diff --git a/docs/source/tutorials/connectomics.ipynb b/docs/source/tutorials/connectomics.ipynb index 62b0388c..73a4abcb 100644 --- a/docs/source/tutorials/connectomics.ipynb +++ b/docs/source/tutorials/connectomics.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "cell_id": "00000-01575947-f75d-4293-bb29-ea52fff8412c", "deepnote_cell_type": "code", @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "cell_id": "00002-58bf3eee-8694-4e03-9bfc-86948a909b2b", "deepnote_cell_type": "code", @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ "0 [Chiang2010] [] " ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -197,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "cell_id": "00004-d857a224-a467-4b77-91c1-4809ac809875", "deepnote_cell_type": "code", @@ -534,7 +534,7 @@ "[158 rows x 13 columns]" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -546,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "cell_id": "00005-94bde190-f01b-43b8-9399-cdf491002d14", "deepnote_cell_type": "code", @@ -821,7 +821,7 @@ "[1359 rows x 11 columns]" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -836,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "cell_id": "00006-e8824075-2f85-4a09-a388-a7486261f9dc", "deepnote_cell_type": "code", @@ -1507,7 +1507,7 @@ "2 None None None " ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1522,7 +1522,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "cell_id": "00007-a3657774-09e1-449b-94d5-26c968c1b737", "deepnote_cell_type": "code", @@ -1797,7 +1797,7 @@ "[1707 rows x 11 columns]" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1812,7 +1812,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "cell_id": "00013-f4ba6007-b441-4122-8315-6202f2e1d906", "deepnote_cell_type": "code", @@ -1830,7 +1830,7 @@ "" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -1864,7 +1864,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "cell_id": "00015-fd8d47b2-711f-41ca-a9b8-709c7f4b9bc6", "deepnote_cell_type": "code", @@ -1907,41 +1907,41 @@ " \n", " \n", " 0\n", - " VFB_jrchjy98\n", - " LC4 (FlyEM-HB:1749258134)\n", - " 56\n", + " VFB_jrchjy9x\n", + " LC4 (FlyEM-HB:1938544937)\n", + " 98\n", " VFB_jrchjtg7\n", " DNp04_R (FlyEM-HB:1405231475)\n", " \n", " \n", " 1\n", - " VFB_jrchk4qn\n", - " PVLP046_R (FlyEM-HB:1600666632)\n", - " 48\n", + " VFB_jrchjy9p\n", + " LC4 (FlyEM-HB:1815070402)\n", + " 97\n", " VFB_jrchjtg7\n", " DNp04_R (FlyEM-HB:1405231475)\n", " \n", " \n", " 2\n", - " VFB_jrchjy9t\n", - " LC4 (FlyEM-HB:2215161310)\n", - " 86\n", + " VFB_jrchjyad\n", + " LC4 (FlyEM-HB:1908226457)\n", + " 93\n", " VFB_jrchjtg7\n", " DNp04_R (FlyEM-HB:1405231475)\n", " \n", " \n", " 3\n", - " VFB_jrchjya4\n", - " LC4 (FlyEM-HB:1877930505)\n", - " 76\n", + " VFB_jrchjya0\n", + " LC4 (FlyEM-HB:5812998136)\n", + " 92\n", " VFB_jrchjtg7\n", " DNp04_R (FlyEM-HB:1405231475)\n", " \n", " \n", " 4\n", - " VFB_jrchk4u3\n", - " PVLP100_R (FlyEM-HB:1375845363)\n", - " 44\n", + " VFB_jrchjyah\n", + " LC4 (FlyEM-HB:1938207942)\n", + " 90\n", " VFB_jrchjtg7\n", " DNp04_R (FlyEM-HB:1405231475)\n", " \n", @@ -1955,41 +1955,41 @@ " \n", " \n", " 82\n", - " VFB_jrchjya3\n", - " LC4 (FlyEM-HB:1907519001)\n", - " 75\n", + " VFB_jrchk063\n", + " LPLC2_R (FlyEM-HB:5813034151)\n", + " 24\n", " VFB_jrchjtg7\n", " DNp04_R (FlyEM-HB:1405231475)\n", " \n", " \n", " 83\n", - " VFB_jrchjy9i\n", - " LC4 (FlyEM-HB:5813069053)\n", - " 48\n", + " VFB_jrchk4oy\n", + " PVLP023_R (FlyEM-HB:1598296804)\n", + " 23\n", " VFB_jrchjtg7\n", " DNp04_R (FlyEM-HB:1405231475)\n", " \n", " \n", " 84\n", - " VFB_jrchjyaa\n", - " LC4 (FlyEM-HB:1906159299)\n", - " 82\n", + " VFB_jrchjrof\n", + " AVLP080_R (FlyEM-HB:5813027276)\n", + " 22\n", " VFB_jrchjtg7\n", " DNp04_R (FlyEM-HB:1405231475)\n", " \n", " \n", " 85\n", - " VFB_jrchjy95\n", - " LC4 (FlyEM-HB:1471601440)\n", - " 41\n", + " VFB_jrchk4p4\n", + " PVLP026_R (FlyEM-HB:1570565128)\n", + " 21\n", " VFB_jrchjtg7\n", " DNp04_R (FlyEM-HB:1405231475)\n", " \n", " \n", " 86\n", - " VFB_jrchjy9d\n", - " LC4 (FlyEM-HB:1627117134)\n", - " 46\n", + " VFB_jrchk4pd\n", + " PVLP031_R (FlyEM-HB:1630342124)\n", + " 20\n", " VFB_jrchjtg7\n", " DNp04_R (FlyEM-HB:1405231475)\n", " \n", @@ -2000,17 +2000,17 @@ ], "text/plain": [ " query_neuron_id query_neuron_name weight target_neuron_id \\\n", - "0 VFB_jrchjy98 LC4 (FlyEM-HB:1749258134) 56 VFB_jrchjtg7 \n", - "1 VFB_jrchk4qn PVLP046_R (FlyEM-HB:1600666632) 48 VFB_jrchjtg7 \n", - "2 VFB_jrchjy9t LC4 (FlyEM-HB:2215161310) 86 VFB_jrchjtg7 \n", - "3 VFB_jrchjya4 LC4 (FlyEM-HB:1877930505) 76 VFB_jrchjtg7 \n", - "4 VFB_jrchk4u3 PVLP100_R (FlyEM-HB:1375845363) 44 VFB_jrchjtg7 \n", + "0 VFB_jrchjy9x LC4 (FlyEM-HB:1938544937) 98 VFB_jrchjtg7 \n", + "1 VFB_jrchjy9p LC4 (FlyEM-HB:1815070402) 97 VFB_jrchjtg7 \n", + "2 VFB_jrchjyad LC4 (FlyEM-HB:1908226457) 93 VFB_jrchjtg7 \n", + "3 VFB_jrchjya0 LC4 (FlyEM-HB:5812998136) 92 VFB_jrchjtg7 \n", + "4 VFB_jrchjyah LC4 (FlyEM-HB:1938207942) 90 VFB_jrchjtg7 \n", ".. ... ... ... ... \n", - "82 VFB_jrchjya3 LC4 (FlyEM-HB:1907519001) 75 VFB_jrchjtg7 \n", - "83 VFB_jrchjy9i LC4 (FlyEM-HB:5813069053) 48 VFB_jrchjtg7 \n", - "84 VFB_jrchjyaa LC4 (FlyEM-HB:1906159299) 82 VFB_jrchjtg7 \n", - "85 VFB_jrchjy95 LC4 (FlyEM-HB:1471601440) 41 VFB_jrchjtg7 \n", - "86 VFB_jrchjy9d LC4 (FlyEM-HB:1627117134) 46 VFB_jrchjtg7 \n", + "82 VFB_jrchk063 LPLC2_R (FlyEM-HB:5813034151) 24 VFB_jrchjtg7 \n", + "83 VFB_jrchk4oy PVLP023_R (FlyEM-HB:1598296804) 23 VFB_jrchjtg7 \n", + "84 VFB_jrchjrof AVLP080_R (FlyEM-HB:5813027276) 22 VFB_jrchjtg7 \n", + "85 VFB_jrchk4p4 PVLP026_R (FlyEM-HB:1570565128) 21 VFB_jrchjtg7 \n", + "86 VFB_jrchk4pd PVLP031_R (FlyEM-HB:1630342124) 20 VFB_jrchjtg7 \n", "\n", " target_neuron_name \n", "0 DNp04_R (FlyEM-HB:1405231475) \n", @@ -2028,7 +2028,7 @@ "[87 rows x 5 columns]" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -2041,6 +2041,346 @@ "upstream_of_DNp04" ] }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelsymbolidtagsdescriptionparents_labelparents_iddata_sourceaccessionxrefstemplatesdatasetlicense
0LC4 (FlyEM-HB:1625080038)LC4VFB_jrchjy9e[Entity, Adult, Anatomy, Cell, Cholinergic, Gl...tracing status-Roughly traced, cropped-False[lobula columnar neuron LC4][FBbt_00003874][neuprint_JRC_Hemibrain_1point1][1625080038][neuprint_JRC_Hemibrain_1point1:1625080038, ne...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
1LC4 (FlyEM-HB:1782668028)LC4VFB_jrchjy9o[Entity, Adult, Anatomy, Cell, Cholinergic, Gl...tracing status-Roughly traced, cropped-False[lobula columnar neuron LC4][FBbt_00003874][neuprint_JRC_Hemibrain_1point1][1782668028][neuronbridge:1782668028, neuprint_JRC_Hemibra...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
2LC4 (FlyEM-HB:1874035952)LC4VFB_jrchjy9z[Entity, Adult, Anatomy, Cell, Cholinergic, Gl...tracing status-Roughly traced, cropped-False[lobula columnar neuron LC4][FBbt_00003874][neuprint_JRC_Hemibrain_1point1][1874035952][neuprint_JRC_Hemibrain_1point1:1874035952, ne...[JRC2018Unisex, JRC_FlyEM_Hemibrain][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
3LC4 (FlyEM-HB:1810956698)LC4VFB_jrchjy9j[Entity, Adult, Anatomy, Cell, Cholinergic, Gl...tracing status-Roughly traced, cropped-False[lobula columnar neuron LC4][FBbt_00003874][neuprint_JRC_Hemibrain_1point1][1810956698][neuprint_JRC_Hemibrain_1point1:1810956698, ne...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
4LC4 (FlyEM-HB:1158187240)LC4VFB_jrchjy8t[Entity, Adult, Anatomy, Cell, Cholinergic, Gl...tracing status-Roughly traced, cropped-False[lobula columnar neuron LC4][FBbt_00003874][neuprint_JRC_Hemibrain_1point1][1158187240][neuronbridge:1158187240, neuprint_JRC_Hemibra...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
..........................................
82PVLP142_R (FlyEM-HB:1876565477)PVLP142_RVFB_jrchk4wk[Entity, Adult, Anatomy, Cell, Cholinergic, In...tracing status-Roughly traced, cropped-False[adult cholinergic neuron, adult posterior ven...[FBbt_00058205, FBbt_20002200][neuprint_JRC_Hemibrain_1point1][1876565477][neuronbridge:1876565477, neuprint_JRC_Hemibra...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
83PVLP151(SCB004)_L (FlyEM-HB:1628973439)PVLP151(SCB004)_LVFB_jrchk4x2[Entity, Adult, Anatomy, Cell, Cholinergic, In...tracing status-Roughly traced, cropped-False[adult posterior ventrolateral protocerebrum n...[FBbt_20002209][neuprint_JRC_Hemibrain_1point1][1628973439][neuronbridge:1628973439, neuprint_JRC_Hemibra...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
84PVLP100_R (FlyEM-HB:1375845363)PVLP100_RVFB_jrchk4u3[Entity, Adult, Anatomy, Cell, GABAergic, Indi...tracing status-Roughly traced, cropped-False[adult posterior ventrolateral protocerebrum n...[FBbt_20002158][neuprint_JRC_Hemibrain_1point1][1375845363][neuronbridge:1375845363, neuprint_JRC_Hemibra...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
85PVLP094_R (FlyEM-HB:1503733177)PVLP094_RVFB_jrchk4tm[Entity, Adult, Anatomy, Cell, GABAergic, Indi...tracing status-Roughly traced, cropped-False[adult posterior ventrolateral protocerebrum n...[FBbt_20002152][neuprint_JRC_Hemibrain_1point1][1503733177][neuprint_JRC_Hemibrain_1point1:1503733177, ne...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
86LPLC2_R (FlyEM-HB:5813034151)LPLC2_RVFB_jrchk063[Entity, Adult, Anatomy, Cell, Cholinergic, In...tracing status-Roughly traced, cropped-False[lobula plate-lobula columnar neuron LPLC2][FBbt_00111763][neuprint_JRC_Hemibrain_1point1][5813034151][neuronbridge:5813034151, neuprint_JRC_Hemibra...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
\n", + "

87 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " label symbol id \\\n", + "0 LC4 (FlyEM-HB:1625080038) LC4 VFB_jrchjy9e \n", + "1 LC4 (FlyEM-HB:1782668028) LC4 VFB_jrchjy9o \n", + "2 LC4 (FlyEM-HB:1874035952) LC4 VFB_jrchjy9z \n", + "3 LC4 (FlyEM-HB:1810956698) LC4 VFB_jrchjy9j \n", + "4 LC4 (FlyEM-HB:1158187240) LC4 VFB_jrchjy8t \n", + ".. ... ... ... \n", + "82 PVLP142_R (FlyEM-HB:1876565477) PVLP142_R VFB_jrchk4wk \n", + "83 PVLP151(SCB004)_L (FlyEM-HB:1628973439) PVLP151(SCB004)_L VFB_jrchk4x2 \n", + "84 PVLP100_R (FlyEM-HB:1375845363) PVLP100_R VFB_jrchk4u3 \n", + "85 PVLP094_R (FlyEM-HB:1503733177) PVLP094_R VFB_jrchk4tm \n", + "86 LPLC2_R (FlyEM-HB:5813034151) LPLC2_R VFB_jrchk063 \n", + "\n", + " tags \\\n", + "0 [Entity, Adult, Anatomy, Cell, Cholinergic, Gl... \n", + "1 [Entity, Adult, Anatomy, Cell, Cholinergic, Gl... \n", + "2 [Entity, Adult, Anatomy, Cell, Cholinergic, Gl... \n", + "3 [Entity, Adult, Anatomy, Cell, Cholinergic, Gl... \n", + "4 [Entity, Adult, Anatomy, Cell, Cholinergic, Gl... \n", + ".. ... \n", + "82 [Entity, Adult, Anatomy, Cell, Cholinergic, In... \n", + "83 [Entity, Adult, Anatomy, Cell, Cholinergic, In... \n", + "84 [Entity, Adult, Anatomy, Cell, GABAergic, Indi... \n", + "85 [Entity, Adult, Anatomy, Cell, GABAergic, Indi... \n", + "86 [Entity, Adult, Anatomy, Cell, Cholinergic, In... \n", + "\n", + " description \\\n", + "0 tracing status-Roughly traced, cropped-False \n", + "1 tracing status-Roughly traced, cropped-False \n", + "2 tracing status-Roughly traced, cropped-False \n", + "3 tracing status-Roughly traced, cropped-False \n", + "4 tracing status-Roughly traced, cropped-False \n", + ".. ... \n", + "82 tracing status-Roughly traced, cropped-False \n", + "83 tracing status-Roughly traced, cropped-False \n", + "84 tracing status-Roughly traced, cropped-False \n", + "85 tracing status-Roughly traced, cropped-False \n", + "86 tracing status-Roughly traced, cropped-False \n", + "\n", + " parents_label \\\n", + "0 [lobula columnar neuron LC4] \n", + "1 [lobula columnar neuron LC4] \n", + "2 [lobula columnar neuron LC4] \n", + "3 [lobula columnar neuron LC4] \n", + "4 [lobula columnar neuron LC4] \n", + ".. ... \n", + "82 [adult cholinergic neuron, adult posterior ven... \n", + "83 [adult posterior ventrolateral protocerebrum n... \n", + "84 [adult posterior ventrolateral protocerebrum n... \n", + "85 [adult posterior ventrolateral protocerebrum n... \n", + "86 [lobula plate-lobula columnar neuron LPLC2] \n", + "\n", + " parents_id data_source \\\n", + "0 [FBbt_00003874] [neuprint_JRC_Hemibrain_1point1] \n", + "1 [FBbt_00003874] [neuprint_JRC_Hemibrain_1point1] \n", + "2 [FBbt_00003874] [neuprint_JRC_Hemibrain_1point1] \n", + "3 [FBbt_00003874] [neuprint_JRC_Hemibrain_1point1] \n", + "4 [FBbt_00003874] [neuprint_JRC_Hemibrain_1point1] \n", + ".. ... ... \n", + "82 [FBbt_00058205, FBbt_20002200] [neuprint_JRC_Hemibrain_1point1] \n", + "83 [FBbt_20002209] [neuprint_JRC_Hemibrain_1point1] \n", + "84 [FBbt_20002158] [neuprint_JRC_Hemibrain_1point1] \n", + "85 [FBbt_20002152] [neuprint_JRC_Hemibrain_1point1] \n", + "86 [FBbt_00111763] [neuprint_JRC_Hemibrain_1point1] \n", + "\n", + " accession xrefs \\\n", + "0 [1625080038] [neuprint_JRC_Hemibrain_1point1:1625080038, ne... \n", + "1 [1782668028] [neuronbridge:1782668028, neuprint_JRC_Hemibra... \n", + "2 [1874035952] [neuprint_JRC_Hemibrain_1point1:1874035952, ne... \n", + "3 [1810956698] [neuprint_JRC_Hemibrain_1point1:1810956698, ne... \n", + "4 [1158187240] [neuronbridge:1158187240, neuprint_JRC_Hemibra... \n", + ".. ... ... \n", + "82 [1876565477] [neuronbridge:1876565477, neuprint_JRC_Hemibra... \n", + "83 [1628973439] [neuronbridge:1628973439, neuprint_JRC_Hemibra... \n", + "84 [1375845363] [neuronbridge:1375845363, neuprint_JRC_Hemibra... \n", + "85 [1503733177] [neuprint_JRC_Hemibrain_1point1:1503733177, ne... \n", + "86 [5813034151] [neuronbridge:5813034151, neuprint_JRC_Hemibra... \n", + "\n", + " templates dataset \\\n", + "0 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "1 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "2 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "3 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "4 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + ".. ... ... \n", + "82 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "83 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "84 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "85 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "86 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "\n", + " license \n", + "0 [https://creativecommons.org/licenses/by/4.0/l... \n", + "1 [https://creativecommons.org/licenses/by/4.0/l... \n", + "2 [https://creativecommons.org/licenses/by/4.0/l... \n", + "3 [https://creativecommons.org/licenses/by/4.0/l... \n", + "4 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labelsymbolidtagsdescriptionparents_labelparents_iddata_sourceaccessionxrefstemplatesdatasetlicense
0LC4 (FlyEM-HB:1625080038)LC4VFB_jrchjy9e[Entity, Adult, Anatomy, Cell, Cholinergic, Gl...tracing status-Roughly traced, cropped-False[lobula columnar neuron LC4][FBbt_00003874][neuprint_JRC_Hemibrain_1point1][1625080038][neuprint_JRC_Hemibrain_1point1:1625080038, ne...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
1LC4 (FlyEM-HB:1782668028)LC4VFB_jrchjy9o[Entity, Adult, Anatomy, Cell, Cholinergic, Gl...tracing status-Roughly traced, cropped-False[lobula columnar neuron LC4][FBbt_00003874][neuprint_JRC_Hemibrain_1point1][1782668028][neuronbridge:1782668028, neuprint_JRC_Hemibra...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
2LC4 (FlyEM-HB:1874035952)LC4VFB_jrchjy9z[Entity, Adult, Anatomy, Cell, Cholinergic, Gl...tracing status-Roughly traced, cropped-False[lobula columnar neuron LC4][FBbt_00003874][neuprint_JRC_Hemibrain_1point1][1874035952][neuprint_JRC_Hemibrain_1point1:1874035952, ne...[JRC2018Unisex, JRC_FlyEM_Hemibrain][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
3LC4 (FlyEM-HB:1810956698)LC4VFB_jrchjy9j[Entity, Adult, Anatomy, Cell, Cholinergic, Gl...tracing status-Roughly traced, cropped-False[lobula columnar neuron LC4][FBbt_00003874][neuprint_JRC_Hemibrain_1point1][1810956698][neuprint_JRC_Hemibrain_1point1:1810956698, ne...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
4LC4 (FlyEM-HB:1158187240)LC4VFB_jrchjy8t[Entity, Adult, Anatomy, Cell, Cholinergic, Gl...tracing status-Roughly traced, cropped-False[lobula columnar neuron LC4][FBbt_00003874][neuprint_JRC_Hemibrain_1point1][1158187240][neuronbridge:1158187240, neuprint_JRC_Hemibra...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
..........................................
82PVLP142_R (FlyEM-HB:1876565477)PVLP142_RVFB_jrchk4wk[Entity, Adult, Anatomy, Cell, Cholinergic, In...tracing status-Roughly traced, cropped-False[adult cholinergic neuron, adult posterior ven...[FBbt_00058205, FBbt_20002200][neuprint_JRC_Hemibrain_1point1][1876565477][neuronbridge:1876565477, neuprint_JRC_Hemibra...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
83PVLP151(SCB004)_L (FlyEM-HB:1628973439)PVLP151(SCB004)_LVFB_jrchk4x2[Entity, Adult, Anatomy, Cell, Cholinergic, In...tracing status-Roughly traced, cropped-False[adult posterior ventrolateral protocerebrum n...[FBbt_20002209][neuprint_JRC_Hemibrain_1point1][1628973439][neuronbridge:1628973439, neuprint_JRC_Hemibra...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
84PVLP100_R (FlyEM-HB:1375845363)PVLP100_RVFB_jrchk4u3[Entity, Adult, Anatomy, Cell, GABAergic, Indi...tracing status-Roughly traced, cropped-False[adult posterior ventrolateral protocerebrum n...[FBbt_20002158][neuprint_JRC_Hemibrain_1point1][1375845363][neuronbridge:1375845363, neuprint_JRC_Hemibra...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
85PVLP094_R (FlyEM-HB:1503733177)PVLP094_RVFB_jrchk4tm[Entity, Adult, Anatomy, Cell, GABAergic, Indi...tracing status-Roughly traced, cropped-False[adult posterior ventrolateral protocerebrum n...[FBbt_20002152][neuprint_JRC_Hemibrain_1point1][1503733177][neuprint_JRC_Hemibrain_1point1:1503733177, ne...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
86LPLC2_R (FlyEM-HB:5813034151)LPLC2_RVFB_jrchk063[Entity, Adult, Anatomy, Cell, Cholinergic, In...tracing status-Roughly traced, cropped-False[lobula plate-lobula columnar neuron LPLC2][FBbt_00111763][neuprint_JRC_Hemibrain_1point1][5813034151][neuronbridge:5813034151, neuprint_JRC_Hemibra...[JRC_FlyEM_Hemibrain, JRC2018Unisex][Xu2020NeuronsV1point1][https://creativecommons.org/licenses/by/4.0/l...
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87 rows × 13 columns

