppers or fruits of Capsicum species rarely have been identified in the paleoethnobotanical record in either Meso- or South America. We report here confirmation of Capsicum sp. residues from pottery samples excavated at Chiapa de Corzo in southern Mexico dated from Middle to Late Preclassic periods (400 BCE to 300 CE). Residues from 13 different pottery types were collected and extracted using standard techniques. Presence of Capsicum was confirmed by ultra-performance liquid chromatography (UPLC)/MS-MS Analysis. Five pottery types exhibited chemical peaks for Capsicum when compared to the standard (dihydrocapsaicin). No peaks were observed in the remaining eight samples. Results of the chemical extractions provide conclusive evidence for Capsicum use at Chiapas de Corzo during a 700 year period (400 BCE-300 CE). Presence of Capsicum in different types of culinary-associated pottery raises questions how chili pepper could have been used during this early time period. As Pre-Columbian cacao products sometimes were flavored using Capsicum, the same pottery sample set was tested for evidence of cacao using a theobromine marker: these results were negative. As each vessel that tested positive for Capsicum had a culinary use we suggest here the possibility that chili residues from the Chiapas de Corzo pottery samples reflect either paste or beverage preparations for religious, festival, or every day culinary use. Alternatively, some vessels that tested positive merely could have been used to store peppers. Most interesting from an archaeological context was the presence of Capsicum residue obtained from a spouted jar, a pottery type previously thought only to be used for pouring liquids. ",PloS one,"['D002211', 'D002212', 'D003296', 'D003297', 'D049690', 'D006801', 'D007198', 'D008800', 'D053719']","['Capsaicin', 'Capsicum', 'Cooking', 'Cooking and Eating Utensils', 'History, Ancient', 'Humans', 'Indians, North American', 'Mexico', 'Tandem Mass Spectrometry']","Prehispanic use of chili peppers in Chiapas, Mexico.","['Q000737', 'Q000737', 'Q000266', None, None, None, None, None, None]","['chemistry', 'chemistry', 'history', None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24236083,2014,0,0,,cacao -0.36,29090642,"Brazil is the sixth largest producer of cocoa beans in the world, after C_te d'Ivoire, Ghana, Indonesia, Nigeria and Cameroon. The southern region of Bahia stands out as the country's largest producer, accounting for approximately 60% of production. Due to damage caused by infestation of the cocoa crop with the fungus Moniliophthora perniciosa, which causes 'witch's broom disease', research in cocoa beans has led to the cloning of species that are resistant to the disease; however, there is little information about the development of other fungal genera in these clones, such as Aspergillus, which do not represent a phytopathogenicity problem but can grow during the pre-processing of cocoa beans and produce mycotoxins. Thus, the aim of this work was to determine the presence of aflatoxin (AF) and ochratoxin A (OTA) in cocoa clones developed in Brazil. Aflatoxin and ochratoxin A contamination were determined in 130 samples from 13 cocoa clones grown in the south of Bahia by ultra-performance liquid chromatography with a fluorescence detector. The method was evaluated for limit of detection (LOD) (0.05-0.90 __g kg","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000348', 'D001938', 'D002099', 'D005506', 'D009793']","['Aflatoxins', 'Brazil', 'Cacao', 'Food Contamination', 'Ochratoxins']","Aflatoxins and ochratoxin A in different cocoa clones (Theobroma cacao L.) developed in the southern region of Bahia, Brazil.","['Q000032', None, 'Q000737', 'Q000032', 'Q000032']","['analysis', None, 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/29090642,2018,1,1,table 1 ,if toxins are included this table reflects aflatoxin types (B1-2 and G1-2) and ochratoxin. -0.36,20557892,"This paper reports a comprehensive sensitive multi-residue liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for detection, identification and quantitation of 73 pesticides and their related products, a total of 98 analytes, belonging to organophosphorus pesticides (OPPs) and carbamates, in foods. The proposed method makes use of a modified QuEChERS (quick, easy, cheap, effective, rigged, and safe) procedure that combines isolation of the pesticides and sample clean-up in a single step. Analysis is performed by liquid chromatography-electrospray ionization-tandem mass spectrometry operated in the multiple reaction monitoring (MRM) mode, acquiring two specific precursor-product ion transitions per target compound. Two main fragment ions for each pesticide were obtained to achieve the identification according to the SANCO guidelines 10684/2009. The method was validated with various food samples, including edible oil, meat, egg, cheese, chocolate, coffee, rice, tree nuts, citric fruits, vegetables, etc. No significant matrix effect was observed for tested pesticides, therefore, matrix-matched calibration was not necessary. Calibration curves were linear and covered from 1 to 20 microg L(-1) for all compounds studied. The average recoveries, measured at 10 microg kg(-1), were in the range 70-120% for all of the compounds tested with relative standard deviations below 20%, while a value of 10 microg kg(-1) has been established as the method limit of quantitation (MLOQ) for all target analytes. Similar trueness and precision results were also obtained for spiking at 200 microg kg(-1). Expanded uncertainty values were in the range 21-27% while the HorRat ratios were below 1. The method has been successfully applied to the analysis of 700 food samples in the course of a baseline monitoring study of OPPs and carbamates.",Journal of chromatography. A,"['D002219', 'D002853', 'D005504', 'D005506', 'D009943', 'D010573', 'D053719']","['Carbamates', 'Chromatography, Liquid', 'Food Analysis', 'Food Contamination', 'Organophosphorus Compounds', 'Pesticide Residues', 'Tandem Mass Spectrometry']",Validation and use of a fast sample preparation method and liquid chromatography-tandem mass spectrometry in analysis of ultra-trace levels of 98 organophosphorus pesticide and carbamate residues in a total diet study involving diversified food types.,"['Q000032', 'Q000379', 'Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000379']","['analysis', 'methods', 'methods', 'analysis', 'analysis', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/20557892,2010,0,0,,no cocoa -0.36,25965784,"An easy extraction method that permits the use of a liquid chromatography-isotopic ratio mass spectrometry (LC-IRMS) system to evaluate __(13)C of vanillin in chocolate products and industrial flavorings is presented. The method applies the determination of stable isotopes of carbon to discriminate between natural vanillin from vanilla beans and vanillin from other sources (mixtures from beans, synthesis, or biotechnology). A series of 13 chocolate bars and chocolate snack foods available on the Italian market and 8 vanilla flavorings derived from industrial quality control processes were analyzed. Only 30% of products considered in this work that declared ""vanilla"" on the label showed data that permitted the declaration ""vanilla"" according to European Union (EU) Regulation 1334/2008. All samples not citing ""vanilla"" or ""natural flavoring"" on the label gave the correct declaration. The extraction method is presented with data useful for statistical evaluation. ",Journal of agricultural and food chemistry,"['D001547', 'D002099', 'D002247', 'D005591', 'D002851', 'D005421', 'D013058', 'D010936', 'D062410', 'D031669']","['Benzaldehydes', 'Cacao', 'Carbon Isotopes', 'Chemical Fractionation', 'Chromatography, High Pressure Liquid', 'Flavoring Agents', 'Mass Spectrometry', 'Plant Extracts', 'Snacks', 'Vanilla']",Easy Extraction Method To Evaluate __13C Vanillin by Liquid Chromatography-Isotopic Ratio Mass Spectrometry in Chocolate Bars and Chocolate Snack Foods.,"['Q000032', 'Q000737', 'Q000737', 'Q000379', 'Q000295', 'Q000032', 'Q000379', 'Q000737', None, 'Q000737']","['analysis', 'chemistry', 'chemistry', 'methods', 'instrumentation', 'analysis', 'methods', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25965784,2015,0,0,,no cocoa -0.36,30007697,"Fermentation and drying are the two crucial processing steps required to produce cocoa beans with desired properties, especially taste and flavor. To understand their impact on the lipid profile of cocoa, the lipid composition of unfermented raw and fermented dried beans from six different origins was investigated using high-performance liquid chromatography-mass spectrometry methods. While the comparison of triacylglycerol profiles across the different origins showed only small variations in individual compound concentrations, the comparison along the fermentation status showed major differences regarding the occurrence of polar lipids. These compounds may serve as biomarkers for the fermentation status of the beans and a simple analytical method suitable for field trials is proposed. Finally, a hypothesis identifying key unsaturated triacylglycerols contributing to the hardness and softness of cocoa butter is presented.","Food research international (Ottawa, Ont.)",[],[],Variation of triacylglycerol profiles in unfermented and dried fermented cocoa beans of different origins.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/30007697,2018,2,3,table 1 , triacylglycerols are in percentages for unfermented and fermented samples -0.35,3163354,"The primary aim of this study was to rank several reference foods (apple drink, caramel, chocolate, cookie, skimmed milk powder, snack cracker, and wheat flake) according to their plaque pH response as monitored in a panel of 12 volunteers by the plaque-sampling method for comparison with data previously reported with other methods used to assess cariogenicity potential. Secondary experiments (using subsets of the panel of subjects) were undertaken in an attempt to elucidate some of the reasons for the observed plaque pH changes. Oral carbohydrate retention was measured at a single time period after food use as total anthrone-positive carbohydrate material, and as specific acidogenic sugars by gas-liquid chromatography after gel-exclusion chromatography. The concentrations of acid anions in the plaque fluid after food consumption were measured by isotachophoresis eight min after food use. According to the plaque pH response, apple-flavored fruit drink and chocolate were the most acidogenic foods and skimmed milk powder the least acidogenic. There were significant correlations (p less than 0.05) between the plaque pH data and lactate-plus-acetate concentrations in plaque fluid, but the correlations between the pH data and any of the carbohydrate retention parameters were not significant.",Journal of dental research,"['D000143', 'D000293', 'D000328', 'D000838', 'D002326', 'D003773', 'D004040', 'D005502', 'D005947', 'D006801', 'D006863', 'D007773', 'D009055', 'D013395']","['Acids', 'Adolescent', 'Adult', 'Anions', 'Cariogenic Agents', 'Dental Plaque', 'Dietary Carbohydrates', 'Food', 'Glucose', 'Humans', 'Hydrogen-Ion Concentration', 'Lactates', 'Mouth', 'Sucrose']","The relationship between plaque pH, plaque acid anion profiles, and oral carbohydrate retention after ingestion of several 'reference foods' by human subjects.","['Q000378', None, None, 'Q000378', None, 'Q000378', 'Q000378', None, 'Q000378', None, None, 'Q000378', 'Q000378', 'Q000378']","['metabolism', None, None, 'metabolism', None, 'metabolism', 'metabolism', None, 'metabolism', None, None, 'metabolism', 'metabolism', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/3163354,1988,,,,no pdf access -0.35,961063,"In Coca-powder fumigated with Ethylene Oxide-1,2(14)C, several derivatives were isolated by extraction and preparative Thin Layer Chromatography. Of the two compounds isolated from the water-extract, the structures have been suggested as N,N-Bis-(Di-Ethoxy-O-Hydroxy-ethyl)-Isoleucyl-Alanyl-Cysteine (MW = 569)and N-Ethoxy-O-Hydroxyethyl)-Tyrosine (MW = 269), based on I.R. and Mass Spectrometry. Their approximate concentrations were found to be 20 and 50 mg/kg respectively.",Zeitschrift fur Lebensmittel-Untersuchung und -Forschung,"['D000596', 'D002099', 'D002855', 'D005030', 'D008970', 'D010455']","['Amino Acids', 'Cacao', 'Chromatography, Thin Layer', 'Ethylenes', 'Molecular Weight', 'Peptides']","[Isolation of the Derivatives from Coca-Powder Fumigated by Ethylene Oxide 1,2-14 C and their Structure Suggested on the Basis of I. R. and Mass-Spectrometry].","['Q000302', 'Q000032', None, 'Q000494', None, 'Q000302']","['isolation & purification', 'analysis', None, 'pharmacology', None, 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/961063,1976,,,, -0.35,1941565,"Both regular and decaffeinated coffees were found to have cholinomimetic actions when tested in urethane-anesthetized rats. These actions were distinct from those of caffeine and reversible by atropine. The bioactive fraction was purified from alcoholic extracts of instant decaffeinated coffee by liquid column chromatography and preparative TLC. The purified compound showed similar pharmacological actions as the starting material. Chromatographic behavior was further characterized by analytical TLC and HPLC. Chromatographic analyses of extracts of green coffee beans and roasted ground coffees showed that the cardioactive compound was only present in roasted coffees. Similar analyses of other commonly consumed beverages, including teas and cocoa, showed that this compound was not present in beverages besides coffee.",Journal of pharmaceutical sciences,"['D000109', 'D000818', 'D001285', 'D001794', 'D002099', 'D002851', 'D002855', 'D003069', 'D008297', 'D010277', 'D051381', 'D011919', 'D013056', 'D013662']","['Acetylcholine', 'Animals', 'Atropine', 'Blood Pressure', 'Cacao', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Coffee', 'Male', 'Parasympathomimetics', 'Rats', 'Rats, Inbred Strains', 'Spectrophotometry, Ultraviolet', 'Tea']",Coffee contains cholinomimetic compound distinct from caffeine. I: Purification and chromatographic analysis.,"['Q000032', None, 'Q000494', 'Q000187', 'Q000032', None, None, 'Q000032', None, 'Q000032', None, None, None, 'Q000032']","['analysis', None, 'pharmacology', 'drug effects', 'analysis', None, None, 'analysis', None, 'analysis', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/1941565,1991,,,,no pdf access -0.35,17440516,To investigate the intake of plant sterols and identify major dietary sources of plant sterols in the British diet.,European journal of clinical nutrition,"['D000328', 'D000368', 'D000704', 'D001939', 'D002849', 'D015331', 'D003430', 'D016208', 'D004034', 'D004041', 'D002523', 'D005260', 'D005504', 'D006801', 'D008297', 'D008875', 'D010840', 'D011446', 'D017678', 'D011795', 'D006113', 'D014675']","['Adult', 'Aged', 'Analysis of Variance', 'Bread', 'Chromatography, Gas', 'Cohort Studies', 'Cross-Sectional Studies', 'Databases, Factual', 'Diet Surveys', 'Dietary Fats', 'Edible Grain', 'Female', 'Food Analysis', 'Humans', 'Male', 'Middle Aged', 'Phytosterols', 'Prospective Studies', 'Sex Distribution', 'Surveys and Questionnaires', 'United Kingdom', 'Vegetables']",Food sources of plant sterols in the EPIC Norfolk population.,"[None, None, None, None, 'Q000379', None, None, None, None, 'Q000032', None, None, 'Q000379', None, None, None, 'Q000008', None, None, None, None, None]","[None, None, None, None, 'methods', None, None, None, None, 'analysis', None, None, 'methods', None, None, None, 'administration & dosage', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/17440516,2008,0,0,,no cocoa -0.35,29885500,"The present study aims to quantify acrylamide in caffeinated beverages including American coffee, Lebanese coffee, espresso, instant coffee and hot chocolate, and to determine their carcinogenic and neurotoxic risks. A survey was carried for this purpose whereby 78% of the Lebanese population was found to consume at least one type of caffeinated beverages. Gas Chromatography Mass Spectrometry analysis revealed that the average acrylamide level in caffeinated beverages is 29,176____g/kg sample. The daily consumption of acrylamide from Lebanese coffee (10.9 __g/kg-bw/day), hot chocolate (1.2 __g/kg-bw/day) and Espresso (7.4 __g/kg-bw/day) was found to be higher than the risk intake for carcinogenicity and neurotoxicity as set by World Health Organization (WHO; 0.3-2 __g/kg-bw/day) at both the mean (average consumers) and high (high consumers) dietary exposures. On the other hand, American coffee (0.37 __g/kg-bw/day) was shown to pose no carcinogenic or neurotoxic risks among the Lebanese community for consumers with a mean dietary exposure. The study shows alarming results that call for regulating the caffeinated product industry by setting legislations and standard protocols for product preparation in order to limit the acrylamide content and protect consumers. In order to avoid carcinogenic and neurotoxic risks, we propose that WHO/FAO set acrylamide levels in caffeinated beverages to 7000____g acrylamide/kg sample, a value which is 4-folds lower than the average acrylamide levels of 29,176____g/kg sample found in caffeinated beverages sold in the Lebanese market. Alternatively, consumers of caffeinated products, especially Lebanese coffee and espresso, would have to lower their daily consumption to 0.3-0.4 cups/day.",Chemosphere,[],[],Carcinogenic and neurotoxic risks of acrylamide consumed through caffeinated beverages among the lebanese population.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29885500,2018,0,0,,no cocoa -0.35,27700988,"In this work, an efficient method for preparative separation of procyanidins from raw cacao bean extract by high-speed counter-current chromatography (HSCCC) was developed. Under the optimized solvent system of n-hexane-ethyl acetate-water (1:50:50, v/v/v) with a combination of head-tail and tail-head elution modes, various procyanidins fractions with different polymerization degrees were successfully separated. UPLC, QTOF-MS and ","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000975', 'D052160', 'D044946', 'D001713', 'D002099', 'D002392', 'D002851', 'D003377', 'D010851', 'D010936', 'D044945', 'D012997', 'D013451']","['Antioxidants', 'Benzothiazoles', 'Biflavonoids', 'Biphenyl Compounds', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Countercurrent Distribution', 'Picrates', 'Plant Extracts', 'Proanthocyanidins', 'Solvents', 'Sulfonic Acids']",Preparative separation of cacao bean procyanidins by high-speed counter-current chromatography.,"['Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000379', 'Q000379', 'Q000737', 'Q000737', 'Q000737', None, 'Q000737']","['chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'methods', 'methods', 'chemistry', 'chemistry', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27700988,2017,0,0,, -0.35,25123980,"Cadmium (Cd) and lead (Pb) concentrations and their relationship to the cocoa content of chocolates commercialized in Brazil were evaluated by graphite furnace atomic absorption spectrometry (GF AAS) after microwave-assisted acid digestion. Several chemical modifiers were tested during method development, and analytical parameters, including the limits of detection and quantification as well as the accuracy and precision of the overall procedure, were assessed. The study examined 30 chocolate samples, and the concentrations of Cd and Pb were in the range of <1.7-107.6 and <21-138.4 ng/g, respectively. The results indicated that dark chocolates have higher concentrations of Cd and Pb than milk and white chocolates. Furthermore, samples with five different cocoa contents (ranging from 34 to 85%) from the same brand were analyzed, and linear correlations between the cocoa content and the concentrations of Cd (R(2) = 0.907) and Pb (R(2) = 0.955) were observed. The results showed that chocolate might be a significant source of Cd and Pb ingestion, particularly for children. ",Journal of agricultural and food chemistry,"['D000818', 'D001938', 'D002099', 'D002104', 'D002417', 'D005506', 'D007854', 'D008892', 'D012639']","['Animals', 'Brazil', 'Cacao', 'Cadmium', 'Cattle', 'Food Contamination', 'Lead', 'Milk', 'Seeds']",Cadmium and lead in chocolates commercialized in Brazil.,"[None, None, 'Q000737', 'Q000032', None, 'Q000032', 'Q000032', 'Q000737', 'Q000737']","[None, None, 'chemistry', 'analysis', None, 'analysis', 'analysis', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25123980,2015,0,0,, -0.35,28460952,"Re-utilization of various agro-industrial wastes is of growing importance from many aspects. Considering the variety and complexity of such materials, compositional data and compliant methodology is still undergoing many updates and improvements. Present study evaluated sugar beet pulp (SBP), walnut shell (WS), cocoa bean husk (CBH), onion peel (OP) and pea pods (PP) as potentially valuable materials for carbohydrate recovery. Macrocomponent analyses revealed carbohydrate fraction as the most abundant, dominating in dietary fibres. Upon complete acid hydrolysis of sample alcohol insoluble residues, developed procedures of high performance thin-layer chromatography (HPTLC) and high performance liquid chromatography (HPLC) coupled with 3-methyl-1-phenyl-2-pyrazolin-5-one pre-column derivatization (PMP-derivatization) were used for carbohydrate monomeric composition determination. HPTLC exhibited good qualitative features useful for multi-sample rapid analysis, while HPLC superior separation and quantification characteristics. Distinctive monomeric patterns were obtained among samples. OP, SBP and CBH, due to the high galacturonic acid content (20.81%, 13.96% and 6.90% dry matter basis, respectively), may be regarded as pectin sources, while WS and PP as materials abundant in xylan-rich hemicellulose (total xylan content 15.53%, 9.63% dry matter basis, respectively). Present study provides new and valuable compositional data for different plant residual materials and a reference for the application of established methodology.","Food research international (Ottawa, Ont.)",[],[],Compositional evaluation of selected agro-industrial wastes as valuable sources for the recovery of complex carbohydrates.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/28460952,2018,2,1,table 1, -0.34,19447973,"The US industry standard for shelf-life of whole milk powder (WMP) is 6 to 9 mo, although previous research has demonstrated flavor changes by 3 mo at ambient storage. This study evaluated the influence of packaging atmosphere, storage temperature, and storage time on WMP shelf-life using sensory and instrumental techniques. Two commercial batches of WMP were repackaged in plastic laminate pouches with air or nitrogen and stored at 2 degrees C or 23 degrees C for 1 yr. Descriptive analysis was conducted using a 10-member trained panel; volatile analysis was performed using solid-phase microextraction with gas chromatography-mass spectrometry. Consumer acceptance (n = 75) was conducted every 3 mo with reconstituted WMP and white and milk chocolate made from each treatment. Data were analyzed using ANOVA with Fisher's LSD, Pearson correlation analysis, and principal component analysis. Air-stored WMP had higher peroxide values, lipid oxidation volatiles, and grassy and painty flavors than nitrogen-flushed WMP. Storage temperature did not affect levels of straight chain lipid oxidation volatiles; 23 degrees C storage resulted in higher cooked and milkfat flavors and lower levels of grassy flavor compared with 2 degrees C storage. Consumer acceptance was negatively correlated with lipid oxidation volatiles and painty flavor. Nitrogen flushing prevented the development of painty flavor in WMP stored up to 1 yr at either temperature, resulting in chocolate with high consumer acceptance. Nitrogen flushing can be applied to extend the shelf life of WMP for use in chocolate; storage temperature also plays a role, but to a lesser extent.",Journal of dairy science,"['D000328', 'D000818', 'D002099', 'D003116', 'D005511', 'D005524', 'D006801', 'D008875', 'D008892', 'D009584', 'D010100', 'D010545', 'D025341', 'D013649', 'D013696', 'D055815']","['Adult', 'Animals', 'Cacao', 'Color', 'Food Handling', 'Food Technology', 'Humans', 'Middle Aged', 'Milk', 'Nitrogen', 'Oxygen', 'Peroxides', 'Principal Component Analysis', 'Taste', 'Temperature', 'Young Adult']",Effect of nitrogen flushing and storage temperature on flavor and shelf-life of whole milk powder.,"[None, None, 'Q000592', None, 'Q000379', None, None, None, 'Q000737', 'Q000737', 'Q000032', 'Q000032', None, None, None, None]","[None, None, 'standards', None, 'methods', None, None, None, 'chemistry', 'chemistry', 'analysis', 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19447973,2009,,,,no pdf access -0.34,22652759,"The enzyme chitinase from Moniliophthora perniciosa the causative agent of the witches' broom disease in Theobroma cacao, was partially purified with ammonium sulfate and filtration by Sephacryl S-200 using sodium phosphate as an extraction buffer. Response surface methodology (RSM) was used to determine the optimum pH and temperature conditions. Four different isoenzymes were obtained: ChitMp I, ChitMp II, ChitMp III and ChitMp IV. ChitMp I had an optimum temperature at 44-73__C and an optimum pH at 7.0-8.4. ChitMp II had an optimum temperature at 45-73__C and an optimum pH at 7.0-8.4. ChitMp III had an optimum temperature at 54-67__C and an optimum pH at 7.3-8.8. ChitMp IV had an optimum temperature at 60__C and an optimum pH at 7.0. For the computational biology, the primary sequence was determined in silico from the database of the Genome/Proteome Project of M. perniciosa, yielding a sequence with 564 bp and 188 amino acids that was used for the three-dimensional design in a comparative modeling methodology. The generated models were submitted to validation using Procheck 3.0 and ANOLEA. The model proposed for the chitinase was subjected to a dynamic analysis over a 1 ns interval, resulting in a model with 91.7% of the residues occupying favorable places on the Ramachandran plot and an RMS of 2.68.",Anais da Academia Brasileira de Ciencias,"['D000363', 'D000595', 'D002688', 'D002850', 'D008954', 'D008969']","['Agaricales', 'Amino Acid Sequence', 'Chitinases', 'Chromatography, Gel', 'Models, Biological', 'Molecular Sequence Data']","Purification, characterization and structural determination of chitinases produced by Moniliophthora perniciosa.","['Q000201', None, 'Q000096', None, None, None]","['enzymology', None, 'biosynthesis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22652759,2012,0,0,, -0.34,19238621,"A survey was undertaken of aflatoxin B1 (AFB1), B2 (AFB2), G1 (AFG1), G2 (AFG2), ochratoxin A (OTA), and fumonisin B1 (FB1), B2 (FB2) and B3 (FB3) contamination of various retail foods in Japan during 2004-05. The mycotoxins were analysed by high-performance liquid chromatography (HPLC), liquid chromatography/mass spectrometry (LC/MS) or high-performance thin-layer chromatography (HPTLC). Aflatoxins (AFs) were detected in ten of 21 peanut butter and in 22 of 44 bitter chocolate samples; the highest level of AFB1, 2.59 microg kg(-1), was found in peanut butter. Aflatoxin contamination was not observed in corn products (n = 55), corn (n = 110), peanuts (n = 120), buckwheat flour (n = 23), dried buckwheat noodles (n = 59), rice (n = 83) or sesame oil (n = 20). OTA was detected in 120 out of 192 samples of oatmeal, wheat flour, rye, buckwheat flour, raw coffee, roasted coffee, raisin, beer, wine and bitter chocolate, but not in rice or corn products. OTA levels in the positive samples were below 13 microg kg(-1). AFs and OTA intakes through the consumption of foods containing cacao were estimated using the data for mycotoxin contamination in bitter chocolate and those for the consumption of foods containing cacao in Japan.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000293', 'D000348', 'D000367', 'D002099', 'D002273', 'D002648', 'D002675', 'D005247', 'D005504', 'D005506', 'D006801', 'D007223', 'D009183', 'D009793', 'D055815']","['Adolescent', 'Aflatoxins', 'Age Factors', 'Cacao', 'Carcinogens', 'Child', 'Child, Preschool', 'Feeding Behavior', 'Food Analysis', 'Food Contamination', 'Humans', 'Infant', 'Mycotoxins', 'Ochratoxins', 'Young Adult']",Aflatoxin and ochratoxin A contamination of retail foods and intake of these mycotoxins in Japan.,"[None, 'Q000032', None, 'Q000737', 'Q000032', None, None, None, 'Q000379', 'Q000032', None, None, 'Q000032', 'Q000032', None]","[None, 'analysis', None, 'chemistry', 'analysis', None, None, None, 'methods', 'analysis', None, None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/19238621,2009,,,, -0.34,21663990,"This paper reports the occurrence of aflatoxigenic fungi and the presence of aflatoxins in 226 cocoa samples collected on Brazilian farms. The samples were taken at various stages of fermentation, drying and storage. A total of 819 potentially aflatoxigenic fungi were isolated using Dichloran 18% Glycerol agar after surface disinfection, and identified by standard techniques. The ability of the fungi to produce aflatoxins was determined using the agar plug technique and TLC. The presence of aflatoxins in cocoa samples was determined by HPLC using post-column derivatization with bromide after immunoaffinity column clean up. The aflatoxigenic fungi isolated were Aspergillus flavus, A. parasiticus and A. nomius. A considerable increase in numbers of these species was observed during drying and storage. In spite of the high prevalence of aflatoxigenic fungi, only low levels of aflatoxin were found in the cocoa samples, suggesting the existence of limiting factors to the accumulation of aflatoxins in the beans.",International journal of food microbiology,"['D000348', 'D001230', 'D001938', 'D002099', 'D002851', 'D005285', 'D005506', 'D005511']","['Aflatoxins', 'Aspergillus', 'Brazil', 'Cacao', 'Chromatography, High Pressure Liquid', 'Fermentation', 'Food Contamination', 'Food Handling']",Aflatoxigenic fungi and aflatoxin in cocoa.,"['Q000032', 'Q000302', None, 'Q000382', None, None, 'Q000032', None]","['analysis', 'isolation & purification', None, 'microbiology', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/21663990,2011,1,1,table 1, -0.34,17164979,Long term cocoa ingestion leads to an increased resistance against UV-induced erythema and a lowered transepidermal water loss.,European journal of nutrition,"['D000293', 'D000328', 'D000368', 'D001628', 'D002099', 'D002392', 'D002851', 'D018592', 'D004305', 'D005260', 'D005419', 'D006801', 'D017078', 'D008833', 'D008875', 'D012867', 'D013057']","['Adolescent', 'Adult', 'Aged', 'Beverages', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Cross-Over Studies', 'Dose-Response Relationship, Drug', 'Female', 'Flavonoids', 'Humans', 'Laser-Doppler Flowmetry', 'Microcirculation', 'Middle Aged', 'Skin', 'Spectrum Analysis']",Consumption of flavanol-rich cocoa acutely increases microcirculation in human skin.,"[None, None, None, None, 'Q000737', 'Q000008', 'Q000379', None, None, None, 'Q000008', None, 'Q000379', 'Q000187', None, 'Q000098', 'Q000379']","[None, None, None, None, 'chemistry', 'administration & dosage', 'methods', None, None, None, 'administration & dosage', None, 'methods', 'drug effects', None, 'blood supply', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/17164979,2007,0,,, -0.34,3369241,"The analytical application of direct pyrolysis (Py) field ionization (FI)-mass spectrometry (MS) und Curie-point pyrolysis gas chromatography-mass spectrometry (Py-GC/FIMS) to various whole foodstuffs is described for the first time. The former technique yields highly differentiated information from the sample in typically 15 min, namely the molecular weight distribution of released volatiles and pyrolysis products in a single spectrum which, owing to the good reproducibility and high significance of the resulting data, has previously been shown to be suitable for the application of chemometric methods. Such mass spectral peaks are further characterized and assigned by high resolution mass measurement and/or by electron ionization after Curie-point pyrolysis and gas chromatographic separation of the components. In this first report, typical results are presented for ground roasted coffee, rosehip tea, wheatmeal biscuit, chocolate drink powder and milk chocolate. The FI mass spectrum obtained from the latter sample is compared with those obtained using the complementary soft ionization techniques of chemical ionization (CI) and direct chemical ionization (DCI).",Zeitschrift fur Lebensmittel-Untersuchung und -Forschung,"['D000818', 'D002099', 'D003069', 'D005504', 'D008401', 'D006358', 'D008892', 'D013057', 'D013662']","['Animals', 'Cacao', 'Coffee', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Hot Temperature', 'Milk', 'Spectrum Analysis', 'Tea']",Fast profiling of food by analytical pyrolysis.,"[None, 'Q000032', 'Q000032', 'Q000379', 'Q000379', None, 'Q000032', 'Q000379', 'Q000032']","[None, 'analysis', 'analysis', 'methods', 'methods', None, 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/3369241,1988,,,, -0.34,17245391,"Indigenous people of the Torres Strait Islands have been concerned about the safety of their traditional seafoods since the discovery of high cadmium levels in the liver and kidney of dugong and turtle in 1996. This study explored links between urinary cadmium levels and consumption frequency of these traditional foods and piloted a community-based methodology to identify potential determinants of cadmium exposure and accumulation. Consultations led to selection of one community for study from which 60 women aged 30 to 50 years participated in health and food frequency survey, urine collection and a routine health check. Urinary cadmium levels were determined by inductively coupled plasma-mass spectrometry; data were analysed using SPSS-14. The geometric mean cadmium level in this group of women was 1.17 (arithmetic mean 1.86) microg/g creatinine with one-third exceeding 2.0 microg/g creatinine. Heavy smoking (>or=300 pack years) was linked to higher cadmium in urine, as was increasing age and waist circumference. Analysis of age-adjusted residuals revealed significant associations (P<0.05) between cadmium level and higher consumption of turtle liver and kidney, locally gathered clams, peanuts, coconut, chocolate and potato chips. Dugong kidney consumption approached significance (P=0.06). Multiple regression revealed that 40% (adjusted r(2)) of variation in cadmium level was explained by the sum of these associated foods plus heavy smoking, age and waist circumference. No relationships between cadmium and pregnancy history were found. This paper presents a novel approach to explore contributions of foods and other factors to exposure to toxins at community level and the first direct evidence that frequent turtle (and possibly dugong) liver and kidney and wild clam consumption is linked to higher urinary cadmium levels among Torres Strait Islander women.",Journal of exposure science & environmental epidemiology,"['D000328', 'D000818', 'D001315', 'D049872', 'D002104', 'D004032', 'D020454', 'D004784', 'D004785', 'D005260', 'D005506', 'D006801', 'D007668', 'D008099', 'D008875', 'D044382', 'D017747', 'D014426']","['Adult', 'Animals', 'Australia', 'Bivalvia', 'Cadmium', 'Diet', 'Dugong', 'Environmental Monitoring', 'Environmental Pollutants', 'Female', 'Food Contamination', 'Humans', 'Kidney', 'Liver', 'Middle Aged', 'Population Groups', 'Seafood', 'Turtles']",Exploring potential dietary contributions including traditional seafood and other determinants of urinary cadmium levels among indigenous women of a Torres Strait Island (Australia).,"[None, None, None, None, 'Q000652', None, None, None, 'Q000652', None, None, None, None, None, None, None, None, None]","[None, None, None, None, 'urine', None, None, None, 'urine', None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/17245391,2007,0,0,,no cocoa -0.34,22483203,"The characterization and authentication of fats and oils is a subject of great importance for market and health aspects. Identification and quantification of triacylglycerols in fats and oils can be excellent tools for detecting changes in their composition due to the mixtures of these products. Most of the triacylglycerol species present in either fats or oils could be analyzed and identified by chromatographic methods. However, the natural variability of these samples and the possible presence of adulterants require the application of chemometric pattern recognition methods to facilitate the interpretation of the obtained data. In view of the growing interest in this topic, this paper reviews the literature of the application of exploratory and unsupervised/supervised chemometric methods on chromatographic data, using triacylglycerol composition for the characterization and authentication of several foodstuffs such as olive oil, vegetable oils, animal fats, fish oils, milk and dairy products, cocoa and coffee.",Analytica chimica acta,"['D002849', 'D002851', 'D002855', 'D016002', 'D005223', 'D005504', 'D013058', 'D009821', 'D025341', 'D014280']","['Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Discriminant Analysis', 'Fats', 'Food Analysis', 'Mass Spectrometry', 'Oils', 'Principal Component Analysis', 'Triglycerides']",Combining chromatography and chemometrics for the characterization and authentication of fats and oils from triacylglycerol compositional data--a review.,"[None, None, None, None, 'Q000032', 'Q000379', None, 'Q000032', None, 'Q000032']","[None, None, None, None, 'analysis', 'methods', None, 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/22483203,2012,0,0,, -0.34,10435077,"A survey on the potential intake of caffeine was carried out in Campinas, SP, Brazil, in the summer of 1993. The survey was based on a representative sample of 600 individuals, 9-80 years old, who were asked about their habitual usage of coffee, tea, chocolate products and carbonated beverages. Caffeine levels in the products were determined by high performance liquid chromatography with a UV-visible detector at 254 nm. Individual daily intakes (mg/kg b.w.) of caffeine were calculated from the consumption data generated by the survey and the caffeine content of the analysed products. Of all those interviewed, 81% consumed soft drinks regularly, 75% coffee, 65% chocolate products and 37% tea. Of the analysed products, coffee showed the highest amount of caffeine. The average and median potential daily intake of caffeine by the studied population were, respectively, 2.74 and 1.85 mg/kg b.w. Coffee, tea, chocolate products and carbonated beverages accounted for median individual daily intakes of 1.90, 0.32, 0.19, and 0.19 mg/kg b.w., respectively. These data show that coffee is the most important vehicle for caffeine intake within the studied population.",Food additives and contaminants,"['D000293', 'D000328', 'D017677', 'D000368', 'D000369', 'D001628', 'D001938', 'D002099', 'D002110', 'D000697', 'D002648', 'D003069', 'D004034', 'D005247', 'D005260', 'D006801', 'D008297', 'D008875', 'D013662']","['Adolescent', 'Adult', 'Age Distribution', 'Aged', 'Aged, 80 and over', 'Beverages', 'Brazil', 'Cacao', 'Caffeine', 'Central Nervous System Stimulants', 'Child', 'Coffee', 'Diet Surveys', 'Feeding Behavior', 'Female', 'Humans', 'Male', 'Middle Aged', 'Tea']",Caffeine daily intake from dietary sources in Brazil.,"[None, None, None, None, None, 'Q000032', None, 'Q000737', 'Q000008', 'Q000008', None, 'Q000737', None, None, None, None, None, None, 'Q000737']","[None, None, None, None, None, 'analysis', None, 'chemistry', 'administration & dosage', 'administration & dosage', None, 'chemistry', None, None, None, None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/10435077,1999,,,, -0.34,18665334,"This study analyzed boron content in commonly consumed foods by Koreans. Boron content was analyzed on 299 different foods using inductively coupled plasma atomic emission spectroscopy. The content of boron in cereals, potatoes, starches, sugars, and confectionaries was 1.11 to 828.56 microg per 100 g. As for beans, nuts, and seeds, the content of boron in acorn starch jelly was 66.15 microg per 100 g and in soybeans 1,642.50 microg per 100 g. In fruits, records show 5.29 to 390.13 microg per 100 g. The content of boron in vegetables was 17.45 to 420.55 microg per 100 g and in mushrooms 2.97 to 526.38 microg per 100 g. As for meats, eggs, milks, and oils, it posted 1.48 to 110.01 microg per 100 g. Fishes, shellfishes, and seaweeds contained 1.20 to 6,300.83 microg per 100 g of boron. Beverages, liquors, seasonings, and processed foods posted 1.06 microg per 100 g in corn cream soup and 2,026.49 microg per 100 g in cocoa. It is suggested that the data for the analysis of boron content in foods need to be more diversified and a reliable food database needs to be compiled based on the findings of the study to accurately determine boron consumption.",Biological trace element research,"['D001628', 'D001895', 'D002523', 'D007887', 'D005504', 'D005638', 'D007723', 'D009754', 'D013054', 'D014675']","['Beverages', 'Boron', 'Edible Grain', 'Fabaceae', 'Food Analysis', 'Fruit', 'Korea', 'Nuts', 'Spectrophotometry, Atomic', 'Vegetables']",Analysis of boron content in frequently consumed foods in Korea.,"['Q000032', 'Q000032', 'Q000737', 'Q000737', 'Q000379', 'Q000737', None, 'Q000737', None, 'Q000737']","['analysis', 'analysis', 'chemistry', 'chemistry', 'methods', 'chemistry', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/18665334,2009,1,1,table 7,boron content -0.34,21279669,"Gallic acid (GA), a key intermediate in the synthesis of plant hydrolysable tannins, is also a primary anti-inflammatory, cardio-protective agent found in wine, tea, and cocoa. In this publication, we reveal the identity of a gene and encoded protein essential for GA synthesis. Although it has long been recognized that plants, bacteria, and fungi synthesize and accumulate GA, the pathway leading to its synthesis was largely unknown. Here we provide evidence that shikimate dehydrogenase (SDH), a shikimate pathway enzyme essential for aromatic amino acid synthesis, is also required for GA production. Escherichia coli (E. coli) aroE mutants lacking a functional SDH can be complemented with the plant enzyme such that they grew on media lacking aromatic amino acids and produced GA in vitro. Transgenic Nicotiana tabacum lines expressing a Juglans regia SDH exhibited a 500% increase in GA accumulation. The J. regia and E. coli SDH was purified via overexpression in E. coli and used to measure substrate and cofactor kinetics, following reduction of NADP(+) to NADPH. Reversed-phase liquid chromatography coupled to electrospray mass spectrometry (RP-LC/ESI-MS) was used to quantify and validate GA production through dehydrogenation of 3-dehydroshikimate (3-DHS) by purified E. coli and J. regia SDH when shikimic acid (SA) or 3-DHS were used as substrates and NADP(+) as cofactor. Finally, we show that purified E. coli and J. regia SDH produced GA in vitro.",Plant molecular biology,"['D000429', 'D056148', 'D004926', 'D005707', 'D018506', 'D031324', 'D010084', 'D030821', 'D012765', 'D021241', 'D014026']","['Alcohol Oxidoreductases', 'Chromatography, Reverse-Phase', 'Escherichia coli', 'Gallic Acid', 'Gene Expression Regulation, Plant', 'Juglans', 'Oxidation-Reduction', 'Plants, Genetically Modified', 'Shikimic Acid', 'Spectrometry, Mass, Electrospray Ionization', 'Tobacco']",Mechanism of gallic acid biosynthesis in bacteria (Escherichia coli) and walnut (Juglans regia).,"['Q000378', None, 'Q000235', 'Q000378', None, 'Q000235', None, 'Q000235', 'Q000031', None, 'Q000235']","['metabolism', None, 'genetics', 'metabolism', None, 'genetics', None, 'genetics', 'analogs & derivatives', None, 'genetics']",https://www.ncbi.nlm.nih.gov/pubmed/21279669,2011,0,0,,no cocoa tested -0.34,2068794,"A high pressure liquid chromatographic (HPLC) method for measuring the theobromine content in cocoa husks, pelleted food and horse urine is described. Starting with 2 ml of urine, concentrations of 500 ng/ml could easily be detected. When feed containing 38.4 mg of theobromine was given twice daily to horses for 2 1/2 days, two days were needed after the last intake before the theobromine concentrations fell below the threshold value of 2 micrograms/ml. The time at which the peak excretion rate occurred varied from 2 to 12 h after the last administration, while the excretion rate seemed to be dependent on the urinary flow. Theobromine could not be detected in plasma after administration in this way.",Veterinary research communications,"['D000821', 'D000818', 'D002851', 'D004300', 'D005260', 'D005506', 'D006736', 'D013805']","['Animal Feed', 'Animals', 'Chromatography, High Pressure Liquid', 'Doping in Sports', 'Female', 'Food Contamination', 'Horses', 'Theobromine']",Urinary excretion of theobromine in horses given contaminated pelleted food.,"['Q000032', None, None, None, None, None, 'Q000378', 'Q000493']","['analysis', None, None, None, None, None, 'metabolism', 'pharmacokinetics']",https://www.ncbi.nlm.nih.gov/pubmed/2068794,1991,,,, -0.34,26525240,"An HPTLC method is proposed to permit effective screening for the presence of three phosphodiesterase type 5 inhibitors (PDE5-Is; sildenafil, vardenafil, and tadalafil) and eight of their analogs (hydroxyacetildenafil, homosildenafil, thiohomosildenafil, acetildenafil, acetaminotadalafil, propoxyphenyl hydroxyhomosildenafil, hydroxyhomosildenafil, and hydroxythiohomosildenafil) in finished products, including tablets, capsules, chocolate, instant coffee, syrup, and chewing gum. For all the finished products, the same simple sample preparation may be applied: ultrasound-assisted extraction in 10 mL methanol for 30 min followed by centrifugation. The Rf values of individual HPTLC bands afford preliminary identification of potential PDE5-Is. Scanning densitometry capabilities enable comparison of the unknown UV spectra with those of known standard compounds and allow further structural insight. Mass spectrometric analysis of the material derived from individual zones supplies an additional degree of confidence. Significantly, the proposed screening technique allows focus on the already known PDE5 Is and provides a platform for isolation and chemical categorization of the newly-synthesized analogs. Furthermore, the scope could be expanded to other therapeutic categories (e.g., analgesics, antidiabetics, and anorexiants) that are occasionally coadulterated along with the PDE5-Is. The method was successfully applied to screening of 45 commercial lifestyle products. Of those, 31 products tested positive for at least one illegal component (sildenafil, tadalafil, propoxyphenyl hydroxyhomosildenafil, or dimethylsildenafil). ",Journal of AOAC International,"['D002099', 'D002214', 'D002638', 'D002855', 'D003069', 'D058110', 'D006801', 'D059625', 'D013058', 'D000432', 'D058986', 'D000068677', 'D012997', 'D013607', 'D000068581', 'D000069058']","['Cacao', 'Capsules', 'Chewing Gum', 'Chromatography, Thin Layer', 'Coffee', 'Counterfeit Drugs', 'Humans', 'Liquid-Liquid Extraction', 'Mass Spectrometry', 'Methanol', 'Phosphodiesterase 5 Inhibitors', 'Sildenafil Citrate', 'Solvents', 'Tablets', 'Tadalafil', 'Vardenafil Dihydrochloride']",Simultaneous Detection of Three Phosphodiesterase Type 5 Inhibitors and Eight of Their Analogs in Lifestyle Products and Screening for Adulterants by High-Performance Thin-Layer Chromatography.,"['Q000737', None, 'Q000032', None, 'Q000737', 'Q000032', None, None, None, 'Q000737', 'Q000302', 'Q000031', 'Q000737', None, 'Q000031', 'Q000031']","['chemistry', None, 'analysis', None, 'chemistry', 'analysis', None, None, None, 'chemistry', 'isolation & purification', 'analogs & derivatives', 'chemistry', None, 'analogs & derivatives', 'analogs & derivatives']",https://www.ncbi.nlm.nih.gov/pubmed/26525240,2015,,,,no pdf access -0.34,12696960,"Partial least squares regression (PLSR) models able to predict some of the wine aroma nuances from its chemical composition have been developed. The aromatic sensory characteristics of 57 Spanish aged red wines were determined by 51 experts from the wine industry. The individual descriptions given by the experts were recorded, and the frequency with which a sensory term was used to define a given wine was taken as a measurement of its intensity. The aromatic chemical composition of the wines was determined by already published gas chromatography (GC)-flame ionization detector and GC-mass spectrometry methods. In the whole, 69 odorants were analyzed. Both matrixes, the sensory and chemical data, were simplified by grouping and rearranging correlated sensory terms or chemical compounds and by the exclusion of secondary aroma terms or of weak aroma chemicals. Finally, models were developed for 18 sensory terms and 27 chemicals or groups of chemicals. Satisfactory models, explaining more than 45% of the original variance, could be found for nine of the most important sensory terms (wood-vanillin-cinnamon, animal-leather-phenolic, toasted-coffee, old wood-reduction, vegetal-pepper, raisin-flowery, sweet-candy-cacao, fruity, and berry fruit). For this set of terms, the correlation coefficients between the measured and predicted Y (determined by cross-validation) ranged from 0.62 to 0.81. Models confirmed the existence of complex multivariate relationships between chemicals and odors. In general, pleasant descriptors were positively correlated to chemicals with pleasant aroma, such as vanillin, beta damascenone, or (E)-beta-methyl-gamma-octalactone, and negatively correlated to compounds showing less favorable odor properties, such as 4-ethyl and vinyl phenols, 3-(methylthio)-1-propanol, or phenylacetaldehyde.",Journal of agricultural and food chemistry,"['D002849', 'D008401', 'D006801', 'D016018', 'D008956', 'D009812', 'D012044', 'D013649', 'D014920']","['Chromatography, Gas', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Least-Squares Analysis', 'Models, Chemical', 'Odorants', 'Regression Analysis', 'Taste', 'Wine']",Prediction of aged red wine aroma properties from aroma chemical composition. Partial least squares regression models.,"[None, None, None, None, None, 'Q000032', None, None, 'Q000032']","[None, None, None, None, None, 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12696960,2003,0,0,, -0.34,29655735,"Fifty-six cocoa bean samples from different origins and status of fermentation were analyzed by a validated hydrophilic interaction liquid chromatography-electrospray ionization-time of flight-mass spectrometry (HILIC-ESI-TOF-MS) method. The profile of the low molecular weight carbohydrate (LMWC) was analyzed by high resolution and tandem mass spectrometry, which allowed the identification of mono-, di-, tri- and tetrasaccharides, sugar alcohols and iminosugars. This study provides, for the first time in a large set of samples, a comprehensive absolute quantitative data set for the carbohydrates identified in cocoa beans (fructose, glucose, mannitol, myo-inositol, sucrose, melibiose, raffinose and stachyose). Differences in the content of carbohydrates were observed between unfermented (range of 0.9-4.9__g/g DM) and fermented (range 0.1-0.5__g/g DM) cocoa beans. The use of multivariate statistical tools allowed the identification of biomarkers suitable for cocoa bean classification according to the status of fermentation, procedure of fermentation employed and number of days of fermentation.",Food chemistry,[],[],"Profiling, quantification and classification of cocoa beans based on chemometric analysis of carbohydrates using hydrophilic interaction liquid chromatography coupled to mass spectrometry.",[],[],https://www.ncbi.nlm.nih.gov/pubmed/29655735,2018,1,2,table 2 ,extract mean values. -0.34,26961599,"Metabolomics is used to assess the compliance and bioavailability of food components, as well as to evaluate the metabolic changes associated with food consumption. This study aimed to analyze the effect of consuming ready-to-eat meals containing a cocoa extract, within an energy restricted diet on urinary metabolomic changes. Fifty middle-aged volunteers [30.6 (2.3) kg m(-2)] participated in a 4-week randomised, parallel and double-blind study. Half consumed meals supplemented with 1.4 g of cocoa extract (645 mg polyphenols) while the remaining subjects received meals without cocoa supplementation. Ready-to-eat meals were included within a 15% energy restricted diet. Urine samples (24 h) were collected at baseline and after 4 weeks and were analyzed by high-performance-liquid chromatography-time-of-flight-mass-spectrometry (HPLC-TOF-MS) in negative and positive ionization modes followed by multivariate analysis. The relationship between urinary metabolites was evaluated by the Spearman correlation test. Interestingly, the principal component analysis discriminated among the baseline group, control group at the endpoint and cocoa group at the endpoint (p < 0.01), although in the positive ionization mode the baseline and control groups were not well distinguished. Metabolites were related to theobromine metabolism (3-methylxanthine and 3-methyluric acid), food processing (L-beta-aspartyl-L-phenylalanine), flavonoids (2,5,7,3',4'-pentahydroxyflavanone-5-O-glucoside and 7,4'-dimethoxy-6-C-methylflavanone), catecholamine (3-methoxy-4-hydroxyphenylglycol-sulphate) and endogenous metabolism (uridine monophosphate). These metabolites were present in higher (p < 0.001) amounts in the cocoa group. 3-Methylxanthine and l-beta-aspartyl-L-phenylalanine were confirmed with standards. Interestingly, 3-methoxy-4-hydroxyphenylglycol-sulphate was positively correlated with 3-methylxanthine (rho = 0.552; p < 0.001) and 7,4'-dimethoxy-6-C-methylflavanone (rho = 447; p = 0.002). In conclusion, the metabolomic approach supported the compliance of the volunteers with the intervention and suggested the bioavailability of cocoa compounds within the meals.",Food & function,"['D000368', 'D000369', 'D002099', 'D002851', 'D019587', 'D004311', 'D005260', 'D006801', 'D008297', 'D013058', 'D055432', 'D008875', 'D009765', 'D010936']","['Aged', 'Aged, 80 and over', 'Cacao', 'Chromatography, High Pressure Liquid', 'Dietary Supplements', 'Double-Blind Method', 'Female', 'Humans', 'Male', 'Mass Spectrometry', 'Metabolomics', 'Middle Aged', 'Obesity', 'Plant Extracts']",The urinary metabolomic profile following the intake of meals supplemented with a cocoa extract in middle-aged obese subjects.,"[None, None, 'Q000737', None, 'Q000032', None, None, None, None, None, None, None, 'Q000178', 'Q000032']","[None, None, 'chemistry', None, 'analysis', None, None, None, None, None, None, None, 'diet therapy', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26961599,2016,0,0,, -0.33,676517,"The nickel content of 260 samples from various types of foods available in the Netherlands was measured by means of flameless atomic absorption spectrometry. In most samples the nickel content was found to be less than 0.5 mg/kg. Two products contained considerably more nickel than all the other foodstuffs, viz. nuts and cacao products, in which nickel concentrations up to 5.1 and 9.8 mg/kg, respectively, were measured. Occasionally nickel contents above 1 mg/kg were found in margarine and sauces.",Zeitschrift fur Lebensmittel-Untersuchung und -Forschung,"['D001628', 'D002099', 'D002523', 'D005504', 'D008460', 'D009426', 'D009532', 'D009754', 'D013054', 'D014675']","['Beverages', 'Cacao', 'Edible Grain', 'Food Analysis', 'Meat', 'Netherlands', 'Nickel', 'Nuts', 'Spectrophotometry, Atomic', 'Vegetables']",Nickel content of various Dutch foodstuffs.,"['Q000032', 'Q000032', 'Q000032', 'Q000379', 'Q000032', None, 'Q000032', 'Q000032', 'Q000379', 'Q000032']","['analysis', 'analysis', 'analysis', 'methods', 'analysis', None, 'analysis', 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/676517,1978,,,, -0.33,7085542,"A collaborative study was conducted using a modified AOAC method (sugars in chocolate) for the determination of fructose, glucose, sucrose, and maltose in presweetened cereals by high pressure liquid chromatography (HPLC). Eight samples consisting of 6 products were analyzed in duplicate by the HPLC method and the AOAC Lane-Eynon method. The AOAC method was modified to use water-alcohol (1 + 1) and Sep-Pak C18 cartridges for sample cleanup. The HPLC results indicate precision comparable to the lane-Eynon method and the chocolate method. The modified HPLC method has been adopted official first action.",Journal - Association of Official Analytical Chemists,"['D002851', 'D004187', 'D002523', 'D009005']","['Chromatography, High Pressure Liquid', 'Disaccharides', 'Edible Grain', 'Monosaccharides']",High pressure liquid chromatographic determination of mono- and disaccharides in presweetened cereals: Collaborative study.,"['Q000379', 'Q000032', 'Q000032', 'Q000032']","['methods', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/7085542,1982,,,, -0.33,11339267,"Individual and geographical variations in ochratoxin A (OA) levels in human blood and milk samples may be due to differences in dietary habits. The purpose of this study was to examine the relationship between OA contamination of human milk and dietary intake. Human milk samples were collected from 80 Norwegian women. The usual food intake during the last year was recorded using a quantitative food frequency questionnaire. The concentration of OA in the human milk was determined by HPLC (detection limit 10 ng/l). Seventeen (21%) out of 80 human milk samples contained OA in the range 10-182 ng/l. The women with a high dietary intake of liver paste (liverwurst, liver p¢t©) and cakes (cookies, fruitcakes, chocolate cakes, etc.) were more likely to have OA-contaminated milk. The risk of OA contamination was also increased by the intake of juice (all kinds). In addition, the results indicate that breakfast cereals, processed meat products, and cheese could be important contributors to dietary OA intake. OA contamination of the milk was unrelated to smoking, age, parity, and anthropometric data other than body weight.",Food additives and contaminants,"['D000328', 'D001827', 'D015992', 'D001835', 'D002273', 'D016009', 'D002851', 'D004032', 'D015930', 'D005260', 'D006801', 'D016015', 'D008895', 'D009793', 'D012621', 'D018709']","['Adult', 'Body Height', 'Body Mass Index', 'Body Weight', 'Carcinogens', 'Chi-Square Distribution', 'Chromatography, High Pressure Liquid', 'Diet', 'Diet Records', 'Female', 'Humans', 'Logistic Models', 'Milk, Human', 'Ochratoxins', 'Seasons', 'Statistics, Nonparametric']",Presence of ochratoxin A in human milk in relation to dietary intake.,"[None, None, None, None, 'Q000032', None, None, None, None, None, None, None, 'Q000737', 'Q000032', None, None]","[None, None, None, None, 'analysis', None, None, None, None, None, None, None, 'chemistry', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11339267,2001,,,, -0.33,27454854,"Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a powerful biotyping tool increasingly used for high-throughput identification of clinical microbial isolates, however, in food fermentation research this approach is still not well established. This study examines the microbial biodiversity of cocoa bean fermentation based on the isolation of micro-organisms in cocoa-producing regions, followed by MALDI-TOF MS in Switzerland. A preceding 6-week storage test to mimic lengthy transport of microbial samples from cocoa-producing regions to Switzerland was performed with strains of Lactobacillus plantarum, Acetobacter pasteurianus and Saccharomyces cerevisiae. Weekly MALDI-TOF MS analysis was able to successfully identify microbiota to the species level after storing live cultures on slant agar at mild temperatures (7_C) and/or in 75% aqueous ethanol at differing temperatures (-20, 7 and 30_C). The efficacy of this method was confirmed by on-site recording of the microbial biodiversity in cocoa bean fermentation in Bolivia and Brazil, with a total of 1126 randomly selected isolates. MALDI-TOF MS analyses revealed known dominant cocoa bean fermentation species with Lact.__plantarum and Lactobacillus fermentum in the lactic acid bacteria taxon, Hanseniaspora opuntiae and S.__cerevisiae in the yeast taxon, and Acet.__pasteurianus, Acetobacter fabarum, Acetobacter ghanensis and Acetobacter senegalensis in the acetic acid bacteria taxon.",Letters in applied microbiology,"['D019342', 'D001419', 'D015373', 'D001838', 'D001938', 'D002099', 'D000431', 'D005285', 'D064307', 'D016533', 'D019032', 'D015003']","['Acetic Acid', 'Bacteria', 'Bacterial Typing Techniques', 'Bolivia', 'Brazil', 'Cacao', 'Ethanol', 'Fermentation', 'Microbiota', 'Mycological Typing Techniques', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Yeasts']",High-throughput identification of the microbial biodiversity of cocoa bean fermentation by MALDI-TOF MS.,"[None, 'Q000302', None, None, None, 'Q000382', None, None, None, None, 'Q000379', 'Q000302']","[None, 'isolation & purification', None, None, None, 'microbiology', None, None, None, None, 'methods', 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/27454854,2017,0,0,, -0.33,19187022,"Food and beverages rich in polyphenols with antioxidant activity are highlighted as a potential factor for risk reduction of lifestyle related diseases. This study was conducted to elucidate total polyphenol consumption from beverages in Japanese people. Total polyphenol (TP) contents in beverages were measured using a modified Folin-Ciocalteu method removing the interference of reduced sugars by using reverse-phase column chromatography. A beverage consumption survey was conducted in the Tokyo and Osaka areas in 2004. Randomly selected male and female subjects (10-59 years old, n = 8768) recorded the amounts and types of all nonalcoholic beverages consumed in a week. Concentration of TP in coffee, green tea, black tea, Oolong tea, barley tea, fruit juice, tomato/vegetable juice, and cocoa drinks were at 200, 115, 96, 39, 9, 34, 69, and 62 mg/100 mL, respectively. Total consumption of beverages in a Japanese population was 1.11 +/- 0.51 L/day, and TP contents from beverages was 853 +/- 512 mg/day. Coffee and green tea shared 50% and 34% of TP consumption in beverages, respectively, and contribution of each of the other beverages was less than 10%. TP contents in 20 major vegetables and 5 fruits were 0-49 mg and 2-55 mg/100 g, respectively. Antioxidant activities, Cu reducing power, and scavenging activities for DPPH and superoxide, of those samples correlated to the TP contents (p < 0.001). Beverages, especially coffee, contributed to a large share of the consumption of polyphenols, as antioxidants, in the Japanese diet.",Journal of agricultural and food chemistry,"['D000293', 'D000328', 'D000975', 'D001628', 'D002648', 'D003069', 'D004032', 'D005260', 'D005419', 'D005638', 'D006801', 'D007564', 'D008297', 'D008875', 'D010636', 'D059808', 'D013662', 'D014675', 'D055815']","['Adolescent', 'Adult', 'Antioxidants', 'Beverages', 'Child', 'Coffee', 'Diet', 'Female', 'Flavonoids', 'Fruit', 'Humans', 'Japan', 'Male', 'Middle Aged', 'Phenols', 'Polyphenols', 'Tea', 'Vegetables', 'Young Adult']",Coffee and green tea as a large source of antioxidant polyphenols in the Japanese population.,"[None, None, 'Q000008', 'Q000032', None, 'Q000737', None, None, 'Q000008', 'Q000737', None, None, None, None, 'Q000008', None, 'Q000737', 'Q000737', None]","[None, None, 'administration & dosage', 'analysis', None, 'chemistry', None, None, 'administration & dosage', 'chemistry', None, None, None, None, 'administration & dosage', None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/19187022,2009,0,0,, -0.32,28241034,"For the first time in the literature, our group has managed to demonstrate the existence of plant RNAs in honey samples. In particular, in our work, different RNA extraction procedures were performed in order to identify a purification method for nucleic acids from honey. Purity, stability and integrity of the RNA samples were evaluated by spectrophotometric, PCR and electrophoretic analyses. Among all honey RNAs, we specifically revealed the presence of both plastidial and nuclear plant transcripts: RuBisCO large subunit mRNA, maturase K messenger and 18S ribosomal RNA. Surprisingly, nine plant microRNAs (miR482b, miR156a, miR396c, miR171a, miR858, miR162a, miR159c, miR395a and miR2118a) were also detected and quantified by qPCR. In this context, a comparison between microRNA content in plant samples (i.e. flowers, nectars) and their derivative honeys was carried out. In addition, peculiar microRNA profiles were also identified in six different monofloral honeys. Finally, the same plant microRNAs were investigated in other plant food products: tea, cocoa and coffee. Since plant microRNAs introduced by diet have been recently recognized as being able to modulate the consumer's gene expression, our research suggests that honey's benefits for human health may be strongly correlated to the bioactivity of plant microRNAs contained in this matrix.",PloS one,"['D000975', 'D002099', 'D028244', 'D040503', 'D004722', 'D035264', 'D006722', 'D035683', 'D009713', 'D010944', 'D016133', 'D018749', 'D012337', 'D060888', 'D012273', 'D017423', 'D013053']","['Antioxidants', 'Cacao', 'Camellia', 'Coffea', 'Endoribonucleases', 'Flowers', 'Honey', 'MicroRNAs', 'Nucleotidyltransferases', 'Plants', 'Polymerase Chain Reaction', 'RNA, Plant', 'RNA, Ribosomal, 18S', 'Real-Time Polymerase Chain Reaction', 'Ribulose-Bisphosphate Carboxylase', 'Sequence Analysis, RNA', 'Spectrophotometry']",Detection of plant microRNAs in honey.,"['Q000378', 'Q000235', 'Q000235', 'Q000235', 'Q000235', 'Q000235', 'Q000032', None, 'Q000235', 'Q000235', None, None, 'Q000235', None, 'Q000235', None, None]","['metabolism', 'genetics', 'genetics', 'genetics', 'genetics', 'genetics', 'analysis', None, 'genetics', 'genetics', None, None, 'genetics', None, 'genetics', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28241034,2017,0,0,, -0.32,9757560,"The antiulcer activity of cacao liquor water-soluble crude polyphenols (CWSP) was examined. CWSP, alpha-tocopherol, sucralfate (500 mg/kg), and cimetidine (250 mg/kg) were orally administered to male SD rats 30 minutes before ethanol treatment. 5 ml/kg of ethanol given intragastrically caused lesions in mucosa of the glandular stomach. CWSP caused a reduction of such hemorrhagic lesions as well as cimetidine and sucralfate which are typical antiulcer drugs, but alpha-tocopherol was less effective. Thiobarbituric acid reactive substances in gastric mucosa significantly increased with ethanol administration. CWSP treatment significantly reduced this change. The administration of ethanol extensively increased myeloperoxidase (MPO) but not xanthine oxidase (XOD) activity. CWSP reduced the activities of both enzymes; they were considered the main sources of oxygen radicals. According to an in vitro study, CWSP directly reducted XOD but not MPO. These results suggest that the antiulcer mechanism of CWSP was not only radical scavenging but also modulation of leukocyte function.","Bioscience, biotechnology, and biochemistry","['D000818', 'D000897', 'D000975', 'D002099', 'D002851', 'D002927', 'D004305', 'D000431', 'D005419', 'D005753', 'D015227', 'D008297', 'D009195', 'D010636', 'D011108', 'D059808', 'D051381', 'D017207', 'D013276', 'D013392', 'D017392', 'D014527', 'D014810', 'D014969']","['Animals', 'Anti-Ulcer Agents', 'Antioxidants', 'Cacao', 'Chromatography, High Pressure Liquid', 'Cimetidine', 'Dose-Response Relationship, Drug', 'Ethanol', 'Flavonoids', 'Gastric Mucosa', 'Lipid Peroxidation', 'Male', 'Peroxidase', 'Phenols', 'Polymers', 'Polyphenols', 'Rats', 'Rats, Sprague-Dawley', 'Stomach Ulcer', 'Sucralfate', 'Thiobarbituric Acid Reactive Substances', 'Uric Acid', 'Vitamin E', 'Xanthine Oxidase']",Effects of polyphenol substances derived from Theobroma cacao on gastric mucosal lesion induced by ethanol.,"[None, 'Q000494', 'Q000378', 'Q000378', None, 'Q000494', None, 'Q000009', None, 'Q000187', None, None, 'Q000037', 'Q000494', 'Q000494', None, None, None, 'Q000188', 'Q000494', 'Q000032', 'Q000032', 'Q000494', 'Q000037']","[None, 'pharmacology', 'metabolism', 'metabolism', None, 'pharmacology', None, 'adverse effects', None, 'drug effects', None, None, 'antagonists & inhibitors', 'pharmacology', 'pharmacology', None, None, None, 'drug therapy', 'pharmacology', 'analysis', 'analysis', 'pharmacology', 'antagonists & inhibitors']",https://www.ncbi.nlm.nih.gov/pubmed/9757560,1998,0,0,,year -0.32,18172716,"Oxalic acid has been shown as a virulence factor for some phytopathogenic fungi, removing calcium from pectin and favoring plant cell wall degradation. Recently, it was published that calcium oxalate accumulates in infected cacao tissues during the progression of Witches' Broom disease (WBD). In the present work we report that the hemibiotrophic basidiomycete Moniliophthora perniciosa, the causal agent of WBD, produces calcium oxalate crystals. These crystals were initially observed by polarized light microscopy of hyphae growing on a glass slide, apparently being secreted from the cells. The analysis was refined by Scanning electron microscopy and the compositon of the crystals was confirmed by energy-dispersive x-ray spectrometry. The production of oxalate by M. perniciosa was reinforced by the identification of a putative gene coding for oxaloacetate acetylhydrolase, which catalyzes the hydrolysis of oxaloacetate to oxalate and acetate. This gene was shown to be expressed in the biotrophic-like mycelia, which in planta occupy the intercellular middle-lamella space, a region filled with pectin. Taken together, our results suggest that oxalate production by M. perniciosa may play a role in the WBD pathogenesis mechanism.",Current microbiology,"['D000363', 'D000595', 'D000818', 'D002099', 'D002129', 'D005656', 'D006867', 'D025301', 'D008855', 'D008859', 'D008969', 'D010935', 'D016415', 'D013052']","['Agaricales', 'Amino Acid Sequence', 'Animals', 'Cacao', 'Calcium Oxalate', 'Fungal Proteins', 'Hydrolases', 'Hyphae', 'Microscopy, Electron, Scanning', 'Microscopy, Polarization', 'Molecular Sequence Data', 'Plant Diseases', 'Sequence Alignment', 'Spectrometry, X-Ray Emission']","Production of calcium oxalate crystals by the basidiomycete Moniliophthora perniciosa, the causal agent of witches' broom disease of Cacao.","['Q000737', None, None, 'Q000382', 'Q000378', 'Q000235', 'Q000235', 'Q000737', None, None, None, 'Q000382', None, None]","['chemistry', None, None, 'microbiology', 'metabolism', 'genetics', 'genetics', 'chemistry', None, None, None, 'microbiology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18172716,2008,0,0,, -0.32,15137812,"Directive 2000/36/EC allows chocolate makers to add up to 5% of only six specific cocoa butter equivalents (CBEs) to cocoa butter (CB). A quantification method based on triacylglycerol (TAG) class analysis by gas chromatography with an unpolar column was set up for routine control purposes of chocolate bars. Mixtures of CBEs/CB were elaborated according to a Placket-Burman experiment design and analyzed by gas chromatography. A matrix was built with the normalized values of TAG classes (C50, C52, C54, and C56) of pure CBs of various origins, homemade CB/CBE mixtures (1 CB type), and mixtures containing CBE with CBs of various origins. A multivariate calibration equation was computed from this matrix using a partial least-squares regression technique. CBE addition can be detected at a minimum level of 2%, and the mathematical model allows its quantification with an uncertainty of 2% with respect to the cocoa butter fats. The model has also been applied for deconvolution and quantification of each CBE of a CBE mixture in chocolate bars.",Journal of agricultural and food chemistry,"['D002099', 'D002182', 'D002849', 'D004041', 'D014280']","['Cacao', 'Candy', 'Chromatography, Gas', 'Dietary Fats', 'Triglycerides']",Alternative method for the quantification by gas chromatography triacylglycerol class analysis of cocoa butter equivalent added to chocolate bars.,"['Q000737', 'Q000032', 'Q000379', 'Q000032', 'Q000032']","['chemistry', 'analysis', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15137812,2004,2,1,table 1, -0.32,15759754,"After the publication of high levels of acrylamide (AA) in food, many research activities started all over the world in order to determine the occurrence and the concentration of this substance in various types of food. As no validated methods were available at that time, interlaboratory studies on the determination of AA in food were of the highest priority. Under the boundary conditions of applying well-established evaluation schemes, the results of 2 studies conducted by the Federal Institute for Risk Assessment (BfR) in Germany and by the European Commission's Directorate General Joint Research Center (JRC) exhibited an overall acceptable performance of the participants in these studies. Nevertheless, many laboratories showed problems in determining AA in food with a complex matrix such as cocoa. The results of analysis also showed a broader variation of AA for samples with low AA concentrations and indicated a bias of the results obtained by gas chromatography-mass spectrometry without derivatization. Improvements of the performance of some laboratories appeared to be necessary.",Journal of AOAC International,"['D020106', 'D001939', 'D002099', 'D002623', 'D005062', 'D005502', 'D005504', 'D008401', 'D005858', 'D012107', 'D018570', 'D011198', 'D013997']","['Acrylamide', 'Bread', 'Cacao', 'Chemistry Techniques, Analytical', 'European Union', 'Food', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Germany', 'Research Design', 'Risk Assessment', 'Solanum tuberosum', 'Time Factors']",Results from two interlaboratory comparison tests organized in Germany and at the EU level for analysis of acrylamide in food.,"['Q000032', None, None, 'Q000379', None, None, 'Q000379', 'Q000379', None, None, None, None, None]","['analysis', None, None, 'methods', None, None, 'methods', 'methods', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/15759754,2005,,,, -0.32,21480674,"Samples of 15 second generation soy-based products (n = 3), commercially available, were analyzed for their protein and isoflavone contents and in vitro antioxidant activity, by means of the Folin-Ciocalteu reducing ability, DPPH radical scavenging capacity, and oxygen radical absorbance capacity. Isoflavone identification and quantification were performed by high-performance liquid chromatography. Products containing soy and/or soy-based ingredients represent important sources of protein in addition to the low fat amounts. However, a large variation in isoflavone content and in vitro antioxidant capacity was observed. The isoflavone content varied from 2.4 to 18.1 mg/100 g (FW), and soy kibe and soy sausage presented the highest amounts. Chocolate had the highest antioxidant capacity, but this fact was probably associated with the addition of cocoa liquor, a well-known source of polyphenolics. This study showed that the soy-based foods do not present a significant content of isoflavones when compared with the grain, and their in vitro antioxidant capacity is not related with these compounds but rather to the presence of other phenolics and synthetic antioxidants, such as sodium erythorbate. However, they may represent alternative sources and provide soy protein, isoflavones, and vegetable fat for those who are not ready to eat traditional soy foods.",Journal of agricultural and food chemistry,"['D000975', 'D004041', 'D007529', 'D009753', 'D045730', 'D030262']","['Antioxidants', 'Dietary Fats', 'Isoflavones', 'Nutritive Value', 'Soy Foods', 'Soybean Proteins']",Nutritional aspects of second generation soy foods.,"['Q000032', 'Q000032', 'Q000032', None, 'Q000032', 'Q000032']","['analysis', 'analysis', 'analysis', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/21480674,2011,0,0,,no cocoa tested -0.32,25213975,"Micellar electrokinetic chromatography (MEKC) has been applied for the determination of 5-hydroxymethylfurfural in several foodstuffs. A 75mM phosphate buffer solution at pH 8.0 containing 100mM sodium dodecylsulphate was used as background electrolyte (BGE), and the separation was performed by applying +25kV in a 50__m I.D. uncoated fused-silica capillary. Good linearity over the range 2.5-250mgkg(-1) (r(2)_©_0.999) and run-to-run and day-to-day precisions at low and medium concentration levels were obtained. Sample limit of detection (0.7mgkg(-1)) and limit of quantification (2.5mgkg(-1)) were established by preparing the standards in blank matrix. The procedure was validated by comparing the results with those obtained with liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Levels of HMF in 45 different foodstuffs such as breakfast cereals, toasts, honey, orange juice, apple juice, jam, coffee, chocolate and biscuits were determined. ",Food chemistry,[],[],5-Hydroxymethylfurfural content in foodstuffs determined by micellar electrokinetic chromatography.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/25213975,2014,0,0,,no cocoa -0.32,10820089,"Catechins, compounds that belong to the flavonoid class, are potentially beneficial to human health. To enable epidemiological evaluation of these compounds, data on their contents in foods are required. HPLC with UV and fluorescence detection was used to determine the levels of (+)-catechin, (-)-epicatechin, (+)-gallocatechin (GC), (-)-epigallocatechin (EGC), (-)-epicatechin gallate (ECg), and (-)-epigallocatechin gallate (EGCg) in 24 types of fruits, 27 types of vegetables and legumes, some staple foods, and processed foods commonly consumed in The Netherlands. Most fruits, chocolate, and some legumes contained catechins. Levels varied to a large extent: from 4.5 mg/kg in kiwi fruit to 610 mg/kg in black chocolate. (+)-Catechin and (-)-epicatechin were the predominant catechins; GC, EGC, and ECg were detected in some foods, but none of the foods contained EGCg. The data reported here provide a base for the epidemiological evaluation of the effect of catechins on the risk for chronic diseases.",Journal of agricultural and food chemistry,"['D002392', 'D002851', 'D005504', 'D006801', 'D009426', 'D013050', 'D013056']","['Catechin', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Humans', 'Netherlands', 'Spectrometry, Fluorescence', 'Spectrophotometry, Ultraviolet']","Catechin contents of foods commonly consumed in The Netherlands. 1. Fruits, vegetables, staple foods, and processed foods.","['Q000032', None, None, None, None, None, None]","['analysis', None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10820089,2000,0,0,, -0.32,26372965,"Chemical analyses of organic residues in fragments of pottery from 18 sites in the US Southwest and Mexican Northwest reveal combinations of methylxanthines (caffeine, theobromine, and theophylline) indicative of stimulant drinks, probably concocted using either cacao or holly leaves and twigs. The results cover a time period from around A.D. 750-1400, and a spatial distribution from southern Colorado to northern Chihuahua. As with populations located throughout much of North and South America, groups in the US Southwest and Mexican Northwest likely consumed stimulant drinks in communal, ritual gatherings. The results have implications for economic and social relations among North American populations. ",Proceedings of the National Academy of Sciences of the United States of America,"['D001106', 'D001628', 'D002099', 'D002110', 'D002562', 'D002851', 'D003466', 'D005502', 'D005843', 'D049690', 'D006801', 'D030017', 'D008800', 'D015206', 'D053719']","['Archaeology', 'Beverages', 'Cacao', 'Caffeine', 'Ceremonial Behavior', 'Chromatography, High Pressure Liquid', 'Cultural Characteristics', 'Food', 'Geography', 'History, Ancient', 'Humans', 'Ilex', 'Mexico', 'Southwestern United States', 'Tandem Mass Spectrometry']",Ritual drinks in the pre-Hispanic US Southwest and Mexican Northwest.,"[None, 'Q000032', None, None, None, None, 'Q000266', None, None, None, None, None, None, None, None]","[None, 'analysis', None, None, None, None, 'history', None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/26372965,2016,0,0,, -0.32,12589329,"This paper offers a review of current scientific research regarding the potential cardiovascular health benefits of flavonoids found in cocoa and chocolate. Recent reports indicate that the main flavonoids found in cocoa, flavan-3-ols and their oligomeric derivatives, procyanidins, have a variety of beneficial actions, including antioxidant protection and modulation of vascular homeostasis. These findings are supported by similar research on other flavonoid-rich foods. Other constituents in cocoa and chocolate that may also influence cardiovascular health are briefly reviewed. The lipid content of chocolate is relatively high; however, one third of the lipid in cocoa butter is composed of the fat stearic acid, which exerts a neutral cholesterolemic response in humans. Cocoa and chocolate contribute to trace mineral intake, which is necessary for optimum functioning of all biologic systems and for vascular tone. Thus, multiple components in chocolate, particularly flavonoids, can contribute to the complex interplay of nutrition and health. Applications of this knowledge include recommendations by health professionals to encourage individuals to consume a wide range of phytochemical-rich foods, which can include dark chocolate in moderate amounts.",Journal of the American Dietetic Association,"['D000924', 'D000975', 'D001682', 'D002099', 'D002318', 'D002851', 'D004041', 'D004043', 'D005419', 'D006801', 'D007109', 'D008903', 'D010840', 'D015539', 'D013229']","['Anticholesteremic Agents', 'Antioxidants', 'Biological Availability', 'Cacao', 'Cardiovascular Diseases', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Dietary Fiber', 'Flavonoids', 'Humans', 'Immunity', 'Minerals', 'Phytosterols', 'Platelet Activation', 'Stearic Acids']",Cocoa and chocolate flavonoids: implications for cardiovascular health.,"['Q000008', 'Q000008', None, 'Q000737', 'Q000378', None, 'Q000008', 'Q000008', 'Q000008', None, 'Q000187', 'Q000008', 'Q000008', 'Q000187', 'Q000008']","['administration & dosage', 'administration & dosage', None, 'chemistry', 'metabolism', None, 'administration & dosage', 'administration & dosage', 'administration & dosage', None, 'drug effects', 'administration & dosage', 'administration & dosage', 'drug effects', 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/12589329,2003,1,1,table 1 and 2, -0.32,2991094,"Correlation studies on patients with myasthenia gravis are reported in which clinical assessment of fatigue and neurophysiological findings are compared to blood levels of pyridostigmine. Measurements using a high-pressure liquid chromatography method (HPLC), give reproducible results. The levels of pyridostigmine in the serum or plasma of healthy controls and of patients show no essential differences. Components of coffee, tea, chocolate and cigarettes can markedly disturb the chromatography by adding additional peaks, so that interpretation becomes difficult or impossible. Blood levels can be measured approximately one hour after oral intake of 60 mg pyridostigmine. Concentrations rise for two to four hours and then decline exponentially. The half-life of pyridostigmine was between 156 and 210 minutes. Despite identical oral dosages, the concentration differed intraindividually and interindividually among patients. While the blood level does not reach its maximum value for 1-1 1/2 to 3 hours, the maximum clinical and neurophysiological effect of pyridostigmine appears 30-60 minutes after ingestion. Variable distribution of cholinesterase inhibitors over the different compartments (blood, synaptic region) is assumed to cause this temporal lag. If the total amount of pyridostigmine is divided into 4-5 doses, the concentration profiles over the course of a day are relatively stable. There is no significant correlation between the variations in blood level throughout one day, and changes in myasthenic symptomatology. Effects of pyridostigmine can be measured at levels as low as 5 ng/ml; at levels above 40 ng/ml further improvement can be detected only rarely. Blood levels were lower if corticosteroids were administered simultaneously; azathioprine had no influence on blood levels. Blood levels assays allow better differentiation of cholinergic and myasthenic crises and the identification of disturbed absorption and interactions with other medications.",Fortschritte der Neurologie-Psychiatrie,"['D000293', 'D000328', 'D000368', 'D001682', 'D002851', 'D004305', 'D004347', 'D004361', 'D005260', 'D006801', 'D007700', 'D008297', 'D008475', 'D008875', 'D009157', 'D011729', 'D009435']","['Adolescent', 'Adult', 'Aged', 'Biological Availability', 'Chromatography, High Pressure Liquid', 'Dose-Response Relationship, Drug', 'Drug Interactions', 'Drug Tolerance', 'Female', 'Humans', 'Kinetics', 'Male', 'Median Nerve', 'Middle Aged', 'Myasthenia Gravis', 'Pyridostigmine Bromide', 'Synaptic Transmission']",[Serum levels of pyridostigmine in myasthenia gravis: methods and clinical significance].,"[None, None, None, None, None, None, None, None, None, None, None, None, 'Q000187', None, 'Q000097', 'Q000097', 'Q000187']","[None, None, None, None, None, None, None, None, None, None, None, None, 'drug effects', None, 'blood', 'blood', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/2991094,1985,,,,no pdf access -0.32,20722952,"Export of cocoa beans is of great economic importance in Ghana and several other tropical countries. Raw cocoa has an astringent, unpleasant taste, and flavor, and has to be fermented, dried, and roasted to obtain the characteristic cocoa flavor and taste. In an attempt to obtain a deeper understanding of the changes in the cocoa beans during fermentation and investigate the possibility of future development of objective methods for assessing the degree of fermentation, a novel combination of methods including cut test, colorimetry, fluorescence spectroscopy, NIR spectroscopy, and GC-MS evaluated by chemometric methods was used to examine cocoa beans sampled at different durations of fermentation and samples representing fully fermented and dried beans from all cocoa growing regions of Ghana. Using colorimetry it was found that samples moved towards higher a* and b* values as fermentation progressed. Furthermore, the degree of fermentation could, in general, be well described by the spectroscopic methods used. In addition, it was possible to link analysis of volatile compounds with predictions of fermentation time. Fermented and dried cocoa beans from the Volta and the Western regions clustered separately in the score plots based on colorimetric, fluorescence, NIR, and GC-MS indicating regional differences in the composition of Ghanaian cocoa beans. The study demonstrates the potential of colorimetry and spectroscopic methods as valuable tools for determining the fermentation degree of cocoa beans. Using GC-MS it was possible to demonstrate the formation of several important aroma compounds such 2-phenylethyl acetate, propionic acid, and acetoin and the breakdown of others like diacetyl during fermentation. Practical Application: The present study demonstrates the potential of using colorimetry and spectroscopic methods as objective methods for determining cocoa bean quality along the processing chain. Development of objective methods for determining cocoa bean quality will be of great importance for quality insurance within the fields of cocoa processing and raw material control in chocolate producing companies.",Journal of food science,"['D000085', 'D000093', 'D002099', 'D003116', 'D003124', 'D003931', 'D005285', 'D005504', 'D005511', 'D008401', 'D005869', 'D015233', 'D010626', 'D025341', 'D011422', 'D011786', 'D012639', 'D013050', 'D019265', 'D055549']","['Acetates', 'Acetoin', 'Cacao', 'Color', 'Colorimetry', 'Diacetyl', 'Fermentation', 'Food Analysis', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Ghana', 'Models, Statistical', 'Phenylethyl Alcohol', 'Principal Component Analysis', 'Propionates', 'Quality Control', 'Seeds', 'Spectrometry, Fluorescence', 'Spectroscopy, Near-Infrared', 'Volatile Organic Compounds']",Ghanaian cocoa bean fermentation characterized by spectroscopic and chromatographic methods and chemometrics.,"['Q000032', 'Q000032', 'Q000737', None, None, 'Q000032', None, 'Q000379', 'Q000379', None, None, None, 'Q000031', None, 'Q000032', None, 'Q000737', None, None, 'Q000032']","['analysis', 'analysis', 'chemistry', None, None, 'analysis', None, 'methods', 'methods', None, None, None, 'analogs & derivatives', None, 'analysis', None, 'chemistry', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20722952,2011,0,0,, -0.31,15237566,The stability and compatibility of tegaserod from crushed tablets in selected beverages and foods were studied.,American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists,"['D001628', 'D002851', 'D004344', 'D004356', 'D005502', 'D005765', 'D007211', 'D012034', 'D012995', 'D013535', 'D013607']","['Beverages', 'Chromatography, High Pressure Liquid', 'Drug Incompatibility', 'Drug Storage', 'Food', 'Gastrointestinal Agents', 'Indoles', 'Refrigeration', 'Solubility', 'Suspensions', 'Tablets']",Stability and compatibility of tegaserod from crushed tablets mixed in beverages and foods.,"[None, None, None, None, None, 'Q000737', 'Q000737', None, None, None, None]","[None, None, None, None, None, 'chemistry', 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/15237566,2004,,,,no pdf access -0.31,22243490,"POSH are polyolefin oligomeric saturated hydrocarbons, such as oligomers from polyethylene or polypropylene. POSH that have migrated into foods are easily mistaken for mineral oil-saturated hydrocarbons (MOSH). In fact, both POSH and MOSH largely consist of highly isomerised branched and possibly cyclic hydrocarbons, both forming humps of unresolved components in gas chromatography. Chromatograms are reported to show typical elution patterns of POSH and help analysts distinguishing POSH from MOSH as far as possible. Since the structures of the POSH are not fundamentally different from those of the MOSH, it would be prudent to apply the evaluation of the MOSH. However, the migration is frequently beyond that for which safety has been demonstrated. This is shown for a few examples, particularly for powdered formula for babies.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D002099', 'D005506', 'D005511', 'D018857', 'D057141', 'D006801', 'D007223', 'D007225', 'D041943', 'D015394', 'D012275', 'D035281', 'D011090', 'D011095', 'D011126', 'D012639', 'D011198', 'D013499', 'D014867', 'D003313']","['Cacao', 'Food Contamination', 'Food Handling', 'Food Packaging', 'Food, Preserved', 'Humans', 'Infant', 'Infant Food', 'Infant Formula', 'Molecular Structure', 'Oryza', 'Plant Tubers', 'Polyenes', 'Polyethylenes', 'Polypropylenes', 'Seeds', 'Solanum tuberosum', 'Surface Properties', 'Water', 'Zea mays']",Migration of polyolefin oligomeric saturated hydrocarbons (POSH) into food.,"['Q000737', None, None, None, 'Q000032', None, None, 'Q000032', 'Q000737', None, 'Q000737', 'Q000737', 'Q000032', 'Q000032', 'Q000032', 'Q000737', 'Q000737', None, 'Q000032', 'Q000737']","['chemistry', None, None, None, 'analysis', None, None, 'analysis', 'chemistry', None, 'chemistry', 'chemistry', 'analysis', 'analysis', 'analysis', 'chemistry', 'chemistry', None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22243490,2012,0,0,, -0.31,24231101,"The regular consumption of flavonoids has been associated with reduced mortality and a decreased risk of cardiovascular diseases. The proanthocyanidins found in plasma are very different from the original flavonoids in food sources. The use of physiologically appropriate conjugates of proanthocyanidins is essential for the in vitro analysis of flavonoid bioactivity. In this study, the effect of different proanthocyanidin-rich extracts, which were obtained from cocoa (CCX), French maritime pine bark (Pycnogenol extract, PYC) and grape seed (GSPE), on lipid homeostasis was evaluated. Hepatic human cells (HepG2 cells) were treated with 25 mg/L of CCX, PYC or GSPE. We also performed in vitro experiments to assess the effect on lipid synthesis that is induced by the bioactive GSPE proanthocyanidins using the physiological metabolites that are present in the serum of GSPE-administered rats. For this, Wistar rats were administered 1 g/kg of GSPE, and serum was collected after 2 h. The semipurified serum of GSPE-administered rats was fully characterized by liquid chromatography tandem triple quadrupole mass spectrometry (LC-QqQ/MS(2)). The lipids studied in the analyses were free cholesterol (FC), cholesterol ester (CE) and triglycerides (TG). All three proanthocyanidin-rich extracts induced a remarkable decrease in the de novo lipid synthesis in HepG2 cells. Moreover, GSPE rat serum metabolites reduced the total percentage of CE, FC and particularly TG; this reduction was significantly higher than that observed in the cells directly treated with GSPE. In conclusion, the bioactivity of the physiological metabolites that are present in the serum of rats after their ingestion of a proanthocyanidin-rich extract was demonstrated in Hep G2 cells. ",The Journal of nutritional biochemistry,"['D000818', 'D002099', 'D002784', 'D005419', 'D056604', 'D056945', 'D006801', 'D050155', 'D028223', 'D024301', 'D010936', 'D044945', 'D051381', 'D017208', 'D015203', 'D044967', 'D014280']","['Animals', 'Cacao', 'Cholesterol', 'Flavonoids', 'Grape Seed Extract', 'Hep G2 Cells', 'Humans', 'Lipogenesis', 'Pinus', 'Plant Bark', 'Plant Extracts', 'Proanthocyanidins', 'Rats', 'Rats, Wistar', 'Reproducibility of Results', 'Serum', 'Triglycerides']",Serum metabolites of proanthocyanidin-administered rats decrease lipid synthesis in HepG2 cells.,"[None, 'Q000737', 'Q000097', 'Q000494', 'Q000493', None, None, 'Q000187', 'Q000737', 'Q000737', 'Q000494', 'Q000097', None, None, None, 'Q000737', 'Q000097']","[None, 'chemistry', 'blood', 'pharmacology', 'pharmacokinetics', None, None, 'drug effects', 'chemistry', 'chemistry', 'pharmacology', 'blood', None, None, None, 'chemistry', 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/24231101,2014,0,0,, -0.31,28605022,"A rapid technique using direct analysis in real-time (DART) ambient ionization coupled to a high-resolution accurate mass-mass spectrometer (HRAM-MS) was employed to analyze stains on an individual's pants suspected to have been involved in a violent crime. The victim was consuming chocolate ice cream at the time of the attack, and investigators recovered the suspect's pants exhibiting splatter stains. Liquid chromatography with mass spectral detection (LC-MS) and stereoscopic light microscopy (SLM) were also utilized in this analysis. It was determined that the stains on the pants contained theobromine and caffeine, known components of chocolate. A shard from the ceramic bowl that contained the victim's ice cream and a control chocolate ice cream sample were also found to contain caffeine and theobromine. The use of DART-HRAM-MS was useful in this case due to its rapid analysis capability and because of the limited amount of sample present as a stain.",Journal of forensic sciences,[],[],Forensic Analysis of Stains on Fabric Using Direct Analysis in Real-time Ionization with High-Resolution Accurate Mass-Mass Spectrometry.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/28605022,2018,0,0,,no cocoa -0.31,22417537,"Cacao (Theobroma cacao L.) is rich in procyanidins, a large portion of which degrades during the natural fermentation process of producing cocoa powder. Recent advances in technology have enabled scientists to produce unfermented cocoa powder, preserving the original profile of procyanidins present in cocoa and allowing for the development of highly concentrated procyanidin-rich extracts. During this process, the anthocyanins naturally present in unfermented cocoa remain intact, producing a violet color in the final extract. The objective of this study was to selectively remove the violet color in procyanidin-rich extracts produced from unfermented cocoa powder, while maintaining the stability and composition of procyanidins present in the matrix. Several processing parameters, including pH fluctuations, enzymatic treatments, and the addition of potassium meta-bisulfite, were explored to influence the color of procyanidin-rich extracts throughout a 60-d shelf life study. The addition of potassium meta-bisulfite (500 ppm) was found to be the most effective means of removing the violet color present in the treated extracts (L*= 71.39, a*= 8.44, b*= 9.61, chroma = 12.79, and hue = 48.8_) as compared to the control (L*= 52.84, a*= 11.08, b*= 2.24, chroma = 11.28, and hue = 11.4_). The use of potassium meta-bisulfite at all treatment levels (200, 500, and 1000 ppm) did not show any significant detrimental effects on the stability, composition, or amount of procyanidins present in the extracts over the shelf life period as monitored by UV-Vis spectrophotometry and HPLC-MS. This research will enable the food industry to incorporate highly concentrated procyanidin-rich extracts in food products without influencing the color of the final product.",Journal of food science,"['D000872', 'D002099', 'D003116', 'D005285', 'D005511', 'D010936', 'D044945', 'D012639', 'D013447']","['Anthocyanins', 'Cacao', 'Color', 'Fermentation', 'Food Handling', 'Plant Extracts', 'Proanthocyanidins', 'Seeds', 'Sulfites']",Selective removal of the violet color produced by anthocyanins in procyanidin-rich unfermented cocoa extracts.,"['Q000737', 'Q000737', None, None, 'Q000379', 'Q000737', 'Q000032', 'Q000737', None]","['chemistry', 'chemistry', None, None, 'methods', 'chemistry', 'analysis', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/22417537,2012,0,0,,no day 0 control -0.3,9583844,"The influence of ascorbic acid on iron absorption from an iron-fortified, chocolate-flavored milk drink (6.3 mg total Fe per serving) was evaluated with a stable-isotope technique in 20 6-7-y-old Jamaican children. Each child received two test meals labeled with 5.6 mg 57Fe and 3.0 mg 58Fe as ferrous sulfate on 2 consecutive days. Three different doses of ascorbic acid (0, 25, and 50 mg per 25-g serving) were evaluated in two separate studies by using a crossover design. Iron isotope ratios were measured by negative thermal ionization mass spectrometry. In the first study, iron absorption was significantly greater (P < 0.0001) after the addition of 25 mg ascorbic acid: geometric mean iron absorption was 1.6% (range: 0.9-4.2%) and 5.1% (2.2-17.3%) for the test meals containing 0 and 25 mg ascorbic acid, respectively. In the second study, a significant difference (P < 0.05) in iron absorption was observed when the ascorbic acid content was increased from 25 to 50 mg: geometric mean iron absorption was 5.4% (range: 2.7-10.8%) compared with 7.7% (range: 4.7-16.5%), respectively. The chocolate drink contained relatively high amounts of polyphenolic compounds, phytic acid, and calcium, all well-known inhibitors of iron absorption. The low iron absorption without added ascorbic acid shows that chocolate milk is a poor vehicle for iron fortification unless sufficient amounts of an iron-absorption enhancer are added. Regular consumption of iron-fortified chocolate milk drinks containing added ascorbic acid could have a positive effect on iron nutrition in population groups vulnerable to iron deficiency.",The American journal of clinical nutrition,"['D000818', 'D001205', 'D002097', 'D002099', 'D002648', 'D018592', 'D005260', 'D005293', 'D005504', 'D005527', 'D006454', 'D006801', 'D007408', 'D007501', 'D007563', 'D008297', 'D008892']","['Animals', 'Ascorbic Acid', 'C-Reactive Protein', 'Cacao', 'Child', 'Cross-Over Studies', 'Female', 'Ferritins', 'Food Analysis', 'Food, Fortified', 'Hemoglobins', 'Humans', 'Intestinal Absorption', 'Iron', 'Jamaica', 'Male', 'Milk']","Influence of ascorbic acid on iron absorption from an iron-fortified, chocolate-flavored milk drink in Jamaican children.","[None, 'Q000008', 'Q000378', None, None, None, None, 'Q000097', None, 'Q000032', 'Q000378', None, 'Q000187', 'Q000008', None, None, None]","[None, 'administration & dosage', 'metabolism', None, None, None, None, 'blood', None, 'analysis', 'metabolism', None, 'drug effects', 'administration & dosage', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9583844,1998,0,0,,no cocoa tested -0.3,24785502,"Surveys were carried out between 2007 and 2009 to determine the aflatoxin B1 content of 3345 commercial Turkish foodstuffs supplied by producers for testing for their own purposes or for export certification. To simplify the reporting of data, foods were categorized as: 1, high sugar products with nuts; 2, nuts and seeds; 3, spices; 4, grain; 5, cocoa products; 6, dried fruit and vegetables; 7, processed cereal products; 8, tea; and 9, baby food and infant formula. Aflatoxin analysis was carried out by high-performance liquid chromatography with fluorescence detection after immunoaffinity column clean-up, with a recoveries ranging from 91% to 99%, depending on the matrix. Of the 3345 samples analysed, 94% contained aflatoxin B1 below the European Union limit of 2 _µg kg(-1), which applies to nuts, dried fruit, and cereals products. The 6% of the 206 contaminated samples were mainly nuts and spices. For pistachios, 24%, 38%, and 42% of the totals of 207, 182, and 24 samples tested for 2007, 2008 and 2009, respectively, were above 2 _µg kg(-1), with 50 samples containing aflatoxin B1 at levels ranging from 10 to 477 _µg kg(-1). ","Food additives & contaminants. Part B, Surveillance","['D016604', 'D003625', 'D004032', 'D002523', 'D004781', 'D005062', 'D005506', 'D005523', 'D005638', 'D005658', 'D006801', 'D007223', 'D041943', 'D009754', 'D014421']","['Aflatoxin B1', 'Data Collection', 'Diet', 'Edible Grain', 'Environmental Exposure', 'European Union', 'Food Contamination', 'Food Supply', 'Fruit', 'Fungi', 'Humans', 'Infant', 'Infant Formula', 'Nuts', 'Turkey']",Surveys of aflatoxin B1 contamination of retail Turkish foods and of products intended for export between 2007 and 2009.,"['Q000032', None, None, 'Q000737', 'Q000032', None, 'Q000032', None, 'Q000737', None, None, None, None, 'Q000737', None]","['analysis', None, None, 'chemistry', 'analysis', None, 'analysis', None, 'chemistry', None, None, None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/24785502,2014,0,0,,cocoa products werent specified -0.3,18727538,"A fast and simple method to determine vitamin B12 in foods is presented. The method allows, in addition to the determination of added cyanocobalamin, the determination of natural vitamin B12 forms, making it also applicable to nonfortified products, especially those that are milk-based. Vitamin B12 is extracted in sodium acetate buffer in the presence of sodium cyanide (100 degrees C, 30 min). After purification and concentration with an immunoaffinity column, vitamin B12 is determined by liquid chromatography with UV detection (361 nm). The method has been validated in analyses of a large range of products: milk- and soy-based infant formulas, cereals, cocoa beverages, health care products, and polyvitamin premixes. The method showed appropriate performance characteristics: linear response over a large range of concentrations, recovery rates of 100.8 +/- 7.5% (average +/- standard deviation), relative standard deviation of repeatability, RSDr, of 2.1%, and intermediate reproducibility, RSDiR, of 4.3%. Limits of detection and quantitation were 0.10 and 0.30 microg/100 g, respectively, and correlation with the reference microbiological assay was good (R2 = 0.9442). The proposed method is suitable for the routine determination of vitamin B12 in fortified foods, as well as in nonfortified dairy products. It can be used as a faster, more selective, and more precise alternative to the classical microbiological determination.",Journal of AOAC International,"['D002846', 'D002851', 'D005527', 'D007120', 'D007202', 'D012015', 'D015203', 'D012997', 'D013056', 'D014805', 'D014815']","['Chromatography, Affinity', 'Chromatography, High Pressure Liquid', 'Food, Fortified', 'Immunochemistry', 'Indicators and Reagents', 'Reference Standards', 'Reproducibility of Results', 'Solvents', 'Spectrophotometry, Ultraviolet', 'Vitamin B 12', 'Vitamins']",Determination of vitamin B12 in food products by liquid chromatography/UV detection with immunoaffinity extraction: single-laboratory validation.,"[None, None, 'Q000032', None, None, None, None, None, None, 'Q000032', 'Q000032']","[None, None, 'analysis', None, None, None, None, None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18727538,2008,,,, -0.3,26572874,"Antioxidant-rich foods scavenge free radicals and other reactive species, decreasing the risk of different non-communicable chronic diseases. The objective of this study was to review the content of total antioxidant capacity of commonly foods comparing with experimental data and to explore the health benefits due to foods with moderate to high TAC. The TAC was analytically measured using the ""Total Antioxidant Capacity"" (NX2332) test from Randox_‰ (UK) by spectrometry at 600 nm. Brazil nut (Bertholletia excelsa), ""guaran"" (Paullinia cupana Kunth) powder, ready to drink boiled coffee (Coffea arabica L.), and milk chocolate (made from seeds of Theobroma cacao) had the highest TAC values, followed by collard greens (Brassica oleracea L.), beets (Beta vulgaris L.), apples (Malus domestica Borkh.), bananas (Musa paradisiaca), common beans (Phaseolus vulgaris), oranges (Citrus sinensis (L.) Osbeck), onions (Allium cepa L.), and lettuce (Lactuca sativa L.). Other foods also showed antioxidant capacity. The binomial antioxidant capacity of foods and health was extensively discussed according to science literature. Based on the high TAC content of Brazil nuts, guaran, coffee, chocolate, collard greens, apples, beets, beans, oranges, onions and other foods, their regular dietary intake is strongly recommended to reduce the risk of chronic non-communicable diseases. ",Current pharmaceutical design,"['D031383', 'D002318', 'D002561', 'D019587', 'D006801', 'D027845', 'D009369', 'D008517']","['Bertholletia', 'Cardiovascular Diseases', 'Cerebrovascular Disorders', 'Dietary Supplements', 'Humans', 'Malus', 'Neoplasms', 'Phytotherapy']",An apple plus a Brazil nut a day keeps the doctors away: antioxidant capacity of foods and their health benefits.,"[None, 'Q000517', 'Q000517', None, None, None, 'Q000517', None]","[None, 'prevention & control', 'prevention & control', None, None, None, 'prevention & control', None]",https://www.ncbi.nlm.nih.gov/pubmed/26572874,2016,,,,no pdf access -0.3,3806705,"Ethyl ether extracts derived from coffee were tested for in vitro estrogenic and in vivo uterotropic activities. Coffee extracts, unlike tea and cocoa, were found to actively compete with 17 beta-estradiol for uterine cytosol binding sites. The biologically active fractions possessed an unique ultraviolet absorbance spectrum that excluded them from containing flavonoid, coumestan, or resorcyclic acid lactone constituents. Coffee extracts administered to immature female mice for 3 d in feeding studies displayed significant (p less than 0.05) uterotropic responses, which were similar to results obtained in mice treated with a standard 17 beta-estradiol dose. Additional studies in mice disclosed that coffee extracts did not reduce the uterotropic effect normally induced by 17 beta-estradiol when administered simultaneously with estradiol. The complete estrogenic effects of coffee constituents, coupled with their failure to inhibit a biological response evoked by estradiol, strongly suggest that coffee contains constituent(s) that are weakly estrogenic.",Journal of toxicology and environmental health,"['D000818', 'D001667', 'D002099', 'D003069', 'D003600', 'D004958', 'D004986', 'D005260', 'D019833', 'D007529', 'D051379', 'D010052', 'D011960', 'D013056', 'D013662', 'D014599']","['Animals', 'Binding, Competitive', 'Cacao', 'Coffee', 'Cytosol', 'Estradiol', 'Ether', 'Female', 'Genistein', 'Isoflavones', 'Mice', 'Ovariectomy', 'Receptors, Estrogen', 'Spectrophotometry, Ultraviolet', 'Tea', 'Uterus']",Studies on the estrogenic activity of a coffee extract.,"[None, None, None, 'Q000633', 'Q000378', 'Q000378', None, None, None, None, None, None, 'Q000378', None, None, 'Q000378']","[None, None, None, 'toxicity', 'metabolism', 'metabolism', None, None, None, None, None, None, 'metabolism', None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/3806705,1987,,,, -0.3,21535643,"Chocolate storage is critical to final product quality. Inadequate storage, especially with temperature fluctuations, may lead to rearrangement of triglycerides that make up the bulk of the chocolate matrix; this rearrangement may lead to fat bloom. Bloom is the main cause of quality loss in the chocolate industry. The effect of storage conditions leading to bloom formation on texture and flavor attributes by human and instrumental measures has yet to be reported. Therefore, the impact of storage conditions on the quality of dark chocolate by sensory and instrumental measurements was determined. Dark chocolate was kept under various conditions and analyzed at 0, 4, and 8 wk of storage. Ten members of a descriptive panel analyzed texture and flavor. Instrumental methods included texture analysis, color measurement, lipid polymorphism by X-ray diffraction and differential scanning calorimetry, triglyceride concentration by gas chromatography, and surface properties by atomic force microscopy. Results were treated by analysis of variance, cluster analysis, principal component analysis, and linear partial least squares regression analysis. Chocolate stored 8 wk at high temperature without fluctuations and 4 wk with fluctuations transitioned from form V to VI. Chocolates stored at high temperature with and without fluctuations were harder, more fracturable, more toothpacking, had longer melt time, were less sweet, and had less cream flavor. These samples had rougher surfaces, fewer but larger grains, and a heterogeneous surface. Overall, all stored dark chocolate experienced instrumental or perceptual changes attributed to storage condition. Chocolates stored at high temperature with and without fluctuations were most visually and texturally compromised. Practical Application: Many large chocolate companies do their own ""in-house"" unpublished research and smaller confectionery facilities do not have the means to conduct their own research. Therefore, this study relating sensory and instrumental data provides published evidence available for application throughout the confectionery industry.",Journal of food science,"['D002099', 'D002152', 'D002182', 'D055598', 'D003116', 'D005260', 'D005410', 'D005511', 'D006358', 'D006801', 'D008297', 'D018625', 'D033362', 'D011786', 'D012677', 'D013499', 'D013649', 'D013997', 'D044366', 'D014280']","['Cacao', 'Calorimetry, Differential Scanning', 'Candy', 'Chemical Phenomena', 'Color', 'Female', 'Flame Ionization', 'Food Handling', 'Hot Temperature', 'Humans', 'Male', 'Microscopy, Atomic Force', 'Powder Diffraction', 'Quality Control', 'Sensation', 'Surface Properties', 'Taste', 'Time Factors', 'Transition Temperature', 'Triglycerides']",Impact of storage on dark chocolate: texture and polymorphic changes.,"['Q000737', None, 'Q000032', None, None, None, None, None, 'Q000009', None, None, None, None, None, None, None, None, None, None, 'Q000032']","['chemistry', None, 'analysis', None, None, None, None, None, 'adverse effects', None, None, None, None, None, None, None, None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/21535643,2011,0,0,, -0.3,21561068,"The physicochemical properties of acidified milk gels after the addition of cocoa flavanols were studied. As the flavanol level increased (from 0 to 2.5 mg/g), syneresis and gel elasticity (tan __) were found to significantly increase and decrease, respectively. Flavanol addition reduced the stress at fracture, with no changes in fracture strain, suggesting that the bond type (i.e., covalent vs noncovalent) was the underlying factor explaining the ease of fracture. Gels made from recombined milks containing the casein fraction of heated milk and the serum of heated flavanol/milk mixtures showed the lowest values of G' and fracture stress. It was concluded that whey proteins/flavanol interactions were responsible for the poor mechanical properties of flavanol-added acidified milk gels. High-performance liquid chromatography analysis of milk sera showed that 60% of the total available monomeric flavanols was found in the serum phase from which 75% was non-associated to whey proteins. Concomitantly, >70% of flavanols with degree of polymerization >3 were found to be associated with the casein fraction.",Journal of agricultural and food chemistry,"['D000818', 'D002099', 'D055598', 'D004548', 'D044950', 'D005782', 'D008892', 'D058105']","['Animals', 'Cacao', 'Chemical Phenomena', 'Elasticity', 'Flavanones', 'Gels', 'Milk', 'Polymerization']",Physicochemical properties of acidified skim milk gels containing cocoa flavanols.,"[None, 'Q000737', None, None, 'Q000737', 'Q000737', 'Q000737', None]","[None, 'chemistry', None, None, 'chemistry', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21561068,2011,0,0,, -0.3,11025151,"Cocoa and chocolate contain the tetrahydroisoquinoline alkaloid salsolinol up to a concentration of 25 microg/g. Salsolinol is a dopaminergic active compound which binds to the D(2) receptor family, especially to the D(3) receptor with a K(i) of 0.48+/-0.021 micromol/l. It inhibits the formation of cyclic AMP and the release of beta-endorphin and ACTH in a pituitary cell system. Taking the detected concentration and the pharmacological properties into account, salsolinol seems to be one of the main psychoactive compounds present in cocoa and chocolate and might be included in chocolate addiction.",Journal of ethnopharmacology,"['D000324', 'D000818', 'D002099', 'D000242', 'D005504', 'D008401', 'D007546', 'D051379', 'D011954', 'D013237', 'D019966', 'D014407', 'D001615']","['Adrenocorticotropic Hormone', 'Animals', 'Cacao', 'Cyclic AMP', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Isoquinolines', 'Mice', 'Receptors, Dopamine', 'Stereoisomerism', 'Substance-Related Disorders', 'Tumor Cells, Cultured', 'beta-Endorphin']",In vitro pharmacological activity of the tetrahydroisoquinoline salsolinol present in products from Theobroma cacao L. like cocoa and chocolate.,"['Q000378', None, 'Q000737', 'Q000378', None, None, 'Q000032', None, 'Q000187', None, 'Q000378', 'Q000187', 'Q000187']","['metabolism', None, 'chemistry', 'metabolism', None, None, 'analysis', None, 'drug effects', None, 'metabolism', 'drug effects', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/11025151,2001,1,1,table 1,only cocoa -0.3,22953918,"Silicon is a trace element for humans, and is absorbed from food in the form of orthosilicic acid. Instant food products are part of a constantly growing market of convenience foods, which have not been evaluated yet as sources of silicon. In this study the total and soluble silicon contents in different instant food products were determined by using graphite furnace atomic absorption spectrometry (GF-AAS). A selection of instant products commercially available in Wroclaw were analyzed: soups, main courses, coffee drinks, jellies and puddings. Total silicon contents in soups, main courses and coffee drinks ranged widely and reached the values: 0.10-30.20, 0.63-37.91 and 0.21-13.37mg/serving, respectively. These products contained 0.05-1.26mg of soluble silicon per serving. The total silicon content in jellies and puddings did not exceed 0.36mg and 2.42mg/serving, respectively. Among the analyzed desserts the highest level of soluble silicon was found in chocolate puddings: 0.36-0.41mg/serving. The silicon level in servings of the studied instant products when prepared with the appropriate amount of water was also estimated. The mean content of silicon determined in samples of drinking water from Wroc_aw and the vicinity, which was used for the estimation, amounted to 7.09mg/l. The total silicon content in ready-to-eat products ranged from 1.32 to 39.21mg/serving. In conclusion, some of the analyzed instant foods contained very high amounts of silicon, however the content of the soluble, and hence available, form of this element was low.",Food chemistry,"['D057140', 'D005504', 'D009753', 'D012825', 'D014131']","['Fast Foods', 'Food Analysis', 'Nutritive Value', 'Silicon', 'Trace Elements']",Instant food products as a source of silicon.,"['Q000032', None, None, 'Q000032', 'Q000032']","['analysis', None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/22953918,2013,0,0,,no cocoa -0.29,18215649,"The spontaneous formation of the neurotoxic carcinogen acrylamide in a wide range of cooked foods has recently been discovered. These foods include bread and other bakery products, crisps, chips, breakfast cereals, and coffee. To date, the diminutive size of acrylamide (71.08 Da) has prevented the development of screening immunoassays for this chemical. In this study, a polyclonal antibody capable of binding the carcinogen was produced by the synthesis of an immunogen comprising acrylamide derivatised with 3-mercaptobenzoic acid (3-MBA), and its conjugation to the carrier protein bovine thyroglobulin. Antiserum from the immunised rabbit was harvested and fully characterised. It displayed no binding affinity for acrylamide or 3-MBA but had a high affinity for 3-MBA-derivitised acrylamide. The antisera produced was utilised in the development of an ELISA based detection system for acrylamide. Spiked water samples were assayed for acrylamide content using a previously published extraction method validated for coffee, crispbread, potato, milk chocolate and potato crisp matrices. Extracted acrylamide was then subjected to a rapid 1-h derivatisation with 3-MBA, pre-analysis. The ELISA was shown to have a high specificity for acrylamide, with a limit of detection in water samples of 65.7 microgkg(-1), i.e. potentially suitable for acrylamide detection in a wide range of food commodities. Future development of this assay will increase sensitivity further. This is the first report of an immunoassay capable of detecting the carcinogen, as its small size has necessitated current analytical detection via expensive, slower, physico-chemical techniques such as Gas or Liquid Chromatography coupled to Mass Spectrometry.",Analytica chimica acta,"['D020106', 'D000818', 'D002273', 'D004797', 'D005260', 'D005504', 'D051379', 'D008807', 'D013997']","['Acrylamide', 'Animals', 'Carcinogens', 'Enzyme-Linked Immunosorbent Assay', 'Female', 'Food Analysis', 'Mice', 'Mice, Inbred BALB C', 'Time Factors']","Development of a high-throughput enzyme-linked immunosorbent assay for the routine detection of the carcinogen acrylamide in food, via rapid derivatisation pre-analysis.","['Q000032', None, 'Q000032', 'Q000379', None, None, None, None, None]","['analysis', None, 'analysis', 'methods', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18215649,2008,0,0,,no cocoa -0.29,16500886,"In the current study, we screened 7 clonal lines from single seed phenotypes of Lamiaceae family for the inhibition of alpha-amylase, alpha-glucosidase and angiotensin converting enzyme (ACE) inhibitory activity. Water extracts of oregano had the highest alpha-glucosidase inhibition activity (93.7%), followed by chocolate mint (85.9%) and lemon balm (83.9%). Sage (78.4 %), and three different clonal lines of rosemary: rosemary LA (71.4%), rosemary 6 (68.4%) and rosemary K-2 (67.8%) also showed significant alpha-glucosidase inhibitory activity. The alpha-glucosidase inhibitory activity of the extracts was compared to selected specific phenolics detected in the extracts using HPLC. Catechin had the highest alpha-glucosidase inhibitiory activity (99.6 %) followed by caffeic acid (91.3 %), rosmarinic acid (85.1%) and resveratrol (71.1 %). Catechol (64.4%), protocatechuic acid (55.7%) and quercetin (36.9%) also exhibited significant alpha-glucosidase inhibitory activity. Results suggested that alpha-glucosidase inhibitory activity of the clonal extracts correlated to the phenolic content, antioxidant activity and phenolic profile of the extracts. The clonal extracts of the herbs and standard phenolics tested in this study did not have any effect on the alpha-amylase activity. We also investigated the ability of the clonal extracts to inhibit rabbit lung angiotensin I-converting enzyme (ACE). The water extracts of rosemary, rosemary LA had the highest ACE inhibitory activity (90.5%), followed by lemon balm (81.9%) and oregano (37.4 %). Lower levels of ACE inhibition were observed with ethanol extracts of oregano (18.5 %) and lemon balm (0.5 %). Among the standard phenolics only resveratrol (24.1 %), hydroxybenzoic acid (19.3 %) and coumaric acid (2.3 %) had ACE inhibitory activity.",Asia Pacific journal of clinical nutrition,"['D000806', 'D000959', 'D002851', 'D003924', 'D004791', 'D000431', 'D065089', 'D006801', 'D006973', 'D007004', 'D066298', 'D019686', 'D008517', 'D010936', 'D014867', 'D000516']","['Angiotensin-Converting Enzyme Inhibitors', 'Antihypertensive Agents', 'Chromatography, High Pressure Liquid', 'Diabetes Mellitus, Type 2', 'Enzyme Inhibitors', 'Ethanol', 'Glycoside Hydrolase Inhibitors', 'Humans', 'Hypertension', 'Hypoglycemic Agents', 'In Vitro Techniques', 'Lamiaceae', 'Phytotherapy', 'Plant Extracts', 'Water', 'alpha-Amylases']",Evaluation of clonal herbs of Lamiaceae species for management of diabetes and hypertension.,"[None, 'Q000627', None, 'Q000188', 'Q000627', None, None, None, 'Q000188', 'Q000627', None, 'Q000737', None, 'Q000032', None, 'Q000037']","[None, 'therapeutic use', None, 'drug therapy', 'therapeutic use', None, None, None, 'drug therapy', 'therapeutic use', None, 'chemistry', None, 'analysis', None, 'antagonists & inhibitors']",https://www.ncbi.nlm.nih.gov/pubmed/16500886,2006,0,0,,no cocoa -0.29,12963011,"High levels of acrylamide have been found in foods heated at high temperatures, especially in carbohydrate rich foods. Several kinds of foods (industrially produced) representing different food/product groups available on the Swedish market have been analysed for acrylamide. A considerable variation in levels of acrylamide between single foodstuffs (different brands) within food categories were found, which also applies for levels in different food categories. Using recent Swedish food consumption data the dietary intake of acrylamide for the Swedish adult population was assessed based on foodstuffs with low to high levels of acrylamide (<30-2300 microg/kg), such as processed potato products, bread, breakfast cereals, biscuits, cookies, snacks and coffee. The estimated dietary intake of acrylamide per person (total population) given as the 5th, 50th and 95th percentile were 9.1, 27 and 62 microg/day respectively, from those food/product groups (mean 31 microg/day). No acrylamide was found in many other foodstuffs analysed and those were therefore not included in the dietary intake assessment of acrylamide. However, an additional minor contribution of a few microg/day of acrylamide from foods/products like poultry, meat, fish, cocoa powder and chocolates cannot be excluded. An average daily intake of 35 microg corresponds to 0.5 microg per kg body weight and day (body weight 70 kg). Risk assessments of acrylamide, made by US EPA and WHO, imply that this dietary intake of acrylamide could be associated with potential health risks.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000178', 'D000293', 'D000328', 'D000368', 'D002853', 'D003625', 'D004032', 'D004435', 'D005260', 'D005504', 'D006801', 'D008297', 'D008875', 'D021241', 'D013548']","['Acrylamides', 'Adolescent', 'Adult', 'Aged', 'Chromatography, Liquid', 'Data Collection', 'Diet', 'Eating', 'Female', 'Food Analysis', 'Humans', 'Male', 'Middle Aged', 'Spectrometry, Mass, Electrospray Ionization', 'Sweden']",Dietary intake of acrylamide in Sweden.,"['Q000032', None, None, None, None, None, None, None, None, None, None, None, None, None, None]","['analysis', None, None, None, None, None, None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12963011,2003,0,0,, -0.29,29083186,"Peanut is an important food allergen, but it cannot currently be reliably detected and quantified in processed foods at low levels. A level of 3 mg protein/kg is increasingly being used as a reference dose above which precautionary allergen labeling is applied to food products. Two exemplar matrices (chocolate dessert and chocolate bar) were prepared and incurred with 0, 3, 10, or 50 mg/kg peanut protein using a commercially available lightly roasted peanut flour ingredient. After simple buffer extraction employing an acid-labile detergent, multiple reaction monitoring (MRM) experiments were used to assess matrix effects on the detection of a set of seven peptide targets derived from peanut allergens using either conventional or microfluidic chromatographic separation prior to mass spectrometry. Microfluidic separation provided greater sensitivity and increased ionization efficiency at low levels. Individual monitored transitions were detected in consistent ratios across the dilution series, independent of matrix. The peanut protein content of each sample was then determined using ELISA and the optimized MRM method. Although other peptide targets were detected with three transitions at the 50 mg/kg peanut protein level in both matrices, only Arah2(Q6PSU2)",Journal of proteome research,[],[],Microfluidic Separation Coupled to Mass Spectrometry for Quantification of Peanut Allergens in a Complex Food Matrix.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29083186,2018,0,0,,no cocoa -0.29,25889873,"Two dimensional electrophoresis and nano-LC-MS were performed in order to identify alterations in protein abundance that correlate with maturation of cacao zygotic and somatic embryos. The cacao pod proteome was also characterized during development. The recently published cacao genome sequence was used to create a predicted proteolytic fragment database. Several hundred protein spots were resolved on each tissue analysis, of which 72 variable spots were subjected to MS analysis, resulting in 49 identifications. The identified proteins represent an array of functional categories, including seed storage, stress response, photosynthesis and translation factors. The seed storage protein was strongly accumulated in cacao zygotic embryos compared to their somatic counterpart. However, sucrose treatment (60 g L(-1)) allows up-regulation of storage protein in SE. A high similarity in the profiles of acidic proteins was observed in mature zygotic and somatic embryos. Differential expression in both tissues was observed in proteins having high pI. Several proteins were detected exclusively in fruit tissues, including a chitinase and a 14-3-3 protein. We also identified a novel cacao protein related to known mabinlin type sweet storage proteins. Moreover, the specific presence of thaumatin-like protein, another sweet protein, was also detected in fruit tissue. We discuss our observed correlations between protein expression profiles, developmental stage and stress responses.",Journal of plant physiology,"['D002099', 'D002853', 'D015180', 'D018507', 'D018506', 'D013058', 'D036103', 'D010940', 'D020543', 'D040901', 'D012639', 'D015053']","['Cacao', 'Chromatography, Liquid', 'Electrophoresis, Gel, Two-Dimensional', 'Gene Expression Regulation, Developmental', 'Gene Expression Regulation, Plant', 'Mass Spectrometry', 'Nanotechnology', 'Plant Proteins', 'Proteome', 'Proteomics', 'Seeds', 'Zygote']","Proteome analysis during pod, zygotic and somatic embryo maturation of Theobroma cacao.","['Q000196', None, None, None, None, None, None, 'Q000378', 'Q000378', 'Q000379', 'Q000235', 'Q000378']","['embryology', None, None, None, None, None, None, 'metabolism', 'metabolism', 'methods', 'genetics', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/25889873,2016,0,0,, -0.29,22664313,"After absorption in the gastrointestinal tract, (-)-epicatechin is extensively transformed into various conjugated metabolites. These metabolites, chemically different from the aglycone forms found in foods, are the compounds that reach the circulatory system and the target organs. Therefore, it is imperative to identify and quantify these circulating metabolites to investigate their roles in the biological effects associated with (-)-epicatechin intake. Using authentic synthetic standards of (-)-epicatechin sulfates, glucuronides, and O-methyl sulfates, a novel LC-MS/MS-MRM analytical methodology to quantify (-)-epicatechin metabolites in biological matrices was developed and validated. The optimized method was subsequently applied to the analysis of plasma and urine metabolites after consumption of dark chocolate, an (-)-epicatechin-rich food, by humans. (-)-Epicatechin-3'-__-d-glucuronide (C(max) 290 _± 49 nM), (-)-epicatechin 3'-sulfate (C(max) 233 _± 60 nM), and 3'-O-methyl epicatechin sulfates substituted in the 4', 5, and 7 positions were the most relevant (-)-epicatechin metabolites in plasma. When plasmatic metabolites were divided into their substituent groups, it was revealed that (-)-epicatechin glucuronides, sulfates, and O-methyl sulfates represented 33 _± 4, 28 _± 5, and 33 _± 4% of total metabolites (AUC(0-24)(h)), respectively, after dark chocolate consumption. Similar metabolites were found in urine samples collected over 24h. The total urine excretion of (-)-epicatechin was 20 _± 2% of the amount ingested. In conclusion, we describe the entire metabolite profile and its degree of elimination after administration of (-)-epicatechin-containing food. These results will help us understand more precisely the mechanisms and the main metabolites involved in the beneficial physiological effects of flavanols.",Free radical biology & medicine,"['D000328', 'D000704', 'D019540', 'D002099', 'D002392', 'D056148', 'D006207', 'D006262', 'D006801', 'D057230', 'D013058', 'D012015', 'D055815']","['Adult', 'Analysis of Variance', 'Area Under Curve', 'Cacao', 'Catechin', 'Chromatography, Reverse-Phase', 'Half-Life', 'Health', 'Humans', 'Limit of Detection', 'Mass Spectrometry', 'Reference Standards', 'Young Adult']",Elucidation of (-)-epicatechin metabolites after ingestion of chocolate by healthy humans.,"[None, None, None, 'Q000378', 'Q000031', 'Q000592', None, None, None, None, 'Q000592', None, None]","[None, None, None, 'metabolism', 'analogs & derivatives', 'standards', None, None, None, None, 'standards', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22664313,2012,0,0,,metabolites -0.29,2209520,"Thoroughbred geldings were fed racehorse cubes containing a predetermined concentration of theobromine in the form of cocoa husk. They were offered 7 kg of cubes per day, divided between morning and evening feed, and food consumption was monitored. Urinary concentrations of theobromine were determined following the consumption of cubes containing 11.5, 6.6, 2.0 and 1.2 mg per kg of theobromine, to verify whether or not such concentrations would produce positive urine tests. Pre-dose urine samples were collected to verify the absence of theobromine before each experiment. It became apparent from the results of the first three administrations that the limit of detection of theobromine, using such procedures, would be reached at a feed level of about 1 mg per kg theobromine. Therefore the final administration, using cubes containing 1.2 mg per kg theobromine, was singled out for additional analytical work and quantitative procedures were developed to measure urinary concentrations of theobromine. It was anticipated that the results would form a basis for discussions relating to the establishment of a threshold value for theobromine in horse urine. The Stewards of the Jockey Club subsequently gave notice that they had established a threshold level for theobromine in urine of 2 micrograms/ml.",Equine veterinary journal,"['D000821', 'D000818', 'D002099', 'D002851', 'D008401', 'D006736', 'D008297', 'D012016', 'D015203', 'D013805']","['Animal Feed', 'Animals', 'Cacao', 'Chromatography, High Pressure Liquid', 'Gas Chromatography-Mass Spectrometry', 'Horses', 'Male', 'Reference Values', 'Reproducibility of Results', 'Theobromine']",The excretion of theobromine in Thoroughbred racehorses after feeding compounded cubes containing cocoa husk--establishment of a threshold value in horse urine.,"[None, None, None, None, None, 'Q000378', None, None, None, 'Q000008']","[None, None, None, None, None, 'metabolism', None, None, None, 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/2209520,1990,,,,no pdf access -0.28,21548445,"Labeling of major food allergens is mandatory for the safety of allergic consumers. Although enzyme-linked immunosorbent assay, polymerase chain reaction, and mass spectrometry are sensitive and specific instruments to detect trace amounts of food proteins, they cannot measure the ability of food constituents to trigger activation of mast cells or basophils.",Journal of investigational allergology & clinical immunology,"['D000485', 'D015703', 'D010367', 'D001491', 'D016022', 'D002648', 'D005260', 'D005512', 'D006801', 'D007073', 'D008297', 'D008407', 'D021183', 'D010980', 'D060149']","['Allergens', 'Antigens, CD', 'Arachis', 'Basophils', 'Case-Control Studies', 'Child', 'Female', 'Food Hypersensitivity', 'Humans', 'Immunoglobulin E', 'Male', 'Mast Cells', 'Peanut Hypersensitivity', 'Platelet Membrane Glycoproteins', 'Tetraspanin 30']",Human basophils: a unique biological instrument to detect the allergenicity of food.,"['Q000276', 'Q000235', 'Q000276', 'Q000276', None, None, None, 'Q000276', None, 'Q000276', None, 'Q000276', 'Q000276', 'Q000235', None]","['immunology', 'genetics', 'immunology', 'immunology', None, None, None, 'immunology', None, 'immunology', None, 'immunology', 'immunology', 'genetics', None]",https://www.ncbi.nlm.nih.gov/pubmed/21548445,2011,0,0,, -0.28,12557249,"Essential oils and their corresponding hydrosols, obtained after distillation of various scented Pelargonium (Geraniaceae) leaves were assessed for their antimicrobial activity in a model food system. Both the essential oils and hydrosols were used at 1000 ppm in broccoli soup, previously inoculated with Enterobacter aerogenes (at 10(5) cfu g(-1)) and Staphylococcus aureus (at 10(4) cfu g(-1)). The results showed a complete inhibition of S. aureus in the broccoli soup by the essential oils of 'Sweet Mimosa', 'Mabel Grey', P. graveolens, 'Atomic Snowflake', 'Royal Oak', 'Attar of Roses' and a lesser effect by 'Chocolate Peppermint' and 'Clorinda'; the hydrosols, however, had a potentiating effect on the bacterial population in the food. Both extracts showed a complete inhibition of S. aureus in the Maximum Recovery Diluent (MRD). Antibacterial activity against E. aerogenes in the broccoli soup was generally very much reduced: only the essential oil of 'Mabel Grey' showed complete inhibition and virtually no reductions in colonies were seen with the other essential oils; the hydrosols again caused an increase in bacterial colonies. All the essential oils, bar Chocolate Peppermint showed complete inhibition of E. aerogenes in MRD, but the hydrosols showed no effect. The results strongly suggest that the residual hydrosols from distillation of these plant essential oils have no potential as antibacterial agents in foods, in contrast to most of the essential oils, which show potential against some micro-organisms, but only in some food systems. The problem of food component interference and its possible management is discussed.",Phytotherapy research : PTR,"['D001937', 'D002849', 'D021902', 'D005516', 'D005520', 'D005517', 'D006801', 'D008826', 'D031316', 'D008517', 'D018515', 'D010938', 'D013211']","['Brassica', 'Chromatography, Gas', 'Enterobacter aerogenes', 'Food Microbiology', 'Food Preservatives', 'Foodborne Diseases', 'Humans', 'Microbial Sensitivity Tests', 'Pelargonium', 'Phytotherapy', 'Plant Leaves', 'Plant Oils', 'Staphylococcus aureus']",The comparative effect of novel Pelargonium essential oils and their corresponding hydrosols as antimicrobial agents in a model food system.,"[None, None, 'Q000187', None, 'Q000008', 'Q000517', None, None, None, None, None, 'Q000494', 'Q000187']","[None, None, 'drug effects', None, 'administration & dosage', 'prevention & control', None, None, None, None, None, 'pharmacology', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/12557249,2003,0,0,,no cocoa -0.28,24804047,"Acrylamide (AA) is a chemical found in starchy foods that have been cooked at high temperatures. The objective of this study is to monitor the levels of AA in a total of 274 samples of potato chips, chips (except potato chips), biscuits, French fries, breakfast cereals, chocolate products, tea, seasoned laver, and nut products sampled in Korean market. These processed foods include (1) potato chips, (2) chips (except potato chips), (3) biscuits, (4) French fries, (5) breakfast cereals, (6) chocolate products, (7) tea, (8) seasoned laver, and (9) nut products. Samples used for this study were cleaned up using HLB Oasis polymeric and Accucat mixed-mode anion and cation exchange solid-phase extraction cartridge. Liquid chromatography-tandem mass spectroscopy (LC-MS/MS) was validated in-house as an efficient analytical method for the routine analysis of AA in various food products. AA was detected with a Fortis dC18 (1.7 __m, 100 mm _ 2.1 mm) column using 0.5% methanol/0.1% acetic acid in water as the mobile phase. Good results were obtained with respect to repeatability (RSDs < 5%). The recoveries obtained for a variety of food matrices ranged between 94.5% and 107.6%. Quantification during routine monitoring was sensitive enough to detect AA at a concentration of 10 __g/kg. A total of 274 food samples were analyzed for AA. The AA levels in the food groups were in the following order: potato chips > French fries > biscuits > tea > chips (except potato chips) > seasoned laver > breakfast cereals > chocolate products > nut products. AA was detected at levels ranging from not detectable to 1435 __g/kg. ",Food science & nutrition,[],[],In-house-validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for survey of acrylamide in various processed foods from Korean market.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/24804047,2014,0,0,,no cocoa -0.28,26590272,"Microbial starter cultures have extensively been used to enhance the consistency and efficiency of industrial fermentations. Despite the advantages of such controlled fermentations, the fermentation involved in the production of chocolate is still a spontaneous process that relies on the natural microbiota at cocoa farms. However, recent studies indicate that certain thermotolerant Saccharomyces cerevisiae cultures can be used as starter cultures for cocoa pulp fermentation. In this study, we investigate the potential of specifically developed starter cultures to modulate chocolate aroma. Specifically, we developed several new S. cerevisiae hybrids that combine thermotolerance and efficient cocoa pulp fermentation with a high production of volatile flavor-active esters. In addition, we investigated the potential of two strains of two non-Saccharomyces species that produce very large amounts of fruity esters (Pichia kluyveri and Cyberlindnera fabianii) to modulate chocolate aroma. Gas chromatography-mass spectrometry (GC-MS) analysis of the cocoa liquor revealed an increased concentration of various flavor-active esters and a decrease in spoilage-related off-flavors in batches inoculated with S. cerevisiae starter cultures and, to a lesser extent, in batches inoculated with P. kluyveri and Cyb. fabianii. Additionally, GC-MS analysis of chocolate samples revealed that while most short-chain esters evaporated during conching, longer and more-fat-soluble ethyl and acetate esters, such as ethyl octanoate, phenylethyl acetate, ethyl phenylacetate, ethyl decanoate, and ethyl dodecanoate, remained almost unaffected. Sensory analysis by an expert panel confirmed significant differences in the aromas of chocolates produced with different starter cultures. Together, these results show that the selection of different yeast cultures opens novel avenues for modulating chocolate flavor.",Applied and environmental microbiology,"['D000085', 'D002099', 'D004952', 'D005285', 'D005421', 'D006358', 'D006801', 'D012441', 'D013649']","['Acetates', 'Cacao', 'Esters', 'Fermentation', 'Flavoring Agents', 'Hot Temperature', 'Humans', 'Saccharomyces cerevisiae', 'Taste']",Tuning Chocolate Flavor through Development of Thermotolerant Saccharomyces cerevisiae Starter Cultures with Increased Acetate Ester Production.,"['Q000378', 'Q000737', 'Q000737', None, 'Q000737', None, None, 'Q000737', None]","['metabolism', 'chemistry', 'chemistry', None, 'chemistry', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/26590272,2016,1,3,table 3, -0.28,28288289,To assess the effect of four different children's drinks on color stability of resin dental composites.,The Journal of clinical pediatric dentistry,"['D001628', 'D003116', 'D003188', 'D006801', 'D013053', 'D013499']","['Beverages', 'Color', 'Composite Resins', 'Humans', 'Spectrophotometry', 'Surface Properties']",Effect of Children's Drinks on Color Stability of Different Dental Composites: An in vitro Study.,"[None, None, 'Q000737', None, None, None]","[None, None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28288289,2017,,,,no pdf access -0.28,25077686,"The influence of thermally induced reaction products of a known dietary bitter compound, catechin, on bitterness perception was investigated. Catechin was reacted in low-moisture simple Maillard models (200 _C for 15 min) consisting of glycine and a reducing sugar (D-glucose, D-xylose, or D-galactose). Based on liquid chromatrography-mass spectrometry (LC-MS) isotopic labeling techniques, eight reaction products were identified and subsequently structurally elucidated by tandem LC-MS/MS and two-dimensional NMR analysis; six were report to be flavan-3-ol-spiro-C-glycosides reaction products. One of the spiro products was reported to significantly suppress the perceived bitterness intensity of a caffeine solution. Additionally, this specific spiro product was further identified in cocoa and reported to increase in concentration during bean roasting.",Journal of agricultural and food chemistry,"['D000328', 'D002099', 'D002392', 'D003296', 'D005260', 'D005421', 'D006358', 'D006801', 'D015416', 'D008297', 'D013058', 'D015394', 'D013649', 'D055815']","['Adult', 'Cacao', 'Catechin', 'Cooking', 'Female', 'Flavoring Agents', 'Hot Temperature', 'Humans', 'Maillard Reaction', 'Male', 'Mass Spectrometry', 'Molecular Structure', 'Taste', 'Young Adult']",Identification of bitter modulating maillard-catechin reaction products.,"[None, 'Q000737', 'Q000737', None, None, 'Q000737', None, None, None, None, None, None, None, None]","[None, 'chemistry', 'chemistry', None, None, 'chemistry', None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25077686,2015,1,1,Fig 3, -0.27,24165745,"Pigments of food and beverages could affect dental bleaching efficacy. The aim of this investigation was to evaluate color change and mineral loss of tooth enamel as well as the influence of staining solutions normally used by adolescent patients undergoing home bleaching. Initial hardness and baseline color were measured on enamel blocks. Specimens were divided into five groups (n=5): G1 (control) specimens were kept in artificial saliva throughout the experiment (3 weeks); G2 enamel was exposed to 10% carbamide peroxide for 6 h daily, and after this period, the teeth were cleaned and stored in artificial saliva until the next bleaching session; and G3, G4, and G5 received the same treatments as G2, but after bleaching, they were stored for 1 h in cola soft drink, melted chocolate, or red wine, respectively. Mineral loss was obtained by the percentage of hardness reduction, and color change was determined by the difference between the data obtained before and after treatments. Data were subjected to analysis of variance and Fisher's test (_±=0.05). G3 and G5 showed higher mineral loss (92.96 _± 5.50 and 94.46 _± 1.00, respectively) compared to the other groups (p ___ 0.05). G5 showed high-color change (9.34 _± 2.90), whereas G1 presented lower color change (2.22 _± 0.44) (p ___ 0.05). Acidic drinks cause mineral loss of the enamel, which could modify the surface and reduce staining resistance after bleaching.",Journal of biomedical optics,"['D000704', 'D000818', 'D001628', 'D002417', 'D003116', 'D003743', 'D006244', 'D010545', 'D013053', 'D058205', 'D017001', 'D014508']","['Analysis of Variance', 'Animals', 'Beverages', 'Cattle', 'Color', 'Dental Enamel', 'Hardness', 'Peroxides', 'Spectrophotometry', 'Tooth Bleaching Agents', 'Tooth Demineralization', 'Urea']",Mineral loss and color change of enamel after bleaching and staining solutions combination.,"[None, None, None, None, None, 'Q000737', 'Q000187', 'Q000494', None, 'Q000494', None, 'Q000031']","[None, None, None, None, None, 'chemistry', 'drug effects', 'pharmacology', None, 'pharmacology', None, 'analogs & derivatives']",https://www.ncbi.nlm.nih.gov/pubmed/24165745,2014,,,, -0.27,11696092,The aim of this study was to identify the causative agent of a smoky/phenolic taint in refrigerated full cream chocolate milk.,Letters in applied microbiology,"['D000818', 'D001547', 'D002099', 'D003080', 'D015169', 'D005516', 'D005519', 'D008401', 'D006139', 'D008892', 'D020638']","['Animals', 'Benzaldehydes', 'Cacao', 'Cold Temperature', 'Colony Count, Microbial', 'Food Microbiology', 'Food Preservation', 'Gas Chromatography-Mass Spectrometry', 'Guaiacol', 'Milk', 'Rahnella']",Formation of guaiacol in chocolate milk by the psychrotrophic bacterium Rahnella aquatilis.,"[None, None, 'Q000382', None, None, None, None, None, 'Q000737', 'Q000382', 'Q000254']","[None, None, 'microbiology', None, None, None, None, None, 'chemistry', 'microbiology', 'growth & development']",https://www.ncbi.nlm.nih.gov/pubmed/11696092,2002,0,0,,no cocoa -0.27,28764077,"Infants and toddlers are highly vulnerable to exposure to lead due to its higher absorption in small children than in adults. This study describes the optimisation and validation of a very sensitive method for the determination of low levels of lead in foods mostly consumed by infants and toddlers. This method, based on inductively coupled plasma-mass spectrometry with a programmable temperature cyclonic spray chamber, attained a limit of quantification (LOQ) of 0.6 or 0.9_µgPbkg",Food chemistry,"['D002675', 'D004032', 'D004034', 'D005247', 'D005502', 'D006801', 'D007223', 'D007854']","['Child, Preschool', 'Diet', 'Diet Surveys', 'Feeding Behavior', 'Food', 'Humans', 'Infant', 'Lead']",Levels of lead in foods from the first French total diet study on infants and toddlers.,"[None, None, None, None, None, None, None, None]","[None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28764077,2017,0,0,,no cocoa tested -0.27,16076092,"A rapid liquid chromatography electrospray ionization tandem mass spectrometry with negative ion detection method was developed and validated to determine cocoa flavonoid metabolites in human plasma and urine after the intake of a standard portion of a cocoa beverage. A chromatographic run time of only 9 min provided clear separation of all metabolites and internal standards. Samples were analyzed in a product-ion scan of m/z 289, 369, and 465 to identify the metabolites and in multiple reaction monitoring acquisition mode to quantify (-)-epicatechin ((-)-Ec) (289/ 245), (-)-epicatechin-glucuronide ((-)-EcG) (465/289), and (-)-epicatechin-sulfate ((-)-EcS) (369/289). One (-)-Ec-G and three (-)-Ec-S were identified and confirmed in urine as the major metabolites, and one (-)-Ec-G was the only metabolite present in plasma volunteers (n = 5) at a mean concentration of 625.7 +/- 198.3 nmol/L at 2 h after consumption of a cocoa beverage containing 54.4 mg of (-)-Ec.",Journal of agricultural and food chemistry,"['D000293', 'D000328', 'D001628', 'D002099', 'D002392', 'D002853', 'D005260', 'D005419', 'D006801', 'D008297', 'D013058', 'D008875', 'D021241']","['Adolescent', 'Adult', 'Beverages', 'Cacao', 'Catechin', 'Chromatography, Liquid', 'Female', 'Flavonoids', 'Humans', 'Male', 'Mass Spectrometry', 'Middle Aged', 'Spectrometry, Mass, Electrospray Ionization']",Rapid liquid chromatography tandem mass spectrometry assay to quantify plasma (-)-epicatechin metabolites after ingestion of a standard portion of cocoa beverage in humans.,"[None, None, None, 'Q000737', 'Q000097', 'Q000379', None, 'Q000008', None, None, 'Q000379', None, None]","[None, None, None, 'chemistry', 'blood', 'methods', None, 'administration & dosage', None, None, 'methods', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16076092,2005,0,0,, -0.27,12059153,"The cocoa roasting process at different temperatures (at 125 and 135 degrees C for 3 min, plus 44 and 52 min, respectively, heating-up times) was evaluated by measuring the initial and final free amino acids distribution, flavor index, formol number, browning measurement, and alkylpyrazines content in 15 cocoa bean samples of different origins. These samples were also analyzed in manufactured cocoa powder. The effect of alkalinization of cocoa was studied. Results indicated that the final concentration and ratio of tetramethylpyrazine/trimethylpyrazine (TMP/TrMP) increased rapidly at higher roasting temperatures. The samples roasted with alkalies (pH between 7.20 and 7.92), such as sodium carbonate, or potassium plus air injected in the roaster during thermal treatment, exhibited a greater degree of brown color formation, but the amount of alkylpyrazines generated was adversely affected. The analysis of alpha-free amino acids at the end of the roasting process demonstrated the importance of the thermal treatment conditions and the pH values on nibs (cocoa bean cotyledons), liquor, or cocoa. Higher pH values led to a lower concentration of aroma and a higher presence of brown compounds.",Journal of agricultural and food chemistry,"['D000596', 'D002099', 'D002254', 'D055598', 'D002627', 'D005511', 'D008401', 'D006863', 'D007202', 'D011188', 'D011719', 'D013053', 'D013649']","['Amino Acids', 'Cacao', 'Carbonates', 'Chemical Phenomena', 'Chemistry, Physical', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Hydrogen-Ion Concentration', 'Indicators and Reagents', 'Potassium', 'Pyrazines', 'Spectrophotometry', 'Taste']",Factors affecting the formation of alkylpyrazines during roasting treatment in natural and alkalinized cocoa powder.,"['Q000032', 'Q000737', None, None, None, None, None, None, None, None, 'Q000032', None, None]","['analysis', 'chemistry', None, None, None, None, None, None, None, None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12059153,2002,1,1,table 1 and 5, -0.26,20100378,"An increasing number of scientific studies support that flavanol-rich foods and beverages such as cocoa can promote human health, and are beneficial agents for the prevention of some diseases. Our previous studies showed that long-term cocoa intake enhances the antioxidant status in lymphoid organs and also modulates lymphocyte functionality in healthy young rats. Cocoa polyphenolic antioxidants seem to be the best candidates for those effects. However, data regarding polyphenol metabolites in tissues after a long-term cocoa intake are scarce. In the present study we mainly focus on the uptake and accumulation of epicatechin metabolites in lymphoid organs, including the thymus, spleen and mesenteric lymphoid nodes, as well as in the liver and testes after a diet rich in cocoa. Ten young weaned Wistar rats were fed randomly with a 10 % (w/w) cocoa diet or a control diet for 3 weeks, corresponding to their infancy and youth. Tissues were treated with a solid-phase extraction and analysed by liquid chromatography-tandem MS. The major compounds recovered in these tissues were glucuronide derivatives of epicatechin and methylepicatechin. The highest concentration of these metabolites was found in the thymus, testicles and liver, followed by lymphatic nodes and spleen. The high amount of epicatechin metabolites found in the thymus supports our previous findings showing its high antioxidant capacity compared with other tissues such as the spleen. Moreover, this is the first time that epicatechin metabolites have been found in high concentrations in the testes, confirming other studies that have suggested the testes as an important site of oxidation.",The British journal of nutrition,"['D000818', 'D002099', 'D002392', 'D004032', 'D005260', 'D008099', 'D008221', 'D008297', 'D051381', 'D017208', 'D013737']","['Animals', 'Cacao', 'Catechin', 'Diet', 'Female', 'Liver', 'Lymphoid Tissue', 'Male', 'Rats', 'Rats, Wistar', 'Testis']",Distribution of epicatechin metabolites in lymphoid tissues and testes of young rats with a cocoa-enriched diet.,"[None, 'Q000378', 'Q000378', None, None, 'Q000378', 'Q000378', None, None, None, 'Q000378']","[None, 'metabolism', 'metabolism', None, None, 'metabolism', 'metabolism', None, None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/20100378,2010,0,0,, -0.26,28130543,"The detection of disaccharides in urine is under investigation to act as a marker for intravenous abuse of disaccharide formulations, like liquid methadone with syrup (sucrose), methadone tablets (lactose and sucrose), or buprenorphine tablets (lactose). As the detection time in urine has not yet been investigated and a routine method for detecting disaccharides is still lacking, a study was performed to estimate the window of detection in urine after intravenous consumption of disaccharides. Furthermore, an analytical LC-MSMS method for the quantification of sucrose and lactose in urine was validated. The method was applied to urine samples of intravenous substitute consumers, with urine being sampled before intravenous use of substitutes and approximately 30 minutes later. Twenty users provided information regarding their most recent prior intravenous consumption. Disaccharides were detectable in all 20 urine samples about 30 minutes after consumption. A cut off for both disaccharides of 40mg/L was used. Based on these conditions 81% of the persons who consumed in a time frame of 24 hours ago showed positive results for disaccharides. The study showed that the validated LC-MSMS method with an easy and fast workup is usable for daily routine in the laboratory. It might be helpful for methadone and buprenorphine prescribing physicians to check whether the opiate maintenance treatment patient takes his or her substitution medicines orally as intended, or continues with intravenous misuse by injecting substitution medicines instead of heroin.",Journal of analytical toxicology,"['D000328', 'D015415', 'D002047', 'D002253', 'D016022', 'D000069956', 'D002853', 'D005260', 'D006801', 'D007785', 'D057230', 'D008297', 'D008691', 'D008875', 'D053610', 'D015203', 'D015813', 'D015819', 'D013395', 'D053719', 'D055815']","['Adult', 'Biomarkers', 'Buprenorphine', 'Carbonated Beverages', 'Case-Control Studies', 'Chocolate', 'Chromatography, Liquid', 'Female', 'Humans', 'Lactose', 'Limit of Detection', 'Male', 'Methadone', 'Middle Aged', 'Opiate Alkaloids', 'Reproducibility of Results', 'Substance Abuse Detection', 'Substance Abuse, Intravenous', 'Sucrose', 'Tandem Mass Spectrometry', 'Young Adult']",Monitoring Intravenous Abuse of Methadone or Buprenorphine in Opiate Maintenance Treatment (OMT): A Simple and Fast LC-MS-MS Method for the Detection of Disaccharides in Urine Samples.,"[None, 'Q000652', 'Q000652', None, None, None, None, None, None, 'Q000652', None, None, 'Q000652', None, 'Q000652', None, 'Q000379', 'Q000652', 'Q000652', None, None]","[None, 'urine', 'urine', None, None, None, None, None, None, 'urine', None, None, 'urine', None, 'urine', None, 'methods', 'urine', 'urine', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28130543,2017,0,0,,no cocoa -0.26,18032884,"The beneficial effects of cocoa polyphenols depend on the amount consumed, their bioavailability and the biological activities of the formed conjugates. The food matrix is one the factors than can affect their bioavailability, but previous studies have concluded rather contradictory results about the effect of milk on the bioavailability of polyphenols.",Annals of nutrition & metabolism,"['D000293', 'D000328', 'D000818', 'D001682', 'D002099', 'D002853', 'D018592', 'D005260', 'D005419', 'D006801', 'D008297', 'D008875', 'D008892', 'D011446', 'D053719']","['Adolescent', 'Adult', 'Animals', 'Biological Availability', 'Cacao', 'Chromatography, Liquid', 'Cross-Over Studies', 'Female', 'Flavonoids', 'Humans', 'Male', 'Middle Aged', 'Milk', 'Prospective Studies', 'Tandem Mass Spectrometry']",Milk does not affect the bioavailability of cocoa powder flavonoid in healthy human.,"[None, None, None, None, None, None, None, None, 'Q000493', None, None, None, None, None, None]","[None, None, None, None, None, None, None, None, 'pharmacokinetics', None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18032884,2008,,,,no pdf access -0.26,18502705,"Flavonoids, a subclass of polyphenols, are major constituents of many plant-based foods and beverages, including tea, wine and chocolate. Epidemiological studies have shown that a flavonoid-rich diet is associated with reduced risk of cardiovascular diseases. The majority of the flavonoids survive intact until they reach the colon where they are then extensively metabolized into smaller fragments. Here, we describe the development of GC-MS-based methods for the profiling of phenolic microbial fermentation products in urine, plasma, and fecal water. Furthermore, the methods are applicable for profiling products obtained from in vitro batch culture fermentation models. The methods incorporate enzymatic deconjugation, liquid-liquid extraction, derivatization, and subsequent analysis by GC-MS. At the level of individual compounds, the methods gave recoveries better than 80% with inter-day precision being better than 20%, depending on the matrix. Limits of detection were below 0.1 microg/ml for most phenolic acids. The newly developed methods were successfully applied to samples from human and in-vitro intervention trials, studying the metabolic impact of flavonoid intake. In conclusion, the methods presented are robust and generally applicable to diverse biological fluids. Its profiling character is useful to investigate on a large scale the gut microbiome-mediated bioavailability of flavonoids.","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D003106', 'D019295', 'D004032', 'D005243', 'D005285', 'D005419', 'D008401', 'D006801', 'D008660', 'D010636', 'D010648', 'D059808', 'D015203', 'D035501']","['Colon', 'Computational Biology', 'Diet', 'Feces', 'Fermentation', 'Flavonoids', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Metabolism', 'Phenols', 'Phenylacetates', 'Polyphenols', 'Reproducibility of Results', 'Uncertainty']",GC-MS methods for metabolic profiling of microbial fermentation products of dietary polyphenols in human and in vitro intervention studies.,"['Q000382', 'Q000379', None, 'Q000737', 'Q000502', 'Q000097', 'Q000379', None, None, 'Q000097', 'Q000097', None, None, None]","['microbiology', 'methods', None, 'chemistry', 'physiology', 'blood', 'methods', None, None, 'blood', 'blood', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18502705,2008,0,0,,no cocoa tested -0.26,27055484,"Analysis of the complex composition of cocoa beans provides fundamental information for evaluating the quality and nutritional aspects of cocoa-based food products, nutraceuticals and supplements. Cameroon, the world's fourth largest producer of cocoa, has been defined as ""Africa in miniature"" because of the variety it habitats. In order to evaluate the nutritional characteristics of cocoa beans from five different regions of Cameroon, we studied their polyphenolic content, volatile compounds and fatty acids composition. The High Performance Thin Layer Chromatography (HPTLC) analysis showed that the Mbalmayo sample had the highest content of theobromine (11.6___mg/g) and caffeic acid (2.1___mg/g), while the Sanchou sample had the highest level of (-)-epicatechin (142.9___mg/g). Concerning fatty acids, the lowest level of stearic acid was found in the Mbalmayo sample while the Bertoua sample showed the highest content of oleic acid. Thus, we confirmed that geographical origin influences the quality and nutritional characteristics of cocoa from these regions of Cameroon. ",International journal of food sciences and nutrition,"['D000975', 'D002099', 'D002109', 'D002163', 'D002392', 'D000069956', 'D002934', 'D004041', 'D019587', 'D005227', 'D005419', 'D063427', 'D006801', 'D009753', 'D025341', 'D012639', 'D013805', 'D014674', 'D055549', 'D014970']","['Antioxidants', 'Cacao', 'Caffeic Acids', 'Cameroon', 'Catechin', 'Chocolate', 'Cinnamates', 'Dietary Fats', 'Dietary Supplements', 'Fatty Acids', 'Flavonoids', 'Food Quality', 'Humans', 'Nutritive Value', 'Principal Component Analysis', 'Seeds', 'Theobromine', 'Vegetable Proteins', 'Volatile Organic Compounds', 'Xanthines']","Nutritional composition, bioactive compounds and volatile profile of cocoa beans from different regions of Cameroon.","['Q000032', 'Q000737', 'Q000032', None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, None, None, None, 'Q000737', 'Q000032', 'Q000032', 'Q000032', 'Q000032']","['analysis', 'chemistry', 'analysis', None, 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', None, None, None, None, 'chemistry', 'analysis', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27055484,2017,2,2,table 1 and 2, -0.26,18024588,"Chemical analyses of residues extracted from pottery vessels from Puerto Escondido in what is now Honduras show that cacao beverages were being made there before 1000 B.C., extending the confirmed use of cacao back at least 500 years. The famous chocolate beverage served on special occasions in later times in Mesoamerica, especially by elites, was made from cacao seeds. The earliest cacao beverages consumed at Puerto Escondido were likely produced by fermenting the sweet pulp surrounding the seeds.",Proceedings of the National Academy of Sciences of the United States of America,"['D000434', 'D001106', 'D001628', 'D002099', 'D002110', 'D002516', 'D004867', 'D005285', 'D018857', 'D008401', 'D049690', 'D006721', 'D006801', 'D007197', 'D013805']","['Alcoholic Beverages', 'Archaeology', 'Beverages', 'Cacao', 'Caffeine', 'Ceramics', 'Equipment Design', 'Fermentation', 'Food Packaging', 'Gas Chromatography-Mass Spectrometry', 'History, Ancient', 'Honduras', 'Humans', 'Indians, Central American', 'Theobromine']",Chemical and archaeological evidence for the earliest cacao beverages.,"['Q000266', None, 'Q000266', 'Q000737', 'Q000032', 'Q000266', None, None, 'Q000266', None, None, None, None, 'Q000266', 'Q000032']","['history', None, 'history', 'chemistry', 'analysis', 'history', None, None, 'history', None, None, None, None, 'history', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18024588,2008,0,0,, -0.26,20598878,"The aim of this work was to conduct the experimental study of pyrolysis of fast-growing aquatic biomass -Lemna minor (commonly known as duckweed) with the emphasis on the characterization of main products of pyrolysis. The yields of pyrolysis gas, pyrolytic oil (bio-oil) and char were determined as a function of pyrolysis temperature and the sweep gas (Ar) flow rate. Thermogravimetric/differential thermogravimetric (TG/DTG) analyses of duckweed samples in inert (helium gas) and oxidative (air) atmosphere revealed differences in the TG/DTG patterns obtained for duckweed and typical plant biomass. The bio-oil samples produced by duckweed pyrolysis at different reaction conditions were analyzed using GC-MS technique. It was found that pyrolysis temperature had minor effect on the bio-oil product slate, but exerted major influence on the relative quantities of the individual pyrolysis products obtained. While, the residence time of the pyrolysis vapors had negligible effect on the yield and composition of the duckweed pyrolysis products.",Bioresource technology,"['D056804', 'D018533', 'D008401', 'D006109', 'D013696', 'D013818', 'D014867']","['Biofuels', 'Biomass', 'Gas Chromatography-Mass Spectrometry', 'Poaceae', 'Temperature', 'Thermogravimetry', 'Water']",Pyrolysis of fast-growing aquatic biomass -Lemna minor (duckweed): Characterization of pyrolysis products.,"[None, None, None, 'Q000254', None, None, None]","[None, None, None, 'growth & development', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/20598878,2010,0,0,,no cocoa -0.26,3941071,"Glycerol-3-phosphate acyltransferase has been purified from the post-microsomal supernatant of cocoa seeds using differential ammonium sulfate solubility along with anion exchange and gel filtration chromatography. Chromatofocusing and isoelectric focusing revealed a series of proteins with acyltransferase activity having isoelectric points close to 5.2. Gel filtration on Sephacryl S-300 in 500 mM NaCl, along with polyacrylamide gel electrophoresis (denaturing and non-denaturing) and immunochemical analysis, gave evidence that the native enzyme has a molecular weight of 2 X 10(5) and consists of an aggregate of 10 Mr 20,000 subunits. The highly purified enzyme carries an acyl donor, probably acyl-CoA, although this is not firmly established. The hydrophobic nature of the purified enzyme was demonstrated by its firm binding to octyl-Sepharose. Mass spectrometric analysis of reaction products revealed the presence of both palmitic and stearic acids. Considering that 1) the fatty acids were derived from the purified enzyme; 2) they were found exclusively in the 1-position of glycerol 3-phosphate; 3) the fatty acid positioning and composition is consistent with that found in cocoa butter, the major storage product of cocoa seeds; and 4) the enzyme is found in the post-microsomal supernatant, it seems reasonable to conclude that the first step in cocoa butter biosynthesis is catalyzed by glycerol-3-phosphate acyltransferase in the cytoplasm of cocoa cotyledon cells.",The Journal of biological chemistry,"['D000217', 'D000818', 'D002099', 'D002850', 'D005992', 'D007525', 'D007700', 'D046911', 'D008970', 'D010945', 'D011817', 'D011863', 'D012639', 'D012995']","['Acyltransferases', 'Animals', 'Cacao', 'Chromatography, Gel', 'Glycerol-3-Phosphate O-Acyltransferase', 'Isoelectric Focusing', 'Kinetics', 'Macromolecular Substances', 'Molecular Weight', 'Plants, Edible', 'Rabbits', 'Radioimmunoassay', 'Seeds', 'Solubility']",Cocoa butter biosynthesis. Purification and characterization of a soluble sn-glycerol-3-phosphate acyltransferase from cocoa seeds.,"['Q000302', None, 'Q000201', None, 'Q000302', None, None, None, None, 'Q000201', None, None, 'Q000032', None]","['isolation & purification', None, 'enzymology', None, 'isolation & purification', None, None, None, None, 'enzymology', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/3941071,1986,0,0,, -0.26,18969491,"An optical chemical sensor based on immobilization of 2-(5-bromo-2-pyridylazo)-5-(diethylamino)phenol (Br-PADAP) in Nafion membrane is described. The membranes were cast onto glass substrates and were used for the determination of nickel in aqueous solutions by spectrophotometry. The sensor system is highly transparent, mechanically stable and showed no evidence of reagent leaching. The influence of several parameters such as pH, ligand concentration, and type and concentration of regenerating solution were optimized. The sensor system showed good sensitivity in the range 0.5-20mugml(-1) with a detection limit of 0.3mugml(-1) Ni(II). The sensor has been incorporated into a home-made flow-through cell for determination of nickel in flowing streams with improved sensitivity, precision and detection limit. The calibration curve in the flow system was linear in the range 0.1-16mugml(-1) with a detection limit of 0.07mugml(-1). The sensor is easily regenerated by dilute nitric acid solution. The proposed method was successfully applied to the determination of nickel content in vegetable oil and chocolate samples and the results were compared with those obtained using atomic absorption spectrometry.",Talanta,[],[],Development of an optical chemical sensor based on 2-(5-bromo-2-pyridylazo)-5-(diethylamino)phenol in Nafion for determination of nickel ion.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/18969491,2012,0,0,,no cocoa -0.25,10563905,"Color-generating reactions of protein-bound lysine with carbohydrates were studied under thermal as well as under physiological conditions to gain insights into the role of protein/carbohydrate reactions in the formation of food melanoidins as well as nonenzymatic browning products in vivo. EPR spectroscopy of orange-brown melanoidins, which were isolated from heated aqueous solutions of bovine serum albumin and glycolaldehyde, revealed the protein-bound 1,4-bis(5-amino-5-carboxy-1-pentyl)pyrazinium radical cation (CROSSPY) as a previously unknown type of cross-linking amino acid leading to protein dimerization. To verify their formation in foods, wheat bread crust and roasted cocoa as well as coffee beans, showing elevated nonenzymatic browning, were investigated by EPR spectroscopy. An intense radical was detected, which, by comparison with the radical formed upon reaction bovine serum albumin with glycolaldehyde, was identified as the protein-bound CROSSPY. The radical-assisted protein oligomerization as well as the browning of bovine serum albumin in the presence of glycolaldehyde occurred also rapidly under physiological conditions, thereby suggesting CROSSPY formation to be probably involved also in nonenzymatic glycation reactions in vivo.",Journal of agricultural and food chemistry,"['D002851', 'D003116', 'D003296', 'D004578', 'D005504', 'D005511', 'D005609', 'D013058', 'D011108', 'D013056', 'D013447']","['Chromatography, High Pressure Liquid', 'Color', 'Cooking', 'Electron Spin Resonance Spectroscopy', 'Food Analysis', 'Food Handling', 'Free Radicals', 'Mass Spectrometry', 'Polymers', 'Spectrophotometry, Ultraviolet', 'Sulfites']",Radical-assisted melanoidin formation during thermal processing of foods as well as under physiological conditions.,"[None, None, None, None, None, None, None, None, 'Q000138', None, 'Q000737']","[None, None, None, None, None, None, None, None, 'chemical synthesis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/10563905,2000,0,0,, -0.25,29899211,"Thaumatin-like protein from banana (designated BanTLP) has been purified by employing a simple protocol consisting of diethylaminoethyl Sephadex (DEAE__Sephadex) chromatography, gel filtration on Sephadex G50, and reversed-phase chromatography. The purified protein was identified by MALDI-TOF mass spectrometry, with an estimated molecular weight of 22.1 kDa. BanTLP effectively inhibited in vitro spore germination of ","Molecules (Basel, Switzerland)",[],[],Antifungal Activity of an Abundant Thaumatin-Like Protein from Banana against ,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29899211,2018,0,0,,no cocoa -0.25,14509366,"We previously reported the inhibitory effect of various methyloxantines and phenolic compounds on tumor-induced angiogenesis and the production of angiogenic growth factors. The aim of the present work was to evaluate the effect of chocolate (CH), food containing substantial amounts of methyloxantine theobromine and polyphenols (mainly catechins), given to mice during pregnancy and the lactation period, on weight of organs, length of limbs, and bone vascular endothelial growth factor (VEGF) concentration (tested by ELISA), in 4-week old offspring. The study was performed on 2-month old Balb/c mice fed during pregnancy and lactation 400 mg of CH daily. Content of polyphenols (catechines) and theobromine in the chocolate was estimated by high liquid perforance chromatography (HPLC). Concentration of VEGF was tested by ELISA. Feeding pregnant mice chocolate produced the following effects: decrease of relative length of limbs and thigh bones in 4-week old progeny and decrease in VEGF content of offspring femoral bones.",Polish journal of veterinary sciences,"['D000284', 'D000821', 'D000818', 'D000831', 'D001842', 'D002099', 'D002851', 'D004797', 'D005260', 'D051379', 'D008807', 'D008517', 'D011247', 'D042461']","['Administration, Oral', 'Animal Feed', 'Animals', 'Animals, Newborn', 'Bone and Bones', 'Cacao', 'Chromatography, High Pressure Liquid', 'Enzyme-Linked Immunosorbent Assay', 'Female', 'Mice', 'Mice, Inbred BALB C', 'Phytotherapy', 'Pregnancy', 'Vascular Endothelial Growth Factor A']",Chocolate feeding of pregnant mice influences length of limbs of their progeny.,"[None, None, None, None, 'Q000187', None, None, None, None, None, None, None, None, 'Q000187']","[None, None, None, None, 'drug effects', None, None, None, None, None, None, None, None, 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/14509366,2003,,,, -0.24,28272800,"Many factors can influence antioxidative and antimicrobial characteristics of plant materials. The quality of cocoa as functional food ingredient is influenced through its processing. The main aim of this study was to test if there is difference in polyphenol content, antioxidant capacity, and antimicrobial activity between nonalkalized and alkalized cocoa powders. To estimate polyphenol and flavonoid content in cocoa samples the spectrophotometric microassays were used. Flavan-3ols were determined with reversed-phase high-performance liquid chromatography (RP-HPLC). Antimicrobial activity against 3 Gram positive bacteria, 4 Gram negative bacteria and 1 strain of yeast was determined using broth microdilution method. Total polyphenol content was 1.8 times lower in alkalized cocoa samples than in natural ones. Epicatechin/catechin ratio was changed due to the process of alkalization in favor of catechin (2.21 in natural and 1.45 in alkalized cocoa powders). Combined results of 3 antioxidative tests (DPPH, FRAP, ABTS) were used for calculation of RACI (Relative Antioxidant Capacity Index) and GAS (Global Antioxidant Score) values that were consistently higher in natural than in alkalized cocoa extracts. Obtained results have shown significant correlations between these values and phenolic content (0.929 ___ r ___ 0.957, P < 0.01). Antimicrobial activity varied from 5.0 to 25.0 mg/ml (MICs), while Candida albicans was the most sensitive tested microorganism. Cocoa powders subjected to alkalization had significantly reduced content of total and specific phenolic compounds and reduced antioxidant capacity (P < 0.05), but their antimicrobial activity was equal for Gram-positive bacteria or even significantly enhanced for Gram-negative bacteria.",Journal of food science,"['D000890', 'D000975', 'D002099', 'D002176', 'D002392', 'D056148', 'D003116', 'D005419', 'D006090', 'D006094', 'D006863', 'D008826', 'D010936', 'D059808', 'D011208', 'D044945']","['Anti-Infective Agents', 'Antioxidants', 'Cacao', 'Candida albicans', 'Catechin', 'Chromatography, Reverse-Phase', 'Color', 'Flavonoids', 'Gram-Negative Bacteria', 'Gram-Positive Bacteria', 'Hydrogen-Ion Concentration', 'Microbial Sensitivity Tests', 'Plant Extracts', 'Polyphenols', 'Powders', 'Proanthocyanidins']","Correlation between Antimicrobial, Antioxidant Activity, and Polyphenols of Alkalized/Nonalkalized Cocoa Powders.","['Q000494', 'Q000494', 'Q000737', 'Q000187', 'Q000494', None, None, 'Q000494', 'Q000187', 'Q000187', None, None, 'Q000494', 'Q000494', None, 'Q000494']","['pharmacology', 'pharmacology', 'chemistry', 'drug effects', 'pharmacology', None, None, 'pharmacology', 'drug effects', 'drug effects', None, None, 'pharmacology', 'pharmacology', None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/28272800,2017,1,1,table 2 and 3, -0.24,19272093,"Synopsis Non-saponifiable lipid fraction (ICSB) extracted from cocoa shell butter was solubilized in dimethylformamide (DMF) and analysed for its biological activity on growth of rat and human fibroblasts. Non-saponifiables (10 mug ml(-1)) partially protected cells from toxicity of DMF (1%) and allowed the growth of fibroblasts cultivated in optimal conditions (10% fetal calf serum-FCS, 37 degrees C) or improved the survival of cells maintained in altered conditions (2.5% FCS, 35 degrees C). At higher concentration (ICSB 50 mug ml(-1), DMF 1%), the protective effect was suppressed. ICSB was fractionated by chromatography into four compounds: sterols, terpenic alcohols, tocopherols and hydrocarbons +/- carotenoids. We found that biological activity of ICSB was mostly due to the major fraction containing sterols.",International journal of cosmetic science,[],[],Non-saponifiable fraction of cocoa shell butter: effect on rat and human skin fibroblasts.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19272093,2012,,,,no pdf access -0.24,28911615,"Candies, chewing gums, dried fruits, jellies, chocolate, and shredded squid pieces imported from 17 countries were surveyed for their aluminum content. The samples were bought from candy shops, supermarkets, and convenience stores, and through online shopping. Sample selection focused on imported candies and snacks. A total of 67 samples, including five chewing gums, seven dried fruits, 13 chocolates, two jellies, two dried squid pieces, and 38 candies, were analyzed. The content of aluminum was analyzed by inductively coupled plasma optical emission spectrometry (ICP OES). The limit of quantitation for aluminum was 1.53__mg/kg. The content of aluminum ranged from not detected (ND) to 828.9__mg/kg. The mean concentrations of aluminum in chewing gums, dried fruits, chocolate, jellies, dried squid pieces, and candies were 36.62__mg/kg, 300.06__mg/kg, 9.1__mg/kg, 2.3__mg/kg, 7.8__mg/kg, and 24.26__mg/kg, respectively. Some samples had relatively high aluminum content. The highest aluminum content of 828.9__mg/kg was found in dried papaya threads imported from Thailand. Candies imported from Thailand and Vietnam had aluminum contents of 265.7__mg/kg and 333.1__mg/kg, respectively. Exposure risk assessment based on data from the Taiwan National Food Consumption Database was employed to calculate the percent provisional tolerable weekly intake (%PTWI). The percent provisional tolerable weekly intake of aluminum for adults (19-50__years) and children (3-6__years) based on the consumption rate of the total population showed that candies and snacks did not contribute greatly to aluminum exposure. By contrast, in the exposure assessment based on the consumers-only consumption rate, the estimated values of weekly exposure to aluminum from dried papaya threads in adults (19-50__years) and children (3-6__years) were 4.18__mg/kg body weight (bw)/wk and 7.93__mg/kg bw/wk, respectively, for 50",Journal of food and drug analysis,"['D000535', 'D002182', 'D006801', 'D062410', 'D013624']","['Aluminum', 'Candy', 'Humans', 'Snacks', 'Taiwan']",Investigation of aluminum content of imported candies and snack foods in Taiwan.,"[None, None, None, None, None]","[None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28911615,2017,1,1,text ,extracted the data from the refered paper (table 3) -0.24,16197573,"Dietary polyphenols are suggested to participate in the prevention of CVD and cancer. It is essential for epidemiological studies to be able to compare intake of the main dietary polyphenols in populations. The present paper describes a fast method suitable for the analysis of polyphenols in urine, selected as potential biomarkers of intake. This method is applied to the estimation of polyphenol recovery after ingestion of six different polyphenol-rich beverages. Fifteen polyphenols including mammalian lignans (enterodiol and enterolactone), several phenolic acids (chlorogenic, caffeic, m-coumaric, gallic, and 4-O-methylgallic acids), phloretin and various flavonoids (catechin, epicatechin, quercetin, isorhamnetin, kaempferol, hesperetin, and naringenin) were simultaneously quantified in human urine by HPLC coupled with electrospray ionisation mass-MS (HPLC-electrospray-tandem mass spectrometry) with a run time of 6 min per sample. The method has been validated with regard to linearity, precision, and accuracy in intra- and inter-day assays. It was applied to urine samples collected from nine volunteers in the 24 h following consumption of either green tea, a grape-skin extract, cocoa beverage, coffee, grapefruit juice or orange juice. Levels of urinary excretion suggest that chlorogenic acid, gallic acid, epicatechin, naringenin or hesperetin could be used as specific biomarkers to evaluate the consumption of coffee, wine, tea or cocoa, and citrus juices respectively.",The British journal of nutrition,"['D000328', 'D001628', 'D015415', 'D002099', 'D002110', 'D002138', 'D016022', 'D002851', 'D032083', 'D032084', 'D003069', 'D004032', 'D005260', 'D005419', 'D006801', 'D008297', 'D010636', 'D059808', 'D012680', 'D021241', 'D018709', 'D013662', 'D013805', 'D013997']","['Adult', 'Beverages', 'Biomarkers', 'Cacao', 'Caffeine', 'Calibration', 'Case-Control Studies', 'Chromatography, High Pressure Liquid', 'Citrus paradisi', 'Citrus sinensis', 'Coffee', 'Diet', 'Female', 'Flavonoids', 'Humans', 'Male', 'Phenols', 'Polyphenols', 'Sensitivity and Specificity', 'Spectrometry, Mass, Electrospray Ionization', 'Statistics, Nonparametric', 'Tea', 'Theobromine', 'Time Factors']",Polyphenol levels in human urine after intake of six different polyphenol-rich beverages.,"[None, None, 'Q000652', None, None, None, None, None, None, None, None, None, None, 'Q000008', None, None, 'Q000008', None, None, None, None, None, None, None]","[None, None, 'urine', None, None, None, None, None, None, None, None, None, None, 'administration & dosage', None, None, 'administration & dosage', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16197573,2005,1,1,table 1,cocoa powder only -0.23,28959635,"Levels of organochlorine pesticides (OCPs) were determined in dried cocoa beans obtained from cocoa produce stores at Ondo and Ile-Ife, Southwestern Nigeria. Cocoa beans samples were sun dried to a constant weight, pulverized and soxhlet extracted with dichloromethane to obtain the OCPs. Qualitative identification and quantitative evaluation of the extracted OCPs after clean-up on silica gel were accomplished with the aid of a Gas Chromatography coupled with an Electron Capture Detector (GC-ECD). Levels of OCPs in cocoa beans from Ondo had a mean range of ND (p, p'-DDE) to 82.17___±__54.53__ng/g (p, p'-DDT) were higher than the OCPs levels in cocoa beans from Ile-Ife with a mean range of 0.37___±__0.63__ng/g (Endrin) to 57.76___±__81.48__ng/g (p, p'-DDT). The higher levels of OCPs detected in the cocoa beans from Ondo could be an indication of higher volume of OCPs application by cocoa farmers in Ondo and its environs since cocoa plantations were more concentrated than Ile-Ife environs. Levels of OCPs determined in the cocoa beans were within the Maximum Residue Limit (MRLs) for OCPs set by the World Health Organization/Food and Agricultural Organization. The study established the presence of OCPs in an important crop of Nigeria. Hence, there is the need to keep monitoring ecotoxicological chemical substances in agricultural food products of Nigeria so as to take steps that ensure health safety of end users.",Toxicology reports,[],[],"Organochlorine pesticide residues in dried cocoa beans obtained from cocoa stores at Ondo and Ile-Ife, Southwestern Nigeria.",[],[],https://www.ncbi.nlm.nih.gov/pubmed/28959635,2017,1,3,"table 3, 4, 5, and 6. Fig 1",pesticides are determined in samples fromt two different regions -0.23,3239114,"The fatty acid composition including trans fatty acids of 12 brands of nut-nougat creams were analyzed by capillary gas chromatography. The creams consisted mainly of sugar and partially hydrogenated vegetable oil. The lipid content, which was quantified gravimetrically, amounted to between 30 and 38.2% in the different brands. The fatty acid composition varied considerably between the different creams. Linoleic acid, the major polyunsaturated fatty acid (PUFA), ranged from 12 to 39%. Palmitic acid (16:0), which was the main fatty acid, varied from 9 to 27%. The total trans fatty acid content of the 12 creams ranged from 0.9 to 12.3%. Only two of the creams contained less than 1% of trans fatty acids; 18:1t was the trans fatty acid found in the greatest amounts, whereas 16:1t and 14:1t were only found in trace amounts. Three samples had amounts of 18:2tt, 18:2ct, and 18:2tc between 0.7 and 1.06%; only small amounts of linoleate isomers were detected in the other creams. Our results show that trans fatty acids are present in every brand of chocolate cream tested. Since the potential risk of arteriosclerosis and cancer resulting from the consumption of trans fatty acids is not yet clear, different ways of production should be used in order to eliminate them from the creams that are a preferred bread spread of infants and children.",Zeitschrift fur Ernahrungswissenschaft,"['D002099', 'D002182', 'D004041', 'D005227', 'D005860', 'D006801', 'D006865', 'D010938', 'D010945']","['Cacao', 'Candy', 'Dietary Fats', 'Fatty Acids', 'Germany, West', 'Humans', 'Hydrogenation', 'Plant Oils', 'Plants, Edible']",Trans fatty acid content of selected brands of West German nut-nougat cream.,"['Q000032', 'Q000032', 'Q000032', 'Q000032', None, None, None, 'Q000032', 'Q000032']","['analysis', 'analysis', 'analysis', 'analysis', None, None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/3239114,1989,,,, -0.23,12488137,"Epicatechin is a flavan-3-ol that is commonly present in green teas, red wine, cocoa products, and many fruits, such as apples. There is considerable interest in the bioavailability of epicatechin after oral ingestion. In vivo studies have shown that low levels of epicatechin are absorbed and found in the circulation as glucuronides, methylated and sulfated forms. Recent research has demonstrated protective effects of epicatechin and one of its in vivo metabolites, 3'-O-methyl epicatechin, against neuronal cell death induced by oxidative stress. Thus, we are interested in the ability of ingested epicatechin to cross the blood brain barrier and target the brain. Rats were administered 100 mg/kg body weight/d epicatechin orally for 1, 5, and 10 d. Plasma and brain extracts were analyzed by HPLC with photodiode array detection and LC-MS/MS. This study reports the presence of the epicatechin glucuronide and 3'-O-methyl epicatechin glucuronide formed after oral ingestion in the rat brain tissue.",Free radical biology & medicine,"['D000284', 'D000818', 'D001682', 'D001921', 'D002392', 'D002851', 'D008297', 'D013058', 'D051381', 'D017208']","['Administration, Oral', 'Animals', 'Biological Availability', 'Brain', 'Catechin', 'Chromatography, High Pressure Liquid', 'Male', 'Mass Spectrometry', 'Rats', 'Rats, Wistar']",Uptake and metabolism of epicatechin and its access to the brain after oral ingestion.,"[None, None, None, 'Q000378', 'Q000008', None, None, None, None, None]","[None, None, None, 'metabolism', 'administration & dosage', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12488137,2004,0,0,,no cocoa tested -0.23,25802220,"In recent years, there has been an increasing interest in identifying and characterizing the yeast flora associated with diverse types of habitat because of the many potential desirable technological properties of these microorganisms, especially in food applications. In this study, a total of 101 yeast strains were isolated from the skins of tropical fruits collected in several locations in the South West Indian Ocean. Sequence analysis of the D1/D2 domains of the large subunit (LSU) ribosomal RNA gene identified 26 different species. Among them, two species isolated from the skins of Cape gooseberry and cocoa beans appeared to represent putative new yeast species, as their LSU D1/D2 sequence was only 97.1% and 97.4% identical to that of the yeasts Rhodotorula mucilaginosa and Candida pararugosa, respectively. A total of 52 Volatile Organic Compounds (VOCs) were detected by Head Space Solid Phase Micro Extraction coupled to Gas Chromatography and Mass Spectroscopy (HS-SPME-GC/MS) from the 26 yeast species cultivated on a glucose rich medium. Among these VOCs, 6 uncommon compounds were identified, namely ethyl but-2-enoate, ethyl 2-methylbut-2-enoate (ethyl tiglate), ethyl 3-methylbut-2-enoate, 2-methylpropyl 2-methylbut-2-enoate, butyl 2-methylbut-2-enoate and 3-methylbutyl 2-methylbut-2-enoate, making them possible yeast species-specific markers. In addition, statistical methods such as Principal Component Analysis allowed to associate each yeast species with a specific flavor profile. Among them, Saprochaete suaveolens (syn: Geotrichum fragrans) turned to be the best producer of flavor compounds, with a total of 32 out of the 52 identified VOCs in its flavor profile. ",International journal of food microbiology,"['D016000', 'D004275', 'D005421', 'D005516', 'D005638', 'D008270', 'D017508', 'D014329', 'D055549', 'D015003']","['Cluster Analysis', 'DNA, Ribosomal', 'Flavoring Agents', 'Food Microbiology', 'Fruit', 'Madagascar', 'Reunion', 'Tropical Climate', 'Volatile Organic Compounds', 'Yeasts']",A comparative study on the potential of epiphytic yeasts isolated from tropical fruits to produce flavoring compounds.,"[None, 'Q000235', 'Q000032', None, 'Q000382', None, None, None, 'Q000032', 'Q000737']","[None, 'genetics', 'analysis', None, 'microbiology', None, None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25802220,2015,0,0,, -0.23,19680899,"The caffeine content of different beverages from Argentina's market was measured. Several brands of coffees, teas, mates, chocolate milks, soft and energy drinks were analysed by high-performance liquid chromatography (HPLC) with ultraviolet detection. The highest concentration level was found in short coffee (1.38 mg ml(-1)) and the highest amount per serving was found in instant coffee (95 mg per serving). A consumption study was also carried out among 471 people from 2 to 93 years of age to evaluate caffeine total dietary intake by age and to identify the sources of caffeine intake. The mean caffeine intake among adults was 288 mg day(-1) and mate was the main contributor to that intake. The mean caffeine intake among children of 10 years of age and under was 35 mg day(-1) and soft drinks were the major contributors to that intake. Children between 11 and 15 years old and teenagers (between 16 and 20 years) had caffeine mean intakes of 120 and 240 mg day(-1), respectively, and mate was the major contributor to those intakes. Drinking mate is a deep-rooted habit among Argentine people and it might be the reason for their elevated caffeine mean daily intake.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000293', 'D000328', 'D000368', 'D000369', 'D000704', 'D001118', 'D001628', 'D002099', 'D002110', 'D002253', 'D000697', 'D002648', 'D002675', 'D003069', 'D003430', 'D004034', 'D005260', 'D006801', 'D008297', 'D008875', 'D011247', 'D011795', 'D013662', 'D055815']","['Adolescent', 'Adult', 'Aged', 'Aged, 80 and over', 'Analysis of Variance', 'Argentina', 'Beverages', 'Cacao', 'Caffeine', 'Carbonated Beverages', 'Central Nervous System Stimulants', 'Child', 'Child, Preschool', 'Coffee', 'Cross-Sectional Studies', 'Diet Surveys', 'Female', 'Humans', 'Male', 'Middle Aged', 'Pregnancy', 'Surveys and Questionnaires', 'Tea', 'Young Adult']",Caffeine levels in beverages from Argentina's market: application to caffeine dietary intake assessment.,"[None, None, None, None, None, None, 'Q000032', 'Q000737', 'Q000008', 'Q000032', 'Q000008', None, None, 'Q000737', None, None, None, None, None, None, None, None, 'Q000737', None]","[None, None, None, None, None, None, 'analysis', 'chemistry', 'administration & dosage', 'analysis', 'administration & dosage', None, None, 'chemistry', None, None, None, None, None, None, None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/19680899,2010,0,0,, -0.22,12654472,"(-)-epicatechin is one of the most potent antioxidants present in the human diet. Particularly high levels are found in black tea, apples, and chocolate. High intake of catechins has been associated with reduced risk of cardiovascular diseases. There have been several reports concerning the bioavailability of catechins, however, the chemical structure of (-)-epicatechin metabolites in blood, tissues, and urine remains unclear. In the present study, we purified and elucidated the chemical structure of (-)-epicatechin metabolites in human and rat urine after oral administration. Three metabolites were purified from human urine including (-)-epicatechin-3'-O-glucuronide, 4'-O-methyl-(-)-epicatechin-3'-O-glucuronide, and 4'-O-methyl-(-)-epicatechin-5 or 7-O-glucuronide, according to 1H- and 13C-NMR, HMBC, and LC-MS analyses. The metabolites purified from rat urine were 3'-O-methyl-(-)-epicatechin, (-)-epicatechin-7-O-glucuronide, and 3'-O-methyl-(-)-epicatechin-7-O-glucuronide. These compounds were also detected in the blood of humans and rats by LC-MS. The presence of these metabolites in blood and urine suggests that catechins are metabolized and circulated in the body after administration of catechin-containing foods.",Free radical biology & medicine,"['D000284', 'D000328', 'D000818', 'D002392', 'D002851', 'D005260', 'D005609', 'D008401', 'D005965', 'D020723', 'D006801', 'D009682', 'D008297', 'D008956', 'D051381', 'D017207', 'D013045', 'D013997']","['Administration, Oral', 'Adult', 'Animals', 'Catechin', 'Chromatography, High Pressure Liquid', 'Female', 'Free Radicals', 'Gas Chromatography-Mass Spectrometry', 'Glucuronates', 'Glucuronic Acid', 'Humans', 'Magnetic Resonance Spectroscopy', 'Male', 'Models, Chemical', 'Rats', 'Rats, Sprague-Dawley', 'Species Specificity', 'Time Factors']",Structures of (-)-epicatechin glucuronide identified from plasma and urine after oral ingestion of (-)-epicatechin: differences between human and rat.,"[None, None, None, 'Q000008', None, None, None, None, 'Q000097', 'Q000097', None, None, None, None, None, None, None, None]","[None, None, None, 'administration & dosage', None, None, None, None, 'blood', 'blood', None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12654472,2004,0,0,, -0.22,10917931,"Diets that are rich in plant foods have been associated with a decreased risk for specific disease processes and certain chronic diseases. In addition to essential macronutrients and micronutrients, the flavonoids in a variety of plant foods may have health-enhancing properties. Chocolate is a food that is known to be rich in the flavan-3-ol epicatechin and procyanidin oligomers. However, the bioavailability and the biological effects of the chocolate flavonoids are poorly understood. To begin to address these issues, we developed a method based on HPLC coupled with electrochemical (coulometric) detection to determine the physiological levels of epicatechin, catechin and epicatechin dimers. This method allows for the determination of 20 pg (69 fmol) of epicatechin, which translates to plasma concentrations as low as 1 nmol/L. We next evaluated the absorption of epicatechin, from an 80-g semisweet chocolate (procyanidin-rich chocolate) bolus. By 2 h after ingestion, there was a 12-fold increase in plasma epicatechin, from 22 to 257 nmol/L (P < 0.01). Consistent with the antioxidant properties of epicatechin, within the same 2-h period, there was a significant increase of 31% in plasma total antioxidant capacity (P < 0.04) and a decrease of 40% in plasma 2-thiobarbituric acid reactive substances (P < 0.01). Plasma epicatechin and plasma antioxidant capacity approached baseline values by 6 h after ingestion. These results show that it is possible to determine basal levels of epicatechin in plasma. The data support the concept that the consumption of chocolate can result in significant increases in plasma epicatechin concentrations and decreases in plasma baseline oxidation products.",The Journal of nutrition,"['D000328', 'D000975', 'D044946', 'D001682', 'D002099', 'D002392', 'D002784', 'D002851', 'D005260', 'D006801', 'D008297', 'D008875', 'D018384', 'D044945']","['Adult', 'Antioxidants', 'Biflavonoids', 'Biological Availability', 'Cacao', 'Catechin', 'Cholesterol', 'Chromatography, High Pressure Liquid', 'Female', 'Humans', 'Male', 'Middle Aged', 'Oxidative Stress', 'Proanthocyanidins']",Epicatechin in human plasma: in vivo determination and effect of chocolate consumption on plasma oxidation status.,"[None, 'Q000493', None, None, 'Q000378', 'Q000008', 'Q000097', None, None, None, None, None, 'Q000187', None]","[None, 'pharmacokinetics', None, None, 'metabolism', 'administration & dosage', 'blood', None, None, None, None, None, 'drug effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/10917931,2000,0,0,, -0.22,15935584,"Caffeine is a mild central nervous stimulant that occurs naturally in coffee beans, cocoa beans and tea leaves. In large doses, it can be profoundly toxic, resulting in arrhythmia, tachycardia, vomiting, convulsions, coma and death. The average cup of coffee or tea in the United States is reported to contain between 40 and 150 mg caffeine although specialty coffees may contain much higher doses. Over-the-counter supplements that are used to combat fatigue typically contain 100-200 mg caffeine per tablet and doses of 32-200mg are included in a variety of prescription drug mixtures. Fatal caffeine overdoses in adults are relatively rare and require the ingestion of a large quantity of the drug, typically in excess of 5 g. Over a period of approximately 12 months our office reported two cases of fatal caffeine intoxication. In the first case, the femoral blood of a 39-year-old female with a history of intravenous drug use contained 192 mg/L caffeine. In the second case, femoral blood from a 29-year-old male with a history of obesity and diabetes contained 567 mg/L caffeine. In both cases, the cause of death was ruled as caffeine intoxication and the manner of death was accidental.",Forensic science international,"['D000328', 'D002110', 'D000697', 'D062787', 'D017809', 'D005260', 'D005554', 'D008401', 'D006801', 'D008297']","['Adult', 'Caffeine', 'Central Nervous System Stimulants', 'Drug Overdose', 'Fatal Outcome', 'Female', 'Forensic Medicine', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Male']",Fatal caffeine overdose: two case reports.,"[None, 'Q000097', 'Q000097', None, None, None, None, None, None, None]","[None, 'blood', 'blood', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/15935584,2005,0,0,,no cocoa tested -0.22,11759010,"The cacao bean husk has been shown to possess two types of cariostatic substances, one showing anti-glucosyltransferase (GTF) activity and the other antibacterial activity, and to inhibit experimental dental caries in rats infected with mutans streptococci. In the present study, chromatographic purification revealed high-molecular-weight polyphenolic compounds and unsaturated fatty acids as active components. The former, which showed strong anti-GTF activity, were polymeric epicatechins with C-4beta and C-8 intermolecular bonds estimated to be 4636 in molecular weight in an acetylated form. The latter, which showed bactericidal activity against Streptococcus mutans, were determined to be oleic and linoleic acids, and demonstrated a high level of activity at a concentration of 30 microgram/mL. The cariostatic activity of the cacao bean husk is likely caused by these biologically active constituents.",Journal of dental research,"['D000891', 'D002099', 'D002327', 'D002392', 'D002845', 'D004791', 'D005231', 'D005936', 'D005964', 'D015394', 'D008970', 'D010936', 'D012639', 'D013295']","['Anti-Infective Agents, Local', 'Cacao', 'Cariostatic Agents', 'Catechin', 'Chromatography', 'Enzyme Inhibitors', 'Fatty Acids, Unsaturated', 'Glucans', 'Glucosyltransferases', 'Molecular Structure', 'Molecular Weight', 'Plant Extracts', 'Seeds', 'Streptococcus mutans']",Identification of cariostatic substances in the cacao bean husk: their anti-glucosyltransferase and antibacterial activities.,"['Q000302', 'Q000737', 'Q000302', 'Q000302', None, 'Q000302', 'Q000302', 'Q000037', 'Q000037', None, None, 'Q000737', None, 'Q000187']","['isolation & purification', 'chemistry', 'isolation & purification', 'isolation & purification', None, 'isolation & purification', 'isolation & purification', 'antagonists & inhibitors', 'antagonists & inhibitors', None, None, 'chemistry', None, 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/11759010,2001,0,0,, -0.22,16819905,"A naturally decaffeinated tea, Camellia sinensis var. ptilophylla (cocoa tea), has long been popular in southern China as a healthy beverage. Our experiments indicate that a single oral administration of 500 mg/kg of cocoa tea extract suppresses increases in plasma triacylgycerol (TG) levels when fed with 5 mL/kg of olive or lard oil in mice and that the inhibition rates are 22.9% and 31.5%, respectively, compared with controls. Under the same condition, cocoa tea extract did not affect the level of plasma free fatty acid. Likewise, the extract reduced the lymphatic absorption of lipids at 250 and 500 mg/kg. Also, cocoa tea extract and polyphenols isolated from cocoa tea inhibit pancreatic lipase. These findings suggest that cocoa tea has hypolipemic activity, which may be due to the suppression of digestive lipase activity by the polyphenols contained within the tea.",Journal of agricultural and food chemistry,"['D000818', 'D028241', 'D002392', 'D002851', 'D004041', 'D004791', 'D005230', 'D005419', 'D005502', 'D000960', 'D008049', 'D008297', 'D051379', 'D008813', 'D000069463', 'D010636', 'D010936', 'D010938', 'D059808', 'D014280']","['Animals', 'Camellia sinensis', 'Catechin', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Enzyme Inhibitors', 'Fatty Acids, Nonesterified', 'Flavonoids', 'Food', 'Hypolipidemic Agents', 'Lipase', 'Male', 'Mice', 'Mice, Inbred ICR', 'Olive Oil', 'Phenols', 'Plant Extracts', 'Plant Oils', 'Polyphenols', 'Triglycerides']",Evaluation of the hypolipemic property of Camellia sinensisVar. ptilophylla on postprandial hypertriglyceridemia.,"[None, 'Q000737', 'Q000494', None, None, 'Q000494', 'Q000097', 'Q000302', None, 'Q000008', 'Q000037', None, None, None, None, 'Q000302', 'Q000008', 'Q000008', None, 'Q000097']","[None, 'chemistry', 'pharmacology', None, None, 'pharmacology', 'blood', 'isolation & purification', None, 'administration & dosage', 'antagonists & inhibitors', None, None, None, None, 'isolation & purification', 'administration & dosage', 'administration & dosage', None, 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/16819905,2006,0,0,, -0.21,11976402,"Excessive peroxidation of biomembranes is thought to contribute to the initiation and progression of numerous degenerative diseases. The present study examined the inhibitory effects of a cocoa extract, individual cocoa flavanols (-)-epicatechin and (+)-catechin, and procyanidin oligomers (dimer to decamer) isolated from cocoa on rat erythrocyte hemolysis. In vitro, the flavanols and the procyanidin oligomers exhibited dose-dependent protection against 2,2'-azo-bis (2-amidinopropane) dihydrochloride (AAPH)-induced erythrocyte hemolysis between concentrations of 2.5 and 40 microM. Dimer, trimer, and tetramer showed the strongest inhibitory effects at 10 microM, 59.4%, 66.2%, 70.9%; 20 microM, 84.1%, 87.6%, 81.0%; and 40 microM, 90.2%, 88.9%, 78.6%, respectively. In a subsequent experiment, male Sprague-Dawley rats (approximately 200 g; n = 5-6) were given a 100-mg intragastric dose of a cocoa extract. Blood was collected over a 4-hr time period. Epicatechin and catechin, and the dimers (-)-epicatechin-(4beta>8)-epicatechin (Dimer B2) and (-)-epicatechin-(4beta>6)-epicatechin (Dimer B5) were detected in the plasma with concentrations of 6.4 microM, and 217.6, 248.2, and 55.4 nM, respectively. Plasma antioxidant capacity (as measured by the total antioxidant potential [TRAP] assay) was elevated (P < 0.05) between 30 and 240 min following the cocoa extract feeding. Erythrocytes obtained from the cocoa extract-fed animals showed an enhanced resistance to hemolysis (P < 0.05). This enhanced resistance was also observed when erythrocytes from animals fed the cocoa extract were mixed with plasma obtained from animals given water only. Conversely, plasma obtained from rats given the cocoa extract improved the resistance of erythrocytes obtained from rats given water only. These results show cocoa flavanols and procyanidins can provide membrane protective effects.","Experimental biology and medicine (Maywood, N.J.)","['D000578', 'D000818', 'D000975', 'D044946', 'D002099', 'D002392', 'D002851', 'D019281', 'D004305', 'D004912', 'D005609', 'D006461', 'D008297', 'D013058', 'D010936', 'D044945', 'D051381', 'D017207']","['Amidines', 'Animals', 'Antioxidants', 'Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Dimerization', 'Dose-Response Relationship, Drug', 'Erythrocytes', 'Free Radicals', 'Hemolysis', 'Male', 'Mass Spectrometry', 'Plant Extracts', 'Proanthocyanidins', 'Rats', 'Rats, Sprague-Dawley']",Inhibitory effects of cocoa flavanols and procyanidin oligomers on free radical-induced erythrocyte hemolysis.,"['Q000494', None, 'Q000032', None, 'Q000737', 'Q000031', None, None, None, 'Q000187', 'Q000494', 'Q000187', None, None, 'Q000494', None, None, None]","['pharmacology', None, 'analysis', None, 'chemistry', 'analogs & derivatives', None, None, None, 'drug effects', 'pharmacology', 'drug effects', None, None, 'pharmacology', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11976402,2002,,,, -0.21,24720622,"In this study, we determined, by atomic absorption spectrophotometry, the potassium amount leached by soaking or boiling foods identified by children suffering from chronic renal failure as ""pleasure food"" and that they cannot eat because of their low-potassium diet, and evaluated whether addition of sodium polystyrene sulfonate resin (i.e. Kayexalate_‰) during soaking or boiling modulated potassium loss. A significant amount of potassium content was removed by soaking (16% for chocolate and potato, 26% for apple, 37% for tomato and 41% for banana) or boiling in a large amount of water (73% for potato). Although Kayexalate_‰ efficiently dose-dependently removed potassium from drinks (by 48% to 73%), resin addition during soaking or boiling did not eliminate more potassium from solid foods. Our results therefore provide useful information for dietitians who elaborate menus for people on potassium-restricted diets and would give an interesting alternative to the systematic elimination of all potassium-rich foods from their diet.",International journal of food sciences and nutrition,"['D000293', 'D001066', 'D002411', 'D002648', 'D003296', 'D004032', 'D005511', 'D006801', 'D007676', 'D057181', 'D011137', 'D011188', 'D014867']","['Adolescent', 'Appetite', 'Cation Exchange Resins', 'Child', 'Cooking', 'Diet', 'Food Handling', 'Humans', 'Kidney Failure, Chronic', 'Pleasure', 'Polystyrenes', 'Potassium', 'Water']",Effects of water soaking and/or sodium polystyrene sulfonate addition on potassium content of foods.,"[None, None, None, None, None, 'Q000523', 'Q000379', None, 'Q000178', None, None, None, None]","[None, None, None, None, None, 'psychology', 'methods', None, 'diet therapy', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24720622,2015,0,0,,no cocoa -0.2,22656242,"2-Substituted-5-methyl-3-oxazolines, a novel class of aroma precursors that are able to release the respective Strecker aldehydes by hydrolysis, were identified. Hydrolysis can take place after the addition of water or with human saliva during mastication, respectively. 2-Isobutyl-, 2-sec-isobutyl-, 2-isopropyl, and 2-benzyl-5-methyl-3-oxazolines were synthesized and structurally identified by means of gas chromatography-mass spectrometry (GC-MS) in the electron impact mode and in the chemical ionization mode as well as by one- and two-dimensional NMR experiments. With these compounds at hand, a variety of stability experiments were performed using headspace-GC-MS or proton transfer reaction-MS techniques on the basis of stable isotope dilution assays, proving the ability to release the respective Strecker aldehydes was dependent on the pH value as well as on the hydrolysis time. After the addition of water at 37 _C, for example, >70 mol % of 3-methylbutanal or >40 mol % of phenylacetaldehyde was liberated from a solution of 2-isobutyl-5-methyl-3-oxazoline or 2-benzyl-5-methyl-3-oxazoline, respectively, after 5 min. Furthermore, the presence of 2-isobutyl-5-methyl-3-oxazoline in dark chocolate containing 70% cocoa was proven by GC-MS.",Journal of agricultural and food chemistry,"['D000447', 'D002099', 'D015394', 'D010080']","['Aldehydes', 'Cacao', 'Molecular Structure', 'Oxazoles']",New insights into the formation of aroma-active strecker aldehydes from 3-oxazolines as transient intermediates.,"['Q000737', 'Q000737', None, 'Q000737']","['chemistry', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22656242,2012,0,0,, -0.2,9662953,"Viscosity and Yield Value of Casson are two chocolate properties. They are very important in the technological processes and they affect to the final product acepptation. In this study viscosity, yield value and fatty acid composition were determined of chocolates elaborated with different fat sources. A correlation study was made between these three variables. Viscosity and yield value were calculated with the Casson's education using a viscometer brookfield and fatty acids composition by gas-chromatography. Positive correlations between viscosity and yield value with stearic and palmitic acids contents have been found. Negative correlations between yield value and lauric content and viscosity and oleic acid content have been observed. The viscosity variations were relationed with total content of cocoa butter of different chocolates.",Nutricion hospitalaria,"['D002099', 'D005224', 'D006801', 'D019301', 'D010938', 'D014783']","['Cacao', 'Fats, Unsaturated', 'Humans', 'Oleic Acid', 'Plant Oils', 'Viscosity']",[Effect on viscosity and yield value of addition of different vegetable fat sources used in chocolate].,"['Q000737', None, None, None, None, None]","['chemistry', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9662953,1998,,,, -0.2,28946303,"Substantial equivalence studies were performed in three Theobroma spp., cacao, bicolor and grandiflorum through chemical composition analysis and protein profiling of fruit (pulp juice and seeds). Principal component analysis of sugar, organic acid, and phenol content in pulp juice revealed equivalence among the three species, with differences in some of the compounds that may result in different organoleptic properties. Proteins were extracted from seeds and pulp juice, resolved by two dimensional electrophoresis and major spots subjected to mass spectrometry analysis and identification. The protein profile, as revealed by principal component analysis, was variable among the three species in both seed and pulp, with qualitative and quantitative differences in some of protein species. The functional grouping of the identified proteins correlated with the biological role of each organ. Some of the identified proteins are of interest, being minimally discussed, including vicilin, a protease inhibitor, and a flavonol synthase/flavanone 3-hydroxylase.",Food chemistry,"['D002099', 'D005638', 'D006801', 'D006899', 'D010940', 'D012639']","['Cacao', 'Fruit', 'Humans', 'Mixed Function Oxygenases', 'Plant Proteins', 'Seeds']",Substantial equivalence analysis in fruits from three Theobroma species through chemical composition and protein profiling.,"[None, None, None, None, None, None]","[None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28946303,2017,1,2,table 1 ,chemical composition of T. cacao -0.2,16508183,"The purpose of this study was to evaluate the bitterness-suppressing effect of three jellies, all commercially available on the Japanese market as swallowing aids, on two dry syrups containing the macrolides clarithromycin (CAM) or azithromycin (AZM). The bitterness intensities of mixtures of the dry syrups and acidic jellies were significantly greater than those of water suspensions of the dry syrups in human gustatory sensation tests. On the other hand, the mixture with a chocolate jelly, which has a neutral pH, was less bitter than water suspensions of the dry syrups. The bitterness intensities predicted by the taste sensor output values correlated well with the observed bitterness intensities in human gustatory sensation tests. When the concentrations of CAM and AZM in solutions extracted from physical mixtures of dry syrup and jelly were determined by HPLC, concentrations in the solutions extracted from mixtures with acidic jellies were higher than those from mixtures with a neutral jelly (almost 90 times higher for CAM, and almost 7-10 times higher for AZM). Thus, bitterness suppression is correlated with the pH of the jelly. Finally, a drug dissolution test for dry syrup with and without jelly was performed using the paddle method. There was no significance difference in dissolution profile. It was concluded the appropriate choice of jelly with the right pH is essential for taste masking. Suitable jellies might be used to improve patient compliance, especially in children. The taste sensor may be used to predict the bitterness-suppressing effect of the jelly.",Chemical & pharmaceutical bulletin,"['D000328', 'D000900', 'D017963', 'D002099', 'D002851', 'D017291', 'D003627', 'D005421', 'D006801', 'D006863', 'D018942', 'D011803', 'D012995', 'D012996', 'D013649']","['Adult', 'Anti-Bacterial Agents', 'Azithromycin', 'Cacao', 'Chromatography, High Pressure Liquid', 'Clarithromycin', 'Data Interpretation, Statistical', 'Flavoring Agents', 'Humans', 'Hydrogen-Ion Concentration', 'Macrolides', 'Quinine', 'Solubility', 'Solutions', 'Taste']",Evaluation of bitterness suppression of macrolide dry syrups by jellies.,"[None, 'Q000009', 'Q000009', None, None, 'Q000009', None, 'Q000737', None, None, 'Q000009', 'Q000494', None, None, 'Q000187']","[None, 'adverse effects', 'adverse effects', None, None, 'adverse effects', None, 'chemistry', None, None, 'adverse effects', 'pharmacology', None, None, 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/16508183,2006,0,0,,no cocoa tested -0.18,22503716,"Human biomonitoring of nickel has gained interest in environmental medicine due to its wide distribution in the environment and its allergenic potential. There are indications that the prevalence of nickel sensitization in children is increased by nickel exposure and that oral uptake of nickel can exacerbate nickel dermatitis in nickel-sensitive individuals. Urinary nickel measurement is a good indicator of exposure. However, data on nickel levels in urine of children are rare. For the first time, the German Environmental Survey on children (GerES IV) 2003-2006 provided representative data to describe the internal nickel exposure of children aged 3-14 years in Germany. Nickel was measured after enrichment in the organic phase of urine by graphite furnace atomic absorption spectrometry with Zeeman background correction. Nickel levels (n=1576) ranged from <0.5 to 15 __g/l. Geometric mean was 1.26 __g/l. Multivariate regression analysis showed that gender, age, socio-economic status, being overweighted, consumption of hazelnut spread, nuts, cereals, chocolate and urinary creatinine were significant predictors for urinary nickel excretion of children who do not smoke. 20.2% of the variance could be explained by these variables. With a contribution of 13.8% the urinary creatinine concentration was the most important predictor. No influence of nickel intake via drinking water and second hand smoke exposure was observed.",International journal of hygiene and environmental health,"['D000293', 'D002099', 'D002648', 'D002675', 'D003367', 'D003404', 'D060766', 'D002523', 'D004784', 'D004785', 'D005260', 'D005506', 'D005858', 'D006306', 'D006801', 'D008297', 'D009532', 'D009754', 'D011795']","['Adolescent', 'Cacao', 'Child', 'Child, Preschool', 'Cotinine', 'Creatinine', 'Drinking Water', 'Edible Grain', 'Environmental Monitoring', 'Environmental Pollutants', 'Female', 'Food Contamination', 'Germany', 'Health Surveys', 'Humans', 'Male', 'Nickel', 'Nuts', 'Surveys and Questionnaires']",Levels and predictors of urinary nickel concentrations of children in Germany: results from the German Environmental Survey on children (GerES IV).,"[None, None, None, None, 'Q000652', 'Q000652', 'Q000032', None, None, 'Q000652', None, None, None, None, None, None, 'Q000652', None, None]","[None, None, None, None, 'urine', 'urine', 'analysis', None, None, 'urine', None, None, None, None, None, None, 'urine', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22503716,2013,0,0,, -0.17,22922535,"Six facultatively anaerobic, non-motile lactic acid bacteria were isolated from spontaneous cocoa bean fermentations carried out in Brazil, Ecuador and Malaysia. Phylogenetic analysis revealed that one of these strains, designated M75(T), isolated from a Brazilian cocoa bean fermentation, had the highest 16S rRNA gene sequence similarity towards Weissella fabaria LMG 24289(T) (97.7%), W. ghanensis LMG 24286(T) (93.3%) and W. beninensis LMG 25373(T) (93.4%). The remaining lactic acid bacteria isolates, represented by strain M622, showed the highest 16S rRNA gene sequence similarity towards the type strain of Fructobacillus tropaeoli (99.9%), a recently described species isolated from a flower in South Africa. pheS gene sequence analysis indicated that the former strain represented a novel species, whereas pheS, rpoA and atpA gene sequence analysis indicated that the remaining five strains belonged to F. tropaeoli; these results were confirmed by DNA-DNA hybridization experiments towards their respective nearest phylogenetic neighbours. Additionally, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry proved successful for the identification of species of the genera Weissella and Fructobacillus and for the recognition of the novel species. We propose to classify strain M75(T) (___=___LMG 26217(T) ___=___CCUG 61472(T)) as the type strain of the novel species Weissella fabalis sp. nov.",International journal of systematic and evolutionary microbiology,"['D001482', 'D001938', 'D002099', 'D004269', 'D004484', 'D005285', 'D005516', 'D005798', 'D056584', 'D008296', 'D008969', 'D009693', 'D010457', 'D010802', 'D012336', 'D017422', 'D058836']","['Base Composition', 'Brazil', 'Cacao', 'DNA, Bacterial', 'Ecuador', 'Fermentation', 'Food Microbiology', 'Genes, Bacterial', 'Leuconostocaceae', 'Malaysia', 'Molecular Sequence Data', 'Nucleic Acid Hybridization', 'Peptidoglycan', 'Phylogeny', 'RNA, Ribosomal, 16S', 'Sequence Analysis, DNA', 'Weissella']",Characterization of strains of Weissella fabalis sp. nov. and Fructobacillus tropaeoli from spontaneous cocoa bean fermentations.,"[None, None, 'Q000382', 'Q000235', None, None, None, None, 'Q000145', None, None, None, 'Q000032', None, 'Q000235', None, 'Q000145']","[None, None, 'microbiology', 'genetics', None, None, None, None, 'classification', None, None, None, 'analysis', None, 'genetics', None, 'classification']",https://www.ncbi.nlm.nih.gov/pubmed/22922535,2013,0,0,, -0.17,1189616,"A procedure based on extraction, column chromatography and precipitation is described for the separation of ethylene oxide-1,2-14C fumigated coca-powder derivatives in 9 different groups. As it was found in wheat [1], the major portion of radioactivity lies in water extract; in coca-powder the major portion of radioactivity is also found in low molecular components.",Zeitschrift fur Lebensmittel-Untersuchung und -Forschung,"['D002099', 'D002250', 'D005027', 'D006868']","['Cacao', 'Carbon Radioisotopes', 'Ethylene Oxide', 'Hydrolysis']","[Group separation of ethylene oxide 1,2-14c fumigated coca-powder derivatives and their distribution of radioactivity (author's transl)].","['Q000032', None, None, None]","['analysis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/1189616,1976,,,, -0.17,11185658,"Cacao is rich in polyphenols such as (-)-epicatechin, and a colored component of cacao (cacao-red) is polyphenol, which is an antioxidant. These properties stimulated an investigation of the effects of cacao liquor polyphenols (CLP) on low-density lipoprotein (LDL) oxidation. The 2.2 '-azobis(4-methoxy-2,4-dimethylvaleronitrile) (AMVN-CH2O)-induced oxidizability of LDL was assessed by monitoring the absorbance at 234 nm. In vitro. 0.1-0.5 mg/dL CLP prolonged the oxidation lag time of LDL in a dose-dependent manner. Compared with the controls, it was prolonged 1.7-fold in the presence of 0.1 mg/dL CLP, 2.9-fold at 0.2 mg/dL, 3.8-fold at 0.3 mg/dL, 5.4-fold at 0.4 mg/dL, and 6.4-fold at 0.5 mg/dL. Furthermore, we enlisted 13 male volunteers to consume 35 g delipidated cocoa. Venous blood samples were taken before and at 2 h and 4 h after consuming the cocoa. The oxidation lag time of LDL before cocoa ingestion was 59.0 +/- 6.3 min, but it was prolonged at 2 h after cocoa (68.3 +/- 6.0 min); before returning to the initial lag time (61.7 +/- 5.7 min) before consumption. Thus we have shown that cocoa inhibited LDL oxidation both in vitro and ex vivo.",Journal of nutritional science and vitaminology,"['D000328', 'D000975', 'D001161', 'D001391', 'D002099', 'D008078', 'D004305', 'D005419', 'D006801', 'D066298', 'D008297', 'D009570', 'D010084', 'D010636', 'D011108', 'D059808', 'D013053', 'D013997']","['Adult', 'Antioxidants', 'Arteriosclerosis', 'Azo Compounds', 'Cacao', 'Cholesterol, LDL', 'Dose-Response Relationship, Drug', 'Flavonoids', 'Humans', 'In Vitro Techniques', 'Male', 'Nitriles', 'Oxidation-Reduction', 'Phenols', 'Polymers', 'Polyphenols', 'Spectrophotometry', 'Time Factors']",Antioxidant effects of polyphenols in chocolate on low-density lipoprotein both in vitro and ex vivo.,"[None, 'Q000494', 'Q000097', 'Q000494', 'Q000737', 'Q000097', None, None, None, None, None, 'Q000494', None, 'Q000494', 'Q000494', None, None, None]","[None, 'pharmacology', 'blood', 'pharmacology', 'chemistry', 'blood', None, None, None, None, None, 'pharmacology', None, 'pharmacology', 'pharmacology', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11185658,2001,,,, -0.16,20153860,"Quality control of cacao beans is a significant issue in the chocolate industry. In this report, we describe how moisture damage to cacao beans alters the volatile chemical signature of the beans in a way that can be tracked quantitatively over time. The chemical signature of the beans is monitored via sampling the headspace of the vapor above a given bean sample. Headspace vapor sampled with solid-phase micro-extraction (SPME) was detected and analyzed with comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GCxGC-TOFMS). Cacao beans from six geographical origins (Costa Rica, Ghana, Ivory Coast, Venezuela, Ecuador, and Panama) were analyzed. Twenty-nine analytes that change in concentration levels via the time-dependent moisture damage process were measured using chemometric software. Biomarker analytes that were independent of geographical origin were found. Furthermore, prediction algorithms were used to demonstrate that moisture damage could be verified before there were visible signs of mold by analyzing subsets of the 29 analytes. Thus, a quantitative approach to quality screening related to the identification of moisture damage in the absence of visible mold is presented.",Journal of chromatography. A,"['D001185', 'D002099', 'D003364', 'D008401', 'D008956', 'D025341', 'D012044', 'D014867']","['Artificial Intelligence', 'Cacao', 'Costa Rica', 'Gas Chromatography-Mass Spectrometry', 'Models, Chemical', 'Principal Component Analysis', 'Regression Analysis', 'Water']",Quantitative assessment of moisture damage for cacao bean quality using two-dimensional gas chromatography combined with time-of-flight mass spectrometry and chemometrics.,"[None, 'Q000737', None, 'Q000379', None, None, None, 'Q000009']","[None, 'chemistry', None, 'methods', None, None, None, 'adverse effects']",https://www.ncbi.nlm.nih.gov/pubmed/20153860,2010,0,0,, -0.16,9554600,"The authors conducted a matched case-control study to investigate the effects of caffeine intake during pregnancy on birth weight. From January to November 1992, in the first 24 hours after delivery, 1,205 mothers (401 cases and 804 controls) were interviewed and their newborns were examined to assess birth weight and gestational age by means of the method of Capurro et al. (J Pediatr 1978;93:120-2). The cases were children with birth weight < 2,500 g and gestational age > or = 28 weeks. Cases and controls were matched for time of birth and hospital of delivery and were recruited from the four maternity hospitals in Pelotas, southern Brazil. Daily maternal caffeine intake during pregnancy for each trimester was estimated. To assess caffeine intake, 10% of the mothers were reinterviewed at their households and samples of reported information on drip coffee and mat© (a caffeine-containing drink widely used in South America) were collected and sent to the laboratory for caffeine determination through liquid chromatography. When instant coffee was reported, the weight of powder was measured using a portable scale, and caffeine intake was estimated from a reference table. Caffeine intake from tea, chocolate, soft drinks, and medicines was estimated from a reference table. Analyses were performed by conditional logistic regression. Crude analyses showed no effect of caffeine on low birth weight, preterm births or intrauterine growth retardation. The results did not change after allowing for confounders.",American journal of epidemiology,"['D001628', 'D001724', 'D002110', 'D016022', 'D003069', 'D005260', 'D005317', 'D005865', 'D006801', 'D007230', 'D007231', 'D007234', 'D015999', 'D011247']","['Beverages', 'Birth Weight', 'Caffeine', 'Case-Control Studies', 'Coffee', 'Female', 'Fetal Growth Retardation', 'Gestational Age', 'Humans', 'Infant, Low Birth Weight', 'Infant, Newborn', 'Infant, Premature', 'Multivariate Analysis', 'Pregnancy']",Caffeine intake and low birth weight: a population-based case-control study.,"['Q000032', 'Q000187', 'Q000008', None, 'Q000737', None, 'Q000139', None, None, None, None, None, None, None]","['analysis', 'drug effects', 'administration & dosage', None, 'chemistry', None, 'chemically induced', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9554600,1998,,,, -0.15,528451,"Aflatoxin was produced in both non-autoclaved and autoclaved Ivory Coast cocoa beans inoculated with Aspergillus parasiticus NRRL 2999 under optimum laboratory growth conditions. Total aflatoxin levels ranged from 213 to 5597 ng/g substrate. Aflatoxin was quantitated by using high pressure liquid chromatography (HPLC). Raw, non-autoclaved cocoa beans, also inoculated with aspergilli, produced 6359 ng aflatoxin/g substrate. Variation in aflatoxin production between bean varieties was observed. Total aflatoxin levels of 10,446 and 23,076 ng/g substrate were obtained on Ivory Coast beans inoculated with A. parasiticus NRRL 2999 and NRRL 3240, respectively. Aflatoxin production on Trinidad and Malaysian beans was 28 and 65 ng aflatoxin/g substrate. These data support previously reported low level natural aflatoxin contamination in cocoa.",Journal - Association of Official Analytical Chemists,"['D000348', 'D001230', 'D002099']","['Aflatoxins', 'Aspergillus', 'Cacao']",Production of aflatoxin in cocoa beans.,"['Q000096', 'Q000378', None]","['biosynthesis', 'metabolism', None]",https://www.ncbi.nlm.nih.gov/pubmed/528451,1980,,,, -0.14,24974581,"The determination of cefaclor in a new, complex chocolate matrix was performed by using a simple sample preparation (dispersion in dilute hydrochloric acid at 80 degrees C, centrifugation, washing with cyclohexane), followed by ion pair HPLC on a Kinetex pentafluorophenyl core-shell stationary phase with UV detection at 265 nm. We obtained good linearity (R2 = 0.9976) and precision (average RSD 0.86%) for the relevant concentration range. The preparations, although hand-made in this pilot phase, showed good uniformity of content. After being stored for four weeks in a refrigerator the preparation did not contain recognizable amounts of decomposition products.",Die Pharmazie,"['D000900', 'D002099', 'D002214', 'D002433', 'D002626', 'D002851', 'D004304', 'D005780', 'D012015', 'D015203', 'D013056']","['Anti-Bacterial Agents', 'Cacao', 'Capsules', 'Cefaclor', 'Chemistry, Pharmaceutical', 'Chromatography, High Pressure Liquid', 'Dosage Forms', 'Gelatin', 'Reference Standards', 'Reproducibility of Results', 'Spectrophotometry, Ultraviolet']",Analysis of cefaclor in novel chocolate-based camouflage capsules.,"['Q000008', None, None, 'Q000008', None, None, None, None, None, None, None]","['administration & dosage', None, None, 'administration & dosage', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24974581,2014,,,, -0.14,16954822,"A proteomic analysis of procyanidin B(2) isolated from cocoa against oxidized low-density lipoprotein-induced lipid-laden macrophage formation was performed. Of approximately 400 detected proteins, 12 were differentially expressed as a result of B(2) treatment. They were subsequently identified by liquid chromatography-electrospray ionization-tandem mass spectrometry and the SWISS-PROT database. Further reverse transcriptase-polymerase chain reaction and Western blot analysis revealed that B(2) strongly inhibited arachidonic acid inflammatory reactions, apoptosis, and their coupled mitogen-activated protein kinase and NF-kappaB pathways. To highlight proteins or genes with similar expressed patterns and similarly biological function induced by B(2) in lipid-laden macrophages, a cluster and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed. The data were mapped to multiple pathways. Further validation of the bioinformatic results revealed that activation of Wnt signaling may contribute to the cardioprotection of B(2). The differentially expressed genes and proteins mentioned above induced by B(2) are through regulating nuclear transcription factors, activating peroxisome proliferator-activated receptor-gamma and inhibiting AP-1 mRNA expressions. These in vitro data help to interpret the beneficial effects of B(2) in reducing the risk of atherosclerosis after consumption of flavonoid-rich foods. Many differentially expressed genes induced by B(2) help to uncover novel targets and may help to target disease interactions in atherosclerosis in the future.",Journal of cardiovascular pharmacology,"['D000595', 'D001094', 'D044946', 'D002392', 'D051546', 'D019281', 'D004734', 'D015870', 'D006801', 'D050356', 'D008077', 'D008264', 'D008969', 'D047495', 'D044945', 'D012333', 'D051127', 'D015398', 'D020298', 'D051153']","['Amino Acid Sequence', 'Arachidonate 5-Lipoxygenase', 'Biflavonoids', 'Catechin', 'Cyclooxygenase 2', 'Dimerization', 'Energy Metabolism', 'Gene Expression', 'Humans', 'Lipid Metabolism', 'Lipoproteins, LDL', 'Macrophages', 'Molecular Sequence Data', 'PPAR gamma', 'Proanthocyanidins', 'RNA, Messenger', 'Scavenger Receptors, Class E', 'Signal Transduction', 'U937 Cells', 'Wnt Proteins']",Inhibitory effects of procyanidin B(2) dimer on lipid-laden macrophage formation.,"[None, 'Q000032', 'Q000494', 'Q000494', 'Q000032', None, None, 'Q000187', None, 'Q000187', 'Q000494', 'Q000187', None, 'Q000502', 'Q000494', 'Q000032', 'Q000032', 'Q000187', None, 'Q000502']","[None, 'analysis', 'pharmacology', 'pharmacology', 'analysis', None, None, 'drug effects', None, 'drug effects', 'pharmacology', 'drug effects', None, 'physiology', 'pharmacology', 'analysis', 'analysis', 'drug effects', None, 'physiology']",https://www.ncbi.nlm.nih.gov/pubmed/16954822,2006,0,0,, +pred,PMID,abstract,journal,mesh_UIds,mesh_terms,paper,qual_UIds,qual_terms,webpage,year,is_useful,usefulness_tier,info_location,comment +0.91,10917927,"Procyanidins are a subclass of flavonoids found in commonly consumed foods that have attracted increasing attention due to their potential health benefits. However, little is known regarding their dietary intake levels because detailed quantitative information on the procyanidin profiles present in many food products is lacking. Therefore, the procyanidin content of red wine, chocolate, cranberry juice and four varieties of apples has been determined. On average, chocolate and apples contained the largest procyanidin content per serving (164.7 and 147.1 mg, respectively) compared with red wine and cranberry juice (22.0 and 31.9 mg, respectively). However, the procyanidin content varied greatly between apple samples (12.3-252.4 mg/serving) with the highest amounts on average observed for the Red Delicious (207.7 mg/serving) and Granny Smith (183.3 mg/serving) varieties and the lowest amounts in the Golden Delicious (92.5 mg/serving) and McIntosh (105.0 mg/serving) varieties. The compositional data reported herein are important for the initial understanding of which foods contribute most to the dietary intake of procyanidins and may be used to compile a database necessary to infer epidemiological relationships to health and disease.",The Journal of nutrition,"['D044946', 'D002099', 'D002392', 'D002851', 'D004032', 'D005504', 'D005638', 'D044945', 'D014920']","['Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Diet', 'Food Analysis', 'Fruit', 'Proanthocyanidins', 'Wine']",Procyanidin content and variation in some commonly consumed foods.,"[None, 'Q000737', 'Q000008', 'Q000379', None, None, 'Q000737', None, 'Q000032']","[None, 'chemistry', 'administration & dosage', 'methods', None, None, 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/10917927,2000,0,0,,no cocoa +0.9,26759032,"Children are vulnerable to heavy metal contamination through consumption of candies and chocolates. Considering this representative samples (69) of candies and chocolates based on cocoa, milk and sugar were analyzed for selected heavy metals by means of flame atomic absorption spectrometry. The average concentration of Zn, Pb, Ni, and Cd was found to be 2.52 _± 2.49, 2.0 _± 1.20, 0.84 _± 1.35, and 0.17 _± 0.22 __g/g respectively. Results indicate that cocoa-based candies have higher metal content than milk- or sugar-based candies. The daily dietary intake of metals for children eating candies and chocolates was also calculated, and results indicated highest intake of Pb and Zn followed by Ni, Cd, and Cu. Comparison of the current study results with other studies around the globe shows that the heavy metal content in candies and chocolates is lower in India than reported elsewhere. However, to reduce the further dietary exposure of heavy metals through candies and chocolates, their content should be monitored regularly and particularly for Pb as children are highly susceptible to its toxicity.",Environmental monitoring and assessment,"['D002182', 'D002648', 'D004032', 'D004781', 'D004784', 'D005506', 'D006801', 'D007194', 'D019216', 'D013054']","['Candy', 'Child', 'Diet', 'Environmental Exposure', 'Environmental Monitoring', 'Food Contamination', 'Humans', 'India', 'Metals, Heavy', 'Spectrophotometry, Atomic']",Heavy metal content in various types of candies and their daily dietary intake by children.,"['Q000032', None, 'Q000706', 'Q000032', 'Q000379', 'Q000032', None, None, 'Q000032', None]","['analysis', None, 'statistics & numerical data', 'analysis', 'methods', 'analysis', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/26759032,2016,1,1,table 3 ,International standars were given by an agency and the paper is referenced in this paper +0.89,11513631,"At present, the commonly used HPLC method for the analysis of caffeine and theobromine contents in aqueous cocoa extracts employs direct application of the extracts on the column. This practice gradually reduces the efficiency of the column and shortens its life. Also, this method gives inflated values due to interfering substances and difficulty in achieving baseline resolution. In the improved method, the interfering cocoa pigments are effectively removed by passing the aqueous extract through a Sep-pak C(18) cartridge. Subsequent injection on a C(18) reverse-phase column employing acetonitrile and water (20:80) as the mobile phase reduces the analysis time without affecting either resolution of the peak or the accuracy of caffeine and theobromine determination or achieving baseline resolution. Therefore, this method is ideally suited for rapid routine analysis of cocoa and its products.",Journal of agricultural and food chemistry,"['D002099', 'D002110', 'D002851', 'D012680', 'D013805', 'D013997']","['Cacao', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Sensitivity and Specificity', 'Theobromine', 'Time Factors']",Improved high-performance liquid chromatography method to determine theobromine and caffeine in cocoa and cocoa products.,"['Q000737', 'Q000032', 'Q000379', None, 'Q000032', None]","['chemistry', 'analysis', 'methods', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/11513631,2001,2,1,table 1 and 2, +0.89,22394229,"This study has examined the occurrence of aflatoxins in 168 samples of different fractions obtained during the processing of cocoa in manufacturing plants (shell, nibs, mass, butter, cake and powder) using an optimised methodology for cocoa by-products. The method validation was based on selectivity, linearity, limit of detection and recovery. The method was shown to be adequate for use in quantifying the contamination of cocoa by aflatoxins B(1), B(2), G(1) and G(2). Furthermore, the method was easier to use than other methods available in the literature. For aflatoxin extraction from cocoa samples, a methanol-water solution was used, and then immunoaffinity columns were employed for clean-up before the determination by high-performance liquid chromatography. A survey demonstrated a widespread occurrence of aflatoxins in cocoa by-products, although in general the levels of aflatoxins present in the fractions from industrial processing of cocoa were low. A maximum aflatoxin contamination of 13.3 ng g(-1) was found in a nib sample. The lowest contamination levels were found in cocoa butter. Continued monitoring of aflatoxins in cocoa by-products is nevertheless necessary because these toxins have a high toxicity to humans and cocoa is widely consumed by children through cocoa-containing products, like candies.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000348', 'D002099', 'D002846', 'D002851', 'D057230']","['Aflatoxins', 'Cacao', 'Chromatography, Affinity', 'Chromatography, High Pressure Liquid', 'Limit of Detection']",Determination of aflatoxins in by-products of industrial processing of cocoa beans.,"['Q000032', 'Q000737', 'Q000379', 'Q000379', None]","['analysis', 'chemistry', 'methods', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/22394229,2012,1,2,table 3 ,the occurrence of aflatoxin in all cocoa by-products +0.88,10868592,"A method was developed for determining fructan inulin in various foods (yogurts, honey cakes, chocolates). Warm water was applied for extraction of samples, and mono- and dissacharides were determined by a thin-layer chromatographic densitometric method. A portion of the test solution was hydrolyzed 30 min with 1% oxalic acid in a boiling water bath. Fructose was determined in the hydrolysate. The amount of inulin in a sample was calculated as the difference between the amount of fructose in the sample before and after hydrolysis. The fructose from sucrose formed during the hydrolysis was also considered. The mean recovery from yogurt fortified with 4% inulin was 95.5 +/- 4.5% (mean +/- standard deviation); from honey cakes extract fortified with 10% inulin, 97.3 +/- 5.5%; and from chocolate extract fortified with 30% inulin, 98.6 +/- 6.6% (6 replicates in all cases). Determination of glucose is not necessary for analyzing fructans with the composition expressed shortened to GFn-1 (G, glucose; F, fructosyl) with the average degree of polymerization 8 < or = n < or = 15.",Journal of AOAC International,"['D000818', 'D002099', 'D002855', 'D005504', 'D005632', 'D006722', 'D006358', 'D006868', 'D007444', 'D008892', 'D015014']","['Animals', 'Cacao', 'Chromatography, Thin Layer', 'Food Analysis', 'Fructose', 'Honey', 'Hot Temperature', 'Hydrolysis', 'Inulin', 'Milk', 'Yogurt']",Determination of inulin in foods.,"[None, 'Q000737', None, 'Q000379', 'Q000032', None, None, None, 'Q000032', 'Q000737', 'Q000032']","[None, 'chemistry', None, 'methods', 'analysis', None, None, None, 'analysis', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/10868592,2000,,,, +0.87,23017398,"This work reports an investigation carried out to assess the natural occurrence of ochratoxin A in 168 samples from different fractions obtained during the technological processing of cocoa (shell, nibs, liquor, butter, cake and cocoa powder) and the reduction of ochratoxin A during chocolate manufacture. Ochratoxin A analyses were performed with immunoaffinity columns and detection by high performance liquid chromatography. Concerning the natural ochratoxin A contamination in cocoa by-products, the highest levels of ochratoxin A were found in the shell, cocoa powder and cocoa cake. The cocoa butter was the least contaminated, showing that ochratoxin A seems to remain in the defatted cocoa solids. Under the technological conditions applied during the manufacture of chocolate in this study and the level of contamination present in the cocoa beans, this experiment demonstrated that 93.6% of ochratoxin A present in the beans was reduced during the chocolate producing.",Food chemistry,"['D002099', 'D002851', 'D003059', 'D005506', 'D005511', 'D009793']","['Cacao', 'Chromatography, High Pressure Liquid', 'Cocos', 'Food Contamination', 'Food Handling', 'Ochratoxins']",Occurrence of ochratoxin A in cocoa by-products and determination of its reduction during chocolate manufacture.,"['Q000737', None, 'Q000737', 'Q000032', None, 'Q000032']","['chemistry', None, 'chemistry', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/23017398,2013,1,1,table 1 and 2, +0.87,15891868,"Chocolate is a complex sample with a high content of organic compounds and its analysis generally involves digestion procedures that might include the risk of losses and/or contamination. The determination of copper in chocolate is important because copper compounds are extensively used as fungicides in the farming of cocoa. In this paper, a slurry-sampling flame atomic-absorption spectrometric method is proposed for determination of copper in powdered chocolate samples. Optimization was carried out using univariate methodology involving the variables nature and concentration of the acid solution for slurry preparation, sonication time, and sample mass. The recommended conditions include a sample mass of 0.2 g, 2.0 mol L(-1) hydrochloric acid solution, and a sonication time of 15 min. The calibration curve was prepared using aqueous copper standards in 2.0 mol L(-1) hydrochloric acid. This method allowed determination of copper in chocolate with a detection limit of 0.4 microg g(-1) and precision, expressed as relative standard deviation (RSD), of 2.5% (n = 10) for a copper content of approximately 30 microg g(-1), using a chocolate mass of 0.2 g. The accuracy was confirmed by analyzing the certified reference materials NIST SRM 1568a rice flour and NIES CRM 10-b rice flour. The proposed method was used for determination of copper in three powdered chocolate samples, the copper content of which varied between 26.6 and 31.5 microg g(-1). The results showed no significant differences with those obtained after complete digestion, using a t-test for comparison.",Analytical and bioanalytical chemistry,"['D002099', 'D003300', 'D011208', 'D012680', 'D013054']","['Cacao', 'Copper', 'Powders', 'Sensitivity and Specificity', 'Spectrophotometry, Atomic']",Determination of copper in powdered chocolate samples by slurry-sampling flame atomic-absorption spectrometry.,"['Q000737', 'Q000032', 'Q000737', None, 'Q000295']","['chemistry', 'analysis', 'chemistry', None, 'instrumentation']",https://www.ncbi.nlm.nih.gov/pubmed/15891868,2007,0,0,,no cocoa +0.86,23828209,"Ultrathin-layer chromatography (UTLC) potentially offers faster analysis, reduced solvent and sample volumes, and lower costs. One novel technique for producing UTLC plates has been glancing angle deposition (GLAD), a physical vapor deposition technique capable of aligning macropores to produce interesting separation properties. To date, however, GLAD-UTLC plates have been restricted to model dye systems, rather than realistic analytes. This study demonstrates the transfer of high-performance thin-layer chromatography (HPTLC) sugar analysis methods to GLAD-UTLC plates using the office chromatography framework. A consumer inkjet printer was used to apply very sharp low volume (3-30__nL) bands of water-soluble analytes (lactose, sucrose, and fructose). Analytic performance measurements extrapolated the limits of detection to be 3-5__ng/zone, which was experimentally proven down to 60-70__ng/band, depending on the sugar. This qualitative analysis of sugars in a commercially available chocolate sample is the first reported application of GLAD-UTLC to food samples. The potential utility of GLAD-UTLC is further exemplified by successful coupling with electrospray ionization mass spectrometry for the first time to characterize underivatized sugars. ",Analytical and bioanalytical chemistry,"['D002099', 'D002855', 'D005504', 'D005632', 'D007281', 'D007785', 'D057230', 'D011327', 'D021241', 'D013395']","['Cacao', 'Chromatography, Thin Layer', 'Food Analysis', 'Fructose', 'Ink', 'Lactose', 'Limit of Detection', 'Printing', 'Spectrometry, Mass, Electrospray Ionization', 'Sucrose']","Inkjet application, chromatography, and mass spectrometry of sugars on nanostructured thin films.","['Q000737', 'Q000379', None, 'Q000032', None, 'Q000032', None, None, None, 'Q000032']","['chemistry', 'methods', None, 'analysis', None, 'analysis', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/23828209,2014,0,0,,no cocoa +0.85,21140662,"A fast and simple chromatographic method to determine biotin in foods is presented. Biotin is extracted using papain (60 degrees C, 1 h). After pH adjustment and filtration, biotin is determined by LC with fluorescence detection using postcolumn reagent avidin-FITC (avidin labeled with fluorescein isothiocyanate). The method has been validated in a large range of products: milk- and soy-based infant formulas, cereals, cocoa-malt beverages, and clinical nutrition products. The method showed recovery rates of 98.1 +/- 5.7% (average +/- SD) in a large range of concentrations. Biotin concentrations determined in infant formula standard reference materials 1846 and 1849 were in agreement with reference values. RSD of repeatability (RSDr) varied from 2.0 to 4.5%, and intermediate reproducibility (RSD(iR)) from 5.8 to 9.4%. LOD and LOQ were 3.0 and 5.0 microg/100 g, respectively. The proposed method is suitable for routine analysis of biotin in fortified foods (infant formulas, infant cereals, cocoa-malt beverages, and clinical nutrition products). It can be used as a faster, more selective, and precise alternative to the classical microbiological determination, and is easily transferable among laboratories.",Journal of AOAC International,"['D001628', 'D001710', 'D002099', 'D002851', 'D002523', 'D005453', 'D006801', 'D007223', 'D041943', 'D015203']","['Beverages', 'Biotin', 'Cacao', 'Chromatography, High Pressure Liquid', 'Edible Grain', 'Fluorescence', 'Humans', 'Infant', 'Infant Formula', 'Reproducibility of Results']","Optimization and validation of an LC-fLD method for biotin in infant formula, infant cereals, cocoa-malt beverages, and clinical nutrition products.","['Q000032', 'Q000032', 'Q000737', 'Q000379', 'Q000737', None, None, None, 'Q000737', None]","['analysis', 'analysis', 'chemistry', 'methods', 'chemistry', None, None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21140662,2010,,,, +0.84,16001548,"A method using normal phase high performance liquid chromatography (NP-HPLC) with UV detection was developed for the analysis of acrylamide and methacrylamide. The method relies on the chromatographic separation of these analytes on a polar HPLC column designed for the separation of organic acids. Identification of acrylamide and methacrylamide is approached dually, that is directly in their protonated forms and as their hydrolysis products acrylic and methacrylic acid respectively, for confirmation. Detection and quantification is performed at 200 nm. The method is simple allowing for clear resolution of the target peaks from any interfering substances. Detection limits of 10 microg L(-1) were obtained for both analytes with the inter- and intra-day RSD for standard analysis lying below 1.0%. Use of acetonitrile in the elution solvent lowers detection limits and retention times, without impairing resolution of peaks. The method was applied for the determination of acrylamide and methacrylamide in spiked food samples without native acrylamide yielding recoveries between 95 and 103%. Finally, commercial samples of french and roasted fries, cookies, cocoa and coffee were analyzed to assess applicability of the method towards acrylamide, giving results similar with those reported in the literature.",Journal of chromatography. A,"['D020106', 'D000178', 'D002851', 'D013056']","['Acrylamide', 'Acrylamides', 'Chromatography, High Pressure Liquid', 'Spectrophotometry, Ultraviolet']",Determination of acrylamide and methacrylamide by normal phase high performance liquid chromatography and UV detection.,"['Q000032', 'Q000032', 'Q000379', None]","['analysis', 'analysis', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/16001548,2005,1,1,table 3 ,only the concentration +0.83,11087482,"An HPLC method, using detection after postcolumn derivatization with p-dimethylaminocynnamaldehyde (DMACA), was developed for the quantitative analysis of individual flavanols in food. This method was applied to flavanol determination in 56 different kinds of Spanish food products, including fruit, vegetables, legumes, beverages (cider, coffee, beer, tea, and wine), and chocolate. The determined compounds corresponded to the catechins and proanthocyanidin dimers and trimers usually present in food and, therefore, they were representative of the flavanols of low degree of polymerization consumed with the diet. The data generated could be used for calculation of the dietary intake of either individual or total flavanols, which would allow the further establishment of epidemiological correlations with the incidence of chronic diseases. Similar flavanol profiles were found in the different samples of a similar type of product, even though important variations could exist in the concentrations of total and individual flavanols among them. This was attributed to factors such as sample origin, stage of ripeness, post-harvesting conservation, and processing. Total flavanol contents varied from nondetectable in most of the vegetables to 184 mg/100 g found in a sample of broad bean. Substantial amounts were also found in some fruits, such as plum and apple, as well as in tea and red wine. Epicatechin was the most abundant flavanol, followed by catechin and procyanidin B2. In general, catechins were found in all the flavanol-containing products, but the presence of gallocatechins was only relevant in pomegranate, broad bean, lentil, grape, wine, beer, and tea, and most of the berries. Galloyled flavanols were only detected in strawberry, medlar, grape, and tea.",Journal of agricultural and food chemistry,"['D001628', 'D044946', 'D002099', 'D002392', 'D002851', 'D007887', 'D005419', 'D005504', 'D005638', 'D007202', 'D010946', 'D044945', 'D013030']","['Beverages', 'Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Fabaceae', 'Flavonoids', 'Food Analysis', 'Fruit', 'Indicators and Reagents', 'Plants, Medicinal', 'Proanthocyanidins', 'Spain']",Quantitative analysis of flavan-3-ols in Spanish foodstuffs and beverages.,"['Q000032', None, 'Q000737', None, 'Q000379', 'Q000737', 'Q000032', None, 'Q000737', None, None, None, None]","['analysis', None, 'chemistry', None, 'methods', 'chemistry', 'analysis', None, 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11087482,2001,1,1,table 1,soluble cacao +0.83,11552746,"Samples of chocolate, cocoa, tea infusions, soft drinks and fruit juice have been examined by, electrothermal atomic absorption spectrometry (ETA-AAS) for the presence of aluminium (Al). Fruit juices and chocolate were analysed after an adequate sample preparation; the other products were evaluated directly. Sampling was performed in duplicate for 248 independent samples. The mean Al concentration in chocolate was 9.2 +/- 7.5 mg kg(-1), and individual values were correlated with the per cent of cocoa in samples (Y = 0.63 + 0.27X, r = 0.78, p < 0.0001). Al concentration in commercial tea infusions ranged from 0.9 to 3.3 mg l(-1) (mean = 1.80 +/- 65 mg l(-1), whereas in laboratory-prepared samples it was 2.7 +/- 0.93 mg l(-1). In soft drinks, the concentrations of Al were lower, ranging from 9.1 to 179 microg l(-1); the highest values were observed in samples of orange squash (mean = 114 +/- 56 microg l(-1)). Apricot juice showed the highest Al level (mean = 602 +/- 190 microg l(-1)), being statistically, different from that of pear (mean = 259 +/- 102 microg l(-1)), but not different from that of peach juice (mean = 486 +/- 269 microg kg(-1)). Toxicologically, the amount of Al deriving from the consumption of these products is far below the acceptable daily intake of 1 mg kg(-1) body weight indicated by the FAO/WHO, and it is a verv low percentage of the normal Al dietary intake.",Food additives and contaminants,"['D000535', 'D001628', 'D002099', 'D002253', 'D002957', 'D005506', 'D006801', 'D013054', 'D013662']","['Aluminum', 'Beverages', 'Cacao', 'Carbonated Beverages', 'Citrus', 'Food Contamination', 'Humans', 'Spectrophotometry, Atomic', 'Tea']",Evaluation of aluminium concentrations in samples of chocolate and beverages by electrothermal atomic absorption spectrometry.,"['Q000032', 'Q000032', 'Q000737', 'Q000032', 'Q000737', None, None, None, 'Q000737']","['analysis', 'analysis', 'chemistry', 'analysis', 'chemistry', None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11552746,2001,,,, +0.82,25174984,"Phenol-specific extracts of 12 Belgian special beers were analyzed by gas chromatography hyphenated to olfactometry (AEDA procedure) and mass spectrometry (single ion monitoring mode). As guaiacol and 4-methylphenol were revealed to be more concentrated in brown beers (>3.5 and >1.1 __g/L, respectively), they are proposed as specific markers of the utilization of dark malts. Analysis of five differently colored malts (5, 50, 500, 900, and 1500 _EBC) allowed confirmation of high levels of guaiacol (>180 __g/L; values given in wort, for 100% specialty malt) and 4-methylphenol (>7 __g/L) for chocolate and black malts only (versus respectively <3 __g/L and undetected in all other worts). Monitoring of beer aging highlighted major differences between phenols. Guaiacol and 4-methylphenol appeared even more concentrated in dark beers after 14 months of aging, reaching levels not far from their sensory thresholds. 4-Vinylphenols and 4-ethylphenols, on the contrary, proved to be gradually degraded in POF(+)-yeast-derived beers. Vanillin exhibited an interesting pattern: in beers initially containing <25 __g/L, the vanillin concentration increased over a 14 month aging period to levels exceeding its sensory threshold (up to 160 __g/L). Beers initially showing an above-threshold level of vanillin displayed a decrease during aging. ",Journal of agricultural and food chemistry,"['D001515', 'D015415', 'D003408', 'D002523', 'D005285', 'D005511', 'D008401', 'D006139', 'D010636', 'D014835']","['Beer', 'Biomarkers', 'Cresols', 'Edible Grain', 'Fermentation', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Guaiacol', 'Phenols', 'Volatilization']",Guaiacol and 4-methylphenol as specific markers of torrefied malts. Fate of volatile phenols in special beers through aging.,"['Q000032', 'Q000737', 'Q000737', 'Q000737', None, None, None, 'Q000737', 'Q000737', None]","['analysis', 'chemistry', 'chemistry', 'chemistry', None, None, None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/25174984,2015,0,0,,no cocoa +0.81,11312867,"The detection threshold of acetaldehyde was determined on whole, lowfat, and nonfat milks, chocolate-flavored milk, and spring water. Knowledge of the acetaldehyde threshold is important because acetaldehyde forms in milk during storage as a result of light oxidation. It is also a degradation product of poly(ethylene terephthalate) during melt processing, a relatively new packaging choice for milk and water. There was no significant difference in the acetaldehyde threshold in milk of various fat contents, with thresholds ranging from 3939 to 4040 ppb. Chocolate-flavored milk and spring water showed thresholds of 10048 and 167 ppb, respectively, which compares favorably with previous studies. Solid phase microextraction (SPME) was verified as an effective method for the recovery of acetaldehyde in all media with detection levels as low as 200 and 20 ppb in milk and water, respectively, when using a polydimethyl siloxane/Carboxen SPME fiber in static headspace at 45 degrees C for 15 min.",Journal of agricultural and food chemistry,"['D000079', 'D000818', 'D020355', 'D002849', 'D005519', 'D006801', 'D008027', 'D008892', 'D010084', 'D013652', 'D014867']","['Acetaldehyde', 'Animals', 'Cholates', 'Chromatography, Gas', 'Food Preservation', 'Humans', 'Light', 'Milk', 'Oxidation-Reduction', 'Taste Threshold', 'Water']","Flavor threshold for acetaldehyde in milk, chocolate milk, and spring water using solid phase microextraction gas chromatography for quantification.","['Q000032', None, 'Q000737', 'Q000379', None, None, None, 'Q000737', None, None, 'Q000032']","['analysis', None, 'chemistry', 'methods', None, None, None, 'chemistry', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11312867,2001,0,0,,no cocoa +0.81,11871386,"This work relates the development of an analytical methodology to simultaneously determine three methylxanthines (caffeine, theobromine, and theophylline) in beverages and urine samples based on reversed-phase high-performance liquid chromatography. Separation is made with a Bondesil C18 column using methanol-water-acetic acid or ethanol-water-acetic acid (20:75:5, v/v/v) as the mobile phase at 0.7 mL/min. Identification is made by absorbance detection at 273 nm. Under optimized conditions, the detection limit of the HPLC method is 0.1 pg/mL for all three methylxanthines. This method is applied to urine and to 25 different beverage samples, which included coffee, tea, chocolate, and coconut water. The concentration ranges determined in the beverages and urine are: < 0.1 pg/mL to 350 microg/mL and 3.21 microg/mL to 71.2 microg/mL for caffeine; < 0.1 pg/mL to 32 microg mL and < 0.1 pg/mL to 13.2 microg/mL for theobromine; < 0.1 pg/mL to 47 microg/mL and < 0.1 pg/mL to 66.3 microg/mL for theophylline. The method proposed in this study is rapid and suitable for the simultaneous quantitation of methylxanthines in beverages and human urine samples and requires no extraction step or derivatization.",Journal of chromatographic science,"['D001628', 'D002110', 'D002138', 'D002851', 'D012680', 'D013056', 'D013805', 'D013806']","['Beverages', 'Caffeine', 'Calibration', 'Chromatography, High Pressure Liquid', 'Sensitivity and Specificity', 'Spectrophotometry, Ultraviolet', 'Theobromine', 'Theophylline']","Simultaneous determination of caffeine, theobromine, and theophylline by high-performance liquid chromatography.","['Q000032', 'Q000032', None, 'Q000379', None, None, 'Q000032', 'Q000032']","['analysis', 'analysis', None, 'methods', None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11871386,2002,,,, +0.81,19754154,"Cocoa-phytochemicals have been related to the health-benefits of cocoa consumption. Metabolomics has been proposed as a powerful tool to characterize both the intake and the effects on the metabolism of dietary components. Human urine metabolome modifications after single cocoa intake were explored in a randomized, crossed, and controlled trial. After overnight fasting, 10 subjects consumed randomly either a single dose of cocoa powder with milk or water, or milk without cocoa. Urine samples were collected before the ingestion and at 0-6, 6-12, and 12-24-h after test-meals consumption. Samples were analyzed by HPLC-q-ToF, followed by multivariate data analysis. Results revealed an important effect on urinary metabolome during the 24 h after cocoa powder intake. These changes were not influenced by matrix as no global differences were found between cocoa powder consumption with milk or with water. Overall, 27 metabolites related to cocoa-phytochemicals, including alkaloid derivatives, polyphenol metabolites (both host and microbial metabolites) and processing-derived products such as diketopiperazines, were identified as the main contributors to the urinary modifications after cocoa powder intake. These results confirm that metabolomics will contribute to better characterization of the urinary metabolome in order to further explore the metabolism of phytochemicals and its relation with human health.",Journal of proteome research,"['D000293', 'D000328', 'D000818', 'D015415', 'D002099', 'D002853', 'D004032', 'D005260', 'D005419', 'D006801', 'D008297', 'D013058', 'D055432', 'D008875', 'D008892', 'D010636', 'D014556', 'D055815']","['Adolescent', 'Adult', 'Animals', 'Biomarkers', 'Cacao', 'Chromatography, Liquid', 'Diet', 'Female', 'Flavonoids', 'Humans', 'Male', 'Mass Spectrometry', 'Metabolomics', 'Middle Aged', 'Milk', 'Phenols', 'Urine', 'Young Adult']",An LC-MS-based metabolomics approach for exploring urinary metabolome modifications after cocoa consumption.,"[None, None, None, 'Q000378', 'Q000737', 'Q000379', None, None, 'Q000737', None, None, 'Q000379', 'Q000379', None, 'Q000737', 'Q000737', 'Q000737', None]","[None, None, None, 'metabolism', 'chemistry', 'methods', None, None, 'chemistry', None, None, 'methods', 'methods', None, 'chemistry', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/19754154,2010,1,1,text,under the experimental section: cocoa powder composition +0.81,24913883,"In this work, a new procedure was developed for the separation and preconcentration of lead(II) and cobalt(II) in several water and foods samples. Complexes of metal ions with 8-hydroxyquinolein (8-HQ) were formed in aqueous solution. The proposed methodology is based on the preconcentration/separation of Pb(II) by solid-phase extraction using paper filter, followed by spectrofluorimetric determination of both metals, on the solid support and the filtered aqueous solution, respectively. The solid surface fluorescence determination was carried out at __em=455 nm (__ex=385 nm) for Pb(II)-8-HQ complex and the fluorescence of Co(II)-8-HQ was determined in aqueous solution using __em=355 nm (__ex=225 nm). The calibration graphs are linear in the range 0.14-8.03_10(4) __g L(-1) and 7.3_10(-2)-4.12_10(3) __g L(-1), for Pb(II) and Co(II), respectively, with a detection limit of 4.3_10(-2) and 2.19_10(-2) __g L(-1) (S/N=3). The developed methodology showed good sensitivity and adequate selectivity and it was successfully applied to the determination of trace amounts of lead and cobalt in tap waters belonging of different regions of Argentina and foods samples (milk powder, express coffee, cocoa powder) with satisfactory results. The new methodology was validated by electrothermal atomic absorption spectroscopy with adequate agreement. The proposed methodology represents a novel application of fluorescence to Pb(II) and Co(II) quantification with sensitivity and accuracy similar to atomic spectroscopies.",Talanta,"['D000818', 'D001118', 'D002099', 'D003035', 'D003069', 'D060766', 'D004784', 'D005453', 'D005506', 'D007854', 'D008892', 'D015125', 'D013054', 'D014874']","['Animals', 'Argentina', 'Cacao', 'Cobalt', 'Coffee', 'Drinking Water', 'Environmental Monitoring', 'Fluorescence', 'Food Contamination', 'Lead', 'Milk', 'Oxyquinoline', 'Spectrophotometry, Atomic', 'Water Pollutants, Chemical']",Sequential determination of lead and cobalt in tap water and foods samples by fluorescence.,"[None, None, 'Q000737', 'Q000032', 'Q000737', 'Q000032', None, None, 'Q000032', 'Q000032', 'Q000737', 'Q000737', None, 'Q000032']","[None, None, 'chemistry', 'analysis', 'chemistry', 'analysis', None, None, 'analysis', 'analysis', 'chemistry', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24913883,2015,1,1,table 3 and 4,Only number 8 which is the cocoa powder +0.81,12537373,"An in vitro model simulating enzymatic activity in the gastrointestinal tract was developed for the assessment of the potential bioaccessibility of Cd and Pb in cocoa powder and liquor. The model was based on the sequential extraction with simulated gastric and intestinal juices; the residue after the latter extraction was further investigated by using, in parallel, solutions of phytase and cellulase. The solubility of Cd and Pb in the corresponding enzymatic extracts was measured by ICP MS. The bioaccessibility of Cd in cocoa varied from 10 to 50% in gastrointestinal conditions. An additional 20 or 30% of Cd could be recovered by phytase and cellulase, respectively. The bioaccessibility of Pb in gastrointestinal conditions did not exceed 5-10%. Only a few percent more of this metal could be recovered by extraction with phytase and cellulase.",The Analyst,"['D001682', 'D002099', 'D002104', 'D004063', 'D005506', 'D006801', 'D007854', 'D013058']","['Biological Availability', 'Cacao', 'Cadmium', 'Digestion', 'Food Contamination', 'Humans', 'Lead', 'Mass Spectrometry']",Development of a sequential enzymolysis approach for the evaluation of the bioaccessibility of Cd and Pb from cocoa.,"[None, 'Q000737', 'Q000032', None, 'Q000032', None, 'Q000032', 'Q000379']","[None, 'chemistry', 'analysis', None, 'analysis', None, 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/12537373,2003,,,, +0.8,27040819,"Ochratoxin A (OTA) is a mycotoxin produced mostly by several species of Aspergillus and Penicillium. OTA is nephrotoxic in all animal species in which it has been tested and is cancerogenic in rodents. It is associated with Balkan endemic nephropathy. It is naturally present in many crop products such as cereals (barley, wheat, maize) and dried fruits, spices, coffee, wine, olives, and cocoa. The aim of this study was to assess the contamination of three Ivoirian spices with OTA (ginger, chili, and pepper) widely consumed by the population. A total of 90 spice samples (ginger: n___=___30; chili: n___=___30; pepper n___=___30) was taken from various sales outlets of Abidjan. OTA was quantified using an HPLC apparatus coupled with a fluorimetric detector. The chili and ginger samples were contaminated with OTA at a mean concentration of 57.48____±___174 and 0.12____±___0.15____g/kg, respectively. No contamination of the pepper samples was detected. Eight (26.67__%) of the chili samples exceeded the maximum limit of 15____g/kg established by European regulation. These results should serve as an alert on the risk to the consumer population of these products that are highly contaminated with OTA. ",Mycotoxin research,"['D002212', 'D002851', 'D007560', 'D005470', 'D020939', 'D009793', 'D029222']","['Capsicum', 'Chromatography, High Pressure Liquid', ""Cote d'Ivoire"", 'Fluorometry', 'Ginger', 'Ochratoxins', 'Piper nigrum']",Occurrence of ochratoxin A in spices commercialized in Abidjan (C_te d'Ivoire).,"['Q000737', None, None, None, 'Q000737', 'Q000032', 'Q000737']","['chemistry', None, None, None, 'chemistry', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27040819,2017,0,0,, +0.8,16608201,"Since recent reports on the role of N-phenylpropenoyl-L-amino acids as powerful antioxidants and key contributors to the astringent taste of cocoa nibs, there is an increasing interest in the concentrations of these phytochemicals in plant-derived foods. A versatile analytical method for the accurate quantitative analysis of N-phenylpropenoyl-L-amino acids in plant-derived foods by means of HPLC-MS/MS and synthetic stable isotope labeled N-phenylpropenoyl-L-amino acids as internal standards was developed. By means of the developed stable isotope dilution assay (SIDA), showing recovery rates of 95-102%, 14 N-phenylpropenoyl-L-amino acids were quantified for the first time in cocoa and coffee samples. On the basis of the results of LC-MS/MS experiments as well as cochromatography with the synthetic reference compounds N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-tryptophan, N-[4'-hydroxy-(E)-cinnamoyl]-L-tryptophan, and N-[4'-hydroxy-3'-methoxy-(E)-cinnamoyl]-L-tyrosine, respectively, were detected for the first time in cocoa powder, and (-)-N-[4'-hydroxy-(E)-cinnamoyl]-L-tyrosine, (-)-N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-tyrosine, N-[4'-hydroxy-3'-methoxy-(E)-cinnamoyl]-L-tyrosine, (+)-N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-aspartic acid, (+)-N-[4'-hydroxy-(E)-cinnamoyl]-L-aspartic acid, N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-tryptophan, N-[4'-hydroxy-(E)-cinnamoyl]-L-tryptophan, and N-[4'-hydroxy-3'-methoxy-(E)-cinnamoyl]-L-tryptophan, respectively, were detected for the first time in coffee beverages.",Journal of agricultural and food chemistry,"['D000596', 'D002099', 'D040503', 'D003903', 'D006358', 'D007201', 'D008279', 'D010666', 'D012639', 'D021241']","['Amino Acids', 'Cacao', 'Coffea', 'Deuterium', 'Hot Temperature', 'Indicator Dilution Techniques', 'Magnetic Resonance Imaging', 'Phenylpropionates', 'Seeds', 'Spectrometry, Mass, Electrospray Ionization']",Quantitative analysis of N-phenylpropenoyl-L-amino acids in roasted coffee and cocoa powder by means of a stable isotope dilution assay.,"['Q000737', 'Q000737', 'Q000737', None, None, None, None, 'Q000737', 'Q000737', None]","['chemistry', 'chemistry', 'chemistry', None, None, None, None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/16608201,2006,1,2,table 2,only the cocoa nibs +0.8,24779628,"Aflatoxins (AFB1, AFB2, AFG1 and AFG2) are immunosuppressant, mutagenic, teratogenic and carcinogenic agents with a widespread presence in foodstuffs. Since human exposure to aflatoxins occurs primarily by contaminated food intake, and given the greater susceptibility of infants to their adverse effects, the quantification of these mycotoxins in infant food based on cereals is of relevance. Aflatoxin levels were determined in 91 Spanish infant cereals classified in terms of non- and organically produced and several types from 10 different manufacturers, using a extraction procedure followed by inmunoaffinity column clean-up step and HPLC with fluorescence detection (FLD) and post-column derivatisation (Kobra Cell system). Daily aflatoxin intake was also assessed. Preliminary analysis showed a valuable incidence of detected infant cereal samples at an upper concentration level than the detection limit for total aflatoxin (66%), corresponding to a 46, 40, 34 and 11% for AFB1, AFB2, AFG1 and AFG2, respectively. Lower aflatoxin values (median, Q1, Q3) in conventional infant cereal (n = 74, AFB1: 0.99], recovery [71-118%], precision [(RSDr and RSDiR)<33%], and trueness [78-117%] were all compliant with the analytical requirements stipulated in the CEN/TR/16059 document. Method ruggedness was proved by a verification process conducted by another laboratory. ",Journal of chromatography. A,"['D002099', 'D002247', 'D002851', 'D003069', 'D006801', 'D007201', 'D007223', 'D007225', 'D007231', 'D059021', 'D009183', 'D011786', 'D012680', 'D017365', 'D053719']","['Cacao', 'Carbon Isotopes', 'Chromatography, High Pressure Liquid', 'Coffee', 'Humans', 'Indicator Dilution Techniques', 'Infant', 'Infant Food', 'Infant, Newborn', 'Laboratory Proficiency Testing', 'Mycotoxins', 'Quality Control', 'Sensitivity and Specificity', 'Spices', 'Tandem Mass Spectrometry']","Combining the quick, easy, cheap, effective, rugged and safe approach and clean-up by immunoaffinity column for the analysis of 15 mycotoxins by isotope dilution liquid chromatography tandem mass spectrometry.","['Q000737', None, 'Q000379', 'Q000737', None, None, None, None, None, None, 'Q000032', None, None, 'Q000032', 'Q000379']","['chemistry', None, 'methods', 'chemistry', None, None, None, None, None, None, 'analysis', None, None, 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/24636559,2014,0,0,, +0.74,19426987,"The quantitative parameters and method performance for a normal-phase HPLC separation of flavanols and procyanidins in chocolate and cocoa-containing food products were optimized and assessed. Single laboratory method performance was examined over three months using three separate secondary standards. RSD(r) ranged from 1.9%, 4.5% to 9.0% for cocoa powder, liquor and chocolate samples containing 74.39, 15.47 and 1.87 mg/g flavanols and procyanidins, respectively. Accuracy was determined by comparison to the NIST Standard Reference Material 2384. Inter-lab assessment indicated that variability was quite low for seven different cocoa-containing samples, with a RSD(R) of less than 10% for the range of samples analyzed.",Journal of chromatography. A,"['D000975', 'D002099', 'D002182', 'D002851', 'D044948', 'D005453', 'D005504', 'D044945']","['Antioxidants', 'Cacao', 'Candy', 'Chromatography, High Pressure Liquid', 'Flavonols', 'Fluorescence', 'Food Analysis', 'Proanthocyanidins']",Method performance and multi-laboratory assessment of a normal phase high pressure liquid chromatography-fluorescence detection method for the quantitation of flavanols and procyanidins in cocoa and chocolate containing samples.,"['Q000032', 'Q000737', 'Q000032', 'Q000295', 'Q000032', None, None, 'Q000032']","['analysis', 'chemistry', 'analysis', 'instrumentation', 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19426987,2009,0,0,,content was given in multiple groups +0.73,23692768,"In this research 12 different varieties of Capsicum cultivars belonging to three species (Capsicum chinense, Capsicum annuum, Capsicum frutescens) and of various colour, shape, and dimension have been characterised by their carotenoids and capsaicinoids content. The berries were cultivated in the region Emilia-Romagna, in Northern Italy. The native carotenoid composition was directly investigated by an HPLC-DAD-APCI-MS methodology, for the first time. In total, 52 carotenoids have been identified and considerable variation in carotenoid composition was observed among the various cultivars investigated. Among the cultivars with red colour, some Habanero, Naga morich and Sinpezon showed an high __-carotene content, whereas Serrano, Tabasco and Jalapeno showed an high capsanthin content and the absence of __-carotene. Habanero golden and Scotch Bonnet showed a high lutein, _±-carotene and __-carotene amounts, and Habanero orange was rich in antheraxanthin, capsanthin and zeaxanthin. Cis-cryptocapsin was present in high amount in Habanero chocolate. The qualitative and quantitative determination of the capsaicinoids, alkaloids responsible for the pungency level, has also been estimated by a validated chromatographic procedure (HPLC-DAD) after a preliminary drying step and an opportune extraction procedure. Results have also been expressed in Scoville units. Dry matter and water activity have also been established on the fresh berries. The dried peppers of each variety were then submitted to the evaluation of the total nitrogen content, measured by a Dumas system, permitting to provide information on the protein content that was found to be in the range between 7 and 16%.",Food chemistry,"['D002212', 'D002338', 'D002851', 'D005638', 'D013058', 'D015394', 'D009812', 'D010936']","['Capsicum', 'Carotenoids', 'Chromatography, High Pressure Liquid', 'Fruit', 'Mass Spectrometry', 'Molecular Structure', 'Odorants', 'Plant Extracts']",Characterization of 12 Capsicum varieties by evaluation of their carotenoid profile and pungency determination.,"['Q000737', 'Q000737', None, 'Q000737', None, None, 'Q000032', 'Q000737']","['chemistry', 'chemistry', None, 'chemistry', None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23692768,2013,0,0,,no cocoa +0.73,22953909,"N-Phenylpropenoyl-l-amino acids (NPA) are among the key contributors to the astringent taste of cocoa. Two fast and easy to use methods (CE and UPLC_‰, both with PDA detection) for routine determination of the main NPA were developed. Crude extracts of defatted seeds were analysed by means of capillary electrophoresis leading to separation in less than 30min. Separation by means of UPLC_‰ was much faster (<4min), however, a preceding SPE clean-up abolishes this benefit in time saving. Thus, the CE- and UPLC_‰-methods are comparable concerning time consumption and provide similar results. Analysis of 18 samples of raw and roasted beans from the global cocoa market originated from 12 countries and 4 continents showed a great variability of NPA content (0.7-3.6mg/g) and qualitative composition of different NPA. Anyway, all samples from cocoa beans showed a comparable NPA pattern. N-[3',4'-dihydroxy-(E)-cinnamoyl]-l-aspartic acid was the most abundant metabolite, followed by N-[4'-hydroxy-(E)-cinnamoyl]-l-aspartic acid and N-[3',4'-dihydroxy-(E)-cinnamoyl]-3-hydroxy-l-tyrosine (clovamide). The analysis of other plant organs (flowers, leaves, fruits) revealed an entirely different situation. NPA were detected in all parts of the fruit, with husk and pulp being clearly dominated by clovamide. In flowers and leaves no NPA were detected; 2-O-caffeoyltartaric acid was shown to be the major caffeic acid metabolite in leaves.",Food chemistry,"['D000596', 'D002099', 'D002851', 'D019075', 'D005843', 'D006801', 'D012639', 'D013649']","['Amino Acids', 'Cacao', 'Chromatography, High Pressure Liquid', 'Electrophoresis, Capillary', 'Geography', 'Humans', 'Seeds', 'Taste']",Fast determination of N-phenylpropenoyl-l-amino acids (NPA) in cocoa samples from different origins by ultra-performance liquid chromatography and capillary electrophoresis.,"['Q000032', 'Q000737', 'Q000379', 'Q000379', None, None, 'Q000737', None]","['analysis', 'chemistry', 'methods', 'methods', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/22953909,2013,1,1,table 1 and Fig. 3,Recorded all the samples that had the NPA detected and quantified +0.73,1141149,"Rapid confirmation of the presence of aflatoxins B-1 and G-1 in foods is provided by reaction with trifluoroacetic acid at the origin of a thin layer chromatographic plate. The procedure has been used successfully with various nuts, grains, coffee and cocoa beans, and other foods.",Journal - Association of Official Analytical Chemists,"['D000348', 'D002855', 'D005504']","['Aflatoxins', 'Chromatography, Thin Layer', 'Food Analysis']",Formation of aflatoxin derivatives on thin layer chromatographic plates.,"['Q000032', 'Q000379', None]","['analysis', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/1141149,1975,,,, +0.73,18781757,"N-Acetylglutamate (NAG) and N-acetylaspartate (NAA) are amino acid derivatives with reported activities in a number of biological processes. However, there is no published information on the presence of either substance in foodstuffs. We developed a method for extracting and quantifying NAG and NAA from soybean seeds and maize grain using ultra performance liquid chromatography-electrospray ionization tandem mass spectrometry (UPLC-ESI-MS/MS). The lower limit of quantification for both NAG and NAA was 1 ng/mL. The method was then utilized to quantify NAG and NAA in other foodstuffs (fruits, vegetables, meats, grains, milk, coffee, tea, cocoa, and others). Both NAG and NAA were present in all of the materials analyzed. The highest concentration of NAG was found in cocoa powder. The highest concentration of NAA was found in roasted coffee beans. Both NAG and NAA were found at quantifiable concentrations in all foods tested indicating that these two acetylated amino acids are common components of the human diet.",Journal of agricultural and food chemistry,"['D001224', 'D002099', 'D002851', 'D040503', 'D005504', 'D005971', 'D012639', 'D013025', 'D021241', 'D053719', 'D003313']","['Aspartic Acid', 'Cacao', 'Chromatography, High Pressure Liquid', 'Coffea', 'Food Analysis', 'Glutamates', 'Seeds', 'Soybeans', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry', 'Zea mays']","N-acetylglutamate and N-acetylaspartate in soybeans (Glycine max L.), maize (Zea mays L.), [corrected] and other foodstuffs.","['Q000031', 'Q000737', None, 'Q000737', None, 'Q000032', 'Q000737', 'Q000737', None, None, 'Q000737']","['analogs & derivatives', 'chemistry', None, 'chemistry', None, 'analysis', 'chemistry', 'chemistry', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/18781757,2008,1,1,table 1,only cocoa powder +0.73,21819158,"Skins from different hazelnut samples were characterized for total polyphenol content, total antioxidant capacity (TAC), and their content in specific polyphenolic compounds. The main polyphenolic subclass, identified and quantified by means of HPLC-MS/MS, comprised monomeric and oligomeric flavan-3-ols, which accounted for more than 95% of total polyphenols. Flavonols and dihydrochalcones were 3.5% while phenolic acids were less than 1% of the total identified phenolics. The TAC values of the skin samples ranged between 0.6 and 2.2 mol of reduced iron/kg of sample, which is about 3 times the TAC of whole walnuts, 7-8 times that of dark chocolate, 10 times that of espresso coffee, and 25 times that of blackberries. By describing the profile of polyphenols present in hazelnut skins, this study provides the basis to further investigate the potential health effects of hazelnut byproduct.",Journal of agricultural and food chemistry,"['D000975', 'D002851', 'D031211', 'D005419', 'D059808', 'D012639', 'D053719']","['Antioxidants', 'Chromatography, High Pressure Liquid', 'Corylus', 'Flavonoids', 'Polyphenols', 'Seeds', 'Tandem Mass Spectrometry']",Polyphenolic composition of hazelnut skin.,"['Q000032', None, 'Q000737', 'Q000032', 'Q000032', 'Q000737', None]","['analysis', None, 'chemistry', 'analysis', 'analysis', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21819158,2012,0,0,,no cocoa +0.72,7430044,"Four duplicate samples of cocoa-containing materials, a practice sample, and standards were submitted to the collaborators for theobromine and caffeine analysis by HPLC. In the method the samples are defatted with petroleum ether, and dried. The fat-free residue is then extracted with water and an aliquot is injected into the chromatograph. Compounds are quantitated by comparison with internal or external standards, either by peak height or peak area. Results for all the analyses showed that few of the values were more than 2 standard deviations from the mean. The method has been adopted as official first action.",Journal - Association of Official Analytical Chemists,"['D002099', 'D002110', 'D002851', 'D011786', 'D013805']","['Cacao', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Quality Control', 'Theobromine']",High pressure liquid chromatographic determination of theobromine and caffeine in cocoa and chocolate products: collaborative study.,"['Q000032', 'Q000032', None, None, 'Q000032']","['analysis', 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/7430044,1981,,,, +0.72,27795344,"Candida sepsis is a life-threatening condition with increasing prevalence. In this study, direct blood culturing on solid medium using a lysis-centrifugation procedure enabled successful Candida species identification by matrix-assisted laser desorption-ionization time of flight mass spectrometry on average 3.8 h (Sabouraud agar) or 7.4 h (chocolate agar) before the positivity signal for control samples in Bactec mycosis-IC/F or Bactec Plus aerobic/F bottles, respectively. Direct culturing on solid medium accelerated candidemia diagnostics compared to that with automated broth-based systems.",Journal of clinical microbiology,"['D000071997', 'D002175', 'D058387', 'D002498', 'D003470', 'D006801', 'D019032', 'D013997']","['Blood Culture', 'Candida', 'Candidemia', 'Centrifugation', 'Culture Media', 'Humans', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Time Factors']",Rapid Detection and Identification of Candidemia by Direct Blood Culturing on Solid Medium by Use of Lysis-Centrifugation Method Combined with Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS).,"['Q000379', 'Q000737', 'Q000175', 'Q000379', 'Q000737', None, 'Q000379', None]","['methods', 'chemistry', 'diagnosis', 'methods', 'chemistry', None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/27795344,2017,0,0,,no cocoa +0.72,23041474,"This study determined exposure of pregnant women to ochratoxin A (OTA). Forty samples of first-void urine samples from Croatian women in the third trimester of pregnancy were analyzed for OTA and its major metabolite ochratoxin alpha (OT_±). The subjects filled a short food frequency questionnaire (FFQ). Analysis was performed by HPLC-FLD following liquid-liquid extraction. All samples were subjected in parallel to enzymatic treatment (__-glucuronidase/aryl sulfatase) to release OTA and OT_± from the conjugates. The median urinary levels of OTA and OT_± before treatment were 0.02 (range: nd-1.07) ng/mL and 0.16 (nd-1.86) ng/mL; the concentrations after enzyme hydrolysis were 0.02 (nd-1.11) ng/mL and 1.18 (0.11-7.57) ng/mL. While OT_± levels increased significantly following enzymatic treatment, evidence for OTA conjugation was weak. The ratio of urinary OT_± medians after and before hydrolysis was 1.5 times higher than previously reported for nonpregnant female subjects, possibly indicating upregulated metabolism and/or elimination of the mycotoxin and metabolites in pregnancy. The mean daily dietary OTA intake calculated from FFQs (1.08_±0.57 ng/kg body weight) was well below the provisional tolerable daily intake and the greatest contributors to intake were cereal products, fruit juices, chocolate and coffee.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000328', 'D000818', 'D001628', 'D002099', 'D002851', 'D003069', 'D003404', 'D004032', 'D002523', 'D005260', 'D005506', 'D005511', 'D005516', 'D005638', 'D006801', 'D008461', 'D009793', 'D011247', 'D011795', 'D013552', 'D027843', 'D014920']","['Adult', 'Animals', 'Beverages', 'Cacao', 'Chromatography, High Pressure Liquid', 'Coffee', 'Creatinine', 'Diet', 'Edible Grain', 'Female', 'Food Contamination', 'Food Handling', 'Food Microbiology', 'Fruit', 'Humans', 'Meat Products', 'Ochratoxins', 'Pregnancy', 'Surveys and Questionnaires', 'Swine', 'Vitis', 'Wine']",Urinary ochratoxin A and ochratoxin alpha in pregnant women.,"[None, None, 'Q000032', 'Q000737', None, 'Q000737', 'Q000652', None, 'Q000737', None, 'Q000032', None, None, 'Q000737', None, None, 'Q000652', None, None, None, 'Q000737', 'Q000032']","[None, None, 'analysis', 'chemistry', None, 'chemistry', 'urine', None, 'chemistry', None, 'analysis', None, None, 'chemistry', None, None, 'urine', None, None, None, 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/23041474,2013,0,0,,intake content +0.71,1830779,"In order to detect the presence of aflatoxin B1 (AFB1), the use of the enzyme-linked immunosorbent assay (ELISA) and recovery test was evaluated. The detection limit of ELISA for AFB1 was 1 pg/assay and the recovery from maize spiked with AFB1 exceeded 80%. AFB1 was detected by ELISA in seven out of twelve samples of imported food products including peanut, almond, red pepper, cocoa bean, black pepper, buckwheat, walnut, adlay, soybean, popcorn, and pistachio nut, and by high performance liquid chromatography (HPLC) in four of the samples. However, the content of AFB1 in these samples was less than 10 ng/g of the minimum value authorized by the Japanese sanitation law. These results demonstrate that ELISA is more sensitive than HPLC and imported food products are broadly contaminated with AFB1.",The Journal of veterinary medical science,"['D016604', 'D000348', 'D002273', 'D002851', 'D004797', 'D005506', 'D011237']","['Aflatoxin B1', 'Aflatoxins', 'Carcinogens', 'Chromatography, High Pressure Liquid', 'Enzyme-Linked Immunosorbent Assay', 'Food Contamination', 'Predictive Value of Tests']",Detection of aflatoxin B1 in imported food products into Japan by enzyme-linked immunosorbent assay and high performance liquid chromatography.,"[None, 'Q000032', 'Q000032', None, None, 'Q000032', None]","[None, 'analysis', 'analysis', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/1830779,1991,,,, +0.71,27176001,"Vanillin (VA), vanillic acid (VAI) and syringaldehyde (SIA) are important food additives as flavor enhancers. The current study for the first time is devote to the application of partial least square (PLS-1), partial robust M-regression (PRM) and feed forward neural networks (FFNNs) as linear and nonlinear chemometric methods for the simultaneous detection of binary and ternary mixtures of VA, VAI and SIA using data extracted directly from UV-spectra with overlapped peaks of individual analytes. Under the optimum experimental conditions, for each compound a linear calibration was obtained in the concentration range of 0.61-20.99 [LOD=0.12], 0.67-23.19 [LOD=0.13] and 0.73-25.12 [LOD=0.15] __gmL(-1) for VA, VAI and SIA, respectively. Four calibration sets of standard samples were designed by combination of a full and fractional factorial designs with the use of the seven and three levels for each factor for binary and ternary mixtures, respectively. The results of this study reveal that both the methods of PLS-1 and PRM are similar in terms of predict ability each binary mixtures. The resolution of ternary mixture has been accomplished by FFNNs. Multivariate curve resolution-alternating least squares (MCR-ALS) was applied for the description of spectra from the acid-base titration systems each individual compound, i.e. the resolution of the complex overlapping spectra as well as to interpret the extracted spectral and concentration profiles of any pure chemical species identified. Evolving factor analysis (EFA) and singular value decomposition (SVD) were used to distinguish the number of chemical species. Subsequently, their corresponding dissociation constants were derived. Finally, FFNNs has been used to detection active compounds in real and spiked water samples.","Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy","['D001547', 'D002138', 'D000069956', 'D005421', 'D005504', 'D016018', 'D015999', 'D016571', 'D010636', 'D013053', 'D014641', 'D014867']","['Benzaldehydes', 'Calibration', 'Chocolate', 'Flavoring Agents', 'Food Analysis', 'Least-Squares Analysis', 'Multivariate Analysis', 'Neural Networks (Computer)', 'Phenols', 'Spectrophotometry', 'Vanillic Acid', 'Water']",Investigating the discrimination potential of linear and nonlinear spectral multivariate calibrations for analysis of phenolic compounds in their binary and ternary mixtures and calculation pKa values.,"['Q000032', None, 'Q000032', 'Q000032', 'Q000379', None, None, None, 'Q000032', 'Q000379', 'Q000032', 'Q000032']","['analysis', None, 'analysis', 'analysis', 'methods', None, None, None, 'analysis', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27176001,2017,0,0,,no cocoa +0.71,28415017,"An accelerated solvent extraction (ASE) procedure for use with gas chromatography-mass spectrometry (GC-MS) was optimized for the determination of eight polycyclic aromatic hydrocarbons (PAHs) in cocoa beans. Plackett-Burman and rotatable central composite design (RCCD) indicated that three variables affected the recoveries of PAHs during the extraction and purification steps: agitation time in the second liquid-liquid partition, weight of silica gel in the column, and volume of hexane for PAH elution from the column. After obtaining the optimal conditions, a single laboratory method validation was performed. Linearity was demonstrated for benzo[a]pyrene in the concentration range from 0.5 to 8.0mgkg","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D002099', 'D004785', 'D005506', 'D008401', 'D057230', 'D011084', 'D012639']","['Cacao', 'Environmental Pollutants', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Limit of Detection', 'Polycyclic Aromatic Hydrocarbons', 'Seeds']",Accelerated solvent extraction method for the quantification of polycyclic aromatic hydrocarbons in cocoa beans by gas chromatography-mass spectrometry.,"['Q000737', 'Q000032', 'Q000032', 'Q000379', None, 'Q000032', 'Q000737']","['chemistry', 'analysis', 'analysis', 'methods', None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28415017,2017,0,0,,all useful data was from spiked samples +0.71,27484307,"The isotopic profile (__(13) C, __(15) N, __(18) O, __(2) H, __(34) S) was used to characterise a wide selection of cocoa beans from different renowned production areas (Africa, Asia, Central and South America). The factors most influencing the isotopic signatures of cocoa beans were climate and altitude for __(13) C and the isotopic composition of precipitation water for __(18) O and __(2) H, whereas __(15) N and __(34) S were primarily affected by geology and fertilisation practises. Multi-isotopic analysis was shown to be sufficiently effective in determining the geographical origin of cocoa beans, and combining it with Canonical Discriminant Analysis led to more than 80% of samples being correctly reclassified. Copyright _© 2016 John Wiley & Sons, Ltd. ",Journal of mass spectrometry : JMS,"['D002099', 'D002247', 'D002980', 'D005843', 'D013058', 'D010103', 'D012639']","['Cacao', 'Carbon Isotopes', 'Climate', 'Geography', 'Mass Spectrometry', 'Oxygen Isotopes', 'Seeds']",Stable isotope composition of cocoa beans of different geographical origin.,"['Q000737', 'Q000032', None, None, None, 'Q000032', 'Q000737']","['chemistry', 'analysis', None, None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27484307,2018,,,, +0.7,22970585,"An international collaborative study was conducted on an HPLC method with fluorescent detection (FLD) for the determination of flavanols and procyanidins in materials containing chocolate and cocoa. The sum of the oligomeric fractions with degree of polymerization 1-10 was the determined content value. Sample materials included dark and milk chocolates, cocoa powder, cocoa liquors, and cocoa extracts. The content ranged from approximately 2 to 500 mg/g (defatted basis). Thirteen laboratories representing commercial, industrial, and academic institutions in six countries participated in the study. Fourteen samples were sent as blind duplicates to the collaborators. Results from 12 laboratories yielded repeatability relative standard deviation (RSDr) values that were below 10% for all materials analyzed, ranging from 4.17 to 9.61%. The reproducibility relative standard deviation (RSD(R)) values ranged from 5.03 to 12.9% for samples containing 8.07 to 484.7 mg/g. In one sample containing a low content of flavanols and procyanidins (approximately 2 mg/g), the RSD(R) was 17.68%. Based on these results, the method is recommended for Official First Action for the determination of flavanols and procyanidins in chocolate, cocoa liquors, powder(s), and cocoa extracts.",Journal of AOAC International,"['D044946', 'D002099', 'D002392', 'D002623', 'D002851', 'D044950', 'D005504', 'D007391', 'D007753', 'D008055', 'D008956', 'D058105', 'D011208', 'D044945', 'D012015', 'D015203']","['Biflavonoids', 'Cacao', 'Catechin', 'Chemistry Techniques, Analytical', 'Chromatography, High Pressure Liquid', 'Flavanones', 'Food Analysis', 'International Cooperation', 'Laboratories', 'Lipids', 'Models, Chemical', 'Polymerization', 'Powders', 'Proanthocyanidins', 'Reference Standards', 'Reproducibility of Results']","Determination of flavanol and procyanidin (by degree of polymerization 1-10) content of chocolate, cocoa liquors, powder(s), and cocoa flavanol extracts by normal phase high-performance liquid chromatography: collaborative study.","['Q000032', 'Q000378', 'Q000032', 'Q000379', 'Q000379', 'Q000032', 'Q000379', None, None, 'Q000032', None, None, 'Q000032', 'Q000032', None, None]","['analysis', 'metabolism', 'analysis', 'methods', 'methods', 'analysis', 'methods', None, None, 'analysis', None, None, 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22970585,2012,,,, +0.7,26768597,"This study investigated the effects of storage and temperature duration on the stability of acrylamide (AA) and 5-hydroxymethylfurfural (HMF) in selected foods with long shelf-life. Products were analysed fresh and stored at temperatures of 4 and 25 _C after 6 and 12 months (with the exception of soft bread samples, which were analysed after 15 and 30 days). The AA and HMF contents were determined with RP-HPLC coupled to a diode array detector (DAD). AA and HMF were not stable in many processed plant products with a long shelf-life. The highest AA reduction and the largest increase in HMF content were observed in the samples stored at a higher temperature (25 _C) for 12 months. It was found that an initial water activity of 0.4 is favourable to HMF formation and that AA reduction may be considerably greater in stored products with a low initial water activity. The kind of product and its composition may also have a significant impact on acrylamide content in stored food. In the final period of storage at 25 _C, acrylamide content in 100% cocoa powder, instant baby foods, 20% cocoa powder and instant coffee was 51, 39, 35 and 33% lower than in products before storage, respectively. It was observed that a large quantity of _µ-NH2 and SH groups of amino acids in some products can be assumed as the reason for the significant AA degradation.","Plant foods for human nutrition (Dordrecht, Netherlands)","['D020106', 'D001939', 'D002099', 'D002851', 'D003069', 'D061353', 'D005662', 'D011208', 'D013696', 'D014867']","['Acrylamide', 'Bread', 'Cacao', 'Chromatography, High Pressure Liquid', 'Coffee', 'Food Storage', 'Furaldehyde', 'Powders', 'Temperature', 'Water']",Effect of Storage on Acrylamide and 5-hydroxymethylfurfural Contents in Selected Processed Plant Products with Long Shelf-life.,"['Q000032', 'Q000032', 'Q000737', None, 'Q000737', None, 'Q000031', 'Q000737', None, 'Q000032']","['analysis', 'analysis', 'chemistry', None, 'chemistry', None, 'analogs & derivatives', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26768597,2017,1,1,table 2,100% cocoa only +0.7,16517524,"Isotope dilution liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-MS/MS) was applied to the quantification of acrylamide in chocolate matrixes (dark chocolate, milk chocolate, chocolate with nuts, chocolate with almonds, and chocolate with wheat best element). The method included defatting with petroleum ether, extracting with aqueous solution of 2 mol l(-1) sodium chloride and clean-up by solid-phase (SPE) with OASIS HLB 6 cm3 cartridges. Acrylamide was detected with an Atlantis dC18 5 microm 210 x 1.5 mm column using 10% methanol/0.1% formic acid in water as the mobile phase. The analytical method was in-house validated and good results were obtained with respect to repeatability (RSD < 3.5%) and recovery (86-93%), which fulfilled the requirements defined by European Union legislation. The acrylamide levels in chocolate were 23-537 microg kg(-1). Therefore, the method was successfully used for the quantitative analysis of acrlyamide in various chocolate products.",Food additives and contaminants,"['D020106', 'D002099', 'D002138', 'D002182', 'D002853', 'D005506', 'D007201', 'D009754', 'D027861', 'D015203', 'D021241', 'D014908']","['Acrylamide', 'Cacao', 'Calibration', 'Candy', 'Chromatography, Liquid', 'Food Contamination', 'Indicator Dilution Techniques', 'Nuts', 'Prunus', 'Reproducibility of Results', 'Spectrometry, Mass, Electrospray Ionization', 'Triticum']",Sensitive isotope dilution liquid chromatography/electrospray ionization tandem mass spectrometry method for the determination of acrylamide in chocolate.,"['Q000032', 'Q000737', None, 'Q000032', 'Q000379', 'Q000032', None, 'Q000737', None, None, 'Q000379', None]","['analysis', 'chemistry', None, 'analysis', 'methods', 'analysis', None, 'chemistry', None, None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/16517524,2006,,,, +0.7,24786625,"Sorbic acid (SA) and benzoic acid (BA) were determined in yoghurt, tomato and pepper paste, fruit juices, chocolates, soups and chips in Turkey by using high-pressure liquid chromatography (HPLC). Levels were compared with Turkish Food Codex limits. SA was detected only in 2 of 21 yoghurt samples, contrary to BA, which was found in all yoghurt samples but one, ranging from 10.5 to 159.9___mg/kg. Both SA and BA were detected also in 3 and 6 of 23 paste samples in a range of 18.1-526.4 and 21.7-1933.5___mg/kg, respectively. Only 1 of 23 fruit juices contained BA. SA was not detected in any chips, fruit juice, soup, or chocolate sample. Although 16.51% of the samples was not compliant with the Turkish Food Codex limits, estimated daily intake of BA or SA was below the acceptable daily intake. ","Food additives & contaminants. Part B, Surveillance","['D019817', 'D001628', 'D002099', 'D002212', 'D002851', 'D005504', 'D005519', 'D005520', 'D005638', 'D007881', 'D018551', 'D008452', 'D013011', 'D014421', 'D015014']","['Benzoic Acid', 'Beverages', 'Cacao', 'Capsicum', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Food Preservation', 'Food Preservatives', 'Fruit', 'Legislation, Food', 'Lycopersicon esculentum', 'Maximum Allowable Concentration', 'Sorbic Acid', 'Turkey', 'Yogurt']",Sorbic and benzoic acid in non-preservative-added food products in Turkey.,"['Q000032', 'Q000032', 'Q000737', 'Q000737', None, None, 'Q000331', 'Q000032', None, None, 'Q000737', None, 'Q000032', None, 'Q000032']","['analysis', 'analysis', 'chemistry', 'chemistry', None, None, 'legislation & jurisprudence', 'analysis', None, None, 'chemistry', None, 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24786625,2014,0,0,,no cocoa +0.7,2808584,"An interface has been developed which permits the on-line coupling of size-exclusion chromatography in tetrahydrofuran with aqueous reversed-phase high-performance liquid chromatography. The interface isolates the required size exclusion chromatography fraction and dilutes it with water to ensure reconcentration of analytes on the reversed-phase column prior to gradient elution. Operational parameters and the influence of analyte polarity have been examined in detail. A predictive system is presented for determining the applicability of the system to any analyte, based on solute retention times on an ODS phase eluted with a methanol-water gradient. The method is illustrated with examples of direct analyses of crude lipid extracts from a snack product for 2,6-di-tert.-4-methylphenol and from chocolate for dibutyl phthalate. Detection limits of ca. 0.5 mg/kg have been achieved.",Journal of chromatography,"['D002850', 'D002851', 'D005503', 'D005506', 'D008970', 'D013056']","['Chromatography, Gel', 'Chromatography, High Pressure Liquid', 'Food Additives', 'Food Contamination', 'Molecular Weight', 'Spectrophotometry, Ultraviolet']",Non-aqueous size-exclusion chromatography coupled on-line to reversed-phase high-performance liquid chromatography. Interface development and applications to the analysis of low-molecular-weight contaminants and additives in foods.,"['Q000379', None, 'Q000032', 'Q000032', None, None]","['methods', None, 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/2808584,1989,,,, +0.7,24830163,"Single-laboratory validation data previously published in the Journal of AOAC INTERNATIONAL 95(2), 500-507 (2012) was reviewed by the Stakeholder Panel on Strategic Food Analytical Methods Expert Review Panel (ERP) at the AOAC INTERNATIONAL Mid-Year Meeting held on March 12-14, 2013 in Rockville, MD. The ERP determined the data presented met the established standard method performance requirement and approved the method as AOAC Official First Action on March 14, 2013. Using high-performance liquid chromatography (HPLC), flavanol enantiomers, (+)- and (-)-epicatechin and (+)- and (-)-catechin, are eluted isocratically using ammonium acetate and methanol mobile phase. The mobile phase is applied to a modified beta-cyclodextrin chiral stationary phase and the flavanols detected by fluorescence. Using several cocoa-based matrices, recoveries for the four enantiomers ranged from 82.2-102.1% at a 50% spike level, and 80.4-101.1% at a 100% spike level. Precision was determined to be 1.46-3.22% for (-)-epicatechin, 3.66-6.90% for (+)-catechin, 1.69-6.89% for (-)-catechin. (+)-Epicatechin was not detected in any of the samples used for this work, so precision could not be determined for this molecule.",Journal of AOAC International,"['D002099', 'D002392', 'D002851', 'D005504', 'D015203', 'D012680', 'D013237']","['Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Stereoisomerism']",Method for the determination of catechin and epicatechin enantiomers in cocoa-based ingredients and products by high-performance liquid chromatography: First Action 2013.04.,"['Q000737', 'Q000737', 'Q000379', 'Q000379', None, None, None]","['chemistry', 'chemistry', 'methods', 'methods', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24830163,2014,,,,no pdf access +0.69,15764339,"A validated high-performance liquid chromatography (HPLC) method with fluorescence detection for the quantitative analysis of ochratoxin A (OTA) in cocoa beans is described. OTA was extracted with methanol-3% sodium hydrogen carbonate solution and then purified with immunoaffinity columns before its analysis by HPLC. The validation of the analytical method was based on the following criteria: selectivity, linearity, limit of detection and quantification, precision (within- and between-day variability) and recovery, robustness and uncertainty. Detection and quantification limits were 0.04 and 0.1 mug kg(-1), respectively. Recovery was 88.9% (relative standard deviation = 4.0%). This method was successfully applied to the measurement of 46 cocoa bean samples of different origins. A total of 63% of cocoa bean samples was contaminated with a level greater than the limit of detection. The means and medians obtained for cocoa bean were 1.71 and 1.12 mug kg(-1), respectively. Surveillance controls should be set up in both crops and factories involved in transformation processes to avoid this mycotoxin in final products.",Food additives and contaminants,"['D002099', 'D002851', 'D005504', 'D005506', 'D006801', 'D009183', 'D009793', 'D015203']","['Cacao', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Food Contamination', 'Humans', 'Mycotoxins', 'Ochratoxins', 'Reproducibility of Results']",Validation of a high-performance liquid chromatography analytical method for ochratoxin A quantification in cocoa beans.,"['Q000737', 'Q000379', 'Q000379', 'Q000032', None, 'Q000032', 'Q000032', None]","['chemistry', 'methods', 'methods', 'analysis', None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15764339,2005,,,, +0.69,12537419,"Catechins are polyphenolic plant compounds (flavonoids) that may offer significant health benefits to humans. These benefits stem largely from their anticarcinogenic, antioxidant, and antimutagenic properties. Recent epidemiological studies suggest that the consumption of flavonoid-containing foods is associated with reduced risk of cardiovascular disease. Chocolate is a natural cocoa bean-based product that reportedly contains high levels of monomeric, oligomeric, and polymeric catechins. We have applied solid-liquid extraction and liquid chromatography coupled with atmospheric pressure chemical ionization-mass spectrometry to the identification and determination of the predominant monomeric catechins, (+)-catechin and (-)-epicatechin, in a baking chocolate Standard Reference Material (NIST Standard Reference Material 2384). (+)-Catechin and (-)-epicatechin are detected and quantified in chocolate extracts on the basis of selected-ion monitoring of their protonated [M + H](+) molecular ions. Tryptophan methyl ester is used as an internal standard. The developed method has the capacity to accurately quantify as little as 0.1 microg/mL (0.01 mg of catechin/g of chocolate) of either catechin in chocolate extracts, and the method has additionally been used to certify (+)-catechin and (-)-epicatechin levels in the baking chocolate Standard Reference Material. This is the first reported use of liquid chromatography/mass spectrometry for the quantitative determination of monomeric catechins in chocolate and the only report certifying monomeric catechin levels in a food-based Standard Reference Material.",Journal of agricultural and food chemistry,"['D001274', 'D002099', 'D002182', 'D002392', 'D002853', 'D013058', 'D012015']","['Atmospheric Pressure', 'Cacao', 'Candy', 'Catechin', 'Chromatography, Liquid', 'Mass Spectrometry', 'Reference Standards']",Quantification of the predominant monomeric catechins in baking chocolate standard reference material by LC/APCI-MS.,"[None, 'Q000737', 'Q000032', 'Q000032', 'Q000379', 'Q000379', None]","[None, 'chemistry', 'analysis', 'analysis', 'methods', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/12537419,2003,0,0,, +0.69,24962135,"Chinese mitten crab (Eriocheir sinensis) from Yangcheng Lake in Jiangsu Province is a popular species due to its unique pleasant aroma and intensive umami taste. In this study, odorants in steamed male E. sinensis were investigated using the headspace-monolithic material sorptive extraction technique coupled with gas chromatography-mass spectrometry-olfactometry (GC-MS-O). A total of 74 volatile compounds were found, and the results of the GC-MS-O analysis, combined with odor activity values, showed that trimethylamine (fishy, ammonia-like odor), (Z)-4-heptenal (mushroom-like odor), and benzaldehyde (paint-like odor) were the important odorants (IOs) in all 4 of the edible parts of steamed male E. sinensis. Furthermore, heptanal (mushroom-like odor) was common to the abdomen, claw, and leg meat but was not found as the IO in the gonad. The abdomen meat also contained 3-methylbutanal (vegetable-like, grassy odor), while 2 additional IOs were found in claw meat (2-methylbutanal, which has a mushroom odor and 3-ethyl-2,5-dimethylpyrazine, which has a chocolate-like, musty odor). Another IO (2-nonanone, chocolate-like odor) was also found in leg meat, while (E)-2-nonenal (green, fruity odor) was the IO found exclusively in the gonad. ",Journal of food science,"['D000818', 'D003386', 'D003296', 'D008401', 'D008297', 'D009812', 'D012903', 'D013649', 'D055549']","['Animals', 'Brachyura', 'Cooking', 'Gas Chromatography-Mass Spectrometry', 'Male', 'Odorants', 'Smell', 'Taste', 'Volatile Organic Compounds']",Characterization of important odorants in steamed male Chinese mitten crab (Eriocheir sinensis) using gas chromatography-mass spectrometry-olfactometry.,"[None, 'Q000737', None, 'Q000379', None, 'Q000032', None, None, 'Q000737']","[None, 'chemistry', None, 'methods', None, 'analysis', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24962135,2015,0,0,,no cocoa +0.69,22849827,"In this work multivariate experiments were conducted to optimize the operating conditions for inductively coupled plasma optical emission spectrometry (ICP OES) for multielemental determinations in chocolate drink powder. The operating conditions were investigated using a 2(3) central composite design, where the variables studied were radio frequency power, nebulization flow rate, and auxiliary argon flow rate. The effects of these parameters on plasma robustness and on signal to background ratio (SBR) were considered in parallel, allowing the evaluation of robustness and detectability using few and fast experiments to select the best conditions for the determination of the analytes. In this case, the proposed experiments were applied to the optimization of a method aimed at the determination of Al, Ba, Cd, Co, Cr, Cu, Fe, Mg, Mn, Mo, Ni, P, Pb, V, and Zn in chocolate drink powder. The compromise conditions that allowed obtaining a robust and sensitive analytical method were radio frequency power of 1200 W, nebulization flow rate of 0.6 L/min, and auxiliary argon flow rate of 0.3 L/min. Using these conditions, recoveries between 95 and 105% and relative standard deviations lower than 5% were obtained for the majority of the analytes. The proposed method was successfully applied to the analysis of 15 samples of chocolate drink powder. The highest concentrations of metallic species were found in diet and light products.",Journal of agricultural and food chemistry,"['D001628', 'D002099', 'D005504', 'D005511', 'D015999', 'D011208', 'D013053', 'D014131']","['Beverages', 'Cacao', 'Food Analysis', 'Food Handling', 'Multivariate Analysis', 'Powders', 'Spectrophotometry', 'Trace Elements']",Multielemental determinations in chocolate drink powder using multivariate optimization and ICP OES.,"['Q000032', 'Q000737', 'Q000379', None, None, 'Q000032', 'Q000379', 'Q000032']","['analysis', 'chemistry', 'methods', None, None, 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/22849827,2013,0,0,,no cocoa +0.69,24401377,"The concentrations of eight trace elements: lead (Pb), cadmium (Cd), chromium (Cr), manganese (Mn), cobalt (Co), arsenic (As), bismuth (Bi) and molybdenum (Mo), in chocolate, cocoa beans and products were studied by ICPMS. The study examined chocolate samples from different brands and countries with different concentrations of cocoa solids from each brand. The samples were digested and filtered to remove lipids and indium was used as an internal standard to correct matrix effects. A linear correlation was found between the level of several trace elements in chocolate and the cocoa solids content. Significant levels of Bi and As were found in the cocoa bean shells but not in the cocoa bean and chocolate. This may be attributed to environmental contamination. The presence of other elements was attributed to the manufacturing processes of cocoa and chocolate products. Children, who are big consumers of chocolates, may be at risk of exceeding the daily limit of lead; whereas one 10 g cube of dark chocolate may contain as much as 20% of the daily lead oral limit. Moreover chocolate may not be the only source of lead in their nutrition. For adults there is almost no risk of exceeding daily limits for trace metals ingestion because their digestive absorption of metals is very poor.",Talanta,"['D002099', 'D013058', 'D012015', 'D014131']","['Cacao', 'Mass Spectrometry', 'Reference Standards', 'Trace Elements']",Trace elements in cocoa solids and chocolate: an ICPMS study.,"['Q000737', 'Q000379', None, 'Q000032']","['chemistry', 'methods', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24401377,2014,1,2,table 3 ,all +0.69,14760852,"Multidimensional analysis of denatured milk proteins is reported using high-performance liquid chromatography (HPLC) combined with dynamic surface tension detection (DSTD). A hydrophobic interaction chromatography (HIC) column (a TSK-Gel Phenyl-5PW column, TosoBiosep), in the presence of 3.0 M guanidine hydrochloride (GdmHCl) as denaturing agent is employed as the mobile phase. Dynamic surface tension is measured through the differential pressure across the liquid-air interface of repeatedly growing and detaching drops. Continuous surface tension measurement throughout the entire drop growth (50 ms to 4 s) is achieved, for each eluting drop of 4 s length, providing insight into both the kinetic and thermodynamic behavior of molecular orientation processes at the liquid-air interface. An automated calibration procedure and data analysis method is applied with the DSTD system, which allows two unique solvents to be used, the HIC mobile phase for the sample and a second solvent (water for example) for the standard, permitting real-time dynamic surface tension data to be obtained. Three-dimensional data is obtained, with surface tension as a function of drop time first converted to surface pressure, which is plotted as a function of the chromatographic elution time axis. Experiments were initially performed using flow injection analysis (FIA) with the DSTD system for investigating commercial single standard milk proteins (alpha-lactalbumin, beta-lactoglobulin, alpha-, beta-, kappa-casein and a casein mixture) denatured by GdmHCl. These FIA-DSTD experiments allowed the separation and detection conditions to be optimized for the HIC-DSTD experiments. Thus, the HIC-DSTD system has been optimized and successfully applied to the selective analysis of surface-active casein fractions (alpha s1- and beta-casein) in a commercial casein mixture, raw milk samples (cow's, ewe's and goat's milk) and other diary products (yogurt, stracchino, mozzarella, parmesan cheese and chocolate cream). The different samples were readily distinguished based upon the selectivity provided by the HIC-DSTD method. The selectivity advantage of using DSTD relative to absorbance detection is also demonstrated.",Journal of chromatography. A,"['D002138', 'D002853', 'D019791', 'D008894', 'D011489', 'D013500']","['Calibration', 'Chromatography, Liquid', 'Guanidine', 'Milk Proteins', 'Protein Denaturation', 'Surface Tension']",Multidimensional analysis of denatured milk proteins by hydrophobic interaction chromatography coupled to a dynamic surface tension detector.,"[None, 'Q000379', 'Q000737', 'Q000737', None, None]","[None, 'methods', 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/14760852,2004,0,0,,no cocoa +0.69,16637672,"The determination of the occurrence and level of cocoa shells in cocoa products and chocolate is an important analytical issue. The recent European Union directive on cocoa and chocolate products (2000/36/EC) has not retained the former limit of a maximum amount of 5% of cocoa shells in cocoa nibs (based on fat-free dry matter), previously authorized for the elaboration of cocoa products such as cocoa mass. In the present study, we report a reliable gas-liquid chromatography procedure suitable for the determination of the occurrence of cocoa shells in cocoa products by detection of fatty acid tryptamides (FATs). The precision of the method was evaluated by analyzing nine different samples (cocoa liquors with different ranges of shells) six times (replicate repeatability). The variations of the robust coefficient of variation of the repeatability demonstrated that FAT(C22), FAT(C24), and total FATs are good markers for the detection of shells in cocoa products. The trueness of the method was evaluated by determining the FAT content in two spiked matrices (cocoa liquors and cocoa shells) at different levels (from 1 to 50 mg/100 g). A good relation was found between the results obtained and the spiking (recovery varied between 90 and 130%), and the linearity range was established between 1 and 50 mg/100 g in cocoa products. For total FAT contents of cocoa liquor containing 5% shells, the measurement uncertainty allows us to conclude that FAT is equal to 4.01 +/- 0.8 mg/100 g. This validated method is perfectly suitable to determine shell contents in cocoa products using FAT(C22), FAT(C24), and total FATs as markers. The results also confirmed that cocoa shells contain FAT(C24) and FAT(C22) in a constant ratio of nearly 2:1.",Journal of agricultural and food chemistry,"['D002099', 'D002849', 'D005227', 'D009536', 'D015203', 'D012639', 'D012680', 'D014363']","['Cacao', 'Chromatography, Gas', 'Fatty Acids', 'Niacinamide', 'Reproducibility of Results', 'Seeds', 'Sensitivity and Specificity', 'Tryptamines']",Development of a gas-liquid chromatographic method for the analysis of fatty acid tryptamides in cocoa products.,"['Q000737', 'Q000379', 'Q000032', 'Q000031', None, 'Q000737', None, 'Q000032']","['chemistry', 'methods', 'analysis', 'analogs & derivatives', None, 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16637672,2006,1,1,table 1 and 4,"the content in cocoa shells and butter, and the cocoa nibs and shells conly." +0.68,12480305,"Indian-made bidi cigarettes sold in the United States are available in a variety of exotic (e.g. clove, mango) and candy-like (e.g. chocolate, raspberry) flavors. Because certain tobacco flavorings contain alkenylbenzenes and other toxic or carcinogenic chemicals, we measured the concentration of flavor-related compounds in bidi tobacco using a previously developed method. Twenty-three brands of bidis were sampled using automated headspace solid-phase microextraction and subsequently analyzed for 12 compounds by gas chromatography-mass spectrometry. Two alkenylbenzene compounds, trans-anethole and eugenol, were found in greater than 90% of the brands analyzed. Methyleugenol, pulegone and estragole were each detected in 30% or more of the brands, whereas safrole and elemicin were not detected in any of the brands. The flavor-related compounds with the highest tobacco concentrations were eugenol (12,000 microg/g tobacco) and trans-anethole (2200 microg/g tobacco). The highest eugenol and trans-anethole concentrations found in bidi tobacco were about 70,000 and 7500 times greater, respectively, than the highest levels previously found in US cigarette brands. Measurement of these compounds is crucial to evaluation of potential risks associated with inhaling highly concentrated flavor-related compounds from bidis or other tobacco products.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000840', 'D001547', 'D052117', 'D003374', 'D005054', 'D005421', 'D008401', 'D007194', 'D039821', 'D014026']","['Anisoles', 'Benzaldehydes', 'Benzodioxoles', 'Coumarins', 'Eugenol', 'Flavoring Agents', 'Gas Chromatography-Mass Spectrometry', 'India', 'Monoterpenes', 'Tobacco']","Concentrations of nine alkenylbenzenes, coumarin, piperonal and pulegone in Indian bidi cigarette tobacco.","['Q000032', 'Q000032', None, 'Q000032', 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000737']","['analysis', 'analysis', None, 'analysis', 'analysis', 'analysis', None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12480305,2003,0,0,,no cocoa +0.68,25833003,"Cocoa contains many compounds such as biogenic amines (BAs), known to influence consumer health. Spermidine, spermidine, putrescine, histamine, tyramine, __-phenylethylamine, cadaverine and serotonine have been found in several cocoa-based products using HPLC with UV detection after derivatisation with dansyl-chloride. Once optimised in terms of linearity, percentage recovery, LOD, LOQ and repeatability, this method was applied to real samples. Total concentrations of BAs ranged from 5.7 to 79.0 _µg g(-)(1) with wide variations depending on the type of sample. BAs present in all samples were in decreasing order: histamine (1.9-38.1 _µg g(-)(1)) and tyramine (1.7-31.7 _µg g(-)(1)), while putrescine (0.9-32.7 _µg g(-)(1)), spermidine (1.0-9.7 _µg g(-)(1)) and spermidine (0.6-9.3 _µg g(-)(1)) were present in most of the samples. Cadaverine, serotonine and __-phenylethylamine were present in a few samples at much lower concentrations. Organic samples always contained much lower levels of BAs than their conventional counterparts and, generally speaking, the highest amounts of BAs were found in the most processed products.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D001679', 'D002099', 'D002851', 'D005504', 'D058871', 'D015203', 'D012680']","['Biogenic Amines', 'Cacao', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Organic Agriculture', 'Reproducibility of Results', 'Sensitivity and Specificity']",Determination of biogenic amine profiles in conventional and organic cocoa-based products.,"['Q000737', 'Q000737', None, 'Q000379', None, None, None]","['chemistry', 'chemistry', None, 'methods', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25833003,2016,1,2,table 3,only th samples 1-3 which are the cocoa powder +0.68,15053518,"A new European legislation (2000/36/CE) has allowed the use of vegetable fats other than cocoa butter (CB) in chocolate up to a maximum value of 5% in the product. The vegetable fats used in chocolate are designated as cocoa butter replacements and are called cocoa butter equivalents (CBE). The feasibility of CBE quantification in chocolate using triacylglycerol (TAG) profiles was conducted by analyzing 55 samples of CBs and 31 samples of CBEs using a liquid chromatograph equipped with an evaporative light scattering detector (HPLC-ELSD). Statistical evaluation of the data obtained has been performed, and a simulation study has been carried out to assess the viability to use this method for quantifying the amount of CBE in real mixtures and in chocolates. The TAGs POP, POS, PLS, and the ratios POP/PLS, POS/PLP (P, palmityl; O, oleyl; S, stearyl; L, linoleyl) are particularly significant to discriminate between CB and CBE. Analysis of 50 mixtures between 5 different CBEs and 10 different CBs at 2 different concentration levels is presented. The data are visualized and interpreted. A mathematical model has been developed to assess the amount of CBE in real mixtures. This predictive model has been successfully applied and validated on dark chocolates including authorized CBE. The results are affected by +/-2.1% absolute average error. In particular, estimations between 10 and 20% of CBE show a very good match. On the other hand, values equal to or smaller than 5% show a larger prediction error (detection limit of the method). For the main purpose of this method (i.e., quantification of CBE at 5% max in chocolate, which represents about 15% of the total fat) this model shows very good results. For milk chocolate, the mathematical model can also be used if TAG are integrated from partition number (PN) 46 to 54. Consequently, the model proposed provides sufficient information to verify the real application of the European legislation.",Journal of agricultural and food chemistry,"['D002099', 'D002851', 'D004041', 'D005060', 'D008433', 'D015203', 'D014280']","['Cacao', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Europe', 'Mathematics', 'Reproducibility of Results', 'Triglycerides']",Triacylglycerol analysis for the quantification of cocoa butter equivalents (CBE) in chocolate: feasibility study and validation.,"['Q000737', None, 'Q000032', None, None, None, 'Q000032']","['chemistry', None, 'analysis', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15053518,2004,0,0,,no quantification nor useful data +0.68,17555636,"Theobromine, theophylline, and caffeine are determined simultaneously by a rapid and selective reversed-phase high-performance liquid chromatography (HPLC) method with UV detection in by-products of cupuacu and cacao seeds. The determination is carried out in the raw and roasted ground cupuacu seeds and in the corresponding powders obtained after pressure treatment. The by-products of both cupuacu seeds and cacao seeds are obtained under the same technological conditions. The HPLC method uses isocratic elution with a mobile phase of methanol-water-acetic acid (80:19:1) (v/v) at a flow rate of 1 mL/min and UV absorbance detection at 275 nm. Total elution time for these analytes is less than 10 min, and the detection limit for all analytes is 0.1 mg/g. The amounts of theobromine and caffeine found in all the cupuacu samples are one or more orders of magnitude lower than those from cacao. Theophylline is found in all cacao samples except for the roasted ground paste, and it is only found in the roasted ground paste in the cupuacu samples.",Journal of chromatographic science,"['D002099', 'D002110', 'D002851', 'D012015', 'D012639', 'D013056', 'D013805', 'D013806']","['Cacao', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Reference Standards', 'Seeds', 'Spectrophotometry, Ultraviolet', 'Theobromine', 'Theophylline']","Determination of theobromine, theophylline, and caffeine in by-products of cupuacu and cacao seeds by high-performance liquid chromatography.","['Q000196', 'Q000032', 'Q000379', None, 'Q000737', None, 'Q000032', 'Q000032']","['embryology', 'analysis', 'methods', None, 'chemistry', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17555636,2007,,,, +0.68,25053037,"Nutritional composition and fatty acids (FA) profile were determined in cocoa and chocolates of different geographical origin and subject to different processing conditions. Cocoa butter was the major nutrient in cocoa beans and carbohydrates were the most important in chocolates. Cocoa composition and FA profile varied depending on geographical origin whilst in chocolates only carbohydrates and fat content varied significantly due to the effect of origin and no significant effect was observed for processing conditions. Both for cocoa and chocolates differences in FA profile were mainly explained as an effect of the geographical origin, and were not due to processing conditions in chocolate. For cocoa, differences in FA profile were found in C12:0, C14:0, C16:0, C16:1, C17:0, C17:1 and C18:0 whilst for chocolates only differences were found in C16:0, C18:0, C18:1 and C18:2. For all samples, C16:0, C18:0, C18:1 and C18:2 were quantitatively the most important FA. Ecuadorian chocolate showed a healthier FA profile having higher amounts of unsaturated FA and lower amounts of saturated FA than Ghanaian chocolate. ",Food chemistry,"['D002099', 'D005227', 'D013058', 'D009753']","['Cacao', 'Fatty Acids', 'Mass Spectrometry', 'Nutritive Value']",Nutritional composition and fatty acids profile in cocoa beans and chocolates with different geographical origin and processing conditions.,"['Q000737', 'Q000737', None, None]","['chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25053037,2015,1,2,table 1 and 4,it has quantified and unquantified data +0.68,11893788,"Maillard reactions are among the most important of the chemical and oxidative changes occurring in food and biological samples that contribute to food deterioration and to the pathophysiology of human disease. Although the association of lipid glycation with this process has recently been shown, the number of lipid glycation products in food and biological materials has not been clear. In this study, we synthesized the Amadori products derived from the glycation of phosphatidylethanolamine (PE), i.e., Amadori-PEs. Dioleoyl PE was incubated with glucose and lactose for 15 days, and the resultant Amadori-PEs were purified and isolated using solid phase extraction followed by HPLC. With this procedure, essentially pure (>98% purity) Amadori-PEs glycated with glucose (Glc-PE) and with lactose (Lac-PE) were obtained and used as standards in the subsequent studies. To determine the presence of Amadori-PEs in food and biological samples, the carbonyl group of Amadori-PEs was ultraviolet (UV)-labeled with 3-methyl-2-benzothiazolinone hydrazone, and the labeled Amadori-PEs were analyzed with normal phase HPLC-UV (318 nm). The detection limit was 4.5 ng (5 pmol) for Glc-PE and 5.3 ng (5 pmol) for Lac-PE. Among the several food samples examined, infant formula and chocolate contained a high amount of both Glc-PE and Lac-PE over wide concentration ranges, such as 1.5-112 microg/g. Testing biological materials showed Amadori-PE (Glc-PE) was detectable in rat plasma.",Journal of lipid research,"['D000818', 'D052160', 'D001769', 'D001774', 'D002851', 'D005260', 'D005504', 'D005947', 'D006031', 'D006801', 'D006835', 'D007785', 'D015416', 'D008297', 'D013058', 'D008895', 'D010714', 'D051381', 'D017207', 'D013844', 'D014466']","['Animals', 'Benzothiazoles', 'Blood', 'Blood Chemical Analysis', 'Chromatography, High Pressure Liquid', 'Female', 'Food Analysis', 'Glucose', 'Glycosylation', 'Humans', 'Hydrazones', 'Lactose', 'Maillard Reaction', 'Male', 'Mass Spectrometry', 'Milk, Human', 'Phosphatidylethanolamines', 'Rats', 'Rats, Sprague-Dawley', 'Thiazoles', 'Ultraviolet Rays']",UV analysis of Amadori-glycated phosphatidylethanolamine in foods and biological samples.,"[None, None, None, 'Q000379', 'Q000379', None, 'Q000379', 'Q000737', None, None, None, 'Q000737', None, None, 'Q000379', 'Q000737', 'Q000032', None, None, 'Q000737', None]","[None, None, None, 'methods', 'methods', None, 'methods', 'chemistry', None, None, None, 'chemistry', None, None, 'methods', 'chemistry', 'analysis', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/11893788,2002,0,0,,no cocoa +0.68,26923226,"Proanthocyanidins (PACs) are naturally occurring flavonoids possessing health beneficial bioactivities. Their quantification often utilizes the 4-dimethylaminocinnamaldehyde (DMAC) spectrophotometric assay with the assumption that molar absorption coefficients (MACs) are similar across the various PAC species. To assess the validity of this assumption, individual PAC monomers and oligomers were examined for their absorbance response with DMAC. Our results have shown that PAC dimers and trimers with interflavan linkage variations exhibited differential absorbance response. Absence of A-type linkage between the terminal and second units in PAC molecule not only impacts absorbance intensity at 640 nm but also elicits a prominent secondary 440 nm absorbance peak. Cranberry (A-type) and cocoa (B-type) oligomeric PACs exhibited differential absorbance (MACs) relationship with degree-of-polymerization. Thus, PAC structural variations have considerable impact on the resulting MAC. The use of DMAC assay in PAC quantification, especially in comparing across specific oligomers and compositions, should not assume MACs are similar. ",Journal of agricultural and food chemistry,"['D002099', 'D002934', 'D019281', 'D005638', 'D015394', 'D010936', 'D058105', 'D044945', 'D012997', 'D013053', 'D029799']","['Cacao', 'Cinnamates', 'Dimerization', 'Fruit', 'Molecular Structure', 'Plant Extracts', 'Polymerization', 'Proanthocyanidins', 'Solvents', 'Spectrophotometry', 'Vaccinium macrocarpon']",Influence of Degree-of-Polymerization and Linkage on the Quantification of Proanthocyanidins using 4-Dimethylaminocinnamaldehyde (DMAC) Assay.,"[None, None, None, 'Q000737', None, 'Q000737', None, 'Q000032', None, 'Q000379', None]","[None, None, None, 'chemistry', None, 'chemistry', None, 'analysis', None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/26923226,2016,0,0,,no real cocoa +0.67,18371766,"The aroma profile of cocoa products was investigated by headspace solid-phase micro-extraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS). SPME fibers coated with 100 microm polydimethylsiloxane coating (PDMS), 65 microm polydimethylsiloxane/divinylbenzene coating (PDMS-DVB), 75 microm carboxen/polydimethylsiloxane coating (CAR-PDMS) and 50/30 microm divinylbenzene/carboxen on polydimethylsiloxane on a StableFlex fiber (DVB/CAR-PDMS) were evaluated. Several extraction times and temperature conditions were also tested to achieve optimum recovery. Suspensions of the samples in distilled water or in brine (25% NaCl in distilled water) were investigated to examine their effect on the composition of the headspace. The SPME fiber coated with 50/30 microm DVB/CAR-PDMS afforded the highest extraction efficiency, particularly when the samples were extracted at 60 degrees C for 15 min under dry conditions with toluene as an internal standard. Forty-five compounds were extracted and tentatively identified, most of which have previously been reported as odor-active compounds. The method developed allows sensitive and representative analysis of cocoa products with high reproducibility. Further research is ongoing to study chocolate making processes using this method for the quantitative analysis of volatile compounds contributing to the flavor/odor profile.",Talanta,"['D002099', 'D005511', 'D008401', 'D009930', 'D015203', 'D052617', 'D014835']","['Cacao', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Organic Chemicals', 'Reproducibility of Results', 'Solid Phase Microextraction', 'Volatilization']",Evaluation of solid-phase micro-extraction coupled to gas chromatography-mass spectrometry for the headspace analysis of volatile compounds in cocoa products.,"['Q000737', None, 'Q000379', 'Q000032', None, 'Q000379', None]","['chemistry', None, 'methods', 'analysis', None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/18371766,2008,2,3,table 1,only the dvb-pdms and DVB/car-pdms fibers and only fot the NCP +0.67,17454113,"A rapid antibody-based assay for the detection of ochratoxin A in cocoa powder is described, involving sequential clean-up and visual detection of the toxin (""clean-up tandem assay column""). The screening test was developed to have a cut-off level of 2 microg kg(-1) and was shown to have false positive and false negative rates of 10 and 2%, respectively. Analysis of six samples can be carried out in the field in approximately 30 min by untrained workers. Using the proposed rapid screening test, 10 retail cocoa powders were found to contain no detectable levels of ochratoxin A (<2 microg kg(-1)). These samples were also found to be negative (<2 microg kg(-1)) when analysed using an LC-MS/MS method.",Food additives and contaminants,"['D001628', 'D002099', 'D002273', 'D002851', 'D005188', 'D005189', 'D005506', 'D009183', 'D009793', 'D053719']","['Beverages', 'Cacao', 'Carcinogens', 'Chromatography, High Pressure Liquid', 'False Negative Reactions', 'False Positive Reactions', 'Food Contamination', 'Mycotoxins', 'Ochratoxins', 'Tandem Mass Spectrometry']",Application and validation of a clean-up tandem assay column for screening ochratoxin A in cocoa powder.,"['Q000032', 'Q000737', 'Q000032', 'Q000379', None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000379']","['analysis', 'chemistry', 'analysis', 'methods', None, None, 'analysis', 'analysis', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/17454113,2007,,,, +0.67,15161179,"A rapid and selective isocratic reversed-phase liquid chromatographic method has been developed at the National Institute of Standards and Technology to simultaneously measure caffeine, theobromine, and theophylline in a food-matrix standard reference material (SRM) 2384, Baking Chocolate. The method uses isocratic elution with a mobile phase composition (volume fractions) of 10% acetronitrile/90% water (pH adjusted to 2.5 using acetic acid) at a flow rate of 1.5 mL/min with ultraviolet absorbance detection (274 nm). Total elution time for these analytes is less than 15 min. Concentration levels of caffeine, theobromine, and theophylline were measured in single 1-g samples taken from each of eight bars of chocolate over an eight-day period. Samples were defatted with hexane, and beta-hydroxyethyltheophylline was added as the internal standard. The repeatability for the caffeine, theobromine, and theophylline measurements was 5.1, 2.3, and 1.9%, respectively. The limit of quantitation for all analytes was <100 ng/mL. The measurements from this method were used in the value-assignment of caffeine, theobromine, and theophylline in SRM 2384.",Journal of agricultural and food chemistry,"['D002099', 'D002110', 'D002851', 'D012015', 'D013805', 'D013806']","['Cacao', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Reference Standards', 'Theobromine', 'Theophylline']","Determination of caffeine, theobromine, and theophylline in standard reference material 2384, baking chocolate, using reversed-phase liquid chromatography.","['Q000737', 'Q000032', None, None, 'Q000032', 'Q000032']","['chemistry', 'analysis', None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15161179,2004,1,1,table 1 and 2,only the 1-6 and NIST from tb 1 and only the caff. And theob. From tb 2 +0.67,22790716,"In order to investigate cadmium contents in foods sold in Japan, cadmium levels in 40 seafood samples and 30 chocolate samples were measured by means of atomic absorption spectrometry and ICP-OES. We first confirmed the validity of the method according to the guidelines of the Ministry of Health, Labour and Welfare. Among 40 seafood samples investigated, cadmium was detected in 31 samples, in which the concentration exceeded half the LOQ (0.025 mg/kg), and the level was ranged from 0.03 to 0.38 mg/kg. We could not find any sample containing cadmium in excess of 2 mg/kg, which the Codex Alimentarius sets as the maximum standard value. Among 30 chocolate samples, cadmium was detected in 21 samples, and the level ranged from 0.025 to 0.54 mg/kg.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D000818', 'D049872', 'D002099', 'D019187', 'D049832', 'D005504', 'D007564', 'D008452', 'D049831', 'D017747', 'D013054']","['Animals', 'Bivalvia', 'Cacao', 'Cadmium Compounds', 'Decapodiformes', 'Food Analysis', 'Japan', 'Maximum Allowable Concentration', 'Octopodiformes', 'Seafood', 'Spectrophotometry, Atomic']","[Surveillance of cadmium level in octopus, squid, clam, short-necked clam and chocolate].","[None, 'Q000737', 'Q000737', 'Q000032', 'Q000737', 'Q000379', None, None, 'Q000737', 'Q000032', 'Q000379']","[None, 'chemistry', 'chemistry', 'analysis', 'chemistry', 'methods', None, None, 'chemistry', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/22790716,2013,,,,The paper wa sin japanese and could be translated with google +0.66,12526004,"Liquid chromatography coupled with ionspray mass spectrometry in the tandem mode (LC/MS/MS) with negative ion detection was used for the identification of a variety of phenolic compounds in a cocoa sample. Gradient elution with water and acetonitrile, both containing 0.1% HCOOH, was used. Standard solutions of 31 phenolic compounds, including benzoic and cinnamic acids and flavonoid compounds, were studied in the negative ion mode using MS/MS product ion scans. At low collisional activation, the deprotonated molecule [M - H](-) was observed for all the compounds studied. For cinnamic and benzoic acids, losses of CO(2) or formation of [M - CH(3)](-*) in the case of methoxylated compounds were observed. However, for flavonol and flavone glycosides, the spectra present both the deprotonated molecule [M - H](-) of the glycoside and the ion corresponding to the deprotonated aglycone [A - H](-). The latter ion is formed by loss of the rhamnose, glucose, galactose or arabinose residue from the glycosides. Different fragmentation patterns were observed in MS/MS experiments for flavone-C-glycosides which showed fragmentation in the sugar part. Fragmentation of aglycones provided characteristic ions for each family of flavonoids. The optimum LC/MS/MS conditions were applied to the characterization of a cocoa sample that had been subjected to an extraction/clean-up procedure which involved chromatography on Sephadex LH20 and thin-layer chromatographic monitoring. In addition to compounds described in the literature, such as epicatechin and catechin, quercetin, isoquercitrin (quercetin-3-O-glucoside) and quercetin-3-O-arabinose, other compounds were identified for the first time in cocoa samples, such as hyperoside (quercetin-3-O-galactoside), naringenin, luteolin, apigenin and some O-glucosides and C-glucosides of these compounds.",Journal of mass spectrometry : JMS,"['D002099', 'D002853', 'D005419', 'D010636', 'D021241']","['Cacao', 'Chromatography, Liquid', 'Flavonoids', 'Phenols', 'Spectrometry, Mass, Electrospray Ionization']",Liquid chromatographic/electrospray ionization tandem mass spectrometric study of the phenolic composition of cocoa (Theobroma cacao).,"['Q000737', None, 'Q000032', 'Q000032', None]","['chemistry', None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/12526004,2003,,,, +0.66,11675670,"Quantitative analyses of fatty acids from five triacylglycerol products, coconut oil, palm kernel oil, palm oil, lard and cocoa butter, were carried out using two analytical methods: matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) and gas chromatography (GC), in an effort to validate the application of MALDI-TOFMS in quantitative fatty acid analysis. For the GC analysis, transmethylated products were used, whereas, for the MALDI-TOF analysis, saponified products were used. Under MALDI-TOF conditions, the acids were detected as sodiated sodium carboxylates [RCOONa + Na](+) consistent with the mode of ionization that was previously reported. Thus, the MALDI-TOF mass spectrum of saponified coconut oil showed the presence of sodiated sodium salts of caprylic acid (7.5 +/- 0.67, m/z 189), capric acid (6.9 +/- 0.83, m/z 217), lauric acid (47.8 +/- 0.67, m/z 245), myristic acid (20.4 +/- 0.51, m/z 273), palmitic acid (9.8 +/- 0.47, m/z 301), linoleic acid (0.9 +/- 0.07, m/z 325), oleic acid (4.8 +/- 0.42, m/z 327) and stearic acid (2.0 +/- 0.13, m/z 329). Saponified palm kernel oil had a fatty acid profile that included caprylic acid (3.5 +/- 0.59), capric acid (4.7 +/- 0.82), lauric acid (58.6 +/- 2.3), myristic acid (20.9 +/- 1.5), palmitic acid (7.2 +/- 1.1), oleic acid (3.8 +/- 0.62) and stearic acid (1.2 +/- 0.15). Saponified palm oil gave myristic acid (0.83 +/- 0.18), palmitic acid (55.8 +/- 1.7), linoleic acid (4.2 +/- 0.51), oleic acid (34.5 +/- 1.5), stearic acid (3.8 +/- 0.26) and arachidic acid (0.80 +/- 0.22). Saponified lard showed the presence of myristic acid (1.5 +/- 0.24), palmitic acid (28.9 +/- 1.3), linoleic acid (13.7 +/- 0.67), oleic acid (38.7 +/- 1.4), stearic acid (12.8 +/- 0.64) and arachidic acid (2.4 +/- 0.35). Finally, for saponified cocoa butter, the fatty acid distribution was: palmitic acid (32.3 +/- 1.0), linoleic acid (2.6 +/- 0.35), oleic acid (34.9 +/- 1.7) and stearic acid (30.3 +/- 1.6). Quantitative gas chromatographic analysis of the corresponding methyl esters from these triacylglycerol products yielded data that were mostly in agreement with the MALDI-TOFMS data. The MALDI-TOF experiment, however, proved to be superior to the GC experiment, particularly with regard to baseline resolution of unsaturated acids. Furthermore, the ability of MALDI-TOFMS to detect low concentrations of fatty acids rendered it more sensitive than the GC methodology.",Rapid communications in mass spectrometry : RCM,"['D000818', 'D002417', 'D002849', 'D004041', 'D005227', 'D010938', 'D019032', 'D014280']","['Animals', 'Cattle', 'Chromatography, Gas', 'Dietary Fats', 'Fatty Acids', 'Plant Oils', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Triglycerides']",Comparative quantitative fatty acid analysis of triacylglycerols using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and gas chromatography.,"[None, None, None, 'Q000032', 'Q000032', 'Q000032', None, 'Q000032']","[None, None, None, 'analysis', 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11675670,2001,,,, +0.66,16719534,"Cocoa and chocolate products from major brands were analyzed blind for total antioxidant capacity (AOC) (lipophilic and hydrophilic ORAC(FL)), catechins, and procyanidins (monomer through polymers). Accuracy of analyses was ascertained by comparing analyses on a NIST standard reference chocolate with NIST certified values. Procyanidin (PC) content was related to the nonfat cocoa solid (NFCS) content. The natural cocoa powders (average 87% of NFCS) contained the highest levels of AOC (826 +/- 103 micromol of TE/g) and PCs (40.8 +/- 8.3 mg/g). Alkalized cocoa (Dutched powders, average 80% NFCS) contained lower AOC (402 +/- 6 micromol of TE /g) and PCs (8.9 +/- 2.7 mg/g). Unsweetened chocolates or chocolate liquor (50% NFCS) contained 496 +/- 40 micromol of TE /g of AOC and 22.3 +/- 2.9 mg/g of PCs. Milk chocolates, which contain the least amount of NFCS (7.1%), had the lowest concentrations of AOC (80 +/- 10 micromol of TE /g) and PCs (2.7 +/- 0.5 mg/g). One serving of cocoa (5 g) or chocolate (15 or 40 g, depending upon the type of chocolate) provides 2000-9100 micromol of TE of AOC and 45-517 mg of PCs, amounts that exceed the amount in a serving of the majority of foods consumed in America. The monomers through trimers, which are thought to be directly bioavailable, contributed 30% of the total PCs in chocolates. Hydrophilic antioxidant capacity contributed >90% of AOC in all products. The correlation coefficient between AOC and PCs in chocolates was 0.92, suggesting that PCs are the dominant antioxidants in cocoa and chocolates. These results indicate that NFCS is correlated with AOC and PC in cocoa and chocolate products. Alkalizing dramatically decreased both the procyanidin content and antioxidant capacity, although not to the same extent.",Journal of agricultural and food chemistry,"['D000975', 'D044946', 'D002099', 'D002392', 'D002851', 'D044945', 'D021241']","['Antioxidants', 'Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Proanthocyanidins', 'Spectrometry, Mass, Electrospray Ionization']",Procyanidin and catechin contents and antioxidant capacity of cocoa and chocolate products.,"['Q000032', 'Q000032', 'Q000737', 'Q000032', None, 'Q000032', None]","['analysis', 'analysis', 'chemistry', 'analysis', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/16719534,2006,1,2,table 3 ,only the powders +0.66,21094947,"The applicability of comprehensive two-dimensional gas chromatography (GC_GC) for flavonoids analysis was investigated by separation and identification of flavonoids in standards, and a complex matrix natural sample. The modulation temperature was optimized to achieve the best separation and signal enhancement. The separation pattern of trimethylsilyl (TMS) derivatives of flavonoids was compared on two complementary column sets. Whilst the BPX5/BPX50 (NP/P) column set offers better overall separation, BPX50/BPX5 (P/NP) provides better peak shape and sensitivity. Comparison of the identification power of GC_GC-TOFMS against both the NIST05 MS library and a laboratory (created in-house) TOFMS library was carried out on a flavonoid mixture. The basic retention index information on high-performance capillary columns with a non-polar stationary phase was established and database of mass spectra of trimethylsilyl derivatives of flavonoids was compiled. TOFMS coupled to GC_GC enabled satisfactory identification of flavonoids in complex matrix samples at their LOD over a range of 0.5-10 __g/mL. Detection of all compounds was based on full-scan mass spectra and for each compound a characteristic ion was chosen for further quantification. This study shows that GC_GC-TOFMS yields high specificity for flavonoids derived from real natural samples, dark chocolate, propolis, and chrysanthemum.",Journal of chromatography. A,"['D047188', 'D005419', 'D005504', 'D008401']","['Chalcones', 'Flavonoids', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry']","Comprehensive two-dimensional gas chromatography, retention indices and time-of-flight mass spectra of flavonoids and chalcones.","['Q000032', 'Q000032', None, 'Q000379']","['analysis', 'analysis', None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/21094947,2011,0,0,,no cocoa +0.66,2745605,"This paper details a high-performance liquid chromatography (HPLC) method for the separation of triacylglycerols, using a 3-micron, 15 cm x 4.6 mm I.D. Spherisorb ODS column and gradient elution with dichloromethane and acetonitrile. The triacylglycerols are detected using a light scattering detector (mass detector). Separations of a number of different edible oils and fats are reported. The procedure offers a possible method for determining cocoa butter equivalents and the adulteration of edible oils and fats by other non-generic fats and oils.",Journal of chromatography,"['D002851', 'D005506', 'D010938', 'D012031', 'D014280']","['Chromatography, High Pressure Liquid', 'Food Contamination', 'Plant Oils', 'Refractometry', 'Triglycerides']",Rapid analysis of triacylglycerols using high-performance liquid chromatography with light scattering detection.,"[None, 'Q000032', 'Q000032', None, 'Q000032']","[None, 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/2745605,1989,,,, +0.66,26651573,"There are few studies about different types of chocolate and their chemical characterization by Fourier transform (FT)-Raman spectroscopy and capillary zone electrophoresis (CZE). The aim of this study was to evaluate the lipid profile of different types of Brazilian chocolate through characterization by FT-Raman spectroscopy and identification and quantification of major fatty acids (FAs) by CZE to confirm FT-Raman spectrometry results. It was found that the main spectroscopic profile difference of the chocolate samples analyzed was related to the presence of saturated or unsaturated FAs. Well defined bands at approximately 1660, 1267, and 1274 cm(-1) corresponding to vibrational modes of unsaturated FAs (UnFAs) were found only in the spectra of samples with cocoa butter in their composition according to label specifications, mainly in dark chocolate samples. The FA identification and quantification by CZE found the presence of stearic (18:0) and palmitic (16:0) acids as the major saturated FAs in all chocolate samples. Dark chocolate samples showed the highest levels of oleic (cis-9 18:1) and linoleic (cis, cis -9,12 18:2) UnFAs monitored and the lowest levels of 14:0 in their chemical composition. Samples coded as 02 (with not only cocoa butter in their composition according to label) had the highest levels of 14:0 (FA not present in cocoa butter composition) corresponding to label information and inferring the presence of other fat sources, called cocoa butter substitutes, mainly for milk and white chocolate samples. This study suggests FT-Raman spectroscopy is a powerful technique that can be used to chemically characterize the chocolate lipid fraction, and CZE is a tool able to confirm Raman spectroscopy results and identify and quantify the major FAs in chocolate samples. ",Journal of AOAC International,"['D002099', 'D019075', 'D005227', 'D013059']","['Cacao', 'Electrophoresis, Capillary', 'Fatty Acids', 'Spectrum Analysis, Raman']","Lipid Characterization of White, Dark, and Milk Chocolates by FT-Raman Spectroscopy and Capillary Zone Electrophoresis.","['Q000737', 'Q000379', 'Q000032', 'Q000379']","['chemistry', 'methods', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/26651573,2016,,,,no pdf access +0.65,16314166,"A rapid and selective cation exchange chromatographic method coupled to integrated pulsed amperometric detection (PAD) has been developed to quantify biogenic amines in chocolate. The method is based on gradient elution of aqueous methanesulfonic acid with post column addition of strong base to obtain suitable conditions for amperometric detection. A potential waveform able to keep long time performance of the Au disposable electrode was set up. Total analysis time is less than 20min. Concentration levels of dopamine, serotonin, tyramine, histamine and 2-phenylethylamine were measured, after extraction with perchloric acid from 2g samples previously defatted twice with petroleum ether. The method was used to determine the analytes in chocolate real matrices and their quantification was made with standard addition method. Only dopamine, histamine and serotonin were found in the analysed real samples. Repeatabilities of their signals, computed on their amounts in the real samples, were 5% for all of them. Repeatabilities of tyramine and phenethylamine were relative to standard additions to real samples (close to 1mg/l in the extract) and were 7 and 3%, respectively. Detection limits were computed with the 3s of the baseline noise combined with the calibration plot regression parameters. They were satisfactorily low for all amines: 3mg/kg for dopamine, 2mg/kg for tyramine, 1mg/kg for histamine, 2mg/kg for serotonin, 3mg/kg for 2-phenylethylamine.",Journal of chromatography. A,"['D001679', 'D002099', 'D002852', 'D004298', 'D004563', 'D004566', 'D005504', 'D006046', 'D006632', 'D008698', 'D010472', 'D010627', 'D015203', 'D012680', 'D012701', 'D014439']","['Biogenic Amines', 'Cacao', 'Chromatography, Ion Exchange', 'Dopamine', 'Electrochemistry', 'Electrodes', 'Food Analysis', 'Gold', 'Histamine', 'Mesylates', 'Perchlorates', 'Phenethylamines', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Serotonin', 'Tyramine']",Determination of biogenic amines in chocolate by ion chromatographic separation and pulsed integrated amperometric detection with implemented wave-form at Au disposable electrode.,"['Q000032', 'Q000737', 'Q000379', 'Q000032', 'Q000379', None, 'Q000379', 'Q000737', 'Q000032', 'Q000737', 'Q000737', 'Q000032', None, None, 'Q000032', 'Q000032']","['analysis', 'chemistry', 'methods', 'analysis', 'methods', None, 'methods', 'chemistry', 'analysis', 'chemistry', 'chemistry', 'analysis', None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16314166,2006,0,0,,chocolate as sample +0.65,18193748,"A simple and rapid method based on ultrasound energy is described for the determination of aluminum (AI) in complex matrixes of chocolate and candy samples by electrothermal atomic absorption spectrometry. The optimization strategy was carried out using multivariate methodologies. Five variables (temperature of the ultrasonic bath; exposure time to ultrasound energy; volumes of 2 acid mixtures, HNO3-H2SO4-H2O2 (1 + 1 + 1) and HNO3-H2O2 (1 + 1); and sample mass) were considered as factors in the optimization process. Interactions between analytical factors and their optimal levels were investigated using fractional factorial and Doehlert matrix designs. Validation of the ultrasonic-assisted acid digestion procedure was performed against standard reference materials, milk powder (SRM 8435) and wheat flour (SRM 1567a). The proposed procedure allowed Al determination with a detection limit of 2.3 microg/L (signal-to-noise = 3) and a precision, calculated as relative standard deviation, of 2.2% for a set of 10 measurements of certified samples. The recovery of Al by the proposed procedure was close to 100%, and no significant difference at the 95% confidence level was found between determined and certified values of Al. The proposed procedure was applied to the determination of Al in chocolate and candy samples. The results indicated that cocoa-based chocolates have higher contents of Al than milk- and sugar-based chocolates and candies.",Journal of AOAC International,"['D000143', 'D000465', 'D000535', 'D002099', 'D002182', 'D003627', 'D006868', 'D007202', 'D013054', 'D014465']","['Acids', 'Algorithms', 'Aluminum', 'Cacao', 'Candy', 'Data Interpretation, Statistical', 'Hydrolysis', 'Indicators and Reagents', 'Spectrophotometry, Atomic', 'Ultrasonics']",Application of fractional factorial design and Doehlert matrix in the optimization of experimental variables associated with the ultrasonic-assisted acid digestion of chocolate samples for aluminum determination by atomic absorption spectrometry.,"['Q000737', None, 'Q000032', 'Q000737', 'Q000032', None, None, None, None, None]","['chemistry', None, 'analysis', 'chemistry', 'analysis', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18193748,2008,,,, +0.65,21409610,"Novel saccharide-based stationary phases were developed by applying non-enzymatic browning (Maillard Reaction) on aminopropyl silica material. During this process, the reducing sugars glucose, lactose, maltose, and cellobiose served as ""ligand primers"". The reaction cascade using cellobiose resulted in an efficient chromatographic material which further served as our model Chocolate HILIC column. (Chocolate refers to the fact that these phases are brownish.) In this way, an amine backbone was introduced to facilitate convenient manipulation of selectivity by additional attractive or repulsive ionic solute-ligand interactions in addition to the typical HILIC retention mechanism. In total, six different test sets and five different mobile phase compositions were investigated, allowing a comprehensive evaluation of the new polar column. It became evident that, besides the so-called HILIC retention mechanism based on partition phenomena, additional adsorption mechanisms, including ionic interactions, take place. Thus, the new column is another example of a HILIC-type column characterized by mixed-modal retention increments. The glucose-modified materials exhibited the relative highest overall hydrophobicity of all grafted Chocolate HILIC columns which enabled retention of lipophilic analytes with high water content mobile phases.",Analytical and bioanalytical chemistry,"['D000327', 'D002099', 'D002853', 'D004187', 'D005947', 'D057927', 'D008024', 'D012822', 'D013499']","['Adsorption', 'Cacao', 'Chromatography, Liquid', 'Disaccharides', 'Glucose', 'Hydrophobic and Hydrophilic Interactions', 'Ligands', 'Silicon Dioxide', 'Surface Properties']",Chocolate HILIC phases: development and characterization of novel saccharide-based stationary phases by applying non-enzymatic browning (Maillard reaction) on amino-modified silica surfaces.,"[None, 'Q000737', None, 'Q000737', 'Q000737', None, None, 'Q000737', None]","[None, 'chemistry', None, 'chemistry', 'chemistry', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21409610,2011,0,0,,no cocoa +0.65,2312513,"A liquid chromatographic (LC) method has been developed to determine the content of polydextrose, a water-soluble 1 calorie/g bulking agent, in food matrixes such as cookies, cakes, fruit spreads, and chocolate toppings. This analysis, which requires use of a blank matrix, provides a feasible means to control the manufacture of foods containing this additive and provides a component for the accurate determination of the caloric value of a particular food product. The method involves aqueous extraction of the polydextrose from the food matrix followed by separation on a carbohydrate analysis column. The LC system uses a mobile phase of 0.005M CaSO4.2H2O and a refractive index detector for quantitation. Polydextrose recoveries from the food matrixes varied from 91.5 to 100.9% with assay precision, expressed as coefficient of variation, ranging from 0.7 to 4.3%. Each error estimate was derived from 5 parallel determinations. The present methodology is precise and selective in contrast to the modified classical phenol-sulfuric acid colorimetric method for assaying carbohydrates, which had been used for polydextrose determination in food matrixes in the past. Because the coefficient of variation frequently exceeded 10%, replicate analyses were necessary to achieve quantitation.",Journal - Association of Official Analytical Chemists,"['D002099', 'D002853', 'D005504', 'D005638', 'D005936', 'D012997']","['Cacao', 'Chromatography, Liquid', 'Food Analysis', 'Fruit', 'Glucans', 'Solvents']",Liquid chromatographic determination of polydextrose in food matrixes.,"['Q000032', None, None, 'Q000032', 'Q000032', None]","['analysis', None, None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/2312513,1990,,,, +0.65,28779575,"A fast separation based on cation-exchange liquid chromatography coupled with high-resolution mass spectrometry is proposed for simultaneous determination of chlormequat, difenzoquat, diquat, mepiquat and paraquat in several food and beverage commodities. Solid samples were extracted using a mixture of water/methanol/formic acid (69.6:30:0.4, v/v/v), while liquid samples were ten times diluted with the same solution. Separation was carried out on an experimental length-modified IonPac CS17 column (2_____15__mm",Journal of separation science,"['D064751', 'D001628', 'D002412', 'D002852', 'D005506', 'D007554', 'D013058', 'D010575', 'D053719']","['Ammonium Compounds', 'Beverages', 'Cations', 'Chromatography, Ion Exchange', 'Food Contamination', 'Isotopes', 'Mass Spectrometry', 'Pesticides', 'Tandem Mass Spectrometry']",Fast analysis of quaternary ammonium pesticides in food and beverages using cation-exchange chromatography coupled with isotope-dilution high-resolution mass spectrometry.,"['Q000032', 'Q000032', None, None, 'Q000032', None, None, 'Q000032', None]","['analysis', 'analysis', None, None, 'analysis', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/28779575,2018,1,1,table 3, +0.65,7698108,"Extracts of several grain-based coffee-substitute blends and instant coffees were mutagenic in the Ames/Salmonella test using TA98, YG1024, and YG1029 with metabolic activation. The beverage powders induced 150 to 500 TA98 and 1,150 to 4,050 YG1024 revertant colonies/g, respectively. Increased sensitivity was achieved using strain YG1024. No mutagenic activity was found in instant hot cocoa products. The mutagenic activity in the beverage powders was shown to be stable to heat and the products varied in resistance to acid nitrite treatment. Differential bacterial strain specificity, and a requirement for metabolic activation suggest that aromatic amines are present. Characterization of the mutagenic activity, using HPLC and the Ames test of the collected fractions, showed the coffee-substitute blends and instant coffees contain several mutagenic compounds. Known heterocyclic amines are not responsible for the major part of the mutagenic activity. The main mutagenic activity in grain-based coffee-substitute blends and instant coffees is due to several unidentified compounds, which are most likely aromatic amines.",Environmental and molecular mutagenesis,"['D000588', 'D001628', 'D002099', 'D018651', 'D002851', 'D003069', 'D002523', 'D005504', 'D005526', 'D006571', 'D006358', 'D006898', 'D009152', 'D009153', 'D011208', 'D012486']","['Amines', 'Beverages', 'Cacao', 'Chicory', 'Chromatography, High Pressure Liquid', 'Coffee', 'Edible Grain', 'Food Analysis', 'Food, Formulated', 'Heterocyclic Compounds', 'Hot Temperature', 'Hydroxylamines', 'Mutagenicity Tests', 'Mutagens', 'Powders', 'Salmonella typhimurium']",Characterization of mutagenic activity in instant hot beverage powders.,"['Q000302', 'Q000032', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', None, 'Q000032', 'Q000302', None, 'Q000302', None, 'Q000302', 'Q000737', 'Q000187']","['isolation & purification', 'analysis', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', None, 'analysis', 'isolation & purification', None, 'isolation & purification', None, 'isolation & purification', 'chemistry', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/7698108,1995,,,, +0.65,28419110,"This work evaluated the effect of cocoa pulp as a malt adjunct on the parameters of fermentation for beer production on a pilot scale. For this purpose, yeast isolated from the spontaneous fermentation of cacha_a (SC52), belonging to the strain bank of the State University of Feira de Santana-Ba (Brazil), and a commercial strain of ale yeast (Safale S-04 Belgium) were used. The beer produced was subjected to acceptance and purchase intention tests for sensorial analysis. At the beginning of fermentation, 30% cocoa pulp (adjunct) was added to the wort at 12_P concentration. The production of beer on a pilot scale was carried out in a bioreactor with a 100-liter capacity, a usable volume of 60 liters, a temperature of 22_C and a fermentation time of 96 hours. The fermentation parameters evaluated were consumption of fermentable sugars and production of ethanol, glycerol and esters. The beer produced using the adjunct and yeast SC52 showed better fermentation performance and better acceptance according to sensorial analysis.",PloS one,"['D001515', 'D019149', 'D002099', 'D002241', 'D002851', 'D004952', 'D000431', 'D005285', 'D005990', 'D006863', 'D010865', 'D012441', 'D052617', 'D013696', 'D013997']","['Beer', 'Bioreactors', 'Cacao', 'Carbohydrates', 'Chromatography, High Pressure Liquid', 'Esters', 'Ethanol', 'Fermentation', 'Glycerol', 'Hydrogen-Ion Concentration', 'Pilot Projects', 'Saccharomyces cerevisiae', 'Solid Phase Microextraction', 'Temperature', 'Time Factors']",Cocoa pulp in beer production: Applicability and fermentative process performance.,"['Q000032', 'Q000382', 'Q000378', 'Q000032', None, 'Q000032', 'Q000032', None, 'Q000032', None, None, 'Q000378', None, None, None]","['analysis', 'microbiology', 'metabolism', 'analysis', None, 'analysis', 'analysis', None, 'analysis', None, None, 'metabolism', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28419110,2017,0,0,,beer containing cocoa pulp +0.64,16881674,"A straightforward stable isotope dilution analysis (SIDA) for the quantitative determination of the di- and trihydroxybenzenes catechol (1), pyrogallol (2), 3-methylcatechol (3), 4-methylcatechol (4), and 4-ethylcatechol (5) in foods by means of liquid chromatography-tandem mass spectrometry was developed. With or without sample preparation involving phenylboronyl solid phase extraction, the method allowed the quantification of the target compounds in complex matrices such as coffee beverages with quantification limits of 9 nmol/L for 4-ethylcatechol, 24 nmol/L for catechol, 3-methyl-, and 4-methylcatechol, and 31 nmol/L for pyrogallol. Recovery rates for the analytes ranged from 97 to 103%. Application of the developed SIDA to various commercial food samples showed that quantitative analysis of the target compounds is possible within 30 min and gave first quantitative data on the amounts of di- and trihydroxybenzenes in coffee beverage, coffee powder, coffee surrogate, beer, malt, roasted cocoa powder, bread crust, potato crisps, fruits, and cigarette smoke and human urine. Model precursor studies revealed the carbohydrate/amino acid systems as well as the plant polyphenols catechin and epicatechin as precursors of catechol and 5-O-caffeoylquinic acid, caffeic acid as a precursor of catechol and 4-ethylcatechol, and gallocatechin, epigallocatechin, and gallic acid as precursors of pyrogallol.",Journal of agricultural and food chemistry,"['D002396', 'D002853', 'D003069', 'D003903', 'D005504', 'D005638', 'D006801', 'D007201', 'D013058', 'D011748', 'D012906', 'D014026']","['Catechols', 'Chromatography, Liquid', 'Coffee', 'Deuterium', 'Food Analysis', 'Fruit', 'Humans', 'Indicator Dilution Techniques', 'Mass Spectrometry', 'Pyrogallol', 'Smoke', 'Tobacco']",Development of a stable isotope dilution analysis with liquid chromatography-tandem mass spectrometry detection for the quantitative analysis of di- and trihydroxybenzenes in foods and model systems.,"['Q000032', 'Q000379', 'Q000737', None, 'Q000379', 'Q000737', None, None, 'Q000379', 'Q000032', 'Q000032', None]","['analysis', 'methods', 'chemistry', None, 'methods', 'chemistry', None, None, 'methods', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/16881674,2006,1,1,table 3,cocoa powder only +0.64,12166966,"New experimental data on the extraction of caffeine from guaran seeds and mat© tea leaves, and theobromine from cocoa beans, with supercritical CO2 were obtained using a high-pressure extraction apparatus. The effect of the addition of ethanol to carbon dioxide on the extraction efficiency was also investigated. Caffeine extraction yields of 98% of the initial caffeine content in both wet ground guaran seeds and mat© tea leaves were obtained. Extractions of caffeine from guaran seeds and mat© tea leaves also exhibited a retrograde behavior for the two temperatures considered in this work. In the removal of theobromine from cocoa beans, a much smaller extraction yield was obtained with longer extraction periods and consequently larger solvent requirements. The results of this study confirm the higher selectivity of CO2 for caffeine in comparison with that for theobromine, and also the influence of other components in each particular natural product on the extraction of methylxanthines. The effect of the addition of ethanol to carbon dioxide on the extraction of methylxanthines was significant, particularly in the extraction of theobromine from cocoa beans. In general, the use of ethanol results in lower solvent and energy requirements and thereby improved extraction efficiency.",Journal of agricultural and food chemistry,"['D002099', 'D002110', 'D002245', 'D025924', 'D000431', 'D030019', 'D018515', 'D029631', 'D012639', 'D013805', 'D014970']","['Cacao', 'Caffeine', 'Carbon Dioxide', 'Chromatography, Supercritical Fluid', 'Ethanol', 'Ilex paraguariensis', 'Plant Leaves', 'Sapindaceae', 'Seeds', 'Theobromine', 'Xanthines']","Extraction of methylxanthines from guaran seeds, mat© leaves, and cocoa beans using supercritical carbon dioxide and ethanol.","['Q000737', 'Q000302', None, None, None, 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000302', 'Q000302']","['chemistry', 'isolation & purification', None, None, None, 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'isolation & purification', 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/12166966,2002,1,1,figure 4,total +0.64,23349790,"The sensory quality and the contents of quality-determining chemical compounds in unfermented and fermented cocoa from 100 cacao trees (individual genotypes) representing groups of nine genotype spectra (GG), grown at smallholder plantings in the municipality of Waslala, Nicaragua, were evaluated for two successive harvest periods. Cocoa samples were fermented using a technique mimicking recommended on-farm practices. The sensory cocoa quality was assessed by experienced tasters, and seven major chemical taste compounds were quantified by near infrared spectrometry (NIRS). The association of the nine, partially admixed, genotype spectra with the analytical and sensory quality parameters was tested. The individual parameters were analyzed as a function of the factors GG and harvest (including the date of fermentation), individual trees within a single GG were used as replications. In fermented cocoa, significant GG-specific differences were observed for methylxanthines, theobromine-to-caffeine (T/C) ratio, total fat, procyanidin B5 and epicatechin, as well as the sensory attributes global score, astringency, and dry fruit aroma, but differences related to harvest were also apparent. The potential cocoa yield was also highly determined by the individual GG, although there was significant tree-to-tree variation within every single GG. Non-fermented samples showed large harvest-to-harvest variation of their chemical composition, while differences between GG were insignificant. These results suggest that selection by the genetic background, represented here by groups of partially admixed genotype spectra, would be a useful strategy toward enhancing quality and yield of cocoa in Nicaragua. Selection by the GG within the local, genetically segregating populations of seed-propagated cacao, followed by clonal propagation of best-performing individuals of the selected GG could be a viable alternative to traditional propagation of cacao by seed from open pollination. Fast and gentle air-drying of the fermented beans and their permanent dry storage were an efficient and comparatively easy precondition for high cocoa quality.",PloS one,"['D044946', 'D044822', 'D018533', 'D002099', 'D002110', 'D002392', 'D005285', 'D005511', 'D005638', 'D014644', 'D005838', 'D009527', 'D044945', 'D011786', 'D012639', 'D019265', 'D013649', 'D013805', 'D014197', 'D014970']","['Biflavonoids', 'Biodiversity', 'Biomass', 'Cacao', 'Caffeine', 'Catechin', 'Fermentation', 'Food Handling', 'Fruit', 'Genetic Variation', 'Genotype', 'Nicaragua', 'Proanthocyanidins', 'Quality Control', 'Seeds', 'Spectroscopy, Near-Infrared', 'Taste', 'Theobromine', 'Trees', 'Xanthines']","Diversity of cacao trees in Waslala, Nicaragua: associations between genotype spectra, product quality and yield potential.","['Q000032', None, None, 'Q000737', 'Q000032', 'Q000032', None, 'Q000379', 'Q000737', None, None, None, 'Q000032', None, 'Q000737', None, None, 'Q000032', 'Q000737', 'Q000032']","['analysis', None, None, 'chemistry', 'analysis', 'analysis', None, 'methods', 'chemistry', None, None, None, 'analysis', None, 'chemistry', None, None, 'analysis', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/23349790,2013,1,1,table 4 , +0.64,28318272,"The odor-active constituents of cocoa pulp have been analyzed by aroma extract dilution analysis (AEDA) for the first time. Pulps of three different cocoa varieties have been investigated. The variety CCN51 showed low flavor intensities, in terms of flavor dilution (FD) factors, in comparison to varieties FSV41 and UF564, for which floral and fruity notes were detected in higher intensities. To gain first insights on a molecular level of how the cocoa pulp odorants affected the odor quality of cocoa beans during fermentation, quantitative measurements of selected aroma compounds were conducted in pulp and bean at different time points of the fermentation. The results showed significantly higher concentrations of 2-phenylethanol and 3-methylbutyl acetate in pulp than in the bean during the different time steps of the fermentation, whereas the reverse could be observed for the odorants linalool and 2-methoxyphenol. The findings of this study constitute a basis for further investigations on the aroma formation of cocoa during fermentation.",Journal of agricultural and food chemistry,"['D002099', 'D005285', 'D005421', 'D008401', 'D009812', 'D012441', 'D012639']","['Cacao', 'Fermentation', 'Flavoring Agents', 'Gas Chromatography-Mass Spectrometry', 'Odorants', 'Saccharomyces cerevisiae', 'Seeds']",Investigations on the Aroma of Cocoa Pulp ( Theobroma cacao L.) and Its Influence on the Odor of Fermented Cocoa Beans.,"['Q000737', None, 'Q000032', None, 'Q000032', 'Q000378', 'Q000737']","['chemistry', None, 'analysis', None, 'analysis', 'metabolism', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28318272,2018,0,0,,if this can be used as unquantified then add them +0.63,19191673,"Procyanidins (PCs) are highly abundant phenolic compounds in the human diet and might be responsible for the health effects of chocolate and wine. Due to low absorption of intact PCs, microbial metabolism might play an important role. So far, only a few studies, with crude extracts rich in PCs but also containing a multitude of other phenolic compounds, have been performed to reveal human microbial PC metabolites. Therefore, the origin of the metabolites remains questionable. This study included in vitro fermentation of purified PC dimers with human microbiota. The main metabolites identified were 2-(3,4-dihydroxyphenyl)acetic acid and 5-(3,4-dihydroxyphenyl)-gamma-valerolactone. Other metabolites detected were 3-hydroxyphenylacetic acid, 4-hydroxyphenylacetic acid, 3-hydroxyphenylpropionic acid, phenylvaleric acids, monohydroxylated phenylvalerolactone, and 1-(3',4'-dihydroxyphenyl)-3-(2'',4'',6''-trihydroxyphenyl)propan-2-ol. Metabolites that could be quantified accounted for at least 12 mol % of the dimers, assuming 1 mol of dimers is converted into 2 mol of metabolite. A degradation pathway, partly different from that of monomeric flavan-3-ols, is proposed.",Journal of agricultural and food chemistry,"['D015102', 'D002851', 'D019281', 'D005243', 'D005285', 'D005707', 'D056604', 'D006801', 'D007783', 'D013058', 'D010936', 'D044945']","['3,4-Dihydroxyphenylacetic Acid', 'Chromatography, High Pressure Liquid', 'Dimerization', 'Feces', 'Fermentation', 'Gallic Acid', 'Grape Seed Extract', 'Humans', 'Lactones', 'Mass Spectrometry', 'Plant Extracts', 'Proanthocyanidins']","Procyanidin dimers are metabolized by human microbiota with 2-(3,4-dihydroxyphenyl)acetic acid and 5-(3,4-dihydroxyphenyl)-gamma-valerolactone as the major metabolites.","['Q000032', None, None, 'Q000382', None, 'Q000302', None, None, 'Q000032', None, 'Q000737', 'Q000737']","['analysis', None, None, 'microbiology', None, 'isolation & purification', None, None, 'analysis', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19191673,2009,0,0,,no cocoa +0.63,17674523,"In the article results of comparative analysis of grated cocoa and cocoa butter samples are presented. The investigation was done by modern instrumental methods such as HPLC, GC, UV- VIS-spectroscopy, and also with application of titrimetric and grarimetric methods. In the analyzed samples contents of total phenolics changes in an interval 1,0-3,2%, including monomeric proantocyanidins 0,6-1,35%; pyrroloquinoline quinine (PQQ) 0,34-0,76 microg/g; phenyl ethylamine from 2,79 to 14,97 microg/g, tyramine from 9,56 to 71,68 microg/g, dopamine from 5,3 to 25,85 microg/g; theobromine from 3,3 to 8%, caffeine from 0,49 to 0,70%; among the amino acids at the greatest quantities were presented glutaminic and asparaginic acids, arginin and leucin; three main fatty acids were determined - palmitinic (31+/-2% rel.), oleinic (35+/-2% rel.) and stearinic (35+/-2% rel.); the main phytosterins were sytosterin (up to 192 mg%) and obtusifoliol (up to 198,5 mg%).",Voprosy pitaniia,"['D000470', 'D000596', 'D001679', 'D001688', 'D002099', 'D002849', 'D002851', 'D004041', 'D005419', 'D010636', 'D010840', 'D059808', 'D013056']","['Alkaloids', 'Amino Acids', 'Biogenic Amines', 'Biological Products', 'Cacao', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Flavonoids', 'Phenols', 'Phytosterols', 'Polyphenols', 'Spectrophotometry, Ultraviolet']",[Biologically active substances in grated cocoa and cocoa butter].,"['Q000032', 'Q000032', 'Q000032', 'Q000302', 'Q000737', None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, None]","['analysis', 'analysis', 'analysis', 'isolation & purification', 'chemistry', None, None, 'analysis', 'analysis', 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/17674523,2007,,,, +0.63,27784867,"Discriminating vegetable oils and animal and milk fats by infrared absorption spectroscopy is difficult due to similarities in their spectral patterns. Therefore, a rapid and simple method for analyzing vegetable oils, animal fats, and milk fats using TOF/MS with an APCI direct probe ion source was developed. This method enabled discrimination of these oils and fats based on mass spectra and detailed analyses of the ions derived from sterols, even in samples consisting of only a few milligrams. Analyses of the mass spectra of processed foods containing oils and milk fats, such as butter, cheese, and chocolate, enabled confirmation of the raw material origin based on specific ions derived from the oils and fats used to produce the final product.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D005223', 'D005504', 'D005511', 'D013058', 'D010938']","['Fats', 'Food Analysis', 'Food Handling', 'Mass Spectrometry', 'Plant Oils']",Analysis of Processed Foods Containing Oils and Fats by Time of Flight Mass Spectrometry with an APCI Direct Probe.,"['Q000032', 'Q000379', None, 'Q000295', 'Q000032']","['analysis', 'methods', None, 'instrumentation', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27784867,2017,,,,Access to pdf in japanese only +0.63,25062492,"Theobroma cacao is a woody and recalcitrant plant with a very high level of interfering compounds. Standard protocols for protein extraction were proposed for various types of samples, but the presence of interfering compounds in many samples prevented the isolation of proteins suitable for two-dimensional gel electrophoresis (2-DE). An efficient method to extract root proteins for 2-DE was established to overcome these problems. The main features of this protocol are: i) precipitation with trichloroacetic acid/acetone overnight to prepare the acetone dry powder (ADP), ii) several additional steps of sonication in the ADP preparation and extractions with dense sodium dodecyl sulfate and phenol, and iii) adding two stages of phenol extractions. Proteins were extracted from roots using this new protocol (Method B) and a protocol described in the literature for T. cacao leaves and meristems (Method A). Using these methods, we obtained a protein yield of about 0.7 and 2.5 mg per 1.0 g lyophilized root, and a total of 60 and 400 spots could be separated, respectively. Through Method B, it was possible to isolate high-quality protein and a high yield of roots from T. cacao for high-quality 2-DE gels. To demonstrate the quality of the extracted proteins from roots of T. cacao using Method B, several protein spots were cut from the 2-DE gels, analyzed by tandem mass spectrometry, and identified. Method B was further tested on Citrus roots, with a protein yield of about 2.7 mg per 1.0 g lyophilized root and 800 detected spots. ",Genetics and molecular research : GMR,"['D000096', 'D002099', 'D015180', 'D059625', 'D013058', 'D018519', 'D019800', 'D018515', 'D010940', 'D018517', 'D012967', 'D012997', 'D013010', 'D014238']","['Acetone', 'Cacao', 'Electrophoresis, Gel, Two-Dimensional', 'Liquid-Liquid Extraction', 'Mass Spectrometry', 'Meristem', 'Phenol', 'Plant Leaves', 'Plant Proteins', 'Plant Roots', 'Sodium Dodecyl Sulfate', 'Solvents', 'Sonication', 'Trichloroacetic Acid']",Efficient method of protein extraction from Theobroma cacao L. roots for two-dimensional gel electrophoresis and mass spectrometry analyses.,"[None, 'Q000737', None, 'Q000379', None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', None, None, None, None]","[None, 'chemistry', None, 'methods', None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25062492,2015,0,0,,roots of the tree +0.63,9246735,"Oral carbohydrate clearance and acid production were monitored over a two hour time period following the ingestion of six foods (chocolate bar, potato chip, oreo cookie, sugar cube, raisin and jelly bean). Each food was evaluated intra-orally in eight volunteers. Oral fluid samples were obtained from each volunteer at 30 min intervals at five different tooth sites using absorbent paper points. The oral fluid samples were analyzed qualitatively and quantitatively for carbohydrates and organic acids using high performance liquid chromatography. Data obtained for each food were averaged and subjected to statistical analysis. The quantity of lactic acid produced 30 min after ingestion was found to be in the following order: (highest) raisin > chocolate bar > sugar cube > jelly bean > oreo cookie > potato chip (least). Two hours after food intake the order had changed significantly: potato chip > jelly bean > sugar cube > chocolate bar > oreo cookie > raisin. A direct linear relationship existed between lactic acid production and the presence of glucose. In foods containing cooked starch prolonged clearance occurs via the intermediate metabolites maltotriose, maltose and glucose. Results indicated that the term 'stickiness', when used to label certain foods such as jelly bean and chocolate bar, should be used cautiously. Foods containing only cooked starch or cooked starch and sugars can be considered as 'sticky', since glucose arising from their intra-oral degradation contributed to acid production over prolonged periods of time.",Zeitschrift fur Ernahrungswissenschaft,"['D000328', 'D002099', 'D002851', 'D004040', 'D019422', 'D005638', 'D006801', 'D007700', 'D007773', 'D012463', 'D011198', 'D013997']","['Adult', 'Cacao', 'Chromatography, High Pressure Liquid', 'Dietary Carbohydrates', 'Dietary Sucrose', 'Fruit', 'Humans', 'Kinetics', 'Lactates', 'Saliva', 'Solanum tuberosum', 'Time Factors']",Intra-oral lactic acid production during clearance of different foods containing various carbohydrates.,"[None, None, None, 'Q000378', 'Q000378', None, None, None, 'Q000378', 'Q000737', None, None]","[None, None, None, 'metabolism', 'metabolism', None, None, None, 'metabolism', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9246735,1997,,,, +0.63,12621878,"The content of fat and fatty acids in 13 selected snack products (nuts and seeds) purchased on the marked in Warsaw region in 2000 have been investigated. The content of fat in examined products varied from 41% to 68%. The fat of nuts and seeds was rich in unsaturated fatty acids, except cocoa product.",Roczniki Panstwowego Zakladu Higieny,"['D002849', 'D004042', 'D005231', 'D006801', 'D009754', 'D011044', 'D012639']","['Chromatography, Gas', 'Dietary Fats, Unsaturated', 'Fatty Acids, Unsaturated', 'Humans', 'Nuts', 'Poland', 'Seeds']",[The content of fat and fatty acids in selected snack products (nuts and seeds)].,"[None, 'Q000032', 'Q000032', None, 'Q000737', None, 'Q000737']","[None, 'analysis', 'analysis', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12621878,2003,,,, +0.63,16019834,"The aim of this study was to investigate the influence of the shelling process on the presence of ochratoxin A (OTA) in cocoa samples. Twenty-two cocoa samples were analysed for the determination of OTA before (cocoa bean) and after undergoing manual shelling process (cocoa nib). In order to determine OTA contamination in cocoa samples, a validated high-performance liquid chromatography (HPLC) method with fluorescence detection was used for the quantitative analysis of ochratoxin A (OTA). In both types of samples, OTA was extracted with methanol-3% sodium hydrogen carbonate solution and then purified using immunoaffinity columns prior to HPLC analysis. Due to the fact that different recovery values were obtained for OTA from both types of samples, a revalidation of the method in the case of cocoa nibs was needed. Revalidation was based on the following criteria: Selectivity, limits of detection and quantification (0.03 and 0.1 microg kg(-1), respectively), precision (within-day and between-day variability) and recovery 84.2% (RSD = 7.1%), and uncertainty (30%). Fourteen of the twenty-two cocoa bean samples (64%) suffered a loss of OTA of more than 95% due to shelling, six samples suffered a loss of OTA in the range 65-95%, and only one sample presented a reduction of less than 50%. The principal conclusion derived from this study is that OTA contamination in cocoa beans is concentrated in the shell; therefore, improvements of the industrial shelling process could prevent OTA occurrence in cocoa final products.",Food additives and contaminants,"['D002099', 'D002851', 'D005504', 'D005506', 'D005511', 'D009793']","['Cacao', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Food Contamination', 'Food Handling', 'Ochratoxins']",Occurrence of ochratoxin A in cocoa beans: effect of shelling.,"['Q000737', 'Q000379', 'Q000379', 'Q000032', 'Q000379', 'Q000032']","['chemistry', 'methods', 'methods', 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16019834,2005,,,, +0.63,15264891,"An improved sample preparation (extraction and cleanup) is presented that enables the quantification of low levels of acrylamide in difficult matrixes, including soluble chocolate powder, cocoa, coffee, and coffee surrogate. Final analysis is done by isotope-dilution liquid chromatography-electrospray ionization tandem mass spectrometry (LC-MS/MS) using d3-acrylamide as internal standard. Sample pretreatment essentially encompasses (a) protein precipitation with Carrez I and II solutions, (b) extraction of the analyte into ethyl acetate, and (c) solid-phase extraction on a Multimode cartridge. The stability of acrylamide in final extracts and in certain commercial foods and beverages is also reported. This approach provided good performance in terms of linearity, accuracy and precision. Full validation was conducted in soluble chocolate powder, achieving a decision limit (CCalpha) and detection capability (CCbeta) of 9.2 and 12.5 microg/kg, respectively. The method was extended to the analysis of acrylamide in various foodstuffs such as mashed potatoes, crisp bread, and butter biscuit and cookies. Furthermore, the accuracy of the method is demonstrated by the results obtained in three inter-laboratory proficiency tests.",Journal of agricultural and food chemistry,"['D020106', 'D002099', 'D002853', 'D003069', 'D004355', 'D013058']","['Acrylamide', 'Cacao', 'Chromatography, Liquid', 'Coffee', 'Drug Stability', 'Mass Spectrometry']","Improved sample preparation to determine acrylamide in difficult matrixes such as chocolate powder, cocoa, and coffee by liquid chromatography tandem mass spectroscopy.","['Q000032', 'Q000737', 'Q000379', 'Q000737', None, 'Q000379']","['analysis', 'chemistry', 'methods', 'chemistry', None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/15264891,2004,1,1,table 3 and 5,only cocoa +0.63,1479781,"The qualitative and quantitative analytical methods were proposed for the simple and rapid determination of triacetin (TAc) in commercial gummy candies and other foodstuffs by gas chromatography (GC), thin layer chromatography (TLC) and infrared spectroscopy (IR). Each extract from the samples was obtained by pretreatment of the foodstuffs as follows: (A) Gummy candy was dissolved in warm water and the solution was extracted with chloroform. The organic (chloroform) layer was separated. (B) Samples (such as ice cream) containing substantial water were mixed with anhydrous Na2SO4 and stirred to sandy appearance and dried. The residue was homogenized with ether, followed by centrifuging, and the organic (ether) layer was separated. (C) Dried samples (such as chocolate and cookie) were smashed, homogenized with ether, and followed by centrifuging, and the organic (ether) layer was separated. (D) Candy was dissolved in warm water and the solution was extracted with ether. The organic (ether) layer was separated. Each organic layer from (A)-(D) was washed with 10% NaHCO3 and evaporated. The residue containing TAc was dissolved in dichloromethane. The extract obtained was subjected to column chromatography on silica gel. The fractions containing TAc were employed in GC with 25% PEG-20M column, TLC, and IR analyses. Recovery of TAc from gummy candy was 99.1 +/- 3.0% and those from other foodstuffs ranged from was 82.1 to 99.4% by GC. Detection limit by this method was 10 ppm. TAc was found to contain at a level as high as 550 ppm in one domestic gummy candy. On the other hand, one imported gummy candy contained no more than 20 ppm of TAc gummy candy.",The Kitasato archives of experimental medicine,"['D001628', 'D002099', 'D002182', 'D002849', 'D002855', 'D005503', 'D005504', 'D007054', 'D012997', 'D013055', 'D014215']","['Beverages', 'Cacao', 'Candy', 'Chromatography, Gas', 'Chromatography, Thin Layer', 'Food Additives', 'Food Analysis', 'Ice Cream', 'Solvents', 'Spectrophotometry, Infrared', 'Triacetin']",Triacetin as food additive in gummy candy and other foodstuffs on the market.,"['Q000032', 'Q000737', 'Q000032', None, None, 'Q000032', None, 'Q000032', None, None, 'Q000032']","['analysis', 'chemistry', 'analysis', None, None, 'analysis', None, 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/1479781,1993,,,, +0.63,19007497,"A reverse-phase liquid chromatography analysis is used to access the quantity of theobromine, (+)-catechin, caffeine, and (-)-epicatechin in Standard Reference Material 2384 Baking Chocolate, cocoa, cocoa beans, and cocoa butter using water or a portion of the mobile phase as the extract. The procedure requires minimal sample preparation. Theobromine, (+)-catechin, caffeine, and (-)-epicatechin are detected by UV absorption at 273 nm after separation using a 0.3% acetic acid-methanol gradient (volume fractions) and quantified using external standards. The limit of detection for theobromine, (+)-catechin, caffeine, and (-)-epicatechin averages 0.08, 0.06, 0.06, and 0.06 microg/mL, respectively. The method when applied to Standard Reference Material 2384 Baking Chocolate; baking chocolate reference material yields results that compare to two different, separate procedures. Theobromine ranges from 26000 mg/kg in cocoa to 140 mg/kg in cocoa butter; (+)-catechin from 1800 mg/kg in cocoa to below detection limits of < 32 mg/kg in cocoa butter; caffeine from 2400 mg/kg in cocoa to 400 mg/kg in cocoa butter, and (-)-epicatechin from 3200 mg/kg in cocoa to BDL, < 27 mg/kg, in cocoa butter. The mean recoveries from cocoa are 102.4 +/- 0.6% for theobromine, 100.0 +/- 0.6 for (+)-catechin, 96.2 +/- 2.1 for caffeine, and 106.2 +/- 1.7 for (-)-epicatechin.",Journal of chromatographic science,"['D002099', 'D002110', 'D002392', 'D002851', 'D004041', 'D015203', 'D013805']","['Cacao', 'Caffeine', 'Catechin', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Reproducibility of Results', 'Theobromine']","Simultaneous determination of theobromine, (+)-catechin, caffeine, and (-)-epicatechin in standard reference material baking chocolate 2384, cocoa, cocoa beans, and cocoa butter.","['Q000737', 'Q000032', 'Q000032', None, 'Q000032', None, 'Q000032']","['chemistry', 'analysis', 'analysis', None, 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19007497,2008,,,, +0.63,3391947,"A method for the quantitative determination of monoethylene glycol (MEG) and diethylene glycol (DEG) in chocolate is described. The procedure involves dissolving the chocolate in hot water, defatting with hexane, removing sugars by precipitation, and analyzing as trimethylsilyl (TMS) ether derivatives by capillary gas chromatography. The use of butan-1,4-diol as an internal standard corrects for recovery, which is between 50 and 60%, to give a relative standard deviation of 10-11% for the determination of both glycols at the level of 50 mg/kg. The presence of MEG and DEG in chocolate is confirmed by full scanning gas chromatography/mass spectrometry of the TMS derivatives.",Journal - Association of Official Analytical Chemists,"['D002099', 'D002482', 'D002849', 'D019855', 'D005026', 'D005511', 'D005519', 'D010945', 'D012997']","['Cacao', 'Cellulose', 'Chromatography, Gas', 'Ethylene Glycol', 'Ethylene Glycols', 'Food Handling', 'Food Preservation', 'Plants, Edible', 'Solvents']",Gas chromatographic determination of monoethylene glycol and diethylene glycol in chocolate packaged in regenerated cellulose film.,"['Q000032', 'Q000032', None, None, 'Q000032', None, None, 'Q000032', None]","['analysis', 'analysis', None, None, 'analysis', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/3391947,1988,,,, +0.63,730638,"A method for determining aflatoxins by high pressure liquid chromatography (HPLC) with fluorescence detection after CB extraction and cleanup has been applied to various foods. Recoveries at 1--15 ppb levels from green coffee and peanut butter was 72--85 and 74--104%, respectively. Precision of the method has been tested for peanut butter. Other products to which the method has been successfully applied include tree nuts, seeds, grains, chocolate-covered peanut butter candy, and roasted, salted-in-shell peanuts. High levels of aflatoxins found in several samples of nuts by this method have been verified by the official thin layer chromatographic (TLC) method. The advantages of this HPLC method are speed, precision, sensitivity, selectivity, and immediate chemical confirmation of aflatoxins B1 and G1. None of the products analyzed required special cleanup procedures. Preparative-scale HPLC was used to isolate purified B1 for toxicity testing.",Journal - Association of Official Analytical Chemists,"['D000348', 'D010367', 'D002851', 'D005504']","['Aflatoxins', 'Arachis', 'Chromatography, High Pressure Liquid', 'Food Analysis']",Reverse phase high pressure liquid chromatographic determination of aflatoxins in foods.,"['Q000032', 'Q000032', 'Q000379', None]","['analysis', 'analysis', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/730638,1979,,,, +0.63,27418182,"Fast methods for the extraction and analysis of various secondary metabolites from cocoa products were developed and optimized regarding speed and separation efficiency. Extraction by pressurized liquid extraction is automated and the extracts are analyzed by rapid reversed-phase ultra high-performance liquid chromatography and normal-phase high-performance liquid chromatography methods. After extraction, no further sample treatment is required before chromatographic analysis. The analytes comprise monomeric and oligomeric flavanols, flavonols, methylxanthins, N-phenylpropenoyl amino acids, and phenolic acids. Polyphenols and N-phenylpropenoyl amino acids are separated in a single run of 33 min, procyanidins are analyzed by normal-phase high-performance liquid chromatography within 16 min, and methylxanthins require only 6 min total run time. A fourth method is suitable for phenolic acids, but only protocatechuic acid was found in relevant quantities. The optimized methods were validated and applied to 27 dark chocolates, one milk chocolate, two cocoa powders and two food supplements based on cocoa extract. ",Journal of separation science,"['D002099', 'D005591', 'D002851', 'D010936', 'D059808', 'D064210']","['Cacao', 'Chemical Fractionation', 'Chromatography, High Pressure Liquid', 'Plant Extracts', 'Polyphenols', 'Secondary Metabolism']",Fast and comprehensive analysis of secondary metabolites in cocoa products using ultra high-performance liquid chromatography directly after pressurized liquid extraction.,"['Q000737', 'Q000379', 'Q000379', 'Q000032', 'Q000737', None]","['chemistry', 'methods', 'methods', 'analysis', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/27418182,2018,1,3,"table S2.1, S2.1 and S3.1",all the contents are in the supplement material for the paper +0.62,8896285,"A high intake of trans fatty acids in children may be disadvantageous because of untoward effects on lipoprotein metabolism and a possible impairment of arachidonic acid synthesis. We measured the trans fatty acid content of different brands of spreads and cold cuts typically consumed by German children because these foods may contribute a considerable portion of total trans fatty acid intake. The highest trans fatty acid contents were found in regular margarines (4.5, 0.0-10.6; median %-wt/wt of fatty acids, minimal-maximal), chocolate spreads (5.5, 0.7-11.1), butter (4.7, 3.7-5.2) and cheese (3.6, 1.8-4.0), while lower values were present in diet margarines (0.2, 0.0-0.4), vegetarian spreads (0.2, 0.1-0.4), peanut butter (0.0, 0.0-0.3) and sausages (1.7, 0.6-6.4). Calculations of typical dietary plans for young children show that food selection and variations in trans fatty acid contents may lead to marked differences in daily trans intake of > 100% (3.1 g/d vs. 1.5 g/d). We propose that trans fatty acid content should be declared on labels of fatty food products to enable the consumer to choose, and further attempts should be made to lower trans fatty acid formation during technical hydrogenation.",Zeitschrift fur Ernahrungswissenschaft,"['D000818', 'D010367', 'D002079', 'D002099', 'D002611', 'D002648', 'D002849', 'D003611', 'D004041', 'D004951', 'D005227', 'D006801', 'D008383', 'D008461']","['Animals', 'Arachis', 'Butter', 'Cacao', 'Cheese', 'Child', 'Chromatography, Gas', 'Dairy Products', 'Dietary Fats', 'Esterification', 'Fatty Acids', 'Humans', 'Margarine', 'Meat Products']",Trans fatty acid contents in spreads and cold cuts usually consumed by children.,"[None, 'Q000737', 'Q000032', 'Q000737', 'Q000032', None, None, 'Q000032', 'Q000032', None, 'Q000032', None, 'Q000032', 'Q000032']","[None, 'chemistry', 'analysis', 'chemistry', 'analysis', None, None, 'analysis', 'analysis', None, 'analysis', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/8896285,1997,,,, +0.62,28207258,"Cocoa is known as an important source of flavan-3-ols, but their fate ""from the bean to the bar"" is not yet clear. Here, procyanidin A2 found in native cocoa beans (9-13 mg/kg) appeared partially epimerized into A2",Journal of agricultural and food chemistry,"['D002099', 'D002392', 'D003296', 'D005285', 'D013058', 'D015394', 'D010936', 'D044945', 'D012639']","['Cacao', 'Catechin', 'Cooking', 'Fermentation', 'Mass Spectrometry', 'Molecular Structure', 'Plant Extracts', 'Proanthocyanidins', 'Seeds']","Procyanidin A2 and Its Degradation Products in Raw, Fermented, and Roasted Cocoa.","['Q000737', 'Q000737', None, None, None, None, 'Q000737', 'Q000737', 'Q000737']","['chemistry', 'chemistry', None, None, None, None, 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28207258,2017,1,1,text under results and discussion first sub section , +0.62,19753497,"We report the migration potential of newly patented low-migration offset printing inks from cardboard food packaging and estimate the potential risk of their migration into food. The complete printing formulation was available and, due to the fact that the solvent compounds in these inks differ from those used in conventional printing inks, the investigation focused on these solvents. Instead of containing mineral and vegetable oils, the low-migration printing inks are based on a novel fatty acid ester. The migration of this alternative solvent was investigated according to DIN EN 14338 in Tenax simulant and in different types of food. For specific detection of the fatty acid ester, LC-MS/MS (APCI) was chosen due to its higher sensitivity and selectivity than GC/MS. Printed packaging materials from three different commercially available food products (meat, chocolate and sweets) were tested. Migration of the fatty acid ester from the packaging into simulants was analysed. For food samples, a clean-up method based on solid-phase extraction was developed and migration of the fatty acid ester into meat, chocolate and sweets was also demonstrated. Levels of contamination of these foods were between 5 and 80 microg fatty acid ester/kg, but levels in food were lower than those in simulants.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D004058', 'D005504', 'D005506', 'D018857', 'D006801', 'D007281', 'D018570', 'D053719']","['Diffusion', 'Food Analysis', 'Food Contamination', 'Food Packaging', 'Humans', 'Ink', 'Risk Assessment', 'Tandem Mass Spectrometry']",Migration of novel offset printing inks from cardboard packaging into food.,"[None, 'Q000379', 'Q000032', 'Q000592', None, None, 'Q000379', 'Q000379']","[None, 'methods', 'analysis', 'standards', None, None, 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/19753497,2010,0,0,,no cocoa +0.62,16843047,"Highly sensitive and interference-free sensitized spectrophotometric method for the determination of Ni(II) ions is described. The method is based on the reaction between Ni(II) ion and benzyl dioxime in micellar media in the presence of sodium dodecyl sulfate (SDS). The absorbance is linear from 0.1 up to 25.0 microg mL-1 in aqueous solution with repeatability (RSD) of 1.0% at a concentration of 1 microg mL-1 and a detection limit of 0.12 ng mL-1 and molar absorption coefficient of 68,600L mol-1 cm-1. The influence of reaction variables including type and amount of surfactant, pH, and amount of ligand and complexation time and the effect of interfering ions are investigated. The proposed procedure was applied to the determination of trace amounts of Ni(II) ion in tap water, river water, chocolate and vegetable without separation or organic solvent extraction.","Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy","['D002413', 'D005504', 'D008024', 'D009532', 'D010091', 'D012680', 'D012967', 'D013053', 'D013501']","['Cations, Divalent', 'Food Analysis', 'Ligands', 'Nickel', 'Oximes', 'Sensitivity and Specificity', 'Sodium Dodecyl Sulfate', 'Spectrophotometry', 'Surface-Active Agents']",Selective and sensitized spectrophotometric determination of trace amounts of Ni(II) ion using alpha-benzyl dioxime in surfactant media.,"['Q000032', 'Q000379', None, 'Q000032', 'Q000737', None, 'Q000737', None, 'Q000737']","['analysis', 'methods', None, 'analysis', 'chemistry', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16843047,2007,0,0,,no cocoa +0.62,24446916,"The occurrence of the bioactive components caffeine (xanthine alkaloid), myosmine and nicotine (pyridine alkaloids) in different edibles and plants is well known, but the content of myosmine and nicotine is still ambiguous in milk/dark chocolate. Therefore, a sensitive method for determination of these components was established, a simple separation of the dissolved analytes from the matrix, followed by headspace solid-phase microextraction coupled with gas chromatography-tandem mass spectrometry (HS-SPME-GC-MS/MS). This is the first approach for simultaneous determination of caffeine, myosmine, and nicotine with a convenient SPME technique. Calibration curves were linear for the xanthine alkaloid (250 to 3000 mg/kg) and the pyridine alkaloids (0.000125 to 0.003000 mg/kg). Residuals of the calibration curves were lower than 15%, hence the limits of detection were set as the lowest points of the calibration curves. The limits of detection calculated from linearity data were for caffeine 216 mg/kg, for myosmine 0.000110 mg/kg, and for nicotine 0.000120 mg/kg. Thirty samples of 5 chocolate brands with varying cocoa contents (30% to 99%) were analyzed in triplicate. Caffeine and nicotine were detected in all samples of chocolate, whereas myosmine was not present in any sample. The caffeine content ranged from 420 to 2780 mg/kg (relative standard deviation 0.1 to 11.5%) and nicotine from 0.000230 to 0.001590 mg/kg (RSD 2.0 to 22.1%). ",Journal of food science,"['D000470', 'D001628', 'D002099', 'D002110', 'D002138', 'D002182', 'D003611', 'D005506', 'D005513', 'D057141', 'D008401', 'D005858', 'D057230', 'D009538', 'D010858', 'D052617', 'D053719', 'D014835']","['Alkaloids', 'Beverages', 'Cacao', 'Caffeine', 'Calibration', 'Candy', 'Dairy Products', 'Food Contamination', 'Food Inspection', 'Food, Preserved', 'Gas Chromatography-Mass Spectrometry', 'Germany', 'Limit of Detection', 'Nicotine', 'Pigmentation', 'Solid Phase Microextraction', 'Tandem Mass Spectrometry', 'Volatilization']","Determination of caffeine, myosmine, and nicotine in chocolate by headspace solid-phase microextraction coupled with gas chromatography-tandem mass spectrometry.","['Q000032', 'Q000032', 'Q000737', 'Q000032', None, 'Q000032', 'Q000032', None, 'Q000379', 'Q000032', None, None, None, 'Q000032', None, None, None, None]","['analysis', 'analysis', 'chemistry', 'analysis', None, 'analysis', 'analysis', None, 'methods', 'analysis', None, None, None, 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24446916,2014,0,0,,chocolates with different content of cocoa +0.62,12926907,Sorghum procyanidins were characterized and quantified from two brown sorghum varieties and their processed products by normal phase HPLC with fluorescence detection. The DP of the procyanidins was determined by thiolysis. Quantification was done by using purified oligomeric and polymeric cocoa procyanidins as external standards. Sorghum procyanidins were composed mostly of high MW (DP > 10) polymers. Significant differences were observed in levels as well as distribution of the different MW procyanidins between the sorghums. Processing of the sorghum brans into cookies and bread significantly reduced the levels of procyanidins; this effect was more pronounced in the higher MW polymers. Cookies had a higher retention of procyanidins (42-84%) than bread (13-69%). Extrusion of sorghum grain resulted in an increase in the levels of procyanidin oligomers with DP /= 6. This suggests a possible breakdown of the high MW polymers to the lower MW constituents during extrusion. Processing changes not only the content of procyanidins in sorghum products but also the relative ratio of the different molecular weights.,Journal of agricultural and food chemistry,"['D044946', 'D001939', 'D002392', 'D002851', 'D005511', 'D006358', 'D008970', 'D006109', 'D011108', 'D044945']","['Biflavonoids', 'Bread', 'Catechin', 'Chromatography, High Pressure Liquid', 'Food Handling', 'Hot Temperature', 'Molecular Weight', 'Poaceae', 'Polymers', 'Proanthocyanidins']",Processing of sorghum (Sorghum bicolor) and sorghum products alters procyanidin oligomer and polymer distribution and content.,"[None, 'Q000032', 'Q000032', None, None, None, None, 'Q000737', 'Q000032', None]","[None, 'analysis', 'analysis', None, None, None, None, 'chemistry', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/12926907,2003,1,1,table 1 ,the rest of the info could be found in the cited paper +0.62,25494681,"Acrylamide (AA) levels in conventional (n = 112) and traditional (n = 43) Colombian foods were analysed by gas chromatography with mass spectrometry (GC/MS) detection. Samples included: infant powdered formula, coffee and chocolate powders, corn snacks, bakery products and tuber-, meat- and vegetable-based foods. There was a wide variability in AA levels among different foods and within different brands of the same food, especially for coffee powder, breakfast cereals biscuits and French fries samples. Among the conventional foods tested, the highest mean AA value was found in bakery products, such as biscuit (1104 _µg kg(-1)) and wafer (1449 _µg kg(-1)), followed by potato chips (916 _µg kg(-1)). On the other hand, among the traditional foods, higher AA amounts were detected in fried platano (2813 _µg kg(-1)) and yuca (3755 _µg kg(-1)) compared to other products. Interestingly, the arepa, a traditional Colombian bakery product made with corn flour, showed a lower AA content (< 75 _µg kg(-1)) when compared with similar bakery products tested, such as soft bread (102-594 _µg kg(-1)), which is a made with wheat flour.","Food additives & contaminants. Part B, Surveillance","['D020106', 'D003069', 'D003105', 'D005506', 'D008401', 'D006801', 'D007223', 'D041943']","['Acrylamide', 'Coffee', 'Colombia', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Infant', 'Infant Formula']",Acrylamide levels in selected Colombian foods.,"['Q000032', 'Q000737', None, 'Q000032', 'Q000379', None, None, 'Q000737']","['analysis', 'chemistry', None, 'analysis', 'methods', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25494681,2016,0,0,,no cocoa +0.62,26138682,"With the revision of the European Tobacco Products Directive (2014/40/EU), characterizing flavors such as strawberry, candy, vanillin or chocolate will be prohibited in cigarettes and fine-cut tobacco. Product surveillance will therefore require analytical means to define and subsequently detect selected characterizing flavors that are formed by supplemented flavors within the complex matrix tobacco. We have analyzed strawberry-flavored tobacco products as an example for characterizing fruit-like aroma. Using this approach, we looked into aroma components to find indicative patterns or features that can be used to satisfy obligatory product information as requested by the European Directive. Accordingly, a headspace solid-phase microextraction (HS-SPME) technique was developed and coupled to subsequent gas chromatography-mass spectrometry (GC/MS) to characterize different strawberry-flavored tobacco products (cigarettes, fine-cut tobacco, liquids for electronic cigarettes, snus, shisha tobacco) for their volatile additives. The results were compared with non-flavored, blend characteristic flavored and other fruity-flavored cigarettes, as well as fresh and dried strawberries. Besides different esters and aldehydes, the terpenes linalool, _±-terpineol, nerolidol and limonene as well as the lactones __-decalactone, __-dodecalactone and __-undecalactone could be verified as compounds sufficient to convey some sort of strawberry flavor to tobacco. Selected flavors, i.e., limonene, linalool, _±-terpineol, citronellol, carvone and __-decalactone, were analyzed further with respect to their stereoisomeric composition by using enantioselective HS-SPME-GC/MS. These experiments confirmed that individual enantiomers that differ in taste or physiological properties can be distinguished within the tobacco matrix. By comparing the enantiomeric composition of these compounds in the tobacco with that of fresh and dried strawberries, it can be concluded that non-natural strawberry aroma is usually used to produce strawberry-flavored tobacco products. Such authenticity control can become of interest particularly when manufacturers claim that natural sources were used for flavoring of products. Although the definition of characterizing flavors by analytical means remains challenging, specific compounds or features are required to be defined for routine screening of reported information. Clarifications by sensory testing might still be necessary, but could be limited to a few preselected samples. ",Archives of toxicology,"['D005062', 'D005421', 'D031985', 'D008401', 'D033161', 'D040541', 'D052617', 'D013237', 'D014026', 'D062789', 'D055549']","['European Union', 'Flavoring Agents', 'Fragaria', 'Gas Chromatography-Mass Spectrometry', 'Government Regulation', 'Marketing', 'Solid Phase Microextraction', 'Stereoisomerism', 'Tobacco', 'Tobacco Products', 'Volatile Organic Compounds']",Toward the stereochemical identification of prohibited characterizing flavors in tobacco products: the case of strawberry flavor.,"[None, 'Q000032', 'Q000737', None, None, 'Q000331', None, None, 'Q000737', 'Q000032', 'Q000032']","[None, 'analysis', 'chemistry', None, None, 'legislation & jurisprudence', None, None, 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26138682,2016,0,0,,no cocoa +0.61,16009604,"A high-performance liquid chromatography (HPLC) method to determine malondialdehyde (MDA) as the 2,4-dinitrophenylhydrazine (DNPH) derivative was applied to biological samples (serum and liver homogenates). Since MDA is considered a presumptive biomarker for lipid peroxidation in live organisms, a model for nutritionally induced oxidative stress (hypercholesterolemic rats) was studied in comparison with normocholesterolemic animals. The effect of diet supplementation with fruits rich in antioxidant polyphenols was assessed. The proposed method showed to be precise and reproducible, as well as sensitive enough to reflect differences in the oxidative status in vivo. A significant decrease of serum and liver MDA concentrations in animals fed diets containing 0.3% of polyphenols from strawberry, cocoa or plum was observed in the normocholesterolemic groups. This reduction was especially noteworthy in the hypercholesterolemic animals, with increased MDA levels indicating enhanced lipid peroxidation in the controls, yet with values parallel to the normocholesterolemic groups in animals fed the polyphenol-rich diets. These results point out the beneficial effects of phenolic antioxidants from fruits in preventing oxidative damage in vivo.","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000818', 'D000975', 'D015415', 'D002851', 'D004032', 'D004195', 'D005638', 'D006937', 'D008099', 'D008297', 'D008315', 'D018384', 'D010636', 'D051381', 'D017208']","['Animals', 'Antioxidants', 'Biomarkers', 'Chromatography, High Pressure Liquid', 'Diet', 'Disease Models, Animal', 'Fruit', 'Hypercholesterolemia', 'Liver', 'Male', 'Malondialdehyde', 'Oxidative Stress', 'Phenols', 'Rats', 'Rats, Wistar']",Determination of malondialdehyde (MDA) by high-performance liquid chromatography in serum and liver as a biomarker for oxidative stress. Application to a rat model for hypercholesterolemia and evaluation of the effect of diets rich in phenolic antioxidants from fruits.,"[None, 'Q000008', 'Q000032', 'Q000379', None, None, 'Q000737', 'Q000097', 'Q000737', None, 'Q000032', 'Q000502', 'Q000008', None, None]","[None, 'administration & dosage', 'analysis', 'methods', None, None, 'chemistry', 'blood', 'chemistry', None, 'analysis', 'physiology', 'administration & dosage', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16009604,2006,0,0,,cocoa in diet +0.61,11324613,"Eight collaborating laboratories assayed 7 blind duplicate pairs of foods for polydextrose content. The 7 test sample pairs ranged from low (2%) to high (95%) levels. The following foods were prepared with polydextrose mixed into the other ingredients and then baked, cooked, or otherwise prepared: milk chocolate candy, iced tea, sugar cookie, grape jelly, soft jellied candy, and powdered drink mix. Collaborators received a polydextrose standard to develop a calibration curve. The method determined polydextrose by ion chromatography, after removal of interfering food components (high molecular weight solubles). Repeatability standard deviations (RSDr) ranged from 3.93 to 9.04%; reproducibility standard deviations (RSDR) ranged from 4.48 to 14.06%. The average recovery was 94%.",Journal of AOAC International,"['D000465', 'D001628', 'D002099', 'D002182', 'D002852', 'D005504', 'D005936', 'D007202', 'D012015', 'D013662', 'D014461']","['Algorithms', 'Beverages', 'Cacao', 'Candy', 'Chromatography, Ion Exchange', 'Food Analysis', 'Glucans', 'Indicators and Reagents', 'Reference Standards', 'Tea', 'Ultracentrifugation']",Determination of polydextrose in foods by ion chromatography: collaborative study.,"[None, 'Q000032', 'Q000737', 'Q000032', None, None, 'Q000032', None, None, 'Q000737', None]","[None, 'analysis', 'chemistry', 'analysis', None, None, 'analysis', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/11324613,2001,,,, +0.61,26041233,"Multi-element stable isotope ratios have been assessed as a means to distinguish between fermented cocoa beans from different geographical and varietal origins. Isotope ratios and percentage composition for C and N were measured in different tissues (cotyledons, shells) and extracts (pure theobromine, defatted cocoa solids, protein, lipids) obtained from fermented cocoa bean samples. Sixty-one samples from 24 different geographical origins covering all four continental areas producing cocoa were analyzed. Treatment of the data with unsupervised (Principal Component Analysis) and supervised (Partial Least Squares Discriminant Analysis) multiparametric statistical methods allowed the cocoa beans from different origins to be distinguished. The most discriminant variables identified as responsible for geographical and varietal differences were the __(15)N and __(13)C values of cocoa beans and some extracts and tissues. It can be shown that the isotope ratios are correlated with the altitude and precipitation conditions found in the different cocoa-growing regions. ",Food chemistry,"['D002099', 'D005285', 'D005843', 'D007554', 'D013058']","['Cacao', 'Fermentation', 'Geography', 'Isotopes', 'Mass Spectrometry']","Multi-element, multi-compound isotope profiling as a means to distinguish the geographical and varietal origin of fermented cocoa (Theobroma cacao L.) beans.","['Q000737', None, None, 'Q000737', 'Q000379']","['chemistry', None, None, 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/26041233,2016,2,3,"table S1, S3 and S5", +0.61,2086709,"A simple method is described for the determination of molecular species of enantiomeric sn-1,2- and sn-2,3-diacylglycerols derived from natural triacylglycerols by Grignard degradation. The method is based on a preparative separation of the enantiomeric diacylglycerols as 3,5-dinitrophenylurethane (DNPU) derivatives by high performance liquid chromatography (HPLC) on a chiral column (25 cm x 4.6 mm ID) containing R-(+)-1-(1-naphthyl)ethylamine as a stationary phase. This is followed by polar capillary gas-liquid chromatography (GLC) of the trimethylsilyl (TMS) ether derivatives of the enantiomeric diacylglycerols derived from the DNPU derivatives using trichlorosilane, which does not cause acyl migration and racemization during the reaction. The cleavage is better than 94% complete. The method was standardized with synthetic sn-1,2- and sn-2,3-dipalmitoyl- and rac-1,2-dioleoylglycerols and was applied to the identification and quantitation of individual molecular species of enantiomeric diacylglycerols generated by Grignard degradation of the triacylglycerols from corn oil, cocoa butter, and lard.",Journal of lipid research,"['D002849', 'D002851', 'D004075', 'D007700', 'D013237', 'D014280']","['Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Diglycerides', 'Kinetics', 'Stereoisomerism', 'Triglycerides']",Determination of molecular species of enantiomeric diacylglycerols by chiral phase high performance liquid chromatography and polar capillary gas-liquid chromatography.,"['Q000379', 'Q000379', 'Q000032', None, None, 'Q000032']","['methods', 'methods', 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/2086709,1991,0,0,,no recordable data +0.61,2613804,"The enantiomers of salsolinol were completely separated as diastereoisomeric derivatives, after reaction with S-1-(1-naphthyl)ethyl isothiocyanate, by reversed-phase high-performance liquid chromatography and quantified by electrochemical detection. Good calibration curves were obtained for the quantification and determination of the enantiomeric composition of salsolinol in human urine. The sensitivity and specificity to the assay also permit the determination of the enantiomeric composition of salsolinol in food such as dried bananas and chocolate.",Journal of chromatography,"['D002099', 'D002851', 'D004563', 'D005638', 'D006801', 'D007546', 'D013237']","['Cacao', 'Chromatography, High Pressure Liquid', 'Electrochemistry', 'Fruit', 'Humans', 'Isoquinolines', 'Stereoisomerism']",Determination of the enantiomeric composition of salsolinol in biological samples by high-performance liquid chromatography with electrochemical detection.,"['Q000032', None, None, 'Q000032', None, 'Q000032', None]","['analysis', None, None, 'analysis', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/2613804,1990,,,, +0.61,10367386,"An immunoaffinity column was prepared from rabbit polyclonal antiserum for the determination of peanut protein from food matrixes. The anti-peanut immunoglobulin G was isolated from antiserum by affinity chromatography on a column coupled with peanut protein and then attached to an AminoLink gel. The column was applied to the determination of peanut protein in chocolate after extraction, immunoaffinity chromatography, and enzyme-linked immunosorbent assay (ELISA). Overall recoveries from chocolate spiked with 0.2-3.2 micrograms/g of peanut protein averaged 77% (range, 72-84%), and the minimum detection limit was 0.1 microgram/g. Chromatography of extracts with the column improved detection limit and eliminated the matrix effect experienced with direct ELISA of chocolate extracts.",Journal of AOAC International,"['D000818', 'D010367', 'D002099', 'D002846', 'D004797', 'D007118', 'D007074', 'D010940', 'D011817', 'D015203']","['Animals', 'Arachis', 'Cacao', 'Chromatography, Affinity', 'Enzyme-Linked Immunosorbent Assay', 'Immunoassay', 'Immunoglobulin G', 'Plant Proteins', 'Rabbits', 'Reproducibility of Results']",An immunoaffinity column for the determination of peanut protein in chocolate.,"[None, 'Q000276', 'Q000737', 'Q000379', None, 'Q000379', None, 'Q000032', None, None]","[None, 'immunology', 'chemistry', 'methods', None, 'methods', None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10367386,1999,,,, +0.61,10995130,"Polydextrose (Litesse) provides physiological effects consistent with dietary fiber. However, AOAC methods for measuring total dietary fiber (TDF) in foods include an ethanol precipitation step in which polydextrose and similar carbohydrates are discarded and therefore not quantitated. This study describes a method developed to quantitate polydextrose in foods. The new method includes water extraction, centrifugal ultrafiltration, multienzyme hydrolysis, and anion exchange chromatography with electrochemical detection. Six foods were prepared with 4 levels of polydextrose to test the ruggedness of the method. Internal validation demonstrated the ruggedness of the method with recoveries ranging from 83 to 104% with an average of 95% (n = 24) and relative standard deviation of recoveries ranging from 0.7 to 13% with an average of 3.3% (n = 24). The value is added to that obtained for dietary fiber content of foods using the AOAC methods, to determine the TDF content of the food.",Journal of AOAC International,"['D000818', 'D000838', 'D001426', 'D001628', 'D002099', 'D002182', 'D002852', 'D004043', 'D000431', 'D005504', 'D005087', 'D005936', 'D006026', 'D006801', 'D006868', 'D007517', 'D013662', 'D014462']","['Animals', 'Anions', 'Bacterial Proteins', 'Beverages', 'Cacao', 'Candy', 'Chromatography, Ion Exchange', 'Dietary Fiber', 'Ethanol', 'Food Analysis', 'Glucan 1,4-alpha-Glucosidase', 'Glucans', 'Glycoside Hydrolases', 'Humans', 'Hydrolysis', 'Isoamylase', 'Tea', 'Ultrafiltration']",Determination of polydextrose as dietary fiber in foods.,"[None, None, None, 'Q000032', 'Q000737', 'Q000032', None, 'Q000032', None, None, 'Q000378', 'Q000032', 'Q000378', None, None, 'Q000378', 'Q000737', None]","[None, None, None, 'analysis', 'chemistry', 'analysis', None, 'analysis', None, None, 'metabolism', 'analysis', 'metabolism', None, None, 'metabolism', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/10995130,2001,,,, +0.61,3398705,"This is the first report confirming the presence of 1,2,3,4-tetrahydroisoquinoline (TIQ) and 1-methyl-1,2,3,4-tetrahydroisoquinoline(1MeTIQ) in a number of foods with a high 2-phenylethylamine content. These compounds were determined by gas chromatography-mass spectrometry. This study also confirmed that 1MeTIQ and TIQ can cross the blood-brain barrier in rat. Thus, these compounds, suspected to have relation to parkinson's disease, may accumulate in the brain from food sources.",Life sciences,"['D000818', 'D001812', 'D001921', 'D002099', 'D002611', 'D055598', 'D002621', 'D005504', 'D008401', 'D007546', 'D008297', 'D010300', 'D010627', 'D051381', 'D011919', 'D044005', 'D014920']","['Animals', 'Blood-Brain Barrier', 'Brain', 'Cacao', 'Cheese', 'Chemical Phenomena', 'Chemistry', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Isoquinolines', 'Male', 'Parkinson Disease', 'Phenethylamines', 'Rats', 'Rats, Inbred Strains', 'Tetrahydroisoquinolines', 'Wine']",Presence of tetrahydroisoquinoline and 1-methyl-tetrahydro-isoquinoline in foods: compounds related to Parkinson's disease.,"[None, None, 'Q000378', 'Q000032', 'Q000032', None, None, None, None, 'Q000032', None, 'Q000209', 'Q000032', None, None, None, 'Q000032']","[None, None, 'metabolism', 'analysis', 'analysis', None, None, None, None, 'analysis', None, 'etiology', 'analysis', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/3398705,1988,,,,no pdf access +0.61,23931630,"Acyl migration is a serious problem in enzymatic modification of fats and oils, particularly in production of cocoa butter equivalent (CBE) through enzymatic acidolysis reaction, which leads to the formation of non-symmetrical triacylglycerols (TAGs) from symmetrical TAGs. Non-symmetrical TAGs may affect the physical properties of final products and are therefore often undesired. Consequently, an accurate method is needed to determine positional isomer TAGs during the production of CBE. A bidimentional high-performance liquid chromatography (HPLC) method with combination of non-aqueous reversed-phase HPLC and silver ion HPLC joining with an evaporative light scattering detector was successfully developed for the analysis of stereospecific TAGs. The best separation of positional isomer standards was obtained with a heptane/acetone mobile-phase gradient at 25 _C and 1 mL/min. The developed method was then used in multidimensional determination of the TAG positional isomers in fat and oil blends and successfully identified the TAGs and possible isomers in enzymatically acidolyzed CBE. ",Journal of agricultural and food chemistry,"['D002851', 'D004041', 'D007536', 'D014280']","['Chromatography, High Pressure Liquid', 'Dietary Fats', 'Isomerism', 'Triglycerides']",Development of an offline bidimensional high-performance liquid chromatography method for analysis of stereospecific triacylglycerols in cocoa butter equivalents.,"['Q000295', 'Q000032', None, 'Q000737']","['instrumentation', 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23931630,2014,0,0,,no cocoa tested +0.61,20543060,"Pectinolytic enzymes play an important role in cocoa fermentation. In this study, we characterized three extracellular pectate lyases (Pels) produced by bacilli isolated from fermenting cocoa beans. These enzymes, named Pel-22, Pel-66, and Pel-90, were synthesized by Bacillus pumilus BS22, Bacillus subtilis BS66, and Bacillus fusiformis BS90, respectively. The three Pels were produced under their natural conditions and purified from the supernatants using a one-step chromatography method. The purified enzymes exhibited optimum activity at 60 degrees C, and the half-time of thermoinactivation at this temperature was approximately 30 min. Pel-22 had a low specific activity compared with the other two enzymes. However, it displayed high affinity for the substrate, about 2.5-fold higher than those of Pel-66 and Pel-90. The optimum pHs were 7.5 for Pel-22 and 8.0 for Pel-66 and Pel-90. The three enzymes trans-eliminated polygalacturonate in a random manner to generate two long oligogalacturonides, as well as trimers and dimers. A synergistic effect was observed between Pel-22 and Pel-66 and between Pel-22 and Pel-90, but not between Pel-90 and Pel-66. The Pels were also strongly active on highly methylated pectins (up to 60% for Pel-66 and Pel-90 and up to 75% for Pel-22). Fe(2+) was found to be a better cofactor than Ca(2+) for Pel-22 activity, while Ca(2+) was the best cofactor for Pel-66 and Pel-90. The amino acid sequences deduced from the cloned genes showed the characteristics of Pels belonging to Family 1. The pel-66 and pel-90 genes appear to be very similar, but they are different from the pel-22 gene. The characterized enzymes form two groups, Pel-66/Pel-90 and Pel-22; members of the different groups might cooperate to depolymerize pectin during the fermentation of cocoa beans.",Applied and environmental microbiology,"['D001407', 'D002099', 'D002118', 'D002413', 'D002845', 'D003001', 'D003067', 'D004269', 'D004795', 'D006358', 'D006863', 'D007501', 'D008969', 'D010368', 'D011133', 'D055550', 'D011994', 'D012639', 'D017422']","['Bacillus', 'Cacao', 'Calcium', 'Cations, Divalent', 'Chromatography', 'Cloning, Molecular', 'Coenzymes', 'DNA, Bacterial', 'Enzyme Stability', 'Hot Temperature', 'Hydrogen-Ion Concentration', 'Iron', 'Molecular Sequence Data', 'Pectins', 'Polysaccharide-Lyases', 'Protein Stability', 'Recombinant Proteins', 'Seeds', 'Sequence Analysis, DNA']",Biochemical properties of pectate lyases produced by three different Bacillus strains isolated from fermenting cocoa beans and characterization of their cloned genes.,"['Q000201', 'Q000382', 'Q000378', 'Q000378', 'Q000379', None, 'Q000378', 'Q000737', None, None, None, 'Q000378', None, 'Q000378', 'Q000737', None, 'Q000235', 'Q000382', None]","['enzymology', 'microbiology', 'metabolism', 'metabolism', 'methods', None, 'metabolism', 'chemistry', None, None, None, 'metabolism', None, 'metabolism', 'chemistry', None, 'genetics', 'microbiology', None]",https://www.ncbi.nlm.nih.gov/pubmed/20543060,2010,0,0,, +0.61,1173811,"A method is described for the determination of total cholesterol in multicomponent foods and also other products such as nonfat dry milk, dried whole egg solids, and certain candy bars. The lipid is extracted from the sample by a mixed solvent and saponified. The unsaponifiable fraction which contains the cholesterol and other sterols is extracted with benzene. An aliquot is evaporated to dryness and the residue is dissolved in dimethylformamide. The sterols are derivatized to form trimethylsilyl (TMS) ethers. The TMS-cholesterol derivative is quantitatively determined by gas-liquid chromatography, using 5alpha-cholestane as an internal standard. Nine laboratories participated in a collaborative study of the determination of total cholesterol in deviled ham sandwich spread, vegetable beef stew, corned beef hash, frozen chicken pot pie, pizza pepperoni, fish sticks, breaded shrimp, chocolate-covered candy bars, dried whole egg solids, and nonfat dry milk and the results are reported here. The coefficient of variation ranged from 5.64 to 23.2%, with an average coefficient of variation of 14.8%.",Journal - Association of Official Analytical Chemists,"['D000818', 'D002182', 'D002784', 'D002849', 'D004531', 'D005504', 'D005525', 'D008460', 'D008722', 'D008892', 'D014675']","['Animals', 'Candy', 'Cholesterol', 'Chromatography, Gas', 'Eggs', 'Food Analysis', 'Food-Processing Industry', 'Meat', 'Methods', 'Milk', 'Vegetables']",Gas-liquid chromatographic determination of total cholesterol in multicomponent foods.,"[None, 'Q000032', 'Q000032', None, 'Q000032', None, None, 'Q000032', None, 'Q000032', 'Q000032']","[None, 'analysis', 'analysis', None, 'analysis', None, None, 'analysis', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/1173811,1975,,,, +0.61,25772568,"A method has been developed for the specific and sensitive determination of Cr(VI) in foods. First, the interactions between Cr(VI) and the matrices were investigated by size-exclusion HPLC-ICP-MS (SEC-ICP-MS). Evidence was found for the complexation of Cr(VI) potentially present with the ligands. For__quantification of Cr(VI), the method was based on an alkaline extraction (NH4OH solution at pH__11.5) followed by Cr(VI) determination by anion-exchange HPLC-ICP-MS. Analytical performances of the method were satisfactory in terms of linearity, specificity, accuracy, repeatability, and intermediate precision. Detection limits ranged from 1 to 10____g/kg, depending on the matrices investigated. The method was then applied for the determination of Cr(VI) in several products (dairy products, flour, chocolate, vegetables, fruits, meat, fish, eggs, and beverages) from different brands and origins. Cr(VI) was found in none of the samples investigated. To further investigate the reason for this absence, a stability study of spiked Cr(VI) was therefore conducted. A semi-skimmed cow milk was selected for this study. Cr(VI) was shown to be unstable in this matrix with a degradation rate increasing with the temperature. ",Analytical and bioanalytical chemistry,"['D002850', 'D002851', 'D002852', 'D002857', 'D005504', 'D005506', 'D015203', 'D012680', 'D021241']","['Chromatography, Gel', 'Chromatography, High Pressure Liquid', 'Chromatography, Ion Exchange', 'Chromium', 'Food Analysis', 'Food Contamination', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Spectrometry, Mass, Electrospray Ionization']",Cr(VI) speciation in foods by HPLC-ICP-MS: investigation of Cr(VI)/food interactions by size exclusion and Cr(VI) determination and stability by ion-exchange on-line separations.,"[None, 'Q000379', 'Q000379', 'Q000032', 'Q000379', 'Q000032', None, None, 'Q000379']","[None, 'methods', 'methods', 'analysis', 'methods', 'analysis', None, None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/25772568,2016,0,0,,no cocoa +0.61,26829387,"Cocoa is an important ingredient for the chocolate industry and for many food products. However, it is prone to contamination by ochratoxin A (OTA), which is highly toxic and potentially carcinogenic to humans. In this work, four different extraction methods were tested and compared based on their recoveries. The best protocol was established which involves an organic solvent-free extraction method for the detection of OTA in cocoa beans using 1% sodium hydrogen carbonate (NaHCO3) in water within 30 min. The extraction method is rapid (as compared with existing methods), simple, reliable and practical to perform without complex experimental set-ups. The cocoa samples were freshly extracted and cleaned-up using immunoaffinity column (IAC) for HPLC analysis using a fluorescence detector. Under the optimised condition, the limit of detection (LOD) and limit of quantification (LOQ) for OTA were 0.62 and 1.25 ng ml(-1) respectively in standard solutions. The method could successfully quantify OTA in naturally contaminated samples. Moreover, good recoveries of OTA were obtained up to 86.5% in artificially spiked cocoa samples, with a maximum relative standard deviation (RSD) of 2.7%. The proposed extraction method could determine OTA at the level 1.5 _µg kg(-)(1), which surpassed the standards set by the European Union for cocoa (2 _µg kg(-1)). In addition, an efficiency comparison of IAC and molecular imprinted polymer (MIP) column was also performed and evaluated.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D002099', 'D002851', 'D005506', 'D005511', 'D009793']","['Cacao', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Food Handling', 'Ochratoxins']",Evaluation of extraction methods for ochratoxin A detection in cocoa beans employing HPLC.,"['Q000737', None, 'Q000032', None, 'Q000032']","['chemistry', None, 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26829387,2016,1,1,table 1a,only batch 1 and 3 +0.6,26675864,"A multi-residue method based on two different extraction procedures was developed and compared with liquid chromatography electrospray ionization tandem mass spectrometry analysis of eighteen water-soluble artificial colours including Tartrazine (E102), Chrysoine (E103), Quinoline Yellow (E104), Yellow 2G (E107), Sunset Yellow (E110), Azorubine (E122), Amaranth (E123), Ponceau 4R (E124), Erythrosine (E127), Red 2G (E128), Allura Red (E129), Patent Blue V (E131), Indigo Carmine (E132), Brilliant Blue (E133), Green S (E142), Fast Green (E143), Brilliant Black (E151), and Black 7984 (E152) in sugar and gummy confectionary, ice-cream, and chocolate sweets. Sample preparation included SPE clean-up and liquid-liquid extraction for ice-cream and chocolate sweets. Accuracy was evaluated by recovery experiments. Correlation between response and concentration was obtained with R(2)>0.98 for all but six colours. Limits of quantification were within the 10-50 __g/kg range for E129; 20-200 __g/kg for E152; 10-250 __g/kg for E103; 10-500 __g/kg for E102, E104, E107, E110, E122, E123, E124, E127, E128, E131, E133; 20-800 __g/kg for E132, 142, 151; and 10-1000 __g/kg for E143. CV for repeatability ranged from 4.0% to 51.0%, while the CV for intermediate reproducibility ranged from 5.8% to 41.4%. Finally, recoveries varied from 84.3% to 166.0%. Together, these demonstrate that the method has been validated for complex matrices and is, thus, fit-for-purpose.",Food chemistry,"['D002182', 'D002851', 'D005505', 'D057230', 'D059625', 'D015203', 'D021241', 'D053719']","['Candy', 'Chromatography, High Pressure Liquid', 'Food Coloring Agents', 'Limit of Detection', 'Liquid-Liquid Extraction', 'Reproducibility of Results', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry']",Determination of 18 water-soluble artificial dyes by LC-MS in selected matrices.,"['Q000032', 'Q000379', 'Q000032', None, None, None, None, None]","['analysis', 'methods', 'analysis', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/26675864,2016,0,0,,no cocoa +0.6,27591614,"The (13)C/(12)C carbon isotope ratio is a chemical parameter with many important applications in several scientific area and the technique of choice currently used for the __(13)C determination is the isotope ratio mass spectrometry (IRMS). This latter is highly accurate (0.1__) and sensitive (up to 0.01__), but at the same time expensive and complex. The objective of this work was to assess the reliability of FTIR and NDIRS techniques for the measurement of carbon stable isotope ratio of food sample, in comparison to IRMS. IRMS, NDIRS and FTIR were used to analyze samples of food, such as oil, durum, cocoa, pasta and sugar, in order to determine the natural abundance isotopic ratio of carbon in a parallel way. The results were comparable, showing a close relationship among the three techniques. The main advantage in using FTIR and NDIRS is related to their cheapness and easy-to-operate in comparison to IRMS. ",Talanta,"['D002247', 'D000069956', 'D019422', 'D005433', 'D005504', 'D013058', 'D010938', 'D013055', 'D013213']","['Carbon Isotopes', 'Chocolate', 'Dietary Sucrose', 'Flour', 'Food Analysis', 'Mass Spectrometry', 'Plant Oils', 'Spectrophotometry, Infrared', 'Starch']",FTIR and NDIR spectroscopies as valuable alternatives to IRMS spectrometry for the __(13)C analysis of food.,"['Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000379', 'Q000379', 'Q000032', 'Q000379', 'Q000032']","['analysis', 'analysis', 'analysis', 'analysis', 'methods', 'methods', 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27591614,2018,2,1,table 2, +0.6,19897953,"Pollution levels of toxic heavy metals (Pb, Cd, Hg) and arsenic in existing food additives used as food colors (40 samples of 15 kinds) were investigated. Heavy metals were detected in 8 samples; Pb in 1 sample (2.8 microg/g), Hg in 8 samples (0.1-3.4 microg/g) and arsenic in 2 samples (1.7, 2.6 microg/g). The Pb level in 1 sample of lac color (2.8 microg/g) exceeded the limit of 2 microg/g proposed by JECFA and Hg levels in 3 samples of cacao color (1.2-3.4 microg/g) exceeded the limit of 1 microg/g in the EU specification.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D001151', 'D002104', 'D005504', 'D005505', 'D005506', 'D007854', 'D008628', 'D019216', 'D013054']","['Arsenic', 'Cadmium', 'Food Analysis', 'Food Coloring Agents', 'Food Contamination', 'Lead', 'Mercury', 'Metals, Heavy', 'Spectrophotometry, Atomic']",[Survey of toxic heavy metals and arsenic in existing food additives (natural colors)].,"['Q000032', 'Q000032', None, 'Q000737', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000379']","['analysis', 'analysis', None, 'chemistry', 'analysis', 'analysis', 'analysis', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/19897953,2010,,,,japanese paper +0.6,19877640,"Organochlorine and organophosphate pesticides in corn muffin mix and cocoa beans were analyzed using disposable pipette extraction (DPX) for rapid cleanup followed by gas chromatography-mass spectrometry (GC-MS). The DPX method in this study used weak anion exchange (WAX) mechanisms to remove the major sample matrix interferences, fatty acids, from the chromatographic analyses. The limits of detection (LOD) were determined to be <10 ppb for all studied pesticides in corn muffin. DPX-WAX exhibited average recoveries reaching 100% for most targeted pesticides, with relative standard deviations below 10%. These results indicate that DPX with weak anion exchange sorbent is effective at eliminating fatty acid interferences in foods of high fat content prior to multiresidue pesticide analysis. Furthermore, the DPX cleanup method takes approximately 2 min to perform. In addition, removal of fatty acids from cocoa beans demonstrates the high capacity of this extraction method for samples containing up to 50% fat.",Journal of agricultural and food chemistry,"['D000097', 'D000327', 'D000837', 'D002099', 'D004041', 'D004209', 'D005227', 'D005504', 'D008401', 'D006843', 'D010755', 'D010573', 'D015203', 'D012639', 'D003313']","['Acetonitriles', 'Adsorption', 'Anion Exchange Resins', 'Cacao', 'Dietary Fats', 'Disposable Equipment', 'Fatty Acids', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Hydrocarbons, Chlorinated', 'Organophosphates', 'Pesticide Residues', 'Reproducibility of Results', 'Seeds', 'Zea mays']",New approach to multiresidue pesticide determination in foods with high fat content using disposable pipette extraction (DPX) and gas chromatography-mass spectrometry (GC-MS).,"[None, None, None, 'Q000737', 'Q000032', None, 'Q000302', 'Q000295', 'Q000379', 'Q000032', 'Q000032', 'Q000032', None, 'Q000737', 'Q000737']","[None, None, None, 'chemistry', 'analysis', None, 'isolation & purification', 'instrumentation', 'methods', 'analysis', 'analysis', 'analysis', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19877640,2010,0,0,, +0.59,18680942,"Ochratoxin A (OTA) is a mycotoxin produced by Aspergillus and Penicillium species, which contaminates cocoa among other food commodities. It has been previously demonstrated that the toxin is concentrated in cocoa shells. The aim of this study was to assay a simple chemical method for ochratoxin A reduction from naturally contaminated cocoa shells. In order to determine the efficiency of the method, a high-performance liquid chromatography method with fluorescence detection was set up beforehand and validated. Ochratoxin A was extracted from cocoa shells with methanol-3% sodium bicarbonate solution and then purified with immunoaffinity columns. The recovery attained was 88.7% (relative standard deviation = 6.36%) and the limits of detection and quantification were 0.06 and 0.2 kg/kg, respectively. For decontamination experiments, the solvent extractor ASE 200 was used. First, aqueous solutions of 2% sodium bicarbonate and potassium carbonate were compared under the same conditions (1,500 lb/in2 at 40 degrees C for 10 min). Higher ochratoxin A reduction was obtained with potassium carbonate (83 versus 27%). Then, this salt was used under different conditions of pressure, temperature, and time. The greatest ochratoxin A reduction was achieved with an aqueous potassium carbonate solution (2%), at 1,000 lb/in2 at 90 degrees C for 10 min. This method could probably be applicable to the cocoa industry because it is fast and relatively economic. From the point of view of human health, the use of potassium carbonate, partially eliminated by rinsing the sample with water, does not likely represent a risk for human health.",Journal of food protection,"['D002099', 'D002254', 'D002851', 'D004305', 'D005453', 'D005504', 'D005506', 'D005511', 'D006801', 'D006874', 'D009793', 'D011188', 'D013696', 'D013997']","['Cacao', 'Carbonates', 'Chromatography, High Pressure Liquid', 'Dose-Response Relationship, Drug', 'Fluorescence', 'Food Analysis', 'Food Contamination', 'Food Handling', 'Humans', 'Hydrostatic Pressure', 'Ochratoxins', 'Potassium', 'Temperature', 'Time Factors']",A simple chemical method reduces ochratoxin A in contaminated cocoa shells.,"['Q000737', 'Q000494', 'Q000379', None, None, None, 'Q000032', 'Q000379', None, None, 'Q000302', 'Q000494', None, None]","['chemistry', 'pharmacology', 'methods', None, None, None, 'analysis', 'methods', None, None, 'isolation & purification', 'pharmacology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18680942,2008,,,, +0.59,16478236,"A method for the separation, isolation, and identification of phytosterols was developed. A commercial phytosterols mixture, Generol 95S, was fractionated first by adsorption silica gel column chromatography and then separated by means of a semipreparative reverse phase high-performance liquid chromatography fitted with a Polaris C8-A column (250 mm x 10 mm i.d., 5 microm) using isocratic acetonitrile:2-propanol:water (2:1:1, v/v/v) as the mobile phase. Milligram scales of six individual phytosterols, including citrostadienol, campesterol, beta-sitosterol, Delta7-avenasterol, Delta7-campesterol, and Delta7-sitosterol, were obtained. Purities of these isolated sterols were 85-98%. Relative response factors (RRF) of these phytosterols were calculated against cholestanol as an authentic commercial standard. These RRF values were used to quantify by gas chromatography-mass spectrometry (GC-MS) the phytosterols content in a reference material, oils, and chocolates.",Journal of agricultural and food chemistry,"['D000327', 'D002099', 'D002845', 'D002851', 'D002855', 'D005504', 'D008401', 'D010840', 'D010938']","['Adsorption', 'Cacao', 'Chromatography', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Phytosterols', 'Plant Oils']",Separation of Delta5- and Delta7-phytosterols by adsorption chromatography and semipreparative reversed phase high-performance liquid chromatography for quantitative analysis of phytosterols in foods.,"[None, 'Q000737', 'Q000379', 'Q000379', None, 'Q000379', None, 'Q000032', 'Q000737']","[None, 'chemistry', 'methods', 'methods', None, 'methods', None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16478236,2006,0,0,,no cocoa +0.59,25051638,"A method was developed and validated for the determination of ochratoxin A (OTA), a fungal metabolite, in cocoa beans of high fat content. The sample was extracted by blending with a 1% sodium bicarbonate solution (pH 10) followed by ultrasonication, and the sample was defatted by treatment with a flocculant. The defatted sample was purified using immunoaffinity column chromatography, and OTA was detected using HPLC with fluorescence detection. The method was fully optimized, validated, and quality controlled using spike recovery analyses, with recoveries of 89-105% over spiking ranges of 320-2.5 ng/g with CV of analyses generally <10% over 4 consecutive years and an LOQ of 0.66 ng/g in cocoa bean samples. This method overcomes the problems posed by the high fat contents of cocoa and chocolate samples with a high degree of reliability.",Journal of AOAC International,"['D002099', 'D002138', 'D002846', 'D002851', 'D009793']","['Cacao', 'Calibration', 'Chromatography, Affinity', 'Chromatography, High Pressure Liquid', 'Ochratoxins']",Determination of ochratoxin A in cocoa beans using immunoaffinity column cleanup with high-performance liquid chromatography.,"['Q000382', None, 'Q000295', 'Q000379', 'Q000032']","['microbiology', None, 'instrumentation', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/25051638,2014,,,,no pdf access +0.59,28946331,"A novel, rapid, simultaneous analysis method for five sugars (fructose, glucose, sucrose, maltose, and lactose) and eight sugar alcohols (erythritol, xylitol, sorbitol, mannitol, inositol, maltitol, lactitol, and isomalt) was developed using UPLC-ELSD, without derivatization. The analysis conditions, including the gradient conditions, modifier concentration and column length, were optimized. Thirteen sugars and sugar alcohols were separated well and the resolution of their peaks was above 1.0. Their optimum analysis condition can be analyzed within 15min. Standard curves for sugars and sugar alcohols with concentrations of 5.0-0.1% and 2.0-0.05% are presented herein, and their correlation coefficients are found to be above 0.999 and the limit of detection (LOD) was around 0.006-0.018%. This novel analysis system can be used for foodstuffs such as candy, chewing gum, jelly, chocolate, processed chocolate products, and snacks containing 0.21-46.41% of sugars and sugar alcohols.",Food chemistry,"['D002241', 'D002851', 'D013402']","['Carbohydrates', 'Chromatography, High Pressure Liquid', 'Sugar Alcohols']",A rapid method for simultaneous quantification of 13 sugars and sugar alcohols in food products by UPLC-ELSD.,"['Q000032', None, 'Q000032']","['analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28946331,2017,0,0,,no cocoa +0.59,17061837,"Bidi cigarettes, small hand-rolled cigarettes produced primarily in India, are sold in the United States in a wide variety of candy-like flavors (e.g. dewberry, chocolate, clove) and are popular with adolescents. Many flavored bidis contain high concentrations of compounds such as eugenol, anethole, methyleugenol, pulegone, and estragole; several of these compounds have known toxic or carcinogenic properties. Clove cigarettes, or kreteks, are another highly flavored tobacco product with high levels of eugenol due to clove buds present in the tobacco filler. In this study, compounds in the burnable portion-the filler and wrapper material actually consumed during the smoking of bidis, kreteks, and U.S. cigarettes-were analyzed. Flavor-related compounds were solvent extracted from the burnable portion of each cigarette with methanol. An aliquot of the methanol extract was heated, and the sample headspace was sampled with a solid-phase microextraction fiber and introduced into a gas chromatograph-mass spectrometer for analysis in selected-ion monitoring mode. High levels of eugenol were detected in five clove-flavored bidi brands ranging from 78.6 to 7130 microg/cigarette (microg/cig), whereas diphenyl ether (128-3550 microg/cig) and methyl anthranilate (154-2360 microg/cig) were found in one grape-flavored bidi brand. A nontobacco herbal bidi brand contained the greatest variety of compounds, including anethole (489-665 microg/cig), eugenol (1670-2470 microg/cig), methyleugenol (27.7-36.6 microg/cig), safrole (32.4-34.4 microg/cig), myristicin (170-247 microg/cig), and elemicin (101-109 microg/cig). Filler from kreteks was found to contain high levels of eugenol, anethole, and coumarin. Flavored bidis and clove cigarettes contain a number of compounds that are present at levels far exceeding those reported in U.S. cigarette tobacco. Research is underway to determine the levels of these compounds delivered in smoke. It is not known what effect inhalation of these compounds has on smokers.",Journal of agricultural and food chemistry,"['D005374', 'D005390', 'D005421', 'D013058', 'D015203', 'D027842', 'D014026']","['Filtration', 'Fires', 'Flavoring Agents', 'Mass Spectrometry', 'Reproducibility of Results', 'Syzygium', 'Tobacco']",Quantification of flavor-related compounds in the unburned contents of bidi and clove cigarettes.,"[None, None, 'Q000032', None, None, 'Q000737', 'Q000737']","[None, None, 'analysis', None, None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/17061837,2007,0,0,,no cocoa +0.59,25722166,"The main procyanidins, including dimeric B2 and B5, trimeric C1, tetrameric and pentameric procyanidins, were isolated from unroasted cocoa beans (Theobroma cacao L.) using various techniques of countercurrent chromatography, such as high-speed countercurrent chromatography (HSCCC), low-speed rotary countercurrent chromatography (LSRCCC) and spiral-coil LSRCCC. Furthermore, dimeric procyanidins B1 and B7, which are not present naturally in the analysed cocoa beans, were obtained after semisynthesis of cocoa bean polymers with (+)-catechin as nucleophile and separated by countercurrent chromatography. In this way, the isolation of dimeric procyanidin B1 in considerable amounts (500mg, purity>97%) was possible in a single run. This is the first report concerning the isolation and semisynthesis of dimeric to pentameric procyanidins from T. cacao by countercurrent chromatography. Additionally, the chemical structures of tetrameric (cinnamtannin A2) and pentameric procyanidins (cinnamtannin A3) were elucidated on the basis of (1)H NMR spectroscopy. Interflavanoid linkage was determined by NOE-correlations, for the first time. ",Food chemistry,"['D044946', 'D002099', 'D002392', 'D002845', 'D009682', 'D015394', 'D010936', 'D011108', 'D044945']","['Biflavonoids', 'Cacao', 'Catechin', 'Chromatography', 'Magnetic Resonance Spectroscopy', 'Molecular Structure', 'Plant Extracts', 'Polymers', 'Proanthocyanidins']","Isolation of dimeric, trimeric, tetrameric and pentameric procyanidins from unroasted cocoa beans (Theobroma cacao L.) using countercurrent chromatography.","['Q000737', 'Q000737', 'Q000737', 'Q000379', None, None, 'Q000737', 'Q000032', 'Q000737']","['chemistry', 'chemistry', 'chemistry', 'methods', None, None, 'chemistry', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25722166,2015,0,0,,no quantif or unquantif data +0.59,10772173,"An AOAC collaborative study was conducted to evaluate the accuracy and reliability of an enzyme assay kit procedure for measuring oligofructans and fructan polysaccharide (inulins) in mixed materials and food products. The sample is extracted with hot water, and an aliquot is treated with a mixture of sucrase (a specific sucrose-degrading enzyme), alpha-amylase, pullulanase, and maltase to hydrolyze sucrose to glucose and fructose, and starch to glucose. These reducing sugars are then reduced to sugar alcohols by treatment with alkaline borohydride solution. The solution is neutralized, and excess borohydride is removed with dilute acetic acid. The fructan is hydrolyzed to fructose and glucose using a mixture of purified exo- and endo-inulinanases (fructanase mixture). The reducing sugars produced (fructose and glucose) are measured with a spectrophotometer after reaction with para-hydroxybenzoic acid hydrazide. The samples analyzed included pure fructan, chocolate, low-fat spread, milk powder, vitamin tablets, onion powder, Jerusalem artichoke flour, wheat stalks, and a sucrose/cellulose control flour. Repeatability relative standard deviations ranged from 2.3 to 7.3%; reproducibility relative standard deviations ranged from 5.0 to 10.8%.",Journal of AOAC International,"['D001894', 'D004798', 'D005504', 'D005630', 'D006026', 'D006868', 'D007202', 'D007444', 'D011786', 'D012996', 'D013053', 'D013393', 'D000516', 'D000520']","['Borohydrides', 'Enzymes', 'Food Analysis', 'Fructans', 'Glycoside Hydrolases', 'Hydrolysis', 'Indicators and Reagents', 'Inulin', 'Quality Control', 'Solutions', 'Spectrophotometry', 'Sucrase', 'alpha-Amylases', 'alpha-Glucosidases']",Measurement of total fructan in foods by enzymatic/spectrophotometric method: collaborative study.,"[None, None, 'Q000379', 'Q000032', 'Q000378', None, None, 'Q000032', None, None, 'Q000379', 'Q000378', 'Q000378', 'Q000378']","[None, None, 'methods', 'analysis', 'metabolism', None, None, 'analysis', None, None, 'methods', 'metabolism', 'metabolism', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/10772173,2000,,,, +0.59,27507506,Cocoa beans are a well-known source of antioxidant polyphenols. Especially individual oligomeric proanthocyanidins demonstrated a significant contribution to the total antioxidant activity of cocoa compared to monomeric compounds. An NP-HPLC-online-DPPH assay was developed for separating the homologous series of oligomeric proanthocyanidins and the simultaneous assessment of their antioxidant capacity in relation to the degree of polymerization (DP). The present study describes the influence of the different stages of a lab-scale chocolate manufacturing process on the content of oligomeric proanthocyanidins and their antioxidant capacity. The sum of the total proanthocyanidin content (___ DP1-DP13) decreased from 30mg epicatechin equivalents per gram non-fat dry matter in raw fresh cocoa beans to 6mg epicatechin equivalents per gram in the final chocolate. The antioxidant capacity decreased accordingly from 25mg epicatechin equivalents per gram non-fat dry matter in raw fresh cocoa beans to 4mg/g in the final chocolate product. ,Food chemistry,"['D000975', 'D001331', 'D002099', 'D000069956', 'D002851', 'D005511', 'D058105', 'D059808', 'D044945']","['Antioxidants', 'Automation', 'Cacao', 'Chocolate', 'Chromatography, High Pressure Liquid', 'Food Handling', 'Polymerization', 'Polyphenols', 'Proanthocyanidins']",Determination of oligomeric proanthocyanidins and their antioxidant capacity from different chocolate manufacturing stages using the NP-HPLC-online-DPPH methodology.,"['Q000737', None, 'Q000737', 'Q000032', 'Q000379', None, None, 'Q000737', 'Q000737']","['chemistry', None, 'chemistry', 'analysis', 'methods', None, None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27507506,2017,1,2,table 1 and 2 , +0.59,14661763,"A confirmatory method for the determination of low levels of acrylamide in different food products is presented. The method entails extraction of acrylamide with water, precipitation of matrix constituents with acetonitrile, and two clean-up steps consecutively over Isolute Multimode and cation-exchange cartridges. The final extract is analyzed by liquid chromatography (LC) coupled to positive electrospray ionization tandem mass spectrometry employing [13C3]-acrylamide as internal standard. For the chromatographic step, a LC column based on a polymethacrylate gel is employed which shows good retention of acrylamide under isocratic flow conditions (k' = 1.2). Mass spectral acquisition is done by selected reaction monitoring, choosing the characteristic transitions m/z 72-->55, 72-->54 and 72-->27. In-house validation data for breakfast cereals and crackers show good precision of the method, with intra- and interassay variation below 10%. The limits of detection for crackers and breakfast cereals, respectively are estimated at 15 and 20 microg/kg, and recoveries of fortified samples ranged between 58 and 76%. Furthermore, the method is applicable to a number of different food products, including biscuits, crisp bread, wafers, confectionery cocoa liquor, and nuts. Finally, the good results obtained in several small-scale interlaboratory tests provided additional confidence in the performance of the method.",Journal of chromatography. A,"['D020106', 'D002851', 'D005504', 'D007554', 'D015203', 'D021241']","['Acrylamide', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Isotopes', 'Reproducibility of Results', 'Spectrometry, Mass, Electrospray Ionization']",Analysis of acrylamide in food by isotope-dilution liquid chromatography coupled with electrospray ionization tandem mass spectrometry.,"['Q000032', 'Q000379', None, None, None, 'Q000379']","['analysis', 'methods', None, None, None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/14661763,2004,0,0,, +0.58,20565928,"Experimental evidences demonstrate that vegetable derived extracts inhibit cholesterol absorption in the gastrointestinal tract. To further explore the mechanisms behind, we modeled duodenal contents with several vegetable extracts.",Lipids in health and disease,"['D002784', 'D002789', 'D005504', 'D007408', 'D013058', 'D010936', 'D014675']","['Cholesterol', 'Cholesterol Oxidase', 'Food Analysis', 'Intestinal Absorption', 'Mass Spectrometry', 'Plant Extracts', 'Vegetables']",When cholesterol is not cholesterol: a note on the enzymatic determination of its concentration in model systems containing vegetable extracts.,"['Q000032', 'Q000378', 'Q000379', None, None, 'Q000737', 'Q000737']","['analysis', 'metabolism', 'methods', None, None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/20565928,2010,2,1,bar plot,the cholesterol bar plot +0.58,422499,"A high pressure liquid chromatographic (HPLC) method has been developed which is fast, simple, specific, and reliable over a wide range of sugar concentrations in a variety of food matrices. With few exceptions, sample preparation is simple, requiring only a water-ethanol extraction, followed by a rapid mini-column cleanup before injection into the HPLC system. The majority of samples can be prepared for analysis within 1--1 1/2 hr, and the following sugars are separated in less than 45 min: fructose, glucose, sucrose, maltose, lactose, melibioals, chocolate products, chocolate sirups, cookies, health food products, molasses, preserves, processed fruits, and soy protein products.",Journal - Association of Official Analytical Chemists,"['D002241', 'D002851', 'D005504']","['Carbohydrates', 'Chromatography, High Pressure Liquid', 'Food Analysis']",High pressure liquid chromatographic determination of sugars in various food products.,"['Q000032', None, None]","['analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/422499,1979,,,, +0.58,21493169,"Rapid, selective and sensitive methods were developed and validated to determine procyanidins, anthocyanins and alkaloids in different biological tissues, such as liver, brain, the aorta vein and adipose tissue. For this purpose, standards of procyanidins (catechin, epicatechin, and dimer B(2)), anthocyanins (cyanidin-3-glucoside and malvidin-3-glucoside) and alkaloids (theobromine, caffeine and theophylline) were used. The methods included the extraction of homogenized tissues by off-line liquid-solid extraction, and then solid-phase extraction to analyze alkaloids, or microelution solid-phase extraction plate for the analysis of procyanidins and anthocyanins. The eluted extracts were then analyzed by ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry, using a triple quadrupole as the analyzer. The optimum extraction solution was water/methanol/phosphoric acid 4% (94/4.5/1.5, v/v/v). The extraction recoveries were higher than 81% for all the studied compounds in all the tissues, except the anthocyanins, which were between 50 and 65% in the liver and brain. In order to show the applicability of the developed methods, different rat tissues were analyzed to determine the procyanidins, anthocyanins and alkaloids and their generated metabolites. The rats had previously consumed 1g of a grape pomace extract (to analyze procyanidins and anthocyanins) or a cocoa extract (to analyze alkaloids) per kilogram of body weight. Different tissues were extracted 4h after administration of the respective extracts. The analysis of the metabolites revealed a hepatic metabolism of procyanidins. The liver was the tissue which produced a greater accumulation of these metabolites.","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000818', 'D000872', 'D002110', 'D002851', 'D008297', 'D044945', 'D051381', 'D017208', 'D015203', 'D012680', 'D053719', 'D013805', 'D014018']","['Animals', 'Anthocyanins', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Male', 'Proanthocyanidins', 'Rats', 'Rats, Wistar', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Tandem Mass Spectrometry', 'Theobromine', 'Tissue Distribution']","Rapid methods to determine procyanidins, anthocyanins, theobromine and caffeine in rat tissues by liquid chromatography-tandem mass spectrometry.","[None, 'Q000032', 'Q000032', 'Q000379', None, 'Q000032', None, None, None, None, 'Q000379', 'Q000032', None]","[None, 'analysis', 'analysis', 'methods', None, 'analysis', None, None, None, None, 'methods', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/21493169,2011,1,1,table 1 ,from suplementary data in the word doc only for cacao +0.58,28432760,"A method validation study for the determination of ochratoxin A in black and white pepper (Piper spp.), nutmeg (Myristica fragrans), spice mix (blend of ginger, turmeric, pepper, nutmeg, and chili), cocoa powder, and drinking chocolate was conducted according to the International Harmonized Protocol of the International Union of Pure and Applied Chemistry. The method is based on the extraction of samples with aqueous methanol, followed by a cleanup of the extract with an immunoaffinity column. The determination is carried out by reversed-phase LC coupled with a fluorescence detector. The study involved 25 participants representing a cross-section of research, private, and official control laboratories from 12 European Union (EU) Member States, together with Turkey and Macedonia. Mean recoveries ranged from 71 to 85% for spices and from 85 to 88% for cocoa and drinking chocolate. The RSDr values ranged from 5.6 to 16.7% for spices and from 4.5 to 18.7% for cocoa and drinking chocolate. The RSDR values ranged from 9.5 to 22.6% for spices and from 13.7 to 30.7% for cocoa and drinking chocolate. The resulting Horwitz ratios ranged from 0.4 to 1 for spices and from 0.6 to 1.4 for cocoa and drinking chocolate according to the Horwitz function modified by Thompson. The method showed acceptable within-laboratory and between-laboratory precision for each matrix, and it conforms to requirements set by current EU legislation.",Journal of AOAC International,"['D000069956', 'D002851', 'D005506', 'D026323', 'D009793', 'D029222', 'D017365']","['Chocolate', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Myristica fragrans', 'Ochratoxins', 'Piper nigrum', 'Spices']","Determination of Ochratoxin A in Black and White Pepper, Nutmeg, Spice Mix, Cocoa, and Drinking Chocolate by High-Performance Liquid Chromatography Coupled with Fluorescence Detection: Collaborative Study.","['Q000032', None, None, 'Q000737', 'Q000032', 'Q000737', 'Q000032']","['analysis', None, None, 'chemistry', 'analysis', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28432760,2017,1,1,table 4 and 5, +0.58,25902989,"Single-laboratory validation data were reviewed by the Expert Review Panel (ERP) of the Stakeholder Panel on Strategic Food Analytical Methods at the AOAC Mid-Year Meeting, March 12-14, 2013, in Rockville, MD. The ERP determined that the data presented met established standard method performance requirements and adopted a method for determination of flavanols and procyanidins (DP 1-10) in cocoa-based ingredients and products by ultra-HPLC as AOAC Official First Action Method 2013.03 on March 14, 2013. The flavanols and procyanidins (DP 1-10) are eluted using a binary gradient (solvents A and B) consisting of 98 + 2 (v/v) acetonitrile-glacial acetic acid (A) and 95 + 3 + 2 (v/v/v) methanol-water-glacial acetic acid (B). The mobile phase is applied to a diol stationary phase. Detection occurs using fluorescence detection. Recovery of flavanols and procyanidins (DP 1-10) from both high- and low-fat matrixes was 98.4-99.8%. Precision was determined for seven different sample types (cocoa extract, cocoa nib, natural cocoa powder, cocoa liquor, alkalized cocoa powder, dark chocolate, and milk chocolate).",Journal of AOAC International,"['D002099', 'D002851', 'D005419', 'D044945']","['Cacao', 'Chromatography, High Pressure Liquid', 'Flavonoids', 'Proanthocyanidins']",Determination of Flavanols and Procyanidins (DP 1-10) in Cocoa-Based Ingredients and Products by UHPLC: First Action 2013.03.,"['Q000737', 'Q000379', 'Q000032', 'Q000032']","['chemistry', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/25902989,2016,,,,no pdf access +0.58,25307999,"As a consequence of the PAH4 (sum of four different polycyclic aromatic hydrocarbons, named benzo[a]anthracene, chrysene, benzo[b]fluoranthene, and benzo[a]pyrene) maximum levels permitted in cocoa beans and derived products as of 2013, an high-performance liquid chromatography with fluorescence detection method (HPLC-FD) was developed and adapted to the complex cocoa butter matrix to enable a simultaneous determination of PAH4. The resulting analysis method was subsequently successfully validated. This method meets the requirements of Regulation (EU) No. 836/2011 regarding analysis methods criteria for determining PAH4 and is hence most suitable for monitoring the observance of the maximum levels applicable under Regulation (EU) No. 835/2011. Within the scope of this work, a total of 218 samples of raw cocoa, cocoa masses, and cocoa butter from several sample years (1999-2012), of various origins and treatments, as well as cocoa and chocolate products were analyzed for the occurrence of PAH4. In summary, it is noted that the current PAH contamination level of cocoa products can be deemed very slight overall. ",Journal of agricultural and food chemistry,"['D002099', 'D002851', 'D005506', 'D011084']","['Cacao', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Polycyclic Aromatic Hydrocarbons']",Quantitation of polycyclic aromatic hydrocarbons (PAH4) in cocoa and chocolate samples by an HPLC-FD method.,"['Q000737', 'Q000379', 'Q000032', 'Q000032']","['chemistry', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/25307999,2015,1,1,"table 2,3 and 4", +0.58,28039812,"The distribution of fatty acid species at the sn-1/3 position or the sn-2 position of triacylglycerols (TAGs) in natural fats and oils affects their physical and nutritional properties. In fats and oils, determining the presence of one or two regioisomers and the identification of structure, where they do have one, as well as their separation, became a problem of fundamental importance to solve. A variety of instrumental technics has been proposed, such as MS, chromatography-MS or pure chromatography. A number of studies deal with the optimization of the separation, but very often, they are expensive in time. In the present study, in order to decrease the analysis time while maintaining good chromatographic separation, we tested different monomeric and polymeric stationary phases and different chromatographic conditions (mobile phase composition and analysis temperature) using Non-Aqueous Reversed Phase Liquid Chromatography (NARP-LC). It was demonstrated that mixed polymeric stationary bonded silica with accessible terminal hydroxyl groups leads to very good separation for the pairs of TAGs regioisomers constituted by two saturated and one unsaturated fatty acid (with double bond number: from 1 to 6). A Nucleodur C18 ISIS percolated by isocratic mobile phase (acetonitrile/2-propanol) at 18_C leads to their separations in less than 15min. The difference of retention times between two regioisomers XYX and XXY are large enough to confirm, as application, the presence of POP, SOP, SOS and PLP and no PPO, SPO, SSO and PPL in Theobroma cacao butter. In the same way, this study respectively shows the presence of SOS, SOP and no SSO, PSO in Butyrospermum parkii butter, POP, SOP, SOS and no PPO, PSO and SSO in Carapa oil and finally POP and no PPO in Pistacia Lentiscus oil.","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D002851', 'D056148', 'D005223', 'D010938', 'D013237', 'D014280']","['Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Fats', 'Plant Oils', 'Stereoisomerism', 'Triglycerides']",Fast non-aqueous reversed-phase liquid chromatography separation of triacylglycerol regioisomers with isocratic mobile phase. Application to different oils and fats.,"['Q000379', 'Q000379', 'Q000737', 'Q000737', None, 'Q000032']","['methods', 'methods', 'chemistry', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28039812,2017,0,0,, +0.58,11559132,"Key aroma components of cooked tail meat of American lobster (Homarus americanus) were studied by gas chromatography-olfactometry (GCO) techniques. Components of low and intermediate volatility were evaluated by aroma extract dilution analysis of solvent extracts prepared by direct solvent extraction-high vacuum distillation and vacuum steam distillation-solvent extraction, whereas headspace volatile components were assessed by GCO of decreasing headspace (static and dynamic modes) samples. Forty-seven odorants were detected by all techniques. 3-Methylbutanal (chocolate, malty), 2,3-butanedione (buttery), 3-(methylthio)propanal (cooked potato), 1-octen-3-one (mushroom), 2-acetyl-1-pyrroline (popcorn), and (E,Z)-2,6-nonadienal (cucumber), were identified as predominant odorants by all four isolation methods. The highly volatile compounds methanethiol (rotten, sulfurous) and dimethyl sulfide (canned corn) were detected by headspace methods only. These eight odorants along with three unknown compounds with crabby, amine, fishy odors were found to predominate in the overall aroma of cooked lobster tail meat.",Journal of agricultural and food chemistry,"['D000818', 'D002849', 'D003296', 'D008121', 'D009812', 'D017747', 'D014835']","['Animals', 'Chromatography, Gas', 'Cooking', 'Nephropidae', 'Odorants', 'Seafood', 'Volatilization']",Aroma components of cooked tail meat of American lobster (Homarus americanus).,"[None, 'Q000379', None, 'Q000737', 'Q000032', None, None]","[None, 'methods', None, 'chemistry', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11559132,2001,0,0,,no cocoa +0.58,11559122,"The fatty acids from cocoa butters of different origins, varieties, and suppliers and a number of cocoa butter equivalents (Illexao 30-61, Illexao 30-71, Illexao 30-96, Choclin, Coberine, Chocosine-Illip©, Chocosine-Shea, Shokao, Akomax, Akonord, and Ertina) were investigated by bulk stable carbon isotope analysis and compound specific isotope analysis. The interpretation is based on principal component analysis combining the fatty acid concentrations and the bulk and molecular isotopic data. The scatterplot of the two first principal components allowed detection of the addition of vegetable fats to cocoa butters. Enrichment in heavy carbon isotope ((13)C) of the bulk cocoa butter and of the individual fatty acids is related to mixing with other vegetable fats and possibly to thermally or oxidatively induced degradation during processing (e.g., drying and roasting of the cocoa beans or deodorization of the pressed fat) or storage. The feasibility of the analytical approach for authenticity assessment is discussed.",Journal of agricultural and food chemistry,"['D002247', 'D002849', 'D004041', 'D005227', 'D005511', 'D013058', 'D014675']","['Carbon Isotopes', 'Chromatography, Gas', 'Dietary Fats', 'Fatty Acids', 'Food Handling', 'Mass Spectrometry', 'Vegetables']",Characterization of cocoa butter and cocoa butter equivalents by bulk and molecular carbon isotope analyses: implications for vegetable fat quantification in chocolate.,"[None, None, 'Q000032', None, 'Q000379', None, 'Q000737']","[None, None, 'analysis', None, 'methods', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11559122,2001,2,1,table 1,exclude the CBEs +0.58,24360428,"Several HPLC and UHPLC developed methods were compared to analyse the natural antioxidants catechins and quercetin used in active packaging and functional foods. Photodiode array detector coupled with a fluorescence detector and compared with LTQ-Orbitrap-MS was used. UHPLC was investigated as quick alternative without compromising the separation, analysis time shortened up to 6-fold. The feasibility of the four developed methods was compared. Linearity up to 0.9995, low detection limits (between 0.02 and 0.7 for HPLC-PDA, 2 to 7-fold lower for HPLC- LTQ-Orbitrap-MS and from 0.2 to 2mgL(-)(1) for UHPLC-PDA) and good precision parameters (RSD lower than 0.06%) were obtained. All methods were successfully applied to natural samples. LTQ-Orbitrap-MS allowed to identify other analytes of interest too. Good feasibility of the methods was also concluded from the analysis of catechin and quercetin release from new active packaging materials based on polypropylene added with catechins and green tea. ",Food chemistry,"['D000975', 'D002099', 'D028241', 'D002392', 'D002851', 'D005419', 'D018857', 'D007700', 'D010936', 'D010969', 'D011794']","['Antioxidants', 'Cacao', 'Camellia sinensis', 'Catechin', 'Chromatography, High Pressure Liquid', 'Flavonoids', 'Food Packaging', 'Kinetics', 'Plant Extracts', 'Plastics', 'Quercetin']",Analytical determination of flavonoids aimed to analysis of natural samples and active packaging applications.,"['Q000032', 'Q000737', 'Q000737', 'Q000032', None, 'Q000032', 'Q000295', None, 'Q000032', 'Q000032', 'Q000032']","['analysis', 'chemistry', 'chemistry', 'analysis', None, 'analysis', 'instrumentation', None, 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24360428,2014,1,1,fig 2,only for cocoa +0.58,26042917,"Flavan-3-ols and proanthocyanidins play a key role in the health beneficial effects of cocoa. Here, we developed a new reversed phased high-performance liquid chromatography-electrochemical detection (HPLC-ECD) method for the analysis of flavan-3-ols and proanthocyanidins of degree of polymerization (DP) 2-7. We used this method to examine the effect of alkalization on polyphenol composition of cocoa powder. Treatment of cocoa powder with NaOH (final pH 8.0) at 92 _C for up to 1 h increased catechin content by 40%, but reduced epicatechin and proanthocyanidins by 23-66%. Proanthocyanidin loss could be modeled using a two-phase exponential decay model (R(2) > 0.7 for epicatchin and proanthocyanidins of odd DP). Alkalization resulted in a significant color change and 20% loss of total polyphenols. The present work demonstrates the first use of HPLC-ECD for the detection of proanthocyanidins up to DP 7 and provides an initial predictive model for the effect of alkali treatment on cocoa polyphenols. ",Journal of agricultural and food chemistry,"['D000468', 'D002099', 'D002851', 'D056148', 'D005511', 'D006358', 'D010936', 'D044945']","['Alkalies', 'Cacao', 'Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Food Handling', 'Hot Temperature', 'Plant Extracts', 'Proanthocyanidins']",Analysis of Cocoa Proanthocyanidins Using Reversed Phase High-Performance Liquid Chromatography and Electrochemical Detection: Application to Studies on the Effect of Alkaline Processing.,"['Q000737', 'Q000737', 'Q000295', 'Q000295', 'Q000379', None, 'Q000032', 'Q000032']","['chemistry', 'chemistry', 'instrumentation', 'instrumentation', 'methods', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26042917,2015,1,1,fig 5,0 time only +0.58,9708288,"Chemical reactions occurring during industrial treatments or storage foods can lead to the formation of epsilon-deoxyketosyl compounds, the Amadori products. Food protein value can be adversely affected by these reactions, and in particular lysine, an essential amino acid having on its side chain a free amino group, can be converted to nonbioavailable N-substituted lysine or blocked lysine. by acid hydrolysis of epsilon-deoxyketosyl compounds, furosine is formed. In this paper furosine prepared from milk-based commercial products has been evaluated by use of a recently developed HPLC method using a microbore column and phosphate buffer as the mobile phase at controlled temperature. Furosine levels have been used, together with protein, total amino acids, and lysine content, as an estimate of protein quality of a few different products such as cooked-cream dessert, yogurt mousse, white chocolate, milk chocolate, milk chocolate with a soft nougat and caramel center, milk chocolate with a whipped white center, chocolate spread, part-skim milk tablets, milk-based dietetic meals, and baby foods. The protein content of the analyzed products ranged from 34.3 gxkg(-1) (milk nougat) to 188.4 g x kg(-1) (milk tablets). The Maillard reaction caused a loss in available lysine that varied from 2.5% (cooked cream) to 36.2% (condensed milk). The contribution to the lysine average daily requirement is heavily affected by this reaction and varied from 13% (milk tablets and soft nougat) to 61% (dietetic meal). Variable results were also obtained for the other essential amino acids.",Journal of food protection,"['D002851', 'D003611', 'D005511', 'D005519', 'D008239', 'D015416', 'D008894', 'D011786']","['Chromatography, High Pressure Liquid', 'Dairy Products', 'Food Handling', 'Food Preservation', 'Lysine', 'Maillard Reaction', 'Milk Proteins', 'Quality Control']",Maillard reaction in mild-based foods: nutritional consequences.,"[None, 'Q000032', 'Q000592', 'Q000592', 'Q000031', None, 'Q000032', None]","[None, 'analysis', 'standards', 'standards', 'analogs & derivatives', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/9708288,1998,,,, +0.57,16910727,"The characteristic aroma-active compounds in raw and cooked pine-mushrooms (Tricholoma matsutake Sing.) were investigated by gas chromatography-olfactometry using aroma extract dilution analysis. 1-Octen-3-one (mushroom-like) was the major aroma-active compound in raw pine-mushrooms; this compound had the highest flavor dilution factor, followed by ethyl 2-methylbutyrate (floral and sweet), linalool (citrus-like), methional (boiled potato-like), 3-octanol (mushroom-like and buttery), 1-octen-3-ol (mushroom-like), (E)-2-octen-1-ol (mushroom-like), and 3-octanone (mushroom-like and buttery). By contrast, methional, 2-acetylthiazole (roasted), an unknown compound (chocolate-like), 3-hydroxy-2-butanone (buttery), and phenylacetaldehyde (floral and sweet), which could be formed by diverse thermal reactions during the cooking process, together with C8 compounds, were identified as the major aroma-active compounds in cooked pine-mushrooms.",Journal of agricultural and food chemistry,"['D000363', 'D002849', 'D008401', 'D006358', 'D006801', 'D007659', 'D009812', 'D012903']","['Agaricales', 'Chromatography, Gas', 'Gas Chromatography-Mass Spectrometry', 'Hot Temperature', 'Humans', 'Ketones', 'Odorants', 'Smell']",Characterization of aroma-active compounds in raw and cooked pine-mushrooms (Tricholoma matsutake Sing.).,"['Q000737', None, None, None, None, 'Q000032', 'Q000032', None]","['chemistry', None, None, None, None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/16910727,2006,0,0,,no cocoa +0.57,27318471,"Cocoa beans contain secondary metabolites ranging from simple alkaloids to complex polyphenols with most of them believed to possess significant health benefits. The increasing interest in these health effects has prompted the need to develop techniques for their extraction, fractionation, separation, and analysis. This work provides an update on analytical procedures with a focus on establishing a gentle extraction technique. Cocoa beans were finely ground to an average particle size of <100____m, defatted at 20___C using n-hexane, and extracted three times with 50__% aqueous acetone at 50___C. Determination of the total phenolic content was done using the Folin-Ciocalteu assay, the concentration of individual polyphenols was analyzed by electrospray ionization high performance liquid chromatography-mass spectrometry (ESI-HPLC/MS). Fractions of bioactive compounds were separated by combining sequential centrifugal partition chromatography (SCPC) and gel permeation column chromatography using Sephadex LH-20. For SCPC, a two-phase solvent system consisting of ethyl acetate/n-butanol/water (4:1:5, v/v/v) was successfully applied for the separation of theobromine, caffeine, and representatives of the two main phenolic compound classes flavan-3-ols and flavonols. Gel permeation chromatography on Sephadex LH-20 using a stepwise elution sequence with aqueous acetone has been shown for effectively separating individual flavan-3-ols. Separation was obtained for (-)-epicatechin, proanthocyanidin dimer B2, trimer C1, and tetramer cinnamtannin A2. The purity of alkaloids and phenolic compounds was determined by HPLC analysis and their chemical identity was confirmed by mass spectrometry. ",Analytical and bioanalytical chemistry,"['D002099', 'D002498', 'D005591', 'D002850', 'D002851', 'D044948', 'D059808', 'D044945', 'D012997', 'D021241']","['Cacao', 'Centrifugation', 'Chemical Fractionation', 'Chromatography, Gel', 'Chromatography, High Pressure Liquid', 'Flavonols', 'Polyphenols', 'Proanthocyanidins', 'Solvents', 'Spectrometry, Mass, Electrospray Ionization']",Extraction of cocoa proanthocyanidins and their fractionation by sequential centrifugal partition chromatography and gel permeation chromatography.,"['Q000737', 'Q000379', 'Q000379', 'Q000379', 'Q000379', 'Q000032', 'Q000032', 'Q000032', None, 'Q000379']","['chemistry', 'methods', 'methods', 'methods', 'methods', 'analysis', 'analysis', 'analysis', None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/27318471,2018,1,1,Fig 3, +0.57,17613050,"A selective and sensitive procedure has been developed and validated for the determination of acrylamide in difficult matrices, such as coffee and chocolate. The proposed method includes pressurised fluid extraction (PFE) with acetonitrile, florisil clean-up purification inside the PFE extraction cell and detection by liquid chromatography (LC) coupled to atmospheric pressure ionisation in positive mode tandem mass spectrometry (APCI-MS-MS). Comparison of ionisation sources (atmospheric pressure chemical ionisation (APCI), atmospheric pressure photoionization (APPI) and the combined APCI/APPI) and clean-up procedures were carried out to improve the analytical signal. The main parameters affecting the performance of the different ionisation sources were previously optimised using statistical design of experiments (DOE). PFE parameters were also optimised by DOE. For quantitation, an isotope dilution approach was used. The limit of quantification (LOQ) of the method was 1 microg kg(-1) for coffee and 0.6 microg kg(-1) for chocolate. Recoveries ranged between 81-105% in coffee and 87-102% in chocolate. The accuracy was evaluated using a coffee reference test material FAPAS T3008. Using the optimised method, 20 coffee and 15 chocolate samples collected from Valencian (Spain) supermarkets, were investigated for acrylamide, yielding median levels of 146 microg kg(-1) in coffee and 102 microg kg(-1) in chocolate.",Food additives and contaminants,"['D020106', 'D002099', 'D002853', 'D003069', 'D013030', 'D053719']","['Acrylamide', 'Cacao', 'Chromatography, Liquid', 'Coffee', 'Spain', 'Tandem Mass Spectrometry']",Determination of acrylamide in coffee and chocolate by pressurised fluid extraction and liquid chromatography-tandem mass spectrometry.,"['Q000032', 'Q000737', 'Q000379', 'Q000737', None, 'Q000379']","['analysis', 'chemistry', 'methods', 'chemistry', None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/17613050,2007,,,, +0.57,20213173,"Cocoa is well-known to be rich in flavan-3-ols. Previous analyses have established that alkaline treatment of cocoa beans results in epimerization of (-)-epicatechin to (-)-catechin and (+)-catechin to (+)-epicatechin. Now, the question is whether both epimers can be absorbed by the human organism. This paper describes sample preparation and an HPLC method for chiral determination of (+)/(-)-catechin from sulfated and glucuronidated metabolites in human plasma. The sample preparation includes enzymatic hydrolysis of the catechin metabolites, and solid-phase extraction (SPE). A PM-gamma-cyclodextrin column is used with a coulometric electrode-array detection (CEAD) system. The recovery of catechin ranges from 89.9 to 96.8%. The limit of detection is 5.9 ng mL(-1) for (-)-catechin and 6.8 ng mL(-1) for (+)-catechin, and the limit of quantification is 12.8 ng mL(-1) for (-)-catechin and 16.9 ng mL(-1) for (+)-catechin. The relative standard deviation of the method ranges from 0.9 to 1.5%. This method was successfully applied to human plasma after consumption of a cocoa drink. In one human self-experiment, (+)-catechin and (-)-catechin were found in human plasma, but metabolism of the two enantiomers differed.",Analytical and bioanalytical chemistry,"['D000328', 'D001628', 'D002099', 'D002392', 'D002851', 'D005260', 'D006801', 'D057230', 'D052616', 'D013237']","['Adult', 'Beverages', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Female', 'Humans', 'Limit of Detection', 'Solid Phase Extraction', 'Stereoisomerism']",Chiral separation of (+)/(-)-catechin from sulfated and glucuronidated metabolites in human plasma after cocoa consumption.,"[None, None, 'Q000378', 'Q000097', 'Q000379', None, None, None, 'Q000379', None]","[None, None, 'metabolism', 'blood', 'methods', None, None, None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/20213173,2010,0,0,, +0.57,10545668,"The presence of carbohydrates and organic acids was monitored in the oral cavity over a 3-hour period following the ingestion of six foods containing cooked starch (popcorn, potato chips, corn flakes, bread stick, hard pretzel and wheat cracker) and compared to a food containing sugar (chocolate-covered candy bar). Oral fluid samples were collected at 30-min intervals from five different tooth sites from 7 volunteers using absorbent paper points. Samples were analyzed for carbohydrates and organic acids using high-performance liquid chromatography. Analytical data for each food were pooled and compared to the results of the sugar food. The amount of lactic acid produced 30 min after ingestion was highest with the potato chips and lowest with the corn flakes. Potato starch contributed more readily to oral lactic acid production than wheat or corn starch. A direct linear relationship existed between lactic acid production and the presence of oral glucose produced from starch, which occurred via the metabolites maltotriose and maltose. Oral clearance of foods containing cooked starch proceeded significantly slower than that of the sugar food, thus contributing to a prolonged period of lactic acid production.",Annals of nutrition & metabolism,"['D019342', 'D002099', 'D002182', 'D002851', 'D004040', 'D019422', 'D005502', 'D005561', 'D005947', 'D006801', 'D007700', 'D019344', 'D008320', 'D009055', 'D012463', 'D013213', 'D014312', 'D014908', 'D003313']","['Acetic Acid', 'Cacao', 'Candy', 'Chromatography, High Pressure Liquid', 'Dietary Carbohydrates', 'Dietary Sucrose', 'Food', 'Formates', 'Glucose', 'Humans', 'Kinetics', 'Lactic Acid', 'Maltose', 'Mouth', 'Saliva', 'Starch', 'Trisaccharides', 'Triticum', 'Zea mays']",Clearance and metabolism of starch foods in the oral cavity.,"['Q000032', None, None, None, 'Q000378', 'Q000378', None, 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000032', 'Q000378', 'Q000737', 'Q000378', 'Q000032', None, None]","['analysis', None, None, None, 'metabolism', 'metabolism', None, 'analysis', 'analysis', None, None, 'analysis', 'analysis', 'metabolism', 'chemistry', 'metabolism', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10545668,1999,,,,no pdf access +0.57,3597580,"A method is described for simultaneous extraction and quantitation of the amines 2-phenylethylamine, tele-methylhistamine, histamine, tryptamine, m- and p-tyramine, 3-methoxytyramine, 5-hydroxytryptamine, cadaverine, putrescine, spermidine and spermine. This method is based on extractive derivatization of the amines with a perfluoroacylating agent, pentafluorobenzoyl chloride, under basic aqueous conditions. Analysis was done on a gas chromatograph equipped with an electron-capture detector and a capillary column system. The procedure is relatively rapid and provides derivatives with good chromatographic properties. Its application to analysis of the above amines in cheese and chocolate products is described.",Journal of chromatography,"['D000588', 'D002099', 'D002611', 'D002849', 'D007202', 'D010945']","['Amines', 'Cacao', 'Cheese', 'Chromatography, Gas', 'Indicators and Reagents', 'Plants, Edible']",Simultaneous extraction and quantitation of several bioactive amines in cheese and chocolate.,"['Q000032', 'Q000032', 'Q000032', None, None, 'Q000032']","['analysis', 'analysis', 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/3597580,1987,,,, +0.57,19924052,"This report describes the characterization of a series of commercially available procyanidin standards ranging from dimers DP = 2 to decamers DP = 10 for the determination of procyanidins from cocoa and chocolate. Using a combination of HPLC with fluorescence detection and MALDI-TOF mass spectrometry, the purity of each standard was determined and these data were used to determine relative response factors. These response factors were compared with other response factors obtained from published methods. Data comparing the procyanidin analysis of a commercially available US dark chocolate calculated using each of the calibration methods indicates divergent results and demonstrate that previous methods may significantly underreport the procyanidins in cocoa-containing products. These results have far reaching implications because the previous calibration methods have been used to develop data for a variety of scientific reports, including food databases and clinical studies.","Molecules (Basel, Switzerland)","['D044946', 'D002099', 'D002392', 'D002851', 'D044945', 'D012015', 'D019032']","['Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Proanthocyanidins', 'Reference Standards', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization']",Characterization of primary standards for use in the HPLC analysis of the procyanidin content of cocoa and chocolate containing products.,"['Q000032', 'Q000737', 'Q000032', 'Q000592', 'Q000032', None, None]","['analysis', 'chemistry', 'analysis', 'standards', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19924052,2010,0,0,, +0.56,15969528,"Application of chromatographic separation and taste dilution analyses recently revealed besides procyanidins a series of N-phenylpropenoyl amino acids as the key contributors to the astringent taste of nonfermented cocoa beans as well as roasted cocoa nibs. Because these amides have as yet not been reported as key taste compounds, this paper presents the isolation, structure determination, and sensory activity of these amino acid amides. Besides the previously reported (-)-N-[3',4'-dihydroxy-(E)-cinnamoyl]-3-hydroxy-L-tyrosine (clovamide), (-)-N-[4'-hydroxy-(E)-cinnamoyl]-L-tyrosine (deoxyclovamide), and (-)-N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-tyrosine, seven additional amides, namely, (+)-N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-aspartic acid, (+)-N-[4'-hydroxy-(E)-cinnamoyl]-L-aspartic acid, (-)-N-[3',4'-dihydroxy-(E)-cinnamoyl]-L-glutamic acid, (-)-N-[4'-hydroxy-(E)-cinnamoyl]-L-glutamic acid, (-)-N-[4'-hydroxy-(E)-cinnamoyl]-3-hydroxy-L-tyrosine, (+)-N-[4'-hydroxy-3'-methoxy-(E)-cinnamoyl]-L-aspartic acid, and (+)-N-[(E)-cinnamoyl]-L-aspartic acid, were identified for the first time in cocoa products by means of LC-MS/MS, 1D/2D-NMR, UV-vis, CD spectroscopy, and polarimetry, as well as independent enantiopure synthesis. Using the recently developed half-tongue test, human recognition thresholds for the astringent and mouth-drying oral sensation were determined to be between 26 and 220 micromol/L (water) depending on the amino acid moiety. In addition, exposure to light rapidly converted these [E]-configured N-phenylpropenoyl amino acids into the corresponding [Z]-isomers, thus indicating that analysis of these compounds in food and plant materials needs to be performed very carefully in the absence of light to prevent artifact formation.",Journal of agricultural and food chemistry,"['D000577', 'D000596', 'D001224', 'D002099', 'D002851', 'D002934', 'D018698', 'D006801', 'D007536', 'D015394', 'D012639', 'D013649', 'D014443']","['Amides', 'Amino Acids', 'Aspartic Acid', 'Cacao', 'Chromatography, High Pressure Liquid', 'Cinnamates', 'Glutamic Acid', 'Humans', 'Isomerism', 'Molecular Structure', 'Seeds', 'Taste', 'Tyrosine']","Isolation, structure determination, synthesis, and sensory activity of N-phenylpropenoyl-L-amino acids from cocoa (Theobroma cacao).","['Q000032', 'Q000032', 'Q000031', 'Q000737', None, 'Q000032', 'Q000031', None, None, None, 'Q000737', None, 'Q000031']","['analysis', 'analysis', 'analogs & derivatives', 'chemistry', None, 'analysis', 'analogs & derivatives', None, None, None, 'chemistry', None, 'analogs & derivatives']",https://www.ncbi.nlm.nih.gov/pubmed/15969528,2005,0,0,, +0.56,26744789,"Gallocatechin gallate (GCG) possesses multiple potential biological activities. However, the content of GCG in traditional green tea is too low which limits its in-depth pharmacological research and application. In the present study, a simple, efficient and environment-friendly chromatographic separation method was developed for preparative enrichment and separation of GCG from cocoa tea (Camellia ptilophylla) which contains high content of GCG. In the first step, the adsorption properties of selected resins were evaluated, and XAD-7HP resin was chosen by its adsorption and desorption properties for GCG. In order to maximize column efficiency for GCG collection, the operating parameters (e.g., flow rate, ethanol concentration, and bed height) were optimized. We found that the best combination was the feed concentration at 20mg/mL, flow rate at 0.75 BV/h and the ratio of diameter to bed heights as 1:12. Under these conditions, the purity of GCG was 45% with a recovery of 89%. In order to obtain pure target, a second step was established using column chromatography with sephadex LH-20 gel and 55% ethanol-water solution as eluent. After this step, the purity of the GCG was 91% with a recovery of 68% finally. ","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D028244', 'D002392', 'D002851', 'D003911', 'D010936', 'D012117']","['Camellia', 'Catechin', 'Chromatography, High Pressure Liquid', 'Dextrans', 'Plant Extracts', 'Resins, Synthetic']",Preparative separation of gallocatechin gallate from Camellia ptilophylla using macroporous resins followed by sephadex LH-20 column chromatography.,"['Q000737', 'Q000031', 'Q000379', 'Q000737', 'Q000737', 'Q000737']","['chemistry', 'analogs & derivatives', 'methods', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/26744789,2016,0,0,,cocoa tea +0.56,26637047,"Direct analysis of microbial cocultures grown on agar media by desorption electrospray ionization mass spectrometry (DESI-MS) is quite challenging. Due to the high gas pressure upon impact with the surface, the desorption mechanism does not allow direct imaging of soft or irregular surfaces. The divots in the agar, created by the high-pressure gas and spray, dramatically change the geometry of the system decreasing the intensity of the signal. In order to overcome this limitation, an imprinting step, in which the chemicals are initially transferred to flat hard surfaces, was coupled to DESI-MS and applied for the first time to fungal cocultures. Note that fungal cocultures are often disadvantageous in direct imaging mass spectrometry. Agar plates of fungi present a complex topography due to the simultaneous presence of dynamic mycelia and spores. One of the most devastating diseases of cocoa trees is caused by fungal phytopathogen Moniliophthora roreri. Strategies for pest management include the application of endophytic fungi, such as Trichoderma harzianum, that act as biocontrol agents by antagonizing M. roreri. However, the complex chemical communication underlying the basis for this phytopathogen-dependent biocontrol is still unknown. In this study, we investigated the metabolic exchange that takes place during the antagonistic interaction between M. roreri and T. harzianum. Using imprint-DESI-MS imaging we annotated the secondary metabolites released when T. harzianum and M. roreri were cultured in isolation and compared these to those produced after 3 weeks of coculture. We identified and localized four phytopathogen-dependent secondary metabolites, including T39 butenolide, harzianolide, and sorbicillinol. In order to verify the reliability of the imprint-DESI-MS imaging data and evaluate the capability of tape imprints to extract fungal metabolites while maintaining their localization, six representative plugs along the entire M. roreri/T. harzianum coculture plate were removed, weighed, extracted, and analyzed by liquid chromatography-high-resolution mass spectrometry (LC-HRMS). Our results not only provide a better understanding of M. roreri-dependent metabolic induction in T. harzianum, but may seed novel directions for the advancement of phytopathogen-dependent biocontrol, including the generation of optimized Trichoderma strains against M. roreri, new biopesticides, and biofertilizers. ",Analytical chemistry,"['D015107', 'D000363', 'D001688', 'D002073', 'D018920', 'D003512', 'D007783', 'D064210', 'D021241', 'D014242']","['4-Butyrolactone', 'Agaricales', 'Biological Products', 'Butanes', 'Coculture Techniques', 'Cyclohexanones', 'Lactones', 'Secondary Metabolism', 'Spectrometry, Mass, Electrospray Ionization', 'Trichoderma']",Imprint Desorption Electrospray Ionization Mass Spectrometry Imaging for Monitoring Secondary Metabolites Production during Antagonistic Interaction of Fungi.,"['Q000031', 'Q000254', 'Q000032', 'Q000737', None, 'Q000737', 'Q000737', None, None, 'Q000254']","['analogs & derivatives', 'growth & development', 'analysis', 'chemistry', None, 'chemistry', 'chemistry', None, None, 'growth & development']",https://www.ncbi.nlm.nih.gov/pubmed/26637047,2016,0,0,, +0.56,15453694,"The flavor of eight cocoa liquors of different origins (Africa, America, and Asia) and different varieties (Fine grades: criollo, trinitario, and nacional. Bulk-basic grade: forastero.) was analyzed by headspace solid-phase microextraction mass spectrometry (HS-SPME-MS). Their procyanidin contents were quantified by HPLC-UV (280 nm). Fine varieties with short fermentation processes proved to contain more procyanidins, while criollo from New Guinea and forastero beans showed the highest aroma levels. The levels of cocoa aroma compounds formed during roasting are shown to vary directly with bean fermentation time and inversely with residual procyanidin content in cocoa liquor. Measurement of antioxidant activity in cocoa liquor proved to be a useful tool for assessing residual polyphenols.",Journal of agricultural and food chemistry,"['D000349', 'D000434', 'D000569', 'D000975', 'D001208', 'D044946', 'D002099', 'D002392', 'D002851', 'D005285', 'D008401', 'D013058', 'D009812', 'D044945', 'D013649']","['Africa', 'Alcoholic Beverages', 'Americas', 'Antioxidants', 'Asia', 'Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Fermentation', 'Gas Chromatography-Mass Spectrometry', 'Mass Spectrometry', 'Odorants', 'Proanthocyanidins', 'Taste']",Relationship between procyanidin and flavor contents of cocoa liquors from different origins.,"[None, 'Q000032', None, 'Q000032', None, 'Q000032', 'Q000737', 'Q000032', None, None, None, 'Q000379', 'Q000032', 'Q000032', None]","[None, 'analysis', None, 'analysis', None, 'analysis', 'chemistry', 'analysis', None, None, None, 'methods', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15453694,2004,1,3,table 1 and Fig. 3 and 6 q,uantified and unquantified +0.56,12881135,"Benzophenone may be present in cartonboard food-packaging materials as a residue from UV-cured inks and lacquers used to print on the packaging. It may also be present if the cartonboard is made from recycled fibres recovered from printed materials. A method has been devised to test for benzophenone in cartonboard packaging materials and to test for migration levels in foodstuffs. Packaging is extracted with solvent containing d10-benzophenone as the internal standard. Foods are extracted with solvent containing d10-benzophenone and the extract defatted using hexane. The extracts are analysed by GC-MS. For analysis of food, the limit of detection was 0.01 mg x kg(-1) and the limit of quantification was 0.05 mg x kg(-1). The calibration was linear from 0.05 to 20 mg x kg(-1). The method for food analysis was validated in-house and it also returned satisfactory results in a blind check-sample exercise organized by an independent laboratory. The methods were applied to the analysis of 350 retail samples that used printed cartonboard packaging. A total of 207 (59%) packaging samples had no significant benzophenone (<0.05 mg x dm(-2)). Seven (2%) were in the range 0.05- 0.2 mg x dm(-2), 60 (17%) were from 0.2 to 0.8 mg x dm(-2) and 76 (22%) were from 0.8 to 3.3 mg x dm(-2). A total of 71 samples were then selected at random from the 143 packaging samples that contained benzophenone, and the food itself was analysed. Benzophenone was detected in 51 (72%) of the foods. Two food samples (3%) were in the range 0.01-0.05 mg kg(-1). A total of 29 (41%) were from 0.05 to 0.5 mg kg(-1), 17 (24%) were from 0.5 to 5 mg x kg(-1) and three (4%) food samples exceeded 5 mg x kg(-1). The highest level of benzophenone in food was 7.3 mg x kg(-1) for a high-fat chocolate confectionery product packaged in direct contact with cartonboard, with room temperature storage conditions and with a high contact area:food mass ratio. When the mass fraction of benzophenone migration was calculated for the different contact and storage regimes involved, the attenuation effects of indirect contact and of low temperature storage were cumulative. Thus, there was a sixfold reduction in migration for indirect contact compared with direct contact, a sixfold reduction for chilled/frozen storage compared with ambient storage, and 40-fold reduction for the two contact conditions combined.",Food additives and contaminants,"['D001577', 'D005506', 'D018857', 'D005519', 'D008401', 'D006801', 'D007281', 'D011786', 'D013696']","['Benzophenones', 'Food Contamination', 'Food Packaging', 'Food Preservation', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Ink', 'Quality Control', 'Temperature']",Benzophenone in cartonboard packaging materials and the factors that influence its migration into food.,"['Q000032', 'Q000032', None, 'Q000379', 'Q000379', None, None, None, None]","['analysis', 'analysis', None, 'methods', 'methods', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12881135,2003,,,, +0.56,29053889,"The interest towards ""substances of emerging concerns"" referred to objects intended to come into contact with food is recently growing. Such substances can be found in traces in simulants and in food products put in contact with plastic materials. In this context, it is important to set up analytical systems characterized by high sensitivity and to improve detection parameters to enhance signals. This work was aimed at optimizing a method based on UHPLC coupled to high resolution mass spectrometry to quantify the most common plastic additives, and able to detect the presence of polymers degradation products and coloring agents migrating from plastic re-usable containers. The optimization of mass spectrometric parameter settings for quantitative analysis of additives has been achieved by a chemometric approach, using a full factorial and d-optimal experimental designs, allowing to evaluate possible interactions between the investigated parameters. Results showed that the optimized method was characterized by improved features in terms of sensitivity respect to existing methods and was successfully applied to the analysis of a complex model food system such as chocolate put in contact with 14 polycarbonate tableware samples. A new procedure for sample pre-treatment was carried out and validated, showing high reliability. Results reported, for the first time, the presence of several molecules migrating to chocolate, in particular belonging to plastic additives, such Cyasorb UV5411, Tinuvin 234, Uvitex OB, and oligomers, whose amount was found to be correlated to age and degree of damage of the containers.",Journal of mass spectrometry : JMS,"['D002138', 'D000069956', 'D002851', 'D005506', 'D018857', 'D006794', 'D006801', 'D057230', 'D010969', 'D011091', 'D015203', 'D053719']","['Calibration', 'Chocolate', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Food Packaging', 'Household Articles', 'Humans', 'Limit of Detection', 'Plastics', 'Polyesters', 'Reproducibility of Results', 'Tandem Mass Spectrometry']","Optimization of mass spectrometry acquisition parameters for determination of polycarbonate additives, degradation products, and colorants migrating from food contact materials to chocolate.","[None, 'Q000032', 'Q000379', 'Q000032', None, None, None, None, 'Q000737', 'Q000737', None, 'Q000379']","[None, 'analysis', 'methods', 'analysis', None, None, None, None, 'chemistry', 'chemistry', None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/29053889,2018,,,, +0.56,19348344,"This study has examined the effects of type of dairy product (whole milk, skim milk, heavy cream) and chocolate matrix (baking, dark, dairy milk, white) on the oral absorption of the chocolate flavanols (+)-catechin and (-)-epicatechin in a small animal model. In the study, each flavanol compound, as a solution in water or a dairy product or as a chocolate dispersion in water, was administered intragastrically to male Sprague-Dawley rats in an amount equal to or equivalent to 350 mg/kg. In each instance, blood samples were collected over a 5 h period, and used to measure plasma total catechin concentrations by HPLC after enzymatic hydrolysis of flavanol conjugates. Pharmacokinetic data were evaluated using a one compartment approach. Whole milk and heavy cream, and to a much lesser extent skim milk, lowered the oral absorption of both (+)-catechin and (-)-epicatechin and altered the AUC, C(max), k(a), k(e) and t1/2 values in direct proportion to their fat, but not to their protein, content. In addition, the t(max) for solutions of (-)-epicatechin in water and skim milk occurred 2 h earlier than from solutions in whole milk and heavy cream. Similarly, dispersions of baking chocolate in water and in whole milk yielded plasma levels of monomeric catechins that were, respectively, about equal to and much lower than those from aqueous solutions of authentic flavanols. A determining role for a chocolate matrix (dark, dairy milk or white chocolate) on the oral absorption of its constitutive monomeric flavanols was suggested by the apparent variability in plasma total catechins levels that existed among them both before and after their spiking with equal amounts of exogenous (+)-catechin and (-)-epicatechin. Such a variability could reflect differences among different chocolates in terms of their physical properties, matrix components, and matrix characteristics imposed by the manufacturing process used for each type of chocolate. In all the experiments, (+)-catechin demonstrated a higher oral absorption than (-)-epicatechin.",Die Pharmazie,"['D000818', 'D019540', 'D002099', 'D002392', 'D002417', 'D002851', 'D003611', 'D044948', 'D005470', 'D008297', 'D008892', 'D008894', 'D051381', 'D017207']","['Animals', 'Area Under Curve', 'Cacao', 'Catechin', 'Cattle', 'Chromatography, High Pressure Liquid', 'Dairy Products', 'Flavonols', 'Fluorometry', 'Male', 'Milk', 'Milk Proteins', 'Rats', 'Rats, Sprague-Dawley']",Assessment of the effect of type of dairy product and of chocolate matrix on the oral absorption of monomeric chocolate flavanols in a small animal model.,"[None, None, 'Q000737', 'Q000097', None, None, 'Q000032', 'Q000737', None, None, 'Q000737', 'Q000032', None, None]","[None, None, 'chemistry', 'blood', None, None, 'analysis', 'chemistry', None, None, 'chemistry', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19348344,2009,,,, +0.56,15453712,"In this work, the occurrence of ochratoxin A (OTA) in 170 samples of cocoa products of different geographical origins was studied. An immunoaffinity column with HPLC separation was developed to quantify low levels of OTA in cocoa bean, cocoa cake, cocoa mass, cocoa nib, cocoa powder, cocoa shell, cocoa butter, chocolate, and chocolate cream with >80% recoveries. The method was validated by performing replicate analyses of uncontaminated cocoa material spiked at three different levels of OTA (1, 2, and 5 microg/kg). The data obtained were related on the acceptable safe daily exposure for OTA. The highest levels of OTA were detected in roasted cocoa shell and cocoa cake (0.1-23.1 microg/kg) and only at minor levels in the other cocoa products. Twenty-six cocoa and chocolate samples were free from detectable OTA (<0.10 microg/kg). In roasted cocoa powder 38.7% of the samples analyzed contained OTA at levels ranging from 0.1 to 2 microg/kg, and 54.8% was contaminated at >2 microg/kg (and 12 samples at >3 microg/kg). Ochratoxin A was detected in cocoa bean at levels from 0.1 to 3.5 microg/kg, the mean concentration being 0.45 microg/kg; only one sample exceeded 2 microg/kg (4.7%). In contrast, 51.2% of cocoa cake samples contained OTA at levels > or =2 microg/kg, among which 16 exceeded 5 microg/kg (range of 5-9 microg/kg). These results indicate that roasted cocoa powder is not a major source of OTA in the diet.",Journal of agricultural and food chemistry,"['D000818', 'D002099', 'D002851', 'D005506', 'D009183', 'D009793', 'D013552']","['Animals', 'Cacao', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Mycotoxins', 'Ochratoxins', 'Swine']",Occurrence of ochratoxin A in cocoa products and chocolate.,"[None, 'Q000737', 'Q000379', 'Q000032', 'Q000032', 'Q000032', None]","[None, 'chemistry', 'methods', 'analysis', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15453712,2004,1,1,table 2,all but the cake +0.56,25466021,"Hazelnut is one of the most appreciated nuts being virtually found in a wide range of processed foods. The simple presence of trace amounts of hazelnut in foods can represent a potential risk for eliciting allergic reactions in sensitised individuals. The correct labelling of processed foods is mandatory to avoid adverse reactions. Therefore, adequate methodology evaluating the presence of offending foods is of great importance. Thus, the aim of this study was to develop a highly specific and sensitive sandwich enzyme-linked immunosorbent assay (ELISA) for the detection and quantification of hazelnut in complex food matrices. Using in-house produced antibodies, an ELISA system was developed capable to detect hazelnut down to 1 mg kg(-1) and quantify this nut down to 50 mg kg(-1) in chocolates spiked with known amounts of hazelnut. These results highlight and reinforce the value of ELISA as rapid and reliable tool for the detection of allergens in foods.",Food chemistry,"['D002099', 'D002138', 'D002182', 'D002853', 'D031211', 'D004591', 'D004797', 'D005504', 'D057230', 'D009754', 'D010940', 'D053719']","['Cacao', 'Calibration', 'Candy', 'Chromatography, Liquid', 'Corylus', 'Electrophoresis, Polyacrylamide Gel', 'Enzyme-Linked Immunosorbent Assay', 'Food Analysis', 'Limit of Detection', 'Nuts', 'Plant Proteins', 'Tandem Mass Spectrometry']",Development of a sandwich ELISA-type system for the detection and quantification of hazelnut in model chocolates.,"['Q000737', None, None, None, 'Q000737', None, 'Q000379', 'Q000379', None, 'Q000737', 'Q000032', None]","['chemistry', None, None, None, 'chemistry', None, 'methods', 'methods', None, 'chemistry', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/25466021,2015,0,0,,no cocoa +0.56,11929301,"After vacuum distillation and liquid-liquid extraction, the volatile fractions of dark chocolates were analyzed by gas chromatography-olfactometry and gas chromatography-mass spectrometry. Aroma extract dilution analysis revealed the presence of 33 potent odorants in the neutral/basic fraction. Three of these had a strong chocolate flavor: 2-methylpropanal, 2-methylbutanal, and 3-methylbutanal. Many others were characterized by cocoa/praline-flavored/nutty/coffee notes: 2,3-dimethylpyrazine, trimethylpyrazine, tetramethylpyrazine, 3(or 2),5-dimethyl-2(or 3)-ethylpyrazine, 3,5(or 6)-diethyl-2-methylpyrazine, and furfurylpyrrole. Comparisons carried out before and after conching indicate that although no new key odorant is synthesized during the heating process, levels of 2-phenyl-5-methyl-2-hexenal, Furaneol, and branched pyrazines are significantly increased while most Strecker aldehydes are lost by evaporation.",Journal of agricultural and food chemistry,"['D000447', 'D002099', 'D002849', 'D005663', 'D008401', 'D006591', 'D006358', 'D009812', 'D011719', 'D012903', 'D013649']","['Aldehydes', 'Cacao', 'Chromatography, Gas', 'Furans', 'Gas Chromatography-Mass Spectrometry', 'Hexobarbital', 'Hot Temperature', 'Odorants', 'Pyrazines', 'Smell', 'Taste']",Use of gas chromatography-olfactometry to identify key odorant compounds in dark chocolate. Comparison of samples before and after conching.,"['Q000032', 'Q000737', None, 'Q000032', None, None, None, None, 'Q000032', None, None]","['analysis', 'chemistry', None, 'analysis', None, None, None, None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11929301,2002,0,0,, +0.55,14643988,"This paper deals with the physicochemical characterization, including thermal behaviour, by differential scanning calorimetry of mango seed almond fat (MAF), alone and in mixtures with cocoa butter (CB). Results showed that mango almond seeds contain about 5.28-11.26% (dw) of fat. The refraction index is 1.466, the saponification index 189.0 and the iodine index 41.76. Fatty acids found in MAF are oleic, stearic, and palmitic acids (40.81%, 39.07% and 9.29% (w/w), respectively) as well as smaller amounts of linoleic, with arachidic, behenic, lignoceric, and linolenic acids, among others. Calorimetric analysis showed that MAF crystallizes between 14.6 and -24.27 degrees C with a DeltaHc of 56.06 J/g and melts between -17.1 and 53.8 degrees C, with fusion maxima at 18.54 degrees C and 40.0 degrees C for the alpha and beta polymorphic forms. Their fusion enthalpies are 70.12 and 115.7 J/g. The MAF solids content profile is very similar to that of CB, both in stabilized and non-stabilized samples. The mixing compatibility was analyzed using isosolids curves of mixtures of different compositions.",Bioresource technology,"['D002152', 'D004041', 'D005223', 'D008401', 'D031022', 'D008800', 'D012639', 'D013696']","['Calorimetry, Differential Scanning', 'Dietary Fats', 'Fats', 'Gas Chromatography-Mass Spectrometry', 'Mangifera', 'Mexico', 'Seeds', 'Temperature']",Mango seed uses: thermal behaviour of mango seed almond fat and its mixtures with cocoa butter.,"[None, 'Q000032', 'Q000737', None, 'Q000737', None, 'Q000737', None]","[None, 'analysis', 'chemistry', None, 'chemistry', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/14643988,2004,2,1,table 2,no units were given in table 2 for fatty profile +0.55,10395610,"The fructans, inulin and oligofructose, were known to possess many of the physiologic properties of dietary fiber (DF) but were not listed as DF on the labels of foods that contained them because they did not precipitate in 78% ethanol as prescribed in the AOAC International methods for DF. In the latter part of 1995, the Food and Drug Administration (FDA) agreed to consider fructans as DF if an AOAC-accepted analytical method could be successfully developed for fructans. Six blind duplicate pairs of foods, containing from 4 to 40% of inulin or oligofructose, were sent to nine collaborators in five countries for assay. These foods included a low fat spread, cheese spread, chocolate, wine gum, dry ice mix powder and biscuits. In the proposed method, the samples were treated with amyloglucosidase and inulinase, and the sugars released were determined by ion-exchange chromatography. The concentration of the fructan was calculated by the difference in sugars present in the two enzymic treatments and the initial sample. The repeatability standard deviations (RSDr) for the inulin and oligofructose ranged from 2.9 to 5.8% and the reproducibility standard deviations (RSDR) for these fructans ranged from 4.7 to 11.1%. The method was accepted by the AOAC as an official first action.",The Journal of nutrition,"['D002852', 'D004043', 'D005504', 'D005630', 'D007444', 'D009844', 'D015203']","['Chromatography, Ion Exchange', 'Dietary Fiber', 'Food Analysis', 'Fructans', 'Inulin', 'Oligosaccharides', 'Reproducibility of Results']",Methods to determine food inulin and oligofructose.,"['Q000379', 'Q000032', 'Q000379', 'Q000032', 'Q000032', 'Q000032', None]","['methods', 'analysis', 'methods', 'analysis', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/10395610,1999,0,0,,no cocoa +0.55,546878,"The quantitative analysis of benzoic and sorbic acid, methyl, ethyl and propyl esters of p-hydroxybenzoic acid and saccharin in foodstuffs is described. These compounds are quantitatively extracted with disposable clean-up columns packed with Extrelut and simultaneously determined by high-performance liquid chromatography on reversed-phase columns. Complicated matrices such as cheese, cake, ketchup and chocolate were tested and recoveries were generally better than 95% in the concentration ranges normally used in the food industry.",Journal of chromatography,"['D001565', 'D002851', 'D005504', 'D005520', 'D062385', 'D012439', 'D013011']","['Benzoates', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Food Preservatives', 'Hydroxybenzoates', 'Saccharin', 'Sorbic Acid']",Determination of food preservatives and saccharin by high-performance liquid chromatography.,"['Q000032', None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000032']","['analysis', None, None, 'analysis', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/546878,1980,,,, +0.55,20557115,"The potential of analytical chemistry to predict sensory qualities of food materials is a major current theme. Standard practice is cross-validation (CV), where a set of chemical and associated sensory data is partitioned so chemometric models can be developed on training subsets, and validated on held-out subsets. CV demonstrates prediction, but is an unlikely scenario for industrial operations, where concomitant data acquisition for model development and test materials would be unwieldy. We evaluated cocoa materials of diverse provenance, and analyzed on different dates to those used in model development. Liquor extracts were analyzed by flow-injection electrospray-mass spectrometry (FIE-MS), a novel method for sensory quality prediction. FIE-MS enabled prediction of sensory qualities described by trained human panelists. Optimal models came from the Weka data-mining algorithm SimpleLinearRegression, which learns a model for the attribute giving minimal training error, which was (-)-epicatechin. This flavonoid likewise dominated partial least-squares (PLS)-regression models. Refinements of PLS (orthogonal-PLS or orthogonal signal correction) gave poorer generalization to different test sets, as did support vector machines, whose hyperparameters could not be optimized in training to avoid overfitting. In conclusion, if chemometric overfitting is avoided, chemical analysis can predict sensory qualities of food materials under operationally realistic conditions.",Analytical chemistry,"['D000465', 'D002099', 'D002392', 'D006801', 'D016018', 'D025341', 'D012684', 'D021241']","['Algorithms', 'Cacao', 'Catechin', 'Humans', 'Least-Squares Analysis', 'Principal Component Analysis', 'Sensory Thresholds', 'Spectrometry, Mass, Electrospray Ionization']",Operationally realistic validation for prediction of cocoa sensory qualities by high-throughput mass spectrometry.,"[None, 'Q000737', 'Q000737', None, None, None, None, 'Q000379']","[None, 'chemistry', 'chemistry', None, None, None, None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/20557115,2010,0,0,, +0.55,8900578,"A method for the determination of aspartame (N-L-alpha-aspartyl-L-phenylalanine methyl ester) and its metabolites, applicable on a routine quality assurance basis, is described. Liquid samples (diet Coke, 7-Up, Pepsi, etc.) were injected directly onto a mini-cartridge reversed-phase column on a high-performance liquid chromatographic system, whereas solid samples (Equal, hot chocolate powder, pudding, etc.) were extracted with water. Optimising chromatographic conditions resulted in resolved components of interest within 12 min. The by-products were confirmed by mass spectrometry. Although the method was developed on a two-pump HPLC system fitted with a diode-array detector, it is straightforward and can be transformed to the simplest HPLC configuration. Using a single-piston pump (with damper), a fixed-wavelength detector and a recorder/integrator, the degradation of products can be monitored as they decompose. The results obtained were in harmony with previously reported tedious methods. The method is simple, rapid, quantitative and does not involve complex, hazardous or toxic chemistry.",Journal of chromatography. A,"['D001218', 'D002851', 'D002852', 'D005504', 'D013058', 'D013056']","['Aspartame', 'Chromatography, High Pressure Liquid', 'Chromatography, Ion Exchange', 'Food Analysis', 'Mass Spectrometry', 'Spectrophotometry, Ultraviolet']",Simple and rapid high-performance liquid chromatographic method for the determination of aspartame and its metabolites in foods.,"['Q000378', 'Q000379', 'Q000379', None, 'Q000379', None]","['metabolism', 'methods', 'methods', None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/8900578,1996,0,0,,no cocoa +0.55,730647,"A method was developed for determining theobromine and caeffine in cocoa and chocolate products by high pressure liquid chromatography. After a simple hot water extraction, both theobromine and caffeine were separated by using a reverse phase C18 column and a mobile phase of methanol-water-acetic acid (20 + 79 + 1). Theobromine and caffeine were quantitated at 280 nm; average recoveries were 98.7 and 95.0%; and coefficients of variation were 2.31 and 3.91%, respectively.",Journal - Association of Official Analytical Chemists,"['D001628', 'D002099', 'D002110', 'D002851', 'D012995', 'D013805']","['Beverages', 'Cacao', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Solubility', 'Theobromine']",High pressure liquid chromatographic determination of theobromine and caffeine in cocoa and chocolate products.,"['Q000032', 'Q000032', 'Q000032', 'Q000379', None, 'Q000032']","['analysis', 'analysis', 'analysis', 'methods', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/730647,1979,,,, +0.55,18503248,"Ochratoxin A (OTA), ochratoxin B (OTB) and citrinin (CIT) in commercial foods were simultaneously determined and confirmed with high-performance liquid chromatography (HPLC) and liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS). The samples examined were made up of cereal, fruit, coffee, and cacao products. The limits of quantification (S/N> or =10) of OTA, OTB and CIT were 0.1 microg/kg or less. Aflatoxins (AF), deoxynivalenol (DON) and fumonisins were also surveyed. Of 157 samples examined, 44 were contaminated with OTA at levels of 0.11 to 4.0 microg/kg. At least 2 positive samples were labeled as domestics. In most positive samples, the OTA level was low, less than 1 microg/kg. The highest incidence of OTA was observed in cacao powder (10/12), followed by instant coffee (5/7), cocoa (5/8) and raisin (6/13). OTB was found in fruit and cacao products containing relatively high levels of OTA. Co-occurrence of OTA, CIT and DON was found in cereal products, and co-occurrence of OTA and AF was found in cacao products. Approximately 30% of naturally contaminated OTA in roasted coffee bean moved into the extract solution when brewed with paper filter.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D000348', 'D002099', 'D002851', 'D002853', 'D002953', 'D003069', 'D002523', 'D005504', 'D005506', 'D005638', 'D037341', 'D009793', 'D053719', 'D014255']","['Aflatoxins', 'Cacao', 'Chromatography, High Pressure Liquid', 'Chromatography, Liquid', 'Citrinin', 'Coffee', 'Edible Grain', 'Food Analysis', 'Food Contamination', 'Fruit', 'Fumonisins', 'Ochratoxins', 'Tandem Mass Spectrometry', 'Trichothecenes']","[Investigation of ochratoxin a, B and citrinin contamination in various commercial foods].","['Q000032', 'Q000737', None, None, 'Q000032', 'Q000737', 'Q000737', 'Q000379', 'Q000032', 'Q000737', 'Q000032', 'Q000032', None, 'Q000032']","['analysis', 'chemistry', None, None, 'analysis', 'chemistry', 'chemistry', 'methods', 'analysis', 'chemistry', 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18503248,2008,,,,japanese paper +0.55,5796351,"Six normal men were fed formula diets containing either highly saturated fat (cocoa butter, iodine value 32) or polyunsaturated fat (corn oil, iodine value 125). The sterol balance technique was used to compare the changes in serum cholesterol concentration with the excretion of fecal steroids. The method used for the analysis of fecal steroids was chemical, with a final identification and quantification by gas-liquid chromatography. It was confirmed that the chemical method for fecal steroid analysis was accurate and reproducible. The three dietary periods were each 3 wk in length. In sequence, cocoa butter (period I), corn oil, and cocoa butter (period III) were fed at 40% of the total calories. All diets were cholesterol free, contained similar amounts of plant sterols, and were identical in other nutrients. Corn oil had a hypocholesterolemic effect. Mean serum cholesterol concentrations were 222 mg/100 ml (cocoa butter, period I), 177 during corn oil, and 225 after the return to cocoa butter. Individual fecal steroids were determined from stools pooled for 7 days. Both neutral steroids and bile acids were altered significantly by dietary polyunsaturated fat. The change in bile acid excretion was considerably greater than the change in neutral steroids. Corn oil caused a greater fecal excretion of both deoxycholic and lithocholic acids. The total mean excretion (milligrams per day) of fecal steroids was 709 for cocoa butter (period I), 915 for corn oil, and 629 for the second cocoa butter period. The enhanced total fecal steroid excretion by the polyunsaturated fat of corn oil created a negative cholesterol balance vis-_-vis the saturated fat of cocoa butter. The hypocholesterolemic effect of polyunsaturated fat was associated with total fecal sterol excretion twice greater than the amount of cholesterol calculated to leave the plasma. This finding suggested possible loss of cholesterol from the tissues as well.",The Journal of clinical investigation,"['D000328', 'D001647', 'D002099', 'D002784', 'D002845', 'D004041', 'D005224', 'D005243', 'D006801', 'D008055', 'D008297', 'D009821', 'D010743', 'D013261', 'D014280', 'D003313']","['Adult', 'Bile Acids and Salts', 'Cacao', 'Cholesterol', 'Chromatography', 'Dietary Fats', 'Fats, Unsaturated', 'Feces', 'Humans', 'Lipids', 'Male', 'Oils', 'Phospholipids', 'Sterols', 'Triglycerides', 'Zea mays']",Cholesterol balance and fecal neutral steroid and bile acid excretion in normal men fed dietary fats of different fatty acid composition.,"[None, 'Q000032', None, 'Q000097', None, 'Q000378', 'Q000378', 'Q000032', None, 'Q000097', None, None, 'Q000097', 'Q000032', 'Q000097', None]","[None, 'analysis', None, 'blood', None, 'metabolism', 'metabolism', 'analysis', None, 'blood', None, None, 'blood', 'analysis', 'blood', None]",https://www.ncbi.nlm.nih.gov/pubmed/5796351,1969,2,1,table 1 part A,only the fatty acids +0.55,22175758,"Procyanidins, as important secondary plant metabolites in fruits, berries, and beverages such as cacao and tea, are supposed to have positive health impacts, although their bioavailability is yet not clear. One important aspect for bioavailability is intestinal metabolism. The investigation of the microbial catabolism of A-type procyanidins is of great importance due to their more complex structure in comparison to B-type procyanidins. A-type procyanidins exhibit an additional ether linkage between the flavan-3-ol monomers. In this study two A-type procyanidins, procyanidin A2 and cinnamtannin B1, were incubated in the pig cecum model to mimic the degradation caused by the microbiota. Both A-type procyanidins were degraded by the microbiota. Procyanidin A2 as a dimer was degraded by about 80% and cinnamtannin B1 as a trimer by about 40% within 8 h of incubation. Hydroxylated phenolic compounds were quantified as degradation products. In addition, two yet unknown catabolites were identified, and the structures were elucidated by Fourier transform mass spectrometry.",Journal of agricultural and food chemistry,"['D000818', 'D002432', 'D066298', 'D007422', 'D013058', 'D008954', 'D015394', 'D044945', 'D013552']","['Animals', 'Cecum', 'In Vitro Techniques', 'Intestines', 'Mass Spectrometry', 'Models, Biological', 'Molecular Structure', 'Proanthocyanidins', 'Swine']",Intestinal metabolism of two A-type procyanidins using the pig cecum model: detailed structure elucidation of unknown catabolites with Fourier transform mass spectrometry (FTMS).,"[None, 'Q000737', None, 'Q000737', None, None, None, 'Q000737', None]","[None, 'chemistry', None, 'chemistry', None, None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/22175758,2012,0,0,, +0.55,21623500,"In order to determine the levels of ochratoxin A (OTA) in cocoa and cocoa products available in Canada, a previously published analytical method, with minor modifications to the extraction and immunoaffinity clean-up and inclusion of an evaporation step, was initially used (Method I). To improve the low method recoveries (46-61%), 40% methanol was then included in the aqueous sodium bicarbonate extraction solvent (pH 7.8) (Method II). Clean-up was on an Ochratest__¢ immunoaffinity column and OTA was determined by liquid chromatography (LC) with fluorescence detection. Recoveries of OTA from spiked cocoa powder (0.5 and 5 ng g(-1)) were 75-84%; while recoveries from chocolate were 93-94%. The optimized method was sensitive (limit of quantification (LOQ) = 0.07-0.08 ng g(-1)), accurate (recovery = 75-94%) and precise (coefficient of variation (CV) < 5%). It is applicable to cocoa and chocolate. Analysis of 32 samples of cocoa powder (16 alkalized and 16 natural) for OTA showed an incidence of 100%, with concentrations ranging from 0.25 to 7.8 ng g(-1); in six samples the OTA level exceeded 2 ng g(-1), the previously considered European Union limit for cocoa. The frequency of detection of OTA in 28 chocolate samples (21 dark or baking chocolate and seven milk chocolate) was also 100% with concentrations ranging from 0.05 to 1.4 ng g(-1); one sample had a level higher than the previously considered European Union limit for chocolate (1 ng g(-1)).","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D002099', 'D002182', 'D002846', 'D002851', 'D005506', 'D005511', 'D006863', 'D057230', 'D009793', 'D015203', 'D012639', 'D013050']","['Cacao', 'Candy', 'Chromatography, Affinity', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Food Handling', 'Hydrogen-Ion Concentration', 'Limit of Detection', 'Ochratoxins', 'Reproducibility of Results', 'Seeds', 'Spectrometry, Fluorescence']",Ochratoxin A in cocoa and chocolate sampled in Canada.,"['Q000737', 'Q000032', None, None, None, None, None, None, 'Q000032', None, 'Q000737', None]","['chemistry', 'analysis', None, None, None, None, None, None, 'analysis', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21623500,2011,1,1,table 3, +0.55,21623503,"For the analysis of blue-green algal food supplements for cylindrospermopsin (CYN), a C18 solid-phase extraction column and a polygraphitized carbon solid-phase extraction column in series was an effective procedure for the clean-up of extracts. Determination of CYN was by liquid chromatography with ultraviolet light detection. At extract spiking levels of CYN equivalent to 25-500 _µg g(-1), blue-green algal supplement recoveries were in the range 70-90%. CYN was not detected in ten samples of food supplements and one chocolate product, all containing blue-green algae. The limit of detection for the method was 16 _µg g(-1), and the limit of quantification was 52 _µg g(-1).","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D001427', 'D002099', 'D002182', 'D002273', 'D002851', 'D000458', 'D019587', 'D057140', 'D005506', 'D057230', 'D052616', 'D013056', 'D014498']","['Bacterial Toxins', 'Cacao', 'Candy', 'Carcinogens', 'Chromatography, High Pressure Liquid', 'Cyanobacteria', 'Dietary Supplements', 'Fast Foods', 'Food Contamination', 'Limit of Detection', 'Solid Phase Extraction', 'Spectrophotometry, Ultraviolet', 'Uracil']",Determination of the cyanobacterial toxin cylindrospermopsin in algal food supplements.,"['Q000032', 'Q000737', 'Q000032', 'Q000032', None, 'Q000378', 'Q000032', 'Q000032', None, None, None, None, 'Q000031']","['analysis', 'chemistry', 'analysis', 'analysis', None, 'metabolism', 'analysis', 'analysis', None, None, None, None, 'analogs & derivatives']",https://www.ncbi.nlm.nih.gov/pubmed/21623503,2011,0,0,,no cocoa +0.54,19722709,"The contents of extractable and unextractable proanthocyanidins were determined in a large number of commercial food products of plant origin available in Finland. Proanthocyanidins were extracted with aqueous acetone-methanol and quantified by normal phase high-performance liquid chromatography (HPLC) according to their degree of polymerization. Unextractable proanthocyanidins were analyzed from the extraction residue by reversed phase HPLC after acid-catalyzed depolymerization as free flavan-3-ols (terminal units) and benzylthioethers (extension units). Proanthocyanidins were detected in 49 of 99 selected food items. The highest contents per fresh weight were determined in chokeberries, rose hips, and cocoa products. Berries and fruits were generally the best sources of proanthocyanidins, whereas most of the vegetables, roots, and cereals lacked them completely. Many of the samples contained a significant proportion of insoluble proanthocyanidins, which need to be quantified as well if total proanthocyanidins are studied. Considerable variation was observed in proanthocyanidin contents in berries, which requires further research.",Journal of agricultural and food chemistry,"['D002851', 'D002523', 'D005387', 'D005638', 'D018517', 'D010945', 'D044945', 'D014675']","['Chromatography, High Pressure Liquid', 'Edible Grain', 'Finland', 'Fruit', 'Plant Roots', 'Plants, Edible', 'Proanthocyanidins', 'Vegetables']",Proanthocyanidins in common food products of plant origin.,"[None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000032', 'Q000737']","[None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19722709,2009,1,1,TABLE 2 CONTINUED, +0.54,15472952,"Modified micellar electrokinetic chromatography (MEKC) analysis of monomeric flavanols (catechin and epicatechin) and methylxanthines (caffeine and theobromine) in chocolate and cocoa was performed by using sodium dodecyl sulfate (SDS) as a principal component of the running buffer. Because of the reported poor stability of catechins in alkaline solutions, acidic conditions (pH 2.5) were chosen and consequently the electroosmotic flow (EOF) was significantly suppressed; this resulted in a fast anodic migration of the analytes partitioned into the SDS micelles. Under these conditions, variations of either pH value in acidic range or SDS concentration, showed to be not suitable to modulate the selectivity. To overcome this limit, use of additives to the SDS-based running buffer was successfully applied and three different systems were optimized for the separation of (+)-catechin, (-)-epicatechin, caffeine, and theobromine in chocolate and cocoa powder samples. In particular, two mixed micelle systems were applied; the first consisted of a mixture of SDS and 3-[(3-cholamidopropyl)dimethylammonio]-1-propansulfonate (CHAPS) with a composition of 90 mM and 10 mM, respectively; the second was SDS and taurodeoxycholic acid sodium salt (TDC) with a composition of 70 mM and 30 mM, respectively. A further MEKC approach was developed by addition of 10 mM hydroxypropyl-beta-cyclodextrin (HP-beta-CD) to the SDS solution (90 mM); it provided a useful cyclodextrin(CD)-modified MEKC. By applying the optimized conditions, different separation profiles of the flavanols and methylxanthines were obtained showing interesting potential of these combined systems; their integrated application showed to be useful for the identification of the low level of (+)-catechin in certain real samples. The CD-MEKC approach was validated and applied to the determination of catechins and methylxanthines in aqueous extracts from four different commercial chocolate types (black and milk) and two cocoa powders.",Electrophoresis,"['D002099', 'D002138', 'D002392', 'D002793', 'D020374', 'D008823', 'D012967', 'D013501', 'D014970']","['Cacao', 'Calibration', 'Catechin', 'Cholic Acids', 'Chromatography, Micellar Electrokinetic Capillary', 'Micelles', 'Sodium Dodecyl Sulfate', 'Surface-Active Agents', 'Xanthines']",Modified micellar electrokinetic chromatography in the analysis of catechins and xanthines in chocolate.,"['Q000737', None, 'Q000032', 'Q000737', 'Q000379', None, 'Q000737', 'Q000737', 'Q000032']","['chemistry', None, 'analysis', 'chemistry', 'methods', None, 'chemistry', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15472952,2005,,,, +0.54,16410875,"This study aimed to evaluate the co-occurrence of caffeine and the extent of its influence as compared to other traditional water quality parameters (microbiological and physico-chemical) in order to characterize it as an efficient indicator of anthropic pollution of urban aquatic environments. Caffeine is an ingredient in a variety of beverages (coffee, tea, and caffeinated soft drinks) and numerous food products (chocolate, pastries, and dairy desserts). Although the human body metabolizes this stimulant efficiently, between 0.5 and 10.0% is excreted, mostly in the urine. Analysis of water samples from the Leopoldina Basin and Guanabara Bay revealed a significant difference between areas not commonly affected by nutrient enrichment or sewage inputs and areas chronically influenced by sewage discharges and elevated eutrophication. Monitoring caffeine will be fundamental in stressed urban aquatic environments where frequent accidental ruptures of sewer lines and discharges of untreated effluents impede effective water quality evaluation with traditional indicators.",Cadernos de saude publica,"['D001938', 'D002110', 'D055598', 'D002627', 'D002851', 'D002947', 'D017753', 'D004784', 'D005618', 'D006801', 'D015999', 'D014865', 'D014874']","['Brazil', 'Caffeine', 'Chemical Phenomena', 'Chemistry, Physical', 'Chromatography, High Pressure Liquid', 'Cities', 'Ecosystem', 'Environmental Monitoring', 'Fresh Water', 'Humans', 'Multivariate Analysis', 'Waste Disposal, Fluid', 'Water Pollutants, Chemical']",Caffeine as an environmental indicator for assessing urban aquatic ecosystems.,"[None, 'Q000032', None, None, None, None, None, 'Q000379', 'Q000737', None, None, None, 'Q000032']","[None, 'analysis', None, None, None, None, None, 'methods', 'chemistry', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16410875,2006,0,0,,no cocoa +0.54,10457651,"The paper describes a simple gas chromatographic method for quantification of ethanol in distillates of chocolate shell pralines and fillings. The samples were prepared in two steps. The first step consisted of ethanol distillation from the product and the second involved capillary gas chromatography of 10% v/v distillate with expected ethanol content between 0.06% and 2.5% w/w. Quantification was carried out using iso-propanol as internal standard. The range of linear method response was 0.05-3.16% w/w of ethanol, which corresponded to products with ethanol content between 0.5 and 31.6% w/w. The detection limit was 0.0158% w/w and the quantification limit was 0.058% w/w of ethanol with the relative standard deviation of 2.5%.",Arhiv za higijenu rada i toksikologiju,"['D002099', 'D002182', 'D002849', 'D000431', 'D005524']","['Cacao', 'Candy', 'Chromatography, Gas', 'Ethanol', 'Food Technology']",Determination of ethanol in chocolate shell pralines and filled chocolates by capillary gas chromatography.,"[None, 'Q000032', 'Q000379', 'Q000032', None]","[None, 'analysis', 'methods', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/10457651,1999,,,, +0.54,11407581,"On-line liquid chromatography-gas chromatography (LC-GC) has been applied to the analysis of steryl esters in cocoa butter. Separation of the steryl esters was achieved after on-line transfer to capillary GC. HPLC removes the large amount of triglycerides and pre-separates the components of interest, thus avoiding time-consuming sample preparation prior to GC analysis. The identities of the compounds were confirmed by GC-MS investigation of the collected HPLC fraction and by comparison of the mass spectra (chemical ionization using ammonia as ionization gas) to those of synthesized reference compounds. Using cholesteryl laurate as internal standard, steryl esters were quantified in commercial cocoa butter samples, the detection limit being 3 mg/kg and the quantification limit 10 mg/kg, respectively. Only slight differences in percentage distributions of steryl esters depending on the geographical origin of the material were observed. The patterns were shown to remain unchanged after deodorization. The method described might be a valuable tool for authenticity assessment of cocoa butter.",Journal of chromatography. A,"['D002849', 'D002851', 'D004952', 'D005069', 'D010938', 'D015203', 'D013229']","['Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Esters', 'Evaluation Studies as Topic', 'Plant Oils', 'Reproducibility of Results', 'Stearic Acids']",Analysis of steryl esters in cocoa butter by on-line liquid chromatography-gas chromatography.,"['Q000379', 'Q000379', 'Q000032', None, 'Q000737', None, 'Q000032']","['methods', 'methods', 'analysis', None, 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11407581,2001,1,1,"table 1, 2 and 3", +0.54,28142090,"The outer portion of the cocoa bean, also known as cocoa husk or cocoa shell (CS), is an agrowaste material from the cocoa industry. Even though raw CS is used as food additive, garden mulch, and soil conditioner or even burnt for fuel, this biomass material has hardly ever been investigated for further modification. This article proposes a strategy of chemical modification of cocoa shell to add value to this natural material. The study investigates the grafting of aryl diazonium salt on cocoa shell. Different diazonium salts were grafted on the shell surface and characterized by infrared spectroscopy and scanning electronic microscopy imaging. Strategies were developed to demonstrate the spontaneous grafting of aryl diazonium salt on cocoa shell and to elucidate that lignin is mainly involved in immobilizing the phenyl layer.",Journal of colloid and interface science,"['D002099', 'D003979', 'D008031', 'D008855', 'D013055']","['Cacao', 'Diazonium Compounds', 'Lignin', 'Microscopy, Electron, Scanning', 'Spectrophotometry, Infrared']",Chemical modification of the cocoa shell surface using diazonium salts.,"['Q000033', 'Q000737', 'Q000737', None, None]","['anatomy & histology', 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28142090,2018,1,1,in text (contains key word), +0.54,23289516,"Proanthocyanidins and ellagitannins, referred to as ""tannins"", exist in many plant sources. These compounds interact with proteins due to their numerous hydroxyl groups, which are suitable for hydrophobic associations. It was hypothesized that tannins could bind to the digestive enzymes _±-amylase and glucoamylase, thereby inhibiting starch hydrolysis. Slowed starch digestion can theoretically increase satiety by modulating glucose ""spiking"" and depletion that occurs after carbohydrate-rich meals. Tannins were isolated from extracts of pomegranate, cranberry, grape, and cocoa and these isolates tested for effectiveness to inhibit the activity of _±-amylase and glucoamylase in vitro. The compositions of the isolates were confirmed by NMR and LC/MS analysis, and tannin-protein interactions were investigated using relevant enzyme assays and differential scanning calorimetry (DSC). The results demonstrated inhibition of each enzyme by each tannin, but with variation in magnitude. In general, larger and more complex tannins, such as those in pomegranate and cranberry, more effectively inhibited the enzymes than did less polymerized cocoa tannins. Interaction of the tannins with the enzymes was confirmed through calorimetric measurements of changes in enzyme thermal stability.",Journal of agricultural and food chemistry,"['D002099', 'D002152', 'D005087', 'D006868', 'D047348', 'D009682', 'D044945', 'D031826', 'D013213', 'D053719', 'D029799', 'D027843', 'D000516']","['Cacao', 'Calorimetry, Differential Scanning', 'Glucan 1,4-alpha-Glucosidase', 'Hydrolysis', 'Hydrolyzable Tannins', 'Magnetic Resonance Spectroscopy', 'Proanthocyanidins', 'Punicaceae', 'Starch', 'Tandem Mass Spectrometry', 'Vaccinium macrocarpon', 'Vitis', 'alpha-Amylases']","Inhibition of _±-amylase and glucoamylase by tannins extracted from cocoa, pomegranates, cranberries, and grapes.","['Q000737', None, 'Q000037', None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000037']","['chemistry', None, 'antagonists & inhibitors', None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'antagonists & inhibitors']",https://www.ncbi.nlm.nih.gov/pubmed/23289516,2014,0,0,, +0.54,17517419,"The retention behaviour of several triacylglycerols (TAGs) and fats on Hypercarb, a porous graphitic carbon column (PGC), was investigated in liquid chromatography (LC) under isocratic elution mode with an evaporative light scattering detector (ELSD). Mixtures of chloroform/isopropanol were selected as mobile phase for a suitable retention time to study the influence of temperature. The retention was different between PGC and non-aqueous reversed phase liquid chromatography (NARP-LC) on octadecyl phase. The retention of TAGs was investigated in the interval 30-70 degrees C. Retention was greatly affected by temperature: it decreases as the column temperature increases. Selectivity of TAGs was also slightly influenced by the temperature. Moreover, this chromatographic method is compatible with a mass spectrometer (MS) detector by using atmospheric pressure chemical ionisation (APCI): same fingerprints of cocoa butter and shea butter were obtained with LC-ELSD and LC-APCI-MS. These preliminary results showed that the PGC column could be suitable to separate quickly triacylglycerols in high temperature conditions coupled with ELSD or MS detector.",Journal of chromatography. A,"['D002853', 'D006108', 'D006358', 'D014280']","['Chromatography, Liquid', 'Graphite', 'Hot Temperature', 'Triglycerides']",Analysis of triacylglycerols on porous graphitic carbon by high temperature liquid chromatography.,"['Q000379', 'Q000737', None, 'Q000032']","['methods', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17517419,2007,2,1,text under 3.4 appication for cocoa butter,maybe there comething in the text +0.54,24577577,"Hazelnut (Corylus avellana L.) is responsible for a significant part of the allergies related to nuts. Still, it is a very much appreciated nut and as consequence is widely used in all types of processed foods, such as chocolates. Correct food labelling is currently the most effective means of preventing the consumption of allergenic ingredients, namely hazelnut, by the sensitised/allergic individuals. Thus, to verify labelling compliance and to ensure allergic patient protection, the development of highly sensitive methodologies is of extreme importance. In this study, three major methodologies, namely enzyme-linked immunosorbent assays (ELISA), liquid chromatography coupled with mass spectrometry and real-time polymerase chain reaction, were evaluated for their performance regarding the detection of hazelnut allergens in model chocolates. The sandwich ELISA and respective antibodies were in-house developed and produced. With sensitivity levels of approximately 1 mg kg(-1) and limits of quantification of 50-100 mg kg(-1), all the performed methods were considered appropriate for the identification of hazelnut in complex foods such as chocolates. To our knowledge, this was the first successful attempt to develop and compare three independent approaches for the detection of allergens in foods.",Analytical and bioanalytical chemistry,"['D000485', 'D002099', 'D031211', 'D018744', 'D004797', 'D005504', 'D009754', 'D010940', 'D060888', 'D053719']","['Allergens', 'Cacao', 'Corylus', 'DNA, Plant', 'Enzyme-Linked Immunosorbent Assay', 'Food Analysis', 'Nuts', 'Plant Proteins', 'Real-Time Polymerase Chain Reaction', 'Tandem Mass Spectrometry']","Assessing hazelnut allergens by protein- and DNA-based approaches: LC-MS/MS, ELISA and real-time PCR.","['Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000379', None, 'Q000737', 'Q000737', 'Q000379', 'Q000379']","['analysis', 'chemistry', 'chemistry', 'chemistry', 'methods', None, 'chemistry', 'chemistry', 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/24577577,2014,0,0,,no cocoa +0.54,26067163,"The natural xanthines caffeine, theobromine, and theophylline are of major commercial importance as flavor constituents in coffee, cocoa, tea, and a number of other beverages. However, their exploitation for authenticity, a requirement in these commodities that have a large origin-based price-range, by the standard method of isotope ratio monitoring by mass spectrometry (irm-MS) is limited. We have now developed a methodology that overcomes this deficit that exploits the power of isotopic quantitative (13)C nuclear magnetic resonance (NMR) spectrometry combined with chemical modification of the xanthines to enable the determination of positional intramolecular (13)C/(12)C ratios (__(13)Ci) with high precision. However, only caffeine is amenable to analysis: theobromine and theophylline present substantial difficulties due to their poor solubility. However, their N-methylation to caffeine makes spectral acquisition feasible. The method is confirmed as robust, with good repeatability of the __(13)Ci values in caffeine appropriate for isotope fractionation measurements at natural abundance. It is shown that there is negligible isotope fractionation during the chemical N-methylation procedure. Thus, the method preserves the original positional __(13)Ci values. The method has been applied to measure the position-specific variation of the (13)C/(12)C distribution in caffeine. Not only is a clear difference between caffeine isolated from different sources observed, but theobromine from cocoa is found to show a (13)C pattern distinct from that of caffeine. ",Analytical chemistry,"['D066241', 'D008745', 'D014970']","['Carbon-13 Magnetic Resonance Spectroscopy', 'Methylation', 'Xanthines']",Position-Specific Isotope Analysis of Xanthines: A (13)C Nuclear Magnetic Resonance Method to Determine the (13)C Intramolecular Composition at Natural Abundance.,"['Q000379', None, 'Q000737']","['methods', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/26067163,2015,2,1,table 5 , +0.54,12166981,"Myosmine has been regarded as a specific tobacco alkaloid until investigations pointed out that nuts and nut products constitute a significant source of myosmine. In the present study it is shown that the occurrence of myosmine is widespread throughout a large number of plant families. Using a method for extraction practicable for all examined foods, quantitative analysis through internal standard addition showed nanograms per gram amounts. Positively tested edibles were staple foods such as maize, rice, wheat flour, millet, potato, and milk and also cocoa, popcorn, tomato, carrot, pineapple, kiwi, and apples. No myosmine was detectable in other vegetables and fruits such as lettuce, spinach, cucumber, onion, banana, tangerines, and grapes. Myosmine is easily nitrosated giving rise to a DNA adduct identical to the esophageal tobacco carcinogen N-nitrosonornicotine. Therefore, the role of dietary myosmine in esophageal adenocarcinoma should be further investigated.",Journal of agricultural and food chemistry,"['D000230', 'D000470', 'D000818', 'D002523', 'D004938', 'D005638', 'D008401', 'D008892', 'D014675']","['Adenocarcinoma', 'Alkaloids', 'Animals', 'Edible Grain', 'Esophageal Neoplasms', 'Fruit', 'Gas Chromatography-Mass Spectrometry', 'Milk', 'Vegetables']","New sources of dietary myosmine uptake from cereals, fruits, vegetables, and milk.","['Q000139', 'Q000008', None, 'Q000737', 'Q000139', 'Q000737', None, 'Q000737', 'Q000737']","['chemically induced', 'administration & dosage', None, 'chemistry', 'chemically induced', 'chemistry', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12166981,2002,1,1,table 1, +0.54,18052039,"Chocolate is often labeled with percent cocoa solids content. It is assumed that higher cocoa solids contents are indicative of higher polyphenol concentrations, which have potential health benefits. However, cocoa solids include polyphenol-free cocoa butter and polyphenol-rich nonfat cocoa solids (NFCS). In this study the strength of the relationship between NFCS content (estimated by theobromine as a proxy) and polyphenol content was tested in chocolate samples with labeled cocoa solids contents in the range of 20-100%, grouped as dark (n = 46), milk (n = 8), and those chocolates containing inclusions such as wafers or nuts (n = 15). The relationship was calculated with regard to both total polyphenol content and individual polyphenols. In dark chocolates, NFCS is linearly related to total polyphenols (r2 = 0.73). Total polyphenol content appears to be systematically slightly higher for milk chocolates than estimated by the dark chocolate model, whereas for chocolates containing other ingredients, the estimates fall close to or slightly below the model results. This shows that extra components such as milk, wafers, or nuts might influence the measurements of both theobromine and polyphenol contents. For each of the six main polyphenols (as well as their sum), the relationship with the estimated NFCS was much lower than for total polyphenols (r2 < 0.40), but these relationships were independent of the nature of the chocolate type, indicating that they might still have some predictive capabilities.",Journal of agricultural and food chemistry,"['D002099', 'D002392', 'D002845', 'D005419', 'D010636', 'D059808', 'D013805']","['Cacao', 'Catechin', 'Chromatography', 'Flavonoids', 'Phenols', 'Polyphenols', 'Theobromine']",Predictive relationship between polyphenol and nonfat cocoa solids content of chocolate.,"['Q000737', 'Q000032', None, 'Q000032', 'Q000032', None, 'Q000032']","['chemistry', 'analysis', None, 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18052039,2008,0,0,,chocolate analysis +0.53,24001847,"New products available for food creations include a wide variety of ""supposed"" food grade aerosol sprays. However, the gas propellants used cannot be considered as safe. The different legislations available did not rule any maximum residue limits, even though these compounds have some limits when used for other food purposes. This study shows a preliminary monitoring of propane, butane and dimethyl ether residues, in cakes and chocolate after spraying, when these gases are used as propellants in food aerosol sprays. Release kinetics of propane, butane and dimethyl ether were measured over one day with sprayed food, left at room temperature or in the fridge after spraying. The alkanes and dimethyl ether analyses were performed by headspace-gas chromatography-mass spectrometry/thermal conductivity detection, using monodeuterated propane and butane generated in situ as internal standards. According to the obtained results and regardingthe extrapolations of the maximum residue limits existing for these substances, different delays should be respected according to the storage conditions and the gas propellant to consume safely the sprayed food. ",Food chemistry,"['D000336', 'D002073', 'D003296', 'D005503', 'D005506', 'D008401', 'D007700', 'D008738', 'D011407']","['Aerosols', 'Butanes', 'Cooking', 'Food Additives', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Kinetics', 'Methyl Ethers', 'Propane']",New trends in the kitchen: propellants assessment of edible food aerosol sprays used on food.,"['Q000032', 'Q000737', 'Q000295', 'Q000737', 'Q000032', None, None, 'Q000737', 'Q000737']","['analysis', 'chemistry', 'instrumentation', 'chemistry', 'analysis', None, None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24001847,2014,0,0,,no cocoa +0.53,28190808,"In the present study, the resolution parameters and correction factors (CFs) of triacylglycerol (TAG) standards were estimated by gas chromatography-flame ionization detector (GC-FID) to achieve the precise quantification of the TAG composition in edible fats and oils. Forty seven TAG standards comprising capric acid, lauric acid, myristic acid, pentadecanoic acid, palmitic acid, palmitoleic acid, stearic acid, oleic acid, linoleic acid, and/or linolenic acid were analyzed, and the CFs of these TAGs were obtained against tripentadecanoyl glycerol as the internal standard. The capillary column was Ultra ALLOY",Journal of oleo science,"['D000074262', 'D002849', 'D004041', 'D005410', 'D057230', 'D000073878', 'D010938', 'D014280']","['Canola Oil', 'Chromatography, Gas', 'Dietary Fats', 'Flame Ionization', 'Limit of Detection', 'Palm Oil', 'Plant Oils', 'Triglycerides']",Quantification of Triacylglycerol Molecular Species in Edible Fats and Oils by Gas Chromatography-Flame Ionization Detector Using Correction Factors.,"[None, None, 'Q000032', None, None, None, 'Q000032', 'Q000032']","[None, None, 'analysis', None, None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28190808,2017,2,2,table 4 , +0.53,17711134,"The objective of present work was to comparison of fat and chosen fatty acid in chocolates with, approachable on national market. In the investigations on fat and fatty acids content in the milk chocolates, there were used 14 chocolates, divided into 3 groups either without, with supplements and stuffing. Crude fat content in the chocolates was determined on Soxhlet automatic apparatus. The saturated ad nsaturated acids content was determined using gas chromatographic method. Content of fat and fatty cids in chocolates were differentiation. The highest crude fat content was finding in chocolates with tuffing (31.8%) and without supplements (28.9%). The sum of saturated fatty acids content in fat above 62%) was highest and low differentiation in the chocolates without supplements. Among of saturated and unsaturated fatty acids depended from kind of chocolates dominated, palmitic, stearic, oleic and, linoleic acids. Supplements of nut in chocolates had on influence of high oleic and linoleic level",Roczniki Panstwowego Zakladu Higieny,"['D002099', 'D002182', 'D002849', 'D004041', 'D005227', 'D005228', 'D005231', 'D005504', 'D008041', 'D010169', 'D010938', 'D011044', 'D013229']","['Cacao', 'Candy', 'Chromatography, Gas', 'Dietary Fats', 'Fatty Acids', 'Fatty Acids, Essential', 'Fatty Acids, Unsaturated', 'Food Analysis', 'Linoleic Acids', 'Palmitic Acids', 'Plant Oils', 'Poland', 'Stearic Acids']",[Fat and fatty acids chosen in chocolates content].,"['Q000737', 'Q000032', None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, 'Q000032', 'Q000032', 'Q000032', None, 'Q000032']","['chemistry', 'analysis', None, 'analysis', 'analysis', 'analysis', 'analysis', None, 'analysis', 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17711134,2008,0,0,,no cocoa +0.53,10208658,"Selenium content of 1028 milk and milk products of Turkey are presented in this study. The selenium content of human milk (colostrum, transitional, and mature milk), various kinds of milk [cow, sheep, goat, buffalo, paper boxes (3%, 1.5%, 0.012% fat), bottled milk, condensed milk (10% fat), mineral added milk (1.6%), and banana, strawberry, and chocolate milk] and milk products (kefir, yogurt, Ayran, various cheese, coffee cream, ice cream, butter, margarine, milk powder, and fruit yogurt) in Turkey were determined by a spectrofluorometric method. The selenium levels of cow milks collected from 57 cities in Turkey were also determined. Selenium levels in cow milk varied with geographical location in Turkey and were found to be lowest for Van and highest for Aksaray. The results [milk (cow, sheep, goat, buffalo and human) and milks products] were compared with literature data from different countries.",Biological trace element research,"['D000328', 'D000818', 'D002079', 'D002611', 'D005260', 'D006801', 'D007054', 'D007774', 'D008892', 'D008895', 'D012643', 'D013050', 'D013997', 'D014421']","['Adult', 'Animals', 'Butter', 'Cheese', 'Female', 'Humans', 'Ice Cream', 'Lactation', 'Milk', 'Milk, Human', 'Selenium', 'Spectrometry, Fluorescence', 'Time Factors', 'Turkey']",Selenium content of milk and milk products of Turkey. II.,"[None, None, 'Q000032', 'Q000032', None, None, 'Q000032', None, 'Q000737', 'Q000737', 'Q000032', None, None, None]","[None, None, 'analysis', 'analysis', None, None, 'analysis', None, 'chemistry', 'chemistry', 'analysis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10208658,1999,0,0,,no cocoa +0.53,9892779,"The aetiology of dental caries is in part related to the retention time of dietary carbohydrates in the oral cavity and their subsequent metabolism by the oral bacteria. Salivary clearance of fermentable carbohydrates from three different foodstuffs was examined in 5 subjects and analyses performed by high-performance anion-exchange chromatography with pulsed amperometric detection. The clearance of glucose, fructose, sucrose, maltose and sorbitol rinses was studied as well as that of chocolate bars, white bread and bananas. Of the sugar rinses studied, sucrose was removed from saliva most rapidly whilst appreciable levels of sorbitol remained even after 1 h. Clearance of residual carbohydrates from bananas and chocolate bars seemed marginally faster than in the case of bread, but sucrose levels still tended to fall more quickly than other carbohydrates studied. Surprisingly, carbohydrate residues from the three foods studied were still present in the mouth even 1 h after ingestion, which is longer than has hitherto been reported.",Caries research,"['D001939', 'D002099', 'D002241', 'D005260', 'D005632', 'D005947', 'D006801', 'D008297', 'D008320', 'D008657', 'D012463', 'D013012', 'D013395', 'D019862']","['Bread', 'Cacao', 'Carbohydrates', 'Female', 'Fructose', 'Glucose', 'Humans', 'Male', 'Maltose', 'Metabolic Clearance Rate', 'Saliva', 'Sorbitol', 'Sucrose', 'Zingiberales']",Human salivary sugar clearance after sugar rinses and intake of foodstuffs.,"['Q000032', None, 'Q000032', None, 'Q000032', 'Q000032', None, None, 'Q000032', None, 'Q000737', 'Q000032', 'Q000032', None]","['analysis', None, 'analysis', None, 'analysis', 'analysis', None, None, 'analysis', None, 'chemistry', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/9892779,1999,,,,no pdf access +0.53,22663977,"(-)-Epicatechin, an abundant dietary polyphenol found mainly in cocoa and tea, is known to extensively undergo metabolism after ingestion giving rise to a complex series of conjugated metabolites including numerous isomers. In the present study, the combination of fractionation, chemical derivatization and various mass spectrometric approaches is described to determine the exact position of sulphate group in methylated epicatechin metabolites. Four O-methyl-(-)-epicatechin-O-sulphate metabolites isolated from human urine samples were derivatized under mild condition using trimethylsilyldiazomethane (TMSD) in the presence of methanol. The resulting methylated reaction products were then analyzed by high resolution and multistage mass spectrometry for the subsequent identification of the sulphate positional isomers. Results show that O-methylation affects the charge delocalization in negatively charged ions and hereby the fragmentation pattern of the sulphate isomers allowing the identification of diagnostic ions. In addition, this study demonstrates that methoxy derivatives of polyphenol metabolites can be prepared using TMSD. Subsequently, the localization of the sulphate group in the polyphenol metabolites can be achieved by analyzing the methoxy derivatives by multistage mass spectrometry. Using an enzymatic reaction for identification of the O-methyl position, and a chemical O-methylation with TMSD follow by high resolution and multistage tandem MS for the identification of the sulphate group position, we were able to identify the previously unknown O-methyl-(-)-epicatechin-O-sulphate. Accordingly, we identified 3'-O-methyl-(-)-epicatechin-5-O-sulphate and 3'-O-methyl-(-)-epicatechin-7-O-sulphate as the main O-methyl-(-)-epicatechin-sulfates(-)-epicatechin metabolites in humans.",Journal of chromatography. A,"['D002392', 'D003978', 'D006801', 'D013058', 'D013463', 'D014297']","['Catechin', 'Diazomethane', 'Humans', 'Mass Spectrometry', 'Sulfuric Acid Esters', 'Trimethylsilyl Compounds']",Identification of O-methyl-(-)-epicatechin-O-sulphate metabolites by mass-spectrometry after O-methylation with trimethylsilyldiazomethane.,"['Q000031', 'Q000031', None, 'Q000379', 'Q000032', 'Q000737']","['analogs & derivatives', 'analogs & derivatives', None, 'methods', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22663977,2012,0,0,, +0.53,25940929,"This note reports an interesting way to rapidly identify bacteria grown from blood culture bottles. Chocolate agar plates were inoculated with 1 drop of the positive blood bottle medium. After a 3-hour incubation, the growth veil was submitted to MALDI-TOF mass spectrometry: 77% of the bacteria present have been correctly identified. ",Journal of microbiological methods,"['D001419', 'D015373', 'D001769', 'D003470', 'D006801', 'D019032', 'D053719']","['Bacteria', 'Bacterial Typing Techniques', 'Blood', 'Culture Media', 'Humans', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Tandem Mass Spectrometry']",MALDI-TOF mass spectrometry for early identification of bacteria grown in blood culture bottles.,"['Q000145', 'Q000379', 'Q000382', 'Q000378', None, 'Q000379', 'Q000379']","['classification', 'methods', 'microbiology', 'metabolism', None, 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/25940929,2016,0,0,,no cocoa +0.53,20299763,"The amount and characterization of phytosterol and other minor components present in three Indian minor seed oils, mahua (Madhuca latifolia), sal (Shorea robusta) and mango kernel (Mangifera indica), have been done. Theses oils have shown commercial importance as cocoa-butter substitutes because of their high symmetrical triglycerides content. The conventional thin layer chromatography (TLC), gas chromatography (GC) & gas chromatography-mass spectroscopy (GC-MS) techniques were used to characterize the components and the high performance thin layer chromatography (HPTLC) technique was used to quantify the each group of components. The experimental data showed that the all the three oils are rich in sterol content and among all the sterols, beta-sitosterol occupies the highest amount. Sal oil contains appreciable amount of cardenolides, gitoxigenin. Tocopherol is present only in mahua oil and oleyl alcohol is present in mango kernel oil. Hydrocarbon, squalene, is present in all the three oils. The characterization of these minor components will help to detect the presence of the particular oil in specific formulations and to assess its stability as well as nutritional quality of the specific oil.",Journal of oleo science,"['D002298', 'D002855', 'D005233', 'D005504', 'D008401', 'D006838', 'D010840', 'D010938', 'D012639', 'D012855', 'D013185', 'D024505']","['Cardenolides', 'Chromatography, Thin Layer', 'Fatty Alcohols', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Hydrocarbons', 'Phytosterols', 'Plant Oils', 'Seeds', 'Sitosterols', 'Squalene', 'Tocopherols']","Analysis of sterol and other components present in unsaponifiable matters of mahua, sal and mango kernel oil.","['Q000032', None, 'Q000032', 'Q000379', None, 'Q000032', 'Q000032', 'Q000737', 'Q000737', 'Q000032', 'Q000032', 'Q000032']","['analysis', None, 'analysis', 'methods', None, 'analysis', 'analysis', 'chemistry', 'chemistry', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20299763,2010,0,0,, +0.53,26833256,"Cocoa tea (Camellia ptilophylla) is a naturally decaffeinated tea plant. Previously we found that cocoa tea demonstrated a beneficial effect against high-fat diet induced obesity, hepatic steatosis, and hyperlipidemia in mice. The present study aimed to investigate the anti-adipogenic effect of cocoa tea in vitro using preadipocytes 3T3-L1. Adipogenic differentiation was confirmed by Oil Red O stain, qPCR and Western blot. Our results demonstrated that cocoa tea significantly inhibited triglyceride accumulation in mature adipocytes in a dose-dependent manner. Cocoa tea was shown to suppress the expressions of key adipogenic transcription factors, including peroxisome proliferator-activated receptor gamma (PPAR __) and CCAAT/enhancer binding protein (C/EBP _±). The tea extract was subsequently found to reduce the expressions of adipocyte-specific genes such as sterol regulatory element binding transcription factor 1c (SREBP-1c), fatty acid synthase (FAS), Acetyl-CoA carboxylase (ACC), fatty acid translocase (FAT) and stearoylcoenzyme A desaturase-1 (SCD-1). In addition, JNK, ERK and p38 phosphorylation were inhibited during cocoa tea inhibition of 3T3-L1 adipogenic differentiation. Taken together, this is the first study that demonstrates cocoa tea has the capacity to suppress adipogenesis in pre-adipocyte 3T3-L1 similar to traditional green tea. ",Scientific reports,"['D041721', 'D017667', 'D050156', 'D000818', 'D028244', 'D002454', 'D002470', 'D002851', 'D005786', 'D051379', 'D020928', 'D010766', 'D010936', 'D013662', 'D014157', 'D014280', 'D014867']","['3T3-L1 Cells', 'Adipocytes', 'Adipogenesis', 'Animals', 'Camellia', 'Cell Differentiation', 'Cell Survival', 'Chromatography, High Pressure Liquid', 'Gene Expression Regulation', 'Mice', 'Mitogen-Activated Protein Kinases', 'Phosphorylation', 'Plant Extracts', 'Tea', 'Transcription Factors', 'Triglycerides', 'Water']",Cocoa tea (Camellia ptilophylla) water extract inhibits adipocyte differentiation in mouse 3T3-L1 preadipocytes.,"[None, 'Q000166', 'Q000187', None, 'Q000737', 'Q000187', 'Q000187', None, 'Q000187', None, 'Q000378', 'Q000187', 'Q000494', None, 'Q000378', 'Q000378', 'Q000737']","[None, 'cytology', 'drug effects', None, 'chemistry', 'drug effects', 'drug effects', None, 'drug effects', None, 'metabolism', 'drug effects', 'pharmacology', None, 'metabolism', 'metabolism', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/26833256,2017,0,0,,cocoa tea +0.53,15080647,"Normal-phase liquid chromatography/mass spectrometry (LC/MS) was used to determine the levels and fate of procyanidins in frozen and canned Ross clingstone peaches as well as in the syrup used in the canning over a 3 month period. Procyanidin oligomers, monomers through undecamers, were identified in Ross clingstone peaches. Optimized methods allowed for the quantitation of oligomers through octamers. The profile of procyanidins in peaches is similar to profiles found in grapes, chocolate, and beverages linked to health benefits such as tea and wine. The monomer content in frozen peeled peaches was found to be 19.59 mg/kg. Dimers (39.59 mg/kg) and trimers (38.81 mg/kg) constituted the largest percent composition of oligomers in the peaches. Tetramers through octamers were present in levels of 17.81, 12.43, 10.62, 3.94 and 1.75 mg/kg, respectively. Thermal processing resulted in an 11% reduction in monomers, a 9% reduction in dimers, a 12% reduction in trimers, a 6% reduction in tetramers, and a 5% reduction in pentamers. Hexamers and heptamers demonstrated an approximate 30% loss, and octamers were no longer detected. Analysis of the syrup after thermal processing indicates that there is a migration of procyanidin monomers through hexamers into the syrup that can account for the losses observed during the canning process. Storage of canned peaches for 3 months demonstrated a time-related loss in higher oligomers and that by 3 months oligomers larger than tetramers are not observed. At 3 months postcanning, levels of monomers had decreased by 10%, dimers by 16%, trimers by 45%, and tetramers by 80%. A similar trend was observed in the canning syrup.",Journal of agricultural and food chemistry,"['D044946', 'D002392', 'D002851', 'D006358', 'D013058', 'D044945', 'D027861']","['Biflavonoids', 'Catechin', 'Chromatography, High Pressure Liquid', 'Hot Temperature', 'Mass Spectrometry', 'Proanthocyanidins', 'Prunus']",Liquid chromatography/mass spectrometry investigation of the impact of thermal processing and storage on peach procyanidins.,"[None, 'Q000032', None, None, None, None, 'Q000737']","[None, 'analysis', None, None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15080647,2004,0,0,, +0.53,25727461,"The scarce availability of nongenetically modified soybeans on the world market represents a growing problem for food manufacturers. Hence, in this study the effects of substituting soybean with sunflower lecithin were investigated with regard to chocolate production. The glycerophospholipid pattern of the different lecithin samples was investigated by high-performance thin-layer chromatography fluorescence detection (HPTLC-FLD) and by HPTLC-positive ion electrospray ionization mass spectrometry (ESI(+)-MS) via the TLC-MS Interface and by scanning HPTLC-matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOFMS). Especially, the contents of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) were of interest due to the influencing effects of these two glycerophospholipids on the rheological parameters of chocolate production. The lecithin substitution led to only slight differences in the rheological parameters of milk and dark chocolate. Limits of detection (LODs) and limits of quantification (LOQs) of seven glycerophospholipids were studied for three detection modes. Mean LODs ranged from 8 to 40 mg/kg for HPTLC-FLD and, using a single-quadrupole MS, from 10 to 280 mg/kg for HPTLC-ESI(+)-MS as well as from 15 to 310 mg/kg for HPTLC-FLD-ESI(+)-MS recorded after derivatization with the primuline reagent. ",Journal of agricultural and food chemistry,"['D000818', 'D002099', 'D002855', 'D005503', 'D006368', 'D054709', 'D008892', 'D013025', 'D021241']","['Animals', 'Cacao', 'Chromatography, Thin Layer', 'Food Additives', 'Helianthus', 'Lecithins', 'Milk', 'Soybeans', 'Spectrometry, Mass, Electrospray Ionization']",Comparison and characterization of soybean and sunflower lecithins used for chocolate production by high-performance thin-layer chromatography with fluorescence detection and electrospray mass spectrometry.,"[None, 'Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000379']","[None, 'chemistry', 'methods', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/25727461,2015,0,0,, +0.53,27132838,"Assessment of the flavanol composition of 41 commercial chocolates was by HPLC-DAD. Among individual flavonols ranged from 0.095 to 3.264mgg(-1), epicatechin was the predominant flavanol accounting for 32.9%. Contrary to catechin, epicatechin was a reliable predictive value of the polyphenol content. Conversely the percentage of theobromine used as a proxy measure for nonfat cocoa solids (NFCS) was not a good predictor of epicatechin or flavanol content. In a further chiral analysis, the naturally occurring forms of cocoa flavanols, (-)-epicatechin and (+)-catechin, was determined joint the occurrence of (+)-epicatechin and (-)-catechin due to the epimerization reactions produced in chocolate manufacture. (-)-Epicatechin, the most bioactive compound and predominant form accounted of 93%. However, no positive correlation was found with% cocoa solids, the most significant quality parameter. ",Food chemistry,"['D044946', 'D002099', 'D002110', 'D002392', 'D000069956', 'D002851', 'D005419', 'D005504', 'D059808', 'D044945', 'D013237', 'D013805', 'D014970']","['Biflavonoids', 'Cacao', 'Caffeine', 'Catechin', 'Chocolate', 'Chromatography, High Pressure Liquid', 'Flavonoids', 'Food Analysis', 'Polyphenols', 'Proanthocyanidins', 'Stereoisomerism', 'Theobromine', 'Xanthines']",Assessment of flavanol stereoisomers and caffeine and theobromine content in commercial chocolates.,"['Q000032', 'Q000737', 'Q000032', 'Q000032', 'Q000032', None, 'Q000032', None, 'Q000032', 'Q000032', None, 'Q000032', 'Q000032']","['analysis', 'chemistry', 'analysis', 'analysis', 'analysis', None, 'analysis', None, 'analysis', 'analysis', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27132838,2017,0,0,,chocolate samples +0.52,28317738,"The presence of 4-methylimidazole (4-MEI), 2-methylimidazole (2-MEI) and 2-acetyl-4-tetrahydroxybutylimidazole (THI) in some foods may result from the usage of caramel colorants E150c and E150d as food additives. This study demonstrates that alkylimidazoles are also byproducts formed from natural constituents in foods during thermal processes. A range of heat-processed foods that are known not to contain caramel colorants were analyzed by isotope dilution LC-MS/MS to determine the contamination levels. Highest 4-MEI concentrations (up to 466_µg/kg) were observed in roasted barley, roasted malt and cocoa powders, with the concomitant presence of 2-MEI and/or THI in some cases, albeit at significantly lower levels. Low amounts of 4-MEI (<20_µg/kg) were also detected in cereal-based foods such as breakfast cereals and bread toasted to a brown color (medium toasted). The occurrence of 4-MEI in certain processed foods is therefore not a reliable indicator of the presence of the additives E150c or E150d.",Food chemistry,"['D002853', 'D005503', 'D005511', 'D007093', 'D013058']","['Chromatography, Liquid', 'Food Additives', 'Food Handling', 'Imidazoles', 'Mass Spectrometry']",Process-induced formation of imidazoles in selected foods.,"['Q000379', 'Q000737', 'Q000379', 'Q000737', 'Q000379']","['methods', 'chemistry', 'methods', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/28317738,2017,1,1,table 2, only cocoa products +0.52,19348428,"Hazelnut is one of the most important items in high-quality food products from Piedmont, Italy. The 'Tonda Gentile delle Langhe' (TGL) variety is acknowledged all over the world as the best one, and it is particularly appreciated when used to provide flavor in chocolate products. Authentication and/or traceability studies must therefore be developed to safeguard this variety against fraud, which can occur when the product is partially or totally substituted with hazelnuts of lower quality. In this work, a classification of hazelnuts from different countries is presented, showing the possibility to discriminate the TGL from other productions on the basis of the distribution of trace elements as determined by means of inductively coupled plasma-mass spectrometry (ICP-MS), with particular reference to lanthanides. Accuracy of the sample treatment procedure was tested by analysis of biological certified materials. Data from elemental analysis were chemometrically treated with an unsupervised method, such as principal component analysis (PCA), allowing for a good discrimination among groups.",Journal of agricultural and food chemistry,"['D031211', 'D005607', 'D007558', 'D028581', 'D013058', 'D009754', 'D012987', 'D014131']","['Corylus', 'Fraud', 'Italy', 'Lanthanoid Series Elements', 'Mass Spectrometry', 'Nuts', 'Soil', 'Trace Elements']","Authentication and traceability study of hazelnuts from piedmont, Italy.","['Q000737', 'Q000517', None, 'Q000032', 'Q000379', 'Q000737', 'Q000032', 'Q000032']","['chemistry', 'prevention & control', None, 'analysis', 'methods', 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19348428,2009,0,0,,no cocoa +0.52,16934398,"A simple, reliable and rapid method for preconcentration and determination of lead using octadecyl bonded silica membrane disk impregnated with Cyanex302 and flame atomic absorption spectrometry is presented. The influence of aqueous phase pH, type of eluent, flow rates of sample solution and eluent, volume of eluent and amount of extractant has been investigated. The break through volume is greater than 4.0 dm(3) with an enrichment factor of more than 400 and a detection limit of 1.0microg dm(-3). The method developed for determination of lead is good as six replicate determinations using 100cm(3) solution containing lead in the range 1-4900microg provides a relative standard deviation (R.S.D.) of 0.4%. The selectivity of the proposed method was confirmed from the interference studies. The developed procedure was successfully applied for the determination of lead in spiked sea water, USGS standard soil sample, sludge and industrial effluents, medicinal formulation, plant, some food products and wine.",Journal of hazardous materials,"['D000327', 'D002099', 'D028241', 'D004785', 'D005504', 'D007220', 'D007854', 'D010721', 'D029222', 'D010936', 'D010946', 'D012623', 'D012822', 'D052616', 'D013054', 'D014920']","['Adsorption', 'Cacao', 'Camellia sinensis', 'Environmental Pollutants', 'Food Analysis', 'Industrial Waste', 'Lead', 'Phosphinic Acids', 'Piper nigrum', 'Plant Extracts', 'Plants, Medicinal', 'Seawater', 'Silicon Dioxide', 'Solid Phase Extraction', 'Spectrophotometry, Atomic', 'Wine']",Solid phase extraction of lead on octadecyl bonded silica membrane disk modified with Cyanex302 and determination by flame atomic absorption spectrometry.,"[None, 'Q000737', 'Q000737', 'Q000032', None, 'Q000032', 'Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000032', 'Q000737', None, None, 'Q000032']","[None, 'chemistry', 'chemistry', 'analysis', None, 'analysis', 'analysis', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'analysis', 'chemistry', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16934398,2007,1,1,table 6,only cocoa powder +0.52,19424684,"A new micro-solid phase extraction (micro-SPE) procedure based on titanium dioxide microcolumns was developed for the selective extraction of phospholipids (PLs) from dairy products before matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) analysis. All the extraction steps (loading, washing, and elution) have been optimized using a synthetic mixture of PLs standard and the procedure was subsequently applied to food samples such as milk, chocolate milk and butter. The whole method demonstrated to be simpler than traditional approaches and it appears very promising for a rapid PLs screening and characterization also in biological matrices.",Analytical and bioanalytical chemistry,"['D003611', 'D010743', 'D052617', 'D019032', 'D053719', 'D014025']","['Dairy Products', 'Phospholipids', 'Solid Phase Microextraction', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Tandem Mass Spectrometry', 'Titanium']",Selective extraction of phospholipids from dairy products by micro-solid phase extraction based on titanium dioxide microcolumns followed by MALDI-TOF-MS analysis.,"['Q000032', 'Q000032', 'Q000295', 'Q000379', 'Q000379', 'Q000737']","['analysis', 'analysis', 'instrumentation', 'methods', 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19424684,2009,0,0,,no cocoa +0.52,10563922,"Monomeric and oligomeric procyanidins present in cocoa and chocolate were separated and identified using a modified normal-phase high-performance liquid chromatography (HPLC) method coupled with on-line mass spectrometry (MS) analysis using an atmospheric pressure ionization electrospray chamber. The chromatographic separation was achieved using a silica stationary phase in combination with a gradient ascending in polarity. This qualitative report confirms the presence of a complex series of procyanidins in raw cocoa and certain chocolates using HPLC/MS techniques. Although both cocoa and chocolate contained monomeric and oligomeric procyanidin units 2-10, only use of negative mode provided MS data for the higher oligomers (i.e., >pentamer). Application of this method for qualitative analysis of proanthocyanidins in other food products and confirmation of this method as a reliable quantitative tool for determining levels of procyanidins in cocoa, chocolate, and other food products are currently being investigated.",Journal of agricultural and food chemistry,"['D044946', 'D002099', 'D002392', 'D002851', 'D013058', 'D044945']","['Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Mass Spectrometry', 'Proanthocyanidins']",Identification of procyanidins in cocoa (Theobroma cacao) and chocolate using high-performance liquid chromatography/mass spectrometry.,"[None, 'Q000737', 'Q000737', None, None, None]","[None, 'chemistry', 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10563922,2000,0,0,, +0.52,25529702,"The extraction capabilities of a Diamond Hydride__¢ phase, as well as silica hydride phases modified with bidentate octadecyl (BDC(18)), phenyl or cholesteryl groups, were evaluated for the analysis of fatty acids, amino acids, sugars and sterols in a dark chocolate extract. These batch adsorption performances were investigated using either methanol or aqueous methanol as the solvent. The compositions of the extracted fractions were assessed by gas chromatography interfaced with quadrupole mass spectrometry (GC-qMS). The batch binding propensities of the various compound classes with silica hydride particles modified with immobilised phenyl groups or larger ligands followed trends predicted from linear solvation energy relationships. Both prediction and experiment revealed that better extraction results could be obtained with the phenyl, BDC(18) and cholesteryl hydride particles for the major chocolate components. Based on these results, separations in micro-pipette tip format with these three types of stationary phase particles have been undertaken.",Food chemistry,"['D000327', 'D002099', 'D002241', 'D005227', 'D005504', 'D008401', 'D000432', 'D017640', 'D052616', 'D013261', 'D014867']","['Adsorption', 'Cacao', 'Carbohydrates', 'Fatty Acids', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Methanol', 'Silicates', 'Solid Phase Extraction', 'Sterols', 'Water']",Comparison of the performance of different silica hydride particles for the solid-phase extraction of non-volatile analytes from dark chocolate with analysis by gas chromatography-quadrupole mass spectrometry.,"[None, 'Q000737', 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000737', None, 'Q000032', 'Q000737']","[None, 'chemistry', 'analysis', 'analysis', None, None, 'analysis', 'chemistry', None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25529702,2015,0,0,,no cocoa tested +0.52,20371968,"This work has been carried out to investigate the conditions which lead to removal of the biogenic amines through the model system. Also, the main goal of this research work is trying to remove biogenic amines; histamine and tyramine, from some Egyptian foods such as tomato, strawberry, banana and mango to prevent their allergy effect. Histamine and tyramine have been affected by pyrogallol, catechol, starch, ascorbic and chlorogenic acids at different levels with different conditions. Some natural additives like glucose, spices, milk, vanillin, starch, orange juice, ascorbic and citric acids, showed an effective effect on disappearance of histamine and tyramine. By studying the effect of some additives on biogenic amines, it was found that tomato showed a decrease in histamine and tyramine concentrations by adding spices. Strawberry and banana showed a clear decrease in histamine and tyramine concentrations by treating them with ascorbic acid. Treating mango by milk led to increase of histamine level while milk with chocolate increases both histamine and tyramine concentrations.",The Journal of toxicological sciences,"['D002851', 'D002855', 'D004534', 'D005504', 'D005511', 'D006632', 'D013053', 'D014439']","['Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Egypt', 'Food Analysis', 'Food Handling', 'Histamine', 'Spectrophotometry', 'Tyramine']","High performance liquid chromatography, thin layer chromatography and spectrophotometric studies on the removal of biogenic amines from some Egyptian foods using organic, inorganic and natural compounds.","['Q000379', 'Q000379', None, 'Q000379', None, 'Q000032', 'Q000379', 'Q000032']","['methods', 'methods', None, 'methods', None, 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20371968,2010,0,0,,no cocoa +0.51,9214759,"The amino acid sequence of 6.5k-arginine/glutamate rich polypeptide (6.5k-AGRP) from the seeds of sponge gourd (Luffa cylindrica) has been determined. The 6.5k-AGRP consists of a 47-residue polypeptide chain containing two disulfide bonds, and a molecular mass calculated to be 5695 Da, which fully coincides with a value of [M+H]+ = m/zeta 5693.39 obtained by matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS). The mass spectrometric evidence indicated that 6.5k-AGRP is also present partially truncated at the C-terminus. In our preparations, approximately half of the polypeptide molecules have the C-terminal sequence Arg-Arg-Glu-Val-Asp; the other half lack Val-Asp and end with the glutamic acid, making a total of 45 residues in the polypeptide chain. The two disulfide bonds connect Cys12 to Cys33 and Cys16 to Cys29. Comparison of the amino acid sequence of 6.5k-AGRP with those of the other known proteins included in the PIR protein sequence database showed that it is related to the amino acid sequence of the N-terminal region encoded by the first exon of the cocoa (Theobroma cacao) and cotton seeds vicilin genes, sharing a characteristic two Cys-Xaa-Xaa-Xaa-Cys motif.","Bioscience, biotechnology, and biochemistry","['D000595', 'D000818', 'D001120', 'D002851', 'D018698', 'D008969', 'D008970', 'D010455', 'D010940', 'D012639', 'D017386', 'D012697', 'D019032', 'D014675']","['Amino Acid Sequence', 'Animals', 'Arginine', 'Chromatography, High Pressure Liquid', 'Glutamic Acid', 'Molecular Sequence Data', 'Molecular Weight', 'Peptides', 'Plant Proteins', 'Seeds', 'Sequence Homology, Amino Acid', 'Serine Endopeptidases', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Vegetables']",Primary structure of 6.5k-arginine/glutamate-rich polypeptide from the seeds of sponge gourd (Luffa cylindrica).,"[None, None, 'Q000737', None, 'Q000737', None, None, 'Q000737', 'Q000737', 'Q000737', None, 'Q000302', None, 'Q000737']","[None, None, 'chemistry', None, 'chemistry', None, None, 'chemistry', 'chemistry', 'chemistry', None, 'isolation & purification', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/9214759,1997,0,0,, +0.51,12607924,"A simple method for the determination of sucralose in various foods using liquid chromatography-electrospray ionization tandem mass spectrometry (LC/MS/MS) was developed. Sucralose was extracted with water or methanol, and the extract was cleaned up on a C18 cartridge, and diluted with water for injection into the LC/MS/MS. The LC separation was performed with a reversed-phase gradient on an ODS column, and the mass spectral acquisition was done in the negative ion mode by applying selected reaction monitoring (SRM). The recoveries of sucralose from various kinds of foods fortified at 100 micrograms/g and 5 micrograms/g were 88.1-96.7% and 92.7-98.5%, respectively. The lower limits of quantification were 0.5 microgram/g in beverage, low-malt beer, yogurt and chocolate and 2.5 micrograms/g in other foods. Forty-three commercial foods containing sucralose were analyzed by this method. Sucralose was detected in all samples at levels of 3.8-481 micrograms/g.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D002853', 'D005504', 'D013058', 'D013395']","['Chromatography, Liquid', 'Food Analysis', 'Mass Spectrometry', 'Sucrose']",[Determination of sucralose in foods by liquid chromatography/tandem mass spectrometry].,"['Q000379', 'Q000379', 'Q000379', 'Q000031']","['methods', 'methods', 'methods', 'analogs & derivatives']",https://www.ncbi.nlm.nih.gov/pubmed/12607924,2003,,,, +0.51,21329356,"Key odorants in roasted pistachio nuts have been determined for the first time. Two different pistachio varieties (Fandooghi and Kerman) have been analyzed by means of headspace solid-phase microextraction (HS-SPME) and gas chromatography-olfactometry (GCO). The aroma extract dilution analyses (AEDA) applied have revealed 46 and 41 odor-active regions with a flavor dilution (FD) factor___64 for the Fandooghi and the Kerman varieties, respectively, and 39 of them were related to precisely identified compounds. These included esters, pyrazines, aldehydes, acids, furans, and phenols. The results show that the Fandooghi variety presents, not only more odor-active regions but also higher FD factors than the Kerman variety that can lead to the conclusion that the first variety has a richer aromatic profile than the second one. The descriptive sensory analysis (DSA) showed that the roasted, chocolate/coffee, and nutty attributes were rated significantly higher in the Fandooghi variety, whereas the green attribute was significantly higher in the Kerman one.",Journal of agricultural and food chemistry,"['D002849', 'D005511', 'D009812', 'D027927', 'D010936', 'D052617', 'D014835']","['Chromatography, Gas', 'Food Handling', 'Odorants', 'Pistacia', 'Plant Extracts', 'Solid Phase Microextraction', 'Volatilization']",Determination of roasted pistachio (Pistacia vera L.) key odorants by headspace solid-phase microextraction and gas chromatography-olfactometry.,"['Q000379', None, 'Q000032', 'Q000737', 'Q000737', 'Q000379', None]","['methods', None, 'analysis', 'chemistry', 'chemistry', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/21329356,2011,0,0,,no cocoa +0.51,18020409,"Ochratoxin A is an important mycotoxin that can enter the human food chain in cereals, wine, coffee, spices, beer, cocoa, dried fruits, and pork meats. Coffee is one of the most common beverages and, consequently, it has a potential risk factor for human health related to ochratoxin A exposure. In this study, coffee and corresponding byproducts from seven different geographic regions were investigated for ochratoxin A natural occurrence by HPLC-FLD, nutritional characterization, and antioxidant activities by spectrophotometric assay. The research focused on composition changes in coffee during the processing step ""from field to cup"". Costa Rica and Indian green coffees were the most contaminated samples, with 13 and 11 microg/kg, respectively, while the Ethiopian coffee was the least contaminated, with 3.8 microg/kg of ochratoxin A. The reduction of ochratoxin A contamination during the roasting step was comparable for any samples that were considered under the recommended level of 4 microg/kg. Total dietary fibers ranged from 58.7% for Vietnam and 48.6% for Ivory Coast in green coffees and ranged from 58.6% for Costa Rica to 61.2% for India in roasted coffee. Coffee silverskin byproduct obtained from Ivory Coast was the highest, with 69.2 and 64.2% of insoluble dietary fibers, respectively.",Journal of agricultural and food chemistry,"['D000975', 'D002273', 'D002851', 'D040503', 'D003069', 'D005506', 'D006358', 'D009183', 'D009793', 'D012639']","['Antioxidants', 'Carcinogens', 'Chromatography, High Pressure Liquid', 'Coffea', 'Coffee', 'Food Contamination', 'Hot Temperature', 'Mycotoxins', 'Ochratoxins', 'Seeds']",Natural occurrence of ochratoxin A and antioxidant activities of green and roasted coffees and corresponding byproducts.,"['Q000032', 'Q000032', None, 'Q000737', 'Q000737', 'Q000032', None, None, 'Q000032', 'Q000737']","['analysis', 'analysis', None, 'chemistry', 'chemistry', 'analysis', None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/18020409,2008,0,0,, +0.51,16859691,"Here, we report on the optimisation and validation of a liquid chromatographic method for the determination of 12 biologically active amines from vegetal food products in a single 40-min run. The suitability of the method was checked in five vegetal products of distinct matrix: spinach (leaves), hazelnut (high protein and fat content), banana, potato (high starch content), and milk chocolate (processed). Sample preparation consisted of a 0.6 M perchloric acid extraction from a minced homogeneous aliquot. For samples with high starch content, a previous mild hydrolytic treatment was required to prevent gel formation. The range of linearity was from 0.1 to 10 mg/l, except for serotonin and spermine (from 0.5 to 10 mg/l), and the correlation coefficient was higher than 0.997 (P < 0.001) for all standard curves. The detection limits and the determination limit were below 0.07 and 0.2 mg/l, respectively, except for spermine, which was 0.14 and 0.4 mg/l. The precision of the method was satisfactory; the relative standard deviation obtained for each amine in each product was acceptable according to Horwitz. Recovery was between 77 and 110% for all amines, irrespective of the product.",Journal of chromatography. A,"['D001679', 'D002851', 'D005504', 'D011073', 'D015203', 'D014675']","['Biogenic Amines', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Polyamines', 'Reproducibility of Results', 'Vegetables']",Improved method for the determination of biogenic amines and polyamines in vegetable products by ion-pair high-performance liquid chromatography.,"['Q000032', 'Q000379', 'Q000379', 'Q000032', None, 'Q000737']","['analysis', 'methods', 'methods', 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16859691,2006,0,0,,no cocoa +0.51,24817360,"The on-line combination of comprehensive two-dimensional liquid chromatography (LC_______LC) with the 2,2'-azino-bis(3-ethylbenzothiazoline)-6 sulphonic acid (ABTS) radical scavenging assay was investigated as a powerful method to determine the free radical scavenging activities of individual phenolics in natural products. The combination of hydrophilic interaction chromatography (HILIC) separation according to polarity and reversed-phase liquid chromatography (RP-LC) separation according to hydrophobicity is shown to provide much higher resolving power than one-dimensional separations, which, combined with on-line ABTS detection, allows the detailed characterisation of antioxidants in complex samples. Careful optimisation of the ABTS reaction conditions was required to maintain the chromatographic separation in the antioxidant detection process. Both on-line and off-line HILIC_______RP-LC-ABTS methods were developed, with the former offering higher throughput and the latter higher resolution. Even for the fast analyses used in the second dimension of on-line HILIC_______RP-LC, good performance for the ABTS assay was obtained. The combination of LC_______LC separation with an on-line radical scavenging assay increases the likelihood of identifying individual radical scavenging species compared to conventional LC-ABTS assays. The applicability of the approach was demonstrated for cocoa, red grape seed and green tea phenolics.",Analytical and bioanalytical chemistry,"['D000975', 'D052160', 'D002099', 'D002623', 'D002851', 'D056148', 'D005609', 'D010636', 'D010936', 'D013451', 'D013662', 'D027843']","['Antioxidants', 'Benzothiazoles', 'Cacao', 'Chemistry Techniques, Analytical', 'Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Free Radicals', 'Phenols', 'Plant Extracts', 'Sulfonic Acids', 'Tea', 'Vitis']",Comprehensive two-dimensional liquid chromatography coupled to the ABTS radical scavenging assay: a powerful method for the analysis of phenolic antioxidants.,"['Q000032', 'Q000032', 'Q000737', 'Q000379', 'Q000379', 'Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000737', 'Q000737']","['analysis', 'analysis', 'chemistry', 'methods', 'methods', 'methods', 'analysis', 'analysis', 'analysis', 'analysis', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24817360,2015,0,0,, +0.51,3356285,"A UK survey of plasticizer levels in retail foods (73 samples) wrapped in plasticized films or materials with plasticized coatings has been carried out. A wide range of different food-types packaged in vinylidene chloride copolymers (PVDC), nitrocellulose-coated regenerated cellulose film (RCF) and cellulose acetate were purchased from retail and 'take-away' outlets. Plasticizers found in these films were dibutyl sebacate (DBS) and acetyl tributyl citrate (ATBC) in PVDC, dibutyl phthalate (DBP), dicyclohexyl phthalate (DCHP), butylbenzyl phthalate (BBP), and diphenyl 2-ethylhexyl phosphate (DPOP) in RCF coatings, and diethyl phthlate (DEP) in cellulose acetate. Foodstuffs analysed included cheese, pate, chocolate and confectionery products, meat pies, cake, quiches and sandwiches. Analysis was by stable isotope dilution GC/MS for DBP, DCHP and DEP, GC/MS (selected ion monitoring) for BBP and DPOP, and GC with flame ionization detection for DBS and ATBC, but with mass spectrometric confirmation. Levels of plasticizers found in foods were in the following ranges: ATBC in cheese, 2-8 mg/kg; DBS in processed cheese and cooked meats, 76-137 mg/kg; 76-137 mg/kg; DBP, DCHP, BBP, and DPOP found individually or in combination in confectionery, meat pies, cake and sandwiches, total levels from 0.5 to 53 mg/kg; and DEP in quiches, 2-4 mg/kg.",Food additives and contaminants,"['D002849', 'D002850', 'D002951', 'D003998', 'D005506', 'D005511', 'D008401', 'D010755', 'D010795', 'D010968', 'D006113']","['Chromatography, Gas', 'Chromatography, Gel', 'Citrates', 'Dicarboxylic Acids', 'Food Contamination', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Organophosphates', 'Phthalic Acids', 'Plasticizers', 'United Kingdom']","Migration from plasticized films into foods. 3. Migration of phthalate, sebacate, citrate and phosphate esters from films used for retail food packaging.","[None, None, 'Q000032', 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000032', 'Q000032', None]","[None, None, 'analysis', 'analysis', 'analysis', None, None, 'analysis', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/3356285,1988,,,, +0.51,24699984,"In the 1980s, a novel tea species, Cocoa tea (Camellia ptilophylla Chang), was discovered in Southern China with surprisingly low caffeine content (0.2% by dry weight). Although its health promoting characteristics have been known for a while, a very limited amount of scientific research has been focused on Cocoa tea. Herein, a systematic study on Cocoa tea and its chemical components, interactions and bioactivities was performed. YD tea (Yunnan Daye tea, Camellia sinensis), a tea species with a high caffeine content (5.8% by dry weight), was used as a control. By UV-Vis spectrometry, High Performance Liquid Chromatography (HPLC), and Flame Atomic Absorption Spectrometry (FAAS) for chemical composition analysis, C-2 epimeric isomers of tea catechins and theobromine were found to be the major catechins and methylxanthine in Cocoa tea, respectively. More gallated catechins, methylxanthines, and proteins were detected in Cocoa tea compared with YD tea. Moreover, the tendency of major components in Cocoa tea for precipitation was significantly higher than that in YD tea. Catechins, methylxanthines, proteins, iron, calcium, and copper were presumed to be the origins of molecular interactions in Cocoa tea and YD tea. The interactions between catechins and methylxanthines were highly related to the galloyl moiety in catechins and methyl groups in methylxanthines. In vitro anti-inflammatory activity assays revealed that Cocoa tea was a more potent inhibitor of nitric oxide (NO) in lipopolysaccharide (LPS)-stimulated macrophage cells (RAW 264.7) than YD tea. This study constructs a solid phytochemical foundation for further research on the mechanisms of molecular interactions and the integrated functions of Cocoa tea. ",Food & function,"['D000818', 'D000893', 'D000975', 'D002110', 'D028244', 'D028241', 'D002392', 'D045744', 'D002681', 'D002851', 'D008070', 'D051379', 'D009569', 'D064209', 'D010936', 'D018515', 'D059808', 'D013662', 'D014970']","['Animals', 'Anti-Inflammatory Agents', 'Antioxidants', 'Caffeine', 'Camellia', 'Camellia sinensis', 'Catechin', 'Cell Line, Tumor', 'China', 'Chromatography, High Pressure Liquid', 'Lipopolysaccharides', 'Mice', 'Nitric Oxide', 'Phytochemicals', 'Plant Extracts', 'Plant Leaves', 'Polyphenols', 'Tea', 'Xanthines']","Interactions among chemical components of Cocoa tea (Camellia ptilophylla Chang), a naturally low caffeine-containing tea species.","[None, None, 'Q000737', 'Q000032', 'Q000737', 'Q000737', 'Q000031', None, None, None, 'Q000009', None, 'Q000009', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737']","[None, None, 'chemistry', 'analysis', 'chemistry', 'chemistry', 'analogs & derivatives', None, None, None, 'adverse effects', None, 'adverse effects', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24699984,2015,0,0,,cocoa tea +0.51,16848541,"Isolation of the volatile fraction from cocoa powder (50 g; 20% fat content) by a careful extraction/distillation process followed by application of an aroma extract dilution analysis revealed 35 odor-active constituents in the flavor dilution (FD) factor range of 8-4096. Among them, 4-hydroxy-2,5-dimethyl-3(2H)-furanone (caramel-like), 2- and 3-methylbutanoic acid (sweaty, rancid), dimethyl trisulfide (cooked cabbage), 2-ethyl-3,5-dimethylpyrazine (potato-chip-like), and phenylacetaldehyde (honey-like) showed the highest FD factors. Quantitation of 31 key odorants by means of stable isotope dilution assays, followed by a calculation of their odor activity values (OAVs) (ratio of concentration to odor threshold) revealed OAVs>100 for the five odorants acetic acid (sour), 3-methylbutanal (malty), 3-methylbutanoic acid, phenylacetaldehyde, and 2-methylbutanal (malty). In addition, another 19 aroma compounds showed OAVs>1. To establish their contribution to the overall aroma of the cocoa powder, these 24 compounds were added to a reconstructed cocoa matrix in exactly the same concentrations as they occurred in the cocoa powder. The matrix was prepared from deodorized cocoa powder, which was adjusted to 20% fat content using deodorized cocoa butter. The overall sensory evaluation of this aroma recombinate versus the cocoa powder clearly indicated that the 24 compounds represented the typical sweet, cocoa-like odor of the real sample.",Journal of agricultural and food chemistry,"['D002099', 'D002849', 'D003903', 'D005519', 'D008401', 'D006801', 'D007201', 'D009812', 'D012639', 'D012903', 'D013649']","['Cacao', 'Chromatography, Gas', 'Deuterium', 'Food Preservation', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Indicator Dilution Techniques', 'Odorants', 'Seeds', 'Smell', 'Taste']",Identification of the key aroma compounds in cocoa powder based on molecular sensory correlations.,"['Q000737', 'Q000379', None, None, None, None, None, 'Q000032', 'Q000737', None, None]","['chemistry', 'methods', None, None, None, None, None, 'analysis', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16848541,2006,1,3,table 3, +0.51,28465162,"Caffeine and caffeic acid are two bioactive compounds that are present in plant foods and are major constituent of coffee, cocoa, tea, cola drinks and chocolate. Although not structurally related, caffeine and caffeic acid has been reported to elicit neuroprotective properties. However, their different proportional distribution in food sources and possible effect of such interactions are not often taken into consideration. Therefore, in this study, we investigated the effect of caffeine, caffeic acid and their various combinations on activities of some enzymes [acetylcholinesterase (AChE), monoamine oxidase (MAO) ecto-nucleoside triphosphate diphosphohydrolase (E-NTPase), ecto-5",Neurotoxicology,"['D000110', 'D000251', 'D000818', 'D001921', 'D002109', 'D002110', 'D000697', 'D003300', 'D004305', 'D004338', 'D007501', 'D008995', 'D051381', 'D017208', 'D013053']","['Acetylcholinesterase', 'Adenosine Triphosphatases', 'Animals', 'Brain', 'Caffeic Acids', 'Caffeine', 'Central Nervous System Stimulants', 'Copper', 'Dose-Response Relationship, Drug', 'Drug Combinations', 'Iron', 'Monoamine Oxidase', 'Rats', 'Rats, Wistar', 'Spectrophotometry']","Effect of caffeine, caffeic acid and their various combinations on enzymes of cholinergic, monoaminergic and purinergic systems critical to neurodegeneration in rat brain-In vitro.","['Q000378', 'Q000378', None, 'Q000187', 'Q000494', 'Q000494', 'Q000493', 'Q000378', None, None, 'Q000378', 'Q000378', None, None, None]","['metabolism', 'metabolism', None, 'drug effects', 'pharmacology', 'pharmacology', 'pharmacokinetics', 'metabolism', None, None, 'metabolism', 'metabolism', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28465162,2018,0,0,, +0.51,7926169,"Beverages of different kinds have been investigated for their content of lead, cadmium, nickel, chromium, arsenic and mercury. About a ten times higher lead concentration was found in wine than in most other beverages. Cocoa was high in cadmium and nickel and some vegetable juices contained high levels of nickel. The daily intake of trace elements from beverages was estimated. Wine was still the most significant source of lead even if the bottles did not have lead capsules. By consumption of half a bottle per day the daily intake of lead would be doubled and it would contribute 12% of Provisional Tolerable Weekly Intake. Cocoa is an important source of cadmium and nickel, and consumption of tea as well as vegetable juices could increase the nickel intake significantly. The data are compared to Danish maximum limits on lead and cadmium.",Food additives and contaminants,"['D001628', 'D002104', 'D005506', 'D006801', 'D007854', 'D009532', 'D013054', 'D014131']","['Beverages', 'Cadmium', 'Food Contamination', 'Humans', 'Lead', 'Nickel', 'Spectrophotometry, Atomic', 'Trace Elements']",Beverages as a source of toxic trace element intake.,"['Q000032', 'Q000008', 'Q000032', None, 'Q000008', 'Q000008', None, 'Q000008']","['analysis', 'administration & dosage', 'analysis', None, 'administration & dosage', 'administration & dosage', None, 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/7926169,1994,,,, +0.5,25032782,"Oligomeric proanthocyanidins were successfully identified by UHPLC-PDA-HRMS(n) in a selection of plant-derived materials (jujube fruit, Fuji apple, fruit pericarps of litchi and mangosteen, dark chocolate, and grape seed and cranberry extracts). The identities of 247 proanthocyanidins were theoretically predicted by computing high-accuracy masses based on the degree of polymerization, flavan-3-ol components, and the number of A type linkages and galloyls. MS(n) fragments allowed characterization on flavan-3-ol based on the monomer, connectivity, and location of A-type bonds. Identification of doubly or triply charged ions of 50 PAs was made on the basis of theoretical calculations. A single catechin standard and molar relative response factors (MRRFs) were used to quantify the well-separated PAs. The ratios of the SIM peak counts were used to quantify each of the unseparated isomers. This is the first report of direct determination of each of the proanthocyanidins in plant-derived foods and proanthocyanidins containing an epifisetinidol unit in grape seeds. ",Journal of agricultural and food chemistry,"['D002851', 'D005638', 'D010936', 'D010944', 'D044945', 'D012639', 'D021241']","['Chromatography, High Pressure Liquid', 'Fruit', 'Plant Extracts', 'Plants', 'Proanthocyanidins', 'Seeds', 'Spectrometry, Mass, Electrospray Ionization']",UHPLC-PDA-ESI/HRMSn profiling method to identify and quantify oligomeric proanthocyanidins in plant products.,"['Q000379', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000379']","['methods', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/25032782,2015,0,0,,no cocoa +0.5,11486386,"An analytical method using GC/MS was developed for bisphenol A (BPA) in foods and BPA was determined in canned foods and fresh foods such as vegetables, fruit and meat. BPA was extracted with acetone from the samples and the extract was concentrated at under 40 degrees C in vacuo to afford an aqueous solution, which was washed with hexane after alkalization and extracted with 50% diethyl ether-hexane after acidification. Extracts were cleaned up on a PSA and/or a C18 cartridge column, and BPA was derivatized with heptafluorobutyric anhydride and determined by GC/MS (SIM). This method was applicable to the detection and determination of BPA residues in food samples at the level of 1 ng/g. Among canned foods, BPA was found in 6 corned beef, 1 chicken, 9 sweet corn and 3 bean samples at the levels of 17-602 ng/g, 212 ng/g, 2.3-75 ng/g and 3.5-26 ng/g, respectively. BPA was also detected in 1 retort soup and 1 retort pack product at the levels of 11 ng/g and 86 ng/g, respectively. As for dairy products, BPA was not detected in butter and milk. Among fresh foods, BPA was detected in 2 fish and 3 liver samples at the levels of trace (tr)-6.2 ng/g and tr-2.2 ng/g, respectively. In vegetables, fruits and chocolates, a trace level of BPA was detected in only 1 chocolate. Traces of BPA were also detected in 3 samples of 6 boxed lunches.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D000818', 'D001559', 'D005396', 'D005504', 'D005519', 'D005638', 'D008401', 'D008460', 'D008461', 'D010636', 'D014675']","['Animals', 'Benzhydryl Compounds', 'Fish Products', 'Food Analysis', 'Food Preservation', 'Fruit', 'Gas Chromatography-Mass Spectrometry', 'Meat', 'Meat Products', 'Phenols', 'Vegetables']",[Determination of bisphenol A in foods using GC/MS].,"[None, None, 'Q000032', 'Q000379', None, 'Q000737', None, 'Q000032', 'Q000032', 'Q000032', 'Q000737']","[None, None, 'analysis', 'methods', None, 'chemistry', None, 'analysis', 'analysis', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11486386,2001,,,, +0.5,17357118,"Fermented cocoa beans (Theobroma cacao L., Sterculiaceae) from different countries of origin (Ecuador, Ghana, Trinidad) and cocoa beans roasted under defined conditions (industrial roasting; 150-220 degrees C for 20 min, dry roasting in conventional oven) were analyzed for their contents of certain chiral hydroxy acids, catechins, and amino acids. Cocoa beans are fermented, dried, and industrially transformed by roasting for the production of chocolate, cocoa powders, and other cocoa-related products. Fermentation and roasting conditions influence the contents of chiral compounds such as hydroxy acids, amino acids, and polyphenols, depending on technological procedures as well as some technical parameters. The aim of this work was to check if the content and nature of the named chiral compounds present both in fermented and roasted cocoa beans could be related to the traditional parameters used to classify the variety of seeds and the degree of fermentation. The extent of racemization of amino acids in fermented cocoa beans was low while it slowly increased during roasting, depending on the temperature applied. L-lactic acid was always higher than the D-form while citric acid was generally the most abundant hydroxy acid detected in beans. A correlation was found between polyphenol content and degree of fermentation, while epimerization of (-)-epicatechin to (+)-catechin was observed during roasting. On the whole, results showed that several chiral compounds could be considered as good quality markers for cocoa seeds and cocoa-related products of different quality and geographic origin.",Chirality,"['D000596', 'D002099', 'D002392', 'D005285', 'D005419', 'D005504', 'D005511', 'D005524', 'D008401', 'D005843', 'D006880', 'D019344', 'D008956', 'D010636', 'D059808', 'D013237']","['Amino Acids', 'Cacao', 'Catechin', 'Fermentation', 'Flavonoids', 'Food Analysis', 'Food Handling', 'Food Technology', 'Gas Chromatography-Mass Spectrometry', 'Geography', 'Hydroxy Acids', 'Lactic Acid', 'Models, Chemical', 'Phenols', 'Polyphenols', 'Stereoisomerism']",GC-MS detection of chiral markers in cocoa beans of different quality and geographic origin.,"['Q000737', 'Q000378', 'Q000737', None, None, None, None, 'Q000379', 'Q000379', None, 'Q000737', 'Q000737', None, None, None, None]","['chemistry', 'metabolism', 'chemistry', None, None, None, None, 'methods', 'methods', None, 'chemistry', 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/17357118,2007,,,, +0.5,27664658,"Nickel is a metal that can be present in products containing hardened edible oils, possibly as leftover catalyst from the vegetable oil hardening process. Nickel may cause toxic effects including the promotion of cancer and contact allergy. In this work, nickel content was determined in hydrogenated vegetable fats and confectionery products, made with these fats, available on the Czech market using newly developed method combining microwave digestion and graphite furnace AAS. While concentrations of 0.086_±0.014mg.kg(-1) or less were found in hydrogenated vegetable fats, the Ni content in confectionery products was significantly higher, varying between 0.742_±0.066 and 3.141_±0.217mg.kg(-1). Based on an average consumer basket, daily intake of nickel from vegetable fats is at least twice as low as intake from confectionery products. Based on results, the levels of nickel in neither vegetable fats nor confectionery products, do not represent a significant health risk. ",Food chemistry,"['D000069956', 'D018153', 'D005223', 'D005506', 'D006801', 'D006865', 'D009532', 'D010938', 'D013054']","['Chocolate', 'Czech Republic', 'Fats', 'Food Contamination', 'Humans', 'Hydrogenation', 'Nickel', 'Plant Oils', 'Spectrophotometry, Atomic']",Determination of nickel in hydrogenated fats and selected chocolate bars in Czech Republic.,"['Q000032', None, 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000032', 'Q000379']","['analysis', None, 'analysis', 'analysis', None, None, 'analysis', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/27664658,2017,0,0,, +0.5,18278822,"A comparison of two methods for the identification and determination of peanut allergens based on europium (Eu)-tagged inductively coupled plasma mass spectrometry (ICP-MS) immunoassay and on liquid chromatography/electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS) with a triple quadrupole mass analyzer was carried out on a complex food matrix like a chocolate rice crispy-based snack. The LC/MS/MS method was based on the determination of four different peptide biomarkers selective for the Ara h2 and Ara h3/4 peanut proteins. The performance of this method was compared with that of a non-competitive sandwich enzyme-linked immunosorbent assay (ELISA) method with ICP-MS detection of the metal used to tag the antibody for the quantitative peanut protein analysis in food. The limit of detection (LOD) and quantitation of the ICP-MS immunoassay were 2.2 and 5 microg peanuts g(-1) matrix, respectively, the recovery ranged from 86 +/- 18% to 110 +/- 4% and linearity was proved in the 5-50 microg g(-1) range. The LC/MS/MS method allowed us to obtain LODs of 1 and 5 microg protein g(-1) matrix for Ara h3/4 and Ara h2, respectively, thus obtaining significantly higher values with respect to the ELISA ICP-MS method, taking into account the different expression for concentrations. Linearity was established in the 10-200 microg g(-1) range of peanut proteins in the food matrix investigated and good precision (RSD <10%) was demonstrated. Both the two approaches, used for screening or confirmative purposes, showed the power of mass spectrometry when used as a very selective detector in difficult matrices even if some limitations still exist, i.e. matrix suppression in the LC/ESI-MS/MS procedure and the change of the Ag/Ab binding with matrix in the ICP-MS method.",Rapid communications in mass spectrometry : RCM,"['D000485', 'D010367', 'D002099', 'D002851', 'D002523', 'D005504', 'D006358', 'D007118', 'D008670', 'D015203', 'D012680', 'D021241', 'D013194']","['Allergens', 'Arachis', 'Cacao', 'Chromatography, High Pressure Liquid', 'Edible Grain', 'Food Analysis', 'Hot Temperature', 'Immunoassay', 'Metals', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Spectrometry, Mass, Electrospray Ionization', 'Staining and Labeling']",Determination of peanut allergens in cereal-chocolate-based snacks: metal-tag inductively coupled plasma mass spectrometry immunoassay versus liquid chromatography/electrospray ionization tandem mass spectrometry.,"['Q000032', 'Q000737', 'Q000737', 'Q000379', 'Q000737', 'Q000379', None, 'Q000379', None, None, None, 'Q000379', 'Q000379']","['analysis', 'chemistry', 'chemistry', 'methods', 'chemistry', 'methods', None, 'methods', None, None, None, 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/18278822,2008,,,, +0.5,25912451,"Development of authenticity screening for Asian palm civet coffee, the world-renowned priciest coffee, was previously reported using metabolite profiling through gas chromatography/mass spectrometry (GC/MS). However, a major drawback of this approach is the high cost of the instrument and maintenance. Therefore, an alternative method is needed for quality and authenticity evaluation of civet coffee. A rapid, reliable and cost-effective analysis employing a universal detector, GC coupled with flame ionization detector (FID), and metabolite fingerprinting has been established for discrimination analysis of 37 commercial and non-commercial coffee beans extracts. gas chromatography/flame ionization detector (GC/FID) provided higher sensitivity over a similar range of detected compounds than GC/MS. In combination with multivariate analysis, GC/FID could successfully reproduce quality prediction from GC/MS for differentiation of commercial civet coffee, regular coffee and coffee blend with 50__wt % civet coffee content without prior metabolite details. Our study demonstrated that GC/FID-based metabolite fingerprinting can be effectively actualized as an alternative method for coffee authenticity screening in industries. ",Journal of bioscience and bioengineering,"['D000818', 'D003069', 'D016002', 'D005410', 'D019649', 'D008401', 'D055442', 'D015999', 'D012015', 'D045949']","['Animals', 'Coffee', 'Discriminant Analysis', 'Flame Ionization', 'Food Industry', 'Gas Chromatography-Mass Spectrometry', 'Metabolome', 'Multivariate Analysis', 'Reference Standards', 'Viverridae']",Application of gas chromatography/flame ionization detector-based metabolite fingerprinting for authentication of Asian palm civet coffee (Kopi Luwak).,"[None, 'Q000737', None, 'Q000379', 'Q000379', 'Q000191', None, None, None, None]","[None, 'chemistry', None, 'methods', 'methods', 'economics', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25912451,2016,0,0,,no cocoa tested +0.5,17909484,"Cocoa contains high levels of different flavonoids. In the present study, the enantioseparation of catechin and epicatechin in cocoa and cocoa products by chiral capillary electrophoresis (CCE) was performed. A baseline separation of the catechin and epicatechin enantiomers was achieved by using 0.1 mol x L(-1) borate buffer (pH 8.5) with 12 mmol x L(-1) (2-hydroxypropyl)-gamma-cyclodextrin as chiral selector, a fused-silica capillary with 50 cm effective length (75 microm I.D.), +18 kV applied voltage, a temperature of 20 degrees C and direct UV detection at 280 nm. To avoid comigration or coelution of other similar substances, the flavan-3-ols were isolated and purified using polyamide-solid-phase-extraction and LC-MS analysis. As expected, we found (-)-epicatechin and (+)-catechin in unfermented, dried, unroasted cocoa beans. In contrast, roasted cocoa beans and cocoa products additionally contained the atypical flavan-3-ol (-)-catechin. This is generally formed during the manufacturing process by an epimerization which converts (-)-epicatechin to its epimer (-)-catechin. High temperatures during the cocoa bean roasting process and particularly the alkalization of the cocoa powder are the main factors inducing the epimerization reaction. In addition to the analysis of cocoa and cocoa products, peak ratios were calculated for a better differentiation of the cocoa products.","Molecules (Basel, Switzerland)","['D000468', 'D002099', 'D002392', 'D002853', 'D019075', 'D005419', 'D013058', 'D013237', 'D013696']","['Alkalies', 'Cacao', 'Catechin', 'Chromatography, Liquid', 'Electrophoresis, Capillary', 'Flavonoids', 'Mass Spectrometry', 'Stereoisomerism', 'Temperature']",(-)-Catechin in cocoa and chocolate: occurrence and analysis of an atypical flavan-3-ol enantiomer.,"[None, 'Q000737', 'Q000032', None, None, 'Q000032', None, None, None]","[None, 'chemistry', 'analysis', None, None, 'analysis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/17909484,2007,0,0,, +0.5,2808244,"Methyl bromide (MB, bromomethane) is determined in a variety of foods by headspace capillary gas chromatography with electron capture detection. The comminuted food sample as an aqueous sodium sulfate slurry is equilibrated with stirring for 1 h at room temperature before a 1 mL headspace aliquot is removed and injected using a modified on-column syringe needle. Methyl bromide is cryogenically focussed at -60 degrees C and then eluted by temperature programming. The procedure requires blending of soft samples, e.g. raisins, prunes, or oranges, and ultrasonic homogenization of hard samples, e.g. wheat, cocoa beans, corn, or nuts, with portions of water and ice so the final temperature of the food-water slurry is less than 1 degree C. A 20 g aliquot (4 g food) is then added to a cold headspace vial containing 4 g sodium sulfate. Losses of MB during a 3.5 min ultrasonic homogenization of wheat were 11% at 0.95 ppb and 4.4% at 4.8 ppb. For flour, cocoa, and finely divided spices, which do not require blending, 4 g is added to the cold headspace vial containing 16 mL cold water and 4 g sodium sulfate. Studies show that comminution of wheat or peanuts must be carried out to release MB trapped within the food so the headspace equilibrium can be attained in 1 h as well as to obtain homogeneous samples and representative sampling. No interferences were noted with the above foods or with many grain-based baking mixes analyzed.(ABSTRACT TRUNCATED AT 250 WORDS)",Journal - Association of Official Analytical Chemists,"['D002611', 'D002849', 'D004041', 'D002523', 'D004563', 'D005433', 'D005504', 'D005638', 'D008401', 'D006842', 'D007202', 'D010316', 'D012987']","['Cheese', 'Chromatography, Gas', 'Dietary Fats', 'Edible Grain', 'Electrochemistry', 'Flour', 'Food Analysis', 'Fruit', 'Gas Chromatography-Mass Spectrometry', 'Hydrocarbons, Brominated', 'Indicators and Reagents', 'Particle Size', 'Soil']",Determination of methyl bromide in foods by headspace capillary gas chromatography with electron capture detection.,"['Q000032', None, 'Q000032', 'Q000032', None, 'Q000032', None, 'Q000032', None, None, None, None, 'Q000032']","['analysis', None, 'analysis', 'analysis', None, 'analysis', None, 'analysis', None, None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/2808244,1989,,,, +0.5,8471852,"A liquid chromatographic method was evaluated for the determination of the intense sweetener acesulfam-K in tabletop sweetener, candy, soft drink, fruit juice, fruit nectar, yogurt, cream, custard, chocolate, and biscuit commercial preparations. Samples are extracted or simply diluted with water and filtered. Complex matrixes need a clarification step with Carrez solutions. An aliquot of the extract is analyzed on a reversed-phase mu Bondapak C18 column using 0.0125M KH2PO4 (pH 3.5)-acetonitrile (90 + 10) as mobile phase. Detection is performed by UV absorbance at 220 nm. Recoveries ranged from 95.2 to 106.8%. With one exception, all analyzed values were within +/- 15% of the declared levels. The repeatabilities and the repeatability coefficients of variation were, respectively, 0.37 mg/100 g and 0.98% for products containing less than 40 mg/100 g acesulfam-K and 2.43 mg/100 g and 1.29% for other products. The same procedure also allowed detection of many food additives or natural constituents, such as other intense sweeteners, organic acids, and alkaloids, in a single run without interfering with acesulfam-K. The method is simple, rapid, precise, and sensitive; therefore, it is suitable for routine analyses.",Journal of AOAC International,"['D001628', 'D002182', 'D002855', 'D005503', 'D005504', 'D006863', 'D012680', 'D013549', 'D013843']","['Beverages', 'Candy', 'Chromatography, Thin Layer', 'Food Additives', 'Food Analysis', 'Hydrogen-Ion Concentration', 'Sensitivity and Specificity', 'Sweetening Agents', 'Thiazines']",Determination of acesulfam-K in foods.,"['Q000032', 'Q000032', None, 'Q000032', 'Q000379', None, None, 'Q000032', 'Q000032']","['analysis', 'analysis', None, 'analysis', 'methods', None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/8471852,1993,,,, +0.5,21427887,"Atole is a Mexican pre-hispanic drink prepared traditionally with corn; however, cereals as wheat, rice and amaranth have also been used. The aim of this study was to determine the physicochemical and sensory properties of an amaranth flour to prepare a drink (atole) mentioned above, in order to determine its nutritive value. Proximate analysis of the amaranth, corn and rice drink flours was determined by means of official techniques of AOAC. Mineral content was carried out by atomic absorption spectrometry. Viscosity was measured in a reometer from 25 to 90 degrees C. The quantitative descriptive profile (QDA) of the amaranth drink was studied by a trained panel of 10 judges. Results showed that the amaranth drink flour presented the highest protein and fat content compared to corn and rice drink flours. Sodium and potassium were the most abundant minerals in all flours studied. Corn and rice drink flours showed a constant viscosity from 20 to 84 degrees C, to 85 degrees C an important increase in this parameter was observed. This increase was detected in the amaranth drink flour to 75 degrees C. Descriptors defined by trained judges for the QDA of the amaranth drink flours were: starch, almond/cherry, caramel, vanilla, strawberry, walnut and chocolate. The amaranth drink flour, compared to corn and rice drink flours, presented the best nutritional profile; it is important to emphasize its protein content.",Archivos latinoamericanos de nutricion,"['D027721', 'D001628', 'D005433', 'D009753', 'D013054', 'D013649', 'D014783']","['Amaranthus', 'Beverages', 'Flour', 'Nutritive Value', 'Spectrophotometry, Atomic', 'Taste', 'Viscosity']","[Physicochemical and sensory properties of flours ready to prepare an amaranth ""atole""].","['Q000737', 'Q000032', 'Q000032', None, None, None, None]","['chemistry', 'analysis', 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/21427887,2011,,,, +0.49,8112343,"Salivary proline-rich proteins have a repetitive primary structure particularly rich in the amino acids proline, glutamine and glycine. One of the biological roles of these proteins is to bind and precipitate polyphenols (vegetable tannins) present in the diet (e.g. tea, coffee, fruit, chocolate) neutralising their harmful actions which include nutritional loss, inhibition of gut enzymes and oesophageal cancer. Two peptides overlapping in sequence, corresponding to the mouse salivary proline-rich protein MP5 repeat sequence: QGPPPQGGPQQRPPQPGNQ and GPQQRPPQPGNQQGPPPQGGPQ have been synthesised and studied in H2O/(2H6)dimethyl sulphoxide (9:1, by vol.) using 1H-NMR spectroscopy. Low-temperature far-ultraviolet CD spectroscopy and NMR conformational parameters indicate that the peptides adopt an extended random coil conformation in solution. There is no evidence for a defined polyproline type II helix in the peptides, despite the high proline content. NMR data show that the trans-proline isomer predominates to at least 90%.",European journal of biochemistry,"['D000595', 'D000818', 'D002851', 'D002942', 'D005973', 'D005998', 'D009682', 'D051379', 'D008969', 'D010446', 'D010455', 'D055232', 'D011487', 'D012471']","['Amino Acid Sequence', 'Animals', 'Chromatography, High Pressure Liquid', 'Circular Dichroism', 'Glutamine', 'Glycine', 'Magnetic Resonance Spectroscopy', 'Mice', 'Molecular Sequence Data', 'Peptide Fragments', 'Peptides', 'Proline-Rich Protein Domains', 'Protein Conformation', 'Salivary Proteins and Peptides']",Conformational study of a salivary proline-rich protein repeat sequence.,"[None, None, None, None, 'Q000737', 'Q000737', None, None, None, 'Q000737', 'Q000737', None, None, 'Q000737']","[None, None, None, None, 'chemistry', 'chemistry', None, None, None, 'chemistry', 'chemistry', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/8112343,1994,0,0,,no cocoa +0.49,27105753,"An ease-of-use protocol for the identification of resistance against third-generation cephalosporins in Enterobacteriaceae isolated from blood culture bottles was evaluated using matrix-assisted laser desorption ionization-time-of-flight mass spectrometry. A cefotaxime hydrolysis assay from chocolate agar subcultures using antibiotic discs and without inoculum standardization was developed for routine work flow, with minimal hands-on time. This assay showed good performance in distinguishing between cefotaxime-susceptible and cefotaxime-resistant strains, with excellent results for Escherichia coli (sensitivity 94.7%, specificity 100%). However, cefotaxime resistance was not detected reliably in Enterobacteriaceae expressing AmpC genes or carbapenemase-producing Klebsiella pneumoniae. ",The Journal of hospital infection,"['D000900', 'D000071997', 'D002439', 'D018550', 'D004755', 'D006868', 'D008826', 'D012680', 'D019032', 'D013997']","['Anti-Bacterial Agents', 'Blood Culture', 'Cefotaxime', 'Cephalosporin Resistance', 'Enterobacteriaceae', 'Hydrolysis', 'Microbial Sensitivity Tests', 'Sensitivity and Specificity', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Time Factors']",Ease-of-use protocol for the rapid detection of third-generation cephalosporin resistance in Enterobacteriaceae isolated from blood cultures using matrix-assisted laser desorption ionization-time-of-flight mass spectrometry.,"['Q000378', None, 'Q000378', None, 'Q000187', None, 'Q000379', None, 'Q000379', None]","['metabolism', None, 'metabolism', None, 'drug effects', None, 'methods', None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/27105753,2017,0,0,,no cocoa +0.49,11210120,"The antioxidant polyphenols in cacao liquor, a major ingredient of chocolate and cocoa, have been characterized as flavan-3-ols and proanthocyanidin oligomers. In this study, various cacao products were analyzed by normal-phase HPLC, and the profiles and quantities of the polyphenols present, grouped by molecular size (monomers to approximately oligomers), were compared. Individual cacao polyphenols, flavan-3-ols (catechin and epicatechin), and dimeric (procyanidin B2), trimeric (procyanidin C1), and tetrameric (cinnamtannin A2) proanthocyanidins, and galactopyranosyl-ent-(-)-epicatechin (2alpha-->7, 4alpha-->8)-(-)-epicatechin (Gal-EC-EC), were analyzed by reversed-phase HPLC and/or HPLC/MS. The profile of monomers (catechins) and proanthocyanidin in dark chocolate was similar to that of cacao liquor, while the ratio of flavan-3-ols to the total amount of monomeric and oligomeric polyphenols in the case of pure cocoa powder was higher than that in the case of cacao liquor or chocolate.","Bioscience, biotechnology, and biochemistry","['D000872', 'D044946', 'D002099', 'D002392', 'D002851', 'D002853', 'D005419', 'D005511', 'D005690', 'D013058', 'D010636', 'D011108', 'D044945', 'D015203']","['Anthocyanins', 'Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Chromatography, Liquid', 'Flavonoids', 'Food Handling', 'Galactose', 'Mass Spectrometry', 'Phenols', 'Polymers', 'Proanthocyanidins', 'Reproducibility of Results']","Analyses of polyphenols in cacao liquor, cocoa, and chocolate by normal-phase and reversed-phase HPLC.","['Q000032', None, 'Q000737', 'Q000031', 'Q000379', None, None, 'Q000379', 'Q000031', 'Q000379', 'Q000032', 'Q000032', None, None]","['analysis', None, 'chemistry', 'analogs & derivatives', 'methods', None, None, 'methods', 'analogs & derivatives', 'methods', 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11210120,2001,2,1,table 4 and 6, +0.49,20480905,"Staphylococcal enterotoxin B (SEB) is an extracellular pyrotoxin produced by Staphylococcus aureus, a known etiologic agent of food poisoning in humans. Lateral flow immunochromatographic devices (LFDs) designed for the environmental detection of SEB were adapted for use in this study to detect SEB in milk containing 2% fat, chocolate-flavored milk, and milk-derived products such as yogurt, infant formula, and ice cream. The advantage of using LFDs in these particular food products was its ease and speed of use with no additional extraction methods needed. No false positives were observed with any of the products used in this study. Dilution of the samples overcame the Hook effect and permitted capillary flow into the membrane. Thus, semisolid products such as ice cream and some yogurts, and products containing thickeners needed to be diluted using a phosphate-buffered saline-based buffer, pH 7.2. SEB was easily detected at concentrations of 5 microg/mL and 500 ng/mL when the LFDs were used. SEB was also reliably detected at concentrations below 5 and 0.25 ng/mL, which may induce serious disease.",Journal of AOAC International,"['D000818', 'D002417', 'D002845', 'D004768', 'D004867', 'D005189', 'D005504', 'D005506', 'D005516', 'D006863', 'D007054', 'D007158', 'D008892', 'D015203', 'D013997', 'D015014']","['Animals', 'Cattle', 'Chromatography', 'Enterotoxins', 'Equipment Design', 'False Positive Reactions', 'Food Analysis', 'Food Contamination', 'Food Microbiology', 'Hydrogen-Ion Concentration', 'Ice Cream', 'Immunologic Techniques', 'Milk', 'Reproducibility of Results', 'Time Factors', 'Yogurt']",Detection of staphylococcal enterotoxin B in milk and milk products using immunodiagnostic lateral flow devices.,"[None, None, 'Q000379', 'Q000032', None, None, 'Q000295', None, None, None, None, None, 'Q000378', None, None, None]","[None, None, 'methods', 'analysis', None, None, 'instrumentation', None, None, None, None, None, 'metabolism', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/20480905,2010,,,, +0.49,22133078,"Chocolate is a key ingredient in many foods such as milk shakes, candies, bars, cookies, and cereals. Chocolate candies are often consumed by mankind of all age groups. The presence of polycyclic aromatic hydrocarbons (PAHs) in chocolate candies may result in health risk to people. A rapid, precise, and economic extraction method was optimized and validated for the simultaneous determination of polycyclic aromatic hydrocarbons in chocolate candy by high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GS-MS) as a confirmatory technique. The method was optimized by using different solvents for liquid-liquid extraction, varying volume of de-emulsifying agent, and quantity of silica gel used for purification. The HPLC separation of 16 PAHs was carried out by C-18 column with mobile phase composed of acetonitrile : water (70 : 30) in isocratic mode with runtime of 20 min. Limit of detection, limit of quantification (LOQ), and correlation coefficients were found in the range of 0.3 to 4 ng g____, 0.9 to 12 ng g____, and 0.9109 to 0.9952, respectively. The exploration of 25 local chocolate candy samples for the presence of PAHs showed the mean content of benzo[a]pyrene as 1.62 ng g____, which representing the need to evaluate effective measures to prevent more severe PAHs contamination in chocolate candies in future.",Journal of food science,"['D002099', 'D002138', 'D002182', 'D002851', 'D056148', 'D004785', 'D005506', 'D008401', 'D007194', 'D057230', 'D059625', 'D008970', 'D011084']","['Cacao', 'Calibration', 'Candy', 'Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Environmental Pollutants', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'India', 'Limit of Detection', 'Liquid-Liquid Extraction', 'Molecular Weight', 'Polycyclic Aromatic Hydrocarbons']",Optimization and validation of an extraction method for the analysis of polycyclic aromatic hydrocarbons in chocolate candies.,"['Q000009', None, 'Q000009', None, None, 'Q000032', None, None, None, None, None, None, 'Q000032']","['adverse effects', None, 'adverse effects', None, None, 'analysis', None, None, None, None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/22133078,2012,0,0,, +0.49,25230186,"Triacylglycerols are responsible for chocolate's peculiar melting behavior: the type and position of fatty acids on the glycerol molecule strongly affect the melting range of cocoa butter. For this reason, the characterization of triglyceride composition in cocoa products is particularly important. In this work, triacylglycerols extracted from cocoa liquor samples were analyzed by matrix-assisted laser desorption/ionization time-of-flight (TOF) and electrospray ionization tandem mass spectrometry (MS/MS) coupled to liquid chromatography. Extracted samples were initially analyzed by direct injection in MS to obtain information on triglyceride molecular weights; relevant MS parameters were optimized, and the possible formation of the adducts [M___+___Na](+) and [M___+___NH(4)](+) was studied. Tandem mass experiments (both with triple quadrupole and TOF/TOF) were performed to study the fragmentation pathways (in particular, the loss of palmitic, stearic and oleic acid) and identify the triacylglycerols in cocoa liquors. Some signals of the spectra obtained with both MS techniques could indicate the presence of diacylglycerols in the cocoa extract, but different experimental evidences demonstrated that they were generated by the in-source fragmentation of triglycerides. A nonaqueous reversed-phase chromatographic separation was also developed and used to support the identification of the analytes; nine triacylglycerols were recognized in the cocoa liquor extracts. The three different batches of Ecuador cocoa liquor did not show significant differences in the triacylglycerol profile.",Journal of mass spectrometry : JMS,[],[],Triacylglycerol profile in cocoa liquors using MALDI-TOF and LC-ESI tandem mass spectrometry.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/25230186,2015,,,, +0.49,22641023,"Cocoa procyanidins (CPs)-gelatin-chitosan nanoparticles were fabricated based on the procyanidin-protein and electrostatic interactions, with an objective to enhance the stability and bioactivity of CPs. The CPs were purified using chromatographic methods and analyzed using HPLC equipped with a fluorescence detector (FLD) and mass spectrometer (MS). The purified CPs had a purity of 53.1% (w/w) and contained procyanidin oligomers (from monomer to decamers) and polymers, with polymers being the predominant component (26.4%, w/w). Different CPs-gelatin-chitosan mass ratios were tested to investigate the effects of formulation on the nanoparticle fabrication. Using CPs-gelatin-chitosan mass ratio of 0.75:1:0.5, the resultant nanoparticles had a particle size of 344.7 nm, zeta-potential of +29.8 mV, particle yield of 51.4%, loading efficiency of 50.1%, and loading capacity of 20.5%. The CPs-gelatin-chitosan nanoparticles were spherical as observed by scanning electron microscopy (SEM). Fourier transform infrared spectroscopy (FTIR) suggested that the primary interaction between the CPs and gelatin was hydrogen bond and hydrophobic interaction, while electrostatic interaction was the main binding force between chitosan and CPs-gelatin nanoparticles. Nanoencapsulation of the CPs significantly improved the stability of the CPs at 60_C. The CPs-gelatin-chitosan nanoparticles showed the same apoptotic effects at lower concentrations in human acute monocytic leukemia THP-1 cells compared with the CPs in solution.",European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V,"['D017209', 'D002099', 'D045744', 'D048271', 'D002851', 'D004337', 'D004355', 'D005780', 'D006801', 'D006860', 'D057927', 'D007948', 'D013058', 'D008855', 'D053758', 'D010316', 'D011108', 'D044945', 'D017550', 'D055672', 'D013696']","['Apoptosis', 'Cacao', 'Cell Line, Tumor', 'Chitosan', 'Chromatography, High Pressure Liquid', 'Drug Carriers', 'Drug Stability', 'Gelatin', 'Humans', 'Hydrogen Bonding', 'Hydrophobic and Hydrophilic Interactions', 'Leukemia, Monocytic, Acute', 'Mass Spectrometry', 'Microscopy, Electron, Scanning', 'Nanoparticles', 'Particle Size', 'Polymers', 'Proanthocyanidins', 'Spectroscopy, Fourier Transform Infrared', 'Static Electricity', 'Temperature']","Preparation, characterization, and induction of cell apoptosis of cocoa procyanidins-gelatin-chitosan nanoparticles.","['Q000187', 'Q000737', None, 'Q000737', None, 'Q000737', None, 'Q000737', None, None, None, 'Q000378', None, None, None, None, 'Q000737', 'Q000008', None, None, None]","['drug effects', 'chemistry', None, 'chemistry', None, 'chemistry', None, 'chemistry', None, None, None, 'metabolism', None, None, None, None, 'chemistry', 'administration & dosage', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22641023,2013,0,0,, +0.49,15712517,"A method for the determination of six kava lactones, methysticin, dihydromethysticin, kawain, dihydrokawain, yangonin and desmethoxyyangonin, in solid foods and beverages has been developed. Solid samples were prepared using methanol extraction, while beverages were extracted using a separate solid phase extraction (SPE) method. After sample preparation, the extracts were analysed using LC-UV or atmospheric pressure photoionization (APPI) LC-MS in the positive mode. Using the method, 10 beverage products, two chocolate products, three unbrewed tea products, three dietary supplements and a drink mix product were analysed. The results obtained using the LC-UV were comparable to those obtained using APPI-LC-MS for most products. Using the SPE method in conjunction with LC-MS, individual kava lactones were detected in drink products at ppb concentrations. Concentrations of total kava lactones ranged between 135-0.035 mg per serving in the food and beverage products tested and between 40-61 mg per serving for the dietary supplement products tested. Results of these analyses as well as extraction efficiency and reproducibility data are reported.",Food additives and contaminants,"['D001628', 'D002853', 'D019587', 'D005504', 'D008401', 'D006801', 'D020901', 'D007783', 'D010936', 'D018517', 'D012997', 'D013056']","['Beverages', 'Chromatography, Liquid', 'Dietary Supplements', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Kava', 'Lactones', 'Plant Extracts', 'Plant Roots', 'Solvents', 'Spectrophotometry, Ultraviolet']",LC-UV and LC-MS analysis of food and drink products containing kava.,"['Q000032', 'Q000379', 'Q000032', 'Q000379', 'Q000379', None, 'Q000737', 'Q000032', 'Q000032', 'Q000737', None, 'Q000379']","['analysis', 'methods', 'analysis', 'methods', 'methods', None, 'chemistry', 'analysis', 'analysis', 'chemistry', None, 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/15712517,2005,,,, +0.48,9547407,"The objective of this study was to determine the urinary excretion of methylxanthines in horses following ingestion of chocolate over eight days. The study was performed in response to gas chromatography-mass spectrometry (GC-MS) confirmation of the presence of caffeine in a positive urine test in a racehorse. The trainer of the horse alleged that he often administered chocolate-coated peanuts as treats to his horses, and he believed that the ingestion of chocolate was responsible for the positive urine test. The urinary excretion of theobromine and caffeine after the ingestion of chocolate-coated peanuts was investigated in three horses. Enzyme-linked immunoassay (ELISA), high-performance liquid chromatography (HPLC), and GC-MS assays were performed on all urine specimens. Theobromine (HPLC) was detected for 72 h and caffeine (GC-MS) for 48 h after chronic ingestion of chocolate-coated peanuts. Methylxanthines were detected by ELISA for 120 h after administration of chocolate.",Journal of analytical toxicology,"['D000821', 'D000818', 'D010367', 'D002099', 'D002110', 'D002851', 'D004300', 'D004797', 'D005260', 'D008401', 'D006736', 'D013805']","['Animal Feed', 'Animals', 'Arachis', 'Cacao', 'Caffeine', 'Chromatography, High Pressure Liquid', 'Doping in Sports', 'Enzyme-Linked Immunosorbent Assay', 'Female', 'Gas Chromatography-Mass Spectrometry', 'Horses', 'Theobromine']",Detection and determination of theobromine and caffeine in urine after administration of chocolate-coated peanuts to horses.,"[None, None, None, 'Q000378', 'Q000652', 'Q000379', 'Q000379', 'Q000379', None, 'Q000379', 'Q000652', 'Q000652']","[None, None, None, 'metabolism', 'urine', 'methods', 'methods', 'methods', None, 'methods', 'urine', 'urine']",https://www.ncbi.nlm.nih.gov/pubmed/9547407,1998,,,, +0.48,24088516,"Triacylglycerol (TAG) molecular species were quantified through high-performance liquid chromatography (HPLC) equipped with a nano quantity analyte detector (NQAD). TAG standard compounds, i.e., 1,3-dipalmitoyl-2-oleoylglycerol (__-POP), 1-palmitoyl-2-oleoyl-3-stearoyl-rac-glycerol (__-POS), and 1,3-distearoyl-2-oleoylglycerol (__-SOS), and natural cocoa butter were used for analyses. NQAD gave the first order equation passing through the origin for all TAG standard compounds. TAG molecular species in cocoa butter were quantified using the calibration curves and the obtained values were almost the same as the reported ones of conventional cocoa butter. Furthermore, a recovery test was also carried out and the values were almost 100. Therefore, HPLC-NQAD can be successfully used for the quantification of TAG molecular species in natural fats and oils. ",Journal of oleo science,"['D002138', 'D002851', 'D004041', 'D005504', 'D053758', 'D036103', 'D014280']","['Calibration', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Food Analysis', 'Nanoparticles', 'Nanotechnology', 'Triglycerides']",Quantification of triacylglycerol molecular species in cocoa butter using high-performance liquid chromatography equipped with nano quantity analyte detector.,"[None, 'Q000295', 'Q000032', 'Q000295', None, 'Q000295', 'Q000032']","[None, 'instrumentation', 'analysis', 'instrumentation', None, 'instrumentation', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24088516,2014,2,1,table 1, +0.48,20953776,"Piceid (3,4',5-trihydroxystilbene-3-__-D: -glucoside) is a stilbene which occurs naturally in various families of plants and has been shown to protect lipoproteins from oxidative damage and to have cancer chemopreventive activity. This paper deals with the determination of piceid in cocoa-containing products by using photo-induced fluorescence and the aid of a multicommutated continuous-flow assembly which was provided with an on-line photoreactor. A strongly fluorescent photoproduct is generated from piceid when it is irradiated under UV light for 30__s, which is retained on Sephadex QAE A-25 and directly monitored on this active solid support at 257/382__nm (__ (exc)/__ (em), respectively). The pre-concentration of the photoproduct of piceid on the solid support greatly improves both sensitivity and selectivity. The influence of different experimental parameters, both chemical (pH, ionic strength) and hydrodynamic (irradiation time, flow rate, photoreactor length, sampling time), was tested. The sample pre-treatment included delipidation with toluene and cyclohexane, stilbene extraction with ethanol/water (80:20, v/v) and clean-up by solid-phase extraction on C(18) cartridges and methanol/water (40:20, v/v) as eluting solution. This procedure allowed the elimination of the aglycon of piceid, resveratrol and other potential interfering species and a recovery of about a 90% piceid. The method was applied to the analysis of piceid in cocoa powder, dark chocolate and milk chocolate. The quantification limits were 1.4, 1.1 and 0.09__mg__kg(-1), respectively. Relative standard deviations ranged from 1.8% to 3.1%. This is the first reported non-chromatographic method for determination of piceid in these foods.",Analytical and bioanalytical chemistry,"['D002099', 'D004867', 'D017022', 'D005960', 'D057230', 'D055668', 'D010946', 'D011827', 'D013050', 'D013267', 'D014466']","['Cacao', 'Equipment Design', 'Flow Injection Analysis', 'Glucosides', 'Limit of Detection', 'Photochemical Processes', 'Plants, Medicinal', 'Radiation', 'Spectrometry, Fluorescence', 'Stilbenes', 'Ultraviolet Rays']",Automatic optosensing device based on photo-induced fluorescence for determination of piceid in cocoa-containing products.,"['Q000737', None, 'Q000295', 'Q000032', None, None, 'Q000737', None, 'Q000295', 'Q000032', None]","['chemistry', None, 'instrumentation', 'analysis', None, None, 'chemistry', None, 'instrumentation', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/20953776,2011,1,1,table 2, +0.48,25240144,"The studied area is located in Western Anatolia and situated on the NE-SW directed U_ak-G_re cross-graben that developed under a crustal extensional regime during the Late Miocene-Pliocene. Silica occurrences have been mostly found as mushroom-shaped big caps. They also show sedimentary structures such as stratification. Silica occurrences are milky white, yellowish white, yellow to chocolate brown and rarely pale blue, bluish gray in color and have no crystal forms in hand specimen. Some of the silica samples show conchoidal fracture. Silica minerals are mostly chalcedony, low-quartz (_±-quartz) and sporadically opal-CT in spectras, according to confocal Raman spectrometry. The silica samples have enrichment of Fe (1000-24,600 ppm), Ca (100-10,200 ppm), P (4-3950 ppm) and Mn (8-3020 ppm). Other striking elements in fewer amounts are Ba (0.9-609.6 ppm), Ni (15.7-182.3 ppm) and Co (18.6-343.1 ppm). In chondrite-normalized spider diagram, silica samples display partial enrichment in LIL elements (Rb, Ba, Th). The __(18)O (__ V-SMOW) values for silica samples vary from 18.4__ to 22.8__ and are similar to low temperature hydrothermal silica. Confocal Raman spectrometry and oxygen isotope indicate that the silica minerals may precipitate from host fluid which is relatively has low temperatures hydrothermal solutions derived from the residual melt of basaltic magma.","Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy","['D001464', 'D002118', 'D019015', 'D005844', 'D007501', 'D008903', 'D010103', 'D010758', 'D011791', 'D012822', 'D013052', 'D013059', 'D014421', 'D014961']","['Barium', 'Calcium', 'Geologic Sediments', 'Geology', 'Iron', 'Minerals', 'Oxygen Isotopes', 'Phosphorus', 'Quartz', 'Silicon Dioxide', 'Spectrometry, X-Ray Emission', 'Spectrum Analysis, Raman', 'Turkey', 'X-Ray Diffraction']",The origin and determination of silica types in the silica occurrences from Altinta_ region (U_ak-Western Anatolia) using multianalytical techniques.,"['Q000032', 'Q000032', 'Q000737', 'Q000379', 'Q000032', 'Q000032', None, 'Q000032', None, 'Q000032', None, None, None, None]","['analysis', 'analysis', 'chemistry', 'methods', 'analysis', 'analysis', None, 'analysis', None, 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25240144,2015,0,0,,no cocoa +0.48,3930303,"A preliminary survey in 1982 of aflatoxin levels in peanut butters indicated that 31 out of 32 samples of major national brand-named products examined contained less than 10 micrograms/kg aflatoxin B1 and that 59% of these were below the limit of detection (2 micrograms/kg). In contrast, of 25 peanut butters from specialist 'Health Food' outlets, 64% contained less than 10 micrograms/kg aflatoxin B1, the remainder ranging from 16 to 318 micrograms/kg, with one sample having a total aflatoxin concentration of 345 micrograms/kg. Subsequent surveys in 1983 and 1984 of 'Health Food' products confirmed that these manufacturers were still experiencing some difficulty in complying with the 30 micrograms/kg total aflatoxin voluntary guideline limit. A further survey in 1984 was carried out of 228 retail samples of nuts and nut confectionery products comprising peanuts (shelled, unshelled, roasted and salted), mixed nuts, almonds (both unblanched and ground), brazils (in shell), hazelnuts (in shell), chocolate-coated peanuts, peanut brittle and coconut ice. The results showed that 74% of the samples contained less than 0.5 microgram/kg of aflatoxin B1 with 3.1% exceeding the guideline tolerance of 30 micrograms/kg total aflatoxins, these being predominantly peanuts and brazils. The highest total levels of aflatoxins observed were in unshelled peanuts containing 4920 micrograms/kg and in a composite sample of visibly moulded brazils containing 17 926 micrograms/kg.",Food additives and contaminants,"['D016604', 'D000348', 'D010367', 'D002851', 'D005511', 'D005516', 'D009754', 'D013050', 'D006113']","['Aflatoxin B1', 'Aflatoxins', 'Arachis', 'Chromatography, High Pressure Liquid', 'Food Handling', 'Food Microbiology', 'Nuts', 'Spectrometry, Fluorescence', 'United Kingdom']","A survey of aflatoxins in peanut butters, nuts and nut confectionery products by HPLC with fluorescence detection.","[None, 'Q000032', 'Q000032', None, None, None, 'Q000032', None, None]","[None, 'analysis', 'analysis', None, None, None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/3930303,1985,,,, +0.48,19251438,"Triacylglycerols were analyzed as cationized species (Li(+), Na(+), K(+)) by high-energy CID at 20 keV collisions utilizing MALDI-TOF/RTOF mass spectrometry. Precursor ions, based on [M + Li](+)-adduct ions exhibited incomplete fragmentation in the high and low m/z region whereas [M + K](+)-adducts did not show useful fragmentation. Only sodiated precursor ions yielded product ion spectra with structurally diagnostic product ions across the whole m/z range. The high m/z region of the CID spectra is dominated by abundant charge-remote fragmentation of the fatty acid substituents. In favorable cases also positions of double bonds or of hydroxy groups of the fatty acid alkyl chains could be determined. A-type product ions represent the end products of these charge-remote fragmentations. B- and C-type product ions yield the fatty acid composition of individual triacylglycerol species based on loss of either one neutral fatty acid or one sodium carboxylate residue, respectively. Product ions allowing fatty acid substituent positional determination were present in the low m/z range enabling identification of either the sn-1/sn-3 substituents (E-, F-, and G-type ions) or the sn-2 substituent (J-type ion). These findings were demonstrated with synthetic triacylglycerols and plant oils such as cocoa butter, olive oil, and castor bean oil. Typical features of 20 keV CID spectra of sodiated triacylglycerols obtained by MALDI-TOF/RTOF MS were an even distribution of product ions over the entire m/z range and a mass accuracy of +/-0.1 to 0.2 u. One limitation of the application of this technique is mainly the insufficient precursor ion gating after MS1 (gating window at 4 u) of species separated by 2 u.",Journal of the American Society for Mass Spectrometry,[],[],The renaissance of high-energy CID for structural elucidation of complex lipids: MALDI-TOF/RTOF-MS of alkali cationized triacylglycerols.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19251438,2009,2,1,text under resutls,key word components of cocoa +0.48,19843177,"A platform based on hydrophilic interaction chromatography in combination with Fourier transform mass spectrometry was developed in order to carry out metabonomics of Drosophila melanogaster strains. The method was able to detect approximately 230 metabolites, mainly in the positive ion mode, after checking to eliminate false positives caused by isotope peaks, adducts and fragment ions. Two wild-type strains, Canton S and Oregon R, were studied, plus two mutant strains, Maroon Like and Chocolate. In order to observe the differential expression of metabolites, liquid chromatography-mass spectrometry analyses of the different strains were compared using sieve 1.2 software to extract metabolic differences. The output from sieve was searched against a metabolite database using an Excel-based macro written in-house. Metabolic differences were observed between the wild-type strains, and also between both Chocolate and Maroon Like compared with Oregon R. It was established that a metabonomic approach could produce results leading to the generation of new hypotheses. In addition, the structure of a new class of lipid with a histidine head group, found in all of the strains of flies, but lower in Maroon Like, was elucidated.",The FEBS journal,"['D000818', 'D001708', 'D002853', 'D004331', 'D005583', 'D006639', 'D008055', 'D013058', 'D055432', 'D011621']","['Animals', 'Biopterin', 'Chromatography, Liquid', 'Drosophila melanogaster', 'Fourier Analysis', 'Histidine', 'Lipids', 'Mass Spectrometry', 'Metabolomics', 'Pteridines']",Towards a platform for the metabonomic profiling of different strains of Drosophila melanogaster using liquid chromatography-Fourier transform mass spectrometry.,"[None, 'Q000737', None, 'Q000378', None, 'Q000737', 'Q000737', None, 'Q000379', 'Q000737']","[None, 'chemistry', None, 'metabolism', None, 'chemistry', 'chemistry', None, 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19843177,2010,0,0,,no cocoa +0.48,16848542,"Sensory-guided decomposition of roasted cocoa nibs revealed that, besides theobromine and caffeine, a series of bitter-tasting 2,5-diketopiperazines and flavan-3-ols were the key inducers of the bitter taste as well as the astringent mouthfeel imparted upon consumption of roasted cocoa. In addition, a number of polyphenol glycopyranosides as well as a series of N-phenylpropenoyl-l-amino acids have been identified as key astringent compounds of roasted cocoa. In the present investigation, a total of 84 putative taste compounds were quantified in roasted cocoa beans and then rated for the taste contribution on the basis of dose-over-threshold (DoT) factors to bridge the gap between pure structural chemistry and human taste perception. To verify these quantitative results, an aqueous taste reconstitute was prepared by blending aqueous solutions of the individual taste compounds in their ""natural"" concentrations. Sensory analyses revealed that the taste profile of this artificial cocktail was very close to the taste profile of an aqueous suspension of roasted cocoa nibs. To further narrow down the number of key taste compounds, finally, taste omission experiments and human dose/response functions were performed, demonstrating that the bitter-tasting alkaloids theobromine and caffeine, seven bitter-tasting diketopiperazines, seven bitter- and astringent-tasting flavan-3-ols, six puckering astringent N-phenylpropenoyl-l-amino acids, four velvety astringent flavonol glycosides, gamma-aminobutyric acid, beta-aminoisobutyric acid, and six organic acids are the key organoleptics of the roasted cocoa nibs.",Journal of agricultural and food chemistry,"['D002099', 'D002851', 'D006358', 'D006801', 'D013058', 'D010936', 'D012639', 'D012677', 'D013649', 'D014867']","['Cacao', 'Chromatography, High Pressure Liquid', 'Hot Temperature', 'Humans', 'Mass Spectrometry', 'Plant Extracts', 'Seeds', 'Sensation', 'Taste', 'Water']",Molecular definition of the taste of roasted cocoa nibs (Theobroma cacao) by means of quantitative studies and sensory experiments.,"['Q000737', None, None, None, None, 'Q000737', 'Q000737', None, None, None]","['chemistry', None, None, None, None, 'chemistry', 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16848542,2006,1,3,table 1,conversion necessary +0.48,21945577,"Two kinds of monoclonal antibodies (MoAbs), OCA-10A and OCA-1B, were prepared based on their specificity to ochratoxin A (OTA) and ochratoxin B (OTB) and on their tolerance to 40% methanol. In an indirect competitive enzyme-linked immunosorbent assay, the half maximal inhibitory concentration (IC(50)) value of OCA-10A was 27ng/mL for OTA and 17ng/mL for OTB, and that of OCA-1B was 28ng/mL for OTA and 13ng/mL for OTB. Immuno-affinity columns (IACs) using these MoAbs were prepared with agarose gel beads. The IAC with OCA-1B showed a NaCl-dependent binding ability to OTA and OTB, while interestingly, the IAC with OCA-10A bound to them without NaCl. The IAC with OCA-10A showed a high methanol tolerance when compared with existing IACs, as expected from the high methanol tolerance of OCA-10A itself. Such tolerance was maintained for the application of the cocoa extract with 70% methanol and the wheat extract with 60% acetonitrile, while the tolerance was slightly altered by interference from the cocoa extract. Examinations with organic solvents at higher concentrations than the allowable level in existing IACs showed that OTA and OTB spiked with wheat, cocoa and red wine could be purified with high recovery. The newly developed IAC is expected to show sufficient clean-up ability for food analyses.","Methods (San Diego, Calif.)","['D000097', 'D000818', 'D000911', 'D000918', 'D000937', 'D002099', 'D002846', 'D004797', 'D005260', 'D006433', 'D007117', 'D007118', 'D020128', 'D000432', 'D051379', 'D008807', 'D009793', 'D055601', 'D012965', 'D012997']","['Acetonitriles', 'Animals', 'Antibodies, Monoclonal', 'Antibody Specificity', 'Antigen-Antibody Reactions', 'Cacao', 'Chromatography, Affinity', 'Enzyme-Linked Immunosorbent Assay', 'Female', 'Hemocyanins', 'Immunization, Secondary', 'Immunoassay', 'Inhibitory Concentration 50', 'Methanol', 'Mice', 'Mice, Inbred BALB C', 'Ochratoxins', 'Organic Chemistry Phenomena', 'Sodium Chloride', 'Solvents']",Development of an immuno-affinity column for ochratoxin analysis using an organic solvent-tolerant monoclonal antibody.,"['Q000737', None, 'Q000737', None, None, 'Q000737', 'Q000295', 'Q000379', None, 'Q000008', None, 'Q000295', None, 'Q000737', None, None, 'Q000008', None, 'Q000737', None]","['chemistry', None, 'chemistry', None, None, 'chemistry', 'instrumentation', 'methods', None, 'administration & dosage', None, 'instrumentation', None, 'chemistry', None, None, 'administration & dosage', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21945577,2012,0,0,, +0.47,20492142,"Cocoa beans were alkalized before or after roasting and made into cocoa liquor before analyzing by SIFT-MS. In both alkalized-before-roasting and alkalized-after-roasting samples, there were significantly higher concentrations of alkylpyrazines for the samples with pH above 7 than pH below 7. At pH 8, the concentrations of 2,3-, 2,5-, and 2,6-dimethylpyrazine (DMP), 2,3,5-trimethylpyrazine (TrMP), 2,3,5,6-tetramethylpyrazine (TMP), and 2,3-diethyl-5-methylpyrazine (EMP) in the samples alkalized-before-roasting were higher than those in the samples alkalized-after-roasting. Volatiles increased under conditions that promoted the Maillard reaction. The partition coefficient was not significantly affected by pH from 5.2 to 8. The ratios of TrMP/DMP and DMP/TMP increased while the ratio of TMP/TrMP decreased as the pH increased. The concentrations of Strecker aldehydes and other volatiles followed a similar pattern as that of the alkylpyrazines. High pH favors the production of alkylpyrazines and Strecker aldehydes.",Journal of food science,"['D000079', 'D000434', 'D000447', 'D002099', 'D005511', 'D005663', 'D006863', 'D013058', 'D011719', 'D055549']","['Acetaldehyde', 'Alcoholic Beverages', 'Aldehydes', 'Cacao', 'Food Handling', 'Furans', 'Hydrogen-Ion Concentration', 'Mass Spectrometry', 'Pyrazines', 'Volatile Organic Compounds']","Alkylpyrazines and other volatiles in cocoa liquors at pH 5 to 8, by Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS).","['Q000031', 'Q000032', 'Q000032', 'Q000737', 'Q000379', 'Q000032', None, 'Q000379', 'Q000032', 'Q000032']","['analogs & derivatives', 'analysis', 'analysis', 'chemistry', 'methods', 'analysis', None, 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20492142,2010,1,2,table 3, +0.47,14745773,"Positional distribution of fatty acyl chains of triacylglycerols (TGs) in vegetable oils and fats (palm oil, cocoa butter) and animal fats (beef, pork and chicken fats) was examined by reversed-phase high-performance liquid chromatography (RP-HPLC) coupled to atmospheric pressure chemical ionization using a quadrupole mass spectrometer. Quantification of regioisomers was achieved for TGs containing two different fatty acyl chains (palmitic (P), stearic (S), oleic (O), and/or linoleic (L)). For seven pairs of 'AAB/ABA'-type TGs, namely PPS/PSP, PPO/POP, SSO/SOS, POO/OPO, SOO/OSO, PPL/PLP and LLS/LSL, calibration curves were established on the basis of the difference in relative abundances of the fragment ions produced by preferred losses of the fatty acid from the 1/3-position compared to the 2-position. In practice the positional isomers AAB and ABA yield mass spectra showing a significant difference in relative abundance ratios of the ions AA(+) to AB(+). Statistical analysis of the validation data obtained from analysis of TG standards and spiked oils showed that, under repeatability conditions, least-squares regression can be used to establish calibration curves for all pairs. The regression models show linear behavior that allow the determination of the proportion of each regioisomer in an AAB/ABA pair, within a working range from 10 to 1000 microg/mL and a 95% confidence interval of +/-3% for three replicates.",Rapid communications in mass spectrometry : RCM,"['D000818', 'D001274', 'D002138', 'D002851', 'D005223', 'D007536', 'D013058', 'D008460', 'D009821', 'D012015', 'D014280']","['Animals', 'Atmospheric Pressure', 'Calibration', 'Chromatography, High Pressure Liquid', 'Fats', 'Isomerism', 'Mass Spectrometry', 'Meat', 'Oils', 'Reference Standards', 'Triglycerides']",Quantitative analysis of triacylglycerol regioisomers in fats and oils using reversed-phase high-performance liquid chromatography and atmospheric pressure chemical ionization mass spectrometry.,"[None, None, None, None, 'Q000737', None, 'Q000379', None, 'Q000737', None, 'Q000032']","[None, None, None, None, 'chemistry', None, 'methods', None, 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/14745773,2004,,,, +0.47,19631941,"The development of an off-line comprehensive 2-dimensional liquid chromatography (2-D-LC) method for the analysis of procyanidins is reported. In the first dimension, oligomeric procyanidins were separated according to molecular weight by hydrophilic interaction chromatography (HILIC), while reversed phase LC was employed in the second dimension to separate oligomers based on hydrophobicity. Fluorescence, UV and electrospray ionisation mass spectrometry (ESI-MS) were employed for identification purposes. The combination of these orthogonal separation methods is shown to represent a significant improvement compared to 1-dimensional methods for the analysis of complex high molecular weight procyanidin fractions, by simultaneously providing isomeric and molecular weight information. The low correlation (r(2)<0.2100) between the two LC modes afforded a practical peak capacity in excess of 2300 for the optimal off-line method. The applicability of the method is demonstrated for the analysis of phenolic extracts of apple and cocoa.",Journal of chromatography. A,"['D001704', 'D002099', 'D002851', 'D005638', 'D027845', 'D010636', 'D010936', 'D044945', 'D012639', 'D021241']","['Biopolymers', 'Cacao', 'Chromatography, High Pressure Liquid', 'Fruit', 'Malus', 'Phenols', 'Plant Extracts', 'Proanthocyanidins', 'Seeds', 'Spectrometry, Mass, Electrospray Ionization']",Off-line comprehensive 2-dimensional hydrophilic interaction x reversed phase liquid chromatography analysis of procyanidins.,"['Q000032', 'Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000032', 'Q000737', 'Q000032', 'Q000737', None]","['analysis', 'chemistry', 'methods', 'chemistry', 'chemistry', 'analysis', 'chemistry', 'analysis', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/19631941,2009,2,1,table 2, +0.47,25167469,"Despite the key role of flavan-3-ols in many foods, very little is yet known concerning the modification of their chemical structures through food processes. Degradation of model media containing (-)-epicatechin and procyanidin B2, either separately or together, was monitored by RP-HPLC-DAD-ESI(-)-MS/MS. Medium composition (aqueous or lipidic) and temperature (60 and 90 _C) were studied. In aqueous medium at 60 _C, (-)-epicatechin was mainly epimerized to (-)-catechin, but it was also oxidized to ""chemical"" dimers, a ""chemical"" trimer, and dehydrodi(epi)catechin A. Unlike oxidation, epimerization was enhanced at 90 _C. In lipidic medium, epimerization proved slow but degradation was faster. Procyanidin B2 likewise proved able to epimerize, especially at 90 _C and in aqueous medium. At high temperature only, the interflavan linkage was cleaved, yielding the same compounds as those found in the monomer-containing model medium. Oxidation to procyanidin A2 was also evidenced. With little epimerization and slow oxidation even at 90 _C, procyanidin B2 proved more stable in lipidic medium. Synergy was also observed: in the presence of the monomer, the dimer degradation rate increased 2-fold at 60 _C. This work states for the first time the presence of newly formed flavan-3-ol oligomers in processed cocoa. ",Journal of agricultural and food chemistry,"['D044946', 'D002099', 'D002392', 'D002851', 'D019281', 'D005419', 'D005511', 'D006358', 'D059808', 'D044945', 'D012996', 'D021241', 'D053719', 'D014867']","['Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Dimerization', 'Flavonoids', 'Food Handling', 'Hot Temperature', 'Polyphenols', 'Proanthocyanidins', 'Solutions', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry', 'Water']","Degradation of (-)-epicatechin and procyanidin B2 in aqueous and lipidic model systems. first evidence of ""chemical"" flavan-3-ol oligomers in processed cocoa.","['Q000032', 'Q000737', 'Q000032', None, None, 'Q000032', 'Q000379', None, 'Q000032', 'Q000032', None, None, None, None]","['analysis', 'chemistry', 'analysis', None, None, 'analysis', 'methods', None, 'analysis', 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25167469,2015,0,0,, +0.47,22468361,"The performance of Gluten-Tec (EuroProxima, Arnhem, The Netherlands) was tested through an interlaboratory study in accordance with AOAC guidelines. Gluten-Tec is a competitive ELISA that detects an immunostimulatory epitope of a-gliadin in dietary food for celiacs. Fifteen laboratories, representing 14 different countries, announced their interest in taking part in this study. Of the 12 laboratories that sent the results within the established timeframe, two submitted inappropriate standard curves and were excluded from the statistical analysis. Four different food matrixes (rice-based baby food, maize bread, chocolate cake mix, and beer) were selected for preparing the test samples. Two gliadin extraction procedures were used: the conventional 60% ethanol, and a new method based on the reducing reagent dithiothreitol. The 38 samples (19 blind duplicates) tested in this study were prepared by diluting the different extracts in order to cover a wide range of gliadin levels. Both sample extraction and dilution were performed by EuroProxima; the present interlaboratory study was focused only on testing the ELISA part of the Gluten-Tec kit protocol. Repeatability values (within-laboratory variance), expressed as RSD(r) ranged from 6.2 to 25.7%, while reproducibility values (interlaboratory variance), expressed as RSD(R), ranged from 10.6 to 45.9%. Both statistical parameters were in the acceptable range of ELISAs under these conditions, and the method will be presented to the Codex Alimentarius as a preferred method for gluten analysis.",Journal of AOAC International,"['D000485', 'D001515', 'D002446', 'D002851', 'D004044', 'D004797', 'D005504', 'D005512', 'D005903', 'D005983', 'D006801', 'D007202', 'D007223', 'D007225', 'D057230', 'D010455', 'D011933', 'D015203']","['Allergens', 'Beer', 'Celiac Disease', 'Chromatography, High Pressure Liquid', 'Dietary Proteins', 'Enzyme-Linked Immunosorbent Assay', 'Food Analysis', 'Food Hypersensitivity', 'Gliadin', 'Glutens', 'Humans', 'Indicators and Reagents', 'Infant', 'Infant Food', 'Limit of Detection', 'Peptides', 'Reagent Kits, Diagnostic', 'Reproducibility of Results']",Validation of a new enzyme-linked immunosorbent assay to detect the triggering proteins and peptides for celiac disease: interlaboratory study.,"['Q000032', 'Q000032', 'Q000139', None, 'Q000032', 'Q000379', None, 'Q000276', 'Q000032', 'Q000032', None, None, None, None, None, 'Q000032', None, None]","['analysis', 'analysis', 'chemically induced', None, 'analysis', 'methods', None, 'immunology', 'analysis', 'analysis', None, None, None, None, None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22468361,2012,,,, +0.47,15759751,"Acrylamide levels in a variety of food samples were analyzed before and after 3 months of storage at 10 degrees-12 degrees C. The analysis was performed by liquid chromatography tandem mass spectrometry (LC/MS/MS) using deuterium-labeled acrylamide as internal standard. Acrylamide was stable in most matrixes (cookies, cornflakes, crispbread, raw sugar, potato crisps, peanuts) over time. However, slight decreases were determined for dietary biscuits (83-89%) and for licorice confection (82%). For coffee and cacao powder, a significant decrease occurred during storage for 3 or 6 months, respectively. Acrylamide concentrations dropped from 305 to 210 microg/kg in coffee and from 265 to 180 microg/kg in cacao powder. On the contrary, acrylamide remained stable in soluble coffee as well as in coffee substitutes. Reactions of acrylamide with SH group-containing substances were assumed as the cause for acrylamide degradation in coffee and cacao. Spiking experiments with acrylamide revealed that acrylamide concentrations remained stable in baby food, cola, and beer; however, recovery levels dropped in milk powder (71%), sulfurized apricot (53%), and cacao powder (17%). These observations suggest that variations in the acrylamide content of food, especially in coffee and cacao, can vary depending on the storage time because special food constituents and/or reaction products can affect the levels.",Journal of AOAC International,"['D020106', 'D001939', 'D002099', 'D050260', 'D002853', 'D003069', 'D003903', 'D002523', 'D005502', 'D005504', 'D005506', 'D005511', 'D008401', 'D013058', 'D013696', 'D013997']","['Acrylamide', 'Bread', 'Cacao', 'Carbohydrate Metabolism', 'Chromatography, Liquid', 'Coffee', 'Deuterium', 'Edible Grain', 'Food', 'Food Analysis', 'Food Contamination', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Mass Spectrometry', 'Temperature', 'Time Factors']",Studies on the stability of acrylamide in food during storage.,"['Q000737', None, None, None, None, None, 'Q000737', None, None, None, None, None, None, None, None, None]","['chemistry', None, None, None, None, None, 'chemistry', None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/15759751,2005,,,, +0.47,17394333,"The development and in-house testing of a method for the quantification of milk fat in chocolate fats is described. A database consisting of the triacylglycerol profiles of 310 genuine milk fat samples from 21 European countries and 947 mixtures thereof with chocolate fats was created under a strict quality control scheme using 26 triacylglycerol reference standards for calibration purposes. Out of the individual triacylglycerol fractions obtained, 1-palmitoyl-2-stearoyl-3-butyroyl-glycerol (PSB) was selected as suitable marker compound for the determination of the proportion of milk fat in chocolate fats. By using PSB values from the standardized database, a calibration function using simple linear regression analysis was calculated to be used for future estimations of the milk fat content. A comparison with the widely used butyric acid method, which is currently used to determine the milk fat content in nonmilk fat mixtures, showed that both methods were equivalent in terms of accuracy. The advantage of the presented approach is that for further applications, i.e., determination of foreign fats in chocolate fats, just a single analysis is necessary, whereas for the same purpose, the C4 method requires two different analytical methods.",Journal of agricultural and food chemistry,"['D000818', 'D002099', 'D002849', 'D005223', 'D008892', 'D011786', 'D014280']","['Animals', 'Cacao', 'Chromatography, Gas', 'Fats', 'Milk', 'Quality Control', 'Triglycerides']",Quantification of milk fat in chocolate fats by triacylglycerol analysis using gas-liquid chromatography.,"[None, 'Q000737', None, 'Q000032', 'Q000737', None, 'Q000032']","[None, 'chemistry', None, 'analysis', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17394333,2007,0,0,,0 +0.46,8197829,"Direct injection of oil or fat into a moderately heated injector enables performance of a kind of headspace technique in the injector: oil or fat is diluted 1:1 with acetone and injected into a vaporizing chamber at 200 degrees C. Components, for example organophosphorus insecticides, evaporate from the oil film on the insert wall and are transferred into the column in the splitless mode; the oil slowly flows along the wall to the bottom of the insert and is retained there in a kind of a bag. Using a flame photometric detector, detection limits are below 10 micrograms/kg.",Zeitschrift fur Lebensmittel-Untersuchung und -Forschung,"['D002849', 'D004041', 'D004042', 'D005506', 'D007306', 'D000069463', 'D009943', 'D010938']","['Chromatography, Gas', 'Dietary Fats', 'Dietary Fats, Unsaturated', 'Food Contamination', 'Insecticides', 'Olive Oil', 'Organophosphorus Compounds', 'Plant Oils']",Determination of organophosphorus insecticides in edible oils and fats by splitless injection of the oil into a gas chromatograph (injector-internal headspace analysis).,"['Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, None, 'Q000032']","['methods', 'analysis', 'analysis', 'analysis', 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/8197829,1994,,,, +0.46,8471853,"A liquid chromatographic procedure already evaluated in a preceding study for the analysis of acesulfam-K is also suitable for the determination of the intense sweetener aspartame in tabletop sweetener, candy, fruit beverage, fruit pulp, soft drink, yogurt, cream, cheese, and chocolate preparations. The method also allows the determination of aspartame's major decomposition products: diketopiperazine, aspartyl-phenylalanine, and phenylalanine. Samples are extracted or diluted with water and filtered. Complex matrixes are centrifuged or clarified with Carrez solutions. An aliquot of the extract is analyzed on a reversed-phase muBondapak C18 column using 0.0125M KH2PO4 (pH 3.5)-acetonitrile ([85 + 15] or [98 + 2]) as mobile phase. Detection is performed by UV absorbance at 214 nm. Recoveries ranged from 96.1 to 105.0%. Decomposition of the sweetener was observed in most food samples. However, the total aspartame values (measured aspartame + breakdown products) were within -10% and +5% of the declared levels. The repeatabilities and the repeatability coefficients of variation were, respectively, 1.00 mg/100 g and 1.34% for products containing less than 45 mg/100 g aspartame and 4.11 mg/100 g and 0.91% for other products. The technique is precise and sensitive. It enables the detection of many food additives or natural constituents, such as other intense sweeteners, organic acids, and alkaloids, in the same run without interfering with aspartame or its decomposition products. The method is consequently suitable for quality control or monitoring.",Journal of AOAC International,"['D000818', 'D001218', 'D001628', 'D002182', 'D002855', 'D004355', 'D005504', 'D005638', 'D008892', 'D013549']","['Animals', 'Aspartame', 'Beverages', 'Candy', 'Chromatography, Thin Layer', 'Drug Stability', 'Food Analysis', 'Fruit', 'Milk', 'Sweetening Agents']",Determination of aspartame and its major decomposition products in foods.,"[None, 'Q000032', 'Q000032', 'Q000032', None, None, 'Q000379', 'Q000737', 'Q000737', 'Q000737']","[None, 'analysis', 'analysis', 'analysis', None, None, 'methods', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/8471853,1993,,,, +0.46,17031994,"Detection of peptides from the peanut allergen Ara h 1 by liquid chromatography-mass spectrometry (LC-MS) was used to identify and estimate total peanut protein levels in dark chocolate. A comparison of enzymatic digestion subsequent to and following extraction of Ara h 1 from the food matrix revealed better limits of detection (LOD) for the pre-extraction digestion (20 ppm) than for the postextraction digestion (50 ppm). Evaluation of LC-MS instruments and scan modes showed the LOD could be further reduced to 10 ppm via a triple-quadrupole and multiple-reaction monitoring. Improvements in extraction techniques combined with an increase in the amount of chocolate extracted (1 g) improved the LOD to 2 ppm of peanut protein. This method provides an unambiguous means of confirming the presence of the peanut protein in foods using peptide markers from a major allergen, Ara h 1, and can easily be modified to detect other food allergens.",Journal of agricultural and food chemistry,"['D000485', 'D000595', 'D052179', 'D002099', 'D002853', 'D005506', 'D006023', 'D013058', 'D008969', 'D010446', 'D010447', 'D010940', 'D012680']","['Allergens', 'Amino Acid Sequence', 'Antigens, Plant', 'Cacao', 'Chromatography, Liquid', 'Food Contamination', 'Glycoproteins', 'Mass Spectrometry', 'Molecular Sequence Data', 'Peptide Fragments', 'Peptide Hydrolases', 'Plant Proteins', 'Sensitivity and Specificity']",Confirmation of peanut protein using peptide markers in dark chocolate using liquid chromatography-tandem mass spectrometry (LC-MS/MS).,"['Q000032', None, None, 'Q000737', None, 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000378', 'Q000032', None]","['analysis', None, None, 'chemistry', None, 'analysis', 'analysis', None, None, 'analysis', 'metabolism', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/17031994,2006,0,0,,no cocoa +0.46,19280160,"The antioxidant potential of commercial beverages against peroxyl radical was determined using the Total Oxyradical Scavenging Capacity (TOSC) assay. Peroxyl radicals generated from thermal homolysis of 2,2'-azobis-amidinopropane oxidize alpha-keto-gamma-methiolbutyric acid to ethylene, which is monitored by gas chromatography. The TOSC of each beverage is quantified from its ability to inhibit ethylene generation relative to a control reaction. Nine different beverages (green tea, jasmine tea, black tea, instant coffee, brewed coffee, cocoa mix, oolong tea, prune juice, and grape juice) were selected for this study. Their antioxidant capacities per a cup-serving (125 mL) were measured and compared to peroxyl radical scavenging capacity provided by a recommended daily dose of ascorbic acid (90 mg) dissolved in the same volume of water. The greatest antioxidant capacity was found in brewed coffee, which was followed, in decreasing order, by prune juice, instant coffee, green tea, cocoa mix, grape juice, jasmine tea, black tea, oolong tea, and ascorbic acid. There was an almost 7-fold difference in the TOSC between brewed coffee and ascorbic acid. The data suggest a potential role for commonly consumed beverages in lowering the risk of pathophysiologies associated with peroxyl radical-mediated events.",Archives of pharmacal research,"['D001628', 'D016166', 'D016014', 'D010084', 'D010545']","['Beverages', 'Free Radical Scavengers', 'Linear Models', 'Oxidation-Reduction', 'Peroxides']",Comparison of peroxyl radical scavenging capacity of commonly consumed beverages.,"['Q000032', 'Q000737', None, None, 'Q000737']","['analysis', 'chemistry', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19280160,2009,0,0,,cocoa mix (hot chocolate) +0.46,28110526,"Jackfruit seeds are an underutilized waste in many tropical countries. This work demonstrates the potential of roasted jackfruit seeds to develop chocolate aroma. Twenty-seven different roasted jackfruit seed flours were produced from local jackfruit by acidifying or fermenting the seeds prior to drying and then roasting under different time/temperature combinations. The chocolate aroma of groups of four flours were ranked by a sensory panel (n = 162), and response surface methodology was used to identify optimum conditions. The results indicated a significant and positive influence of fermentation and acidification on the production of chocolate aroma. SPME/GC-MS of the flours showed that important aroma compounds such as 2,3-diethyl-5-methylpyrazine and 2-phenylethyl acetate were substantially higher in the fermented product and that the more severe roasting conditions produced 2-3 times more 2,3-diethyl-5-methylpyrazine, but less 3-methylbutanal. Moisture, a",Journal of agricultural and food chemistry,"['D000085', 'D000293', 'D000328', 'D031622', 'D000069956', 'D005260', 'D005285', 'D005433', 'D008401', 'D006801', 'D007220', 'D008297', 'D008875', 'D009812', 'D010626', 'D012639', 'D055549', 'D018505', 'D055815']","['Acetates', 'Adolescent', 'Adult', 'Artocarpus', 'Chocolate', 'Female', 'Fermentation', 'Flour', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Industrial Waste', 'Male', 'Middle Aged', 'Odorants', 'Phenylethyl Alcohol', 'Seeds', 'Volatile Organic Compounds', 'Waste Management', 'Young Adult']",Optimization of Postharvest Conditions To Produce Chocolate Aroma from Jackfruit Seeds.,"['Q000032', None, None, 'Q000737', None, None, None, None, 'Q000379', None, None, None, None, 'Q000032', 'Q000031', 'Q000737', 'Q000032', None, None]","['analysis', None, None, 'chemistry', None, None, None, None, 'methods', None, None, None, None, 'analysis', 'analogs & derivatives', 'chemistry', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28110526,2017,0,0,, +0.46,11128210,"Supercritical carbon dioxide can be used to carry out a selective and fast extraction (30 min) of volatile hydrocarbons and 2-alkylcyclobutanones contained in irradiated foods. After elimination of the traces of triglycerides still contained in the extracts on a silica column, the compounds were analysed by gas chromatography-mass spectroscopy (2-alkylcyclobutanones) and gas chromatography-flame ionization detection (volatile hydrocarbons). The present method was applied successfully to freeze-dried samples (1 g or less) of cheese, chicken, avocados and to various ingredients (chocolate, liquid whole eggs) included in non-irradiated cookies. It was faster (4-5 h) than the reference methods EN 1784 (volatile hydrocarbons) and EN 1785 (2-alkylcyclobutanones), which take 1.5 days each. The minimal dose detectable by this method is, in addition, slightly lower than those of the reference methods.",Journal of chromatography. A,"['D003503', 'D005504', 'D005514', 'D008401', 'D006838', 'D012015']","['Cyclobutanes', 'Food Analysis', 'Food Irradiation', 'Gas Chromatography-Mass Spectrometry', 'Hydrocarbons', 'Reference Standards']",Supercritical fluid extraction of hydrocarbons and 2-alkylcyclobutanones for the detection of irradiated foodstuffs.,"['Q000032', None, None, None, 'Q000032', None]","['analysis', None, None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/11128210,2001,0,0,,no cocoa tested +0.46,22705559,"The detection and quantification of polyphenols in biological samples is mainly performed by liquid chromatography in tandem with mass spectrometry (HPLC-MS/MS). This technique requires the use of organic solvents and needs control and maintenance of several MS/MS parameters, which makes the method expensive and time consuming. The main objective of this study was to evaluate, for the first time, the potential of using attenuated total reflection infrared microspectroscopy (ATR-IRMS) coupled with multivariate analysis to detect and quantify phenolic compounds excreted in human urine. Samples were collected from 5 healthy volunteers before and 6, 12 and 24 h after ingestion of 40 g cocoa powder with 250 mL of water or whole milk, and stored at -80 _C. Each sample was centrifuged at 5000 rpm for 10 min and at 4 _C and applied onto grids of a hydrophobic membrane. Spectra were collected in the attenuated total reflection (ATR) mode in the mid-infrared region (4000-800 cm(-1)) and were analyzed by a multivariate analysis technique, soft independent modeling of class analogy (SIMCA). Spectral models showed that IR bands responsible for chemical differences among samples were related to aromatic rings. Therefore, ATR-IRMS could be an interesting and straightforward technique for the detection of phenolic compounds excreted in urine. Moreover, it could be a valuable tool in studies aimed to identify biomarkers of consumption of polyphenol-rich diets.",The Analyst,"['D002099', 'D006801', 'D015999', 'D059808', 'D012016', 'D017550']","['Cacao', 'Humans', 'Multivariate Analysis', 'Polyphenols', 'Reference Values', 'Spectroscopy, Fourier Transform Infrared']",Attenuated total reflection infrared microspectroscopy combined with multivariate analysis: a novel tool to study the presence of cocoa polyphenol metabolites in urine samples.,"['Q000737', None, None, 'Q000378', None, None]","['chemistry', None, None, 'metabolism', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22705559,2012,0,0,, +0.46,17604905,"A cloud point extraction procedure was presented for the preconcentration of copper, nickel and cobalt ions in various samples. After complexation with methyl-2-pyridylketone oxime (MPKO) in basic medium, analyte ions are quantitatively extracted to the phase rich in Triton X-114 following centrifugation. 1.0 mol L(-1) HNO(3) nitric acid in methanol was added to the surfactant-rich phase prior to its analysis by flame atomic absorption spectrometry (FAAS). The adopted concentrations for MPKO, Triton X-114 and HNO(3), bath temperature, centrifuge rate and time were optimized. Detection limits (3 SDb/m) of 1.6, 2.1 and 1.9 ng mL(-1) for Cu(2+), Co(2+) and Ni(2+) along with preconcentration factors of 30 and for these ions and enrichment factor of 65, 58 and 67 for Cu(2+), Ni(2+) and Co(2+), respectively. The high efficiency of cloud point extraction to carry out the determination of analytes in complex matrices was demonstrated. The proposed procedure was applied to the analysis of biological, natural and wastewater, soil and blood samples.",Journal of hazardous materials,"['D000818', 'D002099', 'D002182', 'D002417', 'D002498', 'D003035', 'D003300', 'D004784', 'D004785', 'D005618', 'D006863', 'D008099', 'D000432', 'D009532', 'D010091', 'D011092', 'D011720', 'D012965', 'D012987', 'D013054', 'D018724', 'D013501', 'D013696', 'D014881']","['Animals', 'Cacao', 'Candy', 'Cattle', 'Centrifugation', 'Cobalt', 'Copper', 'Environmental Monitoring', 'Environmental Pollutants', 'Fresh Water', 'Hydrogen-Ion Concentration', 'Liver', 'Methanol', 'Nickel', 'Oximes', 'Polyethylene Glycols', 'Pyrazoles', 'Sodium Chloride', 'Soil', 'Spectrophotometry, Atomic', 'Spinacia oleracea', 'Surface-Active Agents', 'Temperature', 'Water Supply']","Cloud point extraction for the determination of copper, nickel and cobalt ions in environmental samples by flame atomic absorption spectrometry.","[None, None, 'Q000032', 'Q000097', None, 'Q000032', 'Q000032', None, 'Q000032', 'Q000032', None, 'Q000737', 'Q000737', 'Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000032', None, 'Q000737', 'Q000737', None, 'Q000032']","[None, None, 'analysis', 'blood', None, 'analysis', 'analysis', None, 'analysis', 'analysis', None, 'chemistry', 'chemistry', 'analysis', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'analysis', None, 'chemistry', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17604905,2008,0,0,,no cocoa +0.46,27503535,"Complicated urinary tract infections, such as pyelonephritis, may lead to sepsis. Rapid diagnosis is needed to identify the causative urinary pathogen and to verify the appropriate empirical antimicrobial therapy. We describe here a rapid identification method for urinary pathogens: urine is incubated on chocolate agar for 3h at 35_C with 5% CO2 and subjected to MALDI-TOF MS analysis by VITEK MS. Overall 207 screened clinical urine samples were tested in parallel with conventional urine culture. The method, called U-si-MALDI-TOF (urine short incubation MALDI-TOF), showed correct identification for 86% of Gram-negative urinary tract pathogens (Escherichia coli, Klebsiella pneumoniae, and other Enterobacteriaceae), when present at >10(5)cfu/ml in culture (n=107), compared with conventional culture method. However, Gram-positive bacteria (n=28) were not successfully identified by U-si-MALDI-TOF. This method is especially suitable for rapid identification of E. coli, the most common cause of urinary tract infections and urosepsis. Turnaround time for identification using U-si-MALDI-TOF compared with conventional urine culture was improved from 24h to 4-6h.",Journal of microbiological methods,"['D001431', 'D004926', 'D006090', 'D016905', 'D006801', 'D018805', 'D019032', 'D013997', 'D014552', 'D014556']","['Bacteriological Techniques', 'Escherichia coli', 'Gram-Negative Bacteria', 'Gram-Negative Bacterial Infections', 'Humans', 'Sepsis', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Time Factors', 'Urinary Tract Infections', 'Urine']",Identification of urinary tract pathogens after 3-hours urine culture by MALDI-TOF mass spectrometry.,"['Q000295', 'Q000302', 'Q000737', 'Q000175', None, None, 'Q000379', None, 'Q000175', 'Q000382']","['instrumentation', 'isolation & purification', 'chemistry', 'diagnosis', None, None, 'methods', None, 'diagnosis', 'microbiology']",https://www.ncbi.nlm.nih.gov/pubmed/27503535,2017,0,0,,no cocoa +0.46,11962690,"Residual levels of 12 solvents in 87 natural food additives (66 samples of food colours, 19 samples of natural antioxidants and two natural preservatives) collected between 1997 and 1999 were determined by automated head-space GC using FID, with a porous-polymer (PLOT) column. Calibration curves were prepared by the method of standard addition. Confirmation was by manually injected head-space GC using mass spectrometric detection. 1,2-Dichloroethane was found in turmeric colour (natural food colour) collected in 1997 at the concentrations of 8.6 microg g(-1), but was not found in samples collected in 1998 and 1999. Hexane was found in three samples of dunaliella carotene (11, 72 and 75 microg g(-1)), and in chlorophyll at 93 microg g(-1) (both natural food colours). Acetone was found in turmeric colour, annatto colour, dunaliella carotene, kaoliang colour, cacao colour at a concentration between 8.7 and 42 microg g(-1) (all natural food colours).",Food additives and contaminants,"['D000975', 'D002849', 'D005503', 'D005505', 'D005506', 'D005511', 'D005520', 'D008401', 'D006801', 'D012997']","['Antioxidants', 'Chromatography, Gas', 'Food Additives', 'Food Coloring Agents', 'Food Contamination', 'Food Handling', 'Food Preservatives', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Solvents']",Survey of residual solvents in natural food additives by standard addition head-space GC.,"['Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000032', None, 'Q000737', 'Q000379', None, 'Q000032']","['chemistry', 'methods', 'chemistry', 'chemistry', 'analysis', None, 'chemistry', 'methods', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11962690,2002,,,, +0.46,28208630,"Phenolic compounds, which are secondary plant metabolites, are considered an integral part of the human diet. Physiological properties of dietary polyphenols have come to the attention in recent years. Especially, proanthocyanidins (ranging from dimers to decamers) have demonstrated potential interactions with biological systems, such as antiviral, antibacterial, molluscicidal, enzyme-inhibiting, antioxidant, and radical-scavenging properties. Agroindustry produces a considerable amount of phenolic-rich sources, and the ability of polyphenolic structures to interacts with other molecules in living organisms confers their beneficial properties. Cocoa wastes and grape seeds and skin byproducts are a source of several phenolic compounds, particularly mono-, oligo-, and polymeric proanthocyanidins. The aim of this work is to compare the phenolic composition of Theobroma cacao and Vitis vinifera grape seed extracts by high pressure liquid chromatography coupled to a quadrupole time-of-flight mass spectrometer and equipped with an electrospray ionization interface (HPLC-ESI-QTOF-MS) and its phenolic quantitation in order to evaluate the proanthocyanidin profile. The antioxidant capacity was measured by different methods, including electron transfer and hydrogen atom transfer-based mechanisms, and total phenolic and flavan-3-ol contents were carried out by Folin-Ciocalteu and Vanillin assays. In addition, to assess the anti-inflammatory capacity, the expression of MCP-1 in human umbilical vein endothelial cells was measured.",International journal of molecular sciences,"['D000893', 'D000975', 'D002099', 'D002851', 'D004847', 'D005419', 'D056604', 'D006801', 'D062385', 'D010636', 'D010936', 'D044945', 'D012639', 'D021241', 'D019032', 'D027843']","['Anti-Inflammatory Agents', 'Antioxidants', 'Cacao', 'Chromatography, High Pressure Liquid', 'Epithelial Cells', 'Flavonoids', 'Grape Seed Extract', 'Humans', 'Hydroxybenzoates', 'Phenols', 'Plant Extracts', 'Proanthocyanidins', 'Seeds', 'Spectrometry, Mass, Electrospray Ionization', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Vitis']",Cocoa and Grape Seed Byproducts as a Source of Antioxidant and Anti-Inflammatory Proanthocyanidins.,"['Q000737', 'Q000737', 'Q000737', None, 'Q000187', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', None, None, 'Q000737']","['chemistry', 'chemistry', 'chemistry', None, 'drug effects', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28208630,2017,1,2,"fig 3, table 3", +0.46,21107975,"The aim of this work was the determination of peptides, which can function as markers for identification of milk allergens in food samples. Emphasis was placed on two casein proteins (_±- and __-casein) and two whey proteins (_±-lactalbumin and __-lactoglobulin). In silico tryptic digestion provided preliminary information about the expected peptides. After tryptic digestion of four milk allergens, the analytical data obtained by combination of reversed-phase high performance liquid chromatography and quadrupole tandem mass spectrometry (LC-MS/MS) led to the identification of 26 peptides. Seven of these peptides were synthesized and used for calibration of the LC-MS/MS system. Species specificity of the selected peptides was sought by BLAST search. Among the selected peptides, only LIVTQTMK from __-lactoglobulin (m/z 467.6, charge 2+) was found to be cow milk specific and could function as a marker. Two other peptides, FFVAPFPEVFGK from _±-casein (m/z 693.3, charge 2+) and GPFPIIV from __-casein (m/z 742.5, charge 1+), occur in water buffalo milk too. The other four peptides appear in the milk of other species also and can be used as markers for ruminant species milk. Using these seven peptides, a multianalyte MS-based method was developed. For the establishment of the method, it was applied at first to different dairy samples, and then to chocolate and blank samples, and the peptides could be determined down to 1 ng/mL in food samples. At the end, spiked samples were measured, where the target peptides could be detected with a high recovery (over 50%).",Analytical and bioanalytical chemistry,"['D000818', 'D015415', 'D002364', 'D002851', 'D003611', 'D005504', 'D007768', 'D007782', 'D008892', 'D010455', 'D053719']","['Animals', 'Biomarkers', 'Caseins', 'Chromatography, High Pressure Liquid', 'Dairy Products', 'Food Analysis', 'Lactalbumin', 'Lactoglobulins', 'Milk', 'Peptides', 'Tandem Mass Spectrometry']",Selection of possible marker peptides for the detection of major ruminant milk proteins in food by liquid chromatography-tandem mass spectrometry.,"[None, 'Q000737', 'Q000032', None, 'Q000032', None, 'Q000032', 'Q000032', 'Q000737', 'Q000737', None]","[None, 'chemistry', 'analysis', None, 'analysis', None, 'analysis', 'analysis', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21107975,2011,0,0,,no cocoa +0.46,14582980,"Of three different solvents (acetone, ethanol, and methanol) mixed with water and acetic acid, the acetone/water/acetic acid mixture (70:28:2, v/v) proved to be best for extracting dark-chocolate procyanidins. High-performance liquid chromatography coupled with electrospray ionization mass spectrometry (HPLC-MS-ESI) was further used to identify oligomers found in the extract. After HPLC fraction collection, the reduction power of flavanoid fractions was measured in the AAPH [2,2'-azobis(2-amidinopropane)dihydrochloride] assay, where oxidation of linoleic acid is induced in an aqueous dispersion. Even expressed in relative monomeric efficiency units, the oxidation-inhibiting power of polymerized oligomers is much stronger than that of monomers. A comparison with 10 usual antioxidants indicated that oligomers with three or more (epi)catechin units are by far the most efficient.",Journal of agricultural and food chemistry,"['D019342', 'D000096', 'D000578', 'D000975', 'D044946', 'D002099', 'D002392', 'D002851', 'D000431', 'D044948', 'D019787', 'D000432', 'D010936', 'D044945', 'D012997', 'D021241', 'D014867']","['Acetic Acid', 'Acetone', 'Amidines', 'Antioxidants', 'Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Ethanol', 'Flavonols', 'Linoleic Acid', 'Methanol', 'Plant Extracts', 'Proanthocyanidins', 'Solvents', 'Spectrometry, Mass, Electrospray Ionization', 'Water']",Effect of the number of flavanol units on the antioxidant activity of procyanidin fractions isolated from chocolate.,"[None, None, 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', None, None, 'Q000032', 'Q000737', None, 'Q000737', None, None, None, None]","[None, None, 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', None, None, 'analysis', 'chemistry', None, 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/14582980,2004,0,0,, +0.45,28924329,"A computational tool was developed to facilitate proanthocyanidin analysis using data collected by ultra-high-performance liquid chromatography-diode array detection-high resolution accurate mass-mass spectrometry (UHPLC-DAD-HRAM-MS). Both identification and semi-quantitation of proanthocyanidins can be achieved by the developed computational tool. It can extract proanthocyanidin chromatographic peaks, deconvolute the isotopic patterns of A-type, B-type, and multi-charged proanthocyanidins ions, and predict proanthocyanidin structures. Proanthocyanidins were quantified by an external calibration curve of catechin and molar relative response factors (MRRFs) of proanthocyanidins. Quantitation results including concentrations of total proanthocyanidins, individual proanthocyanidins, and proanthocyanidins with different degrees of polymerization and different types of linkage were calculated by the program and exported into an Excel spreadsheet automatically. The program was applied to the analysis of seven plant materials including apple, cranberry, dark chocolate, grape seed extract, jujube, litchi, and mangosteen. The identification results were compared with the results obtained by manual processing. The program can greatly save the time needed for the data analysis of proanthocyanidins.","Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems",[],[],A Computational Tool for Accelerated Analysis of Oligomeric Proanthocyanidins in Plants.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/28924329,2018,0,0,,no cocoa +0.45,29169644,"Food allergy is a considerable heath problem, as undesirable contaminations by allergens during food production are still widespread and may be dangerous for human health. To protect the population, laboratories need to develop reliable analytical methods in order to detect allergens in various food products. Currently, a large majority of allergen-related food recalls concern bakery products. It is therefore essential to detect allergens in unprocessed and processed foodstuffs. In this study, we developed a method for detecting ten allergens in complex (chocolate, ice cream) and processed (cookie, sauce) foodstuffs, based on ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS). Using a single protocol and considering a signal-to-noise ratio higher than 10 for the most abundant multiple reaction monitoring (MRM) transition, we were able to detect target allergens at 0.5mg/kg for milk proteins, 2.5mg/kg for peanut, hazelnut, pistachio, and cashew proteins, 3mg/kg for egg proteins, and 5mg/kg for soy, almond, walnut, and pecan proteins. The ability of the method to detect 10 allergens with a single protocol in complex and incurred food products makes it an attractive alternative to the ELISA method for routine laboratories.",Journal of chromatography. A,"['D000485', 'D000069956', 'D002851', 'D004527', 'D004797', 'D005504', 'D005512', 'D007054', 'D008894', 'D009754', 'D059629', 'D053719']","['Allergens', 'Chocolate', 'Chromatography, High Pressure Liquid', 'Egg Proteins', 'Enzyme-Linked Immunosorbent Assay', 'Food Analysis', 'Food Hypersensitivity', 'Ice Cream', 'Milk Proteins', 'Nuts', 'Signal-To-Noise Ratio', 'Tandem Mass Spectrometry']",Liquid chromatography coupled to tandem mass spectrometry for detecting ten allergens in complex and incurred foodstuffs.,"['Q000032', 'Q000032', 'Q000379', 'Q000032', None, 'Q000379', None, 'Q000032', 'Q000032', 'Q000737', None, None]","['analysis', 'analysis', 'methods', 'analysis', None, 'methods', None, 'analysis', 'analysis', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/29169644,2018,0,0,,no cocoa +0.45,9691293,"A HPLC method is described for the analysis of ochratoxin A at low-ppb levels in samples of artificially contaminated cocoa beans. The samples are extracted in a mixture of methanol-water containing ascorbic acid, adjusted to pH and evaporated to dryness. Samples in this state are then placed onto a Benchmate sample preparation workstation where C18 solid-phase extraction operations are performed. The resulting materials are evaporated to dryness and analyzed by reversed-phase HPLC with fluorescence detection. The method was evaluated for accuracy and precision with R.S.D.s for multiple injections of sample and standard calculated to 1.1% and 2.5% for sample and standard, respectively. Recoveries of ochratoxin A added to cocoa beans ranged from 87-106% over the range of the assay.",Journal of chromatography. A,"['D001322', 'D002099', 'D002851', 'D006863', 'D007202', 'D009183', 'D009793', 'D013050']","['Autoanalysis', 'Cacao', 'Chromatography, High Pressure Liquid', 'Hydrogen-Ion Concentration', 'Indicators and Reagents', 'Mycotoxins', 'Ochratoxins', 'Spectrometry, Fluorescence']",High-performance liquid chromatographic determination of ochratoxin A in artificially contaminated cocoa beans using automated sample clean-up.,"[None, 'Q000737', None, None, None, 'Q000032', 'Q000032', None]","[None, 'chemistry', None, None, None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/9691293,1998,0,0,,artificially contaminated +0.45,27554027,"Sensitive detection of food allergens is affected by food processing and foodstuff complexity. It is therefore a challenge to detect cross-contamination in food production that could endanger an allergic customer's life. Here we used ultra-high performance liquid chromatography coupled to tandem mass spectrometry for simultaneous detection of traces of milk (casein, whey protein), egg (yolk, white), soybean, and peanut allergens in different complex and/or heat-processed foodstuffs. The method is based on a single protocol (extraction, trypsin digestion, and purification) applicable to the different tested foodstuffs: chocolate, ice cream, tomato sauce, and processed cookies. The determined limits of quantitation, expressed in total milk, egg, peanut, or soy proteins (and not soluble proteins) per kilogram of food, are: 0.5mg/kg for milk (detection of caseins), 5mg/kg for milk (detection of whey), 2.5mg/kg for peanut, 5mg/kg for soy, 3.4mg/kg for egg (detection of egg white), and 30.8mg/kg for egg (detection of egg yolk). The main advantage is the ability of the method to detect four major food allergens simultaneously in processed and complex matrices with very high sensitivity and specificity. ",Journal of chromatography. A,"['D000485', 'D000818', 'D010367', 'D002645', 'D002851', 'D004531', 'D005504', 'D005506', 'D005511', 'D008892', 'D030262', 'D053719']","['Allergens', 'Animals', 'Arachis', 'Chickens', 'Chromatography, High Pressure Liquid', 'Eggs', 'Food Analysis', 'Food Contamination', 'Food Handling', 'Milk', 'Soybean Proteins', 'Tandem Mass Spectrometry']",Advances in ultra-high performance liquid chromatography coupled to tandem mass spectrometry for sensitive detection of several food allergens in complex and processed foodstuffs.,"['Q000737', None, 'Q000737', None, 'Q000379', None, 'Q000379', 'Q000032', None, 'Q000737', 'Q000737', 'Q000379']","['chemistry', None, 'chemistry', None, 'methods', None, 'methods', 'analysis', None, 'chemistry', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/27554027,2016,0,0,,no cocoa tested +0.45,29146359,"This study aimed to develop an analytical method for the determination of tryptophan and its derivatives in kynurenine pathway using tandem mass spectrometry in various fermented food products (bread, beer, red wine, white cheese, yoghurt, kefir and cocoa powder). The method entails an aqueous extraction and reversed phase chromatographic separation using pentafluorophenyl (PFP) column. It allowed quantitation of low ppb levels of tryptophan and its derivatives in different fermented food matrices. It was found that beer samples were found to contain kynurenine within the range of 28.7_±0.7__g/L and 86.3_±0.5__g/L. Moreover, dairy products (yoghurt, white cheese and kefir) contained kynurenine ranging from 30.3 to 763.8__g/kg d.w. Though bread samples analyzed did not contain kynurenic acid, beer and red wine samples as yeast-fermented foods were found to contain kynurenic acid. Among foods analyzed, cacao powder had the highest amounts of kynurenic acid (4486.2_±165.6__g/kgd.w), which is a neuroprotective compound.",Food chemistry,"['D001515', 'D002611', 'D002851', 'D056148', 'D043302', 'D000074421', 'D007736', 'D007737', 'D053719', 'D014364', 'D014920']","['Beer', 'Cheese', 'Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Cultured Milk Products', 'Fermented Foods', 'Kynurenic Acid', 'Kynurenine', 'Tandem Mass Spectrometry', 'Tryptophan', 'Wine']",Determination of tryptophan derivatives in kynurenine pathway in fermented foods using liquid chromatography tandem mass spectrometry.,"['Q000032', 'Q000032', 'Q000379', 'Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000379', 'Q000032', 'Q000032']","['analysis', 'analysis', 'methods', 'methods', 'analysis', 'analysis', 'analysis', 'analysis', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/29146359,2018,1,1,table 4 ,cocoa powder tryptophan concentration table +0.45,8069126,"The IUPAC Commission on Oils, Fats, and Derivatives undertook development of a method and collaborative study for the determination of lead in oils and fats by direct graphite furnace-atomic absorption spectrophotometric method. Various types of graphite furnaces were used with or without platform. Twenty-three collaborators from 12 countries participated in the study. The materials tested were edible oils (soybean oil) and fats (cocoa butter) containing lead at 3 concentration levels (low, medium, and high). Each level was represented by 2 batches provided in duplicate (blind coded), so that each collaborator received a total of 24 test samples. Collaborators were instructed to analyze each in duplicate and report both results. Twenty collaborators returned the results of the study. After data from laboratories that did not follow the instructions were excluded, only 16 sets of data were evaluated statistically. The method for determination of lead in oils and fats by direct graphite furnace-atomic absorption spectrophotometry has been adopted first action by AOAC INTERNATIONAL as an IUPAC-AOCS-AOAC method.",Journal of AOAC International,"['D005223', 'D007854', 'D010938', 'D015203', 'D013024', 'D013054']","['Fats', 'Lead', 'Plant Oils', 'Reproducibility of Results', 'Soybean Oil', 'Spectrophotometry, Atomic']",Direct graphite furnace-atomic absorption method for determination of lead in edible oils and fats: summary of collaborative study.,"['Q000737', 'Q000032', 'Q000737', None, 'Q000737', 'Q000379']","['chemistry', 'analysis', 'chemistry', None, 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/8069126,1994,,,, +0.44,16154731,"Our investigations deal with the identification and synthesis of volatile, odoriferous compounds contained in the exhaust gas of food factories and on the biodegradation of alkylpyrazines. Collection of odour emissions samples was performed with a gas sampler equipped with filter tubes containing the styrene-polymer SuperQ. After elution with solvents of different polarity, the extracts were analysed by GC/MS and chemical microreactions. Proposed structures were verified by comparison of analytical data with those of synthetic reference samples. Major components in the exhaust gas of a fat finishing factory were found to be aliphatic aldehydes, strongly dominated by hexanal. The identification of 1,2,3,3-tetramethylcyclohexene shows that for structural proof of target compounds the use of authentic reference samples is indispensable. In the exhaust gas from a chocolate factory, several carbonyl compounds and alkylated pyrazines could be identified. Biodegradation of the latter starts with hydrogenation at the nucleus.","Waste management (New York, N.Y.)","['D002099', 'D002623', 'D004784', 'D019649', 'D008401', 'D009812', 'D009930', 'D014835', 'D014866']","['Cacao', 'Chemistry Techniques, Analytical', 'Environmental Monitoring', 'Food Industry', 'Gas Chromatography-Mass Spectrometry', 'Odorants', 'Organic Chemicals', 'Volatilization', 'Waste Products']","Identification, structure elucidation, and synthesis of volatile compounds in the exhaust gas of food factories.","['Q000737', 'Q000379', 'Q000379', None, None, 'Q000032', 'Q000032', None, 'Q000032']","['chemistry', 'methods', 'methods', None, None, 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16154731,2006,0,0,, +0.44,9675711,"The antioxidative substances contained in cacao liquor, which is one of the major ingredients of chocolate, were separated by column chromatography and high-performance liquid chromatography. Three major compounds were purified and two of them were identified by 1H, 13C NMR and mass spectra as (-)-epicatechin (EC) and (+)-catechin (CA). Their antioxidative activity was measured by monitoring the peroxide value of linoleic acid and the thiobarbituric acid-reactive substance values of erythrocyte ghost membranes and microsomes. EC and CA had strong antioxidative effects in all three methods, but one unidentified peak was found to be less effective. Additionally, we analyzed the polyphenol concentration of cacao liquor extractions produced in several countries. The total polyphenol concentration was 7.0 to 13.0%, catechin concentration was 0.31 to 0.49%, and epicatechin concentration was 0.35 to 1.68% in the extractions. It is believed that chocolate is stable against oxidative deterioration on account of the presence of these polyphenolic compounds, and it is also expected to have a protective role against lipid peroxidation in living systems.",Journal of nutritional science and vitaminology,"['D000434', 'D000818', 'D000975', 'D002099', 'D002392', 'D002851', 'D004910', 'D005419', 'D019787', 'D015227', 'D009682', 'D013058', 'D008862', 'D010084', 'D010636', 'D011108', 'D051381', 'D017392']","['Alcoholic Beverages', 'Animals', 'Antioxidants', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Erythrocyte Membrane', 'Flavonoids', 'Linoleic Acid', 'Lipid Peroxidation', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Microsomes, Liver', 'Oxidation-Reduction', 'Phenols', 'Polymers', 'Rats', 'Thiobarbituric Acid Reactive Substances']",The antioxidative substances in cacao liquor.,"['Q000032', None, 'Q000302', None, 'Q000737', None, 'Q000378', None, 'Q000378', 'Q000187', None, None, 'Q000378', None, 'Q000032', 'Q000032', None, 'Q000378']","['analysis', None, 'isolation & purification', None, 'chemistry', None, 'metabolism', None, 'metabolism', 'drug effects', None, None, 'metabolism', None, 'analysis', 'analysis', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/9675711,1998,,,, +0.44,21698686,"The water-soluble protein profile of the seeds of green, red, and yellow Theobroma cacao L. fruits has been determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF-MS). The seeds were powdered under liquid nitrogen and defatted. The residues were dialyzed and lyophilized. The obtained samples were suspended in the matrix solution of sinapinic acid. The obtained MALDI mass spectra showed the presence of a wide number of proteins with molecular weight ranging from 8000 to 13,000 Da and a cluster of peaks centered at 21,000 Da that were attributed to albumin. The abundance of this peak was found to depend on the different portion of the seed (husk, apical and cortical parts); however, the MALDI mass spectra obtained from the different varieties of cocoa were practically superimposable. Changes in the protein profiles were also observed after the cocoa seeds were treated by fermentation and roasting, which are processes usually employed for the commercial production of cocoa.",Rapid communications in mass spectrometry : RCM,"['D002099', 'D003373', 'D006358', 'D010936', 'D010940', 'D012639', 'D019032']","['Cacao', 'Coumaric Acids', 'Hot Temperature', 'Plant Extracts', 'Plant Proteins', 'Seeds', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization']",The protein profile of Theobroma cacao L. seeds as obtained by matrix-assisted laser desorption/ionization mass spectrometry.,"['Q000737', 'Q000737', None, 'Q000737', 'Q000032', 'Q000737', 'Q000379']","['chemistry', 'chemistry', None, 'chemistry', 'analysis', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/21698686,2011,,,, +0.44,19188605,"Chemical analyses of organic residues in fragments of ceramic vessels from Pueblo Bonito in Chaco Canyon, New Mexico, reveal theobromine, a biomarker for cacao. With an estimated 800 rooms, Pueblo Bonito is the largest archaeological site in Chaco Canyon and was the center of a large number of interconnected towns and villages spread over northwestern New Mexico. The cacao residues come from pieces of vessels that are likely cylinder jars, special containers occurring almost solely at Pueblo Bonito and deposited in caches at the site. This first known use of cacao drinks north of the Mexican border indicates exchange with cacao cultivators in Mesoamerica in a time frame of about A.D. 1000-1125. The association of cylinder jars and cacao beverages suggests that the Chacoan ritual involving the drinking of cacao was tied to Mesoamerican rituals incorporating cylindrical vases and cacao. The importance of Pueblo Bonito within the Chacoan world likely lies in part with the integration of Mesoamerican ritual, including critical culinary ingredients.",Proceedings of the National Academy of Sciences of the United States of America,"['D001106', 'D001628', 'D002099', 'D003466', 'D005843', 'D049691', 'D006801', 'D007198', 'D013058', 'D009516', 'D010164', 'D012931']","['Archaeology', 'Beverages', 'Cacao', 'Cultural Characteristics', 'Geography', 'History, Medieval', 'Humans', 'Indians, North American', 'Mass Spectrometry', 'New Mexico', 'Paleopathology', 'Social Environment']",Evidence of cacao use in the Prehispanic American Southwest.,"[None, None, 'Q000737', None, None, None, None, None, None, None, None, None]","[None, None, 'chemistry', None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19188605,2009,0,0,,no cocoa +0.44,9829045,"The level of styrene migration from polystyrene cups was monitored in different food systems including: water, milk (0.5, 1.55 and 3.6% fat), cold beverages (apple juice, orange juice, carbonated water, cola, beer and chocolate drink), hot beverages (tea, coffee, chocolate and soup (0.0, 0.5, 1, 2, and 3.6% fat), take away foods (yogurt, jelly, pudding and ice-cream), as well as aqueous food simulants (3% acetic acid, 15, 50, and 100% ethanol) and olive oil. Styrene migration was found to be strongly dependent upon the fat content and storage temperature. Drinking water gave migration values considerably lower than all of the fatty foods. Ethanol at 15% showed a migration level equivalent to milk or soup containing 3.6% fat. Maximum observed migration for cold or hot beverages and take-away foods was 0.025% of the total styrene in the cup. Food simulants were responsible for higher migration (0.37% in 100% ethanol). A total of 60 food samples (yogurt, rice with milk, fromage, biogardes, and cheese) packed in polystyrene containers were collected from retail markets in Belgium, Germany, and the Netherlands. The level of styrene detected in the foods was always fat dependent.",Food additives and contaminants,"['D001628', 'D002851', 'D003297', 'D004041', 'D005502', 'D005503', 'D005506', 'D018857', 'D006801', 'D011137', 'D013343', 'D013696']","['Beverages', 'Chromatography, High Pressure Liquid', 'Cooking and Eating Utensils', 'Dietary Fats', 'Food', 'Food Additives', 'Food Contamination', 'Food Packaging', 'Humans', 'Polystyrenes', 'Styrenes', 'Temperature']",Polystyrene cups and containers: styrene migration.,"[None, None, None, None, None, None, 'Q000032', None, None, None, 'Q000032', None]","[None, None, None, None, None, None, 'analysis', None, None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/9829045,1998,,,, +0.44,28070080,"The temperature thawing, as called tempering, of triacylglycerols (TAGs) is an important processing method in food productions, such as chocolates, cream, confections, and spreads. Especially, melt-mediation by temperature thawing is famous in chocolate production for controlling the polymorphic crystalline forms and accelerating crystallization. In the present study, we investigated the _±-melt structure of 1,3-dipalmitoyl-2-oleoyl-sn-glycerol (POP), one of the major continuants of cacao butter, under a phase transition from its melt to __-crystal with in-situ attenuated total reflection-infrared (ATR-IR) spectroscopy. The differential IR spectrum between _±-melt via temperature thawing (_±-melt mediation) and melt via simple cooling revealed that crystal-like local ordered structures remained in part in the _±-melt, and that they acted as nuclei for a rapid phase transition to the __-crystal. The changes to the __-crystal occur in the local ordered structures at first from the glycerol moiety to the acyl chains in the crystallization, providing an important suggestion concerning the mechanism for the acceleration of crystallization to the __-form via _±-melt mediation.",Analytical sciences : the international journal of the Japan Society for Analytical Chemistry,"['D013055', 'D044366', 'D014280']","['Spectrophotometry, Infrared', 'Transition Temperature', 'Triglycerides']","_±-Melt Structure of 1,3-Dipalmitoyl-2-oleoyl-sn-glycerol (POP) under a Thermal Thawing Process Studied by Infrared Spectroscopy.","[None, None, 'Q000737']","[None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28070080,2018,0,0,, +0.44,27613945,"Most electronic cigarettes (e-cigarettes) contain a solution of propylene glycol/glycerin and nicotine, as well as flavors. E-cigarettes and their associated e-liquids are available in numerous flavor varieties. A subset of the flavor varieties include coffee, tea, chocolate, and energy drink, which, in beverage form, are commonly recognized sources of caffeine. Recently, some manufacturers have begun marketing e-liquid products as energy enhancers that contain caffeine as an additive.",Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco,"['D002110', 'D000069956', 'D003069', 'D066300', 'D061215', 'D005421', 'D008401']","['Caffeine', 'Chocolate', 'Coffee', 'Electronic Nicotine Delivery Systems', 'Energy Drinks', 'Flavoring Agents', 'Gas Chromatography-Mass Spectrometry']","Caffeine Concentrations in Coffee, Tea, Chocolate, and Energy Drink Flavored E-liquids.","['Q000032', 'Q000032', 'Q000737', None, 'Q000032', 'Q000032', 'Q000379']","['analysis', 'analysis', 'chemistry', None, 'analysis', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/27613945,2017,0,0,,no cocoa tested +0.43,7228254,"Average-sized portions of a variety of food products were reacted with nitrite under realistically simulated gastric conditions. The aqueous incubation medium contained sodium nitrite (10 mg/l) and potassium thiocyanate to mimic the incoming flux of saliva, as well as pepsin, sodium chloride and hydrochloric acid, reflecting the composition of gastric juice. After incubation for 2 hr at 37 degrees C, volatile N-nitrosamines and N-nitrosamino acids were determined in the reaction mixtures. Nitrosodimethylamine (NDMA) was present in the incubation mixtures of smoked mackerel (8.5 micrograms per portion), canned herring (0.66 micrograms per portion) and beer (0.70 micrograms per 'portion'). Smaller amounts per portion, sometimes of other nitrosamines as well, were observed with canned salmon and anchovy, mustard, yoghurt and coffee brew. Negative results were obtained for canned tuna, soya sauce, ketchup, white bread, 'nasi goreng', tea brew and cocoa milk. Nitrosamino acids were detected in the reaction mixtures of smoked mackerel (58 micrograms per portion), soya sauce (24 micrograms per portion) and canned salmon (6.9 micrograms per portion) and in smaller amounts in those of canned herring, anchovy and cocoa milk. In order to reduce the number of analyses to be performed, most products have been studied only after incubation, so that the nitrosamines and nitrosamino acids found may already have been present -- wholly or partly -- in the original products, before incubation. Such is the case for part of the NDMA in the reaction mixture of smoked mackerel and for all the NDMA in beer. The toxicological implications of these findings remain to be established.",IARC scientific publications,"['D000596', 'D000818', 'D055598', 'D002621', 'D005502', 'D005511', 'D008401', 'D005750', 'D006801', 'D008954', 'D009573', 'D009602']","['Amino Acids', 'Animals', 'Chemical Phenomena', 'Chemistry', 'Food', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Gastric Juice', 'Humans', 'Models, Biological', 'Nitrites', 'Nitrosamines']",Formation of N-nitrosamine and N-nitrosamino acids from food products and nitrite under simulated gastric conditions.,"[None, None, None, None, None, None, None, 'Q000378', None, None, None, None]","[None, None, None, None, None, None, None, 'metabolism', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/7228254,1981,,,, +0.43,2954991,"A high-performance liquid chromatographic (HPLC) method has been developed to allow the determination of patulin, penicillic acid, sterigmatocystin and zearalenone in samples of cocoa beans. When this method is combined with a method that was reported earlier for the determination of ochratoxin A [W. J. Hurst and R. A. Martin, Jr., J. Chromatogr., 265 (1983) 353], it allows for the determination of five mycotoxins. Samples were extracted with an acidic acetonitrile solution, partitioned with hexane to remove fat interferences and then partitioned with chloroform to remove the toxin containing fraction. Interferences were removed by the use of a bonded phase column followed by the final HPLC determination step, which uses a cyano column with a hexane-1-propanol-acetic acid mobile phase with dual channel UV detection at 245 and 280 nm. The method exhibits good linearity, accuracy and precision.",Journal of chromatography,"['D002099', 'D002851', 'D005506', 'D009183', 'D010365', 'D010398', 'D010945', 'D013056', 'D013241', 'D015025']","['Cacao', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Mycotoxins', 'Patulin', 'Penicillic Acid', 'Plants, Edible', 'Spectrophotometry, Ultraviolet', 'Sterigmatocystin', 'Zearalenone']","High-performance liquid chromatographic determination of the mycotoxins patulin, penicillic acid, zearalenone and sterigmatocystin in artificially contaminated cocoa beans.","['Q000032', None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, 'Q000032', 'Q000032']","['analysis', None, 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/2954991,1987,,,, +0.43,20627308,"An analytical method for the determination of US EPA priority pollutant 16 polycyclic aromatic hydrocarbons (PAHs) in edible oil was developed by an isotope dilution gas chromatography-mass spectrometry (GC-MS). Extraction was performed with ultrasonication mode using acetonitrile as solvent, and subsequent clean-up was applied using narrow gel permeation chromatographic column. Three deuterated PAHs surrogate standards were used as internal standards for quantification and analytical quality control. The limits of quantification (LOQs) were globally below 0.5 ng/g, the recoveries were in the range of 81-96%, and the relative standard deviations (RSDs) were lower than 20%. Further trueness assessment of the method was also verified through participation in international cocoa butter proficiency test (T0638) organised by the FAPAS with excellent results in 2008. The results obtained with the described method were satisfying (z ___ 2). The method has been applied to determine PAH in real edible oil samples.",Journal of chromatography. A,"['D000097', 'D002850', 'D004042', 'D005224', 'D005504', 'D008401', 'D007554', 'D011084', 'D052616', 'D014465']","['Acetonitriles', 'Chromatography, Gel', 'Dietary Fats, Unsaturated', 'Fats, Unsaturated', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Isotopes', 'Polycyclic Aromatic Hydrocarbons', 'Solid Phase Extraction', 'Ultrasonics']",Ultrasonication extraction and gel permeation chromatography clean-up for the determination of polycyclic aromatic hydrocarbons in edible oil by an isotope dilution gas chromatography___mass spectrometry.,"[None, 'Q000379', 'Q000032', 'Q000737', 'Q000379', 'Q000379', None, 'Q000032', 'Q000379', 'Q000379']","[None, 'methods', 'analysis', 'chemistry', 'methods', 'methods', None, 'analysis', 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/20627308,2011,2,1,table 4, +0.43,25794741,"An improved micellar electrokinetic capillary chromatography method (MEKC) for the simultaneous determination of ten preservatives in ten different kinds of food samples was reported. An uncoated fused-silica capillary with 50 __m i.d. and 70 cm total length was used. Under the optimized conditions, the linear response was observed in the range of 1.2-200mg/L for the analytes. The limits of detection (LOD, S/N=3) and limits of quantitation (LOQ, S/N=10) ranging from 0.4 to 0.5mg/L and 1.2 to 1.5mg/L, respectively were obtained. The method was used for the determination of sorbic and benzoic acids in two FAPAS_‰ (Food Analysis Performance Assessment Scheme) proficiency test samples (jam and chocolate cake). The results showed that the current method with simple sample pretreatment and small reagent consumption could meet the needs for routine analysis of the ten preservatives in ten types of food products.",Food chemistry,"['D020374', 'D005504', 'D011310']","['Chromatography, Micellar Electrokinetic Capillary', 'Food Analysis', 'Preservatives, Pharmaceutical']",Simultaneous determination of ten preservatives in ten kinds of foods by micellar electrokinetic chromatography.,"['Q000379', 'Q000379', 'Q000494']","['methods', 'methods', 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/25794741,2015,0,0,,no cocoa +0.43,17852509,"Fatty acid compositions of frequently consumed foods in Turkey were analyzed by capillary gas chromatography with particular emphasis on trans fatty acids. The survey was carried out on 134 samples that were categorized as meat products, chocolates, bakery products and others. The meat products except chicken-based foods have trans fatty acids, arising as a result of ruminant activity, with an average content of 1.45 g/100 g fatty acids. The conjugated linoleic acid content of meat and chicken doner kebabs were found higher than other meat products. Chocolate samples contained trans fatty acids less than 0.17 g/100 g fatty acids, with the exceptional national product of chocolate bars and hazelnut cocoa cream (2.03 and 3.68 g/100 g fatty acids, respectively). Bakery products have the highest trans fatty acid contents and ranged from 0.99 to 17.77 g/100 g fatty acids. The average trans fatty acid contents of infant formula and ice-cream, which are milk-based products, were 0.79 and 1.50 g/100 g fatty acids, respectively. Among the analyzed foods, it was found that coffee whitener and powdered whipped topping had the highest saturated fatty acid contents, with an average content of 98.71 g/100 g fatty acids.",International journal of food sciences and nutrition,"['D000818', 'D001939', 'D002099', 'D002611', 'D002849', 'D003611', 'D005227', 'D005247', 'D006801', 'D007223', 'D007225', 'D008460', 'D008461', 'D008892', 'D044242', 'D014421']","['Animals', 'Bread', 'Cacao', 'Cheese', 'Chromatography, Gas', 'Dairy Products', 'Fatty Acids', 'Feeding Behavior', 'Humans', 'Infant', 'Infant Food', 'Meat', 'Meat Products', 'Milk', 'Trans Fatty Acids', 'Turkey']",Fatty acid composition of frequently consumed foods in Turkey with special emphasis on trans fatty acids.,"[None, 'Q000032', 'Q000737', 'Q000032', None, 'Q000032', 'Q000032', None, None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000737', 'Q000032', None]","[None, 'analysis', 'chemistry', 'analysis', None, 'analysis', 'analysis', None, None, None, 'analysis', 'analysis', 'analysis', 'chemistry', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/17852509,2008,0,0,, +0.43,29243642,"In 2006, the French Food Safety Agency (AFSSA) conducted the Second French Total Diet Study (TDS) to estimate dietary exposures to the main minerals and trace elements from 1319 samples of foods typically consumed by the French population. The foodstuffs were analysed by inductively coupled plasma-mass spectrometry (ICP-MS) after microwave-assisted digestion. Occurrence data for lithium, chromium, manganese, cobalt, nickel, copper, zinc, selenium and molybdenum were reported and compared with results from the previous French TDS. The results indicate that the food groups presenting the highest levels of these essential trace elements were ""tofu"" (for Li, Mn, Ni, Cu, Zn and Mo),""fish and fish products"" particularly ""shellfish"" (for Li, Co, Cu, Zn, Se and Mo), ""sweeteners, honey and confectionery"" particularly dark chocolate (for Cr, Mn, Co, Ni and Cu), ""cereals and cereal products"" (for Mn, Ni and Mo) and ""ice cream"" (for Cr, Co and Ni).",Food chemistry,[],[],"Li, Cr, Mn, Co, Ni, Cu, Zn, Se and Mo levels in foodstuffs from the Second French TDS.",[],[],https://www.ncbi.nlm.nih.gov/pubmed/29243642,2017,0,0,,no cocoa +0.43,18458064,"Otitis media caused by nontypeable Haemophilus influenzae (NTHi) is a common and recurrent bacterial infection of childhood. The structural variability and diversity of H. influenzae lipopolysaccharide (LPS) glycoforms are known to play a significant role in the commensal and disease-causing behavior of this pathogen. In this study, we determined LPS glycoform populations from NTHi strain 1003 during the course of experimental otitis media in the chinchilla model of infection by mass spectrometric techniques. Building on an established structural model of the major LPS glycoforms expressed by this NTHi strain in vitro (M. M_nsson, W. Hood, J. Li, J. C. Richards, E. R. Moxon, and E. K. Schweda, Eur. J. Biochem. 269:808-818, 2002), minor isomeric glycoform populations were determined by liquid chromatography multiple-step tandem electrospray mass spectrometry (LC-ESI-MS(n)). Using capillary electrophoresis ESI-MS (CE-ESI-MS), we determined glycoform profiles for bacteria from direct middle ear fluid (MEF) samples. The LPS glycan profiles were essentially the same when the MEF samples of 7 of 10 animals were passaged on solid medium (chocolate agar). LC-ESI-MS(n) provided a sensitive method for determining the isomeric distribution of LPS glycoforms in MEF and passaged specimens. To investigate changes in LPS glycoform distribution during the course of infection, MEF samples were analyzed at 2, 5, and 9 days postinfection by CE-ESI-MS following minimal passage on chocolate agar. As previously observed, sialic acid-containing glycoforms were detected during the early stages of infection, but a trend toward more-truncated and less-complex LPS glycoforms that lacked sialic acid was found as disease progressed.",Infection and immunity,"['D000818', 'D002682', 'D002853', 'D004195', 'D019075', 'D006192', 'D006193', 'D006801', 'D007536', 'D008070', 'D010033', 'D010034', 'D021241']","['Animals', 'Chinchilla', 'Chromatography, Liquid', 'Disease Models, Animal', 'Electrophoresis, Capillary', 'Haemophilus Infections', 'Haemophilus influenzae', 'Humans', 'Isomerism', 'Lipopolysaccharides', 'Otitis Media', 'Otitis Media with Effusion', 'Spectrometry, Mass, Electrospray Ionization']",Application of capillary electrophoresis mass spectrometry and liquid chromatography multiple-step tandem electrospray mass spectrometry to profile glycoform expression during Haemophilus influenzae pathogenesis in the chinchilla model of experimental otitis media.,"[None, None, 'Q000379', None, 'Q000379', 'Q000382', 'Q000378', None, None, 'Q000737', 'Q000382', 'Q000382', 'Q000379']","[None, None, 'methods', None, 'methods', 'microbiology', 'metabolism', None, None, 'chemistry', 'microbiology', 'microbiology', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/18458064,2008,0,0,,no cocoa +0.43,19489609,"Oxidative stress enhances pathological processes contributing to cancer, cardiovascular disease, and neurodegenerative diseases, and dietary antioxidants may counteract these deleterious processes. Because rapid methods to evaluate and compare food products for antioxidant benefits are needed, a new assay based on liquid chromatography-mass spectrometry (LC-MS) was developed for the identification and quantitative analysis of antioxidants in complex natural product samples such as food extracts. This assay is based on the comparison of electrospray LC-MS profiles of sample extracts before and after treatment with reactive oxygen species such as hydrogen peroxide or 2,2-diphenyl-1-picrylhydrazyl radical (DPPH). Using this assay, methanolic extracts of cocoa powder were analyzed, and procyanidins were found to be the most potent antioxidant species. These species were identified using LC-MS, LC-MS/MS, accurate mass measurement, and comparison with reference standards. Furthermore, LC-MS was used to determine the levels of these species in cocoa samples. Catechin and epicatechin were the most abundant antioxidants followed by their dimers and trimers. The most potent antioxidants in cocoa were trimers and dimers of catechin and epicatechin, such as procyanidin B2, followed by catechin and epicatechin. This new LC-MS assay facilitates the rapid identification and then the determination of the relative antioxidant activities of individual antioxidant species in complex natural product samples and food products such as cocoa.",Journal of agricultural and food chemistry,"['D000975', 'D001713', 'D002099', 'D002392', 'D002853', 'D006861', 'D013058', 'D010084', 'D010851', 'D044945', 'D021241']","['Antioxidants', 'Biphenyl Compounds', 'Cacao', 'Catechin', 'Chromatography, Liquid', 'Hydrogen Peroxide', 'Mass Spectrometry', 'Oxidation-Reduction', 'Picrates', 'Proanthocyanidins', 'Spectrometry, Mass, Electrospray Ionization']",Screening antioxidants using LC-MS: case study with cocoa.,"['Q000032', 'Q000737', 'Q000737', 'Q000032', None, 'Q000737', None, None, 'Q000737', 'Q000032', None]","['analysis', 'chemistry', 'chemistry', 'analysis', None, 'chemistry', None, None, 'chemistry', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/19489609,2009,2,1,Fig 3, +0.43,17955976,"A collaborative trial was conducted to validate an analytical approach comprising method procedures for determination of milk fat and the detection and quantification of cocoa butter equivalents (CBEs) in milk chocolate. The whole approach is based on (1) comprehensive databases covering the triacylglycerol composition of a wide range of authentic milk fat, cocoa butter, and CBE samples and 947 gravimetrically prepared mixtures thereof; (2) the availability of a certified cocoa butter reference material for calibration; (3) an evaluation algorithm, which allows reliable quantitation of the milk fat content in chocolate; (4) a subsequent correction to take account of the triacylglycerols derived from milk fat; (5) mathematical expressions to detect the presence of CBEs in milk chocolate; and (6) a multivariate statistical formula to quantitate the amount of CBEs in milk chocolate. Twelve laboratories participated in the validation study. CBE admixtures were detected down to a level of 0.5 g CBE/100 g milk chocolate, without false-positive or -negative results. The applied quantitation model performed well at the statutory limit of 5% CBE addition to milk chocolate, with a prediction error of 0.7%, and HorRat values ranging from 0.8 to 1.5. The relative standard deviation for reproducibility (RSDR) values for quantitation of CBEs in analyses of chocolate fat solutions ranged from 2.2 to 3.8% and for analyses of real chocolate samples, from 4.1 to 4.7%, demonstrating that the whole approach, based solely on chocolate fat blends, is applicable to real milk chocolate samples.",Journal of AOAC International,"['D000818', 'D002099', 'D002138', 'D002623', 'D002849', 'D002853', 'D004041', 'D005223', 'D005504', 'D006112', 'D008892', 'D008962', 'D015203', 'D014280']","['Animals', 'Cacao', 'Calibration', 'Chemistry Techniques, Analytical', 'Chromatography, Gas', 'Chromatography, Liquid', 'Dietary Fats', 'Fats', 'Food Analysis', 'Gravitation', 'Milk', 'Models, Theoretical', 'Reproducibility of Results', 'Triglycerides']",Gas-liquid chromatographic determination of milk fat and cocoa butter equivalents in milk chocolate: interlaboratory study.,"[None, 'Q000737', None, 'Q000379', 'Q000379', 'Q000379', 'Q000378', 'Q000378', 'Q000379', None, 'Q000378', None, None, 'Q000032']","[None, 'chemistry', None, 'methods', 'methods', 'methods', 'metabolism', 'metabolism', 'methods', None, 'metabolism', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17955976,2007,,,, +0.43,17899033,"A liquid chromatography-electrospray-tandem mass spectrometry (LC-ESI-MS-MS) method based on the detection of biomarker peptides from allergenic proteins was devised for confirming and quantifying peanut allergens in foods. Peptides obtained from tryptic digestion of Ara h 2 and Ara h 3/4 proteins were identified and characterized by LC-MS and LC-MS-MS with a quadrupole-time of flight mass analyzer. Four peptides were chosen and investigated as biomarkers taking into account their selectivity, the absence of missed cleavages, the uniform distribution in the Ara h 2 and Ara h 3/4 protein isoforms together with their spectral features under ESI-MS-MS conditions, and good repeatability of LC retention time. Because of the different expression levels, the selection of two different allergenic proteins was proved to be useful in the identification and univocal confirmation of the presence of peanuts in foodstuffs. Using rice crisp and chocolate-based snacks as model food matrix, an LC-MS-MS method with triple quadrupole mass analyzer allowed good detection limits to be obtained for Ara h 2 (5 microg protein g(-1) matrix) and Ara h 3/4 (1 microg protein g(-1) matrix). Linearity of the method was established in the 10-200 microg g(-1) range of peanut proteins in the food matrix investigated. Method selectivity was demonstrated by analyzing tree nuts (almonds, pecan nuts, hazelnuts, walnuts) and food ingredients such as milk, soy beans, chocolate, cornflakes, and rice crisp.",Analytical and bioanalytical chemistry,"['D055516', 'D000485', 'D052179', 'D015415', 'D002853', 'D005504', 'D006023', 'D010446', 'D010940', 'D020033', 'D055314', 'D053719', 'D013997']","['2S Albumins, Plant', 'Allergens', 'Antigens, Plant', 'Biomarkers', 'Chromatography, Liquid', 'Food Analysis', 'Glycoproteins', 'Peptide Fragments', 'Plant Proteins', 'Protein Isoforms', 'Seed Storage Proteins', 'Tandem Mass Spectrometry', 'Time Factors']",Use of specific peptide biomarkers for quantitative confirmation of hidden allergenic peanut proteins Ara h 2 and Ara h 3/4 for food control by liquid chromatography-tandem mass spectrometry.,"[None, 'Q000032', None, 'Q000032', 'Q000379', 'Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, 'Q000379', None]","[None, 'analysis', None, 'analysis', 'methods', 'methods', 'analysis', 'analysis', 'analysis', 'analysis', None, 'methods', None]",,2008,0,0,, +0.43,24574140,"Although proanthocyanidins (PACs) modify dentin, the effectiveness of different PAC sources and the correlation with their specific chemical composition are still unknown. This study describes the chemical profiling of natural PAC-rich extracts from 7 plants using ultra high pressure/performance liquid chromatography (UHPLC) to determine the overall composition of these extracts and, in parallel, comprehensively evaluate their effect on dentin properties. The total polyphenol content of the extracts was determined (as gallic acid equivalents) using Folin-Ciocalteau assays. Dentin biomodification was assessed by the modulus of elasticity, mass change, and resistance to enzymatic biodegradation. Extracts with a high polyphenol and PAC content from Vitis vinifera, Theobroma cacao, Camellia sinensis, and Pinus massoniana induced a significant increase in modulus of elasticity and mass. The UHPLC analysis showed the presence of multiple types of polyphenols, ranging from simple phenolic acids to oligomeric PACs and highly condensed tannins. Protective effect against enzymatic degradation was observed for all experimental groups; however, statistically significant differences were observed between plant extracts. The findings provide clear evidence that the dentin bioactivities of PACs are source dependent, resulting from a combination of concentration and specific chemical constitution of the complex PAC mixtures. ",Journal of dental research,"['D000975', 'D028023', 'D002099', 'D028241', 'D002851', 'D032904', 'D002935', 'D017364', 'D003804', 'D055119', 'D005707', 'D056604', 'D006801', 'D028223', 'D024301', 'D010936', 'D059808', 'D044945', 'D020011', 'D012639', 'D013662', 'D027843']","['Antioxidants', 'Arecaceae', 'Cacao', 'Camellia sinensis', 'Chromatography, High Pressure Liquid', 'Cinnamomum aromaticum', 'Cinnamomum zeylanicum', 'Collagenases', 'Dentin', 'Elastic Modulus', 'Gallic Acid', 'Grape Seed Extract', 'Humans', 'Pinus', 'Plant Bark', 'Plant Extracts', 'Polyphenols', 'Proanthocyanidins', 'Protective Agents', 'Seeds', 'Tea', 'Vitis']",Dentin biomodification potential depends on polyphenol source.,"['Q000494', 'Q000737', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000494', 'Q000033', None, 'Q000032', 'Q000494', None, 'Q000737', 'Q000737', 'Q000032', 'Q000032', 'Q000032', 'Q000494', 'Q000737', 'Q000737', 'Q000737']","['pharmacology', 'chemistry', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'pharmacology', 'anatomy & histology', None, 'analysis', 'pharmacology', None, 'chemistry', 'chemistry', 'analysis', 'analysis', 'analysis', 'pharmacology', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24574140,2014,0,0,, +0.43,1874696,"Jute fibers are treated with about 5-7% of a high boiling mineral oil fraction (""batching oil"") to render them flexible for making fabrics. Foods transported in jute bags are contaminated by this batching oil. A method involving automated on-line LC-GC is described for determining these hydrocarbons in various foods. Complete transfer of the LC fraction to GC is presupposed for obtaining the required sensitivity. Results are given for nuts, coffee, cocoa products, and rice. Contamination ranged between about 5 and 500 ppm.",Journal - Association of Official Analytical Chemists,"['D002099', 'D002849', 'D002853', 'D003069', 'D005506', 'D008899', 'D009754', 'D012275']","['Cacao', 'Chromatography, Gas', 'Chromatography, Liquid', 'Coffee', 'Food Contamination', 'Mineral Oil', 'Nuts', 'Oryza']",Determination of food contamination by mineral oil from jute sacks using coupled LC-GC.,"['Q000032', None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032']","['analysis', None, None, 'analysis', 'analysis', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/1874696,1991,,,, +0.43,23022488,"The hemibiotrophic basidiomycete fungus Moniliophthora perniciosa, the causal agent of Witches' broom disease (WBD) in cacao, is able to grow on methanol as the sole carbon source. In plants, one of the main sources of methanol is the pectin present in the structure of cell walls. Pectin is composed of highly methylesterified chains of galacturonic acid. The hydrolysis between the methyl radicals and galacturonic acid in esterified pectin, mediated by a pectin methylesterase (PME), releases methanol, which may be decomposed by a methanol oxidase (MOX). The analysis of the M. pernciosa genome revealed putative mox and pme genes. Real-time quantitative RT-PCR performed with RNA from mycelia grown in the presence of methanol or pectin as the sole carbon source and with RNA from infected cacao seedlings in different stages of the progression of WBD indicate that the two genes are coregulated, suggesting that the fungus may be metabolizing the methanol released from pectin. Moreover, immunolocalization of homogalacturonan, the main pectic domain that constitutes the primary cell wall matrix, shows a reduction in the level of pectin methyl esterification in infected cacao seedlings. Although MOX has been classically classified as a peroxisomal enzyme, M. perniciosa presents an extracellular methanol oxidase. Its activity was detected in the fungus culture supernatants, and mass spectrometry analysis indicated the presence of this enzyme in the fungus secretome. Because M. pernciosa possesses all genes classically related to methanol metabolism, we propose a peroxisome-independent model for the utilization of methanol by this fungus, which begins with the extracellular oxidation of methanol derived from the demethylation of pectin and finishes in the cytosol.",Fungal genetics and biology : FG & B,"['D000363', 'D000429', 'D000595', 'D002099', 'D005110', 'D005656', 'D015966', 'D000432', 'D008969', 'D010368', 'D010935', 'D021381', 'D016415']","['Agaricales', 'Alcohol Oxidoreductases', 'Amino Acid Sequence', 'Cacao', 'Extracellular Space', 'Fungal Proteins', 'Gene Expression Regulation, Fungal', 'Methanol', 'Molecular Sequence Data', 'Pectins', 'Plant Diseases', 'Protein Transport', 'Sequence Alignment']",A potential role for an extracellular methanol oxidase secreted by Moniliophthora perniciosa in Witches' broom disease in cacao.,"['Q000201', 'Q000737', None, 'Q000382', 'Q000201', 'Q000737', None, 'Q000378', None, 'Q000378', 'Q000382', None, None]","['enzymology', 'chemistry', None, 'microbiology', 'enzymology', 'chemistry', None, 'metabolism', None, 'metabolism', 'microbiology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/23022488,2013,0,0,,gene +0.43,19750020,"In this study, 162 samples were analysed for monomer styrene content with using high performance liquid chromatography (HPLC) method in hot tea, milk, cocoa milk. The monomer styrene content, expressed in microg/l of drink and the level of migration of styrene monomer were varied from 0.61 to 8.15 for hot tea, from 0.65 to 8.30 for hot milk, from 0.71 to 8.65 for hot cocoa milk in GPPS (general purpose polystyrene), from 0.48 to 6.85 for hot tea, from 0.61 to 7.65 for hot milk, from 0.72 to 7.78 for hot cocoa milk in HIPS (high performance polystyrene) cups in different temperatures and times. The estimated limit of detection of (HPLC) method for all samples was 0.001 mg/kg. There is linear regression for styrene monomer from 1 to 10 ng/ml. Several samples spiked with a known amount of styrene monomer. The results of the recovery in study for styrene monomer were determinate to be mean, 96.1 +/- 1.92 to 99.7 +/- 1.15%. The results of this study indicate that styrene monomer from polystyrene disposable into hot and fat drinks was migrated and this migration was highly dependent on fat content and temperature of drinks. The derived concentration of styrene monomer in this study was above the EPA (Environmental protection agency) recommended level, especially in MCLG (Maximum contaminant level goal) standard. More study is needed to further elucidate this finding.",Toxicology mechanisms and methods,"['D001628', 'D002849', 'D002851', 'D004864', 'D005506', 'D006358', 'D011137', 'D013056']","['Beverages', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Equipment and Supplies', 'Food Contamination', 'Hot Temperature', 'Polystyrenes', 'Spectrophotometry, Ultraviolet']",Determination of migration monomer styrene from GPPS (general purpose polystyrene) and HIPS (high impact polystyrene) cups to hot drinks.,"['Q000032', None, None, None, None, None, 'Q000032', None]","['analysis', None, None, None, None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/19750020,2010,,,, +0.42,3680113,"Vitamin D in different fortified foods is determined by using liquid chromatography (LC). Sample preparation is described for fortified skim milk, infant formulas, chocolate drink powder, and diet food. The procedure involves 2 main steps: saponification of the sample followed by extraction, and quantitation by LC analysis. Depending on the sample matrix, additional steps are necessary, i.e., enzymatic digestion for hydrolyzing the starch in the sample and cartridge purification before LC injection. An isocratic system consisting of 0.5% water in methanol (v/v) on two 5 microns ODS Hypersil, 12 X 0.4 cm id columns is used. Recovery of vitamin D added to unfortified skim milk is 98%. The results of vitamin D determination in homogenized skim milk, fortified milk powder, fortified milk powder with soybean, chocolate drink powder, and sports diet food are given.",Journal - Association of Official Analytical Chemists,"['D000818', 'D002099', 'D002853', 'D002523', 'D005504', 'D005526', 'D007202', 'D007225', 'D008892', 'D013056', 'D014807']","['Animals', 'Cacao', 'Chromatography, Liquid', 'Edible Grain', 'Food Analysis', 'Food, Formulated', 'Indicators and Reagents', 'Infant Food', 'Milk', 'Spectrophotometry, Ultraviolet', 'Vitamin D']",Sample preparation and liquid chromatographic determination of vitamin D in food products.,"[None, 'Q000032', None, 'Q000032', None, 'Q000032', None, 'Q000032', 'Q000032', None, 'Q000032']","[None, 'analysis', None, 'analysis', None, 'analysis', None, 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/3680113,1987,,,, +0.42,12124611,"The Maya archaeological site at Colha in northern Belize, Central America, has yielded several spouted ceramic vessels that contain residues from the preparation of food and beverages. Here we analyse dry residue samples by using high-performance liquid chromatography coupled to atmospheric-pressure chemical-ionization mass spectrometry, and show that chocolate (Theobroma cacao) was consumed by the Preclassic Maya as early as 600 bc, pushing back the earliest chemical evidence of cacao use by some 1,000 years. Our application of this new and highly sensitive analytical technique could be extended to the identification of other ancient foods and beverages.",Nature,"['D001106', 'D001531', 'D001628', 'D002099', 'D002516', 'D002851', 'D049690', 'D013058', 'D013805']","['Archaeology', 'Belize', 'Beverages', 'Cacao', 'Ceramics', 'Chromatography, High Pressure Liquid', 'History, Ancient', 'Mass Spectrometry', 'Theobromine']",Cacao usage by the earliest Maya civilization.,"['Q000379', None, 'Q000266', 'Q000737', 'Q000266', None, None, None, 'Q000032']","['methods', None, 'history', 'chemistry', 'history', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12124611,2002,,,, +0.42,9410091,"The quality of three vegetable fats (cocoa butter and two commercial fats) and three roasted nut oils (almond, hazelnut and peanut) used as raw material in the chocolate products manufacturing was studied. The hydroperoxide content, oxidative stability and fatty acid composition were determined and its health repercussion by atherogenicity and thrombogenicity indexes. Two commercial fats and cocoa butter showed higher oxidative stability, atherogenic and thrombogenic properties than oils because of its different fatty acid profiles. Peroxide value was a low reliability parameter of raw material shelf live. Rancimat presented a good correlation with the unsaturation index of different fats and oils, it was a better index than peroxide value. In the chocolate products manufacturing it would be advisable a good raw material selection and formulation in order to get a balance between technological properties, organoleptic qualities and the influence on the health. Those raw material with less primary oxidation and higher oxidative stability were also those of higher atherogenicity and thrombogenicity indexes.",Nutricion hospitalaria,"['D000704', 'D002182', 'D002845', 'D004041', 'D004042', 'D005224', 'D005227', 'D008962', 'D010545', 'D010938']","['Analysis of Variance', 'Candy', 'Chromatography', 'Dietary Fats', 'Dietary Fats, Unsaturated', 'Fats, Unsaturated', 'Fatty Acids', 'Models, Theoretical', 'Peroxides', 'Plant Oils']",[Physico-chemical characteristics of different types of vegetable fats and oils used in the manufacture of candies].,"[None, 'Q000032', None, 'Q000009', 'Q000009', 'Q000009', 'Q000009', None, 'Q000032', 'Q000009']","[None, 'analysis', None, 'adverse effects', 'adverse effects', 'adverse effects', 'adverse effects', None, 'analysis', 'adverse effects']",https://www.ncbi.nlm.nih.gov/pubmed/9410091,1997,,,, +0.42,29389646,"Chocolate is a popular food bearing a number of different classifications that are differentiated by proportions of cocoa solids, milk and cocoa butter. Literature brings evidence that chocolates with a high percentage of cocoa solids contribute to good health maintenance due to the presence of phenolic compounds. On the other hand, it is known that the productive process, including pre-processing, may influence the level of these substances in the finished product. Thus, accurate strategies to measure the levels of this class of molecules that can be highly adaptable throughout the manufacturing process are important to ensure high-quality products. Mass spectrometry is an analytical tool of high sensitivity and specificity that is leading the research in food analysis towards new directions. By using mass spectrometry imaging in direct food analysis, this contribution developed an effective methodology for comparatively establishing the levels of catechin/epicatechin as phenolics content markers for cocoa content in a series of commercial chocolates from a single manufacturer, rendering a versatile tool that can be applied in fast screening of cocoa content in finished products and during manufacturing.","Food research international (Ottawa, Ont.)",[],[],A fast semi-quantitative screening for cocoa content in chocolates using MALDI-MSI.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29389646,2018,0,0,, +0.42,15493674,"A European interlaboratory study was conducted to validate an analytical procedure for the detection and quantification of cocoa butter equivalents in cocoa butter and plain chocolate. In principle, the fat obtained from plain chocolate according to the Soxhlet principle is separated by high-resolution capillary gas chromatography into triacylglycerol fractions according to their acyl-C-numbers, and within a given number, also according to unsaturation. The presence of cocoa butter equivalents is detected by linear regression analysis applied to the relative proportions of the 3 main triacylglycerol fractions of the fat analyzed. The amount of the cocoa butter equivalent admixture is estimated by partial least-squares regression analysis applied to the relative proportions of the 5 main triacylglycerols. Cocoa butter equivalent admixtures were detected down to a level of 2% related to the fat phase, corresponding to 0.6% in chocolate (assumed fat content of chocolate, 30%), without false-positive or -negative results. By using a quantification model based on partial least-squares regression analysis, the predicted cocoa butter equivalent amounts were in close agreement with the actual values. The applied model performed well at the level of the statutory limit of 5% cocoa butter equivalent addition to chocolate with a prediction error of 0.6%, assuming a chocolate fat content of 30%.",Journal of AOAC International,"['D002099', 'D002849', 'D004041', 'D014280']","['Cacao', 'Chromatography, Gas', 'Dietary Fats', 'Triglycerides']",Method validation for detection and quantification of cocoa butter equivalents in cocoa butter and plain chocolate.,"['Q000737', None, 'Q000032', 'Q000032']","['chemistry', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15493674,2005,,,, +0.42,27622657,"According to European legislation, tobacco additives may not increase the toxicity or the addictive potency of the product, but there is an ongoing debate on how to reliably characterize and measure such properties. Further, too little is known on pyrolysis patterns of tobacco additives to assume that no additional toxicological risks need to be suspected. An on-line pyrolysis technique was used and coupled to gas chromatography-mass spectrometry (GC/MS) to identify the pattern of chemical species formed upon thermal decomposition of 19 different tobacco additives like raw cane sugar, licorice or cocoa. To simulate the combustion of a cigarette it was necessary to perform pyrolysis at inert conditions as well as under oxygen supply. All individual additives were pyrolyzed under inert or oxidative conditions at 350, 700 and 1000_C, respectively, and the formation of different toxicants was monitored. We observed the generation of vinyl acrylate, fumaronitrile, methacrylic anhydride, isobutyric anhydride and 3-buten-2-ol exclusively during pyrolysis of tobacco additives. According to the literature, these toxicants so far remained undetectable in tobacco or tobacco smoke. Further, the formation of 20 selected polycyclic aromatic hydrocarbons (PAHs) with molecular weights of up to 278Da was monitored during pyrolysis of cocoa in a semi-quantitative approach. It was shown that the adding of cocoa to tobacco had no influence on the relative amounts of the PAHs formed.",International journal of hygiene and environmental health,"['D031002', 'D000069956', 'D003069', 'D003257', 'D005421', 'D000067030', 'D008401', 'D006035', 'D006722', 'D006358', 'D009930', 'D010936', 'D053149', 'D018517', 'D000068242', 'D031786', 'D013213', 'D014026']","['Acer', 'Chocolate', 'Coffee', 'Consumer Product Safety', 'Flavoring Agents', 'Fruit and Vegetable Juices', 'Gas Chromatography-Mass Spectrometry', 'Glycyrrhiza', 'Honey', 'Hot Temperature', 'Organic Chemicals', 'Plant Extracts', 'Plant Gums', 'Plant Roots', 'Prunus domestica', 'Saccharum', 'Starch', 'Tobacco']",Oxidative and inert pyrolysis on-line coupled to gas chromatography with mass spectrometric detection: On the pyrolysis products of tobacco additives.,"[None, None, None, None, None, None, None, None, None, None, 'Q000032', None, None, None, None, None, None, None]","[None, None, None, None, None, None, None, None, None, None, 'analysis', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/27622657,2017,0,0,,tabacco +0.42,28064480,"Milk powder is an important ingredient in the confectionery industry, but its variable nature has consequences for the quality of the final confectionary product. This paper demonstrates that skim milk powders (SMP) produced using different (but typical) manufacturing processes, when used as ingredients in the manufacture of model white chocolates, had a significant impact on the sensory and volatile profiles of the chocolate. SMP was produced from raw bovine milk using either low or high heat treatment, and a model white chocolate was prepared from each SMP. A directional discrimination test with nave panelists showed that the chocolate prepared from the high heat SMP had more caramel/fudge character (p < 0.0001), and sensory profiling with an expert panel showed an increase in both fudge (p < 0.05) and condensed milk (p < 0.05) flavor. Gas chromatography (GC)-mass spectrometry and GC-olfactometry of both the SMPs and the model chocolates showed a concomitant increase in Maillard-derived volatiles which are likely to account for this change in flavor.",Journal of agricultural and food chemistry,"['D000818', 'D000069956', 'D005504', 'D005511', 'D008401', 'D006801', 'D008892', 'D009812', 'D064367', 'D011208', 'D013649', 'D055549']","['Animals', 'Chocolate', 'Food Analysis', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Milk', 'Odorants', 'Olfactometry', 'Powders', 'Taste', 'Volatile Organic Compounds']",Impact of the Skim Milk Powder Manufacturing Process on the Flavor of Model White Chocolate.,"[None, 'Q000032', 'Q000379', 'Q000379', None, None, 'Q000737', 'Q000032', 'Q000379', 'Q000737', None, 'Q000032']","[None, 'analysis', 'methods', 'methods', None, None, 'chemistry', 'analysis', 'methods', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28064480,2017,0,0,,chocolate +0.42,24444418,"There is a growing interest in studying the nutritional effects of complex diets. For such studies, measurement of dietary compliance is a challenge because the currently available compliance markers cover only limited aspects of a diet. In the present study, an untargeted metabolomics approach was used to develop a compliance measure in urine to distinguish between two dietary patterns. A parallel intervention study was carried out in which 181 participants were randomized to follow either a New Nordic Diet (NND) or an Average Danish Diet (ADD) for 6 months. Dietary intakes were closely monitored over the whole study period, and 24 h urine samples as well as weighed dietary records were collected several times during the study. The urine samples were analyzed by UPLC-qTOF-MS, and a partial least-squares discriminant analysis with feature selection was applied to develop a compliance model based on data from 214 urine samples. The optimized model included 52 metabolites and had a misclassification rate of 19% in a validation set containing 139 samples. The metabolites identified in the model were markers of individual foods such as citrus, cocoa-containing products, and fish as well as more general dietary traits such as high fruit and vegetable intake or high intake of heat-treated foods. It was easier to classify the ADD diet than the NND diet probably due to seasonal variation in the food composition of NND and indications of lower compliance among the NND subjects. In conclusion, untargeted metabolomics is a promising approach to develop compliance measures that cover the most important discriminant metabolites of complex diets. ",Journal of proteome research,"['D000293', 'D000328', 'D000368', 'D002957', 'D003299', 'D004032', 'D005247', 'D005260', 'D005396', 'D005638', 'D006801', 'D008297', 'D055432', 'D008875', 'D019032', 'D016482', 'D014675']","['Adolescent', 'Adult', 'Aged', 'Citrus', 'Cooperative Behavior', 'Diet', 'Feeding Behavior', 'Female', 'Fish Products', 'Fruit', 'Humans', 'Male', 'Metabolomics', 'Middle Aged', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Urinalysis', 'Vegetables']",Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern.,"[None, None, None, 'Q000737', None, 'Q000379', 'Q000523', None, 'Q000656', 'Q000737', None, None, 'Q000295', None, 'Q000379', None, 'Q000737']","[None, None, None, 'chemistry', None, 'methods', 'psychology', None, 'utilization', 'chemistry', None, None, 'instrumentation', None, 'methods', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24444418,2014,0,0,, +0.42,10554262,"The antioxidant components of cacao liquor, which is a major ingredient of chocolate, were isolated with column chromatography and high-performance liquid chromatography. Quercetin and its glucoside were identified by spectrometric methods. Clovamide and deoxyclovamide were characterized by (1)H and (13)C NMR and MS spectrometry. Their antioxidative activity was measured by peroxide value of linoleic acid and thiobarbituric acid reactive-substance value of erythrocyte ghost membranes and microsomes. In the bulk oil system, clovamide had the strongest antioxidative activity but was less active in the other experiments. In the case of the two hydrophilic systems, flavans such as quercetin and epicatechin were more potently effective than the glucosides. It is considered that chocolate is stable against oxidative deterioration due to the presence of these polyphenolic compounds.",Journal of agricultural and food chemistry,[],[],Antioxidative Polyphenols Isolated from Theobroma cacao.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/10554262,1999,0,0,, +0.42,16365716,"Volatiles from chocolate mediate upwind flight behavior in Ephestia cautella and Plodia interpunctella. We used gas chromatography with electroantennographic detection and found 12 active compounds derived from three different chocolate types, i.e., plain, nut-containing, and rum-flavored. Eight of the compounds were identified with mass spectrometry, and the activity of three compounds, ethyl vanillin, nonanal, and phenylacetaldehyde (PAA), was subsequently confirmed in both electrophysiological and behavioral assays. In the electroantennogram experiment, PAA and nonanal were consistently eliciting responses in both species and sexes. Ethyl vanillin was active in males of both species, and also in P. interpunctella females. E. cautella females showed no antennal activity in response to ethyl vanillin. All three volatiles were attractive to E. cautella males and P. interpunctella females in a flight tunnel. E. cautella females were significantly attracted only to ethyl vanillin. P. interpunctella males were attracted to PAA. Ethyl vanillin is a novel insect attractant, whereas both nonanal and phenylacetaldehyde mediate behavior in many insect species. A final experiment revealed that a blend of the three volatiles was required to induce landing in the flight tunnel bioassay, and that the landing rate was dependent on dose. The three-component blend attracted both sexes of P. interpunctella and females of E. cautella, whereas E. cautella males were not attracted.",Journal of chemical ecology,"['D000818', 'D001522', 'D002099', 'D002849', 'D004594', 'D005260', 'D008297', 'D009036', 'D014835']","['Animals', 'Behavior, Animal', 'Cacao', 'Chromatography, Gas', 'Electrophysiology', 'Female', 'Male', 'Moths', 'Volatilization']",Electrophysiological and behavioral responses to chocolate volatiles in both sexes of the pyralid moths Ephestia cautella and Plodia interpunctella.,"[None, None, None, None, None, None, None, 'Q000502', None]","[None, None, None, None, None, None, None, 'physiology', None]",https://www.ncbi.nlm.nih.gov/pubmed/16365716,2006,0,0,,no cocoa +0.42,10725135,"The present work analyzes the lipid fraction from seeds of wild Ecuadorian Theobroma subincanum and selected commercial varieties of Theobroma cacao from Mexico (var. Criollo) and Ecuador (var. Arriba). The lipid fraction was obtained from the seeds through supercritical fluid extraction and analysis performed by preparatory thin-layer chromatography followed by gas chromatography. The results revealed that in T. subincanum the triglycerides contain fatty acids with longer chains. The melting point and peroxide and saponifiable numbers were determined for each Theobroma sample. The results lead to the conclusion that T. subincanum would produce a poorer quality butter than T. cacao. Nevertheless, the results do point toward a significant commercial use of T. subincanum for low-profile products.",Journal of agricultural and food chemistry,"['D002099', 'D004484', 'D006801', 'D008055', 'D019660', 'D008800', 'D010936', 'D012639']","['Cacao', 'Ecuador', 'Humans', 'Lipids', 'Malvaceae', 'Mexico', 'Plant Extracts', 'Seeds']",Lipid composition of wild ecuadorian Theobroma subincanum Mart. seeds and comparison with two varieties of Theobroma cacao L.,"['Q000737', None, None, 'Q000032', 'Q000737', None, 'Q000032', 'Q000737']","['chemistry', None, None, 'analysis', 'chemistry', None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/10725135,2000,2,1,"table 2, 4, 5", +0.41,6521612,"A method for the quantitative analysis of triglyceride species composition of vegetable oils by reversed-phase high performance liquid chromatography (RP-HPLC) via a flame ionization detector (FID) is described. Triglycerides are separated into molecular species via Zorbax chemically bonded octadecylsilane (ODS) columns using gradient elution with methylene chloride in acetonitrile. Identification of species is made by matching the retention times of the peaks in the chromatogram with the order of elution of all of the species that could be present in the sample on the basis of a random distribution of the fatty acids and comparison of experimental and calculated theoretical carbon numbers (TCN). Quantitative analysis is based on a direct proportionality of peak areas. Differences in the response of individual species were small and did not dictate the use of response factors. The method is applied to cocoa butter before and after randomization, soybean oil and pure olive oil.",Lipids,"['D002851', 'D009821', 'D014280', 'D014675']","['Chromatography, High Pressure Liquid', 'Oils', 'Triglycerides', 'Vegetables']",Quantitative analysis of triglyceride species of vegetable oils by high performance liquid chromatography via a flame ionization detector.,"['Q000379', 'Q000032', 'Q000032', 'Q000032']","['methods', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/6521612,1985,,,,no pdf access +0.41,10820090,"Catechins, compounds that belong to the flavonoid class, are potentially beneficial to human health. To enable an epidemiological evaluation of catechins, data on their contents in foods are required. HPLC with UV and fluorescence detection was used to determine the levels of (+)-catechin, (-)-epicatechin, (+)-gallocatechin (GC), (-)-epigallocatechin (EGC), (-)-epicatechin gallate (ECg), and (-)-epigallocatechin gallate (EGCg) in 8 types of black tea, 18 types of red and white wines, apple juice, grape juice, iced tea, beer, chocolate milk, and coffee. Tea infusions contained high levels of catechins (102-418 mg of total catechins/L), and tea was the only beverage that contained GC, EGC, ECg, and EGCg in addition to (+)-catechin and (-)-epicatechin. Catechin concentrations were still substantial in red wine (27-96 mg/L), but low to negligible amounts were found in white wine, commercially available fruit juices, iced tea, and chocolate milk. Catechins were absent from beer and coffee. The data reported here provide a base for the epidemiological evaluation of the effect of catechins on the risk for chronic diseases.",Journal of agricultural and food chemistry,"['D001628', 'D002392', 'D002851', 'D005504', 'D006801', 'D009426', 'D013050', 'D013056']","['Beverages', 'Catechin', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Humans', 'Netherlands', 'Spectrometry, Fluorescence', 'Spectrophotometry, Ultraviolet']","Catechin contents of foods commonly consumed in The Netherlands. 2. Tea, wine, fruit juices, and chocolate milk.","['Q000032', 'Q000032', None, None, None, None, None, None]","['analysis', 'analysis', None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10820090,2000,0,0,, +0.41,24390407,"While metabolomics is increasingly used to investigate the food metabolome and identify new markers of food exposure, limited attention has been given to the validation of such markers. The main objectives of the present study were to (1) discover potential food exposure markers (PEMs) for a range of plant foods in a study setting with a mixed dietary background and (2) validate PEMs found in a previous meal study. Three-day weighed dietary records and 24-h urine samples were collected three times during a 6-month parallel intervention study from 107 subjects randomized to two distinct dietary patterns. An untargeted UPLC-qTOF-MS metabolomics analysis was performed on the urine samples, and all features detected underwent strict data analyses, including an iterative paired t test and sensitivity and specificity analyses for foods. A total of 22 unique PEMs were identified that covered 7 out of 40 investigated food groups (strawberry, cabbages, beetroot, walnut, citrus, green beans and chocolate). The PEMs reflected foods with a distinct composition rather than foods eaten more frequently or in larger amounts. We found that 23__% of the PEMs found in a previous meal study were also valid in the present intervention study. The study demonstrates that it is possible to discover and validate PEMs for several foods and food classes in an intervention study with a mixed dietary background, despite the large variability in such a dataset. Final validation of PEMs for intake of foods should be performed by quantitative analysis. ",Analytical and bioanalytical chemistry,"['D000293', 'D000328', 'D000368', 'D015415', 'D002853', 'D004032', 'D015930', 'D005247', 'D005260', 'D006801', 'D008297', 'D013058', 'D055432', 'D008875', 'D010945', 'D015203', 'D012680', 'D055815']","['Adolescent', 'Adult', 'Aged', 'Biomarkers', 'Chromatography, Liquid', 'Diet', 'Diet Records', 'Feeding Behavior', 'Female', 'Humans', 'Male', 'Mass Spectrometry', 'Metabolomics', 'Middle Aged', 'Plants, Edible', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Young Adult']",Discovery and validation of urinary exposure markers for different plant foods by untargeted metabolomics.,"[None, None, None, 'Q000652', None, 'Q000145', None, None, None, None, None, None, 'Q000379', None, 'Q000145', None, None, None]","[None, None, None, 'urine', None, 'classification', None, None, None, None, None, None, 'methods', None, 'classification', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24390407,2014,0,0,, +0.41,11210035,"Selected volatile compounds of chocolate ice creams containing 0.6, 4.0, 6.0, or 9.0% milk fat or containing 2.5% milk fat, cocoa butter, or one of three fat replacers (Simplesse, Dairy Lo, or Oatrim) were analyzed by gas chromatography and gas chromatography-mass spectrometry using headspace solid-phase microextraction. The headspace concentration of most of the selected volatile compounds increased with decreasing milk fat concentration. Fat replacers generally increased the concentration of volatiles found in the headspace compared with milk fat or cocoa butter. Few differences in flavor volatiles were found between the ice cream containing milk fat and the ice cream containing cocoa butter. Among the selected volatiles, the concentration of 2,5-dimethyl-3(2-methyl propyl) pyrazine was the most highly correlated (negatively) with the concentration of milk fat, and it best discriminated among ice creams containing milk fat, cocoa butter, or one of the fat replacers.",Journal of dairy science,"['D000818', 'D055598', 'D002627', 'D002849', 'D003258', 'D004041', 'D019358', 'D005524', 'D008401', 'D007054', 'D008892', 'D013649']","['Animals', 'Chemical Phenomena', 'Chemistry, Physical', 'Chromatography, Gas', 'Consumer Behavior', 'Dietary Fats', 'Fat Substitutes', 'Food Technology', 'Gas Chromatography-Mass Spectrometry', 'Ice Cream', 'Milk', 'Taste']","Effects of milk fat, cocoa butter, or selected fat replacers on flavor volatiles of chocolate ice cream.","[None, None, None, None, None, 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000737', None]","[None, None, None, None, None, 'analysis', 'analysis', None, None, 'analysis', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/11210035,2001,,,,no pdf access +0.41,11601468,"A simple and rapid gas chromatographic (GC) method was developed for the detection of cocoa butter equivalents (CBEs) in cocoa buffer (CB). It is based on the use of a 5 m nonpolar capillary column for the separation of the main triglycerides of CB according to their acyl/carbon numbers. The GC procedure was optimized to avoid thermal degradation of the triglycerides. By computing the ratio C54/C50 and (C54/C50) x C52 and by 2-dimensional plotting of these values, authentic CB samples were clearly distinguished from samples containing various CBEs. The detection of little as 1% CBE in CB (corresponding to about 0.3% CBE in chocolate) in a model system was shown to be possible. Under real conditions, for a wide range of CBs, about 2.5% CBEs in CB were detected. With this method, quantitation was possible at a concentration of 5% CBEs in CB mixtures, which corresponds to around 1% in chocolate; this value is far below the maximum level of 5% CBEs allowed to be added to chocolate.",Journal of AOAC International,"['D002849', 'D004041', 'D007202', 'D015203', 'D012996', 'D013696', 'D014280']","['Chromatography, Gas', 'Dietary Fats', 'Indicators and Reagents', 'Reproducibility of Results', 'Solutions', 'Temperature', 'Triglycerides']",Development of a rapid method for the detection of cocoa butter equivalents in mixtures with cocoa butter.,"[None, 'Q000032', None, None, None, None, 'Q000032']","[None, 'analysis', None, None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11601468,2002,,,, +0.41,16438304,"Recent concerns surrounding the presence of acrylamide in many types of thermally processed food have brought about the need for the development of analytical methods suitable for determination of acrylamide in diverse matrices with the goals of improving overall confidence in analytical results and better understanding of method capabilities. Consequently, the results are presented of acrylamide testing in commercially available food products--potato fries, potato chips, crispbread, instant coffee, coffee beans, cocoa, chocolate and peanut butter, obtained by using the same sample extract. The results obtained by using LC-MS/MS, GC/MS (El), GC/HRMS (El)--with or without derivatization--and the use of different analytical columns, are discussed and compared with respect to matrix borne interferences, detection limits and method complexities.",Advances in experimental medicine and biology,"['D020106', 'D001966', 'D002849', 'D002851', 'D002853', 'D003069', 'D003296', 'D005502', 'D005504', 'D005506', 'D005511', 'D008401', 'D015233', 'D015203', 'D021241', 'D013997']","['Acrylamide', 'Bromine', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Chromatography, Liquid', 'Coffee', 'Cooking', 'Food', 'Food Analysis', 'Food Contamination', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Models, Statistical', 'Reproducibility of Results', 'Spectrometry, Mass, Electrospray Ionization', 'Time Factors']",Determination of acrylamide in various food matrices: evaluation of LC and GC mass spectrometric methods.,"['Q000032', 'Q000737', 'Q000379', None, 'Q000379', None, None, None, 'Q000379', None, None, None, None, None, None, None]","['analysis', 'chemistry', 'methods', None, 'methods', None, None, None, 'methods', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16438304,2006,,,,no pdf access +0.41,21535747,"Selected ion flow tube-mass spectrometry (SIFT-MS) was used to measure the real-time concentrations of cocoa volatiles in the headspace during roasting. Alkalized and unalkalized Don Homero and Arriba cocoa beans were roasted at 120, 150, and 170 _C in a rotary roaster. The concentrations of total alcohols, acids, aldehydes, esters, ketones, and alkylpyrazines increased, peaked, and decreased within the timeframe used for typical roasting. The concentrations of alkylpyrazines and Strecker aldehydes increased as the roasting temperature increased from 120 to 170 _C. For most of the volatile compounds, there was no significant difference between Arriba and Don Homero beans, but Arriba beans showed higher concentrations of 2-heptanone, acetone, ethyl acetate, methylbutanal, phenylacetaldehyde, and trimethylpyrazine. For unalkalized Don Homero beans (pH 5.7), the time to peak concentration decreased from 13.5 to 7.4 min for pyrazines, and from 12.7 to 7.4 min for aldehydes as the roasting temperature increased from 120 to 170 _C. Also, at 150 _C roasting, the time to peak concentration was shortened from 9 to 5.1 min for pyrazines, and from 9.1 to 5 min for aldehydes as the pH increased from 5.7 to 8.7.",Journal of food science,"['D000079', 'D000085', 'D000096', 'D000447', 'D002099', 'D005511', 'D006863', 'D007659', 'D013058', 'D011719', 'D055549']","['Acetaldehyde', 'Acetates', 'Acetone', 'Aldehydes', 'Cacao', 'Food Handling', 'Hydrogen-Ion Concentration', 'Ketones', 'Mass Spectrometry', 'Pyrazines', 'Volatile Organic Compounds']",Monitoring of cocoa volatiles produced during roasting by selected ion flow tube-mass spectrometry (SIFT-MS).,"['Q000031', 'Q000032', 'Q000032', 'Q000032', 'Q000737', 'Q000379', None, 'Q000032', 'Q000379', 'Q000032', 'Q000378']","['analogs & derivatives', 'analysis', 'analysis', 'analysis', 'chemistry', 'methods', None, 'analysis', 'methods', 'analysis', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/21535747,2011,1,3,table 2, +0.41,16638661,"The objective of this study was to determine the effect of high stearic acid (SA) diets versus high polyunsaturated fatty acid (PUFA) diets on several measures of lipid peroxidation in vivo. Sprague-Dawley rats were fed diets that differed only in the fat source (8% by weight) for 19 weeks. High SA fats were beef tallow (BT) and cocoa butter (CB), high PUFA fats were soybean oil (SO) and menhaden oil (MO). Urine was analyzed for lipophilic aldehydes, the secondary products of lipid peroxidation, by HPLC. Decreases (P<0.05) were found for 4 nonpolar lipophilic aldehydes and related carbonyl compounds (NPC) and 4 polar lipophilic aldehydes and related carbonyl compounds (PC) when the BT-fed group was compared to the SO-fed group. Decreases were also found to be significant for total NPC (P<0.01) and total PC (P<0.05) between BT and SO-fed groups. Serum increase in resistance to oxidation (P<0.01) was found in the BT group when compared to the SO group. The differences in urine and serum measurements in the present experiment indicate lower level of lipid peroxidation in vivo due to the consumption of high SA containing BT diet compared to high PUFA containing SO diet without raising serum triglycerides and cholesterol levels significantly for the BT-fed groups.",International journal of food sciences and nutrition,"['D000447', 'D000818', 'D001835', 'D002851', 'D004041', 'D004042', 'D004435', 'D005223', 'D005227', 'D005260', 'D015227', 'D010084', 'D051381', 'D017207', 'D013229']","['Aldehydes', 'Animals', 'Body Weight', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Dietary Fats, Unsaturated', 'Eating', 'Fats', 'Fatty Acids', 'Female', 'Lipid Peroxidation', 'Oxidation-Reduction', 'Rats', 'Rats, Sprague-Dawley', 'Stearic Acids']",Effect of high stearic acid containing fat on markers for in vivo lipid peroxidation.,"['Q000652', None, 'Q000187', 'Q000379', 'Q000008', 'Q000008', 'Q000187', 'Q000737', 'Q000032', None, 'Q000187', 'Q000187', None, None, 'Q000008']","['urine', None, 'drug effects', 'methods', 'administration & dosage', 'administration & dosage', 'drug effects', 'chemistry', 'analysis', None, 'drug effects', 'drug effects', None, None, 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/16638661,2006,2,1,table 1, +0.41,11027026,"A method for identifying refined vegetable fats added to chocolate (cocoa butter equivalents, CBEs) was combined with established quantitative methods for determining the level of vegetable fat added to cocoa butter with the aim of providing improved precision. The identification of fats was based on the analysis of sterol and triterpene alcohol degradation products formed during the processing of the fat. The procedure was able to successfully discriminate between 95% of pairs of fats from a set (33) of CBE-type vegetable fats. Subsequent analysis of 80 mixtures of four CBEs with chocolate successfully identified, on cross-validation, 94% of the samples. Combining the qualitative procedure with established quantitative methodology, based on the analysis of triacylglycerols, improved the method precision from +/- 2.1% to +/- 0.3% (5% addition of CBE at 95% confidence). Identifying the fat analytically permits the use of quantitative methods for determining the level of added fat in chocolate that have improved precision in comparison with the measurement of an unidentified fat. This may obviate the need to use factory inspection as a means to identify the ingredients of a product and monitor compliance with proposed legislation.",Food additives and contaminants,"['D002099', 'D002182', 'D005504', 'D008401', 'D010938', 'D013261', 'D014280', 'D014315']","['Cacao', 'Candy', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Plant Oils', 'Sterols', 'Triglycerides', 'Triterpenes']",An improved method for the measurement of added vegetable fats in chocolate.,"['Q000737', 'Q000032', 'Q000379', 'Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000032']","['chemistry', 'analysis', 'methods', 'methods', 'analysis', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11027026,2000,,,, +0.4,12613847,"The effects of added conjugated linoleic acid (CLA) on the sensory, chemical, and physical characteristics of 2% total fat (wt/wt) fluid milk were studied. Milks with 2% (wt/wt) total fat (2% CLA, 1% CLA 1% milkfat, 2% milkfat) were made by the addition of cream or CLA triglyceride oil into skim milk followed by HTST pasteurization and homogenization. The effects of adding vitamin E (200 ppm) and rosemary extract (0.1% wt/wt based on fat content) were investigated to prevent lipid oxidation. HTST pasteurization resulted in a significant decrease of the cis-9/trans-11 isomer and other minor CLA isomers. The cis-9/trans-11 isomer concentration remained stable through 2 wk of refrigerated storage. A significant loss of both the cis-9/trans-11 and the cis-10/trans-12 isomers occurred after 3 wk of refrigerated storage. The loss was attributed to lipase activity from excessive microbial growth. No differences were found in hexanal or other common indicators of lipid oxidation between milks with or without added CLA (P > 0.05). Descriptive sensory analysis revealed that milks with 1 or 2% CLA exhibited low intensities of a ""grassy/vegetable oil"" flavor, not present in control milks. The antioxidant treatments were deemed to be ineffective, under the storage conditions of this study, and did not produce significant differences from the control samples (P > 0.05). CLA-Fortified milk had significantly lower L* and b* values compared with 2% milkfat milk. No significant differences existed in viscosity. Consumer acceptability scores (n = 100) were lower (P < 0.05) for CLA-fortified milks compared to control milks, but the addition of chocolate flavor increased acceptability (P < 0.05).",Journal of dairy science,"['D000293', 'D000328', 'D000818', 'D000975', 'D002417', 'D002849', 'D003258', 'D003612', 'D005227', 'D005260', 'D005511', 'D005527', 'D006801', 'D007774', 'D019787', 'D008049', 'D050356', 'D008297', 'D008892', 'D010084', 'D013237', 'D013649', 'D013997']","['Adolescent', 'Adult', 'Animals', 'Antioxidants', 'Cattle', 'Chromatography, Gas', 'Consumer Behavior', 'Dairying', 'Fatty Acids', 'Female', 'Food Handling', 'Food, Fortified', 'Humans', 'Lactation', 'Linoleic Acid', 'Lipase', 'Lipid Metabolism', 'Male', 'Milk', 'Oxidation-Reduction', 'Stereoisomerism', 'Taste', 'Time Factors']",The impact of fortification with conjugated linoleic acid (CLA) on the quality of fluid milk.,"[None, None, None, 'Q000494', 'Q000378', None, None, 'Q000379', 'Q000032', None, 'Q000379', None, None, 'Q000378', 'Q000008', 'Q000378', None, None, 'Q000737', None, None, None, None]","[None, None, None, 'pharmacology', 'metabolism', None, None, 'methods', 'analysis', None, 'methods', None, None, 'metabolism', 'administration & dosage', 'metabolism', None, None, 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12613847,2003,,,,no pdf access +0.4,26174671,"The major constituents of agarwood oils are sesquiterpenes that are obtained from isoprenoid precursors through the plastidial methylerythritol phosphate (MEP) pathway and the cytosolic mevalonate pathway. In this study, a novel full-length cDNA of 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR), which was the second key enzyme in the plastid MEP pathway of sesquiterpenes biosynthesis was isolated from the stem of Aquilaria sinensis (Lour.) Gilg by the methods of reverse transcription polymerase chain reaction (RT-PCR) and rapid amplification of cDNA ends (RACE) technique for the first time, and named as AsDXR. The full-length cDNA of AsDXR was 1768 bp, containing a 1437 bp open reading frame (ORF) encoding a polypeptide of 478 amino acids with a molecular weight of 51.859 kD and the theoretical isoelectric point of 6.29. Comparative and bioinformatic analysis of the deduced AsDXR protein showed extensive homology with DXRs from other plant species, especially Theobroma cacao and Gossypium barbadense, and contained a conserved transit peptide for plastids, and extended pro-rich region and a highly conserved NADPH-binding motif owned by all plant DXRs. Southern blot analysis indicated that AsDXR belonged to a small gene family. Tissue expression pattern analysis revealed that AsDXR expressed strongly in root and stem, but weakly in leaf. Additionally, AsDXR expression was found to be activated by exogenous elicitor of MeJA (methyl jasmonate). The contents of three sesquiterpenes (_±-guaiene, _±-humulene and _”-guaiene) were significantly induced by MeJA. This study enables us to further elucidate the role of AsDXR in the biosynthesis of agarwood sesquiterpenes in A. sinensis at the molecular level. ",Journal of genetics,"['D000085', 'D019747', 'D000595', 'D001483', 'D003001', 'D003517', 'D018076', 'D008401', 'D018628', 'D020869', 'D018506', 'D017343', 'D008969', 'D054883', 'D010802', 'D017433', 'D017434', 'D012333', 'D016415', 'D012717', 'D029645']","['Acetates', 'Aldose-Ketose Isomerases', 'Amino Acid Sequence', 'Base Sequence', 'Cloning, Molecular', 'Cyclopentanes', 'DNA, Complementary', 'Gas Chromatography-Mass Spectrometry', 'Gene Dosage', 'Gene Expression Profiling', 'Gene Expression Regulation, Plant', 'Genes, Plant', 'Molecular Sequence Data', 'Oxylipins', 'Phylogeny', 'Protein Structure, Secondary', 'Protein Structure, Tertiary', 'RNA, Messenger', 'Sequence Alignment', 'Sesquiterpenes', 'Thymelaeaceae']","Molecular cloning, characterization and expression analysis of the gene encoding 1-deoxy-D-xylulose 5-phosphate reductoisomerase from Aquilaria sinensis (Lour.) Gilg.","['Q000494', 'Q000737', None, None, None, 'Q000494', 'Q000235', None, None, None, 'Q000187', None, None, 'Q000494', None, None, None, 'Q000235', None, 'Q000737', 'Q000187']","['pharmacology', 'chemistry', None, None, None, 'pharmacology', 'genetics', None, None, None, 'drug effects', None, None, 'pharmacology', None, None, None, 'genetics', None, 'chemistry', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/26174671,2016,0,0,, +0.4,18484765,"Unbalanced diets generate oxidative stress commonly associated with the development of diabetes, atherosclerosis, obesity and cancer. Dietary flavonoids have antioxidant properties and may limit this stress and reduce the risk of these diseases. We used a metabolomic approach to study the influence of catechin, a common flavonoid naturally occurring in various fruits, wine or chocolate, on the metabolic changes induced by hyperlipidemic diets. Male Wistar rats ( n = 8/group) were fed during 6 weeks normolipidemic (5% w/w) or hyperlipidemic (15 and 25%) diets with or without catechin supplementation (0.2% w/w). Urines were collected at days 17 and 38 and analyzed by reverse-phase liquid chromatography-mass spectrometry (LC-QTOF). Hyperlipidic diets led to a significant increase of oxidative stress in liver and aorta, upon which catechin had no effect. Multivariate analyses (PCA and PLS-DA) of the urine fingerprints allowed discrimination of the different diets. Variables were then classified according to their dependence on lipid and catechin intake (ANOVA). Nine variables were identified as catechin metabolites of tissular or microbial origin. Around 1000 variables were significantly affected by the lipid content of the diet, and 76 were fully reversed by catechin supplementation. Four variables showing an increase in urinary excretion in rats fed the high-fat diets were identified as deoxycytidine, nicotinic acid, dihydroxyquinoline and pipecolinic acid. After catechin supplementation, the excretion of nicotinic acid was fully restored to the level found in the rats fed the low-fat diet. The physiological significance of these metabolic changes is discussed.",Journal of proteome research,"['D000818', 'D000975', 'D001011', 'D001835', 'D002392', 'D002784', 'D002851', 'D003841', 'D004041', 'D004435', 'D005978', 'D005979', 'D005982', 'D008099', 'D008297', 'D008315', 'D013058', 'D015999', 'D009525', 'D010875', 'D011804', 'D051381', 'D017208', 'D014280']","['Animals', 'Antioxidants', 'Aorta', 'Body Weight', 'Catechin', 'Cholesterol', 'Chromatography, High Pressure Liquid', 'Deoxycytidine', 'Dietary Fats', 'Eating', 'Glutathione', 'Glutathione Peroxidase', 'Glutathione Transferase', 'Liver', 'Male', 'Malondialdehyde', 'Mass Spectrometry', 'Multivariate Analysis', 'Niacin', 'Pipecolic Acids', 'Quinolines', 'Rats', 'Rats, Wistar', 'Triglycerides']",A liquid chromatography-quadrupole time-of-flight (LC-QTOF)-based metabolomic approach reveals new metabolic effects of catechin in rats fed high-fat diets.,"[None, 'Q000378', 'Q000187', 'Q000187', 'Q000378', 'Q000097', 'Q000379', 'Q000378', 'Q000494', 'Q000187', 'Q000378', 'Q000378', 'Q000378', 'Q000187', None, 'Q000378', 'Q000379', None, 'Q000378', 'Q000378', 'Q000378', None, None, 'Q000097']","[None, 'metabolism', 'drug effects', 'drug effects', 'metabolism', 'blood', 'methods', 'metabolism', 'pharmacology', 'drug effects', 'metabolism', 'metabolism', 'metabolism', 'drug effects', None, 'metabolism', 'methods', None, 'metabolism', 'metabolism', 'metabolism', None, None, 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/18484765,2008,0,0,, +0.4,14558132,"The triacylglycerol (TAG) composition study of cocoa butter (CB) and cocoa butter equivalents (CBEs) has been performed by gas chromatography (GC) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS). These two techniques provided comparable results. The advantage of the MALDI technique was the detection of each compound comprising the triacylglycerol classes (Cn). Moreover, comparison of the data obtained by these two techniques indicated that TAG relative percentages could be obtained quantitatively with the MALDI technique. These techniques have been applied for the composition determination of CB + CBE mixtures. Encouraging results showed that it is possible to quantify an admixture containing as little as 4% of CBE.",Rapid communications in mass spectrometry : RCM,"['D000349', 'D002099', 'D002849', 'D004041', 'D012996', 'D013020', 'D019032', 'D014280']","['Africa', 'Cacao', 'Chromatography, Gas', 'Dietary Fats', 'Solutions', 'South America', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Triglycerides']",Comparative study of matrix-assisted laser desorption/ionization and gas chromatography for quantitative determination of cocoa butter and cocoa butter equivalent triacylglycerol composition.,"[None, 'Q000737', None, 'Q000032', None, None, None, 'Q000032']","[None, 'chemistry', None, 'analysis', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/14558132,2004,,,, +0.4,28784516,"A comprehensive analysis of cocoa polyphenols from unfermented and fermented cocoa beans from a wide range of geographic origins was carried out to catalogue systematic differences based on their origin as well as fermentation status. This study identifies previously unknown compounds with the goal to ascertain, which of these are responsible for the largest differences between bean types. UHPLC coupled with ultra-high resolution time-of-flight mass spectrometry was employed to identify and relatively quantify various oligomeric proanthocyanidins and their glycosides amongst several other unreported compounds. A series of biomarkers allowing a clear distinction between unfermented and fermented cocoa beans and for beans of different origins were identified. The large sample set employed allowed comparison of statistically significant variations of key cocoa constituents.","Food research international (Ottawa, Ont.)",[],[],Origin-based polyphenolic fingerprinting of Theobroma cacao in unfermented and fermented beans.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/28784516,2017,2,3,table 2, +0.4,1806392,"We have found that many foods are contaminated with mineral oil products used as lubricating oils/greases or as release agents. The mineral oil base of such products usually consists of branched alkanes ranging between C17 and C35. It forms a broad 'hump' of unresolved compounds in the gas chromatogram. Examples of such products are described; contamination is shown for a sample of bread, bonbon, and chocolate, respectively. The results suggest that contamination of foodstuffs with mineral oils does not always receive the required attention. However, there is also a lack of guidelines.",Food additives and contaminants,"['D001939', 'D002099', 'D002182', 'D002849', 'D002851', 'D005506', 'D006838', 'D008899', 'D010577']","['Bread', 'Cacao', 'Candy', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Hydrocarbons', 'Mineral Oil', 'Petrolatum']",Food contamination by hydrocarbons from lubricating oils and release agents: determination by coupled LC-GC.,"['Q000032', 'Q000737', 'Q000032', None, None, 'Q000032', 'Q000032', 'Q000737', 'Q000737']","['analysis', 'chemistry', 'analysis', None, None, 'analysis', 'analysis', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/1806392,1992,,,, +0.4,16152941,"A simple and inexpensive liquid chromatography/mass spectrometry (LC/MS) method was developed for the quantitation of acrylamide in various food products. The method involved spiking the isotope-substituted internal standard (1-C13 acrylamide) onto 6.00 g of the food product, adding 40 mL distilled/deionized water, and heating at 65 degrees C for 30 min. Afterwards, 10 mL ethylene dichloride was added and the mixture was homogenized for 30 s and centrifuged at 2700 x g for 30 min, and then 8 g supernatant was extracted with 10, 5, and 5 mL portions of ethyl acetate. The extracts were combined, dried with sodium sulfate, and concentrated to 100-200 microL. Acrylamide was determined by analysis of the final extract on a single quadrupole, bench-top mass spectrometer with electrospray ionization, using a 2 mm id C18 column and monitoring m/z = 72 (acrylamide) and m/z = 73 (internal standard). For difficult food matrixes, such as coffee and cocoa, a solid-phase extraction cleanup step was incorporated to improve both chromatography and column lifetime. The method had a limit of quantitation of 10 ppb, and coefficients of determination (r2) for calibration curves were typically better than 0.998. Acceptable spike recovery results were achieved in 11 different food matrixes. Precision in potato chip analyses was 5-8% (relative standard deviation). This method provides an LC/MS alternative to the current LC/MS/MS methods and derivatization gas chromatography/mass spectrometry methods, and is applicable to difficult food products such as coffee, cocoa, and high-salt foods.",Journal of AOAC International,"['D000085', 'D020106', 'D002099', 'D002623', 'D002845', 'D002853', 'D003069', 'D005504', 'D005506', 'D008401', 'D013058', 'D011208', 'D012015', 'D015203', 'D011198', 'D013431', 'D013696']","['Acetates', 'Acrylamide', 'Cacao', 'Chemistry Techniques, Analytical', 'Chromatography', 'Chromatography, Liquid', 'Coffee', 'Food Analysis', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Mass Spectrometry', 'Powders', 'Reference Standards', 'Reproducibility of Results', 'Solanum tuberosum', 'Sulfates', 'Temperature']",Quantitation of acrylamide in food products by liquid chromatography/mass spectrometry.,"['Q000032', 'Q000032', None, 'Q000295', None, 'Q000379', None, 'Q000379', None, 'Q000379', 'Q000379', None, None, None, None, 'Q000494', None]","['analysis', 'analysis', None, 'instrumentation', None, 'methods', None, 'methods', None, 'methods', 'methods', None, None, None, None, 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/16152941,2005,,,, +0.39,28566081,"Streptococcus uberis is a gram-positive bacterium that is mostly responsible for mastitis in cattle. The bacterium rarely has been associated with human infections. Conventional phenotyphic methods can be inadequate for the identification of S.uberis; and in microbiology laboratories S.uberis is confused with the other streptococci and enterococci isolates. Recently, molecular methods are recommended for the accurate identification of S.uberis isolates. The aim of this report is to present a lower respiratory tract infection case caused by S.uberis and the microbiological methods for identification of this bacterium. A 66-year-old male patient with squamous cell lung cancer who received radiotherapy was admitted in our hospital for the control. According to the chest X-Ray, patient was hospitalized with the prediagnosis of ''cavitary tumor, pulmonary abscess''. In the first day of the hospitalization, blood and sputum cultures were drawn. Blood culture was negative, however, Candida albicans was isolated in the sputum culture and it was estimated to be due to oral lesions. After two weeks from the hospitalization, sputum sample was taken from the patient since he had abnormal respiratory sounds and cough complaint. In the Gram stained smear of the sputum there were abundant leucocytes and gram-positive cocci, and S.uberis was isolated in both 5% sheep blood and chocolate agar media. Bacterial identification and antibiotic susceptibility tests were performed by VITEK 2 (Biomerieux, France) and also, the bacterium was identified by matrix assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) based VITEK MS system as S.uberis. The isolate was determined susceptible to ampicillin, erythromycin, clindamycin, levofloxacin, linezolid, penicillin, cefotaxime, ceftriaxone, tetracycline and vancomycin. 16S, 23S ribosomal RNA and 16S-23S intergenic spacer gene regions were amplified with specific primers and partial DNA sequence analysis of 16S rRNA polymerase chain reaction (PCR) products were performed by 3500xL Genetic Analyzer (Applied Biosystems, USA). According to the partial 16S rRNA gene sequencing results, bacterium was confirmed as S.uberis. This report makes a significant contribution to the number of case reports of human infections caused by S.uberis as the identification was performed by current microbiological methods in our case. In conclusion, S.uberis should be evaluated as an opportunistic pathogen among the immunosuppressed patients and in addition to phenotypic bacteriological methods, the other recent microbiological methods should also be utilized for the identification.",Mikrobiyoloji bulteni,"['D000368', 'D002176', 'D002180', 'D002294', 'D006801', 'D008175', 'D008297', 'D008826', 'D009894', 'D016133', 'D019032', 'D013183', 'D013290', 'D013291']","['Aged', 'Candida albicans', 'Candidiasis, Oral', 'Carcinoma, Squamous Cell', 'Humans', 'Lung Neoplasms', 'Male', 'Microbial Sensitivity Tests', 'Opportunistic Infections', 'Polymerase Chain Reaction', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Sputum', 'Streptococcal Infections', 'Streptococcus']",[A rarely isolated bacterium in microbiology laboratories: Streptococcus uberis].,"[None, 'Q000302', 'Q000150', 'Q000150', None, 'Q000150', None, None, 'Q000150', None, None, 'Q000382', 'Q000150', 'Q000145']","[None, 'isolation & purification', 'complications', 'complications', None, 'complications', None, None, 'complications', None, None, 'microbiology', 'complications', 'classification']",https://www.ncbi.nlm.nih.gov/pubmed/28566081,2017,0,0,,no cocoa +0.39,7362697,"The effects of dietary stearic and other saturated fatty acids on the fluidity of the plasma lipoproteins were assessed with fluorescence polarization techniques. Rabbits were maintained on diets containing either cocoa butter, milkfat, coconut oil, or corn oil as the only source of fat. Microviscosities eta, of the lipid regions of plasma very low density lipoproteins (VLDL), low density lipoproteins (LDL), and high density lipoproteins (HDL) were determined by measuring the anisotropy of fluorescence from the probe 1,6-diphenyl-1,3,5-hexatriene. The microviscosity values followed the sequence eta HDL greater than eta LDL greater than eta VLDL when the lipoproteins were isolated from the plasma of rabbits fed cocoa butter, milkfat, or corn oil, HDL and LDL consist of an invariant phase in the temperature range 0--50 degrees C regardless of diet. VLDL from rabbits fed milkfat, corn oil, or cocoa butter displayed monophasic behavior in the same range, while VLDL, from rabbits fed coconut oil showed a phase transition at 31.9 +/- 3.7 degrees C. Lipoproteins were less fluid in fasted than in non-fasted rabbits and VLDL and LDL from fasted milkfat-fed rabbits showed phase transitions. Despite the fatty acid compositions of the dietary fats, VLDL and LDL were more fluid from rabbits fed cocoa butter than from rabbits fed corn oil; apparently metabolism influences microviscosity.",Atherosclerosis,"['D000818', 'D004041', 'D006838', 'D008074', 'D008075', 'D008077', 'D008079', 'D008297', 'D011817', 'D013050', 'D014783']","['Animals', 'Dietary Fats', 'Hydrocarbons', 'Lipoproteins', 'Lipoproteins, HDL', 'Lipoproteins, LDL', 'Lipoproteins, VLDL', 'Male', 'Rabbits', 'Spectrometry, Fluorescence', 'Viscosity']",Influence of dietary fats on the fluidity of the lipid domains of rabbit plasma lipoproteins.,"[None, 'Q000494', None, 'Q000097', 'Q000097', 'Q000097', 'Q000097', None, None, None, None]","[None, 'pharmacology', None, 'blood', 'blood', 'blood', 'blood', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/7362697,1980,0,0,, +0.39,19486828,"The daily dietary intake of nickel (Ni) and zinc (Zn) by 42 young children, 21 boys and 21 girls, from 4 to 7 years of age, living in urban and rural areas of Germany and having different food consumption behaviour, was determined by the duplicate method with a 7-day sampling period. Dietary records were also kept by the children's parents for the 7-day sampling period. Individual reported food items were identified, assigned to food groups and, together with known Ni and Zn concentrations of foodstuffs, daily intake rates were calculated. The same method was used for calculations of the energy, fat, protein and carbohydrate intake rates. The levels in the food duplicates, determined by atomic absorption spectrometry, were in the range of 69-2000 microg Ni/kg(dry weight) (geometric mean (GM): 348) and 7.1-43 mg Zn/kg(dry weight) (GM: 17.5). Daily intake rates based on the 294 individual food duplicate analyses were 12-560 microgNi/d (GM: 92.3) and 1.5-11 mgZn/d (GM: 4.63). The results from the dietary records were 35-1050 microg Ni/d (GM: 123) and 1.7-15 mg Zn/d (GM: 5.35). The results of the daily intake rates from both methods showed a correlation with regard to Zn (r=0.56), but no correlation was found between either the Ni intake rates determined with both methods or between the Ni intake rates measured by the duplicate method and calculated intake rates from the dietary records of energy, fat, protein, carbohydrates or drinking water. In the case of nickel, the discrepancies between the methods lead one to suppose that the main factors influencing Ni intake by food are not directly caused by easily assessable food ingredients themselves. It is possible that other factors, such as contaminated drinking water or the transition of Ni from kettles or other household utensils made from stainless steel into the food, may be more relevant. In addition there are some foodstuffs with great variations in concentrations, often influenced by the growing area and environmental factors. Further, some food groups naturally high in Nickel like nuts, cocoa or teas might not have been kept sufficient within the records. In summary, the dietary record method gave sufficient results for Zn, but is insufficient for Ni. Based on the food duplicate analysis, children living in urban areas with consumption of food products from a family-owned garden or the surrounding area and/or products from domestic animals of the surrounding area had about one-third higher Ni levels in their food than children either living in an urban area or children consuming products exclusively from the supermarket. Only slight differences were found with regard to Zn. Compared to the recommendations of the German Society of Nutrition (DGE) (25-30 microgNi/d and 5.0 mgZn/d), the participants of the study had a clearly increased Ni and, in view of the geometric mean value, a nearly adequate Zn intake. Health risks are especially given with regard to the influence of nickel intake by food on dermatitis for nickel-sensitive individuals.",Journal of trace elements in medicine and biology : organ of the Society for Minerals and Trace Elements (GMS),"['D002648', 'D002675', 'D015930', 'D005260', 'D005858', 'D006801', 'D008297', 'D009532', 'D013054', 'D015032']","['Child', 'Child, Preschool', 'Diet Records', 'Female', 'Germany', 'Humans', 'Male', 'Nickel', 'Spectrophotometry, Atomic', 'Zinc']",Dietary intake of nickel and zinc by young children--results from food duplicate portion measurements in comparison to data calculated from dietary records and available data on levels in food groups.,"[None, None, None, None, None, None, None, 'Q000008', 'Q000379', 'Q000008']","[None, None, None, None, None, None, None, 'administration & dosage', 'methods', 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/19486828,2009,0,0,, +0.39,21045839,"The diversity of the chemical structures of dietary polyphenols makes it difficult to estimate their total content in foods, and also to understand the role of polyphenols in health and the prevention of diseases. Global redox colorimetric assays have commonly been used to estimate the total polyphenol content in foods. However, these assays lack specificity. Contents of individual polyphenols have been determined by chromatography. These data, scattered in several hundred publications, have been compiled in the Phenol-Explorer database. The aim of this paper is to identify the 100 richest dietary sources of polyphenols using this database.",European journal of clinical nutrition,"['D000975', 'D002099', 'D016208', 'D002523', 'D005419', 'D005504', 'D005638', 'D009754', 'D010636', 'D010936', 'D059808', 'D017365', 'D027842', 'D014675', 'D014920']","['Antioxidants', 'Cacao', 'Databases, Factual', 'Edible Grain', 'Flavonoids', 'Food Analysis', 'Fruit', 'Nuts', 'Phenols', 'Plant Extracts', 'Polyphenols', 'Spices', 'Syzygium', 'Vegetables', 'Wine']",Identification of the 100 richest dietary sources of polyphenols: an application of the Phenol-Explorer database.,"['Q000032', 'Q000737', None, 'Q000737', 'Q000032', 'Q000706', 'Q000737', None, 'Q000032', 'Q000032', None, None, 'Q000737', 'Q000737', 'Q000032']","['analysis', 'chemistry', None, 'chemistry', 'analysis', 'statistics & numerical data', 'chemistry', None, 'analysis', 'analysis', None, None, 'chemistry', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/21045839,2011,1,1,table 1, +0.39,29146253,"Strategies for achieving global food security include identification of alternative feedstock for use as animal feed, to contribute towards efforts at increasing livestock farming. The presence of theobromine in cocoa pod husks, a major agro-waste in cocoa-producing countries, hinders its utilisation for this purpose. Cheap treatment of cocoa pod husks to remove theobromine would allow largescale beneficial use of the millions of metric tonnes generated annually. The aim of this study was to isolate theobromine-degrading filamentous fungi that could serve as bioremediation agents for detheobromination of cocoa pod husks. Filamentous fungi were screened for ability to degrade theobromine. The most promising isolates were characterized with respect to optimal environmental conditions for theobromine degradation. Secretion of theobromine-degrading enzymes by the isolates was investigated. Theobromine degradation was monitored by HPLC. Of fourteen theobromine-degrading isolates collected and identified by rDNA 5.8S and ITS sequences, seven belonged to Aspergillus spp. and six were Talaromyces spp. Based on the extent of theobromine utilization, four isolates; Aspergillus niger, Talaromyces verruculosus and two Talaromyces marneffei, showed the best potential for use as bioagents for detheobromination. First-time evidence was found of the use of xanthine oxidase and theobromine oxidase in degradation of a methylxanthine by fungal isolates. Metabolism of theobromine involved initial demethylation at position 7 to form 3-methylxanthine, or initial oxidation at position 8 to form 3,7-dimethyuric acid. All four isolates degraded theobromine beyond uric acid. The data suggest that the four isolates can be applied to substrates, such as cocoa pod husks, for elimination of theobromine.",Microbiological research,"['D000821', 'D001234', 'D001673', 'D002099', 'D002851', 'D004271', 'D004275', 'D005658', 'D006863', 'D009584', 'D010084', 'D032901', 'D013696', 'D013805', 'D014969']","['Animal Feed', 'Aspergillus niger', 'Biodegradation, Environmental', 'Cacao', 'Chromatography, High Pressure Liquid', 'DNA, Fungal', 'DNA, Ribosomal', 'Fungi', 'Hydrogen-Ion Concentration', 'Nitrogen', 'Oxidation-Reduction', 'Talaromyces', 'Temperature', 'Theobromine', 'Xanthine Oxidase']",Isolation and characterisation of theobromine-degrading filamentous fungi.,"[None, 'Q000254', None, 'Q000737', 'Q000379', None, 'Q000032', 'Q000145', None, 'Q000378', None, 'Q000254', None, 'Q000737', None]","[None, 'growth & development', None, 'chemistry', 'methods', None, 'analysis', 'classification', None, 'metabolism', None, 'growth & development', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/29146253,2018,0,0,, +0.39,10335542,"When an infant presents severe cyanosis which is not associated with respiratory distress, methaemoglobinemia should always be suspected. In children its main inducers are contaminated water or vegetable broths with high nitrate levels (especially spinach and carrots) used to prepare powdered formula or soups. Children affected with methaemoglobinemia have a peculiar lavender colour. Blood from the heel sticks is chocolate-brown and does not become pink when exposed to room air. Diagnosis can be confirmed by excluding other causes of cyanosis and by spectrophotometric analysis of blood for methaemoglobin. When methaemoglobin's levels reach 60% or more, the patient will collapse and become comatose and may die. Therapy with methylene blue results in prompt relief. In this article we report a case of methaemoglobinemia due to the administration of powdered formula mixed with vegetable broths to a newborn aged 16 days. Furthermore we will present a short review of literature regarding methaemoglobinemia caused by toxic agents over the last 10 years.",La Pediatria medica e chirurgica : Medical and surgical pediatrics,"['D004791', 'D005260', 'D006801', 'D007225', 'D007231', 'D008706', 'D008708', 'D008751', 'D013053']","['Enzyme Inhibitors', 'Female', 'Humans', 'Infant Food', 'Infant, Newborn', 'Methemoglobin', 'Methemoglobinemia', 'Methylene Blue', 'Spectrophotometry']",[Acquired methemoglobinemia: a case report].,"['Q000627', None, None, 'Q000009', None, 'Q000032', 'Q000175', 'Q000627', None]","['therapeutic use', None, None, 'adverse effects', None, 'analysis', 'diagnosis', 'therapeutic use', None]",https://www.ncbi.nlm.nih.gov/pubmed/10335542,1999,,,, +0.39,22970581,"A simple and rapid method using an octadecyl-bonded silica membrane disk impregnated with Cyanex302 is described for the pre-concentration and determination of iron. The influence of various parameters on sorption and elution of Fe(III) were systematically investigated. The sorption of Fe(III) at pH 3.2 was quantitative (99.3 +/- 1.1%). It was completely recovered using 20 mL 5.0 M HCI and determined by flame atomic absorption spectrometry. Breakthrough volume of the modified disk for Fe(III) was >2000 mL, pre-concentration factor was >100, and reusability up to 28 cycles. The LOD and LOQ for Fe(III) were 0.45 microg/L and 1.51 microg/L, respectively, while precision for its determination in terms of RSD was < or =2.1%. This method was applied for Fe(III) determination in milk, fortified flour, cocoa powder, tea, and black pepper. To validate the procedure, EPA Method Standard (QC standard 21) was analyzed for Fe(III).",Journal of AOAC International,"['D000327', 'D000818', 'D002099', 'D002623', 'D005433', 'D005504', 'D006863', 'D007477', 'D007501', 'D008892', 'D009946', 'D010721', 'D029222', 'D012015', 'D015203', 'D012822', 'D013054', 'D013662', 'D013997']","['Adsorption', 'Animals', 'Cacao', 'Chemistry Techniques, Analytical', 'Flour', 'Food Analysis', 'Hydrogen-Ion Concentration', 'Ions', 'Iron', 'Milk', 'Organothiophosphorus Compounds', 'Phosphinic Acids', 'Piper nigrum', 'Reference Standards', 'Reproducibility of Results', 'Silicon Dioxide', 'Spectrophotometry, Atomic', 'Tea', 'Time Factors']",Octadecyl-bonded silica membrane disk modified with Cyanex302 for pre-concentration and determination of iron in food products.,"[None, None, None, 'Q000379', None, 'Q000379', None, None, 'Q000032', None, 'Q000737', 'Q000737', None, None, None, 'Q000737', 'Q000379', None, None]","[None, None, None, 'methods', None, 'methods', None, None, 'analysis', None, 'chemistry', 'chemistry', None, None, None, 'chemistry', 'methods', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22970581,2012,,,, +0.38,29391642,"The effect of the partial replacement of cocoa butter (CB) by cocoa butter equivalent (CBE) in the release of volatile compounds in dark chocolate was studied. The fatty acid profile, triacylglyceride composition, solid fat content (SFC) and melting point were determined in CB and CBE. Chocolate with CB (F1) and with different content of CBE (5 and 10%-F2 and F3, respectively) were prepared. Plastic viscosity and Casson flow limit, particle size distribution and release of volatile compounds using a solid phase microextraction with gas chromatography (SMPE-GC) were determined in the chocolate samples. The melting point was similar for the studied samples but SFC indicated different melting behavior. CBE showed a higher saturated fatty acid content when compared to CB. The samples showed similar SOS triglyceride content (21 and 23.7% for CB and CBE, respectively). Higher levels of POS and lower POP were observed for CB when compared to CBE (44.8 and 19.7 and 19 and 41.1%, respectively). The flow limit and plastic viscosity were similar for the studied chocolates samples, as well as the particle size distribution. Among the 27 volatile compounds identified in the samples studied, 12 were detected in significantly higher concentrations in sample F1 (phenylacetaldehyde, methylpyrazine, 2,6-dimethylpyrazine, 2-ethyl-5-methylpyrazine, 2-ethyl-3,5-dimethylpyrazine, tetramethylpyrazine, trimethylpyrazine, 3-ethyl-2,5-dimethylpyrazine, phenethyl alcohol, 2-acetylpyrrole, acetophenone and isovaleric acid). The highest changes were observed in the pyrazines group, which presented a decrease of more than half in the formulations where part of the CB was replaced by the CBE.",Journal of food science and technology,[],[],Impact of the addition of cocoa butter equivalent on the volatile compounds profile of dark chocolate.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29391642,2018,,,, +0.38,29548447,"The nutritional value of cocoa butter is mainly determined by the composition of triacylglycerols (TAGs). In this paper we have developed a non-aqueous reversed-phase liquid chromatographic method, using ethanol as the mobile phase, coupled to electrospray ionization (ESI) tandem mass spectrometry to identify TAGs in raw cocoa beans from six different origins. Tandem mass spectrometry was adopted to facilitate the identification of TAGs using unique diacylglycerol product ions and neutral losses. Additionally, two-dimensional m/z retention time maps aided the identification of entire homologous series of TAGs. A total of 83 different TAGs were identified in unfermented cocoa beans, 58 of which were not previously reported in cocoa. Thirty-one of these compounds represent a new class of TAGs characterized by the presence of one to three hydroxyl groups on the unsaturated fatty acid chain. To date, this represents the largest number of TAGs identified in cocoa.",Food chemistry,[],[],Characterization of triacylglycerols in unfermented cocoa beans by HPLC-ESI mass spectrometry.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29548447,2018,2,3,table 1 ,extract the different cocoa samples and the lipid amount in % +0.38,30027968,"Recrystallisation occurs frequently in confectionery. More information on sucrose re-crystallisation will aid our understanding of popular foods like chocolate. However, progress has been limited due the lack of a robust method for the production of amorphous sucrose, with known purity. Poor control has led to the glass transition temperatures (Tg's) for amorphous sucrose varying between 48-78 _C in the literature. Our objective was to investigate the recrystallization of sucrose in the presence of lactose, NaCl and water. The purity of sucrose was confirmed by ion chromatography, polarimetry and differential scanning calorimetry. Amorphous sucrose was prepared by freeze-drying 10% w/v aqueous solutions. Fisher (99.7%) and Silver Spoon (98.4%) sucrose samples melted at 186 _± 0.6 _C & 189 _± 0.3 _C respectively. For the Fisher sample the absence of invert sugars and low mineral content allowed the observation of a small endotherm (___ 150 _C). The Tg of amorphous sucrose was 58.3 _± 1.1 _C with a recrystallization enthalpy (_”Hcrys) of 72.8 _± 6.0 J g-1. NaCl reduced both the Tg (54.8 _± 1.8 _C) and the _”Hcrys (35.7 _± 3.8 J g-1) without affecting the onset temperature of sucrose's re-crystallization (Tcrys, 129.5 _± 6.9 _C), suggesting that a proportion of the sample remained amorphous. The presence of water (1.6 _± 0.07%) inside the hermetically sealed pans caused an earlier onset of Tg (52.3 _± 1.3 _C) and Tcrys (85.1 _± 4.0 _C), as well as lowering _”Hcrys (45.2 _± 2.4 J g-1) compared to samples contained in pin-holed pans (where evaporation was possible). The presence of lactose inhibited the crystallization of sucrose completely. On the basis of this study, it is apparent that sucrose crystallization is highly dependent on the presence of other common food ingredients within the matrix.",Food & function,[],[],Crystallisation of freeze-dried sucrose in model mixtures that represent the amorphous sugar matrices present in confectionery.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/30027968,2018,0,0,, +0.38,23122117,"The definition of fat differs in different countries; thus whether fat is listed on food labels depends on the country. Some countries list crude fat content in the 'Fat' section on the food label, whereas other countries list total fat. In this study, three methods were used for determining fat classes and content in bakery products: the Folch method, the automated Soxhlet method, and the AOAC 996.06 method. The results using these methods were compared. Fat (crude) extracted by the Folch and Soxhlet methods was gravimetrically determined and assessed by fat class using capillary gas chromatography (GC). In most samples, fat (total) content determined by the AOAC 996.06 method was lower than the fat (crude) content determined by the Folch or automated Soxhlet methods. Furthermore, monounsaturated fat or saturated fat content determined by the AOAC 996.06 method was lowest. Almost no difference was observed between fat (crude) content determined by the Folch method and that determined by the automated Soxhlet method for nearly all samples. In three samples (wheat biscuits, butter cookies-1, and chocolate chip cookies), monounsaturated fat, saturated fat, and trans fat content obtained by the automated Soxhlet method was higher than that obtained by the Folch method. The polyunsaturated fat content obtained by the automated Soxhlet method was not higher than that obtained by the Folch method in any sample.",Food chemistry,"['D001939', 'D002623', 'D005223', 'D005504', 'D005515']","['Bread', 'Chemistry Techniques, Analytical', 'Fats', 'Food Analysis', 'Food Labeling']",Comparison of different methods to quantify fat classes in bakery products.,"['Q000032', 'Q000379', 'Q000737', 'Q000379', None]","['analysis', 'methods', 'chemistry', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/23122117,2013,0,0,, +0.38,25829576,"In the present study, refined dark chocolate mix was conched with the addition of finely powdered cinnamon in a laboratory-style conching machine to evaluate its aroma profile both analytically and sensorially. The analytical determinations were carried out by a combination of solid phase micro extraction (SPME)-gas chromatography (GC)-mass spectroscopy (MS) and-olfactometry(O), while the sensory evaluation was made with trained panelists. The optimum conditions for the SPME were found to be CAR/PDMS as the fiber, 60___C as the temperature, and 60__min as the time. SPME analyses were carried out at 60___C for 60__min with toluene as an internal standard. 26 compounds were monitored before and after conching. The unconched sample had a significantly higher fruity odor value than the conched sample. This new product was highly acceptable according to the overall inclination test. However some of textural properties, such as coarseness, and hardness were below the general preference. ",Journal of food science and technology,[],[],Effect of cinnamon powder addition during conching on the flavor of dark chocolate mass.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/25829576,2015,0,0,, +0.38,23521141,"The dietary intakes of nine synthetic food colours--amaranth, erythrosine, Allura Red, Ponceau 4R, tartrazine, Sunset Yellow FCF, Fast Green FCF, Brilliant Blue FCF and indigo carmine--permitted in Korea were estimated based on food consumption data for consumers and their concentrations in processed foods. The estimated daily intakes (EDIs) by Korean consumers were compared with the acceptable daily intakes (ADIs) of the colours. Among 704 foods sampled, 471 contained synthetic colours. The most highly consumed synthetic colours were Allura Red and tartrazine; the highest EDI/ADI ratios were found for amaranth, erythrosine and Allura Red. The EDIs of infants and children were higher than those of adults. The main food categories containing colours were beverages and liquor for adults, and beverages, chocolate and ice cream for infants and children. For average Korean consumers, the EDIs were not greater than 2.5% of their corresponding ADIs, although the EDI of a conservative consumer in the upper 95th percentile reached 37% of the ADI.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D002648', 'D002675', 'D002851', 'D004781', 'D005260', 'D005505', 'D006801', 'D007223', 'D008297', 'D056910', 'D013056']","['Child', 'Child, Preschool', 'Chromatography, High Pressure Liquid', 'Environmental Exposure', 'Female', 'Food Coloring Agents', 'Humans', 'Infant', 'Male', 'Republic of Korea', 'Spectrophotometry, Ultraviolet']",Exposure assessment of synthetic colours approved in Korea.,"[None, None, None, None, None, None, None, None, None, None, None]","[None, None, None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/23521141,2013,0,0,, +0.37,19559444,"A simple and direct approach was developed for thermochemolytic analysis of a wide range of biomolecules present in plant materials using an injection port of a gas chromatograph/mass spectrometer (GC/MS) and a novel solids injector consisting of a coiled stainless steel wire placed inside a modified needle syringe. Optimum thermochemolysis (or Thermally Assisted Hydrolysis/Methylation) was achieved by using a suitable methanolic solution of trimethylsulfonium hydroxide (TMSH) or tetramethylammonium hydroxide (TMAH) with an injection port temperature of 350 degrees C. Intact, methylated flavonoids, saccharides, phenolic and fatty acids, lignin dimers and diterpene resin acids were identified. Samples include tea leaves, hemicelluloses, lignin isolates and herbal medicines. Unexpected chromatographic results using TMAH reagent revealed the presence of intact methylated trisaccharides (658 Da) and structurally informative dimer lignin markers.",Journal of chromatography. A,"['D002099', 'D002392', 'D004867', 'D008401', 'D020902', 'D028221', 'D008031', 'D028223', 'D010936', 'D018515', 'D011134', 'D000644', 'D015203', 'D013452', 'D013662', 'D013696']","['Cacao', 'Catechin', 'Equipment Design', 'Gas Chromatography-Mass Spectrometry', 'Hypericum', 'Larix', 'Lignin', 'Pinus', 'Plant Extracts', 'Plant Leaves', 'Polysaccharides', 'Quaternary Ammonium Compounds', 'Reproducibility of Results', 'Sulfonium Compounds', 'Tea', 'Temperature']",Use of an injection port for thermochemolysis-gas chromatography/mass spectrometry: rapid profiling of biomaterials.,"['Q000737', 'Q000032', None, 'Q000295', 'Q000737', 'Q000737', 'Q000032', 'Q000737', 'Q000032', 'Q000737', 'Q000032', 'Q000737', None, 'Q000737', 'Q000737', None]","['chemistry', 'analysis', None, 'instrumentation', 'chemistry', 'chemistry', 'analysis', 'chemistry', 'analysis', 'chemistry', 'analysis', 'chemistry', None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/19559444,2009,2,1,text under section 3.2 , +0.37,27730643,"Rapid and early identification of micro-organisms in blood has a key role in the diagnosis of a febrile patient, in particular, in guiding the clinician to define the correct antibiotic therapy. This study presents a simple and very fast method with high performances for identifying bacteria by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) after only 4__h of incubation. We used early bacterial growth on PolyViteX chocolate agar plates inoculated with five drops of blood-broth medium deposited in the same point and spread with a sterile loop, followed by a direct transfer procedure on MALDI-TOF MS target slides without additional modification. Ninety-nine percentage of aerobic bacteria were correctly identified from 600 monomicrobial-positive blood cultures. This procedure allowed obtaining the correct identification of fastidious pathogens, such as Streptococcus pneumoniae, Neisseria meningitidis and Haemophilus influenzae that need complex nutritional and environmental requirements in order to grow. Compared to the traditional pathogen identification from blood cultures that takes over 24__h, the reliability of results, rapid performance and suitability of this protocol allowed a more rapid administration of optimal antimicrobial treatment in the patients.",Letters in applied microbiology,"['D001420', 'D001431', 'D001769', 'D000071997', 'D006801', 'D012680', 'D019032', 'D013997']","['Bacteria, Aerobic', 'Bacteriological Techniques', 'Blood', 'Blood Culture', 'Humans', 'Sensitivity and Specificity', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Time Factors']",Reducing time to identification of aerobic bacteria and fastidious micro-organisms in positive blood cultures.,"['Q000737', 'Q000379', 'Q000382', None, None, None, 'Q000379', None]","['chemistry', 'methods', 'microbiology', None, None, None, 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/27730643,2017,0,0,,no cocoa +0.37,24780068,"It has been suggested that vitamin D___ is not very prevalent in the human food chain. However, data from a number of recent intervention studies suggest that the majority of subjects had measurable serum 25-hydroxyvitamin D___ (25(OH)D___) concentrations. Serum 25(OH)D___, unlike 25(OH)D___, is not directly influenced by exposure of skin to sun and thus has dietary origins; however, quantifying dietary vitamin D___ is difficult due to the limitations of food composition data. Therefore, the present study aimed to characterise serum 25(OH)D___ concentrations in the participants of the National Adult Nutrition Survey (NANS) in Ireland, and to use these serum concentrations to estimate the intake of vitamin D___ using a mathematical modelling approach. Serum 25(OH)D___ concentration was measured by a liquid chromatography-tandem MS method, and information on diet as well as subject characteristics was obtained from the NANS. Of these participants, 78.7 % (n 884) had serum 25(OH)D___ concentrations above the limit of quantification, and the mean, maximum, 10th, 50th (median) and 90th percentile values of serum 25(OH)D___ concentrations were 3.69, 27.6, 1.71, 2.96 and 6.36 nmol/l, respectively. To approximate the intake of vitamin D___ from these serum 25(OH)D___ concentrations, we used recently published data on the relationship between vitamin D intake and the responses of serum 25(OH)D concentrations. The projected 5th to 95th percentile intakes of vitamin D___ for adults were in the range of 0.9-1.2 and 5-6 __g/d, respectively, and the median intake ranged from 1.7 to 2.3 __g/d. In conclusion, the present data demonstrate that 25(OH)D___ concentrations are present in the sera of adults from this nationally representative sample. Vitamin D___ may have an impact on nutritional adequacy at a population level and thus warrants further investigation.",The British journal of nutrition,"['D015652', 'D000328', 'D000363', 'D002099', 'D016208', 'D004032', 'D019587', 'D004872', 'D005260', 'D005527', 'D055951', 'D006801', 'D007494', 'D008297', 'D008954', 'D009749', 'D009752', 'D009753', 'D014808']","['25-Hydroxyvitamin D 2', 'Adult', 'Agaricales', 'Cacao', 'Databases, Factual', 'Diet', 'Dietary Supplements', 'Ergocalciferols', 'Female', 'Food, Fortified', 'Functional Food', 'Humans', 'Ireland', 'Male', 'Models, Biological', 'Nutrition Surveys', 'Nutritional Status', 'Nutritive Value', 'Vitamin D Deficiency']",Dietary vitamin D___--a potentially underestimated contributor to vitamin D nutritional status of adults?,"['Q000097', None, 'Q000737', 'Q000737', None, 'Q000009', 'Q000032', 'Q000008', None, 'Q000032', 'Q000032', None, None, None, None, None, None, None, 'Q000097']","['blood', None, 'chemistry', 'chemistry', None, 'adverse effects', 'analysis', 'administration & dosage', None, 'analysis', 'analysis', None, None, None, None, None, None, None, 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/24780068,2014,0,0,, +0.37,2433870,"The nickel content of food items consumed in Denmark was estimated on the basis of analysis by atomic absorption spectrophotometry. The highest concentrations (1-10 mg nickel/kg fresh weight) were found in cocoa, licorice, lucerne seeds, dried beans, peanuts, hazel nuts, sunflower seeds, oat meal and wheat bran. A diet instruction sheet is proposed as an aid to reduce the amount of nickel ingested. The nickel intake of 8 normal volunteers for 24-hour periods was measured when they ingested their usual diet, reduced nickel intake by adherence to the diet instruction sheet, and when they made a conscious effort to increase nickel intake. It is concluded that it is possible to reduce daily nickel intake in food items.",Acta dermato-venereologica,"['D003718', 'D004032', 'D005260', 'D005504', 'D006801', 'D008297', 'D009532', 'D013054']","['Denmark', 'Diet', 'Female', 'Food Analysis', 'Humans', 'Male', 'Nickel', 'Spectrophotometry, Atomic']",Nickel in Danish food.,"[None, None, None, None, None, None, 'Q000032', None]","[None, None, None, None, None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/2433870,1987,,,, +0.37,27285570,"Polyphenols play an important role in human health. To address their accessibility to a breastfed infant, we planned to evaluate whether breast milk (BM) (colostrum, transitional, and mature) epicatechin metabolites could be related to the dietary habits of mothers. The polyphenol consumption of breastfeeding mothers was estimated using a food frequency questionnaire and 24 h recalls. Solid-phase extraction-ultra performance liquid chromatography-tandem mass spectrometry (SPE-UPLC-MS/MS) was applied for direct epicatechin metabolite analysis. Their bioavailability in BM as a result of dietary ingestion was confirmed in a preliminary experiment with a single dose of dark chocolate. Several host and microbial phase II metabolites of epicatechin were detected in BM among free-living lactating mothers. Interestingly, a modest correlation between dihydroxyvalerolactone sulfate and the intake of cocoa products was observed. Although a very low percentage of dietary polyphenols is excreted in BM, they are definitely in the diet of breastfed infants. Therefore, evaluation of their role in infant health could be further promoted. ",Journal of agricultural and food chemistry,"['D000328', 'D001942', 'D002099', 'D002392', 'D005260', 'D006801', 'D007223', 'D007774', 'D008297', 'D008895', 'D053719']","['Adult', 'Breast Feeding', 'Cacao', 'Catechin', 'Female', 'Humans', 'Infant', 'Lactation', 'Male', 'Milk, Human', 'Tandem Mass Spectrometry']",Dietary Epicatechin Is Available to Breastfed Infants through Human Breast Milk in the Form of Host and Microbial Metabolites.,"[None, None, 'Q000378', 'Q000032', None, None, None, None, None, 'Q000737', None]","[None, None, 'metabolism', 'analysis', None, None, None, None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/27285570,2017,0,0,, +0.37,22972787,"Flavor is one of the most important characteristics of chocolate products and is due to a complex volatile fraction, depending both on the cocoa bean genotype and the several processes occurring during chocolate production (fermentation, drying, roasting and conching). Alkylpyrazines are among the most studied volatiles, being one of the main classes of odorant compounds in cocoa products. In this work, a mass spectrometric approach was used for the comparison of cocoa liquors from different countries. A headspace solid-phase microextraction gas chromatography-mass spectrometry method was developed for the qualitative study of the volatile fraction; the standard addition method was then used for the quantitative determination of five pyrazines (2-methylpyrazine, 2,3-dimethylpyrazine, 2,5-dimethylpyrazine, 2,3,5-trimethylpyrazine and tetramethylpyrazine). Satisfactory figures of merit were obtained: Limits of quantitation were in the range 0.1-2.7___ng/g; repeatability and reproducibility varied between 3% and 7% and between 8% and 14%, respectively. The total content of the pyrazines was remarkably different in the considered samples, ranging from 99 to 708___ng/g. Tetramethylpyrazine showed the highest concentration in all samples, with a maximum value of 585___ng/g. A preliminary study was also performed on the nonvolatile fraction using LC-MS/MS, identifying some flavanols such as catechin, epicatechin and procyanidins.",Journal of mass spectrometry : JMS,[],[],Characterization of cocoa liquors by GC-MS and LC-MS/MS: focus on alkylpyrazines and flavanols.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/22972787,2013,,,, +0.37,4086433,"A headspace gas chromatographic (GC) method, which can be automated, has been developed for determination of methyl bromide. This method has been applied to wheat, flour, cocoa, and peanuts. Samples to be analyzed are placed in headspace sample vials, water is added, and the vials are sealed with Teflon-lined septa. After an appropriate equilibration time at 32 degrees C, the samples are analyzed within 10 h. A sample of the headspace is withdrawn and analyzed on a gas chromatograph equipped with an electron capture detector (ECD). Methyl bromide levels were quantitated by comparison of peak area with a standard. The standard was generated by adding a known amount of methyl bromide to a portion of the matrix being analyzed and which was known to be methyl bromide free. The detection limit of the method was 0.4 ppb. The coefficient of variation (CV) was 6.5% for wheat, 8.3% for flour, 3.3% for cocoa, and 11.6% for peanuts.",Journal - Association of Official Analytical Chemists,"['D010367', 'D002099', 'D002849', 'D005433', 'D005506', 'D006842', 'D014908', 'D014874', 'D014881']","['Arachis', 'Cacao', 'Chromatography, Gas', 'Flour', 'Food Contamination', 'Hydrocarbons, Brominated', 'Triticum', 'Water Pollutants, Chemical', 'Water Supply']",Headspace gas chromatographic method for determination of methyl bromide in food ingredients.,"['Q000032', 'Q000032', None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032']","['analysis', 'analysis', None, 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/4086433,1986,,,, +0.36,25530151,"Dietary intervention studies have shown that flavanols and inorganic nitrate can improve vascular function, suggesting that these two bioactives may be responsible for beneficial health effects of diets rich in fruits and vegetables. We aimed to study interactions between cocoa flavanols (CF) and nitrate, focusing on absorption, bioavailability, excretion, and efficacy to increase endothelial function. In a double-blind randomized, dose-response crossover study, flow-mediated dilation (FMD) was measured in 15 healthy subjects before and at 1, 2, 3, and 4 h after consumption of CF (1.4-10.9 mg/kg bw) or nitrate (0.1-10 mg/kg bw). To study flavanol-nitrate interactions, an additional intervention trial was performed with nitrate and CF taken in sequence at low and high amounts. FMD was measured before (0 h) and at 1h after ingestion of nitrate (3 or 8.5 mg/kg bw) or water. Then subjects received a CF drink (2.7 or 10.9 mg/kg bw) or a micro- and macronutrient-matched CF-free drink. FMD was measured at 1, 2, and 4 h thereafter. Blood and urine samples were collected and assessed for CF and nitric oxide (NO) metabolites with HPLC and gas-phase reductive chemiluminescence. Finally, intragastric formation of NO after CF and nitrate consumption was investigated. Both CF and nitrate induced similar intake-dependent increases in FMD. Maximal values were achieved at 1 h postingestion and gradually decreased to reach baseline values at 4 h. These effects were additive at low intake levels, whereas CF did not further increase FMD after high nitrate intake. Nitrate did not affect flavanol absorption, bioavailability, or excretion, but CF enhanced nitrate-related gastric NO formation and attenuated the increase in plasma nitrite after nitrate intake. Both flavanols and inorganic nitrate can improve endothelial function in healthy subjects at intake amounts that are achievable with a normal diet. Even low dietary intake of these bioactives may exert relevant effects on endothelial function when ingested together.",Free radical biology & medicine,"['D000328', 'D001794', 'D001916', 'D002099', 'D002851', 'D018592', 'D019587', 'D004305', 'D005419', 'D006801', 'D008297', 'D009566', 'D009569', 'D013270', 'D014664']","['Adult', 'Blood Pressure', 'Brachial Artery', 'Cacao', 'Chromatography, High Pressure Liquid', 'Cross-Over Studies', 'Dietary Supplements', 'Dose-Response Relationship, Drug', 'Flavonoids', 'Humans', 'Male', 'Nitrates', 'Nitric Oxide', 'Stomach', 'Vasodilation']",Interactions between cocoa flavanols and inorganic nitrate: additive effects on endothelial function at achievable dietary amounts.,"[None, 'Q000187', 'Q000187', 'Q000737', None, None, None, None, 'Q000008', None, None, 'Q000008', 'Q000032', 'Q000378', 'Q000187']","[None, 'drug effects', 'drug effects', 'chemistry', None, None, None, None, 'administration & dosage', None, None, 'administration & dosage', 'analysis', 'metabolism', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/25530151,2015,0,0,, +0.36,10956135,"A technique based on solid-phase microextraction, mass spectrometry, and multivariate analysis (SPME-MS-MVA) was used to predict the shelf life of pasteurized and homogenized reduced-fat milk and whole-fat chocolate milk sampled over a 7 month period. Using SPME-MS-MVA, which is essentially a mass spectrometry-based electronic-nose instrument, volatile bacterial metabolites were extracted from milk with SPME (Carboxen-PDMS) and injected into a GC capillary column at elevated temperature. Mass fragmentation profiles from the unresolved milk volatile components were normalized to the intensity of a chlorobenzene internal standard mass peak (m/z 112) and subjected to MVA. Prediction models based on partial least-squares regression of mass intensity lists were able to predict the shelf life of samples to approximately +/-1 day, with correlation coefficients greater than 0.98 for the two types of milk samples. Using principal component analysis techniques, the procedure was also useful for classifying samples that were rendered unpalatable by nonmicrobial sources (contamination by copper and sanitizer) as well as by bacteria.",Journal of agricultural and food chemistry,"['D000818', 'D005511', 'D013058', 'D008892', 'D015999']","['Animals', 'Food Handling', 'Mass Spectrometry', 'Milk', 'Multivariate Analysis']","Shelf-life prediction of processed milk by solid-phase microextraction, mass spectrometry, and multivariate analysis.","[None, None, None, None, None]","[None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10956135,2000,0,0,,no cocoa +0.36,28041933,"Ochratoxin A (OTA) is a mycotoxin (fungal toxin) found in multiple foodstuffs. Because OTA has been shown to cause kidney disease in multiple animal models, several governmental bodies around the world have set maximum allowable levels of OTA in different foods and beverages. In this study, we conducted the first exposure and risk assessment study of OTA for the United States' population. A variety of commodities from grocery stores across the US were sampled for OTA over a 2-year period. OTA exposure was calculated from the OTA concentrations in foodstuffs and consumption data for different age ranges. We calculated the margin of safety (MOS) for individual age groups across all commodities of interest. Most food and beverage samples were found to have non-detectable OTA; however, some samples of dried fruits, breakfast cereals, infant cereals, and cocoa had detectable OTA. The lifetime MOS in the US population within the upper 95% of consumers of all possible commodities was >1, indicating negligible risk. In the US, OTA exposure is highest in infants and young children who consume large amounts of oat-based cereals. Even without OTA standards in the US, exposures would not be associated with significant risk of adverse effects.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000293', 'D000328', 'D000368', 'D000369', 'D002648', 'D002675', 'D002853', 'D004032', 'D005260', 'D005506', 'D006801', 'D007223', 'D007231', 'D008297', 'D008875', 'D009183', 'D009793', 'D018570', 'D053719', 'D014481', 'D055815']","['Adolescent', 'Adult', 'Aged', 'Aged, 80 and over', 'Child', 'Child, Preschool', 'Chromatography, Liquid', 'Diet', 'Female', 'Food Contamination', 'Humans', 'Infant', 'Infant, Newborn', 'Male', 'Middle Aged', 'Mycotoxins', 'Ochratoxins', 'Risk Assessment', 'Tandem Mass Spectrometry', 'United States', 'Young Adult']",A risk assessment of dietary Ochratoxin a in the United States.,"[None, None, None, None, None, None, None, None, None, 'Q000032', None, None, None, None, None, 'Q000032', 'Q000032', None, None, None, None]","[None, None, None, None, None, None, None, None, None, 'analysis', None, None, None, None, None, 'analysis', 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28041933,2017,1,1,table 1, +0.36,30011739,"Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) is an ultra-high resolution mass spectrometry technique used mainly in analysis of unresolved complex mixtures comprising tens of thousands of analytes. For the first time, it was used to analyze samples of raw fermented cocoa beans originating from Cameroon and Ivory Coast. The direct infusion mass spectra of the raw fermented cocoa bean extracts showed 10091 and 10911 peaks, resp., rating cocoa among the most complex organic mixtures ever analyzed. Automated molecular formula calculations could assign 2995 and 2968 of the peaks, resp. to formulae containing only C, H, O, N___3 and S___1 atoms. The formulae were separated into four groups depending on their heteroatom content and the intensities of the groups were compared in class plots, showing the highest population in the CHON species, but the highest abundance in the CHO species. Elemental ratios obtained from the molecular formulae were plotted in an intensity coded three-dimensional modification of the van Krevelen diagram. For the CHO species, the van Krevelen diagram showed that most of the intensity belongs to the lipid, polyphenol and carbohydrate regions of the plot. The biggest difference was observed in the CHON group, assigned as peptide degradation products, where the Ivorian beans showed greater variety and molecular diversity and higher total intensity of the nitrogen containing compounds, in accordance with the fact that the Ivorian beans show generally higher nitrogen content than the Cameroon beans. FTICR-MS proves capable not only for high-throughput comparison of major classes of metabolites from cocoa samples from different origins, but also can give insight into the different molecular formulae comprising these compound classes.","Food research international (Ottawa, Ont.)",[],[],Fourier transform ion cyclotron resonance mass spectrometrical analysis of raw fermented cocoa beans of Cameroon and Ivory Coast origin.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/30011739,2018,0,0,, +0.36,24236083,"The genus Capsicum is New World in origin and represents a complex of a wide variety of both wild and domesticated taxa. Peppers or fruits of Capsicum species rarely have been identified in the paleoethnobotanical record in either Meso- or South America. We report here confirmation of Capsicum sp. residues from pottery samples excavated at Chiapa de Corzo in southern Mexico dated from Middle to Late Preclassic periods (400 BCE to 300 CE). Residues from 13 different pottery types were collected and extracted using standard techniques. Presence of Capsicum was confirmed by ultra-performance liquid chromatography (UPLC)/MS-MS Analysis. Five pottery types exhibited chemical peaks for Capsicum when compared to the standard (dihydrocapsaicin). No peaks were observed in the remaining eight samples. Results of the chemical extractions provide conclusive evidence for Capsicum use at Chiapas de Corzo during a 700 year period (400 BCE-300 CE). Presence of Capsicum in different types of culinary-associated pottery raises questions how chili pepper could have been used during this early time period. As Pre-Columbian cacao products sometimes were flavored using Capsicum, the same pottery sample set was tested for evidence of cacao using a theobromine marker: these results were negative. As each vessel that tested positive for Capsicum had a culinary use we suggest here the possibility that chili residues from the Chiapas de Corzo pottery samples reflect either paste or beverage preparations for religious, festival, or every day culinary use. Alternatively, some vessels that tested positive merely could have been used to store peppers. Most interesting from an archaeological context was the presence of Capsicum residue obtained from a spouted jar, a pottery type previously thought only to be used for pouring liquids. ",PloS one,"['D002211', 'D002212', 'D003296', 'D003297', 'D049690', 'D006801', 'D007198', 'D008800', 'D053719']","['Capsaicin', 'Capsicum', 'Cooking', 'Cooking and Eating Utensils', 'History, Ancient', 'Humans', 'Indians, North American', 'Mexico', 'Tandem Mass Spectrometry']","Prehispanic use of chili peppers in Chiapas, Mexico.","['Q000737', 'Q000737', 'Q000266', None, None, None, None, None, None]","['chemistry', 'chemistry', 'history', None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24236083,2014,0,0,,cacao +0.36,29090642,"Brazil is the sixth largest producer of cocoa beans in the world, after C_te d'Ivoire, Ghana, Indonesia, Nigeria and Cameroon. The southern region of Bahia stands out as the country's largest producer, accounting for approximately 60% of production. Due to damage caused by infestation of the cocoa crop with the fungus Moniliophthora perniciosa, which causes 'witch's broom disease', research in cocoa beans has led to the cloning of species that are resistant to the disease; however, there is little information about the development of other fungal genera in these clones, such as Aspergillus, which do not represent a phytopathogenicity problem but can grow during the pre-processing of cocoa beans and produce mycotoxins. Thus, the aim of this work was to determine the presence of aflatoxin (AF) and ochratoxin A (OTA) in cocoa clones developed in Brazil. Aflatoxin and ochratoxin A contamination were determined in 130 samples from 13 cocoa clones grown in the south of Bahia by ultra-performance liquid chromatography with a fluorescence detector. The method was evaluated for limit of detection (LOD) (0.05-0.90 __g kg","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000348', 'D001938', 'D002099', 'D005506', 'D009793']","['Aflatoxins', 'Brazil', 'Cacao', 'Food Contamination', 'Ochratoxins']","Aflatoxins and ochratoxin A in different cocoa clones (Theobroma cacao L.) developed in the southern region of Bahia, Brazil.","['Q000032', None, 'Q000737', 'Q000032', 'Q000032']","['analysis', None, 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/29090642,2018,1,1,table 1 ,if toxins are included this table reflects aflatoxin types (B1-2 and G1-2) and ochratoxin. +0.36,20557892,"This paper reports a comprehensive sensitive multi-residue liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for detection, identification and quantitation of 73 pesticides and their related products, a total of 98 analytes, belonging to organophosphorus pesticides (OPPs) and carbamates, in foods. The proposed method makes use of a modified QuEChERS (quick, easy, cheap, effective, rigged, and safe) procedure that combines isolation of the pesticides and sample clean-up in a single step. Analysis is performed by liquid chromatography-electrospray ionization-tandem mass spectrometry operated in the multiple reaction monitoring (MRM) mode, acquiring two specific precursor-product ion transitions per target compound. Two main fragment ions for each pesticide were obtained to achieve the identification according to the SANCO guidelines 10684/2009. The method was validated with various food samples, including edible oil, meat, egg, cheese, chocolate, coffee, rice, tree nuts, citric fruits, vegetables, etc. No significant matrix effect was observed for tested pesticides, therefore, matrix-matched calibration was not necessary. Calibration curves were linear and covered from 1 to 20 microg L(-1) for all compounds studied. The average recoveries, measured at 10 microg kg(-1), were in the range 70-120% for all of the compounds tested with relative standard deviations below 20%, while a value of 10 microg kg(-1) has been established as the method limit of quantitation (MLOQ) for all target analytes. Similar trueness and precision results were also obtained for spiking at 200 microg kg(-1). Expanded uncertainty values were in the range 21-27% while the HorRat ratios were below 1. The method has been successfully applied to the analysis of 700 food samples in the course of a baseline monitoring study of OPPs and carbamates.",Journal of chromatography. A,"['D002219', 'D002853', 'D005504', 'D005506', 'D009943', 'D010573', 'D053719']","['Carbamates', 'Chromatography, Liquid', 'Food Analysis', 'Food Contamination', 'Organophosphorus Compounds', 'Pesticide Residues', 'Tandem Mass Spectrometry']",Validation and use of a fast sample preparation method and liquid chromatography-tandem mass spectrometry in analysis of ultra-trace levels of 98 organophosphorus pesticide and carbamate residues in a total diet study involving diversified food types.,"['Q000032', 'Q000379', 'Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000379']","['analysis', 'methods', 'methods', 'analysis', 'analysis', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/20557892,2010,0,0,,no cocoa +0.36,25965784,"An easy extraction method that permits the use of a liquid chromatography-isotopic ratio mass spectrometry (LC-IRMS) system to evaluate __(13)C of vanillin in chocolate products and industrial flavorings is presented. The method applies the determination of stable isotopes of carbon to discriminate between natural vanillin from vanilla beans and vanillin from other sources (mixtures from beans, synthesis, or biotechnology). A series of 13 chocolate bars and chocolate snack foods available on the Italian market and 8 vanilla flavorings derived from industrial quality control processes were analyzed. Only 30% of products considered in this work that declared ""vanilla"" on the label showed data that permitted the declaration ""vanilla"" according to European Union (EU) Regulation 1334/2008. All samples not citing ""vanilla"" or ""natural flavoring"" on the label gave the correct declaration. The extraction method is presented with data useful for statistical evaluation. ",Journal of agricultural and food chemistry,"['D001547', 'D002099', 'D002247', 'D005591', 'D002851', 'D005421', 'D013058', 'D010936', 'D062410', 'D031669']","['Benzaldehydes', 'Cacao', 'Carbon Isotopes', 'Chemical Fractionation', 'Chromatography, High Pressure Liquid', 'Flavoring Agents', 'Mass Spectrometry', 'Plant Extracts', 'Snacks', 'Vanilla']",Easy Extraction Method To Evaluate __13C Vanillin by Liquid Chromatography-Isotopic Ratio Mass Spectrometry in Chocolate Bars and Chocolate Snack Foods.,"['Q000032', 'Q000737', 'Q000737', 'Q000379', 'Q000295', 'Q000032', 'Q000379', 'Q000737', None, 'Q000737']","['analysis', 'chemistry', 'chemistry', 'methods', 'instrumentation', 'analysis', 'methods', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25965784,2015,0,0,,no cocoa +0.36,30007697,"Fermentation and drying are the two crucial processing steps required to produce cocoa beans with desired properties, especially taste and flavor. To understand their impact on the lipid profile of cocoa, the lipid composition of unfermented raw and fermented dried beans from six different origins was investigated using high-performance liquid chromatography-mass spectrometry methods. While the comparison of triacylglycerol profiles across the different origins showed only small variations in individual compound concentrations, the comparison along the fermentation status showed major differences regarding the occurrence of polar lipids. These compounds may serve as biomarkers for the fermentation status of the beans and a simple analytical method suitable for field trials is proposed. Finally, a hypothesis identifying key unsaturated triacylglycerols contributing to the hardness and softness of cocoa butter is presented.","Food research international (Ottawa, Ont.)",[],[],Variation of triacylglycerol profiles in unfermented and dried fermented cocoa beans of different origins.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/30007697,2018,2,3,table 1 , triacylglycerols are in percentages for unfermented and fermented samples +0.35,3163354,"The primary aim of this study was to rank several reference foods (apple drink, caramel, chocolate, cookie, skimmed milk powder, snack cracker, and wheat flake) according to their plaque pH response as monitored in a panel of 12 volunteers by the plaque-sampling method for comparison with data previously reported with other methods used to assess cariogenicity potential. Secondary experiments (using subsets of the panel of subjects) were undertaken in an attempt to elucidate some of the reasons for the observed plaque pH changes. Oral carbohydrate retention was measured at a single time period after food use as total anthrone-positive carbohydrate material, and as specific acidogenic sugars by gas-liquid chromatography after gel-exclusion chromatography. The concentrations of acid anions in the plaque fluid after food consumption were measured by isotachophoresis eight min after food use. According to the plaque pH response, apple-flavored fruit drink and chocolate were the most acidogenic foods and skimmed milk powder the least acidogenic. There were significant correlations (p less than 0.05) between the plaque pH data and lactate-plus-acetate concentrations in plaque fluid, but the correlations between the pH data and any of the carbohydrate retention parameters were not significant.",Journal of dental research,"['D000143', 'D000293', 'D000328', 'D000838', 'D002326', 'D003773', 'D004040', 'D005502', 'D005947', 'D006801', 'D006863', 'D007773', 'D009055', 'D013395']","['Acids', 'Adolescent', 'Adult', 'Anions', 'Cariogenic Agents', 'Dental Plaque', 'Dietary Carbohydrates', 'Food', 'Glucose', 'Humans', 'Hydrogen-Ion Concentration', 'Lactates', 'Mouth', 'Sucrose']","The relationship between plaque pH, plaque acid anion profiles, and oral carbohydrate retention after ingestion of several 'reference foods' by human subjects.","['Q000378', None, None, 'Q000378', None, 'Q000378', 'Q000378', None, 'Q000378', None, None, 'Q000378', 'Q000378', 'Q000378']","['metabolism', None, None, 'metabolism', None, 'metabolism', 'metabolism', None, 'metabolism', None, None, 'metabolism', 'metabolism', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/3163354,1988,,,,no pdf access +0.35,961063,"In Coca-powder fumigated with Ethylene Oxide-1,2(14)C, several derivatives were isolated by extraction and preparative Thin Layer Chromatography. Of the two compounds isolated from the water-extract, the structures have been suggested as N,N-Bis-(Di-Ethoxy-O-Hydroxy-ethyl)-Isoleucyl-Alanyl-Cysteine (MW = 569)and N-Ethoxy-O-Hydroxyethyl)-Tyrosine (MW = 269), based on I.R. and Mass Spectrometry. Their approximate concentrations were found to be 20 and 50 mg/kg respectively.",Zeitschrift fur Lebensmittel-Untersuchung und -Forschung,"['D000596', 'D002099', 'D002855', 'D005030', 'D008970', 'D010455']","['Amino Acids', 'Cacao', 'Chromatography, Thin Layer', 'Ethylenes', 'Molecular Weight', 'Peptides']","[Isolation of the Derivatives from Coca-Powder Fumigated by Ethylene Oxide 1,2-14 C and their Structure Suggested on the Basis of I. R. and Mass-Spectrometry].","['Q000302', 'Q000032', None, 'Q000494', None, 'Q000302']","['isolation & purification', 'analysis', None, 'pharmacology', None, 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/961063,1976,,,, +0.35,1941565,"Both regular and decaffeinated coffees were found to have cholinomimetic actions when tested in urethane-anesthetized rats. These actions were distinct from those of caffeine and reversible by atropine. The bioactive fraction was purified from alcoholic extracts of instant decaffeinated coffee by liquid column chromatography and preparative TLC. The purified compound showed similar pharmacological actions as the starting material. Chromatographic behavior was further characterized by analytical TLC and HPLC. Chromatographic analyses of extracts of green coffee beans and roasted ground coffees showed that the cardioactive compound was only present in roasted coffees. Similar analyses of other commonly consumed beverages, including teas and cocoa, showed that this compound was not present in beverages besides coffee.",Journal of pharmaceutical sciences,"['D000109', 'D000818', 'D001285', 'D001794', 'D002099', 'D002851', 'D002855', 'D003069', 'D008297', 'D010277', 'D051381', 'D011919', 'D013056', 'D013662']","['Acetylcholine', 'Animals', 'Atropine', 'Blood Pressure', 'Cacao', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Coffee', 'Male', 'Parasympathomimetics', 'Rats', 'Rats, Inbred Strains', 'Spectrophotometry, Ultraviolet', 'Tea']",Coffee contains cholinomimetic compound distinct from caffeine. I: Purification and chromatographic analysis.,"['Q000032', None, 'Q000494', 'Q000187', 'Q000032', None, None, 'Q000032', None, 'Q000032', None, None, None, 'Q000032']","['analysis', None, 'pharmacology', 'drug effects', 'analysis', None, None, 'analysis', None, 'analysis', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/1941565,1991,,,,no pdf access +0.35,17440516,To investigate the intake of plant sterols and identify major dietary sources of plant sterols in the British diet.,European journal of clinical nutrition,"['D000328', 'D000368', 'D000704', 'D001939', 'D002849', 'D015331', 'D003430', 'D016208', 'D004034', 'D004041', 'D002523', 'D005260', 'D005504', 'D006801', 'D008297', 'D008875', 'D010840', 'D011446', 'D017678', 'D011795', 'D006113', 'D014675']","['Adult', 'Aged', 'Analysis of Variance', 'Bread', 'Chromatography, Gas', 'Cohort Studies', 'Cross-Sectional Studies', 'Databases, Factual', 'Diet Surveys', 'Dietary Fats', 'Edible Grain', 'Female', 'Food Analysis', 'Humans', 'Male', 'Middle Aged', 'Phytosterols', 'Prospective Studies', 'Sex Distribution', 'Surveys and Questionnaires', 'United Kingdom', 'Vegetables']",Food sources of plant sterols in the EPIC Norfolk population.,"[None, None, None, None, 'Q000379', None, None, None, None, 'Q000032', None, None, 'Q000379', None, None, None, 'Q000008', None, None, None, None, None]","[None, None, None, None, 'methods', None, None, None, None, 'analysis', None, None, 'methods', None, None, None, 'administration & dosage', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/17440516,2008,0,0,,no cocoa +0.35,29885500,"The present study aims to quantify acrylamide in caffeinated beverages including American coffee, Lebanese coffee, espresso, instant coffee and hot chocolate, and to determine their carcinogenic and neurotoxic risks. A survey was carried for this purpose whereby 78% of the Lebanese population was found to consume at least one type of caffeinated beverages. Gas Chromatography Mass Spectrometry analysis revealed that the average acrylamide level in caffeinated beverages is 29,176____g/kg sample. The daily consumption of acrylamide from Lebanese coffee (10.9 __g/kg-bw/day), hot chocolate (1.2 __g/kg-bw/day) and Espresso (7.4 __g/kg-bw/day) was found to be higher than the risk intake for carcinogenicity and neurotoxicity as set by World Health Organization (WHO; 0.3-2 __g/kg-bw/day) at both the mean (average consumers) and high (high consumers) dietary exposures. On the other hand, American coffee (0.37 __g/kg-bw/day) was shown to pose no carcinogenic or neurotoxic risks among the Lebanese community for consumers with a mean dietary exposure. The study shows alarming results that call for regulating the caffeinated product industry by setting legislations and standard protocols for product preparation in order to limit the acrylamide content and protect consumers. In order to avoid carcinogenic and neurotoxic risks, we propose that WHO/FAO set acrylamide levels in caffeinated beverages to 7000____g acrylamide/kg sample, a value which is 4-folds lower than the average acrylamide levels of 29,176____g/kg sample found in caffeinated beverages sold in the Lebanese market. Alternatively, consumers of caffeinated products, especially Lebanese coffee and espresso, would have to lower their daily consumption to 0.3-0.4 cups/day.",Chemosphere,[],[],Carcinogenic and neurotoxic risks of acrylamide consumed through caffeinated beverages among the lebanese population.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29885500,2018,0,0,,no cocoa +0.35,27700988,"In this work, an efficient method for preparative separation of procyanidins from raw cacao bean extract by high-speed counter-current chromatography (HSCCC) was developed. Under the optimized solvent system of n-hexane-ethyl acetate-water (1:50:50, v/v/v) with a combination of head-tail and tail-head elution modes, various procyanidins fractions with different polymerization degrees were successfully separated. UPLC, QTOF-MS and ","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000975', 'D052160', 'D044946', 'D001713', 'D002099', 'D002392', 'D002851', 'D003377', 'D010851', 'D010936', 'D044945', 'D012997', 'D013451']","['Antioxidants', 'Benzothiazoles', 'Biflavonoids', 'Biphenyl Compounds', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Countercurrent Distribution', 'Picrates', 'Plant Extracts', 'Proanthocyanidins', 'Solvents', 'Sulfonic Acids']",Preparative separation of cacao bean procyanidins by high-speed counter-current chromatography.,"['Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000379', 'Q000379', 'Q000737', 'Q000737', 'Q000737', None, 'Q000737']","['chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'methods', 'methods', 'chemistry', 'chemistry', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27700988,2017,0,0,, +0.35,25123980,"Cadmium (Cd) and lead (Pb) concentrations and their relationship to the cocoa content of chocolates commercialized in Brazil were evaluated by graphite furnace atomic absorption spectrometry (GF AAS) after microwave-assisted acid digestion. Several chemical modifiers were tested during method development, and analytical parameters, including the limits of detection and quantification as well as the accuracy and precision of the overall procedure, were assessed. The study examined 30 chocolate samples, and the concentrations of Cd and Pb were in the range of <1.7-107.6 and <21-138.4 ng/g, respectively. The results indicated that dark chocolates have higher concentrations of Cd and Pb than milk and white chocolates. Furthermore, samples with five different cocoa contents (ranging from 34 to 85%) from the same brand were analyzed, and linear correlations between the cocoa content and the concentrations of Cd (R(2) = 0.907) and Pb (R(2) = 0.955) were observed. The results showed that chocolate might be a significant source of Cd and Pb ingestion, particularly for children. ",Journal of agricultural and food chemistry,"['D000818', 'D001938', 'D002099', 'D002104', 'D002417', 'D005506', 'D007854', 'D008892', 'D012639']","['Animals', 'Brazil', 'Cacao', 'Cadmium', 'Cattle', 'Food Contamination', 'Lead', 'Milk', 'Seeds']",Cadmium and lead in chocolates commercialized in Brazil.,"[None, None, 'Q000737', 'Q000032', None, 'Q000032', 'Q000032', 'Q000737', 'Q000737']","[None, None, 'chemistry', 'analysis', None, 'analysis', 'analysis', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25123980,2015,0,0,, +0.35,28460952,"Re-utilization of various agro-industrial wastes is of growing importance from many aspects. Considering the variety and complexity of such materials, compositional data and compliant methodology is still undergoing many updates and improvements. Present study evaluated sugar beet pulp (SBP), walnut shell (WS), cocoa bean husk (CBH), onion peel (OP) and pea pods (PP) as potentially valuable materials for carbohydrate recovery. Macrocomponent analyses revealed carbohydrate fraction as the most abundant, dominating in dietary fibres. Upon complete acid hydrolysis of sample alcohol insoluble residues, developed procedures of high performance thin-layer chromatography (HPTLC) and high performance liquid chromatography (HPLC) coupled with 3-methyl-1-phenyl-2-pyrazolin-5-one pre-column derivatization (PMP-derivatization) were used for carbohydrate monomeric composition determination. HPTLC exhibited good qualitative features useful for multi-sample rapid analysis, while HPLC superior separation and quantification characteristics. Distinctive monomeric patterns were obtained among samples. OP, SBP and CBH, due to the high galacturonic acid content (20.81%, 13.96% and 6.90% dry matter basis, respectively), may be regarded as pectin sources, while WS and PP as materials abundant in xylan-rich hemicellulose (total xylan content 15.53%, 9.63% dry matter basis, respectively). Present study provides new and valuable compositional data for different plant residual materials and a reference for the application of established methodology.","Food research international (Ottawa, Ont.)",[],[],Compositional evaluation of selected agro-industrial wastes as valuable sources for the recovery of complex carbohydrates.