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" - ], - "text/plain": [ - " label symbol id \\\n", - "0 LC4 (FlyEM-HB:1625080038) LC4 VFB_jrchjy9e \n", - "1 LC4 (FlyEM-HB:1782668028) LC4 VFB_jrchjy9o \n", - "2 LC4 (FlyEM-HB:1874035952) LC4 VFB_jrchjy9z \n", - "3 LC4 (FlyEM-HB:1810956698) LC4 VFB_jrchjy9j \n", - "4 LC4 (FlyEM-HB:1158187240) LC4 VFB_jrchjy8t \n", - ".. ... ... ... \n", - "82 PVLP142_R (FlyEM-HB:1876565477) PVLP142_R VFB_jrchk4wk \n", - "83 PVLP151(SCB004)_L (FlyEM-HB:1628973439) PVLP151(SCB004)_L VFB_jrchk4x2 \n", - "84 PVLP100_R (FlyEM-HB:1375845363) PVLP100_R VFB_jrchk4u3 \n", - "85 PVLP094_R (FlyEM-HB:1503733177) PVLP094_R VFB_jrchk4tm \n", - "86 LPLC2_R (FlyEM-HB:5813034151) LPLC2_R VFB_jrchk063 \n", - "\n", - " tags \\\n", - "0 [Entity, Adult, Anatomy, Cell, Cholinergic, Gl... \n", - "1 [Entity, Adult, Anatomy, Cell, Cholinergic, Gl... \n", - "2 [Entity, Adult, Anatomy, Cell, Cholinergic, Gl... \n", - "3 [Entity, Adult, Anatomy, Cell, Cholinergic, Gl... \n", - "4 [Entity, Adult, Anatomy, Cell, Cholinergic, Gl... \n", - ".. ... \n", - "82 [Entity, Adult, Anatomy, Cell, Cholinergic, In... \n", - "83 [Entity, Adult, Anatomy, Cell, Cholinergic, In... \n", - "84 [Entity, Adult, Anatomy, Cell, GABAergic, Indi... \n", - "85 [Entity, Adult, Anatomy, Cell, GABAergic, Indi... \n", - "86 [Entity, Adult, Anatomy, Cell, Cholinergic, In... \n", - "\n", - " description \\\n", - "0 tracing status-Roughly traced, cropped-False \n", - "1 tracing status-Roughly traced, cropped-False \n", - "2 tracing status-Roughly traced, cropped-False \n", - "3 tracing status-Roughly traced, cropped-False \n", - "4 tracing status-Roughly traced, cropped-False \n", - ".. ... \n", - "82 tracing status-Roughly traced, cropped-False \n", - "83 tracing status-Roughly traced, cropped-False \n", - "84 tracing status-Roughly traced, cropped-False \n", - "85 tracing status-Roughly traced, cropped-False \n", - "86 tracing status-Roughly traced, cropped-False \n", - "\n", - " parents_label \\\n", - "0 [lobula columnar neuron LC4] \n", - "1 [lobula columnar neuron LC4] \n", - "2 [lobula columnar neuron LC4] \n", - "3 [lobula columnar neuron LC4] \n", - "4 [lobula columnar neuron LC4] \n", - ".. ... \n", - "82 [adult cholinergic neuron, adult posterior ven... \n", - "83 [adult posterior ventrolateral protocerebrum n... \n", - "84 [adult posterior ventrolateral protocerebrum n... \n", - "85 [adult posterior ventrolateral protocerebrum n... \n", - "86 [lobula plate-lobula columnar neuron LPLC2] \n", - "\n", - " parents_id data_source \\\n", - "0 [FBbt_00003874] [neuprint_JRC_Hemibrain_1point1] \n", - "1 [FBbt_00003874] [neuprint_JRC_Hemibrain_1point1] \n", - "2 [FBbt_00003874] [neuprint_JRC_Hemibrain_1point1] \n", - "3 [FBbt_00003874] [neuprint_JRC_Hemibrain_1point1] \n", - "4 [FBbt_00003874] [neuprint_JRC_Hemibrain_1point1] \n", - ".. ... ... \n", - "82 [FBbt_00058205, FBbt_20002200] [neuprint_JRC_Hemibrain_1point1] \n", - "83 [FBbt_20002209] [neuprint_JRC_Hemibrain_1point1] \n", - "84 [FBbt_20002158] [neuprint_JRC_Hemibrain_1point1] \n", - "85 [FBbt_20002152] [neuprint_JRC_Hemibrain_1point1] \n", - "86 [FBbt_00111763] [neuprint_JRC_Hemibrain_1point1] \n", - "\n", - " accession xrefs \\\n", - "0 [1625080038] [neuprint_JRC_Hemibrain_1point1:1625080038, ne... \n", - "1 [1782668028] [neuronbridge:1782668028, neuprint_JRC_Hemibra... \n", - "2 [1874035952] [neuprint_JRC_Hemibrain_1point1:1874035952, ne... \n", - "3 [1810956698] [neuprint_JRC_Hemibrain_1point1:1810956698, ne... \n", - "4 [1158187240] [neuronbridge:1158187240, neuprint_JRC_Hemibra... \n", - ".. ... ... \n", - "82 [1876565477] [neuronbridge:1876565477, neuprint_JRC_Hemibra... \n", - "83 [1628973439] [neuronbridge:1628973439, neuprint_JRC_Hemibra... \n", - "84 [1375845363] [neuronbridge:1375845363, neuprint_JRC_Hemibra... \n", - "85 [1503733177] [neuprint_JRC_Hemibrain_1point1:1503733177, ne... \n", - "86 [5813034151] [neuronbridge:5813034151, neuprint_JRC_Hemibra... \n", - "\n", - " templates dataset \\\n", - "0 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "1 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "2 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", - "3 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "4 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - ".. ... ... \n", - "82 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "83 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "84 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "85 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "86 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "\n", - " license \n", - "0 [https://creativecommons.org/licenses/by/4.0/l... \n", - "1 [https://creativecommons.org/licenses/by/4.0/l... \n", - "2 [https://creativecommons.org/licenses/by/4.0/l... \n", - "3 [https://creativecommons.org/licenses/by/4.0/l... \n", - "4 [https://creativecommons.org/licenses/by/4.0/l... \n", - ".. ... \n", - "82 [https://creativecommons.org/licenses/by/4.0/l... \n", - "83 [https://creativecommons.org/licenses/by/4.0/l... \n", - "84 [https://creativecommons.org/licenses/by/4.0/l... \n", - "85 [https://creativecommons.org/licenses/by/4.0/l... \n", - "86 [https://creativecommons.org/licenses/by/4.0/l... \n", - "\n", - "[87 rows x 13 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vfb.get_TermInfo(upstream_of_DNp04['query_neuron_id'])" - ] - }, { "cell_type": "markdown", "metadata": { @@ -2734,7 +2734,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "cell_id": "00018-edbac4e5-76ab-4713-b5f1-5030128344f2", "deepnote_cell_type": "code", @@ -2779,9 +2779,9 @@ " 0\n", " VFB_jrchjy8y\n", " LC4 (FlyEM-HB:1249932198)\n", - " 30\n", - " VFB_jrchjup1\n", - " Giant Fiber_R (FlyEM-HB:2307027729)\n", + " 78\n", + " VFB_jrchjtg7\n", + " DNp04_R (FlyEM-HB:1405231475)\n", " \n", " \n", " 1\n", @@ -2795,17 +2795,17 @@ " 2\n", " VFB_jrchjy8y\n", " LC4 (FlyEM-HB:1249932198)\n", - " 11\n", - " VFB_jrchjtg6\n", - " DNp03_R (FlyEM-HB:1565846637)\n", + " 30\n", + " VFB_jrchjup1\n", + " Giant Fiber_R (FlyEM-HB:2307027729)\n", " \n", " \n", " 3\n", " VFB_jrchjy8y\n", " LC4 (FlyEM-HB:1249932198)\n", - " 78\n", - " VFB_jrchjtg7\n", - " DNp04_R (FlyEM-HB:1405231475)\n", + " 11\n", + " VFB_jrchjtg6\n", + " DNp03_R (FlyEM-HB:1565846637)\n", " \n", " \n", "\n", @@ -2813,19 +2813,19 @@ ], "text/plain": [ " query_neuron_id query_neuron_name weight target_neuron_id \\\n", - "0 VFB_jrchjy8y LC4 (FlyEM-HB:1249932198) 30 VFB_jrchjup1 \n", + "0 VFB_jrchjy8y LC4 (FlyEM-HB:1249932198) 78 VFB_jrchjtg7 \n", "1 VFB_jrchjy8y LC4 (FlyEM-HB:1249932198) 65 VFB_jrchjtgf \n", - "2 VFB_jrchjy8y LC4 (FlyEM-HB:1249932198) 11 VFB_jrchjtg6 \n", - "3 VFB_jrchjy8y LC4 (FlyEM-HB:1249932198) 78 VFB_jrchjtg7 \n", + "2 VFB_jrchjy8y LC4 (FlyEM-HB:1249932198) 30 VFB_jrchjup1 \n", + "3 VFB_jrchjy8y LC4 (FlyEM-HB:1249932198) 11 VFB_jrchjtg6 \n", "\n", " target_neuron_name \n", - "0 Giant Fiber_R (FlyEM-HB:2307027729) \n", + "0 DNp04_R (FlyEM-HB:1405231475) \n", "1 DNp11_R (FlyEM-HB:1281324958) \n", - "2 DNp03_R (FlyEM-HB:1565846637) \n", - "3 DNp04_R (FlyEM-HB:1405231475) " + "2 Giant Fiber_R (FlyEM-HB:2307027729) \n", + "3 DNp03_R (FlyEM-HB:1565846637) " ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -2862,7 +2862,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "cell_id": "00021-b41c6c4a-f7aa-4318-b0a8-b7c89f6ec66b", "deepnote_cell_type": "code", @@ -2874,6 +2874,13 @@ "tags": [] }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWarning:\u001b[0m called a non existant id:VFB_00102294\n" + ] + }, { "data": { "text/html": [ @@ -2899,10 +2906,11 @@ " symbol\n", " id\n", " tags\n", - " data_source\n", - " accession\n", + " description\n", " parents_label\n", " parents_id\n", + " data_source\n", + " accession\n", " xrefs\n", " templates\n", " dataset\n", @@ -2912,766 +2920,817 @@ " \n", " \n", " 0\n", - " Uniglomerular mALT DA1 lPN#R6 (FAFB:27295)\n", + " Gad1-F-400088\n", " \n", - " VFB_00101199\n", + " VFB_00009611\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [catmaid_fafb]\n", - 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" VFB_jrchjtdf\n", + " VFB_jrchjtdd\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [neuprint_JRC_Hemibrain_1point1]\n", - " [1734350788]\n", + " tracing status-Roughly traced, cropped-False\n", " [adult fruitless aDT-e (female) neuron, adult ...\n", " [FBbt_00110423, FBbt_00067363]\n", - " [neuprint_JRC_Hemibrain_1point1:1734350788, ne...\n", + " [neuprint_JRC_Hemibrain_1point1]\n", + " [5813039315]\n", + " [neuronbridge:5813039315, neuprint_JRC_Hemibra...\n", " [JRC_FlyEM_Hemibrain, JRC2018Unisex]\n", " [Xu2020NeuronsV1point1]\n", " [https://creativecommons.org/licenses/by/4.0/l...\n", " \n", " \n", - " 20\n", + " 22\n", + " DA1_lPN_R (FlyEM-HB:1734350908)\n", + " DA1_lPN_R\n", + " VFB_jrchjtdb\n", + " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " tracing status-Roughly traced, cropped-False\n", + " [adult antennal lobe projection neuron DA1 lPN...\n", + " [FBbt_00067363, FBbt_00110423]\n", + " [neuprint_JRC_Hemibrain_1point1]\n", + " [1734350908]\n", + " [neuprint_JRC_Hemibrain_1point1:1734350908, ne...\n", + " [JRC_FlyEM_Hemibrain, JRC2018Unisex]\n", + " [Xu2020NeuronsV1point1]\n", + " [https://creativecommons.org/licenses/by/4.0/l...\n", + " \n", + " \n", + " 23\n", " DA1_lPN_R (FlyEM-HB:754534424)\n", " DA1_lPN_R\n", " VFB_jrchjtde\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [neuprint_JRC_Hemibrain_1point1]\n", - " [754534424]\n", + " tracing status-Roughly traced, cropped-False\n", " [adult antennal lobe projection neuron DA1 lPN...\n", " [FBbt_00067363, FBbt_00110423]\n", + " [neuprint_JRC_Hemibrain_1point1]\n", + " [754534424]\n", " [neuronbridge:754534424, neuprint_JRC_Hemibrai...\n", " [JRC2018Unisex, JRC_FlyEM_Hemibrain]\n", " [Xu2020NeuronsV1point1]\n", " [https://creativecommons.org/licenses/by/4.0/l...\n", " \n", " \n", - " 21\n", + " 24\n", " DA1_vPN_R (FlyEM-HB:733316908)\n", " DA1_vPN_R\n", " VFB_jrchjtdh\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [neuprint_JRC_Hemibrain_1point1]\n", - " [733316908]\n", + " tracing status-Roughly traced, cropped-False\n", " [adult antennal lobe projection neuron DA1 vPN]\n", " [FBbt_00067372]\n", + " [neuprint_JRC_Hemibrain_1point1]\n", + " [733316908]\n", " [neuronbridge:733316908, neuprint_JRC_Hemibrai...\n", " [JRC_FlyEM_Hemibrain, JRC2018Unisex]\n", " [Xu2020NeuronsV1point1]\n", " [https://creativecommons.org/licenses/by/4.0/l...\n", " \n", " \n", - " 22\n", + " 25\n", + " DA1_lPN_R (FlyEM-HB:1765040289)\n", + " DA1_lPN_R\n", + " VFB_jrchjtdc\n", + " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " tracing status-Roughly traced, cropped-False\n", + " [adult antennal lobe projection neuron DA1 lPN...\n", + " [FBbt_00067363, FBbt_00110423]\n", + " [neuprint_JRC_Hemibrain_1point1]\n", + " [1765040289]\n", + " [neuronbridge:1765040289, neuprint_JRC_Hemibra...\n", + " [JRC_FlyEM_Hemibrain, JRC2018Unisex]\n", + " [Xu2020NeuronsV1point1]\n", + " [https://creativecommons.org/licenses/by/4.0/l...\n", + " \n", + " \n", + " 26\n", + " DA1_lPN_R (FlyEM-HB:722817260)\n", + " DA1_lPN_R\n", + " VFB_jrchjtda\n", + " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " tracing status-Roughly traced, cropped-False\n", + " [adult antennal lobe projection neuron DA1 lPN...\n", + " [FBbt_00067363, FBbt_00110423]\n", + " [neuprint_JRC_Hemibrain_1point1]\n", + " [722817260]\n", + " [neuprint_JRC_Hemibrain_1point1:722817260, neu...\n", + " [JRC_FlyEM_Hemibrain, JRC2018Unisex]\n", + " [Xu2020NeuronsV1point1]\n", + " [https://creativecommons.org/licenses/by/4.0/l...\n", + " \n", + " \n", + " 27\n", + " DA1_lPN_R (FlyEM-HB:1734350788)\n", + " DA1_lPN_R\n", + " VFB_jrchjtdf\n", + " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " tracing status-Roughly traced, cropped-False\n", + " [adult fruitless aDT-e (female) neuron, adult ...\n", + " [FBbt_00110423, FBbt_00067363]\n", + " [neuprint_JRC_Hemibrain_1point1]\n", + " [1734350788]\n", + " [neuprint_JRC_Hemibrain_1point1:1734350788, ne...\n", + " [JRC_FlyEM_Hemibrain, JRC2018Unisex]\n", + " [Xu2020NeuronsV1point1]\n", + " [https://creativecommons.org/licenses/by/4.0/l...\n", + " \n", + " \n", + " 28\n", " DA1_lPN_R (FlyEM-HB:754538881)\n", " DA1_lPN_R\n", " VFB_jrchjtdg\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [neuprint_JRC_Hemibrain_1point1]\n", - " [754538881]\n", + " tracing status-Roughly traced, cropped-False\n", " [adult fruitless aDT-e (female) neuron, adult ...\n", " [FBbt_00110423, FBbt_00067363]\n", + " [neuprint_JRC_Hemibrain_1point1]\n", + " [754538881]\n", " [neuronbridge:754538881, neuprint_JRC_Hemibrai...\n", " [JRC2018Unisex, JRC_FlyEM_Hemibrain]\n", " [Xu2020NeuronsV1point1]\n", " [https://creativecommons.org/licenses/by/4.0/l...\n", " \n", " \n", - " 23\n", - " Uniglomerular mALT DA1 lPN#L1 (FAFB:4207871)\n", + " 29\n", + " VGlut-F-700040\n", " \n", - " VFB_0010126e\n", + " VFB_00013061\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [catmaid_fafb]\n", - " [4207871]\n", - " [adult fruitless aDT-e (female) neuron, adult ...\n", - " [FBbt_00110423, FBbt_00067363]\n", - " [catmaid_fafb:4207871]\n", - " [JRC2018Unisex]\n", - " [BatesSchlegel2020]\n", - " [https://creativecommons.org/licenses/by-sa/4....\n", + " OutAge: Adult 5~15 days\n", + " [expression pattern fragment, adult antennal l...\n", + " [VFBext_0000004, FBbt_00048096, FBbt_00067362,...\n", + " [FlyCircuit]\n", + " [VGlut-F-700040]\n", + " [FlyCircuit:VGlut-F-700040]\n", + " [adult brain template JFRC2, JRC2018Unisex]\n", + " [Chiang2010]\n", + " []\n", " \n", " \n", - " 24\n", - " VGlut-F-000655\n", + " 30\n", + " fru-M-200319\n", " \n", - " VFB_00006552\n", + " VFB_00000323\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " OutAge: Adult 5~15 days\n", + " [antennal lobe projection neuron of ALl1 linea...\n", + " [FBbt_00067362, FBbt_00067350, VFBext_0000004,...\n", " [FlyCircuit]\n", - " [VGlut-F-000655]\n", - " [adult antennal lobe projection neuron DA1, ad...\n", - " [FBbt_00048096, FBbt_00048096, FBbt_00048096, ...\n", - " [FlyCircuit:VGlut-F-000655]\n", - " [adult brain template JFRC2, JRC2018Unisex]\n", + " [fru-M-200319]\n", + " [FlyCircuit:fru-M-200319]\n", + " [JRC2018Unisex, adult brain template JFRC2]\n", " [Chiang2010]\n", " []\n", " \n", " \n", - " 25\n", - " Multiglomerular mALT lvPN#R46 (FAFB:57179)\n", + " 31\n", + " npf-M-300006\n", " \n", - " VFB_001011zb\n", + " VFB_00001755\n", + " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " OutAge: Adult 5~15 days\n", + " [medial antennal lobe tract projection neuron,...\n", + " [FBbt_00067350, FBbt_00067362, FBbt_00050025, ...\n", + " [FlyCircuit]\n", + " [npf-M-300006]\n", + " [FlyCircuit:npf-M-300006]\n", + " [JRC2018Unisex, adult brain template JFRC2]\n", + " [Chiang2010]\n", + " []\n", + " \n", + " \n", + " 32\n", + " Multiglomerular mALT lvPN#L3 (FAFB:4520615)\n", + " \n", + " VFB_00102dvz\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " Multiglomerular mALT lvPN#L3, FBbt:00049785\n", + " [adult antennal lobe projection neuron DA1 lvPN]\n", + " [FBbt_20003824]\n", " [catmaid_fafb]\n", - " [57179]\n", + " [4520615]\n", + " [catmaid_fafb:4520615]\n", + " [JRC2018Unisex]\n", + " [TaiszGalili2022]\n", + " [https://creativecommons.org/licenses/by-sa/4....\n", + " \n", + " \n", + " 33\n", + " Multiglomerular mALT lvPN#L2 (FAFB:7710838)\n", + " \n", + " VFB_00102dw8\n", + " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " Multiglomerular mALT lvPN#L2, FBbt:00049785\n", " [adult antennal lobe projection neuron DA1 lvPN]\n", " [FBbt_20003824]\n", - " [catmaid_fafb:57179]\n", + " [catmaid_fafb]\n", + " [7710838]\n", + " [catmaid_fafb:7710838]\n", " [JRC2018Unisex]\n", - " [BatesSchlegel2020]\n", + " [TaiszGalili2022]\n", " [https://creativecommons.org/licenses/by-sa/4....\n", " \n", " \n", - " 26\n", - " Uniglomerular mlALT DA1 vPN#R1 (FAFB:1811442)\n", + " 34\n", + " Multiglomerular mALT lvPN#L1 (FAFB:4520197)\n", " \n", - " VFB_0010121x\n", + " VFB_00102dvy\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " Multiglomerular mALT lvPN#L1, FBbt:00049785\n", + " [adult antennal lobe projection neuron DA1 lvPN]\n", + " [FBbt_20003824]\n", " [catmaid_fafb]\n", - " [1811442]\n", - " [adult antennal lobe projection neuron DA1 vPN]\n", - " [FBbt_00067372]\n", - " [catmaid_fafb:1811442]\n", + " [4520197]\n", + " [catmaid_fafb:4520197]\n", " [JRC2018Unisex]\n", - " [BatesSchlegel2020]\n", + " [TaiszGalili2022]\n", " [https://creativecommons.org/licenses/by-sa/4....\n", " \n", " \n", - " 27\n", - " M_lvPNm45_R (FlyEM-HB:757258507)\n", + " 35\n", + " M_lvPNm45_R (FlyEM-HB:792023887)\n", " M_lvPNm45_R\n", - " VFB_jrchk0xk\n", + " VFB_jrchk0xm\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [neuprint_JRC_Hemibrain_1point1]\n", - " [757258507]\n", + " tracing status-Roughly traced, cropped-False\n", " [adult antennal lobe projection neuron DA1 lvPN]\n", " [FBbt_20003824]\n", - " [neuprint_JRC_Hemibrain_1point1:757258507, neu...\n", + " [neuprint_JRC_Hemibrain_1point1]\n", + " [792023887]\n", + " [neuronbridge:792023887, neuprint_JRC_Hemibrai...\n", " [JRC2018Unisex, JRC_FlyEM_Hemibrain]\n", " [Xu2020NeuronsV1point1]\n", " [https://creativecommons.org/licenses/by/4.0/l...\n", " \n", " \n", - " 28\n", - " Cha-F-500006\n", - " \n", - " VFB_00010085\n", - " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [FlyCircuit]\n", - " [Cha-F-500006]\n", - " [adult ALl1 lineage neuron, adult ALl1 lineage...\n", - " [FBbt_00050025, FBbt_00050025, FBbt_00050025, ...\n", - " [FlyCircuit:Cha-F-500006]\n", - " [adult brain template JFRC2, JRC2018Unisex]\n", - " [Chiang2010]\n", - " []\n", - " \n", - " \n", - " 29\n", + " 36\n", " M_lvPNm45_R (FlyEM-HB:757591093)\n", " M_lvPNm45_R\n", " VFB_jrchk0xl\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [neuprint_JRC_Hemibrain_1point1]\n", - " [757591093]\n", + " tracing status-Roughly traced, cropped-False\n", " [adult antennal lobe projection neuron DA1 lvPN]\n", " [FBbt_20003824]\n", + " [neuprint_JRC_Hemibrain_1point1]\n", + " [757591093]\n", " [neuprint_JRC_Hemibrain_1point1:757591093, neu...\n", " [JRC2018Unisex, JRC_FlyEM_Hemibrain]\n", " [Xu2020NeuronsV1point1]\n", " [https://creativecommons.org/licenses/by/4.0/l...\n", " \n", " \n", - " 30\n", - " M_lvPNm45_R (FlyEM-HB:792023887)\n", + " 37\n", + " M_lvPNm45_R (FlyEM-HB:757258507)\n", " M_lvPNm45_R\n", - " VFB_jrchk0xm\n", + " VFB_jrchk0xk\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [neuprint_JRC_Hemibrain_1point1]\n", - " [792023887]\n", + " tracing status-Roughly traced, cropped-False\n", " [adult antennal lobe projection neuron DA1 lvPN]\n", " [FBbt_20003824]\n", - " [neuronbridge:792023887, neuprint_JRC_Hemibrai...\n", + " [neuprint_JRC_Hemibrain_1point1]\n", + " [757258507]\n", + " [neuprint_JRC_Hemibrain_1point1:757258507, neu...\n", " [JRC2018Unisex, JRC_FlyEM_Hemibrain]\n", " [Xu2020NeuronsV1point1]\n", " [https://creativecommons.org/licenses/by/4.0/l...\n", " \n", " \n", - " 31\n", - " Gad1-F-200010\n", + " 38\n", + " Cha-F-300252\n", " \n", - " VFB_00012622\n", + " VFB_00005625\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " OutAge: Adult 5~15 days\n", + " [expression pattern fragment, adult ALl1 linea...\n", + " [VFBext_0000004, FBbt_00050025, FBbt_00067350,...\n", " [FlyCircuit]\n", - " [Gad1-F-200010]\n", - " [adult antennal lobe projection neuron DA1, ad...\n", - " [FBbt_00048096, FBbt_00048096, FBbt_00048096, ...\n", - " [FlyCircuit:Gad1-F-200010]\n", + " [Cha-F-300252]\n", + " [FlyCircuit:Cha-F-300252]\n", " [adult brain template JFRC2, JRC2018Unisex]\n", " [Chiang2010]\n", " []\n", " \n", " \n", - " 32\n", - " Multiglomerular mALT lvPN#L2 (FAFB:7710838)\n", - " \n", - " VFB_00102dw8\n", - " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [catmaid_fafb]\n", - " [7710838]\n", - " [adult antennal lobe projection neuron DA1 lvPN]\n", - " [FBbt_20003824]\n", - " [catmaid_fafb:7710838]\n", - " [JRC2018Unisex]\n", - " [TaiszGalili2022]\n", - " [https://creativecommons.org/licenses/by-sa/4....\n", - " \n", - " \n", - " 33\n", - " fru-M-200319\n", + " 39\n", + " VGlut-F-800329\n", " \n", - " VFB_00000323\n", + " VFB_00005957\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [FlyCircuit]\n", - " [fru-M-200319]\n", + " OutAge: Adult 5~15 days\n", " [antennal lobe projection neuron of ALl1 linea...\n", - " [FBbt_00067362, FBbt_00067362, FBbt_00067362, ...\n", - " [FlyCircuit:fru-M-200319]\n", + " [FBbt_00067362, FBbt_00067350, VFBext_0000004,...\n", + " [FlyCircuit]\n", + " [VGlut-F-800329]\n", + " [FlyCircuit:VGlut-F-800329]\n", " [JRC2018Unisex, adult brain template JFRC2]\n", " [Chiang2010]\n", " []\n", " \n", " \n", - " 34\n", - " Uniglomerular mALT DA1 lPN#L6 (FAFB:2381753)\n", + " 40\n", + " VGlut-F-800317\n", " \n", - " VFB_0010123b\n", + " VFB_00005913\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [catmaid_fafb]\n", - " [2381753]\n", - " [adult antennal lobe projection neuron DA1 lPN...\n", - " [FBbt_00067363, FBbt_00110423]\n", - " [catmaid_fafb:2381753]\n", - " [JRC2018Unisex]\n", - " [BatesSchlegel2020]\n", - " [https://creativecommons.org/licenses/by-sa/4....\n", + " OutAge: Adult 5~15 days\n", + " [antennal lobe projection neuron of ALl1 linea...\n", + " [FBbt_00067362, VFBext_0000004, FBbt_00050025,...\n", + " [FlyCircuit]\n", + " [VGlut-F-800317]\n", + " [FlyCircuit:VGlut-F-800317]\n", + " [adult brain template JFRC2, JRC2018Unisex]\n", + " [Chiang2010]\n", + " []\n", " \n", " \n", - " 35\n", - " Gad1-F-400088\n", + " 41\n", + " VGlut-F-000655\n", " \n", - " VFB_00009611\n", + " VFB_00006552\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " OutAge: Adult 5~15 days\n", + " [adult antennal lobe projection neuron DA1, ad...\n", + " [FBbt_00048096, FBbt_00050025, FBbt_00067362, ...\n", " [FlyCircuit]\n", - " [Gad1-F-400088]\n", - " [expression pattern fragment, adult antennal l...\n", - " [VFBext_0000004, FBbt_00067372]\n", - " [FlyCircuit:Gad1-F-400088]\n", - " [JRC2018Unisex, adult brain template JFRC2]\n", + " [VGlut-F-000655]\n", + " [FlyCircuit:VGlut-F-000655]\n", + " [adult brain template JFRC2, JRC2018Unisex]\n", " [Chiang2010]\n", " []\n", " \n", " \n", - " 36\n", - " Gad1-F-200064\n", + " 42\n", + " VGlut-F-200574\n", " \n", - " VFB_00011249\n", + " VFB_00006638\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " OutAge: Adult 5~15 days\n", + " [medial antennal lobe tract projection neuron,...\n", + " [FBbt_00067350, VFBext_0000004, FBbt_00048096,...\n", " [FlyCircuit]\n", - " [Gad1-F-200064]\n", - " [antennal lobe projection neuron of ALl1 linea...\n", - " [FBbt_00067362, FBbt_00067362, FBbt_00067362, ...\n", - " [FlyCircuit:Gad1-F-200064]\n", - " [adult brain template JFRC2, JRC2018Unisex]\n", + " [VGlut-F-200574]\n", + " [FlyCircuit:VGlut-F-200574]\n", + " [JRC2018Unisex, adult brain template JFRC2]\n", " [Chiang2010]\n", " []\n", " \n", " \n", - " 37\n", + " 43\n", " fru-F-400149\n", " \n", " VFB_00006344\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " OutAge: Adult 5~15 days\n", + " [medial antennal lobe tract projection neuron,...\n", + " [FBbt_00067350, VFBext_0000004, FBbt_00048096,...\n", " [FlyCircuit]\n", " [fru-F-400149]\n", - " [medial antennal lobe tract projection neuron,...\n", - " [FBbt_00067350, FBbt_00067350, FBbt_00067350, ...\n", " [FlyCircuit:fru-F-400149]\n", " [JRC2018Unisex, adult brain template JFRC2]\n", " [Chiang2010]\n", " []\n", " \n", " \n", - " 38\n", - " VGlut-F-700188\n", + " 44\n", + " Cha-F-500006\n", " \n", - " VFB_00010912\n", + " VFB_00010085\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " OutAge: Adult 5~15 days\n", + " [adult ALl1 lineage neuron, expression pattern...\n", + " [FBbt_00050025, VFBext_0000004, FBbt_00048096,...\n", " [FlyCircuit]\n", - " [VGlut-F-700188]\n", - " [antennal lobe projection neuron of ALl1 linea...\n", - " [FBbt_00067362, FBbt_00067362, FBbt_00067362, ...\n", - " [FlyCircuit:VGlut-F-700188]\n", - " [JRC2018Unisex, adult brain template JFRC2]\n", + " [Cha-F-500006]\n", + " [FlyCircuit:Cha-F-500006]\n", + " [adult brain template JFRC2, JRC2018Unisex]\n", " [Chiang2010]\n", " []\n", " \n", " \n", - " 39\n", + " 45\n", " VGlut-F-000280\n", " \n", " VFB_00010758\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " OutAge: Adult 5~15 days\n", + " [adult antennal lobe projection neuron DA1, me...\n", + " [FBbt_00048096, FBbt_00067350, FBbt_00067362, ...\n", " [FlyCircuit]\n", " [VGlut-F-000280]\n", - " [adult antennal lobe projection neuron DA1, ad...\n", - " [FBbt_00048096, FBbt_00048096, FBbt_00048096, ...\n", " [FlyCircuit:VGlut-F-000280]\n", " [adult brain template JFRC2, JRC2018Unisex]\n", " [Chiang2010]\n", " []\n", " \n", " \n", - " 40\n", - " Uniglomerular mALT DA1 lPN#L5 (FAFB:2380564)\n", - " \n", - " VFB_0010122z\n", - " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [catmaid_fafb]\n", - " [2380564]\n", - " [adult antennal lobe projection neuron DA1 lPN...\n", - " [FBbt_00067363, FBbt_00110423]\n", - " [catmaid_fafb:2380564]\n", - " [JRC2018Unisex]\n", - " [BatesSchlegel2020]\n", - " [https://creativecommons.org/licenses/by-sa/4....\n", - " \n", - " \n", - " 41\n", - " Uniglomerular mALT DA1 lPN#L4 (FAFB:2379517)\n", - " \n", - " VFB_0010122y\n", - " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [catmaid_fafb]\n", - " [2379517]\n", - " [adult antennal lobe projection neuron DA1 lPN...\n", - " [FBbt_00067363, FBbt_00110423]\n", - " [catmaid_fafb:2379517]\n", - " [JRC2018Unisex]\n", - " [BatesSchlegel2020]\n", - " [https://creativecommons.org/licenses/by-sa/4....\n", - " \n", - " \n", - " 42\n", - " Multiglomerular mALT lvPN#R44 (FAFB:57158)\n", - " \n", - " VFB_001011z9\n", - " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [catmaid_fafb]\n", - " [57158]\n", - " [adult antennal lobe projection neuron DA1 lvPN]\n", - " [FBbt_20003824]\n", - " [catmaid_fafb:57158]\n", - " [JRC2018Unisex]\n", - " [BatesSchlegel2020]\n", - " [https://creativecommons.org/licenses/by-sa/4....\n", - " \n", - " \n", - " 43\n", - " npf-M-300006\n", + " 46\n", + " VGlut-F-600450\n", " \n", - " VFB_00001755\n", + " VFB_00010984\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [FlyCircuit]\n", - " [npf-M-300006]\n", + " OutAge: Adult 5~15 days\n", " [medial antennal lobe tract projection neuron,...\n", - " [FBbt_00067350, FBbt_00067350, FBbt_00067350, ...\n", - " [FlyCircuit:npf-M-300006]\n", - " [JRC2018Unisex, adult brain template JFRC2]\n", - " [Chiang2010]\n", - " []\n", - " \n", - " \n", - " 44\n", - " VGlut-F-700040\n", - " \n", - " VFB_00013061\n", - " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " [FBbt_00067350, FBbt_00048096, FBbt_00067362, ...\n", " [FlyCircuit]\n", - " [VGlut-F-700040]\n", - " [adult antennal lobe projection neuron DA1, ad...\n", - " [FBbt_00048096, FBbt_00048096, FBbt_00048096, ...\n", - " [FlyCircuit:VGlut-F-700040]\n", + " [VGlut-F-600450]\n", + " [FlyCircuit:VGlut-F-600450]\n", " [adult brain template JFRC2, JRC2018Unisex]\n", " [Chiang2010]\n", " []\n", " \n", " \n", - " 45\n", - " VGlut-F-800329\n", + " 47\n", + " VGlut-F-700188\n", " \n", - " VFB_00005957\n", + " VFB_00010912\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [FlyCircuit]\n", - " [VGlut-F-800329]\n", + " OutAge: Adult 5~15 days\n", " [antennal lobe projection neuron of ALl1 linea...\n", - " [FBbt_00067362, FBbt_00067362, FBbt_00067362, ...\n", - " [FlyCircuit:VGlut-F-800329]\n", + " [FBbt_00067362, FBbt_00050025, FBbt_00048096, ...\n", + " [FlyCircuit]\n", + " [VGlut-F-700188]\n", + " [FlyCircuit:VGlut-F-700188]\n", " [JRC2018Unisex, adult brain template JFRC2]\n", " [Chiang2010]\n", " []\n", " \n", " \n", - " 46\n", - " VGlut-F-800317\n", + " 48\n", + " Gad1-F-200064\n", " \n", - " VFB_00005913\n", + " VFB_00011249\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [FlyCircuit]\n", - " [VGlut-F-800317]\n", + " OutAge: Adult 5~15 days\n", " [antennal lobe projection neuron of ALl1 linea...\n", - " [FBbt_00067362, FBbt_00067362, FBbt_00067362, ...\n", - " [FlyCircuit:VGlut-F-800317]\n", + " [FBbt_00067362, FBbt_00048096, FBbt_00050025, ...\n", + " [FlyCircuit]\n", + " [Gad1-F-200064]\n", + " [FlyCircuit:Gad1-F-200064]\n", " [adult brain template JFRC2, JRC2018Unisex]\n", " [Chiang2010]\n", " []\n", " \n", " \n", - " 47\n", - " Uniglomerular mALT DA1 lPN#L7 (FAFB:3239781)\n", - " \n", - " VFB_0010124l\n", - " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [catmaid_fafb]\n", - " [3239781]\n", - " [adult fruitless aDT-e (female) neuron, adult ...\n", - " [FBbt_00110423, FBbt_00067363]\n", - " [catmaid_fafb:3239781]\n", - " [JRC2018Unisex]\n", - " [BatesSchlegel2020]\n", - " [https://creativecommons.org/licenses/by-sa/4....\n", - " \n", - " \n", - " 48\n", - " Multiglomerular mALT lvPN#R45 (FAFB:57035)\n", - " \n", - " VFB_001011z3\n", - " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [catmaid_fafb]\n", - " [57035]\n", - " [adult antennal lobe projection neuron DA1 lvPN]\n", - " [FBbt_20003824]\n", - " [catmaid_fafb:57035]\n", - " [JRC2018Unisex]\n", - " [BatesSchlegel2020]\n", - " [https://creativecommons.org/licenses/by-sa/4....\n", - " \n", - " \n", " 49\n", - " Uniglomerular mALT DA1 lPN#R5 (FAFB:2863104)\n", + " Gad1-F-200010\n", " \n", - " VFB_0010124e\n", + " VFB_00012622\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", - " [catmaid_fafb]\n", - " [2863104]\n", - " [adult antennal lobe projection neuron DA1 lPN...\n", - " [FBbt_00067363, FBbt_00110423]\n", - " [catmaid_fafb:2863104]\n", - " [JRC2018Unisex]\n", - " [BatesSchlegel2020]\n", - " [https://creativecommons.org/licenses/by-sa/4....\n", + " OutAge: Adult 5~15 days\n", + " [adult antennal lobe projection neuron DA1, ad...\n", + " [FBbt_00048096, FBbt_00050025, FBbt_00067362, ...\n", + " [FlyCircuit]\n", + " [Gad1-F-200010]\n", + " [FlyCircuit:Gad1-F-200010]\n", + " [adult brain template JFRC2, JRC2018Unisex]\n", + " [Chiang2010]\n", + " []\n", " \n", " \n", " 50\n", - " VGlut-F-600450\n", + " VGlut-F-600326\n", " \n", - " VFB_00010984\n", + " VFB_00012155\n", " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " OutAge: Adult 5~15 days\n", + " [expression pattern fragment, medial antennal ...\n", + " [VFBext_0000004, FBbt_00067350, FBbt_00050025,...\n", " [FlyCircuit]\n", - " [VGlut-F-600450]\n", - " [medial antennal lobe tract projection neuron,...\n", - " [FBbt_00067350, FBbt_00067350, FBbt_00067350, ...\n", - " [FlyCircuit:VGlut-F-600450]\n", - " [adult brain template JFRC2, JRC2018Unisex]\n", + " [VGlut-F-600326]\n", + " [FlyCircuit:VGlut-F-600326]\n", + " [JRC2018Unisex, adult brain template JFRC2]\n", " [Chiang2010]\n", " []\n", " \n", @@ -3681,57 +3740,57 @@ ], "text/plain": [ " label symbol id \\\n", - "0 Uniglomerular mALT DA1 lPN#R6 (FAFB:27295) VFB_00101199 \n", - "1 Uniglomerular mALT DA1 lPN#R4 (FAFB:755022) VFB_00101205 \n", - "2 Uniglomerular mALT DA1 lPN#R3 (FAFB:61221) VFB_00101204 \n", - "3 Uniglomerular mALT DA1 lPN#R8 (FAFB:57381) VFB_00101203 \n", - "4 VGlut-F-600326 VFB_00012155 \n", - "5 Uniglomerular mALT DA1 lPN#R7 (FAFB:57353) VFB_00101202 \n", - "6 Uniglomerular mALT DA1 lPN#R1 (FAFB:57323) VFB_00101201 \n", - "7 Uniglomerular mALT DA1 lPN#R2 (FAFB:57311) VFB_00101200 \n", - "8 Uniglomerular mALT DA1 lPN#L3 (FAFB:2345089) VFB_0010122p \n", - "9 Uniglomerular mlALT DA1 vPN#L1 (FAFB:2334841) VFB_0010122m \n", - "10 Uniglomerular mALT DA1 lPN#L2 (FAFB:2319457) VFB_0010122k \n", - "11 Multiglomerular mALT lvPN#L1 (FAFB:4520197) VFB_00102dvy \n", - "12 DA1_lPN_R (FlyEM-HB:1734350908) DA1_lPN_R VFB_jrchjtdb \n", - "13 Multiglomerular mALT lvPN#L3 (FAFB:4520615) VFB_00102dvz \n", - "14 DA1_lPN_R (FlyEM-HB:722817260) DA1_lPN_R VFB_jrchjtda \n", - "15 DA1_lPN_R (FlyEM-HB:5813039315) DA1_lPN_R VFB_jrchjtdd \n", - "16 DA1_lPN_R (FlyEM-HB:1765040289) DA1_lPN_R VFB_jrchjtdc \n", - "17 Cha-F-300252 VFB_00005625 \n", - "18 VGlut-F-200574 VFB_00006638 \n", - "19 DA1_lPN_R (FlyEM-HB:1734350788) DA1_lPN_R VFB_jrchjtdf \n", - "20 DA1_lPN_R (FlyEM-HB:754534424) DA1_lPN_R VFB_jrchjtde \n", - "21 DA1_vPN_R (FlyEM-HB:733316908) DA1_vPN_R VFB_jrchjtdh \n", - "22 DA1_lPN_R (FlyEM-HB:754538881) DA1_lPN_R VFB_jrchjtdg \n", - "23 Uniglomerular mALT DA1 lPN#L1 (FAFB:4207871) VFB_0010126e \n", - "24 VGlut-F-000655 VFB_00006552 \n", - "25 Multiglomerular mALT lvPN#R46 (FAFB:57179) VFB_001011zb \n", - "26 Uniglomerular mlALT DA1 vPN#R1 (FAFB:1811442) VFB_0010121x \n", - "27 M_lvPNm45_R (FlyEM-HB:757258507) M_lvPNm45_R VFB_jrchk0xk \n", - "28 Cha-F-500006 VFB_00010085 \n", - "29 M_lvPNm45_R (FlyEM-HB:757591093) M_lvPNm45_R VFB_jrchk0xl \n", - "30 M_lvPNm45_R (FlyEM-HB:792023887) M_lvPNm45_R VFB_jrchk0xm \n", - "31 Gad1-F-200010 VFB_00012622 \n", - "32 Multiglomerular mALT lvPN#L2 (FAFB:7710838) VFB_00102dw8 \n", - "33 fru-M-200319 VFB_00000323 \n", - "34 Uniglomerular mALT DA1 lPN#L6 (FAFB:2381753) VFB_0010123b \n", - "35 Gad1-F-400088 VFB_00009611 \n", - "36 Gad1-F-200064 VFB_00011249 \n", - "37 fru-F-400149 VFB_00006344 \n", - "38 VGlut-F-700188 VFB_00010912 \n", - "39 VGlut-F-000280 VFB_00010758 \n", - "40 Uniglomerular mALT DA1 lPN#L5 (FAFB:2380564) VFB_0010122z \n", - "41 Uniglomerular mALT DA1 lPN#L4 (FAFB:2379517) VFB_0010122y \n", - "42 Multiglomerular mALT lvPN#R44 (FAFB:57158) VFB_001011z9 \n", - "43 npf-M-300006 VFB_00001755 \n", - "44 VGlut-F-700040 VFB_00013061 \n", - "45 VGlut-F-800329 VFB_00005957 \n", - "46 VGlut-F-800317 VFB_00005913 \n", - "47 Uniglomerular mALT DA1 lPN#L7 (FAFB:3239781) VFB_0010124l \n", - "48 Multiglomerular mALT lvPN#R45 (FAFB:57035) VFB_001011z3 \n", - "49 Uniglomerular mALT DA1 lPN#R5 (FAFB:2863104) VFB_0010124e \n", - "50 VGlut-F-600450 VFB_00010984 \n", + "0 Gad1-F-400088 VFB_00009611 \n", + "1 Uniglomerular mALT DA1 lPN#L1 (FAFB:4207871) VFB_0010126e \n", + "2 Uniglomerular mALT DA1 lPN#L6 (FAFB:2381753) VFB_0010123b \n", + "3 Uniglomerular mALT DA1 lPN#L3 (FAFB:2345089) VFB_0010122p \n", + "4 Uniglomerular mlALT DA1 vPN#L1 (FAFB:2334841) VFB_0010122m \n", + "5 Uniglomerular mALT DA1 lPN#L2 (FAFB:2319457) VFB_0010122k \n", + "6 Uniglomerular mALT DA1 lPN#L7 (FAFB:3239781) VFB_0010124l \n", + "7 Uniglomerular mALT DA1 lPN#L5 (FAFB:2380564) VFB_0010122z \n", + "8 Uniglomerular mlALT DA1 vPN#R1 (FAFB:1811442) VFB_0010121x \n", + "9 Uniglomerular mALT DA1 lPN#R5 (FAFB:2863104) VFB_0010124e \n", + "10 Uniglomerular mALT DA1 lPN#L4 (FAFB:2379517) VFB_0010122y \n", + "11 Uniglomerular mALT DA1 lPN#R4 (FAFB:755022) VFB_00101205 \n", + "12 Uniglomerular mALT DA1 lPN#R7 (FAFB:57353) VFB_00101202 \n", + "13 Uniglomerular mALT DA1 lPN#R1 (FAFB:57323) VFB_00101201 \n", + "14 Uniglomerular mALT DA1 lPN#R3 (FAFB:61221) VFB_00101204 \n", + "15 Uniglomerular mALT DA1 lPN#R2 (FAFB:57311) VFB_00101200 \n", + "16 Uniglomerular mALT DA1 lPN#R8 (FAFB:57381) VFB_00101203 \n", + "17 Multiglomerular mALT lvPN#R45 (FAFB:57035) VFB_001011z3 \n", + "18 Multiglomerular mALT lvPN#R46 (FAFB:57179) VFB_001011zb \n", + "19 Multiglomerular mALT lvPN#R44 (FAFB:57158) VFB_001011z9 \n", + "20 Uniglomerular mALT DA1 lPN#R6 (FAFB:27295) VFB_00101199 \n", + "21 DA1_lPN_R (FlyEM-HB:5813039315) DA1_lPN_R VFB_jrchjtdd \n", + "22 DA1_lPN_R (FlyEM-HB:1734350908) DA1_lPN_R VFB_jrchjtdb \n", + "23 DA1_lPN_R (FlyEM-HB:754534424) DA1_lPN_R VFB_jrchjtde \n", + "24 DA1_vPN_R (FlyEM-HB:733316908) DA1_vPN_R VFB_jrchjtdh \n", + "25 DA1_lPN_R (FlyEM-HB:1765040289) DA1_lPN_R VFB_jrchjtdc \n", + "26 DA1_lPN_R (FlyEM-HB:722817260) DA1_lPN_R VFB_jrchjtda \n", + "27 DA1_lPN_R (FlyEM-HB:1734350788) DA1_lPN_R VFB_jrchjtdf \n", + "28 DA1_lPN_R (FlyEM-HB:754538881) DA1_lPN_R VFB_jrchjtdg \n", + "29 VGlut-F-700040 VFB_00013061 \n", + "30 fru-M-200319 VFB_00000323 \n", + "31 npf-M-300006 VFB_00001755 \n", + "32 Multiglomerular mALT lvPN#L3 (FAFB:4520615) VFB_00102dvz \n", + "33 Multiglomerular mALT lvPN#L2 (FAFB:7710838) VFB_00102dw8 \n", + "34 Multiglomerular mALT lvPN#L1 (FAFB:4520197) VFB_00102dvy \n", + "35 M_lvPNm45_R (FlyEM-HB:792023887) M_lvPNm45_R VFB_jrchk0xm \n", + "36 M_lvPNm45_R (FlyEM-HB:757591093) M_lvPNm45_R VFB_jrchk0xl \n", + "37 M_lvPNm45_R (FlyEM-HB:757258507) M_lvPNm45_R VFB_jrchk0xk \n", + "38 Cha-F-300252 VFB_00005625 \n", + "39 VGlut-F-800329 VFB_00005957 \n", + "40 VGlut-F-800317 VFB_00005913 \n", + "41 VGlut-F-000655 VFB_00006552 \n", + "42 VGlut-F-200574 VFB_00006638 \n", + "43 fru-F-400149 VFB_00006344 \n", + "44 Cha-F-500006 VFB_00010085 \n", + "45 VGlut-F-000280 VFB_00010758 \n", + "46 VGlut-F-600450 VFB_00010984 \n", + "47 VGlut-F-700188 VFB_00010912 \n", + "48 Gad1-F-200064 VFB_00011249 \n", + "49 Gad1-F-200010 VFB_00012622 \n", + "50 VGlut-F-600326 VFB_00012155 \n", "\n", " tags \\\n", "0 [Entity, Adult, Anatomy, Cell, Chemosensory_sy... \n", @@ -3786,277 +3845,330 @@ "49 [Entity, Adult, Anatomy, Cell, Chemosensory_sy... \n", "50 [Entity, Adult, Anatomy, Cell, Chemosensory_sy... \n", "\n", - " data_source accession \\\n", - "0 [catmaid_fafb] [27295] \n", - "1 [catmaid_fafb] [755022] \n", - "2 [catmaid_fafb] [61221] \n", - "3 [catmaid_fafb] [57381] \n", - "4 [FlyCircuit] [VGlut-F-600326] \n", - "5 [catmaid_fafb] [57353] \n", - "6 [catmaid_fafb] [57323] \n", - "7 [catmaid_fafb] [57311] \n", - "8 [catmaid_fafb] [2345089] \n", - "9 [catmaid_fafb] [2334841] \n", - "10 [catmaid_fafb] [2319457] \n", - "11 [catmaid_fafb] [4520197] \n", - "12 [neuprint_JRC_Hemibrain_1point1] [1734350908] \n", - "13 [catmaid_fafb] [4520615] \n", - "14 [neuprint_JRC_Hemibrain_1point1] [722817260] \n", - "15 [neuprint_JRC_Hemibrain_1point1] [5813039315] \n", - "16 [neuprint_JRC_Hemibrain_1point1] [1765040289] \n", - "17 [FlyCircuit] [Cha-F-300252] \n", - "18 [FlyCircuit] [VGlut-F-200574] \n", - "19 [neuprint_JRC_Hemibrain_1point1] [1734350788] \n", - "20 [neuprint_JRC_Hemibrain_1point1] [754534424] \n", - "21 [neuprint_JRC_Hemibrain_1point1] [733316908] \n", - "22 [neuprint_JRC_Hemibrain_1point1] [754538881] \n", - "23 [catmaid_fafb] [4207871] \n", - "24 [FlyCircuit] [VGlut-F-000655] \n", - "25 [catmaid_fafb] [57179] \n", - "26 [catmaid_fafb] [1811442] \n", - "27 [neuprint_JRC_Hemibrain_1point1] [757258507] \n", - "28 [FlyCircuit] [Cha-F-500006] \n", - "29 [neuprint_JRC_Hemibrain_1point1] [757591093] \n", - "30 [neuprint_JRC_Hemibrain_1point1] [792023887] \n", - "31 [FlyCircuit] [Gad1-F-200010] \n", - "32 [catmaid_fafb] [7710838] \n", - "33 [FlyCircuit] [fru-M-200319] \n", - "34 [catmaid_fafb] [2381753] \n", - "35 [FlyCircuit] [Gad1-F-400088] \n", - "36 [FlyCircuit] [Gad1-F-200064] \n", - "37 [FlyCircuit] [fru-F-400149] \n", - "38 [FlyCircuit] [VGlut-F-700188] \n", - "39 [FlyCircuit] [VGlut-F-000280] \n", - "40 [catmaid_fafb] [2380564] \n", - "41 [catmaid_fafb] [2379517] \n", - "42 [catmaid_fafb] [57158] \n", - "43 [FlyCircuit] [npf-M-300006] \n", - "44 [FlyCircuit] [VGlut-F-700040] \n", - "45 [FlyCircuit] [VGlut-F-800329] \n", - "46 [FlyCircuit] [VGlut-F-800317] \n", - "47 [catmaid_fafb] [3239781] \n", - "48 [catmaid_fafb] [57035] \n", - "49 [catmaid_fafb] [2863104] \n", - "50 [FlyCircuit] [VGlut-F-600450] \n", + " description \\\n", + "0 OutAge: Adult 5~15 days \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "5 NaN \n", + "6 NaN \n", + "7 NaN \n", + "8 NaN \n", + "9 NaN \n", + "10 NaN \n", + "11 NaN \n", + "12 NaN \n", + "13 NaN \n", + "14 NaN \n", + "15 NaN \n", + "16 NaN \n", + "17 NaN \n", + "18 NaN \n", + "19 NaN \n", + "20 NaN \n", + "21 tracing status-Roughly traced, cropped-False \n", + "22 tracing status-Roughly traced, cropped-False \n", + "23 tracing status-Roughly traced, cropped-False \n", + "24 tracing status-Roughly traced, cropped-False \n", + "25 tracing status-Roughly traced, cropped-False \n", + "26 tracing status-Roughly traced, cropped-False \n", + "27 tracing status-Roughly traced, cropped-False \n", + "28 tracing status-Roughly traced, cropped-False \n", + "29 OutAge: Adult 5~15 days \n", + "30 OutAge: Adult 5~15 days \n", + "31 OutAge: Adult 5~15 days \n", + "32 Multiglomerular mALT lvPN#L3, FBbt:00049785 \n", + "33 Multiglomerular mALT lvPN#L2, FBbt:00049785 \n", + "34 Multiglomerular mALT lvPN#L1, FBbt:00049785 \n", + "35 tracing status-Roughly traced, cropped-False \n", + "36 tracing status-Roughly traced, cropped-False \n", + "37 tracing status-Roughly traced, cropped-False \n", + "38 OutAge: Adult 5~15 days \n", + "39 OutAge: Adult 5~15 days \n", + "40 OutAge: Adult 5~15 days \n", + "41 OutAge: Adult 5~15 days \n", + "42 OutAge: Adult 5~15 days \n", + "43 OutAge: Adult 5~15 days \n", + "44 OutAge: Adult 5~15 days \n", + "45 OutAge: Adult 5~15 days \n", + "46 OutAge: Adult 5~15 days \n", + "47 OutAge: Adult 5~15 days \n", + "48 OutAge: Adult 5~15 days \n", + "49 OutAge: Adult 5~15 days \n", + "50 OutAge: Adult 5~15 days \n", "\n", " parents_label \\\n", - "0 [adult antennal lobe projection neuron DA1 lPN] \n", - "1 [adult antennal lobe projection neuron DA1 lPN] \n", - "2 [adult antennal lobe projection neuron DA1 lPN] \n", - "3 [adult antennal lobe projection neuron DA1 lPN] \n", - "4 [medial antennal lobe tract projection neuron,... \n", - "5 [adult antennal lobe projection neuron DA1 lPN] \n", - "6 [adult antennal lobe projection neuron DA1 lPN] \n", - "7 [adult antennal lobe projection neuron DA1 lPN] \n", - "8 [adult fruitless aDT-e (female) neuron, adult ... \n", - "9 [adult antennal lobe projection neuron DA1 vPN] \n", + "0 [expression pattern fragment, adult antennal l... \n", + "1 [adult fruitless aDT-e (female) neuron, adult ... \n", + "2 [adult antennal lobe projection neuron DA1 lPN... \n", + "3 [adult fruitless aDT-e (female) neuron, adult ... \n", + "4 [adult antennal lobe projection neuron DA1 vPN] \n", + "5 [adult antennal lobe projection neuron DA1 lPN... \n", + "6 [adult fruitless aDT-e (female) neuron, adult ... \n", + "7 [adult antennal lobe projection neuron DA1 lPN... \n", + "8 [adult antennal lobe projection neuron DA1 vPN] \n", + "9 [adult antennal lobe projection neuron DA1 lPN... \n", "10 [adult antennal lobe projection neuron DA1 lPN... \n", - "11 [adult antennal lobe projection neuron DA1 lvPN] \n", - "12 [adult antennal lobe projection neuron DA1 lPN... \n", - "13 [adult antennal lobe projection neuron DA1 lvPN] \n", - "14 [adult antennal lobe projection neuron DA1 lPN... \n", - "15 [adult fruitless aDT-e (female) neuron, adult ... \n", - "16 [adult antennal lobe projection neuron DA1 lPN... \n", - "17 [adult ALl1 lineage neuron, adult ALl1 lineage... \n", - "18 [medial antennal lobe tract projection neuron,... \n", - "19 [adult fruitless aDT-e (female) neuron, adult ... \n", - "20 [adult antennal lobe projection neuron DA1 lPN... \n", - "21 [adult antennal lobe projection neuron DA1 vPN] \n", - "22 [adult fruitless aDT-e (female) neuron, adult ... \n", - "23 [adult fruitless aDT-e (female) neuron, adult ... \n", - "24 [adult antennal lobe projection neuron DA1, ad... \n", - "25 [adult antennal lobe projection neuron DA1 lvPN] \n", - "26 [adult antennal lobe projection neuron DA1 vPN] \n", - "27 [adult antennal lobe projection neuron DA1 lvPN] \n", - "28 [adult ALl1 lineage neuron, adult ALl1 lineage... \n", - "29 [adult antennal lobe projection neuron DA1 lvPN] \n", - "30 [adult antennal lobe projection neuron DA1 lvPN] \n", - "31 [adult antennal lobe projection neuron DA1, ad... \n", + "11 [adult antennal lobe projection neuron DA1 lPN] \n", + "12 [adult antennal lobe projection neuron DA1 lPN] \n", + "13 [adult antennal lobe projection neuron DA1 lPN] \n", + "14 [adult antennal lobe projection neuron DA1 lPN] \n", + "15 [adult antennal lobe projection neuron DA1 lPN] \n", + "16 [adult antennal lobe projection neuron DA1 lPN] \n", + "17 [adult antennal lobe projection neuron DA1 lvPN] \n", + "18 [adult antennal lobe projection neuron DA1 lvPN] \n", + "19 [adult antennal lobe projection neuron DA1 lvPN] \n", + "20 [adult antennal lobe projection neuron DA1 lPN] \n", + "21 [adult fruitless aDT-e (female) neuron, adult ... \n", + "22 [adult antennal lobe projection neuron DA1 lPN... \n", + "23 [adult antennal lobe projection neuron DA1 lPN... \n", + "24 [adult antennal lobe projection neuron DA1 vPN] \n", + "25 [adult antennal lobe projection neuron DA1 lPN... \n", + "26 [adult antennal lobe projection neuron DA1 lPN... \n", + "27 [adult fruitless aDT-e (female) neuron, adult ... \n", + "28 [adult fruitless aDT-e (female) neuron, adult ... \n", + "29 [expression pattern fragment, adult antennal l... \n", + "30 [antennal lobe projection neuron of ALl1 linea... \n", + "31 [medial antennal lobe tract projection neuron,... \n", "32 [adult antennal lobe projection neuron DA1 lvPN] \n", - "33 [antennal lobe projection neuron