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/28460952,2018,2,1,table 1, +0.34,19447973,"The US industry standard for shelf-life of whole milk powder (WMP) is 6 to 9 mo, although previous research has demonstrated flavor changes by 3 mo at ambient storage. This study evaluated the influence of packaging atmosphere, storage temperature, and storage time on WMP shelf-life using sensory and instrumental techniques. Two commercial batches of WMP were repackaged in plastic laminate pouches with air or nitrogen and stored at 2 degrees C or 23 degrees C for 1 yr. Descriptive analysis was conducted using a 10-member trained panel; volatile analysis was performed using solid-phase microextraction with gas chromatography-mass spectrometry. Consumer acceptance (n = 75) was conducted every 3 mo with reconstituted WMP and white and milk chocolate made from each treatment. Data were analyzed using ANOVA with Fisher's LSD, Pearson correlation analysis, and principal component analysis. Air-stored WMP had higher peroxide values, lipid oxidation volatiles, and grassy and painty flavors than nitrogen-flushed WMP. Storage temperature did not affect levels of straight chain lipid oxidation volatiles; 23 degrees C storage resulted in higher cooked and milkfat flavors and lower levels of grassy flavor compared with 2 degrees C storage. Consumer acceptance was negatively correlated with lipid oxidation volatiles and painty flavor. Nitrogen flushing prevented the development of painty flavor in WMP stored up to 1 yr at either temperature, resulting in chocolate with high consumer acceptance. Nitrogen flushing can be applied to extend the shelf life of WMP for use in chocolate; storage temperature also plays a role, but to a lesser extent.",Journal of dairy science,"['D000328', 'D000818', 'D002099', 'D003116', 'D005511', 'D005524', 'D006801', 'D008875', 'D008892', 'D009584', 'D010100', 'D010545', 'D025341', 'D013649', 'D013696', 'D055815']","['Adult', 'Animals', 'Cacao', 'Color', 'Food Handling', 'Food Technology', 'Humans', 'Middle Aged', 'Milk', 'Nitrogen', 'Oxygen', 'Peroxides', 'Principal Component Analysis', 'Taste', 'Temperature', 'Young Adult']",Effect of nitrogen flushing and storage temperature on flavor and shelf-life of whole milk powder.,"[None, None, 'Q000592', None, 'Q000379', None, None, None, 'Q000737', 'Q000737', 'Q000032', 'Q000032', None, None, None, None]","[None, None, 'standards', None, 'methods', None, None, None, 'chemistry', 'chemistry', 'analysis', 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19447973,2009,,,,no pdf access +0.34,22652759,"The enzyme chitinase from Moniliophthora perniciosa the causative agent of the witches' broom disease in Theobroma cacao, was partially purified with ammonium sulfate and filtration by Sephacryl S-200 using sodium phosphate as an extraction buffer. Response surface methodology (RSM) was used to determine the optimum pH and temperature conditions. Four different isoenzymes were obtained: ChitMp I, ChitMp II, ChitMp III and ChitMp IV. ChitMp I had an optimum temperature at 44-73__C and an optimum pH at 7.0-8.4. ChitMp II had an optimum temperature at 45-73__C and an optimum pH at 7.0-8.4. ChitMp III had an optimum temperature at 54-67__C and an optimum pH at 7.3-8.8. ChitMp IV had an optimum temperature at 60__C and an optimum pH at 7.0. For the computational biology, the primary sequence was determined in silico from the database of the Genome/Proteome Project of M. perniciosa, yielding a sequence with 564 bp and 188 amino acids that was used for the three-dimensional design in a comparative modeling methodology. The generated models were submitted to validation using Procheck 3.0 and ANOLEA. The model proposed for the chitinase was subjected to a dynamic analysis over a 1 ns interval, resulting in a model with 91.7% of the residues occupying favorable places on the Ramachandran plot and an RMS of 2.68.",Anais da Academia Brasileira de Ciencias,"['D000363', 'D000595', 'D002688', 'D002850', 'D008954', 'D008969']","['Agaricales', 'Amino Acid Sequence', 'Chitinases', 'Chromatography, Gel', 'Models, Biological', 'Molecular Sequence Data']","Purification, characterization and structural determination of chitinases produced by Moniliophthora perniciosa.","['Q000201', None, 'Q000096', None, None, None]","['enzymology', None, 'biosynthesis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22652759,2012,0,0,, +0.34,19238621,"A survey was undertaken of aflatoxin B1 (AFB1), B2 (AFB2), G1 (AFG1), G2 (AFG2), ochratoxin A (OTA), and fumonisin B1 (FB1), B2 (FB2) and B3 (FB3) contamination of various retail foods in Japan during 2004-05. The mycotoxins were analysed by high-performance liquid chromatography (HPLC), liquid chromatography/mass spectrometry (LC/MS) or high-performance thin-layer chromatography (HPTLC). Aflatoxins (AFs) were detected in ten of 21 peanut butter and in 22 of 44 bitter chocolate samples; the highest level of AFB1, 2.59 microg kg(-1), was found in peanut butter. Aflatoxin contamination was not observed in corn products (n = 55), corn (n = 110), peanuts (n = 120), buckwheat flour (n = 23), dried buckwheat noodles (n = 59), rice (n = 83) or sesame oil (n = 20). OTA was detected in 120 out of 192 samples of oatmeal, wheat flour, rye, buckwheat flour, raw coffee, roasted coffee, raisin, beer, wine and bitter chocolate, but not in rice or corn products. OTA levels in the positive samples were below 13 microg kg(-1). AFs and OTA intakes through the consumption of foods containing cacao were estimated using the data for mycotoxin contamination in bitter chocolate and those for the consumption of foods containing cacao in Japan.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000293', 'D000348', 'D000367', 'D002099', 'D002273', 'D002648', 'D002675', 'D005247', 'D005504', 'D005506', 'D006801', 'D007223', 'D009183', 'D009793', 'D055815']","['Adolescent', 'Aflatoxins', 'Age Factors', 'Cacao', 'Carcinogens', 'Child', 'Child, Preschool', 'Feeding Behavior', 'Food Analysis', 'Food Contamination', 'Humans', 'Infant', 'Mycotoxins', 'Ochratoxins', 'Young Adult']",Aflatoxin and ochratoxin A contamination of retail foods and intake of these mycotoxins in Japan.,"[None, 'Q000032', None, 'Q000737', 'Q000032', None, None, None, 'Q000379', 'Q000032', None, None, 'Q000032', 'Q000032', None]","[None, 'analysis', None, 'chemistry', 'analysis', None, None, None, 'methods', 'analysis', None, None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/19238621,2009,,,, +0.34,21663990,"This paper reports the occurrence of aflatoxigenic fungi and the presence of aflatoxins in 226 cocoa samples collected on Brazilian farms. The samples were taken at various stages of fermentation, drying and storage. A total of 819 potentially aflatoxigenic fungi were isolated using Dichloran 18% Glycerol agar after surface disinfection, and identified by standard techniques. The ability of the fungi to produce aflatoxins was determined using the agar plug technique and TLC. The presence of aflatoxins in cocoa samples was determined by HPLC using post-column derivatization with bromide after immunoaffinity column clean up. The aflatoxigenic fungi isolated were Aspergillus flavus, A. parasiticus and A. nomius. A considerable increase in numbers of these species was observed during drying and storage. In spite of the high prevalence of aflatoxigenic fungi, only low levels of aflatoxin were found in the cocoa samples, suggesting the existence of limiting factors to the accumulation of aflatoxins in the beans.",International journal of food microbiology,"['D000348', 'D001230', 'D001938', 'D002099', 'D002851', 'D005285', 'D005506', 'D005511']","['Aflatoxins', 'Aspergillus', 'Brazil', 'Cacao', 'Chromatography, High Pressure Liquid', 'Fermentation', 'Food Contamination', 'Food Handling']",Aflatoxigenic fungi and aflatoxin in cocoa.,"['Q000032', 'Q000302', None, 'Q000382', None, None, 'Q000032', None]","['analysis', 'isolation & purification', None, 'microbiology', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/21663990,2011,1,1,table 1, +0.34,17164979,Long term cocoa ingestion leads to an increased resistance against UV-induced erythema and a lowered transepidermal water loss.,European journal of nutrition,"['D000293', 'D000328', 'D000368', 'D001628', 'D002099', 'D002392', 'D002851', 'D018592', 'D004305', 'D005260', 'D005419', 'D006801', 'D017078', 'D008833', 'D008875', 'D012867', 'D013057']","['Adolescent', 'Adult', 'Aged', 'Beverages', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Cross-Over Studies', 'Dose-Response Relationship, Drug', 'Female', 'Flavonoids', 'Humans', 'Laser-Doppler Flowmetry', 'Microcirculation', 'Middle Aged', 'Skin', 'Spectrum Analysis']",Consumption of flavanol-rich cocoa acutely increases microcirculation in human skin.,"[None, None, None, None, 'Q000737', 'Q000008', 'Q000379', None, None, None, 'Q000008', None, 'Q000379', 'Q000187', None, 'Q000098', 'Q000379']","[None, None, None, None, 'chemistry', 'administration & dosage', 'methods', None, None, None, 'administration & dosage', None, 'methods', 'drug effects', None, 'blood supply', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/17164979,2007,0,,, +0.34,3369241,"The analytical application of direct pyrolysis (Py) field ionization (FI)-mass spectrometry (MS) und Curie-point pyrolysis gas chromatography-mass spectrometry (Py-GC/FIMS) to various whole foodstuffs is described for the first time. The former technique yields highly differentiated information from the sample in typically 15 min, namely the molecular weight distribution of released volatiles and pyrolysis products in a single spectrum which, owing to the good reproducibility and high significance of the resulting data, has previously been shown to be suitable for the application of chemometric methods. Such mass spectral peaks are further characterized and assigned by high resolution mass measurement and/or by electron ionization after Curie-point pyrolysis and gas chromatographic separation of the components. In this first report, typical results are presented for ground roasted coffee, rosehip tea, wheatmeal biscuit, chocolate drink powder and milk chocolate. The FI mass spectrum obtained from the latter sample is compared with those obtained using the complementary soft ionization techniques of chemical ionization (CI) and direct chemical ionization (DCI).",Zeitschrift fur Lebensmittel-Untersuchung und -Forschung,"['D000818', 'D002099', 'D003069', 'D005504', 'D008401', 'D006358', 'D008892', 'D013057', 'D013662']","['Animals', 'Cacao', 'Coffee', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Hot Temperature', 'Milk', 'Spectrum Analysis', 'Tea']",Fast profiling of food by analytical pyrolysis.,"[None, 'Q000032', 'Q000032', 'Q000379', 'Q000379', None, 'Q000032', 'Q000379', 'Q000032']","[None, 'analysis', 'analysis', 'methods', 'methods', None, 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/3369241,1988,,,, +0.34,17245391,"Indigenous people of the Torres Strait Islands have been concerned about the safety of their traditional seafoods since the discovery of high cadmium levels in the liver and kidney of dugong and turtle in 1996. This study explored links between urinary cadmium levels and consumption frequency of these traditional foods and piloted a community-based methodology to identify potential determinants of cadmium exposure and accumulation. Consultations led to selection of one community for study from which 60 women aged 30 to 50 years participated in health and food frequency survey, urine collection and a routine health check. Urinary cadmium levels were determined by inductively coupled plasma-mass spectrometry; data were analysed using SPSS-14. The geometric mean cadmium level in this group of women was 1.17 (arithmetic mean 1.86) microg/g creatinine with one-third exceeding 2.0 microg/g creatinine. Heavy smoking (>or=300 pack years) was linked to higher cadmium in urine, as was increasing age and waist circumference. Analysis of age-adjusted residuals revealed significant associations (P<0.05) between cadmium level and higher consumption of turtle liver and kidney, locally gathered clams, peanuts, coconut, chocolate and potato chips. Dugong kidney consumption approached significance (P=0.06). Multiple regression revealed that 40% (adjusted r(2)) of variation in cadmium level was explained by the sum of these associated foods plus heavy smoking, age and waist circumference. No relationships between cadmium and pregnancy history were found. This paper presents a novel approach to explore contributions of foods and other factors to exposure to toxins at community level and the first direct evidence that frequent turtle (and possibly dugong) liver and kidney and wild clam consumption is linked to higher urinary cadmium levels among Torres Strait Islander women.",Journal of exposure science & environmental epidemiology,"['D000328', 'D000818', 'D001315', 'D049872', 'D002104', 'D004032', 'D020454', 'D004784', 'D004785', 'D005260', 'D005506', 'D006801', 'D007668', 'D008099', 'D008875', 'D044382', 'D017747', 'D014426']","['Adult', 'Animals', 'Australia', 'Bivalvia', 'Cadmium', 'Diet', 'Dugong', 'Environmental Monitoring', 'Environmental Pollutants', 'Female', 'Food Contamination', 'Humans', 'Kidney', 'Liver', 'Middle Aged', 'Population Groups', 'Seafood', 'Turtles']",Exploring potential dietary contributions including traditional seafood and other determinants of urinary cadmium levels among indigenous women of a Torres Strait Island (Australia).,"[None, None, None, None, 'Q000652', None, None, None, 'Q000652', None, None, None, None, None, None, None, None, None]","[None, None, None, None, 'urine', None, None, None, 'urine', None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/17245391,2007,0,0,,no cocoa +0.34,22483203,"The characterization and authentication of fats and oils is a subject of great importance for market and health aspects. Identification and quantification of triacylglycerols in fats and oils can be excellent tools for detecting changes in their composition due to the mixtures of these products. Most of the triacylglycerol species present in either fats or oils could be analyzed and identified by chromatographic methods. However, the natural variability of these samples and the possible presence of adulterants require the application of chemometric pattern recognition methods to facilitate the interpretation of the obtained data. In view of the growing interest in this topic, this paper reviews the literature of the application of exploratory and unsupervised/supervised chemometric methods on chromatographic data, using triacylglycerol composition for the characterization and authentication of several foodstuffs such as olive oil, vegetable oils, animal fats, fish oils, milk and dairy products, cocoa and coffee.",Analytica chimica acta,"['D002849', 'D002851', 'D002855', 'D016002', 'D005223', 'D005504', 'D013058', 'D009821', 'D025341', 'D014280']","['Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Discriminant Analysis', 'Fats', 'Food Analysis', 'Mass Spectrometry', 'Oils', 'Principal Component Analysis', 'Triglycerides']",Combining chromatography and chemometrics for the characterization and authentication of fats and oils from triacylglycerol compositional data--a review.,"[None, None, None, None, 'Q000032', 'Q000379', None, 'Q000032', None, 'Q000032']","[None, None, None, None, 'analysis', 'methods', None, 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/22483203,2012,0,0,, +0.34,10435077,"A survey on the potential intake of caffeine was carried out in Campinas, SP, Brazil, in the summer of 1993. The survey was based on a representative sample of 600 individuals, 9-80 years old, who were asked about their habitual usage of coffee, tea, chocolate products and carbonated beverages. Caffeine levels in the products were determined by high performance liquid chromatography with a UV-visible detector at 254 nm. Individual daily intakes (mg/kg b.w.) of caffeine were calculated from the consumption data generated by the survey and the caffeine content of the analysed products. Of all those interviewed, 81% consumed soft drinks regularly, 75% coffee, 65% chocolate products and 37% tea. Of the analysed products, coffee showed the highest amount of caffeine. The average and median potential daily intake of caffeine by the studied population were, respectively, 2.74 and 1.85 mg/kg b.w. Coffee, tea, chocolate products and carbonated beverages accounted for median individual daily intakes of 1.90, 0.32, 0.19, and 0.19 mg/kg b.w., respectively. These data show that coffee is the most important vehicle for caffeine intake within the studied population.",Food additives and contaminants,"['D000293', 'D000328', 'D017677', 'D000368', 'D000369', 'D001628', 'D001938', 'D002099', 'D002110', 'D000697', 'D002648', 'D003069', 'D004034', 'D005247', 'D005260', 'D006801', 'D008297', 'D008875', 'D013662']","['Adolescent', 'Adult', 'Age Distribution', 'Aged', 'Aged, 80 and over', 'Beverages', 'Brazil', 'Cacao', 'Caffeine', 'Central Nervous System Stimulants', 'Child', 'Coffee', 'Diet Surveys', 'Feeding Behavior', 'Female', 'Humans', 'Male', 'Middle Aged', 'Tea']",Caffeine daily intake from dietary sources in Brazil.,"[None, None, None, None, None, 'Q000032', None, 'Q000737', 'Q000008', 'Q000008', None, 'Q000737', None, None, None, None, None, None, 'Q000737']","[None, None, None, None, None, 'analysis', None, 'chemistry', 'administration & dosage', 'administration & dosage', None, 'chemistry', None, None, None, None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/10435077,1999,,,, +0.34,18665334,"This study analyzed boron content in commonly consumed foods by Koreans. Boron content was analyzed on 299 different foods using inductively coupled plasma atomic emission spectroscopy. The content of boron in cereals, potatoes, starches, sugars, and confectionaries was 1.11 to 828.56 microg per 100 g. As for beans, nuts, and seeds, the content of boron in acorn starch jelly was 66.15 microg per 100 g and in soybeans 1,642.50 microg per 100 g. In fruits, records show 5.29 to 390.13 microg per 100 g. The content of boron in vegetables was 17.45 to 420.55 microg per 100 g and in mushrooms 2.97 to 526.38 microg per 100 g. As for meats, eggs, milks, and oils, it posted 1.48 to 110.01 microg per 100 g. Fishes, shellfishes, and seaweeds contained 1.20 to 6,300.83 microg per 100 g of boron. Beverages, liquors, seasonings, and processed foods posted 1.06 microg per 100 g in corn cream soup and 2,026.49 microg per 100 g in cocoa. It is suggested that the data for the analysis of boron content in foods need to be more diversified and a reliable food database needs to be compiled based on the findings of the study to accurately determine boron consumption.",Biological trace element research,"['D001628', 'D001895', 'D002523', 'D007887', 'D005504', 'D005638', 'D007723', 'D009754', 'D013054', 'D014675']","['Beverages', 'Boron', 'Edible Grain', 'Fabaceae', 'Food Analysis', 'Fruit', 'Korea', 'Nuts', 'Spectrophotometry, Atomic', 'Vegetables']",Analysis of boron content in frequently consumed foods in Korea.,"['Q000032', 'Q000032', 'Q000737', 'Q000737', 'Q000379', 'Q000737', None, 'Q000737', None, 'Q000737']","['analysis', 'analysis', 'chemistry', 'chemistry', 'methods', 'chemistry', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/18665334,2009,1,1,table 7,boron content +0.34,21279669,"Gallic acid (GA), a key intermediate in the synthesis of plant hydrolysable tannins, is also a primary anti-inflammatory, cardio-protective agent found in wine, tea, and cocoa. In this publication, we reveal the identity of a gene and encoded protein essential for GA synthesis. Although it has long been recognized that plants, bacteria, and fungi synthesize and accumulate GA, the pathway leading to its synthesis was largely unknown. Here we provide evidence that shikimate dehydrogenase (SDH), a shikimate pathway enzyme essential for aromatic amino acid synthesis, is also required for GA production. Escherichia coli (E. coli) aroE mutants lacking a functional SDH can be complemented with the plant enzyme such that they grew on media lacking aromatic amino acids and produced GA in vitro. Transgenic Nicotiana tabacum lines expressing a Juglans regia SDH exhibited a 500% increase in GA accumulation. The J. regia and E. coli SDH was purified via overexpression in E. coli and used to measure substrate and cofactor kinetics, following reduction of NADP(+) to NADPH. Reversed-phase liquid chromatography coupled to electrospray mass spectrometry (RP-LC/ESI-MS) was used to quantify and validate GA production through dehydrogenation of 3-dehydroshikimate (3-DHS) by purified E. coli and J. regia SDH when shikimic acid (SA) or 3-DHS were used as substrates and NADP(+) as cofactor. Finally, we show that purified E. coli and J. regia SDH produced GA in vitro.",Plant molecular biology,"['D000429', 'D056148', 'D004926', 'D005707', 'D018506', 'D031324', 'D010084', 'D030821', 'D012765', 'D021241', 'D014026']","['Alcohol Oxidoreductases', 'Chromatography, Reverse-Phase', 'Escherichia coli', 'Gallic Acid', 'Gene Expression Regulation, Plant', 'Juglans', 'Oxidation-Reduction', 'Plants, Genetically Modified', 'Shikimic Acid', 'Spectrometry, Mass, Electrospray Ionization', 'Tobacco']",Mechanism of gallic acid biosynthesis in bacteria (Escherichia coli) and walnut (Juglans regia).,"['Q000378', None, 'Q000235', 'Q000378', None, 'Q000235', None, 'Q000235', 'Q000031', None, 'Q000235']","['metabolism', None, 'genetics', 'metabolism', None, 'genetics', None, 'genetics', 'analogs & derivatives', None, 'genetics']",https://www.ncbi.nlm.nih.gov/pubmed/21279669,2011,0,0,,no cocoa tested +0.34,2068794,"A high pressure liquid chromatographic (HPLC) method for measuring the theobromine content in cocoa husks, pelleted food and horse urine is described. Starting with 2 ml of urine, concentrations of 500 ng/ml could easily be detected. When feed containing 38.4 mg of theobromine was given twice daily to horses for 2 1/2 days, two days were needed after the last intake before the theobromine concentrations fell below the threshold value of 2 micrograms/ml. The time at which the peak excretion rate occurred varied from 2 to 12 h after the last administration, while the excretion rate seemed to be dependent on the urinary flow. Theobromine could not be detected in plasma after administration in this way.",Veterinary research communications,"['D000821', 'D000818', 'D002851', 'D004300', 'D005260', 'D005506', 'D006736', 'D013805']","['Animal Feed', 'Animals', 'Chromatography, High Pressure Liquid', 'Doping in Sports', 'Female', 'Food Contamination', 'Horses', 'Theobromine']",Urinary excretion of theobromine in horses given contaminated pelleted food.,"['Q000032', None, None, None, None, None, 'Q000378', 'Q000493']","['analysis', None, None, None, None, None, 'metabolism', 'pharmacokinetics']",https://www.ncbi.nlm.nih.gov/pubmed/2068794,1991,,,, +0.34,26525240,"An HPTLC method is proposed to permit effective screening for the presence of three phosphodiesterase type 5 inhibitors (PDE5-Is; sildenafil, vardenafil, and tadalafil) and eight of their analogs (hydroxyacetildenafil, homosildenafil, thiohomosildenafil, acetildenafil, acetaminotadalafil, propoxyphenyl hydroxyhomosildenafil, hydroxyhomosildenafil, and hydroxythiohomosildenafil) in finished products, including tablets, capsules, chocolate, instant coffee, syrup, and chewing gum. For all the finished products, the same simple sample preparation may be applied: ultrasound-assisted extraction in 10 mL methanol for 30 min followed by centrifugation. The Rf values of individual HPTLC bands afford preliminary identification of potential PDE5-Is. Scanning densitometry capabilities enable comparison of the unknown UV spectra with those of known standard compounds and allow further structural insight. Mass spectrometric analysis of the material derived from individual zones supplies an additional degree of confidence. Significantly, the proposed screening technique allows focus on the already known PDE5 Is and provides a platform for isolation and chemical categorization of the newly-synthesized analogs. Furthermore, the scope could be expanded to other therapeutic categories (e.g., analgesics, antidiabetics, and anorexiants) that are occasionally coadulterated along with the PDE5-Is. The method was successfully applied to screening of 45 commercial lifestyle products. Of those, 31 products tested positive for at least one illegal component (sildenafil, tadalafil, propoxyphenyl hydroxyhomosildenafil, or dimethylsildenafil). ",Journal of AOAC International,"['D002099', 'D002214', 'D002638', 'D002855', 'D003069', 'D058110', 'D006801', 'D059625', 'D013058', 'D000432', 'D058986', 'D000068677', 'D012997', 'D013607', 'D000068581', 'D000069058']","['Cacao', 'Capsules', 'Chewing Gum', 'Chromatography, Thin Layer', 'Coffee', 'Counterfeit Drugs', 'Humans', 'Liquid-Liquid Extraction', 'Mass Spectrometry', 'Methanol', 'Phosphodiesterase 5 Inhibitors', 'Sildenafil Citrate', 'Solvents', 'Tablets', 'Tadalafil', 'Vardenafil Dihydrochloride']",Simultaneous Detection of Three Phosphodiesterase Type 5 Inhibitors and Eight of Their Analogs in Lifestyle Products and Screening for Adulterants by High-Performance Thin-Layer Chromatography.,"['Q000737', None, 'Q000032', None, 'Q000737', 'Q000032', None, None, None, 'Q000737', 'Q000302', 'Q000031', 'Q000737', None, 'Q000031', 'Q000031']","['chemistry', None, 'analysis', None, 'chemistry', 'analysis', None, None, None, 'chemistry', 'isolation & purification', 'analogs & derivatives', 'chemistry', None, 'analogs & derivatives', 'analogs & derivatives']",https://www.ncbi.nlm.nih.gov/pubmed/26525240,2015,,,,no pdf access +0.34,12696960,"Partial least squares regression (PLSR) models able to predict some of the wine aroma nuances from its chemical composition have been developed. The aromatic sensory characteristics of 57 Spanish aged red wines were determined by 51 experts from the wine industry. The individual descriptions given by the experts were recorded, and the frequency with which a sensory term was used to define a given wine was taken as a measurement of its intensity. The aromatic chemical composition of the wines was determined by already published gas chromatography (GC)-flame ionization detector and GC-mass spectrometry methods. In the whole, 69 odorants were analyzed. Both matrixes, the sensory and chemical data, were simplified by grouping and rearranging correlated sensory terms or chemical compounds and by the exclusion of secondary aroma terms or of weak aroma chemicals. Finally, models were developed for 18 sensory terms and 27 chemicals or groups of chemicals. Satisfactory models, explaining more than 45% of the original variance, could be found for nine of the most important sensory terms (wood-vanillin-cinnamon, animal-leather-phenolic, toasted-coffee, old wood-reduction, vegetal-pepper, raisin-flowery, sweet-candy-cacao, fruity, and berry fruit). For this set of terms, the correlation coefficients between the measured and predicted Y (determined by cross-validation) ranged from 0.62 to 0.81. Models confirmed the existence of complex multivariate relationships between chemicals and odors. In general, pleasant descriptors were positively correlated to chemicals with pleasant aroma, such as vanillin, beta damascenone, or (E)-beta-methyl-gamma-octalactone, and negatively correlated to compounds showing less favorable odor properties, such as 4-ethyl and vinyl phenols, 3-(methylthio)-1-propanol, or phenylacetaldehyde.",Journal of agricultural and food chemistry,"['D002849', 'D008401', 'D006801', 'D016018', 'D008956', 'D009812', 'D012044', 'D013649', 'D014920']","['Chromatography, Gas', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Least-Squares Analysis', 'Models, Chemical', 'Odorants', 'Regression Analysis', 'Taste', 'Wine']",Prediction of aged red wine aroma properties from aroma chemical composition. Partial least squares regression models.,"[None, None, None, None, None, 'Q000032', None, None, 'Q000032']","[None, None, None, None, None, 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12696960,2003,0,0,, +0.34,29655735,"Fifty-six cocoa bean samples from different origins and status of fermentation were analyzed by a validated hydrophilic interaction liquid chromatography-electrospray ionization-time of flight-mass spectrometry (HILIC-ESI-TOF-MS) method. The profile of the low molecular weight carbohydrate (LMWC) was analyzed by high resolution and tandem mass spectrometry, which allowed the identification of mono-, di-, tri- and tetrasaccharides, sugar alcohols and iminosugars. This study provides, for the first time in a large set of samples, a comprehensive absolute quantitative data set for the carbohydrates identified in cocoa beans (fructose, glucose, mannitol, myo-inositol, sucrose, melibiose, raffinose and stachyose). Differences in the content of carbohydrates were observed between unfermented (range of 0.9-4.9__g/g DM) and fermented (range 0.1-0.5__g/g DM) cocoa beans. The use of multivariate statistical tools allowed the identification of biomarkers suitable for cocoa bean classification according to the status of fermentation, procedure of fermentation employed and number of days of fermentation.",Food chemistry,[],[],"Profiling, quantification and classification of cocoa beans based on chemometric analysis of carbohydrates using hydrophilic interaction liquid chromatography coupled to mass spectrometry.",[],[],https://www.ncbi.nlm.nih.gov/pubmed/29655735,2018,1,2,table 2 ,extract mean values. +0.34,26961599,"Metabolomics is used to assess the compliance and bioavailability of food components, as well as to evaluate the metabolic changes associated with food consumption. This study aimed to analyze the effect of consuming ready-to-eat meals containing a cocoa extract, within an energy restricted diet on urinary metabolomic changes. Fifty middle-aged volunteers [30.6 (2.3) kg m(-2)] participated in a 4-week randomised, parallel and double-blind study. Half consumed meals supplemented with 1.4 g of cocoa extract (645 mg polyphenols) while the remaining subjects received meals without cocoa supplementation. Ready-to-eat meals were included within a 15% energy restricted diet. Urine samples (24 h) were collected at baseline and after 4 weeks and were analyzed by high-performance-liquid chromatography-time-of-flight-mass-spectrometry (HPLC-TOF-MS) in negative and positive ionization modes followed by multivariate analysis. The relationship between urinary metabolites was evaluated by the Spearman correlation test. Interestingly, the principal component analysis discriminated among the baseline group, control group at the endpoint and cocoa group at the endpoint (p < 0.01), although in the positive ionization mode the baseline and control groups were not well distinguished. Metabolites were related to theobromine metabolism (3-methylxanthine and 3-methyluric acid), food processing (L-beta-aspartyl-L-phenylalanine), flavonoids (2,5,7,3',4'-pentahydroxyflavanone-5-O-glucoside and 7,4'-dimethoxy-6-C-methylflavanone), catecholamine (3-methoxy-4-hydroxyphenylglycol-sulphate) and endogenous metabolism (uridine monophosphate). These metabolites were present in higher (p < 0.001) amounts in the cocoa group. 3-Methylxanthine and l-beta-aspartyl-L-phenylalanine were confirmed with standards. Interestingly, 3-methoxy-4-hydroxyphenylglycol-sulphate was positively correlated with 3-methylxanthine (rho = 0.552; p < 0.001) and 7,4'-dimethoxy-6-C-methylflavanone (rho = 447; p = 0.002). In conclusion, the metabolomic approach supported the compliance of the volunteers with the intervention and suggested the bioavailability of cocoa compounds within the meals.",Food & function,"['D000368', 'D000369', 'D002099', 'D002851', 'D019587', 'D004311', 'D005260', 'D006801', 'D008297', 'D013058', 'D055432', 'D008875', 'D009765', 'D010936']","['Aged', 'Aged, 80 and over', 'Cacao', 'Chromatography, High Pressure Liquid', 'Dietary Supplements', 'Double-Blind Method', 'Female', 'Humans', 'Male', 'Mass Spectrometry', 'Metabolomics', 'Middle Aged', 'Obesity', 'Plant Extracts']",The urinary metabolomic profile following the intake of meals supplemented with a cocoa extract in middle-aged obese subjects.,"[None, None, 'Q000737', None, 'Q000032', None, None, None, None, None, None, None, 'Q000178', 'Q000032']","[None, None, 'chemistry', None, 'analysis', None, None, None, None, None, None, None, 'diet therapy', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26961599,2016,0,0,, +0.33,676517,"The nickel content of 260 samples from various types of foods available in the Netherlands was measured by means of flameless atomic absorption spectrometry. In most samples the nickel content was found to be less than 0.5 mg/kg. Two products contained considerably more nickel than all the other foodstuffs, viz. nuts and cacao products, in which nickel concentrations up to 5.1 and 9.8 mg/kg, respectively, were measured. Occasionally nickel contents above 1 mg/kg were found in margarine and sauces.",Zeitschrift fur Lebensmittel-Untersuchung und -Forschung,"['D001628', 'D002099', 'D002523', 'D005504', 'D008460', 'D009426', 'D009532', 'D009754', 'D013054', 'D014675']","['Beverages', 'Cacao', 'Edible Grain', 'Food Analysis', 'Meat', 'Netherlands', 'Nickel', 'Nuts', 'Spectrophotometry, Atomic', 'Vegetables']",Nickel content of various Dutch foodstuffs.,"['Q000032', 'Q000032', 'Q000032', 'Q000379', 'Q000032', None, 'Q000032', 'Q000032', 'Q000379', 'Q000032']","['analysis', 'analysis', 'analysis', 'methods', 'analysis', None, 'analysis', 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/676517,1978,,,, +0.33,7085542,"A collaborative study was conducted using a modified AOAC method (sugars in chocolate) for the determination of fructose, glucose, sucrose, and maltose in presweetened cereals by high pressure liquid chromatography (HPLC). Eight samples consisting of 6 products were analyzed in duplicate by the HPLC method and the AOAC Lane-Eynon method. The AOAC method was modified to use water-alcohol (1 + 1) and Sep-Pak C18 cartridges for sample cleanup. The HPLC results indicate precision comparable to the lane-Eynon method and the chocolate method. The modified HPLC method has been adopted official first action.",Journal - Association of Official Analytical Chemists,"['D002851', 'D004187', 'D002523', 'D009005']","['Chromatography, High Pressure Liquid', 'Disaccharides', 'Edible Grain', 'Monosaccharides']",High pressure liquid chromatographic determination of mono- and disaccharides in presweetened cereals: Collaborative study.,"['Q000379', 'Q000032', 'Q000032', 'Q000032']","['methods', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/7085542,1982,,,, +0.33,11339267,"Individual and geographical variations in ochratoxin A (OA) levels in human blood and milk samples may be due to differences in dietary habits. The purpose of this study was to examine the relationship between OA contamination of human milk and dietary intake. Human milk samples were collected from 80 Norwegian women. The usual food intake during the last year was recorded using a quantitative food frequency questionnaire. The concentration of OA in the human milk was determined by HPLC (detection limit 10 ng/l). Seventeen (21%) out of 80 human milk samples contained OA in the range 10-182 ng/l. The women with a high dietary intake of liver paste (liverwurst, liver p¢t©) and cakes (cookies, fruitcakes, chocolate cakes, etc.) were more likely to have OA-contaminated milk. The risk of OA contamination was also increased by the intake of juice (all kinds). In addition, the results indicate that breakfast cereals, processed meat products, and cheese could be important contributors to dietary OA intake. OA contamination of the milk was unrelated to smoking, age, parity, and anthropometric data other than body weight.",Food additives and contaminants,"['D000328', 'D001827', 'D015992', 'D001835', 'D002273', 'D016009', 'D002851', 'D004032', 'D015930', 'D005260', 'D006801', 'D016015', 'D008895', 'D009793', 'D012621', 'D018709']","['Adult', 'Body Height', 'Body Mass Index', 'Body Weight', 'Carcinogens', 'Chi-Square Distribution', 'Chromatography, High Pressure Liquid', 'Diet', 'Diet Records', 'Female', 'Humans', 'Logistic Models', 'Milk, Human', 'Ochratoxins', 'Seasons', 'Statistics, Nonparametric']",Presence of ochratoxin A in human milk in relation to dietary intake.,"[None, None, None, None, 'Q000032', None, None, None, None, None, None, None, 'Q000737', 'Q000032', None, None]","[None, None, None, None, 'analysis', None, None, None, None, None, None, None, 'chemistry', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11339267,2001,,,, +0.33,27454854,"Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a powerful biotyping tool increasingly used for high-throughput identification of clinical microbial isolates, however, in food fermentation research this approach is still not well established. This study examines the microbial biodiversity of cocoa bean fermentation based on the isolation of micro-organisms in cocoa-producing regions, followed by MALDI-TOF MS in Switzerland. A preceding 6-week storage test to mimic lengthy transport of microbial samples from cocoa-producing regions to Switzerland was performed with strains of Lactobacillus plantarum, Acetobacter pasteurianus and Saccharomyces cerevisiae. Weekly MALDI-TOF MS analysis was able to successfully identify microbiota to the species level after storing live cultures on slant agar at mild temperatures (7_C) and/or in 75% aqueous ethanol at differing temperatures (-20, 7 and 30_C). The efficacy of this method was confirmed by on-site recording of the microbial biodiversity in cocoa bean fermentation in Bolivia and Brazil, with a total of 1126 randomly selected isolates. MALDI-TOF MS analyses revealed known dominant cocoa bean fermentation species with Lact.__plantarum and Lactobacillus fermentum in the lactic acid bacteria taxon, Hanseniaspora opuntiae and S.__cerevisiae in the yeast taxon, and Acet.__pasteurianus, Acetobacter fabarum, Acetobacter ghanensis and Acetobacter senegalensis in the acetic acid bacteria taxon.",Letters in applied microbiology,"['D019342', 'D001419', 'D015373', 'D001838', 'D001938', 'D002099', 'D000431', 'D005285', 'D064307', 'D016533', 'D019032', 'D015003']","['Acetic Acid', 'Bacteria', 'Bacterial Typing Techniques', 'Bolivia', 'Brazil', 'Cacao', 'Ethanol', 'Fermentation', 'Microbiota', 'Mycological Typing Techniques', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Yeasts']",High-throughput identification of the microbial biodiversity of cocoa bean fermentation by MALDI-TOF MS.,"[None, 'Q000302', None, None, None, 'Q000382', None, None, None, None, 'Q000379', 'Q000302']","[None, 'isolation & purification', None, None, None, 'microbiology', None, None, None, None, 'methods', 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/27454854,2017,0,0,, +0.33,19187022,"Food and beverages rich in polyphenols with antioxidant activity are highlighted as a potential factor for risk reduction of lifestyle related diseases. This study was conducted to elucidate total polyphenol consumption from beverages in Japanese people. Total polyphenol (TP) contents in beverages were measured using a modified Folin-Ciocalteu method removing the interference of reduced sugars by using reverse-phase column chromatography. A beverage consumption survey was conducted in the Tokyo and Osaka areas in 2004. Randomly selected male and female subjects (10-59 years old, n = 8768) recorded the amounts and types of all nonalcoholic beverages consumed in a week. Concentration of TP in coffee, green tea, black tea, Oolong tea, barley tea, fruit juice, tomato/vegetable juice, and cocoa drinks were at 200, 115, 96, 39, 9, 34, 69, and 62 mg/100 mL, respectively. Total consumption of beverages in a Japanese population was 1.11 +/- 0.51 L/day, and TP contents from beverages was 853 +/- 512 mg/day. Coffee and green tea shared 50% and 34% of TP consumption in beverages, respectively, and contribution of each of the other beverages was less than 10%. TP contents in 20 major vegetables and 5 fruits were 0-49 mg and 2-55 mg/100 g, respectively. Antioxidant activities, Cu reducing power, and scavenging activities for DPPH and superoxide, of those samples correlated to the TP contents (p < 0.001). Beverages, especially coffee, contributed to a large share of the consumption of polyphenols, as antioxidants, in the Japanese diet.",Journal of agricultural and food chemistry,"['D000293', 'D000328', 'D000975', 'D001628', 'D002648', 'D003069', 'D004032', 'D005260', 'D005419', 'D005638', 'D006801', 'D007564', 'D008297', 'D008875', 'D010636', 'D059808', 'D013662', 'D014675', 'D055815']","['Adolescent', 'Adult', 'Antioxidants', 'Beverages', 'Child', 'Coffee', 'Diet', 'Female', 'Flavonoids', 'Fruit', 'Humans', 'Japan', 'Male', 'Middle Aged', 'Phenols', 'Polyphenols', 'Tea', 'Vegetables', 'Young Adult']",Coffee and green tea as a large source of antioxidant polyphenols in the Japanese population.,"[None, None, 'Q000008', 'Q000032', None, 'Q000737', None, None, 'Q000008', 'Q000737', None, None, None, None, 'Q000008', None, 'Q000737', 'Q000737', None]","[None, None, 'administration & dosage', 'analysis', None, 'chemistry', None, None, 'administration & dosage', 'chemistry', None, None, None, None, 'administration & dosage', None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/19187022,2009,0,0,, +0.32,28241034,"For the first time in the literature, our group has managed to demonstrate the existence of plant RNAs in honey samples. In particular, in our work, different RNA extraction procedures were performed in order to identify a purification method for nucleic acids from honey. Purity, stability and integrity of the RNA samples were evaluated by spectrophotometric, PCR and electrophoretic analyses. Among all honey RNAs, we specifically revealed the presence of both plastidial and nuclear plant transcripts: RuBisCO large subunit mRNA, maturase K messenger and 18S ribosomal RNA. Surprisingly, nine plant microRNAs (miR482b, miR156a, miR396c, miR171a, miR858, miR162a, miR159c, miR395a and miR2118a) were also detected and quantified by qPCR. In this context, a comparison between microRNA content in plant samples (i.e. flowers, nectars) and their derivative honeys was carried out. In addition, peculiar microRNA profiles were also identified in six different monofloral honeys. Finally, the same plant microRNAs were investigated in other plant food products: tea, cocoa and coffee. Since plant microRNAs introduced by diet have been recently recognized as being able to modulate the consumer's gene expression, our research suggests that honey's benefits for human health may be strongly correlated to the bioactivity of plant microRNAs contained in this matrix.",PloS one,"['D000975', 'D002099', 'D028244', 'D040503', 'D004722', 'D035264', 'D006722', 'D035683', 'D009713', 'D010944', 'D016133', 'D018749', 'D012337', 'D060888', 'D012273', 'D017423', 'D013053']","['Antioxidants', 'Cacao', 'Camellia', 'Coffea', 'Endoribonucleases', 'Flowers', 'Honey', 'MicroRNAs', 'Nucleotidyltransferases', 'Plants', 'Polymerase Chain Reaction', 'RNA, Plant', 'RNA, Ribosomal, 18S', 'Real-Time Polymerase Chain Reaction', 'Ribulose-Bisphosphate Carboxylase', 'Sequence Analysis, RNA', 'Spectrophotometry']",Detection of plant microRNAs in honey.,"['Q000378', 'Q000235', 'Q000235', 'Q000235', 'Q000235', 'Q000235', 'Q000032', None, 'Q000235', 'Q000235', None, None, 'Q000235', None, 'Q000235', None, None]","['metabolism', 'genetics', 'genetics', 'genetics', 'genetics', 'genetics', 'analysis', None, 'genetics', 'genetics', None, None, 'genetics', None, 'genetics', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28241034,2017,0,0,, +0.32,9757560,"The antiulcer activity of cacao liquor water-soluble crude polyphenols (CWSP) was examined. CWSP, alpha-tocopherol, sucralfate (500 mg/kg), and cimetidine (250 mg/kg) were orally administered to male SD rats 30 minutes before ethanol treatment. 5 ml/kg of ethanol given intragastrically caused lesions in mucosa of the glandular stomach. CWSP caused a reduction of such hemorrhagic lesions as well as cimetidine and sucralfate which are typical antiulcer drugs, but alpha-tocopherol was less effective. Thiobarbituric acid reactive substances in gastric mucosa significantly increased with ethanol administration. CWSP treatment significantly reduced this change. The administration of ethanol extensively increased myeloperoxidase (MPO) but not xanthine oxidase (XOD) activity. CWSP reduced the activities of both enzymes; they were considered the main sources of oxygen radicals. According to an in vitro study, CWSP directly reducted XOD but not MPO. These results suggest that the antiulcer mechanism of CWSP was not only radical scavenging but also modulation of leukocyte function.","Bioscience, biotechnology, and biochemistry","['D000818', 'D000897', 'D000975', 'D002099', 'D002851', 'D002927', 'D004305', 'D000431', 'D005419', 'D005753', 'D015227', 'D008297', 'D009195', 'D010636', 'D011108', 'D059808', 'D051381', 'D017207', 'D013276', 'D013392', 'D017392', 'D014527', 'D014810', 'D014969']","['Animals', 'Anti-Ulcer Agents', 'Antioxidants', 'Cacao', 'Chromatography, High Pressure Liquid', 'Cimetidine', 'Dose-Response Relationship, Drug', 'Ethanol', 'Flavonoids', 'Gastric Mucosa', 'Lipid Peroxidation', 'Male', 'Peroxidase', 'Phenols', 'Polymers', 'Polyphenols', 'Rats', 'Rats, Sprague-Dawley', 'Stomach Ulcer', 'Sucralfate', 'Thiobarbituric Acid Reactive Substances', 'Uric Acid', 'Vitamin E', 'Xanthine Oxidase']",Effects of polyphenol substances derived from Theobroma cacao on gastric mucosal lesion induced by ethanol.,"[None, 'Q000494', 'Q000378', 'Q000378', None, 'Q000494', None, 'Q000009', None, 'Q000187', None, None, 'Q000037', 'Q000494', 'Q000494', None, None, None, 'Q000188', 'Q000494', 'Q000032', 'Q000032', 'Q000494', 'Q000037']","[None, 'pharmacology', 'metabolism', 'metabolism', None, 'pharmacology', None, 'adverse effects', None, 'drug effects', None, None, 'antagonists & inhibitors', 'pharmacology', 'pharmacology', None, None, None, 'drug therapy', 'pharmacology', 'analysis', 'analysis', 'pharmacology', 'antagonists & inhibitors']",https://www.ncbi.nlm.nih.gov/pubmed/9757560,1998,0,0,,year +0.32,18172716,"Oxalic acid has been shown as a virulence factor for some phytopathogenic fungi, removing calcium from pectin and favoring plant cell wall degradation. Recently, it was published that calcium oxalate accumulates in infected cacao tissues during the progression of Witches' Broom disease (WBD). In the present work we report that the hemibiotrophic basidiomycete Moniliophthora perniciosa, the causal agent of WBD, produces calcium oxalate crystals. These crystals were initially observed by polarized light microscopy of hyphae growing on a glass slide, apparently being secreted from the cells. The analysis was refined by Scanning electron microscopy and the compositon of the crystals was confirmed by energy-dispersive x-ray spectrometry. The production of oxalate by M. perniciosa was reinforced by the identification of a putative gene coding for oxaloacetate acetylhydrolase, which catalyzes the hydrolysis of oxaloacetate to oxalate and acetate. This gene was shown to be expressed in the biotrophic-like mycelia, which in planta occupy the intercellular middle-lamella space, a region filled with pectin. Taken together, our results suggest that oxalate production by M. perniciosa may play a role in the WBD pathogenesis mechanism.",Current microbiology,"['D000363', 'D000595', 'D000818', 'D002099', 'D002129', 'D005656', 'D006867', 'D025301', 'D008855', 'D008859', 'D008969', 'D010935', 'D016415', 'D013052']","['Agaricales', 'Amino Acid Sequence', 'Animals', 'Cacao', 'Calcium Oxalate', 'Fungal Proteins', 'Hydrolases', 'Hyphae', 'Microscopy, Electron, Scanning', 'Microscopy, Polarization', 'Molecular Sequence Data', 'Plant Diseases', 'Sequence Alignment', 'Spectrometry, X-Ray Emission']","Production of calcium oxalate crystals by the basidiomycete Moniliophthora perniciosa, the causal agent of witches' broom disease of Cacao.","['Q000737', None, None, 'Q000382', 'Q000378', 'Q000235', 'Q000235', 'Q000737', None, None, None, 'Q000382', None, None]","['chemistry', None, None, 'microbiology', 'metabolism', 'genetics', 'genetics', 'chemistry', None, None, None, 'microbiology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18172716,2008,0,0,, +0.32,15137812,"Directive 2000/36/EC allows chocolate makers to add up to 5% of only six specific cocoa butter equivalents (CBEs) to cocoa butter (CB). A quantification method based on triacylglycerol (TAG) class analysis by gas chromatography with an unpolar column was set up for routine control purposes of chocolate bars. Mixtures of CBEs/CB were elaborated according to a Placket-Burman experiment design and analyzed by gas chromatography. A matrix was built with the normalized values of TAG classes (C50, C52, C54, and C56) of pure CBs of various origins, homemade CB/CBE mixtures (1 CB type), and mixtures containing CBE with CBs of various origins. A multivariate calibration equation was computed from this matrix using a partial least-squares regression technique. CBE addition can be detected at a minimum level of 2%, and the mathematical model allows its quantification with an uncertainty of 2% with respect to the cocoa butter fats. The model has also been applied for deconvolution and quantification of each CBE of a CBE mixture in chocolate bars.",Journal of agricultural and food chemistry,"['D002099', 'D002182', 'D002849', 'D004041', 'D014280']","['Cacao', 'Candy', 'Chromatography, Gas', 'Dietary Fats', 'Triglycerides']",Alternative method for the quantification by gas chromatography triacylglycerol class analysis of cocoa butter equivalent added to chocolate bars.,"['Q000737', 'Q000032', 'Q000379', 'Q000032', 'Q000032']","['chemistry', 'analysis', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15137812,2004,2,1,table 1, +0.32,15759754,"After the publication of high levels of acrylamide (AA) in food, many research activities started all over the world in order to determine the occurrence and the concentration of this substance in various types of food. As no validated methods were available at that time, interlaboratory studies on the determination of AA in food were of the highest priority. Under the boundary conditions of applying well-established evaluation schemes, the results of 2 studies conducted by the Federal Institute for Risk Assessment (BfR) in Germany and by the European Commission's Directorate General Joint Research Center (JRC) exhibited an overall acceptable performance of the participants in these studies. Nevertheless, many laboratories showed problems in determining AA in food with a complex matrix such as cocoa. The results of analysis also showed a broader variation of AA for samples with low AA concentrations and indicated a bias of the results obtained by gas chromatography-mass spectrometry without derivatization. Improvements of the performance of some laboratories appeared to be necessary.",Journal of AOAC International,"['D020106', 'D001939', 'D002099', 'D002623', 'D005062', 'D005502', 'D005504', 'D008401', 'D005858', 'D012107', 'D018570', 'D011198', 'D013997']","['Acrylamide', 'Bread', 'Cacao', 'Chemistry Techniques, Analytical', 'European Union', 'Food', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Germany', 'Research Design', 'Risk Assessment', 'Solanum tuberosum', 'Time Factors']",Results from two interlaboratory comparison tests organized in Germany and at the EU level for analysis of acrylamide in food.,"['Q000032', None, None, 'Q000379', None, None, 'Q000379', 'Q000379', None, None, None, None, None]","['analysis', None, None, 'methods', None, None, 'methods', 'methods', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/15759754,2005,,,, +0.32,21480674,"Samples of 15 second generation soy-based products (n = 3), commercially available, were analyzed for their protein and isoflavone contents and in vitro antioxidant activity, by means of the Folin-Ciocalteu reducing ability, DPPH radical scavenging capacity, and oxygen radical absorbance capacity. Isoflavone identification and quantification were performed by high-performance liquid chromatography. Products containing soy and/or soy-based ingredients represent important sources of protein in addition to the low fat amounts. However, a large variation in isoflavone content and in vitro antioxidant capacity was observed. The isoflavone content varied from 2.4 to 18.1 mg/100 g (FW), and soy kibe and soy sausage presented the highest amounts. Chocolate had the highest antioxidant capacity, but this fact was probably associated with the addition of cocoa liquor, a well-known source of polyphenolics. This study showed that the soy-based foods do not present a significant content of isoflavones when compared with the grain, and their in vitro antioxidant capacity is not related with these compounds but rather to the presence of other phenolics and synthetic antioxidants, such as sodium erythorbate. However, they may represent alternative sources and provide soy protein, isoflavones, and vegetable fat for those who are not ready to eat traditional soy foods.",Journal of agricultural and food chemistry,"['D000975', 'D004041', 'D007529', 'D009753', 'D045730', 'D030262']","['Antioxidants', 'Dietary Fats', 'Isoflavones', 'Nutritive Value', 'Soy Foods', 'Soybean Proteins']",Nutritional aspects of second generation soy foods.,"['Q000032', 'Q000032', 'Q000032', None, 'Q000032', 'Q000032']","['analysis', 'analysis', 'analysis', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/21480674,2011,0,0,,no cocoa tested +0.32,25213975,"Micellar electrokinetic chromatography (MEKC) has been applied for the determination of 5-hydroxymethylfurfural in several foodstuffs. A 75mM phosphate buffer solution at pH 8.0 containing 100mM sodium dodecylsulphate was used as background electrolyte (BGE), and the separation was performed by applying +25kV in a 50__m I.D. uncoated fused-silica capillary. Good linearity over the range 2.5-250mgkg(-1) (r(2)_©_0.999) and run-to-run and day-to-day precisions at low and medium concentration levels were obtained. Sample limit of detection (0.7mgkg(-1)) and limit of quantification (2.5mgkg(-1)) were established by preparing the standards in blank matrix. The procedure was validated by comparing the results with those obtained with liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Levels of HMF in 45 different foodstuffs such as breakfast cereals, toasts, honey, orange juice, apple juice, jam, coffee, chocolate and biscuits were determined. ",Food chemistry,[],[],5-Hydroxymethylfurfural content in foodstuffs determined by micellar electrokinetic chromatography.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/25213975,2014,0,0,,no cocoa +0.32,10820089,"Catechins, compounds that belong to the flavonoid class, are potentially beneficial to human health. To enable epidemiological evaluation of these compounds, data on their contents in foods are required. HPLC with UV and fluorescence detection was used to determine the levels of (+)-catechin, (-)-epicatechin, (+)-gallocatechin (GC), (-)-epigallocatechin (EGC), (-)-epicatechin gallate (ECg), and (-)-epigallocatechin gallate (EGCg) in 24 types of fruits, 27 types of vegetables and legumes, some staple foods, and processed foods commonly consumed in The Netherlands. Most fruits, chocolate, and some legumes contained catechins. Levels varied to a large extent: from 4.5 mg/kg in kiwi fruit to 610 mg/kg in black chocolate. (+)-Catechin and (-)-epicatechin were the predominant catechins; GC, EGC, and ECg were detected in some foods, but none of the foods contained EGCg. The data reported here provide a base for the epidemiological evaluation of the effect of catechins on the risk for chronic diseases.",Journal of agricultural and food chemistry,"['D002392', 'D002851', 'D005504', 'D006801', 'D009426', 'D013050', 'D013056']","['Catechin', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Humans', 'Netherlands', 'Spectrometry, Fluorescence', 'Spectrophotometry, Ultraviolet']","Catechin contents of foods commonly consumed in The Netherlands. 1. Fruits, vegetables, staple foods, and processed foods.","['Q000032', None, None, None, None, None, None]","['analysis', None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10820089,2000,0,0,, +0.32,26372965,"Chemical analyses of organic residues in fragments of pottery from 18 sites in the US Southwest and Mexican Northwest reveal combinations of methylxanthines (caffeine, theobromine, and theophylline) indicative of stimulant drinks, probably concocted using either cacao or holly leaves and twigs. The results cover a time period from around A.D. 750-1400, and a spatial distribution from southern Colorado to northern Chihuahua. As with populations located throughout much of North and South America, groups in the US Southwest and Mexican Northwest likely consumed stimulant drinks in communal, ritual gatherings. The results have implications for economic and social relations among North American populations. ",Proceedings of the National Academy of Sciences of the United States of America,"['D001106', 'D001628', 'D002099', 'D002110', 'D002562', 'D002851', 'D003466', 'D005502', 'D005843', 'D049690', 'D006801', 'D030017', 'D008800', 'D015206', 'D053719']","['Archaeology', 'Beverages', 'Cacao', 'Caffeine', 'Ceremonial Behavior', 'Chromatography, High Pressure Liquid', 'Cultural Characteristics', 'Food', 'Geography', 'History, Ancient', 'Humans', 'Ilex', 'Mexico', 'Southwestern United States', 'Tandem Mass Spectrometry']",Ritual drinks in the pre-Hispanic US Southwest and Mexican Northwest.,"[None, 'Q000032', None, None, None, None, 'Q000266', None, None, None, None, None, None, None, None]","[None, 'analysis', None, None, None, None, 'history', None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/26372965,2016,0,0,, +0.32,12589329,"This paper offers a review of current scientific research regarding the potential cardiovascular health benefits of flavonoids found in cocoa and chocolate. Recent reports indicate that the main flavonoids found in cocoa, flavan-3-ols and their oligomeric derivatives, procyanidins, have a variety of beneficial actions, including antioxidant protection and modulation of vascular homeostasis. These findings are supported by similar research on other flavonoid-rich foods. Other constituents in cocoa and chocolate that may also influence cardiovascular health are briefly reviewed. The lipid content of chocolate is relatively high; however, one third of the lipid in cocoa butter is composed of the fat stearic acid, which exerts a neutral cholesterolemic response in humans. Cocoa and chocolate contribute to trace mineral intake, which is necessary for optimum functioning of all biologic systems and for vascular tone. Thus, multiple components in chocolate, particularly flavonoids, can contribute to the complex interplay of nutrition and health. Applications of this knowledge include recommendations by health professionals to encourage individuals to consume a wide range of phytochemical-rich foods, which can include dark chocolate in moderate amounts.",Journal of the American Dietetic Association,"['D000924', 'D000975', 'D001682', 'D002099', 'D002318', 'D002851', 'D004041', 'D004043', 'D005419', 'D006801', 'D007109', 'D008903', 'D010840', 'D015539', 'D013229']","['Anticholesteremic Agents', 'Antioxidants', 'Biological Availability', 'Cacao', 'Cardiovascular Diseases', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Dietary Fiber', 'Flavonoids', 'Humans', 'Immunity', 'Minerals', 'Phytosterols', 'Platelet Activation', 'Stearic Acids']",Cocoa and chocolate flavonoids: implications for cardiovascular health.,"['Q000008', 'Q000008', None, 'Q000737', 'Q000378', None, 'Q000008', 'Q000008', 'Q000008', None, 'Q000187', 'Q000008', 'Q000008', 'Q000187', 'Q000008']","['administration & dosage', 'administration & dosage', None, 'chemistry', 'metabolism', None, 'administration & dosage', 'administration & dosage', 'administration & dosage', None, 'drug effects', 'administration & dosage', 'administration & dosage', 'drug effects', 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/12589329,2003,1,1,table 1 and 2, +0.32,2991094,"Correlation studies on patients with myasthenia gravis are reported in which clinical assessment of fatigue and neurophysiological findings are compared to blood levels of pyridostigmine. Measurements using a high-pressure liquid chromatography method (HPLC), give reproducible results. The levels of pyridostigmine in the serum or plasma of healthy controls and of patients show no essential differences. Components of coffee, tea, chocolate and cigarettes can markedly disturb the chromatography by adding additional peaks, so that interpretation becomes difficult or impossible. Blood levels can be measured approximately one hour after oral intake of 60 mg pyridostigmine. Concentrations rise for two to four hours and then decline exponentially. The half-life of pyridostigmine was between 156 and 210 minutes. Despite identical oral dosages, the concentration differed intraindividually and interindividually among patients. While the blood level does not reach its maximum value for 1-1 1/2 to 3 hours, the maximum clinical and neurophysiological effect of pyridostigmine appears 30-60 minutes after ingestion. Variable distribution of cholinesterase inhibitors over the different compartments (blood, synaptic region) is assumed to cause this temporal lag. If the total amount of pyridostigmine is divided into 4-5 doses, the concentration profiles over the course of a day are relatively stable. There is no significant correlation between the variations in blood level throughout one day, and changes in myasthenic symptomatology. Effects of pyridostigmine can be measured at levels as low as 5 ng/ml; at levels above 40 ng/ml further improvement can be detected only rarely. Blood levels were lower if corticosteroids were administered simultaneously; azathioprine had no influence on blood levels. Blood levels assays allow better differentiation of cholinergic and myasthenic crises and the identification of disturbed absorption and interactions with other medications.",Fortschritte der Neurologie-Psychiatrie,"['D000293', 'D000328', 'D000368', 'D001682', 'D002851', 'D004305', 'D004347', 'D004361', 'D005260', 'D006801', 'D007700', 'D008297', 'D008475', 'D008875', 'D009157', 'D011729', 'D009435']","['Adolescent', 'Adult', 'Aged', 'Biological Availability', 'Chromatography, High Pressure Liquid', 'Dose-Response Relationship, Drug', 'Drug Interactions', 'Drug Tolerance', 'Female', 'Humans', 'Kinetics', 'Male', 'Median Nerve', 'Middle Aged', 'Myasthenia Gravis', 'Pyridostigmine Bromide', 'Synaptic Transmission']",[Serum levels of pyridostigmine in myasthenia gravis: methods and clinical significance].,"[None, None, None, None, None, None, None, None, None, None, None, None, 'Q000187', None, 'Q000097', 'Q000097', 'Q000187']","[None, None, None, None, None, None, None, None, None, None, None, None, 'drug effects', None, 'blood', 'blood', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/2991094,1985,,,,no pdf access +0.32,20722952,"Export of cocoa beans is of great economic importance in Ghana and several other tropical countries. Raw cocoa has an astringent, unpleasant taste, and flavor, and has to be fermented, dried, and roasted to obtain the characteristic cocoa flavor and taste. In an attempt to obtain a deeper understanding of the changes in the cocoa beans during fermentation and investigate the possibility of future development of objective methods for assessing the degree of fermentation, a novel combination of methods including cut test, colorimetry, fluorescence spectroscopy, NIR spectroscopy, and GC-MS evaluated by chemometric methods was used to examine cocoa beans sampled at different durations of fermentation and samples representing fully fermented and dried beans from all cocoa growing regions of Ghana. Using colorimetry it was found that samples moved towards higher a* and b* values as fermentation progressed. Furthermore, the degree of fermentation could, in general, be well described by the spectroscopic methods used. In addition, it was possible to link analysis of volatile compounds with predictions of fermentation time. Fermented and dried cocoa beans from the Volta and the Western regions clustered separately in the score plots based on colorimetric, fluorescence, NIR, and GC-MS indicating regional differences in the composition of Ghanaian cocoa beans. The study demonstrates the potential of colorimetry and spectroscopic methods as valuable tools for determining the fermentation degree of cocoa beans. Using GC-MS it was possible to demonstrate the formation of several important aroma compounds such 2-phenylethyl acetate, propionic acid, and acetoin and the breakdown of others like diacetyl during fermentation. Practical Application: The present study demonstrates the potential of using colorimetry and spectroscopic methods as objective methods for determining cocoa bean quality along the processing chain. Development of objective methods for determining cocoa bean quality will be of great importance for quality insurance within the fields of cocoa processing and raw material control in chocolate producing companies.",Journal of food science,"['D000085', 'D000093', 'D002099', 'D003116', 'D003124', 'D003931', 'D005285', 'D005504', 'D005511', 'D008401', 'D005869', 'D015233', 'D010626', 'D025341', 'D011422', 'D011786', 'D012639', 'D013050', 'D019265', 'D055549']","['Acetates', 'Acetoin', 'Cacao', 'Color', 'Colorimetry', 'Diacetyl', 'Fermentation', 'Food Analysis', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Ghana', 'Models, Statistical', 'Phenylethyl Alcohol', 'Principal Component Analysis', 'Propionates', 'Quality Control', 'Seeds', 'Spectrometry, Fluorescence', 'Spectroscopy, Near-Infrared', 'Volatile Organic Compounds']",Ghanaian cocoa bean fermentation characterized by spectroscopic and chromatographic methods and chemometrics.,"['Q000032', 'Q000032', 'Q000737', None, None, 'Q000032', None, 'Q000379', 'Q000379', None, None, None, 'Q000031', None, 'Q000032', None, 'Q000737', None, None, 'Q000032']","['analysis', 'analysis', 'chemistry', None, None, 'analysis', None, 'methods', 'methods', None, None, None, 'analogs & derivatives', None, 'analysis', None, 'chemistry', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20722952,2011,0,0,, +0.31,15237566,The stability and compatibility of tegaserod from crushed tablets in selected beverages and foods were studied.,American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists,"['D001628', 'D002851', 'D004344', 'D004356', 'D005502', 'D005765', 'D007211', 'D012034', 'D012995', 'D013535', 'D013607']","['Beverages', 'Chromatography, High Pressure Liquid', 'Drug Incompatibility', 'Drug Storage', 'Food', 'Gastrointestinal Agents', 'Indoles', 'Refrigeration', 'Solubility', 'Suspensions', 'Tablets']",Stability and compatibility of tegaserod from crushed tablets mixed in beverages and foods.,"[None, None, None, None, None, 'Q000737', 'Q000737', None, None, None, None]","[None, None, None, None, None, 'chemistry', 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/15237566,2004,,,,no pdf access +0.31,22243490,"POSH are polyolefin oligomeric saturated hydrocarbons, such as oligomers from polyethylene or polypropylene. POSH that have migrated into foods are easily mistaken for mineral oil-saturated hydrocarbons (MOSH). In fact, both POSH and MOSH largely consist of highly isomerised branched and possibly cyclic hydrocarbons, both forming humps of unresolved components in gas chromatography. Chromatograms are reported to show typical elution patterns of POSH and help analysts distinguishing POSH from MOSH as far as possible. Since the structures of the POSH are not fundamentally different from those of the MOSH, it would be prudent to apply the evaluation of the MOSH. However, the migration is frequently beyond that for which safety has been demonstrated. This is shown for a few examples, particularly for powdered formula for babies.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D002099', 'D005506', 'D005511', 'D018857', 'D057141', 'D006801', 'D007223', 'D007225', 'D041943', 'D015394', 'D012275', 'D035281', 'D011090', 'D011095', 'D011126', 'D012639', 'D011198', 'D013499', 'D014867', 'D003313']","['Cacao', 'Food Contamination', 'Food Handling', 'Food Packaging', 'Food, Preserved', 'Humans', 'Infant', 'Infant Food', 'Infant Formula', 'Molecular Structure', 'Oryza', 'Plant Tubers', 'Polyenes', 'Polyethylenes', 'Polypropylenes', 'Seeds', 'Solanum tuberosum', 'Surface Properties', 'Water', 'Zea mays']",Migration of polyolefin oligomeric saturated hydrocarbons (POSH) into food.,"['Q000737', None, None, None, 'Q000032', None, None, 'Q000032', 'Q000737', None, 'Q000737', 'Q000737', 'Q000032', 'Q000032', 'Q000032', 'Q000737', 'Q000737', None, 'Q000032', 'Q000737']","['chemistry', None, None, None, 'analysis', None, None, 'analysis', 'chemistry', None, 'chemistry', 'chemistry', 'analysis', 'analysis', 'analysis', 'chemistry', 'chemistry', None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22243490,2012,0,0,, +0.31,24231101,"The regular consumption of flavonoids has been associated with reduced mortality and a decreased risk of cardiovascular diseases. The proanthocyanidins found in plasma are very different from the original flavonoids in food sources. The use of physiologically appropriate conjugates of proanthocyanidins is essential for the in vitro analysis of flavonoid bioactivity. In this study, the effect of different proanthocyanidin-rich extracts, which were obtained from cocoa (CCX), French maritime pine bark (Pycnogenol extract, PYC) and grape seed (GSPE), on lipid homeostasis was evaluated. Hepatic human cells (HepG2 cells) were treated with 25 mg/L of CCX, PYC or GSPE. We also performed in vitro experiments to assess the effect on lipid synthesis that is induced by the bioactive GSPE proanthocyanidins using the physiological metabolites that are present in the serum of GSPE-administered rats. For this, Wistar rats were administered 1 g/kg of GSPE, and serum was collected after 2 h. The semipurified serum of GSPE-administered rats was fully characterized by liquid chromatography tandem triple quadrupole mass spectrometry (LC-QqQ/MS(2)). The lipids studied in the analyses were free cholesterol (FC), cholesterol ester (CE) and triglycerides (TG). All three proanthocyanidin-rich extracts induced a remarkable decrease in the de novo lipid synthesis in HepG2 cells. Moreover, GSPE rat serum metabolites reduced the total percentage of CE, FC and particularly TG; this reduction was significantly higher than that observed in the cells directly treated with GSPE. In conclusion, the bioactivity of the physiological metabolites that are present in the serum of rats after their ingestion of a proanthocyanidin-rich extract was demonstrated in Hep G2 cells. ",The Journal of nutritional biochemistry,"['D000818', 'D002099', 'D002784', 'D005419', 'D056604', 'D056945', 'D006801', 'D050155', 'D028223', 'D024301', 'D010936', 'D044945', 'D051381', 'D017208', 'D015203', 'D044967', 'D014280']","['Animals', 'Cacao', 'Cholesterol', 'Flavonoids', 'Grape Seed Extract', 'Hep G2 Cells', 'Humans', 'Lipogenesis', 'Pinus', 'Plant Bark', 'Plant Extracts', 'Proanthocyanidins', 'Rats', 'Rats, Wistar', 'Reproducibility of Results', 'Serum', 'Triglycerides']",Serum metabolites of proanthocyanidin-administered rats decrease lipid synthesis in HepG2 cells.,"[None, 'Q000737', 'Q000097', 'Q000494', 'Q000493', None, None, 'Q000187', 'Q000737', 'Q000737', 'Q000494', 'Q000097', None, None, None, 'Q000737', 'Q000097']","[None, 'chemistry', 'blood', 'pharmacology', 'pharmacokinetics', None, None, 'drug effects', 'chemistry', 'chemistry', 'pharmacology', 'blood', None, None, None, 'chemistry', 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/24231101,2014,0,0,, +0.31,28605022,"A rapid technique using direct analysis in real-time (DART) ambient ionization coupled to a high-resolution accurate mass-mass spectrometer (HRAM-MS) was employed to analyze stains on an individual's pants suspected to have been involved in a violent crime. The victim was consuming chocolate ice cream at the time of the attack, and investigators recovered the suspect's pants exhibiting splatter stains. Liquid chromatography with mass spectral detection (LC-MS) and stereoscopic light microscopy (SLM) were also utilized in this analysis. It was determined that the stains on the pants contained theobromine and caffeine, known components of chocolate. A shard from the ceramic bowl that contained the victim's ice cream and a control chocolate ice cream sample were also found to contain caffeine and theobromine. The use of DART-HRAM-MS was useful in this case due to its rapid analysis capability and because of the limited amount of sample present as a stain.",Journal of forensic sciences,[],[],Forensic Analysis of Stains on Fabric Using Direct Analysis in Real-time Ionization with High-Resolution Accurate Mass-Mass Spectrometry.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/28605022,2018,0,0,,no cocoa +0.31,22417537,"Cacao (Theobroma cacao L.) is rich in procyanidins, a large portion of which degrades during the natural fermentation process of producing cocoa powder. Recent advances in technology have enabled scientists to produce unfermented cocoa powder, preserving the original profile of procyanidins present in cocoa and allowing for the development of highly concentrated procyanidin-rich extracts. During this process, the anthocyanins naturally present in unfermented cocoa remain intact, producing a violet color in the final extract. The objective of this study was to selectively remove the violet color in procyanidin-rich extracts produced from unfermented cocoa powder, while maintaining the stability and composition of procyanidins present in the matrix. Several processing parameters, including pH fluctuations, enzymatic treatments, and the addition of potassium meta-bisulfite, were explored to influence the color of procyanidin-rich extracts throughout a 60-d shelf life study. The addition of potassium meta-bisulfite (500 ppm) was found to be the most effective means of removing the violet color present in the treated extracts (L*= 71.39, a*= 8.44, b*= 9.61, chroma = 12.79, and hue = 48.8_) as compared to the control (L*= 52.84, a*= 11.08, b*= 2.24, chroma = 11.28, and hue = 11.4_). The use of potassium meta-bisulfite at all treatment levels (200, 500, and 1000 ppm) did not show any significant detrimental effects on the stability, composition, or amount of procyanidins present in the extracts over the shelf life period as monitored by UV-Vis spectrophotometry and HPLC-MS. This research will enable the food industry to incorporate highly concentrated procyanidin-rich extracts in food products without influencing the color of the final product.",Journal of food science,"['D000872', 'D002099', 'D003116', 'D005285', 'D005511', 'D010936', 'D044945', 'D012639', 'D013447']","['Anthocyanins', 'Cacao', 'Color', 'Fermentation', 'Food Handling', 'Plant Extracts', 'Proanthocyanidins', 'Seeds', 'Sulfites']",Selective removal of the violet color produced by anthocyanins in procyanidin-rich unfermented cocoa extracts.,"['Q000737', 'Q000737', None, None, 'Q000379', 'Q000737', 'Q000032', 'Q000737', None]","['chemistry', 'chemistry', None, None, 'methods', 'chemistry', 'analysis', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/22417537,2012,0,0,,no day 0 control +0.3,9583844,"The influence of ascorbic acid on iron absorption from an iron-fortified, chocolate-flavored milk drink (6.3 mg total Fe per serving) was evaluated with a stable-isotope technique in 20 6-7-y-old Jamaican children. Each child received two test meals labeled with 5.6 mg 57Fe and 3.0 mg 58Fe as ferrous sulfate on 2 consecutive days. Three different doses of ascorbic acid (0, 25, and 50 mg per 25-g serving) were evaluated in two separate studies by using a crossover design. Iron isotope ratios were measured by negative thermal ionization mass spectrometry. In the first study, iron absorption was significantly greater (P < 0.0001) after the addition of 25 mg ascorbic acid: geometric mean iron absorption was 1.6% (range: 0.9-4.2%) and 5.1% (2.2-17.3%) for the test meals containing 0 and 25 mg ascorbic acid, respectively. In the second study, a significant difference (P < 0.05) in iron absorption was observed when the ascorbic acid content was increased from 25 to 50 mg: geometric mean iron absorption was 5.4% (range: 2.7-10.8%) compared with 7.7% (range: 4.7-16.5%), respectively. The chocolate drink contained relatively high amounts of polyphenolic compounds, phytic acid, and calcium, all well-known inhibitors of iron absorption. The low iron absorption without added ascorbic acid shows that chocolate milk is a poor vehicle for iron fortification unless sufficient amounts of an iron-absorption enhancer are added. Regular consumption of iron-fortified chocolate milk drinks containing added ascorbic acid could have a positive effect on iron nutrition in population groups vulnerable to iron deficiency.",The American journal of clinical nutrition,"['D000818', 'D001205', 'D002097', 'D002099', 'D002648', 'D018592', 'D005260', 'D005293', 'D005504', 'D005527', 'D006454', 'D006801', 'D007408', 'D007501', 'D007563', 'D008297', 'D008892']","['Animals', 'Ascorbic Acid', 'C-Reactive Protein', 'Cacao', 'Child', 'Cross-Over Studies', 'Female', 'Ferritins', 'Food Analysis', 'Food, Fortified', 'Hemoglobins', 'Humans', 'Intestinal Absorption', 'Iron', 'Jamaica', 'Male', 'Milk']","Influence of ascorbic acid on iron absorption from an iron-fortified, chocolate-flavored milk drink in Jamaican children.","[None, 'Q000008', 'Q000378', None, None, None, None, 'Q000097', None, 'Q000032', 'Q000378', None, 'Q000187', 'Q000008', None, None, None]","[None, 'administration & dosage', 'metabolism', None, None, None, None, 'blood', None, 'analysis', 'metabolism', None, 'drug effects', 'administration & dosage', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9583844,1998,0,0,,no cocoa tested +0.3,24785502,"Surveys were carried out between 2007 and 2009 to determine the aflatoxin B1 content of 3345 commercial Turkish foodstuffs supplied by producers for testing for their own purposes or for export certification. To simplify the reporting of data, foods were categorized as: 1, high sugar products with nuts; 2, nuts and seeds; 3, spices; 4, grain; 5, cocoa products; 6, dried fruit and vegetables; 7, processed cereal products; 8, tea; and 9, baby food and infant formula. Aflatoxin analysis was carried out by high-performance liquid chromatography with fluorescence detection after immunoaffinity column clean-up, with a recoveries ranging from 91% to 99%, depending on the matrix. Of the 3345 samples analysed, 94% contained aflatoxin B1 below the European Union limit of 2 _µg kg(-1), which applies to nuts, dried fruit, and cereals products. The 6% of the 206 contaminated samples were mainly nuts and spices. For pistachios, 24%, 38%, and 42% of the totals of 207, 182, and 24 samples tested for 2007, 2008 and 2009, respectively, were above 2 _µg kg(-1), with 50 samples containing aflatoxin B1 at levels ranging from 10 to 477 _µg kg(-1). ","Food additives & contaminants. Part B, Surveillance","['D016604', 'D003625', 'D004032', 'D002523', 'D004781', 'D005062', 'D005506', 'D005523', 'D005638', 'D005658', 'D006801', 'D007223', 'D041943', 'D009754', 'D014421']","['Aflatoxin B1', 'Data Collection', 'Diet', 'Edible Grain', 'Environmental Exposure', 'European Union', 'Food Contamination', 'Food Supply', 'Fruit', 'Fungi', 'Humans', 'Infant', 'Infant Formula', 'Nuts', 'Turkey']",Surveys of aflatoxin B1 contamination of retail Turkish foods and of products intended for export between 2007 and 2009.,"['Q000032', None, None, 'Q000737', 'Q000032', None, 'Q000032', None, 'Q000737', None, None, None, None, 'Q000737', None]","['analysis', None, None, 'chemistry', 'analysis', None, 'analysis', None, 'chemistry', None, None, None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/24785502,2014,0,0,,cocoa products werent specified +0.3,18727538,"A fast and simple method to determine vitamin B12 in foods is presented. The method allows, in addition to the determination of added cyanocobalamin, the determination of natural vitamin B12 forms, making it also applicable to nonfortified products, especially those that are milk-based. Vitamin B12 is extracted in sodium acetate buffer in the presence of sodium cyanide (100 degrees C, 30 min). After purification and concentration with an immunoaffinity column, vitamin B12 is determined by liquid chromatography with UV detection (361 nm). The method has been validated in analyses of a large range of products: milk- and soy-based infant formulas, cereals, cocoa beverages, health care products, and polyvitamin premixes. The method showed appropriate performance characteristics: linear response over a large range of concentrations, recovery rates of 100.8 +/- 7.5% (average +/- standard deviation), relative standard deviation of repeatability, RSDr, of 2.1%, and intermediate reproducibility, RSDiR, of 4.3%. Limits of detection and quantitation were 0.10 and 0.30 microg/100 g, respectively, and correlation with the reference microbiological assay was good (R2 = 0.9442). The proposed method is suitable for the routine determination of vitamin B12 in fortified foods, as well as in nonfortified dairy products. It can be used as a faster, more selective, and more precise alternative to the classical microbiological determination.",Journal of AOAC International,"['D002846', 'D002851', 'D005527', 'D007120', 'D007202', 'D012015', 'D015203', 'D012997', 'D013056', 'D014805', 'D014815']","['Chromatography, Affinity', 'Chromatography, High Pressure Liquid', 'Food, Fortified', 'Immunochemistry', 'Indicators and Reagents', 'Reference Standards', 'Reproducibility of Results', 'Solvents', 'Spectrophotometry, Ultraviolet', 'Vitamin B 12', 'Vitamins']",Determination of vitamin B12 in food products by liquid chromatography/UV detection with immunoaffinity extraction: single-laboratory validation.,"[None, None, 'Q000032', None, None, None, None, None, None, 'Q000032', 'Q000032']","[None, None, 'analysis', None, None, None, None, None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18727538,2008,,,, +0.3,26572874,"Antioxidant-rich foods scavenge free radicals and other reactive species, decreasing the risk of different non-communicable chronic diseases. The objective of this study was to review the content of total antioxidant capacity of commonly foods comparing with experimental data and to explore the health benefits due to foods with moderate to high TAC. The TAC was analytically measured using the ""Total Antioxidant Capacity"" (NX2332) test from Randox_‰ (UK) by spectrometry at 600 nm. Brazil nut (Bertholletia excelsa), ""guaran"" (Paullinia cupana Kunth) powder, ready to drink boiled coffee (Coffea arabica L.), and milk chocolate (made from seeds of Theobroma cacao) had the highest TAC values, followed by collard greens (Brassica oleracea L.), beets (Beta vulgaris L.), apples (Malus domestica Borkh.), bananas (Musa paradisiaca), common beans (Phaseolus vulgaris), oranges (Citrus sinensis (L.) Osbeck), onions (Allium cepa L.), and lettuce (Lactuca sativa L.). Other foods also showed antioxidant capacity. The binomial antioxidant capacity of foods and health was extensively discussed according to science literature. Based on the high TAC content of Brazil nuts, guaran, coffee, chocolate, collard greens, apples, beets, beans, oranges, onions and other foods, their regular dietary intake is strongly recommended to reduce the risk of chronic non-communicable diseases. ",Current pharmaceutical design,"['D031383', 'D002318', 'D002561', 'D019587', 'D006801', 'D027845', 'D009369', 'D008517']","['Bertholletia', 'Cardiovascular Diseases', 'Cerebrovascular Disorders', 'Dietary Supplements', 'Humans', 'Malus', 'Neoplasms', 'Phytotherapy']",An apple plus a Brazil nut a day keeps the doctors away: antioxidant capacity of foods and their health benefits.,"[None, 'Q000517', 'Q000517', None, None, None, 'Q000517', None]","[None, 'prevention & control', 'prevention & control', None, None, None, 'prevention & control', None]",https://www.ncbi.nlm.nih.gov/pubmed/26572874,2016,,,,no pdf access +0.3,3806705,"Ethyl ether extracts derived from coffee were tested for in vitro estrogenic and in vivo uterotropic activities. Coffee extracts, unlike tea and cocoa, were found to actively compete with 17 beta-estradiol for uterine cytosol binding sites. The biologically active fractions possessed an unique ultraviolet absorbance spectrum that excluded them from containing flavonoid, coumestan, or resorcyclic acid lactone constituents. Coffee extracts administered to immature female mice for 3 d in feeding studies displayed significant (p less than 0.05) uterotropic responses, which were similar to results obtained in mice treated with a standard 17 beta-estradiol dose. Additional studies in mice disclosed that coffee extracts did not reduce the uterotropic effect normally induced by 17 beta-estradiol when administered simultaneously with estradiol. The complete estrogenic effects of coffee constituents, coupled with their failure to inhibit a biological response evoked by estradiol, strongly suggest that coffee contains constituent(s) that are weakly estrogenic.",Journal of toxicology and environmental health,"['D000818', 'D001667', 'D002099', 'D003069', 'D003600', 'D004958', 'D004986', 'D005260', 'D019833', 'D007529', 'D051379', 'D010052', 'D011960', 'D013056', 'D013662', 'D014599']","['Animals', 'Binding, Competitive', 'Cacao', 'Coffee', 'Cytosol', 'Estradiol', 'Ether', 'Female', 'Genistein', 'Isoflavones', 'Mice', 'Ovariectomy', 'Receptors, Estrogen', 'Spectrophotometry, Ultraviolet', 'Tea', 'Uterus']",Studies on the estrogenic activity of a coffee extract.,"[None, None, None, 'Q000633', 'Q000378', 'Q000378', None, None, None, None, None, None, 'Q000378', None, None, 'Q000378']","[None, None, None, 'toxicity', 'metabolism', 'metabolism', None, None, None, None, None, None, 'metabolism', None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/3806705,1987,,,, +0.3,21535643,"Chocolate storage is critical to final product quality. Inadequate storage, especially with temperature fluctuations, may lead to rearrangement of triglycerides that make up the bulk of the chocolate matrix; this rearrangement may lead to fat bloom. Bloom is the main cause of quality loss in the chocolate industry. The effect of storage conditions leading to bloom formation on texture and flavor attributes by human and instrumental measures has yet to be reported. Therefore, the impact of storage conditions on the quality of dark chocolate by sensory and instrumental measurements was determined. Dark chocolate was kept under various conditions and analyzed at 0, 4, and 8 wk of storage. Ten members of a descriptive panel analyzed texture and flavor. Instrumental methods included texture analysis, color measurement, lipid polymorphism by X-ray diffraction and differential scanning calorimetry, triglyceride concentration by gas chromatography, and surface properties by atomic force microscopy. Results were treated by analysis of variance, cluster analysis, principal component analysis, and linear partial least squares regression analysis. Chocolate stored 8 wk at high temperature without fluctuations and 4 wk with fluctuations transitioned from form V to VI. Chocolates stored at high temperature with and without fluctuations were harder, more fracturable, more toothpacking, had longer melt time, were less sweet, and had less cream flavor. These samples had rougher surfaces, fewer but larger grains, and a heterogeneous surface. Overall, all stored dark chocolate experienced instrumental or perceptual changes attributed to storage condition. Chocolates stored at high temperature with and without fluctuations were most visually and texturally compromised. Practical Application: Many large chocolate companies do their own ""in-house"" unpublished research and smaller confectionery facilities do not have the means to conduct their own research. Therefore, this study relating sensory and instrumental data provides published evidence available for application throughout the confectionery industry.",Journal of food science,"['D002099', 'D002152', 'D002182', 'D055598', 'D003116', 'D005260', 'D005410', 'D005511', 'D006358', 'D006801', 'D008297', 'D018625', 'D033362', 'D011786', 'D012677', 'D013499', 'D013649', 'D013997', 'D044366', 'D014280']","['Cacao', 'Calorimetry, Differential Scanning', 'Candy', 'Chemical Phenomena', 'Color', 'Female', 'Flame Ionization', 'Food Handling', 'Hot Temperature', 'Humans', 'Male', 'Microscopy, Atomic Force', 'Powder Diffraction', 'Quality Control', 'Sensation', 'Surface Properties', 'Taste', 'Time Factors', 'Transition Temperature', 'Triglycerides']",Impact of storage on dark chocolate: texture and polymorphic changes.,"['Q000737', None, 'Q000032', None, None, None, None, None, 'Q000009', None, None, None, None, None, None, None, None, None, None, 'Q000032']","['chemistry', None, 'analysis', None, None, None, None, None, 'adverse effects', None, None, None, None, None, None, None, None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/21535643,2011,0,0,, +0.3,21561068,"The physicochemical properties of acidified milk gels after the addition of cocoa flavanols were studied. As the flavanol level increased (from 0 to 2.5 mg/g), syneresis and gel elasticity (tan __) were found to significantly increase and decrease, respectively. Flavanol addition reduced the stress at fracture, with no changes in fracture strain, suggesting that the bond type (i.e., covalent vs noncovalent) was the underlying factor explaining the ease of fracture. Gels made from recombined milks containing the casein fraction of heated milk and the serum of heated flavanol/milk mixtures showed the lowest values of G' and fracture stress. It was concluded that whey proteins/flavanol interactions were responsible for the poor mechanical properties of flavanol-added acidified milk gels. High-performance liquid chromatography analysis of milk sera showed that 60% of the total available monomeric flavanols was found in the serum phase from which 75% was non-associated to whey proteins. Concomitantly, >70% of flavanols with degree of polymerization >3 were found to be associated with the casein fraction.",Journal of agricultural and food chemistry,"['D000818', 'D002099', 'D055598', 'D004548', 'D044950', 'D005782', 'D008892', 'D058105']","['Animals', 'Cacao', 'Chemical Phenomena', 'Elasticity', 'Flavanones', 'Gels', 'Milk', 'Polymerization']",Physicochemical properties of acidified skim milk gels containing cocoa flavanols.,"[None, 'Q000737', None, None, 'Q000737', 'Q000737', 'Q000737', None]","[None, 'chemistry', None, None, 'chemistry', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/21561068,2011,0,0,, +0.3,11025151,"Cocoa and chocolate contain the tetrahydroisoquinoline alkaloid salsolinol up to a concentration of 25 microg/g. Salsolinol is a dopaminergic active compound which binds to the D(2) receptor family, especially to the D(3) receptor with a K(i) of 0.48+/-0.021 micromol/l. It inhibits the formation of cyclic AMP and the release of beta-endorphin and ACTH in a pituitary cell system. Taking the detected concentration and the pharmacological properties into account, salsolinol seems to be one of the main psychoactive compounds present in cocoa and chocolate and might be included in chocolate addiction.",Journal of ethnopharmacology,"['D000324', 'D000818', 'D002099', 'D000242', 'D005504', 'D008401', 'D007546', 'D051379', 'D011954', 'D013237', 'D019966', 'D014407', 'D001615']","['Adrenocorticotropic Hormone', 'Animals', 'Cacao', 'Cyclic AMP', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Isoquinolines', 'Mice', 'Receptors, Dopamine', 'Stereoisomerism', 'Substance-Related Disorders', 'Tumor Cells, Cultured', 'beta-Endorphin']",In vitro pharmacological activity of the tetrahydroisoquinoline salsolinol present in products from Theobroma cacao L. like cocoa and chocolate.,"['Q000378', None, 'Q000737', 'Q000378', None, None, 'Q000032', None, 'Q000187', None, 'Q000378', 'Q000187', 'Q000187']","['metabolism', None, 'chemistry', 'metabolism', None, None, 'analysis', None, 'drug effects', None, 'metabolism', 'drug effects', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/11025151,2001,1,1,table 1,only cocoa +0.3,22953918,"Silicon is a trace element for humans, and is absorbed from food in the form of orthosilicic acid. Instant food products are part of a constantly growing market of convenience foods, which have not been evaluated yet as sources of silicon. In this study the total and soluble silicon contents in different instant food products were determined by using graphite furnace atomic absorption spectrometry (GF-AAS). A selection of instant products commercially available in Wroclaw were analyzed: soups, main courses, coffee drinks, jellies and puddings. Total silicon contents in soups, main courses and coffee drinks ranged widely and reached the values: 0.10-30.20, 0.63-37.91 and 0.21-13.37mg/serving, respectively. These products contained 0.05-1.26mg of soluble silicon per serving. The total silicon content in jellies and puddings did not exceed 0.36mg and 2.42mg/serving, respectively. Among the analyzed desserts the highest level of soluble silicon was found in chocolate puddings: 0.36-0.41mg/serving. The silicon level in servings of the studied instant products when prepared with the appropriate amount of water was also estimated. The mean content of silicon determined in samples of drinking water from Wroc_aw and the vicinity, which was used for the estimation, amounted to 7.09mg/l. The total silicon content in ready-to-eat products ranged from 1.32 to 39.21mg/serving. In conclusion, some of the analyzed instant foods contained very high amounts of silicon, however the content of the soluble, and hence available, form of this element was low.",Food chemistry,"['D057140', 'D005504', 'D009753', 'D012825', 'D014131']","['Fast Foods', 'Food Analysis', 'Nutritive Value', 'Silicon', 'Trace Elements']",Instant food products as a source of silicon.,"['Q000032', None, None, 'Q000032', 'Q000032']","['analysis', None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/22953918,2013,0,0,,no cocoa +0.29,18215649,"The spontaneous formation of the neurotoxic carcinogen acrylamide in a wide range of cooked foods has recently been discovered. These foods include bread and other bakery products, crisps, chips, breakfast cereals, and coffee. To date, the diminutive size of acrylamide (71.08 Da) has prevented the development of screening immunoassays for this chemical. In this study, a polyclonal antibody capable of binding the carcinogen was produced by the synthesis of an immunogen comprising acrylamide derivatised with 3-mercaptobenzoic acid (3-MBA), and its conjugation to the carrier protein bovine thyroglobulin. Antiserum from the immunised rabbit was harvested and fully characterised. It displayed no binding affinity for acrylamide or 3-MBA but had a high affinity for 3-MBA-derivitised acrylamide. The antisera produced was utilised in the development of an ELISA based detection system for acrylamide. Spiked water samples were assayed for acrylamide content using a previously published extraction method validated for coffee, crispbread, potato, milk chocolate and potato crisp matrices. Extracted acrylamide was then subjected to a rapid 1-h derivatisation with 3-MBA, pre-analysis. The ELISA was shown to have a high specificity for acrylamide, with a limit of detection in water samples of 65.7 microgkg(-1), i.e. potentially suitable for acrylamide detection in a wide range of food commodities. Future development of this assay will increase sensitivity further. This is the first report of an immunoassay capable of detecting the carcinogen, as its small size has necessitated current analytical detection via expensive, slower, physico-chemical techniques such as Gas or Liquid Chromatography coupled to Mass Spectrometry.",Analytica chimica acta,"['D020106', 'D000818', 'D002273', 'D004797', 'D005260', 'D005504', 'D051379', 'D008807', 'D013997']","['Acrylamide', 'Animals', 'Carcinogens', 'Enzyme-Linked Immunosorbent Assay', 'Female', 'Food Analysis', 'Mice', 'Mice, Inbred BALB C', 'Time Factors']","Development of a high-throughput enzyme-linked immunosorbent assay for the routine detection of the carcinogen acrylamide in food, via rapid derivatisation pre-analysis.","['Q000032', None, 'Q000032', 'Q000379', None, None, None, None, None]","['analysis', None, 'analysis', 'methods', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18215649,2008,0,0,,no cocoa +0.29,16500886,"In the current study, we screened 7 clonal lines from single seed phenotypes of Lamiaceae family for the inhibition of alpha-amylase, alpha-glucosidase and angiotensin converting enzyme (ACE) inhibitory activity. Water extracts of oregano had the highest alpha-glucosidase inhibition activity (93.7%), followed by chocolate mint (85.9%) and lemon balm (83.9%). Sage (78.4 %), and three different clonal lines of rosemary: rosemary LA (71.4%), rosemary 6 (68.4%) and rosemary K-2 (67.8%) also showed significant alpha-glucosidase inhibitory activity. The alpha-glucosidase inhibitory activity of the extracts was compared to selected specific phenolics detected in the extracts using HPLC. Catechin had the highest alpha-glucosidase inhibitiory activity (99.6 %) followed by caffeic acid (91.3 %), rosmarinic acid (85.1%) and resveratrol (71.1 %). Catechol (64.4%), protocatechuic acid (55.7%) and quercetin (36.9%) also exhibited significant alpha-glucosidase inhibitory activity. Results suggested that alpha-glucosidase inhibitory activity of the clonal extracts correlated to the phenolic content, antioxidant activity and phenolic profile of the extracts. The clonal extracts of the herbs and standard phenolics tested in this study did not have any effect on the alpha-amylase activity. We also investigated the ability of the clonal extracts to inhibit rabbit lung angiotensin I-converting enzyme (ACE). The water extracts of rosemary, rosemary LA had the highest ACE inhibitory activity (90.5%), followed by lemon balm (81.9%) and oregano (37.4 %). Lower levels of ACE inhibition were observed with ethanol extracts of oregano (18.5 %) and lemon balm (0.5 %). Among the standard phenolics only resveratrol (24.1 %), hydroxybenzoic acid (19.3 %) and coumaric acid (2.3 %) had ACE inhibitory activity.",Asia Pacific journal of clinical nutrition,"['D000806', 'D000959', 'D002851', 'D003924', 'D004791', 'D000431', 'D065089', 'D006801', 'D006973', 'D007004', 'D066298', 'D019686', 'D008517', 'D010936', 'D014867', 'D000516']","['Angiotensin-Converting Enzyme Inhibitors', 'Antihypertensive Agents', 'Chromatography, High Pressure Liquid', 'Diabetes Mellitus, Type 2', 'Enzyme Inhibitors', 'Ethanol', 'Glycoside Hydrolase Inhibitors', 'Humans', 'Hypertension', 'Hypoglycemic Agents', 'In Vitro Techniques', 'Lamiaceae', 'Phytotherapy', 'Plant Extracts', 'Water', 'alpha-Amylases']",Evaluation of clonal herbs of Lamiaceae species for management of diabetes and hypertension.,"[None, 'Q000627', None, 'Q000188', 'Q000627', None, None, None, 'Q000188', 'Q000627', None, 'Q000737', None, 'Q000032', None, 'Q000037']","[None, 'therapeutic use', None, 'drug therapy', 'therapeutic use', None, None, None, 'drug therapy', 'therapeutic use', None, 'chemistry', None, 'analysis', None, 'antagonists & inhibitors']",https://www.ncbi.nlm.nih.gov/pubmed/16500886,2006,0,0,,no cocoa +0.29,12963011,"High levels of acrylamide have been found in foods heated at high temperatures, especially in carbohydrate rich foods. Several kinds of foods (industrially produced) representing different food/product groups available on the Swedish market have been analysed for acrylamide. A considerable variation in levels of acrylamide between single foodstuffs (different brands) within food categories were found, which also applies for levels in different food categories. Using recent Swedish food consumption data the dietary intake of acrylamide for the Swedish adult population was assessed based on foodstuffs with low to high levels of acrylamide (<30-2300 microg/kg), such as processed potato products, bread, breakfast cereals, biscuits, cookies, snacks and coffee. The estimated dietary intake of acrylamide per person (total population) given as the 5th, 50th and 95th percentile were 9.1, 27 and 62 microg/day respectively, from those food/product groups (mean 31 microg/day). No acrylamide was found in many other foodstuffs analysed and those were therefore not included in the dietary intake assessment of acrylamide. However, an additional minor contribution of a few microg/day of acrylamide from foods/products like poultry, meat, fish, cocoa powder and chocolates cannot be excluded. An average daily intake of 35 microg corresponds to 0.5 microg per kg body weight and day (body weight 70 kg). Risk assessments of acrylamide, made by US EPA and WHO, imply that this dietary intake of acrylamide could be associated with potential health risks.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000178', 'D000293', 'D000328', 'D000368', 'D002853', 'D003625', 'D004032', 'D004435', 'D005260', 'D005504', 'D006801', 'D008297', 'D008875', 'D021241', 'D013548']","['Acrylamides', 'Adolescent', 'Adult', 'Aged', 'Chromatography, Liquid', 'Data Collection', 'Diet', 'Eating', 'Female', 'Food Analysis', 'Humans', 'Male', 'Middle Aged', 'Spectrometry, Mass, Electrospray Ionization', 'Sweden']",Dietary intake of acrylamide in Sweden.,"['Q000032', None, None, None, None, None, None, None, None, None, None, None, None, None, None]","['analysis', None, None, None, None, None, None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12963011,2003,0,0,, +0.29,29083186,"Peanut is an important food allergen, but it cannot currently be reliably detected and quantified in processed foods at low levels. A level of 3 mg protein/kg is increasingly being used as a reference dose above which precautionary allergen labeling is applied to food products. Two exemplar matrices (chocolate dessert and chocolate bar) were prepared and incurred with 0, 3, 10, or 50 mg/kg peanut protein using a commercially available lightly roasted peanut flour ingredient. After simple buffer extraction employing an acid-labile detergent, multiple reaction monitoring (MRM) experiments were used to assess matrix effects on the detection of a set of seven peptide targets derived from peanut allergens using either conventional or microfluidic chromatographic separation prior to mass spectrometry. Microfluidic separation provided greater sensitivity and increased ionization efficiency at low levels. Individual monitored transitions were detected in consistent ratios across the dilution series, independent of matrix. The peanut protein content of each sample was then determined using ELISA and the optimized MRM method. Although other peptide targets were detected with three transitions at the 50 mg/kg peanut protein level in both matrices, only Arah2(Q6PSU2)",Journal of proteome research,[],[],Microfluidic Separation Coupled to Mass Spectrometry for Quantification of Peanut Allergens in a Complex Food Matrix.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29083186,2018,0,0,,no cocoa +0.29,25889873,"Two dimensional electrophoresis and nano-LC-MS were performed in order to identify alterations in protein abundance that correlate with maturation of cacao zygotic and somatic embryos. The cacao pod proteome was also characterized during development. The recently published cacao genome sequence was used to create a predicted proteolytic fragment database. Several hundred protein spots were resolved on each tissue analysis, of which 72 variable spots were subjected to MS analysis, resulting in 49 identifications. The identified proteins represent an array of functional categories, including seed storage, stress response, photosynthesis and translation factors. The seed storage protein was strongly accumulated in cacao zygotic embryos compared to their somatic counterpart. However, sucrose treatment (60 g L(-1)) allows up-regulation of storage protein in SE. A high similarity in the profiles of acidic proteins was observed in mature zygotic and somatic embryos. Differential expression in both tissues was observed in proteins having high pI. Several proteins were detected exclusively in fruit tissues, including a chitinase and a 14-3-3 protein. We also identified a novel cacao protein related to known mabinlin type sweet storage proteins. Moreover, the specific presence of thaumatin-like protein, another sweet protein, was also detected in fruit tissue. We discuss our observed correlations between protein expression profiles, developmental stage and stress responses.",Journal of plant physiology,"['D002099', 'D002853', 'D015180', 'D018507', 'D018506', 'D013058', 'D036103', 'D010940', 'D020543', 'D040901', 'D012639', 'D015053']","['Cacao', 'Chromatography, Liquid', 'Electrophoresis, Gel, Two-Dimensional', 'Gene Expression Regulation, Developmental', 'Gene Expression Regulation, Plant', 'Mass Spectrometry', 'Nanotechnology', 'Plant Proteins', 'Proteome', 'Proteomics', 'Seeds', 'Zygote']","Proteome analysis during pod, zygotic and somatic embryo maturation of Theobroma cacao.","['Q000196', None, None, None, None, None, None, 'Q000378', 'Q000378', 'Q000379', 'Q000235', 'Q000378']","['embryology', None, None, None, None, None, None, 'metabolism', 'metabolism', 'methods', 'genetics', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/25889873,2016,0,0,, +0.29,22664313,"After absorption in the gastrointestinal tract, (-)-epicatechin is extensively transformed into various conjugated metabolites. These metabolites, chemically different from the aglycone forms found in foods, are the compounds that reach the circulatory system and the target organs. Therefore, it is imperative to identify and quantify these circulating metabolites to investigate their roles in the biological effects associated with (-)-epicatechin intake. Using authentic synthetic standards of (-)-epicatechin sulfates, glucuronides, and O-methyl sulfates, a novel LC-MS/MS-MRM analytical methodology to quantify (-)-epicatechin metabolites in biological matrices was developed and validated. The optimized method was subsequently applied to the analysis of plasma and urine metabolites after consumption of dark chocolate, an (-)-epicatechin-rich food, by humans. (-)-Epicatechin-3'-__-d-glucuronide (C(max) 290 _± 49 nM), (-)-epicatechin 3'-sulfate (C(max) 233 _± 60 nM), and 3'-O-methyl epicatechin sulfates substituted in the 4', 5, and 7 positions were the most relevant (-)-epicatechin metabolites in plasma. When plasmatic metabolites were divided into their substituent groups, it was revealed that (-)-epicatechin glucuronides, sulfates, and O-methyl sulfates represented 33 _± 4, 28 _± 5, and 33 _± 4% of total metabolites (AUC(0-24)(h)), respectively, after dark chocolate consumption. Similar metabolites were found in urine samples collected over 24h. The total urine excretion of (-)-epicatechin was 20 _± 2% of the amount ingested. In conclusion, we describe the entire metabolite profile and its degree of elimination after administration of (-)-epicatechin-containing food. These results will help us understand more precisely the mechanisms and the main metabolites involved in the beneficial physiological effects of flavanols.",Free radical biology & medicine,"['D000328', 'D000704', 'D019540', 'D002099', 'D002392', 'D056148', 'D006207', 'D006262', 'D006801', 'D057230', 'D013058', 'D012015', 'D055815']","['Adult', 'Analysis of Variance', 'Area Under Curve', 'Cacao', 'Catechin', 'Chromatography, Reverse-Phase', 'Half-Life', 'Health', 'Humans', 'Limit of Detection', 'Mass Spectrometry', 'Reference Standards', 'Young Adult']",Elucidation of (-)-epicatechin metabolites after ingestion of chocolate by healthy humans.,"[None, None, None, 'Q000378', 'Q000031', 'Q000592', None, None, None, None, 'Q000592', None, None]","[None, None, None, 'metabolism', 'analogs & derivatives', 'standards', None, None, None, None, 'standards', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22664313,2012,0,0,,metabolites +0.29,2209520,"Thoroughbred geldings were fed racehorse cubes containing a predetermined concentration of theobromine in the form of cocoa husk. They were offered 7 kg of cubes per day, divided between morning and evening feed, and food consumption was monitored. Urinary concentrations of theobromine were determined following the consumption of cubes containing 11.5, 6.6, 2.0 and 1.2 mg per kg of theobromine, to verify whether or not such concentrations would produce positive urine tests. Pre-dose urine samples were collected to verify the absence of theobromine before each experiment. It became apparent from the results of the first three administrations that the limit of detection of theobromine, using such procedures, would be reached at a feed level of about 1 mg per kg theobromine. Therefore the final administration, using cubes containing 1.2 mg per kg theobromine, was singled out for additional analytical work and quantitative procedures were developed to measure urinary concentrations of theobromine. It was anticipated that the results would form a basis for discussions relating to the establishment of a threshold value for theobromine in horse urine. The Stewards of the Jockey Club subsequently gave notice that they had established a threshold level for theobromine in urine of 2 micrograms/ml.",Equine veterinary journal,"['D000821', 'D000818', 'D002099', 'D002851', 'D008401', 'D006736', 'D008297', 'D012016', 'D015203', 'D013805']","['Animal Feed', 'Animals', 'Cacao', 'Chromatography, High Pressure Liquid', 'Gas Chromatography-Mass Spectrometry', 'Horses', 'Male', 'Reference Values', 'Reproducibility of Results', 'Theobromine']",The excretion of theobromine in Thoroughbred racehorses after feeding compounded cubes containing cocoa husk--establishment of a threshold value in horse urine.,"[None, None, None, None, None, 'Q000378', None, None, None, 'Q000008']","[None, None, None, None, None, 'metabolism', None, None, None, 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/2209520,1990,,,,no pdf access +0.28,21548445,"Labeling of major food allergens is mandatory for the safety of allergic consumers. Although enzyme-linked immunosorbent assay, polymerase chain reaction, and mass spectrometry are sensitive and specific instruments to detect trace amounts of food proteins, they cannot measure the ability of food constituents to trigger activation of mast cells or basophils.",Journal of investigational allergology & clinical immunology,"['D000485', 'D015703', 'D010367', 'D001491', 'D016022', 'D002648', 'D005260', 'D005512', 'D006801', 'D007073', 'D008297', 'D008407', 'D021183', 'D010980', 'D060149']","['Allergens', 'Antigens, CD', 'Arachis', 'Basophils', 'Case-Control Studies', 'Child', 'Female', 'Food Hypersensitivity', 'Humans', 'Immunoglobulin E', 'Male', 'Mast Cells', 'Peanut Hypersensitivity', 'Platelet Membrane Glycoproteins', 'Tetraspanin 30']",Human basophils: a unique biological instrument to detect the allergenicity of food.,"['Q000276', 'Q000235', 'Q000276', 'Q000276', None, None, None, 'Q000276', None, 'Q000276', None, 'Q000276', 'Q000276', 'Q000235', None]","['immunology', 'genetics', 'immunology', 'immunology', None, None, None, 'immunology', None, 'immunology', None, 'immunology', 'immunology', 'genetics', None]",https://www.ncbi.nlm.nih.gov/pubmed/21548445,2011,0,0,, +0.28,12557249,"Essential oils and their corresponding hydrosols, obtained after distillation of various scented Pelargonium (Geraniaceae) leaves were assessed for their antimicrobial activity in a model food system. Both the essential oils and hydrosols were used at 1000 ppm in broccoli soup, previously inoculated with Enterobacter aerogenes (at 10(5) cfu g(-1)) and Staphylococcus aureus (at 10(4) cfu g(-1)). The results showed a complete inhibition of S. aureus in the broccoli soup by the essential oils of 'Sweet Mimosa', 'Mabel Grey', P. graveolens, 'Atomic Snowflake', 'Royal Oak', 'Attar of Roses' and a lesser effect by 'Chocolate Peppermint' and 'Clorinda'; the hydrosols, however, had a potentiating effect on the bacterial population in the food. Both extracts showed a complete inhibition of S. aureus in the Maximum Recovery Diluent (MRD). Antibacterial activity against E. aerogenes in the broccoli soup was generally very much reduced: only the essential oil of 'Mabel Grey' showed complete inhibition and virtually no reductions in colonies were seen with the other essential oils; the hydrosols again caused an increase in bacterial colonies. All the essential oils, bar Chocolate Peppermint showed complete inhibition of E. aerogenes in MRD, but the hydrosols showed no effect. The results strongly suggest that the residual hydrosols from distillation of these plant essential oils have no potential as antibacterial agents in foods, in contrast to most of the essential oils, which show potential against some micro-organisms, but only in some food systems. The problem of food component interference and its possible management is discussed.",Phytotherapy research : PTR,"['D001937', 'D002849', 'D021902', 'D005516', 'D005520', 'D005517', 'D006801', 'D008826', 'D031316', 'D008517', 'D018515', 'D010938', 'D013211']","['Brassica', 'Chromatography, Gas', 'Enterobacter aerogenes', 'Food Microbiology', 'Food Preservatives', 'Foodborne Diseases', 'Humans', 'Microbial Sensitivity Tests', 'Pelargonium', 'Phytotherapy', 'Plant Leaves', 'Plant Oils', 'Staphylococcus aureus']",The comparative effect of novel Pelargonium essential oils and their corresponding hydrosols as antimicrobial agents in a model food system.,"[None, None, 'Q000187', None, 'Q000008', 'Q000517', None, None, None, None, None, 'Q000494', 'Q000187']","[None, None, 'drug effects', None, 'administration & dosage', 'prevention & control', None, None, None, None, None, 'pharmacology', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/12557249,2003,0,0,,no cocoa +0.28,24804047,"Acrylamide (AA) is a chemical found in starchy foods that have been cooked at high temperatures. The objective of this study is to monitor the levels of AA in a total of 274 samples of potato chips, chips (except potato chips), biscuits, French fries, breakfast cereals, chocolate products, tea, seasoned laver, and nut products sampled in Korean market. These processed foods include (1) potato chips, (2) chips (except potato chips), (3) biscuits, (4) French fries, (5) breakfast cereals, (6) chocolate products, (7) tea, (8) seasoned laver, and (9) nut products. Samples used for this study were cleaned up using HLB Oasis polymeric and Accucat mixed-mode anion and cation exchange solid-phase extraction cartridge. Liquid chromatography-tandem mass spectroscopy (LC-MS/MS) was validated in-house as an efficient analytical method for the routine analysis of AA in various food products. AA was detected with a Fortis dC18 (1.7 __m, 100 mm _ 2.1 mm) column using 0.5% methanol/0.1% acetic acid in water as the mobile phase. Good results were obtained with respect to repeatability (RSDs < 5%). The recoveries obtained for a variety of food matrices ranged between 94.5% and 107.6%. Quantification during routine monitoring was sensitive enough to detect AA at a concentration of 10 __g/kg. A total of 274 food samples were analyzed for AA. The AA levels in the food groups were in the following order: potato chips > French fries > biscuits > tea > chips (except potato chips) > seasoned laver > breakfast cereals > chocolate products > nut products. AA was detected at levels ranging from not detectable to 1435 __g/kg. ",Food science & nutrition,[],[],In-house-validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for survey of acrylamide in various processed foods from Korean market.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/24804047,2014,0,0,,no cocoa +0.28,26590272,"Microbial starter cultures have extensively been used to enhance the consistency and efficiency of industrial fermentations. Despite the advantages of such controlled fermentations, the fermentation involved in the production of chocolate is still a spontaneous process that relies on the natural microbiota at cocoa farms. However, recent studies indicate that certain thermotolerant Saccharomyces cerevisiae cultures can be used as starter cultures for cocoa pulp fermentation. In this study, we investigate the potential of specifically developed starter cultures to modulate chocolate aroma. Specifically, we developed several new S. cerevisiae hybrids that combine thermotolerance and efficient cocoa pulp fermentation with a high production of volatile flavor-active esters. In addition, we investigated the potential of two strains of two non-Saccharomyces species that produce very large amounts of fruity esters (Pichia kluyveri and Cyberlindnera fabianii) to modulate chocolate aroma. Gas chromatography-mass spectrometry (GC-MS) analysis of the cocoa liquor revealed an increased concentration of various flavor-active esters and a decrease in spoilage-related off-flavors in batches inoculated with S. cerevisiae starter cultures and, to a lesser extent, in batches inoculated with P. kluyveri and Cyb. fabianii. Additionally, GC-MS analysis of chocolate samples revealed that while most short-chain esters evaporated during conching, longer and more-fat-soluble ethyl and acetate esters, such as ethyl octanoate, phenylethyl acetate, ethyl phenylacetate, ethyl decanoate, and ethyl dodecanoate, remained almost unaffected. Sensory analysis by an expert panel confirmed significant differences in the aromas of chocolates produced with different starter cultures. Together, these results show that the selection of different yeast cultures opens novel avenues for modulating chocolate flavor.",Applied and environmental microbiology,"['D000085', 'D002099', 'D004952', 'D005285', 'D005421', 'D006358', 'D006801', 'D012441', 'D013649']","['Acetates', 'Cacao', 'Esters', 'Fermentation', 'Flavoring Agents', 'Hot Temperature', 'Humans', 'Saccharomyces cerevisiae', 'Taste']",Tuning Chocolate Flavor through Development of Thermotolerant Saccharomyces cerevisiae Starter Cultures with Increased Acetate Ester Production.,"['Q000378', 'Q000737', 'Q000737', None, 'Q000737', None, None, 'Q000737', None]","['metabolism', 'chemistry', 'chemistry', None, 'chemistry', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/26590272,2016,1,3,table 3, +0.28,28288289,To assess the effect of four different children's drinks on color stability of resin dental composites.,The Journal of clinical pediatric dentistry,"['D001628', 'D003116', 'D003188', 'D006801', 'D013053', 'D013499']","['Beverages', 'Color', 'Composite Resins', 'Humans', 'Spectrophotometry', 'Surface Properties']",Effect of Children's Drinks on Color Stability of Different Dental Composites: An in vitro Study.,"[None, None, 'Q000737', None, None, None]","[None, None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28288289,2017,,,,no pdf access +0.28,25077686,"The influence of thermally induced reaction products of a known dietary bitter compound, catechin, on bitterness perception was investigated. Catechin was reacted in low-moisture simple Maillard models (200 _C for 15 min) consisting of glycine and a reducing sugar (D-glucose, D-xylose, or D-galactose). Based on liquid chromatrography-mass spectrometry (LC-MS) isotopic labeling techniques, eight reaction products were identified and subsequently structurally elucidated by tandem LC-MS/MS and two-dimensional NMR analysis; six were report to be flavan-3-ol-spiro-C-glycosides reaction products. One of the spiro products was reported to significantly suppress the perceived bitterness intensity of a caffeine solution. Additionally, this specific spiro product was further identified in cocoa and reported to increase in concentration during bean roasting.",Journal of agricultural and food chemistry,"['D000328', 'D002099', 'D002392', 'D003296', 'D005260', 'D005421', 'D006358', 'D006801', 'D015416', 'D008297', 'D013058', 'D015394', 'D013649', 'D055815']","['Adult', 'Cacao', 'Catechin', 'Cooking', 'Female', 'Flavoring Agents', 'Hot Temperature', 'Humans', 'Maillard Reaction', 'Male', 'Mass Spectrometry', 'Molecular Structure', 'Taste', 'Young Adult']",Identification of bitter modulating maillard-catechin reaction products.,"[None, 'Q000737', 'Q000737', None, None, 'Q000737', None, None, None, None, None, None, None, None]","[None, 'chemistry', 'chemistry', None, None, 'chemistry', None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25077686,2015,1,1,Fig 3, +0.27,24165745,"Pigments of food and beverages could affect dental bleaching efficacy. The aim of this investigation was to evaluate color change and mineral loss of tooth enamel as well as the influence of staining solutions normally used by adolescent patients undergoing home bleaching. Initial hardness and baseline color were measured on enamel blocks. Specimens were divided into five groups (n=5): G1 (control) specimens were kept in artificial saliva throughout the experiment (3 weeks); G2 enamel was exposed to 10% carbamide peroxide for 6 h daily, and after this period, the teeth were cleaned and stored in artificial saliva until the next bleaching session; and G3, G4, and G5 received the same treatments as G2, but after bleaching, they were stored for 1 h in cola soft drink, melted chocolate, or red wine, respectively. Mineral loss was obtained by the percentage of hardness reduction, and color change was determined by the difference between the data obtained before and after treatments. Data were subjected to analysis of variance and Fisher's test (_±=0.05). G3 and G5 showed higher mineral loss (92.96 _± 5.50 and 94.46 _± 1.00, respectively) compared to the other groups (p ___ 0.05). G5 showed high-color change (9.34 _± 2.90), whereas G1 presented lower color change (2.22 _± 0.44) (p ___ 0.05). Acidic drinks cause mineral loss of the enamel, which could modify the surface and reduce staining resistance after bleaching.",Journal of biomedical optics,"['D000704', 'D000818', 'D001628', 'D002417', 'D003116', 'D003743', 'D006244', 'D010545', 'D013053', 'D058205', 'D017001', 'D014508']","['Analysis of Variance', 'Animals', 'Beverages', 'Cattle', 'Color', 'Dental Enamel', 'Hardness', 'Peroxides', 'Spectrophotometry', 'Tooth Bleaching Agents', 'Tooth Demineralization', 'Urea']",Mineral loss and color change of enamel after bleaching and staining solutions combination.,"[None, None, None, None, None, 'Q000737', 'Q000187', 'Q000494', None, 'Q000494', None, 'Q000031']","[None, None, None, None, None, 'chemistry', 'drug effects', 'pharmacology', None, 'pharmacology', None, 'analogs & derivatives']",https://www.ncbi.nlm.nih.gov/pubmed/24165745,2014,,,, +0.27,11696092,The aim of this study was to identify the causative agent of a smoky/phenolic taint in refrigerated full cream chocolate milk.,Letters in applied microbiology,"['D000818', 'D001547', 'D002099', 'D003080', 'D015169', 'D005516', 'D005519', 'D008401', 'D006139', 'D008892', 'D020638']","['Animals', 'Benzaldehydes', 'Cacao', 'Cold Temperature', 'Colony Count, Microbial', 'Food Microbiology', 'Food Preservation', 'Gas Chromatography-Mass Spectrometry', 'Guaiacol', 'Milk', 'Rahnella']",Formation of guaiacol in chocolate milk by the psychrotrophic bacterium Rahnella aquatilis.,"[None, None, 'Q000382', None, None, None, None, None, 'Q000737', 'Q000382', 'Q000254']","[None, None, 'microbiology', None, None, None, None, None, 'chemistry', 'microbiology', 'growth & development']",https://www.ncbi.nlm.nih.gov/pubmed/11696092,2002,0,0,,no cocoa +0.27,28764077,"Infants and toddlers are highly vulnerable to exposure to lead due to its higher absorption in small children than in adults. This study describes the optimisation and validation of a very sensitive method for the determination of low levels of lead in foods mostly consumed by infants and toddlers. This method, based on inductively coupled plasma-mass spectrometry with a programmable temperature cyclonic spray chamber, attained a limit of quantification (LOQ) of 0.6 or 0.9_µgPbkg",Food chemistry,"['D002675', 'D004032', 'D004034', 'D005247', 'D005502', 'D006801', 'D007223', 'D007854']","['Child, Preschool', 'Diet', 'Diet Surveys', 'Feeding Behavior', 'Food', 'Humans', 'Infant', 'Lead']",Levels of lead in foods from the first French total diet study on infants and toddlers.,"[None, None, None, None, None, None, None, None]","[None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28764077,2017,0,0,,no cocoa tested +0.27,16076092,"A rapid liquid chromatography electrospray ionization tandem mass spectrometry with negative ion detection method was developed and validated to determine cocoa flavonoid metabolites in human plasma and urine after the intake of a standard portion of a cocoa beverage. A chromatographic run time of only 9 min provided clear separation of all metabolites and internal standards. Samples were analyzed in a product-ion scan of m/z 289, 369, and 465 to identify the metabolites and in multiple reaction monitoring acquisition mode to quantify (-)-epicatechin ((-)-Ec) (289/ 245), (-)-epicatechin-glucuronide ((-)-EcG) (465/289), and (-)-epicatechin-sulfate ((-)-EcS) (369/289). One (-)-Ec-G and three (-)-Ec-S were identified and confirmed in urine as the major metabolites, and one (-)-Ec-G was the only metabolite present in plasma volunteers (n = 5) at a mean concentration of 625.7 +/- 198.3 nmol/L at 2 h after consumption of a cocoa beverage containing 54.4 mg of (-)-Ec.",Journal of agricultural and food chemistry,"['D000293', 'D000328', 'D001628', 'D002099', 'D002392', 'D002853', 'D005260', 'D005419', 'D006801', 'D008297', 'D013058', 'D008875', 'D021241']","['Adolescent', 'Adult', 'Beverages', 'Cacao', 'Catechin', 'Chromatography, Liquid', 'Female', 'Flavonoids', 'Humans', 'Male', 'Mass Spectrometry', 'Middle Aged', 'Spectrometry, Mass, Electrospray Ionization']",Rapid liquid chromatography tandem mass spectrometry assay to quantify plasma (-)-epicatechin metabolites after ingestion of a standard portion of cocoa beverage in humans.,"[None, None, None, 'Q000737', 'Q000097', 'Q000379', None, 'Q000008', None, None, 'Q000379', None, None]","[None, None, None, 'chemistry', 'blood', 'methods', None, 'administration & dosage', None, None, 'methods', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16076092,2005,0,0,, +0.27,12059153,"The cocoa roasting process at different temperatures (at 125 and 135 degrees C for 3 min, plus 44 and 52 min, respectively, heating-up times) was evaluated by measuring the initial and final free amino acids distribution, flavor index, formol number, browning measurement, and alkylpyrazines content in 15 cocoa bean samples of different origins. These samples were also analyzed in manufactured cocoa powder. The effect of alkalinization of cocoa was studied. Results indicated that the final concentration and ratio of tetramethylpyrazine/trimethylpyrazine (TMP/TrMP) increased rapidly at higher roasting temperatures. The samples roasted with alkalies (pH between 7.20 and 7.92), such as sodium carbonate, or potassium plus air injected in the roaster during thermal treatment, exhibited a greater degree of brown color formation, but the amount of alkylpyrazines generated was adversely affected. The analysis of alpha-free amino acids at the end of the roasting process demonstrated the importance of the thermal treatment conditions and the pH values on nibs (cocoa bean cotyledons), liquor, or cocoa. Higher pH values led to a lower concentration of aroma and a higher presence of brown compounds.",Journal of agricultural and food chemistry,"['D000596', 'D002099', 'D002254', 'D055598', 'D002627', 'D005511', 'D008401', 'D006863', 'D007202', 'D011188', 'D011719', 'D013053', 'D013649']","['Amino Acids', 'Cacao', 'Carbonates', 'Chemical Phenomena', 'Chemistry, Physical', 'Food Handling', 'Gas Chromatography-Mass Spectrometry', 'Hydrogen-Ion Concentration', 'Indicators and Reagents', 'Potassium', 'Pyrazines', 'Spectrophotometry', 'Taste']",Factors affecting the formation of alkylpyrazines during roasting treatment in natural and alkalinized cocoa powder.,"['Q000032', 'Q000737', None, None, None, None, None, None, None, None, 'Q000032', None, None]","['analysis', 'chemistry', None, None, None, None, None, None, None, None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12059153,2002,1,1,table 1 and 5, +0.26,20100378,"An increasing number of scientific studies support that flavanol-rich foods and beverages such as cocoa can promote human health, and are beneficial agents for the prevention of some diseases. Our previous studies showed that long-term cocoa intake enhances the antioxidant status in lymphoid organs and also modulates lymphocyte functionality in healthy young rats. Cocoa polyphenolic antioxidants seem to be the best candidates for those effects. However, data regarding polyphenol metabolites in tissues after a long-term cocoa intake are scarce. In the present study we mainly focus on the uptake and accumulation of epicatechin metabolites in lymphoid organs, including the thymus, spleen and mesenteric lymphoid nodes, as well as in the liver and testes after a diet rich in cocoa. Ten young weaned Wistar rats were fed randomly with a 10 % (w/w) cocoa diet or a control diet for 3 weeks, corresponding to their infancy and youth. Tissues were treated with a solid-phase extraction and analysed by liquid chromatography-tandem MS. The major compounds recovered in these tissues were glucuronide derivatives of epicatechin and methylepicatechin. The highest concentration of these metabolites was found in the thymus, testicles and liver, followed by lymphatic nodes and spleen. The high amount of epicatechin metabolites found in the thymus supports our previous findings showing its high antioxidant capacity compared with other tissues such as the spleen. Moreover, this is the first time that epicatechin metabolites have been found in high concentrations in the testes, confirming other studies that have suggested the testes as an important site of oxidation.",The British journal of nutrition,"['D000818', 'D002099', 'D002392', 'D004032', 'D005260', 'D008099', 'D008221', 'D008297', 'D051381', 'D017208', 'D013737']","['Animals', 'Cacao', 'Catechin', 'Diet', 'Female', 'Liver', 'Lymphoid Tissue', 'Male', 'Rats', 'Rats, Wistar', 'Testis']",Distribution of epicatechin metabolites in lymphoid tissues and testes of young rats with a cocoa-enriched diet.,"[None, 'Q000378', 'Q000378', None, None, 'Q000378', 'Q000378', None, None, None, 'Q000378']","[None, 'metabolism', 'metabolism', None, None, 'metabolism', 'metabolism', None, None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/20100378,2010,0,0,, +0.26,28130543,"The detection of disaccharides in urine is under investigation to act as a marker for intravenous abuse of disaccharide formulations, like liquid methadone with syrup (sucrose), methadone tablets (lactose and sucrose), or buprenorphine tablets (lactose). As the detection time in urine has not yet been investigated and a routine method for detecting disaccharides is still lacking, a study was performed to estimate the window of detection in urine after intravenous consumption of disaccharides. Furthermore, an analytical LC-MSMS method for the quantification of sucrose and lactose in urine was validated. The method was applied to urine samples of intravenous substitute consumers, with urine being sampled before intravenous use of substitutes and approximately 30 minutes later. Twenty users provided information regarding their most recent prior intravenous consumption. Disaccharides were detectable in all 20 urine samples about 30 minutes after consumption. A cut off for both disaccharides of 40mg/L was used. Based on these conditions 81% of the persons who consumed in a time frame of 24 hours ago showed positive results for disaccharides. The study showed that the validated LC-MSMS method with an easy and fast workup is usable for daily routine in the laboratory. It might be helpful for methadone and buprenorphine prescribing physicians to check whether the opiate maintenance treatment patient takes his or her substitution medicines orally as intended, or continues with intravenous misuse by injecting substitution medicines instead of heroin.",Journal of analytical toxicology,"['D000328', 'D015415', 'D002047', 'D002253', 'D016022', 'D000069956', 'D002853', 'D005260', 'D006801', 'D007785', 'D057230', 'D008297', 'D008691', 'D008875', 'D053610', 'D015203', 'D015813', 'D015819', 'D013395', 'D053719', 'D055815']","['Adult', 'Biomarkers', 'Buprenorphine', 'Carbonated Beverages', 'Case-Control Studies', 'Chocolate', 'Chromatography, Liquid', 'Female', 'Humans', 'Lactose', 'Limit of Detection', 'Male', 'Methadone', 'Middle Aged', 'Opiate Alkaloids', 'Reproducibility of Results', 'Substance Abuse Detection', 'Substance Abuse, Intravenous', 'Sucrose', 'Tandem Mass Spectrometry', 'Young Adult']",Monitoring Intravenous Abuse of Methadone or Buprenorphine in Opiate Maintenance Treatment (OMT): A Simple and Fast LC-MS-MS Method for the Detection of Disaccharides in Urine Samples.,"[None, 'Q000652', 'Q000652', None, None, None, None, None, None, 'Q000652', None, None, 'Q000652', None, 'Q000652', None, 'Q000379', 'Q000652', 'Q000652', None, None]","[None, 'urine', 'urine', None, None, None, None, None, None, 'urine', None, None, 'urine', None, 'urine', None, 'methods', 'urine', 'urine', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28130543,2017,0,0,,no cocoa +0.26,18032884,"The beneficial effects of cocoa polyphenols depend on the amount consumed, their bioavailability and the biological activities of the formed conjugates. The food matrix is one the factors than can affect their bioavailability, but previous studies have concluded rather contradictory results about the effect of milk on the bioavailability of polyphenols.",Annals of nutrition & metabolism,"['D000293', 'D000328', 'D000818', 'D001682', 'D002099', 'D002853', 'D018592', 'D005260', 'D005419', 'D006801', 'D008297', 'D008875', 'D008892', 'D011446', 'D053719']","['Adolescent', 'Adult', 'Animals', 'Biological Availability', 'Cacao', 'Chromatography, Liquid', 'Cross-Over Studies', 'Female', 'Flavonoids', 'Humans', 'Male', 'Middle Aged', 'Milk', 'Prospective Studies', 'Tandem Mass Spectrometry']",Milk does not affect the bioavailability of cocoa powder flavonoid in healthy human.,"[None, None, None, None, None, None, None, None, 'Q000493', None, None, None, None, None, None]","[None, None, None, None, None, None, None, None, 'pharmacokinetics', None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18032884,2008,,,,no pdf access +0.26,18502705,"Flavonoids, a subclass of polyphenols, are major constituents of many plant-based foods and beverages, including tea, wine and chocolate. Epidemiological studies have shown that a flavonoid-rich diet is associated with reduced risk of cardiovascular diseases. The majority of the flavonoids survive intact until they reach the colon where they are then extensively metabolized into smaller fragments. Here, we describe the development of GC-MS-based methods for the profiling of phenolic microbial fermentation products in urine, plasma, and fecal water. Furthermore, the methods are applicable for profiling products obtained from in vitro batch culture fermentation models. The methods incorporate enzymatic deconjugation, liquid-liquid extraction, derivatization, and subsequent analysis by GC-MS. At the level of individual compounds, the methods gave recoveries better than 80% with inter-day precision being better than 20%, depending on the matrix. Limits of detection were below 0.1 microg/ml for most phenolic acids. The newly developed methods were successfully applied to samples from human and in-vitro intervention trials, studying the metabolic impact of flavonoid intake. In conclusion, the methods presented are robust and generally applicable to diverse biological fluids. Its profiling character is useful to investigate on a large scale the gut microbiome-mediated bioavailability of flavonoids.","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D003106', 'D019295', 'D004032', 'D005243', 'D005285', 'D005419', 'D008401', 'D006801', 'D008660', 'D010636', 'D010648', 'D059808', 'D015203', 'D035501']","['Colon', 'Computational Biology', 'Diet', 'Feces', 'Fermentation', 'Flavonoids', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Metabolism', 'Phenols', 'Phenylacetates', 'Polyphenols', 'Reproducibility of Results', 'Uncertainty']",GC-MS methods for metabolic profiling of microbial fermentation products of dietary polyphenols in human and in vitro intervention studies.,"['Q000382', 'Q000379', None, 'Q000737', 'Q000502', 'Q000097', 'Q000379', None, None, 'Q000097', 'Q000097', None, None, None]","['microbiology', 'methods', None, 'chemistry', 'physiology', 'blood', 'methods', None, None, 'blood', 'blood', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18502705,2008,0,0,,no cocoa tested +0.26,27055484,"Analysis of the complex composition of cocoa beans provides fundamental information for evaluating the quality and nutritional aspects of cocoa-based food products, nutraceuticals and supplements. Cameroon, the world's fourth largest producer of cocoa, has been defined as ""Africa in miniature"" because of the variety it habitats. In order to evaluate the nutritional characteristics of cocoa beans from five different regions of Cameroon, we studied their polyphenolic content, volatile compounds and fatty acids composition. The High Performance Thin Layer Chromatography (HPTLC) analysis showed that the Mbalmayo sample had the highest content of theobromine (11.6___mg/g) and caffeic acid (2.1___mg/g), while the Sanchou sample had the highest level of (-)-epicatechin (142.9___mg/g). Concerning fatty acids, the lowest level of stearic acid was found in the Mbalmayo sample while the Bertoua sample showed the highest content of oleic acid. Thus, we confirmed that geographical origin influences the quality and nutritional characteristics of cocoa from these regions of Cameroon. ",International journal of food sciences and nutrition,"['D000975', 'D002099', 'D002109', 'D002163', 'D002392', 'D000069956', 'D002934', 'D004041', 'D019587', 'D005227', 'D005419', 'D063427', 'D006801', 'D009753', 'D025341', 'D012639', 'D013805', 'D014674', 'D055549', 'D014970']","['Antioxidants', 'Cacao', 'Caffeic Acids', 'Cameroon', 'Catechin', 'Chocolate', 'Cinnamates', 'Dietary Fats', 'Dietary Supplements', 'Fatty Acids', 'Flavonoids', 'Food Quality', 'Humans', 'Nutritive Value', 'Principal Component Analysis', 'Seeds', 'Theobromine', 'Vegetable Proteins', 'Volatile Organic Compounds', 'Xanthines']","Nutritional composition, bioactive compounds and volatile profile of cocoa beans from different regions of Cameroon.","['Q000032', 'Q000737', 'Q000032', None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, None, None, None, 'Q000737', 'Q000032', 'Q000032', 'Q000032', 'Q000032']","['analysis', 'chemistry', 'analysis', None, 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', None, None, None, None, 'chemistry', 'analysis', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27055484,2017,2,2,table 1 and 2, +0.26,18024588,"Chemical analyses of residues extracted from pottery vessels from Puerto Escondido in what is now Honduras show that cacao beverages were being made there before 1000 B.C., extending the confirmed use of cacao back at least 500 years. The famous chocolate beverage served on special occasions in later times in Mesoamerica, especially by elites, was made from cacao seeds. The earliest cacao beverages consumed at Puerto Escondido were likely produced by fermenting the sweet pulp surrounding the seeds.",Proceedings of the National Academy of Sciences of the United States of America,"['D000434', 'D001106', 'D001628', 'D002099', 'D002110', 'D002516', 'D004867', 'D005285', 'D018857', 'D008401', 'D049690', 'D006721', 'D006801', 'D007197', 'D013805']","['Alcoholic Beverages', 'Archaeology', 'Beverages', 'Cacao', 'Caffeine', 'Ceramics', 'Equipment Design', 'Fermentation', 'Food Packaging', 'Gas Chromatography-Mass Spectrometry', 'History, Ancient', 'Honduras', 'Humans', 'Indians, Central American', 'Theobromine']",Chemical and archaeological evidence for the earliest cacao beverages.,"['Q000266', None, 'Q000266', 'Q000737', 'Q000032', 'Q000266', None, None, 'Q000266', None, None, None, None, 'Q000266', 'Q000032']","['history', None, 'history', 'chemistry', 'analysis', 'history', None, None, 'history', None, None, None, None, 'history', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18024588,2008,0,0,, +0.26,20598878,"The aim of this work was to conduct the experimental study of pyrolysis of fast-growing aquatic biomass -Lemna minor (commonly known as duckweed) with the emphasis on the characterization of main products of pyrolysis. The yields of pyrolysis gas, pyrolytic oil (bio-oil) and char were determined as a function of pyrolysis temperature and the sweep gas (Ar) flow rate. Thermogravimetric/differential thermogravimetric (TG/DTG) analyses of duckweed samples in inert (helium gas) and oxidative (air) atmosphere revealed differences in the TG/DTG patterns obtained for duckweed and typical plant biomass. The bio-oil samples produced by duckweed pyrolysis at different reaction conditions were analyzed using GC-MS technique. It was found that pyrolysis temperature had minor effect on the bio-oil product slate, but exerted major influence on the relative quantities of the individual pyrolysis products obtained. While, the residence time of the pyrolysis vapors had negligible effect on the yield and composition of the duckweed pyrolysis products.",Bioresource technology,"['D056804', 'D018533', 'D008401', 'D006109', 'D013696', 'D013818', 'D014867']","['Biofuels', 'Biomass', 'Gas Chromatography-Mass Spectrometry', 'Poaceae', 'Temperature', 'Thermogravimetry', 'Water']",Pyrolysis of fast-growing aquatic biomass -Lemna minor (duckweed): Characterization of pyrolysis products.,"[None, None, None, 'Q000254', None, None, None]","[None, None, None, 'growth & development', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/20598878,2010,0,0,,no cocoa +0.26,3941071,"Glycerol-3-phosphate acyltransferase has been purified from the post-microsomal supernatant of cocoa seeds using differential ammonium sulfate solubility along with anion exchange and gel filtration chromatography. Chromatofocusing and isoelectric focusing revealed a series of proteins with acyltransferase activity having isoelectric points close to 5.2. Gel filtration on Sephacryl S-300 in 500 mM NaCl, along with polyacrylamide gel electrophoresis (denaturing and non-denaturing) and immunochemical analysis, gave evidence that the native enzyme has a molecular weight of 2 X 10(5) and consists of an aggregate of 10 Mr 20,000 subunits. The highly purified enzyme carries an acyl donor, probably acyl-CoA, although this is not firmly established. The hydrophobic nature of the purified enzyme was demonstrated by its firm binding to octyl-Sepharose. Mass spectrometric analysis of reaction products revealed the presence of both palmitic and stearic acids. Considering that 1) the fatty acids were derived from the purified enzyme; 2) they were found exclusively in the 1-position of glycerol 3-phosphate; 3) the fatty acid positioning and composition is consistent with that found in cocoa butter, the major storage product of cocoa seeds; and 4) the enzyme is found in the post-microsomal supernatant, it seems reasonable to conclude that the first step in cocoa butter biosynthesis is catalyzed by glycerol-3-phosphate acyltransferase in the cytoplasm of cocoa cotyledon cells.",The Journal of biological chemistry,"['D000217', 'D000818', 'D002099', 'D002850', 'D005992', 'D007525', 'D007700', 'D046911', 'D008970', 'D010945', 'D011817', 'D011863', 'D012639', 'D012995']","['Acyltransferases', 'Animals', 'Cacao', 'Chromatography, Gel', 'Glycerol-3-Phosphate O-Acyltransferase', 'Isoelectric Focusing', 'Kinetics', 'Macromolecular Substances', 'Molecular Weight', 'Plants, Edible', 'Rabbits', 'Radioimmunoassay', 'Seeds', 'Solubility']",Cocoa butter biosynthesis. Purification and characterization of a soluble sn-glycerol-3-phosphate acyltransferase from cocoa seeds.,"['Q000302', None, 'Q000201', None, 'Q000302', None, None, None, None, 'Q000201', None, None, 'Q000032', None]","['isolation & purification', None, 'enzymology', None, 'isolation & purification', None, None, None, None, 'enzymology', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/3941071,1986,0,0,, +0.26,18969491,"An optical chemical sensor based on immobilization of 2-(5-bromo-2-pyridylazo)-5-(diethylamino)phenol (Br-PADAP) in Nafion membrane is described. The membranes were cast onto glass substrates and were used for the determination of nickel in aqueous solutions by spectrophotometry. The sensor system is highly transparent, mechanically stable and showed no evidence of reagent leaching. The influence of several parameters such as pH, ligand concentration, and type and concentration of regenerating solution were optimized. The sensor system showed good sensitivity in the range 0.5-20mugml(-1) with a detection limit of 0.3mugml(-1) Ni(II). The sensor has been incorporated into a home-made flow-through cell for determination of nickel in flowing streams with improved sensitivity, precision and detection limit. The calibration curve in the flow system was linear in the range 0.1-16mugml(-1) with a detection limit of 0.07mugml(-1). The sensor is easily regenerated by dilute nitric acid solution. The proposed method was successfully applied to the determination of nickel content in vegetable oil and chocolate samples and the results were compared with those obtained using atomic absorption spectrometry.",Talanta,[],[],Development of an optical chemical sensor based on 2-(5-bromo-2-pyridylazo)-5-(diethylamino)phenol in Nafion for determination of nickel ion.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/18969491,2012,0,0,,no cocoa +0.25,10563905,"Color-generating reactions of protein-bound lysine with carbohydrates were studied under thermal as well as under physiological conditions to gain insights into the role of protein/carbohydrate reactions in the formation of food melanoidins as well as nonenzymatic browning products in vivo. EPR spectroscopy of orange-brown melanoidins, which were isolated from heated aqueous solutions of bovine serum albumin and glycolaldehyde, revealed the protein-bound 1,4-bis(5-amino-5-carboxy-1-pentyl)pyrazinium radical cation (CROSSPY) as a previously unknown type of cross-linking amino acid leading to protein dimerization. To verify their formation in foods, wheat bread crust and roasted cocoa as well as coffee beans, showing elevated nonenzymatic browning, were investigated by EPR spectroscopy. An intense radical was detected, which, by comparison with the radical formed upon reaction bovine serum albumin with glycolaldehyde, was identified as the protein-bound CROSSPY. The radical-assisted protein oligomerization as well as the browning of bovine serum albumin in the presence of glycolaldehyde occurred also rapidly under physiological conditions, thereby suggesting CROSSPY formation to be probably involved also in nonenzymatic glycation reactions in vivo.",Journal of agricultural and food chemistry,"['D002851', 'D003116', 'D003296', 'D004578', 'D005504', 'D005511', 'D005609', 'D013058', 'D011108', 'D013056', 'D013447']","['Chromatography, High Pressure Liquid', 'Color', 'Cooking', 'Electron Spin Resonance Spectroscopy', 'Food Analysis', 'Food Handling', 'Free Radicals', 'Mass Spectrometry', 'Polymers', 'Spectrophotometry, Ultraviolet', 'Sulfites']",Radical-assisted melanoidin formation during thermal processing of foods as well as under physiological conditions.,"[None, None, None, None, None, None, None, None, 'Q000138', None, 'Q000737']","[None, None, None, None, None, None, None, None, 'chemical synthesis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/10563905,2000,0,0,, +0.25,29899211,"Thaumatin-like protein from banana (designated BanTLP) has been purified by employing a simple protocol consisting of diethylaminoethyl Sephadex (DEAE__Sephadex) chromatography, gel filtration on Sephadex G50, and reversed-phase chromatography. The purified protein was identified by MALDI-TOF mass spectrometry, with an estimated molecular weight of 22.1 kDa. BanTLP effectively inhibited in vitro spore germination of ","Molecules (Basel, Switzerland)",[],[],Antifungal Activity of an Abundant Thaumatin-Like Protein from Banana against ,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29899211,2018,0,0,,no cocoa +0.25,14509366,"We previously reported the inhibitory effect of various methyloxantines and phenolic compounds on tumor-induced angiogenesis and the production of angiogenic growth factors. The aim of the present work was to evaluate the effect of chocolate (CH), food containing substantial amounts of methyloxantine theobromine and polyphenols (mainly catechins), given to mice during pregnancy and the lactation period, on weight of organs, length of limbs, and bone vascular endothelial growth factor (VEGF) concentration (tested by ELISA), in 4-week old offspring. The study was performed on 2-month old Balb/c mice fed during pregnancy and lactation 400 mg of CH daily. Content of polyphenols (catechines) and theobromine in the chocolate was estimated by high liquid perforance chromatography (HPLC). Concentration of VEGF was tested by ELISA. Feeding pregnant mice chocolate produced the following effects: decrease of relative length of limbs and thigh bones in 4-week old progeny and decrease in VEGF content of offspring femoral bones.",Polish journal of veterinary sciences,"['D000284', 'D000821', 'D000818', 'D000831', 'D001842', 'D002099', 'D002851', 'D004797', 'D005260', 'D051379', 'D008807', 'D008517', 'D011247', 'D042461']","['Administration, Oral', 'Animal Feed', 'Animals', 'Animals, Newborn', 'Bone and Bones', 'Cacao', 'Chromatography, High Pressure Liquid', 'Enzyme-Linked Immunosorbent Assay', 'Female', 'Mice', 'Mice, Inbred BALB C', 'Phytotherapy', 'Pregnancy', 'Vascular Endothelial Growth Factor A']",Chocolate feeding of pregnant mice influences length of limbs of their progeny.,"[None, None, None, None, 'Q000187', None, None, None, None, None, None, None, None, 'Q000187']","[None, None, None, None, 'drug effects', None, None, None, None, None, None, None, None, 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/14509366,2003,,,, +0.24,28272800,"Many factors can influence antioxidative and antimicrobial characteristics of plant materials. The quality of cocoa as functional food ingredient is influenced through its processing. The main aim of this study was to test if there is difference in polyphenol content, antioxidant capacity, and antimicrobial activity between nonalkalized and alkalized cocoa powders. To estimate polyphenol and flavonoid content in cocoa samples the spectrophotometric microassays were used. Flavan-3ols were determined with reversed-phase high-performance liquid chromatography (RP-HPLC). Antimicrobial activity against 3 Gram positive bacteria, 4 Gram negative bacteria and 1 strain of yeast was determined using broth microdilution method. Total polyphenol content was 1.8 times lower in alkalized cocoa samples than in natural ones. Epicatechin/catechin ratio was changed due to the process of alkalization in favor of catechin (2.21 in natural and 1.45 in alkalized cocoa powders). Combined results of 3 antioxidative tests (DPPH, FRAP, ABTS) were used for calculation of RACI (Relative Antioxidant Capacity Index) and GAS (Global Antioxidant Score) values that were consistently higher in natural than in alkalized cocoa extracts. Obtained results have shown significant correlations between these values and phenolic content (0.929 ___ r ___ 0.957, P < 0.01). Antimicrobial activity varied from 5.0 to 25.0 mg/ml (MICs), while Candida albicans was the most sensitive tested microorganism. Cocoa powders subjected to alkalization had significantly reduced content of total and specific phenolic compounds and reduced antioxidant capacity (P < 0.05), but their antimicrobial activity was equal for Gram-positive bacteria or even significantly enhanced for Gram-negative bacteria.",Journal of food science,"['D000890', 'D000975', 'D002099', 'D002176', 'D002392', 'D056148', 'D003116', 'D005419', 'D006090', 'D006094', 'D006863', 'D008826', 'D010936', 'D059808', 'D011208', 'D044945']","['Anti-Infective Agents', 'Antioxidants', 'Cacao', 'Candida albicans', 'Catechin', 'Chromatography, Reverse-Phase', 'Color', 'Flavonoids', 'Gram-Negative Bacteria', 'Gram-Positive Bacteria', 'Hydrogen-Ion Concentration', 'Microbial Sensitivity Tests', 'Plant Extracts', 'Polyphenols', 'Powders', 'Proanthocyanidins']","Correlation between Antimicrobial, Antioxidant Activity, and Polyphenols of Alkalized/Nonalkalized Cocoa Powders.","['Q000494', 'Q000494', 'Q000737', 'Q000187', 'Q000494', None, None, 'Q000494', 'Q000187', 'Q000187', None, None, 'Q000494', 'Q000494', None, 'Q000494']","['pharmacology', 'pharmacology', 'chemistry', 'drug effects', 'pharmacology', None, None, 'pharmacology', 'drug effects', 'drug effects', None, None, 'pharmacology', 'pharmacology', None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/28272800,2017,1,1,table 2 and 3, +0.24,19272093,"Synopsis Non-saponifiable lipid fraction (ICSB) extracted from cocoa shell butter was solubilized in dimethylformamide (DMF) and analysed for its biological activity on growth of rat and human fibroblasts. Non-saponifiables (10 mug ml(-1)) partially protected cells from toxicity of DMF (1%) and allowed the growth of fibroblasts cultivated in optimal conditions (10% fetal calf serum-FCS, 37 degrees C) or improved the survival of cells maintained in altered conditions (2.5% FCS, 35 degrees C). At higher concentration (ICSB 50 mug ml(-1), DMF 1%), the protective effect was suppressed. ICSB was fractionated by chromatography into four compounds: sterols, terpenic alcohols, tocopherols and hydrocarbons +/- carotenoids. We found that biological activity of ICSB was mostly due to the major fraction containing sterols.",International journal of cosmetic science,[],[],Non-saponifiable fraction of cocoa shell butter: effect on rat and human skin fibroblasts.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19272093,2012,,,,no pdf access +0.24,28911615,"Candies, chewing gums, dried fruits, jellies, chocolate, and shredded squid pieces imported from 17 countries were surveyed for their aluminum content. The samples were bought from candy shops, supermarkets, and convenience stores, and through online shopping. Sample selection focused on imported candies and snacks. A total of 67 samples, including five chewing gums, seven dried fruits, 13 chocolates, two jellies, two dried squid pieces, and 38 candies, were analyzed. The content of aluminum was analyzed by inductively coupled plasma optical emission spectrometry (ICP OES). The limit of quantitation for aluminum was 1.53__mg/kg. The content of aluminum ranged from not detected (ND) to 828.9__mg/kg. The mean concentrations of aluminum in chewing gums, dried fruits, chocolate, jellies, dried squid pieces, and candies were 36.62__mg/kg, 300.06__mg/kg, 9.1__mg/kg, 2.3__mg/kg, 7.8__mg/kg, and 24.26__mg/kg, respectively. Some samples had relatively high aluminum content. The highest aluminum content of 828.9__mg/kg was found in dried papaya threads imported from Thailand. Candies imported from Thailand and Vietnam had aluminum contents of 265.7__mg/kg and 333.1__mg/kg, respectively. Exposure risk assessment based on data from the Taiwan National Food Consumption Database was employed to calculate the percent provisional tolerable weekly intake (%PTWI). The percent provisional tolerable weekly intake of aluminum for adults (19-50__years) and children (3-6__years) based on the consumption rate of the total population showed that candies and snacks did not contribute greatly to aluminum exposure. By contrast, in the exposure assessment based on the consumers-only consumption rate, the estimated values of weekly exposure to aluminum from dried papaya threads in adults (19-50__years) and children (3-6__years) were 4.18__mg/kg body weight (bw)/wk and 7.93__mg/kg bw/wk, respectively, for 50",Journal of food and drug analysis,"['D000535', 'D002182', 'D006801', 'D062410', 'D013624']","['Aluminum', 'Candy', 'Humans', 'Snacks', 'Taiwan']",Investigation of aluminum content of imported candies and snack foods in Taiwan.,"[None, None, None, None, None]","[None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28911615,2017,1,1,text ,extracted the data from the refered paper (table 3) +0.24,16197573,"Dietary polyphenols are suggested to participate in the prevention of CVD and cancer. It is essential for epidemiological studies to be able to compare intake of the main dietary polyphenols in populations. The present paper describes a fast method suitable for the analysis of polyphenols in urine, selected as potential biomarkers of intake. This method is applied to the estimation of polyphenol recovery after ingestion of six different polyphenol-rich beverages. Fifteen polyphenols including mammalian lignans (enterodiol and enterolactone), several phenolic acids (chlorogenic, caffeic, m-coumaric, gallic, and 4-O-methylgallic acids), phloretin and various flavonoids (catechin, epicatechin, quercetin, isorhamnetin, kaempferol, hesperetin, and naringenin) were simultaneously quantified in human urine by HPLC coupled with electrospray ionisation mass-MS (HPLC-electrospray-tandem mass spectrometry) with a run time of 6 min per sample. The method has been validated with regard to linearity, precision, and accuracy in intra- and inter-day assays. It was applied to urine samples collected from nine volunteers in the 24 h following consumption of either green tea, a grape-skin extract, cocoa beverage, coffee, grapefruit juice or orange juice. Levels of urinary excretion suggest that chlorogenic acid, gallic acid, epicatechin, naringenin or hesperetin could be used as specific biomarkers to evaluate the consumption of coffee, wine, tea or cocoa, and citrus juices respectively.",The British journal of nutrition,"['D000328', 'D001628', 'D015415', 'D002099', 'D002110', 'D002138', 'D016022', 'D002851', 'D032083', 'D032084', 'D003069', 'D004032', 'D005260', 'D005419', 'D006801', 'D008297', 'D010636', 'D059808', 'D012680', 'D021241', 'D018709', 'D013662', 'D013805', 'D013997']","['Adult', 'Beverages', 'Biomarkers', 'Cacao', 'Caffeine', 'Calibration', 'Case-Control Studies', 'Chromatography, High Pressure Liquid', 'Citrus paradisi', 'Citrus sinensis', 'Coffee', 'Diet', 'Female', 'Flavonoids', 'Humans', 'Male', 'Phenols', 'Polyphenols', 'Sensitivity and Specificity', 'Spectrometry, Mass, Electrospray Ionization', 'Statistics, Nonparametric', 'Tea', 'Theobromine', 'Time Factors']",Polyphenol levels in human urine after intake of six different polyphenol-rich beverages.,"[None, None, 'Q000652', None, None, None, None, None, None, None, None, None, None, 'Q000008', None, None, 'Q000008', None, None, None, None, None, None, None]","[None, None, 'urine', None, None, None, None, None, None, None, None, None, None, 'administration & dosage', None, None, 'administration & dosage', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16197573,2005,1,1,table 1,cocoa powder only +0.23,28959635,"Levels of organochlorine pesticides (OCPs) were determined in dried cocoa beans obtained from cocoa produce stores at Ondo and Ile-Ife, Southwestern Nigeria. Cocoa beans samples were sun dried to a constant weight, pulverized and soxhlet extracted with dichloromethane to obtain the OCPs. Qualitative identification and quantitative evaluation of the extracted OCPs after clean-up on silica gel were accomplished with the aid of a Gas Chromatography coupled with an Electron Capture Detector (GC-ECD). Levels of OCPs in cocoa beans from Ondo had a mean range of ND (p, p'-DDE) to 82.17___±__54.53__ng/g (p, p'-DDT) were higher than the OCPs levels in cocoa beans from Ile-Ife with a mean range of 0.37___±__0.63__ng/g (Endrin) to 57.76___±__81.48__ng/g (p, p'-DDT). The higher levels of OCPs detected in the cocoa beans from Ondo could be an indication of higher volume of OCPs application by cocoa farmers in Ondo and its environs since cocoa plantations were more concentrated than Ile-Ife environs. Levels of OCPs determined in the cocoa beans were within the Maximum Residue Limit (MRLs) for OCPs set by the World Health Organization/Food and Agricultural Organization. The study established the presence of OCPs in an important crop of Nigeria. Hence, there is the need to keep monitoring ecotoxicological chemical substances in agricultural food products of Nigeria so as to take steps that ensure health safety of end users.",Toxicology reports,[],[],"Organochlorine pesticide residues in dried cocoa beans obtained from cocoa stores at Ondo and Ile-Ife, Southwestern Nigeria.",[],[],https://www.ncbi.nlm.nih.gov/pubmed/28959635,2017,1,3,"table 3, 4, 5, and 6. Fig 1",pesticides are determined in samples fromt two different regions +0.23,3239114,"The fatty acid composition including trans fatty acids of 12 brands of nut-nougat creams were analyzed by capillary gas chromatography. The creams consisted mainly of sugar and partially hydrogenated vegetable oil. The lipid content, which was quantified gravimetrically, amounted to between 30 and 38.2% in the different brands. The fatty acid composition varied considerably between the different creams. Linoleic acid, the major polyunsaturated fatty acid (PUFA), ranged from 12 to 39%. Palmitic acid (16:0), which was the main fatty acid, varied from 9 to 27%. The total trans fatty acid content of the 12 creams ranged from 0.9 to 12.3%. Only two of the creams contained less than 1% of trans fatty acids; 18:1t was the trans fatty acid found in the greatest amounts, whereas 16:1t and 14:1t were only found in trace amounts. Three samples had amounts of 18:2tt, 18:2ct, and 18:2tc between 0.7 and 1.06%; only small amounts of linoleate isomers were detected in the other creams. Our results show that trans fatty acids are present in every brand of chocolate cream tested. Since the potential risk of arteriosclerosis and cancer resulting from the consumption of trans fatty acids is not yet clear, different ways of production should be used in order to eliminate them from the creams that are a preferred bread spread of infants and children.",Zeitschrift fur Ernahrungswissenschaft,"['D002099', 'D002182', 'D004041', 'D005227', 'D005860', 'D006801', 'D006865', 'D010938', 'D010945']","['Cacao', 'Candy', 'Dietary Fats', 'Fatty Acids', 'Germany, West', 'Humans', 'Hydrogenation', 'Plant Oils', 'Plants, Edible']",Trans fatty acid content of selected brands of West German nut-nougat cream.,"['Q000032', 'Q000032', 'Q000032', 'Q000032', None, None, None, 'Q000032', 'Q000032']","['analysis', 'analysis', 'analysis', 'analysis', None, None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/3239114,1989,,,, +0.23,12488137,"Epicatechin is a flavan-3-ol that is commonly present in green teas, red wine, cocoa products, and many fruits, such as apples. There is considerable interest in the bioavailability of epicatechin after oral ingestion. In vivo studies have shown that low levels of epicatechin are absorbed and found in the circulation as glucuronides, methylated and sulfated forms. Recent research has demonstrated protective effects of epicatechin and one of its in vivo metabolites, 3'-O-methyl epicatechin, against neuronal cell death induced by oxidative stress. Thus, we are interested in the ability of ingested epicatechin to cross the blood brain barrier and target the brain. Rats were administered 100 mg/kg body weight/d epicatechin orally for 1, 5, and 10 d. Plasma and brain extracts were analyzed by HPLC with photodiode array detection and LC-MS/MS. This study reports the presence of the epicatechin glucuronide and 3'-O-methyl epicatechin glucuronide formed after oral ingestion in the rat brain tissue.",Free radical biology & medicine,"['D000284', 'D000818', 'D001682', 'D001921', 'D002392', 'D002851', 'D008297', 'D013058', 'D051381', 'D017208']","['Administration, Oral', 'Animals', 'Biological Availability', 'Brain', 'Catechin', 'Chromatography, High Pressure Liquid', 'Male', 'Mass Spectrometry', 'Rats', 'Rats, Wistar']",Uptake and metabolism of epicatechin and its access to the brain after oral ingestion.,"[None, None, None, 'Q000378', 'Q000008', None, None, None, None, None]","[None, None, None, 'metabolism', 'administration & dosage', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12488137,2004,0,0,,no cocoa tested +0.23,25802220,"In recent years, there has been an increasing interest in identifying and characterizing the yeast flora associated with diverse types of habitat because of the many potential desirable technological properties of these microorganisms, especially in food applications. In this study, a total of 101 yeast strains were isolated from the skins of tropical fruits collected in several locations in the South West Indian Ocean. Sequence analysis of the D1/D2 domains of the large subunit (LSU) ribosomal RNA gene identified 26 different species. Among them, two species isolated from the skins of Cape gooseberry and cocoa beans appeared to represent putative new yeast species, as their LSU D1/D2 sequence was only 97.1% and 97.4% identical to that of the yeasts Rhodotorula mucilaginosa and Candida pararugosa, respectively. A total of 52 Volatile Organic Compounds (VOCs) were detected by Head Space Solid Phase Micro Extraction coupled to Gas Chromatography and Mass Spectroscopy (HS-SPME-GC/MS) from the 26 yeast species cultivated on a glucose rich medium. Among these VOCs, 6 uncommon compounds were identified, namely ethyl but-2-enoate, ethyl 2-methylbut-2-enoate (ethyl tiglate), ethyl 3-methylbut-2-enoate, 2-methylpropyl 2-methylbut-2-enoate, butyl 2-methylbut-2-enoate and 3-methylbutyl 2-methylbut-2-enoate, making them possible yeast species-specific markers. In addition, statistical methods such as Principal Component Analysis allowed to associate each yeast species with a specific flavor profile. Among them, Saprochaete suaveolens (syn: Geotrichum fragrans) turned to be the best producer of flavor compounds, with a total of 32 out of the 52 identified VOCs in its flavor profile. ",International journal of food microbiology,"['D016000', 'D004275', 'D005421', 'D005516', 'D005638', 'D008270', 'D017508', 'D014329', 'D055549', 'D015003']","['Cluster Analysis', 'DNA, Ribosomal', 'Flavoring Agents', 'Food Microbiology', 'Fruit', 'Madagascar', 'Reunion', 'Tropical Climate', 'Volatile Organic Compounds', 'Yeasts']",A comparative study on the potential of epiphytic yeasts isolated from tropical fruits to produce flavoring compounds.,"[None, 'Q000235', 'Q000032', None, 'Q000382', None, None, None, 'Q000032', 'Q000737']","[None, 'genetics', 'analysis', None, 'microbiology', None, None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25802220,2015,0,0,, +0.23,19680899,"The caffeine content of different beverages from Argentina's market was measured. Several brands of coffees, teas, mates, chocolate milks, soft and energy drinks were analysed by high-performance liquid chromatography (HPLC) with ultraviolet detection. The highest concentration level was found in short coffee (1.38 mg ml(-1)) and the highest amount per serving was found in instant coffee (95 mg per serving). A consumption study was also carried out among 471 people from 2 to 93 years of age to evaluate caffeine total dietary intake by age and to identify the sources of caffeine intake. The mean caffeine intake among adults was 288 mg day(-1) and mate was the main contributor to that intake. The mean caffeine intake among children of 10 years of age and under was 35 mg day(-1) and soft drinks were the major contributors to that intake. Children between 11 and 15 years old and teenagers (between 16 and 20 years) had caffeine mean intakes of 120 and 240 mg day(-1), respectively, and mate was the major contributor to those intakes. Drinking mate is a deep-rooted habit among Argentine people and it might be the reason for their elevated caffeine mean daily intake.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000293', 'D000328', 'D000368', 'D000369', 'D000704', 'D001118', 'D001628', 'D002099', 'D002110', 'D002253', 'D000697', 'D002648', 'D002675', 'D003069', 'D003430', 'D004034', 'D005260', 'D006801', 'D008297', 'D008875', 'D011247', 'D011795', 'D013662', 'D055815']","['Adolescent', 'Adult', 'Aged', 'Aged, 80 and over', 'Analysis of Variance', 'Argentina', 'Beverages', 'Cacao', 'Caffeine', 'Carbonated Beverages', 'Central Nervous System Stimulants', 'Child', 'Child, Preschool', 'Coffee', 'Cross-Sectional Studies', 'Diet Surveys', 'Female', 'Humans', 'Male', 'Middle Aged', 'Pregnancy', 'Surveys and Questionnaires', 'Tea', 'Young Adult']",Caffeine levels in beverages from Argentina's market: application to caffeine dietary intake assessment.,"[None, None, None, None, None, None, 'Q000032', 'Q000737', 'Q000008', 'Q000032', 'Q000008', None, None, 'Q000737', None, None, None, None, None, None, None, None, 'Q000737', None]","[None, None, None, None, None, None, 'analysis', 'chemistry', 'administration & dosage', 'analysis', 'administration & dosage', None, None, 'chemistry', None, None, None, None, None, None, None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/19680899,2010,0,0,, +0.22,12654472,"(-)-epicatechin is one of the most potent antioxidants present in the human diet. Particularly high levels are found in black tea, apples, and chocolate. High intake of catechins has been associated with reduced risk of cardiovascular diseases. There have been several reports concerning the bioavailability of catechins, however, the chemical structure of (-)-epicatechin metabolites in blood, tissues, and urine remains unclear. In the present study, we purified and elucidated the chemical structure of (-)-epicatechin metabolites in human and rat urine after oral administration. Three metabolites were purified from human urine including (-)-epicatechin-3'-O-glucuronide, 4'-O-methyl-(-)-epicatechin-3'-O-glucuronide, and 4'-O-methyl-(-)-epicatechin-5 or 7-O-glucuronide, according to 1H- and 13C-NMR, HMBC, and LC-MS analyses. The metabolites purified from rat urine were 3'-O-methyl-(-)-epicatechin, (-)-epicatechin-7-O-glucuronide, and 3'-O-methyl-(-)-epicatechin-7-O-glucuronide. These compounds were also detected in the blood of humans and rats by LC-MS. The presence of these metabolites in blood and urine suggests that catechins are metabolized and circulated in the body after administration of catechin-containing foods.",Free radical biology & medicine,"['D000284', 'D000328', 'D000818', 'D002392', 'D002851', 'D005260', 'D005609', 'D008401', 'D005965', 'D020723', 'D006801', 'D009682', 'D008297', 'D008956', 'D051381', 'D017207', 'D013045', 'D013997']","['Administration, Oral', 'Adult', 'Animals', 'Catechin', 'Chromatography, High Pressure Liquid', 'Female', 'Free Radicals', 'Gas Chromatography-Mass Spectrometry', 'Glucuronates', 'Glucuronic Acid', 'Humans', 'Magnetic Resonance Spectroscopy', 'Male', 'Models, Chemical', 'Rats', 'Rats, Sprague-Dawley', 'Species Specificity', 'Time Factors']",Structures of (-)-epicatechin glucuronide identified from plasma and urine after oral ingestion of (-)-epicatechin: differences between human and rat.,"[None, None, None, 'Q000008', None, None, None, None, 'Q000097', 'Q000097', None, None, None, None, None, None, None, None]","[None, None, None, 'administration & dosage', None, None, None, None, 'blood', 'blood', None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12654472,2004,0,0,, +0.22,10917931,"Diets that are rich in plant foods have been associated with a decreased risk for specific disease processes and certain chronic diseases. In addition to essential macronutrients and micronutrients, the flavonoids in a variety of plant foods may have health-enhancing properties. Chocolate is a food that is known to be rich in the flavan-3-ol epicatechin and procyanidin oligomers. However, the bioavailability and the biological effects of the chocolate flavonoids are poorly understood. To begin to address these issues, we developed a method based on HPLC coupled with electrochemical (coulometric) detection to determine the physiological levels of epicatechin, catechin and epicatechin dimers. This method allows for the determination of 20 pg (69 fmol) of epicatechin, which translates to plasma concentrations as low as 1 nmol/L. We next evaluated the absorption of epicatechin, from an 80-g semisweet chocolate (procyanidin-rich chocolate) bolus. By 2 h after ingestion, there was a 12-fold increase in plasma epicatechin, from 22 to 257 nmol/L (P < 0.01). Consistent with the antioxidant properties of epicatechin, within the same 2-h period, there was a significant increase of 31% in plasma total antioxidant capacity (P < 0.04) and a decrease of 40% in plasma 2-thiobarbituric acid reactive substances (P < 0.01). Plasma epicatechin and plasma antioxidant capacity approached baseline values by 6 h after ingestion. These results show that it is possible to determine basal levels of epicatechin in plasma. The data support the concept that the consumption of chocolate can result in significant increases in plasma epicatechin concentrations and decreases in plasma baseline oxidation products.",The Journal of nutrition,"['D000328', 'D000975', 'D044946', 'D001682', 'D002099', 'D002392', 'D002784', 'D002851', 'D005260', 'D006801', 'D008297', 'D008875', 'D018384', 'D044945']","['Adult', 'Antioxidants', 'Biflavonoids', 'Biological Availability', 'Cacao', 'Catechin', 'Cholesterol', 'Chromatography, High Pressure Liquid', 'Female', 'Humans', 'Male', 'Middle Aged', 'Oxidative Stress', 'Proanthocyanidins']",Epicatechin in human plasma: in vivo determination and effect of chocolate consumption on plasma oxidation status.,"[None, 'Q000493', None, None, 'Q000378', 'Q000008', 'Q000097', None, None, None, None, None, 'Q000187', None]","[None, 'pharmacokinetics', None, None, 'metabolism', 'administration & dosage', 'blood', None, None, None, None, None, 'drug effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/10917931,2000,0,0,, +0.22,15935584,"Caffeine is a mild central nervous stimulant that occurs naturally in coffee beans, cocoa beans and tea leaves. In large doses, it can be profoundly toxic, resulting in arrhythmia, tachycardia, vomiting, convulsions, coma and death. The average cup of coffee or tea in the United States is reported to contain between 40 and 150 mg caffeine although specialty coffees may contain much higher doses. Over-the-counter supplements that are used to combat fatigue typically contain 100-200 mg caffeine per tablet and doses of 32-200mg are included in a variety of prescription drug mixtures. Fatal caffeine overdoses in adults are relatively rare and require the ingestion of a large quantity of the drug, typically in excess of 5 g. Over a period of approximately 12 months our office reported two cases of fatal caffeine intoxication. In the first case, the femoral blood of a 39-year-old female with a history of intravenous drug use contained 192 mg/L caffeine. In the second case, femoral blood from a 29-year-old male with a history of obesity and diabetes contained 567 mg/L caffeine. In both cases, the cause of death was ruled as caffeine intoxication and the manner of death was accidental.",Forensic science international,"['D000328', 'D002110', 'D000697', 'D062787', 'D017809', 'D005260', 'D005554', 'D008401', 'D006801', 'D008297']","['Adult', 'Caffeine', 'Central Nervous System Stimulants', 'Drug Overdose', 'Fatal Outcome', 'Female', 'Forensic Medicine', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Male']",Fatal caffeine overdose: two case reports.,"[None, 'Q000097', 'Q000097', None, None, None, None, None, None, None]","[None, 'blood', 'blood', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/15935584,2005,0,0,,no cocoa tested +0.22,11759010,"The cacao bean husk has been shown to possess two types of cariostatic substances, one showing anti-glucosyltransferase (GTF) activity and the other antibacterial activity, and to inhibit experimental dental caries in rats infected with mutans streptococci. In the present study, chromatographic purification revealed high-molecular-weight polyphenolic compounds and unsaturated fatty acids as active components. The former, which showed strong anti-GTF activity, were polymeric epicatechins with C-4beta and C-8 intermolecular bonds estimated to be 4636 in molecular weight in an acetylated form. The latter, which showed bactericidal activity against Streptococcus mutans, were determined to be oleic and linoleic acids, and demonstrated a high level of activity at a concentration of 30 microgram/mL. The cariostatic activity of the cacao bean husk is likely caused by these biologically active constituents.",Journal of dental research,"['D000891', 'D002099', 'D002327', 'D002392', 'D002845', 'D004791', 'D005231', 'D005936', 'D005964', 'D015394', 'D008970', 'D010936', 'D012639', 'D013295']","['Anti-Infective Agents, Local', 'Cacao', 'Cariostatic Agents', 'Catechin', 'Chromatography', 'Enzyme Inhibitors', 'Fatty Acids, Unsaturated', 'Glucans', 'Glucosyltransferases', 'Molecular Structure', 'Molecular Weight', 'Plant Extracts', 'Seeds', 'Streptococcus mutans']",Identification of cariostatic substances in the cacao bean husk: their anti-glucosyltransferase and antibacterial activities.,"['Q000302', 'Q000737', 'Q000302', 'Q000302', None, 'Q000302', 'Q000302', 'Q000037', 'Q000037', None, None, 'Q000737', None, 'Q000187']","['isolation & purification', 'chemistry', 'isolation & purification', 'isolation & purification', None, 'isolation & purification', 'isolation & purification', 'antagonists & inhibitors', 'antagonists & inhibitors', None, None, 'chemistry', None, 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/11759010,2001,0,0,, +0.22,16819905,"A naturally decaffeinated tea, Camellia sinensis var. ptilophylla (cocoa tea), has long been popular in southern China as a healthy beverage. Our experiments indicate that a single oral administration of 500 mg/kg of cocoa tea extract suppresses increases in plasma triacylgycerol (TG) levels when fed with 5 mL/kg of olive or lard oil in mice and that the inhibition rates are 22.9% and 31.5%, respectively, compared with controls. Under the same condition, cocoa tea extract did not affect the level of plasma free fatty acid. Likewise, the extract reduced the lymphatic absorption of lipids at 250 and 500 mg/kg. Also, cocoa tea extract and polyphenols isolated from cocoa tea inhibit pancreatic lipase. These findings suggest that cocoa tea has hypolipemic activity, which may be due to the suppression of digestive lipase activity by the polyphenols contained within the tea.",Journal of agricultural and food chemistry,"['D000818', 'D028241', 'D002392', 'D002851', 'D004041', 'D004791', 'D005230', 'D005419', 'D005502', 'D000960', 'D008049', 'D008297', 'D051379', 'D008813', 'D000069463', 'D010636', 'D010936', 'D010938', 'D059808', 'D014280']","['Animals', 'Camellia sinensis', 'Catechin', 'Chromatography, High Pressure Liquid', 'Dietary Fats', 'Enzyme Inhibitors', 'Fatty Acids, Nonesterified', 'Flavonoids', 'Food', 'Hypolipidemic Agents', 'Lipase', 'Male', 'Mice', 'Mice, Inbred ICR', 'Olive Oil', 'Phenols', 'Plant Extracts', 'Plant Oils', 'Polyphenols', 'Triglycerides']",Evaluation of the hypolipemic property of Camellia sinensisVar. ptilophylla on postprandial hypertriglyceridemia.,"[None, 'Q000737', 'Q000494', None, None, 'Q000494', 'Q000097', 'Q000302', None, 'Q000008', 'Q000037', None, None, None, None, 'Q000302', 'Q000008', 'Q000008', None, 'Q000097']","[None, 'chemistry', 'pharmacology', None, None, 'pharmacology', 'blood', 'isolation & purification', None, 'administration & dosage', 'antagonists & inhibitors', None, None, None, None, 'isolation & purification', 'administration & dosage', 'administration & dosage', None, 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/16819905,2006,0,0,, +0.21,11976402,"Excessive peroxidation of biomembranes is thought to contribute to the initiation and progression of numerous degenerative diseases. The present study examined the inhibitory effects of a cocoa extract, individual cocoa flavanols (-)-epicatechin and (+)-catechin, and procyanidin oligomers (dimer to decamer) isolated from cocoa on rat erythrocyte hemolysis. In vitro, the flavanols and the procyanidin oligomers exhibited dose-dependent protection against 2,2'-azo-bis (2-amidinopropane) dihydrochloride (AAPH)-induced erythrocyte hemolysis between concentrations of 2.5 and 40 microM. Dimer, trimer, and tetramer showed the strongest inhibitory effects at 10 microM, 59.4%, 66.2%, 70.9%; 20 microM, 84.1%, 87.6%, 81.0%; and 40 microM, 90.2%, 88.9%, 78.6%, respectively. In a subsequent experiment, male Sprague-Dawley rats (approximately 200 g; n = 5-6) were given a 100-mg intragastric dose of a cocoa extract. Blood was collected over a 4-hr time period. Epicatechin and catechin, and the dimers (-)-epicatechin-(4beta>8)-epicatechin (Dimer B2) and (-)-epicatechin-(4beta>6)-epicatechin (Dimer B5) were detected in the plasma with concentrations of 6.4 microM, and 217.6, 248.2, and 55.4 nM, respectively. Plasma antioxidant capacity (as measured by the total antioxidant potential [TRAP] assay) was elevated (P < 0.05) between 30 and 240 min following the cocoa extract feeding. Erythrocytes obtained from the cocoa extract-fed animals showed an enhanced resistance to hemolysis (P < 0.05). This enhanced resistance was also observed when erythrocytes from animals fed the cocoa extract were mixed with plasma obtained from animals given water only. Conversely, plasma obtained from rats given the cocoa extract improved the resistance of erythrocytes obtained from rats given water only. These results show cocoa flavanols and procyanidins can provide membrane protective effects.","Experimental biology and medicine (Maywood, N.J.)","['D000578', 'D000818', 'D000975', 'D044946', 'D002099', 'D002392', 'D002851', 'D019281', 'D004305', 'D004912', 'D005609', 'D006461', 'D008297', 'D013058', 'D010936', 'D044945', 'D051381', 'D017207']","['Amidines', 'Animals', 'Antioxidants', 'Biflavonoids', 'Cacao', 'Catechin', 'Chromatography, High Pressure Liquid', 'Dimerization', 'Dose-Response Relationship, Drug', 'Erythrocytes', 'Free Radicals', 'Hemolysis', 'Male', 'Mass Spectrometry', 'Plant Extracts', 'Proanthocyanidins', 'Rats', 'Rats, Sprague-Dawley']",Inhibitory effects of cocoa flavanols and procyanidin oligomers on free radical-induced erythrocyte hemolysis.,"['Q000494', None, 'Q000032', None, 'Q000737', 'Q000031', None, None, None, 'Q000187', 'Q000494', 'Q000187', None, None, 'Q000494', None, None, None]","['pharmacology', None, 'analysis', None, 'chemistry', 'analogs & derivatives', None, None, None, 'drug effects', 'pharmacology', 'drug effects', None, None, 'pharmacology', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11976402,2002,,,, +0.21,24720622,"In this study, we determined, by atomic absorption spectrophotometry, the potassium amount leached by soaking or boiling foods identified by children suffering from chronic renal failure as ""pleasure food"" and that they cannot eat because of their low-potassium diet, and evaluated whether addition of sodium polystyrene sulfonate resin (i.e. Kayexalate_‰) during soaking or boiling modulated potassium loss. A significant amount of potassium content was removed by soaking (16% for chocolate and potato, 26% for apple, 37% for tomato and 41% for banana) or boiling in a large amount of water (73% for potato). Although Kayexalate_‰ efficiently dose-dependently removed potassium from drinks (by 48% to 73%), resin addition during soaking or boiling did not eliminate more potassium from solid foods. Our results therefore provide useful information for dietitians who elaborate menus for people on potassium-restricted diets and would give an interesting alternative to the systematic elimination of all potassium-rich foods from their diet.",International journal of food sciences and nutrition,"['D000293', 'D001066', 'D002411', 'D002648', 'D003296', 'D004032', 'D005511', 'D006801', 'D007676', 'D057181', 'D011137', 'D011188', 'D014867']","['Adolescent', 'Appetite', 'Cation Exchange Resins', 'Child', 'Cooking', 'Diet', 'Food Handling', 'Humans', 'Kidney Failure, Chronic', 'Pleasure', 'Polystyrenes', 'Potassium', 'Water']",Effects of water soaking and/or sodium polystyrene sulfonate addition on potassium content of foods.,"[None, None, None, None, None, 'Q000523', 'Q000379', None, 'Q000178', None, None, None, None]","[None, None, None, None, None, 'psychology', 'methods', None, 'diet therapy', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24720622,2015,0,0,,no cocoa +0.2,22656242,"2-Substituted-5-methyl-3-oxazolines, a novel class of aroma precursors that are able to release the respective Strecker aldehydes by hydrolysis, were identified. Hydrolysis can take place after the addition of water or with human saliva during mastication, respectively. 2-Isobutyl-, 2-sec-isobutyl-, 2-isopropyl, and 2-benzyl-5-methyl-3-oxazolines were synthesized and structurally identified by means of gas chromatography-mass spectrometry (GC-MS) in the electron impact mode and in the chemical ionization mode as well as by one- and two-dimensional NMR experiments. With these compounds at hand, a variety of stability experiments were performed using headspace-GC-MS or proton transfer reaction-MS techniques on the basis of stable isotope dilution assays, proving the ability to release the respective Strecker aldehydes was dependent on the pH value as well as on the hydrolysis time. After the addition of water at 37 _C, for example, >70 mol % of 3-methylbutanal or >40 mol % of phenylacetaldehyde was liberated from a solution of 2-isobutyl-5-methyl-3-oxazoline or 2-benzyl-5-methyl-3-oxazoline, respectively, after 5 min. Furthermore, the presence of 2-isobutyl-5-methyl-3-oxazoline in dark chocolate containing 70% cocoa was proven by GC-MS.",Journal of agricultural and food chemistry,"['D000447', 'D002099', 'D015394', 'D010080']","['Aldehydes', 'Cacao', 'Molecular Structure', 'Oxazoles']",New insights into the formation of aroma-active strecker aldehydes from 3-oxazolines as transient intermediates.,"['Q000737', 'Q000737', None, 'Q000737']","['chemistry', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22656242,2012,0,0,, +0.2,9662953,"Viscosity and Yield Value of Casson are two chocolate properties. They are very important in the technological processes and they affect to the final product acepptation. In this study viscosity, yield value and fatty acid composition were determined of chocolates elaborated with different fat sources. A correlation study was made between these three variables. Viscosity and yield value were calculated with the Casson's education using a viscometer brookfield and fatty acids composition by gas-chromatography. Positive correlations between viscosity and yield value with stearic and palmitic acids contents have been found. Negative correlations between yield value and lauric content and viscosity and oleic acid content have been observed. The viscosity variations were relationed with total content of cocoa butter of different chocolates.",Nutricion hospitalaria,"['D002099', 'D005224', 'D006801', 'D019301', 'D010938', 'D014783']","['Cacao', 'Fats, Unsaturated', 'Humans', 'Oleic Acid', 'Plant Oils', 'Viscosity']",[Effect on viscosity and yield value of addition of different vegetable fat sources used in chocolate].,"['Q000737', None, None, None, None, None]","['chemistry', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9662953,1998,,,, +0.2,28946303,"Substantial equivalence studies were performed in three Theobroma spp., cacao, bicolor and grandiflorum through chemical composition analysis and protein profiling of fruit (pulp juice and seeds). Principal component analysis of sugar, organic acid, and phenol content in pulp juice revealed equivalence among the three species, with differences in some of the compounds that may result in different organoleptic properties. Proteins were extracted from seeds and pulp juice, resolved by two dimensional electrophoresis and major spots subjected to mass spectrometry analysis and identification. The protein profile, as revealed by principal component analysis, was variable among the three species in both seed and pulp, with qualitative and quantitative differences in some of protein species. The functional grouping of the identified proteins correlated with the biological role of each organ. Some of the identified proteins are of interest, being minimally discussed, including vicilin, a protease inhibitor, and a flavonol synthase/flavanone 3-hydroxylase.",Food chemistry,"['D002099', 'D005638', 'D006801', 'D006899', 'D010940', 'D012639']","['Cacao', 'Fruit', 'Humans', 'Mixed Function Oxygenases', 'Plant Proteins', 'Seeds']",Substantial equivalence analysis in fruits from three Theobroma species through chemical composition and protein profiling.,"[None, None, None, None, None, None]","[None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28946303,2017,1,2,table 1 ,chemical composition of T. cacao +0.2,16508183,"The purpose of this study was to evaluate the bitterness-suppressing effect of three jellies, all commercially available on the Japanese market as swallowing aids, on two dry syrups containing the macrolides clarithromycin (CAM) or azithromycin (AZM). The bitterness intensities of mixtures of the dry syrups and acidic jellies were significantly greater than those of water suspensions of the dry syrups in human gustatory sensation tests. On the other hand, the mixture with a chocolate jelly, which has a neutral pH, was less bitter than water suspensions of the dry syrups. The bitterness intensities predicted by the taste sensor output values correlated well with the observed bitterness intensities in human gustatory sensation tests. When the concentrations of CAM and AZM in solutions extracted from physical mixtures of dry syrup and jelly were determined by HPLC, concentrations in the solutions extracted from mixtures with acidic jellies were higher than those from mixtures with a neutral jelly (almost 90 times higher for CAM, and almost 7-10 times higher for AZM). Thus, bitterness suppression is correlated with the pH of the jelly. Finally, a drug dissolution test for dry syrup with and without jelly was performed using the paddle method. There was no significance difference in dissolution profile. It was concluded the appropriate choice of jelly with the right pH is essential for taste masking. Suitable jellies might be used to improve patient compliance, especially in children. The taste sensor may be used to predict the bitterness-suppressing effect of the jelly.",Chemical & pharmaceutical bulletin,"['D000328', 'D000900', 'D017963', 'D002099', 'D002851', 'D017291', 'D003627', 'D005421', 'D006801', 'D006863', 'D018942', 'D011803', 'D012995', 'D012996', 'D013649']","['Adult', 'Anti-Bacterial Agents', 'Azithromycin', 'Cacao', 'Chromatography, High Pressure Liquid', 'Clarithromycin', 'Data Interpretation, Statistical', 'Flavoring Agents', 'Humans', 'Hydrogen-Ion Concentration', 'Macrolides', 'Quinine', 'Solubility', 'Solutions', 'Taste']",Evaluation of bitterness suppression of macrolide dry syrups by jellies.,"[None, 'Q000009', 'Q000009', None, None, 'Q000009', None, 'Q000737', None, None, 'Q000009', 'Q000494', None, None, 'Q000187']","[None, 'adverse effects', 'adverse effects', None, None, 'adverse effects', None, 'chemistry', None, None, 'adverse effects', 'pharmacology', None, None, 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/16508183,2006,0,0,,no cocoa tested +0.18,22503716,"Human biomonitoring of nickel has gained interest in environmental medicine due to its wide distribution in the environment and its allergenic potential. There are indications that the prevalence of nickel sensitization in children is increased by nickel exposure and that oral uptake of nickel can exacerbate nickel dermatitis in nickel-sensitive individuals. Urinary nickel measurement is a good indicator of exposure. However, data on nickel levels in urine of children are rare. For the first time, the German Environmental Survey on children (GerES IV) 2003-2006 provided representative data to describe the internal nickel exposure of children aged 3-14 years in Germany. Nickel was measured after enrichment in the organic phase of urine by graphite furnace atomic absorption spectrometry with Zeeman background correction. Nickel levels (n=1576) ranged from <0.5 to 15 __g/l. Geometric mean was 1.26 __g/l. Multivariate regression analysis showed that gender, age, socio-economic status, being overweighted, consumption of hazelnut spread, nuts, cereals, chocolate and urinary creatinine were significant predictors for urinary nickel excretion of children who do not smoke. 20.2% of the variance could be explained by these variables. With a contribution of 13.8% the urinary creatinine concentration was the most important predictor. No influence of nickel intake via drinking water and second hand smoke exposure was observed.",International journal of hygiene and environmental health,"['D000293', 'D002099', 'D002648', 'D002675', 'D003367', 'D003404', 'D060766', 'D002523', 'D004784', 'D004785', 'D005260', 'D005506', 'D005858', 'D006306', 'D006801', 'D008297', 'D009532', 'D009754', 'D011795']","['Adolescent', 'Cacao', 'Child', 'Child, Preschool', 'Cotinine', 'Creatinine', 'Drinking Water', 'Edible Grain', 'Environmental Monitoring', 'Environmental Pollutants', 'Female', 'Food Contamination', 'Germany', 'Health Surveys', 'Humans', 'Male', 'Nickel', 'Nuts', 'Surveys and Questionnaires']",Levels and predictors of urinary nickel concentrations of children in Germany: results from the German Environmental Survey on children (GerES IV).,"[None, None, None, None, 'Q000652', 'Q000652', 'Q000032', None, None, 'Q000652', None, None, None, None, None, None, 'Q000652', None, None]","[None, None, None, None, 'urine', 'urine', 'analysis', None, None, 'urine', None, None, None, None, None, None, 'urine', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22503716,2013,0,0,, +0.17,22922535,"Six facultatively anaerobic, non-motile lactic acid bacteria were isolated from spontaneous cocoa bean fermentations carried out in Brazil, Ecuador and Malaysia. Phylogenetic analysis revealed that one of these strains, designated M75(T), isolated from a Brazilian cocoa bean fermentation, had the highest 16S rRNA gene sequence similarity towards Weissella fabaria LMG 24289(T) (97.7%), W. ghanensis LMG 24286(T) (93.3%) and W. beninensis LMG 25373(T) (93.4%). The remaining lactic acid bacteria isolates, represented by strain M622, showed the highest 16S rRNA gene sequence similarity towards the type strain of Fructobacillus tropaeoli (99.9%), a recently described species isolated from a flower in South Africa. pheS gene sequence analysis indicated that the former strain represented a novel species, whereas pheS, rpoA and atpA gene sequence analysis indicated that the remaining five strains belonged to F. tropaeoli; these results were confirmed by DNA-DNA hybridization experiments towards their respective nearest phylogenetic neighbours. Additionally, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry proved successful for the identification of species of the genera Weissella and Fructobacillus and for the recognition of the novel species. We propose to classify strain M75(T) (___=___LMG 26217(T) ___=___CCUG 61472(T)) as the type strain of the novel species Weissella fabalis sp. nov.",International journal of systematic and evolutionary microbiology,"['D001482', 'D001938', 'D002099', 'D004269', 'D004484', 'D005285', 'D005516', 'D005798', 'D056584', 'D008296', 'D008969', 'D009693', 'D010457', 'D010802', 'D012336', 'D017422', 'D058836']","['Base Composition', 'Brazil', 'Cacao', 'DNA, Bacterial', 'Ecuador', 'Fermentation', 'Food Microbiology', 'Genes, Bacterial', 'Leuconostocaceae', 'Malaysia', 'Molecular Sequence Data', 'Nucleic Acid Hybridization', 'Peptidoglycan', 'Phylogeny', 'RNA, Ribosomal, 16S', 'Sequence Analysis, DNA', 'Weissella']",Characterization of strains of Weissella fabalis sp. nov. and Fructobacillus tropaeoli from spontaneous cocoa bean fermentations.,"[None, None, 'Q000382', 'Q000235', None, None, None, None, 'Q000145', None, None, None, 'Q000032', None, 'Q000235', None, 'Q000145']","[None, None, 'microbiology', 'genetics', None, None, None, None, 'classification', None, None, None, 'analysis', None, 'genetics', None, 'classification']",https://www.ncbi.nlm.nih.gov/pubmed/22922535,2013,0,0,, +0.17,1189616,"A procedure based on extraction, column chromatography and precipitation is described for the separation of ethylene oxide-1,2-14C fumigated coca-powder derivatives in 9 different groups. As it was found in wheat [1], the major portion of radioactivity lies in water extract; in coca-powder the major portion of radioactivity is also found in low molecular components.",Zeitschrift fur Lebensmittel-Untersuchung und -Forschung,"['D002099', 'D002250', 'D005027', 'D006868']","['Cacao', 'Carbon Radioisotopes', 'Ethylene Oxide', 'Hydrolysis']","[Group separation of ethylene oxide 1,2-14c fumigated coca-powder derivatives and their distribution of radioactivity (author's transl)].","['Q000032', None, None, None]","['analysis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/1189616,1976,,,, +0.17,11185658,"Cacao is rich in polyphenols such as (-)-epicatechin, and a colored component of cacao (cacao-red) is polyphenol, which is an antioxidant. These properties stimulated an investigation of the effects of cacao liquor polyphenols (CLP) on low-density lipoprotein (LDL) oxidation. The 2.2 '-azobis(4-methoxy-2,4-dimethylvaleronitrile) (AMVN-CH2O)-induced oxidizability of LDL was assessed by monitoring the absorbance at 234 nm. In vitro. 0.1-0.5 mg/dL CLP prolonged the oxidation lag time of LDL in a dose-dependent manner. Compared with the controls, it was prolonged 1.7-fold in the presence of 0.1 mg/dL CLP, 2.9-fold at 0.2 mg/dL, 3.8-fold at 0.3 mg/dL, 5.4-fold at 0.4 mg/dL, and 6.4-fold at 0.5 mg/dL. Furthermore, we enlisted 13 male volunteers to consume 35 g delipidated cocoa. Venous blood samples were taken before and at 2 h and 4 h after consuming the cocoa. The oxidation lag time of LDL before cocoa ingestion was 59.0 +/- 6.3 min, but it was prolonged at 2 h after cocoa (68.3 +/- 6.0 min); before returning to the initial lag time (61.7 +/- 5.7 min) before consumption. Thus we have shown that cocoa inhibited LDL oxidation both in vitro and ex vivo.",Journal of nutritional science and vitaminology,"['D000328', 'D000975', 'D001161', 'D001391', 'D002099', 'D008078', 'D004305', 'D005419', 'D006801', 'D066298', 'D008297', 'D009570', 'D010084', 'D010636', 'D011108', 'D059808', 'D013053', 'D013997']","['Adult', 'Antioxidants', 'Arteriosclerosis', 'Azo Compounds', 'Cacao', 'Cholesterol, LDL', 'Dose-Response Relationship, Drug', 'Flavonoids', 'Humans', 'In Vitro Techniques', 'Male', 'Nitriles', 'Oxidation-Reduction', 'Phenols', 'Polymers', 'Polyphenols', 'Spectrophotometry', 'Time Factors']",Antioxidant effects of polyphenols in chocolate on low-density lipoprotein both in vitro and ex vivo.,"[None, 'Q000494', 'Q000097', 'Q000494', 'Q000737', 'Q000097', None, None, None, None, None, 'Q000494', None, 'Q000494', 'Q000494', None, None, None]","[None, 'pharmacology', 'blood', 'pharmacology', 'chemistry', 'blood', None, None, None, None, None, 'pharmacology', None, 'pharmacology', 'pharmacology', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11185658,2001,,,, +0.16,20153860,"Quality control of cacao beans is a significant issue in the chocolate industry. In this report, we describe how moisture damage to cacao beans alters the volatile chemical signature of the beans in a way that can be tracked quantitatively over time. The chemical signature of the beans is monitored via sampling the headspace of the vapor above a given bean sample. Headspace vapor sampled with solid-phase micro-extraction (SPME) was detected and analyzed with comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GCxGC-TOFMS). Cacao beans from six geographical origins (Costa Rica, Ghana, Ivory Coast, Venezuela, Ecuador, and Panama) were analyzed. Twenty-nine analytes that change in concentration levels via the time-dependent moisture damage process were measured using chemometric software. Biomarker analytes that were independent of geographical origin were found. Furthermore, prediction algorithms were used to demonstrate that moisture damage could be verified before there were visible signs of mold by analyzing subsets of the 29 analytes. Thus, a quantitative approach to quality screening related to the identification of moisture damage in the absence of visible mold is presented.",Journal of chromatography. A,"['D001185', 'D002099', 'D003364', 'D008401', 'D008956', 'D025341', 'D012044', 'D014867']","['Artificial Intelligence', 'Cacao', 'Costa Rica', 'Gas Chromatography-Mass Spectrometry', 'Models, Chemical', 'Principal Component Analysis', 'Regression Analysis', 'Water']",Quantitative assessment of moisture damage for cacao bean quality using two-dimensional gas chromatography combined with time-of-flight mass spectrometry and chemometrics.,"[None, 'Q000737', None, 'Q000379', None, None, None, 'Q000009']","[None, 'chemistry', None, 'methods', None, None, None, 'adverse effects']",https://www.ncbi.nlm.nih.gov/pubmed/20153860,2010,0,0,, +0.16,9554600,"The authors conducted a matched case-control study to investigate the effects of caffeine intake during pregnancy on birth weight. From January to November 1992, in the first 24 hours after delivery, 1,205 mothers (401 cases and 804 controls) were interviewed and their newborns were examined to assess birth weight and gestational age by means of the method of Capurro et al. (J Pediatr 1978;93:120-2). The cases were children with birth weight < 2,500 g and gestational age > or = 28 weeks. Cases and controls were matched for time of birth and hospital of delivery and were recruited from the four maternity hospitals in Pelotas, southern Brazil. Daily maternal caffeine intake during pregnancy for each trimester was estimated. To assess caffeine intake, 10% of the mothers were reinterviewed at their households and samples of reported information on drip coffee and mat© (a caffeine-containing drink widely used in South America) were collected and sent to the laboratory for caffeine determination through liquid chromatography. When instant coffee was reported, the weight of powder was measured using a portable scale, and caffeine intake was estimated from a reference table. Caffeine intake from tea, chocolate, soft drinks, and medicines was estimated from a reference table. Analyses were performed by conditional logistic regression. Crude analyses showed no effect of caffeine on low birth weight, preterm births or intrauterine growth retardation. The results did not change after allowing for confounders.",American journal of epidemiology,"['D001628', 'D001724', 'D002110', 'D016022', 'D003069', 'D005260', 'D005317', 'D005865', 'D006801', 'D007230', 'D007231', 'D007234', 'D015999', 'D011247']","['Beverages', 'Birth Weight', 'Caffeine', 'Case-Control Studies', 'Coffee', 'Female', 'Fetal Growth Retardation', 'Gestational Age', 'Humans', 'Infant, Low Birth Weight', 'Infant, Newborn', 'Infant, Premature', 'Multivariate Analysis', 'Pregnancy']",Caffeine intake and low birth weight: a population-based case-control study.,"['Q000032', 'Q000187', 'Q000008', None, 'Q000737', None, 'Q000139', None, None, None, None, None, None, None]","['analysis', 'drug effects', 'administration & dosage', None, 'chemistry', None, 'chemically induced', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9554600,1998,,,, +0.15,528451,"Aflatoxin was produced in both non-autoclaved and autoclaved Ivory Coast cocoa beans inoculated with Aspergillus parasiticus NRRL 2999 under optimum laboratory growth conditions. Total aflatoxin levels ranged from 213 to 5597 ng/g substrate. Aflatoxin was quantitated by using high pressure liquid chromatography (HPLC). Raw, non-autoclaved cocoa beans, also inoculated with aspergilli, produced 6359 ng aflatoxin/g substrate. Variation in aflatoxin production between bean varieties was observed. Total aflatoxin levels of 10,446 and 23,076 ng/g substrate were obtained on Ivory Coast beans inoculated with A. parasiticus NRRL 2999 and NRRL 3240, respectively. Aflatoxin production on Trinidad and Malaysian beans was 28 and 65 ng aflatoxin/g substrate. These data support previously reported low level natural aflatoxin contamination in cocoa.",Journal - Association of Official Analytical Chemists,"['D000348', 'D001230', 'D002099']","['Aflatoxins', 'Aspergillus', 'Cacao']",Production of aflatoxin in cocoa beans.,"['Q000096', 'Q000378', None]","['biosynthesis', 'metabolism', None]",https://www.ncbi.nlm.nih.gov/pubmed/528451,1980,,,, +0.14,24974581,"The determination of cefaclor in a new, complex chocolate matrix was performed by using a simple sample preparation (dispersion in dilute hydrochloric acid at 80 degrees C, centrifugation, washing with cyclohexane), followed by ion pair HPLC on a Kinetex pentafluorophenyl core-shell stationary phase with UV detection at 265 nm. We obtained good linearity (R2 = 0.9976) and precision (average RSD 0.86%) for the relevant concentration range. The preparations, although hand-made in this pilot phase, showed good uniformity of content. After being stored for four weeks in a refrigerator the preparation did not contain recognizable amounts of decomposition products.",Die Pharmazie,"['D000900', 'D002099', 'D002214', 'D002433', 'D002626', 'D002851', 'D004304', 'D005780', 'D012015', 'D015203', 'D013056']","['Anti-Bacterial Agents', 'Cacao', 'Capsules', 'Cefaclor', 'Chemistry, Pharmaceutical', 'Chromatography, High Pressure Liquid', 'Dosage Forms', 'Gelatin', 'Reference Standards', 'Reproducibility of Results', 'Spectrophotometry, Ultraviolet']",Analysis of cefaclor in novel chocolate-based camouflage capsules.,"['Q000008', None, None, 'Q000008', None, None, None, None, None, None, None]","['administration & dosage', None, None, 'administration & dosage', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24974581,2014,,,, +0.14,16954822,"A proteomic analysis of procyanidin B(2) isolated from cocoa against oxidized low-density lipoprotein-induced lipid-laden macrophage formation was performed. Of approximately 400 detected proteins, 12 were differentially expressed as a result of B(2) treatment. They were subsequently identified by liquid chromatography-electrospray ionization-tandem mass spectrometry and the SWISS-PROT database. Further reverse transcriptase-polymerase chain reaction and Western blot analysis revealed that B(2) strongly inhibited arachidonic acid inflammatory reactions, apoptosis, and their coupled mitogen-activated protein kinase and NF-kappaB pathways. To highlight proteins or genes with similar expressed patterns and similarly biological function induced by B(2) in lipid-laden macrophages, a cluster and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed. The data were mapped to multiple pathways. Further validation of the bioinformatic results revealed that activation of Wnt signaling may contribute to the cardioprotection of B(2). The differentially expressed genes and proteins mentioned above induced by B(2) are through regulating nuclear transcription factors, activating peroxisome proliferator-activated receptor-gamma and inhibiting AP-1 mRNA expressions. These in vitro data help to interpret the beneficial effects of B(2) in reducing the risk of atherosclerosis after consumption of flavonoid-rich foods. Many differentially expressed genes induced by B(2) help to uncover novel targets and may help to target disease interactions in atherosclerosis in the future.",Journal of cardiovascular pharmacology,"['D000595', 'D001094', 'D044946', 'D002392', 'D051546', 'D019281', 'D004734', 'D015870', 'D006801', 'D050356', 'D008077', 'D008264', 'D008969', 'D047495', 'D044945', 'D012333', 'D051127', 'D015398', 'D020298', 'D051153']","['Amino Acid Sequence', 'Arachidonate 5-Lipoxygenase', 'Biflavonoids', 'Catechin', 'Cyclooxygenase 2', 'Dimerization', 'Energy Metabolism', 'Gene Expression', 'Humans', 'Lipid Metabolism', 'Lipoproteins, LDL', 'Macrophages', 'Molecular Sequence Data', 'PPAR gamma', 'Proanthocyanidins', 'RNA, Messenger', 'Scavenger Receptors, Class E', 'Signal Transduction', 'U937 Cells', 'Wnt Proteins']",Inhibitory effects of procyanidin B(2) dimer on lipid-laden macrophage formation.,"[None, 'Q000032', 'Q000494', 'Q000494', 'Q000032', None, None, 'Q000187', None, 'Q000187', 'Q000494', 'Q000187', None, 'Q000502', 'Q000494', 'Q000032', 'Q000032', 'Q000187', None, 'Q000502']","[None, 'analysis', 'pharmacology', 'pharmacology', 'analysis', None, None, 'drug effects', None, 'drug effects', 'pharmacology', 'drug effects', None, 'physiology', 'pharmacology', 'analysis', 'analysis', 'drug effects', None, 'physiology']",https://www.ncbi.nlm.nih.gov/pubmed/16954822,2006,0,0,, 0.13,18257943,"The effect of different food matrices on the metabolism and excretion of polyphenols is uncertain. The objective of the study was to evaluate the possible effect of milk on the excretion of (2)-epicatechin metabolites from cocoa powder after its ingestion with and without milk. Twenty-one volunteers received the following three test meals each in a randomised cross-over design with a 1-week interval between meals: (1) 250 ml whole milk as a control; (2) 40 g cocoa powder dissolved in 250 ml whole milk (CC-M); (3) 40 g cocoa powder dissolved in 250 ml water (CC-W). Urine was collected before consumption and during the 0-6, 6-12 and 12-24 h periods after consumption. (2)-Epicatechin metabolite excretion was measured using liquid chromatography-MS. One (2)-epicatechin glucuronide and three (2)-epicatechin sulfates were detected in urine excreted after the intake of the two cocoa beverages (CC-M and CC-W). The results show that milk does not significantly affect the total amount of metabolites excreted in urine. However, differences in metabolite excretion profiles were observed; there were changes in the glucuronide and sulfate excretion rates, and the sulfation position between the period of excretion and the matrix. The matrix in which polyphenols are consumed can affect their metabolism and excretion, and this may affect their biological activity. Thus, more studies are needed to evaluate the effect of these different metabolite profiles on the body.",The British journal of nutrition,"['D000293', 'D000328', 'D000818', 'D002099', 'D002392', 'D018592', 'D005260', 'D005419', 'D005502', 'D020719', 'D006801', 'D016014', 'D008297', 'D008875', 'D008892', 'D010636', 'D059808', 'D013463', 'D055815']","['Adolescent', 'Adult', 'Animals', 'Cacao', 'Catechin', 'Cross-Over Studies', 'Female', 'Flavonoids', 'Food', 'Glucuronides', 'Humans', 'Linear Models', 'Male', 'Middle Aged', 'Milk', 'Phenols', 'Polyphenols', 'Sulfuric Acid Esters', 'Young Adult']",The effects of milk as a food matrix for polyphenols on the excretion profile of cocoa (-)-epicatechin metabolites in healthy human subjects.,"[None, None, None, None, 'Q000031', None, None, 'Q000378', None, 'Q000652', None, None, None, None, None, 'Q000378', None, 'Q000652', None]","[None, None, None, None, 'analogs & derivatives', None, None, 'metabolism', None, 'urine', None, None, None, None, None, 'metabolism', None, 'urine', None]",https://www.ncbi.nlm.nih.gov/pubmed/18257943,2009,0,0,, \ No newline at end of file diff --git a/data/garlic_scoring.csv b/data/garlic_scoring.csv index ae42bbc..bca212e 100644 --- a/data/garlic_scoring.csv +++ b/data/garlic_scoring.csv @@ -1,416 +1,416 @@ -PMID,abstract,journal,mesh_UIds,mesh_terms,paper,qual_UIds,qual_terms,webpage,year,is_useful,usefulness_tier,fold,comments,useful_no_pdf -29693456,"After gas chromatography and mass spectrometry of prepared methanolic extract of Allium sativum, 40 laboratory BALB/c mice were infected intraperitoneally by injection of 1,500 viable protoscoleces. Five months after infection, the infected mice were allocated into four treatment groups, including 1- Albendazole (100 mg/kg); 2- Allium sativum methanolic extract (10 mL/L); 3- A. sativum methanolic extract (10 mL/L) + Albendazole (50 mg /kg); and 4- untreated control group. After 30 days of daily treatment, total number and weight of cysts and size of the largest cyst as well as blood serum bilirubin and liver enzymes were compared between the mice of different groups. The total number and weight of cysts and size of the largest cyst were significantly lower in treated groups A. sativum 10 mL/L + Albendazole 50 and Albendazole 100 in comparison to those of the control group (p < 0.05). The activity of alanine aminotransferase (ALT) enzyme and bilirubin concentration were significantly lower in the mice treated with A. sativum 10 mL/L and A. sativum 10 mL/L + Albendazole 50, when compared to the control group. In addition, bilirubin concentration revealed significant decrease in A. sativum 10 mL/L and A. sativum 10 mL/L + Albendazole 50 groups, when compared to the Albendazole group. In conclusion, administration of A. sativum 10 mL/L improved the anti-hydatidosis activity of Albendazole 50 mg /kg, due to parasitological effects similar to Albendazole 100 mg /kg but less hepatotoxic effects.",Journal of investigative surgery : the official journal of the Academy of Surgical Research,[],[],Allium Sativum Methanolic Extract (garlic) Improve Therapeutic Efficacy of Albendazole Against Hydatid Cyst: In Vivo Study.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29693456,2018,1.0,1.0,,, -29653182,"Meju, a cooked and fermented soy bean based food product, is used as a major ingredient in Korean traditional fermented foods such as Doenjang. We developed a novel type of Meju using single and combined extracts of Allium sativum (garlic clove), Nelumbo nucifera (lotus leaves), and Ginkgo biloba (ginkgo leaves) at 1% and 10% concentrations to improve the safety of Meju-based fermented products. Biogenic amines (BAs) in protein-rich fermented food products pose considerable toxical risks. The objective of this study was to investigate the effects of adding selected plant extracts in Meju samples during fermentation. Nine BAs, including tryptamine, 2-phenylethylamine, putrescine, cadaverine, agmatine, histamine, tyramine, spermidine and spermine, were isolated from Meju samples after sample derivatization with dansyl chloride and analyzed by high performance liquid chromatography. As a result, all tested Meju samples with added plant extracts showed total BAs levels in the range of 20.12 ± 2.03 to 118.42 ± 10.68 mg/100 g, which were below the safety limit set by various regulatory authorities (USFDA/KFDA/EFSA). However, among all tested Meju samples, LOM10 (Meju fermented with Nelumbo nucifera at 10% concentration) showed higher levels of BAs content than others either due to batch-to-batch variability or reduced beneficial microorganisms and/or due to increase in BA forming microorganisms. Also, none of the samples showed the aflatoxin level above the detection limit. Furthermore, all the tested Meju samples improved microbial safety as confirmed by the complete absence of Salmonella species and Staphylococcus aureus. However, some of the Meju samples showed the presence of coliforms (in range of 1.6 × 10",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D001679', 'D002851', 'D005285', 'D005516', 'D059022', 'D005737', 'D020441', 'D057230', 'D031653', 'D010936', 'D045730', 'D013056']","['Biogenic Amines', 'Chromatography, High Pressure Liquid', 'Fermentation', 'Food Microbiology', 'Food Safety', 'Garlic', 'Ginkgo biloba', 'Limit of Detection', 'Nelumbo', 'Plant Extracts', 'Soy Foods', 'Spectrophotometry, Ultraviolet']","Detection of biogenic amines and microbial safety assessment of novel Meju fermented with addition of Nelumbo nucifera, Ginkgo biloba, and Allium sativum.","['Q000032', None, None, None, None, None, None, None, None, 'Q000494', 'Q000382', None]","['analysis', None, None, None, None, None, None, None, None, 'pharmacology', 'microbiology', None]",https://www.ncbi.nlm.nih.gov/pubmed/29653182,2018,0.0,0.0,,, -29542139,"Allicin and soluble solid content (SSC) in garlic is the responsible for its pungent flavor and odor. However, current conventional methods such as the use of high-pressure liquid chromatography and a refractometer have critical drawbacks in that they are time-consuming, labor-intensive and destructive procedures. The present study aimed to predict allicin and SSC in garlic using hyperspectral imaging in combination with variable selection algorithms and calibration models.",Journal of the science of food and agriculture,"['D000465', 'D002138', 'D002623', 'D005737', 'D016018', 'D008962', 'D013057', 'D013441', 'D060388']","['Algorithms', 'Calibration', 'Chemistry Techniques, Analytical', 'Garlic', 'Least-Squares Analysis', 'Models, Theoretical', 'Spectrum Analysis', 'Sulfinic Acids', 'Support Vector Machine']",Hyperspectral imaging for predicting the allicin and soluble solid content of garlic with variable selection algorithms and chemometric models.,"[None, None, 'Q000379', 'Q000737', None, None, 'Q000379', 'Q000737', None]","[None, None, 'methods', 'chemistry', None, None, 'methods', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/29542139,2018,0.0,0.0,,, -29498844,"We analyzed aged garlic extract (AGE) to understand its complex sulfur chemistry using post-column high-performance liquid chromatography with an iodoplatinate reagent and liquid chromatography high resolution mass spectrometry (LC-MS). We observed unidentified peaks of putative sulfur compounds. Three compounds were isolated and identified as γ-glutamyl-γ-glutamyl- S-methylcysteine, γ-glutamyl-γ-glutamyl- S-allylcysteine (GGSAC) and γ-glutamyl-γ-glutamyl- S-1-propenyl-cysteine (GGS1PC) by nuclear magnetic resonance and LC-MS analysis based on comparisons with chemically synthesized reference compounds. GGSAC and GGS1PC were novel compounds. Trace amounts of these compounds were detected in raw garlic, but the contents of these compounds increased during the aging process. Production of these compounds was inhibited using a γ-glutamyl transpeptidase (GGT) inhibitor in the model reaction mixtures. These findings suggest that γ-glutamyl tripeptides in AGE are produced by GGT during the aging process.",Journal of agricultural and food chemistry,"['D002851', 'D005638', 'D005737', 'D010455', 'D010936', 'D013457', 'D053719']","['Chromatography, High Pressure Liquid', 'Fruit', 'Garlic', 'Peptides', 'Plant Extracts', 'Sulfur Compounds', 'Tandem Mass Spectrometry']",Isolation and Identification of Three γ-Glutamyl Tripeptides and Their Putative Production Mechanism in Aged Garlic Extract.,"[None, 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', None]","[None, 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/29498844,2018,0.0,0.0,,, -29433241,"Complexes of amylose (Am) with garlic bioactive components (GBCs) were prepared by milling activating treatment of Am and garlic paste (GP) together. The complex, produced by milling for 2.5h with the garlic (dry basis)/Am ratio of 1:5 (w/w) and water content of 25% (w/w) exhibited significantly higher allicin content (0.49mg/g of complex) than others. The scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transforms infrared (FT-IR), differential scanning calorimetry (DSC), thermogravimetry analysis (TGA), high performance liquid chromatography (HPLC), and gas chromatography-mass spectrometry (GC-MS) techniques were used complex characterization. XRD results indicated that the Am and garlic bioactive components formed the V-type structure. FT-IR and DSC analysis further confirmed the formation of the Am-GBCs complex, and its thermal stability was improved in comparison with garlic powder. According to GC-MS results, all organosulfur compounds (OSCs) in fresh garlic were better retained to Am-GBCs complex. Therefore, the Am-GBCs complexes can have important applications as stable natural flavor compound systems.","Food research international (Ottawa, Ont.)",[],[],Physicochemical characteristics of complexes between amylose and garlic bioactive components generated by milling activating method.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29433241,2018,2.0,1.0,,, -29318305,"In the present research, the applicability of stable isotope (δ13C, δ15N, δ34S, δ18O) and multi-element (P, S, Cl, K, Ca, Zn, Br, Rb, Sr) data for determining the geographical origin of garlic (Allium sativum L.) at the scale of Slovenia was examined. Slovenia is a rather small country (20273 km2) with significant geological and biological diversity. Garlic, valued for its medicinal properties, was collected from Slovenian farms with certified organic production and analyzed by elemental analyzer isotope ratio mass spectrometry combined with energy dispersive X- ray fluorescence spectrometry. Multivariate discriminant analysis (DA) revealed a distinction between four Slovenian macro-regions: the Alpine, Dinaric, Mediterranean and Pannonian. The model was validated through a leave-10%, 20% and 25% out cross validation. The overall success rate of correctly reclassified samples was 77% (on average), indicating that the model and the proposed methodology could be a promising tool for rapid, inexpensive and robust screening to control the provenance of garlic samples.",Acta chimica Slovenica,"['D004602', 'D005737', 'D005843', 'D007554', 'D013058', 'D017524', 'D013052']","['Elements', 'Garlic', 'Geography', 'Isotopes', 'Mass Spectrometry', 'Slovenia', 'Spectrometry, X-Ray Emission']",Geographical Origin Characterization of Slovenian Organic Garlic Using Stable Isotope and Elemental Composition Analyses.,"[None, 'Q000737', None, 'Q000032', None, None, None]","[None, 'chemistry', None, 'analysis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/29318305,2018,,,,, -29215100,"Dietary salt is a vital ingredient associated with sensory performance in processed foods, while reduced salt intake linked to public health is highly desired by consumers and food manufacturers. In this paper, quillaja saponin (QS) based hollow salt particles (∼10 μm) were fabricated by simple spray drying, and utilized as solid carriers to enhance sensory aromas with reduced sodium intake. QS-coated nanodroplets were firstly prepared as a reservoir for flavor oils (lemon and garlic oil), and then served as frameworks to construct hollow salt particles via general spray drying. Headspace gas chromatography-mass spectrometry (DHS-GC-MS) and panel sensory analysis conclude that the hollow salt particles loaded with flavor oils enhance typical aroma attributes and saltiness perception in comparison with their mixture control. The QS-based hollow salt particles could be developed into novel vehicles for improving flavor performance with reduced sodium intake, and furthermore used for delivery of hydrophobic bioactives in food systems.",Food & function,"['D000293', 'D000328', 'D005260', 'D005421', 'D005503', 'D006801', 'D008297', 'D031990', 'D062605', 'D017673', 'D013649', 'D055815']","['Adolescent', 'Adult', 'Female', 'Flavoring Agents', 'Food Additives', 'Humans', 'Male', 'Quillaja', 'Quillaja Saponins', 'Sodium Chloride, Dietary', 'Taste', 'Young Adult']",Quillaja saponin-based hollow salt particles as solid carriers for enhancing sensory aroma with reduced sodium intake.,"[None, None, None, 'Q000737', 'Q000737', None, None, 'Q000737', 'Q000737', 'Q000032', None, None]","[None, None, None, 'chemistry', 'chemistry', None, None, 'chemistry', 'chemistry', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/29215100,2018,0.0,0.0,,, -29181754,"Green and nanoacaricides including essential oil (EO) nanoemulsions are important compounds to provide new, active, safe acaricides and lead to improvement of avoiding the risk of synthetic acaricides. This study was carried out for the first time on eriophyid mites to develop nanoemulsion of garlic essential oil by ultrasonic emulsification and evaluate its acaricidal activity against the two eriophyid olive mites Aceria oleae Nalepa and Tegolophus hassani (Keifer). Acute toxicity of nanoemulsion was also studied on male rats. Garlic EO was analyzed by gas chromatography-mass spectrometry (GC-MS), and the major compounds were diallyl sulfide (8.6%), diallyl disulfide (28.36%), dimethyl tetrasulfide (15.26%), trisulfide,di-2-propenyl (10.41%), and tetrasulfide,di-2-propenyl (9.67%). Garlic oil nanoemulsion with droplet size 93.4 nm was formulated by ultrasonic emulsification for 35 min. Emulsification time and oil and surfactant ratio correlated to the emulsion droplet size and stability. The formulated nanoemulsion showed high acaricidal activity against injurious eriophyid mites with LC",Environmental science and pollution research international,[],[],Formulation and characterization of garlic (Allium sativum L.) essential oil nanoemulsion and its acaricidal activity on eriophyid olive mites (Acari: Eriophyidae).,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29181754,2018,0.0,0.0,,, -29108412,Structures and formation pathways of compounds responsible for blue-green discoloration of processed garlic were studied in model systems. A procedure was developed for isolation of the color compounds and their tentative identification by high-performance liquid chromatography coupled to a diode array detector and tandem mass spectrometry. It was found that the pigment is a mixture of numerous pyrrole-based purple/blue and yellow species. Experiments with isotope-labeled precursors revealed that two molecules of an amino acid are involved in the formation of each color compound. In the purple/blue species (λ,Journal of agricultural and food chemistry,"['D000596', 'D002851', 'D003116', 'D005511', 'D005737', 'D015394', 'D010860', 'D053719']","['Amino Acids', 'Chromatography, High Pressure Liquid', 'Color', 'Food Handling', 'Garlic', 'Molecular Structure', 'Pigments, Biological', 'Tandem Mass Spectrometry']",Allium Discoloration: Color Compounds Formed during Greening of Processed Garlic.,"['Q000737', None, None, None, 'Q000737', None, 'Q000737', None]","['chemistry', None, None, None, 'chemistry', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/29108412,2018,0.0,0.0,,, -28965409,"Aroma extract dilution analysis of distillates prepared by solvent extraction and solvent-assisted flavor evaporation distillation from white Alba truffle (WAT; Tuber magnatum pico) and Burgundy truffle (BT; Tuber uncinatum) revealed 20 odor-active regions in the flavor dilution (FD) factor range of 16-4096 in WAT and 25 in BT. The identification experiments in combination with the FD factors showed clear differences in the overall set of key odorants of both fungi. While 3-(methylthio)propanal (potato-like) followed by 2- and 3-methylbutanal (malty), 2,3-butanedione (buttery), and bis(methylthio)methane (garlic-like) showed the highest FD factors in WAT, 2,3-butanedione, phenylacetic acid (honey-like), and vanillin (vanilla-like) had the highest FD factors in BT. Odor activity values (OAVs, ratio of concentration to odor thresholds), which were calculated on the basis of quantitative data obtained by stable isotope dilution assays, of >1000 for bis(methylthio)methane, 3-methylbutanal, and 3,4-dihydro-2-(H)pyrrol (1-pyrroline) revealed they are key contributors to the aroma of WAT. In BT, 1-pyrroline and 2,3-butanedione showed the highest OAVs of 1530 and 1130, respectively. Aroma recombination experiments successfully mimicked the overall aroma profiles of both fungi when all odorants showing OAVs of >1 were combined. Omission experiments confirmed the amine-like and sperm-like smell of 1-pyrroline, identified for the first time as a key odorant in both truffle species.",Journal of agricultural and food chemistry,"['D001203', 'D005421', 'D008401', 'D006801', 'D009812', 'D012903', 'D055549']","['Ascomycota', 'Flavoring Agents', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Odorants', 'Smell', 'Volatile Organic Compounds']",Characterization of the Key Aroma Compounds in White Alba Truffle (Tuber magnatum pico) and Burgundy Truffle (Tuber uncinatum) by Means of the Sensomics Approach.,"['Q000737', 'Q000737', None, None, 'Q000032', None, 'Q000737']","['chemistry', 'chemistry', None, None, 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28965409,2017,0.0,0.0,,, -28932845,"Garlic (A. sativum) contains a large number of small sulphur (S)-containing metabolites, which are important for its taste and smell and vary with A. sativum variety and growth conditions. This study was designed to investigate the influence of different sulphur-fertilization regimes on low molecular weight S-species by attempting the first sulphur mass balance in A. sativum roots and bulbs using HPLC-ICPMS/MS-ESI-MS/MS. Species unspecific quantification of acid soluble S-containing metabolites was achieved using HPLC-ICP-MS/MS. For identification of the compounds, high resolution ESI-MS (Orbitrap LTQ and q-TOF) was used. The plants contained up to 54 separated sulphur-containing compounds, which constitute about 80% of the total sulphur present in A. sativum. The roots and bulbs of A. sativum contained the same compounds, but not necessarily the same amounts and proportions. The S-containing metabolites in the roots reacted more sensitively to manipulations of sulphur fertilization than those compounds in the bulbs. In addition to known compounds (e.g. γ-glutamyl-S-1-propenylcysteine) we were able to identify and partially quantify 31 compounds. Three as yet undescribed S-containing compounds were also identified and quantified for the first time. Putative structures were assigned to the oxidised forms of S-1-propenylmercaptoglutathione, S-2-propenylmercaptoglutathione, S-allyl/propenyl-containing PC-2 and 2-amino-3-[(2-carboxypropyl)sulfanyl]propanoic acid. The parallel use of ICP-MS/MS as a sulphur-specific detector and ESI-MS as a molecular detector simplifies the identification and quantification of sulphur containing metabolites without species specific standards. This non-target analysis approach enables a mass balance approach and identifies the occurrence of the so far unidentified organosulphur compounds. The experiments showed that the sulphur-fertilization regime does not influence sulphur-speciation, but the concentration of some S-containing compounds in roots is dependent on sulphur fertilization.",Metallomics : integrated biometal science,"['D002851', 'D005737', 'D021241', 'D013457', 'D053719']","['Chromatography, High Pressure Liquid', 'Garlic', 'Spectrometry, Mass, Electrospray Ionization', 'Sulfur Compounds', 'Tandem Mass Spectrometry']",Sulphur fertilization influences the sulphur species composition in Allium sativum: sulphomics using HPLC-ICPMS/MS-ESI-MS/MS.,"['Q000379', 'Q000737', 'Q000379', 'Q000032', 'Q000379']","['methods', 'chemistry', 'methods', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/28932845,2018,0.0,0.0,,, -28911676,"Black garlic produced from fresh garlic under controlled high temperature and humidity has strong antioxidant properties. To determine these compounds, five fractions (from F1 to F5) were separated and purified by elution with chloroform:methanol at different ratios (8:1, 6:1, 4:1, 2:1, and 0:1; v/v). The antioxidant activity of each fraction was analyzed. The results showed that F3 and F4 had higher phenolic contents and stronger 2,2-diphenyl-2-picrylhydrazyl radical scavenging activity than the others. Seven purified individual components were further separated using semipreparation high-performance liquid chromatography from these two intensely antioxidant fractions (F3 and F4), their structures were elucidated by high-performance liquid chromatography coupled to diode array detection, electrospray ionization, mass spectrometry, ",Journal of food and drug analysis,"['D000975', 'D001713', 'D002243', 'D002851', 'D005419', 'D005737', 'D009682', 'D010936', 'D011758']","['Antioxidants', 'Biphenyl Compounds', 'Carbolines', 'Chromatography, High Pressure Liquid', 'Flavonoids', 'Garlic', 'Magnetic Resonance Spectroscopy', 'Plant Extracts', 'Pyrroles']",Composition analysis and antioxidant properties of black garlic extract.,"[None, None, None, None, None, None, None, None, None]","[None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28911676,2017,0.0,0.0,,, -28825644,"This study set out to determine the distribution of sulfur compounds and saponin metabolites in different parts of garlic cloves. Three fractions from purple and white garlic ecotypes were obtained: the tunic (SS), internal (IS) and external (ES) parts of the clove. Liquid Chromatography coupled to High Resolution Mass spectrometry (LC-HRMS), together with bioinformatics including Principal Component Analysis (PCA), Hierarchical Clustering (HCL) and correlation network analyses were carried out. Results showed that the distribution of these metabolites in the different parts of garlic bulbs was different for the purple and the white ecotypes, with the main difference being a slightly higher number of sulfur compounds in purple garlic. The SS fraction in purple garlic had a higher content of sulfur metabolites, while the ES in white garlic was more enriched by these compounds. The correlation network indicated that diallyl disulfide was the most relevant metabolite with regards to sulfur compound metabolism in garlic. The total number of saponins was almost 40-fold higher in purple garlic than in the white variety, with ES having the highest content. Interestingly, five saponins including desgalactotigonin-rhamnose, proto-desgalactotigonin, proto-desgalactotigonin-rhamnose, voghieroside D1, sativoside B1-rhamnose and sativoside R1 were exclusive to the purple variety. Data obtained from saponin analyses revealed a very different network between white and purple garlic, thus suggesting a very robust and tight coregulation of saponin metabolism in garlic. Findings in this study point to the possibility of using tunics from purple garlic in the food and medical industries, since it contains many functional compounds which can be exploited as ingredients.","Molecules (Basel, Switzerland)","['D002851', 'D016000', 'D019295', 'D060146', 'D005737', 'D013058', 'D009928', 'D012503', 'D013457']","['Chromatography, High Pressure Liquid', 'Cluster Analysis', 'Computational Biology', 'Ecotype', 'Garlic', 'Mass Spectrometry', 'Organ Specificity', 'Saponins', 'Sulfur Compounds']",Tissue-Specific Accumulation of Sulfur Compounds and Saponins in Different Parts of Garlic Cloves from Purple and White Ecotypes.,"[None, None, 'Q000379', None, 'Q000737', None, None, 'Q000737', 'Q000737']","[None, None, 'methods', None, 'chemistry', None, None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28825644,2018,0.0,0.0,1.0,, -28719747,"An epidemic fungal disease caused by Fusarium proliferatum, responsible for fumonisin production (FB1, FB2, and FB3), has been reported in the main garlic-producing countries in recent years. Fumonisins are a group of structurally related toxic metabolites produced by this pathogen. The aim of this work was to establish an enzyme-linked immunosorbent assay (ELISA) procedure, mostly applied to cereals, that is suitable for fumonisin detection in garlic and compare these results to those obtained by high-performance liquid chromatography (HPLC) and screening of fresh and dehydrated garlic for toxicological risk. The results show good correlation between the two analytical methods. In fresh symptomatic garlic, fumonisin levels were higher in the basal plates than those in the portions with necrotic spots. Among the 56 commercially dehydrated garlic samples screened, three were positive by ELISA test and only one was above the limit of quantitation. The same samples analyzed by HPLC showed the presence of FB1 in trace amounts that was below the limit of quantitation; FB2 and FB3 were absent. The results are reassuring, because no substantial contamination by fumonisins was found in commercial garlic.",Journal of agricultural and food chemistry,"['D005506', 'D005511', 'D037341', 'D005670', 'D005737', 'D009183', 'D010935']","['Food Contamination', 'Food Handling', 'Fumonisins', 'Fusarium', 'Garlic', 'Mycotoxins', 'Plant Diseases']",Detection of Fumonisins in Fresh and Dehydrated Commercial Garlic.,"['Q000032', None, 'Q000032', 'Q000378', 'Q000737', 'Q000032', 'Q000382']","['analysis', None, 'analysis', 'metabolism', 'chemistry', 'analysis', 'microbiology']",https://www.ncbi.nlm.nih.gov/pubmed/28719747,2017,0.0,0.0,,, -28705396,"Fusarium proliferatum is a polyphagous pathogenic fungus able to infect many crop plants worldwide. Differences in proteins accumulated were observed when maize- and asparagus-derived F. proliferatum strains were exposed to host extracts prepared from asparagus, maize, garlic, and pineapple tissues. Seventy-three unique proteins were up-regulated in extract-supplemented cultures compared to the controls. They were all identified using mass spectrometry and their putative functions were assigned. A major part of identified proteins was involved in sugar metabolism and basic metabolic processes. Increased accumulation of proteins typically associated with stress response (heat shock proteins, superoxide dismutases, and glutaredoxins) as well as others, putatively involved in signal transduction, suggests that some metabolites present in plant extracts may act as elicitors inducing similar reaction as the abiotic stress factors. As a case study, thirteen genes encoding the proteins induced by the extracts were identified in the genomes of diverse F. proliferatum strains using gene-specific DNA markers. Extract-induced changes in the pathogen's metabolism are putatively a result of differential gene expression regulation. Our findings suggest that host plant metabolites present in the extracts can cause biotic stress resulting in elevated accumulation of diverse set of proteins, including those associated with pathogen's stress response.",Fungal biology,"['D000222', 'D005656', 'D005670', 'D015966', 'D013058', 'D058977', 'D010936', 'D020543', 'D013312']","['Adaptation, Physiological', 'Fungal Proteins', 'Fusarium', 'Gene Expression Regulation, Fungal', 'Mass Spectrometry', 'Molecular Sequence Annotation', 'Plant Extracts', 'Proteome', 'Stress, Physiological']",Host extracts induce changes in the proteome of plant pathogen Fusarium proliferatum.,"[None, 'Q000032', 'Q000737', 'Q000187', None, None, 'Q000378', 'Q000032', None]","[None, 'analysis', 'chemistry', 'drug effects', None, None, 'metabolism', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/28705396,2018,0.0,0.0,,, -28683396,The black garlic juice is popular for its nutritive value. Enrichment of antioxidants is needed to make black garlic extract an effective functional ingredient. Five macroporous resins were evaluated for their capacity in adsorbing antioxidants in black garlic juice. XAD-16 resin was chosen for further study due to its high adsorption and desorption ratios. Pseudo-second-order kinetics (q,"Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000327', 'D000975', 'D002852', 'D004912', 'D005737', 'D006461', 'D006801', 'D007475', 'D010936']","['Adsorption', 'Antioxidants', 'Chromatography, Ion Exchange', 'Erythrocytes', 'Garlic', 'Hemolysis', 'Humans', 'Ion Exchange Resins', 'Plant Extracts']",Enrichment of antioxidants in black garlic juice using macroporous resins and their protective effects on oxidation-damaged human erythrocytes.,"[None, 'Q000032', 'Q000295', 'Q000187', 'Q000737', 'Q000187', None, 'Q000737', 'Q000032']","[None, 'analysis', 'instrumentation', 'drug effects', 'chemistry', 'drug effects', None, 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28683396,2017,1.0,1.0,,, -28677661,Right-sided heart failure-often caused by elevated pulmonary arterial pressure-is a chronic and progressive condition with particularly high mortality rates. Recent studies and our current findings suggest that components of Wild garlic (,International journal of molecular sciences,"['D000490', 'D000818', 'D015415', 'D004195', 'D004452', 'D006334', 'D006976', 'D008168', 'D008297', 'D013058', 'D009206', 'D010936', 'D011651', 'D051381', 'D000068677']","['Allium', 'Animals', 'Biomarkers', 'Disease Models, Animal', 'Echocardiography', 'Heart Function Tests', 'Hypertension, Pulmonary', 'Lung', 'Male', 'Mass Spectrometry', 'Myocardium', 'Plant Extracts', 'Pulmonary Artery', 'Rats', 'Sildenafil Citrate']",A Novel Therapeutic Approach in the Treatment of Pulmonary Arterial Hypertension: Allium ursinum Liophylisate Alleviates Symptoms Comparably to Sildenafil.,"['Q000737', None, None, None, None, None, 'Q000175', 'Q000378', None, None, 'Q000378', 'Q000737', 'Q000187', None, 'Q000494']","['chemistry', None, None, None, None, None, 'diagnosis', 'metabolism', None, None, 'metabolism', 'chemistry', 'drug effects', None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/28677661,2018,0.0,0.0,,, -28629219,"Glucosinolates are the most abundant secondary sulfur-containing plant metabolites in the plant family of Brassicaceae. These phytochemicals are well-known for their enzymatic degradation induced by the enzyme myrosinase, resulting in pungent odor impressions derived from their respective degradation products. However, up to now, only little attention has been paid to non-enzymatic thermal degradation and the release of additional aroma-active compounds. Thermal treatment is particularly important in the processing of Brassica vegetables, and thereby, glucosinolates as precursors can act as a natural source of odorants. Application of gas chromatography-olfactometry to the volatile fractions obtained after heat treatment of sinigrin (2-propenyl glucosinolate) in different matrices (phosphate buffer at a pH value of 5, 7, or 9, silicon oil, silica gel (7% water), sea sand, and glycerol) showed a high potential to generate aroma-active compounds, mainly revealing onion- and garlic-like odor impressions deriving from sulfur-containing odorants. A clear dependency of the formation of desired aroma-active compounds upon the respective matrix was found, indicating the need of detailed investigations to obtain knowledge for the best use of glucosinolates as a source of natural aroma compositions. For example, the distillate obtained from sinigrin heat-processed in buffer solution at pH 7 led to the identification of 17 odorants.",Journal of agricultural and food chemistry,"['D001937', 'D002849', 'D003296', 'D005421', 'D005961', 'D006358', 'D009812', 'D064367', 'D014675', 'D055549']","['Brassica', 'Chromatography, Gas', 'Cooking', 'Flavoring Agents', 'Glucosinolates', 'Hot Temperature', 'Odorants', 'Olfactometry', 'Vegetables', 'Volatile Organic Compounds']",Thermally Induced Generation of Desirable Aroma-Active Compounds from the Glucosinolate Sinigrin.,"['Q000737', None, None, 'Q000737', 'Q000737', None, 'Q000032', None, 'Q000737', 'Q000737']","['chemistry', None, None, 'chemistry', 'chemistry', None, 'analysis', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28629219,2018,0.0,0.0,,, -28612465,"Garlic (Allium sativum) is the subject of many studies due to its numerous beneficial properties. Although compounds of garlic have been studied by various analytical methods, their tissue distributions are still unclear. Mass spectrometry imaging (MSI) appears to be a very powerful tool for the identification of the localisation of compounds within a garlic clove.",Phytochemical analysis : PCA,"['D005737', 'D006046', 'D013058', 'D053768']","['Garlic', 'Gold', 'Mass Spectrometry', 'Metal Nanoparticles']",Mass Spectrometry Imaging of low Molecular Weight Compounds in Garlic (Allium sativum L.) with Gold Nanoparticle Enhanced Target.,"['Q000737', 'Q000737', 'Q000379', 'Q000737']","['chemistry', 'chemistry', 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28612465,2018,0.0,0.0,,, -28560773,"Black garlic is increasing its popularity in cuisine around the world; however, scant information exists on the composition of this processed product. In this study, polar compounds in fresh garlic and in samples taken at different times during the heat treatment process to obtain black garlic have been characterized by liquid chromatography coupled to tandem mass spectrometry in high resolution mode. Ninety-five compounds (mainly amino acids and metabolites, organosulfur compounds, and saccharides and derivatives) were tentatively identified in all the analysed samples and classified as a function of the family they belong to. Statistical analysis of the results allowed establishing that the major changes in garlic occur during the first days of treatment, and they mainly affect to the three representative families. The main pathways involved in the synthesis of the compounds affected by heat treatment, and their evolution during the process were studied.",Electrophoresis,"['D000596', 'D002241', 'D002853', 'D016002', 'D005285', 'D005737', 'D006358', 'D010936', 'D053719']","['Amino Acids', 'Carbohydrates', 'Chromatography, Liquid', 'Discriminant Analysis', 'Fermentation', 'Garlic', 'Hot Temperature', 'Plant Extracts', 'Tandem Mass Spectrometry']",Untargeted analysis to monitor metabolic changes of garlic along heat treatment by LC-QTOF MS/MS.,"['Q000032', 'Q000032', 'Q000379', None, None, 'Q000737', None, 'Q000032', 'Q000379']","['analysis', 'analysis', 'methods', None, None, 'chemistry', None, 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/28560773,2017,,,,,True -28481316,Plants of the ,"Molecules (Basel, Switzerland)","['D000890', 'D005419', 'D005737', 'D013058', 'D055432', 'D019697', 'D013457']","['Anti-Infective Agents', 'Flavonoids', 'Garlic', 'Mass Spectrometry', 'Metabolomics', 'Onions', 'Sulfur Compounds']",Phytochemical Profiles and Antimicrobial Activities of Allium cepa Red cv. and A. sativum Subjected to Different Drying Methods: A Comparative MS-Based Metabolomics.,"['Q000032', 'Q000032', 'Q000737', 'Q000379', 'Q000379', 'Q000737', 'Q000032']","['analysis', 'analysis', 'chemistry', 'methods', 'methods', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28481316,2018,0.0,0.0,,, -28475377,"We evaluated organosulphur compounds in Allium vegetables, including garlic, elephant garlic and onion, using high-performance liquid chromatography. Among organosulphur compounds, elephant garlic had considerable γ-glutamyl peptides, and garlic had the highest alliin content. Onion had low level of organosulphur compounds than did elephant garlic and garlic. In addition, antioxidant capacities were evaluated by oxygen radical absorbance capacity (ORAC) values and 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azinobis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) radical scavenging assay. The results showed that garlic had the highest antioxidant capacity, followed by elephant garlic and onion. Furthermore, a positive correlation was observed between antioxidant activities and organosulphur compounds (R > 0.77). Therefore, our results indicate that there was a close relationship between antioxidant capacity and organosulphur compounds in Allium vegetables.",Natural product research,"['D000490', 'D000975', 'D005737', 'D019697', 'D013045', 'D013457']","['Allium', 'Antioxidants', 'Garlic', 'Onions', 'Species Specificity', 'Sulfur Compounds']","Comparative studies of bioactive organosulphur compounds and antioxidant activities in garlic (Allium sativum L.), elephant garlic (Allium ampeloprasum L.) and onion (Allium cepa L.).","['Q000737', 'Q000032', 'Q000737', 'Q000737', None, 'Q000032']","['chemistry', 'analysis', 'chemistry', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28475377,2018,,,,,True -28459863,"The present investigation was conducted to study the true retentions of α-tocopherol, tocotrienols and β-carotene in crown daisy, unripe hot pepper, onion, garlic, and red pepper as affected by various domestic cooking methods, those were, boiling, baking, stir-frying, deep-frying, steaming, roasting, and microwaving. Fatty acid compositions were determined by GC, and HPLC were used for quantification of α-tocopherol, tocotrienols, and β-carotene. True retentions of α-tocopherol in cooked foods were as follows: boiling (77.74-242.73%), baking (85.99-212.39%), stir-frying (83.12-957.08%), deep-frying (162.48-4214.53%), steaming (45.97-179.57%), roasting (49.65-253.69%), and microwaving (44.67-230.13%). Similarly for true retention of β-carotene were: boiling (65.69-313.75%), baking (71.46-330.16%), stir-frying (89.62-362.46%), deep-frying (178.22-529.16%), steaming (50.39-240.92%), roasting (73.54-361.47%), and microwaving (78.60-339.87%).",PloS one,"['D002849', 'D002851', 'D003296', 'D005227', 'D006358', 'D008872', 'D017365', 'D024508', 'D014867', 'D024502', 'D019207']","['Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Cooking', 'Fatty Acids', 'Hot Temperature', 'Microwaves', 'Spices', 'Tocotrienols', 'Water', 'alpha-Tocopherol', 'beta Carotene']",Effect of processing on composition changes of selected spices.,"[None, None, 'Q000379', 'Q000737', None, None, 'Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000737']","[None, None, 'methods', 'chemistry', None, None, 'analysis', 'chemistry', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28459863,2017,1.0,4.0,,, -28407889,"In this work, we synthesized internal standards for four garlic organosulfur compounds (OSCs) by reductive amination with ",Food chemistry,"['D000586', 'D001894', 'D002247', 'D002851', 'D003545', 'D005557', 'D005737', 'D012015', 'D053719']","['Amination', 'Borohydrides', 'Carbon Isotopes', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Formaldehyde', 'Garlic', 'Reference Standards', 'Tandem Mass Spectrometry']",Reductive amination derivatization for the quantification of garlic components by isotope dilution analysis.,"[None, 'Q000737', None, None, 'Q000031', 'Q000737', 'Q000737', None, None]","[None, 'chemistry', None, None, 'analogs & derivatives', 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28407889,2017,0.0,0.0,,, -28373144,"In this work, the efficiency of crude MeOH extracts and soluble glycoprotein fraction of Allium sativum purified by size-exclusion chromatography (SEC) on parasitological, histopathological and some biochemical parameters in Schistosoma mansoni infected mice were investigated. Animals were infected by tail immersion with 100 cercariae/each mouse and divided into five groups in addition to the normal control. The results revealed a significant decrease in mean worm burden in all treated mice especially in the group treated with soluble glycoprotein fraction of A. sativum as compared to infected non-treated control with the disappearance of female worms. Administration of the studied extracts revealed remarkable amelioration in the levels of all the measured parameters in S. mansoni infected mice. In addition, treatment of mice with crude A. sativum MeOH extract and soluble glycoprotein fraction of A. sativum decreased significantly the activities of studied enzymes as compared to the infected untreated group. The highest degrees of enhancement in pathological changes was observed in the treated one with soluble glycoprotein fraction of A. sativum compared to the infected group represented by small sized, late fibro-cellular granuloma, the decrease in cellular constituents and degenerative changes in eggs. In conclusion, A. sativum treatment had effective schistosomicidal activities, through reduction of worm burden and tissue eggs, especially when it was given in purified glycoprotein fraction. Moreover, the soluble glycoprotein fraction of A. sativum largely modulates both the size and the number of granulomas.",Microbial pathogenesis,"['D000469', 'D000818', 'D002850', 'D004195', 'D005260', 'D005737', 'D006023', 'D006099', 'D008099', 'D008297', 'D051379', 'D010270', 'D010936', 'D012550', 'D012555', 'D012556', 'D044967', 'D000637', 'D005723']","['Alkaline Phosphatase', 'Animals', 'Chromatography, Gel', 'Disease Models, Animal', 'Female', 'Garlic', 'Glycoproteins', 'Granuloma', 'Liver', 'Male', 'Mice', 'Parasite Egg Count', 'Plant Extracts', 'Schistosoma mansoni', 'Schistosomiasis mansoni', 'Schistosomicides', 'Serum', 'Transaminases', 'gamma-Glutamyltransferase']",Efficacy of soluble glycoprotein fraction from Allium sativum purified by size exclusion chromatography on murine Schistosomiasis mansoni.,"['Q000097', None, 'Q000379', None, None, 'Q000737', 'Q000737', 'Q000469', 'Q000469', None, None, None, 'Q000494', 'Q000187', 'Q000097', 'Q000494', 'Q000737', 'Q000097', 'Q000097']","['blood', None, 'methods', None, None, 'chemistry', 'chemistry', 'parasitology', 'parasitology', None, None, None, 'pharmacology', 'drug effects', 'blood', 'pharmacology', 'chemistry', 'blood', 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/28373144,2017,0.0,0.0,,, -28249719,"Different ionic liquids (ILs) were assayed as mobile phase modifiers for the separation and determination of selenite [Se(IV)], selenate [Se(VI)], selenomethionine (SeMet) and Se-methylselenocysteine (SeMeSeCys) by reversed-phase high-performance liquid chromatography coupled to hydride generation atomic fluorescence spectrometry (RP-HPLC-HG-AFS). The use of several ILs: 1-butyl-3-methylimidazolium chloride, 1-hexyl-3-methylimidazolium chloride ([C",Journal of chromatography. A,"['D001628', 'D056148', 'D005504', 'D052578', 'D016566', 'D012643', 'D013050']","['Beverages', 'Chromatography, Reverse-Phase', 'Food Analysis', 'Ionic Liquids', 'Organoselenium Compounds', 'Selenium', 'Spectrometry, Fluorescence']",Ionic liquid-assisted separation and determination of selenium species in food and beverage samples by liquid chromatography coupled to hydride generation atomic fluorescence spectrometry.,"['Q000032', 'Q000379', 'Q000379', 'Q000737', 'Q000032', 'Q000032', 'Q000379']","['analysis', 'methods', 'methods', 'chemistry', 'analysis', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/28249719,2017,1.0,3.0,,, -28231115,"We performed a statistical analysis of the concentration of mineral elements, by means of inductively coupled plasma mass spectrometry (ICP-MS), in different varieties of garlic from Spain, Tunisia, and Italy. Nubia Red Garlic (Sicily) is one of the most known Italian varieties that belongs to traditional Italian food products (P.A.T.) of the Ministry of Agriculture, Food, and Forestry. The obtained results suggest that the concentrations of the considered elements may serve as geographical indicators for the discrimination of the origin of the different samples. In particular, we found a relatively high content of Selenium in the garlic variety known as Nubia red garlic, and, indeed, it could be used as an anticarcinogenic agent.","Foods (Basel, Switzerland)",[],[],"Statistical Analysis of Mineral Concentration for the Geographic Identification of Garlic Samples from Sicily (Italy), Tunisia and Spain.",[],[],https://www.ncbi.nlm.nih.gov/pubmed/28231115,2018,1.0,3.0,,, -28183044,"Aged garlic extract (AGE) has been shown to improve hypertension in both clinical trials and experimental animal models. However, the active ingredient of AGE remains unknown. In the present study, we investigated the antihypertensive effects of AGE and its major constituents including S-1-propenylcysteine (S1PC) and S-allylcysteine (SAC) using spontaneously hypertensive rats (SHR) and found that S1PC is an active substance to lower blood pressure in SHR. In addition, the metabolomics approach was used to investigate the potential mechanism of the antihypertensive action of S1PC in SHR. Treatment with AGE (2g/kg body weight) or S1PC (6.5mg/kg body weight; equivalent to AGE 2g/kg body weight) significantly decreased the systolic blood pressure (SBP) of SHR after the repeated administration for 10 weeks, whereas treatment with SAC (7.9mg/kg body weight; equivalent to AGE 2g/kg body weight) did not decrease the SBP. After the treatment for 10 weeks, the plasma samples obtained from Wistar Kyoto (WKY) rats and SHR were analyzed by means of ultra high performance liquid chromatography coupled with high-resolution quadrupole-Orbitrap mass spectrometry. Multivariate statistical analysis of LC-MS data showed a clear difference in the metabolite profiles between WKY rats and SHR. The results indicated that 30 endogenous metabolites significantly contributed to the difference and 7 of 30 metabolites were changed by the S1PC treatment. Furthermore, regression analysis showed correlation between SBP and the plasma levels of betaine, tryptophan and 3 LysoPCs. This metabolomics approach suggested that S1PC could exert its antihypertensive effect by affecting glycine, serine and threonine metabolism, tryptophan metabolism and glycerophospholipid metabolism.","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000596', 'D000818', 'D000959', 'D002853', 'D003545', 'D005227', 'D020404', 'D016014', 'D008297', 'D013058', 'D055442', 'D055432', 'D051381', 'D011918', 'D015203']","['Amino Acids', 'Animals', 'Antihypertensive Agents', 'Chromatography, Liquid', 'Cysteine', 'Fatty Acids', 'Glycerophospholipids', 'Linear Models', 'Male', 'Mass Spectrometry', 'Metabolome', 'Metabolomics', 'Rats', 'Rats, Inbred SHR', 'Reproducibility of Results']",Metabolomic study on the antihypertensive effect of S-1-propenylcysteine in spontaneously hypertensive rats using liquid chromatography coupled with quadrupole-Orbitrap mass spectrometry.,"['Q000097', None, 'Q000494', 'Q000379', 'Q000031', 'Q000097', 'Q000097', None, None, 'Q000379', 'Q000187', None, None, None, None]","['blood', None, 'pharmacology', 'methods', 'analogs & derivatives', 'blood', 'blood', None, None, 'methods', 'drug effects', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28183044,2017,0.0,0.0,,, -28177695,"Diallyl trisulfide (DATS), a major garlic derivative, inhibits cell proliferation and triggers apoptosis in a variety of cancer cell lines. However, the effects of DATS on hepatic stellate cells (HSCs) remain unknown. The aim of this study was to analyze the effects of DATS on cell proliferation and apoptosis, as well as the protein expression profile in rat HSCs. Rat HSCs were treated with or without 12 and 24 μg/mL DATS for various time intervals. Cell proliferation and apoptosis were determined using tetrazolium dye (MTT) colorimetric assay, bromodeoxyuridine (5-bromo-2'-deoxyuridine; BrdU) assay, Hoechst 33342 staining, electroscopy, and flow cytometry. Protein expression patterns in HSCs were systematically studied using 2-dimensional electrophoresis and mass spectrometry. DATS inhibited cell proliferation and induced apoptosis of HSCs in a time-dependent manner. We observed clear morphological changes in apoptotic HSCs and dramatically increased annexin V-positive - propidium iodide negative apoptosis compared with the untreated control group. Twenty-one significant differentially expressed proteins, including 9 downregulated proteins and 12 upregulated proteins, were identified after DATS administration, and most of them were involved in apoptosis. Our results suggest that DATS is an inducer of apoptosis in HSCs, and several key proteins may be involved in the molecular mechanism of apoptosis induced by DATS.",Canadian journal of physiology and pharmacology,"['D000498', 'D000818', 'D017209', 'D002453', 'D049109', 'D002470', 'D005737', 'D005786', 'D055166', 'D040901', 'D051381', 'D013440']","['Allyl Compounds', 'Animals', 'Apoptosis', 'Cell Cycle', 'Cell Proliferation', 'Cell Survival', 'Garlic', 'Gene Expression Regulation', 'Hepatic Stellate Cells', 'Proteomics', 'Rats', 'Sulfides']",Apoptosis of rat hepatic stellate cells induced by diallyl trisulfide and proteomics profiling in vitro.,"['Q000494', None, 'Q000187', 'Q000187', 'Q000187', 'Q000187', 'Q000737', 'Q000187', 'Q000166', None, None, 'Q000494']","['pharmacology', None, 'drug effects', 'drug effects', 'drug effects', 'drug effects', 'chemistry', 'drug effects', 'cytology', None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/28177695,2017,0.0,0.0,,, -28101575,"Diallyl disulfide (DADS) is a primary component of garlic, which has chemopreventive potential. We previously found that moderate doses (15-120 µM) of DADS induced apoptosis and G2/M phase cell cycle arrest. In this study, we observed the effect of low doses (8 µM) of DADS on human leukemia HL-60 cells. We found that DADS could inhibit proliferation, migration and invasion in HL-60 cells, and arrested cells at G0/G1 stage. Then, cell differentiation was displayed by morphologic observation, NBT reduction activity and CD11b evaluation of cytometric flow. It showed that DADS induced differentiation, reduced the ability of NBT and increased CD11b expression. Likewise, DADS inhibited xenograft tumor growth and induced differentiation in vivo. In order to make sure how DADS induced differentiation, we compared the protein expression profile of DADS-treated cells with that of untreated control. Using high resolution mass spectrometry, we identified 18 differentially expressed proteins after treatment with DADS, including four upregulated and 14 downregulated proteins. RT-PCR and western blot assay showed that DJ-1, cofilin 1, RhoGDP dissociation inhibitor 2 (RhoGDI2), Calreticulin (CTR) and PCNA were decreased by DADS. These data suggest that the effects of DADS on leukemia may be due to multiple targets for intervention.",International journal of oncology,"['D000498', 'D000818', 'D039481', 'D059447', 'D002454', 'D002465', 'D049109', 'D004220', 'D004305', 'D015972', 'D018922', 'D006801', 'D007938', 'D051379', 'D020543', 'D023041']","['Allyl Compounds', 'Animals', 'CD11b Antigen', 'Cell Cycle Checkpoints', 'Cell Differentiation', 'Cell Movement', 'Cell Proliferation', 'Disulfides', 'Dose-Response Relationship, Drug', 'Gene Expression Regulation, Neoplastic', 'HL-60 Cells', 'Humans', 'Leukemia', 'Mice', 'Proteome', 'Xenograft Model Antitumor Assays']",Identification of potential targets for differentiation in human leukemia cells induced by diallyl disulfide.,"['Q000008', None, 'Q000235', 'Q000187', 'Q000187', 'Q000187', 'Q000187', 'Q000008', None, 'Q000187', None, None, 'Q000188', None, 'Q000187', None]","['administration & dosage', None, 'genetics', 'drug effects', 'drug effects', 'drug effects', 'drug effects', 'administration & dosage', None, 'drug effects', None, None, 'drug therapy', None, 'drug effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/28101575,2017,0.0,0.0,,, -27979186,"The chemical composition of garlic essential oils (GEOs) extracted from two different cultivars has been characterized using GC-MS analysis. GEO that was extracted from the white-skin cultivar (WGO) had a lower percentage of the major constituents diallyl trisulfide and diallyl disulfide (45.76 and 15.63%) than purple-skin cultivar (PGO) which contained higher percentages (58.53 and 22.38%) of the same components, respectively. Evaluation of the antimicrobial activity of WGO and PGO delivered in organic solvent (isopropanol) showed dose-dependent antimicrobial activity against the tested pathogenic bacteria and fungi, especially with WGO. On the other hand, formulation of both GEOs in water-based emulsions totally suppressed the antimicrobial activity of GEO. Re-formulation of GEOs in water-based microemulsion (particle size 10.1nm) showed better antimicrobial activity than emulsions at the same concentration of GEOs. This study can assist in designing the proper water-based delivery system of GEO for application in food preservation.",Food chemistry,"['D000498', 'D000890', 'D001231', 'D001234', 'D004220', 'D004305', 'D016503', 'D004655', 'D004926', 'D005519', 'D005737', 'D008401', 'D008089', 'D009822', 'D010316', 'D010938', 'D012486', 'D012997', 'D013211', 'D013440', 'D014867']","['Allyl Compounds', 'Anti-Infective Agents', 'Aspergillus flavus', 'Aspergillus niger', 'Disulfides', 'Dose-Response Relationship, Drug', 'Drug Delivery Systems', 'Emulsions', 'Escherichia coli', 'Food Preservation', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Listeria monocytogenes', 'Oils, Volatile', 'Particle Size', 'Plant Oils', 'Salmonella typhimurium', 'Solvents', 'Staphylococcus aureus', 'Sulfides', 'Water']","Chemical composition and antimicrobial activity of garlic essential oils evaluated in organic solvent, emulsifying, and self-microemulsifying water based delivery systems.","['Q000032', 'Q000737', 'Q000187', 'Q000187', 'Q000032', None, None, None, 'Q000187', None, 'Q000737', None, 'Q000187', 'Q000737', None, 'Q000737', 'Q000187', None, 'Q000187', 'Q000032', None]","['analysis', 'chemistry', 'drug effects', 'drug effects', 'analysis', None, None, None, 'drug effects', None, 'chemistry', None, 'drug effects', 'chemistry', None, 'chemistry', 'drug effects', None, 'drug effects', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/27979186,2017,0.0,0.0,,, -27979174,"Garlic is rich in polysulfides, and some of them can be H",Food chemistry,"['D000498', 'D002851', 'D002853', 'D003296', 'D005737', 'D008401', 'D006862', 'D018517', 'D013440', 'D013441']","['Allyl Compounds', 'Chromatography, High Pressure Liquid', 'Chromatography, Liquid', 'Cooking', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Hydrogen Sulfide', 'Plant Roots', 'Sulfides', 'Sulfinic Acids']",Boiling enriches the linear polysulfides and the hydrogen sulfide-releasing activity of garlic.,"['Q000737', None, None, None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000032']","['chemistry', None, None, None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27979174,2017,0.0,0.0,,, -27916960,"The metabolism and excretion of flavor constituents of garlic, a common plant used in flavoring foods and attributed with several health benefits, in humans is not fully understood. Likewise, the physiologically active principles of garlic have not been fully clarified to date. It is possible that not only the parent compounds present in garlic but also its metabolites are responsible for the specific physiological properties of garlic, including its influence on the characteristic body odor signature of humans after garlic consumption. Accordingly, the aim of this study was to investigate potential garlic-derived metabolites in human urine. To this aim, 14 sets of urine samples were obtained from 12 volunteers, whereby each set comprised one sample that was collected prior to consumption of food-relevant concentrations of garlic, followed by five to eight subsequent samples after garlic consumption that covered a time interval of up to 26 h. The samples were analyzed chemo-analytically using gas chromatography-mass spectrometry/olfactometry (GC-MS/O), as well as sensorially by a trained human panel. The analyses revealed three different garlic-derived metabolites in urine, namely allyl methyl sulfide (AMS), allyl methyl sulfoxide (AMSO) and allyl methyl sulfone (AMSOâ‚‚), confirming our previous findings on human milk metabolite composition. The excretion rates of these metabolites into urine were strongly time-dependent with distinct inter-individual differences. These findings indicate that the volatile odorant fraction of garlic is heavily biotransformed in humans, opening up a window into substance circulation within the human body with potential wider ramifications in view of physiological effects of this aromatic plant that is appreciated by humans in their daily diet.",Metabolites,[],[],Detection of Volatile Metabolites Derived from Garlic (Allium sativum) in Human Urine.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/27916960,2018,0.0,0.0,,, -27916569,"Phytocystatins are cysteine proteinase inhibitors present in plants. They play crucial role in maintaining protease-anti protease balance and are involved in various endogenous processes. Thus, they are suitable and convenient targets for genetic engineering which makes their isolation and characterisation from different sources the need of the hour. In the present study a phytocystatin has been isolated from garlic (Allium sativum) by a simple two-step process using ammonium sulphate fractionation and gel filtration chromatography on Sephacryl S-100HR with a fold purification of 152.6 and yield 48.9%. A single band on native gel electrophoresis confirms the homogeneity of the purified inhibitor. The molecular weight of the purified inhibitor was found to be 12.5kDa as determined by SDS-PAGE and gel filtration chromatography. The garlic phytocystatin was found to be stable under broad range of pH (6-8) and temperature (30°C-60°C). Kinetic studies suggests that garlic phytocystatins are reversible and non-competitive inhibitors having highest affinity for papain followed by ficin and bromelain. UV and fluorescence spectroscopy revealed significant conformational change upon garlic phytocystatin-papain complex formation. Secondary structure analysis was performed using CD and FTIR. Garlic phytocystatin possesses 33.9% alpha-helical content as assessed by CD spectroscopy.",International journal of biological macromolecules,"['D000818', 'D002241', 'D002318', 'D015853', 'D005737', 'D006863', 'D007700', 'D008970', 'D055550', 'D017433', 'D013057', 'D013438', 'D013696']","['Animals', 'Carbohydrates', 'Cardiovascular Diseases', 'Cysteine Proteinase Inhibitors', 'Garlic', 'Hydrogen-Ion Concentration', 'Kinetics', 'Molecular Weight', 'Protein Stability', 'Protein Structure, Secondary', 'Spectrum Analysis', 'Sulfhydryl Compounds', 'Temperature']","Insight into the biochemical, kinetic and spectroscopic characterization of garlic (Allium sativum) phytocystatin: Implication for cardiovascular disease.","[None, 'Q000032', 'Q000188', 'Q000737', None, None, None, None, None, None, None, 'Q000032', None]","[None, 'analysis', 'drug therapy', 'chemistry', None, None, None, None, None, None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/27916569,2017,0.0,0.0,,, -27846232,This study was aimed to purify and characterize the Protease inhibitor (PI) from a plant Allium sativum (garlic) with strong medicinal properties and to explore its phytodrug potentials.,PloS one,"['D002851', 'D002942', 'D003902', 'D004591', 'D005737', 'D006863', 'D007700', 'D016877', 'D010455', 'D010940', 'D055550', 'D015843', 'D019032', 'D013696', 'D014361']","['Chromatography, High Pressure Liquid', 'Circular Dichroism', 'Detergents', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Hydrogen-Ion Concentration', 'Kinetics', 'Oxidants', 'Peptides', 'Plant Proteins', 'Protein Stability', 'Serpins', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Temperature', 'Trypsin Inhibitors']",Allium sativum Protease Inhibitor: A Novel Kunitz Trypsin Inhibitor from Garlic Is a New Comrade of the Serpin Family.,"[None, None, 'Q000494', None, 'Q000737', None, None, 'Q000494', 'Q000302', 'Q000302', 'Q000187', 'Q000302', None, None, 'Q000302']","[None, None, 'pharmacology', None, 'chemistry', None, None, 'pharmacology', 'isolation & purification', 'isolation & purification', 'drug effects', 'isolation & purification', None, None, 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/27846232,2017,0.0,0.0,,, -27722608,"The metabolism of selenomethionine (SeMet) in two major selenium (Se) accumulator plants, garlic and Indian mustard, was compared to that of stable isotope labeled selenate. Indian mustard more efficiently transported Se from roots to leaves than garlic. In addition, Indian mustard accumulated larger amounts of Se than garlic. γ-Glutamyl-Se-methylselenocysteine (γ-GluMeSeCys) and Se-methylselenocysteine (MeSeCys) were the common metabolites of selenate and SeMet in garlic and Indian mustard. Indian mustard had a specific metabolic pathway to selenohomolanthionine (SeHLan) from both inorganic and organic Se species. SeMet was a more effective fertilizer for cultivating Se-enriched plants than selenate in terms of the production of selenoamino acids.",Metallomics : integrated biometal science,"['D002851', 'D005737', 'D007287', 'D013058', 'D009149', 'D009930', 'D016566', 'D012643']","['Chromatography, High Pressure Liquid', 'Garlic', 'Inorganic Chemicals', 'Mass Spectrometry', 'Mustard Plant', 'Organic Chemicals', 'Organoselenium Compounds', 'Selenium']","Comparison of the metabolism of inorganic and organic selenium species between two selenium accumulator plants, garlic and Indian mustard.","[None, 'Q000254', 'Q000737', None, 'Q000254', 'Q000737', 'Q000378', 'Q000378']","[None, 'growth & development', 'chemistry', None, 'growth & development', 'chemistry', 'metabolism', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/27722608,2017,0.0,0.0,,, -27765204,"A highly sensitive vortex assisted liquid-liquid microextraction (VA-LLME) method was developed for inorganic Se [Se(IV) and Se(VI)] speciation analysis in Allium and Brassica vegetables. Trihexyl(tetradecyl)phosphonium decanoate phosphonium ionic liquid (IL) was applied for the extraction of Se(IV)-ammonium pyrrolidine dithiocarbamate (APDC) complex followed by Se determination with electrothermal atomic absorption spectrometry. A complete optimization of the graphite furnace temperature program was developed for accurate determination of Se in the IL-enriched extracts and multivariate statistical optimization was performed to define the conditions for the highest extraction efficiency. Significant factors of IL-VA-LLME method were sample volume, extraction pH, extraction time and APDC concentration. High extraction efficiency (90%), a 100-fold preconcentration factor and a detection limit of 5.0ng/L were achieved. The high sensitivity obtained with preconcentration and the non-chromatographic separation of inorganic Se species in complex matrix samples such as garlic, onion, leek, broccoli and cauliflower, are the main advantages of IL-VA-LLME.",Food chemistry,"['D001937', 'D005737', 'D052578', 'D057230', 'D059627', 'D011759', 'D012643', 'D018036', 'D013054', 'D013859']","['Brassica', 'Garlic', 'Ionic Liquids', 'Limit of Detection', 'Liquid Phase Microextraction', 'Pyrrolidines', 'Selenium', 'Selenium Compounds', 'Spectrophotometry, Atomic', 'Thiocarbamates']",Inorganic selenium speciation analysis in Allium and Brassica vegetables by ionic liquid assisted liquid-liquid microextraction with multivariate optimization.,"['Q000737', 'Q000737', 'Q000737', None, 'Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000379', 'Q000032']","['chemistry', 'chemistry', 'chemistry', None, 'methods', 'analysis', 'analysis', 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27765204,2016,0.0,0.0,,, -27725449,"Three major organosulfur compounds of aged garlic extract, S-allyl-L-cysteine (SAC), S-methyl-L-cysteine (SMC), and trans-S-1-propenyl-L-cysteine (S1PC), were examined for their effects on the activities of five major isoforms of human CYP enzymes: CYP1A2, 2C9, 2C19, 2D6, and 3A4. The metabolite formation from probe substrates for the CYP isoforms was examined in human liver microsomes in the presence of organosulfur compounds at 0.01-1 mM by using liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Allicin, a major component of garlic, inhibited CYP1A2 and CYP3A4 activity by 21-45% at 0.03 mM. In contrast, a CYP2C9-catalyzed reaction was enhanced by up to 1.9 times in the presence of allicin at 0.003-0.3 mM. SAC, SMC, and S1PC had no effect on the activities of the five isoforms, except that S1PC inhibited CYP3A4-catalyzed midazolam 1'-hydroxylation by 31% at 1 mM. The N-acetylated metabolites of the three compounds inhibited the activities of several isoforms to a varying degree at 1 mM. N-Acetyl-S-allyl-L-cysteine and N-acetyl-S-methyl-L-cysteine inhibited the reactions catalyzed by CYP2D6 and CYP1A2, by 19 and 26%, respectively, whereas trans-N-acetyl-S-1-propenyl-L-cysteine showed weak to moderate inhibition (19-49%) of CYP1A2, 2C19, 2D6, and 3A4 activities. On the other hand, both the N-acetylated and S-oxidized metabolites of SAC, SMC, and S1PC had little effect on the reactions catalyzed by the five isoforms. These results indicated that SAC, SMC, and S1PC have little potential to cause drug-drug interaction due to CYP inhibition or activation in vivo, as judged by their minimal effects (IC",Biological & pharmaceutical bulletin,"['D000107', 'D002853', 'D003545', 'D065607', 'D003577', 'D006801', 'D008862', 'D010084', 'D053719']","['Acetylation', 'Chromatography, Liquid', 'Cysteine', 'Cytochrome P-450 Enzyme Inhibitors', 'Cytochrome P-450 Enzyme System', 'Humans', 'Microsomes, Liver', 'Oxidation-Reduction', 'Tandem Mass Spectrometry']","Evaluation of the Effects of S-Allyl-L-cysteine, S-Methyl-L-cysteine, trans-S-1-Propenyl-L-cysteine, and Their N-Acetylated and S-Oxidized Metabolites on Human CYP Activities.","[None, None, 'Q000031', 'Q000494', 'Q000378', None, 'Q000378', None, None]","[None, None, 'analogs & derivatives', 'pharmacology', 'metabolism', None, 'metabolism', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/27725449,2017,0.0,0.0,,, -27664612,"Salting-out extraction (SOE) based on lower molecular organic solvent and inorganic salt was considered as a good substitute for conventional polymers aqueous two-phase extraction (ATPE) used for the extraction of some bioactive compounds from natural plants resources. In this study, the ethanol/ammonium sulfate was screened as the optimal SOE system for the extraction and preliminary purification of allicin from garlic. Response surface methodology (RSM) was developed to optimize the major conditions. The maximum extraction efficiency of 94.17% was obtained at the optimized conditions for routine use: 23% (w/w) ethanol concentration and 24% (w/w) salt concentration, 31g/L loaded sample at 25°C with pH being not adjusted. The extraction efficiency had no obvious decrease after amplification of the extraction. This ethanol/ammonium sulfate SOE is much simpler, cheaper, and effective, which has the potentiality of scale-up production for the extraction and purification of other compounds from plant resources. ",Food chemistry,"['D000645', 'D002851', 'D000431', 'D005737', 'D010936', 'D012965', 'D012997', 'D013441', 'D014867']","['Ammonium Sulfate', 'Chromatography, High Pressure Liquid', 'Ethanol', 'Garlic', 'Plant Extracts', 'Sodium Chloride', 'Solvents', 'Sulfinic Acids', 'Water']",Salting-out extraction of allicin from garlic (Allium sativum L.) based on ethanol/ammonium sulfate in laboratory and pilot scale.,"['Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000302', 'Q000737', 'Q000737', 'Q000302', 'Q000737']","['chemistry', 'methods', 'chemistry', 'chemistry', 'isolation & purification', 'chemistry', 'chemistry', 'isolation & purification', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27664612,2016,0.0,0.0,,, -27649517,"Garlic causes a strong garlic breath that may persist for almost a day. Therefore, it is important to study deodorization techniques for garlic breath. The volatiles responsible for garlic breath include diallyl disulfide, allyl mercaptan, allyl methyl disulfide, and allyl methyl sulfide. After eating garlic, water (control), raw, juiced or heated apple, raw or heated lettuce, raw or juiced mint leaves, or green tea were consumed immediately. The levels of the garlic volatiles on the breath were analyzed from 1 to 60 min by selected ion flow tube mass spectrometry (SIFT-MS). Garlic was also blended with water (control), polyphenol oxidase (PPO), rosemarinic acid, quercetin or catechin, and the volatiles in the headspace analyzed from 3 to 40 min by SIFT-MS. Raw apple, raw lettuce, and mint leaves significantly decreased all of the garlic breath volatiles in vivo. The proposed mechanism is enzymatic deodorization where volatiles react with phenolic compounds. Apple juice and mint juice also had a deodorizing effect on most of the garlic volatiles but were generally not as effective as the raw food, probably because the juice had enzymatic activity but the phenolic compounds had already polymerized. Both heated apple and heated lettuce produced a significant reduction of diallyl disulfide and allyl mercaptan. The presence of phenolic compounds that react with the volatile compounds even in the absence of enzymes is the most likely mechanism. Green tea had no deodorizing effect on the garlic volatile compounds. Rosmarinic acid, catechin, quercetin, and PPO significantly decreased all garlic breath volatiles in vitro. Rosmarinic acid was the most effective at deodorization.",Journal of food science,"['D000498', 'D001944', 'D002109', 'D002392', 'D004156', 'D005419', 'D005638', 'D005737', 'D006209', 'D019686', 'D018545', 'D008168', 'D027845', 'D009812', 'D010636', 'D018515', 'D059808', 'D011794', 'D013440', 'D013455', 'D013662', 'D055549']","['Allyl Compounds', 'Breath Tests', 'Caffeic Acids', 'Catechin', 'Catechol Oxidase', 'Flavonoids', 'Fruit', 'Garlic', 'Halitosis', 'Lamiaceae', 'Lettuce', 'Lung', 'Malus', 'Odorants', 'Phenols', 'Plant Leaves', 'Polyphenols', 'Quercetin', 'Sulfides', 'Sulfur', 'Tea', 'Volatile Organic Compounds']","Deodorization of Garlic Breath by Foods, and the Role of Polyphenol Oxidase and Phenolic Compounds.","['Q000032', None, 'Q000378', 'Q000378', 'Q000378', 'Q000378', 'Q000737', 'Q000737', 'Q000517', 'Q000737', 'Q000737', 'Q000378', 'Q000737', None, 'Q000378', 'Q000737', 'Q000378', 'Q000378', 'Q000032', 'Q000032', 'Q000737', 'Q000378']","['analysis', None, 'metabolism', 'metabolism', 'metabolism', 'metabolism', 'chemistry', 'chemistry', 'prevention & control', 'chemistry', 'chemistry', 'metabolism', 'chemistry', None, 'metabolism', 'chemistry', 'metabolism', 'metabolism', 'analysis', 'analysis', 'chemistry', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/27649517,2017,0.0,0.0,,, -27592824,Sulphites are a family of additives regulated for use worldwide in food products. They must be declared on the label if they are present in concentrations greater than 10 mg kg,"Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000490', 'D001937', 'D002853', 'D013447', 'D053719', 'D014675']","['Allium', 'Brassica', 'Chromatography, Liquid', 'Sulfites', 'Tandem Mass Spectrometry', 'Vegetables']",Comparison of multiple methods for the determination of sulphite in Allium and Brassica vegetables.,"['Q000737', 'Q000737', None, 'Q000032', None, 'Q000737']","['chemistry', 'chemistry', None, 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27592824,2017,0.0,0.0,,, -27542503,"Organosulphur compounds (OSCs) present in garlic (Allium sativum L.) are responsible of several biological properties. Functional foods researches indicate the importance of quantifying these compounds in food matrices and biological fluids. For this purpose, this paper introduces a novel methodology based on dispersive liquid-liquid microextraction (DLLME) coupled to high performance liquid chromatography with ultraviolet detector (HPLC-UV) for the extraction and determination of organosulphur compounds in different matrices. The target analytes were allicin, (E)- and (Z)-ajoene, 2-vinyl-4H-1,2-dithiin (2-VD), diallyl sulphide (DAS) and diallyl disulphide (DADS). The microextraction technique was optimized using an experimental design, and the analytical performance was evaluated under optimum conditions. The desirability function presented an optimal value for 600μL of chloroform as extraction solvent using acetonitrile as dispersant. The method proved to be reliable, precise and accurate. It was successfully applied to determine OSCs in cooked garlic samples as well as blood plasma and digestive fluids. ",Food chemistry,"['D002851', 'D005737', 'D059627']","['Chromatography, High Pressure Liquid', 'Garlic', 'Liquid Phase Microextraction']",Development of garlic bioactive compounds analytical methodology based on liquid phase microextraction using response surface design. Implications for dual analysis: Cooked and biological fluids samples.,"['Q000379', 'Q000737', 'Q000379']","['methods', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/27542503,2016,0.0,0.0,,spiked smaples, -27527697,"In this work, binding of garlic component-Diallysulfide (DAS) with major human blood transport protein, Human Serum Albumin (HSA) and its anti- amyloidogenic behavior has been studied by utilizing various spectroscopic and molecular docking strategies. The HSA exhibit significant reduction in fluorescence intensity upon interaction with DAS. DAS quenches the fluorescence of HSA in concentration dependent manner with binding affinity of 1.14×10",International journal of biological macromolecules,"['D000498', 'D000682', 'D001665', 'D005456', 'D006801', 'D007700', 'D062105', 'D011485', 'D017433', 'D012709', 'D013050', 'D013440', 'D013816', 'D013844']","['Allyl Compounds', 'Amyloid', 'Binding Sites', 'Fluorescent Dyes', 'Humans', 'Kinetics', 'Molecular Docking Simulation', 'Protein Binding', 'Protein Structure, Secondary', 'Serum Albumin', 'Spectrometry, Fluorescence', 'Sulfides', 'Thermodynamics', 'Thiazoles']",Anti-amyloidogenic behavior and interaction of Diallylsulfide with Human Serum Albumin.,"['Q000737', 'Q000037', None, 'Q000737', None, None, None, None, None, 'Q000037', None, 'Q000737', None, 'Q000737']","['chemistry', 'antagonists & inhibitors', None, 'chemistry', None, None, None, None, None, 'antagonists & inhibitors', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27527697,2017,0.0,0.0,,, -27453278,"It is well known that Allium sativum has potential applications to clinical treatment of various cancers due to its remarkable ability in eliminating free radicals and increasing metabolism. An allyl-substituted cysteine derivative - S-allyl-L-cysteine (SAC) was separated and identified from Allium sativum. The extracted SAC was reacted with 1-pyrenemethanol to obtain pyrene-labelled SAC (Py-SAC) to give SAC fluorescence properties. Molecular detection of Py-SAC was conducted by steady-state fluorescence spectroscopy and time-resolved fluorescence method to quantitatively measure concentrations of Py-SAC solutions. The ability of removing 1,1-diphenyl-2-picrylhydrazyl (DPPH) and hydroxyl radical using Py-SAC was determined through oxygen radical absorbance capacity (ORAC). Results showed the activity of Py-SAC and Vitamin C (VC) with ORAC as index, the concentrations of Py-SAC and VC were 58.43 mg/L and 5.72 mg/L respectively to scavenge DPPH, and 8.16 mg/L and 1.67 mg/L to scavenge •OH respectively. Compared with VC, the clearance rates of Py-SAC to scavenge DPPH were much higher, Py-SAC could inhibit hydroxyl radical. The ability of removing radical showed a dose-dependent relationship within the scope of the drug concentration. ","Cellular and molecular biology (Noisy-le-Grand, France)","['D000975', 'D001205', 'D001713', 'D003545', 'D016166', 'D005737', 'D017665', 'D010851', 'D011721', 'D013050']","['Antioxidants', 'Ascorbic Acid', 'Biphenyl Compounds', 'Cysteine', 'Free Radical Scavengers', 'Garlic', 'Hydroxyl Radical', 'Picrates', 'Pyrenes', 'Spectrometry, Fluorescence']",Molecular detection and in vitro antioxidant activity of S-allyl-L-cysteine (SAC) extracted from Allium sativum.,"['Q000494', 'Q000494', 'Q000737', 'Q000031', 'Q000494', 'Q000737', 'Q000737', 'Q000737', 'Q000737', None]","['pharmacology', 'pharmacology', 'chemistry', 'analogs & derivatives', 'pharmacology', 'chemistry', 'chemistry', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/27453278,2017,,,,,True -27313155,"Many secondary metabolites in plants are labile compounds which under environmental stress, are difficult to detect and track due to the lack of rapid in situ identification techniques, making plant metabolomics research difficult. Therefore, developing a reliable analytical method for rapid in situ identification of labile compounds and their short-lived intermediates in plants is of great importance.",Phytochemical analysis : PCA,"['D060166', 'D002726', 'D003545', 'D005737', 'D005961', 'D027845', 'D055432', 'D015394', 'D018517', 'D011791', 'D031224', 'D013312', 'D053719', 'D013997']","['Capillary Tubing', 'Chlorogenic Acid', 'Cysteine', 'Garlic', 'Glucosinolates', 'Malus', 'Metabolomics', 'Molecular Structure', 'Plant Roots', 'Quartz', 'Raphanus', 'Stress, Physiological', 'Tandem Mass Spectrometry', 'Time Factors']",In situ Identification of Labile Precursor Compounds and their Short-lived Intermediates in Plants using in vivo Nanospray High-resolution Mass Spectrometry.,"[None, 'Q000737', 'Q000031', 'Q000737', 'Q000737', 'Q000737', None, None, 'Q000737', None, 'Q000737', None, None, None]","[None, 'chemistry', 'analogs & derivatives', 'chemistry', 'chemistry', 'chemistry', None, None, 'chemistry', None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/27313155,2017,0.0,0.0,,, -27300762,"Black garlic is produced through thermal processing and is used as a healthy food throughout the world. Compared with fresh garlic, there are obvious changes in the color, taste, and biological functions of black garlic. To analyze and explain these changes, the contents of water-soluble sugars, fructan, and the key intermediate compounds (Heyns and Amadori) of the Maillard reaction in fresh raw garlic and black garlic were investigated, which were important to control and to evaluate the quality of black garlic. The results showed that the fructan contents in the black garlics were decreased by more than 84.6% compared with the fresh raw garlics, which translated into changes in the fructose and glucose contents. The water-soluble sugar content was drastically increased by values ranging from 187.79% to 790.96%. Therefore, the taste of the black garlic became very sweet. The sucrose content in black garlic was almost equivalent to fresh garlic. The Amadori and Heyns compounds were analyzed by HPLC-MS/MS in multiple reaction monitoring mode using the different characteristic fragment ions of Heyns and Amadori compounds. The total content of the 3 main Amadori and 3 Heyns compounds in black garlic ranged from 762.53 to 280.56 μg/g, which was 40 to 100-fold higher than the values in fresh raw garlic. This result was significant proof that the Maillard reaction in black garlic mainly utilized fructose and glucose, with some amino acids. ",Journal of food science,"['D000596', 'D002241', 'D005511', 'D005630', 'D005632', 'D005737', 'D005947', 'D006358', 'D006801', 'D015416', 'D013395', 'D053719', 'D013649']","['Amino Acids', 'Carbohydrates', 'Food Handling', 'Fructans', 'Fructose', 'Garlic', 'Glucose', 'Hot Temperature', 'Humans', 'Maillard Reaction', 'Sucrose', 'Tandem Mass Spectrometry', 'Taste']","The Comparison of the Contents of Sugar, Amadori, and Heyns Compounds in Fresh and Black Garlic.","['Q000032', None, None, 'Q000032', 'Q000032', 'Q000737', 'Q000032', None, None, None, 'Q000032', None, None]","['analysis', None, None, 'analysis', 'analysis', 'chemistry', 'analysis', None, None, None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/27300762,2017,1.0,2.0,,, -27296605,"Foodborne viruses, particularly human norovirus, are a concern for public health, especially in fresh vegetables and other minimally processed foods that may not undergo sufficient decontamination. It is necessary to explore novel nonthermal techniques for preventing foodborne viral contamination. In this study, aqueous extracts of six raw food materials (flower buds of clove, fenugreek seeds, garlic and onion bulbs, ginger rhizomes, and jalapeño peppers) were tested for antiviral activity against feline calicivirus (FCV) as a surrogate for human norovirus. The antiviral assay was performed using dilutions of the extracts below the maximum nontoxic concentrations of the extracts to the host cells of FCV, Crandell-Reese feline kidney (CRFK) cells. No antiviral effect was seen when the host cells were pretreated with any of the extracts. However, pretreatment of FCV with nondiluted clove and ginger extracts inactivated 6.0 and 2.7 log of the initial titer of the virus, respectively. Also, significant dosedependent inactivation of FCV was seen when host cells were treated with clove and ginger extracts at the time of infection or postinfection at concentrations equal to or lower than the maximum nontoxic concentrations. By comprehensive two-dimensional gas chromatography-mass spectrometry analysis, eugenol (29.5%) and R-(-)-1,2-propanediol (10.7%) were identified as the major components of clove and ginger extracts, respectively. The antiviral effect of the pure eugenol itself was tested; it showed antiviral activity similar to that of clove extract, albeit at a lower level, which indicates that some other clove extract constituents, along with eugenol, are responsible for inactivation of FCV. These results showed that the aqueous extracts of clove and ginger hold promise for prevention of foodborne viral contamination.",Journal of food protection,"['D000818', 'D000998', 'D017927', 'D002415', 'D002460', 'D020939', 'D006801', 'D029322', 'D027842']","['Animals', 'Antiviral Agents', 'Calicivirus, Feline', 'Cats', 'Cell Line', 'Ginger', 'Humans', 'Norovirus', 'Syzygium']","In Vitro Antiviral Activity of Clove and Ginger Aqueous Extracts against Feline Calicivirus, a Surrogate for Human Norovirus.","[None, 'Q000494', 'Q000187', None, None, None, None, 'Q000187', None]","[None, 'pharmacology', 'drug effects', None, None, None, None, 'drug effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/27296605,2017,,,,, -27283666,"In this study, we used liquid chromatography coupled to ion trap mass spectrometry (MS) for the quantification of 11 organosulfur compounds and analysis of their compositional changes in garlic during fermentation using 3 different microbe strains. The calibration curves of all 11 analytes exhibited good linearity (R⩾0.995), and the mean recoveries measured at three concentrations were greater than 81.63% with relative standard deviations of less than 12.79%. Investigation of the compositional changes revealed that the γ-glutamyl peptides content in fermented blanched garlic reduced, whereas the content of the compounds in biosynthesis of S-allyl-l-cysteines from γ-glutamyl peptides increased significantly. Our results also indicated that starter cultures can be used selectively in the production of fermented garlic to increase the amounts of the desired organosulfur compounds. ",Food chemistry,"['D002853', 'D005285', 'D005737', 'D017365', 'D013457', 'D053719']","['Chromatography, Liquid', 'Fermentation', 'Garlic', 'Spices', 'Sulfur Compounds', 'Tandem Mass Spectrometry']",UPLC/ESI-MS/MS analysis of compositional changes for organosulfur compounds in garlic (Allium sativum L.) during fermentation.,"[None, None, 'Q000737', 'Q000032', 'Q000032', None]","[None, None, 'chemistry', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/27283666,2017,0.0,0.0,,, -27275838,"The odor of human breast milk after ingestion of raw garlic at food-relevant concentrations by breastfeeding mothers was investigated for the first time chemo-analytically using gas chromatography-mass spectrometry/olfactometry (GC-MS/O), as well as sensorially using a trained human sensory panel. Sensory evaluation revealed a clear garlic/cabbage-like odor that appeared in breast milk about 2.5 h after consumption of garlic. GC-MS/O analyses confirmed the occurrence of garlic-derived metabolites in breast milk, namely allyl methyl sulfide (AMS), allyl methyl sulfoxide (AMSO) and allyl methyl sulfone (AMSOâ‚‚). Of these, only AMS had a garlic-like odor whereas the other two metabolites were odorless. This demonstrates that the odor change in human milk is not related to a direct transfer of garlic odorants, as is currently believed, but rather derives from a single metabolite. The formation of these metabolites is not fully understood, but AMSO and AMSOâ‚‚ are most likely formed by the oxidation of AMS in the human body. The excretion rates of these metabolites into breast milk were strongly time-dependent with large inter-individual differences. ",Metabolites,[],[],Detection of Volatile Metabolites of Garlic in Human Breast Milk.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/27275838,2016,0.0,0.0,,, -27259073,"Extracts of the bulbs of the two endemic varieties ""Rosato"" and ""Caposele"" of Allium sativum of the Campania region, Southern Italy, were analyzed. The phenolic content, ascorbic acid, allicin content, and in vitro antimicrobial and antifungal activity were determined. Ultra performance liquid chromatography with diode array detector performed polyphenol profile. The polyphenolic extracts showed antioxidant activity (EC50) lower than 120 mg. The amount of ascorbic acid and allicin in the two extracts was similar. Polyphenol extract exhibited antimicrobial activity against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and (only by the extract of Rosato) against Bacillus cereus. The extract of Caposele was more effective in inhibiting the growth of Aspergillus versicolor and Penicillum citrinum. On the other hand, the extract of Rosato was effective against Penicillium expansum. ",Journal of medicinal food,"['D000890', 'D000935', 'D001205', 'D001230', 'D004926', 'D005737', 'D007558', 'D010407', 'D010636', 'D010936', 'D018517', 'D059808', 'D011550', 'D013045', 'D013211', 'D013441']","['Anti-Infective Agents', 'Antifungal Agents', 'Ascorbic Acid', 'Aspergillus', 'Escherichia coli', 'Garlic', 'Italy', 'Penicillium', 'Phenols', 'Plant Extracts', 'Plant Roots', 'Polyphenols', 'Pseudomonas aeruginosa', 'Species Specificity', 'Staphylococcus aureus', 'Sulfinic Acids']","Biochemical Characterization and Antimicrobial and Antifungal Activity of Two Endemic Varieties of Garlic (Allium sativum L.) of the Campania Region, Southern Italy.","['Q000494', 'Q000494', 'Q000032', 'Q000187', 'Q000187', 'Q000737', None, 'Q000187', 'Q000032', 'Q000737', 'Q000737', 'Q000008', 'Q000187', None, 'Q000187', 'Q000032']","['pharmacology', 'pharmacology', 'analysis', 'drug effects', 'drug effects', 'chemistry', None, 'drug effects', 'analysis', 'chemistry', 'chemistry', 'administration & dosage', 'drug effects', None, 'drug effects', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27259073,2017,,,,,True -27008862,"Allicin (diallyl thiosulfinate) from garlic is a highly potent natural antimicrobial substance. It inhibits growth of a variety of microorganisms, among them antibiotic-resistant strains. However, the precise mode of action of allicin is unknown. Here, we show that growth inhibition of Escherichia coli during allicin exposure coincides with a depletion of the glutathione pool and S-allylmercapto modification of proteins, resulting in overall decreased total sulfhydryl levels. This is accompanied by the induction of the oxidative and heat stress response. We identified and quantified the allicin-induced modification S-allylmercaptocysteine for a set of cytoplasmic proteins by using a combination of label-free mass spectrometry and differential isotope-coded affinity tag labeling of reduced and oxidized thiol residues. Activity of isocitrate lyase AceA, an S-allylmercapto-modified candidate protein, is largely inhibited by allicin treatment in vivo Allicin-induced protein modifications trigger protein aggregation, which largely stabilizes RpoH and thereby induces the heat stress response. At sublethal concentrations, the heat stress response is crucial to overcome allicin stress. Our results indicate that the mode of action of allicin is a combination of a decrease of glutathione levels, unfolding stress, and inactivation of crucial metabolic enzymes through S-allylmercapto modification of cysteines.",The Journal of biological chemistry,"['D003545', 'D004926', 'D029968', 'D005737', 'D005978', 'D010936', 'D011499', 'D013438', 'D013441']","['Cysteine', 'Escherichia coli', 'Escherichia coli Proteins', 'Garlic', 'Glutathione', 'Plant Extracts', 'Protein Processing, Post-Translational', 'Sulfhydryl Compounds', 'Sulfinic Acids']",Allicin Induces Thiol Stress in Bacteria through S-Allylmercapto Modification of Protein Cysteines.,"['Q000378', 'Q000187', 'Q000378', 'Q000737', 'Q000378', 'Q000494', 'Q000187', 'Q000378', 'Q000494']","['metabolism', 'drug effects', 'metabolism', 'chemistry', 'metabolism', 'pharmacology', 'drug effects', 'metabolism', 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/27008862,2016,0.0,0.0,,, -27002613,"Garlic is one of the most used seasonings in the world whose beneficial health effects, mainly ascribed to organosulfur compounds, are shared with the rest of the Allium family. The fact that many of these compounds are volatile makes the evaluation of the volatile profile of garlic interesting. For this purpose, three garlic varieties-White, Purple, and Chinese-cultivated in the South of Spain were analyzed by a method based on a headspace (HS) device coupled to a gas chromatograph and mass detector (HS-GC/MS). The main temperatures in the HS were optimized to achieve the highest concentration of volatiles. A total number of 45 volatiles were tentatively identified (among them 17 were identified for the first time in garlic); then, all were classified, also for the first time, and their relative concentration in three garlic varieties was used to evaluate differences among them and to study their profiles according to the heating time. Chinese garlic was found to be the richest variety in sulfur volatiles, while the three varieties presented a similar trend under preset heating times allowing differentiation between varieties and heating time using principal component analysis. Graphical Abstract HS-GC/MS analysis of the volatile profile of garlic.",Analytical and bioanalytical chemistry,"['D005737', 'D008401', 'D006358', 'D055549']","['Garlic', 'Gas Chromatography-Mass Spectrometry', 'Hot Temperature', 'Volatile Organic Compounds']",HS-GC/MS volatile profile of different varieties of garlic and their behavior under heating.,"['Q000737', 'Q000379', None, 'Q000032']","['chemistry', 'methods', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27002613,2017,0.0,0.0,,, -26964532,"The quality of chicken meat, which is one of the most widely consumed meats in the world, has been the subject of research and studies for many years. There are several ways to improve the quality of this type of meat, including changing the concentrations of individual molecular components. Such important components of meat are inter alia, cholesterol, vitamin E, and some fatty acids such as ω-3 and ω-6. Manipulation of ingredient levels may be achieved by enriching chicken feed with elements of different types such as vegetable oils, garlic, or selenium. Thus far, various biochemical and biophysical methods have been used to study quality of different meat types, especially broiler meat. Here, the authors demonstrate the use of high-resolution time-of-flight secondary ion mass spectrometry (TOF-SIMS) mass spectrometry to assess how variations in animal nutrition affect concentrations of specific lipids in the meat, such as cholesterol and vitamin E. In the presented experiment, there were four different dietary treatments. Feed for animals in the first group was supplemented with soy oil in 50%, the second group's feed was supplemented with linseed oil in 50%, a combination of these two oils in the proportion of 44%:56% was used for the third group, and in the reference group, animals were fed with beef tallow. From each group, four individuals were selected for further analysis. Positive and negative ion mass spectra were generated from the pectoralis superficialis muscle tissue of the left carcass side of each one animal. Using TOF-SIMS with a bismuth cluster ion source (Bi3 (+)), and based on characteristic peaks for cholesterol in the positive mode and vitamin E in the negative mode, the authors have illustrated the relationship of these lipids levels to the various feeding regimens. Simultaneously, the authors characterized the varying dependences on the concentrations of measured lipids in fat and muscle fibers. The cholesterol concentration in muscle fibers was the lowest in the group fed with soybean oil and the highest in reference group IV (tallow feed). In the fatty region, the highest level of cholesterol was found in the third group. The highest concentrations of vitamin E were found in the fibers of the first group and the fat region of the second group. The obtained results show that SIMS imaging is a useful approach for assessing changes in lipid concentrations in the meat tissue from animals on different diets and provides a foundation for future research. ",Biointerphases,"['D000821', 'D000818', 'D002645', 'D002784', 'D004032', 'D005504', 'D008460', 'D018629', 'D014810']","['Animal Feed', 'Animals', 'Chickens', 'Cholesterol', 'Diet', 'Food Analysis', 'Meat', 'Spectrometry, Mass, Secondary Ion', 'Vitamin E']",Study of cholesterol and vitamin E levels in broiler meat from different feeding regimens by TOF-SIMS.,"[None, None, None, 'Q000032', 'Q000379', None, None, None, 'Q000032']","[None, None, None, 'analysis', 'methods', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26964532,2016,,,,,True -26948845,"The antifungal activity, kinetics, and molecular mechanism of action of garlic oil against Candida albicans were investigated in this study using multiple methods. Using the poisoned food technique, we determined that the minimum inhibitory concentration of garlic oil was 0.35 μg/mL. Observation by transmission electron microscopy indicated that garlic oil could penetrate the cellular membrane of C. albicans as well as the membranes of organelles such as the mitochondria, resulting in organelle destruction and ultimately cell death. RNA sequencing analysis showed that garlic oil induced differential expression of critical genes including those involved in oxidation-reduction processes, pathogenesis, and cellular response to drugs and starvation. Moreover, the differentially expressed genes were mainly clustered in 19 KEGG pathways, representing vital cellular processes such as oxidative phosphorylation, the spliceosome, the cell cycle, and protein processing in the endoplasmic reticulum. In addition, four upregulated proteins selected after two-dimensional fluorescence difference in gel electrophoresis (2D-DIGE) analysis were identified with high probability by mass spectrometry as putative cytoplasmic adenylate kinase, pyruvate decarboxylase, hexokinase, and heat shock proteins. This is suggestive of a C. albicans stress responses to garlic oil treatment. On the other hand, a large number of proteins were downregulated, leading to significant disruption of the normal metabolism and physical functions of C. albicans.",Scientific reports,"['D000498', 'D000935', 'D002176', 'D016923', 'D015966', 'D005800', 'D008826', 'D012331', 'D017423', 'D013440']","['Allyl Compounds', 'Antifungal Agents', 'Candida albicans', 'Cell Death', 'Gene Expression Regulation, Fungal', 'Genes, Fungal', 'Microbial Sensitivity Tests', 'RNA, Fungal', 'Sequence Analysis, RNA', 'Sulfides']","Antifungal activity, kinetics and molecular mechanism of action of garlic oil against Candida albicans.","['Q000493', 'Q000493', 'Q000187', None, 'Q000187', 'Q000187', None, 'Q000187', None, 'Q000493']","['pharmacokinetics', 'pharmacokinetics', 'drug effects', None, 'drug effects', 'drug effects', None, 'drug effects', None, 'pharmacokinetics']",https://www.ncbi.nlm.nih.gov/pubmed/26948845,2017,0.0,0.0,,, -26921177,"Lipid oxidation causes changes in quality attributes of vegetable oils. Synthetic antioxidants have been used to preserve oils; however, there is interest in replacing them with natural ones. Garlic and its thiosulfinate compound allicin are known for their antioxidant activities. This study assesses a novel formulation, the supercritical fluid extract of garlic, on sunflower oil oxidation during an accelerated shelf-life test.",Journal of the science of food and agriculture,"['D000975', 'D025924', 'D005503', 'D005737', 'D010084', 'D010936', 'D000074242']","['Antioxidants', 'Chromatography, Supercritical Fluid', 'Food Additives', 'Garlic', 'Oxidation-Reduction', 'Plant Extracts', 'Sunflower Oil']",Antioxidant effects of supercritical fluid garlic extracts in sunflower oil.,"['Q000737', None, 'Q000737', 'Q000737', None, 'Q000737', 'Q000737']","['chemistry', None, 'chemistry', 'chemistry', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/26921177,2017,0.0,0.0,,, -26786635,"A soluble glycoprotein was purified to homogeneity from ripe garlic (Allium sativum) bulbs using ammonium sulfate precipitation, Sephadex G-100 gel filtration, and diethylaminoethyl-52 cellulose anion-exchange chromatography. A native mass of 55.7 kDa estimated on gel permeation chromatography and a molecular weight of 13.2 kDa observed on sodium dodecyl sulfate-polyacrylamide gel electrophoresis supported that the glycoprotein is a homotetramer. β-Elimination reaction result suggested that the glycoprotein is an N-linked type. Fourier-transform infrared spectroscopy proved that it contains sugar. Gas chromatography-mass spectrometer analysis showed that its sugar component was galactose. The glycoprotein has 1,1-diphenyl-2-picrylhydrazil free radical scavenging activity and the peroxidation inhibition ability to polyunsaturated fatty acid. These results indicated that the glycoprotein has potential for food additives, functional foods, and even biotechnological and medical applications. ",Preparative biochemistry & biotechnology,"['D004591', 'D005737', 'D008401', 'D006023', 'D066298', 'D012995', 'D017550']","['Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Glycoproteins', 'In Vitro Techniques', 'Solubility', 'Spectroscopy, Fourier Transform Infrared']",Purification and characterization of a soluble glycoprotein from garlic (Allium sativum) and its in vitro bioactivity.,"[None, 'Q000737', None, 'Q000737', None, None, None]","[None, 'chemistry', None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/26786635,2017,,,,,True -26776023,"The thiosulfinate allicin is a labile, bioactive compound of garlic. In order to enrich allicin in a functional food, a delivery system which stabilises the compound and masks its intense flavour is necessary. In the present study allicin was covalently bound to the whey protein β-lactoglobulin and the incorporation of this transporter in a food matrix was tested. The sensory properties of the pure functional ingredient as well as of an enriched beverage were characterised by quantitative descriptive analysis. The concentration of volatile compounds was analysed by headspace gas chromatography-mass spectrometry. The garlic-related organoleptic properties of garlic powder were significantly improved by the binding of allicin in combination with spray drying. After purification of the modified β-lactoglobulin the garlic taste and smell were barely perceptible. β-Lactoglobulin modified with allicin provided a stable functional ingredient that can be used to enrich a broad range of food products. ",Food chemistry,"['D005421', 'D005737', 'D008401', 'D007782', 'D010936', 'D013441']","['Flavoring Agents', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Lactoglobulins', 'Plant Extracts', 'Sulfinic Acids']",β-Lactoglobulin as nanotransporter for allicin: Sensory properties and applicability in food.,"['Q000032', 'Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000737']","['analysis', 'chemistry', 'methods', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/26776023,2016,0.0,0.0,,, -26764333,The chemical assignment of metabolites is crucial to understanding the relation between food composition and biological activity.,The Journal of nutrition,"['D002853', 'D017357', 'D003545', 'D005737', 'D005978', 'D007477', 'D007536', 'D055442', 'D055432', 'D019697', 'D017550', 'D013455', 'D013460']","['Chromatography, Liquid', 'Cyclotrons', 'Cysteine', 'Garlic', 'Glutathione', 'Ions', 'Isomerism', 'Metabolome', 'Metabolomics', 'Onions', 'Spectroscopy, Fourier Transform Infrared', 'Sulfur', 'Sulfur Isotopes']",Chemical Assignment of Structural Isomers of Sulfur-Containing Metabolites in Garlic by Liquid Chromatography-Fourier Transform Ion Cyclotron Resonance-Mass Spectrometry.,"['Q000379', None, 'Q000032', 'Q000737', 'Q000032', 'Q000032', None, None, None, 'Q000737', 'Q000379', 'Q000032', 'Q000032']","['methods', None, 'analysis', 'chemistry', 'analysis', 'analysis', None, None, None, 'chemistry', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26764333,2016,0.0,0.0,,, -26764330,Garlic and its processed preparations contain numerous sulfur compounds that are difficult to analyze in a single run using HPLC.,The Journal of nutrition,"['D000498', 'D002851', 'D003545', 'D004220', 'D005737', 'D006801', 'D007536', 'D013058', 'D010936', 'D013455', 'D013457']","['Allyl Compounds', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Disulfides', 'Garlic', 'Humans', 'Isomerism', 'Mass Spectrometry', 'Plant Extracts', 'Sulfur', 'Sulfur Compounds']",Development of an Analytic Method for Sulfur Compounds in Aged Garlic Extract with the Use of a Postcolumn High Performance Liquid Chromatography Method with Sulfur-Specific Detection.,"['Q000032', 'Q000379', 'Q000031', 'Q000032', 'Q000737', None, None, 'Q000379', 'Q000737', 'Q000032', 'Q000032']","['analysis', 'methods', 'analogs & derivatives', 'analysis', 'chemistry', None, None, 'methods', 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26764330,2016,0.0,0.0,,, -26617005,"Garlic (Allium sativum) is a long-cultivated plant that is widely utilized in cooking and has been employed as a medicine for over 4000 years. In this study, we fabricated standards and internal standards (ISs) for absolute quantification via reductive amination with isotopic formaldehydes. Garlic has four abundant organosulfur compounds (OSCs): S-allylcysteine, S-allylcysteinine sulfoxide, S-methylcysteine, and S-ethylcysteine are abundant in garlic. OSCs with primary amine groups were reacted with isotopic formaldehydes to synthesize ISs and standards. Cooked and uncooked garlic samples were compared, and we utilized tandem mass spectrometry equipped with a selective reaction monitoring technique to absolutely quantify the four organosulfur compounds.",Food chemistry,"['D000586', 'D003545', 'D005557', 'D005737', 'D010936', 'D012015', 'D013454', 'D053719']","['Amination', 'Cysteine', 'Formaldehyde', 'Garlic', 'Plant Extracts', 'Reference Standards', 'Sulfoxides', 'Tandem Mass Spectrometry']",A novel reductive amination method with isotopic formaldehydes for the preparation of internal standard and standards for determining organosulfur compounds in garlic.,"[None, 'Q000031', 'Q000737', 'Q000737', 'Q000032', None, 'Q000032', None]","[None, 'analogs & derivatives', 'chemistry', 'chemistry', 'analysis', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/26617005,2016,1.0,1.0,,, -26502719,"Mutant Allium sativum leaf agglutinin (mASAL) is a potent, biosafe, antifungal protein that exhibits fungicidal activity against different phytopathogenic fungi, including Rhizoctonia solani.",BMC microbiology,"['D000373', 'D000935', 'D017209', 'D002462', 'D002473', 'D002853', 'D005737', 'D025301', 'D053078', 'D050296', 'D008853', 'D051336', 'D050505', 'D025941', 'D017382', 'D012232', 'D053719']","['Agglutinins', 'Antifungal Agents', 'Apoptosis', 'Cell Membrane', 'Cell Wall', 'Chromatography, Liquid', 'Garlic', 'Hyphae', 'Membrane Potential, Mitochondrial', 'Microbial Viability', 'Microscopy', 'Mitochondrial Membranes', 'Mutant Proteins', 'Protein Interaction Mapping', 'Reactive Oxygen Species', 'Rhizoctonia', 'Tandem Mass Spectrometry']","Deciphering the mode of action of a mutant Allium sativum Leaf Agglutinin (mASAL), a potent antifungal protein on Rhizoctonia solani.","['Q000302', 'Q000302', None, 'Q000187', 'Q000187', None, 'Q000737', 'Q000166', 'Q000187', 'Q000187', None, 'Q000187', 'Q000302', None, 'Q000032', 'Q000166', None]","['isolation & purification', 'isolation & purification', None, 'drug effects', 'drug effects', None, 'chemistry', 'cytology', 'drug effects', 'drug effects', None, 'drug effects', 'isolation & purification', None, 'analysis', 'cytology', None]",https://www.ncbi.nlm.nih.gov/pubmed/26502719,2016,0.0,0.0,,, -26245522,"The paper describes the flavonoid composition of the aerial parts (young leaves, YL; adult leaves, AL; stems, ST) of Passiflora loefgrenii Vitta, a rare species native to Brazil, where it is traditionally used as food. Antioxidant potential has also been evaluated. To the best of our knowledge, no phytochemical and biological study on this species has been reported previously.",The Journal of pharmacy and pharmacology,"['D000975', 'D001938', 'D002851', 'D005419', 'D020128', 'D047311', 'D008519', 'D029598', 'D035261', 'D021241', 'D053719']","['Antioxidants', 'Brazil', 'Chromatography, High Pressure Liquid', 'Flavonoids', 'Inhibitory Concentration 50', 'Luteolin', 'Medicine, Traditional', 'Passiflora', 'Plant Components, Aerial', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry']","Phytochemical analysis of Passiflora loefgrenii Vitta, a rich source of luteolin-derived flavonoids with antioxidant properties.","['Q000008', None, 'Q000379', 'Q000008', None, 'Q000008', None, 'Q000737', None, None, None]","['administration & dosage', None, 'methods', 'administration & dosage', None, 'administration & dosage', None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/26245522,2016,0.0,0.0,,, -26161901,"Levels of 3-monochloropropane-1,2-diol (3-MCPD) fatty acid esters were evaluated in commercial deep-fat fried foods from the Brazilian market using a GC-MS method preceded by acid-catalysed methanolysis. A limit of detection of 0.04 mg kg(-1), a limit of quantitation of 0.08 mg kg(-1), mean recoveries varying from 82% to 92%, and coefficients of variation ranging from 2.5% to 5.0% for repeatability and from 3.6% to 6.5% for within-laboratory reproducibility were obtained during in-house validation. The levels of the compounds in the evaluated samples, expressed as free 3-MCPD equivalent, ranged from not detected to 0.99 mg kg(-)(1), and the highest concentrations were observed in samples of chopped onion and garlic. A preliminary estimation of 3-MCPD intake using these occurrence data suggested low risks to human health, but a potential concern may arise in particular cases of consumers of fried food.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D001938', 'D003296', 'D004042', 'D004952', 'D005227', 'D005504', 'D008401', 'D006358', 'D006801', 'D057230', 'D015203', 'D000517']","['Brazil', 'Cooking', 'Dietary Fats, Unsaturated', 'Esters', 'Fatty Acids', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Hot Temperature', 'Humans', 'Limit of Detection', 'Reproducibility of Results', 'alpha-Chlorohydrin']","3-Monochloropropane-1,2-diol fatty acid esters in commercial deep-fat fried foods.","[None, None, None, 'Q000032', 'Q000032', None, None, None, None, None, None, 'Q000032']","[None, None, None, 'analysis', 'analysis', None, None, None, None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26161901,2016,1.0,3.0,,, -26047911,"In the practical application of Bacillus licheniformis γ-glutamyltranspeptidase (BlGGT), we describe a straightforward enzymatic synthesis of γ-L-glutamyl-S-allyl-L-cysteine (GSAC), a naturally occurring organosulfur compound found in garlic, based on a transpeptidation reaction involving glutamine as the γ-glutamyl donor and S-allyl-L-cysteine as the acceptor. With the help of thin layer chromatography technique and computer-assisted image analysis, we performed the quantitative determination of GSAC. The optimum conditions for a biocatalyzed synthesis of GSAC were 200 mM glutamine, 200 mM S-allyl-L-cysteine, 50 mM Tris-HCl buffer (pH 9.0), and BlGGT at a final concentration of 1.0 U/mL. After a 15-h incubation of the reaction mixture at 60 °C, the GSAC yield for the free and immobilized enzymes was 19.3% and 18.3%, respectively. The enzymatic synthesis of GSAC was repeated under optimal conditions at 1-mmol preparative level. The reaction products together with the commercially available GSAC were further subjected to an ESI-MS/MS analysis. A significant signal with m/z of 291.1 and the protonated fragments at m/z of 73.0, 130.1, 145.0, and 162.1 were observed in the positive ESI-MS/MS spectrum, which is consistent with those of the standard compound. These results confirm the successful synthesis of GSAC from glutamine and S-allyl-L-cysteine by BlGGT.",Enzyme and microbial technology,"['D001407', 'D001426', 'D003545', 'D004151', 'D004800', 'D005737', 'D005973', 'D006863', 'D007218', 'D011994', 'D021241', 'D013457', 'D053719', 'D013696', 'D005723']","['Bacillus', 'Bacterial Proteins', 'Cysteine', 'Dipeptides', 'Enzymes, Immobilized', 'Garlic', 'Glutamine', 'Hydrogen-Ion Concentration', 'Industrial Microbiology', 'Recombinant Proteins', 'Spectrometry, Mass, Electrospray Ionization', 'Sulfur Compounds', 'Tandem Mass Spectrometry', 'Temperature', 'gamma-Glutamyltransferase']","Enzymatic synthesis of γ-L-glutamyl-S-allyl-L-cysteine, a naturally occurring organosulfur compound from garlic, by Bacillus licheniformis γ-glutamyltranspeptidase.","['Q000201', 'Q000378', 'Q000031', 'Q000096', 'Q000378', 'Q000378', 'Q000378', None, None, 'Q000378', None, 'Q000378', None, None, 'Q000378']","['enzymology', 'metabolism', 'analogs & derivatives', 'biosynthesis', 'metabolism', 'metabolism', 'metabolism', None, None, 'metabolism', None, 'metabolism', None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/26047911,2016,0.0,0.0,,, -26043852,"Allium sativum is well known for its medicinal properties. The A. sativum lectin 50 (ASL50, 50 kDa) was isolated from aged A. sativum bulbs and purified by gel filtration chromatography on Sephacryl S-200 column. Agar well diffusion assay were used to evaluate the antimicrobial activity of ASL50 against Candida species and bacteria then minimal inhibitory concentration (MIC) was determined. The lipid A binding to ASL50 was determined by surface plasmon resonance (SPR) technology with varying concentrations. Electron microscopic studies were done to see the mode of action of ASL50 on microbes. It exerted antimicrobial activity against clinical Candida isolates with a MIC of 10-40 μg/ml and clinical Pseudomonas aeruginosa isolates with a MIC of 10-80 μg/ml. The electron microscopic study illustrates that it disrupts the cell membrane of the bacteria and cell wall of fungi. It exhibited antiproliferative activity on oral carcinoma KB cells with an IC50 of 36 μg/ml after treatment for 48 h and induces the apoptosis of cancer cells by inducing 2.5-fold higher caspase enzyme activity than untreated cells. However, it has no cytotoxic effects towards HEK 293 cells as well as human erythrocytes even at higher concentration of ASL50. Biological properties of ASL50 may have its therapeutic significance in aiding infection and cancer treatments.",Applied biochemistry and biotechnology,"['D000595', 'D000890', 'D000970', 'D017209', 'D049109', 'D005737', 'D057809', 'D006461', 'D006801', 'D007624', 'D008050', 'D008970', 'D037121', 'D018547', 'D017421']","['Amino Acid Sequence', 'Anti-Infective Agents', 'Antineoplastic Agents', 'Apoptosis', 'Cell Proliferation', 'Garlic', 'HEK293 Cells', 'Hemolysis', 'Humans', 'KB Cells', 'Lipid A', 'Molecular Weight', 'Plant Lectins', 'Plant Stems', 'Sequence Analysis']",Biological Properties and Characterization of ASL50 Protein from Aged Allium sativum Bulbs.,"[None, 'Q000737', 'Q000737', 'Q000187', 'Q000187', 'Q000737', None, 'Q000187', None, None, 'Q000378', None, 'Q000737', 'Q000737', None]","[None, 'chemistry', 'chemistry', 'drug effects', 'drug effects', 'chemistry', None, 'drug effects', None, None, 'metabolism', None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/26043852,2016,0.0,0.0,,, -25976995,"An aqueous aged garlic extract (AGE) was prepared by soaking sliced garlic in water for 20days at room temperature (23-25 °C). In order to locate the antioxidant ingredients of the aqueous AGE, an activity-guided fractionation approach using ABTS assay, DPPH assay and FRAP assay were conducted to guide the fractionation by means of extraction, column chromatography and semi-preparative HPLC. Some phenols and organosulfur compounds were identified as antioxidants in AGE by GC-MS. Furthermore, UV, IR, ESI-MS, NMR and specific rotation experiments led to the identification of l-phenylalanine, l-tryptophan, (3S)-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid, (1S,3S)-1-methyl-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid, and (1R,3S)-1-methyl-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid as the major antioxidants in the AGE. The EC50 values of these purified tetrahydro-β-carboline derivatives were 0.625-1.334 μmol/mL and 1.063-2.072 μmol/mL in ABTS assay and DPPH assay, respectively. It is the first time for us to identify (3S)-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid as an in vitro antioxidant in AGE.",Food chemistry,"['D000975', 'D052160', 'D002243', 'D005591', 'D002851', 'D005737', 'D008401', 'D009682', 'D010636', 'D010649', 'D010936', 'D013451', 'D014364']","['Antioxidants', 'Benzothiazoles', 'Carbolines', 'Chemical Fractionation', 'Chromatography, High Pressure Liquid', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Magnetic Resonance Spectroscopy', 'Phenols', 'Phenylalanine', 'Plant Extracts', 'Sulfonic Acids', 'Tryptophan']","Isolation, purification and identification of antioxidants in an aqueous aged garlic extract.","['Q000032', 'Q000032', 'Q000032', None, None, 'Q000737', None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032']","['analysis', 'analysis', 'analysis', None, None, 'chemistry', None, None, 'analysis', 'analysis', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/25976995,2016,0.0,0.0,,, -25940980,"The beneficial effects of garlic (Allium sativum) consumption in treating human diseases have been reported worldwide over a long period of human history. The strong antioxidant effect of garlic extract (GE) has also recently been claimed to prevent cancer, thrombus formation, cardiovascular disease and some age-related maladies. Using Caenorhabditis elegans as a model organism, aqueous GE was herein shown to increase the expression of longevity-related FOXO transcription factor daf-16 and extend lifespan by 20%. By employing microarray and proteomics analysis on C. elegans treated with aqueous GE, we have systematically mapped 229 genes and 46 proteins with differential expression profiles, which included many metabolic enzymes and yolky egg vitellogenins. To investigate the garlic components functionally involved in longevity, an integrated metabolo-proteomics approach was employed to identify metabolites and protein components associated with treatment of aqueous GE. Among potential lifespan-promoting substances, mannose-binding lectin and N-acetylcysteine were found to increase daf-16 expression. Our study points to the fact that the lifespan-promoting effect of aqueous GE may entail the DAF-16-mediated signaling pathway. The result also highlights the utility of metabolo-proteomics for unraveling the complexity and intricacy involved in the metabolism of natural products in vivo. ",The Journal of nutritional biochemistry,"['D000111', 'D000595', 'D000818', 'D017173', 'D029742', 'D002853', 'D003001', 'D015536', 'D019143', 'D051858', 'D005737', 'D008136', 'D037601', 'D055432', 'D008969', 'D010936', 'D040901', 'D015398', 'D053719', 'D015854', 'D014819']","['Acetylcysteine', 'Amino Acid Sequence', 'Animals', 'Caenorhabditis elegans', 'Caenorhabditis elegans Proteins', 'Chromatography, Liquid', 'Cloning, Molecular', 'Down-Regulation', 'Evolution, Molecular', 'Forkhead Transcription Factors', 'Garlic', 'Longevity', 'Mannose-Binding Lectin', 'Metabolomics', 'Molecular Sequence Data', 'Plant Extracts', 'Proteomics', 'Signal Transduction', 'Tandem Mass Spectrometry', 'Up-Regulation', 'Vitellogenins']",Analysis of lifespan-promoting effect of garlic extract by an integrated metabolo-proteomics approach.,"['Q000378', None, None, 'Q000235', 'Q000235', None, None, None, None, 'Q000235', 'Q000737', 'Q000187', 'Q000378', 'Q000379', None, 'Q000494', 'Q000379', None, None, None, 'Q000235']","['metabolism', None, None, 'genetics', 'genetics', None, None, None, None, 'genetics', 'chemistry', 'drug effects', 'metabolism', 'methods', None, 'pharmacology', 'methods', None, None, None, 'genetics']",https://www.ncbi.nlm.nih.gov/pubmed/25940980,2016,0.0,0.0,,, -25832010,"Terminal residues of pendimethalin and oxyfluorfen applied in autumn sugarcane- and vegetables-based intercropping systems were analyzed in peas (Pisum sativum), cabbage (Brassica oleracea), garlic (Allium sativum), gobhi sarson (Brassica napus), and raya (Brassica juncea). The study was conducted in winter season in 2010-2011 and in 2011-2012 at Ludhiana, India. Pendimethalin at 0.56 kg and 0.75 kg ha(-1) was applied immediately after sowing of gobhi sarson, raya, peas, garlic, and 2 days before transplanting of cabbage seedlings. Oxyfluorfen at 0.17 kg and 0.23 kg ha(-1) was applied immediately after sowing of peas and garlic and 2 days before transplanting of cabbage seedlings intercropped in autumn sugarcane. Representative samples of these vegetables were collected at 75, 90, 100, and 165 days after application of herbicides and analyzed by high-performance liquid chromatograph (HPLC) with diode array detector for residues. The residue level of pendimethalin and oxyfluorfen in mature vegetables was found to be below the limit of quantification which is 0.05 mg kg(-1) for both the herbicides. The soil samples were collected at 0, 7, 15, 30, 45, and 60 days after the application of their herbicides. The residues of herbicides in soil samples were found to be below the detectability limit of 0.05 mg kg(-1) after 60 days in case of pendimethalin and after 45 days in case of oxyfluorfen.",Environmental monitoring and assessment,"['D000814', 'D002851', 'D004784', 'D055768', 'D006540', 'D007194', 'D010573', 'D031786', 'D012987', 'D014675']","['Aniline Compounds', 'Chromatography, High Pressure Liquid', 'Environmental Monitoring', 'Halogenated Diphenyl Ethers', 'Herbicides', 'India', 'Pesticide Residues', 'Saccharum', 'Soil', 'Vegetables']",Harvest time residues of pendimethalin and oxyfluorfen in vegetables and soil in sugarcane-based intercropping systems.,"['Q000032', None, None, 'Q000032', 'Q000032', None, 'Q000032', 'Q000737', 'Q000737', 'Q000737']","['analysis', None, None, 'analysis', 'analysis', None, 'analysis', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25832010,2015,0.0,0.0,,, -25819001,"This study investigated terpene biosynthesis in different tissues (root, protobulb, leaf sheath and blade) of in vitro-grown garlic plants either infected or not (control) with Sclerotium cepivorum, the causative agent of Allium White Rot disease. The terpenes identified by gas chromatography-electron impact mass spectrometry (GC-EIMS) in infected plants were nerolidol, phytol, squalene, α-pinene, terpinolene, limonene, 1,8-cineole and γ-terpinene, whose levels significantly increased when exposed to the fungus. Consistent with this, an increase in terpene synthase (TPS) activity was measured in infected plants. Among the terpenes identified, nerolidol, α-pinene and terpinolene were the most abundant with antifungal activity against S. cepivorum being assessed in vitro by mycelium growth inhibition. Nerolidol and terpinolene significantly reduced sclerotia production, while α-pinene stimulated it in a concentration-dependent manner. Parallel to fungal growth inhibition, electron microscopy observations established morphological alterations in the hyphae exposed to terpinolene and nerolidol. Differences in hyphal EtBr uptake suggested that one of the antifungal mechanisms of nerolidol and terpinolene might be disruption of fungal membrane integrity. ",Phytochemistry,"['D000935', 'D001203', 'D001487', 'D003511', 'D053138', 'D005737', 'D008401', 'D039821', 'D018515', 'D018517', 'D012717', 'D013729']","['Antifungal Agents', 'Ascomycota', 'Basidiomycota', 'Cyclohexanols', 'Cyclohexenes', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Monoterpenes', 'Plant Leaves', 'Plant Roots', 'Sesquiterpenes', 'Terpenes']",Allium sativum produces terpenes with fungistatic properties in response to infection with Sclerotium cepivorum.,"['Q000032', None, 'Q000187', None, None, 'Q000737', None, None, 'Q000737', 'Q000737', None, None]","['analysis', None, 'drug effects', None, None, 'chemistry', None, None, 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25819001,2016,0.0,0.0,,, -25705718,"Aspergillus spp. associated with cashew from the regions of Riyadh, Dammam, and Abha were isolated and three different culture media were used to qualitatively measure aflatoxin production by Aspergillus via UV light (365 nm), which was expressed as positive or negative. The obtained data showed that six isolates of A. flavus and four isolates of A. parasiticus were positive for aflatoxin production, while all isolates of A. niger were negative. Five commercially essential oils (thyme, garlic, cinnamon, mint, and rosemary) were tested to determine their influence on growth and aflatoxin production in A. flavus and A. parasiticus by performing high-performance liquid chromatography (HPLC). The results showed that the tested essential oils caused highly significant inhibition of fungal growth and aflatoxin production in A. flavus and A. parasiticus. The extent of the inhibition of fungal growth and aflatoxin production was dependent on the type and concentration of essential oils applied. The results indicate that cinnamon and thyme oils show strong antimicrobial potential. PCR was used with four sets of primer pairs for nor-1, omt-1, ver-1, and aflR genes, enclosed in the aflatoxin biosynthetic pathway. The interpretation of the results revealed that PCR is a rapid and sensitive method.",TheScientificWorldJournal,"['D000348', 'D000498', 'D031021', 'D000704', 'D000890', 'D001230', 'D002851', 'D017931', 'D005453', 'D005506', 'D017343', 'D027541', 'D009754', 'D009822', 'D016133', 'D012529', 'D013045', 'D013440', 'D046930']","['Aflatoxins', 'Allyl Compounds', 'Anacardium', 'Analysis of Variance', 'Anti-Infective Agents', 'Aspergillus', 'Chromatography, High Pressure Liquid', 'DNA Primers', 'Fluorescence', 'Food Contamination', 'Genes, Plant', 'Mentha', 'Nuts', 'Oils, Volatile', 'Polymerase Chain Reaction', 'Saudi Arabia', 'Species Specificity', 'Sulfides', 'Thymus Plant']",Use of selected essential oils to control aflatoxin contaminated stored cashew and detection of aflatoxin biosynthesis gene.,"['Q000032', None, None, None, 'Q000494', 'Q000737', None, 'Q000235', None, 'Q000032', 'Q000235', None, 'Q000737', 'Q000494', 'Q000379', None, None, None, None]","['analysis', None, None, None, 'pharmacology', 'chemistry', None, 'genetics', None, 'analysis', 'genetics', None, 'chemistry', 'pharmacology', 'methods', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25705718,2016,0.0,0.0,,, -25679258,"Characterization of enzymatic reactions occurring in untreated biological samples is of increasing interest. Herein, the chemical conversion of alliin to allicin, catalyzed by allinase, in raw garlic cloves has been followed in vivo by internal extractive electrospray ionization mass spectrometry (iEESI-MS). Both precursors and products of the enzymatic reaction were instantaneously extracted by infused solution running throughout the tissue and directly electrospray ionized on the edge of the bulk sample for online MS analysis. Compared to the room-temperature (+25 °C) scenario, the alliin conversion in garlic cloves decreased by (7.2 ± 1.4) times upon heating to +80 °C and by (5.9 ± 0.8) times upon cooling to -16 °C. Exposure of garlic to gentle ultrasound irradiation for 3 h accelerated the reaction by (1.2 ± 0.1) times. A 10 s microwave irradiation promoted alliin conversion by (1.6 ± 0.4) times, but longer exposure to microwave irradiation (90 s) slowed the reaction by (28.5 ± 7.5) times compared to the reference analysis. This method has been further employed to monitor the germination process of garlic. These data revealed that over a 2 day garlic sprouting, the allicin/alliin ratio increased by (2.2 ± 0.5) times, and the averaged degree of polymerization for the detected oligosaccharides/polysaccharides decreased from 11.6 to 9.4. Overall, these findings suggest the potential use of iEESI-MS for in vivo studies of enzymatic reactions in native biological matrices. ",Analytical chemistry,"['D000490', 'D013437', 'D002851', 'D005737', 'D008872', 'D009844', 'D010936', 'D011134', 'D021241', 'D013441']","['Allium', 'Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Garlic', 'Microwaves', 'Oligosaccharides', 'Plant Extracts', 'Polysaccharides', 'Spectrometry, Mass, Electrospray Ionization', 'Sulfinic Acids']",Molecular characterization of ongoing enzymatic reactions in raw garlic cloves using extractive electrospray ionization mass spectrometry.,"['Q000378', 'Q000378', None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000379', 'Q000378']","['metabolism', 'metabolism', None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'methods', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/25679258,2015,0.0,0.0,,, -25619986,"A simple and sensitive multiresidue pesticide analysis method was developed and validated for 213 pesticides in leek and garlic based on QuEChERS (quick, easy, cheap, effective, rugged, and safe) procedure combined with gas chromatography-triple quadrupole mass spectrometry. In the QuEChERS method, commercial extraction salt packet, dispersive solid-phase extraction adsorbent packet, and ceramic homogenizer were used to simplify the extraction procedure. The gas chromatography-tandem mass spectrometry (GC-MS/MS) parameters were optimized for analysis of 213 pesticides within a 38-min run time with a limit of quantification for most of the pesticides at 2 μg kg(-1). The coefficient of determination (r(2)) was >0.99 within the calibration linearity range of 2-400 μg kg(-1). Most recoveries at 2, 5, 10, 20, 50, 100, and 200 μg kg(-1) were in the range of 70-120% (n = 6) with associated relative standard deviations (RSDs) of <20%, indicating satisfactory precision. Real leek and garlic samples were analyzed for method application.",Analytical and bioanalytical chemistry,[],[],Multiresidue analysis of 213 pesticides in leek and garlic using QuEChERS-based method and gas chromatography-triple quadrupole mass spectrometry.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/25619986,2015,0.0,0.0,,, -25608858,"Natural organosulfur compounds (OSCs) from Allium sativum L. display antioxidant and chemo-sensitization properties, including the in vitro inhibition of tumor cell proliferation through the induction of apoptosis. Garlic water- and oil-soluble allyl sulfur compounds show distinct properties and the capability to inhibit the proliferation of tumor cells. In the present study, we optimized a new protocol for the extraction of water-soluble compounds from garlic at low temperatures and the production of glutathionyl-OSC conjugates during the extraction. Spontaneously, Cys/GSH-mixed-disulfide conjugates are produced by in vivo metabolism of OSCs and represent active molecules able to affect cellular metabolism. Water-soluble extracts, with (GSGaWS) or without (GaWS) glutathione conjugates, were here produced and tested for their ability to release hydrogen sulfide (H2S), also in the presence of reductants and of thiosulfate:cyanide sulfurtransferase (TST) enzyme. Thus, the TST catalysis of the H2S-release from garlic OSCs and their conjugates has been investigated by molecular in vitro experiments. The antiproliferative properties of these extracts on the human T-cell lymphoma cell line, HuT 78, were observed and related to histone hyperacetylation and downregulation of GAPDH expression. Altogether, the results presented here pave the way for the production of a GSGaWS as new, slowly-releasing hydrogen sulfide extract for potential therapeutic applications. ","Molecules (Basel, Switzerland)","['D055162', 'D045744', 'D049109', 'D002851', 'D056148', 'D003080', 'D005737', 'D005978', 'D006801', 'D006862', 'D016399', 'D008856', 'D008970', 'D010936', 'D019163', 'D012995', 'D013455', 'D013457', 'D013879', 'D013884', 'D014867']","['Biocatalysis', 'Cell Line, Tumor', 'Cell Proliferation', 'Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Cold Temperature', 'Garlic', 'Glutathione', 'Humans', 'Hydrogen Sulfide', 'Lymphoma, T-Cell', 'Microscopy, Fluorescence', 'Molecular Weight', 'Plant Extracts', 'Reducing Agents', 'Solubility', 'Sulfur', 'Sulfur Compounds', 'Thioredoxins', 'Thiosulfate Sulfurtransferase', 'Water']",Glutathione-garlic sulfur conjugates: slow hydrogen sulfide releasing agents for therapeutic applications.,"['Q000187', None, 'Q000187', None, None, None, 'Q000737', 'Q000378', None, 'Q000378', 'Q000473', None, None, 'Q000737', 'Q000494', None, 'Q000378', 'Q000494', 'Q000378', 'Q000037', 'Q000737']","['drug effects', None, 'drug effects', None, None, None, 'chemistry', 'metabolism', None, 'metabolism', 'pathology', None, None, 'chemistry', 'pharmacology', None, 'metabolism', 'pharmacology', 'metabolism', 'antagonists & inhibitors', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25608858,2015,0.0,0.0,,, -25589062,"Heterocyclic amines (HCAs) are known to be suspected human carcinogens produced by high-temperature cooking of protein-rich foods such as meat and fish. In the present study, the influence of numerous food condiments on the formation of HCAs in cooked chicken was investigated. Chicken samples were subjected to pan-frying under controlled temperature. The levels of HCAs DMIP, MeIQx, 4,8-DiMeIQx, PhIP, harman and norharman were found to be between 0.93 and 27.52 ng g(-1), whereas IQ, MeIQ, AαC, MeAαC, Trp-P-1 and Trp-P-2 were found either below the limit of quantification or not detected in the control sample. Nevertheless, for samples cooked using food condiments, the amounts of HCAs (DMIP, MeIQx, 4,8-DiMeIQx and PhIP) were between 0.14 and 19.57 ng g(-1); harman and norharman were detected at higher concentrations up to 17.77 ng g(-1) while IQ and MeIQ were at levels below the limit of quantification; and AαC, MeAαC, Trp-P-1 and Trp-P-2 were not detected in any of the samples. The outcomes revealed that the formation of HCAs (except harman and norharman) diminished after the addition of food condiments. Edible oil contributed to the highest levels of HCA formation, followed by garlic, paprika, pepper and tomato.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000588', 'D000818', 'D002273', 'D002645', 'D002853', 'D003212', 'D003296', 'D006571', 'D006358', 'D008460', 'D053719']","['Amines', 'Animals', 'Carcinogens', 'Chickens', 'Chromatography, Liquid', 'Condiments', 'Cooking', 'Heterocyclic Compounds', 'Hot Temperature', 'Meat', 'Tandem Mass Spectrometry']",Influence of food condiments on the formation of carcinogenic heterocyclic amines in cooked chicken and determination by LC-MS/MS.,"['Q000032', None, 'Q000032', None, None, 'Q000032', 'Q000379', 'Q000032', None, 'Q000032', None]","['analysis', None, 'analysis', None, None, 'analysis', 'methods', 'analysis', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/25589062,2016,0.0,0.0,,, -25438250,"The antimicrobial activities of garlic and other plant alliums are primarily based on allicin, a thiosulphinate present in crushed garlic bulbs. We set out to determine if pure allicin and aqueous garlic extracts (AGE) exhibit antimicrobial properties against the Burkholderia cepacia complex (Bcc), the major bacterial phytopathogen for alliums and an intrinsically multiresistant and life-threatening human pathogen. We prepared an AGE from commercial garlic bulbs and used HPLC to quantify the amount of allicin therein using an aqueous allicin standard (AAS). Initially we determined the minimum inhibitory concentrations (MICs) of the AGE against 38 Bcc isolates; these MICs ranged from 0.5 to 3% (v/v). The antimicrobial activity of pure allicin (AAS) was confirmed by MIC and minimum bactericidal concentration (MBC) assays against a smaller panel of five Bcc isolates; these included three representative strains of the most clinically important species, B. cenocepacia. Time kill assays, in the presence of ten times MIC, showed that the bactericidal activity of AGE and AAS against B. cenocepacia C6433 correlated with the concentration of allicin. We also used protein mass spectrometry analysis to begin to investigate the possible molecular mechanisms of allicin with a recombinant form of a thiol-dependent peroxiredoxin (BCP, Prx) from B. cenocepacia. This revealed that AAS and AGE modifies an essential BCP catalytic cysteine residue and suggests a role for allicin as a general electrophilic reagent that targets protein thiols. To our knowledge, we report the first evidence that allicin and allicin-containing garlic extracts possess inhibitory and bactericidal activities against the Bcc. Present therapeutic options against these life-threatening pathogens are limited; thus, allicin-containing compounds merit investigation as adjuncts to existing antibiotics. ",PloS one,"['D000900', 'D042602', 'D005737', 'D010936', 'D013441', 'D014867']","['Anti-Bacterial Agents', 'Burkholderia cepacia complex', 'Garlic', 'Plant Extracts', 'Sulfinic Acids', 'Water']",Garlic revisited: antimicrobial activity of allicin-containing garlic extracts against Burkholderia cepacia complex.,"['Q000737', 'Q000187', 'Q000737', 'Q000737', 'Q000032', 'Q000737']","['chemistry', 'drug effects', 'chemistry', 'chemistry', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25438250,2016,0.0,0.0,,, -25435627,"Garlic oil which is the main active constituent of garlic has a wide range of pharmacological activities, and a broad antibacterial spectrum. It also has a strong anti-cancer activity, and can significantly inhibit a variety of tumors such as liver cancer, gastric cancer and colon cancer. The objective is to study the extraction process of garlic oil and its antibacterial effects.","African journal of traditional, complementary, and alternative medicines : AJTCAM","['D000498', 'D000900', 'D001412', 'D025924', 'D004926', 'D005737', 'D013211', 'D013440']","['Allyl Compounds', 'Anti-Bacterial Agents', 'Bacillus subtilis', 'Chromatography, Supercritical Fluid', 'Escherichia coli', 'Garlic', 'Staphylococcus aureus', 'Sulfides']",Experimental study on the optimization of extraction process of garlic oil and its antibacterial effects.,"['Q000302', 'Q000302', 'Q000187', 'Q000379', 'Q000187', 'Q000737', 'Q000187', 'Q000302']","['isolation & purification', 'isolation & purification', 'drug effects', 'methods', 'drug effects', 'chemistry', 'drug effects', 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/25435627,2015,0.0,0.0,,, -25435245,"The present study reported on an in situ solvothermal growth method for immobilization of metal-organic framework MOF-5 on porous copper foam support for enrichment of plant volatile sulfides. The porous copper support impregnated with mother liquor of MOF-5 anchors the nucleation and growth of MOF crystallites at its surface, and its architecture of the three-dimensional channel enables accommodation of the MOF-5 crystallite seed. A continuous and well-intergrown MOF-5 layer, evidenced from scanning electron microscope imaging and X-ray diffraction, was successfully immobilized on the porous metal bar with good adhesion and high stability. Results show that the resultant MOF-5 coating was thermally stable up to 420 °C and robust enough for replicate extraction for at least 200 times. The MOF-5 bar was then applied to the headspace sorptive extraction of the volatile organic sulfur compounds in Chinese chive and garlic sprout in combination with thermal desorption-gas chromatography/mass spectrometry. It showed high extraction sensitivity and good selectivity to these plant volatile sulfides owing to the extraordinary porosity of the metal-organic framework as well as the interaction between the S-donor sites and the surface cations at the crystal edges. Several primary sulfur volatiles containing allyl methyl sulfide, dimethyl disulfide, diallyl sulfide, methyl allyl disulfide, and diallyl disulfide were quantified. Their limits of detection were found to be in the range of 0.2-1.7 μg/L. The organic sulfides were detected in the range of 6.0-23.8 μg/g with recoveries of 76.6-100.2% in Chinese chive and 11.4-54.6 μg/g with recoveries of 77.1-99.8% in garlic sprout. The results indicate the immobilization of MOF-5 on copper foam provides an efficient enrichment formats for noninvasive sampling of plant volatiles. ",Analytical chemistry,"['D000490', 'D000498', 'D003300', 'D005737', 'D008401', 'D009942', 'D016062', 'D013440', 'D055549', 'D014961']","['Allium', 'Allyl Compounds', 'Copper', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Organometallic Compounds', 'Porosity', 'Sulfides', 'Volatile Organic Compounds', 'X-Ray Diffraction']",In situ solvothermal growth of metal-organic framework-5 supported on porous copper foam for noninvasive sampling of plant volatile sulfides.,"['Q000737', 'Q000032', 'Q000737', 'Q000737', None, 'Q000737', None, 'Q000032', 'Q000032', None]","['chemistry', 'analysis', 'chemistry', 'chemistry', None, 'chemistry', None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/25435245,2015,1.0,1.0,,, -25423012,"Novel and inexpensive methods of thin-layer chromatography (TLC) were employed for the extraction, characterisation and mechanism of quorum sensing inhibition by ajoene, a component from toluene garlic bulb (Allium sativum L.) extract (TGE). TLC profiling of TGE was carried out using ethyl acetate as solvent. Out of total spots extracted from TLC, four spots exhibited quorum sensing inhibitory (QSI) potential. Among those, spot 5 was identified as Z-ajoene by TLC and confirmed by NMR and MS. HPLC analysis indicated 97.7% purity for purified ajoene. TLC densitometric analysis quantified 221.08 μmol/g of ajoene in TGE and indicated that ajoene is stable at 4°C and at acidic pH. HPTLC profiling showed that ajoene exhibits QSI effect by inhibiting the production of both long-chain acyl homoserine lactones and Pseudomonas quinolone signal (PQS) by P. aeruginosa and also by inactivating PQS molecules.",Natural product research,"['D000900', 'D002855', 'D004220', 'D005737', 'D011550', 'D053038', 'D012997']","['Anti-Bacterial Agents', 'Chromatography, Thin Layer', 'Disulfides', 'Garlic', 'Pseudomonas aeruginosa', 'Quorum Sensing', 'Solvents']",Applications of thin-layer chromatography in extraction and characterisation of ajoene from garlic bulbs.,"['Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000187', 'Q000187', None]","['chemistry', 'methods', 'chemistry', 'chemistry', 'drug effects', 'drug effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/25423012,2015,,,,,True -25420111,"Aged garlic extract (AGE) is widely used as a dietary supplement, and is claimed to promote human health through anti-oxidant/anti-inflammatory activities with hypolipidemic, antiplatelet and neuroprotective effects. Prior studies of AGE have mainly focused on its organosulfur compounds, with little attention paid to its carbohydrate derivatives, such as N-α-(1-deoxy-D-fructos-1-yl)-L-arginine (FruArg). The goal of this study is to investigate actions of AGE and FruArg on antioxidative and neuroinflammatory responses in lipopolysaccharide (LPS)-activated murine BV-2 microglial cells using a proteomic approach. Our data show that both AGE and FruArg can significantly inhibit LPS-induced nitric oxide (NO) production in BV-2 cells. Quantitative proteomic analysis by combining two dimensional differential in-gel electrophoresis (2D-DIGE) with mass spectrometry revealed that expressions of 26 proteins were significantly altered upon LPS exposure, while levels of 20 and 21 proteins exhibited significant changes in response to AGE and FruArg treatments, respectively, in LPS-stimulated BV-2 cells. Notably, approximate 78% of the proteins responding to AGE and FruArg treatments are in common, suggesting that FruArg is a major active component of AGE. MULTICOM-PDCN and Ingenuity Pathway Analyses indicate that the proteins differentially affected by treatment with AGE and FruArg are involved in inflammatory responses and the Nrf2-mediated oxidative stress response. Collectively, these results suggest that AGE and FruArg attenuate neuroinflammatory responses and promote resilience in LPS-activated BV-2 cells by suppressing NO production and by regulating expression of multiple protein targets associated with oxidative stress. ",PloS one,"['D000818', 'D000893', 'D015153', 'D002460', 'D004151', 'D004305', 'D015180', 'D005737', 'D008070', 'D051379', 'D017628', 'D015394', 'D009569', 'D010936', 'D020543', 'D040901', 'D015398', 'D053719', 'D013997']","['Animals', 'Anti-Inflammatory Agents', 'Blotting, Western', 'Cell Line', 'Dipeptides', 'Dose-Response Relationship, Drug', 'Electrophoresis, Gel, Two-Dimensional', 'Garlic', 'Lipopolysaccharides', 'Mice', 'Microglia', 'Molecular Structure', 'Nitric Oxide', 'Plant Extracts', 'Proteome', 'Proteomics', 'Signal Transduction', 'Tandem Mass Spectrometry', 'Time Factors']",Proteomic analysis of the effects of aged garlic extract and its FruArg component on lipopolysaccharide-induced neuroinflammatory response in microglial cells.,"[None, 'Q000494', None, None, 'Q000737', None, None, 'Q000737', 'Q000494', None, 'Q000166', None, 'Q000037', 'Q000494', 'Q000032', None, 'Q000187', None, None]","[None, 'pharmacology', None, None, 'chemistry', None, None, 'chemistry', 'pharmacology', None, 'cytology', None, 'antagonists & inhibitors', 'pharmacology', 'analysis', None, 'drug effects', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25420111,2016,0.0,0.0,,, -25401128,"An acidic peroxidase was extracted from garlic (Allium sativum) and was partially purified threefold by ammonium sulphate precipitation, dialysis, and gel filtration chromatography using sephadex G-200. The specific activity of the enzyme increased from 4.89 U/mg after ammonium sulphate precipitation to 25.26 U/mg after gel filtration chromatography. The optimum temperature and pH of the enzyme were 50°C and 5.0, respectively. The Km and V max for H2O2 and o-dianisidine were 0.026 mM and 0.8 U/min, and 25 mM and 0.75 U/min, respectively. Peroxidase from garlic was effective in decolourizing Vat Yellow 2, Vat Orange 11, and Vat Black 27 better than Vat Green 9 dye. For all the parameters monitored, the decolourization was more effective at a pH range, temperature, H2O2 concentration, and enzyme concentration of 4.5-5.0, 50°C, 0.6 mM, and 0.20 U/mL, respectively. The observed properties of the enzyme together with its low cost of extraction (from local sources) show the potential of this enzyme for practical application in industrial wastewater treatment especially with hydrogen peroxide. These Vat dyes also exhibited potentials of acting as peroxidase inhibitors at alkaline pH range.",TheScientificWorldJournal,"['D002850', 'D004396', 'D005737', 'D006861', 'D007221', 'D009195', 'D062065']","['Chromatography, Gel', 'Coloring Agents', 'Garlic', 'Hydrogen Peroxide', 'Industry', 'Peroxidase', 'Waste Water']",Biobleaching of industrial important dyes with peroxidase partially purified from garlic.,"['Q000379', 'Q000737', 'Q000201', 'Q000737', 'Q000191', 'Q000737', 'Q000737']","['methods', 'chemistry', 'enzymology', 'chemistry', 'economics', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25401128,2015,0.0,0.0,,, -25373222,"The fumigant, contact, and repellent activities of four essential oils extracted from Citrus limonum (Sapindales: Rutaceae), Litsea cubeba (Laurales: Lauraceae), Cinnamomum cassia, and Allium sativum L. (Asparagales: Alliaceae) against 6th instars and adults of the darkling beetle, Alphitobius diaperinus (Panzer) (Coleoptera: Tenebrionidae), one of the main pests of materials and products of Juncus effuses L. (Poales: Juncaceae) during the storage period, were assayed, and chemical ingredients were analyzed with gas chromatography-mass spectrometry in this study. While the major ingredients found in C. limonum and C. cassia were limonene and (E)-cinnamaldehyde, the main constituents of L. cubea were D-limonene, (E)-3,7-dimethyl-,2,6-octadienal, (Z)-3,7-dimethyl,2 ,6-octadienal, and diallyl disulphide (18.20%), while the main constituents of and A. sativum were di-2-propenyl trisulfide and di-2-propenyl tetrasulfide. The fumigation activities of A. sativum and C. limonum on A. diaperinus adults were better than those of the other two essential oilss. The toxicities of A. sativum and C. limonum were almost equitoxic at 96 hr after treatment. Essential oils from Allium sativum and L. cubeba also showed good contact activities from 24 hr to 48 hr, and toxicities were almost equitoxic 48 hr posttreatment. The repellent activities of A. sativum and L. cubeba oils on 6th instars were also observed, showing repellence indexes of 90.4% and 88.9% at 12 hr after treatment, respectively. The effects of A. sativum on AChE activity of 6th instars of A. diaperinus were strongest compared to the other essential oils, followed by C. limonum, L. cubeba, and C. cassia. These results suggest that the essential oils of C. limonum and A. sativum could serve as effective control agents of A. diaperinus.",Journal of insect science (Online),"['D000818', 'D002800', 'D032904', 'D002957', 'D001517', 'D005651', 'D005737', 'D007302', 'D007306', 'D032862', 'D009822', 'D018675']","['Animals', 'Cholinesterase Inhibitors', 'Cinnamomum aromaticum', 'Citrus', 'Coleoptera', 'Fumigation', 'Garlic', 'Insect Repellents', 'Insecticides', 'Litsea', 'Oils, Volatile', 'Toxicity Tests']","Fumigant, contact, and repellent activities of essential oils against the darkling beetle, Alphitobius diaperinus.","[None, 'Q000032', 'Q000737', 'Q000737', None, None, 'Q000737', 'Q000032', 'Q000032', 'Q000737', 'Q000737', None]","[None, 'analysis', 'chemistry', 'chemistry', None, None, 'chemistry', 'analysis', 'analysis', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/25373222,2015,2.0,2.0,,, -25371585,"The medicinal use of garlic is much older than its usage as a food. The medical importance of garlic comes forward for its sulfur-containing components. In this study, it was aimed to compare Kastamonu garlic type with Chinese garlic type based on their aroma profiles.","African journal of traditional, complementary, and alternative medicines : AJTCAM","['D002681', 'D005737', 'D008401', 'D010936', 'D014421', 'D055549']","['China', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Plant Extracts', 'Turkey', 'Volatile Organic Compounds']",Comparitive study on volatile aroma compounds of two different garlic types (Kastamonu and Chinese) using gas chromatography mass spectrometry (HS-GC/MS) technique.,"[None, 'Q000737', None, 'Q000737', None, 'Q000737']","[None, 'chemistry', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25371585,2015,2.0,3.0,,, -25329784,"Sulfur-containing odorants and flavors play an important role in flavor and food industry, especially when meaty, garlic, onion, and vegetable scents are needed. Still, many S-containing flavors also possess fruity scents and may be used in compositions of perfumes that require a fresh and fruity odor perception. They are naturally abundant in various fruits, essential oils, and food. Most of these compounds possess strong scents, and their scent composition is highly dependent on the concentration applied. At higher concentrations, they usually feature repellent off-odors, while their sweet and fruity nature is only experienced at very low concentrations. This represents a challenge for their application in perfumery, as well as in food industry. From a molecular point of view, the endless structural and scent variety of these compounds makes them fascinating, especially as their O-analogs are usually free of any malodors. Here, we report on the investigation of the gas-phase structure and dynamics of the S-containing blackcurrant odorant cat ketone (4-methyl-4-sulfanylpentan-2-one). The work was performed by combining quantum-chemical calculations and molecular-beam Fourier-transform microwave spectroscopy as complementary tools. Using this technique, we are able to determine the structures of sizeable molecules where energy differences are small and conformational distinction is not always possible by quantum-chemical calculations alone. The presented results can be used for further structure-activity correlation studies, as well as for benchmarks to improve theoretical models, especially, as there is still significant interest in characterizing the various conformers of organic molecules in terms of relative energies, structures, and dipole moments.",Chemistry & biodiversity,"['D000818', 'D002415', 'D005421', 'D005638', 'D005663', 'D008401', 'D005740', 'D007659', 'D008872', 'D008956', 'D008968', 'D015394', 'D009812', 'D031965', 'D017550', 'D013237', 'D013329', 'D013455']","['Animals', 'Cats', 'Flavoring Agents', 'Fruit', 'Furans', 'Gas Chromatography-Mass Spectrometry', 'Gases', 'Ketones', 'Microwaves', 'Models, Chemical', 'Molecular Conformation', 'Molecular Structure', 'Odorants', 'Ribes', 'Spectroscopy, Fourier Transform Infrared', 'Stereoisomerism', 'Structure-Activity Relationship', 'Sulfur']",From cats and blackcurrants: structure and dynamics of the sulfur-containing cassis odorant cat ketone.,"[None, None, 'Q000737', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', None, None, None, None, 'Q000032', 'Q000737', None, None, None, 'Q000737']","[None, None, 'chemistry', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', None, None, None, None, 'analysis', 'chemistry', None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25329784,2015,0.0,0.0,,, -25205359,"Garlic is one of the most important bulb vegetables and is mainly used as a spice or flavoring agent for foods. It is also cultivated for its medicinal properties, attributable to sulfur compounds, of which allicin is the most important. However, the stability of allicin in garlic extract is not well understood. In this study, using UPLC, the stability of allicin extracted in water from garlic was evaluated in phosphate buffer at different temperatures under light and dark conditions.",Journal of the science of food and agriculture,"['D000890', 'D002681', 'D002851', 'D005520', 'D061353', 'D005737', 'D006358', 'D006863', 'D008027', 'D009994', 'D010936', 'D018517', 'D013441']","['Anti-Infective Agents', 'China', 'Chromatography, High Pressure Liquid', 'Food Preservatives', 'Food Storage', 'Garlic', 'Hot Temperature', 'Hydrogen-Ion Concentration', 'Light', 'Osmolar Concentration', 'Plant Extracts', 'Plant Roots', 'Sulfinic Acids']","Influence of pH, concentration and light on stability of allicin in garlic (Allium sativum L.) aqueous extract as measured by UPLC.","['Q000032', None, None, 'Q000032', None, 'Q000737', 'Q000009', None, 'Q000009', None, 'Q000737', 'Q000737', 'Q000032']","['analysis', None, None, 'analysis', None, 'chemistry', 'adverse effects', None, 'adverse effects', None, 'chemistry', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/25205359,2016,1.0,1.0,,, -25101875,"The aerotolerant hydrogenosome-containing piscine diplomonad, Spironucleus vortens, is able to withstand high fluctuations in Oâ‚‚ tensions during its life cycle. In the current study, we further investigated the Oâ‚‚ scavenging and antioxidant defence mechanisms which facilitate the survival of S. vortens under such oxidizing conditions. Closed Oâ‚‚ electrode measurements revealed that the S. vortens ATCC 50386 strain was more Oâ‚‚ tolerant than a freshly isolated S. vortens intestinal strain (Sv1). In contrast to the related human diplomonad, Giardia intestinalis, RP-HPLC revealed the major non-protein thiols of S. vortens to be glutathione (GSH, 776 nmol/10â· cells) with cysteine and H2S as minor peaks. Furthermore, antioxidant proteins of S. vortens were assayed enzymatically and revealed that S. vortens possesses superoxide dismutase and NADH oxidase (883 and 37.5nmol/min/mg protein, respectively), but like G. intestinalis, lacks catalase and peroxidase activities. Autofluorescence of NAD(P)H and FAD alongside the fluorescence of the GSH-adduct in monochlorobimane-treated live organisms allowed the monitoring of redox balances before and after treatment with inhibitors, metronidazole and auranofin. Hâ‚‚Oâ‚‚ was emitted into the exterior of S. vortens at a rate of 2.85 pmol/min/10ⶠcells. Metronidazole and auranofin led to depletion of S. vortens intracellular NAD(P)H pools and an increase in Hâ‚‚Oâ‚‚ release with concomitant oxidation of GSH, respectively. Garlic-derived compounds completely inhibited Oâ‚‚ consumption by S. vortens (ajoene oil), or significantly depleted the intracellular GSH pool of the organism (allyl alcohol and DADS). Hence, antioxidant defence mechanisms of S. vortens may provide novel targets for parasite chemotherapy.",Molecular and biochemical parasitology,"['D002851', 'D003545', 'D016828', 'D005978', 'D009097', 'D009247', 'D018384', 'D010100', 'D013312', 'D013447', 'D013482']","['Chromatography, High Pressure Liquid', 'Cysteine', 'Diplomonadida', 'Glutathione', 'Multienzyme Complexes', 'NADH, NADPH Oxidoreductases', 'Oxidative Stress', 'Oxygen', 'Stress, Physiological', 'Sulfites', 'Superoxide Dismutase']",Antioxidant defences of Spironucleus vortens: Glutathione is the major non-protein thiol.,"[None, 'Q000032', 'Q000737', 'Q000378', 'Q000032', 'Q000032', None, 'Q000378', None, 'Q000032', 'Q000032']","[None, 'analysis', 'chemistry', 'metabolism', 'analysis', 'analysis', None, 'metabolism', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/25101875,2015,0.0,0.0,,, -25049964,"The competency of garlic and pennywort to improve broiler chicken growth and influence intestinal microbial communities and fatty acid composition of breast meat were studied. Two hundred forty, ""day-old"" chicks were randomly allocated to 4 treatment groups consisting of 6 replications of 10 chicks in each pen. The groups were assigned to receive treatment diets as follows: i) basal diet (control), ii) basal diet plus 0.5% garlic powder (GP), iii) basal diet plus 0.5% pennywort powder (PW) and iv) 0.002% virginiamycin (VM). Birds were killed at day 42 and intestinal samples were collected to assess for Lactobacillus and Escherichia coli. The pectoralis profundus from chicken breast samples was obtained from 10 birds from each treatment group on day 42 and frozen at -20°C for further analyses. Fatty acid profile of breast muscles was determined using gas liquid chromatography. Feed intake and weight gain of broilers fed with GP, PW, and VM were significantly higher (p<0.05) compared to control. Feeding chicks GP, PW, and VM significantly reduced Escherichia coli count (p<0.05) while Lactobacillus spp count were significantly higher (p<0.05) in the gut when compared to control group on day 42. Supplemented diet containing pennywort increased the C18:3n-3 fatty acid composition of chickens' breast muscle. Garlic and pennywort may be useful in modulating broiler guts as they control the enteropathogens that help to utilize feed efficiently. This subsequently enhances the growth performances of broiler chickens. ",Asian-Australasian journal of animal sciences,[],[],"Effects of two herbal extracts and virginiamycin supplementation on growth performance, intestinal microflora population and Fatty Acid composition in broiler chickens.",[],[],https://www.ncbi.nlm.nih.gov/pubmed/25049964,2014,0.0,0.0,,, -25038704,"Allium genus is a treasure trove of valuable bioactive compounds with potentially therapeutically important properties. This work utilises HPLC-MS and a constrained total-line-shape (CTLS) approach applied to (1)H NMR spectra to quantify metabolites present in onion species to reveal important inter-species differences. Extensive differences were detected between the sugar concentrations in onion species. Yellow onion contained the highest and red onion the lowest amounts of amino acids. The main flavonol-glucosides were quercetin 3,4'-diglucoside and quercetin 4'-glucoside. In general, the levels of flavonols were, higher in yellow onions than in red onions, and garlic and leek contained a lower amount of flavonols than the other Allium species. Our results highlight how (1)H NMR together with HPLC-MS can be useful in the quantification and the identification of the most abundant metabolites, representing an efficient means to pinpoint important functional food ingredients from Allium species. ",Food chemistry,"['D000596', 'D050260', 'D002241', 'D002851', 'D044948', 'D008279', 'D009682', 'D013058', 'D055442', 'D019697', 'D010936']","['Amino Acids', 'Carbohydrate Metabolism', 'Carbohydrates', 'Chromatography, High Pressure Liquid', 'Flavonols', 'Magnetic Resonance Imaging', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Metabolome', 'Onions', 'Plant Extracts']",Quantitative metabolite profiling of edible onion species by NMR and HPLC-MS.,"['Q000737', None, 'Q000737', 'Q000379', 'Q000737', None, 'Q000379', 'Q000379', None, 'Q000737', 'Q000737']","['chemistry', None, 'chemistry', 'methods', 'chemistry', None, 'methods', 'methods', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25038704,2015,1.0,4.0,,, -25012787,"Garlic oil is a kind of fungicide, but little is known about its antifungal effects and mechanism. In this study, the chemical constituents, antifungal activity, and effects of garlic oil were studied with Penicillium funiculosum as a model strain. Results showed that the minimum fungicidal concentrations (MFCs, v/v) were 0.125 and 0.0313 % in agar medium and broth medium, respectively, suggesting that the garlic oil had a strong antifungal activity. The main ingredients of garlic oil were identified as sulfides, mainly including disulfides (36 %), trisulfides (32 %) and monosulfides (29 %) by gas chromatograph-mass spectrometer (GC/MS), which were estimated as the dominant antifungal factors. The observation results by transmission electron microscope (TEM) and scanning electron microscope (SEM) indicated that garlic oil could firstly penetrate into hyphae cells and even their organelles, and then destroy the cellular structure, finally leading to the leakage of both cytoplasm and macromolecules. Further proteomic analysis displayed garlic oil was able to induce a stimulated or weakened expression of some key proteins for physiological metabolism. Therefore, our study proved that garlic oil can work multiple sites of the hyphae of P. funiculosum to cause their death. The high antifungal effects of garlic oil makes it a broad application prospect in antifungal industries.",Applied microbiology and biotechnology,"['D000498', 'D000935', 'D005737', 'D008401', 'D025301', 'D010407', 'D010936', 'D013440']","['Allyl Compounds', 'Antifungal Agents', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Hyphae', 'Penicillium', 'Plant Extracts', 'Sulfides']",Antifungal effect and mechanism of garlic oil on Penicillium funiculosum.,"['Q000737', 'Q000737', 'Q000737', None, 'Q000187', 'Q000187', 'Q000737', 'Q000737']","['chemistry', 'chemistry', 'chemistry', None, 'drug effects', 'drug effects', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25012787,2015,2.0,3.0,,, -24987429,"Objective. To detect the effect of selenium-enriched garlic oil (Se-garlic oil) against cytotoxicity induced by ox-LDL in endothelial cells. Methods. Se-garlic oil was extracted by organic solvent extraction. High performance liquid chromatography (HPLC) was used to detect the content of allicin in the Se-garlic oil. Hydride generation atomic fluorescence spectrometry (HG-AFS) was used to detect the content of Se in the Se-garlic oil. ECV-304 cells were separated into five groups (blank, ox-LDL, and low-, medium-, and high-dose Se-garlic oil). Methyl thiazolyl tetrazolium (MTT) assay was used to detect the cytoactivity of each cell group after culturing for 24, 48, and 72 hours. Flow cytometry (FCM) stained with annexin V-FITC/PI was used to detect the apoptosis of the cells from the blank, Se-garlic oil, ox-LDL, and Se-garlic oil + ox-ldl groups after 48 hours of incubation. Results. The amount of allicin in Se-garlic oil was 142.66 mg/ml, while, in Se, it was 198 mg/kg. When ox-LDL was added to low-, medium-, and high-dose Se-garlic oil, the cell viability rates of ECV-304 cells treated in the three groups were all higher, while the apoptosis rates were significantly lower than those of the ox-LDL group (P < 0.05). However, there was no significant difference between the apoptosis rates of the blank, Se-garlic oil, and Se-garlic oil + ox-LDL groups (P > 0.05). Conclusion. Se-garlic oil could inhibit the cytotoxic effect induced by ox-LDL in endothelial cells. ",Evidence-based complementary and alternative medicine : eCAM,[],[],Effect of Selenium-Enriched Garlic Oil against Cytotoxicity Induced by OX-LDL in Endothelial Cells.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/24987429,2014,0.0,0.0,,, -24948941,"The endophytic fungus strain 0248, isolated from garlic, was identified as Trichoderma brevicompactum based on morphological characteristics and the nucleotide sequences of ITS1-5.8S- ITS2 and tef1. The bioactive compound T2 was isolated from the culture extracts of this fungus by bioactivity-guided fractionation and identified as 4β-acetoxy-12,13- epoxy-Δ(9)-trichothecene (trichodermin) by spectral analysis and mass spectrometry. Trichodermin has a marked inhibitory activity on Rhizoctonia solani, with an EC50 of 0.25 μg mL(-1). Strong inhibition by trichodermin was also found for Botrytis cinerea, with an EC50 of 2.02 μg mL(-1). However, a relatively poor inhibitory effect was observed for trichodermin against Colletotrichum lindemuthianum (EC50 = 25.60 μg mL(-1)). Compared with the positive control Carbendazim, trichodermin showed a strong antifungal activity on the above phytopathogens. There is little known about endophytes from garlic. This paper studied in detail the identification of endophytic T. brevicompactum from garlic and the characterization of its active metabolite trichodermin.",Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology],"['D000935', 'D020171', 'D016000', 'D020231', 'D004271', 'D004275', 'D021903', 'D060026', 'D005737', 'D013058', 'D008826', 'D008969', 'D020648', 'D010802', 'D012340', 'D012232', 'D017422', 'D014242', 'D014243']","['Antifungal Agents', 'Botrytis', 'Cluster Analysis', 'Colletotrichum', 'DNA, Fungal', 'DNA, Ribosomal', 'DNA, Ribosomal Spacer', 'Endophytes', 'Garlic', 'Mass Spectrometry', 'Microbial Sensitivity Tests', 'Molecular Sequence Data', 'Peptide Elongation Factor 1', 'Phylogeny', 'RNA, Ribosomal, 5.8S', 'Rhizoctonia', 'Sequence Analysis, DNA', 'Trichoderma', 'Trichodermin']",Antifungal activity of metabolites of the endophytic fungus Trichoderma brevicompactum from garlic.,"['Q000302', 'Q000187', None, 'Q000187', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000382', None, None, None, 'Q000235', None, 'Q000235', 'Q000187', None, 'Q000737', 'Q000302']","['isolation & purification', 'drug effects', None, 'drug effects', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'microbiology', None, None, None, 'genetics', None, 'genetics', 'drug effects', None, 'chemistry', 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/24948941,2015,0.0,0.0,,, -24926564,"S-Nitrosylation is a redox-based protein post-translational modification in response to nitric oxide signaling and is involved in a wide range of biological processes. Detection and quantification of protein S-nitrosylation have been challenging tasks due to instability and low abundance of the modification. Many studies have used mass spectrometry (MS)-based methods with different thiol-reactive reagents to label and identify proteins with S-nitrosylated cysteine (SNO-Cys). In this study, we developed a novel iodoTMT switch assay (ISA) using an isobaric set of thiol-reactive iodoTMTsixplex reagents to specifically detect and quantify protein S-nitrosylation. Irreversible labeling of SNO-Cys with the iodoTMTsixplex reagents enables immune-affinity detection of S-nitrosylated proteins, enrichment of iodoTMT-labeled peptides by anti-TMT resin, and importantly, unambiguous modification site-mapping and multiplex quantification by liquid chromatography-tandem MS. Additionally, we significantly improved anti-TMT peptide enrichment efficiency by competitive elution. Using ISA, we identified a set of SNO-Cys sites responding to lipopolysaccharide (LPS) stimulation in murine BV-2 microglial cells and revealed effects of S-allyl cysteine from garlic on LPS-induced protein S-nitrosylation in antioxidative signaling and mitochondrial metabolic pathways. ISA proved to be an effective proteomic approach for quantitative analysis of S-nitrosylation in complex samples and will facilitate the elucidation of molecular mechanisms of nitrosative stress in disease. ",Journal of proteome research,"['D000818', 'D002460', 'D007461', 'D008070', 'D051379', 'D058977', 'D010449', 'D011499', 'D040901', 'D013194']","['Animals', 'Cell Line', 'Iodoacetates', 'Lipopolysaccharides', 'Mice', 'Molecular Sequence Annotation', 'Peptide Mapping', 'Protein Processing, Post-Translational', 'Proteomics', 'Staining and Labeling']",Proteomic quantification and site-mapping of S-nitrosylated proteins using isobaric iodoTMT reagents.,"[None, None, 'Q000737', 'Q000494', None, None, None, None, None, None]","[None, None, 'chemistry', 'pharmacology', None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24926564,2015,0.0,0.0,,, -24923489,"A new method for the determination of selenium based on its fluorescence quenching on the hemoglobin-catalyzed reaction of H2 O2 and l-tyrosine has been established. The effect of pH, foreign ions and the optimization of variables on the determination of selenium was examined. The calibration curve was found to be linear between the fluorescence quenching (F0 /F) and the concentration of selenium within the range of 0.16-4.00 µg/mL. The detection limit was 1.96 ng/mL and the relative standard deviation was 3.14%. This method can be used for the determination of selenium in Se-enriched garlic bulbs with satisfactory results.",Luminescence : the journal of biological and chemical luminescence,"['D002138', 'D002384', 'D005453', 'D005504', 'D005737', 'D006454', 'D057230', 'D012643', 'D013050', 'D013816', 'D014443']","['Calibration', 'Catalysis', 'Fluorescence', 'Food Analysis', 'Garlic', 'Hemoglobins', 'Limit of Detection', 'Selenium', 'Spectrometry, Fluorescence', 'Thermodynamics', 'Tyrosine']",Determination of selenium via the fluorescence quenching effect of selenium on hemoglobin-catalyzed peroxidative reaction.,"[None, None, None, 'Q000379', 'Q000737', 'Q000737', None, 'Q000032', 'Q000379', None, 'Q000737']","[None, None, None, 'methods', 'chemistry', 'chemistry', None, 'analysis', 'methods', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24923489,2016,1.0,1.0,,, -24679771,"This work proposes the novel application of a microextraction technique, solid phase microextraction (SPME), coupled to liquid chromatography with UV detection (HPLC-UV) for the analysis of organosulfur compounds (OSCs) in garlic samples. Additionally, a comparative study of OSCs profiles obtained by SPME coupled to HPLC-UV and gas chromatography with flame photometric detector (GC-FPD), respectively; was carried out. This study provided complementary evidence about OSCs's lability and ""artifacts"" formation during the analytical process. Raw, cooked and distilled garlic samples were considered. The target analytes were diallyl disulphide (DADS), diallyl sulphide (DAS), diallyl trisulphide (DATS), allicin, 3-vinyl-4H-1,3-dithiin (3-VD), 2-vinyl-4H-1,2-dithiin (2-VD) and (E)- and (Z)-ajoene, which are the most important OSCs with biological activities present in raw and processed garlic. The coupling of SPME and HPLC showed to be reliable, fast, sensible and selective methodology for OSCs analysis.",Food chemistry,"['D002851', 'D002853', 'D005737', 'D052617']","['Chromatography, High Pressure Liquid', 'Chromatography, Liquid', 'Garlic', 'Solid Phase Microextraction']",Solid phase microextraction coupled to liquid chromatography. Analysis of organosulphur compounds avoiding artifacts formation.,"['Q000379', None, 'Q000737', 'Q000379']","['methods', None, 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/24679771,2015,1.0,2.0,,, -24668756,"A fast and an efficient ultrasound-assisted extraction technique using a lower density extraction solvent than water was developed for the trace-level determination of tebuconazole in garlic, soil and water samples followed by capillary gas chromatography combined with nitrogen-phosphorous selective detector (GC-NPD). In this approach, ultrasound radiation was applied to accelerate the emulsification of the ethyl acetate in aqueous samples to enhance the extraction efficiency of tebuconazole without requiring extra partitioning or cleaning, and the use of capillary GC-NPD was a more sensitive detection technique for organonitrogen pesticides. The experimental results indicate an excellent linear relationship between peak area and concentration obtained in the range 1-50 μg/kg or μg/L. The limit of detection (S/N, 3 ± 0.5) and limit of quantification (S/N, 7.5 ± 2.5) were obtained in the range 0.2-3 and 1-10 μg/kg or μg/L. Good spiked recoveries were achieved from ranges 95.55-101.26%, 96.28-99.33% and 95.04-105.15% in garlic, Nanivaliyal soil and Par River water, respectively, at levels 5 and 20 μg/kg or μg/L, and the method precision (% RSD) was ≤5%. Our results demonstrate that the proposed technique is a viable alternative for the determination of tebuconazole in complex samples. ",Journal of separation science,"['D002849', 'D005737', 'D010575', 'D012987', 'D012989', 'D014230', 'D014465', 'D014874']","['Chromatography, Gas', 'Garlic', 'Pesticides', 'Soil', 'Soil Pollutants', 'Triazoles', 'Ultrasonics', 'Water Pollutants, Chemical']","Fast ultrasound-assisted extraction followed by capillary gas chromatography combined with nitrogen-phosphorous selective detector for the trace determination of tebuconazole in garlic, soil and water samples.","['Q000295', 'Q000737', 'Q000032', 'Q000737', 'Q000032', 'Q000032', 'Q000379', 'Q000032']","['instrumentation', 'chemistry', 'analysis', 'chemistry', 'analysis', 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24668756,2015,0.0,0.0,,, -24668040,"S-Allyl-L-cysteine (SAC), the most abundant organosulfur compound derived from garlic, has multifunctional biological activities that occur via different mechanisms. A sensitive, rapid and simple LC-ESI-MS/MS method using a mixed-mode reversed-phase and cation-exchange column containing C18 silica particles and sulfonic acid cation-exchange particles has been developed and validated for the analysis of SAC in rat plasma. The mobile phase was optimized at 2 mM ammonium acetate buffer (pH = 3.5) and acetonitrile (75:25, v/v). The assay utilized 0.6% acetic acid in methanol to achieve simple and rapid deproteinization. Quantification was conducted using multiple reaction monitoring (MRM) of the transitions of m/z 162.0 → 145.0 for SAC. The standard curve for SAC was linear (r(2) ≥ 0.999) over a range from 5 to 2,500 ng/mL. The intra- and interday precision (relative standard deviation) of the method was not >6.0% at three quality control levels. The limit of quantification (LOQ) was 5.0 ng/mL. After being fully validated, the method was successfully applied to the pharmacokinetic monitoring of SAC in rat plasma.",Journal of chromatographic science,"['D000818', 'D002851', 'D003545', 'D051381', 'D021241', 'D053719']","['Animals', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Rats', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry']",Development and validation of S-allyl-L-cysteine in rat plasma using a mixed-mode reversed-phase and cation-exchange LC-ESI-MS/MS method: application to pharmacokinetic studies.,"[None, 'Q000379', 'Q000031', None, 'Q000379', 'Q000379']","[None, 'methods', 'analogs & derivatives', None, 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/24668040,2015,0.0,0.0,,, -24657412,"In this study, a two-step process combining aqueous two-phase extraction (ATPE) with chromatography was developed for extraction and purification of alliin from garlic powder. The partition coefficient and yield value of alliin in different types of aqueous two-phase system (ATPS) were compared and response surface methodology (RSM) was used for analyzing and optimizing the extraction process. The optimal extraction conditions of 19% (w/w) (NH4)2SO4, 20% (w/w) 1-prpanol, at 30°C, pH 2.35 with 8.54% (w/w) NaCl was chosen based on the higher yield. Compared to the results obtained with the conventional extraction method, this method had an evident advantage on yield (20.4mg/g versus the original yield of 15.0mg/g) and the concentration of alliin in extract solution by ATPE was close to three times of that with conventional extraction. The purification of alliin was carried out with the ammonium form of sulfonic acid cation-exchange resins 001×7. Sample solution with alliin concentration of 1mg/mL was passed through resins and the desorption of alliin was accomplished by water at the flow velocity of 0.5mL/min, 1.5mL/min, respectively. The purity and recovery of alliin after purification were 80% and 76%, respectively. ","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000327', 'D002852', 'D003545', 'D005737', 'D059625', 'D012044', 'D015203', 'D012965']","['Adsorption', 'Chromatography, Ion Exchange', 'Cysteine', 'Garlic', 'Liquid-Liquid Extraction', 'Regression Analysis', 'Reproducibility of Results', 'Sodium Chloride']",Combination of aqueous two-phase extraction and cation-exchange chromatography: new strategies for separation and purification of alliin from garlic powder.,"[None, 'Q000379', 'Q000031', 'Q000737', 'Q000379', None, None, None]","[None, 'methods', 'analogs & derivatives', 'chemistry', 'methods', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24657412,2014,1.0,3.0,,, -24592995,"The ability of foods and beverages to reduce allyl methyl disulfide, diallyl disulfide, allyl mercaptan, and allyl methyl sulfide on human breath after consumption of raw garlic was examined. The treatments were consumed immediately following raw garlic consumption for breath measurements, or were blended with garlic prior to headspace measurements. Measurements were done using a selected ion flow tube-mass spectrometer. Chlorophyllin treatment demonstrated no deodorization in comparison to the control. Successful treatments may be due to enzymatic, polyphenolic, or acid deodorization. Enzymatic deodorization involved oxidation of polyphenolic compounds by enzymes, with the oxidized polyphenols causing deodorization. This was the probable mechanism in raw apple, parsley, spinach, and mint treatments. Polyphenolic deodorization involved deodorization by polyphenolic compounds without enzymatic activity. This probably occurred for microwaved apple, green tea, and lemon juice treatments. When pH is below 3.6, the enzyme alliinase is inactivated, which causes a reduction in volatile formation. This was demonstrated in pH-adjusted headspace measurements. However, the mechanism for volatile reduction on human breath (after volatile formation) is unclear, and may have occurred in soft drink and lemon juice breath treatments. Whey protein was not an effective garlic breath deodorant and had no enzymatic activity, polyphenolic compounds, or acidity. Headspace concentrations did not correlate well to breath treatments. ",Journal of food science,"['D000498', 'D013437', 'D002957', 'D003836', 'D004220', 'D005511', 'D005638', 'D005737', 'D006209', 'D006801', 'D006863', 'D013058', 'D010084', 'D010936', 'D059808', 'D013440', 'D013457', 'D055549']","['Allyl Compounds', 'Carbon-Sulfur Lyases', 'Citrus', 'Deodorants', 'Disulfides', 'Food Handling', 'Fruit', 'Garlic', 'Halitosis', 'Humans', 'Hydrogen-Ion Concentration', 'Mass Spectrometry', 'Oxidation-Reduction', 'Plant Extracts', 'Polyphenols', 'Sulfides', 'Sulfur Compounds', 'Volatile Organic Compounds']",Deodorization of garlic breath volatiles by food and food components.,"['Q000378', 'Q000037', 'Q000737', None, 'Q000378', None, 'Q000737', 'Q000737', 'Q000378', None, None, None, None, 'Q000494', 'Q000494', 'Q000378', 'Q000378', 'Q000378']","['metabolism', 'antagonists & inhibitors', 'chemistry', None, 'metabolism', None, 'chemistry', 'chemistry', 'metabolism', None, None, None, None, 'pharmacology', 'pharmacology', 'metabolism', 'metabolism', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/24592995,2015,0.0,0.0,,, -24491695,"Angelica keiskei is used as popular functional food stuff. However, quantitative analysis of this plant's metabolites has not yet been disclosed. The principal phenolic compounds (1-16) within A. keiskei were isolated, enabling us to quantify the metabolites within different parts of the plant. The specific quantification of metabolites (1-16) was accomplished by multiple reaction monitoring (MRM) using a quadruple tandem mass spectrometer. The limit of detection and limit of quantitation were calculated as 0.4-44 μg/kg and 1.5-148 μg/kg, respectively. Abundance and composition of these metabolites varied significantly across different parts of plant. For example, the abundance of chalcones (12-16) decreased as follows: root bark (10.51 mg/g)>stems (8.52 mg/g)>leaves (2.63 mg/g)>root cores (1.44 mg/g). The chalcones were found to be responsible for the xanthine oxidase (XO) inhibition shown by this plant. The most potent inhibitor, xanthoangelol inhibited XO with an IC50 of 8.5 μM. Chalcones (12-16) exhibited mixed-type inhibition characteristics.",Food chemistry,"['D029969', 'D002851', 'D004791', 'D006801', 'D010636', 'D010936', 'D053719', 'D014969']","['Angelica', 'Chromatography, High Pressure Liquid', 'Enzyme Inhibitors', 'Humans', 'Phenols', 'Plant Extracts', 'Tandem Mass Spectrometry', 'Xanthine Oxidase']",Quantitative analysis of phenolic metabolites from different parts of Angelica keiskei by HPLC-ESI MS/MS and their xanthine oxidase inhibition.,"['Q000737', 'Q000379', 'Q000032', None, 'Q000032', 'Q000032', 'Q000379', 'Q000032']","['chemistry', 'methods', 'analysis', None, 'analysis', 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24491695,2014,0.0,0.0,,, -24267071,"The use of volatile organic compounds (VOCs) emanating from human skin presents great potential for skin disease diagnosis. These compounds are emitted at very low concentrations. Thus, the sampling preparation step needs to be implemented before gas chromatography-mass spectrometry (GC-MS) analysis. In this work, a simple, non-invasive headspace sampling method for volatile compounds emanating from human skin is presented, using thin film as the extraction phase format. The proposed method was evaluated in terms of reproducibility, membrane size, extraction mode and storage conditions. First, the in vial sampling showed an intra- and inter-membrane RSD% less than 9.8% and 8.2%, respectively, which demonstrated that this home-made skin volatiles sampling device was highly reproducible with regard to intra-, inter-membrane sampling. The in vivo sampling was influenced not only by the skin metabolic status, but also by environmental conditions. The developed sampling set-up (or ""membrane sandwich"") was used to compare two different modes of sampling: headspace and direct sampling. Results demonstrated that headspace sampling had significantly reduced background signal intensity, indicating minimized contamination from the skin surface. In addition, membrane storage conditions both before and after sampling were fully investigated. Membranes stored in dry ice for up to 72 h after collection were tested and showed no or minimal change in volatile profiles. This novel skin volatile compounds sampling approach coupled with gas chromatography-mass spectrometry (GC-MS) can achieve reproducible analysis. This technique was applied to identify the biomarkers of garlic intake and alcohol ingestion. Dimethyl sulphone, allyl methyl sulfide and allyl mercaptan, as metabolites of garlic intake, were detected. In addition, alcohol released from skin was also detected using our ""membrane-sandwich"" sampling. Using the same approach, we analyzed skin VOCs from upper back, forearm and back thigh regions of the body. Our results show that different body locations share a number of common compounds (27/99). The area with most compounds detected was the upper back skin region, where the density of sebaceous glands is the highest.",Analytica chimica acta,"['D008401', 'D006801', 'D015203', 'D012867', 'D055549']","['Gas Chromatography-Mass Spectrometry', 'Humans', 'Reproducibility of Results', 'Skin', 'Volatile Organic Compounds']",A non-invasive method for in vivo skin volatile compounds sampling.,"[None, None, None, 'Q000737', 'Q000032']","[None, None, None, 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24267071,2014,0.0,0.0,,, -24243685,"To enhance the utilization of garlic macerated oil as functional foods, oil-soluble organosulfur compounds were investigated using normal-phase high-performance liquid chromatography method. For analysis of compounds, it was simply extracted with 98% n-hexane in 2-propanol followed by sensitive and selective determination of all compounds. These method exhibited excellent linearity for oil-soluble organosulfur compounds with good coefficient (r > 0.999). Average recoveries were in the range of 80.23-106.18%. The limits of quantitation of oil-soluble organosulfur compounds ranged from 0.32 to 9.56 μg mL(-1) and the limits of detection were from 0.11 to 3.16 μg mL(-1). Overall, the precision of the results, expressed as relative standard deviation, ranged from 0.55 to 11.67%. The proposed method was applied to determining the contents of oil-soluble organosulfur compounds in commercial garlic macerated oils. Also, the stability of oil-soluble organosulfur compounds in garlic macerated oil were evaluated during 3 months of storage at four difference temperatures (4, 10, 25 and 35°C). The results showed the studied oil-soluble compounds in garlic macerated oil were stable at 4°C and relatively unstable at 35°C with varied extents degradation. Therefore, these validation data and temperature stability may be useful for quality evaluation of garlic macerated oils.",Journal of chromatographic science,"['D000498', 'D002851', 'D004355', 'D005737', 'D057230', 'D016014', 'D010938', 'D015203', 'D013440', 'D013696']","['Allyl Compounds', 'Chromatography, High Pressure Liquid', 'Drug Stability', 'Garlic', 'Limit of Detection', 'Linear Models', 'Plant Oils', 'Reproducibility of Results', 'Sulfides', 'Temperature']",Validated HPLC method and temperature stabilities for oil-soluble organosulfur compounds in garlic macerated oil.,"['Q000737', 'Q000379', None, 'Q000737', None, None, 'Q000737', None, 'Q000737', None]","['chemistry', 'methods', None, 'chemistry', None, None, 'chemistry', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/24243685,2015,1.0,2.0,,, -24224921,"A multi-residue analytical method was validated for 24 representative pesticides residues in onion, garlic and leek. The method is based on modified QuEChERS sample preparation with a mixture of graphene, primary secondary amine (PSA), and graphitised carbon black (GCB) as reversed-dispersive solid-phase extraction (r-DSPE) material and LC-MS/MS. Graphene was first used as an r-DSPE clean-up sorbent in onion, garlic and leek. The results first show that the mixed sorbent of graphene, PSA and GCB has a remarkable ability to clean-up interfering substances in the r-DSPE procedure when compared with the mixture of PSA and GCB. Use of matrix-matched standards provided acceptable results for tested pesticides with overall average recoveries between 70.1% and 109.7% and consistent RSDs <15.6%. In any case, this method still meets the 1-10 μg kg(-1) detection limit needed for pesticide testing and may be used for qualitative screening applications in which any identified pesticides can be quantified and confirmed by a more intensive method that achieves >70% recovery.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000490', 'D002853', 'D006108', 'D010573', 'D010575', 'D052616', 'D053719']","['Allium', 'Chromatography, Liquid', 'Graphite', 'Pesticide Residues', 'Pesticides', 'Solid Phase Extraction', 'Tandem Mass Spectrometry']","Graphene as dispersive solidphase extraction materials for pesticides LC-MS/MS multi-residue analysis in leek, onion and garlic.","['Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000737', 'Q000379', 'Q000379']","['chemistry', 'methods', 'chemistry', 'chemistry', 'chemistry', 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/24224921,2014,1.0,3.0,,, -24196723,"Colchicine poisoning can occur not only by taking dosage form but also by ingesting a plant containing colchicine. A 39-year-old man presented to the emergency room with nausea, vomiting, and diarrhea 9 hours after ingestion of wild garlic. Symptoms attributed to food poisoning, and he received supportive cares and discharged. However, he was admitted to the hospital because of severe gastrointestinal presentations 4 hours later. He received treatments based on the diagnosis of acute gastroenteritis. The patient was in a fair condition during 30 hours of hospitalization until he suddenly developed respiratory distress and unfortunately died with cardiopulmonary arrest. The deceased body referred to our legal medicine center for determining cause of death and investigating possible medical staff malpractices. Postmortem examination, autopsy, macropathology and micropathology study, and postmortem toxicological analysis were performed. All results were submitted to the medical committee office for decision. The unknown cause of death was disclosed after determination of colchicine in the plant and botanical identification as Colchicum persicum. The committee determined the most probable cause of death as acute cardiopulmonary complications induced by colchicine poisoning and the manner of death as accidental. The medical staff was acquitted of the malpractice.",The American journal of forensic medicine and pathology,"['D015746', 'D000059', 'D000328', 'D002851', 'D003078', 'D003079', 'D003951', 'D003967', 'D004636', 'D053593', 'D005759', 'D006323', 'D006801', 'D007492', 'D008297', 'D011041', 'D012128', 'D014839']","['Abdominal Pain', 'Accidents', 'Adult', 'Chromatography, High Pressure Liquid', 'Colchicine', 'Colchicum', 'Diagnostic Errors', 'Diarrhea', 'Emergency Service, Hospital', 'Forensic Toxicology', 'Gastroenteritis', 'Heart Arrest', 'Humans', 'Iran', 'Male', 'Poisoning', 'Respiratory Distress Syndrome, Adult', 'Vomiting']",Fatal colchicine poisoning by accidental ingestion of Colchicum persicum: a case report.,"['Q000139', None, None, None, 'Q000506', 'Q000506', None, 'Q000139', None, None, 'Q000175', 'Q000139', None, None, None, 'Q000175', 'Q000139', 'Q000139']","['chemically induced', None, None, None, 'poisoning', 'poisoning', None, 'chemically induced', None, None, 'diagnosis', 'chemically induced', None, None, None, 'diagnosis', 'chemically induced', 'chemically induced']",https://www.ncbi.nlm.nih.gov/pubmed/24196723,2014,0.0,0.0,,, -24126836,"Novel imidazole fluorescent ionic liquids with anthracene groups (ImS-FILA) were synthesized for the first time to act as fluorescent probes. They were developed for the determination of superoxide anion radicals (O2 (•-)) in an aqueous system. O2 (•-) was produced by pyrogallol autoxidation. The fluorescence of ImS-FILA was quenched by superoxide anion radicals. The Ï€-bond structure of the fluorescent molecules was oxidized and damaged. This method is very simple and sensitive. The linear range of sensitivity was 1-70 μM ImS-FILA, and the detection limit for reactive oxygen species was 0.1 μM. This method was used to detect superoxide radicals in papaya and garlic, with satisfactory results. Further work is needed to demonstrate the utility of this method in detecting reactive oxygen species in a biological aqueous system. ",Analytical and bioanalytical chemistry,"['D000838', 'D029441', 'D005453', 'D005737', 'D007093', 'D052578', 'D013050', 'D013481']","['Anions', 'Carica', 'Fluorescence', 'Garlic', 'Imidazoles', 'Ionic Liquids', 'Spectrometry, Fluorescence', 'Superoxides']",A novel functional imidazole fluorescent ionic liquid: simple and efficient fluorescent probes for superoxide anion radicals.,"['Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000379', 'Q000737']","['chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24126836,2014,0.0,0.0,,, -24022100,"A comparative study using native garlic peel and mercerized garlic peel as adsorbents for the removal of Pb(2+) has been proposed. Under the optimized pH, contact time, and adsorbent dosage, the adsorption capacity of garlic peel after mercerization was increased 2.1 times and up to 109.05 mg g(-1). The equilibrium sorption data for both garlic peels fitted well with Langmuir adsorption isotherm, and the adsorbent-adsorbate kinetics followed pseudo-second-order model. These both garlic peels were characterized by elemental analysis, Fourier transform infrared spectrometry (FT-IR), and scanning electron microscopy, and the results indicated that mercerized garlic peel offers more little pores acted as adsorption sites than native garlic peel and has lower polymerization and crystalline and more accessible functional hydroxyl groups, which resulted in higher adsorption capacity than native garlic peel. The FT-IR and X-ray photoelectron spectroscopy analyses of both garlic peels before and after loaded with Pb(2+) further illustrated that lead was adsorbed on the through chelation between Pb(2+) and O atom existed on the surface of garlic peels. These results described above showed that garlic peel after mercerization can be a more attractive adsorbent due to its faster sorption uptake and higher capacity.",Environmental science and pollution research international,"['D000327', 'D005737', 'D007700', 'D007854', 'D008855', 'D008956', 'D056951', 'D017550']","['Adsorption', 'Garlic', 'Kinetics', 'Lead', 'Microscopy, Electron, Scanning', 'Models, Chemical', 'Photoelectron Spectroscopy', 'Spectroscopy, Fourier Transform Infrared']",Comparative study of adsorption of Pb(II) on native garlic peel and mercerized garlic peel.,"[None, 'Q000737', None, 'Q000737', None, None, None, None]","[None, 'chemistry', None, 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24022100,2014,2.0,1.0,,, -23993494,"Sofrito is a key component of the Mediterranean diet, a diet that is strongly associated with a reduced risk of cardiovascular events. In this study, different Mediterranean sofritos were analysed for their content of polyphenols and carotenoids after a suitable work-up extraction procedure using liquid chromatography/electrospray ionisation-linear ion trap quadrupole-Orbitrap-mass spectrometry (LC/ESI-LTQ-Orbitrap-MS) and liquid chromatography/electrospray ionisation tandem triple quadrupole mass spectrometry (LC/ESI-MS-MS). In this way, 40 polyphenols (simple phenolic and hydroxycinnamoylquinic acids, and flavone, flavonol and dihydrochalcone derivatives) were identified with very good mass accuracy (<2 mDa), and confirmed by accurate mass measurements in MS and MS(2) modes. The high-resolution MS analyses revealed the presence of polyphenols never previously reported in Mediterranean sofrito. The quantification levels of phenolic and carotenoid compounds led to the distinction of features among different Mediterranean sofritos according to the type of vegetables (garlic and onions) or olive oil added for their production.",Food chemistry,"['D002338', 'D002851', 'D038441', 'D059808', 'D053719']","['Carotenoids', 'Chromatography, High Pressure Liquid', 'Diet, Mediterranean', 'Polyphenols', 'Tandem Mass Spectrometry']",Bioactive compounds present in the Mediterranean sofrito.,"['Q000737', None, None, 'Q000737', None]","['chemistry', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/23993494,2014,0.0,0.0,,, -23925402,"Tellurium (Te) is a widely used metalloid in industry because of its unique chemical and physical properties. However, information about the biological and toxicological activities of Te in plants and animals is limited. Although Te is expected to be metabolized in organisms via the same pathway as sulfur and selenium (Se), no precise metabolic pathways are known in organisms, particularly in plants. To reveal the metabolic pathway of Te in plants, garlic, a well-known Se accumulator, was chosen as the model plant. Garlic was hydroponically cultivated and exposed to sodium tellurate, and Te-containing metabolites in the water extract of garlic leaves were identified using HPLC coupled with inductively coupled plasma mass spectrometry (ICP-MS) or electrospray tandem mass spectrometry (ESI-MS-MS). At least three Te-containing metabolites were detected using HPLC-ICP-MS, and two of them were subjected to HPLC-ESI-MS-MS for identification. The MS spectra obtained by ESI-MS-MS indicated that the metabolite was Te-methyltellurocysteine oxide (MeTeCysO). Then, MeTeCysO was chemically synthesized and its chromatographic behavior matched with that of the Te-containing metabolite in garlic. The other was assigned as cysteine S-methyltellurosulfide. These results suggest that garlic can assimilate tellurate, an inorganic Te compound, and tellurate is transformed into a Te-containing amino acid, the so-called telluroamino acid. This is the first report addressing that telluroamino acid is de novo synthesized in a higher plant. ",Metallomics : integrated biometal science,"['D002851', 'D005737', 'D018527', 'D013058', 'D058955', 'D010936', 'D018515', 'D021241', 'D013691']","['Chromatography, High Pressure Liquid', 'Garlic', 'Hydroponics', 'Mass Spectrometry', 'Metalloids', 'Plant Extracts', 'Plant Leaves', 'Spectrometry, Mass, Electrospray Ionization', 'Tellurium']","Speciation and identification of tellurium-containing metabolites in garlic, Allium sativum.","[None, 'Q000737', None, 'Q000379', 'Q000378', 'Q000737', 'Q000737', None, 'Q000378']","[None, 'chemistry', None, 'methods', 'metabolism', 'chemistry', 'chemistry', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/23925402,2014,0.0,0.0,,, -23865201,"In our screening program for insecticidal activity of the essential oils/extracts derived from some Chinese medicinal herbs and spices, garlic (Allium sativum L.) essential oil was found to possess strong insecticidal activity against overwintering adults of Cacopsylla chinensis Yang et Li (Hemiptera: Psyllidae). The commercial essential oil of A. sativum was analyzed by gas chromatography-mass spectrometry. Sixteen compounds, accounting for 97.44% of the total oil, were identified, and the main components of the essential oil of A. sativum were diallyl trisulfide (50.43%), diallyl disulfide (25.30%), diallyl sulfide (6.25%), diallyl tetrasulfide (4.03%), 1,2-dithiolane (3.12%), allyl methyl disulfide (3.07%), 1,3-dithiane (2.12%), and allyl methyl trisulfide (2.08%). The essential oil of A. sativum possessed contact toxicity against overwintering C. chinensis, with an LC50 value of 1.42 microg per adult. The two main constituent compounds, diallyl trisulfide and diallyl disulfide, exhibited strong acute toxicity against the overwintering C. chinensis, with LC50 values of 0.64 and 11.04 /g per adult, respectively.",Journal of economic entomology,"['D000490', 'D000498', 'D000818', 'D004305', 'D008401', 'D006430', 'D007306', 'D007928', 'D009822', 'D013440']","['Allium', 'Allyl Compounds', 'Animals', 'Dose-Response Relationship, Drug', 'Gas Chromatography-Mass Spectrometry', 'Hemiptera', 'Insecticides', 'Lethal Dose 50', 'Oils, Volatile', 'Sulfides']",Evaluation of acute toxicity of essential oil of garlic (Allium sativum) and its selected major constituent compounds against overwintering Cacopsylla chinensis (Hemiptera: Psyllidae).,"['Q000737', 'Q000494', None, None, None, 'Q000187', 'Q000494', None, 'Q000494', 'Q000494']","['chemistry', 'pharmacology', None, None, None, 'drug effects', 'pharmacology', None, 'pharmacology', 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/23865201,2013,,,,, -23790889,"Cysteine-S-conjugates (CS-conjugates) occur in foods derived from plant sources like grape, passion fruit, onion, garlic, bell pepper and hops. During eating CS-conjugates are degraded into aroma-active thiols by β-lyases that originate from oral microflora. The present study provides evidence for the formation of the CS-conjugates S-furfuryl-l-cysteine (FFT-S-Cys) and S-(2-methyl-3-furyl)-l-cysteine (MFT-S-Cys) in the Maillard reaction of xylose with cysteine at 100°C for 2h. The CS-conjugates were isolated using cationic exchange and reversed-phase chromatography and identified by (1)H NMR, (13)C NMR and LC-MS(2). Spectra and LC retention times matched those of authentic standards. To the best of our knowledge, this is the first time that CS-conjugates are described as Maillard reaction products. Furfuryl alcohol (FFA) is proposed as an intermediate which undergoes a nucleophilic substitution with cysteine. Both FFT-S-Cys and MFT-S-Cys are odourless but produce strong aroma when tasted in aqueous solutions, supposedly induced by β -lyases from the oral microflora. The perceived aromas resemble those of the corresponding aroma-active thiols 2-furfurylthiol (FFT) and 2-methyl-3-furanthiol (MFT) which smell coffee-like and meaty, respectively. ",Food chemistry,"['D003545', 'D005663', 'D015416', 'D013438', 'D014994']","['Cysteine', 'Furans', 'Maillard Reaction', 'Sulfhydryl Compounds', 'Xylose']",Formation of cysteine-S-conjugates in the Maillard reaction of cysteine and xylose.,"['Q000737', 'Q000737', None, 'Q000737', 'Q000737']","['chemistry', 'chemistry', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23790889,2014,0.0,0.0,,, -23765168,"Combined pollution of selenium (Se) and mercury (Hg) has been known in Wanshan district (Guizhou Province, China). A better understanding of how Se and Hg interact in plants and the phytotoxicity thereof will provide clues about how to avoid or mitigate adverse effects of Se/Hg on local agriculture. In this study, the biological activity of Se has been investigated in garlic with or without Hg exposure. Se alone can promote garlic growth at low levels (<0.1 mg L(-1)), whereas it inhibits garlic growth at high levels (>1 mg L(-1)). The promotive effect of Se in garlic can be enhanced by low Hg exposure (<0.1 mg L(-1)). When both Se and Hg are at high levels, there is a general antagonistic effect between these two elements in terms of phytotoxicity. Inductively coupled plasma mass spectrometry (ICP-MS) data suggest that Se is mainly concentrated in garlic roots, compared to the leaves and the bulbs. Se uptake by garlic in low Se medium (<0.1 mg L(-1)) can be significantly enhanced as Hg exposure levels increase (P < 0.05), while it can be inhibited by Hg when Se exposure levels exceed 1 mg L(-1). The synchrotron radiation X-ray fluorescence (SRXRF) mapping further shows that Se is mainly concentrated in the stele of the roots, bulbs and the veins of the leaves, and Se accumulation in garlic can be reduced by Hg. The X-ray absorption near edge structure (XANES) study indicates that Se is mainly formed in C-Se-C form in garlic. Hg can decrease the content of inorganic Se mainly in SeO3(2-) form in garlic while increasing the content of organic Se mainly in C-Se-C form (MeSeCys and its derivatives). Hg-mediated changes in Se species along with reduced Se accumulation in garlic may account for the protective effect of Hg against Se phytotoxicity. ",Metallomics : integrated biometal science,"['D005737', 'D013058', 'D008628', 'D012643', 'D056928']","['Garlic', 'Mass Spectrometry', 'Mercury', 'Selenium', 'X-Ray Absorption Spectroscopy']",Mercury modulates selenium activity via altering its accumulation and speciation in garlic (Allium sativum).,"['Q000378', None, 'Q000494', 'Q000378', None]","['metabolism', None, 'pharmacology', 'metabolism', None]",https://www.ncbi.nlm.nih.gov/pubmed/23765168,2014,1.0,1.0,,, -23722957,"Ambient ionization is the new revolution in mass spectrometry (MS). A microwave plasma produced by a microwave plasma torch (MPT) at atmospheric pressure was directly used for ambient mass spectrometric analysis. H3O(+) and NH4(+) and their water clusters from the background are formed and create protonated molecules and ammoniated molecules of the analytes. In the full-scan mass spectra, both the quasi-molecular ions of the analytes and their characteristic ionic fragments are obtained and provide evidence of the analyte. The successful detection of active compounds in both medicine and garlic proves that MPT has the efficient desorption/ionization capability to analyze solid samples. The obtained decay curve of nicotine in exhaled breath indicates that MPT-MS is a useful tool for monitoring gas samples in real time. These results showed that the MPT, with the advantages of stable plasma, minimal optimization, easy, solvent-free operation, and no pretreatment, is another potential technique for ambient MS.",Journal of mass spectrometry : JMS,[],[],Direct desorption/ionization of analytes by microwave plasma torch for ambient mass spectrometric analysis.