of ALl1 linea... \n", - "34 [adult antennal lobe projection neuron DA1 lPN... \n", - "35 [expression pattern fragment, adult antennal l... \n", - "36 [antennal lobe projection neuron of ALl1 linea... \n", - "37 [medial antennal lobe tract projection neuron,... \n", - "38 [antennal lobe projection neuron of ALl1 linea... \n", - "39 [adult antennal lobe projection neuron DA1, ad... \n", - "40 [adult antennal lobe projection neuron DA1 lPN... \n", - "41 [adult antennal lobe projection neuron DA1 lPN... \n", - "42 [adult antennal lobe projection neuron DA1 lvPN] \n", + "33 [adult antennal lobe projection neuron DA1 lvPN] \n", + "34 [adult antennal lobe projection neuron DA1 lvPN] \n", + "35 [adult antennal lobe projection neuron DA1 lvPN] \n", + "36 [adult antennal lobe projection neuron DA1 lvPN] \n", + "37 [adult antennal lobe projection neuron DA1 lvPN] \n", + "38 [expression pattern fragment, adult ALl1 linea... \n", + "39 [antennal lobe projection neuron of ALl1 linea... \n", + "40 [antennal lobe projection neuron of ALl1 linea... \n", + "41 [adult antennal lobe projection neuron DA1, ad... \n", + "42 [medial antennal lobe tract projection neuron,... \n", "43 [medial antennal lobe tract projection neuron,... \n", - "44 [adult antennal lobe projection neuron DA1, ad... \n", - "45 [antennal lobe projection neuron of ALl1 linea... \n", - "46 [antennal lobe projection neuron of ALl1 linea... \n", - "47 [adult fruitless aDT-e (female) neuron, adult ... \n", - "48 [adult antennal lobe projection neuron DA1 lvPN] \n", - "49 [adult antennal lobe projection neuron DA1 lPN... \n", - "50 [medial antennal lobe tract projection neuron,... \n", + "44 [adult ALl1 lineage neuron, expression pattern... \n", + "45 [adult antennal lobe projection neuron DA1, me... \n", + "46 [medial antennal lobe tract projection neuron,... \n", + "47 [antennal lobe projection neuron of ALl1 linea... \n", + "48 [antennal lobe projection neuron of ALl1 linea... \n", + "49 [adult antennal lobe projection neuron DA1, ad... \n", + "50 [expression pattern fragment, medial antennal ... \n", "\n", " parents_id \\\n", - "0 [FBbt_00067363] \n", - "1 [FBbt_00067363] \n", - "2 [FBbt_00067363] \n", - "3 [FBbt_00067363] \n", - "4 [FBbt_00067350, FBbt_00067350, FBbt_00067350, ... \n", - "5 [FBbt_00067363] \n", - "6 [FBbt_00067363] \n", - "7 [FBbt_00067363] \n", - "8 [FBbt_00110423, FBbt_00067363] \n", - "9 [FBbt_00067372] \n", + "0 [VFBext_0000004, FBbt_00067372] \n", + "1 [FBbt_00110423, FBbt_00067363] \n", + "2 [FBbt_00067363, FBbt_00110423] \n", + "3 [FBbt_00110423, FBbt_00067363] \n", + "4 [FBbt_00067372] \n", + "5 [FBbt_00067363, FBbt_00110423] \n", + "6 [FBbt_00110423, FBbt_00067363] \n", + "7 [FBbt_00067363, FBbt_00110423] \n", + "8 [FBbt_00067372] \n", + "9 [FBbt_00067363, FBbt_00110423] \n", "10 [FBbt_00067363, FBbt_00110423] \n", - "11 [FBbt_20003824] \n", - "12 [FBbt_00067363, FBbt_00110423] \n", - "13 [FBbt_20003824] \n", - "14 [FBbt_00067363, FBbt_00110423] \n", - "15 [FBbt_00110423, FBbt_00067363] \n", - "16 [FBbt_00067363, FBbt_00110423] \n", - "17 [FBbt_00050025, FBbt_00050025, FBbt_00050025, ... \n", - "18 [FBbt_00067350, FBbt_00067350, FBbt_00067350, ... \n", - "19 [FBbt_00110423, FBbt_00067363] \n", - "20 [FBbt_00067363, FBbt_00110423] \n", - "21 [FBbt_00067372] \n", - "22 [FBbt_00110423, FBbt_00067363] \n", - "23 [FBbt_00110423, FBbt_00067363] \n", - "24 [FBbt_00048096, FBbt_00048096, FBbt_00048096, ... \n", - "25 [FBbt_20003824] \n", - "26 [FBbt_00067372] \n", - "27 [FBbt_20003824] \n", - "28 [FBbt_00050025, FBbt_00050025, FBbt_00050025, ... \n", - "29 [FBbt_20003824] \n", - "30 [FBbt_20003824] \n", - "31 [FBbt_00048096, FBbt_00048096, FBbt_00048096, ... \n", + "11 [FBbt_00067363] \n", + "12 [FBbt_00067363] \n", + "13 [FBbt_00067363] \n", + "14 [FBbt_00067363] \n", + "15 [FBbt_00067363] \n", + "16 [FBbt_00067363] \n", + "17 [FBbt_20003824] \n", + "18 [FBbt_20003824] \n", + "19 [FBbt_20003824] \n", + "20 [FBbt_00067363] \n", + "21 [FBbt_00110423, FBbt_00067363] \n", + "22 [FBbt_00067363, FBbt_00110423] \n", + "23 [FBbt_00067363, FBbt_00110423] \n", + "24 [FBbt_00067372] \n", + "25 [FBbt_00067363, FBbt_00110423] \n", + "26 [FBbt_00067363, FBbt_00110423] \n", + "27 [FBbt_00110423, FBbt_00067363] \n", + "28 [FBbt_00110423, FBbt_00067363] \n", + "29 [VFBext_0000004, FBbt_00048096, FBbt_00067362,... \n", + "30 [FBbt_00067362, FBbt_00067350, VFBext_0000004,... \n", + "31 [FBbt_00067350, FBbt_00067362, FBbt_00050025, ... \n", "32 [FBbt_20003824] \n", - "33 [FBbt_00067362, FBbt_00067362, FBbt_00067362, ... \n", - "34 [FBbt_00067363, FBbt_00110423] \n", - "35 [VFBext_0000004, FBbt_00067372] \n", - "36 [FBbt_00067362, FBbt_00067362, FBbt_00067362, ... \n", - "37 [FBbt_00067350, FBbt_00067350, FBbt_00067350, ... \n", - "38 [FBbt_00067362, FBbt_00067362, FBbt_00067362, ... \n", - "39 [FBbt_00048096, FBbt_00048096, FBbt_00048096, ... \n", - "40 [FBbt_00067363, FBbt_00110423] \n", - "41 [FBbt_00067363, FBbt_00110423] \n", - "42 [FBbt_20003824] \n", - "43 [FBbt_00067350, FBbt_00067350, FBbt_00067350, ... \n", - "44 [FBbt_00048096, FBbt_00048096, FBbt_00048096, ... \n", - "45 [FBbt_00067362, FBbt_00067362, FBbt_00067362, ... \n", - "46 [FBbt_00067362, FBbt_00067362, FBbt_00067362, ... \n", - "47 [FBbt_00110423, FBbt_00067363] \n", - "48 [FBbt_20003824] \n", - "49 [FBbt_00067363, FBbt_00110423] \n", - "50 [FBbt_00067350, FBbt_00067350, FBbt_00067350, ... \n", + "33 [FBbt_20003824] \n", + "34 [FBbt_20003824] \n", + "35 [FBbt_20003824] \n", + "36 [FBbt_20003824] \n", + "37 [FBbt_20003824] \n", + "38 [VFBext_0000004, FBbt_00050025, FBbt_00067350,... \n", + "39 [FBbt_00067362, FBbt_00067350, VFBext_0000004,... \n", + "40 [FBbt_00067362, VFBext_0000004, FBbt_00050025,... \n", + "41 [FBbt_00048096, FBbt_00050025, FBbt_00067362, ... \n", + "42 [FBbt_00067350, VFBext_0000004, FBbt_00048096,... \n", + "43 [FBbt_00067350, VFBext_0000004, FBbt_00048096,... \n", + "44 [FBbt_00050025, VFBext_0000004, FBbt_00048096,... \n", + "45 [FBbt_00048096, FBbt_00067350, FBbt_00067362, ... \n", + "46 [FBbt_00067350, FBbt_00048096, FBbt_00067362, ... \n", + "47 [FBbt_00067362, FBbt_00050025, FBbt_00048096, ... \n", + "48 [FBbt_00067362, FBbt_00048096, FBbt_00050025, ... \n", + "49 [FBbt_00048096, FBbt_00050025, FBbt_00067362, ... \n", + "50 [VFBext_0000004, FBbt_00067350, FBbt_00050025,... \n", + "\n", + " data_source accession \\\n", + "0 [FlyCircuit] [Gad1-F-400088] \n", + "1 [catmaid_fafb] [4207871] \n", + "2 [catmaid_fafb] [2381753] \n", + "3 [catmaid_fafb] [2345089] \n", + "4 [catmaid_fafb] [2334841] \n", + "5 [catmaid_fafb] [2319457] \n", + "6 [catmaid_fafb] [3239781] \n", + "7 [catmaid_fafb] [2380564] \n", + "8 [catmaid_fafb] [1811442] \n", + "9 [catmaid_fafb] [2863104] \n", + "10 [catmaid_fafb] [2379517] \n", + "11 [catmaid_fafb] [755022] \n", + "12 [catmaid_fafb] [57353] \n", + "13 [catmaid_fafb] [57323] \n", + "14 [catmaid_fafb] [61221] \n", + "15 [catmaid_fafb] [57311] \n", + "16 [catmaid_fafb] [57381] \n", + "17 [catmaid_fafb] [57035] \n", + "18 [catmaid_fafb] [57179] \n", + "19 [catmaid_fafb] [57158] \n", + "20 [catmaid_fafb] [27295] \n", + "21 [neuprint_JRC_Hemibrain_1point1] [5813039315] \n", + "22 [neuprint_JRC_Hemibrain_1point1] [1734350908] \n", + "23 [neuprint_JRC_Hemibrain_1point1] [754534424] \n", + "24 [neuprint_JRC_Hemibrain_1point1] [733316908] \n", + "25 [neuprint_JRC_Hemibrain_1point1] [1765040289] \n", + "26 [neuprint_JRC_Hemibrain_1point1] [722817260] \n", + "27 [neuprint_JRC_Hemibrain_1point1] [1734350788] \n", + "28 [neuprint_JRC_Hemibrain_1point1] [754538881] \n", + "29 [FlyCircuit] [VGlut-F-700040] \n", + "30 [FlyCircuit] [fru-M-200319] \n", + "31 [FlyCircuit] [npf-M-300006] \n", + "32 [catmaid_fafb] [4520615] \n", + "33 [catmaid_fafb] [7710838] \n", + "34 [catmaid_fafb] [4520197] \n", + "35 [neuprint_JRC_Hemibrain_1point1] [792023887] \n", + "36 [neuprint_JRC_Hemibrain_1point1] [757591093] \n", + "37 [neuprint_JRC_Hemibrain_1point1] [757258507] \n", + "38 [FlyCircuit] [Cha-F-300252] \n", + "39 [FlyCircuit] [VGlut-F-800329] \n", + "40 [FlyCircuit] [VGlut-F-800317] \n", + "41 [FlyCircuit] [VGlut-F-000655] \n", + "42 [FlyCircuit] [VGlut-F-200574] \n", + "43 [FlyCircuit] [fru-F-400149] \n", + "44 [FlyCircuit] [Cha-F-500006] \n", + "45 [FlyCircuit] [VGlut-F-000280] \n", + "46 [FlyCircuit] [VGlut-F-600450] \n", + "47 [FlyCircuit] [VGlut-F-700188] \n", + "48 [FlyCircuit] [Gad1-F-200064] \n", + "49 [FlyCircuit] [Gad1-F-200010] \n", + "50 [FlyCircuit] [VGlut-F-600326] \n", "\n", " xrefs \\\n", - "0 [catmaid_fafb:27295] \n", - "1 [catmaid_fafb:755022] \n", - "2 [catmaid_fafb:61221] \n", - "3 [catmaid_fafb:57381] \n", - "4 [FlyCircuit:VGlut-F-600326] \n", - "5 [catmaid_fafb:57353] \n", - "6 [catmaid_fafb:57323] \n", - "7 [catmaid_fafb:57311] \n", - "8 [catmaid_fafb:2345089] \n", - "9 [catmaid_fafb:2334841] \n", - "10 [catmaid_fafb:2319457] \n", - "11 [catmaid_fafb:4520197] \n", - "12 [neuprint_JRC_Hemibrain_1point1:1734350908, ne... \n", - "13 [catmaid_fafb:4520615] \n", - "14 [neuprint_JRC_Hemibrain_1point1:722817260, neu... \n", - "15 [neuronbridge:5813039315, neuprint_JRC_Hemibra... \n", - "16 [neuronbridge:1765040289, neuprint_JRC_Hemibra... \n", - "17 [FlyCircuit:Cha-F-300252] \n", - "18 [FlyCircuit:VGlut-F-200574] \n", - "19 [neuprint_JRC_Hemibrain_1point1:1734350788, ne... \n", - "20 [neuronbridge:754534424, neuprint_JRC_Hemibrai... \n", - "21 [neuronbridge:733316908, neuprint_JRC_Hemibrai... \n", - "22 [neuronbridge:754538881, neuprint_JRC_Hemibrai... \n", - "23 [catmaid_fafb:4207871] \n", - "24 [FlyCircuit:VGlut-F-000655] \n", - "25 [catmaid_fafb:57179] \n", - "26 [catmaid_fafb:1811442] \n", - "27 [neuprint_JRC_Hemibrain_1point1:757258507, neu... \n", - "28 [FlyCircuit:Cha-F-500006] \n", - "29 [neuprint_JRC_Hemibrain_1point1:757591093, neu... \n", - "30 [neuronbridge:792023887, neuprint_JRC_Hemibrai... \n", - "31 [FlyCircuit:Gad1-F-200010] \n", - "32 [catmaid_fafb:7710838] \n", - "33 [FlyCircuit:fru-M-200319] \n", - "34 [catmaid_fafb:2381753] \n", - "35 [FlyCircuit:Gad1-F-400088] \n", - "36 [FlyCircuit:Gad1-F-200064] \n", - "37 [FlyCircuit:fru-F-400149] \n", - "38 [FlyCircuit:VGlut-F-700188] \n", - "39 [FlyCircuit:VGlut-F-000280] \n", - "40 [catmaid_fafb:2380564] \n", - "41 [catmaid_fafb:2379517] \n", - "42 [catmaid_fafb:57158] \n", - "43 [FlyCircuit:npf-M-300006] \n", - "44 [FlyCircuit:VGlut-F-700040] \n", - "45 [FlyCircuit:VGlut-F-800329] \n", - "46 [FlyCircuit:VGlut-F-800317] \n", - "47 [catmaid_fafb:3239781] \n", - "48 [catmaid_fafb:57035] \n", - "49 [catmaid_fafb:2863104] \n", - "50 [FlyCircuit:VGlut-F-600450] \n", + "0 [FlyCircuit:Gad1-F-400088] \n", + "1 [catmaid_fafb:4207871] \n", + "2 [catmaid_fafb:2381753] \n", + "3 [catmaid_fafb:2345089] \n", + "4 [catmaid_fafb:2334841] \n", + "5 [catmaid_fafb:2319457] \n", + "6 [catmaid_fafb:3239781] \n", + "7 [catmaid_fafb:2380564] \n", + "8 [catmaid_fafb:1811442] \n", + "9 [catmaid_fafb:2863104] \n", + "10 [catmaid_fafb:2379517] \n", + "11 [catmaid_fafb:755022] \n", + "12 [catmaid_fafb:57353] \n", + "13 [catmaid_fafb:57323] \n", + "14 [catmaid_fafb:61221] \n", + "15 [catmaid_fafb:57311] \n", + "16 [catmaid_fafb:57381] \n", + "17 [catmaid_fafb:57035] \n", + "18 [catmaid_fafb:57179] \n", + "19 [catmaid_fafb:57158] \n", + "20 [catmaid_fafb:27295] \n", + "21 [neuronbridge:5813039315, neuprint_JRC_Hemibra... \n", + "22 [neuprint_JRC_Hemibrain_1point1:1734350908, ne... \n", + "23 [neuronbridge:754534424, neuprint_JRC_Hemibrai... \n", + "24 [neuronbridge:733316908, neuprint_JRC_Hemibrai... \n", + "25 [neuronbridge:1765040289, neuprint_JRC_Hemibra... \n", + "26 [neuprint_JRC_Hemibrain_1point1:722817260, neu... \n", + "27 [neuprint_JRC_Hemibrain_1point1:1734350788, ne... \n", + "28 [neuronbridge:754538881, neuprint_JRC_Hemibrai... \n", + "29 [FlyCircuit:VGlut-F-700040] \n", + "30 [FlyCircuit:fru-M-200319] \n", + "31 [FlyCircuit:npf-M-300006] \n", + "32 [catmaid_fafb:4520615] \n", + "33 [catmaid_fafb:7710838] \n", + "34 [catmaid_fafb:4520197] \n", + "35 [neuronbridge:792023887, neuprint_JRC_Hemibrai... \n", + "36 [neuprint_JRC_Hemibrain_1point1:757591093, neu... \n", + "37 [neuprint_JRC_Hemibrain_1point1:757258507, neu... \n", + "38 [FlyCircuit:Cha-F-300252] \n", + "39 [FlyCircuit:VGlut-F-800329] \n", + "40 [FlyCircuit:VGlut-F-800317] \n", + "41 [FlyCircuit:VGlut-F-000655] \n", + "42 [FlyCircuit:VGlut-F-200574] \n", + "43 [FlyCircuit:fru-F-400149] \n", + "44 [FlyCircuit:Cha-F-500006] \n", + "45 [FlyCircuit:VGlut-F-000280] \n", + "46 [FlyCircuit:VGlut-F-600450] \n", + "47 [FlyCircuit:VGlut-F-700188] \n", + "48 [FlyCircuit:Gad1-F-200064] \n", + "49 [FlyCircuit:Gad1-F-200010] \n", + "50 [FlyCircuit:VGlut-F-600326] \n", "\n", " templates dataset \\\n", - "0 [JRC2018Unisex] [Zheng2018] \n", - "1 [JRC2018Unisex] [Zheng2018] \n", - "2 [JRC2018Unisex] [Zheng2018] \n", - "3 [JRC2018Unisex] [Zheng2018] \n", - "4 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", - "5 [JRC2018Unisex] [Zheng2018] \n", - "6 [JRC2018Unisex] [Zheng2018] \n", - "7 [JRC2018Unisex] [Zheng2018] \n", + "0 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", + "1 [JRC2018Unisex] [BatesSchlegel2020] \n", + "2 [JRC2018Unisex] [BatesSchlegel2020] \n", + "3 [JRC2018Unisex] [BatesSchlegel2020] \n", + "4 [JRC2018Unisex] [BatesSchlegel2020] \n", + "5 [JRC2018Unisex] [BatesSchlegel2020] \n", + "6 [JRC2018Unisex] [BatesSchlegel2020] \n", + "7 [JRC2018Unisex] [BatesSchlegel2020] \n", "8 [JRC2018Unisex] [BatesSchlegel2020] \n", "9 [JRC2018Unisex] [BatesSchlegel2020] \n", "10 [JRC2018Unisex] [BatesSchlegel2020] \n", - "11 [JRC2018Unisex] [TaiszGalili2022] \n", - "12 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "13 [JRC2018Unisex] [TaiszGalili2022] \n", - "14 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "15 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "16 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "17 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", - "18 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", - "19 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "20 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "11 [JRC2018Unisex] [Zheng2018] \n", + "12 [JRC2018Unisex] [Zheng2018] \n", + "13 [JRC2018Unisex] [Zheng2018] \n", + "14 [JRC2018Unisex] [Zheng2018] \n", + "15 [JRC2018Unisex] [Zheng2018] \n", + "16 [JRC2018Unisex] [Zheng2018] \n", + "17 [JRC2018Unisex] [BatesSchlegel2020] \n", + "18 [JRC2018Unisex] [BatesSchlegel2020] \n", + "19 [JRC2018Unisex] [BatesSchlegel2020] \n", + "20 [JRC2018Unisex] [Zheng2018] \n", "21 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "22 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", - "23 [JRC2018Unisex] [BatesSchlegel2020] \n", - "24 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", - "25 [JRC2018Unisex] [BatesSchlegel2020] \n", - "26 [JRC2018Unisex] [BatesSchlegel2020] \n", - "27 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", - "28 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", - "29 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", - "30 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", - "31 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", + "22 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "23 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "24 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "25 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "26 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "27 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "28 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "29 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", + "30 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", + "31 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", "32 [JRC2018Unisex] [TaiszGalili2022] \n", - "33 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", - "34 [JRC2018Unisex] [BatesSchlegel2020] \n", - "35 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", - "36 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", - "37 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", - "38 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", - "39 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", - "40 [JRC2018Unisex] [BatesSchlegel2020] \n", - "41 [JRC2018Unisex] [BatesSchlegel2020] \n", - "42 [JRC2018Unisex] [BatesSchlegel2020] \n", + "33 [JRC2018Unisex] [TaiszGalili2022] \n", + "34 [JRC2018Unisex] [TaiszGalili2022] \n", + "35 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "36 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "37 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "38 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", + "39 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", + "40 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", + "41 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", + "42 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", "43 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", "44 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", - "45 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", + "45 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", "46 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", - "47 [JRC2018Unisex] [BatesSchlegel2020] \n", - "48 [JRC2018Unisex] [BatesSchlegel2020] \n", - "49 [JRC2018Unisex] [BatesSchlegel2020] \n", - "50 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", + "47 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", + "48 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", + "49 [adult brain template JFRC2, JRC2018Unisex] [Chiang2010] \n", + "50 [JRC2018Unisex, adult brain template JFRC2] [Chiang2010] \n", "\n", " license \n", - "0 [https://creativecommons.org/licenses/by-sa/4.... \n", + "0 [] \n", "1 [https://creativecommons.org/licenses/by-sa/4.... \n", "2 [https://creativecommons.org/licenses/by-sa/4.... \n", "3 [https://creativecommons.org/licenses/by-sa/4.... \n", - "4 [] \n", + "4 [https://creativecommons.org/licenses/by-sa/4.... \n", "5 [https://creativecommons.org/licenses/by-sa/4.... \n", "6 [https://creativecommons.org/licenses/by-sa/4.... \n", "7 [https://creativecommons.org/licenses/by-sa/4.... \n", @@ -4064,48 +4176,48 @@ "9 [https://creativecommons.org/licenses/by-sa/4.... \n", "10 [https://creativecommons.org/licenses/by-sa/4.... \n", "11 [https://creativecommons.org/licenses/by-sa/4.... \n", - 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"40 [https://creativecommons.org/licenses/by-sa/4.... \n", - "41 [https://creativecommons.org/licenses/by-sa/4.... \n", - "42 [https://creativecommons.org/licenses/by-sa/4.... \n", + "40 [] \n", + "41 [] \n", + "42 [] \n", "43 [] \n", "44 [] \n", "45 [] \n", "46 [] \n", - "47 [https://creativecommons.org/licenses/by-sa/4.... \n", - "48 [https://creativecommons.org/licenses/by-sa/4.... \n", - "49 [https://creativecommons.org/licenses/by-sa/4.... \n", + "47 [] \n", + "48 [] \n", + "49 [] \n", "50 [] " ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -4119,7 +4231,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "cell_id": "00022-304e5471-e66d-41cd-b31c-38c7f624a10a", "deepnote_cell_type": "code", @@ -4135,40 +4247,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "33mWarning:\u001b[0m The following IDs do not match DB &/or id_type constraints: {'VFB_00101199', 'VFB_00005625', 'VFB_00010085', 'VFB_0010121x', 'VFB_jrchjtda', 'VFB_00005913', 'VFB_00006638', 'VFB_0010122m', 'VFB_00102dvz', 'VFB_0010122z', 'VFB_00012622', 'VFB_00101200', 'VFB_001011z9', 'VFB_00010984', 'VFB_00101205', 'VFB_001011zb', 'VFB_00001755', 'VFB_00101201', 'VFB_00000323', 'VFB_0010122k', 'VFB_jrchjtdh', 'VFB_jrchjtdc', 'VFB_0010122y', 'VFB_00005957', 'VFB_jrchk0xm', 'VFB_00009611', 'VFB_00006344', 'VFB_0010124e', 'VFB_00101203', 'VFB_jrchjtdg', 'VFB_0010123b', 'VFB_0010124l', 'VFB_0010122p', 'VFB_00101202', 'VFB_00006552', 'VFB_00011249', 'VFB_jrchk0xk', 'VFB_00012155', 'VFB_00102dvy', 'VFB_00010758', 'VFB_jrchjtdb', 'VFB_jrchk0xl', 'VFB_00101204', 'VFB_00102dw8', 'VFB_00013061', 'VFB_00010912', 'VFB_jrchjtdf', 'VFB_001011z3', 'VFB_0010126e', 'VFB_jrchjtde', 'VFB_jrchjtdd'}\n" + "33mWarning:\u001b[0m The following IDs do not match DB &/or id_type constraints: {'VFB_00009611', 'VFB_00101200', 'VFB_001011z3', 'VFB_jrchjtdg', 'VFB_00006638', 'VFB_0010121x', 'VFB_00102dvy', 'VFB_0010123b', 'VFB_0010124l', 'VFB_jrchjtde', 'VFB_jrchjtdb', 'VFB_00005625', 'VFB_00101204', 'VFB_00101199', 'VFB_0010122y', 'VFB_001011z9', 'VFB_00000323', 'VFB_001011zb', 'VFB_0010126e', 'VFB_jrchjtdf', 'VFB_00011249', 'VFB_00001755', 'VFB_jrchjtdd', 'VFB_00010912', 'VFB_00012155', 'VFB_00101205', 'VFB_00101201', 'VFB_jrchjtdh', 'VFB_00101203', 'VFB_0010122m', 'VFB_0010122z', 'VFB_00005913', 'VFB_00102dw8', 'VFB_jrchk0xk', 'VFB_00010085', 'VFB_00101202', 'VFB_jrchjtdc', 'VFB_jrchjtda', 'VFB_00012622', 'VFB_00005957', 'VFB_00013061', 'VFB_00006344', 'VFB_00006552', 'VFB_0010122p', 'VFB_00102dvz', 'VFB_0010122k', 'VFB_00010758', 'VFB_00010984', 'VFB_jrchk0xl', 'VFB_0010124e', 'VFB_jrchk0xm'}\n", + "[2381753, 7710838, 4520197, 57179, 2379517, 3239781, 57158, 57035, 2345089, 57311, 27295, 2380564, 2319457, 4520615, 57323, 2334841, 755022, 1811442, 61221, 57353, 4207871, 57381, 2863104]\n" ] - }, - { - "data": { - "text/plain": [ - "[2381753,\n", - " 7710838,\n", - " 4520197,\n", - " 57179,\n", - " 2379517,\n", - " 3239781,\n", - " 57158,\n", - " 57035,\n", - " 2345089,\n", - " 57311,\n", - " 27295,\n", - " 2380564,\n", - " 2319457,\n", - " 4520615,\n", - " 57323,\n", - " 2334841,\n", - " 755022,\n", - " 1811442,\n", - " 61221,\n", - " 57353,\n", - " 4207871,\n", - " 57381,\n", - " 2863104]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -4176,7 +4257,9 @@ "\n", "da1_skids = vfb.vfb_id_2_xrefs(DA1['id'], db='catmaid_fafb', reverse_return=True)\n", "da1_skids_int = list(map(int, da1_skids))\n", - "da1_skids_int" + "print(da1_skids_int)\n", + "\n", + "# print is just used so we can see the list as a single line" ] }, { @@ -4192,7 +4275,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "cell_id": "00024-ab443932-b353-48e0-a3e7-75e7c9c8fa6b", "deepnote_cell_type": "code", @@ -4407,7 +4490,7 @@ "[5 rows x 28 columns]" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -4425,7 +4508,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "cell_id": "00025-b2ead253-ecac-4fcd-b77d-2ede4e567b9f", "deepnote_cell_type": "code", @@ -4666,7 +4749,7 @@ "[5 rows x 584 columns]" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -4680,7 +4763,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "cell_id": "00026-8b8c1e47-6f00-4743-aa67-167b21a7209c", "deepnote_cell_type": "code", @@ -4697,13 +4780,7 @@ "output_type": "stream", "text": [ "/Users/robbiecourt/GIT/VFB_connect/.conda/lib/python3.10/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + " warnings.warn(msg)\n", "/Users/robbiecourt/GIT/VFB_connect/.conda/lib/python3.10/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", " warnings.warn(msg)\n" ] @@ -4737,7 +4814,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "cell_id": "00028-f1fdec93-0988-4174-9ba0-ac67f4801be9", "deepnote_cell_type": "code", @@ -4887,7 +4964,7 @@ "4 1811442 5 NaN [356626.0, 167779.0, 158200.0] " ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -4900,7 +4977,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "cell_id": "00029-ec2f0e3d-02fc-4f93-a7c0-257cd1c1e57b", "deepnote_cell_type": "code", @@ -68896,7 +68973,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "cell_id": "00032-abff39ed-238b-4355-b7bf-a8890ccc1e0f", "deepnote_cell_type": "code", @@ -68930,7 +69007,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "cell_id": "00034-b3c86e73-f9f0-4775-a0b4-0e5ae38167ec", "deepnote_cell_type": "code", @@ -69240,7 +69317,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "cell_id": "00036-72792c2e-27eb-4849-a52d-8f63a8f0c92f", "deepnote_cell_type": "code", @@ -69457,7 +69534,7 @@ "4 [LH(R), SLP(R), SNP(R)] " ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -69476,7 +69553,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "cell_id": "00037-73ace4e6-a4e1-4638-8b21-57baa6a38e36", "deepnote_cell_type": "code", @@ -69533,7 +69610,7 @@ " 'VP1d+VP4_l2PN2'], dtype=object)" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -69556,7 +69633,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 42, "metadata": { "cell_id": "00039-806f6ff5-8293-4417-9e29-c7b5202dc840", "deepnote_cell_type": "code", @@ -69568,6 +69645,13 @@ "tags": [] }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWarning:\u001b[0m called a non existant id:VFB_00102294\n" + ] + }, { "data": { "text/html": [ @@ -69593,10 +69677,11 @@ " symbol\n", " id\n", " tags\n", - 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" [neuprint_JRC_Hemibrain_1point1:760250272, neu...\n", - " [JRC_FlyEM_Hemibrain, JRC2018Unisex]\n", + " [5813055048]\n", + " [neuprint_JRC_Hemibrain_1point1:5813055048, ne...\n", + " [JRC2018Unisex, JRC_FlyEM_Hemibrain]\n", " [Xu2020NeuronsV1point1]\n", " [https://creativecommons.org/licenses/by/4.0/l...\n", " \n", " \n", - " 4\n", - " M_vPNml84_R (FlyEM-HB:729544359)\n", - " M_vPNml84_R\n", - " VFB_jrchk0zr\n", - " [Entity, Adult, Anatomy, Cell, GABAergic, Indi...\n", + " 542\n", + " DL2d_adPN_R (FlyEM-HB:1793014716)\n", + " DL2d_adPN_R\n", + " VFB_jrchjte7\n", + " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " tracing status-Roughly traced, cropped-False\n", + " [adult antennal lobe projection neuron DL2d adPN]\n", + " [FBbt_00100389]\n", " neuprint_JRC_Hemibrain_1point1\n", - " [729544359]\n", - " [adult multiglomerular antennal lobe projectio...\n", - " [FBbt_20003859]\n", - " [neuprint_JRC_Hemibrain_1point1:729544359, neu...\n", + " [1793014716]\n", + " [neuprint_JRC_Hemibrain_1point1:1793014716, ne...\n", " [JRC2018Unisex, JRC_FlyEM_Hemibrain]\n", " [Xu2020NeuronsV1point1]\n", " [https://creativecommons.org/licenses/by/4.0/l...\n", @@ -69693,196 +69783,202 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", - 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" [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " 1075\n", + " M_vPNml87_R (FlyEM-HB:572663539)\n", + " M_vPNml87_R\n", + " VFB_jrchk0zw\n", + " [Entity, Adult, Anatomy, Cell, GABAergic, Indi...\n", + " tracing status-Roughly traced, cropped-False\n", + " [adult multiglomerular antennal lobe projectio...\n", + " [FBbt_20003862, FBbt_00058207]\n", " neuprint_JRC_Hemibrain_1point1\n", - " [1788300760]\n", - " [adult antennal lobe projection neuron VA1v adPN]\n", - " [FBbt_00067361]\n", - " [neuprint_JRC_Hemibrain_1point1:1788300760, ne...\n", + " [572663539]\n", + " [neuronbridge:572663539, neuprint_JRC_Hemibrai...\n", " [JRC_FlyEM_Hemibrain, JRC2018Unisex]\n", " [Xu2020NeuronsV1point1]\n", " [https://creativecommons.org/licenses/by/4.0/l...\n", " \n", " \n", - " 1390\n", - " VA1v_vPN_R (FlyEM-HB:887105351)\n", - " VA1v_vPN_R\n", - " VFB_jrchk7d8\n", - " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " 1076\n", + " M_vPNml86_R (FlyEM-HB:5813045696)\n", + " M_vPNml86_R\n", + " VFB_jrchk0zu\n", + " [Entity, Adult, Anatomy, Cell, Individual, Ner...\n", + " tracing status-Roughly traced, cropped-False\n", + " [adult multiglomerular antennal lobe projectio...\n", + " [FBbt_20003861]\n", " neuprint_JRC_Hemibrain_1point1\n", - " [887105351]\n", - " [adult antennal lobe projection neuron VA1v vPN]\n", - " [FBbt_00067371]\n", - " [neuronbridge:887105351, neuprint_JRC_Hemibrai...\n", + " [5813045696]\n", + " [neuprint_JRC_Hemibrain_1point1:5813045696, ne...\n", " [JRC_FlyEM_Hemibrain, JRC2018Unisex]\n", " [Xu2020NeuronsV1point1]\n", " [https://creativecommons.org/licenses/by/4.0/l...\n", " \n", " \n", - " 1391\n", - " VA1v_vPN_R (FlyEM-HB:856071065)\n", - " VA1v_vPN_R\n", - " VFB_jrchk7d9\n", - " [Entity, Adult, Anatomy, Cell, Chemosensory_sy...\n", + " 1077\n", + " M_vPNml88_R (FlyEM-HB:634379734)\n", + " M_vPNml88_R\n", + " VFB_jrchk0zy\n", + " [Entity, Adult, Anatomy, Cell, GABAergic, Indi...\n", + " tracing status-Roughly traced, cropped-False\n", + " [adult GABAergic neuron, adult multiglomerular...\n", + " [FBbt_00058207, FBbt_20003863]\n", " neuprint_JRC_Hemibrain_1point1\n", - " [856071065]\n", - " [adult antennal lobe projection neuron VA1v vPN]\n", - " [FBbt_00067371]\n", - " [neuprint_JRC_Hemibrain_1point1:856071065, neu...\n", - " [JRC_FlyEM_Hemibrain, JRC2018Unisex]\n", + " [634379734]\n", + " [neuronbridge:634379734, neuprint_JRC_Hemibrai...\n", + " [JRC2018Unisex, JRC_FlyEM_Hemibrain]\n", " [Xu2020NeuronsV1point1]\n", " [https://creativecommons.org/licenses/by/4.0/l...\n", " \n", " \n", - " 1400\n", - " VC5_lvPN_R (FlyEM-HB:1849684319)\n", - " VC5_lvPN_R\n", - " VFB_jrchk7e0\n", - " [Entity, Adult, Anatomy, Cell, Cholinergic, In...\n", + " 1078\n", + " M_vPNml87_R (FlyEM-HB:5813013823)\n", + " M_vPNml87_R\n", + " VFB_jrchk0zx\n", + " [Entity, Adult, Anatomy, Cell, GABAergic, Indi...\n", + " tracing status-Roughly traced, cropped-False\n", + " [adult multiglomerular antennal lobe projectio...\n", + " [FBbt_20003862, FBbt_00058207]\n", " neuprint_JRC_Hemibrain_1point1\n", - " [1849684319]\n", - " [adult cholinergic neuron, adult cholinergic n...\n", - " [FBbt_00058205, FBbt_00058205, FBbt_00058205, ...\n", - " [neuronbridge:1849684319, neuprint_JRC_Hemibra...\n", - " [JRC2018Unisex, JRC_FlyEM_Hemibrain]\n", + " [5813013823]\n", + " [neuprint_JRC_Hemibrain_1point1:5813013823, ne...\n", + " [JRC_FlyEM_Hemibrain, JRC2018Unisex]\n", " [Xu2020NeuronsV1point1]\n", " [https://creativecommons.org/licenses/by/4.0/l...\n", " \n", " \n", "\n", - "

339 rows × 12 columns

\n", + "

339 rows × 13 columns

\n", "" ], "text/plain": [ " label symbol id \\\n", - "0 M_vPNml83_R (FlyEM-HB:697145036) M_vPNml83_R VFB_jrchk0zn \n", - "1 M_vPNml84_R (FlyEM-HB:605050789) M_vPNml84_R VFB_jrchk0zo \n", - "2 M_vPNml84_R (FlyEM-HB:791298858) M_vPNml84_R VFB_jrchk0zp \n", - "3 M_vPNml84_R (FlyEM-HB:760250272) M_vPNml84_R VFB_jrchk0zq \n", - "4 M_vPNml84_R (FlyEM-HB:729544359) M_vPNml84_R VFB_jrchk0zr \n", + "538 DC4_vPN_R (FlyEM-HB:1447201088) DC4_vPN_R VFB_jrchjtdy \n", + "539 DA3_adPN_R (FlyEM-HB:1703683361) DA3_adPN_R VFB_jrchjtdn \n", + "540 DA2_lPN_R (FlyEM-HB:1796818119) DA2_lPN_R VFB_jrchjtdm \n", + "541 DC2_adPN_R (FlyEM-HB:5813055048) DC2_adPN_R VFB_jrchjtds \n", + "542 DL2d_adPN_R (FlyEM-HB:1793014716) DL2d_adPN_R VFB_jrchjte7 \n", "... ... ... ... \n", - "1387 VA1v_adPN_R (FlyEM-HB:5813054697) VA1v_adPN_R VFB_jrchk7d6 \n", - "1389 VA1v_adPN_R (FlyEM-HB:1788300760) VA1v_adPN_R VFB_jrchk7d7 \n", - "1390 VA1v_vPN_R (FlyEM-HB:887105351) VA1v_vPN_R VFB_jrchk7d8 \n", - "1391 VA1v_vPN_R (FlyEM-HB:856071065) VA1v_vPN_R VFB_jrchk7d9 \n", - "1400 VC5_lvPN_R (FlyEM-HB:1849684319) VC5_lvPN_R VFB_jrchk7e0 \n", + "1074 M_vPNml89_R (FlyEM-HB:481268653) M_vPNml89_R VFB_jrchk0zz \n", + "1075 M_vPNml87_R (FlyEM-HB:572663539) M_vPNml87_R VFB_jrchk0zw \n", + "1076 M_vPNml86_R (FlyEM-HB:5813045696) M_vPNml86_R VFB_jrchk0zu \n", + "1077 M_vPNml88_R (FlyEM-HB:634379734) M_vPNml88_R VFB_jrchk0zy \n", + "1078 M_vPNml87_R (FlyEM-HB:5813013823) M_vPNml87_R VFB_jrchk0zx \n", "\n", " tags \\\n", - "0 [Entity, Adult, Anatomy, Cell, GABAergic, Indi... \n", - "1 [Entity, Adult, Anatomy, Cell, GABAergic, Indi... \n", - "2 [Entity, Adult, Anatomy, Cell, GABAergic, Indi... \n", - "3 [Entity, Adult, Anatomy, Cell, GABAergic, Indi... \n", - "4 [Entity, Adult, Anatomy, Cell, GABAergic, Indi... \n", + "538 [Entity, Adult, Anatomy, Cell, Chemosensory_sy... \n", + "539 [Entity, Adult, Anatomy, Cell, Chemosensory_sy... \n", + "540 [Entity, Adult, Anatomy, Cell, Chemosensory_sy... \n", + "541 [Entity, Adult, Anatomy, Cell, Chemosensory_sy... \n", + "542 [Entity, Adult, Anatomy, Cell, Chemosensory_sy... \n", "... ... \n", - "1387 [Entity, Adult, Anatomy, Cell, Chemosensory_sy... \n", - "1389 [Entity, Adult, Anatomy, Cell, Chemosensory_sy... \n", - "1390 [Entity, Adult, Anatomy, Cell, Chemosensory_sy... \n", - "1391 [Entity, Adult, Anatomy, Cell, Chemosensory_sy... \n", - "1400 [Entity, Adult, Anatomy, Cell, Cholinergic, In... \n", + "1074 [Entity, Adult, Anatomy, Cell, GABAergic, Indi... \n", + "1075 [Entity, Adult, Anatomy, Cell, GABAergic, Indi... \n", + "1076 [Entity, Adult, Anatomy, Cell, Individual, Ner... \n", + "1077 [Entity, Adult, Anatomy, Cell, GABAergic, Indi... \n", + "1078 [Entity, Adult, Anatomy, Cell, GABAergic, Indi... \n", "\n", - " data_source accession \\\n", - "0 neuprint_JRC_Hemibrain_1point1 [697145036] \n", - "1 neuprint_JRC_Hemibrain_1point1 [605050789] \n", - "2 neuprint_JRC_Hemibrain_1point1 [791298858] \n", - "3 neuprint_JRC_Hemibrain_1point1 [760250272] \n", - "4 neuprint_JRC_Hemibrain_1point1 [729544359] \n", - "... ... ... \n", - "1387 neuprint_JRC_Hemibrain_1point1 [5813054697] \n", - "1389 neuprint_JRC_Hemibrain_1point1 [1788300760] \n", - "1390 neuprint_JRC_Hemibrain_1point1 [887105351] \n", - "1391 neuprint_JRC_Hemibrain_1point1 [856071065] \n", - "1400 neuprint_JRC_Hemibrain_1point1 [1849684319] \n", + " description \\\n", + "538 tracing status-Roughly traced, cropped-False \n", + "539 tracing status-Roughly traced, cropped-False \n", + "540 tracing status-Roughly traced, cropped-False \n", + "541 tracing status-Roughly traced, cropped-False \n", + "542 tracing status-Roughly traced, cropped-False \n", + "... ... \n", + "1074 tracing status-Roughly traced, cropped-False \n", + "1075 tracing status-Roughly traced, cropped-False \n", + "1076 tracing status-Roughly traced, cropped-False \n", + "1077 tracing status-Roughly traced, cropped-False \n", + "1078 tracing status-Roughly traced, cropped-False \n", "\n", " parents_label \\\n", - "0 [adult GABAergic neuron, adult GABAergic neuro... \n", - "1 [adult multiglomerular antennal lobe projectio... \n", - "2 [adult multiglomerular antennal lobe projectio... \n", - "3 [adult multiglomerular antennal lobe projectio... \n", - "4 [adult multiglomerular antennal lobe projectio... \n", + "538 [adult antennal lobe projection neuron DC4 vPN] \n", + "539 [adult antennal lobe projection neuron DA3 adPN] \n", + "540 [adult antennal lobe projection neuron DA2 lPN] \n", + "541 [adult antennal lobe projection neuron DC2 adPN] \n", + "542 [adult antennal lobe projection neuron DL2d adPN] \n", "... ... \n", - "1387 [adult antennal lobe projection neuron VA1v adPN] \n", - "1389 [adult antennal lobe projection neuron VA1v adPN] \n", - "1390 [adult antennal lobe projection neuron VA1v vPN] \n", - "1391 [adult antennal lobe projection neuron VA1v vPN] \n", - "1400 [adult cholinergic neuron, adult cholinergic n... \n", + "1074 [adult multiglomerular antennal lobe projectio... \n", + "1075 [adult multiglomerular antennal lobe projectio... \n", + "1076 [adult multiglomerular antennal lobe projectio... \n", + "1077 [adult GABAergic neuron, adult multiglomerular... \n", + "1078 [adult multiglomerular antennal lobe projectio... \n", "\n", - " parents_id \\\n", - "0 [FBbt_00058207, FBbt_00058207, FBbt_00058207, ... \n", - "1 [FBbt_20003859] \n", - "2 [FBbt_20003859] \n", - "3 [FBbt_20003859] \n", - "4 [FBbt_20003859] \n", - "... ... \n", - "1387 [FBbt_00067361] \n", - "1389 [FBbt_00067361] \n", - "1390 [FBbt_00067371] \n", - "1391 [FBbt_00067371] \n", - "1400 [FBbt_00058205, FBbt_00058205, FBbt_00058205, ... \n", + " parents_id data_source \\\n", + "538 [FBbt_00049771] neuprint_JRC_Hemibrain_1point1 \n", + "539 [FBbt_00100384] neuprint_JRC_Hemibrain_1point1 \n", + "540 [FBbt_00110882] neuprint_JRC_Hemibrain_1point1 \n", + "541 [FBbt_00067354] neuprint_JRC_Hemibrain_1point1 \n", + "542 [FBbt_00100389] neuprint_JRC_Hemibrain_1point1 \n", + "... ... ... \n", + "1074 [FBbt_20003864, FBbt_00058207] neuprint_JRC_Hemibrain_1point1 \n", + "1075 [FBbt_20003862, FBbt_00058207] neuprint_JRC_Hemibrain_1point1 \n", + "1076 [FBbt_20003861] neuprint_JRC_Hemibrain_1point1 \n", + "1077 [FBbt_00058207, FBbt_20003863] neuprint_JRC_Hemibrain_1point1 \n", + "1078 [FBbt_20003862, FBbt_00058207] neuprint_JRC_Hemibrain_1point1 \n", "\n", - " xrefs \\\n", - "0 [neuronbridge:697145036, neuprint_JRC_Hemibrai... \n", - "1 [neuronbridge:605050789, neuprint_JRC_Hemibrai... \n", - "2 [neuprint_JRC_Hemibrain_1point1:791298858, neu... \n", - "3 [neuprint_JRC_Hemibrain_1point1:760250272, neu... \n", - "4 [neuprint_JRC_Hemibrain_1point1:729544359, neu... \n", - "... ... \n", - "1387 [neuronbridge:5813054697, neuprint_JRC_Hemibra... \n", - "1389 [neuprint_JRC_Hemibrain_1point1:1788300760, ne... \n", - "1390 [neuronbridge:887105351, neuprint_JRC_Hemibrai... \n", - "1391 [neuprint_JRC_Hemibrain_1point1:856071065, neu... \n", - "1400 [neuronbridge:1849684319, neuprint_JRC_Hemibra... \n", + " accession xrefs \\\n", + "538 [1447201088] [neuprint_JRC_Hemibrain_1point1:1447201088, ne... \n", + "539 [1703683361] [neuronbridge:1703683361, neuprint_JRC_Hemibra... \n", + "540 [1796818119] [neuprint_JRC_Hemibrain_1point1:1796818119, ne... \n", + "541 [5813055048] [neuprint_JRC_Hemibrain_1point1:5813055048, ne... \n", + "542 [1793014716] [neuprint_JRC_Hemibrain_1point1:1793014716, ne... \n", + "... ... ... \n", + "1074 [481268653] [neuronbridge:481268653, neuprint_JRC_Hemibrai... \n", + "1075 [572663539] [neuronbridge:572663539, neuprint_JRC_Hemibrai... \n", + "1076 [5813045696] [neuprint_JRC_Hemibrain_1point1:5813045696, ne... \n", + "1077 [634379734] [neuronbridge:634379734, neuprint_JRC_Hemibrai... \n", + "1078 [5813013823] [neuprint_JRC_Hemibrain_1point1:5813013823, ne... \n", "\n", " templates dataset \\\n", - "0 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", - "1 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "2 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", - "3 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "4 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "538 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "539 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "540 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "541 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "542 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", "... ... ... \n", - "1387 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "1389 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "1390 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "1391 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", - "1400 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "1074 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "1075 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "1076 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", + "1077 [JRC2018Unisex, JRC_FlyEM_Hemibrain] [Xu2020NeuronsV1point1] \n", + "1078 [JRC_FlyEM_Hemibrain, JRC2018Unisex] [Xu2020NeuronsV1point1] \n", "\n", " license \n", - "0 [https://creativecommons.org/licenses/by/4.0/l... \n", - "1 [https://creativecommons.org/licenses/by/4.0/l... \n", - "2 [https://creativecommons.org/licenses/by/4.0/l... \n", - "3 [https://creativecommons.org/licenses/by/4.0/l... \n", - "4 [https://creativecommons.org/licenses/by/4.0/l... \n", + "538 [https://creativecommons.org/licenses/by/4.0/l... \n", + "539 [https://creativecommons.org/licenses/by/4.0/l... \n", + "540 [https://creativecommons.org/licenses/by/4.0/l... \n", + "541 [https://creativecommons.org/licenses/by/4.0/l... \n", + "542 [https://creativecommons.org/licenses/by/4.0/l... \n", "... ... \n", - "1387 [https://creativecommons.org/licenses/by/4.0/l... \n", - "1389 [https://creativecommons.org/licenses/by/4.0/l... \n", - "1390 [https://creativecommons.org/licenses/by/4.0/l... \n", - "1391 [https://creativecommons.org/licenses/by/4.0/l... \n", - "1400 [https://creativecommons.org/licenses/by/4.0/l... \n", + "1074 [https://creativecommons.org/licenses/by/4.0/l... \n", + "1075 [https://creativecommons.org/licenses/by/4.0/l... \n", + "1076 [https://creativecommons.org/licenses/by/4.0/l... \n", + "1077 [https://creativecommons.org/licenses/by/4.0/l... \n", + "1078 [https://creativecommons.org/licenses/by/4.0/l... \n", "\n", - "[339 rows x 12 columns]" + "[339 rows x 13 columns]" ] }, - "execution_count": 25, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -69890,7 +69986,7 @@ "source": [ "#This will get all ALPNs from ALL datasets on VFB\n", "ALPNs = vfb.get_instances(\"'adult antennal lobe projection neuron'\")\n", - "# Explode the lists in the 'data_source' column into separate rows as each image can potentialy have multiple data sources. NNote this can be done for any column with lists\n", + "# Explode the lists in the 'data_source' column into separate rows as each image can potentialy have multiple data sources. Note this can be done for any column with lists\n", "exploded_ALPNs = ALPNs.explode('data_source')\n", "#Select only rows from Hemibrain1.1 dataset\n", "ALPNs=exploded_ALPNs[exploded_ALPNs['data_source'] == 'neuprint_JRC_Hemibrain_1point1']\n", @@ -69912,7 +70008,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": { "cell_id": "00041-bf3e6251-b103-427e-bafe-aeaad3dd9b8a", "deepnote_cell_type": "code", @@ -70023,7 +70119,7 @@ " ...\n", " \n", " \n", - " 101862\n", + " 85077\n", " 5901222910\n", " 5813086037\n", " 1\n", @@ -70034,7 +70130,7 @@ " {'LH(R)': {'pre': 1, 'post': 1}}\n", " \n", " \n", - " 101863\n", + " 85078\n", " 5901222910\n", " 5813095915\n", " 1\n", @@ -70045,7 +70141,7 @@ " {'MB(R)': {'pre': 1, 'post': 1}, 'CA(R)': {'pr...\n", " \n", " \n", - " 101864\n", + " 85079\n", " 5901222910\n", " 5813129316\n", " 1\n", @@ -70056,7 +70152,7 @@ " {'LH(R)': {'pre': 1, 'post': 1}}\n", " \n", " \n", - " 101865\n", + " 85080\n", " 5901222910\n", " 5901193783\n", " 1\n", @@ -70067,7 +70163,7 @@ " {'LH(R)': {'pre': 1, 'post': 1}}\n", " \n", " \n", - " 101866\n", + " 85081\n", " 5901222910\n", " 5901203780\n", " 1\n", @@ -70079,40 +70175,40 @@ " \n", " \n", "\n", - "

101867 rows × 8 columns

\n", + "

85082 rows × 8 columns