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/23722957,2013,,,,, -23561332,"To investigate the influence of selenium on mercury phytotoxicity, the levels of selenium and mercury were analyzed with inductively coupled plasma-mass spectrometry (ICP-MS) in garlic tissues upon exposure to different dosages of inorganic mercury (Hg(2+)) and selenite (SeO3(2-)) or selenate (SeO4(2-)). The distributions of selenium and mercury were examined with micro-synchrotron radiation X-ray fluorescence (μ-SRXRF), and the mercury speciation was investigated with micro-X-ray absorption near edge structure (μ-XANES). The results show that Se at higher exposure levels (>1mg/L of SeO3(2-) or SeO4(2-)) would significantly inhibit the absorption and transportation of Hg when Hg(2+) levels are higher than 1mg/L in culture media. SeO3(2-) and SeO4(2-) were found to be equally effective in reducing Hg accumulation in garlic. The inhibition of Hg uptake by Se correlates well with the influence of Se on Hg phytotoxicity as indicated by the growth inhibition factor. Elemental imaging using μ-SRXRF also shows that Se could inhibit the accumulation and translocation of Hg in garlic. μ-XANES analysis shows that Hg is mainly present in the forms of Hg-S bonding as Hg(GSH)2 and Hg(Met)2. Se exposure elicited decrease of Hg-S bonding in the form of Hg(GSH)2, together with Se-mediated alteration of Hg absorption, transportation and accumulation, may account for attenuated Hg phytotoxicity by Se in garlic. ",Environmental research,"['D000042', 'D001673', 'D005737', 'D005978', 'D013058', 'D008628', 'D012643', 'D013052', 'D056928']","['Absorption', 'Biodegradation, Environmental', 'Garlic', 'Glutathione', 'Mass Spectrometry', 'Mercury', 'Selenium', 'Spectrometry, X-Ray Emission', 'X-Ray Absorption Spectroscopy']",Selenium inhibits the phytotoxicity of mercury in garlic (Allium sativum).,"[None, None, 'Q000378', 'Q000737', None, 'Q000737', 'Q000737', None, None]","[None, None, 'metabolism', 'chemistry', None, 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/23561332,2013,0.0,0.0,,, -23451371,"Allium sativum L (garlic) is an essential component of many polyherbal oils used in traditional systems of medicine. Allyl disulfide has been a major component found in vegetable oil macerate of garlic, and can be used as reliable marker for determination of garlic in oil macerates of garlic. The HPLC separation of allyl disulfide was achieved on a Phenomenex Luna C18 (25 cm x 4.6 mm id x 5 pm particle size) column using acetonitrile-water-tetrahydrofuran (70 + 27 + 3, v/v/v) mobile phase at a flow rate of 1.0 mL/min. Quantitation was achieved with UV detection at 298 nm over the concentration range 8-48 microg/mL. HPTLC separation of allyl disulfide was achieved on an aluminum-backed layer of silica gel 60 F254 using n-hexane mobile phase. Quantitation was achieved by densitometric analysis at 298 nm over the 200-1200 ng/band concentration range. The methods were validated according to International Conference on Harmonization guidelines.",Journal of AOAC International,"['D000498', 'D002138', 'D002851', 'D002855', 'D004220', 'D007202', 'D057230', 'D010938', 'D010946', 'D012015', 'D015203', 'D013056']","['Allyl Compounds', 'Calibration', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Disulfides', 'Indicators and Reagents', 'Limit of Detection', 'Plant Oils', 'Plants, Medicinal', 'Reference Standards', 'Reproducibility of Results', 'Spectrophotometry, Ultraviolet']",Validation of HPTLC and HPLC methods for the quantitative determination of allyl disulfide in some polyherbal oils.,"[None, None, None, None, 'Q000032', None, None, 'Q000032', 'Q000737', None, None, None]","[None, None, None, None, 'analysis', None, None, 'analysis', 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/23451371,2013,,,,, -23448127,"Activity-guided fractionation was applied on an aged garlic extract (AGE), reported to show strong antioxidant activity, in order to locate the key in vitro antioxidant ingredients by means of the hydrogen peroxide scavenging (HPS) assay as well as the ORAC assay. Besides the previously reported four tetrahydro-β-carbolines, (1R,3S)- and (1S,3S)-1-methyl-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid and (1R,3S)- and (1S,3S)-1-methyl-1,2,3,4-tetrahydro-β-carboline-1,3-dicarboxylic acid, LC-MS/MS, LC-TOF-MS, and 1D/2D-NMR experiments led to the identification of coniferyl alcohol and its dilignols (-)-(2R,3S)-dihydrodehydrodiconiferyl alcohol, (+)-(2S,3R)-dehydrodiconiferyl alcohol, erythro-guaiacylglycerol-β-O-4'-coniferyl ether, and threo-guaiacylglycerol-β-O-4'-coniferyl ether as the major antioxidants in AGE. The purified individual compounds showed high antioxidant activity, with EC50 values of 9.7-11.8 μM (HPS assay) and 2.60-3.65 μmol TE/μmol (ORAC assay), respectively.",Journal of agricultural and food chemistry,"['D000975', 'D005591', 'D002851', 'D005609', 'D005737', 'D006861', 'D009682', 'D013058', 'D010636', 'D010936', 'D013997']","['Antioxidants', 'Chemical Fractionation', 'Chromatography, High Pressure Liquid', 'Free Radicals', 'Garlic', 'Hydrogen Peroxide', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Phenols', 'Plant Extracts', 'Time Factors']",In vitro activity-guided identification of antioxidants in aged garlic extract.,"['Q000032', None, None, 'Q000737', 'Q000737', 'Q000737', None, None, 'Q000032', 'Q000737', None]","['analysis', None, None, 'chemistry', 'chemistry', 'chemistry', None, None, 'analysis', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/23448127,2013,0.0,0.0,,, -23448028,"Larvae of scarab beetles (Coleoptera: Scarabaeidae) are important contaminant and root-herbivore pests of ornamental crops. To develop alternatives to conventional insecticides, 24 plant-based essential oils were tested for their acute toxicity against third instars of the Japanese beetle Popillia japonica Newman, European chafer Rhizotrogus majalis (Razoumowsky), oriental beetle Anomala orientalis (Waterhouse), and northern masked chafer Cyclocephala borealis Arrow. Diluted solutions were topically applied to the thorax, which allowed for calculating LD50 and LD90 values associated with 1 d after treatment. A wide range in acute toxicity was observed across all four scarab species. Of the 24 oils tested, allyl isothiocyanate, cinnamon leaf, clove, garlic, and red thyme oils exhibited toxicity to all four species. Allyl isothiocyanate was the most toxic oil tested against the European chafer, and among the most toxic against the Japanese beetle, oriental beetle, and northern masked chafer. Red thyme was also comparatively toxic to the Japanese beetle, oriental beetle, European chafer, and northern masked chafer. Interspecific variability in susceptibility to the essential oils was documented, with 12, 11, 8, and 6 of the 24 essential oils being toxic to the oriental beetle, Japanese beetle, European chafer, and northern masked chafer, respectively. Analysis of the active oils by gas chromatography-mass spectrometry revealed a diverse array of compounds, mostly consisting of mono- and sesquiterpenes. These results will aid in identifying active oils and their constituents for optimizing the development of plant essential oil mixtures for use against scarab larvae.",Journal of economic entomology,"['D000818', 'D001517', 'D008401', 'D007306', 'D007814', 'D009822']","['Animals', 'Coleoptera', 'Gas Chromatography-Mass Spectrometry', 'Insecticides', 'Larva', 'Oils, Volatile']",Acute toxicity of plant essential oils to scarab larvae (Coleoptera: Scarabaeidae) and their analysis by gas chromatography-mass spectrometry.,"[None, None, None, 'Q000032', None, 'Q000737']","[None, None, None, 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23448028,2013,,,,, -23435922,"The study is aimed to investigate the acaricidal effect of Allium sativum (garlic) and Allium cepa (onion) oils on different stages of Boophilus annulatus hard tick. Engorged B. annulatus females were collected from naturally infected cattle. A number of engorged ticks were incubated at 28 °C and 85 % relative humidity to lay eggs, which were incubated to obtain larvae that were used in the study. The used garlic and onion oils were prepared by steam distillation and were analyzed by gas chromatography. These oils were dissolved in ethanol, methanol alcohols, and, partially, in water. The oils were tested in different concentrations; 1, 2, 5, 10, and 20 %. These concentrations were applied on adult ticks by adult immersion test; on larvae by larval immersion technique and on eggs. The 20, 10, and 5 % of garlic oil dilutions in ethanol and methanol killed all adult ticks and larvae within 24 h. Similar results were obtained for 10 and 20 % garlic oil dissolved in water. The effect of 10 % aqueous solution of garlic oil on embryonated eggs was clear as its addition to these eggs led to their in ability to hatch, deformity in shape, and change in color. The 10 and 20 % onion oil in ethanol and methanol alcohols killed 76-86 % of the adult ticks within 72 h post-application. While, all larvae died within 24 h postsubjected to these two concentrations. These concentrations (10 and 20 %) of onion oil in water killed 56-80 % of the treated ticks. Moreover, 10 % aqueous solution of onion oil prevented hatching of embyonated eggs. We concluded that garlic and onion oils have acaricidal effect on all stages of B. annulatus at concentrations higher than 5 %. Only garlic oil could kill 100 % of adult ticks at concentrations from 5 % in alcohols.",Parasitology research,"['D056810', 'D000498', 'D000818', 'D001681', 'D002417', 'D002418', 'D005260', 'D005737', 'D007814', 'D019697', 'D010938', 'D048494', 'D013440', 'D013984']","['Acaricides', 'Allyl Compounds', 'Animals', 'Biological Assay', 'Cattle', 'Cattle Diseases', 'Female', 'Garlic', 'Larva', 'Onions', 'Plant Oils', 'Rhipicephalus', 'Sulfides', 'Tick Infestations']",Effect of Allium sativum and Allium cepa oils on different stages of Boophilus annulatus.,"['Q000737', 'Q000737', None, 'Q000379', None, 'Q000469', None, 'Q000737', 'Q000187', 'Q000737', 'Q000737', 'Q000187', 'Q000737', 'Q000469']","['chemistry', 'chemistry', None, 'methods', None, 'parasitology', None, 'chemistry', 'drug effects', 'chemistry', 'chemistry', 'drug effects', 'chemistry', 'parasitology']",https://www.ncbi.nlm.nih.gov/pubmed/23435922,2013,0.0,0.0,,, -23416182,"A strategy using reversed-phase high-performance liquid chromatography (HPLC), thin layer chromatography (TLC), mass spectrometry (MS), nuclear magnetic resonance (NMR), chemical synthesis, and MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) cell viability assay to identify allicin as the active anticancer compound in aqueous garlic extract (AGE) is described. Changing the pH of AGE from 7.0 to 5.0 eliminated interfering molecules and enabled a clean HPLC separation of the constituents in AGE. MTT assay of the HPLC fractions identified an active fraction. Further analysis by TLC, MS, and NMR verified the active HPLC fraction as allicin. Chemically synthesized allicin was used to provide further confirmation. The results clearly identify the active compound in AGE as allicin.",Analytical biochemistry,"['D000818', 'D000972', 'D045744', 'D002851', 'D002855', 'D003110', 'D004396', 'D005737', 'D020128', 'D009682', 'D013058', 'D051379', 'D010936', 'D013441', 'D013778', 'D013844']","['Animals', 'Antineoplastic Agents, Phytogenic', 'Cell Line, Tumor', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Colonic Neoplasms', 'Coloring Agents', 'Garlic', 'Inhibitory Concentration 50', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Mice', 'Plant Extracts', 'Sulfinic Acids', 'Tetrazolium Salts', 'Thiazoles']",HPLC-MTT assay: anticancer activity of aqueous garlic extract is from allicin.,"[None, 'Q000494', None, 'Q000379', None, 'Q000188', None, 'Q000737', None, None, None, None, 'Q000032', 'Q000302', None, None]","[None, 'pharmacology', None, 'methods', None, 'drug therapy', None, 'chemistry', None, None, None, None, 'analysis', 'isolation & purification', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/23416182,2013,0.0,0.0,,, -23331069,"Profiles of S-substituted cysteine flavor precursors were determined in 42 Alliaceae species native to South Africa and South America. It was found that the pool of cysteine derivatives present in these plants is remarkably very simple, with S-((methylthio)methyl)cysteine 4-oxide (marasmin) being the principal flavor precursor, typically accounting for 93-100% of the pool. Out of the other cysteine derivatives, only minor quantities of methiin were present in some species. The marasmin-derived thiosulfinate marasmicin (2,4,5,7-tetrathiaoctane 4-oxide), a major sensory-active compound of the freshly disrupted plants, was isolated, and its organoleptic properties were evaluated. Furthermore, sulfur-containing volatiles formed upon boiling of these alliaceous species were studied by GC-MS. The profile of the volatiles formed was relatively simple, with 2,3,5-trithiahexane and 2,4,5,7-tetrathiaoctane being the major components. Despite the traditional belief, ingestion of the marasmin-rich plants was always accompanied by development of a strong ""garlic breath"". We believe that especially several Tulbaghia species deserve to attract much greater attention from the food industry thanks to their pungent garlicky taste and unusual yet pleasant alliaceous smell.",Journal of agricultural and food chemistry,"['D000490', 'D005421', 'D008401', 'D010936', 'D012903', 'D013019', 'D013020', 'D013457', 'D055549']","['Allium', 'Flavoring Agents', 'Gas Chromatography-Mass Spectrometry', 'Plant Extracts', 'Smell', 'South Africa', 'South America', 'Sulfur Compounds', 'Volatile Organic Compounds']",Flavor precursors and sensory-active sulfur compounds in alliaceae species native to South Africa and South America.,"['Q000737', 'Q000032', None, 'Q000032', None, None, None, 'Q000032', 'Q000032']","['chemistry', 'analysis', None, 'analysis', None, None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/23331069,2013,0.0,0.0,,, -23259687,"This study investigates the analysis of thiol compounds using a needle trap device (HS-NTD) and solid-phase microextraction (HS-SPME) derivatized headspace techniques coupled to GC-MS. Thiol compounds and their outgassed products are particularly difficult to monitor in foodstuffs. It was found that with in-needle and in-fiber derivatization, using the derivatization agent N-phenylmaleimide, it was possible to enhance the selectivity toward thiol, which allowed the quantitation of butanethiol, ethanethiol, methanethiol, and propanethiol compounds found in fresh garlic. A side-hole NTD was prepared and packed in house and utilized mixed DVB and Carboxen polymer extraction phases made of 60-80 mesh particles. NTD sampling was accomplished in the exhaustive sampling mode, where breakthrough was negligible. This work demonstrates a new application for a side-hole NTD sampling. A commercial mixed polymer phase of polydimethylsiloxane (PDMS) and divinylbenzene polymer (DVB) SPME fiber was used for SPME extractions. Under optimized derivatization, extraction, and analysis conditions for both NTD-GC-MS and SPME-GC-MS techniques, automated sampling methods were developed for quantitation. Both methods demonstrate a successful approach to thiol determination and provide a quantitative linear response between <0.1 and 10 mg L(-1) (R(2) = 0.9996), with limits of detection (LOD) in the low micrograms per liter range for the investigated thiols. Addition methods using known spiked quantities of thiol analytes in ground garlic facilitated method validation. Carry-over was also negligible for both SPME and NTD under optimized conditions.",Journal of agricultural and food chemistry,"['D004129', 'D005737', 'D008401', 'D015203', 'D052617', 'D013438', 'D014753']","['Dimethylpolysiloxanes', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Reproducibility of Results', 'Solid Phase Microextraction', 'Sulfhydryl Compounds', 'Vinyl Compounds']",Assessment of thiol compounds from garlic by automated headspace derivatized in-needle-NTD-GC-MS and derivatized in-fiber-SPME-GC-MS.,"['Q000737', 'Q000737', 'Q000379', None, 'Q000379', 'Q000737', 'Q000737']","['chemistry', 'chemistry', 'methods', None, 'methods', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23259687,2014,1.0,2.0,,, -23249145,"The phenolic compounds were extracted from green and yellow leaves, stalks, and seeds of garlic ( Allium ursinum L.). The extracts were analyzed by liquid chromatography-photodiode array detector-electrospray ionization-tandem mass spectrometry (LC-PDA-ESI-MS/MS). In total, 21 compounds were detected. The flavonol derivatives were identified on the basis of their ultraviolet (UV) spectra and fragmentation patterns in collision-induced dissociation experiments. On the basis of accurate MS and MS/MS data, six compounds were newly identified in bear's garlic, mainly the kaempferol derivatives. As far as the investigated parts of garlic are concerned, the kaempferol derivatives were found to be predominant in yellow leaves [2362.96 mg/100 g of dry matter (dm)], followed by green leaves (1856.31 mg/100 g of dm). Seeds contained the minimal phenolic compounds, less than stalks. The yellow leaves of A. ursinum possessed a much larger content of compounds acylated with p-coumaric acid than green leaves (1299.97 versus 855.67 mg/100 g of dm, respectively). The stalks and seeds contained much more non-acetylated than acetylated flavonoid glycosides with p-coumaric acid compounds (162.4 versus 62.82 mg/100 g of dm and 105.49 versus 24.18 mg/100 g of dm, respectively).",Journal of agricultural and food chemistry,"['D000490', 'D002853', 'D044948', 'D015394', 'D059808', 'D021241', 'D013056']","['Allium', 'Chromatography, Liquid', 'Flavonols', 'Molecular Structure', 'Polyphenols', 'Spectrometry, Mass, Electrospray Ionization', 'Spectrophotometry, Ultraviolet']",Characterization and content of flavonol derivatives of Allium ursinum L. plant.,"['Q000737', None, 'Q000032', None, 'Q000032', None, None]","['chemistry', None, 'analysis', None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/23249145,2013,1.0,3.0,,, -23218283,"The fructose polymer fructan was extracted from white garlic and fractionated using DEAE cellulose 52 and Sephadex G-100 columns to characterize its chemical composition and protective effect against ultraviolet radiation b (UVB) induced human keratinocyte (HaTaC) damage. Gel permeation chromatography, high performance anion exchange chromatography, infrared spectroscopy and 1D and 2D nuclear magnetic resonance spectroscopy were used to determine the chemical composition and functional characteristics of the garlic fructan (GF). GF was a homogeneous polysaccharide with a molecular weight of 4.54 × 10(3)Da. It was a member of the 1-kestose family, and it was composed of fructose and glucose at a ratio of 14:1. The main chain of GF was composed of (2→1)-β-D-fructopyranose linked to a terminal (2→1)-α-D-glucopyranose at the non-reducing end and a (2→6)-β-D-fructopyranose branched chain. The degree of polymerization was 28. Preliminary tests described herein indicated that GF may be effective in protecting HaTaC from UVB-induced damage.",Carbohydrate polymers,"['D002460', 'D049109', 'D002845', 'D005630', 'D005737', 'D006801', 'D015603', 'D009682', 'D008970', 'D011837', 'D014312']","['Cell Line', 'Cell Proliferation', 'Chromatography', 'Fructans', 'Garlic', 'Humans', 'Keratinocytes', 'Magnetic Resonance Spectroscopy', 'Molecular Weight', 'Radiation-Protective Agents', 'Trisaccharides']",Structure and protective effect on UVB-induced keratinocyte damage of fructan from white garlic.,"['Q000187', 'Q000187', None, 'Q000737', 'Q000737', None, 'Q000528', None, None, 'Q000737', 'Q000737']","['drug effects', 'drug effects', None, 'chemistry', 'chemistry', None, 'radiation effects', None, None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23218283,2013,0.0,0.0,,, -23210417,"A controlled, clinical, double-blind study was conducted to assess the efficacy of a sugar-free chewing gum containing zinc acetate and magnolia bark extract (MBE) on oral volatile sulfur-containing compounds (VSC) versus a placebo sugar-free chewing gum for two hours.",The Journal of clinical dentistry,"['D000293', 'D000328', 'D000704', 'D001944', 'D002638', 'D002849', 'D004311', 'D004338', 'D005260', 'D006209', 'D006801', 'D031566', 'D008297', 'D008875', 'D008517', 'D024301', 'D010936', 'D018709', 'D013457', 'D013549', 'D055815', 'D019345']","['Adolescent', 'Adult', 'Analysis of Variance', 'Breath Tests', 'Chewing Gum', 'Chromatography, Gas', 'Double-Blind Method', 'Drug Combinations', 'Female', 'Halitosis', 'Humans', 'Magnolia', 'Male', 'Middle Aged', 'Phytotherapy', 'Plant Bark', 'Plant Extracts', 'Statistics, Nonparametric', 'Sulfur Compounds', 'Sweetening Agents', 'Young Adult', 'Zinc Acetate']",The effect of zinc acetate and magnolia bark extract added to chewing gum on volatile sulfur-containing compounds in the oral cavity.,"[None, None, None, None, None, None, None, None, None, 'Q000517', None, None, None, None, None, None, 'Q000627', None, 'Q000032', None, None, 'Q000627']","[None, None, None, None, None, None, None, None, None, 'prevention & control', None, None, None, None, None, None, 'therapeutic use', None, 'analysis', None, None, 'therapeutic use']",https://www.ncbi.nlm.nih.gov/pubmed/23210417,2012,,,,, -23160746,"The effects of Cd and HCHs with single and combined forms on Cd and HCHs phytoavailability of Allium sativum L. were investigated. The results indicated that the coexistence of Cd and HCHs presented antagonistic interactions mostly, which might be partly due to the formation of Cd-HCHs complex, compared with single stress. The bioaccumulation of Cd and HCHs in plants depended largely on their concentrations applied in pot soils, and the phytoavailability of HCH isomers was in the sequence: δ- > γ- ≥ β- > α-HCH.",Bulletin of environmental contamination and toxicology,"['D002104', 'D002849', 'D005737', 'D007536', 'D001556']","['Cadmium', 'Chromatography, Gas', 'Garlic', 'Isomerism', 'Lindane']",Accumulation and phytoavailability of hexachlorocyclohexane isomers and cadmium in Allium sativum L. under the stress of hexachlorocyclohexane and cadmium.,"['Q000378', None, 'Q000187', None, 'Q000737']","['metabolism', None, 'drug effects', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23160746,2013,1.0,1.0,,, -23158347,"Garlic has been known for its therapeutic effects for centuries and is used worldwide as a functional food. The concentration of the active molecules could be enhanced by a better knowledge of their biosynthesis. The precursor of these compounds, alliin (a sulfur amino-acid) has been obtained by chemical synthesis. However, this synthesis route also leads to a diastereoisomer as co-product. This work describes the development of an analytical method which allows the separation and quantification of the two diastereoisomers in order to determine in which proportion the natural form can be produced. The HPLC method which was optimized and validated by accuracy profile exploits an original stationary phase consisting of porous graphitic carbon (PGC). Furthermore, the developped method was used to separate the diastereoisomers of methiin, another cysteine sulfoxide, and to analyze an aqueous extract of garlic. The ability to quantify the amount of natural alliin is valuable for further work on garlic molecules and their application for health protection.",Talanta,"['D002851', 'D003545', 'D013237']","['Chromatography, High Pressure Liquid', 'Cysteine', 'Stereoisomerism']",Analysis of the diastereoisomers of alliin by HPLC.,"['Q000379', 'Q000031', None]","['methods', 'analogs & derivatives', None]",https://www.ncbi.nlm.nih.gov/pubmed/23158347,2013,0.0,0.0,,, -23016295,"A method was developed and validated for the simultaneous analysis of 112 pesticide residues in vegetables by gas chromatography coupled with triple quadrupole mass spectrometry (GC-QQQ-MS/MS). It is demonstrated that the optimized conditions could provide a more accurate quantitation and lower limit of quantification of the analysis by dispersive-solid phase extraction (D-SPE) cleanup. The samples were extracted with acetonitrile and toluene (8: 1, v/v), and cleaned up by D-SPE. To every 5 mL extraction solution, 0.8 g MgSO4, 0.05 g graphitized carbon black (GCB), 0.1 g ethylenediamine-N-propyl silyl (PSA) and 0.05 g C18 were added. The extracts were analyzed by GC-QQQ-MS/MS using internal standard method. The recoveries of the 112 pesticides at three spiked levels of 20, 50 and 200 microg/kg were ranged from 53.1% to 138.7%, and among which those of 86 pesticides were from 65.0% to 120.0%. The relative standard deviations (RSD) were less than 12%. The limits of quantifications (LOQs) (signal/noise at 10) were between 1.6 and 13.4 microg/kg. The vegetable samples collected from the market such as garlic chives, cucumber and purple cabbage were analyzed, and the residues of triazophos and fenpropathrin were detected in some of these samples. The method can be applied to the routine analysis for the determination of the 112 pesticides in vegetable samples.",Se pu = Chinese journal of chromatography,"['D005506', 'D008401', 'D010573', 'D052616', 'D014675']","['Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Pesticide Residues', 'Solid Phase Extraction', 'Vegetables']",[Analysis of 112 pesticide residues in vegetables using dispersive-solid phase extraction and gas chromatography-triple quadrupole mass spectrometry].,"['Q000032', 'Q000379', 'Q000032', 'Q000379', 'Q000737']","['analysis', 'methods', 'analysis', 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23016295,2013,,,,, -22985527,"A graphene-supported zinc oxide (ZnO) solid-phase microextraction (SPME) fiber was prepared via a sol-gel approach. Graphite oxide (GO), with rich oxygen-containing groups, was selected as the starting material to anchor ZnO on its nucleation center. After being deoxidized by hydrazine, the Zn(OH)2/GO coating was dehydrated at high temperature to give the ZnO/graphene coating. Sol-gel technology could efficiently incorporate ZnO/graphene composites into the sol-gel network and provided strong chemical bonding between sol-gel polymeric SPME coating and silica fiber surface, which enhanced the durability of the fiber and allowed more than 200 replicate extractions. Results indicated that pure ZnO coated fiber did not show adsorption selectivity toward sulfur compounds, which might because the ZnO nanoparticles were enwrapped in the sol-gel network, and the strong coordination action between Zn ion and S ion was therefore blocked. The incorporation of graphene into ZnO based sol-gel network greatly enlarged the BET surface area from 1.2 m2/g to 169.4 m2/g and further increased the adsorption sites. Combining the superior properties of extraordinary surface area of graphene and the strong coordination action of ZnO to sulfur compounds, the ZnO/graphene SPME fiber showed much higher adsorption affinity to 1-octanethiol (enrichment factor, EF, 1087) than other aliphatic compounds without sulfur-containing groups (EFs<200). Also, it showed higher extraction selectivity and sensitivity toward sulfur compounds than commercial polydimethylsiloxane (PDMS) and polydimethylsiloxane/divinylbenzene (PDMS/DVB) SPME fibers. Several most abundant sulfur volatiles in Chinese chive and garlic sprout were analyzed using the ZnO/graphene SPME fiber in combination with gas chromatography-mass spectrometry (GC-MS). Their limits of detection were 0.1-0.7 μg/L. The relative standard deviation (RSD) using one fiber ranged from 3.6% to 9.1%. The fiber-to-fiber reproducibility for three parallel prepared fibers was 4.8-10.8%. The contents were in the range of 1.0-46.4 μg/g with recoveries of 80.1-91.6% for four main sulfides in Chinese chive and 17.1-122.6 μg/g with recoveries of 73.2-80.6% for three main sulfides in garlic sprout.",Journal of chromatography. A,"['D000490', 'D008401', 'D006108', 'D057230', 'D016014', 'D015203', 'D052617', 'D013438', 'D013440', 'D055549', 'D015034']","['Allium', 'Gas Chromatography-Mass Spectrometry', 'Graphite', 'Limit of Detection', 'Linear Models', 'Reproducibility of Results', 'Solid Phase Microextraction', 'Sulfhydryl Compounds', 'Sulfides', 'Volatile Organic Compounds', 'Zinc Oxide']",Graphene-supported zinc oxide solid-phase microextraction coating with enhanced selectivity and sensitivity for the determination of sulfur volatiles in Allium species.,"['Q000737', None, 'Q000737', None, None, None, 'Q000295', 'Q000032', 'Q000032', 'Q000032', 'Q000737']","['chemistry', None, 'chemistry', None, None, None, 'instrumentation', 'analysis', 'analysis', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22985527,2013,1.0,1.0,,, -22972339,"We produced a single deuterated lachrymatory factor (propanthial S-oxide, m/z = 91) in a model reaction system comprising purified alliinase, lachrymatory factor synthase (LFS), and (E)-(+)-S-(1-propenyl)-L-cysteine sulfoxide ((E)-PRENCSO) in D(2)O. Onion LFS reacted with the degraded products of (E)-PRENCSO by alliinase, but not with those of (Z)-PRENCSO. These findings indicate that onion LFS is an (E)-1-propenylsulfenic acid isomerase.","Bioscience, biotechnology, and biochemistry","['D013437', 'D002384', 'D003545', 'D017666', 'D005737', 'D008401', 'D019746', 'D019697', 'D010940', 'D011522', 'D012996', 'D013237', 'D013454']","['Carbon-Sulfur Lyases', 'Catalysis', 'Cysteine', 'Deuterium Oxide', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Intramolecular Oxidoreductases', 'Onions', 'Plant Proteins', 'Protons', 'Solutions', 'Stereoisomerism', 'Sulfoxides']",Proton transfer in a reaction catalyzed by onion lachrymatory factor synthase.,"['Q000737', None, 'Q000031', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', None, None, None, 'Q000138']","['chemistry', None, 'analogs & derivatives', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', None, None, None, 'chemical synthesis']",https://www.ncbi.nlm.nih.gov/pubmed/22972339,2013,0.0,0.0,,, -22964046,"This paper reports on the development and optimization of a modified Quick, Easy, Cheap Effective, Rugged and Safe (QuEChERS) based extraction technique coupled with a clean-up dispersive-solid phase extraction (dSPE) as a new, reliable and powerful strategy to enhance the extraction efficiency of free low molecular-weight polyphenols in selected species of dietary vegetables. The process involves two simple steps. First, the homogenized samples are extracted and partitioned using an organic solvent and salt solution. Then, the supernatant is further extracted and cleaned using a dSPE technique. Final clear extracts of vegetables were concentrated under vacuum to near dryness and taken up into initial mobile phase (0.1% formic acid and 20% methanol). The separation and quantification of free low molecular weight polyphenols from the vegetable extracts was achieved by ultrahigh pressure liquid chromatography (UHPLC) equipped with a phodiode array (PDA) detection system and a Trifunctional High Strength Silica capillary analytical column (HSS T3), specially designed for polar compounds. The performance of the method was assessed by studying the selectivity, linear dynamic range, the limit of detection (LOD) and limit of quantification (LOQ), precision, trueness, and matrix effects. The validation parameters of the method showed satisfactory figures of merit. Good linearity (Rvalues2>0.954; (+)-catechin in carrot samples) was achieved at the studied concentration range. Reproducibility was better than 3%. Consistent recoveries of polyphenols ranging from 78.4 to 99.9% were observed when all target vegetable samples were spiked at two concentration levels, with relative standard deviations (RSDs, n=5) lower than 2.9%. The LODs and the LOQs ranged from 0.005 μg mL(-1) (trans-resveratrol, carrot) to 0.62 μg mL(-1) (syringic acid, garlic) and from 0.016 μg mL(-1) (trans-resveratrol, carrot) to 0.87 μg mL(-1) ((+)-catechin, carrot) depending on the compound. The method was applied for studying the occurrence of free low molecular weight polyphenols in eight selected dietary vegetables (broccoli, tomato, carrot, garlic, onion, red pepper, green pepper and beetroot), providing a valuable and promising tool for food quality evaluation.",Journal of chromatography. A,"['D000097', 'D000704', 'D002851', 'D016018', 'D057230', 'D008278', 'D008970', 'D059808', 'D015203', 'D012965', 'D052616', 'D014675']","['Acetonitriles', 'Analysis of Variance', 'Chromatography, High Pressure Liquid', 'Least-Squares Analysis', 'Limit of Detection', 'Magnesium Sulfate', 'Molecular Weight', 'Polyphenols', 'Reproducibility of Results', 'Sodium Chloride', 'Solid Phase Extraction', 'Vegetables']",A new and improved strategy combining a dispersive-solid phase extraction-based multiclass method with ultra high pressure liquid chromatography for analysis of low molecular weight polyphenols in vegetables.,"['Q000737', None, 'Q000379', None, None, 'Q000737', None, 'Q000032', None, 'Q000737', 'Q000379', 'Q000737']","['chemistry', None, 'methods', None, None, 'chemistry', None, 'analysis', None, 'chemistry', 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22964046,2013,1.0,2.0,,, -22931231,"A novel low-molecular-weight fructooligosaccharide (LMWF) from garlic ( Allium sativum ) was isolated and identified. The structure and physicochemical properties of the LMWF were determined by chemical and spectroscopic methods, size-exclusion chromatography, atomic force microscopy (AFM), dynamic rheometry, and differential scanning calorimetry (DSC). The results showed that the LMWF was a neo-ketose with a molecular weight of 1770 Da. The LMWF had a (2,1)-linked β-D-Fruf backbone with (2,6)-linked β-D-Fruf side chains, and it was mainly composed of fructose. The branch degree was 18.1%, and the intrinsic viscosity was 3.06 mL/g. The spherical particles of the LMWF were observed by AFM, and their size was relatively uniform. With an increase in the water content, the peak temperature (T(p)), onset temperature (T(o)), and endset temperature (T(c)) increased, while the gelatinization enthalpy (ΔH(gel)) decreased. The LMWF was more stable at a water content of 10%.",Journal of agricultural and food chemistry,"['D002236', 'D055598', 'D005632', 'D005737', 'D015394', 'D008970', 'D009844', 'D010084', 'D013816', 'D014783']","['Carbohydrate Conformation', 'Chemical Phenomena', 'Fructose', 'Garlic', 'Molecular Structure', 'Molecular Weight', 'Oligosaccharides', 'Oxidation-Reduction', 'Thermodynamics', 'Viscosity']",Physicochemical characterization of a low-molecular-weight fructooligosaccharide from Chinese Cangshan garlic (Allium sativum L.).,"[None, None, 'Q000032', 'Q000737', None, None, 'Q000737', None, None, None]","[None, None, 'analysis', 'chemistry', None, None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22931231,2013,0.0,0.0,,, -22885051,"In this work, partially sulfonated polystyrene-titania (PSP-TiO(2)) organic-inorganic hybrid stir bar coating was prepared by sol-gel and blending methods, and a new method of PSP-TiO(2) coating stir bar sorptive extraction (SBSE)-high performance liquid chromatography (HPLC)-inductively coupled plasma mass spectrometry (ICP-MS) was established for the analysis of seleno-amino acids (selenocystine (SeCys(2)), methylseleno-cysteine (MeSeCys), selenomethionine (SeMet) and selenoethionine (SeEt)) and seleno-oligopeptides (γ-glutamyl-Se-methyl-selenocysteine (γ-GluMeSeCys) and selenodiglutathione (GS-Se-SG)) in biological samples. The prepared high polar PSP-TiO(2) hybrid coating avoided the swelling of PSP and cracking of TiO(2) coating by combining the good film-forming property of PSP with the high mechanical strength of TiO(2). The scanning electron microscope (SEM) showed that no obvious swelling and damage occurred for the PSP-TiO(2) hybrid stir bar coating after 30 extraction/desorption cycles. The preparation reproducibility of PSP-TiO(2) coated stir bar, evaluated with the relative standard deviations (RSDs), was in the range of 6.7-12.6% (n=5) in one batch, and 9.9-17.6% (n=7) among different batches. The limits of detection (LODs) of the developed method for six target selenium species were in the range of 50.2-185.5 ngL(-1) (as (77)Se) and 45.9-158.8 ngL(-1) (as (82)Se) with the RSDs within 4.9-11.7%. The dynamic linear range was found to cover three orders of magnitude with correlation coefficient of 0.9995-0.9999. The developed method was applied for the analysis of Certified Reference Material SELM-1 selenium enriched yeast and the determined values were in good agreement with the certified values. The method has also been applied for the analysis of seleno-amino acids and seleno-oligopeptides in human urine and garlic samples. Different from the conventional organic polymer SBSE coatings (such as polydimethylsiloxane, PDMS), the extraction mechanism of PSP-TiO(2) organic-inorganic hybrid SBSE coating was based on the cation exchange interaction, which made it feasible to directly extract high polar seleno-amino acids and seleno-oligopeptides in biological samples without derivatization. This coating could also be suitable for stir bar sorptive extraction of other cationic compounds from the environmental and biological samples.",Journal of chromatography. A,"['D002851', 'D006863', 'D057230', 'D013058', 'D008855', 'D009842', 'D015203', 'D018036']","['Chromatography, High Pressure Liquid', 'Hydrogen-Ion Concentration', 'Limit of Detection', 'Mass Spectrometry', 'Microscopy, Electron, Scanning', 'Oligopeptides', 'Reproducibility of Results', 'Selenium Compounds']",High polar organic-inorganic hybrid coating stir bar sorptive extraction combined with high performance liquid chromatography-inductively coupled plasma mass spectrometry for the speciation of seleno-amino acids and seleno-oligopeptides in biological samples.,"['Q000379', None, None, 'Q000379', None, 'Q000737', None, 'Q000737']","['methods', None, None, 'methods', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22885051,2012,1.0,1.0,,, -22860996,"Garlic has been used throughout history for both culinary and medicinal purpose. Allicin is a major component of crushed garlic. Although it is sensitive to heat and light and easily metabolized into various compounds such as diallyl disulfide, diallyl trisulfide, and diallyl sulfide, allicin is still a major bioactive compound of crushed garlic. The mortality of hepatocellular carcinoma is quite high and ranks among the top 10 cancer-related deaths in Taiwan. Although numerous studies have shown the cancer-preventive properties of garlic and its components, there is no study on the effect of allicin on the growth of human liver cancer cells. In this study, we focused on allicin-induced autophagic cell death in human liver cancer Hep G2 cells. Our results indicated that allicin induced p53-mediated autophagy and inhibited the viability of human hepatocellular carcinoma cell lines. Using Western blotting, we observed that allicin decreased the level of cytoplasmic p53, the PI3K/mTOR signaling pathway, and the level of Bcl-2 and increased the expression of AMPK/TSC2 and Beclin-1 signaling pathways in Hep G2 cells. In addition, the colocalization of LC3-II with MitoTracker-Red (labeling mitochondria), resulting in allicin-induced degradation of mitochondria, could be observed by confocal laser microscopy. In conclusion, allicin of garlic shows great potential as a novel chemopreventive agent for the prevention of liver cancer. ",Journal of agricultural and food chemistry,"['D016588', 'D017209', 'D051017', 'D001343', 'D000071186', 'D002470', 'D002851', 'D016158', 'D056945', 'D006801', 'D053078', 'D008565', 'D008869', 'D010588', 'D015398', 'D013441', 'D058570', 'D053719', 'D025521']","['Anticarcinogenic Agents', 'Apoptosis', 'Apoptosis Regulatory Proteins', 'Autophagy', 'Beclin-1', 'Cell Survival', 'Chromatography, High Pressure Liquid', 'Genes, p53', 'Hep G2 Cells', 'Humans', 'Membrane Potential, Mitochondrial', 'Membrane Proteins', 'Microtubule-Associated Proteins', 'Phagosomes', 'Signal Transduction', 'Sulfinic Acids', 'TOR Serine-Threonine Kinases', 'Tandem Mass Spectrometry', 'Tumor Suppressor Proteins']",Allicin induces p53-mediated autophagy in Hep G2 human liver cancer cells.,"['Q000494', 'Q000187', 'Q000378', 'Q000187', None, 'Q000187', 'Q000379', 'Q000502', 'Q000187', None, 'Q000187', 'Q000378', 'Q000378', 'Q000187', 'Q000187', 'Q000138', 'Q000378', None, 'Q000378']","['pharmacology', 'drug effects', 'metabolism', 'drug effects', None, 'drug effects', 'methods', 'physiology', 'drug effects', None, 'drug effects', 'metabolism', 'metabolism', 'drug effects', 'drug effects', 'chemical synthesis', 'metabolism', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/22860996,2014,0.0,0.0,,, -22610968,"The in vitro antibacterial activity of essential oils (EOs) obtained from fresh bulbs of garlic, Allium sativum L., and leek, Allium porrum L. ( Alliaceae), was studied. A. sativum (garlic) EO showed a good antimicrobial activity against Staphylococcus aureus (inhibition zone 14.8 mm), Pseudomonas aeruginosa (inhibition zone 21.1 mm), and Escherichia coli (inhibition zone 11.0 mm), whereas the EO of A. porrum (leek) had no antimicrobial activity. The main constituents of the garlic EO were diallyl monosulfide, diallyl disulfide (DADS), diallyl trisulfide, and diallyl tetrasulfide. The EO of A. porrum was characterized by the presence of dipropyl disulfide (DPDS), dipropyl trisulfide, and dipropyl tetrasulfide. The antimicrobial activities of the DADS and DPDS were also studied. The results obtained suggest that the presence of the allyl group is fundamental for the antimicrobial activity of these sulfide derivatives when they are present in Allium or in other species (DADS inhibition zone on S. aureus 15.9 mm, P. aeruginosa 21.9 mm, E. coli 11.4 mm).",Phytotherapy research : PTR,"['D000490', 'D000498', 'D000900', 'D004220', 'D004926', 'D008401', 'D009822', 'D010938', 'D011550', 'D013211', 'D013440']","['Allium', 'Allyl Compounds', 'Anti-Bacterial Agents', 'Disulfides', 'Escherichia coli', 'Gas Chromatography-Mass Spectrometry', 'Oils, Volatile', 'Plant Oils', 'Pseudomonas aeruginosa', 'Staphylococcus aureus', 'Sulfides']","The role of diallyl sulfides and dipropyl sulfides in the in vitro antimicrobial activity of the essential oil of garlic, Allium sativum L., and leek, Allium porrum L.","['Q000737', 'Q000737', 'Q000494', None, 'Q000187', None, 'Q000737', 'Q000737', 'Q000187', 'Q000187', 'Q000737']","['chemistry', 'chemistry', 'pharmacology', None, 'drug effects', None, 'chemistry', 'chemistry', 'drug effects', 'drug effects', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22610968,2013,1.0,1.0,,, -22513009,"A bioassay-guided phytochemical analysis of the polar extract from the bulbs of garlic, Allium sativum L., var. Voghiera, typical of Voghiera, Ferrara (Italy), allowed the isolation of ten furostanol saponins; voghieroside A1/A2 and voghieroside B1/B2, based on the rare agapanthagenin aglycone; voghieroside C1/C2, based on agigenin aglycone; and voghieroside D1/D2 and E1/E2, based on gitogenin aglycone. In addition, we found two known spirostanol saponins, agigenin 3-O-trisaccharide and gitogenin 3-O-tetrasaccharide. The chemical structures of the isolated compounds were established through a combination of extensive nuclear magnetic resonance, mass spectrometry and chemical analyses. High concentrations of two eugenol diglycosides were also found for the first time in Allium spp. The isolated compounds were evaluated for their antimicrobial activity towards two fungal species, the air-borne pathogen Botrytis cinerea and the antagonistic fungus Trichoderma harzianum.",Phytochemistry,"['D000935', 'D020171', 'D005737', 'D007558', 'D008826', 'D015394', 'D018517', 'D012503', 'D013150', 'D014242']","['Antifungal Agents', 'Botrytis', 'Garlic', 'Italy', 'Microbial Sensitivity Tests', 'Molecular Structure', 'Plant Roots', 'Saponins', 'Spirostans', 'Trichoderma']","Antifungal saponins from bulbs of garlic, Allium sativum L. var. Voghiera.","['Q000737', 'Q000187', 'Q000737', None, None, None, 'Q000737', 'Q000737', 'Q000737', 'Q000187']","['chemistry', 'drug effects', 'chemistry', None, None, None, 'chemistry', 'chemistry', 'chemistry', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/22513009,2012,0.0,0.0,,units J in Hz, -22500289,"The molecular characteristics of chlorothalonil can cause particular determination difficulties in some vegetable commodities such as leek or garlic. These difficulties are mainly related to the low recoveries obtained using common multi-residue methods (MRMs)--a consequence of the very high interaction level with natural components in the matrix. These shortcomings were pointed out in the last European Proficiency Test for Pesticide Residues on Fruits and Vegetables, where false negatives for chlorothalonil in leek were observed at around 50%. In this study we have evaluated the ethyl acetate, the Dutch mini-Luke and the QuEChERS MRMs to compare their capabilities for chlorothalonil determination using GC-MS/MS in both the electron impact ionization (EI) and negative chemical ionization (NCI) modes. Best recoveries (in the range of 100-120%, with an RSD below 20%) were obtained using the Dutch mini-Luke method. Lower values (52-70%) were obtained for ethyl acetate whereas no recovery was obtained when the QuEChERS method was applied. Furthermore, tomato matrix was also included in the experiments in order to facilitate the comparability of results. Two ionization modes, electron impact ionization (EI) and negative chemical ionization (NCI) in GC-MS/MS, were applied to evaluate their respective advantages and disadvantages for quantification and identification. As expected, NCI showed limits of detection (LODs) 5 to 10 times lower than EI. However, in both cases, the LODs were still below 10 μg kg(-1). The proposed optimal method was applied for chlorothalonil determination in leek and garlic with good results--in accordance with the European Union (EU) Analytical Quality Control (AQC) Guidelines for pesticides analysis.",The Analyst,"['D000085', 'D005591', 'D005506', 'D005638', 'D008401', 'D009570', 'D010573', 'D014675']","['Acetates', 'Chemical Fractionation', 'Food Contamination', 'Fruit', 'Gas Chromatography-Mass Spectrometry', 'Nitriles', 'Pesticide Residues', 'Vegetables']",Determination of chlorothalonil in difficult-to-analyse vegetable matrices using various multiresidue methods.,"['Q000737', None, 'Q000032', 'Q000737', None, 'Q000032', 'Q000032', 'Q000737']","['chemistry', None, 'analysis', 'chemistry', None, 'analysis', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22500289,2012,0.0,0.0,,, -22497489,"In the present study the effects of individual and combined essential oils (EOs) extracted from onion (Allium cepa L.) bulb and garlic (Allium sativum L.) clove on the growth of Aspergillus versicolor and sterigmatocystin (STC) production were investigated. The EOs obtained by hydrodistillation were analyzed by GC/MS. Twenty one compounds were identified in onion EO. The major components were: dimethyl-trisulfide (16.64%), methyl-propyl-trisulfide (14.21%), dietil-1,2,4-tritiolan (3R,5S-, 3S,5S- and 3R,5R- isomers) (13.71%), methyl-(1-propenyl)-disulfide (13.14%), and methyl-(1-propenyl)-trisulfide (13.02%). The major components of garlic EO were diallyl-trisulfide (33.55%), and diallyl-disulfide (28.05%). The mycelial growth and the STC production were recorded after 7, 14, and 21 d of the A. versicolor growth in Yeast extract sucrose (YES) broth containing different EOs concentrations. Compared to the garlic EO, the onion EO showed a stronger inhibitory effect on the A. versicolor mycelial growth and STC production. After a 21-d incubation of fungi 0.05 and 0.11 μg/mL of onion EO and 0.11 μg/mL of garlic EO completely inhibited the A. versicolor mycelial growth and mycotoxins biosynthesis. The combination of EOs of onion (75%) and garlic (25%) had a synergistic effect on growth inhibition of A. versicolor and STC production.",Journal of food science,"['D000498', 'D000890', 'D001230', 'D004220', 'D005506', 'D005516', 'D005519', 'D005737', 'D008401', 'D009822', 'D019697', 'D010938', 'D015203', 'D013170', 'D013241', 'D013440']","['Allyl Compounds', 'Anti-Infective Agents', 'Aspergillus', 'Disulfides', 'Food Contamination', 'Food Microbiology', 'Food Preservation', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Oils, Volatile', 'Onions', 'Plant Oils', 'Reproducibility of Results', 'Spores', 'Sterigmatocystin', 'Sulfides']",Effects of onion (Allium cepa L.) and garlic (Allium sativum L.) essential oils on the Aspergillus versicolor growth and sterigmatocystin production.,"['Q000032', 'Q000494', 'Q000187', 'Q000032', 'Q000517', None, None, 'Q000737', None, 'Q000494', 'Q000737', 'Q000494', None, 'Q000187', 'Q000096', 'Q000032']","['analysis', 'pharmacology', 'drug effects', 'analysis', 'prevention & control', None, None, 'chemistry', None, 'pharmacology', 'chemistry', 'pharmacology', None, 'drug effects', 'biosynthesis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/22497489,2013,2.0,3.0,,, -22473818,"Sulfenic acids play a prominent role in biology as key participants in cellular signaling relating to redox homeostasis, in the formation of protein-disulfide linkages, and as the central players in the fascinating organosulfur chemistry of the Allium species (e.g., garlic). Despite their relevance, direct measurements of their reaction kinetics have proven difficult owing to their high reactivity. Herein, we describe the results of hydrocarbon autoxidations inhibited by the persistent 9-triptycenesulfenic acid, which yields a second order rate constant of 3.0×10(6)  M(-1)  s(-1) for its reaction with peroxyl radicals in PhCl at 30 °C. This rate constant drops 19-fold in CH(3)CN, and is subject to a significant primary deuterium kinetic isotope effect, k(H)/k(D) = 6.1, supporting a formal H-atom transfer (HAT) mechanism. Analogous autoxidations inhibited by the Allium-derived (S)-benzyl phenylmethanethiosulfinate and a corresponding deuterium-labeled derivative unequivocally demonstrate the role of sulfenic acids in the radical-trapping antioxidant activity of thiosulfinates, through the rate-determining Cope elimination of phenylmethanesulfenic acid (k(H)/k(D) ≈ 4.5) and its subsequent formal HAT reaction with peroxyl radicals (k(H)/k(D) ≈ 3.5). The rate constant that we derived from these experiments for the reaction of phenylmethanesulfenic acid with peroxyl radicals was 2.8×10(7)  M(-1)  s(-1); a value 10-fold larger than that we measured for the reaction of 9-triptycenesulfenic acid with peroxyl radicals. We propose that whereas phenylmethanesulfenic acid can adopt the optimal syn geometry for a 5-centre proton-coupled electron-transfer reaction with a peroxyl radical, the 9-triptycenesulfenic is too sterically hindered, and undergoes the reaction instead through the less-energetically favorable anti geometry, which is reminiscent of a conventional HAT.","Chemistry (Weinheim an der Bergstrasse, Germany)","['D000975', 'D005737', 'D015394', 'D010084', 'D010545', 'D021241', 'D013434', 'D013441']","['Antioxidants', 'Garlic', 'Molecular Structure', 'Oxidation-Reduction', 'Peroxides', 'Spectrometry, Mass, Electrospray Ionization', 'Sulfenic Acids', 'Sulfinic Acids']",The reaction of sulfenic acids with peroxyl radicals: insights into the radical-trapping antioxidant activity of plant-derived thiosulfinates.,"['Q000737', 'Q000737', None, None, 'Q000737', None, 'Q000737', 'Q000138']","['chemistry', 'chemistry', None, None, 'chemistry', None, 'chemistry', 'chemical synthesis']",https://www.ncbi.nlm.nih.gov/pubmed/22473818,2012,,,,, -22467307,"Diallyl trisulfide (DATS), a polysulfide constituent found in garlic oil, is capable of the release of hydrogen sulfide (H(2)S). H(2)S is a known cardioprotective agent that protects the heart via antioxidant, antiapoptotic, anti-inflammatory, and mitochondrial actions. Here, we investigated DATS as a stable donor of H(2)S during myocardial ischemia-reperfusion (MI/R) injury in vivo. We investigated endogenous H(2)S levels, infarct size, postischemic left ventricular function, mitochondrial respiration and coupling, endothelial nitric oxide (NO) synthase (eNOS) activation, and nuclear E2-related factor (Nrf2) translocation after DATS treatment. Mice were anesthetized and subjected to a surgical model of MI/R injury with and without DATS treatment (200 μg/kg). Both circulating and myocardial H(2)S levels were determined using chemiluminescent gas chromatography. Infarct size was measured after 45 min of ischemia and 24 h of reperfusion. Troponin I release was measured at 2, 4, and 24 h after reperfusion. Cardiac function was measured at baseline and 72 h after reperfusion by echocardiography. Cardiac mitochondria were isolated after MI/R, and mitochondrial respiration was investigated. NO metabolites, eNOS phosphorylation, and Nrf2 translocation were determined 30 min and 2 h after DATS administration. Myocardial H(2)S levels markedly decreased after I/R injury but were rescued by DATS treatment (P < 0.05). DATS administration significantly reduced infarct size per area at risk and per left ventricular area compared with control (P < 0.001) as well as circulating troponin I levels at 4 and 24 h (P < 0.05). Myocardial contractile function was significantly better in DATS-treated hearts compared with vehicle treatment (P < 0.05) 72 h after reperfusion. DATS reduced mitochondrial respiration in a concentration-dependent manner and significantly improved mitochondrial coupling after reperfusion (P < 0.01). DATS activated eNOS (P < 0.05) and increased NO metabolites (P < 0.05). DATS did not appear to significantly induce the Nrf2 pathway. Taken together, these data suggest that DATS is a donor of H(2)S that can be used as a cardioprotective agent to treat MI/R injury.",American journal of physiology. Heart and circulatory physiology,"['D000498', 'D000818', 'D000975', 'D004305', 'D006862', 'D008297', 'D051379', 'D008810', 'D008929', 'D023421', 'D015428', 'D009206', 'D009569', 'D013440', 'D016277']","['Allyl Compounds', 'Animals', 'Antioxidants', 'Dose-Response Relationship, Drug', 'Hydrogen Sulfide', 'Male', 'Mice', 'Mice, Inbred C57BL', 'Mitochondria, Heart', 'Models, Animal', 'Myocardial Reperfusion Injury', 'Myocardium', 'Nitric Oxide', 'Sulfides', 'Ventricular Function, Left']",The polysulfide diallyl trisulfide protects the ischemic myocardium by preservation of endogenous hydrogen sulfide and increasing nitric oxide bioavailability.,"['Q000494', None, 'Q000494', None, 'Q000378', None, None, None, 'Q000187', None, 'Q000378', 'Q000378', 'Q000378', 'Q000494', 'Q000187']","['pharmacology', None, 'pharmacology', None, 'metabolism', None, None, None, 'drug effects', None, 'metabolism', 'metabolism', 'metabolism', 'pharmacology', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/22467307,2012,0.0,0.0,,, -22434122,"This paper reviews a 10-year experience in establishing a cryopreserved Allium germplasm collection at the genebank of the National Agrobiodiversity Center, Republic of Korea. A systematic approach to Allium cryopreservation included: 1. revealing the most critical factors that affected regeneration after cryostorage; 2. understanding the mechanisms of cryoprotection by analyzing the thermal behavior of explants and cryoprotectant solutions using DSC and influx/efflux of cryoprotectants using HPLC; 3. assessing genetic stability of regenerants; and 4. revealing the efficiency of cryotherapy. Bulbil primordia, i.e. asexual bulbs formed on unripe inflorescences, proved to be the most suitable material for conservation of bolting varieties due to high post-cryopreservation regrowth and lower microbial infection level, followed by apical shoot apices from single bulbs and cloves. A total of 1,158 accessions of garlic as well as some Allium species have been cryopreserved during 2005-2010 using the droplet-vitrification technique with a mean regeneration percentage of 65.9 percent after cryostorage. These results open the door for large-scale implementation of cryostorage and for simplifying international exchange for clonal Allium germplasm.",Cryo letters,"['D000490', 'D002152', 'D002851', 'D003080', 'D015925', 'D003451', 'D055993', 'D010935', 'D018517', 'D018520', 'D010942', 'D012038', 'D056910', 'D058989']","['Allium', 'Calorimetry, Differential Scanning', 'Chromatography, High Pressure Liquid', 'Cold Temperature', 'Cryopreservation', 'Cryoprotective Agents', 'Germ Cells, Plant', 'Plant Diseases', 'Plant Roots', 'Plant Shoots', 'Plant Viruses', 'Regeneration', 'Republic of Korea', 'Vitrification']",Cryobanking of Korean allium germplasm collections: results from a 10 year experience.,"['Q000166', None, None, None, 'Q000379', None, 'Q000166', 'Q000821', 'Q000254', 'Q000254', None, None, None, None]","['cytology', None, None, None, 'methods', None, 'cytology', 'virology', 'growth & development', 'growth & development', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22434122,2012,,,,, -22416880,"The influence of processing, with and without fermentation, on the contents of organosulfur compounds, namely, γ-glutamyl peptides, S-alk(en)yl-L-cysteine sulfoxides (ACSOs), and S-allyl-L-cysteine (SAC), in pickled blanched garlic was evaluated. For each processing type, the effect of the preservation method and storage time was also analyzed. Blanching in hot water (90 °C for 5 min) hardly affected the individual organosulfur compound content. The fermentation and packing steps negatively affected the levels of all compounds except for SAC. The content of this compound increased during storage at room temperature whereas γ-glutamyl peptides and ACSOs were degraded to various extents. The pasteurization treatment itself had no significant effect on the concentrations of organosulfur compounds. Use of the corresponding fermentation brine in the case of the fermented product in conjunction with refrigerated storage was found to be the best method to preserve the levels of organosulfur compounds in pickled garlic stored for up to one year.",Journal of agricultural and food chemistry,"['D002851', 'D005285', 'D005511', 'D005737', 'D007778', 'D013457', 'D013997']","['Chromatography, High Pressure Liquid', 'Fermentation', 'Food Handling', 'Garlic', 'Lactobacillus', 'Sulfur Compounds', 'Time Factors']",Effect of processing and storage time on the contents of organosulfur compounds in pickled blanched garlic.,"[None, None, 'Q000379', 'Q000737', 'Q000378', 'Q000032', None]","[None, None, 'methods', 'chemistry', 'metabolism', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/22416880,2012,1.0,1.0,,, -22320076,"An automatic screening method based on HPLC/time-of-flight (TOF)-MS (full scan) was used for the analysis of 103 non-European fruit and vegetable samples after extraction by the quick, easy, cheap, effective, rugged, and safe method. The screening method uses a database that includes 300 pesticides, their fragments, and isotopical signals (910 ions) that identified 210 pesticides in 78 positive samples, with the highest number of detection being nine pesticides/sample. The concentrations of 97 pesticides were <10 microg/kg, 53 were between 10 and 100 microg/kg, and 60 were at a concentration of >100 microg/kg. Several parameters of the automatic screening method were carefully studied to avoid false positives and negatives in the studied samples; these included peak filter (number of chromatographic peak counts) and search criteria (retention time and error window). These parameters were affected by differences in mass accuracy and sensitivity of the two HPLC/TOF-MS systems used with different resolution powers (15 000 and 7500), the capabilities of which for resolving the ions included in the database from the matrix ions were studied in four matrixes, viz., pepper, rice, garlic, and cauliflower. Both of these mass resolutions were found to be satisfactory to resolve interferences from the signals of interest in the studied matrixes.",Journal of AOAC International,"['D002851', 'D005506', 'D005638', 'D010573', 'D012680', 'D052616', 'D021241', 'D053719', 'D014675']","['Chromatography, High Pressure Liquid', 'Food Contamination', 'Fruit', 'Pesticide Residues', 'Sensitivity and Specificity', 'Solid Phase Extraction', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry', 'Vegetables']",Evaluation of relevant time-of-flight-MS parameters used in HPLC/MS full-scan screening methods for pesticide residues.,"['Q000295', 'Q000032', 'Q000737', 'Q000032', None, None, 'Q000295', 'Q000295', 'Q000737']","['instrumentation', 'analysis', 'chemistry', 'analysis', None, None, 'instrumentation', 'instrumentation', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22320076,2012,,,,, -22284504,"In recent years, the release of information about the preventative and curative properties of garlic on different diseases and their benefits to human health has led to an increase in the consumption of garlic. To meet the requirements of international markets and reach competitiveness and profitability, farmers seek to extend the offer period of fresh garlic by increasing post-harvest life. As a result, the use of maleic hydrazide (1,2-dihydropyridazine-3,6-dione) [MH], a plant growth regulator, has been widespread in various garlic growing regions of the world. The present work was undertaken to develop and validate a new analytical procedure based on MH extraction from garlic previously frozen by liquid nitrogen and submitted to low temperature clean-up. The applicability of the method by analysis of garlic samples from a commercial plantation was also demonstrated. The influence of certain factors on the performance of the analytical methodology were studied and optimized. The approach is an efficient extraction, clean-up and determination alternative for MH residue-quantification due to its specificity and sensitivity. The use of liquid nitrogen during the sample preparation prevents the degradation of the analyte due to oxidation reactions, a major limiting factor. Moreover, the method provides good linearity (r(2): 0.999), good intermediate precision (coefficient of variation (CV): 8.39%), and extracts were not affected by the matrix effect. Under optimized conditions, the limit of detection (LOD) (0.33 mg kg(-1)) was well below the maximum residue level (MRL) set internationally for garlic (15 mg kg(-1)), with excellent rates of recovery (over 95%), good repeatability and acceptable accuracy (CV averaged 5.74%), since garlic is a complex matrix. The analytical performance of the methodology presented was compared with other techniques already reported, with highly satisfactory results, lower LOD and higher recoveries rates. In addition, the extraction process is simple, not expensive, easily executable and requires lower volumes of organic solvent. The proposed methodology removes the need of extensive typical laboratory extraction procedures, reducing the amount of time needed for pesticide analysis and increasing sample throughput. Adopting this method gives food safety laboratories the potential to increase cost savings by a suitable technique in routine testing to determine MH residues in garlic.",Talanta,"['D002849', 'D002851', 'D003080', 'D005504', 'D005737', 'D008300', 'D000432', 'D009584', 'D010573', 'D010937', 'D015203', 'D012680', 'D052616', 'D012997']","['Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Cold Temperature', 'Food Analysis', 'Garlic', 'Maleic Hydrazide', 'Methanol', 'Nitrogen', 'Pesticide Residues', 'Plant Growth Regulators', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Solid Phase Extraction', 'Solvents']",Determination of maleic hydrazide residues in garlic bulbs by HPLC.,"[None, None, None, None, 'Q000737', 'Q000032', 'Q000737', None, 'Q000032', 'Q000032', None, None, None, 'Q000737']","[None, None, None, None, 'chemistry', 'analysis', 'chemistry', None, 'analysis', 'analysis', None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22284504,2012,0.0,0.0,,pesti. Absop, -22161312,"Superoxide dismutases (SODs; EC 1.15.1.1) are key enzymes in the cells protection against oxidant agents. Thus, SODs play a major role in the protection of aerobic organisms against oxygen-mediated damages. Three SOD isoforms were previously identified by zymogram staining from Allium sativum bulbs. The purified Cu, Zn-SOD2 shows an antagonist effect to an anticancer drug and alleviate cytotoxicity inside tumor cells lines B16F0 (mouse melanoma cells) and PAE (porcine aortic endothelial cells). To extend the characterization of Allium SODs and their corresponding genes, a proteomic approach was applied involving two-dimensional gel electrophoresis and LC-MS/MS analyses. From peptide sequence data obtained by mass spectrometry and sequences homologies, primers were defined and a cDNA fragment of 456 bp was amplified by RT-PCR. The cDNA nucleotide sequence analysis revealed an open reading frame coding for 152 residues. The deduced amino acid sequence showed high identity (82-87%) with sequences of Cu, Zn-SODs from other plant species. Molecular analysis was achieved by a protein 3D structural model.",Molecular biotechnology,"['D000595', 'D000818', 'D001483', 'D002457', 'D002853', 'D003001', 'D019610', 'D004317', 'D019008', 'D015180', 'D042783', 'D005737', 'D013058', 'D008546', 'D051379', 'D008958', 'D008969', 'D010455', 'D016133', 'D020033', 'D040901', 'D016415', 'D017422', 'D013482', 'D034421']","['Amino Acid Sequence', 'Animals', 'Base Sequence', 'Cell Extracts', 'Chromatography, Liquid', 'Cloning, Molecular', 'Cytoprotection', 'Doxorubicin', 'Drug Resistance, Neoplasm', 'Electrophoresis, Gel, Two-Dimensional', 'Endothelial Cells', 'Garlic', 'Mass Spectrometry', 'Melanoma, Experimental', 'Mice', 'Models, Molecular', 'Molecular Sequence Data', 'Peptides', 'Polymerase Chain Reaction', 'Protein Isoforms', 'Proteomics', 'Sequence Alignment', 'Sequence Analysis, DNA', 'Superoxide Dismutase', 'Sus scrofa']","Combined proteomic and molecular approaches for cloning and characterization of copper-zinc superoxide dismutase (Cu, Zn-SOD2) from garlic (Allium sativum).","[None, None, None, None, None, 'Q000379', None, 'Q000494', 'Q000187', None, 'Q000166', 'Q000201', None, 'Q000473', None, None, None, 'Q000737', None, 'Q000737', 'Q000379', None, None, 'Q000737', None]","[None, None, None, None, None, 'methods', None, 'pharmacology', 'drug effects', None, 'cytology', 'enzymology', None, 'pathology', None, None, None, 'chemistry', None, 'chemistry', 'methods', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/22161312,2013,0.0,0.0,,, -22127783,"A modified quick, easy, cheap, effective, rugged and safe (QuEChERS) method with multi-walled carbon nanotubes (MWCNTs) as a reversed-dispersive solid-phase extraction (r-DSPE) material combined with gas chromatography-mass spectrometry was developed for the determination of 14 pesticides in complex matrices. Four vegetables (leek, onion, ginger and garlic) were selected as the complex matrices for validating this new method. This technique involved the acetonitrile-based sample preparation and MWCNTs were used as the r-DSPE material in the cleanup step. Two important parameters influencing the MWCNTs efficiency, the external diameters and the amount of MWCNTs used, were investigated. Under the optimized conditions, recoveries of 78-110% were obtained for the target analytes in the complex matrices at two concentration levels of 0.02 and 0.2 mg/kg. In addition, the RSD values ranged from 1 to 13%. LOQs and LODs for 14 pesticides ranged from 2 to 20 μg/kg and from 1 to 6 μg/kg, respectively.",Journal of separation science,"['D005506', 'D008401', 'D037742', 'D010573', 'D052616', 'D014675']","['Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Nanotubes, Carbon', 'Pesticide Residues', 'Solid Phase Extraction', 'Vegetables']",Determination of pesticide residues in complex matrices using multi-walled carbon nanotubes as reversed-dispersive solid-phase extraction sorbent.,"['Q000032', 'Q000379', 'Q000737', 'Q000032', 'Q000295', 'Q000737']","['analysis', 'methods', 'chemistry', 'analysis', 'instrumentation', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22127783,2012,0.0,2.0,,Pesticides, -22097791,"A new idea of solvent transfer technique was developed and applied to determine 283 pesticide residues in garlic by gas chromatography-mass spectrometry (GC-MS) (method I), and the other method using normal phase silica/selective elution technique was applied to determine 6 pesticide residues with strongly polar in garlic by gas chromatography (method II). For the method I, the residues were extracted from homogenized tissue with acetonitrile-water, separated with liquid-liquid partition; the clear supernatant was purified by solvent transfer technique and solid phase extraction (Envi-18 and LC-NH2 columns), then was analyzed by GC-MS. For the method II, the residues were extracted from homogenized tissue using ethyl acetate and sodium sulfate assisted by ultrasonication. The supernatant was purified by solid phase extraction (primary secondary amine (PSA) powder and LC-Si column) prior to GC analysis. The determination was performed by using selected ion monitoring (SIM) mode in GC-MS method and flame photometric detector (FPD) in GC method, then external standard method was used in the quantification. Under the optimal conditions, the detection limits for the two methods (S/N > or = 10) of pesticides were 0.01-0.05 mg/kg, the recoveries carried out by the addition of standards of 0.01-0.20 mg/kg were 52%-163%, among which the recoveries for 88% pesticides were between 70% and 120%; the recoveries of the method II were 70%-111%; while the relative standard deviations were 2.4%-18% and 3.2%-9.3%, respectively. The model of solvent transfer technique and the sensitivity improvement of GC-MS was also studied. The methods are easy, fast, more sensitive, and can meet the requirement of the multiresidual analysis in garlic.",Se pu = Chinese journal of chromatography,"['D002849', 'D005504', 'D005506', 'D005737', 'D008401', 'D010573']","['Chromatography, Gas', 'Food Analysis', 'Food Contamination', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Pesticide Residues']",[Multi-residue determination of 289 pesticides in garlic by gas chromatography and gas chromatography/mass spectrometry].,"['Q000379', 'Q000379', 'Q000032', 'Q000737', 'Q000379', 'Q000032']","['methods', 'methods', 'analysis', 'chemistry', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/22097791,2012,,,,, -22033380,"Chronic lead (Pb(2+)) exposure leads to the reduced lifespan of erythrocytes. Oxidative stress and K(+) loss accelerate Fas translocation into lipid raft microdomains inducing Fas mediated death signaling in these erythrocytes. Pathophysiological-based therapeutic strategies to combat against erythrocyte death were evaluated using garlic-derived organosulfur compounds like diallyl disulfide (DADS), S allyl cysteine (SAC) and imidazole based Gardos channel inhibitor clotrimazole (CLT).",Biochimica et biophysica acta,"['D000818', 'D017209', 'D003022', 'D003545', 'D015536', 'D004912', 'D005260', 'D007854', 'D007855', 'D013058', 'D051379', 'D008807', 'D017382', 'D015398', 'D019014']","['Animals', 'Apoptosis', 'Clotrimazole', 'Cysteine', 'Down-Regulation', 'Erythrocytes', 'Female', 'Lead', 'Lead Poisoning', 'Mass Spectrometry', 'Mice', 'Mice, Inbred BALB C', 'Reactive Oxygen Species', 'Signal Transduction', 'fas Receptor']",S-allyl cysteine in combination with clotrimazole downregulates Fas induced apoptotic events in erythrocytes of mice exposed to lead.,"[None, None, 'Q000494', 'Q000031', 'Q000187', 'Q000187', None, 'Q000633', 'Q000097', None, None, None, None, None, 'Q000378']","[None, None, 'pharmacology', 'analogs & derivatives', 'drug effects', 'drug effects', None, 'toxicity', 'blood', None, None, None, None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/22033380,2012,0.0,0.0,,, -21872076,"A non-chromatographic separation and preconcentration method for Se species determination based on the use of an on-line ionic liquid (IL) dispersive microextraction system coupled to electrothermal atomic absorption spectrometry (ETAAS) is proposed. Retention and separation of the IL phase was achieved with a Florisil(®)-packed microcolumn after dispersive liquid-liquid microextraction (DLLME) with tetradecyl(trihexyl)phosphonium chloride IL (CYPHOS(®) IL 101). Selenite [Se(IV)] species was selectively separated by forming Se-ammonium pyrrolidine dithiocarbamate (Se-APDC) complex followed by extraction with CYPHOS(®) IL 101. The methodology was highly selective towards Se(IV), while selenate [Se(VI)] was reduced and then indirectly determined. Several factors influencing the efficiency of the preconcentration technique, such as APDC concentration, sample volume, extractant phase volume, type of eluent, elution flow rate, etc., have been investigated in detail. The limit of detection (LOD) was 15 ng L(-1) and the relative standard deviation (RSD) for 10 replicates at 0.5 μg L(-1) Se concentration was 5.1%, calculated with peak heights. The calibration graph was linear and a correlation coefficient of 0.9993 was achieved. The method was successfully employed for Se speciation studies in garlic extracts and water samples.",Talanta,"['D005737', 'D006851', 'D007202', 'D052578', 'D059627', 'D009862', 'D009943', 'D011759', 'D018036', 'D013054', 'D013859', 'D014867']","['Garlic', 'Hydrochloric Acid', 'Indicators and Reagents', 'Ionic Liquids', 'Liquid Phase Microextraction', 'Online Systems', 'Organophosphorus Compounds', 'Pyrrolidines', 'Selenium Compounds', 'Spectrophotometry, Atomic', 'Thiocarbamates', 'Water']",Determination of inorganic selenium species in water and garlic samples with on-line ionic liquid dispersive microextraction and electrothermal atomic absorption spectrometry.,"['Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000379', None, 'Q000737', 'Q000737', 'Q000032', 'Q000379', 'Q000737', 'Q000737']","['chemistry', 'chemistry', 'chemistry', 'chemistry', 'methods', None, 'chemistry', 'chemistry', 'analysis', 'methods', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/21872076,2011,1.0,1.0,,, -21864633,"A simple, quick and reliable analytical method for the determination of 1-naphthylacetic acid in garlic and soil has been developed in this study. The residual levels and dissipation rates of 1-naphthylacetic acid in garlic and soil were determined by high performance liquid chromatography-tandem mass spectroscopy (HPLC-MS/MS). The limit of quantification (LOQ) of the developed method was 0.005 mg/kg. The half-lives of 1-naphthylacetic acid in garlic plants and soil were 0.80-1.4 days and 0.94-2.0 days, respectively. The final residues of 1-naphthylacetic acid in garlic, garlic sprout and soil could not be detected and were all below 0.05 mg/kg (the MRL of EU). Results of the ultimate residues in garlic and soil showed that this pesticide is safe to be used under the recommended dosages.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000498', 'D002851', 'D004785', 'D009280', 'D010937', 'D012987', 'D013440', 'D053719']","['Allyl Compounds', 'Chromatography, High Pressure Liquid', 'Environmental Pollutants', 'Naphthaleneacetic Acids', 'Plant Growth Regulators', 'Soil', 'Sulfides', 'Tandem Mass Spectrometry']",Determination and study on dissipation of 1-naphthylacetic acid in garlic and soil using high performance liquid chromatography-tandem mass spectrometry.,"['Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000379']","['chemistry', 'methods', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/21864633,2012,0.0,0.0,,pesticide test on spiked samples, -21845941,"Acaricidal effects of three essential oils extracted from Mexican oregano leaves (Lippia graveolens Kunth), rosemary leaves (Rosmarinus officinalis L.), and garlic bulbs (Allium sativum L.) on 10-d-old Rhipicephalus (Boophilus) microplus (Canestrini) tick larvae were evaluated by using the larval packet test bioassay. Serial dilutions of the three essential oils were tested from a starting concentration of 20 to 1.25%. Results showed that both Mexican oregano and garlic essential oils had very similar activity, producing high mortality (90-100%) in all tested concentrations on 10-d-old R. microplus tick larvae. Rosemary essential oil produced >85% larval mortality at the higher concentrations (10 and 20%), but the effect decreased noticeably to 40% at an oil concentration of 5%, and mortality was absent at 2.5 and 1.25% of the essential oil concentration. Chemical composition of the essential oils was elucidated by gas chromatography-mass spectrometry analyses. Mexican oregano essential oil included thymol (24.59%), carvacrol (24.54%), p-cymene (13.6%), and y-terpinene (7.43%) as its main compounds, whereas rosemary essential oil was rich in a-pinene (31.07%), verbenone (15.26%), and 1,8-cineol (14.2%), and garlic essential oil was rich in diallyl trisulfide (33.57%), diallyl disulfide (30.93%), and methyl allyl trisulfide (11.28%). These results suggest that Mexican oregano and garlic essential oils merit further investigation as components of alternative approaches for R. microplus tick control.",Journal of medical entomology,"['D056810', 'D000818', 'D005737', 'D008401', 'D026863', 'D007814', 'D032411', 'D008800', 'D009822', 'D010938', 'D048494', 'D027542']","['Acaricides', 'Animals', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Ixodidae', 'Larva', 'Lippia', 'Mexico', 'Oils, Volatile', 'Plant Oils', 'Rhipicephalus', 'Rosmarinus']","Acaricidal effect of essential oils from Lippia graveolens (Lamiales: Verbenaceae), Rosmarinus officinalis (Lamiales: Lamiaceae), and Allium sativum (Liliales: Liliaceae) against Rhipicephalus (Boophilus) microplus (Acari: Ixodidae).","['Q000737', None, 'Q000737', None, 'Q000187', 'Q000187', 'Q000737', None, 'Q000737', 'Q000737', 'Q000187', 'Q000737']","['chemistry', None, 'chemistry', None, 'drug effects', 'drug effects', 'chemistry', None, 'chemistry', 'chemistry', 'drug effects', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/21845941,2011,,,,, -21827329,"This study examined the antiradical activity and chemical composition of essential oils of some plants grown in Mosul, Iraq. The essential oils of myrtle and parsley seed contained α-pinene (36.08% and 22.89%, respectively) as main constituents. Trans-Anethole was the major compound found in fennel and aniseed oils (66.98% and 93.51%, respectively). The dominant constituent of celery seed oil was limonene (76.63%). Diallyl disulphide was identified as the major component in garlic oil (36.51%). Antiradical activity was higher in garlic oil (76.63%) and lower in myrtle oil (39.23%). The results may suggest that some essential oils from Iraq possess compounds with antiradical activity, and these oils can be used as natural antioxidants in food applications.",Natural product research,"['D000490', 'D000498', 'D000840', 'D019661', 'D053138', 'D004220', 'D005519', 'D016166', 'D008401', 'D007493', 'D039821', 'D027822', 'D009822', 'D018515', 'D018517', 'D012639', 'D013045', 'D013729']","['Allium', 'Allyl Compounds', 'Anisoles', 'Apiaceae', 'Cyclohexenes', 'Disulfides', 'Food Preservation', 'Free Radical Scavengers', 'Gas Chromatography-Mass Spectrometry', 'Iraq', 'Monoterpenes', 'Myrtaceae', 'Oils, Volatile', 'Plant Leaves', 'Plant Roots', 'Seeds', 'Species Specificity', 'Terpenes']",Essential oil composition and antiradical activity of the oil of Iraq plants.,"['Q000737', 'Q000032', 'Q000032', 'Q000737', 'Q000032', 'Q000032', 'Q000379', 'Q000032', None, None, 'Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', None, 'Q000032']","['chemistry', 'analysis', 'analysis', 'chemistry', 'analysis', 'analysis', 'methods', 'analysis', None, None, 'analysis', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/21827329,2012,,,,, -21756199,"Speciation analysis using high-performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICP MS) is now commonly used to investigate metabolic and toxicological aspects of some metals and metalloids. We have developed a rapid method for simultaneous identification and quantification of metabolites of selenium (Se) compounds using multiple standards labelled with different isotopes. A mixture of the labelled standards was spiked in a selenised garlic extract and the sample was subjected to speciation analysis by HPLC-ICP MS. The selenised garlic contains γ-glutamyl-methylselenocysteine, methylselenocysteine, and selenomethionine and the concentrations of those Se compounds were 723.8, 414.8, and 310.7 ng Se ml(-1), respectively. The isotopically labelled standards were also applied to the speciation of Se in rat urine. Selenate, methylselenonic acid, selenosugar, and trimethyselenium ions were found to be excreted by the present speciation procedure. Multiple standards labelled with different stable isotopes enable high-throughput identification and quantitative measurements of Se metabolites.",Isotopes in environmental and health studies,"['D000818', 'D002851', 'D003903', 'D005737', 'D007201', 'D007553', 'D007554', 'D008297', 'D013058', 'D051381', 'D017208', 'D012643', 'D018036', 'D012680', 'D013997']","['Animals', 'Chromatography, High Pressure Liquid', 'Deuterium', 'Garlic', 'Indicator Dilution Techniques', 'Isotope Labeling', 'Isotopes', 'Male', 'Mass Spectrometry', 'Rats', 'Rats, Wistar', 'Selenium', 'Selenium Compounds', 'Sensitivity and Specificity', 'Time Factors']",Rapid speciation and quantification of selenium compounds by HPLC-ICP MS using multiple standards labelled with different isotopes.,"[None, 'Q000379', 'Q000032', 'Q000737', 'Q000295', 'Q000379', 'Q000032', None, 'Q000379', None, None, 'Q000032', 'Q000032', None, None]","[None, 'methods', 'analysis', 'chemistry', 'instrumentation', 'methods', 'analysis', None, 'methods', None, None, 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/21756199,2011,,,,, -21732172,"A lectin was purified from the leaves of Allium altaicum and corresponding gene was cloned. The lectin namely Allium altaicum agglutinin (AAA) was ~24 kDa homodimeric protein and similar to a typical garlic leaf lectin. It was synthesized as 177 amino acid residues pre-proprotein, which consisted of 28 and 43 amino acid long N and C-terminal signal peptides, respectively. The plant expressed this protein more in scapes and flowers in comparison to the bulbs and leaves. Hemagglutination activity (with rabbit erythrocytes) was 1,428 fold higher as compared to Allium sativum leaf agglutinin (ASAL) although, the insecticidal activity against cotton aphid (Aphis gossypii) was relatively low. Glycan array revealed that AAA had higher affinity towards GlcAb1-3Galb as compared to ASAL. Homology analysis showed 57-94% similarity with other Allium lectins. The mature protein was expressed in E. coli as a fusion with SUMO peptide in soluble and biologically active form. Recombinant protein retained high hemagglutination activity.",The protein journal,"['D000490', 'D000595', 'D000818', 'D001042', 'D001483', 'D002240', 'D003001', 'D004912', 'D004926', 'D006384', 'D006388', 'D007306', 'D008958', 'D008969', 'D010802', 'D018515', 'D037121', 'D011134', 'D011817', 'D011993', 'D025842', 'D016415', 'D053719']","['Allium', 'Amino Acid Sequence', 'Animals', 'Aphids', 'Base Sequence', 'Carbohydrate Sequence', 'Cloning, Molecular', 'Erythrocytes', 'Escherichia coli', 'Hemagglutination', 'Hemagglutinins', 'Insecticides', 'Models, Molecular', 'Molecular Sequence Data', 'Phylogeny', 'Plant Leaves', 'Plant Lectins', 'Polysaccharides', 'Rabbits', 'Recombinant Fusion Proteins', 'SUMO-1 Protein', 'Sequence Alignment', 'Tandem Mass Spectrometry']",Purification and characterization of a lectin with high hemagglutination property isolated from Allium altaicum.,"['Q000737', None, None, 'Q000187', None, None, None, 'Q000187', None, 'Q000187', 'Q000737', 'Q000737', None, None, None, 'Q000737', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', None, None]","['chemistry', None, None, 'drug effects', None, None, None, 'drug effects', None, 'drug effects', 'chemistry', 'chemistry', None, None, None, 'chemistry', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/21732172,2011,0.0,0.0,,, -21645684,"A quantitative immunochemical rapid test for sensitive determination of benzo[a]pyrene (BAP) as a model analyte was developed making use of a handheld reader for results evaluation. The covalent immobilization of antibodies to different Sepharose gels, i.e., CNBr-activated Sepharose 4B and CNBr-activated Sepharose 4 Fast Flow was compared with adsorption to a polyethylene support. The lowest limits of detection (LOD) were 4 ng L(-1) and 40 ng L(-1), respectively, using optimized assay conditions. The developed test was applied to food supplements (garlic, black radish and maca), including a pretreatment procedure. LOD of 9 ng kg(-1) and linear range of 13-80 ng kg(-1) were obtained. Results of BAP determination in naturally contaminated samples were confirmed by high-performance liquid chromatography coupled to fluorescence detection and a good correlation was achieved. We suggest that the developed test format can be used to quantitative detection of the low molecular weight analytes, such as mycotoxins, pesticides, other pollutants in food and environmental samples.",Talanta,"['D055910', 'D001564', 'D002851', 'D019587', 'D005506', 'D005737', 'D029686', 'D057230', 'D031224', 'D012685']","['Antibodies, Immobilized', 'Benzo(a)pyrene', 'Chromatography, High Pressure Liquid', 'Dietary Supplements', 'Food Contamination', 'Garlic', 'Lepidium', 'Limit of Detection', 'Raphanus', 'Sepharose']",New approach to quantitative analysis of benzo[a]pyrene in food supplements by an immunochemical column test.,"[None, 'Q000032', None, 'Q000032', 'Q000032', None, None, None, None, None]","[None, 'analysis', None, 'analysis', 'analysis', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/21645684,2011,1.0,1.0,,Supplements, -21535547,"Encapsulation of garlic oil (GO) in β-cyclodextrin (β-CD) was undertaken to generate a release system of antimicrobial volatiles and tested on microbial growth and sensory quality of fresh-cut tomato. GO volatile profile was characterized by gas chromatography mass spectrometry and to demonstrate the disadvantages of applying free GO to fresh-cut tomato, the effect of different free oil treatments (0, 50, 100, and 200 μg/100 g) on microbial growth and sensorial quality was tested. The effect of GO capsules (0, 0.25, 0.5, and 1 g/100 g) on microbial growth and sensory quality of tomato was also investigated. Allyl disulfide was the most abundant GO compound identified. The release of volatiles from GO: β-CD capsules (12: 88 [w/w] ratio) was evaluated at 100% relative humidity (RH). Close to 70% of GO volatiles were released from capsules when exposed to 100% RH during 5 wk. The most effective antimicrobial concentrations of free oil (100 and 200 μg/100 g) applied to tomatoes did not present acceptable sensory quality for panelists. Tomato was affected by the highest concentration of GO capsules applied, showing the lowest microbial growth and the highest sensory quality. In this context, successful encapsulation in β-CD could stimulate further interest in the use of GO for the control of microbial growth in fresh-cut tomato.",Journal of food science,"['D000328', 'D000498', 'D000890', 'D015169', 'D003692', 'D004339', 'D057140', 'D005260', 'D005519', 'D005638', 'D005658', 'D006090', 'D006094', 'D006801', 'D018551', 'D008297', 'D009812', 'D013440', 'D055549', 'D047392']","['Adult', 'Allyl Compounds', 'Anti-Infective Agents', 'Colony Count, Microbial', 'Delayed-Action Preparations', 'Drug Compounding', 'Fast Foods', 'Female', 'Food Preservation', 'Fruit', 'Fungi', 'Gram-Negative Bacteria', 'Gram-Positive Bacteria', 'Humans', 'Lycopersicon esculentum', 'Male', 'Odorants', 'Sulfides', 'Volatile Organic Compounds', 'beta-Cyclodextrins']",Optimizing the use of garlic oil as antimicrobial agent on fresh-cut tomato through a controlled release system.,"[None, 'Q000737', 'Q000737', None, 'Q000494', None, 'Q000382', None, 'Q000379', 'Q000382', 'Q000187', 'Q000187', 'Q000187', None, 'Q000382', None, None, 'Q000737', 'Q000032', 'Q000737']","[None, 'chemistry', 'chemistry', None, 'pharmacology', None, 'microbiology', None, 'methods', 'microbiology', 'drug effects', 'drug effects', 'drug effects', None, 'microbiology', None, None, 'chemistry', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/21535547,2011,1.0,1.0,,, -21535486,"The formation of pink-red pigments (""pinking"") by various amino acids was investigated by reacting amino acids with compounds present in onion juice. The unknown pink-red pigments were generated and separated using high-performance liquid chromatography (HPLC) and a diode array detector (DAD) in the range of 200 to 700 nm. To generate pink-red pigments, we developed several reaction systems using garlic alliinase, purified 1-propenyl-L-cysteine sulfoxide (1-PeCSO), onion thiosulfinate, natural onion juice, and 21 free amino acids. The compound 1-PeCSO was a key compound associated with pinking in the presence of both the alliinase and amino acids. Numerous naturally occurring pink-red pigments were detected and separated from pink onion juice using the HPLC-DAD system at 515 nm. Most free amino acids, with the exceptions of histidine, serine, and cysteine, formed various pink-red pigments when reacted with onion thiosulfinate. This observation indicated that onion pinking is caused not by a single pigment, but by many. Furthermore, more than one color compound could be produced from a single amino acid; this explains, in part, why there were many pink-red compound peaks in the chromatogram of discolored natural onion juice. We presumed that the complexity of the pink-red pigments was due to the involvement of more than 21 natural amino acids as well as several derivatives of the color products produced from each amino acid. We observed that the pinking process in onion juice is very similar to that of the greening process in crushed garlic, emphasizing that both thiosulfinate from flavor precursors and free amino acids are absolutely required for the discoloration.",Journal of food science,"['D000596', 'D013437', 'D002851', 'D003545', 'D005737', 'D019697', 'D010860', 'D010940', 'D018517', 'D013053', 'D013441', 'D013454']","['Amino Acids', 'Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Onions', 'Pigments, Biological', 'Plant Proteins', 'Plant Roots', 'Spectrophotometry', 'Sulfinic Acids', 'Sulfoxides']",Identification of candidate amino acids involved in the formation of pink-red pigments in onion (Allium cepa L.) juice and separation by HPLC.,"['Q000737', 'Q000378', None, 'Q000031', 'Q000201', 'Q000737', 'Q000737', 'Q000378', 'Q000737', None, 'Q000737', 'Q000737']","['chemistry', 'metabolism', None, 'analogs & derivatives', 'enzymology', 'chemistry', 'chemistry', 'metabolism', 'chemistry', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/21535486,2011,0.0,0.0,,pigmentation, -21495726,"Phytoene synthase (PSY) and phytoene desaturase (PDS), which catalyze the first and second steps of the carotenoid biosynthetic pathway, respectively, are key enzymes for the accumulation of carotenoids in many plants. We isolated 2 partial cDNAs encoding PSY (AsPSY-1 and AsPSY-2) and a partial cDNA encoding PDS (AsPDS) from Allium sativum. They shared high sequence identity and conserved motifs with other orthologous genes. Quantitative real-time PCR analysis was used to determine the expression levels of AsPSY1, AsPSY2, and AsPDS in the bulbils, scapes, leaves, stems, bulbs, and roots of garlic. High-performance liquid chromatography demonstrated that carotenoids were not biosynthesized in the underground organs (roots and bulbs), but were very abundant in the photosynthetic organs (leaves) of A. sativum. A significantly higher amount of β-carotene (73.44 μg·g(-1)) was detected in the leaves of A. sativum than in the other organs.",Journal of agricultural and food chemistry,"['D019883', 'D000595', 'D002338', 'D003001', 'D018076', 'D018744', 'D005737', 'D015870', 'D051232', 'D008969', 'D010088', 'D018515', 'D018517', 'D018547', 'D016133']","['Alkyl and Aryl Transferases', 'Amino Acid Sequence', 'Carotenoids', 'Cloning, Molecular', 'DNA, Complementary', 'DNA, Plant', 'Garlic', 'Gene Expression', 'Geranylgeranyl-Diphosphate Geranylgeranyltransferase', 'Molecular Sequence Data', 'Oxidoreductases', 'Plant Leaves', 'Plant Roots', 'Plant Stems', 'Polymerase Chain Reaction']",Carotenoid accumulation and characterization of cDNAs encoding phytoene synthase and phytoene desaturase in garlic (Allium sativum).,"['Q000737', None, 'Q000032', None, 'Q000032', 'Q000032', 'Q000737', None, None, None, 'Q000737', 'Q000201', 'Q000201', 'Q000201', None]","['chemistry', None, 'analysis', None, 'analysis', 'analysis', 'chemistry', None, None, None, 'chemistry', 'enzymology', 'enzymology', 'enzymology', None]",https://www.ncbi.nlm.nih.gov/pubmed/21495726,2011,1.0,1.0,,, -21491526,"Matrix effect (ME) - ionisation suppression or enhancement - in liquid chromatography/electrospray ionisation mass spectrometry (LC/ESI-MS) is caused by matrix components co-eluting with the analytes. ME has a complex and not fully understood nature. ME is also highly variable from sample to sample making it difficult to compensate for. In this work it was studied whether the background ion signals in scanned mass spectra of the LC effluent at the retention time of the analyte offer some insight into the presence and extent of matrix effect. Matrix effects for six pesticides - thiabendazole, carbendazime, methomyl, aldicarb, imazalil and methiocarb - in garlic and onion samples used in the study varied from 1% (suppression 99%) to 127% (enhancement 27%) depending on the pesticide and sample. Also standards in solvent and solvent blanks were included in the study. The ions most strongly varying from sample to sample - and therefore best describing the changes in sample composition and ME - were selected for quantification according to principal component analysis (PCA) for all six pesticides under study. These ions were used to account for ME via partial least-squares (PLS) regression. The calibration set was constructed from 19 samples and standards and the obtained calibration function was validated with seven samples and standards. The average errors from the test set were from 0.05 to 0.27 mg/kg for carbendazim and imazalil, respectively (the respective average pesticide concentrations were 0.22 and 0.88 mg/kg). The PLS results were significantly more accurate compared to the conventional solvent calibration resulting in average errors from 0.07 to 0.69 mg/kg for carbendazime and methiocarb, respectively.",Rapid communications in mass spectrometry : RCM,[],[],Accounting for matrix effects of pesticide residue liquid chromatography/electrospray ionisation mass spectrometric determination by treatment of background mass spectra with chemometric tools.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/21491526,2011,,,,, -21490929,"Allium sativum leaf agglutinin (ASAL) is a 25-kDa homodimeric, insecticidal, mannose binding lectin whose subunits are assembled by the C-terminal exchange process. An attempt was made to convert dimeric ASAL into a monomeric form to correlate the relevance of quaternary association of subunits and their functional specificity. Using SWISS-MODEL program a stable monomer was designed by altering five amino acid residues near the C-terminus of ASAL.",PloS one,"['D000528', 'D000818', 'D000935', 'D001042', 'D015153', 'D002846', 'D002850', 'D005670', 'D005737', 'D007306', 'D016297', 'D018515', 'D037121', 'D012232', 'D013050']","['Alternaria', 'Animals', 'Antifungal Agents', 'Aphids', 'Blotting, Western', 'Chromatography, Affinity', 'Chromatography, Gel', 'Fusarium', 'Garlic', 'Insecticides', 'Mutagenesis, Site-Directed', 'Plant Leaves', 'Plant Lectins', 'Rhizoctonia', 'Spectrometry, Fluorescence']",Functional alteration of a dimeric insecticidal lectin to a monomeric antifungal protein correlated to its oligomeric status.,"['Q000187', None, 'Q000737', 'Q000187', None, None, None, 'Q000187', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000187', None]","['drug effects', None, 'chemistry', 'drug effects', None, None, None, 'drug effects', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'drug effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/21490929,2011,0.0,0.0,,, -21315911,"An analytical method with the technique of QuEChERS (quick, easy, cheap, effective, rugged and safe) and gas chromatography (GC)/mass spectrometry (MS) in negative chemical ionization (NCI) has been developed for the determination of 17 pyrethroid pesticide residues in troublesome matrices, including garlic, onion, spring onion and chili. Pyrethroid residues were extracted with acidified acetonitrile saturated by hexane. After a modified QuEChERS clean-up step, the extract was analyzed by GC-NCI/MS in selected ion monitoring (SIM) mode. An isotope internal standard of trans-cypermethrin-D(6) was employed for quantitation. Chromatograms of pyrethroids obtained in all these matrices were relatively clean and without obvious interference. The limits of detection (LODs) ranged from 0.02 to 6 μg kg(-1) and recovery yields were from 54.0% to 129.8% at three spiked levels (20, 40 and 60 μg kg(-1) for chili, and 10, 20 and 30 μg kg(-1) for others) in four different matrices depending on the compounds determined. The relative standard deviations (RSDs) were all below 14%. Isomerization enhancement of pyrethroids in chili extract was observed and preliminarily explained, especially for acrinathrin and deltamethrin.",Talanta,"['D005504', 'D005506', 'D008401', 'D057230', 'D016014', 'D010573', 'D011722', 'D015203', 'D013997', 'D014675']","['Food Analysis', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Limit of Detection', 'Linear Models', 'Pesticide Residues', 'Pyrethrins', 'Reproducibility of Results', 'Time Factors', 'Vegetables']",Determination of 17 pyrethroid residues in troublesome matrices by gas chromatography/mass spectrometry with negative chemical ionization.,"['Q000191', 'Q000032', 'Q000191', None, None, 'Q000032', 'Q000032', None, None, 'Q000737']","['economics', 'analysis', 'economics', None, None, 'analysis', 'analysis', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/21315911,2011,0.0,0.0,,, -21056027,"Spironucleus is a genus of small, flagellated parasites, many of which can infect a wide range of vertebrates and are a significant problem in aquaculture. Following the ban on the use of metronidazole in food fish due to toxicity problems, no satisfactory chemotherapies for the treatment of spironucleosis are currently available. Using membrane inlet mass spectrometry and automated optical density monitoring of growth, we investigated in vitro the effect of Allium sativum (garlic), a herbal remedy known for its antimicrobial properties, on the growth and metabolism of Spironucleus vortens, a parasite of tropical fish and putative agent of hole-in-the-head disease. The allium-derived thiosulfinate compounds allicin and ajoene, as well as an ajoene-free mixture of thiosulfinates and vinyl-dithiins were also tested. Whole, freeze-dried garlic and allium-derived compounds had an inhibitory effect on gas metabolism, exponential growth rate and final growth yield of S. vortens in Keister's modified, TY-I-S33 culture medium. Of all the allium-derived compounds tested, the ajoene-free mixture of dithiins and thiosulfinates was the most effective with a minimum inhibitory concentration (MIC) of 107 μg ml(-1) and an inhibitory concentration at 50% (IC(50%)) of 58 μg ml(-1). It was followed by ajoene (MIC = 83 μg ml(-1), IC(50%) = 56 μg ml(-1)) and raw garlic (MIC >20 mg ml(-1), IC(50%) = 7.9 mg ml(-1)); allicin being significantly less potent with an MIC and IC(50%) above 160 μg ml(-1). All these concentrations are much higher than those reported to be required for the inhibition of most bacteria, protozoa and fungi previously investigated, indicating an unusual level of tolerance for allium-derived products in S. vortens. However, chemically synthesized derivatives of garlic constituents might prove a useful avenue for future research.",Experimental parasitology,"['D000490', 'D000818', 'D000981', 'D002245', 'D016828', 'D004220', 'D005393', 'D005398', 'D005399', 'D005612', 'D005737', 'D006859', 'D013058', 'D008795', 'D010101', 'D010936', 'D011529', 'D013441', 'D013457']","['Allium', 'Animals', 'Antiprotozoal Agents', 'Carbon Dioxide', 'Diplomonadida', 'Disulfides', 'Fish Diseases', 'Fisheries', 'Fishes', 'Freeze Drying', 'Garlic', 'Hydrogen', 'Mass Spectrometry', 'Metronidazole', 'Oxygen Consumption', 'Plant Extracts', 'Protozoan Infections, Animal', 'Sulfinic Acids', 'Sulfur Compounds']",Effect of garlic and allium-derived products on the growth and metabolism of Spironucleus vortens.,"['Q000737', None, 'Q000494', 'Q000378', 'Q000187', 'Q000494', 'Q000188', None, None, None, 'Q000737', 'Q000378', None, 'Q000494', 'Q000187', 'Q000494', 'Q000188', 'Q000494', 'Q000494']","['chemistry', None, 'pharmacology', 'metabolism', 'drug effects', 'pharmacology', 'drug therapy', None, None, None, 'chemistry', 'metabolism', None, 'pharmacology', 'drug effects', 'pharmacology', 'drug therapy', 'pharmacology', 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/21056027,2011,2.0,1.0,,[1], -20822860,"The effect of onion and garlic on the formation of two cholesterol oxidation products (COPs): 7-ketocholesterol and 7-hydroxycholesterol was evaluated by comparing their concentrations in meat and gravy samples obtained from three pork dishes prepared in the presence and absence of these flavourings. The concentration of these compounds in meat samples was between 82.4 and 1331.6 ng/g of cooked meat. Gravies contained lower amounts: from 18.3 to 45.6 ng/g of cooked meat. The addition of onion (30 g/100g of meat) caused a decrease in 7-ketocholesterol and 7-hydroxycholesterol concentrations in all of the investigated pork dishes by 9.5-79%, whilst the addition of 15 g of garlic to 100g of meat lowered the concentration by 17 to 88%. The greatest decrease was found in grilled minced chops. The quantitative assessment of 7-ketocholesterol and 7-hydroxycholesterol was carried out by thin-layer chromatography with densitometric detection.",Meat science,"['D000818', 'D000975', 'D002855', 'D005503', 'D005511', 'D005737', 'D006358', 'D006888', 'D007653', 'D008460', 'D008461', 'D019697', 'D010084', 'D028321', 'D013552']","['Animals', 'Antioxidants', 'Chromatography, Thin Layer', 'Food Additives', 'Food Handling', 'Garlic', 'Hot Temperature', 'Hydroxycholesterols', 'Ketocholesterols', 'Meat', 'Meat Products', 'Onions', 'Oxidation-Reduction', 'Plant Preparations', 'Swine']",7-Ketocholesterol and 7-hydroxycholesterol in pork meat and its gravy thermally treated without additives and in the presence of onion and garlic.,"[None, 'Q000032', None, None, 'Q000379', None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, None, 'Q000032', None]","[None, 'analysis', None, None, 'methods', None, None, 'analysis', 'analysis', 'analysis', 'analysis', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/20822860,2011,0.0,0.0,,, -20730643,"A rapid, simple and sensitive multi-residue method was developed and validated for the simultaneous quantification and confirmation of 69 pesticides in fruit and vegetables using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The samples were extracted following the quick, easy, cheap, effective, rugged and safe method known as QuEChERS. Mass spectrometric conditions were individually optimised for each analyte in order to achieve maximum sensitivity in multiple reaction monitoring (MRM) mode. Using the developed chromatographic conditions, 69 pesticides can be separated in less than 17 min. Two selected reaction monitoring (SRM) assays were used for each pesticide to obtain simultaneous quantification and identification in one run. With this method in SRM mode, more than 150 pesticides can be analysed and quantified, but their confirmation is not possible in all cases according to the European regulations on pesticide residues. Nine common representative matrices (zucchini, melon, cucumber, watermelon, tomato, garlic, eggplant, lettuce and pepper) were selected to investigate the effect of different matrices on recovery and precision. Mean recoveries ranged from 70% to 120%, with relative standard deviations (RSDs) lower than 20% for all the pesticides. The proposed method was applied to the analysis of more than 2000 vegetable samples from the extensive greenhouse cultivation in the province of Almeria, Spain, during one year. The methodology combines the advantages of both QuEChERS and LC-MS/MS producing a very rapid, sensitive, accurate and reliable procedure that can be applied in routine analytical laboratories. The method was validated and accredited according to UNE-EN-ISO/IEC 17025:2005 international standard (accreditation number 278/LE1027).","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D053000', 'D002138', 'D002851', 'D005060', 'D005506', 'D005513', 'D005638', 'D057230', 'D010573', 'D015203', 'D021241', 'D053719', 'D014675']","['Analytic Sample Preparation Methods', 'Calibration', 'Chromatography, High Pressure Liquid', 'Europe', 'Food Contamination', 'Food Inspection', 'Fruit', 'Limit of Detection', 'Pesticide Residues', 'Reproducibility of Results', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry', 'Vegetables']",UNE-EN ISO/IEC 17025:2005-accredited method for the determination of pesticide residues in fruit and vegetable samples by LC-MS/MS.,"[None, None, None, None, None, 'Q000379', 'Q000737', None, 'Q000032', None, None, None, 'Q000737']","[None, None, None, None, None, 'methods', 'chemistry', None, 'analysis', None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/20730643,2011,0.0,0.0,,, -20722910,"The effect of milk and milk components on the deodorization of diallyl disulfide (DADS), allyl methyl disulfide (AMDS), allyl mercaptan (AM), allyl methyl sulfide (AMS), and methyl mercaptan (MM) in the headspace of garlic as well as in the mouth- and nose-space after garlic ingestion was investigated using selected ion flow tube-mass spectrometry (SIFT-MS). Fat-free and whole milk significantly reduced the head-, mouth-, and nose-space concentrations of all volatiles. Water was the major component in milk responsible for the deodorization of volatiles. Due to its higher fat content, whole milk was more effective than fat-free milk in the deodorization of the more hydrophobic volatiles diallyl disulfide and allyl methyl disulfide. Milk was more effective than water and 10% sodium caseinate in the deodorization of allyl methyl sulfide, a persistent garlic odor, in the mouth after garlic ingestion. Addition of milk to garlic before ingestion had a higher deodorizing effect on the volatiles in the mouth than drinking milk after consuming garlic. Practical Application: Ingesting beverages or foods with high water and/or fat content such as milk may help reduce the malodorous odor in breath after garlic ingestion and mask the garlic flavor during eating. To enhance the deodorizing effect, deodorant foods should be mixed with garlic before ingestion.",Journal of food science,"['D000328', 'D000498', 'D000818', 'D001944', 'D002364', 'D003836', 'D005223', 'D005260', 'D005511', 'D005737', 'D006209', 'D006801', 'D057927', 'D013058', 'D008892', 'D009994', 'D013438', 'D013440', 'D013997', 'D055549']","['Adult', 'Allyl Compounds', 'Animals', 'Breath Tests', 'Caseins', 'Deodorants', 'Fats', 'Female', 'Food Handling', 'Garlic', 'Halitosis', 'Humans', 'Hydrophobic and Hydrophilic Interactions', 'Mass Spectrometry', 'Milk', 'Osmolar Concentration', 'Sulfhydryl Compounds', 'Sulfides', 'Time Factors', 'Volatile Organic Compounds']",Effect of milk on the deodorization of malodorous breath after garlic ingestion.,"[None, 'Q000032', None, None, 'Q000737', None, 'Q000737', None, None, 'Q000009', 'Q000517', None, None, None, 'Q000737', None, 'Q000032', 'Q000032', None, 'Q000032']","[None, 'analysis', None, None, 'chemistry', None, 'chemistry', None, None, 'adverse effects', 'prevention & control', None, None, None, 'chemistry', None, 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20722910,2011,1.0,1.0,,, -20609277,"The Asian citrus psyllid, Diaphorina citri Kuwayama, vectors Candidatus Liberibacter asiaticus (Las) and Candidatus Liberibacter americanus (Lam), the presumed causal agents of huanglongbing. D. citri generally rely on olfaction and vision for detection of host cues. Plant volatiles from Allium spp. (Alliaceae) are known to repel several arthropod species. We examined the effect of garlic chive (A. tuberosum Rottl.) and wild onion (A. canadense L.) volatiles on D. citri behaviour in a two-port divided T-olfactometer. Citrus leaf volatiles attracted significantly more D. citri adults than clean air. Volatiles from crushed garlic chive leaves, garlic chive essential oil, garlic chive plants, wild onion plants and crushed wild onion leaves all repelled D. citri adults when compared with clean air, with the first two being significantly more repellent than the others. However, when tested with citrus volatiles, only crushed garlic chive leaves and garlic chive essential oil were repellent, and crushed wild onions leaves were not. Analysis of the headspace components of crushed garlic chive leaves and garlic chive essential oil by gas chromatography-mass spectrometry revealed that monosulfides, disulfides and trisulfides were the primary sulfur volatiles present. In general, trisulfides (dimethyl trisulfide) inhibited the response of D. citri to citrus volatiles more than disulfides (dimethyl disulfide, allyl methyl disulfide, allyl disulfide). Monosulfides did not affect the behaviour of D. citri adults. A blend of dimethyl trisulfide and dimethyl disulfide in 1:1 ratio showed an additive effect on inhibition of D. citri response to citrus volatiles. The plant volatiles from Allium spp. did not affect the behaviour of the D. citri ecto-parasitoid Tamarixia radiata (Waterston). Thus, Allium spp. or the tri- and di-sulphides could be integrated into management programmes for D. citri without affecting natural enemies.",Bulletin of entomological research,"['D000490', 'D000818', 'D001522', 'D002957', 'D005260', 'D006430', 'D007303', 'D009043', 'D009822', 'D018515']","['Allium', 'Animals', 'Behavior, Animal', 'Citrus', 'Female', 'Hemiptera', 'Insect Vectors', 'Motor Activity', 'Oils, Volatile', 'Plant Leaves']","Sulfur volatiles from Allium spp. affect Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Psyllidae), response to citrus volatiles.","['Q000187', None, None, 'Q000187', None, 'Q000187', None, None, 'Q000494', 'Q000187']","['drug effects', None, None, 'drug effects', None, 'drug effects', None, None, 'pharmacology', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/20609277,2011,0.0,0.0,,no quantification, -20553188,"Recent studies have shown that deoxygenated human red blood cells (RBCs) converted garlic-derived polysulfides into hydrogen sulfide, which in turn produced vasorelaxation in aortic ring preparations. The vasoactivity was proposed to occur via glucose- and thiol-dependent acellular reactions. In the present study, we investigated the interaction of garlic extracts with human deoxygenated RBCs and its effect on intracellular hemoglobin molecules. The results showed that garlic extract covalently modified intraerythrocytic deoxygenated hemoglobin. The modification identified consisted of an addition of 71 atomic mass units, suggesting allylation of the cysteine residues. Consistently, purified human deoxyhemoglobin reacted with chemically pure diallyl disulfide, showing the same modification as garlic extracts. Tandem mass spectrometry analysis demonstrated that garlic extract and diallyl disulfide modified hemoglobin's beta-chain at cysteine-93 (beta-93C) or cysteine-112 (beta-112C). These results indicate that garlic-derived organic disulfides as well as pure diallyl disulfide must permeate the RBC membrane and modified deoxyhemoglobin at beta-93C or beta-112C. Although the physiological role of the reported garlic extract-induced allyl modification on human hemoglobin warrants further study, the results indicate that constituents of natural products, such as those from garlic extract, modify intracellular proteins.",Journal of medicinal food,"['D004912', 'D005737', 'D006454', 'D006801', 'D010936', 'D011499']","['Erythrocytes', 'Garlic', 'Hemoglobins', 'Humans', 'Plant Extracts', 'Protein Processing, Post-Translational']",Allylation of intraerythrocytic hemoglobin by raw garlic extracts.,"['Q000737', 'Q000737', 'Q000378', None, 'Q000494', 'Q000187']","['chemistry', 'chemistry', 'metabolism', None, 'pharmacology', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/20553188,2010,0.0,0.0,,, -20480390,"A method for the residual pendimethalin in soil and vegetable samples was developed. The method is based on extraction of pendimethalin from samples using microwave-assisted solvent extraction (MASE) with acetone, ethanol, and water as extraction solvent. Extracted pendimethalin samples were analyzed by high-performance liquid chromatography with ultraviolet detector at 240 nm. The MASE parameters, temperature, heating time, and solvent types were optimized with the feasibility of MASE application in the determination of pendimethalin extraction efficiency of pendimethalin from soil and vegetable samples. The maximum temperature that can be used during the heating for MASE is 60°C, where the recovery percentages reached 97%. Linearity for pendimethalin was found in the range of 2-20 μg mL(-1) with limits of detection and limits of quantification of 0.059 and 0.17 μg mL(-1), respectively.",Environmental monitoring and assessment,"['D000814', 'D002851', 'D005737', 'D008872', 'D012987', 'D012997', 'D014874']","['Aniline Compounds', 'Chromatography, High Pressure Liquid', 'Garlic', 'Microwaves', 'Soil', 'Solvents', 'Water Pollutants, Chemical']",Quantification of pendimethalin in soil and garlic samples by microwave-assisted solvent extraction and HPLC method.,"['Q000032', 'Q000379', 'Q000737', None, 'Q000737', 'Q000737', 'Q000032']","['analysis', 'methods', 'chemistry', None, 'chemistry', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20480390,2011,0.0,0.0,,, -20225897,"Through the use of direct analysis in real time mass spectrometry (DART-MS), 2-propenesulfenic acid, an intermediate long postulated as being formed when garlic ( Allium sativum ) is crushed, has been detected for the first time and determined by mass spectrometric methods to have a half-life of <1 s at room temperature. Two other key intermediates, 2-propenesulfinic acid and diallyl trisulfane S-oxide, have also been detected for the first time in volatiles from crushed garlic, along with allicin and related thiosulfinates, allyl alcohol, sulfur dioxide, propene, and pyruvate as coproducts. A commercial dietary supplement containing garlic powder, which was sampled after crushing, was found to contain alliin, methiin, and S-allylcysteine and produced allicin upon addition of water. DART-MS detection of 1-butenesulfenic acid from the ornamental A. siculum is also reported. (Z)-Propanethial S-oxide (onion lachrymatory factor), absent in garlic, is found to be formed from crushed elephant garlic ( Allium ampeloprasum ), consistent with the classification of this plant as a closer relative of leek than of garlic. Mixtures of thiosulfinates, lachrymatory thial S-oxides, and related compounds are directly observed from crushed leek ( Allium porrum ) and Chinese chive ( Allium tuberosum ). Disulfanes and polysulfanes are detected only when the Allium samples are heated, consistent with earlier conclusions that these are not primary products from cut or crushed alliums.",Journal of agricultural and food chemistry,"['D000475', 'D000490', 'D013058', 'D013434', 'D013457']","['Alkenes', 'Allium', 'Mass Spectrometry', 'Sulfenic Acids', 'Sulfur Compounds']","Applications of direct analysis in real time mass spectrometry (DART-MS) in Allium chemistry. 2-propenesulfenic and 2-propenesulfinic acids, diallyl trisulfane S-oxide, and other reactive sulfur compounds from crushed garlic and other Alliums.","['Q000032', 'Q000737', 'Q000379', 'Q000032', 'Q000032']","['analysis', 'chemistry', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20225897,2010,0.0,3.0,,"useful, not collected", -20100526,"Over expression of lectin genes in E. coli often gives inclusion bodies that are solubilised to characterize lectins. We made N-terminal fusion of the Allium sativum leaf agglutinin (ASAL) with SUMO (small ubiquitin related modifier) peptide. The SUMO peptide allowed expression of the recombinant lectin in E. coli, predominantly in soluble form. The soluble fusion protein could be purified by immobilized metal affinity column (IMAC), followed by size exclusion chromatography. The SUMO protease failed to cleave the SUMO peptide from ASAL. This may be due to steric hindrance caused by the homodimer structure of the chimeric ASAL. Some properties like dimerization, haemagglutination and insecticidal properties of the recombinant SUMO-ASAL fusion protein were comparable to the plant derived native lectin. However, glycan array analysis revealed that the carbohydrate binding specificity of the recombinant SUMO-ASAL was altered. Further, the fusion protein was not toxic to E. coli (native ASAL exhibited toxicity). The recombinant lectin was more thermo-labile as compared to the native lectin. Three important findings of this study are: (1) sugar specificity of ASAL can be altered by amino-terminal fusion; (2) anti-E. coli activity of ASAL can be eliminated by N-terminal SUMO fusion and (3) SUMO-ASAL may be a preferred candidate insecticidal protein for the development of transgenic plants.",Journal of biotechnology,"['D000371', 'D000818', 'D003546', 'D018744', 'D052978', 'D005737', 'D006863', 'D007814', 'D037241', 'D046228', 'D018515', 'D010940', 'D011134', 'D011485', 'D055503', 'D055550', 'D011993', 'D025842', 'D018411', 'D013696']","['Agglutination', 'Animals', 'Cysteine Endopeptidases', 'DNA, Plant', 'Disk Diffusion Antimicrobial Tests', 'Garlic', 'Hydrogen-Ion Concentration', 'Larva', 'Mannose-Binding Lectins', 'Microarray Analysis', 'Plant Leaves', 'Plant Proteins', 'Polysaccharides', 'Protein Binding', 'Protein Multimerization', 'Protein Stability', 'Recombinant Fusion Proteins', 'SUMO-1 Protein', 'Spodoptera', 'Temperature']",SUMO fusion facilitates expression and purification of garlic leaf lectin but modifies some of its properties.,"[None, None, 'Q000378', 'Q000302', None, 'Q000737', None, 'Q000187', 'Q000096', None, 'Q000737', 'Q000096', 'Q000378', None, None, None, 'Q000737', 'Q000235', 'Q000187', None]","[None, None, 'metabolism', 'isolation & purification', None, 'chemistry', None, 'drug effects', 'biosynthesis', None, 'chemistry', 'biosynthesis', 'metabolism', None, None, None, 'chemistry', 'genetics', 'drug effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/20100526,2010,0.0,0.0,,, -20079366,"Formation of cholesterol gallstones in gallbladder is controlled by procrystallizing and anticrystallizing factors present in bile. Dietary garlic and onion have been recently observed to possess anti-lithogenic potential in experimental mice. In this investigation, the role of biliary proteins from rats fed lithogenic diet or garlic/onion-containing diet in the formation of cholesterol gallstones in model bile was studied. Cholesterol nucleation time of the bile from lithogenic diet group was prolonged when mixed with bile from garlic or onion groups. High molecular weight proteins of bile from garlic and onion groups delayed cholesterol crystal growth in model bile. Low molecular weight (LMW) proteins from the bile of lithogenic diet group promoted cholesterol crystal growth in model bile, while LMW protein fraction isolated from the bile of garlic and onion groups delayed the same. Biliary LMW protein fraction was subjected to affinity chromatography using Con-A and the lectin-bound and unbound fractions were studied for their influence on cholesterol nucleation time in model bile. Major portion of biliary LMW proteins in lithogenic diet group was bound to Con-A, and this protein fraction promoted cholesterol nucleation time and increased cholesterol crystal growth rate, whereas Con-A unbound fraction delayed the onset of cholesterol crystallization. Biliary protein from garlic/onion group delayed the crystallization and interfered with pronucleating activity of Con-A bound protein fraction. These data suggest that apart from the beneficial modulation of biliary cholesterol saturation index, these Allium spices also influence cholesterol nucleating and antinucleating protein factors that contribute to their anti-lithogenic potential.",Steroids,"['D000818', 'D001646', 'D002784', 'D003208', 'D004032', 'D005737', 'D006801', 'D015227', 'D008297', 'D051379', 'D008970', 'D019697', 'D011485', 'D051381', 'D017208']","['Animals', 'Bile', 'Cholesterol', 'Concanavalin A', 'Diet', 'Garlic', 'Humans', 'Lipid Peroxidation', 'Male', 'Mice', 'Molecular Weight', 'Onions', 'Protein Binding', 'Rats', 'Rats, Wistar']",Effect of dietary garlic and onion on biliary proteins and lipid peroxidation which influence cholesterol nucleation in bile.,"[None, 'Q000737', 'Q000737', 'Q000378', None, 'Q000737', None, None, None, None, None, 'Q000737', None, None, None]","[None, 'chemistry', 'chemistry', 'metabolism', None, 'chemistry', None, None, None, None, None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/20079366,2010,0.0,0.0,,, -20067158,"By the method of capillary gas-liquid chromatography we studied antioxidant properties and stability during the storage of hexane solutions of 14 individual essential oils from black and white pepper (Piper nigrum L.), cardamom (Elettaria cardamomum L.), nutmeg (Myristica fragrans Houtt.), mace (Myristica fragrans Houtt), juniperberry (Juniperus communis L.), seed of fennel (Foeniculum vulgare Mill., var. dulce Thelling), caraway (Carvum carvi L.), dry leaves of cinnamon (Cinnamomum zeylanicum Bl.), marjoram (Origanum majorana L.), laurel (Laurus nobilis L.), ginger (Zingiber officinale L.), garlic (Allium sativum L.), and clove bud (Caryophyllus aromaticus L.). We assessed the antioxidant properties by the oxidation of aliphatic aldehyde (trans-2-hexenal) into the according carbon acid. We established that essential oils of garlic, clove bud, ginger and leaves of cinnamon have the maximal efficiency of inhibition of hexenal oxidation (80-93%), while black pepper oil has the minimal (49%). Antioxidant properties of essential oils with a high content of substituted phenols depended poorly on its concentration in model systems. We studied the changes in essential oils content during the storage of its hexane solutions for 40 days in the light and out of the light and compared it with the stability of essential oils stored for a year out of the light.",Prikladnaia biokhimiia i mikrobiologiia,"['D000975', 'D004355', 'D006586', 'D008027', 'D009822', 'D010944', 'D012639', 'D013997']","['Antioxidants', 'Drug Stability', 'Hexanes', 'Light', 'Oils, Volatile', 'Plants', 'Seeds', 'Time Factors']",[Antioxidant properties of essential oils].,"['Q000737', None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', None]","['chemistry', None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/20067158,2010,,,,, -20015835,"Direct somatic embryo formation (without intervening callus) from garlic clove basal tissue was induced in which the influence of plant growth regulators (PGRs) on various explants was examined. Medium added with 2.0 mg/l 6-benzylaminopurine (BAP) and 0.5 mg/l 2,4-dichlorophenoxyacetic acid (2,4-D) were the most effective PGR combination for somatic embryo induction. It induced embryos directly in 85.5% of the basal clove explant. Callus induction was also obtained from other parts of explant and 2.0 mg/l 2,4-D induced callusing in 86.5% of the inoculated explants. Protein, amino acid and alliin content were measured in callus and in embryos. Somatic embryos had more soluble protein and free amino acid compared to callus. HPTLC analysis revealed that alliin was significantly high in somatic embryos compared to undifferentiated callus tissue; the content was even more in older embryos. The present study of Allium indicates that the event of morphogenetic development including in vitro embryogeny can effectively be analysed by monitoring the changes of biochemical profiles.",Acta biologica Hungarica,"['D002454', 'D002855', 'D003545', 'D005737', 'D010937']","['Cell Differentiation', 'Chromatography, Thin Layer', 'Cysteine', 'Garlic', 'Plant Growth Regulators']",Improved alliin yield in somatic embryos of Allium sativum L. (cv. Yamuna safed) as analyzed by HPTLC.,"[None, 'Q000379', 'Q000031', 'Q000166', 'Q000378']","[None, 'methods', 'analogs & derivatives', 'cytology', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/20015835,2010,,,,no access to pdf, -20004743,"Garlic (Allium sativum), an important medicinal spice, displays a plethora of biological effects including immunomodulation. Although some immunomodulatory proteins from garlic have been described, their identities are still unknown. The present study was envisaged to isolate immunomodulatory proteins from raw garlic, and examine their effects on certain cells of the immune system (lymphocytes, mast cells, and basophils) in relation to mitogenicity and hypersensitivity. Three protein components of approximately 13 kD (QR-1, QR-2, and QR-3 in the ratio 7:28:1) were separated by Q-Sepharose chromatography of 30 kD ultrafiltrate of raw garlic extract. All the 3 proteins exhibited mitogenic activity towards human peripheral blood lymphocytes, murine splenocytes and thymocytes. The mitogenicity of QR-2 was the highest among the three immunomodulatory proteins. QR-1 and QR-2 displayed hemagglutination and mannose-binding activities; QR-3 showed only mannose-binding activity. Immunoreactivity of rabbit anti-QR-1 and anti-QR-2 polyclonal antisera showed specificity for their respective antigens as well as mutual cross-reactivity; QR-3 was better recognized by anti-QR-2 (82%) than by anti-QR-1 (55%). QR-2 induced a 2-fold higher histamine release in vitro from leukocytes of atopic subjects compared to that of non-atopic subjects. In all functional studies, QR-2 was more potent compared to QR-1. Taken together, all these results indicate that the two major proteins QR-2 and QR-1 present in a ratio of 4:1 in raw garlic contribute to garlic's immunomodulatory activity, and their characteristics are markedly similar to the abundant Allium sativum agglutinins (ASA) I and II, respectively.",International immunopharmacology,"['D000373', 'D000818', 'D049109', 'D004591', 'D005122', 'D005737', 'D006023', 'D006386', 'D006636', 'D006801', 'D006969', 'D007155', 'D037102', 'D008214', 'D008264', 'D008407', 'D009569', 'D010940', 'D011485', 'D051381', 'D017382', 'D013481']","['Agglutinins', 'Animals', 'Cell Proliferation', 'Electrophoresis, Polyacrylamide Gel', 'Exudates and Transudates', 'Garlic', 'Glycoproteins', 'Hemagglutination Tests', 'Histamine Release', 'Humans', 'Hypersensitivity, Immediate', 'Immunologic Factors', 'Lectins', 'Lymphocytes', 'Macrophages', 'Mast Cells', 'Nitric Oxide', 'Plant Proteins', 'Protein Binding', 'Rats', 'Reactive Oxygen Species', 'Superoxides']",Identity of the immunomodulatory proteins from garlic (Allium sativum) with the major garlic lectins or agglutinins.,"['Q000737', None, 'Q000187', None, 'Q000166', 'Q000737', 'Q000378', None, 'Q000187', None, 'Q000188', 'Q000737', 'Q000737', 'Q000187', 'Q000187', 'Q000187', 'Q000378', 'Q000737', None, None, 'Q000378', 'Q000378']","['chemistry', None, 'drug effects', None, 'cytology', 'chemistry', 'metabolism', None, 'drug effects', None, 'drug therapy', 'chemistry', 'chemistry', 'drug effects', 'drug effects', 'drug effects', 'metabolism', 'chemistry', None, None, 'metabolism', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/20004743,2010,0.0,0.0,,, -19938491,"A screening method was developed for the determination of 107 pesticide residues in vegetables using off-line dispersive solid-phase extraction (DSPE) and gas chromatography-tandem mass spectrometry (GC-MS/MS). The pesticides interested were extracted from the samples with acetonitrile (saturated by n-hexane) containing 1% acetic acid and simultaneously separated by liquid-liquid partitioning with adding anhydrous magnesium sulfate plus sodium acetate following by a simple cleanup step known as dispersive solid-phase extraction. The extracts were determined by GC-MS/MS using external standard method. The method was reliable and stable that the recoveries of almost all pesticides were in the range from 60% to 130% at the spiked level of 10 microg/kg into four vegetable matrixes (garlic, green bean, radish 8 and spinach) and the relative standard deviations (RSDs) were all not more than 15.3%. The linearity of the method was good between 0.05 mg/L and 1 mg/L, and all limits of quantification (LOQs) less than 10 microg/kg. The method is selective with no interference, especially in the complicated garlic matrix.",Se pu = Chinese journal of chromatography,[],[],[Determination of 107 pesticide residues in vegetables using off-line dispersive solid-phase extraction and gas chromatography-tandem mass spectrometry].,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19938491,2010,,,,, -19807156,"Garlic (Allium sativum) is a medicinal and culinary plant reported to have several positive health effects on cardiovascular diseases, particularly via suppressing platelet activation. Therefore, active compounds inhibiting platelet activation were isolated from garlic extract using a P-selectin expression suppressing activity-guided fractionation technique. Garlic cloves were extracted with methanol, sequentially partitioned using ethyl acetate, and n-butanol. The ethyl acetate portion was fractionated using silica gel chromatography. The fraction with highest P-selectin expression suppressing activity was further purified using HPLC, and the compounds in the fraction were analyzed using MS, MS/MS, and NMR spectroscopic methods. Using NMR spectroscopy, the compound with highest suppressing activity was confirmed as N-feruloyltyramine. At the concentration of 0.05 microM, N-feruloyltyramine was able to suppress P-selectin expression on platelets by 31% (P < 0.016). Since COX enzymes are deeply involved in the regulation of P-selectin expression on platelets, potential effects of N-feruloyltyramine on COX enzymes were investigated. As expected at the concentration of 0.05 microM, N-feruloyltyramine was found to be a very potent compound able to inhibit COX-I and -II enzymes by 43% (P < 0.012) and 33% (P < 0.014), respectively. N-Feruloyltyramine is likely to inhibit COX enzymes, thereby suppressing P-selectin expression on platelets.",Journal of agricultural and food chemistry,"['D002851', 'D003373', 'D016861', 'D005737', 'D009682', 'D013058', 'D019007', 'D018517', 'D010975', 'D014439']","['Chromatography, High Pressure Liquid', 'Coumaric Acids', 'Cyclooxygenase Inhibitors', 'Garlic', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'P-Selectin', 'Plant Roots', 'Platelet Aggregation Inhibitors', 'Tyramine']",Isolation and characterization of N-feruloyltyramine as the P-selectin expression suppressor from garlic (Allium sativum).,"[None, 'Q000302', 'Q000302', 'Q000737', None, None, 'Q000037', 'Q000737', 'Q000302', 'Q000031']","[None, 'isolation & purification', 'isolation & purification', 'chemistry', None, None, 'antagonists & inhibitors', 'chemistry', 'isolation & purification', 'analogs & derivatives']",https://www.ncbi.nlm.nih.gov/pubmed/19807156,2010,0.0,0.0,,, -19783157,"Matrix-enhanced surface-assisted laser desorption ionization mass spectrometry imaging (ME-SALDI MSI) has been previously demonstrated as a viable approach to improving MS imaging sensitivity. We describe here the employment of ionic matrices to replace conventional MALDI matrices as the coating layer with the aims of reducing analyte redistribution during sample preparation and improving matrix vacuum stability during imaging. In this study, CHCA/ANI (alpha-cyano-4-hydroxycinnamic acid/aniline) was deposited atop tissue samples through sublimation to eliminate redistribution of analytes of interest on the tissue surface. The resulting film was visually homogeneous under an optical microscope. Excellent vacuum stability of the ionic matrix was quantitatively compared with the conventional matrix. The subsequently improved ionization efficiency of the analytes over traditional MALDI was demonstrated. The benefits of using the ionic matrix in MS imaging were apparent in the analysis of garlic tissue sections in the ME-SALDI MSI mode.",Journal of the American Society for Mass Spectrometry,[],[],Ionic matrix for matrix-enhanced surface-assisted laser desorption ionization mass spectrometry imaging (ME-SALDI-MSI).,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19783157,2010,0.0,0.0,,, -19768983,"The chemical composition of fresh flowers from Allium ursinum (ramsons, bear's garlic, wild garlic) growing in Bulgaria has been studied. Thymidine (1), adenosine (2), astragalin (kaempferol-3-O-beta-D-glucopyranoside (3), kaempferol-3-O-beta-D-glucopyranosyl-7-O-beta-D-glucopyranoside (4), kaempferol-3-O-beta-D-neohesperoside (5), and kaempferol-3-O-beta-D-neohesperoside-7-O-beta-D-glucopyranoside (6) were isolated from the n-butanol extract and identified by different spectroscopic and spectrometric methods. Thymine (7), uridine (8), uracil (9) and 5-chloro-uridine (10) were detected in the same extract by GC-MS. This is the first report of the occurrence of 1, 2, 4, 7 - 10 in the flowers of A. ursinum. GC-MS of the volatile components of fresh flowers and leaves from the same plant revealed a high content of sulfur compounds, some of which are reported for the first time for A. ursinum. The antimicrobial activities of extracts from fresh flowers and leaves of A. ursinum have been tested; some extracts exhibited moderate antifungal properties.",Natural product communications,"['D020001', 'D000490', 'D000900', 'D000935', 'D002031', 'D002176', 'D035264', 'D005737', 'D008401', 'D044949', 'D009822', 'D010936', 'D018515', 'D013211']","['1-Butanol', 'Allium', 'Anti-Bacterial Agents', 'Antifungal Agents', 'Bulgaria', 'Candida albicans', 'Flowers', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Kaempferols', 'Oils, Volatile', 'Plant Extracts', 'Plant Leaves', 'Staphylococcus aureus']",Chemical composition and antimicrobial activity of wild garlic Allium ursinum of Bulgarian origin.,"[None, 'Q000737', 'Q000737', 'Q000302', None, 'Q000187', 'Q000737', 'Q000737', 'Q000379', 'Q000302', 'Q000737', 'Q000737', 'Q000737', 'Q000187']","[None, 'chemistry', 'chemistry', 'isolation & purification', None, 'drug effects', 'chemistry', 'chemistry', 'methods', 'isolation & purification', 'chemistry', 'chemistry', 'chemistry', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/19768983,2010,,,,, -19734685,"Allicin (allyl 2-propenethiosulfinate), an antibacterial principle of garlic, has drawn much attention, since it has potent antimicrobial activity against a range of microorganisms, including methicillin-resistant Staphylococcus aureus. There have been many reports on the antibacterial properties of allicin, but no quantitative comparison of antibacterial activities between freshly prepared garlic extract and clinically useful antibiotics has been performed. To verify the substantial antibacterial effect of aqueous garlic extract, we compared it with those of allicin and several clinically useful antibiotics using two representative bacteria commonly found in the human environment, Gram-positive S. aureus and Gram-negative Escherichia coli. The garlic extract had more potent anti-staphylococcal activity than an equal amount of allicin. In terms of antibiotic potency against Gram-positive and Gram-negative bacteria, authentic allicin had roughly 1-2% of the potency of streptomycin (vs. S. aureus), 8% of that of vancomycin (vs. S. aureus), and only 0.2% of that of colistin (vs. E. coli). The antibacterial activity of allicin was completely abolished by cysteine, glutathione and coenzyme A, but not by non-SH-compounds. The oxygen in the structure (-S(=O)-S-) of allicin therefore functions to liberate the S-allyl moiety, which might be an offensive tool against bacteria.","Bioscience, biotechnology, and biochemistry","['D000900', 'D002851', 'D005737', 'D006090', 'D006094', 'D013058', 'D008826', 'D010936', 'D013438', 'D013441']","['Anti-Bacterial Agents', 'Chromatography, High Pressure Liquid', 'Garlic', 'Gram-Negative Bacteria', 'Gram-Positive Bacteria', 'Mass Spectrometry', 'Microbial Sensitivity Tests', 'Plant Extracts', 'Sulfhydryl Compounds', 'Sulfinic Acids']",Antibacterial potential of garlic-derived allicin and its cancellation by sulfhydryl compounds.,"['Q000037', None, 'Q000737', 'Q000187', 'Q000187', None, None, 'Q000494', 'Q000494', 'Q000037']","['antagonists & inhibitors', None, 'chemistry', 'drug effects', 'drug effects', None, None, 'pharmacology', 'pharmacology', 'antagonists & inhibitors']",https://www.ncbi.nlm.nih.gov/pubmed/19734685,2009,1.0,1.0,,, -19733738,"Liquid chromatography electrospray mass spectrometry--LC/ESI/MS--a primary tool for analysis of low volatility compounds in difficult matrices--suffers from the matrix effects in the ESI ionization. It is well known that matrix effects can be reduced by sample dilution. However, the efficiency of simple sample dilution is often limited, in particular by the limit of detection of the method, and can strongly vary from sample to sample. In this study matrix effect is investigated as the function of dilution. It is demonstrated that in some cases dilution can eliminate matrix effect, but often it is just reduced. Based on these findings we propose a new quantitation method based on consecutive dilutions of the sample and extrapolation of the analyte content to the infinite dilution, i.e. to matrix-free solution. The method was validated for LC/ESI/MS analysis of five pesticides (methomyl, thiabendazole, aldicarb, imazalil, methiocarb) in five matrices (tomato, cucumber, apple, rye and garlic) at two concentration levels (0.5 and 5.0 mg kg(-1)). Agreement between the analyzed and spiked concentrations was found for all samples. It was demonstrated that in terms of accuracy of the obtained results the proposed extrapolative dilution approach works distinctly better than simple sample dilution. The main use of this approach is envisaged for (a) method development/validation to determine the extent of matrix effects and the ways of overcoming them and (b) as a second step of analysis in the case of samples having analyte contents near the maximum residue limits (MRL).",Analytica chimica acta,[],[],Combating matrix effects in LC/ESI/MS: the extrapolative dilution approach.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19733738,2009,0.0,0.0,,, -19733357,"A novel HPLC method for determination of a wide variety of S-substituted cysteine derivatives in Allium species has been developed and validated. This method allows simultaneous separation and quantification of S-alk(en)ylcysteine S-oxides, gamma-glutamyl-S-alk(en)ylcysteines and gamma-glutamyl-S-alk(en)ylcysteine S-oxides in a single run. The procedure is based on extraction of these amino acids and dipeptides by methanol, their derivatization by dansyl chloride and subsequent separation by reversed phase HPLC. The main advantages of the new method are simplicity, excellent stability of derivatives, high sensitivity, specificity and the ability to simultaneously analyze the whole range of S-substituted cysteine derivatives. This method was critically compared with other chromatographic procedures used for quantification of S-substituted cysteine derivatives, namely with two other HPLC methods (derivatization by o-phthaldialdehyde/tert-butylthiol and fluorenylmethyl chloroformate), and with determination by gas chromatography or capillary electrophoresis. Major advantages and drawbacks of these analytical procedures are discussed. Employing these various chromatographic methods, the content and relative proportions of individual S-substituted cysteine derivatives were determined in four most frequently consumed alliaceous vegetables (garlic, onion, shallot, and leek).",Journal of chromatography. A,"['D000490', 'D053000', 'D002851', 'D003545', 'D003619', 'D019075', 'D005410', 'D008401', 'D018517']","['Allium', 'Analytic Sample Preparation Methods', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Dansyl Compounds', 'Electrophoresis, Capillary', 'Flame Ionization', 'Gas Chromatography-Mass Spectrometry', 'Plant Roots']",Chromatographic methods for determination of S-substituted cysteine derivatives--a comparative study.,"['Q000737', None, None, 'Q000031', None, None, None, None, 'Q000737']","['chemistry', None, None, 'analogs & derivatives', None, None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19733357,2009,0.0,0.0,,, -19680964,"A multi-analyte method for the liquid chromatography-tandem mass spectrometric determination of mycotoxins in food supplements is presented. The analytes included A and B trichothecenes (nivalenol, deoxynivalenol, 3-acetyldeoxynivalenol, 15-acetyldeoxynivalenol, neosolaniol, fusarenon-X, diacetoxyscirpenol, HT-2 toxin and T-2 toxin), aflatoxins (aflatoxin-B(1), aflatoxin-B(2), aflatoxin-G(1) and aflatoxin-G(2)), Alternaria toxins (alternariol, alternariol methyl ether and altenuene), fumonisins (fumonisin-B(1), fumonisin-B(2) and fumonisin-B(3)), ochratoxin A, zearalenone, beauvericin and sterigmatocystin. Optimization of the simultaneous extraction of these toxins and the sample pretreatment procedure, as well as method validation were performed on maca (Lepidium meyenii) food supplements. The results indicated that the solvent mixture ethyl acetate/formic acid (95:5, v/v) was the best compromise for the extraction of the analytes from food supplements. Liquid-liquid partition with n-hexane was applied as partial clean-up step to remove excess of co-extracted non-polar components. Further clean-up was performed on Oasis HLB cartridges. Samples were analysed using an Acquity UPLC system coupled to a Micromass Quattro Micro triple quadrupole mass spectrometer equipped with an electrospray interface operated in the positive-ion mode. Limits of detection and quantification were in the range of 0.3-30 ng g(-1) and 1-100 ng g(-1), respectively. Recovery yields were above 60% for most of the analytes, except for nivalenol, sterigmatocystine and the fumonisins. The method showed good precision and trueness. Analysis of different food supplements such as soy (Glycine max) isoflavones, St John's wort (Hypericum perforatum), garlic (Allium sativum), Ginkgo biloba, and black radish (Raphanus niger) demonstrated the general applicability of the method. Due to different matrix effects observed in different food supplement samples, the standard addition approach was applied to perform correct quantitative analysis. In 56 out of 62 samples analysed, none of the 23 mycotoxins investigated was detected. Positive samples contained at least one of the toxins fumonisin-B(1), fumonisin-B(2), fumonisin-B(3) and ochratoxin A.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D002853', 'D019587', 'D057230', 'D009183', 'D053719']","['Chromatography, Liquid', 'Dietary Supplements', 'Limit of Detection', 'Mycotoxins', 'Tandem Mass Spectrometry']",LC-MS/MS multi-analyte method for mycotoxin determination in food supplements.,"['Q000379', 'Q000032', None, 'Q000032', 'Q000379']","['methods', 'analysis', None, 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/19680964,2010,0.0,0.0,,, -19643074,"Biomarkers in urine can provide useful information about the bioactivation of chemical carcinogens and can be used to investigate the chemoprotective properties of dietary nutrients. N-Nitrosoproline (NPRO) excretion has been used as an index for endogenous nitrosation. In vitro and animal studies have reported that compounds in garlic may suppress nitrosation and inhibit carcinogenesis. We present a new method for extraction and sensitive detection of both NPRO and N-acetyl-S-allylcysteine from urine. The latter is a metabolite of S-allylcysteine, which is found in garlic. Urine was acidified and the organic acids were extracted by reversed-phase extraction (RP-SPE) and use of a polymeric weak anion exchange (WAX-SPE) resin. NPRO was quantified by isotope dilution gas chromatography-mass spectrometry (GC-MS) using [13C5]NPRO and N-nitrosopipecolinic acid (NPIC) as internal standards. This method was used to analyze urine samples from a study that was designed to test whether garlic supplementation inhibits NPRO synthesis. Using this method, 2.4 to 46.0 ng NPRO/ml urine was detected. The method is straightforward and reliable, and it can be performed with readily available GC-MS instruments. N-Acetyl-S-allylcysteine was quantified in the same fraction and detectable at levels of 4.1 to 176.4 ng/ml urine. The results suggest that 3 to 5 g of garlic supplements inhibited NPRO synthesis to an extent similar to a 0.5-g dose of ascorbic acid or a commercial supplement of aged garlic extract. Urinary NPRO concentration was inversely associated with the N-acetyl-S-allylcysteine concentration. It is possible that allyl sulfur compounds found in garlic may inhibit nitrosation in humans.",Analytical biochemistry,"['D000284', 'D002247', 'D003545', 'D005737', 'D008401', 'D006801', 'D016014', 'D009602', 'D015538', 'D012015', 'D013048', 'D018709', 'D013997']","['Administration, Oral', 'Carbon Isotopes', 'Cysteine', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Linear Models', 'Nitrosamines', 'Nitrosation', 'Reference Standards', 'Specimen Handling', 'Statistics, Nonparametric', 'Time Factors']",A gas chromatography-mass spectrometry method for the quantitation of N-nitrosoproline and N-acetyl-S-allylcysteine in human urine: application to a study of the effects of garlic consumption on nitrosation.,"[None, 'Q000378', 'Q000031', None, 'Q000379', None, None, 'Q000652', None, None, None, None, None]","[None, 'metabolism', 'analogs & derivatives', None, 'methods', None, None, 'urine', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19643074,2009,0.0,0.0,,, -19593584,"The severe toxicity, exorbitant cost and the emerging resistance of Leishmania spp. against most of the currently used drugs led to the urgent need for exploiting our traditional Ayurvedic knowledge to treat visceral leishmaniasis. The aim of this study was to evaluate the in vitro anti-leishmanial activity of various extracts from ten traditionally used Indian medicinal plants. The methanolic extract from only two plants, Withania somnifera Dunal (ashwagandha) and Allium sativum Linn. (garlic), showed appreciable activity against Leishmania donovani. Further active compounds from these two plants were isolated and purified based on bioactivity-guided fractionation. HPLC-purified fraction A6 of ashwagandha and G3 of garlic showed consistently high activity with 50% inhibitory concentration (IC(50)) of 12.5 +/- 4 and 18.6 +/- 3 microg/ml against promastigotes whereas IC(50) of 9.5 +/- 3 and 13.5 +/- 2 microg/ml against amastigote form, respectively. The fraction A6 of ashwagandha was identified as withaferin A while fraction G3 of garlic is yet to be identified, and the work is in progress. Cytotoxic effects of the promising fractions and compounds were further evaluated in the murine macrophage (J774G8) model and were found to be safe. These compounds showed negligible cytotoxicity against J774G8 macrophages. The results indicate that fraction A6 of ashwagandha and fraction G3 of garlic might be potential sources of new anti-leishmanial compounds. The in vivo efficacy study and further optimization of these active compounds are in progress.",Parasitology research,"['D000818', 'D000981', 'D002460', 'D005591', 'D002851', 'D007194', 'D020128', 'D007893', 'D008264', 'D051379', 'D021261', 'D010936', 'D010946']","['Animals', 'Antiprotozoal Agents', 'Cell Line', 'Chemical Fractionation', 'Chromatography, High Pressure Liquid', 'India', 'Inhibitory Concentration 50', 'Leishmania donovani', 'Macrophages', 'Mice', 'Parasitic Sensitivity Tests', 'Plant Extracts', 'Plants, Medicinal']",Evaluation of anti-leishmanial activity of selected Indian plants known to have antimicrobial properties.,"[None, 'Q000302', None, None, None, None, None, 'Q000187', 'Q000187', None, None, 'Q000302', 'Q000737']","[None, 'isolation & purification', None, None, None, None, None, 'drug effects', 'drug effects', None, None, 'isolation & purification', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19593584,2009,0.0,0.0,,cell activity, -19550292,"Garlic is generally used as a therapeutic reagent against various diseases, and numerous studies have indicated that garlic and its derivatives can reduce the risk of various types of human cancer. Diallyl trisulfide (DATS), a major member of garlic derivatives, could inhibit the cell proliferation by triggering either cell cycle arrest or apoptosis in a variety of cancer cell lines as shown in many studies. However, whether DATS has the same effect on human osteosarcoma cells remains unknown. In this study, we have attempted to analyze the effects of DATS on cell proliferation, cell cycle, induction of apoptosis, global protein expression pattern in a human osteosarcoma cell line Saos-2 cells, and the potential molecular mechanisms of the action of DATS. Saos-2 cells, a human osteosarcoma cell line, were treated with or without 25, 50, and 100 micromol/l DATS for various time intervals. The cell proliferation, cell cycle progression, and apoptosis were examined in this study. Then, after treatment with or without 50 micromol/l DATS for 48 h, protein add pattern in Saos-2 cells were systematically studied using two-dimensional electrophoresis and mass spectrometry. DATS could inhibit the proliferation of Saos-2 cells in a dose-dependent and time-dependent manner. Moreover, the percentage of apoptotic cell and cell arrest in G0/G1 phase was also dose-dependent and time-dependent upon DATS treatment. A total of 27 unique proteins in Saos-2 cells, including 18 downregulated proteins and nine upregulated proteins, were detected with significant changes in their expression levels corresponding to DATS administration. Interestingly, almost half of these proteins (13 of 27) are related to either the cell cycle or apoptosis. DATS has the ability to suppress cell proliferation of Saos-2 cells by blocking cell cycle progression and inducing apoptosis in a dose and time-dependent manner. The proteomic results presented, therefore, provide additional support to the hypothesis that DATS is a strong inducer of apoptosis in tumor cells. However, the exact molecular mechanisms, how these proteins significantly changed in the Saos-2 cell line upon DATS treatment, should be further studied.",Anti-cancer drugs,"['D000498', 'D000972', 'D017209', 'D002453', 'D045744', 'D049109', 'D004305', 'D015536', 'D015180', 'D020869', 'D015972', 'D006801', 'D007091', 'D013058', 'D012516', 'D011506', 'D040901', 'D013440', 'D015854']","['Allyl Compounds', 'Antineoplastic Agents, Phytogenic', 'Apoptosis', 'Cell Cycle', 'Cell Line, Tumor', 'Cell Proliferation', 'Dose-Response Relationship, Drug', 'Down-Regulation', 'Electrophoresis, Gel, Two-Dimensional', 'Gene Expression Profiling', 'Gene Expression Regulation, Neoplastic', 'Humans', 'Image Processing, Computer-Assisted', 'Mass Spectrometry', 'Osteosarcoma', 'Proteins', 'Proteomics', 'Sulfides', 'Up-Regulation']",A proteomic study on a human osteosarcoma cell line Saos-2 treated with diallyl trisulfide.,"['Q000494', 'Q000494', 'Q000187', 'Q000187', None, 'Q000187', None, 'Q000187', None, None, 'Q000187', None, None, None, 'Q000378', 'Q000378', None, 'Q000494', 'Q000187']","['pharmacology', 'pharmacology', 'drug effects', 'drug effects', None, 'drug effects', None, 'drug effects', None, None, 'drug effects', None, None, None, 'metabolism', 'metabolism', None, 'pharmacology', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/19550292,2009,0.0,0.0,,no garlic sample, -19505565,"Traditionally, garlic (Allium sativum L.; Alliaceae) has been known to boost the immune system. Aged garlic has more potent immunomodulatory effects than raw garlic. These effects have been attributed to the transformed organosulfur compounds; the identity of the immunomodulatory proteins in aged garlic extract (AGE) is not known.",Journal of ethnopharmacology,"['D000818', 'D002852', 'D004396', 'D004591', 'D005737', 'D006023', 'D006386', 'D007155', 'D051379', 'D008807', 'D010936', 'D010940', 'D011485', 'D051381', 'D017208', 'D013154', 'D013601', 'D013778', 'D013844']","['Animals', 'Chromatography, Ion Exchange', 'Coloring Agents', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Glycoproteins', 'Hemagglutination Tests', 'Immunologic Factors', 'Mice', 'Mice, Inbred BALB C', 'Plant Extracts', 'Plant Proteins', 'Protein Binding', 'Rats', 'Rats, Wistar', 'Spleen', 'T-Lymphocytes', 'Tetrazolium Salts', 'Thiazoles']",Identification of the protein components displaying immunomodulatory activity in aged garlic extract.,"[None, None, None, None, 'Q000737', 'Q000378', None, 'Q000737', None, None, 'Q000737', 'Q000737', None, None, None, 'Q000166', 'Q000187', None, None]","[None, None, None, None, 'chemistry', 'metabolism', None, 'chemistry', None, None, 'chemistry', 'chemistry', None, None, None, 'cytology', 'drug effects', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19505565,2009,0.0,0.0,,proteins, -19428347,"Diallyl disulfide (DADS) and diallyl sulfide (DAS) are the major metabolites found in garlic oil and have been reported to lower cholesterol and prevent cancer. The molecular cytotoxic mechanisms of DADS and DAS have not been determined. The cytotoxic effectiveness of hydrogen versus allyl sulfides towards hepatocytes was found to be as follows: NaHS>DADS>DAS. Hepatocyte mitochondrial membrane potential was decreased and reactive oxygen species (ROS) and TBARS formation was increased by all three allyl sulfides. (1) DADS induced cytotoxicity was prevented by the H(2)S scavenger hydroxocobalamin, which also prevented cytochrome oxidase dependent mitochondrial respiration suggesting that H(2)S inhibition of cytochrome oxidase contributed to DADS hepatocyte cytotoxicity. (2) DAS cytotoxicity on the other hand was prevented by hydralazine, an acrolein trap. Hydralazine also prevented DAS induced GSH depletion, decreased mitochondrial membrane potential and increased ROS and TBARS formation. Chloral hydrate, the aldehyde dehydrogenase 2 inhibitor, however had the opposite effects, which could suggest that acrolein contributed to DAS hepatocyte cytotoxicity.",Chemico-biological interactions,"['D000498', 'D000818', 'D016588', 'D002470', 'D002478', 'D004220', 'D008401', 'D022781', 'D008297', 'D051381', 'D017207', 'D017382', 'D013440', 'D017392']","['Allyl Compounds', 'Animals', 'Anticarcinogenic Agents', 'Cell Survival', 'Cells, Cultured', 'Disulfides', 'Gas Chromatography-Mass Spectrometry', 'Hepatocytes', 'Male', 'Rats', 'Rats, Sprague-Dawley', 'Reactive Oxygen Species', 'Sulfides', 'Thiobarbituric Acid Reactive Substances']",The molecular mechanisms of diallyl disulfide and diallyl sulfide induced hepatocyte cytotoxicity.,"['Q000633', None, 'Q000633', None, None, 'Q000633', None, 'Q000187', None, None, None, 'Q000032', 'Q000633', 'Q000032']","['toxicity', None, 'toxicity', None, None, 'toxicity', None, 'drug effects', None, None, None, 'analysis', 'toxicity', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19428347,2009,0.0,0.0,,, -19350826,"To analyze the chemical components and decomposition products in allicin extract of garlic, the chemical components screening and identification were made with HPLC-MS/MS method by full scan TIC MS, HPLC retention time, product MS spectra and chemical reference standards. The stability of the extract in water and alcoholic solutions was also investigated. There were five major components in allicin extract which were all identified as thiosulfinates. The extract was stable for at least 3 months when stored at -20 degrees C as water solution, but obvious decomposition was observed with the increase of alcoholic concentration. The decomposition products were also identified by HPLC-MS/MS.",Yao xue xue bao = Acta pharmaceutica Sinica,"['D002851', 'D004355', 'D005737', 'D010946', 'D021241', 'D013441', 'D053719', 'D013885']","['Chromatography, High Pressure Liquid', 'Drug Stability', 'Garlic', 'Plants, Medicinal', 'Spectrometry, Mass, Electrospray Ionization', 'Sulfinic Acids', 'Tandem Mass Spectrometry', 'Thiosulfates']",[HPLC tandem-mass spectrometric analysis of the chemical components and decomposition products of allicin extract of garlic].,"[None, None, 'Q000737', 'Q000737', None, 'Q000302', None, 'Q000032']","[None, None, 'chemistry', 'chemistry', None, 'isolation & purification', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19350826,2010,,,,, -19336907,"The growing concomitant consumption of drugs and herbal preparations such as garlic, and the numerous reports about the influence of herbal preparations on intestinal transport, led us to evaluate the influence of aged garlic extract on the transport function and electrophysiological parameters of the small intestinal mucosa. Aged garlic extract induced increase of the absolute value of the transepithelial potential difference and of the short-circuit current in both permeability models tested (rat jejunum, Caco-2 cell monolayers) without affecting transepithelial electrical resistance. It also caused a significant increase of the P-glycoprotein and multidrug resistance associated protein 2 mediated effluxes through rat jejunum of marker substrates Rhodamine 123 and 2,4-dinitrophenyl-S-glutathione, respectively. Rhodamine 123 efflux through the Caco-2 cell monolayers was not altered by aged garlic extract, whereas the efflux of 2,4-dinitrophenyl-S-glutathione increased significantly. So altered activity of the important transport proteins could significantly change the pharmacokinetic properties of conventional medicines taken concomitantly with aged garlic extract.",Biological & pharmaceutical bulletin,"['D018435', 'D020168', 'D000818', 'D001693', 'D018938', 'D002851', 'D015194', 'D004594', 'D019793', 'D005737', 'D005978', 'D006801', 'D066298', 'D007413', 'D007583', 'D010936', 'D051381', 'D020112']","['ATP Binding Cassette Transporter, Sub-Family B', 'ATP-Binding Cassette, Sub-Family B, Member 1', 'Animals', 'Biological Transport, Active', 'Caco-2 Cells', 'Chromatography, High Pressure Liquid', 'Diffusion Chambers, Culture', 'Electrophysiology', 'Fluorescein', 'Garlic', 'Glutathione', 'Humans', 'In Vitro Techniques', 'Intestinal Mucosa', 'Jejunum', 'Plant Extracts', 'Rats', 'Rhodamine 123']",Aged garlic extract stimulates p-glycoprotein and multidrug resistance associated protein 2 mediated effluxes.,"['Q000502', 'Q000502', None, 'Q000187', None, None, None, None, None, 'Q000737', 'Q000031', None, None, 'Q000378', 'Q000378', 'Q000494', None, None]","['physiology', 'physiology', None, 'drug effects', None, None, None, None, None, 'chemistry', 'analogs & derivatives', None, None, 'metabolism', 'metabolism', 'pharmacology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19336907,2009,0.0,0.0,,aged garlic, -19271323,"The therapeutic potency of garlic leaf extract obtained from normal and sulphur treated plants was compared. Alliin, the active compound of garlic leaf extract showed 32% increase in yield under sulphur treated conditions. Alliin obtained from leaf extract of plants brought a significant reduction in serum glucose, triglycerides, total lipids, total cholesterol, LDL- and VLDL-cholesterol levels than glibenclamide in alloxan-induced diabetic rats. Alliin from sulphur treated plants was more effective in comparison to that obtained from plants raised in normal conditions. Serum glucose levels showed significant reduction of 50% in rats administered with leaf extract from sulphur treated plants in comparison to 37% noted in rats administered with leaf extract from normal plants. No alteration in HDL-cholesterol was noted. Similarly, alliin obtained from leaf extract of plants lowered the serum enzyme (ALP, AST and ALT) levels towards normal than glibenclamide. The reduction in serum enzyme levels was significant in rats administered with leaf extract of plants raised under sulphur treated conditions in comparison to that raised under normal conditions. The present findings suggest that leaf extract from sulphur treated garlic possess more antidiabetic potential and hence show more therapeutic potency in comparison to extract obtained from normal plants.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000410', 'D000818', 'D001219', 'D001786', 'D002855', 'D003545', 'D005737', 'D008055', 'D008297', 'D010936', 'D018515', 'D051381', 'D017208', 'D013455']","['Alanine Transaminase', 'Animals', 'Aspartate Aminotransferases', 'Blood Glucose', 'Chromatography, Thin Layer', 'Cysteine', 'Garlic', 'Lipids', 'Male', 'Plant Extracts', 'Plant Leaves', 'Rats', 'Rats, Wistar', 'Sulfur']",Sulphur treatment alters the therapeutic potency of alliin obtained from garlic leaf extract.,"['Q000097', None, 'Q000097', 'Q000032', None, 'Q000031', 'Q000737', 'Q000097', None, 'Q000032', 'Q000737', None, None, 'Q000494']","['blood', None, 'blood', 'analysis', None, 'analogs & derivatives', 'chemistry', 'blood', None, 'analysis', 'chemistry', None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/19271323,2009,1.0,1.0,,, -19267240,"A sensitive and simple analytical method has been developed for determination of Sb(III), Sb(V), Se(IV), Se(VI), Te(IV), Te(VI), and Bi(III) in garlic samples by using hydride-generation-atomic-fluorescence spectrometry (HG-AFS). The method is based on a single extraction of the inorganic species by sonication at room temperature with 1 mol L(-1) H2SO4 and washing of the solid phase with 0.1% (w/v) EDTA, followed by measurement of the corresponding hydrides generated under two different experimental conditions directly and after a pre-reduction step. The limit of detection of the method was 0.7 ng g(-1) for Sb(III), 1.0 ng g(-1) for Sb(V), 1.3 ng g(-1) for Se(IV), 1.0 ng g(-1) for Se(VI), 1.1 ng g(-1) for Te(IV), 0.5 ng g(-1) for Te(VI), and 0.9 ng g(-1) for Bi(III), in all cases expressed in terms of sample dry weight.",Analytical and bioanalytical chemistry,"['D000965', 'D001729', 'D005737', 'D006859', 'D007287', 'D007477', 'D018551', 'D018515', 'D018036', 'D013050', 'D013464', 'D013691']","['Antimony', 'Bismuth', 'Garlic', 'Hydrogen', 'Inorganic Chemicals', 'Ions', 'Lycopersicon esculentum', 'Plant Leaves', 'Selenium Compounds', 'Spectrometry, Fluorescence', 'Sulfuric Acids', 'Tellurium']","Determination of total Sb, Se, Te, and Bi and evaluation of their inorganic species in garlic by hydride-generation-atomic-fluorescence spectrometry.","['Q000032', 'Q000032', 'Q000737', 'Q000737', 'Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000032', 'Q000295', None, 'Q000032']","['analysis', 'analysis', 'chemistry', 'chemistry', 'analysis', 'chemistry', 'chemistry', 'chemistry', 'analysis', 'instrumentation', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19267240,2009,1.0,3.0,,, -19200080,"The color-forming ability of amino acids with thiosulfinate in crushed garlic was investigated. We developed reaction systems for generating pure blue pigments using extracted thiosulfinate from crushed garlic and onion and all 22 amino acids. Each amino acid was reacted with thiosulfinate solution and was then incubated at 60 degrees C for 3 h to generate pigments. Unknown blue pigments, responsible for discoloration in crushed garlic cloves (Allium sativum L.), were separated and tentatively characterized using high-performance liquid chromatography (HPLC) and a diode array detector ranging between 200 and 700 nm. Blue pigment solutions exhibited 2 maximal absorbance peaks at 440 nm and 580 nm, corresponding to yellow and blue, respectively, with different retention times. Our findings indicated that green discoloration is created by the combination of yellow and blue pigments. Eight naturally occurring blue pigments were separated from discolored garlic extracts using HPLC at 580 nm. This suggests that garlic discoloration is not caused by only 1 blue pigment, as reported earlier, but by as many as 8 pigments. Overall, free amino acids that formed blue pigment when reacted with thiosulfinate were glycine, arginine, lysine, serine, alanine, aspartic acid, asparagine, glutamic acid, and tyrosine. Arginine, asparagine, and glutamine had spectra that were more similar to naturally greened garlic extract.",Journal of food science,"['D000596', 'D002851', 'D003116', 'D005524', 'D005737', 'D019697', 'D010860', 'D013886', 'D013997']","['Amino Acids', 'Chromatography, High Pressure Liquid', 'Color', 'Food Technology', 'Garlic', 'Onions', 'Pigments, Biological', 'Thiosulfonic Acids', 'Time Factors']",Identification of candidate amino acids involved in the formation of blue pigments in crushed garlic cloves (Allium sativum L.).,"['Q000032', None, None, None, 'Q000737', 'Q000737', 'Q000096', 'Q000032', None]","['analysis', None, None, None, 'chemistry', 'chemistry', 'biosynthesis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/19200080,2009,0.0,0.0,,not quantified, -19170155,"The herbal remedies Natto K2, Agaricus, mistletoe, noni juice, green tea and garlic, frequently used by cancer patients, were investigated for their in vitro inhibition potential of cytochrome P-450 3A4 (CYP3A4) metabolism. To our knowledge, only garlic and green tea had available data on the possible inhibition of CYP3A4 metabolism. Metabolic studies were performed with human c-DNA baculovirus expressed CYP3A4. Testosterone was used as a substrate and ketoconazole as a positive quantitative inhibition control. The formation of 6-beta-OH-testosterone was quantified by a validated HPLC methodology. Green tea was the most potent inhibitor of CYP3A4 metabolism (IC(50): 73 microg/mL), followed by Agaricus, mistletoe and noni juice (1324, 3594, >10 000 microg/mL, respectively). All IC(50) values were high compared with those determined for crude extracts of other herbal remedies. The IC(50)/IC(25) ratios for the inhibiting herbal remedies ranged from 2.15 to 2.67, indicating similar inhibition profiles of the herbal inhibitors of CYP3A4. Garlic and Natto K2 were classified as non-inhibitors. Although Agaricus, noni juice, mistletoe and green tea inhibited CYP3A4 metabolism in vitro, clinically relevant systemic or intestinal interactions with CYP3A4 were considered unlikely, except for a probable inhibition of intestinal CYP3A4 by the green tea product.",Phytotherapy research : PTR,"['D000364', 'D002851', 'D051544', 'D065692', 'D004791', 'D029001', 'D006801', 'D007654', 'D010936', 'D013662', 'D013739', 'D028182']","['Agaricus', 'Chromatography, High Pressure Liquid', 'Cytochrome P-450 CYP3A', 'Cytochrome P-450 CYP3A Inhibitors', 'Enzyme Inhibitors', 'Herbal Medicine', 'Humans', 'Ketoconazole', 'Plant Extracts', 'Tea', 'Testosterone', 'Viscum album']",In vitro inhibition of CYP3A4 by herbal remedies frequently used by cancer patients.,"[None, None, None, None, None, None, None, 'Q000494', 'Q000009', 'Q000009', 'Q000378', 'Q000009']","[None, None, None, None, None, None, None, 'pharmacology', 'adverse effects', 'adverse effects', 'metabolism', 'adverse effects']",https://www.ncbi.nlm.nih.gov/pubmed/19170155,2009,0.0,0.0,,, -19160762,"A gas chromatography-negative chemical ionization mass spectrometric (GC-NCI/ MS) method has been developed for analyzing 14 pesticide residues in sulfur-containing vegetables (scallion, garlic, garlic bolt, leek, etc.). The samples were first heated in a microwave oven to eliminate most of the sulfur-containing interfering impurities and then extracted with acetonitrile. The extracts were further cleaned-up by gel permeation chromatography (GPC) and a primary-secondary amine (PSA) cartridge. The target analytes were determined using GC-NCI/MS in the selected ion monitoring (SIM) mode. The recoveries of all the pesticides (at spiked level of 50 microg/kg) were from 49.2% to 113.1% with the relative standard deviations between 1.42% and 8.70%. The detection limits (S/N = 3) were in the range of 0.5-10.0 microg/kg. The method is selective without interference and suitable for the determination and confirmation of pesticides in the sulfur-containing vegetables.",Se pu = Chinese journal of chromatography,"['D005504', 'D005506', 'D008401', 'D057230', 'D016014', 'D008872', 'D010573', 'D015203', 'D013455', 'D014675']","['Food Analysis', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Limit of Detection', 'Linear Models', 'Microwaves', 'Pesticide Residues', 'Reproducibility of Results', 'Sulfur', 'Vegetables']",[Determination of 14 pesticide residues in sulfur-containing vegetables by gas chromatography-negative chemical ionization mass spectrometry].,"['Q000379', 'Q000032', 'Q000379', None, None, None, 'Q000032', None, 'Q000378', 'Q000737']","['methods', 'analysis', 'methods', None, None, None, 'analysis', None, 'metabolism', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19160762,2010,,,,, -19159814,"A sequential extraction procedure was developed for the fractionation of different classes of selenium species present in garlic. The consecutive steps included leaching with water, extraction of cell-wall bound species after lysis with a mixture of cellulase, chitinase and beta-glucanase completed by a proteolytic attack, extraction with HCl to liberate the residual organic bound species and finally, extractions with sulfite solution and CS(2) to complete the mass balance by the recovery of Se(0) and Se(2-), respectively. Selenium speciation in the aqueous fractions was probed by anion-exchange and ion-pairing reversed-phase HPLC-ICP MS after purification by preparative size-exclusion LC. It was found to be strongly affected by the sample redox conditions. The peak identity was matched with a mixture of 9 compounds expected to be present in allium plants; electrospray QTOF MS turned out to be unsuccessful. Selenite, selenate and selenomethionine were the dominating species present.",Talanta,"['D002851', 'D005737', 'D013058', 'D010447', 'D010936', 'D012643']","['Chromatography, High Pressure Liquid', 'Garlic', 'Mass Spectrometry', 'Peptide Hydrolases', 'Plant Extracts', 'Selenium']",A sequential extraction procedure for an insight into selenium speciation in garlic.,"[None, 'Q000737', None, 'Q000378', None, 'Q000032']","[None, 'chemistry', None, 'metabolism', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19159814,2009,2.0,1.0,,, -19139589,"Thirty elements in garlic sample were determined by inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma atomic emission spectrometry (ICP-AES) after microwave digestion. The concentrations of K, Ca,Na, Sr, and Hg in the present garlic sample were higher than those in rice and wheat, but the concentration of Se in the garlic sample was relatively lower. The extractability of the elements in the garlic sample was also examined; the results showed that most of the elements could be easily extracted by pure water and/or a 0.1 M HNO(3) solution, except for Hg. Furthermore, the size-fractional distribution of the elements in garlic was investigated by pure water extraction and centrifugal ultrafiltration.",Analytical sciences : the international journal of the Japan Society for Analytical Chemistry,"['D002118', 'D002498', 'D005737', 'D008628', 'D008670', 'D012275', 'D011188', 'D012643', 'D012964', 'D012996', 'D013054', 'D013324', 'D053719', 'D014908']","['Calcium', 'Centrifugation', 'Garlic', 'Mercury', 'Metals', 'Oryza', 'Potassium', 'Selenium', 'Sodium', 'Solutions', 'Spectrophotometry, Atomic', 'Strontium', 'Tandem Mass Spectrometry', 'Triticum']",Determination and size-fractional distribution of the elements in garlic.,"[None, None, 'Q000737', None, 'Q000032', 'Q000737', None, None, None, None, 'Q000379', None, 'Q000379', 'Q000737']","[None, None, 'chemistry', None, 'analysis', 'chemistry', None, None, None, None, 'methods', None, 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19139589,2009,1.0,3.0,,, -19053859,"Garlic (Allium sativum) is regarded as both a food and a medicinal herb. Increasing attention has focused on the biological functions and health benefits of garlic as a potentially major dietary component. Chronic garlic administration has been shown to enhance memory function. Evidence also shows that garlic administration in rats affects brain serotonin (5-hydroxytryptamine [5-HT]) levels. 5-HT, a neurotransmitter involved in a number of physiological functions, is also known to enhance cognitive performance. The present study was designed to investigate the probable neurochemical mechanism responsible for the enhancement of memory following garlic administration. Sixteen adult locally bred male albino Wistar rats were divided into control (n = 8) and test (n = 8) groups. The test group was orally administered 250 mg/kg fresh garlic homogenate (FGH), while control animals received an equal amount of water daily for 21 days. Estimation of plasma free and total tryptophan (TRP) and whole brain TRP, 5-HT, and 5-hydroxyindole acetic acid (5-HIAA) was determined by high-performance liquid chromatography with electrochemical detection. For assessment of memory, a step-through passive avoidance paradigm (electric shock avoidance) was used. The results showed that the levels of plasma free TRP significantly increased (P < .01) and plasma total TRP significantly decreased (P < .01) in garlic-treated rats. Brain TRP, 5-HT, and 5-HIAA levels were also significantly increased following garlic administration. A significant improvement in memory function was exhibited by garlic-treated rats in the passive avoidance test. Increased brain 5-HT levels were associated with improved cognitive performance. The present results, therefore, demonstrate that the memory-enhancing effect of garlic may be associated with increased brain 5-HT metabolism in rats. The results further support the use of garlic as a food supplement for the enhancement of memory.",Journal of medicinal food,"['D006916', 'D000818', 'D001362', 'D001921', 'D005737', 'D006897', 'D008568', 'D008517', 'D028321', 'D051381', 'D017208', 'D014364']","['5-Hydroxytryptophan', 'Animals', 'Avoidance Learning', 'Brain', 'Garlic', 'Hydroxyindoleacetic Acid', 'Memory', 'Phytotherapy', 'Plant Preparations', 'Rats', 'Rats, Wistar', 'Tryptophan']",Repeated administration of fresh garlic increases memory retention in rats.,"['Q000097', None, 'Q000187', 'Q000378', None, 'Q000378', 'Q000187', None, 'Q000008', None, None, 'Q000097']","['blood', None, 'drug effects', 'metabolism', None, 'metabolism', 'drug effects', None, 'administration & dosage', None, None, 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/19053859,2009,,,,, -19053158,"A new derivatization-extraction method for preconcentration of seleno amino acids using hollow fiber liquid phase microextraction (HF-LPME) was developed for the separation and determination of seleno amino acids in biological samples by gas chromatography-inductively coupled plasma mass spectrometry (GC-ICP-MS). Derivatization was performed with ethyl chloroformate (ECF) to improve the volatility of seleno amino acids. Parameters influencing microextraction, including extraction solvent, pH of sample solution, extraction time, stirring speed, and inorganic salt concentration have been investigated. Under the optimal conditions, the limits of detection (LODs) obtained for Se-methyl-selenocysteine (SeMeCys), selenomethionine (SeMet), and selenoethionine (SeEth) were 23, 15, and 11 ng Se l(-1), respectively. The relative standard deviations (RSDs) were 14.6%, 16.4%, and 19.4% for SeMeCys, SeMet, and SeEth (c = 1.0 ng ml(-1), n = 7), respectively, and the RSDs for SeMeCys, SeMet could be improved obviously if SeEth was utilized as the internal standard. The proposed method was applied for the determination of seleno amino acids in extracts of garlic, cabbage, and mushroom samples, and the recoveries for the spiked samples were in the range of 96.8-108% and 93.4-115% with and without the use of SeEth as internal standard. The developed method was also applied to the analysis of SeMet in a certified reference material of SELM-1 yeast and the determined value is in good agreement with the certified value.",Journal of mass spectrometry : JMS,"['D000363', 'D001937', 'D005591', 'D002725', 'D003545', 'D005001', 'D005737', 'D008401', 'D016566', 'D017279', 'D012645', 'D012680', 'D012965', 'D014050']","['Agaricales', 'Brassica', 'Chemical Fractionation', 'Chloroform', 'Cysteine', 'Ethionine', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Organoselenium Compounds', 'Selenocysteine', 'Selenomethionine', 'Sensitivity and Specificity', 'Sodium Chloride', 'Toluene']",Separation and determination of seleno amino acids using gas chromatography hyphenated with inductively coupled plasma mass spectrometry after hollow fiber liquid phase microextraction.,"['Q000737', 'Q000737', 'Q000379', 'Q000737', 'Q000031', 'Q000031', 'Q000737', 'Q000379', 'Q000032', 'Q000031', 'Q000032', None, 'Q000737', 'Q000737']","['chemistry', 'chemistry', 'methods', 'chemistry', 'analogs & derivatives', 'analogs & derivatives', 'chemistry', 'methods', 'analysis', 'analogs & derivatives', 'analysis', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19053158,2009,,,,, -19052349,"A wide range of biological activities of garlic in vitro and in vivo have been verified including its antioxidant, antitumor and anti-inflammatory effects. Indoleamine 2,3-dioxygenase (IDO) is an enzyme widely distributed in mammals and is inducible preferentially by IFN-gamma. IDO degrades the essential amino acid tryptophan to form N-formyl kynurenine. In the present in vitro study, the modulatory effect of 14kDa molecule isolated from garlic on IDO induction was tested. Cultures of mononuclear cells were exposed to 14kDa garlic fraction. Then, their proliferation responses and IDO metabolites were measured. A significant down-regulatory effect of garlic on IDO activity was found and also the proliferation responses of mononuclear cells increased. If these results are verified in vivo, an explanation will be provided on how garlic may interfere in IDO induction, which paves the way for elucidating its specific therapeutic effect in preventing tumor progress.","Iranian journal of allergy, asthma, and immunology","['D000818', 'D049109', 'D002470', 'D002851', 'D005737', 'D050503', 'D007737', 'D007963', 'D051379', 'D008807', 'D010084', 'D010936', 'D011506', 'D014364']","['Animals', 'Cell Proliferation', 'Cell Survival', 'Chromatography, High Pressure Liquid', 'Garlic', 'Indoleamine-Pyrrole 2,3,-Dioxygenase', 'Kynurenine', 'Leukocytes, Mononuclear', 'Mice', 'Mice, Inbred BALB C', 'Oxidation-Reduction', 'Plant Extracts', 'Proteins', 'Tryptophan']","The 14kDa protein molecule isolated from garlic suppresses indoleamine 2, 3-dioxygenase metabolites in mononuclear cells in vitro.","[None, 'Q000187', 'Q000187', None, None, 'Q000037', 'Q000378', 'Q000378', None, None, 'Q000187', 'Q000302', 'Q000302', 'Q000378']","[None, 'drug effects', 'drug effects', None, None, 'antagonists & inhibitors', 'metabolism', 'metabolism', None, None, 'drug effects', 'isolation & purification', 'isolation & purification', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/19052349,2009,0.0,0.0,,, -18997429,"The garlic-derived antibacterial principle, alk(en)yl sulfinate compounds, has long been considered as very short-lived substance. However, there are some data showing a rather more stable nature of allicin. We determined here the thermostability of allicin by a systematic analyses employing chemical quantification and an antibacterial activity assay. Allicin in an aqueous extract of garlic was degraded stoichiometrically in proportion to the temperature; we estimated the half-life of allicin to be about a year at 4 degrees C (from 1.8 mg/ml to 0.9 mg/ml) and 32 d at 15 degrees C, but only 1 d at 37 degrees C (from 2.0 mg/ml to 1.0 mg/ml). The half-life values for antibacterial activity showed a similar trend in results: 63 d or more at 4 degrees C for both antibacterial activities, 14 d for anti-staphylococcal activity, and 26 d for anti-escherichia activity at 15 degrees C, but only 1.2 d and 1.9 d for the respective activities at 37 degrees C. Such antibacterial activities were attributable to the major allicin, allyl 2-propenylthiosulfinate. Surprisingly, the decline in the quantity of allicin was not accompanied by its degradation; instead, allicin became a larger molecule, ajoene, which was 3-times larger than allicin.","Bioscience, biotechnology, and biochemistry","['D000900', 'D001681', 'D002851', 'D004926', 'D005737', 'D006207', 'D010936', 'D013211', 'D013441', 'D013696', 'D014867']","['Anti-Bacterial Agents', 'Biological Assay', 'Chromatography, High Pressure Liquid', 'Escherichia coli', 'Garlic', 'Half-Life', 'Plant Extracts', 'Staphylococcus aureus', 'Sulfinic Acids', 'Temperature', 'Water']",Thermostability of allicin determined by chemical and biological assays.,"['Q000737', None, None, 'Q000187', 'Q000737', None, 'Q000737', 'Q000187', 'Q000737', None, 'Q000737']","['chemistry', None, None, 'drug effects', 'chemistry', None, 'chemistry', 'drug effects', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/18997429,2009,1.0,1.0,,, -18952220,"A novel method for determination of S-alk(en)ylcysteine-S-oxides by capillary electrophoresis has been developed and validated. The method is based on extraction of these sulfur amino acids by methanol, their derivatization by fluorenylmethyl chloroformate and subsequent separation by micellar electrokinetic capillary chromatography. Main advantages of the new method are simplicity, sensitivity, high specificity and very low running costs, making it suitable for routine analysis of a large number of samples. Employing this method, the content of S-alk(en)ylcysteine-S-oxides was determined in 12 commonly consumed alliaceous and cruciferous vegetables (e.g. garlic, onion, leek, chive, cabbage, radish, cauliflower and broccoli). The total content of these amino acids in the Allium species evaluated varied between 0.59 and 12.3mg g(-1) fresh weight. Whereas alliin was found only in garlic, isoalliin was the major S-alk(en)ylcysteine-S-oxide in onion, leek, chive and shallot. On the other hand, the cruciferous species analyzed contained only methiin in the range of 0.06-2.45mg g(-1) fresh weight.",Journal of chromatography. A,"['D000490', 'D001937', 'D020374', 'D003545', 'D000432', 'D031224', 'D015203', 'D012680', 'D013454']","['Allium', 'Brassica', 'Chromatography, Micellar Electrokinetic Capillary', 'Cysteine', 'Methanol', 'Raphanus', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Sulfoxides']",Quantitative determination of S-alk(en)ylcysteine-S-oxides by micellar electrokinetic capillary chromatography.,"['Q000737', 'Q000737', 'Q000191', 'Q000031', 'Q000737', 'Q000737', None, None, 'Q000032']","['chemistry', 'chemistry', 'economics', 'analogs & derivatives', 'chemistry', 'chemistry', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18952220,2009,1.0,2.0,,, -18655544,"A novel oligosaccharide was purified from garlic (Allium sativum L.) bulbs via hot water extraction, ammonium sulfate precipitation, gel filtration and ion exchange chromatography. The molecular weight of the oligosaccharide was determined to be 1800. A nuclear magnetic resonance (NMR) study showed that ten fructose molecules were connected by beta1-2 linkage to a terminal glucose. The oligosaccharide had cytotoxic activities against human malignant lymphoma cells (U937) and colon adenocarcinoma cells (WiDr) in vitro. Furthermore, this oligosaccharide significantly suppressed the growth of murine colon adenocarcinoma cells (colon 26) in vivo. The oligosaccharide also stimulated interferon-gamma production by human peripheral blood lymphocyte in vitro, indicating that it may activate the immunological pathways and suppress the growth of tumors in vivo.",Journal of UOEH,"['D000230', 'D000818', 'D002478', 'D003110', 'D004305', 'D019008', 'D005737', 'D006801', 'D007371', 'D008214', 'D008297', 'D051379', 'D008807', 'D008970', 'D009844', 'D013268', 'D014407', 'D020298']","['Adenocarcinoma', 'Animals', 'Cells, Cultured', 'Colonic Neoplasms', 'Dose-Response Relationship, Drug', 'Drug Resistance, Neoplasm', 'Garlic', 'Humans', 'Interferon-gamma', 'Lymphocytes', 'Male', 'Mice', 'Mice, Inbred BALB C', 'Molecular Weight', 'Oligosaccharides', 'Stimulation, Chemical', 'Tumor Cells, Cultured', 'U937 Cells']","Purification, characterization and biological activities of a garlic oligosaccharide.","['Q000473', None, None, 'Q000473', None, None, 'Q000737', None, 'Q000096', 'Q000276', None, None, None, None, 'Q000737', None, None, 'Q000187']","['pathology', None, None, 'pathology', None, None, 'chemistry', None, 'biosynthesis', 'immunology', None, None, None, None, 'chemistry', None, None, 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/18655544,2008,,,,, -18633206,"A simple preprocessing method was developed for multiresidue determination of pesticides in processed agricultural products. Residues were extracted from homogenized samples with acetonitrile in a glass centrifuge tube, followed by salting-out and partitioning with n-hexane. Co-extractives were removed by means of mini-column clean up. Analysis was performed by GC/MS and LC/MS/MS. The prepared sample solutions were examined for matrix effects. Matrix effects had both positive and negative effects on quantitative value. Calibration was achieved by preparing matrix-matched calibration standards to counteract the matrix effects. Of the 235 pesticides spiked at 0.05 or 0.10 microg/g (Method GC), 0.025 or 0.05 microg/g (Method LC) into 8 foods (garlic paste, diced green sweet pepper, green peas paste, celery paste, sweet potato paste, dried adzuki beans, boiled bamboo shoots, tomato paste), recoveries of 214 pesticides were between 50 and 100% and the coefficient of variation was below 20%. This method is useful as a multi-residue analysis method for screening of pesticides in foods.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D002849', 'D003296', 'D005504', 'D013058', 'D010573', 'D053719']","['Chromatography, Gas', 'Cooking', 'Food Analysis', 'Mass Spectrometry', 'Pesticide Residues', 'Tandem Mass Spectrometry']",[Simple preprocessing method for multi-determination of 235 pesticide residues in cooked ingredients of foods by GC/MS and LC/MS/MS].,"[None, None, 'Q000379', None, 'Q000032', None]","[None, None, 'methods', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/18633206,2008,0.0,0.0,,chinese , -18624452,"Potentially toxic acrylamide is largely derived from heat-induced reactions between the amino group of the free amino acid asparagine and carbonyl groups of glucose and fructose in cereals, potatoes, and other plant-derived foods. This overview surveys and consolidates the following dietary aspects of acrylamide: distribution in food originating from different sources; consumption by diverse populations; reduction of the acrylamide content in the diet; and suppression of adverse effects in vivo. Methods to reduce adverse effects of dietary acrylamide include (a) selecting potato, cereal, and other plant varieties for dietary use that contain low levels of the acrylamide precursors, namely, asparagine and glucose; (b) removing precursors before processing; (c) using the enzyme asparaginase to hydrolyze asparagine to aspartic acid; (d) selecting processing conditions (pH, temperature, time, processing and storage atmosphere) that minimize acrylamide formation; (e) adding food ingredients (acidulants, amino acids, antioxidants, nonreducing carbohydrates, chitosan, garlic compounds, protein hydrolysates, proteins, metal salts) that have been reported to prevent acrylamide formation; (f) removing/trapping acrylamide after it is formed with the aid of chromatography, evaporation, polymerization, or reaction with other food ingredients; and (g) reducing in vivo toxicity. Research needs are suggested that may further facilitate reducing the acrylamide burden of the diet. Researchers are challenged to (a) apply the available methods and to minimize the acrylamide content of the diet without adversely affecting the nutritional quality, safety, and sensory attributes, including color and flavor, while maintaining consumer acceptance; and (b) educate commercial and home food processors and the public about available approaches to mitigating undesirable effects of dietary acrylamide.",Journal of agricultural and food chemistry,"['D020106', 'D000293', 'D000328', 'D000368', 'D000369', 'D001215', 'D001216', 'D002648', 'D002675', 'D004032', 'D002523', 'D005260', 'D005504', 'D005511', 'D005947', 'D006358', 'D006801', 'D007223', 'D008297', 'D008875', 'D011198']","['Acrylamide', 'Adolescent', 'Adult', 'Aged', 'Aged, 80 and over', 'Asparaginase', 'Asparagine', 'Child', 'Child, Preschool', 'Diet', 'Edible Grain', 'Female', 'Food Analysis', 'Food Handling', 'Glucose', 'Hot Temperature', 'Humans', 'Infant', 'Male', 'Middle Aged', 'Solanum tuberosum']",Review of methods for the reduction of dietary content and toxicity of acrylamide.,"['Q000008', None, None, None, None, None, 'Q000737', None, None, None, 'Q000737', None, None, 'Q000379', 'Q000737', None, None, None, None, None, 'Q000737']","['administration & dosage', None, None, None, None, None, 'chemistry', None, None, None, 'chemistry', None, None, 'methods', 'chemistry', None, None, None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/18624452,2008,0.0,0.0,,relevant just not garlic, -18616471,"Sodium 2-propenyl thiosulfate, a water-soluble organo-sulfane sulfur compound isolated from garlic, induces apoptosis in a number of cancer cells. The molecular mechanism of action of sodium 2-propenyl thiosulfate has not been completely clarified. In this work we investigated, by in vivo and in vitro experiments, the effects of this compound on the expression and activity of rhodanese. Rhodanese is a protein belonging to a family of enzymes widely present in all phyla and reputed to play a number of distinct biological roles, such as cyanide detoxification, regeneration of iron-sulfur clusters and metabolism of sulfur sulfane compounds. The cytotoxic effects of sodium 2-propenyl thiosulfate on HuT 78 cells were evaluated by flow cytometry and DNA fragmentation and by monitoring the progressive formation of mobile lipids by NMR spectroscopy. Sodium 2-propenyl thiosulfate was also found to induce inhibition of the sulfurtransferase activity in tumor cells. Interestingly, in vitro experiments using fluorescence spectroscopy, kinetic studies and MS analysis showed that sodium 2-propenyl thiosulfate was able to bind the sulfur-free form of the rhodanese, inhibiting its thiosulfate:cyanide-sulfurtransferase activity by thiolation of the catalytic cysteine. The activity of the enzyme was restored by thioredoxin in a concentration-dependent and time-dependent manner. Our results suggest an important involvement of the essential thioredoxin-thioredoxin reductase system in cancer cell cytotoxicity by organo-sulfane sulfur compounds and highlight the correlation between apoptosis induced by these compounds and the damage to the mitochondrial enzymes involved in the repair of the Fe-S cluster and in the detoxification system.",The FEBS journal,"['D000498', 'D017209', 'D002384', 'D002453', 'D002460', 'D049109', 'D006868', 'D008055', 'D009682', 'D013050', 'D013463', 'D013879', 'D013884']","['Allyl Compounds', 'Apoptosis', 'Catalysis', 'Cell Cycle', 'Cell Line', 'Cell Proliferation', 'Hydrolysis', 'Lipids', 'Magnetic Resonance Spectroscopy', 'Spectrometry, Fluorescence', 'Sulfuric Acid Esters', 'Thioredoxins', 'Thiosulfate Sulfurtransferase']",Rhodanese-thioredoxin system and allyl sulfur compounds.,"['Q000494', 'Q000187', None, None, None, None, None, 'Q000096', None, None, 'Q000494', 'Q000378', 'Q000378']","['pharmacology', 'drug effects', None, None, None, None, None, 'biosynthesis', None, None, 'pharmacology', 'metabolism', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/18616471,2008,0.0,0.0,,, -18585988,"Active oxygen species from the photocatalytic reaction in aqueous solution react with luminol to emit strong chemiluminescence (CL), and this can be inhibited by the UV decomposed-products of selenocystine (SeCys) or selenomethionine (SeMet). Based on this phenomenon, a novel hyphenated technique, HPLC-UV/nano-TiO(2)-CL, was established for the determination of SeCys and SeMet. The effects of pH, the UV irradiation time, the TiO(2) coated on the inner surface of the reaction tubing, and the Co(2+) catalyst concentration on the CL intensity and/or chromatographic resolution were systematically investigated. Under these optimized conditions, the inhibited CL intensity has a good linear relationship with the concentration of SeCys in the range of 0.04-10.6 microg mL(-1) or SeMet in the range of 0.05-12.4 microg mL(-1), with a limit of detection (S/N=3) of 6.4 microg L(-1) for SeCys or 12 microg L(-1) for SeMet. As an example, the method was preliminarily applied to the determination of the selenoamino acids in garlic and rabbit serum, with a recovery of 88-104%.","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000818', 'D002851', 'D003553', 'D005737', 'D008163', 'D016566', 'D010777', 'D011817', 'D012645', 'D014025', 'D014466']","['Animals', 'Chromatography, High Pressure Liquid', 'Cystine', 'Garlic', 'Luminescent Measurements', 'Organoselenium Compounds', 'Photochemistry', 'Rabbits', 'Selenomethionine', 'Titanium', 'Ultraviolet Rays']",A novel HPLC-UV/nano-TiO2-chemiluminescence system for the determination of selenocystine and selenomethionine.,"[None, 'Q000379', 'Q000031', 'Q000737', 'Q000379', 'Q000032', None, None, 'Q000032', None, None]","[None, 'methods', 'analogs & derivatives', 'chemistry', 'methods', 'analysis', None, None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18585988,2008,1.0,1.0,,, -18581860,"A reversed-phase high performance liquid chromatographic method with pre-column derivatization for the determination of alliin and its related substances, which are the precursors of garlic's active components, was established. Alliin was derivatized with 6-aminoquinolyl-N-hydroxysuccinimicly carbamate (AQC). The reaction of derivatization was very fast and the derivative was stable. The analysis was carried out on a Kromasil C18 column (250 mm x 4.6 mm, 5 microm) with a gradientelution and detection at 248 nm. The mobile ph ase consisted of 0.1% acetamide (0.03% acetic acid) (A) and the mixture of water and acetonitrile (40: 60, v/v) (B), and the flow rate was set at 1.0 mL/min. The linear calibration was found for alliin within the range of 1.171 9 -1 500 microg/mL (r = 0.999 8). The inter-day and intra-day precision were good with relative standard deviation (RSD) less than 1.8% (n = 5). The recovery was 99.1% with the RSD of 1.9%. The limit of detection was 0.15 microg/mL. The method established is accurate, simple and rapid and suitable for the determination of alliin and related substances.",Se pu = Chinese journal of chromatography,"['D002851', 'D056148', 'D003545', 'D005737', 'D006863', 'D057230', 'D016014', 'D015203']","['Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Cysteine', 'Garlic', 'Hydrogen-Ion Concentration', 'Limit of Detection', 'Linear Models', 'Reproducibility of Results']",[Determination of alliin and its related substances in garlic using pre-column derivatization and reversed-phase high performance liquid chromatography].,"['Q000379', 'Q000379', 'Q000031', 'Q000737', None, None, None, None]","['methods', 'methods', 'analogs & derivatives', 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18581860,2010,,,,, -18565914,"Lactoperoxidase (LP) exerts antimicrobial effects in combination with H(2)O(2) and either thiocyanate (SCN(-)) or a halide (e.g., I(-)). Garlic extract in the presence of ethanol has also been used to activate the LP system. This study aimed to determine the effects of 3 LP activation systems (LP+SCN(-)+H(2)O(2); LP+I(-)+H(2)O(2); LP + garlic extract + ethanol) on the growth and activity of 3 test organisms (Staphylococcus aureus, Pseudomonas aeruginosa, and Bacillus cereus). Sterilized milk was used as the reaction medium, and the growth pattern of the organisms and a range of keeping quality (KQ) indicators (pH, titratable acidity, ethanol stability, clot on boiling) were monitored during storage at the respective optimum growth temperature for each organism. The LP+I(-)+ H(2)O(2) system reduced bacterial counts below the detection limit shortly after treatment for all 3 organisms, and no bacteria could be detected for the duration of the experiment (35 to 55 h). The KQ data confirmed that the milk remained unspoiled at the end of the experiments. The LP + garlic extract + ethanol system, on the other hand, had no effect on the growth or KQ with P. aeruginosa, but showed a small retardation of growth of the other 2 organisms, accompanied by small increases (5 to 10 h) in KQ. The effects of the LP+SCN(-)+H(2)O(2) system were intermediate between those of the other 2 systems and differed between organisms. With P. aeruginosa, the system exerted total inhibition within 10 h of incubation, but the bacteria regained viability after a further 5 h, following a logarithmic growth curve. This was reflected in the KQ indicators, which implied an extension of 15 h. With the other 2 bacterial species, LP+SCN(-)+H(2)O(2) exerted an obvious inhibitory effect, giving a lag phase in the growth curve of 5 to 10 h and KQ extension of 10 to 15 h. When used in combination, I(-) and SCN(-) displayed negative synergy.",Journal of dairy science,"['D000818', 'D001409', 'D002417', 'D002851', 'D015169', 'D004789', 'D004795', 'D000431', 'D005260', 'D005519', 'D005737', 'D006861', 'D007454', 'D007784', 'D008826', 'D008892', 'D010936', 'D011550', 'D013211', 'D013861', 'D013997']","['Animals', 'Bacillus cereus', 'Cattle', 'Chromatography, High Pressure Liquid', 'Colony Count, Microbial', 'Enzyme Activation', 'Enzyme Stability', 'Ethanol', 'Female', 'Food Preservation', 'Garlic', 'Hydrogen Peroxide', 'Iodides', 'Lactoperoxidase', 'Microbial Sensitivity Tests', 'Milk', 'Plant Extracts', 'Pseudomonas aeruginosa', 'Staphylococcus aureus', 'Thiocyanates', 'Time Factors']",Challenge testing the lactoperoxidase system against a range of bacteria using different activation agents.,"[None, 'Q000187', None, None, None, None, None, 'Q000494', None, 'Q000379', 'Q000737', 'Q000494', 'Q000494', 'Q000378', None, 'Q000382', 'Q000494', 'Q000187', 'Q000187', 'Q000494', None]","[None, 'drug effects', None, None, None, None, None, 'pharmacology', None, 'methods', 'chemistry', 'pharmacology', 'pharmacology', 'metabolism', None, 'microbiology', 'pharmacology', 'drug effects', 'drug effects', 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/18565914,2008,0.0,0.0,,no access to PDF, -18494496,"Polish garlic and white and red onions were subjected to blanching, boiling, frying, and microwaving for different periods of time, and then their bioactive compounds (polyphenols, flavonoids, flavanols, anthocyanins, tannins, and ascorbic acid) and antioxidant activities were determined. It was found that blanching and frying and then microwaving of garlic and onions did not decrease significantly the amounts of their bioactive compounds and the level of antioxidant activities ( P > 0.05). The HPLC profiles of free and soluble ester- and glycoside-bound phenolic acids showed that trans-hydroxycinnamic acids (caffeic, p-coumaric, ferulic, and sinapic) were as much as twice higher in garlic than in onions. Quercetin quantity was the highest in red onion among the studied vegetables. The electrophoretic separation of nonreduced garlic and onion proteins after boiling demonstrated their degradation in the range from 50 to 112 kDa.",Journal of agricultural and food chemistry,"['D000975', 'D002851', 'D003373', 'D005419', 'D005511', 'D005737', 'D006358', 'D019697', 'D010636', 'D059808', 'D011794']","['Antioxidants', 'Chromatography, High Pressure Liquid', 'Coumaric Acids', 'Flavonoids', 'Food Handling', 'Garlic', 'Hot Temperature', 'Onions', 'Phenols', 'Polyphenols', 'Quercetin']",Comparison of the main bioactive compounds and antioxidant activities in garlic and white and red onions after treatment protocols.,"['Q000032', None, 'Q000032', 'Q000032', 'Q000379', 'Q000737', None, 'Q000737', 'Q000032', None, 'Q000032']","['analysis', None, 'analysis', 'analysis', 'methods', 'chemistry', None, 'chemistry', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18494496,2008,1.0,1.0,,, -18388403,"A determination method for individual natural vitamin B(6) compounds was developed. The vitamin B(6) compounds were specifically converted into 4-pyridoxolactone (PAL), a highly fluorescent compound, through a combination of enzymatic reactions and HCl-hydrolysis. PAL was then determined by HPLC. Pyridoxal was completely oxidized to PAL with pyridoxal 4-dehydrogenase (PLDH). Pyridoxine and pyridoxamine were totally converted into PAL through a coupling reaction involving pyridoxine 4-oxidase and PLDH, and one involving pyridoxamine-pyruvate aminotransferase and PLDH, respectively. The 5'-phosphate forms and pyridoxine-beta-glucoside were hydrolyzed with HCl, and then determined as their free forms. Pyridoxine 5'-phosphate and pyridoxine-beta-glucoside were not separately determined here. Three food samples were analyzed by this method.",Journal of nutritional science and vitaminology,"['D000429', 'D000818', 'D002212', 'D002645', 'D002851', 'D005504', 'D005737', 'D005960', 'D006851', 'D006868', 'D011735', 'D011736', 'D013997', 'D000637', 'D025101', 'D014803']","['Alcohol Oxidoreductases', 'Animals', 'Capsicum', 'Chickens', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Garlic', 'Glucosides', 'Hydrochloric Acid', 'Hydrolysis', 'Pyridoxic Acid', 'Pyridoxine', 'Time Factors', 'Transaminases', 'Vitamin B 6', 'Vitamin B Complex']",Determination of individual vitamin B(6) compounds based on enzymatic conversion to 4-pyridoxolactone.,"['Q000737', None, None, None, 'Q000379', 'Q000379', None, 'Q000032', 'Q000737', None, 'Q000031', 'Q000031', None, 'Q000737', 'Q000032', 'Q000032']","['chemistry', None, None, None, 'methods', 'methods', None, 'analysis', 'chemistry', None, 'analogs & derivatives', 'analogs & derivatives', None, 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18388403,2008,1.0,1.0,,, -18348048,"Nineteen samples of food in glass jars with twist closures were collected by the national food inspectors at Danish food producers and a few importers, focusing on fatty food, such as vegetables in oil, herring in dressing or pickle, soft spreadable cheese, cream, dressings, peanut butter, sauces and infant food. The composition of the plasticizers in the gaskets was analysed by gas chromatography with flame ionization detection (GC-FID) and gas chromatography-mass spectrometry (GC-MS). Epoxidized soybean oil (ESBO) and phthalates were determined in the homogenized food samples. ESBO was the principal plasticizer in five of the gaskets; in 14 it was phthalates. ESBO was found in seven of the food samples at concentrations from 6 to 100 mg kg(-1). The highest levels (91-100 mg kg(-1)) were in oily foods such as garlic, chilli or olives in oil. Phthalates, i.e. di-iso-decylphthalate (DIDP) and di-iso-nonylphthalates (DINP), were found in seven samples at 6-173 mg kg(-1). The highest concentrations (99-173 mg kg(-1)) were in products of garlic and tomatoes in oil and in fatty food products such as sauce béarnaise and peanut butter. For five of the samples the overall migration from unused lids to the official fatty food simulant olive oil was determined and compared with the legal limit of 60 mg kg(-1). The results ranged from 76 to 519 mg kg(-1) and as a consequence the products were withdrawn from the market.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D002849', 'D003718', 'D004041', 'D004852', 'D005504', 'D005506', 'D018857', 'D006801', 'D010968', 'D011143', 'D013024']","['Chromatography, Gas', 'Denmark', 'Dietary Fats', 'Epoxy Compounds', 'Food Analysis', 'Food Contamination', 'Food Packaging', 'Humans', 'Plasticizers', 'Polyvinyl Chloride', 'Soybean Oil']",Migration of epoxidized soybean oil (ESBO) and phthalates from twist closures into food and enforcement of the overall migration limit.,"['Q000379', None, 'Q000032', 'Q000032', 'Q000379', 'Q000032', None, None, 'Q000737', 'Q000737', 'Q000032']","['methods', None, 'analysis', 'analysis', 'methods', 'analysis', None, None, 'chemistry', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18348048,2008,0.0,0.0,,, -18320173,"A method for the accurate determination of ultratrace selenium species of relevance to cancer research, such as gamma-glutamyl-Se-methylselenocysteine (gamma-glutamyl-SeMC), using species-specific double isotope dilution analysis (IDA) with HPLC-ICP-MS is reported for the first time. The (77)Se-enriched gamma-glutamyl-SeMC spike was produced in-house by collecting the fraction at the retention time of the gamma-glutamyl-SeMC peak from a chromatographed aqueous extract of (77)Se-enriched yeast, pooling the collected fractions and freeze-drying the homogenate. The Se content of this spike was characterised using reverse isotope dilution mass spectrometry (IDMS) and the isotopic composition of this spike was checked prior to quantification of the natural abundance dipeptide species in garlic using speciated IDA. The extraction of the gamma-glutamyl-SeMC species in water was performed in a sonication bath for 2 h after adding an appropriate quantity of (77)Se-enriched gamma-glutamyl-SeMC to 50 mg of garlic to give optimal (78)Se/(77)Se and (82)Se/(77)Se ratios of 1.5 and 0.6, respectively. The effect of ultrasonic nebulisation, in comparison with the loading of the ICP with carbon (through the addition of methane gas on-line), on the detection of Se associated with gamma-glutamyl-SeMC using collision/reaction cell ICP-MS with hydrogen as collision gas was investigated. Sensitivity enhancements of approximately fourfold and twofold were achieved using USN and methane mixed plasma, respectively, in comparison with conventional nebulisation and conventional Ar ICP-MS. However, an approximately twofold improvement in the detection limit was achieved using both approaches (42 ng kg(-1) for (78)Se using peak height measurements). The use of species-specific IDMS enabled quantification of the dipeptide species at ng g(-1) levels (603 ng g(-1) Se) in the complex food matrix with a relative standard deviation (RSD, n = 4) of 4.5%, which was approximately half that obtained using standard addition as a confirmatory technique. Furthermore, good agreement was found between the gamma-glutamyl-SeMC species concentrations obtained using both calibration methods.",Analytical and bioanalytical chemistry,"['D002244', 'D002851', 'D003545', 'D005737', 'D007554', 'D013058', 'D016566', 'D012643', 'D012680', 'D013997']","['Carbon', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Isotopes', 'Mass Spectrometry', 'Organoselenium Compounds', 'Selenium', 'Sensitivity and Specificity', 'Time Factors']",Isotope dilution quantification of ultratrace gamma-glutamyl-Se-methylselenocysteine species using HPLC with enhanced ICP-MS detection by ultrasonic nebulisation or carbon-loaded plasma.,"['Q000737', 'Q000379', 'Q000031', 'Q000737', None, 'Q000379', 'Q000032', 'Q000032', None, None]","['chemistry', 'methods', 'analogs & derivatives', 'chemistry', None, 'methods', 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18320173,2008,0.0,0.0,,spiked samples, -18269349,"Peroxidase POX(1) isoenzyme was purified from garlic (Allium sativum L.) bulb by ammonium sulfate precipitation, gel filtration and anion-exchange chromatography. Native-PAGE profile showed two isoforms, designated POX(1A) and POX(1B). POX(1B) seems to be more attractive for biosensor design since its K(m) (app) for H(2)O(2) is lower than that of POX(1A). In addition to its storage and operational stability, POX(1B) was found to be highly heat-stable, since almost 70% of its activity was conserved at 60 degrees C, whereas full activity was retained at 50 and 40 degrees C for 40 min. The optimal pH was approx. 5 and the optimal temperature was 30 degrees C. Next, gelatin was used as a matrix for enzyme immobilization on a gold electrode surface and electrochemical measurements were performed by using cyclic voltammetry. POX(1B)-based electrodes show great potential for application in H(2)O(2) monitoring of biological samples.",Biotechnology and applied biochemistry,"['D000645', 'D015374', 'D011232', 'D002850', 'D002852', 'D004563', 'D004566', 'D004795', 'D004800', 'D005737', 'D005780', 'D006046', 'D006861', 'D006863', 'D007700', 'D009195', 'D020033', 'D013696']","['Ammonium Sulfate', 'Biosensing Techniques', 'Chemical Precipitation', 'Chromatography, Gel', 'Chromatography, Ion Exchange', 'Electrochemistry', 'Electrodes', 'Enzyme Stability', 'Enzymes, Immobilized', 'Garlic', 'Gelatin', 'Gold', 'Hydrogen Peroxide', 'Hydrogen-Ion Concentration', 'Kinetics', 'Peroxidase', 'Protein Isoforms', 'Temperature']",A new peroxidase from garlic (Allium sativum) bulb: its use in H2O2 biosensing.,"['Q000737', 'Q000379', None, 'Q000379', 'Q000379', None, None, None, None, 'Q000201', 'Q000494', None, 'Q000032', None, None, 'Q000737', 'Q000737', None]","['chemistry', 'methods', None, 'methods', 'methods', None, None, None, None, 'enzymology', 'pharmacology', None, 'analysis', None, None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/18269349,2008,0.0,0.0,,, -18246504,"In this study, an enzyme-linked immunosorbent assay (ELISA) was optimized and applied to the determination of endosulfan residues in 20 different kinds of food commodities including vegetables, dry fruits, tea and meat. The limit of detection (IC(15)) was 0.8 microg kg(-1) and the sensitivity (IC(50)) was 5.3 microg kg(-1). Three simple extraction methods were developed, including shaking on the rotary shaker at 250 r min(-1) overnight, shaking on the rotary shaker for 1 h and thoroughly mixing for 2 min. Methanol was used as the extraction solvent in this study. The extracts were diluted in 0.5% fish skin gelatin (FG) in phosphate-buffered saline (PBS) at various dilutions in order to remove the matrix interference. For cabbage (purple and green), asparagus, Japanese green, Chinese cabbage, scallion, garland chrysanthemum, spinach and garlic, the extracts were diluted 10-fold; for carrots and tea, the extracts were diluted 15-fold and 900-fold, respectively. The extracts of celery, adzuki beans and chestnuts, were diluted 20-fold to avoid the matrix interference; ginger, vegetable soybean and peanut extracts were diluted 100-fold; mutton and chicken extracts were diluted 10-fold and for eel, the dilution was 40-fold. Average recoveries were 63.13-125.61%. Validation was conducted by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). The results of this study will be useful to the wide application of an ELISA for the rapid determination of pesticides in food samples.","Journal of environmental science and health. Part. B, Pesticides, food contaminants, and agricultural wastes","['D002849', 'D003257', 'D018556', 'D004726', 'D004797', 'D005506', 'D008401', 'D006801', 'D007306', 'D010573', 'D012680', 'D014675']","['Chromatography, Gas', 'Consumer Product Safety', 'Crops, Agricultural', 'Endosulfan', 'Enzyme-Linked Immunosorbent Assay', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Insecticides', 'Pesticide Residues', 'Sensitivity and Specificity', 'Vegetables']",Optimization and validation of enzyme-linked immunosorbent assay for the determination of endosulfan residues in food samples.,"['Q000379', None, 'Q000737', 'Q000032', 'Q000379', 'Q000032', 'Q000379', None, 'Q000032', 'Q000032', None, 'Q000737']","['methods', None, 'chemistry', 'analysis', 'methods', 'analysis', 'methods', None, 'analysis', 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/18246504,2008,,,,, -18221910,"Superoxide dismutase (SOD) can enhance the characteristic fluorescence of europium in europium (Eu(3+))-tetracycline (TC) system. According to this, a new spectrofluorimetric determination of SOD was developed. Under the optimum conditions, Eu(3+)-TC formed a ternary complex in close proximity with SOD and then intra-molecular energy transfer from TC-SOD complex to Eu(3+), which resulted in the enhancement of characteristic peak of Eu(3+) at 612 nm. The enhanced fluorescence intensity is in proportion to the concentration of SOD, and the linear range was 0.0553-38.71 microg mL(-1) with the limit of detection of 5.53 ng mL(-1). The developed method was practical, simple, sensitive and relatively free from interference coexisting substances and has been successfully applied to the determination of SOD in the plant and blood samples. The mechanism of fluorescence enhancement between Eu(3+)-TC complex and SOD was also studied.","Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy","['D000818', 'D005063', 'D005737', 'D006801', 'D007202', 'D015335', 'D013050', 'D013482', 'D013552', 'D013752', 'D013997']","['Animals', 'Europium', 'Garlic', 'Humans', 'Indicators and Reagents', 'Molecular Probes', 'Spectrometry, Fluorescence', 'Superoxide Dismutase', 'Swine', 'Tetracycline', 'Time Factors']",Spectrofluorimetric determination of superoxide dismutase using a Europium-tetracycline probe.,"[None, 'Q000378', 'Q000201', None, None, 'Q000378', None, 'Q000032', None, 'Q000378', None]","[None, 'metabolism', 'enzymology', None, None, 'metabolism', None, 'analysis', None, 'metabolism', None]",https://www.ncbi.nlm.nih.gov/pubmed/18221910,2008,0.0,0.0,,relevant just not for garlic, -18207414,"A dual function protein was isolated from Allium sativum bulbs and was characterized. The protein had a molecular mass of 25-26 kDa under non-reducing conditions, whereas two polypeptide chains of 12.5+/-0.5 kDa were observed under reducing conditions. E-64 and leupeptin inhibited the proteolytic activity of the protein, which exhibited characteristics similar to cysteine peptidase. The enzyme exhibited substrate specificity and hydrolyzed natural substrates such as alpha-casein (K(m): 23.0 microM), azocasein, haemoglobin and gelatin. It also showed a high affinity for synthetic peptides such as Cbz-Ala-Arg-Arg-OMe-beta-Nam (K(m): 55.24 microM, k(cat): 0.92 s(-1)). The cysteine peptidase activity showed a remarkable stability after incubation at moderate temperatures (40-50 degrees C) over a pH range of 5.5-6.5. The N-terminus of the protein displayed a 100% sequence similarity to the sequences of a mannose-binding lectin isolated from garlic bulbs. Moreover, the purified protein was retained in the chromatographic column when Con-A Sepharose affinity chromatography was performed and the protein was able to agglutinate trypsin-treated rabbit red cells. Therefore, our results indicate the presence of an additional cysteine peptidase activity on a lectin previously described.",Plant physiology and biochemistry : PPB,"['D000818', 'D002364', 'D003546', 'D004912', 'D005737', 'D005780', 'D006386', 'D006388', 'D006454', 'D010940', 'D011817', 'D017386', 'D013379']","['Animals', 'Caseins', 'Cysteine Endopeptidases', 'Erythrocytes', 'Garlic', 'Gelatin', 'Hemagglutination Tests', 'Hemagglutinins', 'Hemoglobins', 'Plant Proteins', 'Rabbits', 'Sequence Homology, Amino Acid', 'Substrate Specificity']",Isolation and characterization of a dual function protein from Allium sativum bulbs which exhibits proteolytic and hemagglutinating activities.,"[None, 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', None, None, None]","[None, 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18207414,2008,0.0,0.0,,, -18038130,"Two extracellular enzymes (MsP1 and MsP2) capable of efficient beta-carotene degradation were purified from culture supernatants of the basidiomycete Marasmius scorodonius (garlic mushroom). Under native conditions, the enzymes exhibited molecular masses of approximately 150 and approximately 120 kDa, respectively. SDS-PAGE and mass spectrometric data suggested a composition of two identical subunits for both enzymes. Biochemical characterisation of the purified proteins showed isoelectric points of 3.7 and 3.5, and the presence of heme groups in the active enzymes. Partial amino acid sequences were derived from N-terminal Edman degradation and from mass spectrometric ab initio sequencing of internal peptides. cDNAs of 1,604 to 1,923 bp, containing open reading frames (ORF) of 508 to 513 amino acids, respectively, were cloned from a cDNA library of M. scorodonius. These data suggest glycosylation degrees of approximately 23% for MsP1 and 8% for MsP2. Databank homology searches revealed sequence homologies of MsP1 and MsP2 to unusual peroxidases of the fungi Thanatephorus cucumeris (DyP) and Termitomyces albuminosus (TAP).",Applied microbiology and biotechnology,"['D000363', 'D000595', 'D003001', 'D018076', 'D005656', 'D016681', 'D007526', 'D013058', 'D008969', 'D008970', 'D010544', 'D016415', 'D017386', 'D019207']","['Agaricales', 'Amino Acid Sequence', 'Cloning, Molecular', 'DNA, Complementary', 'Fungal Proteins', 'Genome, Fungal', 'Isoelectric Point', 'Mass Spectrometry', 'Molecular Sequence Data', 'Molecular Weight', 'Peroxidases', 'Sequence Alignment', 'Sequence Homology, Amino Acid', 'beta Carotene']",Novel peroxidases of Marasmius scorodonius degrade beta-carotene.,"['Q000201', None, None, 'Q000235', 'Q000737', None, None, None, None, None, 'Q000737', None, None, 'Q000378']","['enzymology', None, None, 'genetics', 'chemistry', None, None, None, None, None, 'chemistry', None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/18038130,2008,0.0,0.0,,, -18025602,"Crude garlic extract contains one Mn-superoxide dismutase designated as SOD1 and two Cu,Zn superoxide dismutases as SOD2 and SOD3. The major isoform SOD2 was purified to homogeneity by Sephacryl S200-HR gel filtration, DEAE Sepharose ion exchange chromatography, and chromatofocusing using PBE 94. SOD2 was purified 82-fold with a specific activity of 4,960 U/mg protein. This enzyme was stable in a broad pH range from 5.0 to 10.0 and at various temperatures from 25 to 60 degrees C. The native molecular mass of SOD2 estimated by high performance liquid chromatography on TSK gel G2000SW column was 39 kDa. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis showed a single band near 18 kDa, suggesting that native enzyme was homodimeric. The isoelectric point as determined by chromatofocusing was 5. Analysis of its N terminal amino acid sequence revealed high sequence homology with several other cytosolic Cu,Zn-SODs from plants. Exposure of cancer cell lines to garlic Cu,Zn-SOD2 led to a significant decrease in superoxide content with a concomitant rise in intracellular peroxides, indicating that the enzyme is active in mammalian cells and could, therefore, be used in pharmacological applications.",Applied biochemistry and biotechnology,"['D000818', 'D000975', 'D045744', 'D002850', 'D002851', 'D004591', 'D005737', 'D006863', 'D007527', 'D051379', 'D018384', 'D013482', 'D013481', 'D013696']","['Animals', 'Antioxidants', 'Cell Line, Tumor', 'Chromatography, Gel', 'Chromatography, High Pressure Liquid', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Hydrogen-Ion Concentration', 'Isoenzymes', 'Mice', 'Oxidative Stress', 'Superoxide Dismutase', 'Superoxides', 'Temperature']","Purification and characterization of a Cu,Zn-SOD from garlic (Allium sativum L.). Antioxidant effect on tumoral cell lines.","[None, 'Q000737', None, None, None, None, 'Q000201', None, 'Q000737', None, 'Q000187', 'Q000737', 'Q000037', None]","[None, 'chemistry', None, None, None, None, 'enzymology', None, 'chemistry', None, 'drug effects', 'chemistry', 'antagonists & inhibitors', None]",https://www.ncbi.nlm.nih.gov/pubmed/18025602,2008,,,,, -17951430,"The consumption of garlic is inversely correlated with the progression of cardiovascular disease, although the responsible mechanisms remain unclear. Here we show that human RBCs convert garlic-derived organic polysulfides into hydrogen sulfide (H(2)S), an endogenous cardioprotective vascular cell signaling molecule. This H(2)S production, measured in real time by a novel polarographic H(2)S sensor, is supported by glucose-maintained cytosolic glutathione levels and is to a large extent reliant on reduced thiols in or on the RBC membrane. H(2)S production from organic polysulfides is facilitated by allyl substituents and by increasing numbers of tethering sulfur atoms. Allyl-substituted polysulfides undergo nucleophilic substitution at the alpha carbon of the allyl substituent, thereby forming a hydropolysulfide (RS(n)H), a key intermediate during the formation of H(2)S. Organic polysulfides (R-S(n)-R'; n > 2) also undergo nucleophilic substitution at a sulfur atom, yielding RS(n)H and H(2)S. Intact aorta rings, under physiologically relevant oxygen levels, also metabolize garlic-derived organic polysulfides to liberate H(2)S. The vasoactivity of garlic compounds is synchronous with H(2)S production, and their potency to mediate relaxation increases with H(2)S yield, strongly supporting our hypothesis that H(2)S mediates the vasoactivity of garlic. Our results also suggest that the capacity to produce H(2)S can be used to standardize garlic dietary supplements.",Proceedings of the National Academy of Sciences of the United States of America,"['D000111', 'D002851', 'D004563', 'D004912', 'D005737', 'D005978', 'D019803', 'D006801', 'D006862']","['Acetylcysteine', 'Chromatography, High Pressure Liquid', 'Electrochemistry', 'Erythrocytes', 'Garlic', 'Glutathione', 'Glutathione Disulfide', 'Humans', 'Hydrogen Sulfide']",Hydrogen sulfide mediates the vasoactivity of garlic.,"['Q000494', None, None, 'Q000187', 'Q000737', 'Q000097', 'Q000097', None, 'Q000097']","['pharmacology', None, None, 'drug effects', 'chemistry', 'blood', 'blood', None, 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/17951430,2008,0.0,0.0,,, -17916975,"Analysis and distribution of Pb and Cd in different mice organs including liver, kidney, spleen, heart and blood were evaluated after treatment with different aqueous concentrations of garlic (12.5-100 mg/l). Atomic absorption spectrometry (AAS) was used for analysis of Pb and Cd in these organs. Treatment of Cd-Pb exposed mice with garlic (12.5-100 mg/l) reduced Pb concentrations by 44.65, 42.61, 38.4, 47.56, and 66.62% in liver, kidney, heart, spleen and blood respectively. Moreover, garlic reduced Cd levels by 72.5, 87.7, 92.6, 95.6, and 71.7% in liver, kidney, heart, spleen and blood respectively. The suppressed immune responses in mice pretreated with Cd-Pb mixture were reversed by 48.85, 55.82, 81.4 and 90.7 in the presence of 100, 50, 25, and 12.5 mg/ml of garlic extract.",Biological trace element research,"['D000818', 'D000917', 'D002104', 'D002105', 'D005260', 'D005737', 'D007854', 'D007855', 'D008297', 'D051379', 'D008807', 'D008517', 'D010936', 'D014018']","['Animals', 'Antibody Formation', 'Cadmium', 'Cadmium Poisoning', 'Female', 'Garlic', 'Lead', 'Lead Poisoning', 'Male', 'Mice', 'Mice, Inbred BALB C', 'Phytotherapy', 'Plant Extracts', 'Tissue Distribution']",Garlic (Allium sativum L.) as a potential antidote for cadmium and lead intoxication: cadmium and lead distribution and analysis in different mice organs.,"[None, 'Q000187', 'Q000493', 'Q000188', None, None, 'Q000493', 'Q000188', None, None, None, None, 'Q000627', 'Q000187']","[None, 'drug effects', 'pharmacokinetics', 'drug therapy', None, None, 'pharmacokinetics', 'drug therapy', None, None, None, None, 'therapeutic use', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/17916975,2007,0.0,0.0,,, -17874834,"The deodorizing effect of the mushroom (Agaricus bisporus) extract on the malodor produced after garlic consumption was investigated using an electronic sensor and sensory evaluation measurements. Comparative gas chromatography analysis revealed that the quantity of methane- and allylthiols that were usually found after garlic solution rinse, significantly fell after mushroom extract rinsing. Furthermore, in-vitro analysis (mixing the garlic solution and mushroom extract) showed that the methanethiol reaction with the mushroom extract proceeded faster than that of the allylthiol. Ab initio calculations implicated an addition reaction as the possible mechanism between the thiol compounds and the polyphenols. In comparison to the methanethiol, the higher activation energy required by allylthiol for a feasible reaction path way with the model acceptor, o-quinone, is expected to contribute to the difference in the rate of the reaction.",Journal of nutritional science and vitaminology,"['D000284', 'D000293', 'D000364', 'D001944', 'D002849', 'D004305', 'D005260', 'D005737', 'D006209', 'D006801', 'D007564', 'D008697', 'D009812', 'D008517', 'D010936', 'D025341', 'D013438']","['Administration, Oral', 'Adolescent', 'Agaricus', 'Breath Tests', 'Chromatography, Gas', 'Dose-Response Relationship, Drug', 'Female', 'Garlic', 'Halitosis', 'Humans', 'Japan', 'Methane', 'Odorants', 'Phytotherapy', 'Plant Extracts', 'Principal Component Analysis', 'Sulfhydryl Compounds']",Studies on the deodorization by mushroom (Agaricus bisporus) extract of garlic extract-induced oral malodor.,"[None, None, 'Q000737', None, None, None, None, 'Q000009', 'Q000139', None, None, 'Q000032', 'Q000517', 'Q000379', 'Q000008', None, 'Q000032']","[None, None, 'chemistry', None, None, None, None, 'adverse effects', 'chemically induced', None, None, 'analysis', 'prevention & control', 'methods', 'administration & dosage', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17874834,2007,0.0,0.0,,, -17855182,"Quorum sensing (QS) controls systems affecting the pathogenicity of many microorganisms; its interruption has an anti-pathogenic effect and can be used in the treatment of bacterial infections. In this study we evaluated QS regulation by Pseudomonas aeruginosa strains and QS inhibition (QSI) by different compounds. The inhibitory activity of 3 macrolide and 3 lincosamide drugs, resveratrol, garlic extract and N-acetylcysteine was tested on 4 P. aeruginosa strains isolated from cystic fibrosis (CF) patients using Chromobacterium violaceum ATCC 12472 as biomonitor. One P. aeruginosa strain, lincomycin and N-acetylcysteine did not show QSI, contrary to other compounds and P. aeruginosa strains. These results indicate that QSI evaluation should be taken into account in the design of new therapeutic strategies to treat P. aeruginosa infections, especially in patients infected by antibiotic-resistant bacteria.","Journal of chemotherapy (Florence, Italy)","['D000111', 'D000900', 'D002851', 'D002861', 'D004353', 'D005737', 'D006801', 'D055231', 'D018942', 'D010936', 'D011550', 'D053038', 'D013267']","['Acetylcysteine', 'Anti-Bacterial Agents', 'Chromatography, High Pressure Liquid', 'Chromobacterium', 'Drug Evaluation, Preclinical', 'Garlic', 'Humans', 'Lincosamides', 'Macrolides', 'Plant Extracts', 'Pseudomonas aeruginosa', 'Quorum Sensing', 'Stilbenes']",Evaluation of different compounds as quorum sensing inhibitors in Pseudomonas aeruginosa.,"['Q000494', 'Q000494', None, 'Q000187', None, 'Q000737', None, None, 'Q000494', 'Q000494', 'Q000187', 'Q000187', 'Q000494']","['pharmacology', 'pharmacology', None, 'drug effects', None, 'chemistry', None, None, 'pharmacology', 'pharmacology', 'drug effects', 'drug effects', 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/17855182,2007,0.0,0.0,,, -17763522,"In the present study, an RP high performance liquid chromatographic method was developed and validated for the determination of allicin in garlic powder and tablets. Chromatographic separation was carried out on an RP-18(e )column (125 mm x 4 mm), using a mobile phase, consisting of methanol-water (50:50 v/v), at a flow rate of 0.5 mL/min and UV detection at 220 nm. Ethylparaben was used as the internal standard. The assay was linear for allicin concentrations of 5.0-60.0 microg/mL. The RSD for precision was <6.14%. The accuracy was above 89.11%. The detection and quantification limits were 0.27 and 0.81 microg/mL, respectively. This method was used to quantify allicin in garlic powder samples. The results showed that the method described here is useful for the determination of allicin in garlic powder and tablets.",Journal of separation science,"['D002853', 'D011208', 'D015203', 'D012680', 'D013056', 'D013441', 'D013607']","['Chromatography, Liquid', 'Powders', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Spectrophotometry, Ultraviolet', 'Sulfinic Acids', 'Tablets']",Validated liquid chromatographic method for quantitative determination of allicin in garlic powder and tablets.,"['Q000379', 'Q000737', None, None, None, 'Q000032', 'Q000737']","['methods', 'chemistry', None, None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/17763522,2008,2.0,1.0,,, -17715891,"This study describes the identification of six allyl esters in a garlic cheese preparation and in a commercial cream cheese. The extracts were prepared by liquid/liquid extraction and concentrated by the SAFE process. The identification of the allyl esters of acetic, butyric, hexanoic, heptanoic, octanoic, and decanoic acids is based on the correlation of their mass spectrometric data and chromatographic retention time data obtained from the extracts with those of authentic standards. In addition to the gas chromatography (GC)/mass spectrometry analysis, the flavor ingredients were characterized by GC sniffing by a trained flavorist. Some of the esters were isolated by preparative GC.",Journal of agricultural and food chemistry,"['D002611', 'D004952', 'D005232', 'D005737', 'D008401', 'D020005']","['Cheese', 'Esters', 'Fatty Acids, Volatile', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Propanols']",Identification of allyl esters in garlic cheese.,"['Q000032', 'Q000032', 'Q000032', 'Q000737', None, 'Q000032']","['analysis', 'analysis', 'analysis', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17715891,2007,0.0,0.0,,garlic cheese, -17628043,"A novel plasma-assisted desorption/ionization (PADI) method that can be coupled with atmospheric pressure sampling mass spectrometry to yield mass spectral information under ambient conditions of pressure and humidity from a range of surfaces without the requirement for sample preparation or additives is reported. PADI is carried out by generating a nonthermal plasma which interacts directly with the surface of the analyte. Desorption and ionization then occur at the surface, and ions are sampled by the mass spectrometer. The PADI technique is demonstrated and compared with desorption electrospray ionization (DESI) for the detection of active ingredients in a range of over-the-counter and prescription pharmaceutical formulations, including nonsterodial anti-inflammatory drugs (mefenamic acid, Ibugel, and ibuprofen), analgesics (paracetamol, Anadin Extra), and Beecham's ""all in one"" cold and flu remedy. PADI has also been successfully applied to the analysis of nicotine in tobacco and thiosulfates in garlic. PADI experiments have been performed using a prototype source interfaced with a Waters Platform LCZ single-quadrupole mass spectrometer with limited modifications and a Hiden Analytical HPR-60 molecular beam mass spectrometer (MBMS). The ability of PADI to rapidly detect active ingredients in pharmaceuticals without the need for prior sample preparation, solvents, or exposed high voltages demonstrates the potential of the technique for high-throughput screening in a pharmaceutical or forensic environment.",Analytical chemistry,"['D000700', 'D000894', 'D001274', 'D005737', 'D006813', 'D009538', 'D004364', 'D019032', 'D013499', 'D013885']","['Analgesics', 'Anti-Inflammatory Agents, Non-Steroidal', 'Atmospheric Pressure', 'Garlic', 'Humidity', 'Nicotine', 'Pharmaceutical Preparations', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Surface Properties', 'Thiosulfates']",Surface analysis under ambient conditions using plasma-assisted desorption/ionization mass spectrometry.,"['Q000032', 'Q000032', None, 'Q000737', None, 'Q000032', 'Q000032', None, None, 'Q000032']","['analysis', 'analysis', None, 'chemistry', None, 'analysis', 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17628043,2007,0.0,0.0,,allicin from citation, -17567142,"Aromas generated in extruded potato snacks without and with addition of 0.25, 0.5, and 1% (w/w) of flavor precursors, cysteine and cystine, were compared and evaluated by descriptive sensory profiling. The results showed that high addition of cysteine (0.5 and 1%) resulted in the formation of undesirable odor and taste described as mercaptanic/sulfur, onion-like, and bitter; on the contrary, addition of cystine even at high concentration gave product with pleasant odor and taste, slightly changed into breadlike notes. GC/O analysis showed cysteine to be a much more reactive flavor precursor than cystine, stimulating formation of 12 compounds with garlic, sulfury, burnt, pungent/beer, cabbage/mold, meatlike, roasted, and popcorn odor notes. Further analysis performed by the AEDA technique identified 2-methyl-3-furanthiol (FD 2048) as a most potent odorant of extruded potato snacks with 1% addition of cysteine. Other identified compounds with high FD were butanal, 3-methyl-2-butenethiol, 2-methylthiazole, methional, 2-acetyl-1-pyrroline, and 3-hydroxy-4,5-dimethyl-2(5H)-furanone. In the case of cystine addition (1%) the highest FD factors were calculated for butanal, 2-acetyl-1-pyrroline, benzenemethanethiol, methional, phenylacetaldehyde, dimethyltrisulfide, 1-octen-3-ol, 1,5-octadien-3-one, and 2-acetylpyrazine.",Journal of agricultural and food chemistry,"['D002849', 'D003545', 'D003553', 'D005511', 'D006801', 'D009812', 'D035281', 'D012677', 'D011198']","['Chromatography, Gas', 'Cysteine', 'Cystine', 'Food Handling', 'Humans', 'Odorants', 'Plant Tubers', 'Sensation', 'Solanum tuberosum']",Effect of cysteine and cystine addition on sensory profile and potent odorants of extruded potato snacks.,"[None, 'Q000008', 'Q000008', 'Q000379', None, 'Q000032', 'Q000737', None, 'Q000737']","[None, 'administration & dosage', 'administration & dosage', 'methods', None, 'analysis', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/17567142,2007,0.0,0.0,,, -17520814,"The characteristics of uptake, transepithelial transport and efflux of Z- and E-ajoenes isolated from the bulbs of Allium sativum were studied. A human colon cell model Caco-2 cell monolayers in vitro cultured had been applied to study the characteristics of uptake, transepithelial transport and efflux of Z- and E-ajoenes. The quantitative determination of Z- and E-ajoenes was performed by high-performance liquid chromatography. Z- and E-Ajoenes can be detected only in the apical side and can be metabolized, but both compounds can not be transported from apical-to-basolateral and basolateral-to-apical directions in cultured Caco-2 cell monolayers. The metabolism of Z- and E-ajoenes in Caco-2 cell monolayers can be partially inhibited by vitamin C as an anti-oxidant, metyrapone as an inhibitor to subtype CYP3A of cytochrome P450 drug metabolism enzymes, and sodium azide as an inhibitor to ATP production. It is shown that neither Z-ajoene nor E-ajoene can pass through Caco-2 cell monolayers, and that they can be metabolized by the cells. The metabolism might be in correlation with cytochrome P450 drugs metabolism enzymes in Caco-2 cell monolayers.",Yao xue xue bao = Acta pharmaceutica Sinica,"['D000975', 'D001205', 'D001692', 'D018938', 'D002462', 'D051544', 'D065692', 'D004220', 'D004791', 'D005737', 'D006801', 'D008797', 'D010946', 'D019810', 'D013237']","['Antioxidants', 'Ascorbic Acid', 'Biological Transport', 'Caco-2 Cells', 'Cell Membrane', 'Cytochrome P-450 CYP3A', 'Cytochrome P-450 CYP3A Inhibitors', 'Disulfides', 'Enzyme Inhibitors', 'Garlic', 'Humans', 'Metyrapone', 'Plants, Medicinal', 'Sodium Azide', 'Stereoisomerism']","[Characteristics of uptake, transport and efflux of Z- and E-ajoenes in Caco-2 cell monolayers in vitro].","['Q000494', 'Q000494', 'Q000187', None, 'Q000187', 'Q000378', None, 'Q000737', 'Q000494', 'Q000737', None, 'Q000494', 'Q000737', 'Q000494', None]","['pharmacology', 'pharmacology', 'drug effects', None, 'drug effects', 'metabolism', None, 'chemistry', 'pharmacology', 'chemistry', None, 'pharmacology', 'chemistry', 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/17520814,2008,,,,, -19071484,"The present paper proposes the application of multiwall carbon nanotubes (MWCNTs) as a solid sorbent for lead preconcentration using a flow system coupled to flame atomic absorption spectrometry. The method comprises the preconcentration of Pb (II) ions at a buffered solution (pH 4.7) onto 30mg of MWCNTs previously oxidized with concentrated HNO(3). The elution step is carried out with 1.0molL(-1) HNO(3). The effect of the experimental parameters, including sample pH, sampling flow rate, buffer and eluent concentrations were investigated by means of a 2(4) full factorial design, while for the final optimization a Doehlert design was employed. Under the best experimental conditions the preconcentration system provided detection and quantification limits of 2.6 and 8.6mugL(-1), respectively. A wide linear range varying from 8.6 up to 775mugL(-1) (r>0.999) and the respective precision (relative standard deviation) of 7.7 and 1.4% for the 15 and 200mugL(-1) levels were obtained. The characteristics obtained for the performance of the flow preconcentration system were a preconcentration factor of 44.2, preconcentration efficiency of 11min(-1), consumptive index of 0.45mL and sampling frequency estimated as 14h(-1). Preconcentration studies of Pb (II) ions in the presence of the majority foreign ions tested did not show interference, attesting the good performance of MWCNTs. The accuracy of the method was assessed from analysis of water samples (tap, mineral, physiological serum and synthetic seawater) and common medicinal herbs submitted to the acid decomposition (garlic and Ginkgo Biloba). The satisfactory recovery values obtained without using analyte addition method confirms the feasibility of this method for Pb (II) ions determination in different type of samples.",Talanta,[],[],Solid-phase extraction system for Pb (II) ions enrichment based on multiwall carbon nanotubes coupled on-line to flame atomic absorption spectrometry.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19071484,2012,0.0,0.0,,, -19071460,"A simple solid phase extraction procedure for speciation of selenium(IV) and selenium(VI) in environmental samples has been proposed prior to graphite furnace atomic absorption spectrometry. The method is based on the solid phase extraction of the selenium(IV)-ammonium pyrrolidine dithiocarbamate (APDC) chelate on the Diaion HP-2MG. After reduction of Se(VI) by heating the samples in the microwave oven with 4moll(-1) HCl, the system was applied to the total selenium. Se(VI) was calculated as the difference between the total selenium content and Se(IV) content. The experimental parameters, pH, amounts of reagents, eluent type and sample volume were optimized. The recoveries of analytes were found greater than 95%. No appreciable matrix effects were observed. The adsorption capacity of sorbent was 5.20mgg(-1) Se (IV). The detection limit of Se (IV) (3sigma, n=11) is 0.010mugl(-1). The preconcentration factor for the presented system was 100. The proposed method was applied to the speciation of selenium(IV), selenium(VI) and determination of total selenium in natural waters and microwave digested soil, garlic, onion, rice, wheat and hazelnut samples harvested various locations in Turkey with satisfactory results. In order to verify the accuracy of the method, certified reference materials (NIST SRM 2711 Montana Soil, NIST SRM 1568a Rice Flour and NIST SRM 8418 Wheat Gluten) were analyzed and the results obtained were in good agreement with the certified values. The relative errors and relative standard deviations were below 6 and 10%, respectively.",Talanta,[],[],Speciation of selenium(IV) and selenium(VI) in environmental samples by the combination of graphite furnace atomic absorption spectrometric determination and solid phase extraction on Diaion HP-2MG.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19071460,2012,1.0,1.0,,, -17283653,"A simple, rapid and sensitive method has been developed and validated for the simultaneous quantification of diallyl trisulfide (DATS) and diallyl disulfide (DADS) in rat blood by gas chromatography with electron-capture detection. The analytes were prevented from degradation by addition of acetonitrile and extraction with hexane before gas chromatographic separation. Two calibration curves for DATS were linear over the range of 10-500 ng/mL and 0.2-20 microg/mL, with typical r values of 0.9989 and 0.9993, respectively. Similarly, two calibration curves for DADS were linear in the concentration range of 50-5000 ng/mL and 1-30 microg/mL, with typical r values of 0.9989 and 0.9983, respectively. The limit of detection was less than 10 ng/mL for DATS and 50 ng/mL for DADS, and the assay was highly reproducible, considering the intra-, inter-day relative standard deviations (R.S.D.) below 12%. The developed procedure was successfully applied for the evaluation of the pharmacokinetics of garlic oil following iv administration at a single dose (10 mg) of garlic oil in rats. The results show that the developed method is suitable for pharmacokinetic and therapeutic purposes of DATS and DADS.",Die Pharmazie,"['D000498', 'D000818', 'D002138', 'D002849', 'D004220', 'D004563', 'D009682', 'D051381', 'D017208', 'D012015', 'D013048', 'D013440', 'D013810']","['Allyl Compounds', 'Animals', 'Calibration', 'Chromatography, Gas', 'Disulfides', 'Electrochemistry', 'Magnetic Resonance Spectroscopy', 'Rats', 'Rats, Wistar', 'Reference Standards', 'Specimen Handling', 'Sulfides', 'Therapeutic Equivalency']",Simultaneous determination of diallyl trisulfide and diallyl disulfide in rat blood by gas chromatography with electron-capture detection.,"['Q000097', None, None, None, 'Q000097', None, None, None, None, None, None, 'Q000097', None]","['blood', None, None, None, 'blood', None, None, None, None, None, None, 'blood', None]",https://www.ncbi.nlm.nih.gov/pubmed/17283653,2007,,,,, -17269787,"New, odorant nitrogen- and sulfur-containing compounds are identified in cress extracts. Cress belongs to the botanical order Brassicales and produces glucosinolates, which are important precursors of nitrogen- and sulfur-containing compounds. Those compounds often present low perception thresholds and various olfactive notes and are thus of interest to the flavor and fragrance chemistry. When the study of organonitrogen and organosulfur compounds is undertaken, Brassicale extracts are one of the matrices of choice. Cress extracts were studied by analytical (GC-MS, GC-FPD) and chemical (fractionation) means to identify new interesting odorant compounds. Two compounds that have never been reported in cress extracts, containing both nitrogen and sulfur, were discovered: N-benzyl O-ethyl thiocarbamate and N-phenethyl O-ethyl thiocarbamate. These two molecules being of organoleptic interest, their homologues were synthesized and submitted to organoleptic tests (static and GC-sniffing). Their odors evolve from garlic and onion over green, mushroom- and cress-like to fresh, spearmint-like. This paper presents the origin, chemical synthesis, and organoleptic properties of a series of O-alkyl thiocarbamates.",Journal of agricultural and food chemistry,"['D019607', 'D002849', 'D008401', 'D006801', 'D009584', 'D009812', 'D010936', 'D012903', 'D013329', 'D013455', 'D013859']","['Brassicaceae', 'Chromatography, Gas', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Nitrogen', 'Odorants', 'Plant Extracts', 'Smell', 'Structure-Activity Relationship', 'Sulfur', 'Thiocarbamates']","Identification of new, odor-active thiocarbamates in cress extracts and structure-activity studies on synthesized homologues.","['Q000737', None, None, None, 'Q000032', 'Q000032', 'Q000737', None, None, 'Q000032', 'Q000032']","['chemistry', None, None, None, 'analysis', 'analysis', 'chemistry', None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17269787,2007,0.0,0.0,,, -17266328,"Odor volatiles in three major lychee cultivars (Mauritius, Brewster, and Hak Ip) were examined using gas chromatography-olfactometry, gas chromatography-mass spectrometry, and gas chromatography-pulsed flame photometric detection. Fifty-nine odor-active compounds were observed including 11 peaks, which were associated with sulfur detector responses. Eight sulfur volatiles were identified as follows: hydrogen sulfide, dimethyl sulfide, diethyl disulfide, 2-acetyl-2-thiazoline, 2-methyl thiazole, 2,4-dithiopentane, dimethyl trisulfide, and methional. Mauritius contained 25% and Brewster contained 81% as much total sulfur volatiles as Hak Ip. Cultivars were evaluated using eight odor attributes: floral, honey, green/woody, tropical fruit, peach/apricot, citrus, cabbage, and garlic. Major odor differences in cabbage and garlic attributes correlated with cultivar sulfur volatile composition. The 24 odor volatiles common to all three cultivars were acetaldehyde, ethanol, ethyl-3-methylbutanoate, diethyl disulfide, 2-methyl thiazole, 1-octen-3-one, cis-rose oxide, hexanol, dimethyl trisulfide, alpha-thujone, methional, 2-ethyl hexanol, citronellal, (E)-2-nonenal, linalool, octanol, (E,Z)-2,6-nonadienal, menthol, 2-acetyl-2-thiazoline, (E,E)-2,4-nonadienal, beta-damascenone, 2-phenylethanol, beta-ionone, and 4-vinyl-guaiacol.",Journal of agricultural and food chemistry,"['D002849', 'D005260', 'D005410', 'D005638', 'D008401', 'D006801', 'D032125', 'D008297', 'D009812', 'D012903', 'D013457', 'D014835']","['Chromatography, Gas', 'Female', 'Flame Ionization', 'Fruit', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Litchi', 'Male', 'Odorants', 'Smell', 'Sulfur Compounds', 'Volatilization']",Comparison of three lychee cultivar odor profiles using gas chromatography-olfactometry and gas chromatography-sulfur detection.,"[None, None, None, 'Q000737', None, None, 'Q000737', None, 'Q000032', None, 'Q000032', None]","[None, None, None, 'chemistry', None, None, 'chemistry', None, 'analysis', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/17266328,2007,0.0,0.0,,no quantification, -17177506,"Cycloalliin, an organosulfur compound found in garlic and onion, has been reported to exert several biological activities and also to remain stable during storage and processing. In this study, we investigated the pharmacokinetics of cycloalliin in rats after intravenous or oral administration. Cycloalliin and its metabolite, (3R,5S)-5-methyl-1,4-thiazane-3-carboxylic acid, in plasma, urine, feces, and organs was determined by a validated liquid chromatography-mass spectrometry method. When administered intravenously at 50 mg/kg, cycloalliin was rapidly eliminated from blood and excreted into urine, and its total recovery in urine was 97.8% +/- 1.3% in 48 h. After oral administration, cycloalliin appeared rapidly in plasma, with a tmax of 0.47 +/- 0.03 h at 25 mg/kg and 0.67 +/- 0.14 h at 50 mg/kg. Orally administered cycloalliin was distributed in heart, lung, liver, spleen, and especially kidney. The Cmax and AUC0-inf values of cycloalliin at 50 mg/kg were approximately 5 times those at 25 mg/kg. When administered orally at 50 mg/kg, cycloalliin was excreted into urine (17.6% +/- 4.2%) but not feces. However, the total fecal excretion of (3R,5S)-5-methyl-1,4-thiazane-3-carboxylic acid was 67.3% +/- 5.9% (value corrected for cycloalliin equivalents). In addition, no (3R,5S)-5-methyl-1,4-thiazane-3-carboxylic acid was detected in plasma (<0.1 microg/mL), and negligible amounts (1.0% +/- 0.3%) were excreted into urine. In in vitro experiments, cycloalliin was reduced to (3R,5S)-5-methyl-1,4-thiazane-3-carboxylic acid during anaerobic incubation with cecal contents of rats. These data indicated that the low bioavailability (3.73% and 9.65% at 25 and 50 mg/kg, respectively) of cycloalliin was due mainly to reduction to (3R,5S)-5-methyl-1,4-thiazane-3-carboxylic acid by the intestinal flora and also poor absorption in the upper gastrointestinal tract. These findings are helpful for understanding the biological effects of cycloalliin.",Journal of agricultural and food chemistry,"['D000818', 'D002853', 'D005243', 'D005737', 'D007700', 'D008297', 'D013058', 'D019697', 'D010875', 'D051381', 'D017207']","['Animals', 'Chromatography, Liquid', 'Feces', 'Garlic', 'Kinetics', 'Male', 'Mass Spectrometry', 'Onions', 'Pipecolic Acids', 'Rats', 'Rats, Sprague-Dawley']","Pharmacokinetics of cycloalliin, an organosulfur compound found in garlic and onion, in rats.","[None, None, 'Q000737', 'Q000737', None, None, None, 'Q000737', 'Q000008', None, None]","[None, None, 'chemistry', 'chemistry', None, None, None, 'chemistry', 'administration & dosage', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/17177506,2007,0.0,0.0,,admin to rats, -17039401,"In our previous study [1], we found that relatively short-term spontaneous fermentation (40 days at 60-70 degrees C, 85-95% relative humidity) potentiates anti-oxidative properties of garlic, in which scavenging activity against hydrogen peroxide was included. Since tetrahydro-beta-carboline derivatives (THbetaCs) that possess hydrogen peroxide scavenging activity have recently been identified in aged garlic extract, THbetaCs were quantitatively analyzed with liquid chromatography-mass spectrometry (LC-MS). (1R, 3S)-1-Methyl-1,2,3,4-tetrahydro-beta-carboline-3-carboxylic acid (MTCC) and (1S, 3S)-MTCC were found in the fermented garlic extract whereas only trace levels of MTCCs were detected in the row garlic extract. Therefore, it is suggested that relatively short-term fermentation potentiates scavenging activity of garlic against hydrogen peroxide by forming THbetaCs, especially MTCCs.","Plant foods for human nutrition (Dordrecht, Netherlands)","['D002243', 'D005285', 'D005737']","['Carbolines', 'Fermentation', 'Garlic']",Increased level of tetrahydro-beta-carboline derivatives in short-term fermented garlic.,"['Q000032', None, 'Q000737']","['analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/17039401,2007,0.0,1.0,,, -17017158,"For the analysis of organosulfur compounds in fresh garlic, a gas chromatographic/mass spectrometric (GC/MS) method is proposed using temperature-programmable cold on-column injection and cold solvent extraction of the fresh garlic. This was carried out under the conditions of cryogenic process from extraction to column separation. Hence, a valid identification can be achieved about the primary components in garlic extract before thermo-degradation. The obtained results showed that 3-vinyl-4H-1, 2-dithiin and 2-vinyl-4H-1, 3-dithiin were the major compounds in the garlic extract with minor amounts of S-methyl methanethiosulfinate, diallyl disulfide, trisulfide-di-2-propenyl. A comparative study of chemical compounds was performed between garlic extract by cold solvent and garlic oil by stream distillation. The degradation and formation of major organosulfur compounds in the garlic extract were also explored.",Se pu = Chinese journal of chromatography,"['D000498', 'D003080', 'D004220', 'D005737', 'D008401', 'D010938', 'D013440', 'D013696']","['Allyl Compounds', 'Cold Temperature', 'Disulfides', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Plant Oils', 'Sulfides', 'Temperature']",[Study of organosulfur compounds in fresh garlic by gas chromatography/mass spectrometry incorporated with temperature-programmable cold on-column injection].,"['Q000032', None, 'Q000032', 'Q000737', 'Q000379', 'Q000737', 'Q000302', None]","['analysis', None, 'analysis', 'chemistry', 'methods', 'chemistry', 'isolation & purification', None]",https://www.ncbi.nlm.nih.gov/pubmed/17017158,2010,,,,, -16999975,"Methiin and alliin are important components of flavors or the precursors of flavors and odors of Allium vegetables. Moreover, they are thought to be beneficial to health. A non-derivative method was developed to analyze these compounds in vegetables by capillary electrophoresis. These compounds in the extracts of Allium and Brassica vegetables were detected indirectly at 225 nm. The analysis of each sample required less than 25 min, and the linear detection range was 5-5000 mg/l. This method was simple compared to the other published methods using high performance liquid chromatography. Moreover, it was possible to detect the peak of pyruvate simultaneously with methiin and alliin using this method. The presence of pyruvate peak is a useful indicator if the blanching of the samples has been insufficient.",Journal of chromatography. A,"['D000490', 'D001937', 'D003545', 'D019075', 'D005737', 'D015203', 'D014675']","['Allium', 'Brassica', 'Cysteine', 'Electrophoresis, Capillary', 'Garlic', 'Reproducibility of Results', 'Vegetables']",Non-derivatized analysis of methiin and alliin in vegetables by capillary electrophoresis.,"['Q000737', 'Q000737', 'Q000031', 'Q000379', 'Q000737', None, 'Q000737']","['chemistry', 'chemistry', 'analogs & derivatives', 'methods', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16999975,2006,1.0,1.0,,, -16835880,"The reproductive health of individuals is severely compromised by HIV infection, with candidiasis being the most prevalent oral complication in patients. Although not usually associated with severe morbidity, oropharyngeal candidiasis can be clinically significant, as it can interfere with the administration of medications and adequate nutritional intake, and may spread to the esophagus. Azole antifungal agents are commonly prescribed for the treatment and prophylaxis of candidal infections, however, the emergence of drug resistant strains and dose limiting toxic effects has complicated the treatment of candidiasis. Consequently, safe and effective and affordable medicine is required to combat this fungus. Commercial garlic (Allium sativum) has been used since time immemorial as a natural antibiotic, however, very little is known about the antifungal properties of two indigenous South African species of garlic, namely Tulbaghia alliacea and Tulbaghia violacea, used as folk medicines for a variety of infections. This study compares the in vitro anticandidal activity of Tulbaghia alliacea, Tulbaghia violacea and Allium sativum extracts. It was found that the greatest concentrations of inhibitory components were extracted by chloroform or water. The IC50 concentrations of Tulbaghia alliacea were 0.007-0.038% (w/v). Assays using S. cerevisiae revealed that the T. alliacea extract was fungicidal, with a killing half-life of approximately 2 h. This inhibitory effect of the T. alliacea extracts was observed via TLC, and may be due to an active compound called marasmicin, that was identified using NMR. This investigation confirms that extracts of T. alliacea exhibit anti-infective activity against candida species in vitro.",Phytotherapy research : PTR,"['D000490', 'D000935', 'D002176', 'D002177', 'D002855', 'D004353', 'D005737', 'D008826', 'D019906', 'D008517', 'D010936', 'D012441']","['Allium', 'Antifungal Agents', 'Candida albicans', 'Candidiasis', 'Chromatography, Thin Layer', 'Drug Evaluation, Preclinical', 'Garlic', 'Microbial Sensitivity Tests', 'Nuclear Magnetic Resonance, Biomolecular', 'Phytotherapy', 'Plant Extracts', 'Saccharomyces cerevisiae']",Tulbaghia alliacea phytotherapy: a potential anti-infective remedy for candidiasis.,"['Q000737', 'Q000737', 'Q000187', 'Q000188', None, None, 'Q000737', None, None, None, 'Q000737', 'Q000187']","['chemistry', 'chemistry', 'drug effects', 'drug therapy', None, None, 'chemistry', None, None, None, 'chemistry', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/16835880,2007,0.0,0.0,,, -16804741,"Garlic (Allium sativum) cloves were stored at ambient temperature and 4 degrees C for periods up to six months to establish the effect of position of the individual clove within the bulb and of low storage temperature on the composition of several flavours precursors and other organic sulphur compounds, measured by gradient High Pressure Liquid Chromatography. Levels of alliin, gamma glutamyl allyl cysteine sulphoxide and gamma glutamyl isoallyl cysteine sulphoxide were statistically significantly higher in outer than in inner cloves. There was no statistically significant change in levels of alliin, the major flavour precursor, in cloves stored at 4 degrees C, remaining in the average range 17.5+/-3.8-39.1+/-7.5 mM. However, isoalliin increased significantly during storage at 4 degrees C, rising from an average 0.6+/-0.2 mM (outer cloves) -- 0.7+/-0.4 mM (inner cloves) to 7.1+/-1.7 mM (outer cloves) -- 4.1+/-0.7 mM (inner cloves). A decline in other sulphur-containing compounds, most likely to be the peptides gamma-glutamyl allylcysteine sulphoxide and gamma-glutamyl isoallylcysteine sulphoxide, occurred at the same time and possibly contributed to the increase in the flavour precursor compounds. The degree of chemical changes during storage will be of interest to the food and pharmaceutical industries.","Plant foods for human nutrition (Dordrecht, Netherlands)","['D002851', 'D003545', 'D005511', 'D005519', 'D005737', 'D006801', 'D013457', 'D013649', 'D013696', 'D013997']","['Chromatography, High Pressure Liquid', 'Cysteine', 'Food Handling', 'Food Preservation', 'Garlic', 'Humans', 'Sulfur Compounds', 'Taste', 'Temperature', 'Time Factors']",Effect of low storage temperature on some of the flavour precursors in garlic (Allium sativum).,"[None, 'Q000031', 'Q000379', 'Q000379', 'Q000737', None, 'Q000032', None, None, None]","[None, 'analogs & derivatives', 'methods', 'methods', 'chemistry', None, 'analysis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16804741,2006,1.0,1.0,,, -16787038,"We determined the changes in the contents of three gamma-glutamyl peptides and four sulfoxides in garlic cloves during storage at -3, 4, and 23 degrees C for 150 days using a validated high-performance liquid chromatography method that we reported recently. When garlic was stored at 4 degrees C for 150 days, marked conversion of the gamma-glutamyl peptides, gamma-L-glutamyl-S-allyl-L-cysteine and gamma-L-glutamyl-S-(trans-1-propenyl)-L-cysteine (GSPC), to sulfoxides, alliin and isoalliin, was observed. Interestingly, however, when garlic was stored at 23 degrees C, a decrease in GSPC and a marked increase in cycloalliin, rather than isoalliin, occurred. To elucidate in detail the mechanism involved, the conversion of isoalliin to cycloalliin in both buffer solutions (pH 4.6, 5.5, and 6.5) and garlic cloves at 25 and 35 degrees C was examined. Decreases in the concentration of isoalliin in both the solutions and the garlic cloves during storage followed first-order kinetics and coincided with the conversion of cycloalliin. Our data indicated that isoalliin produced enzymatically from GSPC is chemically converted to cycloalliin and that the cycloalliin content of garlic cloves increases during storage at higher temperature. These data may be useful for controlling the quality and biological activities of garlic and its preparations.",Journal of agricultural and food chemistry,"['D002851', 'D003545', 'D005519', 'D005737', 'D018517', 'D012996', 'D013457', 'D013696', 'D013997']","['Chromatography, High Pressure Liquid', 'Cysteine', 'Food Preservation', 'Garlic', 'Plant Roots', 'Solutions', 'Sulfur Compounds', 'Temperature', 'Time Factors']",Changes in organosulfur compounds in garlic cloves during storage.,"[None, 'Q000031', 'Q000379', 'Q000737', 'Q000737', None, 'Q000032', None, None]","[None, 'analogs & derivatives', 'methods', 'chemistry', 'chemistry', None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16787038,2006,1.0,1.0,,mol/g , -16786497,"Plant essential oils from 40 plant species were tested for their insecticidal activities against larvae of Lycoriella ingénue (Dufour) using a fumigation bioassay. Good insecticidal activity against larvae of L. ingenua was achieved with essential oils of Chenopodium ambrosioides L., Eucalyptus globulus Labill, Eucalyptus smithii RT Baker, horseradish, anise and garlic at 10 and 5 microL L(-1) air. Horseradish, anise and garlic oils showed the most potent insecticidal activities among the plant essential oils. At 1.25 microL L(-1), horseradish, anise and garlic oils caused 100, 93.3 and 13.3% mortality, but at 0.625 microL L(-1) air this decreased to 3.3, 0 and 0% respectively. Analysis by gas chromatography-mass spectrometry led to the identification of one major compound from horseradish, and three each from anise and garlic oils. These seven compounds and m-anisaldehyde and o-anisaldehyde, two positional isomers of p-anisaldehyde, were tested individually for their insecticidal activities against larvae of L. ingenua. Allyl isothiocyanate was the most toxic, followed by trans-anethole, diallyl disulfide and p-anisaldehyde with LC(50) values of 0.15, 0.20, 0.87 and 1.47 microL L(-1) respectively.",Pest management science,"['D000818', 'D031215', 'D004175', 'D005737', 'D007306', 'D007814', 'D009822', 'D028042', 'D010944']","['Animals', 'Armoracia', 'Diptera', 'Garlic', 'Insecticides', 'Larva', 'Oils, Volatile', 'Pimpinella', 'Plants']","Fumigant activity of plant essential oils and components from horseradish (Armoracia rusticana), anise (Pimpinella anisum) and garlic (Allium sativum) oils against Lycoriella ingenua (Diptera: Sciaridae).","[None, 'Q000737', None, 'Q000737', 'Q000032', None, 'Q000737', 'Q000737', 'Q000737']","[None, 'chemistry', None, 'chemistry', 'analysis', None, 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16786497,2006,2.0,1.0,,, -16770577,"The scope of the work was to investigate the influence of selenate fertilisation and the addition of symbiotic fungi (mycorrhiza) to soil on selenium and selenium species concentrations in garlic. The selenium species were extracted from garlic cultivated in experimental plots by proteolytic enzymes, which ensured liberation of selenium species contained in peptides or proteins. Separate extractions using an aqueous solution of enzyme-deactivating hydroxylamine hydrochloride counteracted the possible degradation of labile selenium species by enzymes (such as alliinase) that occur naturally in garlic. The selenium content in garlic, which was analysed by ICP-MS, showed that addition of mycorrhiza to the natural soil increased the selenium uptake by garlic tenfold to 15 microg g(-1) (dry mass). Fertilisation with selenate and addition of mycorrhiza strongly increased the selenium content in garlic to around one part per thousand. The parallel analysis of the sample extracts by cation exchange and reversed-phase HPLC with ICP-MS detection showed that gamma-glutamyl-Se-methyl-selenocysteine amounted to 2/3, whereas methylselenocysteine, selenomethionine and selenate each amounted to a few percent of the total chromatographed selenium in all garlic samples. Se-allyl-selenocysteine and Se-propyl-selenocysteine, which are selenium analogues of biologically active sulfur-containing amino acids known to occur in garlic, were searched for but not detected in any of the extracts. The amendment of soil by mycorrhiza and/or by selenate increased the content of selenium but not the distribution of detected selenium species in garlic. Finally, the use of two-dimensional HPLC (size exclusion followed by reversed-phase) allowed the structural characterisation of gamma-glutamyl-Se-methyl-selenocysteine and gamma-glutamyl-Se-methyl-selenomethionine in isolated chromatographic fractions by quadrupole time-of-flight mass spectrometry.",Analytical and bioanalytical chemistry,"['D004798', 'D005737', 'D013058', 'D015394', 'D038821', 'D064586', 'D012643', 'D018036', 'D012987', 'D012988']","['Enzymes', 'Garlic', 'Mass Spectrometry', 'Molecular Structure', 'Mycorrhizae', 'Selenic Acid', 'Selenium', 'Selenium Compounds', 'Soil', 'Soil Microbiology']",Uptake and speciation of selenium in garlic cultivated in soil amended with symbiotic fungi (mycorrhiza) and selenate.,"['Q000378', 'Q000378', None, None, 'Q000378', None, 'Q000032', 'Q000032', 'Q000032', None]","['metabolism', 'metabolism', None, None, 'metabolism', None, 'analysis', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/16770577,2007,1.0,1.0,,, -16582584,"Garlic (Allium sativum) has been suggested to affect several cardiovascular risk factors. Its antiatherosclerotic properties are mainly attributed to allicin that is produced upon crushing of the garlic clove. Most previous studies used various garlic preparations in which allicin levels were not well defined. In the present study, we evaluated the effects of pure allicin on atherogenesis in experimental mouse models.","Pathobiology : journal of immunopathology, molecular and cellular biology","['D000818', 'D000975', 'D001011', 'D001057', 'D001161', 'D002784', 'D002853', 'D004578', 'D006801', 'D000960', 'D008077', 'D017737', 'D051379', 'D018345', 'D010084', 'D011973', 'D013441']","['Animals', 'Antioxidants', 'Aorta', 'Apolipoproteins E', 'Arteriosclerosis', 'Cholesterol', 'Chromatography, Liquid', 'Electron Spin Resonance Spectroscopy', 'Humans', 'Hypolipidemic Agents', 'Lipoproteins, LDL', 'Macrophages, Peritoneal', 'Mice', 'Mice, Knockout', 'Oxidation-Reduction', 'Receptors, LDL', 'Sulfinic Acids']",The antiatherogenic effect of allicin: possible mode of action.,"[None, 'Q000302', 'Q000473', 'Q000172', 'Q000097', 'Q000097', None, None, None, 'Q000302', 'Q000097', 'Q000187', None, None, None, 'Q000172', 'Q000302']","[None, 'isolation & purification', 'pathology', 'deficiency', 'blood', 'blood', None, None, None, 'isolation & purification', 'blood', 'drug effects', None, None, None, 'deficiency', 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/16582584,2006,,,,no PDF access, -16529758,"Speciation analysis of selenomethylcysteine (SeMeCys), selenomethionine (SeMet) and selenocystine (SeCys) has been performed using a direct amino acid analysis method with high-performance anion-exchange chromatography (HPAEC) coupled with integrated pulsed amperometric detection (IPAD). Three selenoamino acids could be baseline-separated from 19 amino acids using gradient elution conditions for amino acids and determined under new six-potential waveform. Detection limits for SeMeCys, SeMet and SeCys were 0.25, 1 and 20 microg/L (25 microL injection, 10 times of the baseline noise), respectively. The relative standard deviations (RSDs) of 200 microg/L SeMeCys, SeMet and SeCys were 3.1, 4.1 and 2.8%, respectively (n=9, 25 microL injection). The proposed method has been applied for determination of selenoamino acids in extracts of garlic and selenious yeast granule samples. No selenoamino acids were found in garlic. Both SeMet and SeCys were detected in selenious yeast tablet with the content of 45 and 129 microg Se/g, respectively. Selenoamino acids standards were spiked in garlic and yeast granule samples and the recovery ranged from 90 to 106%.",Journal of chromatography. A,"['D000596', 'D000837', 'D002851', 'D003553', 'D004563', 'D005737', 'D016566', 'D015203', 'D012645', 'D015003']","['Amino Acids', 'Anion Exchange Resins', 'Chromatography, High Pressure Liquid', 'Cystine', 'Electrochemistry', 'Garlic', 'Organoselenium Compounds', 'Reproducibility of Results', 'Selenomethionine', 'Yeasts']",Direct amino acid analysis method for speciation of selenoamino acids using high-performance anion-exchange chromatography coupled with integrated pulsed amperometric detection.,"['Q000032', 'Q000737', 'Q000295', 'Q000031', 'Q000295', 'Q000737', 'Q000032', None, 'Q000032', 'Q000737']","['analysis', 'chemistry', 'instrumentation', 'analogs & derivatives', 'instrumentation', 'chemistry', 'analysis', None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16529758,2006,0.0,0.0,,nothing detected, -16506797,"The properties of garlic (Allium sativum L.) are attributed to organosulfur compounds. Although these compounds change during cultivation and storage, there is no report of their simultaneous analysis. Here, a newly developed analytical method with a rapid and simple sample preparation to determine four sulfoxides and three gamma-glutamyl peptides in garlic is reported. All garlic samples were simply extracted with 90% methanol solution containing 0.01 N hydrochloric acid and prepared for analysis. Alliin, isoalliin, methiin, cycloalliin, and gamma-l-glutamyl-S-methyl-l-cysteine were determined by normal-phase HPLC using an aminopropyl-bonded column. gamma-l-Glutamyl-S-(2-propenyl)-l-cysteine and gamma-l-glutamyl-S-(trans-1-propenyl)-l-cysteine were separated on an octadecylsilane column. The overall recoveries were 97.1-102.3%, and the relative standard deviation values of intra- and interday precision were lower than 2.6 and 4.6%, respectively. This newly developed method offers some advantages over the currently accepted techniques including specificity, speed, and ease of use and would be useful for chemical and biological studies of garlic and its preparations.",Journal of agricultural and food chemistry,"['D002851', 'D003545', 'D005737', 'D015203', 'D012997', 'D013457']","['Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Reproducibility of Results', 'Solvents', 'Sulfur Compounds']",Determination of seven organosulfur compounds in garlic by high-performance liquid chromatography.,"['Q000379', 'Q000031', 'Q000737', None, None, 'Q000032']","['methods', 'analogs & derivatives', 'chemistry', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16506797,2006,1.0,3.0,,, -16484583,"Garlic enriched by selenium (Se) could be an excellent source of dietary Se for cancer chemoprevention. The production of high-Se garlic requires Se-fertilized soil, but such soil may pollute the environment. Hydroponics is a closed system that allows good control over Se fertilization without environmental consequences. We examined the effect of hydroponic cultivation on Se uptake and assimilation in garlic seedlings. Garlic bulbs were grown in the nutrient solution without Se for first 2 wk, and with potassium selenate for an additional week. Sulfate in an ordinary hydroponic solution inhibited the absorption and assimilation of selenate, but when a sulfate-free nutrient was used for Se addition, the garlic seedlings accumulated >1 mg Se, dry weight. Through HPLC inductively coupled plasma MS (HPLC-ICP-MS) analysis, Se-methlyselenocysteine (MeSeCys), gamma-glutamyl-Se-methlyselenocysteine (gamma-GluMeSeCys), selenomethionine, and nonmetabolized selenate were identified in water extracts of the garlic seedlings. The results demonstrate that hydroponic enrichment of Se in garlic seedlings could be a practical means of producing organic Se compounds for nutritional supplements.",The Journal of nutrition,"['D002851', 'D005737', 'D018527', 'D013058', 'D012643']","['Chromatography, High Pressure Liquid', 'Garlic', 'Hydroponics', 'Mass Spectrometry', 'Selenium']",Hydroponic cultivation offers a practical means of producing selenium-enriched garlic.,"[None, 'Q000254', 'Q000379', None, 'Q000378']","[None, 'growth & development', 'methods', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/16484583,2006,0.0,0.0,,, -16484558,"Clinical and experimental evidence indicates that garlic ingestion lowers blood cholesterol levels, and treatment of cells in culture with garlic and garlic-derived compounds inhibits cholesterol synthesis. To identify the principal site of inhibition in the cholesterolgenic pathway and the active components of garlic, cultured hepatoma cells were treated with aqueous garlic extract or its chemical derivatives, and radiolabeled cholesterol and intermediates were identified and quantified. Garlic extract reduced cholesterol synthesis by up to 75% without evidence of cellular toxicity. Levels of squalene and 2,3-oxidosqualene were not altered by garlic, indicating that the site of inhibition was downstream of lanosterol synthesis, and identical results were obtained with 14C-acetate and 14C-mevalonate, confirming that 3-hydroxy-3-methylglutaryl-CoA reductase activity was not affected in these short-term studies. Several methylsterols that accumulated in the presence of garlic were identified by coupled gas chromatography-mass spectrometry as 4,4'-dimethylzymosterol and a possible metabolite of 4-methylzymosterol; both are substrates for sterol 4alpha-methyl oxidase, pointing to this enzyme as the principal site of inhibition in the cholesterolgenic pathway by garlic. Of 9 garlic-derived compounds tested for their ability to inhibit cholesterol synthesis, only diallyl disulfide, diallyl trisulfide, and allyl mercaptan proved inhibitory, each yielding a pattern of sterol accumulation identical with that obtained with garlic extract. These results indicate that compounds containing an allyl-disulfide or allyl-sulfhydryl group are most likely responsible for the inhibition of cholesterol synthesis by garlic and that this inhibition is likely mediated at sterol 4alpha-methyl oxidase.",The Journal of nutrition,"['D000818', 'D000924', 'D045744', 'D002784', 'D004791', 'D005737', 'D007810', 'D008114', 'D009097', 'D010088', 'D008517', 'D010936', 'D051381', 'D013185']","['Animals', 'Anticholesteremic Agents', 'Cell Line, Tumor', 'Cholesterol', 'Enzyme Inhibitors', 'Garlic', 'Lanosterol', 'Liver Neoplasms, Experimental', 'Multienzyme Complexes', 'Oxidoreductases', 'Phytotherapy', 'Plant Extracts', 'Rats', 'Squalene']",Inhibition of sterol 4alpha-methyl oxidase is the principal mechanism by which garlic decreases cholesterol synthesis.,"[None, 'Q000494', None, 'Q000096', 'Q000494', None, 'Q000097', None, 'Q000037', 'Q000037', None, 'Q000494', None, 'Q000378']","[None, 'pharmacology', None, 'biosynthesis', 'pharmacology', None, 'blood', None, 'antagonists & inhibitors', 'antagonists & inhibitors', None, 'pharmacology', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/16484558,2006,0.0,0.0,,, -16484551,"This study used the hydroden peroxide scavenging assay to investigate antioxidant chemical constituents derived and separated from aged garlic extract, a unique garlic extract produced by soaking sliced garlic in an aqueous ethanol solution for >10 mo. Four types of 1, 2, 3, 4-tetrahydro-beta-carboline derivatives (THbetaCs); 1-methyl-1, 2, 3, 4-tetrahydro-beta-carboline-3-carboxylic acid, and 1-methyl-1, 2, 3, 4-tetrahydro-beta-carboline-1, 3-dicarboxylic acid (MTCdiC), from both diastereoisomers, were isolated and identified by use of liquid chromatography-mass spectrometry. All these compounds indicate strong hydrogen peroxide scavenging activities and inhibit 2, 2'-azobis(2-amidinopropane) hydrochloride-induced lipid peroxidation. Particularly, (1S, 3S)-MTCdiC had the most potent hydrogen peroxide scavenging activity, more than ascorbic acid. The (1R, 3S)- and (1S, 3S)-MTCdiC at 50-100 micromol/L and 10-100 micromol/L inhibited LPS-induced nitrite production. Interestingly, THbetaCs were not detected in raw garlic and other processed garlic preparations, but they were generated and increased during the natural aging garlic extraction process. These data suggest that THbetaCs, which are formed during the natural aging process, are potent antioxidants in aged garlic extract and thus may be useful for the prevention of diseases associated with oxidative damage.",The Journal of nutrition,"['D000375', 'D000975', 'D002243', 'D016166', 'D005737', 'D008070', 'D009682', 'D013237']","['Aging', 'Antioxidants', 'Carbolines', 'Free Radical Scavengers', 'Garlic', 'Lipopolysaccharides', 'Magnetic Resonance Spectroscopy', 'Stereoisomerism']",Tetrahydro-beta-carboline derivatives in aged garlic extract show antioxidant properties.,"[None, 'Q000494', 'Q000138', 'Q000494', 'Q000737', 'Q000494', None, None]","[None, 'pharmacology', 'chemical synthesis', 'pharmacology', 'chemistry', 'pharmacology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16484551,2006,1.0,2.0,,, -16435092,"Liquid chromatography (LC) hyphenated with both elemental and molecular mass spectrometry has been used for Se speciation in Se-enriched garlic. Different species were separated by ion-pair liquid chromatography-inductively coupled plasma mass spectrometry (LC-ICP-MS) after hot-water extraction. They were identified by on-line reversed-phase liquid chromatography-electrospray ionization tandem mass spectrometry (RPLC-ESI-MS-MS). Se-methionine and Se-methylselenocysteine were determined by monitoring their product ions. Another compound, gamma-glutamyl-Se-methylselenocysteine, shown to be the most abundant form of Se in the garlic, was determined without any additional sample pre-treatment after extraction and without the need for a synthesized standard. Product ions for this dipeptide were detected by LC-ESI-MS-MS for three isotopes of Se-78 Se, 80Se: and 82Se. The method was extended to the species extracted during in-vitro gastrointestinal digestion. Because both Se-methylselenocysteine and gamma-glutamyl-Se-methylselenocysteine have anticarcinogenic properties, their extractability and stability during human digestion are very important. Garlic was also treated with saliva, to enable detection and analysis of species extracted during mastication. Detailed information on the extractability of selenium species by both simulated gastric and intestinal fluid are given, and variation of the distribution of Se among the different species with time is discussed. Although the main species in garlic is the dipeptide gamma-glutamyl-Se-methylselenocysteine, Se-methylselenocysteine is the main compound present in the extracts after treatment with gastrointestinal fluids. Two more, so far unknown compounds were observed in the chromatogram. The extracted species and their transformations were analysed by combining LC-ICP-MS and LC-ESI-MS-MS. In both the simulated gastric and intestinal digests, Se-methionine, Se-methylselenocysteine, and gamma-glutamyl-Se-methylselenocysteine could be determined by LC-ESI-MS-MS by measuring their typical product ions.",Analytical and bioanalytical chemistry,"['D002853', 'D005737', 'D013058', 'D016566', 'D012643', 'D012680', 'D013997']","['Chromatography, Liquid', 'Garlic', 'Mass Spectrometry', 'Organoselenium Compounds', 'Selenium', 'Sensitivity and Specificity', 'Time Factors']",Liquid chromatography-mass spectrometry (LC-MS): a powerful combination for selenium speciation in garlic (Allium sativum).,"['Q000295', 'Q000737', 'Q000295', 'Q000032', 'Q000032', None, None]","['instrumentation', 'chemistry', 'instrumentation', 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16435092,2007,1.0,1.0,,, -16413559,"The proteinaceous content of garlic (Allium sativum) was characterised according to its amino acid composition by using a gas chromatography-mass spectrometry (GC-MS) analytical procedure. The procedure was tested on fresh and aged garlic samples as well as on reference gilding specimens prepared according to old recipes. The proteinaceous pattern showed a characteristic distribution of amino acids with glutamic acid being the major component. The average amino acidic composition was: glutamic acid (Glu; 29%), aspartic acid (Asp; 17%), serine (Ser; 11%), alanine, glycine, valine, leucine, lysine and phenylalanine (Ala, Gly, Val, Leu, Lys and Phe; 5-6%), isoleucine, proline and tyrosine (Ile, Pro and Tyr; 2-3%), methionine and hydroxyproline (Met and Hyp; 0.5%). In order to distinguish this material from animal glue and egg, which are the other proteinaceous media commonly used in gilding techniques, a database of amino acid percentages of the three proteins was built up and submitted to principal component analysis. Three separate clusters were obtained, allowing the protein identification. The application of the procedure on several gilding samples from Italian wall and easel paintings (13th-17th century) permitted to evidence the use of garlic as a gluing agent.",Journal of chromatography. A,"['D005737', 'D008401', 'D006868', 'D008872', 'D010151', 'D012015']","['Garlic', 'Gas Chromatography-Mass Spectrometry', 'Hydrolysis', 'Microwaves', 'Paintings', 'Reference Standards']",Identification of garlic in old gildings by gas chromatography-mass spectrometry.,"[None, 'Q000379', None, None, None, None]","[None, 'methods', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16413559,2006,2.0,1.0,,, -16386011,"A selective, precise, and accurate high-performance thin-layer chromatographic (HPTLC) method has been proposed for the analysis of garlic and its formulations for their alliin content. The method involves densitometric evaluation of alliin after resolving it by HPTLC on silica gel plates with n-butanol-acetic acid-water (6 + 2 + 2, v/v) as the mobile phase. For densitometric evaluation, peak areas were recorded at 540 nm after derivatizing the resolved bands with ninhydrin reagent. The relation between the concentration of alliin and corresponding peak areas was found to be linear within the range of 250 to 1500 ng/spot. The method was validated for precision (interday and intraday), repeatability, and accuracy. Mean recovery was 98.36%. The method was applied for the quantitation of alliin in bulbs of Allium sativum Linn. (garlic) and its formulations. The proposed TLC method was found to be precise, specific, sensitive, and accurate and can be used for routine quality control of garlic and its formulations.",Journal of AOAC International,"['D002855', 'D003545', 'D003720', 'D005737', 'D012015', 'D012680']","['Chromatography, Thin Layer', 'Cysteine', 'Densitometry', 'Garlic', 'Reference Standards', 'Sensitivity and Specificity']",Development and validation of a thin-layer chromatography-densitometric method for the quantitation of alliin from garlic (Allium sativum) and its formulations.,"['Q000379', 'Q000031', 'Q000379', 'Q000737', None, None]","['methods', 'analogs & derivatives', 'methods', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16386011,2006,,,,, -16377850,"Analysis of peroxidase activity by native polyacrylamide gel electrophoresis (PAGE) from a garlic bulb (Allium sativum L) extract showed two major activities (designated POX1 and POX2). The POX2 isoenzyme was purified to homogeneity by ammonium sulfate precipitation, gel filtration, and cation-exchange chromatography. The purified enzyme was found to be monomeric with a molecular mass of 36.5 kDa, as determined by sodium dodecyl sulfate-PAGE. The optimum temperature ranged from 25 to 40 degrees C and optimum pH was about 5.0. The apparent Km values for guaiacol and H2O2 were 9.5 and 2 mM, respectively. POX2 appeared highly stable since 50% of its activity was conserved at 50 degrees C for 5 h. Moreover POX2 was stable over a pH range of 3.5-11.0. Immobilization of POX2 was achieved by covalent binding of the enzyme to an epoxy-Sepharose matrix. The immobilized enzyme showed great stability toward heat and storage when compared with soluble enzyme. These properties permit the use of this enzyme as a biosensor to detect H2O2 in some food components such as milk or its derivatives.",Applied biochemistry and biotechnology,"['D000818', 'D015374', 'D005737', 'D006861', 'D008892', 'D010544', 'D010940', 'D018517']","['Animals', 'Biosensing Techniques', 'Garlic', 'Hydrogen Peroxide', 'Milk', 'Peroxidases', 'Plant Proteins', 'Plant Roots']","A new thermostable peroxidase from garlic Allium sativum: purification, biochemical properties, immobilization, and use in H2O2 detection in milk.","[None, None, 'Q000201', 'Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000201']","[None, None, 'enzymology', 'analysis', 'chemistry', 'chemistry', 'chemistry', 'enzymology']",https://www.ncbi.nlm.nih.gov/pubmed/16377850,2006,,,,, -16350805,"The volatile oil of garlic was extracted by hydrodistillation method and gas chromatography-mass spectrometry was applied to analyse the compounds in the oil. The best extraction conditions for high-content, effective components were obtained through optimization. The capillary column was an HP-5MS column (25 mm x 0.25 mm i.d. x 0.25 microm); oven temperature increased with a rate of 5 degrees C /min from 80 to 300 degrees C, and then maintained for 20 min; sample size of 1 microL; split ratio of 100:1; carrier gas of helium (1 mL/min). Mass spectra were obtained at 70 eV. The temperatures of injector base, ionization source were maintained at 270 degrees C, 230 degrees C respectively. Under these conditions, twenty compounds in the volatile oil of garlic were isolated and identified, and the content of each was determined. Sulfur-containing compounds were found to be the principal components, of which the major compound was diallyl trisulfide with the content of more than 30%, which is higher than the others in the literature. The experimental results also indicated that hydrodistillation method is an effective method for officinal component extraction. In addition, it was also demonstrated that the garlic volatile oil was stable when stored at 0 degrees C for 6 months.",Se pu = Chinese journal of chromatography,"['D005737', 'D008401', 'D009822']","['Garlic', 'Gas Chromatography-Mass Spectrometry', 'Oils, Volatile']",[Analysis of volatile oil of garlic by gas chromatography-mass spectrometry].,"['Q000737', 'Q000379', 'Q000032']","['chemistry', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16350805,2010,,,,, -16344271,"Garlic is generally used as a therapeutic reagent against various diseases worldwide. Although a great effort is made to understand the pharmaceutical mechanisms of garlic and its derivatives, there are many mysteries to be uncovered. Using proteomic means, herein we have systematically studied the responses of protein expression in BGC823 cells, a gastric cancer cell line, induced by diallyl trisulfide (DATS), a major component of garlic derivatives. A total of 41 unique proteins in BGC823 were detected with significant changes in their expression levels corresponding with DATS administration. Of these proteins, five typical ones, glutathione S-transferase-pi (GST-pi), voltage-dependent anion channel-1 (VDAC-1), Annexin I, Galectin and S100A11, were further examined by Western blotting, resulting in coincident data with the proteomic evidence. Moreover quantitative real-time RT-PCR experiments offered dynamic data of mRNA expression, indicating the responses of Annexin I and GST-pi expression within a short period after DATS treatment. Interestingly, approximately 50% of DATS-sensitive proteins (19/41) in BGC823 are tightly associated with apoptotic pathways. These proteomic results presented, therefore, provide additional support to the hypothesis that garlic is a strong inducer of apoptosis in tumor cells.",Carcinogenesis,"['D000498', 'D017305', 'D017209', 'D045744', 'D015180', 'D015972', 'D005982', 'D006801', 'D007091', 'D013058', 'D040901', 'D020133', 'D019032', 'D013274', 'D013440']","['Allyl Compounds', 'Annexin A1', 'Apoptosis', 'Cell Line, Tumor', 'Electrophoresis, Gel, Two-Dimensional', 'Gene Expression Regulation, Neoplastic', 'Glutathione Transferase', 'Humans', 'Image Processing, Computer-Assisted', 'Mass Spectrometry', 'Proteomics', 'Reverse Transcriptase Polymerase Chain Reaction', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Stomach Neoplasms', 'Sulfides']",A proteomic investigation into a human gastric cancer cell line BGC823 treated with diallyl trisulfide.,"['Q000494', 'Q000378', None, None, None, None, 'Q000378', None, None, None, 'Q000379', None, None, 'Q000378', 'Q000494']","['pharmacology', 'metabolism', None, None, None, None, 'metabolism', None, None, None, 'methods', None, None, 'metabolism', 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/16344271,2006,0.0,0.0,,, -16337756,"Genotoxic effects of acrylamide are supposed to result from oxidative biotransformation to glycidamide. After incubation of rat liver slices with acrylamide we detected free glycidamide using a liquid chromatography tandem mass spectrometric method. Glycidamide formation was diminished in the presence of the cytochrome P450 2E1 inhibitor diallyl sulfide (DAS), which is a specific ingredient of garlic. This may be relevant to human health since the suggested carcinogenic risk of dietary acrylamide may be reduced by concomitant intake of garlic.",Toxicology letters,"['D020106', 'D000498', 'D000818', 'D001711', 'D019392', 'D004852', 'D005737', 'D066298', 'D008099', 'D008297', 'D051381', 'D017208', 'D013440']","['Acrylamide', 'Allyl Compounds', 'Animals', 'Biotransformation', 'Cytochrome P-450 CYP2E1', 'Epoxy Compounds', 'Garlic', 'In Vitro Techniques', 'Liver', 'Male', 'Rats', 'Rats, Wistar', 'Sulfides']",The garlic ingredient diallyl sulfide inhibits cytochrome P450 2E1 dependent bioactivation of acrylamide to glycidamide.,"['Q000378', 'Q000302', None, 'Q000187', 'Q000378', 'Q000378', 'Q000737', None, 'Q000201', None, None, None, 'Q000302']","['metabolism', 'isolation & purification', None, 'drug effects', 'metabolism', 'metabolism', 'chemistry', None, 'enzymology', None, None, None, 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/16337756,2006,0.0,0.0,,, -16277408,"Two garlic subspecies (n = 11), Allium sativum L. var. opioscorodon (hardneck) and Allium sativum L. var. sativum (softneck), were evaluated for their free amino acid composition. The free amino acid content of garlic samples analyzed ranged from 1121.7 to 3106.1 mg/100 g of fresh weight (mean = 2130.7 +/- 681.5 mg/100 g). Hardneck garlic had greater methiin, alliin, and total free amino acids contents compared to softneck garlic. The major free amino acid present in all but one subspecies was glutamine (cv. Mother of Pearl had aspartic acid as the major free amino acid). Cv. Music Pink garlic (a rocambole hardneck variety) contained the most methiin, alliin, and total free amino acids. The solid-phase extraction, alkylchloroformate derivatization, GC-FID, and GC-MS methods used in this study were simple and rapid, allowing 18 free amino acids in garlic to be separated within 10 min.",Journal of agricultural and food chemistry,"['D000596', 'D003545', 'D008401', 'D013454']","['Amino Acids', 'Cysteine', 'Gas Chromatography-Mass Spectrometry', 'Sulfoxides']",Free amino acid and cysteine sulfoxide composition of 11 garlic (Allium sativum L.) cultivars by gas chromatography with flame ionization and mass selective detection.,"['Q000032', 'Q000031', None, 'Q000032']","['analysis', 'analogs & derivatives', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16277408,2006,1.0,3.0,,, -16233877,"The simultaneous speciation of selenium and sulfur in selenized odorless garlic (Allium sativum L. Shiro) and a weakly odorous Allium plant, shallot (Allium ascalonicum), was performed by means of a hyphenated technique, a HPLC coupled with an inductively coupled plasma-mass spectrometry (HPLC-ICP-MS) equipped with an octopole reaction system (ORS). The aqueous extracts of them contained the common seleno compound that was identified as gamma-glutamylmethylselenocysteine by an electrospray ionization-tandem mass spectrometry (ESI-MS/MS). Normal garlic contains alliin as the major sulfur-containing compound, which is the biological precursor of the garlic odorant, allicin. Alliin, however, was not detected in the extracts of the selenized odorless garlic. At least, four unidentified sulfur-containing compounds were detected in odorless garlic and shallot. Moreover, these Allium plants showed chemopreventive effects against human leukemia cells.",Journal of chromatography. A,"['D000490', 'D000603', 'D000972', 'D002851', 'D018922', 'D006801', 'D013058', 'D012643', 'D013455']","['Allium', 'Amino Acids, Sulfur', 'Antineoplastic Agents, Phytogenic', 'Chromatography, High Pressure Liquid', 'HL-60 Cells', 'Humans', 'Mass Spectrometry', 'Selenium', 'Sulfur']",Simultaneous speciation of selenium and sulfur species in selenized odorless garlic (Allium sativum L. Shiro) and shallot (Allium ascalonicum) by HPLC-inductively coupled plasma-(octopole reaction system)-mass spectrometry and electrospray ionization-tandem mass spectrometry.,"['Q000737', 'Q000032', None, 'Q000379', None, None, 'Q000379', 'Q000032', 'Q000032']","['chemistry', 'analysis', None, 'methods', None, None, 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16233877,2006,0.0,0.0,,, -16219763,"Allyl sulfides are characteristic flavor components obtained from garlic. These sulfides are thought to be responsible for their epidemiologically proven anticancer effect on garlic eaters. This study was aimed at clarifying the molecular basis of this anticancer effect of garlic by using human colon cancer cell lines HCT-15 and DLD-1. The growth of the cells was significantly suppressed by diallyl trisulfide (DATS, HCT-15 IC50 = 11.5 microM, DLD-1 IC50 = 13.3 microM); however, neither diallyl monosulfide nor diallyl disulfide showed such an effect. The proportion of HCT-15 and that of DLD-1 cells residing at the G1 and S phases were decreased by DATS, and their populations at the G2/M phase were markedly increased for up to 12 h. The cells with a sub-G1 DNA content were increased thereafter. Caspase-3 activity was also dramatically increased by DATS. Fluorescence-activated cell sorter analysis performed on the cells arrested at the G1/S boundary revealed cell cycle-dependent induction of apoptosis through the transition of the G2/M phase to the G1 phase by DATS. DATS inhibited tubulin polymerization in an in vitro cell-free system. DATS disrupted microtubule network formation of the cells, and microtubule fragments could be seen at the interphase. Peptide mass mapping by liquid chromatography-tandem mass spectrometry analysis for DATS-treated tubulin demonstrated that there was a specific oxidative modification of cysteine residues Cys-12beta and Cys-354beta to form S-allylmercaptocysteine with a peptide mass increase of 72.1 Da. The potent antitumor activity of DATS was also demonstrated in nude mice bearing HCT-15 xenografts. This is the first paper describing intracellular target molecules directly modified by garlic components.",The Journal of biological chemistry,"['D000498', 'D000818', 'D000970', 'D017209', 'D015153', 'D002453', 'D045744', 'D049109', 'D002469', 'D002474', 'D002853', 'D003110', 'D019926', 'D056744', 'D003545', 'D003593', 'D004247', 'D004220', 'D005434', 'D019084', 'D005737', 'D006801', 'D020128', 'D051379', 'D008819', 'D008870', 'D009368', 'D018384', 'D010100', 'D010455', 'D011485', 'D013440', 'D013997', 'D014404']","['Allyl Compounds', 'Animals', 'Antineoplastic Agents', 'Apoptosis', 'Blotting, Western', 'Cell Cycle', 'Cell Line, Tumor', 'Cell Proliferation', 'Cell Separation', 'Cell-Free System', 'Chromatography, Liquid', 'Colonic Neoplasms', 'Cyclin B', 'Cyclin B1', 'Cysteine', 'Cytoplasm', 'DNA', 'Disulfides', 'Flow Cytometry', 'Fluorescent Antibody Technique, Indirect', 'Garlic', 'Humans', 'Inhibitory Concentration 50', 'Mice', 'Mice, Nude', 'Microtubules', 'Neoplasm Transplantation', 'Oxidative Stress', 'Oxygen', 'Peptides', 'Protein Binding', 'Sulfides', 'Time Factors', 'Tubulin']",Diallyl trisulfide suppresses the proliferation and induces apoptosis of human colon cancer cells through oxidative modification of beta-tubulin.,"['Q000737', None, 'Q000494', None, None, None, None, None, None, None, None, 'Q000378', 'Q000378', None, 'Q000031', 'Q000378', 'Q000737', 'Q000737', None, None, None, None, None, None, None, 'Q000378', None, None, 'Q000737', 'Q000737', None, 'Q000737', None, 'Q000737']","['chemistry', None, 'pharmacology', None, None, None, None, None, None, None, None, 'metabolism', 'metabolism', None, 'analogs & derivatives', 'metabolism', 'chemistry', 'chemistry', None, None, None, None, None, None, None, 'metabolism', None, None, 'chemistry', 'chemistry', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16219763,2006,0.0,0.0,,, -16161771,"Selenium (Se) species in Se-enriched shiitake mushroom (Lentinula edodes) were identified and quantified by high performance liquid chromatography with inductively coupled plasma mass spectrometry (HPLC-ICPMS). Two types of Se-enriched shiitake obtained from selenite- or selenate-fertilized mushroom beds were used. More than 80% of Se in both shiitake samples could not be extracted with 0.2 M HCl. Protease digestion released a large amount of selenomethionine from the shiitake enriched with selenite. However, most of the Se in the shiitake enriched with selenate was not released by protease but was released by a cell wall digestive enzyme and most of the Se released was identified as selenate. These results indicate that the main Se species in the shiitake enriched with selenite or selenate is selenomethionine bound to protein or selenate bound to polysaccharides in the cell wall, respectively. Several Se-enriched vegetables grown on a soil fertilized with selenate were also analyzed by HPLC-ICPMS. Four Se species, selenate, Se-methylselenocysteine, selenomethionine, gamma-glutamyl-Se-methylselenocysteine, and an unknown Se compound were detected in the vegetables. The composition of Se species varied with the kinds or parts of vegetables. The main Se species in bulbs, leaves or flowers of the Se-enriched garlic, onions, cabbage and ashitaba were selenate, Se-methylselenocysteine or gamma-glutamyl-Se-methylselenocysteine, while those in fruit bodies of the peppers and pumpkin were selenomethionine bound to protein. Bioavailabilities of Se in the shiitake mushroom enriched with selenite and the vegetables enriched with selenate are expected to be high, but that in shiitake enriched with selenate may be low.",Journal of nutritional science and vitaminology,"['D002851', 'D005308', 'D005527', 'D005737', 'D013058', 'D019697', 'D010447', 'D018517', 'D064586', 'D012643', 'D018036', 'D012645', 'D020942', 'D018038', 'D014675']","['Chromatography, High Pressure Liquid', 'Fertilizers', 'Food, Fortified', 'Garlic', 'Mass Spectrometry', 'Onions', 'Peptide Hydrolases', 'Plant Roots', 'Selenic Acid', 'Selenium', 'Selenium Compounds', 'Selenomethionine', 'Shiitake Mushrooms', 'Sodium Selenite', 'Vegetables']",Composition of chemical species of selenium contained in selenium-enriched shiitake mushroom and vegetables determined by high performance liquid chromatography with inductively coupled plasma mass spectrometry.,"['Q000379', None, 'Q000032', 'Q000737', 'Q000379', 'Q000737', 'Q000378', 'Q000737', None, 'Q000032', 'Q000032', 'Q000032', 'Q000737', 'Q000032', 'Q000737']","['methods', None, 'analysis', 'chemistry', 'methods', 'chemistry', 'metabolism', 'chemistry', None, 'analysis', 'analysis', 'analysis', 'chemistry', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16161771,2006,,,,, -16152948,"A procedure for the determination of traces of total tellurium (Te) in garlic (Allium sativa) is described that combines hydride generation atomic absorption spectrometry with preconcentration of the analyte by coprecipitation. The samples, each spiked with lanthanum nitrate (20 mg/L), are introduced into an Amberlite XAD-4 resin and mixed with ammonium buffer (pH 9.1). Te is preconcentrated by coprecipitation with the generated lanthanum hydroxide precipitate. The precipitate is quantitatively collected in the resin, eluted with hydrochloric acid, and then transferred into the atomizer device. Considering a sample consumption of 25 mL, an enrichment factor of 10 was obtained. The detection limit (3sigma) was 0.03 microg/L, and the precision (relative standard deviation) was 3.5% (n = 10) at the 10 microg/L level. The calibration graph using the preconcentration system for Te was linear with a correlation coefficient of 0.9993. Satisfactory results were obtained for the analysis of Te in garlic samples.",Journal of AOAC International,"['D002138', 'D002623', 'D005737', 'D006851', 'D006863', 'D007811', 'D008872', 'D000644', 'D015203', 'D013054', 'D013691', 'D013997', 'D014131']","['Calibration', 'Chemistry Techniques, Analytical', 'Garlic', 'Hydrochloric Acid', 'Hydrogen-Ion Concentration', 'Lanthanum', 'Microwaves', 'Quaternary Ammonium Compounds', 'Reproducibility of Results', 'Spectrophotometry, Atomic', 'Tellurium', 'Time Factors', 'Trace Elements']",Preconcentration and determination of tellurium in garlic samples by hydride generation atomic absorption spectrometry.,"[None, 'Q000379', 'Q000378', 'Q000032', None, 'Q000494', None, 'Q000494', None, 'Q000295', 'Q000032', None, None]","[None, 'methods', 'metabolism', 'analysis', None, 'pharmacology', None, 'pharmacology', None, 'instrumentation', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16152948,2005,,,,, -15949130,"Garlic extract significantly inhibited the oxidation of methyl linoleate in homogeneous acetonitrile solution, whereas the antioxidant effect of allicin-free garlic extract, prepared by removing allicin by prepared by removing allicin by preparative HPLC, was much lower than that of the garlic extract. These results suggest that the antioxidant properties are mostly attributed to the presence of allicin in the garlic extract. Allicin a major component of the thiosulfinates in garlic extract, was found to be effective for inhibiting methyl linoleate oxidation, but its efficiency was less than that of alpha-tocopherol. Next, the reactivity of allicin toward the peroxyl radical, which is a chain-propagating species, was investigated by direct ESR detection. The addition allicin to 2,2'-azobis(2,4-dimethylvaleronitrile)-peroxyl radical solution caused the signal intensity of the peroxyl radical to dose-dependently decrease, indicating that allicin is capable of scavenging the the peroxyl radical and acting as an antioxidant. Finally, we studied the structure-anioxidant activity relationship for thiosulfinates and suggested that the combination of the allyl group (-CH2CH=CH2) and the -S(O)S- group is necessary for the antioxidant action of thiosulfinates in the garlic extract. In addition, one of the two possible combinations, -S(O)S-CH2CH=CH2, was found to make a much larger contribution to the antioxidant activity of the thiosulfinates than the other, CH2=CH-CH2-S(O)S-.",Redox report : communications in free radical research,"['D000097', 'D000975', 'D002851', 'D004305', 'D004578', 'D005737', 'D008041', 'D008956', 'D004364', 'D010936', 'D010946', 'D012997', 'D013441', 'D013696', 'D013885']","['Acetonitriles', 'Antioxidants', 'Chromatography, High Pressure Liquid', 'Dose-Response Relationship, Drug', 'Electron Spin Resonance Spectroscopy', 'Garlic', 'Linoleic Acids', 'Models, Chemical', 'Pharmaceutical Preparations', 'Plant Extracts', 'Plants, Medicinal', 'Solvents', 'Sulfinic Acids', 'Temperature', 'Thiosulfates']",Antioxidant activity of thiosulfinates derived from garlic.,"['Q000737', 'Q000737', None, None, None, 'Q000378', 'Q000494', None, None, 'Q000494', 'Q000378', None, 'Q000737', None, 'Q000737']","['chemistry', 'chemistry', None, None, None, 'metabolism', 'pharmacology', None, None, 'pharmacology', 'metabolism', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15949130,2006,2.0,1.0,,, -15927923,"Spices are an important group of agricultural commodities being used by many civilizations all over the world to aid flavor, taste and nutritional values in the food. In traditional medical systems, their ability to heal various physical, mental and emotional problems has widely been reported. With this view, HPLC analysis was performed to estimate phenolic acids in 21 spices (asafetida, Bishop's weed, black mustard, coriander, cinnamon, clove, curry leaf, cumin black, cumin, fennel, fenugreek, garlic, ginger, Indian cassia, Indian dill or dill large cardamom, onion, saffron, tamarind, true cardamom, yellow mustard) commonly used in India in different forms. In all, 7 phenolic acids; viz., tannic, gallic, caffeic, cinnamic, chlorogenic, ferulic and vanillic acids could be identified on the basis of their retention time with standard compounds and co-chromatography. Several parts of the spices, for instance, seeds, leaves, barks, rhizomes, latex, stigmas, floral buds and modified stems were used in the study. Maximum amount of tannic and gallic acids was observed in black mustard and clove. Caffeic, chlorogenic and ferulic acids were found maximum in cumin while vanillic and cinnamic acids in onion seeds. The spices are known to significantly contribute to the flavor, taste, and medicinal properties of food because of phenolics.",Journal of herbal pharmacotherapy,"['D000975', 'D018890', 'D002851', 'D006801', 'D062385', 'D007194', 'D008517', 'D010936', 'D010946', 'D017365']","['Antioxidants', 'Chemoprevention', 'Chromatography, High Pressure Liquid', 'Humans', 'Hydroxybenzoates', 'India', 'Phytotherapy', 'Plant Extracts', 'Plants, Medicinal', 'Spices']",Investigation on the phenolics of some spices having pharmacotherapeuthic properties.,"['Q000494', 'Q000379', None, None, 'Q000032', None, None, 'Q000494', 'Q000737', 'Q000032']","['pharmacology', 'methods', None, None, 'analysis', None, None, 'pharmacology', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15927923,2005,,,,, -15913300,"Plant essential oils from 29 plant species were tested for their insecticidal activities against the Japanese termite, Reticulitermes speratus Kolbe, using a fumigation bioassay. Responses varied with plant material, exposure time, and concentration. Good insecticidal activity against the Japanese termite was achived with essential oils of Melaleuca dissitiflora, Melaleuca uncinata, Eucalyptus citriodora, Eucalyptus polybractea, Eucalyptus radiata, Eucalyptus dives, Eucalyptus globulus, Orixa japonica, Cinnamomum cassia, Allium cepa, Illicium verum, Evodia officinalis, Schizonepeta tenuifolia, Cacalia roborowskii, Juniperus chinensis var. horizontalis, Juniperus chinensis var. kaizuka, clove bud, and garlic applied at 7.6 microL/L of air. Over 90% mortality after 3 days was achieved with O. japonica essential oil at 3.5 microL/L of air. E. citriodora, C. cassia, A. cepa, I. verum, S. tenuifolia, C. roborowskii, clove bud, and garlic oils at 3.5 microL/L of air were highly toxic 1 day after treatment. At 2.0 microL/L of air concentration, essential oils of I. verum, C. roborowskik, S. tenuifolia, A. cepa, clove bud, and garlic gave 100% mortality within 2 days of treatment. Clove bud and garlic oils showed the most potent antitermitic activity among the plant essential oils. Garlic and clove bud oils produced 100% mortality at 0.5 microL/L of air, but this decreased to 42 and 67% after 3 days of treatment at 0.25 microL/L of air, respectively. Analysis by gas chromatography-mass spectrometry led to the identification of three major compounds from garlic oil and two from clove bud oils. These five compounds from two essential oils were tested individually for their insecticidal activities against Japanese termites. Responses varied with compound and dose. Diallyl trisulfide was the most toxic, followed by diallyl disulfide, eugenol, diallyl sulfide, and beta-caryophyllene. The essential oils described herein merit further study as potential fumigants for termite control.",Journal of agricultural and food chemistry,"['D000818', 'D005651', 'D005737', 'D008401', 'D007306', 'D020049', 'D009822', 'D010938', 'D027842']","['Animals', 'Fumigation', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Insecticides', 'Isoptera', 'Oils, Volatile', 'Plant Oils', 'Syzygium']",Fumigant activity of plant essential oils and components from garlic (Allium sativum) and clove bud (Eugenia caryophyllata) oils against the Japanese termite (Reticulitermes speratus Kolbe).,"[None, None, 'Q000737', None, None, None, 'Q000737', 'Q000737', 'Q000737']","[None, None, 'chemistry', None, None, None, 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15913300,2005,2.0,1.0,,, -15884836,"A method for determining the country of origin of garlic by comparing the trace metal profile of the sample to an authentic garlic database is presented. Protocols for sample preparation, high-resolution inductively coupled plasma mass spectrometry, and multivariate statistics are provided. The criteria used for making a country of origin prediction are also presented. Indications are that the method presented here may be used to determine the geographic origin of other agricultural products.",Journal of agricultural and food chemistry,"['D000704', 'D003132', 'D005737', 'D008670', 'D012680', 'D014481']","['Analysis of Variance', 'Commerce', 'Garlic', 'Metals', 'Sensitivity and Specificity', 'United States']",Determination of the country of origin of garlic (Allium sativum) using trace metal profiling.,"[None, 'Q000191', 'Q000737', 'Q000032', None, None]","[None, 'economics', 'chemistry', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/15884836,2005,0.0,0.0,,, -15841402,"This document reviews the most relevant mass spectrometry approaches to selenium (Se) speciation in high-Se food supplements in terms of qualitative and quantitative Se speciation and Se-containing species identification, with special reference to high-Se yeast, garlic, onions and Brazil nuts. Important topics such as complexity of Se speciation in these materials and the importance of combining Se-specific detection and molecule-specific determination of the particular species of this element in parallel with chromatography, to understand their nutritional role and cancer preventive properties are critically discussed throughout. The versatility and potential of mass spectrometric detection in this field are clearly demonstrated. Although great advances have been achieved, further developments are required, especially if ""speciated""certified reference materials (CRMs) are to be produced for validation of measurements of target Se-containing species in Se-food supplements.",Analytical and bioanalytical chemistry,"['D000818', 'D004032', 'D019587', 'D006801', 'D013058', 'D012643', 'D012680']","['Animals', 'Diet', 'Dietary Supplements', 'Humans', 'Mass Spectrometry', 'Selenium', 'Sensitivity and Specificity']",Current mass spectrometry strategies for selenium speciation in dietary sources of high-selenium.,"[None, None, None, None, 'Q000379', 'Q000737', None]","[None, None, None, None, 'methods', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/15841402,2007,0.0,0.0,,, -15833378,"Individuals can be classified into rapid or slow acetylators based on the N-acetyltransferase (NAT) activity which is believed to affect cancer risk that is related to environmental carcinogen exposure. Diallyl disulfide (DADS) is a naturally occurring organosulfur compound, from garlic (Allium sativum), which exerts anti-neoplasm activity. In this study, we investigated the inhibitory effects of DADS on NAT activity and gene expresseion (NAT mRNA) in human esophagus epidermoid carcinoma CE 81T/VGH cells. NAT activity was measured by the amounts of N-acetylation of 2-aminofluorene (AF) and non-acetylation of AF by high performance liquid chromatography on cells treated with or without DADS. The amounts of NAT enzymes were examined and analyzed by Western blot. NAT gene expression (NAT mRNA) was examined by polymerase chain reaction and cDNA microarray. DADS decreased the amount of N-acetylation of AF in human esophagus epidermoid carcinoma CE 81T/VGH cells in a dose-dependent manner. Western blot analysis indicated that DADS decreased the levels of NAT protein in CE 81T/VGH cells. PCR and cDNA microarray experiments showed that DADS affected NAT1 mRNA expression in CE 81T/VGH cells. DADS affect NAT activity due to the inhibition of gene expression (NAT1 mRNA) and the decreasing of the protein levels of NAT in CE 81T/VGH cells.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000107', 'D000123', 'D000498', 'D000818', 'D016588', 'D002294', 'D045744', 'D004220', 'D004305', 'D004791', 'D004938', 'D005260', 'D005434', 'D006801', 'D051379', 'D008807', 'D020411', 'D012333', 'D012334', 'D020133']","['Acetylation', 'Acetyltransferases', 'Allyl Compounds', 'Animals', 'Anticarcinogenic Agents', 'Carcinoma, Squamous Cell', 'Cell Line, Tumor', 'Disulfides', 'Dose-Response Relationship, Drug', 'Enzyme Inhibitors', 'Esophageal Neoplasms', 'Female', 'Flow Cytometry', 'Humans', 'Mice', 'Mice, Inbred BALB C', 'Oligonucleotide Array Sequence Analysis', 'RNA, Messenger', 'RNA, Neoplasm', 'Reverse Transcriptase Polymerase Chain Reaction']",Diallyl disulfide inhibits N-acetyltransferase activity and gene expression in human esophagus epidermoid carcinoma CE 81T/VGH cells.,"[None, 'Q000037', 'Q000494', None, 'Q000494', 'Q000201', None, 'Q000494', None, None, 'Q000201', None, None, None, None, None, None, 'Q000096', 'Q000096', None]","[None, 'antagonists & inhibitors', 'pharmacology', None, 'pharmacology', 'enzymology', None, 'pharmacology', None, None, 'enzymology', None, None, None, None, None, None, 'biosynthesis', 'biosynthesis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15833378,2005,0.0,0.0,,, -15790108,"A new type of chiral ligand-exchange stationary phase (CLES) was successfully synthesized by treating silica gel with beta-(3,4-epoxycyclohexyl)ethyltrimethoxy silane and opening the epoxy ring by L-isoleucine. The chiral speciation of DL-selenomethionine (DL-SeMet) by high-performance liquid chromatography (HPLC) with UV absorbance on the CLES column was studied. The influences of the contents of copper ion and methanol as well as the pH value in the mobile phase and temperature of the column on the efficiency of resolution of DL-SeMet were investigated in detail. DL-SeMet could be completely resolved within 40 min under the optimal operating conditions of 0.1 mmol/L Cu2+ at 0.05 mol/L KH2PO4 buffer (pH = 5.5) and 35 degrees C temperature of the column. The limits of detection of D- and L-SeMet were 255 ppb and 286 ppb, respectively. This method was applied to determine the D- and L-enantiomers of DL-SeMet in real samples, such as selenized yeast powder and garlic.",Analytical sciences : the international journal of the Japan Society for Analytical Chemistry,"['D002851', 'D005737', 'D008024', 'D015394', 'D012645', 'D013237', 'D015003']","['Chromatography, High Pressure Liquid', 'Garlic', 'Ligands', 'Molecular Structure', 'Selenomethionine', 'Stereoisomerism', 'Yeasts']",Chiral speciation and determination of DL-selenomethionine enantiomers on a novel chiral ligand-exchange stationary phase.,"['Q000295', 'Q000737', None, None, 'Q000032', None, 'Q000737']","['instrumentation', 'chemistry', None, None, 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15790108,2006,0.0,0.0,,, -15749632,"Garlic consumption is linked with lower incidences of certain cancers perhaps because garlic-derived allyl sulfides inhibit nitrosamine activation by cytochrome P450s. To help evaluate this view, effects of allyl sulfides on O6-methylguanine (O6MG) levels were examined in liver of rats injected with 20 mg/kg of liver carcinogen dimethylnitrosamine (DMN) and killed 3 h later. DNA was isolated and hydrolyzed, and O6MG/guanine ratios were determined by HPLC-fluorescence. Mean inhibition of O6MG formation fell from 89% for 200 to 33% for 12 mg diallyl sulfide (DAS) per kilogram gavaged 18 h before DMN injection. Gavage of DAS 3 or 6 h (instead of 18 h) before DMN injection significantly reduced inhibitions. Mean inhibitions for diallyl disulfide, diallyl sulfoxide, and diallyl sulfone (75-100 mg/kg) gavaged 18 h before DMN were 39%, 72%, and 82%. In lung and kidney, DAS produced mean inhibitions of 98% and 74% compared with 89% in liver. When methylnitrosourea was injected instead of DMN, neither DAS nor DADS inhibited O6MG formation in liver DNA. Feeding 2.5% garlic for 7 days inhibited DMN-induced O6MG formation in liver DNA by 46%, similar to that expected from the estimated yield of allyl sulfides from garlic. Hence, dosing with DAS or feeding garlic may be useful chemopreventive strategies against nitrosamine-induced cancers.",Nutrition and cancer,"['D000498', 'D000818', 'D016588', 'D002851', 'D004247', 'D003849', 'D004128', 'D004305', 'D005737', 'D007668', 'D008099', 'D008168', 'D008297', 'D011897', 'D051381', 'D017207', 'D013440']","['Allyl Compounds', 'Animals', 'Anticarcinogenic Agents', 'Chromatography, High Pressure Liquid', 'DNA', 'Deoxyguanosine', 'Dimethylnitrosamine', 'Dose-Response Relationship, Drug', 'Garlic', 'Kidney', 'Liver', 'Lung', 'Male', 'Random Allocation', 'Rats', 'Rats, Sprague-Dawley', 'Sulfides']",Inhibition by allyl sulfides and crushed garlic of O6-methylguanine formation in liver DNA of dimethylnitrosamine-treated rats.,"['Q000494', None, 'Q000494', 'Q000379', 'Q000187', 'Q000031', 'Q000633', None, 'Q000737', 'Q000737', 'Q000187', 'Q000737', None, None, None, None, 'Q000494']","['pharmacology', None, 'pharmacology', 'methods', 'drug effects', 'analogs & derivatives', 'toxicity', None, 'chemistry', 'chemistry', 'drug effects', 'chemistry', None, None, None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/15749632,2005,,,,, -15686419,"Chemotype analyses and random amplified polymorphic DNA (RAPD) genomic analyses have been applied to the characterization of Allium sativum variety from Voghiera (Ferrara, Italy), a typical Italian product actually demanding the Protected Designation of Origin (PDO). The garlic from Voghiera is characterized by peculiar morphological and composition characteristics. The proximate composition and atomic absorbance spectrometry elemental pattern of this garlic suggested as the chemical composition did not depend on the intrinsic pedologic soil features only, but it was probably connected to some peculiar genetic characters. Amplification of genomic DNA using random primers highlighted a good clustering differentiating of Voghiera Allium sativum from five commercial reference samples used in this study (Piacentino, Serena, France, China, and Adriano varieties), confirming the existence of intervarietal genetic difference. The intravarietal polymorphisms of Voghiera samples were low.",Journal of agricultural and food chemistry,"['D018744', 'D005737', 'D007558', 'D008903', 'D019105']","['DNA, Plant', 'Garlic', 'Italy', 'Minerals', 'Random Amplified Polymorphic DNA Technique']",Chemical and genomic combined approach applied to the characterization and identification of Italian Allium sativum L.,"['Q000032', 'Q000737', None, 'Q000032', None]","['analysis', 'chemistry', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15686419,2005,1.0,2.0,,, -15629528,"A protein designated alliumin, with a molecular mass of 13 kDa and an N-terminal sequence similar to a partial sequence of glucanase, and demonstrating antifungal activity against Mycosphaerella arachidicola, but not against Fusarium oxysporum, was isolated from multiple-cloved garlic (Allium sativum) bulbs. The protein, designated as alliumin, was purified using ion exchange chromatography on DEAE-cellulose, CM-cellulose and Mono S, affinity chromatography on Affi-gel blue gel, and gel filtration on Superdex 75. Alliumin was unadsorbed on DEAE-cellulose, but was adsorbed on Affi-gel blue gel, CM-cellulose and Mono S. Its antifungal activity was retained after boiling for 1 h and also after treatment with trypsin or chymotrypsin (1:1, w/w) for 30 min at room temperature. Alliumin was inhibitory to the bacterium Pseudomonas fluorescens and exerted antiproliferative activity toward leukemia L1210 cells. However, it was devoid of ribonuclease activity, protease activity, mitogenic activity toward mouse splenocytes, and antiproliferative activity toward hepatoma Hep G2 cells.",Peptides,"['D000595', 'D000818', 'D000935', 'D049109', 'D002478', 'D002846', 'D002852', 'D004591', 'D005737', 'D020128', 'D051379', 'D008810', 'D008969', 'D008970', 'D010455', 'D010940', 'D018514', 'D011551', 'D013154']","['Amino Acid Sequence', 'Animals', 'Antifungal Agents', 'Cell Proliferation', 'Cells, Cultured', 'Chromatography, Affinity', 'Chromatography, Ion Exchange', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Inhibitory Concentration 50', 'Mice', 'Mice, Inbred C57BL', 'Molecular Sequence Data', 'Molecular Weight', 'Peptides', 'Plant Proteins', 'Plant Structures', 'Pseudomonas fluorescens', 'Spleen']","Isolation of alliumin, a novel protein with antimicrobial and antiproliferative activities from multiple-cloved garlic bulbs.","[None, None, 'Q000737', 'Q000187', None, None, None, None, 'Q000737', None, None, None, None, None, 'Q000737', 'Q000737', 'Q000737', 'Q000187', 'Q000166']","[None, None, 'chemistry', 'drug effects', None, None, None, None, 'chemistry', None, None, None, None, None, 'chemistry', 'chemistry', 'chemistry', 'drug effects', 'cytology']",https://www.ncbi.nlm.nih.gov/pubmed/15629528,2005,0.0,0.0,,, -15612762,"The sale of botanical dietary supplements in the United States is on the rise. However, limited studies have been conducted on the safety of these supplements. There are reports on the presence of undesired metals in some of the botanical dietary supplements. In this study, echinacea, garlic, ginkgo, ginseng, grape seed extract, kava kava, saw palmetto, and St. John's wort supplements manufactured by Nature's Way, Meijer, GNC, Nutrilite, Solaray, Sundown and Natrol, have been analyzed for lead, mercury, cadmium, arsenic, uranium, chromium, vanadium, copper, zinc, molybdenum, palladium, tin, antimony, thallium, and tungsten using inductively coupled plasma mass spectrometry. All samples were devoid of mercury contamination. Results indicated that the botanical supplements analyzed did not contain unacceptable concentrations of these metals. These supplements were also evaluated for microbial contamination, and most samples analyzed showed the presence of bacteria or fungi or both. Microbes were not counted nor were microbial counts determined in these samples.",Journal of agricultural and food chemistry,"['D001419', 'D019587', 'D004340', 'D005658', 'D008670', 'D028321']","['Bacteria', 'Dietary Supplements', 'Drug Contamination', 'Fungi', 'Metals', 'Plant Preparations']",Evaluation of metal and microbial contamination in botanical supplements.,"['Q000302', None, None, 'Q000302', 'Q000032', 'Q000737']","['isolation & purification', None, None, 'isolation & purification', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15612762,2005,0.0,0.0,,, -15493662,"A method is described for determination of the steroidal saponin, eruboside B, originating in garlic and garlic products as the p-nitrobenzoyl chloride (PNBC) derivative by reversed-phase liquid chromatography (with ultraviolet detection at 260 nm. Proto-eruboside B was extracted from garlic (Allium sativum L.); subjected to solid-phase extraction (SPE) with a C18 cartridge, Florisil column chromatography, and silica gel column chromatography; and then enzymatically converted to eruboside B, which was applied as an external standard. Steroidal saponins in garlic and commercial garlic products were extracted with methanol and purified by SPE cartridges, followed by enzymatic treatment. A frostanol saponin such as proto-eruboside B is enzymatically transformed to a spirostanol saponin, eruboside B. After the derivatization with PNBC, the saponin derivative was chromatographed on a C8 column with a gradient elution of (A) 80% aqueous acetonitrile and (B) 100% acetonitrile. The detection limit of the developed method was 1 microg/g for the samples. The method was applied to the analysis of garlic and garlic health food products available in Japan.",Journal of AOAC International,"['D002853', 'D005504', 'D005737', 'D009579', 'D012503']","['Chromatography, Liquid', 'Food Analysis', 'Garlic', 'Nitrobenzoates', 'Saponins']",Ultraviolet derivatization of steroidal saponin in garlic and commercial garlic products as p-nitrobenzoate for liquid chromatographic determination.,"[None, None, 'Q000737', 'Q000032', 'Q000032']","[None, None, 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15493662,2005,,,,, -15454690,"The sap-sucking homopteran insects, commonly known as aphids and leafhoppers are responsible for a huge amount of lost productivity of mustard, chickpea, cabbage, rice and many other important crops. Due to their unique feeding habits and ability to build up a huge population in a very short time, they are very difficult to control. The objective of the ongoing program is to develop insect-resistant crop species through genetic engineering techniques to combat the yield losses, which necessitates the identification of appropriate control elements. In this direction, mannose-binding 25 kDa lectins have been purified from leaves of garlic, Diffenbachia sequina and tubers of Colocasia esculanta. The purified lectins have been analyzed in SDS-PAGE. The effectiveness of these lectins against chickpea aphids, mustard aphids and green leaf hoppers of rice have been tested. The LC(50) value of each lectin against different insects had been monitored [1,2]. Through immunolocalization analysis, the binding of the lectin had been demonstrated at the epithelial membrane of the midgut of the lectin-treated insects [1]. Receptor proteins of brush border membrane vesicle (BBMV) of the target insects, responsible for binding of the lectin to the midgut of the epithelial layer have been purified and analyzed through ligand assay. Biochemical studies have been undertaken to investigate the lectin-receptor interaction at molecular level.",Glycoconjugate journal,"['D000818', 'D001042', 'D001681', 'D002241', 'D002352', 'D002462', 'D002846', 'D004591', 'D004848', 'D006023', 'D006031', 'D007313', 'D007525', 'D037102', 'D008024', 'D037241', 'D008871', 'D037121', 'D011485']","['Animals', 'Aphids', 'Biological Assay', 'Carbohydrates', 'Carrier Proteins', 'Cell Membrane', 'Chromatography, Affinity', 'Electrophoresis, Polyacrylamide Gel', 'Epithelium', 'Glycoproteins', 'Glycosylation', 'Insecta', 'Isoelectric Focusing', 'Lectins', 'Ligands', 'Mannose-Binding Lectins', 'Microvilli', 'Plant Lectins', 'Protein Binding']",Identification of receptors responsible for binding of the mannose specific lectin to the gut epithelial membrane of the target insects.,"[None, None, None, 'Q000737', 'Q000378', 'Q000378', None, None, 'Q000378', 'Q000737', None, None, None, 'Q000737', None, 'Q000737', 'Q000378', 'Q000378', None]","[None, None, None, 'chemistry', 'metabolism', 'metabolism', None, None, 'metabolism', 'chemistry', None, None, None, 'chemistry', None, 'chemistry', 'metabolism', 'metabolism', None]",https://www.ncbi.nlm.nih.gov/pubmed/15454690,2005,,,,, -15373848,"Allyl isothiocyanate is present in many plants. Allergic contact dermatitis from allyl isothiocyanate is well known but infrequently reported. The aim of this study was to investigate the prevalence of contact allergy to allyl isothiocyanate in patients with suspected contact dermatitis from vegetables and food. 259 such patients were tested at the Department of Dermatology, Gentofte Hospital, Denmark, from 1994 to 2003. Only 2 patients (0.8%) had a positive reaction (+) to allyl isothiocyanate and 43 patients (16.6%) had a ?+ reaction. One of the patients with a positive reaction provided samples of margarine, salad cream, oil and mayonnaise. These were analysed with high-performance liquid chromatography, and a moderate concentration of allyl isothiocyanate (2.5 ppm) was detected in the sample of margarine. This patient was a professional sandwich maker presenting with fingertip dermatitis mimicking 'tulip fingers' or allergic contact dermatitis from garlic and onions. In conclusion, allergic contact dermatitis from allyl isothiocyanate occurs in only a limited number of cases, despite frequent exposure. The large number of ?+ reactions raises the question as to whether the recommended patch test concentration is too low.",Contact dermatitis,"['D000328', 'D015331', 'D003718', 'D017449', 'D009783', 'D005260', 'D005511', 'D005520', 'D008401', 'D006801', 'D017879', 'D012189']","['Adult', 'Cohort Studies', 'Denmark', 'Dermatitis, Allergic Contact', 'Dermatitis, Occupational', 'Female', 'Food Handling', 'Food Preservatives', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Isothiocyanates', 'Retrospective Studies']",Allergic contact dermatitis from allyl isothiocyanate in a Danish cohort of 259 selected patients.,"[None, None, None, 'Q000209', 'Q000209', None, None, 'Q000009', None, None, 'Q000009', None]","[None, None, None, 'etiology', 'etiology', None, None, 'adverse effects', None, None, 'adverse effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/15373848,2005,0.0,0.0,,, -15315393,"Caffeoyl quinic acid (CQA) derivatives in ku-ding-cha, mate, coffee, and related plants were determined by HPLC. One ku-ding-cha contained a large amount of 3,5-dicaffeoylquinic acid (3,5-diCQA, 10.6% in dry weight) as well as 3-CQA (1.7%), 4-CQA (1.1%), 5-CQA (6.3%), 3,4-diCQA (1.8%), and 4,5-diCQA (4.3%). In this ku-ding-cha, the total caffeic acid moiety was 90.3 mmol/100 g of dry weight. The leaves of Ilex latifolia, which is one original species of ku-ding-cha, and another plant of the same genus, I. rotunda, also contained 3,5-diCQA (9.5 and 14.6%), 3-CQA (4.3 and 1.9%), and 5-CQA (4.8 and 3.8%), respectively, whereas raw coffee bean contained 5.5% 5-CQA and other low CQA derivatives. 3,5-DiCQA and 5-CQA with an apple acetone powder (AP) containing polyphenol oxidase showed high capturing activities toward thiols, and two addition compounds between 3,5-diCQA and methane thiol were also identified. Ku-ding-cha indicated extremely strong capturing activities toward methanethiol, propanethiol, and 2-propenethiol in the presence of apple AP. Furthermore, drinking ku-ding-cha reduced the amount of allyl methyl sulfide gas, well-known to persist as malodorous breath long after the ingestion of garlic.",Journal of agricultural and food chemistry,"['D000498', 'D001628', 'D028241', 'D002851', 'D040503', 'D003836', 'D031659', 'D027845', 'D011801', 'D013438', 'D013440']","['Allyl Compounds', 'Beverages', 'Camellia sinensis', 'Chromatography, High Pressure Liquid', 'Coffea', 'Deodorants', 'Ligustrum', 'Malus', 'Quinic Acid', 'Sulfhydryl Compounds', 'Sulfides']",Deodorization with ku-ding-cha containing a large amount of caffeoyl quinic acid derivatives.,"['Q000737', 'Q000032', 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000031', 'Q000737', 'Q000737']","['chemistry', 'analysis', 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'analogs & derivatives', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15315393,2004,0.0,0.0,,, -15289986,"An online UV photolysis and UV/TiO2 photocatalysis reduction device (UV-UV/TiO2 PCRD) and an electrochemical vapor generation (ECVG) cell have been used for the first time as an interface between high-performance liquid chromatography (HPLC) and atomic fluorescence spectrometry (AFS) for selenium speciation. The newly designed ECVG cell of approximately 115 microL dead volume consists of a carbon fiber cathode and a platinum loop anode; the atomic hydrogen generated on the cathode was used to reduce selenium to vapor species for AFS determination. The noise was greatly reduced compared with that obtained by use of the UV-UV/TiO2 PCRD-KBH4-acid interface. The detection limits obtained for seleno-DL: -cystine (SeCys), selenite (Se(IV)), seleno-DL: -methionine (SeMet), and selenate (Se(VI)) were 2.1, 2.9, 4.3, and 3.5 ng mL(-1), respectively. The proposed method was successfully applied to the speciation of selenium in water-soluble extracts of garlic shoots cultured with different selenium species. The results obtained suggested that UV-UV/TiO2 PCRD-ECVG should be an effective interface between HPLC and AFS for the speciation of elements amenable to vapor generation, and is superior to methods involving KBH4.",Analytical and bioanalytical chemistry,"['D002384', 'D002851', 'D004563', 'D005740', 'D036103', 'D010084', 'D010777', 'D012643', 'D013050', 'D014025', 'D014466']","['Catalysis', 'Chromatography, High Pressure Liquid', 'Electrochemistry', 'Gases', 'Nanotechnology', 'Oxidation-Reduction', 'Photochemistry', 'Selenium', 'Spectrometry, Fluorescence', 'Titanium', 'Ultraviolet Rays']",Electrochemical vapor generation of selenium species after online photolysis and reduction by UV-irradiation under nano TiO2 photocatalysis and its application to selenium speciation by HPLC coupled with atomic fluorescence spectrometry.,"[None, 'Q000379', None, None, None, None, None, 'Q000737', 'Q000379', 'Q000737', None]","[None, 'methods', None, None, None, None, None, 'chemistry', 'methods', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/15289986,2006,1.0,1.0,,, -15265743,"Allicin (diallylthiosulfinate), the active substance of garlic, has been shown to possess a variety of biological activities. Mechanistic and pharmacokinetic studies of allicin and its derivatives raise the need for a labeled compound. However, labeling of this volatile and unstable liquid requires delicate handling. Here, we describe a simple method for the preparation of (3)H-labeled allicin. This was achieved by applying synthetic [(3)H]alliin ([2,3-(3)H]allylcysteine sulfoxide) to a column containing immobilized alliinase [EC 4.1.1.4.] from garlic. Purification of [(3)H]allicin was done by differential adsorbtion of the reaction components on a neutral polystyrene resin, Porapak Q. Thiol-containing compounds are known to be the main target of allicin. In this work we demonstrated that [(3)H]allicin can be used for the synthesis of labeled [(3)H]allylmercapto derivatives of SH peptides and proteins. Thus, we prepared [(3)H]S-allylmercaptoglutathione which can be used in metabolic studies. Moreover, we showed that incubation of alliinase with [(3)H]allicin led to modification of 1.4 cysteine residues per subunit of the enzyme.",Analytical biochemistry,"['D000327', 'D002850', 'D013441', 'D014316']","['Adsorption', 'Chromatography, Gel', 'Sulfinic Acids', 'Tritium']",[3H]Allicin: preparation and applications.,"[None, None, 'Q000138', None]","[None, None, 'chemical synthesis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15265743,2005,0.0,0.0,,, -15161196,"The 26S proteasome (multicatalytic protease complex, MPC) was purified from fresh garlic cloves (Allium sativum) to near homogeneity by ion exchange chromatography on DEAE-sephacel, gel filtration on Sepharose-4B, and glycerol density gradient centrifugation. Two alpha-type (20S proteasome ""catalytic core"") subunits were identified by the direct sequencing of peptide fragments (mass fingerprint analysis, Mass Spectrometry Lab, Stanford University) or the sequencing of a cloned cDNA generated using a garlic cDNA library as the template; these subunits were found to have a high homology to those from other plants. Polyacrylamide gel electrophoresis under denaturing conditions separated the garlic MPC into multiple polypeptides having molecular masses in the range of 21-35 (components of the 20S catalytic core) and 55-100 kDa (components of the 19S regulatory units). The banding pattern of the garlic MCP is similar to that of spinach and rat liver with minor differences in some components; however, polyclonal antibodies against mammalian proteasomes failed to significantly stain the enzyme from garlic. This is the first work to identify the garlic proteasome.",Journal of agricultural and food chemistry,"['D000595', 'D001483', 'D003001', 'D018744', 'D005737', 'D008969', 'D010447', 'D011480', 'D046988']","['Amino Acid Sequence', 'Base Sequence', 'Cloning, Molecular', 'DNA, Plant', 'Garlic', 'Molecular Sequence Data', 'Peptide Hydrolases', 'Protease Inhibitors', 'Proteasome Endopeptidase Complex']",The 26S proteasome in garlic (Allium sativum): purification and partial characterization.,"[None, None, None, 'Q000737', 'Q000201', None, 'Q000737', 'Q000494', None]","[None, None, None, 'chemistry', 'enzymology', None, 'chemistry', 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/15161196,2004,0.0,0.0,,, -15139418,"Garlic (Allium sativum L.) is highly consumed worldwide. This crop is mainly known for its flavor and odor, although the many medicinal properties that are attributed to it, including anticarcinogenic, antiatherosclerotic, and antithrombotic potential, among several others, have called the attention of scientists since very early times. It is known that sulfur-containing volatiles are the principal compounds responsible for such properties. The aims of this work were to develop a solventless extraction method for sulfur-containing volatiles from garlic, as well as their chemical characterization. Since garlic volatiles are rather thermolabile, low-pressure hydrodistillation was chosen as the extracting method. The analysis of all compounds was performed on an HP-FFAP chromatographic column mounted in a GC-MS system. For volatile transfer and injection method, solid-phase microextraction was selected, with the use of eight different fibers. The most abundant volatile compound was diallyl disulfide, followed by diallyl trisulfide. Among the 47 totally identified compounds, 18 were linear sulfur-containing volatile compounds, 6 were of non-sulfur nature, and the other 23 were cyclic compounds. However, linear sulfur volatiles accounted for 94% of the total amount.",Journal of chromatography. A,"['D000498', 'D008401', 'D013440']","['Allyl Compounds', 'Gas Chromatography-Mass Spectrometry', 'Sulfides']",Solid-phase microextraction-gas chromatographic-mass spectrometric analysis of garlic oil obtained by hydrodistillation.,"['Q000737', 'Q000379', 'Q000737']","['chemistry', 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15139418,2004,0.0,0.0,,no units , -15137816,"A stable isotope dilution assay was developed for the quantitation of the potent onion odorant 3-mercapto-2-methylpentan-1-ol (1) using mass chromatography and synthesized [(2)H(2)]-3-mercapto-2-methylpentan-1-ol as the internal standard. Application of the newly developed method on onions from different origins revealed amounts between 8 and 32 microg/kg in raw onions, whereas 34-246 microg was found in sliced, stored (50 min), and then cooked onions. In extracts prepared by simultaneous steam distillation-extraction the highest concentrations of 1 were formed, amounting to >1200 microg/kg. The much higher content of 3-mercapto-2-methylpentan-1-ol in cooked onions suggested its formation from specific, yet unkown, precursors enzymatically formed during cutting of raw onions. 1 was for the first time identified and also quantified in other Allium species such as chives, scallions, and leek, whereas surprisingly garlic and bear's garlic did not contain the aroma compound.",Journal of agricultural and food chemistry,"['D000490', 'D003903', 'D008401', 'D007201', 'D009812', 'D000439', 'D013438']","['Allium', 'Deuterium', 'Gas Chromatography-Mass Spectrometry', 'Indicator Dilution Techniques', 'Odorants', 'Pentanols', 'Sulfhydryl Compounds']",Quantitation of the intense aroma compound 3-mercapto-2-methylpentan-1-ol in raw and processed onions (Allium cepa) of different origins and in other Allium varieties using a stable isotope dilution assay.,"['Q000737', None, None, None, 'Q000032', 'Q000032', 'Q000032']","['chemistry', None, None, None, 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15137816,2004,1.0,1.0,,, -15065784,"Garlic and onion, are well known for their medical value, especially in against cancer and anticardiovacular diseases. ""Alliins"" (S-alk(en)yl-L-cysteine sulphoxides) are sources of major active compounds in Allium plants. Se incorporation into garlic significantly increases activities of garlic in cancer prevention and inhibition. Selenomethionine, selenocysteine and Se-methylselenocysteine have been identified in garlic and onion. Previously we identified gamma-glutamyl-Se-methyl-L-selenocysteine, in extracts of garlic cultivated in Se-rich soil [Med. Res. Rev. 16 (1) (1996) 111], suggesting the possible existence of Se-alk(en)yl-L-cysteine selenoxides (Se-""alliins"") in garlic. Several comparative experiments were carried out to demonstrate the existence of Se-""alliins"" in Se-enriched garlic and onion. We found that there was one similar time-dependent Se signal in HPLC-inductively coupled plasma MS chromatograms of cold-water extracts of freeze-dried garlic powder and fresh garlic. This signal was lost when the extracts of garlic powder and fresh garlic were stored for 1 day at >4 degrees C, but remained in fresh onion extract at the same storage conditions. These phenomena and possible mechanisms are discussed. An additional experiment showed that Allium species cultivated in Se-rich soil might contain two different Se-""alliins"".",Journal of chromatography. A,"['D002851', 'D003545', 'D005737', 'D013058', 'D019697', 'D012643', 'D012987']","['Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Mass Spectrometry', 'Onions', 'Selenium', 'Soil']","High-performance liquid chromatographic-inductively coupled plasma mass spectrometric evidence for Se-""alliins"" in garlic and onion grown in Se-rich soil.","['Q000379', 'Q000031', 'Q000737', 'Q000379', 'Q000737', 'Q000032', 'Q000032']","['methods', 'analogs & derivatives', 'chemistry', 'methods', 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15065784,2004,0.0,0.0,,no quantification, -15028723,"The homopteran sucking insect, Lipaphis erysimi (mustard aphid) causes severe damage to various crops. This pest not only affects plants by sucking on the phloem, but it also transmits single-stranded RNA luteoviruses while feeding, which cause disease and damage in the crop. The mannose-binding Allium sativum (garlic) leaf lectin has been found to be a potent control agent of L. erysimi. The lectin receptor protein isolated from brush border membrane vesicle of insect gut was purified to determine the mechanism of lectin binding to the gut. Purified receptor was identified as an endosymbiotic chaperonin, symbionin, using liquid chromatography-tandem mass spectrometry. Symbionin from endosymbionts of other aphid species have been reported to play a significant role in virus transmission by binding to the read-through domain of the viral coat protein. To understand the molecular interactions of the said lectin and this unique symbionin molecule, the model structures of both molecules were generated using the Modeller program. The interaction was confirmed through docking of the two molecules forming a complex. A surface accessibility test of these molecules demonstrated a significant reduction in the accessibility of the complex molecule compared with that of the free symbionin molecule. This reduction in surface accessibility may have an effect on other molecular interactive processes, including ""symbionin virion recognition"", which is essential for such symbionin-mediated virus transmission. Thus, garlic leaf lectin provides an important component of a crop management program by controlling, on one hand, aphid attack and on the other hand, symbionin-mediated luteovirus transmission.",The Journal of biological chemistry,"['D000373', 'D000595', 'D001426', 'D018833', 'D005737', 'D008958', 'D008969', 'D009149', 'D011485', 'D011956', 'D013559']","['Agglutinins', 'Amino Acid Sequence', 'Bacterial Proteins', 'Chaperonins', 'Garlic', 'Models, Molecular', 'Molecular Sequence Data', 'Mustard Plant', 'Protein Binding', 'Receptors, Cell Surface', 'Symbiosis']",The Interactions of Allium sativum leaf agglutinin with a chaperonin group of unique receptor protein isolated from a bacterial endosymbiont of the mustard aphid.,"['Q000378', None, 'Q000235', 'Q000235', 'Q000378', None, None, 'Q000382', None, 'Q000235', None]","['metabolism', None, 'genetics', 'genetics', 'metabolism', None, None, 'microbiology', None, 'genetics', None]",https://www.ncbi.nlm.nih.gov/pubmed/15028723,2004,0.0,0.0,,, -15003558,"This paper presents an automatic spectrofluorimetric method (flow injection spectrofluorimetry) using a novel fluorescent probe named H. Py. Bzt (2-(2-pyridil)-benzothiazoline) for determining superoxide dismutase (SOD) activity. The fluorescent probe was synthesized in house and fully characterized by elemental analysis and by infrared and (1)H nuclear magnetic resonance spectra. It could specially identify and trap O(2)(*-) and was oxidized by O(2)(*-) to form a strong fluorescence product. Based on this reaction, the flow injection spectrofluorimetric method was proposed and successfully used to determine SOD activity. The proposed method has a better selectivity in the determination of reactive oxygen species because the probe can be oxidized only by O(2)(*-) excluding H(2)O(2). As a kind of simple, rapid, precise, sensitive and automatic technique, it was applied to measurement of SOD activity in scallion, garlic, and onion with satisfactory results.",Analytical biochemistry,"['D052160', 'D017022', 'D005456', 'D005609', 'D013050', 'D013482', 'D013481', 'D013844']","['Benzothiazoles', 'Flow Injection Analysis', 'Fluorescent Dyes', 'Free Radicals', 'Spectrometry, Fluorescence', 'Superoxide Dismutase', 'Superoxides', 'Thiazoles']",Study and application of flow injection spectrofluorimetry with a fluorescent probe of 2-(2-pyridil)-benzothiazoline for superoxide anion radicals.,"[None, None, 'Q000737', 'Q000032', 'Q000379', 'Q000378', 'Q000032', None]","[None, None, 'chemistry', 'analysis', 'methods', 'metabolism', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15003558,2004,0.0,0.0,,, -14969516,"A quantitative method is described for the determination of allicin (2-propene-1-sulfinothioic acid S-2-propenyl ester) in garlic, using standard additions of alliin (l-(+)-S-allylcysteine sulfoxide) in conjunction with supercritical fluid extraction (SFE) and high performance liquid chromatography analysis with UV-vis absorbance detection. Optimum CO(2)-SFE conditions provided 96% recovery for allicin with precision of 3% (RSD) for repeat samples. The incorporation of an internal standard (allyl phenyl sulfone) in the SFE step resulted in a modest improvement in recovery (99%) and precision (2% RSD). Standard additions of alliin were converted to allicin in situ by endogenous alliinase (l-(+)-S-alk(en)ylcysteine sulfoxide lyase, EC 4.4.1.4). Complete conversion of the spiked alliin to allicin was achieved by making additions after homogenization-induced conversion of the naturally occurring cysteine sulfoxides to thiosulfinates had taken place, thus eliminating the likelihood of competing reactions. Concentration values for allicin determined in samples of fresh garlic (Allium sativum L. and Allium ampeloprasum) and commercially available garlic powders (Allium sativum L.) by standard addition of alliin were found in all cases to be in statistical agreement (95% confidence interval) with values determined using a secondary allicin standard (concentration determined using published extinction coefficients). This method provides a convenient alternative for assessing the amount of allicin present in fresh and powdered garlic, as alliin is a far more stable and commercially prevalent compound than allicin and is thus more amenable for use as a standard for routine analysis.",Journal of agricultural and food chemistry,"['D013437', 'D002851', 'D025924', 'D003545', 'D005737', 'D012680', 'D013441']","['Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Chromatography, Supercritical Fluid', 'Cysteine', 'Garlic', 'Sensitivity and Specificity', 'Sulfinic Acids']",Quantitative determination of allicin in garlic: supercritical fluid extraction and standard addition of alliin.,"['Q000378', None, None, 'Q000008', 'Q000737', None, 'Q000032']","['metabolism', None, None, 'administration & dosage', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/14969516,2004,0.0,0.0,,, -14763870,"Allicin and allyl-methyl plus methyl-allyl thiosulfinate from acetonic garlic extracts (AGE) have been isolated by high-performance liquid chromatography. These compounds have shown inhibition of the in vitro growth of Helicobacter pylori (Hp), the bacterium responsible for serious gastric diseases such as ulcers and even gastric cancer. A chromatographic method was optimized and used to isolate these thiosulfinates. The method developed has allowed the isolation of natural thiosulfinates extracted from garlic by organic solvents and is an easy and cheap methodology that avoids complex synthesis and purification procedures. The capacity and effectiveness of isolated natural thiosulfinates have been tested, and this has enabled the identification of the main compounds responsible for the bacteriostatic activity shown by AGE origin of these kinds of organosulfur compounds along with ethanolic garlic extracts (EGE). Additionally, microbiological analyses have suggested that these compounds show a synergic effect on the inhibition of the in vitro growth of Hp. The results described here facilitate the process of obtaining garlic extracts with optimal bacteriostatic properties. The product is obtained in a way that avoids expensive purification methods and will allow the design of live tests with the aim of investigating the potential for the use of these garlic derivatives in the treatment of patients with Hp infections.",Biotechnology progress,"['D000900', 'D001673', 'D002455', 'D004355', 'D005737', 'D016480', 'D007700', 'D010936', 'D013441', 'D013696']","['Anti-Bacterial Agents', 'Biodegradation, Environmental', 'Cell Division', 'Drug Stability', 'Garlic', 'Helicobacter pylori', 'Kinetics', 'Plant Extracts', 'Sulfinic Acids', 'Temperature']","Allyl-thiosulfinates, the bacteriostatic compounds of garlic against Helicobacter pylori.","['Q000737', None, 'Q000187', None, 'Q000737', 'Q000166', None, 'Q000737', 'Q000737', None]","['chemistry', None, 'drug effects', None, 'chemistry', 'cytology', None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/14763870,2004,0.0,0.0,,, -14713923,"Garlic (Allium sativum) is one of the most common relishes used in cooking worldwide. Very few garlic allergens have been reported, and garlic allergy has been rarely studied.",The Journal of allergy and clinical immunology,"['D000293', 'D000328', 'D000485', 'D000596', 'D002241', 'D013437', 'D002648', 'D004591', 'D005260', 'D005512', 'D005737', 'D006801', 'D015151', 'D007073', 'D008297', 'D013058', 'D008875', 'D012882']","['Adolescent', 'Adult', 'Allergens', 'Amino Acids', 'Carbohydrates', 'Carbon-Sulfur Lyases', 'Child', 'Electrophoresis, Polyacrylamide Gel', 'Female', 'Food Hypersensitivity', 'Garlic', 'Humans', 'Immunoblotting', 'Immunoglobulin E', 'Male', 'Mass Spectrometry', 'Middle Aged', 'Skin Tests']","Identification and immunologic characterization of an allergen, alliin lyase, from garlic (Allium sativum).","[None, None, 'Q000276', 'Q000032', 'Q000032', 'Q000276', None, None, None, 'Q000453', 'Q000201', None, 'Q000379', 'Q000097', None, None, None, None]","[None, None, 'immunology', 'analysis', 'analysis', 'immunology', None, None, None, 'epidemiology', 'enzymology', None, 'methods', 'blood', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/14713923,2004,0.0,0.0,,, -14667466,"Diallyl sulfide (DAS) is one of the major components of garlic (Allium sativum) and is widely used in the world for food. In this study, DAS was selected for testing the inhibition of arylamine N-acetyltransferase (NAT) activity (N-acetylation of 2-aminofluorene) and gene expression (mRNA NAT) in human colon cancer cell lines (colo 205, colo 320 DM and colo 320 HSR). The NAT activity was examined by high performance liquid chromatography and indicated that a 24 h DAS treatment decreases N-acetylation of 2-aminofluorene in three colon (colo 205, 320 DM and colo 320 HSR) cancer cell lines. The NAT enzymes (protein) were analyzed by western blotting and flow cytometry and it indicated that DAS decreased the levels of NAT in three colon (colo 205, 320 DM and colo 320 HSR) cancer cell lines. The gene expression of NAT (mRNAT NAT) was determined by polymerase chain reaction (PCR), it was shown that DAS affect mRNA NAT expression in examined human colon cancer cell lines. This report is the first to demonstrate that DAS does inhibit human colon cancer cell NAT activity and gene expression.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000498', 'D000972', 'D001191', 'D015153', 'D045744', 'D003110', 'D065607', 'D005434', 'D005737', 'D015972', 'D006801', 'D008517', 'D016133', 'D012333', 'D013440']","['Allyl Compounds', 'Antineoplastic Agents, Phytogenic', 'Arylamine N-Acetyltransferase', 'Blotting, Western', 'Cell Line, Tumor', 'Colonic Neoplasms', 'Cytochrome P-450 Enzyme Inhibitors', 'Flow Cytometry', 'Garlic', 'Gene Expression Regulation, Neoplastic', 'Humans', 'Phytotherapy', 'Polymerase Chain Reaction', 'RNA, Messenger', 'Sulfides']",Inhibition of N-acetyltransferase activity and gene expression in human colon cancer cell lines by diallyl sulfide.,"['Q000008', 'Q000008', 'Q000187', None, 'Q000187', 'Q000201', None, None, None, 'Q000187', None, None, None, 'Q000187', 'Q000008']","['administration & dosage', 'administration & dosage', 'drug effects', None, 'drug effects', 'enzymology', None, None, None, 'drug effects', None, None, None, 'drug effects', 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/14667466,2004,0.0,0.0,,, -14640577,"The extract of garlic skins (peels) showed strong antioxidant activity, and some responsible constituents were isolated and identified. Garlic (Allium sativum L.) has been used as an herbal medicine, but there is no report on the health benefits of the skin or peel. In this study, the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity of garlic skin extract was evaluated. Using chromatographic techniques, the active constituents were isolated and subsequently identified. Analyses by high-performance liquid chromatography coupled with a photodiode array detector (HPLC-PDA) suggested that these compounds were phenylpropanoids, which had a characteristic absorbance at 300-320 nm. Liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance analyses allowed the chemical structures of the isolated constituents to be postulated. The proposed compounds were subsequently synthesized and compared with the constituents in the extract using HPLC-PDA and LC-MS. N-trans-Coumaroyloctopamine, N-trans-feruloyloctopamine, guaiacylglycerol-beta-ferulic acid ether, and guaiacylglycerol-beta-caffeic acid ether were identified as were trans-coumaric acid and trans-ferulic acid. Also, the antioxidant activities of these compounds were determined.",Journal of agricultural and food chemistry,"['D000975', 'D001713', 'D002851', 'D002934', 'D003373', 'D000431', 'D005737', 'D009682', 'D013058', 'D010851', 'D010936', 'D018514']","['Antioxidants', 'Biphenyl Compounds', 'Chromatography, High Pressure Liquid', 'Cinnamates', 'Coumaric Acids', 'Ethanol', 'Garlic', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Picrates', 'Plant Extracts', 'Plant Structures']",Identification of six phenylpropanoids from garlic skin as major antioxidants.,"['Q000032', None, None, 'Q000032', None, None, 'Q000737', None, None, 'Q000737', 'Q000737', 'Q000737']","['analysis', None, None, 'analysis', None, None, 'chemistry', None, None, 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/14640577,2004,0.0,0.0,,, -14633659,"A short-term feeding regimen was designed to analyze the effects of compounds such as diallyl disulfide (DADS), diallylthiosulfinate (allicin) from garlic and butylated hydroxyanisole (BHA) on glutathione S-transferase (GST) expression in the gastrointestinal tract and liver of male mice. After animals were force-fed these compounds, tissue GSTs were purified and individual subunits resolved by HPLC and identified on the basis of mass spectrometry (ESI MS) and immunoreactivity data. The effects of DADS and allicin on GST expression were especially prominent in stomach and small intestine, where there were major coordinate changes in GST subunit profiles. In particular, the transcripts of the mGSTM1 and mGSTM4 genes, which share large segments of common 5'-flanking sequences, and their corresponding subunits were selectively induced. Levels of alpha class subunits also increased, whereas mGSTM3 and mGSTP1 were not affected. The inducible mGSTA5 and non-responsive mGSTM3 subunits had not been identified previously. Liver and colon GSTs were also affected to a lesser extent, but this short-term feeding regimen had no effect on GST subunit patterns from other organs, including heart, brain and testis. Real-time PCR (TaqMan) methods were used for quantitative estimations of relative amounts of the mRNAs encoding the GSTs. Effects on the transcripts generally paralleled changes at the protein level, for the most part, however, the greatest relative increases were observed for those mRNAs that were expressed at low abundance constituitively. Mechanisms by which the organosulfur compounds operate to affect GST transcription could involve reversible modification of certain protein sulfhydryl groups, shifts in reduced glutathione/oxidized glutathione ratios and resultant changes in cellular redox status.",Carcinogenesis,"['D000498', 'D000818', 'D001483', 'D002851', 'D004220', 'D041981', 'D005786', 'D005982', 'D008099', 'D051379', 'D008969', 'D016415', 'D013457']","['Allyl Compounds', 'Animals', 'Base Sequence', 'Chromatography, High Pressure Liquid', 'Disulfides', 'Gastrointestinal Tract', 'Gene Expression Regulation', 'Glutathione Transferase', 'Liver', 'Mice', 'Molecular Sequence Data', 'Sequence Alignment', 'Sulfur Compounds']",Selective expression of glutathione S-transferase genes in the murine gastrointestinal tract in response to dietary organosulfur compounds.,"['Q000378', None, None, None, 'Q000378', 'Q000378', 'Q000502', 'Q000096', 'Q000378', None, None, None, 'Q000378']","['metabolism', None, None, None, 'metabolism', 'metabolism', 'physiology', 'biosynthesis', 'metabolism', None, None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/14633659,2004,0.0,0.0,,, -14586535,"The coupling reaction of 4-aminoantipyrine (4-AAP) with phenol using the superoxide anion radical (O2-*) as oxidizing agent under the catalysis of horseradish peroxidase (HRP) was studied. Based on the reaction, O2-* produced by irradiating vitamin B2 (VB2) was spectrophotometrically determined at 510 nm. Under the optimum experimental conditions, the relationship between A510 and O2-* concentration was linear in the range 9.14x10(-6)-1.2x10(-4) mol L(-1). The detection limit was determined to be 1.37x10(-6) mol L(-1). A possible reaction mechanism was discussed. The effect of interferences and surfactants on the determination of O2-* was also investigated. The proposed method was applied to determine superoxide dismutase activity in garlic, scallion, and onion with satisfactory results.",Analytical and bioanalytical chemistry,"['D002384', 'D004126', 'D005737', 'D006735', 'D006801', 'D006863', 'D019697', 'D019800', 'D013050', 'D013056', 'D013425', 'D013482', 'D013481', 'D014675']","['Catalysis', 'Dimethylformamide', 'Garlic', 'Horseradish Peroxidase', 'Humans', 'Hydrogen-Ion Concentration', 'Onions', 'Phenol', 'Spectrometry, Fluorescence', 'Spectrophotometry, Ultraviolet', 'Sulfanilic Acids', 'Superoxide Dismutase', 'Superoxides', 'Vegetables']",Simple and rapid catalytic spectrophotometric determination of superoxide anion radical and superoxide dismutase activity in natural medical vegetables using phenol as the substrate for horseradish peroxidase.,"[None, 'Q000737', 'Q000737', 'Q000378', None, None, 'Q000737', 'Q000378', 'Q000379', None, 'Q000737', 'Q000032', 'Q000032', 'Q000737']","[None, 'chemistry', 'chemistry', 'metabolism', None, None, 'chemistry', 'metabolism', 'methods', None, 'chemistry', 'analysis', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/14586535,2004,0.0,0.0,,, -14530594,"1,2,3,4-Tetrahydro-beta-carboline derivatives (THbetaCs) are formed through Pictet-Spengler chemical condensation between tryptophan and aldehydes during food production, storage and processing. In the present study, in order to identify the antioxidants in aged garlic extract (AGE), we fractionated it and identified four THbetaCs; 1-methyl-1,2,3,4-tetrahydro-beta-carboline-3-carboxylic acids (MTCC) and 1-methyl-1,2,3,4-tetrahydro-beta-carboline-1,3-dicarboxylic acid (MTCdiC) in both diastereoisomers using liquid chromatography mass spectrometry (LC-MS). Interestingly, these compounds were not detected in raw garlic, but the contents increased during the natural aging process of garlic. In in vitro assay systems, all of these compounds have shown strong hydrogen peroxide scavenging activities. (1S, 3S)-MTCdiC was found to be stronger than the common antioxidant, ascorbic acid. MTCC and MTCdiC inhibited AAPH-induced lipid peroxidation. Both MTCdiCs also inhibited LPS-induced nitrite production from murine macrophages at 10-100 microM. Our data suggest that these compounds are potent antioxidants in AGE, and thus may be useful for prevention of disorders associated with oxidative stress.","BioFactors (Oxford, England)","['D000818', 'D000975', 'D002243', 'D002460', 'D016166', 'D005737', 'D006861', 'D015227', 'D008264', 'D009682', 'D013058', 'D051379', 'D009573', 'D010936', 'D013237', 'D013997']","['Animals', 'Antioxidants', 'Carbolines', 'Cell Line', 'Free Radical Scavengers', 'Garlic', 'Hydrogen Peroxide', 'Lipid Peroxidation', 'Macrophages', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Mice', 'Nitrites', 'Plant Extracts', 'Stereoisomerism', 'Time Factors']",Antioxidant effects of tetrahydro-beta-carboline derivatives identified in aged garlic extract.,"[None, 'Q000494', 'Q000032', None, 'Q000494', 'Q000737', None, 'Q000187', 'Q000187', None, None, None, 'Q000378', 'Q000737', None, None]","[None, 'pharmacology', 'analysis', None, 'pharmacology', 'chemistry', None, 'drug effects', 'drug effects', None, None, None, 'metabolism', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/14530594,2004,,,,error on the link, -12923610,"Several sampling techniques based on steam distillation (SD), simultaneous distillation and solvent extraction (SDE), solid-phase trapping solvent extraction (SPTE), and headspace solid-phase microextraction (HS-SPME) have been compared for the determination of Korean garlic flavor components by gas chromatography-mass spectrometry (GC-MS). Diallyl disulfide (57.88%), allyl sulfide (23.59%), and diallyl trisulfide (11.40%) were found to be the predominant flavor components of garlic samples extracted by SDE whereas these components were at levels of 89.77%, 2.43%, and 3.89% when the same sample was extracted by SD, 97.77%, 0.17%, and 0.10% by SPTE, and 97.85%, 0.01%, and 0.01% by HS-SPME using the 50/30-microm divinyl benzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber. Thermal degradation of components such as allyl methyl sulfide, dimethyl disulfide, and thiirane were observed for SDE and SD but not for SPTE or HS-SPME. HS-SPME had several advantages compared with SD, SDE, and SPTE-rapid solvent-free extraction, no apparent thermal degradation, less laborious manipulation, and less sample requirement. Five different fiber coatings were evaluated to select a suitable fiber for HS-SPME of garlic flavor components. DVB/CAR/PDMS was most efficient among the five types of fiber investigated.",Analytical and bioanalytical chemistry,"['D005421', 'D005737', 'D008401', 'D015203', 'D012997']","['Flavoring Agents', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Reproducibility of Results', 'Solvents']",Comparative study of extraction techniques for determination of garlic flavor components by gas chromatography-mass spectrometry.,"['Q000737', 'Q000737', 'Q000379', None, None]","['chemistry', 'chemistry', 'methods', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12923610,2004,2.0,3.0,,, -12891227,"Garlic (Allium sativum L.) is a commonly used food and herbal supplement. The objective of this study was to assess in healthy volunteers (N = 14) the influence of a garlic extract on the activity of cytochrome P450 (CYP) 2D6 and 3A4. Probe substrates dextromethorphan (CYP2D6) and alprazolam (CYP3A4) were administered orally at baseline and again after treatment with garlic extract (3 x 600 mg twice daily) for 14 days. Urinary dextromethorphan/dextrorphan ratios and alprazolam plasma concentrations were determined by HPLC at baseline and after garlic extract treatment. The ratio of dextromethorphan to its metabolite was 0.044 +/- 0.48 at baseline and 0.052 +/- 0.095 after garlic supplementation. There were no significant differences between the baseline and garlic phases (P > or =.05). For alprazolam, there were no significant differences in pharmacokinetic parameters at baseline and after garlic extract treatment (all P values > or =.05; maximum concentration in plasma, 27.3 +/- 2.6 ng/mL versus 27.3 +/- 4.8 ng/mL; time to reach maximum concentration in plasma, 1.9 +/- 1.4 h versus 2.4 +/- 1.8 h; area under the time-versus-concentration curve, 537 +/- 94 h. ng. mL(-1) versus 548 +/- 159 h. ng. mL(-1); half-life of elimination, 13.7 +/- 4.4 h versus 14.5 +/- 4.3 h). Our results indicate that garlic extracts are unlikely to alter the disposition of coadministered medications primarily dependent on the CYP2D6 or CYP3A4 pathway of metabolism.",Clinical pharmacology and therapeutics,"['D000328', 'D000525', 'D019540', 'D001711', 'D002851', 'D019389', 'D051544', 'D003577', 'D003915', 'D019587', 'D005260', 'D005737', 'D006207', 'D006801', 'D007527', 'D008297', 'D013441']","['Adult', 'Alprazolam', 'Area Under Curve', 'Biotransformation', 'Chromatography, High Pressure Liquid', 'Cytochrome P-450 CYP2D6', 'Cytochrome P-450 CYP3A', 'Cytochrome P-450 Enzyme System', 'Dextromethorphan', 'Dietary Supplements', 'Female', 'Garlic', 'Half-Life', 'Humans', 'Isoenzymes', 'Male', 'Sulfinic Acids']",Effects of garlic (Allium sativum L.) supplementation on cytochrome P450 2D6 and 3A4 activity in healthy volunteers.,"[None, 'Q000493', None, None, None, 'Q000378', None, 'Q000378', 'Q000493', None, None, None, None, None, 'Q000378', None, 'Q000378']","[None, 'pharmacokinetics', None, None, None, 'metabolism', None, 'metabolism', 'pharmacokinetics', None, None, None, None, None, 'metabolism', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/12891227,2003,0.0,0.0,,, -12888387,"A scientific basis for the evaluation of the risk to public health arising from excessive dietary intake of nitrate in Korea is provided. The nitrate () and nitrite () contents of various vegetables (Chinese cabbage, radish, lettuce, spinach, soybean sprouts, onion, pumpkin, green onion, cucumber, potato, carrot, garlic, green pepper, cabbage and Allium tuberosum Roth known as Crown daisy) are reported. Six hundred samples of 15 vegetables cultivated during different seasons were analysed for nitrate and nitrite by ion chromatography and ultraviolet spectrophotometry, respectively. No significant variance in nitrate levels was found for most vegetables cultivated during the summer and winter harvests. The mean nitrates level was higher in A. tuberosum Roth (5150 mg kg(-1)) and spinach (4259 mg kg(-1)), intermediate in radish (1878 mg kg(-1)) and Chinese cabbage (1740 mg kg(-1)), and lower in onion (23 mg kg(-1)), soybean sprouts (56 mg kg(-1)) and green pepper (76 mg kg(-1)) compared with those in other vegetables. The average nitrite contents in various vegetables were about 0.6 mg kg(-1), and the values were not significantly different among most vegetables. It was observed that nitrate contents in vegetables varied depending on the type of vegetables and were similar to those in vegetables grown in other countries. From the results of our studies and other information from foreign sources, it can be concluded that it is not necessary to establish limits of nitrates contents of vegetables cultivated in Korea due to the co-presence of beneficial elements such as ascorbic acid and alpha-tocopherol which are known to inhibit the formation of nitrosamine.",Food additives and contaminants,"['D000490', 'D001530', 'D001937', 'D002212', 'D002852', 'D004032', 'D006801', 'D007391', 'D007723', 'D009566', 'D009573', 'D019697', 'D031224', 'D012621', 'D013025', 'D013056', 'D006113', 'D014675']","['Allium', 'Belgium', 'Brassica', 'Capsicum', 'Chromatography, Ion Exchange', 'Diet', 'Humans', 'International Cooperation', 'Korea', 'Nitrates', 'Nitrites', 'Onions', 'Raphanus', 'Seasons', 'Soybeans', 'Spectrophotometry, Ultraviolet', 'United Kingdom', 'Vegetables']",Survey of nitrate and nitrite contents of vegetables grown in Korea.,"['Q000737', None, 'Q000737', 'Q000737', None, None, None, None, None, 'Q000009', 'Q000032', 'Q000737', 'Q000737', None, 'Q000737', None, None, 'Q000737']","['chemistry', None, 'chemistry', 'chemistry', None, None, None, None, None, 'adverse effects', 'analysis', 'chemistry', 'chemistry', None, 'chemistry', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12888387,2003,,,,, -12852574,"A method is described for determining sulfite in dried garlic. Garlic is extracted with an HCl solution to inhibit the formation of allicin, which interferes with the determination of sulfite. After cleanup of the extract on a C18 solid-phase extraction column, sulfite is converted to hydroxymethylsulfonate (HMS) by adding formaldehyde and heating to 50 degrees C. HMS is determined by reversed-phase ion-pairing liquid chromatography with post-column detection. The post-column reaction system consists of the addition of KOH to convert HMS to sulfite ion, followed by the addition of 5,5'-dithiobis(2-nitrobenzoic acid) to produce 5-mercapto-2-nitrobenzoic acid which is detected spectrophotometrically at 450 nm. Background levels in unsulfited dried garlic equivalent to < 20 ppm SO2 were found. Recoveries of HMS from spiked garlic averaged 94.8% with a coefficient of variation of 3.8%. Sulfite was found in 13 of 21 samples of dried garlic produced in China, with sulfite ranging from 114 to 445 ppm. Sulfite was found in 60% of commercial dried garlic products purchased locally. The suitability of the Monier-Williams method for determining sulfite in garlic is discussed.",Journal of AOAC International,"['D002853', 'D005737', 'D013441', 'D013447']","['Chromatography, Liquid', 'Garlic', 'Sulfinic Acids', 'Sulfites']",Determination of sulfite in dried garlic by reversed-phase ion-pairing liquid chromatography with post-column detection.,"[None, 'Q000737', 'Q000032', 'Q000032']","[None, 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12852574,2003,,,,, -12840173,"Garlic is proposed to have immunomodulatory and anti-inflammatory properties. This paper shows that garlic powder extracts (GPE) and single garlic metabolites modulate lipopolysaccharide (LPS)-induced cytokine levels in human whole blood. GPE-altered cytokine levels in human blood sample supernatants reduced nuclear factor (NF)-kappaB activity in human cells exposed to these samples. Pretreatment with GPE (100 mg/L) reduced LPS-induced production of proinflammatory cytokines interleukin (IL)-1beta from 15.7 +/- 5.1 to 6.2 +/- 1.2 micro g/L and tumor necrosis factor (TNF)-alpha from 8.8 +/- 2.4 to 3.9 +/- 0.8 micro g/L, respectively, whereas the expression of the anti-inflammatory cytokine IL-10 was unchanged. The garlic metabolite diallydisulfide (1-100 micro mol/L) also significantly reduced IL-1beta and TNF-alpha. Interestingly, exposure of human embryonic kidney cell line (HEK293) cells to GPE-treated blood sample supernatants (10 or 100 mg/L) reduced NF-kappaB activity compared with cells exposed to untreated blood supernatants as measured by a NF-kappaB-driven luciferase reporter gene assay. Blood samples treated with extract obtained from unfertilized garlic (100 mg/L) reduced NF-kappaB activity by 25%, whereas blood samples treated with sulfur-fertilized garlic extracts (100 mg/L) lowered NF-kappaB activity by 41%. In summary, garlic may indeed promote an anti-inflammatory environment by cytokine modulation in human blood that leads to an overall inhibition of NF-kappaB activity in the surrounding tissue.",The Journal of nutrition,"['D002460', 'D002851', 'D016207', 'D005737', 'D006801', 'D008070', 'D016328', 'D013056', 'D013457']","['Cell Line', 'Chromatography, High Pressure Liquid', 'Cytokines', 'Garlic', 'Humans', 'Lipopolysaccharides', 'NF-kappa B', 'Spectrophotometry, Ultraviolet', 'Sulfur Compounds']",Garlic (Allium sativum L.) modulates cytokine expression in lipopolysaccharide-activated human blood thereby inhibiting NF-kappaB activity.,"[None, None, 'Q000097', 'Q000737', None, 'Q000494', 'Q000037', None, 'Q000032']","[None, None, 'blood', 'chemistry', None, 'pharmacology', 'antagonists & inhibitors', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12840173,2003,1.0,1.0,,, -12804663,"Nicotine [3-(1-methyl-2-pyrrolidinyl)-pyridine] is a major alkaloid in tobacco products and has proven to be a potential genotoxic compound. Many natural dietary products can suppress the DNA adduction, and hence act as inhibitors of cancer. In this study, we investigated the inhibitory effects of curcumin, garlic squeeze, grapeseed extract, tea polyphenols, vitamin C, and vitamin E on nicotine-DNA adduction in vivo using an ultrasensitive method of accelerator mass spectrometry (AMS). The results demonstrated that all the dietary constituents induced marked dose-dependent decrease in nicotine-DNA adducts as compared with the control. The reduction rate reached about 50% for all agents, except garlic squeeze (40%), even at its highest dose level. Amongst the six agents, grapeseed extract exhibited the strongest inhibition to the DNA adduct formation. Therefore, we may arrive at a point that these dietary constituents are beneficial to prevent the harmful adduct formation, and thus to block the potential carcinogenesis induced by nicotine.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000818', 'D001205', 'D003474', 'D004247', 'D018736', 'D004032', 'D005737', 'D008297', 'D051379', 'D009538', 'D018722', 'D018733', 'D010936', 'D013662', 'D014810', 'D027843']","['Animals', 'Ascorbic Acid', 'Curcumin', 'DNA', 'DNA Adducts', 'Diet', 'Garlic', 'Male', 'Mice', 'Nicotine', 'Nicotinic Agonists', 'Nicotinic Antagonists', 'Plant Extracts', 'Tea', 'Vitamin E', 'Vitis']",Inhibition of nicotine-DNA adduct formation in mice by six dietary constituents.,"[None, 'Q000494', 'Q000494', 'Q000187', 'Q000187', None, None, None, None, 'Q000633', 'Q000633', 'Q000494', 'Q000494', None, 'Q000494', None]","[None, 'pharmacology', 'pharmacology', 'drug effects', 'drug effects', None, None, None, None, 'toxicity', 'toxicity', 'pharmacology', 'pharmacology', None, 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/12804663,2003,0.0,0.0,,, -12721447,"The objective of this study was to obtain purer acid phosphatases than produced by prior art by operating under conditions that improve the final product. The study features are the use of a mild nonionic detergent, 40-80% saturation with (NH4)2SOm4, maintained at low temperature to remove impurity, and the use of chromatografic columns to concentrate the acid phosphatase and remove non-acid phosphatase proteins with lower or higher molecular weights. Acid phosphatase was isolated and purified from garlic seedlings by a streamline method without the use of proteolytic and lipolytic enzymes, butanol, or other organic solvents. Grown garlic seedlings of 10- 15 cm height were homogenized with 0.1 M acetate buffer containing 0.1 M NaCl and 0.1% Triton X-100. After homogenization, the supernatant was filtered with paper filters. Filtrated supernatant was cooled to 4 degrees C, followed by a threestep fractionation of the proteins with ammonium sulfate. The crude enzyme was isolated as a green precipitate that was dissolved in a small amount of 0.1 M acetate buffer containing 0.1 M NaCl and 0.1% Triton X-100. Garlic seedling acid phosphatase was purified with ion-exchange chromatography (DEAE cellulose). The column was equilibrated with 0.1 M acetate buffer. Acid phosphatase was purified 40-fold from the starting material. The specific activity of the pure enzyme was 168 U/mg. A variety of stability and activity profiles were determined for the purified garlic seedling acid phosphatase: optimum pH, optimum temperature, pH stability, temperature stability, thermal inactivation, substrate specificity, effect of enzyme concentration, effect of substrate concentration, activation energy, and effect of inhibitor and activator. The molecular mass of acid phosphatase was estimated to be 58 kDa by sodium dodecyl sulfate polyacrylamide gel electrophoresis. The optimum pH was 5.7 and the optimum temperature was 50 degrees C. The enzyme was stable at pH 4.0-10.0 and 40-60 degrees C. Activation energy was between 10 and 20 kcal, and as Michaelis Menten coefficients, Vm values were 100 and 20 mM/s and Km values were 21.27 and 8.33 mM for paranitrophenylphosphate and paranitrophenyl, respectively. Studies of the effect of metal ions on enzyme activity showed both an activating and a deactivating effect. While Cu, Mo, and Mn showed strong inhibitory effects, Na, Ca, and K were the significant activators of acid phosphatase.",Applied biochemistry and biotechnology,"['D000135', 'D002848', 'D004795', 'D005737', 'D006863', 'D007700', 'D008670', 'D013379', 'D013816']","['Acid Phosphatase', 'Chromatography, DEAE-Cellulose', 'Enzyme Stability', 'Garlic', 'Hydrogen-Ion Concentration', 'Kinetics', 'Metals', 'Substrate Specificity', 'Thermodynamics']",Partial purification and kinetic characterization of acid phosphatase from garlic seedling.,"['Q000302', 'Q000379', None, 'Q000201', None, None, 'Q000494', None, None]","['isolation & purification', 'methods', None, 'enzymology', None, None, 'pharmacology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12721447,2003,,,,, -12703902,"The quality of garlic and garlic products is usually related to their alliin content and allicin release potential. Until now no analytical method was able to quantify simultaneously allicin, its direct precursor alliin (S-allyl-L-cysteine sulfoxide), SAC (S-allyl-L-cysteine) as well as various dipeptides that apparently serve as storage compounds in garlic. It is well known that all these intermediates are involved in the allicin biosynthetic pathway. A simple and rapid HPLC method suitable for routine analysis was developed using eluents containing an ion-pairing reagent. Particularly, heptanesulfonate as ion-pairing reagent guarantees a sufficient separation between alliin and the more retained dipeptides at very low pH. Allicin was eluted after 18 min on a 150 x 3 mm column. Synthetic reference compounds were characterized by the same chromatographic method using a diode-array UV detector and an ion trap mass spectrometer (electrospray ionization) in the multiple MS mode. In routine analysis of garlic bulbs, powders and other products, the diode-array detector is sufficient for a relevant quantification. Our method has been used in studies to improve the quality of garlic and its derived products.",Journal of chromatography. A,"['D013437', 'D002851', 'D003545', 'D004151', 'D005737', 'D015394', 'D021241', 'D013056', 'D013441', 'D013455']","['Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Dipeptides', 'Garlic', 'Molecular Structure', 'Spectrometry, Mass, Electrospray Ionization', 'Spectrophotometry, Ultraviolet', 'Sulfinic Acids', 'Sulfur']","High-performance ion-pair chromatography method for simultaneous analysis of alliin, deoxyalliin, allicin and dipeptide precursors in garlic products using multiple mass spectrometry and UV detection.","['Q000737', 'Q000379', 'Q000031', 'Q000032', 'Q000737', None, None, None, 'Q000032', 'Q000737']","['chemistry', 'methods', 'analogs & derivatives', 'analysis', 'chemistry', None, None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12703902,2003,1.0,1.0,,, -12642382,"(1) Ajoene is a garlic compound with anti-platelet properties and, in addition, was shown to inhibit cholesterol biosynthesis by affecting 3-hydroxy-3-methyl-glutaryl coenzyme A (HMG-CoA) reductase and late enzymatic steps of the mevalonate (MVA) pathway. (2) MVA constitutes the precursor not only of cholesterol, but also of a number of non-sterol isoprenoids, such as farnesyl and geranylgeranyl groups. Covalent attachment of these MVA-derived isoprenoid groups (prenylation) is a required function of several proteins that regulate cell proliferation. We investigated the effect of ajoene on rat aortic smooth muscle cell proliferation as related to protein prenylation. (3) Cell counting, DNA synthesis, and cell cycle analysis showed that ajoene (1-50 micro M) interfered with the progression of the G1 phase of the cell cycle, and inhibited rat SMC proliferation. (4) Similar to the HMG-CoA reductase inhibitor simvastatin, ajoene inhibited cholesterol biosynthesis. However, in contrast to simvastatin, the antiproliferative effect of ajoene was not prevented by the addition of MVA, farnesol (FOH), and geranylgeraniol (GGOH). Labelling of smooth muscle cell cellular proteins with [3H]-FOH and [3H]-GGOH was significantly inhibited by ajoene. (5) In vitro assays for protein farnesyltransferase (PFTase) and protein geranylgeranyltransferase type I (PGGTase-I) confirmed that ajoene inhibits protein prenylation. High performance liquid chromatography (HPLC) and mass spectrometry analyses also demonstrated that ajoene causes a covalent modification of the cysteine SH group of a peptide substrate for protein PGGTase-I. (6) Altogether, our results provide evidence that ajoene interferes with the protein prenylation reaction, an effect that may contribute to its inhibition of SMC proliferation.",British journal of pharmacology,"['D000818', 'D001011', 'D002455', 'D002478', 'D004220', 'D004305', 'D005737', 'D006131', 'D008297', 'D009131', 'D010936', 'D017368', 'D051381', 'D017207']","['Animals', 'Aorta', 'Cell Division', 'Cells, Cultured', 'Disulfides', 'Dose-Response Relationship, Drug', 'Garlic', 'Growth Inhibitors', 'Male', 'Muscle, Smooth, Vascular', 'Plant Extracts', 'Protein Prenylation', 'Rats', 'Rats, Sprague-Dawley']","Ajoene, a garlic compound, inhibits protein prenylation and arterial smooth muscle cell proliferation.","[None, 'Q000166', 'Q000187', None, 'Q000494', None, None, 'Q000494', None, 'Q000166', 'Q000494', 'Q000187', None, None]","[None, 'cytology', 'drug effects', None, 'pharmacology', None, None, 'pharmacology', None, 'cytology', 'pharmacology', 'drug effects', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12642382,2003,0.0,0.0,,, -12595006,"Nitrobenzene (NB), a widely used industrial chemical, is a likely human carcinogen. Many dietary constituents can suppress the DNA-adduction, acting as the inhibitors of cancer. In this study, we investigated the inhibitory effects of vitamin C (VC), vitamin E (VE), tea polyphenols (TP), garlic squeeze, curcumin, and grapestone extract on NB-DNA and NB-hemoglobin (Hb) adductions in mice using an ultrasensitive method of accelerator mass spectrometry (AMS) with 14C-labelled nitrobenzene. All of these dietary constituents showed their inhibitory effects on DNA or Hb adduction. VC, VE, TP and grapestone extract could efficaciously inhibit the adductions by 33-50%, and all of these six agents could inhibit Hb adduction by 30-64%. We also investigated resveratrol, curcumin, VC and VE as inhibitors of NB-DNA adduction in vitro using liquid scintillation counting technique. These agents in the presence of NADPH and S9 components also pronouncedly blocked DNA adduction in a dose-dependent profile. Our study suggests that these seven constituents may interrupt the process of NB-induced chemical carcinogenesis.","Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine","['D000818', 'D000975', 'D002273', 'D004247', 'D018736', 'D019587', 'D006454', 'D008297', 'D051379', 'D009578', 'D012588']","['Animals', 'Antioxidants', 'Carcinogens', 'DNA', 'DNA Adducts', 'Dietary Supplements', 'Hemoglobins', 'Male', 'Mice', 'Nitrobenzenes', 'Scintillation Counting']",Inhibition of nitrobenzene-induced DNA and hemoglobin adductions by dietary constituents.,"[None, 'Q000008', 'Q000633', 'Q000187', None, None, 'Q000187', None, None, 'Q000633', None]","[None, 'administration & dosage', 'toxicity', 'drug effects', None, None, 'drug effects', None, None, 'toxicity', None]",https://www.ncbi.nlm.nih.gov/pubmed/12595006,2003,0.0,0.0,,, -12593760,"1. Diallyl disulphide (DADS), a compound formed from the organosulphur compounds present in garlic, is known for its anticarcinogenic effects in animal models. 2. The aim was to identify and analyse the metabolites produced in vivo after a single oral administration of 200 mg kg(-1) DADS to rats. The organic sulphur metabolites present in the stomach, liver, plasma and urine were measured by gas chromatography coupled with mass spectrometry over 15 days. 3. Data indicate that DADS is absorbed and transformed into allyl mercaptan, allyl methyl sulphide, allyl methyl sulphoxide (AMSO) and allyl methyl sulphone (AMSO(2)), which are detected throughout the excretion period. Overall, the highest amounts of metabolites were measured 48-72h after the DADS administration. AMSO(2) is the most abundant and persistent of these compounds. The levels of all the sulphur compounds rapidly decline within the first week after administration and disappear during the second week. Only AMSO and AMSO(2) are significantly excreted in urine. 4. These potential metabolites are thought to be active in the target tissues. Our data warrant further studies to check this hypothesis.",Xenobiotica; the fate of foreign compounds in biological systems,"['D000284', 'D000498', 'D000818', 'D004220', 'D008401', 'D006801', 'D008297', 'D008698', 'D008956', 'D051381', 'D017208', 'D017550', 'D013440', 'D013441', 'D013997', 'D014018']","['Administration, Oral', 'Allyl Compounds', 'Animals', 'Disulfides', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Male', 'Mesylates', 'Models, Chemical', 'Rats', 'Rats, Wistar', 'Spectroscopy, Fourier Transform Infrared', 'Sulfides', 'Sulfinic Acids', 'Time Factors', 'Tissue Distribution']",In vivo metabolism of diallyl disulphide in the rat: identification of two new metabolites.,"[None, 'Q000378', None, 'Q000378', None, None, None, 'Q000378', None, None, None, None, 'Q000378', 'Q000493', None, None]","[None, 'metabolism', None, 'metabolism', None, None, None, 'metabolism', None, None, None, None, 'metabolism', 'pharmacokinetics', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12593760,2003,0.0,0.0,,, -12567938,"Effective constituents from bulb of Allium stativum were extracted by supercritical-CO2 fluid. These constituents were analyzed by GC-MS. The results showed that oils from SFE-CO2 contained 12 components, two of them were first obtained from the plant.",Zhong yao cai = Zhongyaocai = Journal of Chinese medicinal materials,"['D000490', 'D002245', 'D002849', 'D025924', 'D005737', 'D008401', 'D009822', 'D010946']","['Allium', 'Carbon Dioxide', 'Chromatography, Gas', 'Chromatography, Supercritical Fluid', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Oils, Volatile', 'Plants, Medicinal']",[Supercritical-CO2 fluid extraction of Allium stativum oils].,"['Q000737', None, None, 'Q000379', 'Q000737', None, 'Q000302', 'Q000737']","['chemistry', None, None, 'methods', 'chemistry', None, 'isolation & purification', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12567938,2003,,,,, -12467456,"Helicobacter pylori (Hp) is the bacterium responsible for serious gastric diseases such as ulcers and cancer. The work described here involved the study of the inhibitory power of Allium sativum extracts against the in vitro growth of Hp (Hp ivg). We used purple garlic of the ""Las Pedroñeras"" variety for this study. The effects of two different extraction methods (Soxhlet, stirred tank extractor) and four solvents with different characteristics (water, acetone, ethanol, and hexane) were investigated in terms of the efficiency of the extraction process. Satisfactory results were obtained in most cases in the activity tests, indicating that different extracts gave rise to good inhibitory activity against Hp ivg. The extracts that showed the highest bacteriostatic activities were selected to evaluate the influence of the most important operation variables on the extraction yield: stirring speed, operation time, garlic conditioning, and garlic storage time. The best results were obtained using ethanol and acetone as solvents in a stirred tank. The inhibitory powers of these extracts were compared to those shown by some commercial antibiotics used in the medical treatment of Hp infections. The results of this study show that garlic extracts produce levels of inhibition similar to those of the commercial materials. These extracts were also tested against other common bacteria, and equally satisfactory results were obtained. The research described here represents an important starting point in the fight against and/or prevention of peptic ulcers, as well as other pathologies associated with Hp infections such us gastric cancer. The extracted material can be used by direct application and involves a simple and economical extraction procedure that avoids isolation or purification techniques.",Biotechnology progress,"['D002851', 'D005737', 'D016481', 'D016480', 'D010936', 'D011309', 'D012997', 'D013441']","['Chromatography, High Pressure Liquid', 'Garlic', 'Helicobacter Infections', 'Helicobacter pylori', 'Plant Extracts', 'Preservation, Biological', 'Solvents', 'Sulfinic Acids']",Optimization of Allium sativum solvent extraction for the inhibition of in vitro growth of Helicobacter pylori.,"[None, 'Q000737', 'Q000517', 'Q000187', 'Q000302', None, None, 'Q000032']","[None, 'chemistry', 'prevention & control', 'drug effects', 'isolation & purification', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12467456,2003,1.0,1.0,,, -12463370,"A sensitive method for determining ultratrace volatile Se species produced from Brassica juncea seedlings is described. The use of a new commercially available GC/ ICPMS interface in conjunction with solid-phase micro-extraction is a promising way to perform these studies. The addition of optional gases (O2 and N2) to the argon discharge proved to increase the sensitivity for Se and S as well as for Xe, which as a trace contaminant gas, was used for ICPMS optimization studies. However, the optimization parameters differ when an optional gas is added. In the best conditions, limits of detection ranging from 1 to 10 ppt can be obtained depending on the Se compound and 30 to 300 ppt for the volatile S species. The use of GC/MS with similar sample introduction permits the characterization of several unknown species produced as artifacts from the standards. The method allows the virtually simultaneous monitoring of S and Se species from the headspace of several plants (e.g., onions, garlic, etc.) although the present work is focused on the B. juncea seedlings grown in closed vials and treated with Se. Dimethyl selenide and dimethyl diselenide were detected as the primary volatile Se components in the headspace. Sulfur species also were present as allyl (2-propenyl) isothiocyanate and 3-butenyl isothiocyanate as characterized by GC/MS.",Analytical chemistry,"['D001937', 'D018744', 'D008401', 'D007202', 'D030821', 'D018036', 'D013457']","['Brassica', 'DNA, Plant', 'Gas Chromatography-Mass Spectrometry', 'Indicators and Reagents', 'Plants, Genetically Modified', 'Selenium Compounds', 'Sulfur Compounds']",Simultaneous monitoring of volatile selenium and sulfur species from se accumulating plants (wild type and genetically modified) by GC/MS and GC/ICPMS using solid-phase microextraction for sample introduction.,"['Q000737', 'Q000032', None, None, 'Q000737', 'Q000032', 'Q000032']","['chemistry', 'analysis', None, None, 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12463370,2002,,,,, -12230020,"Two components of garlic, diallyl sulfide (DAS) and diallyl disulfide (DADS), inhibited arylamine N-acetyltransferase (NAT) activity and 2-aminofluorene-DNA adduct in human promyelocytic leukemia cells (HL-60). The NAT activity was measured by high performance liquid chromatography assaying for amounts of N-acetyl-2-aminofluorene (2-AAF) and remaining 2-aminofluorene (2-AF). Cellular cytosols and intact cell suspensions were assayed. The inhibition of NAT activity and 2-AF-DNA adduct formation in human leukemia cells by DAS and DADS were dose-dependent and were directly proportional. The data also indicated that DAS and DADS decrease the apparent values of Km and Vmax from human leukemia cells in both assays. This is the first report of garlic components affecting human leukemia cell NAT activity and 2-AF-DNA adduct formation.",The American journal of Chinese medicine,"['D000498', 'D016588', 'D001191', 'D018736', 'D004220', 'D004305', 'D005449', 'D005737', 'D018922', 'D006801', 'D015473', 'D008517', 'D010938', 'D013440']","['Allyl Compounds', 'Anticarcinogenic Agents', 'Arylamine N-Acetyltransferase', 'DNA Adducts', 'Disulfides', 'Dose-Response Relationship, Drug', 'Fluorenes', 'Garlic', 'HL-60 Cells', 'Humans', 'Leukemia, Promyelocytic, Acute', 'Phytotherapy', 'Plant Oils', 'Sulfides']",Effects of garlic components diallyl sulfide and diallyl disulfide on arylamine N-acetyltransferase activity and 2-aminofluorene-DNA adducts in human promyelocytic leukemia cells.,"['Q000008', 'Q000008', 'Q000187', 'Q000737', 'Q000008', None, 'Q000737', None, 'Q000187', None, 'Q000517', None, 'Q000008', 'Q000008']","['administration & dosage', 'administration & dosage', 'drug effects', 'chemistry', 'administration & dosage', None, 'chemistry', None, 'drug effects', None, 'prevention & control', None, 'administration & dosage', 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/12230020,2003,,,,no pdf access , -12184391,"Caribbean sponges of the genus Ircinia contain high concentrations of linear furanosesterterpene tetronic acids (FTAs) and produce and exude low-molecular-weight volatile compounds (e.g., dimethyl sulfide, methyl isocyanide, methyl isothiocyanate) that give these sponges their characteristic unpleasant garlic odor. It has recently been suggested that FTAs are unlikely to function as antipredatory chemical defenses, and this function may instead be attributed to bioactive volatiles. We tested crude organic extracts and purified fractions isolated from Ircinia campana, I. felix, and I. strobilina at naturally occurring concentrations in laboratory and field feeding assays to determine their palatability to generalist fish predators. We also used a qualitative technique to test the crude volatile fraction from I. felix and I. strobilina and dimethylsulfide in laboratory feeding assays. Crude organic extracts of all three species deterred feeding of fishes in both aquarium and field experiments. Bioassay-directed fractionation resulted in the isolation of the FTA fraction as the sole active fraction of the nonvolatile crude extract for each species, and further assays of subfractions suggested that feeding deterrent activity is shared by the FTAs. FTAs deterred fish feeding in aquarium assays at concentrations as low as 0.5 mg/ml (fraction B, variabilin), while the natural concentrations of combined FTA fractions were > 5.0 mg/ml for all three species. In contrast, natural mixtures of volatiles transferred from sponge tissue to food pellets and pure dimethylsulfide incorporated into food pellets were readily eaten by fish in aquarium assays. Although FTAs may play other ecological roles in Ircinia spp., these compounds are effective as defenses against potential predatory fishes. Volatile compounds may serve other defensive functions (e.g., antimicrobial, antifouling) but do not appear to provide a defense against fish predators.",Journal of chemical ecology,"['D000818', 'D001685', 'D002851', 'D002855', 'D005399', 'D009812', 'D011161', 'D011235', 'D013045']","['Animals', 'Biological Factors', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Fishes', 'Odorants', 'Porifera', 'Predatory Behavior', 'Species Specificity']",Does the odor from sponges of the genus Ircinia protect them from fish predators?,"[None, None, None, None, 'Q000502', None, 'Q000737', None, None]","[None, None, None, None, 'physiology', None, 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12184391,2003,,,,, -12137782,"Allicin (diallylthiosulfinate) is the best known active compound of garlic. It is generated upon the interaction of the nonprotein amino acid alliin with the enzyme alliinase (alliin lyase, EC 4.4.1.4). Previously, we described a simple spectrophotometric assay for the determination of allicin and alliinase activity, based on the reaction between 2-nitro-5-thiobenzoate (NTB) and allicin. This reagent is not commercially available and must be synthesized. In this paper we describe the quantitative analysis of alliin and allicin, as well as of alliinase activity with 4-mercaptopyridine (4-MP), a commercially available chromogenic thiol. The assay is based on the reaction of 4-MP (lambda(max)=324nm) with the activated disulfide bond of thiosulfinates -S(O)-S-, forming the mixed disulfide, 4-allylmercaptothiopyridine, which has no absorbance at this region. The structure of 4-allylmercaptothiopyridine was confirmed by mass spectrometry. The method was used for the determination of alliin and allicin concentrations in their pure form as well as of alliin and total thiosulfinates concentrations in crude garlic preparations and garlic-derived products, at micromolar concentrations. The 4-MP assay is an easy, sensitive, fast, noncostly, and highly efficient throughput assay of allicin, alliin, and alliinase in garlic preparations.",Analytical biochemistry,"['D013437', 'D002851', 'D003545', 'D005737', 'D013058', 'D009579', 'D011725', 'D013056', 'D013438', 'D013441']","['Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Mass Spectrometry', 'Nitrobenzoates', 'Pyridines', 'Spectrophotometry, Ultraviolet', 'Sulfhydryl Compounds', 'Sulfinic Acids']","A spectrophotometric assay for allicin, alliin, and alliinase (alliin lyase) with a chromogenic thiol: reaction of 4-mercaptopyridine with thiosulfinates.","['Q000032', None, 'Q000031', 'Q000737', None, 'Q000737', 'Q000737', 'Q000379', None, 'Q000032']","['analysis', None, 'analogs & derivatives', 'chemistry', None, 'chemistry', 'chemistry', 'methods', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12137782,2003,1.0,1.0,,, -12086179,"Lactobacillus pentosus B235, which was isolated as part of the dominant microflora from a garlic containing fermented fish product, was grown in a chemically defined medium with inulin as the sole carbohydrate source. An extracellular fructan beta-fructosidase was purified to homogeneity from the bacterial supernatant by ultrafiltration, anion exchange chromatography and hydrophobic interaction chromatography. The molecular weight of the enzyme was estimated to be approximately 126 kDa by gel filtration and by SDS-PAGE. The purified enzyme had the highest activity for levan (a beta(2-->6)-linked fructan), but also hydrolysed garlic extract, (a beta(2-->1)-linked fructan with beta(2-->6)-linked fructosyl sidechains), 1,1,1-kestose, 1,1-kestose, 1-kestose, inulin (beta(2-->1)-linked fructans) and sucrose at 60, 45, 39, 12, 9 and 3%, respectively, of the activity observed for levan. Melezitose, raffinose and stachyose were not hydrolysed by the enzyme. The fructan beta-fructosidase was inhibited by p-chloromercuribenzoate, EDTA, Fe2+, Cu2+, Zn2+ and Co2+, whereas Mn2+ and Cu2+ had no effect. The sequence of the first 20 N-terminal amino acids was: Ala-Thr-Ser-Ala-Ser-Ser-Ser-Gln-Ile-Ser-Gln-Asn-Asn-Thr-Gln-Thr-Ser-Asp-Val-Val. The enzyme had temperature and pH optima at 25 degrees C and 5.5, respectively. At concentrations of up to 12% NaCl no adverse effect on the enzyme activity was observed.",Systematic and applied microbiology,"['D000595', 'D000818', 'D001426', 'D002847', 'D003470', 'D005285', 'D005396', 'D005737', 'D006026', 'D006863', 'D007778', 'D012680', 'D013696']","['Amino Acid Sequence', 'Animals', 'Bacterial Proteins', 'Chromatography, Agarose', 'Culture Media', 'Fermentation', 'Fish Products', 'Garlic', 'Glycoside Hydrolases', 'Hydrogen-Ion Concentration', 'Lactobacillus', 'Sensitivity and Specificity', 'Temperature']",Purification and characterisation of an extracellular fructan beta-fructosidase from a Lactobacillus pentosus strain isolated from fermented fish.,"[None, None, 'Q000096', 'Q000379', None, None, 'Q000382', 'Q000737', 'Q000032', None, 'Q000145', None, None]","[None, None, 'biosynthesis', 'methods', None, None, 'microbiology', 'chemistry', 'analysis', None, 'classification', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12086179,2003,,,,, -12005268,"A novel antifungal protein, designated allivin, was isolated from bulbs of the round-cloved garlic Allium sativum var. round clove with a procedure involving ion exchange chromatography on DEAE-cellulose, affinity chromatography on Affi-gel blue gel, ion exchange chromatography on CM-Sepharose and FPLC-gel filtration on Superdex 75. Allivin possessed an N-terminal sequence demonstrating very little similarity to sequences of Allium sativum chitinases and ribosome inactivating proteins. Allivin exhibited a molecular weight of 13 kDa in gel filtration and SDS-polyacrylamide gel electrophoresis. It displayed antifungal activity against Botrytis cinerea, Mycosphaerella arachidicola and Physalospora piricola. It inhibited translation in a cell-free rabbit reticulocyte system with an IC50 of 1.6 microM.",Life sciences,"['D000595', 'D000935', 'D005737', 'D008969', 'D010940']","['Amino Acid Sequence', 'Antifungal Agents', 'Garlic', 'Molecular Sequence Data', 'Plant Proteins']","Purification of allivin, a novel antifungal protein from bulbs of the round-cloved garlic.","[None, 'Q000302', 'Q000737', None, 'Q000737']","[None, 'isolation & purification', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12005268,2002,0.0,0.0,,, -11985847,"Identification and isolation of (R(S)R(C))-S-(methylthiomethyl)cysteine-4-oxide from rhizomes of Tulbaghia violacea Harv. is reported. The structure and absolute configuration of the amino acid have been determined by NMR, MALDI-HRMS, IR, and CD spectroscopy. Its content varied in different parts of the plant (rhizomes, leaves, and stems) between 0.12 and 0.24 mg g(-1) fr. wt, being almost equal in the stems and rhizomes. In addition, S-methyl- and S-ethylcysteine derivatives have been detected in minute amounts (<3 microg g(-1) fr. wt) in all parts of the plant. The enzymatic cleavage of the amino acid and subsequent odor formation are discussed. 2,4,5,7-Tetrathiaoctane-4-oxide, the primary breakdown product, has been detected and isolated for the first time.",Phytochemistry,"['D000490', 'D000596', 'D002852', 'D008401', 'D015394', 'D009812', 'D010087', 'D018514', 'D013057']","['Allium', 'Amino Acids', 'Chromatography, Ion Exchange', 'Gas Chromatography-Mass Spectrometry', 'Molecular Structure', 'Odorants', 'Oxides', 'Plant Structures', 'Spectrum Analysis']",The amino acid precursors and odor formation in society garlic (Tulbaghia violacea Harv.).,"['Q000737', 'Q000737', None, None, None, 'Q000032', 'Q000378', 'Q000737', None]","['chemistry', 'chemistry', None, None, None, 'analysis', 'metabolism', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/11985847,2002,0.0,0.0,,, -11982398,"Fusarium proliferatum is one of a group of fungal species that produce fumonisins and is considered to be a pathogen of many economically important plants. The occurrence of fumonisin B(1) (FB(1)) in F. proliferatum-infected asparagus spears from Germany was investigated using a liquid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS) method with isotopically labeled fumonisin FB(1)-d(6) as internal standard. FB(1) was detected in 9 of the 10 samples in amounts ranging from 36.4 to 4513.7 ng/g (based on dry weight). Furthermore, the capability of producing FB(1) by the fungus in garlic bulbs was investigated. Therefore, garlic was cultured in F. proliferatum-contaminated soil, and the bulbs were screened for infection with F. proliferatum and for the occurrence of fumonisins by LC-MS. F. proliferatum was detectable in the garlic tissue, and all samples contained FB(1) (26.0-94.6 ng/g). This is the first report of the natural occurrence of FB(1) in German asparagus spears, and these findings suggest a potential for natural contamination of garlic bulbs with fumonisins.",Journal of agricultural and food chemistry,"['D027761', 'D002264', 'D002853', 'D005506', 'D037341', 'D005670', 'D005737', 'D005858', 'D021241']","['Asparagus Plant', 'Carboxylic Acids', 'Chromatography, Liquid', 'Food Contamination', 'Fumonisins', 'Fusarium', 'Garlic', 'Germany', 'Spectrometry, Mass, Electrospray Ionization']",Analysis of fumonisin B(1) in Fusarium proliferatum-infected asparagus spears and garlic bulbs from Germany by liquid chromatography-electrospray ionization mass spectrometry.,"['Q000737', 'Q000032', None, None, None, 'Q000378', 'Q000737', None, None]","['chemistry', 'analysis', None, None, None, 'metabolism', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11982398,2002,0.0,0.0,,testing for mold, -11911208,"Extremely high accumulation of allxiin, a phytoalexin derived from garlic, was observed in necrotic tissue areas after long-term storage. The allixin produced recrystallized on the surface of the garlic clove. The amount of allixin produced in raw garlic with necrotic tissue areas was 1400 ng/mg wet garlic, which exceeds the minimum exhibitory concentration of allixin. After approximately 2 years of storage, amount of allixin accumulated reached slightly less than 1% of the dry weight of garlic cloves.",Chemical & pharmaceutical bulletin,"['D002851', 'D005519', 'D005737', 'D009682', 'D011753']","['Chromatography, High Pressure Liquid', 'Food Preservation', 'Garlic', 'Magnetic Resonance Spectroscopy', 'Pyrones']",Allixin accumulation with long-term storage of garlic.,"[None, None, 'Q000378', None, 'Q000737']","[None, None, 'metabolism', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11911208,2002,0.0,0.0,,, -11911198,"The pharmacokinetic behavior of allixin (3-hydroxy-5-methoxy-6-methyl-2-penthyl-4H-pyran-4-one) was investigated in an experimental animal, mice. Allixin was administered using an inclusion compound because the solubility of allixin in aqueous solution is very low. The allixin content in serum and in the organs of administered animals was analyzed by liquid chromatography (LC)-MS. Most of the administered allixin disappeared within 2 h, and the bioavailability of allixin was estimated to be 31% by obtained area under the blood concentration-time curve (AUC). The metabolites of allixin were studied using the metabolic enzyme fraction of liver and liver homogenate. Several new peaks corresponding to allixin metabolites were observed in the HPLC chromatoprofile. The chemical structure of the metabolites was investigated using LC-MS and NMR. Three of them were identified as allixin metabolites having a hydroxylated pentyl group.",Chemical & pharmaceutical bulletin,"['D000818', 'D019540', 'D001682', 'D001711', 'D002851', 'D005737', 'D008099', 'D009682', 'D051379', 'D011753', 'D051381', 'D017208', 'D014018']","['Animals', 'Area Under Curve', 'Biological Availability', 'Biotransformation', 'Chromatography, High Pressure Liquid', 'Garlic', 'Liver', 'Magnetic Resonance Spectroscopy', 'Mice', 'Pyrones', 'Rats', 'Rats, Wistar', 'Tissue Distribution']","Pharmacokinetic study of allixin, a phytoalexin produced by garlic.","[None, None, None, None, None, 'Q000737', 'Q000378', None, None, 'Q000097', None, None, None]","[None, None, None, None, None, 'chemistry', 'metabolism', None, None, 'blood', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11911198,2002,0.0,0.0,,, -11902974,"Aminoethylcysteine ketimine decarboxylated dimer (simply named dimer) is a natural sulfur-containing tricyclic compound detected, until now, in human urine, bovine cerebellum, and human plasma. Recently, the antioxidant properties of this compound have been demonstrated. In this investigation, the presence of aminoethylcysteine ketimine decarboxylated dimer was identified in garlic, spinach, tomato, asparagus, aubergine, onion, pepper, and courgette. Identification of this compound in dietary vegetables was performed using gas chromatography, high-performance liquid chromatography, and gas chromatography-mass spectrometry. Results from GC analysis range in the order of 10(-4) micromol of dimer/g for all the tested vegetables. These results and the lack of a demonstrated biosynthetic pathway in humans might account for a dietary supply of this molecule.",Journal of agricultural and food chemistry,"['D000975', 'D002849', 'D002851', 'D004032', 'D008401', 'D006801', 'D009025', 'D010936', 'D014675']","['Antioxidants', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Diet', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Morpholines', 'Plant Extracts', 'Vegetables']","Identification of aminoethylcysteine ketimine decarboxylated dimer, a natural antioxidant, in dietary vegetables.","['Q000032', None, None, None, None, None, 'Q000032', 'Q000737', 'Q000737']","['analysis', None, None, None, None, None, 'analysis', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11902974,2002,0.0,0.0,,no mol foundecular weight, -11860155,"From the dried bulbs of the lily (Lilium brownii), a protein with strong antifungal and mitogenic activities was isolated. It also exhibited an inhibitory action on the activity of HIV-1 reverse transcriptase. The protein was single-chained and possessed a molecular weight of 14.4 kDa and an N-terminal sequence distinct from chitinases and antimicrobial proteins of garlic, leek and onion which belong to a family closely related to lily. However, there was a small degree of resemblance to cyclophilins and a considerable extent of identity to the 6.5 kDa arginine/glutamate-rich polypeptide from Luffa cylindrica seeds. A nearly homogeneous preparation was obtained after the extract was fractionated on DEAE-cellulose and Affi-gel Blue gel since subsequent chromatography on Mono S and Superdex 75 both yielded a single peak.",Life sciences,"['D000595', 'D000818', 'D000935', 'D002846', 'D004365', 'D004591', 'D005658', 'D054303', 'D006384', 'D027762', 'D008516', 'D051379', 'D008810', 'D008826', 'D008934', 'D008969', 'D010940', 'D018749', 'D012343']","['Amino Acid Sequence', 'Animals', 'Antifungal Agents', 'Chromatography, Affinity', 'Drugs, Chinese Herbal', 'Electrophoresis, Polyacrylamide Gel', 'Fungi', 'HIV Reverse Transcriptase', 'Hemagglutination', 'Lilium', 'Medicine, Chinese Traditional', 'Mice', 'Mice, Inbred C57BL', 'Microbial Sensitivity Tests', 'Mitogens', 'Molecular Sequence Data', 'Plant Proteins', 'RNA, Plant', 'RNA, Transfer']","Isolation of lilin, a novel arginine- and glutamate-rich protein with potent antifungal and mitogenic activities from lily bulbs.","[None, None, 'Q000302', None, 'Q000302', None, 'Q000187', 'Q000037', 'Q000187', 'Q000737', None, None, None, None, 'Q000302', None, 'Q000302', 'Q000037', 'Q000187']","[None, None, 'isolation & purification', None, 'isolation & purification', None, 'drug effects', 'antagonists & inhibitors', 'drug effects', 'chemistry', None, None, None, None, 'isolation & purification', None, 'isolation & purification', 'antagonists & inhibitors', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/11860155,2002,0.0,0.0,,, -11853480,"Five compounds oxidizing canine erythrocytes were isolated from an aqueous ethanol garlic extract by silica gel column chromatography and preparative thin-layer chromatography. On the basis of nuclear magnetic resonance, infrared spectroscopy, and mass spectrometry, they were identified as three known compounds: bis-2-propenyl trisulfide (1), bis-2-propenyl tetrasulfide (2), and bis-2-propenyl pentasulfide (3) as well as two novel compounds, bis-2-propenyl thiosulfonate (4) and trans-sulfuric acid allyl ester 3-allylsulfanyl-allyl ester (5). A mixture of compounds 1-3 and compounds 4 and 5 induced methemoglobin formation in canine erythrocyte suspension in vitro resulting in the oxidation of canine erythrocytes. These groups of characteristic organosulfur compounds contained in garlic probably contribute to oxidations in blood. The constituents of garlic have the potential to oxidize erythrocytes and hemoglobin, suggesting that foods containing quantities of garlic should be avoided for feeding dogs.",Journal of agricultural and food chemistry,"['D000818', 'D002855', 'D004285', 'D004912', 'D005737', 'D010084', 'D010936', 'D013440', 'D013451', 'D013886']","['Animals', 'Chromatography, Thin Layer', 'Dogs', 'Erythrocytes', 'Garlic', 'Oxidation-Reduction', 'Plant Extracts', 'Sulfides', 'Sulfonic Acids', 'Thiosulfonic Acids']",Isolation and identification of organosulfur compounds oxidizing canine erythrocytes from garlic (Allium sativum).,"[None, 'Q000379', None, 'Q000378', 'Q000009', None, 'Q000378', 'Q000097', 'Q000097', 'Q000097']","[None, 'methods', None, 'metabolism', 'adverse effects', None, 'metabolism', 'blood', 'blood', 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/11853480,2002,0.0,0.0,,, -11767087,"Allixin, a phytoalexin isolated from garlic, was induced by irradiating fresh garlic cloves with sunlight or UV light. Induced allixin was analyzed by HPLC, and the accumulated amounts of allixin were 3.1-6.3 microg/g under experimental conditions.",Chemical & pharmaceutical bulletin,"['D000972', 'D002851', 'D005737', 'D008027', 'D011753', 'D013472', 'D014466']","['Antineoplastic Agents, Phytogenic', 'Chromatography, High Pressure Liquid', 'Garlic', 'Light', 'Pyrones', 'Sunlight', 'Ultraviolet Rays']",Allixin induction and accumulation by light irradiation.,"['Q000378', None, 'Q000378', None, 'Q000378', None, None]","['metabolism', None, 'metabolism', None, 'metabolism', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11767087,2002,1.0,1.0,,, -11520428,"Allium vegetables (onions, leeks, chives) and in particular garlic have been claimed to have health-promoting potential. This study was conducted to get insight into the perspectives for monitoring the intake of garlic by a biomarker approach. Chemically, the biomarker results from exposure to gamma-glutamyl-S-allyl-l-cysteine, which is first hydrolysed by gamma-glutamine-transpeptidase resulting in the formation of S-allyl-l-cysteine. The latter compound is subsequently N-acetylated by N-acetyltransferase into S-allyl-mercapturic acid (ALMA) and excreted into urine. The mercapturic acid was measured in urine using gaschromatography with mass spectrometry. Thus the intake of garlic was determined to check the compliance of garlic intake in a placebo-controlled intervention study. Results indicate that S-allyl-mercapturic acid could be detected in 15 out of 16 urine samples of garlic supplement takers, indicating good compliance. In addition, the intake of garlic was also monitored in a cross-section study of vegans versus controls in Finland, in which no differences in garlic consumption nor in ALMA output were recorded between vegans and controls. These data indicate good possibilities for further studies in the field of biomarkers to investigate the putative chemopreventive effects of garlic and garlic-containing products.",The British journal of nutrition,"['D000111', 'D000975', 'D015415', 'D016022', 'D014676', 'D004435', 'D005260', 'D005737', 'D008401', 'D006801', 'D008297', 'D008875', 'D010349', 'D010946', 'D012680']","['Acetylcysteine', 'Antioxidants', 'Biomarkers', 'Case-Control Studies', 'Diet, Vegetarian', 'Eating', 'Female', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Male', 'Middle Aged', 'Patient Compliance', 'Plants, Medicinal', 'Sensitivity and Specificity']",Biomonitoring the intake of garlic via urinary excretion of allyl mercapturic acid.,"['Q000652', 'Q000008', 'Q000652', None, None, None, None, None, None, None, None, None, None, None, None]","['urine', 'administration & dosage', 'urine', None, None, None, None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11520428,2001,0.0,0.0,,, -11486375,"When performing multiresidue analysis of pesticides, the recovery of thiometon was less than 20% from carrots and eggplants, but about 100% from garlic chives and welsh onions. The recovery of thiometon was found to depend on the lot of ethyl acetate. A 2-year-old lot of ethyl acetate caused degradation of thiometon, but a fresh lot of ethyl acetate did not. Analysis showed that ethyl acetate stored for 2 years contained about 5 microL/mL of acetaldehyde. Thiometon was also degraded by acetone or acetonitrile, when acetaldehyde was added to them, in the same manner as by aged ethyl acetate. The fact that the recovery of thiometon from welsh onions was about 100% indicated that some of the mercaptans in allium vegetables may prevent thiometon degradation. Mercaptans such as L-cysteine and 3-mercaptoproionic acid were confirmed to prevent the degradation of thiometon and disulfoton. These findings show that mercaptans may be useful additives for analyzing thiometon and disulfoton.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D000085', 'D005506', 'D008401', 'D007306', 'D063086', 'D014675']","['Acetates', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Insecticides', 'Organothiophosphates', 'Vegetables']",[Degradation of thiometon in ethyl acetate].,"[None, 'Q000032', None, 'Q000032', 'Q000032', 'Q000737']","[None, 'analysis', None, 'analysis', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11486375,2001,,,,, -11410016,"Studies were conducted on the flavonoids (myricetin, quercetin, kaempferol, luteolin, and apigenin) contents of 62 edible tropical plants. The highest total flavonoids content was in onion leaves (1497.5 mg/kg quercetin, 391.0 mg/kg luteolin, and 832.0 mg/kg kaempferol), followed by Semambu leaves (2041.0 mg/kg), bird chili (1663.0 mg/kg), black tea (1491.0 mg/kg), papaya shoots (1264.0 mg/kg), and guava (1128.5 mg/kg). The major flavonoid in these plant extracts is quercetin, followed by myricetin and kaempferol. Luteolin could be detected only in broccoli (74.5 mg/kg dry weight), green chili (33.0 mg/kg), bird chili (1035.0 mg/kg), onion leaves (391.0 mg/kg), belimbi fruit (202.0 mg/kg), belimbi leaves (464.5 mg/kg), French bean (11.0 mg/kg), carrot (37.5 mg/kg), white radish (9.0 mg/kg), local celery (80.5 mg/kg), limau purut leaves (30.5 mg/kg), and dried asam gelugur (107.5 mg/kg). Apigenin was found only in Chinese cabbage (187.0 mg/kg), bell pepper (272.0 mg/kg), garlic (217.0 mg/kg), belimbi fruit (458.0 mg/kg), French peas (176.0 mg/kg), snake gourd (42.4 mg/kg), guava (579.0 mg/kg), wolfberry leaves (547.0 mg/kg), local celery (338.5 mg/kg), daun turi (39.5 mg/kg), and kadok (34.5 mg/kg). In vegetables, quercetin glycosides predominate, but glycosides of kaempferol, luteolin, and apigenin are also present. Fruits contain almost exclusively quercetin glycosides, whereas kaempferol and myricetin glycosides are found only in trace quantities.",Journal of agricultural and food chemistry,"['D002851', 'D005419', 'D044948', 'D010936', 'D010945']","['Chromatography, High Pressure Liquid', 'Flavonoids', 'Flavonols', 'Plant Extracts', 'Plants, Edible']","Flavonoid (myricetin, quercetin, kaempferol, luteolin, and apigenin) content of edible tropical plants.","['Q000379', 'Q000032', None, 'Q000737', 'Q000737']","['methods', 'analysis', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11410016,2001,1.0,3.0,,, -11401577,"Allium sativum agglutinin (ASAI) is a heterodimeric mannose-specific bulb lectin possessing two polypeptide chains of molecular mass 11.5 and 12.5 kDa. The thermal unfolding of ASAI, characterized by differential scanning calorimetry and circular dichroism, shows it to be highly reversible and can be defined as a two-state process in which the folded dimer is converted directly to the unfolded monomers (A2 if 2U). Its conformational stability has been determined as a function of temperature, GdnCl concentration, and pH using a combination of thermal and isothermal GdnCl-induced unfolding monitored by DSC, far-UV CD, and fluorescence, respectively. Analyses of these data yielded the heat capacity change upon unfolding (DeltaC(p) and also the temperature dependence of the thermodynamic parameters, namely, DeltaG, DeltaH, and DeltaS. The fit of the stability curve to the modified Gibbs-Helmholtz equation provides an estimate of the thermodynamic parameters DeltaH(g), DeltaS(g), and DeltaC(p) as 174.1 kcal x mol(-1), 0.512 kcal x mol(-1) x K(-1), and 3.41 kcal x mol(-1) x K(-1), respectively, at T(g) = 339.4 K. Also, the free energy of unfolding, DeltaG(s), at its temperature of maximum stability (T(s) = 293 K) is 13.13 kcal x mol(-1). Unlike most oligomeric proteins studied so far, the lectin shows excellent agreement between the experimentally determined DeltaC(p) (3.2 +/- 0.28 kcal x mol(-1) x K(-1)) and those evaluated from a calculation of its accessible surface area. This in turn suggests that the protein attains a completely unfolded state irrespective of the method of denaturation. The absence of any folding intermediates suggests the quaternary interactions to be the major contributor to the conformational stability of the protein, which correlates well with its X-ray structure. The small DeltaC(p) for the unfolding of ASAI reflects a relatively small, buried hydrophobic core in the folded dimeric protein.",Biochemistry,"['D000373', 'D002151', 'D002352', 'D002942', 'D037222', 'D018548', 'D019281', 'D005737', 'D037102', 'D008351', 'D037241', 'D008956', 'D037121', 'D010940', 'D018517', 'D010946', 'D011489', 'D017510', 'D017433', 'D013050', 'D013816']","['Agglutinins', 'Calorimetry', 'Carrier Proteins', 'Circular Dichroism', 'Collectins', 'Cotyledon', 'Dimerization', 'Garlic', 'Lectins', 'Mannans', 'Mannose-Binding Lectins', 'Models, Chemical', 'Plant Lectins', 'Plant Proteins', 'Plant Roots', 'Plants, Medicinal', 'Protein Denaturation', 'Protein Folding', 'Protein Structure, Secondary', 'Spectrometry, Fluorescence', 'Thermodynamics']",The reversible two-state unfolding of a monocot mannose-binding lectin from garlic bulbs reveals the dominant role of the dimeric interface in its stabilization.,"['Q000737', None, 'Q000737', None, None, None, None, 'Q000737', 'Q000378', 'Q000378', None, None, None, None, 'Q000737', None, None, None, None, None, None]","['chemistry', None, 'chemistry', None, None, None, None, 'chemistry', 'metabolism', 'metabolism', None, None, None, None, 'chemistry', None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11401577,2001,0.0,0.0,,, -11387870,"The aim of this work was to study the physiological mechanisms of dormancy and sprouting during post-harvest of garlic (Allium sativum L.) microbulblets produced by meristem culture of garlic seed cloves. The morphological changes occurring in garlic microbulblets were assessed from harvest till sprouting in relation with peroxidase activity and levels of gibberellins. Also the effect of a cold treatment (30 days at 4 degrees C) given 30 days after harvest was studied. The results showed that during the state of dormancy in garlic microbulblets formation of the leaf primordia and vascular differentiation of the storage leaf occurred, while increases of peroxidase activity and low levels of GA3 (the only active gibberellin identified) were found. At the end of dormancy the sprouting channel was formed, vascular differentiation established, and peaks of soluble peroxidase activity as well as of GA3 were observed. At day 90 post-harvest, garlic microbulblets showed physiologically mature and able to sprout. Further on, bud expansion and decrease of GA3 levels characterized sprouting of the microbulblets. The cold treatment enhanced GA3 levels and anticipated the sprouting process.",Biocell : official journal of the Sociedades Latinoamericanas de Microscopia Electronica ... et. al,"['D002454', 'D002478', 'D005737', 'D008401', 'D005875', 'D010544', 'D018515', 'D018514', 'D010946']","['Cell Differentiation', 'Cells, Cultured', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Gibberellins', 'Peroxidases', 'Plant Leaves', 'Plant Structures', 'Plants, Medicinal']",Morphological changes in garlic (Allium sativum L.) microbulblets during dormancy and sprouting as related to peroxidase activity and gibberellin A3 content.,"[None, None, 'Q000166', None, 'Q000378', 'Q000378', 'Q000166', 'Q000166', None]","[None, None, 'cytology', None, 'metabolism', 'metabolism', 'cytology', 'cytology', None]",https://www.ncbi.nlm.nih.gov/pubmed/11387870,2001,,,,, -11355006,"Fatty acids are known as modulators of the vasoactive properties of the vessel wall and can influence the physical and functional properties of cell membrane. The membrane-bound enzyme Na,K-ATPase plays a central role in endothelial function such as vasoconstriction. In a previous study, we have shown that omega3 fatty acids inhibited Na,K-ATPase activity in human endothelial cells. As Mediterranean diet is known to protect from cardiovascular diseases, we have investigated the effects of Omegacoeur, a Mediterranean nutritional complement consisting of omega3, omega6, omega9 fatty acids, garlic and basil, on Na,K-ATPase activity in human endothelial cells (HUVECs). Cells were incubated for 18 hr with pure lecithin liposomes or Omegacoeur-enriched emulsions (4 mg lecithin/ml). Na,K-ATPase and 5'-nucleotidase activities were determined using coupled assay methods on microsomal fractions obtained from HUVECs. Cell fatty acid composition was evaluated by gas chromatography after extraction of lipids and fatty acids methylation. The results showed that Omegacoeur (0.1 mM) increased Na,K-ATPase activity by 40% without changes in 5'-nucleotidase activity. Cells incubated with Omegacoeur preferentially incorporated linoleic acid. Therefore, linoleic acid or others constituents of Omegacoeur could be responsible of the stimulation of the Na,K-ATPase activity that might be related to changes in endothelial membrane fluidity.","Cellular and molecular biology (Noisy-le-Grand, France)","['D015720', 'D002458', 'D002478', 'D016895', 'D019587', 'D004730', 'D005227', 'D006801', 'D008081', 'D019083', 'D008861', 'D010713', 'D000254']","[""5'-Nucleotidase"", 'Cell Fractionation', 'Cells, Cultured', 'Culture Media, Serum-Free', 'Dietary Supplements', 'Endothelium, Vascular', 'Fatty Acids', 'Humans', 'Liposomes', 'Mediterranean Region', 'Microsomes', 'Phosphatidylcholines', 'Sodium-Potassium-Exchanging ATPase']","Omegacoeur, a Mediterranean nutritional complement, stimulates Na,K-ATPase activity in human endothelial cells.","['Q000378', None, None, None, None, 'Q000166', 'Q000378', None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000378']","['metabolism', None, None, None, None, 'cytology', 'metabolism', None, 'chemistry', None, 'chemistry', 'chemistry', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/11355006,2001,,,,, -11238799,"Aged garlic extract (AGE) has been shown to have antioxidant activity. The organosulfur compounds, S-allyl-L-cysteine and S-allylmercapto-L-cysteine, are responsible, at least in part, for the antioxidant activity of AGE. To identify major active components, we fractionated AGE, using hydrogen peroxide scavenging activity as an antioxidative index. Strong activity in the amino acid fraction was found and the major active compound was identified as N alpha-(1-deoxy-D-fructos-1-yl)-L-arginine (Fru-Arg). Antioxidant activity of Fru-Arg was comparable to that of ascorbic acid, scavenging hydrogen peroxide completely at 50 micromol/L and 37% at 10 micromol/L. Quantitative analysis using the established HPLC system revealed that AGE contained 2.1-2.4 mmol/L of Fru-Arg, but none was detected in either raw or heated garlic juice. Furthermore, it was shown that a minimum of 4 mo aging incubation was required for Fru-Arg to be generated. These findings indicate that the aging process is critical for the production of the antioxidant compound, Fru-Arg. These results may explain some of the variation in benefits among different commercially available garlic preparations.",The Journal of nutrition,"['D000975', 'D001120', 'D002851', 'D016166', 'D005737', 'D006861', 'D015416', 'D009005', 'D010936', 'D010946', 'D013997']","['Antioxidants', 'Arginine', 'Chromatography, High Pressure Liquid', 'Free Radical Scavengers', 'Garlic', 'Hydrogen Peroxide', 'Maillard Reaction', 'Monosaccharides', 'Plant Extracts', 'Plants, Medicinal', 'Time Factors']","N alpha-(1-deoxy-D-fructos-1-yl)-L-arginine, an antioxidant compound identified in aged garlic extract.","['Q000032', 'Q000031', None, None, 'Q000737', 'Q000378', None, 'Q000032', 'Q000032', None, None]","['analysis', 'analogs & derivatives', None, None, 'chemistry', 'metabolism', None, 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11238799,2001,1.0,1.0,,, -11238798,"Various components of garlic and aged garlic extract, including allicin, S-allylcysteine (SAC) and volatile metabolites of allicin were determined in breath, plasma and simulated gastric fluids by HPLC, gas chromatography (GC) or HPLC- and GC-mass spectrometry (MS). Data indicate that allicin decomposes in stomach acid to release allyl sulfides, disulfides and other volatiles that are postulated to be metabolized by glutathione and/or S-adenosylmethionine to form allyl methyl sulfide. SAC can be absorbed by the body and can be determined in plasma by HPLC or HPLC-MS using atmospheric pressure chemical ionization (APCI)-MS.",The Journal of nutrition,"['D000498', 'D001944', 'D002849', 'D002851', 'D003545', 'D005737', 'D008401', 'D005766', 'D005978', 'D006801', 'D010936', 'D010946', 'D012436', 'D013440', 'D013441']","['Allyl Compounds', 'Breath Tests', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Gastrointestinal Contents', 'Glutathione', 'Humans', 'Plant Extracts', 'Plants, Medicinal', 'S-Adenosylmethionine', 'Sulfides', 'Sulfinic Acids']","Determination of allicin, S-allylcysteine and volatile metabolites of garlic in breath, plasma or simulated gastric fluids.","['Q000378', None, 'Q000379', 'Q000379', 'Q000031', 'Q000737', 'Q000379', 'Q000737', 'Q000378', None, 'Q000737', None, 'Q000378', 'Q000378', 'Q000032']","['metabolism', None, 'methods', 'methods', 'analogs & derivatives', 'chemistry', 'methods', 'chemistry', 'metabolism', None, 'chemistry', None, 'metabolism', 'metabolism', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11238798,2001,0.0,0.0,,, -11238797,"The establishment of international monographs for herbs is in progress. Here, we propose both a marker compound and a method for its analysis for the identification of garlic bulbs and their products. The constituents in 26 kinds of fresh edible parts of Allium vegetables and three types of garlic preparations were analyzed. Sulfur compounds are the most characteristic constituents in garlic, but manufacturing processes of garlic products dramatically affect these constituents. Thus, no sulfur compound could be specified as a universal marker of identification applicable for any type of garlic. On the other hand, garlic contains other characteristic compounds, namely, saponins. After analyzing Allium vegetables and garlic preparations, we concluded that sapogenins, especially beta-chlorogenin, may be a viable candidate for identifying and distinguishing garlic from other Allium vegetables.",The Journal of nutrition,"['D000490', 'D002849', 'D002851', 'D002855', 'D003545', 'D005511', 'D005737', 'D008401', 'D010936', 'D010946', 'D012502', 'D012503', 'D013457']","['Allium', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Cysteine', 'Food Handling', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Plant Extracts', 'Plants, Medicinal', 'Sapogenins', 'Saponins', 'Sulfur Compounds']",How to distinguish garlic from the other Allium vegetables.,"['Q000737', None, None, None, 'Q000031', 'Q000379', 'Q000737', None, 'Q000032', None, 'Q000032', 'Q000032', 'Q000032']","['chemistry', None, None, None, 'analogs & derivatives', 'methods', 'chemistry', None, 'analysis', None, 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11238797,2001,0.0,0.0,,, -11237188,"Gas Chromatography-Mass Spectrometry (GC-MS) was the major technique used to determine various metabolites after consumption of dehydrated granular garlic and an enteric-coated garlic preparation, in breath, plasma, and simulated gastric fluids. A special short-path thermal desorption device was used as an introduction technique for the gas chromatograph for the determination of volatiles. These garlic preparations release allicin, which decomposes in stomach acid or with time in the intestine to release allyl sulfides, disulfides and other volatiles, some of which are postulated to be metabolized by glutathione and/or S-adenosylmethionine to form allyl methyl sulfide, the main sulfur containing volatile metabolite. S-Allylcysteine, a non-volatile bioactive component of aged garlic preparations, was determined in human plasma and urine by HPLC-MS using the negative ion atmospheric pressure chemical ionization mode (APcI)- MS. The technique of selected ion monitoring was used for quantitation. A synthetic internal standard of deuterated S-allylcysteine was added to the plasma or urine to ensure recovery and to obtain reliable quantitative data.","BioFactors (Oxford, England)","['D001944', 'D003545', 'D005737', 'D008401', 'D005750', 'D006801', 'D010936', 'D010946', 'D013440', 'D013441']","['Breath Tests', 'Cysteine', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Gastric Juice', 'Humans', 'Plant Extracts', 'Plants, Medicinal', 'Sulfides', 'Sulfinic Acids']",The determination of metabolites of garlic preparations in breath and human plasma.,"[None, 'Q000031', 'Q000378', None, 'Q000502', None, 'Q000493', None, 'Q000032', 'Q000032']","[None, 'analogs & derivatives', 'metabolism', None, 'physiology', None, 'pharmacokinetics', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11237188,2001,,,,error on the link, -11235811,"To search for cytotoxic components from Allium victorialis, MTT assays on each extract and an isolated component, gitogenin 3-O-lycotetroside, were performed against cancer cell lines. Cytotoxicities of most extract were shown to be comparatively weak, though IC50 values of CHCl3 fraction was found to be <31.3-368.4 microg/ml. From the incubated methanol extract at 36 degrees C, eleven kinds of organosulfuric flavours were predictable by GC-MS performance. The most abundant peak was revealed to be 2-vinyl-4H-1,3-dithiin (1) by its mass spectrum. Further, this extract showed significant cytotoxicities toward cancer cell lies. Silica gel column chromatography of the n-butanol fraction led to the isolation of gitogenin 3-O-lycotetroside (3) along with astragalin (4) and kaempferol 3, 4'-di-O-beta-D-glucoside (5). This steroidal saponin exhibited significant cytotoxic activities (IC50, 6.51-36.5 microg/ml) over several cancer cell lines. When compound 3 was incubated for 24 h with human intestinal bacteria, a major metabolite was produced and then isolated by silica gel column chromatography. By examining parent- and prominent ion peak in FAB-MS spectrum of the metabolite, the structure was speculated not to be any of prosapogenins of 3, suggesting that spiroketal ring were labile to the bacterial reaction. These suggest that disulfides produced secondarily are the antitumor principles.",Archives of pharmacal research,"['D000490', 'D000972', 'D001419', 'D004354', 'D005243', 'D005737', 'D006801', 'D007422', 'D010946', 'D013150', 'D014407']","['Allium', 'Antineoplastic Agents, Phytogenic', 'Bacteria', 'Drug Screening Assays, Antitumor', 'Feces', 'Garlic', 'Humans', 'Intestines', 'Plants, Medicinal', 'Spirostans', 'Tumor Cells, Cultured']",Constituents and the antitumor principle of Allium victorialis var. platyphyllum.,"['Q000737', 'Q000737', 'Q000378', None, None, 'Q000737', None, 'Q000378', None, 'Q000737', 'Q000187']","['chemistry', 'chemistry', 'metabolism', None, None, 'chemistry', None, 'metabolism', None, 'chemistry', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/11235811,2001,,,,, -11135419,"A method is described for determining 41 insecticide residues in garlic (Allium sativum L.), including organophosphorus, organochlorine, carbamate, and synthetic pyrethroid insecticides. These insecticides were extracted from samples with acetone and dichloromethane, and co-extractives removed using a charcoal/Celite/alumina column. Analysis was performed by gas chromatography with ion trap mass spectrometry in selective ion storage (SIS) mode. Retention times and specific ions (m/z values) were used to confirm insecticides. Recoveries for most insecticides (blank samples spiked at 0.05, 0.2 and 1 microg mL(-1) levels) ranged from 70% to 110%, the coefficient of variation (CV) of the method was <20% for every case, and the limit of detection (LOD), defined in terms of 3 times baseline noise, varied between 0.01 and 0.16 mg kg(-1), depending on the compound.",Rapid communications in mass spectrometry : RCM,"['D005506', 'D005737', 'D008401', 'D006801', 'D007306', 'D010946']","['Food Contamination', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Insecticides', 'Plants, Medicinal']",Multi-residue determination of 41 insecticides in garlic by gas chromatography and ion trap mass spectrometry using the selective ion storage technique.,"['Q000032', 'Q000737', 'Q000379', None, 'Q000032', None]","['analysis', 'chemistry', 'methods', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/11135419,2001,,,,, -11118647,"The reaction between allicin (diallylthiosulfinate), the active component of garlic and reduced glutathione was investigated. The product of this reaction, mixed disulfide S-allylmercaptoglutathione (GSSA) was separated by high performance liquid chromatography and identified by 1H and (13)C nuclear magnetic resonance and mass spectroscopy. The reaction is fast (with an apparent bimolecular reaction rate constant of 3.0 M(-1) s(-1)). It is pH-dependent, which reveals a direct correlation to the actual concentration of mercaptide ion (GS(-)). Both GSSA and S-allylmercaptocysteine (prepared from allicin and cysteine) reacted with SH-containing enzymes, papain and alcohol dehydrogenase from Thermoanaerobium brockii yielding the corresponding S-allylmercapto proteins, and caused inactivation of the enzymes. The activity was restored with dithiothreitol or 2-mercaptoethanol. In addition, GSSA also exhibited high antioxidant properties. It showed significant inhibition of the reaction between OH radicals and the spin trap 5,5'-dimethyl-1-pyroline N-oxide in the Fenton system as well as in the UV photolysis of H2O2. In ex vivo experiments done with fetal brain slices under iron-induced oxidative stress, GSSA significantly lowered the production levels of lipid peroxides. The similar activity of GSSA and allicin as SH-modifiers and antioxidants suggests that the thioallyl moiety has a key role in the biological activity of allicin and its derivatives.",Biochimica et biophysica acta,"['D000426', 'D000975', 'D002851', 'D003545', 'D004791', 'D005737', 'D005978', 'D007700', 'D009682', 'D010206', 'D010946', 'D013438', 'D013441']","['Alcohol Dehydrogenase', 'Antioxidants', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Enzyme Inhibitors', 'Garlic', 'Glutathione', 'Kinetics', 'Magnetic Resonance Spectroscopy', 'Papain', 'Plants, Medicinal', 'Sulfhydryl Compounds', 'Sulfinic Acids']",S-Allylmercaptoglutathione: the reaction product of allicin with glutathione possesses SH-modifying and antioxidant properties.,"['Q000037', 'Q000737', None, 'Q000031', 'Q000737', None, 'Q000737', None, None, 'Q000037', None, 'Q000737', 'Q000737']","['antagonists & inhibitors', 'chemistry', None, 'analogs & derivatives', 'chemistry', None, 'chemistry', None, None, 'antagonists & inhibitors', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11118647,2001,0.0,0.0,,, -22062166,"The influence of the yeast starter cultures Debaryomyces hansenii and Candida utilis on fermented meat aroma was studied in model minces and in commercial-type fermented sausages. Volatile compounds from model minces and sausages were collected using diffusive and dynamic headspace sampling respectively and were identified by gas chromatography/mass spectrometry (GC/MS). A triangle test was carried out on the sausages to detect whether the yeast influenced the sausage odour. C. utilis demonstrated high metabolic activity in the model minces, producing several volatile compounds, in particularly esters. C. utilis also seemed to ferment the amino acids valine, isoleucine and leucine into compounds important for the aroma of sausages. D. hansenii on the contrary, had very little effect on the production of volatile compounds in the model minces. In the sausage experiment both yeast cultures died out before the ripening process ended and the sensory analysis showed only a slight difference between the sausages. A fungistatic test of the garlic powder added to the sausages indicated that garlic inhibits the growth of the yeast starter cultures.",Meat science,[],[],The influence of Debaryomyces hansenii and Candida utilis on the aroma formation in garlic spiced fermented sausages and model minces.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/22062166,2012,0.0,0.0,,, -10934793,"A thorough investigation of saponin fraction from the underground parts of wild garlic--Allium ursinum L. (Liliaceae) has led to the isolation of 3-[O-alpha-rhamnopyranosyl-(1-->4)-alpha-rhamnopyranosyl-(1-->4)- alpha-rhamnopyranosyl-(1-->4)-beta-glucopyranoside-(1-->)]-3 beta-hydroxypregna-5,16-dien-20-one [1]. The structure of 1 was established by chemical and spectroscopic methods. Compound 1 is reported for the first time.",Acta poloniae pharmaceutica,"['D002240', 'D005737', 'D009682', 'D008969', 'D018517', 'D010946', 'D016339', 'D013055']","['Carbohydrate Sequence', 'Garlic', 'Magnetic Resonance Spectroscopy', 'Molecular Sequence Data', 'Plant Roots', 'Plants, Medicinal', 'Spectrometry, Mass, Fast Atom Bombardment', 'Spectrophotometry, Infrared']",Pregnadienolone glycoside from wild garlic Allium ursinum L.,"[None, 'Q000737', None, None, 'Q000737', None, None, None]","[None, 'chemistry', None, None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10934793,2000,0.0,0.0,,, -10890508,"Off-flavors in foods may originate from environmental pollutants, the growth of microorganisms, oxidation of lipids, or endogenous enzymatic decomposition in the foods. The chromatographic analysis of flavors and off-flavors in foods usually requires that the samples first be processed to remove as many interfering compounds as possible. For analysis of foods by gas chromatography (GC), sample preparation may include mincing, homogenation, centrifugation, distillation, simple solvent extraction, supercritical fluid extraction, pressurized-fluid extraction, microwave-assisted extraction, Soxhlet extraction, or methylation. For high-performance liquid chromatography of amines in fish, cheese, sausage and olive oil or aldehydes in fruit juice, sample preparation may include solvent extraction and derivatization. Headspace GC analysis of orange juice, fish, dehydrated potatoes, and milk requires almost no sample preparation. Purge-and-trap GC analysis of dairy products, seafoods, and garlic may require heating, microwave-mediated distillation, purging the sample with inert gases and trapping the analytes with Tenax or C18, thermal desorption, cryofocusing, or elution with ethyl acetate. Solid-phase microextraction GC analysis of spices, milk and fish can involve microwave-mediated distillation, and usually requires adsorption on poly(dimethyl)siloxane or electrodeposition on fibers followed by thermal desorption. For short-path thermal desorption GC analysis of spices, herbs, coffee, peanuts, candy, mushrooms, beverages, olive oil, honey, and milk, samples are placed in a glass-lined stainless steel thermal desorption tube, which is purged with helium and then heated gradually to desorb the volatiles for analysis. Few of the methods that are available for analysis of food flavors and off-flavors can be described simultaneously as cheap, easy and good.",Journal of chromatography. A,"['D005421', 'D005504']","['Flavoring Agents', 'Food Analysis']",Sample preparation for the analysis of flavors and off-flavors in foods.,"['Q000032', None]","['analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/10890508,2000,0.0,0.0,,only sample preparations, -10888499,"A recent human intervention trial showed that daily supplementation with selenized yeast (Se-yeast) led to a decrease in the overall cancer morbidity and mortality by nearly 50%; past research has also demonstrated that selenized garlic (Se-garlic) is very effective in mammary cancer chemoprevention in the rat model. The goal of this study was to compare certain biological activities of Se-garlic and Se-yeast and to elucidate the differences based on the chemical forms of selenium found in these two natural products. Characterization of organic selenium compounds in yeast (1922 microg/g Se) and garlic (296 microg/g Se) was carried out by high-performance liquid chromatography with inductively coupled plasma mass spectrometry or with electrospray mass spectrometry. Analytical speciation studies showed that the bulk of the selenium in Se-garlic and Se-yeast is in the form of gamma-glutamyl-Se-methylselenocysteine (73%) and selenomethionine (85%), respectively. The above methodology has the sensitivity and capability to account for >90% of total selenium. In the rat feeding studies, supplementation of Se-garlic in the diet at different levels consistently caused a lower total tissue selenium accumulation when compared to Se-yeast. On the other hand, Se-garlic was significantly more effective in suppressing the development of premalignant lesions and the formation of adenocarcinomas in the mammary gland of carcinogen-treated rats. Given the present finding on the identity of selenomethionine and gamma-glutamyl-Se-methylselenocysteine as the major form of selenium in Se-yeast and Se-garlic, respectively, the metabolism of these two compounds is discussed in an attempt to elucidate how their disposition in tissues might account for the differences in cancer chemopreventive activity.",Journal of agricultural and food chemistry,"['D000230', 'D000818', 'D016588', 'D002273', 'D005260', 'D005737', 'D006801', 'D008325', 'D008517', 'D010946', 'D011230', 'D051381', 'D017207', 'D018036', 'D018038', 'D015003']","['Adenocarcinoma', 'Animals', 'Anticarcinogenic Agents', 'Carcinogens', 'Female', 'Garlic', 'Humans', 'Mammary Neoplasms, Experimental', 'Phytotherapy', 'Plants, Medicinal', 'Precancerous Conditions', 'Rats', 'Rats, Sprague-Dawley', 'Selenium Compounds', 'Sodium Selenite', 'Yeasts']",Chemical speciation influences comparative activity of selenium-enriched garlic and yeast in mammary cancer prevention.,"['Q000139', None, 'Q000627', None, None, 'Q000627', None, 'Q000139', None, None, 'Q000139', None, None, 'Q000627', 'Q000627', None]","['chemically induced', None, 'therapeutic use', None, None, 'therapeutic use', None, 'chemically induced', None, None, 'chemically induced', None, None, 'therapeutic use', 'therapeutic use', None]",https://www.ncbi.nlm.nih.gov/pubmed/10888499,2000,0.0,0.0,,, -10885064,"Selenium-enriched plants, such as hyperaccumulative phytoremediation plants (Astragalus praleongus, 517 micrograms g-1 Se, and Brassica juncea, 138 micrograms g-1 Se in dry sample), yeast (1200, 1922 and 2100, micrograms g-1 Se in dry sample), ramp (Allium tricoccum, 48, 77, 230, 252, 405 and 524 micrograms g-1 Se in dry sample), onion (Allium cepa, 96 and 140 micrograms g-1 Se in dry sample) and garlic (Allium sativum, 68, 112, 135, 296, 1355 micrograms g-1 Se in dry sample) were analyzed by HPLC-ICP-MS for their selenium content and speciation after hot water and enzymatic extractions. Reference samples with natural selenium levels, such as onion and garlic controls, cooking garlic powder, baking yeast powder and a commercial garlic supplement were also analyzed. Selected samples were also examined by HPLC-electrospray ionization (ESI)-MS. HPLC was mostly carried out with 0.1% heptafluorobutanoic acid (HFBA) as ion-pairing agent in 1 + 99 v/v methanol-water solution, but 0.1% trifluoroacetic acid (TFA) in 1 + 99 v/v methanol-water solution was also utilized to permit chromatography for compounds that did not elute with HFBA. More than 75% of the total eluting selenium compounds, based upon element specific detection, were identified from retention time data and standard spiking experiments, and between 60 and 85% of compounds were identified by MS, with up to 25% of the total eluting molecular selenium species being unidentified as yet. Limits of quantification (LOQ, defined as the concentration giving an S/N of 10) for HPLC-ICP-MS were in the range 2-50 ng mL-1 Se in the injected extracts for the selenium-enriched samples and 2-10 ng mL-1 Se for the natural selenium level samples. LOQ values for HPLC-ESI-MS were ca. 100 times higher than those measured by HPLC-ICP-MS.",The Analyst,"['D002851', 'D005466', 'D010944', 'D012643']","['Chromatography, High Pressure Liquid', 'Fluorocarbons', 'Plants', 'Selenium']",Selenium speciation in enriched and natural samples by HPLC-ICP-MS and HPLC-ESI-MS with perfluorinated carboxylic acid ion-pairing agents.,"['Q000379', None, 'Q000737', 'Q000032']","['methods', None, 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/10885064,2000,,,,, -10843440,"Phytoestrogens (weak estrogens found in plants or derived from plant precursors by human metabolism) have been hypothesized to reduce the risk of a number of cancers. However, epidemiologic studies addressing this issue are hampered by the lack of a comprehensive phytoestrogen database for quantifying exposure. The purpose of this research was to develop such a database for use with food-frequency questionnaires in large epidemiologic studies.",Cancer causes & control : CCC,"['D000328', 'D000368', 'D002851', 'D016208', 'D016021', 'D004968', 'D005260', 'D005502', 'D005504', 'D005518', 'D006801', 'D007529', 'D013058', 'D008875', 'D009369', 'D048789', 'D028321', 'D010945', 'D011795', 'D014481']","['Adult', 'Aged', 'Chromatography, High Pressure Liquid', 'Databases, Factual', 'Epidemiologic Studies', 'Estrogens, Non-Steroidal', 'Female', 'Food', 'Food Analysis', 'Food Preferences', 'Humans', 'Isoflavones', 'Mass Spectrometry', 'Middle Aged', 'Neoplasms', 'Phytoestrogens', 'Plant Preparations', 'Plants, Edible', 'Surveys and Questionnaires', 'United States']",Assessing phytoestrogen exposure in epidemiologic studies: development of a database (United States).,"[None, None, None, None, None, 'Q000032', None, 'Q000706', None, None, None, None, None, None, 'Q000453', None, None, 'Q000737', None, 'Q000453']","[None, None, None, None, None, 'analysis', None, 'statistics & numerical data', None, None, None, None, None, None, 'epidemiology', None, None, 'chemistry', None, 'epidemiology']",https://www.ncbi.nlm.nih.gov/pubmed/10843440,2000,,,,, -10825863,"The objective of this double-blind clinical study was to investigate the effectiveness of a commercially available dentifrice containing triclosan and a copolymer (Colgate Total Toothpaste) for controlling long-term, i.e., seven-hour and overnight breath odor. In particular, a comparison was made between the level of control of breath odor provided by the test dentifrice, and that provided by a placebo dentifrice which did not contain triclosan or a copolymer. This study followed a two-treatment, two-period crossover design. Prospective subjects were provided with a supply of a commercially available fluoride dentifrice, which was used for a one-week period prior to the two seven-day treatment periods. During each treatment period, subjects were instructed to brush their teeth twice a day, morning and evening, for sixty seconds with their assigned study dentifrice, using the soft-bristled toothbrush which had been provided. On the morning following the seventh day of each treatment period, subjects reported to the clinical facility for overnight breath odor assessments. Directly following this, subjects brushed their teeth, ate and drank normally, and reported once again to the clinical facility at seven hours post-toothbrushing for another breath odor assessment. Prior to the overnight breath odor assessments, subjects refrained from brushing their teeth, rinsing their mouths or using breath mints, and from eating or drinking anything on the morning of the evaluation. Subjects refrained from the use of tobacco products, and from eating onions, garlic, or strong spices throughout the entire study. Breath odor was instrumentally evaluated by measuring the level of volatile sulfur compounds in the mouth air using a 565 Tracor gas chromatograph equipped with a flame photometric detector. Measurements were taken in duplicate, and then averaged. Levels of volatile sulfur compounds were expressed in nanograms per milliliter (ng/ml) of mouth air. The two dentifrices exhibited statistically significant differences (p < 0.05) with respect to both overnight breath odor and seven-hour post-toothbrushing breath odor. The mean overnight breath odor scores were 9.63 ng/ml for Colgate Total Toothpaste, and 12.64 ng/ml for the placebo dentifrice. For seven-hour breath odor, the mean scores were 5.62 ng/ml for Colgate Total Toothpaste, and 7.10 ng/ml for the placebo dentifrice. Thus, the results of this double-blind clinical study on 19 subjects support the conclusion that Colgate Total Toothpaste provides effective seven-hour and overnight control of breath odor.",The Journal of clinical dentistry,"['D000328', 'D000704', 'D000891', 'D002849', 'D045424', 'D018592', 'D003802', 'D004311', 'D005260', 'D005459', 'D006209', 'D006801', 'D008297', 'D008298', 'D008875', 'D011095', 'D011145', 'D011446', 'D012824', 'D014100', 'D016896', 'D014260']","['Adult', 'Analysis of Variance', 'Anti-Infective Agents, Local', 'Chromatography, Gas', 'Complex Mixtures', 'Cross-Over Studies', 'Dentifrices', 'Double-Blind Method', 'Female', 'Fluorides', 'Halitosis', 'Humans', 'Male', 'Maleates', 'Middle Aged', 'Polyethylenes', 'Polyvinyls', 'Prospective Studies', 'Silicic Acid', 'Toothpastes', 'Treatment Outcome', 'Triclosan']",The clinical effectiveness of a dentifrice containing triclosan and a copolymer for providing long-term control of breath odor measured chromatographically.,"[None, None, 'Q000627', None, None, None, 'Q000737', None, None, None, 'Q000188', None, None, 'Q000627', None, 'Q000627', 'Q000627', None, None, None, None, 'Q000627']","[None, None, 'therapeutic use', None, None, None, 'chemistry', None, None, None, 'drug therapy', None, None, 'therapeutic use', None, 'therapeutic use', 'therapeutic use', None, None, None, None, 'therapeutic use']",https://www.ncbi.nlm.nih.gov/pubmed/10825863,2000,,,,, -10737231,"The antibacterial activity of garlic powder against O-157 was tested by using garlic bulbs post-harvested 1 y. O-157 at 10(6-7) cfu/mL perished after incubation for 24 h with a 1% solution of garlic powder. The use of powder from fresh garlic was more effective for antibacterial activity than that from old garlic; the 1% solution of fresh garlic powder eradicating the O-157 in 6 h. The antibacterial activity was resistant to heat treatment of 100 degrees C for 20 min. The water-soluble components of garlic powder were fractionated into three fractions (Fr. 1-3) by Sephadex G-100 column chromatography, among which Fr. 3 showed antibacterial activity against O-157 but the other fractions were scarce in activity. The antibacterial activity was also shown against other types of pathogenic bacteria such as methicillin-resistant Staphylococcus aureus (MRSA), Salmonella enteritidis, and Candida albicans. Thus, the practical use of garlic powder is expected to prevent bacteria-caused food poisoning.",Journal of nutritional science and vitaminology,"['D000900', 'D000890', 'D002176', 'D019453', 'D005737', 'D006801', 'D066298', 'D007223', 'D008826', 'D010946', 'D012477', 'D013211']","['Anti-Bacterial Agents', 'Anti-Infective Agents', 'Candida albicans', 'Escherichia coli O157', 'Garlic', 'Humans', 'In Vitro Techniques', 'Infant', 'Microbial Sensitivity Tests', 'Plants, Medicinal', 'Salmonella enteritidis', 'Staphylococcus aureus']",Antibacterial activity of garlic powder against Escherichia coli O-157.,"[None, None, 'Q000187', 'Q000187', None, None, None, None, None, None, 'Q000187', 'Q000187']","[None, None, 'drug effects', 'drug effects', None, None, None, None, None, None, 'drug effects', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/10737231,2000,,,,, -10720787,"The intravenous administration of a purified fraction (6 microg/kg) to anaesthesized dogs was followed by a significant biphasic diuretic and natriuretic response which reached a maximum at 180 min after injection. Chloride, but not potassium ions, followed the natriuretic profile. No changes were observed in arterial blood pressure or in the electrocardiogram. The purified garlic fraction also induced an inhibitory dose-dependent effect on kidney Na, K-ATPase.",Journal of ethnopharmacology,"['D000490', 'D000818', 'D002851', 'D002852', 'D004231', 'D004232', 'D004285', 'D004573', 'D005260', 'D007668', 'D008297', 'D009318', 'D011506', 'D000254', 'D014563']","['Allium', 'Animals', 'Chromatography, High Pressure Liquid', 'Chromatography, Ion Exchange', 'Diuresis', 'Diuretics', 'Dogs', 'Electrolytes', 'Female', 'Kidney', 'Male', 'Natriuresis', 'Proteins', 'Sodium-Potassium-Exchanging ATPase', 'Urodynamics']",Purification and bioassays of a diuretic and natriuretic fraction from garlic (Allium sativum).,"['Q000737', None, None, None, 'Q000187', 'Q000302', None, 'Q000652', None, 'Q000187', None, 'Q000187', 'Q000378', 'Q000378', 'Q000187']","['chemistry', None, None, None, 'drug effects', 'isolation & purification', None, 'urine', None, 'drug effects', None, 'drug effects', 'metabolism', 'metabolism', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/10720787,2000,0.0,0.0,,, -10624707,"Epidemiological and experimental studies suggest that consumption of garlic may protect against several types of cancer. Moreover, a plausible hypothesis has been proposed that the biological effects of garlic can be attributed to the enhancing action of a variety of organosulfur compounds, present in garlic, on hepatic phase II carcinogen detoxification enzymes. We have used the N-methylnitrosourea (NMU)-induced rat mammary tumor model to test the chemopreventive effects of a water-soluble organosulfur constituent derived from aged garlic, S-allylcysteine (SAC). Rats were fed diets supplemented with 666 and 2,000 ppm SAC beginning seven days before initiation with NMU (55 days of age) to termination (18 wk post-NMU), at which time mammary tumors were enumerated. At neither dose did SAC exert an inhibitory effect on any index of tumor development, including incidence, latency, multiplicity, or volume, compared with untreated controls. Weight gains in all groups were similar. Assay of serum SAC levels in supplemented groups indicated that SAC concentrations were beneath the limits of detection of the high-performance liquid chromatography system used. These results contradict previous animal model studies indicating that SAC acts as an inhibitory agent in experimental mammary tumorigenesis; reasons for this discrepancy include the possibility that SAC may exhibit nonlinear dose effects.",Nutrition and cancer,"['D000230', 'D000818', 'D000970', 'D002273', 'D002851', 'D003545', 'D004032', 'D004195', 'D018572', 'D005260', 'D005737', 'D008325', 'D008770', 'D010946', 'D011897', 'D051381', 'D017207']","['Adenocarcinoma', 'Animals', 'Antineoplastic Agents', 'Carcinogens', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Diet', 'Disease Models, Animal', 'Disease-Free Survival', 'Female', 'Garlic', 'Mammary Neoplasms, Experimental', 'Methylnitrosourea', 'Plants, Medicinal', 'Random Allocation', 'Rats', 'Rats, Sprague-Dawley']","S-allylcysteine, a garlic constituent, fails to inhibit N-methylnitrosourea-induced rat mammary tumorigenesis.","['Q000401', None, 'Q000627', None, None, 'Q000031', None, None, None, None, None, 'Q000401', None, None, None, None, None]","['mortality', None, 'therapeutic use', None, None, 'analogs & derivatives', None, None, None, None, None, 'mortality', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10624707,2000,,,,, -10552426,"Described in this paper is a fiber interface direct headspace mass spectrometric system for the real-time measurement of flavor release. The system was optimized for the detection of the garlic aroma volatile, diallyl disulfide, from water. Parameters investigated included interface temperature, flow rate through the fiber, flow rate through the sample vessel, and sample stir rate. The delay time for detection of sample after introduction into the sample vessel was determined as 43 s. The system proved to be reliable and robust with no loss in sensitivity or contamination of the mass spectrometer over a 6 month period. The technique was applied to a homologous series of aliphatic alcohols from C(2) to C(7). Results showed that as polarity decreased with increasing chain length the release of volatile into the headspace was faster and gave a higher maximum intensity. Release of the garlic aroma volatile from different commercial mayonnaise products clearly showed a decrease in the release of diallyl disulfide as fat content increased. These results demonstrate the potential of using this technique as a tool for understanding the complex interactions that occur between flavor compounds and the bulk food matrix.",Journal of agricultural and food chemistry,"['D000438', 'D000498', 'D005737', 'D013058', 'D010946', 'D012680', 'D013440', 'D013649', 'D013696', 'D013816', 'D014835']","['Alcohols', 'Allyl Compounds', 'Garlic', 'Mass Spectrometry', 'Plants, Medicinal', 'Sensitivity and Specificity', 'Sulfides', 'Taste', 'Temperature', 'Thermodynamics', 'Volatilization']",Use of fiber interface direct mass spectrometry for the determination of volatile flavor release from model food systems.,"['Q000032', 'Q000032', None, 'Q000379', None, None, 'Q000032', None, None, None, None]","['analysis', 'analysis', None, 'methods', None, None, 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10552426,2000,0.0,0.0,,garlic mayo, -10588342,"A new GC method for determination of S-alk(en)ylcysteine sulfoxides, important secondary metabolites occurring in many plant genera, has been developed. The method is based on isolation of the amino acid fraction by ion-exchange chromatography followed by derivatization with ethyl chloroformate at ambient temperature and reduction of derivatized S-alk(en)ylcysteine sulfoxides by sodium iodide. The main advantages of the new method are its high sensitivity, excellent resolution capability, accuracy and reliability, as well as the possibility to identify unknown compounds by means of GC-MS. The content of alliin and other S-alk(en)ylcysteine sulfoxides was determined in nine different samples of garlic (Allium sativum L.) originating from the Czech Republic, France, and China. The total content of S-alk(en)ylcysteine sulfoxide pool ranged between 0.53 and 1.3% fresh weight, with S-allylcysteine sulfoxide (alliin) being predominant. A novel S-alkylcysteine derivative, S-ethylcysteine sulfoxide (ethiin), not previously reported to occur in Allium species, was found in some of the samples examined.",Journal of chromatography. A,"['D002849', 'D002851', 'D003545', 'D005737', 'D007202', 'D010946', 'D019163', 'D013454']","['Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Indicators and Reagents', 'Plants, Medicinal', 'Reducing Agents', 'Sulfoxides']",Gas chromatographic determination of S-alk(en)ylcysteine sulfoxides.,"['Q000379', None, 'Q000031', 'Q000737', None, None, None, 'Q000032']","['methods', None, 'analogs & derivatives', 'chemistry', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/10588342,2000,1.0,2.0,,, -10578481,"Heterocyclic aromatic amines (HAAs), a group of chemicals formed during high-temperature cooking of meat and fish, are potent mutagens and are suspected to play a role in colorectal cancer. A recent study suggested that marinating meat may offer a way to reduce HAA formation. Hawaii's diverse ethnic populations, which are at various risks of colorectal cancer, often use traditional marinades when cooking meat. We compared the HAA content of beef steaks marinated overnight with teriyaki sauce, turmeric-garlic sauce, or commercial honey barbecue sauce with that of unmarinated steaks. The levels of 2-amino-1-methyl-6-phenylimidazo [4,5-b]pyridine (PhIP) and 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx) were determined by liquid-liquid extraction and gas chromatography-mass spectrometry. Beef steaks marinated with teriyaki sauce had 45% and 67% lower PhIP level at 10 minutes (p = 0.002) and 15 minutes (p = 0.001) of cooking time as well as 44% and 60% lower MeIQx levels at 10 minutes (p = 0.008) and 15 minutes (p = 0.001), respectively, than unmarinated meat. Lower levels of PhIP and MeIQx were also observed in meat marinated with turmeric-garlic sauce. In contrast, marinating with barbecue sauce caused a 2.9- and 1.9-fold increase in PhIP (p < or = 0.005) and a 4- and 2.9-fold increase in MeIQx (p < or = 0.001) at 10 and 15 minutes, respectively. Differences in the mutagenic activities of marinated and unmarinated steaks, as measured by the Ames assay, paralleled the differences in PhIP and MeIQx levels. Future studies should test the effects of specific ingredients, including the water content of marinades, and the effect of reapplying barbecue sauce during cooking (to reduce charring) on HAA formation.",Nutrition and cancer,"['D001208', 'D015179', 'D003296', 'D005421', 'D006801', 'D008460', 'D009152', 'D012307', 'D012486', 'D019366']","['Asia', 'Colorectal Neoplasms', 'Cooking', 'Flavoring Agents', 'Humans', 'Meat', 'Mutagenicity Tests', 'Risk Factors', 'Salmonella typhimurium', 'Western World']",Effects of marinating with Asian marinades or western barbecue sauce on PhIP and MeIQx formation in barbecued beef.,"[None, 'Q000139', None, None, None, None, None, None, 'Q000235', None]","[None, 'chemically induced', None, None, None, None, None, None, 'genetics', None]",https://www.ncbi.nlm.nih.gov/pubmed/10578481,2000,,,,, -10564015,"Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a powerful new technique that will have a great impact on food analysis. This study demonstrates the applicability of MALDI-MS performed directly on an aqueous food extract for qualitative and quantitative analysis of food oligosaccharides. 2', 4',6'-Trihydroxyacetophenone was found to be the best matrix for analysis of oligosaccharides in the foods examined. The relationship between laser strength, resolution, and the response factors of individual oligosaccharides using MALDI-MS was investigated. A MALDI-MS method for quantitative analysis of fructooligosaccharides with standard addition of a pure fructooligosaccharide was developed. High performance anion exchange chromatography with pulsed amperometric detection was compared to MALDI-MS for the analysis of fructooligosaccharides. The fructooligosaccharide analyses were performed on red onions, shallots, and elephant garlic.",Journal of agricultural and food chemistry,"['D002236', 'D002240', 'D003505', 'D005504', 'D007202', 'D008969', 'D009844', 'D019032', 'D047408']","['Carbohydrate Conformation', 'Carbohydrate Sequence', 'Cyclodextrins', 'Food Analysis', 'Indicators and Reagents', 'Molecular Sequence Data', 'Oligosaccharides', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'gamma-Cyclodextrins']",Analysis of food oligosaccharides using MALDI-MS: quantification of fructooligosaccharides.,"[None, None, 'Q000737', None, None, None, 'Q000032', 'Q000379', None]","[None, None, 'chemistry', None, None, None, 'analysis', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/10564015,2000,1.0,1.0,,, -10497175,"We report herein for the first time the formation by freshly grown garlic roots and the structural characterization of 14,15-epoxide positional analogs of the hepoxilins formed via the 15-lipoxygenase-induced oxygenation of arachidonic acid. These compounds are formed through the combined actions of a 15(S)-lipoxygenase and a hydroperoxyeicosatetraenoic acid (HPETE) isomerase. The compounds were formed when either arachidonic acid or 15-HPETE were used as substrates. Both the ""A""-type and the ""B""-type products are formed although the B-type compounds are formed in greater relative quantities. Chiral phase high performance liquid chromatography analysis confirmed the formation of hepoxilins from 15(S)- but not 15(R)-HPETE, indicating high stereoselectivity of the isomerase. Additionally, the lipoxygenase was of the 15(S)-type as only 15(S)-hydroxyeicosatetraenoic acid was formed when arachidonic acid was used as substrate. The structures of the products were confirmed by gas chromatography-mass spectrometry of the methyl ester trimethylsilyl ether derivatives as well as after characteristic epoxide ring opening catalytically with hydrogen leading to dihydroxy products. That 15(S)-lipoxygenase activity is of functional importance in garlic was shown by the inhibition of root growth by BW 755C, a dual cyclooxygenase/lipoxygenase inhibitor and nordihydroguaiaretic acid, a lipoxygenase inhibitor. Additional biological studies were carried out with the purified intact 14(S), 15(S)-hepoxilins, which were investigated for hepoxilin-like actions in causing the release of intracellular calcium in human neutrophils. The 14,15-hepoxilins dose-dependently caused a rise in cytosolic calcium, but their actions were 5-10-fold less active than 11(S), 12(S)-hepoxilins derived from 12(S)-HPETE. These studies provide evidence that 15(S)-lipoxygenase is functionally important to normal root growth and that HPETE isomerization into the hepoxilin-like structure may be ubiquitous; the hepoxilin-evoked release of calcium in human neutrophils, which is receptor-mediated, is sensitive to the location within the molecule of the hydroxyepoxide functionality.",The Journal of biological chemistry,"['D015126', 'D001093', 'D002851', 'D005737', 'D006801', 'D019746', 'D009504', 'D018517', 'D010946', 'D011956']","['8,11,14-Eicosatrienoic Acid', 'Arachidonate 15-Lipoxygenase', 'Chromatography, High Pressure Liquid', 'Garlic', 'Humans', 'Intramolecular Oxidoreductases', 'Neutrophils', 'Plant Roots', 'Plants, Medicinal', 'Receptors, Cell Surface']","Formation of 14,15-hepoxilins of the A(3) and B(3) series through a 15-lipoxygenase and hydroperoxide isomerase present in garlic roots.","['Q000031', 'Q000378', None, 'Q000201', None, 'Q000378', 'Q000378', 'Q000201', None, 'Q000378']","['analogs & derivatives', 'metabolism', None, 'enzymology', None, 'metabolism', 'metabolism', 'enzymology', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/10497175,1999,0.0,0.0,1.0,, -10450562,"Volatile sulfur compounds arising from grated raw or heat-treated garlic in both in-vitro and in-vivo tests were gas-chromatographically analyzed. In in-vitro tests, the head-space vapor gas from garlic in a vial was analyzed. It was clarified that allyl mercaptan arising from raw garlic decreased with the passage of time and other volatile low-molecular sulfur compounds (LMSC) did not show remarkable changes. The change of LMSC from heat-treated garlic was also studied. Methyl mercaptan and allyl mercaptan from heat-treated garlic gradually increased to some extent. On the other hand, the quantities of somewhat high-molecular sulfur compounds (HMSC) were much less in heat-treated garlic compared to those of raw garlic. These compounds increased till approx. 60 min and then decreased gradually. In in-vivo tests, human expiration after eating garlic was analyzed. Allyl mercaptan, methyl mercaptan and allyl methyl sulfide in LMSC were detected in significant amounts. The quantities of these compounds arising from heat-treated garlic were smaller than those from raw garlic. These compounds had the tendency of decreasing with the passage of time. On the other hand, almost no HMSC was detected in both raw and heat-treated garlic. By sensory testing, raw garlic showed a stronger smell than heat-treated garlic in both in-vitro and in-vivo tests. GC analysis exhibited higher values of volatile sulfur compounds in raw garlic. That is, the higher the volatile sulfur compound level, the stronger the garlic flavor or malodor.",Journal of nutritional science and vitaminology,"['D000498', 'D002849', 'D004220', 'D005260', 'D005737', 'D008401', 'D006358', 'D006801', 'D009812', 'D010946', 'D013438', 'D013440', 'D013457', 'D014835']","['Allyl Compounds', 'Chromatography, Gas', 'Disulfides', 'Female', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Hot Temperature', 'Humans', 'Odorants', 'Plants, Medicinal', 'Sulfhydryl Compounds', 'Sulfides', 'Sulfur Compounds', 'Volatilization']",Volatile sulfur compounds in human expiration after eating raw or heat-treated garlic.,"['Q000032', None, 'Q000032', None, 'Q000737', None, None, None, None, None, 'Q000032', 'Q000032', 'Q000009', None]","['analysis', None, 'analysis', None, 'chemistry', None, None, None, None, None, 'analysis', 'analysis', 'adverse effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/10450562,1999,,,,, -10234471,"Diallyl sulfide (DAS) and diallyl disulfide (DADS) were used to determine viability and inhibition of arylamine N-acetyltransferase (NAT) activity in human bladder tumor cells. The NAT activity was measured by high performance liquid chromatography assaying for the amounts of N-acetyl-2-aminofluorene (2-AAF) and N-acetyl-p-aminobenzoic acid (N-Ac-PABA) and remaining 2-aminofluorene (2-AF) and p-aminobenzoic acid (PABA). The viability, NAT activity and 2-AAF-DNA adduct formation in human bladder tumor cells was inhibited by DAS and DADS in a dose-dependent manner, i.e. the higher the concentration of DAS and DADS, the higher the inhibition of NAT activity and cell death. The data also indicated that DAS and DADS decrease the apparent values of Km and Vmax from human bladder tumor cells in both systems examined. This report is the first demonstration to show garlic components did affect human bladder tumor cell NAT activity.",Drug and chemical toxicology,"['D000498', 'D016588', 'D000970', 'D001191', 'D004220', 'D004789', 'D005737', 'D006801', 'D010946', 'D013440', 'D014407', 'D001749']","['Allyl Compounds', 'Anticarcinogenic Agents', 'Antineoplastic Agents', 'Arylamine N-Acetyltransferase', 'Disulfides', 'Enzyme Activation', 'Garlic', 'Humans', 'Plants, Medicinal', 'Sulfides', 'Tumor Cells, Cultured', 'Urinary Bladder Neoplasms']",Effects of garlic components diallyl sulfide and diallyl disulfide on arylamine N-acetyltransferase activity in human bladder tumor cells.,"['Q000494', 'Q000494', 'Q000494', 'Q000378', 'Q000494', 'Q000187', None, None, None, 'Q000494', None, 'Q000188']","['pharmacology', 'pharmacology', 'pharmacology', 'metabolism', 'pharmacology', 'drug effects', None, None, None, 'pharmacology', None, 'drug therapy']",https://www.ncbi.nlm.nih.gov/pubmed/10234471,1999,0.0,0.0,,, -10227149,"An organosulfur compound was isolated from oil-macerated garlic extract by silica gel column chromatography and preparative TLC. From the results of NMR, IR, and MS analyses, its structure was determined as E-4,5,9-trithiadeca-1,7-diene-9-oxide (iso-E-10-devinylajoene, iso-E-10-DA). This compound was different from E-4,5,9-trithiadeca-1,6-diene-9-oxide (E-10-devinylajoene, E-10-DA) only in the position of a double bond. Iso-E-10-DA had antimicrobial activity against Gram-positive bacteria, such as Bacillus cereus, B. subtilis, and Staphylococcus aureus, and yeasts at the concentration lower than 100 micrograms/ml, but Gram-negative bacteria were not inhibited at the same concentration. The antimicrobial activity of iso-E-10-DA was inferior to those of similar oil-macerated garlic extract compounds such as E-ajoene, Z-ajoene, and Z-10-DA. From these results, it was suggested that trans structure and/or the position of double bond of iso-E-10-DA reduce the antimicrobial activity.","Bioscience, biotechnology, and biochemistry","['D000900', 'D002855', 'D004220', 'D005737', 'D006094', 'D009682', 'D013058', 'D008826', 'D009821', 'D010936', 'D010946', 'D013055', 'D013454']","['Anti-Bacterial Agents', 'Chromatography, Thin Layer', 'Disulfides', 'Garlic', 'Gram-Positive Bacteria', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Microbial Sensitivity Tests', 'Oils', 'Plant Extracts', 'Plants, Medicinal', 'Spectrophotometry, Infrared', 'Sulfoxides']","An organosulfur compound isolated from oil-macerated garlic extract, and its antimicrobial effect.","['Q000302', None, 'Q000302', 'Q000737', 'Q000187', None, None, None, None, 'Q000737', None, None, 'Q000302']","['isolation & purification', None, 'isolation & purification', 'chemistry', 'drug effects', None, None, None, None, 'chemistry', None, None, 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/10227149,1999,0.0,0.0,,, -10193205,"Alliinase (EC 4.4.1.4) has been isolated from commercially available garlic (Allium sativum L., Alliaceae) powder and was investigated with respect to its use as ingredient of herbal remedies. The enzyme was purified to apparent homogeneity and results were compared with those obtained from a sample of fresh A. sativum var. pekinense. The purification of the enzyme involved a gel filtration step as well as affinity chromatography on concanavalin-A agarose. Vmax using L-(+)-alliin as substrate (252 mumol min-1 mg-1) was at the lower range of data given in the literature (214-390 mumol min-1 mg-1). L-(-)-Alliin was also accepted as substrate (54 mumol min-1 mg-1). Vmax for alliinase from A. sativum var. pekinense was at 332 mumol min-1 mg-1 and 90 mumol min-1 mg-1 for L-(+)- and L-(-)-alliin, respectively. The Km values for alliinase from garlic powder were estimated to be 1.6 mM for L-(+)-alliin and 2.8 mM for L-(-)-alliin. In contrast to literature values, both temperature and pH optima were somewhat higher (36 degrees C and pH 7.0 versus 33 degrees C and pH 6.5, respectively). The enzyme was found to be active in a range from pH 5 to pH 10. Gel electrophoresis gave evidence that the alliinase obtained from garlic powder consisted of two slightly different subunits with molecular weights of 53 and 54 kDa whereas alliinase obtained from fresh garlic consists of two identical subunits. It is assumed that the alliinase gets significantly altered during the drying process of garlic powder but is still capable to convert alliin to allicin.",Planta medica,"['D013437', 'D004591', 'D005737', 'D008517', 'D010946', 'D011208']","['Carbon-Sulfur Lyases', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Phytotherapy', 'Plants, Medicinal', 'Powders']",Quality of herbal remedies from Allium sativum: differences between alliinase from garlic powder and fresh garlic.,"['Q000302', None, 'Q000737', None, None, None]","['isolation & purification', None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10193205,1999,,,,no pdf access, -9882409,"Allicin (diallylthiosulfinate) is the main biologically active component of freshly crushed garlic cloves. It is produced upon the interaction of the nonprotein amino acid alliin with the enzyme alliinase (alliin lyase, EC 4.4.1.4). A simple and rapid spectrophotometric procedure for determination of allicin and alliinase activity, based on the reaction between 2-nitro-5-thiobenzoate (NTB) and allicin, is described. NTB reacts with the activated disulfide bond --S(O)-S--; of allicin, forming the mixed-disulfide allylmercapto-NTB, as characterized by NMR. The method can be used for determination of allicin and total thiosulfinates in garlic preparations and garlic-derived products. The method was applied for determination of pure alliinase activity and for the activity of the enzyme in crude garlic extracts.",Analytical biochemistry,"['D013437', 'D002851', 'D002855', 'D007700', 'D009682', 'D009579', 'D013438', 'D013441', 'D013451']","['Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Kinetics', 'Magnetic Resonance Spectroscopy', 'Nitrobenzoates', 'Sulfhydryl Compounds', 'Sulfinic Acids', 'Sulfonic Acids']",A spectrophotometric assay for allicin and alliinase (Alliin lyase) activity: reaction of 2-nitro-5-thiobenzoate with thiosulfinates.,"['Q000378', None, None, None, None, 'Q000737', None, 'Q000378', 'Q000737']","['metabolism', None, None, None, None, 'chemistry', None, 'metabolism', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/9882409,1999,1.0,1.0,,, -19281347,"Garlic (Allium sativum L.) is used in the household and as an ingredient in many pharmaceutical products. Tissue culture technique provides an excellent source for induction of both chemical and genetic variation in garlic. A callus was induced on root meristem cultured on Murashige and Skoog (MS) medium in the presence of kinetin, indole acetic acid, and 2,4-dichlorophenoxyacetic acid. Shoots with a small bulb were produced on medium containing MS salts, B vitamins, and naphthalene acetic acid. Regenerated plants were transplanted into soil, and a nondivided bulb was formed in the first somaclonal generation (SCI). Plants were normal in their phenotypes in SC2. After four cycles of field cultivation, the selected somaclones (variants) in the fourth generation showed significant differences in bulb character compared with the original plants. Mitotic division and chromosomal abnormalities were investigated in meristimic root tip cells of regenerated plants for the first and fourth regeneration and for control plants. Somaclonal variant metaphase cells had the same chromosome number (2n = 16) as those of the controls. Allicin was measured quantitatively in the regenerated clones by high-performance liquid chromatography. The results showed that some clones contained as much as 14.50 mg/g allicin, compared with 3.80 mg/g in the control plant. This finding suggests that this technique may be useful to improve the allicin content of Egyptian garlic, which could be utilized as a good source for garlic-containing pharmaceutical preparations.",Journal of medicinal food,[],[],Chemical and Genetic Evaluation of Somaclonal Variants of Egyptian Garlic (Allium sativum L.).,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19281347,2012,,,,, -9818412,"A new direct HPLC method with fluorescence detection has been developed for the routine analysis of riboflavin, flavin mononucleotide and flavin-adenine dinucleotide, in wines and other beverages. These compounds are the main agents responsible for the ""taste of light"" that some white wines and other beverages develop when they are exposed to the light, due to the formation of sulfur compounds that produce an anion/garlic odor. A Hewlett-Packard 1100 gradient liquid chromatograph with 1046A fluorescence detector was used. To improve the selectivity, each compound was monitored to fit the best lambda excitation/lambda emission (265/525 nm). A 500 nm cut-off filter was used. The column was a Hypersil C18 ODS, 200 x 2.1 mm, 5 microns particle size. The volume injected was 20 microliters. A constant flow-rate of 0.6 ml/min was used with two solvents: solvent A, 0.05 M buffer NaH2PO4 at pH = 3.0 with H3PO4 and solvent B, acetonitrile. The precision, linearity and sensitivity of this method have been established.",Journal of chromatography. A,"['D001628', 'D002851', 'D005486', 'D005182', 'D012256', 'D012680', 'D014920']","['Beverages', 'Chromatography, High Pressure Liquid', 'Flavin Mononucleotide', 'Flavin-Adenine Dinucleotide', 'Riboflavin', 'Sensitivity and Specificity', 'Wine']","Determination of riboflavin, flavin mononucleotide and flavin-adenine dinucleotide in wine and other beverages by high-performance liquid chromatography with fluorescence detection.","['Q000032', 'Q000379', 'Q000032', 'Q000032', 'Q000032', None, 'Q000032']","['analysis', 'methods', 'analysis', 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/9818412,1998,0.0,0.0,,, -9799098,"Alliinase [S-alk(en)yl-L-cysteine sulfoxide lyase], a pyridoxal-phosphate-(Pxy-P)-dependent enzyme, is responsible for the degradative conversion of S-alk(en)yl-L-cysteine sulfoxide to volatile odorous sulfur-containing metabolites in Allium plants. We have purified alliinase from shoots of Allium tuberosum (Chinese chive) to apparent homogeneity by SDS/polyacrylamide gel electrophoresis. A cDNA clone encoding alliinase was isolated from a cDNA library constructed from whole plants of A. tuberosum by hybridization screening with a synthetic 50-residue oligonucleotide encoding a conserved region of the alliinases from onion and garlic. The isolated cDNA encoded a protein of 476 amino acid residues with a molecular mass of 54083 Da. The deduced amino acid sequence exhibited 66-69% identities with those of reported alliinases from onion, garlic and shallot. The partial amino acid sequence, which was determined for a V8 protease-digested peptide fragment of the purified alliinase, was perfectly matched with the sequence deduced from the cDNA. An expression vector of recombinant alliinase cDNA was constructed in yeast. The catalytically active protein was in the soluble fraction of transformed yeast. Site-directed mutagenesis experiments indicated that Lys280 was essential for the catalytic activity and, thus, a possible Pxy-P-binding residue. The mRNA expression of the alliinase gene comprising a multigene family in the shoots of green plants was twofold higher than that in the roots of green plants; however, the expression in the shoots of etiolated plants was only 13% that in green shoots, although the expression in the roots was not remarkably different between in green and etiolated plants. Immunohistochemical investigation indicated that the alliinase protein is predominantly accumulated in the bundle sheath cells of shoots of A. tuberosum.",European journal of biochemistry,"['D000490', 'D000595', 'D001483', 'D013437', 'D002384', 'D002850', 'D003001', 'D018076', 'D004591', 'D004926', 'D007150', 'D008969', 'D016297', 'D009693', 'D017386']","['Allium', 'Amino Acid Sequence', 'Base Sequence', 'Carbon-Sulfur Lyases', 'Catalysis', 'Chromatography, Gel', 'Cloning, Molecular', 'DNA, Complementary', 'Electrophoresis, Polyacrylamide Gel', 'Escherichia coli', 'Immunohistochemistry', 'Molecular Sequence Data', 'Mutagenesis, Site-Directed', 'Nucleic Acid Hybridization', 'Sequence Homology, Amino Acid']","Alliinase [S-alk(en)yl-L-cysteine sulfoxide lyase] from Allium tuberosum (Chinese chive)--purification, localization, cDNA cloning and heterologous functional expression.","['Q000201', None, None, 'Q000737', None, None, None, None, None, 'Q000235', None, None, None, None, None]","['enzymology', None, None, 'chemistry', None, None, None, None, None, 'genetics', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9799098,1998,0.0,0.0,,, -9770309,An approach using supercritical fluid extraction (SFE) followed by clean-up with a AgNO3-loaded Florisil column was utilized for the analysis of four organochlorine pesticides (OCPs) in garlic. The organic sulfur extracted by SFE from garlic was removed by AgNO3 allowing OCPs to be determined by GC-electron-capture detection without interferences. All OCPs recoveries ranged from 85.0% to 110.0% and relative standard deviations were in the range of 3.9-7.2% for spiked samples. The described method may be used to analyze OCPs in garlic on a routine basis.,Journal of chromatography. A,"['D002849', 'D004563', 'D005737', 'D006843', 'D007306', 'D010573', 'D010946']","['Chromatography, Gas', 'Electrochemistry', 'Garlic', 'Hydrocarbons, Chlorinated', 'Insecticides', 'Pesticide Residues', 'Plants, Medicinal']",Supercritical fluid extraction and off-line clean-up for the analysis of organochlorine pesticide residues in garlic.,"[None, None, 'Q000737', None, 'Q000032', 'Q000032', None]","[None, None, 'chemistry', None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/9770309,1998,0.0,0.0,,pesticides, -9747536,"Supercritical fluid (SF) extracts of homogenized ramp (Allium tricoccum Ait.) were separated and characterized with liquid chromatography coupled with atmospheric pressure chemical ionization mass spectrometric identification. The profiles of SF extracts of aqueous homogenates of ramp bulbs from three different seasons and growing regions revealed that the thiosulfinates were major components. In addition, some of the cepaenes (alpha-sulfinyldisulfides) found in extracts of onion juice, as well as allyl containing cepaenes (2-propenyl l-(2-propenylsulfinyl)propyl disulfide), are present in the ramp extracts. The amount of allicin in ramp bulb homogenates ranged from approximately 10% to 50% of that found in extracts of aqueous garlic homogenates. The greater amount of the methyl 1-propenyl thiosulfinates in the ramp extracts relative to that found in the garlic extracts correlates with the flavor characteristics of ramp bulbs.",Phytochemistry,"['D000490', 'D002853', 'D004220', 'D013058', 'D010936', 'D013438', 'D013441']","['Allium', 'Chromatography, Liquid', 'Disulfides', 'Mass Spectrometry', 'Plant Extracts', 'Sulfhydryl Compounds', 'Sulfinic Acids']",Allium chemistry: identification of organosulfur compounds in ramp (Allium tricoccum) homogenates.,"['Q000737', None, 'Q000032', None, 'Q000032', 'Q000032', 'Q000032']","['chemistry', None, 'analysis', None, 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/9747536,1998,0.0,0.0,,not quantified and only previously reported , -9648236,"A compound showing antimicrobial activity was isolated from an oil-macerated garlic extract by silica gel column chromatography and preparative TLC. On basis of the results of NMR and MS analyses, it was identified as Z-4,5,9-trithiadeca-1,6-diene-9-oxide (Z-10-devinylajoene; Z-10-DA). Z-10-DA exhibited a broad spectrum of antimicrobial activity against such microorganisms as gram-positive and gram-negative bacteria and yeasts. The antimicrobial activity of Z-10-DA was comparable to that of Z-ajoene, but was superior to that of E-ajoene. Z-10-DA and Z-ajoene are different in respect of substitution of the allyl group by the methyl group flanking a sulfinyl group. This result suggests that substitution by the methyl group would also be effective for the inhibition of microbial growth.","Bioscience, biotechnology, and biochemistry","['D000900', 'D004220', 'D005737', 'D008826', 'D010936', 'D010938', 'D010946', 'D013329']","['Anti-Bacterial Agents', 'Disulfides', 'Garlic', 'Microbial Sensitivity Tests', 'Plant Extracts', 'Plant Oils', 'Plants, Medicinal', 'Structure-Activity Relationship']",Antimicrobial activity of a compound isolated from an oil-macerated garlic extract.,"['Q000737', 'Q000737', 'Q000382', None, 'Q000737', 'Q000737', None, None]","['chemistry', 'chemistry', 'microbiology', None, 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9648236,1998,0.0,0.0,,, -9580446,"The controlled fermentation of peeled, blanched garlic, using a starter culture of Lactobacillus plantarum, was studied and compared with that of unblanched garlic. Blanching was carried out in hot water (90 degrees C) for 15 min. The starter grew abundantly in the case of blanched garlic, producing mainly lactic acid and reaching a pH of 3.8 after 7 days, but its growth was inhibited in unblanched garlic. Ethanol and fructose, coming from enzymatic activities of the garlic, and a green pigment were formed during the fermentation of unblanched garlic, but not of blanched garlic. The blanched garlic fermented by L. plantarum, even without a preservation treatment (pasteurization), was microbiologically stable during storage at 30 degrees C in an acidified brine (approximately 3% (w/w) NaCl and pH 3.5 at equilibrium), but fructans were hydrolyzed. The packed fermented product and that obtained by direct packing without fermentation were not significantly different with regard to flavour.",International journal of food microbiology,"['D002241', 'D002849', 'D002851', 'D003116', 'D004755', 'D000431', 'D005285', 'D005519', 'D005630', 'D005737', 'D006358', 'D006863', 'D019344', 'D007778', 'D010946', 'D013030']","['Carbohydrates', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Color', 'Enterobacteriaceae', 'Ethanol', 'Fermentation', 'Food Preservation', 'Fructans', 'Garlic', 'Hot Temperature', 'Hydrogen-Ion Concentration', 'Lactic Acid', 'Lactobacillus', 'Plants, Medicinal', 'Spain']",Lactic acid fermentation and storage of blanched garlic.,"['Q000096', None, None, None, 'Q000254', 'Q000032', None, None, 'Q000032', 'Q000378', None, None, 'Q000096', 'Q000254', None, None]","['biosynthesis', None, None, None, 'growth & development', 'analysis', None, None, 'analysis', 'metabolism', None, None, 'biosynthesis', 'growth & development', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9580446,1998,0.0,0.0,,no control, -9488677,"Two mannose-binding lectins, Allium sativum agglutinin (ASA) I (25 kDa) and ASAIII (48 kDa), from garlic bulbs have been purified by affinity chromatography followed by gel filtration. The subunit structures of these lectins are different, but they display similar sugar specificities. Both ASAI and ASAIII are made up of 12.5- and 11.5-kDa subunits. In addition, a complex (136 kDa) comprising a polypeptide chain of 54 +/- 4 kDa and the subunits of ASAI and ASAIII elutes earlier than these lectins on gel filtration. The 54-kDa subunit is proven to be alliinase, which is known to form a complex with garlic lectins. Constituent subunits of ASAI and ASAIII exhibit the same sequence at their amino termini. ASAI and ASAIII recognize monosaccharides in mannosyl configuration. The potencies of the ligands for ASAs increase in the following order: mannobiose (Manalpha1-3Man) < mannotriose (Manalpha1-6Manalpha1-3Man) approximately mannopentaose < Man9-oligosaccharide. The addition of two GlcNAc residues at the reducing end of mannotriose or mannopentaose enhances their potencies significantly, whereas substitution of both alpha1-3- and alpha1-6-mannosyl residues of mannotriose with GlcNAc at the nonreducing end increases their activity only marginally. The best manno-oligosaccharide ligand is Man9GlcNAc2Asn, which bears several alpha1-2-linked mannose residues. Interaction with glycoproteins suggests that these lectins recognize internal mannose as well as bind to the core pentasaccharide of N-linked glycans even when it is sialylated. The strongest inhibitors are the high mannose-containing glycoproteins, which carry larger glycan chains. Indeed, invertase, which contains 85% of its mannose residues in species larger than Man20GlcNAc, exhibited the highest binding affinity. No other mannose- or mannose/glucose-binding lectin has been shown to display such a specificity.",The Journal of biological chemistry,"['D000595', 'D001665', 'D001667', 'D002236', 'D002240', 'D013437', 'D002352', 'D005737', 'D006023', 'D006026', 'D006384', 'D037102', 'D037241', 'D008362', 'D008969', 'D009844', 'D037121', 'D010946', 'D011485', 'D017421', 'D043324']","['Amino Acid Sequence', 'Binding Sites', 'Binding, Competitive', 'Carbohydrate Conformation', 'Carbohydrate Sequence', 'Carbon-Sulfur Lyases', 'Carrier Proteins', 'Garlic', 'Glycoproteins', 'Glycoside Hydrolases', 'Hemagglutination', 'Lectins', 'Mannose-Binding Lectins', 'Mannosides', 'Molecular Sequence Data', 'Oligosaccharides', 'Plant Lectins', 'Plants, Medicinal', 'Protein Binding', 'Sequence Analysis', 'beta-Fructofuranosidase']",Garlic (Allium sativum) lectins bind to high mannose oligosaccharide chains.,"[None, None, 'Q000502', None, None, 'Q000378', 'Q000378', 'Q000737', 'Q000378', 'Q000378', 'Q000187', 'Q000378', None, 'Q000737', None, 'Q000378', None, None, None, None, None]","[None, None, 'physiology', None, None, 'metabolism', 'metabolism', 'chemistry', 'metabolism', 'metabolism', 'drug effects', 'metabolism', None, 'chemistry', None, 'metabolism', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9488677,1998,0.0,0.0,,, -9399673,"There is a growing need for short-term and cost-effective bioassay to assess the efficacy of potential chemo-preventive agents. We report that the induction of glutathione (GSH) S-transferase pi (mGSTP1-1) by a chemo-preventive agent can be used as a reliable marker to assess its efficacy in retarding chemical carcinogenesis induced by benzo(a)pyrene (BP), which is a widespread environmental pollutant and believed to be a risk factor in human chemical carcinogenesis. This conclusion is based on 1) the relative contribution of mGSTP1-1 of the liver and forestomach of female A/J mice in the detoxification of the ultimate carcinogenic metabolite of BP, (+)-anti-7,8-dihydroxy-9, 10-oxy-7,8,9, 10-tetrahydrobenzo(a)pyrene [(+)-anti-BPDE]; and 2) a positive correlation between the induction of hepatic and forestomach mGSTP1-1 by 5 naturally occurring organosulfides (OSCs) from garlic (diallyl sulfide, diallyl disulfide, diallyl trisulfide, dipropyl sulfide and dipropyl disulfide) and their effectiveness in preventing BP-induced forestomach neoplasia in mice. In the liver, the combined contribution of other GSTs in the detoxification of (+)-anti-BPDE was far less than the contribution of mGSTP1-1 alone. Likewise, in the forestomach, the contribution of mGSTP1-1 far exceeded the combined contribution of other GSTs. Studies on the effects of OSCs against BP-induced forestomach neoplasia revealed a good correlation between their chemo-preventive efficacy and their ability to induce mGSTP1-1 expression in the liver (r = -0.89; p < 0.05) as well as in the forestomach (r = -0.97; p < 0.05). Our results suggest that the induction of mGSTP1-1 may be a reliable marker for evaluating the efficacy of potential inhibitors of BP-induced cancer in a murine model.",International journal of cancer,"['D000498', 'D000818', 'D016588', 'D001564', 'D001681', 'D002851', 'D004220', 'D004790', 'D005260', 'D005737', 'D051549', 'D005982', 'D007527', 'D008099', 'D051379', 'D008805', 'D010946', 'D011407', 'D012044', 'D013270', 'D013274', 'D013440', 'D016896']","['Allyl Compounds', 'Animals', 'Anticarcinogenic Agents', 'Benzo(a)pyrene', 'Biological Assay', 'Chromatography, High Pressure Liquid', 'Disulfides', 'Enzyme Induction', 'Female', 'Garlic', 'Glutathione S-Transferase pi', 'Glutathione Transferase', 'Isoenzymes', 'Liver', 'Mice', 'Mice, Inbred A', 'Plants, Medicinal', 'Propane', 'Regression Analysis', 'Stomach', 'Stomach Neoplasms', 'Sulfides', 'Treatment Outcome']",Induction of glutathione S-transferase pi as a bioassay for the evaluation of potency of inhibitors of benzo(a)pyrene-induced cancer in a murine model.,"['Q000302', None, 'Q000302', 'Q000633', 'Q000191', None, 'Q000302', None, None, 'Q000737', None, 'Q000096', 'Q000096', 'Q000201', None, None, None, 'Q000031', None, 'Q000201', 'Q000139', 'Q000302', None]","['isolation & purification', None, 'isolation & purification', 'toxicity', 'economics', None, 'isolation & purification', None, None, 'chemistry', None, 'biosynthesis', 'biosynthesis', 'enzymology', None, None, None, 'analogs & derivatives', None, 'enzymology', 'chemically induced', 'isolation & purification', None]",https://www.ncbi.nlm.nih.gov/pubmed/9399673,1998,0.0,0.0,,, -9177218,"""Natural"" polyreactive antibodies, which bind in a nonspecific manner to a range of biological molecules both of self- and nonself- origin, are normal constituents of serum and are a significant part of the immune repertoire in many species, including humans. Autoantibodies to sTNF-R (the 55-kDa extracellular domain of the human receptor to tumor necrosis factor alpha) were affinity purified from normal human sera using immobilized sTNF-R. The isolated anti-sTNF-R IgG bound both native and denatured forms of the receptor with low affinity. These antibodies also bound to different proteins and therefore are considered to be polyreactive. We used the anti-sTNF-R antibodies and purified polyreactive antibodies to mannose-specific lectin from garlic (Allium sativum) for screening a peptide library displayed on filamentous M13 phage. After the biopanning procedure, we failed to find epitopes with a consensus sequence; however, we found that proline is the most frequent amino acid in the selected phagotopes. Proline is commonly present at solvent-exposed sites in proteins, such as loops, turns, N-terminal first turn of helix, and random coils. Thus, structures containing proline can serve as conformation-dependent common ""public"" epitopes for polyreactive natural antibodies. Our findings may be important for understanding polyreactivity in general and for the significance of polyreactive natural antibodies in immunological homeostasis.",Proceedings of the National Academy of Sciences of the United States of America,"['D000595', 'D000818', 'D000906', 'D015703', 'D001323', 'D002846', 'D004797', 'D000939', 'D005737', 'D006801', 'D015151', 'D007074', 'D007256', 'D037102', 'D008358', 'D037121', 'D010946', 'D011336', 'D011392', 'D017433', 'D018124', 'D047888']","['Amino Acid Sequence', 'Animals', 'Antibodies', 'Antigens, CD', 'Autoantibodies', 'Chromatography, Affinity', 'Enzyme-Linked Immunosorbent Assay', 'Epitopes', 'Garlic', 'Humans', 'Immunoblotting', 'Immunoglobulin G', 'Information Systems', 'Lectins', 'Mannose', 'Plant Lectins', 'Plants, Medicinal', 'Probability', 'Proline', 'Protein Structure, Secondary', 'Receptors, Tumor Necrosis Factor', 'Receptors, Tumor Necrosis Factor, Type I']",The epitopes for natural polyreactive antibodies are rich in proline.,"[None, None, 'Q000302', 'Q000276', None, None, None, 'Q000737', None, None, None, None, None, 'Q000276', 'Q000276', None, None, None, None, None, 'Q000276', None]","[None, None, 'isolation & purification', 'immunology', None, None, None, 'chemistry', None, None, None, None, None, 'immunology', 'immunology', None, None, None, None, None, 'immunology', None]",https://www.ncbi.nlm.nih.gov/pubmed/9177218,1997,0.0,0.0,,, -9216741,"This study compared heterocyclic aromatic amines in marinated and unmarinated chicken breast meat flame-broiled on a propane grill. Chicken was marinated prior to grilling and the levels of several heterocyclic amines formed during cooking were determined by solid-phase extraction and HPLC. Compared with unmarinated controls, a 92-99% decrease in 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) was observed in whole chicken breast marinated with a mixture of brown sugar, olive oil, cider vinegar, garlic, mustard, lemon juice and salt, then grilled for 10, 20, 30 or 40 min. Conversely, 2-amino-3, 8-dimethylimidazo[4,5-f]quinoxaline (MeIQx) increased over 10-fold with marinating, but only at the 30 and 40 min cooking times. Marinating reduced the total detectable heterocyclic amines from 56 to 1.7 ng/g, from 158 to 10 ng/g and from 330 to 44 ng/g for grilling times of 20, 30 and 40 min, respectively. The mutagenic activity of the sample extracts was also measured, using the Ames/Salmonella assay. Mutagenic activity was lower in marinated samples cooked for 10, 20 and 30 min, but higher in the marinated samples cooked for 40 min, compared with unmarinated controls. Although a change in free amino acids, which are heterocyclic amine precursors, might explain the decrease in PhIP and increase in MeIQx, no such change was detected. Marinating chicken in one ingredient at a time showed that sugar was involved in the increased MeIQx, but the reason for the decrease in PhIP was unclear. PhIP decreased in grilled chicken after marinating with several individual ingredients. This work shows that marinating is one method that can significantly reduce PhIP concentration in grilled chicken.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000588', 'D000818', 'D002645', 'D002851', 'D003296', 'D005504', 'D006571', 'D007093', 'D008460', 'D009152', 'D009153', 'D011810', 'D012486']","['Amines', 'Animals', 'Chickens', 'Chromatography, High Pressure Liquid', 'Cooking', 'Food Analysis', 'Heterocyclic Compounds', 'Imidazoles', 'Meat', 'Mutagenicity Tests', 'Mutagens', 'Quinoxalines', 'Salmonella typhimurium']",Effects of marinating on heterocyclic amine carcinogen formation in grilled chicken.,"['Q000032', None, None, None, 'Q000379', None, 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000032', 'Q000187']","['analysis', None, None, None, 'methods', None, 'analysis', 'analysis', None, None, 'analysis', 'analysis', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/9216741,1997,0.0,0.0,,percentage change, -9147057,"A procedure developed to separate the homodimeric and heterodimeric mannose-binding lectins from bulbs of garlic (Allium sativum L.) and ramsons (Allium ursinum L.) also enabled the isolation of stable lectin-alliinase complexes. Characterization of the individual lectins indicated that, in spite of their different molecular structure, the homomeric and heteromeric lectins resemble each other reasonably well with respect to their agglutination properties and carbohydrate-binding specificity. However, a detailed analysis of the lectin-alliinase complexes from garlic and ramsons bulbs demonstrated that only the heterodimeric lectins are capable of binding to the glycan chains of the alliinase molecules (EC 4.4.1.4). Moreover, it appears that only a subpopulation of the alliinase molecules is involved in the formation of lectin-alliinase complexes and that the complexed alliinase contains more glycan chains than the free enzyme. Finally, some arguments are given that the lectin-alliinase complexes do not occur in vivo but are formed in vitro after homogenization of the tissue.",Glycoconjugate journal,"['D000490', 'D000595', 'D013437', 'D002352', 'D002846', 'D003001', 'D019281', 'D004591', 'D005737', 'D037102', 'D046911', 'D008358', 'D037241', 'D008961', 'D008969', 'D010446', 'D037121', 'D018517', 'D010946', 'D011485', 'D011994', 'D016415', 'D017386']","['Allium', 'Amino Acid Sequence', 'Carbon-Sulfur Lyases', 'Carrier Proteins', 'Chromatography, Affinity', 'Cloning, Molecular', 'Dimerization', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Lectins', 'Macromolecular Substances', 'Mannose', 'Mannose-Binding Lectins', 'Models, Structural', 'Molecular Sequence Data', 'Peptide Fragments', 'Plant Lectins', 'Plant Roots', 'Plants, Medicinal', 'Protein Binding', 'Recombinant Proteins', 'Sequence Alignment', 'Sequence Homology, Amino Acid']",Isolation and characterization of lectins and lectin-alliinase complexes from bulbs of garlic (Allium sativum) and ramsons (Allium ursinum).,"['Q000201', None, 'Q000737', 'Q000737', None, None, None, None, 'Q000201', 'Q000737', None, None, None, None, None, 'Q000737', None, None, None, None, 'Q000737', None, None]","['enzymology', None, 'chemistry', 'chemistry', None, None, None, None, 'enzymology', 'chemistry', None, None, None, None, None, 'chemistry', None, None, None, None, 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9147057,1997,,,,, -9128734,"The microsomal fraction of homogenate of garlic (Allium sativum L.) bulbs contains a divinyl ether synthase which catalyzes conversion of (9Z,11E,13S)-13-hydroperoxy-9, 11-octadecadienoic acid and (9Z,11E,13S,15Z)-13-hydroperoxy-9,11,15-octadecatri eno ic acid into (9Z,11E,1'E,)-12-(1'-hexenyloxy)-9,11-dodecadienoic acid (etherolenic acid) and (9Z,11E,1'E,3'Z)-12-(1',3'-hexadienyloxy)-9,11-dode cadienoic acid (etherolenic acid), respectively. Two isomers of etherolenic acid were isolated. As shown by NMR spectrometry, the double bond configurations of these compounds were (9E,11E,1'E) and (9Z,11Z,1'E). Experiments with linoleic acid (13R,S)-hydroperoxide demonstrated that the S enantiomer was a much better substrate for the divinyl ether synthase compared to the R enantiomer. Incubation of (9Z,11E,13S)-[18O2]hydroperoxy-9,11-octadecadienoic acid led to the formation of etherolenic acid which retained 18O in the ether oxygen. An intermediary role of an epoxyallylic cation in etherolenic acid biosynthesis is postulated.",European journal of biochemistry,"['D002851', 'D003577', 'D005231', 'D005737', 'D007536', 'D015289', 'D008041', 'D008054', 'D009682', 'D008861', 'D010088', 'D010940', 'D010946', 'D013379']","['Chromatography, High Pressure Liquid', 'Cytochrome P-450 Enzyme System', 'Fatty Acids, Unsaturated', 'Garlic', 'Isomerism', 'Leukotrienes', 'Linoleic Acids', 'Lipid Peroxides', 'Magnetic Resonance Spectroscopy', 'Microsomes', 'Oxidoreductases', 'Plant Proteins', 'Plants, Medicinal', 'Substrate Specificity']",On the mechanism of biosynthesis of divinyl ether oxylipins by enzyme from garlic bulbs.,"[None, None, 'Q000096', 'Q000201', None, 'Q000378', 'Q000378', 'Q000378', None, 'Q000201', 'Q000378', None, None, None]","[None, None, 'biosynthesis', 'enzymology', None, 'metabolism', 'metabolism', 'metabolism', None, 'enzymology', 'metabolism', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9128734,1997,0.0,0.0,,protein, -9084912,"The chemoprotective effects of diallyl sulfide (DAS), a flavor component of garlic, have been attributed to its inhibitory effects on CYP2E1-mediated bioactivation of certain carcinogenic chemicals. In addition to being a competitive inhibitor of CYP2E1 in vitro, DAS is known to cause irreversible inhibition of CYP2E1 in rats in vivo. The latter property is believed to be mediated by the DAS metabolite diallyl sulfone (DASO2), which is thought to be a mechanism-based inhibitor of CYP2E1, although the underlying mechanism remains unknown. In order to investigate the nature of the reactive intermediate(s) responsible for the inactivation of CYP2E1 by DAS and its immediate metabolites, the present studies were carried out to detect and identify potential glutathione (GSH) conjugates of DAS and its metabolites diallyl sulfoxide (DASO) and DASO2. By means of ionspray LC-MS/MS, ten GSH conjugates were identified in bile collected from rats dosed with DAS, namely: S-[3-(S'-allyl-S'-dioxomercapto)-2-hydroxypropyl]glutathione (M1, M2; diastereomers), S-[3-(S'-allyl-S'-dioxomercapto)-2-hydroxypropyl]-glutathione (M5), S-[2-(S'-allyl-S'-dioxomercapto)-1-(hydroxymethyl)ethyl]glutathion e (M3, M4; diastereomers), S-[3-(S'-allylmercapto)-2-hydroxypropyl]glutathione (M6), S-(3-hydroxypropyl)-glutathione (M7), S-(2-carboxyethyl)glutathione (M8), allyl glutathionyl disulfide (M9), and S-allylglutathione (M10). With the exception of M6, all of the above GSH conjugates were detected in the bile of rats treated with DASO, while only M3, M4, M5, M7, M8, and M10 were found in the bile of rats treated with DASO2. Experiments conducted in vitro showed that GSH reacted spontaneously with DASO to form conjugates M9 and M10, and with DASO2 to form M10. In the presence of NADPH and GSH, incubation of DAS with cDNA-expressed rat CYP2E1 resulted in the formation of metabolites M6, M9, and M10, while incubation with DASO led to the formation of M3, M4, M5, M9, and M10. When DASO2 acted as substrate, CYP2E1 generated only conjugates M3, M4, M5, and M10. These results indicate that while DAS and DASO undergo extensive oxidation in vivo at the sulfur atom, the allylic carbon, and the terminal double bonds, CYP2E1 preferentially catalyzes oxidation of the sulfur atom to form the sulfoxide and the sulfone (DASO and DASO2). However, it appears that the end product of this sequence, namely, DASO2, undergoes further CYP2E1-mediated activation of the olefinic pi-bond, a reaction which transforms many terminal olefins to potent mechanism-based P450 inhibitors. We hypothesize, therefore, that it is this final metabolic event with DASO2 which leads to autocatalytic destruction of CYP2E1 and which is mainly responsible for the chemoprotective effects of DAS in vivo.",Chemical research in toxicology,"['D000498', 'D000818', 'D000975', 'D001646', 'D002478', 'D002853', 'D019392', 'D065691', 'D008401', 'D005978', 'D009682', 'D008297', 'D051381', 'D017207', 'D013440']","['Allyl Compounds', 'Animals', 'Antioxidants', 'Bile', 'Cells, Cultured', 'Chromatography, Liquid', 'Cytochrome P-450 CYP2E1', 'Cytochrome P-450 CYP2E1 Inhibitors', 'Gas Chromatography-Mass Spectrometry', 'Glutathione', 'Magnetic Resonance Spectroscopy', 'Male', 'Rats', 'Rats, Sprague-Dawley', 'Sulfides']",Metabolism of the chemoprotective agent diallyl sulfide to glutathione conjugates in rats.,"[None, None, 'Q000378', 'Q000378', None, None, 'Q000378', None, None, 'Q000378', None, None, None, None, 'Q000378']","[None, None, 'metabolism', 'metabolism', None, None, 'metabolism', None, None, 'metabolism', None, None, None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/9084912,1997,0.0,0.0,,no garlic, -9046016,"Erythrocyte agglutination by lectins from Allium sativum was inhibited only by mannose of the sugars tested. However, asialofetuin was more effective inhibitor of agglutination as compared to mannose. This led to the use of an asialofetuin-silica affinity column to isolate agglutinins of 110 and 25 kDa (ASA110 and ASA25). While ASA25 is a dimeric protein comprising of subunits of 12.5 and 13.0 kDa, ASA110 is a glycoprotein of two identical subunits of 47 kDa. ASA110 revealed to have a high content of aspartic acid, glycine, leucine and serine but low content of cysteine and methionine. It contains 14 residues of neutral sugars in addition to 43 residues of hexosamines per mole of lectin and requires metal ions for its functional conformation. Serological cross-reactions with other species showed some common epitopes of ASA110 and ASA25 present in A. porrum, A. ascalonicum, Narcissus alba, PHA and Con A but not in A. cepa. ASA110 with CHO cells indicated it to be weakly cytotoxic with LD50 of 160 microg/ml.",Molecular and cellular biochemistry,"['D000596', 'D000818', 'D001212', 'D002846', 'D060748', 'D005737', 'D006168', 'D006384', 'D006801', 'D037102', 'D008970', 'D037121', 'D010946', 'D011487', 'D011817', 'D012822', 'D000509']","['Amino Acids', 'Animals', 'Asialoglycoproteins', 'Chromatography, Affinity', 'Fetuins', 'Garlic', 'Guinea Pigs', 'Hemagglutination', 'Humans', 'Lectins', 'Molecular Weight', 'Plant Lectins', 'Plants, Medicinal', 'Protein Conformation', 'Rabbits', 'Silicon Dioxide', 'alpha-Fetoproteins']",A new high molecular weight agglutinin from garlic (Allium sativum).,"['Q000032', None, None, None, None, 'Q000737', None, 'Q000187', None, 'Q000037', None, None, None, None, None, None, None]","['analysis', None, None, None, None, 'chemistry', None, 'drug effects', None, 'antagonists & inhibitors', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9046016,1997,,,,, -22060942,"The volatile compounds extracted from both traditional and industrial chorizo-a dry fermented sausage-were analysed by gas chromatography/mass spectrometry (GC/MS). One hundred and twenty-six peaks were detected relating to volatile extracts of which 115 were identified. The substances identified belonged to several classes of chemical: acids, alkanes, alcohols, aldehydes, sulphur compounds, ketones, esters, ethers, phenolic compounds, aromatic hydrocarbons, lactones, nitrogen compounds, terpenes, chloroform and benzofurane. Among the major compounds isolated were acetic acid, allyl-1-thiol and phenol. Larger quantities of most of the chemical groups were found in industrial compared to traditional chorizo, except for sulphur compounds. Typical breakdown products derived from lipid autooxidation were virtually negligible in chorizo. Of the chemicals isolated, sulphur compounds, phenols, acids, ethyl esters and carbonyls could have particular importance to the overall chorizo flavour. In addition, the changes in the proportions of volatile compounds during the ripening of chorizo were tracked. Most of the volatiles increased during ripening, especially acids, alcohols, esters, phenols, ketones and terpenes. On comparing the distribution of the sulphur compounds observed in chorizo with that of garlic, some noteworthy differences were observed. The reason for these differences is based upon several transformations of the sulphur compounds derived from garlic during the ripening and storage of chorizo.",Meat science,[],[],Volatile compounds in chorizo and their changes during ripening.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/22060942,2012,1.0,2.0,,, -9096851,"Tellurium (Te) demonstrates properties similar to those of elements known to be toxic to humans, and has applications in industrial processes, which are rapidly growing in importance and scale. It is relevant, therefore, to consider the tellurium physiology, toxicity, and methods for monitoring the element in biological and environmental specimens. Animal studies suggest that up to 25% of orally administered tellurium is absorbed in the gut. There is a biphasic elimination from the circulation with loss of about 50% within a short period, t1/2 = 0.81 d, and slower elimination of the residual Te, t1/2 = 12.9 d. Following a single ip, injection the largest proportion is in the kidney and bone, but with repeated oral administration, Te is found in the heart > > kidney, spleen, bone, and lung. Formation of dimethyl telluride is a characteristic feature of exposure, and gives a pungent garlic-like odor to breath, excreta, and the viscera. The main target sites for Te toxicity are the kidney, nervous system, skin, and the fetus (hydrocephalus). Te can be reliable measured in different specimens by several analytical techniques. Recent work has employed hydride generation atomic absorption spectrometry. Topics for further investigation are proposed.",Biological trace element research,"['D000284', 'D000818', 'D001842', 'D005243', 'D006207', 'D006801', 'D007553', 'D007668', 'D008168', 'D009206', 'D016273', 'D013154', 'D013691', 'D014018']","['Administration, Oral', 'Animals', 'Bone and Bones', 'Feces', 'Half-Life', 'Humans', 'Isotope Labeling', 'Kidney', 'Lung', 'Myocardium', 'Occupational Exposure', 'Spleen', 'Tellurium', 'Tissue Distribution']",Biochemistry of tellurium.,"[None, None, 'Q000187', 'Q000737', None, None, None, 'Q000187', 'Q000187', 'Q000378', None, 'Q000187', 'Q000008', None]","[None, None, 'drug effects', 'chemistry', None, None, None, 'drug effects', 'drug effects', 'metabolism', None, 'drug effects', 'administration & dosage', None]",https://www.ncbi.nlm.nih.gov/pubmed/9096851,1997,,,,no pdf access, -9012771,"Diallyl sulfide (DAS), a major flavour component of garlic, is known to modulate drug metabolism and may protect animals from chemically induced toxicity and carcinogenesis. In this study the effects of DAS on the oxidative metabolism and hepatotoxicity induced by acetaminophen (APAP) in rats were investigated. In the hepatotoxicity evaluation of Fischer 344 rats there was a dose-dependent increase in the odds of mortality rate by APAP (P = 0.009); DAS treatment significantly protected rats from APAP-related mortality (P = 0.026). Liver toxicity determined by lactate dehydrogenase activity was significantly increased by APAP treatment (0.75 g/kg). Pretreatment with DAS protected animals from APAP-induced liver toxicity in a time- and dose-dependent fashion. Treatment of DAS (50 mg/kg) 3 hr after APAP dosing significantly (P < 0.05) protected rats from APAP-induced liver toxicity. The metabolism of APAP (50 microM) in vitro was significantly inhibited by DAS (0.3-1 mM) in liver microsomes isolated from F344 rats. As the effect of DAS on APAP-induced hepatotoxicity in vivo was observed only when DAS was administered before or shortly after (< 3 hr) APAP dosing, data suggested that the protective effect of DAS is mainly at the metabolic activation step of APAP. However, the possibility that DAS may also have effects on other drug metabolism systems, such as glutathione (GSH) and glutathione S-transferases, cannot be ruled out.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000082', 'D000498', 'D018712', 'D000704', 'D000818', 'D016588', 'D000975', 'D002851', 'D004305', 'D005982', 'D007770', 'D008099', 'D008297', 'D011041', 'D051381', 'D011916', 'D012044', 'D013440']","['Acetaminophen', 'Allyl Compounds', 'Analgesics, Non-Narcotic', 'Analysis of Variance', 'Animals', 'Anticarcinogenic Agents', 'Antioxidants', 'Chromatography, High Pressure Liquid', 'Dose-Response Relationship, Drug', 'Glutathione Transferase', 'L-Lactate Dehydrogenase', 'Liver', 'Male', 'Poisoning', 'Rats', 'Rats, Inbred F344', 'Regression Analysis', 'Sulfides']",Protective effects of diallyl sulfide on acetaminophen-induced toxicities.,"['Q000633', None, 'Q000633', None, None, 'Q000494', 'Q000494', None, None, 'Q000378', 'Q000378', 'Q000187', None, 'Q000401', None, None, None, 'Q000494']","['toxicity', None, 'toxicity', None, None, 'pharmacology', 'pharmacology', None, None, 'metabolism', 'metabolism', 'drug effects', None, 'mortality', None, None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/9012771,1997,0.0,0.0,,, -8923805,"Although amphotericin B remains the drug of choice for systemic fungal infections, its use is limited by considerable side effects. In The Peoples' Republic of China, commercial Allium sativum derived compounds are widely used as an antifungal drug to treat systemic fungal infections. To evaluate the scientific merit of using A. sativum derived compounds as antifungal agents, we studied a Chinese commercial preparation, allitridium. This preparation contained mainly diallyl trisulfide as confirmed by high performance liquid chromatography. Allitridium, with and without amphotericin B, was tested to determine its efficacy in killing three isolates of Cryptococcus neoformans. The minimum inhibitory concentration of the commercial preparation was 50 micrograms/ml and the minimum fungicidal concentration was 100 micrograms/ml against 1 x 10(5) organisms of C. neoformans. In addition, the commercial preparation was shown to be synergistic with amphotericin B in the in vitro killing of C. neoformans. This study demonstrates that diallyl trisulfide and other polysulfides possess potent in vitro fungicidal effects and their activity is synergistic with amphotericin B. These observations lend laboratory support for the treatment of cryptococcal infections with both amphotericin B and the Chinese commercial preparation.",Planta medica,"['D000498', 'D000666', 'D000935', 'D003455', 'D004357', 'D005737', 'D006801', 'D016919', 'D008826', 'D010946', 'D013440']","['Allyl Compounds', 'Amphotericin B', 'Antifungal Agents', 'Cryptococcus neoformans', 'Drug Synergism', 'Garlic', 'Humans', 'Meningitis, Cryptococcal', 'Microbial Sensitivity Tests', 'Plants, Medicinal', 'Sulfides']",Enhanced diallyl trisulfide has in vitro synergy with amphotericin B against Cryptococcus neoformans.,"['Q000494', 'Q000494', 'Q000494', 'Q000187', None, None, None, 'Q000382', None, None, 'Q000494']","['pharmacology', 'pharmacology', 'pharmacology', 'drug effects', None, None, None, 'microbiology', None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/8923805,1997,,,,no pdf access, -9174913,An immobilized salicylaldehyde (sal) was used to build various salicylaldehyde-copper-amino acid (Sal-Cu-AA) complexes which are stable at a range of pH values (2.0-11.0). The complexes were found to bind protein molecules as IMAC resins. Thirteen proteins were examined for their binding to a Sal-Cu-Gly column. The efficacy of the Sal-Cu-AA resin for protein separation were demonstrated by two examples. The first was a new purification process for garlic lectins from garlic crude extract. It seems that in this case the Sal-Cu-AA resins were more selective than IDA resin. The second was immobilization of concanavalin A (Con A) on the resin and using the immobilized Con A for affinity chromatography of mannose-rich glycoprotein ovalbumin. The Con A could be later eluted with EDTA or imidazole and the Sal-containing polymer could be recharged again for further use.,Journal of molecular recognition : JMR,"['D000447', 'D000596', 'D000818', 'D013437', 'D002645', 'D002846', 'D003208', 'D003300', 'D005737', 'D006639', 'D037102', 'D010047', 'D037121', 'D010940', 'D010946', 'D012116', 'D012259']","['Aldehydes', 'Amino Acids', 'Animals', 'Carbon-Sulfur Lyases', 'Chickens', 'Chromatography, Affinity', 'Concanavalin A', 'Copper', 'Garlic', 'Histidine', 'Lectins', 'Ovalbumin', 'Plant Lectins', 'Plant Proteins', 'Plants, Medicinal', 'Resins, Plant', 'Ribonuclease, Pancreatic']",Salicylaldehyde-metal-amino acid ternary complex: a new tool for immobilized metal affinity chromatography.,"['Q000737', 'Q000737', None, 'Q000737', None, 'Q000379', 'Q000302', 'Q000737', 'Q000737', 'Q000032', 'Q000737', 'Q000302', None, 'Q000302', None, 'Q000737', 'Q000737']","['chemistry', 'chemistry', None, 'chemistry', None, 'methods', 'isolation & purification', 'chemistry', 'chemistry', 'analysis', 'chemistry', 'isolation & purification', None, 'isolation & purification', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/9174913,1997,,,,, -8871121,"Samples of vegetable oils on the Brazilian market including rape seed, corn, soybean, sunflower, rice, palm and garlic were analysed for benzo(a)pyrene (B(a)P). The analytical method involved liquid-liquid extraction, clean-up on silica gel column and determination by high performance liquid chromatography using fluorescence detection. The limit of detection was 0.5 microgram/kg. Benzo(a)pyrene was detected in almost all samples, at levels up to 58.9 micrograms/kg. The mean levels of B(a)P in rice, sunflower, soybean, corn and palm oils were 1.8, 0.2, 2.2, 10.8 and 2.1 micrograms/kg respectively. No B(a)P was detected in garlic and rape seed oils. The data indicate that the levels of B(a)P found in Brazilian corn oils are relatively higher than those published in the literature for European corn oils.",Food additives and contaminants,"['D001564', 'D001938', 'D002273', 'D002851', 'D005506', 'D010938']","['Benzo(a)pyrene', 'Brazil', 'Carcinogens', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Plant Oils']",Benzo(a)pyrene in Brazilian vegetable oils.,"['Q000032', None, 'Q000032', None, 'Q000032', 'Q000032']","['analysis', None, 'analysis', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/8871121,1997,,,,, -8671745,"Three heterocyclic aromatic amines, 2-amino-3-methyl-imidazo[4, 5-f]quinoline (IQ), 2-amino-3,4-dimethylimidazo[4,5-f]quinoxaline and 2-amino-3,4-dimethylimidazo[4,5-f]quinoline, have been found in boiled pork juice. We have investigated the effect of naturally occurring organosulfur compounds, which are present in garlic and onion, on mutagen formation in boiled pork juice. Six organosulfur compounds - diallyl disulfide (DAD), dipropyl disulfide (DPD), diallyl sulfide (DAS), allyl methyl sulfide (AMS), allyl mercaptan (AM) and cysteine - were added separately to the pork juice before reflux boiling and then the mutagenicity of each sample was examined with the Salmonella typhimurium strain TA98 in the presence of S9 mix. All six compounds were found to inhibit the mutagenicity of boiled pork juice. The greatest inhibitory effect was observed with DAD and DPD, and this was 111-fold higher than that of the lowest, cysteine. To elucidate the inhibitory effect of DAD on mutagen formation in boiled pork juice, the major mutagenic fractions were monitored after HPLC separation by their mutagenicity with S. typhimurium TA98. By comparing the retention times of authentic IQ compounds from boiled pork juice with those following the addition of DAD, we showed that the mutagenicity of three major fractions was significantly inhibited compared with those same fractions in boiled pork juice alone. In addition, the Maillard reaction products (MRPs) in the boiled pork juice with and without the addition of DAD were quantified and identified by capillary gas chromatography and gas chromatography-mass spectrometry. The results show that the reduction in the total amount of MRPs (pyridines, pyrazines, thiophenes and thiazoles) in boiled pork juice after boiling for 12 h is correlated with their mutagenicity. Among the MRPs, tetrahydrothiophene-3-one exhibited the strongest correlation. These data suggest that the inhibition of IQ mutagen formation by DAD is mediated through the reduction of MRPs production.",Mutagenesis,"['D000498', 'D000588', 'D000818', 'D002273', 'D002849', 'D002851', 'D004220', 'D006571', 'D015416', 'D013058', 'D008461', 'D009152', 'D011804', 'D012486', 'D013440', 'D013552']","['Allyl Compounds', 'Amines', 'Animals', 'Carcinogens', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Disulfides', 'Heterocyclic Compounds', 'Maillard Reaction', 'Mass Spectrometry', 'Meat Products', 'Mutagenicity Tests', 'Quinolines', 'Salmonella typhimurium', 'Sulfides', 'Swine']",Naturally occurring diallyl disulfide inhibits the formation of carcinogenic heterocyclic aromatic amines in boiled pork juice.,"[None, 'Q000378', None, 'Q000378', None, None, 'Q000494', 'Q000378', None, None, 'Q000633', None, 'Q000378', 'Q000187', 'Q000494', None]","[None, 'metabolism', None, 'metabolism', None, None, 'pharmacology', 'metabolism', None, None, 'toxicity', None, 'metabolism', 'drug effects', 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/8671745,1996,,,,, -8821433,"From wild garlic Allium ursinum three new flavonoid glycosides were identified as kaempferol 3-O-beta-neohesperidoside-7-O-[2-O-(trans-p-coumaroyl)]-beta -D- glucopyranoside, kaempferol 3-O-beta-neohesperidoside-7-O-[2-O-(trans-feruloyl)]-beta-D- glucopyranoside, kaempferol 3-O-beta-neohesperidoside-7-O-[2-O-(trans-p-coumaroyl)-3-O-b eta-D- glucopyranosyl]-beta-D-glucopyranoside and characterized as the peracetates. Additionally, two known flavonoid glycosides kaempferol 3-O-beta-glucopyranoside and kaempferol 3-O-beta-neohesperidoside were isolated. The isolated compounds showed an inhibition of human platelet aggregation.",Phytochemistry,"['D001792', 'D002240', 'D005419', 'D005737', 'D006027', 'D006801', 'D009682', 'D008969', 'D010936', 'D010946', 'D010975', 'D016339', 'D013056']","['Blood Platelets', 'Carbohydrate Sequence', 'Flavonoids', 'Garlic', 'Glycosides', 'Humans', 'Magnetic Resonance Spectroscopy', 'Molecular Sequence Data', 'Plant Extracts', 'Plants, Medicinal', 'Platelet Aggregation Inhibitors', 'Spectrometry, Mass, Fast Atom Bombardment', 'Spectrophotometry, Ultraviolet']",The flavonoids of Allium ursinum.,"['Q000187', None, 'Q000737', 'Q000737', 'Q000737', None, None, None, 'Q000737', None, 'Q000737', None, None]","['drug effects', None, 'chemistry', 'chemistry', 'chemistry', None, None, None, 'chemistry', None, 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/8821433,1996,0.0,0.0,,not quantified, -8717417,"Garlic has been reported to provide protection against hypercholesterolemic atherosclerosis and ischemia-reperfusion-induced arrhythmias and infarction. Oxygen free radicals (OFRs) have been implicated as causative factors in these diseases and antioxidants have been shown to be effective against these conditions. The effectiveness of garlic in these disease states could be due to its ability to scavenge OFRs. However, the OFR-scavenging activity of garlic is not known. Also it is not known if its activity is affected by cooking. We therefore investigated, using high pressure liquid chromatography, the ability of garlic extract (heated or unheated) to scavenge exogenously generated hydroxyl radical (.OH). .OH was generated by photolysis of H2O2 (1.2-10 mumoles/ml) with ultraviolet (UV) light and was trapped with salicylic acid (500 nmoles/ml). H2O2 produced .OH in a concentration-dependent manner as estimated by .OH adduct products 2,3-dihydroxybenzoic acid (DHBA) and 2,5-DHBA. Garlic extract (5-100 microliters/ml) produced an inhibition (30-100%) of 2,3-DHBA and 2,5-DHBA generated by photolysis of H2O2 (5.00 pmoles/ml) in a concentration-dependent manner. Its activity is reduced by 10% approximately when heated to 100 degrees C for 20, 40 or 60 min. The extent of reduction in activity was similar for the three heating periods. Garlic extract prevented the .OH-induced formation of malondialdehyde in the rabbit liver homogenate in a concentration-dependent manner. It alone did not affect the MDA levels in the absence of .OH. These results indicate that garlic extract is a powerful scavenger of .OH and that heating reduces its activity slightly.",Molecular and cellular biochemistry,"['D000818', 'D002851', 'D016166', 'D005737', 'D005841', 'D006358', 'D062385', 'D017665', 'D015227', 'D008099', 'D008315', 'D010782', 'D010936', 'D010946', 'D011817', 'D012459', 'D020156', 'D012680', 'D013481', 'D014466']","['Animals', 'Chromatography, High Pressure Liquid', 'Free Radical Scavengers', 'Garlic', 'Gentisates', 'Hot Temperature', 'Hydroxybenzoates', 'Hydroxyl Radical', 'Lipid Peroxidation', 'Liver', 'Malondialdehyde', 'Photolysis', 'Plant Extracts', 'Plants, Medicinal', 'Rabbits', 'Salicylates', 'Salicylic Acid', 'Sensitivity and Specificity', 'Superoxides', 'Ultraviolet Rays']",Evaluation of hydroxyl radical-scavenging property of garlic.,"[None, None, None, None, None, None, None, None, 'Q000187', 'Q000378', 'Q000032', None, 'Q000494', None, None, None, None, None, 'Q000302', None]","[None, None, None, None, None, None, None, None, 'drug effects', 'metabolism', 'analysis', None, 'pharmacology', None, None, None, None, None, 'isolation & purification', None]",https://www.ncbi.nlm.nih.gov/pubmed/8717417,1996,,,,, -8875572,"The effect of Allium sativum (Liliacea) on trypanosome-infected mice was examined. At a dose of 5.0 mg/ml, the oily extract from the pulp completely suppressed the ability of the parasites to be infective in the host. Column chromatography of the extract gave four fractions: ethylacetate/methanol, ethylacetate/ethanol, benzene/methanol, and acetic acid/methanol. Among these fractions, the acetic acid/methanol fraction retained the trypanocidal features of the crude extract. It cured experimentally infected mice of trypanosomiasis in 4 days when given at a dose of 120 mg/kg per day. The extract also manifested inhibition of procyclic forms of Trypanosoma brucei brucei and phospholipases from T. congolense, T. b. brucei, T. vivax. The extract appears to be diallyl-disulfide (DAD) and may interfere with the parasites' synthesis of membrane lipids.",Parasitology research,"['D000818', 'D004305', 'D004791', 'D005737', 'D051379', 'D010741', 'D010936', 'D010946', 'D013261', 'D014344', 'D014346']","['Animals', 'Dose-Response Relationship, Drug', 'Enzyme Inhibitors', 'Garlic', 'Mice', 'Phospholipases A', 'Plant Extracts', 'Plants, Medicinal', 'Sterols', 'Trypanocidal Agents', 'Trypanosoma brucei brucei']",Allium sativum-induced death of African trypanosomes.,"[None, None, 'Q000494', 'Q000737', None, 'Q000037', 'Q000494', None, 'Q000032', 'Q000494', 'Q000737']","[None, None, 'pharmacology', 'chemistry', None, 'antagonists & inhibitors', 'pharmacology', None, 'analysis', 'pharmacology', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/8875572,1997,,,,, -8870956,"N-Acetyl-S-allyl-L-cysteine (allylmercapturic acid, ALMA) was previously detected in urine from humans consuming garlic. Exposure of rats to allyl halides is also known to lead to excretion of ALMA in urine. ALMA is a potential biomarker for exposure assessment of workers exposed to allyl halides. It is not known whether garlic consumption can lead to urinary concentrations of ALMA which may interfere with biological monitoring of exposure to allyl halides by determination of urinary ALMA. Therefore, this study was undertaken to determine the cumulative excretion and the excretion kinetics of ALMA in urine of humans consuming garlic. Six human volunteers were given orally two garlic tablets, each containing 100 mg garlic extract (each representing 300 mg fresh garlic). Three of the volunteers consumed additional garlic after the garlic tablet intake. Urine samples were collected up to 24 h after the intake of the garlic tablets. ALMA was identified in the urine using gas chromatography-mass spectrometry (GC-MS) and determined quantitatively with a limit of detection of 0.10 microgram/ml with gas chromatography with sulphur selective detection. The total amount of ALMA found in urine of volunteers who consumed two garlic tablets was 0.43 +/- 0.14 mg (n = 3). In the urine of the three volunteers who consumed not only two garlic tablets but also additional fresh garlic, a significantly higher amount of ALMA was excreted in the urine, 1.4 +/- 0.2 mg (n = 3). The elimination half-life of ALMA, estimated from urinary excretion rate versus time curves, was 6.0 +/- 1.3 h (n = 5). One volunteer, who ate additional garlic, showed an irregular elimination profile and was excluded from this estimation. The highest urinary concentration of ALMA found in this study was 2.2 micrograms/ml. In a preliminary biological monitoring study of exposure in workers with potential exposure to allyl chloride (AC) up to the occupational exposure limit of 1 ppm (8-h TWA), we recently found urinary ALMA concentrations up to 4 micrograms/ml. Based on the results presented here, we conclude that garlic consumption is a potential confounder when monitoring human exposure to allylhalides and other chemicals leading to ALMA excretion when ALMA is used as a biomarker of exposure.",Archives of toxicology,"['D000111', 'D000284', 'D000328', 'D005260', 'D005737', 'D006801', 'D008297', 'D008991', 'D010946']","['Acetylcysteine', 'Administration, Oral', 'Adult', 'Female', 'Garlic', 'Humans', 'Male', 'Monitoring, Physiologic', 'Plants, Medicinal']",Urinary excretion of N-acetyl-S-allyl-L-cysteine upon garlic consumption by human volunteers.,"['Q000031', None, None, None, None, None, None, None, None]","['analogs & derivatives', None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/8870956,1997,,,,, -8806047,"Diallyl sulfone (DASO2) is a metabolite of diallyl sulfide, a compound derived from garlic. The present study investigated the effect of DASO2 as a protective agent against acetaminophen (APAP)-induced hepatotoxicity in mice. Oral administration of DASO2 protected mice against the APAP-induced hepatotoxicity in a dose- and time-dependent manner. When administered 1 hour prior to, immediately after, or 20 minutes after a toxic dose of APAP, DASO2 at a dose of 25 mg/kg completely protected mice from development of hepatotoxicity, as indicated by liver histopathology and serum lactate dehydrogenase levels. Protective effect was observed when DASO2 at a dose as low as 5 mg/kg was given to mice 1 hour prior to APAP administration. Oral administration of DASO2 to mice 1 hour prior to a toxic dose of APAP significantly inhibited the APAP-induced glutathione depletion in the liver. DASO2 treatment also decreased the levels of oxidative APAP metabolites in the plasma without affecting the concentrations of nonoxidative APAP metabolites. In liver microsomes, 0.1 mM of DASO2 caused a 60% decrease in the rate of APAP oxidation to N-acetyl-p-benzoquinone imine, which was determined as glutathione conjugate. This inhibitory effect is mainly due to its inhibition of cytochrome P450 2E1 activity; with an IC50 value equal to 0.11 mM. DASO2 also slightly inhibited the activities of P450s 3A and 1A, with IC50 values > 5 mM. Furthermore, a single oral dose of DASO2 inactivated P450 2E1- and P450 1A-dependent activities in liver microsomes. The results suggest that the protective effect of DASO2 against APAP-induced hepatotoxicity is due to its ability to block acetaminophen bioactivation mainly by the inactivation and inhibition of P450 2E1.",Journal of biochemical toxicology,"['D000082', 'D000284', 'D000498', 'D018712', 'D000818', 'D016227', 'D002851', 'D065691', 'D004305', 'D005978', 'D007097', 'D066298', 'D007770', 'D008099', 'D008297', 'D051379', 'D008862', 'D010084', 'D013450']","['Acetaminophen', 'Administration, Oral', 'Allyl Compounds', 'Analgesics, Non-Narcotic', 'Animals', 'Benzoquinones', 'Chromatography, High Pressure Liquid', 'Cytochrome P-450 CYP2E1 Inhibitors', 'Dose-Response Relationship, Drug', 'Glutathione', 'Imines', 'In Vitro Techniques', 'L-Lactate Dehydrogenase', 'Liver', 'Male', 'Mice', 'Microsomes, Liver', 'Oxidation-Reduction', 'Sulfones']",Protective effect of diallyl sulfone against acetaminophen-induced hepatotoxicity in mice.,"['Q000008', None, 'Q000008', 'Q000008', None, 'Q000378', None, None, None, 'Q000378', 'Q000378', None, 'Q000097', 'Q000187', None, None, 'Q000187', None, 'Q000008']","['administration & dosage', None, 'administration & dosage', 'administration & dosage', None, 'metabolism', None, None, None, 'metabolism', 'metabolism', None, 'blood', 'drug effects', None, None, 'drug effects', None, 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/8806047,1997,,,,, -8759327,"We present an overview of the development and use of our selected-ion flow tube (SIFT) technique as a sensitive, quantitative method for the rapid, real-time analysis of the trace gas content of atmospheric air and human breath, presenting some pilot data from various research areas in which this method will find valuable application. We show that it is capable of detecting and quantifying trace gases, in complex mixtures such as breath, which are present at partial pressures down to about 10 parts per billion. Following discussions of the principles involved in this SIFT method of analysis, of the experiments which we have carried out to establish its quantitative validity, and of the air and breath sampling techniques involved, we present sample data on the detection and quantification of trace gases on the breath of healthy people and of patients suffering from renal failure and diabetes. We also show how breath ammonia can be accurately quantified from a single breath exhalation and used as an indicator of the presence in the stomach of the bacterium Helicobacter pylori. Health and safety applications are exemplified by analyses of the gases of the gases of cigarette smoke and on the breath of smokers. The value of this analytical method in environmental science is demonstrated by the analyses of petrol vapour, car exhaust emissions and the trace organic vapours detected in town air near a busy road. Final examples of the value of this analytical method are the detection and quantification of the gases emitted from crushed garlic and from breath following the chewing of a mint, which demonstrate its potential in food and flavour research. Throughout the paper we stress the advantages of this SIFT method compared to conventional mass spectrometry for trace gas analysis of complex mixtures, emphasizing its selectivity, sensitivity and real-time analysis capability. Finally, we note that whilst the current SIFT is strictly laboratory based, both transportable and portable instruments are under construction and development. These instruments will surely extend the application of this analytical technique into more areas and allow greater exploitation of their on-line and real-time features.",Rapid communications in mass spectrometry : RCM,"['D000388', 'D016902', 'D001944', 'D008401', 'D005740', 'D006801', 'D014028']","['Air', 'Air Pollution, Indoor', 'Breath Tests', 'Gas Chromatography-Mass Spectrometry', 'Gases', 'Humans', 'Tobacco Smoke Pollution']",The novel selected-ion flow tube approach to trace gas analysis of air and breath.,"['Q000032', 'Q000032', 'Q000295', 'Q000379', 'Q000032', None, None]","['analysis', 'analysis', 'instrumentation', 'methods', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/8759327,1996,,,,, -8723021,"The effect of garlic on the serum lipid profile has been the subject of controversy. This study was therefore designed to examine the effects of allicin, an active constituent of garlic, on the lipid profile in a rabbit model.",Coronary artery disease,"['D000818', 'D008076', 'D008078', 'D002851', 'D005737', 'D000960', 'D008055', 'D008297', 'D010946', 'D011446', 'D011817', 'D013441']","['Animals', 'Cholesterol, HDL', 'Cholesterol, LDL', 'Chromatography, High Pressure Liquid', 'Garlic', 'Hypolipidemic Agents', 'Lipids', 'Male', 'Plants, Medicinal', 'Prospective Studies', 'Rabbits', 'Sulfinic Acids']","Alteration of lipid profile in hyperlipidemic rabbits by allicin, an active constituent of garlic.","[None, 'Q000097', 'Q000097', None, None, 'Q000494', 'Q000097', None, None, None, None, 'Q000494']","[None, 'blood', 'blood', None, None, 'pharmacology', 'blood', None, None, None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/8723021,1996,,,,, -8595261,"Alliinase (EC 4.4.1.4) catalyses the production of allicin (thio-2-propene-1-sulfinic acid S-allyl ester), a biologically active compound which is also responsible for the characteristic smell of garlic. It was demonstrated that alliinase which contains 5.5-6% of neutral sugars, gives clear PAS-staining, binds to Con A and can form a complex with garlic mannose-specific lectin (ASA). Evidence that the formation of such a complex is mediated by the interaction of the carbohydrate of the glycoprotein enzyme with the lectin was obtained from a radioligand assay which demonstrated the binding of alliinase to ASA and competitive inhibition of this binding by methyl alpha-D-mannoside. ASA I was shown as the lectin mainly present in the complex with alliinase. The results of this study also demonstrate that alliinase is glycosylated at Asn146 in the sequence Asn146-Met147-Thr148.",Glycoconjugate journal,"['D000595', 'D001216', 'D013437', 'D002846', 'D002850', 'D002918', 'D003488', 'D004591', 'D005737', 'D006020', 'D006031', 'D007700', 'D037102', 'D037241', 'D008969', 'D008970', 'D010449', 'D037121', 'D010946', 'D011487']","['Amino Acid Sequence', 'Asparagine', 'Carbon-Sulfur Lyases', 'Chromatography, Affinity', 'Chromatography, Gel', 'Chymotrypsin', 'Cyanogen Bromide', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Glycopeptides', 'Glycosylation', 'Kinetics', 'Lectins', 'Mannose-Binding Lectins', 'Molecular Sequence Data', 'Molecular Weight', 'Peptide Mapping', 'Plant Lectins', 'Plants, Medicinal', 'Protein Conformation']",Alliinase (alliin lyase) from garlic (Alliium sativum) is glycosylated at ASN146 and forms a complex with a garlic mannose-specific lectin.,"[None, None, 'Q000737', None, None, None, None, None, 'Q000201', 'Q000737', None, None, 'Q000378', None, None, None, None, None, None, None]","[None, None, 'chemistry', None, None, None, None, None, 'enzymology', 'chemistry', None, None, 'metabolism', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/8595261,1996,,,,, -7672118,"Incubations of [1-14C]linoleic acid or [1-14C]-(9Z,11E, 13S)-13-hydropero xy-9,11-octadecadienoic acid (13-HPOD) with juice of garlic bulbs lead to the formation of one predominant labelled product, viz., the novel divinyl ether (9Z,11E, 1'E)-12-(1'-hexenyloxy)-9,11-dodecadienoic acid ('etheroleic acid'). With lesser efficiency [1-14C]alpha-linolenic acid or [1-14C](9Z,11E, 13S,15Z)-13-hydroperoxy-9,11,15-octadecatrienoic acid (13-HPOT) are converted in this way into (9Z,11E,1'E,1'E,3'Z)-12-(1',3'-hexadienyloxy)-9,11- dodecadienoic acid ('etherolenic acid'). Thus, garlic bulbs possess the activity of a new 13-hydroperoxide-specific divinyl ether synthase.",FEBS letters,"['D002851', 'D005231', 'D005737', 'D019787', 'D008041', 'D008042', 'D008054', 'D008084', 'D009682', 'D010946', 'D017962']","['Chromatography, High Pressure Liquid', 'Fatty Acids, Unsaturated', 'Garlic', 'Linoleic Acid', 'Linoleic Acids', 'Linolenic Acids', 'Lipid Peroxides', 'Lipoxygenase', 'Magnetic Resonance Spectroscopy', 'Plants, Medicinal', 'alpha-Linolenic Acid']",The lipoxygenase pathway in garlic (Allium sativum L.) bulbs: detection of the novel divinyl ether oxylipins.,"[None, 'Q000378', 'Q000201', None, 'Q000378', 'Q000378', 'Q000378', 'Q000378', None, None, 'Q000378']","[None, 'metabolism', 'enzymology', None, 'metabolism', 'metabolism', 'metabolism', 'metabolism', None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/7672118,1995,0.0,0.0,,, -8594422,"Garlic has been claimed to be effective against diseases, in the pathophysiology of which oxygen free radicals (OFRs) have been implicated. Effectiveness of garlic could be due to its ability to scavenge OFRs. However, its antioxidant activity is not known. We investigated the ability of allicin (active ingredient of garlic) contained in the commercial preparation Garlicin to scavenge hydroxyl radicals (.OH) using high pressure liquid chromatographic (HPLC) method. .OH was generated by photolysis of H2O2 (1.25-10 mumoles/ml) with ultraviolet light and was trapped with salicylic acid which is hydroxylated to produce .OH adduct products 2,3- and 2,5-dihydroxybenzoic acid (DHBA). H2O2 produced a concentration-dependent .OH as estimated by .OH adduct products 2,3-DHBA and 2,5-DHBA. Allicin equivalent in Garlicin (1.8, 3.6, 7.2, 14.4, 21.6, 28.8 and 36 micrograms) produced concentration-dependent decreases in the formation of 2,3-DHBA and 2,5-DHBA. The inhibition of formation of 2,3-DHBA and 2,5-DHBA with 1.8 micrograms/ml was 32.36% and 43.2% respectively while with 36.0 micrograms/ml the inhibition was approximately 94.0% and 90.0% respectively. The decrease in .OH adduct products was due to scavenging of .OH and not by scavenging of formed .OH adduct products. Allicin prevented the lipid peroxidation of liver homogenate in a concentration-dependent manner. These results suggest that allicin scavenges .OH and Garlicin has antioxidant activity.",Molecular and cellular biochemistry,"['D000818', 'D000975', 'D002851', 'D016166', 'D005737', 'D005841', 'D062385', 'D017665', 'D015227', 'D008099', 'D008315', 'D010946', 'D011817', 'D017382', 'D012459', 'D020156', 'D013441', 'D013607']","['Animals', 'Antioxidants', 'Chromatography, High Pressure Liquid', 'Free Radical Scavengers', 'Garlic', 'Gentisates', 'Hydroxybenzoates', 'Hydroxyl Radical', 'Lipid Peroxidation', 'Liver', 'Malondialdehyde', 'Plants, Medicinal', 'Rabbits', 'Reactive Oxygen Species', 'Salicylates', 'Salicylic Acid', 'Sulfinic Acids', 'Tablets']","Antioxidant activity of allicin, an active principle in garlic.","[None, 'Q000494', None, 'Q000494', 'Q000737', None, 'Q000378', 'Q000378', 'Q000187', 'Q000737', 'Q000032', None, None, 'Q000378', 'Q000378', None, 'Q000302', None]","[None, 'pharmacology', None, 'pharmacology', 'chemistry', None, 'metabolism', 'metabolism', 'drug effects', 'chemistry', 'analysis', None, None, 'metabolism', 'metabolism', None, 'isolation & purification', None]",https://www.ncbi.nlm.nih.gov/pubmed/8594422,1996,,,,, -8537101,"It is known that human serum contains natural antibodies to self and non-self proteins. We wished to determine whether normal human serum contains antibodies to dietary proteins that were never injected. We found that human serum contains antibodies to the two major proteins from cloves of garlic (Allium sativum) which is used as a flavorigard dietary food additive. The antibodies found were directed against alliinase and mannose-specific Allium sativum agglutinin (ASA). The antibodies were purified by affinity chromatography on their corresponding antigens. The purified immunoglobulins were mainly of the IgG and IgM classes and could be divided into two categories--specific and crossreactive. The anti-alliinase antibodies were highly specific, while anti-ASA antibodies were polyreactive. Some of the possible reasons for this difference in specificity are suggested.",Immunology letters,"['D000328', 'D000373', 'D000906', 'D000918', 'D013437', 'D002846', 'D004044', 'D005260', 'D005737', 'D006801', 'D007113', 'D037102', 'D008297', 'D037241', 'D037121', 'D010946']","['Adult', 'Agglutinins', 'Antibodies', 'Antibody Specificity', 'Carbon-Sulfur Lyases', 'Chromatography, Affinity', 'Dietary Proteins', 'Female', 'Garlic', 'Humans', 'Immunity, Innate', 'Lectins', 'Male', 'Mannose-Binding Lectins', 'Plant Lectins', 'Plants, Medicinal']",Natural antibodies to dietary proteins: the existence of natural antibodies to alliinase (Alliin lyase) and mannose-specific lectin from garlic (Allium sativum) in human serum.,"[None, 'Q000097', 'Q000097', None, 'Q000276', None, 'Q000276', None, 'Q000276', None, None, 'Q000276', None, None, None, None]","[None, 'blood', 'blood', None, 'immunology', None, 'immunology', None, 'immunology', None, None, 'immunology', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/8537101,1996,0.0,0.0,,protein, -7646805,"Extracted by n-butanol and separated by two-dimensional TLC, the astragalus saponin 1 in Suanqi Oral Liquid was determined by vanillin-perchloric acid colorimetric method. The recovery and RSD were 96.3% (n = 5) and 0.75% respectively.",Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica,"['D002855', 'D003124', 'D004338', 'D004365', 'D005737', 'D010946', 'D011786', 'D012503']","['Chromatography, Thin Layer', 'Colorimetry', 'Drug Combinations', 'Drugs, Chinese Herbal', 'Garlic', 'Plants, Medicinal', 'Quality Control', 'Saponins']",[Two-dimensional thin layer chromatographic-colorimetric determination of Astragalus saponin 1 in suanqi oral liquid].,"[None, None, None, 'Q000737', 'Q000737', None, None, 'Q000032']","[None, None, None, 'chemistry', 'chemistry', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/7646805,1995,,,,, -7604070,"Aqueous extracts of fresh garlic (Allium sativum L.) inhibited efficiently the activity of adenosine deaminase (ADA) of cultivated endothelial cells. The IC50 value (range between 6 and 120 micrograms per ml) depended on the origin and storage time of the fresh garlic. The aqueous extraction of dried garlic powder showed also an inhibition if ADA activity, but the IC50 value was in the range of 2.5 mg per ml indicating that parts of the active principle were lost during the preparation of the garlic powder. The inhibition of endothelial ADA by garlic extracts seems to contribute to the hypotensive activity and vessel protective effects of A. sativum L.",Die Pharmazie,"['D058892', 'D000818', 'D001011', 'D001794', 'D002417', 'D002460', 'D002851', 'D004730', 'D005737', 'D000960', 'D010936', 'D010946', 'D013441']","['Adenosine Deaminase Inhibitors', 'Animals', 'Aorta', 'Blood Pressure', 'Cattle', 'Cell Line', 'Chromatography, High Pressure Liquid', 'Endothelium, Vascular', 'Garlic', 'Hypolipidemic Agents', 'Plant Extracts', 'Plants, Medicinal', 'Sulfinic Acids']",Inhibition of adenosine deaminase activity of aortic endothelial cells by extracts of garlic (Allium sativum L.).,"[None, None, 'Q000166', 'Q000187', None, None, None, 'Q000187', 'Q000737', 'Q000737', 'Q000494', None, 'Q000737']","[None, None, 'cytology', 'drug effects', None, None, None, 'drug effects', 'chemistry', 'chemistry', 'pharmacology', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/7604070,1995,,,,, -7981966,"Xyloglucans were isolated from the 24% KOH-soluble fraction of the cell walls of bulbs of onion (Allium cepa), garlic (Allium sativum) and their hybrid. The polysaccharides yielded single peaks upon gel filtration with average molecular weights of 65,000 for onion, 55,000 for garlic and 82,000 for the hybrid. Compositional analysis of the oligosaccharide units after digestion with an endo-1,4-beta-glucanase from Streptomyces indicated that the polysaccharides were constructed of four kinds of repeating oligosaccharide unit, namely, a decasaccharide (glucose/xylose/galactose/fucose, 4 : 3: 2 : 1), a nonasaccharide (glucose/xylose/galactose/fucose, 4 : 3 : 1 : 1), an octasaccharide (glucose/xylose/galactose, 4 : 3 : 1), and a heptasaccharide (glucose/xylose, 4 : 3). The xyloglucan from the hybrid contained highly fucosylated units that resembled those from onion rather than from garlic. The analysis also revealed that the xyloglucans from Allium species contain highly substituted xylosyl residues with fucosyl-galactosyl residues, suggesting that these monocotyledonous plants resemble dicotyledons in the structural features of their xyloglucans.",Plant & cell physiology,"['D000490', 'D002240', 'D002850', 'D002852', 'D005737', 'D005936', 'D006824', 'D008969', 'D009844', 'D010946', 'D011134', 'D014990']","['Allium', 'Carbohydrate Sequence', 'Chromatography, Gel', 'Chromatography, Ion Exchange', 'Garlic', 'Glucans', 'Hybridization, Genetic', 'Molecular Sequence Data', 'Oligosaccharides', 'Plants, Medicinal', 'Polysaccharides', 'Xylans']","The oligosaccharide units of the xyloglucans in the cell walls of bulbs of onion, garlic and their hybrid.","['Q000737', None, None, None, 'Q000737', 'Q000737', None, None, 'Q000032', None, 'Q000737', None]","['chemistry', None, None, None, 'chemistry', 'chemistry', None, None, 'analysis', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/7981966,1995,,,,, -7979352,"The garlic plant (Allium sativum) alliinase (EC 4.4.1.4), which catalyzes the synthesis of allicin, was purified to homogeneity from bulbs using various steps, including hydrophobic chromatography. Molecular and biochemical studies showed that the enzyme is a dimer of two subunits of MW 51.5 kDa each. Its Km using synthetic S-allylcysteine sulfoxide (+ isomer) as substrate was 1.1 mM, its pH optimum 6.5, and its isoelectric point 6.35. The enzyme is a glycoprotein containing 6% carbohydrate. N-terminal sequences of the intact polypeptide chain as well as of a number of peptides obtained after cyanogen bromide cleavage were obtained. Cloning of the cDNAs encoding alliinase was performed by a two-step strategy. In the first, a cDNA fragment (pAli-1-450 bp) was obtained by PCR using a mixed oligonucleotide primer synthesized according to a 6-amino acid segment near the N-terminal of the intact polypeptide. The second step involved screening of garlic lambda gt11 and lambda ZAPII cDNA libraries with pAli-1, which yielded two clones; one was nearly full length and the second was full length. These clones exhibited some degree of DNA sequence divergence, especially in their 3' noncoding regions, suggesting that they were encoded by separate genes. The nearly full length cDNA was fused in frame to a DNA encoding a signal peptide from alpha wheat gliadin, and expressed in Xenopus oocytes. This yielded a 50 kDa protein that interacted with the antibodies against natural bulb alliinase. Northern and Western blot analyses showed that the bulb alliinase was highly expressed in bulbs, whereas a lower expression level was found in leaves, and no expression was detected in roots. Strikingly, the roots exhibited an abundant alliinase activity, suggesting that this tissue expressed a distinct alliinase isozyme with very low homology to the bulb enzyme.",Applied biochemistry and biotechnology,"['D000595', 'D000818', 'D001483', 'D015153', 'D013437', 'D002851', 'D003001', 'D003488', 'D003545', 'D018076', 'D005260', 'D005737', 'D005786', 'D006863', 'D066298', 'D007525', 'D008969', 'D008970', 'D009693', 'D010946', 'D016133', 'D011108', 'D012333', 'D011817', 'D013441', 'D014982']","['Amino Acid Sequence', 'Animals', 'Base Sequence', 'Blotting, Western', 'Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Cloning, Molecular', 'Cyanogen Bromide', 'Cysteine', 'DNA, Complementary', 'Female', 'Garlic', 'Gene Expression Regulation', 'Hydrogen-Ion Concentration', 'In Vitro Techniques', 'Isoelectric Focusing', 'Molecular Sequence Data', 'Molecular Weight', 'Nucleic Acid Hybridization', 'Plants, Medicinal', 'Polymerase Chain Reaction', 'Polymers', 'RNA, Messenger', 'Rabbits', 'Sulfinic Acids', 'Xenopus laevis']",Alliin lyase (Alliinase) from garlic (Allium sativum). Biochemical characterization and cDNA cloning.,"[None, None, None, None, 'Q000737', None, None, 'Q000378', 'Q000031', 'Q000737', None, 'Q000201', 'Q000235', None, None, None, None, None, None, None, None, None, 'Q000235', None, 'Q000378', None]","[None, None, None, None, 'chemistry', None, None, 'metabolism', 'analogs & derivatives', 'chemistry', None, 'enzymology', 'genetics', None, None, None, None, None, None, None, None, None, 'genetics', None, 'metabolism', None]",https://www.ncbi.nlm.nih.gov/pubmed/7979352,1994,,,,, -17236056,"A C-S-lyase preparation from ramson, ALLIUM URSINUM L., has been purified to apparent homogeneity. Separation techniques applied were hydrophobic interaction chromatography, anion exchange chromatography, and gel permeation chromatography. A 52-fold purification was obtained. The enzyme could be characterized by a molecular mass of M (r) = 150000 with subunits of 50 000. Its isoelectric point was determined to be at 4.7. The pH-optimum for the substrate-dependent turnover was found at 6.0. The temperature optimum was at 35 degrees C. (+)-Alliin as the substrate caused the highest enzymatic reaction velocity. The lowest K (m) value was observed with (+)- S-propyl- L-cysteine sulfoxide. Inhibitor constants were elaborated for the deoxy-derivatives of the substrates inserted and, likewise, for related amino acids. The protein was sensitive to low concentrations of hydroxylamine, indicating pyridoxal phosphate as a cofactor. Activation energies were determined for the cleavage of alliin, S-propyl- L-cysteine sulfoxide and S-methyl- L-cysteine sulfoxide, and were found to be in the range of 9 to 13 kJ . mol (-1).",Planta medica,[],[],"Purification and Characterization of a C-S-Lyase from Ramson, the Wild Garlic,Allium ursinum.",[],[],https://www.ncbi.nlm.nih.gov/pubmed/17236056,2012,,,,no PDF access, -8053972,"Three groups of 3 rats received oral doses (8 mg/kg) of garlic constituents (alliin, allicin and vinyldithiines (2-vinyl-[4H]-1,3-dithiine and 3-vinyl-[4H]-1,2-dithiine)) in the form of an oil macerate of the 35S-labeled substance. The measured activity was referred to 35S-alliin (35S-alliin equivalents). The blood activity levels in each group were monitored for 72 h. For 35S-allicin and the labeled vinyldithiines the excretion with the urine, feces, and exhaled air was also measured. The distribution among the organs (whole-body autoradiography) and the urinary metabolite pattern (thin-layer chromatography) were also determined. For 35S-alliin the blood activity profile differed considerably from those of 35S-allicin and the labeled vinyldithiines: both the absorption and the elimination of the radioactivity were distinctly faster than for the other garlic constituents, maximum blood levels being reached within the first 10 min and elimination from the blood being almost complete after 6 h. For the other garlic constituents the maximum blood levels were not reached until 30-60 min (35S-allicin) or 120 min (vinyldithiines) p.a. and blood levels > 1000 ng-Eq/ml were still present at the end of the study after 72 h. The mean total urinary and fecal excretion after 72 h was 85.5% (35S-allicin) or 92.3% (labeled vinyldithiines) of the dose. The urinary excretion indicates a minimum absorption rate of 65% (35S-allicin) or 73% (vinyldithiines). It is uncertain whether the 19-21% recovered in the feces was unabsorbed substance or had been excreted via the bile or intestinal mucosa. The exhaled air showed only traces of activity although the whole-body autoradiographs, after fairly long exposure (96 h), showed distinct enrichment of activity in the mucosa of the airways and pharynx. The activity is deposite mainly in the cartilage of the vertebral column and ribs. There was no detectable difference in organ distribution between 35S-allicin and the labeled vinyldithiines. All that could be established from the urinary metabolite pattern was that unchanged 35S-allicin and unchanged labeled vinyldithiines are absent. There is therefore extensive metabolization. The metabolites must have a very polar structure with acid functional groups since satisfactory separation was achievable only with acid solvent systems. Conjugates with sulfuric or glucuronic acid were not detectable. These results reveal no differences in pharmacokinetic behavior between 35S-allicin and the labeled vinyldithiines. A final verdict as to whether the metabolites, which may be pharmacologically active, are identical must await further studies designed to identify the metabolites.",Arzneimittel-Forschung,"['D000818', 'D001345', 'D002855', 'D003545', 'D005243', 'D005260', 'D005737', 'D006571', 'D007553', 'D008297', 'D010946', 'D051381', 'D017207', 'D013441', 'D013457', 'D013462', 'D014753']","['Animals', 'Autoradiography', 'Chromatography, Thin Layer', 'Cysteine', 'Feces', 'Female', 'Garlic', 'Heterocyclic Compounds', 'Isotope Labeling', 'Male', 'Plants, Medicinal', 'Rats', 'Rats, Sprague-Dawley', 'Sulfinic Acids', 'Sulfur Compounds', 'Sulfur Radioisotopes', 'Vinyl Compounds']","[The pharmacokinetics of the S35 labeled labeled garlic constituents alliin, allicin and vinyldithiine].","[None, None, None, 'Q000031', 'Q000737', None, 'Q000737', 'Q000097', None, None, None, None, None, 'Q000097', None, None, 'Q000097']","[None, None, None, 'analogs & derivatives', 'chemistry', None, 'chemistry', 'blood', None, None, None, None, None, 'blood', None, None, 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/8053972,1994,,,,, -7517069,"The multielement (Al, Ca, Cd, Ce, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Si, and Zn) levels of various common vegetables (bean, broccoli, cabbage, cauliflower, lettuce, marrow, onion, parsnip, spinach, sprouts, sweet corn, and tomato); fruits (grape and strawberry); herbs (garlic, lemon balm, marjoram, mint, rosemary and tarragon); local pasture species and surface soils collected from a commercial garden centre located within a distance of 30 m of the London Orbital Motorway (M25) is presented. Comparative values are given from a background area, namely a domestic garden located in the North Yorkshire Dales National Park area. Analysis was undertaken by inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma-source mass spectrometry (ICP-MS) with quality control assessment using four international biological reference materials; BCR:CRM 62 Olive Leaves, NIST 1575 Pine Needles, NIST 1573 Tomato Leaves, and NIST 1572 Citrus Leaves. Inter-analytical method comparison is given using two methods of ICP-MS; namely conventional pneumatic nebulisation of sample solution, and direct solids analysis by laser ablation; and neutron activation analysis methods (NAA). For the elements listed there is a good precision obtained by ICP-MS and NAA. In particular levels of < +/- 1-10% (rsd) are obtained. Comparison of data with certified values and other analytical methods are generally of very good agreement. Lead levels in background areas ranged from 0.0008 to 0.340 microgram/g (fresh weight) for plant material; with the lead magnitude greater for grasses > herbs > vegetables > cereals > fruits. Measured values are in good agreement with reported literature values. The lowest Pb values are for marrow, lettuce, tomato and sweet corn samples (approximately 0.001-0.021 microgram/g). 'Green' leaf material levels were approximately 0.02-0.10 microgram/g (i.e. sprouts and cabbage). Root vegetables contain higher levels, approximately 0.02-0.125 microgram/g (especially carrot), reflecting possible metal uptake from soil. The highest vegetable Pb values are for leek and onion (approximately 0.35 microgram/g). Background values are also provided for nineteen elements (Al, As, B, Ba, Br, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Rb, Se, Sr, V, and Zn). Exposure to motor vehicle activities at a site some 30 m from the M25 shows only significant increases in Pb for unwashed plant material and surface soils. Typically Pb levels of 40-80% can be removed by washing plant surfaces resulting in metal levels similar to background areas.(ABSTRACT TRUNCATED AT 400 WORDS)",The Science of the total environment,"['D000393', 'D004784', 'D007854', 'D008131', 'D010945', 'D015203', 'D012989', 'D001335']","['Air Pollutants', 'Environmental Monitoring', 'Lead', 'London', 'Plants, Edible', 'Reproducibility of Results', 'Soil Pollutants', 'Vehicle Emissions']",Metal dispersion and transportational activities using food crops as biomonitors.,"['Q000032', 'Q000379', 'Q000032', None, 'Q000737', None, 'Q000032', None]","['analysis', 'methods', 'analysis', None, 'chemistry', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/7517069,1994,,,,no PDF access, -8200919,"Supercritical fluid chromatography-mass spectrometry has been used successfully to identify allicin (2-propene-1-sulfinothioic acid S-2-propenyl ester), the predominant thiosulfinate in freshly cut garlic (Allium sativum). A low oven temperature (50 degrees C) and low restrictor tip temperature (115 degrees C) were needed in order to obtain a chemical ionization (CI) mass spectrum of allicin with the protonated molecular ion, m/z 163, as the major ion. The effects of tip temperature on the CI mass spectrum of allicin are presented.",Journal of chromatographic science,"['D002845', 'D005737', 'D008401', 'D013058', 'D010946', 'D013441', 'D013696']","['Chromatography', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Mass Spectrometry', 'Plants, Medicinal', 'Sulfinic Acids', 'Temperature']",Supercritical fluid chromatography of garlic (Allium sativum) extracts with mass spectrometric identification of allicin.,"['Q000379', 'Q000737', None, None, None, 'Q000032', None]","['methods', 'chemistry', None, None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/8200919,1994,,,,, -8223549,"Lectin cDNA clones encoding the two mannose-binding lectins from ramsons (allium ursinum L.) bulbs, AUAI and AUAII (AUA, Allium ursinum agglutinin), were isolated and characterized. Sequence comparison of the different cDNA clones isolated revealed three types of lectin clones called LECAUAG0, LECAUAG1 and LECAUAG2, which besides the obvious differences in their sequences also differ from each other in the number of potential glycosylation sites within the C-terminal peptide of the lectin precursor. In vivo biosynthesis studies of the ramson lectins have shown that glycosylated lectin precursors occur in the organelle fraction of radioactively labeled ramson bulbs. Despite the similarities between the A. ursinum and the A. sativum (garlic) lectins at the protein level, molecular cloning of the two ramson lectins has shown that the lectin genes in A. ursinum are organized differently. Whereas in A. sativum the lectin polypeptides of the heterodimeric ASAI are encoded by one large precursor, those of the heterodimeric AUAI lectin are derived from two different precursors. These results are confirmed by Northern blot hybridization of A. ursinum RNA which, after hybridization with a labeled lectin cDNA, reveals only one band of 800 nucleotides in contrast to A. sativum RNA which yields two bands of 1400 and 800 nucleotides. Furthermore it is shown that the two mannose-binding lectins are differentially expressed.",European journal of biochemistry,"['D000490', 'D000595', 'D001483', 'D015152', 'D002850', 'D003001', 'D018076', 'D004591', 'D006031', 'D037102', 'D008358', 'D008969', 'D037121', 'D017386']","['Allium', 'Amino Acid Sequence', 'Base Sequence', 'Blotting, Northern', 'Chromatography, Gel', 'Cloning, Molecular', 'DNA, Complementary', 'Electrophoresis, Polyacrylamide Gel', 'Glycosylation', 'Lectins', 'Mannose', 'Molecular Sequence Data', 'Plant Lectins', 'Sequence Homology, Amino Acid']",The mannose-specific lectins from ramsons (Allium ursinum L.) are encoded by three sets of genes.,"['Q000235', None, None, None, None, None, 'Q000737', None, None, 'Q000737', 'Q000378', None, None, None]","['genetics', None, None, None, None, None, 'chemistry', None, None, 'chemistry', 'metabolism', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/8223549,1993,0.0,0.0,,, -1516037,"N-Nitroso compounds (NOCs) are known to be strong carcinogens in various animals including primates (Preussman and Stewart, (1984) N-Nitroso Compounds). Human exposure to these compounds can be by ingestion or inhalation of preformed NOCs or by endogenous nitrosation from naturally occurring precursors (Bartsch and Montesano, Carcinogenesis, 5 (1984) 1381-1393; Tannebaum (1979) Naturally Occuring Carcinogens, Mutagens and Modulators of Carcinogenesis; Shephard et al., Food Chem. Toxicol., 25 (1987) 91-108). Several factors present in the diet can modify levels of endogenously formed nitrosamines by acting as catalysts or inhibitors. Compounds in the human diet that alter nitrosamine formation would thus play an important role in carcinogenesis study. Earlier researchers have reported the nitrite scavenging nature of sulphydryl compounds (Williams, Chem. Soc. Rev., 15 (1983) 171-196). We therefore studied the modifying effect of sulphydryl compounds viz., cysteine (CE), cystine (CI), glutathione (GU), cysteamine (CEA), cystamine (CEI), cysteic acid (CIA) and thioglycolic acid (TGA) on the nitrosation of model amines viz., pyrrolidine (PYR), piperidine (NPIP) and morpholine (NMOR). Many of these compounds are present in the food we consume. The present work also describes the inhibitory effect of onion and garlic juices on the nitrosation reactions. Both onion and garlic are known to contain sulphur compounds (Block, Sci. Am., 252 (1985) 114-119). Most of these compounds behave as antinitrosating agents and their inhibitory activity towards formation of carcinogenic nitrosamines, under different conditions is described.",Cancer letters,"['D000490', 'D016588', 'D002849', 'D003538', 'D003543', 'D003544', 'D003545', 'D003553', 'D004032', 'D005737', 'D005978', 'D006801', 'D009025', 'D009602', 'D015538', 'D010880', 'D010946', 'D011759', 'D013438', 'D013864']","['Allium', 'Anticarcinogenic Agents', 'Chromatography, Gas', 'Cystamine', 'Cysteamine', 'Cysteic Acid', 'Cysteine', 'Cystine', 'Diet', 'Garlic', 'Glutathione', 'Humans', 'Morpholines', 'Nitrosamines', 'Nitrosation', 'Piperidines', 'Plants, Medicinal', 'Pyrrolidines', 'Sulfhydryl Compounds', 'Thioglycolates']",Inhibitory effect of diet related sulphydryl compounds on the formation of carcinogenic nitrosamines.,"[None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', None, None, 'Q000737', None, 'Q000037', 'Q000037', 'Q000187', 'Q000037', None, 'Q000037', 'Q000737', 'Q000737']","[None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', None, None, 'chemistry', None, 'antagonists & inhibitors', 'antagonists & inhibitors', 'drug effects', 'antagonists & inhibitors', None, 'antagonists & inhibitors', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/1516037,1992,0.0,0.0,,, -1394291,"Two new mannose-binding lectins were isolated from garlic (Allium sativum, ASA) and ramsons (Allium ursinum, AUA) bulbs, of the family Alliaceae, by affinity chromatography on immobilized mannose. The carbohydrate-binding specificity of these two lectins was studied by quantitative precipitation and hapten-inhibition assay. ASA reacted strongly with a synthetic linear (1----3)-alpha-D-mannan and S. cerevisiae mannan, weakly with a synthetic (1----6)-alpha-D-mannan, and failed to precipitate with galactomannans from T. gropengiesseri and T. lactis-condensi, a linear mannopentaose, and murine IgM. On the other hand, AUA gave a strong reaction of precipitation with murine IgM, and good reactions with S. cerevisiae mannan and both synthetic linear mannans, suggesting that the two lectins have somewhat different binding specificities for alpha-D-mannosyl units. Of the saccharides tested as inhibitors of precipitation, those with alpha-(1----3)-linked mannosyl units were the best inhibitors of ASA, the alpha-(1----2)-, alpha-(1----4)-, and alpha-(1----6)-linked mannobioses and biosides having less than one eighth the affinity of the alpha-(1----3)-linked compounds. The N-terminal amino acid sequence of ASA exhibits 79% homology with that of AUA, and moderately high homology (53%) with that of snowdrop bulb lectin, also an alpha-D-mannosyl-binding lectin.",Carbohydrate research,"['D000490', 'D000595', 'D001667', 'D002240', 'D037102', 'D008351', 'D008358', 'D037241', 'D008969', 'D037121', 'D011971', 'D017385']","['Allium', 'Amino Acid Sequence', 'Binding, Competitive', 'Carbohydrate Sequence', 'Lectins', 'Mannans', 'Mannose', 'Mannose-Binding Lectins', 'Molecular Sequence Data', 'Plant Lectins', 'Receptors, Immunologic', 'Sequence Homology']",New mannose-specific lectins from garlic (Allium sativum) and ramsons (Allium ursinum) bulbs.,"['Q000737', None, None, None, 'Q000737', 'Q000737', 'Q000737', None, None, None, 'Q000737', None]","['chemistry', None, None, None, 'chemistry', 'chemistry', 'chemistry', None, None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/1394291,1992,0.0,0.0,,, -1610423,"The activity of microsomal NADPH-cytochrome-P-450-reductase and NADH-cytochrome-b5-reductase are inhibited after the addition of an aqueous extract of a pharmaceutical preparation of garlic (Allium sativum, L.) to buffer-suspended microsomes. Incubation of garlic extract with isolated pig liver microsomes also decreases the activity of cytochrome P-450-dependent ethoxycoumarin deethylation. As measured by malondialdehyde release, the effects on the enzyme system are evidently not due to lipid peroxidation. No loss of cytochrome P-450 pigment is observed. Moreover, it could be shown that addition of garlic extract displays no protective effect on microsomal lipids when oxidation occurs spontaneously or is enforced by short-wave UV-irradiation. The above findings were reproduced after applying a HPLC-purified preparation of alliin to the incubation mixtures, suggesting that alliin is the active principle for the inhibitory effects observed in vitro.",Arzneimittel-Forschung,"['D000818', 'D002851', 'D003579', 'D042966', 'D005260', 'D005737', 'D066298', 'D015227', 'D008297', 'D008862', 'D009245', 'D009251', 'D010936', 'D010946', 'D013552']","['Animals', 'Chromatography, High Pressure Liquid', 'Cytochrome Reductases', 'Cytochrome-B(5) Reductase', 'Female', 'Garlic', 'In Vitro Techniques', 'Lipid Peroxidation', 'Male', 'Microsomes, Liver', 'NADH Dehydrogenase', 'NADPH-Ferrihemoprotein Reductase', 'Plant Extracts', 'Plants, Medicinal', 'Swine']",In vitro inhibition of cytochrome P-450 reductases from pig liver microsomes by garlic extracts.,"[None, None, 'Q000037', None, None, None, None, 'Q000187', None, 'Q000187', 'Q000037', 'Q000037', 'Q000494', None, None]","[None, None, 'antagonists & inhibitors', None, None, None, None, 'drug effects', None, 'drug effects', 'antagonists & inhibitors', 'antagonists & inhibitors', 'pharmacology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/1610423,1992,,,,, -1776837,"Components of garlic have been shown to inhibit a variety of tumors induced by chemical carcinogens. In this study we determined the effects of ajoene and diallyl sulfide (DAS), two organosulfur compounds of garlic, on the metabolism and DNA binding of aflatoxin B1 (AFB1) using rat liver 9000Xg supernatant as the metabolic activation system. Organosoluble and water-soluble metabolites of [3H]AFB1 were isolated by reverse-phase high performance liquid chromatography (HPLC). The effects of ajoene and DAS on glutathione-S-transferase (GST) were determined using 1-chloro-2,4-dinitrobenzene as the substrate. Ajoene and DAS at 100 mg/ml inhibited [3H]AFB1 binding to calf thymus DNA and adduct formation. They decreased the formation of both organosoluble and water-soluble metabolites of [3H]AFB1. Neither compound significantly affected GST activity. These results indicate that ajoene and DAS affected AFB1 metabolism and DNA binding by inhibiting phase I enzymes and may therefore be considered as potential cancer chemopreventive agents.",Anticancer research,"['D016604', 'D000498', 'D000818', 'D004247', 'D004220', 'D005737', 'D005978', 'D008297', 'D010936', 'D010946', 'D051381', 'D011919', 'D013440']","['Aflatoxin B1', 'Allyl Compounds', 'Animals', 'DNA', 'Disulfides', 'Garlic', 'Glutathione', 'Male', 'Plant Extracts', 'Plants, Medicinal', 'Rats', 'Rats, Inbred Strains', 'Sulfides']",Binding of aflatoxin B1 to DNA inhibited by ajoene and diallyl sulfide.,"['Q000378', None, None, 'Q000378', 'Q000494', None, 'Q000378', None, 'Q000494', None, None, None, 'Q000494']","['metabolism', None, None, 'metabolism', 'pharmacology', None, 'metabolism', None, 'pharmacology', None, None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/1776837,1992,,,,, -1775580,"In garlic (Allium sativum L.) the enzyme alliin lyase catalyzes the cleavage of alliin into allicin which reacts further to furnish ajoene. A simultaneous determination of allicin and ajoene is introduced which, in contrast to the determination of alliin only, allows for the testing of the activity of alliin lyase. It can be demonstrated that at a pH value of less than 3 the enzyme produces only small amounts of allicin. For this reason preparations from garlic should be administered only as enteric-coated formulations.",Planta medica,"['D002855', 'D004220', 'D005737', 'D010936', 'D010946', 'D013441']","['Chromatography, Thin Layer', 'Disulfides', 'Garlic', 'Plant Extracts', 'Plants, Medicinal', 'Sulfinic Acids']",[Formation of allicin from dried garlic (Allium sativum): a simple HPTLC method for simultaneous determination of allicin and ajoene in dried garlic and garlic preparations].,"['Q000379', 'Q000032', 'Q000032', 'Q000032', None, 'Q000032']","['methods', 'analysis', 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/1775580,1992,,,,no PDF access, -1775579,"The content of dialk(en)yl thiosulfinates, including allicin, and their degradation products has been determined by high performance liquid chromatography (HPLC), using the respective determined extinction coefficients, for a number of commercially available garlic products. Quantitation has been achieved for the thiosulfinates; diallyl, methyl allyl, and diethyl mono-, di-, tri-, tetra-, penta-, and hexasulfides; the vinyldithiins; and (E)- and (Z)-ajoene. The thiosulfinates were found to be released only from garlic cloves and garlic powder products. The vinyldithiins and ajoenes were found only in products containing garlic macerated in vegetable oil. The diallyl, methyl allyl, and dimethyl sulfide series were the exclusive constituents found in products containing the oil of steam-distilled garlic. Typical steam-distilled garlic oil products contained about the same amount of total sulfur compounds as total thiosulfinates released from freshly homogenized garlic cloves; however, oil-macerated products contained only 20% of that amount, while garlic powder products varied from 0 to 100%. Products containing garlic powder suspended in a a gel or garlic aged in aqueous alcohol did not contain detectable amounts of these non-ionic sulfur compounds. A comparison of several brands of each type of garlic product revealed a large range in content (4-fold for oil-macerates and 33-fold for steam-distilled garlic oils), indicating the importance of analysis before garlic products are used for clinical investigations or commercial distribution.",Planta medica,"['D000475', 'D000498', 'D002849', 'D002851', 'D005737', 'D010946', 'D013440', 'D013441']","['Alkenes', 'Allyl Compounds', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Garlic', 'Plants, Medicinal', 'Sulfides', 'Sulfinic Acids']",Identification and HPLC quantitation of the sulfides and dialk(en)yl thiosulfinates in commercial garlic products.,"['Q000032', None, None, None, None, None, 'Q000032', 'Q000032']","['analysis', None, None, None, None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/1775579,1992,,,,no PDF access, -17226157,"Reversed-phase high Performance liquid chromatography (C18-HPLC) was used to separate and quantitate all the detectable alkyl and alkenyl thiosulfinates, including configurational isomers, of garlic homogenates. Pure thiosulfinates were synthesized or isolated and identified by (1)H-NMR, and their extinction coefficients determined. Some configurational isomers required Separation by silica-HPLC. Five previously unreported thiosulfinates have been found, four of which contain the TRANS-1-propenyl group and increase several-fold to over half the content of allicin upon storage of garlic bulbs at 4 degrees C with a concomitant decrease in a gamma-glutamyl peptide. The variation in thiosulfinate yield between different countries, stores, bulbs, cloves, and storage times was investigated. A method for standardizing the quantitation of allicin yield from garlic is proposed and compared to other methods of allicin analysis.",Planta medica,[],[],HPLC analysis of allicin and other thiosulfinates in garlic clove homogenates.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/17226157,2013,,,,no PDF access, -1903884,"The effects of two organosulfur compounds of garlic (ajoene and diallyl sulfide) and a crude garlic extract on aflatoxin B1 (AFB1)-induced mutagenesis were determined using rat liver 9,000 g supernatant (S-9) as the activation system and Salmonella typhimurium TA-100 as the tester strain. The effects of these compounds on AFB1 binding to calf thymus DNA were also measured. Metabolites of AFB1 were isolated and analyzed by reverse-phase high-performance liquid chromatography. All these compounds inhibited S-9-dependent mutagenesis induced by AFB1. They also inhibited AFB1 binding to DNA. A significant decrease in organo-soluble metabolites of AFB1 was observed with ajoene and garlic extract. An increase of glucuronide and glutathione conjugates was obtained with garlic extract. The results indicate that garlic compounds tested in this study are antimutagenic and, potentially, anticarcinogenic.",Nutrition and cancer,"['D016604', 'D000348', 'D000498', 'D000704', 'D000818', 'D004247', 'D004220', 'D005737', 'D008297', 'D016296', 'D010936', 'D010946', 'D051381', 'D011919', 'D012486', 'D013440', 'D013950']","['Aflatoxin B1', 'Aflatoxins', 'Allyl Compounds', 'Analysis of Variance', 'Animals', 'DNA', 'Disulfides', 'Garlic', 'Male', 'Mutagenesis', 'Plant Extracts', 'Plants, Medicinal', 'Rats', 'Rats, Inbred Strains', 'Salmonella typhimurium', 'Sulfides', 'Thymus Gland']","Organosulfur compounds of garlic modulate mutagenesis, metabolism, and DNA binding of aflatoxin B1.","[None, 'Q000235', None, None, None, 'Q000378', 'Q000494', None, None, 'Q000187', 'Q000494', None, None, None, None, 'Q000494', None]","[None, 'genetics', None, None, None, 'metabolism', 'pharmacology', None, None, 'drug effects', 'pharmacology', None, None, None, None, 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/1903884,1991,,,,, -2111258,"1. Homogenates of garlic (Allium sativum), onions (Allium cepa) and Allium porum were in vitro incubated with [14C]arachidonic acid. 2. Separation of labelled prostaglandins and thromboxanes were accomplished by thin-layer chromatography (TLC) and the Rf values were compared with those of authentic standards. 3. The prostaglandins identified were 6-keto-PGF1 alpha, PGF2 alpha, TXB2, PGE2 and PGD2. 4. PGE2 and PGD2 were the major metabolites of arachidonic acid among all the members of the Liliaceae family studied. 5. Garlic was found to have the highest capacity to metabolize the [14C]arachidonic acid into prostaglandins and thromboxanes. 6. The synthesis of prostaglandins and thromboxanes, was inhibited by preincubation of homogenates with indomethacin or was completely destroyed by boiling the plant extract prior to incubation with arachidonic acid. This confirmed the presence of cyclooxygenase in these plants.",General pharmacology,"['D000490', 'D002855', 'D005737', 'D010946', 'D011451', 'D011453', 'D013045', 'D013931']","['Allium', 'Chromatography, Thin Layer', 'Garlic', 'Plants, Medicinal', 'Prostaglandin-Endoperoxide Synthases', 'Prostaglandins', 'Species Specificity', 'Thromboxanes']",Comparative study of the in vitro synthesis of prostaglandins and thromboxanes in plants belonging to Liliaceae family.,"['Q000378', None, 'Q000378', None, 'Q000378', 'Q000096', None, 'Q000096']","['metabolism', None, 'metabolism', None, 'metabolism', 'biosynthesis', None, 'biosynthesis']",https://www.ncbi.nlm.nih.gov/pubmed/2111258,1990,,,,no PDF access, -17262455,"An alliin lyase (EC 4.4.1.4) preparation from garlic, ALLIUM SATIVUM L., has been purified to apparent homogeneity. The purification procedure involved liquid chromatography steps on hydroxylapatite, on an anion exchanger, and on a chromatofocussing medium. The enzyme protein was characterized by a relative molecular mass of 108,000, and was found to consist of two equal subunits. Its isoelectric point was determined to be 4.9. The enzyme appeared rather thermolabile. Simulated gastric-intestinal passage by a modified ""half change test"" revealed a high acid lability of the active alliinase protein. K (m)-values for different substrates were in the mM range, and activating energies for the cleavage of different substrates could be determined. A maximal specific activity for synthetic alliin in the range of 490 micromoles per min and mg protein could be achieved at 33 degrees C. There are some significant differences in the characterization of the purified protein compared to results previously reported by others on this enzyme.",Planta medica,[],[],Characterization of an Alliin Lyase Preparation from Garlic (Allium sativum).,[],[],https://www.ncbi.nlm.nih.gov/pubmed/17262455,2013,,,,no PDF access, -17262412,"Combined headspace gas chromatography-mass spectrometry (HSGC-MS) was used in the analysis of garlic volatile compounds. Twenty major components were identified in the gas phases enriched by fresh, sliced garlic cloves ( ALLIUM SATIVUM L, Allioceae, Liliidae). Suspended dry garlic powder and crushed garlic, incubated in vegetable oil, revealed a different pattern since mainly the amounts of di- and trisulfides were decreased. The considerable compositional differences found in the analyses for the gas phase of garlic cloves, kept in oil, are likely associated with the poor stability of allicin in a lipophilic environment; a marked increase in the amounts of 2-propene-1-thiol, acetic acid, and ethanol was observed in the gas phase, whereas trisulfides were present in traces only. The occurrence of 2-propene-1-thiol and diallyl disulfide, the two principal sulfur components in exhaled air, also may indicate a rapid degradation of most garlic volatile components probably caused by the enzymatically active human salivary or digestive system.",Planta medica,[],[],Volatile garlic odor components: gas phases and adsorbed exhaled air analysed by headspace gas chromatography-mass spectrometry.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/17262412,2012,,,,no PDF access, -2729582,"An indirect method for the determination of trace bound selenomethionine (SeMet) has been developed. SeMet reacts with cyanogen bromide (CNBr) quantitatively in the presence of SnCl2 to form CH3SeCN, and after extraction with CHCl3 is acid-digested to form Se(IV). Selenium(IV) reacts with 4-nitro-o-phenylenediamine reagent to form 5-NO2-piazselenol which is then determined by gas chromatography equipped with electron capture detector. The sensitivity of this method (CNBr-piazselenol-GC method) is 6 ng SeMet/g of sample. Trace-bound SeMet in plants and some biological materials has been successfully determined by this method and its content has been compared with the total selenium in the sample.",Analytical biochemistry,"['D000818', 'D002681', 'D002849', 'D005737', 'D007668', 'D012275', 'D010946', 'D012643', 'D012645', 'D013552', 'D014131', 'D014908', 'D003313']","['Animals', 'China', 'Chromatography, Gas', 'Garlic', 'Kidney', 'Oryza', 'Plants, Medicinal', 'Selenium', 'Selenomethionine', 'Swine', 'Trace Elements', 'Triticum', 'Zea mays']",A method for the indirect determination of trace bound selenomethionine in plants and some biological materials.,"[None, None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, 'Q000032', 'Q000032', 'Q000032']","[None, None, None, 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', None, 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/2729582,1989,0.0,0.0,,correct format just not for garlic, -3235614,A liquid chromatographic (LC) method is proposed for the determination of alliin in garlic and garlic products. The method involves heating of the sample with water in a bath of boiling water followed by homogenization and centrifugation. Interfering components are eliminated by use of a Sep-Pak C18 cartridge as a clean up step before injection. The LC system with ultraviolet detection at 210 nm consists of a separation on a Zorbax TMS column and isocratic elution with water as a mobile phase. Fluorometric determination by ion-pairing chromatography with tetra-n-butylammonium bromide on a Nucleosil 5C18 column is also described. The overall recoveries of alliin added to garlic products were greater than 90%. Thin-layer chromatography and enzymatic degradation of alliin were performed for the confirmation of alliin detected in garlic products.,Journal of chromatography,"['D002853', 'D002855', 'D003545', 'D005737', 'D010946', 'D013050', 'D013056']","['Chromatography, Liquid', 'Chromatography, Thin Layer', 'Cysteine', 'Garlic', 'Plants, Medicinal', 'Spectrometry, Fluorescence', 'Spectrophotometry, Ultraviolet']",Liquid chromatographic determination of alliin in garlic and garlic products.,"[None, None, 'Q000031', 'Q000032', None, None, None]","[None, None, 'analogs & derivatives', 'analysis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/3235614,1989,,,,, -3429558,"After consumption of onions or garlic, biological profiles of human urine samples show, in the methylated conjugate fraction, peaks corresponding to the methylates of N-acetyl-S-(2-carboxypropyl) cysteine (1), N-acetyl-S-allylcysteine (2) and hexahydrohippuric acid (3). The compounds 1 and 2 are metabolites of peptides introduced with onions or garlic into the body.",Journal of chromatography,"['D000111', 'D000490', 'D003545', 'D004032', 'D005737', 'D008401', 'D006626', 'D006801', 'D010455', 'D010946']","['Acetylcysteine', 'Allium', 'Cysteine', 'Diet', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Hippurates', 'Humans', 'Peptides', 'Plants, Medicinal']",Unusual conjugates in biological profiles originating from consumption of onions and garlic.,"['Q000031', None, 'Q000031', None, None, None, 'Q000378', None, 'Q000378', None]","['analogs & derivatives', None, 'analogs & derivatives', None, None, None, 'metabolism', None, 'metabolism', None]",https://www.ncbi.nlm.nih.gov/pubmed/3429558,1988,,,,, -3798421,"When added to platelet-rich plasma, aqueous extracts of garlic inhibited platelet aggregation and the release reaction. Subsequent experiments designed to characterize the inhibitory component revealed that the inhibitory activity was i) associated with small molecular-weight components, ii) the inhibitory component possessed the typical garlic odor and contained an abundance of sulfur, iii) the inhibitory activity could be extracted with organic solvents, and iv) temperatures above 56 degrees C and alkaline pH above 8.5 quickly destroyed the inhibitory activity. The Rf value of the major inhibitory component after thin-layer chromatographic separation was similar to that of allicin, an unique thiosulfinate in garlic previously shown to possess strong antibiotic and antifungal properties. Allicin was synthesized. On thin-layer chromatographic plates, allicin co-migrated with the inhibitory component in garlic. At 10 microM concentration, allicin inhibited completely platelet aggregation and the release reaction. Comparative studies suggest that the major platelet aggregation and release inhibitor in garlic may be allicin.",Thrombosis research,"['D000328', 'D002850', 'D002855', 'D005737', 'D006801', 'D008970', 'D010936', 'D010946', 'D010974', 'D013441']","['Adult', 'Chromatography, Gel', 'Chromatography, Thin Layer', 'Garlic', 'Humans', 'Molecular Weight', 'Plant Extracts', 'Plants, Medicinal', 'Platelet Aggregation', 'Sulfinic Acids']",Characterization of a potent inhibitor of platelet aggregation and release reaction isolated from allium sativum (garlic).,"[None, None, None, 'Q000032', None, None, 'Q000032', None, 'Q000187', 'Q000302']","[None, None, None, 'analysis', None, None, 'analysis', None, 'drug effects', 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/3798421,1987,0.0,0.0,,, -3740854,"Alliin lyase from garlic (Allium sativum) has been purified to homogeneity. The purification procedure involves the use of affinity chromatography on concanavalin A-Sepharose 4B. Addition of polyvinylpolypyrrolidone to the homogenizing medium greatly improves the specific activity of the extract. The enzyme is a glycoprotein as seen by its ability to bind to concanavalin A-Sepharose 4B and by its positive periodic acid-Schiff base stain. It has a carbohydrate content of 5.5%. Km values for this enzyme were estimated to be 5.7 mM for S-ethyl-L-cysteine sulfoxide and 3.3 mM for S-allyl-L-cysteine sulfoxide. The molecular weight of this garlic enzyme, as determined by gel filtration, was found to be 85,000; the molecule consists of two equal subunits of Mr 42,000. The amino acid content was found to be similar to that reported previously for onion alliin lyase, although there is twice as much tryptophan in the garlic alliin lyase as in the onion enzyme. By both chemical and spectral methods the enzyme was found to have two molecules of pyridoxal 5-phosphate per enzyme molecule, suggesting one per subunit. There are significant differences in the nature of these findings from those previously reported from this laboratory for the onion enzyme. Studies are in progress to compare further the alliin lyases from garlic and onion.",Archives of biochemistry and biophysics,"['D000596', 'D002241', 'D013437', 'D005737', 'D007700', 'D008190', 'D008970', 'D010946', 'D011732']","['Amino Acids', 'Carbohydrates', 'Carbon-Sulfur Lyases', 'Garlic', 'Kinetics', 'Lyases', 'Molecular Weight', 'Plants, Medicinal', 'Pyridoxal Phosphate']",The C-S lyases of higher plants: preparation and properties of homogeneous alliin lyase from garlic (Allium sativum).,"['Q000032', 'Q000032', 'Q000302', 'Q000201', None, 'Q000302', None, None, 'Q000032']","['analysis', 'analysis', 'isolation & purification', 'enzymology', None, 'isolation & purification', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/3740854,1986,1.0,2.0,,, -2869220,"A patient who made reproduction antique china dolls complained that wherever she touched the dolls' heads when painting them, black speckles appeared after the subsequent firing. Investigation by means of mass spectrometry and X-ray fluorescence showed that the clay was rich in iron, that the patient's sweat contained volatile sulphides whenever she ate garlic, and that the speckles consisted of iron and sulphur. The patient was shown to be a poor sulphoxidiser and was therefore unlikely to be able to excrete sulphur-containing breakdown products of garlic in her urine. The speckling phenomenon, which is not uncommon in 19th-century china dolls, is an example of an occupational hazard where the risk is to the product rather than the patient.","Lancet (London, England)","['D000293', 'D003116', 'D005260', 'D005737', 'D006801', 'D007501', 'D013058', 'D009790', 'D010946', 'D010988', 'D012451', 'D013440', 'D013455', 'D013542']","['Adolescent', 'Color', 'Female', 'Garlic', 'Humans', 'Iron', 'Mass Spectrometry', 'Occupations', 'Plants, Medicinal', 'Play and Playthings', 'Safrole', 'Sulfides', 'Sulfur', 'Sweat']",The case of the black-speckled dolls: an occupational hazard of unusual sulphur metabolism.,"[None, None, None, 'Q000009', None, 'Q000032', None, None, None, None, 'Q000031', 'Q000032', 'Q000378', 'Q000032']","[None, None, None, 'adverse effects', None, 'analysis', None, None, None, None, 'analogs & derivatives', 'analysis', 'metabolism', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/2869220,1986,,,,no PDF access, -6144781,"Garlic has been extracted and separated chromatographically into various fractions which show different degrees of activity as inhibitors of platelet aggregation and smooth muscle. The most potent smooth muscle inhibitor fraction had little activity on platelet aggregation, but microgram ml-1 concentrations greatly reduced the contractions of rat gastric fundus to prostaglandin E2 and acetylcholine. Material in this fraction may contribute to some of the claimed therapeutic effects of garlic involving smooth muscle. Its identity is not known, but is different from allyl sulphide, dimethyl sulphide and diallyl disulphide. These compounds eluted earlier on liquid chromatography than the most active fraction, and they showed only modest inhibitory activity against prostaglandin E2 and acetylcholine on rat fundus.",The Journal of pharmacy and pharmacology,"['D000109', 'D000818', 'D015232', 'D005737', 'D005748', 'D006801', 'D066298', 'D008297', 'D009119', 'D009130', 'D010936', 'D010946', 'D010974', 'D011458', 'D051381']","['Acetylcholine', 'Animals', 'Dinoprostone', 'Garlic', 'Gastric Fundus', 'Humans', 'In Vitro Techniques', 'Male', 'Muscle Contraction', 'Muscle, Smooth', 'Plant Extracts', 'Plants, Medicinal', 'Platelet Aggregation', 'Prostaglandins E', 'Rats']",The effect of garlic extracts on contractions of rat gastric fundus and human platelet aggregation.,"['Q000494', None, None, None, 'Q000187', None, None, None, 'Q000187', 'Q000187', 'Q000032', None, 'Q000187', 'Q000494', None]","['pharmacology', None, None, None, 'drug effects', None, None, None, 'drug effects', 'drug effects', 'analysis', None, 'drug effects', 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/6144781,1984,,,,no PDF access, -6878462,"The odorant allyl sulfide (essence of garlic) dissolved in a corn oil vehicle was injected into rats to induce a conditioned aversion. In subsequent two-choice drinking tests, rats injected with odorant and lithium chloride, and rats injected with odorant and saline avoided drinking from a water bottle paired with the odorant. Because allyl sulfide and saline injections produced symptoms of malaise, we suspect that the odorant served as its own unconditioned stimulus. Rats injected with vehicle and saline showed no differential behavior. In a second experiment, gas chromatography indicated that allyl sulfide was present on the rat's breath within 3 minutes of injection, and was detectable for up to 5 hours post-injection. We conclude that conditioned aversions can be obtained to an intravascular odorant and that one route by which such odorants reach the nose is the breath.",Physiology & behavior,"['D000222', 'D000498', 'D000818', 'D001362', 'D001790', 'D001944', 'D002849', 'D003214', 'D005737', 'D008297', 'D009812', 'D010946', 'D051381', 'D011919', 'D012903', 'D013440']","['Adaptation, Physiological', 'Allyl Compounds', 'Animals', 'Avoidance Learning', 'Blood Physiological Phenomena', 'Breath Tests', 'Chromatography, Gas', 'Conditioning, Classical', 'Garlic', 'Male', 'Odorants', 'Plants, Medicinal', 'Rats', 'Rats, Inbred Strains', 'Smell', 'Sulfides']",Conditioned aversions to an intravascular odorant.,"[None, None, None, 'Q000502', None, None, None, 'Q000502', None, None, 'Q000032', None, None, None, 'Q000502', 'Q000032']","[None, None, None, 'physiology', None, None, None, 'physiology', None, None, 'analysis', None, None, None, 'physiology', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/6878462,1983,,,,no PDF access, -6314793,"A review of current information on the composition, pharmacological actions and mode of death from cow's urine concoction (CUC) toxicity is presented. The concoction is prepared from leaves of tobacco, garlic and basil; lemon juice, rock salt and bulbs of onion. The latter items are soaked in the urine from cows which acts as the vehicle in which the active principles in these constituents dissolve. Over fifty chemical compounds have been identified in CUC. The major compounds it contains are benzoic acid, phenylacetic acid, p-cresol, thymol and nicotine. The chemical composition and pharmacological cations of the individual components of CUC are also reviewed. Observations of CUC poisoning in man and experimental animals showed that the main effects of CUC are severe depression of respiration, cardiovascular system, the central nervous system and hypoglycaemia. These toxic effects acting singly or in combination are believed to be the cause(s) of death from CUC. Management is geared towards correcting these adverse effects.",African journal of medicine and medical sciences,"['D000818', 'D000927', 'D001565', 'D019817', 'D002319', 'D002417', 'D002490', 'D002648', 'D003408', 'D008401', 'D006801', 'D009538', 'D009549', 'D010101', 'D010648', 'D010936', 'D012119', 'D013943']","['Animals', 'Anticonvulsants', 'Benzoates', 'Benzoic Acid', 'Cardiovascular System', 'Cattle', 'Central Nervous System', 'Child', 'Cresols', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Nicotine', 'Nigeria', 'Oxygen Consumption', 'Phenylacetates', 'Plant Extracts', 'Respiration', 'Thymol']","Cow's urine concoction: its chemical composition, pharmacological actions and mode of lethality.","[None, 'Q000032', 'Q000032', None, 'Q000187', None, 'Q000187', None, 'Q000032', None, None, 'Q000032', None, 'Q000187', 'Q000032', 'Q000032', 'Q000187', 'Q000032']","[None, 'analysis', 'analysis', None, 'drug effects', None, 'drug effects', None, 'analysis', None, None, 'analysis', None, 'drug effects', 'analysis', 'analysis', 'drug effects', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/6314793,1983,,,,, -6888523,"The aim of the study was to determine the composition of flavour isolates from garlic and horse-radish, prepared by dichlorodifluoromethane extraction. Gas chromatography, with olfactory determination of the flavour of the resolved components, and gas chromatography combined with mass spectrometry were used. In the garlic extract 19 components comprising mono-, di-, and trisulphides and thiophene derivatives were detected. In the horse-radish extract 14 components including isothiocyanates, thiocyanates and cyanides were identified.",Die Nahrung,"['D002849', 'D005421', 'D005737', 'D008401', 'D010944', 'D010946', 'D013455']","['Chromatography, Gas', 'Flavoring Agents', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Plants', 'Plants, Medicinal', 'Sulfur']",The role of sulphur compounds in evaluation of flavouring value of some plant raw materials.,"[None, 'Q000032', 'Q000032', None, 'Q000032', None, 'Q000032']","[None, 'analysis', 'analysis', None, 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/6888523,1983,,,,, -6625648,"Garlic (Allium sativum L.) water- and ethanol-soluble extracts were prepared and purified by column chromatography. They were tested on garlic-sensitive patients and showed that the allergenic fraction was well located in a few column chromatography fractions. Guinea-pigs were sensitized with garlic water-soluble extracts and tested (open epicutaneous tests) with several fractions. The presence of diallyldisulfide was detected in the sensitizing chromatographic fractions. Guinea-pigs were successfully sensitized to this product and cross-reacted to garlic; animals sensitized to garlic extracts cross-reacted to diallyldisulfide. Both groups reacted to allicin, an oxidized derivative of diallyldisulfide present in garlic. Garlic-sensitive patients showed positive tests to diallyldisulfide, allylpropyldisulfide, allylmercaptan and allicin.",Archives of dermatological research,"['D000485', 'D000498', 'D000818', 'D003877', 'D004220', 'D005737', 'D006168', 'D006801', 'D007004', 'D010328', 'D010936', 'D010946', 'D013441']","['Allergens', 'Allyl Compounds', 'Animals', 'Dermatitis, Contact', 'Disulfides', 'Garlic', 'Guinea Pigs', 'Humans', 'Hypoglycemic Agents', 'Patch Tests', 'Plant Extracts', 'Plants, Medicinal', 'Sulfinic Acids']","Allergic contact dermatitis to garlic (Allium sativum L.). Identification of the allergens: the role of mono-, di-, and trisulfides present in garlic. A comparative study in man and animal (guinea-pig).","['Q000032', None, None, 'Q000209', 'Q000633', 'Q000032', None, None, 'Q000633', None, 'Q000032', None, 'Q000633']","['analysis', None, None, 'etiology', 'toxicity', 'analysis', None, None, 'toxicity', None, 'analysis', None, 'toxicity']",https://www.ncbi.nlm.nih.gov/pubmed/6625648,1983,,,,, -552092,"Oral administration of onion and garlic reportedly decreases platelet aggregation in both human and animal subjects. An oily chloroform extract of onion (Allium Cepa) was prepared and separated by column chromatography on silicic acid into six fractions by elution with solvents of increasing polarity. The least polar fraction contained most of the inhibitory activity towards platelet aggregation induced by either ADP or arachidonic acid. Further purification was afforded by thin-layer chromatography. The specific activity of this major active fraction (I50 per ml of PRP) was approximately 7 units per milligram. Platelets incubated in the presence of onion inhibitor and (1-14C)-arachidonic acid showed striking changes in the pattern of arachidonic acid metabolites formed. Thromboxane B2 synthesis was almost completely suppressed without significant decreases in total hydroxy fatty acid formation. It was concluded that the observed antiplatelet activity of onion relates to the presence of a non-polar, heat stable inhibitor of thromboxane synthesis. This appears to be the first demonstration of this type of inhibitor present in significant quantities in a common foodstuff.",Prostaglandins and medicine,"['D000244', 'D000818', 'D001095', 'D001792', 'D005260', 'D006801', 'D066298', 'D019684', 'D008297', 'D010974', 'D011817', 'D013929', 'D013931']","['Adenosine Diphosphate', 'Animals', 'Arachidonic Acids', 'Blood Platelets', 'Female', 'Humans', 'In Vitro Techniques', 'Magnoliopsida', 'Male', 'Platelet Aggregation', 'Rabbits', 'Thromboxane B2', 'Thromboxanes']",Effects of onion (Allium cepa) extract on platelet aggregation and thromboxane synthesis.,"['Q000494', None, 'Q000097', 'Q000378', None, None, None, None, None, 'Q000187', None, 'Q000097', 'Q000097']","['pharmacology', None, 'blood', 'metabolism', None, None, None, None, None, 'drug effects', None, 'blood', 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/552092,1980,,,,, -719059,"A method has been developed for the purification of alliinase from garlic bulbs. High purity preparations of the enzyme were obtained with specific activity increased 67-fold over that of the homogenate. The preparations were homogeneous on electrophoresis in polyacril gel. Total activity yield was 25%. The native enzyme has a molecular weight of 130.000 and consists of two subunits. Approximately 6 moles of firmly bound pyridoxal phosphate are determined per 1 mole of the purest enzyme (4 equivalents are apparently bound non-specifically outside the active sites). The isoelectric point (pI) of alliinase in 6.2. The enzyme's absorption and circular dichroism spectra have one maximum at 430 nm, in the characteristic range of many pyridoxal-P-containing enzymes. The Km value for the natural substrate, alliin, is 5 . 10(-4) M.","Biokhimiia (Moscow, Russia)","['D013437', 'D002852', 'D002942', 'D003545', 'D004591', 'D005737', 'D007525', 'D008190', 'D008970', 'D010944', 'D010946', 'D011732']","['Carbon-Sulfur Lyases', 'Chromatography, Ion Exchange', 'Circular Dichroism', 'Cysteine', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Isoelectric Focusing', 'Lyases', 'Molecular Weight', 'Plants', 'Plants, Medicinal', 'Pyridoxal Phosphate']",[Alliinase: purification and chief physico-chemical properties].,"['Q000302', None, None, 'Q000031', None, 'Q000201', None, 'Q000302', None, 'Q000201', None, None]","['isolation & purification', None, None, 'analogs & derivatives', None, 'enzymology', None, 'isolation & purification', None, 'enzymology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/719059,1979,,,,, -902266,"Hot-water extraction of defatted garlic-bulbs yielded a mixture of polysaccharides containing a D-galactan, a D-galacturonan, an L-arabinan, a D-glucan, and a D-fructan. A trace of L-rhamnose was also detected in the polysaccharide hydrolyzate. The pectic acid was partially removed by precipitation with aqueous calcium chloride; from the remaining polysaccharide mixture, a pure D-galactan containing 97.3% of D-galactose was isolated by fractional precipitation and repeated chromatography through a column of DEAE-cellulose. Methanolysis and hydrolysis of the permethylated D-galactan yielded 2,3,4,6-tetra-, 2,3,6-tri-, and 2,3,di-O-methyl-D-galactose in the molar proportions of 1:2:1. On periodate oxidation, the D-galactan reduced 1.18 molar equivalents of the oxidant per D-galactosyl residue, and liberated one molar equivalent of formic acid per 4.13 D-galactosyl residues. Smith degradation of the D-galactan was also conducted. From these results, a structure has been assigned to the repeating unit of the D-galactan.",Carbohydrate research,"['D005690', 'D005737', 'D009005', 'D010944', 'D010946', 'D011134']","['Galactose', 'Garlic', 'Monosaccharides', 'Plants', 'Plants, Medicinal', 'Polysaccharides']",Structure of the D-galactan isolated from garlic (Allium sativum) bulbs.,"['Q000032', 'Q000032', 'Q000032', 'Q000032', None, 'Q000302']","['analysis', 'analysis', 'analysis', 'analysis', None, 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/902266,1977,,,,no PDF access, -927476,"The content of S-methylmethionine SMM in the extracts of 53 plant and 13 animal products by means of ion exchange clean-up procedure followed by two dimensional thin layer chromatography has been investigated. It was found that the richest plant SMM sources (in mg/100 g) are cabbage (53-104), kohlrabi (81-110), turnip (51-72), tomatoes (45-83), celery (38-78), leeks (66-75), garlic-leafs (44-64), beet (22-37), raspberries (27) and strawberries (14-25). The animal products are poor in SMM. The control of the plants rich in SMM during a storage for 6 months (autumn, winter) in the soil showed average decreases as follows: celery 38%, kohlrabi 39%, turnip 43%, and leeks 32%. A storage of cabbage with uncontrolled temperature resulted in a decrease of 62%, in a storehouse (0-1 degrees C) of 34% SMM.",Die Nahrung,"['D004355', 'D005504', 'D007700', 'D008715', 'D010944']","['Drug Stability', 'Food Analysis', 'Kinetics', 'Methionine', 'Plants']",[S-Methylmethionine content in plant and animal tissues and stability during storage].,"[None, None, None, 'Q000031', 'Q000032']","[None, None, None, 'analogs & derivatives', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/927476,1978,,,,, -4970593,"1. Alliin lyase (EC 4.4.1.4) was purified up to sevenfold from garlic-bulb homogenates. The enzyme was unstable to storage at -10 degrees , particularly in dilute concentrations, but the addition of glycerol (final concentration 10%, v/v) stabilized the activity completely for at least 30 days. 2. The purified enzyme had an optimum pH for activity at 6.5. The addition of pyridoxal phosphate stimulated the reaction rate and the stimulation became more marked as the purification proceeded. 3. Hydroxylamine (10mum) and cysteine (0.5mm) inhibited the enzyme activity by more than 80%. Spectral studies indicated that cysteine reacted with pyridoxal phosphate bound to the protein. 4. The K(m) values for S-methyl-, S-ethyl-, S-propyl-, S-butyl- and S-allyl-l-cysteine sulphoxides were determined. With S-allyl-l-cysteine sulphoxide the K(m) was 6mm and the V(max.) was greater than those with the other substrates tested. 5. The thioether analogues of the substrates were competitive inhibitors for the lyase reaction. The K(i) decreased with increasing chain length of the alkyl substituent. With S-ethyl-l-cysteine sulphoxide as substrate the K(i) was 33, 8 and 5mm respectively for S-methyl-, S-ethyl- and S-propyl-l-cysteine. 6. The addition of EDTA or Mg(2+), Mn(2+), Co(2+) or Fe(2+) stimulated the reaction rate. Other bivalent cations either had no effect or gave a strong inhibition. In the presence of EDTA no further increase of activity was observed with added Mg(2+).",The Biochemical journal,"['D055598', 'D002621', 'D002850', 'D003035', 'D003080', 'D003545', 'D004355', 'D004492', 'D005737', 'D005990', 'D006863', 'D006898', 'D007501', 'D007700', 'D008190', 'D008274', 'D008345', 'D010946', 'D011732', 'D013053', 'D013454', 'D013997']","['Chemical Phenomena', 'Chemistry', 'Chromatography, Gel', 'Cobalt', 'Cold Temperature', 'Cysteine', 'Drug Stability', 'Edetic Acid', 'Garlic', 'Glycerol', 'Hydrogen-Ion Concentration', 'Hydroxylamines', 'Iron', 'Kinetics', 'Lyases', 'Magnesium', 'Manganese', 'Plants, Medicinal', 'Pyridoxal Phosphate', 'Spectrophotometry', 'Sulfoxides', 'Time Factors']","Purification of the alliin lyase of garlic, Allium sativum L.","[None, None, None, None, None, None, None, None, 'Q000201', None, None, None, None, None, 'Q000037', None, None, None, None, None, None, None]","[None, None, None, None, None, None, None, None, 'enzymology', None, None, None, None, None, 'antagonists & inhibitors', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/4970593,1968,0.0,0.0,,, +PMID,abstract,journal,mesh_UIds,mesh_terms,paper,qual_UIds,qual_terms,webpage,year,is_useful,usefulness_tier,fold,comments,useful_no_pdf +29693456,"After gas chromatography and mass spectrometry of prepared methanolic extract of Allium sativum, 40 laboratory BALB/c mice were infected intraperitoneally by injection of 1,500 viable protoscoleces. Five months after infection, the infected mice were allocated into four treatment groups, including 1- Albendazole (100 mg/kg); 2- Allium sativum methanolic extract (10 mL/L); 3- A. sativum methanolic extract (10 mL/L) + Albendazole (50 mg /kg); and 4- untreated control group. After 30 days of daily treatment, total number and weight of cysts and size of the largest cyst as well as blood serum bilirubin and liver enzymes were compared between the mice of different groups. The total number and weight of cysts and size of the largest cyst were significantly lower in treated groups A. sativum 10 mL/L + Albendazole 50 and Albendazole 100 in comparison to those of the control group (p < 0.05). The activity of alanine aminotransferase (ALT) enzyme and bilirubin concentration were significantly lower in the mice treated with A. sativum 10 mL/L and A. sativum 10 mL/L + Albendazole 50, when compared to the control group. In addition, bilirubin concentration revealed significant decrease in A. sativum 10 mL/L and A. sativum 10 mL/L + Albendazole 50 groups, when compared to the Albendazole group. In conclusion, administration of A. sativum 10 mL/L improved the anti-hydatidosis activity of Albendazole 50 mg /kg, due to parasitological effects similar to Albendazole 100 mg /kg but less hepatotoxic effects.",Journal of investigative surgery : the official journal of the Academy of Surgical Research,[],[],Allium Sativum Methanolic Extract (garlic) Improve Therapeutic Efficacy of Albendazole Against Hydatid Cyst: In Vivo Study.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29693456,2018,1.0,1.0,,, +29653182,"Meju, a cooked and fermented soy bean based food product, is used as a major ingredient in Korean traditional fermented foods such as Doenjang. We developed a novel type of Meju using single and combined extracts of Allium sativum (garlic clove), Nelumbo nucifera (lotus leaves), and Ginkgo biloba (ginkgo leaves) at 1% and 10% concentrations to improve the safety of Meju-based fermented products. Biogenic amines (BAs) in protein-rich fermented food products pose considerable toxical risks. The objective of this study was to investigate the effects of adding selected plant extracts in Meju samples during fermentation. Nine BAs, including tryptamine, 2-phenylethylamine, putrescine, cadaverine, agmatine, histamine, tyramine, spermidine and spermine, were isolated from Meju samples after sample derivatization with dansyl chloride and analyzed by high performance liquid chromatography. As a result, all tested Meju samples with added plant extracts showed total BAs levels in the range of 20.12 ± 2.03 to 118.42 ± 10.68 mg/100 g, which were below the safety limit set by various regulatory authorities (USFDA/KFDA/EFSA). However, among all tested Meju samples, LOM10 (Meju fermented with Nelumbo nucifera at 10% concentration) showed higher levels of BAs content than others either due to batch-to-batch variability or reduced beneficial microorganisms and/or due to increase in BA forming microorganisms. Also, none of the samples showed the aflatoxin level above the detection limit. Furthermore, all the tested Meju samples improved microbial safety as confirmed by the complete absence of Salmonella species and Staphylococcus aureus. However, some of the Meju samples showed the presence of coliforms (in range of 1.6 × 10",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D001679', 'D002851', 'D005285', 'D005516', 'D059022', 'D005737', 'D020441', 'D057230', 'D031653', 'D010936', 'D045730', 'D013056']","['Biogenic Amines', 'Chromatography, High Pressure Liquid', 'Fermentation', 'Food Microbiology', 'Food Safety', 'Garlic', 'Ginkgo biloba', 'Limit of Detection', 'Nelumbo', 'Plant Extracts', 'Soy Foods', 'Spectrophotometry, Ultraviolet']","Detection of biogenic amines and microbial safety assessment of novel Meju fermented with addition of Nelumbo nucifera, Ginkgo biloba, and Allium sativum.","['Q000032', None, None, None, None, None, None, None, None, 'Q000494', 'Q000382', None]","['analysis', None, None, None, None, None, None, None, None, 'pharmacology', 'microbiology', None]",https://www.ncbi.nlm.nih.gov/pubmed/29653182,2018,0.0,0.0,,, +29542139,"Allicin and soluble solid content (SSC) in garlic is the responsible for its pungent flavor and odor. However, current conventional methods such as the use of high-pressure liquid chromatography and a refractometer have critical drawbacks in that they are time-consuming, labor-intensive and destructive procedures. The present study aimed to predict allicin and SSC in garlic using hyperspectral imaging in combination with variable selection algorithms and calibration models.",Journal of the science of food and agriculture,"['D000465', 'D002138', 'D002623', 'D005737', 'D016018', 'D008962', 'D013057', 'D013441', 'D060388']","['Algorithms', 'Calibration', 'Chemistry Techniques, Analytical', 'Garlic', 'Least-Squares Analysis', 'Models, Theoretical', 'Spectrum Analysis', 'Sulfinic Acids', 'Support Vector Machine']",Hyperspectral imaging for predicting the allicin and soluble solid content of garlic with variable selection algorithms and chemometric models.,"[None, None, 'Q000379', 'Q000737', None, None, 'Q000379', 'Q000737', None]","[None, None, 'methods', 'chemistry', None, None, 'methods', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/29542139,2018,0.0,0.0,,, +29498844,"We analyzed aged garlic extract (AGE) to understand its complex sulfur chemistry using post-column high-performance liquid chromatography with an iodoplatinate reagent and liquid chromatography high resolution mass spectrometry (LC-MS). We observed unidentified peaks of putative sulfur compounds. Three compounds were isolated and identified as γ-glutamyl-γ-glutamyl- S-methylcysteine, γ-glutamyl-γ-glutamyl- S-allylcysteine (GGSAC) and γ-glutamyl-γ-glutamyl- S-1-propenyl-cysteine (GGS1PC) by nuclear magnetic resonance and LC-MS analysis based on comparisons with chemically synthesized reference compounds. GGSAC and GGS1PC were novel compounds. Trace amounts of these compounds were detected in raw garlic, but the contents of these compounds increased during the aging process. Production of these compounds was inhibited using a γ-glutamyl transpeptidase (GGT) inhibitor in the model reaction mixtures. These findings suggest that γ-glutamyl tripeptides in AGE are produced by GGT during the aging process.",Journal of agricultural and food chemistry,"['D002851', 'D005638', 'D005737', 'D010455', 'D010936', 'D013457', 'D053719']","['Chromatography, High Pressure Liquid', 'Fruit', 'Garlic', 'Peptides', 'Plant Extracts', 'Sulfur Compounds', 'Tandem Mass Spectrometry']",Isolation and Identification of Three γ-Glutamyl Tripeptides and Their Putative Production Mechanism in Aged Garlic Extract.,"[None, 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', None]","[None, 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/29498844,2018,0.0,0.0,,, +29433241,"Complexes of amylose (Am) with garlic bioactive components (GBCs) were prepared by milling activating treatment of Am and garlic paste (GP) together. The complex, produced by milling for 2.5h with the garlic (dry basis)/Am ratio of 1:5 (w/w) and water content of 25% (w/w) exhibited significantly higher allicin content (0.49mg/g of complex) than others. The scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transforms infrared (FT-IR), differential scanning calorimetry (DSC), thermogravimetry analysis (TGA), high performance liquid chromatography (HPLC), and gas chromatography-mass spectrometry (GC-MS) techniques were used complex characterization. XRD results indicated that the Am and garlic bioactive components formed the V-type structure. FT-IR and DSC analysis further confirmed the formation of the Am-GBCs complex, and its thermal stability was improved in comparison with garlic powder. According to GC-MS results, all organosulfur compounds (OSCs) in fresh garlic were better retained to Am-GBCs complex. Therefore, the Am-GBCs complexes can have important applications as stable natural flavor compound systems.","Food research international (Ottawa, Ont.)",[],[],Physicochemical characteristics of complexes between amylose and garlic bioactive components generated by milling activating method.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29433241,2018,2.0,1.0,,, +29318305,"In the present research, the applicability of stable isotope (δ13C, δ15N, δ34S, δ18O) and multi-element (P, S, Cl, K, Ca, Zn, Br, Rb, Sr) data for determining the geographical origin of garlic (Allium sativum L.) at the scale of Slovenia was examined. Slovenia is a rather small country (20273 km2) with significant geological and biological diversity. Garlic, valued for its medicinal properties, was collected from Slovenian farms with certified organic production and analyzed by elemental analyzer isotope ratio mass spectrometry combined with energy dispersive X- ray fluorescence spectrometry. Multivariate discriminant analysis (DA) revealed a distinction between four Slovenian macro-regions: the Alpine, Dinaric, Mediterranean and Pannonian. The model was validated through a leave-10%, 20% and 25% out cross validation. The overall success rate of correctly reclassified samples was 77% (on average), indicating that the model and the proposed methodology could be a promising tool for rapid, inexpensive and robust screening to control the provenance of garlic samples.",Acta chimica Slovenica,"['D004602', 'D005737', 'D005843', 'D007554', 'D013058', 'D017524', 'D013052']","['Elements', 'Garlic', 'Geography', 'Isotopes', 'Mass Spectrometry', 'Slovenia', 'Spectrometry, X-Ray Emission']",Geographical Origin Characterization of Slovenian Organic Garlic Using Stable Isotope and Elemental Composition Analyses.,"[None, 'Q000737', None, 'Q000032', None, None, None]","[None, 'chemistry', None, 'analysis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/29318305,2018,,,,, +29215100,"Dietary salt is a vital ingredient associated with sensory performance in processed foods, while reduced salt intake linked to public health is highly desired by consumers and food manufacturers. In this paper, quillaja saponin (QS) based hollow salt particles (∼10 μm) were fabricated by simple spray drying, and utilized as solid carriers to enhance sensory aromas with reduced sodium intake. QS-coated nanodroplets were firstly prepared as a reservoir for flavor oils (lemon and garlic oil), and then served as frameworks to construct hollow salt particles via general spray drying. Headspace gas chromatography-mass spectrometry (DHS-GC-MS) and panel sensory analysis conclude that the hollow salt particles loaded with flavor oils enhance typical aroma attributes and saltiness perception in comparison with their mixture control. The QS-based hollow salt particles could be developed into novel vehicles for improving flavor performance with reduced sodium intake, and furthermore used for delivery of hydrophobic bioactives in food systems.",Food & function,"['D000293', 'D000328', 'D005260', 'D005421', 'D005503', 'D006801', 'D008297', 'D031990', 'D062605', 'D017673', 'D013649', 'D055815']","['Adolescent', 'Adult', 'Female', 'Flavoring Agents', 'Food Additives', 'Humans', 'Male', 'Quillaja', 'Quillaja Saponins', 'Sodium Chloride, Dietary', 'Taste', 'Young Adult']",Quillaja saponin-based hollow salt particles as solid carriers for enhancing sensory aroma with reduced sodium intake.,"[None, None, None, 'Q000737', 'Q000737', None, None, 'Q000737', 'Q000737', 'Q000032', None, None]","[None, None, None, 'chemistry', 'chemistry', None, None, 'chemistry', 'chemistry', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/29215100,2018,0.0,0.0,,, +29181754,"Green and nanoacaricides including essential oil (EO) nanoemulsions are important compounds to provide new, active, safe acaricides and lead to improvement of avoiding the risk of synthetic acaricides. This study was carried out for the first time on eriophyid mites to develop nanoemulsion of garlic essential oil by ultrasonic emulsification and evaluate its acaricidal activity against the two eriophyid olive mites Aceria oleae Nalepa and Tegolophus hassani (Keifer). Acute toxicity of nanoemulsion was also studied on male rats. Garlic EO was analyzed by gas chromatography-mass spectrometry (GC-MS), and the major compounds were diallyl sulfide (8.6%), diallyl disulfide (28.36%), dimethyl tetrasulfide (15.26%), trisulfide,di-2-propenyl (10.41%), and tetrasulfide,di-2-propenyl (9.67%). Garlic oil nanoemulsion with droplet size 93.4 nm was formulated by ultrasonic emulsification for 35 min. Emulsification time and oil and surfactant ratio correlated to the emulsion droplet size and stability. The formulated nanoemulsion showed high acaricidal activity against injurious eriophyid mites with LC",Environmental science and pollution research international,[],[],Formulation and characterization of garlic (Allium sativum L.) essential oil nanoemulsion and its acaricidal activity on eriophyid olive mites (Acari: Eriophyidae).,[],[],https://www.ncbi.nlm.nih.gov/pubmed/29181754,2018,0.0,0.0,,, +29108412,Structures and formation pathways of compounds responsible for blue-green discoloration of processed garlic were studied in model systems. A procedure was developed for isolation of the color compounds and their tentative identification by high-performance liquid chromatography coupled to a diode array detector and tandem mass spectrometry. It was found that the pigment is a mixture of numerous pyrrole-based purple/blue and yellow species. Experiments with isotope-labeled precursors revealed that two molecules of an amino acid are involved in the formation of each color compound. In the purple/blue species (λ,Journal of agricultural and food chemistry,"['D000596', 'D002851', 'D003116', 'D005511', 'D005737', 'D015394', 'D010860', 'D053719']","['Amino Acids', 'Chromatography, High Pressure Liquid', 'Color', 'Food Handling', 'Garlic', 'Molecular Structure', 'Pigments, Biological', 'Tandem Mass Spectrometry']",Allium Discoloration: Color Compounds Formed during Greening of Processed Garlic.,"['Q000737', None, None, None, 'Q000737', None, 'Q000737', None]","['chemistry', None, None, None, 'chemistry', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/29108412,2018,0.0,0.0,,, +28965409,"Aroma extract dilution analysis of distillates prepared by solvent extraction and solvent-assisted flavor evaporation distillation from white Alba truffle (WAT; Tuber magnatum pico) and Burgundy truffle (BT; Tuber uncinatum) revealed 20 odor-active regions in the flavor dilution (FD) factor range of 16-4096 in WAT and 25 in BT. The identification experiments in combination with the FD factors showed clear differences in the overall set of key odorants of both fungi. While 3-(methylthio)propanal (potato-like) followed by 2- and 3-methylbutanal (malty), 2,3-butanedione (buttery), and bis(methylthio)methane (garlic-like) showed the highest FD factors in WAT, 2,3-butanedione, phenylacetic acid (honey-like), and vanillin (vanilla-like) had the highest FD factors in BT. Odor activity values (OAVs, ratio of concentration to odor thresholds), which were calculated on the basis of quantitative data obtained by stable isotope dilution assays, of >1000 for bis(methylthio)methane, 3-methylbutanal, and 3,4-dihydro-2-(H)pyrrol (1-pyrroline) revealed they are key contributors to the aroma of WAT. In BT, 1-pyrroline and 2,3-butanedione showed the highest OAVs of 1530 and 1130, respectively. Aroma recombination experiments successfully mimicked the overall aroma profiles of both fungi when all odorants showing OAVs of >1 were combined. Omission experiments confirmed the amine-like and sperm-like smell of 1-pyrroline, identified for the first time as a key odorant in both truffle species.",Journal of agricultural and food chemistry,"['D001203', 'D005421', 'D008401', 'D006801', 'D009812', 'D012903', 'D055549']","['Ascomycota', 'Flavoring Agents', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Odorants', 'Smell', 'Volatile Organic Compounds']",Characterization of the Key Aroma Compounds in White Alba Truffle (Tuber magnatum pico) and Burgundy Truffle (Tuber uncinatum) by Means of the Sensomics Approach.,"['Q000737', 'Q000737', None, None, 'Q000032', None, 'Q000737']","['chemistry', 'chemistry', None, None, 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28965409,2017,0.0,0.0,,, +28932845,"Garlic (A. sativum) contains a large number of small sulphur (S)-containing metabolites, which are important for its taste and smell and vary with A. sativum variety and growth conditions. This study was designed to investigate the influence of different sulphur-fertilization regimes on low molecular weight S-species by attempting the first sulphur mass balance in A. sativum roots and bulbs using HPLC-ICPMS/MS-ESI-MS/MS. Species unspecific quantification of acid soluble S-containing metabolites was achieved using HPLC-ICP-MS/MS. For identification of the compounds, high resolution ESI-MS (Orbitrap LTQ and q-TOF) was used. The plants contained up to 54 separated sulphur-containing compounds, which constitute about 80% of the total sulphur present in A. sativum. The roots and bulbs of A. sativum contained the same compounds, but not necessarily the same amounts and proportions. The S-containing metabolites in the roots reacted more sensitively to manipulations of sulphur fertilization than those compounds in the bulbs. In addition to known compounds (e.g. γ-glutamyl-S-1-propenylcysteine) we were able to identify and partially quantify 31 compounds. Three as yet undescribed S-containing compounds were also identified and quantified for the first time. Putative structures were assigned to the oxidised forms of S-1-propenylmercaptoglutathione, S-2-propenylmercaptoglutathione, S-allyl/propenyl-containing PC-2 and 2-amino-3-[(2-carboxypropyl)sulfanyl]propanoic acid. The parallel use of ICP-MS/MS as a sulphur-specific detector and ESI-MS as a molecular detector simplifies the identification and quantification of sulphur containing metabolites without species specific standards. This non-target analysis approach enables a mass balance approach and identifies the occurrence of the so far unidentified organosulphur compounds. The experiments showed that the sulphur-fertilization regime does not influence sulphur-speciation, but the concentration of some S-containing compounds in roots is dependent on sulphur fertilization.",Metallomics : integrated biometal science,"['D002851', 'D005737', 'D021241', 'D013457', 'D053719']","['Chromatography, High Pressure Liquid', 'Garlic', 'Spectrometry, Mass, Electrospray Ionization', 'Sulfur Compounds', 'Tandem Mass Spectrometry']",Sulphur fertilization influences the sulphur species composition in Allium sativum: sulphomics using HPLC-ICPMS/MS-ESI-MS/MS.,"['Q000379', 'Q000737', 'Q000379', 'Q000032', 'Q000379']","['methods', 'chemistry', 'methods', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/28932845,2018,0.0,0.0,,, +28911676,"Black garlic produced from fresh garlic under controlled high temperature and humidity has strong antioxidant properties. To determine these compounds, five fractions (from F1 to F5) were separated and purified by elution with chloroform:methanol at different ratios (8:1, 6:1, 4:1, 2:1, and 0:1; v/v). The antioxidant activity of each fraction was analyzed. The results showed that F3 and F4 had higher phenolic contents and stronger 2,2-diphenyl-2-picrylhydrazyl radical scavenging activity than the others. Seven purified individual components were further separated using semipreparation high-performance liquid chromatography from these two intensely antioxidant fractions (F3 and F4), their structures were elucidated by high-performance liquid chromatography coupled to diode array detection, electrospray ionization, mass spectrometry, ",Journal of food and drug analysis,"['D000975', 'D001713', 'D002243', 'D002851', 'D005419', 'D005737', 'D009682', 'D010936', 'D011758']","['Antioxidants', 'Biphenyl Compounds', 'Carbolines', 'Chromatography, High Pressure Liquid', 'Flavonoids', 'Garlic', 'Magnetic Resonance Spectroscopy', 'Plant Extracts', 'Pyrroles']",Composition analysis and antioxidant properties of black garlic extract.,"[None, None, None, None, None, None, None, None, None]","[None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28911676,2017,0.0,0.0,,, +28825644,"This study set out to determine the distribution of sulfur compounds and saponin metabolites in different parts of garlic cloves. Three fractions from purple and white garlic ecotypes were obtained: the tunic (SS), internal (IS) and external (ES) parts of the clove. Liquid Chromatography coupled to High Resolution Mass spectrometry (LC-HRMS), together with bioinformatics including Principal Component Analysis (PCA), Hierarchical Clustering (HCL) and correlation network analyses were carried out. Results showed that the distribution of these metabolites in the different parts of garlic bulbs was different for the purple and the white ecotypes, with the main difference being a slightly higher number of sulfur compounds in purple garlic. The SS fraction in purple garlic had a higher content of sulfur metabolites, while the ES in white garlic was more enriched by these compounds. The correlation network indicated that diallyl disulfide was the most relevant metabolite with regards to sulfur compound metabolism in garlic. The total number of saponins was almost 40-fold higher in purple garlic than in the white variety, with ES having the highest content. Interestingly, five saponins including desgalactotigonin-rhamnose, proto-desgalactotigonin, proto-desgalactotigonin-rhamnose, voghieroside D1, sativoside B1-rhamnose and sativoside R1 were exclusive to the purple variety. Data obtained from saponin analyses revealed a very different network between white and purple garlic, thus suggesting a very robust and tight coregulation of saponin metabolism in garlic. Findings in this study point to the possibility of using tunics from purple garlic in the food and medical industries, since it contains many functional compounds which can be exploited as ingredients.","Molecules (Basel, Switzerland)","['D002851', 'D016000', 'D019295', 'D060146', 'D005737', 'D013058', 'D009928', 'D012503', 'D013457']","['Chromatography, High Pressure Liquid', 'Cluster Analysis', 'Computational Biology', 'Ecotype', 'Garlic', 'Mass Spectrometry', 'Organ Specificity', 'Saponins', 'Sulfur Compounds']",Tissue-Specific Accumulation of Sulfur Compounds and Saponins in Different Parts of Garlic Cloves from Purple and White Ecotypes.,"[None, None, 'Q000379', None, 'Q000737', None, None, 'Q000737', 'Q000737']","[None, None, 'methods', None, 'chemistry', None, None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28825644,2018,0.0,0.0,1.0,, +28719747,"An epidemic fungal disease caused by Fusarium proliferatum, responsible for fumonisin production (FB1, FB2, and FB3), has been reported in the main garlic-producing countries in recent years. Fumonisins are a group of structurally related toxic metabolites produced by this pathogen. The aim of this work was to establish an enzyme-linked immunosorbent assay (ELISA) procedure, mostly applied to cereals, that is suitable for fumonisin detection in garlic and compare these results to those obtained by high-performance liquid chromatography (HPLC) and screening of fresh and dehydrated garlic for toxicological risk. The results show good correlation between the two analytical methods. In fresh symptomatic garlic, fumonisin levels were higher in the basal plates than those in the portions with necrotic spots. Among the 56 commercially dehydrated garlic samples screened, three were positive by ELISA test and only one was above the limit of quantitation. The same samples analyzed by HPLC showed the presence of FB1 in trace amounts that was below the limit of quantitation; FB2 and FB3 were absent. The results are reassuring, because no substantial contamination by fumonisins was found in commercial garlic.",Journal of agricultural and food chemistry,"['D005506', 'D005511', 'D037341', 'D005670', 'D005737', 'D009183', 'D010935']","['Food Contamination', 'Food Handling', 'Fumonisins', 'Fusarium', 'Garlic', 'Mycotoxins', 'Plant Diseases']",Detection of Fumonisins in Fresh and Dehydrated Commercial Garlic.,"['Q000032', None, 'Q000032', 'Q000378', 'Q000737', 'Q000032', 'Q000382']","['analysis', None, 'analysis', 'metabolism', 'chemistry', 'analysis', 'microbiology']",https://www.ncbi.nlm.nih.gov/pubmed/28719747,2017,0.0,0.0,,, +28705396,"Fusarium proliferatum is a polyphagous pathogenic fungus able to infect many crop plants worldwide. Differences in proteins accumulated were observed when maize- and asparagus-derived F. proliferatum strains were exposed to host extracts prepared from asparagus, maize, garlic, and pineapple tissues. Seventy-three unique proteins were up-regulated in extract-supplemented cultures compared to the controls. They were all identified using mass spectrometry and their putative functions were assigned. A major part of identified proteins was involved in sugar metabolism and basic metabolic processes. Increased accumulation of proteins typically associated with stress response (heat shock proteins, superoxide dismutases, and glutaredoxins) as well as others, putatively involved in signal transduction, suggests that some metabolites present in plant extracts may act as elicitors inducing similar reaction as the abiotic stress factors. As a case study, thirteen genes encoding the proteins induced by the extracts were identified in the genomes of diverse F. proliferatum strains using gene-specific DNA markers. Extract-induced changes in the pathogen's metabolism are putatively a result of differential gene expression regulation. Our findings suggest that host plant metabolites present in the extracts can cause biotic stress resulting in elevated accumulation of diverse set of proteins, including those associated with pathogen's stress response.",Fungal biology,"['D000222', 'D005656', 'D005670', 'D015966', 'D013058', 'D058977', 'D010936', 'D020543', 'D013312']","['Adaptation, Physiological', 'Fungal Proteins', 'Fusarium', 'Gene Expression Regulation, Fungal', 'Mass Spectrometry', 'Molecular Sequence Annotation', 'Plant Extracts', 'Proteome', 'Stress, Physiological']",Host extracts induce changes in the proteome of plant pathogen Fusarium proliferatum.,"[None, 'Q000032', 'Q000737', 'Q000187', None, None, 'Q000378', 'Q000032', None]","[None, 'analysis', 'chemistry', 'drug effects', None, None, 'metabolism', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/28705396,2018,0.0,0.0,,, +28683396,The black garlic juice is popular for its nutritive value. Enrichment of antioxidants is needed to make black garlic extract an effective functional ingredient. Five macroporous resins were evaluated for their capacity in adsorbing antioxidants in black garlic juice. XAD-16 resin was chosen for further study due to its high adsorption and desorption ratios. Pseudo-second-order kinetics (q,"Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000327', 'D000975', 'D002852', 'D004912', 'D005737', 'D006461', 'D006801', 'D007475', 'D010936']","['Adsorption', 'Antioxidants', 'Chromatography, Ion Exchange', 'Erythrocytes', 'Garlic', 'Hemolysis', 'Humans', 'Ion Exchange Resins', 'Plant Extracts']",Enrichment of antioxidants in black garlic juice using macroporous resins and their protective effects on oxidation-damaged human erythrocytes.,"[None, 'Q000032', 'Q000295', 'Q000187', 'Q000737', 'Q000187', None, 'Q000737', 'Q000032']","[None, 'analysis', 'instrumentation', 'drug effects', 'chemistry', 'drug effects', None, 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28683396,2017,1.0,1.0,,, +28677661,Right-sided heart failure-often caused by elevated pulmonary arterial pressure-is a chronic and progressive condition with particularly high mortality rates. Recent studies and our current findings suggest that components of Wild garlic (,International journal of molecular sciences,"['D000490', 'D000818', 'D015415', 'D004195', 'D004452', 'D006334', 'D006976', 'D008168', 'D008297', 'D013058', 'D009206', 'D010936', 'D011651', 'D051381', 'D000068677']","['Allium', 'Animals', 'Biomarkers', 'Disease Models, Animal', 'Echocardiography', 'Heart Function Tests', 'Hypertension, Pulmonary', 'Lung', 'Male', 'Mass Spectrometry', 'Myocardium', 'Plant Extracts', 'Pulmonary Artery', 'Rats', 'Sildenafil Citrate']",A Novel Therapeutic Approach in the Treatment of Pulmonary Arterial Hypertension: Allium ursinum Liophylisate Alleviates Symptoms Comparably to Sildenafil.,"['Q000737', None, None, None, None, None, 'Q000175', 'Q000378', None, None, 'Q000378', 'Q000737', 'Q000187', None, 'Q000494']","['chemistry', None, None, None, None, None, 'diagnosis', 'metabolism', None, None, 'metabolism', 'chemistry', 'drug effects', None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/28677661,2018,0.0,0.0,,, +28629219,"Glucosinolates are the most abundant secondary sulfur-containing plant metabolites in the plant family of Brassicaceae. These phytochemicals are well-known for their enzymatic degradation induced by the enzyme myrosinase, resulting in pungent odor impressions derived from their respective degradation products. However, up to now, only little attention has been paid to non-enzymatic thermal degradation and the release of additional aroma-active compounds. Thermal treatment is particularly important in the processing of Brassica vegetables, and thereby, glucosinolates as precursors can act as a natural source of odorants. Application of gas chromatography-olfactometry to the volatile fractions obtained after heat treatment of sinigrin (2-propenyl glucosinolate) in different matrices (phosphate buffer at a pH value of 5, 7, or 9, silicon oil, silica gel (7% water), sea sand, and glycerol) showed a high potential to generate aroma-active compounds, mainly revealing onion- and garlic-like odor impressions deriving from sulfur-containing odorants. A clear dependency of the formation of desired aroma-active compounds upon the respective matrix was found, indicating the need of detailed investigations to obtain knowledge for the best use of glucosinolates as a source of natural aroma compositions. For example, the distillate obtained from sinigrin heat-processed in buffer solution at pH 7 led to the identification of 17 odorants.",Journal of agricultural and food chemistry,"['D001937', 'D002849', 'D003296', 'D005421', 'D005961', 'D006358', 'D009812', 'D064367', 'D014675', 'D055549']","['Brassica', 'Chromatography, Gas', 'Cooking', 'Flavoring Agents', 'Glucosinolates', 'Hot Temperature', 'Odorants', 'Olfactometry', 'Vegetables', 'Volatile Organic Compounds']",Thermally Induced Generation of Desirable Aroma-Active Compounds from the Glucosinolate Sinigrin.,"['Q000737', None, None, 'Q000737', 'Q000737', None, 'Q000032', None, 'Q000737', 'Q000737']","['chemistry', None, None, 'chemistry', 'chemistry', None, 'analysis', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28629219,2018,0.0,0.0,,, +28612465,"Garlic (Allium sativum) is the subject of many studies due to its numerous beneficial properties. Although compounds of garlic have been studied by various analytical methods, their tissue distributions are still unclear. Mass spectrometry imaging (MSI) appears to be a very powerful tool for the identification of the localisation of compounds within a garlic clove.",Phytochemical analysis : PCA,"['D005737', 'D006046', 'D013058', 'D053768']","['Garlic', 'Gold', 'Mass Spectrometry', 'Metal Nanoparticles']",Mass Spectrometry Imaging of low Molecular Weight Compounds in Garlic (Allium sativum L.) with Gold Nanoparticle Enhanced Target.,"['Q000737', 'Q000737', 'Q000379', 'Q000737']","['chemistry', 'chemistry', 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28612465,2018,0.0,0.0,,, +28560773,"Black garlic is increasing its popularity in cuisine around the world; however, scant information exists on the composition of this processed product. In this study, polar compounds in fresh garlic and in samples taken at different times during the heat treatment process to obtain black garlic have been characterized by liquid chromatography coupled to tandem mass spectrometry in high resolution mode. Ninety-five compounds (mainly amino acids and metabolites, organosulfur compounds, and saccharides and derivatives) were tentatively identified in all the analysed samples and classified as a function of the family they belong to. Statistical analysis of the results allowed establishing that the major changes in garlic occur during the first days of treatment, and they mainly affect to the three representative families. The main pathways involved in the synthesis of the compounds affected by heat treatment, and their evolution during the process were studied.",Electrophoresis,"['D000596', 'D002241', 'D002853', 'D016002', 'D005285', 'D005737', 'D006358', 'D010936', 'D053719']","['Amino Acids', 'Carbohydrates', 'Chromatography, Liquid', 'Discriminant Analysis', 'Fermentation', 'Garlic', 'Hot Temperature', 'Plant Extracts', 'Tandem Mass Spectrometry']",Untargeted analysis to monitor metabolic changes of garlic along heat treatment by LC-QTOF MS/MS.,"['Q000032', 'Q000032', 'Q000379', None, None, 'Q000737', None, 'Q000032', 'Q000379']","['analysis', 'analysis', 'methods', None, None, 'chemistry', None, 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/28560773,2017,,,,,True +28481316,Plants of the ,"Molecules (Basel, Switzerland)","['D000890', 'D005419', 'D005737', 'D013058', 'D055432', 'D019697', 'D013457']","['Anti-Infective Agents', 'Flavonoids', 'Garlic', 'Mass Spectrometry', 'Metabolomics', 'Onions', 'Sulfur Compounds']",Phytochemical Profiles and Antimicrobial Activities of Allium cepa Red cv. and A. sativum Subjected to Different Drying Methods: A Comparative MS-Based Metabolomics.,"['Q000032', 'Q000032', 'Q000737', 'Q000379', 'Q000379', 'Q000737', 'Q000032']","['analysis', 'analysis', 'chemistry', 'methods', 'methods', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28481316,2018,0.0,0.0,,, +28475377,"We evaluated organosulphur compounds in Allium vegetables, including garlic, elephant garlic and onion, using high-performance liquid chromatography. Among organosulphur compounds, elephant garlic had considerable γ-glutamyl peptides, and garlic had the highest alliin content. Onion had low level of organosulphur compounds than did elephant garlic and garlic. In addition, antioxidant capacities were evaluated by oxygen radical absorbance capacity (ORAC) values and 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azinobis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) radical scavenging assay. The results showed that garlic had the highest antioxidant capacity, followed by elephant garlic and onion. Furthermore, a positive correlation was observed between antioxidant activities and organosulphur compounds (R > 0.77). Therefore, our results indicate that there was a close relationship between antioxidant capacity and organosulphur compounds in Allium vegetables.",Natural product research,"['D000490', 'D000975', 'D005737', 'D019697', 'D013045', 'D013457']","['Allium', 'Antioxidants', 'Garlic', 'Onions', 'Species Specificity', 'Sulfur Compounds']","Comparative studies of bioactive organosulphur compounds and antioxidant activities in garlic (Allium sativum L.), elephant garlic (Allium ampeloprasum L.) and onion (Allium cepa L.).","['Q000737', 'Q000032', 'Q000737', 'Q000737', None, 'Q000032']","['chemistry', 'analysis', 'chemistry', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/28475377,2018,,,,,True +28459863,"The present investigation was conducted to study the true retentions of α-tocopherol, tocotrienols and β-carotene in crown daisy, unripe hot pepper, onion, garlic, and red pepper as affected by various domestic cooking methods, those were, boiling, baking, stir-frying, deep-frying, steaming, roasting, and microwaving. Fatty acid compositions were determined by GC, and HPLC were used for quantification of α-tocopherol, tocotrienols, and β-carotene. True retentions of α-tocopherol in cooked foods were as follows: boiling (77.74-242.73%), baking (85.99-212.39%), stir-frying (83.12-957.08%), deep-frying (162.48-4214.53%), steaming (45.97-179.57%), roasting (49.65-253.69%), and microwaving (44.67-230.13%). Similarly for true retention of β-carotene were: boiling (65.69-313.75%), baking (71.46-330.16%), stir-frying (89.62-362.46%), deep-frying (178.22-529.16%), steaming (50.39-240.92%), roasting (73.54-361.47%), and microwaving (78.60-339.87%).",PloS one,"['D002849', 'D002851', 'D003296', 'D005227', 'D006358', 'D008872', 'D017365', 'D024508', 'D014867', 'D024502', 'D019207']","['Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Cooking', 'Fatty Acids', 'Hot Temperature', 'Microwaves', 'Spices', 'Tocotrienols', 'Water', 'alpha-Tocopherol', 'beta Carotene']",Effect of processing on composition changes of selected spices.,"[None, None, 'Q000379', 'Q000737', None, None, 'Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000737']","[None, None, 'methods', 'chemistry', None, None, 'analysis', 'chemistry', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/28459863,2017,1.0,4.0,,, +28407889,"In this work, we synthesized internal standards for four garlic organosulfur compounds (OSCs) by reductive amination with ",Food chemistry,"['D000586', 'D001894', 'D002247', 'D002851', 'D003545', 'D005557', 'D005737', 'D012015', 'D053719']","['Amination', 'Borohydrides', 'Carbon Isotopes', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Formaldehyde', 'Garlic', 'Reference Standards', 'Tandem Mass Spectrometry']",Reductive amination derivatization for the quantification of garlic components by isotope dilution analysis.,"[None, 'Q000737', None, None, 'Q000031', 'Q000737', 'Q000737', None, None]","[None, 'chemistry', None, None, 'analogs & derivatives', 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28407889,2017,0.0,0.0,,, +28373144,"In this work, the efficiency of crude MeOH extracts and soluble glycoprotein fraction of Allium sativum purified by size-exclusion chromatography (SEC) on parasitological, histopathological and some biochemical parameters in Schistosoma mansoni infected mice were investigated. Animals were infected by tail immersion with 100 cercariae/each mouse and divided into five groups in addition to the normal control. The results revealed a significant decrease in mean worm burden in all treated mice especially in the group treated with soluble glycoprotein fraction of A. sativum as compared to infected non-treated control with the disappearance of female worms. Administration of the studied extracts revealed remarkable amelioration in the levels of all the measured parameters in S. mansoni infected mice. In addition, treatment of mice with crude A. sativum MeOH extract and soluble glycoprotein fraction of A. sativum decreased significantly the activities of studied enzymes as compared to the infected untreated group. The highest degrees of enhancement in pathological changes was observed in the treated one with soluble glycoprotein fraction of A. sativum compared to the infected group represented by small sized, late fibro-cellular granuloma, the decrease in cellular constituents and degenerative changes in eggs. In conclusion, A. sativum treatment had effective schistosomicidal activities, through reduction of worm burden and tissue eggs, especially when it was given in purified glycoprotein fraction. Moreover, the soluble glycoprotein fraction of A. sativum largely modulates both the size and the number of granulomas.",Microbial pathogenesis,"['D000469', 'D000818', 'D002850', 'D004195', 'D005260', 'D005737', 'D006023', 'D006099', 'D008099', 'D008297', 'D051379', 'D010270', 'D010936', 'D012550', 'D012555', 'D012556', 'D044967', 'D000637', 'D005723']","['Alkaline Phosphatase', 'Animals', 'Chromatography, Gel', 'Disease Models, Animal', 'Female', 'Garlic', 'Glycoproteins', 'Granuloma', 'Liver', 'Male', 'Mice', 'Parasite Egg Count', 'Plant Extracts', 'Schistosoma mansoni', 'Schistosomiasis mansoni', 'Schistosomicides', 'Serum', 'Transaminases', 'gamma-Glutamyltransferase']",Efficacy of soluble glycoprotein fraction from Allium sativum purified by size exclusion chromatography on murine Schistosomiasis mansoni.,"['Q000097', None, 'Q000379', None, None, 'Q000737', 'Q000737', 'Q000469', 'Q000469', None, None, None, 'Q000494', 'Q000187', 'Q000097', 'Q000494', 'Q000737', 'Q000097', 'Q000097']","['blood', None, 'methods', None, None, 'chemistry', 'chemistry', 'parasitology', 'parasitology', None, None, None, 'pharmacology', 'drug effects', 'blood', 'pharmacology', 'chemistry', 'blood', 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/28373144,2017,0.0,0.0,,, +28249719,"Different ionic liquids (ILs) were assayed as mobile phase modifiers for the separation and determination of selenite [Se(IV)], selenate [Se(VI)], selenomethionine (SeMet) and Se-methylselenocysteine (SeMeSeCys) by reversed-phase high-performance liquid chromatography coupled to hydride generation atomic fluorescence spectrometry (RP-HPLC-HG-AFS). The use of several ILs: 1-butyl-3-methylimidazolium chloride, 1-hexyl-3-methylimidazolium chloride ([C",Journal of chromatography. A,"['D001628', 'D056148', 'D005504', 'D052578', 'D016566', 'D012643', 'D013050']","['Beverages', 'Chromatography, Reverse-Phase', 'Food Analysis', 'Ionic Liquids', 'Organoselenium Compounds', 'Selenium', 'Spectrometry, Fluorescence']",Ionic liquid-assisted separation and determination of selenium species in food and beverage samples by liquid chromatography coupled to hydride generation atomic fluorescence spectrometry.,"['Q000032', 'Q000379', 'Q000379', 'Q000737', 'Q000032', 'Q000032', 'Q000379']","['analysis', 'methods', 'methods', 'chemistry', 'analysis', 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/28249719,2017,1.0,3.0,,, +28231115,"We performed a statistical analysis of the concentration of mineral elements, by means of inductively coupled plasma mass spectrometry (ICP-MS), in different varieties of garlic from Spain, Tunisia, and Italy. Nubia Red Garlic (Sicily) is one of the most known Italian varieties that belongs to traditional Italian food products (P.A.T.) of the Ministry of Agriculture, Food, and Forestry. The obtained results suggest that the concentrations of the considered elements may serve as geographical indicators for the discrimination of the origin of the different samples. In particular, we found a relatively high content of Selenium in the garlic variety known as Nubia red garlic, and, indeed, it could be used as an anticarcinogenic agent.","Foods (Basel, Switzerland)",[],[],"Statistical Analysis of Mineral Concentration for the Geographic Identification of Garlic Samples from Sicily (Italy), Tunisia and Spain.",[],[],https://www.ncbi.nlm.nih.gov/pubmed/28231115,2018,1.0,3.0,,, +28183044,"Aged garlic extract (AGE) has been shown to improve hypertension in both clinical trials and experimental animal models. However, the active ingredient of AGE remains unknown. In the present study, we investigated the antihypertensive effects of AGE and its major constituents including S-1-propenylcysteine (S1PC) and S-allylcysteine (SAC) using spontaneously hypertensive rats (SHR) and found that S1PC is an active substance to lower blood pressure in SHR. In addition, the metabolomics approach was used to investigate the potential mechanism of the antihypertensive action of S1PC in SHR. Treatment with AGE (2g/kg body weight) or S1PC (6.5mg/kg body weight; equivalent to AGE 2g/kg body weight) significantly decreased the systolic blood pressure (SBP) of SHR after the repeated administration for 10 weeks, whereas treatment with SAC (7.9mg/kg body weight; equivalent to AGE 2g/kg body weight) did not decrease the SBP. After the treatment for 10 weeks, the plasma samples obtained from Wistar Kyoto (WKY) rats and SHR were analyzed by means of ultra high performance liquid chromatography coupled with high-resolution quadrupole-Orbitrap mass spectrometry. Multivariate statistical analysis of LC-MS data showed a clear difference in the metabolite profiles between WKY rats and SHR. The results indicated that 30 endogenous metabolites significantly contributed to the difference and 7 of 30 metabolites were changed by the S1PC treatment. Furthermore, regression analysis showed correlation between SBP and the plasma levels of betaine, tryptophan and 3 LysoPCs. This metabolomics approach suggested that S1PC could exert its antihypertensive effect by affecting glycine, serine and threonine metabolism, tryptophan metabolism and glycerophospholipid metabolism.","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000596', 'D000818', 'D000959', 'D002853', 'D003545', 'D005227', 'D020404', 'D016014', 'D008297', 'D013058', 'D055442', 'D055432', 'D051381', 'D011918', 'D015203']","['Amino Acids', 'Animals', 'Antihypertensive Agents', 'Chromatography, Liquid', 'Cysteine', 'Fatty Acids', 'Glycerophospholipids', 'Linear Models', 'Male', 'Mass Spectrometry', 'Metabolome', 'Metabolomics', 'Rats', 'Rats, Inbred SHR', 'Reproducibility of Results']",Metabolomic study on the antihypertensive effect of S-1-propenylcysteine in spontaneously hypertensive rats using liquid chromatography coupled with quadrupole-Orbitrap mass spectrometry.,"['Q000097', None, 'Q000494', 'Q000379', 'Q000031', 'Q000097', 'Q000097', None, None, 'Q000379', 'Q000187', None, None, None, None]","['blood', None, 'pharmacology', 'methods', 'analogs & derivatives', 'blood', 'blood', None, None, 'methods', 'drug effects', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/28183044,2017,0.0,0.0,,, +28177695,"Diallyl trisulfide (DATS), a major garlic derivative, inhibits cell proliferation and triggers apoptosis in a variety of cancer cell lines. However, the effects of DATS on hepatic stellate cells (HSCs) remain unknown. The aim of this study was to analyze the effects of DATS on cell proliferation and apoptosis, as well as the protein expression profile in rat HSCs. Rat HSCs were treated with or without 12 and 24 μg/mL DATS for various time intervals. Cell proliferation and apoptosis were determined using tetrazolium dye (MTT) colorimetric assay, bromodeoxyuridine (5-bromo-2'-deoxyuridine; BrdU) assay, Hoechst 33342 staining, electroscopy, and flow cytometry. Protein expression patterns in HSCs were systematically studied using 2-dimensional electrophoresis and mass spectrometry. DATS inhibited cell proliferation and induced apoptosis of HSCs in a time-dependent manner. We observed clear morphological changes in apoptotic HSCs and dramatically increased annexin V-positive - propidium iodide negative apoptosis compared with the untreated control group. Twenty-one significant differentially expressed proteins, including 9 downregulated proteins and 12 upregulated proteins, were identified after DATS administration, and most of them were involved in apoptosis. Our results suggest that DATS is an inducer of apoptosis in HSCs, and several key proteins may be involved in the molecular mechanism of apoptosis induced by DATS.",Canadian journal of physiology and pharmacology,"['D000498', 'D000818', 'D017209', 'D002453', 'D049109', 'D002470', 'D005737', 'D005786', 'D055166', 'D040901', 'D051381', 'D013440']","['Allyl Compounds', 'Animals', 'Apoptosis', 'Cell Cycle', 'Cell Proliferation', 'Cell Survival', 'Garlic', 'Gene Expression Regulation', 'Hepatic Stellate Cells', 'Proteomics', 'Rats', 'Sulfides']",Apoptosis of rat hepatic stellate cells induced by diallyl trisulfide and proteomics profiling in vitro.,"['Q000494', None, 'Q000187', 'Q000187', 'Q000187', 'Q000187', 'Q000737', 'Q000187', 'Q000166', None, None, 'Q000494']","['pharmacology', None, 'drug effects', 'drug effects', 'drug effects', 'drug effects', 'chemistry', 'drug effects', 'cytology', None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/28177695,2017,0.0,0.0,,, +28101575,"Diallyl disulfide (DADS) is a primary component of garlic, which has chemopreventive potential. We previously found that moderate doses (15-120 µM) of DADS induced apoptosis and G2/M phase cell cycle arrest. In this study, we observed the effect of low doses (8 µM) of DADS on human leukemia HL-60 cells. We found that DADS could inhibit proliferation, migration and invasion in HL-60 cells, and arrested cells at G0/G1 stage. Then, cell differentiation was displayed by morphologic observation, NBT reduction activity and CD11b evaluation of cytometric flow. It showed that DADS induced differentiation, reduced the ability of NBT and increased CD11b expression. Likewise, DADS inhibited xenograft tumor growth and induced differentiation in vivo. In order to make sure how DADS induced differentiation, we compared the protein expression profile of DADS-treated cells with that of untreated control. Using high resolution mass spectrometry, we identified 18 differentially expressed proteins after treatment with DADS, including four upregulated and 14 downregulated proteins. RT-PCR and western blot assay showed that DJ-1, cofilin 1, RhoGDP dissociation inhibitor 2 (RhoGDI2), Calreticulin (CTR) and PCNA were decreased by DADS. These data suggest that the effects of DADS on leukemia may be due to multiple targets for intervention.",International journal of oncology,"['D000498', 'D000818', 'D039481', 'D059447', 'D002454', 'D002465', 'D049109', 'D004220', 'D004305', 'D015972', 'D018922', 'D006801', 'D007938', 'D051379', 'D020543', 'D023041']","['Allyl Compounds', 'Animals', 'CD11b Antigen', 'Cell Cycle Checkpoints', 'Cell Differentiation', 'Cell Movement', 'Cell Proliferation', 'Disulfides', 'Dose-Response Relationship, Drug', 'Gene Expression Regulation, Neoplastic', 'HL-60 Cells', 'Humans', 'Leukemia', 'Mice', 'Proteome', 'Xenograft Model Antitumor Assays']",Identification of potential targets for differentiation in human leukemia cells induced by diallyl disulfide.,"['Q000008', None, 'Q000235', 'Q000187', 'Q000187', 'Q000187', 'Q000187', 'Q000008', None, 'Q000187', None, None, 'Q000188', None, 'Q000187', None]","['administration & dosage', None, 'genetics', 'drug effects', 'drug effects', 'drug effects', 'drug effects', 'administration & dosage', None, 'drug effects', None, None, 'drug therapy', None, 'drug effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/28101575,2017,0.0,0.0,,, +27979186,"The chemical composition of garlic essential oils (GEOs) extracted from two different cultivars has been characterized using GC-MS analysis. GEO that was extracted from the white-skin cultivar (WGO) had a lower percentage of the major constituents diallyl trisulfide and diallyl disulfide (45.76 and 15.63%) than purple-skin cultivar (PGO) which contained higher percentages (58.53 and 22.38%) of the same components, respectively. Evaluation of the antimicrobial activity of WGO and PGO delivered in organic solvent (isopropanol) showed dose-dependent antimicrobial activity against the tested pathogenic bacteria and fungi, especially with WGO. On the other hand, formulation of both GEOs in water-based emulsions totally suppressed the antimicrobial activity of GEO. Re-formulation of GEOs in water-based microemulsion (particle size 10.1nm) showed better antimicrobial activity than emulsions at the same concentration of GEOs. This study can assist in designing the proper water-based delivery system of GEO for application in food preservation.",Food chemistry,"['D000498', 'D000890', 'D001231', 'D001234', 'D004220', 'D004305', 'D016503', 'D004655', 'D004926', 'D005519', 'D005737', 'D008401', 'D008089', 'D009822', 'D010316', 'D010938', 'D012486', 'D012997', 'D013211', 'D013440', 'D014867']","['Allyl Compounds', 'Anti-Infective Agents', 'Aspergillus flavus', 'Aspergillus niger', 'Disulfides', 'Dose-Response Relationship, Drug', 'Drug Delivery Systems', 'Emulsions', 'Escherichia coli', 'Food Preservation', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Listeria monocytogenes', 'Oils, Volatile', 'Particle Size', 'Plant Oils', 'Salmonella typhimurium', 'Solvents', 'Staphylococcus aureus', 'Sulfides', 'Water']","Chemical composition and antimicrobial activity of garlic essential oils evaluated in organic solvent, emulsifying, and self-microemulsifying water based delivery systems.","['Q000032', 'Q000737', 'Q000187', 'Q000187', 'Q000032', None, None, None, 'Q000187', None, 'Q000737', None, 'Q000187', 'Q000737', None, 'Q000737', 'Q000187', None, 'Q000187', 'Q000032', None]","['analysis', 'chemistry', 'drug effects', 'drug effects', 'analysis', None, None, None, 'drug effects', None, 'chemistry', None, 'drug effects', 'chemistry', None, 'chemistry', 'drug effects', None, 'drug effects', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/27979186,2017,0.0,0.0,,, +27979174,"Garlic is rich in polysulfides, and some of them can be H",Food chemistry,"['D000498', 'D002851', 'D002853', 'D003296', 'D005737', 'D008401', 'D006862', 'D018517', 'D013440', 'D013441']","['Allyl Compounds', 'Chromatography, High Pressure Liquid', 'Chromatography, Liquid', 'Cooking', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Hydrogen Sulfide', 'Plant Roots', 'Sulfides', 'Sulfinic Acids']",Boiling enriches the linear polysulfides and the hydrogen sulfide-releasing activity of garlic.,"['Q000737', None, None, None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000032']","['chemistry', None, None, None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27979174,2017,0.0,0.0,,, +27916960,"The metabolism and excretion of flavor constituents of garlic, a common plant used in flavoring foods and attributed with several health benefits, in humans is not fully understood. Likewise, the physiologically active principles of garlic have not been fully clarified to date. It is possible that not only the parent compounds present in garlic but also its metabolites are responsible for the specific physiological properties of garlic, including its influence on the characteristic body odor signature of humans after garlic consumption. Accordingly, the aim of this study was to investigate potential garlic-derived metabolites in human urine. To this aim, 14 sets of urine samples were obtained from 12 volunteers, whereby each set comprised one sample that was collected prior to consumption of food-relevant concentrations of garlic, followed by five to eight subsequent samples after garlic consumption that covered a time interval of up to 26 h. The samples were analyzed chemo-analytically using gas chromatography-mass spectrometry/olfactometry (GC-MS/O), as well as sensorially by a trained human panel. The analyses revealed three different garlic-derived metabolites in urine, namely allyl methyl sulfide (AMS), allyl methyl sulfoxide (AMSO) and allyl methyl sulfone (AMSOâ‚‚), confirming our previous findings on human milk metabolite composition. The excretion rates of these metabolites into urine were strongly time-dependent with distinct inter-individual differences. These findings indicate that the volatile odorant fraction of garlic is heavily biotransformed in humans, opening up a window into substance circulation within the human body with potential wider ramifications in view of physiological effects of this aromatic plant that is appreciated by humans in their daily diet.",Metabolites,[],[],Detection of Volatile Metabolites Derived from Garlic (Allium sativum) in Human Urine.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/27916960,2018,0.0,0.0,,, +27916569,"Phytocystatins are cysteine proteinase inhibitors present in plants. They play crucial role in maintaining protease-anti protease balance and are involved in various endogenous processes. Thus, they are suitable and convenient targets for genetic engineering which makes their isolation and characterisation from different sources the need of the hour. In the present study a phytocystatin has been isolated from garlic (Allium sativum) by a simple two-step process using ammonium sulphate fractionation and gel filtration chromatography on Sephacryl S-100HR with a fold purification of 152.6 and yield 48.9%. A single band on native gel electrophoresis confirms the homogeneity of the purified inhibitor. The molecular weight of the purified inhibitor was found to be 12.5kDa as determined by SDS-PAGE and gel filtration chromatography. The garlic phytocystatin was found to be stable under broad range of pH (6-8) and temperature (30°C-60°C). Kinetic studies suggests that garlic phytocystatins are reversible and non-competitive inhibitors having highest affinity for papain followed by ficin and bromelain. UV and fluorescence spectroscopy revealed significant conformational change upon garlic phytocystatin-papain complex formation. Secondary structure analysis was performed using CD and FTIR. Garlic phytocystatin possesses 33.9% alpha-helical content as assessed by CD spectroscopy.",International journal of biological macromolecules,"['D000818', 'D002241', 'D002318', 'D015853', 'D005737', 'D006863', 'D007700', 'D008970', 'D055550', 'D017433', 'D013057', 'D013438', 'D013696']","['Animals', 'Carbohydrates', 'Cardiovascular Diseases', 'Cysteine Proteinase Inhibitors', 'Garlic', 'Hydrogen-Ion Concentration', 'Kinetics', 'Molecular Weight', 'Protein Stability', 'Protein Structure, Secondary', 'Spectrum Analysis', 'Sulfhydryl Compounds', 'Temperature']","Insight into the biochemical, kinetic and spectroscopic characterization of garlic (Allium sativum) phytocystatin: Implication for cardiovascular disease.","[None, 'Q000032', 'Q000188', 'Q000737', None, None, None, None, None, None, None, 'Q000032', None]","[None, 'analysis', 'drug therapy', 'chemistry', None, None, None, None, None, None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/27916569,2017,0.0,0.0,,, +27846232,This study was aimed to purify and characterize the Protease inhibitor (PI) from a plant Allium sativum (garlic) with strong medicinal properties and to explore its phytodrug potentials.,PloS one,"['D002851', 'D002942', 'D003902', 'D004591', 'D005737', 'D006863', 'D007700', 'D016877', 'D010455', 'D010940', 'D055550', 'D015843', 'D019032', 'D013696', 'D014361']","['Chromatography, High Pressure Liquid', 'Circular Dichroism', 'Detergents', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Hydrogen-Ion Concentration', 'Kinetics', 'Oxidants', 'Peptides', 'Plant Proteins', 'Protein Stability', 'Serpins', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Temperature', 'Trypsin Inhibitors']",Allium sativum Protease Inhibitor: A Novel Kunitz Trypsin Inhibitor from Garlic Is a New Comrade of the Serpin Family.,"[None, None, 'Q000494', None, 'Q000737', None, None, 'Q000494', 'Q000302', 'Q000302', 'Q000187', 'Q000302', None, None, 'Q000302']","[None, None, 'pharmacology', None, 'chemistry', None, None, 'pharmacology', 'isolation & purification', 'isolation & purification', 'drug effects', 'isolation & purification', None, None, 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/27846232,2017,0.0,0.0,,, +27722608,"The metabolism of selenomethionine (SeMet) in two major selenium (Se) accumulator plants, garlic and Indian mustard, was compared to that of stable isotope labeled selenate. Indian mustard more efficiently transported Se from roots to leaves than garlic. In addition, Indian mustard accumulated larger amounts of Se than garlic. γ-Glutamyl-Se-methylselenocysteine (γ-GluMeSeCys) and Se-methylselenocysteine (MeSeCys) were the common metabolites of selenate and SeMet in garlic and Indian mustard. Indian mustard had a specific metabolic pathway to selenohomolanthionine (SeHLan) from both inorganic and organic Se species. SeMet was a more effective fertilizer for cultivating Se-enriched plants than selenate in terms of the production of selenoamino acids.",Metallomics : integrated biometal science,"['D002851', 'D005737', 'D007287', 'D013058', 'D009149', 'D009930', 'D016566', 'D012643']","['Chromatography, High Pressure Liquid', 'Garlic', 'Inorganic Chemicals', 'Mass Spectrometry', 'Mustard Plant', 'Organic Chemicals', 'Organoselenium Compounds', 'Selenium']","Comparison of the metabolism of inorganic and organic selenium species between two selenium accumulator plants, garlic and Indian mustard.","[None, 'Q000254', 'Q000737', None, 'Q000254', 'Q000737', 'Q000378', 'Q000378']","[None, 'growth & development', 'chemistry', None, 'growth & development', 'chemistry', 'metabolism', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/27722608,2017,0.0,0.0,,, +27765204,"A highly sensitive vortex assisted liquid-liquid microextraction (VA-LLME) method was developed for inorganic Se [Se(IV) and Se(VI)] speciation analysis in Allium and Brassica vegetables. Trihexyl(tetradecyl)phosphonium decanoate phosphonium ionic liquid (IL) was applied for the extraction of Se(IV)-ammonium pyrrolidine dithiocarbamate (APDC) complex followed by Se determination with electrothermal atomic absorption spectrometry. A complete optimization of the graphite furnace temperature program was developed for accurate determination of Se in the IL-enriched extracts and multivariate statistical optimization was performed to define the conditions for the highest extraction efficiency. Significant factors of IL-VA-LLME method were sample volume, extraction pH, extraction time and APDC concentration. High extraction efficiency (90%), a 100-fold preconcentration factor and a detection limit of 5.0ng/L were achieved. The high sensitivity obtained with preconcentration and the non-chromatographic separation of inorganic Se species in complex matrix samples such as garlic, onion, leek, broccoli and cauliflower, are the main advantages of IL-VA-LLME.",Food chemistry,"['D001937', 'D005737', 'D052578', 'D057230', 'D059627', 'D011759', 'D012643', 'D018036', 'D013054', 'D013859']","['Brassica', 'Garlic', 'Ionic Liquids', 'Limit of Detection', 'Liquid Phase Microextraction', 'Pyrrolidines', 'Selenium', 'Selenium Compounds', 'Spectrophotometry, Atomic', 'Thiocarbamates']",Inorganic selenium speciation analysis in Allium and Brassica vegetables by ionic liquid assisted liquid-liquid microextraction with multivariate optimization.,"['Q000737', 'Q000737', 'Q000737', None, 'Q000379', 'Q000032', 'Q000032', 'Q000032', 'Q000379', 'Q000032']","['chemistry', 'chemistry', 'chemistry', None, 'methods', 'analysis', 'analysis', 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27765204,2016,0.0,0.0,,, +27725449,"Three major organosulfur compounds of aged garlic extract, S-allyl-L-cysteine (SAC), S-methyl-L-cysteine (SMC), and trans-S-1-propenyl-L-cysteine (S1PC), were examined for their effects on the activities of five major isoforms of human CYP enzymes: CYP1A2, 2C9, 2C19, 2D6, and 3A4. The metabolite formation from probe substrates for the CYP isoforms was examined in human liver microsomes in the presence of organosulfur compounds at 0.01-1 mM by using liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Allicin, a major component of garlic, inhibited CYP1A2 and CYP3A4 activity by 21-45% at 0.03 mM. In contrast, a CYP2C9-catalyzed reaction was enhanced by up to 1.9 times in the presence of allicin at 0.003-0.3 mM. SAC, SMC, and S1PC had no effect on the activities of the five isoforms, except that S1PC inhibited CYP3A4-catalyzed midazolam 1'-hydroxylation by 31% at 1 mM. The N-acetylated metabolites of the three compounds inhibited the activities of several isoforms to a varying degree at 1 mM. N-Acetyl-S-allyl-L-cysteine and N-acetyl-S-methyl-L-cysteine inhibited the reactions catalyzed by CYP2D6 and CYP1A2, by 19 and 26%, respectively, whereas trans-N-acetyl-S-1-propenyl-L-cysteine showed weak to moderate inhibition (19-49%) of CYP1A2, 2C19, 2D6, and 3A4 activities. On the other hand, both the N-acetylated and S-oxidized metabolites of SAC, SMC, and S1PC had little effect on the reactions catalyzed by the five isoforms. These results indicated that SAC, SMC, and S1PC have little potential to cause drug-drug interaction due to CYP inhibition or activation in vivo, as judged by their minimal effects (IC",Biological & pharmaceutical bulletin,"['D000107', 'D002853', 'D003545', 'D065607', 'D003577', 'D006801', 'D008862', 'D010084', 'D053719']","['Acetylation', 'Chromatography, Liquid', 'Cysteine', 'Cytochrome P-450 Enzyme Inhibitors', 'Cytochrome P-450 Enzyme System', 'Humans', 'Microsomes, Liver', 'Oxidation-Reduction', 'Tandem Mass Spectrometry']","Evaluation of the Effects of S-Allyl-L-cysteine, S-Methyl-L-cysteine, trans-S-1-Propenyl-L-cysteine, and Their N-Acetylated and S-Oxidized Metabolites on Human CYP Activities.","[None, None, 'Q000031', 'Q000494', 'Q000378', None, 'Q000378', None, None]","[None, None, 'analogs & derivatives', 'pharmacology', 'metabolism', None, 'metabolism', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/27725449,2017,0.0,0.0,,, +27664612,"Salting-out extraction (SOE) based on lower molecular organic solvent and inorganic salt was considered as a good substitute for conventional polymers aqueous two-phase extraction (ATPE) used for the extraction of some bioactive compounds from natural plants resources. In this study, the ethanol/ammonium sulfate was screened as the optimal SOE system for the extraction and preliminary purification of allicin from garlic. Response surface methodology (RSM) was developed to optimize the major conditions. The maximum extraction efficiency of 94.17% was obtained at the optimized conditions for routine use: 23% (w/w) ethanol concentration and 24% (w/w) salt concentration, 31g/L loaded sample at 25°C with pH being not adjusted. The extraction efficiency had no obvious decrease after amplification of the extraction. This ethanol/ammonium sulfate SOE is much simpler, cheaper, and effective, which has the potentiality of scale-up production for the extraction and purification of other compounds from plant resources. ",Food chemistry,"['D000645', 'D002851', 'D000431', 'D005737', 'D010936', 'D012965', 'D012997', 'D013441', 'D014867']","['Ammonium Sulfate', 'Chromatography, High Pressure Liquid', 'Ethanol', 'Garlic', 'Plant Extracts', 'Sodium Chloride', 'Solvents', 'Sulfinic Acids', 'Water']",Salting-out extraction of allicin from garlic (Allium sativum L.) based on ethanol/ammonium sulfate in laboratory and pilot scale.,"['Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000302', 'Q000737', 'Q000737', 'Q000302', 'Q000737']","['chemistry', 'methods', 'chemistry', 'chemistry', 'isolation & purification', 'chemistry', 'chemistry', 'isolation & purification', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27664612,2016,0.0,0.0,,, +27649517,"Garlic causes a strong garlic breath that may persist for almost a day. Therefore, it is important to study deodorization techniques for garlic breath. The volatiles responsible for garlic breath include diallyl disulfide, allyl mercaptan, allyl methyl disulfide, and allyl methyl sulfide. After eating garlic, water (control), raw, juiced or heated apple, raw or heated lettuce, raw or juiced mint leaves, or green tea were consumed immediately. The levels of the garlic volatiles on the breath were analyzed from 1 to 60 min by selected ion flow tube mass spectrometry (SIFT-MS). Garlic was also blended with water (control), polyphenol oxidase (PPO), rosemarinic acid, quercetin or catechin, and the volatiles in the headspace analyzed from 3 to 40 min by SIFT-MS. Raw apple, raw lettuce, and mint leaves significantly decreased all of the garlic breath volatiles in vivo. The proposed mechanism is enzymatic deodorization where volatiles react with phenolic compounds. Apple juice and mint juice also had a deodorizing effect on most of the garlic volatiles but were generally not as effective as the raw food, probably because the juice had enzymatic activity but the phenolic compounds had already polymerized. Both heated apple and heated lettuce produced a significant reduction of diallyl disulfide and allyl mercaptan. The presence of phenolic compounds that react with the volatile compounds even in the absence of enzymes is the most likely mechanism. Green tea had no deodorizing effect on the garlic volatile compounds. Rosmarinic acid, catechin, quercetin, and PPO significantly decreased all garlic breath volatiles in vitro. Rosmarinic acid was the most effective at deodorization.",Journal of food science,"['D000498', 'D001944', 'D002109', 'D002392', 'D004156', 'D005419', 'D005638', 'D005737', 'D006209', 'D019686', 'D018545', 'D008168', 'D027845', 'D009812', 'D010636', 'D018515', 'D059808', 'D011794', 'D013440', 'D013455', 'D013662', 'D055549']","['Allyl Compounds', 'Breath Tests', 'Caffeic Acids', 'Catechin', 'Catechol Oxidase', 'Flavonoids', 'Fruit', 'Garlic', 'Halitosis', 'Lamiaceae', 'Lettuce', 'Lung', 'Malus', 'Odorants', 'Phenols', 'Plant Leaves', 'Polyphenols', 'Quercetin', 'Sulfides', 'Sulfur', 'Tea', 'Volatile Organic Compounds']","Deodorization of Garlic Breath by Foods, and the Role of Polyphenol Oxidase and Phenolic Compounds.","['Q000032', None, 'Q000378', 'Q000378', 'Q000378', 'Q000378', 'Q000737', 'Q000737', 'Q000517', 'Q000737', 'Q000737', 'Q000378', 'Q000737', None, 'Q000378', 'Q000737', 'Q000378', 'Q000378', 'Q000032', 'Q000032', 'Q000737', 'Q000378']","['analysis', None, 'metabolism', 'metabolism', 'metabolism', 'metabolism', 'chemistry', 'chemistry', 'prevention & control', 'chemistry', 'chemistry', 'metabolism', 'chemistry', None, 'metabolism', 'chemistry', 'metabolism', 'metabolism', 'analysis', 'analysis', 'chemistry', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/27649517,2017,0.0,0.0,,, +27592824,Sulphites are a family of additives regulated for use worldwide in food products. They must be declared on the label if they are present in concentrations greater than 10 mg kg,"Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000490', 'D001937', 'D002853', 'D013447', 'D053719', 'D014675']","['Allium', 'Brassica', 'Chromatography, Liquid', 'Sulfites', 'Tandem Mass Spectrometry', 'Vegetables']",Comparison of multiple methods for the determination of sulphite in Allium and Brassica vegetables.,"['Q000737', 'Q000737', None, 'Q000032', None, 'Q000737']","['chemistry', 'chemistry', None, 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27592824,2017,0.0,0.0,,, +27542503,"Organosulphur compounds (OSCs) present in garlic (Allium sativum L.) are responsible of several biological properties. Functional foods researches indicate the importance of quantifying these compounds in food matrices and biological fluids. For this purpose, this paper introduces a novel methodology based on dispersive liquid-liquid microextraction (DLLME) coupled to high performance liquid chromatography with ultraviolet detector (HPLC-UV) for the extraction and determination of organosulphur compounds in different matrices. The target analytes were allicin, (E)- and (Z)-ajoene, 2-vinyl-4H-1,2-dithiin (2-VD), diallyl sulphide (DAS) and diallyl disulphide (DADS). The microextraction technique was optimized using an experimental design, and the analytical performance was evaluated under optimum conditions. The desirability function presented an optimal value for 600μL of chloroform as extraction solvent using acetonitrile as dispersant. The method proved to be reliable, precise and accurate. It was successfully applied to determine OSCs in cooked garlic samples as well as blood plasma and digestive fluids. ",Food chemistry,"['D002851', 'D005737', 'D059627']","['Chromatography, High Pressure Liquid', 'Garlic', 'Liquid Phase Microextraction']",Development of garlic bioactive compounds analytical methodology based on liquid phase microextraction using response surface design. Implications for dual analysis: Cooked and biological fluids samples.,"['Q000379', 'Q000737', 'Q000379']","['methods', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/27542503,2016,0.0,0.0,,spiked smaples, +27527697,"In this work, binding of garlic component-Diallysulfide (DAS) with major human blood transport protein, Human Serum Albumin (HSA) and its anti- amyloidogenic behavior has been studied by utilizing various spectroscopic and molecular docking strategies. The HSA exhibit significant reduction in fluorescence intensity upon interaction with DAS. DAS quenches the fluorescence of HSA in concentration dependent manner with binding affinity of 1.14×10",International journal of biological macromolecules,"['D000498', 'D000682', 'D001665', 'D005456', 'D006801', 'D007700', 'D062105', 'D011485', 'D017433', 'D012709', 'D013050', 'D013440', 'D013816', 'D013844']","['Allyl Compounds', 'Amyloid', 'Binding Sites', 'Fluorescent Dyes', 'Humans', 'Kinetics', 'Molecular Docking Simulation', 'Protein Binding', 'Protein Structure, Secondary', 'Serum Albumin', 'Spectrometry, Fluorescence', 'Sulfides', 'Thermodynamics', 'Thiazoles']",Anti-amyloidogenic behavior and interaction of Diallylsulfide with Human Serum Albumin.,"['Q000737', 'Q000037', None, 'Q000737', None, None, None, None, None, 'Q000037', None, 'Q000737', None, 'Q000737']","['chemistry', 'antagonists & inhibitors', None, 'chemistry', None, None, None, None, None, 'antagonists & inhibitors', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/27527697,2017,0.0,0.0,,, +27453278,"It is well known that Allium sativum has potential applications to clinical treatment of various cancers due to its remarkable ability in eliminating free radicals and increasing metabolism. An allyl-substituted cysteine derivative - S-allyl-L-cysteine (SAC) was separated and identified from Allium sativum. The extracted SAC was reacted with 1-pyrenemethanol to obtain pyrene-labelled SAC (Py-SAC) to give SAC fluorescence properties. Molecular detection of Py-SAC was conducted by steady-state fluorescence spectroscopy and time-resolved fluorescence method to quantitatively measure concentrations of Py-SAC solutions. The ability of removing 1,1-diphenyl-2-picrylhydrazyl (DPPH) and hydroxyl radical using Py-SAC was determined through oxygen radical absorbance capacity (ORAC). Results showed the activity of Py-SAC and Vitamin C (VC) with ORAC as index, the concentrations of Py-SAC and VC were 58.43 mg/L and 5.72 mg/L respectively to scavenge DPPH, and 8.16 mg/L and 1.67 mg/L to scavenge •OH respectively. Compared with VC, the clearance rates of Py-SAC to scavenge DPPH were much higher, Py-SAC could inhibit hydroxyl radical. The ability of removing radical showed a dose-dependent relationship within the scope of the drug concentration. ","Cellular and molecular biology (Noisy-le-Grand, France)","['D000975', 'D001205', 'D001713', 'D003545', 'D016166', 'D005737', 'D017665', 'D010851', 'D011721', 'D013050']","['Antioxidants', 'Ascorbic Acid', 'Biphenyl Compounds', 'Cysteine', 'Free Radical Scavengers', 'Garlic', 'Hydroxyl Radical', 'Picrates', 'Pyrenes', 'Spectrometry, Fluorescence']",Molecular detection and in vitro antioxidant activity of S-allyl-L-cysteine (SAC) extracted from Allium sativum.,"['Q000494', 'Q000494', 'Q000737', 'Q000031', 'Q000494', 'Q000737', 'Q000737', 'Q000737', 'Q000737', None]","['pharmacology', 'pharmacology', 'chemistry', 'analogs & derivatives', 'pharmacology', 'chemistry', 'chemistry', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/27453278,2017,,,,,True +27313155,"Many secondary metabolites in plants are labile compounds which under environmental stress, are difficult to detect and track due to the lack of rapid in situ identification techniques, making plant metabolomics research difficult. Therefore, developing a reliable analytical method for rapid in situ identification of labile compounds and their short-lived intermediates in plants is of great importance.",Phytochemical analysis : PCA,"['D060166', 'D002726', 'D003545', 'D005737', 'D005961', 'D027845', 'D055432', 'D015394', 'D018517', 'D011791', 'D031224', 'D013312', 'D053719', 'D013997']","['Capillary Tubing', 'Chlorogenic Acid', 'Cysteine', 'Garlic', 'Glucosinolates', 'Malus', 'Metabolomics', 'Molecular Structure', 'Plant Roots', 'Quartz', 'Raphanus', 'Stress, Physiological', 'Tandem Mass Spectrometry', 'Time Factors']",In situ Identification of Labile Precursor Compounds and their Short-lived Intermediates in Plants using in vivo Nanospray High-resolution Mass Spectrometry.,"[None, 'Q000737', 'Q000031', 'Q000737', 'Q000737', 'Q000737', None, None, 'Q000737', None, 'Q000737', None, None, None]","[None, 'chemistry', 'analogs & derivatives', 'chemistry', 'chemistry', 'chemistry', None, None, 'chemistry', None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/27313155,2017,0.0,0.0,,, +27300762,"Black garlic is produced through thermal processing and is used as a healthy food throughout the world. Compared with fresh garlic, there are obvious changes in the color, taste, and biological functions of black garlic. To analyze and explain these changes, the contents of water-soluble sugars, fructan, and the key intermediate compounds (Heyns and Amadori) of the Maillard reaction in fresh raw garlic and black garlic were investigated, which were important to control and to evaluate the quality of black garlic. The results showed that the fructan contents in the black garlics were decreased by more than 84.6% compared with the fresh raw garlics, which translated into changes in the fructose and glucose contents. The water-soluble sugar content was drastically increased by values ranging from 187.79% to 790.96%. Therefore, the taste of the black garlic became very sweet. The sucrose content in black garlic was almost equivalent to fresh garlic. The Amadori and Heyns compounds were analyzed by HPLC-MS/MS in multiple reaction monitoring mode using the different characteristic fragment ions of Heyns and Amadori compounds. The total content of the 3 main Amadori and 3 Heyns compounds in black garlic ranged from 762.53 to 280.56 μg/g, which was 40 to 100-fold higher than the values in fresh raw garlic. This result was significant proof that the Maillard reaction in black garlic mainly utilized fructose and glucose, with some amino acids. ",Journal of food science,"['D000596', 'D002241', 'D005511', 'D005630', 'D005632', 'D005737', 'D005947', 'D006358', 'D006801', 'D015416', 'D013395', 'D053719', 'D013649']","['Amino Acids', 'Carbohydrates', 'Food Handling', 'Fructans', 'Fructose', 'Garlic', 'Glucose', 'Hot Temperature', 'Humans', 'Maillard Reaction', 'Sucrose', 'Tandem Mass Spectrometry', 'Taste']","The Comparison of the Contents of Sugar, Amadori, and Heyns Compounds in Fresh and Black Garlic.","['Q000032', None, None, 'Q000032', 'Q000032', 'Q000737', 'Q000032', None, None, None, 'Q000032', None, None]","['analysis', None, None, 'analysis', 'analysis', 'chemistry', 'analysis', None, None, None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/27300762,2017,1.0,2.0,,, +27296605,"Foodborne viruses, particularly human norovirus, are a concern for public health, especially in fresh vegetables and other minimally processed foods that may not undergo sufficient decontamination. It is necessary to explore novel nonthermal techniques for preventing foodborne viral contamination. In this study, aqueous extracts of six raw food materials (flower buds of clove, fenugreek seeds, garlic and onion bulbs, ginger rhizomes, and jalapeño peppers) were tested for antiviral activity against feline calicivirus (FCV) as a surrogate for human norovirus. The antiviral assay was performed using dilutions of the extracts below the maximum nontoxic concentrations of the extracts to the host cells of FCV, Crandell-Reese feline kidney (CRFK) cells. No antiviral effect was seen when the host cells were pretreated with any of the extracts. However, pretreatment of FCV with nondiluted clove and ginger extracts inactivated 6.0 and 2.7 log of the initial titer of the virus, respectively. Also, significant dosedependent inactivation of FCV was seen when host cells were treated with clove and ginger extracts at the time of infection or postinfection at concentrations equal to or lower than the maximum nontoxic concentrations. By comprehensive two-dimensional gas chromatography-mass spectrometry analysis, eugenol (29.5%) and R-(-)-1,2-propanediol (10.7%) were identified as the major components of clove and ginger extracts, respectively. The antiviral effect of the pure eugenol itself was tested; it showed antiviral activity similar to that of clove extract, albeit at a lower level, which indicates that some other clove extract constituents, along with eugenol, are responsible for inactivation of FCV. These results showed that the aqueous extracts of clove and ginger hold promise for prevention of foodborne viral contamination.",Journal of food protection,"['D000818', 'D000998', 'D017927', 'D002415', 'D002460', 'D020939', 'D006801', 'D029322', 'D027842']","['Animals', 'Antiviral Agents', 'Calicivirus, Feline', 'Cats', 'Cell Line', 'Ginger', 'Humans', 'Norovirus', 'Syzygium']","In Vitro Antiviral Activity of Clove and Ginger Aqueous Extracts against Feline Calicivirus, a Surrogate for Human Norovirus.","[None, 'Q000494', 'Q000187', None, None, None, None, 'Q000187', None]","[None, 'pharmacology', 'drug effects', None, None, None, None, 'drug effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/27296605,2017,,,,, +27283666,"In this study, we used liquid chromatography coupled to ion trap mass spectrometry (MS) for the quantification of 11 organosulfur compounds and analysis of their compositional changes in garlic during fermentation using 3 different microbe strains. The calibration curves of all 11 analytes exhibited good linearity (R⩾0.995), and the mean recoveries measured at three concentrations were greater than 81.63% with relative standard deviations of less than 12.79%. Investigation of the compositional changes revealed that the γ-glutamyl peptides content in fermented blanched garlic reduced, whereas the content of the compounds in biosynthesis of S-allyl-l-cysteines from γ-glutamyl peptides increased significantly. Our results also indicated that starter cultures can be used selectively in the production of fermented garlic to increase the amounts of the desired organosulfur compounds. ",Food chemistry,"['D002853', 'D005285', 'D005737', 'D017365', 'D013457', 'D053719']","['Chromatography, Liquid', 'Fermentation', 'Garlic', 'Spices', 'Sulfur Compounds', 'Tandem Mass Spectrometry']",UPLC/ESI-MS/MS analysis of compositional changes for organosulfur compounds in garlic (Allium sativum L.) during fermentation.,"[None, None, 'Q000737', 'Q000032', 'Q000032', None]","[None, None, 'chemistry', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/27283666,2017,0.0,0.0,,, +27275838,"The odor of human breast milk after ingestion of raw garlic at food-relevant concentrations by breastfeeding mothers was investigated for the first time chemo-analytically using gas chromatography-mass spectrometry/olfactometry (GC-MS/O), as well as sensorially using a trained human sensory panel. Sensory evaluation revealed a clear garlic/cabbage-like odor that appeared in breast milk about 2.5 h after consumption of garlic. GC-MS/O analyses confirmed the occurrence of garlic-derived metabolites in breast milk, namely allyl methyl sulfide (AMS), allyl methyl sulfoxide (AMSO) and allyl methyl sulfone (AMSO₂). Of these, only AMS had a garlic-like odor whereas the other two metabolites were odorless. This demonstrates that the odor change in human milk is not related to a direct transfer of garlic odorants, as is currently believed, but rather derives from a single metabolite. The formation of these metabolites is not fully understood, but AMSO and AMSO₂ are most likely formed by the oxidation of AMS in the human body. The excretion rates of these metabolites into breast milk were strongly time-dependent with large inter-individual differences. ",Metabolites,[],[],Detection of Volatile Metabolites of Garlic in Human Breast Milk.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/27275838,2016,0.0,0.0,,, +27259073,"Extracts of the bulbs of the two endemic varieties ""Rosato"" and ""Caposele"" of Allium sativum of the Campania region, Southern Italy, were analyzed. The phenolic content, ascorbic acid, allicin content, and in vitro antimicrobial and antifungal activity were determined. Ultra performance liquid chromatography with diode array detector performed polyphenol profile. The polyphenolic extracts showed antioxidant activity (EC50) lower than 120 mg. The amount of ascorbic acid and allicin in the two extracts was similar. Polyphenol extract exhibited antimicrobial activity against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and (only by the extract of Rosato) against Bacillus cereus. The extract of Caposele was more effective in inhibiting the growth of Aspergillus versicolor and Penicillum citrinum. On the other hand, the extract of Rosato was effective against Penicillium expansum. ",Journal of medicinal food,"['D000890', 'D000935', 'D001205', 'D001230', 'D004926', 'D005737', 'D007558', 'D010407', 'D010636', 'D010936', 'D018517', 'D059808', 'D011550', 'D013045', 'D013211', 'D013441']","['Anti-Infective Agents', 'Antifungal Agents', 'Ascorbic Acid', 'Aspergillus', 'Escherichia coli', 'Garlic', 'Italy', 'Penicillium', 'Phenols', 'Plant Extracts', 'Plant Roots', 'Polyphenols', 'Pseudomonas aeruginosa', 'Species Specificity', 'Staphylococcus aureus', 'Sulfinic Acids']","Biochemical Characterization and Antimicrobial and Antifungal Activity of Two Endemic Varieties of Garlic (Allium sativum L.) of the Campania Region, Southern Italy.","['Q000494', 'Q000494', 'Q000032', 'Q000187', 'Q000187', 'Q000737', None, 'Q000187', 'Q000032', 'Q000737', 'Q000737', 'Q000008', 'Q000187', None, 'Q000187', 'Q000032']","['pharmacology', 'pharmacology', 'analysis', 'drug effects', 'drug effects', 'chemistry', None, 'drug effects', 'analysis', 'chemistry', 'chemistry', 'administration & dosage', 'drug effects', None, 'drug effects', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27259073,2017,,,,,True +27008862,"Allicin (diallyl thiosulfinate) from garlic is a highly potent natural antimicrobial substance. It inhibits growth of a variety of microorganisms, among them antibiotic-resistant strains. However, the precise mode of action of allicin is unknown. Here, we show that growth inhibition of Escherichia coli during allicin exposure coincides with a depletion of the glutathione pool and S-allylmercapto modification of proteins, resulting in overall decreased total sulfhydryl levels. This is accompanied by the induction of the oxidative and heat stress response. We identified and quantified the allicin-induced modification S-allylmercaptocysteine for a set of cytoplasmic proteins by using a combination of label-free mass spectrometry and differential isotope-coded affinity tag labeling of reduced and oxidized thiol residues. Activity of isocitrate lyase AceA, an S-allylmercapto-modified candidate protein, is largely inhibited by allicin treatment in vivo Allicin-induced protein modifications trigger protein aggregation, which largely stabilizes RpoH and thereby induces the heat stress response. At sublethal concentrations, the heat stress response is crucial to overcome allicin stress. Our results indicate that the mode of action of allicin is a combination of a decrease of glutathione levels, unfolding stress, and inactivation of crucial metabolic enzymes through S-allylmercapto modification of cysteines.",The Journal of biological chemistry,"['D003545', 'D004926', 'D029968', 'D005737', 'D005978', 'D010936', 'D011499', 'D013438', 'D013441']","['Cysteine', 'Escherichia coli', 'Escherichia coli Proteins', 'Garlic', 'Glutathione', 'Plant Extracts', 'Protein Processing, Post-Translational', 'Sulfhydryl Compounds', 'Sulfinic Acids']",Allicin Induces Thiol Stress in Bacteria through S-Allylmercapto Modification of Protein Cysteines.,"['Q000378', 'Q000187', 'Q000378', 'Q000737', 'Q000378', 'Q000494', 'Q000187', 'Q000378', 'Q000494']","['metabolism', 'drug effects', 'metabolism', 'chemistry', 'metabolism', 'pharmacology', 'drug effects', 'metabolism', 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/27008862,2016,0.0,0.0,,, +27002613,"Garlic is one of the most used seasonings in the world whose beneficial health effects, mainly ascribed to organosulfur compounds, are shared with the rest of the Allium family. The fact that many of these compounds are volatile makes the evaluation of the volatile profile of garlic interesting. For this purpose, three garlic varieties-White, Purple, and Chinese-cultivated in the South of Spain were analyzed by a method based on a headspace (HS) device coupled to a gas chromatograph and mass detector (HS-GC/MS). The main temperatures in the HS were optimized to achieve the highest concentration of volatiles. A total number of 45 volatiles were tentatively identified (among them 17 were identified for the first time in garlic); then, all were classified, also for the first time, and their relative concentration in three garlic varieties was used to evaluate differences among them and to study their profiles according to the heating time. Chinese garlic was found to be the richest variety in sulfur volatiles, while the three varieties presented a similar trend under preset heating times allowing differentiation between varieties and heating time using principal component analysis. Graphical Abstract HS-GC/MS analysis of the volatile profile of garlic.",Analytical and bioanalytical chemistry,"['D005737', 'D008401', 'D006358', 'D055549']","['Garlic', 'Gas Chromatography-Mass Spectrometry', 'Hot Temperature', 'Volatile Organic Compounds']",HS-GC/MS volatile profile of different varieties of garlic and their behavior under heating.,"['Q000737', 'Q000379', None, 'Q000032']","['chemistry', 'methods', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/27002613,2017,0.0,0.0,,, +26964532,"The quality of chicken meat, which is one of the most widely consumed meats in the world, has been the subject of research and studies for many years. There are several ways to improve the quality of this type of meat, including changing the concentrations of individual molecular components. Such important components of meat are inter alia, cholesterol, vitamin E, and some fatty acids such as ω-3 and ω-6. Manipulation of ingredient levels may be achieved by enriching chicken feed with elements of different types such as vegetable oils, garlic, or selenium. Thus far, various biochemical and biophysical methods have been used to study quality of different meat types, especially broiler meat. Here, the authors demonstrate the use of high-resolution time-of-flight secondary ion mass spectrometry (TOF-SIMS) mass spectrometry to assess how variations in animal nutrition affect concentrations of specific lipids in the meat, such as cholesterol and vitamin E. In the presented experiment, there were four different dietary treatments. Feed for animals in the first group was supplemented with soy oil in 50%, the second group's feed was supplemented with linseed oil in 50%, a combination of these two oils in the proportion of 44%:56% was used for the third group, and in the reference group, animals were fed with beef tallow. From each group, four individuals were selected for further analysis. Positive and negative ion mass spectra were generated from the pectoralis superficialis muscle tissue of the left carcass side of each one animal. Using TOF-SIMS with a bismuth cluster ion source (Bi3 (+)), and based on characteristic peaks for cholesterol in the positive mode and vitamin E in the negative mode, the authors have illustrated the relationship of these lipids levels to the various feeding regimens. Simultaneously, the authors characterized the varying dependences on the concentrations of measured lipids in fat and muscle fibers. The cholesterol concentration in muscle fibers was the lowest in the group fed with soybean oil and the highest in reference group IV (tallow feed). In the fatty region, the highest level of cholesterol was found in the third group. The highest concentrations of vitamin E were found in the fibers of the first group and the fat region of the second group. The obtained results show that SIMS imaging is a useful approach for assessing changes in lipid concentrations in the meat tissue from animals on different diets and provides a foundation for future research. ",Biointerphases,"['D000821', 'D000818', 'D002645', 'D002784', 'D004032', 'D005504', 'D008460', 'D018629', 'D014810']","['Animal Feed', 'Animals', 'Chickens', 'Cholesterol', 'Diet', 'Food Analysis', 'Meat', 'Spectrometry, Mass, Secondary Ion', 'Vitamin E']",Study of cholesterol and vitamin E levels in broiler meat from different feeding regimens by TOF-SIMS.,"[None, None, None, 'Q000032', 'Q000379', None, None, None, 'Q000032']","[None, None, None, 'analysis', 'methods', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26964532,2016,,,,,True +26948845,"The antifungal activity, kinetics, and molecular mechanism of action of garlic oil against Candida albicans were investigated in this study using multiple methods. Using the poisoned food technique, we determined that the minimum inhibitory concentration of garlic oil was 0.35 μg/mL. Observation by transmission electron microscopy indicated that garlic oil could penetrate the cellular membrane of C. albicans as well as the membranes of organelles such as the mitochondria, resulting in organelle destruction and ultimately cell death. RNA sequencing analysis showed that garlic oil induced differential expression of critical genes including those involved in oxidation-reduction processes, pathogenesis, and cellular response to drugs and starvation. Moreover, the differentially expressed genes were mainly clustered in 19 KEGG pathways, representing vital cellular processes such as oxidative phosphorylation, the spliceosome, the cell cycle, and protein processing in the endoplasmic reticulum. In addition, four upregulated proteins selected after two-dimensional fluorescence difference in gel electrophoresis (2D-DIGE) analysis were identified with high probability by mass spectrometry as putative cytoplasmic adenylate kinase, pyruvate decarboxylase, hexokinase, and heat shock proteins. This is suggestive of a C. albicans stress responses to garlic oil treatment. On the other hand, a large number of proteins were downregulated, leading to significant disruption of the normal metabolism and physical functions of C. albicans.",Scientific reports,"['D000498', 'D000935', 'D002176', 'D016923', 'D015966', 'D005800', 'D008826', 'D012331', 'D017423', 'D013440']","['Allyl Compounds', 'Antifungal Agents', 'Candida albicans', 'Cell Death', 'Gene Expression Regulation, Fungal', 'Genes, Fungal', 'Microbial Sensitivity Tests', 'RNA, Fungal', 'Sequence Analysis, RNA', 'Sulfides']","Antifungal activity, kinetics and molecular mechanism of action of garlic oil against Candida albicans.","['Q000493', 'Q000493', 'Q000187', None, 'Q000187', 'Q000187', None, 'Q000187', None, 'Q000493']","['pharmacokinetics', 'pharmacokinetics', 'drug effects', None, 'drug effects', 'drug effects', None, 'drug effects', None, 'pharmacokinetics']",https://www.ncbi.nlm.nih.gov/pubmed/26948845,2017,0.0,0.0,,, +26921177,"Lipid oxidation causes changes in quality attributes of vegetable oils. Synthetic antioxidants have been used to preserve oils; however, there is interest in replacing them with natural ones. Garlic and its thiosulfinate compound allicin are known for their antioxidant activities. This study assesses a novel formulation, the supercritical fluid extract of garlic, on sunflower oil oxidation during an accelerated shelf-life test.",Journal of the science of food and agriculture,"['D000975', 'D025924', 'D005503', 'D005737', 'D010084', 'D010936', 'D000074242']","['Antioxidants', 'Chromatography, Supercritical Fluid', 'Food Additives', 'Garlic', 'Oxidation-Reduction', 'Plant Extracts', 'Sunflower Oil']",Antioxidant effects of supercritical fluid garlic extracts in sunflower oil.,"['Q000737', None, 'Q000737', 'Q000737', None, 'Q000737', 'Q000737']","['chemistry', None, 'chemistry', 'chemistry', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/26921177,2017,0.0,0.0,,, +26786635,"A soluble glycoprotein was purified to homogeneity from ripe garlic (Allium sativum) bulbs using ammonium sulfate precipitation, Sephadex G-100 gel filtration, and diethylaminoethyl-52 cellulose anion-exchange chromatography. A native mass of 55.7 kDa estimated on gel permeation chromatography and a molecular weight of 13.2 kDa observed on sodium dodecyl sulfate-polyacrylamide gel electrophoresis supported that the glycoprotein is a homotetramer. β-Elimination reaction result suggested that the glycoprotein is an N-linked type. Fourier-transform infrared spectroscopy proved that it contains sugar. Gas chromatography-mass spectrometer analysis showed that its sugar component was galactose. The glycoprotein has 1,1-diphenyl-2-picrylhydrazil free radical scavenging activity and the peroxidation inhibition ability to polyunsaturated fatty acid. These results indicated that the glycoprotein has potential for food additives, functional foods, and even biotechnological and medical applications. ",Preparative biochemistry & biotechnology,"['D004591', 'D005737', 'D008401', 'D006023', 'D066298', 'D012995', 'D017550']","['Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Glycoproteins', 'In Vitro Techniques', 'Solubility', 'Spectroscopy, Fourier Transform Infrared']",Purification and characterization of a soluble glycoprotein from garlic (Allium sativum) and its in vitro bioactivity.,"[None, 'Q000737', None, 'Q000737', None, None, None]","[None, 'chemistry', None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/26786635,2017,,,,,True +26776023,"The thiosulfinate allicin is a labile, bioactive compound of garlic. In order to enrich allicin in a functional food, a delivery system which stabilises the compound and masks its intense flavour is necessary. In the present study allicin was covalently bound to the whey protein β-lactoglobulin and the incorporation of this transporter in a food matrix was tested. The sensory properties of the pure functional ingredient as well as of an enriched beverage were characterised by quantitative descriptive analysis. The concentration of volatile compounds was analysed by headspace gas chromatography-mass spectrometry. The garlic-related organoleptic properties of garlic powder were significantly improved by the binding of allicin in combination with spray drying. After purification of the modified β-lactoglobulin the garlic taste and smell were barely perceptible. β-Lactoglobulin modified with allicin provided a stable functional ingredient that can be used to enrich a broad range of food products. ",Food chemistry,"['D005421', 'D005737', 'D008401', 'D007782', 'D010936', 'D013441']","['Flavoring Agents', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Lactoglobulins', 'Plant Extracts', 'Sulfinic Acids']",β-Lactoglobulin as nanotransporter for allicin: Sensory properties and applicability in food.,"['Q000032', 'Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000737']","['analysis', 'chemistry', 'methods', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/26776023,2016,0.0,0.0,,, +26764333,The chemical assignment of metabolites is crucial to understanding the relation between food composition and biological activity.,The Journal of nutrition,"['D002853', 'D017357', 'D003545', 'D005737', 'D005978', 'D007477', 'D007536', 'D055442', 'D055432', 'D019697', 'D017550', 'D013455', 'D013460']","['Chromatography, Liquid', 'Cyclotrons', 'Cysteine', 'Garlic', 'Glutathione', 'Ions', 'Isomerism', 'Metabolome', 'Metabolomics', 'Onions', 'Spectroscopy, Fourier Transform Infrared', 'Sulfur', 'Sulfur Isotopes']",Chemical Assignment of Structural Isomers of Sulfur-Containing Metabolites in Garlic by Liquid Chromatography-Fourier Transform Ion Cyclotron Resonance-Mass Spectrometry.,"['Q000379', None, 'Q000032', 'Q000737', 'Q000032', 'Q000032', None, None, None, 'Q000737', 'Q000379', 'Q000032', 'Q000032']","['methods', None, 'analysis', 'chemistry', 'analysis', 'analysis', None, None, None, 'chemistry', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26764333,2016,0.0,0.0,,, +26764330,Garlic and its processed preparations contain numerous sulfur compounds that are difficult to analyze in a single run using HPLC.,The Journal of nutrition,"['D000498', 'D002851', 'D003545', 'D004220', 'D005737', 'D006801', 'D007536', 'D013058', 'D010936', 'D013455', 'D013457']","['Allyl Compounds', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Disulfides', 'Garlic', 'Humans', 'Isomerism', 'Mass Spectrometry', 'Plant Extracts', 'Sulfur', 'Sulfur Compounds']",Development of an Analytic Method for Sulfur Compounds in Aged Garlic Extract with the Use of a Postcolumn High Performance Liquid Chromatography Method with Sulfur-Specific Detection.,"['Q000032', 'Q000379', 'Q000031', 'Q000032', 'Q000737', None, None, 'Q000379', 'Q000737', 'Q000032', 'Q000032']","['analysis', 'methods', 'analogs & derivatives', 'analysis', 'chemistry', None, None, 'methods', 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26764330,2016,0.0,0.0,,, +26617005,"Garlic (Allium sativum) is a long-cultivated plant that is widely utilized in cooking and has been employed as a medicine for over 4000 years. In this study, we fabricated standards and internal standards (ISs) for absolute quantification via reductive amination with isotopic formaldehydes. Garlic has four abundant organosulfur compounds (OSCs): S-allylcysteine, S-allylcysteinine sulfoxide, S-methylcysteine, and S-ethylcysteine are abundant in garlic. OSCs with primary amine groups were reacted with isotopic formaldehydes to synthesize ISs and standards. Cooked and uncooked garlic samples were compared, and we utilized tandem mass spectrometry equipped with a selective reaction monitoring technique to absolutely quantify the four organosulfur compounds.",Food chemistry,"['D000586', 'D003545', 'D005557', 'D005737', 'D010936', 'D012015', 'D013454', 'D053719']","['Amination', 'Cysteine', 'Formaldehyde', 'Garlic', 'Plant Extracts', 'Reference Standards', 'Sulfoxides', 'Tandem Mass Spectrometry']",A novel reductive amination method with isotopic formaldehydes for the preparation of internal standard and standards for determining organosulfur compounds in garlic.,"[None, 'Q000031', 'Q000737', 'Q000737', 'Q000032', None, 'Q000032', None]","[None, 'analogs & derivatives', 'chemistry', 'chemistry', 'analysis', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/26617005,2016,1.0,1.0,,, +26502719,"Mutant Allium sativum leaf agglutinin (mASAL) is a potent, biosafe, antifungal protein that exhibits fungicidal activity against different phytopathogenic fungi, including Rhizoctonia solani.",BMC microbiology,"['D000373', 'D000935', 'D017209', 'D002462', 'D002473', 'D002853', 'D005737', 'D025301', 'D053078', 'D050296', 'D008853', 'D051336', 'D050505', 'D025941', 'D017382', 'D012232', 'D053719']","['Agglutinins', 'Antifungal Agents', 'Apoptosis', 'Cell Membrane', 'Cell Wall', 'Chromatography, Liquid', 'Garlic', 'Hyphae', 'Membrane Potential, Mitochondrial', 'Microbial Viability', 'Microscopy', 'Mitochondrial Membranes', 'Mutant Proteins', 'Protein Interaction Mapping', 'Reactive Oxygen Species', 'Rhizoctonia', 'Tandem Mass Spectrometry']","Deciphering the mode of action of a mutant Allium sativum Leaf Agglutinin (mASAL), a potent antifungal protein on Rhizoctonia solani.","['Q000302', 'Q000302', None, 'Q000187', 'Q000187', None, 'Q000737', 'Q000166', 'Q000187', 'Q000187', None, 'Q000187', 'Q000302', None, 'Q000032', 'Q000166', None]","['isolation & purification', 'isolation & purification', None, 'drug effects', 'drug effects', None, 'chemistry', 'cytology', 'drug effects', 'drug effects', None, 'drug effects', 'isolation & purification', None, 'analysis', 'cytology', None]",https://www.ncbi.nlm.nih.gov/pubmed/26502719,2016,0.0,0.0,,, +26245522,"The paper describes the flavonoid composition of the aerial parts (young leaves, YL; adult leaves, AL; stems, ST) of Passiflora loefgrenii Vitta, a rare species native to Brazil, where it is traditionally used as food. Antioxidant potential has also been evaluated. To the best of our knowledge, no phytochemical and biological study on this species has been reported previously.",The Journal of pharmacy and pharmacology,"['D000975', 'D001938', 'D002851', 'D005419', 'D020128', 'D047311', 'D008519', 'D029598', 'D035261', 'D021241', 'D053719']","['Antioxidants', 'Brazil', 'Chromatography, High Pressure Liquid', 'Flavonoids', 'Inhibitory Concentration 50', 'Luteolin', 'Medicine, Traditional', 'Passiflora', 'Plant Components, Aerial', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry']","Phytochemical analysis of Passiflora loefgrenii Vitta, a rich source of luteolin-derived flavonoids with antioxidant properties.","['Q000008', None, 'Q000379', 'Q000008', None, 'Q000008', None, 'Q000737', None, None, None]","['administration & dosage', None, 'methods', 'administration & dosage', None, 'administration & dosage', None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/26245522,2016,0.0,0.0,,, +26161901,"Levels of 3-monochloropropane-1,2-diol (3-MCPD) fatty acid esters were evaluated in commercial deep-fat fried foods from the Brazilian market using a GC-MS method preceded by acid-catalysed methanolysis. A limit of detection of 0.04 mg kg(-1), a limit of quantitation of 0.08 mg kg(-1), mean recoveries varying from 82% to 92%, and coefficients of variation ranging from 2.5% to 5.0% for repeatability and from 3.6% to 6.5% for within-laboratory reproducibility were obtained during in-house validation. The levels of the compounds in the evaluated samples, expressed as free 3-MCPD equivalent, ranged from not detected to 0.99 mg kg(-)(1), and the highest concentrations were observed in samples of chopped onion and garlic. A preliminary estimation of 3-MCPD intake using these occurrence data suggested low risks to human health, but a potential concern may arise in particular cases of consumers of fried food.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D001938', 'D003296', 'D004042', 'D004952', 'D005227', 'D005504', 'D008401', 'D006358', 'D006801', 'D057230', 'D015203', 'D000517']","['Brazil', 'Cooking', 'Dietary Fats, Unsaturated', 'Esters', 'Fatty Acids', 'Food Analysis', 'Gas Chromatography-Mass Spectrometry', 'Hot Temperature', 'Humans', 'Limit of Detection', 'Reproducibility of Results', 'alpha-Chlorohydrin']","3-Monochloropropane-1,2-diol fatty acid esters in commercial deep-fat fried foods.","[None, None, None, 'Q000032', 'Q000032', None, None, None, None, None, None, 'Q000032']","[None, None, None, 'analysis', 'analysis', None, None, None, None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/26161901,2016,1.0,3.0,,, +26047911,"In the practical application of Bacillus licheniformis γ-glutamyltranspeptidase (BlGGT), we describe a straightforward enzymatic synthesis of γ-L-glutamyl-S-allyl-L-cysteine (GSAC), a naturally occurring organosulfur compound found in garlic, based on a transpeptidation reaction involving glutamine as the γ-glutamyl donor and S-allyl-L-cysteine as the acceptor. With the help of thin layer chromatography technique and computer-assisted image analysis, we performed the quantitative determination of GSAC. The optimum conditions for a biocatalyzed synthesis of GSAC were 200 mM glutamine, 200 mM S-allyl-L-cysteine, 50 mM Tris-HCl buffer (pH 9.0), and BlGGT at a final concentration of 1.0 U/mL. After a 15-h incubation of the reaction mixture at 60 °C, the GSAC yield for the free and immobilized enzymes was 19.3% and 18.3%, respectively. The enzymatic synthesis of GSAC was repeated under optimal conditions at 1-mmol preparative level. The reaction products together with the commercially available GSAC were further subjected to an ESI-MS/MS analysis. A significant signal with m/z of 291.1 and the protonated fragments at m/z of 73.0, 130.1, 145.0, and 162.1 were observed in the positive ESI-MS/MS spectrum, which is consistent with those of the standard compound. These results confirm the successful synthesis of GSAC from glutamine and S-allyl-L-cysteine by BlGGT.",Enzyme and microbial technology,"['D001407', 'D001426', 'D003545', 'D004151', 'D004800', 'D005737', 'D005973', 'D006863', 'D007218', 'D011994', 'D021241', 'D013457', 'D053719', 'D013696', 'D005723']","['Bacillus', 'Bacterial Proteins', 'Cysteine', 'Dipeptides', 'Enzymes, Immobilized', 'Garlic', 'Glutamine', 'Hydrogen-Ion Concentration', 'Industrial Microbiology', 'Recombinant Proteins', 'Spectrometry, Mass, Electrospray Ionization', 'Sulfur Compounds', 'Tandem Mass Spectrometry', 'Temperature', 'gamma-Glutamyltransferase']","Enzymatic synthesis of γ-L-glutamyl-S-allyl-L-cysteine, a naturally occurring organosulfur compound from garlic, by Bacillus licheniformis γ-glutamyltranspeptidase.","['Q000201', 'Q000378', 'Q000031', 'Q000096', 'Q000378', 'Q000378', 'Q000378', None, None, 'Q000378', None, 'Q000378', None, None, 'Q000378']","['enzymology', 'metabolism', 'analogs & derivatives', 'biosynthesis', 'metabolism', 'metabolism', 'metabolism', None, None, 'metabolism', None, 'metabolism', None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/26047911,2016,0.0,0.0,,, +26043852,"Allium sativum is well known for its medicinal properties. The A. sativum lectin 50 (ASL50, 50 kDa) was isolated from aged A. sativum bulbs and purified by gel filtration chromatography on Sephacryl S-200 column. Agar well diffusion assay were used to evaluate the antimicrobial activity of ASL50 against Candida species and bacteria then minimal inhibitory concentration (MIC) was determined. The lipid A binding to ASL50 was determined by surface plasmon resonance (SPR) technology with varying concentrations. Electron microscopic studies were done to see the mode of action of ASL50 on microbes. It exerted antimicrobial activity against clinical Candida isolates with a MIC of 10-40 μg/ml and clinical Pseudomonas aeruginosa isolates with a MIC of 10-80 μg/ml. The electron microscopic study illustrates that it disrupts the cell membrane of the bacteria and cell wall of fungi. It exhibited antiproliferative activity on oral carcinoma KB cells with an IC50 of 36 μg/ml after treatment for 48 h and induces the apoptosis of cancer cells by inducing 2.5-fold higher caspase enzyme activity than untreated cells. However, it has no cytotoxic effects towards HEK 293 cells as well as human erythrocytes even at higher concentration of ASL50. Biological properties of ASL50 may have its therapeutic significance in aiding infection and cancer treatments.",Applied biochemistry and biotechnology,"['D000595', 'D000890', 'D000970', 'D017209', 'D049109', 'D005737', 'D057809', 'D006461', 'D006801', 'D007624', 'D008050', 'D008970', 'D037121', 'D018547', 'D017421']","['Amino Acid Sequence', 'Anti-Infective Agents', 'Antineoplastic Agents', 'Apoptosis', 'Cell Proliferation', 'Garlic', 'HEK293 Cells', 'Hemolysis', 'Humans', 'KB Cells', 'Lipid A', 'Molecular Weight', 'Plant Lectins', 'Plant Stems', 'Sequence Analysis']",Biological Properties and Characterization of ASL50 Protein from Aged Allium sativum Bulbs.,"[None, 'Q000737', 'Q000737', 'Q000187', 'Q000187', 'Q000737', None, 'Q000187', None, None, 'Q000378', None, 'Q000737', 'Q000737', None]","[None, 'chemistry', 'chemistry', 'drug effects', 'drug effects', 'chemistry', None, 'drug effects', None, None, 'metabolism', None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/26043852,2016,0.0,0.0,,, +25976995,"An aqueous aged garlic extract (AGE) was prepared by soaking sliced garlic in water for 20days at room temperature (23-25 °C). In order to locate the antioxidant ingredients of the aqueous AGE, an activity-guided fractionation approach using ABTS assay, DPPH assay and FRAP assay were conducted to guide the fractionation by means of extraction, column chromatography and semi-preparative HPLC. Some phenols and organosulfur compounds were identified as antioxidants in AGE by GC-MS. Furthermore, UV, IR, ESI-MS, NMR and specific rotation experiments led to the identification of l-phenylalanine, l-tryptophan, (3S)-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid, (1S,3S)-1-methyl-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid, and (1R,3S)-1-methyl-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid as the major antioxidants in the AGE. The EC50 values of these purified tetrahydro-β-carboline derivatives were 0.625-1.334 μmol/mL and 1.063-2.072 μmol/mL in ABTS assay and DPPH assay, respectively. It is the first time for us to identify (3S)-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid as an in vitro antioxidant in AGE.",Food chemistry,"['D000975', 'D052160', 'D002243', 'D005591', 'D002851', 'D005737', 'D008401', 'D009682', 'D010636', 'D010649', 'D010936', 'D013451', 'D014364']","['Antioxidants', 'Benzothiazoles', 'Carbolines', 'Chemical Fractionation', 'Chromatography, High Pressure Liquid', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Magnetic Resonance Spectroscopy', 'Phenols', 'Phenylalanine', 'Plant Extracts', 'Sulfonic Acids', 'Tryptophan']","Isolation, purification and identification of antioxidants in an aqueous aged garlic extract.","['Q000032', 'Q000032', 'Q000032', None, None, 'Q000737', None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032']","['analysis', 'analysis', 'analysis', None, None, 'chemistry', None, None, 'analysis', 'analysis', 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/25976995,2016,0.0,0.0,,, +25940980,"The beneficial effects of garlic (Allium sativum) consumption in treating human diseases have been reported worldwide over a long period of human history. The strong antioxidant effect of garlic extract (GE) has also recently been claimed to prevent cancer, thrombus formation, cardiovascular disease and some age-related maladies. Using Caenorhabditis elegans as a model organism, aqueous GE was herein shown to increase the expression of longevity-related FOXO transcription factor daf-16 and extend lifespan by 20%. By employing microarray and proteomics analysis on C. elegans treated with aqueous GE, we have systematically mapped 229 genes and 46 proteins with differential expression profiles, which included many metabolic enzymes and yolky egg vitellogenins. To investigate the garlic components functionally involved in longevity, an integrated metabolo-proteomics approach was employed to identify metabolites and protein components associated with treatment of aqueous GE. Among potential lifespan-promoting substances, mannose-binding lectin and N-acetylcysteine were found to increase daf-16 expression. Our study points to the fact that the lifespan-promoting effect of aqueous GE may entail the DAF-16-mediated signaling pathway. The result also highlights the utility of metabolo-proteomics for unraveling the complexity and intricacy involved in the metabolism of natural products in vivo. ",The Journal of nutritional biochemistry,"['D000111', 'D000595', 'D000818', 'D017173', 'D029742', 'D002853', 'D003001', 'D015536', 'D019143', 'D051858', 'D005737', 'D008136', 'D037601', 'D055432', 'D008969', 'D010936', 'D040901', 'D015398', 'D053719', 'D015854', 'D014819']","['Acetylcysteine', 'Amino Acid Sequence', 'Animals', 'Caenorhabditis elegans', 'Caenorhabditis elegans Proteins', 'Chromatography, Liquid', 'Cloning, Molecular', 'Down-Regulation', 'Evolution, Molecular', 'Forkhead Transcription Factors', 'Garlic', 'Longevity', 'Mannose-Binding Lectin', 'Metabolomics', 'Molecular Sequence Data', 'Plant Extracts', 'Proteomics', 'Signal Transduction', 'Tandem Mass Spectrometry', 'Up-Regulation', 'Vitellogenins']",Analysis of lifespan-promoting effect of garlic extract by an integrated metabolo-proteomics approach.,"['Q000378', None, None, 'Q000235', 'Q000235', None, None, None, None, 'Q000235', 'Q000737', 'Q000187', 'Q000378', 'Q000379', None, 'Q000494', 'Q000379', None, None, None, 'Q000235']","['metabolism', None, None, 'genetics', 'genetics', None, None, None, None, 'genetics', 'chemistry', 'drug effects', 'metabolism', 'methods', None, 'pharmacology', 'methods', None, None, None, 'genetics']",https://www.ncbi.nlm.nih.gov/pubmed/25940980,2016,0.0,0.0,,, +25832010,"Terminal residues of pendimethalin and oxyfluorfen applied in autumn sugarcane- and vegetables-based intercropping systems were analyzed in peas (Pisum sativum), cabbage (Brassica oleracea), garlic (Allium sativum), gobhi sarson (Brassica napus), and raya (Brassica juncea). The study was conducted in winter season in 2010-2011 and in 2011-2012 at Ludhiana, India. Pendimethalin at 0.56 kg and 0.75 kg ha(-1) was applied immediately after sowing of gobhi sarson, raya, peas, garlic, and 2 days before transplanting of cabbage seedlings. Oxyfluorfen at 0.17 kg and 0.23 kg ha(-1) was applied immediately after sowing of peas and garlic and 2 days before transplanting of cabbage seedlings intercropped in autumn sugarcane. Representative samples of these vegetables were collected at 75, 90, 100, and 165 days after application of herbicides and analyzed by high-performance liquid chromatograph (HPLC) with diode array detector for residues. The residue level of pendimethalin and oxyfluorfen in mature vegetables was found to be below the limit of quantification which is 0.05 mg kg(-1) for both the herbicides. The soil samples were collected at 0, 7, 15, 30, 45, and 60 days after the application of their herbicides. The residues of herbicides in soil samples were found to be below the detectability limit of 0.05 mg kg(-1) after 60 days in case of pendimethalin and after 45 days in case of oxyfluorfen.",Environmental monitoring and assessment,"['D000814', 'D002851', 'D004784', 'D055768', 'D006540', 'D007194', 'D010573', 'D031786', 'D012987', 'D014675']","['Aniline Compounds', 'Chromatography, High Pressure Liquid', 'Environmental Monitoring', 'Halogenated Diphenyl Ethers', 'Herbicides', 'India', 'Pesticide Residues', 'Saccharum', 'Soil', 'Vegetables']",Harvest time residues of pendimethalin and oxyfluorfen in vegetables and soil in sugarcane-based intercropping systems.,"['Q000032', None, None, 'Q000032', 'Q000032', None, 'Q000032', 'Q000737', 'Q000737', 'Q000737']","['analysis', None, None, 'analysis', 'analysis', None, 'analysis', 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25832010,2015,0.0,0.0,,, +25819001,"This study investigated terpene biosynthesis in different tissues (root, protobulb, leaf sheath and blade) of in vitro-grown garlic plants either infected or not (control) with Sclerotium cepivorum, the causative agent of Allium White Rot disease. The terpenes identified by gas chromatography-electron impact mass spectrometry (GC-EIMS) in infected plants were nerolidol, phytol, squalene, α-pinene, terpinolene, limonene, 1,8-cineole and γ-terpinene, whose levels significantly increased when exposed to the fungus. Consistent with this, an increase in terpene synthase (TPS) activity was measured in infected plants. Among the terpenes identified, nerolidol, α-pinene and terpinolene were the most abundant with antifungal activity against S. cepivorum being assessed in vitro by mycelium growth inhibition. Nerolidol and terpinolene significantly reduced sclerotia production, while α-pinene stimulated it in a concentration-dependent manner. Parallel to fungal growth inhibition, electron microscopy observations established morphological alterations in the hyphae exposed to terpinolene and nerolidol. Differences in hyphal EtBr uptake suggested that one of the antifungal mechanisms of nerolidol and terpinolene might be disruption of fungal membrane integrity. ",Phytochemistry,"['D000935', 'D001203', 'D001487', 'D003511', 'D053138', 'D005737', 'D008401', 'D039821', 'D018515', 'D018517', 'D012717', 'D013729']","['Antifungal Agents', 'Ascomycota', 'Basidiomycota', 'Cyclohexanols', 'Cyclohexenes', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Monoterpenes', 'Plant Leaves', 'Plant Roots', 'Sesquiterpenes', 'Terpenes']",Allium sativum produces terpenes with fungistatic properties in response to infection with Sclerotium cepivorum.,"['Q000032', None, 'Q000187', None, None, 'Q000737', None, None, 'Q000737', 'Q000737', None, None]","['analysis', None, 'drug effects', None, None, 'chemistry', None, None, 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25819001,2016,0.0,0.0,,, +25705718,"Aspergillus spp. associated with cashew from the regions of Riyadh, Dammam, and Abha were isolated and three different culture media were used to qualitatively measure aflatoxin production by Aspergillus via UV light (365 nm), which was expressed as positive or negative. The obtained data showed that six isolates of A. flavus and four isolates of A. parasiticus were positive for aflatoxin production, while all isolates of A. niger were negative. Five commercially essential oils (thyme, garlic, cinnamon, mint, and rosemary) were tested to determine their influence on growth and aflatoxin production in A. flavus and A. parasiticus by performing high-performance liquid chromatography (HPLC). The results showed that the tested essential oils caused highly significant inhibition of fungal growth and aflatoxin production in A. flavus and A. parasiticus. The extent of the inhibition of fungal growth and aflatoxin production was dependent on the type and concentration of essential oils applied. The results indicate that cinnamon and thyme oils show strong antimicrobial potential. PCR was used with four sets of primer pairs for nor-1, omt-1, ver-1, and aflR genes, enclosed in the aflatoxin biosynthetic pathway. The interpretation of the results revealed that PCR is a rapid and sensitive method.",TheScientificWorldJournal,"['D000348', 'D000498', 'D031021', 'D000704', 'D000890', 'D001230', 'D002851', 'D017931', 'D005453', 'D005506', 'D017343', 'D027541', 'D009754', 'D009822', 'D016133', 'D012529', 'D013045', 'D013440', 'D046930']","['Aflatoxins', 'Allyl Compounds', 'Anacardium', 'Analysis of Variance', 'Anti-Infective Agents', 'Aspergillus', 'Chromatography, High Pressure Liquid', 'DNA Primers', 'Fluorescence', 'Food Contamination', 'Genes, Plant', 'Mentha', 'Nuts', 'Oils, Volatile', 'Polymerase Chain Reaction', 'Saudi Arabia', 'Species Specificity', 'Sulfides', 'Thymus Plant']",Use of selected essential oils to control aflatoxin contaminated stored cashew and detection of aflatoxin biosynthesis gene.,"['Q000032', None, None, None, 'Q000494', 'Q000737', None, 'Q000235', None, 'Q000032', 'Q000235', None, 'Q000737', 'Q000494', 'Q000379', None, None, None, None]","['analysis', None, None, None, 'pharmacology', 'chemistry', None, 'genetics', None, 'analysis', 'genetics', None, 'chemistry', 'pharmacology', 'methods', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25705718,2016,0.0,0.0,,, +25679258,"Characterization of enzymatic reactions occurring in untreated biological samples is of increasing interest. Herein, the chemical conversion of alliin to allicin, catalyzed by allinase, in raw garlic cloves has been followed in vivo by internal extractive electrospray ionization mass spectrometry (iEESI-MS). Both precursors and products of the enzymatic reaction were instantaneously extracted by infused solution running throughout the tissue and directly electrospray ionized on the edge of the bulk sample for online MS analysis. Compared to the room-temperature (+25 °C) scenario, the alliin conversion in garlic cloves decreased by (7.2 ± 1.4) times upon heating to +80 °C and by (5.9 ± 0.8) times upon cooling to -16 °C. Exposure of garlic to gentle ultrasound irradiation for 3 h accelerated the reaction by (1.2 ± 0.1) times. A 10 s microwave irradiation promoted alliin conversion by (1.6 ± 0.4) times, but longer exposure to microwave irradiation (90 s) slowed the reaction by (28.5 ± 7.5) times compared to the reference analysis. This method has been further employed to monitor the germination process of garlic. These data revealed that over a 2 day garlic sprouting, the allicin/alliin ratio increased by (2.2 ± 0.5) times, and the averaged degree of polymerization for the detected oligosaccharides/polysaccharides decreased from 11.6 to 9.4. Overall, these findings suggest the potential use of iEESI-MS for in vivo studies of enzymatic reactions in native biological matrices. ",Analytical chemistry,"['D000490', 'D013437', 'D002851', 'D005737', 'D008872', 'D009844', 'D010936', 'D011134', 'D021241', 'D013441']","['Allium', 'Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Garlic', 'Microwaves', 'Oligosaccharides', 'Plant Extracts', 'Polysaccharides', 'Spectrometry, Mass, Electrospray Ionization', 'Sulfinic Acids']",Molecular characterization of ongoing enzymatic reactions in raw garlic cloves using extractive electrospray ionization mass spectrometry.,"['Q000378', 'Q000378', None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000379', 'Q000378']","['metabolism', 'metabolism', None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'methods', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/25679258,2015,0.0,0.0,,, +25619986,"A simple and sensitive multiresidue pesticide analysis method was developed and validated for 213 pesticides in leek and garlic based on QuEChERS (quick, easy, cheap, effective, rugged, and safe) procedure combined with gas chromatography-triple quadrupole mass spectrometry. In the QuEChERS method, commercial extraction salt packet, dispersive solid-phase extraction adsorbent packet, and ceramic homogenizer were used to simplify the extraction procedure. The gas chromatography-tandem mass spectrometry (GC-MS/MS) parameters were optimized for analysis of 213 pesticides within a 38-min run time with a limit of quantification for most of the pesticides at 2 μg kg(-1). The coefficient of determination (r(2)) was >0.99 within the calibration linearity range of 2-400 μg kg(-1). Most recoveries at 2, 5, 10, 20, 50, 100, and 200 μg kg(-1) were in the range of 70-120% (n = 6) with associated relative standard deviations (RSDs) of <20%, indicating satisfactory precision. Real leek and garlic samples were analyzed for method application.",Analytical and bioanalytical chemistry,[],[],Multiresidue analysis of 213 pesticides in leek and garlic using QuEChERS-based method and gas chromatography-triple quadrupole mass spectrometry.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/25619986,2015,0.0,0.0,,, +25608858,"Natural organosulfur compounds (OSCs) from Allium sativum L. display antioxidant and chemo-sensitization properties, including the in vitro inhibition of tumor cell proliferation through the induction of apoptosis. Garlic water- and oil-soluble allyl sulfur compounds show distinct properties and the capability to inhibit the proliferation of tumor cells. In the present study, we optimized a new protocol for the extraction of water-soluble compounds from garlic at low temperatures and the production of glutathionyl-OSC conjugates during the extraction. Spontaneously, Cys/GSH-mixed-disulfide conjugates are produced by in vivo metabolism of OSCs and represent active molecules able to affect cellular metabolism. Water-soluble extracts, with (GSGaWS) or without (GaWS) glutathione conjugates, were here produced and tested for their ability to release hydrogen sulfide (H2S), also in the presence of reductants and of thiosulfate:cyanide sulfurtransferase (TST) enzyme. Thus, the TST catalysis of the H2S-release from garlic OSCs and their conjugates has been investigated by molecular in vitro experiments. The antiproliferative properties of these extracts on the human T-cell lymphoma cell line, HuT 78, were observed and related to histone hyperacetylation and downregulation of GAPDH expression. Altogether, the results presented here pave the way for the production of a GSGaWS as new, slowly-releasing hydrogen sulfide extract for potential therapeutic applications. ","Molecules (Basel, Switzerland)","['D055162', 'D045744', 'D049109', 'D002851', 'D056148', 'D003080', 'D005737', 'D005978', 'D006801', 'D006862', 'D016399', 'D008856', 'D008970', 'D010936', 'D019163', 'D012995', 'D013455', 'D013457', 'D013879', 'D013884', 'D014867']","['Biocatalysis', 'Cell Line, Tumor', 'Cell Proliferation', 'Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Cold Temperature', 'Garlic', 'Glutathione', 'Humans', 'Hydrogen Sulfide', 'Lymphoma, T-Cell', 'Microscopy, Fluorescence', 'Molecular Weight', 'Plant Extracts', 'Reducing Agents', 'Solubility', 'Sulfur', 'Sulfur Compounds', 'Thioredoxins', 'Thiosulfate Sulfurtransferase', 'Water']",Glutathione-garlic sulfur conjugates: slow hydrogen sulfide releasing agents for therapeutic applications.,"['Q000187', None, 'Q000187', None, None, None, 'Q000737', 'Q000378', None, 'Q000378', 'Q000473', None, None, 'Q000737', 'Q000494', None, 'Q000378', 'Q000494', 'Q000378', 'Q000037', 'Q000737']","['drug effects', None, 'drug effects', None, None, None, 'chemistry', 'metabolism', None, 'metabolism', 'pathology', None, None, 'chemistry', 'pharmacology', None, 'metabolism', 'pharmacology', 'metabolism', 'antagonists & inhibitors', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25608858,2015,0.0,0.0,,, +25589062,"Heterocyclic amines (HCAs) are known to be suspected human carcinogens produced by high-temperature cooking of protein-rich foods such as meat and fish. In the present study, the influence of numerous food condiments on the formation of HCAs in cooked chicken was investigated. Chicken samples were subjected to pan-frying under controlled temperature. The levels of HCAs DMIP, MeIQx, 4,8-DiMeIQx, PhIP, harman and norharman were found to be between 0.93 and 27.52 ng g(-1), whereas IQ, MeIQ, AαC, MeAαC, Trp-P-1 and Trp-P-2 were found either below the limit of quantification or not detected in the control sample. Nevertheless, for samples cooked using food condiments, the amounts of HCAs (DMIP, MeIQx, 4,8-DiMeIQx and PhIP) were between 0.14 and 19.57 ng g(-1); harman and norharman were detected at higher concentrations up to 17.77 ng g(-1) while IQ and MeIQ were at levels below the limit of quantification; and AαC, MeAαC, Trp-P-1 and Trp-P-2 were not detected in any of the samples. The outcomes revealed that the formation of HCAs (except harman and norharman) diminished after the addition of food condiments. Edible oil contributed to the highest levels of HCA formation, followed by garlic, paprika, pepper and tomato.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000588', 'D000818', 'D002273', 'D002645', 'D002853', 'D003212', 'D003296', 'D006571', 'D006358', 'D008460', 'D053719']","['Amines', 'Animals', 'Carcinogens', 'Chickens', 'Chromatography, Liquid', 'Condiments', 'Cooking', 'Heterocyclic Compounds', 'Hot Temperature', 'Meat', 'Tandem Mass Spectrometry']",Influence of food condiments on the formation of carcinogenic heterocyclic amines in cooked chicken and determination by LC-MS/MS.,"['Q000032', None, 'Q000032', None, None, 'Q000032', 'Q000379', 'Q000032', None, 'Q000032', None]","['analysis', None, 'analysis', None, None, 'analysis', 'methods', 'analysis', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/25589062,2016,0.0,0.0,,, +25438250,"The antimicrobial activities of garlic and other plant alliums are primarily based on allicin, a thiosulphinate present in crushed garlic bulbs. We set out to determine if pure allicin and aqueous garlic extracts (AGE) exhibit antimicrobial properties against the Burkholderia cepacia complex (Bcc), the major bacterial phytopathogen for alliums and an intrinsically multiresistant and life-threatening human pathogen. We prepared an AGE from commercial garlic bulbs and used HPLC to quantify the amount of allicin therein using an aqueous allicin standard (AAS). Initially we determined the minimum inhibitory concentrations (MICs) of the AGE against 38 Bcc isolates; these MICs ranged from 0.5 to 3% (v/v). The antimicrobial activity of pure allicin (AAS) was confirmed by MIC and minimum bactericidal concentration (MBC) assays against a smaller panel of five Bcc isolates; these included three representative strains of the most clinically important species, B. cenocepacia. Time kill assays, in the presence of ten times MIC, showed that the bactericidal activity of AGE and AAS against B. cenocepacia C6433 correlated with the concentration of allicin. We also used protein mass spectrometry analysis to begin to investigate the possible molecular mechanisms of allicin with a recombinant form of a thiol-dependent peroxiredoxin (BCP, Prx) from B. cenocepacia. This revealed that AAS and AGE modifies an essential BCP catalytic cysteine residue and suggests a role for allicin as a general electrophilic reagent that targets protein thiols. To our knowledge, we report the first evidence that allicin and allicin-containing garlic extracts possess inhibitory and bactericidal activities against the Bcc. Present therapeutic options against these life-threatening pathogens are limited; thus, allicin-containing compounds merit investigation as adjuncts to existing antibiotics. ",PloS one,"['D000900', 'D042602', 'D005737', 'D010936', 'D013441', 'D014867']","['Anti-Bacterial Agents', 'Burkholderia cepacia complex', 'Garlic', 'Plant Extracts', 'Sulfinic Acids', 'Water']",Garlic revisited: antimicrobial activity of allicin-containing garlic extracts against Burkholderia cepacia complex.,"['Q000737', 'Q000187', 'Q000737', 'Q000737', 'Q000032', 'Q000737']","['chemistry', 'drug effects', 'chemistry', 'chemistry', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25438250,2016,0.0,0.0,,, +25435627,"Garlic oil which is the main active constituent of garlic has a wide range of pharmacological activities, and a broad antibacterial spectrum. It also has a strong anti-cancer activity, and can significantly inhibit a variety of tumors such as liver cancer, gastric cancer and colon cancer. The objective is to study the extraction process of garlic oil and its antibacterial effects.","African journal of traditional, complementary, and alternative medicines : AJTCAM","['D000498', 'D000900', 'D001412', 'D025924', 'D004926', 'D005737', 'D013211', 'D013440']","['Allyl Compounds', 'Anti-Bacterial Agents', 'Bacillus subtilis', 'Chromatography, Supercritical Fluid', 'Escherichia coli', 'Garlic', 'Staphylococcus aureus', 'Sulfides']",Experimental study on the optimization of extraction process of garlic oil and its antibacterial effects.,"['Q000302', 'Q000302', 'Q000187', 'Q000379', 'Q000187', 'Q000737', 'Q000187', 'Q000302']","['isolation & purification', 'isolation & purification', 'drug effects', 'methods', 'drug effects', 'chemistry', 'drug effects', 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/25435627,2015,0.0,0.0,,, +25435245,"The present study reported on an in situ solvothermal growth method for immobilization of metal-organic framework MOF-5 on porous copper foam support for enrichment of plant volatile sulfides. The porous copper support impregnated with mother liquor of MOF-5 anchors the nucleation and growth of MOF crystallites at its surface, and its architecture of the three-dimensional channel enables accommodation of the MOF-5 crystallite seed. A continuous and well-intergrown MOF-5 layer, evidenced from scanning electron microscope imaging and X-ray diffraction, was successfully immobilized on the porous metal bar with good adhesion and high stability. Results show that the resultant MOF-5 coating was thermally stable up to 420 °C and robust enough for replicate extraction for at least 200 times. The MOF-5 bar was then applied to the headspace sorptive extraction of the volatile organic sulfur compounds in Chinese chive and garlic sprout in combination with thermal desorption-gas chromatography/mass spectrometry. It showed high extraction sensitivity and good selectivity to these plant volatile sulfides owing to the extraordinary porosity of the metal-organic framework as well as the interaction between the S-donor sites and the surface cations at the crystal edges. Several primary sulfur volatiles containing allyl methyl sulfide, dimethyl disulfide, diallyl sulfide, methyl allyl disulfide, and diallyl disulfide were quantified. Their limits of detection were found to be in the range of 0.2-1.7 μg/L. The organic sulfides were detected in the range of 6.0-23.8 μg/g with recoveries of 76.6-100.2% in Chinese chive and 11.4-54.6 μg/g with recoveries of 77.1-99.8% in garlic sprout. The results indicate the immobilization of MOF-5 on copper foam provides an efficient enrichment formats for noninvasive sampling of plant volatiles. ",Analytical chemistry,"['D000490', 'D000498', 'D003300', 'D005737', 'D008401', 'D009942', 'D016062', 'D013440', 'D055549', 'D014961']","['Allium', 'Allyl Compounds', 'Copper', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Organometallic Compounds', 'Porosity', 'Sulfides', 'Volatile Organic Compounds', 'X-Ray Diffraction']",In situ solvothermal growth of metal-organic framework-5 supported on porous copper foam for noninvasive sampling of plant volatile sulfides.,"['Q000737', 'Q000032', 'Q000737', 'Q000737', None, 'Q000737', None, 'Q000032', 'Q000032', None]","['chemistry', 'analysis', 'chemistry', 'chemistry', None, 'chemistry', None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/25435245,2015,1.0,1.0,,, +25423012,"Novel and inexpensive methods of thin-layer chromatography (TLC) were employed for the extraction, characterisation and mechanism of quorum sensing inhibition by ajoene, a component from toluene garlic bulb (Allium sativum L.) extract (TGE). TLC profiling of TGE was carried out using ethyl acetate as solvent. Out of total spots extracted from TLC, four spots exhibited quorum sensing inhibitory (QSI) potential. Among those, spot 5 was identified as Z-ajoene by TLC and confirmed by NMR and MS. HPLC analysis indicated 97.7% purity for purified ajoene. TLC densitometric analysis quantified 221.08 μmol/g of ajoene in TGE and indicated that ajoene is stable at 4°C and at acidic pH. HPTLC profiling showed that ajoene exhibits QSI effect by inhibiting the production of both long-chain acyl homoserine lactones and Pseudomonas quinolone signal (PQS) by P. aeruginosa and also by inactivating PQS molecules.",Natural product research,"['D000900', 'D002855', 'D004220', 'D005737', 'D011550', 'D053038', 'D012997']","['Anti-Bacterial Agents', 'Chromatography, Thin Layer', 'Disulfides', 'Garlic', 'Pseudomonas aeruginosa', 'Quorum Sensing', 'Solvents']",Applications of thin-layer chromatography in extraction and characterisation of ajoene from garlic bulbs.,"['Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000187', 'Q000187', None]","['chemistry', 'methods', 'chemistry', 'chemistry', 'drug effects', 'drug effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/25423012,2015,,,,,True +25420111,"Aged garlic extract (AGE) is widely used as a dietary supplement, and is claimed to promote human health through anti-oxidant/anti-inflammatory activities with hypolipidemic, antiplatelet and neuroprotective effects. Prior studies of AGE have mainly focused on its organosulfur compounds, with little attention paid to its carbohydrate derivatives, such as N-α-(1-deoxy-D-fructos-1-yl)-L-arginine (FruArg). The goal of this study is to investigate actions of AGE and FruArg on antioxidative and neuroinflammatory responses in lipopolysaccharide (LPS)-activated murine BV-2 microglial cells using a proteomic approach. Our data show that both AGE and FruArg can significantly inhibit LPS-induced nitric oxide (NO) production in BV-2 cells. Quantitative proteomic analysis by combining two dimensional differential in-gel electrophoresis (2D-DIGE) with mass spectrometry revealed that expressions of 26 proteins were significantly altered upon LPS exposure, while levels of 20 and 21 proteins exhibited significant changes in response to AGE and FruArg treatments, respectively, in LPS-stimulated BV-2 cells. Notably, approximate 78% of the proteins responding to AGE and FruArg treatments are in common, suggesting that FruArg is a major active component of AGE. MULTICOM-PDCN and Ingenuity Pathway Analyses indicate that the proteins differentially affected by treatment with AGE and FruArg are involved in inflammatory responses and the Nrf2-mediated oxidative stress response. Collectively, these results suggest that AGE and FruArg attenuate neuroinflammatory responses and promote resilience in LPS-activated BV-2 cells by suppressing NO production and by regulating expression of multiple protein targets associated with oxidative stress. ",PloS one,"['D000818', 'D000893', 'D015153', 'D002460', 'D004151', 'D004305', 'D015180', 'D005737', 'D008070', 'D051379', 'D017628', 'D015394', 'D009569', 'D010936', 'D020543', 'D040901', 'D015398', 'D053719', 'D013997']","['Animals', 'Anti-Inflammatory Agents', 'Blotting, Western', 'Cell Line', 'Dipeptides', 'Dose-Response Relationship, Drug', 'Electrophoresis, Gel, Two-Dimensional', 'Garlic', 'Lipopolysaccharides', 'Mice', 'Microglia', 'Molecular Structure', 'Nitric Oxide', 'Plant Extracts', 'Proteome', 'Proteomics', 'Signal Transduction', 'Tandem Mass Spectrometry', 'Time Factors']",Proteomic analysis of the effects of aged garlic extract and its FruArg component on lipopolysaccharide-induced neuroinflammatory response in microglial cells.,"[None, 'Q000494', None, None, 'Q000737', None, None, 'Q000737', 'Q000494', None, 'Q000166', None, 'Q000037', 'Q000494', 'Q000032', None, 'Q000187', None, None]","[None, 'pharmacology', None, None, 'chemistry', None, None, 'chemistry', 'pharmacology', None, 'cytology', None, 'antagonists & inhibitors', 'pharmacology', 'analysis', None, 'drug effects', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/25420111,2016,0.0,0.0,,, +25401128,"An acidic peroxidase was extracted from garlic (Allium sativum) and was partially purified threefold by ammonium sulphate precipitation, dialysis, and gel filtration chromatography using sephadex G-200. The specific activity of the enzyme increased from 4.89 U/mg after ammonium sulphate precipitation to 25.26 U/mg after gel filtration chromatography. The optimum temperature and pH of the enzyme were 50°C and 5.0, respectively. The Km and V max for H2O2 and o-dianisidine were 0.026 mM and 0.8 U/min, and 25 mM and 0.75 U/min, respectively. Peroxidase from garlic was effective in decolourizing Vat Yellow 2, Vat Orange 11, and Vat Black 27 better than Vat Green 9 dye. For all the parameters monitored, the decolourization was more effective at a pH range, temperature, H2O2 concentration, and enzyme concentration of 4.5-5.0, 50°C, 0.6 mM, and 0.20 U/mL, respectively. The observed properties of the enzyme together with its low cost of extraction (from local sources) show the potential of this enzyme for practical application in industrial wastewater treatment especially with hydrogen peroxide. These Vat dyes also exhibited potentials of acting as peroxidase inhibitors at alkaline pH range.",TheScientificWorldJournal,"['D002850', 'D004396', 'D005737', 'D006861', 'D007221', 'D009195', 'D062065']","['Chromatography, Gel', 'Coloring Agents', 'Garlic', 'Hydrogen Peroxide', 'Industry', 'Peroxidase', 'Waste Water']",Biobleaching of industrial important dyes with peroxidase partially purified from garlic.,"['Q000379', 'Q000737', 'Q000201', 'Q000737', 'Q000191', 'Q000737', 'Q000737']","['methods', 'chemistry', 'enzymology', 'chemistry', 'economics', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25401128,2015,0.0,0.0,,, +25373222,"The fumigant, contact, and repellent activities of four essential oils extracted from Citrus limonum (Sapindales: Rutaceae), Litsea cubeba (Laurales: Lauraceae), Cinnamomum cassia, and Allium sativum L. (Asparagales: Alliaceae) against 6th instars and adults of the darkling beetle, Alphitobius diaperinus (Panzer) (Coleoptera: Tenebrionidae), one of the main pests of materials and products of Juncus effuses L. (Poales: Juncaceae) during the storage period, were assayed, and chemical ingredients were analyzed with gas chromatography-mass spectrometry in this study. While the major ingredients found in C. limonum and C. cassia were limonene and (E)-cinnamaldehyde, the main constituents of L. cubea were D-limonene, (E)-3,7-dimethyl-,2,6-octadienal, (Z)-3,7-dimethyl,2 ,6-octadienal, and diallyl disulphide (18.20%), while the main constituents of and A. sativum were di-2-propenyl trisulfide and di-2-propenyl tetrasulfide. The fumigation activities of A. sativum and C. limonum on A. diaperinus adults were better than those of the other two essential oilss. The toxicities of A. sativum and C. limonum were almost equitoxic at 96 hr after treatment. Essential oils from Allium sativum and L. cubeba also showed good contact activities from 24 hr to 48 hr, and toxicities were almost equitoxic 48 hr posttreatment. The repellent activities of A. sativum and L. cubeba oils on 6th instars were also observed, showing repellence indexes of 90.4% and 88.9% at 12 hr after treatment, respectively. The effects of A. sativum on AChE activity of 6th instars of A. diaperinus were strongest compared to the other essential oils, followed by C. limonum, L. cubeba, and C. cassia. These results suggest that the essential oils of C. limonum and A. sativum could serve as effective control agents of A. diaperinus.",Journal of insect science (Online),"['D000818', 'D002800', 'D032904', 'D002957', 'D001517', 'D005651', 'D005737', 'D007302', 'D007306', 'D032862', 'D009822', 'D018675']","['Animals', 'Cholinesterase Inhibitors', 'Cinnamomum aromaticum', 'Citrus', 'Coleoptera', 'Fumigation', 'Garlic', 'Insect Repellents', 'Insecticides', 'Litsea', 'Oils, Volatile', 'Toxicity Tests']","Fumigant, contact, and repellent activities of essential oils against the darkling beetle, Alphitobius diaperinus.","[None, 'Q000032', 'Q000737', 'Q000737', None, None, 'Q000737', 'Q000032', 'Q000032', 'Q000737', 'Q000737', None]","[None, 'analysis', 'chemistry', 'chemistry', None, None, 'chemistry', 'analysis', 'analysis', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/25373222,2015,2.0,2.0,,, +25371585,"The medicinal use of garlic is much older than its usage as a food. The medical importance of garlic comes forward for its sulfur-containing components. In this study, it was aimed to compare Kastamonu garlic type with Chinese garlic type based on their aroma profiles.","African journal of traditional, complementary, and alternative medicines : AJTCAM","['D002681', 'D005737', 'D008401', 'D010936', 'D014421', 'D055549']","['China', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Plant Extracts', 'Turkey', 'Volatile Organic Compounds']",Comparitive study on volatile aroma compounds of two different garlic types (Kastamonu and Chinese) using gas chromatography mass spectrometry (HS-GC/MS) technique.,"[None, 'Q000737', None, 'Q000737', None, 'Q000737']","[None, 'chemistry', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25371585,2015,2.0,3.0,,, +25329784,"Sulfur-containing odorants and flavors play an important role in flavor and food industry, especially when meaty, garlic, onion, and vegetable scents are needed. Still, many S-containing flavors also possess fruity scents and may be used in compositions of perfumes that require a fresh and fruity odor perception. They are naturally abundant in various fruits, essential oils, and food. Most of these compounds possess strong scents, and their scent composition is highly dependent on the concentration applied. At higher concentrations, they usually feature repellent off-odors, while their sweet and fruity nature is only experienced at very low concentrations. This represents a challenge for their application in perfumery, as well as in food industry. From a molecular point of view, the endless structural and scent variety of these compounds makes them fascinating, especially as their O-analogs are usually free of any malodors. Here, we report on the investigation of the gas-phase structure and dynamics of the S-containing blackcurrant odorant cat ketone (4-methyl-4-sulfanylpentan-2-one). The work was performed by combining quantum-chemical calculations and molecular-beam Fourier-transform microwave spectroscopy as complementary tools. Using this technique, we are able to determine the structures of sizeable molecules where energy differences are small and conformational distinction is not always possible by quantum-chemical calculations alone. The presented results can be used for further structure-activity correlation studies, as well as for benchmarks to improve theoretical models, especially, as there is still significant interest in characterizing the various conformers of organic molecules in terms of relative energies, structures, and dipole moments.",Chemistry & biodiversity,"['D000818', 'D002415', 'D005421', 'D005638', 'D005663', 'D008401', 'D005740', 'D007659', 'D008872', 'D008956', 'D008968', 'D015394', 'D009812', 'D031965', 'D017550', 'D013237', 'D013329', 'D013455']","['Animals', 'Cats', 'Flavoring Agents', 'Fruit', 'Furans', 'Gas Chromatography-Mass Spectrometry', 'Gases', 'Ketones', 'Microwaves', 'Models, Chemical', 'Molecular Conformation', 'Molecular Structure', 'Odorants', 'Ribes', 'Spectroscopy, Fourier Transform Infrared', 'Stereoisomerism', 'Structure-Activity Relationship', 'Sulfur']",From cats and blackcurrants: structure and dynamics of the sulfur-containing cassis odorant cat ketone.,"[None, None, 'Q000737', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', None, None, None, None, 'Q000032', 'Q000737', None, None, None, 'Q000737']","[None, None, 'chemistry', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', None, None, None, None, 'analysis', 'chemistry', None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25329784,2015,0.0,0.0,,, +25205359,"Garlic is one of the most important bulb vegetables and is mainly used as a spice or flavoring agent for foods. It is also cultivated for its medicinal properties, attributable to sulfur compounds, of which allicin is the most important. However, the stability of allicin in garlic extract is not well understood. In this study, using UPLC, the stability of allicin extracted in water from garlic was evaluated in phosphate buffer at different temperatures under light and dark conditions.",Journal of the science of food and agriculture,"['D000890', 'D002681', 'D002851', 'D005520', 'D061353', 'D005737', 'D006358', 'D006863', 'D008027', 'D009994', 'D010936', 'D018517', 'D013441']","['Anti-Infective Agents', 'China', 'Chromatography, High Pressure Liquid', 'Food Preservatives', 'Food Storage', 'Garlic', 'Hot Temperature', 'Hydrogen-Ion Concentration', 'Light', 'Osmolar Concentration', 'Plant Extracts', 'Plant Roots', 'Sulfinic Acids']","Influence of pH, concentration and light on stability of allicin in garlic (Allium sativum L.) aqueous extract as measured by UPLC.","['Q000032', None, None, 'Q000032', None, 'Q000737', 'Q000009', None, 'Q000009', None, 'Q000737', 'Q000737', 'Q000032']","['analysis', None, None, 'analysis', None, 'chemistry', 'adverse effects', None, 'adverse effects', None, 'chemistry', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/25205359,2016,1.0,1.0,,, +25101875,"The aerotolerant hydrogenosome-containing piscine diplomonad, Spironucleus vortens, is able to withstand high fluctuations in O₂ tensions during its life cycle. In the current study, we further investigated the O₂ scavenging and antioxidant defence mechanisms which facilitate the survival of S. vortens under such oxidizing conditions. Closed O₂ electrode measurements revealed that the S. vortens ATCC 50386 strain was more O₂ tolerant than a freshly isolated S. vortens intestinal strain (Sv1). In contrast to the related human diplomonad, Giardia intestinalis, RP-HPLC revealed the major non-protein thiols of S. vortens to be glutathione (GSH, 776 nmol/10ⷠcells) with cysteine and H2S as minor peaks. Furthermore, antioxidant proteins of S. vortens were assayed enzymatically and revealed that S. vortens possesses superoxide dismutase and NADH oxidase (883 and 37.5nmol/min/mg protein, respectively), but like G. intestinalis, lacks catalase and peroxidase activities. Autofluorescence of NAD(P)H and FAD alongside the fluorescence of the GSH-adduct in monochlorobimane-treated live organisms allowed the monitoring of redox balances before and after treatment with inhibitors, metronidazole and auranofin. H₂O₂ was emitted into the exterior of S. vortens at a rate of 2.85 pmol/min/10ⶠcells. Metronidazole and auranofin led to depletion of S. vortens intracellular NAD(P)H pools and an increase in H₂O₂ release with concomitant oxidation of GSH, respectively. Garlic-derived compounds completely inhibited O₂ consumption by S. vortens (ajoene oil), or significantly depleted the intracellular GSH pool of the organism (allyl alcohol and DADS). Hence, antioxidant defence mechanisms of S. vortens may provide novel targets for parasite chemotherapy.",Molecular and biochemical parasitology,"['D002851', 'D003545', 'D016828', 'D005978', 'D009097', 'D009247', 'D018384', 'D010100', 'D013312', 'D013447', 'D013482']","['Chromatography, High Pressure Liquid', 'Cysteine', 'Diplomonadida', 'Glutathione', 'Multienzyme Complexes', 'NADH, NADPH Oxidoreductases', 'Oxidative Stress', 'Oxygen', 'Stress, Physiological', 'Sulfites', 'Superoxide Dismutase']",Antioxidant defences of Spironucleus vortens: Glutathione is the major non-protein thiol.,"[None, 'Q000032', 'Q000737', 'Q000378', 'Q000032', 'Q000032', None, 'Q000378', None, 'Q000032', 'Q000032']","[None, 'analysis', 'chemistry', 'metabolism', 'analysis', 'analysis', None, 'metabolism', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/25101875,2015,0.0,0.0,,, +25049964,"The competency of garlic and pennywort to improve broiler chicken growth and influence intestinal microbial communities and fatty acid composition of breast meat were studied. Two hundred forty, ""day-old"" chicks were randomly allocated to 4 treatment groups consisting of 6 replications of 10 chicks in each pen. The groups were assigned to receive treatment diets as follows: i) basal diet (control), ii) basal diet plus 0.5% garlic powder (GP), iii) basal diet plus 0.5% pennywort powder (PW) and iv) 0.002% virginiamycin (VM). Birds were killed at day 42 and intestinal samples were collected to assess for Lactobacillus and Escherichia coli. The pectoralis profundus from chicken breast samples was obtained from 10 birds from each treatment group on day 42 and frozen at -20°C for further analyses. Fatty acid profile of breast muscles was determined using gas liquid chromatography. Feed intake and weight gain of broilers fed with GP, PW, and VM were significantly higher (p<0.05) compared to control. Feeding chicks GP, PW, and VM significantly reduced Escherichia coli count (p<0.05) while Lactobacillus spp count were significantly higher (p<0.05) in the gut when compared to control group on day 42. Supplemented diet containing pennywort increased the C18:3n-3 fatty acid composition of chickens' breast muscle. Garlic and pennywort may be useful in modulating broiler guts as they control the enteropathogens that help to utilize feed efficiently. This subsequently enhances the growth performances of broiler chickens. ",Asian-Australasian journal of animal sciences,[],[],"Effects of two herbal extracts and virginiamycin supplementation on growth performance, intestinal microflora population and Fatty Acid composition in broiler chickens.",[],[],https://www.ncbi.nlm.nih.gov/pubmed/25049964,2014,0.0,0.0,,, +25038704,"Allium genus is a treasure trove of valuable bioactive compounds with potentially therapeutically important properties. This work utilises HPLC-MS and a constrained total-line-shape (CTLS) approach applied to (1)H NMR spectra to quantify metabolites present in onion species to reveal important inter-species differences. Extensive differences were detected between the sugar concentrations in onion species. Yellow onion contained the highest and red onion the lowest amounts of amino acids. The main flavonol-glucosides were quercetin 3,4'-diglucoside and quercetin 4'-glucoside. In general, the levels of flavonols were, higher in yellow onions than in red onions, and garlic and leek contained a lower amount of flavonols than the other Allium species. Our results highlight how (1)H NMR together with HPLC-MS can be useful in the quantification and the identification of the most abundant metabolites, representing an efficient means to pinpoint important functional food ingredients from Allium species. ",Food chemistry,"['D000596', 'D050260', 'D002241', 'D002851', 'D044948', 'D008279', 'D009682', 'D013058', 'D055442', 'D019697', 'D010936']","['Amino Acids', 'Carbohydrate Metabolism', 'Carbohydrates', 'Chromatography, High Pressure Liquid', 'Flavonols', 'Magnetic Resonance Imaging', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Metabolome', 'Onions', 'Plant Extracts']",Quantitative metabolite profiling of edible onion species by NMR and HPLC-MS.,"['Q000737', None, 'Q000737', 'Q000379', 'Q000737', None, 'Q000379', 'Q000379', None, 'Q000737', 'Q000737']","['chemistry', None, 'chemistry', 'methods', 'chemistry', None, 'methods', 'methods', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25038704,2015,1.0,4.0,,, +25012787,"Garlic oil is a kind of fungicide, but little is known about its antifungal effects and mechanism. In this study, the chemical constituents, antifungal activity, and effects of garlic oil were studied with Penicillium funiculosum as a model strain. Results showed that the minimum fungicidal concentrations (MFCs, v/v) were 0.125 and 0.0313 % in agar medium and broth medium, respectively, suggesting that the garlic oil had a strong antifungal activity. The main ingredients of garlic oil were identified as sulfides, mainly including disulfides (36 %), trisulfides (32 %) and monosulfides (29 %) by gas chromatograph-mass spectrometer (GC/MS), which were estimated as the dominant antifungal factors. The observation results by transmission electron microscope (TEM) and scanning electron microscope (SEM) indicated that garlic oil could firstly penetrate into hyphae cells and even their organelles, and then destroy the cellular structure, finally leading to the leakage of both cytoplasm and macromolecules. Further proteomic analysis displayed garlic oil was able to induce a stimulated or weakened expression of some key proteins for physiological metabolism. Therefore, our study proved that garlic oil can work multiple sites of the hyphae of P. funiculosum to cause their death. The high antifungal effects of garlic oil makes it a broad application prospect in antifungal industries.",Applied microbiology and biotechnology,"['D000498', 'D000935', 'D005737', 'D008401', 'D025301', 'D010407', 'D010936', 'D013440']","['Allyl Compounds', 'Antifungal Agents', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Hyphae', 'Penicillium', 'Plant Extracts', 'Sulfides']",Antifungal effect and mechanism of garlic oil on Penicillium funiculosum.,"['Q000737', 'Q000737', 'Q000737', None, 'Q000187', 'Q000187', 'Q000737', 'Q000737']","['chemistry', 'chemistry', 'chemistry', None, 'drug effects', 'drug effects', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/25012787,2015,2.0,3.0,,, +24987429,"Objective. To detect the effect of selenium-enriched garlic oil (Se-garlic oil) against cytotoxicity induced by ox-LDL in endothelial cells. Methods. Se-garlic oil was extracted by organic solvent extraction. High performance liquid chromatography (HPLC) was used to detect the content of allicin in the Se-garlic oil. Hydride generation atomic fluorescence spectrometry (HG-AFS) was used to detect the content of Se in the Se-garlic oil. ECV-304 cells were separated into five groups (blank, ox-LDL, and low-, medium-, and high-dose Se-garlic oil). Methyl thiazolyl tetrazolium (MTT) assay was used to detect the cytoactivity of each cell group after culturing for 24, 48, and 72 hours. Flow cytometry (FCM) stained with annexin V-FITC/PI was used to detect the apoptosis of the cells from the blank, Se-garlic oil, ox-LDL, and Se-garlic oil + ox-ldl groups after 48 hours of incubation. Results. The amount of allicin in Se-garlic oil was 142.66 mg/ml, while, in Se, it was 198 mg/kg. When ox-LDL was added to low-, medium-, and high-dose Se-garlic oil, the cell viability rates of ECV-304 cells treated in the three groups were all higher, while the apoptosis rates were significantly lower than those of the ox-LDL group (P < 0.05). However, there was no significant difference between the apoptosis rates of the blank, Se-garlic oil, and Se-garlic oil + ox-LDL groups (P > 0.05). Conclusion. Se-garlic oil could inhibit the cytotoxic effect induced by ox-LDL in endothelial cells. ",Evidence-based complementary and alternative medicine : eCAM,[],[],Effect of Selenium-Enriched Garlic Oil against Cytotoxicity Induced by OX-LDL in Endothelial Cells.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/24987429,2014,0.0,0.0,,, +24948941,"The endophytic fungus strain 0248, isolated from garlic, was identified as Trichoderma brevicompactum based on morphological characteristics and the nucleotide sequences of ITS1-5.8S- ITS2 and tef1. The bioactive compound T2 was isolated from the culture extracts of this fungus by bioactivity-guided fractionation and identified as 4β-acetoxy-12,13- epoxy-Δ(9)-trichothecene (trichodermin) by spectral analysis and mass spectrometry. Trichodermin has a marked inhibitory activity on Rhizoctonia solani, with an EC50 of 0.25 μg mL(-1). Strong inhibition by trichodermin was also found for Botrytis cinerea, with an EC50 of 2.02 μg mL(-1). However, a relatively poor inhibitory effect was observed for trichodermin against Colletotrichum lindemuthianum (EC50 = 25.60 μg mL(-1)). Compared with the positive control Carbendazim, trichodermin showed a strong antifungal activity on the above phytopathogens. There is little known about endophytes from garlic. This paper studied in detail the identification of endophytic T. brevicompactum from garlic and the characterization of its active metabolite trichodermin.",Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology],"['D000935', 'D020171', 'D016000', 'D020231', 'D004271', 'D004275', 'D021903', 'D060026', 'D005737', 'D013058', 'D008826', 'D008969', 'D020648', 'D010802', 'D012340', 'D012232', 'D017422', 'D014242', 'D014243']","['Antifungal Agents', 'Botrytis', 'Cluster Analysis', 'Colletotrichum', 'DNA, Fungal', 'DNA, Ribosomal', 'DNA, Ribosomal Spacer', 'Endophytes', 'Garlic', 'Mass Spectrometry', 'Microbial Sensitivity Tests', 'Molecular Sequence Data', 'Peptide Elongation Factor 1', 'Phylogeny', 'RNA, Ribosomal, 5.8S', 'Rhizoctonia', 'Sequence Analysis, DNA', 'Trichoderma', 'Trichodermin']",Antifungal activity of metabolites of the endophytic fungus Trichoderma brevicompactum from garlic.,"['Q000302', 'Q000187', None, 'Q000187', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000382', None, None, None, 'Q000235', None, 'Q000235', 'Q000187', None, 'Q000737', 'Q000302']","['isolation & purification', 'drug effects', None, 'drug effects', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'microbiology', None, None, None, 'genetics', None, 'genetics', 'drug effects', None, 'chemistry', 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/24948941,2015,0.0,0.0,,, +24926564,"S-Nitrosylation is a redox-based protein post-translational modification in response to nitric oxide signaling and is involved in a wide range of biological processes. Detection and quantification of protein S-nitrosylation have been challenging tasks due to instability and low abundance of the modification. Many studies have used mass spectrometry (MS)-based methods with different thiol-reactive reagents to label and identify proteins with S-nitrosylated cysteine (SNO-Cys). In this study, we developed a novel iodoTMT switch assay (ISA) using an isobaric set of thiol-reactive iodoTMTsixplex reagents to specifically detect and quantify protein S-nitrosylation. Irreversible labeling of SNO-Cys with the iodoTMTsixplex reagents enables immune-affinity detection of S-nitrosylated proteins, enrichment of iodoTMT-labeled peptides by anti-TMT resin, and importantly, unambiguous modification site-mapping and multiplex quantification by liquid chromatography-tandem MS. Additionally, we significantly improved anti-TMT peptide enrichment efficiency by competitive elution. Using ISA, we identified a set of SNO-Cys sites responding to lipopolysaccharide (LPS) stimulation in murine BV-2 microglial cells and revealed effects of S-allyl cysteine from garlic on LPS-induced protein S-nitrosylation in antioxidative signaling and mitochondrial metabolic pathways. ISA proved to be an effective proteomic approach for quantitative analysis of S-nitrosylation in complex samples and will facilitate the elucidation of molecular mechanisms of nitrosative stress in disease. ",Journal of proteome research,"['D000818', 'D002460', 'D007461', 'D008070', 'D051379', 'D058977', 'D010449', 'D011499', 'D040901', 'D013194']","['Animals', 'Cell Line', 'Iodoacetates', 'Lipopolysaccharides', 'Mice', 'Molecular Sequence Annotation', 'Peptide Mapping', 'Protein Processing, Post-Translational', 'Proteomics', 'Staining and Labeling']",Proteomic quantification and site-mapping of S-nitrosylated proteins using isobaric iodoTMT reagents.,"[None, None, 'Q000737', 'Q000494', None, None, None, None, None, None]","[None, None, 'chemistry', 'pharmacology', None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24926564,2015,0.0,0.0,,, +24923489,"A new method for the determination of selenium based on its fluorescence quenching on the hemoglobin-catalyzed reaction of H2 O2 and l-tyrosine has been established. The effect of pH, foreign ions and the optimization of variables on the determination of selenium was examined. The calibration curve was found to be linear between the fluorescence quenching (F0 /F) and the concentration of selenium within the range of 0.16-4.00 µg/mL. The detection limit was 1.96 ng/mL and the relative standard deviation was 3.14%. This method can be used for the determination of selenium in Se-enriched garlic bulbs with satisfactory results.",Luminescence : the journal of biological and chemical luminescence,"['D002138', 'D002384', 'D005453', 'D005504', 'D005737', 'D006454', 'D057230', 'D012643', 'D013050', 'D013816', 'D014443']","['Calibration', 'Catalysis', 'Fluorescence', 'Food Analysis', 'Garlic', 'Hemoglobins', 'Limit of Detection', 'Selenium', 'Spectrometry, Fluorescence', 'Thermodynamics', 'Tyrosine']",Determination of selenium via the fluorescence quenching effect of selenium on hemoglobin-catalyzed peroxidative reaction.,"[None, None, None, 'Q000379', 'Q000737', 'Q000737', None, 'Q000032', 'Q000379', None, 'Q000737']","[None, None, None, 'methods', 'chemistry', 'chemistry', None, 'analysis', 'methods', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24923489,2016,1.0,1.0,,, +24679771,"This work proposes the novel application of a microextraction technique, solid phase microextraction (SPME), coupled to liquid chromatography with UV detection (HPLC-UV) for the analysis of organosulfur compounds (OSCs) in garlic samples. Additionally, a comparative study of OSCs profiles obtained by SPME coupled to HPLC-UV and gas chromatography with flame photometric detector (GC-FPD), respectively; was carried out. This study provided complementary evidence about OSCs's lability and ""artifacts"" formation during the analytical process. Raw, cooked and distilled garlic samples were considered. The target analytes were diallyl disulphide (DADS), diallyl sulphide (DAS), diallyl trisulphide (DATS), allicin, 3-vinyl-4H-1,3-dithiin (3-VD), 2-vinyl-4H-1,2-dithiin (2-VD) and (E)- and (Z)-ajoene, which are the most important OSCs with biological activities present in raw and processed garlic. The coupling of SPME and HPLC showed to be reliable, fast, sensible and selective methodology for OSCs analysis.",Food chemistry,"['D002851', 'D002853', 'D005737', 'D052617']","['Chromatography, High Pressure Liquid', 'Chromatography, Liquid', 'Garlic', 'Solid Phase Microextraction']",Solid phase microextraction coupled to liquid chromatography. Analysis of organosulphur compounds avoiding artifacts formation.,"['Q000379', None, 'Q000737', 'Q000379']","['methods', None, 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/24679771,2015,1.0,2.0,,, +24668756,"A fast and an efficient ultrasound-assisted extraction technique using a lower density extraction solvent than water was developed for the trace-level determination of tebuconazole in garlic, soil and water samples followed by capillary gas chromatography combined with nitrogen-phosphorous selective detector (GC-NPD). In this approach, ultrasound radiation was applied to accelerate the emulsification of the ethyl acetate in aqueous samples to enhance the extraction efficiency of tebuconazole without requiring extra partitioning or cleaning, and the use of capillary GC-NPD was a more sensitive detection technique for organonitrogen pesticides. The experimental results indicate an excellent linear relationship between peak area and concentration obtained in the range 1-50 μg/kg or μg/L. The limit of detection (S/N, 3 ± 0.5) and limit of quantification (S/N, 7.5 ± 2.5) were obtained in the range 0.2-3 and 1-10 μg/kg or μg/L. Good spiked recoveries were achieved from ranges 95.55-101.26%, 96.28-99.33% and 95.04-105.15% in garlic, Nanivaliyal soil and Par River water, respectively, at levels 5 and 20 μg/kg or μg/L, and the method precision (% RSD) was ≤5%. Our results demonstrate that the proposed technique is a viable alternative for the determination of tebuconazole in complex samples. ",Journal of separation science,"['D002849', 'D005737', 'D010575', 'D012987', 'D012989', 'D014230', 'D014465', 'D014874']","['Chromatography, Gas', 'Garlic', 'Pesticides', 'Soil', 'Soil Pollutants', 'Triazoles', 'Ultrasonics', 'Water Pollutants, Chemical']","Fast ultrasound-assisted extraction followed by capillary gas chromatography combined with nitrogen-phosphorous selective detector for the trace determination of tebuconazole in garlic, soil and water samples.","['Q000295', 'Q000737', 'Q000032', 'Q000737', 'Q000032', 'Q000032', 'Q000379', 'Q000032']","['instrumentation', 'chemistry', 'analysis', 'chemistry', 'analysis', 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24668756,2015,0.0,0.0,,, +24668040,"S-Allyl-L-cysteine (SAC), the most abundant organosulfur compound derived from garlic, has multifunctional biological activities that occur via different mechanisms. A sensitive, rapid and simple LC-ESI-MS/MS method using a mixed-mode reversed-phase and cation-exchange column containing C18 silica particles and sulfonic acid cation-exchange particles has been developed and validated for the analysis of SAC in rat plasma. The mobile phase was optimized at 2 mM ammonium acetate buffer (pH = 3.5) and acetonitrile (75:25, v/v). The assay utilized 0.6% acetic acid in methanol to achieve simple and rapid deproteinization. Quantification was conducted using multiple reaction monitoring (MRM) of the transitions of m/z 162.0 → 145.0 for SAC. The standard curve for SAC was linear (r(2) ≥ 0.999) over a range from 5 to 2,500 ng/mL. The intra- and interday precision (relative standard deviation) of the method was not >6.0% at three quality control levels. The limit of quantification (LOQ) was 5.0 ng/mL. After being fully validated, the method was successfully applied to the pharmacokinetic monitoring of SAC in rat plasma.",Journal of chromatographic science,"['D000818', 'D002851', 'D003545', 'D051381', 'D021241', 'D053719']","['Animals', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Rats', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry']",Development and validation of S-allyl-L-cysteine in rat plasma using a mixed-mode reversed-phase and cation-exchange LC-ESI-MS/MS method: application to pharmacokinetic studies.,"[None, 'Q000379', 'Q000031', None, 'Q000379', 'Q000379']","[None, 'methods', 'analogs & derivatives', None, 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/24668040,2015,0.0,0.0,,, +24657412,"In this study, a two-step process combining aqueous two-phase extraction (ATPE) with chromatography was developed for extraction and purification of alliin from garlic powder. The partition coefficient and yield value of alliin in different types of aqueous two-phase system (ATPS) were compared and response surface methodology (RSM) was used for analyzing and optimizing the extraction process. The optimal extraction conditions of 19% (w/w) (NH4)2SO4, 20% (w/w) 1-prpanol, at 30°C, pH 2.35 with 8.54% (w/w) NaCl was chosen based on the higher yield. Compared to the results obtained with the conventional extraction method, this method had an evident advantage on yield (20.4mg/g versus the original yield of 15.0mg/g) and the concentration of alliin in extract solution by ATPE was close to three times of that with conventional extraction. The purification of alliin was carried out with the ammonium form of sulfonic acid cation-exchange resins 001×7. Sample solution with alliin concentration of 1mg/mL was passed through resins and the desorption of alliin was accomplished by water at the flow velocity of 0.5mL/min, 1.5mL/min, respectively. The purity and recovery of alliin after purification were 80% and 76%, respectively. ","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000327', 'D002852', 'D003545', 'D005737', 'D059625', 'D012044', 'D015203', 'D012965']","['Adsorption', 'Chromatography, Ion Exchange', 'Cysteine', 'Garlic', 'Liquid-Liquid Extraction', 'Regression Analysis', 'Reproducibility of Results', 'Sodium Chloride']",Combination of aqueous two-phase extraction and cation-exchange chromatography: new strategies for separation and purification of alliin from garlic powder.,"[None, 'Q000379', 'Q000031', 'Q000737', 'Q000379', None, None, None]","[None, 'methods', 'analogs & derivatives', 'chemistry', 'methods', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24657412,2014,1.0,3.0,,, +24592995,"The ability of foods and beverages to reduce allyl methyl disulfide, diallyl disulfide, allyl mercaptan, and allyl methyl sulfide on human breath after consumption of raw garlic was examined. The treatments were consumed immediately following raw garlic consumption for breath measurements, or were blended with garlic prior to headspace measurements. Measurements were done using a selected ion flow tube-mass spectrometer. Chlorophyllin treatment demonstrated no deodorization in comparison to the control. Successful treatments may be due to enzymatic, polyphenolic, or acid deodorization. Enzymatic deodorization involved oxidation of polyphenolic compounds by enzymes, with the oxidized polyphenols causing deodorization. This was the probable mechanism in raw apple, parsley, spinach, and mint treatments. Polyphenolic deodorization involved deodorization by polyphenolic compounds without enzymatic activity. This probably occurred for microwaved apple, green tea, and lemon juice treatments. When pH is below 3.6, the enzyme alliinase is inactivated, which causes a reduction in volatile formation. This was demonstrated in pH-adjusted headspace measurements. However, the mechanism for volatile reduction on human breath (after volatile formation) is unclear, and may have occurred in soft drink and lemon juice breath treatments. Whey protein was not an effective garlic breath deodorant and had no enzymatic activity, polyphenolic compounds, or acidity. Headspace concentrations did not correlate well to breath treatments. ",Journal of food science,"['D000498', 'D013437', 'D002957', 'D003836', 'D004220', 'D005511', 'D005638', 'D005737', 'D006209', 'D006801', 'D006863', 'D013058', 'D010084', 'D010936', 'D059808', 'D013440', 'D013457', 'D055549']","['Allyl Compounds', 'Carbon-Sulfur Lyases', 'Citrus', 'Deodorants', 'Disulfides', 'Food Handling', 'Fruit', 'Garlic', 'Halitosis', 'Humans', 'Hydrogen-Ion Concentration', 'Mass Spectrometry', 'Oxidation-Reduction', 'Plant Extracts', 'Polyphenols', 'Sulfides', 'Sulfur Compounds', 'Volatile Organic Compounds']",Deodorization of garlic breath volatiles by food and food components.,"['Q000378', 'Q000037', 'Q000737', None, 'Q000378', None, 'Q000737', 'Q000737', 'Q000378', None, None, None, None, 'Q000494', 'Q000494', 'Q000378', 'Q000378', 'Q000378']","['metabolism', 'antagonists & inhibitors', 'chemistry', None, 'metabolism', None, 'chemistry', 'chemistry', 'metabolism', None, None, None, None, 'pharmacology', 'pharmacology', 'metabolism', 'metabolism', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/24592995,2015,0.0,0.0,,, +24491695,"Angelica keiskei is used as popular functional food stuff. However, quantitative analysis of this plant's metabolites has not yet been disclosed. The principal phenolic compounds (1-16) within A. keiskei were isolated, enabling us to quantify the metabolites within different parts of the plant. The specific quantification of metabolites (1-16) was accomplished by multiple reaction monitoring (MRM) using a quadruple tandem mass spectrometer. The limit of detection and limit of quantitation were calculated as 0.4-44 μg/kg and 1.5-148 μg/kg, respectively. Abundance and composition of these metabolites varied significantly across different parts of plant. For example, the abundance of chalcones (12-16) decreased as follows: root bark (10.51 mg/g)>stems (8.52 mg/g)>leaves (2.63 mg/g)>root cores (1.44 mg/g). The chalcones were found to be responsible for the xanthine oxidase (XO) inhibition shown by this plant. The most potent inhibitor, xanthoangelol inhibited XO with an IC50 of 8.5 μM. Chalcones (12-16) exhibited mixed-type inhibition characteristics.",Food chemistry,"['D029969', 'D002851', 'D004791', 'D006801', 'D010636', 'D010936', 'D053719', 'D014969']","['Angelica', 'Chromatography, High Pressure Liquid', 'Enzyme Inhibitors', 'Humans', 'Phenols', 'Plant Extracts', 'Tandem Mass Spectrometry', 'Xanthine Oxidase']",Quantitative analysis of phenolic metabolites from different parts of Angelica keiskei by HPLC-ESI MS/MS and their xanthine oxidase inhibition.,"['Q000737', 'Q000379', 'Q000032', None, 'Q000032', 'Q000032', 'Q000379', 'Q000032']","['chemistry', 'methods', 'analysis', None, 'analysis', 'analysis', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24491695,2014,0.0,0.0,,, +24267071,"The use of volatile organic compounds (VOCs) emanating from human skin presents great potential for skin disease diagnosis. These compounds are emitted at very low concentrations. Thus, the sampling preparation step needs to be implemented before gas chromatography-mass spectrometry (GC-MS) analysis. In this work, a simple, non-invasive headspace sampling method for volatile compounds emanating from human skin is presented, using thin film as the extraction phase format. The proposed method was evaluated in terms of reproducibility, membrane size, extraction mode and storage conditions. First, the in vial sampling showed an intra- and inter-membrane RSD% less than 9.8% and 8.2%, respectively, which demonstrated that this home-made skin volatiles sampling device was highly reproducible with regard to intra-, inter-membrane sampling. The in vivo sampling was influenced not only by the skin metabolic status, but also by environmental conditions. The developed sampling set-up (or ""membrane sandwich"") was used to compare two different modes of sampling: headspace and direct sampling. Results demonstrated that headspace sampling had significantly reduced background signal intensity, indicating minimized contamination from the skin surface. In addition, membrane storage conditions both before and after sampling were fully investigated. Membranes stored in dry ice for up to 72 h after collection were tested and showed no or minimal change in volatile profiles. This novel skin volatile compounds sampling approach coupled with gas chromatography-mass spectrometry (GC-MS) can achieve reproducible analysis. This technique was applied to identify the biomarkers of garlic intake and alcohol ingestion. Dimethyl sulphone, allyl methyl sulfide and allyl mercaptan, as metabolites of garlic intake, were detected. In addition, alcohol released from skin was also detected using our ""membrane-sandwich"" sampling. Using the same approach, we analyzed skin VOCs from upper back, forearm and back thigh regions of the body. Our results show that different body locations share a number of common compounds (27/99). The area with most compounds detected was the upper back skin region, where the density of sebaceous glands is the highest.",Analytica chimica acta,"['D008401', 'D006801', 'D015203', 'D012867', 'D055549']","['Gas Chromatography-Mass Spectrometry', 'Humans', 'Reproducibility of Results', 'Skin', 'Volatile Organic Compounds']",A non-invasive method for in vivo skin volatile compounds sampling.,"[None, None, None, 'Q000737', 'Q000032']","[None, None, None, 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/24267071,2014,0.0,0.0,,, +24243685,"To enhance the utilization of garlic macerated oil as functional foods, oil-soluble organosulfur compounds were investigated using normal-phase high-performance liquid chromatography method. For analysis of compounds, it was simply extracted with 98% n-hexane in 2-propanol followed by sensitive and selective determination of all compounds. These method exhibited excellent linearity for oil-soluble organosulfur compounds with good coefficient (r > 0.999). Average recoveries were in the range of 80.23-106.18%. The limits of quantitation of oil-soluble organosulfur compounds ranged from 0.32 to 9.56 μg mL(-1) and the limits of detection were from 0.11 to 3.16 μg mL(-1). Overall, the precision of the results, expressed as relative standard deviation, ranged from 0.55 to 11.67%. The proposed method was applied to determining the contents of oil-soluble organosulfur compounds in commercial garlic macerated oils. Also, the stability of oil-soluble organosulfur compounds in garlic macerated oil were evaluated during 3 months of storage at four difference temperatures (4, 10, 25 and 35°C). The results showed the studied oil-soluble compounds in garlic macerated oil were stable at 4°C and relatively unstable at 35°C with varied extents degradation. Therefore, these validation data and temperature stability may be useful for quality evaluation of garlic macerated oils.",Journal of chromatographic science,"['D000498', 'D002851', 'D004355', 'D005737', 'D057230', 'D016014', 'D010938', 'D015203', 'D013440', 'D013696']","['Allyl Compounds', 'Chromatography, High Pressure Liquid', 'Drug Stability', 'Garlic', 'Limit of Detection', 'Linear Models', 'Plant Oils', 'Reproducibility of Results', 'Sulfides', 'Temperature']",Validated HPLC method and temperature stabilities for oil-soluble organosulfur compounds in garlic macerated oil.,"['Q000737', 'Q000379', None, 'Q000737', None, None, 'Q000737', None, 'Q000737', None]","['chemistry', 'methods', None, 'chemistry', None, None, 'chemistry', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/24243685,2015,1.0,2.0,,, +24224921,"A multi-residue analytical method was validated for 24 representative pesticides residues in onion, garlic and leek. The method is based on modified QuEChERS sample preparation with a mixture of graphene, primary secondary amine (PSA), and graphitised carbon black (GCB) as reversed-dispersive solid-phase extraction (r-DSPE) material and LC-MS/MS. Graphene was first used as an r-DSPE clean-up sorbent in onion, garlic and leek. The results first show that the mixed sorbent of graphene, PSA and GCB has a remarkable ability to clean-up interfering substances in the r-DSPE procedure when compared with the mixture of PSA and GCB. Use of matrix-matched standards provided acceptable results for tested pesticides with overall average recoveries between 70.1% and 109.7% and consistent RSDs <15.6%. In any case, this method still meets the 1-10 μg kg(-1) detection limit needed for pesticide testing and may be used for qualitative screening applications in which any identified pesticides can be quantified and confirmed by a more intensive method that achieves >70% recovery.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D000490', 'D002853', 'D006108', 'D010573', 'D010575', 'D052616', 'D053719']","['Allium', 'Chromatography, Liquid', 'Graphite', 'Pesticide Residues', 'Pesticides', 'Solid Phase Extraction', 'Tandem Mass Spectrometry']","Graphene as dispersive solidphase extraction materials for pesticides LC-MS/MS multi-residue analysis in leek, onion and garlic.","['Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000737', 'Q000379', 'Q000379']","['chemistry', 'methods', 'chemistry', 'chemistry', 'chemistry', 'methods', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/24224921,2014,1.0,3.0,,, +24196723,"Colchicine poisoning can occur not only by taking dosage form but also by ingesting a plant containing colchicine. A 39-year-old man presented to the emergency room with nausea, vomiting, and diarrhea 9 hours after ingestion of wild garlic. Symptoms attributed to food poisoning, and he received supportive cares and discharged. However, he was admitted to the hospital because of severe gastrointestinal presentations 4 hours later. He received treatments based on the diagnosis of acute gastroenteritis. The patient was in a fair condition during 30 hours of hospitalization until he suddenly developed respiratory distress and unfortunately died with cardiopulmonary arrest. The deceased body referred to our legal medicine center for determining cause of death and investigating possible medical staff malpractices. Postmortem examination, autopsy, macropathology and micropathology study, and postmortem toxicological analysis were performed. All results were submitted to the medical committee office for decision. The unknown cause of death was disclosed after determination of colchicine in the plant and botanical identification as Colchicum persicum. The committee determined the most probable cause of death as acute cardiopulmonary complications induced by colchicine poisoning and the manner of death as accidental. The medical staff was acquitted of the malpractice.",The American journal of forensic medicine and pathology,"['D015746', 'D000059', 'D000328', 'D002851', 'D003078', 'D003079', 'D003951', 'D003967', 'D004636', 'D053593', 'D005759', 'D006323', 'D006801', 'D007492', 'D008297', 'D011041', 'D012128', 'D014839']","['Abdominal Pain', 'Accidents', 'Adult', 'Chromatography, High Pressure Liquid', 'Colchicine', 'Colchicum', 'Diagnostic Errors', 'Diarrhea', 'Emergency Service, Hospital', 'Forensic Toxicology', 'Gastroenteritis', 'Heart Arrest', 'Humans', 'Iran', 'Male', 'Poisoning', 'Respiratory Distress Syndrome, Adult', 'Vomiting']",Fatal colchicine poisoning by accidental ingestion of Colchicum persicum: a case report.,"['Q000139', None, None, None, 'Q000506', 'Q000506', None, 'Q000139', None, None, 'Q000175', 'Q000139', None, None, None, 'Q000175', 'Q000139', 'Q000139']","['chemically induced', None, None, None, 'poisoning', 'poisoning', None, 'chemically induced', None, None, 'diagnosis', 'chemically induced', None, None, None, 'diagnosis', 'chemically induced', 'chemically induced']",https://www.ncbi.nlm.nih.gov/pubmed/24196723,2014,0.0,0.0,,, +24126836,"Novel imidazole fluorescent ionic liquids with anthracene groups (ImS-FILA) were synthesized for the first time to act as fluorescent probes. They were developed for the determination of superoxide anion radicals (O2 (•-)) in an aqueous system. O2 (•-) was produced by pyrogallol autoxidation. The fluorescence of ImS-FILA was quenched by superoxide anion radicals. The π-bond structure of the fluorescent molecules was oxidized and damaged. This method is very simple and sensitive. The linear range of sensitivity was 1-70 μM ImS-FILA, and the detection limit for reactive oxygen species was 0.1 μM. This method was used to detect superoxide radicals in papaya and garlic, with satisfactory results. Further work is needed to demonstrate the utility of this method in detecting reactive oxygen species in a biological aqueous system. ",Analytical and bioanalytical chemistry,"['D000838', 'D029441', 'D005453', 'D005737', 'D007093', 'D052578', 'D013050', 'D013481']","['Anions', 'Carica', 'Fluorescence', 'Garlic', 'Imidazoles', 'Ionic Liquids', 'Spectrometry, Fluorescence', 'Superoxides']",A novel functional imidazole fluorescent ionic liquid: simple and efficient fluorescent probes for superoxide anion radicals.,"['Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000379', 'Q000737']","['chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/24126836,2014,0.0,0.0,,, +24022100,"A comparative study using native garlic peel and mercerized garlic peel as adsorbents for the removal of Pb(2+) has been proposed. Under the optimized pH, contact time, and adsorbent dosage, the adsorption capacity of garlic peel after mercerization was increased 2.1 times and up to 109.05 mg g(-1). The equilibrium sorption data for both garlic peels fitted well with Langmuir adsorption isotherm, and the adsorbent-adsorbate kinetics followed pseudo-second-order model. These both garlic peels were characterized by elemental analysis, Fourier transform infrared spectrometry (FT-IR), and scanning electron microscopy, and the results indicated that mercerized garlic peel offers more little pores acted as adsorption sites than native garlic peel and has lower polymerization and crystalline and more accessible functional hydroxyl groups, which resulted in higher adsorption capacity than native garlic peel. The FT-IR and X-ray photoelectron spectroscopy analyses of both garlic peels before and after loaded with Pb(2+) further illustrated that lead was adsorbed on the through chelation between Pb(2+) and O atom existed on the surface of garlic peels. These results described above showed that garlic peel after mercerization can be a more attractive adsorbent due to its faster sorption uptake and higher capacity.",Environmental science and pollution research international,"['D000327', 'D005737', 'D007700', 'D007854', 'D008855', 'D008956', 'D056951', 'D017550']","['Adsorption', 'Garlic', 'Kinetics', 'Lead', 'Microscopy, Electron, Scanning', 'Models, Chemical', 'Photoelectron Spectroscopy', 'Spectroscopy, Fourier Transform Infrared']",Comparative study of adsorption of Pb(II) on native garlic peel and mercerized garlic peel.,"[None, 'Q000737', None, 'Q000737', None, None, None, None]","[None, 'chemistry', None, 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/24022100,2014,2.0,1.0,,, +23993494,"Sofrito is a key component of the Mediterranean diet, a diet that is strongly associated with a reduced risk of cardiovascular events. In this study, different Mediterranean sofritos were analysed for their content of polyphenols and carotenoids after a suitable work-up extraction procedure using liquid chromatography/electrospray ionisation-linear ion trap quadrupole-Orbitrap-mass spectrometry (LC/ESI-LTQ-Orbitrap-MS) and liquid chromatography/electrospray ionisation tandem triple quadrupole mass spectrometry (LC/ESI-MS-MS). In this way, 40 polyphenols (simple phenolic and hydroxycinnamoylquinic acids, and flavone, flavonol and dihydrochalcone derivatives) were identified with very good mass accuracy (<2 mDa), and confirmed by accurate mass measurements in MS and MS(2) modes. The high-resolution MS analyses revealed the presence of polyphenols never previously reported in Mediterranean sofrito. The quantification levels of phenolic and carotenoid compounds led to the distinction of features among different Mediterranean sofritos according to the type of vegetables (garlic and onions) or olive oil added for their production.",Food chemistry,"['D002338', 'D002851', 'D038441', 'D059808', 'D053719']","['Carotenoids', 'Chromatography, High Pressure Liquid', 'Diet, Mediterranean', 'Polyphenols', 'Tandem Mass Spectrometry']",Bioactive compounds present in the Mediterranean sofrito.,"['Q000737', None, None, 'Q000737', None]","['chemistry', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/23993494,2014,0.0,0.0,,, +23925402,"Tellurium (Te) is a widely used metalloid in industry because of its unique chemical and physical properties. However, information about the biological and toxicological activities of Te in plants and animals is limited. Although Te is expected to be metabolized in organisms via the same pathway as sulfur and selenium (Se), no precise metabolic pathways are known in organisms, particularly in plants. To reveal the metabolic pathway of Te in plants, garlic, a well-known Se accumulator, was chosen as the model plant. Garlic was hydroponically cultivated and exposed to sodium tellurate, and Te-containing metabolites in the water extract of garlic leaves were identified using HPLC coupled with inductively coupled plasma mass spectrometry (ICP-MS) or electrospray tandem mass spectrometry (ESI-MS-MS). At least three Te-containing metabolites were detected using HPLC-ICP-MS, and two of them were subjected to HPLC-ESI-MS-MS for identification. The MS spectra obtained by ESI-MS-MS indicated that the metabolite was Te-methyltellurocysteine oxide (MeTeCysO). Then, MeTeCysO was chemically synthesized and its chromatographic behavior matched with that of the Te-containing metabolite in garlic. The other was assigned as cysteine S-methyltellurosulfide. These results suggest that garlic can assimilate tellurate, an inorganic Te compound, and tellurate is transformed into a Te-containing amino acid, the so-called telluroamino acid. This is the first report addressing that telluroamino acid is de novo synthesized in a higher plant. ",Metallomics : integrated biometal science,"['D002851', 'D005737', 'D018527', 'D013058', 'D058955', 'D010936', 'D018515', 'D021241', 'D013691']","['Chromatography, High Pressure Liquid', 'Garlic', 'Hydroponics', 'Mass Spectrometry', 'Metalloids', 'Plant Extracts', 'Plant Leaves', 'Spectrometry, Mass, Electrospray Ionization', 'Tellurium']","Speciation and identification of tellurium-containing metabolites in garlic, Allium sativum.","[None, 'Q000737', None, 'Q000379', 'Q000378', 'Q000737', 'Q000737', None, 'Q000378']","[None, 'chemistry', None, 'methods', 'metabolism', 'chemistry', 'chemistry', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/23925402,2014,0.0,0.0,,, +23865201,"In our screening program for insecticidal activity of the essential oils/extracts derived from some Chinese medicinal herbs and spices, garlic (Allium sativum L.) essential oil was found to possess strong insecticidal activity against overwintering adults of Cacopsylla chinensis Yang et Li (Hemiptera: Psyllidae). The commercial essential oil of A. sativum was analyzed by gas chromatography-mass spectrometry. Sixteen compounds, accounting for 97.44% of the total oil, were identified, and the main components of the essential oil of A. sativum were diallyl trisulfide (50.43%), diallyl disulfide (25.30%), diallyl sulfide (6.25%), diallyl tetrasulfide (4.03%), 1,2-dithiolane (3.12%), allyl methyl disulfide (3.07%), 1,3-dithiane (2.12%), and allyl methyl trisulfide (2.08%). The essential oil of A. sativum possessed contact toxicity against overwintering C. chinensis, with an LC50 value of 1.42 microg per adult. The two main constituent compounds, diallyl trisulfide and diallyl disulfide, exhibited strong acute toxicity against the overwintering C. chinensis, with LC50 values of 0.64 and 11.04 /g per adult, respectively.",Journal of economic entomology,"['D000490', 'D000498', 'D000818', 'D004305', 'D008401', 'D006430', 'D007306', 'D007928', 'D009822', 'D013440']","['Allium', 'Allyl Compounds', 'Animals', 'Dose-Response Relationship, Drug', 'Gas Chromatography-Mass Spectrometry', 'Hemiptera', 'Insecticides', 'Lethal Dose 50', 'Oils, Volatile', 'Sulfides']",Evaluation of acute toxicity of essential oil of garlic (Allium sativum) and its selected major constituent compounds against overwintering Cacopsylla chinensis (Hemiptera: Psyllidae).,"['Q000737', 'Q000494', None, None, None, 'Q000187', 'Q000494', None, 'Q000494', 'Q000494']","['chemistry', 'pharmacology', None, None, None, 'drug effects', 'pharmacology', None, 'pharmacology', 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/23865201,2013,,,,, +23790889,"Cysteine-S-conjugates (CS-conjugates) occur in foods derived from plant sources like grape, passion fruit, onion, garlic, bell pepper and hops. During eating CS-conjugates are degraded into aroma-active thiols by β-lyases that originate from oral microflora. The present study provides evidence for the formation of the CS-conjugates S-furfuryl-l-cysteine (FFT-S-Cys) and S-(2-methyl-3-furyl)-l-cysteine (MFT-S-Cys) in the Maillard reaction of xylose with cysteine at 100°C for 2h. The CS-conjugates were isolated using cationic exchange and reversed-phase chromatography and identified by (1)H NMR, (13)C NMR and LC-MS(2). Spectra and LC retention times matched those of authentic standards. To the best of our knowledge, this is the first time that CS-conjugates are described as Maillard reaction products. Furfuryl alcohol (FFA) is proposed as an intermediate which undergoes a nucleophilic substitution with cysteine. Both FFT-S-Cys and MFT-S-Cys are odourless but produce strong aroma when tasted in aqueous solutions, supposedly induced by β -lyases from the oral microflora. The perceived aromas resemble those of the corresponding aroma-active thiols 2-furfurylthiol (FFT) and 2-methyl-3-furanthiol (MFT) which smell coffee-like and meaty, respectively. ",Food chemistry,"['D003545', 'D005663', 'D015416', 'D013438', 'D014994']","['Cysteine', 'Furans', 'Maillard Reaction', 'Sulfhydryl Compounds', 'Xylose']",Formation of cysteine-S-conjugates in the Maillard reaction of cysteine and xylose.,"['Q000737', 'Q000737', None, 'Q000737', 'Q000737']","['chemistry', 'chemistry', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23790889,2014,0.0,0.0,,, +23765168,"Combined pollution of selenium (Se) and mercury (Hg) has been known in Wanshan district (Guizhou Province, China). A better understanding of how Se and Hg interact in plants and the phytotoxicity thereof will provide clues about how to avoid or mitigate adverse effects of Se/Hg on local agriculture. In this study, the biological activity of Se has been investigated in garlic with or without Hg exposure. Se alone can promote garlic growth at low levels (<0.1 mg L(-1)), whereas it inhibits garlic growth at high levels (>1 mg L(-1)). The promotive effect of Se in garlic can be enhanced by low Hg exposure (<0.1 mg L(-1)). When both Se and Hg are at high levels, there is a general antagonistic effect between these two elements in terms of phytotoxicity. Inductively coupled plasma mass spectrometry (ICP-MS) data suggest that Se is mainly concentrated in garlic roots, compared to the leaves and the bulbs. Se uptake by garlic in low Se medium (<0.1 mg L(-1)) can be significantly enhanced as Hg exposure levels increase (P < 0.05), while it can be inhibited by Hg when Se exposure levels exceed 1 mg L(-1). The synchrotron radiation X-ray fluorescence (SRXRF) mapping further shows that Se is mainly concentrated in the stele of the roots, bulbs and the veins of the leaves, and Se accumulation in garlic can be reduced by Hg. The X-ray absorption near edge structure (XANES) study indicates that Se is mainly formed in C-Se-C form in garlic. Hg can decrease the content of inorganic Se mainly in SeO3(2-) form in garlic while increasing the content of organic Se mainly in C-Se-C form (MeSeCys and its derivatives). Hg-mediated changes in Se species along with reduced Se accumulation in garlic may account for the protective effect of Hg against Se phytotoxicity. ",Metallomics : integrated biometal science,"['D005737', 'D013058', 'D008628', 'D012643', 'D056928']","['Garlic', 'Mass Spectrometry', 'Mercury', 'Selenium', 'X-Ray Absorption Spectroscopy']",Mercury modulates selenium activity via altering its accumulation and speciation in garlic (Allium sativum).,"['Q000378', None, 'Q000494', 'Q000378', None]","['metabolism', None, 'pharmacology', 'metabolism', None]",https://www.ncbi.nlm.nih.gov/pubmed/23765168,2014,1.0,1.0,,, +23722957,"Ambient ionization is the new revolution in mass spectrometry (MS). A microwave plasma produced by a microwave plasma torch (MPT) at atmospheric pressure was directly used for ambient mass spectrometric analysis. H3O(+) and NH4(+) and their water clusters from the background are formed and create protonated molecules and ammoniated molecules of the analytes. In the full-scan mass spectra, both the quasi-molecular ions of the analytes and their characteristic ionic fragments are obtained and provide evidence of the analyte. The successful detection of active compounds in both medicine and garlic proves that MPT has the efficient desorption/ionization capability to analyze solid samples. The obtained decay curve of nicotine in exhaled breath indicates that MPT-MS is a useful tool for monitoring gas samples in real time. These results showed that the MPT, with the advantages of stable plasma, minimal optimization, easy, solvent-free operation, and no pretreatment, is another potential technique for ambient MS.",Journal of mass spectrometry : JMS,[],[],Direct desorption/ionization of analytes by microwave plasma torch for ambient mass spectrometric analysis.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/23722957,2013,,,,, +23561332,"To investigate the influence of selenium on mercury phytotoxicity, the levels of selenium and mercury were analyzed with inductively coupled plasma-mass spectrometry (ICP-MS) in garlic tissues upon exposure to different dosages of inorganic mercury (Hg(2+)) and selenite (SeO3(2-)) or selenate (SeO4(2-)). The distributions of selenium and mercury were examined with micro-synchrotron radiation X-ray fluorescence (μ-SRXRF), and the mercury speciation was investigated with micro-X-ray absorption near edge structure (μ-XANES). The results show that Se at higher exposure levels (>1mg/L of SeO3(2-) or SeO4(2-)) would significantly inhibit the absorption and transportation of Hg when Hg(2+) levels are higher than 1mg/L in culture media. SeO3(2-) and SeO4(2-) were found to be equally effective in reducing Hg accumulation in garlic. The inhibition of Hg uptake by Se correlates well with the influence of Se on Hg phytotoxicity as indicated by the growth inhibition factor. Elemental imaging using μ-SRXRF also shows that Se could inhibit the accumulation and translocation of Hg in garlic. μ-XANES analysis shows that Hg is mainly present in the forms of Hg-S bonding as Hg(GSH)2 and Hg(Met)2. Se exposure elicited decrease of Hg-S bonding in the form of Hg(GSH)2, together with Se-mediated alteration of Hg absorption, transportation and accumulation, may account for attenuated Hg phytotoxicity by Se in garlic. ",Environmental research,"['D000042', 'D001673', 'D005737', 'D005978', 'D013058', 'D008628', 'D012643', 'D013052', 'D056928']","['Absorption', 'Biodegradation, Environmental', 'Garlic', 'Glutathione', 'Mass Spectrometry', 'Mercury', 'Selenium', 'Spectrometry, X-Ray Emission', 'X-Ray Absorption Spectroscopy']",Selenium inhibits the phytotoxicity of mercury in garlic (Allium sativum).,"[None, None, 'Q000378', 'Q000737', None, 'Q000737', 'Q000737', None, None]","[None, None, 'metabolism', 'chemistry', None, 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/23561332,2013,0.0,0.0,,, +23451371,"Allium sativum L (garlic) is an essential component of many polyherbal oils used in traditional systems of medicine. Allyl disulfide has been a major component found in vegetable oil macerate of garlic, and can be used as reliable marker for determination of garlic in oil macerates of garlic. The HPLC separation of allyl disulfide was achieved on a Phenomenex Luna C18 (25 cm x 4.6 mm id x 5 pm particle size) column using acetonitrile-water-tetrahydrofuran (70 + 27 + 3, v/v/v) mobile phase at a flow rate of 1.0 mL/min. Quantitation was achieved with UV detection at 298 nm over the concentration range 8-48 microg/mL. HPTLC separation of allyl disulfide was achieved on an aluminum-backed layer of silica gel 60 F254 using n-hexane mobile phase. Quantitation was achieved by densitometric analysis at 298 nm over the 200-1200 ng/band concentration range. The methods were validated according to International Conference on Harmonization guidelines.",Journal of AOAC International,"['D000498', 'D002138', 'D002851', 'D002855', 'D004220', 'D007202', 'D057230', 'D010938', 'D010946', 'D012015', 'D015203', 'D013056']","['Allyl Compounds', 'Calibration', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Disulfides', 'Indicators and Reagents', 'Limit of Detection', 'Plant Oils', 'Plants, Medicinal', 'Reference Standards', 'Reproducibility of Results', 'Spectrophotometry, Ultraviolet']",Validation of HPTLC and HPLC methods for the quantitative determination of allyl disulfide in some polyherbal oils.,"[None, None, None, None, 'Q000032', None, None, 'Q000032', 'Q000737', None, None, None]","[None, None, None, None, 'analysis', None, None, 'analysis', 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/23451371,2013,,,,, +23448127,"Activity-guided fractionation was applied on an aged garlic extract (AGE), reported to show strong antioxidant activity, in order to locate the key in vitro antioxidant ingredients by means of the hydrogen peroxide scavenging (HPS) assay as well as the ORAC assay. Besides the previously reported four tetrahydro-β-carbolines, (1R,3S)- and (1S,3S)-1-methyl-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid and (1R,3S)- and (1S,3S)-1-methyl-1,2,3,4-tetrahydro-β-carboline-1,3-dicarboxylic acid, LC-MS/MS, LC-TOF-MS, and 1D/2D-NMR experiments led to the identification of coniferyl alcohol and its dilignols (-)-(2R,3S)-dihydrodehydrodiconiferyl alcohol, (+)-(2S,3R)-dehydrodiconiferyl alcohol, erythro-guaiacylglycerol-β-O-4'-coniferyl ether, and threo-guaiacylglycerol-β-O-4'-coniferyl ether as the major antioxidants in AGE. The purified individual compounds showed high antioxidant activity, with EC50 values of 9.7-11.8 μM (HPS assay) and 2.60-3.65 μmol TE/μmol (ORAC assay), respectively.",Journal of agricultural and food chemistry,"['D000975', 'D005591', 'D002851', 'D005609', 'D005737', 'D006861', 'D009682', 'D013058', 'D010636', 'D010936', 'D013997']","['Antioxidants', 'Chemical Fractionation', 'Chromatography, High Pressure Liquid', 'Free Radicals', 'Garlic', 'Hydrogen Peroxide', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Phenols', 'Plant Extracts', 'Time Factors']",In vitro activity-guided identification of antioxidants in aged garlic extract.,"['Q000032', None, None, 'Q000737', 'Q000737', 'Q000737', None, None, 'Q000032', 'Q000737', None]","['analysis', None, None, 'chemistry', 'chemistry', 'chemistry', None, None, 'analysis', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/23448127,2013,0.0,0.0,,, +23448028,"Larvae of scarab beetles (Coleoptera: Scarabaeidae) are important contaminant and root-herbivore pests of ornamental crops. To develop alternatives to conventional insecticides, 24 plant-based essential oils were tested for their acute toxicity against third instars of the Japanese beetle Popillia japonica Newman, European chafer Rhizotrogus majalis (Razoumowsky), oriental beetle Anomala orientalis (Waterhouse), and northern masked chafer Cyclocephala borealis Arrow. Diluted solutions were topically applied to the thorax, which allowed for calculating LD50 and LD90 values associated with 1 d after treatment. A wide range in acute toxicity was observed across all four scarab species. Of the 24 oils tested, allyl isothiocyanate, cinnamon leaf, clove, garlic, and red thyme oils exhibited toxicity to all four species. Allyl isothiocyanate was the most toxic oil tested against the European chafer, and among the most toxic against the Japanese beetle, oriental beetle, and northern masked chafer. Red thyme was also comparatively toxic to the Japanese beetle, oriental beetle, European chafer, and northern masked chafer. Interspecific variability in susceptibility to the essential oils was documented, with 12, 11, 8, and 6 of the 24 essential oils being toxic to the oriental beetle, Japanese beetle, European chafer, and northern masked chafer, respectively. Analysis of the active oils by gas chromatography-mass spectrometry revealed a diverse array of compounds, mostly consisting of mono- and sesquiterpenes. These results will aid in identifying active oils and their constituents for optimizing the development of plant essential oil mixtures for use against scarab larvae.",Journal of economic entomology,"['D000818', 'D001517', 'D008401', 'D007306', 'D007814', 'D009822']","['Animals', 'Coleoptera', 'Gas Chromatography-Mass Spectrometry', 'Insecticides', 'Larva', 'Oils, Volatile']",Acute toxicity of plant essential oils to scarab larvae (Coleoptera: Scarabaeidae) and their analysis by gas chromatography-mass spectrometry.,"[None, None, None, 'Q000032', None, 'Q000737']","[None, None, None, 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23448028,2013,,,,, +23435922,"The study is aimed to investigate the acaricidal effect of Allium sativum (garlic) and Allium cepa (onion) oils on different stages of Boophilus annulatus hard tick. Engorged B. annulatus females were collected from naturally infected cattle. A number of engorged ticks were incubated at 28 °C and 85 % relative humidity to lay eggs, which were incubated to obtain larvae that were used in the study. The used garlic and onion oils were prepared by steam distillation and were analyzed by gas chromatography. These oils were dissolved in ethanol, methanol alcohols, and, partially, in water. The oils were tested in different concentrations; 1, 2, 5, 10, and 20 %. These concentrations were applied on adult ticks by adult immersion test; on larvae by larval immersion technique and on eggs. The 20, 10, and 5 % of garlic oil dilutions in ethanol and methanol killed all adult ticks and larvae within 24 h. Similar results were obtained for 10 and 20 % garlic oil dissolved in water. The effect of 10 % aqueous solution of garlic oil on embryonated eggs was clear as its addition to these eggs led to their in ability to hatch, deformity in shape, and change in color. The 10 and 20 % onion oil in ethanol and methanol alcohols killed 76-86 % of the adult ticks within 72 h post-application. While, all larvae died within 24 h postsubjected to these two concentrations. These concentrations (10 and 20 %) of onion oil in water killed 56-80 % of the treated ticks. Moreover, 10 % aqueous solution of onion oil prevented hatching of embyonated eggs. We concluded that garlic and onion oils have acaricidal effect on all stages of B. annulatus at concentrations higher than 5 %. Only garlic oil could kill 100 % of adult ticks at concentrations from 5 % in alcohols.",Parasitology research,"['D056810', 'D000498', 'D000818', 'D001681', 'D002417', 'D002418', 'D005260', 'D005737', 'D007814', 'D019697', 'D010938', 'D048494', 'D013440', 'D013984']","['Acaricides', 'Allyl Compounds', 'Animals', 'Biological Assay', 'Cattle', 'Cattle Diseases', 'Female', 'Garlic', 'Larva', 'Onions', 'Plant Oils', 'Rhipicephalus', 'Sulfides', 'Tick Infestations']",Effect of Allium sativum and Allium cepa oils on different stages of Boophilus annulatus.,"['Q000737', 'Q000737', None, 'Q000379', None, 'Q000469', None, 'Q000737', 'Q000187', 'Q000737', 'Q000737', 'Q000187', 'Q000737', 'Q000469']","['chemistry', 'chemistry', None, 'methods', None, 'parasitology', None, 'chemistry', 'drug effects', 'chemistry', 'chemistry', 'drug effects', 'chemistry', 'parasitology']",https://www.ncbi.nlm.nih.gov/pubmed/23435922,2013,0.0,0.0,,, +23416182,"A strategy using reversed-phase high-performance liquid chromatography (HPLC), thin layer chromatography (TLC), mass spectrometry (MS), nuclear magnetic resonance (NMR), chemical synthesis, and MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) cell viability assay to identify allicin as the active anticancer compound in aqueous garlic extract (AGE) is described. Changing the pH of AGE from 7.0 to 5.0 eliminated interfering molecules and enabled a clean HPLC separation of the constituents in AGE. MTT assay of the HPLC fractions identified an active fraction. Further analysis by TLC, MS, and NMR verified the active HPLC fraction as allicin. Chemically synthesized allicin was used to provide further confirmation. The results clearly identify the active compound in AGE as allicin.",Analytical biochemistry,"['D000818', 'D000972', 'D045744', 'D002851', 'D002855', 'D003110', 'D004396', 'D005737', 'D020128', 'D009682', 'D013058', 'D051379', 'D010936', 'D013441', 'D013778', 'D013844']","['Animals', 'Antineoplastic Agents, Phytogenic', 'Cell Line, Tumor', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Colonic Neoplasms', 'Coloring Agents', 'Garlic', 'Inhibitory Concentration 50', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Mice', 'Plant Extracts', 'Sulfinic Acids', 'Tetrazolium Salts', 'Thiazoles']",HPLC-MTT assay: anticancer activity of aqueous garlic extract is from allicin.,"[None, 'Q000494', None, 'Q000379', None, 'Q000188', None, 'Q000737', None, None, None, None, 'Q000032', 'Q000302', None, None]","[None, 'pharmacology', None, 'methods', None, 'drug therapy', None, 'chemistry', None, None, None, None, 'analysis', 'isolation & purification', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/23416182,2013,0.0,0.0,,, +23331069,"Profiles of S-substituted cysteine flavor precursors were determined in 42 Alliaceae species native to South Africa and South America. It was found that the pool of cysteine derivatives present in these plants is remarkably very simple, with S-((methylthio)methyl)cysteine 4-oxide (marasmin) being the principal flavor precursor, typically accounting for 93-100% of the pool. Out of the other cysteine derivatives, only minor quantities of methiin were present in some species. The marasmin-derived thiosulfinate marasmicin (2,4,5,7-tetrathiaoctane 4-oxide), a major sensory-active compound of the freshly disrupted plants, was isolated, and its organoleptic properties were evaluated. Furthermore, sulfur-containing volatiles formed upon boiling of these alliaceous species were studied by GC-MS. The profile of the volatiles formed was relatively simple, with 2,3,5-trithiahexane and 2,4,5,7-tetrathiaoctane being the major components. Despite the traditional belief, ingestion of the marasmin-rich plants was always accompanied by development of a strong ""garlic breath"". We believe that especially several Tulbaghia species deserve to attract much greater attention from the food industry thanks to their pungent garlicky taste and unusual yet pleasant alliaceous smell.",Journal of agricultural and food chemistry,"['D000490', 'D005421', 'D008401', 'D010936', 'D012903', 'D013019', 'D013020', 'D013457', 'D055549']","['Allium', 'Flavoring Agents', 'Gas Chromatography-Mass Spectrometry', 'Plant Extracts', 'Smell', 'South Africa', 'South America', 'Sulfur Compounds', 'Volatile Organic Compounds']",Flavor precursors and sensory-active sulfur compounds in alliaceae species native to South Africa and South America.,"['Q000737', 'Q000032', None, 'Q000032', None, None, None, 'Q000032', 'Q000032']","['chemistry', 'analysis', None, 'analysis', None, None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/23331069,2013,0.0,0.0,,, +23259687,"This study investigates the analysis of thiol compounds using a needle trap device (HS-NTD) and solid-phase microextraction (HS-SPME) derivatized headspace techniques coupled to GC-MS. Thiol compounds and their outgassed products are particularly difficult to monitor in foodstuffs. It was found that with in-needle and in-fiber derivatization, using the derivatization agent N-phenylmaleimide, it was possible to enhance the selectivity toward thiol, which allowed the quantitation of butanethiol, ethanethiol, methanethiol, and propanethiol compounds found in fresh garlic. A side-hole NTD was prepared and packed in house and utilized mixed DVB and Carboxen polymer extraction phases made of 60-80 mesh particles. NTD sampling was accomplished in the exhaustive sampling mode, where breakthrough was negligible. This work demonstrates a new application for a side-hole NTD sampling. A commercial mixed polymer phase of polydimethylsiloxane (PDMS) and divinylbenzene polymer (DVB) SPME fiber was used for SPME extractions. Under optimized derivatization, extraction, and analysis conditions for both NTD-GC-MS and SPME-GC-MS techniques, automated sampling methods were developed for quantitation. Both methods demonstrate a successful approach to thiol determination and provide a quantitative linear response between <0.1 and 10 mg L(-1) (R(2) = 0.9996), with limits of detection (LOD) in the low micrograms per liter range for the investigated thiols. Addition methods using known spiked quantities of thiol analytes in ground garlic facilitated method validation. Carry-over was also negligible for both SPME and NTD under optimized conditions.",Journal of agricultural and food chemistry,"['D004129', 'D005737', 'D008401', 'D015203', 'D052617', 'D013438', 'D014753']","['Dimethylpolysiloxanes', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Reproducibility of Results', 'Solid Phase Microextraction', 'Sulfhydryl Compounds', 'Vinyl Compounds']",Assessment of thiol compounds from garlic by automated headspace derivatized in-needle-NTD-GC-MS and derivatized in-fiber-SPME-GC-MS.,"['Q000737', 'Q000737', 'Q000379', None, 'Q000379', 'Q000737', 'Q000737']","['chemistry', 'chemistry', 'methods', None, 'methods', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23259687,2014,1.0,2.0,,, +23249145,"The phenolic compounds were extracted from green and yellow leaves, stalks, and seeds of garlic ( Allium ursinum L.). The extracts were analyzed by liquid chromatography-photodiode array detector-electrospray ionization-tandem mass spectrometry (LC-PDA-ESI-MS/MS). In total, 21 compounds were detected. The flavonol derivatives were identified on the basis of their ultraviolet (UV) spectra and fragmentation patterns in collision-induced dissociation experiments. On the basis of accurate MS and MS/MS data, six compounds were newly identified in bear's garlic, mainly the kaempferol derivatives. As far as the investigated parts of garlic are concerned, the kaempferol derivatives were found to be predominant in yellow leaves [2362.96 mg/100 g of dry matter (dm)], followed by green leaves (1856.31 mg/100 g of dm). Seeds contained the minimal phenolic compounds, less than stalks. The yellow leaves of A. ursinum possessed a much larger content of compounds acylated with p-coumaric acid than green leaves (1299.97 versus 855.67 mg/100 g of dm, respectively). The stalks and seeds contained much more non-acetylated than acetylated flavonoid glycosides with p-coumaric acid compounds (162.4 versus 62.82 mg/100 g of dm and 105.49 versus 24.18 mg/100 g of dm, respectively).",Journal of agricultural and food chemistry,"['D000490', 'D002853', 'D044948', 'D015394', 'D059808', 'D021241', 'D013056']","['Allium', 'Chromatography, Liquid', 'Flavonols', 'Molecular Structure', 'Polyphenols', 'Spectrometry, Mass, Electrospray Ionization', 'Spectrophotometry, Ultraviolet']",Characterization and content of flavonol derivatives of Allium ursinum L. plant.,"['Q000737', None, 'Q000032', None, 'Q000032', None, None]","['chemistry', None, 'analysis', None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/23249145,2013,1.0,3.0,,, +23218283,"The fructose polymer fructan was extracted from white garlic and fractionated using DEAE cellulose 52 and Sephadex G-100 columns to characterize its chemical composition and protective effect against ultraviolet radiation b (UVB) induced human keratinocyte (HaTaC) damage. Gel permeation chromatography, high performance anion exchange chromatography, infrared spectroscopy and 1D and 2D nuclear magnetic resonance spectroscopy were used to determine the chemical composition and functional characteristics of the garlic fructan (GF). GF was a homogeneous polysaccharide with a molecular weight of 4.54 × 10(3)Da. It was a member of the 1-kestose family, and it was composed of fructose and glucose at a ratio of 14:1. The main chain of GF was composed of (2→1)-β-D-fructopyranose linked to a terminal (2→1)-α-D-glucopyranose at the non-reducing end and a (2→6)-β-D-fructopyranose branched chain. The degree of polymerization was 28. Preliminary tests described herein indicated that GF may be effective in protecting HaTaC from UVB-induced damage.",Carbohydrate polymers,"['D002460', 'D049109', 'D002845', 'D005630', 'D005737', 'D006801', 'D015603', 'D009682', 'D008970', 'D011837', 'D014312']","['Cell Line', 'Cell Proliferation', 'Chromatography', 'Fructans', 'Garlic', 'Humans', 'Keratinocytes', 'Magnetic Resonance Spectroscopy', 'Molecular Weight', 'Radiation-Protective Agents', 'Trisaccharides']",Structure and protective effect on UVB-induced keratinocyte damage of fructan from white garlic.,"['Q000187', 'Q000187', None, 'Q000737', 'Q000737', None, 'Q000528', None, None, 'Q000737', 'Q000737']","['drug effects', 'drug effects', None, 'chemistry', 'chemistry', None, 'radiation effects', None, None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23218283,2013,0.0,0.0,,, +23210417,"A controlled, clinical, double-blind study was conducted to assess the efficacy of a sugar-free chewing gum containing zinc acetate and magnolia bark extract (MBE) on oral volatile sulfur-containing compounds (VSC) versus a placebo sugar-free chewing gum for two hours.",The Journal of clinical dentistry,"['D000293', 'D000328', 'D000704', 'D001944', 'D002638', 'D002849', 'D004311', 'D004338', 'D005260', 'D006209', 'D006801', 'D031566', 'D008297', 'D008875', 'D008517', 'D024301', 'D010936', 'D018709', 'D013457', 'D013549', 'D055815', 'D019345']","['Adolescent', 'Adult', 'Analysis of Variance', 'Breath Tests', 'Chewing Gum', 'Chromatography, Gas', 'Double-Blind Method', 'Drug Combinations', 'Female', 'Halitosis', 'Humans', 'Magnolia', 'Male', 'Middle Aged', 'Phytotherapy', 'Plant Bark', 'Plant Extracts', 'Statistics, Nonparametric', 'Sulfur Compounds', 'Sweetening Agents', 'Young Adult', 'Zinc Acetate']",The effect of zinc acetate and magnolia bark extract added to chewing gum on volatile sulfur-containing compounds in the oral cavity.,"[None, None, None, None, None, None, None, None, None, 'Q000517', None, None, None, None, None, None, 'Q000627', None, 'Q000032', None, None, 'Q000627']","[None, None, None, None, None, None, None, None, None, 'prevention & control', None, None, None, None, None, None, 'therapeutic use', None, 'analysis', None, None, 'therapeutic use']",https://www.ncbi.nlm.nih.gov/pubmed/23210417,2012,,,,, +23160746,"The effects of Cd and HCHs with single and combined forms on Cd and HCHs phytoavailability of Allium sativum L. were investigated. The results indicated that the coexistence of Cd and HCHs presented antagonistic interactions mostly, which might be partly due to the formation of Cd-HCHs complex, compared with single stress. The bioaccumulation of Cd and HCHs in plants depended largely on their concentrations applied in pot soils, and the phytoavailability of HCH isomers was in the sequence: δ- > γ- ≥ β- > α-HCH.",Bulletin of environmental contamination and toxicology,"['D002104', 'D002849', 'D005737', 'D007536', 'D001556']","['Cadmium', 'Chromatography, Gas', 'Garlic', 'Isomerism', 'Lindane']",Accumulation and phytoavailability of hexachlorocyclohexane isomers and cadmium in Allium sativum L. under the stress of hexachlorocyclohexane and cadmium.,"['Q000378', None, 'Q000187', None, 'Q000737']","['metabolism', None, 'drug effects', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23160746,2013,1.0,1.0,,, +23158347,"Garlic has been known for its therapeutic effects for centuries and is used worldwide as a functional food. The concentration of the active molecules could be enhanced by a better knowledge of their biosynthesis. The precursor of these compounds, alliin (a sulfur amino-acid) has been obtained by chemical synthesis. However, this synthesis route also leads to a diastereoisomer as co-product. This work describes the development of an analytical method which allows the separation and quantification of the two diastereoisomers in order to determine in which proportion the natural form can be produced. The HPLC method which was optimized and validated by accuracy profile exploits an original stationary phase consisting of porous graphitic carbon (PGC). Furthermore, the developped method was used to separate the diastereoisomers of methiin, another cysteine sulfoxide, and to analyze an aqueous extract of garlic. The ability to quantify the amount of natural alliin is valuable for further work on garlic molecules and their application for health protection.",Talanta,"['D002851', 'D003545', 'D013237']","['Chromatography, High Pressure Liquid', 'Cysteine', 'Stereoisomerism']",Analysis of the diastereoisomers of alliin by HPLC.,"['Q000379', 'Q000031', None]","['methods', 'analogs & derivatives', None]",https://www.ncbi.nlm.nih.gov/pubmed/23158347,2013,0.0,0.0,,, +23016295,"A method was developed and validated for the simultaneous analysis of 112 pesticide residues in vegetables by gas chromatography coupled with triple quadrupole mass spectrometry (GC-QQQ-MS/MS). It is demonstrated that the optimized conditions could provide a more accurate quantitation and lower limit of quantification of the analysis by dispersive-solid phase extraction (D-SPE) cleanup. The samples were extracted with acetonitrile and toluene (8: 1, v/v), and cleaned up by D-SPE. To every 5 mL extraction solution, 0.8 g MgSO4, 0.05 g graphitized carbon black (GCB), 0.1 g ethylenediamine-N-propyl silyl (PSA) and 0.05 g C18 were added. The extracts were analyzed by GC-QQQ-MS/MS using internal standard method. The recoveries of the 112 pesticides at three spiked levels of 20, 50 and 200 microg/kg were ranged from 53.1% to 138.7%, and among which those of 86 pesticides were from 65.0% to 120.0%. The relative standard deviations (RSD) were less than 12%. The limits of quantifications (LOQs) (signal/noise at 10) were between 1.6 and 13.4 microg/kg. The vegetable samples collected from the market such as garlic chives, cucumber and purple cabbage were analyzed, and the residues of triazophos and fenpropathrin were detected in some of these samples. The method can be applied to the routine analysis for the determination of the 112 pesticides in vegetable samples.",Se pu = Chinese journal of chromatography,"['D005506', 'D008401', 'D010573', 'D052616', 'D014675']","['Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Pesticide Residues', 'Solid Phase Extraction', 'Vegetables']",[Analysis of 112 pesticide residues in vegetables using dispersive-solid phase extraction and gas chromatography-triple quadrupole mass spectrometry].,"['Q000032', 'Q000379', 'Q000032', 'Q000379', 'Q000737']","['analysis', 'methods', 'analysis', 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/23016295,2013,,,,, +22985527,"A graphene-supported zinc oxide (ZnO) solid-phase microextraction (SPME) fiber was prepared via a sol-gel approach. Graphite oxide (GO), with rich oxygen-containing groups, was selected as the starting material to anchor ZnO on its nucleation center. After being deoxidized by hydrazine, the Zn(OH)2/GO coating was dehydrated at high temperature to give the ZnO/graphene coating. Sol-gel technology could efficiently incorporate ZnO/graphene composites into the sol-gel network and provided strong chemical bonding between sol-gel polymeric SPME coating and silica fiber surface, which enhanced the durability of the fiber and allowed more than 200 replicate extractions. Results indicated that pure ZnO coated fiber did not show adsorption selectivity toward sulfur compounds, which might because the ZnO nanoparticles were enwrapped in the sol-gel network, and the strong coordination action between Zn ion and S ion was therefore blocked. The incorporation of graphene into ZnO based sol-gel network greatly enlarged the BET surface area from 1.2 m2/g to 169.4 m2/g and further increased the adsorption sites. Combining the superior properties of extraordinary surface area of graphene and the strong coordination action of ZnO to sulfur compounds, the ZnO/graphene SPME fiber showed much higher adsorption affinity to 1-octanethiol (enrichment factor, EF, 1087) than other aliphatic compounds without sulfur-containing groups (EFs<200). Also, it showed higher extraction selectivity and sensitivity toward sulfur compounds than commercial polydimethylsiloxane (PDMS) and polydimethylsiloxane/divinylbenzene (PDMS/DVB) SPME fibers. Several most abundant sulfur volatiles in Chinese chive and garlic sprout were analyzed using the ZnO/graphene SPME fiber in combination with gas chromatography-mass spectrometry (GC-MS). Their limits of detection were 0.1-0.7 μg/L. The relative standard deviation (RSD) using one fiber ranged from 3.6% to 9.1%. The fiber-to-fiber reproducibility for three parallel prepared fibers was 4.8-10.8%. The contents were in the range of 1.0-46.4 μg/g with recoveries of 80.1-91.6% for four main sulfides in Chinese chive and 17.1-122.6 μg/g with recoveries of 73.2-80.6% for three main sulfides in garlic sprout.",Journal of chromatography. A,"['D000490', 'D008401', 'D006108', 'D057230', 'D016014', 'D015203', 'D052617', 'D013438', 'D013440', 'D055549', 'D015034']","['Allium', 'Gas Chromatography-Mass Spectrometry', 'Graphite', 'Limit of Detection', 'Linear Models', 'Reproducibility of Results', 'Solid Phase Microextraction', 'Sulfhydryl Compounds', 'Sulfides', 'Volatile Organic Compounds', 'Zinc Oxide']",Graphene-supported zinc oxide solid-phase microextraction coating with enhanced selectivity and sensitivity for the determination of sulfur volatiles in Allium species.,"['Q000737', None, 'Q000737', None, None, None, 'Q000295', 'Q000032', 'Q000032', 'Q000032', 'Q000737']","['chemistry', None, 'chemistry', None, None, None, 'instrumentation', 'analysis', 'analysis', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22985527,2013,1.0,1.0,,, +22972339,"We produced a single deuterated lachrymatory factor (propanthial S-oxide, m/z = 91) in a model reaction system comprising purified alliinase, lachrymatory factor synthase (LFS), and (E)-(+)-S-(1-propenyl)-L-cysteine sulfoxide ((E)-PRENCSO) in D(2)O. Onion LFS reacted with the degraded products of (E)-PRENCSO by alliinase, but not with those of (Z)-PRENCSO. These findings indicate that onion LFS is an (E)-1-propenylsulfenic acid isomerase.","Bioscience, biotechnology, and biochemistry","['D013437', 'D002384', 'D003545', 'D017666', 'D005737', 'D008401', 'D019746', 'D019697', 'D010940', 'D011522', 'D012996', 'D013237', 'D013454']","['Carbon-Sulfur Lyases', 'Catalysis', 'Cysteine', 'Deuterium Oxide', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Intramolecular Oxidoreductases', 'Onions', 'Plant Proteins', 'Protons', 'Solutions', 'Stereoisomerism', 'Sulfoxides']",Proton transfer in a reaction catalyzed by onion lachrymatory factor synthase.,"['Q000737', None, 'Q000031', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', None, None, None, 'Q000138']","['chemistry', None, 'analogs & derivatives', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', None, None, None, 'chemical synthesis']",https://www.ncbi.nlm.nih.gov/pubmed/22972339,2013,0.0,0.0,,, +22964046,"This paper reports on the development and optimization of a modified Quick, Easy, Cheap Effective, Rugged and Safe (QuEChERS) based extraction technique coupled with a clean-up dispersive-solid phase extraction (dSPE) as a new, reliable and powerful strategy to enhance the extraction efficiency of free low molecular-weight polyphenols in selected species of dietary vegetables. The process involves two simple steps. First, the homogenized samples are extracted and partitioned using an organic solvent and salt solution. Then, the supernatant is further extracted and cleaned using a dSPE technique. Final clear extracts of vegetables were concentrated under vacuum to near dryness and taken up into initial mobile phase (0.1% formic acid and 20% methanol). The separation and quantification of free low molecular weight polyphenols from the vegetable extracts was achieved by ultrahigh pressure liquid chromatography (UHPLC) equipped with a phodiode array (PDA) detection system and a Trifunctional High Strength Silica capillary analytical column (HSS T3), specially designed for polar compounds. The performance of the method was assessed by studying the selectivity, linear dynamic range, the limit of detection (LOD) and limit of quantification (LOQ), precision, trueness, and matrix effects. The validation parameters of the method showed satisfactory figures of merit. Good linearity (Rvalues2>0.954; (+)-catechin in carrot samples) was achieved at the studied concentration range. Reproducibility was better than 3%. Consistent recoveries of polyphenols ranging from 78.4 to 99.9% were observed when all target vegetable samples were spiked at two concentration levels, with relative standard deviations (RSDs, n=5) lower than 2.9%. The LODs and the LOQs ranged from 0.005 μg mL(-1) (trans-resveratrol, carrot) to 0.62 μg mL(-1) (syringic acid, garlic) and from 0.016 μg mL(-1) (trans-resveratrol, carrot) to 0.87 μg mL(-1) ((+)-catechin, carrot) depending on the compound. The method was applied for studying the occurrence of free low molecular weight polyphenols in eight selected dietary vegetables (broccoli, tomato, carrot, garlic, onion, red pepper, green pepper and beetroot), providing a valuable and promising tool for food quality evaluation.",Journal of chromatography. A,"['D000097', 'D000704', 'D002851', 'D016018', 'D057230', 'D008278', 'D008970', 'D059808', 'D015203', 'D012965', 'D052616', 'D014675']","['Acetonitriles', 'Analysis of Variance', 'Chromatography, High Pressure Liquid', 'Least-Squares Analysis', 'Limit of Detection', 'Magnesium Sulfate', 'Molecular Weight', 'Polyphenols', 'Reproducibility of Results', 'Sodium Chloride', 'Solid Phase Extraction', 'Vegetables']",A new and improved strategy combining a dispersive-solid phase extraction-based multiclass method with ultra high pressure liquid chromatography for analysis of low molecular weight polyphenols in vegetables.,"['Q000737', None, 'Q000379', None, None, 'Q000737', None, 'Q000032', None, 'Q000737', 'Q000379', 'Q000737']","['chemistry', None, 'methods', None, None, 'chemistry', None, 'analysis', None, 'chemistry', 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22964046,2013,1.0,2.0,,, +22931231,"A novel low-molecular-weight fructooligosaccharide (LMWF) from garlic ( Allium sativum ) was isolated and identified. The structure and physicochemical properties of the LMWF were determined by chemical and spectroscopic methods, size-exclusion chromatography, atomic force microscopy (AFM), dynamic rheometry, and differential scanning calorimetry (DSC). The results showed that the LMWF was a neo-ketose with a molecular weight of 1770 Da. The LMWF had a (2,1)-linked β-D-Fruf backbone with (2,6)-linked β-D-Fruf side chains, and it was mainly composed of fructose. The branch degree was 18.1%, and the intrinsic viscosity was 3.06 mL/g. The spherical particles of the LMWF were observed by AFM, and their size was relatively uniform. With an increase in the water content, the peak temperature (T(p)), onset temperature (T(o)), and endset temperature (T(c)) increased, while the gelatinization enthalpy (ΔH(gel)) decreased. The LMWF was more stable at a water content of 10%.",Journal of agricultural and food chemistry,"['D002236', 'D055598', 'D005632', 'D005737', 'D015394', 'D008970', 'D009844', 'D010084', 'D013816', 'D014783']","['Carbohydrate Conformation', 'Chemical Phenomena', 'Fructose', 'Garlic', 'Molecular Structure', 'Molecular Weight', 'Oligosaccharides', 'Oxidation-Reduction', 'Thermodynamics', 'Viscosity']",Physicochemical characterization of a low-molecular-weight fructooligosaccharide from Chinese Cangshan garlic (Allium sativum L.).,"[None, None, 'Q000032', 'Q000737', None, None, 'Q000737', None, None, None]","[None, None, 'analysis', 'chemistry', None, None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22931231,2013,0.0,0.0,,, +22885051,"In this work, partially sulfonated polystyrene-titania (PSP-TiO(2)) organic-inorganic hybrid stir bar coating was prepared by sol-gel and blending methods, and a new method of PSP-TiO(2) coating stir bar sorptive extraction (SBSE)-high performance liquid chromatography (HPLC)-inductively coupled plasma mass spectrometry (ICP-MS) was established for the analysis of seleno-amino acids (selenocystine (SeCys(2)), methylseleno-cysteine (MeSeCys), selenomethionine (SeMet) and selenoethionine (SeEt)) and seleno-oligopeptides (γ-glutamyl-Se-methyl-selenocysteine (γ-GluMeSeCys) and selenodiglutathione (GS-Se-SG)) in biological samples. The prepared high polar PSP-TiO(2) hybrid coating avoided the swelling of PSP and cracking of TiO(2) coating by combining the good film-forming property of PSP with the high mechanical strength of TiO(2). The scanning electron microscope (SEM) showed that no obvious swelling and damage occurred for the PSP-TiO(2) hybrid stir bar coating after 30 extraction/desorption cycles. The preparation reproducibility of PSP-TiO(2) coated stir bar, evaluated with the relative standard deviations (RSDs), was in the range of 6.7-12.6% (n=5) in one batch, and 9.9-17.6% (n=7) among different batches. The limits of detection (LODs) of the developed method for six target selenium species were in the range of 50.2-185.5 ngL(-1) (as (77)Se) and 45.9-158.8 ngL(-1) (as (82)Se) with the RSDs within 4.9-11.7%. The dynamic linear range was found to cover three orders of magnitude with correlation coefficient of 0.9995-0.9999. The developed method was applied for the analysis of Certified Reference Material SELM-1 selenium enriched yeast and the determined values were in good agreement with the certified values. The method has also been applied for the analysis of seleno-amino acids and seleno-oligopeptides in human urine and garlic samples. Different from the conventional organic polymer SBSE coatings (such as polydimethylsiloxane, PDMS), the extraction mechanism of PSP-TiO(2) organic-inorganic hybrid SBSE coating was based on the cation exchange interaction, which made it feasible to directly extract high polar seleno-amino acids and seleno-oligopeptides in biological samples without derivatization. This coating could also be suitable for stir bar sorptive extraction of other cationic compounds from the environmental and biological samples.",Journal of chromatography. A,"['D002851', 'D006863', 'D057230', 'D013058', 'D008855', 'D009842', 'D015203', 'D018036']","['Chromatography, High Pressure Liquid', 'Hydrogen-Ion Concentration', 'Limit of Detection', 'Mass Spectrometry', 'Microscopy, Electron, Scanning', 'Oligopeptides', 'Reproducibility of Results', 'Selenium Compounds']",High polar organic-inorganic hybrid coating stir bar sorptive extraction combined with high performance liquid chromatography-inductively coupled plasma mass spectrometry for the speciation of seleno-amino acids and seleno-oligopeptides in biological samples.,"['Q000379', None, None, 'Q000379', None, 'Q000737', None, 'Q000737']","['methods', None, None, 'methods', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22885051,2012,1.0,1.0,,, +22860996,"Garlic has been used throughout history for both culinary and medicinal purpose. Allicin is a major component of crushed garlic. Although it is sensitive to heat and light and easily metabolized into various compounds such as diallyl disulfide, diallyl trisulfide, and diallyl sulfide, allicin is still a major bioactive compound of crushed garlic. The mortality of hepatocellular carcinoma is quite high and ranks among the top 10 cancer-related deaths in Taiwan. Although numerous studies have shown the cancer-preventive properties of garlic and its components, there is no study on the effect of allicin on the growth of human liver cancer cells. In this study, we focused on allicin-induced autophagic cell death in human liver cancer Hep G2 cells. Our results indicated that allicin induced p53-mediated autophagy and inhibited the viability of human hepatocellular carcinoma cell lines. Using Western blotting, we observed that allicin decreased the level of cytoplasmic p53, the PI3K/mTOR signaling pathway, and the level of Bcl-2 and increased the expression of AMPK/TSC2 and Beclin-1 signaling pathways in Hep G2 cells. In addition, the colocalization of LC3-II with MitoTracker-Red (labeling mitochondria), resulting in allicin-induced degradation of mitochondria, could be observed by confocal laser microscopy. In conclusion, allicin of garlic shows great potential as a novel chemopreventive agent for the prevention of liver cancer. ",Journal of agricultural and food chemistry,"['D016588', 'D017209', 'D051017', 'D001343', 'D000071186', 'D002470', 'D002851', 'D016158', 'D056945', 'D006801', 'D053078', 'D008565', 'D008869', 'D010588', 'D015398', 'D013441', 'D058570', 'D053719', 'D025521']","['Anticarcinogenic Agents', 'Apoptosis', 'Apoptosis Regulatory Proteins', 'Autophagy', 'Beclin-1', 'Cell Survival', 'Chromatography, High Pressure Liquid', 'Genes, p53', 'Hep G2 Cells', 'Humans', 'Membrane Potential, Mitochondrial', 'Membrane Proteins', 'Microtubule-Associated Proteins', 'Phagosomes', 'Signal Transduction', 'Sulfinic Acids', 'TOR Serine-Threonine Kinases', 'Tandem Mass Spectrometry', 'Tumor Suppressor Proteins']",Allicin induces p53-mediated autophagy in Hep G2 human liver cancer cells.,"['Q000494', 'Q000187', 'Q000378', 'Q000187', None, 'Q000187', 'Q000379', 'Q000502', 'Q000187', None, 'Q000187', 'Q000378', 'Q000378', 'Q000187', 'Q000187', 'Q000138', 'Q000378', None, 'Q000378']","['pharmacology', 'drug effects', 'metabolism', 'drug effects', None, 'drug effects', 'methods', 'physiology', 'drug effects', None, 'drug effects', 'metabolism', 'metabolism', 'drug effects', 'drug effects', 'chemical synthesis', 'metabolism', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/22860996,2014,0.0,0.0,,, +22610968,"The in vitro antibacterial activity of essential oils (EOs) obtained from fresh bulbs of garlic, Allium sativum L., and leek, Allium porrum L. ( Alliaceae), was studied. A. sativum (garlic) EO showed a good antimicrobial activity against Staphylococcus aureus (inhibition zone 14.8 mm), Pseudomonas aeruginosa (inhibition zone 21.1 mm), and Escherichia coli (inhibition zone 11.0 mm), whereas the EO of A. porrum (leek) had no antimicrobial activity. The main constituents of the garlic EO were diallyl monosulfide, diallyl disulfide (DADS), diallyl trisulfide, and diallyl tetrasulfide. The EO of A. porrum was characterized by the presence of dipropyl disulfide (DPDS), dipropyl trisulfide, and dipropyl tetrasulfide. The antimicrobial activities of the DADS and DPDS were also studied. The results obtained suggest that the presence of the allyl group is fundamental for the antimicrobial activity of these sulfide derivatives when they are present in Allium or in other species (DADS inhibition zone on S. aureus 15.9 mm, P. aeruginosa 21.9 mm, E. coli 11.4 mm).",Phytotherapy research : PTR,"['D000490', 'D000498', 'D000900', 'D004220', 'D004926', 'D008401', 'D009822', 'D010938', 'D011550', 'D013211', 'D013440']","['Allium', 'Allyl Compounds', 'Anti-Bacterial Agents', 'Disulfides', 'Escherichia coli', 'Gas Chromatography-Mass Spectrometry', 'Oils, Volatile', 'Plant Oils', 'Pseudomonas aeruginosa', 'Staphylococcus aureus', 'Sulfides']","The role of diallyl sulfides and dipropyl sulfides in the in vitro antimicrobial activity of the essential oil of garlic, Allium sativum L., and leek, Allium porrum L.","['Q000737', 'Q000737', 'Q000494', None, 'Q000187', None, 'Q000737', 'Q000737', 'Q000187', 'Q000187', 'Q000737']","['chemistry', 'chemistry', 'pharmacology', None, 'drug effects', None, 'chemistry', 'chemistry', 'drug effects', 'drug effects', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22610968,2013,1.0,1.0,,, +22513009,"A bioassay-guided phytochemical analysis of the polar extract from the bulbs of garlic, Allium sativum L., var. Voghiera, typical of Voghiera, Ferrara (Italy), allowed the isolation of ten furostanol saponins; voghieroside A1/A2 and voghieroside B1/B2, based on the rare agapanthagenin aglycone; voghieroside C1/C2, based on agigenin aglycone; and voghieroside D1/D2 and E1/E2, based on gitogenin aglycone. In addition, we found two known spirostanol saponins, agigenin 3-O-trisaccharide and gitogenin 3-O-tetrasaccharide. The chemical structures of the isolated compounds were established through a combination of extensive nuclear magnetic resonance, mass spectrometry and chemical analyses. High concentrations of two eugenol diglycosides were also found for the first time in Allium spp. The isolated compounds were evaluated for their antimicrobial activity towards two fungal species, the air-borne pathogen Botrytis cinerea and the antagonistic fungus Trichoderma harzianum.",Phytochemistry,"['D000935', 'D020171', 'D005737', 'D007558', 'D008826', 'D015394', 'D018517', 'D012503', 'D013150', 'D014242']","['Antifungal Agents', 'Botrytis', 'Garlic', 'Italy', 'Microbial Sensitivity Tests', 'Molecular Structure', 'Plant Roots', 'Saponins', 'Spirostans', 'Trichoderma']","Antifungal saponins from bulbs of garlic, Allium sativum L. var. Voghiera.","['Q000737', 'Q000187', 'Q000737', None, None, None, 'Q000737', 'Q000737', 'Q000737', 'Q000187']","['chemistry', 'drug effects', 'chemistry', None, None, None, 'chemistry', 'chemistry', 'chemistry', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/22513009,2012,0.0,0.0,,units J in Hz, +22500289,"The molecular characteristics of chlorothalonil can cause particular determination difficulties in some vegetable commodities such as leek or garlic. These difficulties are mainly related to the low recoveries obtained using common multi-residue methods (MRMs)--a consequence of the very high interaction level with natural components in the matrix. These shortcomings were pointed out in the last European Proficiency Test for Pesticide Residues on Fruits and Vegetables, where false negatives for chlorothalonil in leek were observed at around 50%. In this study we have evaluated the ethyl acetate, the Dutch mini-Luke and the QuEChERS MRMs to compare their capabilities for chlorothalonil determination using GC-MS/MS in both the electron impact ionization (EI) and negative chemical ionization (NCI) modes. Best recoveries (in the range of 100-120%, with an RSD below 20%) were obtained using the Dutch mini-Luke method. Lower values (52-70%) were obtained for ethyl acetate whereas no recovery was obtained when the QuEChERS method was applied. Furthermore, tomato matrix was also included in the experiments in order to facilitate the comparability of results. Two ionization modes, electron impact ionization (EI) and negative chemical ionization (NCI) in GC-MS/MS, were applied to evaluate their respective advantages and disadvantages for quantification and identification. As expected, NCI showed limits of detection (LODs) 5 to 10 times lower than EI. However, in both cases, the LODs were still below 10 μg kg(-1). The proposed optimal method was applied for chlorothalonil determination in leek and garlic with good results--in accordance with the European Union (EU) Analytical Quality Control (AQC) Guidelines for pesticides analysis.",The Analyst,"['D000085', 'D005591', 'D005506', 'D005638', 'D008401', 'D009570', 'D010573', 'D014675']","['Acetates', 'Chemical Fractionation', 'Food Contamination', 'Fruit', 'Gas Chromatography-Mass Spectrometry', 'Nitriles', 'Pesticide Residues', 'Vegetables']",Determination of chlorothalonil in difficult-to-analyse vegetable matrices using various multiresidue methods.,"['Q000737', None, 'Q000032', 'Q000737', None, 'Q000032', 'Q000032', 'Q000737']","['chemistry', None, 'analysis', 'chemistry', None, 'analysis', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22500289,2012,0.0,0.0,,, +22497489,"In the present study the effects of individual and combined essential oils (EOs) extracted from onion (Allium cepa L.) bulb and garlic (Allium sativum L.) clove on the growth of Aspergillus versicolor and sterigmatocystin (STC) production were investigated. The EOs obtained by hydrodistillation were analyzed by GC/MS. Twenty one compounds were identified in onion EO. The major components were: dimethyl-trisulfide (16.64%), methyl-propyl-trisulfide (14.21%), dietil-1,2,4-tritiolan (3R,5S-, 3S,5S- and 3R,5R- isomers) (13.71%), methyl-(1-propenyl)-disulfide (13.14%), and methyl-(1-propenyl)-trisulfide (13.02%). The major components of garlic EO were diallyl-trisulfide (33.55%), and diallyl-disulfide (28.05%). The mycelial growth and the STC production were recorded after 7, 14, and 21 d of the A. versicolor growth in Yeast extract sucrose (YES) broth containing different EOs concentrations. Compared to the garlic EO, the onion EO showed a stronger inhibitory effect on the A. versicolor mycelial growth and STC production. After a 21-d incubation of fungi 0.05 and 0.11 μg/mL of onion EO and 0.11 μg/mL of garlic EO completely inhibited the A. versicolor mycelial growth and mycotoxins biosynthesis. The combination of EOs of onion (75%) and garlic (25%) had a synergistic effect on growth inhibition of A. versicolor and STC production.",Journal of food science,"['D000498', 'D000890', 'D001230', 'D004220', 'D005506', 'D005516', 'D005519', 'D005737', 'D008401', 'D009822', 'D019697', 'D010938', 'D015203', 'D013170', 'D013241', 'D013440']","['Allyl Compounds', 'Anti-Infective Agents', 'Aspergillus', 'Disulfides', 'Food Contamination', 'Food Microbiology', 'Food Preservation', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Oils, Volatile', 'Onions', 'Plant Oils', 'Reproducibility of Results', 'Spores', 'Sterigmatocystin', 'Sulfides']",Effects of onion (Allium cepa L.) and garlic (Allium sativum L.) essential oils on the Aspergillus versicolor growth and sterigmatocystin production.,"['Q000032', 'Q000494', 'Q000187', 'Q000032', 'Q000517', None, None, 'Q000737', None, 'Q000494', 'Q000737', 'Q000494', None, 'Q000187', 'Q000096', 'Q000032']","['analysis', 'pharmacology', 'drug effects', 'analysis', 'prevention & control', None, None, 'chemistry', None, 'pharmacology', 'chemistry', 'pharmacology', None, 'drug effects', 'biosynthesis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/22497489,2013,2.0,3.0,,, +22473818,"Sulfenic acids play a prominent role in biology as key participants in cellular signaling relating to redox homeostasis, in the formation of protein-disulfide linkages, and as the central players in the fascinating organosulfur chemistry of the Allium species (e.g., garlic). Despite their relevance, direct measurements of their reaction kinetics have proven difficult owing to their high reactivity. Herein, we describe the results of hydrocarbon autoxidations inhibited by the persistent 9-triptycenesulfenic acid, which yields a second order rate constant of 3.0×10(6)  M(-1)  s(-1) for its reaction with peroxyl radicals in PhCl at 30 °C. This rate constant drops 19-fold in CH(3)CN, and is subject to a significant primary deuterium kinetic isotope effect, k(H)/k(D) = 6.1, supporting a formal H-atom transfer (HAT) mechanism. Analogous autoxidations inhibited by the Allium-derived (S)-benzyl phenylmethanethiosulfinate and a corresponding deuterium-labeled derivative unequivocally demonstrate the role of sulfenic acids in the radical-trapping antioxidant activity of thiosulfinates, through the rate-determining Cope elimination of phenylmethanesulfenic acid (k(H)/k(D) ≈ 4.5) and its subsequent formal HAT reaction with peroxyl radicals (k(H)/k(D) ≈ 3.5). The rate constant that we derived from these experiments for the reaction of phenylmethanesulfenic acid with peroxyl radicals was 2.8×10(7)  M(-1)  s(-1); a value 10-fold larger than that we measured for the reaction of 9-triptycenesulfenic acid with peroxyl radicals. We propose that whereas phenylmethanesulfenic acid can adopt the optimal syn geometry for a 5-centre proton-coupled electron-transfer reaction with a peroxyl radical, the 9-triptycenesulfenic is too sterically hindered, and undergoes the reaction instead through the less-energetically favorable anti geometry, which is reminiscent of a conventional HAT.","Chemistry (Weinheim an der Bergstrasse, Germany)","['D000975', 'D005737', 'D015394', 'D010084', 'D010545', 'D021241', 'D013434', 'D013441']","['Antioxidants', 'Garlic', 'Molecular Structure', 'Oxidation-Reduction', 'Peroxides', 'Spectrometry, Mass, Electrospray Ionization', 'Sulfenic Acids', 'Sulfinic Acids']",The reaction of sulfenic acids with peroxyl radicals: insights into the radical-trapping antioxidant activity of plant-derived thiosulfinates.,"['Q000737', 'Q000737', None, None, 'Q000737', None, 'Q000737', 'Q000138']","['chemistry', 'chemistry', None, None, 'chemistry', None, 'chemistry', 'chemical synthesis']",https://www.ncbi.nlm.nih.gov/pubmed/22473818,2012,,,,, +22467307,"Diallyl trisulfide (DATS), a polysulfide constituent found in garlic oil, is capable of the release of hydrogen sulfide (H(2)S). H(2)S is a known cardioprotective agent that protects the heart via antioxidant, antiapoptotic, anti-inflammatory, and mitochondrial actions. Here, we investigated DATS as a stable donor of H(2)S during myocardial ischemia-reperfusion (MI/R) injury in vivo. We investigated endogenous H(2)S levels, infarct size, postischemic left ventricular function, mitochondrial respiration and coupling, endothelial nitric oxide (NO) synthase (eNOS) activation, and nuclear E2-related factor (Nrf2) translocation after DATS treatment. Mice were anesthetized and subjected to a surgical model of MI/R injury with and without DATS treatment (200 μg/kg). Both circulating and myocardial H(2)S levels were determined using chemiluminescent gas chromatography. Infarct size was measured after 45 min of ischemia and 24 h of reperfusion. Troponin I release was measured at 2, 4, and 24 h after reperfusion. Cardiac function was measured at baseline and 72 h after reperfusion by echocardiography. Cardiac mitochondria were isolated after MI/R, and mitochondrial respiration was investigated. NO metabolites, eNOS phosphorylation, and Nrf2 translocation were determined 30 min and 2 h after DATS administration. Myocardial H(2)S levels markedly decreased after I/R injury but were rescued by DATS treatment (P < 0.05). DATS administration significantly reduced infarct size per area at risk and per left ventricular area compared with control (P < 0.001) as well as circulating troponin I levels at 4 and 24 h (P < 0.05). Myocardial contractile function was significantly better in DATS-treated hearts compared with vehicle treatment (P < 0.05) 72 h after reperfusion. DATS reduced mitochondrial respiration in a concentration-dependent manner and significantly improved mitochondrial coupling after reperfusion (P < 0.01). DATS activated eNOS (P < 0.05) and increased NO metabolites (P < 0.05). DATS did not appear to significantly induce the Nrf2 pathway. Taken together, these data suggest that DATS is a donor of H(2)S that can be used as a cardioprotective agent to treat MI/R injury.",American journal of physiology. Heart and circulatory physiology,"['D000498', 'D000818', 'D000975', 'D004305', 'D006862', 'D008297', 'D051379', 'D008810', 'D008929', 'D023421', 'D015428', 'D009206', 'D009569', 'D013440', 'D016277']","['Allyl Compounds', 'Animals', 'Antioxidants', 'Dose-Response Relationship, Drug', 'Hydrogen Sulfide', 'Male', 'Mice', 'Mice, Inbred C57BL', 'Mitochondria, Heart', 'Models, Animal', 'Myocardial Reperfusion Injury', 'Myocardium', 'Nitric Oxide', 'Sulfides', 'Ventricular Function, Left']",The polysulfide diallyl trisulfide protects the ischemic myocardium by preservation of endogenous hydrogen sulfide and increasing nitric oxide bioavailability.,"['Q000494', None, 'Q000494', None, 'Q000378', None, None, None, 'Q000187', None, 'Q000378', 'Q000378', 'Q000378', 'Q000494', 'Q000187']","['pharmacology', None, 'pharmacology', None, 'metabolism', None, None, None, 'drug effects', None, 'metabolism', 'metabolism', 'metabolism', 'pharmacology', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/22467307,2012,0.0,0.0,,, +22434122,"This paper reviews a 10-year experience in establishing a cryopreserved Allium germplasm collection at the genebank of the National Agrobiodiversity Center, Republic of Korea. A systematic approach to Allium cryopreservation included: 1. revealing the most critical factors that affected regeneration after cryostorage; 2. understanding the mechanisms of cryoprotection by analyzing the thermal behavior of explants and cryoprotectant solutions using DSC and influx/efflux of cryoprotectants using HPLC; 3. assessing genetic stability of regenerants; and 4. revealing the efficiency of cryotherapy. Bulbil primordia, i.e. asexual bulbs formed on unripe inflorescences, proved to be the most suitable material for conservation of bolting varieties due to high post-cryopreservation regrowth and lower microbial infection level, followed by apical shoot apices from single bulbs and cloves. A total of 1,158 accessions of garlic as well as some Allium species have been cryopreserved during 2005-2010 using the droplet-vitrification technique with a mean regeneration percentage of 65.9 percent after cryostorage. These results open the door for large-scale implementation of cryostorage and for simplifying international exchange for clonal Allium germplasm.",Cryo letters,"['D000490', 'D002152', 'D002851', 'D003080', 'D015925', 'D003451', 'D055993', 'D010935', 'D018517', 'D018520', 'D010942', 'D012038', 'D056910', 'D058989']","['Allium', 'Calorimetry, Differential Scanning', 'Chromatography, High Pressure Liquid', 'Cold Temperature', 'Cryopreservation', 'Cryoprotective Agents', 'Germ Cells, Plant', 'Plant Diseases', 'Plant Roots', 'Plant Shoots', 'Plant Viruses', 'Regeneration', 'Republic of Korea', 'Vitrification']",Cryobanking of Korean allium germplasm collections: results from a 10 year experience.,"['Q000166', None, None, None, 'Q000379', None, 'Q000166', 'Q000821', 'Q000254', 'Q000254', None, None, None, None]","['cytology', None, None, None, 'methods', None, 'cytology', 'virology', 'growth & development', 'growth & development', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/22434122,2012,,,,, +22416880,"The influence of processing, with and without fermentation, on the contents of organosulfur compounds, namely, γ-glutamyl peptides, S-alk(en)yl-L-cysteine sulfoxides (ACSOs), and S-allyl-L-cysteine (SAC), in pickled blanched garlic was evaluated. For each processing type, the effect of the preservation method and storage time was also analyzed. Blanching in hot water (90 °C for 5 min) hardly affected the individual organosulfur compound content. The fermentation and packing steps negatively affected the levels of all compounds except for SAC. The content of this compound increased during storage at room temperature whereas γ-glutamyl peptides and ACSOs were degraded to various extents. The pasteurization treatment itself had no significant effect on the concentrations of organosulfur compounds. Use of the corresponding fermentation brine in the case of the fermented product in conjunction with refrigerated storage was found to be the best method to preserve the levels of organosulfur compounds in pickled garlic stored for up to one year.",Journal of agricultural and food chemistry,"['D002851', 'D005285', 'D005511', 'D005737', 'D007778', 'D013457', 'D013997']","['Chromatography, High Pressure Liquid', 'Fermentation', 'Food Handling', 'Garlic', 'Lactobacillus', 'Sulfur Compounds', 'Time Factors']",Effect of processing and storage time on the contents of organosulfur compounds in pickled blanched garlic.,"[None, None, 'Q000379', 'Q000737', 'Q000378', 'Q000032', None]","[None, None, 'methods', 'chemistry', 'metabolism', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/22416880,2012,1.0,1.0,,, +22320076,"An automatic screening method based on HPLC/time-of-flight (TOF)-MS (full scan) was used for the analysis of 103 non-European fruit and vegetable samples after extraction by the quick, easy, cheap, effective, rugged, and safe method. The screening method uses a database that includes 300 pesticides, their fragments, and isotopical signals (910 ions) that identified 210 pesticides in 78 positive samples, with the highest number of detection being nine pesticides/sample. The concentrations of 97 pesticides were <10 microg/kg, 53 were between 10 and 100 microg/kg, and 60 were at a concentration of >100 microg/kg. Several parameters of the automatic screening method were carefully studied to avoid false positives and negatives in the studied samples; these included peak filter (number of chromatographic peak counts) and search criteria (retention time and error window). These parameters were affected by differences in mass accuracy and sensitivity of the two HPLC/TOF-MS systems used with different resolution powers (15 000 and 7500), the capabilities of which for resolving the ions included in the database from the matrix ions were studied in four matrixes, viz., pepper, rice, garlic, and cauliflower. Both of these mass resolutions were found to be satisfactory to resolve interferences from the signals of interest in the studied matrixes.",Journal of AOAC International,"['D002851', 'D005506', 'D005638', 'D010573', 'D012680', 'D052616', 'D021241', 'D053719', 'D014675']","['Chromatography, High Pressure Liquid', 'Food Contamination', 'Fruit', 'Pesticide Residues', 'Sensitivity and Specificity', 'Solid Phase Extraction', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry', 'Vegetables']",Evaluation of relevant time-of-flight-MS parameters used in HPLC/MS full-scan screening methods for pesticide residues.,"['Q000295', 'Q000032', 'Q000737', 'Q000032', None, None, 'Q000295', 'Q000295', 'Q000737']","['instrumentation', 'analysis', 'chemistry', 'analysis', None, None, 'instrumentation', 'instrumentation', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22320076,2012,,,,, +22284504,"In recent years, the release of information about the preventative and curative properties of garlic on different diseases and their benefits to human health has led to an increase in the consumption of garlic. To meet the requirements of international markets and reach competitiveness and profitability, farmers seek to extend the offer period of fresh garlic by increasing post-harvest life. As a result, the use of maleic hydrazide (1,2-dihydropyridazine-3,6-dione) [MH], a plant growth regulator, has been widespread in various garlic growing regions of the world. The present work was undertaken to develop and validate a new analytical procedure based on MH extraction from garlic previously frozen by liquid nitrogen and submitted to low temperature clean-up. The applicability of the method by analysis of garlic samples from a commercial plantation was also demonstrated. The influence of certain factors on the performance of the analytical methodology were studied and optimized. The approach is an efficient extraction, clean-up and determination alternative for MH residue-quantification due to its specificity and sensitivity. The use of liquid nitrogen during the sample preparation prevents the degradation of the analyte due to oxidation reactions, a major limiting factor. Moreover, the method provides good linearity (r(2): 0.999), good intermediate precision (coefficient of variation (CV): 8.39%), and extracts were not affected by the matrix effect. Under optimized conditions, the limit of detection (LOD) (0.33 mg kg(-1)) was well below the maximum residue level (MRL) set internationally for garlic (15 mg kg(-1)), with excellent rates of recovery (over 95%), good repeatability and acceptable accuracy (CV averaged 5.74%), since garlic is a complex matrix. The analytical performance of the methodology presented was compared with other techniques already reported, with highly satisfactory results, lower LOD and higher recoveries rates. In addition, the extraction process is simple, not expensive, easily executable and requires lower volumes of organic solvent. The proposed methodology removes the need of extensive typical laboratory extraction procedures, reducing the amount of time needed for pesticide analysis and increasing sample throughput. Adopting this method gives food safety laboratories the potential to increase cost savings by a suitable technique in routine testing to determine MH residues in garlic.",Talanta,"['D002849', 'D002851', 'D003080', 'D005504', 'D005737', 'D008300', 'D000432', 'D009584', 'D010573', 'D010937', 'D015203', 'D012680', 'D052616', 'D012997']","['Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Cold Temperature', 'Food Analysis', 'Garlic', 'Maleic Hydrazide', 'Methanol', 'Nitrogen', 'Pesticide Residues', 'Plant Growth Regulators', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Solid Phase Extraction', 'Solvents']",Determination of maleic hydrazide residues in garlic bulbs by HPLC.,"[None, None, None, None, 'Q000737', 'Q000032', 'Q000737', None, 'Q000032', 'Q000032', None, None, None, 'Q000737']","[None, None, None, None, 'chemistry', 'analysis', 'chemistry', None, 'analysis', 'analysis', None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22284504,2012,0.0,0.0,,pesti. Absop, +22161312,"Superoxide dismutases (SODs; EC 1.15.1.1) are key enzymes in the cells protection against oxidant agents. Thus, SODs play a major role in the protection of aerobic organisms against oxygen-mediated damages. Three SOD isoforms were previously identified by zymogram staining from Allium sativum bulbs. The purified Cu, Zn-SOD2 shows an antagonist effect to an anticancer drug and alleviate cytotoxicity inside tumor cells lines B16F0 (mouse melanoma cells) and PAE (porcine aortic endothelial cells). To extend the characterization of Allium SODs and their corresponding genes, a proteomic approach was applied involving two-dimensional gel electrophoresis and LC-MS/MS analyses. From peptide sequence data obtained by mass spectrometry and sequences homologies, primers were defined and a cDNA fragment of 456 bp was amplified by RT-PCR. The cDNA nucleotide sequence analysis revealed an open reading frame coding for 152 residues. The deduced amino acid sequence showed high identity (82-87%) with sequences of Cu, Zn-SODs from other plant species. Molecular analysis was achieved by a protein 3D structural model.",Molecular biotechnology,"['D000595', 'D000818', 'D001483', 'D002457', 'D002853', 'D003001', 'D019610', 'D004317', 'D019008', 'D015180', 'D042783', 'D005737', 'D013058', 'D008546', 'D051379', 'D008958', 'D008969', 'D010455', 'D016133', 'D020033', 'D040901', 'D016415', 'D017422', 'D013482', 'D034421']","['Amino Acid Sequence', 'Animals', 'Base Sequence', 'Cell Extracts', 'Chromatography, Liquid', 'Cloning, Molecular', 'Cytoprotection', 'Doxorubicin', 'Drug Resistance, Neoplasm', 'Electrophoresis, Gel, Two-Dimensional', 'Endothelial Cells', 'Garlic', 'Mass Spectrometry', 'Melanoma, Experimental', 'Mice', 'Models, Molecular', 'Molecular Sequence Data', 'Peptides', 'Polymerase Chain Reaction', 'Protein Isoforms', 'Proteomics', 'Sequence Alignment', 'Sequence Analysis, DNA', 'Superoxide Dismutase', 'Sus scrofa']","Combined proteomic and molecular approaches for cloning and characterization of copper-zinc superoxide dismutase (Cu, Zn-SOD2) from garlic (Allium sativum).","[None, None, None, None, None, 'Q000379', None, 'Q000494', 'Q000187', None, 'Q000166', 'Q000201', None, 'Q000473', None, None, None, 'Q000737', None, 'Q000737', 'Q000379', None, None, 'Q000737', None]","[None, None, None, None, None, 'methods', None, 'pharmacology', 'drug effects', None, 'cytology', 'enzymology', None, 'pathology', None, None, None, 'chemistry', None, 'chemistry', 'methods', None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/22161312,2013,0.0,0.0,,, +22127783,"A modified quick, easy, cheap, effective, rugged and safe (QuEChERS) method with multi-walled carbon nanotubes (MWCNTs) as a reversed-dispersive solid-phase extraction (r-DSPE) material combined with gas chromatography-mass spectrometry was developed for the determination of 14 pesticides in complex matrices. Four vegetables (leek, onion, ginger and garlic) were selected as the complex matrices for validating this new method. This technique involved the acetonitrile-based sample preparation and MWCNTs were used as the r-DSPE material in the cleanup step. Two important parameters influencing the MWCNTs efficiency, the external diameters and the amount of MWCNTs used, were investigated. Under the optimized conditions, recoveries of 78-110% were obtained for the target analytes in the complex matrices at two concentration levels of 0.02 and 0.2 mg/kg. In addition, the RSD values ranged from 1 to 13%. LOQs and LODs for 14 pesticides ranged from 2 to 20 μg/kg and from 1 to 6 μg/kg, respectively.",Journal of separation science,"['D005506', 'D008401', 'D037742', 'D010573', 'D052616', 'D014675']","['Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Nanotubes, Carbon', 'Pesticide Residues', 'Solid Phase Extraction', 'Vegetables']",Determination of pesticide residues in complex matrices using multi-walled carbon nanotubes as reversed-dispersive solid-phase extraction sorbent.,"['Q000032', 'Q000379', 'Q000737', 'Q000032', 'Q000295', 'Q000737']","['analysis', 'methods', 'chemistry', 'analysis', 'instrumentation', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/22127783,2012,0.0,2.0,,Pesticides, +22097791,"A new idea of solvent transfer technique was developed and applied to determine 283 pesticide residues in garlic by gas chromatography-mass spectrometry (GC-MS) (method I), and the other method using normal phase silica/selective elution technique was applied to determine 6 pesticide residues with strongly polar in garlic by gas chromatography (method II). For the method I, the residues were extracted from homogenized tissue with acetonitrile-water, separated with liquid-liquid partition; the clear supernatant was purified by solvent transfer technique and solid phase extraction (Envi-18 and LC-NH2 columns), then was analyzed by GC-MS. For the method II, the residues were extracted from homogenized tissue using ethyl acetate and sodium sulfate assisted by ultrasonication. The supernatant was purified by solid phase extraction (primary secondary amine (PSA) powder and LC-Si column) prior to GC analysis. The determination was performed by using selected ion monitoring (SIM) mode in GC-MS method and flame photometric detector (FPD) in GC method, then external standard method was used in the quantification. Under the optimal conditions, the detection limits for the two methods (S/N > or = 10) of pesticides were 0.01-0.05 mg/kg, the recoveries carried out by the addition of standards of 0.01-0.20 mg/kg were 52%-163%, among which the recoveries for 88% pesticides were between 70% and 120%; the recoveries of the method II were 70%-111%; while the relative standard deviations were 2.4%-18% and 3.2%-9.3%, respectively. The model of solvent transfer technique and the sensitivity improvement of GC-MS was also studied. The methods are easy, fast, more sensitive, and can meet the requirement of the multiresidual analysis in garlic.",Se pu = Chinese journal of chromatography,"['D002849', 'D005504', 'D005506', 'D005737', 'D008401', 'D010573']","['Chromatography, Gas', 'Food Analysis', 'Food Contamination', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Pesticide Residues']",[Multi-residue determination of 289 pesticides in garlic by gas chromatography and gas chromatography/mass spectrometry].,"['Q000379', 'Q000379', 'Q000032', 'Q000737', 'Q000379', 'Q000032']","['methods', 'methods', 'analysis', 'chemistry', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/22097791,2012,,,,, +22033380,"Chronic lead (Pb(2+)) exposure leads to the reduced lifespan of erythrocytes. Oxidative stress and K(+) loss accelerate Fas translocation into lipid raft microdomains inducing Fas mediated death signaling in these erythrocytes. Pathophysiological-based therapeutic strategies to combat against erythrocyte death were evaluated using garlic-derived organosulfur compounds like diallyl disulfide (DADS), S allyl cysteine (SAC) and imidazole based Gardos channel inhibitor clotrimazole (CLT).",Biochimica et biophysica acta,"['D000818', 'D017209', 'D003022', 'D003545', 'D015536', 'D004912', 'D005260', 'D007854', 'D007855', 'D013058', 'D051379', 'D008807', 'D017382', 'D015398', 'D019014']","['Animals', 'Apoptosis', 'Clotrimazole', 'Cysteine', 'Down-Regulation', 'Erythrocytes', 'Female', 'Lead', 'Lead Poisoning', 'Mass Spectrometry', 'Mice', 'Mice, Inbred BALB C', 'Reactive Oxygen Species', 'Signal Transduction', 'fas Receptor']",S-allyl cysteine in combination with clotrimazole downregulates Fas induced apoptotic events in erythrocytes of mice exposed to lead.,"[None, None, 'Q000494', 'Q000031', 'Q000187', 'Q000187', None, 'Q000633', 'Q000097', None, None, None, None, None, 'Q000378']","[None, None, 'pharmacology', 'analogs & derivatives', 'drug effects', 'drug effects', None, 'toxicity', 'blood', None, None, None, None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/22033380,2012,0.0,0.0,,, +21872076,"A non-chromatographic separation and preconcentration method for Se species determination based on the use of an on-line ionic liquid (IL) dispersive microextraction system coupled to electrothermal atomic absorption spectrometry (ETAAS) is proposed. Retention and separation of the IL phase was achieved with a Florisil(®)-packed microcolumn after dispersive liquid-liquid microextraction (DLLME) with tetradecyl(trihexyl)phosphonium chloride IL (CYPHOS(®) IL 101). Selenite [Se(IV)] species was selectively separated by forming Se-ammonium pyrrolidine dithiocarbamate (Se-APDC) complex followed by extraction with CYPHOS(®) IL 101. The methodology was highly selective towards Se(IV), while selenate [Se(VI)] was reduced and then indirectly determined. Several factors influencing the efficiency of the preconcentration technique, such as APDC concentration, sample volume, extractant phase volume, type of eluent, elution flow rate, etc., have been investigated in detail. The limit of detection (LOD) was 15 ng L(-1) and the relative standard deviation (RSD) for 10 replicates at 0.5 μg L(-1) Se concentration was 5.1%, calculated with peak heights. The calibration graph was linear and a correlation coefficient of 0.9993 was achieved. The method was successfully employed for Se speciation studies in garlic extracts and water samples.",Talanta,"['D005737', 'D006851', 'D007202', 'D052578', 'D059627', 'D009862', 'D009943', 'D011759', 'D018036', 'D013054', 'D013859', 'D014867']","['Garlic', 'Hydrochloric Acid', 'Indicators and Reagents', 'Ionic Liquids', 'Liquid Phase Microextraction', 'Online Systems', 'Organophosphorus Compounds', 'Pyrrolidines', 'Selenium Compounds', 'Spectrophotometry, Atomic', 'Thiocarbamates', 'Water']",Determination of inorganic selenium species in water and garlic samples with on-line ionic liquid dispersive microextraction and electrothermal atomic absorption spectrometry.,"['Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000379', None, 'Q000737', 'Q000737', 'Q000032', 'Q000379', 'Q000737', 'Q000737']","['chemistry', 'chemistry', 'chemistry', 'chemistry', 'methods', None, 'chemistry', 'chemistry', 'analysis', 'methods', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/21872076,2011,1.0,1.0,,, +21864633,"A simple, quick and reliable analytical method for the determination of 1-naphthylacetic acid in garlic and soil has been developed in this study. The residual levels and dissipation rates of 1-naphthylacetic acid in garlic and soil were determined by high performance liquid chromatography-tandem mass spectroscopy (HPLC-MS/MS). The limit of quantification (LOQ) of the developed method was 0.005 mg/kg. The half-lives of 1-naphthylacetic acid in garlic plants and soil were 0.80-1.4 days and 0.94-2.0 days, respectively. The final residues of 1-naphthylacetic acid in garlic, garlic sprout and soil could not be detected and were all below 0.05 mg/kg (the MRL of EU). Results of the ultimate residues in garlic and soil showed that this pesticide is safe to be used under the recommended dosages.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000498', 'D002851', 'D004785', 'D009280', 'D010937', 'D012987', 'D013440', 'D053719']","['Allyl Compounds', 'Chromatography, High Pressure Liquid', 'Environmental Pollutants', 'Naphthaleneacetic Acids', 'Plant Growth Regulators', 'Soil', 'Sulfides', 'Tandem Mass Spectrometry']",Determination and study on dissipation of 1-naphthylacetic acid in garlic and soil using high performance liquid chromatography-tandem mass spectrometry.,"['Q000737', 'Q000379', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000379']","['chemistry', 'methods', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/21864633,2012,0.0,0.0,,pesticide test on spiked samples, +21845941,"Acaricidal effects of three essential oils extracted from Mexican oregano leaves (Lippia graveolens Kunth), rosemary leaves (Rosmarinus officinalis L.), and garlic bulbs (Allium sativum L.) on 10-d-old Rhipicephalus (Boophilus) microplus (Canestrini) tick larvae were evaluated by using the larval packet test bioassay. Serial dilutions of the three essential oils were tested from a starting concentration of 20 to 1.25%. Results showed that both Mexican oregano and garlic essential oils had very similar activity, producing high mortality (90-100%) in all tested concentrations on 10-d-old R. microplus tick larvae. Rosemary essential oil produced >85% larval mortality at the higher concentrations (10 and 20%), but the effect decreased noticeably to 40% at an oil concentration of 5%, and mortality was absent at 2.5 and 1.25% of the essential oil concentration. Chemical composition of the essential oils was elucidated by gas chromatography-mass spectrometry analyses. Mexican oregano essential oil included thymol (24.59%), carvacrol (24.54%), p-cymene (13.6%), and y-terpinene (7.43%) as its main compounds, whereas rosemary essential oil was rich in a-pinene (31.07%), verbenone (15.26%), and 1,8-cineol (14.2%), and garlic essential oil was rich in diallyl trisulfide (33.57%), diallyl disulfide (30.93%), and methyl allyl trisulfide (11.28%). These results suggest that Mexican oregano and garlic essential oils merit further investigation as components of alternative approaches for R. microplus tick control.",Journal of medical entomology,"['D056810', 'D000818', 'D005737', 'D008401', 'D026863', 'D007814', 'D032411', 'D008800', 'D009822', 'D010938', 'D048494', 'D027542']","['Acaricides', 'Animals', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Ixodidae', 'Larva', 'Lippia', 'Mexico', 'Oils, Volatile', 'Plant Oils', 'Rhipicephalus', 'Rosmarinus']","Acaricidal effect of essential oils from Lippia graveolens (Lamiales: Verbenaceae), Rosmarinus officinalis (Lamiales: Lamiaceae), and Allium sativum (Liliales: Liliaceae) against Rhipicephalus (Boophilus) microplus (Acari: Ixodidae).","['Q000737', None, 'Q000737', None, 'Q000187', 'Q000187', 'Q000737', None, 'Q000737', 'Q000737', 'Q000187', 'Q000737']","['chemistry', None, 'chemistry', None, 'drug effects', 'drug effects', 'chemistry', None, 'chemistry', 'chemistry', 'drug effects', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/21845941,2011,,,,, +21827329,"This study examined the antiradical activity and chemical composition of essential oils of some plants grown in Mosul, Iraq. The essential oils of myrtle and parsley seed contained α-pinene (36.08% and 22.89%, respectively) as main constituents. Trans-Anethole was the major compound found in fennel and aniseed oils (66.98% and 93.51%, respectively). The dominant constituent of celery seed oil was limonene (76.63%). Diallyl disulphide was identified as the major component in garlic oil (36.51%). Antiradical activity was higher in garlic oil (76.63%) and lower in myrtle oil (39.23%). The results may suggest that some essential oils from Iraq possess compounds with antiradical activity, and these oils can be used as natural antioxidants in food applications.",Natural product research,"['D000490', 'D000498', 'D000840', 'D019661', 'D053138', 'D004220', 'D005519', 'D016166', 'D008401', 'D007493', 'D039821', 'D027822', 'D009822', 'D018515', 'D018517', 'D012639', 'D013045', 'D013729']","['Allium', 'Allyl Compounds', 'Anisoles', 'Apiaceae', 'Cyclohexenes', 'Disulfides', 'Food Preservation', 'Free Radical Scavengers', 'Gas Chromatography-Mass Spectrometry', 'Iraq', 'Monoterpenes', 'Myrtaceae', 'Oils, Volatile', 'Plant Leaves', 'Plant Roots', 'Seeds', 'Species Specificity', 'Terpenes']",Essential oil composition and antiradical activity of the oil of Iraq plants.,"['Q000737', 'Q000032', 'Q000032', 'Q000737', 'Q000032', 'Q000032', 'Q000379', 'Q000032', None, None, 'Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', None, 'Q000032']","['chemistry', 'analysis', 'analysis', 'chemistry', 'analysis', 'analysis', 'methods', 'analysis', None, None, 'analysis', 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/21827329,2012,,,,, +21756199,"Speciation analysis using high-performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICP MS) is now commonly used to investigate metabolic and toxicological aspects of some metals and metalloids. We have developed a rapid method for simultaneous identification and quantification of metabolites of selenium (Se) compounds using multiple standards labelled with different isotopes. A mixture of the labelled standards was spiked in a selenised garlic extract and the sample was subjected to speciation analysis by HPLC-ICP MS. The selenised garlic contains γ-glutamyl-methylselenocysteine, methylselenocysteine, and selenomethionine and the concentrations of those Se compounds were 723.8, 414.8, and 310.7 ng Se ml(-1), respectively. The isotopically labelled standards were also applied to the speciation of Se in rat urine. Selenate, methylselenonic acid, selenosugar, and trimethyselenium ions were found to be excreted by the present speciation procedure. Multiple standards labelled with different stable isotopes enable high-throughput identification and quantitative measurements of Se metabolites.",Isotopes in environmental and health studies,"['D000818', 'D002851', 'D003903', 'D005737', 'D007201', 'D007553', 'D007554', 'D008297', 'D013058', 'D051381', 'D017208', 'D012643', 'D018036', 'D012680', 'D013997']","['Animals', 'Chromatography, High Pressure Liquid', 'Deuterium', 'Garlic', 'Indicator Dilution Techniques', 'Isotope Labeling', 'Isotopes', 'Male', 'Mass Spectrometry', 'Rats', 'Rats, Wistar', 'Selenium', 'Selenium Compounds', 'Sensitivity and Specificity', 'Time Factors']",Rapid speciation and quantification of selenium compounds by HPLC-ICP MS using multiple standards labelled with different isotopes.,"[None, 'Q000379', 'Q000032', 'Q000737', 'Q000295', 'Q000379', 'Q000032', None, 'Q000379', None, None, 'Q000032', 'Q000032', None, None]","[None, 'methods', 'analysis', 'chemistry', 'instrumentation', 'methods', 'analysis', None, 'methods', None, None, 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/21756199,2011,,,,, +21732172,"A lectin was purified from the leaves of Allium altaicum and corresponding gene was cloned. The lectin namely Allium altaicum agglutinin (AAA) was ~24 kDa homodimeric protein and similar to a typical garlic leaf lectin. It was synthesized as 177 amino acid residues pre-proprotein, which consisted of 28 and 43 amino acid long N and C-terminal signal peptides, respectively. The plant expressed this protein more in scapes and flowers in comparison to the bulbs and leaves. Hemagglutination activity (with rabbit erythrocytes) was 1,428 fold higher as compared to Allium sativum leaf agglutinin (ASAL) although, the insecticidal activity against cotton aphid (Aphis gossypii) was relatively low. Glycan array revealed that AAA had higher affinity towards GlcAb1-3Galb as compared to ASAL. Homology analysis showed 57-94% similarity with other Allium lectins. The mature protein was expressed in E. coli as a fusion with SUMO peptide in soluble and biologically active form. Recombinant protein retained high hemagglutination activity.",The protein journal,"['D000490', 'D000595', 'D000818', 'D001042', 'D001483', 'D002240', 'D003001', 'D004912', 'D004926', 'D006384', 'D006388', 'D007306', 'D008958', 'D008969', 'D010802', 'D018515', 'D037121', 'D011134', 'D011817', 'D011993', 'D025842', 'D016415', 'D053719']","['Allium', 'Amino Acid Sequence', 'Animals', 'Aphids', 'Base Sequence', 'Carbohydrate Sequence', 'Cloning, Molecular', 'Erythrocytes', 'Escherichia coli', 'Hemagglutination', 'Hemagglutinins', 'Insecticides', 'Models, Molecular', 'Molecular Sequence Data', 'Phylogeny', 'Plant Leaves', 'Plant Lectins', 'Polysaccharides', 'Rabbits', 'Recombinant Fusion Proteins', 'SUMO-1 Protein', 'Sequence Alignment', 'Tandem Mass Spectrometry']",Purification and characterization of a lectin with high hemagglutination property isolated from Allium altaicum.,"['Q000737', None, None, 'Q000187', None, None, None, 'Q000187', None, 'Q000187', 'Q000737', 'Q000737', None, None, None, 'Q000737', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', None, None]","['chemistry', None, None, 'drug effects', None, None, None, 'drug effects', None, 'drug effects', 'chemistry', 'chemistry', None, None, None, 'chemistry', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/21732172,2011,0.0,0.0,,, +21645684,"A quantitative immunochemical rapid test for sensitive determination of benzo[a]pyrene (BAP) as a model analyte was developed making use of a handheld reader for results evaluation. The covalent immobilization of antibodies to different Sepharose gels, i.e., CNBr-activated Sepharose 4B and CNBr-activated Sepharose 4 Fast Flow was compared with adsorption to a polyethylene support. The lowest limits of detection (LOD) were 4 ng L(-1) and 40 ng L(-1), respectively, using optimized assay conditions. The developed test was applied to food supplements (garlic, black radish and maca), including a pretreatment procedure. LOD of 9 ng kg(-1) and linear range of 13-80 ng kg(-1) were obtained. Results of BAP determination in naturally contaminated samples were confirmed by high-performance liquid chromatography coupled to fluorescence detection and a good correlation was achieved. We suggest that the developed test format can be used to quantitative detection of the low molecular weight analytes, such as mycotoxins, pesticides, other pollutants in food and environmental samples.",Talanta,"['D055910', 'D001564', 'D002851', 'D019587', 'D005506', 'D005737', 'D029686', 'D057230', 'D031224', 'D012685']","['Antibodies, Immobilized', 'Benzo(a)pyrene', 'Chromatography, High Pressure Liquid', 'Dietary Supplements', 'Food Contamination', 'Garlic', 'Lepidium', 'Limit of Detection', 'Raphanus', 'Sepharose']",New approach to quantitative analysis of benzo[a]pyrene in food supplements by an immunochemical column test.,"[None, 'Q000032', None, 'Q000032', 'Q000032', None, None, None, None, None]","[None, 'analysis', None, 'analysis', 'analysis', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/21645684,2011,1.0,1.0,,Supplements, +21535547,"Encapsulation of garlic oil (GO) in β-cyclodextrin (β-CD) was undertaken to generate a release system of antimicrobial volatiles and tested on microbial growth and sensory quality of fresh-cut tomato. GO volatile profile was characterized by gas chromatography mass spectrometry and to demonstrate the disadvantages of applying free GO to fresh-cut tomato, the effect of different free oil treatments (0, 50, 100, and 200 μg/100 g) on microbial growth and sensorial quality was tested. The effect of GO capsules (0, 0.25, 0.5, and 1 g/100 g) on microbial growth and sensory quality of tomato was also investigated. Allyl disulfide was the most abundant GO compound identified. The release of volatiles from GO: β-CD capsules (12: 88 [w/w] ratio) was evaluated at 100% relative humidity (RH). Close to 70% of GO volatiles were released from capsules when exposed to 100% RH during 5 wk. The most effective antimicrobial concentrations of free oil (100 and 200 μg/100 g) applied to tomatoes did not present acceptable sensory quality for panelists. Tomato was affected by the highest concentration of GO capsules applied, showing the lowest microbial growth and the highest sensory quality. In this context, successful encapsulation in β-CD could stimulate further interest in the use of GO for the control of microbial growth in fresh-cut tomato.",Journal of food science,"['D000328', 'D000498', 'D000890', 'D015169', 'D003692', 'D004339', 'D057140', 'D005260', 'D005519', 'D005638', 'D005658', 'D006090', 'D006094', 'D006801', 'D018551', 'D008297', 'D009812', 'D013440', 'D055549', 'D047392']","['Adult', 'Allyl Compounds', 'Anti-Infective Agents', 'Colony Count, Microbial', 'Delayed-Action Preparations', 'Drug Compounding', 'Fast Foods', 'Female', 'Food Preservation', 'Fruit', 'Fungi', 'Gram-Negative Bacteria', 'Gram-Positive Bacteria', 'Humans', 'Lycopersicon esculentum', 'Male', 'Odorants', 'Sulfides', 'Volatile Organic Compounds', 'beta-Cyclodextrins']",Optimizing the use of garlic oil as antimicrobial agent on fresh-cut tomato through a controlled release system.,"[None, 'Q000737', 'Q000737', None, 'Q000494', None, 'Q000382', None, 'Q000379', 'Q000382', 'Q000187', 'Q000187', 'Q000187', None, 'Q000382', None, None, 'Q000737', 'Q000032', 'Q000737']","[None, 'chemistry', 'chemistry', None, 'pharmacology', None, 'microbiology', None, 'methods', 'microbiology', 'drug effects', 'drug effects', 'drug effects', None, 'microbiology', None, None, 'chemistry', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/21535547,2011,1.0,1.0,,, +21535486,"The formation of pink-red pigments (""pinking"") by various amino acids was investigated by reacting amino acids with compounds present in onion juice. The unknown pink-red pigments were generated and separated using high-performance liquid chromatography (HPLC) and a diode array detector (DAD) in the range of 200 to 700 nm. To generate pink-red pigments, we developed several reaction systems using garlic alliinase, purified 1-propenyl-L-cysteine sulfoxide (1-PeCSO), onion thiosulfinate, natural onion juice, and 21 free amino acids. The compound 1-PeCSO was a key compound associated with pinking in the presence of both the alliinase and amino acids. Numerous naturally occurring pink-red pigments were detected and separated from pink onion juice using the HPLC-DAD system at 515 nm. Most free amino acids, with the exceptions of histidine, serine, and cysteine, formed various pink-red pigments when reacted with onion thiosulfinate. This observation indicated that onion pinking is caused not by a single pigment, but by many. Furthermore, more than one color compound could be produced from a single amino acid; this explains, in part, why there were many pink-red compound peaks in the chromatogram of discolored natural onion juice. We presumed that the complexity of the pink-red pigments was due to the involvement of more than 21 natural amino acids as well as several derivatives of the color products produced from each amino acid. We observed that the pinking process in onion juice is very similar to that of the greening process in crushed garlic, emphasizing that both thiosulfinate from flavor precursors and free amino acids are absolutely required for the discoloration.",Journal of food science,"['D000596', 'D013437', 'D002851', 'D003545', 'D005737', 'D019697', 'D010860', 'D010940', 'D018517', 'D013053', 'D013441', 'D013454']","['Amino Acids', 'Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Onions', 'Pigments, Biological', 'Plant Proteins', 'Plant Roots', 'Spectrophotometry', 'Sulfinic Acids', 'Sulfoxides']",Identification of candidate amino acids involved in the formation of pink-red pigments in onion (Allium cepa L.) juice and separation by HPLC.,"['Q000737', 'Q000378', None, 'Q000031', 'Q000201', 'Q000737', 'Q000737', 'Q000378', 'Q000737', None, 'Q000737', 'Q000737']","['chemistry', 'metabolism', None, 'analogs & derivatives', 'enzymology', 'chemistry', 'chemistry', 'metabolism', 'chemistry', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/21535486,2011,0.0,0.0,,pigmentation, +21495726,"Phytoene synthase (PSY) and phytoene desaturase (PDS), which catalyze the first and second steps of the carotenoid biosynthetic pathway, respectively, are key enzymes for the accumulation of carotenoids in many plants. We isolated 2 partial cDNAs encoding PSY (AsPSY-1 and AsPSY-2) and a partial cDNA encoding PDS (AsPDS) from Allium sativum. They shared high sequence identity and conserved motifs with other orthologous genes. Quantitative real-time PCR analysis was used to determine the expression levels of AsPSY1, AsPSY2, and AsPDS in the bulbils, scapes, leaves, stems, bulbs, and roots of garlic. High-performance liquid chromatography demonstrated that carotenoids were not biosynthesized in the underground organs (roots and bulbs), but were very abundant in the photosynthetic organs (leaves) of A. sativum. A significantly higher amount of β-carotene (73.44 μg·g(-1)) was detected in the leaves of A. sativum than in the other organs.",Journal of agricultural and food chemistry,"['D019883', 'D000595', 'D002338', 'D003001', 'D018076', 'D018744', 'D005737', 'D015870', 'D051232', 'D008969', 'D010088', 'D018515', 'D018517', 'D018547', 'D016133']","['Alkyl and Aryl Transferases', 'Amino Acid Sequence', 'Carotenoids', 'Cloning, Molecular', 'DNA, Complementary', 'DNA, Plant', 'Garlic', 'Gene Expression', 'Geranylgeranyl-Diphosphate Geranylgeranyltransferase', 'Molecular Sequence Data', 'Oxidoreductases', 'Plant Leaves', 'Plant Roots', 'Plant Stems', 'Polymerase Chain Reaction']",Carotenoid accumulation and characterization of cDNAs encoding phytoene synthase and phytoene desaturase in garlic (Allium sativum).,"['Q000737', None, 'Q000032', None, 'Q000032', 'Q000032', 'Q000737', None, None, None, 'Q000737', 'Q000201', 'Q000201', 'Q000201', None]","['chemistry', None, 'analysis', None, 'analysis', 'analysis', 'chemistry', None, None, None, 'chemistry', 'enzymology', 'enzymology', 'enzymology', None]",https://www.ncbi.nlm.nih.gov/pubmed/21495726,2011,1.0,1.0,,, +21491526,"Matrix effect (ME) - ionisation suppression or enhancement - in liquid chromatography/electrospray ionisation mass spectrometry (LC/ESI-MS) is caused by matrix components co-eluting with the analytes. ME has a complex and not fully understood nature. ME is also highly variable from sample to sample making it difficult to compensate for. In this work it was studied whether the background ion signals in scanned mass spectra of the LC effluent at the retention time of the analyte offer some insight into the presence and extent of matrix effect. Matrix effects for six pesticides - thiabendazole, carbendazime, methomyl, aldicarb, imazalil and methiocarb - in garlic and onion samples used in the study varied from 1% (suppression 99%) to 127% (enhancement 27%) depending on the pesticide and sample. Also standards in solvent and solvent blanks were included in the study. The ions most strongly varying from sample to sample - and therefore best describing the changes in sample composition and ME - were selected for quantification according to principal component analysis (PCA) for all six pesticides under study. These ions were used to account for ME via partial least-squares (PLS) regression. The calibration set was constructed from 19 samples and standards and the obtained calibration function was validated with seven samples and standards. The average errors from the test set were from 0.05 to 0.27 mg/kg for carbendazim and imazalil, respectively (the respective average pesticide concentrations were 0.22 and 0.88 mg/kg). The PLS results were significantly more accurate compared to the conventional solvent calibration resulting in average errors from 0.07 to 0.69 mg/kg for carbendazime and methiocarb, respectively.",Rapid communications in mass spectrometry : RCM,[],[],Accounting for matrix effects of pesticide residue liquid chromatography/electrospray ionisation mass spectrometric determination by treatment of background mass spectra with chemometric tools.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/21491526,2011,,,,, +21490929,"Allium sativum leaf agglutinin (ASAL) is a 25-kDa homodimeric, insecticidal, mannose binding lectin whose subunits are assembled by the C-terminal exchange process. An attempt was made to convert dimeric ASAL into a monomeric form to correlate the relevance of quaternary association of subunits and their functional specificity. Using SWISS-MODEL program a stable monomer was designed by altering five amino acid residues near the C-terminus of ASAL.",PloS one,"['D000528', 'D000818', 'D000935', 'D001042', 'D015153', 'D002846', 'D002850', 'D005670', 'D005737', 'D007306', 'D016297', 'D018515', 'D037121', 'D012232', 'D013050']","['Alternaria', 'Animals', 'Antifungal Agents', 'Aphids', 'Blotting, Western', 'Chromatography, Affinity', 'Chromatography, Gel', 'Fusarium', 'Garlic', 'Insecticides', 'Mutagenesis, Site-Directed', 'Plant Leaves', 'Plant Lectins', 'Rhizoctonia', 'Spectrometry, Fluorescence']",Functional alteration of a dimeric insecticidal lectin to a monomeric antifungal protein correlated to its oligomeric status.,"['Q000187', None, 'Q000737', 'Q000187', None, None, None, 'Q000187', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000187', None]","['drug effects', None, 'chemistry', 'drug effects', None, None, None, 'drug effects', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'drug effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/21490929,2011,0.0,0.0,,, +21315911,"An analytical method with the technique of QuEChERS (quick, easy, cheap, effective, rugged and safe) and gas chromatography (GC)/mass spectrometry (MS) in negative chemical ionization (NCI) has been developed for the determination of 17 pyrethroid pesticide residues in troublesome matrices, including garlic, onion, spring onion and chili. Pyrethroid residues were extracted with acidified acetonitrile saturated by hexane. After a modified QuEChERS clean-up step, the extract was analyzed by GC-NCI/MS in selected ion monitoring (SIM) mode. An isotope internal standard of trans-cypermethrin-D(6) was employed for quantitation. Chromatograms of pyrethroids obtained in all these matrices were relatively clean and without obvious interference. The limits of detection (LODs) ranged from 0.02 to 6 μg kg(-1) and recovery yields were from 54.0% to 129.8% at three spiked levels (20, 40 and 60 μg kg(-1) for chili, and 10, 20 and 30 μg kg(-1) for others) in four different matrices depending on the compounds determined. The relative standard deviations (RSDs) were all below 14%. Isomerization enhancement of pyrethroids in chili extract was observed and preliminarily explained, especially for acrinathrin and deltamethrin.",Talanta,"['D005504', 'D005506', 'D008401', 'D057230', 'D016014', 'D010573', 'D011722', 'D015203', 'D013997', 'D014675']","['Food Analysis', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Limit of Detection', 'Linear Models', 'Pesticide Residues', 'Pyrethrins', 'Reproducibility of Results', 'Time Factors', 'Vegetables']",Determination of 17 pyrethroid residues in troublesome matrices by gas chromatography/mass spectrometry with negative chemical ionization.,"['Q000191', 'Q000032', 'Q000191', None, None, 'Q000032', 'Q000032', None, None, 'Q000737']","['economics', 'analysis', 'economics', None, None, 'analysis', 'analysis', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/21315911,2011,0.0,0.0,,, +21056027,"Spironucleus is a genus of small, flagellated parasites, many of which can infect a wide range of vertebrates and are a significant problem in aquaculture. Following the ban on the use of metronidazole in food fish due to toxicity problems, no satisfactory chemotherapies for the treatment of spironucleosis are currently available. Using membrane inlet mass spectrometry and automated optical density monitoring of growth, we investigated in vitro the effect of Allium sativum (garlic), a herbal remedy known for its antimicrobial properties, on the growth and metabolism of Spironucleus vortens, a parasite of tropical fish and putative agent of hole-in-the-head disease. The allium-derived thiosulfinate compounds allicin and ajoene, as well as an ajoene-free mixture of thiosulfinates and vinyl-dithiins were also tested. Whole, freeze-dried garlic and allium-derived compounds had an inhibitory effect on gas metabolism, exponential growth rate and final growth yield of S. vortens in Keister's modified, TY-I-S33 culture medium. Of all the allium-derived compounds tested, the ajoene-free mixture of dithiins and thiosulfinates was the most effective with a minimum inhibitory concentration (MIC) of 107 μg ml(-1) and an inhibitory concentration at 50% (IC(50%)) of 58 μg ml(-1). It was followed by ajoene (MIC = 83 μg ml(-1), IC(50%) = 56 μg ml(-1)) and raw garlic (MIC >20 mg ml(-1), IC(50%) = 7.9 mg ml(-1)); allicin being significantly less potent with an MIC and IC(50%) above 160 μg ml(-1). All these concentrations are much higher than those reported to be required for the inhibition of most bacteria, protozoa and fungi previously investigated, indicating an unusual level of tolerance for allium-derived products in S. vortens. However, chemically synthesized derivatives of garlic constituents might prove a useful avenue for future research.",Experimental parasitology,"['D000490', 'D000818', 'D000981', 'D002245', 'D016828', 'D004220', 'D005393', 'D005398', 'D005399', 'D005612', 'D005737', 'D006859', 'D013058', 'D008795', 'D010101', 'D010936', 'D011529', 'D013441', 'D013457']","['Allium', 'Animals', 'Antiprotozoal Agents', 'Carbon Dioxide', 'Diplomonadida', 'Disulfides', 'Fish Diseases', 'Fisheries', 'Fishes', 'Freeze Drying', 'Garlic', 'Hydrogen', 'Mass Spectrometry', 'Metronidazole', 'Oxygen Consumption', 'Plant Extracts', 'Protozoan Infections, Animal', 'Sulfinic Acids', 'Sulfur Compounds']",Effect of garlic and allium-derived products on the growth and metabolism of Spironucleus vortens.,"['Q000737', None, 'Q000494', 'Q000378', 'Q000187', 'Q000494', 'Q000188', None, None, None, 'Q000737', 'Q000378', None, 'Q000494', 'Q000187', 'Q000494', 'Q000188', 'Q000494', 'Q000494']","['chemistry', None, 'pharmacology', 'metabolism', 'drug effects', 'pharmacology', 'drug therapy', None, None, None, 'chemistry', 'metabolism', None, 'pharmacology', 'drug effects', 'pharmacology', 'drug therapy', 'pharmacology', 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/21056027,2011,2.0,1.0,,[1], +20822860,"The effect of onion and garlic on the formation of two cholesterol oxidation products (COPs): 7-ketocholesterol and 7-hydroxycholesterol was evaluated by comparing their concentrations in meat and gravy samples obtained from three pork dishes prepared in the presence and absence of these flavourings. The concentration of these compounds in meat samples was between 82.4 and 1331.6 ng/g of cooked meat. Gravies contained lower amounts: from 18.3 to 45.6 ng/g of cooked meat. The addition of onion (30 g/100g of meat) caused a decrease in 7-ketocholesterol and 7-hydroxycholesterol concentrations in all of the investigated pork dishes by 9.5-79%, whilst the addition of 15 g of garlic to 100g of meat lowered the concentration by 17 to 88%. The greatest decrease was found in grilled minced chops. The quantitative assessment of 7-ketocholesterol and 7-hydroxycholesterol was carried out by thin-layer chromatography with densitometric detection.",Meat science,"['D000818', 'D000975', 'D002855', 'D005503', 'D005511', 'D005737', 'D006358', 'D006888', 'D007653', 'D008460', 'D008461', 'D019697', 'D010084', 'D028321', 'D013552']","['Animals', 'Antioxidants', 'Chromatography, Thin Layer', 'Food Additives', 'Food Handling', 'Garlic', 'Hot Temperature', 'Hydroxycholesterols', 'Ketocholesterols', 'Meat', 'Meat Products', 'Onions', 'Oxidation-Reduction', 'Plant Preparations', 'Swine']",7-Ketocholesterol and 7-hydroxycholesterol in pork meat and its gravy thermally treated without additives and in the presence of onion and garlic.,"[None, 'Q000032', None, None, 'Q000379', None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, None, 'Q000032', None]","[None, 'analysis', None, None, 'methods', None, None, 'analysis', 'analysis', 'analysis', 'analysis', None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/20822860,2011,0.0,0.0,,, +20730643,"A rapid, simple and sensitive multi-residue method was developed and validated for the simultaneous quantification and confirmation of 69 pesticides in fruit and vegetables using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The samples were extracted following the quick, easy, cheap, effective, rugged and safe method known as QuEChERS. Mass spectrometric conditions were individually optimised for each analyte in order to achieve maximum sensitivity in multiple reaction monitoring (MRM) mode. Using the developed chromatographic conditions, 69 pesticides can be separated in less than 17 min. Two selected reaction monitoring (SRM) assays were used for each pesticide to obtain simultaneous quantification and identification in one run. With this method in SRM mode, more than 150 pesticides can be analysed and quantified, but their confirmation is not possible in all cases according to the European regulations on pesticide residues. Nine common representative matrices (zucchini, melon, cucumber, watermelon, tomato, garlic, eggplant, lettuce and pepper) were selected to investigate the effect of different matrices on recovery and precision. Mean recoveries ranged from 70% to 120%, with relative standard deviations (RSDs) lower than 20% for all the pesticides. The proposed method was applied to the analysis of more than 2000 vegetable samples from the extensive greenhouse cultivation in the province of Almeria, Spain, during one year. The methodology combines the advantages of both QuEChERS and LC-MS/MS producing a very rapid, sensitive, accurate and reliable procedure that can be applied in routine analytical laboratories. The method was validated and accredited according to UNE-EN-ISO/IEC 17025:2005 international standard (accreditation number 278/LE1027).","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D053000', 'D002138', 'D002851', 'D005060', 'D005506', 'D005513', 'D005638', 'D057230', 'D010573', 'D015203', 'D021241', 'D053719', 'D014675']","['Analytic Sample Preparation Methods', 'Calibration', 'Chromatography, High Pressure Liquid', 'Europe', 'Food Contamination', 'Food Inspection', 'Fruit', 'Limit of Detection', 'Pesticide Residues', 'Reproducibility of Results', 'Spectrometry, Mass, Electrospray Ionization', 'Tandem Mass Spectrometry', 'Vegetables']",UNE-EN ISO/IEC 17025:2005-accredited method for the determination of pesticide residues in fruit and vegetable samples by LC-MS/MS.,"[None, None, None, None, None, 'Q000379', 'Q000737', None, 'Q000032', None, None, None, 'Q000737']","[None, None, None, None, None, 'methods', 'chemistry', None, 'analysis', None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/20730643,2011,0.0,0.0,,, +20722910,"The effect of milk and milk components on the deodorization of diallyl disulfide (DADS), allyl methyl disulfide (AMDS), allyl mercaptan (AM), allyl methyl sulfide (AMS), and methyl mercaptan (MM) in the headspace of garlic as well as in the mouth- and nose-space after garlic ingestion was investigated using selected ion flow tube-mass spectrometry (SIFT-MS). Fat-free and whole milk significantly reduced the head-, mouth-, and nose-space concentrations of all volatiles. Water was the major component in milk responsible for the deodorization of volatiles. Due to its higher fat content, whole milk was more effective than fat-free milk in the deodorization of the more hydrophobic volatiles diallyl disulfide and allyl methyl disulfide. Milk was more effective than water and 10% sodium caseinate in the deodorization of allyl methyl sulfide, a persistent garlic odor, in the mouth after garlic ingestion. Addition of milk to garlic before ingestion had a higher deodorizing effect on the volatiles in the mouth than drinking milk after consuming garlic. Practical Application: Ingesting beverages or foods with high water and/or fat content such as milk may help reduce the malodorous odor in breath after garlic ingestion and mask the garlic flavor during eating. To enhance the deodorizing effect, deodorant foods should be mixed with garlic before ingestion.",Journal of food science,"['D000328', 'D000498', 'D000818', 'D001944', 'D002364', 'D003836', 'D005223', 'D005260', 'D005511', 'D005737', 'D006209', 'D006801', 'D057927', 'D013058', 'D008892', 'D009994', 'D013438', 'D013440', 'D013997', 'D055549']","['Adult', 'Allyl Compounds', 'Animals', 'Breath Tests', 'Caseins', 'Deodorants', 'Fats', 'Female', 'Food Handling', 'Garlic', 'Halitosis', 'Humans', 'Hydrophobic and Hydrophilic Interactions', 'Mass Spectrometry', 'Milk', 'Osmolar Concentration', 'Sulfhydryl Compounds', 'Sulfides', 'Time Factors', 'Volatile Organic Compounds']",Effect of milk on the deodorization of malodorous breath after garlic ingestion.,"[None, 'Q000032', None, None, 'Q000737', None, 'Q000737', None, None, 'Q000009', 'Q000517', None, None, None, 'Q000737', None, 'Q000032', 'Q000032', None, 'Q000032']","[None, 'analysis', None, None, 'chemistry', None, 'chemistry', None, None, 'adverse effects', 'prevention & control', None, None, None, 'chemistry', None, 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20722910,2011,1.0,1.0,,, +20609277,"The Asian citrus psyllid, Diaphorina citri Kuwayama, vectors Candidatus Liberibacter asiaticus (Las) and Candidatus Liberibacter americanus (Lam), the presumed causal agents of huanglongbing. D. citri generally rely on olfaction and vision for detection of host cues. Plant volatiles from Allium spp. (Alliaceae) are known to repel several arthropod species. We examined the effect of garlic chive (A. tuberosum Rottl.) and wild onion (A. canadense L.) volatiles on D. citri behaviour in a two-port divided T-olfactometer. Citrus leaf volatiles attracted significantly more D. citri adults than clean air. Volatiles from crushed garlic chive leaves, garlic chive essential oil, garlic chive plants, wild onion plants and crushed wild onion leaves all repelled D. citri adults when compared with clean air, with the first two being significantly more repellent than the others. However, when tested with citrus volatiles, only crushed garlic chive leaves and garlic chive essential oil were repellent, and crushed wild onions leaves were not. Analysis of the headspace components of crushed garlic chive leaves and garlic chive essential oil by gas chromatography-mass spectrometry revealed that monosulfides, disulfides and trisulfides were the primary sulfur volatiles present. In general, trisulfides (dimethyl trisulfide) inhibited the response of D. citri to citrus volatiles more than disulfides (dimethyl disulfide, allyl methyl disulfide, allyl disulfide). Monosulfides did not affect the behaviour of D. citri adults. A blend of dimethyl trisulfide and dimethyl disulfide in 1:1 ratio showed an additive effect on inhibition of D. citri response to citrus volatiles. The plant volatiles from Allium spp. did not affect the behaviour of the D. citri ecto-parasitoid Tamarixia radiata (Waterston). Thus, Allium spp. or the tri- and di-sulphides could be integrated into management programmes for D. citri without affecting natural enemies.",Bulletin of entomological research,"['D000490', 'D000818', 'D001522', 'D002957', 'D005260', 'D006430', 'D007303', 'D009043', 'D009822', 'D018515']","['Allium', 'Animals', 'Behavior, Animal', 'Citrus', 'Female', 'Hemiptera', 'Insect Vectors', 'Motor Activity', 'Oils, Volatile', 'Plant Leaves']","Sulfur volatiles from Allium spp. affect Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Psyllidae), response to citrus volatiles.","['Q000187', None, None, 'Q000187', None, 'Q000187', None, None, 'Q000494', 'Q000187']","['drug effects', None, None, 'drug effects', None, 'drug effects', None, None, 'pharmacology', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/20609277,2011,0.0,0.0,,no quantification, +20553188,"Recent studies have shown that deoxygenated human red blood cells (RBCs) converted garlic-derived polysulfides into hydrogen sulfide, which in turn produced vasorelaxation in aortic ring preparations. The vasoactivity was proposed to occur via glucose- and thiol-dependent acellular reactions. In the present study, we investigated the interaction of garlic extracts with human deoxygenated RBCs and its effect on intracellular hemoglobin molecules. The results showed that garlic extract covalently modified intraerythrocytic deoxygenated hemoglobin. The modification identified consisted of an addition of 71 atomic mass units, suggesting allylation of the cysteine residues. Consistently, purified human deoxyhemoglobin reacted with chemically pure diallyl disulfide, showing the same modification as garlic extracts. Tandem mass spectrometry analysis demonstrated that garlic extract and diallyl disulfide modified hemoglobin's beta-chain at cysteine-93 (beta-93C) or cysteine-112 (beta-112C). These results indicate that garlic-derived organic disulfides as well as pure diallyl disulfide must permeate the RBC membrane and modified deoxyhemoglobin at beta-93C or beta-112C. Although the physiological role of the reported garlic extract-induced allyl modification on human hemoglobin warrants further study, the results indicate that constituents of natural products, such as those from garlic extract, modify intracellular proteins.",Journal of medicinal food,"['D004912', 'D005737', 'D006454', 'D006801', 'D010936', 'D011499']","['Erythrocytes', 'Garlic', 'Hemoglobins', 'Humans', 'Plant Extracts', 'Protein Processing, Post-Translational']",Allylation of intraerythrocytic hemoglobin by raw garlic extracts.,"['Q000737', 'Q000737', 'Q000378', None, 'Q000494', 'Q000187']","['chemistry', 'chemistry', 'metabolism', None, 'pharmacology', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/20553188,2010,0.0,0.0,,, +20480390,"A method for the residual pendimethalin in soil and vegetable samples was developed. The method is based on extraction of pendimethalin from samples using microwave-assisted solvent extraction (MASE) with acetone, ethanol, and water as extraction solvent. Extracted pendimethalin samples were analyzed by high-performance liquid chromatography with ultraviolet detector at 240 nm. The MASE parameters, temperature, heating time, and solvent types were optimized with the feasibility of MASE application in the determination of pendimethalin extraction efficiency of pendimethalin from soil and vegetable samples. The maximum temperature that can be used during the heating for MASE is 60°C, where the recovery percentages reached 97%. Linearity for pendimethalin was found in the range of 2-20 μg mL(-1) with limits of detection and limits of quantification of 0.059 and 0.17 μg mL(-1), respectively.",Environmental monitoring and assessment,"['D000814', 'D002851', 'D005737', 'D008872', 'D012987', 'D012997', 'D014874']","['Aniline Compounds', 'Chromatography, High Pressure Liquid', 'Garlic', 'Microwaves', 'Soil', 'Solvents', 'Water Pollutants, Chemical']",Quantification of pendimethalin in soil and garlic samples by microwave-assisted solvent extraction and HPLC method.,"['Q000032', 'Q000379', 'Q000737', None, 'Q000737', 'Q000737', 'Q000032']","['analysis', 'methods', 'chemistry', None, 'chemistry', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20480390,2011,0.0,0.0,,, +20225897,"Through the use of direct analysis in real time mass spectrometry (DART-MS), 2-propenesulfenic acid, an intermediate long postulated as being formed when garlic ( Allium sativum ) is crushed, has been detected for the first time and determined by mass spectrometric methods to have a half-life of <1 s at room temperature. Two other key intermediates, 2-propenesulfinic acid and diallyl trisulfane S-oxide, have also been detected for the first time in volatiles from crushed garlic, along with allicin and related thiosulfinates, allyl alcohol, sulfur dioxide, propene, and pyruvate as coproducts. A commercial dietary supplement containing garlic powder, which was sampled after crushing, was found to contain alliin, methiin, and S-allylcysteine and produced allicin upon addition of water. DART-MS detection of 1-butenesulfenic acid from the ornamental A. siculum is also reported. (Z)-Propanethial S-oxide (onion lachrymatory factor), absent in garlic, is found to be formed from crushed elephant garlic ( Allium ampeloprasum ), consistent with the classification of this plant as a closer relative of leek than of garlic. Mixtures of thiosulfinates, lachrymatory thial S-oxides, and related compounds are directly observed from crushed leek ( Allium porrum ) and Chinese chive ( Allium tuberosum ). Disulfanes and polysulfanes are detected only when the Allium samples are heated, consistent with earlier conclusions that these are not primary products from cut or crushed alliums.",Journal of agricultural and food chemistry,"['D000475', 'D000490', 'D013058', 'D013434', 'D013457']","['Alkenes', 'Allium', 'Mass Spectrometry', 'Sulfenic Acids', 'Sulfur Compounds']","Applications of direct analysis in real time mass spectrometry (DART-MS) in Allium chemistry. 2-propenesulfenic and 2-propenesulfinic acids, diallyl trisulfane S-oxide, and other reactive sulfur compounds from crushed garlic and other Alliums.","['Q000032', 'Q000737', 'Q000379', 'Q000032', 'Q000032']","['analysis', 'chemistry', 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/20225897,2010,0.0,3.0,,"useful, not collected", +20100526,"Over expression of lectin genes in E. coli often gives inclusion bodies that are solubilised to characterize lectins. We made N-terminal fusion of the Allium sativum leaf agglutinin (ASAL) with SUMO (small ubiquitin related modifier) peptide. The SUMO peptide allowed expression of the recombinant lectin in E. coli, predominantly in soluble form. The soluble fusion protein could be purified by immobilized metal affinity column (IMAC), followed by size exclusion chromatography. The SUMO protease failed to cleave the SUMO peptide from ASAL. This may be due to steric hindrance caused by the homodimer structure of the chimeric ASAL. Some properties like dimerization, haemagglutination and insecticidal properties of the recombinant SUMO-ASAL fusion protein were comparable to the plant derived native lectin. However, glycan array analysis revealed that the carbohydrate binding specificity of the recombinant SUMO-ASAL was altered. Further, the fusion protein was not toxic to E. coli (native ASAL exhibited toxicity). The recombinant lectin was more thermo-labile as compared to the native lectin. Three important findings of this study are: (1) sugar specificity of ASAL can be altered by amino-terminal fusion; (2) anti-E. coli activity of ASAL can be eliminated by N-terminal SUMO fusion and (3) SUMO-ASAL may be a preferred candidate insecticidal protein for the development of transgenic plants.",Journal of biotechnology,"['D000371', 'D000818', 'D003546', 'D018744', 'D052978', 'D005737', 'D006863', 'D007814', 'D037241', 'D046228', 'D018515', 'D010940', 'D011134', 'D011485', 'D055503', 'D055550', 'D011993', 'D025842', 'D018411', 'D013696']","['Agglutination', 'Animals', 'Cysteine Endopeptidases', 'DNA, Plant', 'Disk Diffusion Antimicrobial Tests', 'Garlic', 'Hydrogen-Ion Concentration', 'Larva', 'Mannose-Binding Lectins', 'Microarray Analysis', 'Plant Leaves', 'Plant Proteins', 'Polysaccharides', 'Protein Binding', 'Protein Multimerization', 'Protein Stability', 'Recombinant Fusion Proteins', 'SUMO-1 Protein', 'Spodoptera', 'Temperature']",SUMO fusion facilitates expression and purification of garlic leaf lectin but modifies some of its properties.,"[None, None, 'Q000378', 'Q000302', None, 'Q000737', None, 'Q000187', 'Q000096', None, 'Q000737', 'Q000096', 'Q000378', None, None, None, 'Q000737', 'Q000235', 'Q000187', None]","[None, None, 'metabolism', 'isolation & purification', None, 'chemistry', None, 'drug effects', 'biosynthesis', None, 'chemistry', 'biosynthesis', 'metabolism', None, None, None, 'chemistry', 'genetics', 'drug effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/20100526,2010,0.0,0.0,,, +20079366,"Formation of cholesterol gallstones in gallbladder is controlled by procrystallizing and anticrystallizing factors present in bile. Dietary garlic and onion have been recently observed to possess anti-lithogenic potential in experimental mice. In this investigation, the role of biliary proteins from rats fed lithogenic diet or garlic/onion-containing diet in the formation of cholesterol gallstones in model bile was studied. Cholesterol nucleation time of the bile from lithogenic diet group was prolonged when mixed with bile from garlic or onion groups. High molecular weight proteins of bile from garlic and onion groups delayed cholesterol crystal growth in model bile. Low molecular weight (LMW) proteins from the bile of lithogenic diet group promoted cholesterol crystal growth in model bile, while LMW protein fraction isolated from the bile of garlic and onion groups delayed the same. Biliary LMW protein fraction was subjected to affinity chromatography using Con-A and the lectin-bound and unbound fractions were studied for their influence on cholesterol nucleation time in model bile. Major portion of biliary LMW proteins in lithogenic diet group was bound to Con-A, and this protein fraction promoted cholesterol nucleation time and increased cholesterol crystal growth rate, whereas Con-A unbound fraction delayed the onset of cholesterol crystallization. Biliary protein from garlic/onion group delayed the crystallization and interfered with pronucleating activity of Con-A bound protein fraction. These data suggest that apart from the beneficial modulation of biliary cholesterol saturation index, these Allium spices also influence cholesterol nucleating and antinucleating protein factors that contribute to their anti-lithogenic potential.",Steroids,"['D000818', 'D001646', 'D002784', 'D003208', 'D004032', 'D005737', 'D006801', 'D015227', 'D008297', 'D051379', 'D008970', 'D019697', 'D011485', 'D051381', 'D017208']","['Animals', 'Bile', 'Cholesterol', 'Concanavalin A', 'Diet', 'Garlic', 'Humans', 'Lipid Peroxidation', 'Male', 'Mice', 'Molecular Weight', 'Onions', 'Protein Binding', 'Rats', 'Rats, Wistar']",Effect of dietary garlic and onion on biliary proteins and lipid peroxidation which influence cholesterol nucleation in bile.,"[None, 'Q000737', 'Q000737', 'Q000378', None, 'Q000737', None, None, None, None, None, 'Q000737', None, None, None]","[None, 'chemistry', 'chemistry', 'metabolism', None, 'chemistry', None, None, None, None, None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/20079366,2010,0.0,0.0,,, +20067158,"By the method of capillary gas-liquid chromatography we studied antioxidant properties and stability during the storage of hexane solutions of 14 individual essential oils from black and white pepper (Piper nigrum L.), cardamom (Elettaria cardamomum L.), nutmeg (Myristica fragrans Houtt.), mace (Myristica fragrans Houtt), juniperberry (Juniperus communis L.), seed of fennel (Foeniculum vulgare Mill., var. dulce Thelling), caraway (Carvum carvi L.), dry leaves of cinnamon (Cinnamomum zeylanicum Bl.), marjoram (Origanum majorana L.), laurel (Laurus nobilis L.), ginger (Zingiber officinale L.), garlic (Allium sativum L.), and clove bud (Caryophyllus aromaticus L.). We assessed the antioxidant properties by the oxidation of aliphatic aldehyde (trans-2-hexenal) into the according carbon acid. We established that essential oils of garlic, clove bud, ginger and leaves of cinnamon have the maximal efficiency of inhibition of hexenal oxidation (80-93%), while black pepper oil has the minimal (49%). Antioxidant properties of essential oils with a high content of substituted phenols depended poorly on its concentration in model systems. We studied the changes in essential oils content during the storage of its hexane solutions for 40 days in the light and out of the light and compared it with the stability of essential oils stored for a year out of the light.",Prikladnaia biokhimiia i mikrobiologiia,"['D000975', 'D004355', 'D006586', 'D008027', 'D009822', 'D010944', 'D012639', 'D013997']","['Antioxidants', 'Drug Stability', 'Hexanes', 'Light', 'Oils, Volatile', 'Plants', 'Seeds', 'Time Factors']",[Antioxidant properties of essential oils].,"['Q000737', None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', None]","['chemistry', None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/20067158,2010,,,,, +20015835,"Direct somatic embryo formation (without intervening callus) from garlic clove basal tissue was induced in which the influence of plant growth regulators (PGRs) on various explants was examined. Medium added with 2.0 mg/l 6-benzylaminopurine (BAP) and 0.5 mg/l 2,4-dichlorophenoxyacetic acid (2,4-D) were the most effective PGR combination for somatic embryo induction. It induced embryos directly in 85.5% of the basal clove explant. Callus induction was also obtained from other parts of explant and 2.0 mg/l 2,4-D induced callusing in 86.5% of the inoculated explants. Protein, amino acid and alliin content were measured in callus and in embryos. Somatic embryos had more soluble protein and free amino acid compared to callus. HPTLC analysis revealed that alliin was significantly high in somatic embryos compared to undifferentiated callus tissue; the content was even more in older embryos. The present study of Allium indicates that the event of morphogenetic development including in vitro embryogeny can effectively be analysed by monitoring the changes of biochemical profiles.",Acta biologica Hungarica,"['D002454', 'D002855', 'D003545', 'D005737', 'D010937']","['Cell Differentiation', 'Chromatography, Thin Layer', 'Cysteine', 'Garlic', 'Plant Growth Regulators']",Improved alliin yield in somatic embryos of Allium sativum L. (cv. Yamuna safed) as analyzed by HPTLC.,"[None, 'Q000379', 'Q000031', 'Q000166', 'Q000378']","[None, 'methods', 'analogs & derivatives', 'cytology', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/20015835,2010,,,,no access to pdf, +20004743,"Garlic (Allium sativum), an important medicinal spice, displays a plethora of biological effects including immunomodulation. Although some immunomodulatory proteins from garlic have been described, their identities are still unknown. The present study was envisaged to isolate immunomodulatory proteins from raw garlic, and examine their effects on certain cells of the immune system (lymphocytes, mast cells, and basophils) in relation to mitogenicity and hypersensitivity. Three protein components of approximately 13 kD (QR-1, QR-2, and QR-3 in the ratio 7:28:1) were separated by Q-Sepharose chromatography of 30 kD ultrafiltrate of raw garlic extract. All the 3 proteins exhibited mitogenic activity towards human peripheral blood lymphocytes, murine splenocytes and thymocytes. The mitogenicity of QR-2 was the highest among the three immunomodulatory proteins. QR-1 and QR-2 displayed hemagglutination and mannose-binding activities; QR-3 showed only mannose-binding activity. Immunoreactivity of rabbit anti-QR-1 and anti-QR-2 polyclonal antisera showed specificity for their respective antigens as well as mutual cross-reactivity; QR-3 was better recognized by anti-QR-2 (82%) than by anti-QR-1 (55%). QR-2 induced a 2-fold higher histamine release in vitro from leukocytes of atopic subjects compared to that of non-atopic subjects. In all functional studies, QR-2 was more potent compared to QR-1. Taken together, all these results indicate that the two major proteins QR-2 and QR-1 present in a ratio of 4:1 in raw garlic contribute to garlic's immunomodulatory activity, and their characteristics are markedly similar to the abundant Allium sativum agglutinins (ASA) I and II, respectively.",International immunopharmacology,"['D000373', 'D000818', 'D049109', 'D004591', 'D005122', 'D005737', 'D006023', 'D006386', 'D006636', 'D006801', 'D006969', 'D007155', 'D037102', 'D008214', 'D008264', 'D008407', 'D009569', 'D010940', 'D011485', 'D051381', 'D017382', 'D013481']","['Agglutinins', 'Animals', 'Cell Proliferation', 'Electrophoresis, Polyacrylamide Gel', 'Exudates and Transudates', 'Garlic', 'Glycoproteins', 'Hemagglutination Tests', 'Histamine Release', 'Humans', 'Hypersensitivity, Immediate', 'Immunologic Factors', 'Lectins', 'Lymphocytes', 'Macrophages', 'Mast Cells', 'Nitric Oxide', 'Plant Proteins', 'Protein Binding', 'Rats', 'Reactive Oxygen Species', 'Superoxides']",Identity of the immunomodulatory proteins from garlic (Allium sativum) with the major garlic lectins or agglutinins.,"['Q000737', None, 'Q000187', None, 'Q000166', 'Q000737', 'Q000378', None, 'Q000187', None, 'Q000188', 'Q000737', 'Q000737', 'Q000187', 'Q000187', 'Q000187', 'Q000378', 'Q000737', None, None, 'Q000378', 'Q000378']","['chemistry', None, 'drug effects', None, 'cytology', 'chemistry', 'metabolism', None, 'drug effects', None, 'drug therapy', 'chemistry', 'chemistry', 'drug effects', 'drug effects', 'drug effects', 'metabolism', 'chemistry', None, None, 'metabolism', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/20004743,2010,0.0,0.0,,, +19938491,"A screening method was developed for the determination of 107 pesticide residues in vegetables using off-line dispersive solid-phase extraction (DSPE) and gas chromatography-tandem mass spectrometry (GC-MS/MS). The pesticides interested were extracted from the samples with acetonitrile (saturated by n-hexane) containing 1% acetic acid and simultaneously separated by liquid-liquid partitioning with adding anhydrous magnesium sulfate plus sodium acetate following by a simple cleanup step known as dispersive solid-phase extraction. The extracts were determined by GC-MS/MS using external standard method. The method was reliable and stable that the recoveries of almost all pesticides were in the range from 60% to 130% at the spiked level of 10 microg/kg into four vegetable matrixes (garlic, green bean, radish 8 and spinach) and the relative standard deviations (RSDs) were all not more than 15.3%. The linearity of the method was good between 0.05 mg/L and 1 mg/L, and all limits of quantification (LOQs) less than 10 microg/kg. The method is selective with no interference, especially in the complicated garlic matrix.",Se pu = Chinese journal of chromatography,[],[],[Determination of 107 pesticide residues in vegetables using off-line dispersive solid-phase extraction and gas chromatography-tandem mass spectrometry].,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19938491,2010,,,,, +19807156,"Garlic (Allium sativum) is a medicinal and culinary plant reported to have several positive health effects on cardiovascular diseases, particularly via suppressing platelet activation. Therefore, active compounds inhibiting platelet activation were isolated from garlic extract using a P-selectin expression suppressing activity-guided fractionation technique. Garlic cloves were extracted with methanol, sequentially partitioned using ethyl acetate, and n-butanol. The ethyl acetate portion was fractionated using silica gel chromatography. The fraction with highest P-selectin expression suppressing activity was further purified using HPLC, and the compounds in the fraction were analyzed using MS, MS/MS, and NMR spectroscopic methods. Using NMR spectroscopy, the compound with highest suppressing activity was confirmed as N-feruloyltyramine. At the concentration of 0.05 microM, N-feruloyltyramine was able to suppress P-selectin expression on platelets by 31% (P < 0.016). Since COX enzymes are deeply involved in the regulation of P-selectin expression on platelets, potential effects of N-feruloyltyramine on COX enzymes were investigated. As expected at the concentration of 0.05 microM, N-feruloyltyramine was found to be a very potent compound able to inhibit COX-I and -II enzymes by 43% (P < 0.012) and 33% (P < 0.014), respectively. N-Feruloyltyramine is likely to inhibit COX enzymes, thereby suppressing P-selectin expression on platelets.",Journal of agricultural and food chemistry,"['D002851', 'D003373', 'D016861', 'D005737', 'D009682', 'D013058', 'D019007', 'D018517', 'D010975', 'D014439']","['Chromatography, High Pressure Liquid', 'Coumaric Acids', 'Cyclooxygenase Inhibitors', 'Garlic', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'P-Selectin', 'Plant Roots', 'Platelet Aggregation Inhibitors', 'Tyramine']",Isolation and characterization of N-feruloyltyramine as the P-selectin expression suppressor from garlic (Allium sativum).,"[None, 'Q000302', 'Q000302', 'Q000737', None, None, 'Q000037', 'Q000737', 'Q000302', 'Q000031']","[None, 'isolation & purification', 'isolation & purification', 'chemistry', None, None, 'antagonists & inhibitors', 'chemistry', 'isolation & purification', 'analogs & derivatives']",https://www.ncbi.nlm.nih.gov/pubmed/19807156,2010,0.0,0.0,,, +19783157,"Matrix-enhanced surface-assisted laser desorption ionization mass spectrometry imaging (ME-SALDI MSI) has been previously demonstrated as a viable approach to improving MS imaging sensitivity. We describe here the employment of ionic matrices to replace conventional MALDI matrices as the coating layer with the aims of reducing analyte redistribution during sample preparation and improving matrix vacuum stability during imaging. In this study, CHCA/ANI (alpha-cyano-4-hydroxycinnamic acid/aniline) was deposited atop tissue samples through sublimation to eliminate redistribution of analytes of interest on the tissue surface. The resulting film was visually homogeneous under an optical microscope. Excellent vacuum stability of the ionic matrix was quantitatively compared with the conventional matrix. The subsequently improved ionization efficiency of the analytes over traditional MALDI was demonstrated. The benefits of using the ionic matrix in MS imaging were apparent in the analysis of garlic tissue sections in the ME-SALDI MSI mode.",Journal of the American Society for Mass Spectrometry,[],[],Ionic matrix for matrix-enhanced surface-assisted laser desorption ionization mass spectrometry imaging (ME-SALDI-MSI).,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19783157,2010,0.0,0.0,,, +19768983,"The chemical composition of fresh flowers from Allium ursinum (ramsons, bear's garlic, wild garlic) growing in Bulgaria has been studied. Thymidine (1), adenosine (2), astragalin (kaempferol-3-O-beta-D-glucopyranoside (3), kaempferol-3-O-beta-D-glucopyranosyl-7-O-beta-D-glucopyranoside (4), kaempferol-3-O-beta-D-neohesperoside (5), and kaempferol-3-O-beta-D-neohesperoside-7-O-beta-D-glucopyranoside (6) were isolated from the n-butanol extract and identified by different spectroscopic and spectrometric methods. Thymine (7), uridine (8), uracil (9) and 5-chloro-uridine (10) were detected in the same extract by GC-MS. This is the first report of the occurrence of 1, 2, 4, 7 - 10 in the flowers of A. ursinum. GC-MS of the volatile components of fresh flowers and leaves from the same plant revealed a high content of sulfur compounds, some of which are reported for the first time for A. ursinum. The antimicrobial activities of extracts from fresh flowers and leaves of A. ursinum have been tested; some extracts exhibited moderate antifungal properties.",Natural product communications,"['D020001', 'D000490', 'D000900', 'D000935', 'D002031', 'D002176', 'D035264', 'D005737', 'D008401', 'D044949', 'D009822', 'D010936', 'D018515', 'D013211']","['1-Butanol', 'Allium', 'Anti-Bacterial Agents', 'Antifungal Agents', 'Bulgaria', 'Candida albicans', 'Flowers', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Kaempferols', 'Oils, Volatile', 'Plant Extracts', 'Plant Leaves', 'Staphylococcus aureus']",Chemical composition and antimicrobial activity of wild garlic Allium ursinum of Bulgarian origin.,"[None, 'Q000737', 'Q000737', 'Q000302', None, 'Q000187', 'Q000737', 'Q000737', 'Q000379', 'Q000302', 'Q000737', 'Q000737', 'Q000737', 'Q000187']","[None, 'chemistry', 'chemistry', 'isolation & purification', None, 'drug effects', 'chemistry', 'chemistry', 'methods', 'isolation & purification', 'chemistry', 'chemistry', 'chemistry', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/19768983,2010,,,,, +19734685,"Allicin (allyl 2-propenethiosulfinate), an antibacterial principle of garlic, has drawn much attention, since it has potent antimicrobial activity against a range of microorganisms, including methicillin-resistant Staphylococcus aureus. There have been many reports on the antibacterial properties of allicin, but no quantitative comparison of antibacterial activities between freshly prepared garlic extract and clinically useful antibiotics has been performed. To verify the substantial antibacterial effect of aqueous garlic extract, we compared it with those of allicin and several clinically useful antibiotics using two representative bacteria commonly found in the human environment, Gram-positive S. aureus and Gram-negative Escherichia coli. The garlic extract had more potent anti-staphylococcal activity than an equal amount of allicin. In terms of antibiotic potency against Gram-positive and Gram-negative bacteria, authentic allicin had roughly 1-2% of the potency of streptomycin (vs. S. aureus), 8% of that of vancomycin (vs. S. aureus), and only 0.2% of that of colistin (vs. E. coli). The antibacterial activity of allicin was completely abolished by cysteine, glutathione and coenzyme A, but not by non-SH-compounds. The oxygen in the structure (-S(=O)-S-) of allicin therefore functions to liberate the S-allyl moiety, which might be an offensive tool against bacteria.","Bioscience, biotechnology, and biochemistry","['D000900', 'D002851', 'D005737', 'D006090', 'D006094', 'D013058', 'D008826', 'D010936', 'D013438', 'D013441']","['Anti-Bacterial Agents', 'Chromatography, High Pressure Liquid', 'Garlic', 'Gram-Negative Bacteria', 'Gram-Positive Bacteria', 'Mass Spectrometry', 'Microbial Sensitivity Tests', 'Plant Extracts', 'Sulfhydryl Compounds', 'Sulfinic Acids']",Antibacterial potential of garlic-derived allicin and its cancellation by sulfhydryl compounds.,"['Q000037', None, 'Q000737', 'Q000187', 'Q000187', None, None, 'Q000494', 'Q000494', 'Q000037']","['antagonists & inhibitors', None, 'chemistry', 'drug effects', 'drug effects', None, None, 'pharmacology', 'pharmacology', 'antagonists & inhibitors']",https://www.ncbi.nlm.nih.gov/pubmed/19734685,2009,1.0,1.0,,, +19733738,"Liquid chromatography electrospray mass spectrometry--LC/ESI/MS--a primary tool for analysis of low volatility compounds in difficult matrices--suffers from the matrix effects in the ESI ionization. It is well known that matrix effects can be reduced by sample dilution. However, the efficiency of simple sample dilution is often limited, in particular by the limit of detection of the method, and can strongly vary from sample to sample. In this study matrix effect is investigated as the function of dilution. It is demonstrated that in some cases dilution can eliminate matrix effect, but often it is just reduced. Based on these findings we propose a new quantitation method based on consecutive dilutions of the sample and extrapolation of the analyte content to the infinite dilution, i.e. to matrix-free solution. The method was validated for LC/ESI/MS analysis of five pesticides (methomyl, thiabendazole, aldicarb, imazalil, methiocarb) in five matrices (tomato, cucumber, apple, rye and garlic) at two concentration levels (0.5 and 5.0 mg kg(-1)). Agreement between the analyzed and spiked concentrations was found for all samples. It was demonstrated that in terms of accuracy of the obtained results the proposed extrapolative dilution approach works distinctly better than simple sample dilution. The main use of this approach is envisaged for (a) method development/validation to determine the extent of matrix effects and the ways of overcoming them and (b) as a second step of analysis in the case of samples having analyte contents near the maximum residue limits (MRL).",Analytica chimica acta,[],[],Combating matrix effects in LC/ESI/MS: the extrapolative dilution approach.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19733738,2009,0.0,0.0,,, +19733357,"A novel HPLC method for determination of a wide variety of S-substituted cysteine derivatives in Allium species has been developed and validated. This method allows simultaneous separation and quantification of S-alk(en)ylcysteine S-oxides, gamma-glutamyl-S-alk(en)ylcysteines and gamma-glutamyl-S-alk(en)ylcysteine S-oxides in a single run. The procedure is based on extraction of these amino acids and dipeptides by methanol, their derivatization by dansyl chloride and subsequent separation by reversed phase HPLC. The main advantages of the new method are simplicity, excellent stability of derivatives, high sensitivity, specificity and the ability to simultaneously analyze the whole range of S-substituted cysteine derivatives. This method was critically compared with other chromatographic procedures used for quantification of S-substituted cysteine derivatives, namely with two other HPLC methods (derivatization by o-phthaldialdehyde/tert-butylthiol and fluorenylmethyl chloroformate), and with determination by gas chromatography or capillary electrophoresis. Major advantages and drawbacks of these analytical procedures are discussed. Employing these various chromatographic methods, the content and relative proportions of individual S-substituted cysteine derivatives were determined in four most frequently consumed alliaceous vegetables (garlic, onion, shallot, and leek).",Journal of chromatography. A,"['D000490', 'D053000', 'D002851', 'D003545', 'D003619', 'D019075', 'D005410', 'D008401', 'D018517']","['Allium', 'Analytic Sample Preparation Methods', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Dansyl Compounds', 'Electrophoresis, Capillary', 'Flame Ionization', 'Gas Chromatography-Mass Spectrometry', 'Plant Roots']",Chromatographic methods for determination of S-substituted cysteine derivatives--a comparative study.,"['Q000737', None, None, 'Q000031', None, None, None, None, 'Q000737']","['chemistry', None, None, 'analogs & derivatives', None, None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19733357,2009,0.0,0.0,,, +19680964,"A multi-analyte method for the liquid chromatography-tandem mass spectrometric determination of mycotoxins in food supplements is presented. The analytes included A and B trichothecenes (nivalenol, deoxynivalenol, 3-acetyldeoxynivalenol, 15-acetyldeoxynivalenol, neosolaniol, fusarenon-X, diacetoxyscirpenol, HT-2 toxin and T-2 toxin), aflatoxins (aflatoxin-B(1), aflatoxin-B(2), aflatoxin-G(1) and aflatoxin-G(2)), Alternaria toxins (alternariol, alternariol methyl ether and altenuene), fumonisins (fumonisin-B(1), fumonisin-B(2) and fumonisin-B(3)), ochratoxin A, zearalenone, beauvericin and sterigmatocystin. Optimization of the simultaneous extraction of these toxins and the sample pretreatment procedure, as well as method validation were performed on maca (Lepidium meyenii) food supplements. The results indicated that the solvent mixture ethyl acetate/formic acid (95:5, v/v) was the best compromise for the extraction of the analytes from food supplements. Liquid-liquid partition with n-hexane was applied as partial clean-up step to remove excess of co-extracted non-polar components. Further clean-up was performed on Oasis HLB cartridges. Samples were analysed using an Acquity UPLC system coupled to a Micromass Quattro Micro triple quadrupole mass spectrometer equipped with an electrospray interface operated in the positive-ion mode. Limits of detection and quantification were in the range of 0.3-30 ng g(-1) and 1-100 ng g(-1), respectively. Recovery yields were above 60% for most of the analytes, except for nivalenol, sterigmatocystine and the fumonisins. The method showed good precision and trueness. Analysis of different food supplements such as soy (Glycine max) isoflavones, St John's wort (Hypericum perforatum), garlic (Allium sativum), Ginkgo biloba, and black radish (Raphanus niger) demonstrated the general applicability of the method. Due to different matrix effects observed in different food supplement samples, the standard addition approach was applied to perform correct quantitative analysis. In 56 out of 62 samples analysed, none of the 23 mycotoxins investigated was detected. Positive samples contained at least one of the toxins fumonisin-B(1), fumonisin-B(2), fumonisin-B(3) and ochratoxin A.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D002853', 'D019587', 'D057230', 'D009183', 'D053719']","['Chromatography, Liquid', 'Dietary Supplements', 'Limit of Detection', 'Mycotoxins', 'Tandem Mass Spectrometry']",LC-MS/MS multi-analyte method for mycotoxin determination in food supplements.,"['Q000379', 'Q000032', None, 'Q000032', 'Q000379']","['methods', 'analysis', None, 'analysis', 'methods']",https://www.ncbi.nlm.nih.gov/pubmed/19680964,2010,0.0,0.0,,, +19643074,"Biomarkers in urine can provide useful information about the bioactivation of chemical carcinogens and can be used to investigate the chemoprotective properties of dietary nutrients. N-Nitrosoproline (NPRO) excretion has been used as an index for endogenous nitrosation. In vitro and animal studies have reported that compounds in garlic may suppress nitrosation and inhibit carcinogenesis. We present a new method for extraction and sensitive detection of both NPRO and N-acetyl-S-allylcysteine from urine. The latter is a metabolite of S-allylcysteine, which is found in garlic. Urine was acidified and the organic acids were extracted by reversed-phase extraction (RP-SPE) and use of a polymeric weak anion exchange (WAX-SPE) resin. NPRO was quantified by isotope dilution gas chromatography-mass spectrometry (GC-MS) using [13C5]NPRO and N-nitrosopipecolinic acid (NPIC) as internal standards. This method was used to analyze urine samples from a study that was designed to test whether garlic supplementation inhibits NPRO synthesis. Using this method, 2.4 to 46.0 ng NPRO/ml urine was detected. The method is straightforward and reliable, and it can be performed with readily available GC-MS instruments. N-Acetyl-S-allylcysteine was quantified in the same fraction and detectable at levels of 4.1 to 176.4 ng/ml urine. The results suggest that 3 to 5 g of garlic supplements inhibited NPRO synthesis to an extent similar to a 0.5-g dose of ascorbic acid or a commercial supplement of aged garlic extract. Urinary NPRO concentration was inversely associated with the N-acetyl-S-allylcysteine concentration. It is possible that allyl sulfur compounds found in garlic may inhibit nitrosation in humans.",Analytical biochemistry,"['D000284', 'D002247', 'D003545', 'D005737', 'D008401', 'D006801', 'D016014', 'D009602', 'D015538', 'D012015', 'D013048', 'D018709', 'D013997']","['Administration, Oral', 'Carbon Isotopes', 'Cysteine', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Linear Models', 'Nitrosamines', 'Nitrosation', 'Reference Standards', 'Specimen Handling', 'Statistics, Nonparametric', 'Time Factors']",A gas chromatography-mass spectrometry method for the quantitation of N-nitrosoproline and N-acetyl-S-allylcysteine in human urine: application to a study of the effects of garlic consumption on nitrosation.,"[None, 'Q000378', 'Q000031', None, 'Q000379', None, None, 'Q000652', None, None, None, None, None]","[None, 'metabolism', 'analogs & derivatives', None, 'methods', None, None, 'urine', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19643074,2009,0.0,0.0,,, +19593584,"The severe toxicity, exorbitant cost and the emerging resistance of Leishmania spp. against most of the currently used drugs led to the urgent need for exploiting our traditional Ayurvedic knowledge to treat visceral leishmaniasis. The aim of this study was to evaluate the in vitro anti-leishmanial activity of various extracts from ten traditionally used Indian medicinal plants. The methanolic extract from only two plants, Withania somnifera Dunal (ashwagandha) and Allium sativum Linn. (garlic), showed appreciable activity against Leishmania donovani. Further active compounds from these two plants were isolated and purified based on bioactivity-guided fractionation. HPLC-purified fraction A6 of ashwagandha and G3 of garlic showed consistently high activity with 50% inhibitory concentration (IC(50)) of 12.5 +/- 4 and 18.6 +/- 3 microg/ml against promastigotes whereas IC(50) of 9.5 +/- 3 and 13.5 +/- 2 microg/ml against amastigote form, respectively. The fraction A6 of ashwagandha was identified as withaferin A while fraction G3 of garlic is yet to be identified, and the work is in progress. Cytotoxic effects of the promising fractions and compounds were further evaluated in the murine macrophage (J774G8) model and were found to be safe. These compounds showed negligible cytotoxicity against J774G8 macrophages. The results indicate that fraction A6 of ashwagandha and fraction G3 of garlic might be potential sources of new anti-leishmanial compounds. The in vivo efficacy study and further optimization of these active compounds are in progress.",Parasitology research,"['D000818', 'D000981', 'D002460', 'D005591', 'D002851', 'D007194', 'D020128', 'D007893', 'D008264', 'D051379', 'D021261', 'D010936', 'D010946']","['Animals', 'Antiprotozoal Agents', 'Cell Line', 'Chemical Fractionation', 'Chromatography, High Pressure Liquid', 'India', 'Inhibitory Concentration 50', 'Leishmania donovani', 'Macrophages', 'Mice', 'Parasitic Sensitivity Tests', 'Plant Extracts', 'Plants, Medicinal']",Evaluation of anti-leishmanial activity of selected Indian plants known to have antimicrobial properties.,"[None, 'Q000302', None, None, None, None, None, 'Q000187', 'Q000187', None, None, 'Q000302', 'Q000737']","[None, 'isolation & purification', None, None, None, None, None, 'drug effects', 'drug effects', None, None, 'isolation & purification', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19593584,2009,0.0,0.0,,cell activity, +19550292,"Garlic is generally used as a therapeutic reagent against various diseases, and numerous studies have indicated that garlic and its derivatives can reduce the risk of various types of human cancer. Diallyl trisulfide (DATS), a major member of garlic derivatives, could inhibit the cell proliferation by triggering either cell cycle arrest or apoptosis in a variety of cancer cell lines as shown in many studies. However, whether DATS has the same effect on human osteosarcoma cells remains unknown. In this study, we have attempted to analyze the effects of DATS on cell proliferation, cell cycle, induction of apoptosis, global protein expression pattern in a human osteosarcoma cell line Saos-2 cells, and the potential molecular mechanisms of the action of DATS. Saos-2 cells, a human osteosarcoma cell line, were treated with or without 25, 50, and 100 micromol/l DATS for various time intervals. The cell proliferation, cell cycle progression, and apoptosis were examined in this study. Then, after treatment with or without 50 micromol/l DATS for 48 h, protein add pattern in Saos-2 cells were systematically studied using two-dimensional electrophoresis and mass spectrometry. DATS could inhibit the proliferation of Saos-2 cells in a dose-dependent and time-dependent manner. Moreover, the percentage of apoptotic cell and cell arrest in G0/G1 phase was also dose-dependent and time-dependent upon DATS treatment. A total of 27 unique proteins in Saos-2 cells, including 18 downregulated proteins and nine upregulated proteins, were detected with significant changes in their expression levels corresponding to DATS administration. Interestingly, almost half of these proteins (13 of 27) are related to either the cell cycle or apoptosis. DATS has the ability to suppress cell proliferation of Saos-2 cells by blocking cell cycle progression and inducing apoptosis in a dose and time-dependent manner. The proteomic results presented, therefore, provide additional support to the hypothesis that DATS is a strong inducer of apoptosis in tumor cells. However, the exact molecular mechanisms, how these proteins significantly changed in the Saos-2 cell line upon DATS treatment, should be further studied.",Anti-cancer drugs,"['D000498', 'D000972', 'D017209', 'D002453', 'D045744', 'D049109', 'D004305', 'D015536', 'D015180', 'D020869', 'D015972', 'D006801', 'D007091', 'D013058', 'D012516', 'D011506', 'D040901', 'D013440', 'D015854']","['Allyl Compounds', 'Antineoplastic Agents, Phytogenic', 'Apoptosis', 'Cell Cycle', 'Cell Line, Tumor', 'Cell Proliferation', 'Dose-Response Relationship, Drug', 'Down-Regulation', 'Electrophoresis, Gel, Two-Dimensional', 'Gene Expression Profiling', 'Gene Expression Regulation, Neoplastic', 'Humans', 'Image Processing, Computer-Assisted', 'Mass Spectrometry', 'Osteosarcoma', 'Proteins', 'Proteomics', 'Sulfides', 'Up-Regulation']",A proteomic study on a human osteosarcoma cell line Saos-2 treated with diallyl trisulfide.,"['Q000494', 'Q000494', 'Q000187', 'Q000187', None, 'Q000187', None, 'Q000187', None, None, 'Q000187', None, None, None, 'Q000378', 'Q000378', None, 'Q000494', 'Q000187']","['pharmacology', 'pharmacology', 'drug effects', 'drug effects', None, 'drug effects', None, 'drug effects', None, None, 'drug effects', None, None, None, 'metabolism', 'metabolism', None, 'pharmacology', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/19550292,2009,0.0,0.0,,no garlic sample, +19505565,"Traditionally, garlic (Allium sativum L.; Alliaceae) has been known to boost the immune system. Aged garlic has more potent immunomodulatory effects than raw garlic. These effects have been attributed to the transformed organosulfur compounds; the identity of the immunomodulatory proteins in aged garlic extract (AGE) is not known.",Journal of ethnopharmacology,"['D000818', 'D002852', 'D004396', 'D004591', 'D005737', 'D006023', 'D006386', 'D007155', 'D051379', 'D008807', 'D010936', 'D010940', 'D011485', 'D051381', 'D017208', 'D013154', 'D013601', 'D013778', 'D013844']","['Animals', 'Chromatography, Ion Exchange', 'Coloring Agents', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Glycoproteins', 'Hemagglutination Tests', 'Immunologic Factors', 'Mice', 'Mice, Inbred BALB C', 'Plant Extracts', 'Plant Proteins', 'Protein Binding', 'Rats', 'Rats, Wistar', 'Spleen', 'T-Lymphocytes', 'Tetrazolium Salts', 'Thiazoles']",Identification of the protein components displaying immunomodulatory activity in aged garlic extract.,"[None, None, None, None, 'Q000737', 'Q000378', None, 'Q000737', None, None, 'Q000737', 'Q000737', None, None, None, 'Q000166', 'Q000187', None, None]","[None, None, None, None, 'chemistry', 'metabolism', None, 'chemistry', None, None, 'chemistry', 'chemistry', None, None, None, 'cytology', 'drug effects', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19505565,2009,0.0,0.0,,proteins, +19428347,"Diallyl disulfide (DADS) and diallyl sulfide (DAS) are the major metabolites found in garlic oil and have been reported to lower cholesterol and prevent cancer. The molecular cytotoxic mechanisms of DADS and DAS have not been determined. The cytotoxic effectiveness of hydrogen versus allyl sulfides towards hepatocytes was found to be as follows: NaHS>DADS>DAS. Hepatocyte mitochondrial membrane potential was decreased and reactive oxygen species (ROS) and TBARS formation was increased by all three allyl sulfides. (1) DADS induced cytotoxicity was prevented by the H(2)S scavenger hydroxocobalamin, which also prevented cytochrome oxidase dependent mitochondrial respiration suggesting that H(2)S inhibition of cytochrome oxidase contributed to DADS hepatocyte cytotoxicity. (2) DAS cytotoxicity on the other hand was prevented by hydralazine, an acrolein trap. Hydralazine also prevented DAS induced GSH depletion, decreased mitochondrial membrane potential and increased ROS and TBARS formation. Chloral hydrate, the aldehyde dehydrogenase 2 inhibitor, however had the opposite effects, which could suggest that acrolein contributed to DAS hepatocyte cytotoxicity.",Chemico-biological interactions,"['D000498', 'D000818', 'D016588', 'D002470', 'D002478', 'D004220', 'D008401', 'D022781', 'D008297', 'D051381', 'D017207', 'D017382', 'D013440', 'D017392']","['Allyl Compounds', 'Animals', 'Anticarcinogenic Agents', 'Cell Survival', 'Cells, Cultured', 'Disulfides', 'Gas Chromatography-Mass Spectrometry', 'Hepatocytes', 'Male', 'Rats', 'Rats, Sprague-Dawley', 'Reactive Oxygen Species', 'Sulfides', 'Thiobarbituric Acid Reactive Substances']",The molecular mechanisms of diallyl disulfide and diallyl sulfide induced hepatocyte cytotoxicity.,"['Q000633', None, 'Q000633', None, None, 'Q000633', None, 'Q000187', None, None, None, 'Q000032', 'Q000633', 'Q000032']","['toxicity', None, 'toxicity', None, None, 'toxicity', None, 'drug effects', None, None, None, 'analysis', 'toxicity', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19428347,2009,0.0,0.0,,, +19350826,"To analyze the chemical components and decomposition products in allicin extract of garlic, the chemical components screening and identification were made with HPLC-MS/MS method by full scan TIC MS, HPLC retention time, product MS spectra and chemical reference standards. The stability of the extract in water and alcoholic solutions was also investigated. There were five major components in allicin extract which were all identified as thiosulfinates. The extract was stable for at least 3 months when stored at -20 degrees C as water solution, but obvious decomposition was observed with the increase of alcoholic concentration. The decomposition products were also identified by HPLC-MS/MS.",Yao xue xue bao = Acta pharmaceutica Sinica,"['D002851', 'D004355', 'D005737', 'D010946', 'D021241', 'D013441', 'D053719', 'D013885']","['Chromatography, High Pressure Liquid', 'Drug Stability', 'Garlic', 'Plants, Medicinal', 'Spectrometry, Mass, Electrospray Ionization', 'Sulfinic Acids', 'Tandem Mass Spectrometry', 'Thiosulfates']",[HPLC tandem-mass spectrometric analysis of the chemical components and decomposition products of allicin extract of garlic].,"[None, None, 'Q000737', 'Q000737', None, 'Q000302', None, 'Q000032']","[None, None, 'chemistry', 'chemistry', None, 'isolation & purification', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19350826,2010,,,,, +19336907,"The growing concomitant consumption of drugs and herbal preparations such as garlic, and the numerous reports about the influence of herbal preparations on intestinal transport, led us to evaluate the influence of aged garlic extract on the transport function and electrophysiological parameters of the small intestinal mucosa. Aged garlic extract induced increase of the absolute value of the transepithelial potential difference and of the short-circuit current in both permeability models tested (rat jejunum, Caco-2 cell monolayers) without affecting transepithelial electrical resistance. It also caused a significant increase of the P-glycoprotein and multidrug resistance associated protein 2 mediated effluxes through rat jejunum of marker substrates Rhodamine 123 and 2,4-dinitrophenyl-S-glutathione, respectively. Rhodamine 123 efflux through the Caco-2 cell monolayers was not altered by aged garlic extract, whereas the efflux of 2,4-dinitrophenyl-S-glutathione increased significantly. So altered activity of the important transport proteins could significantly change the pharmacokinetic properties of conventional medicines taken concomitantly with aged garlic extract.",Biological & pharmaceutical bulletin,"['D018435', 'D020168', 'D000818', 'D001693', 'D018938', 'D002851', 'D015194', 'D004594', 'D019793', 'D005737', 'D005978', 'D006801', 'D066298', 'D007413', 'D007583', 'D010936', 'D051381', 'D020112']","['ATP Binding Cassette Transporter, Sub-Family B', 'ATP-Binding Cassette, Sub-Family B, Member 1', 'Animals', 'Biological Transport, Active', 'Caco-2 Cells', 'Chromatography, High Pressure Liquid', 'Diffusion Chambers, Culture', 'Electrophysiology', 'Fluorescein', 'Garlic', 'Glutathione', 'Humans', 'In Vitro Techniques', 'Intestinal Mucosa', 'Jejunum', 'Plant Extracts', 'Rats', 'Rhodamine 123']",Aged garlic extract stimulates p-glycoprotein and multidrug resistance associated protein 2 mediated effluxes.,"['Q000502', 'Q000502', None, 'Q000187', None, None, None, None, None, 'Q000737', 'Q000031', None, None, 'Q000378', 'Q000378', 'Q000494', None, None]","['physiology', 'physiology', None, 'drug effects', None, None, None, None, None, 'chemistry', 'analogs & derivatives', None, None, 'metabolism', 'metabolism', 'pharmacology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/19336907,2009,0.0,0.0,,aged garlic, +19271323,"The therapeutic potency of garlic leaf extract obtained from normal and sulphur treated plants was compared. Alliin, the active compound of garlic leaf extract showed 32% increase in yield under sulphur treated conditions. Alliin obtained from leaf extract of plants brought a significant reduction in serum glucose, triglycerides, total lipids, total cholesterol, LDL- and VLDL-cholesterol levels than glibenclamide in alloxan-induced diabetic rats. Alliin from sulphur treated plants was more effective in comparison to that obtained from plants raised in normal conditions. Serum glucose levels showed significant reduction of 50% in rats administered with leaf extract from sulphur treated plants in comparison to 37% noted in rats administered with leaf extract from normal plants. No alteration in HDL-cholesterol was noted. Similarly, alliin obtained from leaf extract of plants lowered the serum enzyme (ALP, AST and ALT) levels towards normal than glibenclamide. The reduction in serum enzyme levels was significant in rats administered with leaf extract of plants raised under sulphur treated conditions in comparison to that raised under normal conditions. The present findings suggest that leaf extract from sulphur treated garlic possess more antidiabetic potential and hence show more therapeutic potency in comparison to extract obtained from normal plants.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000410', 'D000818', 'D001219', 'D001786', 'D002855', 'D003545', 'D005737', 'D008055', 'D008297', 'D010936', 'D018515', 'D051381', 'D017208', 'D013455']","['Alanine Transaminase', 'Animals', 'Aspartate Aminotransferases', 'Blood Glucose', 'Chromatography, Thin Layer', 'Cysteine', 'Garlic', 'Lipids', 'Male', 'Plant Extracts', 'Plant Leaves', 'Rats', 'Rats, Wistar', 'Sulfur']",Sulphur treatment alters the therapeutic potency of alliin obtained from garlic leaf extract.,"['Q000097', None, 'Q000097', 'Q000032', None, 'Q000031', 'Q000737', 'Q000097', None, 'Q000032', 'Q000737', None, None, 'Q000494']","['blood', None, 'blood', 'analysis', None, 'analogs & derivatives', 'chemistry', 'blood', None, 'analysis', 'chemistry', None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/19271323,2009,1.0,1.0,,, +19267240,"A sensitive and simple analytical method has been developed for determination of Sb(III), Sb(V), Se(IV), Se(VI), Te(IV), Te(VI), and Bi(III) in garlic samples by using hydride-generation-atomic-fluorescence spectrometry (HG-AFS). The method is based on a single extraction of the inorganic species by sonication at room temperature with 1 mol L(-1) H2SO4 and washing of the solid phase with 0.1% (w/v) EDTA, followed by measurement of the corresponding hydrides generated under two different experimental conditions directly and after a pre-reduction step. The limit of detection of the method was 0.7 ng g(-1) for Sb(III), 1.0 ng g(-1) for Sb(V), 1.3 ng g(-1) for Se(IV), 1.0 ng g(-1) for Se(VI), 1.1 ng g(-1) for Te(IV), 0.5 ng g(-1) for Te(VI), and 0.9 ng g(-1) for Bi(III), in all cases expressed in terms of sample dry weight.",Analytical and bioanalytical chemistry,"['D000965', 'D001729', 'D005737', 'D006859', 'D007287', 'D007477', 'D018551', 'D018515', 'D018036', 'D013050', 'D013464', 'D013691']","['Antimony', 'Bismuth', 'Garlic', 'Hydrogen', 'Inorganic Chemicals', 'Ions', 'Lycopersicon esculentum', 'Plant Leaves', 'Selenium Compounds', 'Spectrometry, Fluorescence', 'Sulfuric Acids', 'Tellurium']","Determination of total Sb, Se, Te, and Bi and evaluation of their inorganic species in garlic by hydride-generation-atomic-fluorescence spectrometry.","['Q000032', 'Q000032', 'Q000737', 'Q000737', 'Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000032', 'Q000295', None, 'Q000032']","['analysis', 'analysis', 'chemistry', 'chemistry', 'analysis', 'chemistry', 'chemistry', 'chemistry', 'analysis', 'instrumentation', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19267240,2009,1.0,3.0,,, +19200080,"The color-forming ability of amino acids with thiosulfinate in crushed garlic was investigated. We developed reaction systems for generating pure blue pigments using extracted thiosulfinate from crushed garlic and onion and all 22 amino acids. Each amino acid was reacted with thiosulfinate solution and was then incubated at 60 degrees C for 3 h to generate pigments. Unknown blue pigments, responsible for discoloration in crushed garlic cloves (Allium sativum L.), were separated and tentatively characterized using high-performance liquid chromatography (HPLC) and a diode array detector ranging between 200 and 700 nm. Blue pigment solutions exhibited 2 maximal absorbance peaks at 440 nm and 580 nm, corresponding to yellow and blue, respectively, with different retention times. Our findings indicated that green discoloration is created by the combination of yellow and blue pigments. Eight naturally occurring blue pigments were separated from discolored garlic extracts using HPLC at 580 nm. This suggests that garlic discoloration is not caused by only 1 blue pigment, as reported earlier, but by as many as 8 pigments. Overall, free amino acids that formed blue pigment when reacted with thiosulfinate were glycine, arginine, lysine, serine, alanine, aspartic acid, asparagine, glutamic acid, and tyrosine. Arginine, asparagine, and glutamine had spectra that were more similar to naturally greened garlic extract.",Journal of food science,"['D000596', 'D002851', 'D003116', 'D005524', 'D005737', 'D019697', 'D010860', 'D013886', 'D013997']","['Amino Acids', 'Chromatography, High Pressure Liquid', 'Color', 'Food Technology', 'Garlic', 'Onions', 'Pigments, Biological', 'Thiosulfonic Acids', 'Time Factors']",Identification of candidate amino acids involved in the formation of blue pigments in crushed garlic cloves (Allium sativum L.).,"['Q000032', None, None, None, 'Q000737', 'Q000737', 'Q000096', 'Q000032', None]","['analysis', None, None, None, 'chemistry', 'chemistry', 'biosynthesis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/19200080,2009,0.0,0.0,,not quantified, +19170155,"The herbal remedies Natto K2, Agaricus, mistletoe, noni juice, green tea and garlic, frequently used by cancer patients, were investigated for their in vitro inhibition potential of cytochrome P-450 3A4 (CYP3A4) metabolism. To our knowledge, only garlic and green tea had available data on the possible inhibition of CYP3A4 metabolism. Metabolic studies were performed with human c-DNA baculovirus expressed CYP3A4. Testosterone was used as a substrate and ketoconazole as a positive quantitative inhibition control. The formation of 6-beta-OH-testosterone was quantified by a validated HPLC methodology. Green tea was the most potent inhibitor of CYP3A4 metabolism (IC(50): 73 microg/mL), followed by Agaricus, mistletoe and noni juice (1324, 3594, >10 000 microg/mL, respectively). All IC(50) values were high compared with those determined for crude extracts of other herbal remedies. The IC(50)/IC(25) ratios for the inhibiting herbal remedies ranged from 2.15 to 2.67, indicating similar inhibition profiles of the herbal inhibitors of CYP3A4. Garlic and Natto K2 were classified as non-inhibitors. Although Agaricus, noni juice, mistletoe and green tea inhibited CYP3A4 metabolism in vitro, clinically relevant systemic or intestinal interactions with CYP3A4 were considered unlikely, except for a probable inhibition of intestinal CYP3A4 by the green tea product.",Phytotherapy research : PTR,"['D000364', 'D002851', 'D051544', 'D065692', 'D004791', 'D029001', 'D006801', 'D007654', 'D010936', 'D013662', 'D013739', 'D028182']","['Agaricus', 'Chromatography, High Pressure Liquid', 'Cytochrome P-450 CYP3A', 'Cytochrome P-450 CYP3A Inhibitors', 'Enzyme Inhibitors', 'Herbal Medicine', 'Humans', 'Ketoconazole', 'Plant Extracts', 'Tea', 'Testosterone', 'Viscum album']",In vitro inhibition of CYP3A4 by herbal remedies frequently used by cancer patients.,"[None, None, None, None, None, None, None, 'Q000494', 'Q000009', 'Q000009', 'Q000378', 'Q000009']","[None, None, None, None, None, None, None, 'pharmacology', 'adverse effects', 'adverse effects', 'metabolism', 'adverse effects']",https://www.ncbi.nlm.nih.gov/pubmed/19170155,2009,0.0,0.0,,, +19160762,"A gas chromatography-negative chemical ionization mass spectrometric (GC-NCI/ MS) method has been developed for analyzing 14 pesticide residues in sulfur-containing vegetables (scallion, garlic, garlic bolt, leek, etc.). The samples were first heated in a microwave oven to eliminate most of the sulfur-containing interfering impurities and then extracted with acetonitrile. The extracts were further cleaned-up by gel permeation chromatography (GPC) and a primary-secondary amine (PSA) cartridge. The target analytes were determined using GC-NCI/MS in the selected ion monitoring (SIM) mode. The recoveries of all the pesticides (at spiked level of 50 microg/kg) were from 49.2% to 113.1% with the relative standard deviations between 1.42% and 8.70%. The detection limits (S/N = 3) were in the range of 0.5-10.0 microg/kg. The method is selective without interference and suitable for the determination and confirmation of pesticides in the sulfur-containing vegetables.",Se pu = Chinese journal of chromatography,"['D005504', 'D005506', 'D008401', 'D057230', 'D016014', 'D008872', 'D010573', 'D015203', 'D013455', 'D014675']","['Food Analysis', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Limit of Detection', 'Linear Models', 'Microwaves', 'Pesticide Residues', 'Reproducibility of Results', 'Sulfur', 'Vegetables']",[Determination of 14 pesticide residues in sulfur-containing vegetables by gas chromatography-negative chemical ionization mass spectrometry].,"['Q000379', 'Q000032', 'Q000379', None, None, None, 'Q000032', None, 'Q000378', 'Q000737']","['methods', 'analysis', 'methods', None, None, None, 'analysis', None, 'metabolism', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19160762,2010,,,,, +19159814,"A sequential extraction procedure was developed for the fractionation of different classes of selenium species present in garlic. The consecutive steps included leaching with water, extraction of cell-wall bound species after lysis with a mixture of cellulase, chitinase and beta-glucanase completed by a proteolytic attack, extraction with HCl to liberate the residual organic bound species and finally, extractions with sulfite solution and CS(2) to complete the mass balance by the recovery of Se(0) and Se(2-), respectively. Selenium speciation in the aqueous fractions was probed by anion-exchange and ion-pairing reversed-phase HPLC-ICP MS after purification by preparative size-exclusion LC. It was found to be strongly affected by the sample redox conditions. The peak identity was matched with a mixture of 9 compounds expected to be present in allium plants; electrospray QTOF MS turned out to be unsuccessful. Selenite, selenate and selenomethionine were the dominating species present.",Talanta,"['D002851', 'D005737', 'D013058', 'D010447', 'D010936', 'D012643']","['Chromatography, High Pressure Liquid', 'Garlic', 'Mass Spectrometry', 'Peptide Hydrolases', 'Plant Extracts', 'Selenium']",A sequential extraction procedure for an insight into selenium speciation in garlic.,"[None, 'Q000737', None, 'Q000378', None, 'Q000032']","[None, 'chemistry', None, 'metabolism', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/19159814,2009,2.0,1.0,,, +19139589,"Thirty elements in garlic sample were determined by inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma atomic emission spectrometry (ICP-AES) after microwave digestion. The concentrations of K, Ca,Na, Sr, and Hg in the present garlic sample were higher than those in rice and wheat, but the concentration of Se in the garlic sample was relatively lower. The extractability of the elements in the garlic sample was also examined; the results showed that most of the elements could be easily extracted by pure water and/or a 0.1 M HNO(3) solution, except for Hg. Furthermore, the size-fractional distribution of the elements in garlic was investigated by pure water extraction and centrifugal ultrafiltration.",Analytical sciences : the international journal of the Japan Society for Analytical Chemistry,"['D002118', 'D002498', 'D005737', 'D008628', 'D008670', 'D012275', 'D011188', 'D012643', 'D012964', 'D012996', 'D013054', 'D013324', 'D053719', 'D014908']","['Calcium', 'Centrifugation', 'Garlic', 'Mercury', 'Metals', 'Oryza', 'Potassium', 'Selenium', 'Sodium', 'Solutions', 'Spectrophotometry, Atomic', 'Strontium', 'Tandem Mass Spectrometry', 'Triticum']",Determination and size-fractional distribution of the elements in garlic.,"[None, None, 'Q000737', None, 'Q000032', 'Q000737', None, None, None, None, 'Q000379', None, 'Q000379', 'Q000737']","[None, None, 'chemistry', None, 'analysis', 'chemistry', None, None, None, None, 'methods', None, 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19139589,2009,1.0,3.0,,, +19053859,"Garlic (Allium sativum) is regarded as both a food and a medicinal herb. Increasing attention has focused on the biological functions and health benefits of garlic as a potentially major dietary component. Chronic garlic administration has been shown to enhance memory function. Evidence also shows that garlic administration in rats affects brain serotonin (5-hydroxytryptamine [5-HT]) levels. 5-HT, a neurotransmitter involved in a number of physiological functions, is also known to enhance cognitive performance. The present study was designed to investigate the probable neurochemical mechanism responsible for the enhancement of memory following garlic administration. Sixteen adult locally bred male albino Wistar rats were divided into control (n = 8) and test (n = 8) groups. The test group was orally administered 250 mg/kg fresh garlic homogenate (FGH), while control animals received an equal amount of water daily for 21 days. Estimation of plasma free and total tryptophan (TRP) and whole brain TRP, 5-HT, and 5-hydroxyindole acetic acid (5-HIAA) was determined by high-performance liquid chromatography with electrochemical detection. For assessment of memory, a step-through passive avoidance paradigm (electric shock avoidance) was used. The results showed that the levels of plasma free TRP significantly increased (P < .01) and plasma total TRP significantly decreased (P < .01) in garlic-treated rats. Brain TRP, 5-HT, and 5-HIAA levels were also significantly increased following garlic administration. A significant improvement in memory function was exhibited by garlic-treated rats in the passive avoidance test. Increased brain 5-HT levels were associated with improved cognitive performance. The present results, therefore, demonstrate that the memory-enhancing effect of garlic may be associated with increased brain 5-HT metabolism in rats. The results further support the use of garlic as a food supplement for the enhancement of memory.",Journal of medicinal food,"['D006916', 'D000818', 'D001362', 'D001921', 'D005737', 'D006897', 'D008568', 'D008517', 'D028321', 'D051381', 'D017208', 'D014364']","['5-Hydroxytryptophan', 'Animals', 'Avoidance Learning', 'Brain', 'Garlic', 'Hydroxyindoleacetic Acid', 'Memory', 'Phytotherapy', 'Plant Preparations', 'Rats', 'Rats, Wistar', 'Tryptophan']",Repeated administration of fresh garlic increases memory retention in rats.,"['Q000097', None, 'Q000187', 'Q000378', None, 'Q000378', 'Q000187', None, 'Q000008', None, None, 'Q000097']","['blood', None, 'drug effects', 'metabolism', None, 'metabolism', 'drug effects', None, 'administration & dosage', None, None, 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/19053859,2009,,,,, +19053158,"A new derivatization-extraction method for preconcentration of seleno amino acids using hollow fiber liquid phase microextraction (HF-LPME) was developed for the separation and determination of seleno amino acids in biological samples by gas chromatography-inductively coupled plasma mass spectrometry (GC-ICP-MS). Derivatization was performed with ethyl chloroformate (ECF) to improve the volatility of seleno amino acids. Parameters influencing microextraction, including extraction solvent, pH of sample solution, extraction time, stirring speed, and inorganic salt concentration have been investigated. Under the optimal conditions, the limits of detection (LODs) obtained for Se-methyl-selenocysteine (SeMeCys), selenomethionine (SeMet), and selenoethionine (SeEth) were 23, 15, and 11 ng Se l(-1), respectively. The relative standard deviations (RSDs) were 14.6%, 16.4%, and 19.4% for SeMeCys, SeMet, and SeEth (c = 1.0 ng ml(-1), n = 7), respectively, and the RSDs for SeMeCys, SeMet could be improved obviously if SeEth was utilized as the internal standard. The proposed method was applied for the determination of seleno amino acids in extracts of garlic, cabbage, and mushroom samples, and the recoveries for the spiked samples were in the range of 96.8-108% and 93.4-115% with and without the use of SeEth as internal standard. The developed method was also applied to the analysis of SeMet in a certified reference material of SELM-1 yeast and the determined value is in good agreement with the certified value.",Journal of mass spectrometry : JMS,"['D000363', 'D001937', 'D005591', 'D002725', 'D003545', 'D005001', 'D005737', 'D008401', 'D016566', 'D017279', 'D012645', 'D012680', 'D012965', 'D014050']","['Agaricales', 'Brassica', 'Chemical Fractionation', 'Chloroform', 'Cysteine', 'Ethionine', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Organoselenium Compounds', 'Selenocysteine', 'Selenomethionine', 'Sensitivity and Specificity', 'Sodium Chloride', 'Toluene']",Separation and determination of seleno amino acids using gas chromatography hyphenated with inductively coupled plasma mass spectrometry after hollow fiber liquid phase microextraction.,"['Q000737', 'Q000737', 'Q000379', 'Q000737', 'Q000031', 'Q000031', 'Q000737', 'Q000379', 'Q000032', 'Q000031', 'Q000032', None, 'Q000737', 'Q000737']","['chemistry', 'chemistry', 'methods', 'chemistry', 'analogs & derivatives', 'analogs & derivatives', 'chemistry', 'methods', 'analysis', 'analogs & derivatives', 'analysis', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/19053158,2009,,,,, +19052349,"A wide range of biological activities of garlic in vitro and in vivo have been verified including its antioxidant, antitumor and anti-inflammatory effects. Indoleamine 2,3-dioxygenase (IDO) is an enzyme widely distributed in mammals and is inducible preferentially by IFN-gamma. IDO degrades the essential amino acid tryptophan to form N-formyl kynurenine. In the present in vitro study, the modulatory effect of 14kDa molecule isolated from garlic on IDO induction was tested. Cultures of mononuclear cells were exposed to 14kDa garlic fraction. Then, their proliferation responses and IDO metabolites were measured. A significant down-regulatory effect of garlic on IDO activity was found and also the proliferation responses of mononuclear cells increased. If these results are verified in vivo, an explanation will be provided on how garlic may interfere in IDO induction, which paves the way for elucidating its specific therapeutic effect in preventing tumor progress.","Iranian journal of allergy, asthma, and immunology","['D000818', 'D049109', 'D002470', 'D002851', 'D005737', 'D050503', 'D007737', 'D007963', 'D051379', 'D008807', 'D010084', 'D010936', 'D011506', 'D014364']","['Animals', 'Cell Proliferation', 'Cell Survival', 'Chromatography, High Pressure Liquid', 'Garlic', 'Indoleamine-Pyrrole 2,3,-Dioxygenase', 'Kynurenine', 'Leukocytes, Mononuclear', 'Mice', 'Mice, Inbred BALB C', 'Oxidation-Reduction', 'Plant Extracts', 'Proteins', 'Tryptophan']","The 14kDa protein molecule isolated from garlic suppresses indoleamine 2, 3-dioxygenase metabolites in mononuclear cells in vitro.","[None, 'Q000187', 'Q000187', None, None, 'Q000037', 'Q000378', 'Q000378', None, None, 'Q000187', 'Q000302', 'Q000302', 'Q000378']","[None, 'drug effects', 'drug effects', None, None, 'antagonists & inhibitors', 'metabolism', 'metabolism', None, None, 'drug effects', 'isolation & purification', 'isolation & purification', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/19052349,2009,0.0,0.0,,, +18997429,"The garlic-derived antibacterial principle, alk(en)yl sulfinate compounds, has long been considered as very short-lived substance. However, there are some data showing a rather more stable nature of allicin. We determined here the thermostability of allicin by a systematic analyses employing chemical quantification and an antibacterial activity assay. Allicin in an aqueous extract of garlic was degraded stoichiometrically in proportion to the temperature; we estimated the half-life of allicin to be about a year at 4 degrees C (from 1.8 mg/ml to 0.9 mg/ml) and 32 d at 15 degrees C, but only 1 d at 37 degrees C (from 2.0 mg/ml to 1.0 mg/ml). The half-life values for antibacterial activity showed a similar trend in results: 63 d or more at 4 degrees C for both antibacterial activities, 14 d for anti-staphylococcal activity, and 26 d for anti-escherichia activity at 15 degrees C, but only 1.2 d and 1.9 d for the respective activities at 37 degrees C. Such antibacterial activities were attributable to the major allicin, allyl 2-propenylthiosulfinate. Surprisingly, the decline in the quantity of allicin was not accompanied by its degradation; instead, allicin became a larger molecule, ajoene, which was 3-times larger than allicin.","Bioscience, biotechnology, and biochemistry","['D000900', 'D001681', 'D002851', 'D004926', 'D005737', 'D006207', 'D010936', 'D013211', 'D013441', 'D013696', 'D014867']","['Anti-Bacterial Agents', 'Biological Assay', 'Chromatography, High Pressure Liquid', 'Escherichia coli', 'Garlic', 'Half-Life', 'Plant Extracts', 'Staphylococcus aureus', 'Sulfinic Acids', 'Temperature', 'Water']",Thermostability of allicin determined by chemical and biological assays.,"['Q000737', None, None, 'Q000187', 'Q000737', None, 'Q000737', 'Q000187', 'Q000737', None, 'Q000737']","['chemistry', None, None, 'drug effects', 'chemistry', None, 'chemistry', 'drug effects', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/18997429,2009,1.0,1.0,,, +18952220,"A novel method for determination of S-alk(en)ylcysteine-S-oxides by capillary electrophoresis has been developed and validated. The method is based on extraction of these sulfur amino acids by methanol, their derivatization by fluorenylmethyl chloroformate and subsequent separation by micellar electrokinetic capillary chromatography. Main advantages of the new method are simplicity, sensitivity, high specificity and very low running costs, making it suitable for routine analysis of a large number of samples. Employing this method, the content of S-alk(en)ylcysteine-S-oxides was determined in 12 commonly consumed alliaceous and cruciferous vegetables (e.g. garlic, onion, leek, chive, cabbage, radish, cauliflower and broccoli). The total content of these amino acids in the Allium species evaluated varied between 0.59 and 12.3mg g(-1) fresh weight. Whereas alliin was found only in garlic, isoalliin was the major S-alk(en)ylcysteine-S-oxide in onion, leek, chive and shallot. On the other hand, the cruciferous species analyzed contained only methiin in the range of 0.06-2.45mg g(-1) fresh weight.",Journal of chromatography. A,"['D000490', 'D001937', 'D020374', 'D003545', 'D000432', 'D031224', 'D015203', 'D012680', 'D013454']","['Allium', 'Brassica', 'Chromatography, Micellar Electrokinetic Capillary', 'Cysteine', 'Methanol', 'Raphanus', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Sulfoxides']",Quantitative determination of S-alk(en)ylcysteine-S-oxides by micellar electrokinetic capillary chromatography.,"['Q000737', 'Q000737', 'Q000191', 'Q000031', 'Q000737', 'Q000737', None, None, 'Q000032']","['chemistry', 'chemistry', 'economics', 'analogs & derivatives', 'chemistry', 'chemistry', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18952220,2009,1.0,2.0,,, +18655544,"A novel oligosaccharide was purified from garlic (Allium sativum L.) bulbs via hot water extraction, ammonium sulfate precipitation, gel filtration and ion exchange chromatography. The molecular weight of the oligosaccharide was determined to be 1800. A nuclear magnetic resonance (NMR) study showed that ten fructose molecules were connected by beta1-2 linkage to a terminal glucose. The oligosaccharide had cytotoxic activities against human malignant lymphoma cells (U937) and colon adenocarcinoma cells (WiDr) in vitro. Furthermore, this oligosaccharide significantly suppressed the growth of murine colon adenocarcinoma cells (colon 26) in vivo. The oligosaccharide also stimulated interferon-gamma production by human peripheral blood lymphocyte in vitro, indicating that it may activate the immunological pathways and suppress the growth of tumors in vivo.",Journal of UOEH,"['D000230', 'D000818', 'D002478', 'D003110', 'D004305', 'D019008', 'D005737', 'D006801', 'D007371', 'D008214', 'D008297', 'D051379', 'D008807', 'D008970', 'D009844', 'D013268', 'D014407', 'D020298']","['Adenocarcinoma', 'Animals', 'Cells, Cultured', 'Colonic Neoplasms', 'Dose-Response Relationship, Drug', 'Drug Resistance, Neoplasm', 'Garlic', 'Humans', 'Interferon-gamma', 'Lymphocytes', 'Male', 'Mice', 'Mice, Inbred BALB C', 'Molecular Weight', 'Oligosaccharides', 'Stimulation, Chemical', 'Tumor Cells, Cultured', 'U937 Cells']","Purification, characterization and biological activities of a garlic oligosaccharide.","['Q000473', None, None, 'Q000473', None, None, 'Q000737', None, 'Q000096', 'Q000276', None, None, None, None, 'Q000737', None, None, 'Q000187']","['pathology', None, None, 'pathology', None, None, 'chemistry', None, 'biosynthesis', 'immunology', None, None, None, None, 'chemistry', None, None, 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/18655544,2008,,,,, +18633206,"A simple preprocessing method was developed for multiresidue determination of pesticides in processed agricultural products. Residues were extracted from homogenized samples with acetonitrile in a glass centrifuge tube, followed by salting-out and partitioning with n-hexane. Co-extractives were removed by means of mini-column clean up. Analysis was performed by GC/MS and LC/MS/MS. The prepared sample solutions were examined for matrix effects. Matrix effects had both positive and negative effects on quantitative value. Calibration was achieved by preparing matrix-matched calibration standards to counteract the matrix effects. Of the 235 pesticides spiked at 0.05 or 0.10 microg/g (Method GC), 0.025 or 0.05 microg/g (Method LC) into 8 foods (garlic paste, diced green sweet pepper, green peas paste, celery paste, sweet potato paste, dried adzuki beans, boiled bamboo shoots, tomato paste), recoveries of 214 pesticides were between 50 and 100% and the coefficient of variation was below 20%. This method is useful as a multi-residue analysis method for screening of pesticides in foods.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D002849', 'D003296', 'D005504', 'D013058', 'D010573', 'D053719']","['Chromatography, Gas', 'Cooking', 'Food Analysis', 'Mass Spectrometry', 'Pesticide Residues', 'Tandem Mass Spectrometry']",[Simple preprocessing method for multi-determination of 235 pesticide residues in cooked ingredients of foods by GC/MS and LC/MS/MS].,"[None, None, 'Q000379', None, 'Q000032', None]","[None, None, 'methods', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/18633206,2008,0.0,0.0,,chinese , +18624452,"Potentially toxic acrylamide is largely derived from heat-induced reactions between the amino group of the free amino acid asparagine and carbonyl groups of glucose and fructose in cereals, potatoes, and other plant-derived foods. This overview surveys and consolidates the following dietary aspects of acrylamide: distribution in food originating from different sources; consumption by diverse populations; reduction of the acrylamide content in the diet; and suppression of adverse effects in vivo. Methods to reduce adverse effects of dietary acrylamide include (a) selecting potato, cereal, and other plant varieties for dietary use that contain low levels of the acrylamide precursors, namely, asparagine and glucose; (b) removing precursors before processing; (c) using the enzyme asparaginase to hydrolyze asparagine to aspartic acid; (d) selecting processing conditions (pH, temperature, time, processing and storage atmosphere) that minimize acrylamide formation; (e) adding food ingredients (acidulants, amino acids, antioxidants, nonreducing carbohydrates, chitosan, garlic compounds, protein hydrolysates, proteins, metal salts) that have been reported to prevent acrylamide formation; (f) removing/trapping acrylamide after it is formed with the aid of chromatography, evaporation, polymerization, or reaction with other food ingredients; and (g) reducing in vivo toxicity. Research needs are suggested that may further facilitate reducing the acrylamide burden of the diet. Researchers are challenged to (a) apply the available methods and to minimize the acrylamide content of the diet without adversely affecting the nutritional quality, safety, and sensory attributes, including color and flavor, while maintaining consumer acceptance; and (b) educate commercial and home food processors and the public about available approaches to mitigating undesirable effects of dietary acrylamide.",Journal of agricultural and food chemistry,"['D020106', 'D000293', 'D000328', 'D000368', 'D000369', 'D001215', 'D001216', 'D002648', 'D002675', 'D004032', 'D002523', 'D005260', 'D005504', 'D005511', 'D005947', 'D006358', 'D006801', 'D007223', 'D008297', 'D008875', 'D011198']","['Acrylamide', 'Adolescent', 'Adult', 'Aged', 'Aged, 80 and over', 'Asparaginase', 'Asparagine', 'Child', 'Child, Preschool', 'Diet', 'Edible Grain', 'Female', 'Food Analysis', 'Food Handling', 'Glucose', 'Hot Temperature', 'Humans', 'Infant', 'Male', 'Middle Aged', 'Solanum tuberosum']",Review of methods for the reduction of dietary content and toxicity of acrylamide.,"['Q000008', None, None, None, None, None, 'Q000737', None, None, None, 'Q000737', None, None, 'Q000379', 'Q000737', None, None, None, None, None, 'Q000737']","['administration & dosage', None, None, None, None, None, 'chemistry', None, None, None, 'chemistry', None, None, 'methods', 'chemistry', None, None, None, None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/18624452,2008,0.0,0.0,,relevant just not garlic, +18616471,"Sodium 2-propenyl thiosulfate, a water-soluble organo-sulfane sulfur compound isolated from garlic, induces apoptosis in a number of cancer cells. The molecular mechanism of action of sodium 2-propenyl thiosulfate has not been completely clarified. In this work we investigated, by in vivo and in vitro experiments, the effects of this compound on the expression and activity of rhodanese. Rhodanese is a protein belonging to a family of enzymes widely present in all phyla and reputed to play a number of distinct biological roles, such as cyanide detoxification, regeneration of iron-sulfur clusters and metabolism of sulfur sulfane compounds. The cytotoxic effects of sodium 2-propenyl thiosulfate on HuT 78 cells were evaluated by flow cytometry and DNA fragmentation and by monitoring the progressive formation of mobile lipids by NMR spectroscopy. Sodium 2-propenyl thiosulfate was also found to induce inhibition of the sulfurtransferase activity in tumor cells. Interestingly, in vitro experiments using fluorescence spectroscopy, kinetic studies and MS analysis showed that sodium 2-propenyl thiosulfate was able to bind the sulfur-free form of the rhodanese, inhibiting its thiosulfate:cyanide-sulfurtransferase activity by thiolation of the catalytic cysteine. The activity of the enzyme was restored by thioredoxin in a concentration-dependent and time-dependent manner. Our results suggest an important involvement of the essential thioredoxin-thioredoxin reductase system in cancer cell cytotoxicity by organo-sulfane sulfur compounds and highlight the correlation between apoptosis induced by these compounds and the damage to the mitochondrial enzymes involved in the repair of the Fe-S cluster and in the detoxification system.",The FEBS journal,"['D000498', 'D017209', 'D002384', 'D002453', 'D002460', 'D049109', 'D006868', 'D008055', 'D009682', 'D013050', 'D013463', 'D013879', 'D013884']","['Allyl Compounds', 'Apoptosis', 'Catalysis', 'Cell Cycle', 'Cell Line', 'Cell Proliferation', 'Hydrolysis', 'Lipids', 'Magnetic Resonance Spectroscopy', 'Spectrometry, Fluorescence', 'Sulfuric Acid Esters', 'Thioredoxins', 'Thiosulfate Sulfurtransferase']",Rhodanese-thioredoxin system and allyl sulfur compounds.,"['Q000494', 'Q000187', None, None, None, None, None, 'Q000096', None, None, 'Q000494', 'Q000378', 'Q000378']","['pharmacology', 'drug effects', None, None, None, None, None, 'biosynthesis', None, None, 'pharmacology', 'metabolism', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/18616471,2008,0.0,0.0,,, +18585988,"Active oxygen species from the photocatalytic reaction in aqueous solution react with luminol to emit strong chemiluminescence (CL), and this can be inhibited by the UV decomposed-products of selenocystine (SeCys) or selenomethionine (SeMet). Based on this phenomenon, a novel hyphenated technique, HPLC-UV/nano-TiO(2)-CL, was established for the determination of SeCys and SeMet. The effects of pH, the UV irradiation time, the TiO(2) coated on the inner surface of the reaction tubing, and the Co(2+) catalyst concentration on the CL intensity and/or chromatographic resolution were systematically investigated. Under these optimized conditions, the inhibited CL intensity has a good linear relationship with the concentration of SeCys in the range of 0.04-10.6 microg mL(-1) or SeMet in the range of 0.05-12.4 microg mL(-1), with a limit of detection (S/N=3) of 6.4 microg L(-1) for SeCys or 12 microg L(-1) for SeMet. As an example, the method was preliminarily applied to the determination of the selenoamino acids in garlic and rabbit serum, with a recovery of 88-104%.","Journal of chromatography. B, Analytical technologies in the biomedical and life sciences","['D000818', 'D002851', 'D003553', 'D005737', 'D008163', 'D016566', 'D010777', 'D011817', 'D012645', 'D014025', 'D014466']","['Animals', 'Chromatography, High Pressure Liquid', 'Cystine', 'Garlic', 'Luminescent Measurements', 'Organoselenium Compounds', 'Photochemistry', 'Rabbits', 'Selenomethionine', 'Titanium', 'Ultraviolet Rays']",A novel HPLC-UV/nano-TiO2-chemiluminescence system for the determination of selenocystine and selenomethionine.,"[None, 'Q000379', 'Q000031', 'Q000737', 'Q000379', 'Q000032', None, None, 'Q000032', None, None]","[None, 'methods', 'analogs & derivatives', 'chemistry', 'methods', 'analysis', None, None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18585988,2008,1.0,1.0,,, +18581860,"A reversed-phase high performance liquid chromatographic method with pre-column derivatization for the determination of alliin and its related substances, which are the precursors of garlic's active components, was established. Alliin was derivatized with 6-aminoquinolyl-N-hydroxysuccinimicly carbamate (AQC). The reaction of derivatization was very fast and the derivative was stable. The analysis was carried out on a Kromasil C18 column (250 mm x 4.6 mm, 5 microm) with a gradientelution and detection at 248 nm. The mobile ph ase consisted of 0.1% acetamide (0.03% acetic acid) (A) and the mixture of water and acetonitrile (40: 60, v/v) (B), and the flow rate was set at 1.0 mL/min. The linear calibration was found for alliin within the range of 1.171 9 -1 500 microg/mL (r = 0.999 8). The inter-day and intra-day precision were good with relative standard deviation (RSD) less than 1.8% (n = 5). The recovery was 99.1% with the RSD of 1.9%. The limit of detection was 0.15 microg/mL. The method established is accurate, simple and rapid and suitable for the determination of alliin and related substances.",Se pu = Chinese journal of chromatography,"['D002851', 'D056148', 'D003545', 'D005737', 'D006863', 'D057230', 'D016014', 'D015203']","['Chromatography, High Pressure Liquid', 'Chromatography, Reverse-Phase', 'Cysteine', 'Garlic', 'Hydrogen-Ion Concentration', 'Limit of Detection', 'Linear Models', 'Reproducibility of Results']",[Determination of alliin and its related substances in garlic using pre-column derivatization and reversed-phase high performance liquid chromatography].,"['Q000379', 'Q000379', 'Q000031', 'Q000737', None, None, None, None]","['methods', 'methods', 'analogs & derivatives', 'chemistry', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18581860,2010,,,,, +18565914,"Lactoperoxidase (LP) exerts antimicrobial effects in combination with H(2)O(2) and either thiocyanate (SCN(-)) or a halide (e.g., I(-)). Garlic extract in the presence of ethanol has also been used to activate the LP system. This study aimed to determine the effects of 3 LP activation systems (LP+SCN(-)+H(2)O(2); LP+I(-)+H(2)O(2); LP + garlic extract + ethanol) on the growth and activity of 3 test organisms (Staphylococcus aureus, Pseudomonas aeruginosa, and Bacillus cereus). Sterilized milk was used as the reaction medium, and the growth pattern of the organisms and a range of keeping quality (KQ) indicators (pH, titratable acidity, ethanol stability, clot on boiling) were monitored during storage at the respective optimum growth temperature for each organism. The LP+I(-)+ H(2)O(2) system reduced bacterial counts below the detection limit shortly after treatment for all 3 organisms, and no bacteria could be detected for the duration of the experiment (35 to 55 h). The KQ data confirmed that the milk remained unspoiled at the end of the experiments. The LP + garlic extract + ethanol system, on the other hand, had no effect on the growth or KQ with P. aeruginosa, but showed a small retardation of growth of the other 2 organisms, accompanied by small increases (5 to 10 h) in KQ. The effects of the LP+SCN(-)+H(2)O(2) system were intermediate between those of the other 2 systems and differed between organisms. With P. aeruginosa, the system exerted total inhibition within 10 h of incubation, but the bacteria regained viability after a further 5 h, following a logarithmic growth curve. This was reflected in the KQ indicators, which implied an extension of 15 h. With the other 2 bacterial species, LP+SCN(-)+H(2)O(2) exerted an obvious inhibitory effect, giving a lag phase in the growth curve of 5 to 10 h and KQ extension of 10 to 15 h. When used in combination, I(-) and SCN(-) displayed negative synergy.",Journal of dairy science,"['D000818', 'D001409', 'D002417', 'D002851', 'D015169', 'D004789', 'D004795', 'D000431', 'D005260', 'D005519', 'D005737', 'D006861', 'D007454', 'D007784', 'D008826', 'D008892', 'D010936', 'D011550', 'D013211', 'D013861', 'D013997']","['Animals', 'Bacillus cereus', 'Cattle', 'Chromatography, High Pressure Liquid', 'Colony Count, Microbial', 'Enzyme Activation', 'Enzyme Stability', 'Ethanol', 'Female', 'Food Preservation', 'Garlic', 'Hydrogen Peroxide', 'Iodides', 'Lactoperoxidase', 'Microbial Sensitivity Tests', 'Milk', 'Plant Extracts', 'Pseudomonas aeruginosa', 'Staphylococcus aureus', 'Thiocyanates', 'Time Factors']",Challenge testing the lactoperoxidase system against a range of bacteria using different activation agents.,"[None, 'Q000187', None, None, None, None, None, 'Q000494', None, 'Q000379', 'Q000737', 'Q000494', 'Q000494', 'Q000378', None, 'Q000382', 'Q000494', 'Q000187', 'Q000187', 'Q000494', None]","[None, 'drug effects', None, None, None, None, None, 'pharmacology', None, 'methods', 'chemistry', 'pharmacology', 'pharmacology', 'metabolism', None, 'microbiology', 'pharmacology', 'drug effects', 'drug effects', 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/18565914,2008,0.0,0.0,,no access to PDF, +18494496,"Polish garlic and white and red onions were subjected to blanching, boiling, frying, and microwaving for different periods of time, and then their bioactive compounds (polyphenols, flavonoids, flavanols, anthocyanins, tannins, and ascorbic acid) and antioxidant activities were determined. It was found that blanching and frying and then microwaving of garlic and onions did not decrease significantly the amounts of their bioactive compounds and the level of antioxidant activities ( P > 0.05). The HPLC profiles of free and soluble ester- and glycoside-bound phenolic acids showed that trans-hydroxycinnamic acids (caffeic, p-coumaric, ferulic, and sinapic) were as much as twice higher in garlic than in onions. Quercetin quantity was the highest in red onion among the studied vegetables. The electrophoretic separation of nonreduced garlic and onion proteins after boiling demonstrated their degradation in the range from 50 to 112 kDa.",Journal of agricultural and food chemistry,"['D000975', 'D002851', 'D003373', 'D005419', 'D005511', 'D005737', 'D006358', 'D019697', 'D010636', 'D059808', 'D011794']","['Antioxidants', 'Chromatography, High Pressure Liquid', 'Coumaric Acids', 'Flavonoids', 'Food Handling', 'Garlic', 'Hot Temperature', 'Onions', 'Phenols', 'Polyphenols', 'Quercetin']",Comparison of the main bioactive compounds and antioxidant activities in garlic and white and red onions after treatment protocols.,"['Q000032', None, 'Q000032', 'Q000032', 'Q000379', 'Q000737', None, 'Q000737', 'Q000032', None, 'Q000032']","['analysis', None, 'analysis', 'analysis', 'methods', 'chemistry', None, 'chemistry', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18494496,2008,1.0,1.0,,, +18388403,"A determination method for individual natural vitamin B(6) compounds was developed. The vitamin B(6) compounds were specifically converted into 4-pyridoxolactone (PAL), a highly fluorescent compound, through a combination of enzymatic reactions and HCl-hydrolysis. PAL was then determined by HPLC. Pyridoxal was completely oxidized to PAL with pyridoxal 4-dehydrogenase (PLDH). Pyridoxine and pyridoxamine were totally converted into PAL through a coupling reaction involving pyridoxine 4-oxidase and PLDH, and one involving pyridoxamine-pyruvate aminotransferase and PLDH, respectively. The 5'-phosphate forms and pyridoxine-beta-glucoside were hydrolyzed with HCl, and then determined as their free forms. Pyridoxine 5'-phosphate and pyridoxine-beta-glucoside were not separately determined here. Three food samples were analyzed by this method.",Journal of nutritional science and vitaminology,"['D000429', 'D000818', 'D002212', 'D002645', 'D002851', 'D005504', 'D005737', 'D005960', 'D006851', 'D006868', 'D011735', 'D011736', 'D013997', 'D000637', 'D025101', 'D014803']","['Alcohol Oxidoreductases', 'Animals', 'Capsicum', 'Chickens', 'Chromatography, High Pressure Liquid', 'Food Analysis', 'Garlic', 'Glucosides', 'Hydrochloric Acid', 'Hydrolysis', 'Pyridoxic Acid', 'Pyridoxine', 'Time Factors', 'Transaminases', 'Vitamin B 6', 'Vitamin B Complex']",Determination of individual vitamin B(6) compounds based on enzymatic conversion to 4-pyridoxolactone.,"['Q000737', None, None, None, 'Q000379', 'Q000379', None, 'Q000032', 'Q000737', None, 'Q000031', 'Q000031', None, 'Q000737', 'Q000032', 'Q000032']","['chemistry', None, None, None, 'methods', 'methods', None, 'analysis', 'chemistry', None, 'analogs & derivatives', 'analogs & derivatives', None, 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18388403,2008,1.0,1.0,,, +18348048,"Nineteen samples of food in glass jars with twist closures were collected by the national food inspectors at Danish food producers and a few importers, focusing on fatty food, such as vegetables in oil, herring in dressing or pickle, soft spreadable cheese, cream, dressings, peanut butter, sauces and infant food. The composition of the plasticizers in the gaskets was analysed by gas chromatography with flame ionization detection (GC-FID) and gas chromatography-mass spectrometry (GC-MS). Epoxidized soybean oil (ESBO) and phthalates were determined in the homogenized food samples. ESBO was the principal plasticizer in five of the gaskets; in 14 it was phthalates. ESBO was found in seven of the food samples at concentrations from 6 to 100 mg kg(-1). The highest levels (91-100 mg kg(-1)) were in oily foods such as garlic, chilli or olives in oil. Phthalates, i.e. di-iso-decylphthalate (DIDP) and di-iso-nonylphthalates (DINP), were found in seven samples at 6-173 mg kg(-1). The highest concentrations (99-173 mg kg(-1)) were in products of garlic and tomatoes in oil and in fatty food products such as sauce béarnaise and peanut butter. For five of the samples the overall migration from unused lids to the official fatty food simulant olive oil was determined and compared with the legal limit of 60 mg kg(-1). The results ranged from 76 to 519 mg kg(-1) and as a consequence the products were withdrawn from the market.","Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment","['D002849', 'D003718', 'D004041', 'D004852', 'D005504', 'D005506', 'D018857', 'D006801', 'D010968', 'D011143', 'D013024']","['Chromatography, Gas', 'Denmark', 'Dietary Fats', 'Epoxy Compounds', 'Food Analysis', 'Food Contamination', 'Food Packaging', 'Humans', 'Plasticizers', 'Polyvinyl Chloride', 'Soybean Oil']",Migration of epoxidized soybean oil (ESBO) and phthalates from twist closures into food and enforcement of the overall migration limit.,"['Q000379', None, 'Q000032', 'Q000032', 'Q000379', 'Q000032', None, None, 'Q000737', 'Q000737', 'Q000032']","['methods', None, 'analysis', 'analysis', 'methods', 'analysis', None, None, 'chemistry', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/18348048,2008,0.0,0.0,,, +18320173,"A method for the accurate determination of ultratrace selenium species of relevance to cancer research, such as gamma-glutamyl-Se-methylselenocysteine (gamma-glutamyl-SeMC), using species-specific double isotope dilution analysis (IDA) with HPLC-ICP-MS is reported for the first time. The (77)Se-enriched gamma-glutamyl-SeMC spike was produced in-house by collecting the fraction at the retention time of the gamma-glutamyl-SeMC peak from a chromatographed aqueous extract of (77)Se-enriched yeast, pooling the collected fractions and freeze-drying the homogenate. The Se content of this spike was characterised using reverse isotope dilution mass spectrometry (IDMS) and the isotopic composition of this spike was checked prior to quantification of the natural abundance dipeptide species in garlic using speciated IDA. The extraction of the gamma-glutamyl-SeMC species in water was performed in a sonication bath for 2 h after adding an appropriate quantity of (77)Se-enriched gamma-glutamyl-SeMC to 50 mg of garlic to give optimal (78)Se/(77)Se and (82)Se/(77)Se ratios of 1.5 and 0.6, respectively. The effect of ultrasonic nebulisation, in comparison with the loading of the ICP with carbon (through the addition of methane gas on-line), on the detection of Se associated with gamma-glutamyl-SeMC using collision/reaction cell ICP-MS with hydrogen as collision gas was investigated. Sensitivity enhancements of approximately fourfold and twofold were achieved using USN and methane mixed plasma, respectively, in comparison with conventional nebulisation and conventional Ar ICP-MS. However, an approximately twofold improvement in the detection limit was achieved using both approaches (42 ng kg(-1) for (78)Se using peak height measurements). The use of species-specific IDMS enabled quantification of the dipeptide species at ng g(-1) levels (603 ng g(-1) Se) in the complex food matrix with a relative standard deviation (RSD, n = 4) of 4.5%, which was approximately half that obtained using standard addition as a confirmatory technique. Furthermore, good agreement was found between the gamma-glutamyl-SeMC species concentrations obtained using both calibration methods.",Analytical and bioanalytical chemistry,"['D002244', 'D002851', 'D003545', 'D005737', 'D007554', 'D013058', 'D016566', 'D012643', 'D012680', 'D013997']","['Carbon', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Isotopes', 'Mass Spectrometry', 'Organoselenium Compounds', 'Selenium', 'Sensitivity and Specificity', 'Time Factors']",Isotope dilution quantification of ultratrace gamma-glutamyl-Se-methylselenocysteine species using HPLC with enhanced ICP-MS detection by ultrasonic nebulisation or carbon-loaded plasma.,"['Q000737', 'Q000379', 'Q000031', 'Q000737', None, 'Q000379', 'Q000032', 'Q000032', None, None]","['chemistry', 'methods', 'analogs & derivatives', 'chemistry', None, 'methods', 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18320173,2008,0.0,0.0,,spiked samples, +18269349,"Peroxidase POX(1) isoenzyme was purified from garlic (Allium sativum L.) bulb by ammonium sulfate precipitation, gel filtration and anion-exchange chromatography. Native-PAGE profile showed two isoforms, designated POX(1A) and POX(1B). POX(1B) seems to be more attractive for biosensor design since its K(m) (app) for H(2)O(2) is lower than that of POX(1A). In addition to its storage and operational stability, POX(1B) was found to be highly heat-stable, since almost 70% of its activity was conserved at 60 degrees C, whereas full activity was retained at 50 and 40 degrees C for 40 min. The optimal pH was approx. 5 and the optimal temperature was 30 degrees C. Next, gelatin was used as a matrix for enzyme immobilization on a gold electrode surface and electrochemical measurements were performed by using cyclic voltammetry. POX(1B)-based electrodes show great potential for application in H(2)O(2) monitoring of biological samples.",Biotechnology and applied biochemistry,"['D000645', 'D015374', 'D011232', 'D002850', 'D002852', 'D004563', 'D004566', 'D004795', 'D004800', 'D005737', 'D005780', 'D006046', 'D006861', 'D006863', 'D007700', 'D009195', 'D020033', 'D013696']","['Ammonium Sulfate', 'Biosensing Techniques', 'Chemical Precipitation', 'Chromatography, Gel', 'Chromatography, Ion Exchange', 'Electrochemistry', 'Electrodes', 'Enzyme Stability', 'Enzymes, Immobilized', 'Garlic', 'Gelatin', 'Gold', 'Hydrogen Peroxide', 'Hydrogen-Ion Concentration', 'Kinetics', 'Peroxidase', 'Protein Isoforms', 'Temperature']",A new peroxidase from garlic (Allium sativum) bulb: its use in H2O2 biosensing.,"['Q000737', 'Q000379', None, 'Q000379', 'Q000379', None, None, None, None, 'Q000201', 'Q000494', None, 'Q000032', None, None, 'Q000737', 'Q000737', None]","['chemistry', 'methods', None, 'methods', 'methods', None, None, None, None, 'enzymology', 'pharmacology', None, 'analysis', None, None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/18269349,2008,0.0,0.0,,, +18246504,"In this study, an enzyme-linked immunosorbent assay (ELISA) was optimized and applied to the determination of endosulfan residues in 20 different kinds of food commodities including vegetables, dry fruits, tea and meat. The limit of detection (IC(15)) was 0.8 microg kg(-1) and the sensitivity (IC(50)) was 5.3 microg kg(-1). Three simple extraction methods were developed, including shaking on the rotary shaker at 250 r min(-1) overnight, shaking on the rotary shaker for 1 h and thoroughly mixing for 2 min. Methanol was used as the extraction solvent in this study. The extracts were diluted in 0.5% fish skin gelatin (FG) in phosphate-buffered saline (PBS) at various dilutions in order to remove the matrix interference. For cabbage (purple and green), asparagus, Japanese green, Chinese cabbage, scallion, garland chrysanthemum, spinach and garlic, the extracts were diluted 10-fold; for carrots and tea, the extracts were diluted 15-fold and 900-fold, respectively. The extracts of celery, adzuki beans and chestnuts, were diluted 20-fold to avoid the matrix interference; ginger, vegetable soybean and peanut extracts were diluted 100-fold; mutton and chicken extracts were diluted 10-fold and for eel, the dilution was 40-fold. Average recoveries were 63.13-125.61%. Validation was conducted by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). The results of this study will be useful to the wide application of an ELISA for the rapid determination of pesticides in food samples.","Journal of environmental science and health. Part. B, Pesticides, food contaminants, and agricultural wastes","['D002849', 'D003257', 'D018556', 'D004726', 'D004797', 'D005506', 'D008401', 'D006801', 'D007306', 'D010573', 'D012680', 'D014675']","['Chromatography, Gas', 'Consumer Product Safety', 'Crops, Agricultural', 'Endosulfan', 'Enzyme-Linked Immunosorbent Assay', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Insecticides', 'Pesticide Residues', 'Sensitivity and Specificity', 'Vegetables']",Optimization and validation of enzyme-linked immunosorbent assay for the determination of endosulfan residues in food samples.,"['Q000379', None, 'Q000737', 'Q000032', 'Q000379', 'Q000032', 'Q000379', None, 'Q000032', 'Q000032', None, 'Q000737']","['methods', None, 'chemistry', 'analysis', 'methods', 'analysis', 'methods', None, 'analysis', 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/18246504,2008,,,,, +18221910,"Superoxide dismutase (SOD) can enhance the characteristic fluorescence of europium in europium (Eu(3+))-tetracycline (TC) system. According to this, a new spectrofluorimetric determination of SOD was developed. Under the optimum conditions, Eu(3+)-TC formed a ternary complex in close proximity with SOD and then intra-molecular energy transfer from TC-SOD complex to Eu(3+), which resulted in the enhancement of characteristic peak of Eu(3+) at 612 nm. The enhanced fluorescence intensity is in proportion to the concentration of SOD, and the linear range was 0.0553-38.71 microg mL(-1) with the limit of detection of 5.53 ng mL(-1). The developed method was practical, simple, sensitive and relatively free from interference coexisting substances and has been successfully applied to the determination of SOD in the plant and blood samples. The mechanism of fluorescence enhancement between Eu(3+)-TC complex and SOD was also studied.","Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy","['D000818', 'D005063', 'D005737', 'D006801', 'D007202', 'D015335', 'D013050', 'D013482', 'D013552', 'D013752', 'D013997']","['Animals', 'Europium', 'Garlic', 'Humans', 'Indicators and Reagents', 'Molecular Probes', 'Spectrometry, Fluorescence', 'Superoxide Dismutase', 'Swine', 'Tetracycline', 'Time Factors']",Spectrofluorimetric determination of superoxide dismutase using a Europium-tetracycline probe.,"[None, 'Q000378', 'Q000201', None, None, 'Q000378', None, 'Q000032', None, 'Q000378', None]","[None, 'metabolism', 'enzymology', None, None, 'metabolism', None, 'analysis', None, 'metabolism', None]",https://www.ncbi.nlm.nih.gov/pubmed/18221910,2008,0.0,0.0,,relevant just not for garlic, +18207414,"A dual function protein was isolated from Allium sativum bulbs and was characterized. The protein had a molecular mass of 25-26 kDa under non-reducing conditions, whereas two polypeptide chains of 12.5+/-0.5 kDa were observed under reducing conditions. E-64 and leupeptin inhibited the proteolytic activity of the protein, which exhibited characteristics similar to cysteine peptidase. The enzyme exhibited substrate specificity and hydrolyzed natural substrates such as alpha-casein (K(m): 23.0 microM), azocasein, haemoglobin and gelatin. It also showed a high affinity for synthetic peptides such as Cbz-Ala-Arg-Arg-OMe-beta-Nam (K(m): 55.24 microM, k(cat): 0.92 s(-1)). The cysteine peptidase activity showed a remarkable stability after incubation at moderate temperatures (40-50 degrees C) over a pH range of 5.5-6.5. The N-terminus of the protein displayed a 100% sequence similarity to the sequences of a mannose-binding lectin isolated from garlic bulbs. Moreover, the purified protein was retained in the chromatographic column when Con-A Sepharose affinity chromatography was performed and the protein was able to agglutinate trypsin-treated rabbit red cells. Therefore, our results indicate the presence of an additional cysteine peptidase activity on a lectin previously described.",Plant physiology and biochemistry : PPB,"['D000818', 'D002364', 'D003546', 'D004912', 'D005737', 'D005780', 'D006386', 'D006388', 'D006454', 'D010940', 'D011817', 'D017386', 'D013379']","['Animals', 'Caseins', 'Cysteine Endopeptidases', 'Erythrocytes', 'Garlic', 'Gelatin', 'Hemagglutination Tests', 'Hemagglutinins', 'Hemoglobins', 'Plant Proteins', 'Rabbits', 'Sequence Homology, Amino Acid', 'Substrate Specificity']",Isolation and characterization of a dual function protein from Allium sativum bulbs which exhibits proteolytic and hemagglutinating activities.,"[None, 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', None, None, None]","[None, 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/18207414,2008,0.0,0.0,,, +18038130,"Two extracellular enzymes (MsP1 and MsP2) capable of efficient beta-carotene degradation were purified from culture supernatants of the basidiomycete Marasmius scorodonius (garlic mushroom). Under native conditions, the enzymes exhibited molecular masses of approximately 150 and approximately 120 kDa, respectively. SDS-PAGE and mass spectrometric data suggested a composition of two identical subunits for both enzymes. Biochemical characterisation of the purified proteins showed isoelectric points of 3.7 and 3.5, and the presence of heme groups in the active enzymes. Partial amino acid sequences were derived from N-terminal Edman degradation and from mass spectrometric ab initio sequencing of internal peptides. cDNAs of 1,604 to 1,923 bp, containing open reading frames (ORF) of 508 to 513 amino acids, respectively, were cloned from a cDNA library of M. scorodonius. These data suggest glycosylation degrees of approximately 23% for MsP1 and 8% for MsP2. Databank homology searches revealed sequence homologies of MsP1 and MsP2 to unusual peroxidases of the fungi Thanatephorus cucumeris (DyP) and Termitomyces albuminosus (TAP).",Applied microbiology and biotechnology,"['D000363', 'D000595', 'D003001', 'D018076', 'D005656', 'D016681', 'D007526', 'D013058', 'D008969', 'D008970', 'D010544', 'D016415', 'D017386', 'D019207']","['Agaricales', 'Amino Acid Sequence', 'Cloning, Molecular', 'DNA, Complementary', 'Fungal Proteins', 'Genome, Fungal', 'Isoelectric Point', 'Mass Spectrometry', 'Molecular Sequence Data', 'Molecular Weight', 'Peroxidases', 'Sequence Alignment', 'Sequence Homology, Amino Acid', 'beta Carotene']",Novel peroxidases of Marasmius scorodonius degrade beta-carotene.,"['Q000201', None, None, 'Q000235', 'Q000737', None, None, None, None, None, 'Q000737', None, None, 'Q000378']","['enzymology', None, None, 'genetics', 'chemistry', None, None, None, None, None, 'chemistry', None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/18038130,2008,0.0,0.0,,, +18025602,"Crude garlic extract contains one Mn-superoxide dismutase designated as SOD1 and two Cu,Zn superoxide dismutases as SOD2 and SOD3. The major isoform SOD2 was purified to homogeneity by Sephacryl S200-HR gel filtration, DEAE Sepharose ion exchange chromatography, and chromatofocusing using PBE 94. SOD2 was purified 82-fold with a specific activity of 4,960 U/mg protein. This enzyme was stable in a broad pH range from 5.0 to 10.0 and at various temperatures from 25 to 60 degrees C. The native molecular mass of SOD2 estimated by high performance liquid chromatography on TSK gel G2000SW column was 39 kDa. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis showed a single band near 18 kDa, suggesting that native enzyme was homodimeric. The isoelectric point as determined by chromatofocusing was 5. Analysis of its N terminal amino acid sequence revealed high sequence homology with several other cytosolic Cu,Zn-SODs from plants. Exposure of cancer cell lines to garlic Cu,Zn-SOD2 led to a significant decrease in superoxide content with a concomitant rise in intracellular peroxides, indicating that the enzyme is active in mammalian cells and could, therefore, be used in pharmacological applications.",Applied biochemistry and biotechnology,"['D000818', 'D000975', 'D045744', 'D002850', 'D002851', 'D004591', 'D005737', 'D006863', 'D007527', 'D051379', 'D018384', 'D013482', 'D013481', 'D013696']","['Animals', 'Antioxidants', 'Cell Line, Tumor', 'Chromatography, Gel', 'Chromatography, High Pressure Liquid', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Hydrogen-Ion Concentration', 'Isoenzymes', 'Mice', 'Oxidative Stress', 'Superoxide Dismutase', 'Superoxides', 'Temperature']","Purification and characterization of a Cu,Zn-SOD from garlic (Allium sativum L.). Antioxidant effect on tumoral cell lines.","[None, 'Q000737', None, None, None, None, 'Q000201', None, 'Q000737', None, 'Q000187', 'Q000737', 'Q000037', None]","[None, 'chemistry', None, None, None, None, 'enzymology', None, 'chemistry', None, 'drug effects', 'chemistry', 'antagonists & inhibitors', None]",https://www.ncbi.nlm.nih.gov/pubmed/18025602,2008,,,,, +17951430,"The consumption of garlic is inversely correlated with the progression of cardiovascular disease, although the responsible mechanisms remain unclear. Here we show that human RBCs convert garlic-derived organic polysulfides into hydrogen sulfide (H(2)S), an endogenous cardioprotective vascular cell signaling molecule. This H(2)S production, measured in real time by a novel polarographic H(2)S sensor, is supported by glucose-maintained cytosolic glutathione levels and is to a large extent reliant on reduced thiols in or on the RBC membrane. H(2)S production from organic polysulfides is facilitated by allyl substituents and by increasing numbers of tethering sulfur atoms. Allyl-substituted polysulfides undergo nucleophilic substitution at the alpha carbon of the allyl substituent, thereby forming a hydropolysulfide (RS(n)H), a key intermediate during the formation of H(2)S. Organic polysulfides (R-S(n)-R'; n > 2) also undergo nucleophilic substitution at a sulfur atom, yielding RS(n)H and H(2)S. Intact aorta rings, under physiologically relevant oxygen levels, also metabolize garlic-derived organic polysulfides to liberate H(2)S. The vasoactivity of garlic compounds is synchronous with H(2)S production, and their potency to mediate relaxation increases with H(2)S yield, strongly supporting our hypothesis that H(2)S mediates the vasoactivity of garlic. Our results also suggest that the capacity to produce H(2)S can be used to standardize garlic dietary supplements.",Proceedings of the National Academy of Sciences of the United States of America,"['D000111', 'D002851', 'D004563', 'D004912', 'D005737', 'D005978', 'D019803', 'D006801', 'D006862']","['Acetylcysteine', 'Chromatography, High Pressure Liquid', 'Electrochemistry', 'Erythrocytes', 'Garlic', 'Glutathione', 'Glutathione Disulfide', 'Humans', 'Hydrogen Sulfide']",Hydrogen sulfide mediates the vasoactivity of garlic.,"['Q000494', None, None, 'Q000187', 'Q000737', 'Q000097', 'Q000097', None, 'Q000097']","['pharmacology', None, None, 'drug effects', 'chemistry', 'blood', 'blood', None, 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/17951430,2008,0.0,0.0,,, +17916975,"Analysis and distribution of Pb and Cd in different mice organs including liver, kidney, spleen, heart and blood were evaluated after treatment with different aqueous concentrations of garlic (12.5-100 mg/l). Atomic absorption spectrometry (AAS) was used for analysis of Pb and Cd in these organs. Treatment of Cd-Pb exposed mice with garlic (12.5-100 mg/l) reduced Pb concentrations by 44.65, 42.61, 38.4, 47.56, and 66.62% in liver, kidney, heart, spleen and blood respectively. Moreover, garlic reduced Cd levels by 72.5, 87.7, 92.6, 95.6, and 71.7% in liver, kidney, heart, spleen and blood respectively. The suppressed immune responses in mice pretreated with Cd-Pb mixture were reversed by 48.85, 55.82, 81.4 and 90.7 in the presence of 100, 50, 25, and 12.5 mg/ml of garlic extract.",Biological trace element research,"['D000818', 'D000917', 'D002104', 'D002105', 'D005260', 'D005737', 'D007854', 'D007855', 'D008297', 'D051379', 'D008807', 'D008517', 'D010936', 'D014018']","['Animals', 'Antibody Formation', 'Cadmium', 'Cadmium Poisoning', 'Female', 'Garlic', 'Lead', 'Lead Poisoning', 'Male', 'Mice', 'Mice, Inbred BALB C', 'Phytotherapy', 'Plant Extracts', 'Tissue Distribution']",Garlic (Allium sativum L.) as a potential antidote for cadmium and lead intoxication: cadmium and lead distribution and analysis in different mice organs.,"[None, 'Q000187', 'Q000493', 'Q000188', None, None, 'Q000493', 'Q000188', None, None, None, None, 'Q000627', 'Q000187']","[None, 'drug effects', 'pharmacokinetics', 'drug therapy', None, None, 'pharmacokinetics', 'drug therapy', None, None, None, None, 'therapeutic use', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/17916975,2007,0.0,0.0,,, +17874834,"The deodorizing effect of the mushroom (Agaricus bisporus) extract on the malodor produced after garlic consumption was investigated using an electronic sensor and sensory evaluation measurements. Comparative gas chromatography analysis revealed that the quantity of methane- and allylthiols that were usually found after garlic solution rinse, significantly fell after mushroom extract rinsing. Furthermore, in-vitro analysis (mixing the garlic solution and mushroom extract) showed that the methanethiol reaction with the mushroom extract proceeded faster than that of the allylthiol. Ab initio calculations implicated an addition reaction as the possible mechanism between the thiol compounds and the polyphenols. In comparison to the methanethiol, the higher activation energy required by allylthiol for a feasible reaction path way with the model acceptor, o-quinone, is expected to contribute to the difference in the rate of the reaction.",Journal of nutritional science and vitaminology,"['D000284', 'D000293', 'D000364', 'D001944', 'D002849', 'D004305', 'D005260', 'D005737', 'D006209', 'D006801', 'D007564', 'D008697', 'D009812', 'D008517', 'D010936', 'D025341', 'D013438']","['Administration, Oral', 'Adolescent', 'Agaricus', 'Breath Tests', 'Chromatography, Gas', 'Dose-Response Relationship, Drug', 'Female', 'Garlic', 'Halitosis', 'Humans', 'Japan', 'Methane', 'Odorants', 'Phytotherapy', 'Plant Extracts', 'Principal Component Analysis', 'Sulfhydryl Compounds']",Studies on the deodorization by mushroom (Agaricus bisporus) extract of garlic extract-induced oral malodor.,"[None, None, 'Q000737', None, None, None, None, 'Q000009', 'Q000139', None, None, 'Q000032', 'Q000517', 'Q000379', 'Q000008', None, 'Q000032']","[None, None, 'chemistry', None, None, None, None, 'adverse effects', 'chemically induced', None, None, 'analysis', 'prevention & control', 'methods', 'administration & dosage', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17874834,2007,0.0,0.0,,, +17855182,"Quorum sensing (QS) controls systems affecting the pathogenicity of many microorganisms; its interruption has an anti-pathogenic effect and can be used in the treatment of bacterial infections. In this study we evaluated QS regulation by Pseudomonas aeruginosa strains and QS inhibition (QSI) by different compounds. The inhibitory activity of 3 macrolide and 3 lincosamide drugs, resveratrol, garlic extract and N-acetylcysteine was tested on 4 P. aeruginosa strains isolated from cystic fibrosis (CF) patients using Chromobacterium violaceum ATCC 12472 as biomonitor. One P. aeruginosa strain, lincomycin and N-acetylcysteine did not show QSI, contrary to other compounds and P. aeruginosa strains. These results indicate that QSI evaluation should be taken into account in the design of new therapeutic strategies to treat P. aeruginosa infections, especially in patients infected by antibiotic-resistant bacteria.","Journal of chemotherapy (Florence, Italy)","['D000111', 'D000900', 'D002851', 'D002861', 'D004353', 'D005737', 'D006801', 'D055231', 'D018942', 'D010936', 'D011550', 'D053038', 'D013267']","['Acetylcysteine', 'Anti-Bacterial Agents', 'Chromatography, High Pressure Liquid', 'Chromobacterium', 'Drug Evaluation, Preclinical', 'Garlic', 'Humans', 'Lincosamides', 'Macrolides', 'Plant Extracts', 'Pseudomonas aeruginosa', 'Quorum Sensing', 'Stilbenes']",Evaluation of different compounds as quorum sensing inhibitors in Pseudomonas aeruginosa.,"['Q000494', 'Q000494', None, 'Q000187', None, 'Q000737', None, None, 'Q000494', 'Q000494', 'Q000187', 'Q000187', 'Q000494']","['pharmacology', 'pharmacology', None, 'drug effects', None, 'chemistry', None, None, 'pharmacology', 'pharmacology', 'drug effects', 'drug effects', 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/17855182,2007,0.0,0.0,,, +17763522,"In the present study, an RP high performance liquid chromatographic method was developed and validated for the determination of allicin in garlic powder and tablets. Chromatographic separation was carried out on an RP-18(e )column (125 mm x 4 mm), using a mobile phase, consisting of methanol-water (50:50 v/v), at a flow rate of 0.5 mL/min and UV detection at 220 nm. Ethylparaben was used as the internal standard. The assay was linear for allicin concentrations of 5.0-60.0 microg/mL. The RSD for precision was <6.14%. The accuracy was above 89.11%. The detection and quantification limits were 0.27 and 0.81 microg/mL, respectively. This method was used to quantify allicin in garlic powder samples. The results showed that the method described here is useful for the determination of allicin in garlic powder and tablets.",Journal of separation science,"['D002853', 'D011208', 'D015203', 'D012680', 'D013056', 'D013441', 'D013607']","['Chromatography, Liquid', 'Powders', 'Reproducibility of Results', 'Sensitivity and Specificity', 'Spectrophotometry, Ultraviolet', 'Sulfinic Acids', 'Tablets']",Validated liquid chromatographic method for quantitative determination of allicin in garlic powder and tablets.,"['Q000379', 'Q000737', None, None, None, 'Q000032', 'Q000737']","['methods', 'chemistry', None, None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/17763522,2008,2.0,1.0,,, +17715891,"This study describes the identification of six allyl esters in a garlic cheese preparation and in a commercial cream cheese. The extracts were prepared by liquid/liquid extraction and concentrated by the SAFE process. The identification of the allyl esters of acetic, butyric, hexanoic, heptanoic, octanoic, and decanoic acids is based on the correlation of their mass spectrometric data and chromatographic retention time data obtained from the extracts with those of authentic standards. In addition to the gas chromatography (GC)/mass spectrometry analysis, the flavor ingredients were characterized by GC sniffing by a trained flavorist. Some of the esters were isolated by preparative GC.",Journal of agricultural and food chemistry,"['D002611', 'D004952', 'D005232', 'D005737', 'D008401', 'D020005']","['Cheese', 'Esters', 'Fatty Acids, Volatile', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Propanols']",Identification of allyl esters in garlic cheese.,"['Q000032', 'Q000032', 'Q000032', 'Q000737', None, 'Q000032']","['analysis', 'analysis', 'analysis', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17715891,2007,0.0,0.0,,garlic cheese, +17628043,"A novel plasma-assisted desorption/ionization (PADI) method that can be coupled with atmospheric pressure sampling mass spectrometry to yield mass spectral information under ambient conditions of pressure and humidity from a range of surfaces without the requirement for sample preparation or additives is reported. PADI is carried out by generating a nonthermal plasma which interacts directly with the surface of the analyte. Desorption and ionization then occur at the surface, and ions are sampled by the mass spectrometer. The PADI technique is demonstrated and compared with desorption electrospray ionization (DESI) for the detection of active ingredients in a range of over-the-counter and prescription pharmaceutical formulations, including nonsterodial anti-inflammatory drugs (mefenamic acid, Ibugel, and ibuprofen), analgesics (paracetamol, Anadin Extra), and Beecham's ""all in one"" cold and flu remedy. PADI has also been successfully applied to the analysis of nicotine in tobacco and thiosulfates in garlic. PADI experiments have been performed using a prototype source interfaced with a Waters Platform LCZ single-quadrupole mass spectrometer with limited modifications and a Hiden Analytical HPR-60 molecular beam mass spectrometer (MBMS). The ability of PADI to rapidly detect active ingredients in pharmaceuticals without the need for prior sample preparation, solvents, or exposed high voltages demonstrates the potential of the technique for high-throughput screening in a pharmaceutical or forensic environment.",Analytical chemistry,"['D000700', 'D000894', 'D001274', 'D005737', 'D006813', 'D009538', 'D004364', 'D019032', 'D013499', 'D013885']","['Analgesics', 'Anti-Inflammatory Agents, Non-Steroidal', 'Atmospheric Pressure', 'Garlic', 'Humidity', 'Nicotine', 'Pharmaceutical Preparations', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Surface Properties', 'Thiosulfates']",Surface analysis under ambient conditions using plasma-assisted desorption/ionization mass spectrometry.,"['Q000032', 'Q000032', None, 'Q000737', None, 'Q000032', 'Q000032', None, None, 'Q000032']","['analysis', 'analysis', None, 'chemistry', None, 'analysis', 'analysis', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17628043,2007,0.0,0.0,,allicin from citation, +17567142,"Aromas generated in extruded potato snacks without and with addition of 0.25, 0.5, and 1% (w/w) of flavor precursors, cysteine and cystine, were compared and evaluated by descriptive sensory profiling. The results showed that high addition of cysteine (0.5 and 1%) resulted in the formation of undesirable odor and taste described as mercaptanic/sulfur, onion-like, and bitter; on the contrary, addition of cystine even at high concentration gave product with pleasant odor and taste, slightly changed into breadlike notes. GC/O analysis showed cysteine to be a much more reactive flavor precursor than cystine, stimulating formation of 12 compounds with garlic, sulfury, burnt, pungent/beer, cabbage/mold, meatlike, roasted, and popcorn odor notes. Further analysis performed by the AEDA technique identified 2-methyl-3-furanthiol (FD 2048) as a most potent odorant of extruded potato snacks with 1% addition of cysteine. Other identified compounds with high FD were butanal, 3-methyl-2-butenethiol, 2-methylthiazole, methional, 2-acetyl-1-pyrroline, and 3-hydroxy-4,5-dimethyl-2(5H)-furanone. In the case of cystine addition (1%) the highest FD factors were calculated for butanal, 2-acetyl-1-pyrroline, benzenemethanethiol, methional, phenylacetaldehyde, dimethyltrisulfide, 1-octen-3-ol, 1,5-octadien-3-one, and 2-acetylpyrazine.",Journal of agricultural and food chemistry,"['D002849', 'D003545', 'D003553', 'D005511', 'D006801', 'D009812', 'D035281', 'D012677', 'D011198']","['Chromatography, Gas', 'Cysteine', 'Cystine', 'Food Handling', 'Humans', 'Odorants', 'Plant Tubers', 'Sensation', 'Solanum tuberosum']",Effect of cysteine and cystine addition on sensory profile and potent odorants of extruded potato snacks.,"[None, 'Q000008', 'Q000008', 'Q000379', None, 'Q000032', 'Q000737', None, 'Q000737']","[None, 'administration & dosage', 'administration & dosage', 'methods', None, 'analysis', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/17567142,2007,0.0,0.0,,, +17520814,"The characteristics of uptake, transepithelial transport and efflux of Z- and E-ajoenes isolated from the bulbs of Allium sativum were studied. A human colon cell model Caco-2 cell monolayers in vitro cultured had been applied to study the characteristics of uptake, transepithelial transport and efflux of Z- and E-ajoenes. The quantitative determination of Z- and E-ajoenes was performed by high-performance liquid chromatography. Z- and E-Ajoenes can be detected only in the apical side and can be metabolized, but both compounds can not be transported from apical-to-basolateral and basolateral-to-apical directions in cultured Caco-2 cell monolayers. The metabolism of Z- and E-ajoenes in Caco-2 cell monolayers can be partially inhibited by vitamin C as an anti-oxidant, metyrapone as an inhibitor to subtype CYP3A of cytochrome P450 drug metabolism enzymes, and sodium azide as an inhibitor to ATP production. It is shown that neither Z-ajoene nor E-ajoene can pass through Caco-2 cell monolayers, and that they can be metabolized by the cells. The metabolism might be in correlation with cytochrome P450 drugs metabolism enzymes in Caco-2 cell monolayers.",Yao xue xue bao = Acta pharmaceutica Sinica,"['D000975', 'D001205', 'D001692', 'D018938', 'D002462', 'D051544', 'D065692', 'D004220', 'D004791', 'D005737', 'D006801', 'D008797', 'D010946', 'D019810', 'D013237']","['Antioxidants', 'Ascorbic Acid', 'Biological Transport', 'Caco-2 Cells', 'Cell Membrane', 'Cytochrome P-450 CYP3A', 'Cytochrome P-450 CYP3A Inhibitors', 'Disulfides', 'Enzyme Inhibitors', 'Garlic', 'Humans', 'Metyrapone', 'Plants, Medicinal', 'Sodium Azide', 'Stereoisomerism']","[Characteristics of uptake, transport and efflux of Z- and E-ajoenes in Caco-2 cell monolayers in vitro].","['Q000494', 'Q000494', 'Q000187', None, 'Q000187', 'Q000378', None, 'Q000737', 'Q000494', 'Q000737', None, 'Q000494', 'Q000737', 'Q000494', None]","['pharmacology', 'pharmacology', 'drug effects', None, 'drug effects', 'metabolism', None, 'chemistry', 'pharmacology', 'chemistry', None, 'pharmacology', 'chemistry', 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/17520814,2008,,,,, +19071484,"The present paper proposes the application of multiwall carbon nanotubes (MWCNTs) as a solid sorbent for lead preconcentration using a flow system coupled to flame atomic absorption spectrometry. The method comprises the preconcentration of Pb (II) ions at a buffered solution (pH 4.7) onto 30mg of MWCNTs previously oxidized with concentrated HNO(3). The elution step is carried out with 1.0molL(-1) HNO(3). The effect of the experimental parameters, including sample pH, sampling flow rate, buffer and eluent concentrations were investigated by means of a 2(4) full factorial design, while for the final optimization a Doehlert design was employed. Under the best experimental conditions the preconcentration system provided detection and quantification limits of 2.6 and 8.6mugL(-1), respectively. A wide linear range varying from 8.6 up to 775mugL(-1) (r>0.999) and the respective precision (relative standard deviation) of 7.7 and 1.4% for the 15 and 200mugL(-1) levels were obtained. The characteristics obtained for the performance of the flow preconcentration system were a preconcentration factor of 44.2, preconcentration efficiency of 11min(-1), consumptive index of 0.45mL and sampling frequency estimated as 14h(-1). Preconcentration studies of Pb (II) ions in the presence of the majority foreign ions tested did not show interference, attesting the good performance of MWCNTs. The accuracy of the method was assessed from analysis of water samples (tap, mineral, physiological serum and synthetic seawater) and common medicinal herbs submitted to the acid decomposition (garlic and Ginkgo Biloba). The satisfactory recovery values obtained without using analyte addition method confirms the feasibility of this method for Pb (II) ions determination in different type of samples.",Talanta,[],[],Solid-phase extraction system for Pb (II) ions enrichment based on multiwall carbon nanotubes coupled on-line to flame atomic absorption spectrometry.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19071484,2012,0.0,0.0,,, +19071460,"A simple solid phase extraction procedure for speciation of selenium(IV) and selenium(VI) in environmental samples has been proposed prior to graphite furnace atomic absorption spectrometry. The method is based on the solid phase extraction of the selenium(IV)-ammonium pyrrolidine dithiocarbamate (APDC) chelate on the Diaion HP-2MG. After reduction of Se(VI) by heating the samples in the microwave oven with 4moll(-1) HCl, the system was applied to the total selenium. Se(VI) was calculated as the difference between the total selenium content and Se(IV) content. The experimental parameters, pH, amounts of reagents, eluent type and sample volume were optimized. The recoveries of analytes were found greater than 95%. No appreciable matrix effects were observed. The adsorption capacity of sorbent was 5.20mgg(-1) Se (IV). The detection limit of Se (IV) (3sigma, n=11) is 0.010mugl(-1). The preconcentration factor for the presented system was 100. The proposed method was applied to the speciation of selenium(IV), selenium(VI) and determination of total selenium in natural waters and microwave digested soil, garlic, onion, rice, wheat and hazelnut samples harvested various locations in Turkey with satisfactory results. In order to verify the accuracy of the method, certified reference materials (NIST SRM 2711 Montana Soil, NIST SRM 1568a Rice Flour and NIST SRM 8418 Wheat Gluten) were analyzed and the results obtained were in good agreement with the certified values. The relative errors and relative standard deviations were below 6 and 10%, respectively.",Talanta,[],[],Speciation of selenium(IV) and selenium(VI) in environmental samples by the combination of graphite furnace atomic absorption spectrometric determination and solid phase extraction on Diaion HP-2MG.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19071460,2012,1.0,1.0,,, +17283653,"A simple, rapid and sensitive method has been developed and validated for the simultaneous quantification of diallyl trisulfide (DATS) and diallyl disulfide (DADS) in rat blood by gas chromatography with electron-capture detection. The analytes were prevented from degradation by addition of acetonitrile and extraction with hexane before gas chromatographic separation. Two calibration curves for DATS were linear over the range of 10-500 ng/mL and 0.2-20 microg/mL, with typical r values of 0.9989 and 0.9993, respectively. Similarly, two calibration curves for DADS were linear in the concentration range of 50-5000 ng/mL and 1-30 microg/mL, with typical r values of 0.9989 and 0.9983, respectively. The limit of detection was less than 10 ng/mL for DATS and 50 ng/mL for DADS, and the assay was highly reproducible, considering the intra-, inter-day relative standard deviations (R.S.D.) below 12%. The developed procedure was successfully applied for the evaluation of the pharmacokinetics of garlic oil following iv administration at a single dose (10 mg) of garlic oil in rats. The results show that the developed method is suitable for pharmacokinetic and therapeutic purposes of DATS and DADS.",Die Pharmazie,"['D000498', 'D000818', 'D002138', 'D002849', 'D004220', 'D004563', 'D009682', 'D051381', 'D017208', 'D012015', 'D013048', 'D013440', 'D013810']","['Allyl Compounds', 'Animals', 'Calibration', 'Chromatography, Gas', 'Disulfides', 'Electrochemistry', 'Magnetic Resonance Spectroscopy', 'Rats', 'Rats, Wistar', 'Reference Standards', 'Specimen Handling', 'Sulfides', 'Therapeutic Equivalency']",Simultaneous determination of diallyl trisulfide and diallyl disulfide in rat blood by gas chromatography with electron-capture detection.,"['Q000097', None, None, None, 'Q000097', None, None, None, None, None, None, 'Q000097', None]","['blood', None, None, None, 'blood', None, None, None, None, None, None, 'blood', None]",https://www.ncbi.nlm.nih.gov/pubmed/17283653,2007,,,,, +17269787,"New, odorant nitrogen- and sulfur-containing compounds are identified in cress extracts. Cress belongs to the botanical order Brassicales and produces glucosinolates, which are important precursors of nitrogen- and sulfur-containing compounds. Those compounds often present low perception thresholds and various olfactive notes and are thus of interest to the flavor and fragrance chemistry. When the study of organonitrogen and organosulfur compounds is undertaken, Brassicale extracts are one of the matrices of choice. Cress extracts were studied by analytical (GC-MS, GC-FPD) and chemical (fractionation) means to identify new interesting odorant compounds. Two compounds that have never been reported in cress extracts, containing both nitrogen and sulfur, were discovered: N-benzyl O-ethyl thiocarbamate and N-phenethyl O-ethyl thiocarbamate. These two molecules being of organoleptic interest, their homologues were synthesized and submitted to organoleptic tests (static and GC-sniffing). Their odors evolve from garlic and onion over green, mushroom- and cress-like to fresh, spearmint-like. This paper presents the origin, chemical synthesis, and organoleptic properties of a series of O-alkyl thiocarbamates.",Journal of agricultural and food chemistry,"['D019607', 'D002849', 'D008401', 'D006801', 'D009584', 'D009812', 'D010936', 'D012903', 'D013329', 'D013455', 'D013859']","['Brassicaceae', 'Chromatography, Gas', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Nitrogen', 'Odorants', 'Plant Extracts', 'Smell', 'Structure-Activity Relationship', 'Sulfur', 'Thiocarbamates']","Identification of new, odor-active thiocarbamates in cress extracts and structure-activity studies on synthesized homologues.","['Q000737', None, None, None, 'Q000032', 'Q000032', 'Q000737', None, None, 'Q000032', 'Q000032']","['chemistry', None, None, None, 'analysis', 'analysis', 'chemistry', None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/17269787,2007,0.0,0.0,,, +17266328,"Odor volatiles in three major lychee cultivars (Mauritius, Brewster, and Hak Ip) were examined using gas chromatography-olfactometry, gas chromatography-mass spectrometry, and gas chromatography-pulsed flame photometric detection. Fifty-nine odor-active compounds were observed including 11 peaks, which were associated with sulfur detector responses. Eight sulfur volatiles were identified as follows: hydrogen sulfide, dimethyl sulfide, diethyl disulfide, 2-acetyl-2-thiazoline, 2-methyl thiazole, 2,4-dithiopentane, dimethyl trisulfide, and methional. Mauritius contained 25% and Brewster contained 81% as much total sulfur volatiles as Hak Ip. Cultivars were evaluated using eight odor attributes: floral, honey, green/woody, tropical fruit, peach/apricot, citrus, cabbage, and garlic. Major odor differences in cabbage and garlic attributes correlated with cultivar sulfur volatile composition. The 24 odor volatiles common to all three cultivars were acetaldehyde, ethanol, ethyl-3-methylbutanoate, diethyl disulfide, 2-methyl thiazole, 1-octen-3-one, cis-rose oxide, hexanol, dimethyl trisulfide, alpha-thujone, methional, 2-ethyl hexanol, citronellal, (E)-2-nonenal, linalool, octanol, (E,Z)-2,6-nonadienal, menthol, 2-acetyl-2-thiazoline, (E,E)-2,4-nonadienal, beta-damascenone, 2-phenylethanol, beta-ionone, and 4-vinyl-guaiacol.",Journal of agricultural and food chemistry,"['D002849', 'D005260', 'D005410', 'D005638', 'D008401', 'D006801', 'D032125', 'D008297', 'D009812', 'D012903', 'D013457', 'D014835']","['Chromatography, Gas', 'Female', 'Flame Ionization', 'Fruit', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Litchi', 'Male', 'Odorants', 'Smell', 'Sulfur Compounds', 'Volatilization']",Comparison of three lychee cultivar odor profiles using gas chromatography-olfactometry and gas chromatography-sulfur detection.,"[None, None, None, 'Q000737', None, None, 'Q000737', None, 'Q000032', None, 'Q000032', None]","[None, None, None, 'chemistry', None, None, 'chemistry', None, 'analysis', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/17266328,2007,0.0,0.0,,no quantification, +17177506,"Cycloalliin, an organosulfur compound found in garlic and onion, has been reported to exert several biological activities and also to remain stable during storage and processing. In this study, we investigated the pharmacokinetics of cycloalliin in rats after intravenous or oral administration. Cycloalliin and its metabolite, (3R,5S)-5-methyl-1,4-thiazane-3-carboxylic acid, in plasma, urine, feces, and organs was determined by a validated liquid chromatography-mass spectrometry method. When administered intravenously at 50 mg/kg, cycloalliin was rapidly eliminated from blood and excreted into urine, and its total recovery in urine was 97.8% +/- 1.3% in 48 h. After oral administration, cycloalliin appeared rapidly in plasma, with a tmax of 0.47 +/- 0.03 h at 25 mg/kg and 0.67 +/- 0.14 h at 50 mg/kg. Orally administered cycloalliin was distributed in heart, lung, liver, spleen, and especially kidney. The Cmax and AUC0-inf values of cycloalliin at 50 mg/kg were approximately 5 times those at 25 mg/kg. When administered orally at 50 mg/kg, cycloalliin was excreted into urine (17.6% +/- 4.2%) but not feces. However, the total fecal excretion of (3R,5S)-5-methyl-1,4-thiazane-3-carboxylic acid was 67.3% +/- 5.9% (value corrected for cycloalliin equivalents). In addition, no (3R,5S)-5-methyl-1,4-thiazane-3-carboxylic acid was detected in plasma (<0.1 microg/mL), and negligible amounts (1.0% +/- 0.3%) were excreted into urine. In in vitro experiments, cycloalliin was reduced to (3R,5S)-5-methyl-1,4-thiazane-3-carboxylic acid during anaerobic incubation with cecal contents of rats. These data indicated that the low bioavailability (3.73% and 9.65% at 25 and 50 mg/kg, respectively) of cycloalliin was due mainly to reduction to (3R,5S)-5-methyl-1,4-thiazane-3-carboxylic acid by the intestinal flora and also poor absorption in the upper gastrointestinal tract. These findings are helpful for understanding the biological effects of cycloalliin.",Journal of agricultural and food chemistry,"['D000818', 'D002853', 'D005243', 'D005737', 'D007700', 'D008297', 'D013058', 'D019697', 'D010875', 'D051381', 'D017207']","['Animals', 'Chromatography, Liquid', 'Feces', 'Garlic', 'Kinetics', 'Male', 'Mass Spectrometry', 'Onions', 'Pipecolic Acids', 'Rats', 'Rats, Sprague-Dawley']","Pharmacokinetics of cycloalliin, an organosulfur compound found in garlic and onion, in rats.","[None, None, 'Q000737', 'Q000737', None, None, None, 'Q000737', 'Q000008', None, None]","[None, None, 'chemistry', 'chemistry', None, None, None, 'chemistry', 'administration & dosage', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/17177506,2007,0.0,0.0,,admin to rats, +17039401,"In our previous study [1], we found that relatively short-term spontaneous fermentation (40 days at 60-70 degrees C, 85-95% relative humidity) potentiates anti-oxidative properties of garlic, in which scavenging activity against hydrogen peroxide was included. Since tetrahydro-beta-carboline derivatives (THbetaCs) that possess hydrogen peroxide scavenging activity have recently been identified in aged garlic extract, THbetaCs were quantitatively analyzed with liquid chromatography-mass spectrometry (LC-MS). (1R, 3S)-1-Methyl-1,2,3,4-tetrahydro-beta-carboline-3-carboxylic acid (MTCC) and (1S, 3S)-MTCC were found in the fermented garlic extract whereas only trace levels of MTCCs were detected in the row garlic extract. Therefore, it is suggested that relatively short-term fermentation potentiates scavenging activity of garlic against hydrogen peroxide by forming THbetaCs, especially MTCCs.","Plant foods for human nutrition (Dordrecht, Netherlands)","['D002243', 'D005285', 'D005737']","['Carbolines', 'Fermentation', 'Garlic']",Increased level of tetrahydro-beta-carboline derivatives in short-term fermented garlic.,"['Q000032', None, 'Q000737']","['analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/17039401,2007,0.0,1.0,,, +17017158,"For the analysis of organosulfur compounds in fresh garlic, a gas chromatographic/mass spectrometric (GC/MS) method is proposed using temperature-programmable cold on-column injection and cold solvent extraction of the fresh garlic. This was carried out under the conditions of cryogenic process from extraction to column separation. Hence, a valid identification can be achieved about the primary components in garlic extract before thermo-degradation. The obtained results showed that 3-vinyl-4H-1, 2-dithiin and 2-vinyl-4H-1, 3-dithiin were the major compounds in the garlic extract with minor amounts of S-methyl methanethiosulfinate, diallyl disulfide, trisulfide-di-2-propenyl. A comparative study of chemical compounds was performed between garlic extract by cold solvent and garlic oil by stream distillation. The degradation and formation of major organosulfur compounds in the garlic extract were also explored.",Se pu = Chinese journal of chromatography,"['D000498', 'D003080', 'D004220', 'D005737', 'D008401', 'D010938', 'D013440', 'D013696']","['Allyl Compounds', 'Cold Temperature', 'Disulfides', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Plant Oils', 'Sulfides', 'Temperature']",[Study of organosulfur compounds in fresh garlic by gas chromatography/mass spectrometry incorporated with temperature-programmable cold on-column injection].,"['Q000032', None, 'Q000032', 'Q000737', 'Q000379', 'Q000737', 'Q000302', None]","['analysis', None, 'analysis', 'chemistry', 'methods', 'chemistry', 'isolation & purification', None]",https://www.ncbi.nlm.nih.gov/pubmed/17017158,2010,,,,, +16999975,"Methiin and alliin are important components of flavors or the precursors of flavors and odors of Allium vegetables. Moreover, they are thought to be beneficial to health. A non-derivative method was developed to analyze these compounds in vegetables by capillary electrophoresis. These compounds in the extracts of Allium and Brassica vegetables were detected indirectly at 225 nm. The analysis of each sample required less than 25 min, and the linear detection range was 5-5000 mg/l. This method was simple compared to the other published methods using high performance liquid chromatography. Moreover, it was possible to detect the peak of pyruvate simultaneously with methiin and alliin using this method. The presence of pyruvate peak is a useful indicator if the blanching of the samples has been insufficient.",Journal of chromatography. A,"['D000490', 'D001937', 'D003545', 'D019075', 'D005737', 'D015203', 'D014675']","['Allium', 'Brassica', 'Cysteine', 'Electrophoresis, Capillary', 'Garlic', 'Reproducibility of Results', 'Vegetables']",Non-derivatized analysis of methiin and alliin in vegetables by capillary electrophoresis.,"['Q000737', 'Q000737', 'Q000031', 'Q000379', 'Q000737', None, 'Q000737']","['chemistry', 'chemistry', 'analogs & derivatives', 'methods', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16999975,2006,1.0,1.0,,, +16835880,"The reproductive health of individuals is severely compromised by HIV infection, with candidiasis being the most prevalent oral complication in patients. Although not usually associated with severe morbidity, oropharyngeal candidiasis can be clinically significant, as it can interfere with the administration of medications and adequate nutritional intake, and may spread to the esophagus. Azole antifungal agents are commonly prescribed for the treatment and prophylaxis of candidal infections, however, the emergence of drug resistant strains and dose limiting toxic effects has complicated the treatment of candidiasis. Consequently, safe and effective and affordable medicine is required to combat this fungus. Commercial garlic (Allium sativum) has been used since time immemorial as a natural antibiotic, however, very little is known about the antifungal properties of two indigenous South African species of garlic, namely Tulbaghia alliacea and Tulbaghia violacea, used as folk medicines for a variety of infections. This study compares the in vitro anticandidal activity of Tulbaghia alliacea, Tulbaghia violacea and Allium sativum extracts. It was found that the greatest concentrations of inhibitory components were extracted by chloroform or water. The IC50 concentrations of Tulbaghia alliacea were 0.007-0.038% (w/v). Assays using S. cerevisiae revealed that the T. alliacea extract was fungicidal, with a killing half-life of approximately 2 h. This inhibitory effect of the T. alliacea extracts was observed via TLC, and may be due to an active compound called marasmicin, that was identified using NMR. This investigation confirms that extracts of T. alliacea exhibit anti-infective activity against candida species in vitro.",Phytotherapy research : PTR,"['D000490', 'D000935', 'D002176', 'D002177', 'D002855', 'D004353', 'D005737', 'D008826', 'D019906', 'D008517', 'D010936', 'D012441']","['Allium', 'Antifungal Agents', 'Candida albicans', 'Candidiasis', 'Chromatography, Thin Layer', 'Drug Evaluation, Preclinical', 'Garlic', 'Microbial Sensitivity Tests', 'Nuclear Magnetic Resonance, Biomolecular', 'Phytotherapy', 'Plant Extracts', 'Saccharomyces cerevisiae']",Tulbaghia alliacea phytotherapy: a potential anti-infective remedy for candidiasis.,"['Q000737', 'Q000737', 'Q000187', 'Q000188', None, None, 'Q000737', None, None, None, 'Q000737', 'Q000187']","['chemistry', 'chemistry', 'drug effects', 'drug therapy', None, None, 'chemistry', None, None, None, 'chemistry', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/16835880,2007,0.0,0.0,,, +16804741,"Garlic (Allium sativum) cloves were stored at ambient temperature and 4 degrees C for periods up to six months to establish the effect of position of the individual clove within the bulb and of low storage temperature on the composition of several flavours precursors and other organic sulphur compounds, measured by gradient High Pressure Liquid Chromatography. Levels of alliin, gamma glutamyl allyl cysteine sulphoxide and gamma glutamyl isoallyl cysteine sulphoxide were statistically significantly higher in outer than in inner cloves. There was no statistically significant change in levels of alliin, the major flavour precursor, in cloves stored at 4 degrees C, remaining in the average range 17.5+/-3.8-39.1+/-7.5 mM. However, isoalliin increased significantly during storage at 4 degrees C, rising from an average 0.6+/-0.2 mM (outer cloves) -- 0.7+/-0.4 mM (inner cloves) to 7.1+/-1.7 mM (outer cloves) -- 4.1+/-0.7 mM (inner cloves). A decline in other sulphur-containing compounds, most likely to be the peptides gamma-glutamyl allylcysteine sulphoxide and gamma-glutamyl isoallylcysteine sulphoxide, occurred at the same time and possibly contributed to the increase in the flavour precursor compounds. The degree of chemical changes during storage will be of interest to the food and pharmaceutical industries.","Plant foods for human nutrition (Dordrecht, Netherlands)","['D002851', 'D003545', 'D005511', 'D005519', 'D005737', 'D006801', 'D013457', 'D013649', 'D013696', 'D013997']","['Chromatography, High Pressure Liquid', 'Cysteine', 'Food Handling', 'Food Preservation', 'Garlic', 'Humans', 'Sulfur Compounds', 'Taste', 'Temperature', 'Time Factors']",Effect of low storage temperature on some of the flavour precursors in garlic (Allium sativum).,"[None, 'Q000031', 'Q000379', 'Q000379', 'Q000737', None, 'Q000032', None, None, None]","[None, 'analogs & derivatives', 'methods', 'methods', 'chemistry', None, 'analysis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16804741,2006,1.0,1.0,,, +16787038,"We determined the changes in the contents of three gamma-glutamyl peptides and four sulfoxides in garlic cloves during storage at -3, 4, and 23 degrees C for 150 days using a validated high-performance liquid chromatography method that we reported recently. When garlic was stored at 4 degrees C for 150 days, marked conversion of the gamma-glutamyl peptides, gamma-L-glutamyl-S-allyl-L-cysteine and gamma-L-glutamyl-S-(trans-1-propenyl)-L-cysteine (GSPC), to sulfoxides, alliin and isoalliin, was observed. Interestingly, however, when garlic was stored at 23 degrees C, a decrease in GSPC and a marked increase in cycloalliin, rather than isoalliin, occurred. To elucidate in detail the mechanism involved, the conversion of isoalliin to cycloalliin in both buffer solutions (pH 4.6, 5.5, and 6.5) and garlic cloves at 25 and 35 degrees C was examined. Decreases in the concentration of isoalliin in both the solutions and the garlic cloves during storage followed first-order kinetics and coincided with the conversion of cycloalliin. Our data indicated that isoalliin produced enzymatically from GSPC is chemically converted to cycloalliin and that the cycloalliin content of garlic cloves increases during storage at higher temperature. These data may be useful for controlling the quality and biological activities of garlic and its preparations.",Journal of agricultural and food chemistry,"['D002851', 'D003545', 'D005519', 'D005737', 'D018517', 'D012996', 'D013457', 'D013696', 'D013997']","['Chromatography, High Pressure Liquid', 'Cysteine', 'Food Preservation', 'Garlic', 'Plant Roots', 'Solutions', 'Sulfur Compounds', 'Temperature', 'Time Factors']",Changes in organosulfur compounds in garlic cloves during storage.,"[None, 'Q000031', 'Q000379', 'Q000737', 'Q000737', None, 'Q000032', None, None]","[None, 'analogs & derivatives', 'methods', 'chemistry', 'chemistry', None, 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16787038,2006,1.0,1.0,,mol/g , +16786497,"Plant essential oils from 40 plant species were tested for their insecticidal activities against larvae of Lycoriella ingénue (Dufour) using a fumigation bioassay. Good insecticidal activity against larvae of L. ingenua was achieved with essential oils of Chenopodium ambrosioides L., Eucalyptus globulus Labill, Eucalyptus smithii RT Baker, horseradish, anise and garlic at 10 and 5 microL L(-1) air. Horseradish, anise and garlic oils showed the most potent insecticidal activities among the plant essential oils. At 1.25 microL L(-1), horseradish, anise and garlic oils caused 100, 93.3 and 13.3% mortality, but at 0.625 microL L(-1) air this decreased to 3.3, 0 and 0% respectively. Analysis by gas chromatography-mass spectrometry led to the identification of one major compound from horseradish, and three each from anise and garlic oils. These seven compounds and m-anisaldehyde and o-anisaldehyde, two positional isomers of p-anisaldehyde, were tested individually for their insecticidal activities against larvae of L. ingenua. Allyl isothiocyanate was the most toxic, followed by trans-anethole, diallyl disulfide and p-anisaldehyde with LC(50) values of 0.15, 0.20, 0.87 and 1.47 microL L(-1) respectively.",Pest management science,"['D000818', 'D031215', 'D004175', 'D005737', 'D007306', 'D007814', 'D009822', 'D028042', 'D010944']","['Animals', 'Armoracia', 'Diptera', 'Garlic', 'Insecticides', 'Larva', 'Oils, Volatile', 'Pimpinella', 'Plants']","Fumigant activity of plant essential oils and components from horseradish (Armoracia rusticana), anise (Pimpinella anisum) and garlic (Allium sativum) oils against Lycoriella ingenua (Diptera: Sciaridae).","[None, 'Q000737', None, 'Q000737', 'Q000032', None, 'Q000737', 'Q000737', 'Q000737']","[None, 'chemistry', None, 'chemistry', 'analysis', None, 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16786497,2006,2.0,1.0,,, +16770577,"The scope of the work was to investigate the influence of selenate fertilisation and the addition of symbiotic fungi (mycorrhiza) to soil on selenium and selenium species concentrations in garlic. The selenium species were extracted from garlic cultivated in experimental plots by proteolytic enzymes, which ensured liberation of selenium species contained in peptides or proteins. Separate extractions using an aqueous solution of enzyme-deactivating hydroxylamine hydrochloride counteracted the possible degradation of labile selenium species by enzymes (such as alliinase) that occur naturally in garlic. The selenium content in garlic, which was analysed by ICP-MS, showed that addition of mycorrhiza to the natural soil increased the selenium uptake by garlic tenfold to 15 microg g(-1) (dry mass). Fertilisation with selenate and addition of mycorrhiza strongly increased the selenium content in garlic to around one part per thousand. The parallel analysis of the sample extracts by cation exchange and reversed-phase HPLC with ICP-MS detection showed that gamma-glutamyl-Se-methyl-selenocysteine amounted to 2/3, whereas methylselenocysteine, selenomethionine and selenate each amounted to a few percent of the total chromatographed selenium in all garlic samples. Se-allyl-selenocysteine and Se-propyl-selenocysteine, which are selenium analogues of biologically active sulfur-containing amino acids known to occur in garlic, were searched for but not detected in any of the extracts. The amendment of soil by mycorrhiza and/or by selenate increased the content of selenium but not the distribution of detected selenium species in garlic. Finally, the use of two-dimensional HPLC (size exclusion followed by reversed-phase) allowed the structural characterisation of gamma-glutamyl-Se-methyl-selenocysteine and gamma-glutamyl-Se-methyl-selenomethionine in isolated chromatographic fractions by quadrupole time-of-flight mass spectrometry.",Analytical and bioanalytical chemistry,"['D004798', 'D005737', 'D013058', 'D015394', 'D038821', 'D064586', 'D012643', 'D018036', 'D012987', 'D012988']","['Enzymes', 'Garlic', 'Mass Spectrometry', 'Molecular Structure', 'Mycorrhizae', 'Selenic Acid', 'Selenium', 'Selenium Compounds', 'Soil', 'Soil Microbiology']",Uptake and speciation of selenium in garlic cultivated in soil amended with symbiotic fungi (mycorrhiza) and selenate.,"['Q000378', 'Q000378', None, None, 'Q000378', None, 'Q000032', 'Q000032', 'Q000032', None]","['metabolism', 'metabolism', None, None, 'metabolism', None, 'analysis', 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/16770577,2007,1.0,1.0,,, +16582584,"Garlic (Allium sativum) has been suggested to affect several cardiovascular risk factors. Its antiatherosclerotic properties are mainly attributed to allicin that is produced upon crushing of the garlic clove. Most previous studies used various garlic preparations in which allicin levels were not well defined. In the present study, we evaluated the effects of pure allicin on atherogenesis in experimental mouse models.","Pathobiology : journal of immunopathology, molecular and cellular biology","['D000818', 'D000975', 'D001011', 'D001057', 'D001161', 'D002784', 'D002853', 'D004578', 'D006801', 'D000960', 'D008077', 'D017737', 'D051379', 'D018345', 'D010084', 'D011973', 'D013441']","['Animals', 'Antioxidants', 'Aorta', 'Apolipoproteins E', 'Arteriosclerosis', 'Cholesterol', 'Chromatography, Liquid', 'Electron Spin Resonance Spectroscopy', 'Humans', 'Hypolipidemic Agents', 'Lipoproteins, LDL', 'Macrophages, Peritoneal', 'Mice', 'Mice, Knockout', 'Oxidation-Reduction', 'Receptors, LDL', 'Sulfinic Acids']",The antiatherogenic effect of allicin: possible mode of action.,"[None, 'Q000302', 'Q000473', 'Q000172', 'Q000097', 'Q000097', None, None, None, 'Q000302', 'Q000097', 'Q000187', None, None, None, 'Q000172', 'Q000302']","[None, 'isolation & purification', 'pathology', 'deficiency', 'blood', 'blood', None, None, None, 'isolation & purification', 'blood', 'drug effects', None, None, None, 'deficiency', 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/16582584,2006,,,,no PDF access, +16529758,"Speciation analysis of selenomethylcysteine (SeMeCys), selenomethionine (SeMet) and selenocystine (SeCys) has been performed using a direct amino acid analysis method with high-performance anion-exchange chromatography (HPAEC) coupled with integrated pulsed amperometric detection (IPAD). Three selenoamino acids could be baseline-separated from 19 amino acids using gradient elution conditions for amino acids and determined under new six-potential waveform. Detection limits for SeMeCys, SeMet and SeCys were 0.25, 1 and 20 microg/L (25 microL injection, 10 times of the baseline noise), respectively. The relative standard deviations (RSDs) of 200 microg/L SeMeCys, SeMet and SeCys were 3.1, 4.1 and 2.8%, respectively (n=9, 25 microL injection). The proposed method has been applied for determination of selenoamino acids in extracts of garlic and selenious yeast granule samples. No selenoamino acids were found in garlic. Both SeMet and SeCys were detected in selenious yeast tablet with the content of 45 and 129 microg Se/g, respectively. Selenoamino acids standards were spiked in garlic and yeast granule samples and the recovery ranged from 90 to 106%.",Journal of chromatography. A,"['D000596', 'D000837', 'D002851', 'D003553', 'D004563', 'D005737', 'D016566', 'D015203', 'D012645', 'D015003']","['Amino Acids', 'Anion Exchange Resins', 'Chromatography, High Pressure Liquid', 'Cystine', 'Electrochemistry', 'Garlic', 'Organoselenium Compounds', 'Reproducibility of Results', 'Selenomethionine', 'Yeasts']",Direct amino acid analysis method for speciation of selenoamino acids using high-performance anion-exchange chromatography coupled with integrated pulsed amperometric detection.,"['Q000032', 'Q000737', 'Q000295', 'Q000031', 'Q000295', 'Q000737', 'Q000032', None, 'Q000032', 'Q000737']","['analysis', 'chemistry', 'instrumentation', 'analogs & derivatives', 'instrumentation', 'chemistry', 'analysis', None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16529758,2006,0.0,0.0,,nothing detected, +16506797,"The properties of garlic (Allium sativum L.) are attributed to organosulfur compounds. Although these compounds change during cultivation and storage, there is no report of their simultaneous analysis. Here, a newly developed analytical method with a rapid and simple sample preparation to determine four sulfoxides and three gamma-glutamyl peptides in garlic is reported. All garlic samples were simply extracted with 90% methanol solution containing 0.01 N hydrochloric acid and prepared for analysis. Alliin, isoalliin, methiin, cycloalliin, and gamma-l-glutamyl-S-methyl-l-cysteine were determined by normal-phase HPLC using an aminopropyl-bonded column. gamma-l-Glutamyl-S-(2-propenyl)-l-cysteine and gamma-l-glutamyl-S-(trans-1-propenyl)-l-cysteine were separated on an octadecylsilane column. The overall recoveries were 97.1-102.3%, and the relative standard deviation values of intra- and interday precision were lower than 2.6 and 4.6%, respectively. This newly developed method offers some advantages over the currently accepted techniques including specificity, speed, and ease of use and would be useful for chemical and biological studies of garlic and its preparations.",Journal of agricultural and food chemistry,"['D002851', 'D003545', 'D005737', 'D015203', 'D012997', 'D013457']","['Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Reproducibility of Results', 'Solvents', 'Sulfur Compounds']",Determination of seven organosulfur compounds in garlic by high-performance liquid chromatography.,"['Q000379', 'Q000031', 'Q000737', None, None, 'Q000032']","['methods', 'analogs & derivatives', 'chemistry', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16506797,2006,1.0,3.0,,, +16484583,"Garlic enriched by selenium (Se) could be an excellent source of dietary Se for cancer chemoprevention. The production of high-Se garlic requires Se-fertilized soil, but such soil may pollute the environment. Hydroponics is a closed system that allows good control over Se fertilization without environmental consequences. We examined the effect of hydroponic cultivation on Se uptake and assimilation in garlic seedlings. Garlic bulbs were grown in the nutrient solution without Se for first 2 wk, and with potassium selenate for an additional week. Sulfate in an ordinary hydroponic solution inhibited the absorption and assimilation of selenate, but when a sulfate-free nutrient was used for Se addition, the garlic seedlings accumulated >1 mg Se, dry weight. Through HPLC inductively coupled plasma MS (HPLC-ICP-MS) analysis, Se-methlyselenocysteine (MeSeCys), gamma-glutamyl-Se-methlyselenocysteine (gamma-GluMeSeCys), selenomethionine, and nonmetabolized selenate were identified in water extracts of the garlic seedlings. The results demonstrate that hydroponic enrichment of Se in garlic seedlings could be a practical means of producing organic Se compounds for nutritional supplements.",The Journal of nutrition,"['D002851', 'D005737', 'D018527', 'D013058', 'D012643']","['Chromatography, High Pressure Liquid', 'Garlic', 'Hydroponics', 'Mass Spectrometry', 'Selenium']",Hydroponic cultivation offers a practical means of producing selenium-enriched garlic.,"[None, 'Q000254', 'Q000379', None, 'Q000378']","[None, 'growth & development', 'methods', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/16484583,2006,0.0,0.0,,, +16484558,"Clinical and experimental evidence indicates that garlic ingestion lowers blood cholesterol levels, and treatment of cells in culture with garlic and garlic-derived compounds inhibits cholesterol synthesis. To identify the principal site of inhibition in the cholesterolgenic pathway and the active components of garlic, cultured hepatoma cells were treated with aqueous garlic extract or its chemical derivatives, and radiolabeled cholesterol and intermediates were identified and quantified. Garlic extract reduced cholesterol synthesis by up to 75% without evidence of cellular toxicity. Levels of squalene and 2,3-oxidosqualene were not altered by garlic, indicating that the site of inhibition was downstream of lanosterol synthesis, and identical results were obtained with 14C-acetate and 14C-mevalonate, confirming that 3-hydroxy-3-methylglutaryl-CoA reductase activity was not affected in these short-term studies. Several methylsterols that accumulated in the presence of garlic were identified by coupled gas chromatography-mass spectrometry as 4,4'-dimethylzymosterol and a possible metabolite of 4-methylzymosterol; both are substrates for sterol 4alpha-methyl oxidase, pointing to this enzyme as the principal site of inhibition in the cholesterolgenic pathway by garlic. Of 9 garlic-derived compounds tested for their ability to inhibit cholesterol synthesis, only diallyl disulfide, diallyl trisulfide, and allyl mercaptan proved inhibitory, each yielding a pattern of sterol accumulation identical with that obtained with garlic extract. These results indicate that compounds containing an allyl-disulfide or allyl-sulfhydryl group are most likely responsible for the inhibition of cholesterol synthesis by garlic and that this inhibition is likely mediated at sterol 4alpha-methyl oxidase.",The Journal of nutrition,"['D000818', 'D000924', 'D045744', 'D002784', 'D004791', 'D005737', 'D007810', 'D008114', 'D009097', 'D010088', 'D008517', 'D010936', 'D051381', 'D013185']","['Animals', 'Anticholesteremic Agents', 'Cell Line, Tumor', 'Cholesterol', 'Enzyme Inhibitors', 'Garlic', 'Lanosterol', 'Liver Neoplasms, Experimental', 'Multienzyme Complexes', 'Oxidoreductases', 'Phytotherapy', 'Plant Extracts', 'Rats', 'Squalene']",Inhibition of sterol 4alpha-methyl oxidase is the principal mechanism by which garlic decreases cholesterol synthesis.,"[None, 'Q000494', None, 'Q000096', 'Q000494', None, 'Q000097', None, 'Q000037', 'Q000037', None, 'Q000494', None, 'Q000378']","[None, 'pharmacology', None, 'biosynthesis', 'pharmacology', None, 'blood', None, 'antagonists & inhibitors', 'antagonists & inhibitors', None, 'pharmacology', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/16484558,2006,0.0,0.0,,, +16484551,"This study used the hydroden peroxide scavenging assay to investigate antioxidant chemical constituents derived and separated from aged garlic extract, a unique garlic extract produced by soaking sliced garlic in an aqueous ethanol solution for >10 mo. Four types of 1, 2, 3, 4-tetrahydro-beta-carboline derivatives (THbetaCs); 1-methyl-1, 2, 3, 4-tetrahydro-beta-carboline-3-carboxylic acid, and 1-methyl-1, 2, 3, 4-tetrahydro-beta-carboline-1, 3-dicarboxylic acid (MTCdiC), from both diastereoisomers, were isolated and identified by use of liquid chromatography-mass spectrometry. All these compounds indicate strong hydrogen peroxide scavenging activities and inhibit 2, 2'-azobis(2-amidinopropane) hydrochloride-induced lipid peroxidation. Particularly, (1S, 3S)-MTCdiC had the most potent hydrogen peroxide scavenging activity, more than ascorbic acid. The (1R, 3S)- and (1S, 3S)-MTCdiC at 50-100 micromol/L and 10-100 micromol/L inhibited LPS-induced nitrite production. Interestingly, THbetaCs were not detected in raw garlic and other processed garlic preparations, but they were generated and increased during the natural aging garlic extraction process. These data suggest that THbetaCs, which are formed during the natural aging process, are potent antioxidants in aged garlic extract and thus may be useful for the prevention of diseases associated with oxidative damage.",The Journal of nutrition,"['D000375', 'D000975', 'D002243', 'D016166', 'D005737', 'D008070', 'D009682', 'D013237']","['Aging', 'Antioxidants', 'Carbolines', 'Free Radical Scavengers', 'Garlic', 'Lipopolysaccharides', 'Magnetic Resonance Spectroscopy', 'Stereoisomerism']",Tetrahydro-beta-carboline derivatives in aged garlic extract show antioxidant properties.,"[None, 'Q000494', 'Q000138', 'Q000494', 'Q000737', 'Q000494', None, None]","[None, 'pharmacology', 'chemical synthesis', 'pharmacology', 'chemistry', 'pharmacology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16484551,2006,1.0,2.0,,, +16435092,"Liquid chromatography (LC) hyphenated with both elemental and molecular mass spectrometry has been used for Se speciation in Se-enriched garlic. Different species were separated by ion-pair liquid chromatography-inductively coupled plasma mass spectrometry (LC-ICP-MS) after hot-water extraction. They were identified by on-line reversed-phase liquid chromatography-electrospray ionization tandem mass spectrometry (RPLC-ESI-MS-MS). Se-methionine and Se-methylselenocysteine were determined by monitoring their product ions. Another compound, gamma-glutamyl-Se-methylselenocysteine, shown to be the most abundant form of Se in the garlic, was determined without any additional sample pre-treatment after extraction and without the need for a synthesized standard. Product ions for this dipeptide were detected by LC-ESI-MS-MS for three isotopes of Se-78 Se, 80Se: and 82Se. The method was extended to the species extracted during in-vitro gastrointestinal digestion. Because both Se-methylselenocysteine and gamma-glutamyl-Se-methylselenocysteine have anticarcinogenic properties, their extractability and stability during human digestion are very important. Garlic was also treated with saliva, to enable detection and analysis of species extracted during mastication. Detailed information on the extractability of selenium species by both simulated gastric and intestinal fluid are given, and variation of the distribution of Se among the different species with time is discussed. Although the main species in garlic is the dipeptide gamma-glutamyl-Se-methylselenocysteine, Se-methylselenocysteine is the main compound present in the extracts after treatment with gastrointestinal fluids. Two more, so far unknown compounds were observed in the chromatogram. The extracted species and their transformations were analysed by combining LC-ICP-MS and LC-ESI-MS-MS. In both the simulated gastric and intestinal digests, Se-methionine, Se-methylselenocysteine, and gamma-glutamyl-Se-methylselenocysteine could be determined by LC-ESI-MS-MS by measuring their typical product ions.",Analytical and bioanalytical chemistry,"['D002853', 'D005737', 'D013058', 'D016566', 'D012643', 'D012680', 'D013997']","['Chromatography, Liquid', 'Garlic', 'Mass Spectrometry', 'Organoselenium Compounds', 'Selenium', 'Sensitivity and Specificity', 'Time Factors']",Liquid chromatography-mass spectrometry (LC-MS): a powerful combination for selenium speciation in garlic (Allium sativum).,"['Q000295', 'Q000737', 'Q000295', 'Q000032', 'Q000032', None, None]","['instrumentation', 'chemistry', 'instrumentation', 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16435092,2007,1.0,1.0,,, +16413559,"The proteinaceous content of garlic (Allium sativum) was characterised according to its amino acid composition by using a gas chromatography-mass spectrometry (GC-MS) analytical procedure. The procedure was tested on fresh and aged garlic samples as well as on reference gilding specimens prepared according to old recipes. The proteinaceous pattern showed a characteristic distribution of amino acids with glutamic acid being the major component. The average amino acidic composition was: glutamic acid (Glu; 29%), aspartic acid (Asp; 17%), serine (Ser; 11%), alanine, glycine, valine, leucine, lysine and phenylalanine (Ala, Gly, Val, Leu, Lys and Phe; 5-6%), isoleucine, proline and tyrosine (Ile, Pro and Tyr; 2-3%), methionine and hydroxyproline (Met and Hyp; 0.5%). In order to distinguish this material from animal glue and egg, which are the other proteinaceous media commonly used in gilding techniques, a database of amino acid percentages of the three proteins was built up and submitted to principal component analysis. Three separate clusters were obtained, allowing the protein identification. The application of the procedure on several gilding samples from Italian wall and easel paintings (13th-17th century) permitted to evidence the use of garlic as a gluing agent.",Journal of chromatography. A,"['D005737', 'D008401', 'D006868', 'D008872', 'D010151', 'D012015']","['Garlic', 'Gas Chromatography-Mass Spectrometry', 'Hydrolysis', 'Microwaves', 'Paintings', 'Reference Standards']",Identification of garlic in old gildings by gas chromatography-mass spectrometry.,"[None, 'Q000379', None, None, None, None]","[None, 'methods', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16413559,2006,2.0,1.0,,, +16386011,"A selective, precise, and accurate high-performance thin-layer chromatographic (HPTLC) method has been proposed for the analysis of garlic and its formulations for their alliin content. The method involves densitometric evaluation of alliin after resolving it by HPTLC on silica gel plates with n-butanol-acetic acid-water (6 + 2 + 2, v/v) as the mobile phase. For densitometric evaluation, peak areas were recorded at 540 nm after derivatizing the resolved bands with ninhydrin reagent. The relation between the concentration of alliin and corresponding peak areas was found to be linear within the range of 250 to 1500 ng/spot. The method was validated for precision (interday and intraday), repeatability, and accuracy. Mean recovery was 98.36%. The method was applied for the quantitation of alliin in bulbs of Allium sativum Linn. (garlic) and its formulations. The proposed TLC method was found to be precise, specific, sensitive, and accurate and can be used for routine quality control of garlic and its formulations.",Journal of AOAC International,"['D002855', 'D003545', 'D003720', 'D005737', 'D012015', 'D012680']","['Chromatography, Thin Layer', 'Cysteine', 'Densitometry', 'Garlic', 'Reference Standards', 'Sensitivity and Specificity']",Development and validation of a thin-layer chromatography-densitometric method for the quantitation of alliin from garlic (Allium sativum) and its formulations.,"['Q000379', 'Q000031', 'Q000379', 'Q000737', None, None]","['methods', 'analogs & derivatives', 'methods', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16386011,2006,,,,, +16377850,"Analysis of peroxidase activity by native polyacrylamide gel electrophoresis (PAGE) from a garlic bulb (Allium sativum L) extract showed two major activities (designated POX1 and POX2). The POX2 isoenzyme was purified to homogeneity by ammonium sulfate precipitation, gel filtration, and cation-exchange chromatography. The purified enzyme was found to be monomeric with a molecular mass of 36.5 kDa, as determined by sodium dodecyl sulfate-PAGE. The optimum temperature ranged from 25 to 40 degrees C and optimum pH was about 5.0. The apparent Km values for guaiacol and H2O2 were 9.5 and 2 mM, respectively. POX2 appeared highly stable since 50% of its activity was conserved at 50 degrees C for 5 h. Moreover POX2 was stable over a pH range of 3.5-11.0. Immobilization of POX2 was achieved by covalent binding of the enzyme to an epoxy-Sepharose matrix. The immobilized enzyme showed great stability toward heat and storage when compared with soluble enzyme. These properties permit the use of this enzyme as a biosensor to detect H2O2 in some food components such as milk or its derivatives.",Applied biochemistry and biotechnology,"['D000818', 'D015374', 'D005737', 'D006861', 'D008892', 'D010544', 'D010940', 'D018517']","['Animals', 'Biosensing Techniques', 'Garlic', 'Hydrogen Peroxide', 'Milk', 'Peroxidases', 'Plant Proteins', 'Plant Roots']","A new thermostable peroxidase from garlic Allium sativum: purification, biochemical properties, immobilization, and use in H2O2 detection in milk.","[None, None, 'Q000201', 'Q000032', 'Q000737', 'Q000737', 'Q000737', 'Q000201']","[None, None, 'enzymology', 'analysis', 'chemistry', 'chemistry', 'chemistry', 'enzymology']",https://www.ncbi.nlm.nih.gov/pubmed/16377850,2006,,,,, +16350805,"The volatile oil of garlic was extracted by hydrodistillation method and gas chromatography-mass spectrometry was applied to analyse the compounds in the oil. The best extraction conditions for high-content, effective components were obtained through optimization. The capillary column was an HP-5MS column (25 mm x 0.25 mm i.d. x 0.25 microm); oven temperature increased with a rate of 5 degrees C /min from 80 to 300 degrees C, and then maintained for 20 min; sample size of 1 microL; split ratio of 100:1; carrier gas of helium (1 mL/min). Mass spectra were obtained at 70 eV. The temperatures of injector base, ionization source were maintained at 270 degrees C, 230 degrees C respectively. Under these conditions, twenty compounds in the volatile oil of garlic were isolated and identified, and the content of each was determined. Sulfur-containing compounds were found to be the principal components, of which the major compound was diallyl trisulfide with the content of more than 30%, which is higher than the others in the literature. The experimental results also indicated that hydrodistillation method is an effective method for officinal component extraction. In addition, it was also demonstrated that the garlic volatile oil was stable when stored at 0 degrees C for 6 months.",Se pu = Chinese journal of chromatography,"['D005737', 'D008401', 'D009822']","['Garlic', 'Gas Chromatography-Mass Spectrometry', 'Oils, Volatile']",[Analysis of volatile oil of garlic by gas chromatography-mass spectrometry].,"['Q000737', 'Q000379', 'Q000032']","['chemistry', 'methods', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16350805,2010,,,,, +16344271,"Garlic is generally used as a therapeutic reagent against various diseases worldwide. Although a great effort is made to understand the pharmaceutical mechanisms of garlic and its derivatives, there are many mysteries to be uncovered. Using proteomic means, herein we have systematically studied the responses of protein expression in BGC823 cells, a gastric cancer cell line, induced by diallyl trisulfide (DATS), a major component of garlic derivatives. A total of 41 unique proteins in BGC823 were detected with significant changes in their expression levels corresponding with DATS administration. Of these proteins, five typical ones, glutathione S-transferase-pi (GST-pi), voltage-dependent anion channel-1 (VDAC-1), Annexin I, Galectin and S100A11, were further examined by Western blotting, resulting in coincident data with the proteomic evidence. Moreover quantitative real-time RT-PCR experiments offered dynamic data of mRNA expression, indicating the responses of Annexin I and GST-pi expression within a short period after DATS treatment. Interestingly, approximately 50% of DATS-sensitive proteins (19/41) in BGC823 are tightly associated with apoptotic pathways. These proteomic results presented, therefore, provide additional support to the hypothesis that garlic is a strong inducer of apoptosis in tumor cells.",Carcinogenesis,"['D000498', 'D017305', 'D017209', 'D045744', 'D015180', 'D015972', 'D005982', 'D006801', 'D007091', 'D013058', 'D040901', 'D020133', 'D019032', 'D013274', 'D013440']","['Allyl Compounds', 'Annexin A1', 'Apoptosis', 'Cell Line, Tumor', 'Electrophoresis, Gel, Two-Dimensional', 'Gene Expression Regulation, Neoplastic', 'Glutathione Transferase', 'Humans', 'Image Processing, Computer-Assisted', 'Mass Spectrometry', 'Proteomics', 'Reverse Transcriptase Polymerase Chain Reaction', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Stomach Neoplasms', 'Sulfides']",A proteomic investigation into a human gastric cancer cell line BGC823 treated with diallyl trisulfide.,"['Q000494', 'Q000378', None, None, None, None, 'Q000378', None, None, None, 'Q000379', None, None, 'Q000378', 'Q000494']","['pharmacology', 'metabolism', None, None, None, None, 'metabolism', None, None, None, 'methods', None, None, 'metabolism', 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/16344271,2006,0.0,0.0,,, +16337756,"Genotoxic effects of acrylamide are supposed to result from oxidative biotransformation to glycidamide. After incubation of rat liver slices with acrylamide we detected free glycidamide using a liquid chromatography tandem mass spectrometric method. Glycidamide formation was diminished in the presence of the cytochrome P450 2E1 inhibitor diallyl sulfide (DAS), which is a specific ingredient of garlic. This may be relevant to human health since the suggested carcinogenic risk of dietary acrylamide may be reduced by concomitant intake of garlic.",Toxicology letters,"['D020106', 'D000498', 'D000818', 'D001711', 'D019392', 'D004852', 'D005737', 'D066298', 'D008099', 'D008297', 'D051381', 'D017208', 'D013440']","['Acrylamide', 'Allyl Compounds', 'Animals', 'Biotransformation', 'Cytochrome P-450 CYP2E1', 'Epoxy Compounds', 'Garlic', 'In Vitro Techniques', 'Liver', 'Male', 'Rats', 'Rats, Wistar', 'Sulfides']",The garlic ingredient diallyl sulfide inhibits cytochrome P450 2E1 dependent bioactivation of acrylamide to glycidamide.,"['Q000378', 'Q000302', None, 'Q000187', 'Q000378', 'Q000378', 'Q000737', None, 'Q000201', None, None, None, 'Q000302']","['metabolism', 'isolation & purification', None, 'drug effects', 'metabolism', 'metabolism', 'chemistry', None, 'enzymology', None, None, None, 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/16337756,2006,0.0,0.0,,, +16277408,"Two garlic subspecies (n = 11), Allium sativum L. var. opioscorodon (hardneck) and Allium sativum L. var. sativum (softneck), were evaluated for their free amino acid composition. The free amino acid content of garlic samples analyzed ranged from 1121.7 to 3106.1 mg/100 g of fresh weight (mean = 2130.7 +/- 681.5 mg/100 g). Hardneck garlic had greater methiin, alliin, and total free amino acids contents compared to softneck garlic. The major free amino acid present in all but one subspecies was glutamine (cv. Mother of Pearl had aspartic acid as the major free amino acid). Cv. Music Pink garlic (a rocambole hardneck variety) contained the most methiin, alliin, and total free amino acids. The solid-phase extraction, alkylchloroformate derivatization, GC-FID, and GC-MS methods used in this study were simple and rapid, allowing 18 free amino acids in garlic to be separated within 10 min.",Journal of agricultural and food chemistry,"['D000596', 'D003545', 'D008401', 'D013454']","['Amino Acids', 'Cysteine', 'Gas Chromatography-Mass Spectrometry', 'Sulfoxides']",Free amino acid and cysteine sulfoxide composition of 11 garlic (Allium sativum L.) cultivars by gas chromatography with flame ionization and mass selective detection.,"['Q000032', 'Q000031', None, 'Q000032']","['analysis', 'analogs & derivatives', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16277408,2006,1.0,3.0,,, +16233877,"The simultaneous speciation of selenium and sulfur in selenized odorless garlic (Allium sativum L. Shiro) and a weakly odorous Allium plant, shallot (Allium ascalonicum), was performed by means of a hyphenated technique, a HPLC coupled with an inductively coupled plasma-mass spectrometry (HPLC-ICP-MS) equipped with an octopole reaction system (ORS). The aqueous extracts of them contained the common seleno compound that was identified as gamma-glutamylmethylselenocysteine by an electrospray ionization-tandem mass spectrometry (ESI-MS/MS). Normal garlic contains alliin as the major sulfur-containing compound, which is the biological precursor of the garlic odorant, allicin. Alliin, however, was not detected in the extracts of the selenized odorless garlic. At least, four unidentified sulfur-containing compounds were detected in odorless garlic and shallot. Moreover, these Allium plants showed chemopreventive effects against human leukemia cells.",Journal of chromatography. A,"['D000490', 'D000603', 'D000972', 'D002851', 'D018922', 'D006801', 'D013058', 'D012643', 'D013455']","['Allium', 'Amino Acids, Sulfur', 'Antineoplastic Agents, Phytogenic', 'Chromatography, High Pressure Liquid', 'HL-60 Cells', 'Humans', 'Mass Spectrometry', 'Selenium', 'Sulfur']",Simultaneous speciation of selenium and sulfur species in selenized odorless garlic (Allium sativum L. Shiro) and shallot (Allium ascalonicum) by HPLC-inductively coupled plasma-(octopole reaction system)-mass spectrometry and electrospray ionization-tandem mass spectrometry.,"['Q000737', 'Q000032', None, 'Q000379', None, None, 'Q000379', 'Q000032', 'Q000032']","['chemistry', 'analysis', None, 'methods', None, None, 'methods', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/16233877,2006,0.0,0.0,,, +16219763,"Allyl sulfides are characteristic flavor components obtained from garlic. These sulfides are thought to be responsible for their epidemiologically proven anticancer effect on garlic eaters. This study was aimed at clarifying the molecular basis of this anticancer effect of garlic by using human colon cancer cell lines HCT-15 and DLD-1. The growth of the cells was significantly suppressed by diallyl trisulfide (DATS, HCT-15 IC50 = 11.5 microM, DLD-1 IC50 = 13.3 microM); however, neither diallyl monosulfide nor diallyl disulfide showed such an effect. The proportion of HCT-15 and that of DLD-1 cells residing at the G1 and S phases were decreased by DATS, and their populations at the G2/M phase were markedly increased for up to 12 h. The cells with a sub-G1 DNA content were increased thereafter. Caspase-3 activity was also dramatically increased by DATS. Fluorescence-activated cell sorter analysis performed on the cells arrested at the G1/S boundary revealed cell cycle-dependent induction of apoptosis through the transition of the G2/M phase to the G1 phase by DATS. DATS inhibited tubulin polymerization in an in vitro cell-free system. DATS disrupted microtubule network formation of the cells, and microtubule fragments could be seen at the interphase. Peptide mass mapping by liquid chromatography-tandem mass spectrometry analysis for DATS-treated tubulin demonstrated that there was a specific oxidative modification of cysteine residues Cys-12beta and Cys-354beta to form S-allylmercaptocysteine with a peptide mass increase of 72.1 Da. The potent antitumor activity of DATS was also demonstrated in nude mice bearing HCT-15 xenografts. This is the first paper describing intracellular target molecules directly modified by garlic components.",The Journal of biological chemistry,"['D000498', 'D000818', 'D000970', 'D017209', 'D015153', 'D002453', 'D045744', 'D049109', 'D002469', 'D002474', 'D002853', 'D003110', 'D019926', 'D056744', 'D003545', 'D003593', 'D004247', 'D004220', 'D005434', 'D019084', 'D005737', 'D006801', 'D020128', 'D051379', 'D008819', 'D008870', 'D009368', 'D018384', 'D010100', 'D010455', 'D011485', 'D013440', 'D013997', 'D014404']","['Allyl Compounds', 'Animals', 'Antineoplastic Agents', 'Apoptosis', 'Blotting, Western', 'Cell Cycle', 'Cell Line, Tumor', 'Cell Proliferation', 'Cell Separation', 'Cell-Free System', 'Chromatography, Liquid', 'Colonic Neoplasms', 'Cyclin B', 'Cyclin B1', 'Cysteine', 'Cytoplasm', 'DNA', 'Disulfides', 'Flow Cytometry', 'Fluorescent Antibody Technique, Indirect', 'Garlic', 'Humans', 'Inhibitory Concentration 50', 'Mice', 'Mice, Nude', 'Microtubules', 'Neoplasm Transplantation', 'Oxidative Stress', 'Oxygen', 'Peptides', 'Protein Binding', 'Sulfides', 'Time Factors', 'Tubulin']",Diallyl trisulfide suppresses the proliferation and induces apoptosis of human colon cancer cells through oxidative modification of beta-tubulin.,"['Q000737', None, 'Q000494', None, None, None, None, None, None, None, None, 'Q000378', 'Q000378', None, 'Q000031', 'Q000378', 'Q000737', 'Q000737', None, None, None, None, None, None, None, 'Q000378', None, None, 'Q000737', 'Q000737', None, 'Q000737', None, 'Q000737']","['chemistry', None, 'pharmacology', None, None, None, None, None, None, None, None, 'metabolism', 'metabolism', None, 'analogs & derivatives', 'metabolism', 'chemistry', 'chemistry', None, None, None, None, None, None, None, 'metabolism', None, None, 'chemistry', 'chemistry', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16219763,2006,0.0,0.0,,, +16161771,"Selenium (Se) species in Se-enriched shiitake mushroom (Lentinula edodes) were identified and quantified by high performance liquid chromatography with inductively coupled plasma mass spectrometry (HPLC-ICPMS). Two types of Se-enriched shiitake obtained from selenite- or selenate-fertilized mushroom beds were used. More than 80% of Se in both shiitake samples could not be extracted with 0.2 M HCl. Protease digestion released a large amount of selenomethionine from the shiitake enriched with selenite. However, most of the Se in the shiitake enriched with selenate was not released by protease but was released by a cell wall digestive enzyme and most of the Se released was identified as selenate. These results indicate that the main Se species in the shiitake enriched with selenite or selenate is selenomethionine bound to protein or selenate bound to polysaccharides in the cell wall, respectively. Several Se-enriched vegetables grown on a soil fertilized with selenate were also analyzed by HPLC-ICPMS. Four Se species, selenate, Se-methylselenocysteine, selenomethionine, gamma-glutamyl-Se-methylselenocysteine, and an unknown Se compound were detected in the vegetables. The composition of Se species varied with the kinds or parts of vegetables. The main Se species in bulbs, leaves or flowers of the Se-enriched garlic, onions, cabbage and ashitaba were selenate, Se-methylselenocysteine or gamma-glutamyl-Se-methylselenocysteine, while those in fruit bodies of the peppers and pumpkin were selenomethionine bound to protein. Bioavailabilities of Se in the shiitake mushroom enriched with selenite and the vegetables enriched with selenate are expected to be high, but that in shiitake enriched with selenate may be low.",Journal of nutritional science and vitaminology,"['D002851', 'D005308', 'D005527', 'D005737', 'D013058', 'D019697', 'D010447', 'D018517', 'D064586', 'D012643', 'D018036', 'D012645', 'D020942', 'D018038', 'D014675']","['Chromatography, High Pressure Liquid', 'Fertilizers', 'Food, Fortified', 'Garlic', 'Mass Spectrometry', 'Onions', 'Peptide Hydrolases', 'Plant Roots', 'Selenic Acid', 'Selenium', 'Selenium Compounds', 'Selenomethionine', 'Shiitake Mushrooms', 'Sodium Selenite', 'Vegetables']",Composition of chemical species of selenium contained in selenium-enriched shiitake mushroom and vegetables determined by high performance liquid chromatography with inductively coupled plasma mass spectrometry.,"['Q000379', None, 'Q000032', 'Q000737', 'Q000379', 'Q000737', 'Q000378', 'Q000737', None, 'Q000032', 'Q000032', 'Q000032', 'Q000737', 'Q000032', 'Q000737']","['methods', None, 'analysis', 'chemistry', 'methods', 'chemistry', 'metabolism', 'chemistry', None, 'analysis', 'analysis', 'analysis', 'chemistry', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/16161771,2006,,,,, +16152948,"A procedure for the determination of traces of total tellurium (Te) in garlic (Allium sativa) is described that combines hydride generation atomic absorption spectrometry with preconcentration of the analyte by coprecipitation. The samples, each spiked with lanthanum nitrate (20 mg/L), are introduced into an Amberlite XAD-4 resin and mixed with ammonium buffer (pH 9.1). Te is preconcentrated by coprecipitation with the generated lanthanum hydroxide precipitate. The precipitate is quantitatively collected in the resin, eluted with hydrochloric acid, and then transferred into the atomizer device. Considering a sample consumption of 25 mL, an enrichment factor of 10 was obtained. The detection limit (3sigma) was 0.03 microg/L, and the precision (relative standard deviation) was 3.5% (n = 10) at the 10 microg/L level. The calibration graph using the preconcentration system for Te was linear with a correlation coefficient of 0.9993. Satisfactory results were obtained for the analysis of Te in garlic samples.",Journal of AOAC International,"['D002138', 'D002623', 'D005737', 'D006851', 'D006863', 'D007811', 'D008872', 'D000644', 'D015203', 'D013054', 'D013691', 'D013997', 'D014131']","['Calibration', 'Chemistry Techniques, Analytical', 'Garlic', 'Hydrochloric Acid', 'Hydrogen-Ion Concentration', 'Lanthanum', 'Microwaves', 'Quaternary Ammonium Compounds', 'Reproducibility of Results', 'Spectrophotometry, Atomic', 'Tellurium', 'Time Factors', 'Trace Elements']",Preconcentration and determination of tellurium in garlic samples by hydride generation atomic absorption spectrometry.,"[None, 'Q000379', 'Q000378', 'Q000032', None, 'Q000494', None, 'Q000494', None, 'Q000295', 'Q000032', None, None]","[None, 'methods', 'metabolism', 'analysis', None, 'pharmacology', None, 'pharmacology', None, 'instrumentation', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/16152948,2005,,,,, +15949130,"Garlic extract significantly inhibited the oxidation of methyl linoleate in homogeneous acetonitrile solution, whereas the antioxidant effect of allicin-free garlic extract, prepared by removing allicin by prepared by removing allicin by preparative HPLC, was much lower than that of the garlic extract. These results suggest that the antioxidant properties are mostly attributed to the presence of allicin in the garlic extract. Allicin a major component of the thiosulfinates in garlic extract, was found to be effective for inhibiting methyl linoleate oxidation, but its efficiency was less than that of alpha-tocopherol. Next, the reactivity of allicin toward the peroxyl radical, which is a chain-propagating species, was investigated by direct ESR detection. The addition allicin to 2,2'-azobis(2,4-dimethylvaleronitrile)-peroxyl radical solution caused the signal intensity of the peroxyl radical to dose-dependently decrease, indicating that allicin is capable of scavenging the the peroxyl radical and acting as an antioxidant. Finally, we studied the structure-anioxidant activity relationship for thiosulfinates and suggested that the combination of the allyl group (-CH2CH=CH2) and the -S(O)S- group is necessary for the antioxidant action of thiosulfinates in the garlic extract. In addition, one of the two possible combinations, -S(O)S-CH2CH=CH2, was found to make a much larger contribution to the antioxidant activity of the thiosulfinates than the other, CH2=CH-CH2-S(O)S-.",Redox report : communications in free radical research,"['D000097', 'D000975', 'D002851', 'D004305', 'D004578', 'D005737', 'D008041', 'D008956', 'D004364', 'D010936', 'D010946', 'D012997', 'D013441', 'D013696', 'D013885']","['Acetonitriles', 'Antioxidants', 'Chromatography, High Pressure Liquid', 'Dose-Response Relationship, Drug', 'Electron Spin Resonance Spectroscopy', 'Garlic', 'Linoleic Acids', 'Models, Chemical', 'Pharmaceutical Preparations', 'Plant Extracts', 'Plants, Medicinal', 'Solvents', 'Sulfinic Acids', 'Temperature', 'Thiosulfates']",Antioxidant activity of thiosulfinates derived from garlic.,"['Q000737', 'Q000737', None, None, None, 'Q000378', 'Q000494', None, None, 'Q000494', 'Q000378', None, 'Q000737', None, 'Q000737']","['chemistry', 'chemistry', None, None, None, 'metabolism', 'pharmacology', None, None, 'pharmacology', 'metabolism', None, 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15949130,2006,2.0,1.0,,, +15927923,"Spices are an important group of agricultural commodities being used by many civilizations all over the world to aid flavor, taste and nutritional values in the food. In traditional medical systems, their ability to heal various physical, mental and emotional problems has widely been reported. With this view, HPLC analysis was performed to estimate phenolic acids in 21 spices (asafetida, Bishop's weed, black mustard, coriander, cinnamon, clove, curry leaf, cumin black, cumin, fennel, fenugreek, garlic, ginger, Indian cassia, Indian dill or dill large cardamom, onion, saffron, tamarind, true cardamom, yellow mustard) commonly used in India in different forms. In all, 7 phenolic acids; viz., tannic, gallic, caffeic, cinnamic, chlorogenic, ferulic and vanillic acids could be identified on the basis of their retention time with standard compounds and co-chromatography. Several parts of the spices, for instance, seeds, leaves, barks, rhizomes, latex, stigmas, floral buds and modified stems were used in the study. Maximum amount of tannic and gallic acids was observed in black mustard and clove. Caffeic, chlorogenic and ferulic acids were found maximum in cumin while vanillic and cinnamic acids in onion seeds. The spices are known to significantly contribute to the flavor, taste, and medicinal properties of food because of phenolics.",Journal of herbal pharmacotherapy,"['D000975', 'D018890', 'D002851', 'D006801', 'D062385', 'D007194', 'D008517', 'D010936', 'D010946', 'D017365']","['Antioxidants', 'Chemoprevention', 'Chromatography, High Pressure Liquid', 'Humans', 'Hydroxybenzoates', 'India', 'Phytotherapy', 'Plant Extracts', 'Plants, Medicinal', 'Spices']",Investigation on the phenolics of some spices having pharmacotherapeuthic properties.,"['Q000494', 'Q000379', None, None, 'Q000032', None, None, 'Q000494', 'Q000737', 'Q000032']","['pharmacology', 'methods', None, None, 'analysis', None, None, 'pharmacology', 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15927923,2005,,,,, +15913300,"Plant essential oils from 29 plant species were tested for their insecticidal activities against the Japanese termite, Reticulitermes speratus Kolbe, using a fumigation bioassay. Responses varied with plant material, exposure time, and concentration. Good insecticidal activity against the Japanese termite was achived with essential oils of Melaleuca dissitiflora, Melaleuca uncinata, Eucalyptus citriodora, Eucalyptus polybractea, Eucalyptus radiata, Eucalyptus dives, Eucalyptus globulus, Orixa japonica, Cinnamomum cassia, Allium cepa, Illicium verum, Evodia officinalis, Schizonepeta tenuifolia, Cacalia roborowskii, Juniperus chinensis var. horizontalis, Juniperus chinensis var. kaizuka, clove bud, and garlic applied at 7.6 microL/L of air. Over 90% mortality after 3 days was achieved with O. japonica essential oil at 3.5 microL/L of air. E. citriodora, C. cassia, A. cepa, I. verum, S. tenuifolia, C. roborowskii, clove bud, and garlic oils at 3.5 microL/L of air were highly toxic 1 day after treatment. At 2.0 microL/L of air concentration, essential oils of I. verum, C. roborowskik, S. tenuifolia, A. cepa, clove bud, and garlic gave 100% mortality within 2 days of treatment. Clove bud and garlic oils showed the most potent antitermitic activity among the plant essential oils. Garlic and clove bud oils produced 100% mortality at 0.5 microL/L of air, but this decreased to 42 and 67% after 3 days of treatment at 0.25 microL/L of air, respectively. Analysis by gas chromatography-mass spectrometry led to the identification of three major compounds from garlic oil and two from clove bud oils. These five compounds from two essential oils were tested individually for their insecticidal activities against Japanese termites. Responses varied with compound and dose. Diallyl trisulfide was the most toxic, followed by diallyl disulfide, eugenol, diallyl sulfide, and beta-caryophyllene. The essential oils described herein merit further study as potential fumigants for termite control.",Journal of agricultural and food chemistry,"['D000818', 'D005651', 'D005737', 'D008401', 'D007306', 'D020049', 'D009822', 'D010938', 'D027842']","['Animals', 'Fumigation', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Insecticides', 'Isoptera', 'Oils, Volatile', 'Plant Oils', 'Syzygium']",Fumigant activity of plant essential oils and components from garlic (Allium sativum) and clove bud (Eugenia caryophyllata) oils against the Japanese termite (Reticulitermes speratus Kolbe).,"[None, None, 'Q000737', None, None, None, 'Q000737', 'Q000737', 'Q000737']","[None, None, 'chemistry', None, None, None, 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15913300,2005,2.0,1.0,,, +15884836,"A method for determining the country of origin of garlic by comparing the trace metal profile of the sample to an authentic garlic database is presented. Protocols for sample preparation, high-resolution inductively coupled plasma mass spectrometry, and multivariate statistics are provided. The criteria used for making a country of origin prediction are also presented. Indications are that the method presented here may be used to determine the geographic origin of other agricultural products.",Journal of agricultural and food chemistry,"['D000704', 'D003132', 'D005737', 'D008670', 'D012680', 'D014481']","['Analysis of Variance', 'Commerce', 'Garlic', 'Metals', 'Sensitivity and Specificity', 'United States']",Determination of the country of origin of garlic (Allium sativum) using trace metal profiling.,"[None, 'Q000191', 'Q000737', 'Q000032', None, None]","[None, 'economics', 'chemistry', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/15884836,2005,0.0,0.0,,, +15841402,"This document reviews the most relevant mass spectrometry approaches to selenium (Se) speciation in high-Se food supplements in terms of qualitative and quantitative Se speciation and Se-containing species identification, with special reference to high-Se yeast, garlic, onions and Brazil nuts. Important topics such as complexity of Se speciation in these materials and the importance of combining Se-specific detection and molecule-specific determination of the particular species of this element in parallel with chromatography, to understand their nutritional role and cancer preventive properties are critically discussed throughout. The versatility and potential of mass spectrometric detection in this field are clearly demonstrated. Although great advances have been achieved, further developments are required, especially if ""speciated""certified reference materials (CRMs) are to be produced for validation of measurements of target Se-containing species in Se-food supplements.",Analytical and bioanalytical chemistry,"['D000818', 'D004032', 'D019587', 'D006801', 'D013058', 'D012643', 'D012680']","['Animals', 'Diet', 'Dietary Supplements', 'Humans', 'Mass Spectrometry', 'Selenium', 'Sensitivity and Specificity']",Current mass spectrometry strategies for selenium speciation in dietary sources of high-selenium.,"[None, None, None, None, 'Q000379', 'Q000737', None]","[None, None, None, None, 'methods', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/15841402,2007,0.0,0.0,,, +15833378,"Individuals can be classified into rapid or slow acetylators based on the N-acetyltransferase (NAT) activity which is believed to affect cancer risk that is related to environmental carcinogen exposure. Diallyl disulfide (DADS) is a naturally occurring organosulfur compound, from garlic (Allium sativum), which exerts anti-neoplasm activity. In this study, we investigated the inhibitory effects of DADS on NAT activity and gene expresseion (NAT mRNA) in human esophagus epidermoid carcinoma CE 81T/VGH cells. NAT activity was measured by the amounts of N-acetylation of 2-aminofluorene (AF) and non-acetylation of AF by high performance liquid chromatography on cells treated with or without DADS. The amounts of NAT enzymes were examined and analyzed by Western blot. NAT gene expression (NAT mRNA) was examined by polymerase chain reaction and cDNA microarray. DADS decreased the amount of N-acetylation of AF in human esophagus epidermoid carcinoma CE 81T/VGH cells in a dose-dependent manner. Western blot analysis indicated that DADS decreased the levels of NAT protein in CE 81T/VGH cells. PCR and cDNA microarray experiments showed that DADS affected NAT1 mRNA expression in CE 81T/VGH cells. DADS affect NAT activity due to the inhibition of gene expression (NAT1 mRNA) and the decreasing of the protein levels of NAT in CE 81T/VGH cells.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000107', 'D000123', 'D000498', 'D000818', 'D016588', 'D002294', 'D045744', 'D004220', 'D004305', 'D004791', 'D004938', 'D005260', 'D005434', 'D006801', 'D051379', 'D008807', 'D020411', 'D012333', 'D012334', 'D020133']","['Acetylation', 'Acetyltransferases', 'Allyl Compounds', 'Animals', 'Anticarcinogenic Agents', 'Carcinoma, Squamous Cell', 'Cell Line, Tumor', 'Disulfides', 'Dose-Response Relationship, Drug', 'Enzyme Inhibitors', 'Esophageal Neoplasms', 'Female', 'Flow Cytometry', 'Humans', 'Mice', 'Mice, Inbred BALB C', 'Oligonucleotide Array Sequence Analysis', 'RNA, Messenger', 'RNA, Neoplasm', 'Reverse Transcriptase Polymerase Chain Reaction']",Diallyl disulfide inhibits N-acetyltransferase activity and gene expression in human esophagus epidermoid carcinoma CE 81T/VGH cells.,"[None, 'Q000037', 'Q000494', None, 'Q000494', 'Q000201', None, 'Q000494', None, None, 'Q000201', None, None, None, None, None, None, 'Q000096', 'Q000096', None]","[None, 'antagonists & inhibitors', 'pharmacology', None, 'pharmacology', 'enzymology', None, 'pharmacology', None, None, 'enzymology', None, None, None, None, None, None, 'biosynthesis', 'biosynthesis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15833378,2005,0.0,0.0,,, +15790108,"A new type of chiral ligand-exchange stationary phase (CLES) was successfully synthesized by treating silica gel with beta-(3,4-epoxycyclohexyl)ethyltrimethoxy silane and opening the epoxy ring by L-isoleucine. The chiral speciation of DL-selenomethionine (DL-SeMet) by high-performance liquid chromatography (HPLC) with UV absorbance on the CLES column was studied. The influences of the contents of copper ion and methanol as well as the pH value in the mobile phase and temperature of the column on the efficiency of resolution of DL-SeMet were investigated in detail. DL-SeMet could be completely resolved within 40 min under the optimal operating conditions of 0.1 mmol/L Cu2+ at 0.05 mol/L KH2PO4 buffer (pH = 5.5) and 35 degrees C temperature of the column. The limits of detection of D- and L-SeMet were 255 ppb and 286 ppb, respectively. This method was applied to determine the D- and L-enantiomers of DL-SeMet in real samples, such as selenized yeast powder and garlic.",Analytical sciences : the international journal of the Japan Society for Analytical Chemistry,"['D002851', 'D005737', 'D008024', 'D015394', 'D012645', 'D013237', 'D015003']","['Chromatography, High Pressure Liquid', 'Garlic', 'Ligands', 'Molecular Structure', 'Selenomethionine', 'Stereoisomerism', 'Yeasts']",Chiral speciation and determination of DL-selenomethionine enantiomers on a novel chiral ligand-exchange stationary phase.,"['Q000295', 'Q000737', None, None, 'Q000032', None, 'Q000737']","['instrumentation', 'chemistry', None, None, 'analysis', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15790108,2006,0.0,0.0,,, +15749632,"Garlic consumption is linked with lower incidences of certain cancers perhaps because garlic-derived allyl sulfides inhibit nitrosamine activation by cytochrome P450s. To help evaluate this view, effects of allyl sulfides on O6-methylguanine (O6MG) levels were examined in liver of rats injected with 20 mg/kg of liver carcinogen dimethylnitrosamine (DMN) and killed 3 h later. DNA was isolated and hydrolyzed, and O6MG/guanine ratios were determined by HPLC-fluorescence. Mean inhibition of O6MG formation fell from 89% for 200 to 33% for 12 mg diallyl sulfide (DAS) per kilogram gavaged 18 h before DMN injection. Gavage of DAS 3 or 6 h (instead of 18 h) before DMN injection significantly reduced inhibitions. Mean inhibitions for diallyl disulfide, diallyl sulfoxide, and diallyl sulfone (75-100 mg/kg) gavaged 18 h before DMN were 39%, 72%, and 82%. In lung and kidney, DAS produced mean inhibitions of 98% and 74% compared with 89% in liver. When methylnitrosourea was injected instead of DMN, neither DAS nor DADS inhibited O6MG formation in liver DNA. Feeding 2.5% garlic for 7 days inhibited DMN-induced O6MG formation in liver DNA by 46%, similar to that expected from the estimated yield of allyl sulfides from garlic. Hence, dosing with DAS or feeding garlic may be useful chemopreventive strategies against nitrosamine-induced cancers.",Nutrition and cancer,"['D000498', 'D000818', 'D016588', 'D002851', 'D004247', 'D003849', 'D004128', 'D004305', 'D005737', 'D007668', 'D008099', 'D008168', 'D008297', 'D011897', 'D051381', 'D017207', 'D013440']","['Allyl Compounds', 'Animals', 'Anticarcinogenic Agents', 'Chromatography, High Pressure Liquid', 'DNA', 'Deoxyguanosine', 'Dimethylnitrosamine', 'Dose-Response Relationship, Drug', 'Garlic', 'Kidney', 'Liver', 'Lung', 'Male', 'Random Allocation', 'Rats', 'Rats, Sprague-Dawley', 'Sulfides']",Inhibition by allyl sulfides and crushed garlic of O6-methylguanine formation in liver DNA of dimethylnitrosamine-treated rats.,"['Q000494', None, 'Q000494', 'Q000379', 'Q000187', 'Q000031', 'Q000633', None, 'Q000737', 'Q000737', 'Q000187', 'Q000737', None, None, None, None, 'Q000494']","['pharmacology', None, 'pharmacology', 'methods', 'drug effects', 'analogs & derivatives', 'toxicity', None, 'chemistry', 'chemistry', 'drug effects', 'chemistry', None, None, None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/15749632,2005,,,,, +15686419,"Chemotype analyses and random amplified polymorphic DNA (RAPD) genomic analyses have been applied to the characterization of Allium sativum variety from Voghiera (Ferrara, Italy), a typical Italian product actually demanding the Protected Designation of Origin (PDO). The garlic from Voghiera is characterized by peculiar morphological and composition characteristics. The proximate composition and atomic absorbance spectrometry elemental pattern of this garlic suggested as the chemical composition did not depend on the intrinsic pedologic soil features only, but it was probably connected to some peculiar genetic characters. Amplification of genomic DNA using random primers highlighted a good clustering differentiating of Voghiera Allium sativum from five commercial reference samples used in this study (Piacentino, Serena, France, China, and Adriano varieties), confirming the existence of intervarietal genetic difference. The intravarietal polymorphisms of Voghiera samples were low.",Journal of agricultural and food chemistry,"['D018744', 'D005737', 'D007558', 'D008903', 'D019105']","['DNA, Plant', 'Garlic', 'Italy', 'Minerals', 'Random Amplified Polymorphic DNA Technique']",Chemical and genomic combined approach applied to the characterization and identification of Italian Allium sativum L.,"['Q000032', 'Q000737', None, 'Q000032', None]","['analysis', 'chemistry', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15686419,2005,1.0,2.0,,, +15629528,"A protein designated alliumin, with a molecular mass of 13 kDa and an N-terminal sequence similar to a partial sequence of glucanase, and demonstrating antifungal activity against Mycosphaerella arachidicola, but not against Fusarium oxysporum, was isolated from multiple-cloved garlic (Allium sativum) bulbs. The protein, designated as alliumin, was purified using ion exchange chromatography on DEAE-cellulose, CM-cellulose and Mono S, affinity chromatography on Affi-gel blue gel, and gel filtration on Superdex 75. Alliumin was unadsorbed on DEAE-cellulose, but was adsorbed on Affi-gel blue gel, CM-cellulose and Mono S. Its antifungal activity was retained after boiling for 1 h and also after treatment with trypsin or chymotrypsin (1:1, w/w) for 30 min at room temperature. Alliumin was inhibitory to the bacterium Pseudomonas fluorescens and exerted antiproliferative activity toward leukemia L1210 cells. However, it was devoid of ribonuclease activity, protease activity, mitogenic activity toward mouse splenocytes, and antiproliferative activity toward hepatoma Hep G2 cells.",Peptides,"['D000595', 'D000818', 'D000935', 'D049109', 'D002478', 'D002846', 'D002852', 'D004591', 'D005737', 'D020128', 'D051379', 'D008810', 'D008969', 'D008970', 'D010455', 'D010940', 'D018514', 'D011551', 'D013154']","['Amino Acid Sequence', 'Animals', 'Antifungal Agents', 'Cell Proliferation', 'Cells, Cultured', 'Chromatography, Affinity', 'Chromatography, Ion Exchange', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Inhibitory Concentration 50', 'Mice', 'Mice, Inbred C57BL', 'Molecular Sequence Data', 'Molecular Weight', 'Peptides', 'Plant Proteins', 'Plant Structures', 'Pseudomonas fluorescens', 'Spleen']","Isolation of alliumin, a novel protein with antimicrobial and antiproliferative activities from multiple-cloved garlic bulbs.","[None, None, 'Q000737', 'Q000187', None, None, None, None, 'Q000737', None, None, None, None, None, 'Q000737', 'Q000737', 'Q000737', 'Q000187', 'Q000166']","[None, None, 'chemistry', 'drug effects', None, None, None, None, 'chemistry', None, None, None, None, None, 'chemistry', 'chemistry', 'chemistry', 'drug effects', 'cytology']",https://www.ncbi.nlm.nih.gov/pubmed/15629528,2005,0.0,0.0,,, +15612762,"The sale of botanical dietary supplements in the United States is on the rise. However, limited studies have been conducted on the safety of these supplements. There are reports on the presence of undesired metals in some of the botanical dietary supplements. In this study, echinacea, garlic, ginkgo, ginseng, grape seed extract, kava kava, saw palmetto, and St. John's wort supplements manufactured by Nature's Way, Meijer, GNC, Nutrilite, Solaray, Sundown and Natrol, have been analyzed for lead, mercury, cadmium, arsenic, uranium, chromium, vanadium, copper, zinc, molybdenum, palladium, tin, antimony, thallium, and tungsten using inductively coupled plasma mass spectrometry. All samples were devoid of mercury contamination. Results indicated that the botanical supplements analyzed did not contain unacceptable concentrations of these metals. These supplements were also evaluated for microbial contamination, and most samples analyzed showed the presence of bacteria or fungi or both. Microbes were not counted nor were microbial counts determined in these samples.",Journal of agricultural and food chemistry,"['D001419', 'D019587', 'D004340', 'D005658', 'D008670', 'D028321']","['Bacteria', 'Dietary Supplements', 'Drug Contamination', 'Fungi', 'Metals', 'Plant Preparations']",Evaluation of metal and microbial contamination in botanical supplements.,"['Q000302', None, None, 'Q000302', 'Q000032', 'Q000737']","['isolation & purification', None, None, 'isolation & purification', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15612762,2005,0.0,0.0,,, +15493662,"A method is described for determination of the steroidal saponin, eruboside B, originating in garlic and garlic products as the p-nitrobenzoyl chloride (PNBC) derivative by reversed-phase liquid chromatography (with ultraviolet detection at 260 nm. Proto-eruboside B was extracted from garlic (Allium sativum L.); subjected to solid-phase extraction (SPE) with a C18 cartridge, Florisil column chromatography, and silica gel column chromatography; and then enzymatically converted to eruboside B, which was applied as an external standard. Steroidal saponins in garlic and commercial garlic products were extracted with methanol and purified by SPE cartridges, followed by enzymatic treatment. A frostanol saponin such as proto-eruboside B is enzymatically transformed to a spirostanol saponin, eruboside B. After the derivatization with PNBC, the saponin derivative was chromatographed on a C8 column with a gradient elution of (A) 80% aqueous acetonitrile and (B) 100% acetonitrile. The detection limit of the developed method was 1 microg/g for the samples. The method was applied to the analysis of garlic and garlic health food products available in Japan.",Journal of AOAC International,"['D002853', 'D005504', 'D005737', 'D009579', 'D012503']","['Chromatography, Liquid', 'Food Analysis', 'Garlic', 'Nitrobenzoates', 'Saponins']",Ultraviolet derivatization of steroidal saponin in garlic and commercial garlic products as p-nitrobenzoate for liquid chromatographic determination.,"[None, None, 'Q000737', 'Q000032', 'Q000032']","[None, None, 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15493662,2005,,,,, +15454690,"The sap-sucking homopteran insects, commonly known as aphids and leafhoppers are responsible for a huge amount of lost productivity of mustard, chickpea, cabbage, rice and many other important crops. Due to their unique feeding habits and ability to build up a huge population in a very short time, they are very difficult to control. The objective of the ongoing program is to develop insect-resistant crop species through genetic engineering techniques to combat the yield losses, which necessitates the identification of appropriate control elements. In this direction, mannose-binding 25 kDa lectins have been purified from leaves of garlic, Diffenbachia sequina and tubers of Colocasia esculanta. The purified lectins have been analyzed in SDS-PAGE. The effectiveness of these lectins against chickpea aphids, mustard aphids and green leaf hoppers of rice have been tested. The LC(50) value of each lectin against different insects had been monitored [1,2]. Through immunolocalization analysis, the binding of the lectin had been demonstrated at the epithelial membrane of the midgut of the lectin-treated insects [1]. Receptor proteins of brush border membrane vesicle (BBMV) of the target insects, responsible for binding of the lectin to the midgut of the epithelial layer have been purified and analyzed through ligand assay. Biochemical studies have been undertaken to investigate the lectin-receptor interaction at molecular level.",Glycoconjugate journal,"['D000818', 'D001042', 'D001681', 'D002241', 'D002352', 'D002462', 'D002846', 'D004591', 'D004848', 'D006023', 'D006031', 'D007313', 'D007525', 'D037102', 'D008024', 'D037241', 'D008871', 'D037121', 'D011485']","['Animals', 'Aphids', 'Biological Assay', 'Carbohydrates', 'Carrier Proteins', 'Cell Membrane', 'Chromatography, Affinity', 'Electrophoresis, Polyacrylamide Gel', 'Epithelium', 'Glycoproteins', 'Glycosylation', 'Insecta', 'Isoelectric Focusing', 'Lectins', 'Ligands', 'Mannose-Binding Lectins', 'Microvilli', 'Plant Lectins', 'Protein Binding']",Identification of receptors responsible for binding of the mannose specific lectin to the gut epithelial membrane of the target insects.,"[None, None, None, 'Q000737', 'Q000378', 'Q000378', None, None, 'Q000378', 'Q000737', None, None, None, 'Q000737', None, 'Q000737', 'Q000378', 'Q000378', None]","[None, None, None, 'chemistry', 'metabolism', 'metabolism', None, None, 'metabolism', 'chemistry', None, None, None, 'chemistry', None, 'chemistry', 'metabolism', 'metabolism', None]",https://www.ncbi.nlm.nih.gov/pubmed/15454690,2005,,,,, +15373848,"Allyl isothiocyanate is present in many plants. Allergic contact dermatitis from allyl isothiocyanate is well known but infrequently reported. The aim of this study was to investigate the prevalence of contact allergy to allyl isothiocyanate in patients with suspected contact dermatitis from vegetables and food. 259 such patients were tested at the Department of Dermatology, Gentofte Hospital, Denmark, from 1994 to 2003. Only 2 patients (0.8%) had a positive reaction (+) to allyl isothiocyanate and 43 patients (16.6%) had a ?+ reaction. One of the patients with a positive reaction provided samples of margarine, salad cream, oil and mayonnaise. These were analysed with high-performance liquid chromatography, and a moderate concentration of allyl isothiocyanate (2.5 ppm) was detected in the sample of margarine. This patient was a professional sandwich maker presenting with fingertip dermatitis mimicking 'tulip fingers' or allergic contact dermatitis from garlic and onions. In conclusion, allergic contact dermatitis from allyl isothiocyanate occurs in only a limited number of cases, despite frequent exposure. The large number of ?+ reactions raises the question as to whether the recommended patch test concentration is too low.",Contact dermatitis,"['D000328', 'D015331', 'D003718', 'D017449', 'D009783', 'D005260', 'D005511', 'D005520', 'D008401', 'D006801', 'D017879', 'D012189']","['Adult', 'Cohort Studies', 'Denmark', 'Dermatitis, Allergic Contact', 'Dermatitis, Occupational', 'Female', 'Food Handling', 'Food Preservatives', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Isothiocyanates', 'Retrospective Studies']",Allergic contact dermatitis from allyl isothiocyanate in a Danish cohort of 259 selected patients.,"[None, None, None, 'Q000209', 'Q000209', None, None, 'Q000009', None, None, 'Q000009', None]","[None, None, None, 'etiology', 'etiology', None, None, 'adverse effects', None, None, 'adverse effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/15373848,2005,0.0,0.0,,, +15315393,"Caffeoyl quinic acid (CQA) derivatives in ku-ding-cha, mate, coffee, and related plants were determined by HPLC. One ku-ding-cha contained a large amount of 3,5-dicaffeoylquinic acid (3,5-diCQA, 10.6% in dry weight) as well as 3-CQA (1.7%), 4-CQA (1.1%), 5-CQA (6.3%), 3,4-diCQA (1.8%), and 4,5-diCQA (4.3%). In this ku-ding-cha, the total caffeic acid moiety was 90.3 mmol/100 g of dry weight. The leaves of Ilex latifolia, which is one original species of ku-ding-cha, and another plant of the same genus, I. rotunda, also contained 3,5-diCQA (9.5 and 14.6%), 3-CQA (4.3 and 1.9%), and 5-CQA (4.8 and 3.8%), respectively, whereas raw coffee bean contained 5.5% 5-CQA and other low CQA derivatives. 3,5-DiCQA and 5-CQA with an apple acetone powder (AP) containing polyphenol oxidase showed high capturing activities toward thiols, and two addition compounds between 3,5-diCQA and methane thiol were also identified. Ku-ding-cha indicated extremely strong capturing activities toward methanethiol, propanethiol, and 2-propenethiol in the presence of apple AP. Furthermore, drinking ku-ding-cha reduced the amount of allyl methyl sulfide gas, well-known to persist as malodorous breath long after the ingestion of garlic.",Journal of agricultural and food chemistry,"['D000498', 'D001628', 'D028241', 'D002851', 'D040503', 'D003836', 'D031659', 'D027845', 'D011801', 'D013438', 'D013440']","['Allyl Compounds', 'Beverages', 'Camellia sinensis', 'Chromatography, High Pressure Liquid', 'Coffea', 'Deodorants', 'Ligustrum', 'Malus', 'Quinic Acid', 'Sulfhydryl Compounds', 'Sulfides']",Deodorization with ku-ding-cha containing a large amount of caffeoyl quinic acid derivatives.,"['Q000737', 'Q000032', 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000031', 'Q000737', 'Q000737']","['chemistry', 'analysis', 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'analogs & derivatives', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15315393,2004,0.0,0.0,,, +15289986,"An online UV photolysis and UV/TiO2 photocatalysis reduction device (UV-UV/TiO2 PCRD) and an electrochemical vapor generation (ECVG) cell have been used for the first time as an interface between high-performance liquid chromatography (HPLC) and atomic fluorescence spectrometry (AFS) for selenium speciation. The newly designed ECVG cell of approximately 115 microL dead volume consists of a carbon fiber cathode and a platinum loop anode; the atomic hydrogen generated on the cathode was used to reduce selenium to vapor species for AFS determination. The noise was greatly reduced compared with that obtained by use of the UV-UV/TiO2 PCRD-KBH4-acid interface. The detection limits obtained for seleno-DL: -cystine (SeCys), selenite (Se(IV)), seleno-DL: -methionine (SeMet), and selenate (Se(VI)) were 2.1, 2.9, 4.3, and 3.5 ng mL(-1), respectively. The proposed method was successfully applied to the speciation of selenium in water-soluble extracts of garlic shoots cultured with different selenium species. The results obtained suggested that UV-UV/TiO2 PCRD-ECVG should be an effective interface between HPLC and AFS for the speciation of elements amenable to vapor generation, and is superior to methods involving KBH4.",Analytical and bioanalytical chemistry,"['D002384', 'D002851', 'D004563', 'D005740', 'D036103', 'D010084', 'D010777', 'D012643', 'D013050', 'D014025', 'D014466']","['Catalysis', 'Chromatography, High Pressure Liquid', 'Electrochemistry', 'Gases', 'Nanotechnology', 'Oxidation-Reduction', 'Photochemistry', 'Selenium', 'Spectrometry, Fluorescence', 'Titanium', 'Ultraviolet Rays']",Electrochemical vapor generation of selenium species after online photolysis and reduction by UV-irradiation under nano TiO2 photocatalysis and its application to selenium speciation by HPLC coupled with atomic fluorescence spectrometry.,"[None, 'Q000379', None, None, None, None, None, 'Q000737', 'Q000379', 'Q000737', None]","[None, 'methods', None, None, None, None, None, 'chemistry', 'methods', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/15289986,2006,1.0,1.0,,, +15265743,"Allicin (diallylthiosulfinate), the active substance of garlic, has been shown to possess a variety of biological activities. Mechanistic and pharmacokinetic studies of allicin and its derivatives raise the need for a labeled compound. However, labeling of this volatile and unstable liquid requires delicate handling. Here, we describe a simple method for the preparation of (3)H-labeled allicin. This was achieved by applying synthetic [(3)H]alliin ([2,3-(3)H]allylcysteine sulfoxide) to a column containing immobilized alliinase [EC 4.1.1.4.] from garlic. Purification of [(3)H]allicin was done by differential adsorbtion of the reaction components on a neutral polystyrene resin, Porapak Q. Thiol-containing compounds are known to be the main target of allicin. In this work we demonstrated that [(3)H]allicin can be used for the synthesis of labeled [(3)H]allylmercapto derivatives of SH peptides and proteins. Thus, we prepared [(3)H]S-allylmercaptoglutathione which can be used in metabolic studies. Moreover, we showed that incubation of alliinase with [(3)H]allicin led to modification of 1.4 cysteine residues per subunit of the enzyme.",Analytical biochemistry,"['D000327', 'D002850', 'D013441', 'D014316']","['Adsorption', 'Chromatography, Gel', 'Sulfinic Acids', 'Tritium']",[3H]Allicin: preparation and applications.,"[None, None, 'Q000138', None]","[None, None, 'chemical synthesis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15265743,2005,0.0,0.0,,, +15161196,"The 26S proteasome (multicatalytic protease complex, MPC) was purified from fresh garlic cloves (Allium sativum) to near homogeneity by ion exchange chromatography on DEAE-sephacel, gel filtration on Sepharose-4B, and glycerol density gradient centrifugation. Two alpha-type (20S proteasome ""catalytic core"") subunits were identified by the direct sequencing of peptide fragments (mass fingerprint analysis, Mass Spectrometry Lab, Stanford University) or the sequencing of a cloned cDNA generated using a garlic cDNA library as the template; these subunits were found to have a high homology to those from other plants. Polyacrylamide gel electrophoresis under denaturing conditions separated the garlic MPC into multiple polypeptides having molecular masses in the range of 21-35 (components of the 20S catalytic core) and 55-100 kDa (components of the 19S regulatory units). The banding pattern of the garlic MCP is similar to that of spinach and rat liver with minor differences in some components; however, polyclonal antibodies against mammalian proteasomes failed to significantly stain the enzyme from garlic. This is the first work to identify the garlic proteasome.",Journal of agricultural and food chemistry,"['D000595', 'D001483', 'D003001', 'D018744', 'D005737', 'D008969', 'D010447', 'D011480', 'D046988']","['Amino Acid Sequence', 'Base Sequence', 'Cloning, Molecular', 'DNA, Plant', 'Garlic', 'Molecular Sequence Data', 'Peptide Hydrolases', 'Protease Inhibitors', 'Proteasome Endopeptidase Complex']",The 26S proteasome in garlic (Allium sativum): purification and partial characterization.,"[None, None, None, 'Q000737', 'Q000201', None, 'Q000737', 'Q000494', None]","[None, None, None, 'chemistry', 'enzymology', None, 'chemistry', 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/15161196,2004,0.0,0.0,,, +15139418,"Garlic (Allium sativum L.) is highly consumed worldwide. This crop is mainly known for its flavor and odor, although the many medicinal properties that are attributed to it, including anticarcinogenic, antiatherosclerotic, and antithrombotic potential, among several others, have called the attention of scientists since very early times. It is known that sulfur-containing volatiles are the principal compounds responsible for such properties. The aims of this work were to develop a solventless extraction method for sulfur-containing volatiles from garlic, as well as their chemical characterization. Since garlic volatiles are rather thermolabile, low-pressure hydrodistillation was chosen as the extracting method. The analysis of all compounds was performed on an HP-FFAP chromatographic column mounted in a GC-MS system. For volatile transfer and injection method, solid-phase microextraction was selected, with the use of eight different fibers. The most abundant volatile compound was diallyl disulfide, followed by diallyl trisulfide. Among the 47 totally identified compounds, 18 were linear sulfur-containing volatile compounds, 6 were of non-sulfur nature, and the other 23 were cyclic compounds. However, linear sulfur volatiles accounted for 94% of the total amount.",Journal of chromatography. A,"['D000498', 'D008401', 'D013440']","['Allyl Compounds', 'Gas Chromatography-Mass Spectrometry', 'Sulfides']",Solid-phase microextraction-gas chromatographic-mass spectrometric analysis of garlic oil obtained by hydrodistillation.,"['Q000737', 'Q000379', 'Q000737']","['chemistry', 'methods', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/15139418,2004,0.0,0.0,,no units , +15137816,"A stable isotope dilution assay was developed for the quantitation of the potent onion odorant 3-mercapto-2-methylpentan-1-ol (1) using mass chromatography and synthesized [(2)H(2)]-3-mercapto-2-methylpentan-1-ol as the internal standard. Application of the newly developed method on onions from different origins revealed amounts between 8 and 32 microg/kg in raw onions, whereas 34-246 microg was found in sliced, stored (50 min), and then cooked onions. In extracts prepared by simultaneous steam distillation-extraction the highest concentrations of 1 were formed, amounting to >1200 microg/kg. The much higher content of 3-mercapto-2-methylpentan-1-ol in cooked onions suggested its formation from specific, yet unkown, precursors enzymatically formed during cutting of raw onions. 1 was for the first time identified and also quantified in other Allium species such as chives, scallions, and leek, whereas surprisingly garlic and bear's garlic did not contain the aroma compound.",Journal of agricultural and food chemistry,"['D000490', 'D003903', 'D008401', 'D007201', 'D009812', 'D000439', 'D013438']","['Allium', 'Deuterium', 'Gas Chromatography-Mass Spectrometry', 'Indicator Dilution Techniques', 'Odorants', 'Pentanols', 'Sulfhydryl Compounds']",Quantitation of the intense aroma compound 3-mercapto-2-methylpentan-1-ol in raw and processed onions (Allium cepa) of different origins and in other Allium varieties using a stable isotope dilution assay.,"['Q000737', None, None, None, 'Q000032', 'Q000032', 'Q000032']","['chemistry', None, None, None, 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15137816,2004,1.0,1.0,,, +15065784,"Garlic and onion, are well known for their medical value, especially in against cancer and anticardiovacular diseases. ""Alliins"" (S-alk(en)yl-L-cysteine sulphoxides) are sources of major active compounds in Allium plants. Se incorporation into garlic significantly increases activities of garlic in cancer prevention and inhibition. Selenomethionine, selenocysteine and Se-methylselenocysteine have been identified in garlic and onion. Previously we identified gamma-glutamyl-Se-methyl-L-selenocysteine, in extracts of garlic cultivated in Se-rich soil [Med. Res. Rev. 16 (1) (1996) 111], suggesting the possible existence of Se-alk(en)yl-L-cysteine selenoxides (Se-""alliins"") in garlic. Several comparative experiments were carried out to demonstrate the existence of Se-""alliins"" in Se-enriched garlic and onion. We found that there was one similar time-dependent Se signal in HPLC-inductively coupled plasma MS chromatograms of cold-water extracts of freeze-dried garlic powder and fresh garlic. This signal was lost when the extracts of garlic powder and fresh garlic were stored for 1 day at >4 degrees C, but remained in fresh onion extract at the same storage conditions. These phenomena and possible mechanisms are discussed. An additional experiment showed that Allium species cultivated in Se-rich soil might contain two different Se-""alliins"".",Journal of chromatography. A,"['D002851', 'D003545', 'D005737', 'D013058', 'D019697', 'D012643', 'D012987']","['Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Mass Spectrometry', 'Onions', 'Selenium', 'Soil']","High-performance liquid chromatographic-inductively coupled plasma mass spectrometric evidence for Se-""alliins"" in garlic and onion grown in Se-rich soil.","['Q000379', 'Q000031', 'Q000737', 'Q000379', 'Q000737', 'Q000032', 'Q000032']","['methods', 'analogs & derivatives', 'chemistry', 'methods', 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/15065784,2004,0.0,0.0,,no quantification, +15028723,"The homopteran sucking insect, Lipaphis erysimi (mustard aphid) causes severe damage to various crops. This pest not only affects plants by sucking on the phloem, but it also transmits single-stranded RNA luteoviruses while feeding, which cause disease and damage in the crop. The mannose-binding Allium sativum (garlic) leaf lectin has been found to be a potent control agent of L. erysimi. The lectin receptor protein isolated from brush border membrane vesicle of insect gut was purified to determine the mechanism of lectin binding to the gut. Purified receptor was identified as an endosymbiotic chaperonin, symbionin, using liquid chromatography-tandem mass spectrometry. Symbionin from endosymbionts of other aphid species have been reported to play a significant role in virus transmission by binding to the read-through domain of the viral coat protein. To understand the molecular interactions of the said lectin and this unique symbionin molecule, the model structures of both molecules were generated using the Modeller program. The interaction was confirmed through docking of the two molecules forming a complex. A surface accessibility test of these molecules demonstrated a significant reduction in the accessibility of the complex molecule compared with that of the free symbionin molecule. This reduction in surface accessibility may have an effect on other molecular interactive processes, including ""symbionin virion recognition"", which is essential for such symbionin-mediated virus transmission. Thus, garlic leaf lectin provides an important component of a crop management program by controlling, on one hand, aphid attack and on the other hand, symbionin-mediated luteovirus transmission.",The Journal of biological chemistry,"['D000373', 'D000595', 'D001426', 'D018833', 'D005737', 'D008958', 'D008969', 'D009149', 'D011485', 'D011956', 'D013559']","['Agglutinins', 'Amino Acid Sequence', 'Bacterial Proteins', 'Chaperonins', 'Garlic', 'Models, Molecular', 'Molecular Sequence Data', 'Mustard Plant', 'Protein Binding', 'Receptors, Cell Surface', 'Symbiosis']",The Interactions of Allium sativum leaf agglutinin with a chaperonin group of unique receptor protein isolated from a bacterial endosymbiont of the mustard aphid.,"['Q000378', None, 'Q000235', 'Q000235', 'Q000378', None, None, 'Q000382', None, 'Q000235', None]","['metabolism', None, 'genetics', 'genetics', 'metabolism', None, None, 'microbiology', None, 'genetics', None]",https://www.ncbi.nlm.nih.gov/pubmed/15028723,2004,0.0,0.0,,, +15003558,"This paper presents an automatic spectrofluorimetric method (flow injection spectrofluorimetry) using a novel fluorescent probe named H. Py. Bzt (2-(2-pyridil)-benzothiazoline) for determining superoxide dismutase (SOD) activity. The fluorescent probe was synthesized in house and fully characterized by elemental analysis and by infrared and (1)H nuclear magnetic resonance spectra. It could specially identify and trap O(2)(*-) and was oxidized by O(2)(*-) to form a strong fluorescence product. Based on this reaction, the flow injection spectrofluorimetric method was proposed and successfully used to determine SOD activity. The proposed method has a better selectivity in the determination of reactive oxygen species because the probe can be oxidized only by O(2)(*-) excluding H(2)O(2). As a kind of simple, rapid, precise, sensitive and automatic technique, it was applied to measurement of SOD activity in scallion, garlic, and onion with satisfactory results.",Analytical biochemistry,"['D052160', 'D017022', 'D005456', 'D005609', 'D013050', 'D013482', 'D013481', 'D013844']","['Benzothiazoles', 'Flow Injection Analysis', 'Fluorescent Dyes', 'Free Radicals', 'Spectrometry, Fluorescence', 'Superoxide Dismutase', 'Superoxides', 'Thiazoles']",Study and application of flow injection spectrofluorimetry with a fluorescent probe of 2-(2-pyridil)-benzothiazoline for superoxide anion radicals.,"[None, None, 'Q000737', 'Q000032', 'Q000379', 'Q000378', 'Q000032', None]","[None, None, 'chemistry', 'analysis', 'methods', 'metabolism', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/15003558,2004,0.0,0.0,,, +14969516,"A quantitative method is described for the determination of allicin (2-propene-1-sulfinothioic acid S-2-propenyl ester) in garlic, using standard additions of alliin (l-(+)-S-allylcysteine sulfoxide) in conjunction with supercritical fluid extraction (SFE) and high performance liquid chromatography analysis with UV-vis absorbance detection. Optimum CO(2)-SFE conditions provided 96% recovery for allicin with precision of 3% (RSD) for repeat samples. The incorporation of an internal standard (allyl phenyl sulfone) in the SFE step resulted in a modest improvement in recovery (99%) and precision (2% RSD). Standard additions of alliin were converted to allicin in situ by endogenous alliinase (l-(+)-S-alk(en)ylcysteine sulfoxide lyase, EC 4.4.1.4). Complete conversion of the spiked alliin to allicin was achieved by making additions after homogenization-induced conversion of the naturally occurring cysteine sulfoxides to thiosulfinates had taken place, thus eliminating the likelihood of competing reactions. Concentration values for allicin determined in samples of fresh garlic (Allium sativum L. and Allium ampeloprasum) and commercially available garlic powders (Allium sativum L.) by standard addition of alliin were found in all cases to be in statistical agreement (95% confidence interval) with values determined using a secondary allicin standard (concentration determined using published extinction coefficients). This method provides a convenient alternative for assessing the amount of allicin present in fresh and powdered garlic, as alliin is a far more stable and commercially prevalent compound than allicin and is thus more amenable for use as a standard for routine analysis.",Journal of agricultural and food chemistry,"['D013437', 'D002851', 'D025924', 'D003545', 'D005737', 'D012680', 'D013441']","['Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Chromatography, Supercritical Fluid', 'Cysteine', 'Garlic', 'Sensitivity and Specificity', 'Sulfinic Acids']",Quantitative determination of allicin in garlic: supercritical fluid extraction and standard addition of alliin.,"['Q000378', None, None, 'Q000008', 'Q000737', None, 'Q000032']","['metabolism', None, None, 'administration & dosage', 'chemistry', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/14969516,2004,0.0,0.0,,, +14763870,"Allicin and allyl-methyl plus methyl-allyl thiosulfinate from acetonic garlic extracts (AGE) have been isolated by high-performance liquid chromatography. These compounds have shown inhibition of the in vitro growth of Helicobacter pylori (Hp), the bacterium responsible for serious gastric diseases such as ulcers and even gastric cancer. A chromatographic method was optimized and used to isolate these thiosulfinates. The method developed has allowed the isolation of natural thiosulfinates extracted from garlic by organic solvents and is an easy and cheap methodology that avoids complex synthesis and purification procedures. The capacity and effectiveness of isolated natural thiosulfinates have been tested, and this has enabled the identification of the main compounds responsible for the bacteriostatic activity shown by AGE origin of these kinds of organosulfur compounds along with ethanolic garlic extracts (EGE). Additionally, microbiological analyses have suggested that these compounds show a synergic effect on the inhibition of the in vitro growth of Hp. The results described here facilitate the process of obtaining garlic extracts with optimal bacteriostatic properties. The product is obtained in a way that avoids expensive purification methods and will allow the design of live tests with the aim of investigating the potential for the use of these garlic derivatives in the treatment of patients with Hp infections.",Biotechnology progress,"['D000900', 'D001673', 'D002455', 'D004355', 'D005737', 'D016480', 'D007700', 'D010936', 'D013441', 'D013696']","['Anti-Bacterial Agents', 'Biodegradation, Environmental', 'Cell Division', 'Drug Stability', 'Garlic', 'Helicobacter pylori', 'Kinetics', 'Plant Extracts', 'Sulfinic Acids', 'Temperature']","Allyl-thiosulfinates, the bacteriostatic compounds of garlic against Helicobacter pylori.","['Q000737', None, 'Q000187', None, 'Q000737', 'Q000166', None, 'Q000737', 'Q000737', None]","['chemistry', None, 'drug effects', None, 'chemistry', 'cytology', None, 'chemistry', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/14763870,2004,0.0,0.0,,, +14713923,"Garlic (Allium sativum) is one of the most common relishes used in cooking worldwide. Very few garlic allergens have been reported, and garlic allergy has been rarely studied.",The Journal of allergy and clinical immunology,"['D000293', 'D000328', 'D000485', 'D000596', 'D002241', 'D013437', 'D002648', 'D004591', 'D005260', 'D005512', 'D005737', 'D006801', 'D015151', 'D007073', 'D008297', 'D013058', 'D008875', 'D012882']","['Adolescent', 'Adult', 'Allergens', 'Amino Acids', 'Carbohydrates', 'Carbon-Sulfur Lyases', 'Child', 'Electrophoresis, Polyacrylamide Gel', 'Female', 'Food Hypersensitivity', 'Garlic', 'Humans', 'Immunoblotting', 'Immunoglobulin E', 'Male', 'Mass Spectrometry', 'Middle Aged', 'Skin Tests']","Identification and immunologic characterization of an allergen, alliin lyase, from garlic (Allium sativum).","[None, None, 'Q000276', 'Q000032', 'Q000032', 'Q000276', None, None, None, 'Q000453', 'Q000201', None, 'Q000379', 'Q000097', None, None, None, None]","[None, None, 'immunology', 'analysis', 'analysis', 'immunology', None, None, None, 'epidemiology', 'enzymology', None, 'methods', 'blood', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/14713923,2004,0.0,0.0,,, +14667466,"Diallyl sulfide (DAS) is one of the major components of garlic (Allium sativum) and is widely used in the world for food. In this study, DAS was selected for testing the inhibition of arylamine N-acetyltransferase (NAT) activity (N-acetylation of 2-aminofluorene) and gene expression (mRNA NAT) in human colon cancer cell lines (colo 205, colo 320 DM and colo 320 HSR). The NAT activity was examined by high performance liquid chromatography and indicated that a 24 h DAS treatment decreases N-acetylation of 2-aminofluorene in three colon (colo 205, 320 DM and colo 320 HSR) cancer cell lines. The NAT enzymes (protein) were analyzed by western blotting and flow cytometry and it indicated that DAS decreased the levels of NAT in three colon (colo 205, 320 DM and colo 320 HSR) cancer cell lines. The gene expression of NAT (mRNAT NAT) was determined by polymerase chain reaction (PCR), it was shown that DAS affect mRNA NAT expression in examined human colon cancer cell lines. This report is the first to demonstrate that DAS does inhibit human colon cancer cell NAT activity and gene expression.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000498', 'D000972', 'D001191', 'D015153', 'D045744', 'D003110', 'D065607', 'D005434', 'D005737', 'D015972', 'D006801', 'D008517', 'D016133', 'D012333', 'D013440']","['Allyl Compounds', 'Antineoplastic Agents, Phytogenic', 'Arylamine N-Acetyltransferase', 'Blotting, Western', 'Cell Line, Tumor', 'Colonic Neoplasms', 'Cytochrome P-450 Enzyme Inhibitors', 'Flow Cytometry', 'Garlic', 'Gene Expression Regulation, Neoplastic', 'Humans', 'Phytotherapy', 'Polymerase Chain Reaction', 'RNA, Messenger', 'Sulfides']",Inhibition of N-acetyltransferase activity and gene expression in human colon cancer cell lines by diallyl sulfide.,"['Q000008', 'Q000008', 'Q000187', None, 'Q000187', 'Q000201', None, None, None, 'Q000187', None, None, None, 'Q000187', 'Q000008']","['administration & dosage', 'administration & dosage', 'drug effects', None, 'drug effects', 'enzymology', None, None, None, 'drug effects', None, None, None, 'drug effects', 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/14667466,2004,0.0,0.0,,, +14640577,"The extract of garlic skins (peels) showed strong antioxidant activity, and some responsible constituents were isolated and identified. Garlic (Allium sativum L.) has been used as an herbal medicine, but there is no report on the health benefits of the skin or peel. In this study, the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity of garlic skin extract was evaluated. Using chromatographic techniques, the active constituents were isolated and subsequently identified. Analyses by high-performance liquid chromatography coupled with a photodiode array detector (HPLC-PDA) suggested that these compounds were phenylpropanoids, which had a characteristic absorbance at 300-320 nm. Liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance analyses allowed the chemical structures of the isolated constituents to be postulated. The proposed compounds were subsequently synthesized and compared with the constituents in the extract using HPLC-PDA and LC-MS. N-trans-Coumaroyloctopamine, N-trans-feruloyloctopamine, guaiacylglycerol-beta-ferulic acid ether, and guaiacylglycerol-beta-caffeic acid ether were identified as were trans-coumaric acid and trans-ferulic acid. Also, the antioxidant activities of these compounds were determined.",Journal of agricultural and food chemistry,"['D000975', 'D001713', 'D002851', 'D002934', 'D003373', 'D000431', 'D005737', 'D009682', 'D013058', 'D010851', 'D010936', 'D018514']","['Antioxidants', 'Biphenyl Compounds', 'Chromatography, High Pressure Liquid', 'Cinnamates', 'Coumaric Acids', 'Ethanol', 'Garlic', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Picrates', 'Plant Extracts', 'Plant Structures']",Identification of six phenylpropanoids from garlic skin as major antioxidants.,"['Q000032', None, None, 'Q000032', None, None, 'Q000737', None, None, 'Q000737', 'Q000737', 'Q000737']","['analysis', None, None, 'analysis', None, None, 'chemistry', None, None, 'chemistry', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/14640577,2004,0.0,0.0,,, +14633659,"A short-term feeding regimen was designed to analyze the effects of compounds such as diallyl disulfide (DADS), diallylthiosulfinate (allicin) from garlic and butylated hydroxyanisole (BHA) on glutathione S-transferase (GST) expression in the gastrointestinal tract and liver of male mice. After animals were force-fed these compounds, tissue GSTs were purified and individual subunits resolved by HPLC and identified on the basis of mass spectrometry (ESI MS) and immunoreactivity data. The effects of DADS and allicin on GST expression were especially prominent in stomach and small intestine, where there were major coordinate changes in GST subunit profiles. In particular, the transcripts of the mGSTM1 and mGSTM4 genes, which share large segments of common 5'-flanking sequences, and their corresponding subunits were selectively induced. Levels of alpha class subunits also increased, whereas mGSTM3 and mGSTP1 were not affected. The inducible mGSTA5 and non-responsive mGSTM3 subunits had not been identified previously. Liver and colon GSTs were also affected to a lesser extent, but this short-term feeding regimen had no effect on GST subunit patterns from other organs, including heart, brain and testis. Real-time PCR (TaqMan) methods were used for quantitative estimations of relative amounts of the mRNAs encoding the GSTs. Effects on the transcripts generally paralleled changes at the protein level, for the most part, however, the greatest relative increases were observed for those mRNAs that were expressed at low abundance constituitively. Mechanisms by which the organosulfur compounds operate to affect GST transcription could involve reversible modification of certain protein sulfhydryl groups, shifts in reduced glutathione/oxidized glutathione ratios and resultant changes in cellular redox status.",Carcinogenesis,"['D000498', 'D000818', 'D001483', 'D002851', 'D004220', 'D041981', 'D005786', 'D005982', 'D008099', 'D051379', 'D008969', 'D016415', 'D013457']","['Allyl Compounds', 'Animals', 'Base Sequence', 'Chromatography, High Pressure Liquid', 'Disulfides', 'Gastrointestinal Tract', 'Gene Expression Regulation', 'Glutathione Transferase', 'Liver', 'Mice', 'Molecular Sequence Data', 'Sequence Alignment', 'Sulfur Compounds']",Selective expression of glutathione S-transferase genes in the murine gastrointestinal tract in response to dietary organosulfur compounds.,"['Q000378', None, None, None, 'Q000378', 'Q000378', 'Q000502', 'Q000096', 'Q000378', None, None, None, 'Q000378']","['metabolism', None, None, None, 'metabolism', 'metabolism', 'physiology', 'biosynthesis', 'metabolism', None, None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/14633659,2004,0.0,0.0,,, +14586535,"The coupling reaction of 4-aminoantipyrine (4-AAP) with phenol using the superoxide anion radical (O2-*) as oxidizing agent under the catalysis of horseradish peroxidase (HRP) was studied. Based on the reaction, O2-* produced by irradiating vitamin B2 (VB2) was spectrophotometrically determined at 510 nm. Under the optimum experimental conditions, the relationship between A510 and O2-* concentration was linear in the range 9.14x10(-6)-1.2x10(-4) mol L(-1). The detection limit was determined to be 1.37x10(-6) mol L(-1). A possible reaction mechanism was discussed. The effect of interferences and surfactants on the determination of O2-* was also investigated. The proposed method was applied to determine superoxide dismutase activity in garlic, scallion, and onion with satisfactory results.",Analytical and bioanalytical chemistry,"['D002384', 'D004126', 'D005737', 'D006735', 'D006801', 'D006863', 'D019697', 'D019800', 'D013050', 'D013056', 'D013425', 'D013482', 'D013481', 'D014675']","['Catalysis', 'Dimethylformamide', 'Garlic', 'Horseradish Peroxidase', 'Humans', 'Hydrogen-Ion Concentration', 'Onions', 'Phenol', 'Spectrometry, Fluorescence', 'Spectrophotometry, Ultraviolet', 'Sulfanilic Acids', 'Superoxide Dismutase', 'Superoxides', 'Vegetables']",Simple and rapid catalytic spectrophotometric determination of superoxide anion radical and superoxide dismutase activity in natural medical vegetables using phenol as the substrate for horseradish peroxidase.,"[None, 'Q000737', 'Q000737', 'Q000378', None, None, 'Q000737', 'Q000378', 'Q000379', None, 'Q000737', 'Q000032', 'Q000032', 'Q000737']","[None, 'chemistry', 'chemistry', 'metabolism', None, None, 'chemistry', 'metabolism', 'methods', None, 'chemistry', 'analysis', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/14586535,2004,0.0,0.0,,, +14530594,"1,2,3,4-Tetrahydro-beta-carboline derivatives (THbetaCs) are formed through Pictet-Spengler chemical condensation between tryptophan and aldehydes during food production, storage and processing. In the present study, in order to identify the antioxidants in aged garlic extract (AGE), we fractionated it and identified four THbetaCs; 1-methyl-1,2,3,4-tetrahydro-beta-carboline-3-carboxylic acids (MTCC) and 1-methyl-1,2,3,4-tetrahydro-beta-carboline-1,3-dicarboxylic acid (MTCdiC) in both diastereoisomers using liquid chromatography mass spectrometry (LC-MS). Interestingly, these compounds were not detected in raw garlic, but the contents increased during the natural aging process of garlic. In in vitro assay systems, all of these compounds have shown strong hydrogen peroxide scavenging activities. (1S, 3S)-MTCdiC was found to be stronger than the common antioxidant, ascorbic acid. MTCC and MTCdiC inhibited AAPH-induced lipid peroxidation. Both MTCdiCs also inhibited LPS-induced nitrite production from murine macrophages at 10-100 microM. Our data suggest that these compounds are potent antioxidants in AGE, and thus may be useful for prevention of disorders associated with oxidative stress.","BioFactors (Oxford, England)","['D000818', 'D000975', 'D002243', 'D002460', 'D016166', 'D005737', 'D006861', 'D015227', 'D008264', 'D009682', 'D013058', 'D051379', 'D009573', 'D010936', 'D013237', 'D013997']","['Animals', 'Antioxidants', 'Carbolines', 'Cell Line', 'Free Radical Scavengers', 'Garlic', 'Hydrogen Peroxide', 'Lipid Peroxidation', 'Macrophages', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Mice', 'Nitrites', 'Plant Extracts', 'Stereoisomerism', 'Time Factors']",Antioxidant effects of tetrahydro-beta-carboline derivatives identified in aged garlic extract.,"[None, 'Q000494', 'Q000032', None, 'Q000494', 'Q000737', None, 'Q000187', 'Q000187', None, None, None, 'Q000378', 'Q000737', None, None]","[None, 'pharmacology', 'analysis', None, 'pharmacology', 'chemistry', None, 'drug effects', 'drug effects', None, None, None, 'metabolism', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/14530594,2004,,,,error on the link, +12923610,"Several sampling techniques based on steam distillation (SD), simultaneous distillation and solvent extraction (SDE), solid-phase trapping solvent extraction (SPTE), and headspace solid-phase microextraction (HS-SPME) have been compared for the determination of Korean garlic flavor components by gas chromatography-mass spectrometry (GC-MS). Diallyl disulfide (57.88%), allyl sulfide (23.59%), and diallyl trisulfide (11.40%) were found to be the predominant flavor components of garlic samples extracted by SDE whereas these components were at levels of 89.77%, 2.43%, and 3.89% when the same sample was extracted by SD, 97.77%, 0.17%, and 0.10% by SPTE, and 97.85%, 0.01%, and 0.01% by HS-SPME using the 50/30-microm divinyl benzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber. Thermal degradation of components such as allyl methyl sulfide, dimethyl disulfide, and thiirane were observed for SDE and SD but not for SPTE or HS-SPME. HS-SPME had several advantages compared with SD, SDE, and SPTE-rapid solvent-free extraction, no apparent thermal degradation, less laborious manipulation, and less sample requirement. Five different fiber coatings were evaluated to select a suitable fiber for HS-SPME of garlic flavor components. DVB/CAR/PDMS was most efficient among the five types of fiber investigated.",Analytical and bioanalytical chemistry,"['D005421', 'D005737', 'D008401', 'D015203', 'D012997']","['Flavoring Agents', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Reproducibility of Results', 'Solvents']",Comparative study of extraction techniques for determination of garlic flavor components by gas chromatography-mass spectrometry.,"['Q000737', 'Q000737', 'Q000379', None, None]","['chemistry', 'chemistry', 'methods', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12923610,2004,2.0,3.0,,, +12891227,"Garlic (Allium sativum L.) is a commonly used food and herbal supplement. The objective of this study was to assess in healthy volunteers (N = 14) the influence of a garlic extract on the activity of cytochrome P450 (CYP) 2D6 and 3A4. Probe substrates dextromethorphan (CYP2D6) and alprazolam (CYP3A4) were administered orally at baseline and again after treatment with garlic extract (3 x 600 mg twice daily) for 14 days. Urinary dextromethorphan/dextrorphan ratios and alprazolam plasma concentrations were determined by HPLC at baseline and after garlic extract treatment. The ratio of dextromethorphan to its metabolite was 0.044 +/- 0.48 at baseline and 0.052 +/- 0.095 after garlic supplementation. There were no significant differences between the baseline and garlic phases (P > or =.05). For alprazolam, there were no significant differences in pharmacokinetic parameters at baseline and after garlic extract treatment (all P values > or =.05; maximum concentration in plasma, 27.3 +/- 2.6 ng/mL versus 27.3 +/- 4.8 ng/mL; time to reach maximum concentration in plasma, 1.9 +/- 1.4 h versus 2.4 +/- 1.8 h; area under the time-versus-concentration curve, 537 +/- 94 h. ng. mL(-1) versus 548 +/- 159 h. ng. mL(-1); half-life of elimination, 13.7 +/- 4.4 h versus 14.5 +/- 4.3 h). Our results indicate that garlic extracts are unlikely to alter the disposition of coadministered medications primarily dependent on the CYP2D6 or CYP3A4 pathway of metabolism.",Clinical pharmacology and therapeutics,"['D000328', 'D000525', 'D019540', 'D001711', 'D002851', 'D019389', 'D051544', 'D003577', 'D003915', 'D019587', 'D005260', 'D005737', 'D006207', 'D006801', 'D007527', 'D008297', 'D013441']","['Adult', 'Alprazolam', 'Area Under Curve', 'Biotransformation', 'Chromatography, High Pressure Liquid', 'Cytochrome P-450 CYP2D6', 'Cytochrome P-450 CYP3A', 'Cytochrome P-450 Enzyme System', 'Dextromethorphan', 'Dietary Supplements', 'Female', 'Garlic', 'Half-Life', 'Humans', 'Isoenzymes', 'Male', 'Sulfinic Acids']",Effects of garlic (Allium sativum L.) supplementation on cytochrome P450 2D6 and 3A4 activity in healthy volunteers.,"[None, 'Q000493', None, None, None, 'Q000378', None, 'Q000378', 'Q000493', None, None, None, None, None, 'Q000378', None, 'Q000378']","[None, 'pharmacokinetics', None, None, None, 'metabolism', None, 'metabolism', 'pharmacokinetics', None, None, None, None, None, 'metabolism', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/12891227,2003,0.0,0.0,,, +12888387,"A scientific basis for the evaluation of the risk to public health arising from excessive dietary intake of nitrate in Korea is provided. The nitrate () and nitrite () contents of various vegetables (Chinese cabbage, radish, lettuce, spinach, soybean sprouts, onion, pumpkin, green onion, cucumber, potato, carrot, garlic, green pepper, cabbage and Allium tuberosum Roth known as Crown daisy) are reported. Six hundred samples of 15 vegetables cultivated during different seasons were analysed for nitrate and nitrite by ion chromatography and ultraviolet spectrophotometry, respectively. No significant variance in nitrate levels was found for most vegetables cultivated during the summer and winter harvests. The mean nitrates level was higher in A. tuberosum Roth (5150 mg kg(-1)) and spinach (4259 mg kg(-1)), intermediate in radish (1878 mg kg(-1)) and Chinese cabbage (1740 mg kg(-1)), and lower in onion (23 mg kg(-1)), soybean sprouts (56 mg kg(-1)) and green pepper (76 mg kg(-1)) compared with those in other vegetables. The average nitrite contents in various vegetables were about 0.6 mg kg(-1), and the values were not significantly different among most vegetables. It was observed that nitrate contents in vegetables varied depending on the type of vegetables and were similar to those in vegetables grown in other countries. From the results of our studies and other information from foreign sources, it can be concluded that it is not necessary to establish limits of nitrates contents of vegetables cultivated in Korea due to the co-presence of beneficial elements such as ascorbic acid and alpha-tocopherol which are known to inhibit the formation of nitrosamine.",Food additives and contaminants,"['D000490', 'D001530', 'D001937', 'D002212', 'D002852', 'D004032', 'D006801', 'D007391', 'D007723', 'D009566', 'D009573', 'D019697', 'D031224', 'D012621', 'D013025', 'D013056', 'D006113', 'D014675']","['Allium', 'Belgium', 'Brassica', 'Capsicum', 'Chromatography, Ion Exchange', 'Diet', 'Humans', 'International Cooperation', 'Korea', 'Nitrates', 'Nitrites', 'Onions', 'Raphanus', 'Seasons', 'Soybeans', 'Spectrophotometry, Ultraviolet', 'United Kingdom', 'Vegetables']",Survey of nitrate and nitrite contents of vegetables grown in Korea.,"['Q000737', None, 'Q000737', 'Q000737', None, None, None, None, None, 'Q000009', 'Q000032', 'Q000737', 'Q000737', None, 'Q000737', None, None, 'Q000737']","['chemistry', None, 'chemistry', 'chemistry', None, None, None, None, None, 'adverse effects', 'analysis', 'chemistry', 'chemistry', None, 'chemistry', None, None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12888387,2003,,,,, +12852574,"A method is described for determining sulfite in dried garlic. Garlic is extracted with an HCl solution to inhibit the formation of allicin, which interferes with the determination of sulfite. After cleanup of the extract on a C18 solid-phase extraction column, sulfite is converted to hydroxymethylsulfonate (HMS) by adding formaldehyde and heating to 50 degrees C. HMS is determined by reversed-phase ion-pairing liquid chromatography with post-column detection. The post-column reaction system consists of the addition of KOH to convert HMS to sulfite ion, followed by the addition of 5,5'-dithiobis(2-nitrobenzoic acid) to produce 5-mercapto-2-nitrobenzoic acid which is detected spectrophotometrically at 450 nm. Background levels in unsulfited dried garlic equivalent to < 20 ppm SO2 were found. Recoveries of HMS from spiked garlic averaged 94.8% with a coefficient of variation of 3.8%. Sulfite was found in 13 of 21 samples of dried garlic produced in China, with sulfite ranging from 114 to 445 ppm. Sulfite was found in 60% of commercial dried garlic products purchased locally. The suitability of the Monier-Williams method for determining sulfite in garlic is discussed.",Journal of AOAC International,"['D002853', 'D005737', 'D013441', 'D013447']","['Chromatography, Liquid', 'Garlic', 'Sulfinic Acids', 'Sulfites']",Determination of sulfite in dried garlic by reversed-phase ion-pairing liquid chromatography with post-column detection.,"[None, 'Q000737', 'Q000032', 'Q000032']","[None, 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12852574,2003,,,,, +12840173,"Garlic is proposed to have immunomodulatory and anti-inflammatory properties. This paper shows that garlic powder extracts (GPE) and single garlic metabolites modulate lipopolysaccharide (LPS)-induced cytokine levels in human whole blood. GPE-altered cytokine levels in human blood sample supernatants reduced nuclear factor (NF)-kappaB activity in human cells exposed to these samples. Pretreatment with GPE (100 mg/L) reduced LPS-induced production of proinflammatory cytokines interleukin (IL)-1beta from 15.7 +/- 5.1 to 6.2 +/- 1.2 micro g/L and tumor necrosis factor (TNF)-alpha from 8.8 +/- 2.4 to 3.9 +/- 0.8 micro g/L, respectively, whereas the expression of the anti-inflammatory cytokine IL-10 was unchanged. The garlic metabolite diallydisulfide (1-100 micro mol/L) also significantly reduced IL-1beta and TNF-alpha. Interestingly, exposure of human embryonic kidney cell line (HEK293) cells to GPE-treated blood sample supernatants (10 or 100 mg/L) reduced NF-kappaB activity compared with cells exposed to untreated blood supernatants as measured by a NF-kappaB-driven luciferase reporter gene assay. Blood samples treated with extract obtained from unfertilized garlic (100 mg/L) reduced NF-kappaB activity by 25%, whereas blood samples treated with sulfur-fertilized garlic extracts (100 mg/L) lowered NF-kappaB activity by 41%. In summary, garlic may indeed promote an anti-inflammatory environment by cytokine modulation in human blood that leads to an overall inhibition of NF-kappaB activity in the surrounding tissue.",The Journal of nutrition,"['D002460', 'D002851', 'D016207', 'D005737', 'D006801', 'D008070', 'D016328', 'D013056', 'D013457']","['Cell Line', 'Chromatography, High Pressure Liquid', 'Cytokines', 'Garlic', 'Humans', 'Lipopolysaccharides', 'NF-kappa B', 'Spectrophotometry, Ultraviolet', 'Sulfur Compounds']",Garlic (Allium sativum L.) modulates cytokine expression in lipopolysaccharide-activated human blood thereby inhibiting NF-kappaB activity.,"[None, None, 'Q000097', 'Q000737', None, 'Q000494', 'Q000037', None, 'Q000032']","[None, None, 'blood', 'chemistry', None, 'pharmacology', 'antagonists & inhibitors', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12840173,2003,1.0,1.0,,, +12804663,"Nicotine [3-(1-methyl-2-pyrrolidinyl)-pyridine] is a major alkaloid in tobacco products and has proven to be a potential genotoxic compound. Many natural dietary products can suppress the DNA adduction, and hence act as inhibitors of cancer. In this study, we investigated the inhibitory effects of curcumin, garlic squeeze, grapeseed extract, tea polyphenols, vitamin C, and vitamin E on nicotine-DNA adduction in vivo using an ultrasensitive method of accelerator mass spectrometry (AMS). The results demonstrated that all the dietary constituents induced marked dose-dependent decrease in nicotine-DNA adducts as compared with the control. The reduction rate reached about 50% for all agents, except garlic squeeze (40%), even at its highest dose level. Amongst the six agents, grapeseed extract exhibited the strongest inhibition to the DNA adduct formation. Therefore, we may arrive at a point that these dietary constituents are beneficial to prevent the harmful adduct formation, and thus to block the potential carcinogenesis induced by nicotine.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000818', 'D001205', 'D003474', 'D004247', 'D018736', 'D004032', 'D005737', 'D008297', 'D051379', 'D009538', 'D018722', 'D018733', 'D010936', 'D013662', 'D014810', 'D027843']","['Animals', 'Ascorbic Acid', 'Curcumin', 'DNA', 'DNA Adducts', 'Diet', 'Garlic', 'Male', 'Mice', 'Nicotine', 'Nicotinic Agonists', 'Nicotinic Antagonists', 'Plant Extracts', 'Tea', 'Vitamin E', 'Vitis']",Inhibition of nicotine-DNA adduct formation in mice by six dietary constituents.,"[None, 'Q000494', 'Q000494', 'Q000187', 'Q000187', None, None, None, None, 'Q000633', 'Q000633', 'Q000494', 'Q000494', None, 'Q000494', None]","[None, 'pharmacology', 'pharmacology', 'drug effects', 'drug effects', None, None, None, None, 'toxicity', 'toxicity', 'pharmacology', 'pharmacology', None, 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/12804663,2003,0.0,0.0,,, +12721447,"The objective of this study was to obtain purer acid phosphatases than produced by prior art by operating under conditions that improve the final product. The study features are the use of a mild nonionic detergent, 40-80% saturation with (NH4)2SOm4, maintained at low temperature to remove impurity, and the use of chromatografic columns to concentrate the acid phosphatase and remove non-acid phosphatase proteins with lower or higher molecular weights. Acid phosphatase was isolated and purified from garlic seedlings by a streamline method without the use of proteolytic and lipolytic enzymes, butanol, or other organic solvents. Grown garlic seedlings of 10- 15 cm height were homogenized with 0.1 M acetate buffer containing 0.1 M NaCl and 0.1% Triton X-100. After homogenization, the supernatant was filtered with paper filters. Filtrated supernatant was cooled to 4 degrees C, followed by a threestep fractionation of the proteins with ammonium sulfate. The crude enzyme was isolated as a green precipitate that was dissolved in a small amount of 0.1 M acetate buffer containing 0.1 M NaCl and 0.1% Triton X-100. Garlic seedling acid phosphatase was purified with ion-exchange chromatography (DEAE cellulose). The column was equilibrated with 0.1 M acetate buffer. Acid phosphatase was purified 40-fold from the starting material. The specific activity of the pure enzyme was 168 U/mg. A variety of stability and activity profiles were determined for the purified garlic seedling acid phosphatase: optimum pH, optimum temperature, pH stability, temperature stability, thermal inactivation, substrate specificity, effect of enzyme concentration, effect of substrate concentration, activation energy, and effect of inhibitor and activator. The molecular mass of acid phosphatase was estimated to be 58 kDa by sodium dodecyl sulfate polyacrylamide gel electrophoresis. The optimum pH was 5.7 and the optimum temperature was 50 degrees C. The enzyme was stable at pH 4.0-10.0 and 40-60 degrees C. Activation energy was between 10 and 20 kcal, and as Michaelis Menten coefficients, Vm values were 100 and 20 mM/s and Km values were 21.27 and 8.33 mM for paranitrophenylphosphate and paranitrophenyl, respectively. Studies of the effect of metal ions on enzyme activity showed both an activating and a deactivating effect. While Cu, Mo, and Mn showed strong inhibitory effects, Na, Ca, and K were the significant activators of acid phosphatase.",Applied biochemistry and biotechnology,"['D000135', 'D002848', 'D004795', 'D005737', 'D006863', 'D007700', 'D008670', 'D013379', 'D013816']","['Acid Phosphatase', 'Chromatography, DEAE-Cellulose', 'Enzyme Stability', 'Garlic', 'Hydrogen-Ion Concentration', 'Kinetics', 'Metals', 'Substrate Specificity', 'Thermodynamics']",Partial purification and kinetic characterization of acid phosphatase from garlic seedling.,"['Q000302', 'Q000379', None, 'Q000201', None, None, 'Q000494', None, None]","['isolation & purification', 'methods', None, 'enzymology', None, None, 'pharmacology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12721447,2003,,,,, +12703902,"The quality of garlic and garlic products is usually related to their alliin content and allicin release potential. Until now no analytical method was able to quantify simultaneously allicin, its direct precursor alliin (S-allyl-L-cysteine sulfoxide), SAC (S-allyl-L-cysteine) as well as various dipeptides that apparently serve as storage compounds in garlic. It is well known that all these intermediates are involved in the allicin biosynthetic pathway. A simple and rapid HPLC method suitable for routine analysis was developed using eluents containing an ion-pairing reagent. Particularly, heptanesulfonate as ion-pairing reagent guarantees a sufficient separation between alliin and the more retained dipeptides at very low pH. Allicin was eluted after 18 min on a 150 x 3 mm column. Synthetic reference compounds were characterized by the same chromatographic method using a diode-array UV detector and an ion trap mass spectrometer (electrospray ionization) in the multiple MS mode. In routine analysis of garlic bulbs, powders and other products, the diode-array detector is sufficient for a relevant quantification. Our method has been used in studies to improve the quality of garlic and its derived products.",Journal of chromatography. A,"['D013437', 'D002851', 'D003545', 'D004151', 'D005737', 'D015394', 'D021241', 'D013056', 'D013441', 'D013455']","['Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Dipeptides', 'Garlic', 'Molecular Structure', 'Spectrometry, Mass, Electrospray Ionization', 'Spectrophotometry, Ultraviolet', 'Sulfinic Acids', 'Sulfur']","High-performance ion-pair chromatography method for simultaneous analysis of alliin, deoxyalliin, allicin and dipeptide precursors in garlic products using multiple mass spectrometry and UV detection.","['Q000737', 'Q000379', 'Q000031', 'Q000032', 'Q000737', None, None, None, 'Q000032', 'Q000737']","['chemistry', 'methods', 'analogs & derivatives', 'analysis', 'chemistry', None, None, None, 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12703902,2003,1.0,1.0,,, +12642382,"(1) Ajoene is a garlic compound with anti-platelet properties and, in addition, was shown to inhibit cholesterol biosynthesis by affecting 3-hydroxy-3-methyl-glutaryl coenzyme A (HMG-CoA) reductase and late enzymatic steps of the mevalonate (MVA) pathway. (2) MVA constitutes the precursor not only of cholesterol, but also of a number of non-sterol isoprenoids, such as farnesyl and geranylgeranyl groups. Covalent attachment of these MVA-derived isoprenoid groups (prenylation) is a required function of several proteins that regulate cell proliferation. We investigated the effect of ajoene on rat aortic smooth muscle cell proliferation as related to protein prenylation. (3) Cell counting, DNA synthesis, and cell cycle analysis showed that ajoene (1-50 micro M) interfered with the progression of the G1 phase of the cell cycle, and inhibited rat SMC proliferation. (4) Similar to the HMG-CoA reductase inhibitor simvastatin, ajoene inhibited cholesterol biosynthesis. However, in contrast to simvastatin, the antiproliferative effect of ajoene was not prevented by the addition of MVA, farnesol (FOH), and geranylgeraniol (GGOH). Labelling of smooth muscle cell cellular proteins with [3H]-FOH and [3H]-GGOH was significantly inhibited by ajoene. (5) In vitro assays for protein farnesyltransferase (PFTase) and protein geranylgeranyltransferase type I (PGGTase-I) confirmed that ajoene inhibits protein prenylation. High performance liquid chromatography (HPLC) and mass spectrometry analyses also demonstrated that ajoene causes a covalent modification of the cysteine SH group of a peptide substrate for protein PGGTase-I. (6) Altogether, our results provide evidence that ajoene interferes with the protein prenylation reaction, an effect that may contribute to its inhibition of SMC proliferation.",British journal of pharmacology,"['D000818', 'D001011', 'D002455', 'D002478', 'D004220', 'D004305', 'D005737', 'D006131', 'D008297', 'D009131', 'D010936', 'D017368', 'D051381', 'D017207']","['Animals', 'Aorta', 'Cell Division', 'Cells, Cultured', 'Disulfides', 'Dose-Response Relationship, Drug', 'Garlic', 'Growth Inhibitors', 'Male', 'Muscle, Smooth, Vascular', 'Plant Extracts', 'Protein Prenylation', 'Rats', 'Rats, Sprague-Dawley']","Ajoene, a garlic compound, inhibits protein prenylation and arterial smooth muscle cell proliferation.","[None, 'Q000166', 'Q000187', None, 'Q000494', None, None, 'Q000494', None, 'Q000166', 'Q000494', 'Q000187', None, None]","[None, 'cytology', 'drug effects', None, 'pharmacology', None, None, 'pharmacology', None, 'cytology', 'pharmacology', 'drug effects', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12642382,2003,0.0,0.0,,, +12595006,"Nitrobenzene (NB), a widely used industrial chemical, is a likely human carcinogen. Many dietary constituents can suppress the DNA-adduction, acting as the inhibitors of cancer. In this study, we investigated the inhibitory effects of vitamin C (VC), vitamin E (VE), tea polyphenols (TP), garlic squeeze, curcumin, and grapestone extract on NB-DNA and NB-hemoglobin (Hb) adductions in mice using an ultrasensitive method of accelerator mass spectrometry (AMS) with 14C-labelled nitrobenzene. All of these dietary constituents showed their inhibitory effects on DNA or Hb adduction. VC, VE, TP and grapestone extract could efficaciously inhibit the adductions by 33-50%, and all of these six agents could inhibit Hb adduction by 30-64%. We also investigated resveratrol, curcumin, VC and VE as inhibitors of NB-DNA adduction in vitro using liquid scintillation counting technique. These agents in the presence of NADPH and S9 components also pronouncedly blocked DNA adduction in a dose-dependent profile. Our study suggests that these seven constituents may interrupt the process of NB-induced chemical carcinogenesis.","Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine","['D000818', 'D000975', 'D002273', 'D004247', 'D018736', 'D019587', 'D006454', 'D008297', 'D051379', 'D009578', 'D012588']","['Animals', 'Antioxidants', 'Carcinogens', 'DNA', 'DNA Adducts', 'Dietary Supplements', 'Hemoglobins', 'Male', 'Mice', 'Nitrobenzenes', 'Scintillation Counting']",Inhibition of nitrobenzene-induced DNA and hemoglobin adductions by dietary constituents.,"[None, 'Q000008', 'Q000633', 'Q000187', None, None, 'Q000187', None, None, 'Q000633', None]","[None, 'administration & dosage', 'toxicity', 'drug effects', None, None, 'drug effects', None, None, 'toxicity', None]",https://www.ncbi.nlm.nih.gov/pubmed/12595006,2003,0.0,0.0,,, +12593760,"1. Diallyl disulphide (DADS), a compound formed from the organosulphur compounds present in garlic, is known for its anticarcinogenic effects in animal models. 2. The aim was to identify and analyse the metabolites produced in vivo after a single oral administration of 200 mg kg(-1) DADS to rats. The organic sulphur metabolites present in the stomach, liver, plasma and urine were measured by gas chromatography coupled with mass spectrometry over 15 days. 3. Data indicate that DADS is absorbed and transformed into allyl mercaptan, allyl methyl sulphide, allyl methyl sulphoxide (AMSO) and allyl methyl sulphone (AMSO(2)), which are detected throughout the excretion period. Overall, the highest amounts of metabolites were measured 48-72h after the DADS administration. AMSO(2) is the most abundant and persistent of these compounds. The levels of all the sulphur compounds rapidly decline within the first week after administration and disappear during the second week. Only AMSO and AMSO(2) are significantly excreted in urine. 4. These potential metabolites are thought to be active in the target tissues. Our data warrant further studies to check this hypothesis.",Xenobiotica; the fate of foreign compounds in biological systems,"['D000284', 'D000498', 'D000818', 'D004220', 'D008401', 'D006801', 'D008297', 'D008698', 'D008956', 'D051381', 'D017208', 'D017550', 'D013440', 'D013441', 'D013997', 'D014018']","['Administration, Oral', 'Allyl Compounds', 'Animals', 'Disulfides', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Male', 'Mesylates', 'Models, Chemical', 'Rats', 'Rats, Wistar', 'Spectroscopy, Fourier Transform Infrared', 'Sulfides', 'Sulfinic Acids', 'Time Factors', 'Tissue Distribution']",In vivo metabolism of diallyl disulphide in the rat: identification of two new metabolites.,"[None, 'Q000378', None, 'Q000378', None, None, None, 'Q000378', None, None, None, None, 'Q000378', 'Q000493', None, None]","[None, 'metabolism', None, 'metabolism', None, None, None, 'metabolism', None, None, None, None, 'metabolism', 'pharmacokinetics', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12593760,2003,0.0,0.0,,, +12567938,"Effective constituents from bulb of Allium stativum were extracted by supercritical-CO2 fluid. These constituents were analyzed by GC-MS. The results showed that oils from SFE-CO2 contained 12 components, two of them were first obtained from the plant.",Zhong yao cai = Zhongyaocai = Journal of Chinese medicinal materials,"['D000490', 'D002245', 'D002849', 'D025924', 'D005737', 'D008401', 'D009822', 'D010946']","['Allium', 'Carbon Dioxide', 'Chromatography, Gas', 'Chromatography, Supercritical Fluid', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Oils, Volatile', 'Plants, Medicinal']",[Supercritical-CO2 fluid extraction of Allium stativum oils].,"['Q000737', None, None, 'Q000379', 'Q000737', None, 'Q000302', 'Q000737']","['chemistry', None, None, 'methods', 'chemistry', None, 'isolation & purification', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12567938,2003,,,,, +12467456,"Helicobacter pylori (Hp) is the bacterium responsible for serious gastric diseases such as ulcers and cancer. The work described here involved the study of the inhibitory power of Allium sativum extracts against the in vitro growth of Hp (Hp ivg). We used purple garlic of the ""Las Pedroñeras"" variety for this study. The effects of two different extraction methods (Soxhlet, stirred tank extractor) and four solvents with different characteristics (water, acetone, ethanol, and hexane) were investigated in terms of the efficiency of the extraction process. Satisfactory results were obtained in most cases in the activity tests, indicating that different extracts gave rise to good inhibitory activity against Hp ivg. The extracts that showed the highest bacteriostatic activities were selected to evaluate the influence of the most important operation variables on the extraction yield: stirring speed, operation time, garlic conditioning, and garlic storage time. The best results were obtained using ethanol and acetone as solvents in a stirred tank. The inhibitory powers of these extracts were compared to those shown by some commercial antibiotics used in the medical treatment of Hp infections. The results of this study show that garlic extracts produce levels of inhibition similar to those of the commercial materials. These extracts were also tested against other common bacteria, and equally satisfactory results were obtained. The research described here represents an important starting point in the fight against and/or prevention of peptic ulcers, as well as other pathologies associated with Hp infections such us gastric cancer. The extracted material can be used by direct application and involves a simple and economical extraction procedure that avoids isolation or purification techniques.",Biotechnology progress,"['D002851', 'D005737', 'D016481', 'D016480', 'D010936', 'D011309', 'D012997', 'D013441']","['Chromatography, High Pressure Liquid', 'Garlic', 'Helicobacter Infections', 'Helicobacter pylori', 'Plant Extracts', 'Preservation, Biological', 'Solvents', 'Sulfinic Acids']",Optimization of Allium sativum solvent extraction for the inhibition of in vitro growth of Helicobacter pylori.,"[None, 'Q000737', 'Q000517', 'Q000187', 'Q000302', None, None, 'Q000032']","[None, 'chemistry', 'prevention & control', 'drug effects', 'isolation & purification', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12467456,2003,1.0,1.0,,, +12463370,"A sensitive method for determining ultratrace volatile Se species produced from Brassica juncea seedlings is described. The use of a new commercially available GC/ ICPMS interface in conjunction with solid-phase micro-extraction is a promising way to perform these studies. The addition of optional gases (O2 and N2) to the argon discharge proved to increase the sensitivity for Se and S as well as for Xe, which as a trace contaminant gas, was used for ICPMS optimization studies. However, the optimization parameters differ when an optional gas is added. In the best conditions, limits of detection ranging from 1 to 10 ppt can be obtained depending on the Se compound and 30 to 300 ppt for the volatile S species. The use of GC/MS with similar sample introduction permits the characterization of several unknown species produced as artifacts from the standards. The method allows the virtually simultaneous monitoring of S and Se species from the headspace of several plants (e.g., onions, garlic, etc.) although the present work is focused on the B. juncea seedlings grown in closed vials and treated with Se. Dimethyl selenide and dimethyl diselenide were detected as the primary volatile Se components in the headspace. Sulfur species also were present as allyl (2-propenyl) isothiocyanate and 3-butenyl isothiocyanate as characterized by GC/MS.",Analytical chemistry,"['D001937', 'D018744', 'D008401', 'D007202', 'D030821', 'D018036', 'D013457']","['Brassica', 'DNA, Plant', 'Gas Chromatography-Mass Spectrometry', 'Indicators and Reagents', 'Plants, Genetically Modified', 'Selenium Compounds', 'Sulfur Compounds']",Simultaneous monitoring of volatile selenium and sulfur species from se accumulating plants (wild type and genetically modified) by GC/MS and GC/ICPMS using solid-phase microextraction for sample introduction.,"['Q000737', 'Q000032', None, None, 'Q000737', 'Q000032', 'Q000032']","['chemistry', 'analysis', None, None, 'chemistry', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12463370,2002,,,,, +12230020,"Two components of garlic, diallyl sulfide (DAS) and diallyl disulfide (DADS), inhibited arylamine N-acetyltransferase (NAT) activity and 2-aminofluorene-DNA adduct in human promyelocytic leukemia cells (HL-60). The NAT activity was measured by high performance liquid chromatography assaying for amounts of N-acetyl-2-aminofluorene (2-AAF) and remaining 2-aminofluorene (2-AF). Cellular cytosols and intact cell suspensions were assayed. The inhibition of NAT activity and 2-AF-DNA adduct formation in human leukemia cells by DAS and DADS were dose-dependent and were directly proportional. The data also indicated that DAS and DADS decrease the apparent values of Km and Vmax from human leukemia cells in both assays. This is the first report of garlic components affecting human leukemia cell NAT activity and 2-AF-DNA adduct formation.",The American journal of Chinese medicine,"['D000498', 'D016588', 'D001191', 'D018736', 'D004220', 'D004305', 'D005449', 'D005737', 'D018922', 'D006801', 'D015473', 'D008517', 'D010938', 'D013440']","['Allyl Compounds', 'Anticarcinogenic Agents', 'Arylamine N-Acetyltransferase', 'DNA Adducts', 'Disulfides', 'Dose-Response Relationship, Drug', 'Fluorenes', 'Garlic', 'HL-60 Cells', 'Humans', 'Leukemia, Promyelocytic, Acute', 'Phytotherapy', 'Plant Oils', 'Sulfides']",Effects of garlic components diallyl sulfide and diallyl disulfide on arylamine N-acetyltransferase activity and 2-aminofluorene-DNA adducts in human promyelocytic leukemia cells.,"['Q000008', 'Q000008', 'Q000187', 'Q000737', 'Q000008', None, 'Q000737', None, 'Q000187', None, 'Q000517', None, 'Q000008', 'Q000008']","['administration & dosage', 'administration & dosage', 'drug effects', 'chemistry', 'administration & dosage', None, 'chemistry', None, 'drug effects', None, 'prevention & control', None, 'administration & dosage', 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/12230020,2003,,,,no pdf access , +12184391,"Caribbean sponges of the genus Ircinia contain high concentrations of linear furanosesterterpene tetronic acids (FTAs) and produce and exude low-molecular-weight volatile compounds (e.g., dimethyl sulfide, methyl isocyanide, methyl isothiocyanate) that give these sponges their characteristic unpleasant garlic odor. It has recently been suggested that FTAs are unlikely to function as antipredatory chemical defenses, and this function may instead be attributed to bioactive volatiles. We tested crude organic extracts and purified fractions isolated from Ircinia campana, I. felix, and I. strobilina at naturally occurring concentrations in laboratory and field feeding assays to determine their palatability to generalist fish predators. We also used a qualitative technique to test the crude volatile fraction from I. felix and I. strobilina and dimethylsulfide in laboratory feeding assays. Crude organic extracts of all three species deterred feeding of fishes in both aquarium and field experiments. Bioassay-directed fractionation resulted in the isolation of the FTA fraction as the sole active fraction of the nonvolatile crude extract for each species, and further assays of subfractions suggested that feeding deterrent activity is shared by the FTAs. FTAs deterred fish feeding in aquarium assays at concentrations as low as 0.5 mg/ml (fraction B, variabilin), while the natural concentrations of combined FTA fractions were > 5.0 mg/ml for all three species. In contrast, natural mixtures of volatiles transferred from sponge tissue to food pellets and pure dimethylsulfide incorporated into food pellets were readily eaten by fish in aquarium assays. Although FTAs may play other ecological roles in Ircinia spp., these compounds are effective as defenses against potential predatory fishes. Volatile compounds may serve other defensive functions (e.g., antimicrobial, antifouling) but do not appear to provide a defense against fish predators.",Journal of chemical ecology,"['D000818', 'D001685', 'D002851', 'D002855', 'D005399', 'D009812', 'D011161', 'D011235', 'D013045']","['Animals', 'Biological Factors', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Fishes', 'Odorants', 'Porifera', 'Predatory Behavior', 'Species Specificity']",Does the odor from sponges of the genus Ircinia protect them from fish predators?,"[None, None, None, None, 'Q000502', None, 'Q000737', None, None]","[None, None, None, None, 'physiology', None, 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12184391,2003,,,,, +12137782,"Allicin (diallylthiosulfinate) is the best known active compound of garlic. It is generated upon the interaction of the nonprotein amino acid alliin with the enzyme alliinase (alliin lyase, EC 4.4.1.4). Previously, we described a simple spectrophotometric assay for the determination of allicin and alliinase activity, based on the reaction between 2-nitro-5-thiobenzoate (NTB) and allicin. This reagent is not commercially available and must be synthesized. In this paper we describe the quantitative analysis of alliin and allicin, as well as of alliinase activity with 4-mercaptopyridine (4-MP), a commercially available chromogenic thiol. The assay is based on the reaction of 4-MP (lambda(max)=324nm) with the activated disulfide bond of thiosulfinates -S(O)-S-, forming the mixed disulfide, 4-allylmercaptothiopyridine, which has no absorbance at this region. The structure of 4-allylmercaptothiopyridine was confirmed by mass spectrometry. The method was used for the determination of alliin and allicin concentrations in their pure form as well as of alliin and total thiosulfinates concentrations in crude garlic preparations and garlic-derived products, at micromolar concentrations. The 4-MP assay is an easy, sensitive, fast, noncostly, and highly efficient throughput assay of allicin, alliin, and alliinase in garlic preparations.",Analytical biochemistry,"['D013437', 'D002851', 'D003545', 'D005737', 'D013058', 'D009579', 'D011725', 'D013056', 'D013438', 'D013441']","['Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Mass Spectrometry', 'Nitrobenzoates', 'Pyridines', 'Spectrophotometry, Ultraviolet', 'Sulfhydryl Compounds', 'Sulfinic Acids']","A spectrophotometric assay for allicin, alliin, and alliinase (alliin lyase) with a chromogenic thiol: reaction of 4-mercaptopyridine with thiosulfinates.","['Q000032', None, 'Q000031', 'Q000737', None, 'Q000737', 'Q000737', 'Q000379', None, 'Q000032']","['analysis', None, 'analogs & derivatives', 'chemistry', None, 'chemistry', 'chemistry', 'methods', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/12137782,2003,1.0,1.0,,, +12086179,"Lactobacillus pentosus B235, which was isolated as part of the dominant microflora from a garlic containing fermented fish product, was grown in a chemically defined medium with inulin as the sole carbohydrate source. An extracellular fructan beta-fructosidase was purified to homogeneity from the bacterial supernatant by ultrafiltration, anion exchange chromatography and hydrophobic interaction chromatography. The molecular weight of the enzyme was estimated to be approximately 126 kDa by gel filtration and by SDS-PAGE. The purified enzyme had the highest activity for levan (a beta(2-->6)-linked fructan), but also hydrolysed garlic extract, (a beta(2-->1)-linked fructan with beta(2-->6)-linked fructosyl sidechains), 1,1,1-kestose, 1,1-kestose, 1-kestose, inulin (beta(2-->1)-linked fructans) and sucrose at 60, 45, 39, 12, 9 and 3%, respectively, of the activity observed for levan. Melezitose, raffinose and stachyose were not hydrolysed by the enzyme. The fructan beta-fructosidase was inhibited by p-chloromercuribenzoate, EDTA, Fe2+, Cu2+, Zn2+ and Co2+, whereas Mn2+ and Cu2+ had no effect. The sequence of the first 20 N-terminal amino acids was: Ala-Thr-Ser-Ala-Ser-Ser-Ser-Gln-Ile-Ser-Gln-Asn-Asn-Thr-Gln-Thr-Ser-Asp-Val-Val. The enzyme had temperature and pH optima at 25 degrees C and 5.5, respectively. At concentrations of up to 12% NaCl no adverse effect on the enzyme activity was observed.",Systematic and applied microbiology,"['D000595', 'D000818', 'D001426', 'D002847', 'D003470', 'D005285', 'D005396', 'D005737', 'D006026', 'D006863', 'D007778', 'D012680', 'D013696']","['Amino Acid Sequence', 'Animals', 'Bacterial Proteins', 'Chromatography, Agarose', 'Culture Media', 'Fermentation', 'Fish Products', 'Garlic', 'Glycoside Hydrolases', 'Hydrogen-Ion Concentration', 'Lactobacillus', 'Sensitivity and Specificity', 'Temperature']",Purification and characterisation of an extracellular fructan beta-fructosidase from a Lactobacillus pentosus strain isolated from fermented fish.,"[None, None, 'Q000096', 'Q000379', None, None, 'Q000382', 'Q000737', 'Q000032', None, 'Q000145', None, None]","[None, None, 'biosynthesis', 'methods', None, None, 'microbiology', 'chemistry', 'analysis', None, 'classification', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/12086179,2003,,,,, +12005268,"A novel antifungal protein, designated allivin, was isolated from bulbs of the round-cloved garlic Allium sativum var. round clove with a procedure involving ion exchange chromatography on DEAE-cellulose, affinity chromatography on Affi-gel blue gel, ion exchange chromatography on CM-Sepharose and FPLC-gel filtration on Superdex 75. Allivin possessed an N-terminal sequence demonstrating very little similarity to sequences of Allium sativum chitinases and ribosome inactivating proteins. Allivin exhibited a molecular weight of 13 kDa in gel filtration and SDS-polyacrylamide gel electrophoresis. It displayed antifungal activity against Botrytis cinerea, Mycosphaerella arachidicola and Physalospora piricola. It inhibited translation in a cell-free rabbit reticulocyte system with an IC50 of 1.6 microM.",Life sciences,"['D000595', 'D000935', 'D005737', 'D008969', 'D010940']","['Amino Acid Sequence', 'Antifungal Agents', 'Garlic', 'Molecular Sequence Data', 'Plant Proteins']","Purification of allivin, a novel antifungal protein from bulbs of the round-cloved garlic.","[None, 'Q000302', 'Q000737', None, 'Q000737']","[None, 'isolation & purification', 'chemistry', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/12005268,2002,0.0,0.0,,, +11985847,"Identification and isolation of (R(S)R(C))-S-(methylthiomethyl)cysteine-4-oxide from rhizomes of Tulbaghia violacea Harv. is reported. The structure and absolute configuration of the amino acid have been determined by NMR, MALDI-HRMS, IR, and CD spectroscopy. Its content varied in different parts of the plant (rhizomes, leaves, and stems) between 0.12 and 0.24 mg g(-1) fr. wt, being almost equal in the stems and rhizomes. In addition, S-methyl- and S-ethylcysteine derivatives have been detected in minute amounts (<3 microg g(-1) fr. wt) in all parts of the plant. The enzymatic cleavage of the amino acid and subsequent odor formation are discussed. 2,4,5,7-Tetrathiaoctane-4-oxide, the primary breakdown product, has been detected and isolated for the first time.",Phytochemistry,"['D000490', 'D000596', 'D002852', 'D008401', 'D015394', 'D009812', 'D010087', 'D018514', 'D013057']","['Allium', 'Amino Acids', 'Chromatography, Ion Exchange', 'Gas Chromatography-Mass Spectrometry', 'Molecular Structure', 'Odorants', 'Oxides', 'Plant Structures', 'Spectrum Analysis']",The amino acid precursors and odor formation in society garlic (Tulbaghia violacea Harv.).,"['Q000737', 'Q000737', None, None, None, 'Q000032', 'Q000378', 'Q000737', None]","['chemistry', 'chemistry', None, None, None, 'analysis', 'metabolism', 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/11985847,2002,0.0,0.0,,, +11982398,"Fusarium proliferatum is one of a group of fungal species that produce fumonisins and is considered to be a pathogen of many economically important plants. The occurrence of fumonisin B(1) (FB(1)) in F. proliferatum-infected asparagus spears from Germany was investigated using a liquid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS) method with isotopically labeled fumonisin FB(1)-d(6) as internal standard. FB(1) was detected in 9 of the 10 samples in amounts ranging from 36.4 to 4513.7 ng/g (based on dry weight). Furthermore, the capability of producing FB(1) by the fungus in garlic bulbs was investigated. Therefore, garlic was cultured in F. proliferatum-contaminated soil, and the bulbs were screened for infection with F. proliferatum and for the occurrence of fumonisins by LC-MS. F. proliferatum was detectable in the garlic tissue, and all samples contained FB(1) (26.0-94.6 ng/g). This is the first report of the natural occurrence of FB(1) in German asparagus spears, and these findings suggest a potential for natural contamination of garlic bulbs with fumonisins.",Journal of agricultural and food chemistry,"['D027761', 'D002264', 'D002853', 'D005506', 'D037341', 'D005670', 'D005737', 'D005858', 'D021241']","['Asparagus Plant', 'Carboxylic Acids', 'Chromatography, Liquid', 'Food Contamination', 'Fumonisins', 'Fusarium', 'Garlic', 'Germany', 'Spectrometry, Mass, Electrospray Ionization']",Analysis of fumonisin B(1) in Fusarium proliferatum-infected asparagus spears and garlic bulbs from Germany by liquid chromatography-electrospray ionization mass spectrometry.,"['Q000737', 'Q000032', None, None, None, 'Q000378', 'Q000737', None, None]","['chemistry', 'analysis', None, None, None, 'metabolism', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11982398,2002,0.0,0.0,,testing for mold, +11911208,"Extremely high accumulation of allxiin, a phytoalexin derived from garlic, was observed in necrotic tissue areas after long-term storage. The allixin produced recrystallized on the surface of the garlic clove. The amount of allixin produced in raw garlic with necrotic tissue areas was 1400 ng/mg wet garlic, which exceeds the minimum exhibitory concentration of allixin. After approximately 2 years of storage, amount of allixin accumulated reached slightly less than 1% of the dry weight of garlic cloves.",Chemical & pharmaceutical bulletin,"['D002851', 'D005519', 'D005737', 'D009682', 'D011753']","['Chromatography, High Pressure Liquid', 'Food Preservation', 'Garlic', 'Magnetic Resonance Spectroscopy', 'Pyrones']",Allixin accumulation with long-term storage of garlic.,"[None, None, 'Q000378', None, 'Q000737']","[None, None, 'metabolism', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11911208,2002,0.0,0.0,,, +11911198,"The pharmacokinetic behavior of allixin (3-hydroxy-5-methoxy-6-methyl-2-penthyl-4H-pyran-4-one) was investigated in an experimental animal, mice. Allixin was administered using an inclusion compound because the solubility of allixin in aqueous solution is very low. The allixin content in serum and in the organs of administered animals was analyzed by liquid chromatography (LC)-MS. Most of the administered allixin disappeared within 2 h, and the bioavailability of allixin was estimated to be 31% by obtained area under the blood concentration-time curve (AUC). The metabolites of allixin were studied using the metabolic enzyme fraction of liver and liver homogenate. Several new peaks corresponding to allixin metabolites were observed in the HPLC chromatoprofile. The chemical structure of the metabolites was investigated using LC-MS and NMR. Three of them were identified as allixin metabolites having a hydroxylated pentyl group.",Chemical & pharmaceutical bulletin,"['D000818', 'D019540', 'D001682', 'D001711', 'D002851', 'D005737', 'D008099', 'D009682', 'D051379', 'D011753', 'D051381', 'D017208', 'D014018']","['Animals', 'Area Under Curve', 'Biological Availability', 'Biotransformation', 'Chromatography, High Pressure Liquid', 'Garlic', 'Liver', 'Magnetic Resonance Spectroscopy', 'Mice', 'Pyrones', 'Rats', 'Rats, Wistar', 'Tissue Distribution']","Pharmacokinetic study of allixin, a phytoalexin produced by garlic.","[None, None, None, None, None, 'Q000737', 'Q000378', None, None, 'Q000097', None, None, None]","[None, None, None, None, None, 'chemistry', 'metabolism', None, None, 'blood', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11911198,2002,0.0,0.0,,, +11902974,"Aminoethylcysteine ketimine decarboxylated dimer (simply named dimer) is a natural sulfur-containing tricyclic compound detected, until now, in human urine, bovine cerebellum, and human plasma. Recently, the antioxidant properties of this compound have been demonstrated. In this investigation, the presence of aminoethylcysteine ketimine decarboxylated dimer was identified in garlic, spinach, tomato, asparagus, aubergine, onion, pepper, and courgette. Identification of this compound in dietary vegetables was performed using gas chromatography, high-performance liquid chromatography, and gas chromatography-mass spectrometry. Results from GC analysis range in the order of 10(-4) micromol of dimer/g for all the tested vegetables. These results and the lack of a demonstrated biosynthetic pathway in humans might account for a dietary supply of this molecule.",Journal of agricultural and food chemistry,"['D000975', 'D002849', 'D002851', 'D004032', 'D008401', 'D006801', 'D009025', 'D010936', 'D014675']","['Antioxidants', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Diet', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Morpholines', 'Plant Extracts', 'Vegetables']","Identification of aminoethylcysteine ketimine decarboxylated dimer, a natural antioxidant, in dietary vegetables.","['Q000032', None, None, None, None, None, 'Q000032', 'Q000737', 'Q000737']","['analysis', None, None, None, None, None, 'analysis', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11902974,2002,0.0,0.0,,no mol foundecular weight, +11860155,"From the dried bulbs of the lily (Lilium brownii), a protein with strong antifungal and mitogenic activities was isolated. It also exhibited an inhibitory action on the activity of HIV-1 reverse transcriptase. The protein was single-chained and possessed a molecular weight of 14.4 kDa and an N-terminal sequence distinct from chitinases and antimicrobial proteins of garlic, leek and onion which belong to a family closely related to lily. However, there was a small degree of resemblance to cyclophilins and a considerable extent of identity to the 6.5 kDa arginine/glutamate-rich polypeptide from Luffa cylindrica seeds. A nearly homogeneous preparation was obtained after the extract was fractionated on DEAE-cellulose and Affi-gel Blue gel since subsequent chromatography on Mono S and Superdex 75 both yielded a single peak.",Life sciences,"['D000595', 'D000818', 'D000935', 'D002846', 'D004365', 'D004591', 'D005658', 'D054303', 'D006384', 'D027762', 'D008516', 'D051379', 'D008810', 'D008826', 'D008934', 'D008969', 'D010940', 'D018749', 'D012343']","['Amino Acid Sequence', 'Animals', 'Antifungal Agents', 'Chromatography, Affinity', 'Drugs, Chinese Herbal', 'Electrophoresis, Polyacrylamide Gel', 'Fungi', 'HIV Reverse Transcriptase', 'Hemagglutination', 'Lilium', 'Medicine, Chinese Traditional', 'Mice', 'Mice, Inbred C57BL', 'Microbial Sensitivity Tests', 'Mitogens', 'Molecular Sequence Data', 'Plant Proteins', 'RNA, Plant', 'RNA, Transfer']","Isolation of lilin, a novel arginine- and glutamate-rich protein with potent antifungal and mitogenic activities from lily bulbs.","[None, None, 'Q000302', None, 'Q000302', None, 'Q000187', 'Q000037', 'Q000187', 'Q000737', None, None, None, None, 'Q000302', None, 'Q000302', 'Q000037', 'Q000187']","[None, None, 'isolation & purification', None, 'isolation & purification', None, 'drug effects', 'antagonists & inhibitors', 'drug effects', 'chemistry', None, None, None, None, 'isolation & purification', None, 'isolation & purification', 'antagonists & inhibitors', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/11860155,2002,0.0,0.0,,, +11853480,"Five compounds oxidizing canine erythrocytes were isolated from an aqueous ethanol garlic extract by silica gel column chromatography and preparative thin-layer chromatography. On the basis of nuclear magnetic resonance, infrared spectroscopy, and mass spectrometry, they were identified as three known compounds: bis-2-propenyl trisulfide (1), bis-2-propenyl tetrasulfide (2), and bis-2-propenyl pentasulfide (3) as well as two novel compounds, bis-2-propenyl thiosulfonate (4) and trans-sulfuric acid allyl ester 3-allylsulfanyl-allyl ester (5). A mixture of compounds 1-3 and compounds 4 and 5 induced methemoglobin formation in canine erythrocyte suspension in vitro resulting in the oxidation of canine erythrocytes. These groups of characteristic organosulfur compounds contained in garlic probably contribute to oxidations in blood. The constituents of garlic have the potential to oxidize erythrocytes and hemoglobin, suggesting that foods containing quantities of garlic should be avoided for feeding dogs.",Journal of agricultural and food chemistry,"['D000818', 'D002855', 'D004285', 'D004912', 'D005737', 'D010084', 'D010936', 'D013440', 'D013451', 'D013886']","['Animals', 'Chromatography, Thin Layer', 'Dogs', 'Erythrocytes', 'Garlic', 'Oxidation-Reduction', 'Plant Extracts', 'Sulfides', 'Sulfonic Acids', 'Thiosulfonic Acids']",Isolation and identification of organosulfur compounds oxidizing canine erythrocytes from garlic (Allium sativum).,"[None, 'Q000379', None, 'Q000378', 'Q000009', None, 'Q000378', 'Q000097', 'Q000097', 'Q000097']","[None, 'methods', None, 'metabolism', 'adverse effects', None, 'metabolism', 'blood', 'blood', 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/11853480,2002,0.0,0.0,,, +11767087,"Allixin, a phytoalexin isolated from garlic, was induced by irradiating fresh garlic cloves with sunlight or UV light. Induced allixin was analyzed by HPLC, and the accumulated amounts of allixin were 3.1-6.3 microg/g under experimental conditions.",Chemical & pharmaceutical bulletin,"['D000972', 'D002851', 'D005737', 'D008027', 'D011753', 'D013472', 'D014466']","['Antineoplastic Agents, Phytogenic', 'Chromatography, High Pressure Liquid', 'Garlic', 'Light', 'Pyrones', 'Sunlight', 'Ultraviolet Rays']",Allixin induction and accumulation by light irradiation.,"['Q000378', None, 'Q000378', None, 'Q000378', None, None]","['metabolism', None, 'metabolism', None, 'metabolism', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11767087,2002,1.0,1.0,,, +11520428,"Allium vegetables (onions, leeks, chives) and in particular garlic have been claimed to have health-promoting potential. This study was conducted to get insight into the perspectives for monitoring the intake of garlic by a biomarker approach. Chemically, the biomarker results from exposure to gamma-glutamyl-S-allyl-l-cysteine, which is first hydrolysed by gamma-glutamine-transpeptidase resulting in the formation of S-allyl-l-cysteine. The latter compound is subsequently N-acetylated by N-acetyltransferase into S-allyl-mercapturic acid (ALMA) and excreted into urine. The mercapturic acid was measured in urine using gaschromatography with mass spectrometry. Thus the intake of garlic was determined to check the compliance of garlic intake in a placebo-controlled intervention study. Results indicate that S-allyl-mercapturic acid could be detected in 15 out of 16 urine samples of garlic supplement takers, indicating good compliance. In addition, the intake of garlic was also monitored in a cross-section study of vegans versus controls in Finland, in which no differences in garlic consumption nor in ALMA output were recorded between vegans and controls. These data indicate good possibilities for further studies in the field of biomarkers to investigate the putative chemopreventive effects of garlic and garlic-containing products.",The British journal of nutrition,"['D000111', 'D000975', 'D015415', 'D016022', 'D014676', 'D004435', 'D005260', 'D005737', 'D008401', 'D006801', 'D008297', 'D008875', 'D010349', 'D010946', 'D012680']","['Acetylcysteine', 'Antioxidants', 'Biomarkers', 'Case-Control Studies', 'Diet, Vegetarian', 'Eating', 'Female', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Male', 'Middle Aged', 'Patient Compliance', 'Plants, Medicinal', 'Sensitivity and Specificity']",Biomonitoring the intake of garlic via urinary excretion of allyl mercapturic acid.,"['Q000652', 'Q000008', 'Q000652', None, None, None, None, None, None, None, None, None, None, None, None]","['urine', 'administration & dosage', 'urine', None, None, None, None, None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11520428,2001,0.0,0.0,,, +11486375,"When performing multiresidue analysis of pesticides, the recovery of thiometon was less than 20% from carrots and eggplants, but about 100% from garlic chives and welsh onions. The recovery of thiometon was found to depend on the lot of ethyl acetate. A 2-year-old lot of ethyl acetate caused degradation of thiometon, but a fresh lot of ethyl acetate did not. Analysis showed that ethyl acetate stored for 2 years contained about 5 microL/mL of acetaldehyde. Thiometon was also degraded by acetone or acetonitrile, when acetaldehyde was added to them, in the same manner as by aged ethyl acetate. The fact that the recovery of thiometon from welsh onions was about 100% indicated that some of the mercaptans in allium vegetables may prevent thiometon degradation. Mercaptans such as L-cysteine and 3-mercaptoproionic acid were confirmed to prevent the degradation of thiometon and disulfoton. These findings show that mercaptans may be useful additives for analyzing thiometon and disulfoton.",Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan,"['D000085', 'D005506', 'D008401', 'D007306', 'D063086', 'D014675']","['Acetates', 'Food Contamination', 'Gas Chromatography-Mass Spectrometry', 'Insecticides', 'Organothiophosphates', 'Vegetables']",[Degradation of thiometon in ethyl acetate].,"[None, 'Q000032', None, 'Q000032', 'Q000032', 'Q000737']","[None, 'analysis', None, 'analysis', 'analysis', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11486375,2001,,,,, +11410016,"Studies were conducted on the flavonoids (myricetin, quercetin, kaempferol, luteolin, and apigenin) contents of 62 edible tropical plants. The highest total flavonoids content was in onion leaves (1497.5 mg/kg quercetin, 391.0 mg/kg luteolin, and 832.0 mg/kg kaempferol), followed by Semambu leaves (2041.0 mg/kg), bird chili (1663.0 mg/kg), black tea (1491.0 mg/kg), papaya shoots (1264.0 mg/kg), and guava (1128.5 mg/kg). The major flavonoid in these plant extracts is quercetin, followed by myricetin and kaempferol. Luteolin could be detected only in broccoli (74.5 mg/kg dry weight), green chili (33.0 mg/kg), bird chili (1035.0 mg/kg), onion leaves (391.0 mg/kg), belimbi fruit (202.0 mg/kg), belimbi leaves (464.5 mg/kg), French bean (11.0 mg/kg), carrot (37.5 mg/kg), white radish (9.0 mg/kg), local celery (80.5 mg/kg), limau purut leaves (30.5 mg/kg), and dried asam gelugur (107.5 mg/kg). Apigenin was found only in Chinese cabbage (187.0 mg/kg), bell pepper (272.0 mg/kg), garlic (217.0 mg/kg), belimbi fruit (458.0 mg/kg), French peas (176.0 mg/kg), snake gourd (42.4 mg/kg), guava (579.0 mg/kg), wolfberry leaves (547.0 mg/kg), local celery (338.5 mg/kg), daun turi (39.5 mg/kg), and kadok (34.5 mg/kg). In vegetables, quercetin glycosides predominate, but glycosides of kaempferol, luteolin, and apigenin are also present. Fruits contain almost exclusively quercetin glycosides, whereas kaempferol and myricetin glycosides are found only in trace quantities.",Journal of agricultural and food chemistry,"['D002851', 'D005419', 'D044948', 'D010936', 'D010945']","['Chromatography, High Pressure Liquid', 'Flavonoids', 'Flavonols', 'Plant Extracts', 'Plants, Edible']","Flavonoid (myricetin, quercetin, kaempferol, luteolin, and apigenin) content of edible tropical plants.","['Q000379', 'Q000032', None, 'Q000737', 'Q000737']","['methods', 'analysis', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11410016,2001,1.0,3.0,,, +11401577,"Allium sativum agglutinin (ASAI) is a heterodimeric mannose-specific bulb lectin possessing two polypeptide chains of molecular mass 11.5 and 12.5 kDa. The thermal unfolding of ASAI, characterized by differential scanning calorimetry and circular dichroism, shows it to be highly reversible and can be defined as a two-state process in which the folded dimer is converted directly to the unfolded monomers (A2 if 2U). Its conformational stability has been determined as a function of temperature, GdnCl concentration, and pH using a combination of thermal and isothermal GdnCl-induced unfolding monitored by DSC, far-UV CD, and fluorescence, respectively. Analyses of these data yielded the heat capacity change upon unfolding (DeltaC(p) and also the temperature dependence of the thermodynamic parameters, namely, DeltaG, DeltaH, and DeltaS. The fit of the stability curve to the modified Gibbs-Helmholtz equation provides an estimate of the thermodynamic parameters DeltaH(g), DeltaS(g), and DeltaC(p) as 174.1 kcal x mol(-1), 0.512 kcal x mol(-1) x K(-1), and 3.41 kcal x mol(-1) x K(-1), respectively, at T(g) = 339.4 K. Also, the free energy of unfolding, DeltaG(s), at its temperature of maximum stability (T(s) = 293 K) is 13.13 kcal x mol(-1). Unlike most oligomeric proteins studied so far, the lectin shows excellent agreement between the experimentally determined DeltaC(p) (3.2 +/- 0.28 kcal x mol(-1) x K(-1)) and those evaluated from a calculation of its accessible surface area. This in turn suggests that the protein attains a completely unfolded state irrespective of the method of denaturation. The absence of any folding intermediates suggests the quaternary interactions to be the major contributor to the conformational stability of the protein, which correlates well with its X-ray structure. The small DeltaC(p) for the unfolding of ASAI reflects a relatively small, buried hydrophobic core in the folded dimeric protein.",Biochemistry,"['D000373', 'D002151', 'D002352', 'D002942', 'D037222', 'D018548', 'D019281', 'D005737', 'D037102', 'D008351', 'D037241', 'D008956', 'D037121', 'D010940', 'D018517', 'D010946', 'D011489', 'D017510', 'D017433', 'D013050', 'D013816']","['Agglutinins', 'Calorimetry', 'Carrier Proteins', 'Circular Dichroism', 'Collectins', 'Cotyledon', 'Dimerization', 'Garlic', 'Lectins', 'Mannans', 'Mannose-Binding Lectins', 'Models, Chemical', 'Plant Lectins', 'Plant Proteins', 'Plant Roots', 'Plants, Medicinal', 'Protein Denaturation', 'Protein Folding', 'Protein Structure, Secondary', 'Spectrometry, Fluorescence', 'Thermodynamics']",The reversible two-state unfolding of a monocot mannose-binding lectin from garlic bulbs reveals the dominant role of the dimeric interface in its stabilization.,"['Q000737', None, 'Q000737', None, None, None, None, 'Q000737', 'Q000378', 'Q000378', None, None, None, None, 'Q000737', None, None, None, None, None, None]","['chemistry', None, 'chemistry', None, None, None, None, 'chemistry', 'metabolism', 'metabolism', None, None, None, None, 'chemistry', None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11401577,2001,0.0,0.0,,, +11387870,"The aim of this work was to study the physiological mechanisms of dormancy and sprouting during post-harvest of garlic (Allium sativum L.) microbulblets produced by meristem culture of garlic seed cloves. The morphological changes occurring in garlic microbulblets were assessed from harvest till sprouting in relation with peroxidase activity and levels of gibberellins. Also the effect of a cold treatment (30 days at 4 degrees C) given 30 days after harvest was studied. The results showed that during the state of dormancy in garlic microbulblets formation of the leaf primordia and vascular differentiation of the storage leaf occurred, while increases of peroxidase activity and low levels of GA3 (the only active gibberellin identified) were found. At the end of dormancy the sprouting channel was formed, vascular differentiation established, and peaks of soluble peroxidase activity as well as of GA3 were observed. At day 90 post-harvest, garlic microbulblets showed physiologically mature and able to sprout. Further on, bud expansion and decrease of GA3 levels characterized sprouting of the microbulblets. The cold treatment enhanced GA3 levels and anticipated the sprouting process.",Biocell : official journal of the Sociedades Latinoamericanas de Microscopia Electronica ... et. al,"['D002454', 'D002478', 'D005737', 'D008401', 'D005875', 'D010544', 'D018515', 'D018514', 'D010946']","['Cell Differentiation', 'Cells, Cultured', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Gibberellins', 'Peroxidases', 'Plant Leaves', 'Plant Structures', 'Plants, Medicinal']",Morphological changes in garlic (Allium sativum L.) microbulblets during dormancy and sprouting as related to peroxidase activity and gibberellin A3 content.,"[None, None, 'Q000166', None, 'Q000378', 'Q000378', 'Q000166', 'Q000166', None]","[None, None, 'cytology', None, 'metabolism', 'metabolism', 'cytology', 'cytology', None]",https://www.ncbi.nlm.nih.gov/pubmed/11387870,2001,,,,, +11355006,"Fatty acids are known as modulators of the vasoactive properties of the vessel wall and can influence the physical and functional properties of cell membrane. The membrane-bound enzyme Na,K-ATPase plays a central role in endothelial function such as vasoconstriction. In a previous study, we have shown that omega3 fatty acids inhibited Na,K-ATPase activity in human endothelial cells. As Mediterranean diet is known to protect from cardiovascular diseases, we have investigated the effects of Omegacoeur, a Mediterranean nutritional complement consisting of omega3, omega6, omega9 fatty acids, garlic and basil, on Na,K-ATPase activity in human endothelial cells (HUVECs). Cells were incubated for 18 hr with pure lecithin liposomes or Omegacoeur-enriched emulsions (4 mg lecithin/ml). Na,K-ATPase and 5'-nucleotidase activities were determined using coupled assay methods on microsomal fractions obtained from HUVECs. Cell fatty acid composition was evaluated by gas chromatography after extraction of lipids and fatty acids methylation. The results showed that Omegacoeur (0.1 mM) increased Na,K-ATPase activity by 40% without changes in 5'-nucleotidase activity. Cells incubated with Omegacoeur preferentially incorporated linoleic acid. Therefore, linoleic acid or others constituents of Omegacoeur could be responsible of the stimulation of the Na,K-ATPase activity that might be related to changes in endothelial membrane fluidity.","Cellular and molecular biology (Noisy-le-Grand, France)","['D015720', 'D002458', 'D002478', 'D016895', 'D019587', 'D004730', 'D005227', 'D006801', 'D008081', 'D019083', 'D008861', 'D010713', 'D000254']","[""5'-Nucleotidase"", 'Cell Fractionation', 'Cells, Cultured', 'Culture Media, Serum-Free', 'Dietary Supplements', 'Endothelium, Vascular', 'Fatty Acids', 'Humans', 'Liposomes', 'Mediterranean Region', 'Microsomes', 'Phosphatidylcholines', 'Sodium-Potassium-Exchanging ATPase']","Omegacoeur, a Mediterranean nutritional complement, stimulates Na,K-ATPase activity in human endothelial cells.","['Q000378', None, None, None, None, 'Q000166', 'Q000378', None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000378']","['metabolism', None, None, None, None, 'cytology', 'metabolism', None, 'chemistry', None, 'chemistry', 'chemistry', 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/11355006,2001,,,,, +11238799,"Aged garlic extract (AGE) has been shown to have antioxidant activity. The organosulfur compounds, S-allyl-L-cysteine and S-allylmercapto-L-cysteine, are responsible, at least in part, for the antioxidant activity of AGE. To identify major active components, we fractionated AGE, using hydrogen peroxide scavenging activity as an antioxidative index. Strong activity in the amino acid fraction was found and the major active compound was identified as N alpha-(1-deoxy-D-fructos-1-yl)-L-arginine (Fru-Arg). Antioxidant activity of Fru-Arg was comparable to that of ascorbic acid, scavenging hydrogen peroxide completely at 50 micromol/L and 37% at 10 micromol/L. Quantitative analysis using the established HPLC system revealed that AGE contained 2.1-2.4 mmol/L of Fru-Arg, but none was detected in either raw or heated garlic juice. Furthermore, it was shown that a minimum of 4 mo aging incubation was required for Fru-Arg to be generated. These findings indicate that the aging process is critical for the production of the antioxidant compound, Fru-Arg. These results may explain some of the variation in benefits among different commercially available garlic preparations.",The Journal of nutrition,"['D000975', 'D001120', 'D002851', 'D016166', 'D005737', 'D006861', 'D015416', 'D009005', 'D010936', 'D010946', 'D013997']","['Antioxidants', 'Arginine', 'Chromatography, High Pressure Liquid', 'Free Radical Scavengers', 'Garlic', 'Hydrogen Peroxide', 'Maillard Reaction', 'Monosaccharides', 'Plant Extracts', 'Plants, Medicinal', 'Time Factors']","N alpha-(1-deoxy-D-fructos-1-yl)-L-arginine, an antioxidant compound identified in aged garlic extract.","['Q000032', 'Q000031', None, None, 'Q000737', 'Q000378', None, 'Q000032', 'Q000032', None, None]","['analysis', 'analogs & derivatives', None, None, 'chemistry', 'metabolism', None, 'analysis', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/11238799,2001,1.0,1.0,,, +11238798,"Various components of garlic and aged garlic extract, including allicin, S-allylcysteine (SAC) and volatile metabolites of allicin were determined in breath, plasma and simulated gastric fluids by HPLC, gas chromatography (GC) or HPLC- and GC-mass spectrometry (MS). Data indicate that allicin decomposes in stomach acid to release allyl sulfides, disulfides and other volatiles that are postulated to be metabolized by glutathione and/or S-adenosylmethionine to form allyl methyl sulfide. SAC can be absorbed by the body and can be determined in plasma by HPLC or HPLC-MS using atmospheric pressure chemical ionization (APCI)-MS.",The Journal of nutrition,"['D000498', 'D001944', 'D002849', 'D002851', 'D003545', 'D005737', 'D008401', 'D005766', 'D005978', 'D006801', 'D010936', 'D010946', 'D012436', 'D013440', 'D013441']","['Allyl Compounds', 'Breath Tests', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Gastrointestinal Contents', 'Glutathione', 'Humans', 'Plant Extracts', 'Plants, Medicinal', 'S-Adenosylmethionine', 'Sulfides', 'Sulfinic Acids']","Determination of allicin, S-allylcysteine and volatile metabolites of garlic in breath, plasma or simulated gastric fluids.","['Q000378', None, 'Q000379', 'Q000379', 'Q000031', 'Q000737', 'Q000379', 'Q000737', 'Q000378', None, 'Q000737', None, 'Q000378', 'Q000378', 'Q000032']","['metabolism', None, 'methods', 'methods', 'analogs & derivatives', 'chemistry', 'methods', 'chemistry', 'metabolism', None, 'chemistry', None, 'metabolism', 'metabolism', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11238798,2001,0.0,0.0,,, +11238797,"The establishment of international monographs for herbs is in progress. Here, we propose both a marker compound and a method for its analysis for the identification of garlic bulbs and their products. The constituents in 26 kinds of fresh edible parts of Allium vegetables and three types of garlic preparations were analyzed. Sulfur compounds are the most characteristic constituents in garlic, but manufacturing processes of garlic products dramatically affect these constituents. Thus, no sulfur compound could be specified as a universal marker of identification applicable for any type of garlic. On the other hand, garlic contains other characteristic compounds, namely, saponins. After analyzing Allium vegetables and garlic preparations, we concluded that sapogenins, especially beta-chlorogenin, may be a viable candidate for identifying and distinguishing garlic from other Allium vegetables.",The Journal of nutrition,"['D000490', 'D002849', 'D002851', 'D002855', 'D003545', 'D005511', 'D005737', 'D008401', 'D010936', 'D010946', 'D012502', 'D012503', 'D013457']","['Allium', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Cysteine', 'Food Handling', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Plant Extracts', 'Plants, Medicinal', 'Sapogenins', 'Saponins', 'Sulfur Compounds']",How to distinguish garlic from the other Allium vegetables.,"['Q000737', None, None, None, 'Q000031', 'Q000379', 'Q000737', None, 'Q000032', None, 'Q000032', 'Q000032', 'Q000032']","['chemistry', None, None, None, 'analogs & derivatives', 'methods', 'chemistry', None, 'analysis', None, 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11238797,2001,0.0,0.0,,, +11237188,"Gas Chromatography-Mass Spectrometry (GC-MS) was the major technique used to determine various metabolites after consumption of dehydrated granular garlic and an enteric-coated garlic preparation, in breath, plasma, and simulated gastric fluids. A special short-path thermal desorption device was used as an introduction technique for the gas chromatograph for the determination of volatiles. These garlic preparations release allicin, which decomposes in stomach acid or with time in the intestine to release allyl sulfides, disulfides and other volatiles, some of which are postulated to be metabolized by glutathione and/or S-adenosylmethionine to form allyl methyl sulfide, the main sulfur containing volatile metabolite. S-Allylcysteine, a non-volatile bioactive component of aged garlic preparations, was determined in human plasma and urine by HPLC-MS using the negative ion atmospheric pressure chemical ionization mode (APcI)- MS. The technique of selected ion monitoring was used for quantitation. A synthetic internal standard of deuterated S-allylcysteine was added to the plasma or urine to ensure recovery and to obtain reliable quantitative data.","BioFactors (Oxford, England)","['D001944', 'D003545', 'D005737', 'D008401', 'D005750', 'D006801', 'D010936', 'D010946', 'D013440', 'D013441']","['Breath Tests', 'Cysteine', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Gastric Juice', 'Humans', 'Plant Extracts', 'Plants, Medicinal', 'Sulfides', 'Sulfinic Acids']",The determination of metabolites of garlic preparations in breath and human plasma.,"[None, 'Q000031', 'Q000378', None, 'Q000502', None, 'Q000493', None, 'Q000032', 'Q000032']","[None, 'analogs & derivatives', 'metabolism', None, 'physiology', None, 'pharmacokinetics', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/11237188,2001,,,,error on the link, +11235811,"To search for cytotoxic components from Allium victorialis, MTT assays on each extract and an isolated component, gitogenin 3-O-lycotetroside, were performed against cancer cell lines. Cytotoxicities of most extract were shown to be comparatively weak, though IC50 values of CHCl3 fraction was found to be <31.3-368.4 microg/ml. From the incubated methanol extract at 36 degrees C, eleven kinds of organosulfuric flavours were predictable by GC-MS performance. The most abundant peak was revealed to be 2-vinyl-4H-1,3-dithiin (1) by its mass spectrum. Further, this extract showed significant cytotoxicities toward cancer cell lies. Silica gel column chromatography of the n-butanol fraction led to the isolation of gitogenin 3-O-lycotetroside (3) along with astragalin (4) and kaempferol 3, 4'-di-O-beta-D-glucoside (5). This steroidal saponin exhibited significant cytotoxic activities (IC50, 6.51-36.5 microg/ml) over several cancer cell lines. When compound 3 was incubated for 24 h with human intestinal bacteria, a major metabolite was produced and then isolated by silica gel column chromatography. By examining parent- and prominent ion peak in FAB-MS spectrum of the metabolite, the structure was speculated not to be any of prosapogenins of 3, suggesting that spiroketal ring were labile to the bacterial reaction. These suggest that disulfides produced secondarily are the antitumor principles.",Archives of pharmacal research,"['D000490', 'D000972', 'D001419', 'D004354', 'D005243', 'D005737', 'D006801', 'D007422', 'D010946', 'D013150', 'D014407']","['Allium', 'Antineoplastic Agents, Phytogenic', 'Bacteria', 'Drug Screening Assays, Antitumor', 'Feces', 'Garlic', 'Humans', 'Intestines', 'Plants, Medicinal', 'Spirostans', 'Tumor Cells, Cultured']",Constituents and the antitumor principle of Allium victorialis var. platyphyllum.,"['Q000737', 'Q000737', 'Q000378', None, None, 'Q000737', None, 'Q000378', None, 'Q000737', 'Q000187']","['chemistry', 'chemistry', 'metabolism', None, None, 'chemistry', None, 'metabolism', None, 'chemistry', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/11235811,2001,,,,, +11135419,"A method is described for determining 41 insecticide residues in garlic (Allium sativum L.), including organophosphorus, organochlorine, carbamate, and synthetic pyrethroid insecticides. These insecticides were extracted from samples with acetone and dichloromethane, and co-extractives removed using a charcoal/Celite/alumina column. Analysis was performed by gas chromatography with ion trap mass spectrometry in selective ion storage (SIS) mode. Retention times and specific ions (m/z values) were used to confirm insecticides. Recoveries for most insecticides (blank samples spiked at 0.05, 0.2 and 1 microg mL(-1) levels) ranged from 70% to 110%, the coefficient of variation (CV) of the method was <20% for every case, and the limit of detection (LOD), defined in terms of 3 times baseline noise, varied between 0.01 and 0.16 mg kg(-1), depending on the compound.",Rapid communications in mass spectrometry : RCM,"['D005506', 'D005737', 'D008401', 'D006801', 'D007306', 'D010946']","['Food Contamination', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Insecticides', 'Plants, Medicinal']",Multi-residue determination of 41 insecticides in garlic by gas chromatography and ion trap mass spectrometry using the selective ion storage technique.,"['Q000032', 'Q000737', 'Q000379', None, 'Q000032', None]","['analysis', 'chemistry', 'methods', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/11135419,2001,,,,, +11118647,"The reaction between allicin (diallylthiosulfinate), the active component of garlic and reduced glutathione was investigated. The product of this reaction, mixed disulfide S-allylmercaptoglutathione (GSSA) was separated by high performance liquid chromatography and identified by 1H and (13)C nuclear magnetic resonance and mass spectroscopy. The reaction is fast (with an apparent bimolecular reaction rate constant of 3.0 M(-1) s(-1)). It is pH-dependent, which reveals a direct correlation to the actual concentration of mercaptide ion (GS(-)). Both GSSA and S-allylmercaptocysteine (prepared from allicin and cysteine) reacted with SH-containing enzymes, papain and alcohol dehydrogenase from Thermoanaerobium brockii yielding the corresponding S-allylmercapto proteins, and caused inactivation of the enzymes. The activity was restored with dithiothreitol or 2-mercaptoethanol. In addition, GSSA also exhibited high antioxidant properties. It showed significant inhibition of the reaction between OH radicals and the spin trap 5,5'-dimethyl-1-pyroline N-oxide in the Fenton system as well as in the UV photolysis of H2O2. In ex vivo experiments done with fetal brain slices under iron-induced oxidative stress, GSSA significantly lowered the production levels of lipid peroxides. The similar activity of GSSA and allicin as SH-modifiers and antioxidants suggests that the thioallyl moiety has a key role in the biological activity of allicin and its derivatives.",Biochimica et biophysica acta,"['D000426', 'D000975', 'D002851', 'D003545', 'D004791', 'D005737', 'D005978', 'D007700', 'D009682', 'D010206', 'D010946', 'D013438', 'D013441']","['Alcohol Dehydrogenase', 'Antioxidants', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Enzyme Inhibitors', 'Garlic', 'Glutathione', 'Kinetics', 'Magnetic Resonance Spectroscopy', 'Papain', 'Plants, Medicinal', 'Sulfhydryl Compounds', 'Sulfinic Acids']",S-Allylmercaptoglutathione: the reaction product of allicin with glutathione possesses SH-modifying and antioxidant properties.,"['Q000037', 'Q000737', None, 'Q000031', 'Q000737', None, 'Q000737', None, None, 'Q000037', None, 'Q000737', 'Q000737']","['antagonists & inhibitors', 'chemistry', None, 'analogs & derivatives', 'chemistry', None, 'chemistry', None, None, 'antagonists & inhibitors', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/11118647,2001,0.0,0.0,,, +22062166,"The influence of the yeast starter cultures Debaryomyces hansenii and Candida utilis on fermented meat aroma was studied in model minces and in commercial-type fermented sausages. Volatile compounds from model minces and sausages were collected using diffusive and dynamic headspace sampling respectively and were identified by gas chromatography/mass spectrometry (GC/MS). A triangle test was carried out on the sausages to detect whether the yeast influenced the sausage odour. C. utilis demonstrated high metabolic activity in the model minces, producing several volatile compounds, in particularly esters. C. utilis also seemed to ferment the amino acids valine, isoleucine and leucine into compounds important for the aroma of sausages. D. hansenii on the contrary, had very little effect on the production of volatile compounds in the model minces. In the sausage experiment both yeast cultures died out before the ripening process ended and the sensory analysis showed only a slight difference between the sausages. A fungistatic test of the garlic powder added to the sausages indicated that garlic inhibits the growth of the yeast starter cultures.",Meat science,[],[],The influence of Debaryomyces hansenii and Candida utilis on the aroma formation in garlic spiced fermented sausages and model minces.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/22062166,2012,0.0,0.0,,, +10934793,"A thorough investigation of saponin fraction from the underground parts of wild garlic--Allium ursinum L. (Liliaceae) has led to the isolation of 3-[O-alpha-rhamnopyranosyl-(1-->4)-alpha-rhamnopyranosyl-(1-->4)- alpha-rhamnopyranosyl-(1-->4)-beta-glucopyranoside-(1-->)]-3 beta-hydroxypregna-5,16-dien-20-one [1]. The structure of 1 was established by chemical and spectroscopic methods. Compound 1 is reported for the first time.",Acta poloniae pharmaceutica,"['D002240', 'D005737', 'D009682', 'D008969', 'D018517', 'D010946', 'D016339', 'D013055']","['Carbohydrate Sequence', 'Garlic', 'Magnetic Resonance Spectroscopy', 'Molecular Sequence Data', 'Plant Roots', 'Plants, Medicinal', 'Spectrometry, Mass, Fast Atom Bombardment', 'Spectrophotometry, Infrared']",Pregnadienolone glycoside from wild garlic Allium ursinum L.,"[None, 'Q000737', None, None, 'Q000737', None, None, None]","[None, 'chemistry', None, None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10934793,2000,0.0,0.0,,, +10890508,"Off-flavors in foods may originate from environmental pollutants, the growth of microorganisms, oxidation of lipids, or endogenous enzymatic decomposition in the foods. The chromatographic analysis of flavors and off-flavors in foods usually requires that the samples first be processed to remove as many interfering compounds as possible. For analysis of foods by gas chromatography (GC), sample preparation may include mincing, homogenation, centrifugation, distillation, simple solvent extraction, supercritical fluid extraction, pressurized-fluid extraction, microwave-assisted extraction, Soxhlet extraction, or methylation. For high-performance liquid chromatography of amines in fish, cheese, sausage and olive oil or aldehydes in fruit juice, sample preparation may include solvent extraction and derivatization. Headspace GC analysis of orange juice, fish, dehydrated potatoes, and milk requires almost no sample preparation. Purge-and-trap GC analysis of dairy products, seafoods, and garlic may require heating, microwave-mediated distillation, purging the sample with inert gases and trapping the analytes with Tenax or C18, thermal desorption, cryofocusing, or elution with ethyl acetate. Solid-phase microextraction GC analysis of spices, milk and fish can involve microwave-mediated distillation, and usually requires adsorption on poly(dimethyl)siloxane or electrodeposition on fibers followed by thermal desorption. For short-path thermal desorption GC analysis of spices, herbs, coffee, peanuts, candy, mushrooms, beverages, olive oil, honey, and milk, samples are placed in a glass-lined stainless steel thermal desorption tube, which is purged with helium and then heated gradually to desorb the volatiles for analysis. Few of the methods that are available for analysis of food flavors and off-flavors can be described simultaneously as cheap, easy and good.",Journal of chromatography. A,"['D005421', 'D005504']","['Flavoring Agents', 'Food Analysis']",Sample preparation for the analysis of flavors and off-flavors in foods.,"['Q000032', None]","['analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/10890508,2000,0.0,0.0,,only sample preparations, +10888499,"A recent human intervention trial showed that daily supplementation with selenized yeast (Se-yeast) led to a decrease in the overall cancer morbidity and mortality by nearly 50%; past research has also demonstrated that selenized garlic (Se-garlic) is very effective in mammary cancer chemoprevention in the rat model. The goal of this study was to compare certain biological activities of Se-garlic and Se-yeast and to elucidate the differences based on the chemical forms of selenium found in these two natural products. Characterization of organic selenium compounds in yeast (1922 microg/g Se) and garlic (296 microg/g Se) was carried out by high-performance liquid chromatography with inductively coupled plasma mass spectrometry or with electrospray mass spectrometry. Analytical speciation studies showed that the bulk of the selenium in Se-garlic and Se-yeast is in the form of gamma-glutamyl-Se-methylselenocysteine (73%) and selenomethionine (85%), respectively. The above methodology has the sensitivity and capability to account for >90% of total selenium. In the rat feeding studies, supplementation of Se-garlic in the diet at different levels consistently caused a lower total tissue selenium accumulation when compared to Se-yeast. On the other hand, Se-garlic was significantly more effective in suppressing the development of premalignant lesions and the formation of adenocarcinomas in the mammary gland of carcinogen-treated rats. Given the present finding on the identity of selenomethionine and gamma-glutamyl-Se-methylselenocysteine as the major form of selenium in Se-yeast and Se-garlic, respectively, the metabolism of these two compounds is discussed in an attempt to elucidate how their disposition in tissues might account for the differences in cancer chemopreventive activity.",Journal of agricultural and food chemistry,"['D000230', 'D000818', 'D016588', 'D002273', 'D005260', 'D005737', 'D006801', 'D008325', 'D008517', 'D010946', 'D011230', 'D051381', 'D017207', 'D018036', 'D018038', 'D015003']","['Adenocarcinoma', 'Animals', 'Anticarcinogenic Agents', 'Carcinogens', 'Female', 'Garlic', 'Humans', 'Mammary Neoplasms, Experimental', 'Phytotherapy', 'Plants, Medicinal', 'Precancerous Conditions', 'Rats', 'Rats, Sprague-Dawley', 'Selenium Compounds', 'Sodium Selenite', 'Yeasts']",Chemical speciation influences comparative activity of selenium-enriched garlic and yeast in mammary cancer prevention.,"['Q000139', None, 'Q000627', None, None, 'Q000627', None, 'Q000139', None, None, 'Q000139', None, None, 'Q000627', 'Q000627', None]","['chemically induced', None, 'therapeutic use', None, None, 'therapeutic use', None, 'chemically induced', None, None, 'chemically induced', None, None, 'therapeutic use', 'therapeutic use', None]",https://www.ncbi.nlm.nih.gov/pubmed/10888499,2000,0.0,0.0,,, +10885064,"Selenium-enriched plants, such as hyperaccumulative phytoremediation plants (Astragalus praleongus, 517 micrograms g-1 Se, and Brassica juncea, 138 micrograms g-1 Se in dry sample), yeast (1200, 1922 and 2100, micrograms g-1 Se in dry sample), ramp (Allium tricoccum, 48, 77, 230, 252, 405 and 524 micrograms g-1 Se in dry sample), onion (Allium cepa, 96 and 140 micrograms g-1 Se in dry sample) and garlic (Allium sativum, 68, 112, 135, 296, 1355 micrograms g-1 Se in dry sample) were analyzed by HPLC-ICP-MS for their selenium content and speciation after hot water and enzymatic extractions. Reference samples with natural selenium levels, such as onion and garlic controls, cooking garlic powder, baking yeast powder and a commercial garlic supplement were also analyzed. Selected samples were also examined by HPLC-electrospray ionization (ESI)-MS. HPLC was mostly carried out with 0.1% heptafluorobutanoic acid (HFBA) as ion-pairing agent in 1 + 99 v/v methanol-water solution, but 0.1% trifluoroacetic acid (TFA) in 1 + 99 v/v methanol-water solution was also utilized to permit chromatography for compounds that did not elute with HFBA. More than 75% of the total eluting selenium compounds, based upon element specific detection, were identified from retention time data and standard spiking experiments, and between 60 and 85% of compounds were identified by MS, with up to 25% of the total eluting molecular selenium species being unidentified as yet. Limits of quantification (LOQ, defined as the concentration giving an S/N of 10) for HPLC-ICP-MS were in the range 2-50 ng mL-1 Se in the injected extracts for the selenium-enriched samples and 2-10 ng mL-1 Se for the natural selenium level samples. LOQ values for HPLC-ESI-MS were ca. 100 times higher than those measured by HPLC-ICP-MS.",The Analyst,"['D002851', 'D005466', 'D010944', 'D012643']","['Chromatography, High Pressure Liquid', 'Fluorocarbons', 'Plants', 'Selenium']",Selenium speciation in enriched and natural samples by HPLC-ICP-MS and HPLC-ESI-MS with perfluorinated carboxylic acid ion-pairing agents.,"['Q000379', None, 'Q000737', 'Q000032']","['methods', None, 'chemistry', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/10885064,2000,,,,, +10843440,"Phytoestrogens (weak estrogens found in plants or derived from plant precursors by human metabolism) have been hypothesized to reduce the risk of a number of cancers. However, epidemiologic studies addressing this issue are hampered by the lack of a comprehensive phytoestrogen database for quantifying exposure. The purpose of this research was to develop such a database for use with food-frequency questionnaires in large epidemiologic studies.",Cancer causes & control : CCC,"['D000328', 'D000368', 'D002851', 'D016208', 'D016021', 'D004968', 'D005260', 'D005502', 'D005504', 'D005518', 'D006801', 'D007529', 'D013058', 'D008875', 'D009369', 'D048789', 'D028321', 'D010945', 'D011795', 'D014481']","['Adult', 'Aged', 'Chromatography, High Pressure Liquid', 'Databases, Factual', 'Epidemiologic Studies', 'Estrogens, Non-Steroidal', 'Female', 'Food', 'Food Analysis', 'Food Preferences', 'Humans', 'Isoflavones', 'Mass Spectrometry', 'Middle Aged', 'Neoplasms', 'Phytoestrogens', 'Plant Preparations', 'Plants, Edible', 'Surveys and Questionnaires', 'United States']",Assessing phytoestrogen exposure in epidemiologic studies: development of a database (United States).,"[None, None, None, None, None, 'Q000032', None, 'Q000706', None, None, None, None, None, None, 'Q000453', None, None, 'Q000737', None, 'Q000453']","[None, None, None, None, None, 'analysis', None, 'statistics & numerical data', None, None, None, None, None, None, 'epidemiology', None, None, 'chemistry', None, 'epidemiology']",https://www.ncbi.nlm.nih.gov/pubmed/10843440,2000,,,,, +10825863,"The objective of this double-blind clinical study was to investigate the effectiveness of a commercially available dentifrice containing triclosan and a copolymer (Colgate Total Toothpaste) for controlling long-term, i.e., seven-hour and overnight breath odor. In particular, a comparison was made between the level of control of breath odor provided by the test dentifrice, and that provided by a placebo dentifrice which did not contain triclosan or a copolymer. This study followed a two-treatment, two-period crossover design. Prospective subjects were provided with a supply of a commercially available fluoride dentifrice, which was used for a one-week period prior to the two seven-day treatment periods. During each treatment period, subjects were instructed to brush their teeth twice a day, morning and evening, for sixty seconds with their assigned study dentifrice, using the soft-bristled toothbrush which had been provided. On the morning following the seventh day of each treatment period, subjects reported to the clinical facility for overnight breath odor assessments. Directly following this, subjects brushed their teeth, ate and drank normally, and reported once again to the clinical facility at seven hours post-toothbrushing for another breath odor assessment. Prior to the overnight breath odor assessments, subjects refrained from brushing their teeth, rinsing their mouths or using breath mints, and from eating or drinking anything on the morning of the evaluation. Subjects refrained from the use of tobacco products, and from eating onions, garlic, or strong spices throughout the entire study. Breath odor was instrumentally evaluated by measuring the level of volatile sulfur compounds in the mouth air using a 565 Tracor gas chromatograph equipped with a flame photometric detector. Measurements were taken in duplicate, and then averaged. Levels of volatile sulfur compounds were expressed in nanograms per milliliter (ng/ml) of mouth air. The two dentifrices exhibited statistically significant differences (p < 0.05) with respect to both overnight breath odor and seven-hour post-toothbrushing breath odor. The mean overnight breath odor scores were 9.63 ng/ml for Colgate Total Toothpaste, and 12.64 ng/ml for the placebo dentifrice. For seven-hour breath odor, the mean scores were 5.62 ng/ml for Colgate Total Toothpaste, and 7.10 ng/ml for the placebo dentifrice. Thus, the results of this double-blind clinical study on 19 subjects support the conclusion that Colgate Total Toothpaste provides effective seven-hour and overnight control of breath odor.",The Journal of clinical dentistry,"['D000328', 'D000704', 'D000891', 'D002849', 'D045424', 'D018592', 'D003802', 'D004311', 'D005260', 'D005459', 'D006209', 'D006801', 'D008297', 'D008298', 'D008875', 'D011095', 'D011145', 'D011446', 'D012824', 'D014100', 'D016896', 'D014260']","['Adult', 'Analysis of Variance', 'Anti-Infective Agents, Local', 'Chromatography, Gas', 'Complex Mixtures', 'Cross-Over Studies', 'Dentifrices', 'Double-Blind Method', 'Female', 'Fluorides', 'Halitosis', 'Humans', 'Male', 'Maleates', 'Middle Aged', 'Polyethylenes', 'Polyvinyls', 'Prospective Studies', 'Silicic Acid', 'Toothpastes', 'Treatment Outcome', 'Triclosan']",The clinical effectiveness of a dentifrice containing triclosan and a copolymer for providing long-term control of breath odor measured chromatographically.,"[None, None, 'Q000627', None, None, None, 'Q000737', None, None, None, 'Q000188', None, None, 'Q000627', None, 'Q000627', 'Q000627', None, None, None, None, 'Q000627']","[None, None, 'therapeutic use', None, None, None, 'chemistry', None, None, None, 'drug therapy', None, None, 'therapeutic use', None, 'therapeutic use', 'therapeutic use', None, None, None, None, 'therapeutic use']",https://www.ncbi.nlm.nih.gov/pubmed/10825863,2000,,,,, +10737231,"The antibacterial activity of garlic powder against O-157 was tested by using garlic bulbs post-harvested 1 y. O-157 at 10(6-7) cfu/mL perished after incubation for 24 h with a 1% solution of garlic powder. The use of powder from fresh garlic was more effective for antibacterial activity than that from old garlic; the 1% solution of fresh garlic powder eradicating the O-157 in 6 h. The antibacterial activity was resistant to heat treatment of 100 degrees C for 20 min. The water-soluble components of garlic powder were fractionated into three fractions (Fr. 1-3) by Sephadex G-100 column chromatography, among which Fr. 3 showed antibacterial activity against O-157 but the other fractions were scarce in activity. The antibacterial activity was also shown against other types of pathogenic bacteria such as methicillin-resistant Staphylococcus aureus (MRSA), Salmonella enteritidis, and Candida albicans. Thus, the practical use of garlic powder is expected to prevent bacteria-caused food poisoning.",Journal of nutritional science and vitaminology,"['D000900', 'D000890', 'D002176', 'D019453', 'D005737', 'D006801', 'D066298', 'D007223', 'D008826', 'D010946', 'D012477', 'D013211']","['Anti-Bacterial Agents', 'Anti-Infective Agents', 'Candida albicans', 'Escherichia coli O157', 'Garlic', 'Humans', 'In Vitro Techniques', 'Infant', 'Microbial Sensitivity Tests', 'Plants, Medicinal', 'Salmonella enteritidis', 'Staphylococcus aureus']",Antibacterial activity of garlic powder against Escherichia coli O-157.,"[None, None, 'Q000187', 'Q000187', None, None, None, None, None, None, 'Q000187', 'Q000187']","[None, None, 'drug effects', 'drug effects', None, None, None, None, None, None, 'drug effects', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/10737231,2000,,,,, +10720787,"The intravenous administration of a purified fraction (6 microg/kg) to anaesthesized dogs was followed by a significant biphasic diuretic and natriuretic response which reached a maximum at 180 min after injection. Chloride, but not potassium ions, followed the natriuretic profile. No changes were observed in arterial blood pressure or in the electrocardiogram. The purified garlic fraction also induced an inhibitory dose-dependent effect on kidney Na, K-ATPase.",Journal of ethnopharmacology,"['D000490', 'D000818', 'D002851', 'D002852', 'D004231', 'D004232', 'D004285', 'D004573', 'D005260', 'D007668', 'D008297', 'D009318', 'D011506', 'D000254', 'D014563']","['Allium', 'Animals', 'Chromatography, High Pressure Liquid', 'Chromatography, Ion Exchange', 'Diuresis', 'Diuretics', 'Dogs', 'Electrolytes', 'Female', 'Kidney', 'Male', 'Natriuresis', 'Proteins', 'Sodium-Potassium-Exchanging ATPase', 'Urodynamics']",Purification and bioassays of a diuretic and natriuretic fraction from garlic (Allium sativum).,"['Q000737', None, None, None, 'Q000187', 'Q000302', None, 'Q000652', None, 'Q000187', None, 'Q000187', 'Q000378', 'Q000378', 'Q000187']","['chemistry', None, None, None, 'drug effects', 'isolation & purification', None, 'urine', None, 'drug effects', None, 'drug effects', 'metabolism', 'metabolism', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/10720787,2000,0.0,0.0,,, +10624707,"Epidemiological and experimental studies suggest that consumption of garlic may protect against several types of cancer. Moreover, a plausible hypothesis has been proposed that the biological effects of garlic can be attributed to the enhancing action of a variety of organosulfur compounds, present in garlic, on hepatic phase II carcinogen detoxification enzymes. We have used the N-methylnitrosourea (NMU)-induced rat mammary tumor model to test the chemopreventive effects of a water-soluble organosulfur constituent derived from aged garlic, S-allylcysteine (SAC). Rats were fed diets supplemented with 666 and 2,000 ppm SAC beginning seven days before initiation with NMU (55 days of age) to termination (18 wk post-NMU), at which time mammary tumors were enumerated. At neither dose did SAC exert an inhibitory effect on any index of tumor development, including incidence, latency, multiplicity, or volume, compared with untreated controls. Weight gains in all groups were similar. Assay of serum SAC levels in supplemented groups indicated that SAC concentrations were beneath the limits of detection of the high-performance liquid chromatography system used. These results contradict previous animal model studies indicating that SAC acts as an inhibitory agent in experimental mammary tumorigenesis; reasons for this discrepancy include the possibility that SAC may exhibit nonlinear dose effects.",Nutrition and cancer,"['D000230', 'D000818', 'D000970', 'D002273', 'D002851', 'D003545', 'D004032', 'D004195', 'D018572', 'D005260', 'D005737', 'D008325', 'D008770', 'D010946', 'D011897', 'D051381', 'D017207']","['Adenocarcinoma', 'Animals', 'Antineoplastic Agents', 'Carcinogens', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Diet', 'Disease Models, Animal', 'Disease-Free Survival', 'Female', 'Garlic', 'Mammary Neoplasms, Experimental', 'Methylnitrosourea', 'Plants, Medicinal', 'Random Allocation', 'Rats', 'Rats, Sprague-Dawley']","S-allylcysteine, a garlic constituent, fails to inhibit N-methylnitrosourea-induced rat mammary tumorigenesis.","['Q000401', None, 'Q000627', None, None, 'Q000031', None, None, None, None, None, 'Q000401', None, None, None, None, None]","['mortality', None, 'therapeutic use', None, None, 'analogs & derivatives', None, None, None, None, None, 'mortality', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10624707,2000,,,,, +10552426,"Described in this paper is a fiber interface direct headspace mass spectrometric system for the real-time measurement of flavor release. The system was optimized for the detection of the garlic aroma volatile, diallyl disulfide, from water. Parameters investigated included interface temperature, flow rate through the fiber, flow rate through the sample vessel, and sample stir rate. The delay time for detection of sample after introduction into the sample vessel was determined as 43 s. The system proved to be reliable and robust with no loss in sensitivity or contamination of the mass spectrometer over a 6 month period. The technique was applied to a homologous series of aliphatic alcohols from C(2) to C(7). Results showed that as polarity decreased with increasing chain length the release of volatile into the headspace was faster and gave a higher maximum intensity. Release of the garlic aroma volatile from different commercial mayonnaise products clearly showed a decrease in the release of diallyl disulfide as fat content increased. These results demonstrate the potential of using this technique as a tool for understanding the complex interactions that occur between flavor compounds and the bulk food matrix.",Journal of agricultural and food chemistry,"['D000438', 'D000498', 'D005737', 'D013058', 'D010946', 'D012680', 'D013440', 'D013649', 'D013696', 'D013816', 'D014835']","['Alcohols', 'Allyl Compounds', 'Garlic', 'Mass Spectrometry', 'Plants, Medicinal', 'Sensitivity and Specificity', 'Sulfides', 'Taste', 'Temperature', 'Thermodynamics', 'Volatilization']",Use of fiber interface direct mass spectrometry for the determination of volatile flavor release from model food systems.,"['Q000032', 'Q000032', None, 'Q000379', None, None, 'Q000032', None, None, None, None]","['analysis', 'analysis', None, 'methods', None, None, 'analysis', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10552426,2000,0.0,0.0,,garlic mayo, +10588342,"A new GC method for determination of S-alk(en)ylcysteine sulfoxides, important secondary metabolites occurring in many plant genera, has been developed. The method is based on isolation of the amino acid fraction by ion-exchange chromatography followed by derivatization with ethyl chloroformate at ambient temperature and reduction of derivatized S-alk(en)ylcysteine sulfoxides by sodium iodide. The main advantages of the new method are its high sensitivity, excellent resolution capability, accuracy and reliability, as well as the possibility to identify unknown compounds by means of GC-MS. The content of alliin and other S-alk(en)ylcysteine sulfoxides was determined in nine different samples of garlic (Allium sativum L.) originating from the Czech Republic, France, and China. The total content of S-alk(en)ylcysteine sulfoxide pool ranged between 0.53 and 1.3% fresh weight, with S-allylcysteine sulfoxide (alliin) being predominant. A novel S-alkylcysteine derivative, S-ethylcysteine sulfoxide (ethiin), not previously reported to occur in Allium species, was found in some of the samples examined.",Journal of chromatography. A,"['D002849', 'D002851', 'D003545', 'D005737', 'D007202', 'D010946', 'D019163', 'D013454']","['Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Cysteine', 'Garlic', 'Indicators and Reagents', 'Plants, Medicinal', 'Reducing Agents', 'Sulfoxides']",Gas chromatographic determination of S-alk(en)ylcysteine sulfoxides.,"['Q000379', None, 'Q000031', 'Q000737', None, None, None, 'Q000032']","['methods', None, 'analogs & derivatives', 'chemistry', None, None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/10588342,2000,1.0,2.0,,, +10578481,"Heterocyclic aromatic amines (HAAs), a group of chemicals formed during high-temperature cooking of meat and fish, are potent mutagens and are suspected to play a role in colorectal cancer. A recent study suggested that marinating meat may offer a way to reduce HAA formation. Hawaii's diverse ethnic populations, which are at various risks of colorectal cancer, often use traditional marinades when cooking meat. We compared the HAA content of beef steaks marinated overnight with teriyaki sauce, turmeric-garlic sauce, or commercial honey barbecue sauce with that of unmarinated steaks. The levels of 2-amino-1-methyl-6-phenylimidazo [4,5-b]pyridine (PhIP) and 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx) were determined by liquid-liquid extraction and gas chromatography-mass spectrometry. Beef steaks marinated with teriyaki sauce had 45% and 67% lower PhIP level at 10 minutes (p = 0.002) and 15 minutes (p = 0.001) of cooking time as well as 44% and 60% lower MeIQx levels at 10 minutes (p = 0.008) and 15 minutes (p = 0.001), respectively, than unmarinated meat. Lower levels of PhIP and MeIQx were also observed in meat marinated with turmeric-garlic sauce. In contrast, marinating with barbecue sauce caused a 2.9- and 1.9-fold increase in PhIP (p < or = 0.005) and a 4- and 2.9-fold increase in MeIQx (p < or = 0.001) at 10 and 15 minutes, respectively. Differences in the mutagenic activities of marinated and unmarinated steaks, as measured by the Ames assay, paralleled the differences in PhIP and MeIQx levels. Future studies should test the effects of specific ingredients, including the water content of marinades, and the effect of reapplying barbecue sauce during cooking (to reduce charring) on HAA formation.",Nutrition and cancer,"['D001208', 'D015179', 'D003296', 'D005421', 'D006801', 'D008460', 'D009152', 'D012307', 'D012486', 'D019366']","['Asia', 'Colorectal Neoplasms', 'Cooking', 'Flavoring Agents', 'Humans', 'Meat', 'Mutagenicity Tests', 'Risk Factors', 'Salmonella typhimurium', 'Western World']",Effects of marinating with Asian marinades or western barbecue sauce on PhIP and MeIQx formation in barbecued beef.,"[None, 'Q000139', None, None, None, None, None, None, 'Q000235', None]","[None, 'chemically induced', None, None, None, None, None, None, 'genetics', None]",https://www.ncbi.nlm.nih.gov/pubmed/10578481,2000,,,,, +10564015,"Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a powerful new technique that will have a great impact on food analysis. This study demonstrates the applicability of MALDI-MS performed directly on an aqueous food extract for qualitative and quantitative analysis of food oligosaccharides. 2', 4',6'-Trihydroxyacetophenone was found to be the best matrix for analysis of oligosaccharides in the foods examined. The relationship between laser strength, resolution, and the response factors of individual oligosaccharides using MALDI-MS was investigated. A MALDI-MS method for quantitative analysis of fructooligosaccharides with standard addition of a pure fructooligosaccharide was developed. High performance anion exchange chromatography with pulsed amperometric detection was compared to MALDI-MS for the analysis of fructooligosaccharides. The fructooligosaccharide analyses were performed on red onions, shallots, and elephant garlic.",Journal of agricultural and food chemistry,"['D002236', 'D002240', 'D003505', 'D005504', 'D007202', 'D008969', 'D009844', 'D019032', 'D047408']","['Carbohydrate Conformation', 'Carbohydrate Sequence', 'Cyclodextrins', 'Food Analysis', 'Indicators and Reagents', 'Molecular Sequence Data', 'Oligosaccharides', 'Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'gamma-Cyclodextrins']",Analysis of food oligosaccharides using MALDI-MS: quantification of fructooligosaccharides.,"[None, None, 'Q000737', None, None, None, 'Q000032', 'Q000379', None]","[None, None, 'chemistry', None, None, None, 'analysis', 'methods', None]",https://www.ncbi.nlm.nih.gov/pubmed/10564015,2000,1.0,1.0,,, +10497175,"We report herein for the first time the formation by freshly grown garlic roots and the structural characterization of 14,15-epoxide positional analogs of the hepoxilins formed via the 15-lipoxygenase-induced oxygenation of arachidonic acid. These compounds are formed through the combined actions of a 15(S)-lipoxygenase and a hydroperoxyeicosatetraenoic acid (HPETE) isomerase. The compounds were formed when either arachidonic acid or 15-HPETE were used as substrates. Both the ""A""-type and the ""B""-type products are formed although the B-type compounds are formed in greater relative quantities. Chiral phase high performance liquid chromatography analysis confirmed the formation of hepoxilins from 15(S)- but not 15(R)-HPETE, indicating high stereoselectivity of the isomerase. Additionally, the lipoxygenase was of the 15(S)-type as only 15(S)-hydroxyeicosatetraenoic acid was formed when arachidonic acid was used as substrate. The structures of the products were confirmed by gas chromatography-mass spectrometry of the methyl ester trimethylsilyl ether derivatives as well as after characteristic epoxide ring opening catalytically with hydrogen leading to dihydroxy products. That 15(S)-lipoxygenase activity is of functional importance in garlic was shown by the inhibition of root growth by BW 755C, a dual cyclooxygenase/lipoxygenase inhibitor and nordihydroguaiaretic acid, a lipoxygenase inhibitor. Additional biological studies were carried out with the purified intact 14(S), 15(S)-hepoxilins, which were investigated for hepoxilin-like actions in causing the release of intracellular calcium in human neutrophils. The 14,15-hepoxilins dose-dependently caused a rise in cytosolic calcium, but their actions were 5-10-fold less active than 11(S), 12(S)-hepoxilins derived from 12(S)-HPETE. These studies provide evidence that 15(S)-lipoxygenase is functionally important to normal root growth and that HPETE isomerization into the hepoxilin-like structure may be ubiquitous; the hepoxilin-evoked release of calcium in human neutrophils, which is receptor-mediated, is sensitive to the location within the molecule of the hydroxyepoxide functionality.",The Journal of biological chemistry,"['D015126', 'D001093', 'D002851', 'D005737', 'D006801', 'D019746', 'D009504', 'D018517', 'D010946', 'D011956']","['8,11,14-Eicosatrienoic Acid', 'Arachidonate 15-Lipoxygenase', 'Chromatography, High Pressure Liquid', 'Garlic', 'Humans', 'Intramolecular Oxidoreductases', 'Neutrophils', 'Plant Roots', 'Plants, Medicinal', 'Receptors, Cell Surface']","Formation of 14,15-hepoxilins of the A(3) and B(3) series through a 15-lipoxygenase and hydroperoxide isomerase present in garlic roots.","['Q000031', 'Q000378', None, 'Q000201', None, 'Q000378', 'Q000378', 'Q000201', None, 'Q000378']","['analogs & derivatives', 'metabolism', None, 'enzymology', None, 'metabolism', 'metabolism', 'enzymology', None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/10497175,1999,0.0,0.0,1.0,, +10450562,"Volatile sulfur compounds arising from grated raw or heat-treated garlic in both in-vitro and in-vivo tests were gas-chromatographically analyzed. In in-vitro tests, the head-space vapor gas from garlic in a vial was analyzed. It was clarified that allyl mercaptan arising from raw garlic decreased with the passage of time and other volatile low-molecular sulfur compounds (LMSC) did not show remarkable changes. The change of LMSC from heat-treated garlic was also studied. Methyl mercaptan and allyl mercaptan from heat-treated garlic gradually increased to some extent. On the other hand, the quantities of somewhat high-molecular sulfur compounds (HMSC) were much less in heat-treated garlic compared to those of raw garlic. These compounds increased till approx. 60 min and then decreased gradually. In in-vivo tests, human expiration after eating garlic was analyzed. Allyl mercaptan, methyl mercaptan and allyl methyl sulfide in LMSC were detected in significant amounts. The quantities of these compounds arising from heat-treated garlic were smaller than those from raw garlic. These compounds had the tendency of decreasing with the passage of time. On the other hand, almost no HMSC was detected in both raw and heat-treated garlic. By sensory testing, raw garlic showed a stronger smell than heat-treated garlic in both in-vitro and in-vivo tests. GC analysis exhibited higher values of volatile sulfur compounds in raw garlic. That is, the higher the volatile sulfur compound level, the stronger the garlic flavor or malodor.",Journal of nutritional science and vitaminology,"['D000498', 'D002849', 'D004220', 'D005260', 'D005737', 'D008401', 'D006358', 'D006801', 'D009812', 'D010946', 'D013438', 'D013440', 'D013457', 'D014835']","['Allyl Compounds', 'Chromatography, Gas', 'Disulfides', 'Female', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Hot Temperature', 'Humans', 'Odorants', 'Plants, Medicinal', 'Sulfhydryl Compounds', 'Sulfides', 'Sulfur Compounds', 'Volatilization']",Volatile sulfur compounds in human expiration after eating raw or heat-treated garlic.,"['Q000032', None, 'Q000032', None, 'Q000737', None, None, None, None, None, 'Q000032', 'Q000032', 'Q000009', None]","['analysis', None, 'analysis', None, 'chemistry', None, None, None, None, None, 'analysis', 'analysis', 'adverse effects', None]",https://www.ncbi.nlm.nih.gov/pubmed/10450562,1999,,,,, +10234471,"Diallyl sulfide (DAS) and diallyl disulfide (DADS) were used to determine viability and inhibition of arylamine N-acetyltransferase (NAT) activity in human bladder tumor cells. The NAT activity was measured by high performance liquid chromatography assaying for the amounts of N-acetyl-2-aminofluorene (2-AAF) and N-acetyl-p-aminobenzoic acid (N-Ac-PABA) and remaining 2-aminofluorene (2-AF) and p-aminobenzoic acid (PABA). The viability, NAT activity and 2-AAF-DNA adduct formation in human bladder tumor cells was inhibited by DAS and DADS in a dose-dependent manner, i.e. the higher the concentration of DAS and DADS, the higher the inhibition of NAT activity and cell death. The data also indicated that DAS and DADS decrease the apparent values of Km and Vmax from human bladder tumor cells in both systems examined. This report is the first demonstration to show garlic components did affect human bladder tumor cell NAT activity.",Drug and chemical toxicology,"['D000498', 'D016588', 'D000970', 'D001191', 'D004220', 'D004789', 'D005737', 'D006801', 'D010946', 'D013440', 'D014407', 'D001749']","['Allyl Compounds', 'Anticarcinogenic Agents', 'Antineoplastic Agents', 'Arylamine N-Acetyltransferase', 'Disulfides', 'Enzyme Activation', 'Garlic', 'Humans', 'Plants, Medicinal', 'Sulfides', 'Tumor Cells, Cultured', 'Urinary Bladder Neoplasms']",Effects of garlic components diallyl sulfide and diallyl disulfide on arylamine N-acetyltransferase activity in human bladder tumor cells.,"['Q000494', 'Q000494', 'Q000494', 'Q000378', 'Q000494', 'Q000187', None, None, None, 'Q000494', None, 'Q000188']","['pharmacology', 'pharmacology', 'pharmacology', 'metabolism', 'pharmacology', 'drug effects', None, None, None, 'pharmacology', None, 'drug therapy']",https://www.ncbi.nlm.nih.gov/pubmed/10234471,1999,0.0,0.0,,, +10227149,"An organosulfur compound was isolated from oil-macerated garlic extract by silica gel column chromatography and preparative TLC. From the results of NMR, IR, and MS analyses, its structure was determined as E-4,5,9-trithiadeca-1,7-diene-9-oxide (iso-E-10-devinylajoene, iso-E-10-DA). This compound was different from E-4,5,9-trithiadeca-1,6-diene-9-oxide (E-10-devinylajoene, E-10-DA) only in the position of a double bond. Iso-E-10-DA had antimicrobial activity against Gram-positive bacteria, such as Bacillus cereus, B. subtilis, and Staphylococcus aureus, and yeasts at the concentration lower than 100 micrograms/ml, but Gram-negative bacteria were not inhibited at the same concentration. The antimicrobial activity of iso-E-10-DA was inferior to those of similar oil-macerated garlic extract compounds such as E-ajoene, Z-ajoene, and Z-10-DA. From these results, it was suggested that trans structure and/or the position of double bond of iso-E-10-DA reduce the antimicrobial activity.","Bioscience, biotechnology, and biochemistry","['D000900', 'D002855', 'D004220', 'D005737', 'D006094', 'D009682', 'D013058', 'D008826', 'D009821', 'D010936', 'D010946', 'D013055', 'D013454']","['Anti-Bacterial Agents', 'Chromatography, Thin Layer', 'Disulfides', 'Garlic', 'Gram-Positive Bacteria', 'Magnetic Resonance Spectroscopy', 'Mass Spectrometry', 'Microbial Sensitivity Tests', 'Oils', 'Plant Extracts', 'Plants, Medicinal', 'Spectrophotometry, Infrared', 'Sulfoxides']","An organosulfur compound isolated from oil-macerated garlic extract, and its antimicrobial effect.","['Q000302', None, 'Q000302', 'Q000737', 'Q000187', None, None, None, None, 'Q000737', None, None, 'Q000302']","['isolation & purification', None, 'isolation & purification', 'chemistry', 'drug effects', None, None, None, None, 'chemistry', None, None, 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/10227149,1999,0.0,0.0,,, +10193205,"Alliinase (EC 4.4.1.4) has been isolated from commercially available garlic (Allium sativum L., Alliaceae) powder and was investigated with respect to its use as ingredient of herbal remedies. The enzyme was purified to apparent homogeneity and results were compared with those obtained from a sample of fresh A. sativum var. pekinense. The purification of the enzyme involved a gel filtration step as well as affinity chromatography on concanavalin-A agarose. Vmax using L-(+)-alliin as substrate (252 mumol min-1 mg-1) was at the lower range of data given in the literature (214-390 mumol min-1 mg-1). L-(-)-Alliin was also accepted as substrate (54 mumol min-1 mg-1). Vmax for alliinase from A. sativum var. pekinense was at 332 mumol min-1 mg-1 and 90 mumol min-1 mg-1 for L-(+)- and L-(-)-alliin, respectively. The Km values for alliinase from garlic powder were estimated to be 1.6 mM for L-(+)-alliin and 2.8 mM for L-(-)-alliin. In contrast to literature values, both temperature and pH optima were somewhat higher (36 degrees C and pH 7.0 versus 33 degrees C and pH 6.5, respectively). The enzyme was found to be active in a range from pH 5 to pH 10. Gel electrophoresis gave evidence that the alliinase obtained from garlic powder consisted of two slightly different subunits with molecular weights of 53 and 54 kDa whereas alliinase obtained from fresh garlic consists of two identical subunits. It is assumed that the alliinase gets significantly altered during the drying process of garlic powder but is still capable to convert alliin to allicin.",Planta medica,"['D013437', 'D004591', 'D005737', 'D008517', 'D010946', 'D011208']","['Carbon-Sulfur Lyases', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Phytotherapy', 'Plants, Medicinal', 'Powders']",Quality of herbal remedies from Allium sativum: differences between alliinase from garlic powder and fresh garlic.,"['Q000302', None, 'Q000737', None, None, None]","['isolation & purification', None, 'chemistry', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/10193205,1999,,,,no pdf access, +9882409,"Allicin (diallylthiosulfinate) is the main biologically active component of freshly crushed garlic cloves. It is produced upon the interaction of the nonprotein amino acid alliin with the enzyme alliinase (alliin lyase, EC 4.4.1.4). A simple and rapid spectrophotometric procedure for determination of allicin and alliinase activity, based on the reaction between 2-nitro-5-thiobenzoate (NTB) and allicin, is described. NTB reacts with the activated disulfide bond --S(O)-S--; of allicin, forming the mixed-disulfide allylmercapto-NTB, as characterized by NMR. The method can be used for determination of allicin and total thiosulfinates in garlic preparations and garlic-derived products. The method was applied for determination of pure alliinase activity and for the activity of the enzyme in crude garlic extracts.",Analytical biochemistry,"['D013437', 'D002851', 'D002855', 'D007700', 'D009682', 'D009579', 'D013438', 'D013441', 'D013451']","['Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Chromatography, Thin Layer', 'Kinetics', 'Magnetic Resonance Spectroscopy', 'Nitrobenzoates', 'Sulfhydryl Compounds', 'Sulfinic Acids', 'Sulfonic Acids']",A spectrophotometric assay for allicin and alliinase (Alliin lyase) activity: reaction of 2-nitro-5-thiobenzoate with thiosulfinates.,"['Q000378', None, None, None, None, 'Q000737', None, 'Q000378', 'Q000737']","['metabolism', None, None, None, None, 'chemistry', None, 'metabolism', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/9882409,1999,1.0,1.0,,, +19281347,"Garlic (Allium sativum L.) is used in the household and as an ingredient in many pharmaceutical products. Tissue culture technique provides an excellent source for induction of both chemical and genetic variation in garlic. A callus was induced on root meristem cultured on Murashige and Skoog (MS) medium in the presence of kinetin, indole acetic acid, and 2,4-dichlorophenoxyacetic acid. Shoots with a small bulb were produced on medium containing MS salts, B vitamins, and naphthalene acetic acid. Regenerated plants were transplanted into soil, and a nondivided bulb was formed in the first somaclonal generation (SCI). Plants were normal in their phenotypes in SC2. After four cycles of field cultivation, the selected somaclones (variants) in the fourth generation showed significant differences in bulb character compared with the original plants. Mitotic division and chromosomal abnormalities were investigated in meristimic root tip cells of regenerated plants for the first and fourth regeneration and for control plants. Somaclonal variant metaphase cells had the same chromosome number (2n = 16) as those of the controls. Allicin was measured quantitatively in the regenerated clones by high-performance liquid chromatography. The results showed that some clones contained as much as 14.50 mg/g allicin, compared with 3.80 mg/g in the control plant. This finding suggests that this technique may be useful to improve the allicin content of Egyptian garlic, which could be utilized as a good source for garlic-containing pharmaceutical preparations.",Journal of medicinal food,[],[],Chemical and Genetic Evaluation of Somaclonal Variants of Egyptian Garlic (Allium sativum L.).,[],[],https://www.ncbi.nlm.nih.gov/pubmed/19281347,2012,,,,, +9818412,"A new direct HPLC method with fluorescence detection has been developed for the routine analysis of riboflavin, flavin mononucleotide and flavin-adenine dinucleotide, in wines and other beverages. These compounds are the main agents responsible for the ""taste of light"" that some white wines and other beverages develop when they are exposed to the light, due to the formation of sulfur compounds that produce an anion/garlic odor. A Hewlett-Packard 1100 gradient liquid chromatograph with 1046A fluorescence detector was used. To improve the selectivity, each compound was monitored to fit the best lambda excitation/lambda emission (265/525 nm). A 500 nm cut-off filter was used. The column was a Hypersil C18 ODS, 200 x 2.1 mm, 5 microns particle size. The volume injected was 20 microliters. A constant flow-rate of 0.6 ml/min was used with two solvents: solvent A, 0.05 M buffer NaH2PO4 at pH = 3.0 with H3PO4 and solvent B, acetonitrile. The precision, linearity and sensitivity of this method have been established.",Journal of chromatography. A,"['D001628', 'D002851', 'D005486', 'D005182', 'D012256', 'D012680', 'D014920']","['Beverages', 'Chromatography, High Pressure Liquid', 'Flavin Mononucleotide', 'Flavin-Adenine Dinucleotide', 'Riboflavin', 'Sensitivity and Specificity', 'Wine']","Determination of riboflavin, flavin mononucleotide and flavin-adenine dinucleotide in wine and other beverages by high-performance liquid chromatography with fluorescence detection.","['Q000032', 'Q000379', 'Q000032', 'Q000032', 'Q000032', None, 'Q000032']","['analysis', 'methods', 'analysis', 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/9818412,1998,0.0,0.0,,, +9799098,"Alliinase [S-alk(en)yl-L-cysteine sulfoxide lyase], a pyridoxal-phosphate-(Pxy-P)-dependent enzyme, is responsible for the degradative conversion of S-alk(en)yl-L-cysteine sulfoxide to volatile odorous sulfur-containing metabolites in Allium plants. We have purified alliinase from shoots of Allium tuberosum (Chinese chive) to apparent homogeneity by SDS/polyacrylamide gel electrophoresis. A cDNA clone encoding alliinase was isolated from a cDNA library constructed from whole plants of A. tuberosum by hybridization screening with a synthetic 50-residue oligonucleotide encoding a conserved region of the alliinases from onion and garlic. The isolated cDNA encoded a protein of 476 amino acid residues with a molecular mass of 54083 Da. The deduced amino acid sequence exhibited 66-69% identities with those of reported alliinases from onion, garlic and shallot. The partial amino acid sequence, which was determined for a V8 protease-digested peptide fragment of the purified alliinase, was perfectly matched with the sequence deduced from the cDNA. An expression vector of recombinant alliinase cDNA was constructed in yeast. The catalytically active protein was in the soluble fraction of transformed yeast. Site-directed mutagenesis experiments indicated that Lys280 was essential for the catalytic activity and, thus, a possible Pxy-P-binding residue. The mRNA expression of the alliinase gene comprising a multigene family in the shoots of green plants was twofold higher than that in the roots of green plants; however, the expression in the shoots of etiolated plants was only 13% that in green shoots, although the expression in the roots was not remarkably different between in green and etiolated plants. Immunohistochemical investigation indicated that the alliinase protein is predominantly accumulated in the bundle sheath cells of shoots of A. tuberosum.",European journal of biochemistry,"['D000490', 'D000595', 'D001483', 'D013437', 'D002384', 'D002850', 'D003001', 'D018076', 'D004591', 'D004926', 'D007150', 'D008969', 'D016297', 'D009693', 'D017386']","['Allium', 'Amino Acid Sequence', 'Base Sequence', 'Carbon-Sulfur Lyases', 'Catalysis', 'Chromatography, Gel', 'Cloning, Molecular', 'DNA, Complementary', 'Electrophoresis, Polyacrylamide Gel', 'Escherichia coli', 'Immunohistochemistry', 'Molecular Sequence Data', 'Mutagenesis, Site-Directed', 'Nucleic Acid Hybridization', 'Sequence Homology, Amino Acid']","Alliinase [S-alk(en)yl-L-cysteine sulfoxide lyase] from Allium tuberosum (Chinese chive)--purification, localization, cDNA cloning and heterologous functional expression.","['Q000201', None, None, 'Q000737', None, None, None, None, None, 'Q000235', None, None, None, None, None]","['enzymology', None, None, 'chemistry', None, None, None, None, None, 'genetics', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9799098,1998,0.0,0.0,,, +9770309,An approach using supercritical fluid extraction (SFE) followed by clean-up with a AgNO3-loaded Florisil column was utilized for the analysis of four organochlorine pesticides (OCPs) in garlic. The organic sulfur extracted by SFE from garlic was removed by AgNO3 allowing OCPs to be determined by GC-electron-capture detection without interferences. All OCPs recoveries ranged from 85.0% to 110.0% and relative standard deviations were in the range of 3.9-7.2% for spiked samples. The described method may be used to analyze OCPs in garlic on a routine basis.,Journal of chromatography. A,"['D002849', 'D004563', 'D005737', 'D006843', 'D007306', 'D010573', 'D010946']","['Chromatography, Gas', 'Electrochemistry', 'Garlic', 'Hydrocarbons, Chlorinated', 'Insecticides', 'Pesticide Residues', 'Plants, Medicinal']",Supercritical fluid extraction and off-line clean-up for the analysis of organochlorine pesticide residues in garlic.,"[None, None, 'Q000737', None, 'Q000032', 'Q000032', None]","[None, None, 'chemistry', None, 'analysis', 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/9770309,1998,0.0,0.0,,pesticides, +9747536,"Supercritical fluid (SF) extracts of homogenized ramp (Allium tricoccum Ait.) were separated and characterized with liquid chromatography coupled with atmospheric pressure chemical ionization mass spectrometric identification. The profiles of SF extracts of aqueous homogenates of ramp bulbs from three different seasons and growing regions revealed that the thiosulfinates were major components. In addition, some of the cepaenes (alpha-sulfinyldisulfides) found in extracts of onion juice, as well as allyl containing cepaenes (2-propenyl l-(2-propenylsulfinyl)propyl disulfide), are present in the ramp extracts. The amount of allicin in ramp bulb homogenates ranged from approximately 10% to 50% of that found in extracts of aqueous garlic homogenates. The greater amount of the methyl 1-propenyl thiosulfinates in the ramp extracts relative to that found in the garlic extracts correlates with the flavor characteristics of ramp bulbs.",Phytochemistry,"['D000490', 'D002853', 'D004220', 'D013058', 'D010936', 'D013438', 'D013441']","['Allium', 'Chromatography, Liquid', 'Disulfides', 'Mass Spectrometry', 'Plant Extracts', 'Sulfhydryl Compounds', 'Sulfinic Acids']",Allium chemistry: identification of organosulfur compounds in ramp (Allium tricoccum) homogenates.,"['Q000737', None, 'Q000032', None, 'Q000032', 'Q000032', 'Q000032']","['chemistry', None, 'analysis', None, 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/9747536,1998,0.0,0.0,,not quantified and only previously reported , +9648236,"A compound showing antimicrobial activity was isolated from an oil-macerated garlic extract by silica gel column chromatography and preparative TLC. On basis of the results of NMR and MS analyses, it was identified as Z-4,5,9-trithiadeca-1,6-diene-9-oxide (Z-10-devinylajoene; Z-10-DA). Z-10-DA exhibited a broad spectrum of antimicrobial activity against such microorganisms as gram-positive and gram-negative bacteria and yeasts. The antimicrobial activity of Z-10-DA was comparable to that of Z-ajoene, but was superior to that of E-ajoene. Z-10-DA and Z-ajoene are different in respect of substitution of the allyl group by the methyl group flanking a sulfinyl group. This result suggests that substitution by the methyl group would also be effective for the inhibition of microbial growth.","Bioscience, biotechnology, and biochemistry","['D000900', 'D004220', 'D005737', 'D008826', 'D010936', 'D010938', 'D010946', 'D013329']","['Anti-Bacterial Agents', 'Disulfides', 'Garlic', 'Microbial Sensitivity Tests', 'Plant Extracts', 'Plant Oils', 'Plants, Medicinal', 'Structure-Activity Relationship']",Antimicrobial activity of a compound isolated from an oil-macerated garlic extract.,"['Q000737', 'Q000737', 'Q000382', None, 'Q000737', 'Q000737', None, None]","['chemistry', 'chemistry', 'microbiology', None, 'chemistry', 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9648236,1998,0.0,0.0,,, +9580446,"The controlled fermentation of peeled, blanched garlic, using a starter culture of Lactobacillus plantarum, was studied and compared with that of unblanched garlic. Blanching was carried out in hot water (90 degrees C) for 15 min. The starter grew abundantly in the case of blanched garlic, producing mainly lactic acid and reaching a pH of 3.8 after 7 days, but its growth was inhibited in unblanched garlic. Ethanol and fructose, coming from enzymatic activities of the garlic, and a green pigment were formed during the fermentation of unblanched garlic, but not of blanched garlic. The blanched garlic fermented by L. plantarum, even without a preservation treatment (pasteurization), was microbiologically stable during storage at 30 degrees C in an acidified brine (approximately 3% (w/w) NaCl and pH 3.5 at equilibrium), but fructans were hydrolyzed. The packed fermented product and that obtained by direct packing without fermentation were not significantly different with regard to flavour.",International journal of food microbiology,"['D002241', 'D002849', 'D002851', 'D003116', 'D004755', 'D000431', 'D005285', 'D005519', 'D005630', 'D005737', 'D006358', 'D006863', 'D019344', 'D007778', 'D010946', 'D013030']","['Carbohydrates', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Color', 'Enterobacteriaceae', 'Ethanol', 'Fermentation', 'Food Preservation', 'Fructans', 'Garlic', 'Hot Temperature', 'Hydrogen-Ion Concentration', 'Lactic Acid', 'Lactobacillus', 'Plants, Medicinal', 'Spain']",Lactic acid fermentation and storage of blanched garlic.,"['Q000096', None, None, None, 'Q000254', 'Q000032', None, None, 'Q000032', 'Q000378', None, None, 'Q000096', 'Q000254', None, None]","['biosynthesis', None, None, None, 'growth & development', 'analysis', None, None, 'analysis', 'metabolism', None, None, 'biosynthesis', 'growth & development', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9580446,1998,0.0,0.0,,no control, +9488677,"Two mannose-binding lectins, Allium sativum agglutinin (ASA) I (25 kDa) and ASAIII (48 kDa), from garlic bulbs have been purified by affinity chromatography followed by gel filtration. The subunit structures of these lectins are different, but they display similar sugar specificities. Both ASAI and ASAIII are made up of 12.5- and 11.5-kDa subunits. In addition, a complex (136 kDa) comprising a polypeptide chain of 54 +/- 4 kDa and the subunits of ASAI and ASAIII elutes earlier than these lectins on gel filtration. The 54-kDa subunit is proven to be alliinase, which is known to form a complex with garlic lectins. Constituent subunits of ASAI and ASAIII exhibit the same sequence at their amino termini. ASAI and ASAIII recognize monosaccharides in mannosyl configuration. The potencies of the ligands for ASAs increase in the following order: mannobiose (Manalpha1-3Man) < mannotriose (Manalpha1-6Manalpha1-3Man) approximately mannopentaose < Man9-oligosaccharide. The addition of two GlcNAc residues at the reducing end of mannotriose or mannopentaose enhances their potencies significantly, whereas substitution of both alpha1-3- and alpha1-6-mannosyl residues of mannotriose with GlcNAc at the nonreducing end increases their activity only marginally. The best manno-oligosaccharide ligand is Man9GlcNAc2Asn, which bears several alpha1-2-linked mannose residues. Interaction with glycoproteins suggests that these lectins recognize internal mannose as well as bind to the core pentasaccharide of N-linked glycans even when it is sialylated. The strongest inhibitors are the high mannose-containing glycoproteins, which carry larger glycan chains. Indeed, invertase, which contains 85% of its mannose residues in species larger than Man20GlcNAc, exhibited the highest binding affinity. No other mannose- or mannose/glucose-binding lectin has been shown to display such a specificity.",The Journal of biological chemistry,"['D000595', 'D001665', 'D001667', 'D002236', 'D002240', 'D013437', 'D002352', 'D005737', 'D006023', 'D006026', 'D006384', 'D037102', 'D037241', 'D008362', 'D008969', 'D009844', 'D037121', 'D010946', 'D011485', 'D017421', 'D043324']","['Amino Acid Sequence', 'Binding Sites', 'Binding, Competitive', 'Carbohydrate Conformation', 'Carbohydrate Sequence', 'Carbon-Sulfur Lyases', 'Carrier Proteins', 'Garlic', 'Glycoproteins', 'Glycoside Hydrolases', 'Hemagglutination', 'Lectins', 'Mannose-Binding Lectins', 'Mannosides', 'Molecular Sequence Data', 'Oligosaccharides', 'Plant Lectins', 'Plants, Medicinal', 'Protein Binding', 'Sequence Analysis', 'beta-Fructofuranosidase']",Garlic (Allium sativum) lectins bind to high mannose oligosaccharide chains.,"[None, None, 'Q000502', None, None, 'Q000378', 'Q000378', 'Q000737', 'Q000378', 'Q000378', 'Q000187', 'Q000378', None, 'Q000737', None, 'Q000378', None, None, None, None, None]","[None, None, 'physiology', None, None, 'metabolism', 'metabolism', 'chemistry', 'metabolism', 'metabolism', 'drug effects', 'metabolism', None, 'chemistry', None, 'metabolism', None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9488677,1998,0.0,0.0,,, +9399673,"There is a growing need for short-term and cost-effective bioassay to assess the efficacy of potential chemo-preventive agents. We report that the induction of glutathione (GSH) S-transferase pi (mGSTP1-1) by a chemo-preventive agent can be used as a reliable marker to assess its efficacy in retarding chemical carcinogenesis induced by benzo(a)pyrene (BP), which is a widespread environmental pollutant and believed to be a risk factor in human chemical carcinogenesis. This conclusion is based on 1) the relative contribution of mGSTP1-1 of the liver and forestomach of female A/J mice in the detoxification of the ultimate carcinogenic metabolite of BP, (+)-anti-7,8-dihydroxy-9, 10-oxy-7,8,9, 10-tetrahydrobenzo(a)pyrene [(+)-anti-BPDE]; and 2) a positive correlation between the induction of hepatic and forestomach mGSTP1-1 by 5 naturally occurring organosulfides (OSCs) from garlic (diallyl sulfide, diallyl disulfide, diallyl trisulfide, dipropyl sulfide and dipropyl disulfide) and their effectiveness in preventing BP-induced forestomach neoplasia in mice. In the liver, the combined contribution of other GSTs in the detoxification of (+)-anti-BPDE was far less than the contribution of mGSTP1-1 alone. Likewise, in the forestomach, the contribution of mGSTP1-1 far exceeded the combined contribution of other GSTs. Studies on the effects of OSCs against BP-induced forestomach neoplasia revealed a good correlation between their chemo-preventive efficacy and their ability to induce mGSTP1-1 expression in the liver (r = -0.89; p < 0.05) as well as in the forestomach (r = -0.97; p < 0.05). Our results suggest that the induction of mGSTP1-1 may be a reliable marker for evaluating the efficacy of potential inhibitors of BP-induced cancer in a murine model.",International journal of cancer,"['D000498', 'D000818', 'D016588', 'D001564', 'D001681', 'D002851', 'D004220', 'D004790', 'D005260', 'D005737', 'D051549', 'D005982', 'D007527', 'D008099', 'D051379', 'D008805', 'D010946', 'D011407', 'D012044', 'D013270', 'D013274', 'D013440', 'D016896']","['Allyl Compounds', 'Animals', 'Anticarcinogenic Agents', 'Benzo(a)pyrene', 'Biological Assay', 'Chromatography, High Pressure Liquid', 'Disulfides', 'Enzyme Induction', 'Female', 'Garlic', 'Glutathione S-Transferase pi', 'Glutathione Transferase', 'Isoenzymes', 'Liver', 'Mice', 'Mice, Inbred A', 'Plants, Medicinal', 'Propane', 'Regression Analysis', 'Stomach', 'Stomach Neoplasms', 'Sulfides', 'Treatment Outcome']",Induction of glutathione S-transferase pi as a bioassay for the evaluation of potency of inhibitors of benzo(a)pyrene-induced cancer in a murine model.,"['Q000302', None, 'Q000302', 'Q000633', 'Q000191', None, 'Q000302', None, None, 'Q000737', None, 'Q000096', 'Q000096', 'Q000201', None, None, None, 'Q000031', None, 'Q000201', 'Q000139', 'Q000302', None]","['isolation & purification', None, 'isolation & purification', 'toxicity', 'economics', None, 'isolation & purification', None, None, 'chemistry', None, 'biosynthesis', 'biosynthesis', 'enzymology', None, None, None, 'analogs & derivatives', None, 'enzymology', 'chemically induced', 'isolation & purification', None]",https://www.ncbi.nlm.nih.gov/pubmed/9399673,1998,0.0,0.0,,, +9177218,"""Natural"" polyreactive antibodies, which bind in a nonspecific manner to a range of biological molecules both of self- and nonself- origin, are normal constituents of serum and are a significant part of the immune repertoire in many species, including humans. Autoantibodies to sTNF-R (the 55-kDa extracellular domain of the human receptor to tumor necrosis factor alpha) were affinity purified from normal human sera using immobilized sTNF-R. The isolated anti-sTNF-R IgG bound both native and denatured forms of the receptor with low affinity. These antibodies also bound to different proteins and therefore are considered to be polyreactive. We used the anti-sTNF-R antibodies and purified polyreactive antibodies to mannose-specific lectin from garlic (Allium sativum) for screening a peptide library displayed on filamentous M13 phage. After the biopanning procedure, we failed to find epitopes with a consensus sequence; however, we found that proline is the most frequent amino acid in the selected phagotopes. Proline is commonly present at solvent-exposed sites in proteins, such as loops, turns, N-terminal first turn of helix, and random coils. Thus, structures containing proline can serve as conformation-dependent common ""public"" epitopes for polyreactive natural antibodies. Our findings may be important for understanding polyreactivity in general and for the significance of polyreactive natural antibodies in immunological homeostasis.",Proceedings of the National Academy of Sciences of the United States of America,"['D000595', 'D000818', 'D000906', 'D015703', 'D001323', 'D002846', 'D004797', 'D000939', 'D005737', 'D006801', 'D015151', 'D007074', 'D007256', 'D037102', 'D008358', 'D037121', 'D010946', 'D011336', 'D011392', 'D017433', 'D018124', 'D047888']","['Amino Acid Sequence', 'Animals', 'Antibodies', 'Antigens, CD', 'Autoantibodies', 'Chromatography, Affinity', 'Enzyme-Linked Immunosorbent Assay', 'Epitopes', 'Garlic', 'Humans', 'Immunoblotting', 'Immunoglobulin G', 'Information Systems', 'Lectins', 'Mannose', 'Plant Lectins', 'Plants, Medicinal', 'Probability', 'Proline', 'Protein Structure, Secondary', 'Receptors, Tumor Necrosis Factor', 'Receptors, Tumor Necrosis Factor, Type I']",The epitopes for natural polyreactive antibodies are rich in proline.,"[None, None, 'Q000302', 'Q000276', None, None, None, 'Q000737', None, None, None, None, None, 'Q000276', 'Q000276', None, None, None, None, None, 'Q000276', None]","[None, None, 'isolation & purification', 'immunology', None, None, None, 'chemistry', None, None, None, None, None, 'immunology', 'immunology', None, None, None, None, None, 'immunology', None]",https://www.ncbi.nlm.nih.gov/pubmed/9177218,1997,0.0,0.0,,, +9216741,"This study compared heterocyclic aromatic amines in marinated and unmarinated chicken breast meat flame-broiled on a propane grill. Chicken was marinated prior to grilling and the levels of several heterocyclic amines formed during cooking were determined by solid-phase extraction and HPLC. Compared with unmarinated controls, a 92-99% decrease in 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) was observed in whole chicken breast marinated with a mixture of brown sugar, olive oil, cider vinegar, garlic, mustard, lemon juice and salt, then grilled for 10, 20, 30 or 40 min. Conversely, 2-amino-3, 8-dimethylimidazo[4,5-f]quinoxaline (MeIQx) increased over 10-fold with marinating, but only at the 30 and 40 min cooking times. Marinating reduced the total detectable heterocyclic amines from 56 to 1.7 ng/g, from 158 to 10 ng/g and from 330 to 44 ng/g for grilling times of 20, 30 and 40 min, respectively. The mutagenic activity of the sample extracts was also measured, using the Ames/Salmonella assay. Mutagenic activity was lower in marinated samples cooked for 10, 20 and 30 min, but higher in the marinated samples cooked for 40 min, compared with unmarinated controls. Although a change in free amino acids, which are heterocyclic amine precursors, might explain the decrease in PhIP and increase in MeIQx, no such change was detected. Marinating chicken in one ingredient at a time showed that sugar was involved in the increased MeIQx, but the reason for the decrease in PhIP was unclear. PhIP decreased in grilled chicken after marinating with several individual ingredients. This work shows that marinating is one method that can significantly reduce PhIP concentration in grilled chicken.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000588', 'D000818', 'D002645', 'D002851', 'D003296', 'D005504', 'D006571', 'D007093', 'D008460', 'D009152', 'D009153', 'D011810', 'D012486']","['Amines', 'Animals', 'Chickens', 'Chromatography, High Pressure Liquid', 'Cooking', 'Food Analysis', 'Heterocyclic Compounds', 'Imidazoles', 'Meat', 'Mutagenicity Tests', 'Mutagens', 'Quinoxalines', 'Salmonella typhimurium']",Effects of marinating on heterocyclic amine carcinogen formation in grilled chicken.,"['Q000032', None, None, None, 'Q000379', None, 'Q000032', 'Q000032', None, None, 'Q000032', 'Q000032', 'Q000187']","['analysis', None, None, None, 'methods', None, 'analysis', 'analysis', None, None, 'analysis', 'analysis', 'drug effects']",https://www.ncbi.nlm.nih.gov/pubmed/9216741,1997,0.0,0.0,,percentage change, +9147057,"A procedure developed to separate the homodimeric and heterodimeric mannose-binding lectins from bulbs of garlic (Allium sativum L.) and ramsons (Allium ursinum L.) also enabled the isolation of stable lectin-alliinase complexes. Characterization of the individual lectins indicated that, in spite of their different molecular structure, the homomeric and heteromeric lectins resemble each other reasonably well with respect to their agglutination properties and carbohydrate-binding specificity. However, a detailed analysis of the lectin-alliinase complexes from garlic and ramsons bulbs demonstrated that only the heterodimeric lectins are capable of binding to the glycan chains of the alliinase molecules (EC 4.4.1.4). Moreover, it appears that only a subpopulation of the alliinase molecules is involved in the formation of lectin-alliinase complexes and that the complexed alliinase contains more glycan chains than the free enzyme. Finally, some arguments are given that the lectin-alliinase complexes do not occur in vivo but are formed in vitro after homogenization of the tissue.",Glycoconjugate journal,"['D000490', 'D000595', 'D013437', 'D002352', 'D002846', 'D003001', 'D019281', 'D004591', 'D005737', 'D037102', 'D046911', 'D008358', 'D037241', 'D008961', 'D008969', 'D010446', 'D037121', 'D018517', 'D010946', 'D011485', 'D011994', 'D016415', 'D017386']","['Allium', 'Amino Acid Sequence', 'Carbon-Sulfur Lyases', 'Carrier Proteins', 'Chromatography, Affinity', 'Cloning, Molecular', 'Dimerization', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Lectins', 'Macromolecular Substances', 'Mannose', 'Mannose-Binding Lectins', 'Models, Structural', 'Molecular Sequence Data', 'Peptide Fragments', 'Plant Lectins', 'Plant Roots', 'Plants, Medicinal', 'Protein Binding', 'Recombinant Proteins', 'Sequence Alignment', 'Sequence Homology, Amino Acid']",Isolation and characterization of lectins and lectin-alliinase complexes from bulbs of garlic (Allium sativum) and ramsons (Allium ursinum).,"['Q000201', None, 'Q000737', 'Q000737', None, None, None, None, 'Q000201', 'Q000737', None, None, None, None, None, 'Q000737', None, None, None, None, 'Q000737', None, None]","['enzymology', None, 'chemistry', 'chemistry', None, None, None, None, 'enzymology', 'chemistry', None, None, None, None, None, 'chemistry', None, None, None, None, 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9147057,1997,,,,, +9128734,"The microsomal fraction of homogenate of garlic (Allium sativum L.) bulbs contains a divinyl ether synthase which catalyzes conversion of (9Z,11E,13S)-13-hydroperoxy-9, 11-octadecadienoic acid and (9Z,11E,13S,15Z)-13-hydroperoxy-9,11,15-octadecatri eno ic acid into (9Z,11E,1'E,)-12-(1'-hexenyloxy)-9,11-dodecadienoic acid (etherolenic acid) and (9Z,11E,1'E,3'Z)-12-(1',3'-hexadienyloxy)-9,11-dode cadienoic acid (etherolenic acid), respectively. Two isomers of etherolenic acid were isolated. As shown by NMR spectrometry, the double bond configurations of these compounds were (9E,11E,1'E) and (9Z,11Z,1'E). Experiments with linoleic acid (13R,S)-hydroperoxide demonstrated that the S enantiomer was a much better substrate for the divinyl ether synthase compared to the R enantiomer. Incubation of (9Z,11E,13S)-[18O2]hydroperoxy-9,11-octadecadienoic acid led to the formation of etherolenic acid which retained 18O in the ether oxygen. An intermediary role of an epoxyallylic cation in etherolenic acid biosynthesis is postulated.",European journal of biochemistry,"['D002851', 'D003577', 'D005231', 'D005737', 'D007536', 'D015289', 'D008041', 'D008054', 'D009682', 'D008861', 'D010088', 'D010940', 'D010946', 'D013379']","['Chromatography, High Pressure Liquid', 'Cytochrome P-450 Enzyme System', 'Fatty Acids, Unsaturated', 'Garlic', 'Isomerism', 'Leukotrienes', 'Linoleic Acids', 'Lipid Peroxides', 'Magnetic Resonance Spectroscopy', 'Microsomes', 'Oxidoreductases', 'Plant Proteins', 'Plants, Medicinal', 'Substrate Specificity']",On the mechanism of biosynthesis of divinyl ether oxylipins by enzyme from garlic bulbs.,"[None, None, 'Q000096', 'Q000201', None, 'Q000378', 'Q000378', 'Q000378', None, 'Q000201', 'Q000378', None, None, None]","[None, None, 'biosynthesis', 'enzymology', None, 'metabolism', 'metabolism', 'metabolism', None, 'enzymology', 'metabolism', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9128734,1997,0.0,0.0,,protein, +9084912,"The chemoprotective effects of diallyl sulfide (DAS), a flavor component of garlic, have been attributed to its inhibitory effects on CYP2E1-mediated bioactivation of certain carcinogenic chemicals. In addition to being a competitive inhibitor of CYP2E1 in vitro, DAS is known to cause irreversible inhibition of CYP2E1 in rats in vivo. The latter property is believed to be mediated by the DAS metabolite diallyl sulfone (DASO2), which is thought to be a mechanism-based inhibitor of CYP2E1, although the underlying mechanism remains unknown. In order to investigate the nature of the reactive intermediate(s) responsible for the inactivation of CYP2E1 by DAS and its immediate metabolites, the present studies were carried out to detect and identify potential glutathione (GSH) conjugates of DAS and its metabolites diallyl sulfoxide (DASO) and DASO2. By means of ionspray LC-MS/MS, ten GSH conjugates were identified in bile collected from rats dosed with DAS, namely: S-[3-(S'-allyl-S'-dioxomercapto)-2-hydroxypropyl]glutathione (M1, M2; diastereomers), S-[3-(S'-allyl-S'-dioxomercapto)-2-hydroxypropyl]-glutathione (M5), S-[2-(S'-allyl-S'-dioxomercapto)-1-(hydroxymethyl)ethyl]glutathion e (M3, M4; diastereomers), S-[3-(S'-allylmercapto)-2-hydroxypropyl]glutathione (M6), S-(3-hydroxypropyl)-glutathione (M7), S-(2-carboxyethyl)glutathione (M8), allyl glutathionyl disulfide (M9), and S-allylglutathione (M10). With the exception of M6, all of the above GSH conjugates were detected in the bile of rats treated with DASO, while only M3, M4, M5, M7, M8, and M10 were found in the bile of rats treated with DASO2. Experiments conducted in vitro showed that GSH reacted spontaneously with DASO to form conjugates M9 and M10, and with DASO2 to form M10. In the presence of NADPH and GSH, incubation of DAS with cDNA-expressed rat CYP2E1 resulted in the formation of metabolites M6, M9, and M10, while incubation with DASO led to the formation of M3, M4, M5, M9, and M10. When DASO2 acted as substrate, CYP2E1 generated only conjugates M3, M4, M5, and M10. These results indicate that while DAS and DASO undergo extensive oxidation in vivo at the sulfur atom, the allylic carbon, and the terminal double bonds, CYP2E1 preferentially catalyzes oxidation of the sulfur atom to form the sulfoxide and the sulfone (DASO and DASO2). However, it appears that the end product of this sequence, namely, DASO2, undergoes further CYP2E1-mediated activation of the olefinic pi-bond, a reaction which transforms many terminal olefins to potent mechanism-based P450 inhibitors. We hypothesize, therefore, that it is this final metabolic event with DASO2 which leads to autocatalytic destruction of CYP2E1 and which is mainly responsible for the chemoprotective effects of DAS in vivo.",Chemical research in toxicology,"['D000498', 'D000818', 'D000975', 'D001646', 'D002478', 'D002853', 'D019392', 'D065691', 'D008401', 'D005978', 'D009682', 'D008297', 'D051381', 'D017207', 'D013440']","['Allyl Compounds', 'Animals', 'Antioxidants', 'Bile', 'Cells, Cultured', 'Chromatography, Liquid', 'Cytochrome P-450 CYP2E1', 'Cytochrome P-450 CYP2E1 Inhibitors', 'Gas Chromatography-Mass Spectrometry', 'Glutathione', 'Magnetic Resonance Spectroscopy', 'Male', 'Rats', 'Rats, Sprague-Dawley', 'Sulfides']",Metabolism of the chemoprotective agent diallyl sulfide to glutathione conjugates in rats.,"[None, None, 'Q000378', 'Q000378', None, None, 'Q000378', None, None, 'Q000378', None, None, None, None, 'Q000378']","[None, None, 'metabolism', 'metabolism', None, None, 'metabolism', None, None, 'metabolism', None, None, None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/9084912,1997,0.0,0.0,,no garlic, +9046016,"Erythrocyte agglutination by lectins from Allium sativum was inhibited only by mannose of the sugars tested. However, asialofetuin was more effective inhibitor of agglutination as compared to mannose. This led to the use of an asialofetuin-silica affinity column to isolate agglutinins of 110 and 25 kDa (ASA110 and ASA25). While ASA25 is a dimeric protein comprising of subunits of 12.5 and 13.0 kDa, ASA110 is a glycoprotein of two identical subunits of 47 kDa. ASA110 revealed to have a high content of aspartic acid, glycine, leucine and serine but low content of cysteine and methionine. It contains 14 residues of neutral sugars in addition to 43 residues of hexosamines per mole of lectin and requires metal ions for its functional conformation. Serological cross-reactions with other species showed some common epitopes of ASA110 and ASA25 present in A. porrum, A. ascalonicum, Narcissus alba, PHA and Con A but not in A. cepa. ASA110 with CHO cells indicated it to be weakly cytotoxic with LD50 of 160 microg/ml.",Molecular and cellular biochemistry,"['D000596', 'D000818', 'D001212', 'D002846', 'D060748', 'D005737', 'D006168', 'D006384', 'D006801', 'D037102', 'D008970', 'D037121', 'D010946', 'D011487', 'D011817', 'D012822', 'D000509']","['Amino Acids', 'Animals', 'Asialoglycoproteins', 'Chromatography, Affinity', 'Fetuins', 'Garlic', 'Guinea Pigs', 'Hemagglutination', 'Humans', 'Lectins', 'Molecular Weight', 'Plant Lectins', 'Plants, Medicinal', 'Protein Conformation', 'Rabbits', 'Silicon Dioxide', 'alpha-Fetoproteins']",A new high molecular weight agglutinin from garlic (Allium sativum).,"['Q000032', None, None, None, None, 'Q000737', None, 'Q000187', None, 'Q000037', None, None, None, None, None, None, None]","['analysis', None, None, None, None, 'chemistry', None, 'drug effects', None, 'antagonists & inhibitors', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/9046016,1997,,,,, +22060942,"The volatile compounds extracted from both traditional and industrial chorizo-a dry fermented sausage-were analysed by gas chromatography/mass spectrometry (GC/MS). One hundred and twenty-six peaks were detected relating to volatile extracts of which 115 were identified. The substances identified belonged to several classes of chemical: acids, alkanes, alcohols, aldehydes, sulphur compounds, ketones, esters, ethers, phenolic compounds, aromatic hydrocarbons, lactones, nitrogen compounds, terpenes, chloroform and benzofurane. Among the major compounds isolated were acetic acid, allyl-1-thiol and phenol. Larger quantities of most of the chemical groups were found in industrial compared to traditional chorizo, except for sulphur compounds. Typical breakdown products derived from lipid autooxidation were virtually negligible in chorizo. Of the chemicals isolated, sulphur compounds, phenols, acids, ethyl esters and carbonyls could have particular importance to the overall chorizo flavour. In addition, the changes in the proportions of volatile compounds during the ripening of chorizo were tracked. Most of the volatiles increased during ripening, especially acids, alcohols, esters, phenols, ketones and terpenes. On comparing the distribution of the sulphur compounds observed in chorizo with that of garlic, some noteworthy differences were observed. The reason for these differences is based upon several transformations of the sulphur compounds derived from garlic during the ripening and storage of chorizo.",Meat science,[],[],Volatile compounds in chorizo and their changes during ripening.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/22060942,2012,1.0,2.0,,, +9096851,"Tellurium (Te) demonstrates properties similar to those of elements known to be toxic to humans, and has applications in industrial processes, which are rapidly growing in importance and scale. It is relevant, therefore, to consider the tellurium physiology, toxicity, and methods for monitoring the element in biological and environmental specimens. Animal studies suggest that up to 25% of orally administered tellurium is absorbed in the gut. There is a biphasic elimination from the circulation with loss of about 50% within a short period, t1/2 = 0.81 d, and slower elimination of the residual Te, t1/2 = 12.9 d. Following a single ip, injection the largest proportion is in the kidney and bone, but with repeated oral administration, Te is found in the heart > > kidney, spleen, bone, and lung. Formation of dimethyl telluride is a characteristic feature of exposure, and gives a pungent garlic-like odor to breath, excreta, and the viscera. The main target sites for Te toxicity are the kidney, nervous system, skin, and the fetus (hydrocephalus). Te can be reliable measured in different specimens by several analytical techniques. Recent work has employed hydride generation atomic absorption spectrometry. Topics for further investigation are proposed.",Biological trace element research,"['D000284', 'D000818', 'D001842', 'D005243', 'D006207', 'D006801', 'D007553', 'D007668', 'D008168', 'D009206', 'D016273', 'D013154', 'D013691', 'D014018']","['Administration, Oral', 'Animals', 'Bone and Bones', 'Feces', 'Half-Life', 'Humans', 'Isotope Labeling', 'Kidney', 'Lung', 'Myocardium', 'Occupational Exposure', 'Spleen', 'Tellurium', 'Tissue Distribution']",Biochemistry of tellurium.,"[None, None, 'Q000187', 'Q000737', None, None, None, 'Q000187', 'Q000187', 'Q000378', None, 'Q000187', 'Q000008', None]","[None, None, 'drug effects', 'chemistry', None, None, None, 'drug effects', 'drug effects', 'metabolism', None, 'drug effects', 'administration & dosage', None]",https://www.ncbi.nlm.nih.gov/pubmed/9096851,1997,,,,no pdf access, +9012771,"Diallyl sulfide (DAS), a major flavour component of garlic, is known to modulate drug metabolism and may protect animals from chemically induced toxicity and carcinogenesis. In this study the effects of DAS on the oxidative metabolism and hepatotoxicity induced by acetaminophen (APAP) in rats were investigated. In the hepatotoxicity evaluation of Fischer 344 rats there was a dose-dependent increase in the odds of mortality rate by APAP (P = 0.009); DAS treatment significantly protected rats from APAP-related mortality (P = 0.026). Liver toxicity determined by lactate dehydrogenase activity was significantly increased by APAP treatment (0.75 g/kg). Pretreatment with DAS protected animals from APAP-induced liver toxicity in a time- and dose-dependent fashion. Treatment of DAS (50 mg/kg) 3 hr after APAP dosing significantly (P < 0.05) protected rats from APAP-induced liver toxicity. The metabolism of APAP (50 microM) in vitro was significantly inhibited by DAS (0.3-1 mM) in liver microsomes isolated from F344 rats. As the effect of DAS on APAP-induced hepatotoxicity in vivo was observed only when DAS was administered before or shortly after (< 3 hr) APAP dosing, data suggested that the protective effect of DAS is mainly at the metabolic activation step of APAP. However, the possibility that DAS may also have effects on other drug metabolism systems, such as glutathione (GSH) and glutathione S-transferases, cannot be ruled out.",Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,"['D000082', 'D000498', 'D018712', 'D000704', 'D000818', 'D016588', 'D000975', 'D002851', 'D004305', 'D005982', 'D007770', 'D008099', 'D008297', 'D011041', 'D051381', 'D011916', 'D012044', 'D013440']","['Acetaminophen', 'Allyl Compounds', 'Analgesics, Non-Narcotic', 'Analysis of Variance', 'Animals', 'Anticarcinogenic Agents', 'Antioxidants', 'Chromatography, High Pressure Liquid', 'Dose-Response Relationship, Drug', 'Glutathione Transferase', 'L-Lactate Dehydrogenase', 'Liver', 'Male', 'Poisoning', 'Rats', 'Rats, Inbred F344', 'Regression Analysis', 'Sulfides']",Protective effects of diallyl sulfide on acetaminophen-induced toxicities.,"['Q000633', None, 'Q000633', None, None, 'Q000494', 'Q000494', None, None, 'Q000378', 'Q000378', 'Q000187', None, 'Q000401', None, None, None, 'Q000494']","['toxicity', None, 'toxicity', None, None, 'pharmacology', 'pharmacology', None, None, 'metabolism', 'metabolism', 'drug effects', None, 'mortality', None, None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/9012771,1997,0.0,0.0,,, +8923805,"Although amphotericin B remains the drug of choice for systemic fungal infections, its use is limited by considerable side effects. In The Peoples' Republic of China, commercial Allium sativum derived compounds are widely used as an antifungal drug to treat systemic fungal infections. To evaluate the scientific merit of using A. sativum derived compounds as antifungal agents, we studied a Chinese commercial preparation, allitridium. This preparation contained mainly diallyl trisulfide as confirmed by high performance liquid chromatography. Allitridium, with and without amphotericin B, was tested to determine its efficacy in killing three isolates of Cryptococcus neoformans. The minimum inhibitory concentration of the commercial preparation was 50 micrograms/ml and the minimum fungicidal concentration was 100 micrograms/ml against 1 x 10(5) organisms of C. neoformans. In addition, the commercial preparation was shown to be synergistic with amphotericin B in the in vitro killing of C. neoformans. This study demonstrates that diallyl trisulfide and other polysulfides possess potent in vitro fungicidal effects and their activity is synergistic with amphotericin B. These observations lend laboratory support for the treatment of cryptococcal infections with both amphotericin B and the Chinese commercial preparation.",Planta medica,"['D000498', 'D000666', 'D000935', 'D003455', 'D004357', 'D005737', 'D006801', 'D016919', 'D008826', 'D010946', 'D013440']","['Allyl Compounds', 'Amphotericin B', 'Antifungal Agents', 'Cryptococcus neoformans', 'Drug Synergism', 'Garlic', 'Humans', 'Meningitis, Cryptococcal', 'Microbial Sensitivity Tests', 'Plants, Medicinal', 'Sulfides']",Enhanced diallyl trisulfide has in vitro synergy with amphotericin B against Cryptococcus neoformans.,"['Q000494', 'Q000494', 'Q000494', 'Q000187', None, None, None, 'Q000382', None, None, 'Q000494']","['pharmacology', 'pharmacology', 'pharmacology', 'drug effects', None, None, None, 'microbiology', None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/8923805,1997,,,,no pdf access, +9174913,An immobilized salicylaldehyde (sal) was used to build various salicylaldehyde-copper-amino acid (Sal-Cu-AA) complexes which are stable at a range of pH values (2.0-11.0). The complexes were found to bind protein molecules as IMAC resins. Thirteen proteins were examined for their binding to a Sal-Cu-Gly column. The efficacy of the Sal-Cu-AA resin for protein separation were demonstrated by two examples. The first was a new purification process for garlic lectins from garlic crude extract. It seems that in this case the Sal-Cu-AA resins were more selective than IDA resin. The second was immobilization of concanavalin A (Con A) on the resin and using the immobilized Con A for affinity chromatography of mannose-rich glycoprotein ovalbumin. The Con A could be later eluted with EDTA or imidazole and the Sal-containing polymer could be recharged again for further use.,Journal of molecular recognition : JMR,"['D000447', 'D000596', 'D000818', 'D013437', 'D002645', 'D002846', 'D003208', 'D003300', 'D005737', 'D006639', 'D037102', 'D010047', 'D037121', 'D010940', 'D010946', 'D012116', 'D012259']","['Aldehydes', 'Amino Acids', 'Animals', 'Carbon-Sulfur Lyases', 'Chickens', 'Chromatography, Affinity', 'Concanavalin A', 'Copper', 'Garlic', 'Histidine', 'Lectins', 'Ovalbumin', 'Plant Lectins', 'Plant Proteins', 'Plants, Medicinal', 'Resins, Plant', 'Ribonuclease, Pancreatic']",Salicylaldehyde-metal-amino acid ternary complex: a new tool for immobilized metal affinity chromatography.,"['Q000737', 'Q000737', None, 'Q000737', None, 'Q000379', 'Q000302', 'Q000737', 'Q000737', 'Q000032', 'Q000737', 'Q000302', None, 'Q000302', None, 'Q000737', 'Q000737']","['chemistry', 'chemistry', None, 'chemistry', None, 'methods', 'isolation & purification', 'chemistry', 'chemistry', 'analysis', 'chemistry', 'isolation & purification', None, 'isolation & purification', None, 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/9174913,1997,,,,, +8871121,"Samples of vegetable oils on the Brazilian market including rape seed, corn, soybean, sunflower, rice, palm and garlic were analysed for benzo(a)pyrene (B(a)P). The analytical method involved liquid-liquid extraction, clean-up on silica gel column and determination by high performance liquid chromatography using fluorescence detection. The limit of detection was 0.5 microgram/kg. Benzo(a)pyrene was detected in almost all samples, at levels up to 58.9 micrograms/kg. The mean levels of B(a)P in rice, sunflower, soybean, corn and palm oils were 1.8, 0.2, 2.2, 10.8 and 2.1 micrograms/kg respectively. No B(a)P was detected in garlic and rape seed oils. The data indicate that the levels of B(a)P found in Brazilian corn oils are relatively higher than those published in the literature for European corn oils.",Food additives and contaminants,"['D001564', 'D001938', 'D002273', 'D002851', 'D005506', 'D010938']","['Benzo(a)pyrene', 'Brazil', 'Carcinogens', 'Chromatography, High Pressure Liquid', 'Food Contamination', 'Plant Oils']",Benzo(a)pyrene in Brazilian vegetable oils.,"['Q000032', None, 'Q000032', None, 'Q000032', 'Q000032']","['analysis', None, 'analysis', None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/8871121,1997,,,,, +8671745,"Three heterocyclic aromatic amines, 2-amino-3-methyl-imidazo[4, 5-f]quinoline (IQ), 2-amino-3,4-dimethylimidazo[4,5-f]quinoxaline and 2-amino-3,4-dimethylimidazo[4,5-f]quinoline, have been found in boiled pork juice. We have investigated the effect of naturally occurring organosulfur compounds, which are present in garlic and onion, on mutagen formation in boiled pork juice. Six organosulfur compounds - diallyl disulfide (DAD), dipropyl disulfide (DPD), diallyl sulfide (DAS), allyl methyl sulfide (AMS), allyl mercaptan (AM) and cysteine - were added separately to the pork juice before reflux boiling and then the mutagenicity of each sample was examined with the Salmonella typhimurium strain TA98 in the presence of S9 mix. All six compounds were found to inhibit the mutagenicity of boiled pork juice. The greatest inhibitory effect was observed with DAD and DPD, and this was 111-fold higher than that of the lowest, cysteine. To elucidate the inhibitory effect of DAD on mutagen formation in boiled pork juice, the major mutagenic fractions were monitored after HPLC separation by their mutagenicity with S. typhimurium TA98. By comparing the retention times of authentic IQ compounds from boiled pork juice with those following the addition of DAD, we showed that the mutagenicity of three major fractions was significantly inhibited compared with those same fractions in boiled pork juice alone. In addition, the Maillard reaction products (MRPs) in the boiled pork juice with and without the addition of DAD were quantified and identified by capillary gas chromatography and gas chromatography-mass spectrometry. The results show that the reduction in the total amount of MRPs (pyridines, pyrazines, thiophenes and thiazoles) in boiled pork juice after boiling for 12 h is correlated with their mutagenicity. Among the MRPs, tetrahydrothiophene-3-one exhibited the strongest correlation. These data suggest that the inhibition of IQ mutagen formation by DAD is mediated through the reduction of MRPs production.",Mutagenesis,"['D000498', 'D000588', 'D000818', 'D002273', 'D002849', 'D002851', 'D004220', 'D006571', 'D015416', 'D013058', 'D008461', 'D009152', 'D011804', 'D012486', 'D013440', 'D013552']","['Allyl Compounds', 'Amines', 'Animals', 'Carcinogens', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Disulfides', 'Heterocyclic Compounds', 'Maillard Reaction', 'Mass Spectrometry', 'Meat Products', 'Mutagenicity Tests', 'Quinolines', 'Salmonella typhimurium', 'Sulfides', 'Swine']",Naturally occurring diallyl disulfide inhibits the formation of carcinogenic heterocyclic aromatic amines in boiled pork juice.,"[None, 'Q000378', None, 'Q000378', None, None, 'Q000494', 'Q000378', None, None, 'Q000633', None, 'Q000378', 'Q000187', 'Q000494', None]","[None, 'metabolism', None, 'metabolism', None, None, 'pharmacology', 'metabolism', None, None, 'toxicity', None, 'metabolism', 'drug effects', 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/8671745,1996,,,,, +8821433,"From wild garlic Allium ursinum three new flavonoid glycosides were identified as kaempferol 3-O-beta-neohesperidoside-7-O-[2-O-(trans-p-coumaroyl)]-beta -D- glucopyranoside, kaempferol 3-O-beta-neohesperidoside-7-O-[2-O-(trans-feruloyl)]-beta-D- glucopyranoside, kaempferol 3-O-beta-neohesperidoside-7-O-[2-O-(trans-p-coumaroyl)-3-O-b eta-D- glucopyranosyl]-beta-D-glucopyranoside and characterized as the peracetates. Additionally, two known flavonoid glycosides kaempferol 3-O-beta-glucopyranoside and kaempferol 3-O-beta-neohesperidoside were isolated. The isolated compounds showed an inhibition of human platelet aggregation.",Phytochemistry,"['D001792', 'D002240', 'D005419', 'D005737', 'D006027', 'D006801', 'D009682', 'D008969', 'D010936', 'D010946', 'D010975', 'D016339', 'D013056']","['Blood Platelets', 'Carbohydrate Sequence', 'Flavonoids', 'Garlic', 'Glycosides', 'Humans', 'Magnetic Resonance Spectroscopy', 'Molecular Sequence Data', 'Plant Extracts', 'Plants, Medicinal', 'Platelet Aggregation Inhibitors', 'Spectrometry, Mass, Fast Atom Bombardment', 'Spectrophotometry, Ultraviolet']",The flavonoids of Allium ursinum.,"['Q000187', None, 'Q000737', 'Q000737', 'Q000737', None, None, None, 'Q000737', None, 'Q000737', None, None]","['drug effects', None, 'chemistry', 'chemistry', 'chemistry', None, None, None, 'chemistry', None, 'chemistry', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/8821433,1996,0.0,0.0,,not quantified, +8717417,"Garlic has been reported to provide protection against hypercholesterolemic atherosclerosis and ischemia-reperfusion-induced arrhythmias and infarction. Oxygen free radicals (OFRs) have been implicated as causative factors in these diseases and antioxidants have been shown to be effective against these conditions. The effectiveness of garlic in these disease states could be due to its ability to scavenge OFRs. However, the OFR-scavenging activity of garlic is not known. Also it is not known if its activity is affected by cooking. We therefore investigated, using high pressure liquid chromatography, the ability of garlic extract (heated or unheated) to scavenge exogenously generated hydroxyl radical (.OH). .OH was generated by photolysis of H2O2 (1.2-10 mumoles/ml) with ultraviolet (UV) light and was trapped with salicylic acid (500 nmoles/ml). H2O2 produced .OH in a concentration-dependent manner as estimated by .OH adduct products 2,3-dihydroxybenzoic acid (DHBA) and 2,5-DHBA. Garlic extract (5-100 microliters/ml) produced an inhibition (30-100%) of 2,3-DHBA and 2,5-DHBA generated by photolysis of H2O2 (5.00 pmoles/ml) in a concentration-dependent manner. Its activity is reduced by 10% approximately when heated to 100 degrees C for 20, 40 or 60 min. The extent of reduction in activity was similar for the three heating periods. Garlic extract prevented the .OH-induced formation of malondialdehyde in the rabbit liver homogenate in a concentration-dependent manner. It alone did not affect the MDA levels in the absence of .OH. These results indicate that garlic extract is a powerful scavenger of .OH and that heating reduces its activity slightly.",Molecular and cellular biochemistry,"['D000818', 'D002851', 'D016166', 'D005737', 'D005841', 'D006358', 'D062385', 'D017665', 'D015227', 'D008099', 'D008315', 'D010782', 'D010936', 'D010946', 'D011817', 'D012459', 'D020156', 'D012680', 'D013481', 'D014466']","['Animals', 'Chromatography, High Pressure Liquid', 'Free Radical Scavengers', 'Garlic', 'Gentisates', 'Hot Temperature', 'Hydroxybenzoates', 'Hydroxyl Radical', 'Lipid Peroxidation', 'Liver', 'Malondialdehyde', 'Photolysis', 'Plant Extracts', 'Plants, Medicinal', 'Rabbits', 'Salicylates', 'Salicylic Acid', 'Sensitivity and Specificity', 'Superoxides', 'Ultraviolet Rays']",Evaluation of hydroxyl radical-scavenging property of garlic.,"[None, None, None, None, None, None, None, None, 'Q000187', 'Q000378', 'Q000032', None, 'Q000494', None, None, None, None, None, 'Q000302', None]","[None, None, None, None, None, None, None, None, 'drug effects', 'metabolism', 'analysis', None, 'pharmacology', None, None, None, None, None, 'isolation & purification', None]",https://www.ncbi.nlm.nih.gov/pubmed/8717417,1996,,,,, +8875572,"The effect of Allium sativum (Liliacea) on trypanosome-infected mice was examined. At a dose of 5.0 mg/ml, the oily extract from the pulp completely suppressed the ability of the parasites to be infective in the host. Column chromatography of the extract gave four fractions: ethylacetate/methanol, ethylacetate/ethanol, benzene/methanol, and acetic acid/methanol. Among these fractions, the acetic acid/methanol fraction retained the trypanocidal features of the crude extract. It cured experimentally infected mice of trypanosomiasis in 4 days when given at a dose of 120 mg/kg per day. The extract also manifested inhibition of procyclic forms of Trypanosoma brucei brucei and phospholipases from T. congolense, T. b. brucei, T. vivax. The extract appears to be diallyl-disulfide (DAD) and may interfere with the parasites' synthesis of membrane lipids.",Parasitology research,"['D000818', 'D004305', 'D004791', 'D005737', 'D051379', 'D010741', 'D010936', 'D010946', 'D013261', 'D014344', 'D014346']","['Animals', 'Dose-Response Relationship, Drug', 'Enzyme Inhibitors', 'Garlic', 'Mice', 'Phospholipases A', 'Plant Extracts', 'Plants, Medicinal', 'Sterols', 'Trypanocidal Agents', 'Trypanosoma brucei brucei']",Allium sativum-induced death of African trypanosomes.,"[None, None, 'Q000494', 'Q000737', None, 'Q000037', 'Q000494', None, 'Q000032', 'Q000494', 'Q000737']","[None, None, 'pharmacology', 'chemistry', None, 'antagonists & inhibitors', 'pharmacology', None, 'analysis', 'pharmacology', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/8875572,1997,,,,, +8870956,"N-Acetyl-S-allyl-L-cysteine (allylmercapturic acid, ALMA) was previously detected in urine from humans consuming garlic. Exposure of rats to allyl halides is also known to lead to excretion of ALMA in urine. ALMA is a potential biomarker for exposure assessment of workers exposed to allyl halides. It is not known whether garlic consumption can lead to urinary concentrations of ALMA which may interfere with biological monitoring of exposure to allyl halides by determination of urinary ALMA. Therefore, this study was undertaken to determine the cumulative excretion and the excretion kinetics of ALMA in urine of humans consuming garlic. Six human volunteers were given orally two garlic tablets, each containing 100 mg garlic extract (each representing 300 mg fresh garlic). Three of the volunteers consumed additional garlic after the garlic tablet intake. Urine samples were collected up to 24 h after the intake of the garlic tablets. ALMA was identified in the urine using gas chromatography-mass spectrometry (GC-MS) and determined quantitatively with a limit of detection of 0.10 microgram/ml with gas chromatography with sulphur selective detection. The total amount of ALMA found in urine of volunteers who consumed two garlic tablets was 0.43 +/- 0.14 mg (n = 3). In the urine of the three volunteers who consumed not only two garlic tablets but also additional fresh garlic, a significantly higher amount of ALMA was excreted in the urine, 1.4 +/- 0.2 mg (n = 3). The elimination half-life of ALMA, estimated from urinary excretion rate versus time curves, was 6.0 +/- 1.3 h (n = 5). One volunteer, who ate additional garlic, showed an irregular elimination profile and was excluded from this estimation. The highest urinary concentration of ALMA found in this study was 2.2 micrograms/ml. In a preliminary biological monitoring study of exposure in workers with potential exposure to allyl chloride (AC) up to the occupational exposure limit of 1 ppm (8-h TWA), we recently found urinary ALMA concentrations up to 4 micrograms/ml. Based on the results presented here, we conclude that garlic consumption is a potential confounder when monitoring human exposure to allylhalides and other chemicals leading to ALMA excretion when ALMA is used as a biomarker of exposure.",Archives of toxicology,"['D000111', 'D000284', 'D000328', 'D005260', 'D005737', 'D006801', 'D008297', 'D008991', 'D010946']","['Acetylcysteine', 'Administration, Oral', 'Adult', 'Female', 'Garlic', 'Humans', 'Male', 'Monitoring, Physiologic', 'Plants, Medicinal']",Urinary excretion of N-acetyl-S-allyl-L-cysteine upon garlic consumption by human volunteers.,"['Q000031', None, None, None, None, None, None, None, None]","['analogs & derivatives', None, None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/8870956,1997,,,,, +8806047,"Diallyl sulfone (DASO2) is a metabolite of diallyl sulfide, a compound derived from garlic. The present study investigated the effect of DASO2 as a protective agent against acetaminophen (APAP)-induced hepatotoxicity in mice. Oral administration of DASO2 protected mice against the APAP-induced hepatotoxicity in a dose- and time-dependent manner. When administered 1 hour prior to, immediately after, or 20 minutes after a toxic dose of APAP, DASO2 at a dose of 25 mg/kg completely protected mice from development of hepatotoxicity, as indicated by liver histopathology and serum lactate dehydrogenase levels. Protective effect was observed when DASO2 at a dose as low as 5 mg/kg was given to mice 1 hour prior to APAP administration. Oral administration of DASO2 to mice 1 hour prior to a toxic dose of APAP significantly inhibited the APAP-induced glutathione depletion in the liver. DASO2 treatment also decreased the levels of oxidative APAP metabolites in the plasma without affecting the concentrations of nonoxidative APAP metabolites. In liver microsomes, 0.1 mM of DASO2 caused a 60% decrease in the rate of APAP oxidation to N-acetyl-p-benzoquinone imine, which was determined as glutathione conjugate. This inhibitory effect is mainly due to its inhibition of cytochrome P450 2E1 activity; with an IC50 value equal to 0.11 mM. DASO2 also slightly inhibited the activities of P450s 3A and 1A, with IC50 values > 5 mM. Furthermore, a single oral dose of DASO2 inactivated P450 2E1- and P450 1A-dependent activities in liver microsomes. The results suggest that the protective effect of DASO2 against APAP-induced hepatotoxicity is due to its ability to block acetaminophen bioactivation mainly by the inactivation and inhibition of P450 2E1.",Journal of biochemical toxicology,"['D000082', 'D000284', 'D000498', 'D018712', 'D000818', 'D016227', 'D002851', 'D065691', 'D004305', 'D005978', 'D007097', 'D066298', 'D007770', 'D008099', 'D008297', 'D051379', 'D008862', 'D010084', 'D013450']","['Acetaminophen', 'Administration, Oral', 'Allyl Compounds', 'Analgesics, Non-Narcotic', 'Animals', 'Benzoquinones', 'Chromatography, High Pressure Liquid', 'Cytochrome P-450 CYP2E1 Inhibitors', 'Dose-Response Relationship, Drug', 'Glutathione', 'Imines', 'In Vitro Techniques', 'L-Lactate Dehydrogenase', 'Liver', 'Male', 'Mice', 'Microsomes, Liver', 'Oxidation-Reduction', 'Sulfones']",Protective effect of diallyl sulfone against acetaminophen-induced hepatotoxicity in mice.,"['Q000008', None, 'Q000008', 'Q000008', None, 'Q000378', None, None, None, 'Q000378', 'Q000378', None, 'Q000097', 'Q000187', None, None, 'Q000187', None, 'Q000008']","['administration & dosage', None, 'administration & dosage', 'administration & dosage', None, 'metabolism', None, None, None, 'metabolism', 'metabolism', None, 'blood', 'drug effects', None, None, 'drug effects', None, 'administration & dosage']",https://www.ncbi.nlm.nih.gov/pubmed/8806047,1997,,,,, +8759327,"We present an overview of the development and use of our selected-ion flow tube (SIFT) technique as a sensitive, quantitative method for the rapid, real-time analysis of the trace gas content of atmospheric air and human breath, presenting some pilot data from various research areas in which this method will find valuable application. We show that it is capable of detecting and quantifying trace gases, in complex mixtures such as breath, which are present at partial pressures down to about 10 parts per billion. Following discussions of the principles involved in this SIFT method of analysis, of the experiments which we have carried out to establish its quantitative validity, and of the air and breath sampling techniques involved, we present sample data on the detection and quantification of trace gases on the breath of healthy people and of patients suffering from renal failure and diabetes. We also show how breath ammonia can be accurately quantified from a single breath exhalation and used as an indicator of the presence in the stomach of the bacterium Helicobacter pylori. Health and safety applications are exemplified by analyses of the gases of the gases of cigarette smoke and on the breath of smokers. The value of this analytical method in environmental science is demonstrated by the analyses of petrol vapour, car exhaust emissions and the trace organic vapours detected in town air near a busy road. Final examples of the value of this analytical method are the detection and quantification of the gases emitted from crushed garlic and from breath following the chewing of a mint, which demonstrate its potential in food and flavour research. Throughout the paper we stress the advantages of this SIFT method compared to conventional mass spectrometry for trace gas analysis of complex mixtures, emphasizing its selectivity, sensitivity and real-time analysis capability. Finally, we note that whilst the current SIFT is strictly laboratory based, both transportable and portable instruments are under construction and development. These instruments will surely extend the application of this analytical technique into more areas and allow greater exploitation of their on-line and real-time features.",Rapid communications in mass spectrometry : RCM,"['D000388', 'D016902', 'D001944', 'D008401', 'D005740', 'D006801', 'D014028']","['Air', 'Air Pollution, Indoor', 'Breath Tests', 'Gas Chromatography-Mass Spectrometry', 'Gases', 'Humans', 'Tobacco Smoke Pollution']",The novel selected-ion flow tube approach to trace gas analysis of air and breath.,"['Q000032', 'Q000032', 'Q000295', 'Q000379', 'Q000032', None, None]","['analysis', 'analysis', 'instrumentation', 'methods', 'analysis', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/8759327,1996,,,,, +8723021,"The effect of garlic on the serum lipid profile has been the subject of controversy. This study was therefore designed to examine the effects of allicin, an active constituent of garlic, on the lipid profile in a rabbit model.",Coronary artery disease,"['D000818', 'D008076', 'D008078', 'D002851', 'D005737', 'D000960', 'D008055', 'D008297', 'D010946', 'D011446', 'D011817', 'D013441']","['Animals', 'Cholesterol, HDL', 'Cholesterol, LDL', 'Chromatography, High Pressure Liquid', 'Garlic', 'Hypolipidemic Agents', 'Lipids', 'Male', 'Plants, Medicinal', 'Prospective Studies', 'Rabbits', 'Sulfinic Acids']","Alteration of lipid profile in hyperlipidemic rabbits by allicin, an active constituent of garlic.","[None, 'Q000097', 'Q000097', None, None, 'Q000494', 'Q000097', None, None, None, None, 'Q000494']","[None, 'blood', 'blood', None, None, 'pharmacology', 'blood', None, None, None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/8723021,1996,,,,, +8595261,"Alliinase (EC 4.4.1.4) catalyses the production of allicin (thio-2-propene-1-sulfinic acid S-allyl ester), a biologically active compound which is also responsible for the characteristic smell of garlic. It was demonstrated that alliinase which contains 5.5-6% of neutral sugars, gives clear PAS-staining, binds to Con A and can form a complex with garlic mannose-specific lectin (ASA). Evidence that the formation of such a complex is mediated by the interaction of the carbohydrate of the glycoprotein enzyme with the lectin was obtained from a radioligand assay which demonstrated the binding of alliinase to ASA and competitive inhibition of this binding by methyl alpha-D-mannoside. ASA I was shown as the lectin mainly present in the complex with alliinase. The results of this study also demonstrate that alliinase is glycosylated at Asn146 in the sequence Asn146-Met147-Thr148.",Glycoconjugate journal,"['D000595', 'D001216', 'D013437', 'D002846', 'D002850', 'D002918', 'D003488', 'D004591', 'D005737', 'D006020', 'D006031', 'D007700', 'D037102', 'D037241', 'D008969', 'D008970', 'D010449', 'D037121', 'D010946', 'D011487']","['Amino Acid Sequence', 'Asparagine', 'Carbon-Sulfur Lyases', 'Chromatography, Affinity', 'Chromatography, Gel', 'Chymotrypsin', 'Cyanogen Bromide', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Glycopeptides', 'Glycosylation', 'Kinetics', 'Lectins', 'Mannose-Binding Lectins', 'Molecular Sequence Data', 'Molecular Weight', 'Peptide Mapping', 'Plant Lectins', 'Plants, Medicinal', 'Protein Conformation']",Alliinase (alliin lyase) from garlic (Alliium sativum) is glycosylated at ASN146 and forms a complex with a garlic mannose-specific lectin.,"[None, None, 'Q000737', None, None, None, None, None, 'Q000201', 'Q000737', None, None, 'Q000378', None, None, None, None, None, None, None]","[None, None, 'chemistry', None, None, None, None, None, 'enzymology', 'chemistry', None, None, 'metabolism', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/8595261,1996,,,,, +7672118,"Incubations of [1-14C]linoleic acid or [1-14C]-(9Z,11E, 13S)-13-hydropero xy-9,11-octadecadienoic acid (13-HPOD) with juice of garlic bulbs lead to the formation of one predominant labelled product, viz., the novel divinyl ether (9Z,11E, 1'E)-12-(1'-hexenyloxy)-9,11-dodecadienoic acid ('etheroleic acid'). With lesser efficiency [1-14C]alpha-linolenic acid or [1-14C](9Z,11E, 13S,15Z)-13-hydroperoxy-9,11,15-octadecatrienoic acid (13-HPOT) are converted in this way into (9Z,11E,1'E,1'E,3'Z)-12-(1',3'-hexadienyloxy)-9,11- dodecadienoic acid ('etherolenic acid'). Thus, garlic bulbs possess the activity of a new 13-hydroperoxide-specific divinyl ether synthase.",FEBS letters,"['D002851', 'D005231', 'D005737', 'D019787', 'D008041', 'D008042', 'D008054', 'D008084', 'D009682', 'D010946', 'D017962']","['Chromatography, High Pressure Liquid', 'Fatty Acids, Unsaturated', 'Garlic', 'Linoleic Acid', 'Linoleic Acids', 'Linolenic Acids', 'Lipid Peroxides', 'Lipoxygenase', 'Magnetic Resonance Spectroscopy', 'Plants, Medicinal', 'alpha-Linolenic Acid']",The lipoxygenase pathway in garlic (Allium sativum L.) bulbs: detection of the novel divinyl ether oxylipins.,"[None, 'Q000378', 'Q000201', None, 'Q000378', 'Q000378', 'Q000378', 'Q000378', None, None, 'Q000378']","[None, 'metabolism', 'enzymology', None, 'metabolism', 'metabolism', 'metabolism', 'metabolism', None, None, 'metabolism']",https://www.ncbi.nlm.nih.gov/pubmed/7672118,1995,0.0,0.0,,, +8594422,"Garlic has been claimed to be effective against diseases, in the pathophysiology of which oxygen free radicals (OFRs) have been implicated. Effectiveness of garlic could be due to its ability to scavenge OFRs. However, its antioxidant activity is not known. We investigated the ability of allicin (active ingredient of garlic) contained in the commercial preparation Garlicin to scavenge hydroxyl radicals (.OH) using high pressure liquid chromatographic (HPLC) method. .OH was generated by photolysis of H2O2 (1.25-10 mumoles/ml) with ultraviolet light and was trapped with salicylic acid which is hydroxylated to produce .OH adduct products 2,3- and 2,5-dihydroxybenzoic acid (DHBA). H2O2 produced a concentration-dependent .OH as estimated by .OH adduct products 2,3-DHBA and 2,5-DHBA. Allicin equivalent in Garlicin (1.8, 3.6, 7.2, 14.4, 21.6, 28.8 and 36 micrograms) produced concentration-dependent decreases in the formation of 2,3-DHBA and 2,5-DHBA. The inhibition of formation of 2,3-DHBA and 2,5-DHBA with 1.8 micrograms/ml was 32.36% and 43.2% respectively while with 36.0 micrograms/ml the inhibition was approximately 94.0% and 90.0% respectively. The decrease in .OH adduct products was due to scavenging of .OH and not by scavenging of formed .OH adduct products. Allicin prevented the lipid peroxidation of liver homogenate in a concentration-dependent manner. These results suggest that allicin scavenges .OH and Garlicin has antioxidant activity.",Molecular and cellular biochemistry,"['D000818', 'D000975', 'D002851', 'D016166', 'D005737', 'D005841', 'D062385', 'D017665', 'D015227', 'D008099', 'D008315', 'D010946', 'D011817', 'D017382', 'D012459', 'D020156', 'D013441', 'D013607']","['Animals', 'Antioxidants', 'Chromatography, High Pressure Liquid', 'Free Radical Scavengers', 'Garlic', 'Gentisates', 'Hydroxybenzoates', 'Hydroxyl Radical', 'Lipid Peroxidation', 'Liver', 'Malondialdehyde', 'Plants, Medicinal', 'Rabbits', 'Reactive Oxygen Species', 'Salicylates', 'Salicylic Acid', 'Sulfinic Acids', 'Tablets']","Antioxidant activity of allicin, an active principle in garlic.","[None, 'Q000494', None, 'Q000494', 'Q000737', None, 'Q000378', 'Q000378', 'Q000187', 'Q000737', 'Q000032', None, None, 'Q000378', 'Q000378', None, 'Q000302', None]","[None, 'pharmacology', None, 'pharmacology', 'chemistry', None, 'metabolism', 'metabolism', 'drug effects', 'chemistry', 'analysis', None, None, 'metabolism', 'metabolism', None, 'isolation & purification', None]",https://www.ncbi.nlm.nih.gov/pubmed/8594422,1996,,,,, +8537101,"It is known that human serum contains natural antibodies to self and non-self proteins. We wished to determine whether normal human serum contains antibodies to dietary proteins that were never injected. We found that human serum contains antibodies to the two major proteins from cloves of garlic (Allium sativum) which is used as a flavorigard dietary food additive. The antibodies found were directed against alliinase and mannose-specific Allium sativum agglutinin (ASA). The antibodies were purified by affinity chromatography on their corresponding antigens. The purified immunoglobulins were mainly of the IgG and IgM classes and could be divided into two categories--specific and crossreactive. The anti-alliinase antibodies were highly specific, while anti-ASA antibodies were polyreactive. Some of the possible reasons for this difference in specificity are suggested.",Immunology letters,"['D000328', 'D000373', 'D000906', 'D000918', 'D013437', 'D002846', 'D004044', 'D005260', 'D005737', 'D006801', 'D007113', 'D037102', 'D008297', 'D037241', 'D037121', 'D010946']","['Adult', 'Agglutinins', 'Antibodies', 'Antibody Specificity', 'Carbon-Sulfur Lyases', 'Chromatography, Affinity', 'Dietary Proteins', 'Female', 'Garlic', 'Humans', 'Immunity, Innate', 'Lectins', 'Male', 'Mannose-Binding Lectins', 'Plant Lectins', 'Plants, Medicinal']",Natural antibodies to dietary proteins: the existence of natural antibodies to alliinase (Alliin lyase) and mannose-specific lectin from garlic (Allium sativum) in human serum.,"[None, 'Q000097', 'Q000097', None, 'Q000276', None, 'Q000276', None, 'Q000276', None, None, 'Q000276', None, None, None, None]","[None, 'blood', 'blood', None, 'immunology', None, 'immunology', None, 'immunology', None, None, 'immunology', None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/8537101,1996,0.0,0.0,,protein, +7646805,"Extracted by n-butanol and separated by two-dimensional TLC, the astragalus saponin 1 in Suanqi Oral Liquid was determined by vanillin-perchloric acid colorimetric method. The recovery and RSD were 96.3% (n = 5) and 0.75% respectively.",Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica,"['D002855', 'D003124', 'D004338', 'D004365', 'D005737', 'D010946', 'D011786', 'D012503']","['Chromatography, Thin Layer', 'Colorimetry', 'Drug Combinations', 'Drugs, Chinese Herbal', 'Garlic', 'Plants, Medicinal', 'Quality Control', 'Saponins']",[Two-dimensional thin layer chromatographic-colorimetric determination of Astragalus saponin 1 in suanqi oral liquid].,"[None, None, None, 'Q000737', 'Q000737', None, None, 'Q000032']","[None, None, None, 'chemistry', 'chemistry', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/7646805,1995,,,,, +7604070,"Aqueous extracts of fresh garlic (Allium sativum L.) inhibited efficiently the activity of adenosine deaminase (ADA) of cultivated endothelial cells. The IC50 value (range between 6 and 120 micrograms per ml) depended on the origin and storage time of the fresh garlic. The aqueous extraction of dried garlic powder showed also an inhibition if ADA activity, but the IC50 value was in the range of 2.5 mg per ml indicating that parts of the active principle were lost during the preparation of the garlic powder. The inhibition of endothelial ADA by garlic extracts seems to contribute to the hypotensive activity and vessel protective effects of A. sativum L.",Die Pharmazie,"['D058892', 'D000818', 'D001011', 'D001794', 'D002417', 'D002460', 'D002851', 'D004730', 'D005737', 'D000960', 'D010936', 'D010946', 'D013441']","['Adenosine Deaminase Inhibitors', 'Animals', 'Aorta', 'Blood Pressure', 'Cattle', 'Cell Line', 'Chromatography, High Pressure Liquid', 'Endothelium, Vascular', 'Garlic', 'Hypolipidemic Agents', 'Plant Extracts', 'Plants, Medicinal', 'Sulfinic Acids']",Inhibition of adenosine deaminase activity of aortic endothelial cells by extracts of garlic (Allium sativum L.).,"[None, None, 'Q000166', 'Q000187', None, None, None, 'Q000187', 'Q000737', 'Q000737', 'Q000494', None, 'Q000737']","[None, None, 'cytology', 'drug effects', None, None, None, 'drug effects', 'chemistry', 'chemistry', 'pharmacology', None, 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/7604070,1995,,,,, +7981966,"Xyloglucans were isolated from the 24% KOH-soluble fraction of the cell walls of bulbs of onion (Allium cepa), garlic (Allium sativum) and their hybrid. The polysaccharides yielded single peaks upon gel filtration with average molecular weights of 65,000 for onion, 55,000 for garlic and 82,000 for the hybrid. Compositional analysis of the oligosaccharide units after digestion with an endo-1,4-beta-glucanase from Streptomyces indicated that the polysaccharides were constructed of four kinds of repeating oligosaccharide unit, namely, a decasaccharide (glucose/xylose/galactose/fucose, 4 : 3: 2 : 1), a nonasaccharide (glucose/xylose/galactose/fucose, 4 : 3 : 1 : 1), an octasaccharide (glucose/xylose/galactose, 4 : 3 : 1), and a heptasaccharide (glucose/xylose, 4 : 3). The xyloglucan from the hybrid contained highly fucosylated units that resembled those from onion rather than from garlic. The analysis also revealed that the xyloglucans from Allium species contain highly substituted xylosyl residues with fucosyl-galactosyl residues, suggesting that these monocotyledonous plants resemble dicotyledons in the structural features of their xyloglucans.",Plant & cell physiology,"['D000490', 'D002240', 'D002850', 'D002852', 'D005737', 'D005936', 'D006824', 'D008969', 'D009844', 'D010946', 'D011134', 'D014990']","['Allium', 'Carbohydrate Sequence', 'Chromatography, Gel', 'Chromatography, Ion Exchange', 'Garlic', 'Glucans', 'Hybridization, Genetic', 'Molecular Sequence Data', 'Oligosaccharides', 'Plants, Medicinal', 'Polysaccharides', 'Xylans']","The oligosaccharide units of the xyloglucans in the cell walls of bulbs of onion, garlic and their hybrid.","['Q000737', None, None, None, 'Q000737', 'Q000737', None, None, 'Q000032', None, 'Q000737', None]","['chemistry', None, None, None, 'chemistry', 'chemistry', None, None, 'analysis', None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/7981966,1995,,,,, +7979352,"The garlic plant (Allium sativum) alliinase (EC 4.4.1.4), which catalyzes the synthesis of allicin, was purified to homogeneity from bulbs using various steps, including hydrophobic chromatography. Molecular and biochemical studies showed that the enzyme is a dimer of two subunits of MW 51.5 kDa each. Its Km using synthetic S-allylcysteine sulfoxide (+ isomer) as substrate was 1.1 mM, its pH optimum 6.5, and its isoelectric point 6.35. The enzyme is a glycoprotein containing 6% carbohydrate. N-terminal sequences of the intact polypeptide chain as well as of a number of peptides obtained after cyanogen bromide cleavage were obtained. Cloning of the cDNAs encoding alliinase was performed by a two-step strategy. In the first, a cDNA fragment (pAli-1-450 bp) was obtained by PCR using a mixed oligonucleotide primer synthesized according to a 6-amino acid segment near the N-terminal of the intact polypeptide. The second step involved screening of garlic lambda gt11 and lambda ZAPII cDNA libraries with pAli-1, which yielded two clones; one was nearly full length and the second was full length. These clones exhibited some degree of DNA sequence divergence, especially in their 3' noncoding regions, suggesting that they were encoded by separate genes. The nearly full length cDNA was fused in frame to a DNA encoding a signal peptide from alpha wheat gliadin, and expressed in Xenopus oocytes. This yielded a 50 kDa protein that interacted with the antibodies against natural bulb alliinase. Northern and Western blot analyses showed that the bulb alliinase was highly expressed in bulbs, whereas a lower expression level was found in leaves, and no expression was detected in roots. Strikingly, the roots exhibited an abundant alliinase activity, suggesting that this tissue expressed a distinct alliinase isozyme with very low homology to the bulb enzyme.",Applied biochemistry and biotechnology,"['D000595', 'D000818', 'D001483', 'D015153', 'D013437', 'D002851', 'D003001', 'D003488', 'D003545', 'D018076', 'D005260', 'D005737', 'D005786', 'D006863', 'D066298', 'D007525', 'D008969', 'D008970', 'D009693', 'D010946', 'D016133', 'D011108', 'D012333', 'D011817', 'D013441', 'D014982']","['Amino Acid Sequence', 'Animals', 'Base Sequence', 'Blotting, Western', 'Carbon-Sulfur Lyases', 'Chromatography, High Pressure Liquid', 'Cloning, Molecular', 'Cyanogen Bromide', 'Cysteine', 'DNA, Complementary', 'Female', 'Garlic', 'Gene Expression Regulation', 'Hydrogen-Ion Concentration', 'In Vitro Techniques', 'Isoelectric Focusing', 'Molecular Sequence Data', 'Molecular Weight', 'Nucleic Acid Hybridization', 'Plants, Medicinal', 'Polymerase Chain Reaction', 'Polymers', 'RNA, Messenger', 'Rabbits', 'Sulfinic Acids', 'Xenopus laevis']",Alliin lyase (Alliinase) from garlic (Allium sativum). Biochemical characterization and cDNA cloning.,"[None, None, None, None, 'Q000737', None, None, 'Q000378', 'Q000031', 'Q000737', None, 'Q000201', 'Q000235', None, None, None, None, None, None, None, None, None, 'Q000235', None, 'Q000378', None]","[None, None, None, None, 'chemistry', None, None, 'metabolism', 'analogs & derivatives', 'chemistry', None, 'enzymology', 'genetics', None, None, None, None, None, None, None, None, None, 'genetics', None, 'metabolism', None]",https://www.ncbi.nlm.nih.gov/pubmed/7979352,1994,,,,, +17236056,"A C-S-lyase preparation from ramson, ALLIUM URSINUM L., has been purified to apparent homogeneity. Separation techniques applied were hydrophobic interaction chromatography, anion exchange chromatography, and gel permeation chromatography. A 52-fold purification was obtained. The enzyme could be characterized by a molecular mass of M (r) = 150000 with subunits of 50 000. Its isoelectric point was determined to be at 4.7. The pH-optimum for the substrate-dependent turnover was found at 6.0. The temperature optimum was at 35 degrees C. (+)-Alliin as the substrate caused the highest enzymatic reaction velocity. The lowest K (m) value was observed with (+)- S-propyl- L-cysteine sulfoxide. Inhibitor constants were elaborated for the deoxy-derivatives of the substrates inserted and, likewise, for related amino acids. The protein was sensitive to low concentrations of hydroxylamine, indicating pyridoxal phosphate as a cofactor. Activation energies were determined for the cleavage of alliin, S-propyl- L-cysteine sulfoxide and S-methyl- L-cysteine sulfoxide, and were found to be in the range of 9 to 13 kJ . mol (-1).",Planta medica,[],[],"Purification and Characterization of a C-S-Lyase from Ramson, the Wild Garlic,Allium ursinum.",[],[],https://www.ncbi.nlm.nih.gov/pubmed/17236056,2012,,,,no PDF access, +8053972,"Three groups of 3 rats received oral doses (8 mg/kg) of garlic constituents (alliin, allicin and vinyldithiines (2-vinyl-[4H]-1,3-dithiine and 3-vinyl-[4H]-1,2-dithiine)) in the form of an oil macerate of the 35S-labeled substance. The measured activity was referred to 35S-alliin (35S-alliin equivalents). The blood activity levels in each group were monitored for 72 h. For 35S-allicin and the labeled vinyldithiines the excretion with the urine, feces, and exhaled air was also measured. The distribution among the organs (whole-body autoradiography) and the urinary metabolite pattern (thin-layer chromatography) were also determined. For 35S-alliin the blood activity profile differed considerably from those of 35S-allicin and the labeled vinyldithiines: both the absorption and the elimination of the radioactivity were distinctly faster than for the other garlic constituents, maximum blood levels being reached within the first 10 min and elimination from the blood being almost complete after 6 h. For the other garlic constituents the maximum blood levels were not reached until 30-60 min (35S-allicin) or 120 min (vinyldithiines) p.a. and blood levels > 1000 ng-Eq/ml were still present at the end of the study after 72 h. The mean total urinary and fecal excretion after 72 h was 85.5% (35S-allicin) or 92.3% (labeled vinyldithiines) of the dose. The urinary excretion indicates a minimum absorption rate of 65% (35S-allicin) or 73% (vinyldithiines). It is uncertain whether the 19-21% recovered in the feces was unabsorbed substance or had been excreted via the bile or intestinal mucosa. The exhaled air showed only traces of activity although the whole-body autoradiographs, after fairly long exposure (96 h), showed distinct enrichment of activity in the mucosa of the airways and pharynx. The activity is deposite mainly in the cartilage of the vertebral column and ribs. There was no detectable difference in organ distribution between 35S-allicin and the labeled vinyldithiines. All that could be established from the urinary metabolite pattern was that unchanged 35S-allicin and unchanged labeled vinyldithiines are absent. There is therefore extensive metabolization. The metabolites must have a very polar structure with acid functional groups since satisfactory separation was achievable only with acid solvent systems. Conjugates with sulfuric or glucuronic acid were not detectable. These results reveal no differences in pharmacokinetic behavior between 35S-allicin and the labeled vinyldithiines. A final verdict as to whether the metabolites, which may be pharmacologically active, are identical must await further studies designed to identify the metabolites.",Arzneimittel-Forschung,"['D000818', 'D001345', 'D002855', 'D003545', 'D005243', 'D005260', 'D005737', 'D006571', 'D007553', 'D008297', 'D010946', 'D051381', 'D017207', 'D013441', 'D013457', 'D013462', 'D014753']","['Animals', 'Autoradiography', 'Chromatography, Thin Layer', 'Cysteine', 'Feces', 'Female', 'Garlic', 'Heterocyclic Compounds', 'Isotope Labeling', 'Male', 'Plants, Medicinal', 'Rats', 'Rats, Sprague-Dawley', 'Sulfinic Acids', 'Sulfur Compounds', 'Sulfur Radioisotopes', 'Vinyl Compounds']","[The pharmacokinetics of the S35 labeled labeled garlic constituents alliin, allicin and vinyldithiine].","[None, None, None, 'Q000031', 'Q000737', None, 'Q000737', 'Q000097', None, None, None, None, None, 'Q000097', None, None, 'Q000097']","[None, None, None, 'analogs & derivatives', 'chemistry', None, 'chemistry', 'blood', None, None, None, None, None, 'blood', None, None, 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/8053972,1994,,,,, +7517069,"The multielement (Al, Ca, Cd, Ce, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Si, and Zn) levels of various common vegetables (bean, broccoli, cabbage, cauliflower, lettuce, marrow, onion, parsnip, spinach, sprouts, sweet corn, and tomato); fruits (grape and strawberry); herbs (garlic, lemon balm, marjoram, mint, rosemary and tarragon); local pasture species and surface soils collected from a commercial garden centre located within a distance of 30 m of the London Orbital Motorway (M25) is presented. Comparative values are given from a background area, namely a domestic garden located in the North Yorkshire Dales National Park area. Analysis was undertaken by inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma-source mass spectrometry (ICP-MS) with quality control assessment using four international biological reference materials; BCR:CRM 62 Olive Leaves, NIST 1575 Pine Needles, NIST 1573 Tomato Leaves, and NIST 1572 Citrus Leaves. Inter-analytical method comparison is given using two methods of ICP-MS; namely conventional pneumatic nebulisation of sample solution, and direct solids analysis by laser ablation; and neutron activation analysis methods (NAA). For the elements listed there is a good precision obtained by ICP-MS and NAA. In particular levels of < +/- 1-10% (rsd) are obtained. Comparison of data with certified values and other analytical methods are generally of very good agreement. Lead levels in background areas ranged from 0.0008 to 0.340 microgram/g (fresh weight) for plant material; with the lead magnitude greater for grasses > herbs > vegetables > cereals > fruits. Measured values are in good agreement with reported literature values. The lowest Pb values are for marrow, lettuce, tomato and sweet corn samples (approximately 0.001-0.021 microgram/g). 'Green' leaf material levels were approximately 0.02-0.10 microgram/g (i.e. sprouts and cabbage). Root vegetables contain higher levels, approximately 0.02-0.125 microgram/g (especially carrot), reflecting possible metal uptake from soil. The highest vegetable Pb values are for leek and onion (approximately 0.35 microgram/g). Background values are also provided for nineteen elements (Al, As, B, Ba, Br, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Rb, Se, Sr, V, and Zn). Exposure to motor vehicle activities at a site some 30 m from the M25 shows only significant increases in Pb for unwashed plant material and surface soils. Typically Pb levels of 40-80% can be removed by washing plant surfaces resulting in metal levels similar to background areas.(ABSTRACT TRUNCATED AT 400 WORDS)",The Science of the total environment,"['D000393', 'D004784', 'D007854', 'D008131', 'D010945', 'D015203', 'D012989', 'D001335']","['Air Pollutants', 'Environmental Monitoring', 'Lead', 'London', 'Plants, Edible', 'Reproducibility of Results', 'Soil Pollutants', 'Vehicle Emissions']",Metal dispersion and transportational activities using food crops as biomonitors.,"['Q000032', 'Q000379', 'Q000032', None, 'Q000737', None, 'Q000032', None]","['analysis', 'methods', 'analysis', None, 'chemistry', None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/7517069,1994,,,,no PDF access, +8200919,"Supercritical fluid chromatography-mass spectrometry has been used successfully to identify allicin (2-propene-1-sulfinothioic acid S-2-propenyl ester), the predominant thiosulfinate in freshly cut garlic (Allium sativum). A low oven temperature (50 degrees C) and low restrictor tip temperature (115 degrees C) were needed in order to obtain a chemical ionization (CI) mass spectrum of allicin with the protonated molecular ion, m/z 163, as the major ion. The effects of tip temperature on the CI mass spectrum of allicin are presented.",Journal of chromatographic science,"['D002845', 'D005737', 'D008401', 'D013058', 'D010946', 'D013441', 'D013696']","['Chromatography', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Mass Spectrometry', 'Plants, Medicinal', 'Sulfinic Acids', 'Temperature']",Supercritical fluid chromatography of garlic (Allium sativum) extracts with mass spectrometric identification of allicin.,"['Q000379', 'Q000737', None, None, None, 'Q000032', None]","['methods', 'chemistry', None, None, None, 'analysis', None]",https://www.ncbi.nlm.nih.gov/pubmed/8200919,1994,,,,, +8223549,"Lectin cDNA clones encoding the two mannose-binding lectins from ramsons (allium ursinum L.) bulbs, AUAI and AUAII (AUA, Allium ursinum agglutinin), were isolated and characterized. Sequence comparison of the different cDNA clones isolated revealed three types of lectin clones called LECAUAG0, LECAUAG1 and LECAUAG2, which besides the obvious differences in their sequences also differ from each other in the number of potential glycosylation sites within the C-terminal peptide of the lectin precursor. In vivo biosynthesis studies of the ramson lectins have shown that glycosylated lectin precursors occur in the organelle fraction of radioactively labeled ramson bulbs. Despite the similarities between the A. ursinum and the A. sativum (garlic) lectins at the protein level, molecular cloning of the two ramson lectins has shown that the lectin genes in A. ursinum are organized differently. Whereas in A. sativum the lectin polypeptides of the heterodimeric ASAI are encoded by one large precursor, those of the heterodimeric AUAI lectin are derived from two different precursors. These results are confirmed by Northern blot hybridization of A. ursinum RNA which, after hybridization with a labeled lectin cDNA, reveals only one band of 800 nucleotides in contrast to A. sativum RNA which yields two bands of 1400 and 800 nucleotides. Furthermore it is shown that the two mannose-binding lectins are differentially expressed.",European journal of biochemistry,"['D000490', 'D000595', 'D001483', 'D015152', 'D002850', 'D003001', 'D018076', 'D004591', 'D006031', 'D037102', 'D008358', 'D008969', 'D037121', 'D017386']","['Allium', 'Amino Acid Sequence', 'Base Sequence', 'Blotting, Northern', 'Chromatography, Gel', 'Cloning, Molecular', 'DNA, Complementary', 'Electrophoresis, Polyacrylamide Gel', 'Glycosylation', 'Lectins', 'Mannose', 'Molecular Sequence Data', 'Plant Lectins', 'Sequence Homology, Amino Acid']",The mannose-specific lectins from ramsons (Allium ursinum L.) are encoded by three sets of genes.,"['Q000235', None, None, None, None, None, 'Q000737', None, None, 'Q000737', 'Q000378', None, None, None]","['genetics', None, None, None, None, None, 'chemistry', None, None, 'chemistry', 'metabolism', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/8223549,1993,0.0,0.0,,, +1516037,"N-Nitroso compounds (NOCs) are known to be strong carcinogens in various animals including primates (Preussman and Stewart, (1984) N-Nitroso Compounds). Human exposure to these compounds can be by ingestion or inhalation of preformed NOCs or by endogenous nitrosation from naturally occurring precursors (Bartsch and Montesano, Carcinogenesis, 5 (1984) 1381-1393; Tannebaum (1979) Naturally Occuring Carcinogens, Mutagens and Modulators of Carcinogenesis; Shephard et al., Food Chem. Toxicol., 25 (1987) 91-108). Several factors present in the diet can modify levels of endogenously formed nitrosamines by acting as catalysts or inhibitors. Compounds in the human diet that alter nitrosamine formation would thus play an important role in carcinogenesis study. Earlier researchers have reported the nitrite scavenging nature of sulphydryl compounds (Williams, Chem. Soc. Rev., 15 (1983) 171-196). We therefore studied the modifying effect of sulphydryl compounds viz., cysteine (CE), cystine (CI), glutathione (GU), cysteamine (CEA), cystamine (CEI), cysteic acid (CIA) and thioglycolic acid (TGA) on the nitrosation of model amines viz., pyrrolidine (PYR), piperidine (NPIP) and morpholine (NMOR). Many of these compounds are present in the food we consume. The present work also describes the inhibitory effect of onion and garlic juices on the nitrosation reactions. Both onion and garlic are known to contain sulphur compounds (Block, Sci. Am., 252 (1985) 114-119). Most of these compounds behave as antinitrosating agents and their inhibitory activity towards formation of carcinogenic nitrosamines, under different conditions is described.",Cancer letters,"['D000490', 'D016588', 'D002849', 'D003538', 'D003543', 'D003544', 'D003545', 'D003553', 'D004032', 'D005737', 'D005978', 'D006801', 'D009025', 'D009602', 'D015538', 'D010880', 'D010946', 'D011759', 'D013438', 'D013864']","['Allium', 'Anticarcinogenic Agents', 'Chromatography, Gas', 'Cystamine', 'Cysteamine', 'Cysteic Acid', 'Cysteine', 'Cystine', 'Diet', 'Garlic', 'Glutathione', 'Humans', 'Morpholines', 'Nitrosamines', 'Nitrosation', 'Piperidines', 'Plants, Medicinal', 'Pyrrolidines', 'Sulfhydryl Compounds', 'Thioglycolates']",Inhibitory effect of diet related sulphydryl compounds on the formation of carcinogenic nitrosamines.,"[None, 'Q000737', None, 'Q000737', 'Q000737', 'Q000737', 'Q000737', 'Q000737', None, None, 'Q000737', None, 'Q000037', 'Q000037', 'Q000187', 'Q000037', None, 'Q000037', 'Q000737', 'Q000737']","[None, 'chemistry', None, 'chemistry', 'chemistry', 'chemistry', 'chemistry', 'chemistry', None, None, 'chemistry', None, 'antagonists & inhibitors', 'antagonists & inhibitors', 'drug effects', 'antagonists & inhibitors', None, 'antagonists & inhibitors', 'chemistry', 'chemistry']",https://www.ncbi.nlm.nih.gov/pubmed/1516037,1992,0.0,0.0,,, +1394291,"Two new mannose-binding lectins were isolated from garlic (Allium sativum, ASA) and ramsons (Allium ursinum, AUA) bulbs, of the family Alliaceae, by affinity chromatography on immobilized mannose. The carbohydrate-binding specificity of these two lectins was studied by quantitative precipitation and hapten-inhibition assay. ASA reacted strongly with a synthetic linear (1----3)-alpha-D-mannan and S. cerevisiae mannan, weakly with a synthetic (1----6)-alpha-D-mannan, and failed to precipitate with galactomannans from T. gropengiesseri and T. lactis-condensi, a linear mannopentaose, and murine IgM. On the other hand, AUA gave a strong reaction of precipitation with murine IgM, and good reactions with S. cerevisiae mannan and both synthetic linear mannans, suggesting that the two lectins have somewhat different binding specificities for alpha-D-mannosyl units. Of the saccharides tested as inhibitors of precipitation, those with alpha-(1----3)-linked mannosyl units were the best inhibitors of ASA, the alpha-(1----2)-, alpha-(1----4)-, and alpha-(1----6)-linked mannobioses and biosides having less than one eighth the affinity of the alpha-(1----3)-linked compounds. The N-terminal amino acid sequence of ASA exhibits 79% homology with that of AUA, and moderately high homology (53%) with that of snowdrop bulb lectin, also an alpha-D-mannosyl-binding lectin.",Carbohydrate research,"['D000490', 'D000595', 'D001667', 'D002240', 'D037102', 'D008351', 'D008358', 'D037241', 'D008969', 'D037121', 'D011971', 'D017385']","['Allium', 'Amino Acid Sequence', 'Binding, Competitive', 'Carbohydrate Sequence', 'Lectins', 'Mannans', 'Mannose', 'Mannose-Binding Lectins', 'Molecular Sequence Data', 'Plant Lectins', 'Receptors, Immunologic', 'Sequence Homology']",New mannose-specific lectins from garlic (Allium sativum) and ramsons (Allium ursinum) bulbs.,"['Q000737', None, None, None, 'Q000737', 'Q000737', 'Q000737', None, None, None, 'Q000737', None]","['chemistry', None, None, None, 'chemistry', 'chemistry', 'chemistry', None, None, None, 'chemistry', None]",https://www.ncbi.nlm.nih.gov/pubmed/1394291,1992,0.0,0.0,,, +1610423,"The activity of microsomal NADPH-cytochrome-P-450-reductase and NADH-cytochrome-b5-reductase are inhibited after the addition of an aqueous extract of a pharmaceutical preparation of garlic (Allium sativum, L.) to buffer-suspended microsomes. Incubation of garlic extract with isolated pig liver microsomes also decreases the activity of cytochrome P-450-dependent ethoxycoumarin deethylation. As measured by malondialdehyde release, the effects on the enzyme system are evidently not due to lipid peroxidation. No loss of cytochrome P-450 pigment is observed. Moreover, it could be shown that addition of garlic extract displays no protective effect on microsomal lipids when oxidation occurs spontaneously or is enforced by short-wave UV-irradiation. The above findings were reproduced after applying a HPLC-purified preparation of alliin to the incubation mixtures, suggesting that alliin is the active principle for the inhibitory effects observed in vitro.",Arzneimittel-Forschung,"['D000818', 'D002851', 'D003579', 'D042966', 'D005260', 'D005737', 'D066298', 'D015227', 'D008297', 'D008862', 'D009245', 'D009251', 'D010936', 'D010946', 'D013552']","['Animals', 'Chromatography, High Pressure Liquid', 'Cytochrome Reductases', 'Cytochrome-B(5) Reductase', 'Female', 'Garlic', 'In Vitro Techniques', 'Lipid Peroxidation', 'Male', 'Microsomes, Liver', 'NADH Dehydrogenase', 'NADPH-Ferrihemoprotein Reductase', 'Plant Extracts', 'Plants, Medicinal', 'Swine']",In vitro inhibition of cytochrome P-450 reductases from pig liver microsomes by garlic extracts.,"[None, None, 'Q000037', None, None, None, None, 'Q000187', None, 'Q000187', 'Q000037', 'Q000037', 'Q000494', None, None]","[None, None, 'antagonists & inhibitors', None, None, None, None, 'drug effects', None, 'drug effects', 'antagonists & inhibitors', 'antagonists & inhibitors', 'pharmacology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/1610423,1992,,,,, +1776837,"Components of garlic have been shown to inhibit a variety of tumors induced by chemical carcinogens. In this study we determined the effects of ajoene and diallyl sulfide (DAS), two organosulfur compounds of garlic, on the metabolism and DNA binding of aflatoxin B1 (AFB1) using rat liver 9000Xg supernatant as the metabolic activation system. Organosoluble and water-soluble metabolites of [3H]AFB1 were isolated by reverse-phase high performance liquid chromatography (HPLC). The effects of ajoene and DAS on glutathione-S-transferase (GST) were determined using 1-chloro-2,4-dinitrobenzene as the substrate. Ajoene and DAS at 100 mg/ml inhibited [3H]AFB1 binding to calf thymus DNA and adduct formation. They decreased the formation of both organosoluble and water-soluble metabolites of [3H]AFB1. Neither compound significantly affected GST activity. These results indicate that ajoene and DAS affected AFB1 metabolism and DNA binding by inhibiting phase I enzymes and may therefore be considered as potential cancer chemopreventive agents.",Anticancer research,"['D016604', 'D000498', 'D000818', 'D004247', 'D004220', 'D005737', 'D005978', 'D008297', 'D010936', 'D010946', 'D051381', 'D011919', 'D013440']","['Aflatoxin B1', 'Allyl Compounds', 'Animals', 'DNA', 'Disulfides', 'Garlic', 'Glutathione', 'Male', 'Plant Extracts', 'Plants, Medicinal', 'Rats', 'Rats, Inbred Strains', 'Sulfides']",Binding of aflatoxin B1 to DNA inhibited by ajoene and diallyl sulfide.,"['Q000378', None, None, 'Q000378', 'Q000494', None, 'Q000378', None, 'Q000494', None, None, None, 'Q000494']","['metabolism', None, None, 'metabolism', 'pharmacology', None, 'metabolism', None, 'pharmacology', None, None, None, 'pharmacology']",https://www.ncbi.nlm.nih.gov/pubmed/1776837,1992,,,,, +1775580,"In garlic (Allium sativum L.) the enzyme alliin lyase catalyzes the cleavage of alliin into allicin which reacts further to furnish ajoene. A simultaneous determination of allicin and ajoene is introduced which, in contrast to the determination of alliin only, allows for the testing of the activity of alliin lyase. It can be demonstrated that at a pH value of less than 3 the enzyme produces only small amounts of allicin. For this reason preparations from garlic should be administered only as enteric-coated formulations.",Planta medica,"['D002855', 'D004220', 'D005737', 'D010936', 'D010946', 'D013441']","['Chromatography, Thin Layer', 'Disulfides', 'Garlic', 'Plant Extracts', 'Plants, Medicinal', 'Sulfinic Acids']",[Formation of allicin from dried garlic (Allium sativum): a simple HPTLC method for simultaneous determination of allicin and ajoene in dried garlic and garlic preparations].,"['Q000379', 'Q000032', 'Q000032', 'Q000032', None, 'Q000032']","['methods', 'analysis', 'analysis', 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/1775580,1992,,,,no PDF access, +1775579,"The content of dialk(en)yl thiosulfinates, including allicin, and their degradation products has been determined by high performance liquid chromatography (HPLC), using the respective determined extinction coefficients, for a number of commercially available garlic products. Quantitation has been achieved for the thiosulfinates; diallyl, methyl allyl, and diethyl mono-, di-, tri-, tetra-, penta-, and hexasulfides; the vinyldithiins; and (E)- and (Z)-ajoene. The thiosulfinates were found to be released only from garlic cloves and garlic powder products. The vinyldithiins and ajoenes were found only in products containing garlic macerated in vegetable oil. The diallyl, methyl allyl, and dimethyl sulfide series were the exclusive constituents found in products containing the oil of steam-distilled garlic. Typical steam-distilled garlic oil products contained about the same amount of total sulfur compounds as total thiosulfinates released from freshly homogenized garlic cloves; however, oil-macerated products contained only 20% of that amount, while garlic powder products varied from 0 to 100%. Products containing garlic powder suspended in a a gel or garlic aged in aqueous alcohol did not contain detectable amounts of these non-ionic sulfur compounds. A comparison of several brands of each type of garlic product revealed a large range in content (4-fold for oil-macerates and 33-fold for steam-distilled garlic oils), indicating the importance of analysis before garlic products are used for clinical investigations or commercial distribution.",Planta medica,"['D000475', 'D000498', 'D002849', 'D002851', 'D005737', 'D010946', 'D013440', 'D013441']","['Alkenes', 'Allyl Compounds', 'Chromatography, Gas', 'Chromatography, High Pressure Liquid', 'Garlic', 'Plants, Medicinal', 'Sulfides', 'Sulfinic Acids']",Identification and HPLC quantitation of the sulfides and dialk(en)yl thiosulfinates in commercial garlic products.,"['Q000032', None, None, None, None, None, 'Q000032', 'Q000032']","['analysis', None, None, None, None, None, 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/1775579,1992,,,,no PDF access, +17226157,"Reversed-phase high Performance liquid chromatography (C18-HPLC) was used to separate and quantitate all the detectable alkyl and alkenyl thiosulfinates, including configurational isomers, of garlic homogenates. Pure thiosulfinates were synthesized or isolated and identified by (1)H-NMR, and their extinction coefficients determined. Some configurational isomers required Separation by silica-HPLC. Five previously unreported thiosulfinates have been found, four of which contain the TRANS-1-propenyl group and increase several-fold to over half the content of allicin upon storage of garlic bulbs at 4 degrees C with a concomitant decrease in a gamma-glutamyl peptide. The variation in thiosulfinate yield between different countries, stores, bulbs, cloves, and storage times was investigated. A method for standardizing the quantitation of allicin yield from garlic is proposed and compared to other methods of allicin analysis.",Planta medica,[],[],HPLC analysis of allicin and other thiosulfinates in garlic clove homogenates.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/17226157,2013,,,,no PDF access, +1903884,"The effects of two organosulfur compounds of garlic (ajoene and diallyl sulfide) and a crude garlic extract on aflatoxin B1 (AFB1)-induced mutagenesis were determined using rat liver 9,000 g supernatant (S-9) as the activation system and Salmonella typhimurium TA-100 as the tester strain. The effects of these compounds on AFB1 binding to calf thymus DNA were also measured. Metabolites of AFB1 were isolated and analyzed by reverse-phase high-performance liquid chromatography. All these compounds inhibited S-9-dependent mutagenesis induced by AFB1. They also inhibited AFB1 binding to DNA. A significant decrease in organo-soluble metabolites of AFB1 was observed with ajoene and garlic extract. An increase of glucuronide and glutathione conjugates was obtained with garlic extract. The results indicate that garlic compounds tested in this study are antimutagenic and, potentially, anticarcinogenic.",Nutrition and cancer,"['D016604', 'D000348', 'D000498', 'D000704', 'D000818', 'D004247', 'D004220', 'D005737', 'D008297', 'D016296', 'D010936', 'D010946', 'D051381', 'D011919', 'D012486', 'D013440', 'D013950']","['Aflatoxin B1', 'Aflatoxins', 'Allyl Compounds', 'Analysis of Variance', 'Animals', 'DNA', 'Disulfides', 'Garlic', 'Male', 'Mutagenesis', 'Plant Extracts', 'Plants, Medicinal', 'Rats', 'Rats, Inbred Strains', 'Salmonella typhimurium', 'Sulfides', 'Thymus Gland']","Organosulfur compounds of garlic modulate mutagenesis, metabolism, and DNA binding of aflatoxin B1.","[None, 'Q000235', None, None, None, 'Q000378', 'Q000494', None, None, 'Q000187', 'Q000494', None, None, None, None, 'Q000494', None]","[None, 'genetics', None, None, None, 'metabolism', 'pharmacology', None, None, 'drug effects', 'pharmacology', None, None, None, None, 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/1903884,1991,,,,, +2111258,"1. Homogenates of garlic (Allium sativum), onions (Allium cepa) and Allium porum were in vitro incubated with [14C]arachidonic acid. 2. Separation of labelled prostaglandins and thromboxanes were accomplished by thin-layer chromatography (TLC) and the Rf values were compared with those of authentic standards. 3. The prostaglandins identified were 6-keto-PGF1 alpha, PGF2 alpha, TXB2, PGE2 and PGD2. 4. PGE2 and PGD2 were the major metabolites of arachidonic acid among all the members of the Liliaceae family studied. 5. Garlic was found to have the highest capacity to metabolize the [14C]arachidonic acid into prostaglandins and thromboxanes. 6. The synthesis of prostaglandins and thromboxanes, was inhibited by preincubation of homogenates with indomethacin or was completely destroyed by boiling the plant extract prior to incubation with arachidonic acid. This confirmed the presence of cyclooxygenase in these plants.",General pharmacology,"['D000490', 'D002855', 'D005737', 'D010946', 'D011451', 'D011453', 'D013045', 'D013931']","['Allium', 'Chromatography, Thin Layer', 'Garlic', 'Plants, Medicinal', 'Prostaglandin-Endoperoxide Synthases', 'Prostaglandins', 'Species Specificity', 'Thromboxanes']",Comparative study of the in vitro synthesis of prostaglandins and thromboxanes in plants belonging to Liliaceae family.,"['Q000378', None, 'Q000378', None, 'Q000378', 'Q000096', None, 'Q000096']","['metabolism', None, 'metabolism', None, 'metabolism', 'biosynthesis', None, 'biosynthesis']",https://www.ncbi.nlm.nih.gov/pubmed/2111258,1990,,,,no PDF access, +17262455,"An alliin lyase (EC 4.4.1.4) preparation from garlic, ALLIUM SATIVUM L., has been purified to apparent homogeneity. The purification procedure involved liquid chromatography steps on hydroxylapatite, on an anion exchanger, and on a chromatofocussing medium. The enzyme protein was characterized by a relative molecular mass of 108,000, and was found to consist of two equal subunits. Its isoelectric point was determined to be 4.9. The enzyme appeared rather thermolabile. Simulated gastric-intestinal passage by a modified ""half change test"" revealed a high acid lability of the active alliinase protein. K (m)-values for different substrates were in the mM range, and activating energies for the cleavage of different substrates could be determined. A maximal specific activity for synthetic alliin in the range of 490 micromoles per min and mg protein could be achieved at 33 degrees C. There are some significant differences in the characterization of the purified protein compared to results previously reported by others on this enzyme.",Planta medica,[],[],Characterization of an Alliin Lyase Preparation from Garlic (Allium sativum).,[],[],https://www.ncbi.nlm.nih.gov/pubmed/17262455,2013,,,,no PDF access, +17262412,"Combined headspace gas chromatography-mass spectrometry (HSGC-MS) was used in the analysis of garlic volatile compounds. Twenty major components were identified in the gas phases enriched by fresh, sliced garlic cloves ( ALLIUM SATIVUM L, Allioceae, Liliidae). Suspended dry garlic powder and crushed garlic, incubated in vegetable oil, revealed a different pattern since mainly the amounts of di- and trisulfides were decreased. The considerable compositional differences found in the analyses for the gas phase of garlic cloves, kept in oil, are likely associated with the poor stability of allicin in a lipophilic environment; a marked increase in the amounts of 2-propene-1-thiol, acetic acid, and ethanol was observed in the gas phase, whereas trisulfides were present in traces only. The occurrence of 2-propene-1-thiol and diallyl disulfide, the two principal sulfur components in exhaled air, also may indicate a rapid degradation of most garlic volatile components probably caused by the enzymatically active human salivary or digestive system.",Planta medica,[],[],Volatile garlic odor components: gas phases and adsorbed exhaled air analysed by headspace gas chromatography-mass spectrometry.,[],[],https://www.ncbi.nlm.nih.gov/pubmed/17262412,2012,,,,no PDF access, +2729582,"An indirect method for the determination of trace bound selenomethionine (SeMet) has been developed. SeMet reacts with cyanogen bromide (CNBr) quantitatively in the presence of SnCl2 to form CH3SeCN, and after extraction with CHCl3 is acid-digested to form Se(IV). Selenium(IV) reacts with 4-nitro-o-phenylenediamine reagent to form 5-NO2-piazselenol which is then determined by gas chromatography equipped with electron capture detector. The sensitivity of this method (CNBr-piazselenol-GC method) is 6 ng SeMet/g of sample. Trace-bound SeMet in plants and some biological materials has been successfully determined by this method and its content has been compared with the total selenium in the sample.",Analytical biochemistry,"['D000818', 'D002681', 'D002849', 'D005737', 'D007668', 'D012275', 'D010946', 'D012643', 'D012645', 'D013552', 'D014131', 'D014908', 'D003313']","['Animals', 'China', 'Chromatography, Gas', 'Garlic', 'Kidney', 'Oryza', 'Plants, Medicinal', 'Selenium', 'Selenomethionine', 'Swine', 'Trace Elements', 'Triticum', 'Zea mays']",A method for the indirect determination of trace bound selenomethionine in plants and some biological materials.,"[None, None, None, 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', 'Q000032', None, 'Q000032', 'Q000032', 'Q000032']","[None, None, None, 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', 'analysis', None, 'analysis', 'analysis', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/2729582,1989,0.0,0.0,,correct format just not for garlic, +3235614,A liquid chromatographic (LC) method is proposed for the determination of alliin in garlic and garlic products. The method involves heating of the sample with water in a bath of boiling water followed by homogenization and centrifugation. Interfering components are eliminated by use of a Sep-Pak C18 cartridge as a clean up step before injection. The LC system with ultraviolet detection at 210 nm consists of a separation on a Zorbax TMS column and isocratic elution with water as a mobile phase. Fluorometric determination by ion-pairing chromatography with tetra-n-butylammonium bromide on a Nucleosil 5C18 column is also described. The overall recoveries of alliin added to garlic products were greater than 90%. Thin-layer chromatography and enzymatic degradation of alliin were performed for the confirmation of alliin detected in garlic products.,Journal of chromatography,"['D002853', 'D002855', 'D003545', 'D005737', 'D010946', 'D013050', 'D013056']","['Chromatography, Liquid', 'Chromatography, Thin Layer', 'Cysteine', 'Garlic', 'Plants, Medicinal', 'Spectrometry, Fluorescence', 'Spectrophotometry, Ultraviolet']",Liquid chromatographic determination of alliin in garlic and garlic products.,"[None, None, 'Q000031', 'Q000032', None, None, None]","[None, None, 'analogs & derivatives', 'analysis', None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/3235614,1989,,,,, +3429558,"After consumption of onions or garlic, biological profiles of human urine samples show, in the methylated conjugate fraction, peaks corresponding to the methylates of N-acetyl-S-(2-carboxypropyl) cysteine (1), N-acetyl-S-allylcysteine (2) and hexahydrohippuric acid (3). The compounds 1 and 2 are metabolites of peptides introduced with onions or garlic into the body.",Journal of chromatography,"['D000111', 'D000490', 'D003545', 'D004032', 'D005737', 'D008401', 'D006626', 'D006801', 'D010455', 'D010946']","['Acetylcysteine', 'Allium', 'Cysteine', 'Diet', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Hippurates', 'Humans', 'Peptides', 'Plants, Medicinal']",Unusual conjugates in biological profiles originating from consumption of onions and garlic.,"['Q000031', None, 'Q000031', None, None, None, 'Q000378', None, 'Q000378', None]","['analogs & derivatives', None, 'analogs & derivatives', None, None, None, 'metabolism', None, 'metabolism', None]",https://www.ncbi.nlm.nih.gov/pubmed/3429558,1988,,,,, +3798421,"When added to platelet-rich plasma, aqueous extracts of garlic inhibited platelet aggregation and the release reaction. Subsequent experiments designed to characterize the inhibitory component revealed that the inhibitory activity was i) associated with small molecular-weight components, ii) the inhibitory component possessed the typical garlic odor and contained an abundance of sulfur, iii) the inhibitory activity could be extracted with organic solvents, and iv) temperatures above 56 degrees C and alkaline pH above 8.5 quickly destroyed the inhibitory activity. The Rf value of the major inhibitory component after thin-layer chromatographic separation was similar to that of allicin, an unique thiosulfinate in garlic previously shown to possess strong antibiotic and antifungal properties. Allicin was synthesized. On thin-layer chromatographic plates, allicin co-migrated with the inhibitory component in garlic. At 10 microM concentration, allicin inhibited completely platelet aggregation and the release reaction. Comparative studies suggest that the major platelet aggregation and release inhibitor in garlic may be allicin.",Thrombosis research,"['D000328', 'D002850', 'D002855', 'D005737', 'D006801', 'D008970', 'D010936', 'D010946', 'D010974', 'D013441']","['Adult', 'Chromatography, Gel', 'Chromatography, Thin Layer', 'Garlic', 'Humans', 'Molecular Weight', 'Plant Extracts', 'Plants, Medicinal', 'Platelet Aggregation', 'Sulfinic Acids']",Characterization of a potent inhibitor of platelet aggregation and release reaction isolated from allium sativum (garlic).,"[None, None, None, 'Q000032', None, None, 'Q000032', None, 'Q000187', 'Q000302']","[None, None, None, 'analysis', None, None, 'analysis', None, 'drug effects', 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/3798421,1987,0.0,0.0,,, +3740854,"Alliin lyase from garlic (Allium sativum) has been purified to homogeneity. The purification procedure involves the use of affinity chromatography on concanavalin A-Sepharose 4B. Addition of polyvinylpolypyrrolidone to the homogenizing medium greatly improves the specific activity of the extract. The enzyme is a glycoprotein as seen by its ability to bind to concanavalin A-Sepharose 4B and by its positive periodic acid-Schiff base stain. It has a carbohydrate content of 5.5%. Km values for this enzyme were estimated to be 5.7 mM for S-ethyl-L-cysteine sulfoxide and 3.3 mM for S-allyl-L-cysteine sulfoxide. The molecular weight of this garlic enzyme, as determined by gel filtration, was found to be 85,000; the molecule consists of two equal subunits of Mr 42,000. The amino acid content was found to be similar to that reported previously for onion alliin lyase, although there is twice as much tryptophan in the garlic alliin lyase as in the onion enzyme. By both chemical and spectral methods the enzyme was found to have two molecules of pyridoxal 5-phosphate per enzyme molecule, suggesting one per subunit. There are significant differences in the nature of these findings from those previously reported from this laboratory for the onion enzyme. Studies are in progress to compare further the alliin lyases from garlic and onion.",Archives of biochemistry and biophysics,"['D000596', 'D002241', 'D013437', 'D005737', 'D007700', 'D008190', 'D008970', 'D010946', 'D011732']","['Amino Acids', 'Carbohydrates', 'Carbon-Sulfur Lyases', 'Garlic', 'Kinetics', 'Lyases', 'Molecular Weight', 'Plants, Medicinal', 'Pyridoxal Phosphate']",The C-S lyases of higher plants: preparation and properties of homogeneous alliin lyase from garlic (Allium sativum).,"['Q000032', 'Q000032', 'Q000302', 'Q000201', None, 'Q000302', None, None, 'Q000032']","['analysis', 'analysis', 'isolation & purification', 'enzymology', None, 'isolation & purification', None, None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/3740854,1986,1.0,2.0,,, +2869220,"A patient who made reproduction antique china dolls complained that wherever she touched the dolls' heads when painting them, black speckles appeared after the subsequent firing. Investigation by means of mass spectrometry and X-ray fluorescence showed that the clay was rich in iron, that the patient's sweat contained volatile sulphides whenever she ate garlic, and that the speckles consisted of iron and sulphur. The patient was shown to be a poor sulphoxidiser and was therefore unlikely to be able to excrete sulphur-containing breakdown products of garlic in her urine. The speckling phenomenon, which is not uncommon in 19th-century china dolls, is an example of an occupational hazard where the risk is to the product rather than the patient.","Lancet (London, England)","['D000293', 'D003116', 'D005260', 'D005737', 'D006801', 'D007501', 'D013058', 'D009790', 'D010946', 'D010988', 'D012451', 'D013440', 'D013455', 'D013542']","['Adolescent', 'Color', 'Female', 'Garlic', 'Humans', 'Iron', 'Mass Spectrometry', 'Occupations', 'Plants, Medicinal', 'Play and Playthings', 'Safrole', 'Sulfides', 'Sulfur', 'Sweat']",The case of the black-speckled dolls: an occupational hazard of unusual sulphur metabolism.,"[None, None, None, 'Q000009', None, 'Q000032', None, None, None, None, 'Q000031', 'Q000032', 'Q000378', 'Q000032']","[None, None, None, 'adverse effects', None, 'analysis', None, None, None, None, 'analogs & derivatives', 'analysis', 'metabolism', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/2869220,1986,,,,no PDF access, +6144781,"Garlic has been extracted and separated chromatographically into various fractions which show different degrees of activity as inhibitors of platelet aggregation and smooth muscle. The most potent smooth muscle inhibitor fraction had little activity on platelet aggregation, but microgram ml-1 concentrations greatly reduced the contractions of rat gastric fundus to prostaglandin E2 and acetylcholine. Material in this fraction may contribute to some of the claimed therapeutic effects of garlic involving smooth muscle. Its identity is not known, but is different from allyl sulphide, dimethyl sulphide and diallyl disulphide. These compounds eluted earlier on liquid chromatography than the most active fraction, and they showed only modest inhibitory activity against prostaglandin E2 and acetylcholine on rat fundus.",The Journal of pharmacy and pharmacology,"['D000109', 'D000818', 'D015232', 'D005737', 'D005748', 'D006801', 'D066298', 'D008297', 'D009119', 'D009130', 'D010936', 'D010946', 'D010974', 'D011458', 'D051381']","['Acetylcholine', 'Animals', 'Dinoprostone', 'Garlic', 'Gastric Fundus', 'Humans', 'In Vitro Techniques', 'Male', 'Muscle Contraction', 'Muscle, Smooth', 'Plant Extracts', 'Plants, Medicinal', 'Platelet Aggregation', 'Prostaglandins E', 'Rats']",The effect of garlic extracts on contractions of rat gastric fundus and human platelet aggregation.,"['Q000494', None, None, None, 'Q000187', None, None, None, 'Q000187', 'Q000187', 'Q000032', None, 'Q000187', 'Q000494', None]","['pharmacology', None, None, None, 'drug effects', None, None, None, 'drug effects', 'drug effects', 'analysis', None, 'drug effects', 'pharmacology', None]",https://www.ncbi.nlm.nih.gov/pubmed/6144781,1984,,,,no PDF access, +6878462,"The odorant allyl sulfide (essence of garlic) dissolved in a corn oil vehicle was injected into rats to induce a conditioned aversion. In subsequent two-choice drinking tests, rats injected with odorant and lithium chloride, and rats injected with odorant and saline avoided drinking from a water bottle paired with the odorant. Because allyl sulfide and saline injections produced symptoms of malaise, we suspect that the odorant served as its own unconditioned stimulus. Rats injected with vehicle and saline showed no differential behavior. In a second experiment, gas chromatography indicated that allyl sulfide was present on the rat's breath within 3 minutes of injection, and was detectable for up to 5 hours post-injection. We conclude that conditioned aversions can be obtained to an intravascular odorant and that one route by which such odorants reach the nose is the breath.",Physiology & behavior,"['D000222', 'D000498', 'D000818', 'D001362', 'D001790', 'D001944', 'D002849', 'D003214', 'D005737', 'D008297', 'D009812', 'D010946', 'D051381', 'D011919', 'D012903', 'D013440']","['Adaptation, Physiological', 'Allyl Compounds', 'Animals', 'Avoidance Learning', 'Blood Physiological Phenomena', 'Breath Tests', 'Chromatography, Gas', 'Conditioning, Classical', 'Garlic', 'Male', 'Odorants', 'Plants, Medicinal', 'Rats', 'Rats, Inbred Strains', 'Smell', 'Sulfides']",Conditioned aversions to an intravascular odorant.,"[None, None, None, 'Q000502', None, None, None, 'Q000502', None, None, 'Q000032', None, None, None, 'Q000502', 'Q000032']","[None, None, None, 'physiology', None, None, None, 'physiology', None, None, 'analysis', None, None, None, 'physiology', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/6878462,1983,,,,no PDF access, +6314793,"A review of current information on the composition, pharmacological actions and mode of death from cow's urine concoction (CUC) toxicity is presented. The concoction is prepared from leaves of tobacco, garlic and basil; lemon juice, rock salt and bulbs of onion. The latter items are soaked in the urine from cows which acts as the vehicle in which the active principles in these constituents dissolve. Over fifty chemical compounds have been identified in CUC. The major compounds it contains are benzoic acid, phenylacetic acid, p-cresol, thymol and nicotine. The chemical composition and pharmacological cations of the individual components of CUC are also reviewed. Observations of CUC poisoning in man and experimental animals showed that the main effects of CUC are severe depression of respiration, cardiovascular system, the central nervous system and hypoglycaemia. These toxic effects acting singly or in combination are believed to be the cause(s) of death from CUC. Management is geared towards correcting these adverse effects.",African journal of medicine and medical sciences,"['D000818', 'D000927', 'D001565', 'D019817', 'D002319', 'D002417', 'D002490', 'D002648', 'D003408', 'D008401', 'D006801', 'D009538', 'D009549', 'D010101', 'D010648', 'D010936', 'D012119', 'D013943']","['Animals', 'Anticonvulsants', 'Benzoates', 'Benzoic Acid', 'Cardiovascular System', 'Cattle', 'Central Nervous System', 'Child', 'Cresols', 'Gas Chromatography-Mass Spectrometry', 'Humans', 'Nicotine', 'Nigeria', 'Oxygen Consumption', 'Phenylacetates', 'Plant Extracts', 'Respiration', 'Thymol']","Cow's urine concoction: its chemical composition, pharmacological actions and mode of lethality.","[None, 'Q000032', 'Q000032', None, 'Q000187', None, 'Q000187', None, 'Q000032', None, None, 'Q000032', None, 'Q000187', 'Q000032', 'Q000032', 'Q000187', 'Q000032']","[None, 'analysis', 'analysis', None, 'drug effects', None, 'drug effects', None, 'analysis', None, None, 'analysis', None, 'drug effects', 'analysis', 'analysis', 'drug effects', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/6314793,1983,,,,, +6888523,"The aim of the study was to determine the composition of flavour isolates from garlic and horse-radish, prepared by dichlorodifluoromethane extraction. Gas chromatography, with olfactory determination of the flavour of the resolved components, and gas chromatography combined with mass spectrometry were used. In the garlic extract 19 components comprising mono-, di-, and trisulphides and thiophene derivatives were detected. In the horse-radish extract 14 components including isothiocyanates, thiocyanates and cyanides were identified.",Die Nahrung,"['D002849', 'D005421', 'D005737', 'D008401', 'D010944', 'D010946', 'D013455']","['Chromatography, Gas', 'Flavoring Agents', 'Garlic', 'Gas Chromatography-Mass Spectrometry', 'Plants', 'Plants, Medicinal', 'Sulfur']",The role of sulphur compounds in evaluation of flavouring value of some plant raw materials.,"[None, 'Q000032', 'Q000032', None, 'Q000032', None, 'Q000032']","[None, 'analysis', 'analysis', None, 'analysis', None, 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/6888523,1983,,,,, +6625648,"Garlic (Allium sativum L.) water- and ethanol-soluble extracts were prepared and purified by column chromatography. They were tested on garlic-sensitive patients and showed that the allergenic fraction was well located in a few column chromatography fractions. Guinea-pigs were sensitized with garlic water-soluble extracts and tested (open epicutaneous tests) with several fractions. The presence of diallyldisulfide was detected in the sensitizing chromatographic fractions. Guinea-pigs were successfully sensitized to this product and cross-reacted to garlic; animals sensitized to garlic extracts cross-reacted to diallyldisulfide. Both groups reacted to allicin, an oxidized derivative of diallyldisulfide present in garlic. Garlic-sensitive patients showed positive tests to diallyldisulfide, allylpropyldisulfide, allylmercaptan and allicin.",Archives of dermatological research,"['D000485', 'D000498', 'D000818', 'D003877', 'D004220', 'D005737', 'D006168', 'D006801', 'D007004', 'D010328', 'D010936', 'D010946', 'D013441']","['Allergens', 'Allyl Compounds', 'Animals', 'Dermatitis, Contact', 'Disulfides', 'Garlic', 'Guinea Pigs', 'Humans', 'Hypoglycemic Agents', 'Patch Tests', 'Plant Extracts', 'Plants, Medicinal', 'Sulfinic Acids']","Allergic contact dermatitis to garlic (Allium sativum L.). Identification of the allergens: the role of mono-, di-, and trisulfides present in garlic. A comparative study in man and animal (guinea-pig).","['Q000032', None, None, 'Q000209', 'Q000633', 'Q000032', None, None, 'Q000633', None, 'Q000032', None, 'Q000633']","['analysis', None, None, 'etiology', 'toxicity', 'analysis', None, None, 'toxicity', None, 'analysis', None, 'toxicity']",https://www.ncbi.nlm.nih.gov/pubmed/6625648,1983,,,,, +552092,"Oral administration of onion and garlic reportedly decreases platelet aggregation in both human and animal subjects. An oily chloroform extract of onion (Allium Cepa) was prepared and separated by column chromatography on silicic acid into six fractions by elution with solvents of increasing polarity. The least polar fraction contained most of the inhibitory activity towards platelet aggregation induced by either ADP or arachidonic acid. Further purification was afforded by thin-layer chromatography. The specific activity of this major active fraction (I50 per ml of PRP) was approximately 7 units per milligram. Platelets incubated in the presence of onion inhibitor and (1-14C)-arachidonic acid showed striking changes in the pattern of arachidonic acid metabolites formed. Thromboxane B2 synthesis was almost completely suppressed without significant decreases in total hydroxy fatty acid formation. It was concluded that the observed antiplatelet activity of onion relates to the presence of a non-polar, heat stable inhibitor of thromboxane synthesis. This appears to be the first demonstration of this type of inhibitor present in significant quantities in a common foodstuff.",Prostaglandins and medicine,"['D000244', 'D000818', 'D001095', 'D001792', 'D005260', 'D006801', 'D066298', 'D019684', 'D008297', 'D010974', 'D011817', 'D013929', 'D013931']","['Adenosine Diphosphate', 'Animals', 'Arachidonic Acids', 'Blood Platelets', 'Female', 'Humans', 'In Vitro Techniques', 'Magnoliopsida', 'Male', 'Platelet Aggregation', 'Rabbits', 'Thromboxane B2', 'Thromboxanes']",Effects of onion (Allium cepa) extract on platelet aggregation and thromboxane synthesis.,"['Q000494', None, 'Q000097', 'Q000378', None, None, None, None, None, 'Q000187', None, 'Q000097', 'Q000097']","['pharmacology', None, 'blood', 'metabolism', None, None, None, None, None, 'drug effects', None, 'blood', 'blood']",https://www.ncbi.nlm.nih.gov/pubmed/552092,1980,,,,, +719059,"A method has been developed for the purification of alliinase from garlic bulbs. High purity preparations of the enzyme were obtained with specific activity increased 67-fold over that of the homogenate. The preparations were homogeneous on electrophoresis in polyacril gel. Total activity yield was 25%. The native enzyme has a molecular weight of 130.000 and consists of two subunits. Approximately 6 moles of firmly bound pyridoxal phosphate are determined per 1 mole of the purest enzyme (4 equivalents are apparently bound non-specifically outside the active sites). The isoelectric point (pI) of alliinase in 6.2. The enzyme's absorption and circular dichroism spectra have one maximum at 430 nm, in the characteristic range of many pyridoxal-P-containing enzymes. The Km value for the natural substrate, alliin, is 5 . 10(-4) M.","Biokhimiia (Moscow, Russia)","['D013437', 'D002852', 'D002942', 'D003545', 'D004591', 'D005737', 'D007525', 'D008190', 'D008970', 'D010944', 'D010946', 'D011732']","['Carbon-Sulfur Lyases', 'Chromatography, Ion Exchange', 'Circular Dichroism', 'Cysteine', 'Electrophoresis, Polyacrylamide Gel', 'Garlic', 'Isoelectric Focusing', 'Lyases', 'Molecular Weight', 'Plants', 'Plants, Medicinal', 'Pyridoxal Phosphate']",[Alliinase: purification and chief physico-chemical properties].,"['Q000302', None, None, 'Q000031', None, 'Q000201', None, 'Q000302', None, 'Q000201', None, None]","['isolation & purification', None, None, 'analogs & derivatives', None, 'enzymology', None, 'isolation & purification', None, 'enzymology', None, None]",https://www.ncbi.nlm.nih.gov/pubmed/719059,1979,,,,, +902266,"Hot-water extraction of defatted garlic-bulbs yielded a mixture of polysaccharides containing a D-galactan, a D-galacturonan, an L-arabinan, a D-glucan, and a D-fructan. A trace of L-rhamnose was also detected in the polysaccharide hydrolyzate. The pectic acid was partially removed by precipitation with aqueous calcium chloride; from the remaining polysaccharide mixture, a pure D-galactan containing 97.3% of D-galactose was isolated by fractional precipitation and repeated chromatography through a column of DEAE-cellulose. Methanolysis and hydrolysis of the permethylated D-galactan yielded 2,3,4,6-tetra-, 2,3,6-tri-, and 2,3,di-O-methyl-D-galactose in the molar proportions of 1:2:1. On periodate oxidation, the D-galactan reduced 1.18 molar equivalents of the oxidant per D-galactosyl residue, and liberated one molar equivalent of formic acid per 4.13 D-galactosyl residues. Smith degradation of the D-galactan was also conducted. From these results, a structure has been assigned to the repeating unit of the D-galactan.",Carbohydrate research,"['D005690', 'D005737', 'D009005', 'D010944', 'D010946', 'D011134']","['Galactose', 'Garlic', 'Monosaccharides', 'Plants', 'Plants, Medicinal', 'Polysaccharides']",Structure of the D-galactan isolated from garlic (Allium sativum) bulbs.,"['Q000032', 'Q000032', 'Q000032', 'Q000032', None, 'Q000302']","['analysis', 'analysis', 'analysis', 'analysis', None, 'isolation & purification']",https://www.ncbi.nlm.nih.gov/pubmed/902266,1977,,,,no PDF access, +927476,"The content of S-methylmethionine SMM in the extracts of 53 plant and 13 animal products by means of ion exchange clean-up procedure followed by two dimensional thin layer chromatography has been investigated. It was found that the richest plant SMM sources (in mg/100 g) are cabbage (53-104), kohlrabi (81-110), turnip (51-72), tomatoes (45-83), celery (38-78), leeks (66-75), garlic-leafs (44-64), beet (22-37), raspberries (27) and strawberries (14-25). The animal products are poor in SMM. The control of the plants rich in SMM during a storage for 6 months (autumn, winter) in the soil showed average decreases as follows: celery 38%, kohlrabi 39%, turnip 43%, and leeks 32%. A storage of cabbage with uncontrolled temperature resulted in a decrease of 62%, in a storehouse (0-1 degrees C) of 34% SMM.",Die Nahrung,"['D004355', 'D005504', 'D007700', 'D008715', 'D010944']","['Drug Stability', 'Food Analysis', 'Kinetics', 'Methionine', 'Plants']",[S-Methylmethionine content in plant and animal tissues and stability during storage].,"[None, None, None, 'Q000031', 'Q000032']","[None, None, None, 'analogs & derivatives', 'analysis']",https://www.ncbi.nlm.nih.gov/pubmed/927476,1978,,,,, +4970593,"1. Alliin lyase (EC 4.4.1.4) was purified up to sevenfold from garlic-bulb homogenates. The enzyme was unstable to storage at -10 degrees , particularly in dilute concentrations, but the addition of glycerol (final concentration 10%, v/v) stabilized the activity completely for at least 30 days. 2. The purified enzyme had an optimum pH for activity at 6.5. The addition of pyridoxal phosphate stimulated the reaction rate and the stimulation became more marked as the purification proceeded. 3. Hydroxylamine (10mum) and cysteine (0.5mm) inhibited the enzyme activity by more than 80%. Spectral studies indicated that cysteine reacted with pyridoxal phosphate bound to the protein. 4. The K(m) values for S-methyl-, S-ethyl-, S-propyl-, S-butyl- and S-allyl-l-cysteine sulphoxides were determined. With S-allyl-l-cysteine sulphoxide the K(m) was 6mm and the V(max.) was greater than those with the other substrates tested. 5. The thioether analogues of the substrates were competitive inhibitors for the lyase reaction. The K(i) decreased with increasing chain length of the alkyl substituent. With S-ethyl-l-cysteine sulphoxide as substrate the K(i) was 33, 8 and 5mm respectively for S-methyl-, S-ethyl- and S-propyl-l-cysteine. 6. The addition of EDTA or Mg(2+), Mn(2+), Co(2+) or Fe(2+) stimulated the reaction rate. Other bivalent cations either had no effect or gave a strong inhibition. In the presence of EDTA no further increase of activity was observed with added Mg(2+).",The Biochemical journal,"['D055598', 'D002621', 'D002850', 'D003035', 'D003080', 'D003545', 'D004355', 'D004492', 'D005737', 'D005990', 'D006863', 'D006898', 'D007501', 'D007700', 'D008190', 'D008274', 'D008345', 'D010946', 'D011732', 'D013053', 'D013454', 'D013997']","['Chemical Phenomena', 'Chemistry', 'Chromatography, Gel', 'Cobalt', 'Cold Temperature', 'Cysteine', 'Drug Stability', 'Edetic Acid', 'Garlic', 'Glycerol', 'Hydrogen-Ion Concentration', 'Hydroxylamines', 'Iron', 'Kinetics', 'Lyases', 'Magnesium', 'Manganese', 'Plants, Medicinal', 'Pyridoxal Phosphate', 'Spectrophotometry', 'Sulfoxides', 'Time Factors']","Purification of the alliin lyase of garlic, Allium sativum L.","[None, None, None, None, None, None, None, None, 'Q000201', None, None, None, None, None, 'Q000037', None, None, None, None, None, None, None]","[None, None, None, None, None, None, None, None, 'enzymology', None, None, None, None, None, 'antagonists & inhibitors', None, None, None, None, None, None, None]",https://www.ncbi.nlm.nih.gov/pubmed/4970593,1968,0.0,0.0,,, diff --git a/notebooks/Data_Statistics.ipynb b/notebooks/Data_Statistics.ipynb index 2c7af46..867321a 100644 --- a/notebooks/Data_Statistics.ipynb +++ b/notebooks/Data_Statistics.ipynb @@ -91,9 +91,2512 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n", + "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(chem_dict, ignore_index = True)\n" + ] + } + ], "source": [ "food_data, food_scoring = load_raw_data(food, load)\n", "\n", @@ -114,7 +2617,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -141,7 +2644,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -158,7 +2661,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -225,9 +2728,19 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_36429/2176026344.py:46: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", + " table['total'] = table.sum(axis=1)\n", + "/tmp/ipykernel_36429/2176026344.py:47: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " table = table.append(table.sum(axis=0), ignore_index = True)\n" + ] + }, { "data": { "text/html": [ @@ -326,7 +2839,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -341,7 +2854,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -364,7 +2877,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -410,16 +2923,42 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_36429/754048652.py:12: UserWarning: \n", + "\n", + "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", + "\n", + "Please adapt your code to use either `displot` (a figure-level function with\n", + "similar flexibility) or `histplot` (an axes-level function for histograms).\n", + "\n", + "For a guide to updating your code to use the new functions, please see\n", + "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", + "\n", + " g1 = sns.distplot(quant_viz['count'], kde=False, label='Quantified', bins=bins)\n", + "/tmp/ipykernel_36429/754048652.py:13: UserWarning: \n", + "\n", + "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", + "\n", + "Please adapt your code to use either `displot` (a figure-level function with\n", + "similar flexibility) or `histplot` (an axes-level function for histograms).\n", + "\n", + "For a guide to updating your code to use the new functions, please see\n", + "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", + "\n", + " g2 = sns.distplot(unquant_viz['count'], kde=False, label='Unquantified', bins=bins)\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -449,7 +2988,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": { "scrolled": false }, @@ -497,16 +3036,42 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_36429/1938569971.py:10: UserWarning: \n", + "\n", + "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", + "\n", + "Please adapt your code to use either `displot` (a figure-level function with\n", + "similar flexibility) or `histplot` (an axes-level function for histograms).\n", + "\n", + "For a guide to updating your code to use the new functions, please see\n", + "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", + "\n", + " g1 = sns.distplot(quant_viz['count'], kde=False, label='Quantified', bins=bins)\n", + "/tmp/ipykernel_36429/1938569971.py:11: UserWarning: \n", + "\n", + "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", + "\n", + "Please adapt your code to use either `displot` (a figure-level function with\n", + "similar flexibility) or `histplot` (an axes-level function for histograms).\n", + "\n", + "For a guide to updating your code to use the new functions, please see\n", + "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", + "\n", + " g2 = sns.distplot(unquant_viz['count'], kde=False, label='Unquantified', bins=bins)\n" + ] + }, { "data": { - "image/png": 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\n", 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TmfZ59uxZdenSxTZpqiT5+vqqXbt2qlKlihITE3Xq1Cnt2rVL0dHRiouLy7Afqx+r2NhY26iRsmXL6rbbblONGjXk4+OjhIQEnThxQjt27FBcXJzCw8PVu3dvbd68OUeH5QAoxgo7OUHBKugRF56enqYk89lnn033i3FERITZpUsXW9tbbrnFTElJybBPR0ZcxMXFmfXr17e1q1mzprlz5067NikpKeacOXNMFxcX08PDI9tfz/JrxMWiRYtsbXx8fMz3338/wxEVmzZtsvu18o033nCohsx07drV1leLFi3y1FdGQkJC7H5R6tmzp3nu3Dm7NnFxceYzzzxjdz/Nnj07w/7SPl/d3d1NwzDM2267zTxw4IBdu2vXrplDhgyxtQ0MDLSNzLjvvvvM8PBwu/Znz561+8Vu1KhRme5T2ud86nNm8uTJZlxcnF27v//+2+757OnpaR46dCjDPo8ePWr6+PjY2jZv3tw8fPiwXZvk5GRz9uzZpouLi63d448/nu39lF8jLjw9PU1vb29z2bJl6V6nBw4csHuejh49OtPt3viLd0b35dGjR81mzZrZ3eeSc4y4iI2NNd3c3Gx9ZDRawJHHo1GjRrY2jz32WKYjaqKjo80vvvjCnDJlSoa353Q0gmnav0en7sujjz5qRkdH27VLSEiw+9U+pyMuUh+7oUOHmlFRUXbtoqKizPvvv9/uubBp0yZL9tGR9+3cvGYcXeeRRx6xtXN1dTXffvvtdKMfDh8+bHuOSzJLly5tHj9+PMP+0u5/6mfqyJEj0432iY2NtXsf9PHxMWNiYhzaNyv2O1V+7n/q83XQoEHmxYsX7dolJyebCQkJOdq3hIQEs0SJErb+n3nmmRyt74g33njD7nn+7LPPpnvPO3funNm9e3e7/dyxY0eG/SUmJprt2rWztfXy8jLffffdDPc9Pj7eXL16tdm/f/8M+7L6sTp58qT5+OOPm6GhoZmO+Ll8+bL59NNP231e53V0EIDigeCimCno4CKz/9inOn/+vOnt7W1rm9kHtSPBxX//+1+7D/Ibvwim9frrr9vVWJDBxZUrV2yHGri6upqbN2/Osr8//vjD9h+r8uXLZ3nIQHZq167t0Jf13Lrzzjtt/bdu3TrLw1smTpxo9x+hK1eupGtz4/O1YsWK5j///JNhf9HR0emGqHbr1i3T/xCFhITY/Qc/MTExw3Y3DskfP358pvt09epVs2HDhra2gwcPzrDdiBEjbG1uueUWu+H4N3r77bdtbV1cXDL8D2NBBBeGYZg//PBDpv2tXbs22/szKSnJ9Pf3t7UbO3Zspv2Fh4enO8TAGYIL0zTNmjVr2voYM2ZMutuzezyio6Ntt1evXj3TwNYReQ0usnsc0sppcCHJ7N27d6avweTkZLNHjx62tpmFqUUpuDh69Khd2Dh//vxM+7t06ZLdcymzwC/t/ksyhwwZkmmf165dszvsZ8WKFQ7tW1Zycl8VxP53797dsi+6p0+ftuvb0cM0HHX58mW7kHry5MmZto2LizNbtGhha9u5c+cM2y1cuNAu4Pjll19yVVt+PFY5MX78eFt/2R2CAgCmaZpMzol8VaFCBb300kuZ3l6pUiX16dPHdj11yHNufPTRR7bLkyZNSjd8Na2nnnoqy6Hs+enjjz+2TXg3atSobE8teuutt9rO4BEREaEff/wx19u+dOmS7bKvr2+u+8nIoUOH7IaWL1iwQB4eHpm2/89//iM/Pz9J0pUrV7R8+fJst/Hiiy+qYsWKGd7m4+Nj91ySrh+2ktmhNW3atLENCY6JibGdHjYrpUqVspvc9EZeXl6aPXu27fq3336bblLFqKgorVy50nb9rbfeyvKxmDRpkm677TZJ14ccf/jhh9nWmR/69u2rnj17Znp77969VblyZUmZ358//fSTzpw5I0kqWbJklocGlC9fXjNnzsxj1fmjTJkytstpTxXpqCtXrtguly9f3uGJfPNDiRIl8u0QDcMwNH/+/Exfgy4uLpo/f75t/3fu3Km9e/fmSy0FZeHChUpJSZEkNWzYUI899limbcuWLWv3frJ8+fJsJ1H08PDQ22+/nentJUqU0JAhQ2zX8/KZmhv5vf+SNHfuXMvObpL2M1Gy/nNx+fLltkkoK1WqpFdffTXTtp6ennr33Xdt14OCguwOBUmV9jPmqaeeUocOHXJVW0E8VlkZPXq07fLPP/+cp74AFA8EF8hXd999d7bH5Ddp0sR2ObOzG2QnOjrabnb6Bx54IMv2bm5uGjp0aK62lVfr1q2zXb7//vsdWqdLly62y1u3bs31tlPPUS9d/6JvpaCgINvlRo0aqWnTplm29/b2tvsPdtr1M5PdnBy333677XJgYKAaNmzocPsTJ05ku/1+/fqpdOnSWbbp2rWr/P39JV2fXf3GxyskJETx8fGSrn9pveeee7Lsz8XFRWPGjLFdd+R+yg+DBw/O8nbDMNSoUSPb9Yxey2lr79Onj8qWLZtln/fdd588PT1zVmgBSPvaSfuaclTqmTUkaf/+/XmeSyIvunfvnu3jkFvt2rXLdu6YunXr2p3BobCe31ZJO1/C6NGjsw2lBgwYoHLlykmS4uPjtX379izbt2/f3hYQZsaKz9Tcyu/9b9iwoW699da8F/o/N75+rf5cTHt/3H///bbXfWZatmypO+64w3b9xtfDqVOn7ELhrMKGnNSWH49V6uffggULNG3aND3xxBN67LHHbH8LFiywtc3N2WAAFD9Mzol8lfYDODPly5e3Xc5tgr9v3z7bLwelS5d26D82WU1Yl5/SftgvXbpU3377bbbrpP5KLV0/fWJulSpVyvYLsdWnItuzZ4/tcrt27Rxap127draJDnfv3p1l2zJlyqhatWpZtkn7BaxBgwbZbj9t+7S/gmfGkedM6oSwqY/Znj171L9/f9vtae+nVq1ayc0t+7fhtPfnnj17ZJpmgf9Kb8VrOe1/Tlu1apVtfz4+Prr99tsdOmVmQUr7ZSe7ICsj7u7uGjBggJYvX67k5GTdddddGjRokAYNGqSOHTva3Y/5rVmzZvnWt6PvsW3atFFISIgk+9dHUWOapt1z3JH3QXd3d7Vs2dI2km737t1ZjmwqqM/U3CiI/bf6+Zr29MaS83wupp6q+8bPxbQTkAYGBtpC8pzKz8fq2rVr+s9//qMPPvgg3YjDzDjaDkDxRnCBfJV2SHVm3N3dbZcTExNztZ2LFy/aLvv7+zv0pa569eq52lZexMTE2H3pSXvueEflZmh6qnLlytnWTz1cxSppH4MaNWo4tE7NmjVtl7P7j4sjz6W0IUBO2zvy3HN0tvm0z62098uN13NzPyUkJCg6OjpXX5jzworXctp9z8l96WzBRdovg6m/QObUnDlztHv3bv35559KTEzU559/rs8//1yGYah+/frq0KGDunfvrj59+uTrmWQqVKiQb31b8XopSi5fvmz3vC+s90ErPlNzoyD23+rn642vX2f/XPznn39sl2+55ZZc15Vfj1VkZKS6dOmS4xEUuRm5BqD44VCRYubGX3czOs1eVlKHuEv2/znKTEH9Kpz2V5KSJUs6tI63t3d+lZMpK379SkpKyvW6af/T8ccff+S5lrTSPgaO3rdp22X3H5ecPpfy47mXm+fWjfuV1/spoz4LghX3Z1F5nWYlNjbWbgRUdsP2M1OxYkXt3LlTM2bMUNWqVW3LTdPUoUOH9OGHH2rQoEGqWrWqXn/9ddtpc62W3dD1vLDi9VKU3PhrvTO8Dxakgth/q5+vlSpVsgsGnf1z0arDPfPrsXr00UdtoYWnp6cefvhhrV69WkeOHFF0dLSSkpJkXj8xgN3hmakjZgEgKwQXxcyNv9bkdFhk2vZWT2KVF2k/wK9everQOrGxsZbXkd2H743/OYiKirJ9iDv6FxwcnOv62rdvb7u8d+9euyAqr9I+Bo7et2nb3Thk1xnl5rl1437l9X7KqM/cKIz/KDrL6zQvfvvtN7sQIS+HnPn4+Gj69Ok6ffq0du7cqdmzZ6t///62SWul679gPv/88xo4cKBM08xT7QXNitdLbhTWl6Abv0jerO+DmSmK++/u7m532FpoaKil/Vv9uZj2el4Oa8mPx+rvv//WihUrJEmurq5av369PvjgA919992qU6eOfHx85OrqamtflENKAIWD4KKYKVWqlN2vC8ePH3d43RsT8vwcYpxTaWs5ffq0Q//Bd2SuiLSjShwZ6ZDdiApfX1+7yQaPHDmSbZ9WSjvJZ3x8vL766ivL+k77GISFhTm0zqlTp2yX035Zc1aO7lfadjfuV17vJw8Pj3T/Yczp81Qq2GPfU+Vm3/Myp0t++OKLL2yXXVxcHD5uPSsuLi5q3ry5Jk+erG+++Ub//POPtm7dajc3ynfffWfp67UgWPF6kax/H84vZcqUsav1Zn0fzExR3f8bJ7+28j3H6s/FSpUq2S47MqF0ZvLjsdq0aZPt/169e/fO9oxpafsCAEcQXBRDac/28Ntvvzm83p9//mmXtufnpG451bBhQ9vp0aKjox0a7pl2kqvMpP2CGBERkW371Am1stKyZUvb5Z9++inb9lbq0KGD3Zk05s6da9mvk2lnsk+daC8727Zts13O7iwkziC7WdSl6wFf2l/tbtyvtPfTr7/+6tAhAGnvpyZNmqQbLp72eRoZGZltcBcWFubQZKRWa9y4se2yI6+/mJgYHThwIB8rypnw8HAtXbrUdr1Hjx75MvIsNRD5+uuv1aNHD9vy1atXp2vrzIcOOPJ6keyfCxm9D+TH+3B+3G+GYdg9xx15H0xKStKvv/5qu14U3gczU1T3/8EHH7Sdujs5OVnz5s2zrG+rPxfTjvA6fPiw3WFrOZEfj9XZs2dtl1NP4Z2VwjybEoCiieCiGOrcubPt8pdffunwsdPLly+3Xfb398/2NHcFqVSpUnZByqeffppl+6SkJLv9yUzNmjVt/8E9evRotkMz0/4am5m+ffvaLn/wwQeKi4vLdh2rGIahZ5991nZ9586dmjNnTo77SUpKSneatrS/Wu3Zs0d79+7Nso9r167ZhpXeuL6zWr16dba/5v7888/6+++/JV3/pfjGX+Tbtm1rG3Vz8eJFff/991n2Z5qmPvnkE9v1jO6n0qVL2yaZu3r1qg4fPpxln448T/ND2veedevW6dKlS1m2X7lypaWHM+WFaZoaOXKk3XvAiy++mK/bNAxDffr0sV1POzFfqrQj6ApyIkZHhISE6NixY1m2OXz4sF3AkfY5kqpWrVq2y45M+ufI8zu/7re0r89PPvkk2xBx9erVtjCmRIkSdqeGLYqK4v5Xq1ZNw4cPt12fO3durg4ZuXjxYrrnZ9r7Y8WKFdl+3u/evVv79u2zXb/x9VCjRg27s6alPaVoTln9WKX+eCRlf5jY1atX7UJgAHAEwUUx9NBDD9mOMzx27Jjmzp2b7TrHjh2z+4I7YcKE/Cov1x588EHb5fnz52d5GMbs2bMdGmZZunRp1atXT9L1L+ufffZZpm337NmjhQsXZtvnww8/bPuV9syZM5owYYLDx66Hh4fneZK+4cOH24UnU6ZM0Ycffujw+qdOnVLXrl3tvkxLUv369e2Ghj7++ONZfiGYNm2aLly4IOn6/Tx06FCHaygs0dHReu655zK9/dq1a3r66adt1/v165fukCpfX1/961//sl1/5plnsjzWd8GCBbYQyMXFRePGjcuwXdqRPEuWLMm0vzNnzui1117L9Pb81KNHD9spba9evaopU6Zk2jYiIkLTp08vqNKyFBMTo/vvv1/r1q2zLRs2bFiuDxOJjo52eGLktEO4Mzo8L+2pL1MDM2dhmqYmTpyY6aiulJQUTZw40fb+17x5czVq1ChduxYtWtgC5NDQUB06dCjTbb733ns6ePBgtrX5+vravmhduHDBsvBi3Lhxtn53796d5Xvr5cuX7YLkIUOGOHTWEGdWVPf/zTfftJ3dJjk5WX369MlRePHLL7+oadOm6YKLoUOH2kYMnTt3Ti+//HKmfSQkJOjxxx+3Xe/cubPt/x9pTZ482XZ59uzZ2rJli8N1pmX1Y5X2LCfff/99lod1PfXUUxkGsQCQJRPF0qRJk0xJpiTTMAxz6tSp5pUrVzJsu3btWrNq1aq29jVr1jSjoqIy7XvkyJG2tosXL862lsWLF9vajxw5Mtdtrl27ZtatW9fWrlatWubOnTvt2qSkpJhz5841XVxcTA8PD1vbGjVqZFrfzJkzbe18fX3NLVu2pGuzbt0608/Pz/T09LS1zerllXZ/JJl9+vQxDx06lGHblJQUMyQkxHz00UfNkiVLmtHR0Zn266iIiAgzMDDQroa+ffuav/76q5mSkpLhOvv37zcnTZpku98yehxCQkJMV1dXW5933323+c8//9i1iY+PN1944QW7bc+ePTvDbZ44ccKhxyiVI8+TtBx5rnbs2NHWJnXfn3rqKTMuLs6u3dmzZ82uXbvatT148GCGfR47dsz08fGxtW3Tpo157NgxuzbJycnm/Pnz7e7Pxx9/PNN9Wbp0qd22v/zyy3Rttm/fbt5yyy2mYRimu7u7rf2JEycy7LNGjRrZtknLkftz0aJFdo/9008/bcbHx9u1OXbsmNmsWTO7+9zR95PspH08p0+fnmXbc+fOmW+99ZZZvXp1u5pbtWplXrt2LdP1snveBgUFmZUrVzZfeukl88CBAxn2kZSUZH766ad27ymfffZZunY7duyw3V65cuUs35tT5fQ9OlXa+y4oKCjbvlMfu2HDhqWrKyoqyhwyZIjd/bphw4ZMt92lSxdbu0aNGpmnT5+2uz0xMdGcNWuW6erqanefdezYMdM+69evb2u3cuXKbPff0fejRx55xNbOzc3NfPfdd83k5GS7NkePHjVbtmxpa1e6dGnz+PHjGfY3ffp0h5+zpnn9+eXI/jsqp+/Dhb3/ubVjxw7T29vb7vk7ceJE8+TJkxm2T0xMNIODg8277747y9fTG2+8Yfc8nzp1arr3vPPnz5u9e/e2u9927NiR6Xbbtm1ra1uyZElzwYIFZkJCQrq28fHx5urVq83+/ftn2JeVj9WlS5fMkiVL2toNGzbMjIyMtGtz+fJlc9y4caYku/vakecVABBcFFPx8fFm586d7T5Mvby8zM6dO5sjR440H3roIfPee+81q1WrZtfG19c3XRhwo8IKLkzz+heztB+chmGYbdq0MceMGWMOHTrU7gvInDlzHPrQjIqKsgtuDMMw27dvb44dO9YcPny43X9+bwwksjJt2jS7toZhmHfccYc5ZMgQ8+GHHzaHDx9udu7c2fT19bVrZ0VwYZqmGR4ebvdFJO2Xnz59+pijRo0yx4wZY/bu3dvuC2zq32OPPZZhv2+99ZZdO09PT7Nnz57muHHjzEGDBpl+fn52tw8YMCDTsMTZgou33nrL9mWsXLly5qBBg8xx48aZPXr0SBdazZs3L8ttr1ixwi6UcHNzMzt37myOHTvWHDJkiOnv72/XX+vWrbP8spyYmGg2atTIbp2mTZuaY8aMMUeOHGk2adLEtnzGjBkOhRL5EVyYpmnee++9dnX6+fmZgwcPNseOHWt27drVdHNzMyWZLVq0MIcOHZqj95PspH08W7RoYT766KO2v0ceecQcNmyY2adPH7NWrVrpnvOSzDFjxmT7GnQkuEjbZ6VKlcyePXuao0aNMseOHWv27dvXrFKlil2bDh06pPtCYZrXA66AgAC71++IESPMyZMnm0899ZT51FNPmStWrLBbp6CCixkzZphly5Y1JZk+Pj7mPffcY44bN86855577II7SebkyZOz3PaOHTtMFxcXu8+rvn37mg899JA5aNAgs2LFirbtvPPOO7Z2WX1xf/HFF23t3N3dzd69e5sTJ0603W9PPfWUXXtH349iY2PNFi1a2O2fv7+/ef/995tjx441O3funO61v2rVqkz7K2rBRWHvf17s2bMn3XuvJLN+/fpm//79bZ/7d911l+25nfYvo8A4OTnZLtyQZJYvX972+dGzZ890nx9z5szJss7Tp0+n+/HB19fX7Nu3rzlu3Dhz1KhRZufOnc3SpUubkswyZcpk2I/Vj9VLL71k11e5cuXMXr16mWPHjjX79OljCytcXV3NTz75JEfPKwAguCjGEhISzMmTJ9v9opnVX+vWrc3Dhw9n229hBhemaZobN260/Sc2oz93d3dzwYIFOfrP2K5du9J94U775+HhYS5YsMA0TdNueXZWrlxpF4pk99eyZct0v/TnRUJCgjl//vx0X5Ky+qtdu7a5aNGiDL9Epfroo49s/2HK7M/V1dWcNGmSmZSUlGk/zhZcBAUFmd99951ZpkyZLJ8Lc+fOzXbbpmmaa9asMStVqpTtfT5kyBAzNjY22/6OHz9u3nLLLZn2YxiG+eKLL5opKSmFGlzEx8fbBRKZPdfPnDmT6y/ZmckorMvuz8XFxezdu7e5fv16h7aR3fN2x44dtnDGkb9BgwZlOiLONE3z+++/T/fFJ+3fja+FggouFi9ebIaEhGT5Hufi4mI+88wzmYaXaS1atMjuS9SNf1WqVDF/+eUXh7+4X7582WzQoEGW931aOXk/io6ONu+7775sH9sqVaqY69aty7KvohZcmGbh7n9eXbx40XzyySfNEiVKOPwabd++vRkcHJxpn4mJieZjjz2W5fNXuh4wOPqajIiIMAcMGOBQfdWqVcu0Hysfq6SkJHPEiBFZ9uPr62t+8803uXpeASje3IRiy93dXbNnz9bkyZO1bNkyBQcH69ChQ4qIiFBCQoLKli0rf39/tWvXTv379y8SkydK1yecOnTokN555x198803On78uFJSUuTv76+uXbvqkUce0e23366TJ0863GfTpk31559/6u2339aaNWt04sQJW5/dunXThAkT1KBBgxzXet9996lfv35asWKFfvrpJ+3cuVMXL15UTEyMvL29Va1aNd16663q0KGDevfurbp16+Z4G1lxd3fX448/rrFjx+rHH3/Uhg0btGPHDl24cEHh4eFydXVV2bJlVadOHbVq1Up9+vRRhw4dsp2R/8EHH1S/fv20cOFC/fDDDzp8+LAuXbqkUqVKqXr16rrrrrs0ZsyYXN1nhe2ee+7Rvn379MEHH2jt2rU6ffq04uPj5e/vrx49euixxx7L8LjkjPTt21dHjx7Vxx9/rLVr1+rgwYMKDw+Xl5eXqlatqs6dO2vEiBFq1aqVQ/3VqlVL+/bt0zvvvKOvv/5ahw8fVnx8vKpWraoOHTrokUcecbiv/OTh4aHPPvtMI0aM0MKFC7V9+3aFh4erfPnyqlevnoYOHaqRI0faZvovyLpKly6tMmXKqFKlSmrSpImaN2+url272o5/t0KrVq104cIF/fzzz9q6dav27NmjY8eOKSIiQsnJySpdurRq166t1q1ba/jw4Xbzl2Skd+/e2rVrl959911t3bpVp06dUkxMjMNz5+SnNm3aaO/evfrwww/19ddf6+TJk4qJiVGVKlXUuXNnTZgwQc2bN3eorzFjxqh169Z6++23tWnTJp07d04lSpRQrVq1NHDgQD388MPy8/NTcHCwQ/2VLl1av/76q95//32tWbNGhw4dUlRUlCXzXfj4+GjlypV64oknbJ+vZ8+e1bVr1+Tn56fbb79dffv21ZgxY+Tt7Z3n7Tmborz/fn5+evvttzVlyhR9//33Wr9+vfbv36+LFy8qMjJSJUuWVPny5XX77berdevWGjRoULafzW5ubnrnnXc0fvx4ffzxx9q4caNOnz6t6OholStXTnXr1lXv3r01btw4u3lrslKuXDl9/fXX2rlzp5YvX67g4GCdOXNGkZGR8vLykr+/vxo3bqyePXtq0KBBmfZj5WPl6uqqTz75RIMHD9aHH36o0NBQRUZGqmzZsgoICFC/fv00ZswYVa1aNUf/BwMASTJMZ/ifDVAITp48aZutvkaNGnyIIkOdOnXS5s2bJUlBQUHq1KlT4RYEAAAAFDOcVQQAAAAAADgtggsAAAAAAOC0CC4AAAAAAIDTIrgAAAAAAABO66YMLq5evapvv/1WDz74oBo2bKjSpUvL29tbjRo10syZMxUTE5NunRkzZsgwjEz/nnvuuULYEwAAAAAAireb8nSoy5cv17hx4yRJt912m3r27KkrV64oJCRE06dP1+eff67NmzerYsWK6dZt166d6tSpk255s2bN8r1uAAAAAABg76YMLjw8PPTII4/oySefVGBgoG35uXPn1KdPH+3Zs0dPPPGEli9fnm7dsWPHatSoUQVYLQpLzZo1xdmAkZ3g4ODCLgEAAAAo1m7KQ0VGjBih9957zy60kKQqVapowYIFkqSvv/5aCQkJhVEeAAAAAABw0E0ZXGSlUaNGkqT4+HhFREQUcjUAAAAAACArN+WhIlk5fvy4JMnd3V3lypVLd/umTZv0+++/Ky4uTv7+/urVqxfzWwAAAAAAUEiKXXAxb948SVLPnj3l6emZ7vZly5bZXZ82bZoGDhyoJUuWyMfHx6FtHDlyJMPliYmJ8vb2Vo0aNXJYNQAAAAAAxVOxOlRk3bp1WrRokdzd3fXKK6/Y3VanTh3NmjVLBw8eVExMjE6fPq3PPvtM1apV01dffaUHHnggz9s3TZN5NQAAAAAAyAHDLCanVTh06JDatWunyMhIzZ07V5MmTXJovXPnzumOO+5QRESEtm3bprZt2+a6htSRGDdOGgoAAAAAADJWLEZcnDlzRj179lRkZKQmT57scGghXT8TyejRoyVJP/30U36VCAAAAAAAMnDTBxfh4eHq1q2bwsLCNHr0aM2aNSvHfaSOkDh37pzV5QEAAAAAgCzc1MFFdHS0evXqpT///FP33nuvFi5cKMMwctxPZGSkJDk8OScAAAAAALDGTRtcxMfHq1+/fvrtt9/Uo0cPff7553J1dc1xP6Zp6ptvvpEkTosKAAAAAEABuymDi+TkZA0ZMkRBQUHq0KGDvv76a3l4eGTaPjw8XEuXLlV8fLzd8piYGD3yyCMKDQ1V5cqVNWDAgPwuHQAAAAAApHFTnlVk3rx5euKJJyRJAwYMUOnSpTNsN2vWLPn5+enkyZOqVauWSpcurVtvvVUBAQGKiorS7t27FRERIV9fX61du1bt2rXLU12cVQQAAAAAgJxxK+wC8kPqnBSSbId5ZGTGjBny8/NT+fLlNWXKFO3YsUNHjx7V77//LldXV9WqVUujRo3Sk08+qWrVqhVE6QAAAAAAII2bcsSFs2LEBQAAAAAAOXNTznEBAAAAAABuDgQXAAAAAADAaRFcAAAAAAAAp0VwAQAAAAAAnBbBBQAAAAAAcFoEFwAAAAAAwGkRXAAAAAAAAKdFcAEAAAAAAJwWwQUAAAAAAHBaBBcAAAAAAMBpuRV2AQAAAABwM1geGlbYJThkaKsAy/sMDw/XvHnztHbtWp04cUIJCQmqWrWqunTpookTJ+r222+3fJuFadSoUfrkk08UFBSkTp062d22YcMGvfzyy9q7d69iYmIkSaZpasmSJRo9erSmT5+uGTNm5FttBbWdgkRwAQAAAADItZ9//lmDBw9WVFSUKlSooI4dO8rT01P79+/XwoUL9fHHH+vVV1/Vc889V9ilOqxmzZo6deqUTNPM0XphYWEaMGCAEhISdNddd6lixYr5VGHxQnABAAAAAMiVnTt3qk+fPkpMTNRrr72mp59+Wm5u//81c926dRo+fLief/55lSxZUhMnTizEaq3z2muv6bnnnlNAgP3olZ9//lmxsbGaNm2aZs6caXfbgAED1Lp1a/n5+RVkqTcF5rgAAAAAAOSYaZoaOXKkEhISNHPmTD333HN2oYUk9e7dW99++60Mw9CUKVMUFlY0DqfJTpUqVVS/fn2VLFnSbvmZM2ckSbfccku6dcqUKaP69esTXOQCwQUAAAAAIMd++OEHHTp0SNWqVdOUKVMybXfnnXdq8ODBiouL03vvvWdbbhiGatasmeE6S5YskWEY6eZoOHr0qGbMmKE2bdqocuXK8vDwkL+/v0aMGKHDhw9n2FfqdpKTk/Xmm2+qbt268vT0VPXq1TVlyhTFx8fb2gYHB8swDJ06dcq2bupf2lpHjRolwzAUHBxst9706dMlSaNHj7atl7oPme2TdD0E+uSTT3TnnXfK19dXXl5eatiwoWbNmqXExMQM92vfvn3q27evypQpozJlyqhbt27avn17hm2LOg4VAQAAAADk2Lp16yRJgwcPlru7e5Zthw4dqi+++ELr1q3T66+/nuttfvTRR3rjjTfUoEEDNW/eXCVKlNAff/yhZcuW6bvvvtOWLVvUsGHDDNcdNmyY1q5dq5YtW6pevXrasmWL3nzzTf3999/69NNPJUmVK1fWyJEj9eWXXyo2NlYjR460rZ/VSInU9X7//Xft3btX7dq1U506dSRJjRs3znKfUlJSdP/992vVqlUqXbq0WrRoIR8fH4WGhuqZZ55RUFCQ1qxZIxeX/x93EBoaqi5duujq1atq3Lix6tevrwMHDqhjx44aNWqUg/dm0UFwAQAAAADIsd9//12S1KxZs2zbprY5ePCgkpKS0h1S4qj+/ftr3Lhxql27tt3yxYsXa8yYMXriiSe0adOmdOudOnVKJUuW1IEDB2wjJ06cOKFmzZrps88+08svv6zatWurfv36WrJkiYKDgxUbG6slS5Y4VFfqejNmzNDevXs1duxYhwOEWbNmadWqVerWrZs+++wzVahQQZIUGxurIUOGaM2aNXr//ff16KOPSroedIwaNUpXr161zbWRatq0aXr11Vcd2m5RwqEiAAAAAIAci4iIkCSHzpyR+mU8JSVFly5dyvU2W7dunS60kK4fmtGuXTsFBwfr8uXLGa77zjvv2B3uUatWLQ0fPlyStGXLllzXlBdJSUl66623VKpUKS1fvtx2P0mSt7e3Fi5cKE9PT/33v/+1LQ8ODtaff/6punXrpjtEZ/r06ekmDL0ZMOICAAAAAJBjqacKdeSUoWnbJCcn52m7MTExWrNmjX7//XddunTJNgfEuXPnZJqmjh07pqZNm9qt4+7urk6dOqXrq27durZ1C8OePXsUHh6uXr16ZXgoSqVKlRQYGKgDBw7o2rVr8vLy0tatWyVdP0THMAy79m5ubho0aJDefvvtAqm/oBBcAAAAAAByzM/PT3/99ZcuXLiQbduLFy9KklxcXFS2bNlcb3PTpk26//77bf1lJDo6Ot2yKlWqyNXVNd1yHx8fSbKboLMgnTx5UtL1iU5vDCFudOnSJVWrVk1nz56VpExHVjDiAgAAAAAASY0aNdK2bdu0a9cuPfDAA1m23bVrlySpdu3aKlGiRLZ9p6SkpFsWExOj++67TxEREZo2bZqGDBmiGjVqyMvLS4ZhaOjQofr8888zHAGSXShQWFJHnwQGBqpt27ZZtvX09JT0/6NXnHWf8gPBBQAAAAAgx3r16qX33ntPX375pd56660szyyyfPlySVLv3r1ty9zd3RUTE5Nh+9OnT6dbtmXLFkVERGjgwIGaOXNmutuPHz+e010odP7+/pKk22+/3eGJQKtWrSpJtlO23igsLMyS2pwJk3MCAAAAAHKsd+/eqlevnv7++2+98cYbmbb75Zdf9OWXX8rDw8N2Zgzp+uEbERERGU7WuX79+nTLIiMjJUnVq1dPd9vRo0e1e/fu3OxGhjw8PCRdnzwzP7Vo0UJlypRRUFCQrly54tA67du3lyR99dVX6UaXJCUl6auvvrK8zsJGcAEAAAAAyDEXFxctWbJE7u7ueumll/TGG2+km3jzhx9+UP/+/WWapubMmaPAwEDbbR07dpQkvfLKK7ZlpmnqtddeU0hISLrtpU6k+fXXX9vNcREVFaUHH3zQNkmnFVJHNfz111+W9ZkRT09PPf3004qKitLAgQMzHEWxb98+rVy50na9c+fOqlu3rv7880/NmjXLru2rr76a6UiMooxDRQAAAADAAkNb3XyTImandevWWr16te6//34999xzevvtt9WmTRt5enpq//79OnTokNzd3TVz5kxNmDDBbt0pU6boyy+/1Ny5cxUcHKzatWtr//79On36tCZMmKD33nvPrn3z5s3VrVs3bdiwQXXr1rWdJSQ4OFh+fn7q16+fvvvuO0v265577tHmzZvVtWtXde7cWd7e3vLz89Prr79uSf9pvfDCC/rjjz/0+eefq169emratKkCAgIUHh6u48eP68SJE+rXr5/+9a9/Sfr/wKhr16569tln9fnnn6t+/fo6cOCA/vzzT40dO1YfffSR5XUWJkZcAAAAAAByrWfPnjpy5IhefPFFValSRZs2bdIXX3yhQ4cOSZJWrVqladOmpVvvtttu06ZNm9SpUycdPnxYGzZsUO3atbV9+3a1aNEiw2199913evHFF1WhQgX98MMP2rVrl+6//37t2LFDvr6+lu3TxIkTNXXqVPn4+Oirr77SokWLtGLFCsv6T8vFxUXLly/Xl19+qc6dO+vIkSP6+uuv9ccff6hSpUqaMWNGukNx2rRpo5CQEPXq1UtHjx7V2rVrVaFCBQUFBaldu3b5UmdhMkxHTroLSxw5ckSS7IZHAQAAAMDN6KmnntLbb7+tevXqadu2bSpfvnxhl4QiihEXAAAAAADLzZo1S0OHDtVff/2lvn376urVq4VdEooo5rgAAAAAAFjOMAwtWbJEDRs21LVr17Rz507bhJxATnCoSAHiUBEAAAAAAHKGQ0UAAAAAAIDTIrgAAAAAAABOi+ACAAAAAAA4LYILAAAAAADgtAguAAAAAACA0yK4AAAAAAAATovgAgAAAAAAOC2CCwAAAAAA4LQILgAAAAAAgNMiuAAAAAAAAE7LrbALAAAAAICbwm+LC7sCxzQfbUk3wcHB6ty5s2rUqKGTJ09m2q5Tp07avHmzFi9erFGjRlmy7eJgyZIlGj16tKZPn64ZM2bY3Xbs2DE9/fTT2rJliy5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\n", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -541,7 +3106,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": { "scrolled": false }, @@ -560,7 +3125,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 24, "metadata": { "scrolled": false }, @@ -569,19 +3134,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "No handles with labels found to put in legend.\n" + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" ] }, { "data": { - "image/png": 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KnTon52XWqme1wudfRMQtDdp+b0TsFxG31ivTouUKnx+oW6pqZOHzRy3O047eOCePpNoD/XbSzeaa2zYinouIQ6L2kwkdv65GxL8i4i8R8XSdthMR/yE9AVQ5P3fNTze04v7C5+VbnMfMShxYNzMzM7MBQ9Ls0CWd4cs9qK4c3JirB3X1lrGFz2u3OM9+TYKx/yp8XrntFvU/G5Ny7gJcEU3SheQA4WH564ykPOpT5ED9ToVJB0TEiw2q3I/Ua7VTisfhqx2slzzo3ncKk34dEU/UKx8RZ1G9uQOwSwuLOTsirmrwezn9yO+iTiqEfBxfUJjU6vG6d0S82+D3fagGmEcAG9Uo893C56Mj4o56lUXEbcA/CpNa2U694QXgwHo/RsTzNNmekuYkDVhZsU+D4PJHVJ+C6ZReO/57yelUz/961+jZ8/vbPbhRNnvhcyspmTphkcLnp1ooX9x3r0fzgU7b1dFzUtIqpDECIB3DO3Zn/0yj62pdEfEa1ZRq85GeNGhFMWC/SN1SZtaQc6ybmZmZ2UAyW+n7VL2S21D+D/QcNUv1IknDSHlXlyXljJ2Nak5VqPaAg9Z6lL1LynncyF2FzyNbqLO/K+aJP6XFea4sfF4N+FPh+1KkR/4h9XhsWGdEvFbK199TxWO8073Vi+v2EXB8C/McC2yQP49qofwZTX4vP6lxZhvlWwn+PEnKq15XRLya99k2edKawDmV3yXNCqxUmKV4M6qeY6kGyFaWNEutpyZ62XlNbihAOv8rN5NG1vj9S1R7qz9PSgNTV0Q8LOlGqgHKnurN479bJC1L6vU8khTgHloqUrmxsKykQTUCyk+QcmvPKWmbiOhO7vFioPa7ki6IiA/qlu6MTxU+v1S3VFWv7bteOifXL3y+ovzUTBt6/boq6ZOkfyssRUo7MwtdxyIobpvlSU+aNFO8YTxv3VJm1pAD62ZmZmY2kLxR+j5LD+qatfS9WUCqYyTNRepZugNT3yyoZ57mRXgwIt5rUqYYIJnmNxN6wRcLnzeS1MoNiOJ6L1j6rTj/A7k3YDM307nAevEYLx+jPbVC4fMDEdFKsOyGwud5JY2IiGcalL+3SX3FJ0Vea5TmoEb52euWqrolIlrpQX0T1cD6CqXfPkf1BtebpBzwzdxNutE3S573c8CNLczXSa0E05qd/8Xj/7YWex3fTOcC6715/LdF0o6kgYmXaHGWGUnbtPw01KlUBzg+KQ+WeSpwZTldUQOnA78iZR3YALhP0nGkns//6YXe4dD172vNpxZKenPf9cY5uWrhc6OnbJrpteuqpKVJYyZsQNeb7o208m8F6LpPe/JvKbOPNQfWzczMzGzAiIjXJX1A9d+xPUnfMmfp+zRJOyBpYeBaYKE2Z20lAN80CBwR76cn14Hp4/8DIwqfN+3G/OXjYHjhc93H+Uue7MZy6ymmN/pEB+uFruv2eCszRMTzkt6lmod4HtLgifU0OwaLvWxbuWlRLD9jC+W7s8+Gl34rfn+ylaBlRHwk6UlgyTyp1eBWJ7WyPd8vfK61Pafn478lObXHP+maEqpVszF1YP23wFdINx9ESl+1cV7WY8B1wOXAORHxeq1KI+IBSXuTnq4RKf3V7/LrdUk3kZ7UOCsiHuxGu5tp5WZVcd/NXqf3fnf1xjlZ7JH/aIfa1rHrqqT1SE/SlJ+QaKbVm/VqXsTMmnGOdTMzMzMbaIr/cV2mB/WU532oZqnOG0c1qP46cCiwHinNxazA4IhQRIiUoqKilX+7dzLX8UDR01735ZsLxZ6Wb9OaTqb8mFj4vFBOgdApxbraaXOxbLOgTTvHYG8cr93ZZ+V1mhbbqTd0Ynv2p+O/1VzRnfZtugbVzwe2J/3NmBMYWrlG5+t08W/SVNfpPJDsmsCewCOlnxchPbl0AvCspD9KmrlWoyLicGAN0gDAxcDy7KS/Ib8HHpB0RU5f01PF/TqshfITC58HAZ/pQBsqeuOc7FTqmo63TdJw0lMNlaD6Y8C+pJszI0j7Y1DhGPxVYfZW43wzFT5P67RVZtON6aGHipmZmZl9vNxAylcLsEoP6inO+3Kjwcba0PA/tJK+RDVlwhvAKhHxQINZ+iI4N9C8RTW4vnxE3NPD+ooBllaCSdDZx+ivL3weRMqde3WH6i6uWzttLpYtp2Pqb7qzz8rr1F+2U190hOsPx/+38udPShoZERM7WH8rflT4/LOI+F2T8k2v0xHxPnA4cLikZUgB8tXzqzKWxrC87NUlrVlr0NiIuB5YX9I8pF7wlTqWp3q8jAZukbRORNxQrqMNzxU+t/IExvWl76sA9/dg+UW9cU52KnVNb7Tt21T/rt0FrNFkYNXu/Fuh2NO+1ZREZlbiHutmZmZmNtAUc6EuIGmNdiuQNAvwtcKkC+sULaZNaKVTSrPe02sVPo9tElQHWLiFZX7cPV/4vHgH6nuh8LnVdD3lPO09cTNdewpvU69gN7S9bnnQvGLPxhfrle0nWt1nxXLldSpupwVVyJ1Uj6RBdD0Oam2nTl9PekNfH/9X0bXnfSeP/6YkLUj1OvIK8Icm5Wdn6nRSDUXEhIg4KiK2jogFSDm6/1kosgqwW5M6XoyIMyJij4hYkZTWZHeqx93MwDHttKuGiYXPCzQrnG+APFaY1FvXrk6dk8W/Ha0MjNxK2zp1XS3+W+E3TYLq0L1/KxQHR5/YjfnNDAfWzczMzGzg+Tddc7nu1Y06vkPXHmrj6pQr9iKbu4V6mz1+X8wH3myQR0i9Gq2xWwqf1+tAfXcXPi8pqZXg5qrNi7QmIt4FxhYmbS3pU3WKt+uuwucl8yC6zRQHpXyuycCl/cGqrQTd6LrP7iz9dg/wYf48K83Pa0gDI1Z6oH6Y6yjr9PWkNxSP/5VzcLKZTh7/j5MG5Kz4rqRWe853QvEa/WBEfFC3ZLIaPcxVHRF3R8TOwN8Lkzdps44XI+LI0nyflfTpHjSteAy3mtblb4XPa3coJU2lLZ0+J28ufB7dg7b1xnW15X8rSBpM9wYPXrLw+e66pcysIQfWzczMzGxAyflqjypM+pqkr7c6fx489MDCpLsi4uI6xYu975ZvUu9KNO/1VsyL2zBYJGkEbQZXOuzdQltaGTSyr5xf+LxN7gnYE/dT7ck4A7BVo8I58P61RmW64VCqg3bOCvyjO5VIWrc06X6q6R0GA9u1UE0x1/RVdUv1HwsAoxoVkPQJuu6zLuuVe4feXpg0poXlFrfTrRFRK2dxp68nveFG4L38+VPAOo0KS1oM+FKH23BQ4fNCwMHdqURSw7bX0fI1OtulG8uop3gt69bNtIi4ia43nntyU+5BqgPizi1p/kaFs2OoDgQuYGx3/n6Ur129dE4Wb+CsJWmpthpZ1RvX1XaOw02BeVtY5hSS5qYavH+T1m70m1kNDqybmZmZ2UD0O+C+wvcTW0kJI2le4FKqvdU/pHFg5NbC5x0b1DsD8OdmywceLXyuG4zNPdD+TnXgsr7wUuFzKwGVvnIG8HD+PAw4qdVAjqRZc1qgKSLiI+C4wqRf5nzG9RxEz/LzTiUiHgV+Wpi0saTj8nHWVF6vv1BKBRERQddesb9oFCyTtBGwcWHS0a0svx/4k6SZGvz+R6o9WZ8FLqhRprjtdpO0XL3KJK0AfK8wqd526vT1pOMi4hXgzMKkuoNp5icD/kwPe2zXaMN1dL15+n1Jv251fknzSDod+Fk3Fv8Y1VQ0y0hatF5BSVsCX23SlqFtDEBcTCNSTC9Ck2tQsdycdL0evVCvbDP5WnhlYdJqLczzOumJsIrPA+eVr7P1SBoiaX+6Br0rOnpORsStpDFbIB3DJ3RnsOheuq62+m+F4cBhLTa1aPXC5ysj4sO6Jc2sIQfWzczMzGzAyYO6bUm1N90w4HJJv809sbrI/1nfgfQo+BKFn34cEbeUyxecQrXn2BclHZSD3sW6FyD1NPwSMLlJ0y+gGrT5iqRDykGrHPw/A9iINDBnX/lv4fMWfdaKJnJAYBeqaQLWAa6VtHK9eSQtJ+n3wBPU7hX8J6o3FuYDLs09c4t1DJF0ECl48x6ddwhweuH7GOAuSZtKGlJrBkkjJO0FPELK0Vwr4Hk48HT+PDdwhaSpek/noOGphUnnRcS1ba/FtPceqTf4uZLmK/6Qg5x/AnYuTN6/TrqPcVRTRwwBLpG0ZrmQpLWAi4HKzZw7gZPrtK3T15PecmBh2csC5+Tr0hSSZgOOBzakd47/PUm95yt+Lul6SaPL26zQpk9L+hUpKPmN7iw0Il6kml5qEHCapC5pUCQNkrQbcCLpuvMu9c0HPCnpUElfqNNuSVqfrk9Slcf9+LekCyRtXi9ILWkh0jFWuT48FBEP1yrbhvMKn1tKlxIRp5GeuqlYD7hX0o710vpImkvSzqRe8gdSO1bVG+fk7lSP9ZVIfzvq7ad5Jf1I0j41fu70dbX49MJ+kqbqBS/p88A1pDzy7f5bobjdzq9bysyaaqnHg5mZmZlZfxMREyStRurZtgDpP9E/BX4s6SbgcVLAZz5SkKqYK/sj4KcR8acmy3hc0tHArnnSvqSc19eSgimLknKbDgGuIPV+rfsYeEQ8IOlEYIc8aW9S+pLbgEnASFJe9SGkfMz70He9hM8A1s+fD8qBn3vpGuz7be7h2qci4nJJu5Dy+w4m5Xy+VdJDpPy3r5AG85uXFHRtmC4mIl6Q9G3gtFzfCsADkq4hBe1mJwUmhpMGpPw5TQY57MY6haStSEH+3fPkZYCzgLck3UI63t7K67MIsBxdg+nFnN6Vel+RtA3pvBlGyp18Z67vPtKxtwpdB4J9CPhW59auVx1F6uG5DvCYpKtJN1DmIu2zYv7jMyLin1PVAETEe5K2JgWuhpOOnSsl3UM1H/HypDzOFZOArSPifWro9PWkt0TE/ZJ+TLXX/DrARElXAk+Rtsdo0nnwai73yw634T1Ja5OC95vnyV8mbZdXJN1K2t6TSelOlmDqPOBTHf8t+jnpyaZBpHP/v5JuIJ37s5J6+1Zu2vyM1EO70eCRnyCNBbKXpJdJ16Snc9s/STpvizf4/sfUTywMIt3E2BB4X9KEXO41YLa8/FWpBqQ/pHrd6IkzSefUTMAmknbJPdkbiogfSZoE/D63aWHS2BHH5H33NPA6KQi9EKlne/GGyVSDdfbSOXmnpG/lts1A2t+3SHqQtJ9eI/3bYWnS9XcQNZ4m6YXr6ljSMbME6cm1EyX9lHRj4d3clpVy2XuAS4Af16mrlk3z+3t0vYFrZu2KCL/88ssvv/zyyy+//BqwL1Jg4m+kAGe08LoPGN1G/TNR7Wle73UeKXgytjBtTJ36hpH+E9yovidJQaRRhWlX16mvaZka80xZVoMyM5DSADRq58hu1NtWe1ups1B2TVKwqZXjIIAJwIgG9W1NCs7Vm/914Ovd2QdtHuMbknpdtrpeLwO/AWZvUOeqpJ7tzeq6DBjepH0T6x0TNcqOLJSd2MK6jymUH1unzAGFMgeQBuW7v8l6nQDM2MLyl2hx298BLNoH15OmZdrdnoWye5GCv/Xa+RwpPUjLdXbj2Bfp5sJDbRz/zwA/AoZ2d/1JT6I0+pvyIfCr3L66xz8pjda7bbT9KmDeGu05r406nge+1sF9cGKh7lFtzvtFmv8dKb7eAv4KfGpanZO5ztGkGyettPE3Derp5HV1iRbquj4fYwcUph3QpN5VCmVP6eT56pdfH8eXe6ybmZmZ2YAWEZOAXSQdTOqFtT6wGCngPlup+OvA2hHxTBv1vyvpq6Qg646knnVzkHrC3UMKbJ0eEZFSDjet721JGwDb5PpWIPX8fJH0H/szSAGfVySNarWdnRYRH0haD/g/UlqFZUm9fWumIekPIuIqSUsCm5FS6axK6tU4O/A2KeD0ACnFxEURcXe9unJ9J0u6ntTzcyNSz8oPSDc+LgSOjojHens/RcSFki4i9ZRdD/gKKZgynPSkxqu5TbeRevSeFxEN04hExM1Kg/VtRzpvKj353ycFTK8HTo6IS3tlpXpRpCdDViYdu1uQrgdzks7ZG4G/R8QVLdb1P6WBRL9JOg++QPWJh0mktCGnk3q/Rwv1dfR60psi4k+SLga+D6xLOubeJj0NdBZwTERMKiCvwmsAACAASURBVKdJ6nAbgjRuwinA2rkdXyb1GJ+HFNh+hRTcvo2UAuTS6GHO6Ig4OvdS35N0w24E8A6pp/WVwL8i4i6ARvspIp7O6clGk87fFUnH43CqTyY9ntt+akRcXqeqTUh/K9YiBUaXIj2pNQvp5scLwH9I16XxkXKdd8rhVJ+c2Bm4utUZIw2mOlrSisAGpO2wMGnfzUzqEf4MKQh+NXBmpIFKG9XZ0XMy13llTvmzFSlv/kq5zqG5jQ8DNwFnRRoDoF49Hbuu5vVcgZTW6+ukXvBDcj3/BcYDp+W/1a2sZsW3C5/7ZCwHs+mJWrzOmJmZmZkNSDnocwPV/3jfRep118nAg5n1EUkHUE1F8quIOKDvWmM2/ZF0GenGxmRg4Yh4vo+bZN2gNLjtk6QbMtdFRNNB382sMQ9eamZmZmbTtUiDt21ENWfrCsDZkob2XavMzMwGjP3z+1Dgh33ZEOuRH5CC6pDGEjCzHnJg3czMzMymexFxO+mx8coAZmsC4yX538NmZmYNRMTNVAe53C2nt7EBRNIcVAe0PTciru3L9phNL/wfCTMzMzP7WIiIS0g5lyu5EL9OGvTUzMzMGtublGN/dtzbeSDaD5iblM5nzz5ui9l0w4OXmpmZmdnHRkScBJzU1+0wMzMbSCLiCappRGyAiYifAD/p63aYTW/cY93MzMzMzMzMzMzMrA0OrJuZmZmZmZmZmZmZtUER0byUmZmZmZmZmZmZmZkB7rFuZmZmZmZmZmZmZtYWB9bNzMzMzMzMzMzMzNrgwLqZmZmZmZmZmZmZWRscWDczM7O6JkyYcO6ECRPO7et2mJmZmZmZmfUnM/R1A8zMzKxfW3To0KFLAx7t3MzMzMzMzKYV9XUDmnGPdTMzMzMzMzMzMzOzNrjHupmZmTX01KS3+OWJV/d1M8zMzMzMzD72xh84qq+bYJl7rJuZmZmZmZmZmZmZtcGBdRsQJM0iaU9JV0l6XtJ7kl6RdJOkAyUtNA3aMEpSSBrb28vqbZLG5HU5oDT9gDx9zDRqx315eQ92uN6QNLHNea7O843sZFv6i+4cv5Im5nkqrw8kvSzpAUnjJe0oaaZebHatNk3TY7Qd9c4rMzMzMzMzM5v+OLBu/Z6kVYGHgD8BXwAmAKcDNwKLAvsD/5O0dp810tomaSVgqfx1CUlf6Mv2WENnAMcD44BrgHeBLYCxwERJG/Rd06YdSWNz4HxUX7fFzMzMzMzMzPqWc6xbvyZpOeBKYGbgYODXEfFW4fdBwKbAH4AF+qSR05e/AKcAz06DZW2f358F5gO2A27tUN1LAe93qC6DH0XExOIESfMCPwd2A86XtFFEXDwN2jItj9F2nQXcDLzY1w0xMzMzMzMzs97lHuvWb0kScBIpqH5AROxXDKoDRMRHEXEmsCJwex80c7oSES9GxAMR8VpvLkfSDMBW+esO+X2rPL3H8jo80om6rLaIeC4ivk96YmQQcJykodNgudPkGO2OiHgtt82BdTMzMzMzM7PpnAPr1p+tBywLPAX8tlHBHNCaUPkuaT5JP5Z0jaSnc0725ySdKWnlWnUU8klL0g8k3SPpbUl31yg7X04L8bykdyTdKWmHWvXm8ktLGifp2dyWpyWdIOkzpXKDJd2Q27FLjXpGSfpQ0pOS5pQ0VNKLuZ1z1Fn2V3J9lzfahrls3fzVkmaUtGtu36t5mf+T9A9JyzSru2Q94JPADRFxOSmtz/A8vVH7NpB0vqRJkiZLekLS2ZI2KpWrm2Nd0nck/VfSu3k/HFlv2+XyS0k6UdIjeZ4XJN0t6XBJ87Wysj05HvPnnSX9Jx9rz0k6RtIn6sw3UtLJkl6S9KakG8vbp8N+DzwOzAtsXqM9wyUdIunBvP1ekXSRpDVqlJ2SB17SvJKOlfSUUm73PXKZqY7RvD9D0pK1Gpjr+iDXNShPm0nStySdI+nRvG1flXStpK1q1BHAjvnrVeqae35kLjNVjnVJ5+Vp69dp2xClvPXvSJq99Nuy+brxdD7en5F0nGqMA6Bkq9z+5/K2flLS5ZJ2q7VsMzMzMzMzM+s+B9atP6sEA0+LiA/anPdrpNQxI4D/AmcDzwCbATdIWrfBvEcDhwKTgHOBR0u/z0VK97A+cDVwHekGwPGSflmuTNJapN702+Q2nJHr3h64XdLqlbIR8WGe/gZwaDFQmAOpJwACdoyIVyJiMin39czAtnXWZ+f8/o8G69yQpFmAK4C/AsuRAuHnAa8CY4BvtlllJQ3MSaX37WuUrbThUOBC0nZ/EDgTeAxYE9inlYVKOgQ4BliclGLoJtJ2uwqYqre1pM8Dd+QyL5BSfdwCDAF+CHymPE8d3T4eJf2BtN1fBy4m7f/vAOdKUqnsorl9W5HSkZxHus6fR8qJ3nH5mD0tf12z1J4lgbuAvYHBpP33H2A0KTi9TZ1qhwO3ka4BNwEXAW83aMa4/F7vHNgqL398RHyUp40EjgVWAZ4AzgHuBlYFTtbUA5AeD1Segrgkf6+83uxB2zYE5gTOi4jXKxMlfYPqdeNZ0rXoOdL5drukz5bq+T1wMrA8cCfp/HgY+Bwtnh9mZmZmZmZm1jrnWLf+bIX8fmc35r0B+FxE/Kc4UdJ6pADVUZIWj4ioMe/XgRUi4t46dW8MXAZsVklNk3sdXwn8QtI5EXF3nj4LKbA2M7BLRBxdaMuepAFZx0taLAfJiYhHJf2ANDDkSZK+GBHvkwLCCwKHRMSVhfYcA+wFfBs4qrS+nyAFvV8iBXO768/A6qQA9OYR8VJhGfOTeiu3JPfK3QR4D/h3nnxqXsYmkmYvBhjzPNuR1vEpYKPifs3beJUWlvslUoD3ZWCNyv6VNDdp361aY7bdSfvuGznlULG+pUg3FlrRk+NxO2CVwjE1DynYvDopkF08Fo4iPQlwFPCDShBZ0s704MZKCypPdVQGo0XSYFLAfX7STYgjK+snaQXSOfR3SZdHxKRSfRuSbmJsExHvtrD88cDvSEHo/Wv8XgngjytMe4H0hMTlhWA7khYhbdP9JY2t5JaPiDGSxpIGTD4oIq5uoV2QAvZvAptKGhYR5RsEU7Utt+EE4B1gnYi4tvDbDqRg/nGkwZyRNBOwBzARWDEiXi6UnwH4UisNnTBhQr1r3qKtzG9mZmZmZmb2ceIe69afzZ3fX2h3xoj4bzmImadfQgr2LQrUS19ycIOgOkCQgpZT8r1HxG2kXsWDgGIKly2ATwHXFYPqeZ7DSL2hFyD1XC7+dnxu54rAryTtmOu6B/hZqez/SAHv5SWtWGrrdsBMwPGVwH27lNKdjCEF+XYoBtXz8p+OiDvaqPKbpGD1RZUAYH6/KE//Ro15fprf9yjv14h4q3SjoZ7v5fdDi/s3r0+9Hr2fzO9T1R8R90dESwNo9vB43L8SVM/zvAj8LX+dkk4l91ZfF3gF+HExWBwRx5KeMugtlZzicxambUxap5Mj4ojiTYOIuAv4NTAL6Rgtm0w6x1oJqhMRTwDXA5+W1OUGiaTFgJWB+yLinsI8L0XEpcXtlKc/Rko9NSivQ49ExDukmwSzkm4oFds2G/BV0j67qPDTD4FhpP14bXGeiDiBdJNs5fxEBcDspCcu7ikG1XP5D8p1mJmZmZmZmVnPuce69WdqXqTBzGkgxfVJvTqHk9J3QErbAikdyH9rzHpuk6rviogHa0w/GdgXWK0wrZLmZdzUxYGUAmXFXO6U0m/fBb6Y63wHeBfYNiLeq1HP0aTey98mBesrvp3fj62z/FasSU7jERFP9aCeikq6lxNL008kBR63I/XGBUDSCFJP6Jci4oweLLeyX/5d/iEiLpX0MinNT9EdwAbACZJ+A9xeDsS2qgfH46U1pv0vvxdzvH85v19YHuQ3O4UWey53Q+VcLfa4Xye/13tS4vr8XivH/J0R8XSbbRhHOo+2IaVqqqj0CD9pqjkASasBo0g962cirUtluy7eZhsatW373Jbieb4Z6WbSiaXzurLtzqlT3/XApqRtd2dETJL0FLCRpH2AcRHxTLuNXGaZZcrpZYApPdmXbrc+MzMzMzMzs+mZA+vWn71IymE9vN0ZJS1LCpCPbFBstjrTn2hS/eN1pk/M7yMK00aUfmtlHgAi4pU86OA5pJ69ezfoSX8W8DywjaS9I+ItSV8g5UO/PiLurzNfKxbM7480LNUCSQsCXyGlUDm/9PN5wGvAKEkLFIL4nVr+CFLg98k6vz/B1IH1P5IC8hvn12uSbsltHxsRb7Sy4B4ej7VuZlRyehfzwleOoXrHb7Pjuifmye/F3tIj8/upkk5tYd6i7rT1NOAIYEtJe+bc7wBbk/b7ycXCSgPWnknK915PvX3SrstJ5+f6kuYq9CqvlaIGqtvuuVIa/bLittuRFLT/A/AHSY8B15Lyyte6OWNmZmZmZmZmPeBUMNafVdJffL5hqZI8oOO/ScGpo0mD+c0ODIoIkQb5gzo94ltNP9GmWrmzW/l9y8LnlerOnHKwH0cKBFYGqezxoKXlxXSgju1I213AFZKur7xIg6NCui7VGtSyE8tvS871PprUE/oPpEFT1yIFcB/M6Vca6sDx2Op61+o1Pq0sn9/vK0wbnN8voutAn+XXFUyt7XMwB6svJqXvWRumDD67JHBDJVd6wcGkfXstqcf6PMAMeZ+sl8v06KmZQts+JI0jMCOweW7bcNKx9CRpAOSiwaT92Gi7HQ8UUxpdCSxGGiT1xFzHjsAlTW5smJmZmZmZmVk3uMe69WcXALsBm0v6cUR80OJ8S+bX7RGxS43fP93Ddi3cZHoxBUPl8yJN5pkqV7ekbUgB5kpu7q0lnR8R4+vU9XdS2phvSzoN2IrUA/y0OuVbVenhvVgP64FqPu05qKYuqWV7UiC7k8t/lhTcXhB4uMbvC9WaKQe2r8+vSkD0z6Se0L+j682PWnr7eKyoHGv1js+a69dTeZDSzfPXqwo/VXraHx0RzdIrdco4UjqhbYFLqN8jHFIalg+BTSLitdJvndon5bbtntt2DOm4mYHUo7x8M+QpUt793csD+TaSy47PL3K++dOALfJArBc1mt/MzMzMzMzMWuce69afXUzqkbkApQE7yyTNLqmSH7gygOJUKTQkzUk1f3F3LS9piRrTt87vNxSmVXqiblunrm1L5QCQtBBwFGkQx23zazJwlKSagdM86OKlpLzsvyb1Xj8pD57YE1eTApAbSpq/u5XkgVWXJqXEmCEiVH6RrklPActI+hxAzhV9PzC3pK/3YD0qOb03L/8gaR2mTgNTU0S8AByQvy7boGhFbx+PFZXjbkNJs9T4fasOLafsJ6Sg/dNAMQf+5fl9015abi3nAW8Am+ZtsCXwPrVvLs0JvFEjqA7Vpz7KKnnQ274pHRG3Ag8Bq+Xzu1HQvyPbLiJupjqWQSvHqpmZmZmZmZm1yIF167dyL87tSGkhDpD0+3LAUMkmwO1UB0F8GPgIGC1p8ULZmUipOFoKoDYwCDhC0rBC3SuSetd/ROqNWvFvUiB5dUnfKbV999zmp0g50ivTB5GCYXMAP4mICRExAfhpnnZCLlPL0fl9j/zek0FLgSmB7RNIgyyOldRl+0kakVNuNFMZtPTUQv7r8rKC6uCi2xd+Oii/H164gVJZ/iySGuXJrqjsl70kLVWYfy6qveO7kPQ9SbWeNtggv7eSC7y3j0cAIuJhUlqVOYGDiseIpJ3o8MClkuaVdCTpJs6HwE6lAThPBx4AxkjaV9KMpfmHSPp6zj/fEfkm0pmkm0qHkG7KXRwRL9Uo/j/gE5K6PHEgaU/SgL21VJ4K+Ew3mzielF7mJ6QbYBMiotaAtYeSBiw+TNLG5R8lzSVpV0kz5+8LSRpTvCbl6UOprktv5tg3MzMzMzMz+9hxYN36tYi4m5Qv+XlgP2CSpMsljZN0Pim9xzmk9B5P5nkmAf8k5bG+R9L5OTXKRFJO5bE9bNb5wFLAI5JOlXQxcBMpmPe7iLij0P63SL3N3wGOkXS7pPGS7iSlE3kL2CYiJhfq3xdYg9Rr9fDC9MNIgdM1gB/Xadt5pJ7DALfl7dcJPySt49rAREkX5HW/hTSY6yaNZpY0A9Ue0yc3KksagBFS6ptBABFxAvAX0n6+R9K1eTteTQp2/qLZCkTE9aTtOQ9wV+G4eCgXubnGbN8DHpV0r6TTJZ0i6a5czzvAr1pYbm8fj0W7AC8A3wfuy9voprz8oxvO2dghksZKOl7SmXkbPJWX8xywUURcVpwhp27ajHReHgQ8LuliSf/ObXqe1MO9aZ76NlV6gH+v9L2sktv+lMLxdC8pIH9YnXnOI+U+P1TS2ZKOza+5u9m2k2oVioiHSDcVZwbOlfSApLPyMu8iXff+SnXw2rlIYyy8IOmafH08m7TtVwVuJd1wMDMzMzMzM7MOcWDd+r2IuIGUX/tHwG3AcqRUDV8mBSd/BSweEcVBEHcB9gYeIw0QuDopUL0SKRDcEy+RepteTuoNOoo0aONOEbF/jfZfQeqZfjKpB+03gXlJQbUVI2JKGpjc8/1XwCvAmGLu5fx5x/zbgblseVkfkgZjhM4NWkpEvEFa1z1JA3h+Bfgq8AngXzTP474u8ClgYk5P0WhZt5F6eY8g7bvK9B+QArVXAMsA3yDlrr+CNBBlK/YiHRsPk1KwfJnUQ35NUqqdsv1J6xe5LRsDw0j57JeLiJtaXG5vHo9T5IDsqqR1+iTwNVIP6U1Jg2d21zdIx952pG01M2mf7wgsEhGX1GnPA6SBTQ8AJgGrARsBw0nH6U5U0550ypVUxyx4E6iZ3z0ixuW23JzbuAHpJs3oBvPcQdoG95KO6W/l12ytNCzvn9sqX2lwkykizgQ+R3rSYsbcvlGkYPo40vlXSWPzCOn6eDUpLc/XqV4fdwdGlZ4mMDMzMzMzM7Me0tRjppnZQJVTQTwDDAZG5IC4mVm3TZgw4d6hQ4cuvfjiizcvbGZmZmZmZtYZ6usGNOMe62bTl91IediPd1DdzMzMzMzMzMysd8zQ1w0ws57J+Z0PJqWX2QB4g2r+aDMzMzMzMzMzM+swB9bNBr7ZSHme3wPuBPaOiKcbz2JmZmZmZmZmZmbd5cC62QAXERMZAHmnzGzgemrSW/zyxKv7uhlmZmZmA8r4A0f1dRPMzKwXOce6mZmZmZmZmZmZmVkbHFg362ckzSPp15LukvSqpLclPSzp75KW6ev22fRN0oKSDpZ0p6SXJb0n6XlJl0raVdKsfd3GjzNJIyWFpKv7ui1mZmZmZmZmH2cOrJv1I5LWBh4Cfg7MD1wDnA+8D3wbuFvSfn3XQpueSfou8D/gx6Tj70bgdOBeYDXgr8DDkubps0aamZmZmZmZmfUDzrFu1k9IWhm4AJgR+AlwSER8UPh9Q+Ak4PeS3o6II/qmpTY9krQzcDTwJrAjcGJEROH3YcBuwP7ArMCLfdFOMzMzMzMzM7P+wD3WzfoBSQKOB4YAv4iIg4pBdYCIuBDYFAjgYEkLT/uW2vRI0gLAkaRja5OIOKEYVAeIiLcj4o/AKsBrfdBMMzMzMzMzM7N+w4F1s/5hA2Ap4Gng4HqFIuJa4DRgJlLv4SkkTcy5lyXpB5LuyfnZ7y6UkaRdJE2Q9K6kpyQdLmk2SVfn+UeW6t1I0r8k3S/pdUlv5bp/KmlouY2SxuR6DpC0kKTxkl6Q9I6k2yVtXG/9JG0o6TJJr+T2PSjpIEmfqFN+hryud0h6M79uzes4uEbdkfPVz1r6TZKuyL/vk6ftk7//tkF7r8plVitNX0vStXlbvSTpDEmL520SksbUqGtWSb+Q9N+8316XdI2kTWuUnZJnW9LMeRs9LmlyXr99882aVn2fdEz9OyKualQwIu6PiFdK7VlQ0jGFNkySdGZ+CqNR22eR9CdJT+bj487i8SFp87w/31LK836EpJlr1Fk89n8o6b58/Dyd56l3/AyTtH8+H96R9Freb1vVKR+SJtb5bcpxX5o+5byStKmkm/P6vCzpZKWbGrXqmydv0+fy8XCXpB1qlc3lJWmr3P7n8vo/KelySbvVm8/MzMzMzMzMuseBdbP+YcP8flpEvN+k7Pj8vkGd348GDgUmAecCjxZ+Oxw4ClgMuAK4Gdg+fx5Sp75/ApuTeilfDFwHLAj8FriwHMAuGAncBnwZuB64C1gROFvSuuXCkn5CSoUzCrgDOBsYBuwL3CLpU6Xyg4FzgCPy+lyeX0vmdTxN0pRrXO7x/1dg0TxP0d7AaOAq0rYDOA6YDOwkaaq0WZIWA74CPBAR1xemfwO4FFg9r/OlwHLArcAitTZUXrdbgF8BcwKX5e8rAmepfl79Ibn+7wD35/bPDxwE/LrOPLVslN/HNyxVu+3LAnfmNrwNnEkaJ2Az4EZJmzdo+xWk4+9u0rH4OdL6ri1pz9yeD0jrOBj4AXBsg+YcCfwReIp0bFTmuUbSbKV2zwZcCxwIfJI0lsENwBeAkyUd3vpWaMmuwBmASOfRm8BWwJXlmwWS5s5t+Q5pfIVzSeffccA+der/PXAysDxpf5wJPEzapvXmMTMzMzMzM7Nuco51s/5h+fx+RwtlK2WWljRjjUD814EVIuLe4sTcq3p3Um7sNSLi/jy9Esj9Yp3lfQ+4LCLeKtQ1Gyno+VVgW+CEGvPtSAp07lVJayPph6Tg/s9JwdJKfSsDvwHeANaOiFvz9KHAiaTA/pHAFoX69yDdkPhvnmdSnmc+UoB5s9z2owrz7EMKoO8k6fyIOFPS50g3CV4FdoyIjwAi4kVJZwDbkALP55TWb2dSkPQfhfWYA/g76ablFhFxWp4+GPgbaQDaWo4Dlgb+APy8sk8lfTpvp99IujAi/lOa74ukGx1LRMSLeZ6VgJuAPSUdFBFv1llmpc1DgM/mr3c2KltjXgHjgHlIgd2fVVLISPomcCrwT0nXRsTzNdp+dW77K3meMXlb/A2YCxgdEdfl30aQblRsI2n/iHiUqW0PfDEi7sjzzErab6NJNy32KpT9HenGxeXAZpXtJGlJ0qDBP5R0ab4h0wm7AutExJV5OcNI592XgK2Bf5XatkRu+5YRMTnPswEpyN6FpJlI58NEYMWIeLnw2wx5GU1NmDDh3jo/LdrK/GZmZmZmZmYfJ+6xbtY/zJ3fJ7VQ9oX8PogUfCw7uBxUz76X3w+tBNUBclCzbo/WiDi7GFTP094A9sxfv1Zn1keBvUu54v8KvAKsmgO6Fd/P63N4JaielzM5//YO8A1J8xfm2T2/71EJqud5ni2sz+6F8kTEO6RA+XvA3yUtSrpBMAT4XkQ8WVqHY/L7zsWJOVi5Y66neFNhc9I+uaQSVM/L/RD4EenGQReSlic9fXAjsF/xRkkOHu9N6nm9c3le4CNg50pQPc9zO3ARqbf/SjXmKZuTdIMAqsdWq0YBywKPAfsX87JHxOmkpw5mA3aqMe+HwLdLaWVOyG1YDPhLJaie63uGFMQHWKNOe/5SCarned4kHT8BfCvfqEHSLMC3SNtv1+LNh4h4gHSTB0rHTw8dVgmq5+W8TfXpiCnrk28GbE/qqb97Jaie57mIlAqqbHZgKHBPMaie5/kgp5AyMzMzMzMzsw5yj3Wz/kGl91bKQgoYlk3VozWr9FqdKjAXEVdJeolqgL/rAqXFSb3DFwNmIQXBK+1YvM7yri73po+IDyQ9SuopPDfwbP5p9fw+jpKImCTpUlIA/0ukFC8LAQsBzxWDlQXnk3qgf0bS8Ih4oVDf3ZJ+TuodficpKHliRJxaY9nXSroP2EDS/BHxdP5pY2Be4NRiUJvG2/j1vB7fKP20Tn4/pzxgaFZJMzNVvnJgYkT8r8b0yrT5avxW1k4u9rLKfjs13zwoO5H0BMXqpPQ0RRMj4uHihIj4SNLjwHBSb+6yR/J7vfU6pTwhIu6XdA/pqZDlSOmJVgRmBm6OiIfqtPsI4MuSVGe/tOvSGtNq7afP57bdEBFP1JjnZFIP9ynyOfIUsJHSGAHj8o2ItiyzzDKfrTU992Rfut36zMzMzMzMzKZn7rFu1j9UgrOfbKHs8PwepN7fZbWCcQAj8jxP1fl9qvnygIiHAg+SUrh8n9T7eEegMpDibOX5snrLqfQOLg58Wmnb43XmmVgoV3yfOFVJIAdCHy+VLTqUlFJndtKAsd+vs1xIqV0GA/9XmFZJ6fKPUtnKsso93ytq7ZuR+f3gPMhllxfVY2OeGvO2s43reZnqDZrhjQrW0HA/MPV+K3q6xjSAtxr8Xvmt3np16vh5lZTTfFbSMdIJtfZVvXMB6p/H9abvSLoe/AF4WtKjksbWGs/AzMzMzMzMzHrOgXWz/uGe/L5iC2UrZe6tNdBpRLzbzTbU6rm8JSkv9dPAN0kDYw6JCFENBtbr8dyJXr7N6mxlGbXKfBZYJn+eB1i4wfzHk1LR/F++0bAgsB4p1U2t3vKN2lVrW1UGf70uL6veq5zjvdFyWhYR7wH35a+f72413fi9O/N0V0+O0Vbb0ezvaav1VNra1vrnJzcWI415cCLpuNoRuETSVE9jmJmZmZmZmVnPOLBu1j9clN+/KWnGJmW3ye8Xt7mMZ0lBuwXq/F5r+mb5fZeIOCMinikE8z/d5vIbeSa3rV6AuzK9kjqmkuZikQZ1LlSaB5gyIOo40o2Bk/L7uEr+7bLce/lUUs/ydUg91wcBx9ZIEVJZ1kLUtmCNaZWezKdHxJgGrx/VW9EOuCC/b9Ow1NSa7Yfyfutt9Y6fyv54pvRes915ENo5SD3ki3nx3yf1Yq+l1r7tjkrbmq3LVCLi9YgYHxE7RMTCpAFinwK2yAOfmpmZmZmZmVmHOLBu1j9cSEq3Mj+wb71CktYg9Rx/jzQQaDtuzO/frFHvKGqnGpkzv9dKbbJFm8tvpDJI5bblHyQNB9YlDTR5I0DOPf0EMK+k0TXm2YjU9geL+dWzg0gDbo6LiO1Jg5cuy9Q5wIsqg5h+iHx4gAAAIABJREFUlxRY/wAYW6Nco208O9V86kWX5/dNGyy/t/0FmEwKwK7ZqKCkJSVVjovKfttS0uAaxbcrlettW5YnSFqSlF/9DeA/efIdpKcQvpDHDyirtPv60s2TZ4G5JdUaNLhTKVfuAN4FVslPR5Rt1WpFEXEzqfc6pGPczMzMzMzMzDrEgXWzfiAiPgLGkHrEHihp33KgMvc4PZvUs3u/iJjY5mL+nt/3lvSZQr2fIOVlrqUyuOJ3JE1JpyFpdWCfNpffyF9JgfMfSlqpsJwhwJHAMODMwuCh5OkAh+Xge2WeeYE/lspUflsH+CEpKL9bnrxb/v7D/PtUcoDyHtJAnAsB50dErV7Yp5HyXK8vacogpZIGAQdTI193rvsKYE1Jh0nq0iNa0iBJ60parVbbOiEingT2IB1b50ravri/cztmlrQHcAupNzfA1cB/ST2/DywdI5uStteb1L4J0Ru+L2mFQhtmIR0DAv4VEZMBIuIt4F+kv4F/zeUq8ywB/Dx/7XL8ANfk9/0L5SXpJ1QHru2RiHiT9ETFDMCfi09S5HzpU93QkrSQpDGShpWmDwUqN0rq5WY3MzMzMzMzs25wYN2sn8gB1k2A10m9p5+RdLakUyXdR+rVPgewf0Qc1o36ryEFsIcDd0s6X9JpwMOka8HNueh7hdmOIKXD2BWYIOlkSdeSAoxHd2c967TtVlKwcnbgJkmXSTo5t21L4CGmHmD0MFIKneWAhySdKeks0s2ApUg3If5WKSxpblKAN4AdIuK1vOxXSbmoAxiby9VyTOFzedDSynq8CnyPdJPgdEnXSRoPPABsTUo9A123MaSe+v8hBbcfl3SFpFMkXQc8B1wCrEQvioijSTcZZgROAJ6VdJ6kcZKuIA2iehgpUP5mnidy218CfgrcK2m8pOuBs0jb4f8i4rnebHvBScAtki7OecUfBtYG7gV+WSr7E1Lv8HWARyX9W9IFpBso8wJHRMQFpXkOJvV030PSXZJOJ+3b/YGjOrge++W2b0Y6tk+WdCXpeK917M0FHAe8IOmavM/OJj1psipwK3BmB9tnZmZmZmZm9rHnwLpZPxIRFwOLA78lpZ0YTQq2zwQcC6wQEb/pwSJ+kF+PkQKKXwJOycv5f/buO1yq6mz/+Pe2EXvHxIaKWBCNxi4WTNRo7LEgWCBRY0kssca88Rdj8hJjeU1iNBobNtAYuzFWROzd6MGILaiIClgQUBHk+f2x1uBmmDln5nDOGcr9ua5zbWfvtdZ+Zk/h8tlrnrUcKbn8cSGeV4FNgTvy8T1INaaPiIi2nLFORAwAdiMl7TclzXaeTJpNv3lEfFDW/qscz3GkhUS/TyrHMYKUIN43/xKg5G/AisC5+SZDcayhwHn5+N+o7IG8HUVKdFd7Hn8HdgYeIS00uwtpcdDNSSU+ICWii30+ICVATyDdRNiUVBpmZeD5/HyupZ1FxEXAWqQZ/+8D25BmSPcgPZ+jgLUiYlyhz0ukRU8vJb039gXWJt3Y6BkRN7Z33AXHkBLmXYA9Se/nC4FtSjdSCnFPALYjJdzHkd5L2wDPAH0j4rjywSNiOOmzMpR0nXYE3iDVMn+6rZ5Evr49SZ/5TqT3wrLA4VT+dckbwEk5rlVJn52ewEjgWKBXXqTWzMzMzMzMzNqIZl57z8zmNZJWIiXhXo+IdRsczmxJ0i9JNzx+ExFntKL/fKRZ6esB3+rAWdxzPUkjgS4RoZbaWv2ampqGd+rUqXu3bpXK0ZuZmZmZmZm1i9n+//E9Y91sHpIXnly4bN/ypDISC5AW8rQyeeHRn5FKuFSb0V5qu5KkzmX7FgR+T0qqD3FS3czMzMzMzMxszrZAowMwsw51PNBX0vOkUjMrkMp4LAE8B5zbwNhmO5J+RCoXsi3wLeD8iBjdQrdtgGslPQe8BSwKfJtUZuYjUrkSMzMzMzMzMzObgzmxbjZvuZmU4P0Oqeb3V6T6zDcB50XE5w2MbXa0HWlh07HABaT63S15FriGlGBfF1gIGE2a6f77iBjZLpGamZmZmZmZmVmHcY11MzMzq6qpqWn42PFTu196zyeNDsXMrGaDzuzV6BDMzMzMbNa4xrqZmZmZmZmZmZmZ2dzEiXUzM6ubpGjhb2iF9iMbE+3sSVL/fF3OqKPPapWur5mZmZmZmZl1LNdYNzOzWXFVlf2vdGgUZmZmZmZmZmYdyIl1MzNrtYjo3+gY5mC3AE8A4xodiJmZmZmZmZnVx4l1MzOzBoiI8cD4RsdhZmZmZmZmZvVzjXUzM2sIJX0kXS/pVUmTJE2Q9JSkoyXNV9b+glxf/Mhmxhye26xVtn9LSbdJGitpsqSRki6StGKFMabXPpe0qqRBud/nkp6RtHsz5/+BpPskfSzpC0kjJJ0laanmzlPh2HKSLpH0vqTPJD0v6ZBmzitJB0galvt8IekdSfdL+mm1fmZmZmZmZmbWOk6sm5lZo3QCBgE7AWOAO4AngfWAC4Erytpfl7cHVhpM0oZAd+DpiHi1sP8g4GFgd2AEcDMwGTgKeE7SOlXiWw14GugJPAI8D2wM3CpppwrnPw34J9ALeBa4FVgEOBV4UtIKVc5TPs6ywKPAT4ApwO2kme1XAidX6fZ7YDCwIfBcfo6vA99upo+ZmZmZmZmZtZJLwZiZWaNMBfYB7oyIL0s7JS0P3AX0k3RFRAwDiIgnJL0B9JTUJSLeKhuvb95eVxhrFeBvQAB7RMSdef98wHnA8cDVwGYV4usHXACcEBFTc7/jgD8CvwLuLZxnU+B3wARgh4h4Ku/vBFwD7JfH2r+G6zIAWAu4DegdEZPzWLuQkuwzkPSN/DxGAhtHxEeFYwsAW9VwTpqamoZXOdS1lv5mZmZmZmZm8xLPWDczs1bLpUwq/c1U+qRcREyNiJuLSfW8fyxwWn64Z1m3QYCAPmVxCDgA+Aq4oXDoMGBhYHApqZ7PMQ34BTAa2FTSFhVCfBM4sZRUzy4EPga2kLRQYf/PSP+m/rGUVM/nmZyPfQ7sI2mlCucpPo/FgINJNx2OLSXV81j/Am6s0G0J0uz/fxeT6rnP1NKNCTMzMzMzMzNrO56xbmZms+KqKvu/rLJ/JrmEy05AF1LpFAGL58PdyppfB5xOmp1+VmH/NsAqwL0R8X7Z/lK/GUTEZEk3Asfldk+UNRkaEVPK+kyV9CapJMyywHs1nGeMpHtJNwm2onJyvOQ7pBsBj0bE2xWOD6bspkIefxSwq6STgesiYnQz56ioR48e61Xan2eyd693PDMzMzMzM7O5mRPrZmbWahHRv7V984zvgZQlisssXnwQESMkPQtsLGn9iHgpH5qpDExWWpx0ZJXxS/tnWsQUGFWlz8S87VR2ngDKy9PUcp6i0vFKSfXm9vcDrgfOBs6W9F9gGDAoIu6t0sfMzMzMzMzMWsmlYMzMrFFOICXVm4BdgBWAhSJCwNq5jSr0KyXP+wJIWhDYl1Ru5ZYq54oWYql0vKU+rdHSmKXnW9e5I2IIsCZpYddrgPlJyfZ7JN3QXF8zMzMzMzMzq58T62Zm1ih7522fiLg7IsYUSq+s0Uy/60m11Pvm2urfJ5VluT0iJpS1LZVEWb3KWF3y9r0qx2s1mpQU71LleK3nKcVbbZxVq3WMiE8jYlBEHBIRXYAtSbPu988Ln5qZmZmZmZlZG3Fi3czMGmXpvH2nwrH9q3WKiPeAB0lJ5p5ULwMD8HDeHlh+IJei2a+sXWs1d57lSTXkpwGPtTDOs8AXwOaSVqlw/IBaA4qIJ0iz1wHWr7WfmZmZmZmZmbXMiXUzM2uUV/P2yOJOSfsCh7TQt5RE/wmwB/ARcHeFdpeTSsT0kbRr4RzzAQOAlYCncxJ6VlxISpwfJ2mTwnkWAi4gLcp6c0S829wgETGR9NwWAP4kaXodd0k7UeGGg6RVJfWXtEjZ/k7A9vlhtdrsZmZmZmZmZtYKTqybmVmjnE0q6XKWpGckDZL0NHAjcH4LfW8mzew+GFgU+HuhjMx0EfE2Kfku4A5JD0saBLwMnAh8QMtJ/BZFxFPA6cASwOOS7pM0GHgd6A28BvysxuF+kfvtDbwmabCkIcC/gEsrtF8GuBIYK+khSddJupX0S4AtgKdI18vMzMzMzMzM2ogT62Zm1hARMQzYGhhCqqm+G/AlsA9pBnhzfT8F7ijsGtRM22uBbYE7gXVJC50uDPwV2DgiXmn9s5jhPANIz+EhYFPgh8Bk0g2EzSPigxrHGUcqcXMZ0AnYi1RD/vA8Vrk3gJOAoaTyOD/M/UcCxwK9IuLLVj4tMzMzMzMzM6tAEdHoGMzMzGw21dTUNLxTp07du3Xr1uhQzMzMzMzMbN6hRgfQEs9YNzMzMzMzMzMzMzOrgxPrZmZmZmZmZmZmZmZ1cGLdzMzMzMzMzMzMzKwOTqybmZmZmZmZmZmZmdVhgUYHYGZmZrO3UWMm8etrhjY6DLN5xqAzezU6BDMzMzMza4FnrJuZWYskRQt/QxsdY3uR1LPwPE9uYBwL5Bheb1QMZmZmZmZmZpZ4xrqZmdXjqir7X5mVQSUdBlwKnB4Rv6twfAfgPuDyiDhsVs7VCgeX/fc5HXx+MzMzMzMzM5vNOLFuZmY1i4j+jY6hI0laCNgfmAaMBdaXtEFEvNjYyMzMzMzMzMyskVwKxszMrLrdgKWBB4HL876Dqzc3MzMzMzMzs3mBE+tmZtbmJO0u6UpJ/5E0QdIkSS9I+kWeBV5s+wipDAzAb8tqtx8k6VpSGRiAQ8uO/0rSlvm/H2omntK4v6jzqZSS6NfmP4C+kqr++ylpMUn/I+m5/NwnSBou6XxJq+Y2l+R4flRljPkkjZQ0VdLKLQWZr8Gtkt6SNFnS+5KelDRA0iJ1PmczMzMzMzMza4FLwZiZWXu4EugEDAdeApYANgd+D3xX0s4RMS23vQsQsBXwPFAss/IGMAxYHtgJeA14rHD83xHxuKR/A9tKWjsiRhQDkTQ/0B+YCgys9QlIWgb4AfA5cFNETJD0HPAd4LvA/RX6rJT3rwN8CDyQz7smcBzwLClBfzHwE+DwfK3K7Qh0Ae6MiFEtxLkHcAupXM2j+W8ZYC3gNOAi4LNan7eZmZmZmZmZtcyJdTMzaw+HA/dExPSErqQlgOuBXYADgEEAETFA0hhSYv3mCouXPi7pTVJifViVxUsvISWQDwNOLju2C7AycEtEvF/Hc+gNLATcEBET8r5rSYn1g6iQWAeuIyXVrwWOKHv+a5FuIBARz0t6CthS0noRMbxsnMPz9lJadkrebhoRLxQPSNoc+LiGMWhqaiqPoaRrLf3NzMzMzMzM5iUuBWNmZjUrK8NS/Fuq2C4ibikmlfO+T4ET8sM92zi0a4GJwCGSFiw7VkrE15KkLiqWgSkZDHwF7FNeYkXSVsB2wGjKkuoAEfFq2Wz6S8riK43TGdgDeI80m78lnYGPypPq+ZxPRsSkGsYwMzMzMzMzszp4xrqZmdXjqir7vyzfkWdo/4A043kx0mzt0g3dbm0ZVC7TMpg003tP4B85hm8BuwLvAPfUOp6krsCWwFjg7sJ53pd0P/D9fJ7BhW475O015Un1Kq4H/g84WNIvImJy3t8PWBC4IiKm1jDOs8ABki4F/lhh9ntNevTosV6l/Xkme/fWjGlmZmZmZmY2t3Ji3czMahYR/VtqI0nA+cCx5NInFSzehmGVXExKrB9OTqwDPyL9W3d5oaZ7LUqz1W+okNy+lpRYP5gZE+ur5O0btZwgIj6TdA3wM+CHhbEOAwK4vMZYfwGsl/sdJmksqQ79LcDgiJjppoeZmZmZmZmZzRqXgjEzs7Z2IGmhzneAfYCVgIUiQsCiuU21hHurRcRzwDPADpK65AT/j0mLel5R53AH5e1Okh4p/gHH52M75rItM4VSx3kuztvDACRtS1p09P6I+G8tA0TEW6S677sAfwHeJ82mHwg8X16mx8zMzMzMzMxmnRPrZmbW1vbO2yMi4uaIGB0RU/K+Ndr53BeT/m07FPguqQzN3RHxTq0D5FrppQU71wJ6lv1tnI8tAPQpdC2dY81az5XLtjwCbJ/Lz9SzaGlxnKkRcXdEHBMRGwCrA0NJJVzKF3M1MzMzMzMzs1nkxLqZmbW1pfO2UjJ7/yp9SuVKqpUoa+l4yWBgPGmm+pF5X2sXLf19RKjSH6l2PHw9sx3g/tI+SQvXcb5LSDP4TyLN8B8L3FZnzDOIiJHAOfnh+rMylpmZmZmZmZnNzIl1MzNra6/m7RG5HAsAknoBJ1bpMzpv127lcSDVLSfVQF8J2JdUFuXOlkOeHuNCfJ38H9xM0/uAccAmktbJ534MGJbP/dfy5LqktSRViv9G4EPSjYCFgavqqYsu6YQqJWl2ztu3ax3LzMzMzMzMzGrjxLqZmbW1PwGfAccAL0kaLOlhYAhwUZU+j5KSywdIGiLpCkmXSdoCICJeB14GtpD0hKQr8/FdK4x1SeG/r6yw+GhzdgWWAV6OiJeqNcpj3pwfFmetHwS8DvQD3pZ0i6R/SHoBeAXYtMJYk4GrCrsuqyNegDOB0ZKelXSDpL9LGkG6/mOB8+ocz8zMzMzMzMxa4MS6mZm1qYj4D7AZaaZ4Z2APYBFS/fDTqvT5nFRe5X7SQpz9SXXSi/XK9wZuz/v65eMbVRjrJWAMaRHRy+sMv1QGprnZ6iWlNgeVZubnWu6bkJLdHwDfB3YklbD5P1Ld80oeyNthETGizph/CvwdWIx0Db9PKp1zNrBBrYugmpmZmZmZmVntFBGNjsHMzKzNSNoWeAh4ICJ2aHQ8tZB0BfAj4OCIuLbR8RQ1NTUN79SpU/du3bo1OhQzMzMzMzObd6jlJo3lGetmZja3+WXe/qWhUdRI0upAX9Is+xsbHI6ZmZmZmZmZ1WCBRgdgZmY2qyRtTZrxvT6pjvlTwG0NDaoFkk4FepBKt3QCfpPrrZuZmZmZmZnZbM6JdTMzmxusA/wYmECqw/7TmP1rne0ObAW8C/wK+GtjwzEzMzMzMzOzWjmxbmZmc7yIuAy4rNFx1CMitm50DGZmZmZmZmbWOk6sm5mZWbNGjZnEr68Z2ugwzNrVoDN7NToEMzMzMzObg3jx0rmApKjw96WkdyRdJ2n9Rsdosw9JA/N7pFcdfeaXdLikYZI+zu+v9yQ9K+lCSbu1QVyr5biGzupYsxDD0BzDao2Kob1I6p+f2xl19Cn/XpkiaZykl/L7aB9JHXqDtjXv344i6YwcW/9Gx2JmZmZmZmZm7csz1ucuVxX+e0lgY6AvsK+knSPiwcaEZXMySQsBdwA7AV8BTwLvAN8ANgSOBr4H3NmoGGslKYC3ImK1jujXwphDge2A1SNiZFuN205K3y3zkb5b1gIOAfoBr0s6MCKealRwHWUOe83MzMzMzMzMrB05sT4XiYj+xceSFgQuBw4G/gRs0ICwbM53DCmpPhLYISLeKB6UtBGwawPiag+HAIuQFpO0rPy7BUBSV2AAsD/woKSeEfFCB4RzGnAW8HYHnKtefwGuB95rdCBmZmZmZmZm1r6cWJ+LRcSUXPbhYGB9SUtFxCcNDsvmPD/M29+VJ9UBIuJ54PmODal9RMTsmKydLeX3Qm9JE4BDgSuA73TAed9jNk1cR8Q4YFyj4zAzMzMzMzOz9uca63O/Dwr/PdONFEmLSfp/uWbyZ5I+lfSQpL0qtJ1eA1vSwpLOkvSWpMmSXpd0qiSV9SnVrG7ub7UK57lE0sg89lhJ/5A004z7Yt1oSatKGpTbfy7pGUm7V7swktbPNejfzecZLenKSvW1lRyQa4y/L+mLXMP+fkk/rXaOCuNsI+kvkl7Mtco/l/RKvpZLVWjfKz+/gZKWkfTXXNt8sqQmST9u5lz7SHoqn+MDSVdLWrHWWAuWz9uxdTzPZut5t1TLXNISkv6Ur/EXkv4j6eeSZvrOkrSspAGShkuaKGm8pFfz892sGE/u0qXs/Te0Wlx19AtJI2u5FqXPEamkCMB/i+OW9ZWkfvl990l+LV+UdJLSL1IqnW8DSXfm6zBe0n2StqzUto2cCEwCNpK0dYV4Wvt5XkvS9fm9O035O0llNdYlLSjpw/w+mekzlNtslvs8Wti3lKRjJN2jr7/HPpR0t6QdKzyHFl8zVaixrvTdGpLWqRLbNyVNlTSq/P0taWtJt0gak+MbKenPkpavMM6Cko7In/lxSt/nI/N74YBK5zYzMzMzMzOz1vOM9bnfxnk7Ls+mnE7SCsAQoDup9MV9pDIYWwK3SDotIs6qMOZCwL3AesBTwH9ICaezgMWBXxXa3k0qIVJuMWCf/N9fFWLaGvgnsAQwHLgdWIk0a/oHknatUit+NeBp4AvgEWCF/DxulbRLRNxb9tz3AQbl5/Is8BjQFegP7C5pu4gYXujye+BUYEIe/xPgW8C3gTWBCyvEVMk5pLrkTaRr34k0y/dUYDdJW0TExAr9lgIeJ9W3fop0/bYFLpc0X0RcVvb8fgZcQLq2D5Fm0e4APAH8u8ZYS0YB3YBDJd0VEVPr7F+vTqRr0zVvFyLVcP8/UjmjH5UaSlqM9JzWBF4D7smHVgX6AG+SrtfrpDrh/UhJ4H8UzvdKM7G0tl9zJuYxdya9T2/K+2aQk6zXA/sBn5Le3xOBzUnvo+0l7R4R0wp9Nidds0WAF3KMPUjvgYGtjLdZETFe0r+AfYHtSZ+PUjyt/TyvTXq+HwIPAksDU6qcf4qkG4EjSN8pl1do1jdvryvs2wL4M2m9gFdJn69VSWWPdpJ0WERckdvW9JpVcR3p++NA4PQKxw8A5gcGlb2WxwJ/BKaR3sPvkl7LY0jfFT3z7P2Sa4DepM/6Y8BnpGu9Den74voa4zUzMzMzMzOzGjixPpeStCSwGanmL6RayOWuJCXVzwZ+FRFTct81SInz3+VE6otl/bYEHgbWKiXrJW1CSkz9XNJZpeRwpcS8JAE354cXRcQ7ef8SwI3AwsB+EfGPQp8dSAm6ayStERFflg3bj5RIPqGU+JV0HCkx9av8fEpjrQ5cDXwO7BgRwwrHDiEl0K7M1w9J3wCOJ90g2DgiPiq0XwDYauZLW9WZwOMR8XFhjE6kBN9PgBNym3J7kpJ5/SJiUu63J3ArKVk3PbGuNNv6XGAysHNEDM37F8ntd6sjXvLY2wN7AG9IuoWUuHssIkbVOVYttgBeBLoV3l9dgWFAf0m3RMTtue2+pKT6BRFxbHEQSZ2BzgAR8QjwiKR+pJtM/WsJpLX9WhhzXH4eQ0lJ2pOqLIR5Eimpfh9wYESMBZC0KDAY2B04inxTJyfiB5KS6jPcFJP0W2a84dXWXiC9FusWzjkrn+cDSN9dx0fEV7TsOlJivS9lifV8XfYHpgJ/LxwaAfSMiMfK2m9EujlxvqS/R8TEOl6zSgaRvn/7UjmxPlPSX9IWwPmkOvJ7lL6D83fnr0jfEX8mvT9Kn/nepJsR20bEF4WxFibdzDMzMzMzMzOzNuRSMHORstIEn5CSyUsBfSPi/LK2GwK7kBKkvygl1QEi4k1SeYf5gcMqnGoacFhxBnxEPAP8i5TU26SFUH8L7EWaiXpcYf+PgW8C5xaTcHn8+4GLSDMwKyWG3wROLJtNfSHwMbCFpIUK+4/LcZ5STKrn81xNSj5vKqlUL3oJ0izqfxeT6rn91PIxmhMRdxWT6nnfZFLifiopgV7Jp8BPSkn13O824CVgVc1YUuXHOd6rS0n13P4z0mzXGcqN1BDzIOAU0o2IVUnX7wbgHUkvSzpa0vz1jFmDk8reX2+Q3jcAxdI7nfN2SIW4x0REUxvH1SHyDZuTSb+Q6FtKqgPk98DhpBsnRxS69QLWIc2+/kPZkL+hfRf7LL1WSxf2zcrneSxwao1JdUiz5N8CemnmckffJf265J6y99R/y5Pqef/zpO+OJUg3lGZJrtv/CLBGTphPJ2lNYFPg5Ygo/pLkF6R/n39SvLEZEQH8jrSmwQ8lLZcPlT4HjxWT6rnP5xHxeC2xNjU1Da/0R/r1iJmZmZmZmZkVOLE+d7mq8Hc9aQb5csDZkrYra1uqIXxbTtaUK5Vz2LTCsZER8WqF/aV936oWoKTewP8AbwD7liXCSzHdWqV7czENLd4cgJT0JiXcFwSWrXCe22o5T0SMIZVD2VXSyRUSd3WRtJKkIyX9UdIVkgYCfwW+JJVcqeSZ8qR+Vumal+pc/72sLRExglYsNBoR55CS6kflcUfmQ+uSkpD/KK8PPQs+ioj7KuwflLdb5Zm7kMr4AAyQtFv+dcHcYCPSZ/eR8hJOABHxAan0TY88Ixm+ft1vLP9M58/CDMntNlZ6PYrnnZXP8/35RlBN8vMdTPo3rbyeeKUyMABIml/STrk2+sVK9dsH8nVCvdrnsV6lc/ct2196fG0hpvlIpY8mAA+UD5Sf66Ok51oq9fUKqVTRjyQdLmnZ8n5mZmZmZmZm1rZcCmYuUqlMRS5r8BBwj6R1I+K/+dBqefsHSeWzW4uWq7CvWvmPUs3hTpUOStqYVGJlAqm8QXmiuBTTk5pxDdS2jql0nvfrOE8/0s2Ks0k3Kv5LKk0yqLx+e3MknUCqt7xQS23L1PP8Son/ajOU3ybVda9LTvBenP+QtBapXMnhpF8g9KFC8rIV3qpy/k8lfUL6FcYSwPiIeEDS+aQZ/3cAX0p6gfRrjcvrKNcxu1ktb3dR2YKmFSxDqr9dy+veXkqfleJnerW8bc3nuTWxXkea6X0gqR5/qczSD0lJ5xlupElaGbiTtE5CNYu3Io5KbiSVbukt6eeFmfh9SDcjBhfaLkuqiQ4wtZZrlz8bhwN/y3+XSBpB+lXQ1RHxRC1B9ujRY71K+/Os9e61jGFmZmZmZmY2r3BifS4XEc9LuoSUAP0ZqcQLpDIvkGqlv9nMEDPNlqXOUiIAkr5JmrnaCdg/Il6u0KwU042khfeqeXIWY5o/t7+6hXbTFy+NiCG5bMOXUr/2AAAgAElEQVRupAUMtyMl2/vlOsy9WzppLgNxHjCeVE99KPB+LgWDpNFUn+1fz/OrNHu4zeVfLfxE0jKkRSN3pfbEemtnt8+UZYyIE/J7fE/STN+epPr4p0jqHRHVZkx3lNY819Jn4TVSuabmTM7bDnndqyjV8C5+rmfl8/xFhX3NiogmSS8C35G0TkS8QnpPLglcW2EG/GWkpPrNpNI5I4AJETFN0k+AS6jwfmuNiPhI0t2kdQp2IN3o/A6pdM8jZTeAStdtAl+vRVHN9JtQETFY0v2kz8FOpO+oo4CjJJ0TEae0xXMxMzMzMzMzs8SJ9XlDaZb62oV9pRnQ/4iIP7fnyfOs0VuBlUl1k++s0nQUKcbfVVgwtS2NItUMPjYiPq21U247KP+VEuU3AvtLGhgR/2phiL3z9lcRcVXxQC7n8c1aY2nBaGAtoAspMVtu1TY6T8lQUmK9OPO4tBjlYjO1TlZpZryK8eXFMJckzT6e4XXLJW5Kvyb4BqkO+7mk5GhHJNan0LrnWk3p89lUx4Kpo/O2S5Xjbf26A9MXSt45P3ywcKijPs9F1wEbkEqs/D+qlIHJC8DuCHxAutFXXst9jXaKbQ/SjPp7qsVGupk5GZhS72K5uRb/ZcBluVzS90nrIZycv6Mq3dA0MzMzMzMzs1ZwjfV5QylJNKmw7/683asDzn8psDlp1ujZzbTrqJja5Dy5vMI1+eH6NXQpLez4ToVj+9FGs2P5unb1fuUHcvmWDcv3N0ct1KLg64UNRxf2vZe3a1UYb22aT/IuK2mHCvv75O1jVdYFACAivoiI83IMnSV1LhyeQutuKLbU7z1S3MtUOLZTlT6lmw+Vxn2a9MuG7fMNhVqUXvd9yl+zvBjqPjWOU6/zgEWBp8sWyezI75iSQaQZ+33zddsVGFOIpWRJ0r9/75Un1fO12pvKmnvNWnIHaRb6Xjmx35v0vrqx2CjXwx8KLCNp21acpzRORMTdwD/zrh6tHcvMzMzMzMzMZubE+lwu11j/SX54V2l/Tgo/QErcnS9psbJ+8+VF/bZmFkg6BTiYVO7h8BaaXwKMBX4p6UcVkoOLSjok10aeFecBnwPnS9q9QszLSDq6tCikpFUl9Ze0SFm7Tny9yGEtNaFLC40eKmnBwjjdSaUo2sqVpATgIZK2KZxnYeBP1P+5v13SMZKWKj8gaRfgyPywWLbiaVL5j11ybf1S++WBy2uI4ZziAoySVgdOzw8vKuzfK/9yoDyujYAVSInMjwuHRgMrVHouLWip30N5W4oRJacBWzUzJsz4SxIAcnmgc0n15G+SNNMsdEkbKC0GXPIg6T22Dqn0U9GvqD6TvVUkrSHpBuBQ0k27Q8uadNTnebqIGEVa+6Ar6TP1DeCGskWSISXbx5MWf+1ZiGl+0i8fZrohlFV9zWqI7XPSZ2Rx0mu7MnB3RHxYofkAYBpwVaXvYEkrSvpp4fFGkn5Y/F7J+5cm3dSE9q2xb2ZmZmZmZjbPcSmYuYikgYWHC5ESaVuQkph38PXs6pIDSYs8Hk9Kwr5ASoStREocLQ/8nK9nwrbG7/P2Y+DiKpOfT4qIcRHxsaS9gduBK4BfS2oilUVYFViXNDN2I6ov5tmiiHhN0kHAtaSk8QjgP6QZ411Ii/QtRJr9+jlpccgrgQslPZPPvSgpYbo88BQt10Imj3EisDswQtLTeeztSOVKNqMNkp8R8aakU4HzgQclDSWVl9iGlKy7k1QrvlarkBZePE/S88BIYEFSAnfd3OayiLi9EMNESeeSynE8kmOA9H58GXgc2LLK+Z4gXf/XJA3J//09YBHSrx6KpV16AcdJehd4nlQiZkVga9L7/vSImFJofztwDPCcpMdItbxHRMQ5LVyDlvr9AdgXOF5SL+AN0q8YViHdCDi6ypj9gEGS7iUleomIw/LxAaT3Yh/S++U5UnJ0OdKvUFYnLch5Q+43TVJ/0g2zsyX1AV4hzVReh1QipDR2XQrfLfORFo5dK48pUrmhvhHxUrFPR32eK7iO9Jk6svB4BhExVdLZwP8CD+X32UekJPQKwIWkckLlWnrNaomtX3Ox5fGGSToO+CPwsFLt+NdINwq6kK7dxBwned9NwPj8HfU+6abMNqTX65ZaFzA1MzMzMzMzs9o4sT536Vf472nAJ6TZm9cAAyNiWrFxRHyQZ/seSSpLsCkpifkeKUl5G/D3WYypNDN552banEFeJDUiHpW0PnACqYzDd4GvSDNF7yQlsGe5TnBE3Czp26RE947ALqRk6WhSsusmctKMlCQ9KcfSnZQAn0iqXf9bUlL5S1oQER9K2pSUhN2OVG/5v6Tk8zn5PG0iIv6Yk82nkpLME0g3UU4hJRPrsS/ptdiRlFAt3XgYA9wCXBkRd1Tod0Y+7xGkxPgHpNnq/4/CrycqmEx6vwwglRFZjnSdLiUlGosGAlOBbUmvy5KkpOJdwPkRMbSs/WmkZPCepPf8AqTZ5i0l1pvtFxHDJX2XdCNpM1Li+1Fgf1LieCb5Pfhz0i85dict7As5+Z0/r30l3ZT3bZL/xpEWrbwKuL5szMclbUW6dlsDa5J+PXAU0I1WJtb5+rtlKunmxWjS4r+3A7dXmBFeiqdDPs9lbgQuIF3PNyKi0uKoRMQASaNINxZ7km6iPUJ6f36nSp9mX7MaDCF9v36L9B1ye7WGEfEXSY+Tbm5uS/q+mEC6CXExM5aQeYL0q4Tvkm6KbkO6mfki6XMzqMb4zMzMzMzMzKxGaqZUsZmZmc3jmpqahnfq1Kl7t27dGh2KmZmZmZmZzTvaai3CduMa62ZmZmZmZmZmZmZmdXBi3czMzMzMzMzMzMysDk6sm5mZmZmZmZmZmZnVwYl1MzMzMzMzMzMzM7M6LNDoAMzMzGz2NmrMJH59zdBGh2HWrgad2avRIZiZmZmZ2RzEM9bNzKxNSYqyv2mSPpH0sKTDJM1WK3tLGprjXG0Wxjgjj9G/jj69cp+BrT2vmZmZmZmZmTWGZ6ybmVl7uSpv5we6Aj2BrYHvAX06KghJAbwVEat11DnNzMzMzMzMbO7mxLqZmbWLiOhffCxpR+Au4ABJ10XEnQ0JbGaHAIsA787CGH8Brgfea5OIzMzMzMzMzGy25lIwZmbWISLiPuCa/HCvRsZSFBFvR8QrETFlFsYYl8cY35axmZmZmZmZmdnsyYl1MzPrSM/n7SrFnUr6SRqW67F/LulFSSdJWrB8EEnLShogabikiZLGS3pV0tWSNstt+ucyMABdyuq+Dy2MVbXGuqTlJP1eUpOkSTm2FyT9r6RlC+2q1liXtJqkwZI+zLE+JmnX5i5Se1wPMzMzMzMzM2s7LgVjZmYdafG8nVzaIWk+UhmV/YBPgaeBicDmwDnA9pJ2j4hpuf1iwBPAmsBrwD15qFVJtdvfBJ4CXifVee8HTAL+UYjjlZYCldQduBdYiVTi5W5Svfi1gV8C9wFDWxijK/AY0Bl4FXgOWB24A7i4Sp/2uh5mZmZmZmZm1kacWDczsw4hScBu+eGLhUMnkZLI9wEHRsTY3H5RYDCwO3AUcGFuvy8piXxBRBxbdo7OpCQ2EfEI8IikfsC48prvLcS6AHATKal+HnBasVSMpI2AsTUMdVGO5yLgmEIy/DDg0ip92uV6mJmZmZmZmVnbcWLdzMzalaT5gTVIs7y3JM1WvzIfWwA4GZgA9I2IcaV+ETFJ0uHAW8ARfJ1ILiWKh5SfKyLGAGPaIOwfAuuQbgCcUkqIF87zfMVeBXm2+k7Ax+VjRMRlkn4EbFXWp2HXo6mpaXiVQ11r6W9mZmZmZmY2L3Fi3czM2kWhvnnRBKBfRLyRH28ELAf8q5hELomIDyS9BvSQtHBEfA48mw8PkDQVuD8ivmjj8HfI20vLk+p16Jm3d0XEpArHr6cssc7sez3MzMzMzMzMrMCJdTMzay9X5e00Uq3wl4CbI+LjQpvV8naXKon4omWAdyPiAUnnA8eTapV/KekFUj30yyNiZBvEXlpc9Y1mWzVvxbx9u8rxSvtXy9sOvx49evRYr9L+PJO9ey1jmJmZmZmZmc0rnFg3M7N2UWNN8/nz9jXSIp/Nmb7gaUScIOkSYE/ge6TZ4ZsBp0jqHRG31h9xRS0lt5ujVowxu18PMzMzMzMzM8OJdTMza6xRedtUz+KiABExAjgbOFvSN4CfAucClwCzmkh+J2/XnIUxRudtlyrHV62wb3a9HmZmZmZmZmZWMF+jAzAzs3na08B4YHtJS7R2kIj4IiLOA94DOkvqXDg8hfpvJN+ft4dJUrMtq3s0b38gadEKxw+osK8jroeZmZmZmZmZzSIn1s3MrGEiYjJpVvVSwE2SZprdLWkDSb0Lj/eStEWFdhsBK5AWSC3WcR8NrCBpqTpCuxl4Ffg2cJakGRLzkjaUtHJzA0TE68ADwNJ5jOn/5kr6ETMvXNpR18PMzMzMzMzMZpFLwZiZWaMNIC2O2QcYIek50sKeywFrAKsDtwE35Pa9gOMkvQs8T1oYdUVga9IN49MjYkph/NuBY4DnJD0GfAGMiIhzqgUUEVMl7QPcB5wCHJT7LgCsDawLbM/XpVuqOYo0c/1nwI75ua0ObA5cDBzZgOthZmZmZmZmZrPIiXUzM2uoiJgG9JV0E3AYsEn+Gwe8BVwFXF/oMhCYCmxLWqBzSeB94C7g/IgYWnaK00gLie4J9Cb92/cQUDWxnuNqkrQhcDKwB7A78FmO6XfAizU8t9fybPLfAzvmGF4C9iIlwGdKrHfA9TAzMzMzMzOzWaSIaHQMZmZmNptqamoa3qlTp+7dunVrdChmZmZmZmY272jtemcdxjXWzczMzMzMzMzMzMzq4MS6mZmZmZmZmZmZmVkdnFg3MzMzMzMzMzMzM6uDE+tmZmZmZmZmZmZmZnVYoNEBmJmZ2ext1JhJ/PqaoY0Ow+YAg87s1egQzMzMzMzMOoRnrJuZ2WxFUpT9TZE0TtJLkgZK2kdS1RvDuc/IDgwZSWfk8/bvyPOamZmZmZmZWWN4xrqZmc2ursrb+YAlgbWAQ4B+wOuSDoyIpxoVnJmZmZmZmZnNu5xYNzOz2VJE9C/fJ6krMADYH3hQUs+IeKGs2brAlPaPcAZ/Aa4H3uvg85qZmZmZmZlZAzixbmZmc4yIeAPoLWkCcChwBfCdsjavNCCuccC4jj6vmZmZmZmZmTWGa6ybmdmc6ERgErCRpK2LByrVWFfSR9L1kl6VNEnSBElPSTpa0nxl7XtVqPVe/jew0L5ijXVJQ/P+1STtJemJfO6PJA2WtHKlJ5fj7SdpmKRPJH0u6UVJJ0lasEL7ZSUNkDRc0kRJ4/PzvFrSZnVeWzMzMzMzMzNrgWesm5nZHCcixkv6F7AvsD3wSAtdOgGDgI+Bl4HngOWALYELgc2A/oX27/N1jfdyWwNdga/qCPlo0s2AZ4C7gU2BA4CNJX07Ij4vNcxJ/uuB/YBPgaeBicDmwDnA9pJ2j4hpuf1iwBPAmsBrwD15qFWBPsCbgGvRm5mZmZmZmbUhJ9bNzGxO9QIpsb5uDW2nAvsAd0bEl6WdkpYH7gL6SboiIobB9HIy/csHkbQFKSH+CfCHOmI9GtgxIobkcRYB7gO2IiW/ryi0PYmUVL8PODAixuY+iwKDgd2Bo0g3BCBdgzWBCyLi2LJ4OwOd64jTzMzMzMzMzGrgxLqZmc2pSjXNl26pYURMBW6usH+spNNISew9gWHVxshlW24h/dvZOyJerSPW80tJ9XzezySdR0qsb0tOrEtaADgZmAD0zbXbS30mSToceAs4gq8T66XE+fTxC33GAGNqCbCpqWl4lUNda+lvZmZmZmZmNi9xYt3MzOZUytuouYO0IbAT0AVYJI+xeD7crZl+CwO3At8Ejo+Ie+uMtVL7UmL+W4V9G5FK1PyrmFQviYgPJL0G9JC0cC4h82w+PEDSVOD+iPiizvjMzMzMzMzMrA5OrJuZ2Zxqubz9qKWGkhYCBpLKrlSzeDPHBgIbA5dHxJ9qjK9oVIV9E/O2U2Hfanm7i6SWbhgsA7wbEQ9IOh84HrgD+FLSC6Rk/uURMbKWAHv06LFepf15Jnv3WsYwMzMzMzMzm1c4sW5mZnOqDfP25RrankBKqjeRSq08B3wcEVMkrQWM4OsZ8DOQdDqwP2mB1KNbGWuts+rnz9vXgMdaaDt5+uARJ0i6hFTO5ntAT9KCrKdI6h0Rt9YZr5mZmZmZmZk1w4l1MzOb40haEtg5P3ywhi57522fiGgqO7ZGM+fZG/gNqa75D4sLn7aT0sz2pojoX0/HiBgBnA2cLekbwE+Bc4FLSGVszMzMzMzMzKyNzNfoAMzMzFrhPGBR4OmIeLyG9qUFTt+pcGz/Sh0kbQBcA3wG7BkRY1sTaJ2eBsYD20taorWDRMQXEXEe8B7QWVLnlvqYmZmZmZmZWe2cWDczszmGpDUk3QAcCkzK21qUFgo9smy8fYFDKpxneeB20gKnh0TEv1sddB0iYjJplvlSwE2SulSIbQNJvQuP95K0RYV2GwErABOAj9svajMzMzMzM7N5j0vBmJnZbEnSwPyf8wFLAGsB65Bqob8G9I2Il2oc7mxS6ZizJO1HSrR3AzYhJbJPKmt/JNAFeB/YQ9IeFcZ8JCIuq/kJ1W4AabHQPsAISc8Bb5MWa10DWB24Dbght+8FHCfpXeB54FNgRWBr0rU7PSKmtEOcZmZmZmZmZvMsJ9bNzGx21S9vp5KSxaOBq0kzyW+PiKm1DhQRwyRtDfwvsBEpSf8SsA9pIdPyxHppEdFvFuKopM0T6xExDegr6SbgMFLyfxNgHKnW+1XA9YUuA0nXaFvSgqVLkm4I3AWcHxFD2zpGMzMzMzMzs3mdIqLRMZiZmdlsqqmpafjY8VO7X3rPJ40OxeYAg87s1egQzMzMzMxs7qBGB9ASz1g3MzOzZq3ceVEGnblho8MwMzMzMzMzm2148VIzMzMzMzMzMzMzszo4sW5mZmZmZmZmZmZmVgcn1s3MzMzMzMzMzMzM6uAa62ZmZtasUWMm8etrhjY6DGsQL0hqZmZmZmY2M89YNzOzuZKkqPNvZAfGtlg+Z1NHndPMzMzMzMzM2o5nrJuZ2dzqqgr7tga6Av8GXig7Nq7dIzIzMzMzMzOzuYIT62ZmNleKiP7l+yQNJCXWb42IMzo4pKJJwLrA5AbGYGZmZmZmZmat5MS6mZlZB4uIAF5pdBxmZmZmZmZm1jqusW5mZlaBpL0lDZH0iaTPJf1H0pmSFi9r11XSp5ImSVq7wjjn5HrqNxT2VayxLmm3vP8vkjpLulzS+5K+kPSipL5VYt1A0mBJb+a2YyQ9J+k8Scu11TUxMzMzMzMzs8SJdTMzszKS/he4mVST/SngdmBJ4HTgUUlLl9pGxBvAscAiwHWSFiyMsz1wIjAKOLKOEJYHngR2ztsngR55/D5lsfYEngb2B94HbgGeARYFTgBWq+O8ZmZmZmZmZlYDJ9bNzMwKJPUCfgl8BGwaETtFRG9SbfZ/AusD5xX7RMRA4B/AxsBv8jhLA1fnJv0i4uM6wtgfeBjoGhF7RsR2wMH52OllbX8OLATsEhFbRUSfiPhBRKwNbACMrOO8ZmZmZmZmZlYD11g3MzOb0TF5+4eI+HdpZ0R8Lulo4HXgIEknRcRHhX5HAFsCp0r6Vx5nZeDciBhSZwxjgaMj4ovC+a+T9FtgXUnLR8TYfKgzMA0YWj5IRLxU6wmbmpqGVznUteaozczMzMzMzOYRnrFuZmY2o23y9rryAxHxNjAMWBDYvOzYR0A/QMCdwH7Av4H/aUUMj0fExAr7X83bbxb2PUv69/xaSRtLUivOZ2ZmZmZmZmZ18Ix1MzOzTNICpPrmXwKjqzQbmbcrlh+IiAckXQYcDkwF+kbEl60IZVSV/aVke6fCvt+Rkvz75b+PJT0B3AFcHRGTajlhjx491qu0P89k717LGGZmZmZmZmbzCs9YNzMza50o3yFpSeD7+eECwIZtNXbVhhEfkhZZ3Z5U+/3NHMNFwMuSVm5lDGZmZmZmZmZWhRPrZmZmWURMJdU3X4gKM9KzLnn7XoVjfwVWBW4EJgMXSVq1reMsFxHTImJoRJwUEZuQarvfkmP5TXuf38zMzMzMzGxe48S6mZnZjB7O2wPLD+TZ39sBU4Any44dCPQBXgQOBk4DlgSultSh/95GxHukEjEA63fkuc3MzMzMzMzmBU6sm5mZzeiCvD1V0vSktKRvABeSFi69Li9WWjrWJR+bDBwUEZOBPwIPkBLxp7RXsJJ+KmmVCod2ydu32+vcZmZmZmZmZvMqL15qZmZWEBFDJQ0Afgk8I+lB4BNgG1J5mCbgxFL7PBv9GtLs9BMi4qU8TkjqB7wEnCnp3oh4rh1C/jlwgaQmYATwFWmx0fWBCXw9c93MzMzMzMzM2ohnrJuZmZWJiP8B9gEeA7YE9uLrJHXP4mx14BekpPsDpFnqxXHeBY4kz3KXtHA7hHsqcDXpZvmOwG75fH8BNoiIF9rhnGZmZmZmZmbzNEVEo2MwMzOz2VRTU9PwseOndr/0nk8aHYo1yKAzezU6BDMzMzMzm/eo0QG0xKVgzMzMrFkrd16UQWdu2OgwzMzMzMzMzGYbLgVjZmZmZmZmZmZmZlYHJ9bNzMzMzMzMzMzMzOrgxLqZmZmZmZmZmZmZWR1cY93MzMyaNWrMJH59zdBGh2FtxIuRmpmZmZmZzTrPWDczszmGpCj7mybpE0kPSzpMksraD8ztejUo5DYlaWh+Pqs1OhYzMzMzMzOzeZlnrJuZ2ZzoqrydH+gK9AS2Br4H9GlUUGZmZmZmZmY2b3Bi3czM5jgR0b/4WNKOwF3AAZKui4g7GxKYmZmZmZmZmc0TXArGzMzmeBFxH3BNfrhXI2MxMzMzMzMzs7mfE+tmZja3eD5vV6l0UNK2koZImiDpU0n/lNS92mCSdpd0j6QPJX0h6VVJv5W0WIW202ufS9pL0hOSJkn6SNJgSStXOccikk6X1CTpc0njJQ2TdEA9T1zSKpIulDRC0mf5vMMlXSJp7XrGMjMzMzMzM7OWObFuZmZzi8XzdnKFY7sDQ4BlgHuA94AfAMMkfbO8saTzgNuBbYEm4J/AQsCvgKGSFq0Sw9HATYCAu4GJwAHAEEkLl51jcWAYcCbQGbgTeBTYDBgs6Y+1POmctH8un/sL4A7gYWAKcDiwZS3jmJmZmZmZmVntnFg3M7M5niQBu+WHL1ZocjxwcERsGBH7At1JCfBlSQnp4lj7AyeQZsCvGxHbRcQ+QDfgb8DGwBlVQjka2DEiNs991gEey33LF1UdkMe6H1gjIvaLiB8AGwJjgOMk/aCGp38YsBxwYkR8OyJ6R8SeEbEhsDopyW5mZmZmZmZmbciLl5qZ2RxL0vzAGsAvSTOzJwNXVmg6KCIGlx5ExFeSBgD7kGalF/0yb/tExMhCnymSjgP2AA6TdGpETCvre35EDCn0+SzPft8qn+eKHPeiwKHANODoiJhY6POKpN8BfwaOJS3K2pzOeTuk/EBEvNVC3+mampqGVznUtdYxzMzMzMzMzOYVnrFuZmZznFzPPICpwKtAf2ACKRn+RoUu91bY92refqswbmfg28B/ImJEeYeI+AJ4BliKNAu9VechzVRfGHgqIl6r0Ke0EGvPPBu/Oc/m7YWStpfkm+ZmZmZmZmZm7cz/821mZnOiq/J2GvAp8BJwc0R8XKX9qPIdETEx56w7FXZ3ydt1c+K+OcsB5cn3mc5DqrNefp4V83ZkpYEj4hNJ44ElgSWA8c3EMRDYCfj/7N13uB1V9cbx70sgAQKoNOmhhWaQ3gmEJiC9Kd1IU5CONAWDyA9BQJAmTYiUAIKISA0t9N5vlBpCDRBqAqGzfn/sfZLJZM7NObfkJuT9PE+e4c7M3rOmnOPjmn3W/glp1PoYSY8CNwEXRsQ7rbQdq0+fPj+oWp9Hsted5NXMzMzMzMxsauTEupmZTXEion+zTRrcr1tejqB69HnRe+04TjP7t7pPRHwN/FTSCcAWwDrAqqTSM0dK2jAiHmwyLjMzMzMzMzNrhRPrZmZm49RGnL/VhuR9M97My4WqNkr6Dmm0+iekEjcTFRFPkCZcPUbSLMAA0iSsfwFWaW/AZmZmZmZmZjaOa6ybmZllEfE6qbzLDyVVJr07yGPAp8DKkqpqte+cl/dGRLOj4ImIUaRJWANYus1RmpmZmZmZmVklJ9bNzMzGdxypJMw/JfUpb5S0iKTd2nOAiPgEuJD0v8NnSepZ6H8x4Kj85xkT60vSLlVxAhsBAl5tT6xmZmZmZmZmNiGXgjEzMyuIiEslLQ0cBjwp6QngZdIkor2AJYCnSInx9jiSVAt9A2CYpLuAnsC6wPTA6RFxQwP9bANcLOkl0iSunwIL5r6/Jo1cNzMzMzMzM7MO5MS6mZlZSUQcLukWYF9gNWAZ4ANSDfaTgCs64BijJa0NHAL8FNgc+AJ4FDg7Ii5vsKs/57jWAPqSkvNvAJcDJ+fa62ZmZmZmZmbWgdSG0q1mZmY2lWhpaRk68qOvljr/lg+7OhTrIIOO7dfVIZiZmZmZmU2MujqAifGIdTMzM2vVfHP2ZNCxy3Z1GGZmZmZmZmaTDU9eamZmZmZmZmZmZmbWBCfWzczMzMzMzMzMzMya4MS6mZmZmZmZmZmZmVkTXGPdzMzMWvX6O58w4JIhXR2G1eHJSM3MzMzMzCY9j1ivICkkxUT2OSbvd8wkCsu+ZSQNrD1rTfzr19Vxt5WkffM5/HpStOtskmYq3ZulW9l3t8J+LZMyzkZJOjnHt20b2s4s6TBJd0t6R9KXkt6XdJ+koyXN0xkxT40k9cn36fqujsXMzMzMzMxsauYR62Zd596KdXMBGwKfAFdXbH+rUyPqApKuBrYBVoqIR7s6nnbYCTiizradO/JAkk4GDgG2i4iq54r/QT0AACAASURBVGSSkbQ2cBUwB/Ax8CAwEvgesDKwOnCEpA0i4v4uC9TMzMzMzMzMrAM5sW7WRSLiAuCC4ro8In1D4N2I6N8FYXWmS4HbgHcmUbtJ5X1gNLCjpCMjYrxfu0iaF1gbeBxYvgvi6zSSVgEGA9MBvwdOjIhPC9u7AdsBJwIetW5mZmZmZmZm3xouBWNmk0REfBgRz0bE+5Oi3SQUwCBgfmCtiu07kb5rL52UQXW2nDS/FOgOHBoRxxST6gAR8XVEXEF6oTBZlsAxMzMzMzMzM2sLJ9Y7gaQZc13hFkmfSvoo1x7evs7+w2s13SX9qtDu5Vy3WHnb8pKuz7WLR0u6VlKvjoght1lO0k15348k3SJpJUn969WTl9Rd0gGSHskxfSLpYUm71+Iu7R/5fLvlc3te0ueSXpN0oqQedWKbSdLvJD0jaYykUZLukrRlK+ezmqR/SxqZjzFc0tlV9Z4LNfP71+lr7D0qrV9F0r8kvZKP8VY+/z9KmqlebO0l6V1JH+freEjhPt+bt3eTtIukqyS9WLhmD0javU6fY2tsS1ohPwsf5uPcJmnFOu02k3S7pDclfSbp9XxvDi/tN16t9HxPg1QGBuARjV+zfPaqdqU+Z5Z0rKT/5fP/UNIdkraayHWT0mdtaI75TUlntOOe1ZLmVSVfdgbeA25urQOlz/eVkkZI+iJfx/MkzVc+B1IZGICrStdsxbxPT0l7KX1fvJzP8YPWrk0bbAUsCrwAnNrajhHxXkQ8WzqPtt67aSQdVGj3oqQDCvutKunmfL4f5c9A5Wj5ZmLQuJr6lS8I6j2nkh6tPc+Sts9/j8nnc7Gk79fpby5JFynVrB+T2/20at+8/zT5M39/bvOppFeVvsf3qNfOzMzMzMzMzNrGpWA6mKSZgTuBFUh1hq8HegLrAn0lrRoRB9ZpeyrwS1KN4pdJ5SNOBHpKGkwquTAcuANYBtgC6CNp6VL5haZjkLQ6qdzGDMATwHPAUqQ64BfVibcncBPQF3g37/sNsBqpxMlK+XyqXAZsCjycj9UXOAyYl1JyMiee7sjxvAHcCsyYj/MvpfIbJ5Ta7AwMJL08uh94jTRqdm9ga0n9yom+ZknaBLiONGL5vnyc7wGLkWptn0uqOd2Z/g5sCwwBhgJf5fXfAy4mJXT/BzwKzA6sAVwgabmI2LdOn31JieJhpGdicWA9YIikZSPixdqOOYl4EvApcE8+3vdJ92oF0vNbz5c5/n5AL9Jz+l5h+2etnbikWYG7gD6k2vPXAd8hPefrSPq/iDiqTvOzgJ+Tnr8XgDWBfYHewEatHbdKRPxX0pPAtpL2jYjPc4x9gKWBs/P51juXXYALgW7AI6RruQSwJ7CZpL6F6345sA7wA9J9f6XQ1bt5uSTp+RtB+nw9RCrFshbp2hwaESc3e54lm+TlFRHxTTMN23nvzgV2JH1PDiM9P6dJ6k767roeeB64nfSZ3xZYQtLyETH2HrQzhrY4DDiYdH9vAlYFdgGWlbRCKba58vn1In3n3076brwc+Gud/k8HfgV8RPouHg3MDayY215Qp52ZmZmZmZmZtYET6x3veFJC8TZgq4j4GEDSEqQkzgGSBkfEjRVttwNWjIihhTZPAr8GdgV+GxGn5W3dScmZdYHtGT/53VQMkqbJ7WcADouIk2odSToaOLbOuZ5ESsJeAuxTOM4cwH+AX0j6T0TcUGrXCxgD9ImI4bnNQsBjwE6SBkTES4X9LyIlav8EHFVLQElamPSy4ThJN0bE03n9/MB5pIT35hFxfeE8TwEOJCWdV65zXo06FBCwckQ8VtwgaWXGTxJ3hp6kBOvSEfFCadsYYHPgpoioJduRNDfpmv1K0oUR8XhFv/sD+0bEWbmNSMnMPUmJwX0K+x5BSuYuExFvFo4zDdVlUcbKyef+SpOX9gJ+3+TkpaeQkqL/AX5ae7kkaRnSi6XfSro1Iu4qtesJ/BhYISL+m9vMRUp4bihp5Yh4uIk4ai4FTiYlnK/J63bJy8vqNZK0JHA+8CGwWUQ8WNi2D+klwPmke01E7Kc0eekPgLPqTF76Oum7YUix5rukxUjJ+P+TdGlEtGcy3OXysuoZmpj23Lv1gGVrz7ykZUkvjn5LmvR3v4g4P2+bnvRSbjVgS9Ikq+2Noa32BNaOiPvycWYmfR8vRxr9/49SbL2AK4BdC995W1MxqbGk75JeYj5H+j4aVdjWnfZ/15mZmZmZmZlZiUvBtELjl1gY7x8woGL/nsDupFHbYxPNAHl09HH5z/3rHPLoWlK90OYG0ujsV2tJ9bztC+Av+c+12xnDuqRR1s+SEoNFx5NGz5fPdU5gj7xtz9JxRgK/yH/+otw226+WVM9tXmZcOY2+heMsC2xMGg1+RHFUZ0QMI5XE6JZjqdmD9JLg8lpSPe//DSkR/CawkqRV68TWqDmBj8pJ9XyshyNidDv7b8QfKpLqRMSYiPhPMame148Ajs5/blGnz5trSfXcJhj33IxNlkualjQy/rliUj23+SYihjR7Mo3Ko413Ar4gPedjf7EREU+RXvoA7Feni8NrSfXc5i1S8hom8kKgFYNIn7udcowCdgCGRcT9rbQ7BOgBHFBMque4zia9IOuXk+INiYi3IuLO8kSqEfE86QVVd9Lnqj1my8uRzTTqoHs39pmPiCdJI7q/AzxZS6rnbZ8BZ+Y/i9+T7Y2hLU6oJdXzcUYDte/04udqNuAnwOekZ6L4nXcNaUR+2ayk78FHi0n13OaLiLi3kQBbWlqGVv0DFmnsFM3MzMzMzMymHk6st+7vrfx7qmL/FUgJ3Yerkp2kkd0Aa+SkW9mtFeuGtbKtNqp77nbGsHpeXl2RiPuacaNvi9YGpiMlYT8vb8zJqdGkcjBlX5JGzZY9n5fF89kgL/9dji2rJYyKx6kl5icYJZxjvaq0X1s9BnxX0t9yyY+ucF1rG5Vq5B+hVFv+IkkDSSVQIJU9qTK4vCIiXiWVZpm7sO4r4GlgdaUa9Qs2H36brUp6/u6OiNcrttee83r3eIJzpPr5a1h+aXEHsEkeQdyPNKFp3dHq2QbA16SR01WqnvGJUtJPaa6Fvxbu/2Z5l3r3v+FDtLFde+9dR3xPtjeGtmj0mVuF9GuyIRHxTkWbyyvWvUL65ci2SnNezNWuSM3MzMzMzMxsolwKphUR0b/eNqWJPJcpra5NkDe8Tn8fSvqINLJyFlIt3KI3Kpp90sC24oSfbYmh1ua1qjbAqxXrFszLvSXtXacdpCR/2YicsC+rjXovnk/tOCdKaq1e9+yF/271GhTWV05o2ITfkOpn7wbspjSp5P3AtcCgqhcOHexLUm3oCUiagTSCuu7krsDMddZXJRohPW/Tl9btRXrxMgAYIOk1Un3wfwDX1XkZ0hEmdo/fJI1GnlPStKWR+2Mi4oOKNlXPX7MuBdYn1fVerbCukqRupOS7gFHV79vGmr21jaV+ZwP+TaqpX0+9+9+od0m1u+dosl17792HFW069HtyIjG0VdXnquqZq8VW9b1buT4ivlaaePkS0ij40yS9SCo1c1lE3NlIgH369PlB1fo8an2pRvowMzMzMzMzm1o4sd45GkkmTrDPRJKQzSYo2xJDvTZV2b5uefkEadRyM5o5l9px7mHcqNQq71asm9hxmoljgl93RMRrklYkldLZlDSKfzNSbfPDJK1eJ4HbUb6I+pNG/paUVH8MOJJ0nz6MiK8kLZ/X18viNnxdIuIRSYuTapZvTLoGO+Z/t0rauM5LlI5SL9ZoZZ/OSvZDeslwNully1LAI7n8Sj3K/76geiRy0XNNxPEXUlJ9MGmOhP8Co3ICdmvgn7R9xHnNk6SXi8uTXiY1qzPuXUd9T7YWQz0T+wVYo/3U7ktT5xIRN0hahPQdtCHps7g7sHueT2H3ZvozMzMzMzMzs9Y5sd6xanWmF6raKOk7pJHin5DKpEwuMYzIywXq9Dl/xbra6MshEXFwG+JsVO04V0fE6Q22eRNYnHQNqpKavfJyRGHdF3k5U3nnPKq4srRCHsk6OP9D0gKkyVbXJdVzP7zBmDvaVnm5bbGWfbZwRx4oIsaQJlS8GsZO/nglqcTJjowrq9GRWn3OSaN+uwPvdHJifzwRMVrSdaQJhaGV0ep5/68kvU2qV75XnjuhI2xJKt2zZbF+eNZR9/8G4GfA9nnC4UYTwZPDvWtLDHW/I7Kq78n2xNarzvZ639PkF3kX539I6kv6XO4maWBE3NNBMZqZmZmZmZlN9VxjvWM9BnwKrCypqn7xznl5byeWyGhLDLWJFbcp136XNA3jkrRFd5LqQm+aE8+d5ba8bK2kSVktebRTeYOk7sB2pf1gXJK9aoLIdUn1mCcq1yKvlaxZupE2neR7pIk036zY9pPOPHCur39e/rORa1BLWDbzou9BUimctSXNV7G99px3RSLx78B7wDvAFQ3sfxvp3Ddt4hh1r1l+xnsC75aT6vnzvV25TRv9i/Qrkt5Aqy/XJM0qaYn85+Rw75qOIb/0eB+YJ08SPVa+rut3UGwPAV/l2KrK7Gxfsa5STqRfmf/syu8jMzMzMzMzs28dJ9Y7UER8AlxIuq5nFZMvkhYDjsp/njGZxXAH8CKwJHBQqcsjqBjhGhFvAANJSbVLJE1Q/1nS6pJ+3OaTScd5ELgdWEfSqZLGGy0qaRpJP5K0ZmH130gvF3aQtElxX+B4Ul3oR3LfNXfl5c7FSTglLUyd+yXpIEnfr9i0UV7Wq5E8KTxPegb2LK6UtCup/ne75WTpLyXNUlrfjXGTzjZyDWrJ/8UbPXZEvE+qIT8d6TkfW8tf0tLAYfnPMxvts6NExM0RMXtEfL/O5JNlfyIlec+W9KPyRklzSNo/P781da9ZTgC/AsxXev5FKhG0chOnU1f+tcbOOfaTJP1O0ng1+PPnc1vSC78+uV2X37t2xHBXbnNEYf9pgD8Ay3ZQbO+SSvVMD5wqaezLE0lbUPECRlJvSTtWXP+ejJuAtSu/j8zMzMzMzMy+dVwKpuMdCaxKSiwOk3QXafTouqREyekRccPkFEOuu/xz4FbgFEk7keo5L0VKtp9PStCWy1TsT0q670Aauf4kKeE3F7AoKYH9F+DGdp7PTqRSKwcCu+bjjMz9L06aPPEg4N58Pq9K2ouU+P+PpPtIE7Mun/d/G9i1eICIGCbp4rz+SUl3k67ZqqSSF9MzYWmGAcDJkp4CXiDVRv5hPsa7wEntPO/2OAFYCzhT0i6kkcVLkpJ/pwCHdMAxZgT+Spoo8TFSMnd6UuJ2XuB/pHswMdfleM7OicP38/oDI+Lj+s04GFiBVNP+JUn3kCbkXZdUxuP4iBjS5DlNchHxdP78XQjcIum/pM/ftKSyH0uRkrlnk36FAHATKaF9lKSVGDeJ7R8i4hXSC6RzSc//XXn78qTP62mkz1JHxP6ApI1Ik9X+HjhU0gOkz+d3Sc/C7KTSU8WJRSeHe9eWGI4HNiFd941Iz/yywJyk6/2LDoxtNdJ33xqSHiSVp+kLnAOUJ4z+PnAZ8LGkR0nfwzMBawKzAkNIz4yZmZmZmZmZdRCPWO9gETGaNGncAFJydXNSMuRRYMeIOGByjCEi7iUlYW4hjULfNLddm5Q8glTeothmDPAjYA/gcdKI1K2ARYCXSKM+T+6A83mblOA+mJTAXolUGmY+0qScv6JUyzoiLiUllq8nJZS3BWYgJYJXiIhnKw61JykhPYo0+V8vUiJthzqh7Ucq9TEjaeLOjUjlcU4GfhgRL7XphDtARNwMrAPcTUr0b0I6r01pLNndiJGklys3k16mbAn0I5VAOQJYZSKJ8Vqsd5MmWXyBdB13z/+mn0i790kTdB5HOrctgNWBB4BtIuK3bTmprhARlwHLkX5tMSPpfvUlJdf/DmyUR4jX9n+J9Ew/SrrmtWs2R95+HrBN3r4i6XkeRvpM3N7Bsd9B+swfQZrQdDlSuaHVSL+cOBpYNCIeKLTp8nvXlhgi4lHSd959wA9I5V/+C6wCtHRgbG+Srt/fSQnyLYGZSb8QOLuiyVDSfA73kV6ebJPbv0BKwm80KecaMDMzMzMzM5saqPNKfdu3haSbSEnjVSPioa6Ox8zMJp2WlpahIz/6aqnzb/mwq0OxOgYd26+rQzAzMzMzM+tomvguXculYAxI9bKBmXMZido6AfuSkuovAg93UXhmZtaF5puzJ4OO7ZAy8mZmZmZmZmbfCk6sW81iwP2SniaVjOhGKu2yMGki0D3DP28wMzMzMzMzMzMzc411G2sYaVK8HsB6pFrXPYBBpFrZQ7ouNDMzMzMzMzMzM7PJh0esGwAR8Q6wT1fHYWZmZmZmZmZmZja5c2LdzMzMWvX6O58w4JIhXR3GVMUTkpqZmZmZmU3eXArGzMzMzMzMzMzMzKwJTqybTQKSZpf0B0lPSPpQ0hhJL0o6T1Kfro5vSiapn6SQNLCrY/k2kDS/pBMlPS7pfUlfSHpb0mBJ+0iaqatjnJpJWjA/70O6OhYzMzMzMzOzqZkT62adTNL6wAvAUcC8wF3A9cCXwJ7Ak5KO6LoIv50kHZMTkP27OpYphaRfAM8Dh5Ge1fuBq4GhwJrAWcCLkmbvsiDNzMzMzMzMzCYDrrFu1okkrQTcAEwHHAmcHBFfFbb/GLgU+KOkMRFxetdEOkV7GFgS+KirA5mSSdoDOAf4GPgZcElERGH7jMCvgKOBmYB3uyJOMzMzMzMzM7PJgUesm3USSQL+DnQHfhcRJxST6gARcSOwJRDAiZJ6TfpIp2wRMSYino2IEV0dy5RK0nzAGaTncPOIuLiYVIex1/kkYBX8EsPMzMzMzMzMpnJOrJt1no1JI6nfAE6st1NE3A1cBUxPGhE8lqThuZyJJO0n6alcn/3Jwj6StLekFkmfSXpd0mmSZpY0JLdfsNTvJpIulPQ/SaMkfZL7/o2kHuUYJfXP/RwjaQFJgySNlPSppEclbVbv/CT9WNKtkj7I8T0n6QRJ363Yd2z5FklLS7out/tE0l2SVq9oM0GNdUnDgQH5z4vy9tq/fhXntJikK3It8W8kbVnoazZJJ0l6Icf/vqSbJf2olXNeT9LdOe73JP1TUu/WytNImknS7yQ9k+/xqHzOW1bsO7bOtqQZ8vV8RdLnSrX7D88vdhq1L+n5+0dE3NnajhHxv4j4oBTP/JLOLcTwjqRr8i82Wou9p6Q/S3otP0uPF58lSdtJejhfx7clnS5phoo+i5+TAyT9N9+rN3KbCZ613G5GSUcrfXY+lfRRvm/b19k/8rNVtW3s81RaP/YzKGlLSQ/m83lf0uVKLzWq+ps9X9O38vPwhKRdq/bN+0vS9jn+t/L5vybpNkm/qtfOzMzMzMzMzNrGiXWzzvPjvLwqIr6cyL6D8nLjOtvPAU4B3gGuA4YVtp0GnA0sCtwOPAjskv+7e53+/gZsRxp5fDNwDzA/8H/AjZK61Wm3IPAIsAZwL/AEsAJwbVWiWdKRpFI4/YDHgGuBGYHDgYckfb/OcVbM57F4Po8XgLWA29XYZK9XA0/l/76P9MuB2r+3Svsuns9pZeBO4FZS/XskzUsqNfNr0rW8Np/z+sAtkg6qOOdtgMFA37zvYOCHuZ+FqoLN1+Eh4PfA93IMD5Gu7b9UvwZ/99z/XsD/cvzzAicAf6jTpsomeTmo1b2qY18aeDzHMAa4hnS/tgLul7RdK7HfTnpWnyTd72VI57t+vraDgK9I59gN2A+4oJVwzgBOAl4H/l1oc5ekmUtxzwzcDRwLzEma9+A+0nNwuaTTGr8KDdkH+Ccg0mfuY2B74I7yywJJs+VY9iI9i9eRPqsXAYfW6f+PwOXAsqT7cQ3wIuma1mtjZmZmZmZmZm3kGutmnWfZvHysgX1r+ywlabqKRPzWwHIRMbS4UtKawP6ketdrRcT/8vpacna1Osf7JXBrRHxS6GtmUiJzU2An4OKKdj8jJS8PrpW1kXQAKbl/FCkBWutvJeA4YDSwfkQ8nNf3AC4hJfbPAH5ScZxfAYdHxJ8K/Z0KHEiaWLPuyF2AiPh1Hjm8DHBBRAxsZfftgTOBAyPi69K2c4CFc7y71+5Lvu63ACdJuj0ins7rvwOcR3pp+ZOIuCqv7wb8lTRZbZWLgKWAPwFHFY6zMOmaHifpxtpxClYjvRRZLCLezW1WBB4ADpJ0QkR83Mq5I6k78IP85+Ot7VvRVsBlwOykxO5vayVkJG0LXAn8TdLdEfF2RexDcuwf5Db987X4KzArsG5E3JO3zUN6UbGjpKMjYhgT2gVYLSIey21mIiXY1yW9tDi4sO/xpBcXtwFb1a6TpCVIEwwfIGlwLtfUEfYBNoiIO/JxZiR9RlcHdgAuLMW2WI79pxHxeW6zMSnJPh5J05M+G8OBFSLi/cK2afMxJqqlpWVonU2LNNLezMzMzMzMbGriEetmnWe2vHyngX1H5uU0pIRi2YnlpHr2y7w8pZZUB8iJyrqjVCPi2mJSPa8bDdRGYG9Rp+kw4JBSrfizgA+AVXOStmbffD6n1ZLq+Tif522fAtvkUeFl9xaT6tlxeblWvfNqo5GkJP54SfWc1N4UGAXsX3zZERH3kpLu3UgJ05rtSPfvllpSPe//NWnU++jywSUtS/qlwv3AEaXjDAMOycfZoyL2b4A9akn13OZR4CbSLwNWbOD8v0caRQ3jnsNG9QOWBl4Gji7WZY+Iq0kj/GcGfl7R9mtgz1JZmYtzDIsCZ9aS6rm/N0lJfKj/DJxZS6rnNh+TnrUAds8vdZDUE9iddP32Kb58iIhnGfes7d/ayTfp1FpSPR9nDOlXKFA4n/wyYBfSSP39a0n13OYmUtmoslmAHsBTxaR6bvNVLjdlZmZmZmZmZh3II9bNOo9Ky0b2hZQELJtglGpWG4k6QbItIu6U9B7jEvzjH1DqTSpXsyjQk5QEr8XRu87xhpRH00fEV5KGkUb/zgbUJhHtm5eXURIR70gaTErgr14R/+CKNu/l85m7TmxtdVtOcpatmZc3RsSHFdsvIY2A7ltY19r9GJXPeZvSpg3y8t/lCUOze/NygnrlwPCIeL5ifW1dI9eqmVrsZbVzv7JitD+ka7R13u+E0rbhEfFicUVEfCPpFWAO0mjuspfyst55XVFeERH/k/QU6RckPySV/VkBmAF4MCJeqBP36cAaklTnvjRrgmea6vu0fI7tvoh4taLN5aQR7mPlz9PrwCaSDgUuyy8imtKnT58fVK3PI9mXarY/MzMzMzMzs28zj1g36zy1UcRzNrDvHHkZpNHfZVUJNoB5cpvX62yfoF2e5PAU4DlSCZd9SSOKf8a4Eiszl9tl9Y5TG/FbnPi0FtsrddoML+zXzHHq1Y1vq9auLYyLs6y2vhh/7b9fa+JYC+bliRp/ktWQFIx7jmavaNvM/ajnfca9zJmjtR0rtOUa1bxRp80nrWyvbat3Xo0+a63GnV+kfATMRBoN3hGq7lW9zw3Ufy7rrf8Z6bvjT8AbkoZJGlg194GZmZmZmZmZtZ9HrJt1nqdIk3yuQBoB25oV8nJo1USnEfFZG2OoGo38U9JI69dJdZkfAEZGxJe5lMvnddpB9Wj69qrqszOOU8/Erm29WKKV7fXaVF3X2kSx9zD+pLRl71asa/d1iogvJP2XVGd9eeon61vtpg3b29KmrdrzPDcax8ReVDfaTy3Wps4/Iu6QtCipfNFGwNqkZPvPJP0jIn7aTH9mZmZmZmZm1jon1s06z02k+tvbSjq0KmFesGNe3tzkMUaQRjzPx7gyGUXzVazbKi/3jojrS9sWbvL4rXkTWAjoRRodX9YrL0dUbJsc1EppLFRn+4J5WYy/9t8L1Gkzf8W6WiL76og4veHoOtYNpMT6jtQvO1RlYtdoUt/jXsAzFetr9+PN0rIy7jwJ7XdII+SLdfG/JI1ir1J1b9uiFluvOtvrPVtExCjSBMSDACStSipL9BNJA3ONdjMzMzMzMzPrAC4FY9Z5biQllOcFDq+3k6S1gG2BL0gTgTbj/rzctqLfflSXD/leXlaVK/lJk8dvTW3iyZ3KGyTNAfyINHnk/eXtHeSLvGzrC8RabfNNJH23YvvOeXlPYV1r92MWxtVTL7otL7dsS5Ad5EzSLxV+Immd1naUtISk2jNUO/efSupWsXvVNepME4zKlrQEqb76aODpvPox0uS5K+e5Bspqcd9bqq8+AphNUtUEwx1VcuUx0q8oVpFUlazfvtGOIuJBxv1aZukOiM3MzMzMzMzMMifWzTpJRHwD9CeNcj1W0uHl5KOkjYFrSeUfjoiI4U0e5ry8PETS4oV+v0uqtVylNmHiXpLGlsiQ1Bc4tMnjt+YsUuL8AEkrFo7THTgDmBG4JiLq1dpur9rI38Vb3auOiBhGGsk9M/AXSdPVtklaDdgb+Bo4u9DsKlKd640kbVPYfxrgRCrqdefk5+3AOpJOlTTeiGhJ00j6kaQ1y207SkS8RioLJOA6SbsUn40cxwySDgQeIo3mBhhCGiG+EOkZLz5PW5ImLv0YGNhZsZfsK2m5Qgw9Sc+agAsj4nOAiPgEuJD0v4Fn5f1qbRYDjsp/nlHq/668PLqwvyQdybiJa9slIj4mTfg7Lem5G1t/PddLn+Dll6QFJPWXNGNpfQ+g9qKkXm12MzMzMzMzM2sDl4Ix60QR8aCkzYErgBOAgyU9QBodvDSwJCn5fHREnNqG/u+SdBbwK+BJSbeTRuKuQ5qY8UFgVcaN3gY4nZTw3wfoJ+lp0qj6NYFTgF83f6aVsT0s6Wjg/4AHJA0h1Qlfg1Q24wXSxKmdZTBp5O9BkvqQEu0BnBQRVaVpqvyCNNp6V2DtfO/mAPqRaqMfEhG1UdBExIeSfglcDlwt6V7SLwNWJE1ieylpNHTxfkAa1T+YlNzeVdKTwEjSfVk8H/Mgxo2i73ARcU5+AfBn4GLgJEmPAKOAuUjP0Yyk7SZJEAAAIABJREFU6/hxbhOSdgLuBH4DbJVjX4B0n78CdouItzor7pJLgYck3UGafHStHPtQYEBp3yNJ57QBMEzSXUBPYF1geuD0iLih1OZE0q8RDsy/CHmJ9Dmen/SCZZ8OOo8jSDXStwJekHQf8P287lzSS52iWYGLSC8JHiWVF+pJSvbPATwMXNNBsZmZmZmZmZkZHrFu1uki4magNynBPIKUuNuclLy7AFguIo5rxyH2y/9eJiUJVycl8tcllYIJ0ijqWjzPAysB/8nbNyfVjf5FRHTkiHUi4njSZIp35WNuTXqp8CdglYh4uyOPVzr2m8AWpJcLawK7AbsDczfRxxukuE8hJYm3Jk00ezuwYUT8uaLNP0iTR96b990Y+C+wCuMmSn2v1OZtUpL3YNILh5VIpWHmA54gvTi5tNG42yoizgYWA04C3gL6kkZI9yGdz97AYhHxbqHNM6RJT88nPUfbkl4GXAusERFXdXbcBfuREua9SPc+SL+c6BsRHxV3jIjRpET1ANILn81J5/sosGNEHFDuPCKGkj5XQ0jXaQNScn014JGOOol8fdcgfT/0ID0LswF7Uv1LlJdIL8SGkF5qbJ3bDwf2B/pFRPlljpmZmZmZmZm1g8YvH2tm3xaS5iUl1l6MiCW7OJypXh4N/jRpktC5J+Eo7m89ScOBXhGhie1rzWtpaRnao0ePpXr3ripHb2ZmZmZmZtYpJvv/j+8R62ZTuDyZ5AyldXOQSkNMCwzqksCmUpLmlTRnad10wB9JSfU7nFQ3MzMzMzMzM5uyuca62ZTvQGBHSU+QSs18n1SaYxbgceDkLoxtatQXuFTS48ArpFrXywDzAO+TypWYmZmZmZmZmdkUzIl1synfNaSk7fKkOt5fk2ou/xM4JSI+7cLYpkaPAZeQEuxLAt1JE36eB/wxIoZ3XWhmZmZmZmZmZtYRXGPdzMzM6mppaRk68qOvljr/lg+7OpQp2qBj+3V1CGZmZmZmZlMS11g3MzMzMzMzMzMzM/s2cWLdzMy6jKSQ5J9OmZmZmZmZmdkUxYl1MzMzQNIxOdHfv6tjMTMzMzMzM7PJmxPrZmZmZmZmZmZmZmZNcGLdzMzMzMzMzMzMzKwJTqybmdkUQdLckg6TdJekNyR9IektSddIWqmVdj0lHSnpcUmjJX0s6b+STpPUK+8zHBiQm1xUq/2e//Ur9beLpHsljZI0RtLTuf/pK449sNaHpLUk3ZFjGCXpBklLtRL3ZpJukfSepM8kPS/pD5JmqnOOh0t6UtKH+RxfknSVpA0bu8JmZmZmZmZm1qhpuzoAMzOzBm0BnAi8CDwDjAIWBbYCNpW0aUQMLjaQNDdwG7AU8D5wB/Blbrc/8CQwELgaWB9YBrgvH6PmrUJ/5wJ7AZ/lvsYA/YDjgc0krRcRn1bEvhlwANAC3AIsDfwYWEVSn4h4q7izpFOAg/NxHgbeBVYAjgI2lrR2RHyS9+0GDAZWB14HhgBfAPMBmwKf5GOamZmZmZmZWQdxYt3MzKYU9wHLRMTTxZV5RPZ1wNmSekdEFDZfQkqqXw7sWUtG53a9gW4AEfFrSceQEusXRMTA8sElbUNKqr8B9IuIF/P6WYAbgDWB3wOHVcR+ILBzRFye23QDrgS2AfYBflc4zk9ISfUngK0jYnhePx1wZo7hGODQ3KQvKan+77z/N4W+vkN6iTBRLS0tQ+tsWqSR9mZmZmZmZmZTE5eCMTOzKUJEPFNOquf1twBXkRLAfWrrJa0MrEcacT5eUj23eyEinm0ihP3z8ne1pHruZxQpOR7ALyV1r2g7qJZUz22+Jo1yB1irtO9v8nKHWlI9t/mSNOr9LWAPSbX/DZ8zL4cUk+q5zUcR8ViD52dmZmZmZmZmDfKIdTMzm2JI6gFsBKwMzAHUkthL52VvUpkYSKVdAC4rJ9XbcNzpgFVJyfNB5e0R8Yykp0kj3pcBHintMrjcBng+L+cuHGfO3P5/EfFcxXE+k/QoqcRLb+A5Ujmbb4BDJb0F3BARo5s7Q+jTp88Pqtbnkex1a8GbmZmZmZmZTY2cWDczsymCpKVJJV8WbGW3mQv/PX9evtQBh5+NlMR/KyI+q7PPcFJSfJ6Kba+XV0TEx5IAehRW98rLJSVFuU3J7MBzEfG8pEOBE0glb76W1EKqLX9RRNQr8WJmZmZmZmZmbeTEupmZTfaUMtD/ICXVz8n/hgEfR0RIOh44ElBF84klqJvRSF9V+zQaQ7e8HEH1KPei98Z2HvFnSVcBWwIbkOquHwIcJGn/iDirweObmZmZmZmZWQOcWDczsynBEvnfoxGxd8X2hSvWvZaXDU3eORHvAV8Ac0maISI+rdinNtp8RDuOUxvZ/lZE9G+mYUS8BpwBnCFpWmB74CLgz5Iui4gP2xGXmZmZmZmZmRV48lIzM5sSfC8vJyipIul7pFHaZbfl5U6SZmzgGF/k5QQvnfPEoQ+SRsTvUBFDH1IZmNHAUw0cq1JEvE6qm/5DSQu1o5+vIuJSUq337sBibe3LzMzMzMzMzCbkxLqZmU0JXiRN0LmupN61lZKmJ5WFmbXcICIeBu4E5gLOLSfXJS0qaYnCqjfzcvE6MZyRl7+XNHaEvKSZgTNJSfdzI+KLqsZNOI5UEuafOWE/HkmLSNqt8Pc6ktaXNE1pv17AkqQyNBO8kDAzMzMzMzOztnMpGDMz63KSHmxl86kRcaWkvwF7Ak9JugP4lFRLvBswEOhf0XYX4A5gZ2BjSfcCX5LKwywD7AY8m/cdDHxGqkveh5RoD+CkiHguIq6WdB6wF9CSYxgD9APmII1oH9CmC1AQEZfmiVoPA56U9ATwMjALqdzMEqRR8RfmJssApwIjJT1GKlszB7AWMD1wWkS8iZmZmZmZmZl1GCfWzcxscrBKK9vmzsu9SUnw3YH1gI9I5V5+C/y8qmFEvCFpJeAgYFvgR8BXpPrrp5GS7rV935S0BfA7YE1gprzpUlJ5FiLiFzk5/0tgbdL/jr6U+zq1Tu31pkXE4ZJuAfYFViMlzz8gjTw/CbiisPv1wGzAOnm/2YCRwD3A2RFxbUfEZGZmZmZmZmbjKCK6OgYzMzObTLW0tAzt0aPHUr179574zmZmZmZmZmYdQ10dwMS4xrqZmZmZmZmZmZmZWROcWDczMzMzMzMzMzMza4IT62ZmZmZmZmZmZmZmTfDkpWZmZtaq19/5hAGXDOnqMKZog47t19UhmJmZmZmZWQfyiHUzMzMzMzMzMzMzsyY4sW5m9i0mKUr/vpH0oaR7JO0habKfZbtRkvrlcxzYRJshuU2/zui/lb4WzH0NabLdcEnR3uObmZmZmZmZWfu4FIyZ2dTh73nZDVgEWANYE1gP2KGrgjIzMzMzMzMzmxI5sW5mNhWIiP7FvyVtANwIbC/psoi4vksCm7I8DCwJfNQBfb2R+xrTAX2ZmZmZmZmZ2STmUjBmZlOhiLgVuCT/uWVXxjKliIgxEfFsRIzogL6+zH292hGxmZmZmZmZmdmk5cS6mdnU64m8nL+2QtLAWs1xSRtKujPXZA9J3y3sN4ekkyU9J+kzSR9IuknSWuWDFGuTS5pV0l8ljZD0uaQWSbvVC1DSapL+LWlk3n+4pLMlzdOhV6IB9WqsSzomr+8vaRVJt+RrNkrSrZJWreirbo11SdNKOlLSC/naDpP0B0ndW4ltFUn/kvRKvk5vSXpY0h8lzdQR529mZmZmZmZm4zixbmY29Zo5Lz+v2LYjcBPQMy8fAQJA0hKkpPwhpJrtNwJPA+sCd0rasc7xvgs8AGyV+7sPWAL4m6Q9yjtL2hm4B9gMeA64Jse6N/B4jmNysjpwNzAf6Zo9B6wP3JVL7zTqcuB4YC7gZuAZ4GDS+U8w2aykTYD7SddpeN7vSWB24Ii8NDMzMzMzM7MO5BrrZmZTIUkCNs1/Pl2xy57A9hFxZaldN+AqYF7gAOCMiKgl3JcDbgXOk3RbRLxT6nML4J/AzyLik9xmC+Ba4GjggsJx5gfOIyXzN6/VgJc0DXAKcCBwMbBymy5A59iTlBA/qnBN9gbOBgZKWiQiPmutA0k7ANsCw4C1IuKNvH4hxiXtyw4lJdxXjojHSv2tDLzXSPAtLS1D62xapJH2ZmZmZmZmZlMTj1g3M5uKSOomqTdwIbAaaQT4RRW73lBOqmebAX2AyyPi9FoCGSAingD+QBrlvnNF21HAXrWkem7zb9KI7AUkLVjYdw9ghnyc6wv7f0Mahf0msFJVmZUu9AowoHRN/go8BMxDGqk/MXvn5dG1pHru52XSta0yJ/BROame2z0cEaMbjN/MzMzMzMzMGuQR62ZmUwFJUbF6NGn0+EsV266r01WtpMm1dbbfm5crVWx7NCLer1j/PLA0MDeplAlA37y8rLxzRHwu6SrSiPm+wIN1YpnU/hkRX1WsvxxYBVgz/3clSdPl/b4Brq7Tz7kV6x8Ddpb0N+DUiGhpNnCAPn36/KBqfR7JvlRb+jQzMzMzMzP7tnJi3cxs6vD3vPyGNHL8GeCaiPigzv6v1lm/YF5eKalqRHtNVV3v1+vs+3Fe9iisq01OOrxOm9r6ST6JaSteqbN+eF5OLNbZgO7AiIj4orwxIkZL+pBUq77oN6QXE7sBu0l6l1Rz/VpgUERU1dA3MzMzMzMzs3ZwYt3MbCoQEf2bbFKvFni3vLwJKNdQL3q2KowmY2ikTVv6nNQmmHB0Ivs1dU4R8ZqkFUmTx24KrE0q2bM5cJik1Vt5gWJmZmZmZmZmbeDEupmZNaM26vyciKhXLqYjvAksDixEKhVT1isvR3RiDM3qVWf9Ann55kTavwt8AcwlqXt51LqkmZlwtDoAuQTN4PwPSQuQauevS6pJf3gjJ2BmZmZmZmZmjfHkpWZm1ozb8nLLTj7OPXm5U3mDpO7AdqX9JgfbSOpWsX77vLyvtcYR8SXwMOl/m7dppZ+JiohXgRPzn0s32s7MzMzMzMzMGuPEupmZNeNqUpmX/pIOzxNujiWpu6StJbU3mfs34FNgB0mbFPqfBjgemBd4JCIml4lLIY1YH1BcIWkvYDXgLeBfDfRRm5z0WElzF/rpBRxd1UDSQZK+X7Fpo7ysVy/fzMzMzMzMzNrIpWDMzKxhEfGVpK2AW4ATgAMkPU2aEHV+YAlSuZKtSBOktvU4r+ak9EDgP5LuA14DlieViHkb2LUdp1J2tqRRdbbdFRGNlFI5HzhC0tbA08CiwErAl8DPI+LTBvq4DNiadP2ek3Q7qfb6+sBdpPrrC5TaDABOlvQU8ELe/4ek6/QucFIDxzUzMzMzMzOzJnjEupmZNSUingWWBY4hTWC6JrAJMAdwN/BzxpWMac9xLgXWAq4HlgS2BWYA/gqskOPoKEsCq9T5t3iDfdxPmjj0LdIkoksCtwP9IuLmRjqIiAB+CvwWGAn8mHStzyAl3KsmNt0PuAKYEdiYNFL9a+Bk4IcR8VKD8ZuZmZmZmZlZg5T+P7yZmZm1haRjSKPGfx4RA7s2mo7X0tIytEePHkv17t27q0MxMzMzMzOzqYe6OoCJ8Yh1MzMzMzMzMzMzM7MmOLFuZmZmZmZmZmZmZtYEJ9bNzMzMzMzMzMzMzJrgxLqZmVk7RMQxEaFvY311MzMzMzMzM6s2bVcHYGZmZpO319/5hAGXDOnqMKZog47t19UhmJmZmZmZWQfyiHXrcJKi9O8bSR9KukfSHpLaPauvpCG57wXbH/HkQVL/fE7HlNYfk9f3n0Rx/Dcf77lJcbwpkaThkqIT+4+J9V94Lo7prDjycb51n7XW5HMd3tVxmJmZmZmZmdnkzSPWrTP9PS+7AYsAawBrAusBO3RVUFafpBWBJfOfi0laOSIe7sqYJrWcQH4ZuCsi+nVpMGZmZmZmZmZmNllyYt06TUT0L/4taQPgRmB7SZdFxPVdEtiU50zgCmDEJDjWLnk5Apgb2BmYqhLrDVoPmK6rgzAzMzMzMzMzs67hUjA2yUTErcAl+c8tuzKWKUlEvBsRz0bER515HEnTAtvnP3fNy+3zeiuIiJci4tmujsPMzMzMzMzMzLqGE+s2qT2Rl/MXV0qaUdLRklokfSrpI0l3S9q+oo8JSJpb0peSXpVU+VxL+lmun3xBab0k7STpdknvSfpM0jBJgyStkffZLre9rJUYLsr77Fxa31PSkZIelzRa0se5jvlpkno1cG51a6xLmk7SPpLuy3Xsx0h6XtL5kvpMrO+SDYE5gfsi4jbgfmCOvL7VuCStLOn6fP1C0rKF/eaXdK6kVyR9LukdSddIWqmizwVz+yH5uv1Z0mv5mXhc0maFfbeT9LCkTyS9Lel0STNU9LmspD9JekzSyBzDMElnS5qnfE6kMjAAa2v8uQIGFvarW2Nd0gKSzpT0Qn6W3stx/qYqvo4k6YYc6wZ1tveUNCp/vnqWtu0l6Zkc8xuSzpD0nTr9FO/TLJJOkfRy/gyeVthvWkn75Wv/cf73sKS9JXWr03dDz7Sk6SXtLunf+X5+mvev+70haWCOu5+kDSXdmduEpO+W9u0u6feSXtK474RjJU1fp+/ZJJ1UuO/vS7pZ0o/q7B/5Oeom6bB8jp/n5/1EST3qHON4SUPztfwot7tY0spVxzEzMzMzMzOzzuGRqDapzZyXn9dWSJoZuBNYARgJXA/0BNYF+kpaNSIObK3TiBgh6Tpga2AjUsmZsj3y8vzCsbuRyqxsm2O6F3gXWADYCvgCuA+4FngL2EbSfhHxfrFjSbMA2wEfAlcX1s8N3AYsBbwP3AF8CSwK7A88CQxs7dzqyYnRm4C+wMfAPcBoYCGgP/AG0NJEl7UyMJcWlqvn9Te00m4t4DzgeWAwMA/wTY5xadI5zw48C1zDuGu7maQdI+Kqij67A7eTavM/CMyUj/MvSRsBSwN/Ah7Jx+wL7AfMBuxU6usI0v1tId3LAJYF9ga2lLRiRLyZ930S+CewDfA2cHOhn3tbuQbk810LuA74DjAM+DfpWV4K+D9gEDB8Yv20wznAj4E9gVsrtm9P+gyeExGfFOI+GTiE9Bm4AxhDuo5rUPisVpgBuAvolZePAx/kPruRzv/HwCjS5wDS5/psYANJ20bEN4U4mnmmFwQuIN2nZ0kli+YiPbN9JS0REcfUiXtH0vfBo/l4i5Cei7GhkD7H65OewydJ5X+OBlaXtGFEfF2Ie17gbmBh4FXS98Ucuf2Gkg6OiFPrxHIZsGmO/7l87ocB85JKMdWOMRPps7Ao8AJwS960AGnOimG4bJOZmZmZmZnZJOPEuk0ykkRKIAE8Xdh0PCmpfhuwVUR8nPdfgpSsO0DS4IioSpYXnUNKrO9JKbGe+1oTaImIhwqbjiQlXZ8BNouIVwptZiVP5BkRX0q6EPgNKdH8l9KxdyQlUM+IiM8K6y8hJVUvB/YsJTN7kyZ2bau/kJJwdwLbRcR7hb7nJSUZG5JfDGxOepHwj7z6ynyMzSXNEhGj6jT/OXB4RPyp1KdIScPZgT8Cv42IyNu2zf3/TdLdEfF2qc/VgCHAYhFRS9T2By4C/grMCqwbEffkbfOQfg2xo6SjI2JYoa/zgIMiYmyNeqVfNRwF/B44DtgNICKulfQkKbH+bHmegNZI+h4pGfsd4CDgL7XzzdvXIiedO9GNwGvAFpLmiIiRpe175mXx5dLqpKT6+8BaETE0r5+NlGRftZXjrQw8ACwcER+Wth1ISqo/A6wfEe/kfucmPbNbAb8kJdlrmnmmR5J+TXFbKTm/UI77aEkDI2J4Rdx7AttHxJV1zmsB0i+6+tSeJUlz5H7XA34FnF7Y/xxSUv0SYPeI+DK3WZOUAD9J0u0RUfzeg/RCYkw+zvBC/I8BO0kaEBEv5X23JSXVz4iI/YudSJqT9GuTdmlpaRlaZ9Mi7e3bzMzMzMzM7NvGpWCs0+VSB72BC0kJ089JCdLaCNXdSSOc96kl1QFyDevj8p/jJZLquA14CdhUUjmpXButfl4hru6khGIAuxWT6vn470fEfYVV5+c492BCtXVjy8zk0gzrkUa6j5dUz/2/0NY63Tk52R/4FNi1mIDMfb8REY810eW2pNHHN9VG4+flTXn9Nq20bQFOqljfjzSy/GXg6GKSOSKuJo3qnZmUmC/7mnTNionoi0nJ1EWBM2tJ9dzfm6QkPqSR7RS23VFMqud130TEsaQR0Fu0cm7N2JM0Svn6iDiteL75mHc3Wydf45eiGe8fMKC8fx5FfQFpxP+upb76AKsAT0TE44VNv8zLU2pJ9dzXe8ChDYS5f0VSHcZ9Zg+sJdVzvyMK/Y79XDf7TEfEexExuJhUz+tfJv06YBpgM6rd0EpSvebY4gua/JKiFvevCnEvTHphOIp0Lb4stLmXlHTvBuxT5zj7FZP/Of7ar0b6FvarJc7vKHcQEe9ERDO/TjEzMzMzMzOzdvKIdes0qq5BPRr4WWEU5gqkxO2DEfFCxf6XkEaGriFJ5WRlUUSEpPOAE0kJuhNyHLUk42eMS1gBrAh8F3gsIh6d2PlExHBJtwAb5/I0D+b+l8vn8VBpROr6eXlZOaneAdYhJetujIjXO6C/WhmYS0rrLyGNZN+Z/DKkwn/q3JdaUvDKYtmMUt9b5/1OKG0bHhEvFldExDeSXiElr6vKnNSeqbnLG/Lo682BPqR7XvulwHTArJJmLZf3aYPa/T63nf0U/b2VbcsCy1Ssv4BUsmQP/p+9O4+3a7r/P/56m4KYpxIlMaQq0pYOoQhBQw0lqFk1xlZrqm9R/VYN3/5Uaaql1JASU0xFSigxxazm4UbNTTWJlKghghjy+f2x1km2k7PPPefeG/cm9/18PM5jO3uvtfbn7LNvPB6fvc5nwbDC/sps9fOq2m+ct1dV7Scixkj6L+kXArW8WutvR9KqpFnfkyNitkQwqdzTW8BahZn1bbqn86zwQaTSKQuTyrhU7oG+Jd2ub2DoK6p3RMTNkt4EvlCIu3L9bip5wHAJcCSfTpJXfET6ZUa15/O2eC9XHiqcLOlj0kz9D+hA/fv3X6fW/jyTvV9HnsvMzMzMzMxsbufEus1JlaTgDNJszqeBa6tmIVcWjxxfa4CIeEvS26TyGksArc34vRD4P+AASb/JCd8hpGTspVXnriyg+hKNOxfYmpSkfDDvm628RjvGb1SHjS1pFWBTUqJzdNXhG0jXfJCkz5ckPF8pGbrud1vY36vGsYklfabVOV459qlFHyXtQUomL1YyJqSZ8+1NrHf4912vFI3SQquzJdYjYpKk0aT68QMj4p68EObepLIjI6u69CL9auPfJad6hfLEepu++/wQ7F+khxy9SL9EaOr6KS2sei2pZnuZxUv2l8Vd8WZETC059i9gaWbF3Z77/NWSh06VX+7MvJcj4nZJp5NK7NwAfJjLFo0B/lxS8sbMzMzMzMzM5hCXgrE5JiKG5td+EXFERPy5KrH9qeaNDNnAOV8nJdvWIM1ihRqLlrbh3BWjgQnAbpIWl7QIqb76VFLN8PaO36yOGHtv0ixfAbdLurfyIi3cCOnfij1L+rc2a7a1GGsdb0uf2UjqTVoctgcpIdkXWDQiFBEi1QeH9Nk7ypz8vht1Tt5W7v2dScnxK6O8Vn5btPe7r9Wm0ev3G1JS/W7S3/pywAL5e90qtyn7Xtsz07tszLK4o87xpu6ViDiStO7DMaSZ7uuQ1gp4TtKQZsYyMzMzMzMzs/ZxYt0626S8Xa3WwTwrdUnSbOSyGaTVKknFAyX1IZXoeD4i7q5qV5mhu2ajwRZqWPcE9gB2zfFdXqwP39bxm9CRY++dt0sCG9V4LZmPf2/2rnXV/W5JCzcCvFpyvCNsQ6o3fkZE/CEiXoyI9wvHV+/Ac83J77tZY4CXgV0kLUX5ryogXX8xa8Z4tVXbcP7WvvviuJXvv9nrtyOpFv/2EXFXrrlemf3d3u91aUlls92r427ts/apat8uEfFcRJwaEVuRHib8lHSPd2QJIjMzMzMzMzNrhRPr1tkeJS1WOCAvcFqtkvS9t1599aKIuAt4llS/+2hS0rBWQvERUvmTr0r6WhMxDycvrkn9hOVtebuXpEWbGL8RY3MM20haua2D5M/dD/gPebZv9Yv078QEoL+kWjW9y1QWF91N0vw1ju9d1W5OWDpvZytzImkT4HM1+nyYt82Wyqp83wc12a/D5b+V80nrFxxPKvUzLiIeqNH83rzdpfqApMGUl4Gpd/5XSOVWVpQ0W6kWSduSvpvn8q9MoPl7emlgasmCsLs2G3MNu1XvkLRVPu8LhQVZK9dv2/wQo9ocu88j4oOIGEZK2q8gaYXW+piZmZmZmZlZx3Bi3TpVXtTzAtK9eJaknpVjkr5AKnMAcGaTQ59LKv9xMGmBwNkWgYyID4HTSYn3P+da4zNJWkbSRjX6TSSVhPk6aUb3k7UWcIyIh4A7gRWBc6uT65LWlPTFJj9XZexJwMWkxOkISZ9KfkrqJemrDQxVmYVetsBoJUl7VVX7Rowl1dVfDThJ0swSGrlsxU6kWtIjmhizWZVFIPeuurdWZtYvG6pNId0za5Q8ECgzPPf9jqRDip83n3Ng/gXGZ+UC0kOCIyh/uASzZjofKWntys58T53ajvNX/mZPl7R8YdwVgdOq2rTlnn4eWErSblXtfkJaCLW9fpl/8VIZdzlmXY+zC3G/DNxIquf+B0kLFvp8k/Rv0CfFPm0haYikDWrsX4/0gGgqUFZqy8zMzMzMzMw6mBPr1hUcS5q5Phh4WdJVkm4EniQlpc+IiBubHPMiZtVRHlWYFVvtZGAUaRHIFyTdKulySfeRFsg8sKRfsezCeXXi+B4pAbg38IqkUZKulvR43j9boqwJh5NqhH8LGC/pRklXSvo7aYHF7et1lrQAsHt+e3kr57oib/eQ1NC/GzkhvxfwBvBzYJykkbl2+3WkRW33i4jJjYzXRtcD40gPQV6U9Je8sOfzpCQveIAoAAAgAElEQVTk/TXi/hC4mXTvPSnpYknDJe1b70QR8V/STOmppITxC/n7uEHSy6Ra4EvXG6Mj5RnVo/Lb6cAlJe3uBX5PKivyuKTRkq4GXshNHqzVrwGnA38Dvky6FtdKuo507dfOsf2pqk8z9/Sv8/YKSXfne2sc8Nt87vZ4hfTvzzhJ10u6hnQ9vkx6WPbHqvY/AP4J7JM/6+WSbiPNUu8JHB0RT7UzpkHAA5Im5HvqMkl3Ag+R/l9+XER81M5zmJmZmZmZmVmDnFi3ThcRU0mlKo4nzfjdHhhIKtWyZ0Qc3oYx3wQez2/LZuoSER+TFnbcD3gYGAAMAVYCrqG8bvFdpFmo7wOX1Rl/IvAN4ARSuYYtSQsrLkRKZt7R4EeqNfZU0szcnwDPka7hdsBSpNnKV7cyxJakma7jI6Ju8jQiHgZeBHoBWzQR49PAV0nfwWLAd4G1SEnVjSKitRjbJSfJB5ISuB+Qrs/apMT3YNLM9FoOICWilyUt2ro/6fq2dr47gXVJD1sWIN1LGwCvkR4gzcmHCLVUFp+9Jif+yxxJmln9Ium6bET6lcJmpKR80/IvILYnJctfJt33W5Lu1R8D342IGVV9Gr6nI+IyYFtS4n9dYGtSvfPNSQ9U2iNI9+rvgS/lGN4G/h+wbf53oxh35e98GPAx6dcYXyNd/60i4nftjAfSLzuGkT7jANK/W6sBNwGbRcQfOuAcZmZmZmZmZtYgNVi22myuIunzpBmurwCrN1qfvYnx9yQl1C+KiKEdObZZR5E0hpQo3ywixnZyODaXamlpGdejR49+ffvWWgbDzMzMzMzMbI5Q6006l2es27zqWHLd9jmQVF+QtCgqwFkdObZZR5E0gFRSZZyT6mZmZmZmZmZmHWuBzg7ArKNIWgs4ClidVE5iArPXcG7P+NuTSnsMANYBrsslUsy6DEmnAKuSyqSIVN/ezMzMzMzMzMw6kBPrNi9ZiVQL+31SDfRDI2JaB47/VWBf0qKXlwGHdODYZh1ld2AVYDxp0cz21hs3MzMzMzMzM7MqrrFuZmZmpVxj3czMzMzMzDpBl6+x7hnrZmZmVteE16Zx/CVjOzuMLmfkSYM6OwQzMzMzMzPrJF681KybkzRY0ihJkyV9KOkNSc9IukzSgZIW6uwY51WSBkkKSSOa6DM+9+lTp83QZsdtC0kj8nkGzcnzdBWVa9/ZcZiZmZmZmZlZ53Ni3awbk3QiMAbYAXgduAG4HfgI2AM4D1imnedoOnncWbpbotjMzMzMzMzMzNrGpWDMuilJXwd+CXwI7BgRN1UdXxk4EJjeCeGZmZmZmZmZmZl1WU6sm3VfO+btVdVJdYCImAic8JlGZGZmZmZmZmZmNhdwKRiz7mv5vH29mU6SBkr6o6SnJL0p6X1Jz0o6RdJSVW1HAHfmt9/PZVYqrxNym7qlYsrKs+R94yUtJOmXOYbpkkbl4wtL2l/SXyW9nON8S9LdknavcZ4Avp/f3lkVa5+qtt+RdEuuR/+BpOcl/Z+kxUo+Qx9Jl+f270q6X9K29a5zR5N0Vv4sB5Ycl6SXJH0iqXfVsZ0lPZSv4X8kXSypV51z1f1uCu2+J+leSe9Iei/fU8dKWrhOjHtJur1w7V+WNFLSRlXt9pB0Rf5upkmamj/DjyTN9v8+SSfkuIdKGiBpdD5HSFq3RhyHK61F8IGkiZLOqL7/C+0XlXScpJZ8Dd8uuw9z+5m13CUdkK/L+0rrIJxb6zySeko6RtIT+T5/N3+fV0vaqtZ5zMzMzMzMzKztPGPdrPuakLc7S/p1RDSaYD8NWBdoAe4AegBfBY4BtpO0QUS8m9veC6wIbAW8lN9XPNHO+CE9HBwFbALcBTwFvJGP9QGGA/8BngUeyrFsCAyU9MWIOKEw1kXAxsAawC3A5MKxyudB0jDgSOCDPOYU4GvAL4CtJW0aEdMK7dcA7gdWAJ4HHgNWI9WzP6edn78Z5wA/IpX3Ob/G8c2B1YGbI+JflZ2SDgHOBD4hXeMpwLeAB4En65yv3neDpHOBg0jX8Q7gPWAQcDLwHUlbRMT7hfbzA1cA3yWVJ7o3x7Iq6dcXHwL35eY9gJHAm8AzpGu+HPBN4CxgADC0JO5NSGsLPE9af6AXMKOqzZk59rHA08CmwKHAppI2joiphbgXJz1c+hrpIdZooCfpeg/Mfy9H1ApE0qnA4cDDwM2ke/cgYO18n1WS7/PnWDck/V2Pzdfj88B2wDTSPW1mZmZmZmZmHcSJdbPu6zLgWFJi8sU8m/ge4AHgmUrSroaTgAci4s3KDkk9gDNISb8jcxsiYrikF0mJ9XsjYmgHf4ZVSEnWtXLpmqLX83lvi4iZiVFJq5ESucdJGhER43OsQ5Vmza8BnBIRY6tPJmlX0ud7HNip0lfSgsAfSZ//BOCoQrezSUn1s4FDK7FIOoDaCe45IiKelnQ/sKGkr0REdVK8MpN9ZkxKM/V/S7rG365cE0mLkpLm29U5Zel3I2ln0rWaCAyKiBfz/iWAG0kPOE4Eji50O5aUVH8a+E5V8n8ZYO1C24+BnYHREfFhod3ywE2kX09cEBF314h7X+CYiDi1zmf7HvDNiHg0j7sY8FdSsvxE0j1ScTIpqX4baS2Dd3OfL5IeOBwuaUytckzA3sD6EfFE7rMc6e9zILAZ6T4mv98wx7BT1f2+JLBmnc8yU0tLy7iSQ2s00t/MzMzMzMysO3EpGLNuKiJeAnYAJgFLAPuQkqotwGRJp9YqORERNxWT6nnfdOAIUkJzhzkde5VjayTViYg3ImJMMcmY9/8T+H+kf/++0+S5fp63e1SS6nnMj0gziycDB1RKjeTZ6luSZk4fXYwlIoaTZrK31T/16XI1M1/AhSV9zs3bA4o7JS0LDCHN7r+hcGg/0uzvi4sPGiLiPdIM7bKHLxU1vxvgsLz9ZSWpnsd9hzSrPoAfSloox7cQ8D95/37FpHru99+IuK/w/uOIuLaYVM/7Xycl6KH8Pm0h/Sqjnj9Wkup53HeBQ3J8++cHTUjqCexPmvH+o8IvOYiIZ4FfVV2PasdVkuq5zxTgT/ntJoV2K+Tt2Br3+9vFWM3MzMzMzMysY3jGulk3FhFjJK0ObA8MBtYH+pMSdUcBO0rasLpMjKSVSUnpL5KS8pWHdB8CfT+j8CElMm+o10DSxqQSIysDCwMCVsqHG45V0grAV4B/RMRzswUS8YGkR0izuPsCzwGVut83FcvDFFxBmmncFtdQKFFTZc3CuYuuAk4H9pZ0dKHUyj6kBPqI/JCgYuNCv0+JiOckPU4qA1RLze8mz+7fIB8fWWPcpyU9RbrWXyGVQfk6sBTwaEQ8UnK+2eTa6FsCvYFFSd/94vlw2Xd/Q51fa1RcUSPuf0h6klQm6cs57q8BiwAPRsQLNca5hPRLj40kqcZ5x9To83zerlTY9wQpeX+UpMnAjcVyNI3q37//OrX255ns/Zodz8zMzMzMzGxe5sS6WTeXZ5tfnV+VchlDSSVN1iSVspi54KWkI4FfAwt9xqHW8lqOfza5BMa1pPIcZRavc6xaZUHPtSsLS9axHCmxXlng85WSdmX7G/HT4qz5IklDqZFYz8n/i0m/LvguKbELaQZ7AH+u6tJI/GWJ9bLvZlnSvTM5Ij4o6TuelFSvnH+VvH2ppP2n5BnuI4A96jQr++4b+U7+VbJ/PCmxXom7V2H/bCLiLUlvA0uSHlC9XdVkwuy9Zj5M6VEY53lJRwGnAJcDn0hqIZWfuTAiykq8mJmZmZmZmVkbuRSMmX1KRLweEaeRFiMF2LZyTNIGwDDgfVLyvQ+wcEQoIgS8OgdCqvfvVFliFuA3pKT63aQZ68sBC+Q4t8pt1EQc8+ftq6SFTuu9Kot0VsZvLRH/WaosmHoAgKQNSbORx9aYVd2e+Ot9N42OWd2m0TiOJCXVW4Ctgc8BC+Xvfq3cpuy7by3uesrGbMtnpYGZ88W2vyPVQj+MVEe+N6l8zlOSftzoOGZmZmZmZmbWGM9YN7MyY/N2ucK+HfP2FxFxUbGxpEWAFdtwnkod7MVKjq9Ssr81OwKfANtHRPVM4NXbMF5l9vDkJhZhnZS3vUuOr9qGONoll3AZCwyStBY1Fi0tmAR8gRR/rVImbYn/DdJ3vqKkRQrlaIoq16vyoObfedvQIpzMuk/3iIiWqmNt+e6r9SYtolqtcj0mVW1XqzVI/lXFksA0oOnSLdUi4t/AmcCZkhYAdifV2/+dpMsi4q32nsPMzMzMzMzMEs9YN+umJLU2W3uNvJ1U2Ld03v6b2e1C7Rm7lcR52YO8SvL0CzViXJbyUiOtWRqYWiOpDrBrSZ/SWCNiAqm8y5cl1UyU1lBZUHObvJBltd0bHKejVRYxPZJ0Lf5LKptT7d683aX6gKQvkMqeNCXXcH+QdK/MVqpFUn9SGZipwJN59yPAW8BXJX2tgdPUu0/Lvvtm7Fa9Q9IXSddjKvBU3v0o6dcdAyTVqum+d97e28zs9EbkBVwvJdV6X4gaf19mZmZmZmZm1nZOrJt1X/8n6dRaSeKcBByW3xYTrpWFE/fPi1BW2vcjlV6ppZKYX6vWwYj4J6mu9Zck7VAYsydpFvUSDXyWWp4HlpL0qSSopJ8Am7UlVuBXpJIw1+QE8KdIWkPSfpX3EfEicDsp0XuKpPkKbfel7QuXtte1wOvAQaRFPS8uqYd+Ielhwz6SBlZ25l8n/IG2/z/kzLw9MS+eWxl3ceCPpKT7uRHxIUDenp73/1nSp37FIGkZScWa8pX79IdV7b5LWqi1vQ6RtF5h3J75Mwm4oHIt84K1F5Cu01nFhyv5wcQv8tvK9WgTSZtJ+lbx/sr7ewNrk8rM1KrXbmZmZmZmZmZt5MS6Wfe1GHAU8JKkf0i6VtKVkh4AniXNWH8UOLHQ50JgMvAd4Lnc/lbgCeAeaizqmBfYfAr4uqSHJF0oabik7QvNTsjbayTdIel60kKV/YHr2/j5fp23V0i6W9JISeOA35KStLXcQEpCDpM0Ksc5PM+cJ88APhVYD3hC0sOSrpJ0s6R/AC+SalwXHUxKYh8CPJPjeIC0UOg5dIKcqL6wsGt4SbuXSbX2FwbulHSbpCtIn7M/MLqN5/8LcB7weaBF0mhJV5G+801JM9qPr+p2MjCKNJv9BUm3Srpc0n3ARAoL7JK+o09IDzMeydf8YdICvWXffTMuBf6ev/crSdfjW8C4GnEfS/o7Ggy8nO+XG0mz8VcEzoiIG9sZz1eAW4HJkv4m6VJJt5D+jpcC/hARk+qOYGZmZmZmZmZNcWLdrPv6FWn27kjgY1JCcydSHeu7gB8DGxZLqUTEG8A3cp+FgO2BlYFfUqOsR8HOpKTo6vmc+1Mo8RIRFwL7Av8ANgIGkJLc3wTebMuHi4jLSAuvPkgq0bE1aUb65pQk6yPiUVJ5jnHAljnO/YHFC22OAbbIY3weGEJKtL8HnAbsVzXmC8AGwFXACsAOpJnNQ4Ar2/LZOsjteXt/RIwraxQRvyeVT3kC2Jj02ceSPtMbZf1aExE/IN0Lj5Puve8ArwH/C2weEe9Vtf+YdB/tRypvMoB0DVcCrmFWeRsi4u4c6x2ke2470sz7nYGz2hpzwaGkhHlv0vcZedyB1aWHImJq/nzHA1NIfzMDSeVt9oyIwzsgntGkv+fnSUn2XYB1SA+7doyIn3TAOczMzMzMzMysQB1c1tXMzOYCks4jzfLeNyJGdHI41oW1tLSM69GjR7++fWuViTczMzMzMzObI1pbG7DTeca6mVk3k2tv702aQd2Zs+bNzMzMzMzMzOZKC3R2AGZm9tmQdBTwZVK970WAn0XE+50blZmZmZmZmZnZ3MeJdTOz7mNbUr3viaSa32d2bjhmZmZmZmZmZnMnJ9bNzLqJiBjU2TGYmZmZmZmZmc0LnFg3MzOzuia8No3jLxnb2WE0beRJgzo7BDMzMzMzM5tHefFSMzPrViStLyny69hOiqFPPv/Yzji/mZmZmZmZmbWPE+tmZtbdfK/kv83MzMzMzMzMGuLEupmZdRuSFgR2AwKYDKwt6audG5WZmZmZmZmZzW2cWDczs+5ka2A54G7gvLzPs9bNzMzMzMzMrClOrJuZWXdSSaJfml8Ae0iav9hI0ohCHfay16CqPv0kXSbpVUkfSpoo6WJJazUbpKSNJV0n6TVJ0yWNl3SGpOVrtF1Q0g8kPSRpiqT3cvvRknZv9txmZmZmZmZm1roFOjsAMzOzz4KkJYHtgOnAXyLiLUkPAQOAwcDNheb3lgwzP7BH3n5SGHsL4AZgEeAxYCzwRVIif0dJ20TEPQ3GeRjwe2AG8BAwEegPHApsJ2mjiHi10OUSUnmbKcD9wHvAysBAYDHgikbOa2ZmZmZmZmaNc2LdzMy6i12BhYFrIuKtvO9SUmJ9bwqJ9YgYDgyvHkDSH0hJ9dHAfXlfT+AyUlL94Ig4p9D+J8DvgJGS1oyI6fUClLQBcDrwCrB9RDyV9wv4BXAScAawS97fh5RUfxjYJCI+KIy1CLBu65fFzMzMzMzMzJrlxLqZmXUXxTIwFVeQEt87SlosIt4t6yxpf+Aw4Blgz4iYkQ/tCnwOuKeYVAeIiNMl7QV8DdiR1meP/4xUpu2gSlI9jxOSfpXH2EnSchExBVghN7m/mFTPfd4HHmjlfDO1tLSMKzm0RqNjmJmZmZmZmXUXrrFuZmbzvDyze2Pgv8BNlf0R8TpwC7AoKWld1n8gcDbwBvCdiJhaODwwby8r6X5pVbuyc8wHbAFMBW6vPh4RQZolPx8pUQ/wLDAN2FfSgZKWrXcOMzMzMzMzM+sYnrFuZmbdwd6AgKsi4sOqY5cC25JmtF9S3VFSb+AaUkJ7l4h4uapJr7wdX3Luyv5eJccrliXVRAf4OFV/KbUcQES8I+lA4Lz8OlfSc8CdwMUR8WAr55ypf//+69Tan2ey92t0HDMzMzMzM7PuwIl1MzPrDvbO2y0kVS9M2qNwbKXiwqC5fvr1wPLAjyLizjrniFZiaO34/Hk7Fbi2lbb/mjloxOWSbgN2ALYENgUOBg6WdFpEHN3KWGZmZmZmZmbWJCfWzcxsniZpALBWfts3v2qZD9gTGJb7iTSD/cvAnyLiTyX9JuXtaiXHe+ftqyXHK6YA04GPImJoK20/JZe0GQ4Mz3FvBVwJHCVpREQ808x4ZmZmZmZmZlafa6ybmdm8rrJo6WkRoVov0kxvmDWzHeAkUt31O0mLlpa5J2/3Kjm+V1W7miLiY2AssIykTeq1bWWciIibgRvzrv5tHcvMzMzMzMzManNi3czM5lmSFgB2y28vr9P0DuA1YF1J/SXtCvwCeJlUV/3jOn2vAv4DDJR0UNX5DwO+AUwArmsg5JOBGcBFkjau8Xl6Sfpx4f16knaStGBVu6WB9fPbVxo4r5mZmZmZmZk1waVgzMxsXrY1qT76cxHxeFmjiPhE0l+AH5Fmre+SD00ChpUsJHpKRDwbEdMk7QXcQFo89CDgeeCLwHrANGDPiJjeWrARcbekw4HfA/dIegp4AViYVFJmbeBd4KzcpbKw6tuSHgEmA0sBA4ElgOuaWcDUzMzMzMzMzBrjxLqZmc3LKmVgrmig7eWkxPpezFpodOP8qmUE8CxARNwu6RvA/wKbk+qyTwEuBX4VEc81GnBE/FHSA8BPgE2A7UkLmk4AzgGuLjR/kDSzfnNSHfmBwJvAU8D5wMhGz2tmZmZmZmZmjVNEtN7KzMzMuqWWlpZxPXr06Ne3b9mar2ZmZmZmZmYdruZPx7sS11g3MzMzMzMzMzMzM2uCE+tmZmZmZmZmZmZmZk1wYt3MzMzMzMzMzMzMrAlOrJuZmZmZmZmZmZmZNWGBzg7AzMzMurYJr03j+EvGdnYYTRt50qDODsHMzMzMzMzmUZ6xbmZm8yRJUfWaIektSfdIOkBSl19h3MzMzMzMzMy6Js9YNzOzed1FeTs/sAawEbAxsAWwR2cFZWZmZmZmZmZzLyfWzcxsnhYRQ4vvJQ0GbgJ2l3RZRIzulMDMzMzMzMzMbK7lUjBmZtatRMStwCX57ZDOjMXMzMzMzMzM5k5OrJuZWXf0eN6uAiDpxlyHfXCtxpJ6SnpH0tuSeuZ9g3KfEZJWlDRc0gRJH0s6IrdZSdLRku6SNFHSh5ImS7pW0jdKzjVeUuT/PkDSU5Lez/3OlbRUSXzHSHoi15F/V9JLkq6WtFUHXC8zMzMzMzMzK3Bi3czMuqPF83Z63p6TtweWtN899xkZEdOqji0PPAxsCzwA/A14Lx/bAfgN0At4GhgFTAJ2BO6TtGVZgJJOBc4C3gFuBgQcBFxfXHhV0vzAGOAUYFlgLKnUzX+A7XAdeTMzMzMzM7MO5xrrZmbWreSk9Hb57VN5exPwb2AHSctHxOtV3SoJ9/NrDLkNcB2wZ0R8UHXsPuArEfFUcWeeRX49cLakvhERNcbdG1g/Ip7IfZYjJe4HApsBd+R2A4ENgb8CO0XEjMJ5lgTWrDG2mZmZmZmZmbWDE+tmZtYt5JndqwM/B75Jmq1+IUBEfCJpOHAisA8wrNCvP7A+8HhEPFZj6OnAoTWS6kTE07ViiYhbJF0N7AX0J81mr3ZcJame+0yR9Kcc2ybMSqyvkLdji0n13Odt4NFaMVRraWkZV3JojUb6m5mZmZmZmXUnTqybmdk8rVKvvMpU4PsR8VJh33DgOOAACol1Zs1WP6/kFI9FxMQ65+8BfBsYQCobs1A+9KW87UvtxPqYGvuez9uVCvueAGYAR0maDNwYEVPL4jEzMzMzMzOz9nNi3czM5nUX5e0MUr3yp4FrI+LNYqOImCRpNDBE0sCIuCcnxfcm1UwfWTL+K2UnlvQlUsmXPnXiW7xk/4Qa+97N2x6FuJ+XdBSpxvrlwCeSWoDbgAsjomwm+qf0799/nVr780z2fo2MYWZmZmZmZtZdePFSMzObp0XE0PzaLyKOiIg/VyfVCyqLmB6QtzsDywBXRsQ7JX1mKwEDM2u5X0VKqp8DrAssAcwXEQJ+XWlaEnetmfY1RcTvSCVbDiPVi+8N/A/wlKQfNzqOmZmZmZmZmTXGiXUzM7NZxgAvA7tIWor6i5a25ov59UhEHBwRT0bE1ELCfPX2hztLRPw7Is6MiO1JJWe+R5ql/7v8WczMzMzMzMysgzixbmZmluWk9/nAIsDxwKbAuIh4oA3DLZ23s5V0kbQ0MLitcbYmIj6OiEuBh0k13b8wp85lZmZmZmZm1h05sW5mZvZpFwAfAkeQyrS0ZbY6wIukGeObS+pb2SlpYVJpmGXaGWdlvM0kfUvSfFX7ewNrA0Hteu1mZmZmZmZm1kZOrJuZmRVExGvAqPx2OnBJO8b5M6mu+pOSRku6GhgPbA6MaHewyVeAW4HJkv4m6VJJtwDPAksBf4iISR10LjMzMzMzMzMDFujsAMzMzLqg24FdgWsi4r/tGOdgUoJ7f2AL4G3gNuB/gX3bG2Q2GlgW2IyUZF8WeB24Bzg7IkbV6WtmZmZmZmZmbaBZa6iZmZkZgKQxpBrom0XE2E4Op1O1tLSMe/3tj/udf8tbnR1K00aeNKizQzAzMzMzM7O2UWcH0Bon1s3MzAokDQAeBJ6JiP6dHU9na2lpGdejR49+ffv2bb2xmZmZmZmZWcfo8ol1l4IxMzMDJJ0CrApsS/of+M87NyIzMzMzMzMz66qcWDczM0t2B1YhLS56dERc37nhmJmZmZmZmVlX5cS6mZkZEBF9OjsGMzMzMzMzM5s7OLFuZmZmdU14bRrHXzK2s8NomhcvNTMzMzMzszllvs4OwMzMuh9JUfWaIektSfdIOkDSHF+kRNIJ+dxDm+gzKPcZ0d6xzMzMzMzMzGzu5RnrZmbWmS7K2/mBNYCNgI2BLYA9OisoMzMzMzMzM7N6nFg3M7NOExFDi+8lDQZuAnaXdFlEjJ6Dp/8jcAXwahcby8zMzMzMzMy6OJeCMTOzLiMibgUuyW+HzOFzTYmIZyPi7a40lpmZmZmZmZl1fU6sm5lZV/N43q4CIOnGXL98cK3GknpKekfS25J65n0za6FLWlHScEkTJH0s6YjcprQuuqQ+ki6X9IakdyXdL2nbsoDLxpI0Nu/vI2mIpAclTZP03zz+50vGk6TvS7o7155/X9JTkn4qacEa7ZeVdLKkcTnetyU9L+liSQPK4jYzMzMzMzOztnFi3czMuprF83Z63p6TtweWtN899xkZEdOqji0PPAxsCzwA/A14r97JJa0B/D2POwW4gfT/yxuAXRv+FJ/2I+AaQMDNwLt5/DskLVJ1/vmAK4ERwFeAR4Bb8mc5DRiV21TaLwY8CBwLLJjb3ga8TapTv00bYzYzMzMzMzOzEq6xbmZmXYYkAdvlt0/l7U3Av4EdJC0fEa9Xdask3M+vMeQ2wHXAnhHxQYNhnA2skLeHRsSMHNsBJedoxI+AwRFxRx5rUeBWYENS8vuCQtufArvk43tVPm+ejX858B3gYOCs3P67wJrAmRFxWPGkklbIn8XMzMzMzMzMOpAT62Zm1ukkzQ+sDvwc+CZptvqFABHxiaThwInAPsCwQr/+wPrA4xHxWI2hp5OS4w0l1fNs9S2BN4GjK0n1HMdwSfuSkuHNOr2SVM9jvSdpWB5rE3JiXdICwFHAVNLDgCmFPtMkHQj8C/gBsxLrlcT5zPELfV4DXmskwJaWlnElh9ZopL+ZmZmZmZlZd+JSMGZm1mly/fEAPgaeB4aSksp7RMRLhabDc5sDqoaozFY/r+QUj0XExCZC2ihvb6pRVgbgiibGKhpTY9/zebtSYd96wHLAvcWkekVE/Ad4AehfKCHzaN6eLGk7SQu3MUYzMzMzMzMza5BnrJuZWWe6KG9nAO8ATwPXRsSbxUYRMUnSaGCIpIERcY+kHsDepJrpI0vGf6XJeHq10q/Z8Som1Nj3bt72KOzrk7db5wcO9SwDTFjI4fEAACAASURBVIyI2yWdDhxBqgP/oaQnSMn8P0fE+EYC7N+//zq19ueZ7P0aGcPMzMzMzMysu3Bi3czMOk1EDG2i+TnAENKs9XuAnUnJ5Qsj4p2SPo3WVa9QJbQm+7Wm0fHmz9sXgPtbaVtZ3JWIOFLSucAOwBakmfcDgKMl7RYRo5qM18zMzMzMzMzqcGLdzMzmFmOAl4FdJB1O/UVL22pS3vYuOb5qB56rlsrM9pYmHzoQEc8BpwKn5nIwPwZ+C5wLOLFuZmZmZmZm1oFcY93MzOYKERGkJPoiwPHApsC4iHigA09zX95uI6lnjeO7d+C5ankYeBvYTNISbR0kIj6IiGHAq8AKklZorY+ZmZmZmZmZNc6JdTMzm5tcAHxIqicuOna2OhHxInA7sDRwiqSZ/5+UtC+wYUeer8b5p5NmmS8FXCNptpnzkr4sabfC+yGSNqjRbj3gc6TFYN+sPm5mZmZmZmZmbedSMGZmNteIiNckjQJ2JdUYv2QOnOZg0sz1Q4DBkh4DVgPWJ9V5/+EcOGfRyaTFQvcAnsvnfwVYDlg9x/JX4MrcfhBwuKSJwOOkRWB7ARuTHqAfFxEfzeGYzczMzMzMzLoVz1g3M7O5ze15e01E/LejB4+IF4ANgKuAFUgLgoq0cOqVdbp21PlnRMSewHeBO4G+wE6kZPt/gBOAYwpdRgDDSPXhB5AWdV0NuAnYLCL+MKdjNjMzMzMzM+tulErWmpmZzR0kjQEGk5LGYzs5nHleS0vLuNff/rjf+be81dmhNG3kSYM6OwQzMzMzMzNrG3V2AK1xYt3MzOYakgYADwLPRET/zo6nO2hpaRnXo0ePfn379u3sUMzMzMzMzKz76PKJdddYNzOzLk/SKcCqwLak/7n+vHMjMjMzMzMzM7PuzIl1MzObG+wOrAKMB46OiOs7NxwzMzMzMzMz686cWDczsy4vIvp0dgxmZmZmZmZmZhVOrJuZmVldE16bxvGXjO3sMAAvSGpmZmZmZmZdw3ydHYCZWUeQFJLm+GrMksbmc/Vpos8Juc/QORbYPERSn3y9xn4G5wpJ45vsU/MeaMtYZmZmZmZmZjZ3cmLdzMzMzMzMzMzMzKwJLgVjZtacfYBFgYmdHcg8bCKwNvDeZ3CutYGPuuBYZmZmZmZmZtaFObFuZtaEiHils2OY10XER8Czn9G5Ouw8HTmWmZmZmZmZmXVtLgVjZvOsYq1uST0l/U7SvyW9L+kxSd8ptN1F0kOSpkn6j6QzJC1SY8zSGuuSNs3H35X0hqTrJH2xTnzrSjpV0qOSXpc0XdLLks6W1KtG+/GVWvJlrxp9tpF0q6Q3JX0g6TlJp0haqkbbmbXgJa0v6RZJb0l6J4+xQVX7XXL7y+p8xgtzm71rfA5JOlTSk5Lek/REPl6zxnqOK3Kca0i6StKUHN/fJPXL7RaQ9HNJz+fP/KKkH5XEV1oXXdJBkp7OY0yUdKakJet81tnGkjQo7x8haRlJf5L0av6uWyTtV2e8PpLOzddrer5H/iLpyyXtt8rf2YTcfpKkeyUdX3YOMzMzMzMzM2sbz1g3s+5gIeB2YA3gQWAxYBPgOknfBr4EnAo8DIwBBgKHAssCezVyAkk7ANcA8wP3A68AA4C/AzeUdPsZ8F2gBbgPCGBd4GBgiKSvR8SkQvu/AMvVGGdFYCtgRlVMxwInAx8DdwFTgI2AY4AdJW0SEf+pMd6GwLnAi8DfgDWBbwGbSNouIm7N7UYBk4GdJR0aEf+tOv8SwC7AWzn2aucA++bY/kH6nhqxGvBQHvcuoC/wbeBrOel8DrA58ADwMrAZcJakjyLi/EZOIOm3wP8A04E7SGVp9iJdv+kNxlm0VI5nyRx75R78s6T5ImJ41fk3Bm4ElgDGAdcDKwM7AdtI2jYi7iy0/yHwpxzbPcDdwPKk8jQnACe2IWYzMzMzMzMzK+HEupl1B98ExgJfiIg3Ic1+Bi4kJSOXATaPiHvysV7A48Ceko6LiJfrDS5pcWA4Kam+Z0RcnvcvkPd/v6TrecBPIuLVwljzAb8gJUJ/Bcyc0RwRP61x7oXzZ4OUqK/s/0buPxX4VkQ8lPf3AC4hJbzPBHatEdeBpIT8LyIicr+DgbOBEZLWiIgPIuIjSRcAPwe+B/yhapw9gZ7AmRHxQY3z7ASsFxHjahyrZx/gd8BRETFDkoALgKGkBygzgH4RMSHHvgVwG/C/QKuJdUkbkpLq/wU2qcQnaVlSkn2DOt3LVB68fD8ipuXxdiA9nDiOdJ9Uzr8EcDWwCLBLRPylcOxbpIT7JZJWj4gP86GfAe8AX4mI8YX2Aga1IV4zMzMzMzMzq8OlYMysO/gEOLCSVM8uBl4nzcb+YyWpDpBniVfKm2zSwPi7kGaS31pJqudxPgZ+Arxbq1NE3FFMqud9MyLiJNICnjs0cO7zgfWBSyLitML+Q0j/xv++klTP40/Px94nzTRfucaY/wKOryTVc78/kWbf9wJ2rDr/DOCAGuNU9g2vcQzgN21IqgO8BBwTETNybEFKtAP0Aw6rJNXz8dtJD0p6q0YJnxp+mLfDivFFxBvAUW2IF1LS+6BKUj2P91fgaWDVqrj2I/0K4bfFpHrucxvpAcfKwHaFQysA/ywm1XP7KM5sr6elpWVcrRfplx5mZmZmZmZmVuDEupl1B+Mj4sXijpyU/Vd+e+vsXXgpb1dqYPyN8/aq6gM5mT+mrKOkZSXtK2mYpD/nWtwjgAWBZSQtU6fvMcDepIT3gVWHB+btbPXPI+K1HNN8pLIv1a7JDwWqVR4aVD4vOZF7C9C/WINd0nrA14C/R8RTJR/h+pL9rRlbI77Krwo+JJWHqdZR3+cY0kz2Zj1SXSone75GXIPzdlTJWPfm7TcK+x4FvqJUP9+JcDMzMzMzM7M5zKVgzKw7mFiyf1qd45VjPRoYv7LQ6Cslx2vul7QHqRzMYnXGXpwaiVxJ25HKtUwAhuSZ6NUxBbMeHlQbX2hXrdk+5wJbk5L7D+Z9lUR/vdIrZderNbN9XxExLVU9YXJlJnuVZr/PAP5dcvwVUvmgZkwo2V/5NUMxrj55+/f8mcoU6+3/mJSIPwY4RtIkUq31vwDXllyTT+nfv/86tfbnWev9WutvZmZmZmZm1p04sW5m3UG083hrKtnPhseR1BsYkfseQaqbPTEi3s/H7yfVhp8tsyqpHzCStFDlkIiY3I7Ym/nsZVne0aTE8W6SjiAtlronqb77laUnrl13vRH1Ym7vdzmnNBPX/Hl7NWnR1DJ/nzl4xFP5vvg2sA2wKbBbft0raYtCPXYzMzMzMzMzaycn1s3M2m9S3vYuOb5qjX3bAAuR6nhXL/oJsHqtgXJpmBtIM9l3j4hH68S0Wo7puRrHK7G+WudYtcrnmFTcGRGfSBoOnADsQUr4LwmcFxE168t3ca+SZo2vArxY43it77MjTQDWAn5Vp4zObPKDilH5VXkAczmptM3+pIV6zczMzMzMzKwDuMa6mVn7VWpe71J9QNJSwJY1+iydt7OVG5G0CfC5GvsXIJX2WJ2UdC2dDU4qAwKwV41xls8xzQDur9F3Z0nz19i/e97eV+PYcPIisTRWBqYrq/d9Dqb5MjDNui1vh7RnkIh4Bjgrv/1SuyIyMzMzMzMzs09xYt3MrP2uJtVB31LSrpWdOTk9jNo11CuLVu4tqWehz8rAOSXnOQPYjDQj+ZetxHQWKXF+uKSvF8ZfCDgTWJRUe7tWffnewPHFHZIOIpWmmQxcV90hjzMa+DqwEfBkRDzSSoxd1bl5e6SktSs7868FTv2Mzv868PO8sO2nSvBI6ilpH0mfz+8XlXRYfohTbDcfsx7qtLWevZmZmZmZmZnV4FIwZmbtFBHv5MTzVcCVkg4jJTIHAMsDlzH7zPHrgXGkRPSLku4DFiYlzp8gzSTfsNJY0irAwfntJ8CFtRa2jIihefuQpOOA/wc8IGksMIWU9F4FeAE4pOQjnQ/8TNJOwFPAmsA3gI+AfSt14Gs4F9gh//d5JW26vIi4V9LvSbXvH5d0G/A+sDnpe30Q2GAOnv9NSTuS7pELgOMltZBK7KwKrA30BNYjlY1ZCPgDcJqkx0iLzC5EurdWBV5m1sMCMzMzMzMzM+sAnrFuZtYBIuIaYDCpBMt6wNbAM6RZ3rPV6c4LSQ4k1b3+ANiOlDA9M4/zUVWXYmmWnYHvl7yK5zg5j3sXKTG+Eyk5eyqwfkT8p+Tj3E9a/HJyIa7bgUERcXOdy3AXKen/PulhwtzsSNKDjBdJ38dGpAcnm5Gu4RwVEfeRyrcMY1ZSf0tgCdIvA3Yj3V8A7wI/zvuXB7bP7d8EjgO+FhFvzumYzczMzMzMzLoTRURnx2BmZl2ApBNIJWD2jYgRbei/JymhflFl5rzN/VpaWsa9/vbH/c6/5a3ODgWAkScN6uwQzMzMzMzMbM6b/Wf6XYxLwZiZWbtJWhA4Or89q15bm/t8foWejDxp3c4Ow8zMzMzMzKzLcGLdzMzaTNL2wBBSPfl1gOsi4uHOjcrMzMzMzMzMbM5yjXUzM2uPrwL7Ar1IZWD269xwzMzMzMzMzMzmPNdYNzMzs1ItLS3jevTo0a9v376dHYqZmZmZmZl1H66xbmZmZnO3Ca9N4/hLxnZ2GIAXLzUzMzMzM7OuwaVgzMy6IUmDJY2SNFnSh5LekPSMpMskHShpoc6OsS0kjZAUkgY12L5Pbj9+Tozfylgn5LGGNtFnUO4zor3nNzMzMzMzM7O2c2LdzKybkXQiMAbYAXgduAG4HfgI2AM4D1imqk/DyWczMzMzMzMzs3mdS8GYmXUjkr4O/BL4ENgxIm6qOr4ycCAwvRPCmxscC5wCvNIBY/0RuAJ4tQPGMjMzMzMzM7PPkBPrZmbdy455e1V1Uh0gIiYCJ3ymEc1FIuJVOigRHhFTgCkdMZaZmZmZmZmZfbZcCsbMrHtZPm9fb6SxpKGSIr/tnUvCVF5jq9ouJumXkp6W9J6kdyTdJWlIjXErtc3HSlpC0jBJ/5T0kaTfF9otIOlQSY9Keje/HpJ0sKT523gN2qysxrqk8Xm/JB2e69V/IGmipDMkLVVjrNIa6/n6XJ5r378r6X5J29aJa0FJP8jXZkq+/uMljZa0e0d8djMzMzMzMzObxTPWzcy6lwl5u7OkX0dEawn2F4GLgO8D04C/FI49W/kPSZ8D7gD6AROBW4FFgW8C10k6NiJOqTH+IsBdQO+8fQx4M485P/BXYBvgHeC23Gdz4GxgsKTvRsSMBj73Z+VM4CBgLPA0sClwKLCppI0jYmprA0haA7gfWAF4nnRNViPVwj+npNslwG6kGfD3A+8BKwMDgcVIJWfMzMzMzMzMrIM4sW5m1r1cRqoTvirwoqRRwD3AA8AzERHFxhFxL3CvpO8DUyJiaMm4F5KS6qcCv4iIjwAkrU5aKPVXkm6KiKeq+g3I5149It6qOnYEKan+NPCtiHgtj7kScCeprM0PSUn2ruJ7wDcj4lFIs/hJDwc2B04EjmxgjLNJSfWzgUMrDw4kHQCcX91YUh9SUv1hYJOI+KBwbBFg3bZ/HDMzMzMzMzOrxYl1M7NuJCJekrQDKRHeC9gnvwBek3QRcHKNJHcpSesCW5NmSv+smJyPiJcl/Q8wCjgAOKzGEIeVnK/S9ohKUj2P+aqko4Drc5uulFj/YyWpDhAR70o6BBgH7J9n7pcuDJtnq29JmrV/dHE2fkQMl7QvsGFVtxXy9v5iUj33eZ/04KJVLS0t40oOrdFIfzMzMzMzM7PuxDXWzcy6mYgYA6wO7EqaAf0UMIOUoD0KeFjS8uUjzGZw3v61esZ7dm/efqPGsVcj4pHqnZJWJc2qnxwRd9ToNxp4C1iryVjntNlKrkTEP4AngSWAL7fSf6O8vSkipjUyPqkkzzRgX0kHSlq2iXjNzMzMzMzMrA08Y93MrBvKs6avzi9ycnoocAKwJnAycGCDw/XJ299I+k2ddsvV2PdKSdteeTu+1sGICEn/ApbKbRtajPUz8K+S/eNJJVl6lRyvqBwvuy6z7Y+IdyQdCJyXX+dKeo5ULufiiHiwtaAB+vfvv06t/Xkme79GxjAzMzMzMzPrLpxYNzMz8iKmp0l6n7QA57ZNdJ8/b+8BXq7TbkqNfR/U2Pep0Bo4fyNtOpuabNfUZ4qIyyXdBuxAKiWzKXAwcLCk0yLi6GbGMzMzMzMzM7P6nFg3M7OisXlba3Z5mQl5+5eIOKOD4piUt6vVabNq3r7aQefsCL1Ji61Wq8Q6qcaxosrx3iXHVy3ZX3k4MhwYLknAVsCVwFGSRkTEM62c28zMzMzMzMwa5BrrZmbdSE641lNZqLI6AfwR5Q9jb8vbIW2Nq1pEvEIqe7KipM2rj0vaFlgaeC4nlLuK3ap3SPoiqQzMVFI9+3ruy9ttJPWscXz3RoKI5GbgxryrfyP9zMzMzMzMzKwxTqybmXUv/yfpVEmzzQSX1BcYlt9eW3V4EvA5SUtV98s1vG8HNpN0uqTFqsadT9KWkjZuMtYz8/b04gKlklYETqtq01UcImm9ypucHD+TVOLlglzbvlREvEi6lksDp0ia+f9pSfsCG1b3kbSepJ0kLVi1f2lg/fy2rGa7mZmZmZmZmbWBS8GYmXUviwGHAz/NC1z+gzQbfVVgAOmB66PAiVX9rgcOBR6TdD+pNvpzEVFJcO8FjAGOAPaR9ARpQdGVgbWA5YGfAPc2EevpwObA1sALku4gJai3ABYHRgF/amK8elaSVG+Rz6Mj4u4GxrkU+HuO9W1gE/j/7N15vF3T+cfxz9eQIKgqfmYxpIbGLOYhpqqxaqqppDVTiqqhrRqqxvrR9qellBBDi5rnMWaNmaRmgoiYBUFieH5/rLWTnZN9zj3n3pvcK77v1+u+trv3Wms/ezgnL89e99nMDQwDjmoyln1IM9d/Dmwo6VFSSZxVgDOBvWvaLwT8Gxgt6WFgFOmlrmsBswJXNvsCUzMzMzMzMzNrjhPrZmbfLMeREucbAcuSXnI5K/ABcBdwOXBORIyr6XcEKan9Q1K5k+ly+1MAIuJNSauSkr4/BvoBPUj1zx8DrgYubSXQiPhS0hbAvsCAHDPAf4HzgLMi4qtWxmygBxNmd1eZvclx9gdeBnYnJcPfA84AjoyI0c0MEBHP53N5ArAh6Zw/RSq18yGTJtYfBH5LegixOCmh/j6p7MzZwMVNxm5mZmZmZmZmTVJEdHUMZmZmX2uShgMLRURbNey/doYOHTrs7dFfLHX2zR90dSgAXHxs/64OwczMzMzMzCa/bv//156xbmZmZg3NP1cvLj52ua4Ow8zMzMzMzKzb8MtLzczMzMzMzMzMzMxa4MS6mZmZmZmZmZmZmVkLXArGzMysgyKid1fHYGZmZmZmZmZTjhPrZmZm1tCIt8Zw1KDBXR0G4JeXmpmZmZmZWffgUjBmZlM5STNJ2l/SLZLekDRO0keS/ivpPEmbS5qmpk9IGt5FIXcpSf3z8Q+sWT8grz96cu2jiX7f2OtiZmZmZmZm1p14xrqZ2VRM0mrA5cC8wGfAEGAkMAPQBxiQf54Elu2SIM3MzMzMzMzMvmacWDczm0pJWg64A+gJnAgcHxEf1bTpDRwM/HRKx/c1dCXwIPBOJ4w1BFgSGN0JY5mZmZmZmZnZFObEupnZVEiSgAtJM9OPiIgTq9pFxHDgAEnnT8HwvpYiYjSdlAiPiE+AZzpjLDMzMzMzMzOb8lxj3cxs6rQJ8D3gFeDkthpHxCONtkvaNtf3vqhBm/Nym51r1veSdISkR3Nt949zfffTJS1UMc7mkm6W9K6kzyQ9J+n3kmauaDs477O3pB0lPZj38UFNu9UkXS3pbUljJQ2X9FdJ87Z1bkpjVNZYlzQwr+8vaWNJ9+ZjfF/SFZKWqBirbo31fL5OkvRqPv5nJB2cH5bUi22jfM5G5OMbmeM4qtnjMzMzMzMzM7PmObFuZjZ12jgvL4+IrzphvKuAUcDWkmav3ShpVmBb4ANSTfdi/TyksifHAwuRStPcBIwDDgDWrRnnVOAaYG1gKHA90AP4LTBYUq868R0BDMrjXpf7FmPuDNwDbA48C1wBjAX2AR6tSny307aleK8l1bL/EfCgpKbq10vqCdwCHArMmMcZTirl8391+uxNOqfrAE8D/waGAb2Bo9t5LGZmZmZmZmbWgEvBmJlNnYpE7mOdMVhEfC7pXODXwE+AP9U02RHoBfwlIj4rrR8ELAVcAuwREWOKDZL6ANOWft+OVO/9MWCrXKYGSdOTksp7khLFv6oIcRdgvYi4q7xS0gLA34EAtoiI6/L6aYBTgQOBC4CVmzwVjewL7BkRZ+d9CDgBOAw4F1ixiTEOBlYnPYz4fi4/g6QVgDvr9Dkc+BBYtjhnpf33b8+BmJmZmZmZmVljTqybmU2d5sjLyhdtVpUgAf4WEf9pMObZpCTu7kyaWN89L88p7WNlYH3STPeJkuoAEfF8zRi/zssdygninNT/BbAFsLukwypm4f+jNqleimtGYFCRVM9jfiXpcGA7oJ+kVSPiwTrH3az7i6R63kdIOpL00GEFSatFxANtjLFPXh5UJNXzWI9KOoM0M7/WXMBz5XNW7J/6yfhJDB06dFidTYs2O4aZmZmZmZnZN4VLwZiZTZ2KetxRZ/uuFT8LNxowJ25vBvpKWnX8jqTlSbOx/xMRT5a6bJCXF9Um1ScJVpqLNMv+6Yh4tmLfnwEPA7MBfSqGuKbO0GsVMVSMORa4rKZdR/yzYh+fk0qzAKzZqLOkBYEFgNcj4v6KJpfU6foIsKykEyU5CW5mZmZmZmY2BXjGupnZ1OkdYHEmzFyfSESMfxFmnr2+a5PjnkWq374HUMzw3iMvz65pu0BevtjEuMVLTJeUVO9hQGEOUq30slfrtC1eTjq8zvZifdMvMW3glQ7uo9he71jqrd+PVAP/MOAwSSNJNeUvB65otsZ+3759v1e1Ps9kX6qZMczMzMzMzMy+KZxYNzObOj0BrAGsAFzcieNeB4wAfizpQOALUqmTj4B/1enTVqIcJtRaf4P08s5G3q1Y91nFulZiaCbG9lLbTSZqVy+WyvUR8aSkpYAfAJuQXmL64/xzr6T1I2JcC/GamZmZmZmZWRucWDczmzrdSHqZ5jaSDm121nJbIuJLSeeQXiK6AzAW+Bbw94j4uKb5a3m5WBNDj8jLURExoBNCLYwkzdxfGHiuYnsxU/6NTtjXQnXWL1iKpZFie71x6q0vSuVclX/IifZLSOVndgP+1sa+zczMzMzMzKwFrrFuZjZ1ugF4mpSMPayTxz4H+JJUAqZeGRiA2/JyJ0kzNRowIkaQyrssI6lhrfcW3VPEULtBUg9g25p2HfHjin1MB2ydf72vUeeIeIX0gGE+SatVNNm+2UAi4r/AGfnXpZvtZ2ZmZmZmZmbNcWLdzGwqlGeo/4Q0o/wPkk6QNEttO0kLAd9tcezXSSVhViKVm3kiIh6uaDcEuBOYGzirNrkuaTFJS5RWHUcqCfNvSX0rYl1U0s9aiRX4B/ApsIOkTUtjTQMcD8wHPBQRD9bp34o1yvFJEnAMacb6E3VeSFrrrLw8VdKspbGWI9VSn4ikmSQdIGm2mvXTAN/Pv9arzW5mZmZmZmZm7eRSMGZmU6mIeETSBqSXWB4OHChpCKnkyIzA/MDypIesw4DHWxj+LOCH+b//3qDdT4A7gJ2BjSXdC3xOKg+zLPAz4Jkc74WSlgYOBR6X9BjwMjAraeb9EqTa8ec2G2REvCppT2AgcK2k+0glalYglYh5E9il2fHa8DfgHEl7kV7YugzwPVL9+Z82OcYpwGbAasCLku4EZgHWIz0k2KemfQ/gT8Apkh4lvSi1B+mhx4LAS0xI1puZmZmZmZlZJ/GMdTOzqVhE3AssChwE3E9KTm8NrA/0Ai4CtgCWjYhnWhj6LlI5mE/zGPX2/zrQj1ST/Q3SLOqNSMnf00lJ93L7w3Js15AS/1uSkv+fkJLOrc5YJyIuBNYmzbJfEtiG9GDhb8CKLR53I5eSzuWXpIcO8wNXA6tGxGNNxjoW2AD4I+mvDX4ILAL8Fvh5RZePSTPZrwPmzPtfD3gfOJJ0fO+3/5DMzMzMzMzMrIoioqtjMDOzrxlJO5IS6ud38stGv3YkDQR2BdaNiMFdG03nGzp06LC3R3+x1Nk3f9DVoQBw8bH9uzoEMzMzMzMzm/zU1QG0xaVgzMysJZKmJ5VrgQkvyLSp2Pxz9eLiY5fr6jDMzMzMzMzMug0n1s3MrCmStiCVZlmZVDv8yoh4qGujMjMzMzMzMzOb8lxj3czMmrUC6SWc85LKwLRc79zMzMzMzMzMbGrgGutmZmZW19ChQ4f17NlzqT59+nR1KGZmZmZmZvbN4RrrZmZm9vU24q0xHDVo8GQZ2y8jNTMzMzMzs68jl4IxMzMzMzMzMzMzM2uBE+tmk5mkkNSw5pKko3O7o6dQWPY1Iql/vj8GToF9LSnpz5KGShotaayk1yVdI2kXST0mdwxW35S8F8zMzMzMzMysPifWzaYykoa3lci37qX0YGVAF8dxDPAUsD8wC3AncAXwMvAD4Hzgv10WoJmZmZmZmZlZN+Ea62Zm3d8QYElg9OTagaTjgN8AbwI/i4gbarZ/GzgE+NXkisHMzMzMzMzM7OvCiXUzs24uIj4Bnplc40vqB/wa+BRYNyKerojhfeA3km6cXHGYmZmZmZmZmX1duBSMWTenZFdJd0v6QNKnkp6UdIik6Uvt+ucSMAvl36P0M7xmzB6SfiHpIUkfSRojaYik3SSpIobIJWZ6SPqdpGdy7e2ratr9RNK9kj6U9EmO8whJM1SMOY+kQyXdlWt4j5M0StIVOdFbdS6G51gkaX9JT+T9PF7TbhNJt0p6X9Jnkp6VdKKk2SrGHF+GRdKKkm7M5/k9SZdKmj+36yXplBzD4Yt/vgAAIABJREFUZ7kG+TZ1rtcOkv4p6bl8bj/K53dfSdPUtB8OHJV/Pa/muvXPbRrW1Za0saTrJL2Vr8urkq6StGlV+wq/BAT8uSqpXhYR91bsfylJF0l6I1/H1yVdIGnxirbjj0XSXJL+ka/7x/neWb3Udu98D30q6TVJR9Wev9yufH8eI+nFfI1eknRs1f2X+30nX9Pnc/v3JN0k6fsVbXvn/QyuM1ZlOR+VSjNJ2r10PKMknVV1T5b2d4mkd/O5ub/R9ZQ0vaS98n32Tv5cDM/3xfb1+pmZmZmZmZlZ+3jGulk3lpOI/wS2BT4EHgI+BlYBTgHWlbR5RHwFjCLVwN4G6JX/u/BOacxewI3AWnn9vcBXwGrAOUA/YO+KcKYBrgLWBu4CngTeLY17FrAn8BlwB/AJ0B84Hthc0voR8WlpvB8CJwEvkOp6fwgsBvwI2EzSZhFxS51Tcybw0xzH08D4F2pKOiLv84u8/R1gDeAw4EeS1o6INyvGXCWP+wJwG7As6bwvK2ll4FZgEeABUs3xdYBLJW0cETeXxukJXAy8T6pH/igwB+n8ngGsDAwotb8c2CDv7768/8KoOsc/nqRTgYOBL3NsI4B5gXWB2YDr2+g/Dal+OjnulkhaH7gWmJF0rIOBJYCfkM73JhFxT0XXb+d4Z8jLeUjX6dZ8vvfMP/8hXY91gKNJ1/o3VaEw4VzeDjwOrA8cCawuaaOI+LIU93zA3aRr+irp3p4z999I0sERcVqr56MeSScDvyB9hm8CVs/Ht6SkdSIiSm0XBe4H5gKeI53XhUnn+cw6uxgE/Jh0v99P+vzNR/qcz0z6HjEzMzMzMzOzTuLEuln3dggpuXsrsFNEvA3jk+OXAJsD+wBnRMQzwIA8y7lXRAyoM+YppGTbIGDfiPg4jzknKXG3l6RrI6I2IbsAMBZYPCJeL2+QtDUpSfg60D8iXsjrZyUldtcEjgEOLXW7D1g2Ip6sGWsj4Brgr5L6lBOOJVsBy0fEsJq+/YDjgI+ADSJiSF7fMx/vtsBfgO0qxtwbOCgiTs99pgduICVa7wfeAvrkkihI2o30IOLXQDmx/gWwNXBdRIwrxTZnHm9XSedGxN0AEXGIpKNJifVzImJgRWyVJO1MSqqPADYtn8t8j6zSxDCLAN8iXdthbbSt3X8v4CJSUn2fiDiztO0g4H+BiyUtFhFja7pvAfwLGBARn+U+R5Nm71+aY1qpuMaSlgIeAw6UdEJx35YsSHr40zciXsp95iQ95Fkf2A/4c6n9mfnYBwG7RcTnuc+apOt5iqTba+/PDtgZWCUiHs/7mYP0QGEt0kOQO0pt/0pKqv8V2D8/OEPS7sDZtQNL6k1Kqj8ErF2cz7xtRmC5ZgIcOnRoveu/aDP9zczMzMzMzL5JXArGbArRxCU+JvphQimQcvvpSC+K/AjYsUiqA0TEGGAPUjJ0rxZimAvYnTTjeo9ycjKPX4xVb8wjapPq2QF5+bsiqZ7H/BDYFwhgb0k9Stueqkpa5tnfl5GSeX3rxHFSbVI9+znpe+30Iqmexxybt30KbJ1nK9e6u0iq5z6fMyERuzjpfL1faj+QNDt4NZVK8kTEFxFxRTmpnte/DRyRf/1hneNq1a/z8sDacxkRYyLijoo+tb6Tl++XZ3Q3aTvgf4B7ykn1vP/TgEeA+Ul/hVBrNLB3OQlMSsQHsBRwZPkaR8R/SQ9pZgJWqhPPsUVSPfd5mwkvW92vWC9pEWAz0l9JHFAk1XOfe0lJ92lJ925nObJIquf9vAP8Lf+6dim2RYHvk/7i4dAiqZ77nEN6yFNrrry8v+Z8EhGfRsQDnXMIZmZmZmZmZlbwjHWzKef8BtuWI81YLlueVELkxpyEm0hEvCnpeaCvpBlryqzUsw4wPXBTxQxiIuIJSR+RysFMspk0o30iOam8at4+SSmRiHhK0pOk41uWNKu26NuTVIZkZVIZjiLxvnRe9iGVial1TZ3jWysvL6qI4y1Jt5CS2quTkvdlt1aMVyRph5cfGOTxvlSqj74S6Tq9Ud4uaTlSgnQhUjJYwCx5c5868TdN0rzAksC7EfHvjgzVgb51z3d2IbBibldbiuThiPigvCIiPpT0Lul8Vl2PF/Nynjr7m6TcSUTcJOl94LuS5szJ9jXz5htqY8gGkf4SYK2Kbe1VVdboubwsH88apdjGVPT5J+n+LXsGGAP8VNIw4IqIeHeSnm3o27fv96rW55nsS7U6npmZmZmZmdnUzIl1symkQWmWogRGbWK9d15uXLz8sIHZSWVY2lKMuY+kfRq0m7Fi3VtVyXjSjOcewKja2bIlw0nHN2+xQtLSpAR57+ouwIREdK1X66yfl5Tgf6VBHEW7WlXnb0yDbeXtPYsVeVb+QGCHOn2g/nG1YoG8fLFhq7YVD22+LWnaFmetF+dxeJ3txfpmzzekczpHne2TnO+S9yPiozpjvkKq6T4v8DYdi7u9RlSsK/5ipHw8xT7r3eOTrM8PJPYA/p5/zpL0LHAncEFEPNi+kM3MzMzMzMysHifWzbqvafPyearLP5RVJbwbjfkY6eWjraiXNC+0lfwf30aSSHW0e5PKbpxJmh3+cUSEpONJZVMqZ1M3SOA3qyrWRvE3c2yFg0lJ9aGkMiSPkpK+n0v6LvAsHZsl3pHYqrxEKsvyLeB7tH5fNBNDq+ebOrX126ve+a63j2hje5WGpdVaOJ4i1paOPyIukXQb6S8yvk/665R9SA/RTomIQxsOYGZmZmZmZmYtcWLdrPsqZrgObTTbvZ1jDo6IgztpzHeBccDcDUrSLJSXRbmUJfLPwxFRNXN+kXbGMhJYOO/v2SbimByKeuI7RMTQmm3tPa4qr+XlYh0ZJCK+knQzqV76jrSWWB+ZlwvX2T4lznfh25JmqTNrfcGaONqKu3dNe0j3OMDMdfosUGd9q4rYFqqzfcE664ua8ucA5+SHVxuRXhD7K0kDc516MzMzMzMzM+sEfnmpWff1EGkm8bqSZm2h3zgY//LTWncCXwKbSZq2YnvL8osfHyTNtJ2k/ImkvqQyMB8BT+TV387LScpjSPo2sGE7w7knL3eqGHdO0kzer2j7LwA6oji21yq2bVenT5G0bfphZ0SMBJ4GviNpq+bDq1S8NPQASUs2aiipXN+77vmuWX9Pne2d7ce1KyRtRLomz0fEW3n1vXm5qaTZKsbZOS/Lcb8DfA4sXPvZyuV/1ulI4CX35eUmknpVbN++mUEiuYn0wleo/yJgMzMzMzMzM2sHJ9bNuqlcz/yPwGzAvyVNMoNV0jKSapOJxYzXxSvGfJ1U/7sPMEjSHBVjri5pkxbD/UteHiNp/KxsSbMA/0dKup8VEUUC+QVSgns9SX1K7WcglYWZvcX9F87I4/5C0kqlcXvkGGcivdixmXr07VW8kHLv8kpJ2wC71OlT95q14cS8PF3SRC+elNRL0nrNDBIR/wFOJtXWv6Pq+kv6lqRjSA9nCpcCbwJrSdqzpv0BpJfgjgCubPJ4Oup3knqXYpiDdFwAfy3WR8RLpITzLMCf8gt4iz6rkUqofFnTZxzpAdLswH6l9tMDp1F/9ntL8ktybyc9DDhR0vh/pyX9lElfXIqk5SVtVT6OvP7bwCr513o1283MzMzMzMysHVwKxqx7Ox5YijQT/FlJj5ISZHOQyoosDFxNKvdQuIY0e/Z2SXeSXvj4TkQcnrcfkPvuQJq5/jgpsTs3qazIfMCfgBuaDTIiLpf0d2BPYKikO4BPgP7AnKSE5FGl9m9J+gewB/BEbv8psBapDvxAYECz+y+NO0TSkcAfgAckDSbNNF6DVKrjeeDnrY7bopOBH5CSotuSEu19gJVID0oOqehzC6mG/UF5hv9I0gzyUyKiqqQNABFxgaR+pGN6QtL9pET2vMDypFr6dzQZ9xHAF3l5vaRXcv9PgflJCdoepHNY7H+MpJ2Aa0kvzNwzH+8Sef9jgB3rvPS2s71KKmMzTNLtpNnl65EeTN1JesBTthdpRvouwDqSHiDdq/1J9+AvI6K2LM6xwM2kBxk/BkYBK5Ie2JwP7NpJx7IPaeb6z4EN8+d+YdI1OJOahzaksjH/BkZLejjHNRvp8zQrcKVfYGpmZmZmZmbWuTxj3awbi4ivImJHYBtScrAPsBUp2f4mcDRwWE23PwPHAR8DWwO7USofERGfkEqi7E56sWZfUl3wRYEXgUNJCeBWY92LlKR8jJTY3xx4C/gNsF7eb9k+wC+Bl4H1SUnA20gJ6Fda3X8pjuOBzYC7SDOmtyK93PVkYJWIeLO9Yze5/7uBNUkJ7UVyLONI1+KMOn1Gkl46+WDu+zPSdZunif3tT7p+t5Ou5dakJOztwEktxB0R8VtgmRznJ6Trsg3p3riZVCLlezX9bied50tICfhtSA9pLgRWjIgpVQYm8r5PB5YmnffRpIcsm0bEFzVxv57jPpX0QGErUpL8dmCjiPjfSXYQcRuwBalM0wqk+/zBPM7wTjuQiOeBVUl/ETAX6d4QsCUTP0QrPAj8FniE9FcP25I+R0+Skv31ShCZmZmZmZmZWTspIro6BjMzs3aTFMArEdG7q2OZGg0dOnRYz549l+rTp0/bjc3MzMzMzMw6h7o6gLZ4xrqZmZmZmZmZmZmZWQucWDczMzMzMzMzMzMza4ET62ZmZmZmZmZmZmZmLZiuqwMwMzPriIjo9nXXvu5GvDWGowYNnixjX3xs/8kyrpmZmZmZmdnk5BnrZmZmZmZmZmZmZmYtcGLdzMymCpJCUkyB/Ryd9zWgE8YKScM7HpWZmZmZmZmZTUlOrJuZmZmZmZmZmZmZtcCJdTMzMzMzMzMzMzOzFjixbmZmZmZmZmZmZmbWAifWzcxsqiWpd65jPljSjJJOlPSKpLGSXpB0mCTV6btO7vexpHclXSlpiTb2t4Cks0r7eEvSFZL6tSP2pSVdJOn1PNZISedJ6l3RVpK2l3S3pFGSPpP0mqTbJO3X6r7NzMzMzMzMrDEn1s3M7JugB3ALsCfwNHAnMB9wIvD72saSfgjcDqwDPJH7LgP8B1isageSlgYezfv4BLgCeB74EXC/pG2bDVbS1sDDwI7AG8A1wChgAPCwpO/VdDkBuARYLsdwBfACsCzwq2b3a2ZmZmZmZmbNma6rAzAzM5sCVgPuAb4bEe8ASFoJeAA4SNKJEfFxXj8LcA4wLbBjRFyS10+X1+9aO3ie9X4RMAcpyf2biIi8bRvgX8A/JN0dEW82ClTSwsAFwKfAhhFxd2nbLsD5wHnAynndDMCBwHBgxYh4r9R+OmD1Zk7Q0KFDh9XZtGgz/c3MzMzMzMy+STxj3czMvgm+AnYvkuoAEfEwcCMwE7BSqe22pAT5rUVSPbf/AjgI+Lhi/P7A0sDLwJFFUj33uxy4CpgF+GkTsf4ix3RoOamex7ogj9VP0gp59axAT+CJclK9iLl2DDMzMzMzMzPrOM9YNzOzb4LhEfFcxfpi3TyldWvm5aW1jSPifUm3AFvVbForL/8VEV9W7GdQ7rMWqfxMIxvm5dV1tt8LbAn0Ax6NiLckjQA2lfQr4KKIGNnGPibRt2/f2vIywPiZ7Eu1Op6ZmZmZmZnZ1Mwz1s3M7JtgRJ31xezznqV18+blq3X6VK0v+gyv06dYP2+d7WW983JUfvHqRD/AH/P2OUp9dgXeB04GXpf0kqSBkr7fxP7MzMzMzMzMrEWesW5mZt8E0XaT8dSOPs3up5kxp83tLmij3fia6BFxh6TFgM2AH5BeurorsKukSyPix03s18zMzMzMzMya5MS6mZnZxIoyKgvV2b5ggz4L1+lTjPVGE/sfQXph6AER8WET7QHIbS/OP0haFbgM2E7SwIi4sdmxzMzMzMzMzKwxl4IxMzOb2L15uW3tBkmzAVXlVe7Jyx9LmrZi+8417Rq5LS+3bKJtXRHxIKm2O6QXq5qZmZmZmZlZJ3Fi3czMbGKXAe8B35e0XbEyJ8xPBWau6DMYeIo0Y/1YSSr125L04tKPgYFN7P9U4FPgNEmb126UNLukfSXNmH9fUNIASTPVtOsJrJt/rVcv3szMzMzMzMzawaVgzMzMSiLiQ0l7ApcC/5J0ACkxvTIwJ3ARsFNNn5C0E3An8GvgR5IeJ5WNWQP4AvhZRIxqYv/PS9oZuBC4RtKzwNOk2u8LAUsBPUglXz4FZgfOA86Q9DCplEwvYPUc7xDgivafETMzMzMzMzOr5RnrZmZmNSLi38CGpNItywMbA/8FVgNeqNPnKWAF4GzSrPZtgMWBq4A1IuKyFvZ/BbAscBYwfd5/f6AnKbG/GTA6N38ROIQ0a35B0uz4NYDhwAFA/4gY1+y+zczMzMzMzKxtioiujsHMzMy6qaFDhw7r2bPnUn369OnqUMzMzMzMzOybQ2036VqesW5mZmZmZmZmZmZm1gIn1s3MzMzMzMzMzMzMWuDEupmZmZmZmZmZmZlZC5xYNzMzMzMzMzMzMzNrwXRdHYCZmZl1byPeGsNRgwZPlrEvPrb/ZBnXzMzMzMzMbHLqlBnrkkJStNHm6Nzu6M7Yp31zSRpY3HOln08lPSPpNElzd3WMzZA0ouI4Gv180dUxd4Skq/JxLDcl+k1ukrYsXZuRkup+n0q6uNT29CkZZ7MkPZ7jm60dfReRdKqkJyS9L2mcpDck3SBpd0kzTo6Yv4kkHZiv04FdHYuZmZmZmZnZN5lnrNvX2X3AC/m/5wJWBQ4Etpe0WkQMnxJBSBoI7AqsGxGDW+h6KTB7zbrlgWWA54H7a7Z92c4QuzVJHwBERMsJ3W5kHmA94LbaDZJmBn7YmTuT9DiwLPDtiPigM8duRyy/BE4ApgdGAvcAY4B5gfWBjYHfSVoiIj7pskDNzMzMzMzMzDqRE+v2dXZORAwsfpE0J3ADsBLwR2CbLoqrKRFxcO06SceREut3R8TuUz6qyWo/4HDg5SnUb0p5jPRAZGcqEuvAVsBMwKPAClMwrslO0iHAKcD7wJ4RcXnN9lmBA4BfAz0AJ9bNzMzMzMzMbKrgl5faVCMi3gZ+mX/dVNL0XRmPTSwiXo+IZyJi7JToNwU9BzwEbFWn5MnOwFjgsika1WQmaQnSTPXPge/XJtUBIuLDiDgOWJN0DszMzMzMzMzMpgrdIrEuaSZJR0oammtlj5Z0t6Tt67QfXtR0l7Rfqd/Lkg6VpLxtBUnXSXpP0ke5VvNCnRFD7rO8pBtz29GSbpbUT9KAevXkJfWQ9AtJD+WYxkgaImm3Iu6a9pGPd9p8bM9JGivpNUknSepZJ7aZJf1O0lOSPpH0oaS7JG3Z4HhWk3S1pLfzPoZL+qukeSvaFjXzB9QZa/w1qlm/iqQrJb2S9zEqH/8JuWRGRz2WlzMAc9TsexNJt+Ya0J9JelbSiVU1pZVsn++BUbn9a5Juk7RfqV2QysAA3KmJa6L37oTjmYSk5fL4V0n6jqQzJL0q6YvinpM0p6SDJd2e4x6br+u1ktaqM+74GtuSdpT0SP4svK1U136uij4z5Pv5kfw5GyPppRzbD2vaTlQrXblGOfAt4Fs15+7xev1qxlxU0nlK9eqL++mfkpZu47zNIun0Ur9nJO3f4qUouxCYBdiiZp9zk0rEXA80LNkiaTtJd5Tuz/9K+q2kGWqPgVQGBuD90jn7oNRuQUm/kXSvUv33cXlZeW7a6UDSXz2dGxEPN2oYEY9GxKc1x9veazdbvudHKn23/UfS+qW2u0h6NG8bqfQ9WfmQrcUYipr6lTXyq+7THGvkz9b0ko6S9GLe18uSjpE0bZ3xVlb6vvoo3xPXV30GSu2/pQnf+R/mn+clXSJp7Xr9zMzMzMzMzKx9urwUjKRZgDuBFYG3geuAXqRk1FqSVo2Iype0SToN2Bt4kFQmYh3gJKCXpFuAW4DhwB2kRNQPgb6Sli4nedoTg6TVSWUfZiQlc58FlgLuBc6rE28v4EZgLeCd3PYrYDXgHKBfPp4qFwGbAUPyvtYCDgXmI82ILe/nf/IxLwW8DtxKKkWxGnClpCMi4sSaPjsDA0kPW+4HXiOVrdiHNBO3f0Q8Uye2pkjaFLgGCFJ99PuBbwPfJZX6OAv4uCP7ICU3C+NnyEo6Ajge+AK4i3T+1wAOA34kae2IeLPU94S87SPSdfqAVEd7WWAx4Izc7nzSbNxFgZuBUaUxOnosbZmVdA5nB+4Gps3xQqptfSrp/n86t1uYdA9tLGn7qhnG2a+Bg0izsG8k1a7fFVhWUr+IKL9E9UrgB6Tjvpt0zucHNsjbr24Q/6uk87dj/v3imm0NSVqF9BmfFXgq738x4MfAlpK2jIibKrrORLoHFiB9nnoCawN/ltQzIv7Y1r4r/JN0vncC/lVavwPpulwI/E+DYzkH2I1Um/whUmmVlYHfAxtK2jAixgHvkc7ZFqTPziXAuDxMuczKjsBxpO+Kx/O4i5POzRaSNoiI2hr+rdo0Ly9u2KpCB65dL1IN9zmB/5Du/TWBG3LyeBPSd8kQ0vfe2qTvyZlJJYU6I4b2mJb0WVgjx/YM6d+r3wHfAX5eE9v6pLJWPUj3w4ukckP3k+41atr3yPEvQ/q38DbSOxkWIJUiejNvNzMzMzMzM7NO0uWJdVKyc0VSIuBHEfExjC8zcBfwC0m3RMQNFX23BVaKiGGlPo8DhwC7AL+JiNPzth6kJOF6wPZMnPxuKQZJ0+T+MwKHRsQpxUCSjgSOrXOsp5AS4oOAfUv7mRO4FthL0rURcX1Nv4VISbO+xQs5JS0MPALsJOmoiHix1P48UlL9ZOC3EfF57rMIKZF0nKQbIuLJvH4B4O+khPcWEXFd6ThPJc1MvYCU6OuIXwECVo6IR8obJK0MvNvB8QE2z8vXI+K9PHY/UpLxI2CDiBiS1/ckXYttgb8A2+X1M5COeTiwYjFO3jYdsHrxe0QMUHp56aLAiS2+vLSj1iUlD1eIiDE12x4D+tXOJJa0Buke+Iukq4t7o8ZuwNoR8UDuMyspKbccKaF7RV6/HCmpfgfwg/JYSn998L1GwUfEo8AA5b+iiIgBzRx0Hn96UlJ5VmCiB0WSfgb8AxgkadGI+LCm+4bATaSXzY7OffqTHq4dIelPdc5Lo2N5S9JtwA8kfSciint5Z9JDmRuAn9Y5lj1J5/w+YLuIGJnXzwCcS0rO/wr4Q0S8Sjpnj5MS6/vWeXnprcBVtQ/DJG1NemnuX0jfee0iaQ7SAxRIteNb6duRa7cBafb/aqXvzwOB00gPBmcH1iju+/w9+QSwp6RjIuKtToihPfqSHjotWbq+S5K+w/eSdGwpth75WHoA+0TEmXn9NMDpQNVfVmxCSqpfAAyIiPF/LSRpdmDBZoIcOnTosDqbFm2mv5mZmZmZmdk3SaeWgtHEpRwm+gGOqmjfi5RQ+opSohkgJ4SOy78eUGeXRxZJ9VKf60kzUl8tkup52zjgT/nXdToYw3qkWdbPkF6SWXY8FS9ZVCqjsXvetkfNft4G9sq/7lXbN9u/SKrnPi+TZsFCStYX+1kO2Jg0s/HwcoIwIl4i1SCfNsdS2J30kOCSIqme239Fmv05EugnadU6sTVrLmB0bVI972tIRHxU0acpSqVPfkp6mADwt9Lmn5Pu9dOLpHre59i87VNga0nz5U2zkmYxP1FOquc+X0REd5n5+RXw84qkOhHxbFV5joi4j/TgZW7STPQqxxdJ9dznQ+DP+ddySYmiNMw9tYnoiPg4Iv7T9JG0bjPSDPwna//6IiLOJSX752DCbPiyscDuRVI99xlMmgk9O208EGjgQmB6JjygWYL0Vx+XtVEf/ghSnfLti6Rrjukz0l+MjKb+90KliHik6i9MIuLfpAeMK+QHau31nbz8rPxd1qSOXLvPqPn+JH3WPwWWID3cGn/f5+/Jy0kPkVcv9elIDO21V831fbpBbPMDDxVJ9dy++D5+p2Ls4rN4Zzmpnvu9FxGPV/QxMzMzMzMzsw7o7Brr5zf4eaKi/YqkhO6QiHi+YvugvFxDmrT+OGlWZq2XGmwrZnXP08EYiiTI5RVJjC/JM3prrENKut1UlWSLiCdIM6r7VfT9HBhcsf65vCwfz4Z5eXVtbNm9eVneT5GYv6girvJLFytrc7fgEWA2Sf+Q1LeDYwGcV3pw8xZpdu+spPutnCxrdHxvkWZwT0O+rnndCNILUH+lihrz3cSzEfFcvY1KNZ03lfR7SWcp1UkfCKySm/Sp0/WWinVV99pTpDIk+ynVtZ6kVv1kVFzTemVIBtW0KxsWEa9XrK86xlZcSSr/U5Rm+kleXljdHCT1AXoDD0fEiNrtOfn/FLBALvHUNEkzStpG6T0CZ5eufzH7uN71b2r4DvTt6LV7o7wif0cV17PZ7/2OxNAe71U9UKT6nlszLy+tbRwRn5DKadUq/mrgd5K2kjRTe4Ls27fv96p+mHAOzczMzMzMzCzr1FIwjUo5KL1Ucdma1UXCcnid8T6QNJr0csNZSTM3y6qSY2Oa2FZ+4Wd7Yij6vFbVh+r60L3zch9J+9TpBynJX+uNnLCvVczaLB9PsZ+TJJ3UYD/lF3s2PAel9R1NMP8aWBr4GfAzSe+QZtZfBVzcxqzeKvcBL+T//gx4BbixYnbmvKQyN6/UGWd4qV1hV1It45OBkyW9TCqHcnFEVCWeK0lak4n/OqBwSERUzTxtRd065JIWI70rYPEG/Weps36SBC8V91pEvCHp56TZ7OcDX0l6mjTbd2Au9TK5dOSerTo+qP48NS0iPpF0FbBzLru0I+meu6dBt955uZoqXvZbYw5Srew25frh/ya9g6Geete/GcW9O4OkmVuctd6Ra1f1vQ6d/L3fRgzt0co9V+yz3ud7kvUR8bCkY4Dfkq7755KeJD1oOLfOQ2MzMzMzMzMz64DuUGMdUtKz5TZ1ZmS3MmZHY6jXp2o257R5+RjwZLNBtbGfKsV+7mHC7P0qVUndtvbror97AAAgAElEQVTTShyT/DVERLwmaSVSKZ3NSLP4NyfV7T5U0uoR8X4L+zgnIga20L4t448vIu7IyenNSHXE1yEl23eVdGlE/LjJMRfL/WodTfU1aMVnDbZdSEqqDyKVQHoe+DgivpJ0KOklv/VmHTd9nSPibEnXkV4M/H1SqZj9gf0l/SYijm92rHZqzz3b6ndDKy4kzVg/g5Q0P6GN76ni8/oK1X+VUtZUqaRcP/xyUlL9VFK97uHAmIgISX8llZhp96zziHhH0ghSyZIVaN+LMSfHtevs7/0Ofee1c5ziurR0LBFxtKQLgC1JLy9ei/QXWYdI2i0iLmhlPDMzMzMzMzNrrKsT60W92YWrNkr6Fmmm+BiaTCpNoRiKUgT1XghXVbu4mLE4OCIObkeczSr2c3lE/LlhywlGkpKwCzOhNEHZQnlZLsEwLi9nrm0saVpSDe9JRMQXpFIjt+S2C5Jqfq9Hqh98WJMxt2Ik6dgWAp6t2F51fEVt8YvzD7nG/GXAdpIGRsSNbe04J/4Htjfw9pA0D6ncy/PArhWJ3UU6c3+5NMeZwJn5BYs/JJ2zYyVdUFXipBM0/NxS55pOAbcBo0gPY6BBGZisODcvNfqLnxatREp43x4Rh1Rs76zrfwOwJ2lmfiuJ9e5w7doTQ93vvKwjNevLitgWqrO97otI83s0/hf43/wS1J+SatCfIemf+V0jZmZmZmZmZtYJOrvGeqseIb10buVca7hWUav43jZmfU7pGO7Py61ra7/nxOKPKsa5E/gS2CwnnieX2/Jyyxb6FKUqdqrdkJMz29a0gwkJp+9WjLceqZ58myLiVdLsaUhlYiaHRsc3J2mm9VdMuK6VIuJBJtReLsdaJKu6+kFV4dt5+Xrt5ybXXt5scu04Ir6KiCtJ9/u0wFJNdBtH6+euuKb1Xi65c027KSKXbBoIvAvcHRH/baPLMNJnabUWa6g3uueK6z9Jqar80GXN2vXtdDrwBams00qNGkpaXlJR5qo7XLv2xFD3O0/S/MCSnRPa+PdgbFu7IZ/DLZoZJCLGRcRZpJdsz8yEskNmZmZmZmZm1gm6NLEeEWNIL5ychjSjrlexTdJ3SfViAf7SzWK4g1Tbe0ngoJohD6diRmh+WeJA0gsDB0mao7aNpNUlbdLug2F88vd2YF1Jp0maaHalpGkkfT/X/i78g/RwYQdJm5bbAseTSko8lMcu3JWXO0vqXeqzCHWul6SD6iQPi9m9dWuGd9AZpMT5L8oJwPzQ4C/ATMAVxQstJS0oaUDtCwAl9QTWrYi1mGHaqJ75lPQKMBZYVdL49xrkEiF/pnHd7aZJWi2/HHW6mvVzA8vlX5u5piOBXpJaies64GVgmVzaprz/XYENSMntS1oYs1NExBERMUdErNNE2yB9xmYArqh6uCdpSUk/qVnd6J4r/upkE0njZ1Hn74JzgF4VfVoWEU8DR5Ieot0saevaNpJ6STqc9D6Eoo54d7h27YlhKPAesKak/qX2s5H+DWlXbf46sb1Oeti7R2k/It0rVf92bCxp7YoHvUuQEurjSH9JYWZmZmZmZmadpDvMsD0CWBXYEHhJ0l2kxM96pGTTnyPi+u4UQ0R8KemnpBfDnSppJ1KJkaVIyfazgT2YMKu0cAAp6b4Daeb646QE2dykWtzzkeph39DB49mJVGrlQGCXvJ+38/iLA3OSHgjcm4/nVUl7khL/10q6jzTbdYXc/k1gl/IOIuKlXM93F+BxSXeTztmqwPWk81ZbyuAo4I+SniCVKRGwTN7HO8ApHTzuShExRNKRwB+AByQNzvtbg1S+4Xng56Uus5PK05wh6WFSuY5ewOqkczcEuKLU/lrgd6R7YUMm1E4/LCLenRzH1EhEjJH0Z+BXwBBJdwAfko53FuDvpBIeHbU46Ty9l8/T26RztzbpfA2MiGeaGOca0ouNH5R0D/AJ8GpEHFuvQ0R8LmlH4GbSi3p3Is3+XhRYmfRg4ScRUfvC424nIv5PUl9gL2CYpEdJNdFnJyVF+5AeZA0qdbuGVHLnGkm3kl6C+UlEHBARL0i6jDTj+b/5+n9Bek/AZ6SX8m7fSbGfmF+6ehxwuaSRwMOk0lnzkL4PZmDCw55uce3aE0Pucxyp1Mot+d+JsfkYR5D+WmiDTohtbP735Xrg75J2B14Elid9pw4EBtR0W4X0/Toq3z8fAP9DqrPeAzg6l7YyMzMzMzMzs07S1aVgiIiPSAmfo0gJyS1IyYCHgR0j4hfdMYaIuJdUUuFmUuJrs9x3HVISCdKMx3KfT0hlR3YHHgX6ksrGLEpKnBwK/LETjudNUrLnYFLSuB+pNMz8pJen7kdN7eeIuJCUEL2O9HBgG2BGUn3eFeskSPcATiQlbTciJX2OJz04qLI/Kak3E7Axaab6l6RjXiYiXmzXATchv0RzM1KCsh+wFSkpdjKwSj5nhReBQ0gvk1wwt12DlOw8AOhfrlUcEY+QSkcMI13f3fLPLJPreJpwOOkFlc+S7sl1SaVuVgae7qR93EF6CetTwPdIidzlgIdIidvdmhznD6R74Is8xm6kc95Q/guKFYHzSbN4tyEloi8FVm6mBn53ERF7A5sCN5Hqfm9Feuj0Aen8/Lymy3nAr0nfOT8inbPyw6+dSX9tM4L02VyVlIzvR3pQ1pmxn0T6zjidNKO7P+laLEEqCbQbsHhEfFrq0+XXrj0xRMRppO/PF0nflyuQvtPWJj1M6KzYbiV9bm8nfbY2Jf27sibwREWXS0kltYbnmLYhlay5Hdg4Io7prNjMzMzMzMzMLNHkK13+zSXpRlLSeNWI+E9Xx2NmZtZeQ4cOHdazZ8+l+vSpeg2JmZmZmZmZ2WShtpt0rS6fsf51JWl2SQvVrJOk/UlJ9RdIJUPMzMzMzMzMzMzMbCrSHWqsf119F7hf0pPAS8C0pNIui5BeBLpH+M8BzMzMzMzMzMzMzKY6nrHefi8BZwI9gfVJNcN7AheTanYP7rrQzMzMzMzMzMzMzGxy8Yz1doqIt4B9uzoOMzMzMzMzMzMzM5uynFg3MzOzhka8NYajBg2eLGNffGz/yTKumZmZmZmZ2eTkUjDfcJJCUsNa8JKOzu2OnkJh2VRK0sDinpN0VoN2PSS9X2rbfwqG2RRJvXNsg2vW98/rB3bCPga2evySBrT38yqpl6SDJN0p6U1J4/J1eEDSsZIWbHVM61yShrf1nW1mZmZmZmZmk58T62bWVbaV1KPOts2A2TprR/WS4DaBpFWB54H/BVYGhgKXA/cDiwJHAs9J2qDLgjQzMzMzMzMz6yacWDezrvAY8G1gkzrbdwa+BJ6YYhF1T0cASwJDJudOJC0D3AHMA5wEzBUR60fEjhGxKTA3sDUwAph/csZiZmZmZmZmZvZ14MS6mXWFi4GvgJ1qN0iajZRwvx0YNYXj6lYi4o2IeCYiPplc+5Ak4EJgRuDoiDg8IsbUxPFVRFwBrAg8PLliMTMzMzMzMzP7unBi3TpE0kySjpQ0VNKnkkZLulvS9nXaj68PLGm/Ur+XJR2ak3xIWkHSdZLek/SRpKskLdQZMeQ+y0u6MbcdLelmSf0a1afOdb9/IemhHNMYSUMk7VbEXdM+8vFOm4/tOUljJb0m6SRJPevENrOk30l6StInkj6UdJekLRscz2qSrpb0dt7HcEl/lTRvRduiZv6AOmNV1nCWtIqkKyW9kvcxKh//CZJmrhdbHSOAu4DNJH2rZtt2QE9SsreuZs9TvpYv51/X0YS67RPVQZe0lqT/k/Rkriv+qaRnJJ2Yk/0dJmk6SftLekTSx/lniKR9JE1b0b5ujXVJy+TPSHEP3ypptXaEtRGwNOma/KFRw4gYHRFDa+Lo0u+A8jmStLGke/N5fV/SFZKWqHc8kn6S23+Y76EnJR0haYaKtoPzfnpXbKtXb3/8Z03S0pKuyXGNyffq6nXimi7H8bykzyS9JOn3ql86qbM/n2ZmZmZmZmbWBifWrd0kzQLcDRwLzAVcB9xHqs98iaTTG/Q9Dfgj8DZwG/AdUgmKoyWtAdwDLEwqTzEK+CFwu6QZOxpDTmbdB/wAeBG4gVTq4l5g1Trx9spxng70zm0HA4sB5wB/q3eswEXA70iJy1uAWYBDgX9U7Od/gP8Ax5BKpdyaf18RuFLS4RV9diadr82BZ4ErgLHAPsCjjRKLzZK0KanW9ubA8LyPx4E5gMPzslUXATOQSoyU7QR8AlzZIJ5WztPjwL/zf78JnF/6ubfU7hRgd2Ac6b67HZgVOAy4t6PJyZw4vxr4M+m+uS3/LAH8FbhMUlPfyZJWAR4ANgVeYsI9fBdQmaxtYNO8vCwivmilY3f4DijZFrge6AFcC4wEfgQ8KGnZiv2fBVxAumfuyX3nAY4H7miwn/ZYCXgQWJx0Xz0PrJ2Pp29F+0tyHHMDNwFPAQeTPndVD/Emx+fTzMzMzMzMzBqYrqsDsK+140lJqduAH0XExwA5kXsX8AtJt0TEDRV9twVWiohhpT6PA4cAuwC/iYjT87YewI3AesD2wHntjSEnLs8jlb04NCJOKQaSdCQpQVjlFGAtYBCwb2k/c5KSeHtJujYirq/ptxApSdw3IobnPgsDjwA7SToqIl4stT8PWAo4GfhtRHye+yxCSsofJ+mGiHgyr18A+DsQwBYRcV3pOE8FDiQlD1euc1zN+hUpobdyRDxS3iBpZeDddox5OfB/pHrq5+axFiSd539GxMea9A8BCk2fp4i4StLjpAT+MxExoM6YxwIPRMT7pWPrSUqE70lKbNa7P5pxIKnEzVPABhHxVt7HPMCdpCTw3qQke1352g4EZgKOiIgTS9t+D/y2xbiWz8tHW+wH3eM7oLAvsGdEnJ37CDiB9GDk3BwnedvWpGv6OtA/Il7I62clJdjXJD20ObQd56TKfsBhEXFyKYbTSPfEofl4i/U7ANuQHpisHRGv5/ULkx5iVNW475TP59ChQ4fV2bRoM/3NzMzMzMzMvkk8Y92A8WVLKn+Aoyra9wJ2I9XJHp9oBoiIZ4Dj8q8H1NnlkUVCrdTnelKy8NUioZa3jQP+lH9dp4MxrAd8F3iGNFu27HgmlAwpH+tcpJnMLwN71OznbWCv/OtetX2z/Yukeu7zMhPKnKxV2s9ywMakmaeHF8ni3Ocl4JfAtDmWwu6khwSXFEn13P4r0kzVkUA/SZUz8VswFzC6NmmX9zUkIj5qdcCIGE2a4byOpPny6p1ICcK6ZWDaeZ6aieeGclI9rxtLSn5+QZox3RHFfXhgkVTP+3iDlBgtt2mkP2mW+3OkGd5lxwCvthjXd/Ly7VY6dYfvgBr3F0n13CeAI4HXgBU0cZmcIqbfFUn13OdDUoI+gL0blV5p0b3lpHpWnJ+1a9bvk5dHFkn1HNvLwO/rjN/pn08zMzMzMzMza8yJdSuc3+DniYr2K5ISukMi4vmK7YPycg1VTzu+tWLdSw22FbO65+lgDEWZjMtz4m28iPiSVEKh1jrA9MBNOdE6kYh4AvgI6FfR93NSyZhaz+Vl+Xg2zMura2PLirIl5f0UifmLKuIaC1xW0669HgFmk/SPOqUr2utC0vfQjvn3nYC3SLPO62nPeWqKpPkk7S3pdEnnKtVg/xupPEyfVscrjbsgsCAwKiLuqGhyHfABsHj+K4hG1szLyyru4S9IfwnQUngtti90h++Asn/WrsgPXYoyQGsCSJqeVPIpSC/Rre3zFPAkqWTTJCVk2mmS+zki3iXNJB9/PDm2VUgPK6qu4yV1xu+Uz2ffvn2/V/XDhHNvZmZmZmZmZplLwRgADcpjFC9/rE0wFS/FHF5nvA8kjQa+RapTPbqmyeuT9mJME9vKL/xsTwxFn9eq+lA927d3Xu4jaZ+K7YWqmsxv5IR9rWJ2b/l4iv2cJKl2JnJZuV5yw3Pw/+zdedhtY/3H8fcnIlPGpCIHkUSUDBlPipIhkjJliEiFEKmI+vXzU4g0UeSYUzJkypRjTMaDo4zHIfOUeeb7++N7L2edfdbez977PM95zuHzuq7nWmevda/7vte0z3V9172/d239JJOY9uj75ASXXwW+KukxcsT46cCJTS8cunQOGVzcXNKFwIeBXw6Q63tEWfZyngYkaTcydchgjVKuG+heDUn3AHOUsp1Gj1d1tRuZ3uuI9cfI3N8DBfTb9WN808Yp9B1Qd0+b9VX/qv7OTV7jhyLixQ77LM3kPzeV+9qsf5YJvxio9+3BMkp/IhHxjKQnyfukbqieTzMzMzMzMzNrw4F1m1xNI4YHLNNmpHEvdU5uH9rt0zSydrqyvIEcydqLXo6laucyJozcbfJYH+300o9JfskSEf+R9HEylc665Cj+9YD1gT0lrdSaRqUbEfGKpD+TucX3L6vbpoEpJuc8NSqpcg4mg7/bk78yeKgKSEp6gPYjpXvR1/PSorpHe31O2hkDrAx8jIHPfZOp4Tugk3Yj8gfjWlQG+vVXt/X0dW2H6vk0MzMzMzMzs/YcWLd+PVCWCzVtlDQ7OVL1OTJNytTShwfL8v1t6lygYV012nR0ROzWRz+7VbVzSkQc1uU+D5CjjRdiQnqZugXL8sHaumok7KythSVNB8zX1FAZRX5++avSmxxNBvP2IieJ7MfxZGD9s8AdEXH1AOX7OU8D2bAs946IY+obJM1Em3PSg473alHdkw92KFOva8E229vd2+2cTU6uubGkPQf4tUBTP4bzO6BuoPNR9fdx8hmYT9JMEfFCh7q6em5o/t7ox2O1vs3QOmpd0mxMOlodGNLn08zMzMzMzMwaOMe69es64AVgeUlNuae3KMvLBxiZOqX7cGVZbtSa91nS25gQYK27GHgNWLcEnofKhWW5QQ/7XFaWm7duKBMvbtxSDiYECxdrqG8NMp/8gCLiXiZMnrlUN/u0qecKctT048BRXezSz3mqApTtXibOWZZNKYI2pv885MAb5+peMmC6Rut2SeuUPtxWJsTtpMoh33QPTw9s1GP3/gbcAswP/KBTQUnvlPTh8nFq+A6o+3LripbzcQW8kXf9KvKabtqwz5JkGphnmHh+iU7PzVp997qm9O1q8v/mpuu4SQ91DcrzaWZmZmZmZmbNHFi3vkTEc8AfyHvo15JmqbZJWgzYu3z85VTWh78DdwIfAnZtqXIvYOGGdu4HRpGTVx4naZLc3ZJWkvS5vg8m27kKuAj4pKRDJE00MlbS2yStJWmV2uqjyODmpiU4+0ZZMrXK+4BrSt2VS8pyC0kjavssTJvrJWlXSe9u2PTZsuw1r/dEIuKjETFPRHTKmV6V7ec8PUZOJLtIm5cj1Wj/bcsEklVdSzAhODm5qnN7SH2CUknzAQe2lOnkYrK/iwPfadm2N+1HbjcqQe8tgBeB/ST9X/1ZKn2UpPWBaymTwk4N3wEtVpb01XqfgR+RI9ZvjIgra2WrPv2o3PfVPrMBvyKD7ke0jBivnpvdJc1c2+fTwLcH8TiOKMsfS6pPbLogsE/TDkP9fJqZmZmZmZnZpJwKxibH94AVgTWBcZIuAWYhRz2/AzgsIs6emvoQEa9J2ga4ADhY0ubAbcASZLD998DXmDDCubIzGXTflBy5PoZMLTEf8AEygP0LcjLOybE5mcrh28CWpZ1HS/3VBJO7UkYtR8S9krYnA/9nSrqCHHX9sVL+YWDLegMRMU7SsWX9GEmXkudsRTItyDuYNDi7L3CQpBuBO8jA40dKG48xITA8pfR6nl6W9Dcy7/SNkq4nr/EVEXE0mTJj97L9NknXAHORuapPB5anx4B1g0PI+3Jt4A5JfyfP46eA2Uo7vx2okoh4XdLW5MuFn0naFLgVWJIMth8JbNdLxyJiTAkQ/4V8wbSzpH+Q98/swMeBd5PB9/qo/qnhO6DyW+BISTsAd5H354fJkefb1AtGxCmSfkfm0x9brsXzwEjy3rmKvOfrTgL2BFYC/l3ukQXIc/NzJn3J0a8TgC+Qv565TdJF5H3yaTK4H0ya7mdqez7NzMzMzMzM3vQ8Yt36FhHPkIHHfcngzfrAquSo1s0iYpepsQ8RcTmwCnAeOQp93bLv6sA9pdjjLfs8T6Z72A64ngxibggsQgbx9gQOGoTjeZgMVO5GBsiWI1OezE9OnvpNWiaYjIjjgdWAs8iXA18EZiIDjctGxK0NTX0NOAB4GvgMGTTen4bUGMVOwB+BmcnA8GfJ9DgHAR+JiLv6OuA+9XOeyGt3HDA3sBmwLXnNiYjHSx0nAjOQ99H7gB/S/pz02ufXSr27kJOufoa8p24r/f1iRLzeZV3/IAO855IvdtYlXyx8kpLypI/+XVHq+g5wDRmY/RI5sel4cvT3ohFxUW2fYf8OqPlTaf814PPkvXAGsGJE3NBaOCJ2IF8u3cCEyT4fIdPhrFGe+Xr5F8iXICeRL0I+R/4f+mXg14N1EOUXBF8u/Xi0tLMMOcr+CzRPbDpVPZ9mZmZmZmZmbwWaMqlvzaYNks4lg1IrRsQ/h7s/ZtaZpFHAVsAnI2L08PbmzWns2LG3zDjjjEssumhTKn0zMzMzMzOzITFZ891NCR6xbm85kuYq+Yrr6yRpJzKofic5gaCZmZmZmZmZmZnZJJxj3d6KFgOulHQTmZJjOjK1y8LkRKBfC/+Uw8zMzMzMzMzMzNrwiHV7KxoHHA7MSOZMXrv8+0RgBaeTMDMzMzMzMzMzs048Yt3eciLiEeAbw90PM5t8EbE1sPUwd8PMzMzMzMzM3mIcWDczM7OO7nvkOfY9bvSQ1H3ij0cOSb1mZmZmZmZmQ2mKp4KRFJI65q+WtF8pt98U6pa9RUj6V7m3bhug3NZN96CkUWX9yCHs4+jSxoiW9eMHenaGQ+15HehvxFTQ15A0fpDqGlE7ttckva9D2T1rZUcPRvuDbXLubUlzS9pH0j8kPSrpFUmPSbqkHPu7hqDLb0mSRpbrNGq4+2JmZmZmZmb2VuYR6/aWIenjwIfKx8UkLR8RVw9nn95kbgTGdNj+7FA2XgL3dwOXRMTIoWyrwduATYGD2mzfYjAbK0HVrYBPDvecAJLWB44FZgeeBP4JPAHMDawIrAb8QNJKEXHLsHXUzMzMzMzMzGwQObBubyVfKcsHgfeQwc5pKbD+KeDtw92JDk6PiP2GuxPDYDwZVN6ChsC6pKWApYDrgY9N0Z4NMUmfAU4DXgd2B34ZEa/Uts9Anpf9AY9aNzMzMzMzM7M3jSmeCsZsOEiaHtikfNyyLDcp66cJEXFXRNw63P2wSbwEnAIsLenDDdurFzrHT7kuDT1JM5Mj1d8GbBcRP68H1QEi4uWI+AOwLPkCwszMzMzMzMzsTWGaC6xLmrnk8h0r6QVJT0m6VNImbcq/kZda0jdr+91dcv+qbPuYpLMkPSHpGUmnS1pwMPpQ9vmopHNL2acknSdpuXa5vMs+M0jaRdI1pU/PSbpa0rZVv1vKRzne6cqx3S7pJUn/kfRTSTO26duskn4o6WZJz0t6uuRG3qDD8XxC0hkln/JLpd3fSHpvQ9kqB/fWbepqzB0uaQVJp0m6p7TxUDn+/5M0a7u+tfEZYF7gioi4ELiSHEH7mR7raVSu1SnlOP9cRuoi6T3lWlwi6X5JL5fjOFXScj22Mcl50oQ836MlzSTpgNr5ulPSd5vulbLvuyQdJOk2SS9K+m+5R1fr/0x0fSxzSNqpPAdVfx+X9DdJa7bZ543c85I2k3RVeS6eLM/P3aXo6po4t/uohrp6eka6UAXNJ0r5IqlKEXMX8I9OFUhapdzvj9SeqcPUkp+83ANblY8XqyGPfT/ntw9bks/UPyPimE4FI+L+iBjfchxzSzpQ0h3l/nui9G+tpjo04ftteuX3753K799/S9qmVm4NSReX77H/SjpW0txt6uy6D/VnrU1djd9zmvj/oO0k3VT6/ZCkIyTN0aa+EZJOKtftWUlXSlqnqWwp/3ZJOyi/Ix9TfpePV/6/1vb/JjMzMzMzMzPrzzQVWJc0G3Ap8GMyoHMWcAWwPHCSpEM77HsImabhUeBCMv/vT4H9JK0MXAYsBPwdeAj4PHCRpJkmtw+SViplPksG2M4B5gMuJ3MQN/V3ltLPQ4ERpexo4APAkcBv2x0rcALwQ+A+4HxgNmBP4KiGdt5N5kT+ETAncEH5vCxwmqS9GvbZgjxf6wG3AaeSo3Z3BK6XtHiHvnWlBJCuLG2ML22MAeYB9irLXrSOGj6+ZX3flEH+s4GNyGvz5Yh4uWz+PHmfvRe4GTgdeADYELiiXRCxDzOQ13p74N/AxcD7gAOA/2no8+LADWT6junIe/ImYA0yWLvZIPWrnRWBw8ic93eQ6URuA9YCzpP01Q77fg84DniZfP7GkvfGX8r2h4Fjan+XN9TR9TPSpcuAe4HNpIleZKwOzF/aa0vSzuT3ynrAncBfgReAnYB/SnpPrfgx5PcIwHlMfKxVHvvJOb/dqoK8J/a6o3Ki16uB75D37unk/fjp0r9dO+z+J2AP8hxcSn5v/0HSNpK+SJ6T2cjvsufIZ/z0lusyuX3omaSfAb8Gngb+Boh8Xv/a0LdFyO/hTYDHgDPJ/6/PBL7UponjgMPJ83EleQ/9B1gV+PpgHouZmZmZmZmZTXs51vcnA74XAhtGxLPwRpDwEmAXSedHxDkN+24MfLyaPK/sM4YMqmwJ/CAiDi3bZgDOJYOMmwBH99uHMmL1aGAmYM+IOLCqSNI+ZIC+yYFkQOQ44Bu1dt5FBld2kHRmRJzdst+CwPPAktUIUUkLAdcBm0vaNyLuqpU/GlgC+Bmwd5XKQdLCZMDxJ5LOiYibyvoFgN8BAawfEWfVjvNg4Ntkeojl2xxXt/YgA0/LR8R19Q2Slgce77YiSe8E1icDsX8qq08GfgGsL+mdEfF0P52UNBcZlF4B+FlEfLelyBXA0tX5q+33GTLw9RtJi0bEJCP2e/QJMri7WEQ8Vtr4ODlKeldJB+olpl0AACAASURBVNTuoemAP5OB913IvNjViNqPkgHJ30m6MCIemcx+tXMbsHJEXFlfWdr/O3CIpD9VfW6xJbBGRFzSsu8Y8uXGrRGxdYe2e31GBhQRIelE8qXPqmTAFyaMYD8BmKtpX0krAoeQgfn1a8+agL3J74jDyO8wImJr5Sj8RYAD2kxeOjnnt1sfLcvr+9j3cGBh8vtt29r3zipkYPxASRe1PjfktXsGWCIi7iv7fJI8pv8lA+SbRMRfyrZ3kkHmVYCR5Aunye1Dv7YAVoiIMaWdecjnc1WgOobKb8gXt78BdoqI18s+2wG/b61Y+UuFLwPXAKtFxIu1bTMBywzSMZiZmZmZmZlZMWwj1jVx+oKJ/oB9G8rPAmxLTpL3jXpAqOSd/kn5uHObJvepguq1fc4GZgburYLqZdvLZNAVcsTp5PRhDWAx4FYmndhwfyakr6gf67zAdmXb11raeRTYoXzcoXXfYqd62oWIuJsJI7RXrbWzDLA2GXjaq54fOSLGMWE083a1urcjXxKcVAXVS/nXyaDiA8ByJVg4OeYFnmoNqpe2ro6IZ3qo64ulz+dGxBOljifIlyczkcHYnpURr5eRQfW9GoLqRMTNTYG5iDiPDG4vAizZT/stXifzXD9Wa+Na8hhnBj5eK7teafOkiDisHtSPiBvIEe6z0JLWpAv7dniux9QLRsTdrUHfWvu/Bt5JBhubHNUaVO9DV89Ij44ry80BJL2DvLeuiYjbO+y3F/ldvH39XinX5SfkKOovlEBsVybz/HarSq/yaC87lZd265Ijt3du+d65nAx4Twd8o00VO1dB9bLPxWRw/z3A2VVQvWx7mnwRCBN/l09uH/qxTxVUL+08xoRfHr2RfqmMVl8L+C/5Mvb12j5Hkt/XreYtyyvrQfWyzwsR0TENUWXs2LG3NP2R31NmZmZmZmZmVjOcI9Y75eRdBli6Zd2yZBD0qoi4o2Gf48hRnStLUsMI4Asa9hnXYVs1YrWegqGfPqxUtp3S2qeIeE3SqWQAu2514O3A3yLipdZGIuJGSc8ATTm6XyFTxrSqAnv146lyLZ/RZsR0lUKj3k4VdJwktUVEvCTpz+Qo6FWBqxrq7NZ1wBaSjgIOiYixk1FXle7luJb1x5Ej2bdg4l8ldGNRYBSwABkQnWQUaUWZt/uz5Cj+d5GjagGWqtV1c4/ttxrfJnjb6bqf3qaupuvejRvJX4E0ubd1RRk5/ynyGZkPeEfZtGjLstVfe+xXq16eka5FxL/KC4SNJe1EvsCYnQ6TlpZfenyKHIV9UUOdIekKcnT4suRI6q5MxvkdaquU5TkR8WTD9uOA3Wh+wfEy+cugVuOAj9H9d/nk9KFf5zesa7rnVq717bmGff7IhP9XKreSaW+2kXQLcGpEdP2rHjMzMzMzMzPr3bAF1julalBORNgaWK8mxRzfpr4nJT1FBrLeCTzVUuT+ht2e62JbfTLDfvpQ7fOfpn1oCDiSOdUBdpS0Y5v9IIP8rR6MiNca1lej3uvHU7XzU0k/7dBOfaRsx3NQWz/JJKY9+j4ZeP4q8FVJj5EjNU8HTmx64dCkpK5ZHXiSzMdddyZ5jUZKmr8+CrYLvyGfn+8OEFRfigwEj+hQ12w9tNtOu753uu4nSzq5Q5295rE/PSL266agpPnJ69H6nNe1Oy9Nz0wvenlGenU8+cuUz5EvbF4lA6HtzA1UE/G+quZ5ZitdX4/JPL/depxMJ/QuMvVMtybnO+Sh+gjumkH9Lh+gD/1qekab7rmqzXb3+STrI+JpSV8jR+f/DjhC0m1k6ptjI6Krl5xLLrnkh5vWl1HrS3RTh5mZmZmZmdlbxbSWYx0yt3fPZQbIYd1rfut++tBun6ZI2nRleQM5oWQvejmWqp3LmDB6v8ljDesGaqeXfkySkigi/lNyhK9BpmxYnRwBvD6wp6SVIuK/XdS9BXmORU5G2679zcg88906ueyzq6QzImKSwGLJkf0nMpB9ePkbBzxbRiLvT07E2TGa2qV+rvu5QKcc6rf2350BHUkGfU8lJ3e9DXgmIl6XtD1wBO3Py4tt1ndrcvPZd3IieR/tRI6KviA656mvrsUz5Lno5J4e+jE557dbY8jA+sdoniB2IO2uQ3TYPpjfO/32oZ2OqdUG+D+orrouPR1LRJwk6UJywuS1yO/MHckXtAdGxJ691GdmZmZmZmZmnU1LgfUHynKhpo2SZidHij9HBqmmlj48WJbvb1PnAg3rqpGNoyNitz762a2qnVMi4rAu93kA+CB5DppSjyxYlg/W1r1clrO2lK3SVczX1FBEvEqmTzi/lH0/mbJlDTIv9SQ5zRtUecJnZ0KKhSZfobfA+pHkxKS/AS6WNLIhFcvi5e/aiGj65cHCPbQ3mKrrfnhETG5alZ6VuQrWBB4GvtQweny4zstki4gHJf0d+HRZ1TYNTPEY8BLwSqdf8fRiCp7fs4F1gE3JFFjd6vg9yoRfVDzYZvtg6KcPbb/Hiqbv8n5UfVuwzfZ2/5dUc3AcCRxZXux9hnwJuIekURHxr0Hqo5mZmZmZmdlb3rBNXtqH64AXgOUlNeUGrgKol/cwMnBK9KGaaG4jtQyXLvmVN2yo52LgNWDdEngeKheW5QY97HNZWW7eukHSDMDGLeVgQnBqsYb61iDzyQ8oIu4lR9/ChPzkbUlalkxf8DAwfUSo9Y98Bu4DlpTUKW1GU39+C3yLzI/8d0kfaCkyZ1lOkgJC0pxMyHU+pfVz3QfT7OR5nyQli6TpaX4mulEFPof7heHRZJqU+2ifxx544+XRaGAuSat1Ktui07EO1fltdSw5cemKkrbqVFDSeyWNKB+r0e3rSJqjoXj1PXpZw7bB0k8fHiPz8y9UzuMbynff6gyOK8ryc+UlSatNuqkk0t/IFyAwOJMkm5mZmZmZmVkxzQTWyyRufyD7/Ot6wEHSYsDe5eMvp7I+/B24E/gQsGtLlXvRMHo0Iu4nJ8ZcFDhO0iS5lSWtJOlzfR9MtnMVOWHiJyUdImmikZiS3iZpLUmr1FYfRb5c2FTSOvWywP5kaohrWnL6VpMNblELriFpYdpcL0m7Snp3w6bPlmU3ebarSUtPbpNTu0rP8KeW8l2LiF+Tk7W+jwyu16/nncDrwBr1FzGS3kGmhZmr1/YGySlkmpetJX1X0kQvNiTNIOkLJT/8UHiEzG2/pKQ3fkVQXiL9jOYXMN2oAp+LDPELqY4i4sSImCciFoiI57vYZX/yPjmm5VkD3ghKf7NldTWq+YMN9Q3V+Z1I+T7cmuz7keWZbb2Xppe0JflSckTZbxwZ7J0N+EV9H0mfINOXvEb+GmRI9NOHiHiZnJB5LuCbtfJvBw6h/ej3Xvt2J/m9PCdwQPlurdrahkknLkXSR8sz23r+5wRWKB8nd24CMzMzMzMzM6sZ7pGdvfoesCI50necpEuAWchRz+8ADouIszvsP8X7EBGvlWDIBcDBkjYn8x0vQQbbfw98jQkjUCs7k0H3TcmR62PIYNp8wAfIQO4vgHMm83g2J1OtfBvYsrTzaKn/g+TEhLtSRnhGxL0lR/Mo4ExJV5ATs36slH8Y2LLeQESMk3RsWT9G0qXkOVuRDG69g0nTHuwLHCTpRuAOMu/wR0objwEHdjqoMqK0Gtl50gDn4I/AbuTLgj3bTI7YVkQcVoJfh5DB9dUj4p6IeETSUeT1vbGkCHkBWJXMrT2KDExOURHxqqQNgfOAA4BdJN0EPE2ms1gcmIMc2XxzD1VvUH9x0uCwiLi+tP8z4H+BS8p5eYIMAL4b+DW1wGW3IuJlSX8jc/HfKOl68rm6IiKO7rW+KSUiLpW0C3AocFm5Fncw4bn4EDnJ5a9ru50J/JD8TlmTCfMgfDciHh+K89um7+dI+iJwDPBz4IeSrirtzV3anIOcPLiea34HcjT4lsDqkv5BfteMJJ+N3SOi1/kletVPH35MPjeHSvoy8BCwLDAzeQ46jtzvwY7kyPVvAWuWe3kh8nweDny9pfyCwF+ApyRdW/o1B/ld807gtG4nMDUzMzMzMzOz7kwzI9YBIuIZ8uf2+5KBpPXJwMG1wGYRscvU2IeIuJycyPA8chT6umXf1ZkwIeHjLfs8T05Atx1wPfkz/g2BRYC7gD2BgwbheB4mA9y7kcG85cgUIfOTk6d+k5Y80RFxPLAacBYZ9PsiMBPwW2DZiGia9PJrZBD3aTLv74LkSN1N23RtJzLgPTOwNjlS/TXymD8SEXcNcGhrkUHE8QMFlCLiGnJ0+XuBTw1Qb7s6DgW+Qx7XxSUfPGSAbHfg7lL3qmQqlo/T22SUg6pco2WA/ciA5ypkvux3AZcC2zAhZUy3liYDi+3+3sgNHRH7l3U3kbnvPw3cSN6L1/Z3VEA+L8eRQd3NgG0ZvBQdQyYifkUGTU8gRyqvD3yCHA1+ODkhZb38dWS6klvIe33b8jdb2T5U57ep76eRLwH3I18aLg98ibzHbwL2ABap5/cuv8pZDjgYeBX4Ahmgvgj4TET8fDD72KbfPfchIi4kr8015MvE1clR7MsB4wexb3eQ1+pPwLzk9Rf53Xxywy5Xkb+Yuo58+bgxE87/VuT1MDMzMzMzM7NBpKFLR27dkHQuGTReMSL+Odz9MTMzqxs7duwtM8444xKLLto0tYiZmZmZmZnZkNDARYbXNDVifVolaS5JC7ask6SdyKD6ncDVw9I5MzMzMzMzMzMzM+vJtJZjfVq1GHBlyZ08jszfuySZPuEF4Gvhnw6YmZmZmZmZmZmZTRM8Yn3KGEfmSZ6RzLO9dvn3icAKETF6+LpmZmZmZmZmZmZmZr3wiPUpICIeAb4x3P0wMzMzMzMzMzMzs8nnwLqZmZl1dN8jz7HvcaOHpO4TfzxySOo1MzMzMzMzG0pOBWNmNpWTFC1/r0h6TNLNkkZJ2khS2xelZZ/xLetGlvWjhrr/7UzpPkiaTtKPJd0l6eXhPn4zMzMzMzMzm3Z5xLqZ2bTjmLJ8GzA7OTHylsBWwJ2SNo+Iq4erc9OAXYB9gAeAU4EXgcuHtUdmZmZmZmZmNk1yYN3MbBoREVu3rpO0CLA/8CXgYkkrR8SYlmIfAl4Z+h5O9TYoy1UjYtyw9sTMzMzMzMzMpmlOBWNmNg2LiLsi4svAUcDMwB8aytwaEXdN8c5NfeYHcFDdzMzMzMzMzCaXA+tmZm8OuwPPAR+VtEp9Q1OO9Zbtc0n6raQHJb0kaaykr7aUWa7Uc0WHevYtZfZuWT9C0kmSHpf0rKQrJa3ToZ5RpZ6Rkj4j6WJJT5Z1c9TKLSHphNLvlyXdL+lYSR9sqg9YqHY+qr8RA50jSVuX7fu1rB9d1SFpM0lXSXpG0pO1MutI+oOkf0t6WtJzkm6U9H1JM3ZqS9L7JZ0o6VFJL0i6VtJ6bfq4gqTTJN1TruFDkq6W9H+SZm13rs3MzMzMzMysPw6sm5m9CUTEU8C55eMne9h1DuAfwIbANcAVwOLAUZK2q9V/DXAdsJKkD7dWIultwDbAa8DRtfWLAP8ENgEeA84k/+85k0xf08lm5ZhmKctrgCj1fgq4tpR5APgL8AjwFeBaSavW6rmczE//XPl8TO3v2QH60I3vAccBLwNnAWNr244CNgaeAv4GXAYsAPwvcI6k6drUOYI83pVL/28AlgVOl7RWvWB5SXElsB4wnswfPwaYB9irLM3MzMzMzMxsEDnHupnZm8cY4ItkTvVufZ4MSm8VEc8BSPo8cDo50eeRtbJHAL8DtgN2balnLWBB4MyIuL+2/jfAvGW5U0S8XtrYDvj9AH37GrBJRJxcXylpFuAEYCZgx4g4vLZtV+DnwImSPhARL0XEkcCRkkYCszTlqp9MWwJrRMQlDdu+DlxQndvSx9mAE4F1gc2BYxv22wr4JbBbRLxa9tsFOBTYGzi/VnYPQMDyEXFdvRJJywOP93lcZmZmZmZmZtaGA+tmZm8ej5XlnD3s8zSwfT3wGxFnSLoZWErSiIgYXzadCBwEfEXSXhHxUq2eanT7G8HyMlp9LeC/wJ5VUL20caSkbYCVOvTt7NagevEl4N3AZfWgeqn3EEmbk6O7NwT+2KH+wXJUm6A6EXF6w7pnyguAdckXG02B9XHA7lVQvfg1sC+woqQZIuLlsn5e4KnWoHpp6+puD2Ls2LG3tNm0SLd1mJmZmZmZmb1VOBWMmdmbh8oyetjn2oh4omH97WX5nmpFCb6fAMxNBq2zUWleYH0yJcs5tTpWLstz6oH7moGC3n9ts75K83JCm+3Ht5Qbau36CYCkRSXtIumXJd/6KPLXAACLttltdES8Ul9RguzjgLeT16ByHTCHpKMkLdnXEZiZmZmZmZlZTzxi3czszaPKpd0UKG/nvjbrq9zjrRNsHg7sSKZpqQLjW5PB3j9ExGu1su8ty3vbtNFu/UDbq3rHt9lerX9vm+2DrbGfkkSO8N+VCS89Ws3WZn0v1+X7wFLAV4GvSnqMzLl+OnBiyy8L2lpyySUnyZ0Pb4xkX6KbOszMzMzMzMzeKjxi3czszWOZsvxXD/v0MrqdiLgJuAr4ZEn1ArBtqeeoluL9jKCve3Gg7kzm9m4N9H9lu35+GdgNuJ/Mff8+YIaIEBMC4+0C7l33PSL+A3wc+AyZl/0BciLTPwBjJPWSGsjMzMzMzMzMuuDAupnZm4Ck2YHPlo8XD3Fzh5MB4W0lrQ4sRk7QOb6l3ANluWCbet7fZ/tVvQu12V6192APdb4CzNpm2wI91FNXpcvZMSL+EhEP1NK7LNxnnY0i4tWIOD8ido6IpYERwN+BxYG9BrMtMzMzMzMzM3Ng3czszeJgYBbgmoj4xxC39SdyQtKtybQwUJu0tOaKsvycpFkatm/SZ/uXleXmbbZv3lKuGw8Cc0uaq2HbWj3UU1eNFP9Pw7Yv9VlnVyLiXuCn5eNSQ9mWmZmZmZmZ2VuRA+tmZtMwSQtLOplMx/JcWQ6piHgBOJac2PTLwKPAGQ3l7gQuIgPMB0h64/8cSdsAK/XZhT8BDwOrStq+vkHSzsByZI7y03qo85KyrCYVRel7k9HPagLY7Uu+9areVYE9+qxzEpJ2lfTuhk3VLxgGymVvZmZmZmZmZj3y5KVmZtMISaPKP98GvJNMwbI4mZblDmCziLh5CnXnCGCX8u9RtRQnrXYkR65/C1hT0vVkCpcVyJQyX++14Yh4TtLmwJnAESW4fjt5Lj5KvmDYrNtJO4ufknnQvy1pJHAXOdJ7AeA3wDd67SdwGDmq/xvASEk3kXnWVyF/YfCdPupssi9wkKQbyftAwEeADwKPAQcOUjtmZmZmZmZmVnjEupnZtGOr8rcpsCrwGjlyfCNgiYi4dkp1JCL+zYRc50d2KHcHsCI5ynxe4PNk4HcD4OTJaP8icmT6ScD8ZFB8PuB4YNmI6CUNDBFxC7AGMJp8YbEmGVz/BHBNn328vfTxTGAeYH0yj/sOETFoI9aBnYA/AjMDa5Mj1V8DDgI+EhF3DWJbZmZmZmZmZgYoIoa7D2ZmNo2RtBI5Ev2SiBg5zN2xITR27NhbHn3q1SV+f96TQ1L/iT8eOST1mpmZmZmZ2TRNAxcZXk4FY2Zm/fh+Wf5qWHthU8T8887CiT9eZri7YWZmZmZmZjbVcGDdzMy6UkapbwssCSwPXAecOqydMjMzMzMzMzMbBg6sm5lZtxYDvgo8Q+YN/1ZEvD68XTIzMzMzMzMzm/IcWDczs65ExChg1DB3w8zMzMzMzMxs2DmwbmZmZh3d98hz7Hvc6CGp25OXmpmZmZmZ2bTobcPdAbPhICkkxQBl9ivl9hvivowu7YwYynamNpK+W10HSZ8YxHpHlTpH9rDP1lPiWg8nSeMHuudbyu9Xuz7V3zOS/iPp/LJ9xND1uLFPI0o/Rk/JdrtV+jZ+uPthZmZmZmZmZkPPgXUzGy5b1P79lWHrhQ3kRuCY8nc2cCewArAvcJekn0uaYRj7N0VIGlkC56OGuy9mZmZmZmZmNvycCsbMpjhJHwWWBB4C3g18SdIuEfHKIFT/PeAA4N5BqMvg9IjYr75C0vTAJsChwK7kNdx8CvTlfuBDwPNToK1+fAgYjHvYzMzMzMzMzKZyHrFuZsOhGqF+BHAZMDfwucGoOCIejIhbI2JqDb5O8yLi1Yg4HlgFeBbYTNL6U6DdV8q1nSpfmpS+3TXc/TAzMzMzMzOzoefAulkPJJ1d0kGs2Wb7LJKelvSUpFlatm0v6WZJL0q6X9IvJc3epp43cklLeqekgyXdLekVSYfWyk0vaSdJ10l6tvxdLWlHSdO11Pm5UuedkmZt2SZJF5Xte5R1e5TP/9vhfFxcyqwy8Nl7Y5/pgE3Lx+PLH0ycGqZpv/dL+pWkO8o5fLwc6/clzVQr1zbHuqSPSDqrXJ+nJF3QKb97uZ7flTRG0pPl/N4l6c+SPtPl8UrSppL+KOl2Sc+VXOVXS/qGpEm+h2v5zbeWtJSkv0r6b9n3EkkrtWlreknfq52jcZL+Z6hStUTEreSodYCdG/ojSVtJurScvxck3STpO5Le3lB+fDlulfv6RknPSxpTtk+SY13S7mXdAe36Kemc1udW0qrlfrqpnNsXJN0q6QBJc7TsPwq4uHzcShPnnd+vVm6iHOuSNirr/tihb78pZb7Wsn5WST9Ufmc8r/xeuUTSBm3q+ZCk48r9+aKkR8t9e6ik97Rr38zMzMzMzMz648C6WW8OL8uvtdm+CTAbcGJEPFetlHQQOTp7UeDvwD/I1BkXAzN2aG8m4BJgG2AM8Ffgv6XO6YAzgMOADwAXlr/Fgd8Af64HbSPiHODXwCJln7rdgTVKfw4u644GXgK2Uab+mIikDwCrA7dGxOUdjqHVmsB8wD8j4k7gz6Wd9VoDmrW2VgNuAr5Jfm+dQZ7DeYD/JVORdCRphbLPOsA44JzSj0uASQLV5fyeT6aVmRsYXfZ5GFiXCS8HBjIjcCKwFvAIcCbwT+DD5PX4Q4d9Pw5cBXwQuAi4A1gNuEjSkg3lTwL2L8f1N+BmYDfgVEBd9rdXVdB45XoAv9x7JwOjgKWBa4HzgHcBBwKnN71UKA4n78NHyHt+XIf2TwJeBzaVNMkxSpqHvOceIp+9yoHAdsDLZf1FwDuB7wKXa+KXT5eXvgPcxYSc88eQz2U7ZwFPAeu31Ff1bXpg49KHU2rr303eIz8C5gQuKJ+XBU6TtFdLPR8DriO/Ux4FTivlZwB2Ie8fMzMzMzMzMxtEzrFu1ptzgP8An5f0roh4tGV7FXD/fbWijC7eHXgCWC0ibinr5yYDeit2aG95Mhi8cEQ82bLt22T6lJuBT0fEI6Xe95AB8g2Br5NB9soeZAB9G0lnRcSpkpYmg9NPAltFxOsAEfGYpL8Am5HB6DNa2t+ODNb+nt5UaWCOL+08Kels4AvAF4Ej64UlzUkGHWcn83n/IiKitn01ysuGdkoAdxQwM/C9iDigtu1/gL0bdluVDLifAXyhOi9ln9nJlxndeBXYCDgrIl6u1fEu8n7aStIfIuLShn2/CXw3In5W2+8Q8trvCWxZW78pef7GkffZ/WX9QsClwPxd9rdX/yZfjLwDGAHcXtZ/hwwaXwBsXj0ryl9ynASsB+xIvlxo9QXgo9Wz0klEPFBGsK9Bpqa5rKXIl8j/6/4YEa/V1v8Y+EdEvHHvSJqRfOm0PflC4seljSMl3Ql8Brg8IrYeqF9lv5fKM/RV8nk8rqXIWuTLodPr/SBfai0B/AzYu5p7QNLC5Muen0g6JyJuKuV3Jl/CbRQRp9YbkPQh8tk2MzMzMzMzs0HkEev2ltaS0mGiP2Df1vIlMHckORJ0y5a6lgRWAG6IiOtrm75elgfXA4UR8TgZ6B7Izg1BdZiQeuPbVVC91Ptgrd6J0nNExAtkoPxl4HeSFiFHU88AfD0i/tPSxhFluV19ZRlpu1Wp59gujqHab1ZgAzLYXE+PUaWD+cokO+XLineRgelD60H1ckyXRsRTAzQ9khzJfzvw05ZtP6J5otN5y3J0Pahe2nwqIq4boM2q7KsRcWo9qF7WP0pOtArw+Ta7X14Pqhc/KcvVWtbvWJb7VEH10s7dwP9009d+lHNTBYXnhDfujz2AZ4DN6i+gyi85vkYG43doU+1Puwmq15xQlk0TqG7WUqbqxzktwWwi4iXypcWrtL8mveqpb5KWAdYGrgT2qk/oGxHjyJd00zHxM1ndq/UR+dU+/y7fCQMaO3bsLU1/5K9czMzMzMzMzKzGI9btre6YDtuWIVNYtDoS2IcMbB1cW1+NVv9dS/kq//ifWiuKiPMlPQHM1aYPD0bEta0rJb0feD/wUERMEkwjU1A8CXywdWR9RIyRtDc5GvZ6Mv3FcRFxckP/LpX0L2BtSe+rBWzXI9ONnBwRj7Xpe5ONyFHjZ7XsdzY5on9VSQtGxD21bZ8uyyPoX3UN/twQmH9V0inkCOW6MWSKkT0kPQScHRHP9NuBEjBdC1iQPAci0wZBpghqcn7rioh4XNLjwBt5s5X5ylco/T2ldR9yhPjknL+BVClYqnP7UXIk9rlN90dEPCzpDmBJSTOVFz51f+2x/b+QI9+/KGmn2gjv95O/Ori9zXP0PvJeXpx8DqqXzS/T/pr0ajRwP/BpSfPWflkyMxm8f5p8XitVHvgzWu/Vokq7tFxt3XVkMP5YST8Brm19GWRmZmZmZmZmg8uBdXtL65TSoUxKOElgvaSeOAvYQNKqEXFZSSGxBfA8OQK87r1kwLF1NHjlXtoH1ptGUld1Aoxv2hgRIekeYI5StjVlzcHAl8mczfcD32rTDuSLgkPJdBbVyOdJUt50aaI0MLX+vizpz+QI5s3JPOGVBcryrh7bqqvOV7vzOcn6iLhdOZHrAWRg+jVJY8k89kd3O6K65B0fReec7LO1WX9fm/XPknnfK3OTvzp4sHVkPEBEPCPpSfJ+GFQlzc6ctd+P1gAAIABJREFU5eMTZTmiLNcuv/7oZC7yHqxrd50aRcRTJZ3QRsBnyTz2kCPCRcto9dLv3YD/I8/bkImI18vkpbuTz9wvy6b1gVnJe+nF2i4jyvKnklp/XVE3T+3fB5Ivj9Yrf09J+icZsB/V7QuhJZdc8sNN68uo9SW6qcPMzMzMzMzsrcKBdbP+HE6mNNmOzOm8ERkgPDoinh7Edl4cYPtAQct2ZT4MVJNfzkOOor65zf7HkAHIr5bRsPOTuabH0ZB6op0yOviT5eN3JO3UUqRKZ7EFEwfWK90ca9vm+6kjIn5eAv4bkCOJVyUDpLtK2jkimvKDt9qNDKqPJdOjXA/8NyJekbQYcFutf5N0ocuu9nV8g2QJMjj9PBNe9ExXlneQKU06eal1RUuguVsnkM/hZkwcWIeWl12SViRfLj1F5lMfTf7646Wy/QFqvwgYBCeQ981mTAisN6aoYcK5u4zOk7a+8UuAiHha0hrAymRgfSTwKfIXEt8rLwAn58WUmZmZmZmZmbVwYN2sP+eTQa+NJe1C5xHcD5KjUBcA7mzY/v4+2n+gLBfqUKaqd6L8ymV0/QnAjOTI8S2AEyQtVwUW68rkoicDW5PB5U+QKTOObJOqop3NmZBq4+Mdyn1I0rK1HOb/IVN1fAC4tYf26qrztWCb7W2vQck7/0vglyV3+Cbk5JI/l3RCm/z3dRuW5aYRMbZl28ID7Nutx8j0JfNJmqF11Lqk2RiC0erFJmV5eUS8Wv5djbQf2+1En4PgbDL90foll/+CwFLAPyOi9bmrrsneETFROihJM5FpjgZNRNwg6d/AimUC0v+SL6ceJCcarqvO3SkRcVgPbQSZJuZyeGNy3F+QL3X2J0fLm5mZmZmZmdkg8eSlZn0oQazfAzORk5yuDtwSEf9oKF7lRN64dYOkNWmfBqZT+/eS6TLmKyNVW+tdh0zPcVs9v3pxABlwPCEivkKO5l2qrG+nys+9A5kS5lUyvUkvtijLdSJCTX9MGKm+RW2/C8ty+x7bq6uuwUaSJhodXoLlG3VTSZmI9HjgGnKU9mJd7FalSWlKBfSlbtrtol+vAFeT3+lNx7JJw7rJJmlxYJfy8Re1TdeQo8E/KemdQ9F2q/Iy4RQyf/0GTJgsdJI0MHS+JhvT/AuC6mVFvy+kq35sVtqYATipIRd6db9v0Gc7wBuT4+5XPi41OXWZmZmZmZmZ2aQcWDfr3x/IYNu3yUBcu3zjVVB6N0kfqlZKmoucQLRfVUqJQ8ro1Kre+cicy/Uy1bY1yUDovcA3y+pvls+7lO2TiIirgBuBL5Cju8+KiAebyjYpE3cuBTwOXNCh6ElluamkKiXGkeSI7PUkfashML6qpNkH6MLFwO3kyPfvtGzbm4aR7JI+KenTJYd4ff2CwIfItCvtcqDX3V6WX2+p54vAll3s363qPvuxpPrEpguSk+0OGknTS9qcTFcyK3BsRJxTbS+/fDiIHCX/l9KH1jo+ImmwR1FXwevNyZcJrwGTTMrLhGuybZn4terTEkC7vObVrx4+2GffqnQ0m9M+DUz1rF1EvpQ4pIy+f4Okt0laS9IqtXVfl9T065W1y7KnnPVmZmZmZmZmNjAH1s36FBGPAKeXjy8Bx7Updzk5+ec8wA2Szip5u+8oRa7qswuHAOcCHwHukHSqpNPIoOGHSt9+WxWWNDc5yjyALSPiqdK/J4GtyvpRpVyTI2r/7nfS0lPK6OpGJVXKLcC7ybQzRMQT5MjuZ8gXBXdIOlnSmZLGAZcyYQRyu3pfJ1PZvAD8TNL1kk6UdBPwfTJ432pp8iXAQ5LOlXS8pPPIdDRzAL+IiAca9mv1MzLAe4Cka0u71wB/Jq/hYDkBOI1MmXObpNMknU6ez5vpP7i6gaRR5e+Pki4iX5AcT/7a4mByroFW+5MvSj5d+nNl2f/Cct1upPOErv24hHzZ8VkyTdIF5TltdTTwEJmP/LZyP10AjCFfFtzTukNEjAduAj4u6WpJR0s6UtL63XQsIu4m880vTv7C5daIuL5N8c1LW98G7pF0UTl3l5V+n8fE6ZS+DoyTdIukU0rZG8jvnReAH3XTRzMzMzMzMzPrngPrZpPnorL8SwkAt7MbsCOZY31NcpLBP5GTeU6S17wbEfEasD45An0cmbN5LXIyzG8CX2xJM/E74L3AQRFxSUtdo8kA6XtLuSbVsd5HBva6UkaeVwHUP3axS1WmCsYTERcDy5S+TU+myVgReAT4Hhls7Kik6VmJfBnxAWBd4FHyGlzRsMtZwE/IFxVLk+k7PkwGXjeMiF27OBYi4lJgFXKi14VLuy+TKVu6mfy0KyU90ZeBH5DH9TnynP2S/KVBvxObLk2+eNmK7PsHgX+SaUYWjojvNL0siYjXI2Iz4IvkLwYWLf1YAni47P/dPvvUqJyDk2qrTmxT7nFgubJ9BvI5eh/wQzoH+zciX1gtTP7aYFvgYz10sT5CvbFvpX8Pk/f3buQLuOXIe35+4Aby+T6+tss+5C9ogpy0dD0yJc7vgI+0SVFlZmZmZmZmZpNBvc09aGZ1ks4nA+WfLMHpNy1J3wf+F/hRROw3zN0xsylk7Nixtzz61KtL/P68gebp7c+JPx45JPWamZmZmZnZNK1p/rOpigPrZn2StDyZxuVfEbHkcPdnKJUJKG8F5gYW6jIFipm9CYwdO/aWGWeccYlFF110uLtiZmZmZmZmbx1TfWB9+uHugNm0RtIB5ASe65AP+feHt0dDR9I2ZD7o1YD3AIc4qG5mZmZmZmZmZm91Dqyb9W4TYAFgPLBnRPx1eLszpFYnc2s/Subq/t7wdsfMzMzMzMzMzGz4ObBu1qOIGDHcfZhSImJrYOth7oaZmZmZmZmZmdlUxYF1MzMz6+i+R55j3+NGD0ndnrzUzMzMzMzMpkVvG+4ODDZJIanjjKyS9ivl9ptC3bI3OaXNJJ0t6SFJL5fluZI2l9TXhAuStp7a71VJI6rnTtIdkhpf2EkaVcpsMqX7OBBJI2vH8KSkd3Qo+5ta2VFTsJtdkzS69G9EH/suIOmnkq6X9ES5lx+WdL6kb0iadfB7bN2qPW+jh7svZmZmZmZmZm9lb7rAutmUJmlO4BLgBGAt4FbgFODfwKeA44FLSrnWfauXPFtPuR4PqQ8AXxnuTkym2YF1mzZIejvwpcFsbHKC4INN0g7A7cCewPuAK8l7+RZgFeDXwJ2S5hm2TpqZmZmZmZmZTQUcWDebDCXQ+jdgVeBiYKGIGBkRm0XEJ4GFgL+X7X9rN5r7TeKFstx7Gj7Om4DXgC3abF8bmBu4for1aAqRtB1wOPAqOWHtfBGxbrmX1wDmIQPuMwMetW5mZmZmZmZmb2kOrJtNnt2B5YGbgXUi4r76xoi4nxz9PLaU232K93DKuRc4B1iYDMxOix4kX4Ss3fQLAzLg/jpw4hTt1RCTND/wSyCA9SPi2IiYKKVWRDwfEQcCKwBPDUM3zczMzMzMzMymGg6st5A0s6R9JI2V9IKkpyRd2i4vtKTxVU53Sd+s7Xe3pD2r3NqSPibprJKz+BlJp0tacDD6UPb5aMnn/VT5O0/Scp1ydEuaQdIukq4pfXpO0tWStm3KCV7qGS9punJst0t6SdJ/Sk7mGdv0bVZJP5R0s6TnJT0t6RJJG3Q4nk9IOkPSo6WN8SW39XsbynZMp1K/Ri3rV5B0mqR7ShsPleP/v27ySJdR2TuXj9+NiBeaypX1e5aPu0iaruoXsG9Zf3Qtb3dIGtnQ3vslnVjOyQuSrpW0Xof+LSXpBEn3l+N7QNLRTSlH6udQ0vLlXn28rFtmoHNRs19Z7l1G83dF0tySDlTmaH+xPCd/k7RWm/J93YtdOh6YgZaUL5LeCawHjAbuH+B41ivP4OPleG6X9D/1+0olVzawell1d/0eqJV7TznGS8q1rPL3nyppuck4zrpvAe8A/hQRF3cqGBH/joj/thzvApKOqD1Lj7Trn2o5wiXNIunn5bq9oMzrvl6t7MblmXxOmef9MEkzNdQ5vtQp5Xfav8p5v7/sM0fTsaj37/soz23TtsbvWtVS/UjaQNJV5XiekHSS8qVGU33zlHP6kPJ78wZJWzaVLeUlaZPS/4fK8f9H0oWSvtluPzMzMzMzMzPrjwPrNZJmAy4FfgzMC5wFXEGOND5J0qEd9j0EOAh4FLiQTBfxU2A/SSsDlzEhLchDwOeBi1qDRP30QdJKpcxngbvIUcPzAZcDK7bp7yyln4cCI0rZ0WSO7COB37Y7VjKX+A+B+4DzgdnIwPFRDe28G/gn8CNgTuCC8nlZ4DRJezXsswV5vtYDbgNOBV4CdgSul7R4h751RdI6ZP7o9YDxpY0xZLqLvcpyIMsA7wEeB84boOx5wBOlfBWoPgW4sfz7CuCY2t9DLfuPAK4BViav1Q3kOTy9KfgsaSPgWmAzchT2X0udWwPXSvpwm36uVuofQV7bS8kR2l2JiGvIe3ZEaWtAkt4HXA18hwxon04e36eB8yTt2mH3ru/FHpwKPA9s3rJ+IzL4fHynnSUdTJ7v1chfKpxNHtfewOjy7AE8S17rh8vnvzDxPVD5PPld8l7ylxGnAw8AGwJXtHv50KN1yrLnkfiSliJT42xPnrdTgTtK/66UtHGbXWcALiJz8o8BrgKWJr8XPl2u+4lkaprzgemAncjvp3Z+CRxI3g9n1Pa5pHy31vvd9/d9n75BXmOR6aOeBTYB/t7w/8DcpS/bA6+Q99NTwNHAHm3q/z/gJPL75XryOtxJntN2+5iZmZmZmZlZn6bVPMhDZX8yWHkhsGFEPAtQArmXkKONz4+Icxr23Rj4eETcUttnDBks3BL4QUQcWrbNAJwLrEEGVo7utw+S3lb2nwnYs6RqoGzbhwwaNTmQzPt9HPCNWjvvAs4EdpB0ZkSc3bLfgmTwbMmIGF/2WQi4Dthc0r4RcVet/NHAEsDPgL0j4pWyz8JksOwnks6JiJvK+gWA3zEhJcVZteM8GPg2cCwZ/Joce5ABruUj4rr6BknLk8HygVQB8jER0TH4HBGvS7qBnMx0GeC6iPhOGd26NHBkRIzqUMVWZNBwt4h4tfRzF/LFyN7kuaz6vxB5jl4A1oyIS2vbtiSDtkfTfA63IUff/6zT8QxgPzL9zQ8kjaqueQeHk+ljjgO2rd0jq5AvJA6UdFF1j9T0ei92JSKelfRX4MuSFoyIe8qmLYAXyeDo55r2lfQlYDfyxcAXav16O/ArMlC6H7BHRDwGbC1pNPBu4DtV+RZXAEu3Hr+kz5AB199IWrQ1dUu3yvdR9aKlp9zxkkS+3JiHDOz+oOqHpC8CJwNHSbo0Ih5u2f0T5Mu8xaoR8MpfnRxNvtibC1gjIi4r295LntfNJO0TEeMauvQV4BPVM638hcAZ5Hftj8hrU5mc7/t+fIN8Hv9e2pmZfNG4ErAp8IeWvi1W+v7liHip7LM2ec0nIukd5HfjeGDZiHiitm360oaZmZmZmZmZDaI37Yh1TZxWY6I/JqTfqJefBdiWHJ37RqAZICJuBX5SPu7cum+xTxVUr+1zNjnR371VUL1sexn4RflYpYHotw9rkAGYW8kR83X7A3c3HOu8wHZl29da2nkU2KF83KF132KnegAwIu5mwijeVWvtLENO9nglsFc9wFqCYruTI0q3q9W9HfmS4KQqqF7Kv06OJH8AWE5S40j8HswLPNUaVC9tXR0Rz3RRx9xl+UiXbT5alt2Mhm81Dti9CqoXvwb+C6xYgqOVXcj7bs96UB0gIo4lRzwvJ+ljDe2MJV+69K2c07+Sge+vdipbXrCsCzwN7Nxyj1xOBt2nI4OSTbq6F/twPPniZbPSz/cBI4EzI+LpDvt9vyw3benXK+R1eQjYrrwo6kpE3NzwUoGIOA/4M7AIsGS39TWYkzxWmHCPdmsksBT5XbJPPbgfEaeQ99ps5AubVq+R3z/1tDLHlj58APhVFVQv9T1ABvEhfw3Q5Ff1Z7p8t32LfFG3rUqKoEH4vu/HIVVQvbTzPPmyEGrHU14GfIUcqb9zFVQv+5xLXvNW7wRmBG6sB9XLPq+2fg+0M3bs2Fua/sh7zMzMzMzMzMxq3rSBdSZOqdD6d2ND+WXJgO7VEXFHw/bjynLlMkqz1QUN68Z12FaNpH3PZPahGol4SsNkg6+R6QBarQ68HfhbPWhT2+9G4BmgKX/zK+Qo01a3l2X9eNYsyzPajKa9vCzr7VTB0BNaylL6+ueWcv26DphD0lGS+g1KqmXZbfl+RhaPbh35XYLs48hrOXdt0xvnvU1dTee9cma/I59b7Ece5w9agv6tVinLcyLiyYbt1T3fdL17uRd7dR4Z4K3SwWxGfl+2TQNTXlgtDfw7Im5r3R4R/8/encd9Ntf/H388E7LvQjIja5oWIWSbKOpL9p1sWVIRFdEvkco3SaQvKSrrCFkSyj6yZsk2yjrGOpaRdQjD6/fH6/2ZOXPmfK7r87mua+aaxvN+u83tuM55v895n7Vbr/f783r/h0zPMy+wTDeNkTSrpE0k/VjSbySdIukUMqhNt/ur774fdVv35ezyvanr6f6NiYiHqitKB1rrFwKdfjer/lBfERH/Ir/5cwMfK6v7+73vi8sb1jU9q58sbft7RDzWUOes+oqIeJZMf7OhpAPUMBeFmZmZmZmZmQ2sGTYVTETs0m5bJf1GVSsQMabN/l6U9BIwDxmgealWpGkyw/EdbKtOstiXNrTqPN5UB2gKzAwty70l7d2mHmRwp25smwBaa8Rn9XxaxzlS0pE9HKc6grvHa1BZ39/A0XfJoORuwG6SxpEj6y8ERjR1ODQYV5YLd3jMhcqykzQzdU+0Wd/TdX+6l5hg08j5puelaxFxh6Q/AZuSI4Pb5ezvz/3u+Fksufzrufnvi4ifNB00IiZIOhv4evnlxY7kfftLm3ZCjtAH+LAaJsutWZCcP6BXJYf5RUy6r03m6mFbb/5NdoKIfEbbPWtN+nP/2k0A2+13s+rRNuvHkCmYWu3o7/e+L5qua9P722pbu3ex3fqdyY6FnwI/lfQImUN+REQ0BfWnMGzYsMa5F8qo9RU62YeZmZmZmZnZu8UMG1jvh05G605RppdRvt2OAO5LG9rVaYqszlSWdwBTpJjo8rg9aR3nOiaN3m8yrmFdb8fpph1T/DIjIh6XtDKZSmcjchT/F4GNgQMlfbqWoqJJ65cPn5D0np7yrJfUHxNzsnfR9olN7qLsTKX8ab2Uu7dh3X+6OE5vDiMn3vyupN/1Urbd+UUP27u5Jp+nknapuBZoDKwXZ5BpRP6XHOn8q17yxbee97E0j06u6qhzpYyWPocMqp9Y/o0GXo2IkHQEcDD9GHUeEW9K+ieZZ/2TdBdYn7ibPmwfyHe8N+2uT5++92309guwTvfTp1+2RMTVkpYmv2et531nYGdJ50TENt3sz8zMzMzMzMx65sD6JE+V5ZJNGyXNQ45eHE+mSZle2jC2LJdos88PNqxrBc5GRsQ3G7YPlNZx/hgRx3VY5ylgOfIaPNCwvTUqeGxl3ZtlOWe9sKSZgEWaDlRSqVxe/iFpCXLixHXJfO7f6aWtd5A5sxcBNqDn0cyfJydjfJrmVEQD6QkyJ/K+veQDn6oi4i5JFwCbM3ke/aoen3kmjdIe22Z7p20Z3oc6f5f0IHnvoIc0MEXreX+6p1/MdGn58u+2iGj6dcmHBug4l5CB9e1pmByzB73dv6b3dWoaAtzTsL71fXyqtuz2e/8WDd+Zoulb2xettg1ps73dt57yvo8o/yhzUZwLbF0mEu7pG2VmZmZmZmZmXZiRc6x363bgdeBTkpryFe9YltcPUA7qgWrDjWW5RT0XcBklvVnDfq4hJw7cqASep5Yry3LTLuq0Jivcob6h5OreqlYOJgXtlm3Y37pkDvJelXzGrZQ1H+2pbCk/Afhl+fNISU2pcyjrW/s9rjYBaatTYCA7ufpy3aeWw8iRtwfTnL6jle99Q0nzNmxvPfPXNWybFn5Hji6/MyJu7KlgRDxBpnf5mKR2geYmPT0D85XlFKPIJc3HpHz6/fV/wBtkAPYzPRWUtHw5Nky6L9u0+ZZM6/s3xahsScuTvxZ5hUm/0Onr934ssICk+RvqrN/nVk/udvKXI6tKagrWb9vpjiLiZibli+/1m2ZmZmZmZmZmnXNgvYiI8WQQ7T3A8ZLmaG2TtCzwvfLnLxuqD2YbrgYeAj4M7F/b5UE0jGiNiCeBU8gJD0+XNEWubUmflvQ/fT4ZJgZ1rgI+I+kYSZON9JT0HknrS1qzsvq3ZMBrO0kbVssCRwAfAG4t+265tix3lDS0UudDtLlfkvaX9P6GTa3RyZ3mGv8ZGQj7KHCxpA/UjrMYcDEwrJT7Wa1+a3Tqch0erxNHk9fwGElfrG+UNL+kr7brCBhIEXEPcB553zZp2D6aHC09F/ALSRM7QSStDuxNdgKdMLXb2iQifhIRC0bEih1W+RGZEua8pklxJS0labfa6p6egYeAd4B1qwFgSe8j08I0BXi7FhGPA/uRaUgukvSlho662STtB/ydHM0NOXnsPeTI78OrdSRtSv5a4VXyezMtfF3SxHtVvqG/JM/rd625E/rxvW99aw6plJekg5k0kXS/RMSr5OTN7yXfiepcAesDW9frSFpC0i6SZq+tnxVodZQMyPwJZmZmZmZmZpacCmZyBwOrkaNAR0u6FpiDHPX8PnK08SXTUxsi4m1JuwJXAEdL2oEcNbsCGWw/CdiDSaNiW/Ylg+7bkSPX7yQDfIsAS5OB0F8Al/bzfHYgU63sB+xUjvNc2f9y5GSJ+1NGLkfEY5L2JANxf5Z0Azkx6ydL+WeAnaoHiIjRkk4r6++U9Dfymq1GBm3fx5RpFQ4FfibpLuBBMvD2sXKMccBRnZxcyU+9AZk+Y13gkdLmseS1XJMcMX8jsHFDju7LydGp+5dA7FPkCO+jIqKjyS0b2vSgpB3J1CUXSbof+Fc5xyHkszELmS7i9b4co0s/ALageTJcgL3IEc07AetIuol8LoaTQepvRUS3cwEMiog4o0w2eiD5LN4BPEJOgDmETOtyFxnUbbmIzIU9QtLllIkyI2L3iHhW0m/Jd/guSVeT92wt8tqcAuwyQG0/sXRg/ZzMz3+UpFuBl8lneTVgdvIZfbXUifLNuYacEHiz8o4vAawBTAB2i4inB6KNHTgD+Hu5Ti8Ba5e230u+81V9+d4fCWwJ7CdpOPAw2an2QbLz56sDdB4HkTnSNwMeLN+U95d1vyY7nKrmJ9NYHS/pNvIXDnOQwf6FgFuA8weobWZmZmZmZmaGR6xPJiJeIQMXh5LB1Y3JANZtwPYR8Y3psQ0RcT0ZwL2MHIW+Uam7DvBoKfZ8rc5rZOqC3YF/kCOqNyNzcz9MBgbro6v7cj7PkMGrb5IB7FXIFCWLkznKv0Ytd3VEnEEGxC4mOwe2JIOyvwJWioj7Gg61BzkR5ctkvvMh5Aj37do0bR/gD2Sg8AvkSPW3yXP+WEQ83MU5Pk/eoy+RvyBYobR5WPl7J2CtUq5e9ylyJPfN5D3cDfgysGinx2/TpvOBj5NBuJnJcxxOpmM5k3xGXurPMbpoyygyz3O77U+Sz8XRZCB2c2Al8tcOG0TEz6dFOwdKRHwHWI8MmC9OPu8rAq+RHTa71cqfT3YuPUFOoPvl8q9lb+BbZIB+PfJZuxJYmUnv90C1/QQypdJR5HwAa5EjpIeRnV97A8tGxLhKnXvIjq+TyPzjW5IdVBcCa0RE23s/FexDBsyHkO9VAMeT799kz3sfv7X3koH3keR1+hz5vVwduHWgTqJc3zWAk8l3dlNgAfI799OGKg8D3y7tWoJ8h9YAxpCdqMMjot65amZmZmZmZmb9oKmXLtymB5L+QgaNV4uIvw92e8zMBpqkMcCQiFBvZa17o0aNuve5lyascNJlL06V/Y84fPhU2a+ZmZmZmZn9V5vu/z++U8HMAMpEenNFxKOVdQK+TgbVHyJTAZiZmXVt8YXnYMThnxjsZpiZmZmZmZlNNxxYnzEsC9wo6W5gNJl7eRiZQ/11YI/wTxPMzMzMzMzMzMzMBoRzrM8YRgMnkrl41yPzac9KTk65akSMHLymmZmZmZmZmZmZmc1YPGJ9BhARzwJfHex2mJkNhogYOthtMDMzMzMzM7N3FwfWzczMrEdPPDueQ08fOVX27clLzczMzMzM7L+RU8FMxySFpB5zo0s6rJQ7bBo1y94lJP2zPFv391Jul6ZnUNIpZf3wqdjGkeUYQ2vrx/T27tTKt84hJN3XS9lLK2UP61PDp7Juz79W98OSjpM0StJLkt6Q9KSkiyTtJGmWgW6vdU7S8PLsnTLYbTEzMzMzMzN7N3Ng3cymIGll4MPlz2UlfWow2zONLVfOfwqSFgY+N5AH608QfKBJ+gFwD7APMBdwDXA+8AjweeBU4J+D1kAzMzMzMzMzs+mEA+tm1uRLZTm2LHccrIb00XpM6hjoxh1l2e58tyVTaP2jL42ankn6EfB9YBywYUQMiYhNI2K7iFgTeD9wBLDEYLbTzMzMzMzMzGx64MC6mU1G0nvJADLATmW5bVn/XyEiHo6IHlO6tHEL8AB5vjM1bN8ReAn4c3/aN72RtArwXeB14DMRcWm9TES8EBH/D1h3WrfPzMzMzMzMzGx648D6DEzS7JIOKbmSXy/5kv8mads25SempJD0tUq9RyQdKEll2yclXSzp35JekXShpCED0YZSZ0VJfyllX5J0maRV2uXyLnVmkfQNSbeWNo2XdIukL7faXSsf5XxnKuf2QMkl/bikIyXN2qZtc0r6vqR7JL0m6WVJ10ratIfzWV3SnyQ9V44xRtIJkhZrKNvKmb9Lm301pg2RtKqkCyQ9Wo7xdDn//5U0Z7u2tbEBsDBwQ0RcCdwILFTW91u5V38s53luK2e3pEXLvbi25PR+s5xk8Q+rAAAgAElEQVTH+SXw280x+pNe5UxydPZna/tcBlgFOA/4Tw/HlqSdy3P+Ynnu75b0bUkzV8oNL20cUv6Oyr8xlXJLl+fipnI93pT0hKTTJC3bx3Os+xYg4LiI+FdPBSPi+oZzXkHSmZLGlvY9Wdq3XEPZiTnCJS0s6bflvF6VdL2kT1fKfqVcu9fLu3mopCn+d6vyPs8i6QeSHpb0H0mjJR0u6X1N5yJpAUlHSXqwlP+3pL9KWr+h7NBynJFt9tX47mry7+rulfN5WtKvJc3bZn9DJZ0l6flybW6UtGFT2VJ+Zkl7lfd+nPL7NEb5rW77vTUzMzMzMzOzvnFgfQYlaS7gb8DhZJD0YuAG4FPAWZKO7aHuMcDPgOeAK4EFgCOBwyStAVwHLAlcDTwNbAJcJWm2/rahBNVuIPM5PwxcCiwCXA+s1qa9c5R2HgsMLWVHAksDJwO/aneuZBD1+8ATwOVkXukDgd82HOf9wN+BHwDzAVeUv1cCLpB0UEOdHcnr9UXgfjJf9RvA3sA/JC3fQ9s6UoJtN5ZjjCnHuBNYEDioLLvRSgNzRm35pYayXSlB/kuALch7s01EvFk2b0I+Z4uReb4vBJ4CNgNuaAp2TiVnluUOtfWt9DBn0EYJ+p4NnAJ8HLgNuIzsmDgKuLASGH6azFk+vvx9auXfHyu73R04FJi77O8i4GXyftwq6WNdnV1zmz9f/hzRh/rrlXZtT96v84BnS/tuk7RWm6rzATeVY98EjALWAK6Q9BFJvwCOAf5Nvt/zAIcBP2zXFPK6HUDmgb8EmB84BLhYtV8gSPoA+QuFbwOzkM/bHWSHymWS9u/mOvRG0k+B48l799fS3j2Bi6TJO/8kLUV+W7YlU/P8mfzf6z8DW7c5xOnAieS3+UbyOXkcWAv4ykCei5mZmZmZmZllrmCbMR1BBnyvBDaLiFcBSiD3WuAbki5vSvkAbAWsHBH3VurcSQagdgL+X0QcW7bNAvyFTA+xLfD7vrahBPh+D8wGHBgRR7V2JOkQMkDf5CgyeHQ68NXKcRYiA1F7SfpzRFxSqzcEeA0YFhFjSp0lgduBHSQdGhEPV8r/HlgB+CnwvYh4q9T5EBmU/5GkSyPi7rL+g8BvgAA2joiLK+d5NLAfcBrZ0dAfB5BBuk9FxO3VDcpJR5/vdEeS5gY2Bt4EzimrzwZ+AWwsae6IeLkvjZQ0P9lRsirw04j4Tq3IDcDHW9evUm8DMkh4gqRlImKqTvQZEQ9LuhnYTNLsEfFa2bQD8CT57K7apvq3yffnCmCHiHiunMMcwFlk58fewPElVc0ukoYDc0TELm32eSFwUu1ZRNKuwO/IDqX+pGf5EBm0fgO4t5uK5bzOJN/ZvSPixMq2/YGfAyMkLR0Rb9Sqb0w+W7tExH9KncPIToRzSpuq36EVyMD3fpL+t/WeVyxBBp+HRcToUmchsgNwPeBrwHGV8ieWcz8d+HLlfV6T7Aw5StJV9eexH3YEVo2IO8txFiQ7FNYCPlPa2XIC2Rl5ArBPRLxT6uwOnFTfsaShwDbArcDaretZts0GfGKAzsHMzMzMzMzMCo9Y/y+gyVNETPaPDELVy88BfBl4h0qgGaAE835U/ty3zSEPaQWzKnUuAWYHHmsF1cu2N8mgK8A6/WzDusCywH3kiPmqI4BHGs51YXJE7yPAHrXjPAfsVf7cq1632KcVVC91HmHSiOSJI20lfQL4AjkS9KBWEK7UGU2m0piptKVldzLgeFYrqF7Kv0OOJH8KWEVS40j8LiwMvFQPqpdj3RIRr3Sxry1Lm/8SEf8u+/g32XkyGznSvGtldPB1ZED6oIagOhFxT1MQMyIuA84FlgKG9eX4fXAGMCc5ih5Jq5fjj2gFOeuUOegPAF4Btm8F1QEiYjywBxm8bvcsNoqIm+tB9bL+92RnxHBJ83Szz5oFyvKFiHi7y7pbk2lzrqsG1Uv7jiE7qRYnf3VQ9xLwlWoQmAzEB9mBVf8OtUahzw6s3KY9h7eC6qXOc+Q9gQysAxM7wzYiR4/vW3ufryeD7jMBX2175t07pBVUL8cZx6Rf06xdadtSwPrAC2QH4zuVOieT36C6hcvyxtr1JCJej4ibOmngqFGj7m36Rz77ZmZmZmZmZlbhwPp/h1N7+HdXQ/mVyCDoLRHxYMP208tyjXoKguKKhnWje9jWCvot2s82tHIr/7E+KrkE/M5v2M86wMzAXxtGxBIRd5GBzqYc3W+RKWPqHijL6vl8riz/1GbEdCvvdPU4rcD8mbWylLaeWyvXV7cD8ypzVfc38NxK93J6bX3r7x3p3jJkAHh5YM+IOLJdQUmzStpE0o8l/UaZh/sU4KOVfU0LZ5PPR+t8e00DA6xIpt25vgRNJxMRzwAPAsPqaZN6o8ztv50y//9JleuyKPlrhf4EPpu+AZ1q+4wXU3RSVdwWES9WV5RfQ7R+YdHpt6bqD/UVEfFXMki9bBnBDrBmWV5ab0PRet77+25WXd6wrulbs0ZZXlo6ZOqmOEeyM3I8sKukPSQt0FDGzMzMzMzMzAaQU8H8F+ghRUQrdcLHa6tbk2KOabO/FyW9RKZamJscOVr1ZEO18R1sq0742Zc2tOo83lQHeKxh3dCy3FvS3m3qQQb568a2GaHbGvVePZ/WcY6U1DYwzOT5zHu8BpX1U0xi2qXvkoHn3YDdJI0jR7VeSI6wnqLDoUlJXbMO8CKZD7/qz+Q9Gi5p8Yh4oov2nUB+a74TEVOksagc/6NkypehPexrri6O22cRMU7SZcDnlZPMbg00jqivGFqWX1DvE6fOT/O7NAVJ65LB1IV6KNaf69LqBJhP0kxdjlrvzzPe7vzHk+9Rp9+alhd6+HXGo2RO98XIuSOm1btZ1fTONH1rWsds+t41ro+IlyXtQaae+g3wa0n3A9cAp0XEzZ00cNiwYR9pWl9Gra/QyT7MzMzMzMzM3i0cWJ+xdZKLeooyveSw7ja/dV/a0K5O08ja1oSEdwDd5kLu5lxax7mOSaP3m0wxUrmD43TTjil+ZRIRj0tamUylsxEZHP8imcP6QEmfjogXOtj3juQ1FjkZbbvjb0/mme/U2aXO/pL+FBH31wuUXy2cQwanTyz/RgOvRkRIOgI4mP6Nru7WGeT1/C0Z6D2q5+ITn5EHaU7XUdVpZ8ec5HVZgJy08ywySPx6uS4jgO3o33UZTXaazAN8hO7fI+jbM95jnQHOpd/u+rQ7RvSyvUmPvwDr4nxabe3q/CPiLElXkumL1ie/A3uTnY5HRcSB3ezPzMzMzMzMzHrmwPqM6amyXLJpY8nHPA85+rOb/NtTuw1jy3KJNvv8YMO61ijQkRHxzT60s1Ot4/wxIo7rseQkTwHLkdfggYbtQ8pybGXdm2U5Z72wpJmARZoOFBETyFQTl5eyS5CTra5L5nOfIqd5g1a6k3mYlI6iyZfoLrB+MpkK5gTgGknDI6J+PZYv/26LiKZfHnyoi+MNlIvIHNyfJ+cKGNFL+dYzMqqnX5l0aS0yqH5eRHy/YXu/r0tEvFNG529NdoB0E1jv8T2n+RmfWuaTNFebUeutb0qrHb21e2itPPTwbhZN36e+aLVtSJvt7b6PrZzyJwMnl86qDciOrQMknVLy1JuZmZmZmZnZAHCO9RnT7cDrwKckNeWkbgVQrx/gUaH9bUNrlO8W9dzvkt5D8wSI1wBvAxuVwPPUcmVZbtpFnevKcof6BkmzAFvVysGkQN6yDftbl8wn36uIeAxopaz5aE9lS3tWIlM9PAO8NyJU/0d+L54gc4TX0w/11p5fAV8nc0lfLWnpWpH5ynKKdBmS5mNSjvtpJiJeJ0etPw9c0kH6m1vJkd+fkTR3F4d6EyZOflrXui5TpEcq1/CTXRynJ61JQ/eV9OGeCkr6dOXPts94bf11bbYPtG3qKyRtQF7HByPi2bK6NSfChpLmbdhP6/tUbfc4Mu/+kvV7Vd7ndRgYN5Tl/5RJoOu27WQnkf5KTvgK027iXzMzMzMzM7N3BQfWZ0Blwrvfkff3+GpwRtKywPfKn7+cztpwNfAQ8GFg/9ouD6JhdG5EPAmcQk5qebqkBetlJH1a0v/0+WTyODcDV5FB02NKio7qMd4jaX1Ja1ZW/5bsXNhO0obVssARwAeAW2v5j68tyx0lDa3U+RBt7pek/SW9v2HT58uyXa7mqtakpWe3y7FdOkDOqZXvWEQcD3yDPO+ryzm1PESOCl+32hEj6X1kWpj5uz3eQIiIr0XEghGxcQdl3wB+BswLnCdpihHHkj4mqR78bY1QXq5ht62R/ZtXJt6kBIN/S4cdLb2JiL+Tv0KYjbw3U7wvkuaR9AOyM6vlHLIzZi1Je9bK70tO5vsEcMFAtLMD36+9Nwsy6dcVJ7TWR8RoMuA8F/ALSTNX6qxOplB5u1bnTeBm8ln8WqX8zMAxtB/93pWIeIj81swH/KR8L1rH2pVJkzxTWb+ipM2r51HWzwesWv7s5DtgZmZmZmZmZh1yKpgZ18HAauRI39GSrgXmIEc9vw84LiIu6aH+NG9DRLxdAkdXAEdL2gG4nxxJ/WHgJGAPJqVkaNmXDLpvR45cv5MMVi4CLE0Gcn8BXNrP89mBTLWyH7BTOc5zZf/LkZNL7k8ZDRsRj5Vg4ynAnyXdQI48/mQp/wywU/UAETFa0mll/Z2S/kZes9XIQOD7mDJFxKHAzyTdReb3FvCxcoxx9JIbvIy+bY2CPauXa/AH4JtkZ8GBEfFOL+UnExHHlUDhMWQAd52IeDQinpX0W/L+3iXparJTYi0yd/kpwC7dHGuQHEE+r9sB90v6BxnQXJB8RpcE/kSm52i5iBztfJWka8j0SOMi4qCIuE3SFeQ79ICkkaXOcPLe/onMqT0QDgYmlOUlkh4l5y54HVicDNDOQj5jQHaglff0z+SEmXuSnQHLAyuWc9m+0wl0++kxMo3NvZKuIkeXr0t2dFwD/F+t/F7kiPSdgHUk3US+w8PJZ+5bDZPVHg5cBhxbOkieBlYCZgdOBXYeoHPZmxy5/nXgc+U5WpK8BycCX6mVHwKcB7wk6bbSrnnJ92du4IJOJzA1MzMzMzMzs854xPoMquQZXocMuo4jJ7JcC7iNDHR9Y3psQ0RcD6xJBq+WISePHFf282gp9nytzmvkZH27A/8gUx5sBiwFPAwcSI4k7u/5PEMGuL9JBhdXIVPDLE4GIL9Gpg6p1jkDWBu4mOwc2JIcFfwrYKWIuK/hUHsAPyHze29ABs2OIIO1TfYhA96zA18gR6q/TZ7zxyLi4V5ObX3g/cCY3oJvEXErObp8MWC9Xvbbbh/HAt8mz+uakg8eMpj4LeCRsu+1yBQ8KzPp3k/XIuKdiNievM/XkM/w5kxKs3MYU+a7Pw74EfAqsAXwZSZP97EJ8GOyE+cLZCD3D+Sz+OIAtj0i4ntkp8zxwGvkfdiSfJcuI1OkfKRW7yryXTiLfBe2JDu1ziCf8WmVBibKsY8l0x9tRKbm+TGwYZmHoNruJ0u7jyY7FDYnr+1VwAYR8fMpDhBxJfkdu5XsIFuHHMW+CjBmwE4k4kHy/p4DLEw+AyK/N2c3VLmZ/BXQ7WSH2lbke3M3GezfeqDaZmZmZmZmZmZJUy/FttnAkvQXMmi8WkldYWaGpAAejYihg92WGdGoUaPufe6lCSucdNmA9eNMZsThw6fKfs3MzMzMzOy/mnovMricCsamK5LmB+aKiEcr60SmRPg8OVr6lkFqnpnZu9LiC8/BiMM/MdjNMDMzMzMzM5tuOLBu05tlgRsl3Q2MJnMdDyPzU78O7BH+mYWZmZmZmZmZmZkNIudYt+nNaHJyvlnJ/M5fKP89Alg1IkYOXtPMzMzMzMzMzMzMPGLdpjMR8Szw1cFuh5n994iI6T7vmpmZmZmZmZnNWBxYNzMzsx498ex4Dj19ZEdlPRmpmZmZmZmZvRs4FYzZdEbScEkhaUwv5UaWcrtMm5bZQJM0tNzDkV3Uad334T2UGd7tfvtC0mHvpmewcu2HDnZbzMzMzMzMzGxwObBuZlNVX4LHg+XdFig2MzMzMzMzM7O+cWDdzMzMzMzMzMzMzKwLDqybmZmZmZmZmZmZmXXBgXWzGYykMZKi/Pfuku6W9LqkpyX9WtK8DXXmkPQdSXdKelHSq5IelnSupA1qZT8h6aeSbpf0nKQ3JI2WdIKkxWplDwMeKX+uU9KstP6dUsr0mCqmXXqW1nkq7SPpLkmvSbqzbJek7ST9QdIDksZLekXSLZK+Kuk99f0Bh5Y/f19r6/Ba2TUlXSDp2XL+YyQdJ2mhNuewYLn2T5c23iFpp6ayU4ukA8q5/LiHMteUMmvW1q9T8ou/Kun5cu7L97CfHu9Npdz/SLpC0guS/iPpfkk/aXpGK3W+IOniyrV/TNKFkjasldtQ0u8k/UvSy+X+3yXpu5JmbdjvLqXNh0latjw3z0h6R9KmDeV3LO/Aa6Utp0r6QJs2v7dch9vLNXy1PId7S5qpofzEXO6SNpV0c2n/vyWdJWnxhjozS9qr7HdcadeYcq22bXc9zczMzMzMzKxv3jvYDTCzqUPST4FvALcCfwU+DewJfFjSOhHRCr7PBFxetj8BjATeBBYHNgLGA5dVdn0QsCUwCrgBCOATwN7AppJWjoinStk7gfOALYBnSjtarh+gUz0R2BW4FvgXMEtZPyswAngB+CfwD2BBYHXgeOBTwC6V/fwR+Czw8XJeD1W2Pd36D0n7AscC7wC3AE8Cw4B9gI0krRERYyvlFyj7W5a8vhcBiwC/L22fVn4P/BDYVdKhETGhulHS0sA6wH0RcX1l/SbkPZwJuBF4jLx2fwf+3Msx290bJB0MHAFMKNvHAWsA3wE2k7R2RDxTa+PRwDeBt4GbyOu5GPAZYF7gkkrx3wJzAPcC9wBzl3b/GFhP0voR8XZDm5cj35nngWuA+YC3amW+DXwVuA74E7AasBOwrqTVI+KJSptnKmX+B3gZuLJsWhc4AficpC0j4p2GtnwV+BZwG/nurAJsC6wk6eMR8Xql7OnANuR1vBF4DfgAsBYwJ/CHhv2bmZmZmZmZWR85sG4249oRWDUiWiO4FySDkWuRgcirS7m1yKD6n4DNqwE+SfMAS9f2+xtg/1rw+D3A94AfAD8CdgOIiAvLKOUtyIDtLgN8jgCbAytGxL219RPKcS+OiDcrbV0IuBTYWdLvIuJvpa3fVo6w/zhwckScUj+QpNWAY8jg8sYRcXdZL/L8DweOA7aqVDuCDKr/CdgmIt4odb5ABtmniYgYJ+k8YHtgw9Keqt0BASe1VkiaCziZDKpvHxFnlfXvLet37uWwjfdG0irkc/IK8NmIuKWsn5UMEG8F/BLYulJnRzKo/gSwYeval21zAKvWjv0V4IqIGF87nxFkh9EOwGkNbd4W+D9gvzaBd4C9gI0i4tKy35nJjosdyPu/eaXsfmRQ/Z5yrs+WOouSgfvNSltPaDjOV4HPRcTVpc7swBXk+7od8LuyfigZVL8VWDsi/lM559nIji8zMzMzMzMzG0BOBWM24zqkFVSHDKwCvyp/rl0pt3BZjqyPmo2IlyLi9tq6q6tB9bLunYg4nBy9vclAnUCHjmwIqhMREyLi/GpQvax/Dji4/NltWw8iv5t7VgO7ZfT/j4A7gM1LJwaS5gS+RAb5920F1UudvwDndnn8qlbalin+kQHbJr8uy92rK0ugfGfylwrVYPNW5Cj/K1pB9dL2CcD+wKu9tLHx3gBfJ6/jsa2getnvG2Xb68AWtdQq3y3L/arXvtQb3wo+V9ZdWA2ql3WvlHZD+3v/HPCdHoLqAOe0guplv2+Rvw4ZD2xSa/e+lXY/W6kzFjigVqbumOp5RcRrwNHlz6Z3+MZqUL3UeT0iburhXCYaNWrUvU3/gKU6qW9mZmZmZmb2buIR62Yzrssb1j1QlotW1t1JpjU5QNLTwCUlANlWSW+yMZkCZV5yRDPAzMD8kuaPiH/3p/Fd6HHUt6RPAOsDQ4DZyVHZc5XNy3R6kDIqfz1ylPVV9e0REZJuAFYEViLT53wSmA24ISIea9jtWeTI4764jEqKmppFgA3qKyPib5L+CXxB0gci4smy6YulztmlA6allWv9nIZ9vSDpciYfnV3X7t6sVZZnNuz32bLfTciR2ecqc/d/GHg+Is7r4XiTkbQMOVp8aTItzHvI+w/t7/2VJYDdkynSqkTE85KuADattHsJYAng6Xrgv7gYeBFYTtJCpdOnqtN3+D4yqL+rpHuB8yPi+V7OwczMzMzMzMz6wYF1s+lPdFiuFSBsV/6JhnWtEcYTJ2+MiAckHQD8hAz0vi1pFJkL+vcNaTy2I9PBzNlD2+YCplVgvSlgjaRZgFPoOXA9Vw/b6hZg0jlPyOwvbS1Ylq3JXBvb2MP6TvwkIkY2bVBOtjpFYL34DZkjfjcy5zrAHmV5Uq1sf9vfbvti5HP7aJvtY2rH/2BZPtzL8YCJqXl+Ro5Ob3ej2t37Tu5Jp+1erLZ+MqUz5lGyc2oxcrR8Vafv8MuS9iDv7W+AX0u6n/zlwmkRcXPbM6kYNmzYR5rWl1HrK3SyDzMzMzMzM7N3C6eCMZv+tCYknKOXcrOX5fimja3JSTsRET8n0z3sS+YfH0JOmni3pK+1ykkaQgarZyVzRy8DzB4RigiROdyhfTCzL3r8TtVTX1R8kwyqjwK+ALwfmKW0c7lSppt2tkblvwKc2su/VuC1t86PwXAq+YztpvRBMgg/mkl591v61f4e7k3Hu+jl73a2Ie//k+REux9g0r1vBaTb3fv+tLndPjtpd1OZbt7hs4APkZ0kfwTmJycUvqlMZGxmZmZmZmZmA8gj1s2mP4+X5YKS5o6Il9uU+1BZNo1q7VpEPE5OGPnLknN7W3JCxp9LOjMiXiTTaswCHB0Rv+ihTd1o5UBvNwL+g23W92azstwuIkbVtvWlneOAN4C3upiE9amyHNJm+xJ9aEe/RMSLks4GdgE+B6xOdl6c3NAZM7Xa/xSwZNnv/Q3bW8dr5fJvvRP1iXTbad37vSPi4tq2vtz7uiHA3Q3rW9fjqdpyyR721aoztocyHSmpZE4GTi6j9jcAzibTPJ0SEf/s7zHMzMzMzMzMLHnEutl0pkxq+FD5c6OmMpLWIEekvkpOmDnQbZgQEWcAt5KB9GXLpvnK8vF6HUlrk6PC61qB83YdeeOAt4AlS0C/us9ZgHW6a/1EbdsKbN2mTtu2lgk7R5I55Neub2/jdnIE9KplZHjdth3uZ6C1JjHdi0wJM4H8JULd9WW5VX2DpHnJ3PV9cV1Z7tCw34XKft8BbgSIiKeAfwELSOopp3tLX+59N7apr5A0P9nuoPxyo+TVfwxYRNK6DXU2LG29vyG/er9E+itwSVk1bCD3b2ZmZmZmZvZu58C62fSpNRr8SEnLVzdIWhQ4ofx5YkS80Z8DSfqMpM+WyTmr64eQE0YGk0bFtyZO3FHSHJWyHwBObHOIVuB8KUkz1TdGxJvAzWRHQTXtzMzAMfQ82rcnrbZ+pbpS0pbATm3qtEYYL9dm+xFkwPdUSWvWN0parJo6JyJeJSfofC/wC0mzVsquz8AEebtWcm7fRU48ugRwcenQqTuXzJW/vqSJbS338Wh6zrPfk+PJ6/gNSStX9jsL+auJ2ckJOJ+s1PlJWR4rabJc4JLmqAWuW/d+T1WS4UtaCzigj22u2lrSxBz2pUPoGDJ900URUf0VyS/L8pjSadCqswhwVK1Mn0haUdLm5Z2prp8PWLX82Z98/mZmZmZmZmZW41QwZtOn44E1yBHNd0u6gcwXvRCwFjAbcC1wyAAc6+NkUPA5SbcDz5fjrA28Dzi2jBgGuAi4F1gZeKi0633AZ4A7yRHGn67uPCLelPRX4IvAXZL+QY4MvyEifl+KHQ5cRgZNtwGeBlYiA6ynAjv34bx+Cnwe+Imkrchg6zKl7T8Dvt1Q53JyhPn+koaRgfYAjoqI+yPib5K+QU7+eZ2ku4EHyzVodUS8St6/loPIUfebAQ+Wa/b+su7XZB7swfBrJnXQ1CctBSZOirkncA5wtqR9yQDtp8hn5EwaRp33JiJukXQI8GMyB/hIsgNmDTL1z4PA12t1TpO0Sll/l6QbyQ6fxYAVyV9utHLEH0emuvkqMLzcpw8Aa5IdAk33vhu/Af4i6W/kM7Ia2QH0FDlPQdUxwLpknv8HJV1N5mJfj5xA9ULgV/1szxDgPOAlSbeR78+85LdibuCCTicwNTMzMzMzM7POeMS62XSo5Lrenpx88xoyjcM2wCrAbWQw9nMDMDkkwMXAj8jA88fJtB8fIdN1bBYR+1fa9SYZrPsVGYDeiAwm/5LM1/1Wm2PsDpwOLFDO68tUUrxExJXAxmTqmU+WbTeX8x3Tl5OKiL+RgdSrybzaG5EB/S2YPPBdrfMUsEk59ppkmpQvA4tWyvwfOQr4TDKNx8ZknvJ3yFH7m9T22QoYn0xOnLlpuQ57kMH/wXJVWT5Bdmo0iojzyHt7HRnA/gLwT/KcH2pXrzcRcQR5T64l7/PmZA77nwKrRsQzDXX2ITsoriLfiS3IgPZVwJGVcg+Uff4ZWJC8R3MCe0XEQIxY/xmwKzBPac/c5PO9akn/Um3z2+X43yAniN2ATBlzP/kLjS0j4p1+tudm4Htk6qHlyHd4ZTIP/M4M0i8jzMzMzMzMzGZkmnKuOjMzm9FJ+i45YvwHEXHYIDfHpmOjRo2697mXJqxw0mUvdlR+xOHDp2p7zMzMzMzM7F1BvRcZXA6sm5m9y0iaG7iPHDm/ZCXVj9kURo0ade+ss866wjLLLDPYTTEzMzMzM7N3j+k+sO4c62Zm7xKSdiXT7KxNprc5xkF1MzMzMzMzM7PuOce6mdm7xzpkzu05ybz4Bw9uc8zMzMzMzMzM/jt5xLqZ2btEROwC7DLIzTAzMzMzMzMz+6/nwNwetcQAACAASURBVLqZmZn16Ilnx3Po6SM7KuvJS83MzMzMzOzdwKlgzMzMzMzMzMzMzMy64MC6mZkNOElR+/eOpBclXSdpd0nT/ezeTSQNLeczsrZ+eFl/yuC0zMzMzMzMzMymJaeCMTOzqenUspwJWApYA1gTWA/YbrAaZWZmZmZmZmbWHw6sm5nZVFMmTJ1I0ueAS4FtJZ0ZERcPSsMG3i3Ah4GXBrshZmZmZmZmZjb1ORWMmZlNMxFxBXB6+XPTwWzLQIqI1yLivogYO9htMTMzMzMzM7Opz4F1MzOb1u4oyw8CSLqk5Cf/XFNhSXNIelnSS5LmKOsm5jSXtIikkyU9IWmCpP1KmVNKmeGSNpB0TcnzHpLmrex/BUlnShor6U1JT0o6TdJynZ5Quxzrkg4r63eR9FFJF0l6QdJ4SddK+nQP+1xT0gWSnpX0hqQxko6TtFBD2Zkl7SXpFknjJL1Wyl8sadtOz8PMzMzMzMzMOuPAupmZTWtzleUbZXliWe7Rpvy2pc6IiBhf27YQcCuwIXAT8BfgtVqZ7cv6OcryViAAJK0H3FbKPAWcBzwLfAm4TdJaXZ5bOysDNwPLAVcBDwJrA1dJGlYvLGlf4G/AF4GHgIuA14F9gL9LWrRW5XTyOi4J3FjKPw6sBXxlgM7BzMzMzMzMzArnWDczs2lGkoCNyp93l+WlZBB4E0kLRcRztWqtgPtJDbv8H+ACYPuI+E+bw+4BbBsRZ9faMgdwJjAbsHdEnFjZtj/wc2CEpKUj4g3652vAdyLip5VjHAPsBxwI7FRZvxpwDPAYsHFE3F3WC/gecDhwHLBVWT8U2IbsMFi7eh0kzQZ8opMGjho16t42m5bqpL6ZmZmZmZnZu4lHrJuZ2VQnaSZJywC/A1YnR6v/HiAi3gZOBmahEmAu9YYBqwJ3RMQ/Gnb9BrBPD0F1gEvqQfVia+D9wHXVoHpp0zHA7cDiwGa9n2Gvrq8G1YsfleXatfUHkf/7vGcrqF7aFKXOHcDmkhYsmxYuyxvr1yEiXo+Imwag/WZmZmZmZmZW4RHrZmY21UiKhtWvADtHxMOVdScDhwC7A0dX1rdGq/+mzSH+ERFP9tKMi9qsb6V5ObPN9jOAlUq5P/RyjN5cXl8REc9Leh6YmNZF0nuA9chrdFVDnZB0A7BiadtlwH3AeGBXSfcC50fE8902cNiwYR9pWl9Gsq/Q7f7MzMzMzMzMZmQOrJuZ2dR0alm+A7wM3EMGfl+oFoqIpyRdDGwqaa2IuE7SrMCOZM70EW32/1gHbWhXZrGyHNNme2v9Ym22d+OJNutfBRao/L0AMGf57wmZ/aWtBQEi4mVJe5CdD78Bfi3pfuAa4LSIuLk/DTczMzMzMzOzKTmwbmZmU01E7NJF8ROBTclR69cBWwDzA7+PiJfb1OkpBUynZZpG1XezvROd7mOmsnwFOL+Xso9O3HnEWZKuBDYB1gfWAfYG9pZ0VEQc2GV7zczMzMzMzKwHDqybmdn04nJgNLCVpG/Q86SlA+GpslyyzfYhZTl2Kh2/yTgyb/xbXXZKUCZ9PRk4uUx0ugFwNnCApFMi4p8D3VgzMzMzMzOzdytPXmpmZtOFMjnnScBswKHkqOt7p+Lkm9eV5Q5ttu9QKzfVRcQEYCQwv6T6pKbd7Cci4q/AJWXVsAFonpmZmZmZmZkVDqybmdn05HfAm8B+gJh6o9UBzgGeAdaStGd1g6R9gVXI3OgXTMU2NDmCzEl/qqQ16xslLSbpa5W/V5S0uaSZa+XmA1Ytf3aSi97MzMzMzMzMOuRUMGZmNt2IiGclXQhsTaZEOX0qHmu8pB2AP5MTfu4JPAAsD6wIjAe2j4g3plYb2rTrbyUVzrHAdZLuBh4E3kemp/kwOenp8aXKEOA84CVJtwFPA/MCawFzAxd4AlMzMzMzMzOzgeUR62ZmNr25qizPi4h/T80DRcRV5Mj0s4DFgS2BRYAzgJUiYpqlgam16//I0eZnAvMBGwOrkyPZTyQnKW25GfgecDuwHLAVsDJwN7Az2UlhZmZmZmZmZgNImdLWzMxs+iDpcuBzwGciYuQgN+ddb9SoUffOOuusKyyzzDKD3RQzMzMzMzN799BgN6A3HrFuZmbTDUmfAj5LTlo6cpCbY2ZmZmZmZmbWyDnWzcxs0En6CbAEsCHZK/3dwW2RmZmZmZmZmVl7Dqybmdn0YFvgg8AY4MCIuGhwm2NmZmZmZmZm1p4D62ZmNugiYuhgt8Hae+LZ8Rx6+siOyo44fPjUbIqZmZmZmZnZdME51s3MzMzMzMzMzMzMuuDAuvWZpDkk7S/pGknPSHpT0guSbpJ0uKQlpkEbhksKSadM7WN1S9LI0rahU/k4p5TjDO+izi6lzmH93VdfSZq1PC8h6bIB3O/Qss+RXdYbIykGqh3Tm3b3vJc6Ufv3lqRxku4pz8oWkqbpL5+m5TPaLUmHlbbtMthtMTMzMzMzM7Opy6lgrE8krQacDywKvAbcDDwDzAOsAqwGHChpo4i4ctAaatOzLwLzlv9eT9KiETF2MBtkbZ1alu8h3/FlgZ2AnYGHJO0QEbcMVuOmldJZsw6wZESMGdzWmJmZmZmZmdlgcmDduibpY8DVwGzAkcAPI2J8Zft7gE2BnwKLD0ojpw87AbMDT07l4xwM/AR4bDrbV2++VJZjyQ6a7YCfD8B+nwQ+THb42ACIiF3q6yQtBRwBbA1cI2mNiLhzGjRnWj6j3fo/4A/kM21mZmZmZmZmMzCngrGuSBJwBhlUPywiDqoG1QEi4p2IOB9YCbhtEJo5XYiIxyLivoh4ayofZ2w5Tr8DyQO5r55IWgD4AjAe2Kus/lL7Gp2LiLfKOUyPgdcZRkQ8HBHbAL8lO5B+N42OO02e0b6IiHGlbS8NdlvMzMzMzMzMbOpyYN26tQHwUeAJ4Mc9FYyIlyJiVOtvSYtKOlDStZKeLDnZn5Z0vqRVmvbRynuttI+kuyS9JmmKkbFl/6eUfO+vS/qHpJ3atU/SCpLOlDS2tOVJSadJWq5WbiZJN5R27N2wn+GS3pb0uKT5Kusbc6yXdWMkvVfSIZIeKu39l6RdK+XWLfnrXy65yE8rAen68dvmnJb0MUkXS3qp/LtC0uo9XJOe9jWHpIPLdX1F0quS/inpWElD2u2zjW2BmYELgYvJ5+kTkj7SQ9skaQdJV0l6XtJ/JI2WNELSGpVybXOsl2t+sKQHK/V/KGmWHo67qqQLJD0q6Y3yzN4i6X8lzdnJyUpauuTfvqnUf1PSE+WeLtumTus5mam8Nw+U4z8u6UhJs7ap19U9HwDfIjtIVpS0ZkN7hkr6dTmXNyQ9J+mPyl++1MtOzAMvaVlJfyjv8zuSNi1lJntGJc1ceR7mre+zlPlUqXNDZd28ym/KZZV7+7ykv0r6XMM5BJkGBuARVXLPV8pNkWNdmY8+JC3fpm2LSJpQnof31LatWZ69Z0v7xkg6TtJCDfuZWdJe5dkcp/xOjinPwrZNxzYzMzMzMzOzvnNg3bq1YVmeGxETuqy7CZk6ZjHgHjKo+hSwGXCDpPV7qHsicDTwLHARMLq2fX4yz/vngZHAdWQHwKmSDq3vTNJ65Gj67Usbziv7/hJwm6S1WmUj4u2y/hXg6GqArATyTgME7BwRL3RwHVrOAQ4AHgb+BiwJ/E7SrpK2BC4D5gKuIAOXXwIulKROdi5pVeAm8p6NBi4FFgGuBT7dRTuRtChwC5n6YwiZCuivwJvAvsBnutkfk0annxERAYyora8ffybyep0BrAHcQT4/T5PPzx4dHvescg6LlPbfA3yTnC9giusqaUPgRjIf/JhS7k5gQeCgsuzE7sChwNzkc3cR8DJ5vrc2BZkrzgS+T3Y+XE4+EweSI8Xr7R2we96pMjr7L+XPyZ6DEmi/C9gTeJU87weBzYGbJbV7bpYDbgU+BVxDvgONv/wovwg5F5gV2KLN/rYvyzMr61YDjiPTBj0IXADcD6wPXCZpt0rZV8k888+Uv88rf7f+9aR1zB3abN8WmAkYERHvtFZK2pf8LnwReIi8dq8D+wB/L+9k1enkd3JJ8pm9CHgcWAv4Si9tNDMzMzMzM7MuOce6dWvFsvxHH+reAHw8Iu6urpS0ARkEOkHSMiXQWrc5sGJE3Ntm318kg2+btVLTKEfBXw18X9KfWvmfJc1BBrtmA/aOiBMrbdmfzPM9QtLSEfEGQESMlrQPcApwhqTVS0Dv18AHgZ9FxNVdXIshZKB+hYh4ohz7M6W9PwZmAbaNiPPKtrnJYNmawHAy2NhWGfl6Cpmi4+CI+Ell2w+B73XRVsig3QpkYHqPWk79ZcjAYEdK+VXJIOUVlf0fCGwv6eCGZ+BgYEsyEP7FiHi0sr/5yeBob8fdruxjNLB2RDxZ1i9JBjCb5gM4gAy4fyoibq/t71PA870dt7gQOCkiHq7tY1cyhcqxwLoN9YaQueKHtSbLLO29HdhB0qGtfU6Fe96NO8lrO/E+lGf2XPI92yoi/ljZ9lngEuB0SR+KiDdr+9uWzFe+X+nY6s2ZZEqh7al1OJTrsjUwgeycabkfWCMibqyVX5F8D4+RdE5EvBoR44BdlL+CeD/w7S4mLx1BduZsDxzSsH2KoL9ycuhjyDzyG7e+maVT7XvA4WSnwFZl/VBgG7IzYu2I+E9lX7MBn+ikoaNGjWr3fV2qk/pmZmZmZmZm7yYesW7daqUiea7bihFxTz2oXtZfRgbglgKGtal+ZA9BdYAA9qkGfCPiVuB48jmvpnDZmgyOXVcNqpc6x5BBy8XJkdDVbaeWdq4E/EDSzmVfdwH/r4e2tbNvK6he9n8N2WGxKHBJK6hetr0M/Kb8uQ69Gw4sDzxA/kqg6gd0MfFjCSCvR44OnyyoXtr2YETc1+n+mDQq/Q+toGlJGXQ32UkxvHb8Wch0IwHsVg2ql7r/jogb6F3rGTikFVQv9R8BftimzsLAS/Wgeql3S0S80sFxiYib60H1sv73ZIfTcEnztKm+TzWIW9p7RvlzrUq54QzQPe+DcWU5X2XdbuRo+Z9Vg+oAEXElcALwAWCjhv09B3ynw6A6wPXAo+R1XKy2bV3ynbqsBMhbbXikHlQv6+8gvxtz0/0vMaZQcv1fD3yoBMwnkrQ0sArwz4i4q7LpIPK7tWf1m1k6nH5E/mJjc0mtX0wsXJY3VoPqpc7rEXFTf8/DzMzMzMzMzCbnEevWrY7SkLStnHmhP0+meFiIHJkNmbYFYBlyVHLdRb3s+o6IuL9h/VnAd8iR3i2tYOSZUxYHMmi5Uin3h9q2vYDVyz5fB/4D7NAw4rY3b5LpOepGA59k0kjuqlZgtp4CoknrfM+tj/6OiAmS/kimQOnEZ8vyzHpQvY9aKTFOr60/HTgK2JHJR+SvDMwL3B4RfZoMV9LM5Cj5d4A/NhQ5i/z1Qd3twI6SfgscU50zoA9tmJP8ZcUnyNRFM5dNi5Lv1VJM+UuQt8jURnUPVOq2DOQ971bru1A9bitP+YVt6lwP7EcGls+vbbuym8lJIyIknUUGpLclf3XS0pQGJhudKYbWI9PkLAK8r2xaprbsrzPJ78n2ZMqqettaHSWtEfbrkb9ouaq+o3KuN5C/HlqJTBl1H5kualdJ9wLnR0Snv6aYaNiwYY1zHJSR7Ct0uz8zMzMzMzOzGZkD69atcWT+4ykmz+uNpI+SAfKhPRSbq8363kbbPtpm/ZiyrI5iXay2rZM6AETEC5K+BvwJmAP4Vi8j6dt5uppPuaIVuH6yh22Nk1bWtNre7rp1M3r5g2U5xYjrbpWc2x8C7msYBT6CHGm9paSvVUbeDsTxFyA7ccY2dYJExCuSXiQD+FXfJTt9dgN2kzSOTMlzIZkT+41ODi5pXbKTpqf3punZH9tm1ParZVl9FgbynnerNXL635V1Q8vy771MC9CUp74vbT2TDKzvQAmsl468zcl350/VwpIWJyfO/XgP+2z3PerWuWTqlm0k7V+5p9uRnRFnVcouALQmxZ3QybWLiJcl7UH+quU3wK8l3U92UJ0WETf3sA8zMzMzMzMz6wMH1q1bd5KTR36SyijL3pTcwOeQwbYTy7/RwKtlBOYRZB7txihSPb3BAGnK5d7J9m0q/73yND52p5pGEPfXQOyrlQZmfknXN2x/i0zBsTGT58Pu7/H7dD0i4nFJK5PpRDYi0/B8sbTvQEmf7m3C2jJS/RwyYPpDMoj6KPB6efZHkAHWpme/m/ZOjXveqVYO739W1rXy7p9L5olv5+8N67p+3yNilKS7gU9KWr6kJ9oQmIecJLfehpPJoPr5ZIfO/cArEfGOpD3JXzD06xc6lbb9W9Jfyefms+TkqJ8kU/dcX8vX3rpurzDlSP66iR2KEXGWpCvJSaLXJ5/VvYG9JR0VEQcOxLmYmZmZmZmZWXJg3bp1CfA1YCtJB0bEhA7rLV/+3RYRezds/1A/2zWkl/VPVda1/nvJXuqMrW+QtD2ZvqGV93g7SRdH/H/27jzerun+//jrjYih5qlUCRpjlCJKTTHPQ82z0FJDq9TsS6lOqKEov9IihsQQ1JASc0w1xXzVTEpMScyz4PP7Y62T7Jzsc+859557b+S+n4/Hfeycvdde+7OHez189jqfFUMaiLUrVM6x1nVZqIG+XsvLH7Q/nAmjh7fLH+dlYl3oMrsxMbHejOOPI5Xf+a6k6atHrUuahclHqwOpjApwS/5B0kLAhaRk+5GkskCtWYOUVL86In5bsr2jz35FM+953XJt+I3yx2IJn9Gkb7f8oWxuhU4yGPgh6Xf0t9QoA5MnMF6fNIHu9iXfCmjWPamObQvSiPqba8VGela/AMZHxMBGDhARY0kvDP6ZX2ZuCFwBHCZpUET8t9UOzMzMzMzMzKxunrzUGjUceJo0uWerE3ZKmlVSpWZvZVLD0SXt5mBiPeb2Wl7S4iXrd8rL4uSW9+TlLpTbpaodMCGheg4p6bVL/vkCOEdSrWRmd6mMBt9GVbUkJE0HbNNAX7fl5S6SZupATJuRnoOHI0JlP6TR6p8DGxYmZhwJvE8aibxiew4cEeOBh0h/88rOfccG+nqViZODLtta26zy7L9WvSFPXrlCvcduQzPveSNOJZVFerhqkszKc7NVJx23zBDSiP2dJc1KGrE+phBLxWykZ2GyUjv5Wv2UcpUXMu15KX0DaRT6VjmxvwPpGxpDi43yi5wRpG91rNmO41T6iYgYTnoZCrUnhjYzMzMzMzOzdnBi3RqSJ0XclZT8PF7Sn3OSaAIlW5ASov3z6hdJE0euI6lvoe0MpLIwc3YwtGmAM4uJ35yEPSAftzgx5ZWkkapr5JIPxdgPzDGPBv5VWD8NaXLN2YCjIqIlT2R5dF53cW4zpbiTNMHlksChVduOofao5slExEO5v++SajdPklyX9ANJS9bRVaUMzGW1GkTER8CNpIk9d8zrvgROJ5XlOF/S94v7SJpT0mp1HL/yDJwgacKkn/mlyLFlO0g6WNJ8JZsqI7TrqQVemWh0a0kTaqxLmh04n4mTmHZU0+55PSQtKukK4GekGuY/q2pyLjAWOFrSniXJ/pkl7Z5rnTdFRIwG7iZNBHsSaTLSK0q+WTMG+ADoV3x28mSmJwNlL+lg4rcClmhHbJ+RSrvMApxCejk5vMYko38i/d26KM9LMAlJC+S5HiqffyRp6zxJb7HdHKRJe6Fza+ybmZmZmZmZ9ThTUiLQviUi4nFSneC3SaUwxki6TdJgScNIJVSuI006+VreZwwpiTgr8ISkYZKGkiYKXQcY1MGwhgFLAS9JuiLXM76flMT6U3GizIj4hDTa/DNSonikpCGSHgXOICUJd66amPIIYE3SyNe/FtafDtyet00xNYzzxKgDSed4sqRH8zk+SXoZ8M8Gu9yNlLTdFXhV0rWShkp6LK9fpbWdJc0JbExKFlbXTq92eV7uWlj3J9KEocsBL0i6VdJlku4jTfS6dx3nMJj0suQHwHOS/iXpWtI3MJ6iPPF4HPBGvn5XSLpS0rPAwaSSHX9p66ARMRK4lVSK5fl83H8Br5AmHL2utf3r1Qn3fAJJg/LPxfne/5f0smx74AVgQEQ8VRXPe6SR358AFwCv5N/7qyU9TPr7cRHlk5d2RKW0yr5Vn4uxfUVKoE8H3CXpFkmX53PaFzi7Rt/X5+WQ/Pz/U1Ij17XN2HJ8dwO/Jv0NvUfSE5Kuytev8qz+sbDLwsDVwNj8t/jS/Ld4FKmszb88gamZmZmZmZlZczmxbu0SEfeREpSHAg+T6hpvT5rYdBTwO6BvRNxe2G0/4BBSQnFdUu3p20gTgP6PjnkHWDX3tzYwgDSR4p4RMdlo5BxXf9Lo6QWBbUkjsi8FVoyICWVg8sj33wHvAQPzqP1KPwHskbed0N5SJZ0hl+X4CXAT6V5tRhpBvDaTlsapp6/XSdfreNKLkw1I9ZunJ71ouKONLnbMbe/JfbVmGKlkxo8r5X1yInQbYC/S87YyqcTI/KSE4rnlXU1yDkEqv/F/pOuwCWnSzbOArSmf9PNXpET/TKQXAxsBX5NGHP8wIl5q67jZlqRE6Njcz4q531VIZW6aopn3vMoe+Wcn0u/t18DFpHuydH55UBbPfaRyOaeSEv7rkJ6dWUn3eQcmnfC0GYaSSjQBvBQRZZOjEhF/Ip3Tk6S/W+sBT5DuSa3zuYb0UmU0aRLbnzH5SP3W3MHEuRs+ZmKivuxYfyONNh9MKie0Belv3Dekb/lsWWj+AOlbCY+QRtNvR/q7+mQ+x+0biNHMzMzMzMzM6qBCjtDMzMxsEi0tLU/37t176b59+7bd2MzMzMzMzKw51HaT7uUR62ZmZmZmZmZmZmZmDXBi3czMzMzMzMzMzMysAU6sm5mZmZmZmZmZmZk1wIl1MzMzMzMzMzMzM7MGTNfdAZiZmdmUbfSYTzjukhF1tR1ywoDODMXMzMzMzMxsiuAR6zbVkjSzpIMl3SnpbUlfSnpP0v2STpC0UFX74yWFpIHdFHK3kzQwX4Pju+h4N+XjfSBphib2O0pSNLjPoBzLgGbFMSWR1Cef34gG9hmR96n8fJ1/h16UdLWkAyTN1olhl8XUpc9oIyQNyLEN6u5YzMzMzMzMzKxzObFuUyVJqwAvAKcBKwMtwFXAf4DFgGOB5yWt121B9nCS5gPWzx9nBbboxnCsdTcDFwGXAHcA7wKbA38DXuspL6P88s3MzMzMzMzMKlwKxqY6kn5ISv7NCJwE/D4iPilsnwbYCjgZWLCw69+Ay4E3uy7aKc6/gAeAcV1wrJ2BaUnXe35gV+DKJvW9LtCrSX0ZnBgRI4or8kj1g4FjgAsl9YqIf3RBLF35jDbqIWAp4IPuDsTMzMzMzMzMOpcT6zZVkSTgUlJS/fiI+F11m4j4BrhG0u3A9wvrxzFlJuu6TER8QNclBXfLy32AK4CNJM2d70OHRMRLHe3DWpefleMlvUgayX6mpOsj4u0uOO4UmbiOiE+BZ7s7DjMzMzMzMzPrfC4FY1ObDYFlgdHAH1trGBEfRERL5XOtMg+FOtN9JG0l6QFJn0h6V9Jlkhas7rtYr1vSmpLukPSRpA8l/VvS0iX7zC7pV5JulvQ/SV9IekfScEnrV7cviW0HSQ9L+lTS65JOljR9brdYjnVM3n5HHtlf3V/N+tVKdpF0e47rc0kvSxoiabXWrnVJX0sDPwJeA/4NXEsaYb5DG/utKulKSW/k6/N6vl67VrWrWWNd0jaSHpL0mVLt/YslLdDKMb8v6WxJz+Vr966kpyWdK2mJOs+3o/e2rucu7zd3ju2tHO9jknavJ872iIhLgXuBGYC9S+L5jqTfSnoqx/OhpLskbVXSdkIdeEmzSjpV0iuSxkv6a24z2TMq6Ya8bqOyGCVNn6/bZ5JmzeskaSdJl0t6Pl/bj/Kzsb/SN1uKfYwCjssfL9SktecH5DaT1ViXdFZet2+ta5ifp5C0eMn1ODc/z19IGivpqrLf3dx+w/yMjc7t35B0r6TjytqbmZmZmZmZWfs5sW5Tm03zcmhEfNXkvvcHrgYEDAc+BnYE7pA0Y419NieVpZmTVKf6TWAT4G5J361quwpwJqmUxAukkhfPARsAN0vaq5XYfk0aqT8+H2d64DDgH5L6kkpnrAzcA7wIrA3cqVTnvE2SpiWVabkUWA14jJQMfwv4KSUJ1TZUEr1DIiJyvzBxFHtZDAcB9wHbkV6cXEMaHbwC8Ic6z+OXpFr7K5Dq7Y8A1iNdn7lK2i8IPEq6958DN5Cu4XjSOa9az3Hp2L2t+7mTNBfpGu2TY7yeNLr7QtLz0Fkuz8u1q+KZD3gQ+B0wB3Br/rwi8C9JR9bob0bgLmBP4HHSebzXyvEH5+UuNbZvko9/Q0R8mNf1BoaQ7sEY0r19EFgGOBu4oKqPq4An8r/vI9Wcr/y81d7YJC0PLA08HBHPF9avno+3D+meX096drYGHpBUfa33JT0fawHPkJ6Zp4E+wPGtxGdmZmZmZmZm7eBSMDa1+VFePtoJfe8PrB8RdwBImomUKPwJsBOTJ+IADgJ2jYjL8j7TksqebJP7+22h7XPAahHxn2IHkn5ESs6fLunKiPi45Dg/A9aJiHvyPt8lJSR3A/oDFwOHRcQ3kgQMIiW392fiKNzWHAVsCzwFbB4R/yvENycpYVyXPBJ45/yxklC/BXgb+LGkvhHxQtU+a5Imov0I2LJY71tpVP4kScYax+0DnAJ8AWxU6SPfx2uBzUp2+zkwN3BIRJxW1d/C1P83tCP3tpHn7k/A4sB1wA4R8UXeZ2NSYrazPJ6X1c/BhaSk8cnAMRExPsezKOme/0HSjRHxZNV+KwP3A4tGxPt1HP86UvJ5K0kz5ZIsRZXnbXBh3Vek38NhEfFlZaWkeYAbgT0kXRARdwNExKF5lPxywD8jYlAdcRERD0h6CVhN0sLF351aseVR9UNJLxi2i4ir2BUTsAAAIABJREFUCtvWI33L4xJJixZiPxL4EFguIkYV2gsYUE+sLS0tT9fYtFg9+5uZmZmZmZn1JB6xblObyqjjsZ3Q9+mV5CZMqKd8av64Zo19hlSS6nmfr0nJz8n2iYhXqhOvef1jpBG0s1I7gXx6Jame93mLlKgTafT6Ebm2PHmEeCXutWr0N0FOXB8CBLBXdWIwIt6NiPva6qdgAKm2/ROVUjz5ulyRt+9ass+R+VxOqJ5EMyK+jIib6zjuXqRRyhcX+8j38Vek86s2b17eUb0hIv5Xby33JtzbNp87Sd8hvUj5CjiwklTP+9xEStR2lkpd/DkK8SwPbEz6ZsCRlaR6judl0jM1LenlRZkD60yqExGfkb4F8B1gi+I2SbOQXpq8B9xU2OeriLimmFTP68eSXiQBbFnP8eswhPT87lQVm0jfPig+/5Ce1e8CpxST6jm+24BzgO8x6cugeYFXikn13D4i4s7mnIaZmZmZmZmZVXjEuk1t1Il931KyrlK6Yf5m7JNHtK9LGo38XVLdaoC+Vctqt5asezkvR5SUxakkhGvFXbQSMDvwSESMrKN9WyrlXi6pWn8JcCCpZMaEUfT5mgzIH8/rwHFXz8srqzdExHOSHiOViCl6JC/PlnQMcE97Swx14N7W+wytQBrhfF9EvFqyz2VUJXabqPJ7V3w5Uakdf11+mVPt3rzsX7LtzXY8a4NJz9bOTCxNA6lU0YzAJdVJdJjwAmADYGFgJtK5zJI317onjRoMHJtjO7Gwfg3SS6Zb8suwisq1u7ZGf/eSvg3Tn1QSCdKzurqkE4F/tGcC3379+i1Ttj6PZJ9sXggzMzMzMzOznsyJdZvajAOWAObphL5Hl6yrlO7oXe8+EfFxGqg66T65nvcwUqmJWmapsf71knWf1NoWEZ+UxVDD9/Oy4URdtVwTfBvgG1KitxjTSEnPAktK+klhhPfcpMTomIj4qAOHr0xQWpZ0rqyvTqwPIiVdtyeNWv9U0kjSyOcLImJMPQfu4L2t97mr5/w6y9x5+W5hXZ+8PEnSSXXsW9SeWG8jlRPaSNKcEVGJpawMTOWbGINo/WVDrXvSkPzi5hFgRUnLRsRTrcXGxGv3YP49raV47Q4gJeKPAI6Q9AZpPoCrgGsq31gxMzMzMzMzs+ZwYt2mNo+TJtdcgYn1u5ulbNRtM/f5Jynxeg1wEqku90e5Lvo+wLnUHpHf2nHaE3dn9bMVKVn5BXBlSdJwzrzclVRCpJnHLxtV3apcomaHPAp4S1K5llVIJViOkrRhRDxQR1eddW+LGj6/Jlo+L/9bWDdtXt7DxG9PlBlXsu7zRgOIiK8lXUH61sN2wLm5Xvq6wGs5jqLfkJLqLaSJXR8F3ouI8ZIWJ92jZn4DZjBp0tadSc9OL9K8BZUyNkWVazcUqK4XX/Rg5R8R8aSkpYGNSJO1rgXskH/ulbRu2Yh9MzMzMzMzM2sfJ9ZtavNv0sjN7SQd3t6yHV1N0syk8g9vA9vnhG7Rol0f1QSv5eUPmtBXpQxMb9ILkFp2kHRQTgSOIyUf55M0SwdGrb9BmthzYeCFku0L1dox10J/DDg+Tyx5HCkxewbw49YO2oX39o28XLjG9prn1wQ75mWxlndlpP1VEXFmJx67aDATywmdS0oqT0ea66D6hcNP83KnSq3/gs74fbsc+Auws6SjgQ1Jc0JcUfJMjyZ98+YPJRO71hQRn5NGrV8LkBPtl5HKIP0M+H8dPQkzMzMzMzMzSzx5qU1thgNPAwsC/9daQ0mzSiqtKdwNZiP9Pr5ZnXiVNB0Tk4DdYSTwPrCCpBXb24mkeUkJ5i+BOSNCZT+kkepzkia+rIwaH5G72bsD51Gp6b1dSWyLM3HUdasi4kPgaNLI8GXr2KWr7u0jpJHeP5b0/ZLtO5as6zBJu5JeknxKGplfcVtebtUZxy0TEQ+RXpqsLmkhapdagYkTrb5Wsm37GoeojPhu+KV0RLxJevGwEOl6tRZbU65dRPyXNDku1PesmpmZmZmZmVmdnFi3qUoelborKcF4vKQ/5xHDEyjZgpQwLps4sTuMAT4A+kmaMJI7T3h5MmmkdbfIo8ZPJ5XFOL86aStpzmLMrdiZlJAcHhHvtdKuMvHkboV1J5ES2cdKWqPq+L0kbVjH8S8kJUZ3L/aR676fQcnfQ0m7SepX0tdGpOtRTy3wLrm3EfExKUk7HXCGpAn11yVV6sQ3jaTZJB1HqlMO8MuIGFuI5wHgdmBtSadL+k7V/tNI2kDS6jTXENK9OQpYFWgp1DQvqkwAu29VXNsCu9fou/KtgCXaGVslib4PsAWpJv3wknbnAmOBoyXtqaqaSZJmlrR7rt2PpJkkHShp9qp205DmCIDOrbFvZmZmZmZm1uM4sW5TnYh4HFiPVHrjSGCMpNskDZY0DHgTuI40KWfZaNUul0vWnExKit4l6RZJlwMvkhJ/Z7e2fxf4E6m8xHLAC5JulXSZpPtIk6PWM5K8kii/rNVWcCXwNbBZJVEYEXcBhwOzA3dLelDSEEm3kZKd57Z18Ih4mTSx4wzAnfmZqFzjfqTJRattAzwl6UVJ/8rH/A+pJvbXpJHrbR23K+/tkbnfn5Lu02WS7iBNtvqPjvQraVD+uUrSA6QXBseTJsndPSIuLNlvF+BJ4CDgf5Jul3S5pHuAt4CbgZU6EFeZSvK6kjCvNdfCyaR7eKKkkfnePkyqa356jX1uIb20O1jSTZLOl/RPSfUm2q/J++8GzAxcGRHjqxvlF08/JV3bC4BXJA2TdHWO8W3gIiZOXjo96eXQ25Luz/f9auAV0jP8MnX8jpiZmZmZmZlZ/ZxYt6lSRNxHqgl+KPAw8EPSiN3VgFHA74C+EXF7d8VYLSL+BOxBSkSuRno58ARpssyR3RhaJTm8DbAX6XquTCpTMT9wNW0k7SQtRZpQ9lPghjaO9Tap9EtvCmVbIuIUYADppcgipIkflySVQGkzwZ37+CvpOXicVHd63XysVYB3SnY5jZT4/ghYg5TsnJf0cqB/RFxT53G75N5GxLjc/z9J128rUh3vvUmJ5PbakBT/bqTY5yG9iDgA+H5EXFIjnrdJ5/gbUomW/jmmBUk16w+gyZMMR8QLpGcU0rccSl/kRMTdpGfgDlJN9c1I32jYhhovOyLiDdIktg/kffci1S6fv87YPmTS539IK23vI5VvOZU0x8A6pNHns5Ku/Q5MnCz2Y9K1HEa6N1vk9u8BxwIrtvEtETMzMzMzMzNrkCafz83MzMwsaWlpebp3795L9+3bt7tDMTMzMzMzs55DbTfpXh6xbmZmZmZmZmZmZmbWACfWzczMzMzMzMzMzMwa4MS6mZmZmZmZmZmZmVkDnFg3MzMzMzMzMzMzM2vAdN0dgJmZmU3ZRo/5hOMuGVFX2yEnDOjMUMzMzMzMzMymCB6xbmYGSJpb0u8lPSbpfUmfSnpR0nmS+nV3fJ1BUh9JIWlEJ/U/IPdf/PlM0luS7pd0hqRV6th/UGfE18pxB+bjHl+ybTFJ/5I0TtI3ud2A9l5LSaMkRbNiNzMzMzMzM7Ou4RHrZtbjSVoPGArMDowF7gK+AJYF9gb2knRMRJzYfVF+q70NDM//ng6YE1gOWAU4UNItwB4R8VY3xVcXSdMAVwHLAw8ALwDfAFN03GZmZmZmZmbWfE6sm1mPJqk/8G+gF3AUcEpEfFXYvglwKfBnSZ9GxJndE2mneB1YCvi0k4/zbEQMrF4paQ3gTGAD4E5JP46IDwtNHsrxfdDJ8dWrDympfk9ErFncIKkXXXMtzczMzMzMzGwK4FIwZtZjSRJwETA98NuIOLGYVAeIiBuBrYAATpK0cNdH2jkiYnxEPBsRr3bT8e8BVgOeApYEjq/a/mmO781uCK/Mgnn5cvWG7r6WZmZmZmZmZta1nFg3s55sY9Io49eBk2o1ioi7SaViZgAOKG6r1MhW8itJT+T67I8X2kjSfpJaJH0uabSkv0qaRdKIvH+fqn43lXSBpGckfSjpk9z30ZJ6V8dYrAsuaSFJQySNzTXNR0ravGSfVuuCS1pV0pWS3pD0haTXJd0saddWrmlDIuJT4OD8cR9JMxSOX1pjPV/PHSXdneu1fy7pNUm3Saq+P6XXN2+ruy56roN+V/64R6Fm/Ii2+pI0naSjJL2QY31ZqZ7/9G0cc1lJg/N1/yLfhwtrnEvd18TMzMzMzMzMOs6lYMysJ9skL4dGxPg22g4Bticl4w8v2f53YE9S8vUZ0ij4ir8CB5Lqtt8OfAbsBvwE+Ipy5wMzA0+TRnTPCqwM/BFYV9IGEfF1yX59gIeBz4F7gfmAVYFrJW0cEbe0cZ4ASDoIOA1Q7u8uYF5gBWAJUnmcpoiI2yWNBeYB+gP3tLHLn4EjgI9I5/g+MD+pbvsPgLObFVvBRcB3gQ2Bl/JxAZ6tY9/LgG2Bj0m15gX8BvhR/vdkJG1DeuamBx4B/gMsBgwENpe0VkQ8XdilO66JmZmZmZmZWY/lxLqZ9WTL5+UjdbSttFlaUq+SRPzWwI+qkp1IWp2UVB8HrBkRz+T1cwC3kpLeZfYFbo2ITwp9zUJKtm4G7AJcXLLfHsBZwG8qZW0k/ZqU3D8GaDOxLmlNUlL9I2DLiBhR2DY9sHZbfbTDE8B6pG8Q1Eys5xHtBwGjgBUj4t3CtulILyuaLiIGShpASqzfW1YzvoyknUhJ9ZdJ9//1vH4R4G4mlpcp7rMI6d5+BqyfvzFR2bY7Kcl/IelFS9OuSUtLy9M1Ni1Wz/5mZmZmZmZmPYlLwZhZTzZXXo6po+3YvJwGmLNk+0nVSfVs37w8tZJUB4iI94DDah0sIq4tJtXzuo+YWDZlyxq7vgwcUlUr/mzgPWCVtsqPZEeSRlKfUEyq5xi+jIib6+ijUePyco422s0K9AaeKCaQc2xfFZPQU4j98vLYSlIdICJeAX5fY59fAzMBh1efT0RcDFwL9Je0Ql79bbsmZmZmZmZmZt96HrFuZj2Zqpb1tIU0kWm162vsVxktPLR6Q0TcKekdJib4Jz2g1JdUruYHpLIw0xTi6FvjeCOqR9NHxFeSXgZWzMeqORmopGmBAfnjebXadYLKeZVd2wkiYoyk0cCmkg4DBkfEG50eXTtI6gX8GPgGuKqkyWXAuSXr18/L62p0fS9pQt3+wKPNuib9+vVbpmx9Hsm+dKP9mZmZmZmZmU3NnFg3s55sHKle+Lx1tJ0nL4M0+rvaqzX2WyDvM7rG9lepSqxLEnAKaXR6raT/LDXW1zrOx3k52cSnVeYGZgTG5BHyXWXuvHy31VbJHsDlwMnAyZJeIZVVGVJvDfkuMhepRvqbEfFl9caI+EjS+8DsVZv65OVb6VGoae7Cv78t18TMzMzMzMxsquDEupn1ZE8Aq5FGcl/SRtsV8/LpsolOI+LzdsZQljndgTS55WhS7ez7gbERMT6Xcvmixn7QxojvBjSrn3otl5f/bathRNwh6QekWvMbAWuREst7SLoyInao85idXQ6trlH4JabN+5TV0C+aUHqoidfEzMzMzMzMzOrgxLqZ9WQ3AfsD20o6rCxhXrBzXg5v8BhvkkYgLwi8VLJ9sskrgZ/m5X4RMaxq26INHr9R40iTZs4naZauGLUuaT3S6OuPqG8iWSLiQ9JErkNyH6uQyu1sL2lQRNyUm1ZGin+npJvvdyTuOozLx/+upOmrR63nyWirR6tDeqGyGHBgPs+6NHBNzMzMzMzMzKyDPHmpmfVkNwLPAd8DjqjVSNKawLakJOnZDR7jP3m5bUm/A5i0nEdFZQLP10q2bd/g8RsSEV8DI/LHvTvzWACSZgJOyx//HhFftKefiHiAid86WLawqVJPfvGS3TZoz7EaiGk88BDpv7XblDTZscaut+XlVh08fq1rYmZmZmZmZmYd5MS6mfVYEfENMBAYD5wg6Yg8eecEkjYGriWV9TgyIkY1eJjKBKCHSFqi0O/spHrYZZ7Py31UKLItaQ3gsAaP3x4nkUqRHJuPOYGkXpI2bMZBJK0O3EdK+j4N/L6OfRaSNDAn5IvrewNr54/Fevd35eUhxX3yKPmDOhB+vSqTk54gaf7C8RcGjq2xz6mkbw2cLmnz6o2S5pS0v6QZ8+dGr4mZmZmZmZmZdZBLwZhZjxYRD0jagjTx44nAbyTdT6pjviywFPANcGxEnN6O/u+SdDZwAPC4pNtJSdO1gVHAA8AqTCxZAnAmKeG/PzBA0pOkUfWrk5KuhzZ+pg3HfDjwF+BuSQ+RytjMS6qF/gkTJ9isx5KSBuV/T0cakb8c6ZwgldcZWGfZmTmBC4GzJY0klU2ZGfgJaYLZh4BrCu0vAw7P25+R9DCpBMxKpJHynXotgcHA1qTyPs/l+y9gPVLSP4CFijtExAuSdgUuBa6X9BzwTN5vYWBp0qSoQ0jPUqPXxMzMzMzMzMw6yCPWzazHi4jhQF/gj6TSIesAWwAzAP8EfhQRf+jAIX6Vf14B1iclPC/Px5mblFx9rxDP80B/4Ia8fQtSjfBfRERXjFgnIk4BBgDXAYuQStksSaqBfnSD3c1Hnkgz97MSqczNmcCqEbFxRLxdZ18vkZLhI0gJ6a1JE9COAg4EBhRrmUfEZ8C6pAT7LMAmpP/27UDjZX0aFhGRj/V/wNh8/OWBs3LspRObRsQ1pJcP5wK9gI1J96M3KVm/GfBBbt7QNTEzMzMzMzOzjlP6f34zM+tqkr5HSn6+GBFLdXM4ZqVaWlqe7t2799J9+/bt7lDMzMzMzMys51DbTbqXR6ybmXUySUtW6mEX1s1DKt8xHamkh5mZmZmZmZmZfUu4xrqZWec7CNhZ0mOkUjPzASsAswKPAqd0Y2xmZmZmZmZmZtYgJ9bNzDrfNcACpGT6j4GvSXWxrwZOzXXAzczMzMzMzMzsW8KJdTOzThYRtwC3dHccZmZmZmZmZmbWHE6sm5mZWatGj/mE4y4ZUVfbIScM6MxQzMzMzMzMzKYInrzUzHoUSXNL+r2kxyS9L+lTSS9KOk9Sv+6OrzNI6iMpJI1ocr8Dcr+N/AxqoP+ReZ+5mxl3HccdJ+njGtsOl/SspC9ybMPy+qvy55UaOM4v8z6HNit2MzMzMzMzM+saHrFuZj2GpPWAocDswFjgLuALYFlgb2AvScdExIndF+W3ylvARSXrtwVmBm7ObYru7eygOouk3YGTgHHAdcCnwOPdGpSZmZmZmZmZdQsn1s2sR5DUH/g30As4CjglIr4qbN8EuBT4s6RPI+LM7om0U7wOLEVKBDdNRDwLDKxeL2kAKbF+YkSM6MAhtgFmBN7rQB/NtFVebhoRD1VtOxA4BhjVpRGZmZmZmZmZWbdwKRgzm+pJEmlk9fTAbyPixGJSHSAibiQlTgM4SdLCXR9p54iI8RHxbES82t2xNCIi/pfj/rq7Y8kWzMuXqzdExBs51s+7OCYzMzMzMzMz6wZOrJtZT7AxacT266RSHqUi4m5SqZgZgAOK2ySNyvWwJelXkp7I9dkfL7SRpP0ktUj6XNJoSX+VNIukEXn/PlX9birpAknPSPpQ0ie576Ml9a6OUdLA3M/xkhaSNETSWEmf5Zrkm5fs02qNdUmrSrpS0hu5dvjrkm6WtGsr17RDJJ2SY9pW0uqShkt6N6/7QW5TWmNd0iKS/iHp+Xze70h6StI5khYptNss7/+3GjHUVRe9EivQP68aW6gZv1JbfUnqn6/nR0p1/W+StEIbx5xB0qGSHpX0cf65v9Y9qfeamJmZmZmZmVlzuBSMmfUEm+Tl0IgY30bbIcD2pGT84SXb/w7sSarP/gxpFHzFX0klQb4Abgc+A3YDfgJ8RbnzSWVTngaeAmYFVgb+CKwraYMaI7b7AA8Dn5Pqls8HrApcK2njiLiljfMEQNJBwGmAcn93AfMCKwBLkMrjdKaNSOVkWoDhwEKkbw2Uykn3kcBswKPA9aRyMX2A/YA7gFeaHOPDpG88bAbMBVxOuseQ6q3XlMviDAd657hfBJYD7iM9a2X7zAbcQnoO3ibdk2lI9/cSSctHxKGF9t1xTczMzMzMzMx6NCfWzawnWD4vH6mjbaXN0pJ6lSTitwZ+FBFPF1dKWp2UVB8HrBkRz+T1cwC3kpKiZfYFbo2ITwp9zUJKum4G7AJcXLLfHsBZwG8qZW0k/ZqU3D+GlJhtlaQ1SUn1j4Ati/XQJU0PrN1WH03wM+CAiDinzvb7khLIv4iI84obJC1G7RcY7RYRVwBXSBpJSqz/KiJaTajneHoBg0hJ9V9FxN/yegGnAgfX2PUsUlL9POCgiPgs77cAcCNwiKQbIuKu3L7Lr4mZmZmZmZlZT+fEupn1BHPl5Zg62o7Ny2mAOUkjhotOqk6qZ/vm5amVpDpARLwn6TDSqOHJRMS1Jes+knQwKbG+JeWJ9ZeBQ6pqxZ8NHAesImn6iPiy7JgFR5JGqp9QPclo3vfmNvZvhgcbSKpDGk0PJdczIl5qTkhNszGwMPBYJakOEBEh6WjSS5N5izso1fbfhfRtiAOK9zci3pB0AOkbCr8gjWSHJl2TlpaWsucaYLF6+zAzMzMzMzPrKVxj3cx6AlUt62kL5SVJrq+x30/ycmj1hoi4E3in5gGlvpJ+LemsXG99EHBs3ty3xm4jqkfT5yTsy0AvJr5MqHXMaYEB+eN5rTTtbLWuZy2VbxScJ2mNfB5TqtXz8srqDXmS08leqgDrkP7bPKx6gt3sQWA8E+u9w7frmpiZmZmZmZlNFTxi3cx6gnGkeuHzttUQmCcvA3ivZPurNfZbIO8zusb2V6lKdueSIKeQSoLUSvrPUmN9reN8nJeTTXxaZW5SHe4xEfFRG207U63rWcvfgXWBzUmlaj6W9DCpRMoFEfFuk+PriAXystY5lq3vk5eH5W861FKc0LUp16Rfv37LlK3PI9mXrqcPMzMzMzMzs57CiXUz6wmeAFYDVgQuaaPtinn5dNlEp3mkcXuUJc53AH5DSpIfBNwPjI2I8bnG+Rc19oNWJvhsULP6aa+GrmdEfAFsIWllJiaSV8/LoyStGxGP19ldZ39rq3LvGrnGldHmD5HKwdQy4bo1+ZqYmZmZmZmZWR2cWDeznuAmYH9gW0mHlSXMC3bOy+ENHuNN0mjjBYGyutYLlqz7aV7uFxHDqrYt2uDxGzUO+AyYT9Is3TxqvWER8RAp+Yyk2YE/k+rcn0oavQ1QqTH/nRrdfL8zYwTeyMuFa2xfqGRd5ZsIN0fEbxs5WJ3XxMzMzMzMzMyawDXWzawnuBF4DvgecEStRpLWBLYlJWTPbvAY/8nLbUv6HcCkpTsq5sjL10q2bd/g8RsSEV8DI/LHvTvzWJ0tIt4H/i9/XLaw6c28XLx6H0kLAP06ObR783K7kuP3Jk1MW+120gj3LXKpoHZp5ZqYmZmZmZmZWRM4sW5mU72I+AYYSJr08QRJR1RP8ChpY9JkkgKOjIhRDR6mMgHoIZKWKPQ7O3ByjX2ez8t9iklUSWsArdXXbpaTSEncY/MxJ5DUS9KGXRBDQyTtWby+BRvnZbFu+TPAWGBVSesV+pgNOB+YodMCTW4ivTRZQdJ+heML+CMwX/UOEfECabLT5YDzJc1R3K5kTUnrF9Y1ck3MzMzMzMzMrAmcWDezHiEiHgC2AD4ETgTekHStpCsk/Zc0qn024NiIOL0d/d9FGuU+D/C4pGGShgIvkv7WPpCbflnY7UzgE1KZmhZJl0m6G7iLNCFlp8oxHw7MDtwt6UFJQyTdRipjcm5nx9AOuwDPSnpO0jU53geBS0kvTo6pNIyIr4Df5483SrpV0g2ke7IgcHNnBhoRX5Je6HwJnCPpIUlDgBbgl8CFNXb9Bane/p7AK5JGSLpc0l3A66TnY61C+7qviZmZmZmZmZk1hxPrZtZjRMRwoC9ptPCbwDqkZPsMwD+BH0XEHzpwiF/ln1eA9YGfAJfn48xNGh3+XiGe54H+wA15+xakeuC/iIiuGLFORJwCDACuAxYhlbJZEngEOLorYmjQiaSXDp+Tkss/BeYELgZWyPd4gog4C9iPVPd+TdLktEOBNYCPOzvYiLgjH/c2YGlgU1JyfHXg0Rr7fEA6t/2Ap4DlSefZB3iWNOFtsVRRQ9fEzMzMzMzMzDpOEdHdMZiZTdUkfQ8YBbwYEUt1czhmDWlpaXm6d+/eS/ft27e7QzEzMzMzM7Oeo93zjnUVj1g3M2sSSUtKmrFq3Tykkh/TAUO6JTAzMzMzMzMzM2uq6bo7ADOzqchBwM6SHiOVmpkPWAGYlVT245RujM3MzMzMzMzMzJrEiXUzs+a5BliAlEz/MfA1qbb31cCpEfFZN8ZmZmZmZmZmZmZN4sS6mVmTRMQtwC3dHYeZmZmZmZmZmXUuJ9bNzMysVaPHfMJxl4yoq+2QEwZ0ZihmZmZmZmZmUwRPXmpmZmZmZmZmZmZm1gAn1s2mYJLmlvR7SY9Jel/Sp5JelHSepH5NPtYgSSFpQDP7beD4oyRFyfqQNKobQpqqSDo+X8uBdbbvk9sXf76QNEbSI5LOlbS+JHVy6NVxjcix9OnK49aju3+HzMzMzMzMzKzrOLFuNoWStB7wAnAM8D3gLmAYMB7YG3hc0pHdF6H1EJ8AF+WfK4EHgTmBfUj15B+WtHj3hdd1ar38MTMzMzMzM7OexzXWzaZAkvoD/wZ6AUcBp0TEV4XtmwCXAn+W9GlEnNmEwx4FnAi82oS+mmkp0ssE6x7jImJg9UpJywGnAesAd0vqHxGvdUE8uwMzAa93wbEaNaX+DpmZmZmZmZlZk3nEutkUJpfWuAiYHvhtRJxYTKoDRMSNwFZAACdJWqijx42INyPi2Yj4tKN9NVOO6aXujsMmFRFPABuQRq3PB5zRRcd9NT8TU9zLlin1d8jMzMzMzMzMms+JdbMpz8akUdqvAyfVahQRdwNDgRmA/YvbWqtLLmlg3n581foruSw3AAAgAElEQVTS+tDF8heSfi7pSUmfSXor19mevcZx5pL0F0kvSPpc0ruShkvaoI3zr+5nsnORNCCvHyRpTkn/T9KbuQZ4i6S9WumvT457VG4/VtJVkn5Yo/2Gkm6WNDq3f0PSvZKOa+Aclpd0cq5NPjb387KkcyQtUCPGyPXEZ5R0oqT/5f1elHRErdrmktbK+30s6R1J/5K0ZL2xNiIivgYOIL3g2arsBY+kZSUNlvR64fpdWFYjvVgHXtLKkoblcwhJy+c2k9RYlzSvpK9y/6X/TZO0fd5ncGHd/JIOl3RX3vfL/Exfk78xUtx/QP4dWDh/LtadH1VoN8nvkKReOf7PW/k9WTnvc1/Jts3zs1fp43mlORe+U9J25vxcPK40H8PHkl6SNFTShmXHNjMzMzMzM7P2c2LdbMqzSV4OrWNU7pCqfTqNpJOBs4EPgeGASHW2r69O8kr6HvAQcChp5P21wGPAesDNkg5uUlizA/cDPwUeBu4DlgTOl/TzknNYHXgix/0xcD2pjv3WwAOS1q5qv28+17WAZ4CrgaeBPsDxDcR5JPAbYNoc442k67cfMLIsuZ5NTxoRvk8+/p2kevsnAr8vOb8tgdtzvE/kfX9Iqov+gwbirVtEvAg8Qjqftari2QYYCewMvEm63m8BA0nnvUyNbtcE7iVd51uAu4Fvahx/DHAbsACwdlmbfHyAwYV1W5JeXC0APEV6Rt8gPUv3adIXQG+RvkXySf58UeHnqhrHJP/+DgV6A9s0EBuSTiVdrzWBFlJpqOlJcy6MkDRzoe20pOt0IjAXMIL0jL0NbAbsVCtGMzMzMzMzM2sf11g3m/Isn5eP1NG20mYZSdNVl4xpsl2BH0fE4wCS5iYltdcgJTTvKLT9O7AocAnws8oLgpzYvhn4i6TbI+LJDsa0JSnZvUdEfJKPsSUpSXos8M9KQ0mzkpKcMwLbRcRVhW3rkRKXl0haNCK+zJuOJL1IWC4iRhXaCxjQQJznAQdHxJuFPqYhJUl/B/wBKBtlvypwD7B4RIzL+61Euu4HSzoxIj7O62fJ5zstsHNEXJbXT5fX79FAvI16HFiJ9E0L8nEXAS4GPgPWz9+wqGzbnZSUvhBYuaS/PYEjIuLkOo8/GNiQlKS+vbghjxTfGBhHSj5X3Ee6r09Wtd+QlNA+R1LfSJ4FBuaR6DOX1ZxvI7Zf5NjOrzrWNMD2wFekiWEr67cnvYh5DNi68uxJ6gX8jfSi5XjgsLzLGsBPgOty+28Kfc1GnS9VWlpanq6xabF69jczMzMzMzPrSTxi3WzKM1dejqmj7di8nAaYs3PCmeDYSlIdICd6/1/+uGZlvaRFSaNkPwQOLI66j4h7SUn3aakqX9NOHwL7VJLq+RjXkUYgL1RVbmQv4LukiWAnGWUcEbcB55BGg29W2DQv8EoxqZ7bR0TcWW+QEXFHMame130TESeQSv5sWWPXb4CfV5Lqeb+RwE2kCTxXKrTdDpgbuLWSVM/tvwIOJo3Q7yyV+OYorPt1jvHwYlI9x3Qx6eVHf0krlPTXAvylgeP/C/gU2EZS76pt25JGel9RfPEUEU+VvdiJiJtJL2AWA/o1EEMt9wL/AwaUfDNhHWB+4ObiPQaOzsudis9e/l36NWkE/c8LpW/mzcsRxaR63ueDiKjnJZ2ZmZmZmZmZNcAj1s2mPKpa1tMWUrK6M91Ssu75vJy/sG71vLwxIt4v2ecS0mjcNZoQ08iIeLdGXMvmuEbldevn5bU1+roXOAjoD1yT1z0CrC7pROAfHZlEVdJcwBakZO3sTLxfvYA5Jc1Zci6jIuJ5Jtfadb+yqi0R8Z6kW0glbzpD5TmMwrrK9b6uxj73kibg7Q88WrXthoiIyXcpFxEfS7oe2BHYlIn3D2qUWgHISfiNSKPm5yEl4CE9OwB9SS9p2i0iQtJlpG8/7Aic1lpskuYFlgOeiYjnSvr7XNJI0gugvsBzpG8MfAMcJukt4N8R8VGjsfbr16+0NE8eyb50o/2ZmZmZmZmZTc2cWDeb8owDlmDiKNTWzJOX3wDvdVpEyeiSdZVR0MVRwpVRuaNq9FNZX6uueCPKYoLyuPrk5YMqn/ezYu7Cvw8gJeKPAI6Q9AapNMtVwDXVo4NrkbQTqRzMZJNOFswCVCfWGzm/yvV8tcY+tdY3Q+WaFePvk5dvNXC9K9oT62BS4noXcmI9jxBfi/Stg/uLjSUtSyr50ofaZmlHHLViOzLHdlo+fm/Si45PmPTlw8J5uVSeMLU1cwPPRcTzkg4j1Vi/DPhaUgup9vyFEVGrxIuZmZmZmZmZtZMT62ZTnieA1YAVSaO7W7NiXr4UEZ/X2X+7SkA1MoK4sksb6xvtr5FjlKmMEB9KKhtSy4MTOo94UtLSpFHNm5CStDvkn3slrVuox15K0sLAINKo7oNItdxfj4jP8vb/kGqpl2WfGzm/slHjXaUyL8B/C+umzbFc3Ma+ZUnfep/loptJL6U2lTRbRHxASrRPw+QTg4o0sr8PqTTR34GXgY/zCPM/AUdR37dG2hQRLZKeBFaQtGSu2b4pMBtwaUQUn8fKc/om5d8SKXqncIzTJA0lfQtgfdI3Qg4h1eI/MCLObsa5mJmZmZmZmVnixLrZlOcmUv3xbSUdVqxRXqJSSuLGqvXjqT06+vsdjK8tb+TlIjW298nLN2ts7yyjSd8E+EMjk6bmFxbX5h9yov0yUumVnzGxznwtm5BKjJwaEWeUbF+03ljaULnuC9fYvlCTjjMJSX2BFUjfmijWUh9NqlN+YER82BnHLoqI8TmxvB+wDXABE38/hlQ1XzL/jIyI/Uq6a9Y9KRoM/DDH9Ftql6ipfEvhrQYnSSUiXgPOAs7Kk9buSJog9jRJg2uUZjIzMzMzMzOzdvDkpWZTnhtJdZO/RypBUkrSmqSJGb8EqkejvgnMJalsQtMNmhRnLffm5aaSZi/Zvmte3tPJcVS7LS+36kgnEfFfJl7vZVtrm1Um9HytekO+h/N1JJ6CynXfruQ4s9MJ913StMDfSCO7r46IYumaplzvBlWS1DtLWpz0jY5HI+KZqnaVezJZqR1JczCxPny1L3Ob9ryUHkIawb+zpFlJI9bHMPE6AZCv4XPADyXVejnVpoj4KiIuBR4mvdhZvL19mZmZmZmZmdnknFg3m8Lkut0DSaPOT5B0RE5gTiBpY9IIagEHR8QLVd3clZfHFvaRpKOAn3RW7AAR8TKp3MkswBmSehViWJU0ovhr4JzOjKPEucBY4GhJe6qq8LekmSXtLmnB/HkmSQdWvxyQNA0Tk9T11AKvTDS6q6SZC/18j1SCpFmGkmqcbyBp+8JxpgVOpfX67g2T9ENSqZINSC9yDq5qcirwGXC6pM1L9p9T0v6SZmxiWP8h1fBfm1QGBUomLQVeJI2wXyePuK/ENAPpnpS9kIKJ3wpYotHAcsL8btIo/pOAGYArIuKrkuZ/IJWEuVpSv+qNkhaTtFfh89qS1svPZrHdwsBSpIR+rXr9ZmZmZmZmZtYOLgVjNgWKiAckbQFcTpqQ8DeS7ge+II2SXoqUeP9tRJQlqE8ijWY/SNIA4KW83/dJCe39O/kUfkEakb47sFaOfR5gAClheEgj5ViaISLek/RT0oSVFwDH5QkevyCVSVkKmBn4ESkJOT1wBvAXSY+SErbTAyvl9i+TkvVtuZ5UR3wl4EVJ95GSqmsDj5OSwR1+2RERH0rah1Q7/ApJB5IS/yuTrv1g0uSZjZpb0qD872lJdcGXYWK5lIeBnSPi9ap4XpC0K3ApcL2k54BnSC+DFgaWJl3PIaQEfIfl+uhDgKOBfUjJ88tL2o2RdD6wN/CEpDtyDGvkcxxEerlV7XpSnf3bJd1Jmnh0XEQcWWeIg/P++xY+l53HpXly1cOBxyU9BrwCzEq6dkuS5mK4IO+yHHA6MFbSI6Ta6/MAa5Ketb9GxBuYmZmZmZmZWdM4sW42hYqI4Xk07a+BzYB1SKPAK7aLiOtq7Pu0pHWAP5MSq4sC9wHbkxLHnSoiXpfUnzQB5FbA1qQJQ28n1Rpva1LGzorrvpyw/A2pFMc6pNHzbwDDgGuYOAHnx8ABwLqkxOUPSaVA/gf8A/hbPTWrI+JLSWsAfwQ2Jt3L10m1sE9g8vr4HTm/qyWtDxxPKoOyDOkFx5Gke98eMwN75H+PBz4gJezPA64Cbqs1sW1EXCNpOdLo8fVJ5/856XoPBq7O/TXTYFJiHWBEKwnl/YBnSXXy181x3Ab8H7BnjX3OJJWR2YlUx70X6XmoN7E+lHTfe5MmHH6wVsOIOELSzcAvSZPbLge8R3rp8xcmfWEwDJiL9LJmufzvsaR7f05EXFtnfDUtOO/MDDlh+bYbmpmZmZmZmfUQqpEPMbMplKRTSYnh54DVIuKdbg7JzKZiLS0tT/fu3Xvpvn37tt3YzMzMzMzMrDnUdpPu5RrrZt8+h5LKZywBDJM0UzfHY2ZmZmZmZmZm1qO4FIzZt0yuIz0QeBKYEejPxMlKzczMzMzMzMzMrJM5sW72LRQR40kTlJqZmZmZmZmZmVkXc2LdzMzMWjV6zCccd8mIutoOOWFAZ4ZiZmZmZmZmNkVwjXUzsxKSosGfUd0dcyMkfSfH3dKTYpC0p6THJX3W3edvZmZmZmZmZt9eHrFuZlbuopJ1qwOLAU8Aj1dtG9feA0k6BTgE2C4irmpvP9Y6SWsB5wOfADcBHwKjuzUoMzMzMzMzM/tWcmLdzKxERAysXidpECmxfm1EHN/FIVnHbQkI+FlEXNndwZiZmZmZmZnZt5dLwZiZWU+xYF6+3K1RmJmZmZmZmdm3nhPrZmadQNIKkq6Q9KakLyWNlnSepAWr2o0jlYEBGFpVt32l3OaX+fOhkpaRdLWksZK+kbRebrOkpD9IekjS25K+kPSqpAskLdJGrL0kHSvppbzfKEm/lzRdoc0skj6S9L6kGWv0s2mO89qq9bNKOj1fg88ktUjav5V42jzf3G5eSWcU4h4naVgu+TJZf8B2edXDhWu8bW4zMn+euySefnnbsKr1p1T6kLS6pOGS3s3rfpDb9Jd0mqTHJL0j6XNJL+a4523tWLkG/WmSXsvn95ykg2pcs0Uk/UPS8/kavyPpKUnntHX/zczMzMzMzKxxLgVjZtZkknYDLgCmBR4G7gGWBPYGNpe0RkS8mJtfBqwNLAOMAP5X6Kq6bvtywO+A14HbgHmAr/K2XwL7A08DDwFfAv2APYGtJK0SEc+XhDsNcA2wFvAg8Ez+9zG5/30BIuIjSUOAfUgJ6otL+vp5Xv6jcC1mBu4EVgDeBm4A5gDOyOfcmprnm5PFd5NGob+Sz2F+YGNgE0n7RsR5uZ9nSDXzBwALA8OAd/K2UW3EUI+NgIFACzAcWAiIvO04YEPgqRzvNMDywIHAlpJWioiy+vwzkp6HPqT7Mj2wJnC6pBki4sRKw5zEHwnMBjwKXJ/37wPsB9xBukZmZmZmZmZm1iROrJuZNZGkpUiJ5feBzSPigcK2/YGz8/a1ASLiV3ny0mWAs9uYvHRX4CTg6Ij4pmrb5cBfImJCYl6SSMn2vwGnAFuU9LkU8AWwdESMzvstATwC7C3pdxHxZm77d1JifW+qEuuSvgtsRpoMdHhh09GkpPpdwGYR8XFu/xPg1lbOta3zvYCUVD8XOCAivs79rgf8GzhL0p0R8f/Zu+9wuapyj+PfH713UCFAgIsUg4iIdAih9yIgImLooFIEKSLtAldBCFIEAem9C0ivoYiAdBJ6iaETSuhJSHjvH2ttspnsOWfm1Eny+zzPefaZtdfea+1ZM5Ocd69514sRcSdwp6SrSIH1/42IR9ppuxk75T6cVrHvJGDHiHi3KMjfBDgaOBA4jBRkrzWA9PysFREj83Grkp7HAyX9JSJG57q7k4Lqu5VuJhRtLcL4my9mZmZmZmZm1kWcCsbMrGvtB0wL7F0OqgPkwOsdQH9J3+3AuV8DDq0IMhMR95eD6rksIuJU4ElgPUnT1jnv7kVQPR/3PHAF6d+IlUvlj5Nm4K8iafGacwwk3aw9pxTknoIUiIf0fHxaOtcDwNkduV5JS5Fmn78H7Fu0l897B3AeaYb3bu2cv6s8VCeoTkTcXg6q57KxwO9JN182rXPOMaRFVkeWjruPNIt9NmCpUt0ipcxdFe2/XPu6qGfIkCFDq35IC/aamZmZmZmZWYkD62ZmXWttYBwp5UmV+/N2uQ6c+5aI+LLeTkmzStpO0nGSzpJ0nqTzSKlXpibN1q71cUQ8VFFepI35Tk356XlbpH0pZsbvBHxFmkleWByYC3g+Ip6saOPSeteS1bveVfP22oj4vGL/hTX1utv1be3MueB3znnmz85jci7p+VpA0jQVhz0XEa9VlFeNy6N5e6akVSVN2WT/zczMzMzMzKxJTgVjZtZFckBzfkDAxyneXNcEi2Q2YHgbbW9ACijP0cbxM1eUvV5RBlDMLq+d5X4ZcALwS0kHR8QYUlqb/yEFwsuzo+dtp991r6ed/cV5h9XZX5TPW2d/V2trXHYkpeKpXPA1m5nxOd8LzYzL6cCawMaksfhU0n+Am0jfIPigjba/1q9fv8qc93nW+pKNnMPMzMzMzMxscuHAuplZ11H+GUP7s7Gf78D5R1U2Ks2R25sJ+ANwNSmNyhcREZKuJwVdqyL9UVFWV0R8LulC0mKpmwJXUrFoadG1jrRRUnm95e50cn+j2vt2V71xWRw4E/iStIjobcCbETEq73+KlNKlU+OSc61vIunHjA+ur5K3v5e0ZkQ80ej5zMzMzMzMzKx9DqybmXWRiBgr6R1gTmDXPJu7J6wJzAKcHxF/rNi/cBe3dzopsL6LpDuBLYB3mDD9zZt5W5WCBmCBDrZfnHehOvuL9t6qs79KMVYzkXK3l83fxHnKNgamBI6OiNMr9tfrf4dExMPAwwCSZgP+RFrYdBDpNWJmZmZmZmZmXcQ51s3MutYdpJuWGzVxTBHU7ejNztnzdoKc3HmhzyU6eN5KETEU+BewFnAYKS3JeRX50J8lpThZLPej1jYd7MJ9ebuZpBkq9m9XU68RRRC+alHZdZo4T1lb47IBKYjfLfKip3/ID6ueezMzMzMzMzPrBAfWzcy61p9JqT9OkzRBQFbS3JL2klT+/C1mYC/WwTaLBS23llQEc5E0F3AW3fNZfzophcnepLQlZ9dWiIivcvsAJ0qasdS3FSgtgNqMiHgauIeUp35QebFOSQOAHUg3K85o4rT35O3+kr7OXy5pY9Ks744oxuWXkqYrnXMh4JQOnnMCknaQVPXaWT9v28tlb2ZmZmZmZmZNcmDdzKwLRcRTpMDu7MCtkoZKukbS9ZKeAN4ATuKbn783k4Lxh0i6QdJZ+adeCpVa9wAPkGZbv5jbuw54GZgBuKVrru4brmL8gpuDI+LFOvWOBp4EBgAvS7pc0q2k2eTnd6L9HUgLfO4OvCDpUkl3AbcD0wB7tdGnKucC/yXNwn9W0lWSHgGuo+NB8CtIY7Aq8JKkKyXdDDwDvAp0Vd7znwPPSXo+j/0lkh4CLiK/rrqoHTMzMzMzMzPLHFg3M+tiEXExsAxpFvcMwIak4OpUpGDyehExtlT/ZWBL4BGgP7BT/pm7wfYCWBc4HhgJbAAsDZxHWsTys85f1QRtjiIF82HCRUvL9T4FVgdOBr4iLXg6P7AfcGAn2n8V+BEp6D0F8BPgB6SbCAMiopnZ6kTEJ7mfV5FuimxACkpvQnoeO9LHz4EVSbP2x5Fyri8CHEd6TYzryHkrHEP6BsEo0jVsDswBXAD8MCK648aKmZmZmZmZ2WRNKR5jZmbWOEmzkvKSfwHMGxGje7lL1k2GDBkydMRHY5f8+60jG6p/yZH9u7U/ZmZmZmZmNllQb3egPR1dKM/MzCZv+wLTAyc7qD7p6zPPjFxy5A96uxtmZmZmZmZmLcOBdTMza4ikeYEjgflIqWc+AAb1aqfMzMzMzMzMzHqBA+tmZtaoOUi530cDDwH7RMSI3u2SmZmZmZmZmVnPc2DdzMwaEhFDmAhynJmZmZmZmZmZdTcH1s3MzKxNr7/7GYdfOLihul681MzMzMzMzCYHU/R2B8zMbOIgKWp+vpI0UtJ9knaWpJr6R+R6A3upy+3K/RvW5DGD83F9O3suMzMzMzMzM5s4eca6mZk16/y8nRJYBFgZWAVYE/hZb3XKzMzMzMzMzKynOLBuZmZNiYiB5ceS1gZuAraRdHFE3JB3/RW4DHirZ3vYlCWAL1vwXGZmZmZmZmbWwpwKxszMOiUibgcuzA83K5W/FxHPRcRHvdOz9uX+vdxq5zIzMzMzMzOz1ubAupmZdYXH83b+oqBejvVyjnJJ20l6VNLnkt6VdL6k+eo1ImljSbdKel/SKEkvSDpK0kwVdcvtbCvpQUmfSBpZqlM3L7qkXSU9ndt5Q9IpkmZto28TnEtS/1x+nqQ5JP1N0luSRksaImnHNs7XV9IZkobl+iMkXSXp+3Xqr5ufm9dz/Tcl3S/p8HptmJmZmZmZmVnHOLBuZmZdYea8Hd3EMb8DLgA+Ba4DPgO2Bx6U1Ke2sqRBwPXAasAQ4EZgGuAQYLCkGeu083vSjPoxwA352DZJOh44A1gUuAv4N/Bz4G5g2oavcLzZ8jk2B/4D/AtYHDhb0s4V7a8CPAnsSnp+rgdeBLYgPT9r1NTfHbgFWB14FrgaGAr0BY7oQH/NzMzMzMzMrA3OsW5mZp0iScBG+eFTTRy6G7BRRNyUzzM1cC4pgH0yKYhctLE1sC9pZvwWETGsdMxfSQHoI4D9K9rZHhgQEfc0eD0rAfsBHwCrRcTQXD4nKci+QhPXWNiUFOz+ZUR8ls+3KXAtcChwVqn9WYArgemBrSLiqtK+tUg3FC6UtHBEjMm7DgI+BpYunptcX0D/DvTXzMzMzMzMzNrgwLqZmXWIpCmBhYGDgRVJs9XPbeIUVxRBdYCI+FLS3qQ87ZtKmi8i3si7D87bn5UDx6VjNgF2lnRgRHxV087ZjQbVs93zdlARVM9tvS9pf+DWJs5V+BjYtQiq5/NdJ+lpYClJfUvXtSPwbeBP5aB6PuYOSacB+5BuZlyTd80DvFB+bnL9IM2yb9eQIUOG1tm1SCPHm5mZmZmZmU1OnArGzMyaknOGBzAWeAEYCHxCCno3s3jnZbUFEfE+cDvp36eVcnvzAEsDz0bE8xXHjAIeIaVbWbSineub6BPAKnl7RUVbt5FmsjfrkYioOu6FvP1OqWztvL22zrnuz9vlSmWPAktLOkaSA+FmZmZmZmZm3cwz1s3MrFnn5+1XpJnYTwPXRMSHTZ7nv3XKh+XtvHm7YN4ukQP6bZkLqA2+D2+yX/MCAbxWZ/9wYI4mz/l6nfJP87act71v3j6UMrnUNVfp91+TAvEHAgdKehO4D7iKNDa1s/gn0K9fv+9VleeZ7Eu2d7yZmZmZmZnZ5MSBdTMza0pEDOzmJmqjyVPm7VvAbe0c+35F2ahO96jz2rshUFZc75XA523Ue+jrk0c8JWlJYD1gA9Iipj/NP/dLWrOUj93MzMzMzMzMOsmBdTMz6y0LUr3Y6QJ5+2beFrO93+6BoD6kAH5fYH7gpYr9C1SUdaXXgcWAoyOi4cVgc0qca/MPOdB+KSm1zU7A37q+q2ZmZmZmZmaTJ+dYNzOz3vLT2gJJcwDrkGZ4/xsgIl4npXf5vqSFeqBfRQ7zrSr6tzbNp4Fp1h15u1lnThIRzwCn5odLdapHZmZmZmZmZvYNDqybmVlv2VrSusUDSVMBfwFmBK7PAfXC0aQUKVdL6ld7IkmLSNqxi/p1Rt7uK2mJUhtzAH/uojbaa38EcLCkHVSTaF3SjJK2l9QnP55B0l6SZqupNwXpJgU0n2fezMzMzMzMzNrgVDBmZtZbzgRulnQvKe3LCsBC+fe9yhUj4iJJSwEHAE9Iehx4FZiFlFJmceBJ4JzOdioi7pd0IrAP8LikO4AvgAGkAPWDua/dIiI+lLQ5cD3peg6XNAQYTUpDswTp5sMypLQx0wAnAcdJeoy0+Os0wI9y/VcYf7PAzMzMzMzMzLqAZ6ybmVlvOR7YAZgV2JwUJL8QWD4iJphhHREHAmuSAs59SKlSliEt8Hkc0FUz1gH2BfYg5VhfG1gZuAJYgxTg7lYR8S9S+pZBjA/qr0N6jm4gpdF5Jlf/FPh1Lp8b2CTX/xA4FFg2Ij7s7j6bmZmZmZmZTU4UEb3dBzMzm4xIGgysDiwUEcN6tzfWniFDhgwd8dHYJf9+68iG6l9yZP9u7Y+ZmZmZmZlNFtR+ld7lVDBmZmbWpj7zzMglR/6gt7thZmZmZmZm1jKcCsbMzMzMzMzMzMzMrAkOrJuZmZmZmZmZmZmZNcGpYMzMrEdFRP/e7oOZmZmZmZmZWWc4sG5mZmZtev3dzzj8wsEN1fXipWZmZmZmZjY5cCqYDpA0l6SjJD0uaaSkzyW9JOlMSf16u3/dQVJfSSFpcDedv38+f/lnrKS3JV0naY2KY4ZJiu7oT09qlesojcF5PdTe33J74yT16cLzDs7n7dvEMUfkYwZ2VT9aTb6+YU3UP6/m/fiVpI/y6/Wfkg6Q9K1u7HJVn3r0NdqM7v6MNDMzMzMzM7PW4sB6kyStBbwIHALMB9wD3AB8CewCPCHpoN7r4UTvHeD8/HMVMBLYBLhT0h692THrOpKmAbbOD6cAtu3F7ljb/kV6P14A3Aa8DqwJHAsMl3SgJPVi/3qEpIE5cH5Eb/fFzMzMzMzMzHqfU8E0QdJywI3A1MDvgeMjYmxp/wbARcCfJH0eESf3Tk+7xRvAEsDn3dzOcxExsHiQA3aHAUcAgyRdHRHvdnMfJlcPk8b4ox5oa0NgDuAt4DvAdsCfu+jc2wMzkF6z1nlnRcR55QJJ0wM7A8fkn1mBg3ugLz35Gm1WT31GmpmZmZmZmVkL8Iz1Bj1XUvYAACAASURBVOUA7/nANMBhEXFMOagOEBE3AZsBARwracGe72n3iIgvI+K5iBjew+0GcBTwMjA9sE5Ptj85iYjP8xi/1QPN/SJv/wAMA5aStHRXnDgihufr+LIrzmcTiogvIuIU0g2SccDvu2r82mm3J1+jTemtz0gzMzMzMzMz6x0OrDdufdJsxDdIKRAqRcS9wJXAdMCvy/uKXNpK9pT0ZM7P/kSpjiTtIWmIpFGSXpd0oqSZ6+WOlrShpHMkPSvpY0mf5XMfLGna2j6WUxpIWkDSJZJGSPpC0iOSNq44ps38wZJWlHSFpDcljZb0hqRbJW3XxnPakIj4CngyP5y/Tvs7S3oqX8Pbks6QNFudunNKOk7Si/k5/kDSLZIqg/aS5pd0qqTn83h9IGlobmOxUr2vnyNJs0g6SdJruY1nJf1WUpvvuW6+jsivwWkkHSbpuTxW1+b9beavlrS+pBskvZuPGy7pWkkbtnVNFeeZnRSQ/YKU7ueSvKvN14qkJSWdK+m/uf13JN0rae+aenVzrEtaPe//VNL7kv4hafE22pxT0h/zeH+qlGP8BUkXSPpxg9c7naSdlNYKeCWP7cjc923qHFPkN+8vaTVJd0n6JL+/b5S0ZJ3jZpR0bB6bUXmM95W6J1VLRAwGLs0P96zozzSS9pb0n9z/zyQ9nJ+PCfrUkdeopFNy2e71+pnHLyR9t1TW8Oem0ufeufnh4fpm7vmBuc4En5GS9stlx7TRt5tynbVryueWdLzS584oSR9KulnSanXOs3x+PRfvj7fzc/0nSTPVa9/MzMzMzMzMOsaB9cZtkLdXNjATtggUrl9n/+nAIOBd4HrgldK+E4HTgP8B7gQeJM3uvZM0W77K2cBWpPQItwD3kQLQ/wfcJGnKOsf1Bf4DrAzcDzwOLAtcqzrB2SqS9iHlYd6KlH/5GuA54IfA0Y2epx0z5+3oivb/DJwKfEy6fgG7AtfXBu8kzUdKJ/E70vN5Lem61wJulfTbmvp9gMeAXwGjgH+Snt8ip/6KFX2dFriLlJLkYeB2YEHgBNJYVerO6yiZItc9gPQtgOtI6VjaJGkQcBOwHvA8aYxfBdYA9m/v+Bo/zX2+PiI+AS7M5duqzo0HSVuRxmEg8Elu/wlgEdJ7pl2SNiW9j1Yn3ai5Dfg+8BDp/VZbfybS++/3pPRPtwJ3kN5nP2P8Z0J7+gJnAcsDw0nP+RPACsClajtn98ak19Icuf23crv3Svp2TX+nzdd0AOnbHf8kfRvgGOCvDfa1Iy7L228sMCxpRtLzdSLpObgfGEx6rs8C/lbnfM2+Ri/O259X7ZT0A2BJ4D8R8UJpVzOfm7eQPuMgvXbOL/281EbfLgW+An5W50bCXMDawNukcS7KFye9n/cDpiS9954CBgB3S9q25jwbAg+QXi/DGP/+mAs4KG/NzMzMzMzMrAs5x3rjfpC3jzZQt6izpKSpKwLxWwDLRMTQcqGkVYC9gPeA1SLi2Vw+Oyk4WxXEBdgduD0iPiuda2ZSgH8jUsDpgorjfgmcAuxbpLVRmv17Imlx1tvau9A8e/IEUrBz0zyDtdg3DTXBto6QNA8pKAkpuFRrO2D5iHgi158L+Dewam7/rlLd04GFScHcnYqxyc/9rcBxku6MiKKdnUlBqf0i4oSafi1I9XtohdzPRSPivVx3EeBeYKCkf0TE9T18HYX5STcnFouIhnKQK33rYF/STZMNy+fMwdPl6x1bR5EG5iKAiHhO0mOkGzEDSMHYcvuLkl6/UwA/jYgrSvumoIEAd34/nEUKUm4bEZfm8qly+S8rDtuSFAQ+JSL2qjnfPMA87V5pMgJYF7gjf/uiOMdCpDE9VNJ5ETGs4th9gO1K/Z0SuBz4Celmz2GluvsCK5FuuKwTER/lY34I3N1gXzui+MbNwpKmiYgx+fFxpNfuhcCvIuLT3J+5SUH/3ST9MyJurDlfU6/RiHhQ0svAypIWjIj/1lQpgtAX15Q3/LkZEcdIept0E/LaiDiivX7l497MM9gHAKuQgvdlW5M+Qy6LiHG5D1OSvvU0H7A36fUXed8ypH8LzpR0R2m9if1JN+J+HBHf+DdK6ZsV7zfSXzMzMzMzMzNrnGesN27OvG1k4cwReTsFaaZprWNrg+pZkcpgUBFUB4iID2ljVnBEXFsODuWyT4Bi1vKmdQ59hRQwLueKPxX4EFghB8bbcxApoHNkOaie+zAmIm5t4ByVlFJoLE+a1T8LaaZ0VYDw0CIYndt9j/GzYb9OmyBpYVLA7GNgr/INj4i4nxSsnpIUsCwUwdNyULs45r8R8XKd7v+uCKrnui+TcsVDTYqgHrqOst83GlTPikUp96kN1EfEZxExwXNTT+77SqSbR7eUdl2Ut7+Y4KD0Op4OOKMcVM/tfxURNzTQ9FakGyS3F0HqfPzYfP5PK45pa+zfjYghDbRLRLwfEbeVg+q5/FXS7OgpSDONq1xS099xwB/zw9qUIHvk7W+LoHo+5jHS+7q7vFf6fXb4+sbDzqRvNexSBNVzf0YAu+WHu1Gt2dfoJaTPoZ+VC/Ms8W1IeeAvL+/rxOdms9qaUV8V9N8Y6AdcGhEnF0H13L/HSZ8jM/LN1EnzAB/VBtXzMQ/n62rXkCFDhlb9kL4ZYmZmZmZmZmYlDqw3TjXbRupCWsi0VtVsZUgBR0izFb8hIu6mjVmHkhZVymV8Ss4bfB5waN69aJ3DBtfOps+BxldIqS/mrDxqfJtTAv3zwzPbqtuE1YvcxaQc3A+SZkS/BGxWzOqsUTWzvkj58J1S2Sp5e1NEjKw4pkhJsmqprAhUnSppjTzDuT0fRMTtFeVFiqCVqtJC0L3XUQjSbOGGSJqXtLbA+xFxdaPHtaEInF9Wc0PnUlLwcwtJM9Qcs1bentGJdovn7IraHfnGVdVzX4z9HyVtJGm6TrSPpFUkHSLpb0q54s8jBfyh/nu0odeEpAVIM73fiIgHKo65tKKsq1R93q1O+gy5JSImSN8UEU+SvuWyXMX5mnqNZkVgetua8lVJz8udEfF27UEd/Nxs1tWkNFJbSpq61PYCpM/8FyLikVL9Itf6tXXOd3/elp+7R4HZJJ0tqV/XdNvMzMzMzMzM2uJUMI17D1iMxtI/zJ23QZr9XWt4nePmzce8Xmf/cGqC3TlAezxplmW9oP/MdcrrtVPMLp1g4dMac5FyOb/b6IzIBrzD+JnMY0k3Ex4Ebmgjt33VdVRdw7x5O6zOeYryeUtl5wHrkFI23AV8LukR4GbgnFIqhrLaVBQARMTHkkYCs5Fm4H9UU6U7r6PwblWgsw3FYrH1ZuY3q5hle1G5MCLelnQHKWXKZoy/CVHuQ3ktgmYVz0W9994E5RFxp6S/kNKx/BMYo7TQ8G3A2XVSt0xA0qyknNcD2qjW8Hs0Ij7N92WqXhMNX18XKufvLj7v+ubtHpL2oL7pK8qafY0SEc9LehRYVtJSEfF03lWZBqaTn5tNiYiPJN1ISt+zHuNvGmyb265NUdM3by+XdDn1lZ/3g4GlgB2BHSW9R8q5fi3pWw8NPZ/9+vX7XlV5nrVeuWCumZmZmZmZ2eTKgfXGPUnKr7ss42cE17Ns3g6tCgZHxKgO9qEqAPRTxue/3oeUk3tERHyZU7mMrnMcVM+m74iuOg/AcxExsKnGS6kSGj2knfJy6oVxwE8lHUNKDbEGKYf6asDvJa0bEQ820Xbdbzx053WUdPS11+kxlrQi4xcJHVQxab8IoG/HNwPrRfud6UPRWFPniIh9JZ1BGvs1SZ8BPwYOkPTTiKg3q7jsWFJQ/V5STvQhwMiIGKe0SPCtdP492t71deV7tFax/sSLpc+7YuHPx6leF6EtHX2NXkz67N2W9N6cmpQn/wvgHzV1O/O52dG+/ST3rRxYhwlf68VzdzNtpx57rvglIl6T9CPS62wj0jcGNgY2Ib1WV8rfzDAzMzMzMzOzLuLAeuNuJuWs3lLS/m3MnobxAZNb2qhT5S3SbMU+VM8Q7lNRtnne7lGRa3rhJttv1nukoNW3JM3chbPWu8ubebtQnf198/at2h05t/HjwBGSZgEOJwXmTmLCxTsXqDp5Pm5W4DNSfvSO6vB1dMBrefs/bdZqTDl/+spt1FtH0rci4p1SHxYl5XluKK95heI5W7DO/soxgzQbGvgz8OecDubXpNnOZ1A/XUfZ5qQ0N5uUc59nXfUebe/66pV3hW3ytrz+QTHTfnBE7NuNbZddRlowdVtJB5O+/TAncHnFZ1NPf27eCIwENpE0E2k8lgIeioiXauoWz93pUb3IcaWcWum2/FOkmjmXFGw/CDiwU1dgZmZmZmZmZt/gHOuNu4m0eOZ8tBGgkLQaaZbkGJpfMLDIjbxlxXn7882v/hdmz9vXKvZt3WT7TcmzuQfnh7t0Z1tdpMhNvKGk2Sr2F2lK7mvrJBHxMSn1QpCCY7XmlLRWRXmxsOIDHZidXtYl19GIiHgTeJZ0TVt09Dx59nDxeuwXEar6Ic3enZLxwVqAO/J21462z/jnbKvaHfk5XKeRk0TEqIgYRLppMU9epLM9swOfVATVoYveoxHxX1JAdr78zYBa21SUdVr+XNqG9F44pbTrbtLNhI3yWgzdLiLeyu0uQLpxU5kGJuvI5+aYvG36hnREjAGuAmYgpToqFjKt6lvxet+s2XZq2hxO+rYEVH9OmZmZmZmZmVknOLDeoIj4ChgIfAkcKenA2oCRpPVJM1gFHNRoDuaSYgHQ/SQtVjrvbKQZs1WKhQx3LS+IKWlVYP8m2++IY0lBtUNzm1+TNLWkdXugDw2JiFdIM0dnBk6qWUhwRWAPUjDwtFL5L+osBrgeaZzr5a4+TtLX+fAlLcT4RRFPqz6k+66jk47J2xMlfSMHs6QZJbWVO7ywIWn28FMRMbSNepflbXl2+4mk9CC7S/pJTftTSNqggfavBD4gzYb/OnCa38ODgJlqD5C0maQVKsqXAb5FWnyzkfQaL5AWlvxpzXl+S0ot1FWKxV0H5W9HFO38gDTLvstImk7Sb0ivwymBoyLi628TRMQbpPUJFgUulDTBTUFJKzU4ds0oAtW7ktKgfED1N4c68rlZfCtgsTr7G+3bz0k3I8YBVTnUryKleRmY/52ZurxT0jSStpC0VKnst5K+VXGu9fK2O3Psm5mZmZmZmU2WnAqmCRHxoKRNSMG/Y4B9Jf2blI93KWAJ4Cvg0Ij4SwfOf4+kU0lBsCck3UlKtbIGaUHKB0n5vceUDjuZFPD/FdBf0lOkWfWrkAKGv2v+Spvu8wGkFAz3SnqYlMZmHmBpUtqTvt3ZhybtRprJvT2weh6/uYH+pADhfhFRzgn9E+ACSS8DT5PGoy9pHMaRZq7XehCYBnhR0l359zVJs1UvajAvd1dfR4dFxAWSlgN+Azwp6QHS7Oh5gWVIKXLuauc0RaD80nbq3UoKVi8raYmIeDYiXpC0I3A+cJWkIaSUMLOT3nfz0k4+7Lxw7K7AFaRFIfciBRt/THreLmb8LOJCf2BvSW/ka/w4t7UK6abkoe2khCr8ibRY62WSfk167pYGFgf+QlpAsyscR8qvvSLwsqS7STdfBgBnk264dMTOeWY6pNfwt0m5zGcgffYdQEqNU2svUlqVn5Fmrj9BCk5/m5RaaD5SKqWbOtivKtcAf2P86+3COmPUkc/NB0k5z7eUNJi0mO5XpEWMH6ioX+se0tgXwe5bqhY/joixkjYnvReOIb0GnyK9/uYnvW5mI6WzKRZpPRw4XtKTwIuk98P3STcB3iO9NszMzMzMzMysC3nGepMi4hbSLMz/I6WDGECaGTkdcBawTEQc3Ykm9sw/rwJrAyuRAvkDSKlggtIs2Yh4AViOtCDeXLkvMwG7RURPzFgnIo4nBSGvI+X93pIU/HmU6sBzr8kzaZcjBc/GAluQgoR3AutGxAk1h5xASunzCbAqKZg1DylAvFxEXFPRzGjSeF1KCnKuS0o58TtSMK83rqOz7e1JuvY7gX6kGw4L5cfHtnFo8Y2LDfPDy9qqm1NmFM/pdqXyS0nXewlp5vtPSItmvkgK4DZyDVeT3lP3kW4IrA88Qxqj2jzXkGZcDyIFg3/M+Gu+CVgjIk5qsN2LSdf/YO7z+vmcA4CGc2g30M5oYC1SkHs0acHVhYFDSDdFOmpl4JekmzjrkVKt3E0KqC8QEcdVpTaKiM9JKXZ2Bh4jvW42J+XKf5n6AfkOy2ma/lkqql0YtKjX9OdmXnR6Q+B20jgOBHYCvttg34Jv3liq7Fuu+1xu4whSMH+V3PbcpEVwd2B8yhhI/2ZcRrrZsT5pnMaRnt/vR0TVmh1mZmZmZmZm1gnqXKpn6ymS5iPNWn8pIpbo5e5YBUl9STdE7omI/r3aGTOzLjJkyJChIz4au+Tfbx3ZUP1Ljuzfrf0xMzMzMzOzyUKb2QlagVPBtBhJiwP/jYgvSmVzA+eSxqvuLEczM7Pu0GeeGbnkyB/0djfMzMzMzMzMWoYD661nH2BbSY+TUs18C/ghMAspnUKXpk4wMzMzMzMzMzMzs+Y4sN56riEtkPhDYHlSntyXgauBQeWZ7GZmZmZmZmZmZmbW8xxYbzERcRtwW2/3w5oXEcOYCPI/mZmZmZmZmZmZWec4sG5mZmZtev3dzzj8wsEN1fXipWZmZmZmZjY5mKK3O2BmvUtSNPkzrLf73AxJM+V+D5lc+iDpPUmf9kRbPU1Sv/xc3tBL7f8mt/+7mvLjc/mWvdEvMzMzMzMzM+tZnrFuZudXlK0CLAI8CTxRs++9jjYk6XhgP2CriLiqo+cxMzMzMzMzMzPrTQ6sm03mImJgbZmk80iB9Wsj4oge7pJ13gpMut9IegFYAmi1GfnHAmcBb/R2R8zMzMzMzMys+zmwbmY2iYmIl3q7D90lIsYAz/V2P2pFxAhgRG/3w8zMzMzMzMx6xqQ6o9HMeoikH0q6XNJbksZIel3SmZL61NR7j5QGBuDKmrztP8p1vs5fLel7kq6WNELSV5LWynUWl3S0pIclvSNptKThks6RtFA7fZ1a0qGSXs7HDZN0lKSpSnVmlvSJpJGSpq9zng1zP6+tKZ9F0l/yc/CFpCGSftVGf9q93lxvHkknlfr9nqQbJK1e57yVOdYlfV/SpZJekTRK0ruSHpM0SNJcFfU3l3RXfi6+kPSspCMlzVyn3crrr5djvr285FXXUS/Hes1zuYikKyW9L+lzSQ9KWqeqjY5cZ51zVF6LpEdy+VyStsmPP8/XdoGkb9U535SSdpH0L0kf5WMel7SnpCkr6n8r9+EZSZ/la3lO0rmSftDodZiZmZmZmZlZYxxYN7MOk/QL4CFgK2A4cC3wAbAL8B9J/1OqfikwNP8+mJTbvfipzdu+NPAwsBRwB3AXMDbv+w1wMDB9rnMD8AWwA/CopO/W6e4UwDXA/sArwO3AnMAhwF+LShHxCXAJMGu+rio75+3fS8/FjMDdwD6kbwP9E3gLOImUJqQtda833yx4FNgLUL6GIcD6wN2Sdm3n3EX/Vgb+A2wNvA38A3gEmBHYF+hbU///clur5L5dT3pODgX+JWn2mvqduf6utBjpOpcF7iE9V8sDN0papbZys9fZCQcAFwFfAjcDo4FfALdLmrqmT1MD1wFnAkuS3mN3APMBJwOXSVKp/uz5mosbVzeTXkOf5ja+vkljZmZmZmZmZl3DqWDMrEMkLUEKLI8ENo6IB0v7fgWcmvevARAReyotXvo94NR2Fi/djhSMPTgivqrZdxlwXET8t9SegF+RAuTHA5tUnHMJUjBzyYh4PR+3GClovYuk/42It3Ld04FdSTcILqi57m8DGwGvA7eUdh0M/JAUzN0oIj7N9VciBfHb0tb1ngP0Ac4Afh0R4/J51wJuBE6RdHdEvNhOG78FpgHWjYjbaq5pKVIQvHjcP1/PB8CAiHgyl08PXAlsCAwCduyi6+9KO1PzXEr6A3B07uMGRcUOXmdH7QKsHhH/ym3MTHqulgE2B64o1T00t309MDAiPszHzApcBWwJbM/4hYd/DswP/CkiDi43Kuk7wGxd0H8zMzMzMzMzK/GMdTPrqP2AaYG9y0F1gIg4jTTDtn8bM8jb8hpwaEWQmYi4vxxUz2UREacCTwLrSZq2znl3L4Lq+bjnSQHNKYCVS+WPk2YAryJp8ZpzDCTdlDynFOSeghSIh/R8fFo61wPA2R253hzw7k+a0b9v0V4+7x3AeaRg+W7tnB9gHuAr0rcFviEino6I8rcG9szbY4tgc673BekGxpfAdpLmyP3s7PV3paFMeINiEPA5sGp5pjdNXmcnHVME1XMbnwAn5oerFeWSZgD2Jo359kVQPR/zEbATaRzLYz5P3t5V22hEvBURzzbSwSFDhgyt+iEtZGxmZmZmZmZmJQ6sm1lHrQ2MI6X8qHJ/3i7XgXPfEhFf1tspaVZJ20k6TtJZks6TdB4wOzA1sGDFYR9HxEMV5S/k7Xdqyk/P2yLtSzEzvghsnlOquzgwF/B8OUBbcmm9a8nqXe+qeXttRHxesf/CmnpteZT0mX+RpGVrAsz12r24dkdEDAfuJT3Py+fizl5/V7qz9gZFRIwi3byYKf8Umr3Ozritoqzqtbc8MAtwdw6kV/VrOFAew0fz9nhJbd1YMjMzMzMzM7Mu4lQwZta0vHji/KSc3x+3HaNlgkUxGzC8jbY3IAWU25pFXLXo5OsVZZDyUEOafV92GXAC8EtJB0fEGFJam/8hBcLLs+bnbaffda+nnf3FeYfV2V+Uz1tnf9nRpKDtVvnnQ0kPkm6MXBARnwEoLeQ6NzAGeLPBdjt7/V2pkXH+pIPX2dX9qnrt9c3brSRFO+ecBfgoIq6TdCbpWwM3A6MkPU5KVXRO+VsabenXr9/3qsrzrPUlGzmHmZmZmZmZ2eTCgXUz6wjlnzG0Pxv5+Q6cf1Rloyklx6WkWcd/AK4mzUT+IiJC0vXAxrlvtdoLUn6zcsTnki4kLZa6KSnn9gSLlhZd60gbJZXXW+5OJ/cTEe/nxTtXI+WI7w+sS1oE9SBJKzcagK1ot7PXX09HvlXV1X3oqnM2eo4p8/YZUjqitpRTA+0m6SRgM2AAsBKwImlsN4+IW5vsr5mZmZmZmZm1wYF1M2taRIyV9A4wJ7Brns3dE9YkzdI9PyL+WLF/4S5u73RSYH0XSXcCWwDvMGH6m2LGc1UKGoAFOth+cd6F6uwv2nurzv5vyClSBuefYmHLU0mLZ/4vsFMe2xGk2dzzAm800G5Hr7943cxUu0PSjKTUPp/VObZTOnidPaG4ufFoRAxs5sCIeIYUkP9jztW+H3AkcBrOk25mZmZmZmbWpZxj3cw66g7SzbmNmjimCKR29Kbe7Hn7Wu2OvNDnEh08b6WIGAr8C1gLOIyUsuO8inzozwLvA4vlftTapoNduC9vN8uB0lrb1dRrSkS8RUoRA1Dud3G+n9ceI6kPsDppYc8iZ31Hr78IWFctcLtO/Z53mWavsyc8AHwBrCNp+o6eJCI+j4ijgE+Aheu8fszMzMzMzMysgxxYN7OO+jMp6HiapAmCoJLmlrSXpPLnTDGzebEOtlks9ri1pCLIjqS5gLPons+000mpTvYmpfM4u7ZCngl+Vn54Yp5tXfRtBUoLoDYjIp4G7iHlqR+Uc9sX5x0A7EC6WXFGe+eS9GtJ81fsWj9vy3nQT8nbA8uBcknTkWa4Tw1cHBEf5H529PrvydsdJM1XOmZxYFB719QFmrrOnhARHwMnA98Criw/L6X+/VDSFqXHW0latqLeyqT1Bt4jBevNzMzMzMzMrIs4FYyZdUhEPCVpB+Ac4FZJz5DyqU9FSv2xJCkweRrwVT7sZlIw/hBJywFv5/KjahYDrece0ozelYAXJd1LykndnxQYvgVYr/NX9w1XASeS0t4MjogX69Q7Orc9AHhZ0j3AbPnx34E9Otj+DsC9wO6kWcwPk4Kuq5MC/nu00aey3wKnSBpCGqdxpDFaijSruZi5TkQMlvRH4GDgEUl3AyOBVUlpU4aQ0oyUNX39+TV0DSnFztOS7iOl+lmBtHjsLMB0DVxbh3TwOnvCoaSbT5uRXuePkVLEzE1K6bIgcDFwTa6/LrCTpOHAE6RFUecHVs77fx8R3ZF73szMzMzMzGyy5RnrZtZhEXExsAxpFvcMwIakoORUwPnAehExtlT/ZWBL4BFSMHyn/DN3g+0FKYh4PCkAugGwNHAesArdkI87IkaRgvkw4aKl5XqfkoLdJ5NuJGxKCm7uBxzYifZfBX5Eml09BfAT4AekmwgDIqLd2erZgcAFpLFZm5TCZ2rgr8D3I+KJmnb/kNt6gLQI5maMD8CvXDuLuxPXvx1wAmlG9XqkgPahpNdFt2v2OnuoT1+SbjZsC9xPSnG0BbA4KRf8IcDhpUPOJN38GUG6hp+QnvtrSddwFmZmZmZmZmbWpeRJbGZm9UmalZQL/Atg3ogY3ctdmuhImokUrB4aEf16uz/WnCFDhgwd8dHYJf9+68iG6l9yZP9u7Y+ZmZmZmZlNFtTbHWiPU8GYmbVtX2B64GQH1W1y1WeeGbnkyB/0djfMzMzMzMzMWoYD62ZmNSTNCxwJzEdKPfMBPbOYplkrmn/MmDG8+GIjqfzNzMzMzMzMOm/06NHX9+vXb5Pe7kdbHFg3M5vQHKQc36OBh4B9ImJE73bJrNdMHxFfjR49+rne7oh1iUXy9uVe7YV1FY/npMdjOmnxeE5aPJ6TFo/npMdjOmlZhLQ2X0tzYN3MrEZEDGEiyOU1scgLm/r5nHi9ANCvX7/v9XZHrPOGDBkyFDyekwqP56THYzpp8XhOWjyekxaP56THYzppKcaz1U3R2x0wMzMzMzMzMzMzM5uYOLBuZmZmZmZmZmZmZtYEB9bNzMzMzMzMzMzMzJrgwLqZmZmZmZmZmZmZWRMcWDczMzMzMzMzMzMztsCOFwAAH+VJREFUa4Iiorf7YGZmZmZmZmZmZmY20fCMdTMzMzMzMzMzMzOzJjiwbmZmZmZmZmZmZmbWBAfWzczMzMzMzMzMzMya4MC6mZmZmZmZmZmZmVkTHFg3MzMzMzMzMzMzM2uCA+tmZmZmZmZmZmZmZk1wYN3MzMzMzMzMzMzMrAkOrJuZmdkEJE0n6X8lvSBplKQ3JZ0jqU9v980mJGlZSQdJukbSG5JC0qgGjtte0sOSPpX0gaSbJK3UE322+iTNIGkzSWdLekrSx5I+k/SkpMMkzdTGsR7TFiRp3/z+fFHSR5JGS/qvpPMlfa+N4zyeEwFJc0h6N3/2PtdOXY9pi5E0OI9dvZ/16hznsWxhkr4t6S/5/7Jf5DF6VNKf69TfSNI9+TP64/z7Rj3db/smSf3beX8WP4dVHOv3aIuStIKkqyW9LenLPD53StqyTv0pJO0j6en8fh4h6UpJS/Z03yfoW0T0dh/MzMyshUiaDrgTWAl4C7gP6Av8GBgBrBgRL/daB20Ckq4FNq0pHh0R07VxzAnAb4EvgNuA6YA1AQFbRcQ/uqm71g5JOwN/zw+HAs8As5DekzMDzwGrR8S7Ncd5TFuUpPeAGYGngDdy8feA7wJjgM0i4uaaYzyeEwlJ5wHbk8bm+YhYvE49j2kLkjQYWB24Gvi0osqgiHi65hiPZQuTtCJwEzAb6d/QIaR/P5cE+kTEVDX19wJOAsYCdwCjgXWA6YG9I+Lknuu9lUlaHDiozu4pge3y7wMi4u7ScX6PtihJWwGXkSZ7PwK8DMwLrJzLjo2Ig0r1BVwBbAmMJP2dOhewGjAKWCMiHurJayhzYN3MzMy+QdKRwKHAv4F1IuLTXL4vMAi4NyJW78UuWg1JBwIzAP/JP2/TRmBd0gDSf0rfJ90oeTGXrwgMJv0RslBEfNj9vbdakrYHVgD+UoxNLv8OcCOwDHBpRGxb2ucxbWGSVgYejYhRNeV7AKcBbwILRMS4XO7xnEhIWpMUiDsT2JU6gXWPaesqBdYXiohhDdT3WLYwSfOSbkpPC/y8NoAq6ccR8XDp8Xdz/XGkAN2/S+UPALMCS5b/PbbWIGl90g2U14C+EfFVLvd7tEVJmor0f565gW0i4vLSvhWBu0jv3UWLiVySdgTOBl4EVo2Id3L5T4CrSIH5xSNibE9eS8GpYMzMzOxrkqYG9swPf10E1QEi4gTSbMvVJC3bG/2zahFxbEQcHhE3FP/ZbMd+eXt0+Q/F/Mfk6aQ/Infshq5aAyLigoj4Ve0f8RHxFvDr/HALSdOUdntMW1hE/Ks2qJ7L/wa8RJqptVhpl8dzIiBpetJ4PAMc3051j+mkw2PZ2o4hzVQ/oGpWcjmonu0NTAWcXgTVc70XgP/L+/bqvu5aJxSz1S8uguqZ36Ota3FSUP25clAdvh6fW0nfKij/rVmM5wHlv3Mi4mrgemARJvzmbo9xYN3MzMzKViH9MfJyRDxesf+qvN2457pkXSmn+lkzP7yqoorHuLU9mbfTAnOCx3QSMC5vx4DHcyJzOOkP+j2AL+tV8phOOjyWrU3S7MDWwEfAWQ0eVuRRrxrPK/PW49liJM3I+GDqRaVyv0db2+gG630AIGkhUgqnL0jf2qzV6+M5VftVzMzMbDKydN4+Vmf/YzX1bOKzOCkoOyIiXq/YX4zx93uuS9aEhfP2S/IfHXhMJ1o57c9iwAvAK7nY4zkRkPR90iy6cyPiXkl926juMZ047CRpTuAr0nvy2ogYXlPHY9naViaNzx3Al3khxFWAqUnrk1xRnvEqaTZggfxwggklEfF6XiNjQUmzRsRH3X0B1rAtSGuXPB4RQ0vlfo+2tlfyz+KSto6IK4odORXMusCrwL25uPibc0hEVN3A7vW/TR1YNzMzs7Lij4uq/4iWyxeos99aX5tjHBGfSRoJzC5p5oj4pOe6Zg3YO29viYhi1o/HdCIhaX/SoqUzAkvk398Eti19jd3j2eIkTUFaYHgkcEADh3hMJw6H1Dw+XtJREXFUqcxj2dq+l7fvAPcBK9bs/5OkHSKimIlejOeHEfFZnXO+TloocQHg6Tp1rOcVaWAurCn3e7SFRcQ4SQOBfwKX5/8XvQx8h3QT7GHgFxExJh/S8n+bOhWMmZmZlc2Ut5/X2f9ZTT2b+LQ3xuBxbkmSNgB2Is1WP7S0y2M68VgX+CWwJSkA9BopqP5oqY7Hs/XtCfwY2D8i3m+gvse0td0L/IKU1mcG0rdI/gCMBY6UtHeprseytc2et9uTZiTvRMrnvBBwAumm5kX5Gyfg8ZwoSfo2Kd3LOODSmt0e0xYXEfeRFox+FfgR8FNgNdK43EGacFBo+b9NHVg3MzOzMuVttLPfJl7tjXG5jrUISUuQcoiKFMx7srw7bz2mLS4i1ooIkYI/qwHPA4Ml/aFUzePZwiTNDxwN3BMR5zV6WN56TFtQRBwWERdFxCsR8UVEvBARfwQ2y1X+Ny9UCx7LVjdl3k4F7BsR50TEexExLCL2I+Vjnobx3zTxeE6ctiWN9e0R8XbNPo9pi5P0M+AhYDiwPCko/l3STZJDgDskTV1Uz9u2xrNXObBuZmZmZcXXIWess3+GvP20B/pi3aO9MQaPc0uR1Ae4hRSMPSEiTqqp4jGdyETEyDxjawPgUeAoScvl3R7P1nYaKTC3RxPHeEwnQhFxG/AIMCuwQi72WLa2Yny+As6v2H9O3vavqe/xnLjUSwMDHtOWJmlR0ntzBLBhRDwcEZ9FxIsRsRspRcyKwA75kPbGsyjvtbF0YN3MzMzKikW6+tTZ36emnk182hxjSTMCswEjnXey90maC7idlDvyXOB3FdU8phOpvBDX5aQZWRvnYo9na9uI9JX0v0kaXPwAl+X9C5TKi6+me0wnXi/m7Xfy1mPZ2obl7duldUiq9s+Tt8V4zp7Hror/79tC8jf4liEFUq+tqOL3aGvbhrSY8C111jUoFjPtn7ct/7epFy81MzOzsiK9xA/r7C/Kn+qBvlj3eB4YDcwtqU9E1C4G5DFuEZJmBm4GFgeuAXaJiKqvwnpMJ27v5e3ceevxbH2zkfLDVpm+tK/4e9tjOvEqcnYXsyE9lq3t8bydXZIq/s2cM28/hfTtIUnDSTevlwHuL1fO3xibCxgeER91X7etCb/I22sioirvtt+jra0IhH9cZ39RPkfeFn+b9pM0dZ6QUNbr4+kZ62ZmZlb2L+AjYBFJy1Ts3zJvb+i5LllXiogvgLvywy0rqniMW4CkaYHrSIs63Qr8LCLGVdX1mE70iiDsy+DxbHURoaof0uKIAM+XykfmYzymEyFJcwOr5oePgcey1UXE06QFEacn5W6u1T9vHyuV3Zi3VeO5Vd56PFuAJJHyq0N1Ghi/R1tfkRP/R3X2F2nxhgFExKvAs6T39IYV9Xt9PB1YNzMzs69FxBjgr/nhX8tfi5W0L/B94P6I+E9v9M+6zAl5e0jOdQiApBWB3UizRc7ujY4ZSJqStIDTGsB9wBb5vdkWj2mLkrSqpJ9KmqqmfGpJe5Jm331BSglT8HhOejymLUjSCpLWyAG7cnlf4B+k/L3X18x69Vi2tmPz9uScTg0AScsC++WHp5fqnwSMA3aXtEKp/qLAH/K+k7u1x9aoVYEFgTcZHzyv4vdo67oub1eT9I21SvL777f54VWlXcV4/lnSPKX6WwCbkG6mVaUF6hGq/japmZmZTa4kTQcMJs30eYsU2FswP34fWCEiXuq1DtoEJG0IHFoqWh4I4OFS2VERcWPpmBOBvUm5gm8nLca3NmnixdYRcXV399uqSdobODE//Af1vy77u4go0oh4TFuUpIGk/PjvkRYqfZ+UWmApUt7mUcAvI+KKmuM8nhORHIh9lTRjffE6dTymLab0/nwLeIE0m7IPsCwwHTAUGBAR79Yc57FsUZKmIK15sBXwAfAAMBOwEmmc/h4Ru9Yc81tS8G4saTzHAOuQZsnuGxF/6bELsLoknQnsAhwXEQe0U9fv0RYl6TjGrxk0FHgGmJe0aOkUwJl5IdOi/hSkQPvmwIfAnaT/R61OSvuzZkQ80GMXUMOBdTMzM5uApOmB35O+bjk/6T8xtwCHRsRrvdk3m1ApMNCWHSLivIrjfgMsAXwJPAgcHRH3T3C09RhJRwCHN1B1oYgYVnPsQDymLUXSQsDOpD8AFyb9MTiG9DXnu4CT692s9HhOPBoJrOd6A/GYtoy8EOKepBvS85Nyqn9GSj1wJfC3nFqi6tiBeCxbUg7E7U767F2MNNngSeD0iKhMISJpY2B/Uq51gCdIAdzru7/H1p6cIu8t0nt06YhoN6e236OtS9LmpPfossCswCek99xZEXFJRf0pSTdKdgQWIX1O3wMcFhFDe6rfVRxYNzMzMzMzMzMzMzNrgnOsm5mZmZmZmZmZmZk1wYF1MzMzMzMzMzMzM7MmOLBuZmZmZmZmZmZmZtYEB9bNzMzMzMzMzMzMzJrgwLqZmZmZmZmZmZmZWRMcWDczMzMzMzMzMzMza4ID62ZmZmZmZmZmZmZmTXBg3czMzMzMzMzMzMysCQ6sm5mZmZmZmZmZmZk1wYF1MzMzMzMzMzMzM7MmOLBuZmZmZmZmZmZmZtYEB9bNzMzMzMwmMZKOkBT5p29v92diIWkLSbdKGiFprJ9DayWSBufX47De7ouZmcFUvd0BMzMzMzOzzshBz1dLRSOBhSPiw3aOux9YGSAi1F39s4mDpCOAw7vp3KsCGwGrAfMDcwJfAR8AzwAPAJdHxHPd0b6ZmZl1PQfWzczMzMxsUjMbcDCwf293xCYOkuYF/pAfvgQcArwAfJnL3ujgeZcFTgRWqVNlBqAPsA5whKQ7gP0j4omOtGdmZmY9x4F1MzMzMzObFP1G0ikRMby3O2IThTUZ//fxvhHxz86eUNLPgbOA6XLRi8BVwL+Bd3PZt4GVgE2BxYC1gH2AgZ1t38zMzLqXA+tmZmZmZjYpGQHMTQpmHokDlNaYPqXfn+/sySStA1xAWtdsLLAfcGpEjKuofp2kg4AtgGM727aZmZn1DC9eamZmZmZmk5LbgIfy77+Q9P/t3Xn4XdO9x/H3J4bUFA0RtPRGTXXVELOomUart4bgoh5DHimlSa+n1Kx56iqqqoaLam+kpapNo61ylRpiLNqiGiUhFTWUGkoiguJ7/1jr+O3snL3POfkNieTzep71/PY5a521195n7zxPvnud79pgfg7GPjD6F7bf7k5Hkj4MXEXX/7f3j4gLKoLqAEQyEdgEuL47+zczM7O+4cC6mZmZmZktbL6W//YDzprXTiSNlxSSoo2203PbSRX1kcv4/HpdSRdLekLSG5Kek3SdpE+VPvchSV+SdI+kF3PbP0s6QVL/Zvuq2P/iuZ+7cz+zJU2RdJ6kVdvsYwlJh0m6VtIzkt6U9Jqkh3M/a9R8dkjhHIzN720p6XJJ0/JxhaSN2z2mUv8DJZ1cOE9vS3pB0i2SxkhaquJzje+3uGjpk4Wxvj/eDnyFtDgpwA9zwLwtETEjIiZU1UtaR9L5kibnc/+mpKcl/VzS3nV9S9qhcEyH5vd2kTSx8H3+VdL3y9+lpFUknZ6vvZl533dI2q/FPifl/U3PrwcV+pmRyx8kHdvu9SxpU0mXSZqax/KGpCclXSFp5xafHVs4B0M6GXuT+jnu+Xxs3yico5mSHpB0oqSl2ziufpJGSbpT0j8lzcr36HckfazV5wv9bJbPzyN5DI17YbKkayQdLWm11j2ZmVkrTgVjZmZmZmYLlYi4Q9L1wO7AZyVtHxG3z+9xNUgaQUoTUgy2LUXXeA+PiHE54P0rYPNSF58EzgR2kzQ8It5qscsBwO2kXN5F6+RymKS9IuK2mjFvAEwE1i5V9Qc2yOVoSWMi4tIW40HSScDp9MBkL6W0K1cDA0tVg4GdcjlW0h4R8WB399eGUYXtc3qqU6V0Macz9//jV8tlhKTbgRER8XIb/Z1N10OohjWAw3Nfu0TEA5KGAb8gnc+ibYFtJQ2NiBPb2N9GpNn4Hy1VbZrLSEm7RkTThWIlLQacB3wZUKl6SC4HSZoAHBIRs1uNqadI2hz4JfCRUtXQXPaVtFNEvFrx+QGkc1Ne5LZ4j45oYxxjgdOY+/wMzmV9YC9SuqyxrfozM7N6nrFuZmZmZmYLoxOA9/L2t+bnQEo2BK4EXiLNbN4a2Ao4GXiDFBC7VNI6pEDdxsAlwHBSmpD9gIdzX9szd2C0mctIQfXfkPJ4b0oK4l+V65cHrpP0iWYflvTvwN2koPrbpAU5D8hjH5aPYxqwBHCJpINajGcP4AxgOjAm97EVcBTwShvHUxzbMFJAciAQpAcWu+dj3Au4NjddHZgkac1SF42HApcU3hteeH8D4OIOxrMuXYHjaRHxSCfHU9PvcaSHKYsDs0jnbwfSQ5eRzHlN/FbSki26HEW6du4BDgI2Iz2A+EGuHwhcJenjpPPbDzgF2C7vczTpGgY4QdJ2Lfa3NHANabHWS4Bd8z4PJC3mCrAecJOkDzXtAS7I+1Xe9wnANqRr52jS9QSwLzBBUjm43FtWBa4jHePpwI6k6+8A4KHcZigVD1nyOK+hK6j+KOnhxhak7/gs0gOsCcwduC/282nSLy8EPEP6N6Xx78ZWwP7AucDUeTlIMzNrIiJcXFxcXFxcXFxcXFw+sIU0UzVyubLw/uWF9/dp8rm7GvUV/Y6vqy+1nZ7bTqqoj0J5EBjYpM0+hTYvAP8CdmnSbnng2dzueWCxJm3GlvZ5TsW4ji60mWvspIDq5Fz/V2DNin6WKZzPl4Hlar6jIAVTl+3m994PeLzQ5xcq2p1SaHNrRZvi+RrSjTEdUOjnJz10fa9JeqARpAcPn2zSZkng/wr7Pq1Jmx1K38E4oF+TdheVrsO/Aas3abcl6eFVABMqxj6p0Ne7wOcrvsefFtqNbdJmu0L9k8BHK+6LPxbaHdyd77kw9ukV9dMLfT3b7N7I98XU3OZNYIUmbQ4p9HMHsFSTNluQHqhE1ZiAH+a6Wc3OT6ntij1xbbq4uLgs6sUz1s3MzMzMbGF1GimYBfBNSQtKKszDIuKfTd6fSJppCiltw0URcXO5UUS8Rgr6A6xMmulbZwrQNFVHRPwPcGt+ub3mznG+Nyl9BMCoiJhW0c8s4Mj8cgXSQ4IqARwaEa+3GHcr/wGslbeviogfV7Q7g65Z0TtKGtrN/dYZVNh+oYf6/DLp1wAAx0fE5HKDiHibFKCdmd8a3WLW+vPAURHxXpO67xa2BwOjI+LpJvu8j67zukPtESSXR8S15TfzGL5I168Vjmoy9mMK21+MJuli8n1xEF2/VPlqG2PqKWOa3Rv5vjg/v+xP+pVH2Vfy33eoSGETEffTer2IxloJU5qdn1J/LVMFmZlZaw6sm5mZmZnZQikHAy/ML9dmztzX88vkiHioWUVEBF2pIyCljKnyQGG7nN6kbFxEvFNTf1lhe7dSXSOv8z8i4pa6neSAbyNgV84VXXRvREyp66tNwwvblXnd83ktpnMZXtW2BwwobHf3wUFDY7yzqLkmIuJF4Gf55SBSCpAqP4+IN5tVRMQTdI39NeDXNf00rsNBkpavaQdzXmflfb5G19hXojD2nFu9sSjptIj4bU0/jwKNtQI2lLRKizH1hBmktE1V7i9sr1WskDSYlCYG0q8pnqzp5/stxtEIpq8vaZsWbc3MrAc4sG5mZmZmZguzM4HG7PCvS1pmfg4GeKxFfXEm+6NtthtQ2Sq5t4P6jUp1W+a/gyVFqwKsmNuvSrWeWkB0w/z3HeYMXjZzd2G7fIw9aUZhe9nudpZnbjdy3z/YbDZzSbvH2e51OLViVnu5HdRfh2/T+nuvug7XAJbL27+jtb76rhumRMS7NfXF2eHlc7RhYbv2Po2I5+nKI9/M/5J+DbIkcIekmyQdI2nLNnLum5nZPHBg3czMzMzMFlo55cqZ+eXK9G16iGZmtah/P4gZEW+00w5YrEWfrVKSPF/YHlSqG9zis1WWrqnraIHSGo0g/qsR8VaLtn9v8rne8FJhe+Ue6G8gaTFKmPN7qtLucbZ7HdZdg8V2UH8dvhIR/2rRV9V1uGJFmyp99V03dOccFcf3jzb2VXn8EXEXKR3Qq6RYz67Ad0gB+1cl3SxpVM3isGZm1qEFJcegmZmZmZlZb7kQGA2sDhwr6dKIaCeItbCIbrRt/J/xz8CBHfRTF7itm93biUbAuZ3j6+QcdEcxRc/mPdCfCtt9dQy9oZ2xVx3rwnIOmun02FRXGRFXSPoVaY2DXYFhwMeApUjpdHYGTpa0Z1VKKjMza58D62ZmZmZmtlCLiDclnQZcTkopcSop0N7K+zNNJfVrkRJjfqeYqbMKMLVFfUN5UcMXgdVIqV0eyfnKFxQvAesCAyX1bzFr/SOF7V5buDEipkh6Lu9vTUnrR8Qj3ejyFVLAVdSn12kotlmQFqhcUdLiLXL9F2f4F8de/BVAd89B8R5u9Qv+vrini+Nr5xcOLdtExAxgXC5IWo0UZB9FWjz134BrJa3dxi89zMyshlPBmJmZmZnZouBHwOS8fYSkVgt+AswsbK9Q1UjSSsydQmVBslUH9X8q1f0x/x1Ez8zA7kkP57+L03pswwrb5WPsacVFJr/WnY4i4m268qEPbSONR18eZyeWpH4xVai+Dp+k617cuo191Z2Ddu/pfsA6beyru4rjq71PJa0MDOl0BxHxTERcDmwD3JjfXp36BYbNzKwNDqybmZmZmdlCL882PyG/XAI4o42PTSts1wVuD5rXcfWRwyTV/Vr58ML2TaW6iYXtU3tuSD3ixsL2ES3afqnic73hAroW9TxY0oh2PyhpgKR9Sm83xrsM8IWazw4C9ssvX2LOtDQLglFVFZIGAPvml3OMPS8Mekt+uaaknWv6WRfYKb98OC/4WdTuPb0HrRcF7rackqqRkmUnSUNqmleevzb3Fcx5f6/Unf7MzMyBdTMzMzMzW0RExPXA7fnlfqQ0InVuLWwfJ2muxRklbQJ8o2dG2Gs+Afx3swpJRwK75Jd3RkQ5GPsTumZMf07S2ZIq8zxLWlLSyDy7trf9Gngibx8k6T8rxnQiabYuwG0R8WBvDioiXiEFwBtpR66WNLrZ9VMkaU9SQPlzpaqLgMbCn2dLWq/JZ5cAxtMVDL4wz3ZfkIyUtHv5zTw7/FK6FvK8pMnYzytsXyZprpQwkpYDrqQrznFukzHcDTT6HiNprkV2Ja1BWpehr1yQ/y4OjG/2qwRJm9H1YLApSQfmc1BV3w8YXnhrWlVbMzNrj3Osm5mZmZnZouR44F5Szura9C0RMVnSraQZsDsCN0q6APgbabbnZ4Aj8+sVWHBngN4HHC9pA1KakqdIedUPpGu2/WzSscwhIt6RtDdwD/BhUmqT3SSNIwWBZ5Ly1q9NSsGxJylAujbwQi8eExHxnqRDSA9LFgeukjQc+Fne98eAQ/OYAGbQzVm/HYztBkmHAZcB/UnB09GSJgC/AxqL564CbEmaIb1+fu+uUl/TJJ0MfIt0bu+TdB5wM+l7Wx84Btgof+RB4OxeOrR59SLwOvBLSd8DfkGa1b8Oab2DRvqWx4Azyx+OiDskXQwcBXwceEjSOaRA+bvApsBxwBr5I9cDVzTp52VJVwIjSQ+c7pR0NvA4sDzpPh9DCr5PpW/SwYwHDgZ2ALYH/iDpXNKCwUuTguH/BbwJPEe6t5r5JvA9STeQ7olHSed4GWAt0jFvm9veGxG/74VjMTNbpDiwbmZmZmZmi4yIuE/SRKDd9BwjgUmk3MY751I0FdidrlQVC6IjgEuAz+ZSNgPYOyL+0uzDEfGopC2Aq0l5sjcEvluzv7dy6XURcU+eBX01MBA4LJeyp4E9IqLPZulGxI8kPUY6V1uTAqIntfjYb4BvN+nrnDzj/XTSg4zTcim7HRixAC5K+QawF3ADcHQuZVOAT0fE7Io+xpCC6KOBwcA5Fe0mAIfULLT7VWBoLpsAPy3VPw98Pvff64H1iAhJe5EeBgwjPSgZV2r2GulXNqdSHVgHWJaUUmffmja/B/ae5wGbmdn7nArGzMzMzMwWNScB77TTMCKeIs2GPZM0A3Q2aZb2Q8DJwCZ9GaydR6+RZsKOIc2WfoUU+H4COB9YLyJqHwxExOPAZqSZ1VeQZvjOJAU6XyUtJHoFaYb4KhHxdG8cSMXYbgLWBE4hHd/LpNQpLwK3kWb7rtvbKWAqxnZ/RAwjzUb+NnA/adbxW6Rr6RlS3uuv5zF+JiImV/R1FinoegHwCOn8v5X7uAbYB9gxIl7uzWOaVxHxJ2Bj4CzgL6QZ7K+TfvlwPLBx3XUTEe9GxBhSbvQfkK7BWaSZ3NOBHwO7RMR+NcF5IuJV0sKdJ5Jm988iBf4fJd3nG/b1bO48pu1Ivxq5h3TPziYd44Wkc9Pq4d2ngENIM+AfAJ4lzbyfTTo/E4H9ga0i4u89fhBmZosgVT/ENTMzMzMzMzObN5ImkR7qPBURQ+bvaMzMzHqWZ6ybmZmZmZmZmZmZmXXAgXUzMzMzMzMzMzMzsw44sG5mZmZmZmZmZmZm1gEH1s3MzMzMzMzMzMzMOuDAupmZmZmZmZmZmZlZBxQR83sMZmZmZmZmZmZmZmYfGJ6xbmZmZmZmZmZmZmbWAQfWzczMzMzMzMzMzMw64MC6mZmZmZmZmZmZmVkHHFg3MzMzMzMzMzMzM+uAA+tmZmZmZmZmZmZmZh1wYN3MzMzMzMzMzMzMrAMOrJuZmZmZmZmZmZmZdcCBdTMzMzMzMzMzMzOzDjiwbmZmZmZmZmZmZmbWAQfWzczMzMzMzMzMzMw64MC6mZmZmZmZmZmZmVkHHFg3MzMzMzMzMzMzM+uAA+tmZmZmZmZmZmZmZh1wYN3MzMzMzMzMzMzMrAP/D+kg2Se/crclAAAAAElFTkSuQmCC\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -600,7 +3163,8 @@ "# Extract ordered keys and corresponding values, then plot\n", "terms = [str(k) for k in counter.keys()]\n", "term_counts = list(counter.values())\n", - "ax = sns.barplot(term_counts, terms, color='b')\n", + "\n", + "ax = sns.barplot(x = term_counts, y = terms, color='b')\n", "\n", "ax.set_title(f'Quantified Compound Class ({food.capitalize()})', fontsize=16)\n", "\n", @@ -618,7 +3182,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -657,7 +3221,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -674,19 +3238,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -838,7 +3400,7 @@ "\n", "with sns.axes_style('whitegrid'):\n", "\n", - " ax = sns.barplot(term_counts, terms, palette=colors)\n", + " ax = sns.barplot(x =term_counts, y = terms, palette=colors)\n", " ax.set_xlabel('Number of Papers', fontsize=14)\n", " ax.set_title(title, fontsize = 16)\n", " ax.title.set_position([0, 1.05])\n", @@ -864,31 +3426,25 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\software\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:362: SettingWithCopyWarning: \n", + "/tmp/ipykernel_36429/1579583286.py:5: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " self.obj[key] = _infer_fill_value(value)\n", - "C:\\software\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:543: SettingWithCopyWarning: \n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " usda_mod.at[idx, 'usda_amount'] = cdh.__unit_handler__(row['Nutr_Val'], row['unit'] + '/100g', 'mg/100g')\n", + "/tmp/ipykernel_36429/1579583286.py:9: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " self.obj[item] = s\n", - "C:\\software\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " if __name__ == '__main__':\n" + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " usda_mod['units'] = 'mg/100g'\n" ] } ], @@ -908,7 +3464,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -921,19 +3477,19 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "No handles with labels found to put in legend.\n" + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -995,7 +3551,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1012,7 +3568,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1024,8 +3580,8 @@ "Dep. Variable: usda_amount R-squared: 0.558\n", "Model: OLS Adj. R-squared: 0.543\n", "Method: Least Squares F-statistic: 37.82\n", - "Date: Wed, 16 Sep 2020 Prob (F-statistic): 9.18e-07\n", - "Time: 23:42:12 Log-Likelihood: -67.295\n", + "Date: Sun, 29 Jan 2023 Prob (F-statistic): 9.18e-07\n", + "Time: 01:25:08 Log-Likelihood: -67.295\n", "No. Observations: 32 AIC: 138.6\n", "Df Residuals: 30 BIC: 141.5\n", "Df Model: 1 \n", @@ -1042,17 +3598,9 @@ "Kurtosis: 3.074 Cond. No. 6.92\n", "==============================================================================\n", "\n", - "Warnings:\n", + "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\software\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n", - " return ptp(axis=axis, out=out, **kwargs)\n" - ] } ], "source": [ @@ -1072,7 +3620,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1084,8 +3632,8 @@ "Dep. Variable: usda_amount R-squared: 0.745\n", "Model: OLS Adj. R-squared: 0.724\n", "Method: Least Squares F-statistic: 35.08\n", - "Date: Wed, 16 Sep 2020 Prob (F-statistic): 7.00e-05\n", - "Time: 23:42:12 Log-Likelihood: -29.501\n", + "Date: Sun, 29 Jan 2023 Prob (F-statistic): 7.00e-05\n", + "Time: 01:25:27 Log-Likelihood: -29.501\n", "No. Observations: 14 AIC: 63.00\n", "Df Residuals: 12 BIC: 64.28\n", "Df Model: 1 \n", @@ -1102,7 +3650,7 @@ "Kurtosis: 2.806 Cond. No. 5.81\n", "==============================================================================\n", "\n", - "Warnings:\n", + "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] }, @@ -1110,8 +3658,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\software\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1416: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=14\n", - " \"anyway, n=%i\" % int(n))\n" + "/home/heba/anaconda3/lib/python3.9/site-packages/scipy/stats/_stats_py.py:1772: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=14\n", + " warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n" ] } ], @@ -1137,7 +3685,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1148,7 +3696,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1157,26 +3705,24 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "No handles with labels found to put in legend.\n" + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1199,7 +3745,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1208,26 +3754,24 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "No handles with labels found to put in legend.\n" + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], diff --git a/notebooks/Molecule_Embedding.ipynb b/notebooks/Molecule_Embedding.ipynb index 3bb5972..a7bd12c 100644 --- a/notebooks/Molecule_Embedding.ipynb +++ b/notebooks/Molecule_Embedding.ipynb @@ -53,6 +53,11 @@ "metadata": {}, "outputs": [], "source": [ + "import sys\n", + "sys.path.append('FoodMine/config')\n", + "sys.path.append('FoodMine/src')\n", + "\n", + "\n", "from config import mfp\n", "from src.plot_utils import clean_plot\n", "from src.data_loader import load_health\n", @@ -68,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -91,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -114,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -125,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -142,14 +147,16 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "389it [02:13, 2.91it/s]\n" + "7it [00:00, 68.98it/s][01:38:39] WARNING: not removing hydrogen atom without neighbors\n", + "[01:38:39] WARNING: not removing hydrogen atom without neighbors\n", + "302it [00:00, 1274.19it/s]\n" ] } ], @@ -174,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -191,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -206,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -241,16 +248,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(37133, 87295)" + "(37148, 87295)" ] }, - "execution_count": 8, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -260,13 +267,13 @@ "\n", "dim_embedding = 100\n", "\n", - "model = Word2Vec(fingerprints, size=dim_embedding, sg=1, window=5, min_count=1, workers=4)\n", + "model = Word2Vec(fingerprints, vector_size=dim_embedding, sg=1, window=5, min_count=1, workers=4)\n", "model.train(fingerprints, total_examples=model.corpus_count, epochs=model.epochs)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -357,17 +364,534 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\software\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3248: DtypeWarning: Columns (13,14,17,45,50,51,54,55,62,63,64,65,66,67,68,69,88) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " if (await self.run_code(code, result, async_=asy)):\n", - "C:\\software\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:40: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", - "of pandas will change to not sort by default.\n", - "\n", - "To accept the future behavior, pass 'sort=False'.\n", - "\n", - "To retain the current behavior and silence the warning, pass 'sort=True'.\n", - "\n", - "C:\\software\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3248: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " if (await self.run_code(code, result, async_=asy)):\n" + "/tmp/ipykernel_38693/2986734704.py:31: DtypeWarning: Columns (13,14,17,45,50,51,54,55,62,63,64,65,66,67,68,69,88) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " foodb_class = pd.read_csv(mfp('data/compounds.csv'), encoding='latin1')[['id', 'name', 'superklass', 'klass', 'subklass']]\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " new_row = pd.Series()\n", + "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", + "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/data_loader.py:313: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " hdata = pd.read_csv('data/CTD_chemicals_diseases.csv', skiprows=skip).reset_index()\n" ] } ], @@ -391,7 +915,7 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -401,9 +925,20 @@ }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/anaconda3/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n", + " warnings.warn(\n", + "/home/heba/anaconda3/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n", + " warnings.warn(\n" + ] + } + ], "source": [ "tsne = TSNE(n_components=2)\n", "tsne_fit = tsne.fit_transform(dr_data)" @@ -411,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": 232, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -427,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -447,34 +982,41 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 23, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_38693/3082833711.py:36: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " cbar.ax.set_yticklabels([int(round(np.exp(t))) for t in ticks])\n" + ] + }, { "data": { "text/plain": [ - "[Text(1, 0, '1'),\n", - " Text(1, 0, '3'),\n", - " Text(1, 0, '7'),\n", - " Text(1, 0, '20'),\n", - " Text(1, 0, '55'),\n", - " Text(1, 0, '148')]" + "[Text(1, 0.0, '1'),\n", + " Text(1, 1.0, '3'),\n", + " Text(1, 2.0, '7'),\n", + " Text(1, 3.0, '20'),\n", + " Text(1, 4.0, '55'),\n", + " Text(1, 5.0, '148'),\n", + " Text(1, 6.0, '403')]" ] }, - "execution_count": 17, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -519,7 +1061,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -531,7 +1073,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -625,7 +1167,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -654,29 +1196,27 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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xxx948803PWrz1VdfwcvLK1vjERERERERkXMM4BMREeWCokWLYseOHejSpQtOnToFADh37pxDEHfw4MH48ssv7dLH+Pn5OfSn1Wqxfv16dOzYEZcuXQIAnDlzBmfOnLGr16xZMyxduhRz5szJ6VN6Jvz9/bFr1y4MHz4c8+bNg8ViQUpKCrZs2eK0vk6ng1KpzOVZutamTRscPHgQly9flssSEhKQkJDgso2Xlxc++OADfPDBB1CpcuZXM0EQ8NlnnyEyMjJb7c+ePYuzZ8961Obzzz9nAJ+IiIiIiCiHMYBPRESUS8qXL4+jR49i7ty5WLx4MU6fPo3k5GSUKFECDRs2xJAhQ9CuXTsAwKNHjwCkB2L9/f2d9le5cmX8+eefmDFjBlatWoVz587BaDSiePHiqFWrFvr06YMePXrkWFA4t3h5eWH27Nl45513sHDhQmzfvh0JCQl4+PAhNBoNSpQogZCQELRr1w69evVyeoMjrwwZMgRDhgzBqVOnEBcXh/j4eJw9exZXrlzBkydPIIoi/Pz8ULx4cYSEhKB169aIiopCQEBAjs/lpZdeQmhoKA4ePJjjfRMREREREVHuEMT8+Nw5ERHRc+zChQuoXLkyAKBKlSoer4QmIiIiIiIiov8GbmJLRESUzyxbtkz+umHDhnk4EyIiIiIiIiLKSwzgExER5SNXrlzB119/LX8fHR2dh7MhIiIiIiIiorzEAD4REVEu6dWrF1auXAmDweD0+O+//46wsDA8fvwYAFCrVi106NAhF2dIRERERERERPkJc+ATERHlkqCgIFy5cgV+fn6oV68egoKCoNPp8ODBAxw8eBBXr16V6/r4+GDv3r2oU6dO3k2YiIiIiIiIiPIUA/hERES5RArgZ6VChQpYtmwZ6tevnwuzIiIiIiIiIqL8igF8IiKiXHL58mWsWrUKe/bswaVLl3D//n08ePAAGo0GgYGBqF+/PiIiItCnTx+o1eq8ni4RERERERER5TEG8ImIiIiIiIiIiIiI8iFuYktERERERERERERElA8xgE9ERERERERERERElA8xgE9ERERERERERERElA8xgJ9PdenSBV26dMnraRARERERERERERFRHlHl9QTIuYsXL+b1FIiIiIiIiIiIiIgoD3EFPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhERERERERERERFRPsQAPhEREREREREReUQQBAQFBeX1NLItO/MPDw+HIAhISEh4JnPKa7t27YIgCIiJicmR/saOHQtBELBgwYIc6S8zCxYsgCAIGDt27DMfy1M5fV3p+cMAPhEREREREREREVE25OaNCno+qfJ6AkRERERERERErtxJuYO5x+di99U4JBmT4KfxQ8uy4RhUexCK+RTL6+k9t86cOQO1Wp3X08i2f/v8yV7Xrl3RuHFjFClSJK+n4iA0NBRnzpxBgQIF8noq9C/FAD4RERERERER5TtppjQM3zYcP51cAJPVZHdsa8JWfLp3LGJCBmBa22nQqXR5NMvnV9WqVfN6Ck/l3z5/slegQIF8GyD39vbm+42eClPoEBEREREREVG+kmZKw4vLOmLu8TkOwXuJyWrCnGOz0XFpB6SZ0nJ5hs4dOHAAL730EgIDA6HVahEUFIQ33ngDN2/edKhrm7P7/Pnz6N27N4oVKwaFQoE1a9bI9ZYtW4aGDRvCy8sLxYoVw4ABA3Dnzh3ExMRAEATs2rXLrt89e/bgzTffREhICAICAuDl5YWqVati9OjRePz4scM8bPNzP3z4EK+//jpKlCgBrVaLmjVrYt68eU7PNbMc8qdPn8aAAQNQrlw5aLVaFCtWDC1atMC0adPcuo6iKGLx4sXo3bs3KleuDB8fH/j5+SE0NBTff/89rFary7abNm1CREQEihYtCq1Wi7JlyyIyMhK//fab2/OfPXs2atWqBZ1Oh1KlSmHYsGF48uSJyzHPnDmDfv36oWLFitDpdAgMDESdOnUwYsQI3Lp1y61zvnXrFiZNmoSWLVuiVKlS0Gg0KF68OLp164ZDhw45bRMUFARBEAAAc+fORUhICLy8vFC8eHHExsY6fb0BICEhAdHR0ShcuDB8fX3RtGlTh+vjibi4OISHh8PX1xeFCxdG165dcfbs2UzbJCcn47PPPkOtWrXg7e0Nf39/tGzZ0u69bztfQRAQHh6OxMREjBw5EuXLl4darcaIESMAOM+B37lzZwiCgM2bNzudg9FoRKFCheDl5YXExEQAnr/3goKC8OmnnwIABgwYAEEQ5P+kf5vOcuAPGzYMgiBg1qxZLq9RjRo1IAgCzp8/73A9YmNjERQUBK1Wi8DAQERFReHEiRNO+/n999/xwgsvoHTp0tBqtShZsiTCwsLkeVP+xwA+Ef0r6P+6j1vv7sLVbmtxpeta3HxzO1L/uAVRFPN6akRERERElMOGbxuOuGtxbtWNuxaHEdtHPNsJueGXX35B8+bNsX79elSpUgXdunWDVqvFzJkzUa9ePZcBzXPnzqFhw4Y4ePAgWrVqhXbt2smpXaZOnYpevXrhzz//RLNmzdCqVSts3boVjRs3xqNHj5z2995772Hu3LnQaDRo3bo12rRpg8TEREycOBFhYWFITk522u7x48do0qQJVq9ejYYNG6JZs2Y4e/YsBg0ahLlz57p9HZYvX4569ephwYIF8PPzQ7du3VCnTh1cvHhRDrZmxWAwoE+fPtiyZQuKFi2Kzp07o1GjRvjrr78wdOhQDBw40Gm7kSNH4sUXX8TmzZvl16B8+fLYuXMnvvrqK7fGfvfddxEbG4sLFy6gdevWaNKkCRYtWoRWrVrBYDA41D969Cjq16+PRYsWITAwEF27dkWjRo1gNBoxbdo0nDt3zq1x165di/fffx83b95ErVq1EBkZiZIlS2L16tVo1qwZtmzZ4rLtqFGjMHToUPj7+6NDhw4QRRGzZ89Gly5dHP5mvnjxIho1aoQlS5agSJEi6Ny5M6xWKzp37oxly5a5NdeM827Tpg3i4uJQu3ZttG/fHidOnECjRo3w999/O21z584dNGrUCJ988gkePXqEdu3aoVGjRjhy5Ai6du2KL7/80mm7tLQ0tGzZEvPnz0edOnXQpUsXBAQEuJxb3759AQCLFi1yenzjxo149OgROnfuDH9/fwCev/eioqJQu3ZtAECzZs3Qv39/+b/ixYtne27Hjh3D6dOn0bBhQ1SuXFku37t3L2rXro3Zs2fD19cXXbp0QXBwMFatWoXGjRtj586ddv3MmjULHTp0QFxcHKpVq4bu3bujRo0aSEhIyJcb/pILIuVL1atXF6tXr57X0yDKc2mn74uXwpeKJ/G10/8u1FsoJu+9ntfTJCIiIiKiHHIr6ZaomagWhQlw+z/NRLV4O/l2ns356tWropeXl6hSqcT169fL5RaLRRwxYoQIQGzYsKFdm/nz54sARADim2++KZrNZrvjFy9eFDUajajT6cTdu3fL5WlpaWKnTp3ktjt37rRr99tvv4kPHz60K9Pr9eKrr74qAhA//fRTu2M7d+6U++revbuYnJwsH1uzZo0IQCxbtqzDOQMQy5UrZ1d2/vx5UafTiWq1Wly6dKndMYvFYndtMmMymcSVK1eKBoPBrvzu3btigwYNRABiXFyc3bGff/5ZBCCWLl1aPH78uN2x5ORkcfv27VnOf9++fSIAsVChQuKpU6fk8vv374shISHydbp8+bJ8rH///iIAceXKlQ7ncfr0afHmzZtunfOJEycc5i2Korh582ZRo9GIFStWFK1Wq92xcuXKiQDEEiVKiH/++adcfu/ePbFSpUoiAIfzbt++vQhAfOONN0SLxSKXz5kzRz6//v37uzXnxMREsUiRIiIA8ddff5XLTSaTfF0AiPPnz7dr17FjRxGAOGrUKNFoNMrlFy9eFCtWrCgqlUq7a3H58mW5ryZNmoiPHj1ymIv07+mTTz6Ry1JTU0VfX1/R19dXTElJcWjTo0cPEYC4Zs0au7l7+t775JNPnJ6nRPo3lvG6VqxYURQEQUxISHBo895774kAxKlTp8plT548EYsXLy6q1Wpx+fLldvW3bt0qajQasVSpUnZzL1eunOjv72/3nhVFUbRareKOHTuczpfyH67AJ6J8K+3wbVxqshgpu665rKM/ehcJrZfh8boLMBgM0Ov1MBgMMJvNXJ1PRERERPQv9OOJH12mzXHFZDXhx+M/PqMZZW3u3LlIS0tDdHQ0IiIi5HKFQoEvv/wSJUuWxKFDhxAfH+/QNjAwEBMnToRSqbQrnzdvHoxGI/r374/mzZvL5TqdDtOmTYNC4Tyk8+KLLzqsStZqtZg6dSpUKhXWrl3rtJ2/vz9mz54NHx8fueyll15CrVq1cPXqVSQkJGR5HaZMmQK9Xo/Y2Fj07NnT7phCobC7NplRqVTo1q0bNBqNXXlgYCAmTJgAAA7nMX78eADpTy2EhITYHfPx8UHr1q2zHFdKZzJy5EjUqFFDLi9cuLDLFfx3794FAKf9V6tWDSVKlMhyXACoVauWw7wB4IUXXkCPHj1w8eJFnDp1ymnbcePGoU6dOvL3RYoUweuvvw4A2L17t1x+8eJFbNmyBQEBAZg0aZLde2jw4MFo2rSpW3OVLF++HPfv30e7du0QHR0tl6tUKkyZMgW+vr4ObY4dO4ZNmzahadOm+PLLL+02Eq5QoQK++eYbWCwWl099fPvttyhYsKBb8/Py8kLXrl2RnJyMdevW2R1LSkrChg0bEBAQgI4dO9rN3dP3Xnb16dNHTtljSxRFLFmyBEqlEr169ZLL582bh9u3b+Pdd99FVFSUXZu2bdvijTfewI0bN7Bhwwa5/O7duyhfvrxDuihBENCqVascOQ969hjAJ6J8yXQ/FQkRq2F94viIYkai0Yob0RuRfOoOjEYjDAYDUlNTkZqaCovFkguzJSIiIiKinLL7qnupczKKu7orZyfigT179gD4Jy2GLa1Wix49etjVs9W2bVt4e3s7lO/fvx8A5La2KlasiLp167qcz40bNzBr1iyMGDECAwcORExMDF5//XVoNBpcuHDBaZsGDRqgUKFCDuVS+g53crlv27YNABAbG5tlXXccO3YMkyZNwtChQzFgwADExMRg5syZAGB3Hjdv3sSZM2dQuHBhdO/ePdvj7d27FwAcbj4AQPv27Z1en/r16wMAXnnlFRw8eDDT/PxZMRgMWLt2LT788EO8+uqriImJQUxMDE6ePAkALl+79u3bO5Q5e9327dsHIP0mj+2NGknv3r09mm9m1ysgIMDpvLZu3Qog/eaQlL/fVlhYGAA4zftfokQJNGjQwKM5Sv8mf/31V7vy1atXIy0tDT169HAI1gPuv/eehqu57dmzB9euXUObNm3s0vBI1y4yMtJpf86uXf369XH8+HGMHj0aFy9ezJF5U+5T5fUEiIgkacfu4uHM40jecRXmW8kQU8xutxVTzUiacRKFpv6zMsVisSA1NRXe3t4Oq1mIiIiIiCh/SjIm5Wq7nCBtUutqU1Sp3NlmtmXLls20zzJlyjg9XrZsWRw5csShfPLkyfjggw9gNBqzmrad0qVLOy2XVlE7y/+e0bVr6U9PV6hQwaOxMzIajYiJiXFYmWwrKemf11sat2LFik817s2bNyEIQqbX/OHDh3Zl7733Hvbu3Yv169dj/fr1KFCgABo1aoSIiAjExMTAz8/PrbFPnjyJLl26ZPqkg+0523L22jl73aT3lKv3nKtyV7LTn3R+77//Pt5//32Xfd+/f/+p5wek3yArVqwYNm/ejIcPH8o3YaSgecabbp6+955GlSpVUL9+fRw5cgQnT55ErVq1Mp2bdO0aNWqUab+2127GjBmIjIzExIkTMXHiRJQsWRLNmzdHVFQUunXr5vJJHspfGMAnojxneaTHtZc3Innj5afqJ2XZ3yj4WSMo/P+5ey6KIvR6Pby9vZ3e3SciIiIiovzFT+NewDOn2uWkrP7mcHZcp9Nlq09nKUPj4+MxcuRIFChQALNnz0Z4eDiKFy8OrVYLAChZsqTLlfQ59feSIAhP3dfkyZOxePFi1KxZE1999RXq1auHgIAAqNVqnD9/HlWqVHF6/nnxN5+/vz927NiBffv2Yf369di1axe2b9+OLVu2YMKECdizZ0+WNxZEUUTPnj2RkJCA1157Da+99hoqVKgAX19fCIKAMWPGYMKECS7TxLp73lL7nLpO2elPekq+efPmmd7oKVKkiENZVv9WnJHS0Hz77bdYvnw5YmNjce/ePWzfvh1lypSxS08FZP+9l119+/bFkSNH8Ouvv2LChAkwmUxYsWKFnP7HlnTtevTo4fSpHYltgD8kJASnT5/G5s2bsXHjRsTFxWHp0qVYunQpwsLCsH37dqdPIFD+wgA+EeUpS6IBl1stg/74vafuS0wxw3DwDrza2q+WsFgssFgsUKn4I4+IiIiIKL9rUbYltiZs9bhdy7LhOT8ZN5UsWRLnzp3D5cuX5dQltq5cuQIAbudDl+qeO3cOV69eRXBwsMNxadW5rdWrVwMAPv/8c/Tv39/uWFpaGm7fvu32+NlRpkwZXLhwARcvXkTNmjWz3Y90HlIg1dalS5ecjgsAf//9d7bHBNKveUJCAq5du4ZKlSo5HL969arTdoIgICwsTE5hcu/ePQwfPhyLFy/GmDFjsHTp0kzHPXv2LM6ePYsGDRrIaVpsOTvn7ChZsiSAf96PGbk6v5zsT3paICoqCm+99ZZH42VX37598e2332LRokWIjY3F0qVLYTab0adPH4ebD56+955W79698d577+HXX3/F+PHj8fvvv+PBgwfo1auXw9MbpUuXxrlz5/DRRx853S/BFZ1Oh8jISDn1zunTpxEdHY29e/fixx9/lPdLoPyLz0kQUZ66OXR7jgTvJdYnzh8TzZgLXxRFmM1mbnxLRERERJTPDAoZBLVCnXVFG2qFGoNqD3pGM8qatIp30aJFDseMRiOWL19uV88d0oaiK1ascDh26dIl/Pnnnw7ljx49AuA87c7y5cuf+d87bdu2BQDMnj37qfrJ7DyWLVvmUFayZElUq1YNDx48wKpVq7I9rhSAl14vW1u3bnVIn+NKYGAgxo4dCwBy/vrMSOfrLBXOo0eP5NznT6tZs2YAgI0bNyIlJcXh+JIlSzzqL7Pr9fjxY2zZssWhXHqPrFmzxqOxnkZoaCiCg4Oxd+9eXL161WWKGsDz9x4AeQW72ex+GmBJiRIl0KpVK1y9ehX79u3LdG45de2qV6+OoUOHAnDv/Ul5jwF8IsozppvJeLL4bI72KertPzAtFgtMJpO8ua3ZbIbZbJY3uTUYDNz4loiIiIgoHynuWxz9a8V41CYmZACK+RR7NhNyw6BBg+Dl5YXFixfjt99+k8utVivGjBmDGzduoGHDhmjcuLHbfQ4YMABqtRoLFiyQN7QFAL1ejxEjRjjdLFVa/f/jjz/CZDLJ5adPn84033hOGTFiBHQ6HWbNmoWVK1faHbNardi4caNb/UjnMWvWLLvyFStWYOHChU7bjB49Wp7DX3/9ZXcsJSUFO3bsyHJcafPdyZMn48yZM3L5w4cPMWrUKKdtZs2ahcuXHdPBbtq0CYB7edsrVaoEhUKBHTt22G2Qqtfr8dprr7l948Cdcdq0aYNHjx5h9OjRdu+h+fPn273P3NGjRw8UKlQIW7ZssQtuWywWjBw5EsnJyQ5tGjdujDZt2mDnzp14++23HepYrVZs2bJF3iA3p/Tp0weiKGLChAk4cOAAatasKeect5Wd9570JMK5c+eyNTcpWD979mysW7cOhQoVQocOHRzqxcbGIjAwEOPHj8f8+fMdbsilpKRg4cKFuH79OgAgNTUV3377LR4/fmxXT7rGQPb2FaDcxwA+EeUq0WTBkxXncWPw70hotxyw5OwKkMdjD8F04TGsVisMBgMMBgNMJhMsFgsMBgPS0tLw5MkTu19mbUkb3zKIT0RERESUd6a1nYaWZVq6VbdlmZaY1nbaM55R5sqWLYvZs2dDFEV07twZzZs3R58+fVC9enV88803KFasmMvgnyuVKlXC+PHjodfr0aJFC7Rr1w69e/dGcHAwjh8/js6dOwOAXf7qAQMGoHjx4li/fj2qVKmCXr16oV27dqhTpw6aN2+OcuXK5eh5Z1S5cmXMmzcPQHqKlFq1aiE6OhodOnRAmTJl0KlTJ7f6GTVqFJRKJUaPHo0GDRqgT58+aNiwIXr06IG3337baZtXXnkFb775Jq5du4batWujRYsW6NOnD8LDw1GyZEl89tlnWY4bFhaGESNG4P79+6hbty4iIiLQo0cPOYWRsxsws2bNQoUKFVCjRg1ERUWhd+/eqFu3LkaMGAEvLy988sknWY5btGhRDBo0CImJiahdu7Y8blBQEHbs2IGYmJgs+3DXzJkzERgYiOnTp6N69ero06cPmjRpgkGDBuG1117zqC9/f3/Mnj0bCoUCvXr1QlhYGPr06YMqVapgxYoVTleRA+lPqoSEhGDq1KkoV64c2rRpg969e6N58+YoXrw4XnjhBRw+fDgnTlcmzUUKzL/88stO62Xnvde+fXvodDpMmTIFHTt2xKBBgzB48GC3A/rdunWDTqfDzz//jJSUFPTs2RNqteNTSAEBAVi9ejV8fHwwcOBAlC9fHhEREejevTsaNmyIYsWKoX///vImtkajEcOHD0exYsXQpEkTREdHo3v37ihfvjxWrlyJChUqyDetKH9jAJ+IcoUoingw40+cKzcH13qsx6MfT8FwOmdWEdiy3k3D3ajNSHuQLAfhBUGAQqGAIAgwGo0wmUwwGAwOK1ZsV+snJyfDZDIxpQ4RERERUR7wUnthY89NGFLnVZfpdNQKNYbUeRWbem2GTuX55pY57eWXX8bu3bsRERGBM2fOYMWKFUhLS8Prr7+OI0eOoGrVqh73+e6772Lx4sWoXbs29uzZgx07dqBVq1aIj49HWloaAKBw4cJy/cKFC+PQoUPo06cPjEYj1q1bhxs3buCzzz7D4sWLc+xcMxMdHS3P4cGDB1i5ciWOHTuG4OBgfPvtt2710aJFC+zduxetW7fGpUuXsGHDBmg0GqxcuVJO/eHMd999h9WrV6NNmzY4deoUVq5cicuXL6NNmzZuP4EwefJkzJw5E5UqVcLWrVuxb98+9OzZEzt37pQ3A7Y1btw4DBw4EIIgYPv27Vi/fj1SU1Px6quv4sSJE2jSpIlb486cORPffPMNypcvj+3bt2PPnj1o27YtDh8+nKM3XoKDgxEfH4+ePXvi7t27WLt2LURRxJo1a9CrVy+P++vevTu2bt2K5s2b488//8SmTZtQvXp1HDhwwOk+AgBQrFgxxMfHY/LkyQgODsahQ4ewZs0aXL9+HXXr1sWMGTNcBtizKzg4GA0bNgSQHiOIjo52Wi87772SJUti7dq1aNy4Mfbu3Yt58+bhxx9/dLlhdEb+/v7yDTkg/WkBV5o1a4aTJ09i5MiR8PLywo4dO7BlyxYkJiYiIiICS5cuRfXq1QEAvr6+mDFjBiIiInDv3j2sW7cOO3bsQEBAAMaNG4cjR44gICDArTlS3hJERqfypRo1agCAw2NfRP9Goiji9tu78GDa0Vwb0+eLBvAaWAUAoFarodFoIIoi0tLS5KC8UqmEVquF1WqVV+lLBEGAl5cXlEoldDodlEplrs2diIiIiIj+cSflDn48/iPiru5CkjEJfho/tCwbjkG1B+Vp2py8lJKSgqCgIPkJY/69QkT036XK6wkQ0X/fw5nHczV4DwD6BeehG1AZKpVKDt5brVa7FfUWiwVms9npSnupPpCeN87b25u/FBMRERER5YFiPsUwpukYjGk6Jq+nkusuXbqEwoULo0CBAnJZcnIyXn/9ddy/fx8DBw7k3ylERP9xDOAT0TMlmiy498UfuT6u5UIiFDf00FUpIgfnRVGEIAjy10B6TjhXbNvp9Xp4e3vL7YmIiIiIiJ61ZcuWYezYsahfvz5Kly6NR48e4c8//8T9+/cRFBSE8ePH5/UUiYjoGWMAn4ieqcT1l2C+6bjzfG5QJlvloL3tCntBEKBUKmE2m2G1WiEIgl0+fNuc+RKLxQKLxQKVij82iYiIiIgod7Rp0wbHjh1DfHw8/vzzT4iiiLJly6J///54//33ERgYmNdTJCKiZ4yRKCJ6ppI3X867wb2U8sa1ZrMZoijCaDTKQX2lUgmVSgWj0eiwoa1KpYJCYb/PNwP4RET0byCKonzj2fYzT6lUOjxJ5kldIiLKfQ0bNsSSJUvyehpERJSHGIkiomfK8tiQJ+MK/hqoyxeAXq+XN6e1XXkvpcXRaDRyEN+uvSAgNTUVKpVKXpGfMchPRESU31gsFrvPPlsZN2b3pK6EAX8iIiIiotzFAD4RPVMKb3WejOvTrQLMSisspn+CEqIoQqVSQRRFmEwmKBQKmEwmAOmBCimAIaXPMRgM8qp7URShVCqh0Wi4SRQREeVLFosFqampDhuzZzzu7e0NAG7XfZqAPxERERERPR1F1lWIiLLPu3GJvBm3RyWYzWaHcimIL6XPEUURZrNZXjkolUuBfdsVhkB6sMNZ4IKIiCgvSU+WuQrIZ6znSV1p1X1mn4FZHSciIiIiouxhAJ+InqkCfatB4Zv7q/B9QkvY5bC3Wq2wWCywWq2wWq3Q6XTQarXyakGVSgWdTgcAcp58ID14IQX9pc1w3Ql6EBER5SYprY07jEaj23UtFgvMZrPHAX8iIiIiIsoZDOAT0TOl9NMg4NWQXB3TL6IClL4aaLVaaDQauVyhUMhBfb1eD5PJBLVaDYVCAYvFIqfMyUihUECj0cgBCU+CJERERLnBk88l6Wa2u8xms0cBf35GEhERERHlHAbwieiZKzY+DD6tyuTaeAVja0EQBFitVjkVjpTXHvhnVb3ZbIbRaHSZ114QBGg0Guh0OofVhAxOEBFRbpM+uwwGA/R6PQwGg7wxu6er3j2p7+lnHj8jiYiIiIhyDjexJaJnTqFVodxvXXGt5wYkbbj0TMfStigBZcviEARBDmpkJAXygX9SA/j4+MhBfynnvUKhsMt/b4vpAYiIKDdltYGsbdo4dzj7bHPlWd4cICIiIiKizHEFPhHlCoWXGmXXRkJdvsAzG0NVvwh8ZodBb0gPcLgKTigUCrsV91Kg3jbgL31vu3LflieBDyIioqfhzgayRqPR7c8m25Ry7nD2lFpmpD1jXD0tQERERERE7mMAn4hyjaAQUPitutlur6xeENA4/tgSAnXwHhmCgivaQlFAI+ezV6lULoMOSqUSgiBApVJBpVLBarXKAQfpP6VSaZf7PmN7IiKiZ83djWEFQYDJZHIriO8qdZwzSqUSarVnm9ELgoDU1FSkpqbCYDDAaDTCYDDIZUyxQ0RERETkPqbQIaJcFTCoFu5+vA/WZJPHbQUfFQod6Yq0jVdguZsGqAQoyvtB1boEvPy9AcBuo1mr1QqdTgej0eiw6k+hUMDb2xsKhQImkwlKpRJKpRJms1kO7GcWvGcAn4iIcoMnm8K6s6peEATodDoAQGpqqt3nnHQjW0onp1AooFar5c89d+ahVCphMBhc3nCQnibw9vbmZykRERERkRu4Ap+IcpXSTwNtSGC22loTTUAhDVS9gqAdVg3a16tC3aEUBI1jmhuFQiHnt9doNPDy8oJOp4NWq4VOp4OXlxe0Wq0cqFer1fD29oaXlxe8vLxcBu+lwAdT6BARUW7wZLW6QqHI8ukzKXBu+zWQ/vlmNBqRlpYGvV4v3/jW6/VIS0uDWq3O8rNPoVDIwf/MuPtUARHRv01CQgIEQUB4eHheT+U/acGCBRAEAWPHjnW7DV8TIvov4Ap8Isp1qkCvbLUTfFQug+oKhQJms1kuk1Lk2AYSMq5MlMqVSiV0Oh1UKlWWmwTqdDquGCQiolzjaZBbEAR4e3vLK/etVissFouc915KEycIghzEN5lMSElJAQCoVCqHvWKkfrRaLUwmk8vPSLVaDb1e79Y8pfmpVPxzhIiyZrgDXJsLPIwDzEmAyg8oFA6UGQRoi+X17IiIiJ4t/sZMRLnOu1kpJK296HE7TcOiEARBfsRfolarYbVa5e+l4IO7QQHblDhSMEMKLNgGOaSbAkRERLnF088d6XNSEASYzWY52C59ThqNRocb0rap5FwRRREmkwk6nQ5msxkmkwlWq1VOs6NWq2EyeZYejwF8IsqKJQ04PRy4vgAQM/yIub8VuDAWKD0AqD4NUOryYoaUm7p27YrGjRujSJEieT0VIqJcxd+YiSjXBQyogbsf74No8GwTO79B1WD9/8C8IAiwWCwQBAFqtRpms1kOJCgUCnkTW9vAvjPOUuLYbm5LRESUm0RRtLuJLH3vyaazUp55d/LQS/27w2q1OvRrtVphMBjkPWQ8wRQ6RJQZSxpwqGP6qntXRBNwbTaQcg5ouAlQZu9BX/qXKFCgAAoUKJDX0yAiynXMgU9EuU5VxBsF+9fwqI36hVKwlvGSAwV6vV7eiFYKdEiBd6PRCJPJBL1en2nO3oz5f4mIiPKSFFhPTU2FwWCQN2GXPvuyuimtVCqhUCjcyi8v5aF3N3gvCAL0ej2MRqPLuUur8t3Fp9qIKDOnh2cevLf1MA44PeKZTsdjiYmJGD58OMqUKQOdTodq1aphypQpTn9OJicn47PPPkOtWrXg7e0Nf39/tGzZEmvWrHGoa5vTPS0tDaNHj0a5cuWg1WpRqVIlTJw40eEzIDw83O4JLWf/JSQkOIwTGxuLoKAgaLVaBAYGIioqCidOnHCYk21u+qtXr6JPnz4IDAyEl5cXGjRogPXr17u8Ths3bkS7du0QEBAAnU6HKlWqYPTo0Xj8+HGm42R0//59xMbGonjx4vD29kbdunWxcOFCl+OKooglS5agRYsWKF68OHQ6HcqUKYO2bdtixowZLtsREeUFLi8lojxRYmo4DH/dR+q+m1nWVVYtiMLft4SgVkMURbtc9kajEWq1GkqlElar1S4PvkqlklPgaLVah7y/TIlDRET5hatV86IoQqPRQK/Xw2AwQKvVOuzpAvzzRJmU897dMZ315axvo9GYZX1BEGAymaDRaNwa22q1Qq/X83OZiBwYbqenzfHE9flA5c/yR058g8GA1q1b4+LFi2jdujWMRiO2b9+Od955BydOnMD8+fPlunfu3EHr1q1x+vRplCpVCu3atUNqaioOHDiArl27YsKECRg9erTDGEajEe3bt8dff/2F0NBQVKtWDXFxcRg9ejSSkpLw+eefy3U7dOiAoKAghz6Sk5OxcuVKALBb1LR371506tQJiYmJqFGjBrp06YIbN25g1apV2LhxI3777Te0atXKob+EhAQ0bNgQOp0OYWFhuHPnDg4cOIDIyEhs2rQJ7du3t6s/YcIEjBkzBiqVCi1btkSRIkWwb98+TJw4EatXr8bu3btRrFjWL+iDBw/QrFkznD9/HqVLl0aXLl1w+/ZtDBgwAK+99prTNh988AEmTpwIPz8/hIWFoWDBgrh16xaOHz+Ov//+G0OHDs1yXCKi3MIAPhHlCYWXGkFbonAjdiueLDoDuFgoqO1QGoVnhkNV6J/nYVUqFQwGg5yPVwrq265mUSqV0Gg0ctDeZDLB29ubgQEiIsp3pNXwrlbNi6IInU4nr8i3DZBLKyeVSiVMJpN8IzvjfjGuuBPsF0XR7ga5K9JcMkv5Y7VaYTKZ5M3nbefIzeKJSHLtR8ec91kRTentKo15NnPyRHx8PEJCQnDhwgU5X/vFixfRokULLFiwAF27dkWXLl0AAAMGDMDp06cxatQofP7551Cr1QCAS5cuoX379vjoo4/w4osvIiQkxG6MAwcOoHnz5jh//rw8xuHDh9GkSRNMmTIFo0ePhq+vLwA4vQEgiiK6desGAHjjjTdQpkwZAOlPDvTo0QNpaWlYvnw5oqKi5Dbbtm1Dp06d0K9fP1y6dMnhhu1PP/2EYcOGYfLkyXI60mnTpmHEiBH4/PPP7QL4hw4dwkcffQQ/Pz9s27YNoaGhANJvfvTr1w/Lly/HsGHDsGzZsiyv95gxY3D+/Hm89NJLWLp0KbRaLQBg06ZN8nW2pdfrMXXqVAQFBeHIkSMoVKiQfMxsNmP//v1ZjklElJuYQoeI8ozCW40yP7+IypcGo8joUOgaFoO6ckFo6wWiwJshKPpHdxRb2tEueA8ACoUCWq0WgiDAarXCaDTKKwKlnPg6nc4uKCCl2SEiIspv3PmMklbiazQaqFQqaDQaOcgjBcWNRiMMBgPS0tJgNBqzvGktrYKX0t+4moPVapU/UzNbgS/N0RUpFRAA+SZ7xvmkpqby85qI3E6d49BuV45O46l8/fXXdputVqxYER9//DEAyClajh07hk2bNqFp06b48ssv5Z/rAFChQgV88803sFgsmDt3rkP/CoUCc+fOtRujQYMG6NixI1JTU3H48OFM5/fxxx9jzZo1aNWqFaZNmyaXz5s3D7dv38a7775rF7wHgLZt2+KNN97AjRs3sGHDBoc+pTnb7iU2dOhQBAQEID4+3i4N2/Tp02G1WjFixAg5eA8AWq0W06dPh5eXF1auXIkbN25keh7Jycn4+eefoVKp8O2338rBewDo2LEjevTo4dAmMTERBoMBtWvXtgveA+mLxVq0aJHpmEREuY0BfCLKc5qgAig+oTkqHXwZVc4NQvCRV1D06xbQVS3kso3i/zezlQIYCoUCOp0OXl5eToMCgHurDImIiHKbu59P0mebUqmUN3B39nknPXkmpafJSAqkS0+zSfWlsoz5mW3HzWp1vPS0gLN6ZrMZKpXK4SZ7xvbu5PAnov82c1LutstphQoVQrt27RzK+/TpAwDYv38/RFHE1q1bAQAvvfSS05/XYWFhANJXq2cUFBSEypUrO5RLZbdu3XI5v6VLl+KLL75AxYoVsWLFCruAuzSnyMhIp20zm1N4eLjdTQggPSBeoUIFmEwmPHjwQC7fs2cPAKBv374O/RQtWhTt27eH1WrNcjX80aNHkZaWhkaNGqFs2bIOx6Ojo532X7p0afz222/46quvcPNm1mldiYjyEgP4RJQvefKHu/TIvrQqMLOgABERUX7j6edTZkFu2xXyFovFYSW+FLyXNn+XnlyTAu4Wi8UhiG9bzx0KWOFtfQxv811okWb31ICrm+y2+NQcEan8crddTitXrpzTcn9/fxQsWBDJyclITEyUN459//33nW4sK62uv3//vkNfpUuXdjqGlDZHeuIpoyNHjmDAgAHw8/PDunXrHFagS3Nq1KiR0zlJq/Kfdk43b96EIAgur5WUsz+r4Lp03FnwPrPyn376CQEBARg1ahRKlSqFChUqICYmBlu2bMl0PCKivMAc+ESUL3maq96d+sx/T0RE+ZGnn0+iKLoMcEur5KXjZrPZLvAu7R0DpK+KlHLl2+bYl1bkS2kIpJWZWW14K6TchObKWmiub4RgfAIV/v+PjcC6MJXvCnORZhAV7t0EsFgsditCiej5UqglcH9rNtqF5/hUclzGNJ8A0Lx5c1SoUMFlG9s0OZLs/G1z+/ZtREZGwmAwYNmyZahevbpDHWlOPXr0gLe3t8u+GjVqlCNzykpWfUrX09OxW7dujb///hsbNmzA5s2bERcXh59++gk//fQTevbsiaVLl2Z7zkREOY2/FRNRvuTOBna2ee+zCiq42ycREVFu8/TzKasV7Gq1Ws5bL4oirFYrFAqF3cp2283epT6ltHRSW2nVvEKhQFpaWqar4tVXf4Pu5NcQRCd17v0J9b0/oShQBakNJ0DUOQaiPD1HIvpvKzMIuPCpZxvZCur0dvnB1atXnZYnJibiyZMn8PHxgb+/v7xiPSoqCm+99dYznZPBYEBkZCSuX7+OiRMnIiIiwmm90qVL49y5c/joo48cNs7NSSVLlsTly5dx5coVVKlSxeH4lStXAAAlSpTIsh/b+hm5ei2A9Cci+vTpI6c2io+PR48ePbBs2TLExMSgY8eObp0LEdGzxhQ6RJQvuZNnV6ojrSB82v6IiIhyiyiKMJvNMBgMMJn+iVC583mWVR1ps3fpc08KhlutVpebvdvWUygUdp+x0j4z0rjSprfSf8qrm+B1YqLz4L3UNwDlk3Pwjn8HMGWdpJpPzRE937TFgdIxnrUpPQDQFnsm0/HYgwcPsG3bNofyxYsXAwCaNm0KQRDQtm1bAMCaNWue+ZyGDBmCP/74Ay+//DJGjRrlsl5uzal58+YAgEWLFjkcu3fvHrZs2QKFQoGmTZtm2k/9+vWh0+nwxx9/4Nq1aw7HlyxZ4vacGjdujH79+gEATp486XY7IqJnjQF8IsqXBEGwCxa4otFooNVqM12p525fREREucFisSA1NRWpqalyAN9qtUKv1zvkrLelUCigVqvtAuiuVsVLQXytVgu1Wi1/Xma22btEGl/aCFfa7Far1cJsNsNoNMrjm1Puw/vU5CzPWTojZXICtOfmZVmfN92JqPq09FQ67ijUMr1+fvLee+/Zbdp6+fJljBs3DgDwxhtvAEgPGLdp0wY7d+7E22+/jeTkZLs+rFYrtmzZgr179z7VXCZNmoSff/4ZjRo1wpw5czKtGxsbi8DAQIwfPx7z5893+LxISUnBwoULcf369aea09ChQ6FQKDBt2jQcPnxYLjcajRg2bBhSU1PRrVs3lCpVKtN+fH190bdvX5jNZgwfPtwuz/6WLVuwbNkyhzZXr17FggULkJqaalduMBiwc+dOAK5z5xMR5QWm0CGifEupVMLb2xt6vd5pgEKpVEKn0wFAlnUYCCAiovxACt5nDIgoFApoNBo5aJ5xhbxSqZSD/ADkXPXSMbVa7TSdnFKphFarhSAIMJvN8ua1CoVCzn9vSxAEORe+SqWSN7OVylUqlVwuiiJ0N3dCsDrfKNEVzfVNMFQdAqic51bmU3NEBABKL6DhJuD0COD6fOfpdAR1+sr76tMApS7Xp+hS48aNYTQaERwcjNatW8NoNGL79u1ITU3Fyy+/jMjISLnuokWL0L59e0ydOhULFy5EnTp1EBgYiBs3buDcuXO4d+8epkyZgrCwsGzP54MPPgAABAQE4LXXXnNa5+uvv0aRIkUQEBCA1atXo0uXLhg4cCA+/fRT1KxZE1qtFlevXsWZM2eQkpKCP//80+Wmte4IDQ3FuHHj8OGHH6JJkyYIDw9HkSJFsG/fPly7dg3BwcGYPn26W319+eWXiIuLw+rVqxEcHIxmzZrhzp07iIuLQ2xsLGbOnGlX/+HDhxgwYACGDh2KBg0aoHTp0khJScH+/ftx7949hIaGolu3btk+NyKinMYAPhHla1IQX8rbK4oiBEGQ/7iXVgm6U4eIiCgviaIIvV7vcvW7tGreYrHAarXKgXdBEORV8EB6MF2lUsmpd2zrZwziS4F36TNSWpko9WG7Gl8QBLsb4rZ7zUir7s1ms904mmsbPb4OgjkV6lu7YCrzoty/lKsfgPyUAT/DiUjpBdT6Aaj8GXDtR+DhLsCcBKj80jesLTMo/6TNsaXVarF582aMGTMGa9aswf3791G+fHkMGTIEI0aMsKtbrFgxxMfHY9asWVi6dCkOHToEo9GIEiVKoG7dunjppZfQs2fPp5qP9PN18+bNLuuMHTtW3iy3WbNmOHnyJCZPnozffvsNO3bsgFKpRMmSJREREYFu3bo53QDXU2PGjEHt2rUxZcoUHDp0CGlpaShbtixGjRqF0aNHIyAgwK1+pMD/hx9+iHXr1mHNmjWoXLky5syZg9atWzsE8CtWrIivv/4aO3bswOnTp3Hw4EH4+vqifPny+PjjjzF48GBoNJqnPj8iopwiiNwhKl+qUaMGAOCvv/7K45kQERERUU4wm80Oj+tnxtvbG0qlEqmpqQ5PmWUMtgP/rLaXiKIor9yXGAwGhzbS02xSkN5ZX2lpaXYr/rVaLSBa4fdbawj4p393GSr1g6HqELsV/1KKIOnmAJ+iIyIiIiLiCnwiIiIiolzhKl99VvWdtRNFETqdTg5+w5QKzY0d0D08BIU5CYJSB2uByjCUiQB8y8jt1Gq1nP5G6ttoNEKj0aT3A8gb3Ups60ttLBYLlApkK3gPAGqlAKtaLacE0mg0DoF6Kd2QdCODiIiIiOh5xAA+EREREVEu8PTBV1EUMw36i6IIjVoNnytLob3wCwSL/ep+5f0jUF9cDFPRxtDXHg1RW0hO02O7Aa7ZbIZarZZX7GfMp+9s3larFUqlGlZtISgMDz06LwAQvIv+fx+Z57uX0g55e3sznQ4RERERPZccd7oiIiIiIqIc52kA2tkms3ZEEdoTX0N3drZD8F6uAkB9Nx4++96AoL8P4J9c+1qtFmq1GiqVCqIoymXO8ui7YirZ1qNzSu9QCUup1m4/kSCt+CciIiIieh4xgE9ERERElAs8SQMjBc3NZrO8Sa1tOQCor6yB5uo6ZLauX6qtSL0Jr8MfAzY3BJRKpRysF0XRbtNaWwqFwmUQ31TuJbfPSVa6FSzawh41YQCfiIiIiJ5XDOATEREREeWCrNLFSARBgMlkgsFgkP+v1+uRlpYGo9GYHkwXLdBeXJxe342xRQCqx39B+fA4gPQUOAaDQR7DarU6jmEzH5XKPvOmFOy3+paBoXyUPEaW81D7AiFvZCudEBERERHR84gBfCIiIiKiXCAIAnQ6XaYpaQRBgF6vlwPktkF/URRhMpmg1+uhuncQirTb7o/9///XJKyWg/fSqnYpQK9QKOzGkOYpiqK8yaxCoYBGo4EgCPKTAYbqQ2Eq01Eew1WoXVT7IS10Iiy+5bKVToiIiIiI6HnETWyJiIiIiJ4xaUNai8UCpVIJs9kMwD7PvbTyPuMmsmq1GlarVa5nsVgg3D2SrXkoHxyDyWSyW9GuUqkgCILdOBaLBUajERqNBqIoQhRFeHl5ITU1FUaj0a5PQRBgrv4uvAMbQnVpBVSPT9ufu9ILptLtYagQDdGnJES9HhqNxrN5e5B+iIiIiIjov+S5DuCnpqZiy5YtWL9+PQ4dOoSEhARYLBZUqlQJ3bt3xzvvvANfX1+nbRcuXIjp06fj9OnT0Gg0aNy4MT766CM0bdo0l8+CiIiIiPIzi8UCvV7vkMfdYrHIK9ptc9FnJG06azKZ5D5EU3K25iKYU+3moVQq5SB9xnHMZjPUanV6O0GAwWCQv5dy8kvzA4Ckws2hK9UOwuPzUCZdAixGiBp/mIs0ANQ+ductje1Obnt3Uw8REREREf0XCeJznFBy7ty5GDJkCACgRo0aqF69OhITE7F//34kJSWhatWqiIuLQ9GiRe3avfPOO5gyZQq8vLzQvn176PV6bN++HaIoYvny5ejatetTz61GjRoAgL/++uup+yIiIiKivGGxWJCampppDndBEODt7Q2z2QyDwZBlf1arFd7nfoBXwjKP52PVFMStJovkGwdS8N6WKIry5rlardYu0O5yM1uTSX56wFmfGWm1WqhUKrevDQP4RERERPS8eq5z4Gs0Grz++us4f/48Tp06hWXLlmHz5s04d+4c6tati7Nnz2LEiBF2bXbs2IEpU6agcOHCOH78ONasWYPNmzdj9+7dUCqVGDBgAB49epQ3J0RERERE+YYoitDr9VkGs6V6tqvaXVEqlVCr1RAD62VrTqZCdaBWq+WV9BlXwFutVhiNRnljW+k/vV4Po9GY5RzNZrPD+VosFjnAL63ut1qtUCqVDsF5qa7Uj1artUsnRERERET0vHmufxt+5ZVX8P333yM4ONiuvESJEpgxYwYAYNWqVXZ5Pr/55hsAwEcffWTXrkmTJnjttdfw5MkTzJs3LxdmT0RERET5mZTz3t26njwYay7aCKJ3CbfrSz2bgrrKK+xNJhMMBoMclM+4uS2QvgLeNi++bX2n44iiQ38Gg8EugC99L+0H4O3tDZ1OJ/ehVCrlTXX1ej1SU1Pdvo5ERERERP81z3UAPzO1a9cGABgMBjx48AAA5FQ5ABAVFeXQRipbv359Ls2SiIiIiPIrT4POrtLTOK+shKXyywD+Cc5nWh2AuWB1WAvXsSsXRVHe1Dbj5raCIEChUNiVSfUysl0lL216m/FmgG2/AOTAvBToB9KD9xn3A5DSEDGIT0RERETPIwbwXbh06RIAQK1Wo1ChQgCAs2fPwmAwIDAwEKVLl3ZoU69e+qPMJ06cyL2JEhEREVG+5OlWU4IgOE0nY5t6xrauvkwXGMt2RmZhf2kGVu8SSGvwOQSFAiqVyq6OtGFtxgC5SqWCIAgONxacPVlgu9GsIAgONwOc9SulDvIkzdBzvH0XERERET2nGMB3Ydq0aQCADh06QKvVAgCuXr0KAE6D9wDg4+ODggUL4tGjR0hKSsqdiRIRERFRvuTRivr/r6/T6eTV685SzxgMBoiiCIVCAasoQl/rXeirDIGo8nbeJwBz0cZIaTYToq4IRFGERqNx2BTWWUBe2oxWoVA4nIuzNDpqtVquazabnc7Htl9pXNt0lZnxJCUREdG/RVBQkMefF/nVggULIAgCxo4dm9dTeS6MHTsWgiBgwYIFbrfZtWsXBEFATEzMM5tXXgsPD4cgCEhISMjrqdjJqX8f+eE1FAQBQUFBHrXJr6/Lv4Uq6yrPn40bN+LHH3+EWq3GuHHj5PLk5GQAgLe38z+QgPQg/uPHj5GcnAw/P78sx6pRo4bT8osXL6JixYoezpyIiIiI8ouMQXJ360tpZKTc8xIppY2Ul/7/C2EM7gdj+e5Q39gK1Z19EIyJEBUaGH0qIK10J2gK2+/3JIoidDodjEajvFms1J8gCPImt9LYgiBApVI5TZ1jS6FQwMvLC6IoyivsbeeuUqnsgvcA5E1y3b1WFovF4QkCIvrvu6M3Yl7CXey5l4gkswV+KiVaBPpjQFBRFNNp8np6RET/Gbt27UKrVq3Qv39/j24O0bPF334zOHPmDF5++WWIooivvvpKzoUPwO6PGFf4WC8RERERAf+klXFn1bgUtE9LS4PVaoVGo4FarZaD+LbBe2nFul0wXOUNU7mXYCr3EgDIq/YBQGW12uWoByCvxJfGACCny5E2uZXGlMayWq2wWCxQKBR2NxikelIAXxrX2dwz/q7s6e/O/F2b6PmSZrHgnWMJWHjlHkwZ/v1vu/sE405fR/+gQEyuXR46JRMM0PPlzTffRO/evVGihPub2hPlhDNnzkCtVuf1NJ4rDODbuH79Ojp06IBHjx7hnXfewfDhw+2OSyvqU1JSXPaRmpoKAPD19XVrzL/++stpuauV+URERET07yClxElNTbVbCCKteJeC20qlElqtVg6QA/8Eqp0F3gHAbDZn+odTxnaAY0Ddtq5arUZqaqq8It/2HKSV8zqdDmazGQaDAXq93m5Ri0qlktNOSuWu5p7xGnniv5JmgoiylmaxoPPes9h9P9FlHZMoYu7luzifpMf6sKrw8vDJJ6J/syJFiqBIkSJ5PQ16DlWtWjWvp/Dc4S3q/3f//n20a9cOV69exYABA/D111871ClbtiyA9EC/MykpKXj8+DEKFizoVvocIiIiIvpvUyqV8Pb2hlKplFfPp6WlQa/XyznuLRaL/LW7bNPeuBpXqVRCpVLBbDbbjanX65GWlgaj0SjfQDCZTA4pe6RxTCaTHLA3m81QKBTy6n21Wg2tVis/DSAtZgEg35CwXeGfkbRy312epiUion+vd44lZBq8t7X7fiJGHk94thPKQkJCAgRBQHh4OBITEzF8+HCUKVMGOp0O1apVw5QpUzL9uQ0Ac+fORUhICLy8vFC8eHHExsbi8ePHTusajUZMmzYNDRs2hJ+fH3x8fBAaGooff/zR5Q3ToKAgWCwWTJo0CZUrV4ZWq0WZMmXw/vvvw2AwyHUNBgOKFCkCb29vPHnyxOn4cXFxEAQBbdu2zfLaxMTEQBAE7Nq1C5s2bUJYWBh8fX0REBCAbt264ezZsw5tbPOFnz9/Hr1790axYsWgUCiwZs0aud7GjRvRrl07BAQEQKfToUqVKhg9erTT62abM/6PP/7ACy+8gIIFC8Lf3x/t2rVDfHy8QxtRFLF48WL07t0blStXho+PD/z8/BAaGorvv//e6WtqO87JkyfRpUsXBAQEwMfHBy1btsT+/ftdXquff/4ZYWFh8Pf3h7e3N0JCQjBhwgTo9fpMx8koISEB0dHRKFy4MHx9fdG0aVP89ttvLsc1mUz44YcfEBoaKr/2QUFBiIiIwJIlS1y2y+i3337DwIEDUa1aNfj7+8PHxwe1a9fG+PHj7d5jEtvX+erVq+jTpw8CAwPh5eWFBg0aYP369S7Hmj17NmrVqgWdTodSpUph2LBhLt+vmcnO+0LiyfvPmU6dOkEQBGzdutXp8ZSUFPj7+6NAgQKZLiaWLFmyBBqNBqVKlbJbMHzv3j28++67qFKlCnQ6HQICAtCxY0fs3r3brn1MTAxatWoFAPjpp5/kpzMz5u7PLAd+dl6XhIQExMbGIigoCFqtFoGBgYiKisKJEyec1v/999/xwgsvoHTp0tBqtShZsiTCwsLw6aefZnmN/q0YwAeQlJSEjh074uzZs+jWrRvmzJnj9A+MKlWqQKvV4t69e06D+EePHgUAhISEPPM5ExEREdG/g+0KeyB9tboU+NZqtVAoFLBYLA6r2l2RAt5ZpZPRaDR2ee5tSYF5o9Eor/pXq9Uux5eC87apd6T/bIPqFosFKSkpMJvNLm8YZLw2Go17+aulmxJE9N93W2/Ewiv3PGrzU8I93NG7tyn2s2QwGNC6dWssXLgQoaGhaNeuHa5cuYJ33nkHgwYNctlu1KhRGDp0KPz9/dGhQweIoojZs2ejS5cuDj/DU1JS0LZtW4wYMQIJCQkICwtDeHg4/v77bwwePBivv/66y3H69u2Lzz77DKVLl0b79u2RlJSESZMm2c1Nq9Wif//+SEtLw6JFi5z2M3fuXADAkCFD3L42y5cvR6dOnWA0GtG5c2eULFkSq1evRuPGjXH8+HGnbc6dO4eGDRvi4MGDaNWqFdq1ayc/gTZhwgR06tQJu3btQv369REZGYnU1FRMnDgRjRo1wp07d5z2uX//frRo0QLXr19Hx44dUaVKFWzbtg0tW7Z0CKQaDAb06dMHW7ZsQdGiRdG5c2c0atQIf/31F4YOHYqBAwe6PN/Dhw+jcePGOHfuHNq0aYPg4GDs3r0bbdq0walTpxzqx8bG4pVXXsGRI0fQvHlzdOrUCbdu3cKYMWPQunVrpKWluXWdL168iEaNGmHJkiUoUqQIOnfuDKvVis6dO2PZsmVO2/Tr1w+vvfYaLl++jKZNm6JLly4oU6YM9uzZg1mzZrk1LgAMGjQIy5cvR4ECBdChQwc0b94c165dw4cffogXX3zRZVrBhIQENGzYEPv27UNYWBjq1q2LI0eOIDIyElu2bHGo/+677yI2NhYXLlxA69at0aRJEyxatAitWrVyeqPAHZ68L4Dsv/9svfbaawCAOXPmOD2+ZMkSJCUloU+fPvDx8cm0r5kzZ6Jv374oW7Ys9u7dK2f2OHv2LOrWrYtvvvkGFosFL774IkJCQrBjxw60atUKv/76q9xHWFgYXnjhBQBAxYoV0b9/f/m/OnXqZHk+2Xld9u7di9q1a2P27Nnw9fVFly5dEBwcjFWrVqFx48bYuXOnXf1Zs2ahQ4cOiIuLQ7Vq1dC9e3fUqFEDCQkJ/+0NtMXnnF6vF1u1aiUCEF944QXRYDBkWr9jx44iAHHKlCkOx9566y0RgDhp0qSnnlf16tXF6tWrP3U/RERERJS3rFarmJycLD558iTT/+7fvy/ev39fTExMtD/2+LH45OEd8cnjh3LZ3bt3xUePHsl1Hz16JD58+FB89OiR+OTJEzEpKUm8f/++ePv2bfHu3bvirVu3HP67e/eu+OTJE/HevXtyv48ePXJa/969e+L169fFBw8euJz/o0ePxNu3b4u3b98Wnzx5It6+fdvpmNKcExMTRbPZLJrNZsdzzvCfVJeIng/jz1wT1Sv2e/zfhDPX8mzOly9fFgGIAMSQkBDx3r178rG///5bLFmypAhAXLt2rV27cuXKiQDEEiVKiH/++adcfu/ePbFSpUoiAHH79u12bV5//XURgNivXz8xKSlJLr97967YqFEjEYC4YcMGuzbS3KpVqyZevnxZLr906ZIYEBAgAhD//vtvufzcuXMiALFOnToO5/ro0SNRp9OJhQsXFvV6vVw+f/58EYD4ySef2NXv37+/PP7s2bPlcqvVKr7//vsiALFevXp2baS+AIhvvvmmw2fAwYMHRYVCIfr5+Yl//PGHXK7X68UePXqIAMQePXrYtfnkk0/kPseMGSNarVb52Pfffy8CEEuWLCmmpaXJ5SaTSVy5cqVDrOju3btigwYNRABiXFycy3EmTpxod2zEiBHya2drxYoVIgCxVKlS4oULF+TyJ0+eiGFhYSIA8b333nM6zvz58+3K27dvLwIQ33jjDdFiscjlc+bMkefVv39/uVx67zZs2NDu3EVRFFNTU8X9+/eL7lq9erWYnJxsV5aYmChGRESIAMSffvrJ7pjt6zxs2DDRZDLJx6ZOnSoCEJs3b27XZt++fSIAsVChQuKpU6fk8vv374shISFyf7bv88xk532Rnfefs38fZrNZLFOmjKjRaMS7d+86zE3693zkyBG5bOfOnQ6v4bhx4+SfPbdu3bLrv2bNmiIAcdq0aXbndvToUbFw4cKij4+PeOfOnUz7zwiAWK5cObuy7LwuT548EYsXLy6q1Wpx+fLldv1t3bpV1Gg0YqlSpez+/ZUrV0709/d3eH2tVqu4Y8cOl3P+t3uuV+BbLBZER0dj586daN68OVatWpXlCqB33nkHAPD555/jwoULcvmBAwfwww8/wN/fP9O76kRERET0fLFYLG5tZKtQKP5ZLS+KUD48Aa+jn8FvU3v4b2oP/99awScuBuqEVVDDCJVK5ZCSR1rtbjAYIAiCvMpfq9XarZqXyqxWK8xmszw/hULhUF+j0UChUECpVGa66t9kMkEURTm9j1ardVgxb7FYYDQa7VILKZVKeHl5yRvjZky7Y1uXiJ4Pe+65lzono93ZbJfTvv76a7vc5BUrVsTHH38MAJgxY4bTNuPGjbNb4VqkSBF5Jb1tmou7d+9i7ty5KF++PObMmWO3/15gYCB++OEHAJD/n9F3331nl/qifPnyePnllwEAe/bskcsrV66MVq1a4dixYzhy5IhdH7/88gv0ej369+8v73/ijqZNm9qt2BcEAePGjUOZMmVw9OhRHDhwwKFNYGAgJk6c6PAZMH36dFitVowYMQKhoaFyuVarxfTp0+Hl5YWVK1fixo0bDn2WK1cOn376qd1TYa+//joaNWqEmzdvYvXq1XK5SqVCt27dHGJFgYGBmDBhAgBg7dq1Ts83LCwMo0aNsiv76KOPAMAhdcm3334LAPjss89QqVIludzf3x/ff/89BEHArFmz5I3sXbl48SK2bNmCgIAATJo0yS5N3eDBg9G0aVOHNnfv3gWQ/vrodDq7Y15eXmjSpEmmY9qKjIx0WCnu5+eHKVOmAHB9rSpUqIBvvvkGKtU/W3UOHToUAQEBiI+Ptztv6YmAkSNH2u0fWbhwYXz11VduzzUjT94XT/P+s6VUKjF48GAYjUYsXLjQ7tipU6fwxx9/oG7duqhXr57T9qIo4u2338bHH3+Mpk2bIi4uDsWLF5ePr1+/HqdOnUJ0dDTeeustu3OrW7cuPv74Y6SkpOCXX35x7yJlIjuvy7x583D79m28++67iIqKsjvWtm1bvPHGG7hx4wY2bNggl9+9exfly5d3SOEjCIKc/ue/6LkO4E+fPl3+B1ikSBG88cYbiImJcfjv/v37cpu2bdti+PDhePDgAerUqYPIyEi8+OKLaNGiBUwmE+bNm4dChQrl1SkRERERUT7jTvAeSP8jTqFQwGpIgtfBUfDZ/ybUN7dBsP7zyLEy6RK8Tk1FgV29INyOzzRvvdlslgMeSqXSadobqW3GHL629VUqVZapfTLepBBF0enNALVaLefQl+Yg7QEgnYsUxDebzQDg9EYAEf23JZnd+7mZU+1yUqFChdCuXTuH8j59+gBIT9Ph7GZo+/btHcoqV64MALh165ZcFhcXB5PJhA4dOjgNnteuXRt+fn44dOiQwzG1Wo3w8HC3xgFcp/eQvh88eLBDX5np3bu30zl1794dQHoqjYzatm0Lb29vh3LpZkPfvn0djhUtWhTt27eH1Wp1mm++e/fudoFiSXR0tMt5HDt2DJMmTcLQoUMxYMAAxMTEYObMmQBgt7jTlrPXtHDhwihcuLDdtTaZTIiPj4cgCPL7xFatWrUQEhKCpKQkl6mGJPv27QMAvPjii05Trjh7DapWrQofHx/Mnz8fc+bMwYMHDzIdIysXLlzAtGnTMGzYMAwcOBAxMTEYN26cfMyZ8PBwOTWSRKVSoUKFCjCZTHZzkl6fnj17OvTTvn37bMfkPHlfPM37L6PBgwdDpVLJaakk0r+zV1991Wk7s9mMmJgYTJ06FR06dMDWrVtRsGBBuzpS6p/IyEinfYSFhQGA058XnsrO65Kd+dWvXx/Hjx/H6NGjcfHixaed9r+G4zvzOfLo0SP5a9s7aRmNHTvW7u751KlTUadOHUyfPh1bt26FWq1GmzZt8NFHH8lvLiIiIiIiIOtc9bbUCit8D74P1aNTEAG4CpsLpmR4H3ofCJ0EY6F6dgF4Kae+2WyG0WiUN5h12o8bOffdqZfxBoBtfWe566X6FotFzq3vrK4oikhLS+MKfKLnjJ8qe//es9suJ5UrV85pub+/PwoWLIjHjx8jMTERBQoUsDteunRphzbS6nrb3NEJCQkA0vNdSwFkZ5zlSy9RooTTn6XOxgGArl27olixYvj111/xzTffwMfHBwcPHsSJEycQFhaGatWquRzfGVfXRlpJe/PmTYdjZcuWddrm5s2bEAQhW3160sZoNCImJgaLFy922gZI31fRGWevKZB+vW0D0g8ePIDRaETx4sUdVsDbzu348eNOz8eWdNzVdXNW7u/vjzlz5uDVV1/Fq6++itjYWFSpUgWtWrXCK6+8gsaNG2c6pkQURbz77ruYMmWKy987snOtAPv3pvTalylTxmmbsmXL4uHDh27N2ZYn74unef9lVLJkSURERGDNmjXYs2cPmjdvDoPBgF9++QXe3t5Ob+oAwNKlS2E2m1G7dm2sW7fO4QYI8M/Pi169eqFXr14u52C7cDm7svO6SPNr1KhRpn3bzm/GjBmIjIzExIkTMXHiRJQsWRLNmzdHVFQUunXrZvfUyX/Jcx3AHzt2bLY3OJBW5xMRERERZcbdIDkAeF38OcvgvdyvaIH30bGwtFkOpdp+daIUIDebzU7/oJMolUoIgmC3el5KlyORjknpcSwWS6bBdEEQsvzjSepLr9dneYNDquft7e3RtSSif6/mgf7YdveJx+1aBPo/g9nknMx+3rn78036mVy3bl2EhIR4NL6nP0PVajUGDBiAL7/8EsuWLcOAAQOytXltVjK7Lq4C2u7y5JydzWPy5MlYvHgxatasia+++gr16tVDQEAA1Go1zp8/jypVqjz1TXJP6mdVR5qLp2NHR0ejbdu2WLt2LbZs2YK4uDj5JtF7772HSZMmZdnH0qVLMXnyZJQuXRpTp05FkyZNEBgYCLVaDaPRCK1Wm2PXKjd5shAjI3fP67XXXsOaNWswd+5cNG/eHCtXrsTDhw8xYMAA+Ps7/7kWFhaGv//+G8ePH8eMGTMwYsQIhzrSz4uOHTuiaNGiLsevWrWqW/PMadL8evTo4fRJG4ltgD8kJASnT5/G5s2bsXHjRsTFxWHp0qVYunQpwsLCsH379izTo/8bPdcBfCIiIiKiZ83tleMWA9RX1gPIOngvEUxJUN/YDlO5znblUgBdSknjLKAuCIKct94277w0Z6VSKafGkYL60sp+KcWOs36llDtZBarc3RsA+CdFj7NH24nov2dAUFF8fvo6TJ48wSQIGBDkOkCVW65eveq0PDExEU+ePIGPj4/LgJw7pJXK4eHhmDx5crb7cderr76KiRMnYs6cOejRoweWLFmCAgUKoEePHh73deXKFafl0jUrWbKk232VLFkSly9fxpUrV1ClShWXY5UoUeKp5iFla5CC+LYuXbrk9nwzU7hwYWg0Gty+fRtpaWnw8vJyOWdn52NLmntW5+hMYGAgBg8ejMGDB0MURfz+++/o1asXvvrqK8TExKB69eqZji1dq5kzZyIiIsLuWE5dKyD9GiQkJODatWt2+wVIMjvHzHjyvnia958z7du3R4UKFbB8+XJMmzZNTp+T2Y2ycuXKYe7cuWjZsiXefvttKJVKDBs2zK6O9PPitddeQ5cuXdyaS3Zl53UpXbo0zp07h48++sijG5I6nQ6RkZFy6p3Tp08jOjoae/fuxY8//ijvH/Jf8t98roCIiIiIKJ9wlkLGGdXt3VCYPF9xqrm6zq0xbTeIFQQBRqNRTrGTcZW+2WxGamqq3Way3t7ecsDeNm898M8NA6VSKafssVgsMJlM8n+2wXrpZoAnPK1PRP9exXUavFIu0KM2/YMCUUyX96suHzx4gG3btjmUSylYmjZt+lSrjVu1agWlUokNGzbkys/F8uXLo3379jhw4AA+/vhjJCUl4eWXX3YaZM7K0qVLHcrMZjNWrlwJAGjWrJnbfTVv3hwAsGjRIodj9+7dw5YtW6BQKJxu2rpy5Uqn127JkiUO85BSLztLC7Js2TK355sZtVqNxo0bQxRFp6l6Tp06hePHj8PPzw+1a9fOtC9p7hs3bkRKSorDcekcsyIIAjp06IBOnTrJc8hKblwr4J+86MuXL3c4tnXr1mylzwE8e188zfvPGUEQMGTIEKSlpeHTTz9FXFwcatSokeUGwhUrVsTOnTtRsmRJvPXWWw5ptdq2bQsAWLNmjVvzACCvXpf2InJXdl6X7MzPmerVq2Po0KEAgJMnTz5VX/kVA/hERERERM+QIAjQ6XRZBmyUSdlbnaZIuuy0XNo01mw2Iy0tDXq9HgaDAQaDAUlJSTAajfIqep1OB7VaLc9RWvFusVig0Wig0+lgtVrt6kmb5QL/BO51Op0c3DcYDHYBfKlMEAQolUqPH0l/mkfYiejfZ3KdILQo4t5K9RZF/DG5dvlnPCP3vffee3Y5zi9fvixv4vnGG288Vd+lSpVCTEwMLly4gH79+jnNXb1//35s3LjxqcaxJW1mO3XqVACeb14r2bdvH+bNmyd/L4oiPvnkE1y9ehW1a9d2O9gJAEOHDoVCocC0adNw+PBhudxoNGLYsGFITU1Ft27dUKpUKYe2V65cwaeffmpXNnv2bBw4cADFixdH165d5XJpg99Zs2bZ1V+xYgUWLlzo9nyzIq2c/uSTT+xWqyclJeHNN9+EKIqIjY3NMjVIpUqV0KZNGzx69AijR4+2e7pu/vz5TjdV/fPPP7Fq1Sr5M13y6NEj/PHHHwBc59S3JV2r2bNn231m79mzB1999VWW7d0VGxsLID290ZkzZ+Tyhw8fYtSoUdnu15P3xdO8/1wZOHAgNBoNpk6dClEU3U5TFRwcjJ07d6JEiRIYOnSo3abTUVFRqFq1KhYsWICJEyc6vMZGoxGrVq2yC3pLTxqcO3fO7bkD2XtdYmNjERgYiPHjx2P+/PkOv+ulpKRg4cKFuH79OgAgNTUV3377LR4/fmxXz2q1YsuWLQDce6/+GzGAT0RERET0jCmVSnkjVilIbrsiXqFQQK3IZoDaagKcBLelVe5Wq9XuDyJRFGE2m+UAvRRM12g08PLyklfk234ttbetp9PpoFKpoFKp4O3tDX9/f1itVhgMhkxXhQqCID8F4In8nB+XiHKel1KJ9WFVMbh8Uahd/PtXCwIGly+KDWHVoFPmj/BG48aNoVAoEBwcjKioKHTp0gU1a9bEjRs38PLLL8spH57Gt99+i1atWmHx4sWoUKECWrRogd69eyM8PBylS5dGs2bN5GBWTujcubMciGzYsCHq1KmTrX5ef/11DB48GI0aNUKfPn1Qq1YtjB8/Hn5+fpg/f75HfYWGhmLcuHFITExEkyZN0K5dO0RHR6NSpUpYunQpgoODMX36dKdthwwZgi+//BI1a9ZEnz59EBoaitjYWKjVasyfP9/u6YJRo0ZBqVRi9OjRaNCgAfr06YOGDRuiR48eePvtt7N1HZyJiorCq6++iuvXr6NmzZqIiIhAz549UbFiRcTFxaFx48YOwWVXZs6cicDAQEyfPh3Vq1dHnz590KRJEwwaNEi+GWPrypUr6N69OwIDA9G2bVu8/PLLiIiIQFBQEC5duoSuXbu6tZHtW2+9BR8fH3z//feoWbMmoqOj0aJFC7Rs2dLpuNkVFhaGESNG4P79+6hbty4iIiLQo0cPBAcHA4Dbm+5m5Mn74mnef64ULVpU/vmg1WrRr18/t9tWrlwZO3bsQNGiRREbGyvfKFOpVFi9ejXKlCmD0aNHo1y5cujQoQN69uyJJk2aoFixYujevTsuXrwo9xUUFISQkBAcPnwYoaGhGDBgAAYPHox16xyf+LSVndclICAAq1evho+PDwYOHIjy5csjIiIC3bt3R8OGDVGsWDH0799fvlFpNBoxfPhwFCtWDE2aNEF0dDS6d++O8uXLY+XKlahQoYJ8I+G/Jn98whERERER/ccplUpotVoAkAPoUnDdarXCrPTLVr+ipgCQIbglpcgRBAFarRZarVZeka9QKOTAu9VqletlXPVkG+h3RQrGA7DrN2OwXRAEqNVqeSW/Xq/PcqPbjNzeS4CI/jO8lEp8X68iLr1YD5/VKIO2RQugUSFftC1aAJ/VKINLL9bD9/Uq5pvgPZAeeNuxYweio6Nx4MAB/P777yhTpgy+/vprLFiwIEfG8Pb2xpYtWzB37lzUq1cPp06dwurVq3Hx4kVUrFgRkyZNwrvvvpsjYwHpP39btGgB4Ok2r+3ZsyfWrVsHpVKJtWvX4vr163jppZcQHx+PunXretzfmDFjsGHDBrRs2RKHDh3CqlWroNVqMWrUKPzxxx8oVqyY03ZNmzZFXFwcihcvjg0bNuDMmTNo06YNdu3ahQ4dOtjVbdGiBfbu3YvWrVvj0qVL2LBhAzQaDVauXCmn7MgpP/zwAxYuXIi6desiLi4O69evR9GiRfHFF19gx44dmW7yaSs4OBjx8fHo2bMn7t69i7Vr10IURaxZswa9evVyqN+4cWN8/vnnqF+/Ps6dO4fly5fj8OHDCAkJwU8//eR2+pvKlSvj0KFD6Ny5M+7fv49169YhOTkZP/zwQ46uwAfSV3nPnDkTlSpVwtatW7Fv3z707NkTO3fulH/X8pQn7wsg+++/zLRp0wYA0L17dxQqVMijtlWrVsXOnTsRGBiIIUOG4KeffpLLjx07hrFjx6Jo0aLYu3cvfvvtN9y7dw8tWrTA/Pnz5VQ2kpUrVyIyMhKXLl3CwoUL8eOPP+Lo0aNZziE7r0uzZs1w8uRJjBw5El5eXtixYwe2bNmCxMREREREYOnSpfL+C76+vpgxYwYiIiJw7949rFu3Djt27EBAQADGjRuHI0eOICAgwKPr9m8hiHwWNV+qUaMGAOCvv/7K45kQERERUU6wWCxITU11mQpGkXwVvrtehgj3N7EFAGPZLtCHOAZq9Ho9NBqNQ6BcSmkjEQTBblVZxuNarVYOnks3Bsxms3we0o0B26C9dFNCFEUIggCFQuFwk8DLywtGo9GtHM7SEwxchU9E+VVCQgLKly+Pli1bYteuXXk9nRyVmpqKkiVLwmKx4ObNm/Dz8+yGc0xMDH766Sfs3LkT4eHhz2aSbhg7diw+/fRTzJ8/HzExMXk2D8pf8tP7on379ti6dWue/1uh/Cf/3KYmIiIiIvqPEkURer0+0zzuVt+yMBep73bwXurJGBQJAHapeaS0PO6s1ZGC7a5I/QqCAL1eD5PJ5LRfo9EIvV4vf69QKKBUKuUbCBnbmEwmKBQKebNbV4F8d/cQICKiZ2PGjBl48uQJ+vfv73Hwnojcc/DgQWzbtg01atRg8J4cqPJ6AkRERERE/3VSvvmsGKoMgvLBCQiiKdN60ip9U6l2QMHKUCkUchBcCtybzWaYzWYolUo5dQ4Ap6lrbIPrtsdtV8+7Wi1vW99iscBoNEKj0bi8eWC1WuW5KpVKqFQqGI1GGI3G9L0AbOaqVCqh0+mYPoeIKJc9ePAA77//Pm7fvo1NmzbBz88PH3zwQV5Pi+g/Z/To0bh69Sp+++03iKKI8ePH5/WUKB9iAJ+IiIiI6BlzJ3gPAJaAmkir9zG8/hwHweo6iC8AMAc2QlrIKAiiiJSUFKjVaqhU6b/e266ol3Lta7VaeVW8tMGt3J/N6nbb41I+e+mGQEZSXVtmsxlqtdrpvKVNbkVRlOtIG+Oq1Wp53lJaHttNfz0liqJ840RK5SPNl6v5iYgyl5SUhB9//BEajQb16tXDN998I29kS0Q5Z8mSJbh27RqCgoIwadIkdOnSJa+nRPkQc+DnU8yBT0RERPTfodfrYTQa3a6vTT4P7fkFEG/ugwD7X9etuqIwBnWFsUIvCEo1jEYjTCaT3Sa5AJCWlma3Ct72uMVigcFggMViscuBLwXjpVXyOp1OTrFjmx4HgLxBrrRaXuoTAHQ6ndOV/s7GdOZpc95bLBbo9XqnN064qp+IiIiI/k24Ap+IiIiI6BnzOBBduBYQPh2WxwkwJ2yDYEqEqNDA6l8J5qKNACE9+Gy7Ml5abS6tMFepVHab0UrHBUGA2WyW56T4//Q7VqtVTrejVqvh7e0tp7rJuOYnY1oeqUxaue9sjZBtGiHblf3OSHWlJwo8kdVmwdJxb29vBvGJiIiIKN9jAJ+IiIiI6BnzNFAs1bd4lYCxQk+X9axWq12gWgrCS2lprFar3Sp025QyAKDRaKDT6RzSzKjVaoiiKG9CKwiCHPyX0vA4I6XBcXbDQkqPo1QqM82RbztXTwP47mwWbFvvaVb5ExERERHlBgbwiYiIiIieMWd5521Jq9Gl4Le0qt6dQHRmx3Q6HYxGI8xms91qfWmFvhRIl1bSS6lyXG1EKwX0XVEoFNBqtVCpVHZ5+KUx1Wq1W8H7rM7NFXc3C7atm51V/kREREREuYW/rRIRERERPWOCIECn0zlN7SIIghxkBwCtVguj0Qij0SgH110Fs7NaPW67QazUv0KhkFfVZ1y9L20wa7Va7TaiFQQBgiDAYDDY5b0H/tkkV6LRaODl5SVvIiut7Jfm4G5gPjsr490N3tvWZwCfiIiIiPIz/rZKRERERJQLpI1ZbTdXFQRB/t5ZXnmr1Qqj0ShvJptRxkC8s9Xxtu1sg9UZ+zOZTHKZFMSX+rNNyWMymaDVauWvbYPmUmodvV4PnU5nt6mu2Wy2y8mflezkp/d01X52VvkTEREREeUm18+/EhERERFRjpKC+N7e3nIQXEo7k3Flu1QfAIxGo9MV6VIqHKluZkFvaXNbZ5ylnskY3JZS8igUClgsFhgMBrsbEWq1Wr7RIG0UmzG4725Q3pO6tjxdtc/890RERESU33EFPhERERFRLrINukuB78yo1Wo5nU5G0sp4qZ60qa202ay0Ol8ax9UK+Iz56qW5ORtPuvGg1WqdjmVb13aj2MzSCGUcV6fTZSu4nt3NgomIiIiI8isG8ImIiIiI8oC7+doVCoUcpHd1XAryS3nugX9uFOh0OnllvLspbKSgvDNWqxVms9khd7yzoLy0sl/awFf62nYzXdt2SqUSOp0u24H1rDYLdlaXiIiIiCg/YwCfiIiIiCgPeJJ/XQrSq9Vqu41hpY1lgfTNY5VKpd1qeoVCIX8vrZI3Go12x50FsVUqlUNwXSKt8neXyWSyS7cjsVgs8s0JaR6ZpflxR26s8iciIiIiyk0M4BMRERER5QFPg8cKhQIqlUpe+S6KokOg2tmqcqmetKLearVmmpteqVRCo9G4DICLouhydX5GVqsVBoPBaX1pTLPZDG9v7xxbDe9ss+CMx59mlT8RERERUW7iJrZERERERHngafO1O9t41hmr1Yq0tDR55b5Op4NarZZvIFgsFphMJqhUKruNaAXDI6ivboDmwkJo/v4Vqjv7AavZo9Qz7qTskXLle7KqPysZNwvWaDTQarVyGYP3RPRvJggCgoKC8noaOSomJgaCIGDXrl125eHh4RAEAQkJCXkyL8pbQUFBfFqOCFyBT0RERESUJzzJ1y4IAiwWC8xmMwRBsMsjnxWTyQSr1Qqr1QqFQiFvfJtx01s5fU3iFWgvLID61i4IVvsAvFUXCGvF7kgtGwUoXeflB9JvDEhjZkW6GZExr/7TkPYAyMk+iShvpOIOzmAubiEOJiRBDT+URDiqYhC8USyvp0dERPRM8bdZIiIiIqI84E6+dindjUajkXPdS0RRzDKdje0qfdsxpK9t24qiCF3SGaj2vgPBnOy0P4X+HhR/zYLP7XikNPwSUHm7HNtqtWaaS9/ZXBlsJyJbZqRhH4bjHBbACvsbitexFYcxFlUwAM0wDSro8miW9CwtXLgQqampKFWqVF5PhYgozzCFDhERERFRHpFSvThL6SKtmneVj14URRgMBrtNa531IcnqEXRF8jWo9rkO3tvN+8ExeB8dC2QSmJc2qHU3NU5OptAhon8/M9LwGzriDOY4BO8lVphwBrPxGzrAjLRcniHlhrJly6Jq1apQq9V5PRUiojzDAD4RERERUR5ylq9do9HIOeldBbal1fPu5JkXBCHLVDaavxdCMGUdvJeo7sZD/ei402PSRrGeBOWZ45aIbO3DcNxCnFt1byEO+zDi2U4oCwkJCRAEAeHh4UhMTMTw4cNRpkwZ6HQ6VKtWDVOmTHF5w/XatWuIjY1FuXLloNVqUbRoUXTr1g2HDh1ya+zly5dDEAT07dvXZZ0BAwZAEAT88ssvduUpKSmYMGEC6tWrBz8/P/j6+qJ69eoYMWIErly5AgAYOnQoBEHAnDlznPYtiiIqVqwIpVIpt5FcvXoVb775JoKDg6HT6VC4cGGEhoZi/PjxSEvL+qZLZjnw79+/jw8++AA1a9aEj48PChYsiDp16uDDDz/EgwcPsuw7O/2kpqZi3LhxqFmzJry8vFCgQAG0aNECS5Yscdq3bQ73GTNmyO3Kly+PSZMmyZ+TR48eRUREBAoVKgQ/Pz9ERkY6XEvAfq+ATZs2ISwsDL6+vggICEC3bt1w9uxZl+f5888/IywsDP7+/vD29kZISAgmTJgAvV7vUDez6277Xrc1duxYCIKABQsW4OTJk+jSpQsCAgLg4+ODli1bYv/+/U7nZTabMWHCBPk9UqFCBXz88ccwGo0uz+WPP/5A165d5X8zxYsXR2hoKD744AMkJ7v/uwzRvwUD+EREREREeUzK167VaqHT6aBSqeT89Fm1cWczWymVjcu+jI+hvrnD43nrrq11uVGsp6slubEsEUlScRvnsMCjNucwH6m482wm5AGDwYDWrVtj4cKFCA0NRbt27XDlyhW88847GDRokEP9kydPol69epg9eza8vb3RrVs3BAcHY/Xq1WjatCmWL1+e5ZiRkZEoXrw4Vq5ciYcPHzocT0xMxPLly1GwYEFERUXJ5bdu3UJoaCjGjBmDK1euoHXr1ujQoQM0Gg2+/fZb7Ny5EwDw2muvAYDLAP6OHTtw6dIltG/fHuXKlZPLd+/ejZCQEMyYMQNWqxUvvfQSmjRpgvv37+PDDz/EnTvZf71Onz6NOnXq4Msvv8TDhw/RoUMHhIeHw2AwYPz48Th58mSO95OUlIQWLVrgf//7H+7evYuIiAg0a9YMBw8eRHR0NEaMGOFynLfffhvvvvsuAgMD0bZtWzx48ADvv/8+xo4di3379qF58+a4fPkyWrdujeLFi2Pt2rVo06aNy5scy5cvR6dOnWA0GtG5c2eULFkSq1evRuPGjXH8uOPN9djYWLzyyis4cuQImjdvjk6dOuHWrVsYM2YMWrdu7dbNFHcdPnwYjRs3xrlz59CmTRsEBwdj9+7daNOmDU6dOuVQPzo6GmPGjMHt27fRoUMH1KpVC5MnT0a3bt2c/h7022+/oWnTpli/fj2CgoLQrVs31KlTB/fv38eXX36J+/fv59i5EOUXTDJJRERERJTPuLOxrbQZrZRqx1kAXKFQQKlUZpnKRnXngMOGtW65HgdYTHC2LsiTTXqlukREAHAGP7pMm+OKFSacxY+ohzHPaFbuiY+PR0hICC5cuIAiRYoAAC5evIgWLVpgwYIF6Nq1K7p06QIg/ed437595RXgX3zxhXyzdcWKFejVqxcGDRqEFi1aoFgx15v1qtVqDBw4EOPHj8fPP/+M4cOH2x3/9ddfkZKSgmHDhkGn+2evgH79+uH06dOIjo7GnDlz4OPjIx+7cOGC/PO7Vq1aaNq0Kfbv34/jx4+jdu3adv1Lgf0hQ4bIZY8ePUJUVBSePHmCKVOmYPjw4XY3knfv3o2AgAD3L6wNs9mM7t2748aNGxg5ciQmTJhgd9P4zz//RGBgYI73M2bMGBw5cgRt27bF6tWr4evrCwA4e/YsWrZsiWnTpqF9+/Z48cUXHcZavnw5Dh8+jBo1asht6tSpg6+//hoLFy7EF198Id8AMBqN6NixI3bs2IElS5ZgwIABDv19//33mD17tnzNRVHEBx98gIkTJ2LgwIE4cuSIXHflypWYPXs2SpUqhV27dqFSpUoA0m/sdOrUCXv37sUnn3yCSZMmZXnN3DFjxgxMnDgRo0aNksvefvttTJ06FZMmTcLChQvl8sWLF2PFihWoUKECdu/eLe91cPnyZbRo0QLXr1936P+rr76CKIo4ePAg6tevb3fs4MGDKFy4cI6cB1F+whX4RERERET5jCd546UV+xKLxQKTyQSz2QyFQgEvL68s+xGMj7M1T0E0Iy3xDgwGA4xGIwwGA1JTU5Gamgqr1QqdTpdlahxpM1+m0CEiibupczK6iV05O5Fs+vrrr+XgPQBUrFgRH3/8MYD04KZk165dOHnyJMqXL49x48bZ/RyMiopCZGQkkpKSMH/+/CzHHDJkCBQKBebOnetwTCobPHiwXHbw4EFs374dxYsXdwjeA0BwcDCqVq0qfx8bG2vXl+TBgwdYs2YNihUrhs6dO8vlc+bMwb179xAREYERI0Y4/Ixv0aIFChQokOV5ObNq1SqcPXsWISEhmDRpksMTX3Xr1kXp0qVztJ+UlBT8+OOPUCgU+P777+XgPQBUrVoVH330EQDg22+/dTrWuHHj5OC91KZTp05ITU1F2bJl7VbvazQa+SZMXJzzfwtNmza1u2EiCALGjRuHMmXK4OjRozhw4IB8TJrTZ599JgfvAcDf3x/ff/89BEHArFmzMk1Z44mwsDC74D0A+frs3r3brnzmzJkA0q+P7UbF5cuXl//NZHT37l0UKFDAIXgPAKGhofDz83uq+RPlRwzgExERERHlM54Es6Ugvu2qSqVSCZVKBYVCAbPZDJPJlHmfiuxvDig6aWuxWJCamgoALjfpleaZ2XEiej6ZkJSr7XJSoUKF0K5dO4fyPn36AAD2798v36Tds2cPAKBXr15Ofw7269fPrl5mgoKC8MILL+DUqVOIj4+Xy//8808cOXIEjRo1QkhIiFy+bds2AEDfvn0dgvfO9OzZE4UKFcIvv/xil25l4cKFMBgMiImJsQuAS/1Lgf+cJPUt3bTIjX6OHDmCtLQ0hIaGIjg42OG49Frt27fP6U14Z++JChUquDxWsWJFAOlpjpzp3bu3Q5larUb37t0BAHv37gWQvk9OfHw8BEGQ34O2atWqhZCQECQlJTlNvZMd7du3dygrXLgwChcubHc+JpMJf/zxBxQKhV1qJ0l0dLTT/uvXr4/Hjx9j0KBBTlPyEP0XMYBPRERERJTPeBrQFgQBBoNBbisFIkRRhEKhgEKhgNFodBrEVyqVUBepkq15WrVFAJWv02OiKEKv10OhUDhs0mubK5/BeyLKSI3sraDNbrucZJsD3pa/vz8KFiyI5ORkJCYmAgBu3rwJID347oxULtXLihQst81V7yy9DZC+cS7wT6A4KzqdDq+88goeP36MFStWyOVz586FIAgO+f097d8TOdW3J/1k9VoVLFgQBQoUsHt9bdmuLpdIN04yOyZ9tmfk6n2W8T3z4MEDGI1GFCtWzO5Gf2Ztnparpx98fX3tVvnbzk2j0TjU9/PzQ8GCBR3Kx48fj9q1a2PevHmoVasWAgMD8dJLL2H+/PkurxfRvx0D+ERERERE+YwnOeGVSiVMJlOmaXcUCoW8MtI2iO7l5QWNRgNz4bqwepf0eJ6mshFAJiv7pQ12M27Sq9Vqs9xYl4ieXyXQMlvtSiI8ZyeSw1z9nHYn1Zg7IiIiULp0aSxduhRJSUlIS0vDr7/+Cj8/P/Tq1eup+gb+2cxWSqOzf/9+nD59GuHh4U5XpXvav6dyqm9P+nGnrrM6mW4kn4PXKLvvMU/mYbVac6Qfaa6enn+ZMmVw+PBh/P777xg2bBhKliyJ9evXY+DAgahTpw4ePXrkUX9E/wYM4BMRERER5TPu5oUXBAFqtdqtjWKB9D+WlUqlnDffaDQiLS0NBqMZxnKR6XXcnKMoqGEs2znLeu7OjYhIUg2DoIBnqb0UUKMqBmVd8Rm7evWq0/LExEQ8efIEPj4+8Pf3BwCULJl+4/Ty5ctO21y5cgUAUKJECbfGViqVGDx4MFJSUrB48WIsW7YMT548QXR0tF3OdiA9CAoAf//9t1t9A0CVKlUQHh6O3bt349y5cy5X92e3f3flVN+e9JPVa/XkyRP59c2NHOzSeyMj6f0nzbdw4cLQaDS4ffu2XeojZ33Zvs+kFfHJyckO9aUnF55WkSJF5Lk5y7+flJSEx48fO22rUqnQvn17fPvttzh+/DgSEhLQunVrnD17Fl9++WWOzI8oP2EAn4iIiIgoH8oqP7x03N0NbyXSqvjU1FS74LqxfBTMgaEQ4F4QXx/yLkSvwCzreTo/IiJvFEcVxHjUpgoGwBvFns2EPPDgwQM5t7qtxYsXA0jffFS6Odu8eXMAwNKlS53e7Pzll1/s6rlj8ODBUCqVmDNnTqYB9rZt2wIAFi1aJO9Z4g4pTc/kyZOxbNkyFCpUCN26dXPZ/+zZs93u211S33Pnzn2qzxhP+qlfvz68vLxw8OBBXLhwweG49FqFhYXlytNlS5cudSgzm81YuXIlAKBZs2YA0vPiN27cGKIoyu9BW6dOncLx48fh5+eH2rVry+VSMP/8+fMObbZs2ZIj56BWqxEaGgqr1SrP29aSJUvc7qts2bJ4//33AQAnT57MkfkR5ScM4BMRERER5VNSkD6z/PGeBi+k3PQO7RQqpDb4HKbiLZFZ6EFUqJFWZwxMZTq6NR7T5BBRdjTDNLdT6ZRASzTDtGc8I/e99957ePDggfz95cuXMW7cOADAG2+8IZeHh4ejVq1auHz5Mv73v//Z/Vxes2YNVq1aBV9fX8TExLg9dqlSpRAREYHDhw9j3759qF27Nho0aOBQLzQ0FK1atcLt27cRGxvrEMT/+++/cfbsWYd23bp1Q2BgIGbPno3U1FS88sor0Gq1DvUGDx6MIkWKYP369Zg+fbrDZ86ePXvw5MkTt88r4xwqV66M48ePY/To0TCbzXbHjx07huvXr+doPz4+Phg4cCCsViuGDh2KlJQUud758+fx+eefAwCGDRuWrXPy1L59+zBv3jz5e1EU8cknn+Dq1auoXbs2mjZtKh+T5vTJJ5/g0qVLcnlSUhLefPNNiKKI2NhYuzz0LVum/9v75ptv7N4b27Ztw9SpU3PsPKQbQv/73//sNri9cuWK/G8moylTpuDOnTsO5Zs3bwaQHswn+q9hAJ+IiIiIKB/LKn+8pwFyURRdp7VR6pBW/zOkNJ0BY6l2EBU2m8p5FwNqvQFLp/Uwle7g9njcpJaIskMFL3TCJlTDqy7T6SigRjW8ik7YDBWcb9CZ2xo3bgyFQoHg4GBERUWhS5cuqFmzJm7cuIGXX34ZkZGRcl1BELBo0SIULlwY48ePR40aNdCnTx+EhYWha9euUCgUmDdvHooXL+7RHKSgKAC8+uqrLuv9/PPPqFy5Mn755ReULVsWkZGR6NGjB+rWrYvKlSsjPj7eoY1Go8GAAQPk7wcPHuy070KFCmHZsmXw8/PDsGHDEBwcjF69eqFz586oUKECWrRoke1c5SqVCitXrkTx4sUxadIklCtXDj169EDXrl1RvXp11K1b1620OJ72M2HCBNSvXx9bt25FhQoV0LNnT3Tq1Am1a9fG7du38dZbb6FTp07ZOidPvf766xg8eDAaNWqEPn36oFatWhg/fjz8/Pwwf/58u7pRUVF49dVXcf36ddSsWRMRERHo2bMnKlasiLi4ODRu3BiffvqpXZvo6GhUqVIF+/fvR7Vq1RAVFYVGjRrhhRdesLsJ9bT69u2Lrl274u+//0aVKlXQtWtXREZGokaNGqhVq5bTYPynn36KkiVLol69eujVqxd69uyJqlWrYsqUKShSpAjee++9HJsfUX7BAD4RERER0b+YpwHyLFfsCwIshWpBX/djJHXcAkPnrUDPA8BLm4Far0LpW8yjDXYZwCei7FLBCy3xA17GNYTiC5RGOxRDY5RGO4TiC7yMa2iJH/JN8B5I3yh8x44diI6OxoEDB/D777+jTJky+Prrr7FgwQKH+rVq1cLRo0cxZMgQJCcnY8WKFTh37hwiIyOxb98+9OjRw+M5tGzZEkqlEl5eXujbt6/LeqVKlcKhQ4cwduxYlChRAlu2bMHvv/8Oo9GIESNGoHXr1k7btWnTBkB6OqAaNWq47L9Vq1Y4duwYXn31VZjNZqxZswbx8fEoWrQoJkyY4PGNCVs1a9bEsWPHMHLkSPj4+GD9+vWIi4uDVqvFRx99hJCQkBzvx8/PD3Fxcfj0009RpEgRrFu3Dnv27EGDBg3w66+/Ytq03HsKpGfPnli3bh2USiXWrl2L69ev46WXXkJ8fDzq1q3rUP+HH37AwoULUbduXcTFxWH9+vUoWrQovvjiC+zYsQPe3t529b28vLB9+3ZER0cjKSkJGzduhNVqxdKlSzF06NAcOw9BELB06VJ88cUXCAwMxMaNG3Hs2DEMGzYMq1atcrpI4bvvvkPv3r2RmpqKTZs2YfPmzVAqlXj33Xdx4sQJVKxYMcfmR5RfCCKTUuZL0ofgX3/9lcczISIiIqL8TBRFh3z2riiVSigUCphMJrf712g00Onsg2NSDv3M/pQQBCHTHP5ERP8lCQkJKF++PFq2bIldu3bl6Vx+/fVX9O3bF/3793d60+Bpvfrqq5gzZw7mz5/vUXofenoxMTH46aefsHPnToSHh+f1dIgol3AFPhERERHRv5ggCNDpdFmm0pHqKRSe/QngrF93N9hl8J6IKHeZTCZMmjQJAHJ0pbTkypUr+OWXX1CkSBH06tUrx/snIiJHqryeABERERERPR0pYK7X652uxFcqldDpdNna9DarIP3/sXfn8TFd///AX3cmyWQjIiKriEjEThD7kliLiAixJErQWqqKqqWqRfVLtZ/aqqXaokrtayxFSWy1xL5TaxBqiSDrTGbO7w+/uc2YSTIhkbRez8fDo3XOuee+752Z4H3PvI9Wq4VWq4UQApIkyWVzuHktEdHrs2nTJmzYsAFHjhzBuXPn0KVLFwQGBhbY/F9//TVOnz6NnTt3Ij09HV9++SVsbGwKbH4iIsoZE/hERERERP8B5ibU9b83t+RObqvo9RvsWljwnxVEREXp+PHjWLRoERwdHREVFYW5c+cW6PxbtmzBnj174OHhgcmTJ2PYsGEFOj8REeWMNfCLKdbAJyIiIqLCwhr2RERERET/DqyBT0RERET0hmENeyIiIiKifwd+15WIiIiI6A3EGvZERERERMUfE/hERERERG8o1rAnIiIiIireWEKHiIiIiIiIiIiIiKgYYgKfiIiIiIiIiIiIiKgYYgKfiIiIiIiIiIiIiKgYYgKfiIiIiIiIiIiIiKgYYgKfiIiIiIiIiIiIiKgYYgKfiIiIiIiIiIiIiKgYYgKfiIiIiIiIiOgV3LhxA5IkISgoqKhDIfrX4efnvyc6OhqSJCEuLq6oQ/lPsCjqAIiIiIiIiIiIcpT+CLi6Drh/DNCkAZa2gEs9wKcLYONU1NERvVFu3LiBChUqoEWLFkzOEr0mTOATERERERERUfGTlQEc+wq4vgnQZRn23TsEnJkP+HQG6o4BlKqiiZGIiKiQMYFPRERERERERMVLVgYQN/T5qvuc6LKAK2uBpzeAoO8AC+vXFh4REdHrwhr4RERERERERFS8HPsq9+R9dvePAce/Ltx48iE9PR3jxo1D+fLloVKp4Ovri+nTp0MIYXL8+fPnERUVBTc3N1hZWcHDwwN9+vTBpUuXjMbGxcVBkiRER0fj/v37GDBgAFxdXWFvb4+mTZvizz//lMfOnz8fNWvWhI2NDcqVK4fJkydDp9O9cgwAIITAvHnzUL16dVhbW8PT0xMjRozAs2fPEBQUBEmScOPGDaPjbty4gUGDBsHb2xsqlQrOzs7o1q0bTp8+bTR28eLFkCQJkyZNQkJCAiIjI+Hs7AwbGxvUq1cPMTExJmMDgDNnziAqKgoeHh5QqVRwd3dHv379TMYEAFlZWfj2229Rt25d2Nvbw97eHvXr18e8efOg1WqNxnt7e0OSJJNzZX+NstNoNPjhhx9Qv359lClTBra2tvD29kZISAhWrFiR47Vkl71WfGpqKj788EOUK1cONjY2qFOnjsE9Wb16NerXrw87Ozu4uLjggw8+QHp6usl5U1JS8Pnnn6NGjRqwtbVFyZIl0aJFC2zYsMFg3KRJk1ChQgUAwJ49eyBJkvwr+/Xu27cP77//PmrWrAlHR0fY2NigcuXKGDduHJKTk826VnNoNBp8//33aNKkCUqVKgVbW1tUqlQJ7777Ls6ePWs0/tdff0XTpk1RsmRJ2NraombNmpg2bRoyMjKMxmav4f7HH3+gRYsWKFGiBMqWLYt3330XT548AQDcv38fgwYNgru7O6ytrVG/fn2TpYWyv58vX76Mrl27wsnJCXZ2dmjSpAm2bt2a43UePHgQnTt3hrOzM1QqFby9vfHee+8hMTHRaOykSZMgSRIWL15sci5T793s79mkpCQMGTIEbm5uUKlUqF69OhYuXJhjbGvXrkX9+vVhY2MDFxcX9OnTx2Rcerdu3cLQoUPh7+8PW1tblC5dGtWqVcOgQYNy/HlDAAQVS1WrVhVVq1Yt6jCIiIiIiIiIXq+0B0IsryvEslrm/1peV4i0h0UUsBDXr18XAESjRo1E06ZNhaOjo2jXrp1o166dsLa2FgDEJ598YnTcH3/8IWxsbAQAUadOHdGzZ09Ru3ZtAUDY29uLvXv3GoyPjY0VAERoaKjw8fER7u7uIiwsTDRo0EAAELa2tuLs2bPigw8+ENbW1qJFixYiJCRElChRQgAQ48ePf+UYhBDigw8+EACESqUSHTp0EF27dhWlS5cWgYGBolGjRgKAuH79usEx+/btEyVLlhQARLVq1US3bt1Eo0aNhCRJwsbGRuzevdtg/KJFiwQA0bdvX1G2bFnh5eUlwsLC5PkVCoXYvn27UWxr1qwRVlZWAoCoW7eu6NatmwgICBAAhJOTkzh79qzB+KysLNGhQwcBQJQsWVJ07txZdO7cWb5nXbp0EVqt1uCY8uXLi5xSavrXqG/fvgbtPXr0EABEmTJlRKdOnUSPHj1E06ZNRcmSJUWLFi1MzvWi7O+zBg0aiDJlyoiQkBARFBQkFAqFUCqVYufOnWLGjBnCwsJCNGrUSISFhQknJycBQERGRhrNee/ePVG1alUBQHh4eIjQ0FDRunVrYWdnJwCIadOmyWPXr18vunbtKgAIFxcX0bdvX/nXjz/+KI9r0KCBUKlUom7duiI8PFx07NhRuLm5ya/9s2fPTF6XufdBCCFSUlJEs2bN5Pdp+/btRffu3UVgYKCwsLAQEydONBg/cOBAAUBYW1uLDh06iG7duokyZcrI9zMtLc1gfN++fQUA8cEHHwiFQiFfi6enpxzrgwcPTH4OVSqVOH36tMF8+vdz7969hYODg6hQoYLo2bOnaN68uZAkSUiSJBYtWmR0nb/++qtQKpVCkiTRpEkT0bNnT1GpUiX5Nbhw4YLB+IkTJwoAJucSwvR7V/+e7dy5s6hUqZJwcXERnTp1EsHBwUKpVAoABq+v3rfffisACKVSKVq2bCm6d+8u3NzcRLly5URISIgAIGJjY+Xxt27dku95zZo1Rffu3UVoaKioVatWjtdPzzGBX0wxgU9ERERERERvpDML8pe81/86a5xgel30CUgAolmzZuLBgwdyX3x8vLCwsBC2trYGicuUlBTh4uIiAIh58+YZzDdjxgwBQHh6eoqMjAy5XZ9oAyB69Ogh0tPT5T594q5q1arCw8PDIFF97tw5YWVlVSAx7Nu3T05Enz9/Xm5PSkoSdevWlePLnsB/8uSJcHV1FZaWlmL16tUG59m5c6ewsrISHh4eIjMzU27XJzwBiGHDhgmNRiP3zZo1S77X2V27dk3Y2toKBwcHsWfPHoO+X375RQAQgYGBBu3/+9//BABRo0YN8ffff8vtiYmJwt/fXwAQ3333ncEx+U3g698fgYGBBq+ZEEKkpaWJP//80+RcL8r+PgsKChJJSUlyn/5++fr6itKlSxs8eLlz544oW7asACCuXr1qMGf79u0FADFmzBihVqvl9qtXr4qKFSsKpVIpTp06ZRRDbsn2LVu2GMQmhBAZGRlyEn3y5Mkmrys/CfwBAwYIACI4OFg8fGj48O727dvi6NGj8u/XrFkjP6D466+/5PYnT56Ipk2bCgBi9OjRBnPoE/hKpVKsWbNGbn/69KmoXr26/Fl78XM4YcIEAUD06dPHYL7s7+c+ffoYvJ9jYmKEUqkUdnZ2IjExUW5PSEgQNjY2wsLCQsTExMjtWq1WjBgxwuT7+VUS+ABE165dRUpKity3YcMGAUB4eXkZHHP9+nWhUqmESqUySNKnpqaKNm3ayPNl79PH9s033xjFdePGDXHlyhWTMRMT+MUWE/hERERERET0Rto16OUS+LsGFUm4QvyTgFQoFOLSpUtG/Z06dTJKZi1cuNBkElpPnwxfvny53KZPtDk4OIjHjx8bjH/y5ImQJEkAEAsXLjSar0uXLgUSQ1RUlNHKbL3du3ebTODPnDlTABAff/yxyfPok5Fr166V2/QJTx8fH4PEshBCaDQa4ejoKCwtLQ2S/sOHDxcAxA8//GDyPGFhYQKAOHbsmNzm5eUlAIhdu3YZjd+0aZMAIPz9/Q3a85vAP3z4sAAghg8fbvIYc+nfZ0ql0iARLcTzpK6zs7MAID777DOjY0eOHGmU2D1x4oQAIBo3bix0Op3RMfrk7bBhw4xiyE+yXS8tLU1YWFiIOnXqmLwuc+dMTEwUSqVS2NjYiFu3buU5vnnz5gKA+Pnnn436Tp8+LSRJEiVKlDB4L+kT+C8m4oUQYs6cOTl+DpOTk4UkSaJ8+fIG7fr3s729vdHDDSH++YbG1KlT5bbPPvtMABBvv/220fiMjAzh7u4uAIiDBw/K7a+SwC9ZsqR49OiR0TE1atQw+kx/+umnAoB49913jcZfvHhR/lmU/efNkCFDBABx4sQJk7FRzlgDn4iIiIiIiIiKD03a6z2uAHl7e6NSpUpG7fq2u3fvym379u0DAERFRZmcq3fv3gbjsqtXrx5KlSpl0FayZEk4OTkBANq0aWN0TMWKFQskBn2d/YiICKPxwcHBcgzZ7dy5EwAQFhZm8jxNmzYFAMTHxxv1BQUFwdLS0qDNwsICPj4+0Gg0ePTokdF5OnfubNZ5EhISkJCQAFdXV7Rs2dJofEhICEqVKoVLly7hwYMHJuc0R+XKlWFnZ4dFixbhxx9/NIj5ZXh7e8PX19egTaFQoHz58gDMf/2z3y9TNf1ze13ycufOHcyfPx8jRoxA//79ER0djSFDhsDKygp//fVXvufLLjY2FlqtFh06dICnp2euYzUaDQ4dOgRJkhAZGWnUX6NGDdSsWRPPnj3DqVOnjPpN3UsfHx8Apj+HDg4OcHJyMrjP2bVt2xaOjo5G7b169QIA7N+/X27L7fOpUqnkz6CpnxEvo169eihdurRRu6mfX/o4u3fvbjTe398fAQEBRu1169YFAAwdOhSxsbHIysoqkLjfBBZFHQARERERERERkczS9vUeV4BySiba29sDADIzM+U2/UaP3t7eJo/Rt5vaENLDw8PkMXZ2dnj48KHJfjs7uwKJITExEZIk5XitXl5eRglq/eaxDRo0MHmM3sOHD43a8nNP9edxdXU16zx5Xb8kSShfvjySk5ORmJgIZ2fnXOfNScmSJfHjjz9i4MCBGDhwIAYNGgR/f38EBwejT58+aNiwYb7my+31z6nf1Ouvv19jx47F2LFjczyfqdclNzNmzMDHH38MtVqdr+PMdevWLQD/PJTIzaNHj6BWq+Hq6gpra2uTY7y9vXHq1CmzP2u53Wd9f073TP+QxVQMgPFnLXufOce8ipf5+eXl5WXyGC8vLxw/ftygLTo6Gjt27MCqVavQsmVL2Nraol69emjfvj369++PsmXLFsRl/CcxgU9ERERERERExUfZusC9Q/k/zqVewceST6ZWMb/qMab6X+aYgo4hJ0IIozatVgvg+ap9W9ucH7SYSvDn59xarRaSJKFPnz65jqtWrVq+z2FuHDqdzmR7r1690Lp1a2zcuBE7duzAnj17MG/ePMybNw+jR4/GV199Zdb85sRibqz616VZs2byqnJTypQpY3Zshw4dwqhRo+Dg4IAFCxYgKCgIrq6uUKlUAAB3d/ccV6fnV37eGy/7Gud23Mt83nNi6nNj7nnyE0dO78/8zqOPNz/HKJVKrFy5EuPGjcPGjRsRGxuLQ4cOYe/evZg2bRq2b9+e74dZbwom8ImIiIiIiIio+KjYBTj7A6DLR3kFhQXg06XwYioE7u7uAIDr16+b7L958yYAwM3NrVjF4Obmhhs3buD27dsmV0Dfvn3bqM3T0xOXLl3ChAkTULNmzYII3SRPT09cvXoVc+bMQcmSJfMcn9f1A8/L7ACG98DKygoAkJKSIq9O1tOvDjfF2dkZ77zzDt555x0IIbB9+3b06NEDX3/9NaKjo1G1atU8Yy5I+hXX3bp1wwcffFAgc65fvx4A8MUXX6Bv374Gfenp6bh3794rn6NcuXIAgCtXruQ51snJCVZWVrh37x7S09NhY2NjNOZ1fNZePNeL9O8z/XtS//+XLl3C9evXTZbmMhV39vfmi7RabYHcf31sly9fxs2bN+Hn52fUr78eUwICAhAQEIBJkybh6dOnmDx5MmbMmIHhw4fj8OHDBRLffw1r4BMRERERERFR8WFTBqgQmr9jfDoDNsa114uzZs2aAQCWLVtmsl/frh9XXGJo3LgxAGDNmjVG4+Pi4kyWDmndujUAYMOGDa8Ub17yex4vLy94eXnh3r172L17t1H/li1b8PjxY/j7+xuUz9EnTC9fvmx0zI4dO8w6tyRJeOutt9CxY0cAwNmzZ806riC9zOuiTxDnVL/88ePHAP5Jsme3evXqXFeamysoKAhKpRJbt27FnTt3ch1raWmJhg0bQgiB5cuXG/WfPXsWp06dQokSJVCrVq1Xji0vO3bsQHJyslG7PrYmTZrIbbl9PtVqNVavXm0wDsj9vbl7925oNJqXDz4b/f4I+hiyu3z5Mk6ePGnWPCVLlsTUqVMhSRLOnDlTILH9FzGBT0RERERERETFS90xz0vpmKNs3efj/2W6d+8OFxcX7Nu3DwsWLDDomzNnDuLj4+Hp6YkuXQrvmwUvE8PAgQMBAN988w0uXboktycnJ2PMGNOvw6BBg+Ds7IypU6di0aJFRknc1NRULFmyxOTq/fwYNWoUbGxsMHLkSMTExBj1JyUl4fvvv0d6errcNmzYMADAyJEjDTaqvXfvHkaPHm0wRq9FixYAgGnTpsllaABg6dKlWLFihdF5T5w4gXXr1hklTx8/fiyvOM6plnhhatiwIVq1aoXY2FiMHDnSaNW2TqfDjh07DDZWLVOmDCwtLXH16lWDa9fTrxT/+eefDa73/PnzudbZzw93d3f06dMH6enpiI6ORlJSkkF/YmKiQf11/es3ceJEXLt2TW5/9uwZ3n//fQghMGjQIPnhRGFKSUnBhx9+aPAAZOvWrVi9ejVsbW0NvrUwYMAA2NjYYPny5diyZYvcrtPpMH78eNy5cweBgYEGZWf0782lS5fKexwAwLVr14zex6+iX79+sLKywpIlSww20U1PT8fw4cNNlur59ddfTT6o+v333yGEKJLPwL8FE/hEREREREREVLxYWANB3wG+XZ+XxzFFYfG8P/h7QKl6vfEVADs7Oyxbtgw2NjYYNGgQ6tWrh8jISNSpUwfDhw+HnZ0dfvvtN7l2eHGJoUWLFhg6dCgePHiA2rVrIyQkBBEREfD19YVOp5OTidmToY6Ojli/fj3s7OzQv39/VKhQASEhIejatSsCAwPh4uKCvn375nuz1Bf5+flh6dKlSE9PR2hoKCpXrowuXbogLCwMAQEBcHNzw9ChQw024xw5ciTat2+P06dPw8/PD+Hh4ejSpQsqVaqECxcuICwsDEOGDDE4z9ChQ+Hs7Iw1a9agatWqiIiIQO3atREdHY3hw4cbxXXz5k107doVzs7OaN26NXr37o2QkBB4e3vj2rVr6NKlS5HV/l62bBlq1qyJWbNmoXz58mjVqhV69uyJZs2awdXVFe3atcPRo0fl8VZWVnjrrbdw79491KpVC3369ME777yDRYsWAXie2HV1dUVMTAz8/f3Ro0cPtGnTBrVr10azZs1y3MQ1v2bPno1GjRrhjz/+gLe3Nzp27IgePXqgQYMGKF++PDZt2iSP7datGwYOHIjbt2+jevXqCAkJQffu3VGxYkXs2bMHDRs2xOTJkwskrrxERUVh3bp1qFSpEnr16oWgoCCEhIRAq9Vi9uzZBhvjenl5YcGCBRBCoFOnTmjWrBkiIyNRtWpVfPPNN3BxccGSJUsM5vfx8UGfPn3w+PFj1K5dG6GhoWjVqhVq1KiBmjVrFtj99/HxwfTp05GRkYHg4GC0bt0aPXv2hK+vL86ePYuQkBCjY9auXYsaNWrA19cXXbp0QWRkJBo3bowuXbpAqVRi6tSpBRLbfxET+ERERERERERU/FhYA/U/BTpvB2q9D7g2BJxqPv9vrfeft9f/9F+ZvNdr1aoV4uPj0atXL9y+fRtr1qzBvXv30Lt3bxw7dqxQy+e8Sgzffvstvv32W1SoUAE7d+7En3/+iZ49e2L37t14+PAhJEmCo6OjwTFNmjTBmTNn5FXyu3fvxo4dO/D06VOEhIRg5cqVBVIDPjw8HKdOncKgQYOg0Wiwbds2xMXFITMzE1FRUdi8eTMcHBzk8UqlEps2bcLs2bPh4+OD7du3Y8eOHfD398d3332HNWvWQKEwTJ+5uLhg7969CAkJwd27d7Ft2zY4ODhg586dCA01Lv/UsGFDfPHFF6hbty4uXbqE1atX4+jRo6hZsyZ++eUXrFq16pWv+2W5uLjg0KFDmDFjBvz8/BAfH48NGzbg9u3bCAgIwHfffYfevXsbHPPTTz/h7bffxqNHj/Dbb7/h559/xp49ewA8rzkfHx+PyMhIqNVqbNq0CXfu3MHnn39usoTNyypRogRiY2Mxc+ZM+Pv7Y8+ePdi8eTOSk5PRv39/REREGIz/4YcfsGTJEgQEBGDPnj2IiYlB2bJl8X//93/YvXt3rpsrFyRfX18cPHgQNWvWxPbt23HkyBE0bNgQMTExeOedd4zG9+7dW36vXbhwAWvWrEF6ejqGDBmCY8eOoXLlykbH/Pjjjxg3bhxKliyJ7du34+bNmxg/fnyB3n8AGDFiBFatWoXatWtj//792LVrF4KCgnDo0CE4ORmXNPvwww8xdOhQlChRAvv27cP69etx//599OrVC/Hx8QgPDy/Q+P5LJFEQxaeowOl3RD937lwRR0JERERERERElLc7d+7A29sbvr6+uHDhQlGHQ1RsLF68GP369cPEiRMxadKkog6H/mW4Ap+IiIiIiIiIiMx28eJFgzryAPDgwQP069cPWVlZiIyMLKLIiIj+e3IoJEdERERERERERGRs1qxZ+O233+S68n///TeOHz+Op0+fok6dOvjoo4+KOkQiov8MJvCJiIiIiIiIiMhs4eHhSExMxPHjx3H48GEolUpUrFgRXbt2lWvcExFRwWAN/GKKNfCJiIiIiIiIiIiI3mysgU9EREREREREREREVAwxgU9EREREREREREREVAwxgU9EREREREREREREVAwxgU9EREREREREREREVAwxgU9EREREREREREREVAwxgU9EREREREREREREVAwxgU9EREREREREREREVAwxgU9EREREREREREREVAwxgU9EREREREREREREVAwxgU9ERERERERE9IokSYK3t7dB240bNyBJEoKCggrtHHkJCgqCJEm4ceNGgcRQ3MTFxUGSJERHR5t9jLe3NyRJMmp/mfub33PkJjo6GpIkIS4uzuxjFi9eDEmSMGnSJIP2SZMmQZIkLF682KD9v/5+yEtBvsaF7WXeQ/TfxAQ+EREREREREREREVExZFHUARARERERERER5ShVA5y9D9x+Cmh0gKUCKFcSqFYWsLMs6uhy5eHhgQsXLsDW1rZA5rtw4QIsLYv3Nf+bFeT93bVrFzQaTYHM9TLef/999OzZE25ubkUWAxEVDCbwiYiIiIiIiKj4ydIBsTeA8w8BnTDsS3gKHLwDVHMGgsoDFsWzwIClpSUqV65cYPMV5FxkrCDvb8WKFQtsrpdRpkwZlClTpkhjIKKCUTz/hCMiIiIiIiKiN1eWDlh/ETj7wDh5r6cTwJn7z8dl6V5vfGbKqQZ+9rrlly9fRteuXeHk5AQ7Ozs0adIEW7duNTlfbvW7FyxYgBo1asDa2hoeHh4YNmwYnjx5kmNsFy5cwNtvv42KFSvC2toazs7OqF27NkaMGIG7d++adX13797FV199hRYtWsDDwwNWVlZwdXVFeHg44uPjTR6Tva73Tz/9hJo1a8LGxgaurq4YNGgQkpOTTR5348YN9OrVC05OTrC3t0fjxo2xZcsWs+I0l6n7m73G/t27dxEdHQ0XFxfY2NigTp06WLJkicm5cqtfvnbtWtSvXx82NjZwcXFBnz59kJiYWKDXklMN/Jw8efIEzZs3hyRJGDFiBIT453MXExODdu3awcnJCdbW1qhUqRI+/fRTpKSkmB1P9s9CamoqPvzwQ5QrV06+jzExMfLY1atXo379+rCzs4OLiws++OADpKenm5z31q1bGDRoEMqXLw+VSoWyZcvm+v7LzZkzZxAVFQUPDw+oVCq4u7ujX79+ue4XsG3bNoSEhKBs2bJQqVTw8vJCWFiYwXszr30aXmbvgxs3bmDQoEHw9vaGSqWCs7MzunXrhtOnT5scv337drRr1w6enp7ytTVt2hSTJ082+5xUdJjAJyIiIiIiIqLiJfYGcPuZeWNvPwPibhZqOIXl6tWrqF+/Pk6cOIG2bduiXr16OHjwIEJCQsxOvALARx99hEGDBuGvv/5Cy5Yt0ahRIyxbtgzBwcHIzMw0Gn/8+HHUrVsXy5Ytg7OzM7p06YIGDRpArVZj9uzZuHTpklnn3bhxI8aOHYvExETUqFEDYWFhcHd3x/r169GkSRPs2LEjx2PHjBmDoUOHomTJknjrrbcghMCCBQsQGhpqkDzW36cGDRpgxYoVKFOmDDp16gSdTodOnTph1apVZt+nV5GUlISGDRvi999/R1BQEJo1a4YzZ86gb9+++UqCzp07F926dcPx48fRuHFjBAUF4Y8//kDDhg3x6NGjQryCnP39998ICgrCvn37MHnyZMyaNUt++DBq1CiEhoZi7969qF69Ojp27Ai1Wo0vvvhCTsbnh1qtRqtWrfDrr7+idu3aaNiwIU6dOoUuXbrgjz/+wMyZMxEZGQkLCwu0bdsWWq0W3377Ld555x2juc6cOYM6depgwYIFsLW1RXh4OPz8/LB+/Xo0btwYq1evNjuutWvXol69evjtt9/g5uaG0NBQuLq6YvHixahXrx7OnTtndMyoUaPQoUMH/P777/D390d4eDgqVKiA2NhYfP311/m6L/mxf/9+1KpVCwsWLIC9vT1CQ0Ph5+eHdevWoWHDhoiNjTUYP3/+fLz11lvYs2cPqlSpgq5du6JatWq4ceOG0ebHVEwJKpaqVq0qqlatWtRhEBEREREREb1eKZlCzDosxIxD5v+adViIFHWRhg1AlC9f3qDt+vXrAoBo0aKFQfuiRYsEAAFA9OnTR2g0GrkvJiZGKJVKYWdnJxITE/M8x4EDBwQAUbp0aXH27Fm5/eHDh6JmzZryea5fvy739e3bVwAQa9euNbqO8+fPG503J6dPnxanTp0yav/999+FlZWVqFixotDpdAZ95cuXFwCEm5ubOHHihNz+4MED4evrKwCIXbt2GRzTtm1bAUC89957QqvVyu0//vijfH19+/Y1K+bsMbzI1P2NjY2Vz9GmTRuRkpIi9x05ckTY29sLhUJhcC05neP69etCpVIJlUolYmNj5fbU1FTRpk0b+TzZ+/Kify9NnDjRoH3ixIkCgFi0aJFBe4sWLQzeD9evXxe+vr5CkiTx7bffGoxduXKlACACAgIM3j9qtVoMHDhQABAfffSRWXHqPwsARFBQkEhKSjK6Bl9fX1G6dGmxd+9eue/OnTuibNmyAoC4evWq3K7T6USNGjUEAPHxxx8bvM9Wr14tFAqFKFGihLh3755BHKZe42vXrglbW1vh4OAg9uzZY9D3yy+/CAAiMDDQoP3XX38VAISnp6fRZyAlJcXgPax/D+X0HtV/Hl983U29h548eSJcXV2FpaWlWL16tUHfzp07hZWVlfDw8BCZmZkG85QsWdLgNRTi+T3cvXu3yZioeOEKfCIiIiIiIiIqPnIrm5MTnQDO3S+ceAqRvb09Zs2aBQuLf7YoDAkJQbdu3ZCammrWKvz58+cDeL4auFq1anK7k5NTjquA799/fq9atmxp1FelShWzNz6tUaMGatasadTerl07RERE4OrVqzh79qzJY6dMmYLatWvLvy9TpgyGDBkCANi7d6/cfvXqVezYsQOOjo746quvoFD8k8p655130LhxY7NifVWSJOHbb7+FnZ2d3BYYGIihQ4dCp9Nh3rx5ec6xcOFCZGZmok+fPgZllWxtbfHtt9/mWHKnsJw7dw5NmjTBjRs38Ouvv+L999836J86dSoAYPny5QalhSwtLTF79my4urrip59+gk5nfgkrpVKJH3/8EY6OjnJbnz594OzsjCtXruD9999Hs2bN5D53d3dERUUBMHxfxMXF4cyZM6hQoQKmTJlicO+6deuGsLAwPHv2DIsWLcozptmzZyMtLQ1fffUVmjdvbtDXp08fhIWFIT4+HsePH5fb9fdm1qxZRp8BOzs7k5+tgrBw4ULcu3cPH330Ebp162bQ17p1a7z33nu4c+cONm/eLLffv38fFSpUMCoPJUkSgoODCyVOKlhM4BMRERERERFR8XH76csdd+sljytCbdu2NUhk6vXq1QvA81IZedGP6d69u8n5S5cubdRet25dAM+Tk0eOHMlXAvZFmZmZ2LhxIz755BMMHDgQ0dHRiI6OxpkzZwAAf/31l8nj2rZta9RWqVIlADCowX/gwAEAQIcOHQyS53o9e/Z86djzIyAgAP7+/kbtBfVa+fv7IyAg4BWjNN+hQ4fQvHlzPH78GBs2bJCT5Hr379/HqVOnUKVKFZPXbW1tjXr16iE5OTnH19gUb29v+Pr6GrQpFAqUL18eANCmTRujY/QbAmd/X+zbtw8A0KNHDyiVSqNj3n77bYNxudm5cycAoHPnzib7mzZtCgByXf3ExERcuHABTk5O6Nq1a57zFyR9rGFhYSb7X4wVeP55P3XqFMaNG4erV68WeoxU8CzyHkJERERERERE9JpoXjKZ/LLHFSF90vJF+pWy5mxsmpiYCEmSUK5cOZP9Xl5eSEpKMmgbPXo09u/fj5iYGMTExMDBwQENGjRASEgIoqOjUaJECbPiP3PmDEJDQ3Pd5PPZM9N7GXh6ehq12dvbA4BB3X79PfDy8jI5T07tBa2gXisg92vJvsobeL6/wcOHDw3amjZtarImfH68/fbbyMrKwsqVK9GxY0ej/ps3n+8rceHChTy/GfDw4UOTSX5TPDw8TLbrH86Y6tf3mXpf5LSpc35eF/3719XVNddx+tfh1q1bAP55sPA66WNt0KBBruOyv2e+++47hIWFYfr06Zg+fTrc3d3RrFkzdOvWDeHh4QbfaqHiiQl8IiIiIiIiIio+LF8ymfSyxxVDQuSzhFA+lSxZErt378aBAwcQExODuLg47Nq1Czt27MC0adOwb9++PJOTQgh0794dN27cwODBgzF48GD4+PjA3t4ekiRh/PjxmDZtWo7XYm65GP3xr7u8TGF4mWtZs2aNnEzP7lUT+L169cKvv/6Kzz77DM2bNzdKXmu1WgCAm5ubyW9LZOfk5GT2efO69vy+zgUxn1arhSRJ6NOnT67jspeoMnduc+TnGzD61yUiIgK2trY5jsue4K9ZsybOnz+P33//HVu3bsWePXuwcuVKrFy5Ek2bNsWuXbtgZWX18hdAhY4JfCIiIiIiIiIqPjxLAgkvUQ6nXMmCj6WQmUrMAkBCQgKA5/W/8+Lm5oYbN27g1q1bRqVJss/1IkmS0LRpU7nkxoMHDzB8+HAsX74c48ePx8qVK3M978WLF3Hx4kXUq1fPZP33a9eu5Rm7OfT3IK97VdhyOr++3ZzXyt3dHZcvX8bNmzfh5+dn1G/qWnL7dsOr+Pzzz+Hu7o7p06ejVatWiI2NRdmyZeV+/TckXF1dzdqL4XXT3+/r16+b7Ne/Lubs5+Dp6YmrV69izpw5KFky758j+m+7XLlyxaxY9cnxlJQUk/36Ff3m8PT0xKVLlzBhwgST+0/kxNraGmFhYXLpnfPnz6NXr17Yv38/fv75Z3n/CSqe/juPp4mIiIiIiIjo36+6M6DI58pWhQRUK5v3uGJmx44dSE5ONmpfvnw5AKBJkyZ5zqFPwK9evdqob+fOnUblc3Li7OyMSZMmAYBcvz43jx8/BmC6FM7jx4/lWt2vSn8Ptm7ditTUVKP+FStWFMh58nLy5ElcvnzZqL2gXqvLly/j5MmTrxZkPn355ZcYPXo0zp8/j1atWhmUXfH09IS/vz9Onz6dY5K8KOk3ul25cqW8Kj27pUuXGozLTevWrQEAGzZsMOvc7u7uqFKlCh49eoR169blOV7/EMHU++fRo0dGZZMKMtacVK1aFUOHDgVg3uedihYT+ERERERERERUfNhZAVXL5O+Yas6AnWXhxFOIUlJS8OGHHyIrK0tu27p1K1avXg1bW1v07ds3zzkGDRoEAJgxYwYuXLggtyclJWHMmDEmj5k/f77JpOy2bdsAmFdX3tfXFwqFArt37zbYxDQjIwODBw82+8GBOedp1aoVHj9+jHHjxhmUG1m0aBH+/PPPAjlPXnQ6HT744AOkpaXJbceOHcN3330HhUIhvw656devH6ysrLBkyRKDzVXT09MxfPjwV9pM+GV99dVX+PDDD3H27Fm0atUKjx49kvsmTJgArVaLrl274uzZs0bHXr16FQsXLnyd4cqCgoJQo0YNXL9+HZ999plBqaYNGzZg3bp1sLe3R3R0dJ5zjRo1CjY2Nhg5ciRiYmKM+pOSkvD9998jPT1dbhs3bhwAYMSIETh37pzB+NTUVOzevVv+fYUKFeDl5YUzZ85g48aNBuPeffddPH1q/jeOBg0aBGdnZ0ydOhWLFi0yKlGVmpqKJUuW4Pbt2wCAtLQ0zJkzx+hBoU6nw44dOwC8vn0k6OWxhA4RERERERERFS/B3kByBnDb9AaoBjxLAEGmNxgt7qKiorBu3TrExcWhQYMGuHv3Lvbu3QshBGbPnp3jhp/ZNW3aFCNGjMCsWbMQEBCA1q1bw8bGBrt374aXlxcaNmyIQ4cOGRwzf/58DBkyBFWrVkWVKlVgYWGBS5cu4eTJk7CxscHEiRPzPG/ZsmUxYMAA/Pjjj6hVqxZatmwJGxsb7Nu3D1qtFtHR0QVWemXevHlo0qQJ5s6di507d6JOnTq4fv06Dh8+jMGDB2P+/PkFcp7chISE4PTp06hYsSKaN2+OJ0+eYPfu3dBoNJgwYQLq1q2b5xw+Pj6YPn06Ro4cieDgYAQFBaFMmTLYt28fFAoFQkJCsHnz5kK/lhd988030Ol0mDVrFlq3bo3du3fD0dERvXv3xpkzZ/DVV1+hdu3aCAgIQIUKFfD06VPcvHkTFy9eRK1atdC/f//XHrMkSVi2bBmCg4MxdepUrF+/HrVr10ZCQgIOHDgACwsLLFy4MM+NaQHAz88PS5cuRe/evREaGgp/f39UqVIFQgjcvHkT58+fh1qtRmRkJGxsbAAAffr0QXx8PObOnYtatWqhcePG8PT0RGJiIk6cOIGAgAC0bNlSPsekSZPQv39/dO3aFc2bN4e9vT2OHDmCkiVLIjQ0FJs2bTLruh0dHbF+/XqEhoaif//+mDx5MqpXrw6VSoWEhARcuHABqampOHHiBDw9PaFWqzF8+HCMHj0aderUgbe3N9RqNY4ePYqEhAT4+PiY9fCJihZX4BMRERERERFR8WKhALpUBmqUzbmcjkJ63t+l8vPx/0K+vr44ePAgatasie3bt+PIkSNo2LAhYmJi8rVJ6YwZMzBv3jz4+vpi586dOHDgALp3747Y2FioVCqj8VOmTEH//v0hSRJ27dqFmJgYpKWlYeDAgTh9+jQaNWpk1nnnzZuHb775BhUqVMCuXbuwb98+tG7dGkePHkX58gX3UMXPzw+HDh1C9+7dcf/+fWzcuBFCCGzYsAE9evQosPPkxsnJCQcPHkTr1q0RGxuLuLg4VK1aFYsWLcKUKVPMnmfEiBFYtWoVateujf3792PXrl0ICgrCoUOH8rUZbEGbOXMmhg0bhpMnT6JNmzbyiu3p06dj165dCA0Nxe3bt7FhwwacOHECtra2GD16dJGtwAeAGjVq4Pjx43j33XeRkpKCNWvW4NKlSwgLC8OBAwcQERFh9lzh4eE4deoUBg0aBI1Gg23btiEuLg6ZmZmIiorC5s2b4eDgYHDMt99+i/Xr16NVq1Y4e/Ys1q5di+vXr6NVq1YYO3aswdh+/fph0aJFqFKlCg4cOIAjR46gU6dOOHjwIBwdHfN13U2aNMGZM2fkbw7s3r0bO3bswNOnTxESEoKVK1eiatWqAAB7e3t89913CAkJwYMHD7Bp0yb5Ac2UKVNw7NixfJ+fXj9JFPbW5vRS9Dtbv/g1HCIiIiIiIqI3SqoGOHcfuPUU0OgAS8XzDWurlf1Xls0BgMWLF6Nfv36YOHGiXHeeiqe4uDgEBwejb9++xXIzVyL672MJHSIiIiIiIiIqvuwsgfoez38RERG9Yf6d3zEjIiIiIiIiIiIiIvqPYwKfiIiIiIiIiIiIiKgYYg38Yoo18ImIiIiIiIiIiIjebFyBT0RERERERERERERUDDGBT0RERERERERERERUDDGBT0RERERERERERERUDDGBT0RERERERERERERUDDGBT0RERERERERERERUDDGBT0RERERERERERERUDDGBT0RERERERERERERUDDGBT0RERERERERERERUDDGBT0RERERERERERERUDDGBT0RERERERERERERUDDGBT0REREREREREhWrx4sWQJAmTJk0q6lDoBZMmTYIkSVi8eLFBe3R0NCRJQlxcXJHERUUrKCgIkiThxo0bRR3KG8+iqAMgIiIiIiIiIsrJs+QnOPrHHlw/fxGZ6RlQ2VjDp1oV1G3VHCVKORR1eFTMTJo0CZMnT8aiRYsQHR1d1OEQEb0yJvCJiIiIiIiIqNjRZKqxeeFSHI/dD61Wa9B35dQ57Fq5HnVaNkNI/yhYWlkVUZRE/13Tpk3DuHHj4OXlVdShEL3RmMAnIiIiIiIiomJFk6nG4v/7BtfPXcxxjFarRfzOODxMvIfoT0bBUsUkPlFBcnNzg5ubW1GHQfTGYw18IiIiIiIiIipWNi9cmmvyPrvr5y5i86JlhRyReQ4ePIjOnTvD2dkZKpUK3t7eeO+995CYmGg0NntN+MuXL6Nnz55wcXGBQqHAhg0b5HGrVq1CYGAgbGxs4OLign79+uHvv//OsT75vn378P7776NmzZpwdHSEjY0NKleujHHjxiE5Odkojri4OEiShOjoaCQlJWHIkCFwc3ODSqVC9erVsXDhwgK53uwSEhIQGRkJZ2dn2NjYoF69eoiJiclx/JkzZxAVFQUPDw+oVCq4u7ujX79+RrW5vb29MXnyZABAv379IEmS/OvF+7R//3506dIFZcuWlWP/4IMP8ODBA6PzZ7/X27dvR3BwMEqVKgVJkuR7mpWVhW+//RZ169aFvb097O3tUb9+fcybN8/oGyR6Go0G33//PZo0aYJSpUrB1tYWlSpVwrvvvouzZ88CAL7++mtIkoRPPvkkx/sTHBwMSZKwf/9+g/aHDx/i448/RvXq1WFnZ4dSpUqhdu3a+OSTT/Do0aMc5zN13S9KTU3FtGnTUKdOHZQoUQL29vaoWrUqRowYgZs3b+Y598vMk997nL2G+8qVKxEYGAhbW1t4eHhgzJgxUKvVAICrV6+iV69eKFu2LGxtbdGyZUucPn3aaL7sewUcPnwY7dq1Q6lSpVCyZEm0adMGhw4dyvE6t27dijZt2sDR0RHW1tbw9/fP8TOZ194DkiTB29vboC37z5P8fr4WLFiAGjVqwNraGh4eHhg2bBiePHmS4/gLFy7g7bffRsWKFWFtbQ1nZ2fUrl0bI0aMwN27d3M8jl4eE/hEREREREREVGw8e5yM47H78x6YzfHd+/AsOeeE0+uwdOlSNGvWDDExMfD390d4eDhUKhXmzZuHOnXq4OJF0w8kLl26hMDAQBw5cgTBwcFo06YNLC0tAQCzZs1Cjx49cOLECTRp0gTBwcHYuXMnGjZsiMePH5ucb/To0fjpp59gZWWFli1bolWrVnj69CmmT5+Opk2bIiUlxeRxycnJaNSoEdavX4/AwEA0adIEFy9exIABA/DTTz8V2PXeuHEDgYGBOHDgAJo2bYqAgAAcO3YMYWFh2LFjh9H4tWvXol69evjtt9/g5uaG0NBQuLq6YvHixahXrx7OnTsnj+3WrRtq1aoFAGjSpAn69u0r/3J1dZXHzZkzB82bN0dMTAx8fX0RGhoKGxsbfPvtt2jQoEGOScjffvsN7du3R2pqKtq3b4/AwEBIkgStVovOnTvjgw8+wJUrV9C6dWu0bt0aFy9exHvvvYeIiAjodDqDuVJTU9GqVSsMHToUp0+fRuPGjdGpUyeUKlUKixcvxpo1awA8fxChUqmwaNEiZGVlGcV05coV7NmzB5UrV0bTpk3l9vPnz6N27dr48ssvkZSUhLfeegtBQUHIzMzE1KlTcebMGZPXaI67d++ifv36GD9+PG7evImWLVvirbfegpWVFebMmYPY2NgCn+dl7rHe7Nmz0bt3b1haWqJdu3ZQq9X4+uuv8e677+Kvv/5Cw4YNceTIETRr1gy+vr6IjY1FcHAw/v77b5Pz/fnnn2jevDlu376N9u3bw9/fH3/88QdatGiBnTt3Go2fNm0aOnbsiLi4ONStWxdhYWFIS0vD9OnT0aBBgxzP8zLy+/n66KOPMGjQIPz1119o2bIlGjVqhGXLliE4OBiZmZlG448fP466deti2bJlcHZ2RpcuXdCgQQOo1WrMnj0bly5dKrBroWwEFUtVq1YVVatWLeowiIiIiIiIiF6r3as3io/D++T7V+yaTUUWc0JCgrCxsREWFhYiJiZGbtdqtWLEiBECgAgMDDQ4ZtGiRQKAACDef/99kZWVZdB/9epVYWVlJaytrcXevXvl9vT0dNGxY0f52NjYWIPjtmzZIpKSkgzaMjIyxMCBAwUAMXnyZIO+2NhYea6uXbuKlJQUuW/Dhg0CgPDy8irQ6x02bJjQaDRy36xZswQA0axZM4Njrl27JmxtbYWDg4PYs2ePQd8vv/xi8jwTJ04UAMSiRYuEKQcPHhQKhUKUL19enDp1Sm7X6XTi888/FwBEt27dDI7p27evHPuKFSuM5vzf//4nAIgaNWqIv//+W25PTEwU/v7+AoD47rvvDI4ZMGCAACCCg4PFw4cPDfpu374tjh49Kv8+MjJSABAbNmwwOvfYsWMFAPHNN9/IbRqNRlSuXFkAEKNGjRJqtdrgmOPHj4tbt27Jv8/pnumv+8X3WKtWrQQA0atXL4P3ixBCXL58WVy4cMEoTlPyM8/L3OMWLVoIAKJEiRIGn6G7d+8KFxcXIUmSqFKlivjwww+FVqsVQjx/H/Tp00cAEJ999pnBfPr7BECMHz9e6HQ6ue/7778XAIS7u7tIT0+X248cOSIUCoUoUaKEOHz4sNyekZEhIiIiBAARERFhcJ6c7rseAFG+fHmDtpf5fB04cEAAEKVLlxZnz56V2x8+fChq1qwpz3f9+nWj2NauXWsU1/nz50ViYqLJmOnVcAU+ERERERERERUb18+bVzrnRdfOXSjgSMz3008/IT09Hb169UJISIjcrlAo8OWXX8Ld3R3x8fEmS2w4Oztj+vTpUCqVBu0LFy6EWq1G37590axZM7nd2toas2fPhkJhOqXToUMHODo6GrSpVCrMmjULFhYW2Lhxo8njSpYsiQULFsDOzk5u69y5M2rUqIGEhASDcjWvcr0+Pj745ptvYGHxz7aMQ4cOhaOjIw4dOiSXNQGer5xOS0vDV199hebNmxvM06dPH4SFhSE+Ph7Hjx83eU2mfPnll9DpdFiwYAFq1qwpt0uShAkTJiAgIADr1q3Dw4cPjY7t2LEjevToYdQ+Z84cAM+/MVG2bFm53c3NDV9//bXBGOD5yvPFixfDxsYGS5YsgZOTk8F8Hh4eqFu3rvz7QYMGAYDRNyGysrLwyy+/wMrKCn369JHb161bh4sXL6JmzZr46quv5G906AUEBMDT0zOHO5S7I0eOYNeuXXB1dcWPP/5o8H4BAD8/P1SuXLnA58nvPc5u5MiRBp8hV1dXREVFQQgBtVqN6dOny58nSZIwatQoAMCePXtMzle+fHlMnjwZkiTJbUOGDEGDBg2QmJiI9evXy+1z586FTqfDiBEjUL9+fbldpVJh7ty5sLGxwdq1a3Hnzp1c7pb58vP5mj9/PgBg1KhRqFatmtzu5OQk39MX3b9/HwDQsmVLo74qVapwz4RCwgQ+ERERERERERUbmekZr/W4grBv3z4AQFRUlFGfSqVCRESEwbjsWrduDVtbW6P2P//8EwDkY7OrWLEiAgICcoznzp07mD9/PkaMGIH+/fsjOjoaQ4YMgZWVFf766y+Tx9SrVw+lS5c2aq9UqRIAGJSVeZXrDQoKMkooW1hYwMfHBxqNxqA2u74cSefOnU3GrC8ZEx8fb7L/RTqdDrt27UKJEiXQqlUro35JktCkSRPodDocO3bMqD80NNSoLSEhAQkJCXB1dTWZ1AwJCUGpUqVw6dIlub5+bGwstFotOnToYFYivXnz5qhatSq2bdtmkOiNiYnBvXv30KVLF5QpU0Zu/+OPPwAA7777bo4Pel6Wfu6oqCijpHthzfMy9zi7Nm3aGLX5+PgAeP5+zJ7sBp5/vgDkWEqpa9euRscAQK9evQDAYC+C3D4rZcuWRdu2baHT6eTP+6vKz+dLH2f37t2N5mnbtq3Jnwf6B0t9+vTBkSNHcixbRAXL+N1GRERERERERFREVDbWr/W4gqDftPXFjSX19O2mNnf18vLKdc5y5cqZ7Pfy8jKZZJ4xYwY+/vhjg5W25sgpkWxvbw8ABvWwX+V683Me/ar/7PXrTTG1Wt6UR48eyXsAmErA5jWnqdcqr3shSRLKly+P5ORkJCYmwtnZGbdu3QLwT6LYHAMHDsSIESOwcOFCfPrppwCAH3/8EcDzRH12LzO/uQpq7vzM8zL3ODsPDw+jY/QPDXLrM1UDHni+At8UU+/7xMREOT5zj3kV+f0cS5KU68+YpKQkg7bRo0dj//79iImJQUxMDBwcHNCgQQOEhIQgOjoaJUqUKJDrIENM4BMRERERERFRsVGhamVcOXUu74Ev8KlWpRCiyZ/sJTXM7be2zv3BQ05zCiGM2g4dOoRRo0bBwcEBCxYsQFBQEFxdXaFSqQAA7u7uOa4qziv2lznGVH9+zqPVaiFJkkF5GFOyl//Iaz4AKFGiBMLDw3MdayrhmttrZc51vTgmP/eib9+++Pjjj7Fw4UJMmDABt2/fxvbt2+Hj42NyVXp+58+vgpo7P/O8zD3O67iCvEemPpPmMjeOvFa8F+ZrDjwvtbV7924cOHAAMTExiIuLw65du7Bjxw5MmzYN+/btK5QHR286JvCJiIiIiIiIqNio16o5dq/aICdbzaFUKlG3VfO8BxYSd3d3XLp0CdevX5dLzmR38+ZNAMhXfWg3NzdcunQJCQkJ8PPzM+rXr2DOTl97+4svvkDfvn0N+tLT03Hv3j2zz5+bwrheUzw9PXH16lXMmTMHJUuWfKW5AKBMmTJQqVSwtLTE4sWLX3k+4Pm9AIDr16/nOCYhIQHAP/dDv+L5ypUrZp+nVKlS6NGjBxYvXoydO3fi4MGD0Ol0eOedd4ySti8zv7kKau78zPMy97gw6d/fOcWgj1f//9evX8fNmzfh7++f41zZ47aysgIA+dsi2Zn63L8sNzc33LhxA7du3YKvr69Rv/56XiRJEpo2bSqXsHrw4AGGDx+O5cuXY/z48Vi5cmWBxUjPsQY+ERERERERERUbJRxLoU5w03wdU6dlM5Qo5VBIEeVNv0HmsmXLjPrUajVWr15tMM4cjRs3BgCsWbPGqO/atWs4ceKEUfvjx48BmC67s3r16ldaIZxdYVyvKa1btwYAbNiwwexj9MnPrKwsoz4LCwsEBQUhKSkJe/fufaXY9Ly8vODl5YV79+5h9+7dRv1btmzB48eP4e/vL5d2CQoKglKpxNatW/O1eal+M9sffvgBCxcuhIWFBaKjo43G6e/bTz/9VGCv+YtzL1u2DGlpaa9lnpe5x4Vp7dq1Jh8wrlixAgDQpEkTuS23z8qDBw+wY8cOKBQK+fMO/JPMv3z5stExO3bseLXgs9En4PWf1+x27txpVD4nJ87Ozpg0aRIA4MyZMwUWH/2DCXwiIiIiIiIiKlZC+vdGhWqVzRpboVplhPQ33iDydRowYABsbGywfPlybNmyRW7X6XQYP3487ty5g8DAQDRs2NDsOfv16yevFM++wWVGRgZGjBhhspSGfjX8zz//DI1GI7efP38eY8eOfZlLM6kwrteUUaNGwcbGBiNHjkRMTIxRf1JSEr7//nukp6fLbfrVz5cuXTI55/jx46FQKNC3b1+DzUb1EhMT8d133+UrzmHDhgEARo4cabCJ6r179zB69GiDMfoY+/Tpg/T0dERHRxslShMTE3H8+HGj8zRs2BC1atXCunXrkJCQgJCQEJMrzsPDw1GpUiWcOnUK48aNM3qYcfLkSdy+fTtf16hXv359BAcH4969exg0aJBR8v3KlSu4ePFigc+T33tcmG7evInJkycbtC1YsAAHDx6Eq6srunTpIrcPHToUCoUCs2fPxtGjR+V2tVqNYcOGIS0tDeHh4Qa1+Fu0aAEAmDdvnsGms8ePH5f3PygI+gdCM2bMwIULF+T2pKQkjBkzxuQx8+fPN/lNiG3btgHIeU8PejVM4BMREb3hhBDIyspCZmYmMjIykJmZiaysLGi1WmRmZiIlJQVPnz5FSkoKMjMz86y7SERERPSqLFVWiP5kFALbPF+pbIpSqURgmyBETxgFy/+/6rqoeHl5YcGCBRBCoFOnTmjWrBkiIyNRtWpVfPPNN3BxccGSJUvyNaevry+mTp2KjIwMNG/eHG3atEHPnj3h5+eHU6dOoVOnTgD+WXEOPE/6u7q6IiYmBv7+/ujRowfatGmD2rVro1mzZjlupFkcrtcUPz8/LF26FOnp6QgNDUXlypXRpUsXhIWFISAgAG5ubhg6dKjBxpxt27aFtbU1Zs6cifbt22PAgAF455135IR+8+bNMXv2bNy6dQvNmjVDrVq10K1bN4SEhKBGjRrw8vLCJ598kq84R44cifbt2+P06dPw8/NDeHg4unTpgkqVKuHChQsICwvDkCFDDI6ZPXs2GjVqhD/++APe3t7o2LEjevTogQYNGqB8+fLYtGmTyXPpk66A8ea1ehYWFli7di1cXV3x1VdfoXz58oiIiECXLl1QtWpVBAQEvFIJnF9//RWVKlXC0qVL4eXlhbCwMERERCAgIACVKlXCoUOHCnyel7nHheXdd9/Fl19+ierVqyMyMhL169fHoEGDYGlpiUWLFsHGxkYeW79+fUyZMgVPnz5Fo0aN0KZNG/Tq1Qu+vr5YuXIl/Pz8MHfuXIP5g4OD0aJFC1y5cgVVq1ZFeHg4mjVrhoYNG+Ltt98usOto2rQpRowYgYcPHyIgIAAhISGIiIiQS3aZegA3f/58+Pj4oFq1aujWrRt69uyJgIAAjBgxAjY2Npg4cWKBxUf/YAKfiIjoDabVapGWloa0tDRkZmZCrVYjMzMTz549w6NHj5CSkoLU1FSkpaUhJSUFycnJSE5OhlqtLurQiYiI6D/OUmWFLoP7YcyCmWgb2Q2+taqhXKWK8K1VDW0ju2HMgpnoMrhfkSfv9Xr37o29e/ciJCQEFy5cwJo1a5Ceno4hQ4bg2LFjqFzZvG8UZPfRRx9h+fLlqFWrFvbt24fdu3cjODgYhw4dkledOzk5yeOdnJwQHx+PyMhIqNVqbNq0CXfu3MHnn3+O5cuXF9i1AoVzvaaEh4fj1KlTGDRoEDQaDbZt24a4uDhkZmYiKioKmzdvhoPDP+WT3N3dsXHjRjRs2BD79+/HwoUL8fPPPxts3vv+++/j8OHDiIqKwuPHj7Fp0yYcPHgQCoUCgwcPxsaNG/MVo1KpxKZNmzB79mz4+Phg+/bt2LFjB/z9/fHdd99hzZo1UCgMU3AlSpRAbGwsZs6cCX9/f+zZswebN29GcnIy+vfvj4iICJPnatWqFYDn+wO0a9cux5iqV6+OkydPYtSoUbCzs0NMTAz27NkDlUqFCRMmoGbNmvm6xuw8PDwQHx+PSZMmwc3NDTt27MD27duhVqsxYsSIHDfVfZV5XuYeF5bGjRtjz549cHV1xebNm3HhwgW0atUKcXFxeOutt4zGjx8/Hps3b0aLFi0QHx+PdevWQaVSYcyYMTh8+DBcXFwMxkuShI0bN2Lw4MGQJAlbt27F48ePMWfOHHz99dcFei0zZszAvHnz4Ovri507d+LAgQPo3r07YmNj5c2vs5syZQr69+8PSZKwa9cuxMTEIC0tDQMHDsTp06fRqFGjAo2PnpNEQRfDogKh30H93LlzRRwJERH9V+mT9y/+VSArKwtpaWnQarWwsLCASqUy+tqtUqmEg4ODwYovIiIiIno9UlNT4e3tjfT0dDx58iTHbynQf8/UqVPxySefYOLEiXLdcXo9Jk2ahMmTJ2PRokUm9x4gKixcgU9ERPQGEkIgIyPDKHmvb9dvypSVlQWNRmO0mkWr1SIlJYXldIiIiIgK0bVr1/DkyRODtpSUFAwePBgPHz5Ejx49mLx/gzx9+hRz586FlZUVBg4cWNThENFrYlHUARAREdHrp9Vq5SR9dllZWUar7TUaDSwtLY2S9RqNBhqNxuRXK4mIiIjo1a1atQqTJk1C3bp14enpicePH+PEiRN4+PAhvL29MXXq1KIOkV6DRYsWYc+ePdi7dy/u3r2LkSNHypv1EtF/HxP4REREbyB98l6r1Rok5rOyskyuyhdCyKvw9f1CCCbwiYiIiApRq1atcPLkSRw6dAgnTpyAEAJeXl7o27cvxo4dC2dn56IOkV6DPXv24JdffoGzszOGDRuGadOmFXVIRPQasQZ+McUa+EREVJjS0tKQmppqtApfq9VCrVbLyXqFQgFJkmBtbQ1JkqDT6SBJEoQQcru9vX1RXAIRERERERHRfx5X4BMREb1htFotNBoNtFqtvLpekiRIkgSFQgGdTgchBKysrOQEv0ajQWZmpvx7CwsLWFhYQKFQQKvVsvYqERERERERUSFgAp+IiOgNot+kVqfTQaPRGJTPkSQJVlZWUCgUUCgUUKvVUCqV0Ol00Ol0Bqv1s7KyoNPpoFKpkJaWBltbWybxiYiIiIiIiAqYoqgDICIiotdHo9EgLS0NGo0GFhYWEEIYJOizsrJgYWEh18bPysqCJElQq9VGtfH1JXX0DwVYlY+IiIiIiIioYL3xCfxjx47hyy+/RHh4ODw8POR6vnlZsmQJ6tevD3t7e5QuXRodOnTAn3/++RoiJiIiejlCCKSnp0Or1UKr1cr17fUJfIVCAY1GA6VSKSfu9TXvMzMz5XHA89r4VlZWcuJf/4uIiIiIiIiICs4bX0JnypQp2LhxY76O+fDDDzFz5kzY2Nigbdu2yMjIwM6dO7Fjxw6sXr0aXbp0KaRoiYiIXp4+yS6EgEajAQC5ZE72tszMTFhaWkKlUkGj0SAjIwMAoNPpoFQqDUrtZG/XarWwsHjj/2pBREREREREVGDe+H9lN2rUCLVq1UJgYCACAwPh6uqa6/jdu3dj5syZcHJywsGDB+Hn5wcAOHjwIIKCgtCvXz8EBQXB0dHxdYRPRERkNn3yXv9fAFCr1XIyXl/DXpIkaLVaeQW+lZWVvNGtvj6+qXI5LKFDREREREREVLDe+AT+2LFj8zX+m2++AQBMmDBBTt4Dzx8EDB48GHPmzMHChQsxatSoAo2TiIjoVekT7Nk3rtVLTU2FhYUFsrKyYGlpifT0dFhbWyMrK0tO7Ash5Br5AORkvp4kSa/hKoiIiIiIiIjeHG98Dfz8yMjIwK5duwAA3bp1M+rXt8XExLzWuIiIiMyhT7DrE/L6NiGEvJltVlaW3Jd9tb3+V3b63+uT+NnnJSIiIiIiIqJXxwR+Ply8eBGZmZlwdnaGp6enUX+dOnUAAKdPn37doREREeVJX7/ewsLCoFyOvjyOPiGv0WhgZWWVY0377Il8pVJp8IuIiIiIiIiICg4T+PmQkJAAACaT9wBgZ2eHUqVK4fHjx3j27NnrDI2IiChP+iS7EAKWlpZycj57Eh94XivfysoKAAzG6enHSZIES0tLSJIEa2trltAhIiIieoNIkgRvb++iDoOI6D/vja+Bnx8pKSkAAFtb2xzH2NnZITk5GSkpKShRokSec1arVs1k+9WrV1GxYsWXC5SIiMgESZJgY2MDtVotr64XQkCpVEKj0UCpVEKr1UKhUBgk8VUqFRQKBbKysqDT6SBJEpRKJWxsbGBpaQlra2uuviciIqJCk/V3KpJ+OoPUPbehe6aGooQV7ILKofSA6rBwsSvq8IiIiAoVE/j5oC8ZkNsKwxfrA1PB02+imJWVBa1WKyef9KtEuQKUiChnlpaWsLW1RUZGhlzvXgghl8yxsrKS6+Hrf69QKAxW6yuVSigUCtjY2PDnLhERERUaXboGd4fH4vHic4BGZ9CXuvMm7k/6E479qsNtdjAU1kxvvG4XLlyApaVlUYdBRPSfxz/h8kG/oj41NTXHMWlpaQAAe3t7s+Y8d+6cyfacVua/6bRaLTIyMuTEU/YHJhYWFlCpVLC1teVKUCKiHOjL3ejL6Oh0OgghoFKpoFarIYSAWq2GTqeDUqmETqczWHWvX5XPn7VERERUmHTpGtxovw5pe27nPEijw+MFp5F5KQne28KhsGEy+XWqXLlyUYdARPRGYA38fPDy8gIA3L5t+i8QqampSE5ORqlSpcwqn0P5o9VqkZ6ejrS0NGg0GqNvO2RlZSEtLQ0pKSnQarVFFCURUfGnL3+jX0mv39xWXwrH2toaKpXKoNa9/iGpQqFg8p6IiIgK3d3hsbkn77NJ23Mbd0fEFW5AZjp48CA6d+4MZ2dnqFQqeHt747333kNiYqLR2MWLF0OSJEyaNAmXL19Gz5494eLiAoVCgQ0bNsjjVq1ahcDAQNjY2MDFxQX9+vXD33//jejoaEiShLi4OIN59+3bh/fffx81a9aEo6MjbGxsULlyZYwbNw7JyclGccTFxUGSJERHRyMpKQlDhgyBm5sbVCoVqlevjoULF5q81txq4J8/fx79+vVD+fLloVKp4OLigubNm2P27Nnm3koAwNatW9GmTRs4OjrC2toa/v7+Jq+jR48ekCQJY8eONZrj4sWLsLW1RcmSJXHt2jUAQPXq1SFJEi5fvmzyvDdu3IBCoYCfn59B7kGj0eD//u//4OvrC2tra/j4+GDSpEnQaDTw9vbO8ZupZ86cQVRUFDw8PKBSqeDu7o5+/frhxo0bRmMnTZoESZKwePFinDlzBqGhoXB0dISdnR1atGiBP//808y7949r166he/fuKF26NOzt7dG0aVNs377d4LXPLvt7a/v27QgODkapUqUgSZJ877OysvDtt9+ibt26sLe3h729PerXr4958+aZzMnkdn/MiWPbtm1o2rQp7O3t4ejoiPDwcFy8eDHf94Lo34gJ/Hzw9/eHSqXCgwcPTCbxjx8/DgCoWbPm6w7tP08IgYyMDGRmZuaanNevHM3IyGA5IyKiXCiVStja2sLW1hYqlQpWVlawsrJCyZIl4eDgAAcHB5QsWRL29vbyf21sbJi8JyIiokKnuZf6vGxOPjxedBZZf+f8bfnXYenSpWjWrBliYmLg7++P8PBwqFQqzJs3D3Xq1Mkx2Xjp0iUEBgbiyJEjCA4ORps2beTSNLNmzUKPHj1w4sQJNGnSBMHBwdi5cycaNmyIx48fm5xv9OjR+Omnn2BlZYWWLVuiVatWePr0KaZPn46mTZvK+/u9KDk5GY0aNcL69esRGBiIJk2a4OLFixgwYAB++ukns+/D6tWrUadOHSxevBglSpRAeHg4ateujatXr2LEiBFmzzNt2jR07NgRcXFxqFu3LsLCwpCWlobp06ejQYMG+Pvvv+Wx8+fPh6enJ/73v/8ZPNDQaDSIjIxEeno65syZAx8fHwDAoEGDACDH6/r5558hhMA777wjJ52FEIiIiMCECRNw//59tG/fHjVq1MA333yDiIiIHK9j7dq1qFevHn777Te4ubkhNDQUrq6uWLx4MerVq5djZYajR4+iYcOGuHTpElq1agU/Pz/s3bsXrVq1wtmzZ82+j3/99RcaNGiA1atXo2zZsujUqRO0Wi06dOiAtWvX5nrsb7/9hvbt2yM1NRXt27dHYGAgJEmCVqtF586d8cEHH+DKlSto3bo1WrdujYsXL+K9995DREQEdDpdrnPnx+rVq9GxY0eo1Wp06tQJ7u7uWL9+PRo2bIhTp04V2HmIii1BBgAIlUqVY3/79u0FADFz5kyjvg8++EAAEF999dUrx1G1alVRtWrVV57nv0Kj0YgnT56Ie/fuibt37xr8unP2urg64Q9xqd0KcbH5UnGp40pxa8afIvNxalGHTURERERERPn09xcHxRn8L9+//v6/Q0UWc0JCgrCxsREWFhYiJiZGbtdqtWLEiBECgAgMDDQ4ZtGiRQKAACDef/99kZWVZdB/9epVYWVlJaytrcXevXvl9vT0dNGxY0f52NjYWIPjtmzZIpKSkgzaMjIyxMCBAwUAMXnyZIO+2NhYea6uXbuKlJQUuW/Dhg0CgPDy8jK6ZgCifPnyBm2XL18W1tbWwtLSUqxcudKgT6vVGtyb3Bw5ckQoFApRokQJcfjwYYPriIiIEABERESEwTG7d+8WCoVClCtXTjx+/FgIIcSYMWMEANGtWzeDscnJycLW1laULVtWqNVqg76srCzh4eEhLCwsxL179+T2X3/9VQAQvr6+IjExUW5PSEgQ5cuXl+9hdteuXRO2trbCwcFB7Nmzx6Dvl19+Mfm+mDhxojzX9OnTDfr076W33347t9tnoFWrVgKAGDZsmNBqtXL7kiVL5PP07dvX4Ji+ffvKfStWrDCa83//+58AIGrUqCH+/vtvuT0xMVH4+/sLAOK7774zOEZ/j0zRvwdzi2PBggVyu06nE2PHjhUARJ06dcy9FUT/WlyBn08ffvghAOCLL77AX3/9JbcfPHgQP/zwA0qWLIkBAwYUVXj/WVqtVq7TrCfSspAy5jCS6qxH2hcnod5+G5q996DecgvJH/6JK+V/xt8TD0BoC+6pLxERERERERWuVDNL5xgdF3ergCMx308//YT09HT06tULISEhcrtCocCXX34Jd3d3xMfH49ChQ0bHOjs7Y/r06Ubfcly4cCHUajX69u2LZs2aye3W1taYPXs2FArTKZ0OHTrA0dHRoE2lUmHWrFmwsLDAxo0bTR5XsmRJLFiwAHZ2dnJb586dUaNGDSQkJJgs9/KimTNnIiMjA4MGDUL37t0N+hQKhcG9yc3cuXOh0+kwYsQI1K9f3+A65s6dCxsbG6xduxZ37tyR+4KDgzFq1CjcunUL7733HuLi4vC///0P7u7u+OGHHwzmd3BwQI8ePXD//n1s2rTJoG/btm24c+cOQkND4eLiIrfPnz8fADBlyhS4ubnJ7eXKlcPEiRNNXsfs2bORlpaGr776Cs2bNzfo69OnD8LCwhAfHy9XdMiuadOmGDNmjEHbhAkTAAB79+41eb4XXblyBbt27ULp0qUxbdo0g/fM22+/bRTTizp27IgePXoYtc+ZMwfA82+IlC1bVm53c3PD119/bTCmIDRu3Bjvvvuu/HtJkjBlyhSUK1cOx48fx8GDBwvsXETF0RufwN+yZQsaNmwo/wIAtVpt0LZlyxZ5fOvWrTF8+HA8evQItWvXRlhYGDp06IDmzZtDo9Fg4cKFKF26dFFdzn+WEMIoef+k5y5k/HoFUJtO0OueqvHg80O43Xsrk/hERERERET/Erpn6td6XEHYt28fACAqKsqoT6VSySVW9OOya926NWxtbY3a9bXOTZVnqVixIgICAnKM586dO5g/fz5GjBiB/v37Izo6GkOGDIGVlZXBYsTs6tWrZzKfUalSJQDA3bt3czyf3h9//AHgnxI1Lyu3+1m2bFm0bdsWOp3OqB78F198gYCAACxfvhydO3eGEAK//PKLyesaPHgwAODHH380aNf/PnvCWKPRID4+HgqFAuHh4UZz5VRCZ+fOnQCePwgxpWnTpgCA+Ph4o762bdsatTk5OcHJycms1wL45z3UoUMHgwczecWtFxoaatSWkJCAhIQEuLq6omXLlkb9ISEhKFWqFC5duoQHDx6YFWdeevbsadRmaWmJrl27AgD2799fIOchKq4sijqAovbgwQMcPnzYoE0IYdD24g+cWbNmoXbt2pg7dy527twJS0tLtGrVChMmTJB/+FLBkiTJYLOTlNGHkRX/0Kxjn6y4BFXl0ig7sXFhhUdEREREREQFRFHC6rUeVxD0m9TmtKmrvt3UZrZeXl65zlmuXDmT/V5eXjh27JhR+4wZM/Dxxx9Drc7fAw1PT0+T7fb29gCAzMzMPOe4dev5tyD0teZfVmJiIiRJQvny5U3253Q/rayssHjxYtSqVQtPnz7FsGHD0Lp1a5Nz1K9fHwEBAdi5cydu3ryJ8uXL4+7du9i6dSu8vLwMEuiPHj2CWq2Gm5sbrKyM32f6jVVf3JdA/60FV1fXXK/34UPj/EZur8ejR49ynU/PnPdQbkz15/Ve179uycnJSExMhLOzs1mx5ia/7wOi/5o3PoEfHR1ttMt1YR5HL0epVEKhUECSJGTdeIrM9TfydfzDmcdR5qNAKOwsCydAIiIiIiIiKhB2LTyRuvNm/o8LMp2kfJ2yLzwzt9/a2vql5sz+LXW9Q4cOYdSoUXBwcMCCBQsQFBQEV1dXqFQqAIC7u3uOq7fzit1cLy7AK0ymzrNy5Ur5/0+cOAGdTpdjuaFBgwZh8ODBWLhwISZPnoxFixYhKysLAwYMMHlMbtdl6vXQarWQJAl9+vTJ9TqqVauWr3PlV37eQ9nl9t40Jz5zr+FlN7zNK36i/4o3voQO/TsolUoolUpYWFg8L5uTz5/RuieZSF5xsXCCIyIiIiIiogLjOKAGYJnPdIWlAqUHVC+cgMzg7u4OALh+/brJ/ps3nz+QyF47PS/6sQkJCSb79avds1u/fj2A56Vk+vbti/Lly8vJ+/T0dNy7d8/s87+McuXKQQiBq1evvtI87u7uEELI9+1FOd3Pffv2yXsOtGzZEvv378eXX36Z43mioqJQokQJLFy4EFlZWfj555+hUCjQv39/g3FOTk6wtLTEvXv3TH6zISUlBcnJyUbtnp6eEEJgzpw5WLx4cY6/wsLC8rgjL+dl3kN5yeu9nv182V8f/TcXUlJS8h1HTu8D/Xn0MRH9VzGBT8WeTqfD91fuodaeiyi/5y8c+z3/KzEAIHW36T+wiIiIiIiIqPiwdLWDY7TxiuTcOParDgsX4xrfr4t+k9lly5YZ9anVaqxevdpgnDkaN35eBnbNmjVGfdeuXcOJEyeM2vUlXEyVTFm9enWhr1jWl6tZsGDBK82T2/188OABduzYAYVCId8jAHj69CnefvttCCGwaNEiLF26FE5OTpg0aRKOHj1q8jz29vaIjIzE7du3MXr0aFy7dg3t27c3Kl9jaWmJwMBA6HQ6+SFJdqZeI+Cf+7Fhwwazrrug6e/P1q1bkZaWZtSfU9y58fLygpeXF+7du4fdu3cb9W/ZsgWPHz+Gv7+/QfkcfTL/8uXLRsfs2LEj13Nm/1aFXlZWFtauXQsAaNKkSb6ugejfhgl8Ktb+uJcMx03xGHn6BhLS1VALAbs07UvNpXtSdBsaERERERERkfncZgfDtoXpGuAvsm3hCbfZwYUcUe4GDBgAGxsbLF++HFu2bJHbdTodxo8fjzt37iAwMBANGzY0e85+/frB0tISixcvNtisNSMjAyNGjDBZdkS/4ezPP/8MjUYjt58/fx5jx459mUvLlxEjRsDa2hrz58+Xk6t6Op0OW7duNWueoUOHQqFQYPbs2QbJd7VajWHDhiEtLQ3h4eHw8PCQ+9577z3cvHkTw4YNQ9u2beHm5oYff/wRGo0GUVFRJhPYwD+b2c6aNQuA4ea12ek35v3ss88Mvslw+/ZtfP755yaPGTVqFGxsbDBy5EjExMQY9SclJeH7779Henp6Lnfj5fn5+SE4OBhJSUkYP368wQOcZcuWYc+ePS8177BhwwAAI0eONNg38t69exg9erTBGL0WLVoAAKZNmwat9p+8ztKlS7FixYpcz3fgwAEsXLhQ/r0QAhMnTkRCQgJq1apl8CCH6L+ICXwqtn6/9xgdD1xAutbwLyVp1sqXmk9hz/r3RERERERE/wYKG0t4bwuH48CaOZfTsVTAcWBNeP/eFQrrot3iz8vLCwsWLIAQAp06dUKzZs0QGRmJqlWr4ptvvoGLiwuWLFmSrzl9fX0xdepUZGRkoHnz5mjTpg169uwJPz8/nDp1Cp06dQIAg01V+/XrB1dXV8TExMDf3x89evRAmzZtULt2bTRr1izHzUALSqVKleREa7du3VCjRg306tULb731FsqVK4eOHTuaNU/9+vUxZcoUPH36FI0aNUKbNm3Qq1cv+Pr6YuXKlfDz88PcuXPl8StWrMCyZctQrVo1TJ8+XW7v0qUL+vXrh8uXL+PDDz80ea7atWujfv36AJ6vEs8pxrfffhudOnXC5cuX4e/vj/DwcISFhaFKlSqoXr06vLy8YGlpmHfw8/PD0qVLkZ6ejtDQUFSuXBldunRBWFgYAgIC4ObmhqFDh5q1QfDLmjdvHkqXLo3Zs2ejatWqiIyMROPGjfH222/jvffeAwCTG/PmZuTIkWjfvj1Onz4NPz8/hIeHo0uXLqhUqRIuXLiAsLAwDBkyxOCYoUOHwtnZGWvWrEHVqlURERGB2rVrIzo6GsOHD8/1fEOGDME777yDBg0aIDIyEjVq1MDUqVNRokQJLFq0KH83hOhfiAl8KpbUOh26/nnJZKn7k5VLvNSctk1YE42IiIiIiOjfQmFjCY8f2qDyrYEo+39NYdemPGwausGuTXmU/b+mqHxrIDx+aFPkyXu93r17Y+/evQgJCcGFCxewZs0apKenY8iQITh27BgqV66c7zk/+ugjLF++HLVq1cK+ffuwe/duBAcH49ChQ/KqbScnJ3m8k5MT4uPjERkZCbVajU2bNuHOnTv4/PPPsXz58gK71tz06tVLjuHRo0dYu3YtTp48CT8/P8yZM8fsecaPH4/NmzejRYsWiI+Px7p166BSqTBmzBgcPnwYLi4uAJ7XTx8yZAisrKywdOlSo41X58yZAx8fH/zwww/YtGmTyXO1atUKwPMHIBYWpt9PkiRhzZo1+Pzzz1GmTBls2bIFp06dwogRI7Bq1Sr8/fffBq+FXnh4OE6dOoVBgwZBo9Fg27ZtiIuLQ2ZmJqKiorB582Y4ODiYfV/yy9/fH4cPH0a3bt1w7949bNy4EQCwefNmeeW6qbhzo1QqsWnTJsyePRs+Pj7Yvn07duzYAX9/f3z33XdYs2aN0SbALi4u8ufj7t272LZtGxwcHLBz506Ehobmer7u3btj06ZNUCqV2LhxI27fvo3OnTvj0KFDCAgIyFfsRP9GkuCWzcWSfgfyc+fOFXEkRWPi2QRMu3THZF+FW2lYO/IkBABz92RX2FnC/84gKB1UBRYjERERERERUVFITU2Ft7c30tPT8eTJEyiVL/dNdXpejqVy5cr466+/cOXKFfj4+OR7jsOHD6Nhw4Z46623sG3btkKIsnAMGTIE8+fPx4oVK9CjR4+iDsdIdHQ0fvnlF8TGxiIoKKiowyEqMlyBT8XSj9f/zrHvejlb7K3raHbyHgAcB9Zk8p6IiIiIiIj+Va5du4YnT54YtKWkpGDw4MF4+PAhevToweT9K1qzZg0uX76MDh065Jm8P3PmjMHeAgBw48YNuVxMZGRkocX5sjIyMnDhwgWj9rVr1+Knn36Cg4OD2aWNiKhoFI/vmRG94JE6K9f+z973xc+fnkXF2+l5rsS3a+0Fly+bFWh8RERERERERIVt1apVmDRpEurWrQtPT088fvwYJ06cwMOHD+Ht7Y2pU6cWdYj/Wu+88w6Sk5OxefNmKJXKHDeizW706NE4evQoatWqhbJly+L27ds4evQoMjIy0KFDB/Tu3fs1RJ4/ycnJqFq1KipXrgw/Pz9YWlriwoULuHDhAhQKBb7//nvY29sXdZhElAsm8KlYyquu09MSlhgwpTomfXcFQUcfmx6klODYvzrcvm0JhRVXJBAREREREdG/S6tWrXDy5EkcOnQIJ06cgBACXl5e6Nu3L8aOHQtnZ+eiDvFf6+eff4aFhQUqVaqEKVOmoE6dOnkeEx0dDZ1OhzNnzmD//v2wsrJCjRo1EBkZiaFDh0KS8lMr4PVwcHDAqFGjsHPnTuzfvx/Pnj1D6dKl0blzZ3z00Udo2rRpUYdIRHlgDfxi6k2vga9aezDPJL5e+Tvp6LrzHmpefoYyGsDXvSTsWpdH6YE1YenOp8hERERERERERET078QV+FQsedmqcDMt06yxNz1sMCO6AgBgZk1vtPVzK8zQiIiIXishBLRaLbRaLYQQkCQJSqUSSqXyta3yKg4xEBERERERvYmYwKdiaYy/B4aeuJavY1QKCUMquhRSRERERK9fVlYW0tPTodFooP/SpEKhkJPn1tbWhb5xnUajQWZmJjQaDXQ6nXx+hUIBSZJeSwxERERERERvKibwqVh628sJY0/fQIpWZ/Yx71ZwgUKhKMSoiIiIXh+1Wo1nz55Bo9EYrHIXQkCpVMLS0hI6nQ62traFlkDPyMjA06dPkZmZCZ3unz+TFQoFVCoVbG1tkZaWVqgxEBERERERvcmY7aRi5UGmBpPOJaDS9pP5St63cC6JGbUrFGJkREREr09WVhZSUlKg1WphYWEhJ/AlSYKFhQWEEMjMzIRWq0VGRgYKY0sjtVqN5ORkpKenGyTvAUCn0yE9PR3Pnj2DJEmFFgMREREREdGbjivwqdi48DQNIfsv4Fa62uxjlBLQx8sZP9TzLcTIiIiIXh8hhJwQN5WglyQJlpaWsLS0hEajgUKhkBP9BUWn0yElJQWZmbnvR6NWq+UV+AUdAxERERERETGBT8VEYroaHfZfwB0zk/cOlkq85+OKT6p6woplc4iI6D9Ev1lsRkYGsrKyIEkSFAoFhBDyL7VaDZ1OB5VKJY83lTx/2c1n9XXvzZGZmQlra2sm8ImIiIiIiAoB/5VFxcK0i7fNTt4DwBONFl08nZi8JyKi/xytVgu1Wg0hBKysrOSkvSRJkCRJTshnZWVBoVDIyX1T82RkZECr1Rr15bUBrn7DWnPodDr5AQEREREREREVLGY/qcg91WRhWcKDfB/3w9V7hRANERFR0dLpdJAkCUIIpKenIzU1FWlpaUhJSUFaWhq0Wi0sLS0hSZK8we2Lq+m1Wq081pS8+s1N3uvpHzAQERERERFRwWICn4pczN3HSMnKX6IAAFbceggdV/sREdF/jFarRWZmplxCR7/iXqfTQaPRID09HRkZGfJmtkIIg5X02Wvo5ya3cQqFwiAhn/3/X6zHr/9vTqv5iYiIiIiI6OWxhA4VucR8lM7JLlWrwxONFo5WfBsTEdF/g76+vVqthkKhgEajMTlOrVbLdexfTJ7rE/7myKl+vqWlJZRKJXQ6nVwix9SqfIVCIW+oywQ+ERERERFRweMKfCpyr/KF+6eaLHx35S4GHbuKvkf+wqhT17HnwRPW4SUion+lrKwsg7I4ilz2esnMzDSZODc3eZ/beH1SXqFQ5Ji8B56X2rGwsICVlRVL6BAREeVTXFwcJElCdHS0QfukSZMgSRIWL15caOeWJAne3t6vPM/ixYshSRImTZpk9jE3btyAJEkICgp65fMXV9HR0ZAkCXFxcQUyn7e392v7u1ZQUBAkScKNGzdey/nyo6DvK9G/BZcuU5Hzsbd+qeNUCgk1d55CutYwqfDtlXuoUsIGn1fzQmeP0gURIhER0Wuh0WjkkjhZWVlycj6n1e/6lfPZV9Hn9yF2TiV07O3t5W8C5BSDSqWCra0tNBqNXJefiIiooGWkAzevAg/vA1kawMISKOMClPcBrG2KOjoiKije3t64efMmF2USvYAJfCpyHVwdUcbKAg/VWfk6LlMnAJj+oX7hWToiDl3CrFreeM/XrQCiJCIiKnxardagLI5+Q1tLS0u53r1CoTCoUS9JkkECP79J9JzG65P4CoUCmZmZ0Gq18j+mFAoFrK2tYWdnJ5fYMVWKh4iI6FVos4DTx4CE64B44Tnyg3vAxTPPk/g16gKs5FY0unTpgoYNG6JMmTJFHQoVkCVLliAtLQ0eHh5FHYqRadOmYdy4cfDy8irqUIheK/4ri4qctVKBfhXK4utLiQU+94hTN1DBzhrt3RwLfG4iIqKCJkkShBCwsLCQS+noN6vVJ/aBf1bNK5VKObGvl99a9DmN15fOsbW1hbW1tZzA1z9cUCgUBqvymcAnIqKCpM0C/owDHt3PeYzQATeuAM+eAo2DACX/GHrtHBwc4ODgUNRhUAEqzslxNzc3uLlxkSa9eVgDn4qFsf4eqF7StlDmnnrxdqHMS0REVND0G9MKIQw2ktUn6fWbyiqVSoO689lX0SuVSrOT+LmN1T8U0CfplUolLCwscizrw686ExFRQTp9LPfkfXaP7gNnjhduPObYsmUL+vfvjypVqqBkyZKws7NDrVq1MHXqVGRmZr7y/EIIDB8+HJIkoXnz5njy5AkA4OTJkxgzZgzq1q0LZ2dnqFQq+Pj44L333kNiYv4Xym3duhVt2rSBo6MjrK2t4e/vj3HjxiE5OdlobG418B8+fIhBgwbB1dUVtra2CAgIwJIlS3K9vhUrVqB58+ZwdXWFtbU1ypUrh9atW+O7774zO/59+/bh/fffR82aNeHo6AgbGxtUrlw5x2vIvhdBUlIShgwZAjc3N6hUKlSvXh0LFy7M8Vxr165F/fr1YWNjAxcXF/Tp0+el7jnwfC+kadOmwc/PD9bW1vDx8cGnn34KtVqd63FnzpxBVFQUPDw8oFKp4O7ujn79+pmsYZ99f4UjR44gJCQETk5OkCQJJ0+eBGBcA//+/fuwsLCAh4dHjvsirVq1CpIkISoqSm67e/cuvvrqK7Ro0QIeHh6wsrKCq6srwsPDER8fb3C8/jW4efMmAMgLV17cq+HFGvgajQZOTk6wtrY2+doCwJEjRyBJEpo0aWLUFxMTg3bt2slzVKpUCZ9++ilSUlKMxqampmL69OmoXbs2SpUqBXt7e1SsWBERERHYvn27yXMTFRQm8KlQCCGQlZWFzMxMpKenIy0tDenp6cjIyEBmZiaysrIM/qFf0tICW5tVQZ1SdgUey+GkFJx4bPzDl4iIqLixsLCAhYUFJEmCTqeDQqGASqWClZUVLC0tYWVlBZVKZVRGJ3sSXpIkWFtb51lKJ69xBVWKh4iIKL8y0p+XzcmPm9eeH1eUBgwYgNWrV8PBwQFvvfUWmjVrhlu3buGTTz5Bhw4d8r3RfHZZWVno27cv5syZg44dO2L79u3yyvcvv/wSM2bMgFarRZMmTdChQwcIITBv3jzUq1cvXwnladOmoWPHjoiLi0PdunURFhaGtLQ0TJ8+HQ0aNMDff/9t1jyPHj1CkyZNsGDBAlhaWiI0NBQODg7o168fvv76a5PHfPzxx+jVqxdOnjyJOnXqIDw8HL6+vjh16lSOx5gyevRo/PTTT7CyskLLli3RqlUrPH36FNOnT0fTpk1NJmcBIDk5GY0aNcL69esRGBiIJk2a4OLFixgwYAB++ukno/Fz585Ft27dcPz4cTRu3BhBQUH4448/0LBhQzx69MjsePV69eqF8ePH4969e3jrrbdQo0YNzJgxA+Hh4TkulFi7di3q1auH3377DW5ubggNDYWrqysWL16MevXq4dy5cyaP27t3L5o2bYobN26gbdu2aN68ubzv0YvKli2L1q1bIzExEbGxsSbH/PbbbwBgkMDfuHEjxo4di8TERNSoUQNhYWFwd3fH+vXr0aRJE+zYsUMe6+rqir59+8LO7nlOqG/fvvKvbt265XjPLC0tERERgczMTKxdu9bs2ABg1KhRCA0Nxd69e1G9enV07NgRarUaX3zxBYKCgpCamiqP1Wq1aNu2LcaNG4dHjx4hKCgIHTp0gIuLCzZv3ozly5fnGCNRQeAXzKjAabVaZGRkyF+912g0ck1fCwsLWFlZyRv0WVtby0kHV2sr7AmqjpW3H+KHq/cQ//ifH5bWCgkRnk5YdycJqVrTT3xz8/vfyQhwtC+wayQiIioMFhYWsLa2llfZZ2U93x8me+kcnU4HCwsLOZFvahW9UqmEra2t/Ofxi178M9iUgirFQ0RElF83rxrXvM+L0D1P4vtXK5yYzDF//ny0adNGTkICwLNnzxAZGYnNmzdj2bJl6NOnT77nTU9PR/fu3bF582ZERkbil19+MShbN3DgQMycOdOgtIhOp8MXX3yBiRMnYsKECbmuIteLj4/HhAkTUKJECfzxxx+oX78+ACAzMxNvv/02Vq9ejWHDhmHVqlV5zjV+/HhcvnwZnTt3xsqVK6FSqQAA27ZtQ2hoqNH4jIwMzJo1C97e3jh27BhKly4t92VlZeHPP//M85x6n332GRo1agRHx39K6WZmZuKDDz7AggULMGPGDHz22WdGx23cuBFdu3bFL7/8Ir+GGzduRFhYGKZMmYJ33nlHHnvjxg189NFHUKlU+P333xEUFAQASEtLQ1hYGDZv3mx2vACwfPlyrFmzBj4+Pti7d69cf/769eto3rw5bt82rixw/fp19OnTBzY2Nti5cyeaN28u9y1ZsgR9+/ZFv379cOTIEaNjFy1ahOnTp2PMmDFmxRcVFYXt27fjt99+Q6tWrQz6kpOTsW3bNpQpUwZt27aV25s0aYJTp06hZs2aBuO3b9+O0NBQvPfee/jrr78gSRIqV66MxYsXIy4uDqmpqVi8eLFZcelj++GHH/Dbb79hwIABBn06nQ6rVq2ChYUFunfvLrevWrUKM2bMQEBAANatWyev8tdoNHj//fexYMECTJo0SX5wtG/fPvz555/o3Lkz1q1bZ/Cw48mTJ7hy5YrZ8RK9DK7ApwKl1WqRlpYmJ+/1m94Bz5MOGo0GGRkZ8oZ7+rF6KqUCfcqXxYGWNXGlfR38GVwDR1vXxO2QelhQz/elkvcAkJzPDXKJiIiKgn5VvI2NDWxsbOR/nGevc29lZQVra2t5BX5Oq+j1SXxbW1t5Fb9KpZLb8kq4F1QpHiIiovx6aGbpHKPjzFscXmjCwsIMkvcAUKJECcycORPA82Rwfj158gRvvfUWNm/ejKFDh2Lp0qVGe860bNnSqC64QqHAZ599Bg8PD7PPO3fuXOh0OowYMUJO3gOASqXC3LlzYWNjg7Vr1+LOnTu5zpOSkoJff/0VFhYWmDNnjpy8B4D27dsjIiLC6JinT58iMzMTtWrVMkjeA88XOGRPTuelQ4cOBsl7/TXMmjULFhYWOd6PkiVLYsGCBQavYefOnVGjRg0kJCQYlKRZuHAhMjMz0adPHzl5DwC2trb49ttv8/3NxHnz5gEApkyZYrB5bIUKFfDpp5+aPGb27NlIS0vDV199ZXR/+vTpg7CwMMTHx+P4ceP6UtWrV8fo0aPNjq9Lly6wtbXF2rVrjcpBrVmzBmq1Gj169DB4b9aoUcMoeQ8A7dq1Q0REBK5evYqzZ8+aHUNOmjZtivLlyyMuLs7o2ya7d+/G3bt30a5dO4ONlqdOnQrg+YOT7CV6LC0tMXv2bLi6uuKnn36SSwbdv//8h1JQUJDRNxUcHBxQt27dV74OotxwBT69MiEEtFqtvPI+KysLCoXCqEyOnlarhVqtllfiZ2RkwNbW1ugPOC9bFbxs//mDXggBlUJCpi7/NXZtlHxWRURE/w5KpRI2NjaQJAmWlpZQq9Xyw2590l6pVMLS0jLPVfT6b7+9zOay+ocDaWlpuda3N7dkDxERkbmyNK/3uIL0119/YevWrbhy5QpSU1PlvWz0fflx//59BAcH48SJE/j000/x+eef5zj20aNH2LRpE86ePYvk5GT57w4ajQZJSUlISkoySoy/aN++fQCMS40Az8uotG3bFhs3bsSff/5pMgmvd/z4caSnp6NJkyYmN0Tt1auXUcmRsmXLwtPTE1u2bMHXX3+NqKgouLu75xpvbu7cuYOYmBhcvHgRT58+lROxVlZWOb4O9erVM3mPKlWqhDNnzuDu3btysnf//v0AYLCqW8/f3x8BAQEmE+emaDQaHD58GAqFwmS5mF69emHQoEFG7Tt37gTw/CGDKU2bNsWGDRsQHx+POnXqGPR16tQpX393s7e3R2hoKFasWIEtW7YgPDxc7supRA3w/JsPv//+O44cOYIHDx7I9fzPnDkD4PlnokaNGmbHYYokSejVqxe+/PJLrFixAh9++GGusd2/fx+nTp1ClSpV4O/vbzSftbU16tWrh82bN+Ovv/6Cv78/ateuDYVCga+//hqurq7o2LEjSpQo8UpxE+UHE/j0SrKXywGef+1N/5eTrKwseTO+F2VlZcHS0lKeQ6vV5plckCQJ9RztceDRs3zHWY/lc4iI6F9Ev3peq9XCysoKOp0OWq1Wrn2v30y2sJPmBVGKh4iIKL8sLF/vcQVBCIGPPvoIM2fOzPHB97Nn+fu37CeffIKsrCwMGTIk1+T98uXLMXDgwBxru+vPnVcCPzExEZIkoXz58ib79cnrvGrq6/tNJe9za//ll1/Qs2dPjBkzBmPGjEGFChXQvHlzREZGGpRmycuMGTPw8ccf57n564s8PT1NttvbP88nZF95bs41AjAqQgABAABJREFUmpvAf/ToEdRqNdzc3GBlZWXUX6JECZQqVcpok1b9NwJcXV1znf/hw4cm48uvqKgorFixAsuWLZMT+ImJidizZw8qVKiARo0aGYw/c+YMQkNDTW6mq5ffz0RusX355ZdYtmyZnMDPzMzEunXrYGdnZ/CQQ79R7oULF/L8u/TDhw/h7++PSpUq4euvv8a4cePQq1cvKJVKVK9eHa1bt0a/fv1QrVoR1u6iNwIT+PTS9CVwhBC4na7G35ka6NRqlLO2RAmlJJfRsbS0NPqhqK/hq//qkTkJfAAY6OOS7wS+l60V2rs55j2QiIioGHmV1fMFKfvDBK1WCyGE/C2A1/EQgYiI3jxlygIP7r3EcS4FH4u5Vq5ciRkzZsDT0xOzZs1Co0aN4OzsLH+bTqVS5fqNNlPCw8OxYcMG/Prrr4iMjETTpk2Nxty8eRPR0dEQQmDWrFno2LEjPDw8YGNjAwBo3LgxDh48mO9z5yavP/v158rv3xFatmyJK1euYPPmzfj999+xZ88e/PLLL/jll1/QvXt3rFy5Ms85Dh06hFGjRsHBwQELFixAUFAQXF1d5TI+7u7uuHv37ktdV3Yve40FOZd+r8G89lUwlVy2trbO17kAyGVotmzZgidPnsDBwQErVqyATqczWn0vhED37t1x48YNDB48GIMHD4aPjw/s7e0hSRLGjx+PadOmFdj7snr16qhZsyaOHz+OixcvonLlynKcvXv3hq2trTxWvyjFzc0tzwdDTk5O8v9/+OGHiIiIwIYNG7Bz507s27cP33zzDWbOnIk5c+Zg6NChBXItRKYwgU8vRQiBp2lpWHPnMRbeTsKxJ+lyn5UEhJSxQ6STDWrZW5lMzqdrdTiWlIpnOgF7pQJ1nBRwz1YXLyfhHk6YcDYBt9LNf5I+tKIblEwuEBERvbTi8jCBiIjeDOUrAhfP5m8jW0kBlPcpvJjysn79egDPa5mHhIQY9F27du2l5mzfvj2ioqLQrVs3dOjQAdu3bzda5bx161ao1WqMGjUKw4cPN5ojP+d2d3fH9evXcfPmTZOlRfQrl1+st29qnuzjX5SQkJDjsSVLlkRkZCQiIyMBPE/IR0REYNWqVYiOjkb79u1zPbf+dfjiiy/Qt29fg7709HTcu/cST4ZMcHd3x+XLl3Hz5k34+fkZ9ed2jS8qU6YMrKyscO/ePbnccHbPnj0zWn0PPP/GwNWrVzFnzhyULFky39eQX5aWloiIiMC8efOwdu1a9O/fXy5Ro3+99C5evIiLFy+iXr16cn3/7F72M5GbqKgonD59Gr/99hs+//zzHEv76L9p4erqmq/NcgGgXLlyGDZsGIYNG4asrCysWLEC/fr1w4cffoioqCiUKlWqIC6FyAgLg9NLuZuWgfaHrmLouTsGyXsAUAtg3YNUdLv4EO9deYSRfz1Ev/P38N7F+5h+IwnjrzxEYPwtdDmRgD6nbiH8+E347zqL/vFXcCI5NdfzqpQKrGnkD3sL8966XTxK4wO/3P9yQUREREVLv/F9SkoKnj59ipSUFGRmZsr1aomI6M1ibQN4VcjfMeV9nh9XVB4/fgzgeYLvRatWrXrpeUNDQ7Fq1SpkZGTgrbfewuHDh80+7969e/H33+bv7NusWTMAwLJly4z6Hjx4gB07dkChUKBx48a5zlO3bl1YW1vj8OHDuHXrllH/ihUrzI6pYcOGePvttwH8Uzc9N7ndj9WrVxfYim/9tyFWr15t1Hf58mWcPHnS7LksLS1Rv3596HQ6rF271qg/p/vVunVrAMCGDRvMPter0ifDf/vtN1y+fBnHjh1DnTp1UKVKFYNx+tfBVFmix48fy/X7X6R/eJGVlZXv2CIjIyFJEn777Tc8ffoUW7ZsQdmyZeX7pOfp6Ql/f3+cPn0a169fz/d59CwsLNC7d28EBgZCrVbj8uXLLz0XUV6YwKd8e6bRIvTPSzj+ND3PsTuTM7ExKR27Hqdj86NUfHfnCZb+/QxPtYb/IM/UCSxNeIDGu0/jx2v3cDQpBb/fe4zY+09w94XV9gGO9tjdojoq2uW8Yl8BYLCPC5bW9+PqeyIiomJMrVYjOTkZycnJSElJQVpaGlJSUuS2/NavJSKi/4aadQGnsuaNdSoL1KhbuPHkpVKlSgCABQsWGCSJ9+3bh6+//vqV5g4LC8OKFSuQlpaGdu3a4ejRo0bnXbp0KVJT/1kQd+fOHQwePDhf5xk6dCgUCgVmz55tcA61Wo1hw4YhLS0N4eHh8PDwyHUee3t7REVFISsrC8OHDzeoHb9jxw6TDzQSEhKwePFipKWlGbRnZmYiNjYWgHl12/X34+eff4ZG88+uxufPn8fYsWPzPN5c/fr1g5WVFZYsWSJv/gs8X+U/fPjwfC9C0G9S+9lnnxmU+Ll58yamTJli8phRo0bBxsYGI0eORExMjFF/UlISvv/+e6Sn5527MVfjxo3h7e2N2NhYfPPNNwBMb17r6+sLhUKB3bt3G2wanJGRgcGDByMpKcnk/Ppvb1y6dCnfsXl6eqJ58+a4evUqxo4di4yMDPTo0cPkN0gnTJgArVaLrl274uzZs0b9V69excKFC+Xfx8bG4o8//jB6XW/evCnX0s9pDwWigsAEPuXbN5fv4LQZyfuXoRXA0BPX0Tj2DEIPXES7fedRYesxdDt4EbH3n8jjapeyw9l2AdjQuDI6uDrCy9YKZVWWqFLCBmP9PXC5fR3MCfCBpeK/8xYXQiArKwuZmZnIyMhAZmYmsrKyCrSWIRER0eukVqvx5MkTqNVqoz/PhBAG/URE9GZRWgCNgwBv3+flcUyRFM/7GwcDRb2f+gcffAA7Ozt8//33qF69Onr16oXmzZujRYsW+U6kmxIeHo7ly5cjNTUVbdu2lTdIDQ0NRbVq1XD06FH4+vqiW7duCAkJQaVKleDo6Jjnavns6tevjylTpuDp06do1KgR2rRpg169esHX1xcrV66En58f5s6da9ZcX375JXx9fbF+/Xr4+fmhV69eaNmyJdq3b493333XaHxSUhL69esHZ2dntGjRAlFRUQgLC0O5cuVw6NAh1K9fX944NTf9+vWDq6srYmJi4O/vjx49eqBNmzaoXbs2mjVrluMGvfnl4+OD6dOnIyMjA8HBwWjdujV69uwJX19fnD171qiMUl6ioqLQpUsXXLlyBf7+/ujSpQvCwsJQrVo11KhRw+TDCz8/PyxduhTp6ekIDQ1F5cqV5eMCAgLg5uaGoUOHGjxAeVWSJCEyMhI6nQ4LFiyAQqFAz549jcaVLVsWAwYMwNOnT1GrVi2EhIQgIiIC3t7e2L17N6Kjo03OHxoaCgBo1aoVevXqhXfeeQfjxo0zOz79w4T58+cb/P5FvXv3xpgxY3DixAnUrl0bgYGB6N69O9566y1UqVIFvr6+mDNnjjz+1KlTaNOmDVxdXdG+fXv07t0b7dq1Q+XKlZGcnIzhw4fLDx+ICsN/J7tJr4Vap8PP1++/1nPqAGxKfIx2+87jk7M35X/gKyUJHdwcsaFJZVxpXxe3Q+rhVNvamFLdC162edfT/zfRbxj87NkzpKSkIDU1FSkpKXj27BnS0tLkTViIiIj+LXQ6HVJSUvL8M0yr1SIlJYXldIiI3kBKC6B2faBdZ6BKLcDZFXB0ev7fKrWet9euX/TJe+D5yu/4+Hh06tQJDx8+xKZNm5CSkoIffvjhlVfg63Xr1g3Lli3D06dP0aZNG5w8eRJWVlbYt28fhgwZAmtra2zevBkXLlzAsGHDsHPnTlhaWubrHOPHj8fmzZvRokULxMfHY926dVCpVBgzZgwOHz4MFxfzdgouU6YMDhw4gHfeeQeZmZnYsGEDHj16hB9//BFjxowxGl+xYkX873//Q1BQEBISErBu3TocOHAA3t7emDNnDuLi4oxqw5vi5OSE+Ph4REZGQq1WY9OmTbhz5w4+//xzLF++PF/3Ii8jRozAqlWrULt2bezfvx+7du1CUFAQDh06ZLD5qTkkScLKlSvxf//3f3B2dsbWrVtx8uRJDBs2DOvWrctxg9vw8HCcOnUKgwYNgkajwbZt2xAXF4fMzExERUVh8+bNcHBwKIjLlWVPigcFBeWYuJ43bx6++eYbVKhQAbt27cK+ffvQunVrHD16NMcHKR988AEmTJgAe3t7rF27Fj///HO+Si5FRETIGxZXrFgRDRo0yHHs9OnTsWvXLoSGhuL27dvYsGEDTpw4AVtbW4wePdpgBX5ISAgmTJiASpUq4dSpU1i9ejXOnTuHZs2aYf369Zg5c6bZMRK9DElw+W6xpN8l/Ny5c0UciaGYxCR0PZj/rzIVpM+qemJCFeN6dv9V+sSFWq02meRQKpWwsrKCvb09lMXhb65ERERmyMzMRHJyslnfJJMkCaVKlZL/QUZERERERPSm4Ap8ypfrqQX31auXNeX8bdxIzSjqMF4LIQTS0tKQkZGR4wpFrVaLjIwMpKWlsZwOERH9a2g0GrP/3BJCGNSxJSIiIiIielMwgU/5oisGCWIB4Mfrfxd1GK+Fvua9PsGh/9qcVqtFVlaWQVJfXxOfiIjo3yC/JXH4kJqIiIiIiN5ETOBTvrjb5K9+XmFZevNBUYfwWmg0GoOkfGZmJlJTU+VNbDMyMpCamion77k6kYiI/i0U+dxoPqfar0RERERERP9lFkUdAP17ZOkEtt1LLuowAAB3MzTI0glYKP7b/5jPvsI+PT09x9WKGo0GWq0Wtra2rys0IiL6lxJCQKvVQqvVQggBSZKgVCqhVCpfa5Lc0tISkiSZXQM/v5vwERERERER/RcwgU9mG3XqOpYlPCzqMN4okiRBkiRkZmb+P/buOzyKan3g+He2bwoBEkogQOi99x6qqAiIoFIUUH6ioghW5NrhWi8iqMBFRbAhAoLY6b1Xkd57LwlJtu/8/sidcTe7m+xuNpDA+TwPj2bmzMzZOjvvvOc9uZYacLvdOBwO3G53yFmNgiAIwu1BmTcl0KToJpPphk2Irtfr0ev12O32oNsKgiAIgiAIgiDcbkSUTwjK9msZTDlScOrOlzUbbvnse0ANogRTGkej0YhJ/gRBEISAXC4XmZmZOU6KntP6SNNoNMTExOR6w0Cr1RITEyNuTguCIAiCIAiCcFsSV0JCUP57+NzN7oKXhyqUuNlduCF0Ol1Q5QzcLjfXL1/l9KGjnDl6HKdDTGYrCIIg/EOWZaxWa67laoJtFykGg4G4uDgMBoPP+U6SJK/1giAIgiAIgiAItyNRQkfIlUuW+f5kwSmdowGGVix1s7txQ+h0OoxGIxaLxWsyW0Xm9XT2bdjK/o3byEy7ri6PjitC087taX5HR+Lii0e0Ty4cnGMtFs4hoaMo1YmnbkSPIQiCIESWUvM+lLY63Y35mWgwGChatCgOhwOHw6HW5VfK5ojMe0EQBEEQBEEQbmcigC/k6qrdSaYr5/rreSUBweb6vVWnPOWjjPnZnQJDkiQ0Gg0mkwm73Y7T6VRr4Z87epwlM2Zjs1h9tstITWPFvJ9Z9+si+j//NNUa5j3AbuUKf/Mxe5hGJme81pWkOXUYTlUGIImBPYIgCAVOqGVxbmQAH7LK6RiNRozG2+P8LgiCIAiCIAiCECwRaRNyld+V5l+tUpLlLSpT0Zz78PhXaibxQrUy+dyjgkUpK6DX6zGbzZjNZtIuXObPL77zG7z3ZLfa+Prdjzi6Z3+e+pDKIX6kKVt4wyd4D3CBjSzjYRbzAC5seTqWIAiCEHmhlsS5USV0BEEQBEEQBEEQhJyJAL6Qq6IGHXH6nCeYy4u/r1upE2tiafNKvFWtlE8gXy9J3J8Uz4r2tXmtVrmgasLfSvR6vVctfEmSWDF7AU57cJPVupxOfpz8hZq5HyoLF/mFrqRxJNe2R5jLCoYiBz2eQhAEQbgR/NWXB3C73bhcLjVg73muEQRBEARBEARBEG4+UUJHyJVWkniofAk+yaeJbK84sob1F9FrebJCAo+Xj2dPuo0rdidGrYaaRWNIKhKTL8cuDDQaDTExMaSmpuJyuTh37AQXT54OaR+Xz57n8K49VK1fJ+Tj7+B9rnM06PYH+YZaDCORNiEfSxAEQcgfWu0/N+IlScJut+N2u5EkCbfbjSzLaDQadDoder3eq70gCIIgCIIgCIJw84gMfCEoj1UqnW/7Nmq8s/w0kkSdWBPt4mNoXjSK0lGmfDt2YWEwGIiLi8NgMLB3/Zaw9rF16aqQt3FiYR/TQ95uN1NC3kYQBEHIP1qtFq1WiyRJWK1WZFnG6XSSmZmJxWLBarWSmZnJ9evXyczMDHvUlhA65bWw2WxYrVZsNhtOp1OUMRIEQRAEQRAEARABfCFINYqY+VfNpHzZd62YwAF6JeAgZAXxixYtSsa1tLC2v3LhYsjbnOQPbFwJebsjzMGJJeTtBEEQhPwhSRJGoxGHw4EkSdhsNux2u98gscvl4vr16zidzpvQ09uLy+UiMzNTvZGi/FOWhTr5sCAIgiDcSJIkkZycfLO74aUg9kkQIuWNN95AkiRmzJhxs7si3GCihI4QtNdqJuFwy7y/P7TyLTmRgIFli/lfJ0mYTCZRh9eDJElhZ+TJ7tC3yyC819qNAyuXiKFcWNsLgiAIOZNlmRP7D7Hxz2WcPHAYh92GKSqKqg3q0PyOTiSU8T9yTqvV4nA4/AbnPbP0HQ4HFouFmJgYcR7OJy6XC4vF4jfjXpIkdDodsixjNptFMoMgCLe9TGs6e47t4PTF49iddgw6A2VLVKBWcgOiTLdvuVUhOCtWrKBDhw4MGjRIBD4FQSiURABfCJokSYyrU567E4sx+dBZfjx9BYfHxWadGBNpThcnrMFNrgpwR4lYkqMMPsu1Wi0mk0lcsGbjcrmIKRoX1rZx8f5vlOREIvznPy/bCoIgCIFdu3SZWf/5lJMHD3stT7tyjQunzrD2l0XUa92c3sMfxWA0qutdLpcajNfr9T4BY43Ge2Cmw+HA5XKh04mfi5EmyzJWqxWLxeI3y16WZRwOhzpPQVRUlLiRIgjCbcnpcrBqxyL2Hd+JW/Yu73bywlE27VlFzeT6tK1/BzqtOF/daHv37kWv19/sbngpiH0SBEHIK3GGE0LWMj6WlvGxvJeWzsHUDByyTAmDjmSznqMWO3duOsplR+5DvsubDfy3aVXMOo0aJICsSVs1Go2aIeiZEXi7c7lc1G7VlL/XbQp52wbtW4e8TREqhbwNgJ4YTCSEta0gCIIQ2LVLl/nvmHGkXs65vNlfazeSeuUqj7z6Anpj1o1yWZa9JqzNjSzLIoCfT5xOJ1arNdcSOS6XC6vVisFgEMEIQRBuO06Xg4VrZnHm0omAbdyym91Ht3P1+mV6tOmHTiu+K2+kGjVq3Owu+CiIfRIEQcgrUQNfCFu8UU/DODPNikZRMcqAJElUijLyS9OKVDL7ZtV7qh8XxfKUOpQ06LDb7TidTtxuNzabjevXr5OWlkZGRgZ2u13UgfUgyzLJNatRrFSJkLYrUrwoNZs2DPl4ZelENKHPfVCVh9CS83tAEARBCI0sy3z/4eRcg/eK43sP8Mc3P6h/h1OGTUykmj+cTmfQcwyE0lYQBOFWsmrHohyD957OXDrB6p2L8rlHwVm/fj09e/akRIkSGI1GkpOTefLJJzlz5oxP2xkzZiBJEm+88QYHDhzgwQcfpFSpUmg0GhYsWKC2++GHH2jatClms5lSpUoxZMgQzp8/z+DBg5EkiRUrVnjtd/Xq1Tz11FPUq1ePYsWKYTabqVGjBqNHj+batWs+/VixYgWSJDF48GCuXLnCE088QWJiIkajkTp16jB9+nS/jzWnevN79uxhyJAhVKhQAaPRSKlSpWjXrh0TJ04M6nmMVJ8GDx5Mhw4dAJg5cyaSJKn/3njjDQCOHTuGJEmkpKSQlpbGc889R8WKFdHr9YwcOdLrMQ0YMIDExEQMBgNly5bl4YcfZv/+/X77IssyU6ZMoU6dOphMJpKSkhg5ciTXr18nJSUFSZI4duyYz3bHjh1j2LBhJCcnYzQaKVGiBH369OGvv/7yaev5Hjpx4gT9+/enRIkSmM1mmjRpws8//xzU8529399++y2dOnUiPj4ek8lEpUqV6N+/P2vXrvVp/9tvv9GlSxeKFSuGyWSievXqAd9rnjXct27dyp133knRokUpXrw4999/P6dOnQIgIyODF154geTkZEwmE3Xq1GHu3Lk++/N8n5w9e5bBgwdTqlQpzGYzjRo14quvvgr4OEN5PT2fZ3/8vZ6e7yuLxcLo0aPVz0OVKlV47733Av7WXrlyJSkpKcTExBAfH8+9997Lvn37Aj6Wy5cvM2bMGGrXrk1MTAxxcXFUq1aNhx9+mE2bQk8AFQoekVIlhC1QeZuq0UbWtqrCHxev88XJy2y8lolDBqNGIqVEEZ6oXJo7ShcDt5vMzEw1I9Bms6lfXp5Dx00mkzrJW1RU1G1dVkeSJCSNhq4D+/LDhKnIbncwG3HP0IfCet406KjF42zmlZC2q80TIR9LEARByNmpg0c4sf9QSNtsXbqKLg/2xhQdFfJoNo1GI0a/5ROXyxX0zRFlJIQgCMLtJMOazr7jO0PaZu+xnTSv1f6m1sT/5ptvGDx4MG63m1atWlGuXDm2bdvGlClT+PHHH1mxYoXfDPH9+/fTtGlT4uPj6dChA1evXlVHXn300UeMGjUKrVZLSkoKCQkJLF68mBUrVlCvXj2//XjhhRfYsWMHderUoWPHjthsNrZt28Z7773HL7/8woYNG4iJ8X2erl27RsuWLUlNTaVZs2akp6ezatUqHn30UdxuN0OHDg3qeZgzZw4PPfQQNpuN2rVr06pVK65cucLff//NyJEjeeaZZ4J+TvPapzZt2nDu3Dn+/PNPKleuTJs2bdR1DRo08GprsVho3749x48fp3379jRq1IhixbJK0S5dupR77rkHi8VCo0aNSElJYd++fXz99dfMnz+f3377jbZt23rtb+TIkUyaNAmj0UinTp0wm818/fXXrFu3LuAIxzVr1nD33XeTlpZG7dq16dGjB6dPn+bHH3/kt99+49dff1VvSHg6duwYTZs2xWQy0aZNG86fP8/69evp1asXv//+O127dg3m6cblcvHggw8yd+5cjEYjbdq0ISEhgRMnTjB//nwMBgOtW/8zuv+dd95hzJgx6HQ62rdvT0JCAmvXruW9995j/vz5rFq1ilKlSvkcZ+PGjTz++ONUqVKFzp07s3PnTubMmcPOnTvZtGkTXbp04ciRI7Rs2ZKKFSuycuVK7r//fn7//XfuuOMOn/1duXKFFi1aYLPZSElJ4erVqyxfvpxBgwZx9OhRXn/9da/24bye4bLb7XTt2pXdu3fTrFkzatasycqVKxk9ejTXr19n3LhxXu1/+ukn7rvvPlwuF61ataJ8+fJs2rSJ5s2bc8899/jsPz09nRYtWnDo0CGqVq2qPj8nTpxg1qxZVKpUiWbNmkXksQg3jwjgC2FTStv4u6jUayTuKVWEe0oVyQrQazQU9ZgIT5ZlMq1W9eLV4XD4vZB1uVzY7XYMBoNaL/Z2rgOrBOEr1qlJz8cH8/O0mbicgS/qJY2G3k8MoXbzJmEfsx6jOM5CLhDcXdtGvEI8dcM+niAIguDfxj+XhbyN3WZj+/I1tCzfAO3+SxRJtxEty7hi9WRWjsGeaAI/51TlHH873zTPT2IkhCAIQs72HtvhU/M+N27ZzZ5jO2hSo03ujfPByZMneeyxx5AkiYULF9K9e/esfrndPPfcc3z00UcBs2G///57nnrqKT766COvc++RI0d46aWXMJlMLFq0SA0oWq1W+vTpw8KFC/325bXXXqNly5Zq8BnAZrMxYsQIpk2bxocffshrr73ms50SOJw5cybR0dHqsl69ejF27NigAvgHDx7k4Ycfxu12M3v2bO6//351ndvt5rfffst1H5Hs09ChQ6lSpQp//vknbdq0yXES202bNtGyZUuOHDlC0aJF1eUZGRkMGDAAi8XClClTePzxx9V1EyZM4Nlnn6V///4cOnQI4//mH1qzZg2TJk0iISGBVatWUbNmTQCuXr1Kly5dWL9+vc/x09LS6Nu3LxaLhTlz5tCnTx913ZIlS7j77rt56KGHOHLkCAaD94j3mTNn8vTTT/Phhx+qNwcmTpzIyJEjGTduXNAB/HfeeYe5c+dSt25dfv75ZypUqKCuu3LlCnv37lX/3rx5M6+88gqxsbEsWbJEDRLbbDYeeugh5syZw9NPP80PP/zgc5ypU6cyYcIEdYSDw+HgrrvuYsmSJbRq1YqSJUty8OBB9T38xRdfMHToUN5++22/Afyff/6ZLl26MH/+fPV9snnzZjp27Mhbb71Fz5491Rs24byeebF+/Xratm3LgQMHSEjIKjW8ZcsWWrZsyYQJExg9erR6Q+369esMHToUl8vFd999R79+/YCsEZlDhw5l5syZPvufO3cuhw4d4umnn2bSpEle6y5cuMCFCxfy/BiEm0+U0BHCJkkSJpMp12C6RqOhSLagu8vlUgP/nv/vye1243K5sNlsOJ1Otd3tnIXmGUyp0bQhg19/kXptW6DLVhdXq9NSt3Uznnj3NRp3bJenY+qJ4i5+pzS5/xBuyGia8laejicIgiD4l33S2mCd+n0LLD2KdOo6mmt29KkOTKcyKb7yAgm/nEF/yebVXpIk9Hq9CODno1CfV/E6CIJwuzl98fgN3S4SPv/8cywWC/369VOD95B1Pfzuu+9SpkwZNm/ezIYNG3y2LVGiBO+9957P9/306dOx2+0MGjTIKxvYZDIxceLEgHPa3HXXXV7BewCj0chHH32ETqfjp59+8rtdkSJFmDZtmhoABejZsyd169blxIkTfsu9ZDdhwgSsVivDhg3zCt5D1nPh+dwEIxJ9CsWkSZO8gveQVcLo/PnztG3b1ivYCzBq1CgaN27MqVOnmD9/vrp86tSpADz33HNq8B6gWLFifPDBB36PPX36dM6dO8fzzz/vFbwH6Ny5M08++SSnT5/ml19+8dm2UqVKjB8/3iuzf/jw4RQrVowNGzZgt9tzfex2u53x48cjSRLTp0/3Ct4DFC9e3Cv7/pNPPsHtdjNy5EivDG+j0cgnn3yC2Wxm3rx5nD592udY7dq18ypPpNfrGTFiBJA1IuWzzz7zeg8PHjyYhIQE1q9fj8Ph8NmfJEl8/PHHXu+Tpk2bMnz4cNxuN1OmTFGXh/N65oVGo+Hzzz9Xg/cATZo04c477yQzM5MtW7aoy+fMmcOlS5fo0qWLGrwH0Ol0TJgwwe/IGSVA37FjR591JUuWpE6dOhF5HMLNJQL4Qp5otdocy9oEWu8ZhHdnKwMjyzJOp1OdxNbhcOBwOLDb7dhsNr9f1reL7DdNSiSV4a5HBvDUhHH0HfU4PR8fTN+Rj/PUhH9z/zOPk1S5YkSOa6I497CMzswiEe9hZFqMVONh7mUDzXkHidtzdIQgCEJ+c9hyv/Dyx57DeVOX7qT4svMYzluBrPO20WhEq9XmfJNelsHqhAwHuELLkBSyLlKVC2zPyYUB9TnP/l8loUFk4wuCcDuwO8M854W5XSSsXr0agAEDBvisMxqN9O3b16udp86dOxMVFeWzfN26dQDqtp4qV65Mw4aB5zk7ffo0U6dOZeTIkTzyyCMMHjyYJ554AoPBwMGDB/1u06RJE4oXL+6zvFq1agCcPXs24PEUS5YsAWDYsGG5tg1GJPoUrMTERJo08R29ntNrCzBw4ECvdpDza9ehQwfi4+N9li9evBiAXr16+T2OUv5n8+bNPutSUlJ8JrzX6XRUqlQJh8PB5cuX/e7T05YtW7h27RqNGjXy+zxkl9PzUrJkSbp27Yrb7VafC09dunTxWVapUiUAkpOTqVKlitc6rVZLcnIyDoeDS5cu+WzbsGFDqlev7rNcCYKvWbMmqH6D/9czL5KTk9X3qyd/72Gln9lvfkHWzR9/IykaN24MwJgxY/jll1+wWq0R6bdQsIgSOkKeKUF6JTtelmUkSVIz9/xd/Ae6+FRq32dfr/ytZOQbDIbbNhtNeb6tVqt6I8QUHUXlerXV9SaTKeLPjxY9VXiQKjxIOqexcB4NemIoj5G4iB5LEARB8GWKjoJLuV98ZWfW5Tz0V3LJFF1zkbTeFdEYDTmfR67bYNdF+PtCVvAeQCNBpaLQoBQkFfFbkkfwJkkSGo1Gne/Hc7lWq0Wv16vrzGYzLpdLncg2v87zgiAIBYlBZ8i9UQS3iwRlktpAk7oqy/1NZlu+fPkc91muXDm/68uXL8/WrVt9ln/44Ye8/PLLQWVde0pKSvK7XMn6tdlsftd7OnnyJPBPMDavItGnYOX2OoTy2p45cwZJkgL2v3z58j5BdWU0QfPmzXPsp78AdiRfu8qVK+faFv55jNkz9RU5vefLli3rs0zJnve3znO9v8cSSh/y8lkNRyivjXLMQO9Ff8s7derEqFGj+Oijj7jnnnswGAw0aNCArl278uijjwZ8nELhIgL4QkRIkoROpws4EYu/9v4EmtRNkiSv5aIWfug3TSIphrLE4P+kKgiCIOSPag3rce74yZC3q1rM/wWAJ43dTcxxCzQpGvg8svsi8pKjSO5s52m3DIeuZv2rWBTuqgIGEVwOxOVyYbFY0Ov1GI1G7Ha7ekNembDW6XSq641Go9dvIJfLRWZmZo4jIAVBEAq7siUqcPLC0bC2u9lyuxbzt95kMoW1T3/Xzhs2bOC5554jLi6OadOmkZKSQunSpdVa3mXKlAmYtR6p60hJkiK6rxsl3Nch2PWeAs0BCFlZ+/5GZCj8Bfgj+TxF+jn3t7+cjnGjXvNIvp7ZK0uEu5/sIzKD9eGHHzJs2DB++uknli5dytq1a9m0aRPvv/8+s2fPDjiqQyg8RABfuKEsLhdzTl3miyPn2X/dgkOWKaHXcnd8FA+WjKGkxn/wXqPR4HQ61f9XAtfB3jC4FYV600QQBEEo3Jp17cCqn37LKl8TpCKGaGoVTw6qrW73ZWiehAzq3DPKDWLd/qtolx7PsUiaDEhHr8HCA9CrOuhEpcbsZFnGarWqF2dmsxmtVovdblcv/JRMe51OR1RUlN8LQmU/t3MygyAIt7aayQ3YtGdVSBPZaiQNtZIb5F+nclGmTBn279/P0aNH/ZbLOH48qz5/YmJi0PtMTExk//79nDhxgqpVq/qsVzKmPSl1u8eNG8egQYO81lksFs6dOxf08cNRrlw5Dh48yOHDh2+Z2ttlypQB4OhR/zeV/L22iYmJHDt2jFOnTvnNaD916pTPsqSkJPbv388rr7xCvXr1ItH1kCgjPQ4dOhRU+zJlynD06FGOHz/ut3xNOO/5cCnHCrRceQ09/z+U11OZNDg9Pd3vNv4+i+FQ+hbo8Zw4cSLgttWrV+fFF1/kxRdfxGq18umnn/L8888zbNgwEcC/BYgrK+GGWXz+GlV+38bQLYdZfyWdKw4X151ujlgcfHwqlbbbTvPuyVTc2QITer1evXjV6XTqhertPJmtIAiCcPspXqoEjdq3zr2hh/ZJjdBqgszSTrPhum4lMzOTzMxMbDYbdrsd+5V0NMsDXywoJLKC+JxMg235GxworJQEBIUsyxgMBqKjo4mOjsZsNmM2m4mOjkaSpByzubLvSxAE4VYSbYqhRoX6IW1TM7k+USbfCR5vFGWS2W+//dZnnd1uZ86cOV7tgtGqVSsA5s6d67PuyJEjbN++3Wf51atXAf9ld+bMmZPvc6l07twZgGnTpuXrcUKhBF+Vm+Shyum19Vzu+drm9NqtWLHCbxkc5blbsGBBWP3MqyZNmlC0aFG2bdvmtzRTdjk9LxcvXmTRokVoNBr1uchPO3bs4MCBAz7LZ82aBeA1+W44r6cSzPd3DOUmWyQo8xwo3xeerl27xqJFi4Laj8lk4rnnniMxMZELFy6oE90KhZcI4As3xB/nrtJz7T4u2gKfMN3AF+cz+NfxVPVHhU6nUwP4Wq0Wg8GgrhOTuAmCIAi3m57DBlGpbq2g2rYqU48WiaFlvlnSMnyCwob913zL5gSg5oL/dT6rtI7gxV/AXfk9o9Fo1L+VERA5BfAD7U8QBOFW0a5BV8ok5F4GDqBMQnna1r8jn3uUs0cffRSz2cysWbP49ddf1eVut5sxY8Zw+vRpmjZtSosWLYLe55AhQ9Dr9cyYMcNrIlCr1crIkSP9nieU7P8vvvgCh8dE9nv27OGll14K56GFZOTIkZhMJqZOncq8efO81rndbn777bd870N2Slbz/v37w9r+/vvvp1SpUqxevdrnxsSkSZPYvHkzSUlJ3Hvvveryxx57DIDx48d7HffatWu8+OKLfo8zbNgwSpQowdtvv82XX37pE/PIyMjgq6++8pu9HwkGg4FRo0YhyzKPPvqoT1b5lStXWLt2rfr38OHD0Wg0TJw4kS1btqjL7XY7Tz/9NJmZmfTu3TtgTftIcrvdjBgxgszMTHXZ1q1b+fTTT9FoNF6TKofzejZt2pSoqCh+//13r5sbFy9e5NFHH831N1uw+vbtS/HixVm0aBE//PCDutzlcvHcc8/5HQGwYMECNmzY4LN8+/btnD9/ntjYWIoVKxaR/gk3jwjgC/nuks3BgI0HcQYZcJ97KZP5VywYDAaMRiMulwu9Xo/JZPI6gYkh44IgCMLtRm8wMPhfz9K2510Yo8x+2xQxRNOjcjvurtg66HOlcnaV9dl+Gsoy+v3XQu/odTscTw19u1tcbskHsix7tQmmvSAIwq1Kp9XTo00/aldsiEbyH7rQSBpqV2xIjzb90WlvbmnR8uXLM23aNGRZ5p577qFt27b079+fWrVqMX78eEqVKsVXX30V0j6rVKnC22+/jdVqpV27dnTp0oUHH3yQqlWrsnPnTu655x7gnwxzyAr6ly5dmp9//pnq1avzwAMP0KVLFxo0aEDbtm0DTvYZKdWqVWP69OkA9OnTh7p169KvXz+6detGuXLluPvuu/P1+P4kJydTr149tmzZQrNmzRgyZAhDhw5l4cKFQW0fHR3Nt99+i9lsZtiwYTRp0oT+/fvTqFEjnnnmGaKjo/nuu+/UeQYA2rdvz/Dhw7l48SINGjSge/fu9O3blypVquB2u9UbOZ6vXbFixZg/fz7R0dE88sgjVKxYke7du3PffffRtGlTSpUqxaBBg/xm70fKmDFj6NWrFzt37qRq1ap06dKFfv360bp1a8qWLctnn32mtm3WrBljx44lLS2Nli1bqm2rVKnC7NmzqVq1Kp988km+9dVT9+7d2bt3L5UrV+aBBx6gW7dutGzZkuvXrzNmzBgaN26stg3n9YyJieH555/H6XTSpk0b7rzzTu68806qVauGLMu0bNkyIo+jSJEiTJs2DY1GwwMPPECbNm3o378/1atXZ+7cuQwYMMBnmxUrVtCyZUuSkpK45557GDBgAB06dKBZs2a43W7Gjh2LXq+PSP+Em0cE8IV8N+PYBa47Q8sQ++qiBZPJhE6nw2w2e2XeK8TEbYIgCMItLdMBm07D3L3w7d8wZw+sOYnO4ubOhx9g9LSP6DVsMI0bNqVuQmWalq5F/xp38ELTh2iRWCekG90S4CpuRDZ5n1slp4wmM7zh5ly1hLfdLSzU5INITq4mCIJQGOm0ejo0upvBd42gRe0UypWsSKniZSlXsiItaqcw+K4RdGh0900P3isGDhzIqlWr1GDi3LlzsVgsPPHEE2zdupUaNWqEvM/nn3+eWbNmUb9+fVavXs2yZcvo0KEDGzZswGLJOtfGx8er7ePj49m8eTP9+/fHbrezcOFCTp8+zVtvvaWWE8lv/fr1U/tw+fJl5s2bx44dO6hatSqTJk26IX3Ibt68efTq1YsjR47w1Vdf8cUXX7Bt27agt+/UqRObN2+mX79+nDp1irlz53Lu3DkGDhzI1q1b/ZZG+vjjj/n444+pWLEiixcvZt26dTz44IMsW7aMS5cuIUmST2Z069at2bVrF8899xxms5lly5axaNEi0tLS6N69O7Nnz6ZWreBGY4ZDp9Mxb948pk+fTtOmTdm0aRMLFizg7Nmz3HfffV6Z7JAV8P/ll19o3749mzdv5scff8RoNPLiiy+yceNGSpUqlW999RQfH8/69evp3Lkzy5cvZ8WKFdSqVYsvv/ySsWPH+rQP5/V84403+OCDD0hKSmLp0qX8/fffPProoyxevNjrRkxe3XfffSxevJi2bduyfft2fv/9d2rVqsX69eupUqWKT/vBgwfz3HPPUaZMGTZt2sS8efM4evQod911F8uXL+eZZ56JWN+Em0eSRepOgVS7dm0Adu/efZN7kjeyLFPjj+0czbSFvO3PDZNoXjxWHVLuSavVionbBEEQhFuTyw0rT8CuC4HL0FQrDp0rglEHvx6EA1fyfFhL69I4avxzESlJEo40C8V/8D/BV65al4NmZXJvdxtxOp1eQ7uzc7lc2GxZv5kkScJs9j/KQhEVFSUmsxcEQbhNZWRkkJycjMViITU1VSS4FSKnT58mOTmZKlWqsHfv3pvdnUJtxYoVdOjQgUGDBjFjxoyb3R1ByDciA1/IV9ccrrCC9wA7Uy3YbDafWmKSJGEymUTwXhAEQbj1uNyw8ADszKWG/IErMGcv2JxgDTND3oMco8dROc57mSzj0LiRw/21GC2G6man1WpzDLB4rtfpdDn+1sltX4IgCMKt4ciRI6SmepelS09P5/HHH+fSpUs88MAD4nxQQO3bt08dJaG4ePEiQ4YMwel00r9//5vUM0EQChuRsiPkK4sr/Ik8LP8LXNjtdrRaLZIkodVqMZlMfrPyBUEQBKHQW3sSjgVZO/5iJiw6Avo8XrSbdTi6V4Js9e/dbjeyBJllzUSfDLEcjlaCSkXz1q9bkJKEkJmZGbB+vVKj1F/5wOz7EckMgiAIt74ffviBN954g8aNG5OUlMTVq1fZvn07ly5dIjk5mbfffvtmd1EI4KOPPuK7776jYcOGJCYmcv78ebZt20ZaWhqNGjXi+eefv9ldFAShkBABfCFfFc1DUKFUtBmdTofL5UKSJDUTzWq14nA4MJlMItNAEARBuHXYnLDzQmjbHLoKDUuHf8zyRaBTRTQxOshW2kUJHqdXiiL6pAWZrFr5QakWD2aRge+PUgbQarXicvnOEaTX64mKisLhcPhdryQziN9AgiAIt4dOnTqxY8cONmzYwPbt25FlmfLlyzNo0CBeeuklSpQocbO7KATQu3dvzpw5w7Zt29i4cSNarZbKlStz3333qTXuBUEQgiFq4BdQt0oNfIBuq/aw7GKQ2YT/o5VgY7NkSv0vG1Cv1/vUeJUkiaioKHEBKwiCINwadpyD5cdD365iHBwN7TwLQI9qUDmr5r0sy2RmZnoFjN1ud9ZNc7ud4uuuEH3WFlQQXzZokPrXhWKm0Pt0G5FlGZfLhcvlQpZldaShMuowt/WCIAiCIAiCINweRB0SId8Nqxz6rOPd4mMoqcu6eFX+ZSfLMlarNeDwckEQBEEoVE5dD2+7cIL35YuowXvwX5JFo9Gg1Woxmkxca1Eca0ljrsF7t16D4+7KIngfBGV0odFoxGQyYTQavere57ZeEARBEARBEITbgwjgC/nunsTiNCoaHXR7o0ZieFJcru0kScLlcmGxWLBardhsNpxOpwjoC4IgCIWTw7dcSr4oYoRulX0WK6VdlJFtGo1GvVludTk43SyGK3VicET5/nyUNWCpFENa93LoyhfN70cgCIIgCIIgCIJw27jhNfCHDx/Ovn37WLp06Y0+tHCT6DQS81vVoMuq3RxIt+bY1iBJTK1dhjrRBq/l2SetlSQJu92O0+lEp9OpE76BqA0rCIIgFFKGG3DeSoyB7lUh23lWoQTxHQ4HGRkZaDQadDodGo0Gh8PB1UomrlY0Yr7owJQpo9NocRkk3MlxuA0a4uLixETzgiAIgiAIgiAIEXTDA/jbtm1j06ZNN/qwwk2WaDawqkMdxuw6wXcnLmJ1+2bJtygaxatVS9Ek1ojNZlOXK/VeFcpEtv4mdgNwuVxkZmaK+viCIAhC4ZJUBA5cyZ99x+jh7qpZAfwgSrA4HA71/KvT6XA4HGg0Gux2e9bot5IGLIDZbM4qvQPExcRgMPi/MSAIgiAIgiAIgiCE54YH8IXbV3GDnqmNK/N23fLMOnGJfWmZWOwOShi09CwVR63YrHq5brdb3UaSJK/seiXzPlDwXqEM+Y+KihK1YgVBEITCoWYCrDkBdnfubUOV4YAYQ1DBe2XiVIVGo1HrrxsMBq9JVTUaDWazGaPRKDLvBUEQBEEQBEEQ8kHYAfwffvghrO0uX74c7iGFW0Rxg57hVRKBrCBB9mx6jUajBgX0er1XQECWZZxOp9f+AgUMlACETifuUwmCIAiFgEEL9UvD5jOR37cMnLmeVf8+F4FukmcfEafQaDQieC8IgiAIgiAIgpBPwo5sPvjgg2FlNisZW4IA/9TaVYLtyvtDkiS/k9G63W6v5crQflmW1XVK8F+Z5FYE8AVBEIRCo1USXLHA4auR37cjuMz+UCeDF5PHC4IgCIIgCIIg5J+wI5tarRa3283AgQNDyrr69ddfRRa+4EWSJHQ6nVegXafTkZmZ6RMU8PxbkiQMBoM6mW32ddn3KQiCIAgFnkbKmmR2zUnYeR6cESynE+QkuaEmWojEDEEQBEEQBEEQhPwTdnSzTp06/PXXX4wePZqaNWsGvV3Lli1FAF/IlZKZn728jhIk0Gq1avDe31B/WZZxOBxYrVYMBoOYzFYQBEEoPDQStCsPzcrAnktwIhXsLtBrsiah3XMJUm257yf7PsvGBtU01HOmOMcKgiAIgiAIgiDkn7ALljZr1gyALVu2RKwzguBJCeJHRUVhNBoxGAwYjUaMRiMmkwmn05nrZLYAVqv1th/eb0nP4PyJU5w7cQpLesbN7o4gCIIQDJMOGpWGXtXh/lpwbw1okQQNSoe+r8rFsiaxDUKgWvd5bSsIgiAIt7Jjx44hSRIpKSm3dR9upOTkZDESUBCE20LYGfjNmjXjs88+Y9OmTTz00ENBbyfL8m0fTBWCl70UjlLr3uVy+Uxmm50SVLhdJ7OVZZnDu/aw8Y9l7N28Dbc7qwyDRqOhRtOGtLijE5Xr1RI/eARBEAqbWglZE91mOoJrLwGNE4PevSRJmEwmv6Xs/LUT5xFBEARBEARBEIT8E3ZEs1evXiQkJFCqVKmQttuwYUO4hxQENViQnp6ea1BBr9erf99uAXynw8n8qdPZvmKtzzq3282ejVvZs3ErDdq1pPeTj6LzeK4EQRCEAs6ky8rKn7s3q7RObrpUyiq9E4JApew815tMJpF9LwiCINwQmWnn2b/2c84eXInDdh29MZbEailUb/UoUUVCi0kIgiAIQmETdkQzPj6enj17RrIvghAUrVaLXq8PWP9eWe85uXI4oz5kWVaz92VZRpIkNau/IGcbyrLMvE8/Z+fq9bm23bFqPW6Xm/tHPh7SZNSCIAjCTVYqGh6sBYuPwtl0/22KGCGlQlb5nDAoQfzCeC4UBEEQbg1Ou4X1c57hwIYZuF3eI89O71vMtl/foFrLIbTsOxGd3nSTeikIgiAI+atAROx++ukn3nrrrZvdDaEQ0Wq1aj18vV6v/lOWZQ9GhxpkcLlcZGZmkpmZic1mw263Y7PZ1GXB1N6/WfZu3h5U8F7x19qN7Nm0LR97JAiCIOSL+Ch4sDYMqAP1S0GFOChXBGrEZ2XoP1I/7OC9Qillp8w/YzQa0el0IngvCIIg5Dun3cIfn97JvrWf+QTvFW6Xg31rpvHHJ91w2i03uIeBWSwWRo8eTYUKFTAajVSpUoX33nvPK7Hs7Nmz6PV6ypcvr5Y7zW7mzJlIksTQoUO9ll+6dIlhw4ZRunRpoqKiaNiwIV999VXA/rzxxhtIksSMGTPYtGkT3bt3Jz4+HkmS2LFjh9ru66+/pk2bNhQpUoSoqCjq1avHO++8g9Vq9btfWZb59ttv6dSpE/Hx8ZhMJipVqkT//v1Zu3atV7tZs2bx4IMPUq1aNaKjo4mNjaVZs2ZMnjzZ7+MPts/K/idOnEitWrUwmUyULVuWESNGcO3aNb/9zszMZOzYsdSpUwez2UxcXBzt2rXj+++/99ves9b+559/Tr169TCbzZQuXZphw4b5PU5GRgbvvfceDRo0oGjRosTExFC5cmX69u3Ln3/+6fc4giAIgRSIAP6CBQt48803b3Y3hEJEGbKvZNsr/wIN5Q9liL8SvA8UpM9t/c224Y8lYWyzNB96IgiCINwQJaOhYzL0rgF9asKdVaBiURBBdkEQBKEQWz/nGc4eXBlU27MHV7J+7sj87VCQ7HY7Xbt2Zdq0adSsWZMOHTpw+vRpRo8ezauvvqq2S0xMpEePHpw8eZI//vjD774+//xzAP7v//5PXXb58mVat27NtGnT0Ov19OjRg7i4OIYMGcIHH3yQY99WrVpFmzZtOHbsGF27dqVdu3Zq8tuwYcN4+OGH2bp1K23btuXuu+/m7NmzjBkzho4dO2KxeN8gcblc3H///QwcOJC1a9fSsGFDevXqRenSpZk/fz6fffaZ2tZms9G/f38WLVpEyZIlueeee2jevDm7d+9m+PDhPPLII2H1WfH000/zwgsvkJSURM+ePXG5XHz88ce0b9+e69eve7W9fv067dq147XXXuPChQt0796d1q1bs2nTJvr168fIkSMD9uXFF19k+PDhFClShG7duiHLMtOmTaNHjx5eN2dcLhddu3Zl9OjRXL58mZSUFO666y5KlSrFL7/8wqxZs3J8nQRBELK7fYqCC7cUzwlqg20bDFmWsVqtuZbcUdpFRUUVqCzEaxcvc2jn7pC3O7JrD1fOX6R4qRL50CtBEARBuH0U1hJ8giAIBUlm6jkObJgR0jYH1n9J4+5v3fSa+OvXr6dt27YcOHCAhIQEALZs2ULLli2ZMGECo0ePJiYma26axx9/nB9//JHPPvuMu+66y2s/+/btY82aNdSpU4fmzZury8eMGcOBAwfo2bMns2fPxmg0AvD777/To0ePHPv25Zdf8t577/Hiiy96LZ83bx7Tpk2jbNmyrFixgipVqgCQlpbG3XffzZo1a3j99dd5//331W3eeecd5s6dS926dfn555+pUKGCuu7KlSvs3btX/Vun0zFv3jy6d++OwWBQl1+8eJG77rqLmTNn8sgjj9CuXbug++zp66+/Zv369TRu3BiA9PR0evbsybJly3j99df58MMPvZ6/rVu30rlzZ+bPn6++Fvv27aN9+/ZMnDiRrl27+rweAN988w0bN26kQYMGQNZIiJYtW7J69WqWL19Ox44dAVi9ejXr1q2jZ8+e/Pjjj143HFJTUzl06FDAxyIIguBPgcjAF4RQKZPZ5nYhHGw7hXLBHem2N8qV8xduyraCIAiCIBTuEnyCIAgFyf51XwQsmxOI2+Vg/7ov8qlHwdNoNHz++edq8B6gSZMm3HnnnWRmZrJlyxZ1eefOnalcuTK//PIL586d89qPkn3/2GOPqcvS09P5+uuv0el0TJo0SQ3eA9x555307ds3x77VqVOHF154wWf5pEmTAHjrrbfU4D1AkSJFmDx5MpIkMXXqVOx2O5A1ymD8+PFIksT06dO9gvcAxYsXp3Xr1urfOp2O3r17ewXvAUqUKME777wDZJVWDqXPnp566ik1eA8QExPDJ598giRJfPHFF9hsNiCrrM0XX3yBRqNh8uTJavAeoEaNGrzyyitez0d2Y8eOVYP3AAkJCTzxxBNA1kgBxYULWdfWKSkpPqMF4uLivPoqCIIQDBHAFwotZXK9nMrm5LTen1AvrAvahXig2onBkPOwrSAIgiDc7rKX2HO5XDgcDvWf3W4XQfzbiCzLOJ1ObDYbVqsVm82G0+nMdZSnIAhZgi2d47PdgRWR7UgYkpOTqVatms9yZdnZs2fVZZIk8dhjj+F0OpkxY4a63G6389VXX2EymRg4cKC6fNu2bVgsFpo3b0758uV9jtGvX78c+3bPPff4JLc5HA42bNiAJEn079/fZ5u6detSr149rl+/zs6dO4GsEQXXrl2jUaNGNGnSJMdjetqxYwfvv/8+w4cPZ8iQIQwePJgpU6YAcPDgwaD7nN2DDz7os6xmzZrUr1+ftLQ0/vrrLwC2bt2KxWKhWbNmVK1a1Webhx56CIC1a9f6/b7u2rWrzzJ/r2uDBg3QaDR88MEHfP/99z5lfARBEEIlSugIhZoSpI/UUPVQL6oK2kVYkeLhT1YYm4dtBUEQhMgRJVgKH88SfG63G4fD4TdQ73Q6gazMQPFa3rpcLhdWq9Xve0Cr1WIymUJKMBGE25HDFl7AM9ztIikpKcnvciXbW8kGVwwZMoRXX32Vzz//nJdeeglJkliwYAEXL15k4MCBFCv2z3XamTNnAPwG73NantP6y5cvY7fbKV26NCaTye92ycnJ7Ny5Uz3+yZMnAahcuXKOx1PY7XYGDx6cY+33QEHu3B4T4DMCQJGcnMyOHTvUfiv/TU5O9tu+aNGixMXFkZqaSlpaGnFxcV7r/b22/l7XatWq8cEHHzB69Gj69euHVqulTp06dO7cmSFDhlC7du1cH5MgCIInkYEvFHqSJKHT6TAajZhMJoxGIzqdLqwL41C3KWgX3yXKJlKmov8fLzkpXaEcpcqVzYceCYIgCKEQJVgKJ+WGi9vtxmazBXydXC4XFosFhyO0shBC4SDLMna7nbS0NKxWq98bOdlHagiC4J/eGHtDt4ukUK8RS5QoQe/evTl8+DArVqwA/E9eC/8kkIV7HRooQB/sPrO3CbYfH374IbNmzaJOnTr8/vvvnD9/HrvdjizL7N+/HwicHJdTn3MTaJ/hPNZgt1M8++yzHD58mEmTJnHXXXdx/Phxxo8fT7169fj000+D3o8gCAKIAL4geAk1G6qgZU9JkkSLbp1C3q55t04F7maEIAjC7Sa3wJ4I/BVcymvicDhyHZ3ndrvVbH3h1qF8Pq9fv47NZlNLJ9lsNmw2m1eZQ88RG4Ig+JdYtX1421VLiWxHbpDHH38cgM8++4xjx46xZMkSqlWr5jOpa5kyZQA4fvy43/2cOHEi5GPHx8djMBg4d+4cFovFbxvleImJiQCUK1cOIOjJWOfPnw/ArFmz6NatGyVLlkSv1wNw5MiRkPscqH/ZKc+H8rwp/z169Kjf9qmpqaSmphIdHU1sbN5vBpUrV46nn36ahQsXcvHiRb7++ms0Gg3PPvss165dy/P+BUG4fYgAviB4UEoURLrtjdSgfSvKVQtuKCNAUtVKNEppnXtDQSgk0q5e49Sho5w+coyMtJs/jFoQghFsQM+znTXTwvrfl/D56+8ycdS/+OSF15gz6b8c23tABAZvMM+yR8EIpa1Q8HneXFPKJGVfnz2IL94DgpCz6q0eRaPVh7SNRquneqtH86lH+at9+/bUqFGDH3/8kffffx9Zln2y7wEaN26MyWRi48aNahkbT99//33Ix9br9bRo0QJZlv2WuPn777/ZuXMnsbGx1K9fH8ialLdo0aJs27aNrVu35nqMq1evAv8E/j398MMPIfc5u9mzZ/ss27dvHzt27CA2NpZ69eoBWc+f2Wxm06ZNfmvuf/PNNwC0adMm4gluOp2OgQMH0rRpU+x2OwcOHIjo/gVBuLWJAL4geJAkCZPJlOvJOth2N4NOr+fhl0eRVLVSrm3LVq7Iwy+PQm8w3ICeCUL+cbvc/L1+M5+/8R7vDn2GyS+9wacvvM47j47gm/cnceiv3SKgKRRowQTzlElRrVYrS+f8xDtDn+Hnz7/myN97OX/iFGeOHGf7ynVMe+XffPLCa1w4deYG9V6QJCmkieQlSRLB21tE9vkPAp1rZFn2Kp0kSZKaoS8muRUEX1FxpanWYnBI21RrOYSoIqXyp0M3wLBhw7DZbEyZMgW9Xs+gQYN82sTExDBgwACcTifPPPOMV931RYsWhR0Mf/rppwF4/fXXvTLir1+/zlNPPYUsywwbNgzD/64bDQYDo0aNQpZlHn30UZ+bCVeuXGHt2rXq38pEr1OnTvVqN3fuXL766quw+uzpk08+Yfv27erfGRkZPP3008iyzCOPPILRaAQgOjqaRx55BLfbzfDhw8nIyFC3OXDgAOPGjfN6PsK1fPlylixZ4vPb4Pjx4+zduxdJkgLOlSAIguCPmMRWuCkK8gR9ysS4hXnysegisfzfmy+z4c+lbPpzOZfPnfdaX7xUCZp17UiLOzth+N+PGUEorGwWC7PGf8qB7bt81rndbvZs3MqejVtp0qkdPYcNLtCfXeHWk56axq61G7l26QqSBEVLJFCvdXOiYmO82uUUzPWcFFWWZVb/+As7lq3J8bhnj57gv2PGMfSt0SQm5z75m5A3ofx+kSQJjUYjArW3CM+bb7m9pkpbnU6H3W7H4XCg03lfjhWG35mCcKO07DuR1AsHOHtwZa5tE6u2p2XfiTegV/ln0KBBvPzyy1itVnr16kWJEiX8tnv33XdZuXIl8+fPp2rVqrRu3Zrz58+zcuVKhg0bxpQpU0I+dp8+fXjssceYNm0aderUoWPHjkRFRbFixQouXrxIixYtePPNN722GTNmDNu3b2fBggVUrVqVtm3bkpCQwIkTJ9i2bRsPPPAArVtnjfR+8cUX+eOPPxg9ejRz5syhWrVqHDx4kC1btvD888/zn//8J/QnzMPAgQNp3rw5HTt2JC4ujlWrVnHu3Dlq167t0+933nmHDRs2sHjxYipVqkT79u3JyMhg2bJlWK1WRowYwd13352n/uzcuZNRo0ZRokQJGjduTHx8PBcvXmTVqlVYrVZGjhyplvMRBEEIRoEI4A8dOpSUlJSb3Q3hBnG5XAU+OK4E8QvqTYZg6I0G2va4k9bd7+DE/kNcu3gJgLiEeCrUqIpGIwbgCIWfy+nk2/c/5tBfu3Ntu2XpKpAk7n18SKH4DAuF29ULl1g8ay671m3Gla2kxm8zZlGvTXO69u9DkeLFgMCBP2VSVGX9/s07cg3eKywZGXz9zkeM+vhdMdIqn4VSVk+n0yFJkvgeukV4/p4N9jVVfgcr9Z89ud1uMjMzvd4nhen3pyBEks5gptvw31k/dyQH1n+J2+U7AbhGq6dayyG07DsRnT78yU4LgmLFitGwYUPWr1/vt3yOIiEhgbVr1/Kvf/2LhQsXsmDBAqpVq8Znn31Gx44dwwrgA/z3v/+lTZs2TJ06lZUrV+J0OqlcuTIjR45k1KhRmM1mr/Y6nY558+Yxc+ZMpk+fzqZNm7Db7SQmJnLfffcxbNgwtW27du1Ys2YN//rXv9i+fTsHDhygbt26zJs3j0aNGuU5gP/xxx9TsWJFPv/8c44ePUrx4sUZPnw4Y8eOJS4uzqttbGwsK1euZPz48cyePZuFCxdiMBho0qQJTz75JP369ctTXwC6d+/O5cuXWb58OTt37uTy5cuUKFGCtm3b8uSTT9KrV688H0MQhNuLJEc4/efSpUt88803bN68mUuXLtGpUydefPFFIKt22pEjR+jcuTNRUVGRPOwtp3bt2gDs3p17UKowUWqE5vS2kySJqKiomx7EFwSh4Nu0aDkL/jsjpG0efeMlKtetlT8dyiac0UZpV67y19qNpF2+iqTRULxUCeq1bo45JvqG9FnIu3PHTzL9rQ9Iv5aaY7u4+OI8+sZLJJQprU50mZ3NZvPK7p317iQuhVgap++Ix2jYXsx1kt/sdjupqak5jqZQEhVkWSYqKson+1oofKxWK3a7Xf3bYrEE/J2rJE+43W7cbjcGg0E9F2g0GjUz3+l0otFo1JIPUHCSXAThZslMO8/+dV9w9sAKHLbr6I2xJFZLoXqrRwt12RxPp06dokKFCpQvX54jR46Im3aCIAiCKqJXDd9//z2PPfYYGRkZaqCibNmy6vqDBw/Sp08fZsyYwUMPPRTJQwuFQKgT9EVFRYkfLYIgBCTLMhv+WBrydhv+WOoTwM+Psl6hjja6dOYcf347h72btvnUy/xtxizqt2tJ1/59iIkrElZ/hBsjPTWNGePG5xq8B0i9fIUZ4/7DUx+8hc7omyGfvS7+uaMnQg7eA2z8c5kI4N8Aer1eLcGXvZa5JEnodDoMBgOyLIeUsS8UbJ7nCOV19qx170mj0agjapR5EJT3iU6nw2q1qvtRPv/K+0RJghFJLsLtKqpIKRp2G0PDbmNudlfyzTvvvKPWZhfXwYIgCIKniNXQWL16NQMHDsRoNDJhwgQ2b97sE6jt3r07cXFx/Pjjj5E6rFCIBDNBXzhtBUG4PZ05coxzx0/m3jCbvZu2kXk9Xf1bCYpkZmZis9nUSQXT09NJS0vDYrGEPLGgss9A32PZ1588eJgpo99i94YtfifCdNjtbFmykimj3+LqhYshPmLhRtr451LSrlwNuv2V8xfZsnSV34Bu9vfC2SPHwurTif2HxDn1BlAmuDcajZjNZvX/TSYTZrNZDd4r7URw5tbg+bmVZRmDweA3wC5JErIs4/xfSS2dTqeeVzQaDQ6HQ62LryzP/h0QbDKMIAiFx/79+xk6dCgdO3Zk8uTJJCUl8cQTT9zsbgmCIAgFTMQC+O+88w56vZ4lS5YwYsQIGjdu7NNGr9dTo0aNW64sjBCcUIMH2dsrFz02mw2r1YrNZssxqBZqe0EQCpcr58MLZLvdbq5dugz4BtIlScJut2OxWLBarVgsFtLS0khPT88xIO8p1NFGVy9eYubbE7BkZOS676sXLjJj3HjsfkqtCDefy+lk8+IVIW+38c9lyLKca1DXbrMHXJcbRx62FYKn1Woxm81otVo0Go36X0DNvBcZ1LeW7DfflM+yXq/3yc5XJrDV6/Ve5wglgK+0k2UZWZZxOBzqBNYKkeQiCLeWs2fP8sUXX7Bhwwbat2/Pb7/9RnS0KJsoCIIgeItYCZ0NGzbQokUL6tevn2O7cuXKsWvXrkgdVihEQg2ce7bPqRSFMlzZc6Iv8K4b7EnUEBWEW0NebsYpwRHPQLskSX6/Z5QgikajCap8Qaijjdb+/CeZadeD7vvF02fZuWo9TbukBL2NcGOcPnyMtCvXQt7u8rnzXDh1htLlk9QSLNnfQ5IkERXmBb2kkTCYjH7XWRwWfjn8C8dTjyMhUSGuAndXvhuz3uy3vZA7JUgf6bJcQsGkjKjwnONJycTX6/W43W5kWUaj0SDLstek1Mr2Sqa9VqvF5XJht9vRarVek9wqf2s0GvVGgCAIhV9KSopIMBMEQRByFbFffhaLhfj4+FzbpaWliQuX21Sor7vSPtDEt263W81K8pwUTsmg9cx68yRqiArCrSEuvljY2xYpVtQr0K58b+RU8kb5rsltjo5QMiMdNjvbVqwJuf+/fPktLpeLhu1bYTSLQGtBkRHCjZjslJs4nsFfu92ORqNBkiQ0Gg2V69VixZyfQt53pdo1fc6Hly2XeWf9O3z513SuWr1L/ug1ehqUasiUO6bQqHSjsB/T7UxJLhBB1tuD8rn1vPnmWR5H+U0aqDa+EuB3OBxeZXM8zzMulwu3243RaBTBPkEQBEEQhNtMxK4qKlSowF9//ZVjG6fTyV9//UWVKlUidVihEMktWK5cmEDWBYvRaMTtdvstReF2u70ymJRAh9FoVGtYO51OjEaj3yC+mChXEAq/ctWqUKxkiZBrwlepX5vYYkWxeZSh8axLHIjb7VazI3PKfgwlsHLq4GGsGZlBt1c4bHYWfvYVf37zA/cMfYhGKW1C3ocQeTqDPvdGQWyrBH+1Wq06wTJAiaQyJFWtxKmDR0Lad4s7O3n9fezaMbrO7sKhq4f8tne4HWw+u4kmMxrTObkzX9/zDaWiS+V6HIfLwcKDC9l0dhOZjkyKmopyV+W7aFGmhTjXCrc8fyMv4J8gvFLKUcmg96Qs81fz3uVyqTfxlBFhJpPpxjwoQRAEQRAEoUCIWA387t27c/jwYT799NOAbT788EPOnTvHvffeG6nDCoWIvwn64J9gvBJ4VybvstvtZGZm+s1W8pzgS+F0OnG73WoQTrnICUTUEBWEwk2j0dD8jo4hb9fijqxgpud3iFLiIFg5fXeEEqjMvJ573fuc2CxW5n78GRv+WJqn/QiRUapc2bAC1VqdjoTE0j7L/U122uLuLiHtu2S5stRo0lD9+7rtOnfNuTNg8D67JceW0PKrFpy+fjpgG5fbxbvr3yV5SgX6LujDBxvf59Ntn/DvdeNo/XUrGn/ZiPn754fUb0EojJSbb0ajEb1ej8vlUn/jut1u7Ha7V417yDoX6XQ6NXjvdrvV/3c6nTidTjUxRZZl3G63uCEmCIIgCIJwm4lYAH/06NEkJiYyYsQIHnroIebOnQvAhQsX+OWXXxg6dChjxoyhfPnyjBgxIlKHFQoRJRChZMQrwXaHw6FmFkFWoN9gMHhNQuuZkZRT4N0z4ym3tsp6QRDyhyzLrD+9nuGLhnPvvF70nncvzy97nt0XIzeRefM7OpKYXD7o9tUb1adG06xgpmcAJC9zdGQXbGmuI7v2sGXJipCOG8jPn3/NmSPHI7IvIXxFihejZtOGuTfMpl7rZkTFxvhdl33S0yr169Dh/p5B7TcuvjiDxozyek9+tvMz9l3eF1L/jqUe4955vXDLbp91DpeDPvPvY8zKlzmbftbv9jsu7OC++b35YOMHIR1XEAqr7BOkA2o5LJfL5TXBrecyt9uNRqPxO8LL5XLhcDhE+UdBEARBEITbkCRHsIjivn376Nu3L7t371azS5Qfp7IsU6NGDebPn0/16tUjdchbVu3atQHYvTtyga6CQLmgUYYRO51OdWiwwWDAaDSi0+nU4JhSQgdQJ6p1u905TmabPeter9d7TQLmyWAwiGHIgpAPVp9czTOLR7Djwg6/69uXa8/kO6ZQM6Fmno+VdvUaM8eN5+yxEzm2q9qgLv2ffwqjOesz73Q6yczMKl/j+V0TiNFoVAMnRqMRo9H/pKCyLPsEbjzXbV2ykjULfsOaacn1sYWiUYc29Hnq/yK6TyF0h3ft5Ys33g1pmyfefY1yVSvn2EYppaHcqN61diNLZ88n7fJV38aSRPVG9eg1bDBx8cXVxW7ZTY1p1YPOvs9u0YOL6Zzc2bvvfz7Bf7dPDXofs3p+zwM1Hwjr+IJwM2T/7OU2IXGgc4Ay14ryO1Wpi+/521ZZ75nY4kmn02E2m9V/giAIgiAIwu0hogF8yAqCLFy4kCVLlnDs2DFcLhdJSUl07tyZPn36iKyRIN2KAfzsk9E6HA7sdrt6MaRcEGWfjDYjIwNZltHr9Wg0GnVocfYLJ+UmgGdda8g5gJ9TEE4QBP9cLhdH/95H6uXLgESxUiVIrllNDTb8fPBn+i7og91lz3E/RY1FWfTgYpokNslzn+xWGxv/XMrGRcu5cu6C17rEiuVp0a0TjVLaoPXIasweZLFYLAEz67Vardd3RVRUlFeGZEY6HDsIly6A0wlarUxccTtlytsxR/2vTILbzfzJ0zmwdWeeH68/Or2elz+fiDkmOl/2LwRv0bdzWfHjz0G17TqgDym97wnrOC6Xi/1bd/L3+s1kpKah1esoVa4sTTq3J760b8367ee203hG+JPS3lvtXub1/lH9+9i1Y1SeWgmZ4H9KVi1Wlb2P7UMjRWwQqCDkG5fL5TUxrSflN2v2axvPm8PZSZKE1WpVR6G6XC4MBgN6vZ60tDQMBoPf+VgkSVJ/z7pcLmJjY4mKiorMgxQEQRAEQRAKvIgH8IXIuNUC+P6ykZR699np9XqMRiMWiwVJktSgv+eEfkoNUc8hyBqNBpPJpF4Yee4vUAA/exBOEITArJkW1v26iE2LlpN2xTvr12g2U79tCyrd05CW37XE4gwuu7xUdCn+HrqbeHN8RProdrs5eeAwaVeuImk0FC9ZgsSK5QPWC1ZuLAJemZGelEm1Pct8KRNgOxywfQOcORmoRzIlyzipUc/Csu/nsG3pqkg8zID+b+wYKtYSo9xuNlmWWTHvZ5bOnu8zKaVCo9Vy58MP0Lr7HTesX38c+YO7frgz7O3jjHFcHXVN/XvMyjG8u/6dkPfze98/6Fqpa651vEPNfBaESMqeeOKPJElERUWpk9AqWfQul0vNrs++vUajISMjA5vNpiaoGI1G0tLSgKzRoUp2vvK+90xgkSSJIkWKiAx8QRAEQRCE24iIXAo3RCgTxio1PpWLH71ej93+Tyavkq2vXNgbDAavIcjKRY9yoeNvCDIEnlRXEARfqZev8OXY/3DhpP+JLG0WC5sWLWfsoXexxAdfGuZ8xnm+2PkFL7Z4MSL91Gg0VKhRNej2SjDearViMBh8SnRptVp19A94TyrqdMCK37Oy7wOTuHBGz9Vzl/I9eA/gyDYCSbg5JEmiQ58eNExpzebFK9m+ci1pl68AULREPI06tKVJ5/YUKVb0hvbLoDHkafs0W5pXecSfDy4Maz8/7p1Hm9Jt/GYvK8LJfBaEcPi7UaTRaNRRorltm5mZqQbw4Z8EFaW0ozKvE2R9N9hsNjXw7/m71WQyqZPVOp1OdDqdWlbSMytfKSkpCIIgCIIg3D4iHsDPzMxky5YtnD171qeUiaeHH3440ocWCrBQJotVhhUrAXllUi+FkoHncrnQarU4nU41Q1+5YHI6nWg0GrXkhWdWrTI5mBKEEwQhZzaLlZnjxgcM3iusWjt/Fw29tvbU7VN4rtlzaDU3JxinBPFdLhc6nU7NnlS+azzbeQYN1y3PLXj/j0M7ludH132Yo0X5nIKkaEI8Xfr1pku/3l4BvJulWvFqSEghlbzxFGuM9er/ZcvlsPZzxXpFzW72nKBXkVvmc07bCoXfjRx5kdONIrvdjk6nC5gIAlm/We12e8D5lJTfqEajEVmW1WC8LMtqNr3yT/m9qwTrld/CnsfXarUYDAbx+1UQBEEQBOE2E9EA/muvvcaECRMC1n0E1B/iIoB/e/F3ER7ogki5qFG4XC71wsftdqvBep1Oh9VqRaPReGU3KeuVizLPeqJKNlROF2NC4SZKLkTepsXLOXfiVK7tTsdcxKEN/mad4ljqMQ5fO0y14tVu2uunfDcoN/dy68PlC3DlUvD7v3x8XT71/B/RcUVIrFgh348jhKcgfP8kFUnizsp38tvh38Lavm1SW6+/TbrwJoE36bJKf8iyjNVqVUtSeS4LJvM5+7a3s1vl3HcjR17kdKPIs0a9Zwm17BwOh8+IT6Xco/KbVdmXXq9Xg/cKz4x/z2M5HA414A//nKOU37vixpUgCIIgCMLtJWIB/Pfff59x48ah0+no3r071apVIyYmJlK7Fwo5fxePyoVl9os05WLGMyAvy7I6ua0S3He5XGpte3/1RT2XKZlRSqmdYLP30vfC5RXgvA66WCjeFmLrhP74hRtDlFyIPLfbzcY/lwXV1qbNedLanFyzXiswr59nMD+QXduD358su3Har0egZzmzXE9nxY8/0/mBe/P9WELhNbzRU2EH8J9o9KTX3w1KNuBY6rGQ91MvoZ76/0rQWfm8hVpyz3Pb21VB+e7Mqxs58iK3G0Wev0EdDofXJOae/VGec89Au8vlwmazeZW9sdls6rxLnr9xPW+yKCNJldJtyk0BJfFE2U6UgBSEguvYsWNUrFiR9u3bs2LFipvdHUEQbpDBgwczc+ZMli9fTkpKys3ujpBHBfW7PGJXPJ999hlms5nVq1fTqFGjSO1WuEUEutBQLlA8L6CUWqCeWfN6vV5to2Q42Ww2v9llntsCap18hedFWaDsvYt/wOH34MoK3z4XawOVXoRS94TwBAj5Trmw98yaUy5+lWw4UXIhdKcPH+XKuQtBtdW7wj+lmLSmQlMyw+WCayFVDpFA0oDsfzLTSHG73Sz7YQGXz53ngWcez9djCYVXt0rdeLLRcCZv+zSk7eqUqMMdFb0n3B3W8HEWHFwQ0n7MWjMPVH/Aa1n2AH4obscAvme2vdvtxmq1Aqj10pXfVcpvG4vFgtlsvunfnTm50SMvst8oUp5L8E06Udoqz5/SVlnuWQZHuZGSfQJbpdSOZ/lH5fet8rg852FRyutoNBr1d6/ymooSkMLt6nJaKgvXrmbbwf1k2qxEGU00rlaDe1q1Ib5I3M3uniAIhYQItguFVcSueE6ePEnHjh1F8F7wK1C2vVKnXpm0VsksUjKRlCHgytBi5YJGuXhVgrOeF6XZL8qUi1l/w5/9Ze8d+Q/seyHwY7m6BraugWrjoMq/8vCkCBGjXNA7HA6sVqvPEHXl/WQymW65kgv5XTbh+pVrQbdNzEhA45Zwa0Krr51gTqCcuVyhKZlx7Upo7SVJwhiVgC0juBshebVz1XpKlUsipXf3G3I8oXCRJIlJXSZh0Bj4aMuEoLYpE1OGn+5b6DNPRZeKXahRvAb7ruwL+vj9avSjqLGo17LsJUVCEWr7ws4z216SJOx2O06nE61W63fSVc/fUzf7uzMnwYy8UH7/ORwOLBaLeq4L53ynHMvtdqu/QT0ppRiVQLzy+9OzreecTbIsY7PZ1HVKcD57lr5Sbkev16u/bxXKaFPlNfW8CQOFazSFIESS1W7nwzmz+HXDWpzZPqub9u3hs19/onvLNjzbtx/G/90UEwRBEIRbTcQKgZcuXTpSuxJuQcFkDCkXR8oQYmXiWUmSsNls2Gw2dbnT6VTrjvrbT3Y5XeB7XrSd/jrn4L2nA6/AiWnBtRXylzJcPTMzU60b60m54M/MzFQz4G4FSlZ6ZmYmNpsNu92uPg+ZmZkReZxSCPNFxDjNVL1WPuRjDKn7CFqCC0iEUl4jv5w6Hvo2JSqmhHWsynVrUTKpTMjbrZj3c1jHE24PGknDh50/ZOcjf9EssVmObduXa8+6h9ZTsWhFv/uZc+9cn4B8IPUT6vNWq7E+yz1/G4QaiC2oAWmFMmmpzWbDarWqpVXCufGgfOd7BoSV4L2y/+xBfM/zn/K7qiAKJnhvt9uxWCxYrVYsFkuezndKMN0z6O5JSfxQflP4a6u895RRohaLxW/ygF6vV8uy6fV6NBqN+l9//TIYDJjNZqKiojCZTBiNRqKiogrECDRBuNGsdjujPv2In9au8gneK5wuFwvWrGTkJxOw2sMv5ygIgiAIBVnEAvgPPvggmzZt4urVq5HapXCL0Wq1PhcfygWR2+3GaDQSHR2tZhhpNBrsdjvp6ek+F+ieWcZ2u91v0D57+0DU4c0O2PdiaI9p/xhwWUPbRog8JfM+twt4l8uFxWIp0EGMYGUP5IS6PljxiaVCat/0fE0IIS5l1pl5tO6jIR3jZgfwr4Ywea2iRHJbJE3og946P9gbRxgXo3arlZ2r14e8nXB7qVuyLhsGbeTyM1d4L+V9Usqn0KBkQ1qVbcUTjZ5kxyM7WT5gBeXjAt+Yq12iNqsGrqZSXKUcj9UhqSM/9VxIjN53fiTP3wWhBij9/aZIT08nLS2N9PR09TfGzRDJm6z+Ssx4ZoUrpQOVUVn++pI9wFyQ5NQvpTSNvxv0EP75LtD+AK8sec9Rbtn7pUws63Q61fI32SmjRfV6PQaDAZPJ5FUqR9mXQsm8j46Oxmw2YzQa1VEUgnC7+XDOLLYd3B9U220H9zNh7vf53KPgWSwWRo8eTYUKFTAajVSpUoX33nsv4PfOnj17GDBgAImJiRgMBsqWLcvDDz/M/v2+j3/FihVIksTgwYO5cOECjz76KKVLlyYmJoY2bdqwbt06te3UqVOpV68eZrOZcuXK8eabbwY8L4bSB8j6vpoyZQp16tTBZDKRlJTEyJEjuX79OikpKUiSxLFjx3y2O3bsGMOGDSM5ORmj0UiJEiXo06cPf/31l0/bGTNmIEkSb7zxBidOnKB///6UKFECs9lMkyZN+PnnwEkru3btYsCAAZQtWxaj0UiZMmUYMmSI3z4BOJ1OPv74Yxo3bkxMTAwxMTE0a9aMKVOm+P1+T05ODvjd7PkaeXI4HPz3v/+lWbNmJCQkEBUVRXJyMt27d+f774N7/x47dgxJkkhJSblh77MrV67wxBNPkJiYiNFopE6dOkyfPj2o/noaPHgwkiSxYsUKlixZQvv27YmNjaVkyZL83//9H6mpqQBcuHCBYcOGUaZMGUwmE82aNcuxFvmaNWu49957KVmyJEajkeTkZEaMGMHFixe92kmSxMyZMwHo0KGDVxk85X1x7do1Pv74Y+644w71eY2Pj6dbt24sXrw45Meckz179jBkyBD1OKVKlaJdu3ZMnDjRp+3JkycZNmyY2rZkyZL07t2bzZs3+7T1fI9kZGTw7LPPUq5cOcxmM40aNfL63MyZM4dmzZoRHR1NqVKlGDFiBBaLxWefyvtdlmUmTpxIrVq1MJlMlC1blhEjRnDt2jW/jzEzM5OxY8dSp04dzGYzcXFxtGvXLuD7XZIkkpOT/a7z/D7w5Pl9s2DBAlq0aEF0dDTFixenX79+nDp1yu/+Ll26xLBhwyhdujRRUVE0bNiQr776ym9byPrO+/7772nXrh2lS5fGZDJRrlw5OnfuzKefhlaeNBwRC+C/8cYb1KpVi969e3P48OFI7Va4xShB/KioKAwGQ1ZpCaMRs9mMwWDwqnOv/O1yuXA4HF6ZSspFkDLBrXIRplxkedaGVWqIBqKcdM8vANu50B6P4zKcmxPy0yBEmNPp9Jr3IFJtC6pQawXnJWBTMqkM5atXCbp9UnpJOp9oGlRbnUbHdz1mUT42tKz9mx2ACuftozcWIbnhoJC26fxAb5KqVOTqhTDuGACbl6wMazvh9lPMXIwXWrzAsv7L2fbINtY8tJZPu35KvZL1ct+YrPr4ex/bx5xec+lUoRNxxjh0Gh0JpgTur/oAS+5byo/3/Og3Uz97GbxQJuj0bGu327l27RrXrl0jPT2dzMxM0tPT1WX2G5yVGembrP5GH8myrGaJZ2/rLzhTEEYwBRIoAKJk3ufW71DPd0pZnJy4XC6MRqNaRif79gaDgaioKK8RFYGee61Wi06nU0cRKDddlNEEdrtdPYa/hBdBuB1dTk3l1w1rQ9rml/VruJyWmk89Cp7dbqdr165MmzaNmjVr0qFDB06fPs3o0aN59dVXfdovXbqUJk2a8N1331GmTBnuu+8+SpYsyddff02TJk1YvXq13+NcvXqVli1b8scff9CyZUvq1KnD2rVr6dKlC7t37+aZZ55h1KhRFC9enM6dO5Oamsobb7wRsT6MHDmSJ598kkOHDtGpUydatGjB119/TadOnQKed9esWUP9+vWZNm0aMTEx9OjRg6pVq/Ljjz/SokULli9f7ne7Y8eO0bRpU9auXUubNm1o2LAhW7dupVevXixatMin/bx589THk5iYSI8ePShdujQzZsygSZMm7N6926u9y+WiZ8+ejBgxgkOHDtG5c2c6d+7Mvn37ePLJJ+nbt29EEgIeeughHn/8cY4ePUqrVq3o0aMH5cqVY/Xq1UydOjWkfd2o99m1a9do2bIl8+fPp2nTprRu3Zp9+/bx6KOP8vnnn4f1PMyfP5877riDjIwMunbtitFo5PPPP6dnz55cunSJli1b8ssvv9C8eXMaNGjA5s2b6datG7t27fLZ16RJk2jXrh0///wzVapUoUePHpjNZj7++GOaN2/O2bNn1baDBg2icuXKANxxxx0MGjRI/RcTk5VksmHDBkaMGMHevXupWrUq9957L9WrV2fRokXccccdYd248GfOnDk0atSIGTNmEBsbS+/evWnQoAGHDx9m5MiRXm137dpFo0aNmDZtGlFRUfTu3ZuqVasyf/58WrVqxZw5/oNSdrudTp068fXXX9OgQQNatGjBzp07uffee1myZAkTJkygf//+6HQ6unbtisvl4uOPP2bo0KEB+/3000/zwgsvkJSURM+ePdVt2rdvz/Xr173aXr9+nXbt2vHaa69x4cIFunfvTuvWrdm0aRP9+vXzeZx5NXnyZO677z5kWaZbt27ExMTw/fff07FjR5+bEpcvX6Z169ZMmzYNvV5Pjx49iIuLY8iQIXzwwQd+9//yyy/Tr18/duzYQaNGjejduzdVqlRh586dAbeJpIjVwL/rrrtwu92sXbuWGjVqkJycTFJSkt8f5JIksXTp0kgdWihklIwlwOuCxd9Fl2d9UKPRqF6sajQatY4ooE7i5nkBpfy/MoFpoItD5QLp7A/hPZ4zs6HsQ+FtK0RG9omQc6IMhS/MQgnCeE6+F26t/BZ3dubE/kNB96/JhZoYXQaWV9xGpuR79x6gbGxZvrhrOl0rdsVmswW9b7j5JTPCnS8zsUo76jVxsXDazFyzTe8Y0Jd2995N5vX0MHsJGalpYW8rCKHSa/XcV+M+7qtxH/BPgDq393r28nrKslC2tdvtpKam+v1elGVZXR8XF+c1qX1+yY8JWf09NiUI7e84gZYV1El/AwWrlTJB2QU7r1FOlJKNgSjHNhgM6gSzyvnTM6ifPWjvL1PfYDB4jRRUyuh4but0OomOjkav19/085wgFAQL160OWDYnEKfLxc/r1jC429351KvgrF+/nrZt23LgwAESEhIA2LJlCy1btmTChAmMHj1aDRZmZGQwYMAALBYLU6ZM4fHHH1f3M2HCBJ599ln69+/PoUOHMBqNXsdZuHAhDzzwADNmzMBkMgFZiZVvvvkm999/P6mpqWzZsoXatWsDWdm+DRs25KOPPuLll1/OUx/WrFnDpEmTSEhIYNWqVdSsWRPIuqnQpUsX1q/3HQmalpZG3759sVgszJkzhz59+qjrlixZwt13381DDz3EkSNHfM7XM2fO5Omnn+bDDz9Uv+cnTpzIyJEjGTduHF27dlXbHj16lIcffhiz2czixYtp166duu6rr75i0KBBDBkyhE2bNqnLP/roI3777Tfq1q3LkiVLKFmyJABnz56lQ4cOzJ8/n6lTp/Lkk08GetlzdezYMWbPnk3Tpk1ZtWqV+ppB1oiNHTt2hLS/G/U+++mnn7jvvvuYOXMm0dHR6rJevXoxduzYHIO9gXz66af88MMP3Hdf1u/G69ev06pVK1auXEn79u1p2rSp1/v61VdfZdy4cfznP/9RM+ghK9g+atQoypcvz8KFC6lXLyv5RJZlxo0bx2uvvcaIESPUAPeMGTMYPHgwhw8fZvTo0X4nsa1evTpr166lVatWXsu3b99Ox44dGTVqFPfff7/63Ibj4MGDPPzww7jdbmbPns3999+vrnO73fz222/q37IsM2DAAC5dusTLL7/Mv//9b/V3wty5c3nggQd49NFHadeuHaVKeY+eX79+PSkpKRw4cIBixYqpz8GQIUN44oknuHLlCsuWLaNt27YAnDlzhoYNG/Ldd98xduxYKlXyHWH79ddfs379eho3bgxAeno6PXv2ZNmyZbz++ut8+OGHatsxY8awdetWOnfuzPz589XnbN++fbRv356JEyfStWtX7rrrrrCfS0+TJ09m8eLFdOzYEcjK/u/SpQvr1q1j1qxZPPLII159O3DgAD179mT27Nnq+/7333+nR48ePvu2Wq189NFHJCcns3XrVooXL66uczqdXiOf8kvEMvBXrFjBunXr1IuDw4cPs3LlSlasWOH3nyDkFoDMflGqBO+VdQaDQQ0+eg4fVwKTkHVxptVqsVqtATOilLa28+E9DnuY2wmRowx7y2m9crGd24iMwiCcMgF5KeNQr3VzajRpENIx616uzJrOy/jirul0rNCR6sWrUzO+JndXvpu5987jyONH6Vox64d2Xkpm3AxFi+fexp9yFaF51w6MnPgOLbp1Qp/twsRgMtKgfWvGfDGJdvfe/b9lJn+7CsrF02fZt2VH2NsLQl7klkWc0/pQtnW73aSnpwdVQi09Pf2G3MAN5yZrbvwF5EM9l2k0mps+gimQQCMv/N2gyGmURrDPu1JrPrfziXIsZUJbz8QSZRSo0WgMWNNeKQvpdDq9+qa0VUrrKOV6boUSf4IQKcGWzslu64HgJ1bPLxqNhs8//1wNqgI0adKEO++8k8zMTLZs2aIu/+GHHzh//jxt27b1CqoCjBo1isaNG3Pq1Cnmz5/vc5y4uDimTp3qFQh+9tlnkSSJPXv2MHbsWDV4D1CrVi3uvvvuiPRByRZ/7rnn1OA9QLFixQJmok6fPp1z587x/PPPewXvATp37syTTz7J6dOn+eWXX3y2rVSpEuPHj/e6STt8+HCKFSvGhg0bvDL+J06cSGZmJu+//75X8B7g4YcfplevXmzevJlt27apyydNmgRkBfKV4D1AYmKi+niUNuG6cOECAK1atfJ6zQDMZjMtW7YMaX836n1WpEgRpk2bpgbvAXr27EndunU5ceJEwJJEORkwYIAavAeIjY3lscceA+D06dM+7+vnn38eSZJYudJ7hPG7776L2+1m2rRpavAesq7/X3nlFRo2bMiPP/7IpUvBj2iuWLGiT/AeoGHDhgwfPpy0tLSAI0WCNWHCBKxWK8OGDfMK3kPW69q9e3f17xUrVrBr1y4qVqzI2LFjveIeffr0oVevXly/fp0vv/zS5zharZbPPvtMDd5D1megRIkSHDp0iKeeekoN3gOUKVOGAQMGALBq1Sq/fX/qqafU4D1ATEwMn3zyCZIk8cUXX6jJeRkZGXzxxRdoNBomT57sdcOjRo0avPLKK0DeP1eeRo0apQbvAaKionjuued8Hk96ejpff/01Op2OSZMmed20uvPOO+nbt6/PvtPS0rDZbNSvX98reA9Zccfs3zX5IWJRrKNHjwb978iRI5E6rFCIhXoR6W9iUrPZ7DMMWpZl9Hq9OvGXknGc/aIoe+afRh/mA9G5w56QTogMJZMtO61Wi8FgQKfTodFo0Ol06numMAvlvZbTJH0QXBkHjUZD/+efolKdmgHbZNf5wd40aNqCIfWGsKTfUvY+to/d/7eHn/v+Qu/qvdFr/3m9wi2ZcbMkVw1vu2r/u34qUTaRHv/3MK/M/JRnP36PER+OY/RnH/HGt9O4f8RjRMcVUbfR6XUYzeawjud2u/n63Y/YvXFL7o0FIR94ls0zGo0YDIagJ+QMdlvPSe9zE0rbvHC5XLjTHaR/s5+rr27k6svrSf1wB44j/kfFBBN0DjSiNdTvzoJ6/vM3GgP8Z7P7O98Hap/T8WRZxmQy+c14V46j1KvPbS4lvV6vlodUJqs1Go0YjUYkSfIaRZDbDYiCWuZIEG60TFt4E42Fu10kJScnU61aNZ/lyjLPkh5K2RIlaJbdwIEDvdp5atKkCUWLFvVaVqRIEeLj4wHo0qWLzzZK+ZC89kHJNvUX6OrQoYPaB09K/fBevXr5PU6bNm0A/Nb0TklJ8fn+1+l0VKpUCYfDweXLl32O07Nnz6COc+LECU6cOEHp0qW9gn+K7t27U7RoUfbv3+9TUz0UNWrUIDo6mi+//JLPPvvMq8/huJHvs+wBy0DHCZa/96aS7e3vfR0XF0d8fLzXsdxuN0uXLiU2NpZOnTr57E+SJFq3bo3b7Wbr1q0h9c/lcrFo0SLeeOMNHn/8cQYPHszgwYPVwP3BgwdD2l92S5YsAWDYsGG5tlVekwceeMDv74eHHnrIq52n5ORkqlTxLoer0WioUKECEPx3hKcHH3zQZ1nNmjWpX78+aWlp6lwWW7duxWKx0KxZM6pW9b2AVvq9du3aiMXSPEfiKPy9T7dt24bFYqF58+aUL+9bzrdfv34+y0qWLElSUhK//vorH3zwAWfOnIlIn0MRsTG0yhtAEIIV6kWkv/bKRZUy/FzJslbK7HiW5vAsJaJkRHl+AcbUgsvLQn8cpqpOMjOtfvcp3BjKnAhud9bNFOXCW6kvq9zgUTLeDAaD+l4ojEL57DgcjhyDHRBcGQedXs+jb7zEul8Xsei7uThs/utaGs0muvbvQ8u7fH8MBBJOyYybqWgxiC8Jly8Ev01SBTBH/fP31QuX2LR4Obs3bCE9NQ2tTkep8kk065JCrWaN0en/OT03aNeSjX+G8eVE1ms7e8JUnp/8AUWKF8t9A0GIMKVsXjhlW4LZVpkDJ3umtr/RVsrN/OxDwyPJlWbjwphVpH29FznN+2ZB6pubMXVOouirTTE0+CdbLpiLFn/nKyWLXKfT+ZSZyV6WSDkPFOTznnLTxrPUjOfjULLVg5nXKJhjwT/PoV6v91six7N0Y06UkaFut1sd/em5TnmNc7sBARTYMkeCcKNFGcMbhRjudpGUlJTkd7mSgep5jaoEgQJN2qgs9xcsKlu2rN9toqOjuXTpkt/1ShZ1Xvtw5swZJEkK+FjLly/vE6BWMrWbN2/udxuFv2zpUJ5T5TilS5cO6ji5PX5JkqhQoQLXrl3jzJkzlChRIsf9BlKkSBE+++wzHnvsMR577DGGDRtG9erV6dChAw8//DAtWrQIaX836n0WynGyT9oLWTdsst+0yem9mdv7WnH58mXS07PKjeZ23gwlA//UqVN0796dnTt3BmyTvdZ7qE6ePAngt0RNdvn1HRFovb/vCE+BYr/Jycns2LFD7Udu/S5atChxcXGkpqaSlpZGXFyc33ah8Pdezenz4C94n9PymTNn8uCDD/Liiy/y4osvUrFiRdq1a0f//v393jyINPHrULhpcruI9LxYClT2xO12q8OalQtyz9r4yrBm5eJJyabyl4VWbigc/ySURyADEqWHZAUylUxmMfHYjadkz3lONmez2bwCGtkvqAvzaxVsn5XausGUWQimdrAkSbTufgct7+zC3+s3s2nJCq5duIQkQWzxYtRv04IG7VqGlTHuL3CTfX1BukHWpBWsWgSWzNzbFomDev+b19flcvHbjFls+H2JT9DuyK49HNm1hyLFi/LAqCepWKs6AF0H9GHT4uXI7vAyE5wOB5+9+g6PvvkSRRN8s6EEoTBTbtwG+t7Ifr7Pz9FyzouZHOs8F+tfgbPzrEtOcX7tWRK+6YK5czkguKCzZymX7MxmMxaLRR1x6FnmxTPoXRBGMOVGORco5yTlN12wow1CHZHgOWeSvxs+gFqHOZiJdKOionzaes7JlNsNCM/2gnC7a1S1Opv27Ql5u8bVauRDb0ITTrJJbtsEGokVyX5Ecn/+vsuU78a+ffuq35f++Avwh3Jsl8uFJEk8/PDDObbzLC8U7DGC7Uegkn39+vWjc+fO/PTTTyxatIiVK1cyZcoUpkyZwgsvvMD7778f1P5D6Uso24TzPvPkWZ9ekZyc7BPAz638bTCU95MyAWxOQkk4Hjp0KDt37qR379689NJLVK9endjYWDQaDdOmTWPYsGEROVfnVgbYX/tQ10f6OyIngZ6TG/G5CnU/nokVoejYsSOHDh3il19+4Y8//mDlypXMnDmTmTNncv/99zN79uyQ9heqiAXwDx48qE6iUbFiRXX5pk2beOaZZ9i1axcVKlTg7bffDjiUSbi95HQxmn29UgJFuZBTMqSUicEcDofPhHFK5p7BYFAvlpQgrtPpxOFwqCd3rVaLuZaOYm11XF0d7IdYIqaRi5hG3uV7gp2QTogcjUZDTEwMqamp6g0bpayO8j5R3htKBndhfq1y++wolExAz8efk2Cz/jRaDfXaNKdem5yzZ0KVPXAT6mS7N5I5Ctp2gU2r4dqVwO1KlIamrcFgyHo95kyaxl9rNuS477Qr1/jyrfcZ/OoLVKpdA3N0NL0eG8z8qb51DYN1+dx5pox+i8fGjSG+dKncNxCEQkAJ8OZUIsztdnuVSMmv7xHZ5eZ4zwU5Bu/VthYXlx5aQqklPTHULh5U0DnQSCUlOcGz9IoSwFeC9p7bF6Tv0UA8R14ooyuCKSsTyg2KUEd+AUG31Wg0XucxJZkglL4JggA9WrXl898WhjSRrU6r5Z5WbfKxV5FXpkwZIKsksT/Hjx8HsmqxF6Q+JCYmcuzYMU6dOqWW3PB06tQpn2VJSUns37+fV155xateeaQlJSVx+PBhJk2aRJEiRXJtn9vjh6wyO+D9HCg3eNPT030mNFUyrP0pUaIEQ4cOZejQociyzJ9//skDDzzABx98wODBg6lVq1aufQ7VjXqf3cib0AkJCWrC5owZMyKyz4yMDBYvXkypUqX44YcffM7dkSoHXq5cOQ4ePMjhw4epU6dOjm0LwndE9uPVrVvXZ7nyGVH6m1u/U1NTSU1NJTo6mtjYWHW5Xq9XR1Zkl9PnKhRK35TnLjvlsfhTpEgR+vfvT//+/YGsiZT79u3LDz/8wODBg7nzzjsj0kd/IlYDf/z48TzyyCNewZ+LFy/StWtXNm7ciMViYe/evfTt2zfHoSjC7SOYi0llUi+j0YjNZsNisWC1WrHZbFitVvVfoKHlSmBfOYYsy6Snp6vDdDIyMkhPTyctLY20tDSqfmJBnxDcSUcbJ1NtqoXs3Rf1S28Og8FAXFwcOp0Om82m/nhQLv6V4LDnd1Rhfa2CDcRoNBoMBkPQP6QKQtafErgxGo3qPBbKTYiCJioa2t8BbTpB2fJgMoNOnxXcL18pa13rjmD4X7WOTYuW5xq8VzgdTr59fxI2iwWApl1S6PX4kDz19/rVa8z89wScDmfujQWhgFNuwuZW3kSWZa9yLLmVLwnX9V+PYFkffA1YOdNJ2oc7Qp4DxN/IMaWWu8FgUL83ld9POW1XGAR7vgvnBkUokyWH0jb7eSzU0WOF8XUShPwQHxfH3S1ah7RN95ZtiC+S9zIMN5IygeS3337rd72y3HOiyYLQB2WSz7lz5/q0X7Fihd+SJZ07dwZgwYIFeepvbkI9Tvny5Slfvjznzp1j2TLfspW//vorV69epXr16l7lc5SA6YEDB3y2WbRoUVDHliSJbt26cffddwPw999/B7VdqArC+yzSdDodKSkpXLlyJeCEq/4oN16ylx+ErKCy2+0mMTHR53zsdDr9TvIbDuU9Om3atFzbKq/J7Nmz/cYuvvnmG692+c1flvm+ffvYsWMHsbGx6s25xo0bYzab2bRpk985A5R+t2nTxuv3W2JiIpcvX+bKFd8suWA/V7lp3LgxJpOJjRs3+r0p8P333we9rxYtWqj1/Hft2hWR/gUSsQD+mjVrqFevHuXKlVOXTZ8+nbS0NJ577jksFgvz58/H5XIxfvz4SB1WKORyuyDS6/XExsZis9nUOrcKSZJwuVw4HA5sNluOE4LZ7Xa1Jn5mZqbPl7US7JcTM6j3eybm5JwDmYaybur+mkFUdf9DeApjUPhW4DnJoRLEMJlMREdHEx0d7Te7vLC+VsEEE0wmU0hB+bwGyZVMQ+UGm1LGqCDcGMgvkgQJpaBpG+h2L3TvC3f0gkYtoJhHtRq32826X0P7wWFJz2D7ynXq3826pNB1gO9EYaG4dOYsezaFNomTIBREyg1YzxJ6ObVVMvHzK4B/ZXLoySmZPx1Fl+oKK+icfXJfs9lM0aJFiYmJCXnC4IIulOB5uPvObbJkWZbV8kTKqFBlotrcnuPCNlG7IBQkz/btR6Oq1YNq26hqdZ7t6zvxYEF3//33U6pUKVavXu0TzJs0aRKbN28mKSmJe++9t0D14bHHHgOyEjn379+vLr927Rovvvii3+MMGzaMEiVK8Pbbb/Pll1/6XCNkZGTw1Vdf+c3eD8Vzzz2H2Wxm1KhR/Pzzzz7rr1y5wuTJk7H8L1EG4OmnnwZg1KhRXhPVnjt3jhdeeMGrjaJ9+/YAvPPOO17XlN98843fAOD27dv58ccfcTi858i5evUqGzduBALX3s6rgvA+yw9jxoxBo9EwaNAg1qxZ47P+zJkzfPrpp17LlOxrz/etomTJksTFxfH333+zdu1adbnL5eLFF1/0e7MmHCNHjsRkMjF16lTmzZvntc7tdvPbb7+pf6ekpFC3bl2OHj3Ka6+95vW5WbBgAT/++CMxMTF+5x/ID5988gnbt29X/87IyODpp59GlmUeeeQR9Xd5dHQ0jzzyCG63m+HDh5ORkaFuc+DAAcaNGwcE/lyNHTtWXSbLMu+88446eXZexcTEMGDAAJxOJ88884xXffxFixbxww8/+Gxz4sQJZsyYQWamdw1dm82mTm6cX59fRcRK6Jw9e5Z27dp5Lfv9998xGo28/vrrGAwGevbsSYsWLdiwIbgMROH2kFPZDI1Gg8ViUWuGZq95pVxMOZ1Odai4v7pYbrcbu93uVevUH5fLhb6ilcabIfWXKE5MkUjd9M/66PouEofaKdHHgTZw2b5bOmBZ0CkZlsEGaQrza5VbyRnlBldqRjpnLl/G5XZRNCaWsvEJfgNGeQkauFyuQlO//mY4unsfl86cC3m7TX8uo0W3TurfxUom5NA6OBt+X0K91pEtfyQIN5ryXeN2u9XvQbvd/+TakPU9FBMTE9ScIKFyZzpIX3Qs9A0dbjIXn8Q4qHbubT3kNLmvRqO5JSdAzc8Sa7lNlhzo/KaM7sttlFhhm6hdEAoSk8HAhOEjmTD3e35Zv8ZvOR2dVkv3lm14tm8/jPl0kzY/RUdH8+2333LPPfcwbNgwpk2bRrVq1di3bx/bt28nOjqa7777Ll8nYA+nD+3bt2f48OF8+umnNGjQgE6dOmE2m1m+fDnJyclq3EfJdgYoVqwY8+fPp0ePHjzyyCO8+eab1KlTB6PRyIkTJ9i7dy8ZGRls37494MSpwahatSrffPMNAwcOpEePHlSvXp2aNWsiyzLHjx9nz5492O12+vfvj/l/c3eNGjWKZcuW8fvvv1O1alU6duyILMssXbqU69ev06tXL5544gmv4wwfPpypU6cyd+5catWqRb169Th48CB///03zzzzDBMmTPBqf/z4ce677z7i4uJo0qQJpUuX5tq1a6xevZq0tDTuvffekCeyDVZBeJ/lh3bt2jFx4kRGjhxJ27ZtqVevHlWrVsVqtXL8+HH27t1LTEwMw4cPV7e55557eOutt3juuedYvHgxCQlZ11fvvfce8fHxvPjii/zrX/+iffv2dOzYkeLFi7Nx40bOnz+vvufzqlq1akyfPp1BgwbRp08f6tSpQ506dbh69Sq7du3izJkzXnXav/32Wzp06MDbb7/N/PnzadCgASdOnGDt2rXodDqmT5+e66TNkTJw4ECaN29Ox44diYuLY9WqVZw7d47atWvz5ptverV955132LBhA4sXL6ZSpUq0b9+ejIwMli1bhtVqZcSIEeroE8VLL73E3Llz+eijj1ixYgWVK1dm165dnDx5kieffJLJkydH5HG8++67rFy5kvnz51O1alVat27N+fPnWblyJcOGDWPKlCle7a9cucKQIUMYPnw4TZo0ISkpiYyMDNatW8fFixdp1qxZrnMx5FXEfuFbrVa1RiRk/djdsmULLVq08KoHpsxMLAieAl08eU5Mlz0rSblzrQTtHQ6HWv/c3/6DraHqdDrRm10kDnRRbrAOZwZkXrLhMtjQRgf/eISbI9KTNRV0OQUedh09zPfLFrN6105cHp+LKmXKck+LNnRu2BjD/y508pL1p0zgHCgwcatM8KyU4ggneHT6cOCamjk5d+IUTocTnT7r9a1ctxZanQ6XnyGfwTq29wBOhwNdIbzIFQSF5/eN2+0mNjaWzMxMbDab1+8AJUM/JibGK4gQSa6r1qx57cPZ9pIl90Ye8vI9VNjlFmjPD+Ge3/y9TkajUZ1/KTtxo1sQAjMZDLzc/2Ee696Tn9etYeuBfWTarEQZTTSuVoN7WhW+sjnZderUic2bN/Pvf/+bZcuW8ddff5GQkMDAgQN55ZVXqF49uFEIN7oPH3/8MTVq1GDy5MlqIPTBBx/k7bffpnHjxkiSRLFixby2ad26Nbt27eLDDz/k119/ZdmyZWi1WsqUKUP37t3p3bt3RGrA9+7dm507dzJ+/HgWL17M77//jslkokyZMgwYMEANpCu0Wi0LFy5k8uTJzJgxgz///BOAWrVqMWTIEIYNG+aTBFCqVClWrVrFCy+8wMqVKzl9+jSNGzdm8eLFSJLkE8Bv0aIF48aNY9myZezfv5/Vq1dTrFgx6tWrx//93/+pNbXzS0F4n+WHp556ipYtWzJhwgRWrVrFwoULiY2NJSkpiccff5y+fb1HMDdu3JhvvvmG8ePHs2jRInUkxiuvvEJ8fDxjxowhKSmJjz76iLVr12I2m2nTpg1vvfUW27Zti1i/+/XrR61atXj//fdZvnw58+bNo3jx4tSoUYPRo0d7ta1bty7btm1j3Lhx/PHHH8ydO5e4uDh69erFyy+/TLNmzSLWr9x8/PHHVKxYkc8//5yjR49SvHhxhg8fztixY70+U5A1wfDKlSsZP348s2fPZuHChRgMBpo0acKTTz5Jv36+o6Zq167NsmXLePnll9m0aRNHjhyhdevW/PDDD16Z/3mVkJDA2rVr+de//sXChQtZsGAB1apV47PPPqNjx44+AfzKlSvzn//8h2XLlrFnzx42bdpETEwMFStW5NVXX2Xo0KH5dq2hkOQIpZ/WqFEDs9msPqErVqygY8eOvPHGG7z22mtqu969e7NmzRouXLgQicPespQZ0Xfv3n2Te3Jz2Ww2r+EsnhwOhxrEVy6UlHrfnm9rZeJau90edLa1yWTCbDard6CdTqfPUJmcZK+1Ltw44rXKCmZ9smAu3y75M8d2VcqUZdzg/yMhrmjYwXVZlsnMzAx6gsHCOGkw5H2EwdIf5rN09oKwjv3aV1MwRf8z5OeHif9lx6q8DR/815efEF0kNveGglBA+ft9oIzU8zeaTyl3kh+cly3sSwgvGyjx007EP9kgqLZipNONFe75LbfXSa/Xq79Vb6cbMIIg3D5Onz5NcnIyVapUYe/evTe7O4IgREBycjLHjx8v1BUMCruIjSPu1KkTf/31FxMnTuSvv/7ilVdeQZIkevbs6dVu165dXnXyBSEnwX45eGZlKeVTlIt15S5YKBdG2W8CiPqlhYd4rWDqz/NzDd4DHDpzmjHTpyH/L4AQjlAmAi6skwYrGZaB+p7begCzx0i0UGX/Fmx/793ojXm7u28wFa7hsYKQnb/vLCXzXilrorRRSp3kW1+Km9CXC++GmKl+idwbEfh7Rvlt43A4uH79OhaL5Zafe+RGCef8Fsz5wmazodPpCvxE7YIgCLnZt2+fVx15gIsXLzJkyBCcTme+Z5QLgiDcTiIWwH/55ZcpXrw4zz77LA0bNmTdunXcf//91K9fX22ze/duDh8+TOvWoc0mL9y+crqg8VfHVqn9qgTwlQt2SZJCqnsrSZLXsYOtSyrql958t/trdfjMKWb++VvuDf/nyLkzfLd8cdjHCzUgX9gC+LIsY7Vacw2G5dauesN6Yfdh7MNPMHbQk/w2cxZ2q5VS5ZPo/9xTaMMcOVK2cjL6fB7eJwj5rSDdrJUkiWLDQv+MG+skENWqTK7tAn2/SJKE3W7HYrFgtVqxWq2kp6eTmZkZdOa4EFioz5/T6YzI+UIQBKGw+OijjyhVqhTt27fnwQcfpEOHDlSpUoXFixfTqFEjnn/++ZvdRUEQhFtGxAL4SUlJ7Nixg1dffZXHH3+czz//nG+//darzfbt2+nZsyf3339/pA4r3EJkWcbpdGKz2bBarerQ+EAB1uwX5DkF6T0z8WRZxu12e00K4knZT/aLfWV4tOdy53W4OFfH6ckGzn1mJHNNFBpuvYzuwsbfaxXK+sJs3qoVIW/z09pVOMKsqR5qAKKwBSwiNcIgPrEUVRvUDbsflvQM1iz8gzcfeoLtK9dSvXF9Hhs3BqPZlPvG2TS/o1PujQShgCtoN2uLD62LZA7tplr8iIZB9cvfd4skSVitVhwOh9f3arCZ4IWFO8OBdddFMjefw37k2g09h4R6rGDnWoLCOyJNEATBU+/evUlJSeHw4cMsWLCATZs2UaFCBd544w1WrlypThArCIIg5F3ECj8/8sgjtGnThjfeeCNgm4EDB+J0Opk+fTpt2rSJ1KGFW0BO9UKdTqdaLzQ7ZdJaWZbVIcj+MtQMBgN2u91rUlxlnTLUXtlOCfb7C+4qgd+M4y4OvyNx7jsNrnTvi29zBSj/OCSPBG3osTUhQpTX6naa7M/pcvH7pvUhb3clLY11u3fRvn7DkLe9kZMGy7LM0d372LhoOWePHsdhtxMVG0ONxg1p1jWFuPjiYe87kHBGGASaU6H9vXdzcMeuPPVHdruZM2kasizTKKUNvZ98lFnjPw16+yLFi1GvTfM89UEQCgrlez6SdeFlWcbpdqLXhjbJs65UNEnf3MXJPguDmtA2rl8Nij0a3E09f8F7u90e8PtJKRmkZHoXxrlHrLsvceXTHVz7eg/udIe63FgngeJP1qfYw7XRROfvRNyhPmeekycHI6fzhSAIQmHQtWtXunbterO7IQjCDXDs2LGb3YXbXsR+Nc6YMQPICuTnZO3atcycOZPp06dH6tBCIadkiQXKdNJoNFitVkwmk08bjUaD0Wj0msDWkzJZmM1mQ6/XYzAYvIYtK1n/siyj1+vV/Wk0Gmw2m9+A7/W/JDZ302E75//xWI7D/pfh4u/Q+GfQF8njEySEzXNuhNtBWmYGmVZrWNuevXwprO1CHcUQ7qiHS2fOMWv8p5w9dsJreeqlK5w9eoKVP/5Ms64duHtI/7BLy/gTyREG8Yml8tod1bxPv6BW8ybUbdWM04ePsWrBr7luY4oy8/DLozDk00SegnAzROJmrd1l58f9PzJ1+xTWn16Pw+3ArDPTObkzTzR6kq4Vu6KRch+0Gte7KtL8npx6+HfcafaA7Yo/UZ/ESR2RNMEFiLN/ryi/XYKhPC+F6Tx4ecoOzj61DNy+36e2vy9x9smlXP5oG8m/98ZQqWi+9SOc81soQfzCNiJNEARBEARBuHkiVkInWHa7/ZYsWyGEJ5g6oBqNBr1ej8Ph8HshrtfriYuLw2w2q5PWGo1GoqKiMJvN6vByWZYxm81ERUX5XMi63W41U9/pdOJwOLDb7dhsNq9astbTsLkbAYP3nq6sgu19QQ4tIUsQwpaXYEC4W96IOtQXTp1h6pixPsF7T263mw1/LGXW+E9xuyL3oYvkCINIBmtkt5vF380B4I6BfblrUL8cy+mUrlCOx/79CmUqVYhYHwShoFBu1hqNxpAnBv374t/UnFaD/gv7serkKhzurGxvi9PCz4d+5q4f7qTlVy04m342qL4U6VmF6qeGkTi5E6YGJZBMOtBK6MrEED+qMVX3D6HM5M5IutDm5fGkjDxUSrYo/wIFjwtTqZar03dx9smlfoP3nuwHrnK00xwc5zLyrS+hnt9CmWsJ8jYiTRAEQRAEQbi93NB0HFmW2bZtGyVKlLiRhxUKsGBrgGo0GjQaDYb/TbwYKMMue2A+e8kcWZbVIL/T6VQD9xqNBqfT6TN5rWc/MzMzOf5+NLZzwV+gXVoEF/+AkncFvYkghC0uOhqj3oDNETjzM5CSRYuFdUylvnROo2g824VcksDl5pv3JpF5PT2o9ns2bWPVT7+S0vuekI4TSCRHGEQXiUWr0+EKc76B7LatWMs9jz6EJEm06dGNpl1S2LF6PX+v20R6aho6vY4SZcvQrGsHKtSoKoJFgpDN3kt7af9tO65ar+bYbvPZzbT/th3rHlpPQlRCrvvVxhqIf6IB8U80AP75zRKu7N8rbrfb5/eNZ1tDtkmqC0umt/OyhTPDlwbd3nEsjfNjVpM0vVtE++G6auXqjN1c/+UIzisWMGjQN0wgZkgNDDV9S7Up57dQn2eR0CQIgiAIgiAEK08B/I4dO3r9/ccff/gsUzidTg4fPsy5c+d46KGH8nLYAsFqtfLOO+8wa9YsTpw4QfHixenWrRtvvfUWSUlJN7t7hUY4WWHGEMo/+Nu/Z617hc1mU7PXAmVQOdNlTs8M/QL8+GQRwBduDJ1WR5cmTfll/dqQtisSFUXrOvXCPm5+1KFW7N+2k0tngst8Vaz/dTFte9wZkVI6yk1Cq8XC5bPncdjsGM1mEsqUQuNnouucHqPeYKBuq2bsWLUuz/0CsGVavP42mk0079qB5l07RGT/gnArk2WZ/gv75Rq8Vxy6eoinFg3n+16zQz5WXm+eKd8tyu+UnJIfJEnC6XSqiQ+ROP6Ncu3Lv5Gtof0uTJ21j9L/aY+ueN4nSpRdbs6/upbLE7b69MO26Tzp/92NqUNZ4qemoC0dBXif32RZVl+n3IQ7Ik0QBEEQBEG4PeUpurFixQr1/yVJ4ty5c5w7F7i2iF6vp3v37vznP//Jy2FvOqvVSqdOnVi3bh2JiYn07NmTY8eO8eWXX/LLL7+wfv16KleufLO7WShEsr50uO09L4Rzan91mQ5XaugXwRd/A0eaqIUv3Bh92nUMOYDfvWUbTNkyNj3Jspxrfen8mjR445/LQt7m+rVU9mzeTt2WTcM6pqcr5y+w/rclbFu+GqtHwDymWBwN2remUYc2RBWJDXqEQYtunSIWwBcEIXyrT65m54WdIW0zb/88Tl8/TdnYsvnUK/88Rzop5QQlSfI7749Op8PtduNwONSEh8ISKL46/e+Qt5GtLlK/30/8kw3ydGzZLXPq4d9J/W5fju2sy09zvutCyi27D2P5OK/zW36PSBMEQRAEQRBuX3mqgX/06FGOHj3KkSNHkGWZPn36qMuy/zt9+jTp6en89NNPJCTkPvy4IHv77bdZt24dLVu25MCBA8yePZuNGzcyfvx4Ll68mOtEvsI/IlFfWnZB5hFI+wsyj3nXnA9m/541Y3Nq7zgf5oWWDI7w5gcVhJDVrJBM77YpQbdPKlGSQXcEHiKilI/KzMzEZrP5nRtCEU4damUyRpvNhtVqxWazqRNLA5w8eDjox+Lp5IFDYW3nadf6zUwcOYZ1vy7yCt4DpF9NZc2C3/jsX//m7JHjREVFBRUkK1+9Co07tstz3wAQwR9BCNu0Hf8NeRuX7OKLnV/kQ29yp9VqMRqNaDQaZFlGr9er65TvXr1er353KjdTC1Omt/1IaljbOY6Gt52nS+O35Bq8VziPX+dc/z/93pxWbmYHes5zWy8IgiAIgiAI/uQpA79ChX8mw3v99ddp2LCh17JbkcPh4OOPPwbg008/JSYmRl337LPPMnPmTFatWsXWrVtp3LjxzepmoZGX+tK283DyMzjxX7Ce+qdNVGUo/wSUewS0scHvX6mFH3C9PuCq3Pedh20FIVTP3d8fm8PBrxtyzsQvV7IUE58aRdGYWL/rleB9oExCZX24wQiXy5Vr2R2HLfR6/gB2qy2s7RT7t+3k+w8nIweYFFJhSc9g1gcf8+iboylXpVJQ++41bBAupzPPmfjxpUvmaXtBuJ3turgrrO3+DnO7SJBlGYPBoP5Xo9Goc/n4y8gHClemdy4T1wYiu/JW4192uLg8YWtI21g2niVzzWmi2/qWzcyvEWmCIAiCIAjC7Stik9i+/vrrkdpVgbZmzRquXbtG5cqVadiwoc/6Pn368Ndff/Hzzz+LAH4QPOu6BtsW4NJS2NYbnGm+7TIPw77n4cj70PhnLYbawe1fyRQOFKw0V1MCeTIQ/MWXLg6MpYJuHjJZhisr4cx3YD0Dkhaiq0LSIxBbK/+OK9w4sixzdPc+zh0/idPhICo2lmqN6lGkWFG/7XVaLa8+NIR29Rowd+UyNu/f67U+MT6B3m3bc2+bFGKjogIe02q15lqGSmkXFRUVUlAi2JsDpugo0q+Fnl1pjokOeRv12E4n86dMzzV4r7BbbSz87CuG/fsVn4m0/dHqdPQd8Ri1mjViwx9LOfL33ly38adDnx5hbScIAlid1hu6XSTIsqx+Z0qSRFRUFHa73WvUkrJOp9OFPffIzaIrG4PjmJ8fdrnQl43JvVEO0n46jPNsRsjbXZm8w28AH/55DYI5JwiCIAiCIAhCbsSvyhDt3JlVL7VRo0Z+1yvLlXZCzsKpF3p1HWy5G9y5JNjaL8DmrhLNVprQVgq8f41Gg1arVbPaAinSwoW5mgvLgdAuhpMGgyZwefE8ubgI9o6EdD/xv6PjIb4D1J4KMdXy5/hC/nK73GxctIz1vy3xmchVg0RFVxkapFeiRFxJYu+uSNyDNdBEZQ33kCSJlAaNSGnQiFMXL3D8/DmcLhfFixShVoWKaHMYbQLkOElioLbBBipCuTlQqU5N/lqzIaj9eqraoG7I2yj2bN5O2pVrIW1z+tBRjh84RKWa1YO6kSFJEnVaNqVOy6ZcPnueJT/MZ+eq9UEfLyauCI1S2oTUR0EQ/pEQlcDBqwfD2u5myf7domTi6/V63G63mumt0WhyHVVYEBUdWIuL40L8vtdKxD1YPU/HzVx9KvdGfmSsOp2n4wqCIAiCIAhCsEQAP0QnTpwAICnJf8aNslxpJ+ROGWqcWykNrVaL7Iadg3MP3iucqbDnCS3NVwfev8FgwGAw5BqslCQoO8zJoedCCOBLUP7x4JuH4sz3sHNg1hwAgVxeDutbQvNlUKR+/vQjFJb0DLatWMP+bTuxZmSiNxpJqlKJZl1TiC+dj8MUCiGX08n3E6awe8MWv+vdyBzWnuZY9Bnark2mzI8HOfvcSkq81JSEl5p5BXqSSpQkqURo5VaCDd57tg82gB/KzYEGHVqHHMAvWa4sFWuFH9DZtmx1WNvtWLmOCtWqhJxxGZ9YigeeeRy3y82utRtzbW8wmXjqP2+F1UdBELL0qnYv608Hf9NMcW+13vnQm+D4y6ZXboRmD9bLslyosu8Bij1Wl4tvbwyplE6RHpXRl/VfBi5YruvhlWpzp4e3nSAIgiAIgiCESgTwQ5Seng5AVICyE9HR0V7tclO7dm2/yw8fPkzlypXD6GHhFGy90EtLIDPEhLlr6yFjl5bY+oH373a7gxoFUHG4jtSlcPG34I5d4wOIqRFaf4ORuh12Ppxz8F7huAKb74J2e0FfJPJ9CYbb5WbxrLms+3UxDrv3Be/R3ftYvfB3ajVtSO8nHyUqNm9D4XMiy3KhqUn702dfBQzee3LpZFY3P0aXVZUpfg3Ov7wG+9E0ykztnKfHlFt2vHp8j+fS4XCg0WjQaDTodLqAz2soNwcSK1agct1aHN61J+htOvTpkafHfuXCxbC2S7102edGhizLHNt7gM2Ll3PmyPH/lUCKoWbTRjTp3J7YonFq237PPkmJxNKsWvgbTrvD7zHKVErmkddeyNfPiSDcDobUHcJrq17F5gp+vozyRcpzV+XAk37nt3DLDhYWhnJFKPFqCy6+GdyNFW0xI6Xebeu90OqEPRfh74twzZpV9TDGADXioW5JKGL03U+c77JgaIrk0/BKQRAEQRAEQchGBPBD5Fl7NKf1QuiCqRd66ovw9n3yC6j9ceD9hzIKoNHcrOD5ubk5HFCTFbyvOCq8/ubmyAcg+4/v+WU7A6dnQvLT+dOfnLhdbmZ/NIVd6zYFbiTL7Nm0jYunz/LYuH8RXSRv2XT+BDNhakEJdpw/eZotS1YG3d6lk/mr5nlS1lcE4Oq0vzDVjid+hP9SX8HILQDudrtxOBxq4N7ucWNGq9Wi1+vR6/V+n9dQviclSaL300P59p2JnDl6PNf2nR/sTf02LYLev19hfo/Lsvdju3TmHN9PmMyZI979vnL+IqcOHWXZDwtoeXcXug18AI02K3u2c7/edHygF1uXrWLLklVYMjLQ6fWUqViBbg89QEzRm3QXThBuALfbzaGdu9n45zJOHDiEw2bHHB1F1QZ1aN6tE2UrJUfsWAlRCTzf/AX+vW5c0NuMa/dvtJqbd54Ip+xgYVPy9Za40+1cHp/zpLLaBDMVfrkXY7Xi/yzccxGWHgNntvlL0myw6QxsPgNNy0CrpKxhlf8T3bE8lz/aFnJfYzqWD3kbQRAEQRAEQQiHCOCHKDY2K7CYkeF/sqvMzEwAYmKCy47cvXu33+WBMvNvdxmHwtsu83DubYIdBaA1Q8Mf4OpqOD4Zzv34TzBdXzxr8tjywyC6Snh9zY3tQi43DwI4PhkqPOV1zXpDrJj/c87Bew8XT59l9oQpPPL6ixHtQ7ATpkZFRRWIIP7GP5eFvM2Z0tdJj7ITk5mVEXjx/c0Uf7IBki68Gsg5PQ9utxubzYZWq8Vms+FyuTAY/slEdLlcuP83Aazb7fZ5XkMNKkXFxPB/Y1/mtxmz2L5yHU6H792rYiUT6PTAvRGpCx+XUJyLp8/m3jCbIsWLqo/t4umz/PeVf5OZdj1ge5fLxZqFf5B25Rr3PzNMLYGh0Who2jmFpp1TfEaNKM97QRw1Igh5cfnseb55fxLnT3jXI7dbrWxZuootS1dRvXF9HnjmcUzR/kdBhurNtm9yNv0s0//KPTvgnZR3GVhnYESOmxehJBwURpIkkfifFGI6lufypO2k/3nMa722uIlij9Qh/plG6JM8bvb/fQEWH8155zJZgXy7C1IqqD+IYu+qiL58LI4Tgb+v/Sk+vEFI7QVBEARBEAQhXCKAH6Ly5bOybU6d8j/hlbJcaSdEVjAlY/KyXTCjALLaQfF2Wf9cNnBcBkkPhuLw/+zdd3gc1bnH8e/sbFeX3Hvv3aYag+kkEDokkECogZBA7k3v9ab3CqEEQid0DKHbNGNsjI2x5d6b3GT17btz/xArq6yknZVktd/nefSAZs45c3Yt7Wrf8857jA7+zHz4LXvZ97UsatYbBHdb+IcevaBfNBLh3edfsdVn80fF7Nm6vd0yLdPdMBUgGAzW/dt3ZnmddBc8GjBg5+ByJm2qrXUf21NN1YIt5F40NqM5tFSqoX7mfSwWw+l0YhhGg+fYsqy6kjqhUAi/339kEcxmYMk0TZweDxd98TrO/tzlrHjjHUq27SAaieLLzmLiMTMZN2NaXRZ7W808eS6bV6VeXG3J1JOOq3vOHvjVn1oM3tf30TvvMWTMSE761DkNjnenu0ZE2qJ0337u+N7/UVNR2WK7DR+s4p6f/JobfvIdPD5vm6/rMBzc9Ym7OGbgMfxh2e9Tbmp7zMBj+O4J3+OCcRe0+Xpt0XgxzzTNuverrl4SLhM5nxxFzidHEdlaTuijgySCMZx9fPhPGozD52rYuCyE9fo20n7UH+6HobkwpjZ73zAd9PnmMZR8Of3F86xTh+I7ZkDa7UVERERE2kIBfJumT6/dCXTFitS32iaPT5s27ajNqTfxDoaqVfb7uQcksCyjzR9qLas2837X3VCzCawYeAbCoCug/8UdH7yH2o157at93NX7gngGeY5a0G/NkvcJVKW3H0R9S19eyMVfvA6ozeDe9OFqlr/2FodK9pGIx8nOz2PaScczY94JLQZxLMsiEokQjUbrAhwOh6NJsNkwDCKRCLFYDLfb3eD5OdqB0kQi0WoQqzkhT6zB99Wv78w4gN9cqYZkAMnpdBIKheqCSKkWSOpvVlu/Nnxb6jj7c7KbBLrb25QTj+GF+x629bPbf/gQhowZhWmarHt/JYf27rN1zcXPv8yJnzyrbhGiu901IpIpy7J45Pd/T/t1b8+W7fz3349w0c3Xtsv1DcPgppk3ceOMG1m4YyFL9iyhOlJNniePM0eeyTEDj2mX67RFb17Mc4/Kxz0qv8U2iQ/34Ui02KSpD/fXBfABCm+ZQbi4lMO3t/5HpmdiIUMfO69HLJSIiIiISPegAL5Nc+fOJS8vjy1btrBy5UpmzpzZ4PwTT9TWNjnvvPM6Y3o93qAr099AtpYFGBReHCQQoE0fcitWwEfXQNXqpucOLAB3P5j4Oxh8VUbDp83MakNn39EN+m1fuyGjftuK1wOwc8Nm/vOXf3J434EG5w/uKWFb8Xpeuv9Rzv7c5Rx/zulNxkgGPMLhcIP67Mm7LNxud11Qv35gJJFINHhujnag1DAMHKZJwsZGr0kOq2EwIVEZaaZlelKVakgkEnULIC0F75NisRiJRIJQKITL5aoLyHflOs4ut5tPXf85HvvTHWm1N51Ozvrc5fh8PgzDyKgEUsWhw2xYsYqJx8xM+66RZLv6dzeIdDfb121ssk9Ea1a+sZizP3tZu27m7DAcnDHiDM4YcUa7jdketJjXMisax1ibwcbjuyrhcBAKfcDHpXv+fjquEbkc+tUy4mUpNjd2GOReMpZBd5yB8+N+IiIiIiJHQ/vUG+hF3G43X/7ylwH48pe/3KAW/h/+8Ac++ugjTjrpJI45pvMztnqiAZeCu6+dHgbe0XHyTonXfchNJ+u3sbJ34b2TUwfvkyIHaje33fYn28Pbkn88pH+f+BGeIQk8gy1bJWXaKhwMZdQvEgyxtXg9d//oV02C943Hf+6u+1n4+LMNjtf/t278OJOlXZLPQTAYJBKJ1NVtT/W8HM3nzDAM+g0ZlFHfvMqGdyM4sl3NtExfMojv9/vxeDy43W68Xm9dqanmnhPLsojFYkSj0bqvcDhMIBCo2yukpYBT8rqdFZCaPu8ELrjpmlYD4y6Pm0u/chPjpk+pm+vOjZlt1rFzQ235jvp3LrTGTluRrmjZK4ts94lFo6x4450OmE3XYncx72i8R3U18fIgRsRu+v3HDgYafGsYBn2/eSzj99zE4HvPJvfisWTNH0r2J0fS9wfHM277DQz7z6cUvBcRERGRo04Z+Bn4/ve/z2uvvca7777L2LFjmTdvHjt27GDp0qUUFRVx7733dvYUeyzTAxP/CKvS3UfOYTH6NyGMj5eqMslYjZbB8gsgnnrf4ibW/S/kzoCi+WnO0Sb/COj7STj4gr1+A66J1JX4qV8GpSNlWqPY5fXw0K//knKz0lRee/QpBo8ewfhZ09MKZCQ3A00G82OxI6VnHA4HTqezbkPRpKP1nAEcc8YpLLjnQVt93BGToXvzGhzzzxvcLvNpvDdEOBxuUoaovkQiUXfXg9vtrlsYSSQSOBwOEolEXcZoOhtHd5bjzjqVwaNG8O4LL7P63feJ1/s5cXu9TJ93PCeeexb9hgxqMNdIKMOFq3Dtc2Y3IH+0fi5FOsKeLdsz6mc3a787ymQxr7e9FiQisdYbNSeWOvDv8LkouGYKBddMyXxsEREREZF21Lv+ym8nXq+XRYsW8ctf/pKHH36YZ555hoKCAj7/+c/zs5/9jKFDh3b2FHu0wZ+tDaqvvY3aCjnNMFwWY28PUnBGww+/dj/k7roXoofszXHb7zsugA8w6mv2AviJ3Er2feGnFBhXkW2NBNo36BcJhVn1zhK2r91AOBjC4/MyaspEho4fw/uvvWl7PH9OdouZ96ksXvAy42ZOa1Lz3ul01mXXw5EMfIBIJNIgwzsZhA2Hw3g8npRB/KMRHJl5ylxeefiJ9O5gqK0SxajtBTjjR+Zr9vOTe3Fm9e9bkny+Uu0lkNxkMfn8JhIJotFoXVmiRCJRtxDgcDiorq7G4/HUlTTq7IB9KkPGjOTyr9zMJ6+5kr1bdxAJhfBm+Rk6dhQeX+osTK/fT7A6zRW/Rv0A21m0vTHrVnqOWCSzUl/RcNtKhHUHWsxrXcJT+7738VuhPb7e9VyJiIiISPelv1wz5PP5+OlPf8pPf/rTzp5KrzTiy5A7E7b9AQ48C1a9z7iG26LPhVEGfylC9ozU2VXpfsi1LNh5u/35HXgBAttrs+XbU5wwu3iF/ae+R/Dlw/D7C/G+fjpGvPnHYnlCHHzyYsJDX2e7dRcnBx6mf3xeuwT94vE4C//zDEv++xqhQMNb0Ve++S7eLD8uj9t2oOXQnhLbc9n8UTG7tmwjuzC/Sc17oC6QH4vF6oL7yYzw+jXdk/8fjUbxeDwNrnG0AqXeLD+X3foFHvztX2t/CJvzccSioMzH1HX9G5zq8z+zcHja/yU+mR2fDMYng/XJ5yz5HMVisQbtkt9Ho1GCwSAOhwOXy1UX3O/qGzFm5+UybubUtNqOnT6FjxYvtX2NMdMnA9heyOiKCx8i6fLlZFF+qDSjfj2dFvNaZ2S7iRd5MUtt3vnkdsCQ3I6ZlIiIiIhIO1MNfOm2CufC7Cdh/naY8lCEsf8IMuHBAMesrWb8XaFmg/eQ/ofccAkEMilnbUHZ4gz6NSPEYZbyHe5nEC9xPiv5BQfPuoODL5/D3h3Dqfje/5HwN834jY7awoHXziB8xuu13xuVvOn/NGWO1W0O+sVjMR7+7d9Y9MRzTYL3dfOuCdgO3uf3LSIUCGY0p10bt6SseZ9IJAiHwzgcjgYZjclgfbJsS/2a7qlKFxzNQOmk42Zz5de/jMvjbr6RAf0OZnHaOyNxxY8EvnMvG0efbx3bIfNKbiwLteVxkgH3+vsNJBdGTNNsUJIoHA7XlS5KLqYk74xoyx4VXc1xKTZVbs2AYUMYMXEcgO1FjK666CGSjgmzZ2bUb+KcGe07kS5Ii3mtM51OIhML7Hec1Bfceu0UERERke5BGfjS7fmGQP9LLMLh9OqlQ/ofcmNVmc6qbX3rq2IHz3MmFWxKeT4+eC8V//cDaq66n8Lr/oWjKpfYmM1U33A3obNfBrPhQkbMqGGl9wecxyspx0sGV1urSf7yg4+z7v0V7fMgP5ZXVEj5wfQzMb3V/Riw7VTyD0zGGfVT+b6HrfPdFF1djX/Kkbq4hmHgcDiIxWJ12eCWZeFwOOoy8E3TbBL8T2aHJ9kNlNZshsqVEA+CqxCKTgFnTvr9pxw/hxETx7H89TdZ9soblB+sreVkWDBwXw7jthYxcH8OxseFAwyPSdFtM+n/y3kYjo4L5CQ3mA2FQni9XkKhUN1dD8nnOhm8T2bhJxKJur0GDMPAMIwmmwxnskdFVzRi4jhGTp7AtuL1afeZf+n5dY85+fuWzmJGsq1Id3XsWfN586kFdYt56cj3ZDO+Ir/jJtVFaDGvdaZpEh5XQHxNKWZ5mgkDHhNmDezYiYmIiIiItCPD6o3323YDkyfXllIoLi7u5Jl0D7FYjEAzWeCp+P3+tErohEpg4aDM5jT9IRh8ZWZ9kyJU8hTHUU76gcBWfVx25TPWBvKNcQ1OxeNxQqFQysBh/RIngapqfnXjV4hF0988zsCBEXORcIabnjMMJh8/h4N7Sti/c3erYzlibsasuI5+u09otk3OSWFG/KMCV98jQaFEIoHb7a4r9WKaJm63m0gkkvKuDJfLhcvlAo4ErdMJLB94Abb/GQ692vC4mQ2Dr67dw8A/qtVhGrAsi3AgSDQaxdgZovLuYoLL92EFYpgFXnLOHUn+tVNwFqWuy94Rkos9yZ+ZZACufpZ9PB7H7XYTDAbrnuPkwgnUliNLZvQnpfv72ZXVVFZx1w9/yYFde1pte/qnL+L0yy9scCx5R0JLb9GGYeD3+3tl0E56lmfv/DdLX16YdvuLx8xnzoBJcNJQOCbDN+luwLKstO9MsvMe1dPE43GC+8vx/3cHjqpWkjncJlw4HgbbWE0XEREREelk3TtCIvKxjspY9QyArAlQYzd+7oDCk232SaGY29s3eA91u7xtNh5hDj+qO9xawDB53u/388HCt2wF7wEsEgxfdwGOuIey/h8Rc9dgxjzklI2i/475jJqa4D87v9HqOI64i8mLv0Fe6TgsrLrs88ZXq3rHw8bzCxn33OG6IH6ynI7D4cCyLJxOJy6Xi1gs1uLPTrJsTGuBEcuCDd+Grb9JfT5eDTv/AXsfgtnP2Nvo2DAMvFl+vAD5eWT/pX9rXTpccpNgp9PZYKPa5Ma1yTaWZTX7c5XqOe3qGzEe3neAjxYvpbKsHIfDQdHA/syYdwK+7CM1ubNyc7jp59/j+Xse5KN3lqb8+crrU8gZn76I2ac1fbGof5dDawtqIt3dudd+lop9h1i/6qNW2546dE5t8B5gyW6YMxB6aNA6+d6TzmJeOu9RPZVpmvj65xO80Ilr6T5cWyow4o3K6RlgjCqAuUPhKC50i4iIiIi0B2Xgd1HKwLevozJWt/8V1t5mby79L4LZT9nr01iCOI8whiq2t22gZkzkRk7hTsB+lt+jv/s7Wz6y/7NZUDKdyUu+2uz59cf+nUNDlrU4xuiVVzNw2+ktBO8byjk5zNjHyoCGJXEcDgder7euTFAkEqkrrZPk8Xhwu91pB0o3/xw2fr/VZkBtNv4JiyF3Wnrtu7JkbXuo/Vmqrq6uK5eTzLYPhY5sMJg8ZpomPp+v7i6HpORz3tWUbN/Jyw8+zsYPVzfZXNjldjN93vGc9dnLyM5ruDFiVVk5Hyx6m5Ltu4hFIvhzspl4zCzGz57e6s9VuiWtRLq7+FPrePPthSwpWU1NtOk+KH18+Zw6dDYz+41veOL0ETCt8xc0O1K6d8f1dnWvlzVhHJvKMCojOAwDI9uDMaEIcjytDyIiIiIi0gV13RRHEZs6KmN18NWw5Ze1G9qmxQGjvm7rEikdYFmHBe8BDI48D6k2bG1OPB4nWN10w9y0+rpaLnM0atVnKR30AZYj9VyckSz67ZgHkFbwHiyq3vIQXOfENzFWt6GqYRi4XK66YL1lWbjdblwuF4lEoq68js/nq2vfmlAJbPpxGlP6WLwa1n/D4piXuv/Gg/V/p+o/t/WDzvUla+An6+Q31hWfjy2r1/HAr/5IJNS0BBRANBJh+etvsbV4PTf8+Nvk9y2qO5dTkM/8iz+V0XXr3+Ug0pOZe6o5bdgcTh4yk+LSreyq2k8kHsXn9DC2YCij84akfm34aH+PD+An/77RYl7L6l4v85wwJ6v1DiIiIiIi3YQiAtKjdMSHXFcezHkelp4GsYrW20+5AwpOzGDyjQTZ1/ZBWpDLkSLs6Qbvk9xed0bXdMRbzn5zh/Mp2ju72Sz8fjtOwkzYuXbtv3fpA9mM/G0NhmHg8/ka1GtPql+fPZM7NXbdDVYaVYVcHCKbD/GzEfOVIMUeB95JRRTcOJX8qyZh5na/DMH6JaySv28ul6tuo9rkxrbJ8kVOpxPTNOv+m2q8rqS0ZD8P/vrPzQbv6zu87wD3/fz3fOk3P8blzuz3RKRXite+JjsdJtP7jmV637Hp9Qvae//qrrSYJyIiIiLSezVNfRTp5pIfcj0eD16vF4/Hk3YWdXPyZtWWO8lvfs9UfMNh1pMw7MaML9OA0aHraybjuKruO7uVtAaPsbkD68dyDo9ptU3fnc2vfuQcHp3RdWuW125Gm/yZ8Pl8zQaJk4tAdoPIex9urUWcQl5iIP8mh1WYfFwiIpogtOogJV9eyIahd1K5YIut67ZmQ+kGvvvmd7ny2Su44tnP8I2F3+CjA63Xmbajfv3l5N0MyUB98v+Tv4fJ/zocjialc8DeHhVHy1vP/pdwsGlJj+Yc2LWH1YtbLgUlIu1Ef8mKiIiIiEgPpzQekTTlTIYT34WKFbDrHghsgkQUPANh0JXQ7xNgtGPcMZ9x7TdYI4OZj58Bdd/bXdyYc/rJLF7wcpM64C1KOBiw7ZRWm7lD+c2eM2OZZafHAw2DzB1xp0Z4T0tnE/ThefxsxoJmi/8kKiPsvPBZhj11PrkXtL7Y0ZL1peu57dVbeW37a03O/X7Z75g7ZC5/PuMvzBowq03XSWpcwsrr9TbYV8Dr9TbY7NblcjUpn9MVN2IM1QT48K13bfd77+XXmXXqSR0wI5EeyuuEoL3N0QHI63r7ZYiIiIiIiLQnBfBFbMqbVfvV0fIZz0DmUcLb7T72cfyywfd2M56LBvRnyvFzWLPk/bT79N19HJ5QYesNjUSzp2LulmvoN8eVT5Os+nYvR9DCU5jDh60G7+skLHZ/9r+M234Dzj7+jKayvGQ5Zz16JuXh8mbbLN69mJMfmsdzlyzgtBGnZXSdxhovjCRr4devg994o+D6fbviRoxb1qwlGo7Y7rd701aqyivIyc/rgFmJ9ECT+sIH6W42U8/coe0/FxERERERkS5ENx6LdGGT+VK7j9mfufTjmAbH7JQtSba96IvXMmDYkJYbfxynzSofzugPP5/W+P4xzYe4y/utSWuMxvqd7ejwwLC/2eo+FtmsBNII3n8sUROl7N7ijOZxMHCQ8x4/t8XgfVIgGuDipy5ie/n2jK6VSuMSVj6fD7/fj8/nw+fzkZ2djd/vx+Px4Ha78Xg8+P3+jMoWHQ2BqupO6SvS65wwOP0XyaQsFwzM7pDpiIiIiIiIdBUK4It0YaO5jDFc0a5jzuBrTY6lW7qkfjtfVhY3/uy7TJg9o4UOULh3FlPf+jbOmK+VmdVG+6d9vx+nfeYiPL6m7Q8NWUbUbTMo6oBhN3d8SZah16U+7mEnLsqxt8sAHL5jle29CQD+ufKfHAgcSLt9ZaSSv3zwF9vXyVRH7FHRkdqyEa02sRWxwWXCKcPTb28AF3RcqTkREREREZGuQiV0RLowAwench8B9rGXRW0ebwQXMIILUp5rXMM81fnGJU582Vlc/d3/Zf+uPSx9eSHbi9cTDoXw+HyMnDwB9z2nEX9vUJqzM/CNjZN/SoJjjdOYMX8uW1auYd2yFQSqqnC6XPQfNoRh42OU/Cb9xzz46gSOflFCobbXuW/JoM/B+m9DvKrhcTcHAfuJpdGtFViBGEZW041eoXbj4cY1/C3D4q4P77Q99/s+upf/O/n/8Dq9vLXzLTaXbyaeiDM4ZzBnjDgDr7P31pgeONJGQLEef24OeUUF7TwbkR5u5gCIJ+DtXS23cxhw8QTor+x7ERERERHp+RTAF+niTNycx6s8xynsY3HG44zgfE7nYYwWbrzJdHPX/kMHc/4NVzU5XjkHlsxrGtROxeG1GPuPEMlL+Px+jj1zPseddWqDdpYF5iHY/a/Wx8yfH2f4r2sIh5s+zvaut+7Khal3wYefaXjcoOliSLoSoRiOFAH8eDyecqGluLSYXVWtBL5SKA+X8/WFX+fVba+wpXxLg3N9fH24bvr1fP3Yr9PH38f22N1d/6GDGTFxHNvXbbTVb85pJ2O21/4KIr3JnEEwugDe3Ak7yqH+tiguB0zoA/OGgke/X42lWtjtqEVrERERERE5evTpR6QbcGByLq/wGp9mB8/b6juQeUzmFkZxGY6Wdlr9WHtu7po7HY59GZafD9FDzbczcywmPhgg99h43RyaK+ljGDD1bsieAFt/C5GDKcbLthhwbZThPwzhSFHFJB6PEwgE2r3u+qBPgxWBj64HK/rxtWitdFAzTAMzz9PkcHLuqcrrVIQrMrsWcMfK21MePxQ8xG/e+zWPr/sPL3/mFcYUjMn4Gt3V8Z84w1YA33Q6ObbRwpOI2FDggwvHQyIB5WEIxiDHDblNXxOlVnMLu9B1NwkXEREREZH0GFYmRZalw02ePBmA4uLMNrKUnilBnM08QjH/YD9LGpwrZCqTuYX+nECQAxgY5DCCPDo/4Bo5BLvugZ13QHD7kePuQQkGXBthwOejuPvXvhTZCTTEw7DvCTj0KsQqwMyG/BMsCi4OYPhbz3xP3nHQUmaiZVmEi0uJbK8AC1zDcvBO69tin5otsPMfsPteSJRVMYi7MGxWwc+5cAzDn25Y7siyLAKBQMoADcCHB1Yy/4n5tq5jx6j8USz9/DKKfEUddo320J5ZqPF4nJqaGl554D+sWPh2Wn0uu+0LzDxlbiZTF5EOEovFiEQiRCIR4vE4pmnidrtxu93tsmDdmVpa2E0yDKPLbhYuIiIiIiItUwC/i1IAX1pTxnqq2IZFgiyGUMQ0DNuV1o8uKw6B7RCrrM2Sdw+NYxnte6t/LBYjEAik3d7v99cFb+oHfhORGFWPbKTyn2sIrWi4Kax3Wl8Kb5lO/jWTcbRQxiEehuAO2P/F5wgu3GTrcYx45RKyzxzR4Fhrjy0QDTD+3+OpilTaupYd3z/xB/z05J922Pht1Z5ZqPUXTKxEgjefep6l/32t2SCZ2+fl4i9ex7S5x7XpMYhI+woGg1RUVBAMBkkkjtTkcTgc+Hw+8vLy8KXYOL07aG1ht750Fq1FRERERKTrUQC/i1IAXyQz4XCYcOOi9y3weDx4PJ4Ggd9EVYRDV71GaNGeFvv6TxrMsGcvwFnYcuAn+OEBtp74CFYwltacsk4bxohXL8VwNAyypPPYvvH2N7hrtf2NbNM1IGsA22/ZgdtMUZuok7V3Fmr9BZNEIkE4HKbycBlr3lnKhuUfUlNRicPhIK9vH6bMPZZZ8+dSUFSkDFeRLiQYDFJaWtria6fH46GoqKhbBvHbsmgtIiIiIiLdgwL4XZQC+CKZCYVCRCKRtNu73W5cLldd4NeKJTh42UuEFrYcvE/ynzSYEa9fhsPdctC26oWt7Lz0OaxQy1mSvjn9GfHKpZgF3ibn0nlsm8o2ceJjJxBNRFuffIZe/vQrnDnyzA4bPxMdkYVaf8EkHA63OrbX68XlcinDVaSLiMViHDp0iJqamlbbZmVl0adPn24X3M500VpERERERLoPR2dPQESkPdkNnBqGQSgUqsvaDjyzNe3gPUDgnT2U39f6QlvOuaMY9fZnyP7ESFJVOjILvfT55jGMfOPTKYP3ybm2ZmzBWP5+2j86tJxSSXVJh42dimVZxGIxwuEwoVCIcDhMLBZrkGmfLH2UjnTbJse3097OPESkY0UiEYLBYFptg8GgrcXfrsJuHo7ydkREREREup/ulWYkItKKTMqX1A+4Vt+91nb/w7d/SMGNU1sNsPvmDGDEfy8mvKWcyqc2Edtfg8PjxDOxkNxLxuLwuVrsn+5ju3zc5XhNL//71v9QGixN2SbblU11tDqt8RpzmS3Ps7G2bCqbbk17u0HzeDzeaqZtcm71a2an0z6dsUWk40UikbR/fxOJBJFIBL/f38Gzal+ZLFqLiIiIiEj3ogiDiPQoycBwuqVU6mcjxrZXEl6y3/Y1Qx8eJLzmEN6pfdNq7xmdT99vHGP7OnYe20XjLuKSKZfwxIYnuO+je9lesR0LiyE5Q/jclKs4f8z5jLpjJKFYyPY8xhaMTbttWzaVba2mffK83+/vkCxUO4tBhmHgcDjSHltEOp7dhb10g/1did1Fa+3RISIiIiLS/SiALyI9imEYeL3etDYz9Xq9RKNHasXH9rZeJ7k5kR2VaQfwM2X3sZmmyVVTruKqKVelbPeZiVdw3+p7bc1hRr8ZzB4wO622dgLwjYNKlmU1KG3UnGQ7uxnv6WShJhdM6v+MNMfpdGIYRt0dBiLS+ewGq5OLcN2J3UVrBfBFRERERLqf7vdJRUSkFclNSpsLVNQ/3yDY2obA696bXyPw/r6M+6fLzmNrzS2zbrF9/VtmfymtALXdAHzjdnZr2tuVzvOTXAhpbXHANE3cbnfdY1CATKRrcLvdaQflHQ4Hbre7g2fU/pKvU629LqfbTkREREREuh4F8EWkR0oGsv1+Px6PB7fbjcfjqTuWDLLWD7Y6h2ZnfL3Ynmq2zX+MmsXpb4CbqVSPzeVy4XQ6cTgcxGKxJpu8pjJn4By+fcJ30r7uuaPP5Zqp16TVtq2bytoNyluWlXbg3E4WqmmaZGVl4Xa7mwS+DMPA5XLh9XobBO8VwBfpGtxuNz6fL622Pp+vWwbwoX0XdkVEREREpOtRCR0R6dLiAahYDtFyMLMhdwa4C9PraxgGTqezxQzq+uUHnEOy8ZwyiPCbezOaqxWIsfOCZxi3+XrMfG9GY6Qr+dgMw8i4xjzAz0/+OQ7DwS/e/XmL17t43MU88KkHcTrSe9to66aymdSRt1NeyE4WqtPpJDc3l2Aw2GAjXofDUVc2J9OxRaTjOJ1O8vLyiMVihMPhZtt5PB7y8vK69ebTySB9phuGi4iIiIhI12VY2m2vS5o8eTIAxcXFnTwTkc4R2Abb/wK774VYxZHjDg8M/AyM+ArkzWyfa9Wv1R54bhuHrnqtTeMN/POpFN02q30m14LWasxDbVA5nczLD/d/yO0rb+fh4oeoidbuBWAaJheMvYCbZ32R04efbisAFAqFiEQiabdP3iGRDD5Fo1HC4TAOhyOtrFGPx1PXvy0LGi3pyLFFpOMEg0EqKioIBoMNNqp1OBz4fD7y8vLSztQXERERERE52hTA76IUwJfe7OCrsOISiFc138YwYcodMPSG9rlmMjgbi8Y49PnXCT63LeOx3OMLGLvu2g7NeLQsi0AgkPbGhX6/P635hGIh9tfsJ27F6efvR7Y7s7JC4XC4xYzXxlwuF4lEosHjCQaDdaVxXC5Xi7Ws/X5/XfasZVkdloXakWOLSMeJxWJEIhEikQiJRKKu5r3b7e7WmfciIiIiItLzKYDfRSmAL71V+TJ4bz4kgum1n/kYDLy8fa6dDM5Ga8Lsu/ZVap7ekvFYE0pvwVnYcRmdsViMQCCQdvv6Ae6jwc78EokEpmk2uJPAMAwikQjRaLTue4/HkzKIb2eBQkRERERERESkO9EmtiLSpRR/Of3gPcCaWyAeap9rJ+vK+/KyGPHEBeRfNznjsRLV0faZVDMyqTF/NNnZzLV+SYsky7Jwu911Y1iWVRfMr09150VERERERESkJ1MAX0S6jPL3oeJ9e32ipbDvifafi+EwyDppSMb9zTxPO86mKbs3Tx3tm63SDaxbloXL5Uo5P8uy8Hq9uFwuDMOoK12TlMy8V915EREREREREempFMAXkS5jzwOZ9dv97/adR1LWqUMhg8Ru3zEDOjyAbzfjvDMy1FsLsJumidvtbnFxIZmJ7/P58Hq9OJ1OPB4Pfr9fwXsRERERERER6fG0a5eIdBmhXUe3X2vcI/LIOXcUVc9vtdWv8EszOmZCgBVPwMcbp9rRWYHuZBC/uY1fW9ro1rKgeoWDwEYTKwKufhb9znTgyenYxRERERERERERka5CAXwR6ToyTRK32S+4G3bdBYffglgVuPKg6FQYegN4BjRsm3XWcFsBfLOPj7zLx9mbUCvCW8opu2MV5Q+uI7avBhwG7pG5ZH1uPP7PjcXs5295Tjbq0XeE5N4CqTbRTXVngBWH/Q+6KLnLTc3qhvPeVGAx6OoYI74KWUNN1b4XERERERERkR5NAXwR6TJ8wzu2X6waim+BvQ/XBonrK10Im34CQ66DSX8G01tbvqX8X2tszSURjGHF26fevJWw2P/ttzj02+WNLmIR2VJB5CfLKPvlcgp/cyLZ105MOUZX3+S18cJCIgwbrvNR+rzr4yMW9VdoYmUGO//sZN9jCaY+E6TPHI/K6IiIiIiIiIhIj6Ua+CLSZQz5fIb9rm29Tawalp1RW2e/cfA+yYrBrjth+SchHoLgkr2EPjxoay5WTZSKh9bZ6pNyHMui5MuvNw3eNxZJcPh/3qHqzuImp7rDJq/17w6wLNj0Ze/HwfvkIkjqhYfIPgcfXeClbGOwwca2IiIiIiIiIiI9iQL4ItJl5M6AghPt9XH3hwEXtd5uzRehfGl6Y5YugnVfg/KH19ubzMcy7Vdf1TObOXz7qrTbl31rCWytwe12d6tNXuvfIVC13OTgf9w0zrpvTnS/g52/cxMKhVrcCFdEREREREREpLtSAF9EupTJ/wAzJ83GDph6FzjcLTcL7qotm2PH7nsgsr3aXqePxfZm1q++0r+stNchYVF991q8Xi8ejwen09lly+Y0lrxTYP89yX/I9Od94DEX4bK4svBFREREREREpEdSDXwR6VJyp8MxL8IH50P0cPPtDDdMvw/6f6r1MXfdCSTszSMRhuCuDNc4zbYFzsMbDlPzxi7b/crvX8uA35yMw+9qvXEXYyRMDj5l//lO1BgcfslJ1lXxlJvkioh0ioQFW8tgzUEoC9a+B2W5YHwRTOwD3qP7ehXZUUnZXR8RfH8fiUAMs8BDzrmjyPvsRMzsVlbBRURERESkUynaISJdTuFcmLcGdt5RG3wP7ztyzpkLgz8Pw78E2ePTG+/wW5nNI1yTn9GLpGdMQWYX/Fho9aGM+iWqIkR2VOKdWNSm63eGaDkkwpktfERKHCqhIyJdx+5KeGkLVEUaHq8MQ0k11ju7MOYOgZkDoIPvlIodDrL3pteofHLjka1FPla1YCv7vvEWfb4xh77fOx7D0T3u2hIRERER6W0UwBeRLsk7EMb9BMZ8D6qKIVYOZhZkTwZnlr2xYlWZzSFcOBnnlmW2+xVcPyWzC37MCsU6pW9nMtpQqt9w0m3KBYlID7ejAp7ZUJuB3wwjloA3d0IwBnOHdthUYocCbDv5McLrmr+dLVEV4cAP3yWyrYLB95yt11IRERERkS5INfBFpEtzuCFvJhSdCvnH2g/eAzjTranfiDmgkOyzhtu71pBscj41OrMLJq/b15953z6+Nl27s7jywd0ns76+0Ykuv1mviPQCgSg8v6nF4H0Dy/bWltnpILs+80KLwfv6yu8tpvSPH3TYXEREREREJHMK4ItIj1d4avPnnJSTw3LyeIs8FuNnPVCbxV50Kgz8++npB8VdDvrfdQbhWIRwOEwsFsuotEvWyYMxC7y2+7ln9cUclMEKRxdgOGDIdfb7uQcm6HO2pQC+iHS+NQchYm9DbeuDkg6ZSuD9fdS8vtNWn0O//wArqg3BRURERES6GgXwRaTHG3Zj0xItbvbRl6cYyD0U8CZ5vE8e79GHFxjMneQ732HQZ2J4xhQwcuFlOIdkt3gNI9tF30fOwjyxL5FIbQA/EAgQCASIx+0FRBw+F/nXTbb7MMm+fmJG1+sqht2EjXel2oWRAddG8WV7VfZBRDqXZcHq/fa6AMbuKjgcbPfpHL79Q9t9YnurqXxuS7vPRURERERE2kYBfBHp8byDYfDVR773sYV+PIqPbaQK+5oEyY0tZc9ljxOvDOOd2pexxdcw8K+n4ZlY2KCtc1AWed+ZzcDll+E7s2kt43g8nlFQvc//zMbsm345HNekAvyXjMayLEKhULfc1NU/Cib8Np2WFmCQc0yccd92KvteRDpfTRQqI623q6fu/aekuv2ns2hXRv2qXtrevhMREREREZE2M6zuGOXpBSZPrs2+LS4u7uSZiPQM8SAsOxuq395Pfx7BQXoB9exPjGT4CxfVZXhblkV0dxWJ8jCG30m0j5OE0frLqGma+P1+W5niweX72H7Ok8RLQy22c47Opd9z5za4S8Dv9+N0dr99yi0Ltv0e1n+TZJJ9SgUnW8x+GtyFyrwXkS6gPAT3rsqs72kjYHr/dp3Ouj5/b/W9IxVHvodx66/F2b97lmMTEREREemJlIEvIr2C6YNjX4YBw5ekHbwHqH5xG4F39tR9bxgG7qG5eKf2xRyek1bwHmoz8e1m4fvmDGDUe1eS9+nx4Gz6cm1ku8i+fiL9X7ugSYmf7lpGxzBg1Ndh3hoY/iUwG21AXHQGzHoKjnvdUPBeRLoOTxvuBGpL32Y4ctwZ9UuUh9n+yadI2KzlLyIiIiIiHaf7pWeKiGQofrASY9dW2/0O/+NDsuYNaTqezSB5PB63nRXvGVPA0EfPo2B7KRWPbyBeEgDTwDkiF/8FI3Hkpg7SxINRIvvCkLBw9vPjyHLZum5ny5kEk/8GE34H4RJIhMHdD9yFrfcVETnqvE7o54cDAVvdLAcYQ3LbfTpZJw+hfPvajPqGVhxg54XPMuyJT+Hwd6/3DhERERGRnkgBfBHpNaoWbIGE/aphlU9vxrKsJuVv7FYga0vFMtfAbHJumtJqu8iqQ1TdvZbA41uwgrHag04HuReNofCWGWSdMqRbbfhqesE/srNnISLtzbKsujuTkq+vpmlimma3eo2qYxi1ZXBe3Wav35hCyM4sW74lhbfMoPz+zAL4UHv32bbTH2fEy5dg5nracWYiIiIiImKXSuiISK8ROxjMqJ8VjvP83+/nwO69DY7bDTK1JSjV2katVsKi/EfL2Hfy09Tcv+FI8B4glqDy8Y1sP/U/7L7yBRKhWPMDiYh0sOTm3oFAgJqaGqqrq6mpqak71l1LgDG+CPLSD3ZbDgNjzqAOmYrv2AH4T25655gdwfdK2HXFC91yU3QRERERkZ5EAXwR6TUc3szrDL/3+kL+9JXv8Mw/7yMeqw2AtxZUb8xu+8Z9W+pf/qNlVP6p9Q0UKx7dwO4rX8CKJzKei4hIpuLxOMFgkKqqKkpLSykrK6OiooKysjJKS0upqqoiGAx2zyC+y4SLxkMLJcuSoXDLAOOc0dBBm8UahsGw/5yH2c/fpnGq/7uN4LJ97TQrERERERHJhAL4ItLthEpg5z9h089gy69h//OQSCOp3Dutb0bXq8gOYX38arnslUX85y93kkgkWg2q12enbSqGYeD1elNm8YeX7afqLx+lPVbl05spf2hdxnMREcmEZVmEQiHKy8upqakhkWi4kJhIJKipqaG8vJxQKNQ9M78LfPCZyTAqn1SzNwCrjw/j4gm1GfsdyNk/i/4/PbHN45T+bWU7zEZERERERDKlGvgi0m1UFcPmn8K+p8BqFLD3DoFht8Cor4GjmXLC2WePwDUsh+jOKlvX3TzqcIPvVy9eyphpkzjmjPl4vV4CgUCLgaaWgu92mKaJ3+8nFAo1yE6turPY9liH//4hBVdPbtN8RETsiEajVFVVEQ6HW2wXDoepqqrC5XLhdrd/ffgOl+uBC8ZjVISg+BCUh2r3X8lywfgijIHZtTXzjwL/iW0v0VP5xEZ44JPtMBsREREREcmEMvBFpFs4tBDePR5K/tM0eA8Q2g0bvwvvnwOxmtRjGKaDwi/NsHXdqDPO1mFlTY4v+e9rWJZVF1RvLru+tfN2Jcfz+/14PB7MgEXgGZubJgLBZfsIfXSwXeYkIpKOaDRKMJjeXiTBYJBoNNrBM+pgeV44cQh8cgycNxZOHQGDco5a8B7AO7UvvuMHtmkMKxQnejDQTjMSERERERG7FMAXkS6vai18cAHEq1tvW7oIPrwSmkuI7/O/s8k+Z0Ra17WwWHzMTqLuprWY9+3Yxa6NW4CmQXW3243H46k71l7B+yTDMHA6nbUB/AMRiGZWzz68qenChIhIR4lGo03K5jQnkUh0/wB+F1F068w2j1F+n/07vUREREREpH0ogC8iXd7mn6YXvE868FxtwL8iRdlew2Uy7OkLyLtiQotjRJxx3jxhO3sHNl9up2THriPj1guqe71ePB4PTqezzWVzWmNlGLxva18REbvSDd5n2l5Sy7tiAvlXT2rTGFULtrTTbERERERExC7VwBeRLi28D/Y9ab/fgQW1X0WnwYxHwNPvyDmH18nQh8+lzzfmsO+Pyyh7dC3uqJOEYVGRE2LzyMNsG1ZGzNVy8CgW6bzs0MB22PMABNdmZbwS6xqY1Z5TEhFpkd27kdr77qXeyjAMBt9zNobfSdkd6W94Xl/8cKidZyUiIiIiIulSAF9EurT9z6WueZ+u0oWw5CQ4YTF4+jY855vZn8F3nsVdNU9gJMAyABsJ81m52ZlPLEM1m2DdV+HAC4AFkE0/BuNlj61xnEOy8c8d3BFTbJFlWRws30d1sBIDg5ysfIpy+3b4nQoi0vncbjemaTbYhLs5pml2zw1suyjD6WDw7WdS9fJ2Ytsqbfd3+PSRQURERESks+ivcRHp0iIH2j5GYBN8dDUc82LTc26vh7HTp7Bp1RpbYzpdTsbNnNb2ydlQsRKWnQnR0obHD3ApBgkchPCxhRw+xMXhFsfKuXw6hvPoVVGLxaMUb/uQNVuXU1bV8AH0zR/I1NGzmTBsGg6HKruJ9FRutxu/309VVfOlyZL8fr8C+B2g4HOTOPiz92z3887o13ojERERERHpEArgi0iXZrRT/ObgS7Wb4eakKAN83Dmn2w7gT5t7PP6cljPw47EYa99fybKXF7J781ai4Qje7CzGz5rG8WefzpCxo9LOPI8cguWfbBq8r+XEAuK4qWYm1czEzzoKeQUHTW9fiFLA5qdn0Pe74C5K6/JtEgzXsGDxYxwo25vy/MHyEhZ+8Dybd6/lnOMvxe1U0E6kJzJNk9zcXGKxGOFwOGWNe4fDgcfjITc3VyV0OkDhF6Zx8Ofvgc3tBQpvnt4xExIRERERkVYp1VFEurScye031s47Uh+fMHsGw8aPAYeB4XK0WkbH4/Ny8kXnNjhmWVZdUCoUCrFz0xb+cOu3eOR3f2PL6rWEgyESiQSByipWvrGY27/zU+77v98TrKlJe+7hfWk1BSwCTOQgF2I1epmPks8BLqZmm5dtv093vMzF4tEWg/f17dy/lZeXPkXC0saVIj2V1+ulsLCQrKwsXC4XDoej7svlcpGVlUVhYSFer7ezp9ojuYbkkHfZeFt9/HMH4Zvdv4NmJCIiIiIirTEsy7I6exLS1OTJtVHL4uLiTp6JSOdKxOCNURDa1fax8o6FuUsbHgtHQqzf+REfbV5ORU1t2RkrYRHaU0NgUwXhfYEG7d1eD1d9+38YPfVIKn88HicUCtXVdT64ey8P/uJPhIPBVuc0ePQIbvjJd/D4mg9WJWLwxkgI7U73kUJtgXyDXJaQz7vE8VHNVKqYQwJf7WPpB6fuBNNjZ1x7Vm1+n7dXvWyrzznHX8KYwRM7aEYi0laWZRGPx4nH41iWhWEYmKaJaZpp31UUj8eJRCJEIpG6Mdxud12dfOk4scNBtp30KOF1LZdaA3AOzGLUe1fiHpZ7FGYmIiIiIiKpqISOiHRpDicMvwU2fKftY8UbJbvv3L+Fl5Y+RSQabnDccBj4hmbjG5pNeH+Aw2+XQNRiwpyZnHnFxQwYPvTImPE4gUCA5FqolUjw9N/vSSt4D7Bny3ZeevA/XHDj1c22qVptN3gPydsIKjmGCP0IMZzGL/mRA1D6GvQ7N0V3m1IF9BwOB2u2Lrc91potHyiAL9JFNV6wrM80Tbxeb1oBeNM08fl8+Hy+jpimtMBZ6GPkm59m56ULCLzV/JuLd1pfhj1zgYL3IiIiIiKdTAF8EenyRn4VDr0Gpa+3bRxX/pH/37V/KwsWP4bVUrkWCzz9/Yy6dCqfOuEz9Bs0qOFpyyIUClH/RqZtxes5vM/ezrsrFr3N2VdeijfLn/J8tMzWcI04CTG62bOhPW0Zu1ZzAb1DFfubbFibjt0Ht1MdrCTbp6CRSFeQXKCLRqOEQrXlwBwOR5NAfXJB0+/3K4u+i3P29TPyjcsJvLuXw//4kOrXdpKoCOPIduGfO5jCW2aQfeZwDEd6d1SIiIiIiEjHUQBfRLo8hxtmPwurPgf7n8l8nD5n1/43Govy8rJnWg7eQ10t/CAB1h9Y3SSAn8w4r2/londszysajrDyzcWc8MkzU543OzBB1WhjjK3xHQj1BcLVGY9bE6xSAF+kC0gu0CUSCSKRCNFotO5csmxO/d9/h8NBKBTC7/enXU5HOodhGGTNHUzW3MGdPRUREREREWmBNrEVkW7BmQWznoLj34EBl9vvbzhh2I21/79pdzGhSKDlDo2s27GKSCzS4FiqEhL7d9qudQNAyfadzZ7LngSODgri+0dl3jfVHQjtR4E/kc6WXKBLlsaKx+N1QXnLsgiHwwQCAWKxGNFolGg0SjgcJhgMNgj0CyTiCWoqq6gqryAWjXX2dEREREREpBtRBr6IdBuGAYVza782T4ON30+/r5kNS08D30jYNacMJjnA2UoGfj3RWITNu4qZNHJm3bFUgetMAzPRSPPBLlceDLoSdt+T0dDN8o2EwlMy75/qDoT62pJBn+NX9r1IZ0ou0CU13t8iEonU/f4bhoHT6ax7TYzFYgSDwboM/d6sdN9+lr28iA8WvU2gqvauJNPpZMoJx3D8OacxbPxY3akgIiIiIiItUgBfRLql0d+Bms2w57702sfKobocqteB97+nMqxgDge/+DzBWVvSvubhqkMNvk8VdPFlZxGorEp7zCR/TnaL50fc2v4B/OFfBKMN92G1FLwHKMjuQ2FOXw5XHbQ17ogBY/F7W34+RKRjxePxurI5sViMeDxOLFa7QJlIJDBNE7fbTTQaJR6PNwnUJxKJXl1Kx7Is3lnwEi/d/1iTxd54LMaqt5ew6u0lTJt7HJd8+QZcbncnzVRERERERLo6ldARkW7JcMC0e2DsL+OYRS0HklNxluUw4JefJmvJhLT7xOMNs+tTZZaOnTHV9lwAJh07q8XzudNh1HczGjqlghNh+K1tG6Ol0jnJYN+YgZNsjzt19Oy2TEtE2kFyw9pYLIZlWQ2+DMMgEokQCoVwOp0kEokmrweGYbR6l05P9vazL/Livx9ttcTYR4uX8ugfbicRT/+OMBERERER6V2UgS8i3VJVoIJVm5exbtIqwn+LkvXeBHyrR+I8mIdvzQgMq/WMT8Ny0O+PFxNYtJlETojIsANUzf+IRF7q+vg+T1aD75PlIeoHqGacOpf3XnwNbNSF7zNoIKOmTGy13YSfw6FXoHJ52kOnVHgKzH4aTG/bxkmVVZtIJOoycgGGFI1me/5mDpTvSWvMcUOnMKz/6LZNTETaxLKsuhI5yY1rHQ4H0WiURKI20Ox01v4JGY1G64L1yWPJMjtAg+O9xf5de3jpwf+k3X7d+ytY/vqbHHvWqR04KxERERER6a6UgS8i3U5J6W4eff1uPty0lHA0BK44NfOKOXTL88QLq9IK3icZCQdZK8aR8+Y0ih44g+Ff+Ap9/3I+jqqm0e3RQxoG2Q3DwOv1Nghk5/cpYsYpJ9p6PGdecXHaJSaOfwPy0x2+0Q0ChfNh5mNw7GvgKrAzw2aGT1EyIxwON1jQcDgcnDD+dPrnD2l1vHFDp3D6nE/1ynIbIl1JPB4nGo022Yw2+buZLK0TiUSwLKuu/n00Gq37vv5mt73N0pdet7WIC/Dei6/1yudKRERERERa17tSokSk2yurPMSCxY8QiYabnHNU+ch6137JlvqMuEnOW9PwbB5EyU8eIF5QA8CgPsMoyu3bpL1pmvj9fkKhUF3g+szPXkp1eSWbP1zd6vU+8fnPMPXEY9OenzMLjnsdNv0Idt5ZW9u/sZzpMO5nUHgyBHeCFQPPAPAOTPsyaWl8B0IyeNeYy+lm/vRz2Ve2k/W7PmJ/WcNs/OEDxjB11ByGDxit4L1IFxCNRolEInXZ9lC7Ma3D4cCyrLrjyVJZbrcbh8NRVyPf7XbXvRb0tt/pWDTKyjcX2+63b+dudm/aytBxugOpOYmaKOUPr6PsrtWE1x/GiiZw9veTd/k4Cm+ejntUfmdPUURERESkQyiAL9JLbagK8tbBSqpicXKcJif1yWFirr+zp9WqxWteTxm8B/BsHYAj2j4va+69fej/68vZ+4t7cZgOjp88v9m2ySB+st6z5XZzxdduYckLr7L05YVUlZU36TNo5HBOu/zCVmvfp7yeFyb8Gsb+CEr+A5UfQjwIrkLofz7kHw/JmJkrs5L8aUnegRAIBOo2uUw5X9PE6/EyvP9YhvcfS02oijgxnE4nOf48srRhrUiXkvx9djgcdcF6y7IabFYbj8exLKsusB+Px3G73bhcLmKxWF27VHuF9GSVpWWEg6GM+h7cU6IAfjOqXtjK7qv+S7ys4ft/dEclh367nEO/W07RbbMY8PtTMEzdYCwiIiIiPYsC+CK9zKv7y/n9hr0sPFjR5NwpfXP56thBfGJgO9RX6QCVNeVsL9nU7Hkj7Gq3a1lYeDcPJuujscy7aRqD+gxrsb1hGDidzga1nk+77AJOuehc1r2/kt2btxEJh/FnZzFu1jSGjm17trnphyHXtGmINksuXlRXV2MYRoMM/ORzUj8bFyDLm4PH48Hj8XTGlEWkFYlEgkQiURd8r5+JH4/H6363k1wuF263m1gsRiwWwzCMujt0elsAvy2b9sZjsdYb9TKWZVH+zCb2XPo8JFooMWRB6Z9XED8cYvC/z0n7/TUWCbBt5VNUHtqClYiTlT+YkTMvwZvdp50egYiIiIhI2ymAL9KL/GnjXr65ekez5988WMmbByv52eRhfGvC4KM4s/Rs2l3c4vmEP3VmfiYMaj/8j1t2AWN+lvlur6bTyZQTjmHKCce019S6HNM08Xg8GIZBIpHAsqy6TSwbB/WTVOtZpOsyDKPud7d+xn19iUQCh8NRV/8+Fos1WcBrvEdIb5Cdl5t53/zM+/ZE8Xicmp1l7L3qxZaD9/WUP7CWrPlDKLiu5dvPwoFyVvz3p2xc8i8iwYYJDUse/wojZ13O7PN+Qm6fkRnPX0RERESkvegeU5Fe4qGdB1sM3tf3g+Kd3Lttf5PjsWoIH4B4ZtUB2qw6WNXi+fDovSR87Tu5yoVerETr7Xq7ZJDO4XBgmiYOR+3bS3OB+t4W1BPpThwOBy5X7R1NlmXVfe90OjFNE6fTicfjweFwNCizA0fuvPH7/b0u+x7Al53FmOmTbffzZvkZPc1+v54qHo8TCASouG8tVo29OxMO/ukDQqFQk0WlpEBFCc/9fi5rFv6xSfAeIB4Ls3nZAzzz62M4tHNFxo9BRERERKS9KIAv0gtEEwm+m2bwPul7a3YSjieIVsD2v8Fbk+GVHHi9P7zsg6WnQcmTkIh20KRTSGbFN8fyRama/1G7XtOKQbwmzbYfZ6GGw2FCoRDhcLjZAEJPYzdQ1xsDeyLdRbIkTv0yOcm7a5L/H4/Hcblc+Hw+3G43brcbr9eLz+frtcH7pOPPPt12n9mnzsOtsmJA7XtpKBQikUhQfd862/0jq0upemcXgUCAQCDQ4O6ReDTMS/84l/KSta2OE64p5ZnfHMdHr/+BWLSTMhdERERERFAAX6RXeG5vGSUhe5H2Q5EY9y8s5Y3RsPZWqG70Wbd0Eay8FBbPgeCudpxsC3Kz8lttU3Hu+yS87VdKB8Dha71NMlswEAgQDoeJRCKEw+GUAYSeyE6t695YF1ukO3G5XBiGgcfjwe1212XaJzkcjroNa5NldpJ33iQz9HuzCXNmMmrKxLTb5xbmM++CT3TgjLqX5IbwibIw8Z3VGY0R+ai0bqz678FbPniU0l0r0x7HSsRY+uTXeOZXc6g+vDOjuYiIiIiItJUC+CK9wOO7D2XU799vlBItbblN1UewdH5taZ2ONm7oFByOlgO/sQFl7P/akyRctbfcW7Qt+z13NjhaiUU1DhDYPd8TpFvvurfWxRbpThwOB9nZ2XXBeZ/PR3Z2Nn6/H5/PV/c7nEgk8Hq9dZn5+v2u5TAdfPabtzJs/JhW2+bk5/H5732N3MKuuXl8Z0i+V1qhzN8zrdCRsjvJjH7Lslj71j8yGq+spJgX/nw6oerM/p4SEREREWmL3p0iJdJLHAhnVuemLCe9foGtsPYrMPORjC6TNr83i7FDJrJh55oW2wVnbqXkxw/Q555z8Gwd2KZrDr+l5fP1AwPptPP7/T02uGWaJn6/n1AolHKxwjRNvF6vsu9FugG3201eXh7V1dVEo7XvBaZpEovFiMVidSVzkq9n+v1uyJeVxfU/+hbvLHiJZa8soqL0cIPzbq+XmfPnMv/i88grKuykWXYsy7J4e9fbPLT2IfZU7cY0TEblj+baadcyrd+0FvsBOPIzLykUfH03VX9fQ/xgEMPtwDW+AN+1Azi4fVnGY1Ye3Mz7z36XeZ+9M+MxREREREQyYVi9oThzNzR5cu1GZsXFxZ08E+kJzn6rmEUHK233m7guh1/+YEpabQ0nnLoTvG2Ll7eqOljFE4vupTqYxuOxYK7z07hfG0twFwR3QNWq9K/lGQDzt4Dpb75NLBYjEAikPabf7+/x5SUsy6orgWBZFoZh1JXY6KmLFyI9VSKRIBqNEo1GG/w+J3+X9fvdung8zuYP13CoZD+JRJycgnwmzJ6B159GfbZuZG/VXu5edTdL977H7qrdbK/YTlUk9ebz84bM4x9n387kvk037g2Hw4TDtaXwDlz4X0KL9rTL/GqK9vDR5X9s0ximy8dnf7kXjz+/XeYkIiIiIpKOnh1FEhEAJuT4MgrgD96TfnDBisHmn8Hkv0NHxnCyfTlcOO9zLFj8CBU1Zc22MwyDU2aew5RRY+HCj+dowUfXwZ77Wr+OmQWzn2k5eA/YLosTj8d7fADfMAycTmePf5wivYHD4cDj8eDRBqsZM02T8bOnM76zJ9JBqsJVfPmVL/Hw2oeJW+m9J769+21OenAur3zmVY4ZeEyDc/Xv4si+YVK7BfCNeNvfk+LRIFuWP8qkk29uhxmJiIiIiKRHNfBFeoFrR/a31+Hj+3LOeq2frW47b4cVF0Osxt7l7MrPKeTTp9/AyTPOoSCnT4NzLqebqaPmcMUZNzFl1OwG5wwDpt0Do75Ve8dAc/yj4Pg3If+41udi9yYm3fQkIiI9RWW4klMfns8DxQ+kHbxPqghX8KnHz6M02HCznfobnfvOGYZ7dt92maunugBHpO0LUZUHN7fDbERERERE0qf0SJFeYEZ+FicUZrHkcJqRdQPGbM5i7OZsm1ey2P+MwfJz4ZiXwezAhE23y8O00XOYOmo2lTVlhCJBTNNFXlYBLqer2X6GAyb8Ckb+D+y6G0qegMhBcHggZwoMuwn6ngNGmmWc7ZaMUIkJERHpKa594RpW7F+Rcf8DgQPcs+oevnn8N+uOJTdDDgQC4HTQ99GzOPCpF4iuL2/TXM2Ym74bZ7F/ypI2jWPZXKgQEREREWkrZeCL9BL/nDmKQld6UensoMlX/jYGA7vBZgOwOPwmbPlF07OWZRGLxQiHw4RCIcLhMLFYrE1Z6YZhkJddSP/CwfTJ69di8L4+zwAY832Y9yGcvgdO3QpznoN+56YfvAdsb9ioDR5FpLvpiNdu6f7Wl67n6Y1Pt3mcO1beTjzRMCie3BTdNE3Mfn76v3w+2ddPxMhqW+7RgDVzwWrbQro/b1Cb+ouIiIiI2KVNbLsobWIr7c2yLJbvP8ynP9jG7lC02XYDPU7+tHYsnm/lt+l6rn4Wp2yL4/LVbmwYj8cJhUIpa8abponX6+2WwW3LsggEAmnVwk8GJJSFLyLdRU997Za2CcfCXPjkhby87aV2GW/jTZsYUzCmyfHGm6InKiPUPLGF6MZyiCVw9PURKQ9Q+af0d6gvmfYW2+c+l+FMDY45/+fsWP0cVaXbwLLIKhjK2GOvYuzxn9fmtiIiIiLSIRTA76IUwJeOEI/HOVRVzdMl5fxr92E+rAzVnZua4+W6IYVcMjCf3FA2b44wsSJtu974ewMMuMzC5XIRDodbzNY0DKMu2667icfjBAKBHvv4RKR30mubNGZZFn98/4/8+r1fcTBwsN3Gff+a5cweMLv1hins/9kSDv7wXVt99k1ezPaTnsVyJGz1MxxOrEQs5Tmn289xF/9eG9yKiIiISLtTDXyRXsQ0TfrkZHOVy8mVgwuojsWpiiXIdjrIcZpHsikLTEZ/Bzb/pG3XC6w1icdrSy643e4W21qWRSgU6pYZ6snMemWpikhPkXxNbi3Pozu/dos9lmXxpVe+xB0rb2/3sbNddvfcOcLV12+7z4DiuRRsn8KmMx6katC2tPs1F7wHiEUCLH70i8TC1Uw78+u25yQiIiIi0hzVwBfpZZLBZr/fT1GWn+G5WfTJ8tcdSwaZx/4Iht3StmslQrUZnNFolEgk0mpwJ3mbfHdU/3n1eDy43W48Hk+T51VEpDuw83rcnV+7JX1//eCvHRK875/Vn1H5ozLun33uSHDYXzzy1OQx+dkv0n/1iUcOtsN9yUuf/gb7tixu+0AiIiIiIh9TAF+kFzIMA6fTicfjwev14vF4cDqdDQLshgGT/wbT7wcyjD27iiwSidrb09Pd8LA7BIGa29ARaPV5FRHpDuy+FneH127JXDQe5dfv/apDxr5+2vW4zPQ2oE/FPTSXrPlDMupr4GDkOxcx7qXPk7tnFDR6u3Z5cygYNMX2uGsW/Tmj+YiIiIiIpKISOiLSLMOAwVfBoTdgz7/s9LQAg8Lzjtxqblm1wXyHo+V1w66+LYc2dBSR3sDua3FXf+2Wtnl207OUVJe0+7hOh5NgKMiaA2uY0s9+oDzJNSoPFu7KqK+BQdG2qRRtm0ogfz+BPnvxHFvIoG+dSZ/hc3j8x+Nsj7n9w6cJVOzDnzcgozmJiIiIiNSnDHwRadWILyX/L90AjUH+/Bj+sQ03h0snwNOVs9WTGzo2l2na2nkRke7C7mtxV37tlrZ7ZdsrGff14GaGMY6TjOlMNcbgrHdbXywR448r/8i0f03ljEdO5+1db2d0jfb6+fOX96fP5pnkPDaCQUXzCVTsJRqutj2OlYhRumdVu8xJREREREQBfJFewkq03qY5ebNgwKVQe295GkF4l8Ww74QBGmTcp/MBu6tmr9vd0FHZqCLSndl9Le6qr93SPirC5bb7jGAgvzG/zG73Ala47+ct9z9Z5X6QHe5n+Yl5IwPp06D9wh0LOf2R03hk7SO2r2U42/kjTdyi4tENxCKBjIdoS18RERERkfoUwBfpoSwLShfBisvglVx40YSX/LD0dCh5AhJRe+NN/zcUnQ5NCsQeuSJQG7wff1eQ3ONrs9BN08Q0TQzDaLV8TrJtV6QNHUWkN7HzetyVX7ulfWS5sm21/5RjHmvcj/B15+coMvIanBto9OEHzutZ436Yk42ZDc7FEjE+//zVtjPx3eMLbbVPR3RPNR5/Qcb929JXRERERKQ+BfBFeqDwAXjvZFh6Gux7AmJVtccTQShdCCsvg7cmQNWapn2b26DV4bM45r8w9ifgGZAqu9wg/9QYU18I0OeiWIMzLpcLl8vVYga+YRh4vd4uVYah/nMRDAbrNuRNZ44K4ItId5bua3JXfO2W9nfikBPTbnuaMYcnnL/Eb3hbbFdg5PKC6w/MMsY3OB5LxPjJOz+2Nb/8z03E8LTzIpID8gdMJLtwuO2unqxC+o04rn3nIyIiIiK9lgL4Ij1MtAyWzoeyd1puF9haG+SvXnfkWLKGeyAQIBwOE4lECIfDdccsM87YH8KpOw1mPgFjfgQjvp5g5M9CzP6gminPBMg9LvXmrtnZ2c1m4Jumid/v71IZnKmei1AoRDAYJBKJtBqsUgkdEenuWntt7oqv3dIxrph4BdlpZOE7cHCX67u4DGda42YZPu5wfrvJ8YU7FrLu0LoUPVJzFvnIu2JC2u3T4R6Zh8NhMvHkL9ruO/6E63C6fe06HxERERHpvRTAF+lhim9rGJRvSbQMVlxeW27HzgatDhcMvATG/Rgm/dbBuO+4yB6fOqCdDPC43W78fj9+vx+Px4Pb7cbj8dQd60oBoJaeC8uyiEajhEKhVu8oEBHp7pKv4d3htVs6TpY7i1tmf6nVdp9wnMBIY1Da41qWxRzHRI4xJjU59+ymZ23NccBvT8Y9Jt9Wn+YYXpO8z9QuCEyYeyP+vPQfk8dfwOT5t7bLPEREREREQAF8kR4lvA9KHrPXp3oNlC5q2wat6QZ4DMPA6XTi8Xjwer243W5CiRBRuwX5O1Bzj7Hx3QPxeLzFTHwFtUSkp2j82u3xeHA6nVqo7GV+Ou+nTO07tcU21zjOszVm8mfoWrNpv0PBQ7bGcvbxM3LR5Xin97XVL5W8z0zAWVSbQe/NKuScL/0Xb3afVnqBy5vDWTc/R3bhsDbPQUREREQkSQF8kR5k931gZRAL3/lPq80btKYb4LEsi3d3v8tVCz5H9u+zyP1DDt7fehj/z3H8funvKQ2W2n8A7ai5x5dqk8ZYLJZy0UMbOoqISFdWGizloeKH+PP7f+afK//Je3vea3UR3226efeqJQzKbj4b3U72fX3DjYFNjnmdLdfQT8U1JIdRyz7LkIc+ge/4pmOmNcbwXPr/4qQGx4qGTOeCb7zHsKmfgmYWrgZPOIPzv7aYAWNOSnleRERERCRT6RWoFJFuoXJVZv32P2NQvSWLgtNiDLg2gmdwyx/i4/E4Tqf9l4+aSA1XLfgcz2x6psm5TWWb+Mair/Ojt3/IFZOupCpSSVWkijxPHqcOP40rJ11JljvL9jXtamkhw+VykUgk6oIclmWRSCQaZOdrQ0cREemq1peu59dLfsWj6x4lHA83ODet7zRunXMb1067FoeROscny51F8Q1rOec/Z7N079Im5zN950vVb1rfabbHCX54gMO3r6LioXUkamozGhw5LnIvG4/Zz0/pb96HRPN/47jHFjD8xYtxDWxa7z+372jO/uJzVJVuZ9PSB6gq3QaWRVbBUMYc+1ny+49PMaKIiIiISNsZlnZa7JImT54MQHFxcSfPRLqTDy6C/c+0cRDTov+VUUb/LoSjmeQ3t9uN12svMy4Sj/DJ/3yChTsWZjStPE8el4y/hJH5o6iKVJHjzmHekHnMGzqvXYPloVCISCTS7PlEIkE0Gq0L9Hs8nrpse9M08Xq9yr4XEZEuZ+H2hVz01IVURapabHfFpCv493n343Q0v1AfjoW588M7+esHf2Fz2ea6438y/5fbnJ+2Pbd74s9xY+wXdd/38/dj55d24TbdafW34glK/vcNDv91ZYvt8q+fguF0UPHgkQA/gGdSEYVfmkH+1ZMws9O7poiIiIjI0aIMfJEexN16edbWxQ32P+AmtNPB5McDODxNm2QSMP/Ne7/JOHgPUBGu4F8f/avJ8cl9JvP1477B1VOubpdAfmtjOBwOPB4P8XicRCKBy+XC5XLVlc1R5r2IiHQ1Hx34iAuePJ+aaE2rbR9Z+wh5nnz+cfY/mm2zpXwLH+xbzq7KXQ2O/0/8jzyWeI3bzMu53HFGq++JlmVhGAYPxF9scPymmTenH7y3LPZ+8TXK7lrdatvye9ZQ9LXZjN93M5Et5VjBGGYfH+7R+Xr/FhEREZEuSxn4XZQy8CUTB16A5fb2j2uGBRgM+lKYUb8INznr9/ttldCJxqOMvH0Ee6v3tsfkUrptzlf44+l/bPMH8FgsRiAQSLu93edCRETkaLvgifNZsHmBrT5rb1zHhKIJTY6/uOVFLnvmUgLRlt8rv+i4hL86v9ZsOZ6k4sRWpkavrPt+/rD5vHj5S3icKTIIUqhcsIWd5z+TVtukkW9cTtYpQ231ERERERHpLNrEVqQH6XsO+Ea0x0i1QfB997mJVTQ8k8kGrf/d8t8ODd4D/GX5n/nt0t+2eRw7j0+b1YqISFexYt8Kbnn5Fs569ExOe/hUrnz2Cp7d+Cybyzbz/ObnbY93x8o7mhx7v+R9Lnn64laD9wC3J57kB/F/ttgmYkX5UuzIe/fF4y5mwaXPpx28Bzj8t5bL5qRS+vcPbfcREREREeksysDvopSBL5na8zCs+mz7jTfqt0EGfaG2TqxhGPj9fttB61+8+wu+/9b32m9Szchx57Dny3vJdjfdfM6OeDxOIBCgpZfHTJ8LERGR9rT20Fpu+O/1vLf3vZTn8zx5VIQrUp5rSf+s/pTcuq/BsTMeOd1WOTwTk23upxli9GtyLmRF+HTseyx2F3PBuAu5ZdYtzB4w29YcI9sr2Djyblt9AHA6mLD3Jpx9/fb7ioiIiIgcZar7INLDDL4SQrtgw7fbZ7yqZU74QrRNG7RG4s1vCtueqiJVPFT8EDfNvKlN45imid/vJxQK1W1W2/i8NqsVEZHOtnLfSk5/5DTKw+XNtskkeA9woOYACStRVwJn3aF1tveyiRPnrviz/MR5Y8MTw3LxzB/OUwWLMB2Zv5eG1x/OrGMsQWRzuQL4IiIiItItqISOSA80+lsw6ynIndX2saygA7/f36Zs877+vm2fSJqe2vBku4yTDOL7/X48Hg9utxuPx9Pm50JERKQ9VIYrOe+Jc1sM3reF6TAxOLKvzGPrHstonMcSrx75Jt8LF46DSyZiFPnbFLwHsKKJzPtGmi7Qi4iIiIh0RcrAF+mhBlwE/S+EimVw8BXY8VeIHLQ/jqfApK17tJ4/9ny+8tptJKzMP2in61DwULuNZRgGTqdTm9SKiEiX88CaByipLumw8ccXjm+wMfy+mn0ttG5eiVEKswfAuCIY0LYSd405+2eeQd+WviIiIiIiR5My8EV6MMOA/ONg7A9gyDWZjVF0RtvnMTR3KOePOb/tA6XB6/QeleuIiIh0FsuyuH3lPzr0GjdMb1j2xunIbDHb5fbAycPbPXgP4JvdH9fIPNv9vNP64h5f2O7zERERERHpCArgi/QSw26GenfCp8VVBAMvb5/r/+ikH+N3dXy22+Q+Uzr8GiIiIp3pQOAAaw+t7bDxs1xZfH7q5xscG1c4LqOx+vj7tMeUUjJMB4U3T7Pdr/CW6Q3uLjhqYgkoPggvbYHnNsKLm+Gj/aByPiIiIiLSAtWFEOkl/KNg0JWw96H0+4z8qoXljBMOx7EsC8MwME0T0zRtf/Cd3n86T1z0JJc+fQmBaMDm7NN388ybO2xsERGRrqAyXNmh49/zyX+R781vcOyzkz/LtxZ9k3A8bGusjYc38sO3fsikPpN4eO3D7KjYjmmYDM0ZygXjLuQzkz7TpgX+wpumU3b3GiKbytJq753Wl/yrJmV8vYxYFizdCytKINwoWL++FN7eCdP7wwlDwFR+lYiIiIg0ZFiWZXX2JKSpyZMnA1BcXNzJM5GeJB6AZWdB2eLW2w66OsGYvwVJWE2zwkzTxOv12t7I1bIsVpSs4Gfv/ozntyxo95r4Jw4+kXeuSuPBiYiIdAPRvdVUPrOZ2P4aDLeJZ2IROeeN4kDkIIP+NjDtcfxhD2esnsqq4dvZ0bf5vWI8pod/nXsvV0y6IuX56164lvtW32f3YbQoz53H9+f+gK8e+9WMs+Ij2yrYfuYTRLaUt9jOM6mIEa9cgmtwTkbXyUjCqs2431DaetuR+fCpsQrii4iIiEgDCuB3UQrgS0eJB2Hd12D3vZAINT3vKoDh/5tgwG01YDT/8mAYBn6/v0EQ37Is4vE48XjTjP1EIkEoFCIer10Q2F21mxe3v8jB4EF8Lh+jC0fznw3/4fnNCzJ6XH39fVly9XuMyh+VUX8REZGuIlR8iAM/XkLl05sg3vC92Dkgi/ybpjJ/4HVsLNvY4jhm3MEtr53Fle+eRFbES8wR560J63js+HdZMvZI34FZA7lhxo3cOP1GhuQOaXa8g4GDnHj/CWwp39K2B5jCrbNv409n/CnjIH7sUICDv1xG+b3FxMtS/IEDZJ81nKKvzCL7nJEYjqNUQufd3bB0T/rtZ/SHU0d02HREREREpPtRAL+LUgBfOlqkFHb/G8qXQLwGnPnQ5wwY+GmLMIG6QHtLTNPE7/djGAbxeLxBgL5xu1gs1uKH8uSCwKbyTdy+4nbe2vkWNdFqPKaHfTX7KQ01nzE4tmAsz126gPFF49N67CIiIl1V9cKd7LzgGRLV0RbbPfa5Vfx88gPNnnckDH77yFWcuSZ1jfiAO0yFL4A75mTIMRMY+eKlOLytV9fcVbmLkx+cx47KHa22tevOT9zFDdNvaNMYh+9dw94vvFpbb74Z/lOGMOyp83EW+tp0rVZF4nDXCojYuOPQYcCNM8Hv6rh5iYiIiEi3ogB+F6UAvnSWWCxGIJB+jfpkAD8QCJDq5cQwDCKRCLFYDI/Hg8PR/G3hpmni8/kIBoMNFgLiiTiv7nyVu9fcxVu73yKSiOByuDhx0Il8ac6XuWDsBbhMfdAVEZHuLbSulK3HPtRq8B6g0hvk/O/8lsPu1PXwb335HG584wwsLIw0drEvuH4Kg+8+O615XvnsFTy67tG02toxtmAs676wHoeRWQmZ8kfXs/uKF9Jq65vTn5FvfBpHVgf+/bBqPyzcbr/f3CFw7OB2n46IiIiIdE/axFZEGkgn876+WCxWVzInFcuyiMViWJZFNBrF4/E0e91kln44HK4rvwNgOkzOGXEO54w4B8uyiCQiuB3uuqx9p6mXMhER6f4O/t97aQXvAXJDPv7+z2u54St3U5OoaXDOH/Zw5bsnAaQVvAcou6+Yfj85Ma368IeCzd8V1xabyjbx2vbXOGvkWbb7xkqD7Lnu5bTbB5fv58D/vceAX86zfa2kLWVbuGPlHbyz+22qIlXkefI4dfhpfGH6FxiWNwx2VGQ28I4KBfBFREREpI52SBKRBuzelJNIJFoM+icSiboxk/XxG58Ph8OEw2FCoRDBYLDuv40z8aE2o99jeurK8dhdcBAREemKYgcCVDzeck37xibvHcqCBT9lWt+GZXLOWzmLrIjX3gTiFofv/Citpi5Hx2Wtv7nzzYz6ld+7BisYs9Wn7O7VJEL2+gBUhCq47OlLGfvPMfx+2e9Yuncpaw+tZcmeJfzi3Z8z6o6RXPP85wkEq2yPDUBYf9uIiIiIyBEK4ItIA3Y3j0skWq7r2nhBoH77ZPA+FovVfSWD/NFolFAoRCAQIBY78uE6Ob/kwkE0Gq3L8BcREemuqp7fAlEbtdI/1ucdi0XeR3jrs29z9ZSrOWHwCZyz59iM5lDz+s602k0ompjR+OmoDKcuCdSaw3ettt0nfihI5TObbfWpCFVw6sPzeXLDk822SVgJ7l9zP2fv+wJBK/WGui1y6iOaiIiIiByhvw5FpIFk2Zr2at/SgkA0GiWRSBCNRusy6Q3DaBCMj8VidZvjJuvpJ7P0k5n7gUCAQCC9jXdFRES6otjBYMZ9997wKsf7ZnHfef9m8VXvckr+3IzGiVdE0mrX1o1mW5Ljab2ET2NWPEFkY1lG1wuvP2yr/RdeupEPD3yYVtvFwRX8T+xP9ic1KNt+HxERERHpsRTAF5EGTNNMO4hvmmaLm9ICOByOlEH8ZKZ94/r5TmfTevbJDPtQKEQ0Gm3QPnn9eDzeoUH8eCLOgZoDlFSXUBOpoSxURjSeXp1iERGR1hhuewvo9VmhGGX3Ftd978h2ZzSOIzu90jgT+0zk9OGnZ3SN1pw89GTbfax45nfhWZH0/27YWr6VJ9Y/YWv8fyde4KBlc3FhWn977UVERESkR1MAX0QaMAwDr9fbaimdZLt0MvDrB+WTAfdEItGkfr7L5SIej+NyNQwgOByOuuB9fY0XG5JB/vYopxONR3ly/ZPMe+Ak/L/z4fqNkwF/7c/gvw0i5w/ZFP2pEM9v3Zzw7+N5YM0DhGIZ3CIvIiLyMe+Uojb1L6tXvz5r7qCMxsg6Kf2NU+/8xF308/fL6DrNGZU/KqMNbB1uE0e+J6NrOvr60m5754d3YmHvb4wIUf4VX5B+h9EFkG9z/wIRERER6dEUwBeRJkzTxO/3Nxucr3++tYx9y7Jwu90p2zbOvHe5XMRiMVwuV4Ogv2maTTLvDcNoEuiH1Bvl2vXBvg8Y98+xXPbMpSzes7jF4PzSkqV8/vmrmXTXRNYcXNOm64qISO+VdfpwXKPyMu4f2VyOFa19/8u/dgqGx35Gf8HN09NuOzJ/JG989k1G5Y+yfZ3mfP24b+AwMvt4knf5ePudTAP/+SPTbv7u7sX2rwEsdhW33gig0Atntd/zKSIiIiI9gwL4IpJSMkjv9/vxeDy43W48Hk/dsWQgPp2Mfcuy8Pl8eL1NM8oMw6gbOxl4j8fjddd0OBxNMupN08Tj8TRbvqctAfwP9n3AqQ/NZ0flDlv9tldsZ/5Dp7ChdEPG1xYRkd7LcBgUfXlmm8awIrWb4DqLfBRcN8VW39yLx+IZnW+rz4SiCay5oZj7zv03xw5suHFulivL1lg3z/wiN824yVaf+gq/mP7iQ5Lvk8NxDk5/ntWRatvXAKjuA4wtbLnRiDy4fBJ4m5YSFBEREZHeTX8hikizkuVvUtWlry8Z7E9uNpvqvNfrxeFw1GXIO51ODMOoK6UTi8Xq2luWRSwWwzRN3G43icTHAYmPs/RbK9uTaQmdSDzCxU9dRHU0sw/oh0OHufr5q3jv6qWtliASERFprOjWmWBl8NIAAG4ySURBVBy+ezWRtaW2+xp+J4b/yPv1gD/MJ1RcSuCt3a329U7ry+B77JeuAfA6vVw99Wqunno11ZFqKsOVZLuzsSyLy565lNe2v9Zif5fDxffn/oDvn/j9Nr13+mb0I/+ayZTfl162u5HtIu+7s21dM9eTm9Hc8nz5cN5YKAvCRwdgZyVE4uBywJCc2pr3ffwZjS0iIiIiPZ8C+CLSLpJB/Pob0xqGUVc2J/kBObkgYFkW8XicmpqaZsdMJBJYloXL5SKRSKQVvAcyDgA8vfFpdlXuyqhv0vsl7/N+yfscO+jY1huLiIjUYzgdDH/2AjaN+xc2S62Td9n4Bu9/Dq+TES9dzN5bXqf8/rWQSD1g7kVjGPyvszHboe56tjubbHd23fcvXv4ST218ittX/IM3dr7RoG3/rP5cMelKfnLST8jx5GR0vdDqgxy+8yNCqw9hheKYRV48M/sSXnmwxX5Gtou+j5yFe1JhWn9XJJ02/HTe2vWW7Xmeltzwt8AHpwy33V9EREREejfDao/dHqXdTZ48GYDi4jRrZop0Q7FYjIqKiiab09aXzN4Ph8O43e60xvX7/a3eNZDK6Q+fxqKdi2z3a+zaaddxzyfvafM4IiLSO+289Dkqn9xkq8+opVfiP3ZgynORXZWU3bma6td3kKiI4Mh24Z83mMKbpuMZW9AeU27Vnqo9lFSXYBomw/KGUeTLfNPeyPYK9lz7MjVvpF50N/xOjGwXiQPBhic8JlkXjyL3qzNwjcuvSz5Id+F/b9VeRtw+nFgi1nrjj2W5stj9pT3keTPf30BEREREejdl4ItIp3E6neTk5FBdXZ1yk1qn04nb7cYwDDweT1qlcVrbVLclH+z/IKN+ja07tLZdxhERkd6p349PpPrl7SSqm1/gri/3snH4jhnQ7Hn30Fz6/2wu/X82t72maNvgnMEMzhnc5nHCm8vYNu8xYvuav4PPCsSwAjH8l43Gc9wArHAMR5EX31nDMItq7zRIZw+fxgblDOKaqddy96q70u5zy6wvKXgvIiIiIm2iTWxFpFO53W5yc3PJysrC6/Xi8Xjwer34fL66TWx9Pl9aGXKZfBivLxgNtt4oDeF4uF3GERGR3sk7pQ/DnrkAR7ar1bbZ54xgyL/P6RV7r1ixBDs/9UyLwfv6Ao9vwVHgJvfL08i+Ylxd8D6ZeZ/Jgv9fzvwLpydL4rTigrEX8PNTfm77GiIiIiIi9SmALyKdzul0kpWVRXZ2Nn6/vy6QnwzcJ7PqW/qw3ZYP40mFvsKM+9bXz9+vXcYREZHeK/v04Yx670ryrphQu9lpI66ReQz43SkMf+5CHL7WA/09QeVzWwivP2yrT/Vf1uB2u3G73Xg8Hvx+f5v+XvA6vTx/2QvcOvs2fE5fyjbZ7my+dfy3efyiJ3A6dMOziIiIiLSNauB3UaqBL5JacvPbljbKzdSNL97IPavubvMc7/zEXdww/YY2jyMiIgIQ219D5YItxA8GMTwmnil9yD5jOIaj52fd17ftjMepeX2n7X6j3r0C/wmD2n0+ZaEy/r3637y96y2qI9XkenI5dfhpXDX5qow35hURERERaUwB/C5KAXyRo2/FvhXMuW92m8bI8+Sx+0t7yHJntdOsREREMmNZFpGNZcQOBXH4nLjHFmDmpLchfFdjWRbF7j9BLGG7b/9fnETf7xzX/pMSERERETkKdE+niMjHZg2YxYVjL+SZTc9kPMaPT/qJgvciItKp4lURyu9dQ+k/PiSyoazuuOF3kn/lRAq/PAPf9O5V7s0KxzMK3kPt8yEiIiIi0l2pBr6ISD33f+oB5g45KaO+3zr+29w257Z2npGIiEj6wpvL2DLzAUq+sqhB8B7ACsQou3s1W2Y+QOlfVnTSDDNjeEyMFHsBpMPM7Z53HdhlWRaxWIxwOEwoFCIcDhOLxdAN1yIiIiLdmwL4IiL1ZLuzeeXTr3Dt1GsxSK+2sN/p595P3scv5/+yzXX4RUREMhXdW8320x4nsqW85YYWlHxlEYf/ueqozKs9GIaB/5QhGfXNmj+0nWfT9cTjcQKBAIFAgHA4TCQSIRwO1x2Lx+OdPUURERERyZAC+CIijfhcPu45918s/fwy+vr6ttj2uEHHsfvLe/j8tM8fpdmJiIiktv+77xDdVZV2+5KvLCJ2KNCBM2pfRbfMsN3HO7MfvuMGtv9kupBk8L65IH1r50VERESka+vVAfyamhoeeOABbr31Vo499lg8Hg+GYfCrX/2q1b67d+/muuuuY9CgQXi9XsaNG8cPf/hDQqHQUZi5iBwNcwbOYc+te3nyoqc4c8SZ5HnycDqc9PX35arJV7Hk6vd496ol5HvzO3uqIiLSy8UOBah4dL2tPlY4Ttm/1nTQjNpfzqdG45lYaKtPn28ec1TujovuqaL61e1UvbCVwLISrHhm9frtsiyLUCjUapmcdNuJiIiISNfTqzex3bRpE1dffbXtflu2bOGEE07g4MGDTJkyhXnz5rF8+XJ+9rOf8dprr7Fo0SI8Hk8HzFhEjjanw8lF4y/iovEXdfZUREREmlXx6IbajV5tKr+3mL7fPLYDZtT+DKeDYQsuYtu8R4mV1LTavs93jiX/MxM6dE7Vr26n9K8rqXp+K9SLjbuG51J40zQKb56OWeDtsOvH4/EWM+sNw8CyLCzLIhKJYBgGbrcb0zRV9k9ERESkm+jVGfg5OTlcf/31/POf/2TFihV873vfS6vfddddx8GDB7nttttYvXo1jz32GBs2bOCiiy5iyZIl/OIXv+jgmYuIiIiIHBHZVnFU+3UWz+h8Ri25kqxTm69rbxZ6GfjX0+j/88w2pU+HZVmUfPUNtp/1JFULGgbvAaI7Ktn/3XfYPPtBwhsPd9g8WgreOxyOBpvaRqNRwuEwwWBQJXVEREREupFenYE/evRo7r777rrvn3322Vb7vP/++7z11lv069eP3/zmN3XHnU4nt99+O88//zx//etf+f73v4/L5eqQeYuIiIiINJBpZZRuWFHFPTyXkQsvJ1R8iLI7PyK0phQrFMPs6yP34rHkXTYOh69j/w4/8IPFlP7xg1bbRbdVsP2MJxi17LO4BmS1+zyaK4njcDioqakhHA43aBOLxfB4PLjdbgKBAH6/H9M0231eIiIiItJ+enUAPxPPP/88AJ/61KealMnp378/8+bNY+HChSxevJj58+d3wgxFREREpLdxDcnOqJ8zw35dgXdyHwb++bSjft3wxsMc/PnStNtHd1Vx4MfvMviOM9t9LqnK4DgcDqqrqwmHw03OWZZFNBolkUjg9XoJhUL4/X6V0xERERHpwnp1CZ1MrFq1CoBZs2alPJ88nmwnIiIiItLR8j4zAUz7Qdj8qyZ1wGx6tsN3fGS7T8WD64hXNA2ot1Xj7HnDMAiHw0QikZTtk4H6eDxOJBIhkUiolI6IiIhIF6cMfJt27twJwJAhQ1KeTx5PthMRERER6WiuQdnkXjSWyic2pt/JNCi8cWrHTaqHKn9gre0+iZoolU9touDaKe06F9M0MU2zLgifzLBPVVrHNE0cjiP5W7FYDJfLRTwex+nsWh8LQ2tLqXxyI7GDQQy3A++UPuRdNh5HlkqUioiISO/Ttf5S6waqq6sB8Pv9Kc9nZWU1aNeayZMnpzy+ZcsWRo8encEMRURERKQ36v/Lk6hZtJN4aSit9v1+ciKuwTkdPKueJRGMEj8UzKhvdFdVO8+mNqPe6/USCASwLItEIkEikUjZrnG2frJ9c3X0O0PN4j0c+P5iat7Y1eRcyf+8QcH1U+j/07kK5IuIiEiv0q0D+Jdeeilr1qyx1ef+++/n2GOPzfiayT9wm6sT2ZX+ABYRERGR3sMzpoARL1/K9k8+RfxAoMW2fb59LH2/e1yz5xOROPHDIQwDzCIfhrNh5c3g8n1UL9pFoiqCI8dN1slD8B07oOfXUm/L4+ugp8Y0Tfx+P6FQiHg83uTfIJmln+rfxrKsLvNvVvHERnZd+QJEmy5AACQqwpT+4QMCb+1mxCuXYhZ4j/IMRURERDpHtw7gb9++nQ0bNtjqEwi0/GGmNTk5tVlKNTU1LY6fnZ3ehmDFxcUpjzeXmS8iIiIi0hzf7P6M+fAqSv+6krK7VxM/WC9b3ICcc0dRdNtMss8ckbJ/YFkJh/+xiorHNmCFYgA4ctzkXzWRwltmEN5QxqFfLSP4/r4mfb2z+tH3m8eQ9+kJHfHQugSH14nZz9/qAkkqruG5HTCjWskgvmEYxONxLMuqC87XL5vTWKrM/M4QWFrC7s82H7yvL7h8PzsvW8CIVy/tMosPIiIiIh2pWwfwly9fftSvOWzYMFauXMnu3btTnk8eHzZs2NGcloiIiIgIAK6B2Qz4xTz6/egEAktKiB8K4vA58Uztg3tY6iCyFY2z95bXKbt7dZNziaoIh/+xisP/WNXidUMrDrDrMy8Q/PAg/X9xUo8NrhZ8fhKHfmvvc4gjx03uRWM7aEa1DMPA7XYTi8WwLKvVzWkNw8DpdHaJAP6Bny7BirQevE+qeX0nVa/vIPeMER03KREREZEuolsH8DvD9OnTefbZZ1mxYkXK88nj06ZNO5rTEhEREZFuZO2htdz14V2sK11LJB6hn78fl064jAvGXoDLbJ/63g6Pk+z5Q1ttZ1kWu697mYoH17XLdQ/9ahmugVkU3TarXcbragpvns6h3y0HG5Uz86+ehJnj7rhJfSxZLsflcrVa397lcuH1ejt9oSWytZzqF7fZ7nfo7yvIOnVoytr+JZveZN1bt3NgxzLikQBuXz5Dp3ySifO+SH7/ce01dREREZGjQgF8m84991x++tOfsmDBAsLhMB6Pp+7c/v37efvtt8nLy+Okk07qxFmKiIiISFe0rXwbN754Awt3LGxy7j/r/8PA7IH8av6vuWrKVUdtTpVPbGy34H3SgR8voeCGqTj8PW+zUfeofPr95EQO/PDdtNq7RuXR70cndPCsaiU3tU0kEng8HqLRaMpMfJfLRXZ2Nk5n538crHphq63FkKTAf3cQDATJys6qW4SoPLSV1+66lNJdKxu0DVYdoGLhRtYs/BOj53yGkz93D063vz2mLyIiItLhmi+IKCkde+yxzJ07lwMHDvCtb32r7ngsFuOWW24hGo1y66234nL1vA8rIiIiIpK5DaUbOPGBE1IG75NKqkv4/PNX87ulvztq8yr924ftPma8LETFY/b2qupO+n7/ePp+r/lNgJPc4wsY+dplOPsevWBxsh6+y+XC4/Hg8XhwuVy4XC7cbjdZWVnk5eXhdnf8HQHpiJWGMusYSRCrCNUtUFQe2sZzv5vbJHjf2Jblj/Li3z5BLJrhdUVERESOMsNq6b7KXuCiiy6ipKQEqK1fv2fPHoYOHcqgQYMAGDhwIE8//XSDPps2beKEE06gtLSUqVOnMmnSJN5//322bt3KcccdxxtvvIHX623TvJKb2Da3ya2IiIiIdB+hWIipd09hS/mWtPu8cNl/+cToT3TgrCC84TCbJtzbIWNnf3IkI164uEPG7ipq3tnN4b99SMWTmyB2pIa7e1wBhbfMoOC6KUeldE4qyTr4yU1tkxvWmqbZ6WVz6jv4q6Xs/847GfUdeuh6vNk+3G43z/xqDod2pS5zmsqU0/6XEy79Q0bXFRERETmaOv+eyU62cuVKduzY0eDYrl272LVrFwDDhw9v0mfs2LGsXLmSH/7wh7z00ks8/fTTDB06lO9///t897vfbXPwXkRERER6lifWP2EreA/wm/d+3fEB/PWHO2zs+MFgh43dVWSdNISsk4Yw8GCAyKYyEqE4zn5+PJOLOj1Intyk1joQIvTRQaxgDLPQi+/4gRiervMx0DujX0b9XBMLMFwOLMti36a3bAXvATYsvovZ5/0Etzcno+uLiIiIHC1d5y+3TrJ9+/aM+g0dOpR77+2YbCURERER6VluX/kP233e3PUmxQeLmdx3cgfMqJYVTbTeKEOGx2y9UQ/h7Os/qmVy0lHz5i5K/7yCyue2QPzITddmHx8FN0yl6NaZuAZld+IMa2WfORzXyDyi2yrs9btuIlC7ULHunTtsXzcarmbzsoeYdPLNtvuKiIiIHE2qgS8iIiIi0oHCsTBL9izJqO+inYvaeTYNOft3XNDZO6VPh40tzbMsiwM/XcK2+f+h8unNDYL3APFDQQ79ahmbZ9xPYFlJJ83yCMN0UHTrTHt98txkfXosUFvz/8D2ZRld+9DO5Rn1ExERETmaFMAXEREREelAlZHKzPuGM++bDv8Jg3AO6Zgs7IKbpnXIuNKy0j9+wIEfvdtqu/jBIDvOeZLwho4ro5SuottmknPhmPQaOw363Hc6jjx3XU3/WCSQ0XVjkZqM+omIiIgcTQrgi4iIiIh0oBx35jW229I3HYbTQeFN09t9XP/cQfgyrG0umYsdCNjaEDZeFmbfN9/qwBmlxzAdDH3sPPKvm9JiO0ehh35PfgLfaUMwDAOv14thGHj8BRld1+3LrJ+IiIjI0aQAvoiIiIhIB/I6vcwZMCejvicPPbmdZ9NU0Zdm4B6T327jmX19DP53x26+K6mV/Ws1ViRuq0/V81uJ7OjYOz3S4XCbDLnnbMasvYai22biHJQFLgdGtgv37L4U/u1kBhVfiXf+YEzTxO/3Y5q1+ywMnfzJjK45dLJ+TkVERKTrUwBfRERERKSDfXHWLbb7nDj4RKb3b//s+MbMAi/DX7oE18i8Vtu2tjGte3Q+o97+DJ7R+e00O7Gj/IF19jslLCoePtLPsixisRjhcJhQKEQ4HCYWi2FZVguDtB/vxCIG/vk0Juy5mcnh/2F82S0MX/xpCq+fiq8gC7/f3yB4DzBxnv2NaLMLhzN0SmaBfxEREZGjSQF8EREREZEO9umJn2Zo7lBbfb5+3Dc6aDZNeUbnM3rplRR9dTZmgbfJeeeALPr+8HjG772JoY9/iqxTh4LDqD3pMPCfPIShj57LmLXX4BlfeNTmLQ1Fd1Vl1m93bb94PE4gECAQCBAOh4lEIoTD4bpj8bi97P62MgwDp9OJx+PB6/Xi8XhwOp0YhtGgXV6/MYw59nO2xp75iR/gcLS8ICUiIiLSFTg7ewIiIiIiIj2d3+VnwaXPc+rD8ykLlbXa/gdzf8iF4y5MfTJhwfZyKD4IlWGwgBw3TOwDowvAzCxHx9nXz8Dfz6f//82l6oVtRHdXYTgMXCPzyDlnBIarNtiZd+k48i4dh5WwSASiOPwuDIfRyuhyVGT6z2AYdcH75jLtk+cbZ78faZCAQ0EIx8BtQqGv9r9Hybwr76SmfA8lGxe12nbG2d9hwtzrj8KsRERERNrOsI7WvZBiy+TJkwEoLi7u5JmIiIiISHtZd2gdVz9/FR/s+yDl+XxPPj87+f/40uwvpR5gZwW8uq02cJ9KlgtOGwFjlAXfG22ecT+hVQdt9+v/m5Px3zIprQz7ZP35uiz46gh8tB9WH4RA9EhDt6N2UWnGgNpg/lEQj4ZZvuAHrHvnn0RDTev6ZxeNYNYnfsD4E687KvMRERERaQ8K4HdRCuCLiIiI9EyWZbF071Lu/PBONhxeTzgWpl9WPy4dfxmfmfQZ/C5/6o5bymDBxtqM+9acNQom923XeUvXd+hPH7Dvf9+w18npYMzW64jkpZ++7/f7cTqdsKcKntsAoRYC/w4Dzh4FE/rYm1cbRMM1bFn+CAe3LyMaqcHjL2DopE8wZPI5KpsjIiIi3Y4C+F2UAvgiIiIiUmdXBTy5Pr3gPdQGTa+YDP2yOnRa0rXEy0KsH/JPrEAs7T65l4+j//1nEQ43c1dHCh6PB09lDB5bC9FEep3OHwujdWeIiIiIiF3axFZEREREpCs7UAPPbEg/eA+1dfJX7kurqWVZxGIxwuEwoVCIcDhMLBZrtha6dF1mgZdBfzs97fbOgVkM+O0ptv+tLcuChTvSD94DvLa9tk4+1JaA+nAfLNkN7++t3dMhoZ83ERERkVS0ia2IiIiISFcVjMLT6yGWQXBzQymcMhy8zf/JH4/HCYVCKWufm6aJ1+tNvWGpdFkF104hEYxRcuvCFoPirmE5DH/xEtzDcm1l3wM4SkOwt8rexAJR+KAE9tXA1rKmC1K5HpjRH2YOqL2DREREREQABfBFRERERLqujw6AjXIoDcQt2F8Nw/NTno7FYgSDQeLxOJZlYRgGDocDwzCwLIt4PE4gEMDv9yuI380U3TKDrJMGU/q3lZQ/tK5BSR336HwKvzidguunYOZ7AWz/+zo3lmU2scW7mz9XGYa3dsKuSjhvLDg/vlm8Ivzxps0WZHugwJvZtUVERES6KQXwRURERES6ooRVG8Bvi0jqEiexWIzKykqi0WiD8imGYeAwHbyz723e2P0GVZEqst05zB85n/PHno/ToY8P3YV3Wl8G33kWA353CpGNZSQCMcwiL56JRRiNMtxN08Q0zZR3YjRmmiZGdbSjpg3byuG1rTCqAFbth92NMv0HZMH0/jC+CMyPg/zlIdhXDbEEeJwwJAd8ro6bo4iIiMhRpL/ARURERES6ooMBqI60bQxP08zqeDxOTU0NkUjDsS3L4qGND/G31X9lR9WOBuf+9uFfGZwzmP895qv8zzH/g8PQVlrdhZnrwTdnQIttDMPA6/USCARarIefbNfhBW7WldZ+pbKvBvZthbWHYHo/WH0QdlQ0bGMaMK4I5gyEPv6Onq2IiIhIh1IAX0RERESkKwplWDonyeWA/lkNDlmWRSgUIhaLNTn+4/d/xF1r72p2uD1Ve/j6wq/x4f6V3HvufZgOldXpSUzTxO/3N9kTwUpYxPfWYAQT+Prl4Mh2QI6nE2f6sV2VtV+pxC1Ydwg2lsK5Y2B0YVpDJktH1S8rlTwOtQsYybsVkudEREREOpoC+CIiIiIiXZGzjVnuE/vUlhOpJxmcbOzutXe1GLyv78HiBxmaO4yfn/Lzts1PupxkED8ejxPeV03FvcVU3lVMbNeRMjaeiYX0/eI08jtvmumLW/D8Zrh0IgzOablpow2dDcMgEokQi8VwOBy4XC4cjtrfyXQ3eD68dw3r3rqdnWteIBwow+n203f4MUycdzNDJp2NQ4tgIiIikgbDaukeSek0kydPBqC4uLiTZyIiIiIinSIUgztX1AYh7TINrM9OIZ7nbpBNHI/HicViRKNRotHox5cJMefx2RwOH057eK/Ty54v76XAW2B/btLlVS/cyc6LnyNREW62zeivz8Q3KKvZ811K/yy4ckqzp5MbNtfPtG98J4JhGHg8nrogvmEYzW7wHA3X8Ob917Bt5RPNXjO//wTOvOlp8gdMyPRRiYiISC+h4pUiIiIiIl2R11lbxzsD8U+MJuC1CAQChMNhIpEI4XC47qt++Y/ndyywFbyH2qD/vR/dm9HcpGsLvLuHHZ98qsXgPcDexzaRiKbeJLnL2V9Tu8ltCsmyUvWD95FIpMmdKpZl1S16peqXFIsEefFv57QYvAco37+e5343l/J96zN5RCIiItKLKIAvIiIiIj2GZVnEYjHC4TChUIhwOEwsFmtxY86uwrIs3tz5Jt9c9E1ufPFGbnv1Nh7IWUjIajmQ2lh8/jACA9wpS+XUz8JPBvFf3fVqRvN9fvOCjPpJ12UlLHZ//iWscNOfncaCu6rZcXdx9wnir0+9KW7jslLJ15B02qYqSbV8wffZv+WdtKYUDhzm1bsuwUp0k+dQREREOoVq4IuIiIhIj9C4hnV96das7iyPr3+cn7zzY9YeWtvk3FddBdwUv4AfmtfjNlwtjmOdNITQuFysFM8BgMPhwDAMEokEpmmSSCQoC5dnNOfSYOqAqHRf1a9sJ7K5PO32NZsq2PSrDxh995k491VBvcC/5TBIDM7BOHYQjhc2t31T5raqiaQ83Pj1IpFItLjgl/zdqd/f6az9WB0NVbN+cXp7SSSVl6xlz/rXGDLpLFv9REREpPdQBr6IiIiIdHvJGtapgvfpnO9Mv37v13z6mctTBu8BSqNl/CJ+H+fFvtZ8Nr5pwGkjiM/s3+JjNAyjLtgYj8dxuVz4nL6M5u1yuNhXvY9IPHVgVLqfsrtX2+4TLQtzcHEJic9NpdLvYe+iPWz7x2rWf3cJ6y77LxtmPUj1oVAHzNYmh5HycONgva27dSwLKxSFQBTiCbYsf4RoqKr1fo2sfft2231ERESk91AGvoiIiIh0a83Vom6und/vb1ADvjP9Z91/+M4b306r7WuJZdxU9Ff+7fghVH0cyM9xw6S+MLkveJ3Ewy2X27EsC7fbTSKRqNvcdmb/mby66xXbc1+xfwWD/jYQj+nh8gmX88VZt3D84ONtjyNdR2RTeUb9QqsOsOW4hwmvb7qXQrw0xI6fLWPkLVPxD89p4wzbIM+b8nDj14J0XhuM6iju9WW4NpbjCH68YGYaHAy8ntHUDu38IKN+IiIi0jsYVncoCNoLTZ48GYDi4uJOnomIiIhI1xaLxQgEAmm39/v9dVnoncmyLCbdNZENhzfY6rf+CxsYVzgu5blQKEQk0npGfHKjTsMw2Bfcx7QHp5Kw2l6H+5qp13DHOf/EbbrbPJYcfZum3Ee42H5pJMPnxAq2XCLH4TUZevUEciYUNN/IaUCsgz6eXjcD8jxNDqd6/QgGgw0XBOMW3l0BPHsCuMujOCqjpArzL9r3EzZXvWh7ap6sIq7+7SHb/URERKR3UAkdEREREenW7JbF6SpldBbtWGQ7eA9wx8o7mj2X7p0FyUx8n8/H6D6juWjMRbbnkcp9q+/jmuc/3y6LAXL0OQdmZdSvteA9QCIUZ8edxWy/dx2Jkfm1ZZ+SCrxwyjC4YSaMys9oDi0alZ8yeA+1+2PUr2mfSCTqNnuOx2J4N1TQ99nd5C85hG9nALOZ4D2A18zNaHqerMKM+omIiEjvoAC+iIiIiHRrdm8o7So3oC7YvCCjfs+30M/OJr2WZeFyufB4PPzzk3cyoWhCRvNp7NF1j/LYusfaZSw5uvKvnNjh16heXUpZSRBuPQZumQ23HQPXTIdZA8Hngk+Oad8gvt8F80c0e9qIJfBtrsL/3x34n9hM1tNbyXv7AJ6SENkflpG/ohwznN6C1LCsuRlNcfjUT2XUT0RERHoHBfBFREREpFuzW8++q9S/Lws1rReejsPB5vs1ziZuSf22hb5C3vzsW5w89OSM5tTYP1b8vV3GkaMr79PjMQtS14pvT2X3rAHDAI8TzEYfSV0mfGocfGI0DEpRM7+fH84cCZ8aC85WPs7meuDSiU2z7yvCsOYALNgIt3+AY+EOnCUBnBVRXOVRPDtr6PP2IfI2pV+aC2CQbw55rmG2+gBMnHez7T4iIiLSe3R+8U8RERERkTawk3WeSfuO4nX6Murnd/mbPWcYBl6vl0Ag0OKdBsl29Rcz+vr7sujKN1iyZwlffPlmVh9cndH8ABbvXszaQ2uZ1GdSxmPI0efwu+j/25PZe4P9TY3tiGwub2UiBkzoU/tVFoLKMGBBthsKfbXBf4Brs2H1gdqvmuiR/kU+mN4fJvYBd73f9z1V8P5e2NbK9TNkGA5mFd3Aon0/TLvPmGM/R16/sR0yHxEREekZFMAXERERkW4tmUmeTm17OxnqHW32gNkZ9ZvVf1bK45sOb+L9kvcJxALkunI5rt9xFLibbhhqmiZerzfl82AYBiv2r2hT8D5pfel6BfC7ocLrpxI/HGL/N99quaHTQdYpQ6h5faf9i8Rt7JFQ4K39SiXbDScMgeMGQ1UEonHwOiHLdSTIn7TmALy2DTq4gtaYnLOoiu5leWnze1UkDZ5wBvM+e1fHTkhERES6PQXwRURERKRba0vWeWe6YtIVfH3h16iKVNnqd9PMhuU2/rvlv/xx2R94fcfrDY57TA+Xjr+U/539VSYVTcIwjLoFjOaeg6pwFd998zv2Hkgzoolo642kS+r7jWPwze5P6R8+oOq/WxsGvZ0O8i4dS5+vzaHqpe0ZBfCdg7Lbb7JQm7HfzCa1WBYs3QNL9rTvNVsws/Aacl2DWVF6N+XRHU3Oe/wFTJx3M7PO/TGm033U5iUiIiLdkwL4IiIiItLtmaaJ3+8nFAqlzMRvKeu8s2S7s7l+2vX8afmf0u4zvnA8Z486G6jdhPaHb/+Qn7/7fynbhuNhHlr7EE9tfIpHLniU88ee3+r4DxY/SHWkOu35tKS/v3+7jCOdI/u0YWSfNozI9goCS/aSqI5i5nnwnzwE14AsABz5Hg78YLHtsfM/2/Gb5QJQHoLnNkBp6Ohcr57ROWcyKvsMSqbtY2fFO4QDZTjdfvoOn8OoWZfjdGdWQktERER6H8NqKU1JOs3kyZMBKC4u7uSZiIiIiHQflmURj8eJx+NYlpVW1nlnicfjHK48zIXPXcCSkiWtti/wFvD2596pK0vz+6W/5xuLvp7Wtdymm9evWMjcIXNbbDf/oVN4a1crpVPSMCh7ENu+uB2X6WrzWNK1bT/nSapf3p5+B9Ng/M4v4MokCz+egOooJBLgc9WWy0myrNoa96v2w+4qiMQhZqNUT0dwOeDGmbWb9YqIiIhkSH9JiIiIiEiPYRgGTqcTp7Nr/5lrWRahUAiP6eHx857gi6/fzIKtC5ptP7ZgLE9f8kxd8P5w8DA/ePv7aV8vEo/w9YVfY8nV77XYrqS6JO0xW3LjjC8oeN9L9P/1PALv7CFRk17JpH4/ON5+8L4sCKsOwNqDEK53h83QXJjWDwZmwwuboaR97h5pNxP7KHgvIiIibaa/JkREREREjrLkXQIA2a5sHjjnQdYcWsO/iu/hlR2vUB4ux+f0MbPvTK6bcj0XTLgAj/tIje/7Vt9HKGavLMjSvUv5YN8HLW6e63S0/ePBiNwRfHn2l9s8jnQBsQRsK68tRWNZtZvGjipokPnum96PEY+dS9lvlkE0QbwmSvXGchKhI4F2h9ckf04/+pw7Alc/P9y3qnZj2in9YGR+bQ375qzaD4u2p958dldl7ZdpQLyL3Vjuc8Ixgzp7FiIiItIDKIAvIiIiInKUJEv8hEIhotHajGWHw4FpmkzpM4U/nPLHZjo2/PbRtY9kdP1H1j7SYgB/XOE41pWuy2hsgKE5w3ju4gUU+YoyHkPayLJgX83HpWQqa0vJuEwYnAPT+8OgbGitnFQkDu/vhTUHIBBreM7pgAlFcNwg2FMNK/fh31+D/8JRdU0SkTjlKw5yaOFuciYV0v+8ETicjtqTFeHa/5aFYGs55HrgnNG182vso/2wcHvrj7mLBe8tr0n0k6Nw+E3Mj0t5iYiIiGRKAXwRERERkaOgfuA+FosRDtcGMg3DwOVy4XK5cDgcKfs23rZqb/XejOZQ0kq/66ffwLP/396dx0dV3f8ff9/ZM1kJ+74KyL5UEAUXFJW6I7i0Xy1VW1espba1tW51/2rtT6vWtrYuqN9SrWBRFLWlbrghKhAEgRDZBMISIJnMfn9/xDtkkplkZrKSvJ6PB4+v3HvOvWfISb/J+577OetfTvu6Oa4cXTz0Es0dN1d9O/XNaGxoBJWhqlIyWw7EHw9EpHV7qv50z5HOOEJmtjPxfhGBqIyF66RdvsT3CEel1aXSl7uTBuc2l12FR3dTh6O7qd7o+kBA+ueX0jlDpD75h46XB6WlX6f80VsD024o0D9XlSM7SLmS3edrlRtoAwCAwwsBPgAAANDEIpGIysvLFQwGFY1GZRiGwuFDK5uj0agikYg8Hk/CEL/mCt5MS904jLr7TR8wXf3z+2vT/k0pX3Nkx5FaMuMNeZ3e2IbBaAH+sPSPNdLeekorfVMuc36RKs/qr7C7xlyLmsp+9WvZd1XWf78UVr2nvO48YkqL1kuXjq7anFaqWv0fbV0r65MJ9MlWZQ+PAj2zZLq+nf+BgOx2u5xOp6LRqLxeL98bAAAgI4mX+AAAAABoFKZpyufzye/3x1Y722y2uFA+EokoGAzGVuXXVDP4G9RhUEZjGVhPP7vNrmfOnCe33V1nO0vnrM6aN/1ZeZ1eGYYhj8dDuZCW8tam+sP7bxkHgnIt3VrruGPTAdl3VSYsN9/kghGpqPTQ31eXJm/b2gQi8vfPORTefysSiSgQCMTevqn5Jg0AAEAqCPABAACAJmSVy6ke3kWjUTmdzrh2pmkqFArFNre1JFrVfumoy9Ieh82wafbI2fW2O7bXsXp11mLlu/PrbNcnt49eOftV9cvrJ7vdzgrjlnQgIG3Ym3JzU5JjW4VsZfEPjFxf7pOUxsr5xvbFrqoa/uGodDDYUqNIiynJvdMve3ko8flq39c1v7cBAABSQYAPAAAANCGr5n11VoDvcMSXtIlEInFtk61qP2/Ieeqa3TWtcZw16Cz1ye+TUtup/aZq/RUbdO8J96lffr+4c6M6j9KjJz+qTy9ZoVHdR8nr9RLet7TVu2ptdFwXazY51+47dKwyLMfOFlp9bzkQqNo0t4VWqpsZPLmwutgPhJO2scJ7AnwAAJAJauADAAAATShZaBeJROR2u2Wz2RQKhWIr9KPRqCTVufml2+HWU6c/rTNeOF0Rs/5QsHtOd/2/kx9Ka9ydvJ30i6N/oRsm3qAd5TtUEapQgadAnbI6USantdlZkVE3++5DJXeMyqoAusW/sqGI5HVILpsUjDbbbYMD8hQalK/sN7Zk1N+op15/NBqlhA4AAMgIAT4AAADQhJKF3aZpKhwOx210aZqmPB5PbEV7XUH5qQNO1UszFuiif10oX8iXtN2AggFafP5rKa++r8lm2NQjt0dGfZEZ0zS1ZVexNmz9Uj5/hWw2uwrzOmpYvzHKy+5Qu0M4s6DbqN7P1uLRfRWPQzIMaUgnadWuJruNqUMPKwKjChU4qqts+wO1zqUq6qn/DRQefAEAgEwQ4AMAAABNyArik62+jUajikajMgxDhmHI5XLVKq2TyH7/fpXsL9GYLmO0Yd8G7fPvUyh6qA736C6jddW4q/X9Yd9Xtiu70T4PmtbGbWu1bNW/tb9iX9zx4u3S8rXvq1/3I3TCmOnK8eYdOunJ7Nc6030odI7mOGU6DBnhFlwl3sV76LOM6tKkAb4hKZLnUnBMR4WOKJAkRfNcihS6Zd+beDPpZMLZdoUKXXXfzzAoMwUAADJCgA8AAAA0IYfDIYfDoVAo8SaXFtM0Y23ra3fPB/fo7g/uSrryfnSX0Zp/9j80uOPgjMdd1/3X7lmrUl+pXHaXBhcOVmFWYaPfpz1aXfyp/vvZa3W2KflmvV4o26EZx12i/JxvV+P3L5A27KuzXyLh3rmH/uKwKTQoX661ZWlfp9GMrravQ5dsaURnaXVp412/MEvqlq2w01Cgm0eRntlVq/0thqHgkR2U9f6OtFbhVw7KrfcNBqfTSYAPAAAyQoAPAAAANCGHwyGPx6NoNJq0Hn4gEtB233bJKfVw9FCfvD4Jy22Ypqnr3rxOj654pM57frHrCx377DF65/vv6shORzbK5ygPluupVU/pjyse05d7vowdd9qcmjV0lq4Zf60m9ZzUKPdqj9ZvLao3vLdUVB7UK8v+rgtP/rHsNrs0pKP0zmYpkPomqabdUPCI/LhjwSM7tFyAn++u+hzVTe0nBSPSV3vr7uuxSyO6SF/tkQ4Ea5/PdUnju0tjulYF9uGwIr5DD7+sN2Si0agCA3LlXLNXjn0JrpNAJM+pwJACSWatOveGYchms8npdCorK4sSOgAAICOGyU46rdLw4cMlSUVFRS08EgAAADRUJBJRZWWlAoGAwuFwLOQrOVCip9c9pfkb5qssUBZrP7LzSF0x+gpdOPQieR3eWPmNeWvm6bLFl6Z834EFA7XmR1/KaXc2aPwlZSX67gvTtXbP2jrb3XLsrbp18q0ElWkq9x3QvCWPKhJNPYCXpFMmnKvBvat+b9DH26X369+A1VpZHhjdUYHvdKl13rVyjzyfNF3pmoSyndKsI6UOWbXPmaa0Zrf0+Q5pV403Tlx2aVinqnA+zy1FTamkTNp6sCr4d9mknnlVbyhUWyFvmqZ8Pp8ikYgMw1AwGFQ4XLWJbzgcluELq9M7pXIdqPvrEc1zat+J3WQv9CoQCCgQCNQK8N1ut3JycmKltOx2e737WwAAAFRHgN9KEeADAAC0LZFIRH6/X5FIRNFoVH9f93f95O3r4urW1zQgf4BePONFDcgfKNM0dfTfJ2rdvnVp3ffvZ8/X+Ueen1LbkrISvbDuBe0o3yGHzaHBhYN1Ut+TNO3vJ2tj2caUrnHX8XfrV5N+ldYY27NoNKpn3/ijDlSkXwKne8feOu+EH1T9xTSlf5ekVDc+OqRQlVO6K2LW3vzWbrMp68v9sr2/tSrtT6ZnrvRNeVVonkyXbGl8t6qHC3sqE7fpX1C10j7PXfegTVPaWVF1nYgpeR1Sn/yqED8D1kO1ysrKuDdjrFJX4Qq/CjZUKm9zQPZA/L9T1GVTZf9shcZ2kSPXo8rKqs9mGEZsFb61+t5msykSicjj8cTCfbvdLo/HQ0kdAACQEgL8VooAHwAAoO0xTVORSETz18zXxYv/J6U+vXJ66c3z3tLGso064+XT077n8b2P19Lv/7fONit3rdRv3r5Jr258VWaN1NZhOBQ2wynfz2bYtOGKjepX0C/tsbY320q/1hsfL1SF/2DG17jynF/KYb1hYZrSFzul5d9IBxOUgMl2SuO6SeO7y1RViB2JRGKBc9zq8P2BqocBRbsk37dff6etqszNqK5S12zpQEBauUtavUuqrDZHeuZW1bMf1EGy26rGte2gtG6P5AtVrYbv4JGGd5byPRl/9oYwTVPl5eWxh2rWMSvAj0QistlsUsRU7p6InJVRmVFTEa9dgR5ZksMml8sV+7dzOBxx4b31bxuNVoX/Tqcz1l6qCvu9Xi8hPgAAqBcBfitFgA8AANA2HQwcVO9He+lA8EDKfWYdMUtDC4fqjo/uSPt+DptDgZ8Hk5bs+E/Jf3T2P89SRagi7Wsnc+OkX+nu4+9utOu1RV/v2KBXP3hB0TTL5tR06enXy+vJiT8YNaVN+6TNB74tJWOvCtUHfhuop8M0pVC06ppue/ymr9XvVxGUwqaU5ZA8rX+rtXA4LN+3dfCtoD0UCsUC9+qbTlthu1Vmx+J2uxUMBuVwOOR0HipTVb0kT/VrZGXFlwiy2+3yer2U0wEAAHVK86c3AAAAAA3xbNGzaYX3krRw40KV+kozul84GlYgEkh4bt2edTr3pXMaNbyXpKdWPtmo12trDvr267UP/9ng8F6SnA5X7YM2QxpYKJ3YTzp1YNX/Hdwx/fBeqgrsXfaqUD5Z0GwzpFx31ar6wyC8lxRXNsdut8vpdMrpdMbK3lQP1a0Nbqsfq14ux2azxV23Znhf/Ro1x5BsY2sAAAALAT4AAADQjJ5c+be0+4SiIW3cX5zR/Vx2l9z2xPXF7/vwPh0MZl6+JZkdFTsUCCd+aABpVfGnCkeS732Qqo55nRMH+KhXohfRrSDeKidUX3tJsbJDlpohfX3XIMAHAAD1OTyWRwAAAABtRKqbwdaU5cisVvhJfU9KWKJjb+Ve/f3L/8vomqmoWUsfVSKRsNZs+rxRrtWzc79GuU57lOh7wgrjI5GIHA5HbM8Kq71V316SHA5HrE1D7klFWwAAUB9W4AMAAADNwDRNhcNhhaOpbwhbXaG7UGM6j0m731Xjrk54/M2SN+UP+zMaS31shq1JVva3BfvK98gf9DXKtToVdG2U67RHyTaPdTqdsbDe6XTK4XDIZrPJ4XDENvi1/u5yueLK59TF6pfoOAAAQF0I8AEAAIAmFolE5PP55PP51N3bPaNrdM/pobnjfpZWn9FdRmv6gOkJz+327c5oHKmImlHd+u6tTXb9w1koFGy0a7mdmb2Vgdqlbyw2m01utzt2zul0KicnR6Zpxja5tYL8cDiscDgcVzYnWaDvcDiSrvoHAACoCwE+AAAA0ISs8N4qxTFr8PkZXWfW4Jk6a+BZumnCb1Jq3zevr/41c5HstsQBoSfDkjyperZong4E0tustz1wOhPvR5CJjnldGu1a7Y1hGPJ4PElDdbfbHdvY1tqoNisrS16vVw5HVSVaa5W+FexbfWuG8na7XS6Xq1a5nGQPEQAAAKojwAcAAACaiGma8vv9ccHdJcMukcOW3lZUU3tP1YD8gZKkXxz1C/3ltCfUI6dHwraGDJ016Cx9cMmH6p3XO+k1R3cZndYY0lUeLNeLa19s0nscjjrkdpTXk9Pg6/Tq3E8FuYWNMKL2y263y+v1xkJ0wzBkGIaCwaD8fr+i0agikYjC4bCCwaAikUitwD8ajcrhcMT6+P3+WLkcwzDkdDrl8Xhqhfd1PUAAAACojk1sAQAAgCYSiURiK+8tXb1d9aujfqU7ProjpWtkO7N1y1G3KhwOy+l0KisrS5eNuUyXjLxE/1r/L7301T+127dbHodHwzuP0I9G/0j9C/rXe93x3care3Z3fVPxTUafLRXF+4ub7NqHK7vNruH9xuiTte816DqjB01opBG1b1aIHwqF5PP5VFlZqWg0GgvWre87p9MZK5djBfLhcFh+v1/hcDjuOlaob626T7Ty3uPxsPoeAACkhAAfAAAAaCI1w3trc8zrx/xU+wMH9PDnD9XZP8+Vp3mnPqtRXUbFamsHAoGqlb12p84bep7OG3peRmMzDEM3TPy5fvafuRn1T0XUjNbfqB0aPmC8vtj4iYKhQEb9Rw78jvp1P6KRR9V+RaNRBQIBhUIhGYYRC9YjkYhM01QwGFQ0GpXb7Y6txrfZbHGlscLhsEKhkCTF1cvPycmJfd9b17Zq6AMAAKSCEjoAAABAE6m+8tYqzVFZWalAIKAbx9yoeSc/q+N7nFCrX64zVz8a8SO9M+sdHd/7+Fh4b5pmrZr6DXH9Uderd27yMjsN1Su3V5Nd+3CWk5Wr706aJYc9/fVU44Yco+NGn0oAnKLK5Tu09bIlWj/qaX11xF+1ccJz2nX7MoW2l0s6VOYqHA7X+T1lBfQ2my22eW3N9tZqe+tPOByWz+eLldFxu91JN7MFAABIxjBrvs+HVmH48OGSpKKiohYeCQAAADIVCARiK+atmtqW6j+Glxwo0ZqyNYo6ourg6qCjux2tXFdunde2SnY0NAwsKSvRyL+OUEWookHXqclld2nLNVvV2du5Ua/bluzcu11LV7yq3ft31tnO68nR0D4jNbz/OOXndGim0R3eglsOaOtFr8r3/vbEDRw2Ff54pDo/MEWVoarV99YKeotV/16SbP6Icjf75d0bkRkMy3TaVNnRqfK+WYq6qx6wZWVl1SqZYxiGCgoK5HY33ubFAACgfSHAb6UI8AEAAA5/4XBYlZWVsXIa0Wg0VkrDZrPFjlmys7Nr1cuui9frlcPR8KqYX+39Sic9P1Xbyrc1+FqW7w//vuad+WyjXa+tMk1TO/Zu05cln+tARZlMmcrJytPQPiPVs3M/yZBsBi9OpyO4+YCKj/k/hbeV19s2+/R+KnzmJIXNSK0APxqNKuQPqnDlAeV8XSkjQUUo0yaV9/Nq36g8eXOzY4F/dTk5OcrJafjGxQAAoH2iBj4AAADQRKxa18FgUMFgsNbKXGtzzEgkIrvdHqu5nermlpFIpFEC/MGFg3XluKt08zu/afC1JKmLt4vumHJno1yrrTMMQ9079lL3jpQbagymaWrLef9KKbyXpIpXS2S//zNl3zCq1jmbaajrsn3ylAaT9jeiUm6xT66KiPaf6JESvBDDmjkAANAQLOUAAAAAmkg0Gk0Y3kuKbY4ZCATkcDjkdDplmmbcivz6NFYwaJqmnlz5t0a5Vvec7nr9giXqV9CvUa4HpMP3zlZVLq+7JFFN5X8ukhGs/X2X9+leeUqDqu+7zJTk3hlQ9qe7E56n5j0AAGgIAnwAAACgCVibY5qmKafTmXRVvVUz22azpR30NVYwuMu3S8VlxQ26RhdvF/1q0q/16ewVGtN1TKOMC0jX3j9+kXaf6N6AAos2x32P2iojytpUtYq/vu8y67x3Y7kMf/zGttabNgAAAJmihA4AAADQBCKRiCKRqjDPMAw5HA7ZbLZaZXRsNlusdI5VFz9VqZbaqY8v5Mu47+yRP9TFIy7WMT2PkdvBRp1oWb5lSTatrUfg453KOn9gbJ+KrOLyhDXv62JEJW9xuSqG5ceOWWWyAAAAMkWADwAAADQBK7y32Q699Fr9v6uzSufY7fZYKZ362O32Rgvw89359TdK4sS+J+rEvic2yjiAVEQDYR3453odWLBB4VKfbB6HPKM6qcOPRilaHqr/AgmYvrBsNpvcbrdCoZBcO/0ZXce1yx8L8O12u3JycpJ+3wMAAKSCAB8AAABoAlYIbwXtVqBfV3srQKysrKwzxDcMQx6Pp9FK6OQ58zSuyzit2LUirX52w66T+p7UKGMAUrHnj59r163LFCmtjDtevqREu+9fLiMrs19xXR2zYw/E3G637HV/uyZlhMxY2ZycnBy5XK7MLgQAAPAtlgIAAAAATaB6uO50OusN2202mzwej+x2u9xut0zTVDgcVigUigv/7Xa7vF5vnavvrb6BQEB+v1+BQEDhcDjpQ4FIJKLLRlye5ieUzh18rnrk9ki7H5CJnTe9p2+u/net8L46szKc0bVzT+knr9crr9crt9stw53ZgwCbx6mCggIVFBQQ3gMAgEZBgA8AAAA0gbgNMb9dWZ8sdHc4HMrKypIk+Xw++f1+2Ww2ORwOORyHgkSPx1NveB+JROTz+eTz+RQIBBQMBhUIBGLHEr0JYJqmZgyaoQH5A1L+fE6bUz8/+hexv4ciIe0o36GdFTsVjmYWogLJlP3flyq9+6Mmubazb55yv9s/tleF2+2WvU9mZaUcfQvkdrspmwMAABoNP1UAAAAA9Uh3RbtUu0a9FeK73e7YxpZOp1Nut1tZWVmy2WyxgN0wjFhd/Gg0Glu9HwwG69zk1grvk5XrSXbeMAxlObL0wukvqnt293r/PeyGXU+d8bSO6n6UVuxYocsXX66C3+erxyPd1f0P3VT4/zroqiVXaeWulfVeqzJUqS0Htuib8m8I/lGLGYnqwCsbtf2qt5rsHp1/NUGGvcavxiO6SLY0S1TZDGl4l8YbGAAAgCTDTGWHLDS74cOHS5KKiopaeCQAAADtWyQSkd/vTxiK2+32WNmbZH19Pl+99eyzsrIUCARi4X0wGKz1gMBaHWytwq9Zksc0zTrD+5rjrn6NcDgsn88nSdp6cKt+8d4v9HrJa4qatR8WDO84Qg9MfUAn9z9Z1791vR5d8Uid9/rZhBt034n3yWYcCkhN09TSr5fqj589poVfLVTErBpzgbtAPxg5W1eNu0qDCwfX+znQth1cXKzt1/xboZIDTXaPjj8dr26/Oz5xias3iqWi0tQvNqKzNC31t1gAAABSQYDfShHgAwAAtLxUA3irrI1pmopEIopEIjLNqs0sDcOoVcfeYj0AsMJ3wzCSPiyo3ic/P79Wfe3qIXwqvF5vrDxPovB/88HNeu7L57Rx/waFoiF18XbVzEEzNannJHm9Xl255Eo98cVfUrrXdd/5if7fyf9PkuQL+XTxov/Rgq8WJG1vyNC9J96nGybc0Ggb9eLwUvb8l9p68WtStAG/rtoNGQ6bzEDt7ydHt2x1vmmiCq8Zk3yOhSLSS2ul7eX136tnrjRjqOTgJXcAANC4CPBbKQJ8AACAprWjfIdKfaXyODzqnddbHocn7ny6K9rdbndsFX2i806nU6ZpxoJ9q8SOYRixWvXBYFChUKje+2VlZSkvLy8ueAwEAgoEAil88ipWOR9LOg8rFhcv1tn/PCvle0nSWxf+W1N6T9FZL56pJZuWpNTn3hPu0y+q1dlH++BfWaoN33lWCiUvF5WqId9cqf3PrpHvkx0yfWHZCz3KPX2Acs8ZJJsr+V4SMaGItPRraU2plOhbw2ZIwzpJJ/YjvAcAAE3CUX8TAAAAoG0IhAP6x9p/6I8rHtOH2z+MHc935+sHI2fr6nFXx0q3WCvpUxGNRlVRUZF0JW8kElE4HJbL5UrYxgr2w+HUasBbY6u+wW2663JqtrfK6qRSLqi+sjmJPLbiUa3Zsybl8F6SfvXfG3XmoDN1ZKcj074fDk/B4jJtuejVRgnvDY9Dzm7Z6nTDUZldwDSlbQelQFjKdUv+cNUxt13K80h986vK5uS46r8WAABAhgjwAQAA0C5sP7hdZ7xwuj7f9Xmtc/sD+/Xw8of06KeP6A+nPKIrx16Zcnhv1ayXJKfTWet8NBqNldCxQnwrPLdCccMwFI1GUw7hrVI91QP8dErNWGPx+/0yDEM2my02VrvdLpvNFntTwGazxb0tUFJWojc2vZHyvSwvr39ZX+z6Iq0+pkz98bM/6uFpD6d9Pxx+Dr5arK9n/UuqTO17rz7Zx/XMvPOuCmnxBmmfv/a5UFTyhaVeuVJ27e95AACAxsQ7fgAAAGjz9vn36eS/n5QwvK8uYkZ09ZKr9MQXT6QVpidbOR+NRuPK6tTcmNYqW2MYRlor6K2AvbpkG+kmGk8wGFQ0GlUwGFRlZaX279+vAwcOxJXxCYfDikajcjgccjgcsQcEX+37KuVxVhcxI9pYtjHtfs+sflrBSDCje+LwUfH2Fm2e8XKjhfeSVHj1mMw67qyQXliTOLy3RE3p4+3SW5uqVuUDAAA0EQJ8AAAAtHm3vnur1u5Zm3L7OW9cq9LK0pTa1rVyPhQKxZ0zTVPRaHxpENM0FQqFUgrgJcWthk90vK5xWg8TrEDeOhYOhxUKhWIr8i3WA4bqbyOEIvXX6G9MBwIHtLNiZ7PeE83LNE1tu+JNmcGGl82xuIcWKvf0Ael3DEell9dJqY5ldWnVHwAAgCZCgA8AAIA2rTxYrqdXPZVWn0AkoHlr5qXU1grorTI0lmQ19BOF/ZFIRE6ns9Y1ajIMI1amp2ZYbxhGrBxPItbDBLvdHivjU/MBQyQSUTAYjLuGaZry+/2xdl2zu9Y5xqYQijbvQwM0r4r/bFZw3b5Gu56jq1d9Fp0jI5NNZdftkSrSnG8rvmEVPgAAaDLUwAcAAECb9uLaF3UweDDtfk+uelLXjLgmVh7HqgVfk2EYCVe/11xpX719IjabTVlZWXVuImuF/MlW21sb0ZaVl2nB+gVas6dIgUhQhe5CndrrVB3Z6chYeJ/sAUM4HK5Vy7/6prnjuo3TgIIBKi4rTvg5kumT11ebD3ydVh9JMmSoc1bntPvh8FH29JpGu5Z3ck/1mjddrn75mV1gZQZve+z1S1sPSr3zMrsnAABAHQjwAQAA0KZtKNuQUb/iso2SURXEW0F39RDdUl/pm5qBfbJV9qZpyuv1SlKs/nz1PtZ96lppXxmq1B3L7tBfPv+z9lTuiTt31yd36riex+vXE36to7sfnfQBg1XmJ9EbBQ6HQzbDpivHXqVfLP15nZ+7prkT5urWd2/R/sD+tPqdPvB05bpz0+qDw0vw6wMNvoZnXBf1fOIUZY1twBsiUVPaUZFZ32/KCfABAECToIQOAAAA2rRINLNNMU2ZikQj8ng8cjqdMgxDkUhEgUAgFn7b7XZlZWXVWrEuVQXt1Td/NQxDLpcraYBvreT3er1yuVxyOp2xP1Z4b51P9NBgj2+Pxj05Vvd+cE+t8N7yzra3debLZ2jhhgV1f/YE5UCqH/vxmB9raMehdV6jutFdRuvy0Zdr9sgfptzHctW4q9Pug8NMA8vP2PJc6vuvcxoW3ktSpAE1+MONt/kuAABAdQT4AAAAaNN65PTIqF+XrC5y2BwyTVMul0tZWVnyeDyxED4rK0ter1cOh6PWingr7Pf5fKqsrJTf71cwGFQoFFIgEEi4er5mSO/1euV2u+VyueR2u2PHapXqMaP63w/vU69He2rd3nX1fq5QNKQfvfUjfbLzk6RtEo2v+rE8d55eO/91HdHhiHrvN7zTcL06a7G8Tq9+PvHnaX09Thtwmk4dcGrK7XF4cvbNfOW64bGrz0tnydmzEd7ScNgkW+ISV/Vy83I7AABoGgT4AAAAaNNmDZ0lhy39cG3W4PNj/119o9rqpWysULv6ynjDMGJ17G02W2wlvtPpVDQaVSgUkt/vjwvEa9a0t/q43W55PB653e641fyWcDSsi16+UDf+90YFIoGUP1soGtLvPnsg4TnDMBK+JVB9fKZpqmd2T/33wrf10/E/VaGnsFb7zt7O+tWkX+u9/3lfPXKrQvseuT302vmvq3tO93rHeEKfEzT/7H/IZvArS1vX4ZJhGfVz9slV//+cr5yT+jbOQAxD6pth7fxM+wEAANTDMBO9H4sWN3z4cElSUVFRC48EAADg8HfRyxdq/pfz0+rz6fdWaGDBwKTn3W633G533LFoNKqKigoFg0FJim0Wa/13dU6nUy6XS5KSlsWpz8/+/TP9/pMH0+5n+WjWR+qd3SfhuKqP13pAYb1ZUHOj3cpwpd78+k1t922X0+lU/4L+mj5gutyO+H8fy7aD23Tfh/fpmVVP60Awvv55//z+umrc1Zozfk7S/mhbzKip9UOfVHD9vpT72Ltna8iWH8tmb+QHPMX7pJe/Sq9Pz1zp/MweQgAAANSHAL+VIsAHAABoPF/t/UqTnjla+/ypBYRXjbpa90y+p842LpdLHo8n7lg4HJbP54s7Zq26rx54S1Ur3bOzs5WVlZVReL+zYqf6PNpboWgo7b6WOyfdpcuGXhYL6+12uzweT1x4bxhG7AGDVRaorl8hqrevT3mwXK8Vv6Yd5TvktDt1RIcjdGLfE1l13w6V/2ezSk79pxROoQ69w6Z+r81QzsmNtPK+uqgp/b1I2pnGZrYzhkh9Cxp/LAAAAJIo1AcAAIA2b3DhYC0+/zWd8cLpSTd4tVx85MW685g7671mojrxNUN6qarsjtvtViQSiW1+ax13OBwZhfeS9Ncv/tqg8F6SyoL75Ha7FQ6HY5vs1lx57/F4ZLfbZZqm/H5/neG9pFg7a8V+XXJcOZo1dFaDPgPahpypfdTnxTO15cJXZPqTbwhreOzq9dzpTRPeS1U18M8aLL3wpVTmr7/9iX0J7wEAQJNiaQsAAADahYk9Jmr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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -710,7 +1250,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -724,7 +1264,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/notebooks/Paper_Citations.ipynb b/notebooks/Paper_Citations.ipynb index c16a87d..ddcd62d 100644 --- a/notebooks/Paper_Citations.ipynb +++ b/notebooks/Paper_Citations.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -156,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -191,20 +191,20 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "g_food_data = pd.read_pickle(mfp('misc_save/garlic_food_data.pkl'))\n", - "g_food_info = pd.read_csv(mfp('data/garlic_scoring.csv'), encoding='latin1')\n", + "g_food_info = pd.read_csv('data/garlic_scoring.csv', encoding='latin_1')\n", "\n", "c_food_data = pd.read_pickle(mfp('misc_save/cocoa_food_data.pkl'))\n", - "c_food_info = pd.read_csv(mfp('data/cocoa_scoring.csv', encoding='latin1'))" + "c_food_info = pd.read_csv('data/cocoa_scoring.csv', encoding='latin_1')" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -215,9 +215,31 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Errno 1] [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1129)\n" + ] + }, + { + "ename": "UnboundLocalError", + "evalue": "local variable 'data' referenced before assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_39046/576058552.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Retrieve citation ids from Microsoft Academic Graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mg_papers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_citations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg_PMIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'paper'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mc_papers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_citations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc_PMIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'paper'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#g_papers.to_pickle(mfp('misc_save/garlic_msft.pkl'))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/tmp/ipykernel_39046/1890607011.py\u001b[0m in \u001b[0;36madd_citations\u001b[0;34m(df, target)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mcitations_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpapers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mID\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcitations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_citations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# Float implies that the ID is NaN, aka it did not recognize a paper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/tmp/ipykernel_39046/2436934745.py\u001b[0m in \u001b[0;36mget_citations\u001b[0;34m(paper)\u001b[0m\n\u001b[1;32m 55\u001b[0m })\n\u001b[1;32m 56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m \u001b[0mloaded_eval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery_API\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;31m# If there are issues with retrieving info, like no interpretations returned, return 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/tmp/ipykernel_39046/2436934745.py\u001b[0m in \u001b[0;36mquery_API\u001b[0;34m(mode, params, headers)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[Errno {0}] {1}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'data' referenced before assignment" + ] + } + ], "source": [ "# Retrieve citation ids from Microsoft Academic Graph\n", "g_papers = add_citations(g_PMIDs, 'paper')\n", @@ -229,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -242,9 +264,30 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Errno 1] [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1129)\n" + ] + }, + { + "ename": "UnboundLocalError", + "evalue": "local variable 'data' referenced before assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_39046/2807372739.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mg_citation_ids\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtitles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/tmp/ipykernel_39046/2436934745.py\u001b[0m in \u001b[0;36mget_title\u001b[0;34m(ID)\u001b[0m\n\u001b[1;32m 24\u001b[0m })\n\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mloaded_eval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery_API\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/tmp/ipykernel_39046/2436934745.py\u001b[0m in \u001b[0;36mquery_API\u001b[0;34m(mode, params, headers)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[Errno {0}] {1}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'data' referenced before assignment" + ] + } + ], "source": [ "start = time.time()\n", "\n", @@ -265,40 +308,27 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0 at 0.055836995442708336 min\n", - "50 at 1.2028738657633464 min\n", - "100 at 2.3527114470799764 min\n", - "150 at 3.50199187596639 min\n", - "200 at 4.597352143128713 min\n", - "250 at 5.795228282610576 min\n", - "300 at 6.960369209448497 min\n", - "350 at 8.074555778503418 min\n", - "400 at 9.23499865134557 min\n", - "450 at 10.397917222976684 min\n", - "500 at 11.508652718861898 min\n", - "550 at 12.667752373218537 min\n", - "600 at 13.829023762543995 min\n", - "650 at 14.948043950398763 min\n", - "700 at 16.120840458075204 min\n", - "750 at 17.280420589447022 min\n", - "800 at 18.44827857812246 min\n", - "850 at 19.62849095662435 min\n", - "900 at 20.791995211442313 min\n", - "950 at 21.903584138552347 min\n", - "1000 at 23.066848842302957 min\n", - "1050 at 24.29008613030116 min\n", - "1100 at 25.61460832754771 min\n", - "1150 at 26.776571385065715 min\n", - "1200 at 27.93707577387492 min\n", - "1250 at 29.04781185388565 min\n", - "1300 at 30.221344435214995 min\n" + "[Errno 1] [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1129)\n" + ] + }, + { + "ename": "UnboundLocalError", + "evalue": "local variable 'data' referenced before assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_39046/876643916.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mc_citation_ids\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtitles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/tmp/ipykernel_39046/2436934745.py\u001b[0m in \u001b[0;36mget_title\u001b[0;34m(ID)\u001b[0m\n\u001b[1;32m 24\u001b[0m })\n\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mloaded_eval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery_API\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/tmp/ipykernel_39046/2436934745.py\u001b[0m in \u001b[0;36mquery_API\u001b[0;34m(mode, params, headers)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[Errno {0}] {1}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'data' referenced before assignment" ] } ], @@ -322,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -332,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -490,9 +520,17 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 at 0.047705264886220296 min\n" + ] + } + ], "source": [ "g_ct.title = g_ct.title.apply(greek_letter_converter)\n", "\n", @@ -865,7 +903,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -879,7 +917,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/notebooks/Paper_Screening.ipynb b/notebooks/Paper_Screening.ipynb index 410d447..4057ac9 100644 --- a/notebooks/Paper_Screening.ipynb +++ b/notebooks/Paper_Screening.ipynb @@ -1484,13 +1484,7 @@ "305it [01:06, 4.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "306it [01:06, 3.93it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + " self.data = self.data.append(data_row, ignore_index=True)\n", "307it [01:07, 3.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "308it [01:07, 3.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", @@ -1838,15 +1832,925 @@ "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'output' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_31499/2415424161.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'output' is not defined" - ] + "data": { + "text/html": [ + "
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PMIDpaperjournalyearabstractmesh_termsmesh_UIdsqual_termsqual_UIdswebpage
034893232Chitosan nanoparticles loaded with garlic esse...Carbohydrate polymers2021In this study, garlic essential oil (GEO) has ...[][][][]https://www.ncbi.nlm.nih.gov/pubmed/34893232
131588747Metabolomics for Biomarker Discovery in Fermen...Journal of agricultural and food chemistry2019Fermented black garlic has multiple beneficial...[Animals, Biomarkers, Cardiovascular Diseases,...[D000818, D015415, D002318, D042783, D005260, ...[None, analysis, genetics, drug effects, None,...[None, Q000032, Q000235, Q000187, None, Q00003...https://www.ncbi.nlm.nih.gov/pubmed/31588747
231387036Identification of the cooked off-flavor in hea...Food chemistry2019The main contributors to the cooked off-flavor...[Cooking, Flavoring Agents, Fruit and Vegetabl...[D003296, D005421, D000067030, D008401, D00635...[None, analysis, analysis, None, None, chemist...[None, Q000032, Q000032, None, None, Q000737, ...https://www.ncbi.nlm.nih.gov/pubmed/31387036
331250635Characterization of Key Aroma-Active Compounds...Journal of agricultural and food chemistry2019Black garlic is a new garlic product produced ...[Adult, Flavoring Agents, Garlic, Gas Chromato...[D000328, D005421, D005737, D008401, D006801, ...[None, chemistry, chemistry, None, None, analy...[None, Q000737, Q000737, None, None, Q000032, ...https://www.ncbi.nlm.nih.gov/pubmed/31250635
430105074Probabilistic Risk Assessment of Inorganic Ars...Evidence-based complementary and alternative m...2020Total and inorganic arsenic contents in ten co...[][][][]https://www.ncbi.nlm.nih.gov/pubmed/30105074
529433241Physicochemical characteristics of complexes b...Food research international (Ottawa, Ont.)2019Complexes of amylose (Am) with garlic bioactiv...[Amylose, Calorimetry, Differential Scanning, ...[D000688, D002152, D002851, D004220, D005511, ...[chemistry, None, None, None, methods, chemist...[Q000737, None, None, None, Q000379, Q000737, ...https://www.ncbi.nlm.nih.gov/pubmed/29433241
629161814Changes in the Aromatic Profile, Sugars, and B...Journal of agricultural and food chemistry2018Black garlic is an elaborated product obtained...[Antioxidants, Ascorbic Acid, Carbohydrates, C...[D000975, D001205, D002241, D002851, D005419, ...[chemistry, chemistry, None, methods, chemistr...[Q000737, Q000737, None, Q000379, Q000737, Q00...https://www.ncbi.nlm.nih.gov/pubmed/29161814
728911676Composition analysis and antioxidant propertie...Journal of food and drug analysis2017Black garlic produced from fresh garlic under ...[Antioxidants, Biphenyl Compounds, Carbolines,...[D000975, D001713, D002243, D002851, D005419, ...[None, None, None, None, None, None, None, Non...[None, None, None, None, None, None, None, Non...https://www.ncbi.nlm.nih.gov/pubmed/28911676
828719747Detection of Fumonisins in Fresh and Dehydrate...Journal of agricultural and food chemistry2017An epidemic fungal disease caused by Fusarium ...[Food Contamination, Food Handling, Fumonisins...[D005506, D005511, D037341, D005670, D005737, ...[analysis, None, analysis, metabolism, chemist...[Q000032, None, Q000032, Q000378, Q000737, Q00...https://www.ncbi.nlm.nih.gov/pubmed/28719747
928560773Untargeted analysis to monitor metabolic chang...Electrophoresis2017Black garlic is increasing its popularity in c...[Amino Acids, Carbohydrates, Chromatography, L...[D000596, D002241, D002853, D016002, D005285, ...[analysis, analysis, methods, None, None, chem...[Q000032, Q000032, Q000379, None, None, Q00073...https://www.ncbi.nlm.nih.gov/pubmed/28560773
1027592824Comparison of multiple methods for the determi...Food additives & contaminants. Part A, Chemist...2017Sulphites are a family of additives regulated ...[Allium, Brassica, Chromatography, Liquid, Sul...[D000490, D001937, D002853, D013447, D053719, ...[chemistry, chemistry, None, analysis, None, c...[Q000737, Q000737, None, Q000032, None, Q000737]https://www.ncbi.nlm.nih.gov/pubmed/27592824
1127313155In situ Identification of Labile Precursor Com...Phytochemical analysis : PCA2017Many secondary metabolites in plants are labil...[Capillary Tubing, Chlorogenic Acid, Cysteine,...[D060166, D002726, D003545, D005737, D005961, ...[None, chemistry, analogs & derivatives, chemi...[None, Q000737, Q000031, Q000737, Q000737, Q00...https://www.ncbi.nlm.nih.gov/pubmed/27313155
1227300762The Comparison of the Contents of Sugar, Amado...Journal of food science2017Black garlic is produced through thermal proce...[Amino Acids, Carbohydrates, Food Handling, Fr...[D000596, D002241, D005511, D005630, D005632, ...[analysis, None, None, analysis, analysis, che...[Q000032, None, None, Q000032, Q000032, Q00073...https://www.ncbi.nlm.nih.gov/pubmed/27300762
1327296605In Vitro Antiviral Activity of Clove and Ginge...Journal of food protection2017Foodborne viruses, particularly human noroviru...[Animals, Antiviral Agents, Calicivirus, Felin...[D000818, D000998, D017927, D002415, D002460, ...[None, pharmacology, drug effects, None, None,...[None, Q000494, Q000187, None, None, None, Non...https://www.ncbi.nlm.nih.gov/pubmed/27296605
1425371585Comparitive study on volatile aroma compounds ...African journal of traditional, complementary,...2015The medicinal use of garlic is much older than...[China, Garlic, Gas Chromatography-Mass Spectr...[D002681, D005737, D008401, D010936, D014421, ...[None, chemistry, None, chemistry, None, chemi...[None, Q000737, None, Q000737, None, Q000737]https://www.ncbi.nlm.nih.gov/pubmed/25371585
1525329784From cats and blackcurrants: structure and dyn...Chemistry & biodiversity2015Sulfur-containing odorants and flavors play an...[Animals, Cats, Flavoring Agents, Fruit, Furan...[D000818, D002415, D005421, D005638, D005663, ...[None, None, chemistry, chemistry, chemistry, ...[None, None, Q000737, Q000737, Q000737, None, ...https://www.ncbi.nlm.nih.gov/pubmed/25329784
1623259687Assessment of thiol compounds from garlic by a...Journal of agricultural and food chemistry2014This study investigates the analysis of thiol ...[Dimethylpolysiloxanes, Garlic, Gas Chromatogr...[D004129, D005737, D008401, D015203, D052617, ...[chemistry, chemistry, methods, None, methods,...[Q000737, Q000737, Q000379, None, Q000379, Q00...https://www.ncbi.nlm.nih.gov/pubmed/23259687
1722610968The role of diallyl sulfides and dipropyl sulf...Phytotherapy research : PTR2013The in vitro antibacterial activity of essenti...[Allium, Allyl Compounds, Anti-Bacterial Agent...[D000490, D000498, D000900, D004220, D004926, ...[chemistry, chemistry, pharmacology, None, dru...[Q000737, Q000737, Q000494, None, Q000187, Non...https://www.ncbi.nlm.nih.gov/pubmed/22610968
1822284504Determination of maleic hydrazide residues in ...Talanta2012In recent years, the release of information ab...[Chromatography, Gas, Chromatography, High Pre...[D002849, D002851, D003080, D005504, D005737, ...[None, None, None, None, chemistry, analysis, ...[None, None, None, None, Q000737, Q000032, Q00...https://www.ncbi.nlm.nih.gov/pubmed/22284504
1921535547Optimizing the use of garlic oil as antimicrob...Journal of food science2011Encapsulation of garlic oil (GO) in β-cyclodex...[Adult, Allyl Compounds, Anti-Infective Agents...[D000328, D000498, D000890, D015169, D003692, ...[None, chemistry, chemistry, None, pharmacolog...[None, Q000737, Q000737, None, Q000494, None, ...https://www.ncbi.nlm.nih.gov/pubmed/21535547
2019768983Chemical composition and antimicrobial activit...Natural product communications2010The chemical composition of fresh flowers from...[1-Butanol, Allium, Anti-Bacterial Agents, Ant...[D020001, D000490, D000900, D000935, D002031, ...[None, chemistry, chemistry, isolation & purif...[None, Q000737, Q000737, Q000302, None, Q00018...https://www.ncbi.nlm.nih.gov/pubmed/19768983
2119053859Repeated administration of fresh garlic increa...Journal of medicinal food2009Garlic (Allium sativum) is regarded as both a ...[5-Hydroxytryptophan, Animals, Avoidance Learn...[D006916, D000818, D001362, D001921, D005737, ...[blood, None, drug effects, metabolism, None, ...[Q000097, None, Q000187, Q000378, None, Q00037...https://www.ncbi.nlm.nih.gov/pubmed/19053859
2218952220Quantitative determination of S-alk(en)ylcyste...Journal of chromatography. A2009A novel method for determination of S-alk(en)y...[Allium, Brassica, Chromatography, Micellar El...[D000490, D001937, D020374, D003545, D000432, ...[chemistry, chemistry, economics, analogs & de...[Q000737, Q000737, Q000191, Q000031, Q000737, ...https://www.ncbi.nlm.nih.gov/pubmed/18952220
2317269787Identification of new, odor-active thiocarbama...Journal of agricultural and food chemistry2007New, odorant nitrogen- and sulfur-containing c...[Brassicaceae, Chromatography, Gas, Gas Chroma...[D019607, D002849, D008401, D006801, D009584, ...[chemistry, None, None, None, analysis, analys...[Q000737, None, None, None, Q000032, Q000032, ...https://www.ncbi.nlm.nih.gov/pubmed/17269787
2417017158[Study of organosulfur compounds in fresh garl...Se pu = Chinese journal of chromatography2010For the analysis of organosulfur compounds in ...[Allyl Compounds, Cold Temperature, Disulfides...[D000498, D003080, D004220, D005737, D008401, ...[analysis, None, analysis, chemistry, methods,...[Q000032, None, Q000032, Q000737, Q000379, Q00...https://www.ncbi.nlm.nih.gov/pubmed/17017158
2516413559Identification of garlic in old gildings by ga...Journal of chromatography. A2006The proteinaceous content of garlic (Allium sa...[Garlic, Gas Chromatography-Mass Spectrometry,...[D005737, D008401, D006868, D008872, D010151, ...[None, methods, None, None, None, None][None, Q000379, None, None, None, None]https://www.ncbi.nlm.nih.gov/pubmed/16413559
2616277408Free amino acid and cysteine sulfoxide composi...Journal of agricultural and food chemistry2006Two garlic subspecies (n = 11), Allium sativum...[Amino Acids, Cysteine, Gas Chromatography-Mas...[D000596, D003545, D008401, D013454][analysis, analogs & derivatives, None, analysis][Q000032, Q000031, None, Q000032]https://www.ncbi.nlm.nih.gov/pubmed/16277408
2715161196The 26S proteasome in garlic (Allium sativum):...Journal of agricultural and food chemistry2004The 26S proteasome (multicatalytic protease co...[Amino Acid Sequence, Base Sequence, Cloning, ...[D000595, D001483, D003001, D018744, D005737, ...[None, None, None, chemistry, enzymology, None...[None, None, None, Q000737, Q000201, None, Q00...https://www.ncbi.nlm.nih.gov/pubmed/15161196
2815065784High-performance liquid chromatographic-induct...Journal of chromatography. A2004Garlic and onion, are well known for their med...[Chromatography, High Pressure Liquid, Cystein...[D002851, D003545, D005737, D013058, D019697, ...[methods, analogs & derivatives, chemistry, me...[Q000379, Q000031, Q000737, Q000379, Q000737, ...https://www.ncbi.nlm.nih.gov/pubmed/15065784
2914969516Quantitative determination of allicin in garli...Journal of agricultural and food chemistry2004A quantitative method is described for the det...[Carbon-Sulfur Lyases, Chromatography, High Pr...[D013437, D002851, D025924, D003545, D004220, ...[metabolism, None, None, administration & dosa...[Q000378, None, None, Q000008, None, Q000737, ...https://www.ncbi.nlm.nih.gov/pubmed/14969516
3011767087Allixin induction and accumulation by light ir...Chemical & pharmaceutical bulletin2002Allixin, a phytoalexin isolated from garlic, w...[Antineoplastic Agents, Phytogenic, Chromatogr...[D000972, D002851, D005737, D008027, D011753, ...[metabolism, None, metabolism, None, metabolis...[Q000378, None, Q000378, None, Q000378, None, ...https://www.ncbi.nlm.nih.gov/pubmed/11767087
3111486375[Degradation of thiometon in ethyl acetate].Shokuhin eiseigaku zasshi. Journal of the Food...2001When performing multiresidue analysis of pesti...[Acetates, Food Contamination, Gas Chromatogra...[D000085, D005506, D008401, D007306, D063086, ...[None, analysis, None, analysis, analysis, che...[None, Q000032, None, Q000032, Q000032, Q000737]https://www.ncbi.nlm.nih.gov/pubmed/11486375
3211238797How to distinguish garlic from the other Alliu...The Journal of nutrition2001The establishment of international monographs ...[Allium, Chromatography, Gas, Chromatography, ...[D000490, D002849, D002851, D002855, D003545, ...[chemistry, None, None, None, analogs & deriva...[Q000737, None, None, None, Q000031, Q000379, ...https://www.ncbi.nlm.nih.gov/pubmed/11238797
3310737231Antibacterial activity of garlic powder agains...Journal of nutritional science and vitaminology2000The antibacterial activity of garlic powder ag...[Anti-Bacterial Agents, Anti-Infective Agents,...[D000900, D000890, D002176, D019453, D005737, ...[None, None, drug effects, drug effects, None,...[None, None, Q000187, Q000187, None, None, Non...https://www.ncbi.nlm.nih.gov/pubmed/10737231
3410588342Gas chromatographic determination of S-alk(en)...Journal of chromatography. A2000A new GC method for determination of S-alk(en)...[Chromatography, Gas, Chromatography, High Pre...[D002849, D002851, D003545, D005737, D007202, ...[methods, None, analogs & derivatives, chemist...[Q000379, None, Q000031, Q000737, None, None, ...https://www.ncbi.nlm.nih.gov/pubmed/10588342
3510234740Stabilization and pharmaceutical use of alliin...Die Pharmazie1999In recent years, numerous clinical trials were...[Buffers, Carbon-Sulfur Lyases, Drug Stability...[D002021, D013437, D004355, D005079, D005737, ...[None, chemistry, None, None, chemistry, None,...[None, Q000737, None, None, Q000737, None, None]https://www.ncbi.nlm.nih.gov/pubmed/10234740
3610193205Quality of herbal remedies from Allium sativum...Planta medica1999Alliinase (EC 4.4.1.4) has been isolated from ...[Carbon-Sulfur Lyases, Electrophoresis, Polyac...[D013437, D004591, D005737, D008517, D010946, ...[isolation & purification, None, chemistry, No...[Q000302, None, Q000737, None, None, None]https://www.ncbi.nlm.nih.gov/pubmed/10193205
378870956Urinary excretion of N-acetyl-S-allyl-L-cystei...Archives of toxicology1997N-Acetyl-S-allyl-L-cysteine (allylmercapturic ...[Acetylcysteine, Administration, Oral, Adult, ...[D000111, D000284, D000328, D005260, D005737, ...[analogs & derivatives, None, None, None, None...[Q000031, None, None, None, None, None, None, ...https://www.ncbi.nlm.nih.gov/pubmed/8870956
387604070Inhibition of adenosine deaminase activity of ...Die Pharmazie1995Aqueous extracts of fresh garlic (Allium sativ...[Adenosine Deaminase Inhibitors, Animals, Aort...[D058892, D000818, D001011, D001794, D002417, ...[None, None, cytology, drug effects, None, Non...[None, None, Q000166, Q000187, None, None, Non...https://www.ncbi.nlm.nih.gov/pubmed/7604070
397517069Metal dispersion and transportational activiti...The Science of the total environment1994The multielement (Al, Ca, Cd, Ce, Cr, Cu, Fe, ...[Air Pollutants, Environmental Monitoring, Lea...[D000393, D004784, D007854, D008131, D010945, ...[analysis, methods, analysis, None, chemistry,...[Q000032, Q000379, Q000032, None, Q000737, Non...https://www.ncbi.nlm.nih.gov/pubmed/7517069
4017262412Volatile garlic odor components: gas phases an...Planta medica2012Combined headspace gas chromatography-mass spe...[][][][]https://www.ncbi.nlm.nih.gov/pubmed/17262412
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" + ], + "text/plain": [ + " PMID paper \\\n", + "0 34893232 Chitosan nanoparticles loaded with garlic esse... \n", + "1 31588747 Metabolomics for Biomarker Discovery in Fermen... \n", + "2 31387036 Identification of the cooked off-flavor in hea... \n", + "3 31250635 Characterization of Key Aroma-Active Compounds... \n", + "4 30105074 Probabilistic Risk Assessment of Inorganic Ars... \n", + "5 29433241 Physicochemical characteristics of complexes b... \n", + "6 29161814 Changes in the Aromatic Profile, Sugars, and B... \n", + "7 28911676 Composition analysis and antioxidant propertie... \n", + "8 28719747 Detection of Fumonisins in Fresh and Dehydrate... \n", + "9 28560773 Untargeted analysis to monitor metabolic chang... \n", + "10 27592824 Comparison of multiple methods for the determi... \n", + "11 27313155 In situ Identification of Labile Precursor Com... \n", + "12 27300762 The Comparison of the Contents of Sugar, Amado... \n", + "13 27296605 In Vitro Antiviral Activity of Clove and Ginge... \n", + "14 25371585 Comparitive study on volatile aroma compounds ... \n", + "15 25329784 From cats and blackcurrants: structure and dyn... \n", + "16 23259687 Assessment of thiol compounds from garlic by a... \n", + "17 22610968 The role of diallyl sulfides and dipropyl sulf... \n", + "18 22284504 Determination of maleic hydrazide residues in ... \n", + "19 21535547 Optimizing the use of garlic oil as antimicrob... \n", + "20 19768983 Chemical composition and antimicrobial activit... \n", + "21 19053859 Repeated administration of fresh garlic increa... \n", + "22 18952220 Quantitative determination of S-alk(en)ylcyste... \n", + "23 17269787 Identification of new, odor-active thiocarbama... \n", + "24 17017158 [Study of organosulfur compounds in fresh garl... \n", + "25 16413559 Identification of garlic in old gildings by ga... \n", + "26 16277408 Free amino acid and cysteine sulfoxide composi... \n", + "27 15161196 The 26S proteasome in garlic (Allium sativum):... \n", + "28 15065784 High-performance liquid chromatographic-induct... \n", + "29 14969516 Quantitative determination of allicin in garli... \n", + "30 11767087 Allixin induction and accumulation by light ir... \n", + "31 11486375 [Degradation of thiometon in ethyl acetate]. \n", + "32 11238797 How to distinguish garlic from the other Alliu... \n", + "33 10737231 Antibacterial activity of garlic powder agains... \n", + "34 10588342 Gas chromatographic determination of S-alk(en)... \n", + "35 10234740 Stabilization and pharmaceutical use of alliin... \n", + "36 10193205 Quality of herbal remedies from Allium sativum... \n", + "37 8870956 Urinary excretion of N-acetyl-S-allyl-L-cystei... \n", + "38 7604070 Inhibition of adenosine deaminase activity of ... \n", + "39 7517069 Metal dispersion and transportational activiti... \n", + "40 17262412 Volatile garlic odor components: gas phases an... \n", + "\n", + " journal year \\\n", + "0 Carbohydrate polymers 2021 \n", + "1 Journal of agricultural and food chemistry 2019 \n", + "2 Food chemistry 2019 \n", + "3 Journal of agricultural and food chemistry 2019 \n", + "4 Evidence-based complementary and alternative m... 2020 \n", + "5 Food research international (Ottawa, Ont.) 2019 \n", + "6 Journal of agricultural and food chemistry 2018 \n", + "7 Journal of food and drug analysis 2017 \n", + "8 Journal of agricultural and food chemistry 2017 \n", + "9 Electrophoresis 2017 \n", + "10 Food additives & contaminants. Part A, Chemist... 2017 \n", + "11 Phytochemical analysis : PCA 2017 \n", + "12 Journal of food science 2017 \n", + "13 Journal of food protection 2017 \n", + "14 African journal of traditional, complementary,... 2015 \n", + "15 Chemistry & biodiversity 2015 \n", + "16 Journal of agricultural and food chemistry 2014 \n", + "17 Phytotherapy research : PTR 2013 \n", + "18 Talanta 2012 \n", + "19 Journal of food science 2011 \n", + "20 Natural product communications 2010 \n", + "21 Journal of medicinal food 2009 \n", + "22 Journal of chromatography. A 2009 \n", + "23 Journal of agricultural and food chemistry 2007 \n", + "24 Se pu = Chinese journal of chromatography 2010 \n", + "25 Journal of chromatography. A 2006 \n", + "26 Journal of agricultural and food chemistry 2006 \n", + "27 Journal of agricultural and food chemistry 2004 \n", + "28 Journal of chromatography. A 2004 \n", + "29 Journal of agricultural and food chemistry 2004 \n", + "30 Chemical & pharmaceutical bulletin 2002 \n", + "31 Shokuhin eiseigaku zasshi. Journal of the Food... 2001 \n", + "32 The Journal of nutrition 2001 \n", + "33 Journal of nutritional science and vitaminology 2000 \n", + "34 Journal of chromatography. A 2000 \n", + "35 Die Pharmazie 1999 \n", + "36 Planta medica 1999 \n", + "37 Archives of toxicology 1997 \n", + "38 Die Pharmazie 1995 \n", + "39 The Science of the total environment 1994 \n", + "40 Planta medica 2012 \n", + "\n", + " abstract \\\n", + "0 In this study, garlic essential oil (GEO) has ... \n", + "1 Fermented black garlic has multiple beneficial... \n", + "2 The main contributors to the cooked off-flavor... \n", + "3 Black garlic is a new garlic product produced ... \n", + "4 Total and inorganic arsenic contents in ten co... \n", + "5 Complexes of amylose (Am) with garlic bioactiv... \n", + "6 Black garlic is an elaborated product obtained... \n", + "7 Black garlic produced from fresh garlic under ... \n", + "8 An epidemic fungal disease caused by Fusarium ... \n", + "9 Black garlic is increasing its popularity in c... \n", + "10 Sulphites are a family of additives regulated ... \n", + "11 Many secondary metabolites in plants are labil... \n", + "12 Black garlic is produced through thermal proce... \n", + "13 Foodborne viruses, particularly human noroviru... \n", + "14 The medicinal use of garlic is much older than... \n", + "15 Sulfur-containing odorants and flavors play an... \n", + "16 This study investigates the analysis of thiol ... \n", + "17 The in vitro antibacterial activity of essenti... \n", + "18 In recent years, the release of information ab... \n", + "19 Encapsulation of garlic oil (GO) in β-cyclodex... \n", + "20 The chemical composition of fresh flowers from... \n", + "21 Garlic (Allium sativum) is regarded as both a ... \n", + "22 A novel method for determination of S-alk(en)y... \n", + "23 New, odorant nitrogen- and sulfur-containing c... \n", + "24 For the analysis of organosulfur compounds in ... \n", + "25 The proteinaceous content of garlic (Allium sa... \n", + "26 Two garlic subspecies (n = 11), Allium sativum... \n", + "27 The 26S proteasome (multicatalytic protease co... \n", + "28 Garlic and onion, are well known for their med... \n", + "29 A quantitative method is described for the det... \n", + "30 Allixin, a phytoalexin isolated from garlic, w... \n", + "31 When performing multiresidue analysis of pesti... \n", + "32 The establishment of international monographs ... \n", + "33 The antibacterial activity of garlic powder ag... \n", + "34 A new GC method for determination of S-alk(en)... \n", + "35 In recent years, numerous clinical trials were... \n", + "36 Alliinase (EC 4.4.1.4) has been isolated from ... \n", + "37 N-Acetyl-S-allyl-L-cysteine (allylmercapturic ... \n", + "38 Aqueous extracts of fresh garlic (Allium sativ... \n", + "39 The multielement (Al, Ca, Cd, Ce, Cr, Cu, Fe, ... \n", + "40 Combined headspace gas chromatography-mass spe... \n", + "\n", + " mesh_terms \\\n", + "0 [] \n", + "1 [Animals, Biomarkers, Cardiovascular Diseases,... \n", + "2 [Cooking, Flavoring Agents, Fruit and Vegetabl... \n", + "3 [Adult, Flavoring Agents, Garlic, Gas Chromato... \n", + "4 [] \n", + "5 [Amylose, 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https://www.ncbi.nlm.nih.gov/pubmed/27296605 \n", + "14 https://www.ncbi.nlm.nih.gov/pubmed/25371585 \n", + "15 https://www.ncbi.nlm.nih.gov/pubmed/25329784 \n", + "16 https://www.ncbi.nlm.nih.gov/pubmed/23259687 \n", + "17 https://www.ncbi.nlm.nih.gov/pubmed/22610968 \n", + "18 https://www.ncbi.nlm.nih.gov/pubmed/22284504 \n", + "19 https://www.ncbi.nlm.nih.gov/pubmed/21535547 \n", + "20 https://www.ncbi.nlm.nih.gov/pubmed/19768983 \n", + "21 https://www.ncbi.nlm.nih.gov/pubmed/19053859 \n", + "22 https://www.ncbi.nlm.nih.gov/pubmed/18952220 \n", + "23 https://www.ncbi.nlm.nih.gov/pubmed/17269787 \n", + "24 https://www.ncbi.nlm.nih.gov/pubmed/17017158 \n", + "25 https://www.ncbi.nlm.nih.gov/pubmed/16413559 \n", + "26 https://www.ncbi.nlm.nih.gov/pubmed/16277408 \n", + "27 https://www.ncbi.nlm.nih.gov/pubmed/15161196 \n", + "28 https://www.ncbi.nlm.nih.gov/pubmed/15065784 \n", + "29 https://www.ncbi.nlm.nih.gov/pubmed/14969516 \n", + "30 https://www.ncbi.nlm.nih.gov/pubmed/11767087 \n", + "31 https://www.ncbi.nlm.nih.gov/pubmed/11486375 \n", + "32 https://www.ncbi.nlm.nih.gov/pubmed/11238797 \n", + "33 https://www.ncbi.nlm.nih.gov/pubmed/10737231 \n", + "34 https://www.ncbi.nlm.nih.gov/pubmed/10588342 \n", + "35 https://www.ncbi.nlm.nih.gov/pubmed/10234740 \n", + "36 https://www.ncbi.nlm.nih.gov/pubmed/10193205 \n", + "37 https://www.ncbi.nlm.nih.gov/pubmed/8870956 \n", + "38 https://www.ncbi.nlm.nih.gov/pubmed/7604070 \n", + "39 https://www.ncbi.nlm.nih.gov/pubmed/7517069 \n", + "40 https://www.ncbi.nlm.nih.gov/pubmed/17262412 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ diff --git a/src/data_loader.py b/src/data_loader.py index 53f285d..2ca00f8 100644 --- a/src/data_loader.py +++ b/src/data_loader.py @@ -4,9 +4,9 @@ import time import config -import tools.chemidr.labeler as lbr -import tools.chemidr.id_map as id_map -import collected_data_handling as cdh +import src.tools.chemidr.labeler as lbr +import src.tools.chemidr.id_map as id_map +import src.collected_data_handling as cdh report = False diff --git a/src/file_downloader.py b/src/file_downloader.py index 4c8950c..6e10034 100644 --- a/src/file_downloader.py +++ b/src/file_downloader.py @@ -5,9 +5,11 @@ cwd = os.getcwd() + def unzip_file(dirzip): - with ZipFile(dirzip, 'r') as zip: - zip.extractall() + with ZipFile(dirzip, 'r') as zip: + zip.extractall() + def download_core_data(): url = 'https://drive.google.com/uc?id=1urQmygS0vrbWN_4nU6R44q85XkPomYLD' @@ -16,6 +18,7 @@ def download_core_data(): unzip_file(output) + def download_intermediate_data(): url = 'https://drive.google.com/uc?id=1Enc3FOXDb8R2gGGVnn73FOQEAYqePn9M' output = os.path.join(cwd, "misc_save.zip") @@ -23,20 +26,22 @@ def download_intermediate_data(): unzip_file(output) + def download_chemidr_intermediate_data(): url = 'https://drive.google.com/uc?id=1vl5TSitI30bT5V_wkFfil8mqvQzvFhNn' output = os.path.join(cwd, "src/tools/intermediate_data.zip") - + gdown.download(url, output, quiet=False) - with ZipFile(output, 'r') as zip: - zip.extractall(path = os.path.join(cwd, "src/tools")) + with ZipFile(output, 'r') as zip: + zip.extractall(path=os.path.join(cwd, "src/tools")) + def make_copypath_and_copy(src, dst): dst = f'{dst}/{src}' src = f'data/{src}' - print(src,dst) + print(src, dst) copyfile(src, dst) @@ -56,4 +61,4 @@ def download_all_data(): if __name__ == "__main__": - download_all_data() \ No newline at end of file + download_all_data() diff --git a/stats/fm_usda_overlap_perc_cocoa.txt b/stats/fm_usda_overlap_perc_cocoa.txt new file mode 100644 index 0000000..98ed0d2 --- /dev/null +++ b/stats/fm_usda_overlap_perc_cocoa.txt @@ -0,0 +1 @@ +perc cocoa fm data used w/ usda: 0.05351170568561873 01/29/2023 \ No newline at end of file diff --git a/stats/fm_usda_r2_cocoa.txt b/stats/fm_usda_r2_cocoa.txt new file mode 100644 index 0000000..df510f9 --- /dev/null +++ b/stats/fm_usda_r2_cocoa.txt @@ -0,0 +1 @@ +FM-USDA log R2 cocoa: 0.5576625681157634 01/29/2023 \ No newline at end of file diff --git a/stats/fm_usda_r2_r_cocoa.txt b/stats/fm_usda_r2_r_cocoa.txt new file mode 100644 index 0000000..133e9b0 --- /dev/null +++ b/stats/fm_usda_r2_r_cocoa.txt @@ -0,0 +1 @@ +FM-USDA removed paper log R2 cocoa: 0.7451095518300979 01/29/2023 \ No newline at end of file diff --git a/stats/unique_chems_cocoa.txt b/stats/unique_chems_cocoa.txt new file mode 100644 index 0000000..c806b07 --- /dev/null +++ b/stats/unique_chems_cocoa.txt @@ -0,0 +1 @@ +Num unique fm chems cocoa: 284 01/29/2023 \ No newline at end of file From 28e06e4b627d96398e117a0485858aa6e14dee2c Mon Sep 17 00:00:00 2001 From: hebamuh68 Date: Wed, 8 Feb 2023 07:50:32 +0200 Subject: [PATCH 3/4] test everything is working well: T --- notebooks/Data_Statistics.ipynb | 2675 +------------------- notebooks/Molecule_Embedding.ipynb | 562 +--- notebooks/Paper_Citations.ipynb | 97 +- notebooks/Paper_Screening.ipynb | 1578 ++++++------ notebooks/Phenol_Explorer_Comparison.ipynb | 4 +- src/collected_data_handling.py | 1 - src/pubmed_util.py | 3 +- src/try.py | 16 - stats/fm_usda_overlap_perc_cocoa.txt | 2 +- stats/fm_usda_r2_cocoa.txt | 2 +- stats/fm_usda_r2_r_cocoa.txt | 2 +- stats/unique_chems_cocoa.txt | 2 +- 12 files changed, 872 insertions(+), 4072 deletions(-) delete mode 100644 src/try.py diff --git a/notebooks/Data_Statistics.ipynb b/notebooks/Data_Statistics.ipynb index 867321a..9490c74 100644 --- a/notebooks/Data_Statistics.ipynb +++ b/notebooks/Data_Statistics.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -23,12 +23,15 @@ "import seaborn as sns\n", "import matplotlib as mpl\n", "import matplotlib.patches as mpatches\n", - "%matplotlib inline" + "%matplotlib inline\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -38,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -64,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -91,2512 +94,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 22, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n", - "/home/heba/Graduation project/FoodMine/src/collected_data_handling.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " df = df.append(chem_dict, ignore_index = True)\n" - ] - } - ], + "outputs": [], "source": [ "food_data, food_scoring = load_raw_data(food, load)\n", "\n", @@ -2617,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -2644,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -2661,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -2728,19 +228,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 26, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_36429/2176026344.py:46: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", - " table['total'] = table.sum(axis=1)\n", - "/tmp/ipykernel_36429/2176026344.py:47: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " table = table.append(table.sum(axis=0), ignore_index = True)\n" - ] - }, { "data": { "text/html": [ @@ -2839,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -2854,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -2877,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -2923,37 +413,9 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_36429/754048652.py:12: UserWarning: \n", - "\n", - "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", - "\n", - "Please adapt your code to use either `displot` (a figure-level function with\n", - "similar flexibility) or `histplot` (an axes-level function for histograms).\n", - "\n", - "For a guide to updating your code to use the new functions, please see\n", - "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", - "\n", - " g1 = sns.distplot(quant_viz['count'], kde=False, label='Quantified', bins=bins)\n", - "/tmp/ipykernel_36429/754048652.py:13: UserWarning: \n", - "\n", - "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", - "\n", - "Please adapt your code to use either `displot` (a figure-level function with\n", - "similar flexibility) or `histplot` (an axes-level function for histograms).\n", - "\n", - "For a guide to updating your code to use the new functions, please see\n", - "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", - "\n", - " g2 = sns.distplot(unquant_viz['count'], kde=False, label='Unquantified', bins=bins)\n" - ] - }, { "data": { - "image/png": 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\n", 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" ] @@ -2988,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 30, "metadata": { "scrolled": false }, @@ -3036,37 +498,9 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_36429/1938569971.py:10: UserWarning: \n", - "\n", - "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", - "\n", - "Please adapt your code to use either `displot` (a figure-level function with\n", - "similar flexibility) or `histplot` (an axes-level function for histograms).\n", - "\n", - "For a guide to updating your code to use the new functions, please see\n", - "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", - "\n", - " g1 = sns.distplot(quant_viz['count'], kde=False, label='Quantified', bins=bins)\n", - "/tmp/ipykernel_36429/1938569971.py:11: UserWarning: \n", - "\n", - "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", - "\n", - "Please adapt your code to use either `displot` (a figure-level function with\n", - "similar flexibility) or `histplot` (an axes-level function for histograms).\n", - "\n", - "For a guide to updating your code to use the new functions, please see\n", - "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", - "\n", - " g2 = sns.distplot(unquant_viz['count'], kde=False, label='Unquantified', bins=bins)\n" - ] - }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3106,7 +540,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 31, "metadata": { "scrolled": false }, @@ -3125,7 +559,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 33, "metadata": { "scrolled": false }, @@ -3139,7 +573,7 @@ }, { "data": { - "image/png": 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qVaqEN2/e4NixY+Io2dDQUKSkpGDz5s1K+7p58yZatGiB169fi9Nq166NevXqQSKR4MqVK+I2Xbp0Cc2aNcOJEydQrVq1fOMsCc+ePYOnpyeePXuGsmXLonnz5jAzM0NCQgKOHj2K9+/fAwB++eUX1KhRAz179lTa15IlS/D999+Ln7W1tdGyZUtYWVnh9evXOHr0KJ4/f46OHTtixIgRxbYNx44dkxnpWxLnvyqkI8y1tbVRs2ZN2NnZwcjICIIg4Pnz5zh37hwSEhLw+fNnjBs3DgAwduxYhX1dvHgR3bt3R0ZGBgBAV1cXLi4usLW1hba2NpKSknD//n3ExsYqfRhqZmYmOnTogEuXLonTatWqhVq1aqFs2bL49OkTXrx4gWvXruH58+fFsg+ysrLEB4oCgKura57t4+Li8ODBA/FzSRy7krgmN23ahL59+8r8QqZatWqoX78+jIyM8P79e/z999/4+++/kZWVJY7Mz624v1efPXsm/v2pUqUKqlevjooVK0JHRwfv3r1DbGws/v77bwDZvzzz9PRETEyM0mdm5JSzLM+RI0eQmZmp9FkDRKSCUk3hExEREREpkXOUV7du3Qrdz8KFC2VGcj1//lyuTUmNRJ8wYYIQGxurtJ+XL18Kffr0Eftq1aqV0rY5R0Fra2sLAIRGjRoJN2/elGmXlZUlzJ8/X2ab8xrFn7OdqlRZpqCjtlXp8/Dhw4Kampo4MnTatGnCx48f5dpduXJFZoTyt99+q7C/169fC8bGxmI7Jycn4c6dOzJt0tLShLFjxwoAZEbrFnUk+tSpU8W+1NTUhA8fPhSpv9zevHkjWFpaiuuoWrWqcOHCBbl269evF3R1dcV2nTp1UtpnzutES0tLMDQ0FHbu3CnXbsGCBWI7DQ0NYfbs2QIAoXbt2sLff/8t0/bTp0+Cv7+/2L5y5cpCVlaWwvXnHIkuPRaenp7Cs2fPZNqlpqYKI0aMkDmnVq9erbDPtLQ0oW7dumK7ihUrCgcOHJBrd+DAAZlzpX79+kpHuJf0SHTp9T927FghOTlZpl1iYqLg4eEhtq1SpYrS/Xnr1i2xL+m+zP39mJSUJH5H5Tz/izoS/ZtvvhH7MjExKVJfiqg6Ev3bb78VoqKihJSUFIXzMzIyhDVr1gj6+vri986DBw8Utu3SpYvM36w3b94obPfp0ychKipKGDRokNy8nCOHzc3NhZiYGKWxX79+XRg3bpxw7tw5pW1U8ffff4vrrFChQr7t161bJ3Nt5b6mi6okrsnLly/L/GrGyclJ6b59/vy5MGvWLIW/PCiJ79VVq1YJixYtEp48eaK0zbVr14SGDRuK/U2dOlVp29wsLCzE5a5du6byckQkj0l0IiIiIvoqaWhoiP/xmzx5cqH7OXr0qMx/+E+fPi3XpiSSXgXRrl07sb8bN24obJO7BIu9vX2eidfu3buLbQcPHqy03T8liZ6ZmSnY29uLbdavX59nf8+fPxdMTEzExNfjx4/l2owfP17sz8zMTGHpG6nBgwfLxFjUJHrOJKKtrW2R+lJk0qRJYv9ly5YV4uLilLbdvn27Sjddcl4nEolEOHr0qNI+27RpI9OnmZmZ0rIGHz58ECpUqCC2VZbcyplElybCUlNTlcYQHBwstrWwsBA+f/4s12b16tViG01NTeHixYtK+zt//rzM91JYWJjCdiWdRAcgjB8/Xml/L168EJO+ee3PXr16iW1q166tNJGcmZkptG/fXmb9RU2it2rVSuzL3d29SH0pUpylZwRBEDZt2iT2N3bsWIVtpOewtrZ2oW+KjRo1SlzPihUrihKyyjZu3Cius1mzZvm2z30DMDMzs1jjKYlrslmzZmKbhg0bFvr4lMT3qqrevXsnljEzNzcXMjIyVFou57WmbP8QkWr4YFEiIiIi+uokJSWJP4kHgPLlyxe6r3Llysl8fvPmTaH7Kik5H7J5+PBhlZaZMWMGDAwMlM7v37+/+P7ChQuFju1rsXv3bty9excA0KpVq3xLfpiZmWHkyJEAsh8AFxERITM/KysLa9asET9PmTIFxsbGSvvLb38XVM7zsGzZssXWLwAIgoDly5eLn3/66SdUqlRJafuuXbuiXbt24uelS5fmuw5vb2+Zh8fmlruEyIQJE5SWMzAwMECHDh3Ez6qer3PmzIGOjo7S+bNmzYK+vj6A7JIJUVFRcm2WLVsmvh88eDAaNGigtL9GjRphwIAB4mdV9lNJqFixIiZNmqR0vqmpab778+3bt9i+fbv4edasWdDV1VXYn5qaGhYsWFCoh94qU5Lnf0no3r27eP0r+45OSkoCAOjp6RX6u0LaBwCVyioVh4cPH4rvrays8m2f89gZGhrm+xDSgirua/LcuXPig3YlEgnCwsIKdXy+xPdqXoyMjMSyaM+fP8eNGzdUWs7S0lJ8n/NYE1HBsSY6EREREX11Pnz4IPNZmggrjNz/WZbWC/6SUlJSEBMTg9jYWLx+/RofPnwQa6AC2fXDpa5evZpvfzo6OujYsWOebZycnMT3jx49KnDMX5ucdd39/PxUWsbDw0N8f+rUKfzwww/i55s3b+LFixcAAA0NjXz7NDIykqmvX1Q5z/HiTM4DstumpqaGwMDAfJcJDg7Gvn37AADR0dH5tu/WrVue82vVqiXz2cfHR+X2qiR6rK2t4e7unmebsmXLokuXLggPDweQXYe7S5cu4vyPHz/i4sWL4uecN56UCQ4OFpNhFy5cQHJycpG+nwqjU6dOed48ALKvf+mNI0XX/5kzZ5CWlgYgO+neunXrPPuzs7ND06ZNxWRkUZXk+V9YsbGxuHz5Mh49eoSkpCRx/0hJbyLExsYiKytLLnlcqVIl3L9/H2/fvkV4eHihaoXnTMouW7YMHTp0gIZGyaZtXr58Kb6vUKFCvu1L8tiVxDW5f/9+8X2rVq1Qo0aNQsX2Jb5XX716hZiYGNy8eRNv375FcnKyzLMDcu6bq1evonbt2vn2mfPmsDR+IiocJtGJiIiI6KtTpkwZmc/JycmF7kv6cE+p/JJPxenNmzeYNGkS1q5dK3djQJmEhIR82zg4OEBLSyvPNjmTIaVx46C4nT17VnwfFRWl0s2GnNv9+PFjmXk5l3d0dISRkVG+/bm4uBRbEj3nOZ77HC2qK1euiO8dHR1VSow1a9ZMfP/ixQs8e/YMFhYWStvXrFkzz/5y/gLEyMhIZjRkfu1zjsZVpnHjxiqNjG7SpImYRM+5XwDg2rVr4s0sAwMD1KlTJ9/+6tWrB319fSQnJyMzMxPXrl1D06ZN812uOKmSOMvv+s95/jdq1Eil0cQuLi7FlkQvyfO/oMLCwvDbb7/hzp07KrX//Pkz3r9/L/crp549e4oPH+7duzc2bdqEnj17wsPDA+bm5ir13b17d0yePBlZWVnYt28fatSogX79+qFdu3aoU6dOsY/6BmT/vir7NUJOJXnsSuKajImJEd/n9euZ/JTk9+qNGzcwbtw47Nu3T+YGe15U+bcCIHtMi/JvKSJiEp2IiIiIvkKGhobQ0NAQS7oUpQTL27dvZT5/qdIBcXFxcHNzQ3x8fIGWUyXZrkrCV1NTU3yfszTOP9WzZ8/E95GRkQVePvd58Pr1a/F9Xj/Jz8na2rrA61UmZ4mid+/eFVu/gOy22djYqLSMqakpdHR08OnTJwDZCZq8kuj5nYM5R8+qcr7mbP/58+d82xfmmOXcL7k/W1tbq5SgVFNTg7W1NW7dugVA9URWcSro9a9of/6bz39VCYKAb775Rqask6o+fPggl0SfOHEijh8/jtOnT0MQBOzevRu7d+8GAFSuXBnNmzeHp6cnunTpAkNDQ4X9Ojo6Ys6cOfjhhx8gCALu3r2LCRMmYMKECTA0NESTJk3g7u6Orl27wsHBoeAbnQ9VbkzlPHZJSUkKR+UXVklckzlH2lepUqVYYivO79UDBw6gS5cucr98yI+qN+ZzjmQnoqJhTXQiIiIi+irl/E/q9evXC91P7mXt7e0L3VdBBAQEiAl0Q0NDjBo1CgcOHMDDhw/x8eNHZGZmQhAECIKAY8eOictlZWXl23dx1ib+pyjqaPrcNxJyjqDU09NTqY/iLNtha2srvo+Pjy/WEZ05+ypIzDnb5pegKcg5WBLna2GOWe5t+hL7qSQUx/78ms5/VWs7F7cVK1bIJNA7duyIdevW4fr163j79i3S0tLE72hBEGT+Jin6ntbT08OxY8cwb948VK1aVWbew4cPsXbtWvTt2xfm5uYYM2YMUlNTFcY1YsQInDhxAm3atJFJIiclJeHAgQMYP348HB0d0apVK8TGxhZ1N8gc15SUlHzb5zx2WVlZuH37dpFjkCqJa7K4ys+URGyvX79Gz549xQR65cqVMXPmTJw+fRrPnj1DSkoKsrKyxHNw8uTJ4rKq/FsBgJjAL2jcRCSPI9GJiIiI6KvUrFkz3L9/H0D2g8EKK+ey5cuXV3nUZV7y+8/rmTNnxLIHZcqUwblz5+Do6Ki0fWkk4v5p9PX1xUT61atXUbdu3SL1lzOZokriCCjen8K7urqK77OysnDx4sV8a3yrKue2FSTmnG1zl1T62hTmmOXepq9lP6maDCtOX8P5v2rVKgDZdaAfPXokk5z9EmbPni2+//XXXzFhwoQ826vyPa2pqYkRI0ZgxIgRuH79Ok6cOIGTJ0/i5MmT4rMvUlJSMHv2bJw8eRLHjh1TWELF1dUV+/fvR0JCAo4fPy72cfXqVfF8OXr0KBo3boxDhw7JlA0pKDMzM/G9Kr+syPndBWT/ja1evXqh159TSVyTxVV+piRiW7Fihfh3zcnJCSdOnMgz0V+YfyvkHEGvalkhIlKMI9GJiIiI6KuUs3bpkydPcOLEiQL3kZycjJ07d4qf27dvr7BdQUuf5Dcq+siRI+L7oKCgPBPoQHbpF8qbqamp+P7u3btF7q9ixYrie1VL7uSuq14ULi4uMiOApXW7i0Nhtu3Vq1cyIxZzPozua6TqduVsl3ubcu6nx48fq1T2ICsrS+Y8ULSfivv7pCSU9vnfsmVLmRH1xXn+q+Lx48fi90i5cuUwduzYPNsnJSXJlYTKT61atfDdd99h48aNePLkCa5cuYJvvvlGnH/u3DksWbIkzz6MjY3RrVs3zJ8/H5cuXcLLly+xcOFC8bxLTU3FoEGDChRXbjlvXjx58kSl9pUrVxY/l9R3V3Fdkzn/dqjy0GJVYiuu79Wc/1b48ccf8x0pX5h/K+R8cPmXvlFF9G/DJDoRERERfZV69OghU3t17ty5Be5j+fLlMiPPAgICFLbLOTosMTEx337z+wl9zvrd+T2AEUChbhD81zRu3Fh8f+DAgSL3V69ePfH9rVu3VEpk5nxAXVHp6OggKChI/Lxx40aZ2r1F4eTkJL6/deuWSs8UyPnASDMzszzroX8NYmJiVEqw5Txm9evXl5lXt25dqKurA8geoapKaYxr166JI0vV1dUV/iKiuL9PSkLO8//ChQsqjYYvzvPfxsYG7dq1Ez8vW7ZM5RHxxSHnd7SDg4NMTX5FTp06VeTa0vXq1cPKlSsxcOBAcdquXbsK1IexsTGGDRsms9zff/+NBw8eFDqunOewqqVZvv32W/H94cOHi+0cLolr0sXFRXx/9OjRQsdWEt+rBfm3QmZmZqEe7CutFQ/IXvdEVHBMohMRERHRV0lPTw/fffed+Hnnzp3Yvn27ysvHxcVh0qRJ4mcnJye0bdtWYduco+quXr2aZ78XL17MdzRbzjq2+SWGnj17VuBESnHS0dER36vyQMfS0rFjR/F9eHg4Xr16VaT+qlevLo5QzMjIwKZNm/Js//79e5lfNRSHUaNGicm7jx8/YsCAAYXq5+DBgzKfq1evLpZoyMzMxPr16/PtI2dt6Jy/AvlaPXnyBNHR0Xm2effuncwxy71dBgYGaNiwofg5NDQ03/Xm3E/Ozs4KawwX9/dJSWjatCm0tLQAZD948dChQ3m2v3fvHs6cOVOsMYSEhIjv4+PjMW7cuEL1k1/sihTkOxoAli5dWuB1KJPzu6ywN86aNGkic5O5KDfgHBwcxIfVJiYmyoxcVmbQoEHiQ7oFQUBQUFCh/n7k/u4qiWsy582aI0eO4ObNmwWOEyiZ79WCnIeRkZF48eKFquECyD6e0kS9gYGBSjf1iUg5JtGJiIiI6Ks1YcIE1KhRQ/zcp08flUZtv3jxAl5eXuIodHV19TyTIM7OzuL7sLAwpe0yMjLw/fff57v+KlWqiO/zSrxmZmZi4MCB4kPFSkOFChXE96okT0pLt27dYGdnByA72dC7d2+VkzYfP36Uq2GrpqaGfv36iZ9//vnnPOsBh4SEFOvDP4Hs8+S3334TP+/evRv9+vVTqQQIkL1dQ4cOlSvnIJFIZEa7/vLLL3ke26ioKOzevVv8PHjwYFU3oVT98MMPMqUSchszZox43M3NzdGhQwe5Njn33ZIlS/DXX38p7e/KlSv4888/xc/K9lNxf5+UhHLlysHHx0f8nNeDLgVBwPfff1/kkdi5NW/eXOZG6eLFi/HTTz+pvHxCQgK6d++OX3/9tcDrrly5slhO5vr16+LzNxTZvHkz9uzZk2d/aWlpKn8/5CwFkrNECKBaTXIAePv2rcz6cvdTEGpqavDw8BA/nzp1Kt9lDA0NsXz5cvHz5cuX0alTJ5Vrhaenp2Pq1KkyCW6p4r4mnZ2dxZrxgiCgb9++hfouL4nvVVX/rfD69WuMHDmyoCHj5MmT4nsPDw9xlD8RFZJARERERPQVi42NFYyMjAQAAgBBU1NTmDBhgpCQkCDXNi0tTQgLCxNMTEzE9gCEOXPm5LmOR48eCWpqamL7cePGCRkZGTJtHj9+LLRp00YAIGhra4tt16xZI9ffzZs3BYlEIrYZNWqUkJKSItPm+fPnQpcuXQQAgr6+vti2RYsWCmM8duxYvm1yy7kPlGnbtq3YZubMmcXWb0HjVaXPQ4cOCerq6mI7FxcX4fz580rbX7t2TQgJCRHKlSsnxMbGys1/9eqVUKFCBbE/Jycn4e7duzJt0tLShHHjxgkABC0trQIfg/xkZWUJ3bt3l9n+WrVqCTt27BDS0tIULvP06VNhzpw54nluY2Mj1+bNmzeCpaWl2KeDg4Nw5coVuXabNm2SOf86deqkNFYbGxux3cOHD/PcrocPH4ptFcWX25o1a8T2gYGBCttMnjxZbCM9Fq1btxaePXsm0+7Tp0/CyJEjZfbpypUrFfaZlpYm1K1bV2xnZmYmHD16VK7d4cOHZb5X6tevL6Snpyvss7i/TwRBEAIDA/Ntk5Mq+/PGjRsy627durXw/PlzmTZJSUlCnz595M5/ZX0WVFpamtC0aVOZY9WsWTPhyJEjcvtM6v79+8KkSZOEMmXKKL0WVdl+FxcXmWv/1q1bMvMzMzOFxYsXC5qamoK6urqgo6Oj9Px/+PChULZsWeGHH34Qzp07p3B9WVlZwr59+4Ty5cuL/fz6668ybVq2bCm0b99eiIiIED5+/Kiwn7i4OMHLy0vsw97eXmG7gli9erXY38CBA1VebtSoUTLHzsbGRggNDRWSk5MVtk9MTBRWrFgh2NraKv2+L4lr8tKlSzLnupOTk9Lj9Pz5c2HWrFnC77//LjevuL9X//zzT7GNtra2sG7dOoWxV69eXe7fCpMnT1bYZ07Dhw8X2y9fvjzf9kSUNybRiYiIiOirFxsbK1hZWcn8Z11DQ0No3ry50Lt3b6F///5Cu3btZJLtAAQ1NTVhxowZKq3ju+++k1m2UqVKQu/evYXg4GChZcuWYgKpVatWQu/evfNNaPXt21emP3Nzc6Fz585CcHCw4OnpKfZXpkwZmf9If+kk+ooVK8Q2EolEaNmypTB06FBh1KhR4uvNmzcF7rckkuiCIAjLly+XSaRLk0g9evQQBg0aJPTt21fw8vKSu5GiKIkuCIKwfft2mf7U1dUFDw8PITg4WOjRo4dQsWJFAci+efP7778XexJdEAQhIyNDJtkhfenr6wseHh5CQECAMHDgQMHb21uoW7euzA0aadJdkePHjwt6enoyx9fFxUXo37+/0Lt3b8He3l5uP7569UppnF9TEn3EiBFC5cqVxeRTmzZthAEDBgjdunWTSVICELp165bnum/cuCEeZ+mrbt26QmBgoBAYGCiT0AMgmJiYCLdv386zz+L+PimJJLogCMKCBQtk4tTW1hbatWsnDBgwQPD29hYMDQ0FAELZsmVl9n9xJdEFQRBSUlIEX19fufO/XLlyQps2bYQ+ffoIwcHBQqdOnQQHBwe5dh07dizU9h8+fFjmZoempqbg7u4u9O/fX+jRo4dgbm4uzvv111/zPP9znu8AhPLlywutWrUS+vbtKwwYMEDo0qWLeL5KX9WqVZNLlLdo0UImHicnJ6Fnz57CwIEDhV69eglNmzaViVldXV3Yt29fkY/Bu3fvxJsEZmZmQmZmpsrLzpw5UyYm6XnUvHlzwc/PTxg4cKDQrVs3oVGjRnLf3QYGBgr7LIlrcv369YKGhobMcg4ODoKfn58waNAgwc/PT6hTp464Ld9//73Cforze/XTp09CtWrVZNpXr15d8PPzE4KCgoSGDRvKbP/YsWPFz6ok0StVqiQA2TfAcv8dJ6KCYxKdiIiIiP4RXr58KQwePFjuP8HKXtWrVxeOHDmicv+pqalC+/bt8+yzY8eOwtu3b1VKaCUnJ8uMFlT0srKyEk6dOqVSwrmkkuifP38WWrZsmWecuRNGqvRbUkl0QRCEo0ePyiUq8nrVrFlTePr0qdL+wsPDBQMDA6XLlylTRti2bVuhjkFBREVFCU5OTipvV7ly5YSJEycK79+/V9rn2bNnhSpVquTbl6enZ54JdEH4upLokydPFm7evCk4OjrmuV19+vRROjo1p9u3b6u07+vXry/cu3cv3/6K+/ukpJLogiAIc+bMkRllnvtlamoqnDx5skB9FlRWVpawbt06wc7OTuXz39zcXJg1a5bw6dOnQm//0qVL8/yboqamJkyaNEnIysrK8/x/8uSJzEjn/F7u7u5yo/4FQRA6duyoch8mJiZCZGRkUXe9KOfNnGPHjhVo2TNnzuT7dyTnS09PT/juu++EFy9eKO2zuK9JQRCEI0eOyN3MUPaaOHGi0n6K83v19u3b+fbVrFkz4cmTJ3LfgXmJiYkR2/bs2VOl/UNEecv7EdRERERERF8JExMTLF26FOPGjUNkZCT279+Pe/fu4dWrV/jw4YNMW0NDQxw+fBgWFhYq96+jo4M9e/Zg48aNCAsLw+XLl/H+/XuYmJigbt26CAoKQvfu3cU6uvnR09PDvn37EB4ejrCwMFy5cgVJSUkwNjZGlSpV0K1bNwQFBaFcuXL5PiCxJGloaODAgQNYvXo1tm3bhtjYWLx58wbp6emlFlN+WrZsiVu3bmHHjh2IiopCTEwMXrx4gaSkJOjp6cHU1BSOjo5o2rQp2rVrh3r16uXZX69eveDq6oqFCxciKioK8fHx0NDQgLW1Ndq3b4/BgwejcuXKJX6c2rdvj3bt2uHkyZM4cOAAjh8/jqdPn+L169f4/PkzypYtC2trazRq1AitWrVCp06doK2tnWefLi4uuHnzJtavX4/IyEhcvXoVr169gqamJszMzODq6opevXrBy8urRLetJDg6OuLChQtYvXo1IiIicO/ePbx9+xYmJiZo2rQpBg4ciFatWqnUV7Vq1XDx4kVs3boV27Ztw/nz58WH15qYmKBx48bo3r07unXrptJ3QHF/n5SkH374AW3btsXixYtx8OBBPH36FHp6erCxsUHXrl0xaNAgmJiY4N69eyUWg0QiQe/eveHn54fDhw/j4MGDOH36NJ4/f46EhAQIgoBy5crB1tYWjRo1Qtu2beHl5VXkGs+DBw9Gs2bNMG/ePBw7dgzPnj2Drq4uLC0t4eHhgf79+8PJySnffiwtLZGYmIijR4/i5MmTuHTpEu7du4fXr18jPT0dZcqUgY2NDRo1aoSePXvC09NTYT+7du3ClStXcOTIEZw7dw43b97EkydPkJycDG1tbVSsWBF16tRB+/bt4e/vD0NDwyJtf04jRowQH5a5cuVKuLu7q7xskyZNcPToUVy6dAn79u3D0aNHERcXh4SEBKSmpsLIyAgWFhZo0KAB3N3d4ePjAwMDgzz7LO5rEsiuC3779m1s2rQJe/bswcWLF/Hq1SukpaXByMgIdnZ2aNKkCbp27YrmzZsr7ac4v1erVauGK1euYMmSJdi+fTtu376N9PR0mJmZoXbt2vD394evr6/4EGpVrVixQnxfWs9eIPq3kQhCMT8dhIiIiIioFNy7dw/NmjUT/5Pt5OSE6OjoYk0yEFHpmTJlCn7++WcAwOTJkzFlypTSDYjoX6Z169Y4fPgwtLW1ERcXB1NT09IOiQrh7du3sLa2RnJyMpo3b67SA9mJKH9qpR0AEREREVFxsLOzQ1RUlDi67cqVK/D29kZaWlopR0ZERPT1mzp1KgAgLS0NCxYsKOVoqLAWLVqE5ORkAMC0adNKORqifw8m0YmIiIjoX6Nhw4bYunUrNDU1AQDHjh2Dv78/srKySjkyIiKir5uLiwu6d+8OAFiyZAkSExNLOSIqqPfv32PhwoUAgM6dO8PNza2UIyL692ASnYiIiIj+Vdq0aYPVq1eLNVK3b9+Ob7/9tpSjIiIi+vrNmTMHenp6SEpK4ijmf6AZM2YgMTER2tramDdvXmmHQ/SvwgeLEhEREdG/Tu/evdG7d+/SDoOIiOgfpVKlSmIpEPrnmT59OqZPn17aYRD9K3EkOhERERERERERERGREkyiExEREREREREREREpIREEQSjtIIiIiIiIiIiIiIiIvkYciU5EREREREREREREpAST6ERERERERERERERESjCJTkRERERERERERESkBJPoREREhLi4OMTFxZV2GERERERERERfHY3SDoCIiIhKX3p6emmHQERERERERPRV4kh0IiIiIiIiIiIiIiIlOBKdiIiIAABPXiVj8rro0g6DiIiIiIjoPy/8F/fSDoFy4Eh0IiIiIiIiIiIiIiIlmESnr05ycjLmzZuHli1bwtTUFFpaWihXrhyaNGmCSZMmIT4+vsRjiI6OhkQiQVBQUImvq6RJJBLY2trKTHv06BEkEgnc3d2/SAwzZ86ERCKBRCLB2bNni61fRduWH3d3d0gkEjx69KjY4viaFObctbW1FY+PRCKBhoYGypcvD0dHR/j7+yMsLAyfPn0quaAVmDJlCiQSCUJDQ7/oelURGhoKiUSCKVOmlHYoRERERERERPQFMIlOX5WYmBjY29vjhx9+wPnz51GrVi10794dTZs2xf379zF16lRUq1YNhw8fLu1QqQDWr18vvl+3bl0pRkJ56datGwIDAxEQEIAWLVpAR0cHERERCAoKgq2tLfbt21faIX4RQUFBkEgkiI6OLu1QiIiIiIiIiOgrwJro9NX466+/4OHhgdTUVIwbNw4//fQT9PX1xflZWVmIjIzE2LFj8eTJk1KM9J/P0tISN2/ehJ6eXomv68qVK7h+/TrMzMzw8uVLREREYMGCBdDU1Cxy3zdv3iyWfijb7Nmz5Ub2v3jxAtOmTcOSJUvQsWNHREVFoW3btiUey9ChQ+Hn5wdzc/MSX1dBde3aFS4uLjA2Ni7tUIiIiIiIiIjoC+BIdPoqCIKA3r17IzU1FVOmTMGMGTNkEugAoKamBh8fH1y6dAkNGzYspUj/HTQ1NeHo6IhKlSqV+LqkI88HDRqE5s2bIzExEXv37i2Wvh0dHVG1atVi6YsUMzMzw+LFizF16lRkZWWhX79+SEtLK/H1Ghsbw9HREUZGRiW+roIyMjKCo6Mjk+hERERERERE/xFMotNX4cCBA4iNjYWVlRUmTpyYZ1sjIyPUqlVL/Pz8+XP8/vvvaNGiBSwtLaGlpQUzMzP4+PjgwoULCvuQ1oAWBAGLFi1C3bp1oaenh3r16sm1ff78OYKCgmBqagpdXV3Ur18fa9euVRrfjRs3EBAQAHNzc2hpacHS0hJ9+/bF7du3Zdr98ccfkEgkaNasGTIzM2XmpaamombNmpBIJNiyZQsAYMiQIZBIJFixYoXC9QqCgKpVq0JdXR1xcXFK4wPyr4l+9uxZ9OjRAxYWFtDW1oalpSXatGkjU5ZFFZmZmdi4cSMAoHfv3ujduzcA5NvPvn370LFjR5iYmEBbWxuVKlWCt7c3oqKiZNrlVRN9+fLlqF27NnR0dGBpaYlhw4bh/fv3Std58+ZN9OnTB1WrVoWOjg4qVqyIevXqYcSIEXj+/LlK21uUcxEAVq5ciTp16kBXVxdmZmYYNGgQ3r17p3C5R48eoVevXqhQoQIMDAzQtGlTuf1TnMaPHw8bGxu8ePFCPCdzev36NUaPHg0HBwfo6OigXLlyaNeuHU6cOCHXNmfd9hcvXiA4OBhWVlbQ0NDA/PnzASiuiV67dm1IJBLcunVLYYwvXryAhoYGrKyskJWVBQD49OkTVq1ahS5duqBKlSrQ1dVF2bJl4ebmhk2bNsn1IZFIEBYWBgBo2bKlTK14aR19RTXRO3XqBIlEgv379yuMLT09HeXLl4euri6SkpJk5sXGxiIgIACWlpbQ1taGhYUF+vXrp7BuvyAI2LRpE9zc3GBmZgYdHR1YW1vD09MTS5YsUbhuIiIiIiIiIioaJtHpqyBN/vn6+kJDo2BVhnbu3Ilx48bh2bNnqF27Nry9vWFhYYEdO3agWbNmOHjwoNJlBw8ejFGjRsHExASdO3dGlSpVZOa/efMGLi4u2L9/P9zd3dG8eXPExsYiMDAQP//8s1x/R44cQcOGDREeHg4LCwt069YNJiYmWLduHRo2bIiTJ0+Kbb/77jt07NgRZ86cwa+//irTz5gxY3Djxg0EBgbC19dXjBWA0iT60aNH8eDBA3h5ecHGxka1nafA/Pnz0axZM2zZsgVWVlbw8fGBo6MjLl++jB9//LFAfR06dAgvXrxA48aNYWdnB19fX2hra2P37t1Kk8OjRo1C+/btsX//fjg4OMDHxweVK1fGsWPHMGvWLJXWO3r0aAwaNAh3796Fh4cHmjRpgg0bNqBly5YKR1FfvnwZDRo0wIYNG1CxYkV07doVjRs3Rnp6OhYsWCB3A0SZopyLY8eOxZAhQ2BoaIi2bdtCEAQsX74cnTt3hiAIMm3v37+Pxo0bY9OmTTA2NkanTp2QlZWFTp06ISIiQqVYC0pdXV08F48dOyYz79atW3BycsKcOXOQmZmJ9u3bo06dOjh69ChatmyJ8PBwhX2+fv0ajRo1QlRUFJo0aYJ27drlWWIoICAAALBhwwaF8zdt2oTMzEz4+/tDTS37z9ujR48QHByMc+fOoVKlSujSpQvq1auHmJgY9OrVS+7hoIGBgeKvG9q0aYPAwEDxZWBgUOjY9u7di7dv36JTp04wNDQUp2/btk38zjA3N0fnzp1hZmaG0NBQNGzYEH///bdMP+PHj0evXr1w9epV1K9fHz4+PrCzs8O1a9dUvj6IiIiIiIiIqGBYE52+CleuXAEA1K9fv8DLNmvWDNeuXUOdOnVkph84cACdO3fGd999h7t374qjfXPavn07rly5gpo1ayrse/fu3WjdujV27Nghlpe5cOECPDw88Msvv4gJOQBITk5GQEAAUlNTsXTpUjHpDQDz5s3DDz/8AH9/f9y7dw/a2toAgFWrVqF27dqYOnUq2rZtC2dnZ+zbtw9LlixB5cqVsWjRIrGP2rVro2nTpjhz5gyuXbuGunXrysQqTa4PGDCggHvw/06cOIEffvgBZcqUwc6dO2VGqqenp8slT/MjLeUiHYFetmxZdOjQAdu3b8fWrVsRHBws0379+vWYO3curKysEBUVJXNMk5OTce7cuXzXeebMGcyZMwfly5fHiRMnxGObmJgIDw8PxMTEyC2zcOFCpKamYtu2bfDx8ZGZd/PmTZQtW1al7S3Kubh+/XqcO3dOPJ8SEhLQpEkTnDx5EseOHYOHh4fY9rvvvsOrV6/w3XffYdGiRWLCeOXKlUU6/vmRxnbz5k1xWmZmJnx9ffH06VMsWLAAw4YNE7fvypUraN26NQYOHAhPT0+YmJjI9Ld371507doV4eHh0NHRyXf9/v7+mDBhAsLDwzF16lS5+dJkvTShDQAVK1bEgQMH4OnpKe4nAHj48CE8PDwwdepU8cGpQPYo86CgINy/fx8hISFKf62RW5cuXWBgYIDIyEikpKTI3QxQFNvDhw/Rt29f6Orq4tChQ3BzcxPnrV27FoGBgejXrx/Onz8PIHtU/fz582Fra4tLly6hfPnyYvuMjAycOXNGpVjv3r2rcHp6erpKyxMRERERERH913AkOn0VEhMTAWQnvAqqdu3acklLIHsUqa+vL+7fv4/r168rXHbcuHFKE+hAdmmHRYsWydRnb9SoEYYMGYKsrCwsXbpUnB4REYGXL1+iefPmMgl0ABg5ciQaNGiAJ0+eYMeOHeJ0ExMTrF69GhkZGejduzcePnyIfv36QV1dHevXr0eZMmVk+hk0aBCA7GRpTomJiYiMjISpqSk6deqkdHvyM2PGDAiCgEmTJsklD7W0tNCmTRuV+/r48SMiIyOhoaEBPz8/cbo0oS5NsOf022+/AcgeDZ/7mOrr68skkpX5888/AWSPaM95bCtUqKB0pO6rV68AQGH/1atXV/nhlkU5F6dOnSpTTsjY2BjffvstAMiURLl//z4OHjyIcuXK4ffff5dJDAcHB6Np06YqxVoY0hrgb9++Faft3r0b169fR69evTB8+HCZGwROTk746aefkJycrLCEj7a2NhYtWqRSAh0AKlWqBFdXVzx48EDuZsi9e/dw4cIF1KhRQ+YGU4UKFeDl5SWznwCgcuXKmDhxIrKysrB7926V1p8XXV1ddO3aFR8/fsSuXbtk5n348AF79uwRS9xILViwACkpKfj9999lEugA0LdvX3h7e+PChQu4fPkyACApKQlpaWmoW7euTAIdADQ0NOT6ICIiIiIiIqLiwSQ6fRVyl6soqLS0NOzcuRMTJ07EwIEDERQUhKCgIMTGxgJQPvKyc+fOefbr5OQEBwcHuem9evUCAJw6dUqcJi3VknOkaU7S5HHOki4A0KFDB3GEcr169fDy5UtMmDBBYTK0R48eKF++PNavX4/U1FRx+tq1a5GWloagoCBoamrmuU3KZGZmIjo6GgAwcODAQvWR07Zt25CSkoK2bdvKPICxQ4cOKF++PE6ePClTu/3Zs2e4efMmKlSogG7duhV6vdJj0qNHD7l5Xl5ecslHAGjQoAGA7MTl+fPnxXrahVHYc9HLy0tuWrVq1QBApib76dOnAQDt27eXe/guAJkbFsVNep3mTJQfOnQIAODt7a1wGVdXVwBQWBO+fv36sLS0LFAM0usrd4kY6WfpdZbbqVOnMG3aNHz77bfo168fgoKCxNruyo5JQSmLbceOHUhNTYWvry+0tLTE6dJ916VLF4X95d53JiYm4q80Zs2ahWfPnhUqTnt7e4WvnLERERERERER0f+xnAt9FYyNjXH79m28fv26wMvGxsaic+fOCh/CJ/XhwweF0ytVqpRn38pqi0tLP+RMYknfK3vQpaJlpGbPno2dO3fi6dOncHJywqRJkxT2oaOjg759+2L+/PnYunUr+vTpAyB7ZLpEIsE333yT5/bkJSEhAampqTAxMZEbAV8YuUu5SGlpacHX1xfLli3Dhg0bMGHCBADA48ePAUCsR11Yz549g0QigbW1tcL5lSpVwps3b2SmjRkzBqdOncLu3buxe/duGBkZoXHjxujYsSOCgoJU3h9FORetrKzkpklrcOes4y49f5Sdu/md00WRkJAAADI3IqTb2rNnT/Ts2TPfZXMqTKy+vr4YPnw4Nm/ejHnz5kFdXR0AsHHjRkgkEvEGl9T79+/h4+ODo0ePKu1T2TEpKE9PT5iammL//v148+aNuJ8UlXIB/r/vzMzM8uw3574LCwuDn58fxo4di7Fjx6Jy5cpwc3ODv7+/whsxRERERERERFR0HIlOXwVpGQtp2QJVCYKAHj164NGjRxg8eDCuXr2KpKQkZGVlQRAEjB8/XmyniKplJApCUb3r/OafPHlSTI7Gx8fneTNBWipGWtLlzJkzuHHjBtzd3WFvb1/YsPOMr6CePn0q1k+fPXs2XF1dZV7ShKaiEh/Fsf6CMjQ0xNGjR3Hy5EmMHTsWDg4OOHLkCIYPHw4HBwfcv38/3z6Kei6qut2KRoN/KVevXgUA1KhRQ5yWmZkJAGjXrp3MQzhzv1q1aiXXX2Guv/Lly6Nt27Z49eoVDh8+DCD7e+PWrVto1qyZ3E2scePG4ejRo3Bzc0N0dDQSEhKQkZEBQRBw4MABAEX/JYyUuro6evbsic+fP4uj3F+/fo0jR47A2toazZs3l2mfmZkJiUSS534LDAyUKUvk4eGBe/fuYcOGDejTpw8yMzMRFhaGNm3a5HkTg4iIiIiIiIgKjyPR6avQoUMHLFmyBFu2bMHvv/8ODQ3VTs1bt27h1q1baNiwoUx9cqkHDx4UKa6c5UYUTbewsBCnSd8/fPgwz2Vy19dOTExEUFAQ1NTU0KNHD2zcuBFBQUHYv3+/wkSpg4MD3N3dER0djdu3bxfLA0WB7F8D6Orq4uXLl/jw4UORRqNv2LBBLIly8eJFpe1u3ryJS5cuoUGDBuLI8Xv37hV6vUD2/n306BEeP34MOzs7ufnx8fEKl5NIJGKSH8hOfn7//ffYuHEjJkyYgM2bN+e53pI+F6Wk55myc1PZ9hVVZmammBhu2bKlOF06gn7w4MH5lkcqLgEBAdi1axc2bNiANm3aKB3pDWSXUlFXV8euXbtgZGQkM6+4jknu2BYuXIgNGzZg0KBB2Lx5MzIyMuDv7y93PVtZWeH+/ftYuHAhDA0NVV6HoaEh/P394e/vDwCIiYmBr68vIiIiEBQUJFN3nYiIiIiIiIiKjiPR6avQtm1b1KxZE0+ePMGvv/6aZ9ukpCT8/fffAP7/gENFpTDevn0r1hwurKtXr+LOnTty0zdu3AgAaNasmThNOsp0w4YNCvuSTs89GnXAgAF4/vw5QkJCsH79ejRv3hwHDx7EwoULlcYlfcDo3LlzERERgfLly8PHx6cAWyZPXV1dfJioNDFfWNIR5lFRURAEQeFLWsZF2tbCwgLVq1dHYmIitm/fXuh1S5Pg0oRvTocOHZIr5aJMxYoVMWXKFAAQ65nnpaTPRSnpObd3714kJyfLzd+0aVOxrCe36dOnIz4+HpaWljI16z09PQEAkZGRJbJeRTp16oQyZcogMjISycnJ2Lx5MzQ1NeHr6yvX9u3btyhTpoxcAh3IfhiwItLa4BkZGQWOzdnZGfb29jh16hTi4+PzTPAX175zcXERSzupcq4SERERERERUcEwiU5fBYlEgvXr10NHRwdTpkzB+PHj5RKEgiBg165daNiwofigPTs7O6ipqeHo0aMyDwf89OkTBg8erHLCVJmsrCwMHz4cKSkp4rRLly5hyZIlUFNTE5PZQPaDLE1NTXHy5EksX75cpp+FCxfiwoULsLKyQteuXcXpK1euxI4dO9CgQQNMnjwZampqWLt2LQwNDRESEoLr168rjMvHxwcVK1bE8uXLkZKSgr59+0JbW7tI2wpkl76QSCSYOnWq3ANQP3/+LJa/yMvVq1cRGxuLChUqoHXr1krbSWtXb9y4USwJEhISAgAYMWKEeKNEKjk5Oc+61lI5bzDcvHlTnP7mzRuMHTtW4TJ//vmnwl8Q7Nu3D4BqtbtL+lzMuZ5WrVrh7du3CAkJkXkI6po1a3DmzJliWY/UixcvMGzYMPz0009QV1fHmjVrZB5A2b17dzg6OiI0NBQzZ87E58+fZZZPT0/H9u3bizW5q6urCx8fH3z48AGjR4/GkydP0LZtW1SoUEGubbVq1fDu3Tu5XxLMmzdPLDmUm3S0/+3btwsVn7+/PwRBwPTp03H27FnUqlULtWvXlms3atQo6OrqYuTIkdi9e7fc/Ddv3uCPP/4QHyIcHx+P0NBQme8jILtmvnRbSrImPhEREREREdF/FZPo9NWoV68eDh8+DFNTU8yYMQMmJibw9PREQEAAOnbsCHNzc3Tp0gWPHz8WS3+YmJjgm2++QVJSEurWrYuOHTvC19cXtra2OHr0KIKCgooUU8eOHXHz5k1UrVoVPXv2RNu2bdGkSRN8+PABEyZMQIMGDcS2+vr62LBhA3R1dTFo0CA0bNgQ/v7+qF+/Pr7//nvo6+sjPDxcTHbfu3cPI0aMgJ6eHjZs2ABNTU0A2Q8gXbRoET59+oSAgACZh0pKaWlpoV+/fuLn4ODgIm2nVIsWLfD777/j3bt3cHNzQ+PGjeHv7w9PT09YWFjI3DRQRvpA0e7du4vbpEitWrVQs2ZNvHz5Uhyl3bdvXwwdOhSPHz9G3bp1xQcmuru7w8LCAr/88ku+63d1dcWIESOQkJAAJycn8ZyQ1ot3cXGRW+bPP/9ElSpVULNmTXTv3h1+fn5wcnLCiBEjoKuri8mTJ+e73pI+F3NaunQpKlasiMWLF6NGjRrw9/dHkyZN8M0334g18wtj9OjRCAoKQmBgIHx8fODk5AQrKyssXrwYZmZmiIqKkrsxoqGhgR07dsDa2hohISGwsbFB27Zt0aNHDzRp0gSmpqbo1q2bSnXlC0I6svvPP/+U+ZybtBa9n5+feD7VrFkTo0ePxsiRIxUu06lTJ0gkEowaNQre3t4IDg5GcHAwEhMTCxVb7ofrStnb22P9+vVITU1F586d4ejoiK5du8Lb2xtOTk4wNzfHkCFDxO+AN2/eoF+/fqhYsSJatGiBgIAAeHt7w9raGjExMXB2di7yL1KIiIiIiIiISB6T6PRVadasGe7du4fZs2ejUaNG+OuvvxAREYHTp0/D1tYWkydPxt27d2UeUrh06VLMmTMHlStXxpEjR3Dy5El4enri4sWLsLGxKVI8FSpUwNmzZ+Hp6Yljx44hOjoaNWrUwJo1azB16lS59q1atcKFCxfQq1cvPHnyBFu3bsWLFy/Qu3dvXLp0SSzlkpGRgYCAACQnJ2P27NlwcHCQ6adv377w9fXFX3/9JZY9UbQuAGjatKnMgweLavTo0YiOjkaXLl3w8OFDbN26Fbdu3UKDBg3w22+/5blsZmamWOrGz88v33VJ20gT7wCwaNEi7NixA61atcL169exbds2PHz4EK1atcK4ceNU2oa5c+di6dKlsLOzw6FDh3D69Gn06NEDx44dUzhif+rUqejfvz8kEgmOHDmC3bt3IyUlBQMHDsRff/2FJk2aqLTekjwXc7K3t0dMTAx69OiBV69eYefOnRAEAZGRkUV6uOS2bdsQFhaG9evX49ixY0hNTYWvry/CwsLw8OFDtGnTRuFyjo6OuHr1KqZMmQITExOcOnUKUVFReP36Ndzc3LBmzRqxdElx8fDwEJ8vYGBgoLQee0BAAKKiouDi4oKrV69i3759sLCwwNGjR5Uu06BBA6xfvx41a9bEwYMHsWrVKqxatQofPnxQKTZ7e3s0atQIQPavbKS/ulDEx8cH165dw6BBg/D582fs27cP0dHRSEtLQ0BAAPbs2SOWoqlatSpmz54Nd3d3xMfHY/v27eJ348KFCxEdHS3zKwEiIiIiIiIiKh4SQRCE0g6CiApu4MCBWLFiBdasWVOso5yJ6L9JWoZI+qsNIiIiIiIiIsrGJDrRP1BcXByqV68OfX19xMfHQ1dXt7RDIqJ/OCbRiYiIiIiIiBTTKO0AiEh1s2bNwl9//YVDhw4hNTUVM2bMYAKdiIiIiIiIiIioBDGJTvQPEhUVhePHj8PS0hI///wzhg0bVtohERERERERERER/asxiU70DxIdHV3aIRAREREREREREf2nMIlOREREAIAnr5IxeV10aYdBRERE9I8S/ot7aYdAREQlTK20AyD6L0tISMBPP/0EJycnlC1bFnp6erCzs8PAgQNx/fr10g6P/sUeP36McePGoX79+ihfvjy0tLRgamoKLy8v/PHHH/j48WNph/if9ujRI0gkEri7u5d2KERERERERET/eUyiE5WSw4cPw97eHtOmTcPTp0/RokULdOzYEZqamlixYgXq1auHGTNmlHaY9C+0bNkyVKtWDb///juePn2Kpk2bonv37qhZsyZOnTqFIUOGwM7ODgkJCaUdKhERERERERFRqWM5F6JScOHCBXTo0AGfP3/G9OnTMXr0aGho/P9y3Lt3L3r37o3x48dDT08Pw4cPL8Vo6d9k5cqVGDx4MAwMDBAWFoY+ffpAIpGI81NSUrBkyRJMnToVHz9+hLGxcSlGS0RERERERERU+jgSnegLEwQBgYGBSE9Pxy+//IKQkBCZBDoAtG/fHpGRkZBIJBg3bhzi4uJKKVr6N3ny5AmGDRsGiUSCXbt2oW/fvjIJdADQ09PDmDFjcO7cORgZGZVSpEREREREREREXw8m0Ym+sH379uHmzZuwtLTEuHHjlLZzc3ODr68vPn36hCVLlsjMs7W1hUQigSAIWLRoEerWrQs9PT3Uq1dPbPPq1SsMGDAApqam0NPTQ/369REeHq601vK7d++waNEitGnTBjY2NtDW1kaFChXQtm1bHDp0SGGM7u7ukEgkePToESIjI+Hi4gJ9fX2UL18evXr1wpMnTxQul5KSgqlTp6JWrVrQ1dWFkZER3NzcsGnTJqX74/Hjxxg0aJAYm4mJCXx8fHDhwgWZdnv37oVEIoGdnZ1cXW9BENCqVStIJBLMmjULADBr1ixIJBJMnDhR6bpbtmwJiUSCU6dOyUw/cuQI3NzcoK+vjwoVKqBbt264e/cupkyZAolEgtDQULm+Pn78iF9++QW1a9eGnp4eDA0N0aJFC0RGRsq1zXmsUlNTERISIm6/nZ0dZs6cCUEQlMad2+LFi/Hp0yf06NEDLVu2zLNt9erVUa5cOZlpqh6D3LEnJyfjhx9+gLW1NXR1dVG/fn3s3r1bbLtlyxY4OztDX18fpqamGD58OFJTU+X6zHneL1iwADVq1ICOjg4sLS0xfPhwvHv3TuG2FPR8k0gksLW1VTgvNDQUEokEU6ZMkZle2GshISEBgwYNgpmZGfT09ODk5IS1a9cqbAtkn8ObNm2Cm5sbzMzMoKOjA2tra3h6esp9TxARERERERFR8WASnegL27t3LwDA19cXmpqaebb19/cHkJ14V2Tw4MEYNWoUTExM0LlzZ1SpUgVAdmKuadOmWLlyJbS1tdG5c2cYGRmhT58+mDdvnsK+YmJiMHz4cNy8eRP29vbo2rUrHBwccPDgQbRp0warV69WGucff/yBbt26QRAEtG3bFgYGBti0aRM8PDzkkqEfPnyAm5sbJk2ahFevXqFjx45o1qwZzp8/j169emHEiBFy/cfGxqJ+/fpYvnw59PT04OPjA3t7e+zYsQNNmzbFli1bxLbt27fHkCFDcP/+fbkyOHPmzMHRo0fRsmVLjBo1CgDQr18/aGtrY82aNcjIyJBb971793D8+HE4OjrC1dVVnL5t2zZ4eXnh5MmTcHJygpeXF/766y84Ozvj4cOHCvfTy5cv0bhxY0yePBlv375F69at0bhxY1y6dAldu3ZVWgM/PT0dXl5eWL58OapXr46WLVvi6dOnCAkJwU8//aT4oCgQFRUF4P/nVUEU5Bjkjr1Vq1ZYt24d6tWrBxcXF1y7dg1du3bF4cOHMW/ePPj7+0NDQwNeXl7IzMzEokWLEBwcrDSWYcOGYcyYMbCyskKXLl3EZVq0aIEPHz7ItC3M+VYUBbkWEhMT0axZMyxfvhyampriddqvXz/xJk9u48ePR69evXD16lXUr18fPj4+sLOzw7Vr15QuQ0RERERERERFw5roRF/Y1atXAQANGjTIt620zY0bN/D582e5pPv27dtx5coV1KxZU2Z6SEgI7t+/j65du2Ljxo3Q1tYGkD1yun379grX5eDggNOnT6Np06Yy069cuQIPDw+MHDkSPXr0gIGBgdyyf/zxBw4dOgQPDw8A2SN/W7dujTNnzmDjxo3o37+/2HbChAm4dOkSPD09sWPHDrG/W7duoUWLFliwYAG8vLzEOAVBQEBAABISEjB+/Hj8+uuvYgmSrVu3omfPnvjmm2/g5uYGU1NTANmjy48ePYo1a9agY8eO8PHxwbVr1zBx4kSULVsWYWFhUFPLvodobGyMbt26ITw8HFFRUejSpYvMtq1cuRKCIGDAgAHitPfv32PgwIHIyspCREQEfH19AQCZmZn49ttvsWLFCoX7uF+/frhx4wbGjh2LadOmicfzwYMH8PLywo8//oj27dujTp06MsudPXsWzZs3x507d8Qa5RcvXkSTJk0wb948hISEKDwuOaWnp+Pvv/8GANSvXz/PtrkV5hjkjN3d3R137twRR7aHhoaiX79++Pbbb/HmzRscPXoUzZs3BwA8e/YMTk5OCA8Px9SpU8UbQzmtW7cOZ8+eFa+Pjx8/okuXLjh69CgmT56MuXPnim0Ler4VVUGvhTt37qBLly7YvHmzeJ3u27cPnTt3luv706dPmD9/PmxtbXHp0iWUL19enJeRkYEzZ84UyzYQERERERERkSyORCf6whITEwEAJiYm+batWLEiACArKwtv3ryRmz9u3Di5BPrHjx+xYcMGaGhoYMGCBWJiDgBatWoFPz8/heuqXLmyXAIdAJycnDBkyBAkJSXh2LFjCpcdOXKkmDQEsutqS0d6nzhxQpyenJyMVatWQU1NDX/88YdM4tfR0RE//vgjAGDhwoXi9OjoaMTGxqJy5cqYOnWqTA3v7t27w9vbGx8+fMCaNWvE6bq6uggPD4eWlhYGDhyI+/fvw9/fH+np6fjzzz9hbW0tE/+gQYMAZCfMc8rIyEBYWBi0tLTQt29fcfqWLVvw5s0btGnTRkygA4C6ujpmz56NMmXKyO2jq1evYt++fWjatClmzJghc0OkSpUqmDNnDjIzM+ViAAA1NTWsXLlS5iGfDRs2RLt27ZCSkoKLFy/KLZPb27dvxdIv0vNKVYU5BlLq6upYsWKFTGmYvn37omLFirh37x6GDh0qJtABwMLCAgEBAQBkz52chg4dKnMTysDAAIsXL4ZEIsGqVauQlpYGoHDnW1Gpei18/PgR69atg4aGBhYuXChznbZr107mvJJKSkpCWloa6tatK5NABwANDQ24ubmpFOPdu3cVvtLT0wu0rURERERERET/FUyiE31h0kSmKrWsc7bJ/QBIAApHq16+fBmfPn2Ci4uLXLIYgMLknFRmZiYOHjyIKVOmYPDgwQgKCkJQUJCYPL97967C5by8vOSmVatWDQDw/PlzcdqlS5eQmpoKZ2dn2Nvbyy3Tp08fAMDp06fFbT958iQAoGfPnlBXV1e6jLSdVL169TBt2jQkJiaifv36uHHjBvr06YOePXvK9eHm5oYaNWpg3759ePr0qTh99+7dePHiBbp27SqTwJaO+FW0Lw0NDRXuD2ld+S5duig8ltJSMYrqi9va2or7MydF+1iZgtROz62wxwDIjt3Ozk5mmpqaGmxsbAAArVu3llumatWqAJRvl6IbQdWrV0fdunWRlJSEv/76C0DhzreiUvVauHz5MlJTU9G4cWNUqlRJbplevXrJTTMxMYGVlRWioqIwa9YsPHv2rFhiJiIiIiIiIqK8MYlO9IVJk7GvXr3Kt+3r168BZCfQcz/kEYDC5Js0saYoga5sGQB48uQJGjRogDZt2uDnn3/GsmXLEBYWhrCwMDFpnLvetJSVlZXcNOmoX+mo4JyxKXtoY9myZWFkZISPHz8iKSlJpWWk0xUlFEeNGoUGDRogKSkJlpaWWLx4scI+AGDgwIHIzMyUqf0uLcuSs5RLznUVZB8/evQIQPavByQSidxLel4kJCTILato/wKK97Ey5cuXF5P30vNKVUU5BpaWlgqX0dfXVzpfOk/ZdkkT8PnFUZjzragKei0oux6VTQ8LC0O5cuUwduxYWFpaokqVKggKCsLBgwdVjtHe3l7hS0tLS+U+iIiIiIiIiP5LmEQn+sLq1q0LIHuUbH6kbWrWrKnwIaQ6OjpKl1U02hlQPiI5ODgY165dg4+PD86dO4d3794hMzMTgiBg2bJleS6rbF0FjS2vNvkto2j+33//jevXrwPITk7HxcUpXT4wMBC6urpYvXo1BEHA48ePceDAAVSpUkWmPIcqMSnaT5mZmQCA5s2bIzAwUOkrd032vNZTEFpaWqhRowaA7FHQhVGYY1CYZQqrKOenqnFkZWUVSz/SWAu6/R4eHrh37x42bNiAPn36IDMzE2FhYWjTpo3CX1kQERERERERUdHxwaJEX1i7du3wxx9/YOvWrZg1a5bC5LhUeHg4AKBt27Yq929ubg4AiI+PVzj/8ePHctOSk5Nx6NAhmJqaIiIiQq5kx4MHD1Ref14sLCwAAA8fPlQ4//3793j//j309fXFuuL5LSNNjEu3WyotLQ0BAQFIS0tD7969sX79egQEBODChQsy9aelypYti549eyI0NBSHDh3C2bNnkZWVheDgYLlEZ2H2sXSEcvfu3TF8+HCFy5W0Dh064O+//0Z4eLjCUkDKFPYYlJS4uDjUrl1bbrr0eEjjLcz5BgCampr4+PGjwmUUHdvCkMam7MaOsnMLyC4Z5O/vD39/fwBATEwMfH19ERERgaCgILRr165YYiQiIiIiIiKibByJTvSFtW/fHg4ODnj69ClmzpyptN2JEyewdetWaGlpYciQISr3X79+fWhrayMmJgZPnjyRm79161a5ae/fv0dWVhbMzc3lEugZGRnYsWOHyuvPS4MGDaCrq4vz588rrK++fv16ANn1waWJa+lDJzdv3iyO5la0TM6HUwJASEgIYmNjERAQgHXr1sHf3x+xsbEICQlRGp/0AaPLli3D6tWroaGhgaCgILl20gewKtqXSUlJYv3znDw9PQEAkZGRStdf0oYOHQptbW1EREQofUis1K1bt/D27VsAhT8GJWXz5s1y027duoWrV6+iTJkyqFOnDoDCnW9A9s2AxMREhQ/zLUjZlLw0aNAAOjo6OHfunMLE/KZNm1Tuy8XFRazvHhsbWyzxEREREREREdH/MYlO9IWpqakhNDQUmpqamDRpEmbOnCmXmNy3bx+8vb0hCAJmzJihtKazImXKlIG/vz8yMjIwcuRIpKeni/Oio6OxceNGuWVMTExgZGSE69ev4/Tp0+L0zMxMjB07Fnfu3Cn4hiqgr6+P/v37IysrC0OGDEFycrI4786dO5g2bRoAYNiwYeJ0d3d31K5dGw8fPsSkSZNkSnZERkZi+/btMDAwkEl2Hzp0CAsWLEClSpWwZMkSAMCSJUtQqVIlLFiwQGGSG8hORtatWxfbt29HfHw8OnbsqHB0ta+vL8qVK4f9+/dj27Zt4vSsrCyMGzdOYX1tFxcXtGrVCseOHcPIkSPlRjpnZWXh4MGDOHXqVF67sEisra0xf/58CIKAzp07Y926dXIlUFJTUzF//nw0btwY79+/B1C4Y1CSFi9ejCtXroifk5OTMWzYMAiCgP79+4u/NCjM+QYALVq0AABMnTpVnCYIAqZPny4+H6CoDAwMEBAQgIyMDHz//fcy9dIPHjyIiIgIuWXi4+MRGhqKlJQUmelpaWniTRFltdSJiIiIiIiIqPCYRCcqBS4uLti1axcMDQ0REhICCwsLeHt7o2fPnqhRowbat2+P9+/fY+rUqRg5cmSB+58xYwYqV66MrVu3wt7eHr169UKrVq3QqlUrDBw4EABkHiKooaGBsWPHIiMjAy1atICXlxf8/PxgZ2eHP//8s0Aj4fMzffp0NGjQAIcOHUKVKlXQo0cPdOjQAXXr1sWLFy8wfPhwdOjQQWwvkUiwYcMGVKhQAb/99htq1qwJf39/uLq6omvXrlBTU8Pq1athZmYGAEhMTERQUBAkEgnWrl0LIyMjANnlWsLCwiCRSBAUFITExESF8UlHowPyDxSVKlu2LP7880+oqamhe/fuaN68Ofz9/eHo6IiNGzeid+/eACD3oMYNGzagTp06mD9/PmxsbNCqVSv4+fmhefPmMDMzQ5s2bXDx4sXC71wVDB48GEuWLMHnz5/Rt29fmJubo1OnTggICECrVq1gbGyMkSNHwsDAQHwgZkGPQUnr3bs3GjdujLZt26Jnz56ws7PD4cOHUbNmTfz8888ybQt6vgHZD3/V1dXF/Pnz4eTkhO7du8PR0RFTp07Fd999V2zbMWPGDNjZ2WHHjh3iderh4YF27dopPPfevHmDfv36oWLFimjRogUCAgLg7e0Na2trxMTEwNnZGT4+PsUWHxERERERERFlYxKdqJS0bdsWd+/excSJE2Fubo6jR49i165d+PTpE4KDg3HlyhX8+OOPherbxMQEZ8+eRf/+/ZGamorIyEgkJiZizZo18PPzAwBUqFBBZpkJEyYgLCwMderUwenTp3H48GHUrVsXMTExaNiwYZG3V6pMmTI4fvw4fv75ZxgbG2PXrl04efIkGjZsiPDwcCxYsEBumdq1a+Py5csYMGAAPn78iK1bt+L27dvw9vbG6dOn4evrK7YdOHAgnj17htGjR4sjiqXc3d0xatQoPHv2TLyZkFurVq0AZNcwb9OmjdLt6NGjB/bv3w9XV1dcunQJ+/btQ40aNXDu3Dnxga+597GpqSliYmIwd+5c2Nvb48KFC4iMjMSTJ0/g5OSEJUuWiAn4kvTdd9/hzp07GDNmDMzMzHDy5ElERETg+vXrcHV1xdKlS3Hnzh0YGxuLyxTkGJS0RYsWYfr06YiLi8POnTshkUgwZMgQnDx5UrxpIlWY861mzZo4evQo3N3dcefOHRw6dAhVq1bF2bNn0ahRo2LbDmNjY5w+fRrBwcFIS0sTr9MVK1Zg7Nixcu2rVq2K2bNnw93dHfHx8di+fTtOnz4NW1tbLFy4ENHR0XI3boiIiIiIiIio6CRC7t/yE9G/2syZMxESEoIZM2Zg3LhxpR3OV+e3337DxIkTMXnyZEyZMqXAy2dlZaFOnTr4+++/8fz58y82Ovu/wNbWFnFxcXIlaKh43L17F09eJWPFgXelHQoRERHRP0r4L+6lHQIREZUwJtGJ/qUuX76M+vXry0w7ceIEOnXqhJSUFNy5cweVK1cupei+TklJSXB0dERiYiIePnwICwsLpW2fPn0KTU1NmJiYiNM+f/6MH3/8Eb///js8PDxw5MiRLxH2fwaT6CVL+vBVe3v7Uo6EiIiIiIiI6OuiUdoBEFHJaNq0KSwsLFC9enXo6+vj3r174sMYpTXTKduaNWtw/PhxnDhxAs+fP8fIkSPzTKADwMmTJ9G7d2/Ur18fNjY2SE5OxrVr1/Ds2TOUL18eixYt+kLRExERERERERFRSWJNdKJ/qfHjx8PY2Bjnzp3Djh07EBcXBy8vL+zatYtlXHI5fvw4wsLC8PHjRwwbNgzTp0/Pd5kGDRqgT58+ePPmDfbv348jR45AS0sLAwcOxKVLl1CjRo0vEDkREREREREREZU0lnMhIiIilnMhIiIiIiIiUoLlXIiIiAgA8ORVMiaviy7tMIiIVMaH+RERERHRl8ByLkRERERERERERERESjCJTkRESkkkkjxf7u7ucu1tbW1LJdavVWhoKCQSCaZMmaLyMo8ePVK4f4mIiIiIiIjoy2M5FyIiyldgYKDC6Y6Ojl84EiIiIiIiIiKiL4tJdCIiyldoaGhph/CP1bVrV7i4uMDY2Li0QyEiIiIiIiKiQmASnYiIqAQZGRnByMiotMMgIiIiIiIiokJiTXQiIipRgiBg48aN8PPzQ7Vq1aCvr48yZcrA2dkZf/zxB7KysmTaDxs2DBKJBH/++afSPmvWrAmJRII7d+7ITD979iy6dOmCihUrQltbG7a2tvjuu+/w7NkzuT5y1iqPj4+Hv78/KlasCF1dXTRs2BC7d+9Wuv69e/eidevWKFeuHHR0dODg4ICQkBC8e/cuz/XklpCQgEGDBsHMzAx6enpwcnLC2rVrla5XEARs2rQJbm5uMDMzg46ODqytreHp6YklS5YoXY6IiIiIiIiICo9JdCIiKlFpaWnw9/fHwYMHYWJigk6dOqFx48b4+++/MWTIEPTv31+mfUBAAABgw4YNCvu7evUqbty4gUaNGqFatWri9PXr16N58+bYvXs3HBwc4OPjA21tbSxduhT169fHrVu3FPb36NEjNGrUCKdPn4arqyucnJxw6dIleHt74+DBg3Ltp0+fjg4dOiA6OhoNGjSAt7c3UlJSMHPmTDRu3BgvX75Uab8kJiaiWbNmWL58OTQ1NdG5c2cYGRmhX79+mDVrlsJlxo8fj169euHq1auoX78+fHx8YGdnh2vXrildhoiIiIiIiIiKhuVciIioRGloaGDbtm3o2LEjtLS0xOmvX79G+/btERYWhv79+8PNzQ0A4OLigqpVq+L06dOIi4uDjY2NTH/h4eEA/p9sB4DHjx9j4MCBkEgk2LVrFzp27AgAyMrKwqhRozB//nz07dsX58+fl4svLCwMw4YNw9y5c6Ghkf1nccGCBRgxYgSmTZsGLy8vse2FCxfw448/okyZMjh8+DCcnZ0BZN8o6NOnD7Zs2YJhw4YhIiIi3/0yYcIE3LlzB126dMHmzZuhra0NANi3bx86d+4s1/7Tp0+YP38+bG1tcenSJZQvX16cl5GRgTNnzuS7TgC4e/euwunp6ekqLU9ERERERET0X8OR6ERElC+JRKLwpah8SW4aGhrw8fGRSaADQMWKFTF9+nQAwM6dO2Xm+fv7i2VgcpKWM1FXV0fPnj3F6StXrkRqaip69eolJtABQE1NDTNmzICFhQUuXLiAmJgYufiqVKmCOXPmiAl0ABgyZAjKlSuHmJgYmeTy4sWLkZWVhREjRogJdADQ1tbG4sWLoauri23btuHp06d57pOPHz9i3bp10NDQwMKFC8UEOgC0a9cOvr6+csskJSUhLS0NdevWlUmgA9n7WHoTgoiIiIiIiIiKF0eiExFRvgIDAxVOz50Yz8vVq1dx8OBBxMXFISUlBYIg4MOHDwDkR0cHBARg6tSpCA8PR0hIiDj95MmTePz4Mby8vGBmZiYzXbpcbtra2vD19cWCBQtw8uRJuLi4yMx3d3eHpqamzDQNDQ1UqVIFly5dQmJiIszNzfNdj4mJCby8vLBz506cOXNGYSJc6vLly0hNTUWzZs1QqVIlufm9evWSu4FgYmICKysrREVFYdasWQgICICFhYXSdShjb2+vcHr2Mfhc4P6IiIiIiIiI/u2YRCcionyFhoYWetn09HQEBQXJJYVzkibTpRwcHNCgQQNcunQJsbGxqF27NgDFpVwAiA8OtbW1Vdi/dLqiB4xaWVkpXMbAwABAdqmWnOuRSCRyJWZUWY+ieBUl0POaHhYWBj8/P4wdOxZjx45F5cqV4ebmBn9/f5myM0RERERERERUfFjOhYiIStTcuXOxceNG1KpVC/v27cPLly+Rnp4OQRBw+/ZtANllWnKTJsqlifPPnz9j69at0NXVRdeuXRWuSyKR5BmLovn5LVMY+fUp3d6CrtvDwwP37t3Dhg0b0KdPH2RmZiIsLAxt2rSRKW9DRERERERERMWHSXQiIipRO3bsAABs3LgRbdu2hYmJiVg+5cGDB0qX8/Pzg7q6OsLDwyEIAg4cOIDExER07twZZcqUkWkrLWvy8OFDhX3FxcUBgFiWpbAsLCwgCILYX2HXI41XWT/x8fFKlzU0NIS/vz/Wrl2LuLg4nD17FlZWVoiIiMC+fftU2QwiIiIiIiIiKgAm0YmIqES9ffsWAGBtbS03LyIiQuly5ubmaNmyJeLj43H69GmlpVwAoHnz5gCADRs2yM1LT0/Hli1bZNoVVl7ref36NQ4ePAg1NTU0bdo0z34aNGgAHR0dnDt3Do8fP5abv2nTJpVjcnFxQZ8+fQAAsbGxKi9HRERERERERKphEp2IiEpUtWrVAAB//vmnzPStW7di7dq1eS4rTZgvX74cu3btQvny5dG2bVu5dt988w10dXWxceNGREVFidOzsrIwYcIEPH36FI0aNZJ7qGhBDRkyBGpqaliwYAEuXrwoTk9PT8ewYcOQkpICHx8fWFpa5tmPgYEBAgICkJGRge+//16m7vrBgwcV3lyIj49HaGgoUlJSZKanpaXh2LFjAJTXUiciIiIiIiKiwmMSnYiIStTYsWOhrq6OkJAQNGzYEP7+/mjUqBF8fX0xcuTIPJf18fGBjo4O1q1bh+TkZPTo0UMsBZNTpUqVsHz5cgiCgE6dOqF58+bw9/dHjRo1MGfOHJiamuabsFeFs7Mzpk6diqSkJDRp0gStW7dGr169YGdnh82bN8Pe3h6LFy9Wqa8ZM2bAzs4OO3bsgL29PXr16gUPDw+0a9cOAwYMkGv/5s0b9OvXDxUrVkSLFi0QEBAAb29vWFtbIyYmBs7OzvDx8SnyNhIRERERERGRLCbRiYioRLm5ueHUqVPw8PDAgwcPsGfPHmhpaWHbtm0YMmRInssaGhqiU6dO4md/f3+lbXv37o0TJ06gY8eOuHnzJrZu3YrU1FR8++23uHTpEhwdHYtleyZMmIA9e/agRYsWuHDhArZv3w5tbW2MHTsW586dg6mpqUr9GBsb4/Tp0wgODkZaWhoiIyORmJiIFStWYOzYsXLtq1atitmzZ8Pd3R3x8fHYvn07Tp8+DVtbWyxcuBDR0dHQ0tIqlm0kIiIiIiIiov+TCIIglHYQREREVLru3r0LALC3ty/lSIiIiIiIiIi+LhyJTkRERERERERERESkBJPoRERERERERERERERKMIlORERERERERERERKQEk+hEREREREREREREREpolHYARERE9HV48ioZk9dFl3YYRP8Z4b+4l3YIRERERESkAo5EJyIihSQSSZ4vd3f30g6x2D1//hwaGhqQSCQYNGhQqcURHR0NiUSCoKCgUouBiIiIiIiIiLJxJDoREeUpMDBQ4XRHR8dC9RcdHY2WLVsiMDAQoaGhcvMfPXqEypUro0WLFoiOji7UOgprw4YNyMzMBABERERg4cKF0NbW/qIxEBEREREREdHXhUl0IiLKk6JE97/VunXrAADm5uZ4/vw5oqKi4OPjU8pREREREREREVFpYjkXIiIiALGxsfjrr79ga2uLadOmAfh/Up2IiIiIiIiI/ruYRCcioiI5efIkhg4dijp16qBcuXLQ1dWFo6MjQkJC8O7dO5m2QUFBaNmyJQAgLCxMpsb6lClTMGXKFFSuXBkAcPz4cZn5QUFBeP78OTQ1NVGpUiVkZWUpjEfab3BwcIG2Y+3atQCAgIAAdO/eHbq6uti7dy/evHmjdJn4+HgMHToU9vb20NHRQYUKFeDs7IzffvsNqampAIBatWpBIpHgzp07Cvt49OgR1NTUYG9vD0EQ8oxREARs2rQJbm5uMDMzg46ODqytreHp6YklS5YUaHuJiIiIiIiISDVMohMRUZGMGTMGK1euhJaWFjw8PNCqVSskJSVh5syZcHV1xcePH8W2rq6uaNOmDQCgatWqCAwMFF/16tVDvXr10K1bNwCAqampzHxXV1eYm5ujc+fOePz4Mfbv368wnpUrVwIABgwYoPI2ZGVlITw8HADQu3dvGBoaolOnTkhPT0dERITCZU6cOIE6depgyZIlyMrKQpcuXdCkSRMkJCRg4sSJePnyJQCIDyiVxpXbqlWrIAgCgoODIZFI8oxz/Pjx6NWrF65evYr69evDx8cHdnZ2uHbtGmbNmqXy9hIRERERERGR6iRCfsPeiIjoP0ma0M3vz8TevXvRpEkTlCtXTpyWlpaG4cOHY/ny5fj5558xadIkcV5RHyx66NAheHl5wdvbGzt27JCZd+vWLVSvXh21atVCbGysytsq7bNBgwa4ePEiAGD37t3o3LkzmjVrhlOnTsm0f/v2LRwcHPD69WvMmzcP33//vUwC/MSJE6hbty6MjIzw/v17WFhYwMDAAE+ePIGmpqbYLjMzEzY2Nnj58iWePHkCU1NTpfvo06dPKFu2LMzNzXHp0iWUL19e7CcjIwNnzpyBm5ubytuc2927d/HkVTJWHHhX6D6IqGDCf3Ev7RCIiIiIiEgFHIlORER5yllSJedLWqqlffv2Mgl0ANDW1sb8+fOhoaGBnTt3Fms8np6eqFq1Kvbs2YMXL17IzJOO9h44cGCB+pTWPu/du7c4rW3btjA2Nsbp06fx4MEDmfYrVqzA69ev0bFjR4wYMUJuBLmbmxuMjIwAAEZGRujZsydevXqFXbt2ybTbt28fnj59is6dO4sJdGWSkpKQlpaGunXryiTQAUBDQ0PlBPrdu3cVvtLT01VanoiIiIiIiOi/RqO0AyAioq9bYGCgwulaWlri+6dPn2L37t24desWkpKSxHrlWlpauHv3brHGI5FIMHDgQIwbNw6hoaEICQkBAKSnp2Pt2rXQ0dGRSYbnJzk5GTt27IC6ujp69eolTtfU1ETPnj2xZMkSbNiwAT/99JM47/DhwwD+X6olP4MHD8aaNWuwYsUKsVwNkJ2MB1QrPWNiYgIrKytERUVh1qxZCAgIgIWFhUrrJyIiIiIiIqLCYxKdiIjypKjkSk5z587F+PHjv+hI5n79+uGnn37CypUrMW7cOEgkEkRGRuL169fo3bu33Mj4vOzYsQMfP35E27Zt5UaD9+7dG0uWLMH69etlkuiPHz8GkF3XXRXOzs5wcnLCoUOHEBcXBxsbGzx//hx79+5FpUqV4OXlpVI/YWFh8PPzw9ixYzF27FhUrlwZbm5u8Pf3V7kPe3t7hdOzb3Z8VqkPIiIiIiIiov8SlnMhIqJCi4mJwahRo6Crq4vQ0FA8evQInz59giAIEAQB5ubmJbLeihUrwsfHB/fv3xfrphfmgaLA/0u53LhxA66urjKv0aNHAwDu3LmD8+fPyy2b34NAcxo0aBCysrKwevVqAMCaNWuQkZGBb775Bmpqqv059vDwwL1797Bhwwb06dMHmZmZCAsLQ5s2bdCzZ0+VYyEiIiIiIiIi1TGJTkREhSZ9sOe0adMQGBgIGxsbaGtrAwBSU1PlapYXp8GDBwPILony6NEjHD58GNWqVSvQwzWfP3+OI0eOAADi4+Nx+vRpuZeUNNkOANbW1gCAe/fuqbyugIAAlClTBqtXr0ZGRgZWrVoFNTU19O/fX+U+AMDQ0BD+/v5Yu3Yt4uLicPbsWVhZWSEiIgL79u0rUF9ERERERERElD8m0YmIqNDevn0L4P9J5Zy2bNkCQRDkpktrqWdkZCjsM7/5Ui1atICjoyO2b9+O33//HYIgFHgUenh4ODIzM+Hr6yuOns/9ktZ037x5sxiTp6cnAGD58uUqr8vAwAD+/v548uQJxowZgwcPHqBdu3awsrIqUMy5ubi4oE+fPgCA2NjYIvVFRERERERERPKYRCciokKrVq0aAGDVqlX4/Pn/9bRv3LiBcePGKVxG+jDM27dvK5xvbGwMTU1N3L9/H5mZmXmuf9CgQUhLS8PSpUuhqamp9CGoykhHl+d8oGhudnZ2qF+/Pl6/fo39+/cDAIKDg2FsbIzdu3dj8eLFcjcLTp48iffv38v1JR09P3/+fAAFKz0THx+P0NBQpKSkyExPS0vDsWPHAACVKlVSuT8iIiIiIiIiUg2T6EREVGj9+vWDmZkZdu/eDQcHB/Ts2ROtW7dGvXr10Lx5c9jY2MgtY2trizp16uDixYtwdnZGv379EBwcjF27dgHIHonetm1bvHjxAnXr1kXfvn0RHByMNWvWyPUVGBgIHR0dAIC3tzcqVqyocuzXr1/HtWvXYGhoiPbt2+fZ1s/PDwCwfv16AED58uURERGBMmXKYNiwYbC3t0fPnj3RqVMnVKlSBW5ubuIo/Zzq1asHZ2dnAIC5uTk6dOigcrxv3rxBv379ULFiRbRo0QIBAQHw9vaGtbU1YmJi4OzsDB8fH5X7IyIiIiIiIiLVMIlORESFVqFCBVy4cAH+/v5IT0/Hrl278PTpU/zyyy/YuHGj0uW2bdsGb29vPHjwAGvXrsWqVatw+fJlcf7KlSvRp08fJCYmIjw8HKtWrcLx48fl+ilXrhycnJwAFPyBomvXrgUAdO3aVazjroyfnx8kEgl27dqFpKQkAEDLli1x9epVDBw4EBkZGYiMjERMTAxMTEwwffp0mJmZKeyrVatWALJvQGhoaKgcb9WqVTF79my4u7sjPj4e27dvx+nTp2Fra4uFCxciOjpaLIVDRERERERERMVHIigqWEtERPQP8OTJE9jY2KBSpUp48OABJBJJaYeUJ0EQ4OjoiLt37+LevXuoUqVKaYckunv3Lp68SsaKA+9KOxSi/4zwX9xLOwQiIiIiIlKB6kPgiIiIvjLTp09HVlYWhgwZ8tUn0AFg69atuHPnDjp06PBVJdClrEz0Ef5LvdIOg4iIiIiIiOirwiQ6ERH9o9y+fRuzZs3CgwcPcOzYMVhZWeHbb78t7bDyFBwcjHfv3mHPnj1QV1fHL7/8UtohEREREREREZGKmEQnIqJ/lOfPn2PVqlXQ1dVFixYtsGjRIujr65d2WHlatWoVNDQ0UK1aNUydOhX169cv7ZCIiIiIiIiISEVMohMR0T+Ku7s7/mmP8/inxUtERERERERE/8ckOhEREQEAnrxKxuR10aUdBlGJ4sM8iYiIiIiooNRKOwAiIiIiIiIiIiIioq8Vk+j/IBKJRO6lpaUFa2trBAQEIDY2trRDpK9IUFAQJBIJoqOjVV4mMzMTK1asgJubG8qVKwctLS2Ym5ujQYMGGDJkCPbs2VPkuB49egSJRAJ3d/ci91VY7u7ukEgkePToUanFUFJCQ0MhkUgwZcoUlZfJ/b2iqakJY2Nj1K5dG0FBQdi2bRsyMjJKLmgFCnP+filTpkyBRCJBaGhoaYdCRERERERERF8Ay7n8AwUGBorv379/j0uXLiE8PBxbt27F/v370bJly1KMjv6p0tPT0alTJxw8eBDq6upo3LgxrK2t8enTJ1y9ehV//PEHjhw5go4dO5Z2qPmSSCSwsbEpcJK8sMvlxd3dHcePH8fDhw9ha2tbbP2WBOl3S1ZWFt6/f487d+5g7dq1CAsLg52dHTZs2ABnZ+dSjrLk/ZOOGRERERERERGVPCbR/4Fyj378/PkzvvnmG6xbtw7ff/89/vrrr9IJjP7RFi1ahIMHD8LW1haHDx9G1apVZeZfuXIFUVFRpRRd8Vq7di1SUlJgaWlZ2qF8VRSNrL5//z4mTJiAiIgItGzZEqdPn0a9evVKPJbp06cjJCQElSpVKvF1FdTQoUPh5+cHc3Pz0g6FiIiIiIiIiL4AJtH/BTQ1NTFlyhSsW7cOsbGxePfuHcqWLVvaYdE/zPbt2wEAP/74o1wCHQCcnJzg5OT0pcMqEV9jYvZrVbVqVWzevBllypTBqlWr0L9/f1y+fLnE12tubv7VJqmNjY1hbGxc2mEQERERERER0RfCmuj/EqampuJ7RbWLP378iF9++QW1a9eGnp4eDA0N0aJFC0RGRsq1zVmzOjU1FSEhIbCxsYG2tjbs7Owwc+ZMCIIgs4y0xnRer9wlMh49eoRBgwbB1tYW2traqFixIrp3765wJH3OOs/x8fHw9/dHxYoVoauri4YNG2L37t1K901sbCwCAgJgaWkJbW1tWFhYoF+/fgpLdgiCgE2bNsHNzQ1mZmbQ0dGBtbU1PD09sWTJEqXryO3kyZMYOnQo6tSpg3LlykFXVxeOjo4ICQnBu3fv5NpHR0dDIpEgKCgIb968wbfffgtzc3Noa2ujVq1aWL16tdJ1bdu2Dc7OztDV1YWpqSn69u2LZ8+eqRyr1OvXrwEAFStWVHmZ/Opv51d7PCkpCd9//z2sra2ho6OD6tWrY968ecjKypJrm5iYiAkTJqBmzZowMDCAkZERqlWrhr59++L8+fMy8QBAXFyczPmXswZ77rhUXU4ikSgt75F7X0ivo+PHjwMAKleuLNNvToIgICwsDG5ubihbtix0dXVRp04dzJ49G58/f1a4vr/++gsdO3aEkZERjIyM0Lp1a5w9e1Zh2+IwZ84c6Ovr48qVKzh16pTc/MJez3fu3IGfnx9MTU2hpqYmfiflron++fNnVKhQATo6OgqvIQA4f/48JBIJmjVrJk579+4dFi1ahDZt2ojfYxUqVEDbtm1x6NAhuW1Q5Zgpqoleu3ZtSCQS3Lp1S2FsL168gIaGBqysrOTO71OnTqFr164wMTGBtrY2bG1tMXz4cPGazOnz589YtmwZnJ2dYWxsDD09Pdja2qJjx47YtGmTwnUTERERERERUdFwJPq/xKVLlwAoHiH58uVLeHh44MaNG7C0tETr1q2RkpKCs2fPomvXrmLZhNzS09Ph5eWFv//+G87OzqhevTqOHz+OkJAQfPjwAdOmTRPbtm3bVmFy8ePHj9i2bRsAQF1dXZx+6tQpdOjQAUlJSahZsyY6d+6Mp0+fYvv27di7dy+ioqIU1nZ/9OgRGjVqBB0dHbi6uuLly5c4e/YsvL29sW/fPnh5ecm037ZtG/z9/ZGeno4GDRqgadOmuH//PkJDQ7F7924cP34cNWvWFNuPHz8eM2fORJkyZeDq6oqyZcvi+fPnuHbtGu7du4chQ4aocDSAMWPG4OrVq6hVqxY8PDyQlpaGy5cvY+bMmdizZw9iYmJgYGAgt9y7d+/QpEkTvH//Hs7Ozvj48SNOnDiBb775BllZWQgODpZpv3jxYgwbNgzq6upo0aIFjI2NcfjwYbi4uKBu3boqxSplZWWFu3fvYtWqVWjfvj00NEr26yEtLQ0eHh64f/8+PDw8kJ6ejiNHjuCHH37AX3/9hTVr1ohtP378CBcXF9y7dw/29vZo06YNACA+Ph4bN25ElSpV4OzsDDs7OwQGBiIsLAz6+vro3r272Iejo6PSWAq7XF4MDAwQGBiI/fv34+XLl+jWrZvCY56VlQU/Pz9s2bIFhoaGaNSoEQwMDHDu3DmMGTMGx44dw+7du6Gm9v97nufOnYOHhwdSUlJQr149ODo64vr162jRogWCgoIKFW9+jIyM0K5dO2zduhXHjh2Dq6urOK+w1/Pt27fRqFEjVKhQAS1btsTbt2+hqampcP2amprw9fXFsmXLsG3bNnzzzTdybcLDwwEAAQEB4rSYmBgMHz4c1tbWqFatGpo0aYL4+HgcPHgQBw8exMqVK9G/f38Aqh8zRQICAjB+/Hhs2LABU6dOlZu/adMmZGZmwt/fX+ZYLly4ECNGjICamhqcnZ1haWmJ69evY9GiRdizZw9Onz4tMyK/T58+2Lx5M4yNjdG0aVPo6enh6dOnOHnyJD5+/Ag/Pz+V4iUiIiIiIiIi1TGJ/g/3/v17nD9/HkOHDgUATJgwQa5Nv379cOPGDYwdOxbTpk0Tk1QPHjyAl5cXfvzxR7Rv3x516tSRWe7s2bNo3rw57ty5IybmL168iCZNmmDevHkICQkRE0yKkvCCIMDHxwcA8N1338Ha2hpA9uhjX19fpKamYsuWLTIJy8OHD6NDhw7o06cPHjx4AC0tLZk+w8LCMGzYMMydO1dM8i5YsAAjRozAtGnTZJLoDx8+RN++faGrq4tDhw7Bzc1NnLd27VoEBgaiX79+4ijmT58+Yf78+bC1tcWlS5dQvnx5sX1GRgbOnDmj/EDkMmnSJDRp0gTlypUTp6WlpWH48OFYvnw55s6di0mTJsktt3PnTnTr1k1M5kqneXt7Y+rUqTJJ9EePHmH06NHQ1tbG/v37xRHTKSkp8Pb2xp49e1SOFwCCg4Nx7Ngx7Nq1C1WrVkXXrl3RtGlTNG3aFFZWVgXqSxUxMTGoU6cO7t69K55f9+/fh5ubG0JDQ9G1a1d07twZALB161bcu3cPw4YNw8KFC2X6efXqFV69egUAcHV1haurK8LCwmBsbKywxrcihV0uL9J+3N3d8fLlS8yePVvhjabZs2djy5YtaN26NTZs2CD+EiA5ORm9evXC7t27sXTpUvEGTlZWFoKCgpCSkiJ3A+ynn36SublV3OrVq4etW7fi5s2b4rSiXM+bNm3C0KFDMX/+fJmbbMoEBARg2bJlCA8Pl0uiZ2VlISIiAhoaGujRo4c43cHBAadPn0bTpk1l2l+5cgUeHh4YOXIkevToAQMDA5WPmSL+/v6YMGECwsPDFSbRlSX4R44ciUqVKmHXrl3id7AgCJg2bRomTZqE4cOHY8uWLQCyr/nNmzejUaNGOHHiBHR0dMS+UlNTcfXqVZVivXv3rsLp6enpKi1PRERERERE9F/Dci7/QDnLC5QtWxZeXl549+4dwsPDMXLkSJm2V69exb59+9C0aVPMmDFDZpRnlSpVMGfOHGRmZmLlypVy61FTU8PKlStlRrY3bNgQ7dq1Q0pKCi5evJhnnD/99BMiIyPRsmVLLFiwQJy+evVqvHjxAqNHj5ZJuAGAp6cnvvvuOzx9+lRhElgac85R0kOGDEG5cuUQExMjkwRasGABUlJS8Pvvv8sk0AGgb9++8Pb2xoULF8T6zklJSUhLS0PdunVlEugAoKGhIddHXtq3by+TQAcAbW1tzJ8/HxoaGti5c6fC5QwNDbF8+XIxgQ4AXbp0Qe3atREfHy9TFmX16tVIS0tD3759ZUqO6OnpYdGiRXIlQ/Lj7++P33//Hbq6uoiPj8eCBQvQs2dPWFtbo0aNGvjjjz+QmZlZoD7zM3v2bJnzq2rVqvjpp58AQKZ8jjRJ7uHhIdeHiYkJatWqVaxxfSkZGRmYNWsWypQpg/DwcJlSOvr6+lixYgW0tbWxbNkycXp0dDRu3bqFatWqYdy4cTL9TZ48uUTrvUuP1du3b8VpRbmeK1asiJkzZ6qUQAeyb3bY2NggOjparmTR0aNH8fz5c7Rp00bmnKpcubJcAh3IrvE/ZMgQJCUl4dixYyqtPy+VKlWCq6srHjx4gJiYGJl59+7dw4ULF1CjRg2ZX4jMmDEDWVlZWL58ucxNTIlEgh9//BFOTk7Yvn07EhISAPz/OmjatKlMAh0AdHV10aRJkyJvBxERERERERHJYxL9HygwMFB8+fn5oUmTJkhISMDYsWPFWr5S0pq/Xbp0UZhUlZZkuHDhgtw8W1tbVKtWTW66dNrz58+Vxrh582b8+uuvqFq1KrZu3SqT9JbG5O3trXDZvGJyd3eXK/egoaGBKlWq4PPnz0hMTJRbT5cuXVRaj4mJCaysrBAVFYVZs2YVqq54Tk+fPsWff/6JESNGoH///ggKCsK3334LLS0tpSNBGzZsKJfABxTvc2ld6pyjbqUcHBwK9RDQMWPGID4+HkuXLkWPHj3EUbg3b97EkCFD0L17d4X1ygujfPnyaN26tdx0f39/AMCZM2fE2vsNGjQAkP1Liz179uDTp0/FEkNpu3LlChISEuDq6qrwQZWmpqawt7fH9evXkZqaCuD/x93X11fumtbQ0JBLZBcn6fHIud6iXM+enp7Q09NTef0SiQS9evVCVlaWXP1vRSO9pTIzM3Hw4EFMmTIFgwcPRlBQEIKCgsTkubLrsaCk65bGkju23r17i9OysrJw5MgRlClTBq1atZLrS1rbPSsrSyzX5ejoCH19faxZswYrVqyQ+b4rCHt7e4Wv3L8UICIiIiIiIqJsLOfyD6So1MSVK1fQokULtGnTBjdv3kTlypUBQBy5PG7cOLlRqzlJRzrmpKyEh7SES1pamsL5ly5dQr9+/VCmTBns2rVLLiksjalx48ZK4ymOmKTrMTMzU3k9YWFh8PPzw9ixYzF27FhUrlwZbm5u8Pf3l6u3npe5c+di/PjxBS6PUJDtkyb5lY08rlSpkjjKviCMjY0xePBgDB48GABw584dzJ49GytWrEBkZCQ2btyoMFFZUDY2NgqnGxoaomzZsnj37h2SkpJgZGSEVq1aYeTIkZg/fz46deoELS0t1KtXD15eXvjmm29ULrnxtZGeo/v27cv3lwNv3ryBpaWlSse9pEivlZzXdFGu58LEGhAQgBkzZmDDhg344YcfAGRfF9u3b4e+vr7cTbMnT56gY8eOuHbtmtI+P3z4UOA4FPH19cXw4cOxefNmzJs3Txxhv3HjRvEGgFRiYiI+fvwIAPk+f0C67wwNDbFixQoMHDgQAwcOxKBBg+Dg4ICWLVuib9++cHFxKZbtICIiIiIiIiJZTKL/Szg5OWHQoEGYPXs2Fi9ejDlz5gCAWH6jefPmqFKlitLlFY2CLWg5EAB48eIFvL29kZaWhoiICNSoUUOujTQmX1/fPEehKkrKFSSmzMxMSCQS9O3bN892OR8s6uHhgXv37mHPnj3Yv38/jh8/jrCwMISFhaFHjx7YvHlzvuuNiYnBqFGjYGRkhOXLl8Pd3R1mZmbQ1tYGAFhYWCgdxV+Q7VM0KrgkVKtWDcuXL8ebN2+wbds2REVFqZxEL+yodem25TR37lwMGjQIO3fuxJEjR3D69GmcP38ev//+OzZv3qx0JPSXUphtlV4L9vb2CkuO5CQ9f77UcVdEWnM753VdlOs5d0kSVdSqVQt16tTB5cuXcevWLTg6OiIqKgrv379H79695WIIDg7GtWvX4OPjg3HjxsHBwQFlypSBmpoali9fjkGDBik83wqjfPnyaNu2LXbt2oXDhw+jTZs2Ypyurq4yN3uk+61MmTLisyOUyXnDqVevXvD09MTOnTtx8OBBHD9+HEuXLsXSpUsxZswY/P7778WyLURERERERET0f0yi/4tIR5/fvn1bnCYd2dy9e3cMHz68RNeflpYGb29vPHnyBDNnzkTHjh0VtrOyssLt27fx448/yj3MtDhZWVnh/v37WLhwIQwNDVVeztDQEP7+/mJZkZiYGPj6+iIiIgJBQUFo165dnsvv2LEDADBt2jQEBgbKzEtNTcWLFy8KuCWKWVhY4M6dO4iLi4O9vb3c/Pj4+GJZj5S7uzu2bdsmM6JYWv5BOqI2t8ePHyvtT1l8SUlJeP/+PfT19eWOm4ODg/grgU+fPmHJkiUYPXo0Bg0a9EWS6JqamoXaVmWk12etWrVUfpiphYUFACAuLk7h/OI+7lLv37/H/v37AQAtW7YUp3+p6zmngIAA/PXXXwgPD8cvv/yitJRLcnIyDh06BFNTU0RERMjVXn/w4EGJxLZr1y5s2LABbdq0URqbsbExtLW1oampWeAH2VasWBHBwcEIDg6GIAg4cOAAevbsiVmzZiEoKEjhzUsiIiIiIiIiKjzWRP8XkSaEcj6U0tPTEwAQGRlZ4usfMGAAzp07h969e2Ps2LFK232pmIprPS4uLujTpw8AIDY2Nt/20ocuWltby83bsmVLsY16ldaa3rJli9y8O3fuiKOGVZVfXPfv3wfw/yQuAJibm4vry+327dt5JnQTExP/x959RkV1tX8f/w6CdLBgA6IoIiaxF2JBRVTsPSZ2sSR6xxSNNSZ3YtTEXhNLoiZoVOy9V7B3Jfq3Ye+xxYYFhXle8DC34wwCimLi77MWK2Gfffa5Tplxretsrs3atWst2sPDw4GExROfNdvawcGBbt26kStXLq5cuWJadBESkt2PHz9+5vlYk9x+uXLl4vr169y4ccNi2+rVq63uk/iiwdq4pUuXxt3dnQ0bNnD79u0UxZh43+fNm2dxzx4/fsy8efNSNE5qdevWjZiYGEqXLm22gOWr/I5J1Lx5cwwGAzNmzOD27dssW7aM7Nmzm2JJdOvWLeLj48mVK5dFAv3x48emF15Pe9Y9S07dunVxdXVl4cKFxMTEMGvWLOzs7GjSpIlZP1tbW4KCgrhx4wYbN25M9XESGQwGatSoQe3atQE4ePDgc48lIiIiIiIiItYpif4vsW/fPn799VcAatWqZWovU6YMVapUYcOGDXTt2tViFm18fDyrV682LVb4vIYMGcIff/zBe++9x8SJE5/Zt2PHjmTLlo0ff/yR33//3SIRGBMTw9SpUzl//vwLxdStWzccHR3p2rUrS5Yssdh+48YNxo0bZ1qw8ezZs4SFhXHv3j2zfg8fPjQtQJiSGs6Ji4BOnjyZR48emdoPHTr0zLr0qdW2bVsyZszI1KlT2bRpk6n9/v37fPHFF6kuL1KvXj1++uknbt68abFtxYoVTJgwAcCs9ETp0qVxcnJixYoVpsUPAa5evUr79u2TjaFHjx5miyOeOnWK/v37A/DJJ5+Y2hcuXMj27dst9t+3bx9//fUXrq6uZM6c2dTu6enJX3/9ZfVcniW5/SpVqgRgihESXj4MHDiQrVu3JjkmmP+FSCJ7e3u6d+/OzZs3ady4sdXZ5X/++adZGaHKlStToEABjhw5wrBhw8z6DhgwIMkZ6s/r5MmTfPjhh0yePBlnZ2cmT55stv1VfZ6f5O3tTcWKFTlx4gS9evXiwYMHfPjhhxa1xbNnz467uzsHDx5ky5Ytpva4uDh69uxp9eUPPPueJcfR0ZFGjRpx584dunfvzvnz56lRowZZs2a16NunTx9sbGxo06aN1e/gixcvMnbsWNPv+/btY/78+WbfK5Dw4m7Hjh3Ay62JLyIiIiIiIvKmUjmXf6DQ0FDT/8fGxnLmzBm2b99OfHw8devWNc2aTjR9+nRCQkIYNWoUU6dOpVixYmTLlo0LFy5w9OhRrl69ysiRI00zXJ/HV199BUDmzJlNC1I+bdiwYXh4eJA5c2YWLFhAvXr1aNeuHd9//z2FChXC3t6es2fPcvjwYWJiYti3b1+SC22mhJ+fH9OmTaNly5bUq1cPf39/3n77bYxGI2fOnOHQoUPExsbSvHlzHB0duXHjBm3btqVz586UKlUKb29vYmJi2Lp1K1evXiUgICDZ2sWQkNwePnw4S5Yswd/fn9KlS3Pjxg0iIyNp0KABO3fuTJNEZ758+Rg8eDBdu3alcuXKBAUF4eHhwaZNm7CxsaFOnTosXbo0xeOdO3eOzz//nG7dulG8eHF8fHx49OgRR44c4fDhw0BCfel69eqZ9nFxcaF79+7069ePwMBAgoKCgIQSOO+88w5ly5Zl27ZtVo9XpkwZYmNj8fPzIzg4mNjYWNatW8e9e/do2bKlWXmWiIgIRo8ejZeXF8WLF8fNzY2LFy+yefNm4uPj6d+/P3Z2dqb+iS8ESpQoQbly5XBwcMDf358ePXo88xokt1+vXr2YO3cuo0aNIiIiAl9fXw4cOMC5c+f45JNPGDdunNUxp0yZYlqc1t3dHYBJkyYBCYnUQ4cOER4ejr+/PyVKlCB37txcu3aNkydPcurUKerXr8+HH34IgI2NDWFhYVSpUoWePXsSHh5OwYIFOXjwIEeOHKFDhw6msVMr8bslPj6e27dvc+zYMY4cOYLRaMTPz48ZM2ZQuHBhs31e1ef5aS1atCAyMtL0csdanX5bW1t69uzJ119/TaVKlQgODiZLlizs2LGDv/76i86dO5slqRMld89SEtuUKVOeGRtAxYoVGT16NF26dKFChQoUKVIEPz8/Hjx4wJkzZzh8+DAuLi507twZSCjh07hxY9zd3SlVqhQ5c+bk5s2bbNq0idu3b9OwYUMtLioiIiIiIiLyEiiJ/g80ZcoU0//b2NiQKVMmKlasSKtWrQgNDcXGxvwPDHLkyMH27duZMGECs2bNYteuXcTGxpIrVy6KFy9O/fr1+eCDD14opsQZx4k1k63p27evaQHT8uXLc+DAAUaMGMGyZctYv349GTJkwNPTkzp16tCoUaM0qevbqFEjoqKiGD58OGvWrGHFihU4ODjg6elJixYtTAkpAF9fX4YNG8b69es5dOgQO3fuxMXFhbx58/Lf//6XDh06mMo8PEvWrFnZtWsXvXr1IjIyksWLF5M3b1769etHjx498PX1feHzStSlSxe8vLwYPHgwmzdvxtXVlZCQEIYMGcLXX3+dqrHmzp3LsmXLWLNmDceOHTO9ZMiePTsNGzakbdu21K1b12K/vn374urqyi+//MK6devIkSMH7du3p1+/fmZ/FfE0e3t7Vq5cSZ8+fVi4cCHXrl0jb968fPTRR3Tp0sWsb2hoKLa2tmzcuJGdO3dy69YtcubMSa1atejataspeZ9o4MCBGI1GFi1axKxZs3j8+DGVKlVKNome3H7vvvsu69ev56uvvmLnzp2cPHmS8uXLM3v2bPbt22d1zEaNGjFy5EgmTpzIkiVLePjwIfC/hKyNjQ0zZsygcePGTJo0id27d7N79248PDzIkycPbdq0oWnTpmZjli1blq1bt9KnTx82b97M8ePHKV26NOPHjyc6Ovq5k+iJ3y22tra4ubnh6elJ69atqVevHvXq1bOY6Z3oVX2en9SkSRM+++wzHj58iK+vr9WFSyHhJYW3tzejRo1iy5YtODo6EhgYSL9+/di7d6/VfZK7Z8kJDg4mV65cXLp0CRcXF7MXT0/79NNPKVu2LCNHjmTjxo0sXrwYV1dXvL296dSpk1kZmDJlyjBgwADWr1/P0aNH2bRpE5kzZ6ZIkSJ89NFHpnUcRERERERERCRtGYxpVaBZRERE/rGio6M5fyWGiatupncoIi/VjH5B6R2CiIiIiIj8wyiJLiIiIkRHRwMJpbBERERERERE5H+0sKiIiIiIiIiIiIiISBKURBcRERERERERERERSYKS6CIiIiIiIiIiIiIiSbBN7wBERETk9XD+Sgzf/RGR3mGIvFRaWFRERERERFJLM9FFROS5GQwGsx8bGxsyZcpEhQoVmDRpEq/T2tV9+/bFYDAQFhb23GNERERgMBgIDQ1N1X4GgwEfH5/nPq6IiIiIiIiIpB/NRBcRkRfWpk0bAOLi4jhx4gRbtmxh8+bNrFu3jvDw8FcSQ1BQEJGRkZw6dUoJaxERERERERFJM0qii4jIC3t6dveaNWuoVasWM2fOpEWLFtSpUyd9AnvCp59+StOmTcmVK9dzjxEQEMDhw4dxd3dPw8hERERERERE5HWmci4iIpLmqlWrRqtWrQBYuHBh+gbz/3l4eFCwYMEXSoA7OTlRsGDBF0rEi4iIiIiIiMg/i5LoIiLyUhQvXhyAc+fOmbVv3ryZhg0bkj17duzt7fHx8eHzzz/n6tWrFmM8evSIX375hYCAADw8PHBycsLHx4c6deowc+ZMAE6fPo3BYCAyMhKAvHnzmtVpT/SsmuiPHj1i3LhxlC9fnkyZMuHk5ESBAgX46KOPOHjwoKnfs2qix8TE0KtXL3Lnzo2DgwMFCxZkxIgRydaFT+vrISIiIiIiIiJpS+VcRETkpbhz5w4A9vb2prYxY8bQpUsXbGxsCAgIwMvLi4MHD/LTTz+xdOlStmzZYjbLu1WrVsyaNQsPDw/KlSuHk5MTFy5cYNOmTdy9e5emTZvi4uJCmzZtWLlyJX/99ReNGzfGxcUlxXHGxMRQs2ZNNm3ahIuLCxUqVMDV1ZVTp04RFhaGl5cXhQoVeuYYDx8+JCQkhK1bt+Lh4UHdunW5c+cOvXv35sSJE0nu9zKuh4iIiIiIiIikLSXRRUQkzRmNRpYuXQpAkSJFANi+fTtdu3Yld+7cLF682NRuNBoZMGAA3377LZ9//jlz5swBEmaYz5o1i9KlS7Nx40YcHBxM49+/f5/9+/cDCWVawsLCCAoK4q+//mLYsGGpWlj0iy++YNOmTVSuXJk5c+aQNWtW07YLFy5w+fLlZMcYMWIEW7duJSAggNWrV5tKxuzdu5fKlStb3edlXQ8RERERERERSVsq5yIiImkmLi6O6Oho2rVrx7Zt27C3t6dt27YADBo0iPj4eH799VdTwhjAYDDwzTffULx4cebPn8+1a9cAuHLlCgDlypUzSxgDODo6UrZs2ReO99KlS4SFheHo6MjUqVPNEugAXl5elCxZMtlxxo8fD8DIkSPNaq6XKFGCzp07W90nva5HdHS01Z/Y2NgU7S8iIiIiIiLyplESXUREXlhi/XFbW1sKFChAWFgYrq6uhIeH4+vrS3x8POvWrcPV1ZUqVapY3b98+fLEx8ezZ88eAAoWLIizszO///47EydO5Pr162ke94YNG4iLi6NWrVp4e3s/1xhnz57l3LlzeHl5Ua5cOYvtzZo1s2h7Xa+HiIiIiIiIiFhSORcREXlhbdq0AcDGxgY3NzcKFy5Mo0aNyJw5MwDXr1/n7t27ANjaPvufnsSZ125ubkycOJGPP/6Yjz/+mI4dO+Lv70/lypVp3bo1ZcqUeeG4Exc99fX1fe4xLl68CEDu3LmtbrfWnp7Xw8/Pz2p7dHQ08ChFY4iIiIiIiIi8SZREFxGRFxYWFvbM7XFxcQC4urrSqFGjZ/bNkyeP6f+bNWtG1apVWbRoEatXryYyMpLx48czfvx4evTowZAhQ144dkiY+f28jEbjM8ew1v66Xw8RERERERER+R8l0UVE5KXz8PDA3t4eOzu7ZBPuT8uWLRsdOnSgQ4cOGI1GVq1axYcffsjQoUMJDQ3lnXfeee643nrrLQCOHz/+3GN4enoCcObMGavbrbW/rtdDRERERERERCypJrqIiLx0tra2BAUFcePGDTZu3Pjc4xgMBmrUqEHt2rUBOHjwoGlbxowZAXj8+HGKxwsKCiJDhgwsX76cCxcuPFdMefLkwdvbmwsXLrBt2zaL7TNnzrRoexXXQ0RERERERETShpLoIiLySvTp0wcbGxvatGnD5s2bLbZfvHiRsWPHmn7ft28f8+fP59Ej8zrdf//9Nzt27ADM640nzgg/evRoimPy9PSkdevW3L9/n9DQUG7cuGER0969e5Mdp2PHjgB069aN27dvm9r3799vdk5PetnXQ0RERERERETShsq5iIjIK1GxYkVGjx5Nly5dqFChAkWKFMHPz48HDx5w5swZDh8+jIuLC507dwYSyqA0btwYd3d3SpUqRc6cObl58yabNm3i9u3bNGzY0GwxzXr16jFlyhSaN29OSEgI7u7uAEyaNOmZcY0ePZojR46wdu1afHx8qFChAi4uLpw+fZq9e/fy9ddfU6JEiWeO0aNHD5YuXcq2bdvw9fWlcuXK3Llzh/Xr19O+fXvGjx//yq+HiIiIiIiIiKQNzUQXEZFX5tNPP2XHjh20aNGCv//+m8WLF7Nt2zZsbGzo1KkTixYtMvUtU6YMAwYMoGTJkhw9epQ5c+awe/duihQpwpQpU5g9e7bZ2I0aNWLkyJF4e3uzZMkSJk+ezOTJk5ONydXVlQ0bNjBy5Ej8/f2JjIxk6dKl3Lx5k3bt2tGkSZNkx7C3t2ft2rV0794de3t7Fi1axMmTJxkwYAA///xzulwPEREREREREUkbBqPRaEzvIERERCR9RUdHc/5KDBNX3UzvUEReqhn9gtI7BBERERER+YdREl1ERESIjo4GwM/PL50jEREREREREXm9qJyLiIiIiIiIiIiIiEgSlEQXEREREREREREREUmCkugiIiIiIiIiIiIiIklQEl1EREREREREREREJAm26R2AiIiIvB7OX4nhuz8i0jsM+YeY0S8ovUMQERERERF5JTQTXURE0o3BYDD7sbOzw8PDg8KFCxMaGsq8efN4/PjxM/f38fF5dQEDffv2xWAwEBYW9kqPKyIiIiIiIiLpQzPRRUQk3bVp0waA+Ph4bt26xbFjx5g6dSpTpkwhf/78TJ8+nYCAgHSOUkRERERERETeRAaj0WhM7yBEROTNZDAYALD2T9GJEyfo06cPs2fPxsnJiS1btlCsWDGzPkeOHMHOzg5fX99XES4A165d49q1a+TKlQt3d/dXdtyXLTo6mvNXYpi46mZ6hyL/ECrnIiIiIiIibwrNRBcRkdeSr68vs2bNwtXVlcmTJ9OuXTv27t1r1qdgwYKvPC4PDw88PDxe+XFFREREREREJH2oJrqIiLzWhg8fjrOzM/v27WPz5s1m26zVRDcajYSHh9O0aVMKFCiAs7Mzrq6uBAQEMG7cOOLj4836R0REWNRmf/onNDTU1D+pmuhBQUEYDAZOnz7NwoULKVOmDM7OzmTJkoVmzZpx/vx5q+dnNBqZMmUKFStWJFOmTDg6OlKkSBGGDRvGo0ePLPpfv36dPn368O677+Li4oK7uzsFChSgdevW7Ny5M+UXVkRERERERERSRDPRRUTktebu7k7NmjWZO3cuGzZsIDAw8Jn9Hz58SPPmzcmcOTPvvPMOJUqU4Nq1a2zbto3OnTuzc+dOswR4zpw5TTXZn7Z582ZOnDhBhgwZUhzvuHHjGD58OKVKlaJGjRrs2rWLmTNnsmfPHqKionB0dDT1jY+Pp2nTpsyZMwc3NzdKly6Ni4sLO3bsoEePHmzYsIElS5ZgY5Pwzvvu3buUKVOG48eP4+fnR/Xq1QE4e/Ys4eHh5MuXT7XjRURERERERNKYkugiIvLaK1asGHPnzuXw4cPJ9rW1tWXevHnUqVOHjBkzmtqvXr1KrVq1mDJlCu3ataNixYpAQkmYp2eVA2zfvp2ZM2eSKVMmevXqleJYx40bx5o1awgODgbg3r17VKtWja1btxIeHk67du1MfYcNG8acOXOoVq0a06dPJ1u2bADExMTQrFkzlixZwvjx4+ncuTMAc+fO5fjx43z22WeMGTPG7LhXrlzhypUrKY5TRERERERERFJG5VxEROS1l1iD/O+//062r62tLY0aNTJLoANky5aNgQMHArBo0aJnjnH+/HkaNmzI48ePmTVrFgUKFEhxrF27djUl0AGcnJzo1q0bABs3bjS1P378mKFDh+Lq6sqMGTNMCXQAZ2dnJk6ciL29Pb/88oupPTFJ/uT4ibJnz06hQoWSjS86OtrqT2xsbIrPUURERERERORNopnoIiLy2jMajUBCDfSU2r9/P6tXr+bMmTPcu3cPo9HInTt3gIREclLu379PgwYNuHz5MqNGjSIkJCRVsVrrn5iEv3Tpkqlt3759XLt2jZo1a1pdqDRHjhz4+flx8OBB7t+/j6OjIyVLlgSgT58+2NraUrVqVRwcHFIVn4iIiIiIiIikjpLoIiLy2rt27RoAWbJkSbZvbGwsoaGhhIeHJ9knMZluTWhoKHv27KF9+/Z88cUXqY7V29vbos3FxQVIqNee6PTp0wCsWLEi2ZcDN27cwMvLiypVqtC1a1dGjRpF3bp1yZgxI8WKFSMkJIT27dtbLLJqjZ+fn9X2hBcLlguZioiIiIiIiLzplEQXEZHX3v79+wF45513ku07YsQIwsPDKVSoEEOHDqVEiRJkzpwZOzs7jh07hr+/v2lm+9P69+/P7NmzCQwMZNy4cc8Va0pny8fFxQEJSe1y5co9s6+9vb3p/0eMGEHHjh1ZtGgR69atY8uWLezcuZMhQ4Ywa9YsGjRo8Fxxi4iIiIiIiIh1SqKLiMhr7datW6xcuRKAypUrJ9t/wYIFAKZE+pNOnjz5zP2+++478uTJw/z58y1qqqe1xBnrhQoVsrqw6bP4+/vTs2dPevbsyYMHDxg7dizdu3enY8eOSqKLiIiIiIiIpDEtLCoiIq+1bt26ERMTQ+nSpSlbtmyy/RMXH33rrbcsts2ePdvqPn/++SetWrXCycmJRYsWmS3y+bKULl0ad3d3NmzYwO3bt597HAcHB7p160auXLm4cuWKafFREREREREREUkbSqKLiMhr6eTJk3z44YdMnjwZZ2dnJk+enKL9EhfxnDBhgln73LlzmTp1qkX/q1evUq9ePe7du8fUqVMpWrToiwefAvb29nTv3p2bN2/SuHFjzpw5Y9Hnzz//ZNasWabfFy5cyPbt2y367du3j7/++gtXV1cyZ878UuMWERERERERedOonIuIiKS70NBQAOLj47l9+zbHjh3jyJEjGI1G/Pz8mDFjBoULF07RWD179mTlypX07t2bOXPmUKBAAaKjo9m9ezfdu3dn2LBhZv0nTJjAmTNnyJkzJ4sXL2bx4sUWYwYGBtKhQ4cXPs+n9enTh0OHDhEeHo6/vz8lSpQgd+7cXLt2jZMnT3Lq1Cnq16/Phx9+CEBERASjR4/Gy8uL4sWL4+bmxsWLF9m8eTPx8fH0798fOzu7NI9TRERERERE5E2mJLqIiKS7KVOmAGBra4ubmxuenp60bt2aevXqUa9ePWxtU/7PVcWKFdm8eTNff/01+/bt49ixYxQuXJh58+ZRokQJiyR64gKfly9fNsVhzctIotvY2DBjxgwaN27MpEmT2L17N7t378bDw4M8efLQpk0bmjZtauofGhqKra0tGzduZOfOndy6dYucOXNSq1YtunbtSlBQUJrHKCIiIiIiIvKmMxiNRmN6ByEiIiLpKzo6mvNXYpi46mZ6hyL/EDP6BaV3CCIiIiIiIq+EkugiIiJCdHQ0AH5+fukciYiIiIiIiMjrRQuLioiIiIiIiIiIiIgkQUl0EREREREREREREZEkKIkuIiIiIiIiIiIiIpIE2/QOQERERF4P56/E8N0fEekdhqQTLRQqIiIiIiJinWaii4iIiIiIiIiIiIgkQUl0ERH51zEYDKn68fHxeWmxnD59GoPBQFBQ0Es7hoiIiIiIiIi8PCrnIiIi/zpt2rSxaNu8eTMnTpygaNGiFCtWzGybh4fHK4pMRERERERERP5plEQXEZF/nbCwMIu20NBQTpw4QYMGDejbt+8ri8XLy4vDhw/j5OT0yo4pIiIiIiIiImlHSXQREZGXyM7OjoIFC6Z3GCIiIiIiIiLynFQTXURE3njXr1+nR48e+Pn54eDgQJYsWahRowarV68267d//37s7e3JmjUrFy5csBinbdu2GAwGOnfubGpLqiZ6WFgYBoOBvn37cvbsWZo3b062bNlwdHSkVKlSLFmyxGqsO3bsoGHDhuTJkwd7e3ty5sxJQEAAX331FXfv3n3xiyEiIiIiIiIiZpREFxGRN9qFCxcICAhg2LBhxMbG0qBBA4oXL87atWupXr06I0eONPUtVqwYP/zwAzdu3KBNmzYYjUbTtrlz5xIWFkbBggUZNmxYio9/+vRpSpcuzZYtWwgMDKR48eLs2bOHBg0aWCTxly1bRrly5ViyZAk+Pj40atSIYsWKce3aNQYNGsS1a9de/IKIiIiIiIiIiBmVcxERkTdap06dOHnyJK1atWLy5MnY2dkBCQuRVq9enR49elClShWKFCkCQLdu3VixYgXr1q1j5MiRfPnll1y4cIGOHTtiZ2fHjBkzcHR0TPHxp0yZwmeffcaIESOwtU34Z3n06NF06dKFAQMGEBISYuo7dOhQjEYjO3fupGTJkmbj7Ny5k6xZsyZ7vOjoaKvtsbGxKY5ZRERERERE5E2imegiIvLGOnnyJEuXLsXNzY0xY8aYEugAgYGBdOrUibi4OMaNG2dqNxgMTJkyhcyZM9OnTx+ioqJo06YNN27coH///hQvXjxVMeTLl4/hw4ebEugAnTt3JnPmzGzfvt0suX3lyhXc3d0tEugAAQEBuLq6purYIiIiIiIiIpI8JdFFROSNtXnzZgBq1apFpkyZLLa3atUKgE2bNpm1e3t788svv/Dw4UMqVqzIunXrqFSpEj169Eh1DEFBQWbJewBbW1vy5cvHo0ePuH79uqm9ZMmS3Lx5k/bt23Pw4MFUHwvAz8/P6k/GjBmfazwRERERERGRfzsl0UVE5I118eJFAHx8fKxuT2xP7PekJk2a0LBhQ27fvo2zszNTp07Fxib1/6x6e3tbbXdxcQHg4cOHprYff/yRokWL8ttvv1G4cGGyZctG/fr1+f333836iYiIiIiIiEjaURJdRETeeAaD4Znt1rZfunTJNEP93r17HDlyJE2Pbc1bb73F7t27WbVqFZ999hmenp4sWbKEdu3aUaxYMf7+++/nikFEREREREREkqYkuoiIvLE8PT0BOHXqlNXtp0+fBiBXrlxm7UajkdDQUK5du0bz5s2xsbEx/f6y2draEhISwpgxY4iKiuL06dMEBwdz5MgRBg0a9NKPLyIiIiIiIvKmURJdRETeWIGBgQAsW7aMmzdvWmyfNm0aABUqVDBrHzNmDKtXr6ZChQr88ccf9OrVi0uXLvHRRx+99Jifljt3bnr16gXAgQMHXvnxRURERERERP7tlEQXEZE3Vr58+ahduzZ37tzhiy++4NGjR6Zt27ZtY/z48WTIkIFPPvnE1H7w4EF69+6Nm5sbf/zxBzY2NvTt25eSJUuycOFCJk2a9NLiHTlyJH/99ZdF+8qVK4GEhLqIiIiIiIiIpC3b9A5AREQkPf3yyy9UqFCBqVOnEhkZSdmyZbl69SoRERHExcUxfPhwihQpAiQs8tmiRQsePHjAr7/+Sp48eQCws7Nj2rRplChRgi5duhAUFET+/PnTPNbvv/+e7t27U7RoUfz8/DAajfz5558cPXoUDw8PevTokebHFBEREREREXnTaSa6iIi80by8vNi1axfdunXD1taW+fPns2fPHqpUqcKqVav48ssvTX2/+uor/vzzT5o0aUKrVq3MxilYsCDDhg0jJiaGli1b8vjx4zSP9aeffqJp06bcu3ePFStWsHLlSjJkyED37t35888/8fX1TfNjioiIiIiIiLzpDEaj0ZjeQYiIiEj6io6OBsDPzy+dIxERERERERF5vWgmuoiIiIiIiIiIiIhIEpREFxERERERERERERFJgpLoIiIiIiIiIiIiIiJJUBJdRERERERERERERCQJtukdgIiIiLwezl+J4bs/ItI7DEkjM/oFpXcIIiIiIiIi/wqaiS4iIq8lg8Fg9mNjY0OmTJmoUKECkyZNwmg0mvUPDQ3FYDAQERGRPgGnsaCgIAwGA6dPn07vUERERERERETeaJqJLiIir7U2bdoAEBcXx4kTJ9iyZQubN29m3bp1hIeHp3N0IiIiIiIiIvJvpyS6iIi81sLCwsx+X7NmDbVq1WLmzJm0aNGCOnXqpE9gIiIiIiIiIvJGUDkXERH5R6lWrRqtWrUCYOHChekbjIiIiIiIiIj86ymJLiIi/zjFixcH4Ny5c1a3b9y4keDgYFxdXXFzc6N27docOnQoyfGWLFlC9erVyZo1Kw4ODhQoUID//ve/3L1716Lvk7XKFy5cSJkyZXB2diZLliw0a9aM8+fPWz3GvXv36N+/P4UKFcLR0RF3d3cqVqzIzJkzU3Xu586do3Pnzvj7++Pk5ESWLFl499136dixI0ePHk3VWCIiIiIiIiKSPCXRRUTkH+fOnTsA2NvbW2xbsmQJwcHB3Lhxg+rVq5MrVy6WL19OxYoVuXz5skX/bt26Ua9ePTZu3EihQoWoXbs2sbGxDBgwgKCgIGJiYqzGMG7cOBo3bozRaKRGjRq4uLgwc+ZMgoODuX//vkW8FStW5Ntvv+XKlSvUqVOH8uXLs3PnTpo1a0aXLl1SdN7nz5+nRIkSjBs3DgcHB+rWrUuFChWws7Nj4sSJbNu2LUXjiIiIiIiIiEjKKYkuIiL/KEajkaVLlwJQpEgRi+2jRo3ijz/+YP/+/cydO5dDhw7RuHFjrl+/zrhx48z6zp49mxEjRlC8eHEOHz5MZGQk8+bNIzo6mo8//pg9e/bQt29fq3GMGzeONWvWsGPHDubNm8eRI0coV64c0dHRFgue9unThz179lC1alVOnjzJnDlzWL58Ofv37yd79uyMHj2a5cuXJ3vukyZN4tq1awwfPpyoqChmzZrFokWL2L9/P6dOnaJChQopvIoiIiIiIiIiklJKoouIyD9CXFwc0dHRtGvXjm3btmFvb0/btm0t+jVv3pxmzZqZfs+QIQN9+vQBEsq8POnHH38EIDw8HB8fH1O7nZ0do0ePJmfOnEyaNIn4+HiL43Tt2pXg4GDT705OTnTr1s3iODExMUyePBkbGxvGjRuHi4uLaVvBggX55ptvABgzZkyy1+DKlSsAZsdNlCdPHnx9fZMdIzo62upPbGxssvuKiIiIiIiIvImURBcRkdeawWDAYDBga2tLgQIFCAsLw9XVlfDwcKtJ45CQEIu2AgUKAHDp0iVT25UrV4iKiuLtt9/G39/fYh8HBwdKlSrFzZs3iY6Ofu7j7Nmzh/v37xMQEICfn5/FPomLpG7ZsgWj0Wix/UklS5YEoHPnzmzYsIHHjx8/s7+IiIiIiIiIvDjb9A5ARETkWdq0aQOAjY0Nbm5uFC5cmEaNGpE5c2ar/b29vS3aEmd/P3z40NR25swZAA4fPozBYHhmDNeuXbNItKf0OBcvXgQwm+n+pEyZMuHu7s6tW7e4ffs27u7uScYRGhrK6tWrmT17NsHBwTg5OVGqVClq1qxJu3btyJ49+zPPA7CayAf+/4uCR8nuLyIiIiIiIvKmURJdRERea2FhYanqn1xCPFFcXBwAuXLlsjqr/ElZs2Z97uOkpn9yfTJkyMCsWbPo3bs3ixYtYsOGDWzfvp2NGzcycOBAVq1aRZkyZVIVl4iIiIiIiIg8m5LoIiLyRkqcSZ4zZ85UJ+pTw9PTE4BTp05Z3X7r1i1u3bqFs7Mzrq6uKRqzePHiFC9enL59+3L79m2+//57RowYwRdffMGOHTvSLHYRERERERERUU10ERF5Q3l7e+Pv78+ff/6ZZII7LZQsWRJHR0d27txptbb6tGnTAAgMDEz17HYANzc3fvzxRwwGAwcOHHjheEVERERERETEnJLoIiLyxvrmm2+Ii4ujcePGHDx40GL7iRMn+O23317oGM7OzrRr1474+Hg6d+5MTEyMaduxY8cYMGAAAJ999lmyY/3xxx9W41y5ciVGo5HcuXO/UKwiIiIiIiIiYknlXERE5I3VsmVLDhw4wJAhQyhWrBjFixcnb9683L59mzNnznDkyBGKFi1Ku3btXug4AwcOZPv27axZs4Z8+fJRqVIlYmJiWL9+PQ8ePODzzz+ndu3ayY4zb948Wrduja+vL4ULF8bR0ZHTp0+zfft2MmTIwI8//vhCcYqIiIiIiIiIJSXRRUTkjTZ48GCqV6/Ozz//zLZt24iKiiJz5sx4e3vTo0cPmjZt+sLHcHV1JTIykuHDhzNr1iwWL15MxowZKVWqFJ988gnNmjVL0Thffvkl3t7ebNmyhU2bNhETE4OXlxfNmjWje/fuFC9e/IVjFRERERERERFzBqPRaEzvIERERCR9RUdHc/5KDBNX3UzvUCSNzOgXlN4hiIiIiIiI/CsoiS4iIiKmRU/9/PzSORIRERERERGR14sWFhURERERERERERERSYKS6CIiIiIiIiIiIiIiSVASXUREREREREREREQkCbbpHYCIiIi8Hs5fieG7PyLSOwx5Bi0WKiIiIiIi8uppJrqIiIiIiIiIiIiISBKURP//DAYDBoPhmX369u2LwWCgb9++ryYo+dcJDQ01PWsp/YmIiEjvsFMlLCzsuT4nz7vfq/Dk/di2bVuS/WbPnm3q5+Pj8+oCTIXE77GwsLBU7xsTE8PIkSOpXLkyOXLkIGPGjGTOnJmyZcvy7bffcvbs2bQP+A11+vRpDAYDQUFB6R2KiIiIiIiIyBtP5VxEXqHAwECLtsuXL7Nq1SqcnZ15//33LbbnzJnzVYT20oWGhjJlyhQ2bNjwj04MTp8+nbJly1rdNm3atDQ9Vt++ffn+++/5/fffCQ0NTdOxU2v79u00atSIS5cu4eTkRJkyZciRIwe3bt1i165dbN++nSFDhrB06VKqVq2arrGKiIiIiIiIiKQlJdFFXqEOHTrQoUMHs7aIiAhWrVqFh4fHc80Oft00bNiQMmXK4OHh8Ur2e1Xs7e3x9fVl1qxZjBo1Cltb86/P69evs3LlSkqUKMHevXvTKcqX488//yQ4OJj79+/Tq1cv/vvf/+Ls7GzaHh8fz8KFC+nZsyfnz59Px0hFRERERERERNKeyrmISJpyd3enYMGCqU6GP+9+r1KLFi24du0aq1atstg2a9YsHj16RMuWLdMhspfHaDTSsmVL7t+/T9++fRk0aJBZAh3AxsaGRo0asWfPHkqVKpVOkYqIiIiIiIiIvBxKoqeRe/fu0b9/fwoVKoSjoyPu7u5UrFiRmTNnWu3v4+NjqsE+duxY03558+ZlyJAhGI1GAPbu3UudOnXIkiULrq6uNGjQgDNnzqRJDAD79u2jZs2auLu74+7uTvXq1dm1a9cz61PHxsYyevRoSpcujaurK87OzgQEBDB58mRT3E9KrA8dFxfHkCFDKFCgAPb29rz11lv06tWLhw8fWo3t7t279OvXj8KFC+Pk5ISbmxuVKlVi4cKFSZ7Ptm3bqF+/PtmyZcPe3h4fHx8++eQTLl68aNE3udrQT96jJ+3YsYOGDRuSJ08e7O3tyZkzJwEBAXz11VfcvXs3ydieR2IMRqORn376iaJFi+Lk5ESxYsWAhARneHg4TZs2pUCBAjg7O+Pq6kpAQADjxo0jPj7eYswnz/vAgQPUq1ePzJkz4+zsTKVKldi6davVWFatWkX16tXx9vbG3t4eT09PAgMD+f777836WXt2DAYDU6ZMAaBy5cpmNcZPnz6d5H6JXuTzNWnSJIoUKYKjoyM5c+akY8eO3Lx58xlXPWktWrTAYDBYLdsybdo0XFxcqF+//jPHOHDgAC1atMDLy8t0Hdu2bWu6Dk+eQ+K1bdu2rdU6+Q8ePGDy5MnUr1+ffPny4ejoSKZMmZL93KfGqlWrOHDgAN7e3nz99dfP7Ovu7k6hQoXM2v6J343Pqmmf1HMaFBRkep4XLlxImTJlcHZ2JkuWLDRr1izJGfrXrl2jY8eO5MyZEycnJ4oXL87UqVOt9oWEz/zMmTOpWLEiOXPmxMHBgbfeeouqVasyduzYJPcTERERERERkeenci5p4M6dO1SuXJk9e/aQLVs26tSpQ0xMDOvXr2fTpk1s376dUaNGWd23a9euTJgwgTJlypA3b14iIyPp1asXMTExhISEEBISgo+PD8HBwURFRbFo0SIOHjzIgQMHcHR0fKEYtm7dStWqVbl//z7FixfH39+fQ4cOERgYSNu2ba3GGxMTQ82aNdm0aRMeHh4EBgZiY2PDtm3b6NChA7t27WLChAlW923RogVLly4lICAAf39/Nm3axJAhQ7hw4YJFUvKvv/4iODiYQ4cO4eXlRbVq1bh37x7btm2jYcOGDBw4kN69e5vtM23aNEJDQ4mPj6dcuXK89dZb7N27l/HjxzN//nwiIiIoWLBgMnfz2ZYtW0a9evUwGAyUL1+ecuXK8ffff3Ps2DEGDRpEx44dcXFxeaFjWNOpUyd+//13KlWqxNtvv01sbCwADx8+pHnz5mTOnJl33nmHEiVKcO3aNbZt20bnzp3ZuXNnki8Jdu/eTefOnfH29qZKlSocP36cjRs3UqVKFXbt2mWWDJ0wYQL/+c9/sLe3p0KFClSsWJGrV69y+PBh+vbty3fffffM+Nu0acPmzZs5ceIE1atXN6vzntz1epHPV8+ePU0vfGrUqMHWrVv59ddfOXz4MJGRkckuJvy0PHnyUL58eRYvXszdu3dNsZ86dYpt27bRunVrnJycktx/3rx5NG/enNjYWEqWLEm5cuU4ceIEYWFhLFmyhMjISN59910A3n//fdauXUtUVBTly5cnf/78pnESr9/p06fp0KEDOXLkoGDBggQEBHD58mW2bt3Kpk2bOHLkyAsv1Lps2TIAmjRpYlHCJjn/1O/GFzFu3DiGDx9OqVKlqFGjBrt27WLmzJns2bOHqKgos9iuX79O+fLlOXbsGN7e3tSrV4/Lly/Ttm1bOnXqZHX8r776isGDB+Pq6kpgYCCZMmXi0qVLREVFcfz4cTp37pxm5yIiIiIiIiIiCZRETwN9+vRhz549VK1alQULFpgSa0eOHKFSpUqMHj2akJAQatWqZbHvnDlz2L17tylxduTIEYoVK8awYcOYOnUqP/zwA126dAESZoDXrFmT9evXM3PmTLNEd2pjiI+Pp23btty/f58hQ4bQo0cP01j9+/fn22+/tXquPXr0YNOmTbRq1Ypx48aZjnP16lXq1q3LL7/8Qt26daldu7bZfmfOnMHJyYmDBw+aZnieOnWKkiVLMn36dL7//nt8fX1N/du2bcuhQ4fo2bMnAwYMwM7ODoCTJ08SEhLCN998Q61atShSpAgA586d4+OPP8ZgMLB48WLq1KljOs9u3boxatQoWrduzc6dO5O7nc80dOhQjEYjO3fupGTJkmbbdu7cSdasWV9o/KTMnz+fffv2mZ6TRLa2tsybN486deqQMWNGU/vVq1epVasWU6ZMoV27dlSsWNFizLFjxzJ48GB69uxpauvatSujRo1iyJAhZrNhBw0ahJubG1FRUWYzdI1Go2lW9LOEhYURGhrKiRMn6N27d6oWFn2Rz9e0adPYsWOHaeb+tWvXKFu2LJs2bWLDhg0EBwenOI5ELVu2ZPPmzcyfP5/WrVubjgMJL4qScurUKVq3bo2joyNr1qwxuydTp06lTZs2tG3b1vSMDhs2jL59+xIVFUWHDh2sLiyaLVs2Vq1aRdWqVbGx+d8fFp06dYrg4GD69+9PaGhokrOqU2Lfvn0AlChRItX7/hO/G1/UuHHjWLNmjenZunfvHtWqVWPr1q2Eh4fTrl07s9iOHTtG/fr1mTVrFvb29gCsWLGCevXqWYz94MEDRo0ahY+PD3v27CFLliymbY8fP07yr0ieFh0dbbU98eWciIiIiIiIiJhTOZenPFky4emfp8tWQMLM7MmTJ2NjY2OWVAYoWLAg33zzDQBjxoyxerz+/fubJUYLFixI7dq1uXfvHrlz5zYliQAyZszIF198AUBkZOQLxbB+/XqOHTtGwYIF6d69u1lMffr0IW/evBaxXrlyhUmTJpE3b14mTpxodpxs2bLxyy+/AJj++7SffvrJLJmXN29eU/3oTZs2mdr379/PihUrKFeuHIMGDTIl0AHy5cvH8OHDiYuLY9KkSab2SZMmcf/+fZo1a2ZKoENCreZBgwbh6enJrl272L59u9XYUurKlSu4u7tbJNABAgICcHV1faHxk9KrVy+LBDokJNEbNWpklkCHhPsxcOBAABYtWmR1zMDAQLMEOmB6VjZu3GjWfuXKFfLmzWuRjDUYDFSuXDlV55IaafH5SkygA3h4ePCf//wHsDzHlPrggw/ImDEj06dPN7VNnz6dnDlzUqVKlST3Gz16NPfu3WPIkCEWLzVat25NgwYN2LVrV6oWJc2aNSshISFmCXRI+Gx9/fXXxMfHs2TJkhSPZ83169eBhGcqNf6p340vqmvXrmYvZ5ycnOjWrRtg/szdvXuXP/74A1tbW8aMGWNKoAPUrFmTJk2aWIx9+/ZtHj58SNGiRc0S6JDwXWDtZZmIiIiIiIiIvDgl0Z/Spk2bJH+KFi1q0X/Pnj3cv3+fgIAA/Pz8LLa3atUKgC1btlitF16tWjWLtnz58iW5LXG29qVLl14ohsQZi++//75FSYsMGTLQqFEji3EiIyN59OgRNWrUMEv4JCpatCiurq7s2rXLYpudnZ3V2ccFChSwOJ81a9YAUL9+favlNgIDAwHMjpOYhLc2E9je3t6UkHoyWf88SpYsyc2bN2nfvj0HDx58obFSw9qs1Cft37+fIUOG0LlzZ9q2bUtoaCjjx48Hkp51GhISYtGWNWtWsmbNanY/IOG8o6Ki6N27NydOnHjOs0i9F/18WTtHa89camTOnJlatWqxbt06Ll++zK5duzh69CjNmjUjQ4YMSe735HNtjbXnOqU2b97MgAED+M9//mO6/3PmzAGSvv8pZe26psQ/9bvxRaX0mdu7dy/379/nvffeI3fu3Bb7NGvWzKIte/bseHt7s2zZMoYOHWp1rYeU8PPzs/rz9Ms4EREREREREUmgci5PSap+NGAqrfCkxCRGUuUSMmXKhLu7O7du3eL27du4u7ubbffy8rLYx9nZOdltTy7G+TwxJO7z1ltvWd3HWlInceHD8ePHmxK01ty/f9+iLVeuXFYTjIkzQ588n8Tj9OrVi169eiV5nGvXrpn+P7lrkNj+vEmnRD/++CMHDhzgt99+47fffsPDw4Ny5crRoEEDmjdvbvXlQlqwdj8gofxCaGgo4eHhSe57584dq+3e3t5W211cXEyzjxONHTuWBg0aMHjwYAYPHoynpycVKlTg/fffp1GjRhYzodPKi36+rJ2jtWcutVq2bMnChQuZOXMmp06dMrU9S+Jz/WQ9eGuefK6Tc+vWLRo1asT69euT7JPU/U8pDw8Pjh49ytWrV1O13z/1u/FFpfSZS4wtqc92Uu1TpkyhadOm9OzZk549e5I3b14qVqxI8+bNrSbwRUREREREROTFKYmeRlKyQKG1Ps/aL7WLHj5PDEntY21WZlxcHADFixc31SJPy9iePk6FChVMM0+t8fDwSPVxUhNHfHy8Rdtbb73F7t27Wb9+PUuXLiUyMpIlS5awePFihgwZwtatW8mcOXOKj5FSDg4OVttHjBhBeHg4hQoVYujQoZQoUYLMmTNjZ2fHsWPH8Pf3T3KGbWquRZEiRTh06BArV65k+fLlREZGMmvWLGbNmkVgYCDr1q17qbNYX8bn60XUqVOHTJkyMXXqVC5evMjbb7+dbM3wuLg4DAaDqY56UqyV7UlKr169WL9+PRUrVqRfv34UKlSITJkykSFDBlavXk316tVfeIZ1sWLF2LJlC3v37k32RYE1/9TvxqRY+154nnES70tqzyU4OJjjx4+zdOlSVq5cSWRkJFOmTGHKlCl88MEHzJo1K1XjiYiIiIiIiEjylER/QZ6engCm2ahPu3XrFrdu3cLZ2fml1ct+nhhy5coFwNmzZ63uc+7cOYu2xBmWQUFBjBgx4oXjTkricd5//30+//zzFO3j6enJ0aNHOXXqlKl0wpPOnDkD/O+8AVPS9+7duxb94+LiuHz5stVj2draEhISYpr1efbsWdq2bcv69esZNGgQgwcPTlHMaWHBggUApkT6k06ePJmmx3JwcKBBgwY0aNAAgEOHDtGsWTM2b97M5MmTTbXG09Lr8Pmyxt7envfff99Ulz8lz6m3tzcnTpxgzJgxuLm5pUkcCxYsIEOGDCxevNhiFnVa3f/atWszduxY5syZw5AhQ7C1Tdk/G6/DvXveGOzs7Kx+L4D178YXiS3xu+lpSX03A7i5udG8eXOaN28OwPbt22nSpAmzZ88mNDSUmjVrpkmMIiIiIiIiIpJANdFfUMmSJXF0dGTnzp1Waw9PmzYNSKh3/LJmxT5PDOXKlQNg3rx5FjNV4+PjTcnZJ1WuXJkMGTKwdOlS02zxl6Fq1aoALFy4MMX7VKhQAcBsscdEsbGxpvrQif3gfwn1Y8eOWeyzfv16Hj16lKJj586d21R25sCBAymOOS38/fffgPWyPLNnz36px37nnXfo3LkzkLLzTnxp8fjx4xQf43X4fCWldevWZM2aFQ8PD6u1+J/2PM91ctfs77//xtXV1WoZkrS6/zVq1ODdd9/l/Pnz/PDDD8/se/v2bf7v//4PeD3u3fPGkCtXLq5fv86NGzcs9lm9enWaxebg4MCOHTusJuZnzpyZ4rHKlCljqu/+qr+DRERERERERN4ESqK/IGdnZ9q1a0d8fDydO3cmJibGtO3YsWMMGDAAgM8+++y1iiE4OJj8+fNz+PBhRo4caTbeoEGDrM5i9fLyIjQ0lOjoaFq1amW1dvPWrVtZvnz5C51PmTJlqFKlChs2bKBr164WM0Lj4+NZvXo1mzdvNrW1b98eR0dHwsPDWbZsmVnfPn36cOHCBUqXLk2ZMmVM2ypVqgQkJNIS61VDwgzepO7XyJEj+euvvyzaV65cCSRdx/hlSZx1P2HCBLP2uXPnMnXq1DQ5xr179xgzZgw3b940a0+8D5Cy806ceXv06NEUH/t1+HwlpUKFCly7do2rV6+SJ0+eZPt369YNR0dHunbtypIlSyy237hxg3HjxpmtKZDcNStQoAA3b960KOExcuRINmzYkJrTSZLBYGDatGk4ODjQt29fvvrqK7P7AAmlSRYvXkypUqVMC6O+DvfueWNI/G7o37+/qc1oNDJw4EDToswvysXFhRYtWvD48WO++OILs3rpq1evtvoS5OzZs4SFhXHv3j2z9ocPH5ru96v+DhIRERERERF5E6icSxoYOHAg27dvZ82aNeTLl49KlSoRExPD+vXrefDgAZ9//jm1a9d+rWLIkCEDv//+O9WqVaNbt25Mnz4df39/Dh06xOHDh/noo4+YOHGiRZ3rMWPGcPLkScLDw1m6dCnFihXD09OTy5cvc/z4cS5cuMAXX3xBrVq1Xuh8pk+fTkhICKNGjWLq1KkUK1aMbNmyceHCBdMihyNHjiQwMBBISBz9+uuvhIaGUrduXcqXL89bb73F3r17OXr0KDly5LBIKufLl4/WrVubxq9YsSIxMTFs376d2rVr8+DBA4tSC99//z3du3enaNGi+Pn5YTQa+fPPPzl69CgeHh706NHjhc47tXr27MnKlSvp3bs3c+bMoUCBAkRHR7N79266d+/OsGHDXvgYsbGxfPHFF/To0YMSJUrg4+NDbGwsu3fv5uzZs+TLl4+OHTsmO07dunXp168f3bp1Y82aNaaa9oMHDyZr1qxJ7vc6fL7Sgp+fH9OmTaNly5bUq1cPf39/3n77bYxGI2fOnOHQoUPExsbSvHlzHB0dAQgJCcHBwYGRI0dy8OBBPD09MRgM9OjRA39/f7766itatmxJ06ZNGTt2LN7e3kRFRXHkyBG6du1q8YLseRUrVoy1a9fSuHFjBg0axJgxYyhbtiw5cuTg1q1b7N69m7/++gsHBwezv4p4He7d88TQq1cv5s6dy6hRo4iIiMDX15cDBw5w7tw5PvnkE8aNG5cmsQ0aNIjIyEgWLFiAn58f5cuX56+//iIyMpKOHTtaLOB848YN2rZtS+fOnSlVqhTe3t7ExMSwdetWrl69SkBAAI0aNUqT2ERERERERETkfzQTPQ24uroSGRnJ999/j4eHB4sXL2bTpk2UKlWKGTNmMHr06NcyhsDAQDZv3kz16tWJjo5m6dKleHh4EBkZaZpZ+3Ry08nJidWrVzNp0iRKlCjBwYMHWbBgASdOnMDX15chQ4bQvXv3Fz6fHDlysH37dkaMGIGfnx+7du1i4cKFnD9/nuLFizN27FiLRQ5btmzJxo0bqVOnDocPH2bu3Lncv3+f//znP+zZs4eCBQtaHGfixIn07t0bNzc3Vq1axZkzZ+jTpw/h4eFW4/rpp59o2rQp9+7dY8WKFaxcuZIMGTLQvXt3/vzzT3x9fV/43FOjYsWKbN68meDgYE6ePMnSpUvJmDEj8+bNM5VaeVEuLi6MHTuWOnXqcPXqVRYvXsz69evJnDkz/fv3Z8+ePSlaTLVkyZJMmzaNd999l9WrVzN58mQmT57MnTt3nrnf6/D5SiuNGjUiKiqKjh078ujRI1asWEFERAQPHz6kRYsWLF261Kw0i6enJ4sWLaJMmTJs3ryZ3377jcmTJ3Pp0iUAWrRowbJlyyhTpgz79+9nxYoVeHp6sn79eurVq5emsZcvX57jx48zbNgwSpcuzZ9//sns2bPZsmULPj4+fPfdd0RHR1OlShXTPq/DvXueGN59913Wr19PUFAQx44dY82aNfj6+rJt2zZKly6dZrF5eHiwZcsWOnTowMOHD1m4cCHXr19n4sSJ9OzZ06K/r68vw4YNIygoiLNnzzJ//nzT9R8zZgwREREvdYFfERERERERkTeVwfh0QWwRoGbNmqxcuZLt27fz3nvvpXc4IiLykiXWjffz80vnSEREREREREReL5qJ/ga7ceOGRbkSo9HITz/9xMqVK8mfPz8BAQHpFJ2IiIiIiIiIiIhI+lNN9DfYsWPHKFeuHEWKFCFfvnzExcVx8OBBTp48iaOjIxMnTsRgMKR3mCIiIiIiIiIiIiLpRjPR32D58uWjU6dOPHz4kHXr1rFixQoePnxI8+bN2bFjB0FBQekdooiIiIiIiIiIiEi6Uk10ERERUU10ERERERERkSSonIuIiIgAcP5KDN/9EZHeYbxRZvQLSu8QREREREREJBkq5yKSxq5du8Z///tfihcvTqZMmXByciJ//vx8/PHHHDx4ML3D+0cLDQ3FYDAQERGR3qH8412/fp3+/ftTtmxZsmXLhp2dHR4eHlSqVIkhQ4Zw9erV9A7xjWcwGPDx8UnvMERERERERETeeEqii6ShtWvX4ufnx4ABA7hw4QKVKlWiTp062NnZMXHiRIoVK8agQYPSO8x/HR8fHy2CmwqLFy/G19eXb7/9liNHjlCyZEmaNGlCyZIl2b9/P7169SJ//vz83//9X3qHKiIiIiIiIiKS7lTORSSN7Nq1i9q1a/Po0SMGDhxI9+7dsbX930ds+fLltGzZkq+++gonJyc+//zzdIz2n2ngwIH07t2b3Llzp3co/1irVq2iYcOG2NjYMHz4cD777DPs7OxM22NjY5k2bRp9+vTRbHQREREREREREbSwqEiaMBqNvPvuuxw+fJj+/fvzzTffWO23ceNGgoKCsLe358iRI+TJk+cVR/rv5OPjw5kzZ9DX2bPdu3ePvHnzcuXKFcLCwmjTpk2SfS9cuMCjR49UTiQdGQwG8uTJw+nTp1/J8aKjozl/JYaJq26+kuNJAtVEFxERERERef2pnItIGlixYgWHDx/Gy8uLXr16JdmvYsWKNGnShAcPHjB27FizbYklSYxGIz/99BNFixbFycmJYsWKmfpcuXKFjz76iBw5cuDk5ESJEiWYMWMGp0+fxmAwEBQUZDbmzZs3+emnn6hevTp58uTB3t6erFmzUqNGDdasWWM1xqCgIAwGA6dPn2bhwoWUKVMGZ2dnsmTJQrNmzTh//rzV/e7du0f//v0pVKgQjo6OuLu7U7FiRWbOnGm1/5MlWCZNmkSRIkVwdHQkZ86cdOzYkZs3b1rs83RN9IiICAwGA2fOnAESko6JP08mf588pxkzZlCmTBlcXV3JlCnTc8cPsG/fPmrWrIm7uzvu7u5Ur16dXbt2ERYWhsFgoG/fvhb7xMbGMnr0aEqXLo2rqyvOzs4EBAQwefJkqy8BEs8lLi6OIUOGUKBAAezt7Xnrrbfo1asXDx8+TDK+p02dOpUrV67w3nvvPTOBDuDl5WWRQL9+/To9evTAz88PBwcHsmTJQo0aNVi9erXVMRJjf/z4Mf379yd//vw4Ojry9ttv8/vvv5v6rV+/nsqVK+Pm5kbmzJlp3bo1169ftxjvyfs4bdo0SpYsiZOTE9mzZ6dNmzZcuHDBahyPHz/mp59+omTJkri4uODi4kJAQADjx48nLi7Oov+zygMlPnOhoaFm7U8+mxs3biQ4OBhXV1fc3NyoXbs2hw4dsjpeTEwMvXr1Infu3Dg4OFCwYEFGjBjxzBdCq1atonr16nh7e2Nvb4+npyeBgYF8//33Se4jIiIiIiIiIs9PSXSRNLB8+XIAmjRpYlYaw5rmzZsDCYl3azp16kS3bt3Inj079erVI1++fEDCgqXlypVj0qRJ2NvbU69ePdzd3WnVqhUjR460Otb27dv5/PPPOXz4MH5+fjRs2BB/f39Wr15N9erV+e2335KMc9y4cTRu3Bij0UiNGjVwcXFh5syZBAcHc//+fbO+d+7coWLFinz77bdcuXKFOnXqUL58eXbu3EmzZs3o0qVLksfp2bMnnTt3xs3NjRo1amA0Gvn111+pV69esjPLc+bMSZs2bXB2dgagTZs2pp/333/fov/AgQNp1aoVGTNmpE6dOhQqVOi549+6dSvly5dn5cqV+Pr6UqtWLS5fvkxgYCDbt2+3Gm9MTAxVq1alS5cunD59msDAQIKCgjh+/DgdOnTgP//5T5Ln2qJFC/r164e3tzchISHcuXOHIUOG0L59+2deoyctW7YM+N8zmBoXLlwgICCAYcOGERsbS4MGDShevDhr166levXqST6DAB988AFDhw7F19eXihUrcurUKdq1a8fvv//O3LlzqV69Onfu3KFatWo4Ozvzxx9/0KBBgyTv/7Bhw2jdujUuLi7Ur18fZ2dnpk6dSpkyZSxe8sTFxVG/fn0+//xzjh8/TtWqValatSpHjhzhk08+oUmTJsTHx6f6eiRlyZIlBAcHc+PGDapXr06uXLlYvnw5FStW5PLly2Z9Hz58SEhICEOGDOH+/fvUrVsXHx8fevfuzaeffmp1/AkTJlCjRg0iIyN5++23ady4Me+++y6nT5+2+tJGRERERERERF6caqKLpIH9+/cDULJkyWT7JvY5dOgQjx49ski6z58/n3379vHuu++atffu3ZsTJ07QsGFDwsPDsbe3B2DdunXUqlXL6rH8/f3ZsmUL5cqVM2vft28fwcHBdO3alQ8++AAXFxeLfceNG8eaNWsIDg4GEmZqV6tWja1btxIeHk67du1Mffv06cOePXuoWrUqCxYsMI135MgRKlWqxOjRowkJCbEa57Rp09ixY4dpxv21a9coW7YsmzZtYsOGDabjW1OwYEHCwsKIiIggJiaGsLCwJPtCwkzs9evXU6lSJbP21MYfHx9P27ZtuX//PkOGDKFHjx6msfr378+3335r9fg9evRg06ZNtGrVinHjxpmOc/XqVerWrcsvv/xC3bp1qV27ttl+Z86cwcnJiYMHD5pmh586dYqSJUsyffp0vv/+e3x9fZ957pBw3wFKlCiRbN+nderUiZMnT9KqVSsmT55sem43b95M9erV6dGjB1WqVKFIkSIWsbu6unLo0CG8vb0BTPf166+/JjY2lpkzZ9K4cWMAbt++Tbly5di8eTMRERFUrlzZIpZffvmFpUuXmu7Ho0ePaNu2LdOnT+fzzz9n/vz5pr6jRo1i+fLlFC5cmLVr15I9e3YALl26ROXKlVmwYAETJkzgk08+SfU1sWbUqFFMmzaNZs2aAQlJ/A8//JB58+Yxbtw4+vXrZ+o7YsQItm7dSkBAAKtXr8bd3R2AvXv3Wj1vgEGDBuHm5kZUVJTZXwoYjUbTX2iIiIiIiIiISNrSTHSRNJBYeiIxQfcs2bJlAxISsTdu3LDY3qtXL4sE+t27d5k+fTq2traMHj3alEAHqFKlCk2bNrV6rLx581ok0AGKFy9O586duX37Nhs2bLC6b9euXc0S2E5OTnTr1g1IqO2eKCYmhsmTJ2NjY2OWGIaEJHdiffgxY8ZYPU7//v3NStZ4eHiYZmQ/eZy00L59e4sE+vPEv379eo4dO0bBggXp3r272Xh9+vQhb968Fse+cuUKkyZNIm/evEycONHsONmyZeOXX34BMP33aT/99JNZ0jRv3ry0bNkSgE2bNqXk9E3PaeIzmFInT55k6dKluLm5MWbMGLMXP4GBgXTq1Im4uDjGjRtndf8xY8aYEugAlStXpkSJEly6dInatWubEugAbm5ufPzxxwBERkZaHe+DDz4weyFjZ2fH6NGjcXZ2ZtGiRWZlXRLv26hRo8w+n7ly5WLo0KFmfdJC8+bNTQl0gAwZMtCnTx/A8nkeP348ACNHjjQl0CHhJUfnzp2tjn/lyhXy5s1rUWrHYDAkmXh/WnR0tNWf2NjYFO0vIiIiIiIi8qZREl0kDSSWnUjJwpZP9rFWd7levXoWbXv37uXBgweUKVOGt956y2J7kyZNkjxeXFwcq1evpm/fvnTq1InQ0FBCQ0NNyfPo6Gir+4WEhFi0FShQAEiYxZtoz5493L9/n4CAAPz8/Cz2adWqFQBbtmyxen1Sepy0YO3aPk/8W7duBeD999+3uIcZMmSgUaNGFuNERkby6NEjatSoYfYSJFHRokVxdXVl165dFtvs7Ows6t3Dy7tOT9u8eTMAtWrVMqsjnyjxGllL5mfMmNHixQVgKlNUrVo1i22Js+qTOi9rL42yZs1KtWrViI+PN92fs2fPcvbsWXLmzGn1Lxrq1KlDpkyZOHr0KFevXrV6rNRK6fN89uxZzp07h5eXl9UXXU8m4p9UsmRJoqKiTH+ZIiIiIiIiIiIvn8q5iKQBDw8Pjh49ypUrV5Ltm5isMxgMZM6c2WJ77ty5LdouXrwIYDWBntQ+AOfPn6dOnTpERUUlGc+dO3estj85czhR4uzpJxezTIzt6ZmxiTJlyoS7uzu3bt3i9u3bZjNuU3OctPCsa5ua+J/nfpw+fRpImH2cOAPZmqfrzUPCrOkMGTJYtKf2OmXNmpULFy5w9epV/P39U7QPJH+NEtsT+z0pZ86c2NhYvq9NrGPv5eWV5LakzitPnjwpiiO5uA0GA3ny5OHmzZtcvHgx1TP0rUnt5yapz25S7WPHjqVBgwYMHjyYwYMH4+npSYUKFXj//fdp1KiR1Wv9NGsviyDxhdqjZPcXERERERERedMoiS6SBooWLcqWLVvYs2ePaVZuUvbs2QPAu+++a3URUgcHhyT3tTZzHZKeAd+hQweioqJo1KgRvXr1wt/fH1dXV2xsbPj111/p2LFjkvsmdazUxpZcn9Qe50U8z7V9Vp/U3I+4uDggoZTO03XDU3vc51WsWDEuXLjA3r17CQwMTPX+ScWR2P489zct7/+LPMspjSO5RUhTOk5irMld06cVKVKEQ4cOsXLlSpYvX05kZCSzZs1i1qxZBAYGsm7dOjJmzJiiGEREREREREQkZZREF0kDNWvWZNy4ccydO5ehQ4daTY4nmjFjBgA1atRI8fi5cuUCEkpAWHPu3DmLtpiYGNasWUOOHDmYPXu2xUzmkydPpvj4z+Lp6QkkLHRpza1bt7h16xbOzs64urqmyTHT0vPE/zz3I3GGclBQECNGjHjhuJ9H7dq1WbZsGeHh4Xz++ecp3i+5a5Q4yz7xurxsZ86csfoiIvF+JMabXNxP7vNk7IlJ6Lt371osumvt3j6PxNjOnDljdXtS7ZDwMqhBgwY0aNAASFikuFmzZmzevJnJkyeb1hQQERERERERkbShmugiaaBWrVr4+/tz4cIFBg8enGS/jRs3MnfuXDJmzJjkwoHWlChRAnt7e7Zv38758+ctts+dO9ei7datW8THx1stBfL48WMWLFiQ4uM/S8mSJXF0dGTnzp1W66tPmzYNSFiA8mXNOk9Mej5+/DjV+z5P/Ik1rOfNm2cx+zk+Pt7qta1cuTIZMmRg6dKlplnpr1rr1q3Jli0b27dvZ8qUKc/se/HiRVNyPHHW+rJly7h586ZF38RrVKFChTSNNymzZs2yaLtx4warV6/GYDBQtmxZIKEkSu7cubl8+TLr16+32GfZsmX8/fff+Pv7m5VySUyoHzt2zGKf1atXp8k55MmTB29vby5cuMC2bdssts+cOTPFY73zzjum75MDBw6kSXwiIiIiIiIi8j9KooukARsbG8LCwrCzs+Pbb79l8ODBFonSFStW0KBBA4xGI4MGDUqyTrM1rq6uNG/enMePH9O1a1diY2NN2yIiIggPD7fYJ3v27Li7u3Pw4EG2bNliao+Li6Nnz55WE4TPw9nZmXbt2hEfH0/nzp2JiYkxbTt27BgDBgwA4LPPPkuT41mTOKv36NGjqd73eeIPDg4mf/78HD58mJEjR5qNN2jQIKuz/L28vAgNDSU6OppWrVpx7do1iz5bt25l+fLlqT6HlHJ2diYsLAwbGxs6dOjAyJEjefTIvAb248ePmTp1KiVLljQl0fPly0ft2rW5c+cOX3zxhdk+27ZtY/z48WTIkIFPPvnkpcX+pNmzZ7Nq1SqzmLt27UpMTAz16tUzq0ueeN+6du1qtnjo5cuX6dGjh1mfRIkLoQ4cONDsczxt2rRUJbeT07FjRwC6devG7du3Te379+9n7NixFv3v3bvHmDFjLF5kxMfHm5L7SdVSFxEREREREZHnp3IuImmkTJkyLF68mKZNm9K7d29GjBhB2bJlsbe358CBAxw+fBgbGxv69+9P165dUz3+oEGDiIiIYO7cuezcuZNy5cpx5coVIiIi+OSTT/j555/NaiHb2trSs2dPvv76aypVqkRwcDBZsmRhx44d/PXXX3Tu3Nlqou55DBw4kO3bt7NmzRry5ctHpUqViImJYf369Tx48IDPP/+c2rVrp8mxrKlXrx6RkZFUqVKFypUr4+zsjIeHB4MGDXop8WfIkIHff/+datWq0a1bN6ZPn46/vz+HDh3i8OHDfPTRR0ycONGiNvWYMWM4efIk4eHhLF26lGLFiuHp6cnly5c5fvw4Fy5c4IsvvqBWrVppen2eVKtWLebOnUubNm348ssv6devH2XKlCFLlixcv36dHTt2cPPmTTJlykT27NlN+/3yyy9UqFCBqVOnEhkZSdmyZbl69SoRERHExcUxfPjwVNd6f14ff/wxNWvWpGLFinh6erJ9+3ZOnTqFp6cnY8aMMevbtWtX1q9fz4oVK/Dz8yM4OBij0ci6deu4c+cODRo0sCh/0rlzZyZMmMDcuXN55513KFKkCNHR0Rw8eJAvvvjC4sXJ8+rRowdLly5l27Zt+Pr6UrlyZe7cucP69etp3769xQK0sbGxfPHFF/To0YMSJUrg4+NDbGwsu3fv5uzZs+TLl8+UmBcRERERERGRtKOZ6CJpqEaNGkRHR/P111+TK1cu1q9fz+LFi3nw4AEdOnRg3759fPPNN881dvbs2dm2bRvt2rXj/v37LFy4kOvXr/P777/TtGlTALJmzWq2T58+fZgyZQpFihRhy5YtrF27lqJFi7J9+3ZKlSr1wuebyNXVlcjISL7//ns8PDxYvHgxmzZtolSpUsyYMYPRo0en2bGs+fzzz/nmm29wcXFh3rx5TJ48OVUzhp8n/sDAQDZv3kz16tWJjo5m6dKleHh4EBkZSZ48eQDL++Hk5MTq1auZNGkSJUqU4ODBgyxYsIATJ07g6+vLkCFD6N69+4tdjBRo2LAhJ0+epG/fvvj7+7Nz505mz57N7t27KVKkCEOHDuXEiRO88847pn28vLzYtWsX3bp1w9bWlvnz57Nnzx6qVKnCqlWr+PLLL1963Im6d+/O77//zq1bt1iwYAG3b9+mVatW7Nixw2ImdoYMGVi8eDGjR48mX758rFq1itWrV+Pv78/YsWOZO3cuNjbm/xTmyJGDjRs3UqdOHS5dusSKFStwd3dnzZo11KtXL83Ow97enrVr19K9e3fs7e1ZtGgRJ0+eZMCAAfz8888W/V1cXBg7dix16tTh6tWrLF68mPXr15M5c2b69+/Pnj17yJw5c5rFJyIiIiIiIiIJDManC/qKyD/O4MGD6d27N4MGDaJXr17pHc4br2bNmqxcuZLt27fz3nvvpXc4/xpBQUFERkZy6tSpVJVDkpSJjo7m/JUYJq66md6hvFFm9AtK7xBEREREREQkGSrnIvIPsnfvXkqUKGHWtnHjRn788UdsbW354IMP0imyN8+NGze4c+eOadY5gNFo5Oeff2blypXkz5+fgICAdIxQJPW8szszo1+x9A5DRERERERE5LWiJLrIP0i5cuXw9PTk7bffxtnZmePHj7Nv3z4goWZ63rx50znCN8exY8coV64cRYoUIV++fMTFxXHw4EFOnjyJo6MjEydOxGAwpHeYIiIiIiIiIiLyglQTXeQf5KuvvsLDw4MdO3awYMECzpw5Q0hICIsXL1YZl1csX758dOrUiYcPH7Ju3TpWrFjBw4cPad68OTt27CAoKCi9QxQRERERERERkTSgmugiIiJCdHQ0AH5+fukciYiIiIiIiMjrReVcREREBIDzV2L47o+I9A7jH0+LhYqIiIiIiPy7qJyLiIiIiIiIiIiIiEgSlEQXEZGXzmAwaKFVEREREREREflHUhJdRETeKH379sVgMBAWFpbeoYiIiIiIiIjIP4CS6CIiIiIiIiIiIiIiSVASXUREREREREREREQkCUqii4jIa+XSpUsMGTKESpUq4eXlRcaMGcmZMyeNGjVi165dSe4XExPDwIEDKVGiBK6urri4uPDOO+/QpUsXzpw5A4CPjw/ff/89AG3btjXVajcYDERERJiN98cffxAYGIibmxtOTk4UKVKEgQMH8uDBA4tjh4aGmsbYuHEjwcHBuLq64ubmRu3atTl06FCScS9ZsoTq1auTNWtWHBwcKFCgAP/973+5e/eu1XMcPHgwxYoVI1OmTLi4uODr60uTJk1YtWpVSi6viIiIiIiIiKSSbXoHICIi8qRFixbRq1cv8ufPT+HChXFzc+P48eMsWLCApUuXsnTpUkJCQsz2uXTpElWrVuXQoUNkyZKF4OBg7OzsOH78OGPGjKFYsWKEhoby/vvvs3btWqKioihfvjz58+c3jZEzZ07T/3fs2JFff/0VBwcHgoODcXJyIiIigj59+rBkyRLWrVuHo6OjRexLlixh9OjRFCpUiOrVq3PgwAGWL1/Ojh07OHjwoNkxALp168aIESNwcHAgICAADw8P9uzZw4ABA1ixYgWRkZE4OzsDEBcXR0hICFu3bsXb25ugoCAyZszI+fPnWbp0Kc7OzlSvXj0tb4WIiIiIiIiIoCS6iIi8ZsqXL09UVBRFihQxa1+1ahX16tXjk08+ITo6GoPBYNrWqlUrDh06RLNmzZg4caIp8QwQHR1NXFwcAMOGDaNv375ERUXRoUMHQkNDLY4/b948fv31V7y8vIiIiDAl2m/fvk3t2rXZvHkz3333HUOGDLHYd9SoUUybNo1mzZoBCYnvDz/8kHnz5jFu3Dj69etn6jt79mxGjBhB8eLFmT9/Pj4+PgA8evSITz/9lF9//ZW+ffsydOhQADZt2sTWrVupX78+8+fPx8bmf39MduvWLY4fP56i6xsdHW21PTY2NkX7i4iIiIiIiLxpVM5FREReK4ULF7ZIoANUr16dJk2acOLECQ4ePGhq37lzJ+vWrSNnzpwWCXQAPz8/ChYsmOLjjxkzBoB+/fqZzVR3c3Nj3LhxGAwGJkyYYDXp3Lx5c1MCHSBDhgz06dMHgI0bN5r1/fHHHwEIDw83JdAB7OzsGD16NDlz5mTSpEnEx8cDcOXKFQCCgoLMEugA7u7ulCxZMsXnKCIiIiIiIiIpp5noIiLy2nn48CErV65k586dXL161ZSwPnDgAJAwm7pw4cIArF27FoAWLVpYJNBT69GjR2zfvh2DwUDz5s0tticm+KOiooiKiqJ06dJm258uMwNQoEABIKHkTKIrV64QFRXF22+/jb+/v8U+Dg4OlCpViqVLlxIdHY2/vz/FihXDxsaGoUOHkjNnTmrXro2rq2uqz9HPz89qe8IM9UepHk9ERERERETk305JdBERea0cOHCAevXqcfr06ST73Llzx/T/586dA8DX1/eFj339+nViY2PJmTMnDg4OVvv4+PgQFRXFxYsXLbZ5e3tbtLm4uAAJLwYSJS50evjwYbOyNNZcu3YNf39/ChQowNChQ+nduzfNmjUjQ4YMFCpUiKpVq9K2bVvefffdFJ+niIiIiIiIiKSckugiIvLaMBqNfPDBB5w+fZpOnTrRqVMn8uXLh4uLCwaDgT59+jBw4ECMRqPFvsklo1MjJWNZ65PSGBJrtOfKlcvq7PUnZc2a1fT/X375JU2aNGHhwoWsWbOGTZs2MXz4cEaOHMmYMWPo3Llzio4vIiIiIiIiIimnJLqIiLw2jhw5wpEjRyhVqhTjx4+32H7y5EmLtrfeegsgxQtrPkvWrFnJmDEjly9f5v79+zg6Olr0SZxFnitXruc+TuKM9Zw5cxIWFpaqfd966y0+++wzPvvsMx4/fszMmTNp27YtX375JS1atCBTpkzPHZeIiIiIiIiIWNLCoiIi8tr4+++/AetlUf7++2/WrFlj0V61alUApk+fzr1795I9RsaMGQF4/PixxTY7OzvKlCmD0WgkPDzcYvvBgweJiorC1dWVokWLJnuspHh7e+Pv78+ff/7JqVOnnnscW1tbWrZsSenSpYmNjeXYsWPPPZaIiIiIiIiIWKckuoiIvDby58+PjY0N69ev//8LXSZ48OABnTp14saNGxb7BAQEULlyZS5fvkzHjh0tEunHjx/nyJEjpt89PT0BOHr0qNUYPvvsMwC+++47s5nvd+7c4dNPP8VoNNKxY0dTMv55ffPNN8TFxdG4cWMOHjxosf3EiRP89ttvpt83bNjA2rVriY+PN+t35swZU211ay8fREREREREROTFqJyLiIi8MmXKlElyW9euXfnwww9p3749EydOpGjRogQHB+Po6MimTZuIi4sjNDTUavmTP/74g+DgYKZNm8aKFSsIDAzEzs6O48ePExUVxW+//UbBggUBCAkJwcHBgZEjR3Lw4EE8PT0xGAz06NEDf39/3n//fT7++GN+/fVXChUqRHBwME5OTkRERHD16lXKlCnD999//8LXomXLlhw4cIAhQ4ZQrFgxihcvTt68ebl9+zZnzpzhyJEjFC1alHbt2gEQFRVF165dyZYtGyVLliRr1qxcvXqVjRs38uDBA7p06WJ6QSAiIiIiIiIiaUdJdBEReWV27NiR5LZLly4BMH78eAoWLMjkyZNZt24d7u7uVK1alR9++IHff//d6r5eXl7s2rWLkSNHMnfuXFavXo2trS1vvfUWXbp0ITg42NTX09OTRYsW0a9fPzZv3szdu3eBhKS2v78/AL/88guBgYFMmDCByMhIHj9+jK+vL126dKFr165Wa6U/j8GDB1O9enV+/vlntm3bRlRUFJkzZ8bb25sePXrQtGlTU986depw/fp1NmzYQFRUFNevXydbtmxUqFCBTz75hAYNGqRJTCIiIiIiIiJizmA0Go3pHYSIiIikr8TyOX5+fukciYiIiIiIiMjrRTXRRURERERERERERESSoCS6iIiIiIiIiIiIiEgSlEQXEREREREREREREUmCkugiIiIiIiIiIiIiIkmwTe8ARERE5PVw/koM3/0Rkd5h/OPN6BeU3iGIiIiIiIhIGtJMdBGRfxGDwWD2Y2NjQ6ZMmahQoQKTJk3CaDSmd4hp4vTp0xgMBoKCglK8T2hoKAaDgbCwsJcy/rMYDAZ8fHxStU9QUBAGg4HTp0+nSQwiIiIiIiIi8nw0E11E5F+oTZs2AMTFxXHixAm2bNnC5s2bWbduHeHh4ekcnYiIiIiIiIjIP4eS6CIi/0JPz7Zes2YNtWrVYubMmbRo0YI6deqkT2D/EF5eXhw+fBgnJ6c0Ge/w4cPY2dmlyVgiIiIiIiIi8mqpnIuIyBugWrVqtGrVCoCFCxembzD/AHZ2dhQsWJDcuXOnyXgFCxbE19c3TcYSERERERERkVdLSXQRkTdE8eLFATh37pypLbFWd2xsLP369aNgwYLY29vToEEDU5+7d+/Sr18/ChcujJOTE25ublSqVMlqMv7JWuL379+nd+/e5MmTB3t7e/Lnz8/gwYOTrMt+6NAhWrRoQa5cuciYMSNeXl60bt2ao0ePpul1SImkaqKHhYVhMBjo27cvx44do3HjxmTNmhVnZ2fKly/P8uXLrY73rJrov/76K4ULF8bBwQEvLy8+++wzbt26lWRshw8fplWrVvj6+uLg4EC2bNkoVqwYXbp04dKlS897yiIiIiIiIiKSBJVzERF5Q9y5cwcAe3t7s/b4+HgaNGjAxo0bqVSpEkWKFCFr1qwA/PXXXwQHB3Po0CG8vLyoVq0a9+7dY9u2bTRs2JCBAwfSu3dvi2PFxsYSEhLC//3f/xEQEMDbb79NZGQkvXv35s6dOwwYMMCs/7p166hbty7379+nRIkSBAUFceTIEf744w8WLFjA8uXLqVChwku6Mql34sQJAgICyJIlCyEhIVy8eJFNmzZRp04dfvvtN0JDQ1M0Tvfu3Rk+fDj29vYEBwfj5OTE9OnT2bJli8V9Ati7dy+BgYE8ePCAgIAAAgICuHPnDidPnmT06NE0aNCAXLlypfHZioiIiIiIiLzZlEQXEXkDGI1Gli5dCkCRIkXMtp07dw57e3uOHj2Kl5eX2ba2bdty6NAhevbsyYABA0x1vU+ePElISAjffPMNtWrVshhz27ZtVKhQgWPHjuHh4QHA7t27KVu2LCNHjqR37964uLgAEBMTQ4sWLbh//z7jx4+nU6dOpnFGjhzJl19+SfPmzTl+/LjVxHJ6mDZtGq1bt2by5MnY2ib8U7p06VIaNGjAp59+SvXq1ZNNZm/dupXhw4eTJUsWNm7cyLvvvgvA9evXCQ4OZvv27Rb7jBkzhvv37zNv3jwaNWpktu3w4cNkypQpbU5QRERERERERExUzkVE5F8sLi6O6Oho2rVrx7Zt27C3t6dt27YW/QYOHGiRQN+/fz8rVqygXLlyDBo0yGxhzHz58jF8+HDi4uKYNGmSxXg2NjZMmjTJlEAHKFWqFDVr1uTevXvs3r3b1D579mz++usvKlSoYJZAB+jatSslS5bk/PnzLFiw4LmvQ1pzcXFh1KhRpgQ6QJ06dXj//feJiYmxWNjVmgkTJgDQrVs3UwIdIGvWrAwdOtTqPleuXAEgODjYYtvbb7+dolno0dHRVn9iY2OT3VdERERERETkTaQkuojIv5DBYMBgMGBra0uBAgUICwvD1dWV8PBwiwUuDQYDdevWtRhjzZo1ANSvXx+DwWCxPTAwEIBdu3ZZbPPx8aFAgQIW7YltT9bu3rRpEwAtWrSwei4tW7Y06/c6CAkJIXPmzBbtzZo1A2Dz5s3JjpHY54MPPrA6fpYsWSzaS5YsCUDr1q3ZuXMn8fHxqYpbRERERERERFJP5VxERP6F2rRpAyTMCHdzc6Nw4cI0atTIauI3e/bsVsuknD59GoBevXrRq1evJI917do1izZvb2+rfRNLuDx8+NDUdvHiRYAkF95MbE/s9zrIkyeP1fbUxHrx4kUMBgNvvfWW1e25c+fmxo0bZm09evRg8+bNLFmyhCVLluDu7s57771HnTp1CA0NxdXVNdnj+vn5WW2Pjo4GHiW7v4iIiIiIiMibRkl0EZF/oZSUE0nk4OBgtT0uLg6AChUqkC9fviT3f7JkSyJrM9eTk9w+zzPmq2Y0Gl/q+G5ubqxfv54tW7awZMkSIiIiWLduHatXr2bgwIFs2rTJ4i8NREREREREROTFKIkuIiJWJc4mf//99/n8889f2nE8PT0BOHXqlNXtZ86cAUhRve9XJTGmp509exb43zk9S65cuTh9+jTnzp0jf/78SY71NIPBQGBgoKmcztWrV/niiy8IDw+nT58+zJo1K6WnISIiIiIiIiIpoJroIiJiVdWqVQFYuHDhSz1OhQoVAJg+fbrV7Yntif1eB6tXr+bmzZsW7eHh4QCUL18+2TESk+Bz5syx2LZmzRqLUi5JyZYtG3379gXgwIEDKdpHRERERERERFJOSXQREbGqTJkyVKlShQ0bNtC1a1fu3r1rtj0+Pp7Vq1enaBHNZ/nggw/IkSMHmzZt4tdffzXbNmbMGHbt2oW3tzcNGzZ8oeOkpbt37/Lll1/y+PFjU9vy5cuZM2cOTk5Oppr0z9KxY0cARowYweHDh03tN27coGfPnlb3mTBhgtUZ+ytWrAAS6qiLiIiIiIiISNpSORcREUnS9OnTCQkJYdSoUUydOpVixYqRLVs2Lly4wNGjR7l69SojR440zap+Hs7OzkyfPp26devSsWNHfv31VwoUKMCRI0fYt28fzs7OzJgxw+rip8+jf//+TJgwweq2AgUKMHXq1GTHaNGiBfPnzyciIoL33nuPS5cusXHjRoxGI6NHj8bLyyvZMQIDA+nSpQujRo2iePHiVK1aFUdHR9avX0/u3LkpU6YM27dvN9tnwoQJ/Oc//+Gdd97h7bffxtbWlqNHj7J//34cHR357rvvUnYRRERERERERCTFlEQXEZEk5ciRg+3btzNhwgRmzZrFrl27iI2NJVeuXBQvXpz69evzwQcfvPBxqlSpwq5du/jhhx9Yv349f/75Jx4eHrRs2ZJvvvkGf3//NDibBCdPnuTkyZNWtz148CBFY+TPn59t27bx1VdfsWrVKh48eECZMmXo06cPderUSXEsI0aMwN/fn59//pk1a9aQNWtWPvjgAwYOHEiDBg0s+vfv35+FCxeyY8cO1q1bR2xsLN7e3nz88cf06NHDam11EREREREREXkxBqPRaEzvIERERP4JwsLCaNu2Ld99952pDvm/RXR0NOevxDBx1c30DuUfb0a/oPQOQURERERERNKQZqKLiIgIAN7ZnZnRr1h6hyEiIiIiIiLyWtHCoiIiIiIiIiIiIiIiSVASXUREREREREREREQkCaqJLiIiIkRHRwPg5+eXzpGIiIiIiIiIvF5UE11EREQAOH8lhu/+iEjvMP7xtLCoiIiIiIjIv4vKuYiIiIiIiIiIiIiIJEFJdEkTBoPB7MfGxoZMmTJRoUIFJk2axItWDerbty8Gg4GwsLC0Cfg1cPr0aQwGA0FBQWbtERERGAwGQkNDX0kc//nPfzAYDGTIkIHz58+/kmP+kyR1n9JK4v1ObvygoCAMBgMREREvJY5EBoMBHx+fl3qM10XiNT19+nR6hyIiIiIiIiIirzEl0SVNtWnThjZt2tCiRQveeecdtmzZwkcffUTz5s3TOzSxIjY2ltmzZwMQHx/PjBkz0jmiVy8sLAyDwUDfvn3TOxQREREREREREXkNqSa6pKmnZ4qvWbOGWrVqMXPmTFq0aEGdOnXSJ7B/kICAAA4fPoy7u/tLP9ayZcu4ceMGuXLl4tKlS0ybNo2ePXu+9OP+k3h5eXH48GGcnJzSOxQREREREREREUkHmokuL1W1atVo1aoVAAsXLkzfYP4hnJycKFiwILly5Xrpx/rjjz8A+OGHH/Dx8eHAgQNERUW99OP+k9jZ2VGwYEFy586d3qGIiIiIiIiIiEg6UBJdXrrixYsDcO7cObP2P/74g8DAQNzc3HBycqJIkSIMHDiQBw8epGjcQoUKYTAYOHbsmNXtp0+fxsbGBj8/P4ua7Nu2beODDz7A09MTe3t7vLy8qF69OtOmTQPg0qVL2NnZkTt3buLj462OP2XKFAwGAx06dDBrNxqNTJ8+nSpVqpA1a1YcHBzIly8fzZs3Z8uWLcmeV3I10VesWEGdOnXInj079vb25M6dmwYNGrBs2bJkx37S33//zbJly3B0dOT99983ldxJvAbPiuvy5ct06NABb29vbG1tGTVqlKnftm3bqF+/PtmyZcPe3h4fHx8++eQTLl68aDHmk6VUTpw4wQcffICHhwdubm7UrFmTQ4cOAfD48WN+/PFHChQogIODA/nz52fcuHFW41y2bBnt2rXj7bffxs3NDWdnZ4oWLcqPP/7Iw4cPzfoGBQXRtm1bAL7//nuzuv6Jf1WRXE305J6ll+Xhw4d4eHjg5OTErVu3rPaJjIzEYDBQtWpVs/aYmBh69epF7ty5cXBwoGDBgowYMSLJtQuevE/Hjh2jadOm5MiRAxsbG7OXY8uXL6datWpkzpwZBwcH/P396d27Nzdv3kzyPFL6PKfmvqYm5kTTpk2jZMmSODk5kT17dtq0acOFCxesxvz48WN++uknSpYsiYuLCy4uLgQEBDB+/Hji4uIs+j9Ze33hwoWUKVMGZ2dnsmTJQrNmzayuRfDo0SN++eUXAgICTPfZx8eHOnXqMHPmzCSvp4iIiIiIiIikPSXR5aW7c+cOAPb29qa2jh070rp1a/bs2UOFChWoXbs2ly5dok+fPgQHB3P//v1kx+3YsSMAkyZNsrp98uTJGI1GOnTogMFgMLWPGjWK8uXLM2fOHLy9vWnUqBEFCxZk7969fPPNNwDkypWLevXqce7cOVauXGl1/MTjfvTRR6a2uLg4PvjgA1q2bMmWLVsoXrw4DRo0IGfOnCxYsICJEycme17P0q1bN2rVqsXKlSvx9/enUaNG5M2blw0bNjB06NBUjTVr1ixiY2OpV68erq6upr8YmDFjRpIvDgCuXr1K6dKlWbZsGWXLlqVmzZqmUifTpk2jQoUKLFmyxBSfvb0948ePp0SJEhw5csTqmKdOnSIgIIA9e/ZQqVIlfHx8WLlyJUFBQVy+fJn333+fQYMGkTdvXoKCgjh37hydO3e2ej3bt2/PnDlzcHd3p0aNGlSoUIFz587x9ddfU6tWLbMkZ40aNShfvjwARYsWNdX0b9OmDfnz50/2GqbkWXpZ7O3tadOmDffv32f69OlW+1h7Rh8+fEhISAhDhgzh/v371K1bFx8fH3r37s2nn376zGMePXqU0qVLs3PnTipXrky1atWws7MDYODAgdSuXZuIiAhKlixJgwYNuHfvHoMHD+a9997jr7/+shgvNc9zau5rSmNONGzYMFq3bo2Liwv169fH2dmZqVOnUqZMGYsEd1xcHPXr1+fzzz/n+PHjVK1alapVq3LkyBE++eQTmjRpkuTnZ9y4cTRu3Bij0UiNGjVwcXFh5syZVr/zWrVqRadOnTh16hTlypWjXr16vPXWW2zatIkJEyY88z6JiIiIiIiISNpSTXR5qYxGI0uXLgWgSJEiAMybN49ff/0VLy8vIiIiTMnK27dvU7t2bTZv3sx3333HkCFDnjl269at6d27N1OmTOGHH34wS4zFxcXx+++/Y2trazaje+PGjXz55Ze4urqyaNEis9nFsbGxbNiwwfR7p06dmD9/PhMnTqRWrVpmxz5y5AibN2+mUKFCvPfee6b2gQMHMnfuXAoXLsySJUvIkyePaduNGzc4fPhwCq+cpWnTpjFixAi8vb1ZtmyZ6XpCwsziHTt2pGq8xFIuLVu2BKBgwYKUKFGCvXv3sn79eovZy4mWL19Ow4YNmTFjBg4ODqb2c+fO8fHHH2MwGFi8eLGp/n18fDzdunVj1KhRtG7dmp07d1qMOXXqVL788kuGDh2KjY0NRqORdu3aERYWRpUqVbCxseHQoUN4e3sDsG7dOqpWrcoPP/xgliAGmDBhAtWqVcPZ2dnUdufOHZo3b87SpUuZPn06rVu3BqB3797kzJmTLVu20KBBg1QtLpqaZ+ll6dixIyNGjGDixIl88sknZttu3rzJ3LlzyZo1Kw0aNDC1jxgxgq1btxIQEMDq1atNtff37t1L5cqVn3m8mTNn8umnnzJq1CgyZMhgat+1axfffPMNrq6urF27loCAACAhYd+qVSvmzJnDZ599ZlrEFlL/PKfmvqYk5if98ssvLF261PQ5f/ToEW3btmX69Ol8/vnnzJ8/39R31KhRLF++nMKFC7N27VqyZ88OJPz1SuXKlVmwYAETJkywuB+QkERfs2YNwcHBANy7d49q1aqxdetWwsPDadeuHZDw1w+zZs2idOnSbNy40exzdv/+ffbv32/1PFIqOjraantsbOwLjSsiIiIiIiLyb6WZ6PJSxMXFER0dTbt27di2bRv29vamshljxowBoF+/fmazfd3c3Bg3bhwGg4EJEyYkm9Bxd3fnww8/5MqVKyxevNhs24oVK7hw4QL16tUjR44cpvZBgwZhNBr59ttvLcpzZMyYkerVq5t+r1q1Kr6+vixdupTLly+b9U2c4fvxxx+b2mJjYxk+fDgGg4HffvvNLIEOkCVLFtOs5+fx448/AglJvCcTjgDOzs6mxFxKnDx5kq1bt+Lh4UGNGjVM7YkJ9cQEuzX29vb89NNPZok9SLgm9+/fp1mzZmYLyNrY2DBo0CA8PT3ZtWsX27dvtxjT19eXwYMHY2OT8JVkMBj48ssvATh06BBjxowxJdABqlSpQvHixTlz5gynT582G6tBgwZmiVYAV1dXRo4cCcCiRYuSPLfUSM2zlBKJpVeS+omMjLTYp0CBAlSuXJn9+/ezZ88es23Tpk3jwYMHtGnTxuyvQMaPHw/AyJEjzRavLVGiBJ07d35mjNmyZWPw4MEWyeiff/6Z+Ph4unTpYkqgQ8Kz8vPPP+Po6Mi8efPMyqOk9nl+3vuaVMxP+uCDD8xelNnZ2TF69GicnZ1ZtGiRWdyJ31+jRo0yJdAh4a9XEmfPJ/Z5WteuXc3Oy8nJiW7dugEJL2USXblyBYBy5cpZfM4cHR0pW7ZskuciIiIiIiIiImlPM9ElTT1ZNiWRq6srU6ZMwdfXl0ePHrF9+3YMBoOpBveTChcuTJEiRYiKiiIqKorSpUs/83idOnXi999/Z+LEiTRu3NjUnljm4+lSKxEREYB58vtZ5/Lxxx/Tq1cvwsLC6N27N5CQLJ86dSoODg6mpDPA7t27uXnzJiVLlqRUqVLJjp8aFy9e5PDhw2TNmtXsPJ9XYpK8adOm2Nr+72ugWbNm9OjRg/nz5zN+/HhTmZYnlShRAi8vL4v2TZs2AdCiRQuLbfb29jRp0oTRo0ezadMmypQpY7Y9KCjILA6AfPnyAQkJ6UqVKlmM6evry759+7h06RI+Pj5m26Kjo1m+fDnHjx8nJiaG+Ph4U73vpGbhpkZqn6WUyJEjh9kLjaetXLnSakmUTp06sWHDBiZOnEjJkiVN7YmfgSdr9p89e5Zz587h5eVFuXLlLMZq1qwZAwcOTDKGqlWrWn0mnnXvs2fPTkhICIsWLWLr1q00adLkuZ/n57mvScX8pKZNm1q0Zc2alWrVmlyvCAABAABJREFUqrFw4UJT3GfPnuXs2bPkzJnT6kurOnXqkClTJo4ePcrVq1fJli2b2faQkBCLfQoUKAAkzGRPVLBgQZydnfn999959913adSoEVmzZn3mOaSGn5+f1faEa/gozY4jIiIiIiIi8m+hJLqkqTZt2gAJs4/d3NwoXLgwjRo1InPmzABcv36d2NhYcubMaTHDMpGPjw9RUVFWF6J8WkBAAMWLF2fNmjWcOXOGPHnycOnSJZYvX07u3LnNklbXrl3j/v37ZM+eHVdX1xSdT9u2bfnvf//LpEmT6NWrFwaDgYULF3L16lVatmxpOi/438Kpvr6+KRo7NdJ67MRFL598CQCQM2dOqlatyqpVq1i4cKHVFx25c+e2Ombi/Xo6oZ0osd3afbWWlE+cdZwzZ07TDHVr259cVNJoNNK9e3dGjhyZ5CKZiTX6X8TzPEvJKViwoGkxU2uCgoKsJtEbNmxIjhw5mDFjBsOHD8fZ2ZmdO3fy559/EhgYyNtvv23qm3jtk7qHSbUnt/3ixYsYDAaLv75I9PS9T+3z/CL3NblzAlIcd3LPeOI1uHnzJhcvXrRIoj/51xSJXFxcAPPn2M3NjYkTJ/Lxxx/z8ccf07FjR/z9/alcuTKtW7e2eAklIiIiIiIiIi+XyrlImgoLCyMsLIzffvuNUaNG0b59e7NEcyJrM9afpw8k1IWOj4/nt99+A+D333/n8ePHtG/f3mryNaXjQkIpiEaNGnHixAnTzGNrizU+7/iplRZjb9u2jePHjwMJCzsGBgaa/STWbU9MtD8tqZcfKY3R2vZn7ZOac541axYjRozAy8uLuXPncuHCBWJjYzEajaYkZVJJ2OfxMu91StnZ2dG2bVvu3Lljqjme1DOaeO5JxZ3c+SR375Pz9PgpvX4vcl9fJOakxnze76/UPC/NmjXj5MmTTJw4kffff58bN24wfvx4ypYtS8+ePVM8joiIiIiIiIi8OCXR5ZXKmjUrGTNm5PLly9y/f99qnzNnzgAJNYZTokWLFri6uvLbb7/x+PFjJk+ejI2NjWmRvkQeHh44Ojry119/pWo2cqdOnYCE8hinT59m7dq1FChQgIoVK5r1e+uttwBMCeq0lJZjP1nvfMuWLRY/Z8+eBWD16tVWZz4nxdPTE4BTp05Z3Z7a+/o8FixYACTU/W7cuDGenp6mBWdPnjyZZsd53mfpZUlc0HXixIncvXuXmTNn4u7uTpMmTcz6Jd6jxHvxtKTak+Pp6YnRaEx23MR7n9rn+WXf16TiTvwsJF635J7xJ/dJi+c8W7ZsdOjQgdmzZ3P58mVWrFiBm5sbQ4cO5dChQy88voiIiIiIiIikjJLo8krZ2dlRpkwZjEYj4eHhFtsPHjxIVFQUrq6uFC1aNEVjuri40Lx5c86fP0+PHj04efIkNWvWtCidkCFDBtMCkIn1olOiUqVKFCxYkPnz5zNkyBCMRqPVWeilSpUiU6ZM7N2712KRxxfl6enJ22+/zfXr15k/f/5zj/Po0SPTbOWDBw9iNBqt/jRv3py4uDhmzpyZ4rErVKgAwPTp0y22xcbGMmfOHLN+L8Pff/8N/C9J+6TE835axowZAXj8+HGKj/O8z9LLkjdvXkJCQti2bRv//e9/uXPnDi1btsTR0dGsX548efD29ubChQts27bNYpzU3O8nPeveX716ldWrV2NjY2Oqw57a5/l57mtqzJo1y6Ltxo0brF69GoPBYFrIM3fu3OTOnZvLly+zfv16i32WLVvG33//jb+/v0UplxdlMBioUaMGtWvXBhI+vyIiIiIiIiLyaiiJLq/cZ599BsB3331nNov0zp07fPrppxiNRjp27GhKbqZE4mzxUaNGAUmXWkmsa96/f3/TYoiJHj16xKpVq6zu17FjRx4+fMj48eOxs7Mz1X5/UsaMGenatStGo5H27dub6j4nunHjBlu2bEnxOT0tcWHTLl268H//939m22JiYqwm9Z62bNkyrl+/TpEiRXj33XeT7Je40OKTs9aT0759exwdHQkPD2fZsmWm9vj4ePr06cOFCxcoXbr0S63nnLhI46+//mpWimPTpk0MHTrU6j6Js4uPHj2aqmM977P0sjz9GXhyQdEndezYEUgo5XP79m1T+/79+xk7duxzHbtz587Y2NgwevRodu/ebWqPjY3ls88+4969ezRq1Mis9n1qnufnua+pMXv2bLP79fjxY7p27UpMTAz16tUzeyGX+P3VtWtXrl69amq/fPkyPXr0MOvzvPbt28f8+fN59Mh8kc+///6b/8fefYfXfPd/HH+eiJwMMRMiCWLE3iO1QgTRamjsESPaGNVqqVHV3q1WS9Hard5mqL23oggxg6pyWzFito3YYoTk/P7wy6njnBCrMV6P6zrXxWe+v99z7rvX9f5+vL/bt28H0lbrXURERERERESeDr1YVP51TZs2pVOnTowbN46SJUsSGBiIs7MzkZGRnDt3jsqVK/Pll18+0pply5bFz8+P6OhocufObT6teb+aNWsyZMgQevfuTY0aNfDz86NgwYLExcWxZ88eXFxciI2NtZrXvn17PvnkE27evElISEiqp0z79evH7t27WbRoEb6+vvj7++Pm5sbJkyf57bffaNGiBdWqVXuka0vRrl07duzYwZgxYyhTpgxVq1bF29ubs2fPsnv3bsqVK0dgYOAD10hJirdq1eqB4+rVq0e2bNnYtWsXBw4csHg5ZWry5s3LuHHjCAsLo0GDBlSrVo08efLw22+/cejQIXLlysXUqVPTfsGP4YMPPiAiIoIff/yRyMhISpcuzZkzZ9i0aRM9e/bku+++s5pTuXJlcubMybx58wgICKBAgQLmckApJ6dtedzf0rPSoEEDvLy8zA8rypYta3Nc7969WbZsGVu3bqVgwYLUqlWLq1evsm7dOt555x3Gjh37yHv7+fkxYMAAPv30U6pUqUJAQABubm5s3ryZU6dO4evry5gxYyzmPMrv+XG+10fRqVMn3njjDWrUqIGnpyfbtm3j+PHjeHp6MmrUKIuxPXr0YN26daxcuRJfX18CAwMxmUysXbuWq1evEhISwrvvvvtE8Zw4cYImTZqQJUsWKlasiIeHB5cuXSIqKoorV67QqFEjvVxURERERERE5F+kk+iSLv773/8ydepUypUrx4YNG1i6dCk5c+bkm2++Yd26dTg7Oz/ymrVr1wagQ4cO2Nun/nyoV69eREZG8tZbb3H8+HHmzZvHwYMHqVChAgMHDrQ5J1u2bJQrVw5I/ZQ7gL29PfPnz2fSpElUqlSJ6OhoFi1axJ9//kmTJk3Mp4Af1+jRo1m4cCG1a9dm3759zJ8/n+PHj1O7dm0+/vjjB869dOmS+YR4yknz1Dg4ONC4cWMg9ReM2tKmTRs2btxIcHAwBw4cYN68edy4cYN3332XXbt2UbRo0TSv9TgKFy7Mjh07aNCgAfHx8SxZsoRr167x3//+N9UTy46Ojixfvpy6devy+++/ExERwcSJEzl8+PBD93uc39KzkiFDBnOd/gf9Ro1GI7/++iu9evXCaDSyePFijh07xtdff22V6H4U/fr1Y9myZdSsWZMdO3awYMECjEYjffr0Yfv27eTKlctqTlp/z4/zvT6KXr16MXnyZC5fvszChQu5cuUKbdu2Zfv27VYnvjNkyMCSJUsYOXIkBQoUYNWqVaxevZoiRYrwww8/MG/ePJsvNH4UlStX5uuvv6ZChQocOnSIuXPnsnPnTkqXLs2UKVOeSgkbEREREREREUk7g+nefxsv8oIymUwULVqUmJgYjhw5QoECBZ7q+qdPnyZfvnzkzZuXY8eOYTAYnur6Ik/q+vXreHp6kpSUxNmzZ3F1dU3vkOQFExMTA4Cvr286RyIiIiIiIiLyfNFJdHkpzJs3j8OHD1O/fv2nnkAHGDRoEMnJybz33ntKoMtz6YcffuDy5cu0b99eCXQRERERERERkadIJ9HlhRYeHs6lS5dYtmwZd+7cITo6mvLlyz+VtQ8dOsTQoUM5duwY69evx9vbm4MHD+Li4vJU1hd5UufPn+fjjz/mr7/+YuXKlbi4uHDgwAGLF3iKpJVOoouIiIiIiIjYpheLygtt4sSJ2NvbU7hwYQYMGPDUEugAf/75JxMnTsTJyYmaNWsyevRoJdDluXL16lUmTpyIg4MD5cuX5/vvv1cCXURERERERETkKdNJdBEREdFJdBEREREREZFU6CS6iIiIAHA6LoEvfo5M7zCeOzO+CkjvEERERERERCQd6cWiIq+QNWvWEBISgoeHBw4ODuTIkYPixYsTGhrK+PHjSUxMTO8QX1qRkZEYDAbCwsLSPMfHxweDwUBsbGyqYyIiIh553ccRFhaGwWAgMjLyme7zPEi5p/3790/vUERERERERETkOaAkusgr4osvviAoKIjFixfj7u5OgwYNqF27NhkzZmTmzJl06tSJCxcuPNEej5MoTi+vUlJYREREREREREQen8q5iLwCdu7cyVdffYWDgwMLFy6kfv36Fv1nzpxh/PjxGI3GdIpQRERERERERETk+aQkusgrYOHChQA0b97cKoEO4OXlpdIVIiIiIiIiIiIiNqici8gr4Ny5cwC4u7s/0ryoqCjef/99SpcuTbZs2XBycqJo0aL07duXS5cuWYwNCwujVq1aAEyZMgWDwWD+pCToH1buJbUSKwaDAR8fHxITE/nqq68oWrQoRqORkJAQAG7evMnEiRN56623KFCgAE5OTmTNmpUaNWowa9Ysq30MBgNTpkwBoFatWhax3l9/fOnSpdSrV48cOXLg6OhI4cKF+c9//sO1a9dsXkNsbCytWrUiR44cZMqUiapVq7J8+fLUb/Iz8N5772EwGBg/frzNfpPJRMGCBcmQIQMnTpyw6Js/fz5+fn44OTmRK1cu2rVrx9mzZ1Pd62HfDcCpU6fo3Lkz+fLlw2g0kjNnTho3bsyOHTtSXffkyZO8//77+Pr64ujoSI4cOfDz82PgwIHcuHHDPO7IkSP079+fKlWqmGv9e3t7065dOw4fPvzYMac4fPgwTZo0IUeOHLi4uFCtWjVWrFiRatxbt27lrbfewt3dHaPRiI+PD127drV5D++tvX7y5Elat26Nu7s7Tk5OVKxYkaVLl9rcY/v27TRq1Mh8Pz08PPDz8+OTTz5J9XcpIiIiIiIiIo9PSXSRV4C3tzdwN0GaklBPi969ezNhwgQcHBwIDAykdu3aXLlyhcGDB1O9enWLhF316tWpV68eAAULFqR9+/bmT9myZZ/4GpKTkwkJCWHIkCEULFiQt956i9y5cwN3E9fh4eFs376dvHnz8tZbb1G2bFm2bdtGq1atrE7Zt2/fnoIFCwJQr149i1gzZcpkHtezZ08aNmzIxo0bKVmyJG+++SaJiYl8/fXXBAQEkJCQYLHu0aNHee2115g1axZubm40aNCA5ORkGjRowJw5c574HqRVly5dAFJNoq9bt45jx44RFBREvnz5zO1jxoyhadOm/Pbbb1StWpWAgAB+/fVXKleuzPnz51Pd70Hfzd69eylfvjzjxo3D2dmZxo0b4+vry8KFC6latSpz5861Wm/jxo2ULl2aH374geTkZN566y2qVKlCfHw8n376KX///bd57IQJE/jyyy+5cuUKFStWpGHDhmTOnJmff/6ZSpUq8ccffzxyzCmOHj2Kn58fu3fvJigoiIoVK7J161aCg4OJiIiwWnPatGn4+/uzdOlSihQpQuPGjTEajYwdO5by5ctz8OBBm7HExsZSqVIlNm/eTPXq1SlXrhy7du0iJCSE1atXW4xdvnw5VatWZenSpfj4+NC4cWPKli1LfHw83377LfHx8al+TyIiIiIiIiLyeFTOReQVEBoayqBBgzh58iSFChUiJCQEf39/qlSpQvHixTEYDDbnff7551SpUoVs2bKZ227dusUHH3zAuHHjGDZsGJ9//jkA4eHhFCpUiFWrVlG9enWbScYncerUKYxGI4cOHcLLy8uiz93dnVWrVlGnTh3s7P55Nnj8+HECAwMZMGAAYWFh+Pj4AHdPAIeFhXH06FH69u1LQECA1X5z5sxh2LBhlCtXjgULFpjn3r59m/fff59x48bRv39/hg4dap7TtWtX4uLi6Nq1K6NHjzbHMmHCBDp27PhU78eDlCpViqpVq7Jlyxb27NlDmTJlLPpTkuv3xhQbG0uvXr0wGo388ssv5nty/fp1QkJCWLZsWar7pfbdmEwmQkNDiY+P55NPPuGbb74x/9bmzZtHixYteOedd6hRowa5cuUC4OLFizRt2pTLly8zfPhwPvzwQ4vf58aNGy1+jyEhIXTs2NH8UCTF5MmTefvtt+nevTvr1q1Lc8z3mjZtGu3atWPixInY29/9z+WyZcsICQnh/fffp169eubE+6lTp+jUqRMGg4ElS5YQHBwM3E3W9+zZkxEjRtCuXTuio6Ot9pkyZQrdunVj2LBh5n1GjhxJ9+7d+frrrwkKCjKPHTp0KCaTiejoaCpUqGCxTnR0NDly5LB5LSIiIiIiIiLy+HQSXeQVULBgQRYvXoynpydXrlxh6tSpdOzYkZIlS+Lh4UGfPn2syrMA1K9f3yJhCWA0GhkxYgT29vYsXrz4X7qCuwYNGmQz4ZkjRw6CgoIsEugA+fPn59NPPyU5OTnV0hipGThwIAAzZ840J9ABMmbMyMiRI/Hw8GDChAkkJycDd08tr169mmzZsjFkyBCLWMLDw6lateoj7X//ddxbcubeT4cOHWzO6dy5M3A3gX+v8+fPs2jRInLlykWDBg3M7ZMmTeLWrVu0a9fO4qGCs7Mzo0ePTvVBSwpb301kZCR79+4lf/78DBgwwGKNpk2bEhISwtWrV5k8ebK5ffz48Zw7d47g4GC6d+9utW+NGjXIkiWL+e+VK1e2SqADdOjQgWrVqhEZGcnly5fTHPO9MmXKZP6tpwgODqZp06YkJCRYPCiaMGECN27coFWrVuYEOoCdnR3ffvstnp6e7Nixg23btlntU6BAAb7//nuLfd577z2yZcvGtm3bSExMNLfHxcWRJUsWqwQ6gJ+fH66urqleT4qYmBibn3v3EREREREREZF/6CS6yCsiKCiIY8eOsWTJEtasWcP27dvZt28fcXFxDB06lIULF7Jlyxaruulnzpxh6dKlHDx4kCtXrpiTxg4ODsTExPxr8RsMBoukry2bNm0iMjKSM2fOcPPmTUwmE3/++SfAI8UaFxfHnj17KFasGEWKFLHqd3R0pGLFiixbtoyYmBiKFCnC5s2bgbsPHlxcXKzmtGzZki1btqQ5hns1adLEoszMvY4cOWLe+17NmzenR48eTJs2jSFDhuDk5ATA1KlTuXXrFmFhYWTMmNE8ftOmTeZ59ytSpAjlypXjt99+sxlDat9NVFQUAC1atCBDhgxW/W3btmXBggVERUXRt29fAH799Vfgn4cAaXHt2jWWLl3K77//zoULF7h9+zYAf/75JyaTiaNHj1K+fPk0xXyvoKAgq4dIAK1atWL27Nnme3bvtYaGhlqNNxqNNGvWjJEjRxIVFUXlypUt+gMCAiy+CwB7e3sKFCjArl27OH/+vPnEe4UKFZg2bRrvvPMOPXr0oGTJkg+8BhERERERERF5ckqii7xCUpJ5zZo1A+6+cDQiIoL+/ftz5MgR+vXrZ1FHe9iwYXzyySfPxQnVnDlzYjQabfZdvnyZxo0b2yzbkeLq1atp3ivlZZsHDhx46Ans+Ph4ihQpYn5xZN68eW2OS609Lb777juL0/D3ioiIsJlEd3R0pF27dowYMYJ58+bRtm1b4O6JaYPBwDvvvGMxPi3xp5ZET+27SVkztdhT2u996eapU6cAbJ4ut2XdunW0bNnygbX+bX33D/o9pbi3Xvy9bMX9ONeaIuWdBfdLeXBy69Ytc9vAgQPZu3cvkyZNYtKkSbi5uVG1alVCQkJo3br1Q68JwNfX12b73QdNtx86X0RERERERORVo3IuIq8wd3d3evfuzeDBg4G7Ly1MsW3bNnr27ImTkxMRERHExsaaT3ebTCarlzA+DSmn3G1xdHRMte/jjz9m3bp11KhRg8jISOLj47lz5w4mk4lVq1YBd+tzp1VSUhIAuXPntnjpqK1PSg3qlPUflnT/N6W8YDSlpMuWLVvYv38/AQEBVonUJ4n/Qd9NWta01Z+WOK5du0bz5s05d+4c//nPf9i/fz8JCQkkJydjMplo1aoVYPu7f1jMD/Kg39KzutYUefLkYefOnaxatYpu3brh6enJ0qVLefvttylbtiwXL15M81oiIiIiIiIikjY6iS4i5hrY8fHx5raFCxcC8PXXX9O+fXuL8Tdu3OCvv/565H0cHByAu8lPW1JOIT+qhQsXkiFDBpYsWWJRLxvg2LFjj7xeyslgDw+PNL8g1dPTE/jnFPv9Tp48+chxPKkiRYoQEBBAZGQkhw4dsvlC0RSenp4cPnyYEydO2Dyp/Djxp9yT48eP2+xPuVf3PpDJkycPBw8e5MiRIxQtWvSB60dFRXH+/HmaNGnCV199ZdX/ON+9rfjul3IvUq4v5c+HDh3i+PHjFC5cONW1nsbDJ3t7e4KCgswvHD158iQdOnRg3bp1fPvtt+aHYiIiIiIiIiLydOgkusgr4GGnsI8ePQpYJgVTTrTmyZPHavzcuXNtrpmSJL9z547NfVISiIcPH7bqO3/+fKrlQh7m4sWLuLq6WiXQAebMmWNzzoNi9fb2pkiRIvzxxx+pJoDvV61aNQBWrFhBQkKCVf+sWbPStM7TllJbfNiwYcyZM4fs2bPTuHFjq3HVq1cH7n639zt8+DC///77I+/t7+8PwOzZs82n++81bdo0i3EAderUAWDcuHEPXf9Bv9EjR4489u8pxerVq22+cHfmzJnAP985/HMN06dPtxqfmJhovq/3XuvTkjdvXj7++GMA9u7d+9TXFxEREREREXnVKYku8gr4z3/+Q58+fWwmhGNiYujZsyeARXI15TTtxIkTzS9qBNi/f785YXe/lCT8oUOHbPbnz5+fvHnzsnfvXhYvXmxuT0hIoGPHjly5cuURr+yfWC9dusTs2bMt2ocPH8769esfK9bPPvuMpKQkmjRpwr59+6z6jx49yqRJk8x/L1SoELVr1+bixYv07dvXojTN5MmTH/ulok+qcePGuLu7M27cOK5fv067du1s1s3u0KEDDg4OTJ061fySTLj7rw4+/PDDB5baSU1AQAClSpXi+PHjfP755xYPXhYtWsSCBQvIlCkTYWFh5vbw8HDc3NxYunQpY8aMsXpYExUVxeXLl4F/fqMLFiywqIl+6dIl3nnnHYvf7eO4du0aH330kcWDlhUrVjB37lycnZ0t/oXGO++8g5OTEzNnzrQoi5ScnEy/fv04c+YMlSpVsnqp6KMaPnw4f//9t1X7L7/8AjxZ7X0RERERERERsU1JdJFXwLVr1xg6dCgFCxakWLFiNG7cmBYtWlClShWKFi3K0aNHqVChAl988YV5TocOHfDw8GDp0qUUKVKEFi1aULduXcqWLYu/v7/Nly76+PhQunRpdu7ciZ+fHx06dCA8PJwlS5aYx/Tv3x+AJk2aEBgYSMOGDSlYsCD79u2jYcOGj3V9n3zyCQAtW7akRo0atG7dmhIlStCrVy969Ohhc06DBg0wGAz07NmTkJAQwsPDCQ8P5/z58wC0adOGPn36sHv3bsqWLUulSpVo3rw5r7/+OsWKFaNQoUKMGjXKYs2xY8fi7u7OmDFjKF68OK1bt6ZKlSq888475vrk/zYHBwc6dOhg/nt4eLjNcQUKFGDw4MHcvHmTWrVqUadOHVq2bEmhQoXYt28fwcHBj7y3wWBg+vTp5MiRg4EDB1KiRAlat25N9erVadSoEXZ2dkyaNAkPDw/znOzZszNnzhxcXV3p1q0bvr6+tGjRggYNGlCgQAFq1KhhPoFesWJF6taty8mTJylcuDCNGjWiUaNG5M+fn7Nnz/LWW289csz3Cg0NZcGCBRQuXJhWrVoREBBAcHAwSUlJjBw5Ei8vL/PYvHnzMm7cOEwmEw0aNMDf35/WrVtTvHhxvv/+e3LlysXUqVOfKB6AL7/8Ek9PT8qXL0+LFi1o3rw5RYsWZfjw4bi5udG7d+8n3kNERERERERELCmJLvIK+Oyzz5g6dSqtW7fG3t6eDRs2sGDBAo4cOULNmjX54Ycf2LJli0U5lBw5crBjxw5at25NYmIiS5Ys4cyZM3z11Vfmcha2zJ8/n5CQEI4dO8bUqVOZOHGiRVmNDh06MHnyZIoVK8bmzZuJjo6mQYMGbN26lWzZsj3W9YWGhrJ8+XIqV67M77//zsqVK/H09GTdunWpJuYrVKjAtGnTKFGiBKtXr2bixIlMnDiRq1evmscMHjyYtWvX0rBhQ06fPs2iRYvYvXs3zs7O9O7d2+IkOoCvry/btm2jefPmxMXFsXjxYkwmE4sWLaJFixaPdW1PQ+3atQGoWrUqJUqUSHVc9+7dmTNnDmXLlmXTpk2sXbuWgIAAtm3bZn6B6qMqVaoUv/32Gx07duTatWvMmzePQ4cOERISwubNm2nWrJnVnFq1avH777/TqVMn7ty5w6JFi9i2bRs5c+Zk0KBBFkn3xYsX8+mnn+Lu7s7KlSvZtWsXLVu2ZNu2bWTNmvWxYk5RqFAhtm7dSunSpVm1ahXR0dFUrlyZpUuX2nwY0aZNGzZu3EhwcDAHDhxg3rx53Lhxg3fffZddu3Y9tMZ7WowePZqWLVty/fp1Vq5cyS+//EKGDBno1asXf/zxBwULFnziPURERERERETEksH0sGLJIiLyQuvUqRPjx49n8uTJFqVTRO4VExPD6bgExq+6lN6hPHdmfBWQ3iGIiIiIiIhIOlISXUTkJXbixAmKFSuGi4sLJ0+exMnJKb1DkudUTEwMcPdfVIiIiIiIiIjIP+zTOwAREXn6hg4dyh9//MGaNWu4ceMG3377rRLoIiIiIiIiIiKPQUl0EZGX0PLly9mwYQNeXl58+eWXdOvWLb1DEhERERERERF5ISmJLiLyEoqMjEzvEEREREREREREXgpKoouIiAgAp+MS+OLnyPQO47Ho5Z8iIiIiIiLyrNildwAiIiIiIiIiIiIiIs8rJdFFROSltH37dgwGAwaDgUGDBqVLDLGxsRgMBgICAtJlfxERERERERF5ckqii4jIS+nnn3+2+WcRERERERERkUehJLqIiLx0bt++zezZszEYDHh4eHDgwAF+++239A5LRERERERERF5ASqKLiMhLZ+XKlcTHx1OjRg06deoE6DS6iIiIiIiIiDweJdFFROSlk5Iwb9OmDW3atAFg5syZJCUlWYwLCwsz101P7RMZGWkxZ//+/YSGhpI7d24cHBzw8vKiXbt2HDp06JHj3LRpE40aNSJnzpwYjUZ8fHz44IMPOHfunNXY27dv89///hc/Pz/c3NxwdnbGx8eH4OBgZs2a9ch7i4iIiIiIiEja2Kd3ACIiIk/T5cuXWbZsGUajkaZNm5I1a1b8/PyIjo5mzZo1vP766+ax1atXt7lGUlKSOemeIUMGc/vatWtp0KABN27coHz58gQEBHDw4EF+/vlnFi5cyIoVK/D3909TnKNGjaJ79+7Y2dnh5+eHl5cX+/btY/To0SxbtozNmzeTO3du8/i2bdsye/Zs3NzcqFq1Ks7Ozpw5c4aoqCiuXbtGy5YtH/OOiYiIiIiIiMiDKIkuIiIvlTlz5nDz5k2aNGlC1qxZgbsn0qOjo5k2bZpFEj08PJzw8HCrNT788EOSkpIIDg6mWrVqACQkJBAaGsqNGzcYO3YsXbp0MY8fPnw4H330Ea1bt+bIkSMYjcYHxrht2zZ69OhB3rx5WbJkCaVLlwbAZDLx9ddf8/nnn/PBBx8wd+5cAGJjY5k9ezaVKlVi48aNODo6mte6ceMGv//+e5rvT0xMjM32xMTENK8hIiIiIiIi8ipRORcREXmp3FvKJUXLli2xt7dn4cKFXLt27YHzJ06cyKhRoyhevDgzZszAzu7ufyrnzJnD33//jb+/v0UCHaBHjx5UqFCB06dPs3DhwofG+O2335KcnMy4cePMCXQAg8HAZ599Rrly5ViwYAHx8fEAxMXFAVC1alWLBDqAk5MTVapUeeieIiIiIiIiIvJ4lEQXEZGXRmxsLJs2bSJ79uzUr1/f3O7u7k69evW4fv36A5PcUVFRdO3alRw5crB06VJcXV0t+gBCQ0Ntzk1J2qeMS01ycjJr167F1dWV2rVrW/UbDAaqVatGcnIyu3btAqBo0aK4uLgwefJkxo8fz/nz5x+4x4P4+vra/Dg4ODz2miIiIiIiIiIvMyXRRUTkpTFt2jRMJhPNmze3SgqnJLlTTqrf78SJEzRp0oTk5GTmzp1LgQIFLPrPnj0LgI+Pj835Ke0p41Jz/vx5rl27xtWrV7G3t7f5MtMxY8YAmE+iZ86cmfHjx5OcnEynTp1wd3enWLFidO3alW3btj1wPxERERERERF5MqqJLiIiL41p06YBd18Aev9LQ2/dumXu+/PPPy1e2pmQkEDDhg05d+4cP/74I7Vq1Up1D4PB8MAYHtaflJQEgKurK40bN37g2Hz58pn/3KpVK+rUqcPixYtZvXo1GzZsYOzYsYwdO5bevXszZMiQB64lIiIiIiIiIo9HSXQREXkpREdHc+jQIeDuyzNTe4FmcnIyM2bMoGfPnsDdl3m2bduWP/74g3fffZd3333X5jxPT08Ajh8/brP/xIkTABbJeVvc3NwwGo1kzJiRiIiIh17Xvdzd3c0vQzWZTKxatYoWLVowdOhQwsLCKF68+COtJyIiIiIiIiIPp3IuIiLyUkgp09K7d29MJpPNz+rVq4F/TqwDfP755yxcuJBatWoxatSoVNf39/cHYPr06Tb7U9pTxqXG3t6egIAALly4wMaNG9N+gfcxGAy8/vrrvPnmmwDs27fvsdcSERERERERkdQpiS4iIi+8O3fuMHv2bOBu2ZPUBAYGkjNnTn7//Xf27dvHnDlz+PrrrylQoABz587F3j71f6DVvHlzcuXKRVRUFOPGjbPoGzVqFDt27MDb25tGjRo9NN5+/fphZ2dH+/bt2bRpk1X/2bNn+eGHH8x/3717NwsWLOD27dsW4y5evMj27dsByJs370P3FREREREREZFHp3IuIiLywlu5ciXnzp2jSJEilCtXLtVxGTJkoGnTpvz4449MmzaNuXPnAndLtaSUd7lf3759KVq0KC4uLkyfPp0GDRrQuXNnxo0bR+HChTl48CC7d+/GxcWFGTNmYDQaHxpvjRo1GDlyJN27d8ff35/SpUvj6+vLzZs3OXHiBAcOHCBTpky89957wD8vPc2SJQsVK1bEw8ODS5cuERUVxZUrV2jUqBGVK1d+jDsnIiIiIiIiIg+jJLqIiLzwUkq5tGzZ8qFjW7VqxY8//sj06dPNLwHdtGmTzRPhAGFhYRQtWhSA2rVrs2PHDr755hvWrVvHH3/8gZubG23atOGzzz6jSJEiaY75/fffp0qVKgwfPpyNGzeyZMkSXF1d8fb2pkuXLjRr1sw8tnLlynz99desW7eOQ4cOERUVRbZs2ShdujQdO3akdevWad5XRERERERERB6NwWQymdI7CBEREUlfKS9i9fX1TedIRERERERERJ4vqokuIiIiIiIiIiIiIpIKJdFFRERERERERERERFKhJLqIiIiIiIiIiIiISCqURBcRERERERERERERSYV9egcgIiIiz4fTcQl88XNkeofxWGZ8FZDeIYiIiIiIiMhLSifRRUTkhWYwGCw+dnZ2ZM2aFX9/fyZMmIDJZErvEEVERERERETkBaaT6CIi8lJo3749AElJSRw9epTNmzezadMm1q5dy8yZM9M5OhERERERERF5USmJLiIiL4WIiAiLv69Zs4b69esza9YsQkNDCQ4OTp/AREREREREROSFpnIuIiLyUqpbty5t27YFYNGiRekbjIiIiIiIiIi8sJREFxGRl1a5cuUAOHXqFABvvvkmBoOBNWvW2ByfkJBA5syZyZIlCwkJCQBERkZiMBgICwvjr7/+Ijw8HG9vb+zt7RkxYgQAf/75J0OGDKFmzZp4eXnh4OCAh4cHjRs3ZseOHTb38vHxwWAwADBhwgRKly6Nk5MTHh4edO7cmUuXLtmMb/DgwZQtW5asWbOSKVMmChYsSLNmzVi1atWT3CoRERERERERSYWS6CIi8tK6evUqAEajEYAuXboAMH78eJvjZ82axdWrV2ndujUuLi4WfefOnaNSpUosX76cKlWq8MYbb+Ds7AzA4sWL+fjjjzl79iylSpUiJCQET09PFi5cSLVq1Vi9enWqMfbp04f33nuPzJkz8/rrr2MymRg3bhwNGza0eClqUlISQUFB9O3bl/PnzxMQEED9+vXJlSsXy5YtU913ERERERERkWdENdFFROSlZDKZWLZsGQClS5cGoH79+uTJk4fFixdz7tw53N3dLeakJNc7duxotd6KFSto1KgRM2bMwNHR0aKvWrVq7Nmzx7xPilWrVtGwYUO6du1KTEyM+eT5vaZNm8b27dspW7YsAPHx8VSpUoWoqCjWr19PYGAgAFFRUWzZsoW33nqLBQsWYGf3z3Pwy5cvc+TIkUe5PSIiIiIiIiKSRjqJLiIiL5WkpCRiYmJ4++232bp1K0ajkQ4dOgCQIUMGwsPDSUxMZOrUqRbz9u3bx/bt2ylXrhzly5e3WtdoNDJ69GirBDpAqVKlrBLoAPXq1aNZs2YcPXqUffv22Yx3wIAB5gQ6gJubG++++y4AGzduNLfHxcUBEBAQYJFAB8iSJQsVKlSwuf79YmJibH4SExPTNF9ERERERETkVaOT6CIi8lKwdcrb1dWVKVOmULBgQXNbeHg4AwYMYMKECfTs2dPcnnIKvVOnTjbXL1++PF5eXqnuf+vWLX755Reio6M5d+6cOSm9d+9e4G7yulSpUlbzgoKCrNoKFy4M3K21nqJs2bLY2dkxdOhQPDw8ePPNN3F1dU01HhERERERERF5OpREFxGRl0L79u0BsLOzI3PmzJQqVYrGjRuTLVs2i3Genp4EBwezaNEioqKi8Pf359atW0ybNg1nZ2dat25tc/28efOmuvfevXtp2LAhsbGxqY5Jqc9+P29vb6u2TJkyAXcT8ykKFy7M0KFD6du3L61atSJDhgyULFmSOnXq0KFDB0qUKJHq3vfy9fW12R4TEwPcTtMaIiIiIiIiIq8SlXMREZGXQkREBBEREUyaNIkRI0bwzjvvWCXQU6S8YHTChAkAzJ8/nwsXLtCiRQsyZ85sc46tMi5wt/Z68+bNiY2NpUuXLvz+++9cuXKF5ORkTCYTn3zyiXmcLbZO0Kfmo48+4ujRo4waNYr69etz4sQJvv/+e0qXLs0PP/yQ5nVEREREREREJO2URBcRkVdOUFAQBQoUYO7cuVy6dOmBLxR9mIMHD3Lw4EEqVqzI2LFjKVOmDK6urubk+LFjx55q7Hny5KFbt24sWbKEc+fO8fPPP2NnZ8dHH33EpUuXnupeIiIiIiIiIqIkuoiIvIIMBgMdO3bkxo0bfPnll2zYsIESJUpQpUqVR17r4sWLgO2yLBcvXmTNmjVPHG9q7O3tadOmDZUqVSIxMZHDhw8/s71EREREREREXlVKoouIyCvp7bffxsHBgREjRmAymR7rFDpAoUKFsLOzY926df9fV/yumzdv0qVLFy5cuPBU4l2/fj2//vorycnJFu0nTpzgwIEDGAwGm4l8EREREREREXkySqKLiMgrKWfOnISEhABgNBpp27btY6/zzjvvcOXKFcqUKUNwcDDNmjXDx8eHdevWERYW9lTi3bNnD3Xr1sXDw4M33niDNm3aUK9ePYoWLcqlS5f48MMP8fT0fCp7iYiIiIiIiMg/7NM7ABERkfRSu3Zt5syZQ5MmTciePftjrzN27FiKFi3KxIkTWbt2LVmyZKFOnTp88803TJ48+anEGhwczPnz51m/fj179uzh/PnzuLu74+/vT9euXc0PBERERERERETk6TKYTCZTegchIiKSHoKCglizZg3r168nICAgvcNJVzExMZyOS2D8qkvpHcpjmfFVQHqHICIiIiIiIi8pJdFFROSVFB0dTeXKlSlevDj79u1L73DSXUo9d19f33SOREREREREROT5onIuIiLySunbty8nT55k+fLlmEwmBg4cmN4hiYiIiIiIiMhzTEl0ERF5pcyaNYtTp07h4+PDkCFDaNiwYXqHJCIiIiIiIiLPMSXRRUTklRIbG5veIYiIiIiIiIjIC0RJdBEREQHgdFwCX/wcmd5hPBa9WFRERERERESeFbv0DkBERERERERERERE5HmlJLqIiDxTBoPB4mNnZ0fWrFnx9/dnwoQJmEymZx5D//79MRgMREREpHlOZGQkBoOBsLCwJ15LRERERERERF5cKuciIiL/ivbt2wOQlJTE0aNH2bx5M5s2bWLt2rXMnDkznaMTEREREREREbFNSXQREflX3H9ye82aNdSvX59Zs2YRGhpKcHDwM9v7/fffp2XLluTOnfu5WktEREREREREnn8q5yIiIumibt26tG3bFoBFixY9073c3NwoWrQoWbJkea7WEhEREREREZHnn5LoIiKSbsqVKwfAqVOnAHjzzTcxGAysWbPG5viEhAQyZ85MlixZSEhIACxrl//111+Eh4fj7e2Nvb09I0aMAB5cxzw2NpZWrVqRI0cOMmXKRNWqVVm+fHmqMae2VkBAAAaDgdjYWBYtWkTlypVxcXEhe/bstGrVitOnT9tcz2QyMWXKFGrUqEHWrFlxcnKidOnSfPfdd9y+fdtq/Pnz5+nXrx8lSpQgU6ZMZMmShcKFC9OuXTuio6NTjVtEREREREREHo+S6CIikm6uXr0KgNFoBKBLly4AjB8/3ub4WbNmcfXqVVq3bo2Li4tF37lz56hUqRLLly+nSpUqvPHGGzg7Oz9w/6NHj/Laa68xa9Ys3NzcaNCgAcnJyTRo0IA5c+Y81jX9+OOPNGnSBJPJxOuvv06mTJmYNWsWgYGB3Lhxw2JscnIyLVq0ICwsjD179lCxYkXq1avHuXPn6N27NyEhISQnJ5vHX7t2jcqVKzNo0CBu375NvXr1qFOnDlmyZGHmzJmsWLHisWIWERERERERkdSpJrqIiKQLk8nEsmXLAChdujQA9evXJ0+ePCxevJhz587h7u5uMSclud6xY0er9VasWEGjRo2YMWMGjo6OaYqha9euxMXF0bVrV0aPHo2d3d1nyxMmTLC5R1r8+OOPrFmzhsDAQACuX79O3bp12bJlCzNnzuTtt982j/3uu++YO3cudevWZfr06ebrTUhIoFWrVixdupSxY8fy3nvvATBv3jyOHDlCt27dGDVqlMW+cXFxxMXFPTS+mJgYm+2JiYmPdb0iIiIiIiIiLzudRBcRkX9VUlISMTExvP3222zduhWj0UiHDh0AyJAhA+Hh4SQmJjJ16lSLefv27WP79u2UK1eO8uXLW61rNBoZPXp0mhPoR48eZfXq1WTLlo0hQ4aYE+gA4eHhVK1a9bGur0ePHuYEOoCzszM9e/YEYOPGjeb2O3fuMHToUFxdXZkxY4bFAwMXFxfGjx+P0Wjkv//9r7k9JUl+7/opcubMScmSJR8rZhERERERERFJnZLoIiLyrzAYDBgMBuzt7SlcuDARERG4uroyc+ZMChYsaB4XHh6Ovb09EyZMsJifcgq9U6dONtcvX748Xl5eaY5n8+bNwN3T7/eXhgFo2bJlmte6V1BQkFVb4cKFAfjzzz/Nbbt37yY+Pp7q1avj5uZmNSdXrlz4+vqyb98+cxmYChUqANCvXz+WLVvGzZs3Hzk+X19fmx8HB4dHXktERERERETkVaByLiIi8q9o3749AHZ2dmTOnJlSpUrRuHFjsmXLZjHO09OT4OBgFi1aRFRUFP7+/ty6dYtp06bh7OxM69atba6fN2/eR4rn7NmzD5z3qOul8Pb2tmrLlCkTALdu3TK3xcbGArBy5UoMBsMD17xw4QJeXl7Url2bHj16MGLECBo0aICDgwNly5YlKCiId955Bx8fn8eKWURERERERERSpyS6iIj8KyIiItI8tkuXLixatIgJEybg7+/P/PnzuXDhAh06dCBz5sw256S1jEsKk8kE8NAE9qNK63pJSUnA3ZPhDysdk/LiVYBhw4bRuXNnFi9ezNq1a9m8eTPR0dEMGTKE2bNnExIS8tixi4iIiIiIiIg1JdFFROS5ExQURIECBZg7dy4jR4584AtFH5enpycAJ06csNl/8uTJp7aXLSkn1kuWLPlIDxgAihQpQp8+fejTpw83b97khx9+oFevXnTu3FlJdBEREREREZGnTDXRRUTkuWMwGOjYsSM3btzgyy+/ZMOGDZQoUYIqVao8tT2qVasGwIoVK0hISLDqnzVr1lPby5ZKlSqRJUsW1q9fz5UrVx57HUdHR3r27Enu3LmJi4szv3xURERERERERJ4OJdFFROS59Pbbb+Pg4MCIESMwmUxP9RQ6QKFChahduzYXL16kb9++JCcnm/smT57Mli1bnup+9zMajfTq1YtLly7RpEkTmyfi//jjD2bPnm3++6JFi9i2bZvVuN27d/P333/j6upqVWNeRERERERERJ6MyrmIiMhzKWfOnISEhDBnzhyMRiNt27Z96nuMHTuWatWqMWbMGNasWUP58uU5fvw427dvp0uXLvz0009Pfc979evXj/379zNz5kyKFClC+fLlyZs3L/Hx8Rw7dozjx4/z1ltv0aJFCwAiIyMZOXIkXl5elCtXjsyZM3P27Fk2bdpEcnIyAwYMIGPGjM80ZhEREREREZFXjU6ii4jIc6t27doANGnShOzZsz/19X19fdm2bRvNmzcnLi6OxYsXYzKZWLRokTlx/SzZ2dkxY8YM5s2bR61atYiJiWHBggXs37+fXLly0b9/fwYPHmweHxYWRs+ePfH09CQ6Opr58+dz/Phx6tevz/r16/nwww+fecwiIiIiIiIirxqDyWQypXcQIiIitgQFBbFmzRrWr19PQEBAeofzUouJiQHuPlgQERERERERkX/oJLqIiDyXoqOj+fXXXylRooQS6CIiIiIiIiKSblQTXUREnit9+/bl5MmTLF++HJPJxMCBA9M7JBERERERERF5hSmJLiIiz5VZs2Zx6tQpfHx8GDJkCA0bNkzvkERERERERETkFaYkuoiIPFdiY2PTOwQRERERERERETMl0UVERASA03EJfPFzZHqHAcCMrwLSOwQRERERERERQC8WFZEXjMFgwGAwPPN9AgICMBgMj3Qqun///hgMBiIiIp5ZXC+T2NhYDAbDv/LSUIPBgI+PzyPNSe038DhriYiIiIiIiMiLS0l0EREREREREREREZFUqJyLiIgNU6dO5fr163h5eaV3KC8tLy8vDhw4gLOz8zPf68CBA2TMmPG5W0tEREREREREnn9KoouI2JA3b970DuGllzFjRooWLfqv7PU09/m3YhYRERERERGR54PKuYjIC+/e2toJCQl89NFH5MmTBycnJ8qXL8/SpUvNY+fOnYufnx8uLi7kypWLDz74gBs3blit+aCa6Bs2bCAgIIBMmTKRI0cOGjVqxMGDB1ON7/fff6dPnz5UqFABd3d3jEYjBQoUoGvXrpw9e9ZqvI+Pj7n2e2qf+61YsYK6deuSLVs2HB0dKVKkCH379uXSpUtWY++t3b59+3bq1atH1qxZyZw5M3Xr1mXbtm0W4+fOnYvBYCA0NDTVa+zQoQMGg4Fp06ZZXYfJZGL06NGUKVMGZ2dnypYtC6ReEz0iIgKDwUD//v05evQozZs3x83NjcyZM/PGG2+wf/9+AO7cucPAgQMpXLgwjo6OFCpUiB9//NFmfA+qYz5u3DhKlSqFo6MjXl5edOvWjcuXL6d6rbbWioyMxGAwEBYWxoULF3j33XfJnTs3RqORkiVLMmnSpFTXi42NpXPnzvj4+GA0GnF3d6dp06b88ccfNsevWrWKevXq4e3tjdFoxNPTk+rVq/Pll1+muoeIiIiIiIiIPD6dRBeRl0ZiYiK1a9fm6NGjVK5cmWvXrrFx40YaNWrEL7/8wt69e+nTpw+VKlUiKCiIqKgoRo8ezfnz55k+fXqa9li8eDFNmjQhKSmJqlWrkjdvXqKjo3nttddo0KCBzTnffvst8+bNo2TJklSrVg2DwcDvv//O2LFjWbRoETt37sTT09M8vmnTpsTHx1ut89dff7Fq1Srs7Cyffw4aNIh+/fphb29PzZo1cXNzY/PmzQwePJiFCxeyceNGcuXKZbXeli1b6Ny5M4UKFeKNN97gyJEj/Prrr2zcuJFly5ZRt25dAEJCQvDw8GD+/PmMHj2a7NmzW6xz5coV5s6dS9asWWnatKnVPl26dGHy5MnUrFmTYsWKkZiY+PAbDRw/fhw/Pz+yZs1KzZo1iYmJ4ZdffmHXrl388ccfdOnShXXr1lGlShUKFCjA+vXree+998iYMSMdO3ZM0x69evXi+++/x2g0EhgYiLOzM9OnT2fz5s0YjcY0rXGvS5cuUaVKFS5fvoyfn5/5N/jOO++QnJxMeHi4xfhNmzbx5ptvcuXKFUqUKEHDhg05c+YMCxYsYMWKFSxfvpxatWqZx//000+8++67GI1G/P39qVGjBufOnePAgQP079+fL7744pFjFhEREREREZEHUxJdRF4aW7duJSAggMOHD5MtWzbg7qnmDh068O6773LhwgXWrVuHv78/AGfPnqVcuXLMmDGDAQMGUKBAgQeuf/XqVcLDw0lKSmLGjBm0atUKuHsiOjw8nClTptic16lTJ4YPH07u3LnNbcnJyXz99dd88cUXfPbZZxYnlb/77jurNW7evGk+sf3tt9+a23fs2MFnn32Gq6srv/76K35+fgDcunWLtm3bMnfuXLp168acOXOs1hw/fjz9+vXj66+/Np9uHzt2LF27diUsLIyjR4/i6OhIxowZefvttxk4cCA///wzH374ocU6M2bMICEhgW7duuHo6Gi1z4IFC9i9ezclSpSweX9SM3XqVD766COGDh2KnZ0dJpOJt99+m4iICGrXro2dnR379+/H29sbgLVr11KnTh2++eabNCXRt2zZwvfff0/27NnZuHGjOb7z588TGBhodSI/LVIeskyZMgUXFxdzW0hICAMGDLBIol+5coVmzZpx48YN5s6da/EA4tdff+XNN9+kbdu2HDt2DAcHB+Dud585c2b27NljcRreZDIRGRn5yPGKiIiIiIiIyMOpnIuIvDQyZMjA+PHjzQl0gHbt2uHu7s6RI0d4//33zQl0AE9PT3OJko0bNz50/blz5xIfH0/dunXNCXQAe3t7hg8fTqZMmWzOCwwMtEigA9jZ2fH555/j5eXF4sWLH7p3x44d2b59O23btqV3797m9jFjxpCcnEz37t3NCXQAo9HImDFjcHJyYv78+Zw5c8ZqzXz58vHll19alId59913ee211zh79iwLFy602N/Ozo4JEyZYrZPSdv8p6xQff/zxIyfQAQoWLMjgwYPNJ+8NBgMfffQRAPv372fUqFHmBDpA7dq1KVeuHCdOnLBZhud+P/30EwA9e/a0iC9HjhwMHTr0keMFyJw5M+PGjTMn0AHeeustSpUqxcmTJy3imjRpEn/99Re9evWyOsFfp04dunbtypkzZ1i2bJm5PS4ujvz581uVkzEYDBYn1h8kJibG5iet/0JARERERERE5FWjJLqIvDR8fHwoVKiQRZudnR358uUDMJcnuVfBggUB+PPPPx+6/qZNmwBo3ry5VV+2bNkICgpKde758+eZPHkyPXv25J133iEsLIywsDBu377NhQsXuHDhQqpzBw8ezLRp03jttdcYP368RV9UVBSAzXrlOXPmJCgoiOTkZLZs2WLV36RJE+ztrf9BUsoDgpTrhbv3tl69euzbt8/ihPbu3bvZtWsXr732GqVLl7YZf8OGDVO9tgcJCAiwii/lXws4ODhQs2ZNqzlP6/sMCgqyKluTFhUrVrQ5r3DhwlZxrVmzBrhbLseW6tWrA3f/tUGKChUqsGfPHvr27cvRo0cfOT4REREREREReXQq5yIiLw0vLy+b7Smngm31p/TdunXroeunvAQ0b968NvtTa585cyadOnXi2rVrqa599epVm8nXZcuW0a9fP7y9vVm0aJFVne6zZ89iMBjMDwrul3Ji2dYLTB91TufOnVm5ciXjx4+ncuXKAOak/oPKp6R2Xx7mQd+Xh4eHVW34e/vT+n0aDAby5Mljsz9v3rwPfLhhy70n4++V8q8U7o0r5VT6a6+99sA1762P/8MPPxASEsLgwYMZPHgwnp6e+Pv707RpUxo3bmzzntzP19fXZntMTAxw+6HzRURERERERF41SqKLyEvj3rIkj9P/MCaT6ZHXOXHiBGFhYZhMJkaMGMGbb76Jl5cXTk5OAFStWpWtW7ea177X/v37ad26NUajkUWLFuHh4fHYsT9KzLZiAQgODsbb25vZs2czYsQI7O3tmTFjBq6urrRo0SLV9WzVSU+LB8X8pN/ls/IocSUlJQHQrFkznJ2dUx13b5K9dOnS7N+/n19++YUVK1awYcMGZs+ezezZs6levTpr1641108XERERERERkadDSXQRkTTy9PQE7ibGbTl58qRV24oVK0hMTKRnz55WL+QEOHbsmM21Lly4QIMGDbh69SqzZs2iQoUKqcZ0/PhxTpw4QZEiRaz6U2K9vyZ7Wq4j5XpTZMiQgfDwcPr378/MmTMxGo1cvnyZTp06pVoP/nmWO3duYmNjOXXqlFUZILD9fT5N3t7eHDp0iM8++yzVUji2ODo6EhISYi4Ds3//flq1asWmTZuYOHEi77777jOKWEREREREROTVpJroIiJplFKjeu7cuVZ9ly5dYvXq1VbtFy9eBLBZMmTjxo38/fffVu137tyhadOmHDt2jM8+++yBp7xTXpQ6ffp0q75z586xevVq7OzsqFq1qlX//Pnzzaeh7zVr1iwAqlWrZtUXHh5ufoFrWkq5PM8e9H2uWbPmkUu5PKo6deoAsGjRoidap3jx4rz33nsA7N2790nDEhEREREREZH7KIkuIpJGzZo1I3v27KxevZo5c+aY25OSkujZs6fNmucpL5ScNm0aCQkJ5vYzZ87QpUsXm/t88MEHrF+/npCQEL766qsHxvTee+9hZ2fHyJEj2blzp7k9MTGRbt26cf36dRo3bmyzvviJEyf48ssvLdrGjRvH1q1b8fDwoFGjRlZzvLy8CA4OZufOnWzevJkyZcpQsWLFB8b4vOrcuTMAw4YN48CBA+b2Cxcu0KdPn39lf3d3dwYOHMjkyZOtyugkJCQwdepUTp8+DcD169cZNWoUly5dshiXnJxsfoDzuPXnRURERERERCR1KuciIpJGmTNnZty4cTRv3pwWLVowatQo8ubNS3R0NOfOnSM0NNTqRHjDhg0pUaIEO3fupFChQlSrVo2bN2+yfv16ypYtS9WqVdmyZYt5/KlTpxg7dixwt3xKhw4dbMYSEREBgJ+fHwMGDODTTz+lSpUqBAQE4ObmxubNmzl16hS+vr6MGTPG5hodO3bk22+/ZcGCBZQuXZojR46wY8cOMmbMyOTJk8112+/XuXNnFi9eDECnTp0e6R4+T6pXr0737t0ZMWIE5cqVo06dOjg5ObFu3Try5s1L5cqV2bZt2zPbP1u2bCxcuJCGDRvy9ttv8+WXX1KyZEmMRiMnT57kwIEDJCQksHv3bry9vUlMTOTDDz+kd+/elC9fHh8fHxITE9m5cycnT56kQIEC5gcDIiIiIiIiIvL06CS6iMgjaNKkCWvWrMHf35/du3ezcuVKihcvztatW23W1XZwcCAqKop3330XR0dHli1bxoEDB+jWrRtr1qwhY8aMFuPvLa8yf/58pkyZYvNzr379+rFs2TJq1qzJjh07WLBgAUajkT59+rB9+3Zy5cpl81qqVq3Khg0b8PDwMMdVu3ZtIiMjef3111O9BzVr1iRDhgw4OTkRGhr6KLfvuTNs2DDGjh1LoUKFWLNmDZs3b6Z58+asX78eo9H4zPevVq0ae/fupWfPnuYE/urVq7ly5QrBwcHMnj2b4sWLA5ApUyZ++OEHgoODOXfuHEuWLGHdunVky5aNAQMGsGvXLrJly/bMYxYRERERERF51RhM9//7cRERean179+fL7/8ksmTJxMWFvbI82fMmEFoaCjt27c3n4iXF19MTAyn4xIYv+pSeocCwIyvAtI7BBERERERERFA5VxEROQR3L59myFDhgCYX2YpLw/vnC7M+KpseochIiIiIiIi8lxREl1ERB5qyZIlLFq0iOjoaP73v//RqFEjKlWqlN5hiYiIiIiIiIg8c6qJLiIiD/Xbb78xefJkzp49S2hoKJMmTUrvkERERERERERE/hWqiS4iIiLExMQA4Ovrm86RiIiIiIiIiDxfdBJdRERERERERERERCQVqokuIiIiAJyOS+CLnyPTOwwAZnwVkN4hiIiIiIiIiAA6iS4i8tJbs2YNISEheHh44ODgQI4cOShevDihoaGMHz+exMTE9A7xkcXGxmIwGAgICEjznLCwMAwGAxEREc9k/QcxGAz4+Pg80pyAgAAMBgOxsbFPJQYREREREREReTxKoouIvMS++OILgoKCWLx4Me7u7jRo0IDatWuTMWNGZs6cSadOnbhw4YLFnMdJ+IqIiIiIiIiIvKxUzkVE5CW1c+dOvvrqKxwcHFi4cCH169e36D9z5gzjx4/HaDSmU4TPLy8vLw4cOICzs/NTWe/AgQNkzJjxqawlIiIiIiIiIv8uJdFFRF5SCxcuBKB58+ZWCXS4myju37//vxzViyFjxowULVr0qa33NNcSERERERERkX+XyrmIiLykzp07B4C7u3uaxkdERGAwGAA4ceIEBoPB/Lm/Nvi1a9f46quvKFWqFM7OzmTOnJmaNWuyaNEiq3XvrS9+5coVevbsSf78+cmYMSPdu3c3j9u/fz+hoaHkzp0bBwcHvLy8aNeuHYcOHXqs638SqdVET7lH/fv35/DhwzRp0oQcOXLg4uJCtWrVWLFihc31HlQiZ9y4cZQqVQpHR0e8vLzo1q0bly9fTjW2AwcO0LZtWwoWLIijoyPu7u6ULVuW7t278+effz7uJYuIiIiIiIhIKnQSXUTkJeXt7Q3A/Pnz+eSTTx6aTC9UqBDt27dnypQpuLi40LRpU3PfvSep//77bwIDA9m/fz9eXl7UrVuX69evs3XrVho1asSgQYPo27ev1fo3btygZs2anDhxgpo1a1K+fHmyZcsGwNq1a2nQoAE3btygfPnyBAQEcPDgQX7++WcWLlzIihUr8Pf3fxq35ak4evQofn5+ZM+enaCgIM6ePUtUVBTBwcFMmjSJsLCwNK3Tq1cvvv/+e4xGI4GBgTg7OzN9+nQ2b95ss8zOb7/9RvXq1bl58yZ+fn74+flx9epVjh07xsiRIwkJCSF37txP+WpFREREREREXm1KoouIvKRCQ0MZNGgQJ0+epFChQoSEhODv70+VKlUoXry4+dR5iurVq1O9enWmTJmCm5sbERERNtft0KED+/fvp0+fPnz99dfmWt/Hjh0jKCiIzz77jPr161O6dGmLedHR0VSpUoVjx46RNWtWc3tCQgKhoaHcuHGDsWPH0qVLF3Pf8OHD+eijj2jdujVHjhx5buq3T5s2jXbt2jFx4kTs7e/+p3TZsmWEhITw/vvvU69evYcms7ds2cL3339P9uzZ2bhxIyVKlADg/PnzBAYGsm3bNqs5o0aN4saNG8yfP5/GjRtb9B04cMDivoqIiIiIiIjI06FyLiIiL6mCBQuyePFiPD09uXLlClOnTqVjx46ULFkSDw8P+vTpw6VLlx5pzd9//52VK1dStWpVvv32W4uXZRYoUIDvv/+epKQkJkyYYHP+qFGjrBK9c+bM4e+//8bf398igQ7Qo0cPKlSowOnTp8013p8HmTJlYsSIEeYEOkBwcDBNmzYlISEh1QcQ9/rpp58A6NmzpzmBDpAjRw6GDh1qc05cXBwAgYGBVn3FihVL0yn0mJgYm5/ExMSHzhURERERERF5FSmJLiLyEgsKCuLYsWPMmTOHjh07Urp0aezs7IiLi2Po0KFUqlTJXDs9LdasWQPAW2+9ZXWSHe6eZgfYsWOHVV/u3LmpWLGiVXtUVBRw9+S8LW3atLEY9zwICgoyl6K5V6tWrQDYtGnTQ9dIGdO8eXOb62fPnt2qvUKFCgC0a9eO6OhokpOTHyluEREREREREXl0SqKLiLzkjEYjzZo1Y9y4cezZs4e//vqLIUOG4OzszJEjR+jXr1+a14qNjQXg448/tnjxaMrHzc0NgPj4eKu5efPmtbnm2bNnAVJ98WZKe8q450G+fPlstj9KrGfPnsVgMJAnTx6b/bbuV+/evQkICGDp0qW89tprZM+enXr16jF69GiuXr2apth9fX1tfhwcHNI0X0RERERERORVo5roIiKvGHd3d3r37o2TkxPdunVj+fLlaZ6blJQEgL+/PwUKFEh1XEoy/V6Ojo4PXNvWyfZH6X8emEymZ7p+5syZWbduHZs3b2bp0qVERkaydu1aVq9ezaBBg4iKiqJgwYLPNAYRERERERGRV42S6CIir6iAgADA9qnx1Hh7ewPQtGlTPvjgg6cSh6enJwDHjx+32X/ixAmANNX7/rekxHS/kydPAv9c04Pkzp2b2NhYTp06RaFChVJd634Gg8H8EliAc+fO8eGHHzJz5kz69evH7Nmz03oZIiIiIiIiIpIGKuciIvKSetip6KNHjwLWCd+MGTNy584dm3Pq1KkDwKJFi548wP/n7+8PwPTp0232p7SnjHserF692uZLWWfOnAlAtWrVHrpGShJ87ty5Vn1r1qzhwoULaYrF3d2d/v37A7B37940zRERERERERGRtFMSXUTkJfWf//yHPn362DzhHRMTQ8+ePQFo3LixRZ+npyd///23zSRx5cqVqV27NuvXr6dHjx5cu3bNoj85OZnVq1en6cWaKZo3b06uXLmIiopi3LhxFn2jRo1ix44deHt706hRozSv+axdu3aNjz76yOJhw4oVK5g7dy7Ozs60b9/+oWt07twZgGHDhnHgwAFz+4ULF+jTp4/NOT/99JPN73PlypVA6nXnRUREREREROTxqZyLiMhL6tq1a4wcOZLvvvuOIkWKUKxYMTJmzMjJkyeJjo4mOTmZChUq8MUXX1jMa9iwIaNHj6Z8+fJUrVoVR0dHihQpQu/evYG7J8ODgoIYMWIEU6dOpWzZsri7u3PmzBkOHTrEuXPnGD58uPmk9cO4uLgwffp0GjRoQOfOnRk3bhyFCxfm4MGD7N69GxcXF2bMmIHRaHwq92XAgAH89NNPNvsKFy7M1KlTH7pGaGgoCxYsIDIyktdee40///yTjRs3YjKZGDlyJF5eXg9do3r16nTv3p0RI0ZQrlw56tSpg5OTE+vWrSNv3rxUrlyZbdu2Wcz56aefePfddylevDjFihXD3t6eQ4cO8fvvv+Pk5GT1XYqIiIiIiIjIk1MSXUTkJfXZZ59RoUIFVq1axZ49e9iwYQNXrlwha9as1KxZk6ZNmxIeHo6Dg4PFvEGDBmEymVi8eDGzZ8/mzp071KxZ05xEz5UrF9u2beOnn35i9uzZ7Nixg8TERHLnzk25cuV46623aN68+SPFWrt2bXbs2ME333zDunXr+OOPP3Bzc6NNmzZ89tlnFClS5Kndl2PHjnHs2DGbfTdv3kzTGoUKFWLr1q188sknrFq1ips3b1K5cmX69etHcHBwmmMZNmwYRYoUYcyYMaxZs4YcOXLQvHlzBg0aREhIiNX4AQMGsGjRIrZv387atWtJTEzE29ubTp060bt3b5u11UVERERERETkyRhMDyuaKyIiIgBERETQoUMHvvjiC3Md8pdFTEwMp+MSGL/qUnqHAsCMrwLSOwQRERERERERQCfRRURE5P9553Rhxldl0zsMERERERERkeeKXiwqIiIiIiIiIiIiIpIKJdFFRERERERERERERFKhmugiIiJCTEwMAL6+vukciYiIiIiIiMjzRTXRRUREBIDTcQl88XNkeocB6MWiIiIiIiIi8vxQORcRERERERERERERkVQoiS4i8hK5fv06o0ePJigoiNy5c+Pg4ICrqyvFixenQ4cOLF26lOTkZIs5BoMBHx+f9Ak4nQUEBGAwGIiNjbVo9/HxwWAwPNM9HqR///4YDAYiIiKeSgwiIiIiIiIi8vhUzkVE5CWxdetWmjZtytmzZ3F0dMTPzw9PT09u3rxJTEwMERERREREULp0afbs2ZPe4YqIiIiIiIiIvBCURBcReQn8/vvvBAYGcuvWLfr27Uu/fv1wdXW1GBMbG8uwYcOYPHlyOkX54li7di23b99+KmtNnTqV69ev4+Xl9VTWExEREREREZF/l5LoIiIvOJPJRJs2bbh58yaDBg2ib9++Nsf5+PgwatQo2rdv/y9H+OIpWLDgU1srb968T20tEREREREREfn3qSa6iMgLbsWKFfzvf/8jX7589OnT56HjK1So8MD+uXPnYjAYCA0NTXVMhw4dMBgMTJs2zaI9ISGBQYMGUb58eVxdXcmUKRPFixene/funDhxwmqdpUuXUq9ePXLkyIGjoyOFCxfmP//5D9euXbMae29t8RkzZlC5cmVcXV3JmjWrecz169cZMGAAJUuWxMnJiSxZslCjRg1mzZr1kLtiyVZN9NjYWAwGAwEBAVy5coUPP/yQPHny4OjoSLFixRg+fLhVvfn7477fhg0bCAgIIFOmTOTIkYNGjRpx8ODBVOM6f/48/fr1o0SJEmTKlIksWbJQuHBh2rVrR3R09CNdo4iIiIiIiIikjU6ii4i84FauXAlA06ZNsbN78mejISEheHh4MH/+fEaPHk327Nkt+q9cucLcuXPJmjUrTZs2Nbf/+eef1KlTh/3795M9e3YCAwPJmDEjR44cYdSoUZQtW5awsDDz+J49ezJs2DBz/XY3Nzd27drF119/zcqVK9mwYQMuLi5W8Q0aNIgJEyZQrVo1goODOXXqFABXr16lVq1a7Nq1C3d3d4KDg0lISGDdunVERUWxbds2RowY8cT359atWwQGBnL06FECAwNJTExk7dq1fPTRR/zxxx9pLpezePFimjRpQlJSElWrViVv3rxER0fz2muv0aBBA6vx165do3Llyhw5cgRfX1/q1asHwMmTJ5k5cyYFChTAz8/via9PRERERERERCwpiS4i8oJLeUlouXLlnsp6GTNm5O2332bgwIH8/PPPfPjhhxb9M2bMICEhgW7duuHo6Ghub9u2Lfv376dVq1aMHz/eIgEeExNDUlKS+e9z5sxh2LBhlCtXjgULFuDj4wPA7du3ef/99xk3bhz9+/dn6NChVvFNnTqVdevWUbNmTYv2fv36sWvXLurUqcPChQvJlCkTAAcPHqRmzZqMHDmSoKAg6tev/0T3Z9u2bZQuXZqYmBjc3NwAOHr0KDVq1CAiIoJGjRrRsGHDB65x9epVwsPDSUpKYsaMGbRq1QqAO3fuEB4ezpQpU6zmzJs3jyNHjtCtWzdGjRpl0RcXF0dcXFya4o+JibHZnpiYmKb5IiIiIiIiIq8alXMREXnBxcfHA5gTuvcLCwuz+mzfvv2Ba3bs2BE7OzsmTJhg1ZfSFh4ebm6Ljo5m7dq1eHh4WCXQAXx9fSlatKj57wMHDgRg5syZ5gQ63E3gjxw5Eg8PDyZMmGCzPMo777xjlUBPSEhg4sSJ2NnZ8eOPP5oT6ABFixbls88+A7BKPj+u7777zuJ+FyxYkP/85z8A/PDDDw+dP3fuXOLj46lbt645gQ5gb2/P8OHDLeJPkZIkDwwMtOrLmTMnJUuWfOTrEBEREREREZGHUxJdROQFZzKZAKxqeKeYMmWK1ef48eMPXNPHx4d69eqxb98+tm3bZm7fvXs3u3bt4rXXXqN06dLm9l9//RWA0NBQmyVY7hUXF8eePXsoVqwYRYoUsep3dHSkYsWKXLp0yeapaVunvHft2sWNGzfw8/PD19fXqr9t27YAbN682Xy/Hlf27NmpW7euVXvr1q0B2LJly0P32LRpEwDNmze36suWLRtBQUFW7Sm17Pv168eyZcu4efPmI8cOdx9o2Po4ODg81noiIiIiIiIiLzsl0UVEXnApJ6JTTqTfz2QymT/t27dP87qdO3cGYPz48ea2lD937NjRYmxKXfKCBQs+dN2UF4weOHAAg8Fg87Ns2bJUrylv3rxWbWfPngWwONV+r6xZs5IlSxauXbvGlStXHhrjg+TLl89me+bMmcmaNWua9kiJ19a1pNZeu3ZtevTowcGDB2nQoAFZsmThtdde4z//+Y/Nl5aKiIiIiIiIyNOhmugiIi+4MmXKsHnzZn777TfzaeinITg4GG9vb2bPns2IESOwt7dnxowZuLq60qJFC5tzUjsNf6+U2ui5c+e2eeL6Xjly5LBqu7cO++Psn5Yxjyutp9wf9q8HUjNs2DA6d+7M4sWLWbt2LZs3byY6OpohQ4Ywe/ZsQkJCHjVkEREREREREXkIJdFFRF5wb7zxBj/++CPz5s1jyJAh2Nk9nX9klCFDBsLDw+nfvz8zZ87EaDRy+fJlOnXqZFWzO0+ePAAcOXLkoet6e3sD4OHhQURExFOJ1dPTEyDVMjWXL1/m8uXLuLi44Orq+kR7nTx50mb7lStXzHtkzpw5TfGmnMpP6x4ARYoUoU+fPvTp04ebN2/yww8/0KtXLzp37qwkuoiIiIiIiMgzoHIuIiIvuPr161OsWDFOnDjB4MGDn+ra4eHhZMiQgfHjx6daygWgTp06AEyfPp3r168/cE1vb2+KFCnCH3/88dDa7GlVoUIFnJyciI6OtllHfdq0aQBUr179iU+inz9/3lwD/l4zZ84EoGrVqg/do3r16sDdF4ze79KlS6xevTpNsTg6OtKzZ09y585NXFyc+eWjIiIiIiIiIvL0KIkuIvKCs7Oz4+eff8ZoNPLpp5/yySefcPXqVatxJ06c4PDhw4+0tpeXF8HBwezcuZPNmzdTpkwZKlasaDXOz8+PWrVq8ddff9G5c2erRPqRI0c4ePCg+e+fffYZSUlJNGnShH379lmtd/ToUSZNmpTmOF1cXHj77bdJTk7mvffeIyEhwdx3+PBhvv76awC6deuW5jUfpHfv3pw/f9789+PHjzNgwAAAunbt+tD5zZo1I3v27KxevZo5c+aY25OSkujZsyfXrl2zmrNo0SKLl7ym2L17N3///Teurq5ky5btcS5HRERERERERB5A5VxERF4CFSpU4Ndff6Vp06Z8++23jBgxAj8/Pzw9Pblx4wanT59m9+7dJCcnU6JECcqWLZvmtVNqcAN06tQp1XE///wzgYGBTJs2jZUrV1K9enUyZszIkSNH2LNnD5MmTaJo0aIAtGnThr179zJkyBDKli1LuXLlyJ8/P1euXOHEiRMcPHiQMmXK8Pbbb6c5zkGDBrFt2zbWrFlDgQIFqFmzJgkJCaxbt46bN2/ywQcf8Oabb6Z5vdRUrlyZxMREfH19CQwMJDExkbVr13L9+nXatGmTppIqmTNnZty4cTRv3pwWLVowatQo8ubNS3R0NOfOnSM0NJTp06dbzImMjGTkyJF4eXlRrlw5MmfOzNmzZ9m0aRPJyckMGDCAjBkzPvH1iYiIiIiIiIglJdFFRF4S1atX5+jRo4wfP56lS5eyb98+tm7ditFoxNvbm9DQUJo1a0b9+vXJkCFDmtetWbMmGTJkwMHBgdDQ0FTHeXl5sWPHDoYPH868efNYvXo19vb25MmTh+7duxMYGGgxfvDgwdSrV48xY8awdetW9uzZQ7Zs2fD29qZ37960bNnyka7f1dWVDRs28P333zN79myWLFmCg4MDFStWpGvXrrRq1eqR1kuN0Wjkl19+oV+/fixatIj4+Hjy589Px44d6d69e5rXadKkCWvWrKF///7s2rWL//3vf/j7+/Ptt99anE5PERYWhr29PRs3biQ6OprLly/j4eFB/fr16dGjBwEBAU/l+kRERERERETEksFkMpnSOwgREXl+zZgxg9DQUNq3b//UXgT6IoqNjSV//vzUrFmTyMjI9A7nqUupJe/r65vOkYiIiIiIiIg8X1QTXUREUnX79m2GDBkCwHvvvZfO0YiIiIiIiIiI/PtUzkVERKwsWbKERYsWER0dzf/+9z8aNWpEpUqV0jssEREREREREZF/nU6ii4iIld9++43Jkydz9uxZQkNDmTRpUnqHJCIiIiIiIiKSLlQTXURERFQTXURERERERCQVKuciIiIiAJyOS+CLnyOfydozvgp4JuuKiIiIiIiIPGsq5yLyFBkMBgwGwwPH9O/fH4PBQP/+/f+doOSFERkZicFgICws7JnvdeDAAT744ANKlixJlixZMBqNeHl50bBhQ6ZOnUpiYuIzj0FS92/+FkRERERERETkwZREF3mB+fj4PDRpL8+PlAcoERER6RrHF198QalSpRg9ejRXr16lVq1aNG7cmPz58/PLL7/Qvn17ihcvnq4xioiIiIiIiIg8L1TORUTkOeHn58eBAwfIkiXLM9vjs88+45tvviFXrlxMmjSJ+vXrW/RfvHiR7777jqFDhz6zGEREREREREREXiRKoouIPCecnZ0pWrToM1t/x44dDBw4ECcnJ9avX0+xYsWsxmTLlo1vvvmGN95445nFISIiIiIiIiLyIlE5F5HniMlkYsqUKdSoUYOsWbPi5ORE6dKl+e6777h9+7Z5XEq95BMnTgD/1GI3GAz4+PhYrJmYmMjIkSOpVKkSrq6uuLi44Ofnx8SJEzGZTFYxpKyRmJjIV199RdGiRTEajYSEhJjHnDp1is6dO5MvXz6MRiM5c+akcePG7Nixw+Z1LV++nLfffptixYqROXNmXFxcKFOmDAMHDuTWrVtW4yMiIsx14w8fPkzLli3JlSsXdnZ2LFq0yDxuxYoV1K1bl2zZsuHo6EiRIkXo27cvly5dslrz3lIqu3bt4o033iBr1qxkz56d5s2bc/r0aQASEhLo3bs3Pj4+ODo6UrJkSebNm2fzu5o5cyYtW7akcOHCuLi44Orqip+fHz/++CPJyckW4318fPjyyy8B6NChg8V3FhkZCTy8DvbKlSsJDg4mZ86cGI1G8ubNS0hICMuXL7c5/n7ff/89JpOJDz74wGYC/V7Vq1e3atu/fz+hoaHkzp0bBwcHvLy8aNeuHYcOHbIae++1xMXF8c477+Dh4UGmTJmoXr06W7ZsMY/96aefKF26NE5OTuTJk4cvv/zS6v6B5W/ziy++oGDBgjg6OlKgQAE+//xzbt68afNazp8/T+/evfH19cXR0ZHs2bPz+uuvs3r1aquxsbGxGAwGAgICbK6VWkmee0srTZgwwXw9Hh4edO7c2eZvMmW/Vq1akSNHDjJlykTVqlUf+H3evn2b//73v/j5+eHm5oazszM+Pj4EBwcza9asVOeJiIiIiIiIyOPTSXSR50RycjItW7Zk7ty5ZM6cmUqVKpEpUya2b99O7969Wb9+PUuXLsXOzg4PDw/at2/PvHnzSEhIoH379uZ13NzczH9OSEjgjTfeICoqCjc3N6pXr46dnR1bt24lPDycHTt28NNPP9mMJSQkhI0bN1KzZk1Kly5Njhw5ANi7dy+BgYHEx8dTtGhRGjduzMmTJ1m4cCFLly5lxowZNGvWzGK9d955h4SEBEqUKEGpUqW4cuUK0dHRfPrpp6xdu5bVq1eTIUMGqzgOHTpEpUqVyJEjB7Vq1eLixYtkzJgRgEGDBtGvXz/s7e2pWbMmbm5ubN68mcGDB7Nw4UI2btxIrly5rNbcvn07Xbp0oVChQtSpU4c9e/Ywd+5c9uzZQ3R0NHXr1uXYsWNUqVKF/Pnzs2HDBpo3b87KlSupV6+eeZ1bt27RunVrsmXLRvHixSlfvjzx8fFs3bqV9957j+joaItEa9OmTfn111/Zs2cP1apVo1ChQuY+Dw+Ph/086NmzJ8OGDSNDhgxUqVIFb29vzp49y/r167l06RJvvvnmA+cnJyfzyy+/ANC6deuH7ne/tWvX0qBBA27cuEH58uUJCAjg4MGD/PzzzyxcuJAVK1bg7+9vNe/ixYtUqVKFmzdvUqVKFf788082b95M3bp1iY6OZty4cYwbN47XXnuNOnXqsGHDBvr3709iYiLffPON1Xomk8l8L2vXrk3ZsmVZu3YtAwYMYMuWLaxatcrit3TmzBlq1KjBsWPHzA8dzp07x6+//sqqVasYNmwYPXr0eOT7kZo+ffqYH1q9/vrrbNmyhXHjxnHgwAE2bNhg8Q6Do0ePUrVqVeLi4ihcuDDly5fn+PHjNGjQgC5duthcv23btsyePRs3NzeqVq2Ks7MzZ86cISoqimvXrtGyZcundi0iIiIiIiIi8v9MIvLUAKaH/c/qiy++MAGmL774wqJ98ODBJsBUt25dU1xcnLn92rVrpgYNGpgA05gxYyzm5MuX74H7vfvuuybA1LZtW9PVq1fN7XFxcabXXnvNBJiWLVtm8xoKFSpkOn36tEVfcnKyqVSpUibA9Mknn5iSk5PNfXPnzjXZ2dmZXF1dTX/99ZfFvIULF5quXbtm0XblyhVTcHCwCTBNmTLFom/y5MnmON5//33TnTt3LPqjo6PNe23fvt3cfvPmTVOzZs1MgKlZs2YWc1LuO2AaPny4uT0xMdFUp04dE2AqXry4KSAgwHThwgVz/4QJE0yAqUaNGhbr3b592zR//nzTrVu3LNrj4uJMFStWNAGmDRs22Ixh8uTJJlvWr19vAkzt27e3aP/5559NgMnb29u0Z88ei75r166Z1q5da3O9e8XExJgAk9FotLqfD3Pt2jVTrly5TIBp7NixFn3Dhg0zx3bz5k2rawFMLVq0MN24ccPcl3IfihcvbvLy8jLt27fP3Pe///3P5ODgYHJ2drb4zZpM//w2vb29TUePHjW3x8XFmUqWLGkCTCNHjrSYk/Iba9u2rSkxMdHcHhUVZXJ2djZlyJDB4p4eP37cBJhq1qxp816k9h2m/G8xd+7cpt27d5vbz507ZypUqJAJsPqegoKCTICpa9eupqSkJHP7+PHjzdd6728hJbZKlSpZ3E+TyWS6fv26acuWLTZjTqvDhw+b1m3abWr1n/XP5CMiIiIiIiLyolI5F5Fn4N5SHfd/Ukp63OvOnTsMHToUV1dXZsyYgbu7u7nPxcWF8ePHYzQa+e9//5vmGOLi4pgwYQL58+dn/PjxZMqUydzn7u5uXiu1NQcNGoSXl5dFW2RkJHv37iV//vwMGDDA4lRt06ZNCQkJ4erVq0yePNliXkhICC4uLhZtrq6uDB8+HIDFixfbjMHd3Z3BgwdbnVIfM2YMycnJdO/eHT8/P3O70WhkzJgxODk5MX/+fM6cOWO1Zo0aNejevbv57xkzZuSDDz4A7p58Hz9+PNmyZTP3h4WF4ebmxtatWy1K6tjb29O4cWMcHBysYh40aNADr+tRDRw4EIARI0ZQunRpiz4XFxcCAwMfusb58+eBuzXPbZ36f5A5c+bw999/4+/vb3VCukePHlSoUIHTp0+zcOFCq7lZsmThp59+wtHR0dz20UcfYTAY2L9/PwMGDKBEiRLmvuLFi/Pmm29y/fp1du7caTOezz//nAIFCpj/7u7ubn4R6g8//GBuP3bsGMuWLSNz5syMGjXK/K8Y4G65mi5dupCUlMSPP/74SPfjQQYMGEDZsmXNf3dzc+Pdd98FYOPGjeb2o0ePsnr1arJly8aQIUOws/vnP8fh4eFUrVrVau24uDgAqlatanE/AZycnKhSpUqaYoyJibH5SUxMTPN1ioiIiIiIiLxKVM5F5Bm4t7zK/X7//Xf27Nlj0bZ7927i4+N54403LMqxpMiVKxe+vr7s27ePGzdu4OTk9NAYNmzYwO3bt3n99dcxGo1W/WXKlMHV1dVmHXODwUCDBg2s2qOiogBo0aKFzURs27ZtWbBgAVFRUfTt29eiLyYmhhUrVnDkyBESEhJITk4212SPiYmxeQ116tTB2dk51ThCQ0Ot+nLmzElQUBCLFy9my5YtVqVl6tatazUnJSHr4+NjUWYFIEOGDPj4+LBz507i4+PJnTu3Rf/vv//O6tWrOXHiBNevX8dkMnH16tUHXtejOHv2LAcOHCBHjhw0adLksdcx2ah/n1YPut8Abdq0YdeuXURFRVmVE6lYsSJZs2a1aMucOTM5cuQgPj7e5vdRsGBBAP7880+b+9kqWfL666+TLVs2Dh8+zLlz53B3d2fTpk0A1K9f3yoGuPt7HTZsmPn6noagoCCrtsKFCwOW17N582ZzbPc/YIK713hv3XiAokWL4uLiwuTJkylRogSNGzc2l1kSERERERERkWdHSXSRZ+D+lw7eq3///lZJ9NjYWODuiyPvPd1ty4ULF6xOiNuSsubYsWMZO3ZsquNu3Lhh1Zby4sr7nT17FsDq5aUpUtpTxsHd5G2vXr0YPnx4qonclKTz/fLmzWuz/ezZsxgMBvLly5fmOFLYuncpSczU7mtK/70vQU1MTCQsLIyZM2fanAOpX9ejOHXqFPBPYvlxpTycuXjxIklJSY90Gv1xvvcUD7qn8fHxD/w+bL10Nlu2bLi6utpcM1++fFy8eJGzZ8/i7u7+RHE/Lm9vb6u2lH8Fcu/1pOyZ2m/cVnvmzJkZP348nTp1olOnTnTu3JkiRYpQq1Yt2rVrR+XKldMUo6+vr832uw99btvsExEREREREXmVKYku8hxISkoC7ia3bJVxuJet5PaD1ixXrpxVCZCHub9UxP0elui/t3/27NkMGzYMb29vRowYQZUqVXB3dydjxowkJiZiNBpTTa4/LI6HsRXng2J/2HXda9iwYcycOZOSJUsydOhQypcvT7Zs2ciYMSOHDx+mSJEiT3T6+0lis6VAgQJkyZKFy5cv87///e+RfxNpieFR73da+h9Favc7tT1S2h8lhuTk5Af2p3WtlFgf9fpbtWpFnTp1WLx4MatXr2bDhg3mB2W9e/dmyJAhj7SeiIiIiIiIiDyckugiz4GU06slS5Z84Cn2x1kzICCAYcOGPZU1PT09ATh+/LjN/hMnTgBYlDxJqZM9duxYgoODLcYfO3bsseM4fvw4J06coEiRImmK42lLua6URPq9Hve6bMmTJw8AR44ceaJ17OzsqFevHnPmzGHGjBmPlER/nO/9Wbl48SJXr161eRr95MmTFnE8LO6Uf61xb9wpNe6vXbtmc07Kvwx4Uimxpdy7+6Vciy3u7u6Eh4cTHh6OyWRi1apVtGjRgqFDhxIWFkbx4sWfSowiIiIiIiIicpdeLCryHKhUqRJZsmRh/fr1XLlyJc3zUhJ+d+7cseqrVasWGTJkYNmyZeZT6U/K398fuHu63Naa06ZNsxgHd5Oe8E8y+F5z5sx5ojimT59u1Xfu3DlWr16NnZ3dQ0/1P4nHua4HfV+p8fT0pFixYpw/f54FCxY8RqT/SHmh56hRozhw4MADx95bj/tB9/ve9nu/92dp9uzZVm2rVq3i4sWL+Pr6kjNnTuDuy0MBli9fzqVLl6zm2Pq9urm5kTFjRo4fP271PSUmJrJhw4ancg3VqlUDYMWKFSQkJFj1z5o1K03rGAwGXn/9dd58800A9u3b91TiExEREREREZF/KIku8hwwGo306tWLS5cu0aRJE5unU//44w+r5GHKadZDhw5Zjffy8iIsLIyYmBjatm1LfHy81ZgtW7awYsWKNMcZEBBAqVKlOH78OJ9//rlF+YxFixaxYMECMmXKRFhYmLk95aWK48aNsxgfFRXF0KFD07z3vd577z3s7OwYOXIkO3fuNLcnJibSrVs3rl+/TuPGjdNUO/5xpVzXTz/9ZNE+b948pk6danPOg76vB0l5SWv37t353//+Z9GXkJDAunXr0rTOa6+9Rp8+fbhx4waBgYE2v/vLly/zxRdfUKtWLXNb8+bNyZUrF1FRUYwbN85i/KhRo9ixYwfe3t40atToka7rcX311VfmU+QA8fHx9OnTB4CuXbua2wsUKMCbb77J1atX+fDDD7l9+59631u3bmXs2LFkyJDBYo6DgwOVK1fmwoUL/PDDD+b227dv06NHj1RPtT+qQoUKUbt2bS5evEjfvn0tysRMnjzZ6qWicPcFxAsWLLC4Drj7QGf79u1A6jXWRUREREREROTxqZyLyHOiX79+7N+/n5kzZ1KkSBHKly9P3rx5iY+P59ixYxw/fpy33nqLFi1amOc0bNiQDRs2ULt2bWrVqoWLiwtubm58++23wN0E57Fjx5g5cybLli2jbNmyeHp68tdff3HkyBHOnDnDhx9+SP369dMUo8FgYPr06dSqVYuBAweycOFCypYty8mTJ9m8eTP29vZMmjQJDw8P85wPPviAiIgIfvzxRyIjIyldujRnzpxh06ZN9OzZk+++++6R75Wfnx8DBgzg008/pUqVKgQEBODm5sbmzZs5deoUvr6+jBkz5pHXfRR9+vThl19+oW/fvsydO5fChQsTExPDzp076dWrl83rCgoKwtHRkeHDh7Nv3z48PT0xGAz07t3bZlmaFO3atWPHjh2MGTOGMmXKULVqVby9vTl79iy7d++mXLlyBAYGpinuQYMGYW9vz6BBg3jzzTfJly8f5cqVw8nJidOnT7N9+3YSExMtXj7p4uLC9OnTadCgAZ07d2bcuHEULlyYgwcPsnv3blxcXJgxY0aa6/U/ibx581K6dGlKlChB7dq1yZgxI+vWrePSpUvUqlWL999/32L8f//7X/z9/Zk6dSobNmygSpUqnDt3jsjISJKSkvj++++tStt8/vnn1KtXj+7duzN79mw8PDzYtWsX169fp3379kyZMuWpXMvYsWOpVq0aY8aMYc2aNZQvX57jx4+zfft2unTpYvWA5sSJEzRp0oQsWbJQsWJFPDw8uHTpElFRUVy5coVGjRql+eWiIiIiIiIiIpJ2Ooku8pyws7NjxowZzJs3j1q1ahETE8OCBQvYv38/uXLlon///gwePNhizgcffMBnn31GpkyZmD9/PhMnTrQoA+Hs7Mzq1auZMGEC5cuXZ9++fSxcuJCjR49SsGBBhgwZQq9evR4pzlKlSvHbb7/RsWNHrl27xrx58zh06BAhISFs3ryZZs2aWYwvXLgwO3bsoEGDBsTHx7NkyRKuXbvGf//738c+iQ53HzosW7aMmjVrsmPHDhYsWIDRaKRPnz5s376dXLlyPfbaaVGjRg02bdpEYGAgx44dY9myZTg4ODB//nzee+89m3M8PT1ZvHgxlStXZtOmTUyaNImJEyfy559/PnS/0aNHs3DhQmrXrs2+ffuYP38+x48fp3bt2nz88cdpjttgMPD111/zxx9/8N577+Hs7MzatWuZN28eR48epV69ekybNs3qxHvt2rXZsWMHrVq14vTp08ybN4+//vqLNm3asGvXrn+tlIvBYGDevHl0796dvXv3smzZMrJkycKnn37K8uXLsbe3fDbs5eXFjh076NmzJ/b29ixYsIBdu3ZRu3ZtVq1axUcffWS1R506dViyZAmVKlXit99+Y8OGDVSuXJkdO3bg4+Pz1K7F19eXbdu20bx5c+Li4li8eDEmk4lFixZZPCxLUblyZb7++msqVKjAoUOHmDt3Ljt37qR06dJMmTLlscsjiYiIiIiIiMiDGUz31lcQERF5ThkMBvLly2dRykWenpiYGE7HJTB+1aVnsv6MrwKeyboiIiIiIiIiz5rKuYiIiAgA3jldmPFV2fQOQ0REREREROS5onIuIiIiIiIiIiIiIiKpUBJdRERERERERERERCQVKuciIiIvBL3CQ0RERERERETSg5LoIiIiAsDpuAS++DnymaytF4uKiIiIiIjIi0rlXEREREREREREREREUqEkuoiIvFAMBgMGg+GZ79O/f38MBgMRERFPvJbBYMDHx+eJ1xERERERERGRf5+S6CIiIiIiIiIiIiIiqVASXUREREREREREREQkFUqii4iIiIiIiIiIiIikQkl0ERF54cXGxmIwGAgICODGjRv07duXfPnyYTQaKVSoEIMHD8ZkMtmcu2HDBgICAsiUKRM5cuSgUaNGHDx48IH7nTp1is6dO5v3yJkzJ40bN2bHjh2PHPvevXsJDQ3Fy8sLo9GIp6cnHTp0IDY21mqsyWRi1qxZ1KhRAw8PDxwdHcmTJw916tThhx9+eOS9RUREREREROThlEQXEZGXRmJiIkFBQYwbN45ixYpRq1Ytzpw5Q9++ffnPf/5jNX7x4sXUrl2bDRs2UKZMGYKCgvjjjz947bXXOHLkiM099u7dS/ny5Rk3bhzOzs40btwYX19fFi5cSNWqVZk7d26a450/fz4VK1ZkxowZ5M6dm4YNG+Lh4UFERAQVK1bkf//7n8X4Tz75hFatWvH7779Tvnx5GjduTKFChdizZw9Dhw59tJslIiIiIiIiImliMKV2NE9EROQ5ZDAYACxOlsfGxpI/f34A/P39WbBgAW5ubgDs3LmTKlWq4ODgwN9//02mTJkAuHr1KgUKFCA+Pp4ZM2bQqlUrAO7cuUN4eDhTpkwBYPLkyYSFhZn3LFOmDHv37uWTTz7hm2++Mcczb948WrRogYuLCzExMeTKlcsi5nz58lmcLj9+/DglS5YkY8aMLFmyhBo1apj7pk6dSvv27alUqRLR0dEA3Lx5k6xZs5I7d2527dpF9uzZzePv3LnDli1bLNZITUxMjM32xMRE4i7dZvyqSw9d43HM+CrgmawrIiIiIiIi8qzpJLqIiLw07OzsmDBhgjmBDlCxYkXeeOMNrl+/zs6dO83tc+fOJT4+nrp165oT6AD29vYMHz7cnGy/V2RkJHv37iV//vwMGDDAnEAHaNq0KSEhIVy9epXJkyc/NNaRI0dy/fp1hgwZYpX8bteuHSEhIezYsYPffvsNgCtXrnDr1i3KlCljkUBPiTktCXQREREREREReXRKoouIyEvDx8eHwoULW7WntP3555/mtk2bNgHQvHlzq/HZsmUjKCjIqj0qKgqAFi1akCFDBqv+tm3bWox7kDVr1gDw1ltv2eyvXr06gLnOes6cOfH29mb58uUMHTqUs2fPPnQPW3x9fW1+HBwcHms9ERERERERkZedkugiIvLS8Pb2ttmecqr81q1b5raUJHTevHltzrHVnjLHx8fH5pyU9rQkuFNKu3h4eGAwGKw+vXr1AiA+Pt48Z8qUKWTLlo0+ffrg5eVFgQIFCAsLY/Xq1Q/dT0REREREREQej316ByAiIvK03Fte5WFSaqo/ypy07pOWNZOSkjAYDLRr1+6B40qUKGH+c2BgIEeOHGHZsmX88ssvbNiwgSlTpjBlyhSaN2/O7Nmz03YBIiIiIiIiIpJmSqKLiMgrydPTE4ATJ07Y7D958mSqc44fP25zTspauXPnfuj+3t7eHD16lFGjRpE5c+Y0xQyQOXNmWrduTevWrQHYtm0bzZo1Y86cOYSFhfHGG2+keS0REREREREReTiVcxERkVdSSs3xuXPnWvVdunTJZokUf39/AGbPnk1SUpJV/7Rp0yzGPUidOnUAWLRoUZpjtqVy5crmWux79+59orVERERERERExJqS6CIi8kpq1qwZ2bNnZ/Xq1cyZM8fcnpSURM+ePbl27ZrVnICAAEqVKsXx48f5/PPPzSVh4G4yfMGCBWTKlImwsLCH7t+zZ0+cnJzo0aMHS5cuteq/cOECP/74Izdu3ADunoyPiIjg+vXrFuNu3brF+vXrgdTru4uIiIiIiIjI41M5FxEReSVlzpyZcePG0bx5c1q0aMGoUaPImzcv0dHRnDt3jtDQUKZPn24xx2AwMH36dGrVqsXAgQNZuHAhZcuW5eTJk2zevBl7e3smTZqEh4fHQ/f39fVl2rRptGnThoYNG1KkSBGKFSuGyWTixIkT7N+/n8TERFq3bo2TkxMXLlygQ4cOvPfee1SsWBFvb28SEhLYsmUL586dw8/Pj8aNGz+r2yUiIiIiIiLyytJJdBEReWU1adKENWvW4O/vz+7du1m5ciXFixdn69atFCpUyOacUqVK8dtvv9GxY0euXbvGvHnzOHToECEhIWzevJlmzZqlef/GjRuzZ88eOnfuzO3bt1m5ciWRkZHcunWL0NBQli1bRpYsWQAoWLAg3333HQEBAZw8eZIFCxawefNmfHx8GDVqFJGRkTg4ODyV+yIiIiIiIiIi/zCY7v236CIiIvJKiomJAe6ekBcRERERERGRf+gkuoiIiIiIiIiIiIhIKpREFxERERERERERERFJhZLoIiIiIiIiIiIiIiKpUBJdRERERERERERERCQV9ukdgIiIiDwfTscl8MXPkc9k7RlfBTyTdUVERERERESetadyEt1gMGAwGB44pn///hgMBvr37/80tpRXWFhYmPk3l/JxcnKiaNGi9OjRg7/++iu9Q0xVyv8OHuUTERGR3mE/ksjISAwGA2FhYf/KvH+Dj4+P+fuYOXNmquOio6MtvrvnUURExGP/f/Ht27eZMGEC9evXx9PTE6PRSJYsWShfvjw9e/bkwIEDTz/gV5jBYMDHxye9wxARERERERF55amci7ywqlWrRvv27Wnfvj21atUiLi6OESNGUK5cOWJjY/+VGB41IVm2bFlzzCmfJk2amPvv72vfvj2FChV6RtH/u1IeILxoDwXuN3369FT7pk2b9lT3epKE99N2+PBhSpUqRceOHVm7di0FCxakcePG1KxZk/j4eIYNG0bJkiWZMmVKeocqIiIiIiIiIvJUqZyLvLDCw8MtTi2fO3eO+vXrs3PnTnr16sW8efPSL7hUhISEEBISYtEWGxvL/PnzAV74BDOAn58fBw4cIEuWLP/KvH9TuXLlWLVqFefOncPd3d2i786dO8yePZvixYtz9OhRbt26lU5RPn1nz57F39+fuLg4wsLC+O6778iRI4fFmHXr1tGrVy+OHz+eTlGKiIiIiIiIiDwbOokuLw13d3e+//57AJYvX87t27fTOaJXk7OzM0WLFiV37tz/yrx/U5s2bbhz5w5z5syx6lu9ejVxcXG0adMmHSJ7tjp37mxOoE+ePNkqgQ4QGBjI1q1bCQ4OTocIRURERERERESeneciiX79+nUGDBhAyZIlcXJyIkuWLNSoUYNZs2bZHJ9Snxjghx9+MM/Lnz8/Q4YMwWQyAfDbb78RHBxM9uzZcXV1JSQkhBMnTjyVGAB2797NG2+8QZYsWciSJQv16tVjx44dDyzBkJiYyMiRI6lUqRKurq64uLjg5+fHxIkTzXHfK6UmblJSEkOGDKFw4cIYjUby5MnDxx9/nOpp12vXrvHVV19RqlQpnJ2dyZw5MzVr1mTRokWpXs/WrVt56623cHd3x2g04uPjQ9euXTl79qzV2IeV5rj3O7rX9u3badSoEfny5cNoNOLh4YGfnx+ffPIJ165dSzW2tCpXrhwAN2/eJD4+3ty+f/9+QkNDyZ07Nw4ODnh5edGuXTsOHTpkc520xBkQEECHDh0A+PLLL59JHfOAgAAMBgOxsbHMmDGDypUr4+rqStasWc1jli9fzttvv02xYsXInDkzLi4ulClThoEDB9r8fdz7+zx58iStW7fG3d0dJycnKlasyNKlSx/7noDt2uY+Pj58+eWXAHTo0MHiXkVGRqY6L8WdO3cYPXo0FSpUIFOmTGTKlAk/Pz/Gjh1LUlLSA+/bokWLqFy5Mi4uLmTPnp1WrVpx+vTpNNx9ayEhIWTKlMlm2ZZp06ZhMBho3br1A9eIjY2lc+fO+Pj4YDQacXd3p2nTpvzxxx9W1/Cw35fJZGLmzJm0bNmSwoUL4+LigqurK35+fvz4448kJyc/1nXe68CBAyxbtgwnJyeGDRv2wLFGo5GKFStatD3Jdzd79mwqVaqEs7MzXl5e9OnTh8TERACOHj1Kq1atyJkzJ87OzgQGBlrdw8eNIbX//4LUf6cp72qIjIxk48aNBAYG4urqSubMmXnzzTfZv3+/zfUSEhL4+OOPyZs3L46OjhQtWpRhw4bZ/O9BilWrVlGvXj28vb0xGo14enpSvXp18//GREREREREROTpSvdyLlevXqVWrVrs2rULd3d3goODSUhIYN26dURFRbFt2zZGjBhhc26PHj346aefqFy5Mvnz52fDhg18/PHHJCQkEBQURFBQED4+PgQGBrJnzx4WL17Mvn372Lt3L05OTk8Uw5YtW6hTpw43btygXLlyFClShP3791O9enVz4ut+CQkJvPHGG0RFReHm5kb16tWxs7Nj69athIeHs2PHDn766Sebc0NDQ1m2bBl+fn4UKVKEqKgohgwZwpkzZ6wSen///TeBgYHs378fLy8v6taty/Xr19m6dSuNGjVi0KBB9O3b12LOtGnTCAsLIzk5mapVq5InTx5+++03xo4dy4IFC4iMjKRo0aIP+TYfbPny5TRs2BCDwUC1atWoWrUqFy9e5PDhw3z77bd07tyZTJkyPdEeV69eNf/ZaDQCsHbtWho0aMCNGzcoX748AQEBHDx4kJ9//pmFCxeyYsUK/P39HznO119/nTt37rB582bKlClD2bJlzWs87TrmgwYNYsKECVSrVo3g4GBOnTpl7nvnnXdISEigRIkSlCpViiv/x959R0V1pg8c/w5VQMQCioiKIGASu4JKEcSuRLFGrFgSu9FY4yaxZSOS2GNM7BrsGhtYI4oVayysBRUVG/aCWBC4vz/8zSzjzCBVzeb5nMPZ49vuc2vOvved5z55wuHDh/nXv/7Fzp072b59O8bGxjpjXrlyBQ8PDwoUKICPjw+3b9/m4MGDBAUFsWXLFho1apTtY2JI27Zt+fPPPzl58iTe3t5ax8fe3j7TfU9LS6Nly5Zs3ryZQoUK0aBBA+B1+pB+/fqxY8cO1qxZg5GR7jvBX375hcmTJ1OzZk2aNGnCkSNHWLFiBceOHePkyZNaz4GssLS0JCgoiPDwcC5duoSLiwvw+t7esGEDvr6+lC1b1mD/ffv20bx5c548ecInn3xCixYtuHHjBn/88QebN28mMjKSevXqAWTp+nr58iUdO3akSJEifPzxx1SvXp179+5x8OBB+vfvz+HDh3P9Qmfz5s2aeIoUKZKtvrk5d9OnT+fnn3/Gw8ODxo0bs2/fPn788Udu377NN998g5eXF4UKFcLX15cLFy6wa9cu6tWrx5kzZyhRokSexJATmzZtYvr06VSsWJHGjRtz+vRpNm/ezKFDh4iNjdW63l++fEmjRo04cOAAtra2fPrppyQlJTFq1CguXbqkd/xff/2Vvn37Ym5ujq+vL3Xr1uXu3bucPXuWsWPHMmbMmDzZDyGEEEIIIYQQQmSg5AFAedtQY8aMUQBlzJgxWuUDBgxQAKVBgwZKUlKSpvzs2bNK8eLFFUCJjIzU6lO2bFkFUEqVKqXExsZq9TE3N1csLS0VJycnZerUqZq6ly9fKgEBAQqgLFiwIFcxpKWlKW5ubgqghIWFaY01fvx4zfF4c1/79u2rAEqXLl20tnPnzh2lVq1aCqBERERo9VGP9dFHHymXL1/WlMfHxytFihRRAOXixYtafZo2baoAyogRI5SUlBRN+aVLlxQXFxfF2NhYOXnypKY8ISFBsbCwUExMTJRNmzZp7efgwYMVQPHw8NDahvp8Lly4UNFHfY4y8vPzU1QqlXL06FGd9ocOHVKePHmid6w3devWzeC2f/31V821oSiK8vTpU6VEiRIKoMyePVur7ZQpUxRAcXR0VF68eJGjOBcuXKj3XGfH5cuXDd5Dfn5+CqAUKFBA2b17t97+69atU54+fapV9uTJEyUwMFABlMWLF2vVqWMGlIEDByqvXr3S1E2bNk0BFF9fX504snpMdu3apQBKt27dtNq97Zox1O+nn35SAKVSpUrK7du3NeU3b95U3N3dFUCZNWuWTryAYmVlpezcuVNTnpycrHh5eSmAMn/+fL1x6KO+nm/duqVs3bpVAZRx48Zp6pcsWaIAym+//aYoiqKYm5vrnM/Hjx8r9vb2iqmpqbJ69Wqtuh07dihmZmZKqVKllJcvX2rK33Z9vXr1Slm7dq1WH0V5/UypWbOmAijR0dFaddm9Zjt16qQAyoQJE7LUPqPcnDtra2tlz549mvJbt24pJUqUUFQqlfLRRx8pX331lZKWlqYoiqKkp6crXbt2VQDlu+++y3UM+p5faoauU/VzycjISFm2bJmmPDU1VWnTpo0CKN9++61Wnx9++EEBFE9PT+XRo0ea8mPHjimFChVSAKVs2bI6sRUqVEjrvwfqYxAVFaU35qyKi4tTovb9pQR/uytf/oQQQgghhBBCiL+rPE3nkjHdwJt/+n5mnpyczPz58zEyMuKXX37RWslaoUIFvvnmGwBmzJihd3sTJkzgk08+0erTvHlznj17RpkyZRg8eLCmzszMjC+//BKA6OjoXMUQFRVFXFwcFSpUYNiwYVoxjR49mnLlyunEeufOHebNm0e5cuWYO3eu1nbs7Oz47bffADT/+6aZM2fi5OSk+Xe5cuU0uZf37t2rKT9x4gRbtmzBy8uL0NBQTE1NNXXOzs5MnjyZtLQ05s2bpymfN28ez58/Jzg4WCufsZGREaGhoTg4OHDkyBFiYmL0xpZVd+7cwcbGhho1aujUeXp6Ym1tneOx7969y8KFCxkxYgQAffv2BWDVqlXcvn0bX19f+vTpo9VnyJAh1KhRg+vXr7Nu3bp3EmdO9ezZEz8/P711QUFBWFlZaZVZW1szdepUADZs2KC3n/p6MDH57w9S+vfvT5EiRYiJidGkzYD3e0zU9960adMoXry4prxkyZL8+OOPWm3eNGTIEAICAjT/trS0ZOjQoQDs2bMnR/E0aNAAe3t7li5dqikLDw/HzMyMdu3aGey3YMECEhMTGTZsGG3bttUZs1+/fty4cYOIiIgsx2JiYkLr1q0xMzPTKrezs2PixImA4fOfVffv39eMmV25PXcZfyFib29Pp06dUBSFlJQUJk2apFk9rlKpNOc14/M9tzHkRMeOHQkODtb829jYmNGjRwO619zs2bMBmDp1qtYHdatXr07//v31jn/nzh3KlSun9d8DeH0M1L9ieJsLFy7o/ct4zwshhBBCCCGEEOK/8nQSvVu3bgb/qlSpotP+2LFjPH/+HE9PT1xdXXXqu3TpAsD+/fv15odt2LChTpmzs7PBOnXqhVu3buUqhgMHDgCvU1S8mTfX2NiY1q1b64wTHR3Nq1evaNKkiSbNSEZVqlTB2tqaI0eO6NSZmpri7++vU+7m5qazPzt27ACgZcuWenP6+vj4AGhtRz0J36lTJ5325ubmmonBjJP1OVGjRg0ePXpEz549iY2NzdVYoJ1bu3jx4vTo0YMnT57QrVs3TbqazPYN0PsiIq/jzAstWrTItP7ChQtMnz6dgQMH0qNHD0JCQpgwYYKmTh9/f3+tlyzwelLW2dmZV69eaSZP4f0dk4SEBBISErC3t9eaDFcLDAykcOHCnD9/nrt37+rUZ0xJo6bvvskOY2NjOnToQFxcHEeOHCExMZGdO3fSvHnzTNOdqO/NoKAgvfX67s2sOnHiBGFhYfTv35/u3bsTEhKimaA1dP6zSt+zNytye+4ye777+/trvfwB/c/33MaQE1m95hISErh27RqlSpXCy8tLp0/GifiMatSowcmTJzNN+SKEEEIIIYQQQoi8lac50TPLvTt27FhOnjypVab+YOWbK+rUChcujI2NDY8fP+bJkydaK/UASpUqpdNHvSI3s7qMH1vMSQzqPqVLl9bbp0yZMjplV65cAV6vPFRPbunz/PlznbKSJUvqzWmtXs2ecX/U2xk5ciQjR440uJ2MH9182zFQl+v7wGh2/PDDD5w+fZoFCxawYMECbG1t8fLyIigoiI4dO+p9uZCZjLm1CxQoQNmyZWnatKlW7uic7FtexXnv3j2dXyoA9OrVSzNhmlX6ril4PcE5bNgwpk6danCyM2Oe+IwcHR31luu7rvL63GXV286fSqWibNmyPHr0iJs3b+qslta3j/r2L7s6d+7MtGnTWLp0KWXLliUtLU3zQsYQ9b1Zq1atTNtlvDffJiUlhZCQEJYvX26wjaHzn1W2trYA2Z5kzu25exfP97fFkBNZvebUsRm6tw2Vz5o1i6CgICZNmsSkSZNwcHDA19eXtm3b0rp16yzldtf3whjUL1xevbW/EEIIIYQQQgjxT/PePywK6F0xnZU2mfXLypi5jcFQH32TmWlpaQBUq1aNypUr53lsb27H19dXs2pTH/XEWHa2k5040tPTdcpKly7N0aNHiYqKIiIigujoaDZt2sTGjRsJCwvjwIED2fpwYa9evQgJCclS2+zsW17F+fTpUxYvXqxT7u/vn+1J9AIFCugtX7lyJVOmTMHR0ZFp06ZRp04d7OzsMDU1JSUlBXNzc4OT69k5n3l97rIrP54RuVGjRg0++ugjVqxYQcmSJSlcuDDNmzfPtI/63mzXrh2WlpYG271tkj2jKVOmsHz5cipWrMiPP/5I9erVKVKkCKampsTFxeHu7p7jleRqVatWZenSpRw/fjxH/f+uz3dD9D3bcjKO+rwYam+ovHLlypw5c4atW7eyefNmoqOjWblyJStXrsTHx4edO3fqpPcRQgghhBBCCCFE7rzXSXQHBwcALl++rLf+8ePHPH78GCsrq3zLuZyTGEqWLAm8/jm+PteuXdMpU69O9Pf3Z8qUKbmO2xD1dtq2bcugQYOy1MfBwYHz589z+fJlTdqBjK5evQr8d78BzSTN06dPddqnpaWRmJiod1smJiY0atRIk/IgISGB7t27ExUVRWhoKJMmTcpSzFn1tvOrb9/yKk4nJ6dcT2C+jTqX++zZs7Xy2QPEx8fn6bbe9bmDt58/dRygew7zW6dOnfjmm2+4ffs2n3/++VtX4zs6OnL+/Hm++eabbL9IM0R9/tUT6Rnl1flv1qwZw4cPZ+vWrTx8+DDLL0s+hHOX0xgyPt8yfr8C9D/fcxOb+hn0JkPl8PqlWlBQkCY10JkzZwgODmbfvn3Mnz9f800IIYQQQgghhBBC5I08zYmeXTVq1MDCwoLDhw/rzdsbHh4OvM4VnJ8rSrMbgzp/7dq1a3UmSdPT07U+UqlWr149jI2NiYiI0KxIzQ8NGjQAYP369Vnuo/54X8YPJaqlpKSwevVqrXbw3wmnuLg4nT5RUVG8epW1lABlypTRpJ05ffp0lmPOqsz2LWN5xn3Tx1Cc6sm21NTUXMeaEw8fPgT0pxZatWpVvm47u+cuJ8eqTJkylClThsTERKKionTqIyMjefjwIe7u7nmSiiM7OnXqhK2tLcWKFaNr165vbZ+Te/Ntx+xdnP+PP/6YZs2a8fz5c83HOw1JSUnh6NGjwIdx7nIaQ2bPt+3bt+dJbGXLlsXR0ZEbN25w8OBBnfoVK1ZkeayPP/5Y8yHS/HiOCiGEEEIIIYQQ/3TvdRLdysqKHj16kJ6eTv/+/UlOTtbUxcXF8f333wMwcODADyqGgIAAypcvz9mzZ5k6darWeKGhoXpXgJYqVYqQkBAuXLhAly5d9OY9PnDgAJs3b87V/tSuXZv69euza9cuhgwZorNSPD09ne3bt7Nv3z5NWc+ePbGwsGD58uVERkZqtR09ejQ3btzAw8OD2rVra+r8/PyA1y8Z1Lme4fXqV0Pna+rUqdy+fVunfOvWrYDhHMC50b59e0qUKMHevXuZM2eOVt2MGTM4cuQIjo6OtGrVKkdxqleTnj9/Ps9jzwr1LwfmzJmj9UJn7969/Pjjj3m2nbw4dzk9VurraciQIVp5uRMTExk+fLhWm3fJycmJu3fvcu/evSyl5+nduzd2dnb88MMPLFy4UOcFXHJyMkuWLOH69euasrcdM/X5//XXX7XK16xZw5IlS7K1P5n57bffsLW1ZeHChfTo0UPro7Nqe/bswcvLi4iICE3Zh3DuchKD+vk2ceJErZee4eHh2ZrcfpvevXsDMHToUJ48eaIpP3HiBLNmzdJp/+zZM2bMmMGjR4+0ytXPdcif56gQQgghhBBCCPFP995zok+cOJGYmBh27NiBs7Mzfn5+JCcnExUVxYsXLxg0aNBbcw2/6xiMjY1ZuHAhDRs2ZOjQoSxduhR3d3fOnDnD2bNn+fzzz5k7d65OXtoZM2YQHx/P8uXLiYiIoGrVqjg4OJCYmMjFixe5ceMGX375Jc2aNcvV/ixdupRGjRoxbdo0lixZQtWqVbGzs+PGjRucP3+eu3fvMnXqVM3EX5kyZZgzZw4hISF8+umneHt7U7p0aY4fP8758+cpUaKEzoScs7MzXbt21Yxft25dkpOTiYmJoXnz5rx48UInHcG4ceMYNmwYVapUwdXVFUVROHXqFOfPn8fW1lYzoZWXrKysWLp0KZ9++im9e/dmzpw5uLm5ce7cOf766y+srKxYtmyZViqO7MRZu3Ztihcvzpo1a/D398fZ2RkjIyN69Oih+cVCfho0aBCLFi3il19+Yffu3VSuXJkbN26wb98+hg4dyk8//ZQn28mLc9eoUSMKFCjA1KlTiY2NxcHBAZVKxfDhw3F3dzfYb8iQIURFRbFlyxZcXV0JCAhAURR27txJUlISQUFBf4v0FUWKFGHdunW0aNGCHj16MG7cOCpWrIi5uTkJCQmcPXuW5ORk/vrrL01aprddXyNGjGDr1q2MGjWK1atX4+bmxoULFzh69CjDhg3Ls/Pv6OjI3r17adGiBQsXLmTp0qXUqlULR0dHkpOTOXnyJFevXsXY2FgrjdSHcO5yEkP//v359ddfWbNmDR9//DGVK1fmwoULxMbG8uWXX+q8PM2p4cOHExERwcGDB3FxcaFevXokJSURFRVFz549dT5CnZKSwpdffsnw4cOpXr06Tk5OmtX/CQkJODs7aybmhRBCCCGEEEIIkXfe60p0AGtra6Kjoxk3bhy2trZs3LiRvXv3UrNmTZYtW8b06dM/yBh8fHzYt28fjRs35sKFC0RERGBra0t0dDRly5YFoFixYlp9LC0t2b59O/PmzaN69erExsaybt06Ll26hIuLC2FhYQwbNizX+1OiRAliYmKYMmUKrq6uHDlyhPXr13P9+nWqVavGrFmz6Ny5s1afzp07s2fPHgIDAzl79ixr1qzh+fPn9O3bl2PHjlGhQgWd7cydO5dRo0ZRqFAhtm3bxtWrVxk9ejTLly/XG9fMmTPp0KEDz549Y8uWLWzduhVjY2OGDRvGqVOncHFxyfW+61O/fn2OHDlCcHAw169fZ82aNSQmJtK5c2eOHTumk8olO3EWKFCAyMhIGjZsyIkTJ1i0aBHz58/XmwYiP7i5uXHkyBE+/fRT7t27x8aNG3n69Cm//fZbnq5Ez4tz5+DgwIYNG6hduzb79u1jwYIFzJ8/n1u3bmXaz9jYmI0bNzJ9+nScnZ3Ztm0b27dvx93dnVmzZrFmzRqMjN77oyxLvL29OX36NEOHDsXCwoKoqCi2b9/OkydPCAwMZOXKlXz88cea9m+7vurWrcu+ffsICAggPj6eiIgIzMzMWLt2rSa9R16pUKECsbGx/Pbbb9SrV4+4uDjWrFnDrl27KFq0KEOHDuXMmTNaqW0+hHOXkxhKlCiheR7eunWLLVu2YGNjw44dO2jRokWexWZubs6ff/7JsGHDMDc3Z8OGDcTHx/P999/z888/67QvWLAgs2bNIjAwkLt377Jx40aioqIoUqQIEyZM4NixY/n6gV8hhBBCCCGEEOKfSqXk95cP/4GaNm3K1q1biYmJoVatWu87HCGEEOKtLly4wPU7yczd9ihfxl823j9fxhVCCCGEEEIIIfKbTKLn0IMHD0hKStKsOgdQFIWff/6ZQYMGUb58eeLi4vLtg6hCCCFEXlJ/XNvV1fU9RyKEEEIIIYQQQnxY3ntO9L+ruLg4vLy8qFy5Ms7OzqSlpREbG0t8fDwWFhbMnTtXJtCFEEIIIYQQQgghhBDib+7vkUj4A+Ts7EyfPn14+fIlO3fuZMuWLbx8+ZKOHTty6NAh/P3933eIQgghhBBCCCGEEEIIIXJJ0rkIIYQQQtK5CCGEEEIIIYQQBkg6FyGEEEIAcP1OMmN+350vY8uHRYUQQgghhBBC/F1JOhchhBBCCCGEEEIIIYQQwgCZRP8HUalUb/3Y6dixY1GpVIwdO/bdBCX+Z4WEhGiuud69extsl5KSQpEiRTRtd+/e/e6CzKIrV66gUql0vnWwe/duVCoVISEhud6G+nhlZ/8XLVqU4/s1OTmZqVOnUq9ePUqUKIGZmRlFihShTp06fPfddyQkJGR7TJG3nJyc5APVQgghhBBCCCHEB0Am0YUQ+W716tWkpKTorYuIiODRo0d5ti1DE97iv2JiYnB1deWrr77i8OHDVKxYkbZt2+Ll5cWlS5eYMGECbm5u/Pnnn+87VCGEEEIIIYQQQoj3TibRhRD5qlq1ajx8+JDNmzfrrQ8PD8fY2JgqVaq848g+LBMnTuTs2bN4enrm63ZOnTpFQEAAt27dYuTIkdy5c4edO3eybNkyIiMjSUxMZO3atTg6OnL9+vV8jUUIIYQQQgghhBDi70Am0YUQ+apjx44YGRmxdOlSnbpHjx6xefNm6tevj729/XuI7sNRsmRJKlSogKWlZb5tQ1EUOnfuzPPnzxk7diyhoaFYWVlptTEyMqJ169YcO3aMmjVr5lssQgghhBBCCCGEEH8XMokusuzZs2dMmDCBihUrYmFhgY2NDXXr1mXFihV622fM5ztr1ixNv3LlyhEWFoaiKAAcP36cwMBAihYtirW1NUFBQVy9ejVPYgD466+/aNq0KTY2NtjY2NC4cWOOHDmSaT7plJQUpk+fjoeHB9bW1lhZWeHp6cn8+fM1cWekUqlwcnIiLS2NsLAw3NzcMDc3p3Tp0owcOZKXL1/qje3p06eMHz+eSpUqYWlpSaFChfDz82P9+vUG9+fgwYO0bNkSOzs7zM3NcXJyol+/fty8eVOnrTrH/aJFi/SOZSjn8qFDh2jVqhVly5bF3Nwce3t7PD09+frrr3n69KnB2PRxdHTEz8+PiIgIHj9+rFW3atUqXr58SefOnTMdI6vHaezYsZQrVw6A6OhoTZ71N/OW7927lwEDBlC5cmWKFCmChYUFFSpUYNSoUXmWWiY1NZWZM2dSo0YNChYsSMGCBfH09GT27NmkpaXptM8sJ/qpU6cIDAzUXMMNGzbk4MGD2Y5p27ZtnD59GkdHR/71r39l2tbGxoaKFStqlb3vZ0DGY7RlyxZ8fHwoWLAgRYoUoXXr1pw7d87g/vz+++/4+PhQqFAhLC0tqVy5MhMnTuTFixc6bf39/VGpVFy5ckWnzlC6oIz32unTp2nRogVFihTBysoKPz8/Dhw4oDeu1NRUJk6ciKurKwUKFMDZ2Zlvv/3WYPojyNv7UwghhBBCCCGEEG8nk+giS5KSkqhbty7fffcdd+7cITAwEG9vbw4fPkxwcDCDBw822HfIkCEMGzYMOzs7GjRowP379xk5ciRjx45l//79+Pr6cvnyZQICArC3t2fDhg3Ur1+f58+f5zqGAwcO4O3tzdatW3FxcaFZs2YkJibi4+NDTEyM3niTk5Np0KABgwcP5sqVK/j4+ODv78/Fixfp1asXffv2NbivnTp1Yvz48Tg6OtKoUSOSkpIICwujZ8+eOm1v375NrVq1GDNmDA8fPqRhw4bUqlWLY8eO0apVK0JDQ3X6hIeH4+vry6ZNm3B3d6d169aYm5sze/ZsqlevnukkYlZFRkbi5eXFpk2bcHJyonXr1lStWpV79+4RGhrKvXv3sj1mp06dePHiBWvXrtUqX7p0KZaWlrRq1cpg3+wcp6pVq9KmTRsASpQoQbdu3TR/Pj4+mnbDhw9n3rx5mJmZERAQQP369Xny5AmTJk3Cx8cn1xORaWlptGzZkkGDBnHx4kUaNGhAgwYNOHfuHP369aNdu3akp6dnaaxDhw5Rp04dIiMjcXZ21lzDmU3MGhIZGQlAu3btMDExyVbfD+EZoLZ69WqaN29OSkoKn376KQ4ODqxbt47atWtz8uRJnfa9e/ema9euHDt2DF9fX5o3b86tW7cYPXo0AQEBBreTE0ePHqV27dqcP3+e+vXr4+rqyp49e6hfvz6xsbE67YODgxk9ejSJiYk0adKESpUqMWXKFFq3bq33hV1+3J9CCCGEEEIIIYR4C0X8YwDK2075mDFjFEAZM2aMVvmAAQMUQGnQoIGSlJSkKT979qxSvHhxBVAiIyO1+pQtW1YBlFKlSimxsbFafczNzRVLS0vFyclJmTp1qqbu5cuXSkBAgAIoCxYsyFUMaWlpipubmwIoYWFhWmONHz9eczze3Ne+ffsqgNKlSxet7dy5c0epVauWAigRERFafdRjffTRR8rly5c15fHx8UqRIkUUQLl48aJWn6ZNmyqAMmLECCUlJUVTfunSJcXFxUUxNjZWTp48qSlPSEhQLCwsFBMTE2XTpk1a+zl48GAFUDw8PLS2oT6fCxcuVPRRn6OM/Pz8FJVKpRw9elSn/aFDh5QnT57oHetN3bp1UwBl+fLlyqNHj5QCBQoo9erV09RfvXpVUalUSnBwsKIoitK4cWMFUHbt2qU1TnaP0+XLlxVA8fPzMxhbZGSk8uDBA62yFy9eKF988YUCKOPGjdOqMzTmrl27FEDp1q2bVvlPP/2kAEqlSpWU27dva8pv3rypuLu7K4Aya9Ysvccr4/6npaUpFSpUUABl4sSJWu2/+eYbg9ewId7e3gqg/P7771lqn9GH8AxQHyNAmTNnjqY8PT1dGTlypAIo1atX1+qzZs0aTQwXLlzQlD9+/Fjx8fFRAGX48OFaffz8/BRA615WM3QtqO81QJk0aZJWnfr+7NKli1b5smXLFEBxdnZWrl+/rimPj49XHB0d9T6z8+L+jIuL0/sXGxurRO37Swn+dle+/AkhhBBCCCGEEH9XshL9Hyhjios3/8aNG6fTPjk5mfnz52NkZMQvv/xCwYIFNXUVKlTgm2++AWDGjBl6tzdhwgQ++eQTrT7Nmzfn2bNnlClTRmsFq5mZGV9++SXwOh1HbmKIiooiLi6OChUqMGzYMK2YRo8erUn7kdGdO3eYN28e5cqVY+7cuVrbsbOz47fffgPQ/O+bZs6ciZOTk+bf5cqV06Qq2bt3r6b8xIkTbNmyBS8vL0JDQzE1NdXUOTs7M3nyZNLS0pg3b56mfN68eTx//pzg4GACAwM15UZGRoSGhuLg4MCRI0cMrrDPqjt37mBjY0ONGjV06jw9PbG2ts72mDY2NgQGBhIdHc2NGzeA16vQlf/P0W1ITo5TVjRr1owiRYpolZmbmzNt2jRMTEzYsGFDtsZ7k/o6nDZtGsWLF9eUlyxZkh9//FGrTWZ2797NuXPncHNzY+TIkVp1Y8aMoUyZMtmK6/79+8Drazk7PoRnQEZeXl58/vnnmn+rVComTJhA6dKlOX78uFaqG3VM48ePp3z58pryQoUK8csvv6BSqfj1118zTZ+SHT4+PowYMUKrTH189uzZo1U+e/Zs4PXxKVWqlKa8XLlyfPvtt3rHz4/7UwghhBBCCCGEEJmTSfR/oIwpLt78q1Klik77Y8eO8fz5czw9PXF1ddWp79KlCwD79+/Xm36gYcOGOmXOzs4G61xcXAC4detWrmJQp7po27atTt5vY2NjWrdurTNOdHQ0r169okmTJpibm+vUV6lSBWtra44cOaJTZ2pqqpMnGcDNzU1nf3bs2AFAy5Yt9eYkV6ceybgd9SR8p06ddNqbm5vTrl07rXY5VaNGDR49ekTPnj31pp/Iqc6dO5Oens6yZcuA15PoxYsXp1GjRgb75OQ4ZdWNGzf49ddfGTx4MD169CAkJIS+fftiZmbGhQsXsj2eWkJCAgkJCdjb2xMQEKBTHxgYSOHChTl//jx3797NdKx9+/YBr9OvvLn/JiYmtG3bNlux6bs/s+JDeAZk1KFDB50yU1NTTSof9XF79eoVMTExqFQqOnbsqNOnUqVKVK5cmaSkJL1pYHJC3/VcrFgxihUrprU/r1694tChQxgZGek9j8HBwXrHz4v709XVVe+fmZlZjsYTQgghhBBCCCH+12UvKa74n2DoI5Pw+uN4b04mqT9YmXGFdUaFCxfGxsaGx48f8+TJE2xsbLTqM66wVLOysnprXcaPceYkBnWf0qVL6+2jbxWv+kOCs2fP1qwS1UdfDuWSJUtibGysU65etZtxf9TbGTlypM4K44wy5jd+2zFQl+v7wGh2/PDDD5w+fZoFCxawYMECbG1t8fLyIigoiI4dO+p9uZAVzZo1o1ixYixdupQGDRrwn//8h4EDB2aamzsnxykrpkyZwtdff51nq48zett5UqlUlC1blkePHnHz5s1MV4WrxzK04jy7K9FtbW2zNHlvKI73+QzIqGzZsnrL37wH7t+/T0pKCvb29hQoUMBgn5MnT+b6vlFzdHTUW16wYEHNLwEyxlayZEm9k9fW1tYULlxY50O3+XV/CiGEEEIIIYQQwjCZRBdZpm8lcFbaZNYvK2PmNgZDffStmE1LSwOgWrVqVK5cOc9je3M7vr6+mhW5+tja2mZ7O9mJQ9/HLUuXLs3Ro0eJiooiIiKC6OhoNm3axMaNGwkLC+PAgQM6qVCywtTUlHbt2vHrr78yevRogExTuUDujpMhMTExDB06FBsbG+bMmYO/vz/29vaayUcHBweDK6CzI6f3S0bqazS794khVatWZf/+/Rw/fvytx16fD+EZkBlDK+3z4lyove2DsFkdJ6fnNr/uTyGEEEIIIYQQQhgmk+jirRwcHAC4fPmy3vrHjx/z+PFjrKys8i0fb05iKFmyJPA6vYY+165d0ylTryL19/dnypQpuY7bEPV22rZty6BBg7LUx8HBgfPnz3P58mVNipiMrl69Cvx3vwHNCtenT5/qtE9LSyMxMVHvtkxMTGjUqJEmNUVCQgLdu3cnKiqK0NBQJk2alKWY39S5c2d+/fVXtm7diqurK56enpm2z8lxept169YB8P3339OtWzetuufPnxs8Jln1tmsV/ntNZjxXmY2lPreGxsmq5s2bM2vWLFavXk1YWFimvwLQF8f7fAZk9LbjoY63WLFimJmZkZiYyPPnz7GwsDA4VlbvG33PjZywtbXVxJaSkqKzGj0pKUlnFbpaft2fQgghhBBCCCGE0E9yoou3qlGjBhYWFhw+fFhvrujw8HDgdX7qvFxVmtsYvLy8AFi7dq3OCtX09HTNZGpG9erVw9jYmIiICM0q6PzQoEEDANavX5/lPr6+vsDrXOJvSklJYfXq1Vrt4L8Tg3FxcTp9oqKiePXqVZa2XaZMGU06ldOnT2c55jd5e3tTtWpVihUrRs+ePd/aPifHST0ZmZqaqrf+4cOHgP40P6tXr85x3nC1MmXKUKZMGRITE4mKitKpj4yM5OHDh7i7u7/1A5/qnO/6ruHU1FTWrl2brdiaNGnCJ598wvXr1/n3v/+dadsnT57wn//8B/gwngEZrVy5Uqcs4/Hw9vYGXv/6oXbt2iiKwvLly3X6xMbGcvLkSaytrbW+B5HZfbN9+/Y82QdTU1M8PT1JT0/Xex5XrFiR5bHy6v4UQgghhBBCCCGEfjKJLt7KysqKHj16kJ6eTv/+/UlOTtbUxcXF8f333wMwcODADyqGgIAAypcvz9mzZ5k6darWeKGhocTHx+tsp1SpUoSEhHDhwgW6dOmiN9f2gQMH2Lx5c672p3bt2tSvX59du3YxZMgQnRWv6enpbN++XfOBRICePXtiYWHB8uXLiYyM1Go7evRobty4gYeHB7Vr19bU+fn5Aa8nOdX5xQHi4+MNnq+pU6dy+/ZtnfKtW7cC2c/D/aa//vqLe/fuZZrjXC0nx8nW1hZTU1MuXbqk90WIehX//PnztV4inDlzJksxZYX62A4ZMkQr/3hiYiLDhw/XapOZevXq4ebmxrlz5/jpp5+06r7//nuDK7INUalUhIeHU6BAAcaOHcvXX3+tdS/B6zQjGzdupGbNmpoPtn4Iz4CM9u/fz4IFC7RiHjNmDAkJCVSpUkXzAi1jTGPGjNG655OSkhgwYACKotC7d2+tleDq+2by5Mk8e/ZMU/7nn38ybdq0PNuP3r17A/Ddd99ppRC6evUqEyZM0Nsnv+9PIYQQQgghhBBC6JJ0LiJLJk6cSExMDDt27MDZ2Rk/Pz+Sk5OJiorixYsXDBo0iObNm39QMRgbG7Nw4UIaNmzI0KFDWbp0Ke7u7pw5c4azZ8/y+eefM3fuXJ00CjNmzCA+Pp7ly5cTERFB1apVcXBwIDExkYsXL3Ljxg2+/PJLmjVrlqv9Wbp0KY0aNWLatGksWbKEqlWrYmdnx40bNzQff5w6dapmNXKZMmWYM2cOISEhfPrpp3h7e1O6dGmOHz/O+fPnKVGiBEuWLNHahrOzM127dtWMX7duXZKTk4mJiaF58+a8ePFCZyJ23LhxDBs2jCpVquDq6oqiKJw6dYrz589ja2urmQR+V7J7nMzMzGjSpAmbNm2iSpUqVK9eHTMzM7y9venevTvdu3dn8uTJbNq0CXd3dzw8PHjw4AHR0dEEBQVx+PDhbE9Ov2nIkCFERUWxZcsWXF1dCQgIQFEUdu7cSVJSEkFBQfTt2/et4xgZGbFo0SLq16/PiBEjWL58ORUqVCA2NpZz587Rq1cv5s2bl63Yqlatyp9//kmbNm0IDQ1lxowZ1KlThxIlSvD48WOOHj3K7du3KVCggNZq/Q/hGaDWt29fevXqxW+//YaLiwunTp3iP//5D9bW1ixcuFCrbdu2bfniiy+YM2cOFStWJCAgAEtLS3bv3s3du3epXbs248aN0+oTHBysyS/+0Ucf4eHhwbVr1zh69ChfffWVzguNnOrUqRN//PEH69atw93dnfr166MoCn/++Sd+fn6oVCqdlD0f2v0phBBCCCGEEEL8E8hKdJEl1tbWREdHM27cOGxtbdm4cSN79+6lZs2aLFu2jOnTp3+QMfj4+LBv3z4aN27MhQsXiIiIwNbWlujoaMqWLQu8zpuckaWlJdu3b2fevHlUr16d2NhY1q1bx6VLl3BxcSEsLIxhw4blen9KlChBTEwMU6ZMwdXVlSNHjrB+/XquX79OtWrVmDVrls7HHzt37syePXsIDAzk7NmzrFmzhufPn9O3b1+OHTtGhQoVdLYzd+5cRo0aRaFChdi2bRtXr15l9OjRetNbAMycOZMOHTrw7NkztmzZwtatWzE2NmbYsGGcOnUKFxeXXO97duTkOM2bN48uXbpw//59li1bxvz584mOjgZen+8jR47QsWNHUlJS2LhxIzdu3GD8+PEGj0l2GRsbs3HjRqZPn46zszPbtm1j+/btuLu7M2vWLNasWYORUdYev3Xq1OHAgQM0bdqUixcvEhERgZ2dHbt27dKkLckub29vLl68yE8//YSHhwenTp1i1apV7N+/HycnJ8aMGcOFCxeoX7++ps+H8AxQa9++PRs3bsTY2JgNGzZw/fp1WrZsSUxMDNWqVdNp/9tvv7FkyRKqVaum+RBn8eLF+fe//01UVBSWlpZa7S0sLNi5cyfBwcEkJSWxefNm0tPTWblyJf3798+z/VCpVKxcuZJ///vf2NnZsXnzZk6cOMHAgQP5448/9KbG+dDuTyGEEEIIIYQQ4p9ApeQ2AbAQf1NNmzZl69atxMTEUKtWrfcdjhDiLUJCQli8eDG7du3C39//fYfzP0ed797V1fU9RyKEEEIIIYQQQnxYZCW6+J/24MEDndQciqIwc+ZMtm7dSvny5fH09HxP0QkhhBBCCCGEEEIIIT50khNd/E+Li4vDy8uLypUr4+zsTFpaGrGxscTHx2NhYcHcuXP1pkwQQgghhBBCCCGEEEIIkJXo4n+cs7Mzffr04eXLl+zcuZMtW7bw8uVLOnbsyKFDhyQlhBBCCCGEEEIIIYQQIlOSE10IIYQQkhNdCCGEEEIIIYQwQNK5CCGEEAKA63eSGfP77nwZe9l4/3wZVwghhBBCCCGEyG/5ms5FpVK9Nd/02LFjUalUjB07Nj9DEf9AH3/8MSqVCnd390zbLVq0SO81GBISgkqlYvfu3fkS35UrV1CpVDopZXbv3o1KpSIkJCRftpsbTk5Omvva0N+HkCLH0DnNKfVzSqVS0bhx40zbqq87lUrFokWL8mT7eU2lUuHk5JSjvocOHeLzzz/Hzc0Na2trChQogJOTE+3bt2fdunWkp6fnbbD/YPn9DBJCCCGEEEIIIUTWyEp08T/p6NGjnD17Fnj9cdHDhw/j6en5nqP639GmTRsKFiyot65ChQr5vv2xY8cybtw4Fi5c+M5fNuzcuZPExETs7e116o4fP6657vKKSqWibNmyXLlyJU/Hza5Xr17Rv39/5s6dC4CbmxsNGjTAzMyMy5cvs3btWlavXk1AQAA7d+58r7EKIYQQQgghhBBC5CWZRBf/k37//XcASpYsya1btwgPD//bTKJ7enpy9uxZbGxs3ncoBv300085Xsn8d1atWjX++usvVqxYweDBg3Xqw8PDAahevTrHjx9/x9Hlr+7du7N06VLc3NxYuHAhXl5eWvU3b95k/PjxbN++/T1FKIQQQgghhBBCCJE/8jWdixDvQ2pqKitWrABgyZIlAKxYsYLU1NT3GVaWWVpaUqFCBUqWLPm+QxFvaNGiBYUKFWLp0qU6dWlpaaxYsQI3Nzc8PDzeQ3T5Z+3atSxdupQSJUqwZ88enQl0AAcHB3799VfNCywhhBBCCCGEEEKI/xUf9CT6s2fPmDBhAhUrVsTCwgIbGxvq1q2rmSB9kzpfM8CsWbM0/cqVK0dYWBiKogCvUy4EBgZStGhRrK2tCQoK4urVq3kSA8Bff/1F06ZNsbGxwcbGhsaNG3PkyJFM8zSnpKQwffp0PDw8sLa2xsrKCk9PT+bPn6+JOyN1TuO0tDTCwsJwc3PD3Nyc0qVLM3LkSF6+fKk3tqdPnzJ+/HgqVaqEpaUlhQoVws/Pj/Xr1xvcn4MHD9KyZUvs7OwwNzfHycmJfv36cfPmTZ226tzRhnJBZzxHGR06dIhWrVpRtmxZzM3Nsbe3x9PTk6+//pqnT58ajE2fbdu2cefOHby9vWnQoAFeXl7cvXuXbdu2ZWscQ1JSUmjbti0qlYp27dqRkpICwN69exkwYACVK1emSJEiWFhYUKFCBUaNGsWjR4+yPL6hnOgZj+3p06dp0aIFRYoUwcrKCj8/Pw4cOGBwzH379tGqVSuKFy+uOYeDBg3i7t27OTkE2XLixAlGjBhBjRo1NNeQs7OzwWsoY674J0+eMHToUMqVK4epqSmDBw/GycmJcePGAa9XR2fMx64vd3RCQgIdO3bEzs4OCwsLatasyaZNm3K0LwUKFKBNmzYcPXqU8+fPa9Xt3LmTW7du0blz50zHyOq9rn5eAFy9etVg3vnsHt+c+PHHH4HX12CJEiUybevt7a1Tlp1nSMbn5KVLl2jfvj22trYUKlSIpk2bcubMGeD1y7IffvgBNzc3ChQoQPny5fnll18MxpXfz7GM9+2DBw/o27cvJUuWxNzcnIoVK7JgwQKDsa1duxZPT08sLCwoUaIEXbt2zfTcXbt2jf79++Pu7o6lpSVFixblk08+oXfv3jrXpRBCCCGEEEIIIXLvg51ET0pKom7dunz33XfcuXOHwMBAvL29OXz4MMHBwXpTKagNGTKEYcOGYWdnR4MGDbh//z4jR45k7Nix7N+/H19fXy5fvkxAQAD29vZs2LCB+vXr8/z581zHcODAAby9vdm6dSsuLi40a9aMxMREfHx8iImJ0RtvcnIyDRo0YPDgwVy5cgUfHx/8/f25ePEivXr1om/fvgb3tVOnTowfPx5HR0caNWpEUlISYWFh9OzZU6ft7du3qVWrFmPGjOHhw4c0bNiQWrVqcezYMVq1akVoaKhOn/DwcHx9fdm0aRPu7u60bt0ac3NzZs+eTfXq1Tl37pzB2LIqMjISLy8vNm3ahJOTE61bt6Zq1arcu3eP0NBQ7t27l63x1Cth1ZOZ6v/NixWyT58+pXnz5qxdu5ZevXqxcuVKzMzMABg+fDjz5s3DzMyMgIAA6tevz5MnT5g0aRI+Pj7ZfhlgyNGjR6lduzbnz5+nfv36uLq6smfPHurXr09sbKxO+xkzZlC3bl02bdpE+fLladGiBRYWFsycOZNatWpx69atPInLkNDQUKZMmUJaWhre3t40a9YMRVGYPXs2NWvWNDhZ+Pz5c/z8/Fi4cCFVq1bVvDRo27YtVapUAV5P2Hbr1k3z92ae8itXruDh4cH+/fvx8fGhWrVqHDt2jKCgoBynHenUqROAzmp09b/V9fpk514vX7483bp1A8DKykprP5s0aaJpl9Pjm1X37t3j8OHDqFQqOnTokO3+OX2GXL58GU9PT44dO4afnx9OTk5s3boVf39/EhMTadu2LaGhoZQrVw5/f3/NxLI6Z3texJATjx49ok6dOqxbtw4PDw+8vb05d+4cPXv2ZN68eTrtf/75Z9q2bcvx48fx8vLC39+fP//8k9q1a3P//n2d9tevX6d69er88ssvFChQgE8//RRfX19MTU2ZO3cuBw8ezLN9EUIIIYQQQgghxP9T8hGgvG0TY8aMUQBlzJgxWuUDBgxQAKVBgwZKUlKSpvzs2bNK8eLFFUCJjIzU6lO2bFkFUEqVKqXExsZq9TE3N1csLS0VJycnZerUqZq6ly9fKgEBAQqgLFiwIFcxpKWlKW5ubgqghIWFaY01fvx4zfF4c1/79u2rAEqXLl20tnPnzh2lVq1aCqBERERo9VGP9dFHHymXL1/WlMfHxytFihRRAOXixYtafZo2baoAyogRI5SUlBRN+aVLlxQXFxfF2NhYOXnypKY8ISFBsbCwUExMTJRNmzZp7efgwYMVQPHw8NDahvp8Lly4UNFHfY4y8vPzU1QqlXL06FGd9ocOHVKePHmidyx9Hj9+rFhYWChmZmbK/fv3FUVRlPv37yumpqaKhYWF8vjxY50+Cxcu1HteunXrpgDKrl27NOOoz8eIESN0xomMjFQePHigVfbixQvliy++UABl3LhxWnWXL19WAMXPz0+rfNeuXQqgdOvWTatcfWwBZdKkSVp16vPRpUsXrfKDBw8qRkZGStmyZbXObXp6uuaabNu2rc6+GKI+fxmvubfZuXOncvPmTa2ytLQ0Zdy4cQqgdO/eXatOfVwApU6dOsrDhw91xnzbdaY+p4AycOBA5dWrV5q6adOmKYDi6+ub5X1Qb2/ixIlKWlqaUqpUKcXZ2VlT/+zZM8Xa2lqpU6eOoiiK0rt3b73x5fReL1u2rMHYsnt8szJmRjt27FAAxcXFJUvtM8rJMyTjufvqq6+UtLQ0RVFeX7MhISEKoHz88cdKxYoVlWvXrmn6/fnnn3r36109x9T3LaC0adNGefr0qaZu/fr1CqCUKVNGq8/ly5cVc3NzxdzcXPOcURRFSU5OVho2bKgZL2OdOrbJkyfrxHXlyhWd5352xMXFKVH7/lKCv92VL39CCCGEEEIIIcTf1TtZiZ4xDcGbf+q0DBklJyczf/58jIyM+OWXXyhYsKCmrkKFCnzzzTfA6xW2+kyYMIFPPvlEq0/z5s159uwZZcqU0VpBbmZmxpdffglAdHR0rmKIiooiLi6OChUqMGzYMK2YRo8eTbly5XRivXPnDvPmzaNcuXLMnTtXazt2dnb89ttvAJr/fdPMmTO1PvBYrlw5zcrrvXv3aspPnDjBli1b8PLyIjQ0FFNTU02ds7MzkydPJi0tTWul5Lx583j+/DnBwcEEBgZqyo2MjAgNDcXBwYEjR44YXGGfVXfu3MHGxoYaNWro1Hl6emJtbZ3lsdasWcPz589p2rQpRYsWBaBo0aI0bdqU58+fs3bt2hzFeOPGDXx9fTl06BChoaFMmjRJp02zZs0oUqSIVpm5uTnTpk3DxMSEDRs25Gjbb/Lx8WHEiBFaZerrcc+ePVrloaGhpKenM2fOHCpXrqwpV6lUfPPNN1SrVo0//vgj26v9y5UrZ/CenjZtmlbbgIAAnfzuRkZGfPfdd5QqVSrT4zJjxgwKFy6crdgyUl/bJib//YZy//79KVKkCDExMZpUPNlhZGREcHAw8fHxmlW/69evJykpKdNULrm91w3JzfHNCvVqaDs7u2z3zc0zxMXFhUmTJmFk9Po/UyqViq+++gqAM2fOMGPGDBwdHTXt69evT7Vq1bh69SpXrlzJkxhyolChQsyZMwcrKytNWcuWLalUqRIJCQlasS1YsICXL1/StWtXrRQ9lpaWzJw5U2/qqzt37gCvz/ubypYti4uLy1tjvHDhgt6/nNwPQgghhBBCCCHEP4HJ25vknjolgT4nTpzg5MmTWmXHjh3j+fPn1K5dG1dXV50+Xbp0YdCgQezfvx9FUXQmGho2bKjTx9nZ2WCdetIhY1qLnMSgzkmtzpedkbGxMa1bt2by5Mla5dHR0bx69YomTZpgbm6us50qVapgbW3NkSNHdOpMTU21Jl7U3NzcdPZnx44dwOvJHH0TMz4+PgBa21FPwutLT2Fubk67du2YPn06e/fupXbt2jptsqpGjRqEh4fTs2dPhgwZQsWKFXM8ljplS5cuXbTKu3TpwsaNGwkPD6d79+7ZGvPChQuEhIRw7do15syZw+eff26w7Y0bN9i0aRPnzp3jyZMnpKenA69f1ly4cCGbe6Nfo0aNdMqKFStGsWLFtM55eno6O3fuxNramvr16+v0UalUeHt789dff3Hs2DEaN26c5RjatGmjNQmc0ccff6xTdv/+fTZu3EhsbCyPHj0iLS0NgFevXvHgwQMePHigeemhVrJkSWrWrJnlmPTx9/fXemEEYGJigrOzM8eOHeP+/fs5+oBr586d+emnnwgPD6dOnTqEh4djamrKZ599ZrBPbu71t8nJ8c0qRc83GbIqN88Qf39/rZcf8N/nuJmZGX5+fjpjuri48Ndff3Hr1i3Ny8V39RxTq1mzpt5j7ebmxunTp7Vi27dvHwDt27fXae/u7k61atU4fvy4Vrn6ZWP//v35/vvv8fX11TlOQgghhBBCCCGEyFvv5P95G/o4G7z+gNubk+jqHL4ZV1hnVLhwYWxsbHj8+DFPnjzBxsZGq75UqVI6fdSrAjOry/gxzpzEoO5TunRpvX3KlCmjU6ZelTh79mxmz56ttx+gk68dXk8yGhsb65SrJzcz7o96OyNHjmTkyJEGt5NxRfLbjoG6PLc5l3/44QdOnz7NggULWLBgAba2tnh5eREUFETHjh31Tjjqc+3aNaKjoylcuLDWilOATz/9FBsbG3bv3s3169e1VrC+Tb9+/UhNTWXSpEmZTqBPmTKFr7/+Ot9XcxqKvWDBglo5lO/fv6/Jw/62SbbsrkT/6aefDF4Xb1q+fDlffPFFpjnhk5KSdCYe9d0v2ZXZsQIMfoD3bapUqUKlSpVYtWoV//rXv9i+fTtNmzalWLFiBvvk5l7PTE6Pb1bZ2toC5OgjtLl5hmT2rLa3t9esUNdXn51neV49x9Syc82pt2noWi9TpozOJHpISAjbt29n1apVBAQEYGlpSc2aNWnatCk9evSgePHib41R34th4P9f9L16a38hhBBCCCGEEOKf5oNevqZvxXRW2mTWLytj5jYGQ330rehUrxitVq2aVrqNvIrtze34+vpqVnPqo54wy852shOHemV2RqVLl+bo0aNERUURERFBdHQ0mzZtYuPGjYSFhXHgwAGdNCn6hIeHoygKiqLoXXmt3v6yZct00qFk5rPPPmPZsmVMnTqVli1b4u7urtMmJiaGoUOHYmNjw5w5c/D398fe3l7zAsDBwSHPPuCZ1eOtPufW1ta0bt0607Zly5bNdVz6XL16lZCQEBRFYdq0aTRv3pxSpUphYWEBgJeXFwcPHtR7bxQoUCDX28/u/Z4dnTp1YtSoUfTs2ZPU1NRMU7lA7u51Q3JzfLOqatWqAMTHx/Po0aMcpdfJyTMkL5/jOY3BEH3PsZyMoz4v2eljbGzMypUrGTVqFBs2bGDXrl3ExMSwZ88eJk6cyLZt2/JkRb0QQgghhBBCCCH+64OcRHdwcADg8uXLeusfP37M48ePsbKyyla+7PyOQZ0WIiEhQW+fa9eu6ZSpVy36+/szZcqUXMdtiHo7bdu2ZdCgQVnq4+DgwPnz57l8+bImRUxGV69eBdBKh2FmZgagd1VsWloaiYmJerdlYmJCo0aNNKlKEhIS6N69O1FRUQZzkL8pPDwceH1u9u/fb7Dd77//nq1J9F69euHt7U2/fv2oV68eu3fv1jke69atA+D777/XSV/0/Plzg/udn2xtbTE3N8fU1DTTX4Pkp82bN5OSksLQoUM13x7IKD4+/j1ElTc6derE119/zdatWylUqBAtWrTItH1+3Ovv4vja2tri6enJ4cOHWbFiBX369Mly35w8Q/Lau3yO5SS2uLg4rl69qnd1uKH/lsDrlzHVqlVj7NixPHnyhHHjxjFlyhS+/PJLDh06lCfxCSGEEEIIIYQQ4rV38mHR7KpRowYWFhYcPnxYbx5p9WSpj49Pvq00zUkMXl5eAKxdu1Zn5Wd6erpmojWjevXqYWxsTEREhGalan5o0KAB8PoDiFnl6+sLwNKlS3XqUlJSWL16tVY7+O9EVFxcnE6fqKgoXr3KWqqAMmXKaNLOnD59+q3tjx07xpkzZyhRogSpqamaFekZ/9LT03F0dCQ2NlYnhdDb9O3bl59//plbt24REBDAxYsXteofPnwI6E/ls3r16lytBM4pExMT/P39efDggc4HR9+VzI7Lnj17uH37do7GVU9ypqam5jy4XHJ0dKR58+YUK1aMzp07v3XlfE7vdVNTU4P7mV/H903qDyWPHTtW82FLQ9TfhoCcPUPy2vt8jr2N+lsU6hgyiouL48SJE1kap1ChQvzwww+oVKosPS+FEEIIIYQQQgiRPR/kJLqVlRU9evQgPT2d/v37k5ycrKmLi4vj+++/B2DgwIEfVAwBAQGUL1+es2fPMnXqVK3xQkND9a4KLVWqFCEhIVy4cIEuXbrozU194MABNm/enKv9qV27NvXr12fXrl0MGTJEZ4Vleno627dv13zoDqBnz55YWFiwfPlyIiMjtdqOHj2aGzdu4OHhoZU6QP2xv/DwcE0OaHi9ItbQ+Zo6dareyb6tW7cCWcuNrf6g6GeffaY3Tzy8Tpmg/oCfun129O/fn+nTp3Pjxg0CAgK0zqd6hev8+fO1JtjOnDmTaQ76/DZ69GiMjIzo1q2b1rlVu3nzJrNmzcq37auPS3h4uNY9dOPGjWytaH6T+pci58+fz12AubRp0ybu3buXpWOY03vdwcGB27dv8+jRI532+XV839SuXTs6dOjA7du3qVu3LgcPHtRpk5iYyIABA7TS2uTkGZLX3tVzLCe6d++OmZkZS5Ys0XwAFV7/euXLL7/Umzbm999/JzY2Vqd869atKIqSJ98SEEIIIYQQQgghhLYPMp0LwMSJE4mJiWHHjh04Ozvj5+dHcnIyUVFRvHjxgkGDBtG8efMPKgZjY2MWLlxIw4YNGTp0KEuXLsXd3Z0zZ85w9uxZPv/8c+bOnatZRas2Y8YM4uPjWb58OREREVStWhUHBwcSExO5ePEiN27c4Msvv6RZs2a52p+lS5fSqFEjpk2bxpIlS6hatSp2dnbcuHGD8+fPc/fuXaZOnapZHVmmTBnmzJlDSEgIn376Kd7e3pQuXZrjx49z/vx5SpQowZIlS7S24ezsTNeuXTXj161bl+TkZGJiYmjevDkvXrzQpE9QGzduHMOGDaNKlSq4urqiKAqnTp3i/Pnz2NraMnz48Ez3KzU1lRUrVgAQHBycadsOHTowZcoUli9fTlhYmN6PE2Zm0KBBpKenM2TIEAICAoiOjqZs2bJ0796dyZMns2nTJtzd3fHw8ODBgwdER0cTFBTE4cOHdfb7Xahbty7Tp09n8ODB+Pr6UrlyZVxdXTXn4ezZsxQsWJD+/ftna9xhw4ZpPpT4JktLS3755RcAWrRowSeffMLRo0cpX7483t7evHjxgl27dlG1alW8vLy0Vi5nVaNGjShQoABTp04lNjYWBwcHVCoVw4cP15uz/kORk3u9RYsWzJw5k+rVq+Pl5UWBAgVwd3dn+PDh+XZ89VmyZAmWlpYsWLAALy8vKlSowMcff4ypqSlXrlzh6NGjpKWl0bBhQ02fnDxD8tq7eo7lhLOzM5MmTWLIkCHUq1cPf39/bG1t2bt3L0ZGRgQGBhIREaHVZ+3atXTt2hUXFxcqVaqEhYUFV65cISYmBmNjY3744YdcxyWEEEIIIYQQQghtH+RKdHj9McTo6GjGjRuHra0tGzduZO/evdSsWZNly5Yxffr0DzIGHx8f9u3bR+PGjblw4QIRERHY2tpqJlsBihUrptXH0tKS7du3M2/ePKpXr05sbCzr1q3j0qVLuLi4EBYWpkmnkBslSpQgJiaGKVOm4OrqypEjR1i/fj3Xr1+nWrVqzJo1S+fjiJ07d2bPnj0EBgZy9uxZ1qxZw/Pnz+nbty/Hjh2jQoUKOtuZO3cuo0aNolChQmzbto2rV68yevRoli9frjeumTNn0qFDB549e8aWLVvYunUrxsbGDBs2jFOnTuHi4pLpfm3fvp3bt2/j5OT01hWtHh4elC9fnps3b7Jz5863HDH9Bg8ezE8//cTVq1epV68eCQkJFCtWjCNHjtCxY0dSUlLYuHEjN27cYPz48Qb3+10ZMGAAhw4dolOnTjx8+JCNGzdy8OBBjIyM6NOnDxs2bMj2mGvXrmXx4sV6/5YtW6ZpZ2Zmxt69e+nbty8FChQgIiKCs2fPMnDgQHbs2IGpqWmO9snBwYENGzZQu3Zt9u3bx4IFC5g/f36efbw1v+TkXp84cSIDBgwgNTWVlStXMn/+fM2K6vw6vvqYmpoyf/58Dhw4QI8ePUhNTWXr1q2sX7+exMRE2rRpw4YNG9i2bZtWv5w8Q/Lau3iO5dTgwYNZtWoVVatWZd++fezcuRN/f39iYmJ0/lsB8NVXX9G/f3+sra3Zu3cv69at486dOwQHB3PkyJG3fkRYCCGEEEIIIYQQ2adS3key5n+opk2bsnXrVmJiYqhVq9b7DkcIIYTQuHDhAtfvJDN326N8GX/ZeP98GVcIIYQQQgghhMhvMomexx48eEBSUpJm1TmAoij8/PPPDBo0iPLlyxMXF5dvH0QVQgghckL9EW1XV9f3HIkQQgghhBBCCPFh+WBzov9dxcXF4eXlReXKlXF2diYtLY3Y2Fji4+OxsLBg7ty5MoEuhBBCCCGEEEIIIYQQfxMfbE70vytnZ2f69OnDy5cv2blzJ1u2bOHly5d07NiRQ4cO4e/v/75DFEIIIYQQQgghhBBCCJFFks5FCCGEEJLORQghhBBCCCGEMEDSuQghhBACgOt3khnz++58GVs+LCqEEEIIIYQQ4u9K0rkIIYQQQgghhBBCCCGEEAbIJLoQQnxAVCqV1p+pqSm2trZUqlSJkJAQ1q5dS2pqaqb9nZyctMp2796NSqUiJCQkf4PPxLuO4dixYzRq1IjChQtrjuWVK1feybaFEEIIIYQQQgjxv0XSuQghxAeoW7duAKSnp/P48WPi4uJYsmQJixcvpnz58ixduhRPT8/3HOWHKSkpiRYtWnDr1i38/f0pXbo0KpWKggULvu/QhBBCCCGEEEII8Tckk+hCCPEBWrRokU7ZpUuXGD16NKtWraJevXrs37+fqlWrarU5e/Yspqam7ybID9SRI0e4efMmXbp0YcmSJe87HCGEEEIIIYQQQvzNSToXIYT4m3BxcWHlypX07NmTZ8+e0aNHD502FSpUwMXF5T1E9+G4fv06AM7Ozu85EiGEEEIIIYQQQvwvkEl0IYT4m5k8eTJWVlb89ddf7Nu3T6tOX070jB48eEDfvn0pWbIk5ubmVKxYkQULFmi1OXLkCCqVCm9vb4PjjBs3DpVKxffff69VfuXKFYKDgylWrBgFCxbEy8uLyMhIg+OEhISgUqnYvXs327Zto169epo85o8ePQIgNTWVmTNnUqNGDQoWLEjBggXx9PRk9uzZpKWlaW1bpVJpUuGoY8yYi33s2LGoVCq9K/0BnJycUKlUWmUZ87knJibSq1cvHB0dMTExYdq0aQDcunWLsLAw/Pz8KFWqFGZmZtjb29O6dWuOHDny1m3NmzePypUrY2Fhgb29Pb1799bsf0bJyclMmjSJqlWrUrhwYQoWLIiLiwvt2rVj27ZtBo+zEEIIIYQQQgghck7SuQghxN+MjY0NTZs2Zc2aNezatQsfH58s9Xv06BF16tTh8ePHeHp68vTpU/bs2UPPnj1JT0+nV69eAHh4eFCjRg0OHDjAf/7zHz755BOtcdLT01m4cCHGxsZ0795dU37p0iW8vLy4c+cObm5uVK9encuXL/Ppp5/Sp0+fTGNbtmwZ8+bNo2bNmjRt2pRLly6hUqlIS0ujZcuWbN68mUKFCtGgQQMAoqKi6NevHzt27GDNmjUYGRlRsGBBunXrxsWLF9m/fz9VqlTRpLvJ6jHKzN27d/Hw8CA1NRUfHx9evHiBpaUlABs2bGDkyJGUL1+eSpUqUahQIS5evMi6deuIiIggIiKCRo0a6R13xIgRTJ8+HQ8PD5o0acKBAweYM2cOZ8+eJTo6WjPRnpaWRqNGjThw4ACOjo74+/tjZmbG9evXiYiIwMrKisaNG+d6P4UQQgghhBBCCKFNJtGFEOJvqGrVqqxZs4azZ89muc+GDRto06YNixcvxsrKSlMWFBTEhAkTNJPoAL179+aLL75g3rx5TJ06VWuc7du3c/XqVT799FNKlSqlKe/Xrx937tyhX79+zJw5EyOj1z92mjdvHp9//nmmsc2dO5cVK1bw2WefaZVPnjyZzZs3U6lSJf7880+KFy8OvF75Xa9ePdatW8evv/5Kv379sLW1ZdGiRSxatIj9+/cTFBTE2LFjs3x83mbz5s20atWKZcuWUaBAAa06b29vTp48SeXKlbXKt23bRosWLejXrx8XLlzQWeUOEB4ezqFDhzQT/vfu3aNOnTrs3buXXbt2ERAQAMDevXs5cOAALVu25I8//tAcX4DHjx9z8eLFLO3HhQsX9JanpKRkqb8QQgghhBBCCPFPI+lchBDib8jW1haAhw8fZrlPoUKFmDNnjmYCHaBly5ZUqlSJhIQErly5oinv2LEjhQoV4vfff+fly5da48ybNw9Aa2L80qVLbN++nSJFihAWFqY1wdurVy+8vLwyja158+Y6E+gAM2bMAGDatGmaCXSAkiVL8uOPP2q1yW/m5ubMnDlTZwIdoFKlSjoT6ACNGzemXbt2XLp0idjYWL3jTpgwQesDsba2tvTt2xeAPXv2aMrv3LkDgL+/v9bxhde/TqhRo0a290kIIYQQQgghhBBvJyvRhRDib0hRFAC9K5sNqVmzJkWLFtUpd3Nz4/Tp09y6dUuTT93KyopOnToxe/Zs1q1bR4cOHYDXE7kbN27EwcGBZs2aacbYv38/AM2aNdOapFfr0KEDBw4cMBhbixYtdMoSEhJISEjA3t5esxo7o8DAQAoXLsz58+e5e/cudnZ2mR+AXKpevbrWyvs3vXz5kq1bt3L48GHu3r2rWdl9+vRp4PUK8EqVKun005fmxc3NDXi94l6tatWqGBkZ8eOPP2Jvb0/z5s2xtrbO9n64urrqLX+9Qv1VtscTQgghhBBCCCH+18kkuhBC/A3du3cPQO+kuCGOjo56ywsWLAigs+K8T58+zJ49m7lz52om0RctWsSrV6/o0aMHxsbGmrY3b94EoEyZMnq3Yag8s3r1mIY+lKpSqShbtiyPHj3i5s2b+T6Jntk+nD59mhYtWmit5n9TUlKS3nJ950XfOXFzc+PHH39k1KhRBAcHY2xsTMWKFWnQoAHdu3fXyV0vhBBCCCGEEEKIvCHpXIQQ4m/oxIkTAHz88cdZ7pOdVesAlStXpnbt2uzatYtLly4BMH/+fFQqFT179tRqm5OV8RnpS5GilpUxc7rdN6WnpxusMxSjoii0b9+eK1eu0KdPH06cOMGTJ09IT09HURS+/vprTTt9shP7V199xaVLl5gxYwbNmjXj6tWrTJ48mcqVKzNr1qwsjyOEEEIIIYQQQoisk0l0IYT4m3n8+DFbt24FoF69evm6rT59+qAoCvPnzyc6Opq4uDgaNmyoszrcwcEBgKtXr+odJyEhIdvbVo95+fJlg23U45YsWTJLY5qZmQHw9OlTnbq0tDQSExOzGybnzp3j3Llz1KxZk9mzZ1OlShWsra01k+Px8fHZHjMzpUuXZuDAgWzcuJG7d+/y+++/Y2RkxFdffcWjR4/ydFtCCCGEEEIIIYSQSXQhhPjbGTp0KMnJyXh4eFCnTp183Vb79u0pUqQIixYtYvbs2YD2B0XVvL29Adi8eTPJyck69StWrMj2tsuUKUOZMmVITEwkKipKpz4yMpKHDx/i7u6e5VQu6sn2uLg4nbqoqChevcp+TnD1x131pWV5+PAhO3bsyPaYWWViYkLnzp3x8PAgJSVF734JIYQQQgghhBAid2QSXQgh/ibi4+P57LPPmD9/PlZWVsyfPz/ft2lhYUHXrl25desWK1euxM7OjpYtW+q0K1++PPXr1+fhw4eMGjVKKy3KwoULM/2oaGYGDhwIwJAhQ7h7966mPDExkeHDh2u1yQo/Pz8AwsPDtfKXx8fHZ2ucjMqXL4+RkRFRUVH//3HO1168eEGfPn148OBBjsZ9065du/jzzz91Us5cvXqVs2fPolKpDOa9F0IIIYQQQgghRM7Jh0WFEOIDFBISArzO0f3kyRPi4uI4d+4ciqLg6urKsmXLqFSp0juJpXfv3kyfPl0Tl6mpqd52s2fPxtvbm59//pkdO3ZQvXp1Ll++zKFDh+jTpw+//vprtrc9ZMgQoqKi2LJlC66urgQEBKAoCjt37iQpKYmgoCD69u2b5fGcnZ3p2rUrS5YsoWrVqtStW5fk5GRiYmJo3rw5L168MJiSxpDixYvTs2dP5s6dS5UqVQgICMDCwoK9e/eSlpZGSEgIixYtyuae6zp58iRDhgzBzs6OGjVqUKxYMe7evcuePXt48eIFgwcP1qTAEUIIIYQQQgghRN6RSXQhhPgALV68GHidrqNQoUI4ODjQtWtXWrRoQYsWLTAxeXeP748++ggHBwdu3rxJr169DLZzdXUlJiaGr7/+mh07drBhwwYqVarE+vXrKVSoUI4m0Y2Njdm4cSO//PILixYtYtu2bcDrD6p2796d3r17Y2SUvR9VzZ07FwcHB5YuXcq2bdsoXbo0o0ePZtSoUbi4uGQ7Rnj9AqFChQrMnz+fnTt3YmNjQ4MGDfj3v//NwoULczTmmwIDA7l//z67du3i5MmT3L9/Hzs7O3x9fenXrx9BQUF5sh0hhBBCCCGEEEJoUymKorzvIIQQQny4Dhw4gLe3N35+fuzevft9hyPyiToVjaur63uORAghhBBCCCGE+LBITnQhhBCZ+uGHHwAYMGDAe45ECCGEEEIIIYQQ4t2TdC5CCCF0HDhwgPnz5xMbG8vhw4epUaMGrVu3ft9hCSGEEEIIIYQQQrxzMokuhBBCR1xcHAsWLMDa2ppPP/2Un3/+Odu5x4UQQgghhBBCCCH+F0hOdCGEEEJITnQhhBBCCCGEEMIAWYkuhBBCCACu30lmzO+782XsZeP982VcIYQQQgghhBAiv8lv88U/gkqlQqVSZdpm7NixqFQqxo4dm6+x+Pv7o1KpuHLlSr5u50PSt29fVCoVxsbGXL9+PU/GvHLlCiqVCn9//2z1c3Jyeuu18He2aNGibF/H6vtD/WdqaoqtrS2VKlUiJCSEtWvXkpqamn9B6xESEoJKpWL37t3vdLtZoX5WLFq06H2HIoQQQgghhBBCiHdAJtGFEPkqJSWFVatWAZCens6yZcvec0TCkG7dutGtWzeCg4Px9vYmNTWVJUuW0LZtWz766CMOHz78vkN8J/6JL7qEEEIIIYQQQghhmKRzEULkq8jISB48eEDJkiW5desW4eHhjBgxItfjlipVirNnz2JpaZkHUQpA78rqS5cuMXr0aFatWkW9evXYv38/VatWzfdYJk6cyKhRoyhTpky+byu7BgwYQIcOHShZsuT7DkUIIYQQQgghhBDvgKxEF0Lkq99//x2Af//73zg5OXH69GlOnjyZ63FNTU2pUKHCBznJ+r/ExcWFlStX0rNnT549e0aPHj3eyXZLlixJhQoVPsiXJLa2tlSoUAEbG5v3HYoQQgghhBBCCCHeAZlEFyITzZs3R6VSsWPHDr31ycnJFCpUCBsbG5KTk7Xq5syZQ6VKlShQoAClSpVi4MCBPH78WO84GfN7P3nyhKFDh1KuXDlMTU0ZPHiwpt2ZM2fo1KkTJUuWxMzMjFKlStG1a1fOnz+vNd4vv/yCSqXC29ubtLQ0rbrnz5/zySefoFKpWL16NQD9+/dHpVIxd+5cvfEpioKLiwvGxsZcvXo102OW0cOHD4mMjMTCwoK2bdvSsWNHAMLDww32URSFpUuXUr9+fYoVK0aBAgVwdnamY8eO7N+/X+8xe1NqaioTJ07E1dVV0//bb78lJSXF4HYPHTpEq1atKFu2LObm5tjb2+Pp6cnXX3/N06dPs7S/Fy9eZOzYsdSpUwd7e3vMzMxwdHSka9euxMXF6e2jUqlwcnIiLS2NsLAw3NzcMDc3p3Tp0owcOZKXL1/q7Xfq1CkCAwOxsbHBxsaGhg0bcvDgwSzFmROTJ0/GysqKv/76i3379unUX7lyhd69e+Pk5IS5uTl2dna0bduWU6dO6bTNmLc9Li6ODh06UKJECYyMjFi/fj2gmxP91atXmuvh0aNHemM8fPiw5rpXe/ToETNnzqRx48aac1usWDGaNGmic1+rr6no6GgAypUrp5UrXk1fTvRKlSqhUqk4d+6c3tgSExMxMTHB0dGR9PR0rbp9+/bRqlUrihcvjrm5OU5OTgwaNIi7d+/qjPPq1St+++03PD09sbW1xdLSEicnJwIDA1mxYoXebQshhBBCCCGEECJ3ZBJdiEz06dMHwODk8ooVK0hKSqJjx45YWVlpyocNG0bv3r25cOECAQEB1KlTh6VLl1KvXj2Dk6LweoLbz8+PhQsXUrVqVVq0aEGRIkUA2LlzJzVr1mTZsmU4ODjQpk0bihcvzu+//07NmjXZu3evZpx+/foRGBjIgQMH+Pe//621jeHDh3PmzBm6detGu3btsrSfUVFRxMfH06hRI8qWLfu2w6axcuVKUlJSaNGiBdbW1nTp0gWAZcuW6UwkAqSlpdG+fXs6d+7M/v37qVatGkFBQdjb27Nu3TqD8b0pODiY0aNHk5iYSJMmTahUqRJTpkyhdevWKIqi0z4yMhIvLy82bdqEk5MTrVu3pmrVqty7d4/Q0FDu3buXpe3OmzePcePG8eTJE2rWrEmLFi0oVKgQv//+Ox4eHnonlNU6derE+PHjcXR0pFGjRiQlJREWFkbPnj112h46dIg6deoQGRmJs7MzzZo1IzExET8/Pw4cOJClWLPLxsaGpk2bArBr1y6tun379lGlShXmzJlDwYIFadGiBa6urvzxxx/Url1bp73a+fPn8fDw4PDhw9SrV4+GDRtiamqqt62pqSnt2rXj5cuXrF27Vm8bdb79Tp06acpiYmIYNGgQZ8+exdXVlVatWuHu7s727dtp3LgxCxYs0LQtWLAg3bp1o0SJEgC0adNGkye+W7dumR4f9TaXLl2qt37FihWkpaXRsWNHjIz++5/eGTNmULduXTZt2kT58uVp0aIFFhYWzJw5k1q1anHr1i2tcbp06UKfPn24fPkyXl5etGjRgtKlS7N3715+/fXXTGMUQgghhBBCCCFEDilC/AMAytsu9zFjxiiAMmbMGE1ZamqqUrp0acXMzEy5c+eOTp9atWopgHLs2DFN2f79+xVAKVq0qBIbG6spv3fvnlK5cmVNLJcvX9bUXb58WVNep04d5eHDh1rbefr0qVKiRAkFUGbPnq1VN2XKFAVQHB0dlRcvXmjKb9++rRQvXlwxMTFRDh06pCiKomzevFkBlHLlyilPnjzRGsfLy0sBlBMnTujs52effaYAytq1aw0fQD3UY27atElTVr16dQVQduzYodN+woQJCqBUqlRJuXLlilbd/fv3lX379mn+rT5mfn5+Wu2WLVumAIqzs7Ny/fp1TXl8fLzi6Oio91rw8/NTVCqVcvToUZ2YDh06pHOsDDl48KBy8eJFnfIFCxYogFKvXj2dOnU8H330kdY1ER8frxQpUkQBtMZMS0tTKlSooADKxIkTtcb65ptvNONlvI7fJiv3h6Ioyvfff68ASnBwsKbs8ePHir29vWJqaqqsXr1aq/2OHTsUMzMzpVSpUsrLly815QsXLtRsc8CAAUpqaqrOtrp166YAyq5duzRle/bsUQAlICBAp31aWppSsmRJxcTERLl7966mPD4+Xtm/f79O++PHjyuFCxdWChUqpCQlJWnV+fn56dyjGamfFQsXLtSUXb16VVGpVIqzs7PePh4eHjr318GDBxUjIyOlbNmyysmTJzXl6enpyvjx4xVAadu2raZcfc17eHgoz58/1xr/2bNnyoEDB/RuO6vi4uKUqH1/KcHf7sqXPyGEEEIIIYQQ4u9KVqKLf5SMqRne/Bs3bpxOe2NjY3r16kVKSgpLlizRqouNjeXQoUNUq1aN6tWra8rVq0GHDh3KJ598oikvVqwYP/7441tjnDFjBoULF9YqW7VqFbdv38bX11ezalxtyJAh1KhRg+vXr7Nu3TpNefHixVmwYAGpqal07tyZy5cv0717d4yNjQkPD8fa2lprnN69ewOvV1NndP/+fdavX0+JEiX49NNP3xq/Wnx8PAcOHMDW1pYmTZpoyjt37gz8N1e6WkpKCpMnT0alUrFgwQKdFe9FixbVStNhyOzZswGYMGECpUqV0pSXK1eOb7/9Vm+fO3fuYGNjQ40aNXTqPD09dY6VIbVr18bFxUWnvHv37nh7e7N7926DKX1mzpyJk5OTVrzqY5XxVwa7d+/m3LlzuLm5MXLkSK0xxowZk6854m1tbYHXaXrUFixYQGJiIsOGDaNt27Za7Rs0aEC/fv24ceMGEREROuPZ2dkxadIkjI2Ns7R9Hx8fypYty+7du7l586ZWXVRUFLdu3aJx48aaOOH1cfTy8tIZq1q1avTv358nT54YXCmfHWXKlMHHx4f4+HhiYmK06i5evMiRI0f4+OOPqVKliqY8NDSU9PR05syZQ+XKlTXlKpWKb775hmrVqvHHH39ofglx584dALy8vChQoIDWNiwsLKhTp06WYr1w4YLev8zSHQkhhBBCCCGEEP9kMoku/lEypmZ48y/j5FZGvXr1wsTERGdyWZ1a5IsvvtAqV+eLbt++vc5YjRo1omjRogbjK1myJDVr1tQpV0+iZkxTkZG+yVZ4ndO9X79+XLhwgapVq3L79m1Gjx6td1Kxffv2FC1alPDwcJ4/f64pX7JkCS9fviQkJMRgqg191JPkHTp0wMTERFMeHByMsbExf/zxB8+ePdOUHz16lEePHlG9enW9xyArXr16xaFDhzAyMtKZ0FVvW58aNWrw6NEjevbsSWxsbI62rfb06VOWL1/OyJEj+fzzzwkJCSEkJIRbt26hKAqXLl3S6WNqaqo3t7ubmxuAVkoP9fXVrl07rTzdACYmJnr3O68o/58KJ+N21XnFg4KC9Pbx8fEB4MiRIzp1DRo0yNaHQ1UqFcHBwaSnp+vk/9aXykUtLS2N7du3M3bsWPr06aM5J+rJ8wsXLmQ5hsyot62O5c3Y1PcpQHp6Ojt37sTa2pr69evrjKXO7Z6ens6xY8cAqFChAlZWVixcuJC5c+dy//79PIlbCCGEEEIIIYQQmTN5exMh/ndk/BDgm8aOHcvJkyd1yh0cHAgMDGT9+vXs3bsXX19fXr58SXh4OJaWlpqPZardvHkTlUpF6dKl9W6nTJkyPHjwwGCdPupVtxlXKmekLn9zdS7ATz/9xIYNG7hx4wbVqlXju+++0ztGgQIF6Nq1K9OmTWPNmjWa/OXz5s1DpVLpzc2dGfXHQzNOHALY29vToEEDtm3bxvr16zXH79q1awB6V3Jn1f3790lJSdF8ePVN1tbWFC5cWOfDlD/88AOnT59mwYIFLFiwAFtbW7y8vAgKCqJjx46Ym5tnaftRUVF06NBB7wch1ZKSknTKSpYsqXc1dsGCBQG08uirz7GhayU/V6KrV0RnfBF05coVAGrVqpWlvhnlJNZOnToRGhrK0qVL+eqrr4DXx+ePP/7AysqKli1barW/fv06gYGBeu9tNX3nJCfatWvHoEGDWLlyJVOnTtWc0+XLl2teAKjdv39f88HajC+Z9FEfu0KFCjF37ly++OILvvjiC3r37o27uzv16tWja9eu1K5dO0txurq66i1//TLhVZbGEEIIIYQQQggh/klkEl2ILOjTpw/r169n3rx5+Pr6snbtWh48eED37t0pVKhQnm3nzRQNb3pz5XFW6vfu3auZeE1ISODu3buULFlSb/8+ffowbdo05s2bR5cuXThw4ABnzpyhXr16Bife9Dl48CAXL14EXqe1eZN6wjw8PFznJcTb9jEz+lZKZ0Xp0qU5evQoUVFRREREEB0dzaZNm9i4cSNhYWEcOHBA84FXQ54+fUr79u25f/8+3377LcHBwZQtWxYLCwtUKhUdO3Zk+fLlej9smp14c7qPeeHEiRMAfPzxx5qytLQ04PUEcmaryvVNsr/tetenYsWKVK5cmePHj3Pu3DkqVKhAZGQkjx8/pnPnzjox9OrVi5MnT9K6dWtGjhyJu7s71tbWGBkZMWfOHHr37q33nORE0aJFadKkCRs3buTPP/+kcePGmjh9fHy0XoKpj5u1tTWtW7fOdNyMqY2Cg4Np0KABGzZsYPv27URHRzN79mxmz57N8OHDCQsLy5N9EUIIIYQQQgghxH/JJLoQWdCoUSOcnZ1ZvXo106dP16Ry+fzzz3XalixZkitXrnDt2jXKly+vU5+QkJDt7Ts4OABw+fJlvfVXr17VbDuj+/fvExISgpGREe3bt2f58uWEhISwdetWvZOw7u7u+Pv7s3v3bs6fP5/pfmYmY77z/fv3G2y3fft2bt++TYkSJTQr99WT7zlha2uLmZkZiYmJpKSk6KxGT0pK0lmFrmZiYkKjRo1o1KgR8Po8de/enaioKEJDQ5k0aVKm2967dy/379+nTZs2jB8/Xqc+Pj4+Zzv1BvW1oD7nb8rJ9ZUVjx8/ZuvWrQDUq1dPU+7o6Mj58+f55ptvtPJ656dOnTpx6tQpli1bxvjx4w2mcklOTmbHjh2UKFGCVatW6az2z6tz8mZsGzduZOnSpTRu3NhgbLa2tpibm2NqaprpL2T0sbOzo1evXvTq1QtFUdi2bRufffYZP/74IyEhIVovOYQQQgghhBBCCJF7khNdiCxQqVR8/vnnPH/+nHHjxhEdHc0nn3yi90N+6hzQq1ev1qnbsWOHwVQumfH19QVg6dKleuvV5ep2ap9//jm3bt1i1KhRhIeH4+vry/bt25kxY4bBbak/MDplyhRWrVpF0aJF37pSNqNXr16xatUq4PXHVxVF0fvXsWNH0tLSNLmta9asSeHChTl+/LgmB3R2mZqa4unpSXp6OmvXrtWpfzOPdmbKlCmj+XDn6dOn39pe/bFNfWl8Ll68yPHjx7O87cyor6+1a9fqrKBOTU3Vu995YejQoSQnJ+Ph4aF13Tdo0ACA9evX58t29enYsSMqlYply5bx5MkTIiMjKV68uCYWtcePH5Oenq43XU5qaqrWh3gzUr98SU1NzXZsn376KdbW1qxfv57k5GRWrlyJqakp7dq102pnYmKCv78/Dx48YM+ePdnejppKpaJJkyY0b94cINc5/YUQQgghhBBCCKFLJtGFyKIePXpgZmbGtGnTUBTF4OrsjJPQZ8+e1ZQ/ePCAESNG5Gjb7du3p0SJEuzdu5c5c+Zo1c2YMYMjR47g6OhIq1atNOXz5s1j3bp11KhRgzFjxmBkZMSSJUsoVKgQo0aNMjjZ1rp1a+zs7JgzZw7Pnj2ja9euWc4JDhAZGcn9+/epXLkyn3zyicF2HTp0AP67at3MzIwhQ4agKAo9e/bUpHxRe/DgQaar2tXUx/+7777T+iDn1atXmTBhgt4+U6dO5fbt2zrl6pXXWcndrf4I6B9//KGVE139wdJXr/Im13S9evVwc3Pj3Llz/PTTT1p133//vcEV6jkVHx/PZ599xvz587GysmL+/Pla9b1798bOzo4ffviBhQsX6kzsJycns2TJEq5fv55nMTk6OlK3bl0uXbrEyJEjefHiBZ999plObvHixYtjY2NDbGys1rWTlpbGiBEjiIuL0zu+erX/+fPnsx2bhYUFrVu3JikpiWHDhnH9+nWaNGlCsWLFdNqOHj0aIyMjunXrpvlgbEY3b95k1qxZmn//9ddf/PHHHzrX0sOHDzl06BCQvznxhRBCCCGEEEKIfyqZRBcii4oXL05QUBAA5ubmmg9vvsnHx4fBgwdz7949qlWrRmBgIO3atdPkFM/qx/8ysrKyYunSpVhYWNC7d29q1qxJx44dqV69Ol9++SVWVlYsW7ZMM9l98eJFBg8ejKWlJUuXLsXU1BR4/QHSmTNn8uLFCzp16qT1wUo1MzMzunfvrvl3r169shWrelI840cU9WncuDFFihTh2LFjmpcNo0ePJigoiJMnT+Lq6krDhg0JDg7G29ubUqVKadLLZKZTp060atWKixcv4u7uTqtWrQgKCuKTTz6hUqVKeicZx40bh4ODA9WrV+ezzz6jffv2VKhQgalTp2Jra8vw4cPfut2aNWvSsGFDEhIScHNzo1WrVrRq1Ypy5cpx8+ZNnQ9e5pSRkRGLFi3CwsKCESNGUL16dTp27EjlypX54Ycfsn2+MgoJCSEkJISuXbsSFBTExx9/TPny5Vm1ahWurq7s3r2bSpUqafUpUqQI69atw8rKih49elCuXDkCAwNp06YNHh4elChRgm7duun9sGhuqNOj/Prrr1r/zsjExIQRI0aQmpqKn58fjRo1okOHDpQvX55ff/2V/v376x27RYsWwOsV7+3atdOkTsnL2ADq1q3L9OnTuXbtGr6+vlSpUoW2bdsSGBiouVb/9a9/adpfvXqVNm3aYGdnR4MGDejcuTOBgYE4OTkRHx9Pq1atcvR8EUIIIYQQQgghROZkEl2IbKhfvz4Abdq0oWjRogbbTZkyhdmzZ1O+fHl27NjB/v37ad++Pbt27crWqu43t33kyBGCg4O5fv06a9asITExkc6dO3Ps2DFNKpfU1FQ6depEcnIyP/30E+7u7lrjdO3alXbt2nHq1ClGjx6d6X56eXllupr8TY8ePSIyMhL470pzQ8zMzDRpYsLDw4HXk55r165lwYIFeHh4cPjwYdavX8+tW7do06aNZpV5ZlQqFStXruTf//43dnZ2bN68mRMnTjBw4ED++OMPvbngZ86cSYcOHXj27Blbtmxh69atGBsbM2zYME6dOoWLi0uW9n/Dhg3861//ws7Oji1btnDs2DE6dOhATEwMhQsXztIYWVGnTh0OHDhA06ZNuXjxIhEREdjZ2bFr1y68vb1zPO7ixYtZvHgxy5cvZ+/evRgbG9O1a1fWrl3LmTNnqFmzpt5+3t7enD59mqFDh2JhYUFUVBTbt2/nyZMnBAYGsnLlyjzP092uXTvNveTi4qL3w6Xw+sXM4sWLqVy5Mvv37+fPP/+kSpUqxMTEGNyf1q1bM3XqVBwdHdm0aRPz58/XWYGfmYCAAM33CQoWLKiZlNdnwIABHDp0iE6dOvHw4UM2btzIwYMHMTIyok+fPmzYsEHTtnbt2nz//ffUqFGD8+fPs3r1ao4ePUrlypVZvHixJo2SEEIIIYQQQggh8pZKefO390IIgxo1asSOHTvYtWsX/v7+7zucfPPFF18wd+5cFi5cSEhIyPsORwjxDly4cIHrd5KZu+1Rvoy/bLx/vowrhBBCCCGEEELkN5lEFyKLDh8+TO3atfn444//pz/ed/XqVT766COsrKxISEjAwsLifYckhHgHLly4AKBJPSWEEEIIIYQQQojXTN7eRIh/tlGjRpGQkEBkZCSKovDDDz+875DyxY8//sipU6fYsWMHz58/JzQ0VCbQhRBCCCGEEEIIIcQ/nkyiC/EWK1as4Nq1azg5OREWFpZpfuO/s8jISKKjoylVqhTjxo1j4MCB7zskIYQQQgghhBBCCCHeO0nnIoQQQghJ5yKEEEIIIYQQQhggK9GFEEIIAcD1O8mM+X13vowtHxYVQgghhBBCCPF3ZfS+AxBCCCGEEEIIIYQQQgghPlR/20l0lUqFSqXKtM3YsWNRqVSMHTv23QQl/ucpisKyZcto3rw59vb2mJmZYW9vT9OmTVm6dCk5zY60aNGiD/5avXLliua+c3V1JTU1VW+7kJAQVCoVK1aseMcRvt3u3bs1+1C4cGFevHhhsG2/fv00bUNCQt5dkNng7++PSqXiypUr2e577do1Ro4cSfXq1SlatChmZmaUKFGCRo0a8csvv/D06dO8D1hkmfp+8/f3f9+hCCGEEEIIIYQQ/3h/20l0Id61hw8f4ufnR6dOndi+fTsVKlSgbdu2fPTRR+zcuZPOnTvj5+fHw4cPdfqqX+gsWrTo3QeeDy5evMjvv//+vsPIlcePHxMREaG37tWrV6xatSpPt5ebCe+89ttvv+Hm5kZYWBg3btzAy8uLtm3b8sknn7Bv3z769+9P+fLluXfv3vsOVQghhBBCCCGEEOK9k0l0IbLg1atXNGnShL1791KvXj0uX77M7t27WbZsGbt27eLy5csEBASwd+9emjRpYnCV9v8CCwsLAL7//vu/7X5WrlwZY2NjwsPD9dZv2bKF+/fvU7169XccWf6bN28effr0wcTEhMWLF5OYmEhERATLli0jKiqKe/fuERYWxrNnz2Q1uhBCCCGEEEIIIQQyiS5ElkyePJnDhw9TqVIlIiMjcXR01KovVaoUERERVKxYkcOHDzN58uT3FGn+K1OmDM2aNSM+Pp7Fixe/73BypGTJkgQEBLBlyxa9vxwIDw/HyMiIjh07vofo8s/169cZOHAgKpWKjRs30rVrV520WJaWlgwfPpxDhw5hY2PzniIVQgghhBBCCCGE+HD8YyfRnz17xoQJE6hYsSIWFhbY2NhQt25dg3mcnZycNJNNs2bN0vQrV64cYWFhmlzYx48fJzAwkKJFi2JtbU1QUBBXr17NkxgA/vrrL5o2bYqNjQ02NjY0btyYI0eOZJpTOyUlhenTp+Ph4YG1tTVWVlZ4enoyf/58vTm8VSoVTk5OpKWlERYWhpubG+bm5pQuXZqRI0fy8uVLvbE9ffqU8ePHU6lSJSwtLSlUqBB+fn6sX7/e4P4cPHiQli1bYmdnh7m5OU5OTvTr14+bN2/qtH1bSpSM5yijQ4cO0apVK8qWLYu5uTn29vZ4enry9ddfZ2mlbWpqKjNmzABg0qRJmpXYb7KwsCAsLAyA6dOnk5aWpolr3LhxAHTv3l2TZ1ulUrF7926dcRISEujYsSN2dnZYWFhQs2ZNNm3aZDC+06dP06lTJ0qVKoW5uTkODg50795db9qQjMfw8OHDBAYGUqxYMVQqFSdOnHjrscg4Drxejf7q1ass97t//z7Dhw/H1dWVAgUKULRoUZo0acL27dv1ts/ptZgVnTt3JiUlRSdty5MnT9i0aRP+/v6UKlUq0zE2bdpE48aNKVasGAUKFMDNzY1vv/1W67pS57aOjo4GoFy5clrXgNqtW7cICwvDz8+PUqVKafLtt27dmiNHjuR4PzP6+eefefHiBe3bt6devXqZtv3oo48oUqSIVtm1a9fo3bu35l4qXry4wfgy5vROTk7mq6++onTp0lhYWFC9enWta3r16tV4enpiZWVFiRIlGDRoEM+fP9cZU32PK4rC9OnT+fjjjylQoAClSpVi0KBBPHr0SO++ZPdZq77u9DH0rM2Yrmf9+vXUrl0bKysrihYtSnBwMNevX9c73r179+jduzf29vZYWlpSrVo1lixZorctvP4uw4oVK6hbty729vYUKFCA0qVL06BBA2bNmmWwnxBCCCGEEEIIIXLuHzmJnpSURN26dfnuu++4c+cOgYGBeHt7c/jwYYKDgxk8eLDBvkOGDGHYsGHY2dnRoEED7t+/z8iRIxk7diz79+/H19dXk9rD3t6eDRs2UL9+fZ0JoZzEcODAAby9vdm6dSsuLi40a9aMxMREfHx8iImJ0RtvcnIyDRo0YPDgwVy5cgUfHx/8/f25ePEivXr1om/fvgb3tVOnTowfPx5HR0caNWpEUlISYWFh9OzZU6ft7du3qVWrFmPGjOHhw4c0bNiQWrVqcezYMVq1akVoaKhOn/DwcHx9fdm0aRPu7u60bt0ac3NzZs+eTfXq1Tl37pzB2LIqMjISLy8vNm3ahJOTE61bt6Zq1arcu3eP0NDQLOV8PnHiBLdu3aJYsWI0btw407aNGzemaNGi3Lp1SzMp3bZtW6pUqQKAt7c33bp10/zZ29tr9b9y5QoeHh7s378fHx8fqlWrxrFjxwgKCtI70bx27Vpq1qzJsmXLKFmyJC1atMDe3p5FixZRs2ZN/vOf/+iNc8+ePfj4+HDlyhUaNWpE3bp1MTLK+uPAw8ODwMBArly5kuU87zdu3MDT05OffvqJlJQUgoKCqFatGn/++SeNGzdm6tSpBvtm51rMqtatW2NpacnSpUu1yteuXcuLFy/o3Llzpv2HDh1KixYt2LNnDxUrVqR58+akpKTw/fffayaOAQoWLEi3bt0oUaIEAG3atNG6BtQ2bNjAyJEjuXnzJpUqVSIoKAgHBwfWrVuHt7e3wRcN2REZGQmQoxX2p0+fpnr16syZMwdLS0tat26Nq6sr69atw8vLi9WrV+vtl5KSQv369fn999+pWrUqtWvX5uTJk7Rq1Yo///yTqVOn0rFjR0xMTGjUqBFpaWnMnDmTXr16GYxl4MCBDB8+HEdHR1q2bKnp4+fnR1JSklbb3Dzvc+KXX36hTZs2KIpCkyZNKFiwICtWrCAgIEDnvwP379/H29ubOXPmYGpqSosWLbCxsaF79+78+OOPesf/+uuvCQ4O5sSJE1SvXp3WrVtTvnx5Tp48abCPEEIIIYQQQgghckn5mwKUt4U/ZswYBVDGjBmjVT5gwAAFUBo0aKAkJSVpys+ePasUL15cAZTIyEitPmXLllUApVSpUkpsbKxWH3Nzc8XS0lJxcnJSpk6dqql7+fKlEhAQoADKggULchVDWlqa4ubmpgBKWFiY1ljjx4/XHI8397Vv374KoHTp0kVrO3fu3FFq1aqlAEpERIRWH/VYH330kXL58mVNeXx8vFKkSBEFUC5evKjVp2nTpgqgjBgxQklJSdGUX7p0SXFxcVGMjY2VkydPasoTEhIUCwsLxcTERNm0aZPWfg4ePFgBFA8PD61tqM/nwoULFX3U5ygjPz8/RaVSKUePHtVpf+jQIeXJkyd6x8po7ty5CqDUr1//rW0VRVHq16+vAMq8efOyHPvChQs1x33gwIHKq1evNHXTpk1TAMXX11erT3x8vGJpaanY2Ngo0dHRWnWLFy/O9BgCyqRJk7K0P2qXL19WAMXd3V1RFEU5evSoAihly5bVOufdunVTAGX58uVa/QMDAzXXYsb2e/fuVSwtLXWuEUXJ2bWYmV27dimA0rhxY0VRFKVDhw6KSqVSrly5omkTEBCgFChQQHn8+LGyfPlyBVC6deumNc7KlSsVQKlWrZpWXCkpKcoXX3yhAMqwYcO0+vj5+SmAVvuMTp06pbP/iqIoW7duVczMzBQXFxclPT09W2Nm9PLlS0WlUimAcu3atbe2zyg9PV2pVKmSAihff/21VhyrV69WjIyMFGtrayUxMVFTrr5eAMXf31958OCBpk59vZcvX14pWrSosmfPHk3djRs3NM/AS5cuacWhvscLFSqkdU8nJSVpnrVDhgzR6pOT5736utZHHfubz1r1ubCyslJ27typKU9OTla8vLwUQJk/f75WH/W10rJlS+XFixea8s2bNysmJiYKoPj5+WnKnz9/rpibmytOTk7K/fv3tcZ69eqVznPAkLi4OL1/sbGxStS+v5Tgb3fly58QQgghhBBCCPF39bdfiZ4xLcKbf+oUGhklJyczf/58jIyM+OWXXyhYsKCmrkKFCnzzzTcAmvQdb5owYQKffPKJVp/mzZvz7NkzypQpo7Wq0czMjC+//BJAk8ohpzFERUURFxdHhQoVGDZsmFZMo0ePply5cjqx3rlzh3nz5lGuXDnmzp2rtR07Ozt+++03AM3/vmnmzJlaKQ3KlSunWZ27d+9eTfmJEyfYsmULXl5ehIaGYmpqqqlzdnZm8uTJpKWlMW/ePE35vHnzeP78OcHBwQQGBmrKjYyMCA0NxcHBgSNHjhhcYZ9Vd+7cwcbGhho1aujUeXp6Ym1t/dYx7t+/D0Dx4sWztE07OzuALK1yf5P6eJmYmGjK+vfvT5EiRYiJiSElJUVTPn36dJ49e0ZYWBh169bVGqdr164EBQVx5MgRjh8/rrOdihUrMnz48GzHl1GNGjVo0aIFV69eZcGCBZm2jY+PJyIigkKFCjFjxgyta8THx4c+ffqQlpbGL7/8ord/Vq/F7OrcuTOKorBs2TLg9Wr53bt38+mnn1KoUCGD/X744QcAli9frhWXqakp06dPx97ennnz5pGenp7lWCpVqkTlypV1yhs3bky7du24dOkSsbGxWR7vTQ8fPtSkb1Jfo1m1e/duTp8+Tbly5ZgwYYJWGpq2bdsSFBREUlISCxcu1OlrbGzM3LlztVLDdO3aFTs7Oy5evMiAAQPw9fXV1Dk4ONCpUyfg9S8m9BkwYIDWPV2wYEF+/vlnVCoV8+fP16T5yS2F6moAAQAASURBVO3zPieGDBlCQECA5t+WlpYMHTpUZ3+ePn3K77//jomJCTNmzMDc3FxT17RpU9q1a6cz9pMnT3j58iVVqlShaNGiWnUmJiY6zwEhhBBCCCGEEELkjb/9JHrGtAhv/qlTaGR07Ngxnj9/jqenJ66urjr1Xbp0AWD//v1684U3bNhQp8zZ2dlgnYuLC/A633FuYjhw4ADwesLqzbzfxsbGtG7dWmec6OhoXr16RZMmTbQmaNSqVKmCtbW13nzGpqam+Pv765S7ubnp7M+OHTsAaNmypd6c5D4+PgBa21FPfKonyzIyNzfXTCDlZoIUXk/0Pnr0iJ49e+Z4AlJ9DvRdD5m113cs3sbf319rghleT445Ozvz6tUrzYQ+aB93ffQdd7VPP/00R/G9SZ1j/d///rfWBP+b9u3bB0CzZs0oXLiwTr36mtd3vrNzLWZX48aNsbOz06R0WbZsGenp6Zmmcrlz5w4nT57ko48+wt3dXae+QIEC1KxZk0ePHnHhwoVsxfPy5Us2bNjAv/71L7744gtCQkIICQnh9OnTANkeL6OsXr/6qM/LZ599hrGxsU59ZufPycmJ8uXLa5UZGRlRtmxZIOvPzYw6dOigU/bRRx9RpUoVnjx5wqlTp4DcP+9zolGjRjpl+q7V48eP8/z5c2rVqkWZMmV0+gQHB+uUFS9eHEdHRyIjI/nxxx/1fjsiK1xdXfX+mZmZ5Wg8IYQQQgghhBDif53J25t82DLLxzx27FhOnjypVaaedDD00bjChQtjY2PD48ePefLkCTY2Nlr1+j40aGVl9da6jB9AzEkM6j6lS5fW20ffJIz6w5KzZ89m9uzZevsBej/gV7JkSb2TZeqVnBn3R72dkSNHMnLkSIPbybgy+23HQF2e00kitR9++IHTp0+zYMECFixYgK2tLV5eXgQFBdGxY0e9LxfeZGtrC7yePM2Ku3fvAlCsWLFsx+vo6Ki3PLPj/mZe9TfpWxGv73rJiWrVqtGyZUvWr1/P/PnzDebYz835zs61GBoaqpNLv0KFCowaNUrvdk1MTPjss8/4+eefOXHiBOHh4RQrVoymTZvqbQ9oPhR89uzZt76IuHfvnt6Jdn1Onz5NixYt9H4QVu3NfN/ZUbRoUc1HOe/evWvwWtMnN+fP0MdZs/vczEg9Aa8vjhMnTmjiyO3zPif0HVd916o6NkP3oqHyxYsX06FDB0aMGMGIESMoV64cdevWpWPHjnon8IUQQgghhBBCCJF7f/tJ9JzKyipcfW0y65fdlb05icFQH32rKNPS0oDXE5360kTkNrY3t+Pr66tZla+PejI6O9vJThz6UmeULl2ao0ePEhUVRUREBNHR0WzatImNGzcSFhbGgQMHtNJM6KP+RcOJEydIT0/P9AOc6enpmg+KVq1aNcuxq2X3uKtUKrp27Zppu4zph9QKFCiQ7dgMGTt2LBs2bOCHH36gR48embY1tH/q8uzec2/aunWrVuokAD8/P4OT6PA6pcvPP//M119/zalTp+jbt6/OrwEyUl/vJUuWfOukZVZfpCiKQvv27bly5Qp9+vShT58+ODs7U7BgQVQqFaNHj2bixIm5Wi1tZmbGxx9/zH/+8x+OHz+erUl0tZzcr3l5j7+NoeOT0+e9Pm9L0ZPVcXL6i5WAgAAuXrxIRESE5npfvHgxixcvpn379qxcuTJb4wkhhBBCCCGEEOLt/nGT6A4ODgBcvnxZb/3jx495/PgxVlZWWcqX/a5iKFmyJAAJCQl6+1y7dk2nTD1J5u/vz5QpU3IdtyHq7bRt25ZBgwZlqY+DgwPnz5/n8uXLmlQHGalX+6r3G9CkGnj69KlO+7S0NBITE/Vuy8TEhEaNGmkmPBMSEujevTtRUVGEhoYyadKkTGOtVq0a9vb2JCYmsm3btkxXKW/dupUHDx5gb2+vN51QXnJ0dOTSpUvMmDEj0/zd+a1KlSq0atWKP/74QyvvfUZvu+bVq68znu+c2L17d7b71KpVC1dXV7Zu3QqQaSoX+O/1bm9vn+kvYbLj3LlznDt3jpo1a+r91Uh8fHyebKd58+b85z//YdmyZbRo0SLL/d52/vTdr/np6tWrVKpUSadc/XxUx5vT572pqane5wzof9bmhDo29bF7k6FnPUChQoXo2LEjHTt2BCAmJoZ27dqxatUqQkJCMn1GCSGEEEIIIYQQIvv+9jnRs6tGjRpYWFhw+PBhvfmFw8PDgdf5pPNyhWRuY/Dy8gJg7dq1Oqst09PTWbdunc449erVw9jYmIiICM3q2fzQoEEDANavX5/lPuoPCapzUWeUkpLC6tWrtdrBfyfo4uLidPpERUXx6tWrLG27TJkymrQz6lzTmTExMWHgwIHA65Q1+tLfwOu0OOpxBw0apPVxUPULgNTU1CzFmBU5Oe75RZ0bfeLEiXpTcKjzs0dGRvLo0SOdevU1n/F8v0s9evSgWLFiVK1aVXOvGeLo6Ii7uzunTp0yODmrT2bXwMOHDzVj66tT57/PrQEDBmBubs6qVavYtWtXpm3PnTuniUt9XlauXKn3WfKuz5++1dbnzp3jxIkTWFtba355k9PnfcmSJbl//z4PHjzQ6bN9+/Y82YcaNWpQoEABDh06pHdifsWKFVkeq3bt2pr87ll5pgkhhBBCCCGEECJ7/nGT6FZWVvTo0YP09HT69+9PcnKypi4uLo7vv/8eQDNp+qHEEBAQQPny5Tl79ixTp07VGi80NFTvStVSpUoREhLChQsX6NKli97c2AcOHGDz5s252p/atWtTv359du3axZAhQ3RWcKanp7N9+3bNxyUBevbsiYWFBcuXLycyMlKr7ejRo7lx4wYeHh7Url1bU+fn5we8nvjKmDc6Pj7e4PmaOnUqt2/f1ilXrzrOam7wYcOGUaNGDU6fPk1gYCA3btzQqr958yaBgYHExsZSo0YNhg0bplWvXnV6/vz5LG0vK4YOHYqFhQVDhgxh06ZNOvUPHjzgl19+MTjpn5cqVapEmzZtuHHjBhs2bNCpd3Z2pnnz5iQlJfHll19qvfA4ePAgs2fPxtjYmH79+uV7rPqMGjWKe/fu8ddff2Wp/TfffENaWhpt2rTR+8HaS5cusWDBAq2yzK6B8uXLY2RkRFRUlNZk74sXL+jTp4/eydycKF26NNOmTUNRFFq0aMHvv/+u81Lu+fPnTJs2jVq1avH48WPg9a9ZKlWqxOXLl/nuu++0+qxfv54//viDggULEhISkidxvs3PP/+sda6Sk5MZOHAgiqLQo0cPzbcOcvq8Vz9rJkyYoClTFIWJEydqPvKcWwULFqRTp06kpqby5Zdfar182r59O6tWrdLpk5CQwKJFi3j27JlW+cuXLzUvRfLqewdCCCGEEEIIIYT4r39cOheAiRMnEhMTw44dO3B2dsbPz4/k5GSioqJ48eIFgwYNonnz5h9UDMbGxixcuJCGDRsydOhQli5diru7O2fOnOHs2bN8/vnnzJ07V7PaVW3GjBnEx8ezfPlyIiIiqFq1Kg4ODiQmJnLx4kVu3Ljxf+zdeVjN6f/48eeptKosSSmkJLsihhIVsjdEqAxZBjNmYazDzNjmY2kGg8F87Gt2su8qyb7rY9/3XZZQqffvD79zpuOcUkQzX6/HdXWN7vt+3/frvZzmuu73fV4333//PU2aNHmv81m4cCEBAQH88ccfzJs3D3d3d4oUKcKNGzc4c+YM9+7dY/z48ZoVySVKlGDatGmEh4fTvHlzvL29KV68OIcPH+bMmTMULVqUefPmaY3h7OxMhw4dNP3XqVOHpKQk9u7dS9OmTXn58qVOaoRhw4bRt29fqlSpgqurK4qicPz4cc6cOYONjQ39+vXL1vkZGxuzefNmAgMD2bFjB6VKlcLb2xt7e3tu377Nrl27SE1NxcvLizVr1ujk1A4ICMDU1JTx48eTkJBAsWLFUKlU9OvXL9sbT77J1dWVBQsW0L59ewIDA3Fzc6NcuXIoisKVK1c4efIkKSkphIaGYmZm9k5j5MSQIUNYsWJFppP2//3vf/Hx8WHevHnExsZSq1Yt7t27R0xMDGlpaYwdOzbHufvzSvv27Tlx4gQRERG4u7vj4eFBqVKlePLkCVeuXOH06dNUqVJFK0d8YGAgc+fO1WwAqd7EcsaMGdja2tKlSxemT59OlSpV8Pf3x8zMjLi4ONLS0ggPD8+11DE9evQgPT2dH374gQ4dOtCvXz+qV6+OlZUVt2/fZu/evTx//pxixYppNsRUqVQsXLgQPz8/Ro4cyapVq3B3d+fq1avEx8djZGTErFmz3rrJbW5p3749n332Gf7+/lhbW7Nz505u375NhQoVGDZsmFbbd/l7P2DAAJYvX84ff/xBTEwMLi4unDhxgmvXrvH1118zZcqUXDmP0aNHExsby6pVq3B1dcXb25s7d+4QGxtL9+7ddVL7PHz4kE6dOtGzZ088PT1xdHQkKSmJ3bt3c+/ePWrUqEFQUFCuxCaEEEIIIYQQQoi/fXIr0QEsLS2JjY1l2LBh2NjYsGbNGuLi4vD09CQyMpIJEyb8I2OoXbs2u3btomHDhpw7d45169ZhY2NDbGwsJUuWBHQ3MjQ3N2fLli3MmDGDqlWrkpCQwKpVq7hw4QIuLi5ERETorJp+F0WLFmXv3r2MGzcOV1dXDhw4QFRUFNevX8fDw4PJkyfr5Jpu3749O3fupFmzZpw6dYrly5fz4sULvvrqKw4dOkTZsmV1xpk+fToDBw7EysqKzZs3c+XKFQYNGsSiRYv0xjVp0iTatWvH8+fP2bhxI5s2bcLQ0JC+ffty/PhxXFxcsn2OhQsXJi4ujvnz5+Pv78/JkydZvnw5CQkJ+Pv7M2/ePOLi4vRuJlmsWDFWr15NzZo12bVrF7NmzWLmzJncunUr2+PrExQUxLFjx+jevTupqals3LiRmJgYkpOTCQsLY926dZrJ2g+tYsWKBAcHZ1rv4ODAgQMH6NOnD0ZGRqxcuZJDhw5Rr149Nm/ezA8//PBR4swtY8aMYfv27QQGBnL9+nWioqI4cuQI5ubm9OvXT2clelBQEOPHj8fR0ZG1a9cyc+ZMZs6cqamfOnUqY8eOpVSpUmzfvp24uDjq16/PwYMHNZ/v3PL1119z9uxZ+vXrh52dHXFxcSxdupSEhARq167N1KlTOXv2rNZmwJUqVeLw4cN8+eWXPHv2jOXLl3PmzBlatGhBfHx8lvc+t02aNIlRo0Zx5coVVq9ejUqlomfPnsTFxek87+/yt7ZChQrs2LEDX19fzp49y9atW3FxcWHPnj1Ur149187DxsaG+Ph4unbtSnJyMlFRUTx48IDp06fTv39/nfYuLi78/vvv+Pr6cvXqVVauXEl8fDxOTk5MnDiRmJgYnRepQgghhBBCCCGEeH8q5c3v8ot/pcaNG7Np0yb27t3LZ599ltfhCCFErnNycuLKlSs6KWhE7lCnEnJ1dc3jSIQQQgghhBBCiH+WT3Il+r/Vw4cPddKVKIrCpEmT2LRpE6VLl6ZGjRp5FJ0QQgghhBBCCCGEEEL83/NJ5kT/tzp79ixeXl5UrlwZZ2dn0tLSSEhI4OLFi5iZmTF9+nRUKlVehymEEEIIIYQQQgghhBD/Z8hK9H8RZ2dnevToQXJyMtu3b2fjxo0kJycTGhrKvn378PX1zesQhRBCCCGEEEIIIYQQ4v8UyYkuhBBCCMmJLoQQQgghhBBCZELSuQghhBACgOt3kxgyP+aD9B053PeD9CuEEEIIIYQQQnxoks7lH0KlUr01n/nQoUNRqVQMHTr04wQlPhnly5dHpVLh5uaWZbs5c+bofQbDw8NRqVTExMR8kPguX76MSqXSSVkUExODSqUiPDw82335+vpqPm+jRo3KtN2tW7cwMjLStL18+fK7Bf8Bvcv5qymKwtKlS2nVqhXFixfH1NQUS0tLKlSowFdffcX+/ftzP2CRIx/6cyWEEEIIIYQQQojskUl0IT5xBw8e5NSpU8DrzWs/pcnThQsXZlq3aNEi0tLScm2s95nwzm137tzB29ubtm3bsnr1aooVK8bnn39O/fr1SU1N5a+//uKzzz5jxIgReR2qEEIIIYQQQgghRJ6TdC5CfOLmz58PgL29Pbdu3WLBggXUqFEjj6PKnho1anDq1Cmsra1zfKyHhwdHjhzh6NGjuLu769QvWLCAggULUqBAAS5dupQL0f4zPHv2DF9fX06fPk3Tpk2ZMmUKJUqU0Gpz8OBB+vfvz4ULF/IoSiGEEEIIIYQQQoh/DlmJLsQn7NWrVyxevBiAefPmAbB48WJevXqVl2Flm7m5OWXLlsXe3j7Hx7Zv3x7Qvxr91KlTHDlyhODgYIyNjd87zn+SH3/8kdOnT1O/fn1Wr16tM4EO4OnpybZt2+jevXseRCiEEEIIIYQQQgjxzyKT6P9HPH/+nBEjRlCxYkXMzMywtramTp06mgnSNzk5OWlysE+ePFlzXKlSpYiIiEBRFAAOHz5Ms2bNKFSoEJaWlrRo0YIrV67kSgwAR44coXHjxlhbW2NtbU3Dhg05cOBAprm3AVJSUpgwYQLVq1fH0tISCwsLatSowcyZMzVxZ6RSqXByciItLY2IiAjKlCmDiYkJxYsXZ8CAASQnJ+uN7dmzZwwfPpxKlSphbm6OlZUVdevWJSoqKtPz2bNnD59//jlFihTBxMQEJycnvv76a27evKnTVp3jfs6cOXr7yniPMtq3bx8tW7akZMmSmJiYYGdnR40aNfjxxx959uxZprHps3nzZu7evYu3tzf169fHy8uLe/fusXnz5hz1k5mUlBRat26NSqUiODiYlJQUAOLi4vjmm2+oXLkyBQsWxMzMjLJlyzJw4EASExOz3f/7pEjx8vLC2dmZRYsWkZ6erlWnXp2vnmjPzL179+jbty9ubm6YmppSsGBBGjduzM6dO7XahYeH4+fnB8DcuXM1edbffMbXr19P586dKVeuHFZWVlhYWFClShVGjhyZ6XOaEw8fPmTmzJkATJw4EUNDw0zbGhgYUKtWLZ3y+fPnU7t2baysrDA3N6dy5cqMGjWKly9f6rTNmNN727Zt1K1bF0tLS2xtbfnyyy95/PgxAHfv3qV79+4UK1YMU1NTatSooTcPeMa/C2fPnqVVq1YULlwYCwsLvL292bBhQ6bn86E/mxmfxYcPH/LVV19hb2+PiYkJFStWZNasWZnGtmLFCmrUqIGZmRlFixalQ4cOeuNSu3btGj179sTNzQ1zc3MKFSpEhQoV6N69O2fOnMn0OCGEEEIIIYQQQrwbmUT/P+Dp06fUqVOHX375hbt379KsWTO8vb3Zv38/ISEh9OrVK9Nje/fuTd++fSlSpAj169fnwYMHDBgwgKFDhxIfH4+Pjw+XLl3C398fOzs7Vq9eTb169Xjx4sV7x7B79268vb3ZtGkTLi4uNGnShNu3b1O7dm327t2rN96kpCTq169Pr169uHz5MrVr18bX15fz58/TtWtXvvrqq0zPNSwsjOHDh+Po6EhAQABPnz4lIiKCLl266LS9c+cOn332GUOGDOHRo0c0aNCAzz77jEOHDtGyZUtGjx6tc8yCBQvw8fFh7dq1uLm5ERQUhImJCVOnTqVq1aqcPn0609iya/369Xh5ebF27VqcnJwICgrC3d2d+/fvM3r0aO7fv5+j/t6cLFb/V13+Pp49e0bTpk1ZsWIFXbt2ZcmSJZpV3f369WPGjBkYGxvj7+9PvXr1ePLkCWPGjKF27do5fhnwrkJDQ7lx44bWhK2iKERGRlKyZElq166d6bGnT5/Gw8ODsWPHkpaWRpMmTahcuTI7duzAz8+PyMhITdvatWvTsGFDAFxcXOjYsaPmJ2MqmS5durBs2TKsra1p1KgRPj4+XLt2jcGDB9OkSZP3ztEeHR3Nixcv8PDwoFy5cjk+vnv37nTo0IFDhw7h4+ND06ZNuXXrFoMGDcLf31/n74LaqlWraNiwIUlJSQQEBGBiYsKMGTP4/PPPuX//PrVq1WLdunV89tlnuLu7c+DAARo1asSJEyf09nfhwgVq1KjBkSNHCAgIwNPTkz179tCsWTO9E98f47OplpiYSK1atVi1ahXVq1fH29ub06dP06VLF2bMmKHT/s8//6R169YcPnwYLy8vfH192bZtGzVr1uTBgwc67a9fv07VqlWZMmUKpqamNG/eHB8fH/Lly8f06dPZs2dPrp2LEEIIIYQQQggh/j9F/CMAyttux5AhQxRAGTJkiFb5N998owBK/fr1ladPn2rKT506pdja2iqAsn79eq1jSpYsqQCKg4ODkpCQoHWMiYmJYm5urjg5OSnjx4/X1CUnJyv+/v4KoMyaNeu9YkhLS1PKlCmjAEpERIRWX8OHD9dcjzfP9auvvlIA5YsvvtAa5+7du8pnn32mAMq6deu0jlH3Va5cOeXSpUua8osXLyoFCxZUAOX8+fNaxzRu3FgBlP79+yspKSma8gsXLiguLi6KoaGhcuzYMU351atXFTMzM8XIyEhZu3at1nn26tVLAZTq1atrjaG+n7Nnz1b0Ud+jjOrWrauoVCrl4MGDOu337dunPHnyRG9f+jx+/FgxMzNTjI2NlQcPHiiKoigPHjxQ8uXLp5iZmSmPHz/WOWb27Nl670vHjh0VQImOjtb0o74f/fv31+ln/fr1ysOHD7XKXr58qXTr1k0BlGHDhmnVXbp0SQGUunXrapVHR0crgNKxY8dsn3fdunUVQNmzZ49y+vRpBVA6deqkqd+5c6cCKD/++KOiKIri5uamAFrPzqtXr5SKFSsqgDJhwgQlPT1dU3f48GGlcOHCioWFhXLnzp0cxbpq1Srl2bNnWmVPnjxRmjVrpgDK3Llz3+v8Bw8erABKly5dstU+o+XLl2v+Zpw7d05T/vjxY6V27doKoPTr10/rGPVzYWhoqCxfvlzrnNTXr3z58krbtm2VFy9eaOp/+uknBVA6dOig1Z/6+VPXpaamaurWrl2rGBoaKhYWFsrNmzc15R/rs6m+F4DSqlUrrfsYFRWlAEqJEiW0jrl06ZJiYmKimJiYaD47iqIoSUlJSoMGDTT9ZaxTxzZ27FiduC5fvqzztywnzp49q+zYdUQJ+Tn6g/wIIYQQQgghhBD/VrIS/R8mY5qHN3+GDRum0z4pKYmZM2diYGDAlClTyJ8/v6aubNmy/PTTT8Dr1A36jBgxggoVKmgd07RpU54/f06JEiW0VpAbGxvz/fffAxAbG/teMezYsYOzZ89StmxZ+vbtqxXToEGDKFWqlE6sd+/eZcaMGZQqVYrp06drjVOkSBH++9//Amj++6ZJkybh5OSk+b1UqVKalddxcXGa8qNHj7Jx40a8vLwYPXo0+fLl09Q5OztrVh5nXFU6Y8YMXrx4QUhICM2aNdOUGxgYMHr0aIoVK8aBAwcyXWGfXXfv3sXa2ppq1arp1NWoUQNLS8ts97V8+XJevHhB48aNKVSoEACFChWicePGvHjxghUrVrxTjDdu3MDHx4d9+/YxevRoxowZo9OmSZMmFCxYUKvMxMSEP/74AyMjI1avXv1OY+eUm5sbnp6erFixQpOOZMGCBUDWqVzWrl1LQkICISEhfPfdd1qpPTw8PPj5559JSkrS9JVdLVq0wMLCQqvM0tKS8ePHA7z3dVGvbC5SpEiOj1V/focPH07p0qU15VZWVkyZMgWVSsVff/2lSdmTUVhYGK1atdL8bmlpSbdu3YDXz8tff/2Fqamppr5v376oVCqtvzMZ5c+fX/OsqDVr1ozWrVuTlJSktRr9Y3021aysrJg2bZrWffz888+pVKkSV69e5fLly5ryWbNmkZycTIcOHfD19dWUm5ubM2nSJL3pnO7evQuAv7+/Tl3JkiVxcXF5a4znzp3T+6Pv3gkhhBBCCCGEEELSufzjZEzz8OZPlSpVdNofOnSIFy9eUKNGDVxdXXXqv/jiCwDi4+P15gtv0KCBTpmzs3OmdeoJmlu3br1XDLt37wbQ5MvOyNDQkKCgIJ1+YmNjSU1NpVGjRpiYmOjUV6lSBUtLSw4cOKBTly9fPq1JKrUyZcronM/WrVuB1xNf+iax1Ck+Mo6jnoQPCwvTaW9iYkJwcLBWu3dVrVo1EhMT6dKlCwkJCe/Vlzpli/r+qKl/z+kEMLyenFOnr5g2bRoDBgzItK168rRXr1507tyZ8PBwvvrqK4yNjTl37lyOx35X7du358mTJ6xdu5aUlBSWLVuGh4cH5cuXz/QY9TPSokULvfX6npHsOnfuHBMmTODbb7/VXJcRI0Zo6t6Hvr8B2ZGamsrevXtRqVSEhobq1FeqVInKlSvz9OlTjh07plOf1d8ZT09PChQooFVnbW1N4cKFtT6XGQUEBOi8hAEICQkBYNeuXZqyj/XZVPP09NS8lMpI398adZxt2rTRae/m5oaHh4dOufoFWs+ePYmOjv7XbAIshBBCCCGEEEL8mxm9vYn4mDLbyA5eb3b35gSVevO5jCusMypQoADW1tY8fvyYJ0+eYG1trVXv4OCgc4x6BWVWdRk3OXyXGNTHFC9eXO8xJUqU0ClTr+CcOnUqU6dO1XscoDcvs729vd5NFNWr2TOej3qcAQMGZDkJnDH/+Nuugbo8q80Cs2PkyJGcOHGCWbNmMWvWLGxsbPDy8qJFixaEhobqfbmgz7Vr14iNjaVAgQJaq3MBmjdvjrW1NTExMVy/fh1HR8dsx/f111/z6tUrxowZw5dffplpu3HjxvHjjz/+I1a+hoSE0LdvXxYuXIiRkRGPHj3SfHsiM+pnpG3btrRt2zbTdjnJUa8oCn379mX8+PGZTnY/ffo02/3pY2NjA7zeEDUnHjx4QEpKCnZ2dlorxjNycnLi2LFjep/xnP6dUddndv1KliyZaQyg/Tn7WJ9Ntcw+L/r+1qjH1Pf3Tl1++PBhrbLw8HC2bNnC0qVL8ff3x9zcHE9PTxo3bkznzp2xtbV9a4z6XnaC+iVN6luPF0IIIYQQQgghPjUyif5/hL4V09lpk9Vx2enzfWPI7Bh9k4jqTRU9PDyoXLlyrsf25jg+Pj6a1bL6qCckczJOTuJIT0/XKStevDgHDx5kx44drFu3jtjYWNauXcuaNWuIiIhg9+7delfovmnBggUoioKiKNSrVy/T8SMjI+nfv3+2Y27bti2RkZGMHz+ezz//HDc3N502e/fupU+fPlhbWzNt2jR8fX2xs7PTvAAoVqxYpiuQPwRbW1vq16/Pxo0befr0KYaGhpoVzZlRPyONGzfOctKybNmy2Y5jyZIljBs3DkdHR/744w9q1apFkSJFyJcvHykpKZiYmLzzSnI19Samb07MZtc/4e9MVrK6Ph/6s/ku/ajjzckxhoaGLFmyhIEDB7J69Wqio6PZu3cvO3fuZNSoUWzevJmaNWtmuz8hhBBCCCGEEEK8nUyi/8sVK1YMgEuXLumtf/z4MY8fP8bCwiJH+bI/dAz29vYAXL16Ve8x165d0ylTr/D09fVl3Lhx7x13ZtTjtG7dmu+++y5bxxQrVowzZ85w6dIlTdqGjK5cuQL8fd7wOsc8wLNnz3Tap6Wlcfv2bb1jGRkZERAQQEBAAPD6Gnbq1IkdO3ZkmoP8TepULY8fPyY+Pj7TdvPnz8/RJHrXrl3x9vbm66+/xs/Pj5iYGJ3rsWrVKgB+/fVXOnbsqFX34sWLTM/7Q2rfvj2bNm1ix44dNGjQQOs+6aN+Rnr06EFgYGCuxKC+LlOnTtX5dsDFixdzZQx/f39MTU05cuQIp0+fzvYkf+HChTE2Nub27du8ePECMzMznTb6nvEPRT3Wm9R/T9R/k9T//lifzZwqVqwYZ8+e5cqVK3pXh2f29xFev0z08PBg6NChPHnyhGHDhjFu3Di+//579u3blyvxCSGEEEIIIYQQ4jXJif4vV61aNczMzNi/f7/efMnqydLatWvn6orP943By8sLgBUrVuisHk1PT9dMKGbk5+eHoaEh69at06wE/hDq168PQFRUVLaP8fHxAWDhwoU6deo82xnbwd+TdmfPntU5ZseOHaSmZi+tQokSJTRpZ06cOPHW9ocOHeLkyZMULVqUV69eaVakZ/xJT0/H0dGRhIQEvTmus/LVV1/x559/cuvWLfz9/Tl//rxW/aNHjwD9qXyWLVv23qut30XLli1xdHSkcOHChIeHv7X9uzwj6onZzHJYZ3Vdli5dmu1xslKoUCE6d+4MwLfffpvl50hRFM1mm/ny5aNmzZooisKiRYt02qqfE0tLS717N+S2LVu2kJiYqFOujs3b21tTlpefzbdR585Xx5DR2bNnOXr0aLb6sbKyYuTIkahUqmz9DRBCCCGEEEIIIUTOyCT6v5yFhQWdO3cmPT2dnj17kpSUpKk7e/Ysv/76K/B6wuyfFIO/vz+lS5fm1KlTjB8/Xqu/0aNH61156+DgQHh4OOfOneOLL77Qmy959+7dbNiw4b3Op2bNmtSrV4/o6Gh69+6tsxo1PT2dLVu2aG1e2KVLF8zMzFi0aBHr16/Xajto0CBu3LhB9erVtdIs1K1bF3j9kkGdYxterzrO7H6NHz+eO3fu6JRv2rQJyDy3ckbqDUXbtm2rN088vE4vod7sUN0+J3r27MmECRO4ceMG/v7+WvdTvRp45syZWpORJ0+ezDIH/Ydkbm7OtWvXuH//vt6NM9/UunVrypYty5w5cxgzZozOpGpKSgorV67UmtBUr44+c+aM3j7V12XatGlaLxLi4uL47bffcnxOmRk9ejSurq5s27aNFi1a6P3Wx7FjxwgICOCvv/7SlKmfySFDhmjdz6dPn/LNN9+gKArdu3fXvCz4kJ49e8YPP/yg9UJiw4YNLFu2DHNzc61vOHysz+a76NSpE8bGxsybN09rY9MXL17w/fff600bM3/+fL2bCm/atAlFUbL1N0AIIYQQQgghhBA5I5Po/weMGjWKatWqsXXrVpydnWnTpg1NmzalSpUq3L59m++++46mTZv+o2IwNDRk9uzZmJqa0qdPH6pVq0ZoaCju7u4MGzZMsynlmxNyEydOxM/Pj0WLFuHs7EydOnVo164dvr6+ODo64u3tzZYtW977fBYuXEjlypX5448/KFmyJPXq1aNdu3b4+PhgZ2dHw4YNOXjwoKZ9iRIlNJOfzZs3x8fHh9DQUMqXL8/YsWMpWrQo8+bN0xrD2dmZDh068OjRI9zd3QkMDKRevXpUqlSJypUr6908cdiwYRQrVoyqVavStm1b2rRpQ9myZRk/fjw2Njb069cvy/N69eoVixcvBnhr3u927doBr1f3ZpUDOjPfffcd48eP59q1a/j7+2vSZnTq1Ak7OzvWrl2Lm5sbbdu2pUGDBri7u+Pj45PpppH/JEZGRqxatYrixYszcOBASpYsSaNGjWjTpg21atWiaNGitGrVigsXLmiOcXJyonLlyhw8eJAaNWrQqVMnunbtypo1a4DX18vCwoIpU6ZQsWJFQkJCqFOnDnXr1qVHjx65FrulpSWxsbHUqFGDdevWUapUKWrWrEm7du0ICgqiXLlyuLu7s23bNq0UI61bt6Zbt25cv36dihUr0qxZM9q0aYOLiwuxsbHUrFmTYcOG5VqcWQkLC2PlypWUKVOGkJAQfH19adasGWlpaUyYMEFrs9KP9dl8F87OzowZM4aXL1/i5+dH/fr1adeuHaVLlyYhIUEnrQ+8/vZOpUqVKF26NC1btiQ0NBQvLy9atmyJoaEhI0eOzJXYhBBCCCGEEEII8TeZRP8/QD0pNmzYMGxsbFizZg1xcXF4enoSGRnJhAkT/pEx1K5dm127dtGwYUPOnTvHunXrsLGxITY2VjNJVbhwYa1jzM3N2bJlCzNmzKBq1aokJCSwatUqLly4gIuLCxEREfTt2/e9z6do0aLs3buXcePG4erqyoEDB4iKiuL69et4eHgwefJk2rdvr3VM+/bt2blzJ82aNePUqVMsX76cFy9e8NVXX3Ho0CG9+aenT5/OwIEDsbKyYvPmzVy5coVBgwbpTZkBMGnSJNq1a8fz58/ZuHEjmzZtwtDQkL59+3L8+HFcXFyyPK8tW7Zw584dnJyc3rr5YPXq1SldujQ3b95k+/btb7li+vXq1Yvff/+dK1eu4Ofnx9WrVylcuDAHDhwgNDSUlJQU1qxZw40bNxg+fHim5/1PVLZsWY4ePcrQoUOxtbVl165drF+/nnv37lGnTh1mz56tSfuitmLFClq0aMHFixeZN28eM2fO1GzyWaZMGQ4cOEDz5s25f/8+a9as4dmzZ/z3v//N1ZXo8DpdyZ49e1i0aBHNmzfn+vXrrFq1ii1btmBgYMBXX33FwYMHGTx4sNZx//3vf5k3bx4eHh6aTW1tbW35z3/+w44dOzA3N8/VODNTunRp9uzZQ+XKldm8eTP79++nZs2arF27lq5du+q0/xifzXfVq1cvli5diru7O7t27WL79u34+vqyd+9enb9/AD/88AM9e/bE0tKSuLg4Vq1axd27dwkJCeHAgQMEBQXlanxCCCGEEEIIIYQAlZIXCYiFeIvGjRuzadMm9u7dy2effZbX4Qgh/gHmzJlDp06dGDJkCEOHDs3rcP7POXfuHNfvJjF9c+IH6T9yuO8H6VcIIYQQQgghhPjQjPI6APHpevjwIU+fPtVKjaAoCn/++SebNm2idOnS1KhRIw8jFEKIT4ujrQWRw93zOgwhhBBCCCGEEOIfRSbRRZ45e/YsXl5eVK5cGWdnZ9LS0khISODixYuYmZkxffp0VCpVXocphBBCCCGEEEIIIYT4hElOdJFnnJ2d6dGjB8nJyWzfvp2NGzeSnJxMaGgo+/btw9fXN69DFEIIIYQQQgghhBBCfOIkJ7oQQgghOHfuHACurq55HIkQQgghhBBCCPHPIulchBBCCAHA9btJDJkfk+32slmoEEIIIYQQQohPgaRzEUIIIYQQQgghhBBCCCEyIZPoQuShmJgYVCoVTk5OWbbz9fVFpVIxZ86cjxKXyH2XL19GpVLlKNe/+r7HxMRk2kb9DH3oPQSGDh36yTyD6msaHh6e16EIIYQQQgghhBDiH0Am0YUQueZdJorzyqc0KSyEEEIIIYQQQggh3p1MogshhBBCCCGEEEIIIYQQmZBJdCGEEEIIIYQQQgghhBAiEzKJLsS/mJOTEyqVCoAZM2ZQuXJlzMzMsLOzo3v37iQmJuock5SUxJgxY3B3d6dAgQLkz58fFxcXgoOD2bx5s1bbo0eP0r9/f6pVq0aRIkUwMTHB2dmZr7/+mps3b2q1HTp0KKVKlQIgNjYWlUql+VHnln5bupfMUqyoz1NRFCZNmkSVKlUwNzfH3d0dAEVRWLRoEe3ataNMmTJYWFhgaWlJjRo1mDJlCunp6Tr9DRs2DIBOnTppxfpm/vFdu3bRsmVLbG1tMTExwcnJie+++4579+7pPYf79+/TvXt37OzsMDc3x8PDg3nz5ult+6H89ttvqFQqBg8enGkbPz8/VCoVu3bt0iqPjY3F19eX/PnzU7hwYVq2bMnp06cz7edt9wbgwYMH9OvXD1dXV0xNTSlUqBCNGjViy5YtmfZ7//59fvzxRypWrIiFhQUFChTA3d2dwYMH8+DBA027W7duERERQd26dXFwcMDY2Bg7OzuCgoI4cODAO8ecsf/w8HCKFi2KmZkZVatWzfJ+njx5krCwMOzt7TE2NsbBwYEOHTpw5swZnbYZc68/fPiQr776Cnt7e0xMTKhYsSKzZs3SO8apU6f44osvcHFxwdTUlCJFiuDu7k6vXr24detWprEJIYQQQgghhBDi3RjldQBCiPfXv39/JkyYQPXq1WnUqBG7d+9m2rRpnDp1SjOhDZCWlkZAQAC7d+/G0dERX19fjI2NuX79OuvWrcPCwoKGDRtq+h09ejTLly+nYsWKeHt7o1KpOHr0KFOnTiUqKoqDBw9SrFgxANzd3WnVqhUrVqygaNGiNGrUSNNP7dq1c+U8e/TowezZs6lbty7lypUjJSUFgOTkZEJDQylYsCDly5enatWq3L9/nz179tCzZ0/279+vNTHfunVrtm3bxrFjx/D29qZ06dKaOjs7O82/J06cSK9evTAwMKBGjRo4ODiQkJDApEmTWLduHfHx8djb22vaP3jwAG9vb86ePYujoyOBgYHcvn2bTp060aNHj1y5BtnRqVMnfv75Z2bPns2wYcMwMtL+U3/+/HliY2MpW7as1r1ZvXo1rVq1Ii0tDS8vL0qUKMH+/fv57LPPaN68eZZjZnZvbty4QZ06dbh48SIlSpSgRYsW3Lt3j23btrF582bGjRtH7969tfo6efIkAQEB3LhxA3t7exo1akRaWhpnzpxh5MiRNGjQQPMiZvXq1QwYMIDSpUtTqVIlrKysOH/+PKtWrWLdunWsW7eOgICAHMWs9vDhQ2rWrElycjK+vr48evSI6OhoOnbsyKVLlxgyZIhW++3bt9O8eXNevHhB1apV8fX15fTp08yfP59Vq1axYcMGfHx8dOJITEykVq1aPH78mBo1avDs2TN27txJly5dSE9Pp2vXrpq2hw8fpnbt2rx8+ZIaNWpQo0YNnj59ysWLF5kwYQItWrTQeiaFEEIIIYQQQgiRCxQhRJ6Jjo5WAKVkyZJZtqtbt64CKLNnz9YqL1mypAIo9vb2ypEjRzTl9+7dU0qXLq0Ayvbt23XG+/zzz5W0tDStvhITE5WDBw9qlW3fvl25efOmVllaWpoybNgwBVA6deqkVXfp0iUFUOrWrav3PN5WP2TIkCzP08bGRklISNA5LjU1VVmxYoWSnJysVX737l3F09NTAZTY2NhsjaW2Z88excDAQClZsqRy7NgxTXl6eroyfPhwBVBat26tdUy3bt001/fly5ea8g0bNihGRkZZnrs+6vseHR2daRv1PX2z39DQUAVQoqKidI4ZMGCAAihjx47VlD158kSxsbFRACUyMlJTnpqaqnTs2FEB3uneNGvWTAGUL774QklJSdGUx8XFKebm5oqhoaHW9U1NTVXKli2rAEqfPn20jlEURTl8+LBy7do1ze/Hjx/XOl5t06ZNirGxseLi4qKkp6fnKGb1NQWUBg0aKM+ePdPU7d+/X8mfP79iYGCg9Zl79uyZUrRoUQVQpk6dqtXfuHHjFEBxdHTUei4yjtOqVSutcaKiohRAKVGihFZf6nuxYsUKnbhPnjyp83nV5+zZs3p/EhISlB27jighP0dn+0cIIYQQQgghhPgUSDoXIf4PGDFihFYqChsbG7766isAdu7cqSm/e/cuAL6+vhgYaH/8ra2tqVatmlaZv7+/zqpWAwMDfvnlFxwcHFi9enVunsZbDRgwgAoVKuiUGxkZERQUhLGxsVZ5kSJFGDVqFECOYx09ejTp6elMmzaNypUra8pVKhU//fQTHh4erFy5kvv37wPw7Nkz5s+fj5GRERMnTsTExERzTOPGjQkODs7R+BmpU6/o+/Hz89N7TPfu3YHXaX4yevXqFXPnzsXY2JgOHTpoypctW8b9+/dp0KABISEhmnIjIyPGjx9P/vz5s4xR3725ePEi69atw8rKiokTJ5IvXz5NXe3atenRowdpaWlMmTJFU75y5UpOnz5N5cqViYiI0DoGwMPDA0dHR83vlSpV0ro/ag0bNiQ4OJgLFy6QkJCQ7ZgzUqlUTJo0CQsLC01Z9erV6dmzJ+np6UydOlVTvnTpUu7cuYOPj4/Otw569+5NtWrVuH79OqtWrdIZx8rKimnTpmmN8/nnn1OpUiWuXr3K5cuXNeXqz7C/v79OP+XKlZNV6EIIIYQQQgghxAcg6VyE+D9AX7qKMmXKAGjlSHZ3d8fAwIDffvsNOzs7mjZtiqWlZZZ9P3jwgDVr1pCQkEBiYiJpaWkApKam8vDhQx4+fEihQoVy8WwyFxgYmGX90aNH2bJlC1euXOH58+coisLTp08BOHfuXLbHSU9PZ/v27VhaWlKvXj2depVKhbe3N0eOHOHQoUM0bNiQw4cP8+LFC7y9vSlRooTOMSEhISxatCjbMWTUsGFDrTQzGd2+fVsnlz1AnTp1KF++PBs3buTGjRs4ODgAsHbtWm7fvk3btm2xsbHRtFfnRm/Tpo1OXwULFiQgIICVK1dmGqO+e6Pus0mTJhQoUECn/osvvmDcuHHExcVpyrZt2wbAl19+qfOiJzPJycls2rSJ/fv3c+/ePU1alhMnTgCv732lSpWyFXNGHh4euLm56ZSHhIQwZswYrXzy6nMICwvT21f79u05dOgQcXFxtGvXTqvO09NT72eoTJkynDhxglu3buHk5ARAtWrV2LhxIx06dOCnn37C09Mz29dJzdXVVW/5689Iao76EkIIIYQQQgghPgUyiS5EHlLnKn8bRVGybJ9xZa6aeuVwcnKypqxMmTL89ttvDBw4kJCQEAwNDalYsSL169enU6dOOqtyFy1aRLdu3Xj27FmmsT19+vSjTaLrm5wGSElJITw8PMtJavVkenY8ePBAc85v5hN/k3olunqj1cxizKw8OwYOHJjpZqwxMTF6J9EBunXrRq9evZg1axY///wzANOnTwdeT1Jn9L7x66tX96meAH6TujzjJrXXrl0DwMXFJcvx1E6cOEFgYKDWau03ZXbv33ZOJUuW1FuuL+53OVc1fZ9f0P8Z7tevH7t27WLt2rWsXbsWa2trPvvsM5o1a0Z4ePhbX4oJIYQQQgghhBAi5ySdixB5yMzMDICkpKQs2z1//hxAK91DRtmdjAf44YcfuHDhAhMnTqRJkyZcuXKFsWPHUrlyZSZPnqxpd+XKFcLDw0lOTuaPP/7g3LlzmtXdiqJQq1Yt4O8J/tyQnp6eZb2pqane8nHjxrFo0SIqVqzIxo0buXPnDikpKSiKwpkzZ3Icp3q1vaWlJR07dszyRz3R+rYXHXmhY8eOmJmZMWvWLBRF4dq1a2zevBlnZ2eddCDvG39m9yarPtXl+uqzE4eiKLRp04bLly/To0cPjh49ypMnT0hPT0dRFH788UdNu5zG/K7eFve7nqualZUVO3bsIC4ujv79++Pm5sb27dv57rvvcHNz48KFCzmOWQghhBBCCCGEEFmTlehC5KHixYsDr1czP3nyBCsrK73tLl68CGS+YvVdxv3222/59ttvefXqFYsXL6ZTp0788MMPhIWFUaBAATZs2EBKSgp9+vTh+++/zzSmnFDnLM9sZbt6FXJOqfNMqyfSM3qXOG1sbDAxMSFfvnzMmTMnW8cUK1YMeP3yQZ+rV6/mOI73VaBAAdq2bcucOXPYunUre/bsIT09na5du+pM3H6I+NV9Xrp0SW+9evV4xjze6s/E+fPn39r/6dOnOX36NJ6enlr5ydXe5d5nlNm1UJerzy/jvzM7V/UxuZGzXKVSUbt2bWrXrg3AvXv3+P7771m0aBGDBg1iyZIl7z2GEEIIIYQQQggh/iYr0YXIQ/b29pQuXRqAdevW6W0THx/Pw4cPyZ8/Px4eHrkeg5GREe3bt6d69eqkpKRw9uxZAB49egT8PamZ0c6dO7lz545OuXqS/NWrV3rHsrGxIV++fFy6dEmnTUpKCrGxse90DlnFunTpUr3HZBWrkZERvr6+PHz4UGtj1qxUq1YNU1NT9u3bp/dlwOLFi7PVT25TbzD63//+l1mzZmFkZER4eLhOO/WE7LJly3TqEhMT2bJlS47HVve5fv16EhMTdeoXLFgAgI+Pj6asfv36wOsNUd/27QH1fdf3cunRo0ds3bo1xzFndPToUc3nISN12iBvb29NmfocFi5cqLcvdXnGc80tRYoUYejQocDfeeCFEEIIIYQQQgiRe2QSXYg8pl7lPWDAAE6fPq1Vd+vWLb7++msAevTogYmJyXuNFR0dzbZt23TSply5coVTp06hUqk0E5LqjUkXLFiglW7mxo0b9OjRQ2//6knyCxcuaFKiZGRsbEzNmjV5+PChVuqY1NRUevfunekq3rdRx/rXX39plS9fvpx58+bpPUa9clid7uVNgwYNwsDAgI4dO2ptIKl28+ZNrXPInz8/YWFhvHr1iu+//14rj/WWLVsyncz/0GrWrEmVKlVYuXIlV69epVmzZnpXQwcHB1OoUCGdWNPS0ujTp0+WefEz4+zsTNOmTXn69Cnff/89qal/b1q5Z88epk6diqGhoeYZBwgKCqJMmTIcO3aMgQMH6rzkOHr0KNevXwegdOnSGBgYsGPHDq2NY1++fEmPHj14+PBhjmPOKD09ne+++06TTgng0KFDTJ48GQMDA80LCni9IWvRokWJi4tj2rRpWv1MnDiRAwcO4OjoSMuWLd8rpr/++kvv52Tjxo3A++XeF0IIIYQQQgghhH4yiS5EHuvZsyft2rXj+vXrVK5cGT8/P9q3b0/Dhg1xcXHh+PHj1K1blxEjRrz3WMeOHaNBgwbY2dnRuHFjzThly5YlMTGR77//XjO5HBgYSIUKFTh48CClS5emdevWNGvWjDJlylCwYEG8vLx0+jc2NqZRo0bcvn2bKlWq0KFDB7p27crs2bM1bX755RcMDAzo1asXXl5eBAUFUbp0aZYuXUrHjh3f6bz69++PoaEhAwcOxNPTk9DQUKpXr05wcDC9e/fWe0xAQACmpqaMHz+exo0b06VLF7p27aqZVK9Tpw4TJkzg2rVr+Pj4UKVKFc01qFSpEiVKlGDw4MFafY4ePZrSpUuzatUqXF1dCQkJwd/fn8aNG+ts5PkxZZzszSwOKysrpk2bhoGBAW3btqV27dqEhobi5ubG8uXLCQsLe6ex//vf/1KqVCnmzZunuSb169fHx8eHpKQkIiIiqFy5sqa9kZERK1aswM7OjoiICEqWLElwcDAtW7akfPnyeHh4aFK92Nra0qVLF548eUKVKlVo1qwZwcHBODk5sWPHDr0r7nOiWbNmnDp1ChcXF9q2bUujRo2oVasWT58+ZdCgQVSrVk3T1sLCgoULF2JmZkb37t01z2HVqlX5/vvvsbCwIDIy8r1fhP311184OztToUIFWrduTbt27fDw8KBXr16YmZkxZMiQ9+pfCCGEEEIIIYQQumQSXYg8plKpiIyMZNGiRfj5+ZGQkMCSJUs4cOCAJtfz1q1bc2UTxGbNmvHTTz9pVvouW7aM//3vf/j4+LBq1SrGjx+vaWtsbExcXBxfffUVpqamrFu3jlOnTvHtt9+ydetW8uXLp3eMGTNm8MUXX/DgwQMiIyOZOXOmVpqW+vXrs2bNGqpXr87hw4eJjY2lZs2aHDhwACcnp3c6rzp16rBr1y78/f25ePEi69atw9jYmBUrVtCzZ0+9xxQrVozVq1dTs2ZNdu3axaxZs5g5cya3bt3StPnmm2/Yt28fYWFhPHr0iDVr1rBnzx4MDAzo0aMHq1ev1urTxsaG+Ph4unbtSnJyMlFRUTx48IDp06fTv3//dzq33FCvXj3gddqThg0bZtquVatWbN26FR8fH44cOcLGjRspX748e/bs0aQdyikHBwcOHDhAnz59MDIyYuXKlRw6dIh69eqxefNmfvjhB51jKlasyNGjR+nTpw8WFhasXbuW2NhYTExM+Omnn7Qm3adOncrYsWMpVaoU27dvJy4ujvr163Pw4EHNpq/vqnDhwuzZs4f69esTHR1NTEwM5cuXZ/bs2XpfatWrV48DBw4QEhLC9evXWb58Obdv36Z9+/YcOnQoV1K5jBgxgs6dO6NSqdi+fTtr167l+fPndOvWjePHj2s2/BVCCCGEEEIIIUTuUSlvSzorhBDiX23kyJEMHjyYIUOGaHJnC/EmdUocV1fXPI5ECCGEEEIIIYT4Z5FJdCGE+D/syZMnlC1blgcPHnDp0iVNuh4h3iST6EIIIYQQQgghhH5GeR2AEEKI3Dd79mxiY2PZuXMnt27donfv3jKBLoQQQgghhBBCCPEOJCe6EEL8HxQbG8vcuXN59uwZ3377LaNGjcrrkIQQQgghhBBCCCH+lSSdixBCCCEknYsQQgghhBBCCJEJSecihBBCCACu301iyPyYbLePHO77oUIRQgghhBBCCCH+MSSdixBCiPeiUqm0fgwMDChQoAA+Pj7MmDGDf+sXnlQqFU5OTlplly9fRqVS4evrmycxCSGEEEIIIYQQ4uOTlehCCCFyRceOHQFIS0vjwoULxMfHs2vXLrZv386iRYvyODohhBBCCCGEEEKIdyOT6EIIIXLFnDlztH7funUrTZo0YfHixYSFhdGsWbO8CSwXOTg4cOrUKczNzfM6FCGEEEIIIYQQQnwkks5FCCHEB9GgQQO++OILAKKiovI2mFySL18+ypYtS4kSJfI6FCGEEEIIIYQQQnwkMokuhBDig/Hw8ADg2rVrADRt2hSVSsXWrVv1tk9KSsLKygpra2uSkpIAiImJQaVSER4ezu3bt+natSuOjo4YGRnxxx9/AH/nL09JSWH48OGULVsWExMTWrRooen72rVrdO/enZIlS2JiYoKtrS1BQUEcOHAg2+eTWU70OXPmoFKpGDp0KFevXiU0NJQiRYpgZmaGp6cna9euzbTPEydOEBYWhoODAyYmJhQrVoxOnTpx+fJlnbaKorB48WLq1KmDnZ0dpqamFC9enPr16zN58uRsn4cQQgghhBBCCCGyTybRhRBCfDBPnz4FwMTEBIAePXoAMH36dL3tFy9ezNOnTwkNDcXCwkKr7t69e1SvXp3169dTq1YtGjdurJVWJT09nRYtWhAREYGLiwuff/459vb2wOuJ6qpVqzJt2jTMzc0JCgrC1dWVVatW4eXlxbJly3LlfC9fvkz16tWJj4+ndu3aeHh4cOjQIVq0aMGWLVt02q9YsQJPT08iIyOxt7cnMDAQOzs75syZg6enJ//73/+02v/444+EhIRw9OhRqlatSlBQEKVLl+bYsWP89ttvuXIOQgghhBBCCCGE0CY50YUQQnwQiqKwbt06ACpXrgxAkyZNKF68OKtXr+bevXsUKVJE6xj15PqXX36p09+GDRto2bIlkZGRmJqa6tRfu3YNExMTzpw5g4ODg1YcYWFh3L9/nx9//JH//Oc/qFQqAJYvX07btm3p0qULderUoWjRou91znPnzuXbb79l3LhxGBm9/l/shAkT6NWrF7/++isBAQGatpcuXaJDhw6YmZmxdetW6tSpo6mbN28eHTt2pFOnTuzfvx+Aly9f8scff+Dk5MShQ4coVKiQpv2rV6/YvXv3e8UuhBBCCCGEEEII/WQluhBCiFyVlpbGuXPn6Ny5M3v27MHExIROnToBYGhoSNeuXUlJSWHevHlaxyUkJLBv3z48PDyoWrWqTr8mJiZMmjRJ7wS62qhRo7Qm0OF1OpgTJ05QqlQpRowYoZlAB2jdujUtWrTg6dOnzJ49+31OGwBnZ2fGjh2rmUAH6NmzJwULFmTv3r2kpKRoyidMmMDz58+JiIjQmkAH6NChAy1atODAgQMcPnwYgCdPnpCcnEyVKlW0JtABjIyMdPrIzLlz5/T+ZIxNCCGEEEIIIYQQf5NJdCGEELlCpVKhUqkwMjKiTJkyzJkzB0tLSxYtWoSLi4umXdeuXTEyMmLGjBlax6tXoXfr1k1v/1WrVtWZIH9z/ObNm+uUx8XFAdC2bVsMDQ116tWbn6rbvQ9fX1/y5cunVWZkZISzszOpqak8ePBAU67OC//555/r7at27doAmpzttra2ODo6sn79en777Tdu3rz53vEKIYQQQgghhBDi7SSdixBCiFzRsWNHAAwMDLCysqJSpUoEBQVRsGBBrXbFihWjWbNmREVFERcXh4+PD8nJySxYsABzc3NCQ0P19l+iRIksx7e1tdXkXs9IPdns5OSk9zh1eW5MSjs6Ouotz58/PwDJycmaMvXGoXZ2dln2ef/+fc2/586dS7t27ejfvz/9+/enVKlS1KlTh9DQUK1UMVlxdXXVW37u3DkgNVt9CCGEEEIIIYQQnxKZRBdCCJEr5syZk+22PXr0ICoqihkzZuDj48OKFSt4+PAhnTp1wsrKSu8xWaVxyU59xjQu71KfHTnpIy0tDZVKRYcOHbJsV6FCBc2//f39OX/+POvWrWPTpk3ExsYyd+5c5s6dS5s2bViyZMk7xy6EEEIIIYQQQgj9ZBJdCCHERxcQEICzszPLli1jwoQJWW4o+r6KFSsGvN7IU58rV64AYG9vn+tjZ8XR0ZELFy4wceLETF8c6GNlZUVoaKhmxf7evXsJDg5m6dKlhIeH07hx4w8VshBCCCGEEEII8UmSnOhCCCE+OpVKxZdffsmLFy8YNmwYsbGxVKhQgVq1auX6WD4+PgAsWbKEtLQ0nfoFCxZotftY6tevD0BUVNR79VOzZk1NXvcTJ068b1hCCCGEEEIIIYR4g0yiCyGEyBOdO3fG2NiYP/74A0VRPsgqdHi92WelSpW4dOkSv/zyC4qiaOqioqJYuXIl+fPnJzw8/IOMn5k+ffpgZmZG7969Wbt2rU79w4cPmTJlCi9evADg6tWrzJkzh+fPn2u1S05OJjo6Gnh73nghhBBCCCGEEELknKRzEUIIkSdsbW1p0aIFS5cuxcTERLOaOrepVCoWLlyIn58fI0eOZNWqVbi7u3P16lXi4+MxMjJi1qxZb93gM7e5urqyYMEC2rdvT2BgIG5ubpQrVw5FUbhy5QonT54kJSWF0NBQzMzMNDnje/bsiaenJ46OjiQlJbF7927u3btHjRo1CAoK+qjnIIQQQgghhBBCfApkJboQQog8U69ePQBatWpFoUKFPtg4lSpV4vDhw3z55Zc8e/aM5cuXc+bMGVq0aEF8fDzBwcEfbOysBAUFcezYMbp3705qaiobN24kJiaG5ORkwsLCWLduHdbW1gC4uLjw+++/4+vry9WrV1m5ciXx8fE4OTkxceJEYmJiMDY2zpPzEEIIIYQQQggh/i9TKRm/1y6EEEJ8RAEBAWzdupXo6Gh8fX3zOpxP2rlz57h+N4npmxOzfUzkcN8PFo8QQgghhBBCCPFPIZPoQggh8sT+/fupWbMm5cuXJyEhIa/D+eSdO3cOeJ1mRgghhBBCCCGEEH+TnOhCCCE+qoEDB3L16lXWr1+PoiiMHDkyr0MSQgghhBBCCCGEyJRMogshhPioFi9ezLVr13ByciIiIoLAwMC8DkkIIYQQQgghhBAiUzKJLoQQ4qO6fPlyXocghBBCCCGEEEIIkW0GeR2AEEIIIYQQQgghhBBCCPFPJSvRhRBCCAHA9btJDJkfk+32kcN9P1QoQgghhBBCCCHEP4asRBfZkpSUxPjx4/Hz86No0aIYGxtTsGBBatWqxS+//MLVq1c/eAwxMTGoVCrCw8M/+Fg5MWfOHFQqFUOHDv2g47zr+atUKpycnHKlr3f11VdfoVKpMDQ05Pr167nS5+XLl1GpVPj6+uboOCcnJ1QqVa7E8E/0Ls+jSqXS+smXLx82NjZUqlSJ8PBwVqxYwatXrz5c0HqEh4ejUqmIiYn5qONmx9ChQ1GpVMyZMyevQxFCCCGEEEIIIcRHIJPo4q327t2Lq6srP/zwA/v376dixYq0bt0aLy8vLly4wIgRIyhTpgzbtm3L61DFP1BKSgpLly4FID09ncjIyDyOSGSmY8eOdOzYkZCQELy9vXn16hXz5s2jdevWlCtXjv379+d1iB+Fr68vKpVKcrcLIYQQQgghhBACkHQu4i2OHz+Ov78/L168YMCAAfz8889YWFho6tPT04mKiqJ///65tsL436Zly5bUrFkTGxubDzpOjRo1OHXqFNbW1v+ovt5m/fr1PHz4EHt7e27dusWCBQvo37//e/fr4ODAqVOnMDc3z4UoBaB3ZfWFCxcYNGgQS5cuxc/Pj/j4eNzd3T94LKNGjWLgwIGUKFHig4+VU9988w3t2rXD3t4+r0MRQgghhBBCCCHERyCT6CJTiqLQvn17Xrx4wdChQxkyZIhOGwMDA4KCgqhXrx7Xrl3LgyjznrW19UeZjDY3N6ds2bL/uL7eZv78+QD85z//Yfjw4Zw4cYJjx45RpUqV9+o3X758H+0cPmUuLi4sWbIES0tLZs6cSefOnTl8+PAHH9fe3v4fO0ltY2PzwV+aCSGEEEIIIYQQ4p9D0rmITG3evJkTJ07g6OjI4MGDs2xrbW1NxYoVNb/funWLiIgI6tati4ODA8bGxtjZ2REUFMSBAwf09qHOVa0oCpMmTaJKlSqYm5vrXfV669YtwsPDKVq0KGZmZlStWpV58+ZlGt/JkycJCwvD3t4eY2NjHBwc6NChA2fOnNFqN2XKFFQqFd7e3qSlpWnVvXjxggoVKqBSqVi2bJmmPLMc1BlzOm/bto26detiaWmJra0tX375JY8fPwbg7t27dO/enWLFimFqakqNGjX05oHOKo95UlISAwYMoESJEpiamlK2bFnGjRuHoih6r8fbcqJv3LiRZs2aYWtri4mJCSVKlKBFixasX79eb/vMPHr0iPXr12NmZkbr1q0JDQ0FYMGCBZkeoygKCxcupF69ehQuXBhTU1OcnZ0JDQ0lPj5e0y6rnOivXr1i1KhRuLq6ao7/+eefSUlJyXTcffv20bJlS0qWLImJiQl2dnbUqFGDH3/8kWfPnmXrfM+fP8/QoUOpVasWdnZ2GBsb4+joSIcOHTh79qzeY9Q569PS0oiIiKBMmTKYmJhQvHhxBgwYQHJyst7jjh8/TrNmzTQvcRo0aMCePXuyFee7GDt2LBYWFhw5coRdu3bp1F++fJnu3bvj5OSEiYkJRYoUoXXr1hw/flynbcbPzNmzZ2nXrh1FixbFwMCAqKgoQDcnempqquZ5SExM1Bvj/v37NZ9ftcTERCZNmkTDhg0197Zw4cI0atSIrVu36pyDSqUiNjYWgFKlSmnlilfTlxO9UqVKqFQqTp8+rTe227dvY2RkhKOjI+np6Vp1u3btomXLlprPm5OTE9999x337t3T6Sc1NZX//ve/1KhRAxsbG8zNzXFycqJZs2YsXrxY79hCCCGEEEIIIYR4PzKJLjKlnjANDg7GyChnX1pYvXo1AwYM4ObNm1SqVIkWLVpQrFgxVq1ahbe3N1u2bMn02B49etCnTx9sbW0JDAzE2dlZq/7hw4fUrFmTTZs24evri4+PDydOnKBjx44MGzZMp7/t27fj6elJZGQkxYoVo1WrVtja2jJ//nw8PT2Ji4vTtP36669p1qwZu3fv5j//+Y9WP/369ePkyZN07NiR4ODgbF+LVatW0bBhQ5KSkggICMDExIQZM2bw+eefc//+fWrVqsW6dev47LPPcHd358CBAzRq1IgTJ05kq//k5GQCAgKIiIjgxYsXNG/eHCcnJwYOHMg333yT7TjV+vTpQ5MmTdi0aRNubm4EBQVRqlQpoqOj+e2333LU15IlS0hJSSEwMBBLS0u++OILACIjI3UmEgHS0tJo06YN7du3Jz4+Hg8PD1q0aIGdnR2rVq1i+vTp2Ro3JCSEQYMGcfv2bRo1akSlSpUYN24cQUFBel8srF+/Hi8vL9auXYuTkxNBQUG4u7tz//59Ro8ezf3797M17owZMxg2bBhPnjzB09OTwMBArKysmD9/PtWrV9c7oawWFhbG8OHDcXR0JCAggKdPnxIREUGXLl102u7bt49atWqxfv16nJ2dadKkCbdv36Zu3brs3r07W7HmlLW1NY0bNwYgOjpaq27Xrl1UqVKFadOmkT9/fgIDA3F1dWXlypXUrFlTp73amTNnqF69Ovv378fPz48GDRqQL18+vW3z5ctHcHAwycnJrFixQm8bdb79sLAwTdnevXv57rvvOHXqFK6urrRs2RI3Nze2bNlCw4YNmTVrlqZt/vz56dixI0WLFgWgVatWmjzxHTt2zPL6qMdcuHCh3vrFixeTlpZGaGgoBgZ//6934sSJ1KlTh7Vr11K6dGkCAwMxMzNj0qRJfPbZZ9y6dUurny+++IIePXpw6dIlvLy8CAwMpHjx4sTFxfHXX39lGaMQQgghhBBCCCHekSJEJry9vRVAmT9/fo6PPX78uHLs2DGd8k2bNinGxsaKi4uLkp6erlVXsmRJBVBsbGyUhIQEnWOjo6MVQAGUBg0aKM+ePdPU7d+/X8mfP79iYGCgHDlyRFP+7NkzpWjRogqgTJ06Vau/cePGKYDi6OiovHz5UlN+584dxdbWVjEyMlL27dunKIqibNiwQQGUUqVKKU+ePNHqZ/bs2QqgDBkyRKu8Y8eOCqAYGhoqy5cv15Q/efJEqVixogIo5cuXV9q2bau8ePFCU//TTz8pgNKhQwe959+xY0et8pEjRyqAUqNGDSUxMVFTfujQIcXKykoBlJIlS2arr/nz52uuyZv379mzZ8r27duVnPDy8lIAZe3atZqyqlWrKoCydetWnfYjRoxQAKVSpUrK5cuXteoePHig7Nq1S/P7pUuXFECpW7euVrvIyEgFUJydnZXr169ryi9evKg4OjpqnqGM6tatq6hUKuXgwYM6Me3bt0/nnmdmz549yvnz53XKZ82apQCKn5+fTp06nnLlyimXLl3SirdgwYIKoNVnWlqaUrZsWQVQRo0apdWX+tnR9zxmRd810efXX39VACUkJERT9vjxY8XOzk7Jly+fsmzZMq32W7duVYyNjRUHBwclOTlZU67+zADKN998o7x69UpnLPXnJzo6WlO2c+dOBVD8/f112qelpSn29vaKkZGRcu/ePU35xYsXlfj4eJ32hw8fVgoUKKBYWVkpT58+1aqrW7euAmjdj4yGDBmiAMrs2bM1ZVeuXFFUKpXi7Oys95jq1asrgHL06FFN2Z49exQDAwOlZMmSWp+39PR0Zfjw4QqgtG7dWlOufuarV6+u9TdDURTl+fPnyu7du/WOnV1nz55Vduw6ooT8HJ3tHyGEEEIIIYQQ4lMgK9FFph48eABAkSJFcnxspUqVqFy5sk55w4YNCQ4O5sKFCyQkJOg9dsCAAVSoUCHTvlUqFZMmTdLa4LR69er07NmT9PR0pk6dqilfunQpd+7cwcfHhx49emj107t3b6pVq8b169dZtWqVptzW1pZZs2bx6tUr2rdvz6VLl+jUqROGhoYsWLAAS0vLbF8HeL1CtVWrVprfLS0t6datGwA3btzgr7/+wtTUVFPft29frZQSb6M+3/Hjx2vlZq9atSo9e/bMUawjR44E4I8//tC5fxYWFvj7+2e7r4sXL7J7925sbGxo1KiRprx9+/bA37nS1VJSUhg7diwqlYpZs2ZRsmRJrfpChQpppenIjPp6jBgxAgcHB015qVKl+Pnnn/Uec/fuXaytralWrZpOXY0aNbJ9z2vWrImLi4tOeadOnfD29iYmJkaTxudNkyZNwsnJSSte9bXK+G2JmJgYTp8+TZkyZRgwYIBWH0OGDPmgG3Gq84A/evRIUzZr1ixu375N3759ad26tVb7+vXr8/XXX3Pjxg3WrVun01+RIkUYM2YMhoaG2Rq/du3alCxZkpiYGG7evKlVt2PHDm7dukXDhg218pWXKlUKLy8vnb48PDzo2bMnT548yXSlfE6UKFGC2rVrc/HiRfbu3atVd/78eQ4cOED58uW19gIYPXo06enpTJs2TevzplKp+Omnn/Dw8GDlypWab0LcvXsXAC8vL62/GQBmZmbUqlUrW7GeO3dO709W6Y6EEEIIIYQQQohPmUyii0wpmeTTzq7k5GRWr17N4MGD6datG+Hh4YSHh2vSlJw7d07vcYGBgVn26+HhgZubm055SEgIgFa+ZvXkY8b0Dhnpm6QEaNq0KV9//TXnzp3D3d2dO3fuMGjQIL2TcW/ToEEDnTJ1ihpPT08KFCigVWdtbU3hwoV10jjoc/XqVa5du4aDg4Pe2NTXJDtu3rzJqVOnKFy4sNak/7tST5K3a9dOKx1QSEgIhoaGrFy5kufPn2vKDx48SGJiIlWrVsXT0/OdxkxNTWXfvn0YGBjoTOiqx9anWrVqJCYm0qVLl0xf7mTXs2fPWLRoEQMGDODLL7/UPPe3bt1CURQuXLigc0y+fPn05nYvU6YMgNazoH6+g4ODtfJ0AxgZGek979yi/puQcVx1XvEWLVroPaZ27doAevdCqF+/Pubm5tkeX6VSERISQnp6uk7+b32pXNTS0tLYsmULQ4cOpUePHpp7op48z+xvUU6px1bH8mZs6r83AOnp6Wzfvh1LS0vq1aun05c6t3t6ejqHDh0CoGzZslhYWDB79mymT5+uedEphBBCCCGEEEKIDytnia7FJ8XGxoYzZ87o3dzubU6cOEFgYCCXL1/OtM3Tp0/1lr9tJe2bK5TV1Kt4M65QVf874wrftx2j9vvvv7N69Wpu3LiBh4cHv/zyS5ZxZSbjamg19Sp6fXXq+uzk4VbHndk1y8mq5GvXrgHoXUn9LtSbh2acOASws7Ojfv36bN68maioKM1mo7kx/oMHD0hJSdFsIPsmS0tLChQooLMx5ciRIzlx4gSzZs1i1qxZ2NjY4OXlRYsWLQgNDcXExCRb4+/YsYN27dpl+ZnR99zb29vrXY2dP39+AK3NRXPznueU+pksVKiQpkz9Gf/ss8+ydWxG7xJrWFgYo0ePZuHChfzwww/A6+uzcuVKLCws+Pzzz7XaX79+nWbNmnHs2LFM+8zsb1FOBQcH891337FkyRLGjx+vuaeLFi3SvABQe/DggWbD2rftOaG+dlZWVkyfPp1u3brRrVs3unfvjpubG35+fnTo0IGaNWtmK05XV1e95a9fJqRmqw8hhBBCCCGEEOJTIpPoIlPu7u7Ex8dz+PBhnYnQrCiKQps2bbh8+TI9evSgR48eODs7kz9/flQqFYMGDWLUqFGZrnR/M01BbnhzxW526uPi4jQTllevXuXevXvY29vn6thvi+tt9K0Mft/+3zcmgD179nD+/Hng9Ualb1JPmC9YsEAziZ4b47/temSmePHiHDx4kB07drBu3TpiY2NZu3Yta9asISIigt27d1OwYMEs+3j27Blt2rThwYMH/Pzzz4SEhFCyZEnMzMxQqVSEhoayaNEivc99TuJ913PMDUePHgWgfPnymrK0tDTg9QRyVqvK9U2yv8tnvWLFilSuXJnDhw9z+vRpypYty/r163n8+DHt27fXiaFr164cO3aMoKAgBgwYgJubG5aWlhgYGDBt2jS6d+/+3t+6UStUqBCNGjVizZo1bNu2jYYNG2rirF27ttbLPPV1s7S0JCgoKMt+M744DAkJoX79+qxevZotW7YQGxvL1KlTmTp1Kv369SMiIiJXzkUIIYQQQgghhBB/k0l0kammTZsyefJkli1bRkRExFtXS6qdPn2a06dP4+npqZWfXO3ixYvvFdeVK1eyLC9WrJimTP3vS5cuZXnMm5PjDx48IDw8HAMDA9q0acOiRYsIDw9n06ZNeTJ5mRn1+b3tmmRH8eLFATST3+8jY77z+Pj4TNtt2bKFO3fuULRo0VwZ38bGBmNjY27fvk1KSorOavSnT5/qrEJXMzIyIiAggICAAOD1i5NOnTqxY8cORo8ezZgxY7IcOy4ujgcPHtCqVSuGDx+uU/++z73a2+751atXc2WcNz1+/JhNmzYB4Ofnpyl3dHTkzJkz/PTTT3r3QfgQwsLCOH78OJGRkQwfPjzTVC5JSUls3bqVokWLsnTpUp3V/rl1T96Mbc2aNSxcuJCGDRtmGpuNjQ0mJibky5ePOXPm5GiMIkWK0LVrV7p27YqiKGzevJm2bdvy22+/ER4ervWSQwghhBBCCCGEEO9PcqKLTDVq1IgKFSpw/fp1/vOf/2TZ9smTJ/zvf/8D/t500NHRUafdo0ePNDmU39XRo0c5e/asTvmiRYsAtDaf9PHxAWDhwoV6+1KXq9upffnll9y6dYuBAweyYMECfHx82LJlCxMnTnyv2HNbyZIlcXR05MaNG+zZs0en/s280VkpVqwY5cqV48GDB6xcufKdY0pNTWXp0qUAJCQkoCiK3p/Q0FDS0tI0Marzwx8+fFiTAzqn8uXLR40aNUhPT2fFihU69Tm5HiVKlNBs3KnO458V9XOvfhmQ0fnz5zl8+HC2x86KOsf4ihUrdFZQv3r1Su9554Y+ffqQlJRE9erVtTawrF+/PgBRUVEfZFx9QkNDUalUREZG8uTJE9avX4+tra0mFrXHjx+Tnp6uN13Oq1evtDYUzkj98uXVq1c5jq158+ZYWloSFRVFUlISS5YsIV++fAQHB2u1MzIywtfXl4cPH7Jz584cj6OmUqlo1KgRTZs2BXjvnP5CCCGEEEIIIYTQJZPoIlMqlYoFCxZgamrK0KFD+fHHH0lKStJqoygKa9aswdPTU7NxYOnSpTEwMGDHjh1aG/a9fPmSHj168PDhw/eKKz09ne+++05rU8pDhw4xefJkDAwM6N69u6a8TZs2FC1alLi4OKZNm6bVz8SJEzlw4ACOjo60bNlSUz5jxgxWrVpFtWrVGDJkCAYGBsybNw8rKysGDhz4j5ukUp9vnz59ePLkiab86NGjTJ48OUd9DRw4EIBevXppXoqoJSUlsWPHjrf2sX79eh48eEDlypWpUKFCpu3atWsH/L1q3djYmN69e6MoCl26dNGkfFF7+PBhlqva1dTX45dfftHakPPKlSuMGDFC7zHjx4/nzp07OuXqldfZyd2t3gR05cqVWjnR1RuWpqbmTq5pPz8/ypQpw+nTp/n999+16n799dccffsgOy5evEjbtm2ZOXMmFhYWzJw5U6u+e/fuFClShJEjRzJ79mydif2kpCTmzZvH9evXcy0mR0dH6tSpw4ULFxgwYAAvX76kbdu2Ot+WsbW1xdramoSEBK1nJy0tjf79++t9GQd/r/Y/c+ZMjmMzMzMjKCiIp0+f0rdvX65fv06jRo0oXLiwTttBgwZhYGBAx44dtTZEVrt586bWZ/jIkSOsXLlS51l69OgR+/btAz5sTnwhhBBCCCGEEOJTJelcRJbc3d3Ztm0brVq1YvTo0UycOJFatWpRtGhRHj9+zMGDB7lz5w6mpqaaFbi2trZ06dKF6dOnU6VKFfz9/TEzMyMuLo60tDTCw8NznL4go2bNmnH8+HFcXFyoU6cOjx8/ZseOHaSmpvLTTz9RrVo1TVsLCwsWLlxI8+bN6d69O9OmTdNMQB45cgQLCwsiIyM1G0eeP3+eXr16YW5uzsKFC8mXLx/wegPSSZMm0bFjR8LCwti/f3+2N5v80Pr168e6devYs2cPLi4u+Pn58fTpU3bs2EGXLl30ptTJTIcOHThw4AB//vknVapUwcvLC0dHR27evMmRI0fw8PDA398/yz7Uk+IZN1HUp2HDhhQsWJBDhw5x6tQpypUrx6BBgzhy5AhRUVG4urri4+ODjY0NV69e5fDhw7Rt21brmwb6hIWFsXLlSlatWoWbmxv16tVDURS2bdtG3bp1UalUOilPhg0bRt++falSpQqurq4oisLx48c5c+YMNjY29OvX763XztPTkwYNGrB161bKlCmDr68vADExMdjY2PD555+zevXqt/bzNgYGBsyZM4d69erRv39/Fi1aRNmyZUlISOD06dN07dqVGTNmvFPf4eHhwOsXVU+ePOHs2bOcPn0aRVFwdXUlMjKSSpUqaR1TsGBBVq1aRWBgIJ07d2bYsGFUrFgRExMTrl69yqlTp0hKSuLIkSN6v53yrsLCwoiNjeWvv/7S/P4mIyMj+vfvz+DBg6lbty7+/v4UKlSIffv2cefOHXr27Kn3RVNgYCBz584lNDSUgIAArK2tAbJ9XcPCwpg7d26WsQHUqVOHCRMm0KtXL3x8fKhcuTKurq68fPmSK1eucOrUKfLnz0/Pnj2B1y+CWrVqhbW1NZ6entjZ2ZGYmEhcXBxPnjyhZcuW2d5cVAghhBBCCCGEENknK9HFW3l7e3P+/Hl+//13qlevzvHjx1m6dCnx8fE4OTkxZMgQzp07R7169TTHTJ06lbFjx1KqVCm2b99OXFwc9evX5+DBg1qb5L2LwoULs2fPHurXr090dDQxMTGUL1+e2bNn611pXK9ePQ4cOEBISAjXr19n+fLl3L59m/bt23Po0CFNKpdXr14RFhZGUlISv//+O25ublr9dOjQgeDgYI4fP86gQYPe6xxyk4mJCdu2baNv376YmJiwevVqLl68yK+//sqff/6Z4/4mTZrEqlWrqFevHgkJCaxYsYJLly5Rr149TXqTzCQmJrJ+/Xrg75XmmTE2NtZsqLhgwQLg9aTnihUrmDVrFtWrV2f//v1ERUVx69YtWrVqpfUtg8yoVCqWLFnCf/7zH4oUKcKGDRs4evQo3377LStXrtSb037SpEm0a9eO58+fs3HjRjZt2oShoSF9+/bVvLDJjtWrVzN48GCKFCnCxo0bOXToEO3atWPv3r0UKFAgW31kR61atdi9ezeNGzfm/PnzrFu3jiJFihAdHf3WlwxZmTt3LnPnzmXRokXExcVhaGhIhw4dWLFiBSdPnsTT01Pvcd7e3pw4cYI+ffpgZmbGjh072LJlC0+ePKFZs2YsWbIk1/N0BwcHa15kubi46N24FF6v9p47dy6VK1cmPj6ebdu2UaVKFfbu3Zvp+QQFBTF+/HgcHR1Zu3YtM2fO1FmBnxV/f3/NPgv58+cnMDAw07bffPMN+/btIywsjEePHrFmzRr27NmDgYEBPXr00HrxUrNmTX799VeqVavGmTNnWLZsGQcPHqRy5crMnTtXk0ZJCCGEEEIIIYQQuUulvPndeyGEEEJ8cs6dO8f1u0lM35yY7WMih/t+sHiEEEIIIYQQQoh/CplEF0IIIYRmDwtXV9c8jkQIIYQQQgghhPhnkXQuQgghhBBCCCGEEEIIIUQmZBJdCCGEEEIIIYQQQgghhMiETKILIYQQQgghhBBCCCGEEJkwyusAhBBCCPHPcP1uEkPmx2S7vWwsKoQQQgghhBDiUyAr0cW/XlJSEuPHj8fPz4+iRYtibGxMwYIFqVWrFr/88gtXr17Vaj906FBUKhVz5szJm4D/AebMmYNKpWLo0KEfZbzGjRujUqmwtrbm5cuXudJnTEwMKpWK8PDwHB2nUqlwcnLKlRj+iXL6fF++fBmVSqX1Y2Jigq2tLdWqVaN79+5s3bqVj70Hta+vLyqVisuXL3/UcbMjPDwclUpFTExMXocihBBCCCGEEEKIj0BWoot/tb179xIUFMStW7cwNzenZs2aFC1alMePH3PgwAH27t1LREQE69ato379+nkd7ifpzp07bN26FYAnT56wZs0a2rRpk8dRiTdZWFjQunVrANLS0khMTCQhIYFp06Yxbdo0qlWrRmRkJGXKlMnjSD88Jycnrly58tFfHAghhBBCCCGEEOKfSSbRxb/W8ePH8ff358WLFwwYMICff/4ZCwsLTX16ejpRUVH079+f69eva8q/+eYb2rVrh729fV6E/Y/QsmVLatasiY2NzQcfKzIykrS0NOzt7bl16xYLFizIlUn0GjVqcOrUKaytrXMhSmFjY6N39fqxY8f44Ycf2LFjB3Xq1OHAgQMUL178g8czb948nj9/joODwwcfK6dGjRrFwIEDKVGiRF6HIoQQQgghhBBCiI9A0rmIfyVFUWjfvj0vXrxg6NChjB49WmsCHcDAwICgoCAOHTqEp6enptzGxoayZct+0pOv1tbWlC1b9qNMos+fPx+AadOmYW5uzqZNm7h///5792tubk7ZsmU/6ZchH0OVKlXYsmULAQEB3Llzh++///6jjFuiRAnKli1Lvnz5Psp4OWFvb0/ZsmUxNzfP61CEEEIIIYQQQgjxEcgkuvhX2rx5MydOnMDR0ZHBgwdn2dba2pqKFStqfs8sZ3TGHMxRUVHUrFkTCwsLChUqREhIiNZqdrWMuZF37tyJv78/lpaWWFlZ0bRpU06ePKlzTGJiIpMmTaJhw4aULFkSExMTChcuTKNGjTRpT96UMbYlS5ZQvXp1zM3NcXBwoH///qSkpABw4cIFQkJCsLW1xdzcHH9/f44fP67TX1Y50RVFYeHChdSrV4/ChQtjamqKs7MzoaGhxMfHZ3WpdZw8eZIjR45QvHhxmjZtSosWLUhNTWXJkiWZHpOamsqUKVPw9vamQIECmJubU6ZMGb788ksSEhI07bLKiZ6UlMSAAQMoUaIEpqamlC1blnHjxmWZnmPz5s00bNgQR0dHTExMKFasGLVr12bYsGHZPt+jR4/Sv39/qlWrRpEiRTAxMcHZ2Zmvv/6amzdv6rRX5yP39fXlxYsXDBw4UPNMlC5dmjFjxmQac2xsLL6+vuTPn5/ChQvTsmVLTp8+ne1Yc8LQ0JDJkyejUqmIiorS2WcA4MSJE4SFheHg4KC5fp06ddKb0zzjZ3D//v00a9aMwoULo1KpOHr0KKCbE/3u3bsYGRnh4OBAenq63jiXLl2KSqUiLCxMU3br1i0iIiKoW7cuDg4OGBsbY2dnR1BQEAcOHNA6Xv1MXblyBUArT3zGPPpv5kRPTU3VfFYSExP1xrZ//35UKhXe3t46dWvXrqVhw4aaPsqUKcPPP//Ms2fPdNomJSUxZswY3N3dKVCgAPnz58fFxYXg4GA2b96sd2whhBBCCCGEEEK8H5lEF/9K69evByA4OBgjo9zNSjRlyhRatWqFoig0atSI/Pnzs3jxYk3qGH3Wrl2Lv78/Dx8+pGHDhtjb27Nhwwbq1KnD7du3tdru3buX7777jlOnTuHq6krLli1xc3Njy5YtNGzYkFmzZmUa24QJE2jfvj358uWjYcOGpKSk8Ntvv/Hll19y7tw5atasyf79+/Hx8aF06dJER0fj5+fHnTt3snXuaWlptGnThvbt2xMfH4+HhwctWrTAzs6OVatWMX369OxfSF6n5AAIDQ1FpVLRvn174O/V6W9KSkqiXr169OzZk+PHj+Pl5UXz5s0pUKAAc+bMYfny5W8dMzk5mYCAACIiInjx4gXNmzfHycmJgQMH8s033+g95q+//qJRo0bExsZSrlw5WrVqRYUKFbh8+XKONl8dPXo048aNIy0tDW9vb5o0aYKiKEydOhVPT0+9E+kAKSkpBAQEMG3aNMqVK4efnx83btxg4MCB/PzzzzrtV69eTb169YiNjaVKlSoEBARw/PhxPvvsM86fP5/teHOidOnSVKtWDUVRiI2N1apbsWIFnp6eREZGYm9vT2BgIHZ2dsyZMwdPT0/+97//6e1z586d1K5dm8uXLxMQEECdOnUwMND/vyVbW1vq16/PzZs3iY6O1tsmMjISQGsSffXq1QwYMICbN29SqVIlWrRoQbFixVi1ahXe3t5s2bJF09bOzo6OHTtqvtXSsWNHzY86X7w++fLlIzg4mOTkZFasWJHt2AD69OlDYGAgO3fupGLFijRt2pSUlBR+/fVXfH19SUpK0rRNS0sjICCAgQMH8uDBA3x9fWnSpAlFixZl3bp1LFq0KNMYhRBCCCGEEEII8R4UIf6FvL29FUCZP39+jo8dMmSIAiizZ8/WKq9bt64CKBYWFsr27ds15UlJSYqXl5cCKDNnztQ6pmPHjgqgGBgYKJGRkZryV69eKa1atVIA5eeff9Y65uLFi0p8fLxOXIcPH1YKFCigWFlZKU+fPtUbm6WlpbJz505N+a1bt5SiRYsqKpVKKVeunPLDDz8oaWlpiqIoSnp6utKhQwcFUH755Ret/mbPnq0AypAhQ7TKR4wYoQBKpUqVlMuXL2vVPXjwQNm1a5dO3JlJS0tTihcvrgDKiRMnNNelaNGiCqCcPXtW55guXboogOLn56fcv39fq+769evKwYMHNb9HR0crgNKxY0etdiNHjlQApUaNGkpiYqKm/NChQ4qVlZUCKCVLltQ6pmTJkoqVlZVy6dIlrfL09HRlx44d2T7n7du3Kzdv3tQqS0tLU4YNG6YASqdOnbTqLl26pAAKoPj4+Cj37t3T1B04cEAxMjJSzM3NtZ6HJ0+eKDY2Ngqg9cylpqZqnkd9z3dm1DG8eU306dq1qwIoP/74o6bs4sWLirm5uWJtba3ExsZqtZ87d64CKNWrV9cqV38GAWXMmDF6x1I/8xnvybx58xRA6dy5s077R48eKcbGxoqNjY2SmpqqKT9+/Lhy7NgxnfabNm1SjI2NFRcXFyU9PV2rrmTJkkpW/3tUX+fo6GhN2c6dOxVA8ff312mflpam2NvbK0ZGRlr3eMmSJQqgeHh4aJ1nSkqK0q1bNwVQ+vbtqylXP/Off/655nOulpiYqPX5eBdnz55Vduw6ooT8HJ3tHyGEEEIIIYQQ4lMgK9HFv9KDBw8AKFKkSK733bt3b/z9/TW/m5ub06dPH+D1yll9QkNDCQkJ0fxuaGjIoEGD9B5TqlQpvLy8dPrw8PCgZ8+ePHnyJNOVtr1798bHx0fzu52dHWFhYSiKQkpKCmPGjNGs5FWpVJq431w5rE9KSgpjx45FpVIxa9YsSpYsqVVfqFAhvakoMhMTE8O1a9eoUqWKJp2OoaEhbdu2BWDBggVa7W/dusWcOXMwMzNj3rx5FC5cWKvewcGBatWqvXXcqVOnAjB+/HitvPdVq1alZ8+eeo+5e/cupUqV0krZAa+voZ+f31vHVPP399fJ0W5gYMAvv/yCg4MDq1ev1nucgYEBM2bM0MpR7+npSePGjXn+/DkHDx7UlC9btoz79+/ToEEDrWfOyMiI8ePHkz9//mzHm1Pq+B49eqQpmzBhAs+fPyciIoI6depote/QoQMtWrTgwIEDHD58WKe/ihUr0q9fv2yP37JlS8zNzVmxYgXJycladcuXLyclJYW2bdtqfTulUqVKVK5cWaevhg0bEhwczIULF7TSBL2r2rVrU7JkSWJiYnS+cbBjxw5u3bpFw4YNte7xyJEjAVi0aJHWs5cvXz4mTJiAnZ0dM2bM0KSvuXv3LvA61c2bK/atra2z9fkAOHfunN4fdVooIYQQQgghhBBCaJNJdPGvpGSR2/p9BQQE6JSVKVMGeD3RmxvHpKWlsWXLFoYOHUqPHj0IDw8nPDxcM3l+7tw5veM0aNBAp8zZ2Rl4PbH2ZmobFxeXLOPO6ODBgyQmJlK1alWtjVjflTplyxdffKFVrv594cKFWuXR0dGkpaXRpEkTHB0d32nMq1evcu3aNRwcHPS+qMg46ZxRtWrVOHbsGAMHDuTChQvvNLbagwcPmD17Nn369KFLly6ae5uamsrDhw95+PChzjFOTk6a5yUjfc/Qrl27AGjTpo1O+4IFC+p9FnOL+nOnUqk0Zeo8/p9//rneY2rXrg2gk38coHnz5lp9vU3+/PkJDAzk8ePHmpROapmlS4HXKX5Wr17N4MGD6datm+aenDhxAsj885YTKpWKkJAQ0tPTWbx48Vtju3v3LseOHaNcuXK4ubnp9GdqaoqnpyeJiYma+Nzd3TEwMOC3335j8eLFPH369L3jFkIIIYQQQgghxNvlbjJpIT4SGxsbzpw5w71793K9b30TuOrVvW+ufn2XY65fv06zZs04duxYpjFkNjnm4OCgU6bO35xVXWZxZ3Tt2jXg74n39/HixQtWrFiBgYGBzsS1p6cnZcuW5fTp0+zevVsz2Z0b46tXAJcoUUJvfWblkydPpkWLFowZM4YxY8ZQrFgxfHx8aN26NUFBQZnm6X7TokWL6Natm94NIdWePn1KoUKFtMoye2mg7xl613PMDffv3wfQil+98aednV22js3oXWINCwtj8eLFLFy4kKCgIOD1NYmNjaVUqVLUqlVLq/2JEycIDAzUu8GpWm5NRoeFhTF69GgWLlzIDz/8ALy+dytXrsTCwkLrRYN689JTp0699UXC/fv3cXNzo0yZMvz2228MHDiQkJAQDA0NqVixIvXr16dTp05UqFAhW3G6urrqLX89WZ+arT6EEEIIIYQQQohPiUyii38ld3d34uPjOXz4sGazytySk5Wx73JM165dOXbsGEFBQQwYMAA3NzcsLS0xMDBg2rRpdO/ePdOV9lmN8y5xf6h+oqKiePr0KSYmJnpXTKtXYy9YsEBnxfj7jK9vpXR2+q5cuTInT55k06ZNbNiwgdjYWJYsWcKSJUuoXbs227dvx9jYOMuxr1y5Qnh4OIqi8Mcff9C0aVMcHBwwMzMDwMvLiz179ui9tzk557ed44d09OhRAMqXL68pS0tLQ6VS0aFDhyyP1TfBa2pqmuMY1ClR1q9fz+PHj7G2tmbx4sWkp6frrEJXFIU2bdpw+fJlevToQY8ePXB2diZ//vyoVCoGDRrEqFGjcu2bLRUrVqRy5cocPnyY06dPU7ZsWU2c7du3x9zcXNM2LS0NAHt7+7d+eyBjaqMffviB4OBgoqKi2Lp1K3FxcYwdO5bx48czceLETFMWCSGEEEIIIYQQ4t3JJLr4V2ratCmTJ09m2bJlRERE6KQx+adKSkpi69atFC1alKVLl2JoaKhVf/HixTyKDIoXLw7A+fPn37svdSqX5ORk4uPjM223ZMkS/vjjD4yNjXNl/GLFigF/r/J9U2bl8HpCt0WLFrRo0QKAkydPEhISwq5du5g5cyZfffVVlmNv2LCBlJQU+vTpw/fff69Tn1v39m3nePXq1VwZ503nzp3j8OHDGBgYaOU+d3R05MKFC0ycOBErK6sPMnZG+fLlIzg4mKlTp7JixQo6d+6sSZcSGhqq1fb06dOcPn0aT09PTa78jD7E5y0sLIzjx48TGRnJ8OHDM00zo/72gZ2dHXPmzMnRGMWLF+fbb7/l22+/5dWrVyxevJhOnTrxww8/EBYWRoECBXLjVIQQQgghhBBCCPH/SU508a/UqFEjKlSowPXr1/nPf/6TZdsnT57wv//97yNFlrXHjx+Tnp6Ovb29zgT6q1evWLVqVR5F9jrNSoECBTh8+DCHDh16537u3r3L1q1bMTY25uHDhyiKovfHy8uLhw8fsnHjRuB1TndDQ0M2bNjAjRs33mnskiVL4ujoyI0bN9izZ49O/Zu5qrNSvnx5zapede7srKg321S/DMho586d3LlzJ9tjZ0WdY3zZsmU6dYmJiWzZsiVXxskoLS2Nb775BkVRaNWqlVb6mfr16wOvv33wsagnpCMjIzl79iyHDh2iatWqlCtXTqud+p7oS5fz6NEjTT73N6m/dfDq1ascxxYaGopKpSIyMpInT56wfv16bG1tNddJzdHRETc3N44fP86lS5dyPI6akZER7du3p3r16qSkpHD27Nl37ksIIYQQQgghhBD6ySS6+FdSqVQsWLAAU1NThg4dyo8//khSUpJWG0VRWLNmDZ6enno3NcwLtra2WFtbk5CQoLVCOy0tjf79++fpBJixsTG9e/dGURS6dOmiyVGu9vDhwyxXlatFRkby6tUrGjVqRMGCBTNt165dO+DvVevFihWjQ4cOvHjxgvDwcJ0NOG/evMnhw4ffOn737t0B6NOnD0+ePNGUHz16lMmTJ+u0f/78ORMnTiQxMVGrPD09XTMhnZ3c3epNQBcsWKD1LN64cYMePXq89fjsCg4OplChQmzZsoWlS5dqytPS0ujTp0+W+djfxfHjxwkICGDLli3Y29szfvx4rfo+ffpgZmZG7969Wbt2rc7xDx8+ZMqUKbx48SLXYvLy8sLJyYno6GjGjh0L6N9QtHTp0hgYGLBjxw6tzUNfvnxJjx499G7yCn+v9j9z5kyOY3N0dKROnTpcuHCBAQMG8PLlS9q2bav32zI//fQTaWlptGrVioSEBJ36CxcuMGvWLM3v0dHRbNu2jfT0dK12V65c0eRWf9dNeYUQQgghhBBCCJG5f0cODCH0cHd3Z9u2bbRq1YrRo0czceJEatWqRdGiRXn8+DEHDx7kzp07mJqa6l0dnBeMjIzo378/gwcPpm7duvj7+1OoUCH27dvHnTt36Nmzp96J3o9l0KBBHDlyhKioKFxdXfHx8cHGxoarV69y+PBh2rZti7e3d5Z9qCfF39xQ9E1t2rShd+/erFu3jsTERAoUKMCECRM4ffo027Ztw8nJCR8fH/Lnz8/ly5c5fPgwgwcPpmrVqln2269fP9atW8eePXtwcXHBz8+Pp0+fsmPHDrp06aKT1iMlJYXvv/+efv36UbVqVZycnEhJSeHgwYNcvXoVZ2dnzcR8VgIDA6lQoQIHDx6kdOnSeHt78/LlS6Kjo3F3d8fLy4vdu3e/tZ+3sbKyYtq0abRp04a2bdsyceJESpQowf79+7l37x5hYWEsXLgwx/3ev3+f8PBw4PWE/OPHj/nf//6nSXlSvXp1IiMjdTawdXV1ZcGCBbRv357AwEDc3NwoV64ciqJw5coVTp48SUpKCqGhoZr88O9LpVIRGhrKyJEjmTZtGgYGBpqXMhnZ2trSpUsXpk+fTpUqVfD398fMzIy4uDjS0tIIDw/Xm0olMDCQ2NhY6tWrh5+fHxYWFtjY2DB69OhsxRcWFkZsbCx//fWX5nd92rdvz4kTJ4iIiMDd3R0PDw9KlSrFkydPuHLlCqdPn6ZKlSp07twZgGPHjtG7d2+KFClCtWrVKFy4MPfu3WPnzp28fPmSXr16aV4ACCGEEEIIIYQQIvfISnTxr+bt7c358+f5/fffqV69OsePH2fp0qXEx8fj5OTEkCFDOHfuHPXq1cvrUDUGDRrE3LlzqVy5MvHx8Wzbto0qVaqwd+9ePD098zQ2IyMjVqxYwaxZs6hevTr79+8nKiqKW7du0apVq7dOJp86dYrDhw9jbm5O8+bNs2xbtGhRfH19SU5O1qQmsbS0JDo6mvHjx+Pm5kZsbKxmkr1z584EBwe/9RxMTEzYtm0bffv2xcTEhNWrV3Px4kV+/fVX/vzzT532+fPnZ/LkyTRr1ox79+6xZs0aduzYQcGCBRkxYgSHDh3KckW9mrGxMXFxcXz11VeYmpqybt06Tp06xbfffsvWrVvJly/fW/vIrlatWrF161Z8fHw4cuQIGzdupHz58uzZs4fSpUu/U59JSUnMnTuXuXPnsmTJEvbs2UOBAgXo1q0bW7ZsYd++fZn2HRQUxLFjx+jevTupqals3LiRmJgYkpOTCQsLY926dVhbW7/PKevIODHt6+ub6eTx1KlTGTt2LKVKlWL79u3ExcVRv359Dh48SMmSJfUe89133/HTTz+RP39+VqxYwcyZM3OUCig4OBgTExMAXFxc+OyzzzJtO2bMGLZv305gYCDXr18nKiqKI0eOYG5uTr9+/bRWojdr1oyffvqJMmXKcOzYMZYtW8b//vc/fHx8WLVqlc63BIQQQgghhBBCCJE7VIqiKHkdhBBCCCHy1rlz57h+N4npmxOzfUzkcN8PFo8QQgghhBBCCPFPIZPoQgghhNDkjXd1dc3jSIQQQgghhBBCiH8WSecihBBCCCGEEEIIIYQQQmRCJtGFEEIIIYQQQgghhBBCiEzIJLoQQgghhBBCCCGEEEIIkQmZRBdCCCGEEEIIIYQQQgghMmGU1wEIIYQQ4p/h+t0khsyPyXb7yOG+HyoUIYQQQgghhBDiH0NWogshPjn379/n559/xsPDgwIFCmBubk7p0qXp1q0bCQkJeR3eBzF06FBUKhVz5sz5IP2Hh4ejUqk0PwYGBlhbW+Pk5ETz5s2JiIjgzp07bz0+Jibmg8SXGV9fX1QqFZcvX9apW7RoEdWqVcPc3ByVSoWTkxPwbtcyJiYGlUpFeHh4rsQthBBCCCGEEEKIj0dWogshPinbtm0jODiYxMREihQpQt26dTExMeHEiRNMnz6dWbNm8euvvzJw4MC8DvVfydvbm9KlSwOQlJTErVu32L59O+vWrePnn39m+PDh9O/fH5VKlceRZu3AgQO0b98eU1NTAgICKFCgADY2NnkdlhBCCCGEEEIIIfKATKILIT4ZBw4coGnTpqSmpjJq1Cj69u2LkdHffwY3bNhA+/bt+fHHHzE3N+e7777Lw2hz1zfffEO7du2wt7f/oON07dpVZ7X1ixcvmDFjBgMHDmTgwIE8fvyYkSNHarUZNWoUAwcOpESJEh80vuxau3Yt6enpTJo0ic6dO2vVfaxrKYQQQgghhBBCiH8GSecihPgkKIpCx44dSUlJYfjw4QwcOFBrAh2gSZMmREVFoVKpGDBgAFeuXMmjaHOfjY0NZcuWxdra+qOPbWZmxrfffsv69esxNDRk1KhRHDt2TKuNvb09ZcuWxdzc/KPHp8/169cBcHZ21qnLy2sphBBCCCGEEEKIj08m0YUQn4SNGzdy6tQpHBwcGDBgQKbt6tSpQ3BwMC9fvmTy5MladU5OTqhUKhRFYdKkSVSpUgVzc3Pc3d01be7evcuXX35J0aJFMTc3p2rVqkRGRnL58mVUKhW+vr5afSYmJjJp0iQaNmxIyZIlMTExoXDhwjRq1IitW7fqjTFjHu+oqChq1qyJhYUFhQoVIiQkRDMBnFFWebxTU1OZMmUK3t7emhzxZcqU4csvv8zVHPG+vr6EhIQAMGnSJK26zHKiP3jwgEGDBlGhQgXy58+PtbU1ZcqUoUOHDuzfv1/TLrPrq5bdPOZz5sxBpVIxe/ZsAPz8/DR53tXHZtXX5cuXCQkJoXDhwuTPnx8vLy/Wr1+f5ZiKojB37lzq1KlDgQIFMDMzo3Llyvz++++kpqbqtM/uNRFCCCGEEEIIIUTukHQuQohPwoYNGwAIDg4mX758WbYNDQ1l6dKlbNy4kYiICJ36Hj16MHv2bOrWrUu5cuVISUkBXm9Y6uXlxYULFyhevDh+fn7cuXOHL774gm+++UbvWHv37uW7776jePHilClThlq1anH16lW2bNnCli1bmDFjhk46EbUpU6YwduxYPD09adSoEQcOHGDx4sUcOnSIY8eOYWZm9tbrkpSUROPGjYmLiyN//vz4+PhgaWnJpUuXmDNnDg4ODlSsWPGt/WRXu3btWLBgAdHR0W9t++zZM2rWrMn58+dxdXWlYcOGAFy9epVFixbh7OxMjRo1ci02gNKlS9OxY0d27drFhQsXaNiwIXZ2dpq6rFy4cAEvLy/u3r1LmTJlqFq1KpcuXaJ58+b06NFD7zHp6em0a9eOZcuWYWVlRfXq1cmfPz/79u2jX79+REdHs3btWgwMXr/zzotrIoQQQgghhBBCfOpkEl0I8Uk4evQoANWqVXtrW3WbkydPkpqaqjPpvnLlSo4cOUKFChW0ygcOHMiFCxdo2bIlixYtwsTEBIDt27fTpEkTvWO5ubkRHx+Pl5eXVvmRI0fw9/end+/etGnThvz58+scO2XKFLZu3Yq/vz8Az58/p0GDBuzevZtFixZlOvme0ffff09cXBx+fn4sW7aMwoULa+pu3LjB7du339pHTqhX7V+8eJGUlBSMjY0zbbt8+XLOnz/Pt99+y8SJE7Xq7t69y927d3M1NoDatWtTu3ZtwsPDuXDhAgMHDsx0dfubvv76a+7evcvXX3/NpEmTNBPfM2bM4Msvv9R7zO+//86yZcto0KABCxcupEiRIsDrlxshISGsXbuWqVOn0rNnTyBvrokQQgghhBBCCPGpk3QuQohPwoMHDwCwtbV9a1v1RGZ6ejoPHz7UqR8wYIDOBPqzZ89YuHAhRkZGTJgwQTOBDlCvXj3atWund6xSpUrpTKADeHh40LNnT548eZLpqu3evXtrJtABzM3N6dOnDwA7d+58y1nCrVu3mDNnDmZmZsybN09rAh3AwcEhWy8dcsLGxkbz70ePHmXZVj0hnPEc1WxtbXN1hfz7unDhAlu2bKFgwYJERERoJtDh9War+u7xq1ev+O2337C0tCQyMlLz3AFYWFgwffp0TExM+O9//6spz41rcu7cOb0/6m9UCCGEEEIIIYQQQpusRBdCfBIURdH6b3baAqhUKp36wMBAnbLDhw/z8uVLateuTfHixXXqg4ODmTdvnt7x0tLS2L59O7t37+b27du8fPkSeD3ZmfG/bwoICNApK1OmDPB6gvxtoqOjSUtLo0mTJjg6Or61fW5427XNSD2BP2jQIIyMjKhfvz6mpqYfNL53FR8fD7zenNbCwkKnvl27duzevVur7MiRI9y/f5/GjRtrvVxQK1q0KK6uriQkJPDixQvMzMz+VddECCGEEEIIIYT4v0Im0YUQnwQbGxvOnDmTrXQX9+7dA15P8hYsWFCnvkSJEjplN2/eBNA7gZ7ZMQDXr1+nWbNmHDt2LNN4nj59qrdc38S3Ou1LcnJypv2pXbt2DQAXF5e3ts0t9+/f1/xb37XNqF69evTu3Zs//viD5s2bY2xsjLu7OwEBAXTp0gUnJ6cPHG32qe9/ZvdZX/nly5eB15vevu2FwsOHD3FwcMiVa+Lq6qq3/PXLGt2NTIUQQgghhBBCiE+dTKILIT4JVapUIT4+nkOHDvHFF19k2fbQoUMAVKhQQe8mpFmt/M1sMjSzFfBdu3bl2LFjBAUFMWDAANzc3LC0tMTAwIBp06bRvXv3TI9928RrduVWP9mhzk3v6ur61g1eAcaNG0f37t1ZvXo127dvJz4+nv379xMREcGSJUto0aJFtsZNT09/j6jfTn2PcnIt09LSgNfXQl+6l4wypgfKrWsihBBCCCGEEEKI7JFJdCHEJ6Fx48ZMmTKF5cuX89tvv2U5gRsZGQlAo0aNst2/vb09AFevXtVbr171nVFSUhJbt26laNGiLF26FENDQ636ixcvZnv8d6FeNX/+/PkPOk5GixcvBsDPzy/bx7i5udG/f3/69+/Py5cvmTx5Mn379qV79+6aCWP1BqXPnj3T24e+65+bihUrBsCVK1f01ut7LtTfJKhYsSJz5szJ0XjZuSZCCCGEEEIIIYTIHbKxqBDik9CkSRPc3Ny4ceMGY8aMybTdzp07Wb58OcbGxvTs2TPb/VetWhUTExP27t3L9evXdeqXL1+uU/b48WPS09Oxt7fXmUB/9eoVq1atyvb478LX1xdDQ0M2bNjAjRs3PuhYADExMSxevBiVSsW33377Tn2YmprSp08f7O3tuXv3riY9j42NDfny5ePSpUu8evVK65iUlBRiY2PfO/6seHt7A7BhwwaSkpJ06tUvDzKqXr061tbWREdH8+TJk3ceO7NrIoQQQgghhBBCiNwhk+hCiE+CgYEBc+bMIV++fPzyyy+MGTNGk05DbePGjbRo0QJFURg9enSOcm5bWloSGhrKq1ev6N27NykpKZq6mJgYFi1apHOMra0t1tbWJCQkaDamhNdpPvr378/Zs2dzfqI5UKxYMTp06MCLFy8IDw/n4cOHWvU3b97k8OHD7z3Oy5cv+fPPP2natClpaWn8/PPPVKxY8a3HRUVFsXfvXp3yI0eOcOfOHSwtLTV51Y2NjalZsyYPHz5k8uTJmrapqan07t2bS5cuvfd5ZKV06dLUq1ePR48eMXDgQK30MbNnz9bZVBRep2jp27cviYmJtGrVSu8q9uPHj7NkyRLN7zm5JkIIIYQQQgghhMgdks5FCPHJqFmzJmvWrKFdu3YMHDiQcePGUatWLUxMTDhx4gSnTp3CwMCAESNG0Lt37xz3P3r0aGJiYli+fDn79+/Hy8uLu3fvEhMTw9dff82ff/6pSTsCYGRkRP/+/Rk8eDB169bF39+fQoUKsW/fPu7cuUPPnj21JoQ/hAkTJnD69Gm2bduGk5MTPj4+5M+fn8uXL3P48GEGDx5M1apVs93fjBkziImJAeD58+fcvn2bQ4cO8fz5c0xMTIiIiKBv377Z6ismJoYJEybg4OCAh4cHVlZW3Lx5k127dpGens6IESO00vL88ssvNGzYkF69erFkyRLs7Ow0Y3fs2JG5c+fm6Nrk1NSpU/H29ubPP/9k69atVK1alUuXLrFv3z569OjBX3/9pXPMoEGDOHnyJIsWLcLNzY2qVatSokQJ7t+/z8WLF7l06RKff/45bdu2fadrIoQQQgghhBBCiPcnk+hCiE9Ko0aNOHfuHBMmTGDdunXs2LGD1NRU7O3t6dq1K99++y2VK1d+p75tbW3Zs2cPgwYNYu3atURFReHm5sbs2bNxcXHhzz//pHDhwlrHDBo0CEdHR/744w/i4+MxMzOjdu3aDB8+PFdWgb+NpaUl0dHRTJ06lYULFxIbG4uiKDg6OtK5c2eCg4Nz1F98fDzx8f+PvTuPq6ra/z/+OgiCoDiiAuaM4CzOAypOaIqGIiqmQWZqmZlpSpZlw9cxNe2mpZZD5ZBzTlcccR4zh3JWHCvnCQcQ9u8Pf+dcj+ccRUXBej8fDx7F2muv9dl7nUP3fs46n70Bk8lE1qxZyZUrF/Xq1aNu3bpERUWRN2/eVI8VHR2Ns7Mza9euZevWrVy5coX8+fPTtGlTevXqRXBwsFX/hg0b8ssvv/DJJ5/w66+/4uHhQcOGDRk6dOgj1xx/HH5+fmzevJn333+f5cuXs2DBAsqWLcv8+fPx9PS0m0R3cnJi2rRphIeHM3HiRLZv38727dvJkycPhQoVIioqinbt2ln6P+o9EREREREREZEnZzIMw0jvIERE/umGDh1KTEwMQ4YMoV+/fukdjoiNQ4cOcepsAhOWXU71OdM+DX5q8YiIiIiIiIhkFEqii4ikoV9//dWm/MnatWtp3rw5N27c4ODBgxQpUiSdohNx7NChQ8DdHfUiIiIiIiIi8j8q5yIikoZq1qyJj48PJUuWxMPDg8OHD7Nz507gbs10JdBFRERERERERJ4vSqKLiKSh999/n8WLF7NlyxauXLmCp6cnISEhvPXWWzRv3jy9wxMRERERERERkUekci4iIiKici4iIiIiIiIiDmgnuoiIiABw6mwCH/+wJtX99WBRERERERER+TdwSu8AREREREREREREREQyKiXRReRf4fz58wwYMIDAwEBy5MiBu7s7xYsXp0uXLuzduze9w3sqBg4ciMlkYvLkyWk6bnR0NCaT6ZF+1qxZ43C8+Ph4TCYTwcHBaRrnw6xZswaTyUR0dLTNsXPnzvHKK6/g7e1NpkyZrO6jyWSicOHCjzRXcHAwJpOJ+Pj4J45bRERERERERJ4tlXMRkX+8FStWEBERweXLl/Hy8qJu3bq4urqyZ88eJkyYwPfff8/nn39OTExMeof6XAgKCrJp++uvv1i2bBkeHh60bt3a5nj+/PmfRWhp5rXXXmPhwoWUK1eOBg0a4OzsTPHixdM7LBERERERERFJB0qii8g/2rZt22jWrBlJSUkMHjyYPn364Oz8vz99S5YsoUOHDrz//vu4u7vz9ttvp2O0aeutt96iXbt2eHt7p+m4nTt3pnPnzlZta9asYdmyZeTJk+eRd777+vqyb98+3N3d0zDKx5eYmMiSJUsoXLgwO3fuxMnJ+ktb+/btw8XFJZ2iExEREREREZFnTeVcROQfyzAMoqKiSExM5NNPPyUmJsYqgQ7QtGlT5s+fj8lkol+/fhw/fjydok17efLkISAggOzZs6d3KA/k4uJCQEAABQsWTO9QgLu76pOTkylUqJBNAh0gICCAYsWKpUNkIiIiIiIiIpIelEQXkX+spUuXsm/fPnx9fenXr5/DfnXq1CEiIoJbt27x9ddfWx0rXLgwJpMJwzD46quvKF++PO7u7lSoUMHS5+zZs7z++uvky5cPd3d3KlasyLRp0xzW+r58+TJfffUVjRs3plChQri6upI7d26aNGnC8uXL7cZ4b03t+fPnU716dTw8PMiVKxeRkZGcOnXK5pwH1URPSkpi7Nix1KpVy1IjvkSJErz++utpXiP+YffwQTXRly1bRuPGjSlQoACurq74+PgQFBTEJ598YtXPXKfdUe311NYxL1y4MIUKFQIgLi7OUtP93nMfNNb48eMpW7Ysbm5u+Pr60qNHD65cufLAOePj4+natSuFCxfG1dUVLy8vWrduze7du+32T+09EREREREREZG0oSS6iPxjLVmyBICIiIiHlt9o3749cDfxbk+3bt3o3bs3efPmpUWLFhQtWhS4+8DSmjVrMnHiRFxdXWnRogXZs2enY8eOjBo1yu5Ymzdv5u2332bfvn34+fnRsmVL/P39iY2NpXHjxnz//fcO4xw7dizh4eEYhkGTJk3ImjUrM2bMoH79+ty8efOh9wQgISGBBg0a0L17d3bv3k3NmjVp3rw5OXLkYPLkycyePTtV4zwqR/fQkW+++YYmTZoQFxdHyZIlCQ8Pp3Tp0sTHxzNw4MCnEmPr1q0JDw8HIF++fERFRREVFWW3zvv9+vTpQ9euXTl06BD169enRo0a/PTTT9SrV4/bt2/bPWf9+vWUL1+e8ePHkzVrVlq0aIGfnx9z586levXqrF692qp/etwTERERERERkX871UQXkX+s3377DYBKlSo9tK+5zx9//EFSUpJN0n3u3Lns3LmT0qVLW7XHxMRw5MgRWrZsyfTp03F1dQVg5cqVNG3a1O5c/v7+bNiwgZo1a1q179y5k/r169OrVy/atGlD1qxZbc4dO3Ysy5cvp379+gDcuHGDRo0asXHjRqZPn06nTp0eeq09e/Zk3bp11KtXj1mzZpE7d27LsdOnT/PXX389dIzH4egeOjJkyBA8PT3ZtWuX1c5vwzAc7jh/Ul988QXx8fHMmTOHgICAVNd337hxIyNGjCBXrlysXbvWco0XLlygfv36bN682eacq1evEhERwc2bN5k1a5ZVon7FihU0a9aMjh07cvToUTJnzgykzT05dOiQ3fbExMRUnS8iIiIiIiLyb6Od6CLyj3XhwgUA8ubN+9C+Xl5eAKSkpHDx4kWb4/369bNJ/l6/fp2ffvoJZ2dnRo8ebUmgAzRo0IB27drZnatIkSI2CXSAwMBAunfvztWrV212IJv16tXLkkAHcHd3p3fv3gCsXbv2IVcJf/75J5MnTyZLlixMnTrVKoEOdx/ymZoPHR6HvXv4IGfPnqVIkSI2pVNMJhP16tVL4+iezDfffANA7969ra4xd+7cDB8+3O4533//PX/99Rd9+vSx2enesGFD3nzzTU6fPs2iRYss7c/TPRERERERERH5p1ASXUT+sQzDsPpnavrC3YTk/Vq0aGHT9uuvv3Lr1i2qV6/OCy+8YHM8IiLC4XzJycnExsYycOBAunXrRnR0NNHR0ZbkuaPdwiEhITZtJUqUAO4myB9m9erVJCcn07RpUwoUKPDQ/mnJ3j18kEqVKrFr1y7Lbv+MbP369QC0adPG5lhISAi5cuWyaTfXvw8LC7M7ZlBQEADbtm2ztKXFPfHz87P7Y97tLiIiIiIiIiLWVM5FRP6x8uTJw4EDBzh79uxD+547dw64m0DPmTOnzfGCBQvatJ05cwbAbgLd0TkAp06dIjQ0lF27djmM59q1a3bb7SW+zWVfHNXdvtfJkycBKFas2EP7pjVH98ORr7/+mrCwMIYOHcrQoUPx8fGhdu3atG7dmlatWuHklHE+Bz5z5gwmk+mBr4X7v+EQHx8PQLVq1R449vnz5y3//jzdExEREREREZF/CiXRReQfq3z58mzYsIEdO3bQsWPHB/bdsWMHAKVLl7b7EFI3NzeH59rbuQ6Od8B37tyZXbt20apVK/r164e/vz/ZsmXDycmJ8ePH07VrV4fnOprrUaXVOI/iQffQnnLlyvHHH3/w3//+lyVLlhAXF8fMmTOZOXMmQUFBrFy5MlW7p1NSUh435KcqOTkZuPuNBXd3d4f97k2yp9U9EREREREREZHUUxJdRP6xXnzxRcaOHcvs2bMZPny43eS42bRp0wBo0qRJqsf39vYG4MSJE3aPm3d93yshIYHly5eTL18+fv75ZzJlymR1/OjRo6me/3GYd0ofPnz4qc6TVtzc3AgLC7OUPPnjjz+IjIxk/fr1fPfdd7zxxhsAlsTx9evXbcawtw5pzdvbm/j4eE6ePEnx4sVtjtt7jRQoUIADBw7w4YcfUq5cuVTPldp7IiIiIiIiIiJpQ9/7FpF/rKZNm+Lv78/p06cZOnSow35r165l9uzZZM6cme7du6d6/IoVK+Lq6srmzZs5deqUzfHZs2fbtF25coWUlBS8vb1tEuh37txh3rx5qZ7/cQQHB5MpUyaWLFnC6dOnn+pcT0OpUqUsa7Rnzx5Lu/kDjYMHD9qcExsb+9TjMtcvnzVrls2x5cuX231YbcOGDQGYP3/+E83t6J6IiIiIiIiISNpQEl1E/rGcnJyYPHkyLi4ufPTRRwwdOtRSQsNs6dKlhIWFYRgGQ4YMoXDhwqkeP1u2bLRv3547d+7Qq1cvEhMTLcfWrFnD9OnTbc7Jmzcv2bNnZ+/evWzYsMHSnpycTN++fe0mgdOSj48Pr7zyCjdv3iQ6OtomuXvmzBl+/fXXpxpDaty4cYMxY8Zw+fJlq/aUlBRLUvzeGut169YFYNy4cVy4cMHS/uuvvzJgwICnHm/Xrl0BGDlyJPv27bO0X7x4kb59+zo8x8vLi0GDBjFp0iSbEj4JCQlMnTrV8gHNo94TEREREREREUkbSqKLyD9a9erV+eWXX/D09CQmJgYfHx/CwsJo27YtpUqVomnTply5coXPPvuMXr16PfL4Q4YMoUiRIsyePRs/Pz8iIyNp0KABDRo0oEuXLgBWNaqdnZ3p27cvd+7coW7duoSEhNCuXTuKFy/ON99880g74R/X6NGjqVGjBitWrKBw4cI0a9aMtm3bUq1aNQoVKsQvv/zy1GN4mMTERHr27Em+fPmoUaMGkZGRhIeHU6RIEebMmUPRokUtiWuAevXqUbduXQ4fPkypUqVo1aoVtWvXpnr16g+th58WgoKCeOeddzh//jyBgYGEhoYSERGBn58fcPd1eL+cOXMyb948PDw86NSpE0WKFCE0NJTw8HCqVKlCvnz5iIqKsjxY9FHviYiIiIiIiIikDSXRReQfr0mTJhw6dIgPPvgAb29vVq1axS+//MKtW7fo3LkzO3fu5MMPP3yssfPmzcumTZvo1KkTN2/eZP78+Vy4cIFJkybRrl07AHLnzm11Tv/+/ZkyZQrlypVjw4YNrFixgvLly7N582YqV678xNf7MNmyZWP16tWMGjUKf39/4uLiWLRoEZcvX6ZTp05EREQ89RgeJmvWrHz99deEhoZy7tw5fvnlF1atWkXOnDn57LPP2LFjBzlz5rT0N5lMLFiwgG7dumEymViyZAmXLl1izJgxDB8+/JnEPHLkSMaNG0fx4sVZvnw5GzZsoE2bNqxevRpXV1e759SqVYs9e/bQu3dvsmTJwqpVq4iNjeXq1auEhoYyc+ZMSpUq9Vj3RERERERERETShsm4//vjIiKSJoYOHUpMTAxDhgyhX79+6R2OyAMdOnQIwLJ7XkRERERERETu0k50EZEnZK+G+Nq1axk0aBDOzs60adMmHaISEREREREREZG04JzeAYiIPO9q1qyJj48PJUuWxMPDg8OHD7Nz507gfzXTRURERERERETk+aQkuojIE3r//fdZvHgxW7Zs4cqVK3h6ehISEsJbb71F8+bN0zs8ERERERERERF5AqqJLiIiIqqJLiIiIiIiIuKAdqKLiIgIAKfOJvDxD2tS3X/ap8FPKxQRERERERGRDEMPFhURERERERERERERcUBJdJEM4Pz58wwYMIDAwEBy5MiBu7s7xYsXp0uXLuzduzdN54qOjsZkMrFmzZo0HTe1ChcujMlksmk3mUwULlz42Qf0DzNw4EBMJhOTJ09OVf/4+HhMJpPVj6urK3nz5qVSpUp07dqV5cuX86wrfwUHB2MymYiPj3+m86ZGer+HREREREREROTZUhJdJJ2tWLECPz8/Pv/8c06fPk3dunUJDQ3FxcWFCRMmUKFCBYYMGZLeYco/nIeHB1FRUURFRdGmTRuqVavGxYsXGT9+PCEhIVSpUoWDBw+md5jPhKMPekRERERERETk30k10UXS0bZt22jWrBlJSUkMHjyYPn364Oz8v7flkiVL6NChA++//z7u7u68/fbbTzzn4MGDiYmJoWDBgk88Vlrat28fLi4u6R3Gv1aePHns7l7ftWsX7777LqtWraJOnTps27aNF1544anHM3XqVG7cuIGvr+9Tn+tRZdT3kIiIiIiIiIg8HdqJLpJODMMgKiqKxMREPv30U2JiYqwS6ABNmzZl/vz5mEwm+vXrx4kTJ554Xm9vbwICAnB3d3/isdJSQEAAxYoVS+8w5D7ly5cnNjaWkJAQ/v77b3r27PlM5i1YsCABAQEZ8oOVjPoeEhEREREREZGnQ0l0kXSydOlS9u3bh6+vL/369XPYr06dOkRERHDr1i3Gjh1rdexBdcQnT56MyWRi4MCBVu2O6jnfW8Ji4sSJlCtXjixZspA/f366du3K5cuX7c5z4cIF3nvvPfz8/HBzcyNXrlw0adKE2NjYB17//exdy5o1azCZTERHR3Px4kXeeOMNvL29cXV1pUyZMnz//fcOx4uPj6dr164ULlwYV1dXvLy8aN26Nbt377bbf9myZTRu3JgCBQrg6uqKj48PQUFBfPLJJ6m+ht9++42+fftSqVIlvLy8cHV1pWjRorz55pucOXPGbowmk4ng4GBu3rxJTEwMhQoVwtXVleLFizN06FCHtcjj4uIIDg4ma9as5M6dm5YtW7J///5Ux/ooMmXKxNdff43JZGL+/Pl2P8zZs2cPL7/8Mr6+vpb79+qrr9qtaX5v3fatW7cSGhpK7ty5MZlM/Pbbb4BtTfSzZ8/i7OyMr68vKSkpduP8+eefMZlMvPzyy5a2P//8k2HDhlG3bl18fX3JnDkz+fPnp1WrVmzbts3qfPPr7fjx4wBWdeLvfW3e/x5KSkoid+7cuLm5OXyfbN26FZPJRK1atWyOLVy4kMaNG1vGKFGiBAMGDOD69es2fRMSEhg6dCgVKlQgR44cZM2alWLFihEREcGyZcvszi0iIiIiIiIiT0ZJdJF0smTJEgAiIiIeutu2ffv2Vuc8TX379qV79+54enrSpEkTDMNg/PjxtGjRwiahe/r0aapWrcoXX3xBYmIiYWFhBAYGsmLFCho3bsyoUaPSJKbLly9To0YN5s2bR5UqVahVqxb79+/ntddeY+LEiTb9169fT/ny5Rk/fjxZs2alRYsW+Pn5MXfuXKpXr87q1aut+n/zzTc0adKEuLg4SpYsSXh4OKVLlyY+Pt7mQ4gHGTJkCCNHjiQ5OZlatWrRtGlTDMNg3LhxVK5c2W4iHSAxMZGQkBDGjx9PyZIlqVevHqdPnyYmJoYBAwbY9F+wYAENGjQgLi6O8uXLExISwu7du6lWrRqHDx9OdbyPonjx4lSqVAnDMIiLi7M6NmfOHCpXrsy0adPw9vamRYsW5M+fn8mTJ1O5cmV+//13u2OuXbuWoKAg4uPjCQkJoU6dOjg52f/PUt68eWnYsCFnzpyxWT+zadOmAVgl0RcsWEC/fv04c+YMZcuWJSwsDB8fH+bNm0etWrWsPuzJnz8/UVFReHh4AFhqxEdFRdG6dWuH98bFxYWIiAhu377NnDlzUh0bQO/evWnRogVr166lTJkyNGvWjMTERD7//HOCg4NJSEiw9E1OTiYkJISYmBguXLhAcHAwTZs2JV++fCxatIjp06c7jFFEREREREREnoAhIumiVq1aBmD88MMPD+178uRJAzCcnJyMpKQkSztgFCpUyO45kyZNMgDj448/tmqPiooyAGP16tVW7YUKFTIAw9vb29i5c6el/dy5c0bx4sUNwFi5cqXVOaGhoQZgdOzY0UhMTLS0r1u3znB3dzcyZcpk7Nq1y+4897N3LatXrzYAAzDCw8ON69evW47Nnz/fAIyCBQtanXPlyhUjf/78houLizFr1iyrY8uXLzcyZ85s+Pr6Grdv37aKydPT0zh27JhV/5SUFGPVqlU2sTqycuVK48yZM1ZtycnJxieffGIAxquvvmp17NixY5brq127tnHu3DnLsW3bthnOzs6Gu7u7ce3aNUv71atXjTx58hiAMW3aNEt7UlKSZW0BY9KkSamK2RyDo9fRvTp37mwAxvvvv29pO3r0qOHu7m5kz57diIuLs+o/ZcoUAzCqVKli1f7xxx9b4hw6dKjduerWrWsAVmsydepUAzA6depk0//SpUtG5syZjTx58li9R3bv3m3zGjQMw/jvf/9rZM6c2ShWrJiRkpJidczRa9TM3nto7dq1BmDUr1/fpn9ycrLh7e1tODs7W63xzJkzDcAIDAy0us7ExESjS5cuBmD06dPH0m5+P7z00ktGcnKy1RyXL182tm/f7jDmex08eNDuz969e41V63cakQNWp/pHRERERERE5N9AO9FF0smFCxeAuztsH8bLywuAlJQULl68+FTj+uyzz6hQoYLl9zx58vDGG28Ad3cOmx09epRFixbh6enJmDFjrHbTBwUF0a1bN5KTk21K0DwOT09Pxo8fb9khDPDSSy9RtmxZTpw4YVUy5Pvvv+evv/6iT58+NruHGzZsyJtvvsnp06dZtGiRpf3s2bMUKVLEppyMyWSiXr16qY6zfv36eHt7W7U5OTnx0Ucf4evry4IFC+ye5+TkxMSJE8mTJ4+lrXLlyrz44ovcuHGD7du3W9pnzZrF+fPnadSoEZGRkZZ2Z2dnRo0aRdasWVMd76Myx3fp0iVL2+jRo7lx4wbDhg2jTp06Vv1feeUVwsLC2LZtG7/++qvNeGXKlOG9995L9fwtW7bE3d2dOXPmcPv2batjs2fPJjExkbZt21o9W6Bs2bKUK1fOZqzGjRsTERHBkSNH2Lt3b6pjcCQoKIhChQqxZs0am28crFq1ij///JPGjRtbrfGgQYMAmD59utVrz8XFhdGjR5M/f34mTpxoKV9z9uxZ4G6pm/t37GfPnp1KlSo98XWIiIiIiIiIiC0l0UXSifH/S6MYDmpe2+sLd0s6PE0hISE2bSVKlADu1pc2W79+PXD34ac5cuSwOadjx44ArFu37oljqly5Mrly5UpVXMuXLwcgLCzM7lhBQUEAVvWwK1WqxK5du4iJieHIkSNPFOuFCxeYNGkSvXv35rXXXiM6Opro6GiSkpK4ePGi3Q9BChcubLmWez3ovrdp08amf86cOe2uX1oxvw7NtfPhf/f7pZdesnuOvftt1rx5c6uxHsZcmufKlSssXrzY6pijcikAt2/fZsGCBXzwwQd06dLFsiZ79uwB4NChQ6mOwRGTyURkZCQpKSnMmDHjobGdPXuWXbt2UbJkSfz9/W3Gc3Nzo3Llyly+fNkSX4UKFXBycmL48OHMmDGDa9euPVasfn5+dn8yZ878WOOJiIiIiIiI/NM5P7yLiDwNefLk4cCBA5bdpQ9y7tw54O6O5Zw5cz7VuAoUKGDTZt7dfO/uX/NuW0cPNjW3O6oD/qQxOYrLvCu9WrVqDxzz/Pnzln//+uuvCQsLY+jQoQwdOhQfHx9q165N69atadWqlcM63febPn06Xbp0sftASLNr167ZfCDwKNdnvp8FCxa0e46j9rRgvmf3xm++3/nz50/Vufd6nFhffvllZsyYwU8//USrVq2Au/ckLi6OIkWKUKNGDav+e/bsoUWLFnYfcGr2uMloe7ENGTKEn376iXfffRe4u3Zz587Fw8PD6oMG88NL9+3b99APEs6fP4+/vz8lSpRg+PDhxMTEEBkZSaZMmShTpgwNGzbk1VdfpXTp0mlyHSIiIiIiIiJiTUl0kXRSvnx5NmzYwI4dOyy7th3ZsWMHAMWKFcPNzS1V45tLQDyqR9kZ/KD+5vZHHe9JYzLv1I+IiMDd3d1hv3uT7OXKleOPP/7gv//9L0uWLCEuLo6ZM2cyc+ZMgoKCWLly5UN36R4/fpzo6GgMw+DLL7+kWbNm+Pr6kiVLFgBq1qzJpk2b7H7z4FGuz95u8Gflt99+A6BUqVKWtuTkZEwmE6+88soDz7WX4E3ta/le5pIoixcv5sqVK2TPnp0ZM2aQkpJiswvdMAzatGlDfHw83bp1o1u3bhQtWpSsWbNiMpno378/gwcPTtW3QVKjTJkylCtXjl9//ZX9+/cTEBBgibNDhw5Wr0fz69Tb2/uh3x7InTu35d/fffddIiIimD9/PsuXL2fdunWMGDGCUaNGMWbMGLp3754m1yIiIiIiIiIi/6Mkukg6efHFFxk7diyzZ89m+PDhVjXF72cuB9G0aVOrdhcXF4e7nk+ePJl2wdrh4+MDwLFjx+weN+/8vb9G+NNWoEABDhw4wIcffmi3FrYjbm5uhIWFWcrA/PHHH0RGRrJ+/Xq+++47S114R5YsWUJiYiK9e/emZ8+eNsePHj36SNfhiPm+m3cy3+/EiRNpMs/9Dh06xK+//oqTk5NV7fMCBQpw5MgRxowZg6en51OZ+14uLi5EREQwbtw45syZQ6dOnSzvj/bt21v13b9/P/v376dy5cqMGzfOZqy0WpN7vfzyy+zevZtp06bx6aefOiwzY/72Qf78+Zk8efIjzfHCCy/Qo0cPevTowZ07d5gxYwavvvoq7777Li+//LLd8koiIiIiIiIi8vhUE10knTRt2hR/f39Onz7N0KFDHfZbu3Yts2fPJnPmzDa7TL29vblw4YLdOtuxsbFpHvO9zLWuFy9ezOXLl22O//jjjwDUrl37qcZxv4YNGwIwf/78JxqnVKlSlvttrp39IOaHbb7wwgs2x9auXcvff//9RPGYme/7rFmzbI5dvnz5qax7cnIyb731FoZhEB4eblV+Jq3u96MwJ6SnTZvGwYMH2bFjBxUrVqRkyZJW/cxrYq9czqVLlyz13O9n/tbBnTt3Hjm29u3bYzKZmDZtGlevXmXx4sXkzZvXcp/MChQogL+/P7t373b4QVRqODs706FDB6pUqUJiYiIHDx587LFERERERERExD4l0UXSiZOTE5MnT8bFxYWPPvqIoUOH2jw0dOnSpYSFhWEYBqNGjcLPz8/qeN26dQH47LPPLG2GYTB48GA2btz4VOMvWrQozZo149q1a/Ts2ZOkpCTLsU2bNjFu3DgyZcrEm2+++VTjuF/Xrl3x8vJi0KBBTJo0yaZUR0JCAlOnTuXUqVMA3LhxgzFjxth8EJCSkmJJSKemdrf5IaA//vgjCQkJlvbTp0/TrVu3J7kkKxEREeTKlYvY2Fh+/vlnS3tycjK9e/d+YD32x7F7925CQkKIjY3F29ubUaNGWR3v3bs3WbJkoVevXixcuNDm/IsXLzJ27Fhu3ryZZjHVrFmTwoULs3r1akaMGAHYf6Bo8eLFcXJyYtWqVVYPD7116xbdunWz++ET/G+3/4EDBx45tgIFClCnTh2OHDlCv379uHXrFm3btsXZ2faLXx9++CHJycmEh4ezd+9em+NHjhzh+++/t/y+evVqVqxYYVOq6fjx45ba6o7q64uIiIiIiIjI41M5F5F0VL16dX755RfatWtHTEwMI0eOpEaNGri6urJnzx727duHi4sLn376qd1kdL9+/Zg9ezZffvkla9asoVixYuzZs4eTJ0/y5ptvMnbs2Kca/7fffkvt2rWZOnUqcXFx1KhRg3PnzrFmzRqSk5MZMWLEI5VUSQs5c+Zk3rx5tGjRgk6dOvHJJ59QpkwZXF1dOXHiBPv27SMhIYGdO3dSoEABEhMT6dmzJ++99x4VK1akcOHCJCYmsn37dk6cOEHRokXp2rXrQ+dt0aIFpUuXZvv27RQvXpxatWpx69YtVq9eTYUKFahZs2aafLDh6enJ+PHjadOmDW3btmXMmDEULFiQrVu3cu7cOV5++WV++umnRx73/PnzREdHA3cT8leuXOH333+3lDypUqUK06ZNw9fX1+o8Pz8/fvzxRzp06ECLFi3w9/enZMmSGIbB8ePH+eOPP0hMTKR9+/aW+vBPymQy0b59ewYNGsT48eNxcnKiXbt2Nv3y5s3La6+9xoQJEyhfvjz169cnS5YsrFu3juTkZKKjo+2WUmnRogVxcXE0aNCAevXq4eHhQZ48eRgyZEiq4nv55ZeJi4vjm2++sfxuT4cOHdizZw/Dhg2jQoUKBAYGUqRIEa5evcrx48fZv38/5cuXp1OnTgDs2rWLXr164eXlRaVKlcidOzfnzp1j7dq13Lp1i3feecfyAYCIiIiIiIiIpB3tRBdJZ02aNOHQoUN88MEHeHt7s2rVKn7++Wf27dsH3C3bMWDAALvnli5dmlWrVhEcHMzBgwdZvnw5xYoVY9OmTVSpUuWpx+7r68u2bdvo3bs3zs7OzJ07lx07dtCgQQOWLVvGu++++9RjsKdWrVrs2bPHskt61apVxMbGcvXqVUJDQ5k5c6bl4ZhZs2bl66+/JjQ0lHPnzvHLL7+watUqcubMyWeffcaOHTvImTPnQ+fMnDkz69at44033sDNzY1Fixaxb98+evTowfLlyx9Y8/5RhYeHs3z5cmrXrs3OnTtZunQppUqVYtOmTRQvXvyxxkxISGDKlClMmTKFmTNnsmnTJnLkyEGXLl2IjY1ly5YtDsdu1aoVu3btomvXriQlJbF06VLWrFnD7du3efnll1m0aBHZs2d/kku2cW9iOjg42GHyeNy4cYwYMYIiRYqwcuVK1q1bR8OGDdm+fTuFChWye87bb7/Nhx9+SNasWZkzZw7fffcdM2bMSHVsERERuLq6AncfBnzvQ2zvN3ToUFauXEmLFi04deoU8+fPZ+fOnbi7u/Pee+9Z7UQPDQ3lww8/pESJEuzatYtZs2bx+++/U7t2bebNm2fzLQERERERERERSRsm4/5aByKSIfTu3ZuRI0fi7+/Phg0byJ07d3qHJCL/YOaSN/eXjRIRERERERH5t9NOdJEM6osvvqB9+/YcOHCA0NBQbty4kd4hiYiIiIiIiIiI/OuoJrpIBmUymZg8eTLlypXj5s2bbNu2zfIgUREREREREREREXk2VM5FREREVM5FRERERERExAHtRBcREREATp1N4OMf1qS6/7RPg59WKCIiIiIiIiIZhmqii4iIiIiIiIiIiIg4oCS6iMh9TCbTI/0ULlw4vUN+qIEDB1rq7P9bYpg+fTqVKlXC3d39uVknEREREREREcl4VM5FROQ+UVFRNm3r16/nyJEjlC9fngoVKlgdy5MnzyONbzKZKFSoEPHx8U8QpTzItm3b6NChA25uboSEhJAjR45HXicREREREREREVASXUTEhr2d0tHR0Rw5coSwsDAGDhz4zGOSR7Nw4UJSUlL46quv6NSpU3qHIyIiIiIiIiLPMZVzERGRf5xTp04BULRo0XSORERERERERESed0qii4g8ocTEREaPHk2VKlXIli0bHh4eVK1ale+++w7DMCz9Jk+ejMlkAuD48eNWddWDg4Mt/QoXLozJZMIwDL766ivKly+Pu7u7pYyMYRhMnz6ddu3aUaJECTw8PMiWLRtVq1Zl7NixpKSkPDDePXv20KJFC3LmzImHhwd169Zl48aNVn2GDx+OyWTigw8+cDhOvXr1MJlMrF+/3qo9Li6O4OBgsmbNSu7cuWnZsiX79+93OM7DrhfgwoULvPfee/j5+eHm5kauXLlo0qQJsbGxVmOZ7/GkSZOsYry3FntwcDAmk8luOZ34+Hib9QDreu5bt24lNDSU3LlzYzKZ+O233wD47bff6Nu3L5UqVcLLywtXV1eKFi3Km2++yZkzZx44182bN4mJiaFQoUK4urpSvHhxhg4davX6MTt58iTdu3fH398fd3d3cuXKRenSpenatSsHDhxweJ9FRERERERE5PGonIuIyBNISEjgxRdfZN26deTJk4egoCCcnJzYtGkTnTt3Ztu2bXzzzTcAFC9enKioKKZMmYKHhwetW7e2jBMQEGAzdrdu3Zg0aRJ169alZMmSJCYmAnD79m3at29Pzpw5KVWqFBUrVuT8+fNs2rSJ7t27s3XrVocP79y+fTvdu3enQIECNGjQgMOHD7N27VoaNGjAtm3bKFOmDACvvvoqAwYMYNKkSXzyySc4O1v/5+Lw4cPExcUREBBAUFCQpX3BggWEh4eTnJxMzZo1KViwIFu3bqVatWo0b978gffS0fWePn2aOnXqcPToUQoWLEhYWBjnzp1jxYoVLFu2jJEjR9KrVy+re2yuYd+4cWPy589vOfak1q5dS5cuXShRogQhISGcOXMGJ6e7n0cPGTKE2bNnU6ZMGWrVqmVJsI8bN4758+ezfft2fHx8bMZMTEwkJCSE33//napVq1KyZEni4uKIiYnh2rVrfP7555a+p06dsqx3uXLlaN68Obdu3eL48eNMmDCBGjVq4O/v/8TXKSIiIiIiIiL/oyS6iMgTeO+991i3bh0dO3Zk7NixZM2aFYBz587RvHlzvv32W5o3b06zZs0ICgoiKCiIKVOmkCdPHoeJbrO5c+eyc+dOSpcubdXu7OzMnDlzCA0NJXPmzJb2c+fO0bRpU6ZMmUKnTp2oU6eOzZhff/01Q4cOpW/fvpa2Xr168eWXXzJs2DCmTp0K3H1Yanh4ONOmTWPx4sW89NJLVuNMnDgRwzB4/fXXLW3Xrl2jc+fOJCcnM23aNCIjIwG4c+cOnTt3ZsqUKY91vd26dePo0aN07NiR7777DhcXF+Duw14bN27Me++9R4MGDShXrpzlHptr2MfExNjsKn8SkyZNsrl/Zl26dGHUqFF4e3tb2lJSUvj888/5+OOP+fDDD/n+++9tztu0aRO1a9fm4MGDloefbt++nRo1ajBq1ChiYmIsr6uJEydy/vx5RowYwbvvvms1zvHjx7lz585Dr+HQoUN2280fWoiIiIiIiIiINZVzERF5TGfPnmXixIkUKVKECRMmWBKdAF5eXnz77bcAln8+qn79+tkklOFuEr1Vq1ZWCXTznIMHDwbu7gi3JygoyCYB/OGHHwJ3d1nfq2vXrsDdxO297ty5w5QpU8icOTOvvPKKpX3WrFmcP3+eRo0aWRLo5nhHjRpldX9Se71Hjx5l0aJFeHp6MmbMGEsC3Xwt3bp1Izk5mbFjxz5w7LRSpkwZ3nvvPbvH6tevb5VAB3BycuKjjz7C19fX4Zo4OTkxceJESwIdoHLlyrz44ovcuHGD7du3W9rPnj1rmet+hQoVolixYo98TSIiIiIiIiLyYNqJLiLymOLi4khKSqJJkya4urraHC9fvjzZsmVj27ZtjzV+ixYtHnj8t99+IzY2luPHj3Pjxg0Mw+DatWuA493GISEhNm25c+cmd+7c/Pnnn1btderUoVSpUixdupTTp0/j6+sLwMKFC/nrr79o27atVeLXXBu9TZs2NnPkzJmTkJAQ5s6d+0jXax6zadOm5MiRw+Z4x44dGTlyJOvWrXM4blpq3ry5pa69PRcuXOCXX35h7969XL58meTkZACSkpK4ePEiFy9eJFeuXFbnFC5cmBIlStiMZW67d10qVaoEQPfu3fn888+pXbu2Tamdh/Hz87Pbfvc1k/RIY4mIiIiIiIj8GyiJLiLymMwPphw3bhzjxo1z2O/mzZuPNX7BggXtticmJhIdHc306dMdnmtOpt+vQIECdtuzZs3KhQsXbNq7dOnCO++8w/fff8+AAQMAmDBhAoBVKRfA8vBMR3E7an/QcfOYhQsXtnuOud3egzufhgddw/Tp0+nSpQvXr1932OfatWs2SfQHrQncrYFvFh0dTWxsLD///DP169fH3d3dsmu9U6dO5M2b91EuR0RERERERERSQUl0EZHHZN5lHBgYSLly5dJ8fDc3N7vtI0eOZPr06ZQpU4bhw4dTsWJFcubMiYuLCwcPHsTf3x/DMOye+6Bd1PZERUXx/vvv8/333/Phhx9y6tQpli1bRtGiRW1KipjnfNQ5zBxd74PGNLc/7pz3S0lJeeBxRzEeP36c6OhoDMPgyy+/pFmzZvj6+pIlSxYAatasyaZNm+yuy6PEnilTJmbOnElMTAwLFixg9erVbN68mbVr1zJ48GCWLVtG9erVUz2eiIiIiIiIiDyckugiIo/JvIM4ODiYkSNHPrN5582bB2BJpN/r6NGjaTpXjhw5aNu2LZMnT2b58uVs2rSJlJQUOnfubJP89fHxAe4mlO05ceLEI89vHvPYsWN2j5u/DXB/LfIHMdeSt7dj/OTJk48Y4V1LliwhMTGR3r1707NnT5vjab0ugYGBBAYGMnDgQK5evconn3zCyJEj6dmzJ1u2bEnTuURERERERET+7fRgURGRx1SvXj0yZcrEokWLLLvSU8PFxYU7d+489ryXLl0C4IUXXrA59vPPPz/2uI6YHzD67bff8v333+Ps7Ex0dLRNv6CgIODuA0bvd/nyZWJjYx95bvOYixcv5vLlyzbHf/zxRwBq166d6jHNCfeDBw/aHHucGOHBa7J27Vr+/vvvxxo3NTw9PRk0aBAmk4k9e/Y8tXlERERERERE/q2URBcReUy+vr5ER0dz6NAhOnbsyPnz5236bNy4kSVLlli1+fj48Pfff9tNCqeG+YGT33zzjVX77NmzmTp16mON+SDVq1enfPnyzJ07lxMnThAaGmp353dERAS5cuWy1Ow2S05Opnfv3g+sFe5I0aJFadasGdeuXaNnz54kJf3vwZebNm1i3LhxZMqUiTfffDPVY9atWxeAESNGcOPGDUv7ihUr+PLLLx85Rvjfmvz4448kJCRY2k+fPk23bt0ea0x7fvjhB/bu3WvT/t///hfDMB5ad15EREREREREHp2S6CIiT2DMmDHUq1eP6dOnU7RoUerUqUO7du0IDg6mQIEC1KpVy2Z3c4sWLbhz5w4VK1akQ4cOdO7cmeHDh6d6zr59+5IpUyZiYmKoXLky7du3p0qVKkRERNCrV6+0vkTgf7vRwfaBomaenp6MHz8eJycn2rZtS1BQEO3bt8ff35/Zs2fz8ssvP9bc3377LUWKFGHq1Kn4+fkRGRlJw4YNqV27NgkJCQwbNuyRatJHRkbi7+/Pxo0bKVmyJK1bt6ZatWo0btz4kZLx92rRogWlS5dm+/btFC9enNatWxMaGkqJEiXImTMnNWvWfKxx7zdnzhzKli1L8eLFadmyJe3bt6dmzZq0bNmSTJkyMWjQoDSZR0RERERERET+R0l0EZEn4O7uTmxsLBMnTqRixYrs3buXefPmceTIEYoVK8awYcPo06eP1TmDBw/mrbfe4s6dO8ycOZPvvvuOxYsXp3rOOnXqsH79eurXr8/Ro0dZtGgRmTNnZs6cOXTv3j2tLxGABg0aAHfrwDdu3Nhhv/DwcJYvX07t2rXZuXMnS5cupVSpUmzatInixYs/1ty+vr5s27aN3r174+zszNy5c9mxYwcNGjRg2bJlvPvuu480XpYsWVi5ciWRkZFcu3aNJUuWkJKSwsyZMx/7/mXOnJl169bxxhtv4ObmxqJFi9i3bx89evRg+fLluLi4PNa493v33Xfp3r072bJlY926dcybN4+zZ88SGRnJtm3baNWqVZrMIyIiIiIiIiL/YzIMw0jvIEREJGMbNGgQH3zwAR9//DEDBw5M73DkKTh06BAAfn5+6RyJiIiIiIiISMaiJLqIiDzQ1atXCQgI4MKFCxw7dgwfH5/0DkmeAiXRRUREREREROxzTu8AREQkY5o0aRJxcXGsXbuWP//8k169eimBLiIiIiIiIiL/OqqJLiIidsXFxTFlyhSuX79Ojx49GDx4cHqHJCIiIiIiIiLyzKmci4iIiKici4iIiIiIiIgDKuciIiIiAJw6m8DHP6xJdf9pnwY/rVBEREREREREMgyVcxERERsmk8nqx8nJiRw5clC7dm0mTpzI/V9iMplMFC5cOH2CTYWBAwdiMpmYPHlyqs9Zs2YNJpOJ6OjoJx5LRERERERERJ5f2okuIiIORUVFAZCcnMyRI0fYsGED69evZ+XKlUyfPj2doxMRERERERERefqURBcREYfu3229fPlymjZtyowZM3j55ZcJDQ0FYN++fbi4uKRDhKnz1ltv0a5dO7y9vTPUWCIiIiIiIiKS8amci4iIpFqjRo3o2LEjAPPnz7e0BwQEUKxYsXSK6uHy5MlDQEAA2bNnz1BjiYiIiIiIiEjGpyS6iIg8ksDAQABOnjxpabNXE/3emuJ//vkn0dHR5MuXjyxZslCxYkWmTp3qcI5z587Rp08f/P39cXNzI2fOnLz44ousXbvWpu+98/z111907tyZAgUK4OzszJdffgk8uI55fHw8kZGR5M6dm6xZs1KzZk0WL17sMDZHYwUHB2MymYiPj2f+/PlUr14dDw8PcuXKRWRkJKdOnbI7nmEYTJkyhTp16pAjRw6yZMlCuXLl+OKLL0hKSrLpf+HCBfr370/p0qXJmjUr2bNnp0SJErzyyits3brVYdwiIiIiIiIi8nhUzkVERB7JtWvXAHB1dU1V/4sXL1K9enVu375NcHAwly5dYvXq1URFRXHs2DE+/vhjq/779++nYcOGnD59mmLFitG0aVMuXLjAqlWriI2N5YcffqB9+/Y285w7d44qVapw584dgoKCuHXrFu7u7g+M7ciRI9SsWZOzZ89SokQJKlasyLFjx2jevDndunVL5R2xNnbsWEaMGEHlypVp0qQJ27ZtY8aMGezYsYNdu3aRJUsWS9+UlBTatWvHrFmz8PT0pEqVKmTNmpUtW7bw3nvvsXr1ahYuXIiT093PvK9fv0716tU5fPgwfn5+NG7cGIATJ04wffp0ihYtStWqVR8rbhERERERERGxT0l0ERFJNcMwWLRoEQDlypVL1TkLFy6kUaNGzJs3Dw8PDwC2bdtG/fr1+fTTT3nppZeoUKECcPcBphEREZw+fZrRo0fTo0cPTCYTADt37qRRo0Z06dKFhg0bkjdvXqt5lixZQsuWLZk2bRpubm6piu3NN9/k7NmzvPnmm3z11VeWZPXEiRN5/fXXUzXG/caOHcvy5cupX78+ADdu3KBRo0Zs3LiR6dOn06lTJ0vfL774glmzZtGoUSN++uknvLy8AEhISCAyMpKFCxcybtw4unfvDsDs2bM5fPgwPXr0YMyYMVbznj17lrNnzz5WzCIiIiIiIiLimMq5iIjIQyUnJ3Po0CE6derEpk2bcHV15dVXX03VuSaTia+++sqSQAeoUqUK3bt3JyUlhXHjxlnaFy5cyN69e4mMjOTtt9+2JNDhbhmZAQMGkJCQwI8//mgzj6urK1999VWqE+hHjhwhNjaWnDlzMmzYMEsCHaBz587UrFkzVePcr1evXpYEOoC7uzu9e/cGsCpHc+fOHYYPH062bNmYNm2aJYEO4OHhwYQJE3B1deXbb7+1tJuT5PeOb5Y3b17KlCnz0PgOHTpk9ycxMfHRL1ZERERERETkX0BJdBERcchkMmEymXB2dqZEiRJMnjyZbNmyMX369FQ/SDQwMBB/f3+b9sjISADWr19vaVu+fDkAYWFhdscKCgoC7u5kv1/FihXx9fVNVUwAGzZsAKBp06ZWCX6zdu3apXqse4WEhNi0lShRAoA///zT0rZz507Onz9PUFAQefLksTknX758+Pn5sXfvXm7evAlApUqVAOjfvz+LFi3i1q1bjxWjiIiIiIiIiKSeyrmIiIhDUVFRADg5OeHp6UnZsmVp1aoVOXPmTPUYhQoVsttufhDpmTNnLG3x8fEAtG3blrZt2zoc8/z58zZtBQsWTHVM987r6LxHHc+sQIECNm1Zs2YF4Pbt25Y287UuXbrUase9PRcvXsTX15cGDRrQq1cvvvzyS5o3b07mzJmpUKECISEhvPbaazYPd7XHz8/PbvuhQ4cA2weZioiIiIiIiPzbKYkuIiIOTZ48+ZnOl5ycDMCLL75oU/P8XgEBATZtqS3jYmYYBsBDE9iPKrXjma/Vz8/voaVj7n2I68iRI+natSsLFixg5cqVbNiwga1btzJs2DBmzpzpcBe/iIiIiIiIiDweJdFFROSpOn78+APbfXx8LG3mXdzdunWjRYsWTzUu87yO4jtx4sRTnd98rWXKlHnkDyv8/f3p27cvffv25datW3z99df06dOHrl27KokuIiIiIiIiksZUE11ERJ6q3377jYMHD9q0T58+HYBatWpZ2ho2bAjA/Pnzn3pc5nmXLFlCQkKCzfEZM2Y81fmrVKlC9uzZWb16NVevXn3scdzc3Ojduzfe3t6cPXvW8vBREREREREREUkbSqKLiMhTlZKSwttvv82NGzcsbTt27ODrr7/GycmJrl27Wtpbt25NQEAAkydPZujQoSQlWdfoTkxMZO7cuezZs+eJ4ypevDgNGjTg0qVLxMTEkJKSYjk2adIkNm7c+MRzPIirqyt9+vTh8uXLhIeH290Rv3v3bmbOnGn5ff78+WzevNmm386dO/n777/Jli3bI9WrFxEREREREZGHUzkXERF5qkJDQ9m9ezfFihWjTp06XLlyhVWrVpGUlMSHH35IpUqVLH2dnZ2ZN28ejRs3JiYmhtGjR1OuXDk8PT05efIk+/fv5/Lly8ybN4+yZcs+cWzjxo2jVq1a/Oc//2H58uVUrFiRY8eOsWXLFrp168Y333zzxHM8SP/+/fnjjz+YPn06/v7+VKxYkYIFC3L+/HmOHj3KsWPHeOmllywPWV2zZg2jR4/G19eXwMBAPD09OXPmDOvXryclJYXPPvsMFxeXpxqziIiIiIiIyL+NkugiIvJU5c6dm02bNtGvXz+WLVvG1atXKVWqFO+88w7R0dE2/QMCAvjtt98YM2YM8+bNY/369RiGgbe3N3Xq1KFly5aWsi9Pys/Pj82bN/P++++zfPlyFixYQNmyZZk/fz6enp5PPYnu5OTEtGnTCA8PZ+LEiWzfvp3t27eTJ08eChUqRFRUFO3atbP0j46OxtnZmbVr17J161auXLlC/vz5adq0Kb169SI4OPipxisiIiIiIiLyb2QyDMNI7yBEROSfZ82aNdSrV4+oqKhHfnCmPHuHDh3i1NkEJiy7nOpzpn0a/NTiEREREREREckotBNdREREACiQ14Npn1ZI7zBEREREREREMhQ9WFRERERERERERERExAEl0UVEREREREREREREHFA5FxEReSqCg4PRYzdERERERERE5HmnJLqIiIgAcOpsAh//sCbV/fVgUREREREREfk3UDkXEREREREREREREREHlERPhfPnzzNgwAACAwPJkSMH7u7uFC9enC5durB37970Du+pGDhwICaTicmTJz+V8aOjozGZTFY/WbJkISAggF69evHXX39Z9V+zZg0mk4no6OinEs+zkpGuw7wGa9aseepz3b59m5w5c2IymWjcuHGajfs4r9OMtAZPS3BwMCaTifj4+FT1nzx5ss370cPDAx8fH4KDg+nXrx+///770w3aDpPJROHChZ/5vKlRuHBhTCZTeochIiIiIiIiIs+AkugPsWLFCvz8/Pj88885ffo0devWJTQ0FBcXFyZMmECFChUYMmRIeof53KpVqxZRUVFERUVRr149zp49y5dffklgYGCqE4CS8S1cuJDLly8DsHLlSv7888/0DUjsKlasmOX9+NJLL1GmTBl+//13hg0bRpkyZejQoQNXr15N7zCfuvj4eEwmE8HBwekdioiIiIiIiIhkAKqJ/gDbtm2jWbNmJCUlMXjwYPr06YOz8/9u2ZIlS+jQoQPvv/8+7u7uvP322+kYbdp66623aNeuHd7e3k91ns6dO1vtCD537hxNmzZl+/bt9OnTh9mzZz/V+f/NBg8eTExMDAULFnzqc/3www8AeHt78+effzJ9+nTefffdJx73Wb1O/y2CgoJsdvUbhsHixYvp0aMHP/30E6dOnWL58uW4uLg89Xj27dv3TOZ5HCtXriQpKSm9wxARERERERGRZ0A70R0wDIOoqCgSExP59NNPiYmJsUqgAzRt2pT58+djMpno168fx48fT6do016ePHkICAgge/bsz3ReLy8vRowYAcDixYuVpHqKvL29CQgIwN3d/anOc+HCBZYuXYqHhwfffvst8L+k+pNKr9fpv4nJZCI0NJQtW7bg4+NDXFwc48aNeyZzBwQEUKxYsWcy16MqVqwYAQEB6R2GiIiIiIiIiDwDSqI7sHTpUvbt24evry/9+vVz2K9OnTpERERw69Ytvv76a6tj5pq5hmHw1VdfUb58edzd3alQoYKlz9mzZ3n99dfJly8f7u7uVKxYkWnTpjksJ3D58mW++uorGjduTKFChXB1dSV37tw0adKE5cuX243x3vrI8+fPp3r16nh4eJArVy4iIyM5deqUzTkPqjWdlJTE2LFjqVWrlqVGfIkSJXj99dfTpEZ8YGAgALdu3eL8+fM2xy9evMgbb7yBt7c3rq6ulClThu+//97heJs2beKll17Cy8sLV1dXChcuzJtvvsmZM2fs9l+2bBmNGzemQIECuLq64uPjQ1BQEJ988olVv3vv0ZYtW2jcuDE5cuTA09OTRo0asXnz5gde59O8DnON64EDB3Lw4EHatWtHvnz5cHJyYv78+cCDa6InJCQwePBgKlasSLZs2ciaNSulSpXinXfeeeQPi2bMmEFSUhJhYWGEhoZSoEABfvvttwfW2D5//jzvv/8+ZcqUwcPDgxw5clChQgU++OADLly4YOn3oNdpfHw8kZGR5M6dm6xZs1KzZk0WL17scM6kpCS+/fZbqlatSp48eXB3d6dw4cKEhoYyY8aMVF/v4sWL6dSpEyVLlsTT0xMPDw/Kly/PoEGDuH37tk3/e9fqxIkTtG/fHi8vL7JkyULlypVZuHChw7nGjx9P2bJlcXNzw9fXlx49enDlypVUx/oo8ubNy6effgrAmDFj7PZZuHAhjRs3Jnfu3Li5uVGiRAkGDBjA9evXbfre+3dp2rRpVK9enWzZspEjRw5Ln/tros+ZMweTyUS7du0cxvnmm29iMpmYMGGCpe23336jb9++VKpUyfL+KVq0qN33z8CBAylSpAgAcXFxVnXi7/3WzP010Xfs2IHJZKJ69eoOYxs2bBgmk4kPPvjAqj0xMZHRo0dTpUoVsmXLhoeHB1WrVuW7777DMAybcU6ePEn37t3x9/fH3d2dXLlyUbp0abp27cqBAwcczi8iIiIiIiIij0dJdAeWLFkCQERExEPLCbRv3x64m3i3p1u3bvTu3Zu8efPSokULihYtCtxNFNasWZOJEyfi6upKixYtyJ49Ox07dmTUqFF2x9q8eTNvv/02+/btw8/Pj5YtW+Lv709sbCyNGzd+YBJ27NixhIeHYxgGTZo0IWvWrMyYMYP69etz8+bNh94TuJtcbdCgAd27d2f37t3UrFmT5s2bkyNHDiZPnpwm5VeuXbtm+XdXV1erY5cvX6ZGjRrMmzePKlWqUKtWLfbv389rr73GxIkTbcb68ccfqV27NgsXLsTf359WrVrh6urKuHHjqFixIvv377fq/80339CkSRPi4uIoWbIk4eHhlC5dmvj4eAYOHGg33o0bN1KnTh1OnTrFiy++iL+/PytWrKBu3boOP9h42tdhduDAAapUqcLWrVupV68ejRo1eujr+c8//6Rq1ar079+f48ePU79+fZo0aULmzJkZM2YMq1evfuD59zPvOu/QoQMmk8nyfnG0G/2PP/6wPGvg4sWLNGnShODgYG7fvs2gQYPYs2fPQ+c8cuQI1apVY8aMGeTJk4fmzZuTkpJC8+bN+fnnn+2e07FjR7p168axY8eoWbMmLVq04IUXXmDdunV88803qb7e1157jVmzZpE9e3aaNGlC7dq1OXnyJB988AFNmzYlOTnZ7nnx8fFUqVKFDRs2EBQURGBgIDt27CAsLIzY2Fib/n369KFr164cOnSI+vXrU6NGDX766Sfq1atnN1mfFtq0aYOTkxNHjhzh5MmTVsd69+5NixYtWLt2LWXKlKFZs2YkJiby+eefExwcTEJCgt0xBw8eTMeOHcmcOTOhoaGUKVPG4fyhoaFkz56dX375xW5i/s6dO8yaNYvMmTPTunVrS/uQIUMYOXIkycnJ1KpVi6ZNm2IYBuPGjaNy5cpWifQKFSoQHh4OQL58+Sw14qOioggKCnIYW6VKlQgICGDLli0cOXLEbp9p06YB//tvBtz9m9qwYUPeeecd4uPjCQoKIjg4mMOHD9O5c2feeOMNqzFOnTpFxYoVGTt2LG5ubjRv3pzatWtbntOxadMmhzGKiIiIiIiIyGMyxK5atWoZgPHDDz88tO/JkycNwHBycjISExMt7YUKFTIAI0+ePMbevXttznvttdcMwGjZsqVx69YtS/uKFSuMzJkzG4BRt25dq3OOHj1qbNiwwWasX3/91ciRI4fh6elpXLt2zepY3bp1DcDw8PAwVq5caWlPSEgwatasaQDGd999Z3XOxx9/bADGpEmT7MZcr1494/z581bHTp06ZWzfvt3+TbpPVFSU3fENwzC++eYbAzB8fX0tbatXrzYAAzDCw8ON69evW47Nnz/fAIyCBQtajXPixAkjS5YshrOzs7Fw4UJLe3JysvHOO+8YgFGlShWrcwoVKmR4enoax44ds2pPSUkxVq1aZdVmvkeA0b9/fyMlJcVybOzYsQZg+Pj4GDdv3nzm1zFp0iTLPG+99ZZx584d437mNVi9erVVe4MGDQzAiIyMtIrPMAzj4MGDxr59+2zGcuTgwYMGYOTLl88Sw549ewzAeOGFF6zumWEYRlJSkhEQEGAARu/eva3eT4Zx93V+8uRJy++OXqchISEGYLz55ptGcnKypX3ChAmW+xIVFWVpP3bsmOU+3rtehmEYN27cMDZu3Jjqa543b57Nfbt69aoRGhpqAMaUKVOsjt27Vj169DCSkpIsx7788ksDMGrXrm11zoYNGwzAyJUrl9XflvPnzxvlypWzjHf/69gRcwz33hNHihcvbgDGsmXLLG0zZ840ACMwMNBqzsTERKNLly4GYPTp08dqHPPfJTc3N2PNmjV25wKMQoUKWbV16tTJAIypU6fa9F+8eLEBGGFhYVbtK1euNM6cOWPVlpycbHzyyScGYLz66qtWx8yvh/v//t7L/Pf9Xp9++qkBGJ9++qlN/z/++MMAjAoVKli1v/HGGwZgdOzY0epv99mzZ41q1aoZgLFo0SJLu/k1P2LECJs54uPjjcOHDzuM2ezgwYN2f/bu3WusWr/TiBywOtU/IiIiIiIiIv8G2onugLlkRN68eR/a18vLC4CUlBQuXrxoc7xfv36ULl3aqu369ev89NNPODs7M3r0aKsd1w0aNHBYrqBIkSLUrFnTpj0wMJDu3btz9epVhzuFe/XqRf369S2/u7u707t3bwDWrl37kKu8u0N58uTJZMmShalTp5I7d26r476+vlSqVOmh4zhy7tw5Jk2aRN++fQFsdmACeHp6Mn78eDw8PCxtL730EmXLluXEiRPEx8db2idOnMjNmzeJjIwkNDTU0u7k5MSQIUPw8fFh27ZtVmVXzp49S5EiRaxKSMDdshL16tWzG3ehQoX45JNPrEo7vPHGG1SrVo0zZ84wb968Z34dZl5eXgwdOpRMmTLZjf1+W7duZeXKleTPn58JEyZYxQfg5+f3SHWgzbvN27VrZ4mhTJkylCtXjpMnT9qUkpk7dy779++nXLlyDBs2zGbXfGBgIAUKFHjgnEeOHCE2NpacOXMybNgwnJz+92euc+fOdt8/Z8+eBaBmzZq4ublZHcuSJQs1atRI3QUDYWFhNvctW7Zslm+XLFiwwO55RYsWZcSIEVbPXujevTs5c+Zk8+bNJCYmWtrNO+N79+5t9bcld+7cDB8+PNWxPo48efIAcOnSJUvboEGDAJg+fbrVe8fFxYXRo0eTP39+Jk6cSEpKis14r732GnXr1k31/C+//DIAP/30k80x805vcx+z+vXr2zx81snJiY8++ghfX1+Ha/KoHhSbue3e2M6ePcvEiRMpUqQIEyZMIGvWrJZjXl5elmcImP9pPsd8TfcrVKhQhq0hLyIiIiIiIvI8UxLdAeP/16E17NSjddQXsEqkmrVo0cKm7ddff+XWrVtUr16dF154weZ4RESEw/mSk5OJjY1l4MCBdOvWjejoaKKjoy3J80OHDtk9LyQkxKatRIkSwN0E+cOsXr2a5ORkmjZt+tBEZmq9+uqrlnrDefPmpVOnTly9epWoqChiYmJs+leuXJlcuXLZtNu7jnXr1gG2CTW4WybGfI/N/eBuSYZdu3YRExPjsCTD/cLDw20eOgsQGRkJwPr165/5dZg1bNjwkR4cumLFCstc9yeCH4c5cdixY0erdvPvP/74o935X3/9davk96PYsGEDcPfBv/auwd4HVAEBAXh4eDBp0iQmTJhgVXf9cRw6dIjRo0fTo0cPOnXqRHR0NJ999pnlmD3BwcE2Hxo4OztTtGhRkpKSrGIyv6batGljM05ISIjd11ZaMf+9M/+tO3v2LLt27aJkyZL4+/vb9Hdzc6Ny5cpcvnzZ7rXb+/v4IMHBwfj6+rJixQpLQhngxo0bLFiwAE9PT6sPm8wuXLjApEmT6N27N6+99prl72ZSUhIXL160+wHooypatCjVq1fnwIED/Prrr1bHZsyYgZOTk9XrLy4ujqSkJJo0aWJTugqgfPnyZMuWjW3btlnazB9Udu/endWrV3Pnzp1HjtPPz8/uT+bMmR95LBEREREREZF/A9vMnwB3d1seOHDAKknjyLlz54C7SaWcOXPaHC9YsKBNm7kGr70EuqNz4G493NDQUHbt2uUwnntrit/LXuLbvPMxNTWUzTWQ03KnY61atShevDhwN9lWqFAhXnzxRauHr97LUfLe3nWY7/H9u8rNzO331kP++uuvCQsLY+jQoQwdOhQfHx9q165N69atadWqld3EbqFChVI9/rO6DjNHryNH0nKN169fz9GjRwkICLD5hkL79u3p168fs2fP5uuvv7bs/k6L+c33wdG122v39PRkwoQJdOnShS5dutC1a1f8/f2pV68er7zyygMfFnkvwzDo06cPo0aNcvgB3KO8P8Hxa8JkMj3w70daJIXtMT/s15yoNz9odt++fXY/RLz/3PsT7Y/6GjUnokeMGMHMmTPp0aMHgKVO+quvvmrzbYLp06fTpUsXu3XUza5du5YmHz68/PLLbN68mZ9++omKFSsCd59lceTIEerVq2e1zuZvnIwbN45x48Y5HPPeZ1ZER0cTGxvLzz//TP369XF3d6dy5cq8+OKLdOrUKVXfnhIRERERERGRR6MkugPly5dnw4YN7Nixw2YX7f127NgBQOnSpe0+tPH+hM69HCWdHCXgOnfuzK5du2jVqhX9+vXD39+fbNmy4eTkxPjx4+natavDcx+W4EqttBoH7l5PdHT0U537Yefce7xcuXL88ccf/Pe//2XJkiXExcUxc+ZMZs6cSVBQECtXrkz1bs0HfYvhaV+H2YNee08yV2qYS7lcvHjR7gMZXVxcuHr1Kr/88ovNjuonmf/+ndKpFRkZScOGDVmwYAGxsbHExcVZkpvvvfcew4YNe+gYM2fOZOTIkRQoUIAvv/ySGjVq4OXlhYuLC4mJibi6uj719+fTdPXqVY4ePQpAqVKlACwPSvX29rb7bZd73V8CCh7vNfryyy8zYsQIpk2bZkmiOyrlcvz4caKjozEMgy+//JJmzZrh6+tLlixZgLslfDZt2pSqbx2lRtu2benVqxczZsxg+PDhODk5OYzNfO8CAwMpV65cqsbPlCkTM2fOJCYmhgULFrB69Wo2b97M2rVrGTx4MMuWLUv1hz4iIiIiIiIikjpKojvw4osvMnbsWGbPns3w4cPtJsfNzAmSJk2apHp8c33eEydO2D1u3pF7r4SEBJYvX06+fPn4+eefbepcm5NbT4t51+vhw4ef6jxpxcfHhwMHDnDs2DFLmZR7mXfQ3l8r2c3NjbCwMMLCwgD4448/iIyMZP369Xz33Xc2tdrN49zPvLY+Pj7pch2PI63W+Pbt28yaNQu4W+7jQd/o+OGHHyxJ9LSY33y/H7Yu9nh5edG5c2c6d+6MYRgsW7aMtm3bMnz4cKKjoy2JY0fM9e/HjRtnU1IkLd+f3t7exMfHc/LkScs3Oe71oGt8Ej///DOGYVCiRAnLfTbvrM6fPz+TJ09+KvPeLzAwkJIlS7J582aOHj1Kzpw5WbZsGd7e3jbPLliyZAmJiYn07t2bnj172oyV1n83vby8aNSoEUuXLmXNmjXUrVuXn3/+GVdXV8LDw636mu9dcHAwI0eOfKR5AgMDCQwMZODAgVy9epVPPvmEkSNH0rNnT7Zs2ZJm1yMiIiIiIiIiqonuUNOmTfH39+f06dMMHTrUYb+1a9cye/ZsMmfOTPfu3VM9fsWKFXF1dWXz5s2cOnXK5vjs2bNt2q5cuUJKSgre3t42CfQ7d+7YfYBlWgoODiZTpkwsWbKE06dPP9W50kLt2rUB+w/5S0xMtCR5zf0cKVWqlGVt9+zZY3N8zpw5lh2l95oxYwZwt2TNk0ir60iNhg0bWua6cePGY4+zaNEiLl26RJUqVTAMw+7P1atXcXNzY9myZZYSIeb5J06c+Ng7g833e8mSJSQkJNgcN6/Lw5hMJpo0aUKzZs0A2Lt370PPMT9s016ZlZ9//jlV86aGeWe/ee3vtXz58qdSyuXs2bN8/PHHAFbJ6AIFCuDv78/u3bs5duxYms/riHlX97Rp05g1axaJiYlERkbalFx60JqsXbuWv//+26bd/G2Tx6k3fn9sK1eu5O+//6ZZs2bkyJHDql+9evXIlCkTixYtsvs3JLU8PT0ZNGgQJpPJ7t8oEREREREREXkySqI74OTkxOTJk3FxceGjjz5i6NChNkmOpUuXEhYWhmEYDBkyxGHNanuyZctG+/btuXPnDr169SIxMdFybM2aNUyfPt3mnLx585I9e3b27t1reXgi3C0J0LdvXw4ePPjoF/oIfHx8eOWVV7h58ybR0dE2ibozZ87YPEwvPb322mtkyZKF6dOns3jxYkt7SkoK/fv35/Tp01SpUsVS+uDGjRuMGTOGy5cvW42TkpJCbGwsYL9+8/Hjx/nkk0+s2saPH8+mTZvInz8/LVu2fKbX8SSqVq1KvXr1+Ouvv+jatatNIv3w4cPs37//oeOYS7mYH65qT7Zs2WjatClJSUmWxHarVq0oUaKE5eGu9ycxf/vtN7sfOt2rePHiNGjQgEuXLhETE0NKSorl2KRJk9i4caPNOTt37mTu3LkkJSVZtV+6dMmyqzc1tbvN3xQYP3681YcA69atY/jw4Q89P7W6du0KwMiRI9m3b5+l/eLFi/Tt2zfN5oG75XGWLFlC9erVOXPmDPXr16dLly5WfT788EOSk5MJDw+3+2HDkSNH+P7779M0rvbt2wN3P/BxVC4F/rcmP/74o9WHKqdPn6Zbt252x86TJw8uLi4cOXLksZLbYWFheHh4MGfOHCZNmuQwNl9fX6Kjozl06BAdO3a0fJh0r40bN7JkyRLL7z/88IPde/zf//4XwzAeuca8iIiIiIiIiDyckugPUL16dX755Rc8PT2JiYnBx8eHsLAw2rZtS6lSpWjatClXrlzhs88+o1evXo88/pAhQyhSpAizZ8/Gz8+PyMhIGjRoQIMGDSxJqnvrbzs7O9O3b1/u3LlD3bp1CQkJoV27dhQvXpxvvvnmkXbCP67Ro0dTo0YNVqxYQeHChWnWrBlt27alWrVqFCpUiF9++eWpx5BaBQsWtCQzmzdvTu3atWnfvj2lSpVixIgR5MuXj6lTp1r6JyYm0rNnT/Lly0eNGjWIjIwkPDycIkWKMGfOHIoWLWpJXt7r9ddfZ8iQIZQpU4b27dtTtWpVunbtiouLC5MmTbLUXn5W1/GkfvjhB0qUKMGPP/5IwYIFCQsLIyIigsDAQEqUKMHmzZsfeP7FixdZunQpTk5ONrXO79euXTvgboIT7r7G58yZQ/78+Rk2bBiFChUiIiKCli1bUqpUKQIDA1NV6mXcuHF4eXnxn//8h1KlStG+fXtq1KjBa6+9Zjdxevz4ccLDw/Hy8qJhw4Z06NCB0NBQChcuzNGjR2nZsmWqPqR4++238fDwYOzYsZQpU4bIyEjq1KlD3bp1HSZsH0dQUBDvvPMO58+fJzAwkNDQUCIiIvDz8wN47A9U1q9fT3R0NNHR0bRv357GjRuTN29emjVrxrFjx+jYsSPz58/H2dm6EliHDh3o27cvO3fupEKFClSpUoU2bdrQpEkTSpYsSfHixRkzZswTX/e9ihQpQs2aNdm/fz9xcXEEBARYHuR5rxYtWlC6dGm2b99O8eLFad26NaGhoZQoUYKcOXNSs2ZNm3MyZ85MkyZN+OuvvyhfvjyvvPIKnTt3tiTEH8bDw4OXXnqJy5cvM2PGDLJnz275RsP9xowZQ7169Zg+fTpFixalTp06tGvXjuDgYAoUKECtWrUsH+LB3W++lC1bluLFi9OyZUvat29PzZo1admyJZkyZWLQoEGpvIMiIiIiIiIiklpKoj9EkyZNOHToEB988AHe3t6sWrWKX375hVu3btG5c2d27tzJhx9++Fhj582bl02bNtGpUydu3rzJ/PnzuXDhApMmTbIkF+9/EF///v2ZMmUK5cqVY8OGDaxYsYLy5cuzefNmKleu/MTX+zDZsmVj9erVjBo1Cn9/f+Li4li0aBGXL1+mU6dOREREPPUYHkWHDh1Yu3YtoaGh7Nu3j9mzZ3Pz5k3eeOMNduzYQUBAgKVv1qxZ+frrrwkNDeXcuXP88ssvrFq1ipw5c/LZZ5+xY8cOcubMaTNHzZo1iYuLI3/+/CxatIh9+/bRoEED1qxZ80h18tPqOp6Ur68v27ZtY+DAgXh7exMbG8uyZctITEzknXfeoX79+g88f8aMGSQmJlK7dm18fX0f2Dc0NJRs2bKxZcsWyzcpypQpw2+//Ubv3r3x8PBg4cKFxMXF4erqyocffpiqBzD6+fmxefNm2rRpw9mzZ1mwYAGGYTB//nzatm1r07969ep8/vnnVKpUiQMHDjBr1iy2b99OuXLlmDJlSqpLsZQoUYJt27bRvHlzzp8/zy+//ML169f59ttv03QnOtzdhT5u3DiKFy/O8uXL2bBhA23atGH16tW4uro+1phHjhxhypQpTJkyhfnz57Nnzx5KlSpF37592bt3L1OnTiVbtmx2zx06dCgrV66kRYsWnDp1ivnz57Nz507c3d1577330nwnOljv7jbvTL9f5syZWbduHW+88QZubm6W92iPHj1Yvny5w+ddTJw4kY4dO3LhwgWmTZvGd999R1xc3GPFFh4e7nBN3N3diY2NZeLEiVSsWJG9e/cyb948jhw5QrFixRg2bBh9+vSx9H/33Xfp3r072bJlY926dcybN4+zZ88SGRnJtm3baNWqVapjFBEREREREZHUMRmPW3hYnqqhQ4cSExPDkCFD6NevX3qHI3YMHDiQTz75hEmTJhEdHZ3e4YiIPJFDhw4BWL7RICIiIiIiIiJ3aSd6OrNXQ3zt2rUMGjQIZ2fnh5bDEBEREREREREREZGnx/nhXeRpqlmzJj4+PpQsWRIPDw8OHz7Mzp07gf/VTBcRERERERERERGR9KEkejp7//33Wbx4MVu2bOHKlSt4enoSEhLCW2+9RfPmzdM7PBEREREREREREZF/NdVEFxEREdVEFxEREREREXFAO9FFREQEgFNnE/j4hzWp7j/t0+CnFYqIiIiIiIhIhqEHi4r8i5lMpkf6KVy4cHqH/FADBw7EZDIxefLkf0UMhQsXxmQyPfV50kN8fDwmk4ng4OB0mX/y5MmYTCYGDhxo1Z4RXmMiIiIiIiIi8uxoJ7rIv1hUVJRN2/r16zly5Ajly5enQoUKVsfy5MnzSOObTCYKFSpEfHz8E0QpIiIiIiIiIiKSfpREF/kXs7eTNjo6miNHjhAWFmazA1cynpUrV5KUlJTeYTwVvr6+7Nu3D3d39/QOxcpbb71Fu3bt8Pb2Tu9QREREREREROQZUBJdROQ5VqxYsfQO4alxcXEhICAgvcOwkSdPnkf+VoaIiIiIiIiIPL9UE11EUi0xMZHRo0dTpUoVsmXLhoeHB1WrVuW7777DMAxLP3MtaYDjx49b1VW/t761uZ63YRh89dVXlC9fHnd3d0sZGcMwmD59Ou3ataNEiRJ4eHiQLVs2qlatytixY0lJSXlgvHv27KFFixbkzJkTDw8P6taty8aNG636DB8+HJPJxAcffOBwnHr16mEymVi/fr1Ve1xcHMHBwWTNmpXcuXPTsmVL9u/f73Cch10vwIULF3jvvffw8/PDzc2NXLly0aRJE2JjYx845v327dtHx44dKVasGG5ubnh5eVGhQgXeeecd/vzzT5v+S5YsoVGjRuTMmRM3Nzf8/f2JiYnh8uXLdudNSEigX79+FCxYEDc3NwICAhg5ciSGYditn/+wOuL2rsNRTfR7a5WfOHGC9u3b4+XlRZYsWahcuTILFy60O8fjXKc9jq4lODgYk8lEfHw88+fPp3r16nh4eJArVy4iIyM5deqU3fEMw2DKlCnUqVOHHDlykCVLFsqVK8cXX3xh91sGFy5coH///pQuXZqsWbOSPXt2SpQowSuvvMLWrVtTfR0iIiIiIiIikjpKootIqiQkJNCwYUPeeecd4uPjCQoKIjg4mMOHD9O5c2feeOMNS9/ixYtb6q17eHgQFRVl+WnSpInN2N26daN3797kzZuXFi1aULRoUQBu375N+/btiY2NJW/evDRv3pxq1arx+++/0717dzp16uQw3u3bt1O9enUOHDhAgwYN8PPzY+3atTRo0IC9e/da+r366qu4uroyadIk7ty5YzPO4cOHiYuLIyAggKCgIEv7ggULaNCgAXFxcZQvX56QkBB2795NtWrVOHz48APvpaPrPX36NFWrVuWLL74gMTGRsLAwAgMDWbFiBY0bN2bUqFEPHNfs119/pVKlSvz00094eXnRsmVLqlWrZvkQ5MCBA1b9Bw8eTLNmzVizZg2VKlUiLCyMGzduMHToUKpVq8bff/9t1f/27duEhIQwbNgwbt68SfPmzSlcuDAxMTG89dZbqYoxLcTHx1OlShU2bNhAUFAQgYGB7Nixg7CwMLsfOjzqdT6usWPHEh4ejmEYNGnShKxZszJjxgzq16/PzZs3rfqmpKTQtm1boqOj2bVrF5UrV6Zx48acO3eO9957j7CwMKsPi65fv0716tUZPHgwSUlJNG7cmIYNG5I9e3amT5/OkiVL0uQaREREREREROQehojIPaKiogzA+Pjjj63a33jjDQMwOnbsaFy7ds3SfvbsWaNatWoGYCxatMjqHMAoVKiQw7kKFSpkAEaePHmMvXv32hxPSkoy5syZY9y+fduq/ezZs0blypUNwIiLi7M69vHHHxuAARhDhw61OvbOO+9YruFe7du3NwBj/vz5NjH069fPAIwRI0ZY2q5evWrkyZPHAIxp06ZZxWu+f4AxadKkR7re0NBQS3yJiYmW9nXr1hnu7u5GpkyZjF27dtkd817mGObMmWMzxx9//GGcOXPG8vvWrVsNJycnI1u2bMaWLVss7bdu3TIiIiIMwIiIiLAaY9CgQQZgVK1a1bh8+bKlfceOHYanp6fddTevy/335EHXcezYMQMw6tata9U+adIkyz3u0aOHkZSUZDn25ZdfGoBRu3Ztq3Me5zrN89z/XnB0LXXr1jUAw8PDw1i5cqWlPSEhwahZs6YBGN99953VOUOHDjUAo1GjRsbZs2ct7devXzeaN29uAMZ//vMfm5h69Ohh3O/vv/829uzZY9OeWgcPHjRWrd9pRA5YneofERERERERkX8D7UQXkYc6e/YsEydOpEiRIkyYMIGsWbNajnl5efHtt98CWP75qPr160fp0qVt2p2dnWnVqhWZM2e2avfy8mLw4MHA3R3h9gQFBdG3b1+rtg8//BCAtWvXWrV37doVgIkTJ1q137lzhylTppA5c2ZeeeUVS/usWbM4f/48jRo1IjIy0ireUaNGWd2f1F7v0aNHWbRoEZ6enowZMwYXFxera+nWrRvJycmMHTv2gWPD3fUCqF+/vs2xkiVLWj0Q8z//+Q8pKSm88847VK1a1dLu6urKf/7zH7JkycKcOXM4ffq05di4ceMAGDVqFNmzZ7e0V6xYke7duz80vrRStGhRRowYgbPz/x7v0b17d3LmzMnmzZtJTEy0tD/OdT6uXr16Wd17d3d3evfuDVi/9u7cucPw4cPJli0b06ZNw8vLy3LMw8ODCRMm4OrqavW+etDa5s2blzJlyjw0vkOHDtn9ufd+iYiIiIiIiMj/KIkuIg8VFxdHUlISTZo0wdXV1eZ4+fLlyZYtG9u2bXus8Vu0aPHA47/99hvDhg2je/fuvPrqq0RHR1sSuYcOHbJ7TkhIiE1b7ty5yZ07t01N8Dp16lCqVCmWLl1qlURduHAhf/31Fy1btrR6kKS5NnqbNm1s5siZM6fdue9l73rNYzZt2pQcOXLYHO/YsSMA69ate+DYAJUqVQKw1Mh+UO1483gvv/yyzbG8efMSEhJCSkqKpZb8iRMnOHnyJL6+vtSsWdPmnHs/VHjagoODrT5sgLsfZBQtWpSkpCQuXLhgaX/U63wS9ta/RIkSAFavvZ07d3L+/HmCgoLsPqg0X758+Pn5sXfvXksZGPPa9u/fn0WLFnHr1q0njldEREREREREHsz54V1E5N8uPj4euLsD2Zy8tuf+es+pVbBgQbvtiYmJREdHM336dIfnXrt2zW57gQIF7LZnzZrVKrlq1qVLF9555x2+//57BgwYAMCECRMAeP311636njlz5oFxO2p/0HHzmPc/kNPM3G7u9yDvvfce69evZ+HChSxcuJDs2bNTrVo1QkNDiY6OJlu2bFbzmkwmChUqlKp5n/Ta09KD1hju1m43e9TrTOu47MVkfl8tXbrU7sNh73Xx4kV8fX1p0KABvXr14ssvv6R58+ZkzpyZChUqEBISwmuvvebw9XMvPz8/u+13P5CyfZCpiIiIiIiIyL+dkugi8lDJyckABAYGUq5cuTQf383NzW77yJEjmT59OmXKlGH48OFUrFiRnDlz4uLiwsGDB/H398cwDLvnPiwpeb+oqCjef/99vv/+ez788ENOnTrFsmXLKFq0qE3pDPOcjzqHmaPrfdCY5vbUzOnp6cmqVavYsGEDCxcuZM2aNaxcuZLY2FgGDx7MunXrKFas2CPFbJ73Ydf+uPfkQbvlHxZTWkqLMVM7hvl95efnZ3dX/73u/QbIyJEj6dq1KwsWLGDlypVs2LCBrVu3MmzYMGbOnElYWNhjxy4iIiIiIiIitpREF5GHMu+sDQ4OZuTIkc9s3nnz5gFYEun3Onr0aJrOlSNHDtq2bcvkyZNZvnw5mzZtIiUlhc6dO9skRX18fAA4fvy43bFOnDjxyPObxzx27Jjd4+Zdy/fWM38Qk8lEUFAQQUFBAJw7d46ePXsyffp0+vfvz8yZMy3zHjt2jOPHj+Pv728zjvkazfM+7NodtZvr2l+/ft3mWHJyMn/99VeqrutxPep1Pgvm91WZMmWYPHnyI53r7+9P37596du3L7du3eLrr7+mT58+dO3aVUl0ERERERERkTSmmugi8lD16tUjU6ZMLFq0yLJ7NjVcXFy4c+fOY8976dIlAF544QWbYz///PNjj+uI+QGj3377Ld9//z3Ozs5ER0fb9DMnpmfNmmVz7PLly8TGxj7y3OYxFy9ezOXLl22O//jjjwDUrl37kceGuw9jHThwIAB79uyxtJvH++mnn2zOOXfuHLGxsTg5OVl2ShcqVIgCBQpw+vRpNm3aZHPOjBkz7M5vTk4fPHjQ5tiqVatISnq6ZUQe9TqfhSpVqpA9e3ZWr17N1atXH3scNzc3evfujbe3N2fPnrU8fFRERERERERE0oaS6CLyUL6+vkRHR3Po0CE6duzI+fPnbfps3LiRJUuWWLX5+Pjw999/200Kp4b5YYzffPONVfvs2bOZOnXqY435INWrV6d8+fLMnTuXEydOEBoaandnckREBLly5SI2NtYqmZ+cnEzv3r3t7rZ+mKJFi9KsWTOuXbtGz549rZLKmzZtYty4cWTKlIk333zzoWN98803dne0L126FLCuW969e3ecnJwYPXo027dvt7QnJibSo0cPbty4QatWrfD19bUcM3/Y0Lt3b6vk72+//cbXX39tN6a6desCdz8MMO+qh7vfKOjRo8dDr+lJPc51Pm2urq706dOHy5cvEx4ebncX/+7duy3fGgCYP38+mzdvtum3c+dO/v77b7Jly0bOnDmfatwiIiIiIiIi/zZKootIqowZM4Z69eoxffp0ihYtSp06dWjXrh3BwcEUKFCAWrVq2ezAbtGiBXfu3KFixYp06NCBzp07M3z48FTP2bdvXzJlykRMTAyVK1emffv2VKlShYiICHr16pXWlwj8L0EMtg8UNfP09GT8+PE4OTnRtm1bgoKCaN++Pf7+/syePZuXX375seb+9ttvKVKkCFOnTsXPz4/IyEgaNmxI7dq1SUhIYNiwYamqSf/NN99QtGhRSpcuTevWrWnXrh2BgYG88847ZMmShY8//tjSt2rVqnz22WdcvXqVGjVq0KhRIyIjIylevDgzZ87Ez8+P//znP1bjv/fee1SrVo1NmzZRrFgx2rRpw4svvki1atVo37693ZiKFi3KK6+8wqVLl6hQoQItWrSgQYMGlC1blnLlyjl84GdaeZzrfBb69+9PZGQkK1aswN/fn5o1a9KuXTsaNmxI0aJFKV++vNWDddesWUONGjUoUKAAzZs35+WXX6ZevXpUrVqVlJQUPvvsM1xcXJ75dYiIiIiIiIj8kymJLiKp4u7uTmxsLBMnTqRixYrs3buXefPmceTIEYoVK8awYcPo06eP1TmDBw/mrbfe4s6dO8ycOZPvvvuOxYsXp3rOOnXqsH79eurXr8/Ro0dZtGgRmTNnZs6cOXTv3j2tLxGABg0aAHfrVTdu3Nhhv/DwcJYvX07t2rXZuXMnS5cupVSpUmzatInixYs/1ty+vr5s27aN3r174+zszNy5c9mxYwcNGjRg2bJlvPvuu6ka57PPPqNTp06YTCZWrlzJwoULuXHjBl26dGH37t3UqFHDqn///v1ZtGgRdevWZdu2bcydOxdXV1f69u3Lli1byJcvn1V/V1dXVqxYQZ8+fXB1dWXBggUcPXqUzz///IGJ6AkTJhATE4OnpyfLli3j+PHj9O/f3ypJ/DQ96nU+C05OTkybNo3Zs2dTr149Dh06xNy5c/njjz/Ily8fAwcOZOjQoZb+0dHR9O7dGx8fH7Zu3cqcOXM4duwYTZs2ZfXq1fTs2fOZX4OIiIiIiIjIP53JMAwjvYMQEckoBg0axAcffMDHH39sqSEuj8ZkMlGoUCGrsi2S8R06dIhTZxOYsOxyqs+Z9mnwU4tHREREREREJKNQEl1E5P+7evUqAQEBXLhwgWPHjuHj45PeIT2XlER/Ph06dAgAPz+/dI5EREREREREJGNxTu8ARETS26RJk4iLi2Pt2rX8+eef9OrVSwl0+ddJSkrCMAxLMl1ERERERETkacucOfNTf05aWlBNdBH514uLi2PKlClcv36dHj16MHjw4PQOSeSZS0lJQV9O++dITEwkMTExvcOQNKQ1/WfRev6zaD3/WbSe/zxa038Wrec/S2JiIgkJCekdRqqonIuIiIionMs/jNbzn0dr+s+i9fxn0Xr+s2g9/3m0pv8sWs9/ludpPbUTXURERERERERERETEASXRRUREREREREREREQcUBJdRERERERERERERMQBJdFFRERERERERERERBxQEl1ERERERERERERExAGTYRhGegchIiIiIiIiIiIiIpIRaSe6iIiIiIiIiIiIiIgDSqKLiIiIiIiIiIiIiDigJLqIiIiIiIiIiIiIiANKoouIiIiIiIiIiIiIOKAkuoiIiIiIiIiIiIiIA0qii4iIiIiIiIiIiIg4oCS6iIiIiIiIiIiIiIgDSqKLiIj8i926dYuPP/6YEiVK4Obmho+PD506deLUqVPpHZo4sGPHDoYMGUKrVq3w9fXFZDLh5ub20POmTp1K1apVyZo1K7ly5aJp06Zs3LjxGUQsjty4cYP58+fz2muvUa5cOTw9PfHw8KB8+fJ8+umnXL9+3eG5Ws+MaeTIkbRq1Qo/Pz+yZ8+Oq6srhQoVIioqit9//93heVrP58PFixfJmzcvJpOJgICAB/bVmmZMwcHBmEwmhz///e9/7Z6n9czY/vrrL3r16kWJEiXIkiULuXLlolKlSvTt29duf61nxrNmzZoHvjfNP59++qnNuVrPjGvz5s2Eh4eTP39+XFxcyJUrFw0aNGD27NkOz8nI62kyDMNI7yBERETk2bt16xYNGjRg48aNeHt7U7t2beLj49m6dSteXl5s2rSJYsWKpXeYcp+wsDAWLFhg1ebq6sqtW7ccnvPuu+8yatQosmTJQkhICLdu3WLlypUYhsGsWbNo2bLl0w5b7Jg4cSKvv/46AKVLl6ZUqVJcvXqVjRs3cu3aNQICAoiLiyNv3rxW52k9M648efKQkJBAuXLl8PX1BeD333/n4MGDZM6cmfnz5/Piiy9anaP1fH5ER0czdepUDMPA39+f/fv32+2nNc24goODiYuLIzw8nKxZs9oc7927N2XLlrVq03pmbJs2baJp06ZcvnyZUqVKUaZMGa5du8Yff/zBqVOnuHPnjlV/rWfGtH//foYMGWL3WHJyMj/++CMAq1atol69epZjWs+Ma9asWbRr146UlBQqV65MsWLFOHPmDBs2bCAlJYV+/frZrHmGX09DRERE/pUGDBhgAEaNGjWMa9euWdpHjBhhAEadOnXSMTpxZMiQIcZHH31kLFy40Pjrr78MwHB1dXXYf+XKlQZg5M6d2zh48KClfePGjUbmzJmN7NmzGxcvXnwWoct9pkyZYrzxxhtW62IYhnHmzBkjMDDQAIzIyEirY1rPjG39+vXGzZs3bdrHjh1rAIaPj49x584dS7vW8/mxYsUKAzC6dOliAIa/v7/dflrTjK1u3boGYBw7dixV/bWeGdvp06eNHDlyGFmyZDHmzp1rc3zLli1Wv2s9n09LliwxAOOFF14wkpOTLe1az4wrKSnJ8PLyMgBjxowZVsc2btxouLm5GSaTyTh8+LCl/XlYTyXRRURE/oUSExONHDlyGIDx66+/2hwvV66cARjbt29Ph+jkUTwsid60aVMDMEaNGmVz7O233zYA44svvniKEcrj2Lhxo2Vtb9++bWnXej6/ihcvbgDG77//bmnTej4fbty4YRQvXtwoVaqUcfDgwQcm0bWmGdujJtG1nhlbx44dDcD46quvUtVf6/l8at++vQEYMTExVu1az4xrz549BmAEBATYPf7SSy8ZgDFz5kxL2/OwnqqJLiIi8i+0fv16Ll++TLFixQgMDLQ53rp1awAWLlz4rEOTNGT+CiT8b03vpXXOuMqXLw/A7du3uXBZuf89AAAj5klEQVThAqD1fN5lypQJgMyZMwNaz+fJJ598wpEjRxg3bhwuLi4O+2lN/1m0nhnbpUuX+Pnnn8mePTudO3d+aH+t5/MpISHBUsawQ4cOlnatZ8bm6uqaqn65cuUCnp/1dE7X2UVERCRd7Nq1C4CKFSvaPW5uN/eT59P+/fu5ffs2Xl5eFChQwOa4eZ137979rEOThzh69CiA5SFMoPV8nk2dOpUDBw5QokQJihYtCmg9nxe7d+9mxIgRvPrqq9SpU4f4+HiHfbWmz4/vvvuOCxcu4OTkRIkSJQgLC6NgwYJWfbSeGduGDRu4ffs2DRs2xMXFhdmzZ7N+/XqSkpIICAigTZs25MuXz9Jf6/l8mjt3LgkJCQQGBlK6dGlLu9YzYytatChFixZl//79/Pzzz7Rp08ZybNOmTSxbtowiRYpQp04d4PlZTyXRRURE/oVOnDgBYPd/pNzbbu4nz6eHrbOHhwc5cuTg0qVLXLt2jWzZsj3L8OQBRo8eDUCTJk0su3m0ns+P4cOH8/vvv5OQkMC+ffv4/fff8fHxYdq0aTg53f0ysNYz40tJSeH1118nR44cDBs27KH9tabPj88//9zq9z59+jBgwAAGDBhgadN6Zmy///47APny5aN27dps2rTJ6vj777/PpEmTiIiIALSezyvzA0U7duxo1a71zNgyZcrE5MmTad68OW3btmX48OEUK1aMP//8k/Xr11O1alV++OEHy7fznpf1VDkXERGRf6Hr168D4O7ubve4h4eHVT95Pj1snUFrnREtWbKE7777DhcXFz777DNLu9bz+bFs2TKmTJnC7Nmz+f3333nhhReYNm0alSpVsvTRemZ8X331FVu3bmX48OHkzp37of21phlfnTp1+OGHHzhy5Ag3btzgwIED/N///R/Ozs589NFHlg8wQeuZ0V26dAm4+02f3bt3891333Hu3DmOHTvGu+++S0JCAh06dLDsXNV6Pn/++usvVq5cSaZMmYiMjLQ6pvXM+GrXrk1cXBxFihRh+/btzJw5k7Vr1+Lh4UHDhg3x8fGx9H1e1lNJdBERkX8hwzAAMJlMDzwuz7eHrfO9fSRj2LdvHx06dMAwDIYPH26pjQ5az+fJihUrMAyDS5cusXbtWvz9/QkODub//u//LH20nhnbyZMn+fDDD6lbty7R0dGpOkdrmvF9+umndOjQgaJFi5IlSxZKlChB//79mT9/PgAff/wxN2/eBLSeGV1ycjIAd+7cYeTIkXTq1Ik8efJQuHBhRowYQevWrUlMTLR8i0Tr+fyZNm0aycnJNGrUiPz581sd03pmfNOnT6datWoULFiQLVu2cP36dQ4ePEhkZCSff/45DRs2JCkpCXh+1lNJdBERkX8h81fgEhIS7B6/ceMGAFmzZn1mMUnae9g6g9Y6Izl16hRNmjTh0qVLvPvuu/Ts2dPquNbz+ZMjRw5q167NkiVLqFSpEgMGDGDbtm2A1jOje/PNN0lMTGTcuHGpPkdr+vwKCQmhcuXKXLlyhc2bNwNaz4zOvD5OTk5ERUXZHO/UqRMAa9asseqv9Xx+OCrlAlrPjO7QoUNERUXh5eXF4sWLqVq1Kh4eHvj5+fHtt9/SvHlzNm3axKRJk4DnZz2VRBcREfkXMj8869SpU3aPm9vvf8iWPF8ets4JCQlcvnyZHDlyqFZkOjt//jyNGjXixIkTvPrqq3zxxRc2fbSezy8XFxfatm2LYRgsXLgQ0HpmdIsWLcLd3Z033niD4OBgy0+7du2Au/VbzW3mr5ZrTZ9vfn5+APz555+A1jOjK1y4MAD58+e3PDvE3vGzZ88CWs/nzb59+9i5cydZs2YlLCzM5rjWM2ObMWMGSUlJNGnSxFKG5V7mB42aP+R6XtZTDxYVERH5FzKXiPj111/tHje3lytX7pnFJGnP398fV1dXzp07x6lTp2we1qN1zhiuXbvGiy++yP79+2nVqhUTJkyw+3VWrefzLU+ePACcO3cO0Ho+Dy5fvkxcXJzdYzdv3rQcu3PnDqA1fd6Za2ybdzlqPTO2wMBA4O66GYZh89/NCxcuAFrP59UPP/wAQKtWrezWydZ6ZmzmZLinp6fd4+b2ixcvAs/PemonuoiIyL9QrVq1yJ49O0eOHGHnzp02x2fPng1AaGjosw5N0lCWLFmoX78+8L81vZfWOf3dvn2bl156ie3bt9O4cWOmT59OpkyZ7PbVej7fzAnXYsWKAVrPjM4wDLs/x44dA+7+H35zW44cOQCt6fPs3LlzrFu3DoCKFSsCWs+MrmzZshQpUoSbN2+yZcsWm+PmHa5az+ePYRhMmzYNsF/KBbSeGZ25hv327dvtHjeXtjN/Y+S5WU9DRERE/pU++OADAzBq1qxpXL9+3dI+YsQIAzCCgoLSMTpJLcBwdXV1eHz58uUGYOTOnds4ePCgpX3jxo2Gq6ur4enpaVy4cOFZhCr3uXPnjtGyZUsDMGrXrm0kJCQ89BytZ8a1du1aY8aMGUZSUpJVe2JiojFmzBjDycnJyJIli3HixAnLMa3n8+fYsWMGYPj7+9s9rjXNuDZt2mSsWrXKSElJsWo/duyYUatWLQMwWrRoYXVM65mxffPNNwZgVKlSxTh37pylffv27UaOHDkMwJg1a5alXev5fIiLizMAw8fHx0hOTnbYT+uZce3YscMADMAYO3as1bFNmzYZHh4eBmAsX77c0v48rKeS6CIiIv9SN2/eNKpVq2YAhre3t9GmTRvL77lz5zYOHTqU3iGKHYsWLTKqVatm+QEMk8lk1bZo0SKrc3r27GkAhru7u/HSSy8ZL774ouHs7Gw4OTkZs2fPTqcrkS+//NLyfzBatmxpREVF2f25NzFgGFrPjGrSpEkGYOTJk8do3Lix0b59eyMkJMTw9vY2AMPNzc2YOXOmzXlaz+fLw5LohqE1zajM71Fvb2+jbt26Rtu2bY1atWoZbm5uBmCULl3a+Pvvv23O03pmXMnJyUZERIQBGLly5TJCQ0ON4OBgI3PmzAZgvP766zbnaD0zvtdff90AjPfee++hfbWeGVefPn0s/zu3dOnSRkREhFGrVi3DycnJAIwuXbrYnJPR11NJdBERkX+xGzduGAMGDDCKFStmZM6c2ciXL58RFRVltVNSMhZzEuBBP5MmTbJ7XqVKlQx3d3cje/bsRuPGjY1169Y9+wsQi48//vihawkYx44dszlX65nxHD161Ojfv79Rq1Ytw9vb23BxcTE8PDyM0qVLGz169HjgB5Naz+dHapLohqE1zYj++OMP44033jAqVqxoeHl5Gc7Ozkb27NmN6tWrGyNGjDBu3Ljh8FytZ8aVnJxsfP3110ZgYKDh7u5ueHh4GDVr1jSmTp3q8BytZ8Z169YtI2fOnAZg7Nq1K1XnaD0zrrlz5xohISFG7ty5DWdnZyNnzpxGvXr1jJ9++snhORl5PU2GYRhpUBVGREREREREREREROQfRw8WFRERERERERERERFxQEl0EREREREREREREREHlEQXEREREREREREREXFASXQREREREREREREREQeURBcRERERERERERERcUBJdBERERERERERERERB5REFxERERERERERERFxQEl0EREREREREREREREHlEQXEREREREREREREXFASXQREREREREREREREQeURBcRERERERERERERcUBJdBERERERkefUwIEDMZlMmEwm4uPj0zuc58bcuXNp3LgxXl5eODs76x5KhhIcHIzJZKJw4cLpHYqIiPx/SqKLiIiIiMhzIT4+3pLsNJlM5MyZk0uXLj30vKCgIMs5IgMHDiQ8PJzY2FjOnz9PcnJymo29bt06+vXrR40aNShQoABZsmTBw8ODF154gcaNG/PJJ5+wf//+NJtPREREng3n9A5ARERERETkcVy+fJlBgwYxfPjw9A5FnhNnzpzh//7v/wAoXrw4n3/+OSVKlMDFxQUAX1/fxxp3x44dvPPOO6xfv97u8Rs3bnDq1CliY2MZOHAgDRs2ZPjw4VSoUOGx5hMREZFnS0l0ERERERF5bv3nP/+hR48eFCxYML1DkefAypUruXPnDgAjR46kefPmTzzmTz/9ROfOnbl16xYAfn5+tG7dmho1apA3b14A/vrrLzZu3MiCBQs4cOAAK1as4Msvv2Ty5MlPPL+IiIg8fUqii4iIiIjIc8fLy4tz585x69YtPvroIyUjJVVOnTpl+Xd/f/8nHi82NpZXXnmFlJQUnJ2dGTFiBN27dydTpkw2fV966SWGDBnC3Llz6dev3xPPLSIiIs+OaqKLiIiIiMhzJyQkhGrVqgHwww8/sGfPnnSOSJ4Ht2/ftvx75syZn2isy5cv0759e1JSUgCYMWMGb7/9tt0EupnJZCI8PJxff/2VZs2aPdH8IiIi8uwoiS4iIiIiIs+lYcOGAZCSkkJMTMxjjxMdHZ3qB48WLlwYk8lEcHCw3ePmcaKjowE4cOAAb775JsWLF8fd3R0fHx9CQ0NtamffunWLcePGUbNmTby8vHB3d6ds2bIMGTLEKvH7MHfu3GHcuHHUqlULLy8vsmTJgr+/P7169eLPP/9M1RhJSUlMmjSJFi1aUKBAAdzc3MiePTvlypWjV69eHDt2zOG59z78deDAgQBs2bKFV199lWLFiuHu7o7JZOK3335L9TXd69KlS/zf//2f5T5lzpyZfPny0aBBA8aMGcPNmzftnmeO6ZNPPrG0FSlSxOpBteZ4U2v06NFcuHABgKioKMLDw1N9rqenJxEREQ6PHzx4kJ49e1KmTBmyZ8+Om5sbL7zwAq1bt2bu3LkPHHvNmjWWazJ/Q2PFihWEh4db1rNo0aK8/vrrNmv5119/MWDAAMqWLUu2bNnInj07derU4eeff37gnMHBwZhMJgoXLgzA+fPnLeN4enri6elJ5cqV+eKLL1L9et6xYwddunShRIkSZMuWDXd3d4oUKULH/9fe3QdFVfVxAP8uIAiIvKioKIGDSASZUCov2qaZ5tiLaTilomhGUkEvlkpWPFqELzUmmFEUSENYSiZTTpHyZoGggCiZIhSGZaQgKkqJwHn+2Nn7LOzeu4vg6/P9zOzMbe855/7O4V5nOvu754SGIjs7W7Huf/7zH2kMjh8/3qXYO+v8zNfX1+PNN9+UxsjOzg7+/v6Ii4tDc3Oz0X61t7cjKSkJ48ePh6OjI2xtbeHl5YWXX34ZtbW1RutrlZSUIDw8HD4+PrCzs5OeBV9fX8yYMQMffPBBhzcviIiomwQREREREdFNoKamRgAQAMScOXOEEEJMmzZN+i4vL89gveDgYKmMIfPnz1c8r8vNzU0AEGq12uB5bTvz588XGRkZwsbGRvpO96NSqcSnn34qhBDi5MmTYvTo0QbLaa/177//GrxeTEyMVO7gwYMiKChIth17e3uRk5Oj2L9Dhw4JT09P2TYAiF69eokPP/zQYH3dv1FMTIyIjY0VZmZmem0cOHDA6Fh3lpWVJRwdHRVjc3V1FWVlZXp1leroxtsVQ4YMker+/PPPXe6PnLi4OGFhYaEYq1qtFvX19Qbr5+bmSuVSUlLE0qVLZdtxdHQUpaWlQgghCgoKhLOzs2zZ5cuXy8asVqsFAOHm5ibKy8s7jE3nj7e3t/jjjz9k22ptbRWRkZFCpVIpjkFISIhobm422Ibuc1FTU6M43rqxG6L7zO/bt0+4uLjIxuTn5ycaGxtlr3Xu3Dkxbtw42foODg4iOzvbaEwxMTFGx+dK7mkiIpLHTHQiIiIiIrpprV69GmZmmv+tWbp06XWO5n8OHTqEuXPnon///tiwYQP27t2LoqIixMbGwsbGBkIILF68GMeOHcP06dNRXl6OiIgIZGVloaysDFu3bsXIkSMBAPn5+VLWvZLw8HAUFhbiwQcfxPbt21FaWoqdO3di9uzZAIBz587hoYcewtGjRw3W/+WXXxAcHIyqqipYWlpi0aJF2LJlC/bu3YvCwkJs2LABHh4euHz5MiIiIpCWlqYYT2ZmJlasWAF3d3fEx8ejsLAQRUVF2LRpE5ycnLo0noWFhZg2bRoaGxuhUqkwb9487Ny5E6Wlpfj666/xyCOPAABOnDiB++67D7/++muH+hUVFaioqEBERIT0XVZWlvR9RUUFnn32WZPjqaysxJ9//gkA8PDwgI+PT5f6I2fdunWIjo5Ga2srbG1tsWLFCuTl5WH//v1ITk7ucE888MADaGlpUWwvKSkJa9euRVBQENLS0lBSUoKcnBwsWrQIgCazf/bs2fjtt98wbdo0tLe34+2338aePXuwf/9+JCQkoH///gA0z9qePXsUr9fc3IwZM2agrq4OERER2LVrF0pKSpCeno7AwEAAwJEjRzB58mRpI9bOoqKikJCQACEE+vfvj9WrV6OgoABFRUX44IMPpIzxbdu2ISQkBEIIk8e3O/766y889NBDaG5uxhtvvIHc3FyUlpZiy5YtGDVqFADgwIEDePXVVw3WF0JgxowZ0lso3t7e+OSTT7Bv3z7k5eVh+fLluHTpEkJCQnDy5EnZOH744QesXLkSQggMHToUsbGx0r8bRUVF+OKLL7BkyRKMGDGix8eAiOj/2nWdwiciIiIiIjKRoUx0IYQICwuTvt+2bZteveuRiQ5AjBo1Spw5c0avzLZt26Qyzs7OwsLCQuzatUuv3NmzZ6Ws14EDB4rW1la9MroZtwDEK6+8YjCujRs3dshi7qytrU34+PgIAGLYsGGiurraYDsXLlyQxtPJyUmcP3++w3ndvxEAERAQIJqamgy2Zaq2tjYxfPhwqc20tDSD5d566y2pzIQJEwyW6UqGspL09HSpnSeeeOKK29FVXV0tevXqJWWIV1RU6JW5dOmSmDp1qnTtlStX6pXRzUQHIBYsWCDa2tr0yj333HMd7kNXV1dRW1urV66oqEjKen788ccNxq7NnAYgzMzMRGZmpl6ZtrY2MWvWLMUs6fz8fOm8u7u7wYz1s2fPCn9/f6lcamqqXpmrkYkOQLi4uBh8Ni5cuCC9wWFlZSUaGhr0ymzevFlqZ/z48Qaz6IuLizu8vWIopnnz5gkAwsbGRjGjXwgh+7YCERF1HTPRiYiIiIjoprZq1Sr07t0bAPDaa6+htbX1OkekkZKSAkdHR73vtWtTA8CpU6fw/PPPY9KkSXrl7O3tpbXV//77bxw5ckTxel5eXoiLizN47rnnnsPEiRMBaLKYO69Jvn37dhw+fBiAJnvZw8PDYDu2trZITEwEAJw5cwYZGRmy8WjX5O7Tp49i3MZ88803qK6uBgDMnj0bc+bMMVhuxYoVUrZzbm4uDhw40K3rKqmvr5eOBw4c2CNtbty4EZcvXwYArFmzBr6+vnplLC0tkZqaCjs7OwBAQkKCYjb6oEGDsGnTJultDV0vvviidHzq1CkkJCTA1dVVr9zYsWOlcc3LyzPajwULFkhvBugyMzPDxx9/LL2FsGnTJr3Y169fLx1//PHHGDJkiF479vb2SEtLk/r03nvvGY2pp8THxxt8NmxtbfHCCy8A0Gxeu3fvXr0yGzZsAABYWFggNTUV1tbWemXGjBljdH8H7d4GXl5eBsdHV79+/RTPExGR6TiJTkRERERENzVXV1dERkYCAKqqqpCUlHSdIwJ8fX2lJR46U6lUHc7NnTtXth1/f3/puPMSJZ0tXLgQFhYWsufDw8Ol4++//77Dua+++goA4OzsjPvvv1/xOr6+vtLkXOcNUnUFBATAy8tLsS1TZGVlSceLFy+WLadSqTosyaJbr6edP39eOu7ujwRa2nhtbW0V74kBAwZg1qxZADST+WVlZbJlH3/8cekHps6GDx8uxW5vb4+HH35Yth3tfVhfX49z584p9kP3PuvM3t5eiv306dMdYm9ra5M2DPXw8MADDzwg2463tzcmTJgAQLN0Ul1dnWJMPaFv376YPn267PkxY8ZIx9offbROnTol/agzceJEDBs2TLadp59+WjEO7cT54cOHUVBQYCxsIiLqIZxEJyIiIiKim150dLSU9b1y5UpcvHjxusZz++23K57XzVD39vY2qZzuxK0hAQEBJp8/ePBgh3PFxcUANJN9KpXK6KehoQHA/7JiDfHz81OMx1SHDh0CoMng1Z2oNCQ4OFg67tzHntS3b1/p+MKFC91ur6WlRVqr3s/Pz2CWsi5T+2nqfThixAiD2eqdywHK96GlpaXRv7vcfVhTU4OmpiYAkDLflVyrv7WWl5cXzM3NZc/rZn13HiPtPQwYf04HDRokrftuyFNPPQWVSoWWlhbce++9mDx5MtavX4/i4mKja+QTEdGV4yQ6ERERERHd9BwdHREdHQ1As/TJtVziwRBbW1vF87oTljY2NiaVa2trU2zT2LIigwYNko51lyMBNJPnV6K5uVn2XFc3D5WjnbB3cHCAlZWVYtnBgwfr1bsatJttApr7rbsaGxulDTJ1/05yTO2nqfeh0j2oWw5Qvg+dnJzQq1cvxbbk7kPdfvTkGPSU7oyRbnzOzs5Gr6XU/3HjxiE1NRUODg5ob2/Hrl278PLLLyMgIAAODg6YNGkSkpKSZDduJSKiKyP/rh8REREREdFNJDIyEgkJCThx4gTeffddLF682KQJq1uFSqW64rLadeTvvPNOpKenm9yO0iStUtZuV2gnl03pX1fGoDt0l9nZv39/t9vT9hG4dn24GkyJXa6vt8oYGNLVvumWNyQ0NBSPPvooMjIysGvXLhQWFqK2thb//PMPsrOzkZ2djdjYWOzYsUN2WSkiIuoaZqITEREREdEtoXfv3li1ahUAoKmpCW+99ZZJ9XQzSNvb2xXLXu9lYpQYWxda93znDQcHDBgAQLM8i4+PD3x9fU36KK3t3FO0Wd+NjY24dOmSYtmTJ09Kx1dzU0UvLy+4uLgA0KxVr92U9Uo5OTlJk6tKS+Ro6Za5kTaPbGhoMLqxr27mvm7sutn93R2DG+2Z1o3PlDcXTCnTt29fLFy4EFu2bMHvv/+OEydOIDk5WVoK5/fff8cjjzxi9JkhIiLTcBKdiIiIiIhuGfPmzYOvry8A4KOPPjK6GScA2NnZScdnzpyRLXf69Gm9ZVBuJEVFRSafv+uuuzqcu/vuuwFoltfoiczqnjRy5EgAmmx5Y7EVFhZKx5372NN0N4Bcu3Ztt9qytLSU1i8/cOCA0aU4rmU/u6KlpUVxo1NA/j4cNmyY9Czu3bvX6LWUxsDUZ7q9vR3Hjh0zeq3u0o3P2HP6999/4/jx412+xtChQ7FgwQIUFBRgypQpAIATJ04obv5LRESm4yQ6ERERERHdMszMzLB69WoAwOXLl7FixQqjdTw8PKRjpUnatLS07gd4FaWkpChmAX/yySfS8eTJkzucmzlzpnRsagb/taKdEAQ0P4wo+fDDDw3WuxqioqKkDTc/++wzfPXVVybXPX/+PDIyMjp8p4334sWL+Pzzz2Xr1tfXY+vWrQA02du6S8vcCJKSkmTPnT9/Htu2bQOgH7u5uTnuv/9+AJrs/uzsbNl2KisrkZOTA0DzI0vnNcRNfaYzMzONbtjbE5ydnaVlVXJychQnyZXGzxQqlarD83369OlutUdERBqcRCciIiIiolvKtGnToFarAQBbt25FZWWlYvmJEydKx+vWrTO4cWJZWRnefPPNng20hx09ehSvv/66wXOJiYnYvXs3AGD8+PF6E69PPvmklAn97bffYtmyZYrrMre0tCA5OblHNtU05uGHH8bw4cMBaH7I+PLLLw2Wi4uLQ0FBAQBgwoQJ8PPzu6pxOTk54fPPP5eWDnniiSeQkJBgdAPYHTt2wN/fH99++22H759//nlpU85ly5bhyJEjenUvX76MsLAwaeI3MjISlpaWPdGdHpOcnIydO3fqfd/e3o7FixdLm2xGREToxf7SSy9Jx+Hh4QaXdWlqasLcuXOlZVqWLFmiVyY4OFhqOz4+3uAGuDU1NYiMjOxCz7onKioKgOaNirCwMINvG5SUlEg/AspJT09HU1OT7Pn29nZkZWVJ/637gwIREV05bixKRERERES3nDVr1iAgIABCCKNLsPj6+mLixInIyclBbm4upkyZgqioKNx22204ffo0vvvuOyQmJuK2227DmTNnbtjMzrFjx2LNmjWoqKjA008/DTc3N9TV1SE9PV3Kore2tkZiYqJeXQsLC2zfvh1BQUE4e/Ys1q5di++//x4LFy6Ev78/7Ozs0NTUhKqqKhQWFmLHjh1oaGhAVVUVBg4ceFX7ZWZmhtTUVKjVarS2tmL27NnIysrCrFmzMHDgQNTW1mLz5s3YsWMHAM1a0d3N5jXV1KlTkZKSgvDwcFy6dAlRUVFISEhASEgIAgMDpY1t6+rqUFxcjMzMTGn99HHjxnVoy8PDA7GxsVi6dCkaGhowduxYvPTSS5g0aRKsra1x+PBhrF+/HgcPHgQA+Pn5YdmyZdekn6YaMGAA+vTpg+nTp+OZZ57BY489BkdHRxw7dgwJCQnSEiy33347oqOj9erfe++9ePbZZ7Fp0yb89ttvGDVqFF599VUEBwfD3NwcpaWlWLduHWpqagBofjALDQ3Va6dfv36YO3cukpOTcfToUYwfPx7Lli2Dp6cnzp07h9zcXMTHx8PS0hIjRoy4Jku6hIWF4bPPPkNeXh7y8/Nxzz33YMmSJbjzzjvR3NyMrKwsvP/+++jduzdcXFxQVVVlsJ3XXnsNzzzzDKZOnQq1Wg1vb284Ojri4sWLqK6uRnJyMn788UcAQEBAAEaPHn3V+0ZE9H9BEBERERER3QRqamoEAAFAzJkzx2j5mTNnSuW1HznHjx8X7u7ueuW1nxEjRojq6mrh5uYmAAi1Wm2wHW35+fPnK8Y2f/58ozEJIURubq5ULiUlRe98TEyMdL68vFwEBgbK9qFv375i9+7ditc7duyY8Pf3l21D92NlZSVqa2s71Nf9G8XExCheq6uysrKEo6OjYkyurq6irKxMtg3d8aqpqemx2IqLixXHvvPnwQcfFBUVFQbbiouLExYWFor11Wq1qK+vN1jf2D2jy9j9rGVs3NRqtQAg3NzcRHl5uRg8eLBs7F5eXnr3ja7W1lYRGRlpdAxDQkJEc3OzbDuNjY3Cz89Ptv6gQYPEvn37OsTenTEy5d5vbGwUQUFBsjHZ29uL3bt3K8akjcfYZ/To0eLkyZOKMRMRkem4nAsREREREd2S3nnnHVhYmPbyrZubG0pLSxEdHQ1vb29YW1vDzs4Oo0aNQmxsLMrKym74ZRHs7e2Rn5+P+Ph4BAYGwsnJCVZWVhg+fDheeOEFHDlyRFpzWo6npydKSkqQmZmJ0NBQeHp6ws7ODubm5nBwcMDIkSMRGhqKzZs3o66uDq6urteod5p13H/99Ve8/fbbCAwMRL9+/dCrVy8MGDAAEyZMwPvvv4/KysqrvoyLIWPGjEFhYSHy8vLwyiuvYMyYMXBxcYGVlRWsra0xdOhQTJ48GStXrkRlZSW+++47aQPczpYvX47Dhw8jKioKPj4+sLOzg5WVFYYOHYoZM2YgIyMDubm56Nev3zXupWnuuusulJeXY/ny5bjjjjvQp08f9OnTB/7+/lizZg3Ky8sV7xtzc3PEx8dj//79WLRoETw9PWFra4vevXvD3d0dc+bMwe7du7F161ZYW1vLtuPg4ICffvoJcXFx8PPzg62tLWxsbODt7Y3o6GgcOnTommdpOzg4YM+ePUhMTERQUBDs7e1hbW0NT09PREZGory83Ogz+tNPPyE1NRVhYWHw9/fHkCFDYGlpCWtra7i7u2PmzJn44osvUFRUhMGDB1+jnhER3fpUQigsdEdEREREREREpOC+++5Dfn4+3NzcFDfNJCIiulkxE52IiIiIiIiIiIiISAYn0YmIiIiIiIiIiIiIZHASnYiIiIiIiIiIiIhIBifRiYiIiIiIiIiIiIhkcBKdiIiIiIiIiIiIiEiGSgghrncQREREREREREREREQ3ImaiExERERERERERERHJ4CQ6EREREREREREREZEMTqITEREREREREREREcngJDoRERERERERERERkQxOohMRERERERERERERyeAkOhERERERERERERGRDE6iExERERERERERERHJ4CQ6EREREREREREREZEMTqITEREREREREREREcngJDoRERERERERERERkQxOohMRERERERERERERyeAkOhERERERERERERGRDE6iExERERERERERERHJ4CQ6EREREREREREREZGM/wIvGHo0vEuIQwAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -3182,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -3221,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -3238,12 +672,12 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -3327,14 +761,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 37, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -3426,28 +860,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 38, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_36429/1579583286.py:5: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " usda_mod.at[idx, 'usda_amount'] = cdh.__unit_handler__(row['Nutr_Val'], row['unit'] + '/100g', 'mg/100g')\n", - "/tmp/ipykernel_36429/1579583286.py:9: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " usda_mod['units'] = 'mg/100g'\n" - ] - } - ], + "outputs": [], "source": [ "usda_mod = usda[(usda.unit != 'IU') & (usda.chem_id.notnull())]\n", "\n", @@ -3464,7 +879,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -3477,7 +892,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -3489,7 +904,7 @@ }, { "data": { - "image/png": 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" ] @@ -3551,7 +966,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -3568,7 +983,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -3580,8 +995,8 @@ "Dep. Variable: usda_amount R-squared: 0.558\n", "Model: OLS Adj. R-squared: 0.543\n", "Method: Least Squares F-statistic: 37.82\n", - "Date: Sun, 29 Jan 2023 Prob (F-statistic): 9.18e-07\n", - "Time: 01:25:08 Log-Likelihood: -67.295\n", + "Date: Wed, 08 Feb 2023 Prob (F-statistic): 9.18e-07\n", + "Time: 07:24:02 Log-Likelihood: -67.295\n", "No. Observations: 32 AIC: 138.6\n", "Df Residuals: 30 BIC: 141.5\n", "Df Model: 1 \n", @@ -3685,7 +1100,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -3696,7 +1111,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -3705,7 +1120,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -3717,7 +1132,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3745,7 +1160,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -3754,7 +1169,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -3766,7 +1181,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/notebooks/Molecule_Embedding.ipynb b/notebooks/Molecule_Embedding.ipynb index a7bd12c..28af1fb 100644 --- a/notebooks/Molecule_Embedding.ipynb +++ b/notebooks/Molecule_Embedding.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ "\n", "from gensim.models import Word2Vec\n", "from sklearn.manifold import TSNE\n", - "from sklearn.decomposition import PCA" + "from sklearn.decomposition import PCA\n" ] }, { @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -357,544 +357,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_38693/2986734704.py:31: DtypeWarning: Columns (13,14,17,45,50,51,54,55,62,63,64,65,66,67,68,69,88) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " foodb_class = pd.read_csv(mfp('data/compounds.csv'), encoding='latin1')[['id', 'name', 'superklass', 'klass', 'subklass']]\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:14: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " new_row = pd.Series()\n", - "/tmp/ipykernel_38693/2986734704.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(new_row, ignore_index=True)\n", - "/tmp/ipykernel_38693/2986734704.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " consolidated_data = consolidated_data.append(temp, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/data_loader.py:313: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " hdata = pd.read_csv('data/CTD_chemicals_diseases.csv', skiprows=skip).reset_index()\n" - ] - } - ], + "outputs": [], "source": [ "food_mine = pd.read_pickle(mfp('misc_save/' + food + '_quant_fm.pkl')).rename(columns={'pubchem_id' : 'chem_id_p'})\n", "food_mine['source'] = 'FoodMine'\n", @@ -925,20 +390,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/anaconda3/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n", - " warnings.warn(\n", - "/home/heba/anaconda3/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "tsne = TSNE(n_components=2)\n", "tsne_fit = tsne.fit_transform(dr_data)" @@ -1264,7 +718,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/notebooks/Paper_Citations.ipynb b/notebooks/Paper_Citations.ipynb index ddcd62d..4c93c85 100644 --- a/notebooks/Paper_Citations.ipynb +++ b/notebooks/Paper_Citations.ipynb @@ -215,31 +215,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Errno 1] [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1129)\n" - ] - }, - { - "ename": "UnboundLocalError", - "evalue": "local variable 'data' referenced before assignment", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_39046/576058552.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Retrieve citation ids from Microsoft Academic Graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mg_papers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_citations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg_PMIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'paper'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mc_papers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_citations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc_PMIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'paper'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#g_papers.to_pickle(mfp('misc_save/garlic_msft.pkl'))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/tmp/ipykernel_39046/1890607011.py\u001b[0m in \u001b[0;36madd_citations\u001b[0;34m(df, target)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mcitations_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpapers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mID\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcitations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_citations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# Float implies that the ID is NaN, aka it did not recognize a paper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/tmp/ipykernel_39046/2436934745.py\u001b[0m in \u001b[0;36mget_citations\u001b[0;34m(paper)\u001b[0m\n\u001b[1;32m 55\u001b[0m })\n\u001b[1;32m 56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m \u001b[0mloaded_eval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery_API\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;31m# If there are issues with retrieving info, like no interpretations returned, return 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/tmp/ipykernel_39046/2436934745.py\u001b[0m in \u001b[0;36mquery_API\u001b[0;34m(mode, params, headers)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[Errno {0}] {1}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'data' referenced before assignment" - ] - } - ], + "outputs": [], "source": [ "# Retrieve citation ids from Microsoft Academic Graph\n", "g_papers = add_citations(g_PMIDs, 'paper')\n", @@ -264,30 +242,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Errno 1] [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1129)\n" - ] - }, - { - "ename": "UnboundLocalError", - "evalue": "local variable 'data' referenced before assignment", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_39046/2807372739.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mg_citation_ids\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtitles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m 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"\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'data' referenced before assignment" - ] - } - ], + "outputs": [], "source": [ "start = time.time()\n", "\n", @@ -306,50 +263,6 @@ "#pd.DataFrame({'id' : g_citation_ids, 'title' : titles}).to_pickle(mfp('misc_save/garlic_citation_titles.pkl'))" ] }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Errno 1] [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1129)\n" - ] - }, - { - "ename": "UnboundLocalError", - "evalue": "local variable 'data' referenced before assignment", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_39046/876643916.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mc_citation_ids\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtitles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/tmp/ipykernel_39046/2436934745.py\u001b[0m in \u001b[0;36mget_title\u001b[0;34m(ID)\u001b[0m\n\u001b[1;32m 24\u001b[0m })\n\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mloaded_eval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery_API\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/tmp/ipykernel_39046/2436934745.py\u001b[0m in \u001b[0;36mquery_API\u001b[0;34m(mode, params, headers)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[Errno {0}] {1}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'data' referenced before assignment" - ] - } - ], - "source": [ - "start = time.time()\n", - "\n", - "# Retrieve paper titles from MAG\n", - "titles = []\n", - "c = 0\n", - "for p in c_citation_ids:\n", - " titles.append(get_title(p))\n", - " if not c % 3:\n", - " time.sleep(3)\n", - " \n", - " if not c % 50:\n", - " print(f'{c} at {(time.time()-start)/60} min')\n", - " c+=1\n", - "\n", - "#pd.DataFrame({'id' : c_citation_ids, 'title' : titles}).to_pickle(mfp('misc_save/cocoa_citation_titles.pkl'))" - ] - }, { "cell_type": "code", "execution_count": 23, @@ -917,7 +830,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/notebooks/Paper_Screening.ipynb b/notebooks/Paper_Screening.ipynb index 4057ac9..0a5413e 100644 --- a/notebooks/Paper_Screening.ipynb +++ b/notebooks/Paper_Screening.ipynb @@ -12,6 +12,15 @@ "execution_count": 1, "metadata": {}, "outputs": [], + "source": [ + "import pandas as pd\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], "source": [ "import os\n", "os.chdir('..')" @@ -19,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -28,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -36,10 +45,10 @@ "output_type": "stream", "text": [ "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=fresh%20garlic&retmax=1000000\n", - "ids 378\n", - "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=36673488,36661532,36558083,36374623,36296587,36241263,36235435,36231461,36122930,36091965,35940117,35822789,35798823,35729146,35637890,35628468,35627064,35507844,35480459,35480455,35436379,35355410,35293213,35211974,35172234,35168162,35125657,35062505,35057517,35011342,34893232,34828984,34819925,34795722,34713965,34527070,34521004,34443625,34395025,34164770,33929534,33785394,33740130,33681348,33638666,33605386,33571752,33549282,33415587,33389861,33325167,33317599,33295000,33242796,33221098,33177859,33142731,33123325,33088979,32963075,32900002,32884733,32328266,32291787,32184427,32178294,32156160,32142159,32107396,32010343,32010338,31991938,31901831,31743742,31721940,31588747,31577841,31516325,31509980,31387036,31382578,31338077,31250635,31031431,30931955,30929787,30923456,30744360,30704623,30603687,30570073,30497454,30457057,30412324,30374433&retmode=xml\n", - "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=30366271,30356168,30320007,30311619,30263743,30169119,30105074,29753990,29621132,29579944,29433241,29391611,29331459,29161814,29024904,28934070,28911676,28911544,28873605,28719747,28560773,28425131,28196294,28078257,28051097,28017989,27976376,27904380,27784931,27778523,27753097,27592824,27584700,27313155,27300762,27296605,27263111,27182249,27043510,27011724,27008423,26969520,26954136,26889365,26786785,26776039,26690030,28433269,26471590,26440842,26212875,26139864,26060559,26019632,26017222,26003845,25941212,25838894,25745260,25745247,25631559,25573280,25532343,25493198,25371585,25329784,25284945,25141133,25133543,25124136,24991105,24598083,26761498,24006751,23755406,23700562,23679240,23623137,23600691,23583806,23578652,23527659,23387242,23292331,26770698,25050258,23259687,23244152,23050048,24471087,22668601,22625420,22610968,22507958,22473701&retmode=xml\n", - "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=22353229,22284504,23983369,21812650,21794123,21729078,21726146,21721585,21631395,21615122,21553301,21538625,21535547,21325240,21290188,21269249,21184771,21170253,21118053,21086547,20951625,20924970,20739164,20633941,20538890,20202327,20192846,19929845,19895494,19878318,19827749,19768983,19735176,19653315,19601391,19382351,19174616,19120662,19053859,19019552,19019099,18952220,20416582,18844255,18588510,18489116,18334029,18205306,17966138,17767872,17918162,19070102,17523869,17472490,17472489,17396504,17330154,17269787,17219900,17189767,17146719,17123005,17075725,17017158,16910057,16715809,16584547,16500553,16484578,16484574,16413559,16405290,16380980,16366855,16298867,16287614,16277408,16230689,16223688,16121720,16076102,16041728,15916949,15796617,15769123,15749368,15718030,15616341,15615431,15380914,15216390,15161196,15065784,15056375&retmode=xml\n", + "ids 379\n", + "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=36721742,36673488,36661532,36558083,36374623,36296587,36241263,36235435,36231461,36122930,36091965,35940117,35822789,35798823,35729146,35637890,35628468,35627064,35507844,35480459,35480455,35436379,35355410,35293213,35211974,35172234,35168162,35125657,35062505,35057517,35011342,34893232,34828984,34819925,34795722,34713965,34527070,34521004,34443625,34395025,34164770,33929534,33785394,33740130,33681348,33638666,33605386,33571752,33549282,33415587,33389861,33325167,33317599,33295000,33242796,33221098,33177859,33142731,33123325,33088979,32963075,32900002,32884733,32328266,32291787,32184427,32178294,32156160,32142159,32107396,32010343,32010338,31991938,31901831,31743742,31721940,31588747,31577841,31516325,31509980,31387036,31382578,31338077,31250635,31031431,30931955,30929787,30923456,30744360,30704623,30603687,30570073,30497454,30457057,30412324&retmode=xml\n", + "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=30374433,30366271,30356168,30320007,30311619,30263743,30169119,30105074,29753990,29621132,29579944,29433241,29391611,29331459,29161814,29024904,28934070,28911676,28911544,28873605,28719747,28560773,28425131,28196294,28078257,28051097,28017989,27976376,27904380,27784931,27778523,27753097,27592824,27584700,27313155,27300762,27296605,27263111,27182249,27043510,27011724,27008423,26969520,26954136,26889365,26786785,26776039,26690030,28433269,26471590,26440842,26212875,26139864,26060559,26019632,26017222,26003845,25941212,25838894,25745260,25745247,25631559,25573280,25532343,25493198,25371585,25329784,25284945,25141133,25133543,25124136,24991105,24598083,26761498,24006751,23755406,23700562,23679240,23623137,23600691,23583806,23578652,23527659,23387242,23292331,26770698,25050258,23259687,23244152,23050048,24471087,22668601,22625420,22610968,22507958&retmode=xml\n", + "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=22473701,22353229,22284504,23983369,21812650,21794123,21729078,21726146,21721585,21631395,21615122,21553301,21538625,21535547,21325240,21290188,21269249,21184771,21170253,21118053,21086547,20951625,20924970,20739164,20633941,20538890,20202327,20192846,19929845,19895494,19878318,19827749,19768983,19735176,19653315,19601391,19382351,19174616,19120662,19053859,19019552,19019099,18952220,20416582,18844255,18588510,18489116,18334029,18205306,17966138,17767872,17918162,19070102,17523869,17472490,17472489,17396504,17330154,17269787,17219900,17189767,17146719,17123005,17075725,17017158,16910057,16715809,16584547,16500553,16484578,16484574,16413559,16405290,16380980,16366855,16298867,16287614,16277408,16230689,16223688,16121720,16076102,16041728,15916949,15796617,15769123,15749368,15718030,15616341,15615431,15380914,15216390,15161196,15065784,15056375&retmode=xml\n", "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=15031746,14969516,14959768,14727763,12847923,12738093,12706595,12701369,12697379,12570662,12396965,12174037,12098946,12042427,11933151,11906464,11833727,11802218,11767087,11486375,11466175,11454685,11434986,11385050,11365438,11271766,11238807,11238803,11238797,11049697,10882191,10737231,21214446,10552475,10588342,10524347,10354821,10235193,10234740,10193205,10098897,10072338,9726786,9637953,9625398,9246703,8739190,8729671,9772707,8870956,8603796,8560468,8607564,7480084,7604070,7666832,7517069,8170288,8183725,8302920,8439494,1470664,1285693,1531110,1665257,1742542,2065395,1831097,1855874,2083173,17221429,2686739,2793233,17262437,17262412,30991489,28310369,3207435,30965409,3702985,3924535,24318347,6471131,6877039,6870217,30913616,6154673,28223570,973445,1183776,4796677,4738589,5102504,14377534&retmode=xml\n" ] }, @@ -47,761 +56,769 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " info = info.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n" ] }, @@ -809,7 +826,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "['fresh garlic'] Document Info (378 entries) Retrieved in 0.313481072584788 min\n", + "['fresh garlic'] Document Info (379 entries) Retrieved in 0.34865829944610593 min\n", "Creating features...\n" ] }, @@ -819,65 +836,65 @@ "text": [ "0it [00:00, ?it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "1it [00:00, 3.79it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "1it [00:00, 5.97it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "2it [00:00, 4.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "3it [00:00, 8.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "4it [00:00, 8.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "5it [00:00, 7.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "6it [00:00, 6.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "6it [00:00, 7.65it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "7it [00:01, 7.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "8it [00:00, 9.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "9it [00:01, 8.99it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "10it [00:01, 10.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "11it [00:01, 10.40it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "12it [00:01, 7.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "13it [00:01, 7.53it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "14it [00:01, 8.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "14it [00:01, 7.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "16it [00:01, 10.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "16it [00:01, 9.33it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "18it [00:02, 10.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "18it [00:02, 10.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "20it [00:02, 9.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "20it [00:02, 11.53it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "22it [00:02, 5.73it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "22it [00:02, 7.60it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "24it [00:03, 6.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "24it [00:03, 6.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "25it [00:03, 5.60it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "25it [00:03, 6.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "26it [00:03, 4.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "26it [00:03, 5.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "27it [00:04, 4.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "27it [00:03, 4.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "28it [00:04, 4.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "29it [00:04, 5.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "30it [00:04, 6.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "30it [00:04, 5.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n" ] }, @@ -885,67 +902,67 @@ "name": "stderr", "output_type": "stream", "text": [ - "31it [00:04, 5.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "31it [00:04, 5.76it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "32it [00:04, 5.01it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "32it [00:04, 5.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "33it [00:04, 5.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "33it [00:05, 4.78it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "34it [00:05, 5.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "35it [00:05, 6.78it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "36it [00:05, 6.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "36it [00:05, 6.27it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "37it [00:05, 6.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "37it [00:05, 5.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "38it [00:05, 5.01it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "39it [00:05, 6.98it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "40it [00:06, 6.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "40it [00:06, 5.51it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "41it [00:06, 5.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "41it [00:06, 4.33it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "42it [00:06, 4.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "42it [00:06, 3.49it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "43it [00:07, 3.40it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "44it [00:07, 4.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "45it [00:07, 4.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "45it [00:07, 4.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "46it [00:07, 4.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "46it [00:07, 4.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "47it [00:07, 4.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "47it [00:07, 4.45it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "48it [00:08, 4.40it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "48it [00:07, 5.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "49it [00:08, 5.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "49it [00:08, 5.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "50it [00:08, 5.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "50it [00:08, 4.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "51it [00:08, 4.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "51it [00:08, 4.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "52it [00:08, 4.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "53it [00:08, 4.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "54it [00:09, 4.82it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "54it [00:09, 5.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "55it [00:09, 5.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "55it [00:09, 4.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "56it [00:09, 4.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "57it [00:09, 4.89it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "58it [00:10, 4.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "59it [00:09, 6.78it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "60it [00:10, 6.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "61it [00:10, 6.36it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n" ] }, @@ -953,69 +970,67 @@ "name": "stderr", "output_type": "stream", "text": [ - "62it [00:10, 5.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "62it [00:10, 6.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "63it [00:10, 5.78it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "64it [00:10, 5.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "65it [00:11, 5.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "65it [00:11, 5.61it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "66it [00:11, 5.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "67it [00:11, 7.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "68it [00:11, 7.31it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "68it [00:11, 5.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "69it [00:11, 5.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "69it [00:11, 5.07it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "70it [00:12, 4.97it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "70it [00:12, 4.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "71it [00:12, 4.80it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "72it [00:12, 6.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "73it [00:12, 6.58it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "73it [00:12, 6.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "74it [00:12, 6.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "74it [00:12, 6.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "75it [00:12, 6.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "76it [00:12, 7.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "77it [00:13, 7.27it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "77it [00:12, 6.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "78it [00:13, 6.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "79it [00:13, 8.49it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "80it [00:13, 8.58it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "80it [00:13, 6.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "81it [00:13, 7.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "82it [00:13, 8.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "83it [00:13, 8.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "83it [00:13, 7.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "84it [00:13, 7.82it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "85it [00:13, 7.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "86it [00:14, 7.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "86it [00:14, 5.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "87it [00:14, 5.65it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "88it [00:14, 7.50it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "89it [00:14, 7.45it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "89it [00:14, 6.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "90it [00:14, 6.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "91it [00:14, 8.41it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "92it [00:14, 7.51it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "92it [00:15, 8.36it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n" ] }, @@ -1023,67 +1038,67 @@ "name": "stderr", "output_type": "stream", "text": [ - "94it [00:15, 8.49it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "93it [00:15, 7.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "95it [00:15, 8.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "95it [00:15, 8.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "96it [00:15, 7.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "96it [00:15, 8.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "97it [00:15, 6.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "97it [00:15, 8.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "98it [00:15, 6.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "99it [00:16, 5.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "100it [00:16, 5.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "101it [00:16, 5.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "102it [00:16, 5.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "102it [00:16, 4.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "103it [00:17, 4.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "103it [00:17, 4.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "104it [00:17, 4.37it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "104it [00:17, 4.06it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "105it [00:17, 4.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "105it [00:17, 3.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "106it [00:17, 3.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "106it [00:17, 3.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "107it [00:18, 3.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "107it [00:18, 4.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "108it [00:18, 4.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "108it [00:18, 3.50it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "109it [00:18, 3.51it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "109it [00:18, 3.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "110it [00:18, 3.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "110it [00:18, 3.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "111it [00:19, 3.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "112it [00:19, 5.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "113it [00:19, 5.05it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "113it [00:19, 5.09it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "114it [00:19, 5.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "114it [00:19, 5.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "115it [00:19, 5.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "115it [00:19, 4.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "116it [00:20, 4.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "116it [00:20, 5.01it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "117it [00:20, 4.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "118it [00:20, 5.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "119it [00:20, 5.33it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "120it [00:20, 4.82it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "119it [00:20, 5.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "121it [00:20, 5.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "120it [00:20, 5.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "122it [00:21, 6.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "121it [00:21, 4.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "123it [00:21, 6.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "122it [00:21, 5.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "123it [00:21, 6.33it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n" ] }, @@ -1091,67 +1106,67 @@ "name": "stderr", "output_type": "stream", "text": [ - "125it [00:21, 6.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "124it [00:21, 6.56it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "126it [00:21, 6.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "127it [00:21, 7.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "128it [00:21, 7.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "128it [00:21, 6.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "129it [00:22, 6.19it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "129it [00:22, 6.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "130it [00:22, 6.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "130it [00:22, 5.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "131it [00:22, 5.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "131it [00:22, 4.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "132it [00:23, 4.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "132it [00:22, 4.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "133it [00:23, 4.19it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "134it [00:23, 4.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "135it [00:23, 4.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "136it [00:23, 4.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "137it [00:24, 4.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "137it [00:23, 4.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "138it [00:24, 4.58it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "138it [00:24, 4.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "139it [00:24, 4.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "139it [00:24, 4.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "140it [00:24, 4.09it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "140it [00:24, 4.78it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "141it [00:24, 4.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "141it [00:24, 5.22it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "142it [00:25, 5.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "142it [00:25, 4.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "143it [00:25, 4.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "143it [00:25, 4.76it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "144it [00:25, 4.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "144it [00:25, 4.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "145it [00:25, 4.75it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "146it [00:25, 6.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "147it [00:25, 6.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "147it [00:25, 5.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "148it [00:26, 5.61it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "149it [00:26, 6.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "150it [00:26, 4.97it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "150it [00:26, 6.07it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "151it [00:26, 4.53it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "151it [00:26, 4.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "152it [00:26, 4.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "152it [00:27, 4.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "153it [00:27, 4.33it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "153it [00:27, 4.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "154it [00:27, 4.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "155it [00:27, 4.22it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "154it [00:27, 4.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n" ] }, @@ -1159,67 +1174,67 @@ "name": "stderr", "output_type": "stream", "text": [ - "156it [00:27, 4.31it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "155it [00:27, 4.04it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "156it [00:28, 4.19it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "157it [00:28, 3.80it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "157it [00:28, 4.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "158it [00:28, 3.58it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "158it [00:28, 3.80it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "159it [00:28, 3.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "159it [00:28, 3.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "160it [00:29, 3.51it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "161it [00:29, 3.95it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "162it [00:29, 3.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "163it [00:29, 5.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "164it [00:29, 5.12it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "164it [00:29, 4.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "165it [00:30, 4.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "165it [00:30, 4.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "166it [00:30, 4.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "167it [00:30, 4.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "168it [00:31, 4.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "168it [00:30, 4.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "169it [00:31, 4.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "169it [00:31, 3.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "170it [00:31, 3.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "170it [00:31, 3.25it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "171it [00:32, 3.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "172it [00:31, 4.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "173it [00:32, 4.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "174it [00:32, 4.89it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "175it [00:32, 4.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "175it [00:32, 4.82it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "176it [00:32, 4.58it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "177it [00:33, 4.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "177it [00:33, 4.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "178it [00:33, 4.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "178it [00:33, 4.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "179it [00:33, 4.12it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "179it [00:33, 4.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "180it [00:33, 4.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "181it [00:33, 6.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "182it [00:34, 6.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "182it [00:33, 5.28it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "183it [00:34, 5.27it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "184it [00:34, 6.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "185it [00:34, 5.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "186it [00:34, 5.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "185it [00:34, 6.05it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n" ] }, @@ -1227,65 +1242,65 @@ "name": "stderr", "output_type": "stream", "text": [ - "187it [00:34, 4.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "186it [00:34, 5.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "188it [00:35, 4.37it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "187it [00:35, 5.12it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "188it [00:35, 4.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "190it [00:35, 4.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "189it [00:35, 4.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "192it [00:36, 4.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "191it [00:36, 4.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "193it [00:36, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "194it [00:36, 3.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "193it [00:36, 4.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "195it [00:36, 4.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "194it [00:36, 3.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "196it [00:37, 3.76it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "195it [00:37, 3.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "197it [00:37, 3.56it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "196it [00:37, 4.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "198it [00:37, 3.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "197it [00:37, 3.80it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "199it [00:38, 3.58it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "198it [00:38, 3.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "200it [00:38, 2.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "199it [00:38, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "201it [00:39, 2.92it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "200it [00:38, 3.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "202it [00:39, 3.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "201it [00:39, 2.75it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "203it [00:39, 3.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "202it [00:39, 2.95it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "204it [00:39, 3.28it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "203it [00:39, 3.21it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "205it [00:40, 3.29it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "204it [00:39, 3.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "206it [00:40, 3.50it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "205it [00:40, 3.29it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "207it [00:40, 3.39it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "206it [00:40, 3.31it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "208it [00:41, 3.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "207it [00:40, 3.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "209it [00:41, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "208it [00:41, 3.36it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "210it [00:41, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "209it [00:41, 3.45it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "211it [00:41, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "210it [00:41, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "212it [00:41, 3.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "211it [00:41, 3.73it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "213it [00:42, 2.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "212it [00:42, 3.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "214it [00:42, 3.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "213it [00:42, 3.88it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "215it [00:43, 3.29it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "214it [00:42, 2.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "216it [00:43, 3.25it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "215it [00:43, 3.01it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n" ] }, @@ -1293,65 +1308,65 @@ "name": "stderr", "output_type": "stream", "text": [ - "217it [00:43, 3.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "216it [00:43, 3.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "218it [00:43, 3.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "217it [00:43, 3.29it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "219it [00:44, 3.50it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "218it [00:44, 3.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "220it [00:44, 3.68it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "219it [00:44, 3.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "221it [00:44, 4.06it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "220it [00:44, 3.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "222it [00:44, 3.93it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "221it [00:44, 3.53it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "223it [00:45, 3.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "222it [00:45, 3.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "224it [00:45, 3.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "223it [00:45, 3.75it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "225it [00:45, 3.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "224it [00:45, 3.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "226it [00:45, 4.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "225it [00:46, 3.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "227it [00:46, 5.00it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "226it [00:46, 3.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "228it [00:46, 5.88it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "227it [00:46, 3.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "229it [00:46, 4.36it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "228it [00:46, 4.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "230it [00:46, 3.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "229it [00:46, 5.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "231it [00:47, 3.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "230it [00:47, 3.88it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "232it [00:47, 3.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "231it [00:47, 3.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "233it [00:47, 3.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "232it [00:47, 3.39it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "234it [00:47, 3.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "233it [00:48, 3.60it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "235it [00:48, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "234it [00:48, 3.88it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "236it [00:48, 4.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "235it [00:48, 3.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "237it [00:48, 4.68it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "236it [00:48, 3.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "238it [00:48, 3.79it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "237it [00:49, 4.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "239it [00:49, 3.68it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "238it [00:49, 4.75it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "239it [00:49, 3.88it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "241it [00:49, 4.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "240it [00:49, 3.79it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "242it [00:49, 4.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "243it [00:49, 4.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "242it [00:50, 4.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "244it [00:50, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "243it [00:50, 5.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "245it [00:50, 3.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "244it [00:50, 4.39it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "246it [00:50, 3.68it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "245it [00:50, 4.00it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n" ] }, @@ -1359,65 +1374,65 @@ "name": "stderr", "output_type": "stream", "text": [ - "247it [00:51, 3.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "246it [00:51, 3.51it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "248it [00:51, 3.67it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "247it [00:51, 3.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "249it [00:51, 3.61it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "248it [00:51, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "250it [00:52, 3.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "249it [00:51, 3.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "251it [00:52, 3.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "250it [00:52, 4.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "251it [00:52, 3.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "253it [00:52, 4.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "252it [00:52, 3.60it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "254it [00:52, 4.06it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "255it [00:53, 3.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "254it [00:53, 4.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "256it [00:53, 2.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "255it [00:53, 4.89it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "256it [00:53, 4.07it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "258it [00:54, 4.31it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "257it [00:53, 3.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "259it [00:54, 4.22it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "260it [00:54, 3.76it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "259it [00:54, 5.28it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "261it [00:54, 3.73it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "260it [00:54, 5.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "262it [00:55, 3.95it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "261it [00:54, 4.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "263it [00:55, 3.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "262it [00:54, 4.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "264it [00:55, 3.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "263it [00:55, 4.36it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "265it [00:56, 2.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "264it [00:55, 3.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "266it [00:56, 3.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "265it [00:55, 3.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "267it [00:56, 3.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "266it [00:56, 3.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "268it [00:57, 3.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "267it [00:56, 3.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "269it [00:57, 3.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "268it [00:56, 3.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "270it [00:57, 3.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "269it [00:57, 3.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "271it [00:57, 3.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "270it [00:57, 3.68it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "272it [00:58, 3.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "271it [00:57, 3.75it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "273it [00:58, 3.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "272it [00:57, 3.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "274it [00:58, 3.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "273it [00:58, 3.92it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "275it [00:59, 3.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "274it [00:58, 4.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "276it [00:59, 3.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "275it [00:58, 3.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n" ] }, @@ -1425,125 +1440,131 @@ "name": "stderr", "output_type": "stream", "text": [ - "277it [00:59, 3.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "276it [00:58, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "277it [00:59, 3.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "278it [00:59, 3.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "279it [00:59, 4.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "280it [00:59, 4.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "280it [00:59, 5.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "281it [00:59, 5.61it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "281it [01:00, 5.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "282it [00:59, 5.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "282it [01:00, 4.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "283it [01:00, 5.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "283it [01:00, 4.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "284it [01:00, 4.67it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "284it [01:00, 4.01it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "285it [01:00, 4.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "285it [01:01, 4.00it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "286it [01:00, 4.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "286it [01:01, 3.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "287it [01:01, 3.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "287it [01:01, 3.67it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "288it [01:01, 3.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "288it [01:02, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "289it [01:01, 3.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "289it [01:02, 3.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "290it [01:02, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "290it [01:02, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "291it [01:02, 3.80it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "291it [01:03, 3.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "292it [01:02, 3.45it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "292it [01:03, 3.07it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "293it [01:02, 3.31it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "293it [01:03, 2.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "294it [01:03, 3.29it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "294it [01:04, 3.05it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "295it [01:03, 3.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "295it [01:04, 3.80it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "296it [01:03, 4.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "297it [01:04, 4.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "298it [01:03, 5.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "298it [01:04, 4.45it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "299it [01:04, 4.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "299it [01:04, 4.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "300it [01:04, 5.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "300it [01:05, 3.97it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "301it [01:04, 4.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "301it [01:05, 3.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "302it [01:05, 3.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "302it [01:05, 3.53it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "303it [01:05, 3.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "304it [01:06, 4.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "305it [01:06, 4.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "305it [01:05, 4.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "306it [01:05, 4.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "306it [01:06, 3.93it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "307it [01:06, 4.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "307it [01:07, 3.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "308it [01:06, 3.78it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "308it [01:07, 3.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "309it [01:06, 3.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "309it [01:07, 3.39it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "310it [01:06, 4.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "310it [01:08, 2.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "311it [01:07, 3.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "311it [01:08, 2.25it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "312it [01:07, 3.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "312it [01:09, 2.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "313it [01:08, 2.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "313it [01:09, 2.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "314it [01:08, 3.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "314it [01:10, 2.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "315it [01:08, 3.25it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "315it [01:10, 2.19it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "316it [01:09, 3.12it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "316it [01:11, 2.37it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "317it [01:09, 3.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "317it [01:11, 2.49it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "318it [01:09, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "318it [01:11, 2.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "319it [01:09, 3.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "319it [01:12, 2.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "320it [01:10, 3.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "320it [01:12, 2.09it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "321it [01:10, 3.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "321it [01:13, 2.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "322it [01:10, 3.40it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "322it [01:13, 2.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "323it [01:10, 4.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "323it [01:13, 2.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "324it [01:11, 4.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "324it [01:14, 2.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "325it [01:11, 3.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "325it [01:14, 2.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "326it [01:11, 3.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "326it [01:14, 3.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "327it [01:15, 3.21it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "328it [01:11, 4.56it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "328it [01:15, 2.37it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "329it [01:12, 4.19it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "329it [01:15, 2.88it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "330it [01:12, 4.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "331it [01:16, 3.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "332it [01:12, 5.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "332it [01:16, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "333it [01:16, 3.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "334it [01:17, 2.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "335it [01:17, 2.67it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "334it [01:12, 6.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "336it [01:18, 2.50it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "335it [01:13, 4.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n" ] }, @@ -1551,65 +1572,65 @@ "name": "stderr", "output_type": "stream", "text": [ - "337it [01:18, 2.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "336it [01:13, 4.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "338it [01:19, 2.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "337it [01:13, 4.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "339it [01:19, 3.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "338it [01:14, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "340it [01:19, 3.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "339it [01:14, 4.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "341it [01:20, 2.60it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "340it [01:14, 4.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "342it [01:20, 2.65it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "341it [01:14, 4.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "343it [01:20, 3.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "342it [01:15, 3.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "344it [01:20, 3.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "343it [01:15, 3.31it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "345it [01:21, 3.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "344it [01:15, 3.76it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "346it [01:21, 2.89it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "345it [01:15, 4.09it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "347it [01:22, 3.04it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "346it [01:16, 4.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "348it [01:22, 3.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "347it [01:16, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "349it [01:22, 3.12it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "348it [01:16, 4.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "350it [01:23, 2.61it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "349it [01:16, 4.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "351it [01:23, 2.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "350it [01:17, 4.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "352it [01:23, 3.05it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "351it [01:17, 4.00it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "353it [01:23, 3.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "352it [01:17, 4.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "354it [01:24, 3.56it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "353it [01:17, 4.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "355it [01:24, 3.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "354it [01:17, 4.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "356it [01:24, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "355it [01:18, 5.04it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "357it [01:25, 3.09it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "356it [01:18, 4.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "358it [01:25, 3.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "357it [01:18, 5.19it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "359it [01:25, 3.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "358it [01:18, 4.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "360it [01:25, 3.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "359it [01:19, 4.39it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "361it [01:26, 3.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "360it [01:19, 4.60it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "362it [01:26, 3.79it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "361it [01:19, 4.79it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "363it [01:26, 4.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "362it [01:19, 4.67it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "364it [01:26, 4.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "363it [01:19, 4.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "366it [01:27, 5.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "365it [01:20, 5.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n" ] }, @@ -1617,29 +1638,33 @@ "name": "stderr", "output_type": "stream", "text": [ - "367it [01:27, 5.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "367it [01:20, 6.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "368it [01:27, 4.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "368it [01:20, 6.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "369it [01:27, 5.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "369it [01:20, 5.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "370it [01:27, 4.92it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "370it [01:21, 5.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "371it [01:28, 5.29it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "371it [01:21, 5.04it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "372it [01:28, 5.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "372it [01:21, 5.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "373it [01:28, 5.98it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "373it [01:21, 5.67it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "374it [01:28, 5.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "374it [01:21, 5.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " self.data = self.data.append(data_row, ignore_index=True)\n", + "375it [01:21, 5.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "376it [01:28, 8.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "377it [01:22, 7.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " self.data = self.data.append(data_row, ignore_index=True)\n", - "378it [01:28, 4.26it/s]\n", + "379it [01:22, 4.61it/s]\n", "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", @@ -1728,7 +1753,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Data Converted in 1.5174651225407918 min\n", + "Data Converted in 1.4211973190307616 min\n", "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=34893232,31588747,31387036,31250635,30105074,29433241,29161814,28911676,28719747,28560773,27592824,27313155,27300762,27296605,25371585,25329784,23259687,22610968,22284504,21535547,19768983,19053859,18952220,17269787,17017158,16413559,16277408,15161196,15065784,14969516,11767087,11486375,11238797,10737231,10588342,10234740,10193205,8870956,7604070,7517069,17262412&retmode=xml\n" ] }, @@ -1736,87 +1761,87 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:166: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " info = info.append(new_row, ignore_index=True)\n" ] } @@ -1828,7 +1853,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -2748,7 +2773,7 @@ "40 https://www.ncbi.nlm.nih.gov/pubmed/17262412 " ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -2757,12 +2782,21 @@ "output" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exporting output" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "output.to_csv(f\"data/{search_terms[0]}_scoring.csv\")" + ] } ], "metadata": { diff --git a/notebooks/Phenol_Explorer_Comparison.ipynb b/notebooks/Phenol_Explorer_Comparison.ipynb index 65870f8..20bc4e7 100644 --- a/notebooks/Phenol_Explorer_Comparison.ipynb +++ b/notebooks/Phenol_Explorer_Comparison.ipynb @@ -368,7 +368,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -382,7 +382,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/src/collected_data_handling.py b/src/collected_data_handling.py index 181d9b2..2eb09e0 100644 --- a/src/collected_data_handling.py +++ b/src/collected_data_handling.py @@ -56,7 +56,6 @@ def build_data_dict(df): return data_df - def dict_to_df(data_dict): """ Converts a constructed data dictionary to a pandas dataframe diff --git a/src/pubmed_util.py b/src/pubmed_util.py index 5477ce5..d5e7ae2 100644 --- a/src/pubmed_util.py +++ b/src/pubmed_util.py @@ -11,6 +11,7 @@ # Imports from directory from .filter import Filter + def filter_results(search_terms): """ Receives search terms to classify usefulness of pubmed documents from search results. @@ -339,7 +340,7 @@ def __safe_urlopen__(url): response.content : str (maybe bytes) or None Returns the response of a url query if it exists, else None """ -#Heba ======================================> + # Heba ======================================> print(url) try: diff --git a/src/try.py b/src/try.py deleted file mode 100644 index 90df465..0000000 --- a/src/try.py +++ /dev/null @@ -1,16 +0,0 @@ -"""import os - -os.system("jupyter notebook") - -docker run -v D:\m:/MyProjectFiles/in -v D:\o:/MyProjectFiles/out brain-docker - -os.system("some_command < input_file | another_command > output_file") - -os.system("docker run -v < D:\m:/MyProjectFiles/in | -v D:\o:/MyProjectFiles/out > D:\o:/MyProjectFiles/out ") -""" - -import subprocess - -with open("/tmp/output.log", "a") as output: - subprocess.call("docker run -v D:\m:/MyProjectFiles/in -v D:\o:/MyProjectFiles/out brain-docker", shell=True, - stdout=output, stderr=output) diff --git a/stats/fm_usda_overlap_perc_cocoa.txt b/stats/fm_usda_overlap_perc_cocoa.txt index 98ed0d2..ecb8a52 100644 --- a/stats/fm_usda_overlap_perc_cocoa.txt +++ b/stats/fm_usda_overlap_perc_cocoa.txt @@ -1 +1 @@ -perc cocoa fm data used w/ usda: 0.05351170568561873 01/29/2023 \ No newline at end of file +perc cocoa fm data used w/ usda: 0.05351170568561873 02/08/2023 \ No newline at end of file diff --git a/stats/fm_usda_r2_cocoa.txt b/stats/fm_usda_r2_cocoa.txt index df510f9..69c166c 100644 --- a/stats/fm_usda_r2_cocoa.txt +++ b/stats/fm_usda_r2_cocoa.txt @@ -1 +1 @@ -FM-USDA log R2 cocoa: 0.5576625681157634 01/29/2023 \ No newline at end of file +FM-USDA log R2 cocoa: 0.5576625681157634 02/08/2023 \ No newline at end of file diff --git a/stats/fm_usda_r2_r_cocoa.txt b/stats/fm_usda_r2_r_cocoa.txt index 133e9b0..1015694 100644 --- a/stats/fm_usda_r2_r_cocoa.txt +++ b/stats/fm_usda_r2_r_cocoa.txt @@ -1 +1 @@ -FM-USDA removed paper log R2 cocoa: 0.7451095518300979 01/29/2023 \ No newline at end of file +FM-USDA removed paper log R2 cocoa: 0.7451095518300979 02/08/2023 \ No newline at end of file diff --git a/stats/unique_chems_cocoa.txt b/stats/unique_chems_cocoa.txt index c806b07..7619b1b 100644 --- a/stats/unique_chems_cocoa.txt +++ b/stats/unique_chems_cocoa.txt @@ -1 +1 @@ -Num unique fm chems cocoa: 284 01/29/2023 \ No newline at end of file +Num unique fm chems cocoa: 284 02/08/2023 \ No newline at end of file From 8838bdcedc7d36a0d0855ee9bbea94a43c67654c Mon Sep 17 00:00:00 2001 From: hebamuh68 Date: Wed, 8 Feb 2023 07:53:16 +0200 Subject: [PATCH 4/4] test everything is working well: T --- notebooks/Paper_Screening.ipynb | 1812 +------------------------------ 1 file changed, 3 insertions(+), 1809 deletions(-) diff --git a/notebooks/Paper_Screening.ipynb b/notebooks/Paper_Screening.ipynb index 0a5413e..3eda1cc 100644 --- a/notebooks/Paper_Screening.ipynb +++ b/notebooks/Paper_Screening.ipynb @@ -37,1815 +37,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=fresh%20garlic&retmax=1000000\n", - "ids 379\n", - "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=36721742,36673488,36661532,36558083,36374623,36296587,36241263,36235435,36231461,36122930,36091965,35940117,35822789,35798823,35729146,35637890,35628468,35627064,35507844,35480459,35480455,35436379,35355410,35293213,35211974,35172234,35168162,35125657,35062505,35057517,35011342,34893232,34828984,34819925,34795722,34713965,34527070,34521004,34443625,34395025,34164770,33929534,33785394,33740130,33681348,33638666,33605386,33571752,33549282,33415587,33389861,33325167,33317599,33295000,33242796,33221098,33177859,33142731,33123325,33088979,32963075,32900002,32884733,32328266,32291787,32184427,32178294,32156160,32142159,32107396,32010343,32010338,31991938,31901831,31743742,31721940,31588747,31577841,31516325,31509980,31387036,31382578,31338077,31250635,31031431,30931955,30929787,30923456,30744360,30704623,30603687,30570073,30497454,30457057,30412324&retmode=xml\n", - "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=30374433,30366271,30356168,30320007,30311619,30263743,30169119,30105074,29753990,29621132,29579944,29433241,29391611,29331459,29161814,29024904,28934070,28911676,28911544,28873605,28719747,28560773,28425131,28196294,28078257,28051097,28017989,27976376,27904380,27784931,27778523,27753097,27592824,27584700,27313155,27300762,27296605,27263111,27182249,27043510,27011724,27008423,26969520,26954136,26889365,26786785,26776039,26690030,28433269,26471590,26440842,26212875,26139864,26060559,26019632,26017222,26003845,25941212,25838894,25745260,25745247,25631559,25573280,25532343,25493198,25371585,25329784,25284945,25141133,25133543,25124136,24991105,24598083,26761498,24006751,23755406,23700562,23679240,23623137,23600691,23583806,23578652,23527659,23387242,23292331,26770698,25050258,23259687,23244152,23050048,24471087,22668601,22625420,22610968,22507958&retmode=xml\n", - "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=22473701,22353229,22284504,23983369,21812650,21794123,21729078,21726146,21721585,21631395,21615122,21553301,21538625,21535547,21325240,21290188,21269249,21184771,21170253,21118053,21086547,20951625,20924970,20739164,20633941,20538890,20202327,20192846,19929845,19895494,19878318,19827749,19768983,19735176,19653315,19601391,19382351,19174616,19120662,19053859,19019552,19019099,18952220,20416582,18844255,18588510,18489116,18334029,18205306,17966138,17767872,17918162,19070102,17523869,17472490,17472489,17396504,17330154,17269787,17219900,17189767,17146719,17123005,17075725,17017158,16910057,16715809,16584547,16500553,16484578,16484574,16413559,16405290,16380980,16366855,16298867,16287614,16277408,16230689,16223688,16121720,16076102,16041728,15916949,15796617,15769123,15749368,15718030,15616341,15615431,15380914,15216390,15161196,15065784,15056375&retmode=xml\n", - "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=15031746,14969516,14959768,14727763,12847923,12738093,12706595,12701369,12697379,12570662,12396965,12174037,12098946,12042427,11933151,11906464,11833727,11802218,11767087,11486375,11466175,11454685,11434986,11385050,11365438,11271766,11238807,11238803,11238797,11049697,10882191,10737231,21214446,10552475,10588342,10524347,10354821,10235193,10234740,10193205,10098897,10072338,9726786,9637953,9625398,9246703,8739190,8729671,9772707,8870956,8603796,8560468,8607564,7480084,7604070,7666832,7517069,8170288,8183725,8302920,8439494,1470664,1285693,1531110,1665257,1742542,2065395,1831097,1855874,2083173,17221429,2686739,2793233,17262437,17262412,30991489,28310369,3207435,30965409,3702985,3924535,24318347,6471131,6877039,6870217,30913616,6154673,28223570,973445,1183776,4796677,4738589,5102504,14377534&retmode=xml\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['fresh garlic'] Document Info (379 entries) Retrieved in 0.34865829944610593 min\n", - "Creating features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0it [00:00, ?it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "1it [00:00, 5.97it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "2it [00:00, 4.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "4it [00:00, 8.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "6it [00:00, 6.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "7it [00:01, 7.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "9it [00:01, 8.99it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "11it [00:01, 10.40it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "13it [00:01, 7.53it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "14it [00:01, 7.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "16it [00:01, 9.33it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "18it [00:02, 10.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "20it [00:02, 11.53it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "22it [00:02, 7.60it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "24it [00:03, 6.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "25it [00:03, 6.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "26it [00:03, 5.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "27it [00:03, 4.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "28it [00:04, 4.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "30it [00:04, 5.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "31it [00:04, 5.76it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "32it [00:04, 5.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "33it [00:05, 4.78it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "34it [00:05, 5.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "36it [00:05, 6.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "37it [00:05, 6.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "38it [00:05, 5.01it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "40it [00:06, 6.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "41it [00:06, 5.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "42it [00:06, 4.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "43it [00:07, 3.40it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "45it [00:07, 4.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "46it [00:07, 4.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "47it [00:07, 4.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "48it [00:08, 4.40it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "49it [00:08, 5.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "50it [00:08, 5.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "51it [00:08, 4.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "52it [00:08, 4.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "54it [00:09, 4.82it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "55it [00:09, 5.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "56it [00:09, 4.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "58it [00:10, 4.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "60it [00:10, 6.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "62it [00:10, 6.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "63it [00:10, 5.78it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "65it [00:11, 5.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "66it [00:11, 5.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "68it [00:11, 7.31it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "69it [00:11, 5.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "70it [00:12, 4.97it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "71it [00:12, 4.80it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "73it [00:12, 6.58it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "74it [00:12, 6.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "75it [00:12, 6.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "77it [00:13, 7.27it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "78it [00:13, 6.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "80it [00:13, 8.58it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "81it [00:13, 7.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "83it [00:13, 8.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "84it [00:13, 7.82it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "86it [00:14, 7.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "87it [00:14, 5.65it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "89it [00:14, 7.45it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "90it [00:14, 6.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "92it [00:15, 8.36it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "93it [00:15, 7.42it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "95it [00:15, 8.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "96it [00:15, 8.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "97it [00:15, 8.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "98it [00:15, 6.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "100it [00:16, 5.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "102it [00:16, 5.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "103it [00:17, 4.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "104it [00:17, 4.37it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "105it [00:17, 4.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "106it [00:17, 3.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "107it [00:18, 3.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "108it [00:18, 4.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "109it [00:18, 3.51it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "110it [00:18, 3.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "111it [00:19, 3.66it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "113it [00:19, 5.05it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "114it [00:19, 5.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "115it [00:19, 5.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "116it [00:20, 4.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "117it [00:20, 4.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "119it [00:20, 5.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "120it [00:20, 5.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "121it [00:21, 4.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "122it [00:21, 5.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "123it [00:21, 6.33it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "124it [00:21, 6.56it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "126it [00:21, 6.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "128it [00:21, 7.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "129it [00:22, 6.19it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "130it [00:22, 6.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "131it [00:22, 5.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "132it [00:23, 4.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "133it [00:23, 4.19it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "135it [00:23, 4.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "137it [00:24, 4.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "138it [00:24, 4.58it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "139it [00:24, 4.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "140it [00:24, 4.09it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "141it [00:24, 4.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "142it [00:25, 5.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "143it [00:25, 4.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "144it [00:25, 4.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "145it [00:25, 4.75it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "147it [00:25, 6.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "148it [00:26, 5.61it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "150it [00:26, 6.07it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "151it [00:26, 4.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "152it [00:27, 4.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "153it [00:27, 4.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "154it [00:27, 4.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "155it [00:27, 4.04it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "156it [00:28, 4.19it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "157it [00:28, 4.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "158it [00:28, 3.80it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "159it [00:28, 3.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "160it [00:29, 3.51it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "162it [00:29, 3.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "164it [00:29, 5.12it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "165it [00:30, 4.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "166it [00:30, 4.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "168it [00:31, 4.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "169it [00:31, 4.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "170it [00:31, 3.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "171it [00:32, 3.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "173it [00:32, 4.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "175it [00:32, 4.82it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "176it [00:32, 4.58it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "177it [00:33, 4.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "178it [00:33, 4.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "179it [00:33, 4.12it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "180it [00:33, 4.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "182it [00:34, 6.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "183it [00:34, 5.27it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "185it [00:34, 6.05it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "186it [00:34, 5.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "187it [00:35, 5.12it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "188it [00:35, 4.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "189it [00:35, 4.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "191it [00:36, 4.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "193it [00:36, 4.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "194it [00:36, 3.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "195it [00:37, 3.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "196it [00:37, 4.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "197it [00:37, 3.80it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "198it [00:38, 3.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "199it [00:38, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "200it [00:38, 3.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "201it [00:39, 2.75it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "202it [00:39, 2.95it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "203it [00:39, 3.21it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "204it [00:39, 3.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "205it [00:40, 3.29it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "206it [00:40, 3.31it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "207it [00:40, 3.52it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "208it [00:41, 3.36it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "209it [00:41, 3.45it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "210it [00:41, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "211it [00:41, 3.73it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "212it [00:42, 3.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "213it [00:42, 3.88it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "214it [00:42, 2.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "215it [00:43, 3.01it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "216it [00:43, 3.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "217it [00:43, 3.29it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "218it [00:44, 3.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "219it [00:44, 3.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "220it [00:44, 3.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "221it [00:44, 3.53it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "222it [00:45, 3.91it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "223it [00:45, 3.75it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "224it [00:45, 3.38it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "225it [00:46, 3.32it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "226it [00:46, 3.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "227it [00:46, 3.74it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "228it [00:46, 4.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "229it [00:46, 5.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "230it [00:47, 3.88it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "231it [00:47, 3.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "232it [00:47, 3.39it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "233it [00:48, 3.60it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "234it [00:48, 3.88it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "235it [00:48, 3.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "236it [00:48, 3.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "237it [00:49, 4.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "238it [00:49, 4.75it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "239it [00:49, 3.88it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "240it [00:49, 3.79it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "242it [00:50, 4.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "243it [00:50, 5.03it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "244it [00:50, 4.39it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "245it [00:50, 4.00it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "246it [00:51, 3.51it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "247it [00:51, 3.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "248it [00:51, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "249it [00:51, 3.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "250it [00:52, 4.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "251it [00:52, 3.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "252it [00:52, 3.60it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "254it [00:53, 4.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "255it [00:53, 4.89it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "256it [00:53, 4.07it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "257it [00:53, 3.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "259it [00:54, 5.28it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "260it [00:54, 5.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "261it [00:54, 4.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "262it [00:54, 4.23it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "263it [00:55, 4.36it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "264it [00:55, 3.46it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "265it [00:55, 3.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "266it [00:56, 3.10it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "267it [00:56, 3.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "268it [00:56, 3.62it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "269it [00:57, 3.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "270it [00:57, 3.68it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "271it [00:57, 3.75it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "272it [00:57, 3.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "273it [00:58, 3.92it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "274it [00:58, 4.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "275it [00:58, 3.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "276it [00:58, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "277it [00:59, 3.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "278it [00:59, 3.84it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "280it [00:59, 4.96it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "281it [00:59, 5.61it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "282it [00:59, 5.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "283it [01:00, 5.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "284it [01:00, 4.67it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "285it [01:00, 4.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "286it [01:00, 4.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "287it [01:01, 3.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "288it [01:01, 3.87it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "289it [01:01, 3.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "290it [01:02, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "291it [01:02, 3.80it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "292it [01:02, 3.45it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "293it [01:02, 3.31it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "294it [01:03, 3.29it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "295it [01:03, 3.24it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "296it [01:03, 4.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "298it [01:03, 5.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "299it [01:04, 4.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "300it [01:04, 5.15it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "301it [01:04, 4.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "302it [01:05, 3.90it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "303it [01:05, 3.70it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "305it [01:05, 4.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "306it [01:05, 4.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "307it [01:06, 4.13it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "308it [01:06, 3.78it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "309it [01:06, 3.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "310it [01:06, 4.16it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "311it [01:07, 3.69it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "312it [01:07, 3.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "313it [01:08, 2.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "314it [01:08, 3.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "315it [01:08, 3.25it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "316it [01:09, 3.12it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "317it [01:09, 3.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "318it [01:09, 3.71it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "319it [01:09, 3.59it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "320it [01:10, 3.81it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "321it [01:10, 3.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "322it [01:10, 3.40it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "323it [01:10, 4.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "324it [01:11, 4.08it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "325it [01:11, 3.85it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "326it [01:11, 3.43it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "328it [01:11, 4.56it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "329it [01:12, 4.19it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "330it [01:12, 4.94it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "332it [01:12, 5.64it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "334it [01:12, 6.54it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "335it [01:13, 4.72it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "336it [01:13, 4.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "337it [01:13, 4.18it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "338it [01:14, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "339it [01:14, 4.20it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "340it [01:14, 4.63it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "341it [01:14, 4.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "342it [01:15, 3.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "343it [01:15, 3.31it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "344it [01:15, 3.76it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "345it [01:15, 4.09it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "346it [01:16, 4.44it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "347it [01:16, 3.83it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "348it [01:16, 4.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "349it [01:16, 4.35it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "350it [01:17, 4.57it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "351it [01:17, 4.00it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "352it [01:17, 4.47it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "353it [01:17, 4.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "354it [01:17, 4.48it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "355it [01:18, 5.04it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "356it [01:18, 4.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "357it [01:18, 5.19it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "358it [01:18, 4.17it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "359it [01:19, 4.39it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "360it [01:19, 4.60it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "361it [01:19, 4.79it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "362it [01:19, 4.67it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "363it [01:19, 4.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "365it [01:20, 5.11it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "367it [01:20, 6.14it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "368it [01:20, 6.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "369it [01:20, 5.02it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "370it [01:21, 5.26it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "371it [01:21, 5.04it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "372it [01:21, 5.34it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "373it [01:21, 5.67it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "374it [01:21, 5.55it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "375it [01:21, 5.30it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "377it [01:22, 7.86it/s]/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:143: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " self.data = self.data.append(data_row, ignore_index=True)\n", - "379it [01:22, 4.61it/s]\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/filter.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " selected_articles = selected_articles.append({'PMID': PMID}, ignore_index=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data Converted in 1.4211973190307616 min\n", - "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=34893232,31588747,31387036,31250635,30105074,29433241,29161814,28911676,28719747,28560773,27592824,27313155,27300762,27296605,25371585,25329784,23259687,22610968,22284504,21535547,19768983,19053859,18952220,17269787,17017158,16413559,16277408,15161196,15065784,14969516,11767087,11486375,11238797,10737231,10588342,10234740,10193205,8870956,7604070,7517069,17262412&retmode=xml\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n", - "/home/heba/Graduation project/FoodMine/src/pubmed_util.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " info = info.append(new_row, ignore_index=True)\n" - ] - } - ], + "outputs": [], "source": [ "search_terms = ['fresh garlic']\n", "output = filter_results(search_terms)" @@ -2815,7 +1009,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.10.6" } }, "nbformat": 4,