\n", "" ], "text/plain": [ - " bodyId_pre bodyId_post weight type_pre type_post instance_pre \\\n", - "0 635062078 1671292719 390 DP1m_adPN lLN2T_c DP1m_adPN_R \n", - "1 635062078 1704347707 326 DP1m_adPN lLN2T_c DP1m_adPN_R \n", - "2 542634818 1704347707 322 DM1_lPN lLN2T_c DM1_lPN_R \n", - "3 635062078 1640922516 320 DP1m_adPN lLN2T_e DP1m_adPN_R \n", - "4 724816115 1670916819 318 DP1l_adPN lLN2P_a DP1l_adPN_R \n", - "... ... ... ... ... ... ... \n", - "101862 5901222910 5813086037 1 DM2_lPN None DM2_lPN_R \n", - "101863 5901222910 5813095915 1 DM2_lPN KCg-m DM2_lPN_R \n", - "101864 5901222910 5813129316 1 DM2_lPN LHAV6a1_b DM2_lPN_R \n", - "101865 5901222910 5901193783 1 DM2_lPN LHAV4g4_a DM2_lPN_R \n", - "101866 5901222910 5901203780 1 DM2_lPN LHAV4g11 DM2_lPN_R \n", + " bodyId_pre bodyId_post weight type_pre type_post instance_pre \\\n", + "0 635062078 1671292719 390 DP1m_adPN lLN2T_c DP1m_adPN_R \n", + "1 635062078 1704347707 326 DP1m_adPN lLN2T_c DP1m_adPN_R \n", + "2 542634818 1704347707 322 DM1_lPN lLN2T_c DM1_lPN_R \n", + "3 635062078 1640922516 320 DP1m_adPN lLN2T_e DP1m_adPN_R \n", + "4 724816115 1670916819 318 DP1l_adPN lLN2P_a DP1l_adPN_R \n", + "... ... ... ... ... ... ... \n", + "85077 5901222910 5813086037 1 DM2_lPN None DM2_lPN_R \n", + "85078 5901222910 5813095915 1 DM2_lPN KCg-m DM2_lPN_R \n", + "85079 5901222910 5813129316 1 DM2_lPN LHAV6a1_b DM2_lPN_R \n", + "85080 5901222910 5901193783 1 DM2_lPN LHAV4g4_a DM2_lPN_R \n", + "85081 5901222910 5901203780 1 DM2_lPN LHAV4g11 DM2_lPN_R \n", "\n", - " instance_post conn_roiInfo \n", - "0 lLN2T_c(Tortuous)_R {'AL(R)': {'pre': 390, 'post': 390}, 'AL-DP1m(... \n", - "1 lLN2T_c(Tortuous)_R {'AL(R)': {'pre': 324, 'post': 324}, 'AL-DP1m(... \n", - "2 lLN2T_c(Tortuous)_R {'AL(R)': {'pre': 322, 'post': 322}, 'AL-DM1(R... \n", - "3 lLN2T_e(Tortuous)_R {'AL(R)': {'pre': 317, 'post': 316}, 'AL-DP1m(... \n", - "4 lLN2P_a(Patchy)_R {'AL(R)': {'pre': 318, 'post': 318}, 'AL-DP1l(... \n", - "... ... ... \n", - "101862 None {'LH(R)': {'pre': 1, 'post': 1}} \n", - "101863 KCg-m_R {'MB(R)': {'pre': 1, 'post': 1}, 'CA(R)': {'pr... \n", - "101864 LHAV6a1_b_R {'LH(R)': {'pre': 1, 'post': 1}} \n", - "101865 LHAV4g4_a_R {'LH(R)': {'pre': 1, 'post': 1}} \n", - "101866 LHAV4g11_R {'LH(R)': {'pre': 1, 'post': 1}} \n", + " instance_post conn_roiInfo \n", + "0 lLN2T_c(Tortuous)_R {'AL(R)': {'pre': 390, 'post': 390}, 'AL-DP1m(... \n", + "1 lLN2T_c(Tortuous)_R {'AL(R)': {'pre': 324, 'post': 324}, 'AL-DP1m(... \n", + "2 lLN2T_c(Tortuous)_R {'AL(R)': {'pre': 322, 'post': 322}, 'AL-DM1(R... \n", + "3 lLN2T_e(Tortuous)_R {'AL(R)': {'pre': 317, 'post': 316}, 'AL-DP1m(... \n", + "4 lLN2P_a(Patchy)_R {'AL(R)': {'pre': 318, 'post': 318}, 'AL-DP1l(... \n", + "... ... ... \n", + "85077 None {'LH(R)': {'pre': 1, 'post': 1}} \n", + "85078 KCg-m_R {'MB(R)': {'pre': 1, 'post': 1}, 'CA(R)': {'pr... \n", + "85079 LHAV6a1_b_R {'LH(R)': {'pre': 1, 'post': 1}} \n", + "85080 LHAV4g4_a_R {'LH(R)': {'pre': 1, 'post': 1}} \n", + "85081 LHAV4g11_R {'LH(R)': {'pre': 1, 'post': 1}} \n", "\n", - "[101867 rows x 8 columns]" + "[85082 rows x 8 columns]" ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -70138,7 +70234,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": { "cell_id": "00043-7809795f-0834-412d-a132-e71cb36f90dc", "deepnote_cell_type": "code", @@ -70214,58 +70310,58 @@ " ...\n", " \n", " \n", - " 40631\n", - " M_vPNml50\n", - " WEDPN4\n", + " 32238\n", + " M_lvPNm44\n", + " LHAV4a2\n", " 1\n", " \n", " \n", - " 40632\n", - " M_vPNml50\n", - " WEDPN12\n", + " 32239\n", + " VL2a_adPN\n", + " LHAV4g16_b\n", " 1\n", " \n", " \n", - " 40633\n", - " M_vPNml50\n", - " V_ilPN\n", + " 32240\n", + " M_lvPNm44\n", + " LHAV3f1\n", " 1\n", " \n", " \n", - " 40634\n", - " M_vPNml50\n", - " VP4+VL1_l2PN\n", + " 32241\n", + " M_lvPNm44\n", + " LHAV3e5\n", " 1\n", " \n", " \n", - " 40635\n", + " 32242\n", " Z_vPNml1\n", " mALD2\n", " 1\n", " \n", " \n", "\n", - "

40636 rows × 3 columns

\n", + "

32243 rows × 3 columns

\n", "" ], "text/plain": [ - " type_pre type_post weight\n", - "0 DC3_adPN KCg-m 3670\n", - "1 VM5d_adPN KCg-m 3219\n", - "2 DC1_adPN KCg-m 3215\n", - "3 VL2a_adPN KCg-m 3096\n", - "4 DA1_lPN KCg-m 3078\n", - "... ... ... ...\n", - "40631 M_vPNml50 WEDPN4 1\n", - "40632 M_vPNml50 WEDPN12 1\n", - "40633 M_vPNml50 V_ilPN 1\n", - "40634 M_vPNml50 VP4+VL1_l2PN 1\n", - "40635 Z_vPNml1 mALD2 1\n", + " type_pre type_post weight\n", + "0 DC3_adPN KCg-m 3670\n", + "1 VM5d_adPN KCg-m 3219\n", + "2 DC1_adPN KCg-m 3215\n", + "3 VL2a_adPN KCg-m 3096\n", + "4 DA1_lPN KCg-m 3078\n", + "... ... ... ...\n", + "32238 M_lvPNm44 LHAV4a2 1\n", + "32239 VL2a_adPN LHAV4g16_b 1\n", + "32240 M_lvPNm44 LHAV3f1 1\n", + "32241 M_lvPNm44 LHAV3e5 1\n", + "32242 Z_vPNml1 mALD2 1\n", "\n", - "[40636 rows x 3 columns]" + "[32243 rows x 3 columns]" ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -70290,7 +70386,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "cell_id": "00045-de5f57a4-02f2-4f06-9ef1-3c5d54b44dea", "deepnote_cell_type": "code", @@ -70641,7 +70737,7 @@ "[1927 rows x 13 columns]" ] }, - "execution_count": 28, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -70657,7 +70753,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -70813,7 +70909,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "cell_id": "00046-bca99962-caab-4192-9740-bf955e489ffa", "deepnote_cell_type": "code", @@ -70828,7 +70924,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "914c33640c6d4b1fb35264fdab9aa1a7", + "model_id": "7cd849c8613d4331b3dfb5e08b846acd", "version_major": 2, "version_minor": 0 }, @@ -70915,7 +71011,7 @@ "4 542634818 332690153 CA(R) 20" ] }, - "execution_count": 30, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -70928,7 +71024,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": { "cell_id": "00047-803f385f-a7ef-4344-829f-3f86e66d31b6", "deepnote_cell_type": "code", @@ -70944,13 +71040,13 @@ "data": { "text/plain": [ "roi\n", - "CA(R) 14006\n", - "NotPrimary 52\n", - "SLP(R) 31\n", + "CA(R) 19333\n", + "NotPrimary 88\n", + "SLP(R) 35\n", "Name: weight, dtype: int64" ] }, - "execution_count": 31, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -70963,7 +71059,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "cell_id": "00048-d923226d-8497-4fdd-a38f-568ea82d3b8e", "deepnote_cell_type": "code", @@ -70981,13 +71077,13 @@ "Text(0, 0.5, 'PN to KC synapses')" ] }, - "execution_count": 32, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -71004,7 +71100,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "cell_id": "00049-d31ef6c3-bb72-4f3c-b1cc-d057849bac26", "deepnote_cell_type": "code", @@ -71024,7 +71120,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "cell_id": "00050-d145250c-51cf-4afc-984d-66b3e2becef3", "deepnote_cell_type": "code", @@ -71052,16 +71148,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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//fcBAFlZWdDR0YGRkZHEuebm5sjKyqrNYRNRA1LZHuCVBeWZmZkMyomIiIiUgEE3Xv+y6uXlJXGsKlluDw8PpKam4uzZs4oaGhFRlcgKyn18fGRuNwZUHpQDDMyJiIiIaoJzulH1UvI3zZw5E4cPH8Yff/yBli1bCsctLCzw8uVL5ObmSmS7s7OzYWFhUVtDJiKqsqpkySsLyoGqBeZlGKATERERvSZX0F3d7bbqg9LSUsyaNQv79+/H6dOn0aZNG4n27t27Q1tbGydOnICrqysAIC0tDZmZmRCJRMoYMhGRTJUF5W+qrIy9DDPnRERERK/JFXQ3RB4eHoiOjsYvv/yCpk2bCvO0DQ0Noa+vD0NDQ0yePBleXl5o0aIFmjVrhlmzZkEkEnERNSJSe9UJ0AHZQXpiYiIGDhwo9VpTU1OZGXgG7URERKQOWF5eTeHh4QCAvn37Shzftm0bJkyYAAAIDQ1Fo0aN4OrqCrFYDGdnZ6xfv76OR0pEpHyygnRPT0+FLQ7HgJyIiIhUBRdSq6bS0tJKz9HT00NYWBjCwsLqYERERPWTPCu2c7V2IiIiUhXMdBMRkVqqbqn7m6q6IBwRERGRvJjpJiKieqey0vSUlBSp88mLioqgra0t9VrONSciIqLqYKabiIjqncqy4LLmk3OuOREREdUmrl5ORERUDZxrTkRERNXB8nIiIqJaxLnmRERE9CaWlxMREdUReeaaA7LnkzNLTkREpJqY6SYiIqneDhJzcnIksrEM9KpHnrnmAEvXiYiI1BEz3UREJFVVgkSqOyxdJyIiUj8MuomIiOqBykrX365SeBOz4ERERIrDoJuIiKge4KrqREREqolBNxERUQMgKyiXZ+9xgEE5ERGRLFxIjYiIqIGTJ0sOyA7KGZATEVFDx0w3ERERySRPUJ6YmFjjbdAABu1ERKT+mOkmIiIiucgKyuXZBg1gFp2IiNQfM91ERESkNMyiExFRfcegm4iIiFSWorLo169fx4sXL6QG7QzYiYiotjDoJiIionpJnoC9MrJWcyciInoTg24iIiKit1Q21zwnJ4fbqBERUZVwITUiIiKit8gz17yy0nWg8vJ1gIE7EVF9wUw3ERERUTUpcsX269evo6SkBGKxWGbgDgD5+fkoLS2Fjo4OdHV1qzb4/1WVwF8aPhAgIqo6Bt1EREREdaiyLHp15pvLOze9pjinnYio6hh0ExERETVglWXeK1LZnPa3MTNORA2ZXEE3EREREam3yjLvVVFZ4J6ZmcmF54ioweJCakRERERq4u3g9u2Ms7KCV3kDd5arE1F9xvLyavrjjz+watUqJCcn4/79+9i/fz+GDx8utJeWlsLf3x+bN29Gbm4uPvroI4SHh+O9995T3qCJiIioXqjKfHAiIlItDLqr6fnz5+jSpQsmTZqEESNGlGtfuXIlvv/+e0RGRqJNmzbw8/ODs7Mzrl27Bj09PSWMmIiIiEi55Nn3nKXnRKTuGHRX0+DBgzF48OAK20pLS7FmzRr4+vpi2LBhAIAff/wR5ubmOHDgAP7zn//U5VCJiIiIBNICX2kBb20Gu/Lse8754ESk7hh016J//vkHWVlZcHJyEo4ZGhrCwcEBCQkJDLqJiIhIaao777ouS9Vlja2yLHliYqLU/cwr24ucATsR1QUG3QDEYjHEYrHEMV1dXejq6larn6ysLACAubm5xHFzc3OhjYiIiIiqTp59zbmqOhGpAgbdAIKDgxEYGChxzN/fHwEBAcoZEBERERHJTZ6ydoBZdCKqHQy68foHrpeXl8Sx6ma5AcDCwgIAkJ2dDUtLS+F4dnY2unbtKtcYiYiIiKh2yZNFrwxXkieiMgy6UbNS8oq0adMGFhYWOHHihBBkP336FOfOncN///tfufsnIiIiItUgz4rsADPhRA0Jg+5qys/PR3p6uvD6n3/+QUpKClq0aAFra2vMnTsXS5cuxXvvvSdsGWZlZSWxlzcRERERqbfqLkz3NmbCiRoOBt3VdOHCBfTr1094XVaW7u7uju3bt+Obb77B8+fPMW3aNOTm5qJ379747bffuEc3ERERqbS3M7dvZ2qZma0e7k1ORGUYdFdT3759UVpaKrVdQ0MDixcvxuLFi+twVERERETyqcr8Zqo6eTLh/KyJ6hcG3UREREREdUxWJpzzwYnqFwbdRERERER1TFYmnFuZEdUvDLqJiIiIiFSIPFuZVRawZ2ZmMotOVMcYdBMRERER1ROVBezMohPVPQbdREREREQNhCKz6LICdkB20M6AneozBt1ERERERFQpeQJ2QHbQzoCd6jMG3UREREREpHCygnZ5AnbOUydVx6CbiIiIiIhUGvc9J3UmV9BNRERERESkTJXNNU9JSanR4nDMkFNtYaabiIiIiIjUlqLmmssqW2dATtXBoJuIiIiIiBqsmpSus2SdqoNBNxERERERUQWkZcFzcnLg6emJ69evo6SkRDj+drk6M+IEcE43ERERERFRheQtXWdGnABmuomIiIiIiIgURqO0tLS0phe/ePGiNseiNPr6+soeAhEREREREdVDjZQ9ACIiIiIiIvo/AQEB0NDQkPjTvn17oT0rKwvjxo2DhYUFmjRpgg8++AB79+6V6CMoKAiOjo5o3LgxjIyMyt3j8uXLcHNzQ6tWraCvr48OHTpg7dq15c4LCwtDhw4doK+vDzs7O/z444/lzlmzZg3s7Oygr6+PVq1awdPTU2Iu/B9//AEXFxdYWVlBQ0MDBw4ckLi+qKgICxYsQKdOndCkSRNYWVlh/PjxuHfvnnDO7du3MXnyZLRp0wb6+vp499134e/vj5cvX8r83DQ0NNCkSZNKP3NFYnk5ERERERGRirG3t8fx48eF11pa/xe6jR8/Hrm5uTh48CBMTEwQHR2N0aNH48KFC+jWrRsA4OXLlxg1ahREIhEiIiLK9Z+cnAwzMzNERUWhVatWiI+Px7Rp06CpqYmZM2cCAMLDw+Hj44PNmzejR48eSEpKwtSpU9G8eXO4uLgAAKKjo+Ht7Y2tW7fC0dERN2/exIQJE6ChoYGQkBAAwPPnz9GlSxdMmjQJI0aMKDeWgoICXLx4EX5+fujSpQuePHmCOXPm4PPPP8eFCxcAADdu3EBJSQk2btwIW1tbpKamYurUqXj+/DlWr14NAJg3bx5mzJgh0feAAQPQo0ePGn8daoNc5eWyNqFXJ3p6esoeAhEREREREYDXGdsDBw4gJSWlwnYDAwOEh4dj3LhxwjFjY2OsWLECU6ZMkTh3+/btmDt3LnJzcyu9r4eHB65fv46TJ08CABwdHfHRRx9h1apVwjlff/01zp07h7NnzwIAZs6cievXr+PEiRNSz3mThoYG9u/fj+HDh8scy/nz59GzZ0/8+++/sLa2rvCcVatWITw8HLdu3aqw/fLly+jatSv++OMP9OnTR+b9FImZbmqw3NzcZLb//PPPNe570aJFMtsXL15c476pbslakfTDDz+Uea08P9zXrVsns73sCTSpN7FYLLNdV1e3xn1v375dZvuECRNq3DdRZe7cuSOzvWXLlnU0koahqKhIapu2tnYdjoRq099//w0rKyvo6elBJBIhODhYCD4dHR2xa9cuDB06FEZGRoiJiUFhYSH69u0r1z3z8vLQokUL4bVYLC6XoNTX10dSUhKKioqgra0NR0dHREVFISkpCT179sStW7dw9OhRiQcCNR2LhoZGhaXx0sb7ti1btqBdu3ZKDbgBBt1EREREREQKJxaLyz1s1dXVrfABq4ODA7Zv3w47Ozvcv38fgYGB6NOnD1JTU9G0aVPExMRgzJgxMDY2hpaWFho3boz9+/fD1ta2xuOLj4/Hrl27cOTIEeGYs7MztmzZguHDh+ODDz5AcnIytmzZgqKiIjx8+BCWlpYYO3YsHj58iN69e6O0tBSvXr3CjBkz8O2339Z4LIWFhViwYAHc3NzQrFmzCs9JT0/HDz/8IJSWV9THjh074O3tXeNx1Bbu0w3un1efyMpKEhEREREpS3BwMAIDAyWO+fv7IyAgoNy5gwcPFv6/c+fOcHBwgI2NDWJiYjB58mT4+fkhNzcXx48fh4mJCQ4cOIDRo0cjLi4OnTp1qvbYUlNTMWzYMPj7+2PgwIHCcT8/P2RlZaFXr14oLS2Fubk53N3dsXLlSjRq9HpN7tOnT2PZsmVYv349HBwckJ6ejjlz5mDJkiXw8/Or9liKioowevRolJaWIjw8vMJz7t69i0GDBmHUqFGYOnVqhefs378fz549g7u7e7XHUNvkmtMtq5RFnbDshupaSUmJzPayH2J1TVXHpaql1vJ8XllZWTKvbdy4scx2aU99a4OPj4/Utt27d8u8Nj09vbaHU6/FxcXJbFd2ORyROpL1s1lZ/44RAdXLdFekR48ecHJywpQpU4SFxOzt7YV2Jycn2NraYsOGDRLXVTan+9q1a+jXrx+mTJmCoKCgCs8pKipCdnY2LC0tsWnTJixYsAC5ublo1KgR+vTpg169eknM+46KisK0adOQn59f7u+drDndZQH3rVu3cPLkSRgbG5c75969e+jbty969eqF7du3S/17PWDAADRr1gz79++vsL0usbwczHTXJ8x0ExEREZEqqk6A/bb8/HxkZGRg3LhxKCgoAFD+IZKmpmalCYG3Xb16Ff3794e7u7vUgBt4naQsW4th586d+Oyzz4T7FxQUVDgWAKhOfrcs4P77779x6tSpCgPuu3fvol+/fujevTu2bdsmNeD+559/cOrUKRw8eLDK91ckBt1goFafyfoelaPIA4B8T9KV9aQ9LS1NZrudnZ3C7i1rcbnKFpZT1UXD5Pk6WlhY1OJIaldwcHCN2qi8yv7OMZNNVPuYzab6YN68eXBxcYGNjQ3u3bsHf39/aGpqws3NDUZGRrC1tcX06dOxevVqGBsb48CBA4iNjcXhw4eFPjIzM/H48WNkZmaiuLhYWAnd1tYWBgYGSE1NRf/+/eHs7AwvLy+hCk9TUxOmpqYAgJs3byIpKQkODg548uQJQkJCkJqaisjISOE+Li4uCAkJQbdu3YTycj8/P7i4uAjBd35+vkQ13D///IOUlBS0aNEC1tbWKCoqwsiRI3Hx4kUcPnwYxcXFwnhatGgBHR0d3L17F3379oWNjQ1Wr16NBw8eCP29/XvV1q1bYWlpKVGmr0yc042aZbovX76MCxcuoKCgACYmJujXr59K/xLdUPABChERERGpuzt37sDNzQ2PHj2CqakpevfujcTERCEYPnr0KLy9veHi4oL8/HzY2toiMjISQ4YMEfpYtGiRRHBctn/3qVOn0LdvX+zZswcPHjxAVFQUoqKihPNsbGxw+/ZtAEBxcTG+++47pKWlQVtbG/369UN8fDxat24tnO/r6wsNDQ34+vri7t27MDU1hYuLi0Tm/MKFC+jXr5/w2svLCwDg7u6O7du34+7du0JWumvXrhKfRdl4Y2NjkZ6ejvT09HI7ILyZTCspKcH27dsxYcIEIehXNrnmdFe3fEFVVfeJ6K5duzB+/Hhs2LABDg4OWLNmDXbv3o20tDSYmZkpaJRUE6qa6VYWdc10E6krZf6dIyIiItUgV9Atb9CiKqpbJu/g4IAePXoIizuVlJSgVatWmDVrlkosSU+kimQthqaq5eMN1YoVK6S2LViwoA5HQkTySkhIkNqWnJws81obGxupbS4uLjUek7xUddFPIiJpWF6O6pWXFxcX4/z58zAzM5O4ztDQEJs3b0Z2drYihkhVxPJyIiIiIiJSJXJluuuL6izff+/ePbzzzjuIj4+HSCQSjn/zzTc4c+YMzp07p/Dxkmp48+v/NlmZBWVidoDqq/r6vf31119Lbfvuu+8Udt+MjAyZ7e+++67C7k1ERFTfKCTT7enpWe2MY2XX1KTPqhowYAD+/PNPiWMODg4VBlX5+fkAgDVr1iAmJkY4HhcXhzt37nD7MSVjppuIiIiIiFSJWpaX+/j4oLCwsNb6a9myJfLy8iSOSct0v3z5Etu2bYObm5vEhu7u7u545513GPQ1IGWrR9Y1Nzc3me0///yz1DZ1zfYRVaa+fm8rMpstCzPZREREtUctg+7CwsJaDW49PT3RrFmzKp2ro6OD7t2748SJE0LQXVJSghMnTnAxKCIiIiIiIpKgkKBbT0+v2mXWOTk5ihhKlVR3vMbGxggPD8fNmzdhYWGBixcv4uHDh7h16xbLy5WMlQZEREREVB/cvXsXCxYswK+//oqCggLY2tpi27Zt+PDDDwEAAQEB2LlzJ/7nf/5HSAwGBQXBwcEBAHD79m0sWbIEJ0+eRFZWFqysrPDll19i4cKF0NHREe7z119/wcPDA+fPn4epqSlmzZqFb775psIx7dy5E25ubhg2bBgOHDggHM/Pz4e3tzcOHDiAR48eoU2bNpg9ezZmzJghnFNYWIivv/4aO3fuhFgshrOzM9avXw9zc3MAwPbt2zFx4sQK75udnQ0zMzOcPn1aYr/vMvfv34eFhUX1PuA6pJCgOzg4uNrXKDNYrcl4161bh1WrVuH06dPo2rUrzpw5I3yDk+ooKiqS2qapqSnz2srKVQ8ePFijMVXFnj17pLbJKh+vTEpKisz2rl271rjvyshamImlrLXr0KFDMtuVtdVPZQ/F+NCydvHzLu/OnTtS21q2bFmHIyFVJWtRRkVOY3ny5InM9ubNmyvs3qSanjx5go8++gj9+vXDr7/+ClNTU/z9998S3wvt2rXDunXr0LZtW7x48QKhoaEYOHAg0tPTYWpqihs3bqCkpAQbN26Era0tUlNTMXXqVDx//hyrV68GADx9+hQDBw6Ek5MTNmzYgCtXrmDSpEkwMjLCtGnTJMZ0+/ZtzJs3D3369Ck3Xi8vL5w8eRJRUVFo3bo1fv/9d3z11VewsrLC559/DuD1vztHjhzB7t27YWhoiJkzZ2LEiBHC2lpjxozBoEGDJPqdMGECCgsLYWZmJnE8LS1NolL57XZVo5bl5devX1eJXxZGjBgh/P/OnTuxc+dOJY6GAGa6iYiIiEj9rVixAq1atcK2bduEY23atJE4Z+zYsRKvQ0JCEBERgb/++gsDBgzAoEGDJILYtm3bIi0tDeHh4ULQvWPHDrx8+RJbt26Fjo4O7O3tkZKSgpCQEImgu7i4GF988QUCAwMRFxeH3NxciXvHx8fD3d0dffv2BQBMmzYNGzduRFJSEj7//HPk5eUhIiIC0dHR6N+/PwBg27Zt6NChAxITE9GrVy/o6+tDX19f6PPBgwc4efIkIiIiyn0+ZmZmMDIyqvoHqmRqGXSXlJQwuKIq0dbWVvYQauTEiRNS20aOHFnjfhWZya4Ms9mS5M1Glz01rogiqzDkoQoPS+uaMrcyU9XP+/bt2zLbW7durbB7a2mp5a89VIeUtSijvJlsWZV96vq7kKqS9VkDtfd5Hzx4EM7Ozhg1ahTOnDmDd955B1999RWmTp1a4fkvX77Epk2bYGhoiC5dukjtNy8vDy1atBBeJyQk4OOPP5YoN3d2dsaKFSvw5MkT4Xtz8eLFMDMzw+TJkxEXF1euX0dHRxw8eBCTJk2ClZUVTp8+jZs3bwoxW3JyMoqKiuDk5CRc0759e1hbWyMhIQG9evUq1+ePP/6Ixo0bV/i7b9euXSEWi/H+++8jICAAH330kdT3rArq53KvREREREREKkQsFuPp06cSf8RicYXn3rp1C+Hh4Xjvvfdw7Ngx/Pe//8Xs2bMRGRkpcd7hw4dhYGAAPT09hIaGIjY2FiYmJhX2mZ6ejh9++AHTp08XjmVlZQlzqsuUvc7KygIAnD17FhEREdi8ebPU9/bDDz+gY8eOaNmyJXR0dDBo0CCEhYXh448/FvrS0dEpl502NzcX7vO2iIgIjB07ViL7bWlpiQ0bNmDv3r3Yu3cvWrVqhb59++LixYtSx6YKGsQj39reYoxUFysgiIiIiEgVBQcHIzAwUOKYv78/AgICyp1bUlKCDz/8EMuWLQMAdOvWDampqdiwYQPc3d2F8/r164eUlBQ8fPgQmzdvxujRo3Hu3Llyc5zv3r2LQYMGYdSoUVKz5RV59uwZxo0bh82bN0sN5oHXQXdiYiIOHjwIGxsb/PHHH/Dw8ICVlZVEdruqEhIScP36dfz0008Sx+3s7GBnZye8dnR0REZGBkJDQ8udq0rUNuiuTiCdk5ODHTt2KHhERLVn4cKFyh4CKdjQoUPlun7gwIFS265evSrzWnt7+xrfV9ZCVID6LkYVHR0tte3tOXPVUVmp6oMHD2S2m5qa1vjelVHWglGKLB+vjDwr2164cEFqW9lKwoqgzCkKynLs2DGZ7c7OznU0EvXBEvK6I89n7ePjAy8vL4ljurq6FZ5raWmJjh07Shzr0KED9u7dK3GsSZMmsLW1ha2tLXr16oX33nsPERER8PHxEc65d+8e+vXrB0dHR2zatEniegsLC2RnZ0scK3ttYWGBjIwM3L59W2LaW9nPJS0tLaSlpcHKygrffvst9u/fL/x+07lzZ6SkpGD16tVwcnKChYUFXr58idzcXIlsd3Z2doU/m7ds2YKuXbuie/fuFX4+b+rZsyfOnj1b6XnKpLZBd3X26vbx8VHZuW1Uu5jpJiIiIiJVpKurKzXIfttHH32EtLQ0iWM3b96EjY2NzOtKSkokStbv3r2Lfv36oXv37ti2bVu5B3UikQgLFy5EUVGR8EAhNjYWdnZ2aN68OfT19XHlyhWJa3x9ffHs2TOsXbsWrVq1QmFhIYqKisr1rampKQTo3bt3h7a2Nk6cOAFXV1cAr1cgz8zMhEgkkrguPz8fMTExVd5hKiUlBZaWllU6V1nUNuiujppsCUb1n6zMFgAkJibKbP/+++9rczgS1DVbqChvPq2tiDr+HZc3OzVz5sxaGkn11NfvTXmy2fJQZCa7MvUxQ6pIisxmy9IQv07MZBO9XgzT0dERy5Ytw+jRo5GUlIRNmzYJmernz58jKCgIn3/+OSwtLfHw4UOEhYXh7t27GDVqFIDXAXffvn1hY2OD1atXS1RXlWWXx44di8DAQEyePBkLFixAamoq1q5dKySy9PT08P7770uMrSxTXXZcR0cHn3zyCebPnw99fX3Y2NjgzJkz+PHHHxESEgIAMDQ0xOTJk+Hl5YUWLVqgWbNmmDVrFkQiUblF1Hbt2oVXr17hyy+/LPe5rFmzBm3atIG9vT0KCwuxZcsWnDx5Er///ru8H7lCqUzQraenJzMbnZOTU4ejUV8Nff46M91EREREpO569OiB/fv3w8fHB4sXL0abNm2wZs0afPHFFwBeZ5Fv3LiByMhIPHz4EMbGxujRowfi4uKEaWSxsbFIT09Henp6uYfmpaWlAF4Hw7///js8PDzQvXt3mJiYYNGiReX26K7Mzp074ePjgy+++AKPHz+GjY0NgoKCMGPGDOGc0NBQNGrUCK6urhCLxXB2dsb69evL9RUREYERI0ZUuCXYy5cv8fXXX+Pu3bto3LgxOnfujOPHj6Nfv37VGm9d0ygt+8RVnKenpxBQDRw4EPb29gywKvDm50SyqXKmmyTVx0w3ERERETUMKpPprg0NPcsLsCLgbRXtI1jm7UUj3tauXTuZ7bIWemFpXO16/vy5sodARERERFQj9Srors7iavVVQ180rqF//YmIiIiISLXUq6CbWGb7tj59+tSoTdkOHTokte3NLRsaispK+d9e3fNNu3fvlnmtr69vjcZERKpnxYoVMtsXLFhQRyMhIkVT1raHRDXRYIJulp43DMx0ExERERGRKmkwQTdLz4mIiIiIiKiuNZigm6guubm5yWz/+eefZbY3xBJyedjZ2UltU9Xy8adPn8psb9asmcLunZWVJbO9bO9Oqt/WrVsntU1Z+8DLi+XjRA0HS8hJnTDoVkMslZeO1QxEREREVB88e/YMfn5+2L9/P3JyctCtWzesXbsWPXr0AAAEBARg586d+J//+R/o6Oige/fuCAoKgoODg9DHxYsXsWDBApw/fx6amppwdXVFSEgIDAwMAADbt2/HxIkTK7x/dnY2zMzMcPbsWSxYsAA3btxAQUEBbGxsMH36dInFm4uLixEQEICoqChkZWXBysoKEyZMgK+vLzQ0NMr1PWPGDGzcuBGhoaGYO3cuAOD27dtYsmQJTp48KfTx5ZdfYuHChdDR0SnXR3p6Orp16wZNTU3k5ubW9GOuE/Uy6K4oKC3bSqs+BKw5OTnYsWOHsodBMvz555/KHgKpOEVmsitTHzPZd+7ckdnesmXLOhqJ+lDXbDYRUUMxZcoUpKam4qeffoKVlRWioqLg5OSEa9eu4Z133kG7du2wbt06tG3bFi9evEBoaCgGDhyI9PR0mJqa4t69e3BycsKYMWOwbt06PH36FHPnzsWECROwZ88eAMCYMWMwaNAgiftOmDABhYWFMDMzAwA0adIEM2fOROfOndGkSROcPXsW06dPR5MmTTBt2jQArxeyDA8PR2RkJOzt7XHhwgVMnDgRhoaGmD17tkT/+/fvR2JiIqysrCSO37hxAyUlJdi4cSNsbW2RmpqKqVOn4vnz51i9erXEuUVFRXBzc0OfPn0QHx9fq5+7ItTLoLui+dtlT2Lqw9zuhr4tmCzq/rUlIiIiInrx4gX27t2LX375BR9//DGA15ntQ4cOITw8HEuXLsXYsWMlrgkJCUFERAT++usvDBgwAIcPH4a2tjbCwsKEcvwNGzagc+fOSE9Ph62tLfT19aGvry/08eDBA5w8eRIRERHCsW7duqFbt27C69atW2Pfvn2Ii4sTgu74+HgMGzYMQ4cOFc75+eefkZSUJDHGu3fvYtasWTh27JhwbplBgwZJPABo27Yt0tLSEB4eXi7o9vX1Rfv27TFgwAAG3aQY3BZM9WVmZip7CDUia6syoH7ONd+0aZPM9rJ/TKhqlDVPWN5MdlxcnNQ2Vd5ekIiI6qdXr16huLgYenp6Esf19fVx9uzZcue/fPkSmzZtgqGhIbp06QIAEIvF0NHRkZj/XhZgnz17Fra2tuX6+fHHH9G4cWOMHDlS6tguXbqE+Ph4LF26VDjm6OiITZs24ebNm2jXrh0uX76Ms2fPIiQkRDinpKQE48aNw/z582Fvb1+lzyEvLw8tWrSQOHby5Ens3r0bKSkp2LdvX5X6UTauQEBERERERKRgYrEYT58+lfgjFosrPLdp06YQiURYsmQJ7t27h+LiYkRFRSEhIQH3798Xzjt8+DAMDAygp6eH0NBQxMbGwsTEBADQv39/ZGVlYdWqVXj58iWePHkCb29vAJDo400REREYO3asRPa7TMuWLaGrq4sPP/wQHh4emDJlitDm7e2N//znP2jfvj20tbXRrVs3zJ07F1988YVwzooVK6ClpVWu3Fya9PR0/PDDD5g+fbpw7NGjR5gwYQK2b9+u1Kl61aU2mW49PT2hpFraN2dVrs/JyakX87qpYiwvJyIiIiJVFBwcjMDAQIlj/v7+CAgIqPD8n376CZMmTcI777wDTU1NfPDBB3Bzc0NycrJwTr9+/ZCSkoKHDx9i8+bNGD16NM6dOwczMzPY29sjMjISXl5e8PHxgaamJmbPng1zc/MKV39PSEjA9evX8dNPP1U4nri4OOTn5yMxMRHe3t6wtbUVduyJiYnBjh07EB0dDXt7e6SkpGDu3LmwsrKCu7s7kpOTsXbtWly8eLHChdXedvfuXQwaNAijRo3C1KlTheNTp07F2LFjhZJ7daFRWlpaquxBVNfAgQNhb29f4bzt0NBQ4b8VKQvcGZxRZU6dOiWzvV+/fnU0kupJSEiQ2iYSiepwJJIuXLggte3DDz+Uq29ZD+J0dXXl6ltZWPauOmR97wLyf/8SqaqrV69KbatqaSgR/R+xWFzudxZdXd1Kf1d5/vw5nj59CktLS4wZMwb5+fk4cuRIhee+9957mDRpEnx8fCSOZ2dno0mTJtDQ0ECzZs2wc+dOjBo1SuKcyZMn4+LFi7h06VKl72Xp0qX46aefkJaWBgBo1aoVvL294eHhIXFOVFQUbty4gTVr1sDLy0si2C8uLkajRo3QqlUr3L59Wzh+79499O3bF7169cL27dslrjEyMkJ+fr7wurS0FCUlJdDU1MSmTZswadKkSseuDGqT6VYkZr7rDz5MISIiIiJVVJUAuyJNmjRBkyZN8OTJExw7dgwrV66Uem5JSUmFyQhzc3MAwNatW6Gnp4dPP/1Uoj0/Px8xMTFVXjvq7fsUFBSUy55ramqipKQEADBu3Dg4OTlJtDs7O2PcuHESW5bdvXsX/fr1Q/fu3bFt27ZyfSYkJKC4uFh4/csvv2DFihWIj4/HO++8U6WxKwODbtSPFc2pYrK+rpWtAK+qmeyyH17SKCub/eZiGhXx9fVV2L1VNZstz+JczGSXp6zFzpjJpoaK2Wz1Iet3g4rKiEk9HDt2DKWlpbCzs0N6ejrmz5+P9u3bY+LEiXj+/DmCgoLw+eefw9LSEg8fPkRYWBju3r0rkcFet24dHB0dYWBggNjYWMyfPx/Lly+HkZGRxL127dqFV69e4csvvyw3jrCwMFhbW6N9+/YAgD/++AOrV6+WmJvt4uKCoKAgWFtbw97eHpcuXUJISIiQeTY2NoaxsbFEv9ra2rCwsICdnR2A1wF33759YWNjg9WrV+PBgwfCuWXbnXbo0EGijwsXLqBRo0Z4//33q/vx1qkGF3Tr6emp7crSRERERETUMOTl5cHHxwd37txBixYt4OrqiqCgIGhra6O4uBg3btxAZGQkHj58CGNjY/To0QNxcXESD8ySkpLg7++P/Px8tG/fHhs3bsS4cePK3SsiIgIjRowoF4wDrx/q+Pj44J9//oGWlhbeffddrFixQmKBsx9++AF+fn746quvkJOTAysrK0yfPh2LFi2q8vuNjY1Feno60tPTy+1KooYzoiU0uKA7ODi41ve4Znm66mDFAhERERHVB6NHj8bo0aMrbNPT06vSdlk//vhjle4la6/rWbNmYdasWTKvb9q0KdasWYM1a9ZU6X4AJOZxA8CECRMwYcKEKl9f02uUocEF3dLIEzjn5ORgx44dtTwiqg2yHrDIWiAGUN2yOlUtEzt+/LjMdkWWl6sq7u9cu/h51p0XL17IbK9oKxkiUp6ioiKpbao6BYuoIWHQ/b/kmddd25nzijCbXjXMdBMRERERkSppkEH3m3t+A68z1WZmZrXWnyIwm14zGRkZUtv+/PNPmdeqaqZbVY0cOVJhfR86dEhmu4uLi8LuXR/duXNHZvvb86io4WEmm0i9qGoVHBG91iCD7reXwpc3YK7q0vry8PHxqZOMurpjppuIiIiIiFRJgwy61VFdBPZERERERERUuxh0U70ma19fS0tLufqWtS91Q1w0bObMmQrrm+XjtYvl40RE9Yu2trayh0BEMjDoJgD1Z6E2lpcTERERkboLCAhAYGCgxDE7OzvcuHEDAJCVlYX58+cjNjYWz549g52dHRYuXAhXV1fh/KCgIBw5cgQpKSnQ0dFBbm6uRH+XL1/G8uXLcfbsWTx8+BCtW7fGjBkzMGfOHInzwsLCsG7dOty+fRvW1tZYuHAhxo8fL3HOmjVrEB4ejszMTJiYmGDkyJEIDg6Gnp4eACA8PBzh4eHCNmH29vZYtGgRBg8eDAB4/Pgx/P398fvvvyMzMxOmpqYYPnw4lixZAkNDQwDA9u3bMXHixAo/r+zsbJiZmWHfvn0IDw9HSkoKxGIx7O3tERAQAGdn52p8+rWPQXcDIy24rq8LtYlEIqltle0jWFl2tUePHjUZEhGRgIvaEVFtKCkpkdrGRdbUl729vcSWrFpa/xe6jR8/Hrm5uTh48CBMTEwQHR2N0aNH48KFC+jWrRsA4OXLlxg1ahREIhEiIiLK9Z+cnAwzMzNERUWhVatWiI+Px7Rp06CpqSlUMIaHh8PHxwebN29Gjx49kJSUhKlTp6J58+bC78rR0dHw9vbG1q1b4ejoiJs3b2LChAnQ0NBASEgIgNf/ni1fvhzvvfceSktLERkZiWHDhuHSpUuwt7fHvXv3cO/ePaxevRodO3bEv//+ixkzZuDevXvYs2cPAGDMmDEYNGiQxHuYMGECCgsLhUWx//jjD3z66adYtmwZjIyMsG3bNri4uODcuXPC56IMDLrxevXxzMxMZQ+jTkjbGq2+LNTGTDcRERER1QdaWlqwsLCosC0+Ph7h4eHo2bMngNdTG0NDQ5GcnCwEl2WZ8u3bt1fYx6RJkyRet23bFgkJCdi3b58QdP/000+YPn06xowZI5xz/vx5rFixQgi64+Pj8dFHH2Hs2LEAgNatW8PNzQ3nzp0T+n47mRUUFITw8HAkJibC3t4e77//Pvbu3Su0v/vuuwgKCsKXX36JV69eQUtLC/r6+hK7azx48AAnT56UeKDwdlJt2bJl+OWXX3Do0CEG3coWHBxcLwJOeXChNiIiIiIi1fH333/DysoKenp6EIlECA4OhrW1NQDA0dERu3btwtChQ2FkZISYmBgUFhaib9++ct0zLy8PLVq0EF6LxWKhRLyMvr4+kpKSUFRUBG1tbTg6OiIqKgpJSUno2bMnbt26haNHj2LcuHEV3qO4uBi7d+/G8+fPZVal5uXloVmzZhIZ/jf9+OOPaNy4scxta0tKSvDs2TOJ96QMDLrfUl/mNkuTk5Oj7CHUKVkl82/Pa6muxo0by3W9LGKxWGqbrq6uwu5L9YOs/emB10+PSTWwfJyIagNLyNWDWCwu9zuerq5uhb/bOTg4YPv27bCzs8P9+/cRGBiIPn36IDU1FU2bNkVMTAzGjBkDY2NjaGlpoXHjxti/fz9sbW1rPL74+Hjs2rULR44cEY45Oztjy5YtGD58OD744AMkJydjy5YtKCoqwsOHD2FpaYmxY8fi4cOH6N27N0pLS/Hq1SvMmDED3377rUT/V65cgUgkQmFhIQwMDLB//3507NixwrE8fPgQS5YswbRp06SONyIiAmPHjpXIfr9t9erVyM/Px+jRo6v5adQuBt1vkVZ+XV+UZfTr68OF+vy1IyIiIiL1FRwcXG5xNH9/fwQEBJQ7t2yBMQDo3LkzHBwcYGNjg5iYGEyePBl+fn7Izc3F8ePHYWJiggMHDmD06NGIi4tDp06dqj221NRUDBs2DP7+/hg4cKBw3M/PD1lZWejVqxdKS0thbm4Od3d3rFy5UnjYc/r0aSxbtgzr16+Hg4MD0tPTMWfOHCxZsgR+fn5CX3Z2dkhJSUFeXh727NkDd3d3nDlzplzg/fTpUwwdOhQdO3as8LMBgISEBFy/fh0//fST1PcUHR2NwMBA/PLLL8Kcb2Vh0N3A6OnpwdPTs94unPY2WVmke/fuybz26tWrtT2cKlNUNltWBl2R96W6xUw2ySstLU1mu52dXR2NhIiqggupqQcfHx94eXlJHKvq715GRkZo164d0tPTkZGRgXXr1iE1NRX29vYAgC5duiAuLg5hYWHYsGFDtcZ17do1DBgwANOmTSu37a2+vj62bt2KjRs3Ijs7G5aWlti0aROaNm0KU1NTAK8D83HjxmHKlCkAgE6dOuH58+eYNm0aFi5cKHwP6ujoCJn47t274/z581i7di02btwo3O/Zs2cYNGgQmjZtiv3790vdDm/Lli3o2rUrunfvXmH7zp07MWXKFOzevRtOTk7V+jwUgUF3A1M2d7u+LJz2Nma6iYiIiEgVSSslr4r8/HxkZGRg3LhxKCgoAFD+gYqmpqbMBzAVuXr1Kvr37w93d3cEBQVJPU9bW1tIZu3cuROfffaZcP+CgoIKxwIApaWlUvssKSmRSAg9ffoUzs7O0NXVxcGDB8vNJS+Tn5+PmJgYqWtS/fzzz5g0aRJ27tyJoUOHSr1/XWLQ3UA1lIXTZM0DkdVWFYcOHarxtZX9QFTUU2lmsqsvJSVFatu+fftkXrt48eJaHg0pw4MHD2S2lz3pr09UNZN94cIFme0ffvhhHY2ESLUwm13/zJs3Dy4uLrCxscG9e/fg7+8PTU1NuLm5wcjICLa2tpg+fTpWr14NY2NjHDhwALGxsTh8+LDQR2ZmJh4/fozMzEwUFxcLv9PY2trCwMAAqamp6N+/P5ydneHl5YWsrCwArwPmsn/bbt68iaSkJDg4OODJkycICQlBamoqIiMjhfu4uLggJCQE3bp1E8rL/fz84OLiIgTfPj4+GDx4MKytrfHs2TNER0fj9OnTOHbsGIDXAffAgQNRUFCAqKgoPH36FE+fPgXw+t/Zsn4AYNeuXXj16hW+/PLLcp9bdHQ03N3dsXbtWjg4OAjvSV9fX9jvWxkYdBMREREREamQO3fuwM3NDY8ePYKpqSl69+6NxMREIRg+evQovL294eLigvz8fNja2iIyMhJDhgwR+li0aJFEcFy2ZdapU6fQt29f7NmzBw8ePEBUVBSioqKE82xsbHD79m0Ar1ca/+6775CWlgZtbW3069cP8fHxaN26tXC+r68vNDQ04Ovri7t378LU1BQuLi4SmfOcnByMHz8e9+/fh6GhITp37oxjx47h008/BQBcvHhR2GLs7cXg/vnnH4n7RUREYMSIETAyMir3uW3atAmvXr2Ch4cHPDw8hOPu7u5St06rCwy6/9ebc52Vpb4ublaXWF5OREREROpu586dMtvfe+89iX2tK7J9+3aZgWZAQIDUhcrKdOjQAZcuXZJ5jpaWFvz9/eHv7y/1nDf30q5I3759ZZaivyk+Pl5q2+nTp6vUR11j0P2/ysqtPT09lRb8NpTFzeoLExOTGl/LMrDatWnTJqlt8k4j6Nq1q9S2U6dO1bhfLmqnPupj+bi6Yvk4KZqs6ST8WUBENaWWQbeif+gpa9uw+rq4WV1ippuIiIiIiFSJWgbdyt5nTVEayuJm9YWDg4PC+l66dKnUtre3clAVCQkJMttFIpHC7i1vNrum5HlIdv36dZntsjLs8qqPC4OVLcQijbOzc437fvHihcx2fX39GvdNRKpFHX/+NVTcJo3UiVoG3XWB86vVEzPdRERERESkShh0S6GsEnMiIiIiIiKqPxh0v0VPTw+ZmZn1toS9oSnb7qAib249UJHKylVl7WNbWd+VbVmgqiXksty9e1fZQ1Ariiwfr0x9LJ+Up3y8MiwfJyJSPSwhJ3XCoPstwcHBCl3MjGXrisXqBCIiIiJSdwEBAQgMDJQ4Zmdnhxs3bgAAsrKyMH/+fMTGxuLZs2ews7PDwoUL4erqKpwfFBSEI0eOICUlBTo6OsjNzZXo7/Lly1i+fDnOnj2Lhw8fonXr1pgxYwbmzJkjcV5YWBjWrVuH27dvw9raGgsXLsT48eMlzlmzZg3Cw8ORmZkJExMTjBw5EsHBwdDT05PoZ9WqVcjKykKXLl3www8/oGfPnhL9JCQkYOHChTh37hw0NTXRtWtXHDt2THgAXtl7AgANDY1yx37++Wf85z//kfJpKx6D7jrGsvW6lZaWJrWtsmy0IjNn77//fo2vrWybqn79+tW4b3lkZWUp5b5ERERE9ZG9vT2OHz8uvNbS+r/Qbfz48cjNzcXBgwdhYmKC6OhojB49GhcuXEC3bt0AAC9fvsSoUaMgEokq3Cc7OTkZZmZmiIqKQqtWrRAfH49p06ZBU1MTM2fOBACEh4fDx8cHmzdvRo8ePZCUlISpU6eiefPmcHFxAQBER0fD29sbW7duhaOjI27evIkJEyZAQ0MDISEhAIBdu3bBy8sLGzZsgIODA9asWQNnZ2ekpaUJFcYJCQkYNGgQfHx88MMPP0BLSwuXL1+WqGqo7D2V2bZtGwYNGiS8NjIyqu7HX6sYdFeitjPTOTk5cvfBbLl0fKBBRERERPWBlpYWLCwsKmyLj49HeHi4kCn29fVFaGgokpOThaC7LFMubVrjpEmTJF63bdsWCQkJ2LdvnxB0//TTT5g+fTrGjBkjnHP+/HmsWLFCCLrj4+Px0UcfYezYsQBeJ7bc3Nxw7tw5oe+QkBBMnToVEydOBABs2LABR44cwdatW+Ht7Q3g9a4ws2fPFl4D5adzVvaeyhgZGUn97JSBQXclajszXRul68yWExERERHVb3///TesrKygp6cHkUiE4OBgWFtbAwAcHR2xa9cuDB06FEZGRoiJiUFhYSH69u0r1z3z8vLQokUL4bVYLJYoEQder3WSlJSEoqIiaGtrw9HREVFRUUhKSkLPnj1x69YtHD16FOPGjQPwOjudnJwMHx8foY9GjRrByclJ2HI2JycH586dwxdffAFHR0dkZGSgffv2CAoKQu/evav9Pjw8PDBlyhS0bdsWM2bMwMSJEyssO68rDLorULaYmqL6ljfwro1seUNx/vx5qW2KLB+vzG+//Saz/cMPP5TadvXqVZnXKqu8vOyJKBERERGVJxaLIRaLJY7p6upCV1e33LkODg7Yvn077OzscP/+fQQGBqJPnz5ITU1F06ZNERMTgzFjxsDY2BhaWlpo3Lgx9u/fD1tb2xqPLz4+Hrt27cKRI0eEY87OztiyZQuGDx+ODz74AMnJydiyZQuKiorw8OFDWFpaYuzYsXj48CF69+6N0tJSvHr1CjNmzMC3334LAHj48CGKi4thbm4ucT9zc3NhjvqtW7cAvJ7Lvnr1anTt2hU//vgjBgwYgNTUVLz33ntVfh+LFy9G//790bhxY/z+++/46quvkJ+fj9mzZ9f4s5EXg+4KKHIxteDgYLn78PHxUehib+qMFQBEREREpIqCg4PLLY7m7++PgICAcucOHjxY+P/OnTvDwcEBNjY2iImJweTJk+Hn54fc3FwcP34cJiYmOHDgAEaPHo24uDh06tSp2mNLTU3FsGHD4O/vj4EDBwrH/fz8kJWVhV69eqG0tBTm5uZwd3fHypUrhbnWp0+fxrJly7B+/Xo4ODggPT0dc+bMwZIlS+Dn51el+5eUlAAApk+fLpSgd+vWDSdOnMDWrVurFUO9ec9u3brh+fPnWLVqFYNuqp7aCNwbClXdekueccmTUX7y5InM9ubNm9e4b0WqbN7OhAkTpLZV9gP2+++/r8GIiIiIiKrHx8cHXl5eEscqynJXxMjICO3atUN6ejoyMjKwbt06pKamwt7eHgDQpUsXxMXFISwsDBs2bKjWuK5du4YBAwZg2rRp5X5H1dfXx9atW7Fx40ZkZ2fD0tISmzZtQtOmTYUtSP38/DBu3DhMmTIFANCpUyc8f/4c06ZNw8KFC2FiYgJNTU1kZ2dL9J2dnS3Mu7a0tAQAdOzYUeKcDh06yF2B7ODggCVLlkAsFlf5865tDLprCRc3Uw3MdBMRERGRKpJWSl4V+fn5yMjIwLhx41BQUACg/F7lmpqaQsa4qq5evYr+/fvD3d0dQUFBUs/T1tZGy5YtAQA7d+7EZ599Jty/oKCgwrEAQGlpKXR0dNC9e3ecOHECw4cPB/A6s33ixAkhmdS6dWtYWVmV23no5s2bEln/mkhJSUHz5s2VFnADDLqrpCoBdU5ODnbs2FFHI6KqkvWD5+0fDg2BqmayK5Ofn1/ja5nJrl3Hjh2T2a7MtRJkqeyXEFX9eRAXFye1rU+fPnU4EiIiqkvz5s2Di4sLbGxscO/ePfj7+0NTUxNubm4wMjKCra0tpk+fjtWrV8PY2BgHDhxAbGwsDh8+LPSRmZmJx48fIzMzE8XFxUhJSQEA2NrawsDAAKmpqejfvz+cnZ3h5eUlbP+qqakpZLFv3ryJpKQkODg44MmTJwgJCUFqaioiIyOF+7i4uCAkJATdunUTysv9/Pzg4uIiBN9eXl5wd3fHhx9+iJ49e2LNmjV4/vy5UEquoaGB+fPnw9/fH126dEHXrl0RGRmJGzduYM+ePVV+T4cOHUJ2djZ69eoFPT09xMbGYtmyZZg3b57CvlZVwaC7CqqyWjjnWBMRERERUW24c+cO3Nzc8OjRI5iamqJ3795ITEwUguGjR4/C29sbLi4uyM/Ph62tLSIjIzFkyBChj0WLFkkEx2VbiZ06dQp9+/bFnj178ODBA0RFRSEqKko4z8bGBrdv3wYAFBcX47vvvkNaWhq0tbXRr18/xMfHo3Xr1sL5vr6+0NDQgK+vL+7evQtTU1O4uLhIZM7HjBmDBw8eYNGiRcjKykLXrl3x22+/SSyuNnfuXBQWFsLT0xOPHz9Gly5dEBsbi3fffbfK70lbWxthYWHw9PREaWkpbG1the3KlEmjtLS0VKkjqIGyAPftQNjT0xOhoaHCf+W9R1lfFd3rbSwvVw1vf52Y6a4f1q1bJ7OdK6fXHWa66xYz3UREROqPmW4pyrb2ysnJgZmZWaXnc3Ez1bRlyxapbdOmTZN5rbu7u8z2N5+ykWIpMqiWZ5G2hkhVg+rKqGpQXRkG1kREROqPQbcUZUF0RWXjzGqrLi6kRkREREREqoRBdw1UZY43qQZZ2exDhw7JvPajjz6q7eFU2YsXL6S26evr1+FIJEVHR0ttGzt2bB2OpPYwk01EREREiqSe9XZEREREREREaoCZ7nqCJe+vsQKBiIiIiIhUCYPuKnozqM3JyanSeXWJ+4TXvvfee09p91ZmCbks7dq1U/YQqBYkJCTIbBeJRHU0EpJHZVNkXFxc6mgkREREJAuD7ip6cx63rD25lTXf28fHh3uFg5luIiIiIlJ/4eHhCA8PF/bLtre3x6JFizB48GAAr2OOr7/+Gjt37oRYLIazszPWr18vse/17Nmz8eeffyI1NRUdOnRASkqKxD3S0tIwY8YMXLt2DXl5ebCyssLYsWPh7+8PbW1tAMDVq1exaNEiJCcn499//0VoaCjmzp0r0U9xcTECAgIQFRWFrKwsWFlZYcKECcL+3WWuX7+OBQsW4MyZM3j16hU6duyIvXv3wtraWqK/0tJSDBkyBL/99hv279+P4cOHV/k9qSoG3fUEtyyrmKy9eSvLAlW2H7E8Ktsz+M8//5TapswthD788EOl3ZuqrrIM6NChQ+toJKRI8mayZf0cUuQWa1evXpXZbm9vr7B7P3nyRGpb8+bNFXZfIlWWlZUltc3CwqIOR0JvatmyJZYvX4733nsPpaWliIyMxLBhw3Dp0iXY29vD09MTR44cwe7du2FoaIiZM2dixIgR5X6HnDRpEs6dO4e//vqr3D20tbUxfvx4fPDBBzAyMsLly5cxdepUlJSUYNmyZQCAgoICtG3bFqNGjZKa4FuxYgXCw8MRGRkJe3t7XLhwARMnToShoSFmz54NAMjIyEDv3r0xefJkBAYGolmzZrh69Sr09PTK9bdmzRqJYP1tst6TqmLQrSCcY60czHQTERERkbp7+8FqUFAQwsPDkZiYiJYtWyIiIgLR0dHo378/AGDbtm3o0KEDEhMT0atXLwDA999/DwB48OBBhQFq27Zt0bZtW+G1jY0NTp8+jbi4OOFYjx490KNHDwCAt7d3hWONj4/HsGHDhIf6rVu3xs8//4ykpCThnIULF2LIkCFYuXKlcOzdd98t11dKSgq+++47XLhwAZaWluXaK3tPqopBdw3o6elJfdJTNt+b24oREREREZG8iouLsXv3bjx//hwikQjJyckoKiqCk5OTcE779u1hbW2NhIQEIeiurvT0dPz2228YMWJEta5zdHTEpk2bcPPmTbRr1w6XL1/G2bNnERISAuB1ZdWRI0fwzTffwNnZGZcuXUKbNm3g4+MjUTpeUFCAsWPHIiwsrN5VWTDoroSenh4yMzNhZmYmHJNVys151apFkSWS8qhsXMosISdJyirBLZvDJU3r1q2ltnEBrbp14cIFqW2VTcmQVdYJKLa0U1k/HxVZPl4ZlpBTfSXPv1XqGtwo699neYjFYojFYoljurq60NXVrfD8K1euQCQSobCwEAYGBti/fz86duyIlJQU6OjowMjISOJ8c3PzSv9dqYijoyMuXrwIsViMadOmYfHixdW63tvbG0+fPkX79u2hqamJ4uJiBAUF4YsvvgDwOimZn5+P5cuXY+nSpVixYoUQ3J86dQqffPIJgNdxlKOjI4YNG1bt96DqGHRXIjg4WO0C6YZc2s7qAiIiIiJSRcHBwQgMDJQ45u/vj4CAgArPt7OzQ0pKCvLy8rBnzx64u7vjzJkztT6uXbt24dmzZ7h8+TLmz5+P1atX45tvvqny9TExMdixYweio6Nhb2+PlJQUzJ07F1ZWVnB3dxcekAwbNkyIq7p27Yr4+Hhs2LABn3zyCQ4ePIiTJ0/i0qVLtf7+VAGD7lpWVnoua1sxRWNpe+3YvHmzzHZnZ+c6Ggkpk7KelsvKZJNqkWeBQXXNMBGRalHVzK4iqeN79vHxgZeXl8QxaVluANDR0YGtrS0AoHv37jh//jzWrl2LMWPG4OXLl8jNzZXIdmdnZ9fo35VWrVoBADp27Iji4mJMmzYNX3/9NTQ1Nat0/fz58+Ht7Y3//Oc/AIBOnTrh33//RXBwMNzd3WFiYgItLS107NhR4roOHTrg7NmzAICTJ08iIyOjXPbe1dUVffr0wenTp6v9vlQJg+5aVlZ6Xp3seG1nppUZ8Nelij43PmwgIiIiIlUkq5S8KkpKSiAWi9G9e3doa2vjxIkTcHV1BfB6+6/MzEyIRCK5xlhSUoKioiKUlJRUOeguKCgo9xBEU1NTyHDr6OigR48eSEtLkzjn5s2bsLGxAfC6RH3KlCkS7Z06dUJoaGi9mDbHoFsF1HZmWt3K4WuKGX0iIiIiqo98fHwwePBgWFtb49mzZ4iOjsbp06dx7NgxGBoaYvLkyfDy8kKLFi3QrFkzzJo1CyKRSGIRtfT0dOTn5yMrKwsvXrwQ9rTu2LEjdHR0sGPHDmhra6NTp07Q1dXFhQsX4OPjgzFjxgj7dL98+RLXrl0T/v/u3btISUmBgYGBkIV3cXFBUFAQrK2tYW9vj0uXLiEkJASTJk0SxjJ//nyMGTMGH3/8Mfr164fffvsNhw4dEjLYFhYWFWbpra2t0aZNmyq/J1XFoLsekrW6en0ib0a/sr2ymzRpIlf/REREREQ1kZOTg/Hjx+P+/fswNDRE586dcezYMXz66acAXld3NmrUCK6urhCLxXB2dsb69esl+pgyZYrEHPBu3boBAP755x+0bt0aWlpaWLFiBW7evInS0lLY2Nhg5syZEnHEvXv3hOsAYPXq1Vi9ejU++eQTIWD+4Ycf4Ofnh6+++go5OTmwsrLC9OnTsWjRIuG6//f//h82bNiA4OBgzJ49G3Z2dti7dy969+5drc+lsvekqjRKS0tLlT2I6ir7Rng7y+np6YnQ0FDhv4q+nyzVKRnPycnBjh07ajS2hkze8vLKgu6JEyfKbI+MjKzyvYiIiIiIqGFipltBZG0r9raGkJVWhKp8xvJsJ/Hvv//KbF+xYoXUtgULFsgeWCUSEhKktsk7V4eIiIjqF3XcPouoIWHQrYYa8pZgleEcbyIiIiIiUiUMulVAdedgsxy96mQ93Y2Li5N5bWUZ5bLtFRSB2Wz1ULZ4hzRdu3atk3EQEVHDVlxcLLWNmW4i5WPQrQKqU4oOsBydiIiIiIhIXTDoVkMNZXXymmB5ORERERERqRIG3VWgp6eHzMxMZQ9DUN3MeEMmq4T88uXLMq+V53OubGV0ZZV6HTp0SGa7i4tLHY1E0vbt22W2T5gwoU7GUV0sHyciIlVQtqcyEakmBt1VEBwcXOeZZS6WVjPMdBMRERGRugsICEBgYKDEMTs7O9y4cQMAkJWVhfnz5yM2NhbPnj2DnZ0dFi5cCFdXV+H8oKAgHDlyBCkpKdDR0UFubq5Ef5cvX8by5ctx9uxZPHz4EK1bt8aMGTMwZ84cifPCwsKwbt063L59G9bW1li4cCHGjx8vcc6aNWsQHh6OzMxMmJiYYOTIkQgODoaenh4AIDw8HOHh4bh9+zYAwN7eHosWLcLgwYOFPgoLC/H1119j586dEnuPm5ubV3m8Z8+exYIFC3Djxg0UFBTAxsYG06dPV3qVMINuOSgyMOZiabWjT58+Utv09fUVdl9VXbRELBYrrG9Z25wBsheH69GjR20Ph+qhq1evSm2zt7eXeW1GRobM9nfffbdGYyIi9ZKfny+z3cDAoI5GQlQ5e3t7HD9+XHitpfV/odv48eORm5uLgwcPwsTEBNHR0Rg9ejQuXLiAbt26AQBevnyJUaNGQSQSISIiolz/ycnJMDMzQ1RUFFq1aoX4+HhMmzYNmpqamDlzJoDXwbKPjw82b96MHj16ICkpCVOnTkXz5s2FCsno6Gh4e3tj69atcHR0xM2bNzFhwgRoaGggJCQEANCyZUssX74c7733HkpLSxEZGYlhw4bh0qVLwr/hnp6eOHLkCHbv3g1DQ0PMnDkTI0aMwJ9//lnl8TZp0gQzZ85E586d0aRJE5w9exbTp09HkyZNMG3atNr+ElUZg245FBYWKiyzquynMUREREREpDxaWlqwsLCosC0+Ph7h4eHo2bMnAMDX1xehoaFITk4Wgu6yTLm0aXyTJk2SeN22bVskJCRg3759QhD7008/Yfr06RgzZoxwzvnz57FixQoh6I6Pj8dHH32EsWPHAgBat24NNzc3nDt3Tuj77SmMQUFBCA8PR2JiIuzt7ZGXl4eIiAhER0ejf//+AIBt27ahQ4cOSExMRK9evao03m7dugnvv2ws+/btQ1xcHINuKo+LpdUMy8uJiIiIqD74+++/YWVlBT09PYhEIgQHB8Pa2hoA4OjoiF27dmHo0KEwMjJCTEwMCgsL0bdvX7numZeXhxYtWgivxWKxUCJeRl9fH0lJSSgqKoK2tjYcHR0RFRWFpKQk9OzZE7du3cLRo0cxbty4Cu9RXFyM3bt34/nz50IlZHJyMoqKiuDk5CSc1759e1hbWyMhIQG9evWq0njfdunSJcTHx2Pp0qVV/gwUgUF3Nb1ZUp6Tk6Ow+3CxNMX78MMPlT0EqWSVwspTBjty5MgaX1sZefYW37Vrl8z2xYsX17hvqj8qKyGXJT09XWa7osrLy+auSdO6dWuF3JeIKsbycVImsVhcbqqfrq4udHV1y53r4OCA7du3w87ODvfv30dgYCD69OmD1NRUNG3aFDExMRgzZgyMjY2hpaWFxo0bY//+/bC1ta3x+OLj47Fr1y4cOXJEOObs7IwtW7Zg+PDh+OCDD5CcnIwtW7agqKgIDx8+hKWlJcaOHYuHDx+id+/eKC0txatXrzBjxgx8++23Ev1fuXIFIpEIhYWFMDAwwP79+9GxY0cAr+eo6+jowMjISOIac3NzZGVlVXm8ZVq2bIkHDx7g1atXCAgIwJQpU2r8udQGtQy6lbma+Jsl5VXNRHNRtLrDTDcRERERqaLg4OByi6P5+/sjICCg3LlvLjDWuXNnODg4wMbGBjExMZg8eTL8/PyQm5uL48ePw8TEBAcOHMDo0aMRFxeHTp06VXtsqampGDZsGPz9/TFw4EDhuJ+fH7KystCrVy+UlpbC3Nwc7u7uWLlypbCG0enTp7Fs2TKsX78eDg4OSE9Px5w5c7BkyRL4+fkJfdnZ2SElJQV5eXnYs2cP3N3dcebMGSHwro3xlomLi0N+fj4SExPh7e0NW1tbuLm5Vfs+tUUtg25lrCYuD0XO/SbZZs+eLbXt+++/r8ORVE9DW9TJzMxM2UOoVy5cuCCzXZWrPBTF2dlZKfdlJpuI6oKsrUpVdXHXhsjHxwdeXl4SxyrKclfEyMgI7dq1Q3p6OjIyMrBu3TqkpqYKVWBdunRBXFwcwsLCsGHDhmqN69q1axgwYACmTZsGX19fiTZ9fX1s3boVGzduRHZ2NiwtLbFp0yY0bdoUpqamAF4H5uPGjROyyZ06dcLz588xbdo0LFy4UPge1NHRETLx3bt3x/nz57F27Vps3LgRFhYWePnyJXJzcyWy3dnZ2eXmtcsab5k2bdoIY8nOzkZAQACDbnVV1XnXiixDL8Ns+mt8uEFEREREqkhaKXlV5OfnIyMjA+PGjUNBQQGA8g9UNDU1ZT6AqcjVq1fRv39/uLu7IygoSOp52traaNmyJQBg586d+Oyzz4T7FxQUVDgWACgtLZXaZ0lJiVBu3717d2hra+PEiRPCtmdpaWnIzMyUmMJY1fFKu4+yMOiWQ1XnXddFVp7ZdCIiIiKi+mHevHlwcXGBjY0N7t27B39/f2hqasLNzQ1GRkawtbXF9OnTsXr1ahgbG+PAgQOIjY3F4cOHhT4yMzPx+PFjZGZmori4GCkpKQAAW1tbGBgYIDU1Ff3794ezszO8vLyEudOamppCFvvmzZtISkqCg4MDnjx5gpCQEKSmpiIyMlK4j4uLC0JCQtCtWzehvNzPzw8uLi5C8O3j44PBgwfD2toaz549Q3R0NE6fPo1jx44BAAwNDTF58mR4eXmhRYsWaNasGWbNmgWRSCQsolaV8YaFhcHa2hrt27cHAPzxxx9YvXq1zOrXusCguw7UxUrkdZFNV0fjx4+v8bXy7DtN1ZObm6vsIdQrDbF8nIioIWMJef1z584duLm54dGjRzA1NUXv3r2RmJgoBJdHjx6Ft7c3XFxckJ+fD1tbW0RGRmLIkCFCH4sWLZIIjsu20jp16hT69u2LPXv24MGDB4iKikJUVJRwno2NjbAQaHFxMb777jukpaVBW1sb/fr1Q3x8vMT0KV9fX2hoaMDX1xd3796FqakpXFxcJDLROTk5GD9+PO7fvw9DQ0N07twZx44dw6effiqcExoaikaNGsHV1RVisRjOzs5Yv3690F6V8ZaUlMDHxwf//PMPtLS08O6772LFihWYPn26HF8N+WmUysr5qzBPT89ymd2yYxW11db9FNF3bWB5+Wtvf21kzW2tLDBh0F13KtvGQdp8HSIiIiIiVcdMdz3BLcYq9ujRoxpfq8ygetGiRVLb6uP2WQyqG4ayEjJplLXYGREREZEiMehWEGaelUMVqxCIiIiIiKjhYtCtIFzYTDVcu3ZNapsqZ9XGjBmj7CE0GCxtrzuq/HeOiIiISFG46gIRERERERGRgjDTXUVlK5CXrRJeWfl4VVYTZwl67WN1ARERERERqRIG3VVUtlBZ2dZflZWPV2WLMJagK96dO3eUPYQKVTauoqKiOhoJsXy8fpC1UwHAbdSIqH4rKSmR2sbtxIiUj0G3glRlb25F7K3d0LPnfIhBRERERESqhEG3glRlC6+qZMOri9lzSQUFBcoeQoVatmwpV7ss8uxNTqSu+L2tOiqr5GnWrJlc7UTyyM/Pl9luYGBQRyOpXcxm1z8BAQEIDAyUOGZnZ4cbN24AALKysjB//nzExsbi2bNnsLOzw8KFC+Hq6iqcHxQUhCNHjiAlJQU6OjrIzc2V6O/y5ctYvnw5zp49i4cPH6J169aYMWMG5syZI3FeWFgY1q1bh9u3b8Pa2hoLFy7E+PHjJc5Zs2YNwsPDkZmZCRMTE4wcORLBwcHQ09OT6GfVqlXIyspCly5d8MMPP6Bnz57l3ntpaSmGDBmC3377Dfv378fw4cOFttmzZ+PPP/9EamoqOnTogJSUlHLX//XXX/Dw8MD58+dhamqKWbNm4ZtvvpH5eSsag24lqko2vLoUkT0nIiIiIqK6ZW9vj+PHjwuvtbT+L3QbP348cnNzcfDgQZiYmCA6OhqjR4/GhQsX0K1bNwDAy5cvMWrUKIhEIkRERJTrPzk5GWZmZoiKikKrVq0QHx+PadOmQVNTEzNnzgQAhIeHw8fHB5s3b0aPHj2QlJSEqVOnonnz5nBxcQEAREdHw9vbG1u3boWjoyNu3ryJCRMmQENDAyEhIQCAXbt2wcvLCxs2bICDgwPWrFkDZ2dnpKWlwczMTGJca9asgYaGhtTPZdKkSTh37hz++uuvcm1Pnz7FwIED4eTkhA0bNuDKlSuYNGkSjIyMMG3atKp+9LWOQbcSVSUbXl3SgviGUnbOLD8RERER1QdaWlqwsLCosC0+Ph7h4eFCptjX1xehoaFITk4Wgu6yTPn27dsr7GPSpEkSr9u2bYuEhATs27dPCLp/+uknTJ8+XdjOtm3btjh//jxWrFghBN3x8fH46KOPMHbsWABA69at4ebmhnPnzgl9h4SEYOrUqZg4cSIAYMOGDThy5Ai2bt0Kb29v4byUlBR89913uHDhAiwtLcuN+fvvvwcAPHjwoMKge8eOHXj58iW2bt0KHR0d2NvbIyUlBSEhIQy6SfEaatn5gwcPlD2ECiUkJMhsF4lENe6bZbZEpEzyTI8hUjR1LR+n+kEsFkMsFksc09XVha6uboXn//3337CysoKenh5EIhGCg4NhbW0NAHB0dMSuXbswdOhQGBkZISYmBoWFhejbt69cY8zLy0OLFi0kxvxmiTgA6OvrIykpCUVFRdDW1oajoyOioqKQlJSEnj174tatWzh69CjGjRsH4HXGPTk5GT4+PkIfjRo1gpOTk8TvxAUFBRg7dizCwsKkPmyoTEJCAj7++GPo6OgIx5ydnbFixQo8efIEzZs3r1G/8mLQXc9IK1mvatm5umfEG+KDBSIiIiJSfcHBweXmafv7+yMgIKDcuQ4ODti+fTvs7Oxw//59BAYGok+fPkhNTUXTpk0RExODMWPGwNjYGFpaWmjcuDH2798PW1vbGo8vPj4eu3btwpEjR4Rjzs7O2LJlC4YPH44PPvgAycnJ2LJlC4qKivDw4UNYWlpi7NixePjwIXr37o3S0lK8evUKM2bMwLfffgsAePjwIYqLi2Fubi5xP3Nzc2GOOvC6YtfR0RHDhg2r8XvIyspCmzZtyt2nrI1BN9UKaSXrVZ07Xt8y4qampjW+trLFgOTJ5siTySYiIiIi9ePj4wMvLy+JY9Ky3IMHDxb+v3PnznBwcICNjQ1iYmIwefJk+Pn5ITc3F8ePH4eJiQkOHDiA0aNHIy4uDp06dar22FJTUzFs2DD4+/tj4MCBwnE/Pz9kZWWhV69eKC0thbm5Odzd3bFy5UphAb/Tp09j2bJlWL9+PRwcHJCeno45c+ZgyZIl8PPzq9L9Dx48iJMnT+LSpUvVHrs6YNDdQFR10bayjLi6Zrzr0wMDIiIiIqo/ZJWSV8bIyAjt2rVDeno6MjIysG7dOqSmpsLe3h4A0KVLF8TFxSEsLAwbNmyoVt/Xrl3DgAEDMG3aNPj6+kq06evrY+vWrdi4cSOys7NhaWmJTZs2oWnTpkJyy8/PD+PGjcOUKVMAAJ06dcLz588xbdo0LFy4ECYmJtDU1ER2drZE39nZ2UIZ+cmTJ5GRkQEjIyOJc1xdXdGnTx+cPn26Su/FwsKiwvuUtSkLg+4GoqqLtpUF5vUt401EREREpK7y8/ORkZGBcePGCVvivr1VnKamJkpKSqrV79WrV9G/f3+4u7sjKChI6nna2tpClefOnTvx2WefCfcvKCiocCzA6+2/dHR00L17d5w4cULY/qukpAQnTpwQFmzz9vYWgvYynTp1QmhoqLBgW1WIRCIsXLhQmG8OALGxsbCzs1NaaTnAoJveUpYRry9bj+3atUtqW3h4uMxruRgQEclLkdNUiIgaMlnBZX3Yt3zevHlwcXGBjY0N7t27B39/f2hqasLNzQ1GRkawtbXF9OnTsXr1ahgbG+PAgQOIjY3F4cOHhT4yMzPx+PFjZGZmori4WNjT2tbWFgYGBkhNTUX//v3h7OwMLy8vZGVlAXgdMJdlsW/evImkpCQ4ODjgyZMnCAkJQWpqKiIjI4X7uLi4ICQkBN26dRPKy/38/ODi4iIE315eXnB3d8eHH36Inj17Ys2aNXj+/LmwmrmFhUWFmWhra2uJOdrp6enIz89HVlYWXrx4Ibynjh07QkdHB2PHjkVgYCAmT56MBQsWIDU1FWvXrlV6MpFBdzXVt6D0bWUZcR8fn1rfQ7wuKPsvFBERERGRvO7cuQM3Nzc8evQIpqam6N27NxITE4Vg+OjRo/D29oaLiwvy8/Nha2uLyMhIDBkyROhj0aJFEsFx2VZip06dQt++fbFnzx48ePAAUVFRiIqKEs6zsbHB7du3AQDFxcX47rvvkJaWBm1tbfTr1w/x8fFo3bq1cL6vry80NDTg6+uLu3fvwtTUFC4uLhKZ8zFjxuDBgwdYtGgRsrKy0LVrV/z222/lFlerzJQpU3DmzJly7+mff/5B69atYWhoiN9//x0eHh7o3r07TExMsGjRIqVuFwYAGqWlpaVKHUENeXp6lguwyo5V1FYX9yci6datWye1ray0iEgVyfreBfj9S0SkDPU90031CzPdCqauC5KpKz4IISIiIiIiVcKgW8G4IBkREREREVHDxaBbDlXJYtfXud/q4tChQ1LbqrMSYm07deqUzPZ+/frV0UjqTlFRkbKHQP8rLS1NZrudnV0djUQ9sHyciFRdQyy1rq/vi+onBt1yqEoWW10XJFNXrCogIiIiIiJVwqBbwaq6PzYphqxsdmX7GFb2BFVWtrCyTKEiM9mysuiV3XfPnj0y20eOHFmjMQFAnz59anwt1S51zWSXbQtSka5du9bZOIiIiIiqg0G3GuBibFXHTDcREREREakSBt1KUN0gOicnBzt27FDgiBomeecCxcbGSm1TZiZRnix6RkZGLY5E0ocffqiwvhUpPz9fapuBgUEdjoSYzSYiqhjnN9c/4eHhCA8PF/bLtre3x6JFizB48GDhnISEBCxcuBDnzp2DpqYmunbtimPHjkFfXx8A8PnnnyMlJQU5OTlo3rw5nJycsGLFClhZWQl9/PXXX/Dw8MD58+dhamqKWbNm4ZtvvqlwTDt37oSbmxuGDRuGAwcOAHi9Zo+vry+OHj2KW7duwdDQEE5OTli+fLlwn9OnT0v9/TQpKQk9evQAABw7dgz+/v64evUq9PT08PHHH+O7774T9gS/f/8+vv76a1y4cAHp6emYPXs21qxZU9OPuE4x6FaC6q5ozjnhREREREQNR8uWLbF8+XK89957KC0tRWRkJIYNG4ZLly7B3t4eCQkJGDRoEHx8fPDDDz9AS0sLly9flngA069fP3z77bewtLTE3bt3MW/ePIwcORLx8fEAgKdPn2LgwIFwcnLChg0bcOXKFUyaNAlGRkaYNm2axHhu376NefPmlZsuWFBQgIsXL8LPzw9dunTBkydPMGfOHHz++ee4cOECAMDR0RH379+XuM7Pzw8nTpwQkjL//PMPhg0bBi8vL+zYsQN5eXnw9PTEiBEjcPHiRQCAWCyGqakpfH191a66lUG3GtDT02PgXUXq9heQiIiIiOhtb69LFBQUhPDwcCQmJsLe3h6enp6YPXs2vL29hXPerrR8M36wsbGBt7c3hg8fjqKiImhra2PHjh14+fIltm7dCh0dHdjb2yMlJQUhISESQXdxcTG++OILBAYGIi4uDrm5uUKboaFhuerPdevWoWfPnsjMzIS1tTV0dHRgYWEhtBcVFeGXX37BrFmzoKGhAQBITk5GcXExli5dKjw4mDdvHoYNGyaMt3Xr1li7di0AYOvWrTX5WJWGQbecajLfurrbiHExtporK8mpSFmpSk3Vx22EWrVqpewhqJz6WEL+5MkTme3Nmzevo5FUj7IWUjt27JjMdmdnZ4Xdm4ioKl68eCG1razUuL5Rx23SxGIxxGKxxDFdXV3o6urKvK64uBi7d+/G8+fPIRKJkJOTg3PnzuGLL76Ao6MjMjIy0L59ewQFBaF3794V9vH48WPs2LEDjo6O0NbWBvC6PP3jjz+Gjo6OcJ6zszNWrFiBJ0+eCL8PLF68GGZmZpg8eTLi4uIqfZ95eXnQ0NCAkZFRhe0HDx7Eo0ePMHHiROFY9+7d0ahRI2zbtg0TJkxAfn4+fvrpJzg5OQnjVWcMumtIT08PmZmZMDMzq3Z21dPTk4ujKQgz3URERESkioKDgxEYGChxzN/fHwEBARWef+XKFYhEIhQWFsLAwAD79+9Hx44dkZiYCAAICAjA6tWr0bVrV/z4448YMGAAUlNT8d577wl9LFiwAOvWrUNBQQF69eqFw4cPC21ZWVlo06aNxD3Nzc2FtubNm+Ps2bOIiIiQ+eD7TYWFhViwYAHc3NzQrFmzCs+JiIiAs7MzWrZsKRxr06YNfv/9d4wePRrTp09HcXExRCIRjh49WqX7qjoG3TUUHBxc45LvsoCdi6MpnrzZ7Ibm1q1byh4C1QFVzWRXRlkLqdXXTLasigdlfo/Iqiyor18LRZK1QOa7775bhyMhRaqv2WxZVDWbLYuPjw+8vLwkjsnKctvZ2SElJQV5eXnYs2cP3N3dcebMGSHLP336dCFb3K1bN5w4cQJbt26VqJKdP38+Jk+ejH///ReBgYEYP348Dh8+LJR1y/Ls2TOMGzcOmzdvhomJSaXnFxUVYfTo0SgtLUV4eHiF59y5cwfHjh1DTEyMxPGsrCxMnToV7u7ucHNzw7Nnz7Bo0SKMHDkSsbGxVRqvKmPQrQTyBOxERERERKR+qlJK/iYdHR3Y2toCeF1+ff78eaxdu1aYx92xY0eJ8zt06IDMzEyJYyYmJjAxMUG7du3QoUMHtGrVComJiRCJRLCwsEB2drbE+WWvLSwskJGRgdu3b0vMLy8L+LW0tJCWliY8vCsLuP/991+cPHlSapZ727ZtMDY2xueffy5xPCwsDIaGhli5cqVwLCoqCq1atcK5c+fQq1evqn1oKopBt5JwcTTFYHk5EREREdVHJSUlEIvFaN26NaysrJCWlibRfvPmTYktxSq6HoAwr1wkEmHhwoXCQmXA6y1x7ezs0Lx5c+jr6+PKlSsSffj6+uLZs2dYu3atsBZQWcD9999/49SpUzA2Nq7w/qWlpdi2bRvGjx9fbp52QUFBueoFTU1NiXGrMwbdSsLF0ZSvsrkpqroncHR0tMz2sWPH1rhvX1/fGl8rjxUrVshsX7BgQR2NhKjhUNVpBiwhr10NsYS8IZbUq+OiYiSbj48PBg8eDGtrazx79gzR0dE4ffo0jh07Bg0NDcyfPx/+/v7o0qULunbtisjISNy4cQN79uwBAJw7dw7nz59H79690bx5c2RkZMDPzw/vvvsuRCIRgNe/MwYGBmLy5MlYsGABUlNTsXbtWiGJpaenh/fff19iXGWLo5UdLyoqwsiRI3Hx4kUcPnwYxcXFyMrKAgC0aNFCYpG2kydP4p9//sGUKVPKvd+hQ4ciNDQUixcvFsrLv/32W9jY2KBbt27CeWW/v+fn5+PBgwdISUmBjo5Ouay/qmHQrSRcSE0xmOkmIiIiInWXk5OD8ePH4/79+zA0NETnzp1x7NgxfPrppwCAuXPnorCwEJ6ennj8+DG6dOmC2NhY4cFS48aNsW/fPvj7++P58+ewtLTEoEGD4OvrK5S4Gxoa4vfff4eHhwe6d+8OExMTLFq0qNwe3bLcvXsXBw8eBFA+YXXq1Cn07dtXeB0REQFHR0e0b9++XD/9+/dHdHQ0Vq5ciZUrV6Jx48YQiUT47bffJNYseDMAT05ORnR0NGxsbGTuWKQKNEpLS0uVPYia8PT0LBdglR2rqE1RYwBqFujV1Rip/lm6dKnM9jdXgnzbhAkTank0DRu3kiIiIiKiyjDTrSCVZbKrulc3M+LVwwcZRERERESkShh0K0hhYaHMALCqi6hV1g8RERERERGpLgbdcijbb5tUl6zFVF69eiXzWjs7u9oejuDt1Sarc+8uXbrIvPbNbR1IsVg+TkRERESVYdAtB3n2267qlmFVLUOvioZQqs6qACIiIiIiUiUMupWkqluG1eZe3g2xVF3W1iDK3PNPnix62d6KpHyyKimA+rs1DRGRKmuIW4YRkWpj0K3iqpoRr4qKsub1Lfvd0B4qEBERERGRamPQreKqmhGvioqC94aY/SYiIiIiUmUBAQEIDAyUOGZnZ4cbN24AALKysjB//nzExsbi2bNnsLOzw8KFC+Hq6iqcHxQUhCNHjiAlJQU6OjrIzc2V6O/Ro0f44osv8Ndff+HRo0cwMzPDsGHDsGzZMjRr1kw47/Tp0/Dy8sLVq1fRqlUr+Pr6SmxDGxwcjH379uHGjRvQ19eHo6MjVqxYIVHZuWnTJkRHR+PixYt49uwZnjx5AiMjI4nxfP7550hJSUFOTg6aN28OJycnrFixAlZWVgBexy0zZsxAcnIyrl+/js8++wwHDhyQ6GPfvn0IDw9HSkoKxGIx7O3tERAQoPR1eBh0NyAVZc1rc864umnUqJGyh1AjO3fulNk+cuTIOhoJsUyRqPry8/NlthsYGNTRSKi+4s9mqi/s7e1x/Phx4bWW1v+FbuPHj0dubi4OHjwIExMTREdHY/To0bhw4QK6desGAHj58iVGjRoFkUiEiIiIcv03atQIw4YNw9KlS2Fqaor09HR4eHjg8ePHiI6OBgD8888/GDp0KGbMmIEdO3bgxIkTmDJlCiwtLYVA9syZM/Dw8ECPHj3w6tUrfPvttxg4cCCuXbuGJk2aAAAKCgowaNAgDBo0CD4+PhW+3379+uHbb7+FpaUl7t69i3nz5mHkyJGIj48HABQXF0NfXx+zZ8/G3r17K+zjjz/+wKeffoply5bByMgI27Ztg4uLC86dOyd8LsrAoLsBqShrLm/puqqVpzNrT0RERET1gZaWFiwsLCpsi4+PR3h4OHr27AkA8PX1RWhoKJKTk4XgsixTvn379gr7aN68Of773/8Kr21sbPDVV19h1apVwrENGzagTZs2+O677wAAHTp0wNmzZxEaGioE3b/99ptEv9u3b4eZmRmSk5Px8ccfAwDmzp0L4HXWXJo34xIbGxt4e3tj+PDhKCoqgra2Npo0aYLw8HAAwJ9//lkucw8Aa9askXi9bNky/PLLLzh06BCDblIeeeeM5+TkYMeOHbU4otpVHxdT6dq1a42vrWzxOHXN/hOR+mAmm4gaKrFYXG5BXF1dXejq6lZ4/t9//w0rKyvo6elBJBIhODgY1tbWAABHR0fs2rULQ4cOhZGREWJiYlBYWIi+ffvWeHz37t3Dvn378MknnwjHEhIS4OTkJHGes7OzEERXJC8vDwDQokWLGo/l8ePH2LFjBxwdHaGtrV3jfkpKSvDs2TO5xlIbGHQ3cPLOGffx8anVFdblxUw3EREREami4ODgcvO0/f39ERAQUO5cBwcHbN++HXZ2drh//z4CAwPRp08fpKamomnTpoiJicGYMWNgbGwMLS0tNG7cGPv374etrW21x+Xm5oZffvkFL168gIuLC7Zs2SK0ZWVlwdzcXOJ8c3NzPH36FC9evIC+vr5EW0lJCebOnYuPPvoI77//frXHsmDBAqxbtw4FBQXo1asXDh8+XO0+3rR69Wrk5+dj9OjRcvUjLwbdJJfaXOhNEX799VepbV999ZXMa1U16+vr61vja+V9T4sWLZLatnjxYrn6JpIH5wkTEZGq8/HxgZeXl8QxaVnuwYMHC//fuXNnODg4wMbGBjExMZg8eTL8/PyQm5uL48ePw8TEBAcOHMDo0aMRFxeHTp06VWtcoaGh8Pf3x82bN4Uxrl+/vvpvEICHhwdSU1Nx9uzZGl0/f/58TJ48Gf/++y8CAwMxfvx4HD58GBoaGtXuKzo6GoGBgfjll19gZmZWo/HUFgbdcpJWnt2QFygjIiIiIiJJskrJK2NkZIR27dohPT0dGRkZWLduHVJTU2Fvbw8A6NKlC+Li4hAWFoYNGzZUq28LCwtYWFigffv2aNGiBfr06QM/Pz9YWlrCwsIC2dnZEudnZ2ejWbNm5bLcM2fOxOHDh/HHH3+gZcuWNXqfJiYmMDExQbt27dChQwe0atUKiYmJEIlE1epn586dmDJlCnbv3l2uPF4ZGHTLSVqmV5VKrhVNlRZTY3k5EREREdU3+fn5yMjIwLhx41BQUACgfAWjpqZmpev3VKbs+rK55yKRCEePHpU4JzY2ViIILi0txaxZs7B//36cPn0abdq0kWsM0sZSVT///DMmTZqEnTt3YujQobUyFnkx6FYzqhTgllH1xdSkUdXycQC4c+eO1LaaPjmsDWXbPhCpGpaPE5Gyyfq3G1Dsv9+yAi1V/n2HpJs3bx5cXFxgY2ODe/fuwd/fH5qamnBzc4ORkRFsbW0xffp0rF69GsbGxjhw4ABiY2Ml5kBnZmbi8ePHyMzMRHFxMVJSUgAAtra2MDAwwNGjR5GdnY0ePXrAwMAAV69exfz58/HRRx+hdevWAIAZM2Zg3bp1+OabbzBp0iScPHkSMTExOHLkiHAfDw8PREdH45dffkHTpk2RlZUFADA0NBSy4VlZWcjKykJ6ejoA4MqVK2jatCmsra3RokULnDt3DufPn0fv3r3RvHlzZGRkwM/PD++++65EgH/t2jW8fPkSjx8/xrNnz4T3VLbQcHR0NNzd3bF27Vo4ODgIY9HX14ehoWGtf52qikG3miksLFS5bK4qLaamap8NEREREVF13blzB25ubnj06BFMTU3Ru3dvJCYmwtTUFABw9OhReHt7w8XFBfn5+bC1tUVkZCSGDBki9LFo0SJERkYKr8u2zDp16hT69u0LfX19bN68GZ6enhCLxWjVqhVGjBgBb29v4Zo2bdrgyJEj8PT0xNq1a9GyZUts2bJF2C4MgLCN19srp2/btg0TJkwA8HrrsTcXkSvbSqzsnMaNG2Pfvn3w9/fH8+fPYWlpiUGDBsHX11eiJH/IkCH4999/y72n0tJSAMCmTZvw6tUreHh4wMPDQzjP3d1d6tZpdUGjtGyEasbT07NcgFV2rKK2uqaoMajCe6OG6/PPP5fadvDgwTocCVH1PHnyRGZ78+bN62gkROXdvn1baltZtolIFma6iVQbM90Kcv36dYVkf2trgTZVLFOvDXwgQUREREREqoRBt4KUlJQoLNNdG1SxTJ2IiIiIiKi+YdCtZqRtUVZd3NKMakJV5u6TbJWtXNoQSw1VtXyc+4urj7LFeCpiYWEhV98sISd5NcSf60TqhEG3mpG2RVl1VTd4UpdydGbviYiIiIhIlTDobqCqmzFX123BZGE2sPqSkpKktvXr168OR0Ky8HtXfTCTXT0ZGRky2999912F3VvebDYRETVcDLobqOpmzFVpWzBZmOkmIiIiIiJVwqBbQcr20KsvaqusnYiIiIiIZCsuLkZAQACioqKQlZUFKysrTJgwAb6+vtDQ0EBRURF8fX1x9OhR3Lp1C4aGhnBycsLy5cthZWUl9HPx4kUsWLAA58+fh6amJlxdXRESEiJRaZWZmYn//ve/OHXqFAwMDODu7o7g4GBoab0OFe/fv4+vv/4aFy5cQHp6OmbPno01a9ZIjHf79u2YOHGixDFdXd1y01OvX7+OBQsW4MyZM3j16hU6duyIvXv3wtraGo8fP4a/vz9+//13ZGZmwtTUFMOHD8eSJUtgaGgo9HHixAn4+fnhypUraNKkCdzd3REUFCSMVxWp7sjUnJmZmbKHQJWorAT36tWrMtsvX74stW3s2LE1GpOqW7BggbKHQHUgJSVFalvXrl3rbBxEb5O3fPzFixdS2/T19eXqm1THnTt3pLa1bNmyDkciKS0tTWqbnZ1dHY6k7hQVFUlt09bWrsORqJ8VK1YgPDwckZGRsLe3x4ULFzBx4kQYGhpi9uzZKCgowMWLF+Hn54cuXbrgyZMnmDNnDj7//HNcuHABAHDv3j04OTlhzJgxWLduHZ4+fYq5c+diwoQJ2LNnD4DXwf3QoUNhYWGB+Ph43L9/H+PHj4e2tjaWLVsGABCLxTA1NYWvr6/MqtJmzZpJfJ9raGhItGdkZKB3796YPHkyAgMD0axZM1y9ehV6enrCeO/du4fVq1ejY8eO+PfffzFjxgzcu3dPGO/ly5cxZMgQLFy4ED/++CPu3r2LGTNmoLi4GKtXr669L0AtY9CtQtRlsTJVxvJyIiIiIlJ38fHxGDZsGIYOHQrg9S4HP//8s7C+jqGhIWJjYyWuWbduHXr27InMzExYW1vj8OHD0NbWRlhYmJBs2rBhAzp37oz09HTY2tri999/x7Vr13D8+HGYm5uja9euWLJkCRYsWICAgADo6OigdevWWLt2LQBg69atUsesoaEhc/2LhQsXYsiQIVi5cqVw7M2Hqe+//z727t0r0RYUFIQvv/wSr169gpaWFnbt2oXOnTtj0aJFAABbW1usXLkSo0ePhr+/P5o2bVqlz7euMehWoOoG0fVxsTJlk2eLF3t7e5nthw8frtGYiKri0KFDMttdXFwUdm9ms6m+Yja7YVBmNluW+prNloXZbElisRhisVjimK6uLnR1dcud6+joiE2bNuHmzZto164dLl++jLNnzyIkJERq/3l5edDQ0ICRkZFwPx0dHYnqzrKfg2fPnoWtrS0SEhLQqVMnmJubC+c4Ozvjv//9L65evYpu3bpV+f3l5+fDxsYGJSUl+OCDD7Bs2TLh9+mSkhIcOXIE33zzDZydnXHp0iW0adMGPj4+GD58uMz31KxZM6F0XCwWC5nxN99TYWEhkpOT0bdv3yqPty4x6FagwsLCamVe1WWxMlXGTDcRERERqaLg4GAEBgZKHPP390dAQEC5c729vfH06VO0b98empqaKC4uRlBQEL744osK+y4sLMSCBQvg5uaGZs2aAQD69+8PLy8vrFq1CnPmzMHz58/h7e0N4PU8beB1gurNgBuA8FpW8uptdnZ22Lp1Kzp37oy8vDysXr0ajo6OuHr1Klq2bImcnBzk5+dj+fLlWLp0KVasWIHffvsNI0aMwKlTp/DJJ5+U6/Phw4dYsmQJpk2bJhxzdnbGmjVr8PPPP2P06NHIysrC4sWLJd6TKmLQrUK4WFntU+QWL2PGjJHaVjaXRpoPP/ywtocjOHXqlNQ2buulPhSZySZqqGSt1VFZdZOqiouLk9nep0+fOhoJqaqnT5/KbC8L0NSNrK1fVXXrTB8fH3h5eUkcqyjLDQAxMTHYsWMHoqOjYW9vj5SUFMydOxdWVlZwd3eXOLeoqAijR49GaWkpwsPDheP29vaIjIyEl5cXfHx8oKmpidmzZ8Pc3LzWPyORSASRSCS8dnR0RIcOHbBx40YsWbJE+HoNGzZMSDJ27doV8fHx2LBhQ7mg++nTpxg6dCg6duwo8VBi4MCBWLVqFWbMmIFx48ZBV1cXfn5+iIuLU9mvOwCo7siIiIiIiIjqCV1dXTRr1kzij7Sge/78+fD29sZ//vMfdOrUCePGjYOnp2e5JF1ZwP3vv/8iNja23EOUsWPHIisrC3fv3sWjR48QEBCABw8eoG3btgBeJ6iys7Mlril7LU/ySltbG926dUN6ejoAwMTEBFpaWujYsaPEeR06dEBmZqbEsWfPnmHQoEFo2rQp9u/fX26agpeXF3Jzc5GZmYmHDx9i2LBhACC8J1XETLeC6OnpITExkeXidYzl5URERESk7goKCsplbjU1NSUy/GUB999//41Tp07B2NhYan9lJeNbt26Fnp4ePv30UwCvM9RBQUHIyckRdl8qC97fDpCro7i4GFeuXMGQIUMAADo6OujRo0e5Vfxv3rwJGxsb4fXTp0/h7OwMXV1dHDx4sNz87TIaGhrC1mg///wzWrVqhQ8++KDG41U0Bt0KEhwcjIEDBzIIrMdat25dozZFYwm5enh7IZW3SXvyTaTqli5dKrPd19e3jkZSnrqWkMvC8vG6k5CQILP9zdJaVaKu5eMNnYuLC4KCgmBtbQ17e3tcunQJISEhmDRpEoDXAffIkSNx8eJFHD58GMXFxcIc7BYtWkBHRwfA6xXNHR0dYWBggNjYWMyfPx/Lly8XFlsbOHAgOnbsiHHjxmHlypXIysqCr68vPDw8JH4XKdtOND8/Hw8ePEBKSgp0dHSEwHzx4sXo1asXbG1tkZubi1WrVuHff//FlClThD7mz5+PMWPG4OOPP0a/fv3w22+/4dChQzh9+jSA1wH3wIEDUVBQgKioKDx9+lSYHmFqagpNTU0AwKpVqzBo0CA0atQI+/btw/LlyxETEyO0qyIG3QqUn5/PTHcd40MOIiIiIlJ3P/zwA/z8/PDVV18hJycHVlZWmD59urBV1t27d3Hw4EEA5XcdOXXqlLCKd1JSEvz9/ZGfn4/27dtj48aNGDdunHCupqYmDh8+jP/+978QiURo0qQJ3N3dhcXJyry5inlycjKio6NhY2OD27dvAwCePHmCqVOnIisrC82bN0f37t0RHx8vkS3/f//v/2HDhg0IDg7G7NmzYWdnh71796J3794AgIsXL+LcuXMAXm8F9qZ//vlHSGr9+uuvCAoKglgsRpcuXfDLL79g8ODBNfiU645GaWlpqbIHUROenp7lAqyyYxW1KcMnn3yCM2fOKHsYJMWePXtkto8cObLGfXNhG6LylLkNGhFRVchaCFWRi6ASUf3GhdQUiOWhREREREREDRvLy1WYj48PCgsLlT0MtaIKFQ5ERERERERlGHQrmDyBc05ODnbs2FHLI6IyTZs2VVjfle3Trazy8vz8fJntBgYGdTSSuvP2Kplvs7Ozq6OREMvHCWiYP4dIfbCEnIgUgUG3ApmamqKwsLDG2deqLMLGbLgkZrqJiIiIiEiVMOhWoLK97mpKT0+v0sCb2fCaa9y4scL6lvdJ+alTp6S2ybMlWH3NIJVtY1GRt1f0JCLlqq8/h4iIiKRh0K3CgoODKz3Hx8eH25K9gZluIiIiIiJSJQy61VxVAnMiIiIiIlIvz549g5+fH/bv34+cnBx069YNa9euRY8ePQAAGhoaFV63cuVKzJ8/HwAQFBSEI0eOICUlBTo6OsjNzS13/uzZs/Hnn38iNTUVHTp0KFdBWFhYiBkzZiA5ORnXr1/HZ599hgMHDpTrZ8eOHVi5ciX+/vtvGBoaYvDgwVi1ahWMjY0BAH379q1wO+UhQ4bgyJEjKCoqgq+vL44ePYpbt27B0NAQTk5OWL58OaysrMpdJxaL4eDggMuXL+PSpUsqXd3IoFuB9PT0kJmZqexhkBSKXMyssr4rW9yrQ4cOtTmces/GxkbZQ6hQSUmJ1LZGjbhjI6mvpUuXSm3z9fWVea2svxeA7L8bRUVFMq/V1taW2a4sDx48kNluampaRyMhInUyZcoUpKam4qeffoKVlRWioqLg5OSEa9eu4Z133sH9+/clzv/1118xefJkuLq6CsdevnyJUaNGQSQSISIiQuq9Jk2ahHPnzuGvv/4q11ZcXAx9fX3Mnj0be/furfD6P//8E+PHj0doaChcXFxw9+5dzJgxA1OnTsW+ffsAAPv27cPLly+Fax49eoQuXbpg1KhRAICCggJcvHgRfn5+6NKlC548eYI5c+bg888/r3CR4m+++QZWVla4fPmyjE9RNTDoVqDg4GB4enpysbM6xPJyIiIiIlJ3L168wN69e/HLL7/g448/BgAEBATg0KFDCA8Px9KlS2FhYSFxzS+//IJ+/fqhbdu2wrHAwEAAwPbt26Xe6/vvvwfw+gFhRUF3kyZNEB4eDuB1cF1RtjwhIQGtW7fG7NmzAQBt2rTB9OnTsWLFCuGcFi1aSFyzc+dONG7cWAi6DQ0NERsbK3HOunXr0LNnT2RmZsLa2lo4/uuvv+L333/H3r178euvv0p9b6qCQXcdkGcFc1JdsrYFq2whNXXdpkpVM7fNmzeX2iZPVk1eDTGbLetnXWXrT9y+fVtme+vWrWswIlKEyrLZssjz90JVM9mVYSZbdbx48UJmu76+fh2NhBoisVgMsVgscUxXVxe6urrlzn316hWKi4uhp6cncVxfXx9nz54td352djaOHDmCyMjI2h10FYlEInz77bc4evQoBg8ejJycHOzZswdDhgyRek1ERAT+85//oEmTJlLPycvLg4aGBoyMjIRj2dnZmDp1Kg4cOKDQhZFrk9oG3RWt7J2Tk6Ok0VSO2e66wYcbRERERKSKgoODhcxzGX9/fwQEBJQ7t2nTphCJRFiyZAk6dOgAc3Nz/Pzzz0hISIDt/2fvvuOqLP/Hj78YMhLFhQoOHCigKZiagTtNXKjlSs09ojIVi5QcgIZI7tQwC5USt7hXONNEc4SFGxx8RJAUt4II/P7wx/31COew4Rx4Px+PHnnu67qvc91HEN73+31fl41Nhv5BQUGUKVOGjz76qKCmr1HLli0JDg6mf//+JCYm8vLlS1xdXVm6dGmm/f/66y8iIiI0lrwnJiYyadIkBgwYQNmyZQFIS0tj2LBhuLm50axZsyxv2GsLnQ26M1tATBtX8U5/rrty5coSEAohhBBCCFFCeXp6MnHiRJVjmWW50/3222+MGDGCatWqYWBgwDvvvMOAAQM4c+ZMhr4rVqxg0KBBGTLjheXChQuMHz+e6dOn4+LiQmxsLB4eHri5uWUaWAcGBtKoUSPefffdTMdLTk6mX79+pKWlKaXtAIsXL+bx48d4enoW2LUUBJ0NunVF+nPdovjJ617cuuj69etq2+rWrVuIM8m+kljiXZTy8u9dUZaP6+Je71ktyKirj7EcPXpUbVuNGjU0niuPIIisSPm4KErqSsnVqVu3LkeOHOHp06c8evQIS0tL+vfvr/LMNrz6d/Py5cusX78+v6ecbX5+frRs2VJZNb1x48aULl2a1q1b891332Fpaan0ffr0KevWrWPGjBmZjpUecN+8eZODBw8qWW6AgwcPEhYWluFzbNasGYMGDSqy8vqsSNBdBKTUvOBINYEQQgghhChOSpcuTenSpbl//z779u3j+++/V2kPDAykadOmODg4FNEMX608bmioGloaGBgAr0rCX7dx40aSkpL45JNPMoyTHnBfvXqVQ4cOKduNpfvhhx9UdtC4ffs2Li4urF+/nhYtWuTX5eQ7CbqLgCysJrKiaYXJYcOG5XrcvC5Upa3Z7KISFxensf3NVUVF3mj6+s1rhrOostlRUVEa2zV9z+lqJjsrBbmdoxCi+NDWxV3z0759+0hLS8PW1pbIyEg8PDyws7Nj+PDhSp9Hjx6xceNG5s2bl+kY0dHRJCQkEB0dTUpKilLZZWNjg5mZGQCRkZE8efKEuLg4nj9/rvRp0KABRkZGwKvy8RcvXpCQkMDjx4+VPuk/P11dXRk9ejQBAQFKefmECRN49913M+yxHRgYSK9evTIE1MnJyfTp04ezZ8+yc+dOUlJSlN+1KlSogJGRkcoK5oByDXXr1qV69eo5+HQLlwTdhUCb9usu7ll2uZkhhBBCCCGKg4cPH+Lp6cmtW7eoUKECvXv3xtfXV2Unh3Xr1pGWlsaAAQMyHWP69OkqJddNmjQB4NChQ7Rr1w54tR/4kSNHMvS5fv26clO7a9eu3Lx5M0Of9Cz2sGHDePz4MUuWLOGrr76iXLlyvP/++ypbhsGrR6OOHTvG77//nmGuMTExbN++Hch4M/z1+eoivbQ38/06zN3dnQULFij/1yavz0nd/AojII6Pjyc4OLhA36O4uH//vsZ2TdtUZeW///7T2L569Wq1bbJGgO4oqrvwsvWW7tD0LDkUXQb+0aNHGttff75OCCGEEJpJpluLFEbZuQRsQgghhBBCCFF4JOguYTLb37w40bYKByGEEEIIIUTJJkF3Ebh48WKmgW98fHyBv3dm+5uLzOWlfDwrFhYWGtt79OhRYO8tCs/rz0e96c1FRd6Ul0WytLV8XFO5fXb8888/atu0dVuvrORl3ufPn9fY3rBhw1yPLeXjQgghRP6RoLuQvJ5hfv78eaYZ2ZxmoIv7omi5IZluIYQQQgghhDaRoLuQvJ5h7tSpU6Z9clr6LYuiFV+3b99W2ybbdumON/erfF1x3e5Jk7wuHqer2eyCkpdMthAFLSwsTGO7k5NTIc1ECCGKngTdWiSnpd/F+dlsIYQQQgghhCgOJOjWYcV9UbTckPJyIYQQQgih67y9vfHx8VE5Zmtry6VLl1SOpaWl0bVrV/bu3cuWLVvo1auX0nbgwAGmTZvGv//+S+nSpRk6dCi+vr5KJd6NGzeoXbt2hvcOCwvjvffeA6Bdu3aZrlHTtWtXdu3aRXJyMlOnTmX37t1cu3YNc3NzOnbsyOzZs1XWr0lISODLL79kx44d6Ovr07t3bxYtWoSZmRnwahcnNzc3zpw5w8WLF+nevTtbt25Vec9hw4ap7DmerkGDBlmuc1LUJOjWYbIoWvGV/g+d0G2tW7dW23br1i2N51avXj2/pyOEiqLaR17kzKpVqzS2v/322xrbmzVrlo+zyT4pHxci7xo2bMj+/fuV15k9trZw4UL09PQyHD937hxdu3ZlypQp/Prrr8TExODm5kZKSgpz585V6bt//36VR5YqVqyo/DkkJIQXL14or+/du4eDgwN9+/YF4NmzZ5w9e5Zp06bh4ODA/fv3GT9+PD169OD06dPKeYMGDSI2NpbQ0FCSk5MZPnw4Y8aMYc2aNQCkpKRgamrKuHHj2Lx5c6afx6JFi5g9e7by+uXLlypz0WYSdBchWQgt/0mmWwghhBBCFAeGhoZUrVpVbXt4eDjz5s3j9OnTWFpaqrStX7+exo0bM336dABsbGz4/vvv6devH15eXpQpU0bpW7FiRbXvU6FCBZXX69at46233lICXXNzc0JDQ1X6LFmyhHfffZfo6Ghq1qzJxYsX2bt3L6dOnVJuBC5evJiuXbsyd+5crKysKF26NAEBAQD8+eefPHjwIMNczM3NMTc3V15v3bqV+/fvM3z4cLWfkbaQoLsIJSYmSpCow8LDw9W2XbhwQeO5AwcO1Nh+584dtW2SAc3o0KFDatvat29fiDPJvocPH2psl79nUdAkm60bhg0bVtRTEELkk6SkJJKSklSOGRsbY2xsnGn/q1evYmVlhYmJCU5OTvj5+VGzZk3gVYZ54MCBLF26NNOAOSkpCRMTE5VjpqamJCYmcubMGdq1a6cc79GjB4mJidSvX59vvvlG49a1gYGBfPzxx5QuXVptn4cPH6Knp0e5cuWAV+Xq5cqVU6m86dixI/r6+pw8eZIPP/xQ7ViaBAYG0rFjR6ytrXN1fmGSoLsIZLVHc16U9Oy53MQQQgghhBDayM/PL8Nz2l5eXnh7e2fo26JFC1atWoWtrS2xsbH4+PjQunVrIiIiKFOmDO7u7jg7O9OzZ89M38vFxYWFCxeydu1a+vXrR1xcHDNmzAAgNjYWADMzM+bNm0fLli3R19dn8+bN9OrVi61bt2YaeP/1119EREQQGBio9hoTExOZNGkSAwYMoGzZsgDExcVRuXJllX6GhoZUqFCBuLg49R+YBrdv32bPnj1Kebq2k6C7CLz5RZefJHsuhBBCCCGE9vH09GTixIkqx9Rlubt06aL8uXHjxrRo0QJra2s2bNiAhYUFBw8e5O+//1b7Xp06dWLOnDm4ubkxePBgjI2NmTZtGkePHlUqnSpVqqQyn+bNm3P79m3mzJmTadAdGBhIo0aNePfddzN9z+TkZPr160daWppSKl5QgoKCKFeunMrCcdpMgm4tkh9Z6vj4+HyajchKZGSk2rasysez8mbpkdCsfPnyRT2FHJM9loUQQoiSRVMpeVbKlStH/fr1iYyM5N9//yUqKkop307Xu3dvWrduzeHDhwGYOHEi7u7uxMbGUr58eW7cuIGnpyd16tRR+z4tWrTI8Iw2wNOnT1m3bp2SLX9TesB98+ZNDh48qGS5AapWrZohRnn58iUJCQkan1lXJy0tjRUrVjB48GCMjIxyfH5RkKBbi+RHltrT07NEbyMmWX4hhBBCCFHcPHnyhKioKAYPHky/fv0YNWqUSnujRo1YsGABrq6uKsf19PSUrbvWrl1LjRo1eOedd9S+T3h4eIZF2QA2btxIUlISn3zySYa29ID76tWrHDp0SGX1c3i1m8GDBw84c+YMTZs2BeDgwYOkpqbSokWL7H0Arzly5AiRkZGMHDkyx+cWFQm6i9jr2e38yFLLNmKFp0+fPgU2dt26dQts7OJI08J1jo6OGs8tiVt3ZbWXZXHMwt+4cUNje61atQplHkIIIUR2fP3117i6umJtbc3t27fx8vLCwMCAAQMGYGFhkWmGuGbNmir7bs+ZM4fOnTujr69PSEgIs2fPZsOGDRgYGACvSrSNjIxo0qQJ8Gp7sBUrVvDLL79kGDswMJBevXplCKiTk5Pp06cPZ8+eZefOnaSkpCjPaVeoUAEjIyPs7e3p3Lkzo0ePZtmyZSQnJzN27Fg+/vhjlb28L1y4wIsXL0hISODx48fKosVv/i4XGBhIixYtstwyUZtI0F3EXs9ua8pQl/QF0rJLMt1CCCGEEELX3bp1iwEDBnDv3j0sLCxo1aoVJ06cyNGCzHv27MHX15ekpCQcHBzYtm2byrPiADNnzuTmzZsYGhpiZ2fH+vXrMySWLl++zLFjx/j9998zvEdMTAzbt28HMgbHhw4dUlZJDw4OZuzYsXTo0AF9fX169+7NDz/8oNK/a9eu3Lx5U3mdfjMgLS1NOfbw4UM2b97MokWLsv05aAMJunWELJBW+IpjZiwsLExju5OTUyHNJH+9edc1J4pjJjsrxTGTnRVd/H4tqU6fPq227fXtZoQQojhbt25djvq/HpimO3jwoMZzhg4dytChQ7Mc29bWNtPx4dXPV3Vtr6tQoUKWK41n9bs3vNqr+9mzZ1n20zYSdBcBExMT3N3diY+PV1nJPP14ZmSBNCGEEEIIIYTQPRJ0F4H0567fDLA1PY+dWTAuJecZSTWAEEIIIYQQQptI0K3DpOS8YMXGxmpsL126tNq2hIQEjefa2trmak55ldfy8fSFMTKTmy0f8kteystF8aDpaxOK9utT5IyUkAuRc6mpqWrb0vdkFkIUHQm6dURmpedZlZyXxEy43IQQQgghhBBCaBMJunVEZqXnWe3JHR8fT3BwcEFOS6dpuisMrxZ80ETT6pE5WVlSl2hrtvDx48dF8r5JSUka242NjQtpJrqhIBfy09avTSGEEEIICbp1WFZ7cmsKyIUQQgghhBBCFDwJuosxTauhF1dSXi6EEEIIIXRdQEAAAQEByjZaDRs2ZPr06Sr7bIeFhTFlyhROnjyJgYEBjo6O7Nu3D1NTUwB69OhBeHg48fHxlC9fno4dO+Lv74+VlRUA3t7e+Pj4ZHjvt956i6dPnwLw888/8+uvvxIREQFA06ZNmTVrFu+++67S/86dO0yaNInff/+dBw8e0KZNGxYvXky9evWUPp9++in79+/n9u3bmJmZ4ezsjL+/P3Z2dkqfcePG8eeffxIREYG9vT3h4eFqP5/IyEiaNGmCgYEBDx48yNmHWwQk6C7GssqEl3QXL17U2F6Qexnfv39fY3v58uUL7L2Lo/bt2xfJ+wYFBWlsHzNmTK7HjoqK0thet27dXI9dVHR1H3ghhBCisFWvXp3Zs2dTr1490tLSCAoKomfPnvz99980bNiQsLAwOnfujKenJ4sXL8bQ0JBz586pLJzXvn17vv32WywtLYmJieHrr7+mT58+HD9+HICvv/4aNzc3lfft0KEDzZs3V14fPnyYAQMG4OzsjImJCf7+/nTq1Inz589TrVo10tLS6NWrF6VKlWLbtm2ULVuW+fPn07FjRy5cuKAsPNy0aVMGDRpEzZo1SUhIwNvbm06dOnH9+nUMDAyU9xsxYgQnT57kn3/+UfvZJCcnM2DAAFq3bq1ci7aToLuIXbx4scRlowuSZLqFEEIIIYSuc3V1VXnt6+tLQEAAJ06coGHDhri7uzNu3DgmT56s9Hlzd5zXYwxra2smT55Mr169SE5OplSpUpiZmWFmZqb0OXfuHBcuXGDZsmXKsTfXh/rll1/YvHkzBw4cYMiQIVy9epUTJ04QERGhJKwCAgKoWrUqa9euZdSoUYBqIqJWrVp89913ODg4cOPGDSWR8MMPPwDw33//aQy6p06dip2dHR06dJCgW2RPamqqBIoF6Pnz52rbbt26pfFcGxsbje15WSSrIDPZO3bsUNv25j/gxUVRbWWWl0x2VnQxky2Kj4KstJDt3URRKq6VZrItmG5ISkrKsAirsbFxlr9TpqSksHHjRp4+fYqTkxPx8fGcPHmSQYMG4ezsTFRUFHZ2dvj6+tKqVatMx0hISCA4OBhnZ2dKlSqVaZ9ffvmF+vXr07p1a7VzefbsGcnJycqCw+nXY2JiovTR19fH2NiYY8eOKUH3654+fcrKlSupXbs2NWrU0Hjtbzp48CAbN24kPDyckJCQHJ1blCToLkImJiZZrn6cH0rS1mFyA0MIIYQQQmgjPz+/DM9Qe3l54e3tnWn/f//9FycnJxITEzEzM2PLli00aNCAEydOAK+eyZ47dy6Ojo78+uuvdOjQgYiICJVnqSdNmsSSJUt49uwZ7733Hjt37sz0vRITEwkODlbJnGdm0qRJWFlZ0bFjRwDs7OyoWbMmnp6e/PTTT5QuXZoFCxZw69YtYmNjVc798ccf+eabb3j69Cm2traEhoZiZGSk8f1ed+/ePYYNG8bq1aspW7Zsts/TBhJ0FyE/Pz86depU4O+TmJgowagQQgghhBBFyNPTk4kTJ6oc05TltrW1JTw8nIcPH7Jp0yaGDh3KkSNHlG1vP/30U4YPHw5AkyZNOHDgACtWrFBZ18nDw4ORI0dy8+ZNfHx8GDJkCDt37kRPT0/lvbZs2cLjx48ZOnSo2vnMnj2bdevWcfjwYSWzXapUKUJCQhg5ciQVKlTAwMCAjh070qVLF9LS0lTOHzRoEB988AGxsbHMnTuXfv368eeff6pkyTUZPXo0AwcOpE2bNtnqr00k6NYhuc1Yx8fHF8BsdEP66o2ZyeoOWXJyssZ22YNZe0g5asmgaZ9vWaQtfxXk4w3y/SqKkq6Wj2clPQjLjJSea4/slJK/zsjISHncsWnTppw6dYpFixYp2egGDRqo9Le3tyc6OlrlWKVKlahUqRL169fH3t6eGjVqcOLEiQw/N3/55Re6d+9OlSpVMp3L3LlzmT17Nvv376dx48YqbU2bNlVuDrx48QILCwtatGhBs2bNVPqZm5tjbm5OvXr1eO+99yhfvjxbtmxhwIAB2fo8Dh48yPbt25k7dy4AaWlppKamYmhoyPLlyxkxYkS2xikKEnTrkNxmrD09PUvMYm2S0RdCCCGEEMVRamoqSUlJ1KpVCysrKy5fvqzSfuXKFZUtxTI7H8jweOv169c5dOgQ27dvz/S877//Hl9fX/bt25chkH6dubk5AFevXuX06dPMnDlTbd+0tDTS0tJy9KhtWFgYKSkpyutt27bh7+/P8ePHqVatWrbHKQoSdJcAsnVY5goyM5bVIkQnT57U2D5w4MBcv3dhrBMgRFHQ9D1bHLdYE0IIUXJ5enrSpUsXatasyePHj1mzZg2HDx9m37596Onp4eHhgZeXFw4ODjg6OhIUFMSlS5fYtGkT8Op3zVOnTtGqVSvKly9PVFQU06ZNo27duhl+nq5YsQJLS8tMA3Z/f3+mT5/OmjVrqFWrlrIY5usrn2/cuBELCwtq1qzJv//+y/jx4+nVq5fyGO21a9dYv349nTp1wsLCglu3bjF79mxMTU3p2rWr8l6RkZE8efKEuLg4nj9/ruzT3aBBA4yMjLC3t1eZ2+nTp9HX1+ftt9/Onw+9AEnQraUyKyXPzzLx4rq4mmS6hRBCCCGErouPj2fIkCHExsZibm5O48aN2bdvHx988AEAEyZMIDExEXd3dxISEnBwcCA0NFS5yfzWW28REhKCl5cXT58+xdLSks6dOzN16lSVEvfU1FRWrVrFsGHDVPbLThcQEMCLFy/o06ePyvHXF4CLjY1l4sSJ3LlzB0tLS4YMGcK0adOUviYmJhw9epSFCxdy//59qlSpQps2bTh+/DiVK1dW+o0aNYojR44or5s0aQK8ysTXqlUrbx9oEZOgW0tlVkqenyXisriaEEIIIYQQ2ikwMDDLPpMnT1a72nijRo04ePBglmPo6+vzv//9T237jRs3shxj3LhxjBs3Tm27lZUVu3fvznKcw4cPZ9nndcOGDWPYsGE5OqeoSNBdjOQke12SF1dLl16yoo6jo2Oux87q76Fp06a5HjsrjRo1UtuW1eJw6vZtFELbSfm4EKIkk8XShNBuEnQXIznJXhfXxdUkey+EEEIIIYTQJhJ06xATExONgXJOsteyuFreMtlZadiwYYGNnZVVq1apbZO/d+0hVQdCmz158kRje/riOUIIIYTImgTdOiSrgEldQF5cF03LjGS6hRBCCCGEENpEgu4SQBZNy1z6XoXq5OX5qLCwMI3tBbldWf/+/QtsbJF/ijKTLVlMkRX5GhDaLKuFnXR9lWORPZp+j5Nn3IW2kaC7iFlYWCh/fj0jnZuFztSVn8uiaUIIIYQQQghRNCToLmKv7033ekY6N4ucqSs/z48F03SlRF0y+kIIIYQQQtfVqlWLmzdvZjj++eefs3TpUqKiovj66685duwYSUlJdO7cmcWLF1OlShWl75UrV/Dw8ODPP//kxYsXNG7cmJkzZ9K+fXulz7hx4/jzzz+JiIjA3t4+0919NmzYwKxZs7hy5QoWFhaMHTsWDw8PlT5JSUnMmDGD1atXExcXh6WlJdOnT2fEiBEAhISEMGvWLCIjI0lOTqZevXp89dVXDB48WGWcixcvMmnSJI4cOcLLly9p0KABmzdvpmbNmkqfsLAwpkyZwsmTJzEwMMDR0ZF9+/Zhamqaq8+6MEjQXQJktQBbdsTHxxMcHJxPM9IOWZUeXb58WWO7ra2t2jZLS8tczUnbSSlX8ZDVIm5CCKHNpHxcQPH/vePUqVOkpKQoryMiIvjggw/o27cvT58+pVOnTjg4OCh7cU+bNg1XV1dOnDihfDbdu3enXr16HDx4EFNTUxYuXEj37t2JioqiatWqytgjRozg5MmT/PPPPxnmsWfPHgYNGsTixYvp1KkTFy9eZPTo0ZiamjJ27FilX79+/bhz5w6BgYHY2NgQGxur8ntjhQoVmDJlCnZ2dhgZGbFz506GDx9O5cqVcXFxASAqKopWrVoxcuRIfHx8KFu2LOfPn8fExEQZJywsjM6dO+Pp6cnixYsxNDTk3LlzWv/1IEF3CZAfK1bryhZjkukWQgghhBC67vVHUAFmz55N3bp1adu2LaGhody4cYO///6bsmXLAhAUFET58uU5ePAgHTt25O7du1y9epXAwEAaN26sjPHjjz8SERGhBN0//PADAP/991+mQfdvv/1Gr169cHNzA6BOnTp4enri7+/PF198gZ6eHnv37uXIkSNcu3aNChUqABlvjrVr107l9fjx4wkKCuLYsWNK0D1lyhS6du3K999/r/SrW7euynnu7u6MGzeOyZMnK8c0JcK0hQTdIlt0daup+/fvq20rX768xnPT/9FQ5+jRo2rbWrdurXliBSizsqB0ed0mTdvvIorsyepr//z582rbCnI7PG1d4C0qKkpj+5u/EAghhBD56cWLF6xevZqJEyeip6dHUlISenp6GBsbK31MTEzQ19fn2LFjdOzYkYoVK2Jra8uvv/7KO++8g7GxMT/99BOVK1emadOm2X7vpKQk3nrrLZVjpqam3Lp1i5s3b1KrVi22b99Os2bN+P777/ntt98oXbo0PXr0YObMmZmWfKelpXHw4EEuX76Mv78/8KqacteuXXzzzTe4uLjw999/U7t2bTw9PenVqxfwqvL25MmTDBo0CGdnZ6KiorCzs8PX15dWrVrl4pMtPPIbtBBCCCGEEEIUsKSkJB49eqTyX1JSUpbnbd26lQcPHjBs2DAA3nvvPUqXLs2kSZN49uwZT58+5euvvyYlJYXY2FgA9PT02L9/P3///TdlypTBxMSE+fPns3fv3ixvvr/OxcWFkJAQDhw4QGpqKleuXGHevHkAyntdu3aNY8eOERERwZYtW1i4cCGbNm3i888/Vxnr4cOHmJmZYWRkRLdu3Vi8eDEffPAB8CqgfvLkCbNnz6Zz5878/vvvfPjhh3z00UccOXJEeR8Ab29vRo8ezd69e3nnnXfo0KEDV69ezfY1FQXJdGs5XVnATFtIebkQQgghhNBGfn5++Pj4qBzz8vLC29tb43mBgYF06dIFKysr4FXp+caNG/nss8/44Ycf0NfXZ8CAAbzzzjtKVWJaWhpffPEFlStX5ujRo5iamvLLL7/g6urKqVOnsr3+0OjRo4mKiqJ79+4kJydTtmxZxo8fj7e3t/Jeqamp6OnpERwcjLm5OQDz58+nT58+/Pjjj0q2u0yZMoSHh/PkyRMOHDjAxIkTqVOnDu3atVOe/+7Zs6fySKujoyPHjx9n2bJltG3bVunz6aefMnz4cACaNGnCgQMHWLFihVZX5krQraXSFz8rjguYFSZNZaHNmjXTeO6jR480thdkCXleSteL6yJuovAUZAm5Jtq6N7SUjwshtJ0sdKobPD09mThxosqx10vEM3Pz5k32799PSEiIyvFOnToRFRXF3bt3MTQ0pFy5clStWpU6deoAcPDgQXbu3Mn9+/eV575//PFHQkNDCQoKUnkmWhM9PT38/f2ZNWsWcXFxWFhYcODAAQDlvSwtLalWrZoScAPY29uTlpbGrVu3qFevHvDqa9HGxgZ4FVBfvHgRPz8/2rVrR6VKlTA0NKRBgwYq729vb8+xY8eU9wEy7RMdHZ2t6ykqEnRrqfQ7NQW5eFlxzKJLplsIIYQQQmgjY2PjLIPsN61cuZLKlSvTrVu3TNsrVaoEvAqy4+Pj6dGjBwDPnj0DMt500dfX13iTRh0DAwOqVasGwNq1a3FyclIWe2vZsiUbN27kyZMnyg30K1euoK+vT/Xq1dWOmZqaqpTXGxkZ0bx58wy7B125cgVra2vg1eJsVlZWmfbp0qVLjq+pMEnQreXyY7svdSSLrllRZrfefvvtXJ8rW6kIXaVpATfQnIHPqjIl/S6/EEIUR5LNLp5SU1NZuXIlQ4cOxdBQNWxbuXIl9vb2WFhYEBYWxvjx43F3d1dW8nZycqJ8+fIMHTqU6dOnY2pqys8//8z169dVAvjIyEiePHlCXFwcz58/VxbkbdCgAUZGRty9e5dNmzbRrl07EhMTWblyJRs3blSeswYYOHAgM2fOZPjw4fj4+HD37l08PDwYMWKEUlru5+dHs2bNqFu3LklJSezevZvffvuNgIAAZRwPDw/69+9PmzZtaN++PXv37mXHjh0cPnwYeJV19/DwwMvLCwcHBxwdHQkKCuLSpUts2rSpIP4K8o0E3VquIJ9N0JVtwHJCMt1CCCGEEKI42L9/P9HR0YwYMSJD2+XLl/H09CQhIYFatWoxZcoUld/rK1WqxN69e5kyZQrvv/8+ycnJNGzYkG3btuHg4KD0GzVqlEoA3aRJEwCuX7+uJHKCgoL4+uuvSUtLw8nJicOHD/Puu+8q55iZmREaGsqXX35Js2bNqFixIv369eO7775T+jx9+pTPP/+cW7duYWpqip2dHatXr6Z///5Knw8//JBly5bh5+fHuHHjsLW1ZfPmzSork0+YMIHExETc3d1JSEjAwcGB0NBQrX8UTC8tLS2tqCeRX9zd3VmwYIHyf13w+lx1ad664vTp02rbsnqmuyjlZauzN0tuXqcL+xiKkksy3UIIIYQojiTTLYo1Tc+RFKTly5drbB8zZozG9pxs5fAmXQyssyoJ6tOnTyHNRBSlvCzgpq1BtdwMEEIIIYQE3SVAcVwwTR2pFBBCCCGEEEJoEwm6i9jrC6XFx8dn2T83AXRJXjAtLi5ObVvVqlUL7H3btm1bYGPnxY0bNzS2F9UibOnPDwlR3OQ1k52+oE1mHB0d8zS2EKL4SE5OVttWqlSpQpyJECIzEnQXsdcXSsvOomaJiYk5zuYWxwXT1JFMtxBCCCGEEEKbSNBdAhTkCuhCCCGEEEIIIdSToFvLZFU+np0SdPF/8lJ++d9//2lst7CwUNsWGhqq8dyiWuysKPfwllXVRXH1/PlztW3p+5PmlpSQCyGyQ0rIhdBuEnRrmazKx3NSJl6SFlBLJ+XlQgghhBCiOIiJiWHSpEns2bOHZ8+eYWNjw8qVK5Vtb729vVm3bh3/+9//MDIyomnTpvj6+tKiRQtljISEBL788kt27NiBvr4+vXv3ZtGiRZiZmQGvkiJubm5cuHCBhw8fYmVlxcCBA/Hy8lJu5rRr105lL+90Xbt2ZdeuXQDo6elleg3ff/89Hh4eyutdu3YxY8YM/vnnH0xMTGjbti1bt27NcN69e/dwcHAgJiaG+/fvU65cOQBCQkIICAggPDycpKQkGjZsiLe3Ny4uLjn/gAuRBN3FWG6e/xb/R1MmOyv16tXT2J6X/Yh1lWSzRVHStCAZ5C2jnNdsthBCCPGm+/fv07JlS9q3b8+ePXuwsLDg6tWrKtvK1q9fnyVLllCnTh2eP3/OggUL6NSpE5GRkcrvsYMGDSI2NpbQ0FCSk5MZPnw4Y8aMYc2aNcCrKokhQ4bwzjvvUK5cOc6dO8fo0aNJTU1l1qxZwKtA98WLF8r7pgfEffv2VY7FxsaqzH/Pnj2MHDmS3r17K8c2b97M6NGjmTVrFu+//z4vX74kIiIi0+sfOXIkjRs3JiYmRuX4H3/8wQcffMCsWbMoV64cK1euxNXVlZMnT2r1wrwSdOuY11c7z0p6KXpJynjLTQYhhBBCCKHr/P39qVGjBitXrlSO1a5dW6XPwIEDVV7Pnz+fwMBA/vnnHzp06MDFixfZu3cvp06dUrLjixcvpmvXrsydOxcrKyvq1KlDnTp1lDGsra05fPgwR48eVY5VqFBB5X3WrVvHW2+9pRJ0v7kr0LZt22jfvr0y9suXLxk/fjxz5sxh5MiRSr8GDRpkuPaAgAAePHjA9OnT2bNnj0rbwoULVV7PmjWLbdu2sWPHDgm6Rf7JyaJo6cF5Sc54F9UWGhUrVtTYXlSZ7H379mls1/bSHCFyq0yZMkU9BSGEKDCyZVjxs337dlxcXOjbty9HjhyhWrVqfP7554wePTrT/i9evGD58uWYm5vj4OAAQFhYGOXKlVMCboCOHTuir6/PyZMn+fDDDzOMExkZyd69e/noo4/Uzi0wMJCPP/6Y0qVLZ9p+584ddu3aRVBQkHLs7NmzxMTEoK+vT5MmTYiLi8PR0ZE5c+bw9ttvK/0uXLjAjBkzOHnyJNeuXdP8IQGpqak8fvw4w40BbaNf1BMQBSc9Ky6LrwkhhBBCCFG0kpKSePTokcp/SUlJmfa9du0aAQEB1KtXj3379vHZZ58xbtw4lUAWYOfOnZiZmWFiYsKCBQsIDQ2lUqVKAMTFxVG5cmWV/oaGhlSoUIG4uDiV487OzpiYmFCvXj1at27NjBkzMp3XX3/9RUREBKNGjVJ7nUFBQZQpU0YlcE8PoL29vZk6dSo7d+6kfPnytGvXjoSEBOXzGTBgAHPmzKFmzZpqx3/d3LlzefLkCf369ctW/6Iime5iLD0rnlU5enEqPy+pGX0hhBBCCKHd/Pz88PHxUTnm5eWFt7d3hr6pqak0a9ZMea66SZMmREREsGzZMoYOHar0a9++PeHh4dy9e5eff/6Zfv36cfLkyQzBdlbWr1/P48ePOXfuHB4eHsydO5dvvvkmQ7/AwEAaNWrEu+++q3asFStWMGjQIExMTFSuB2DKlCnKc94rV66kevXqbNy4kU8//RRPT0/s7e355JNPsjXnNWvW4OPjw7Zt23J8vYVNgu4SIKvnwOPj4wkODi7EGRWeoiqpunHjhsb218t8MpP+D1Nm9PVzX6BSHBdo01Xq7mynMzY2LqSZFA+avmcA6tatW0gzEaJkiIqK0theEr/n7t+/r7H99QWw8puUkOsGT09PJk6cqHJM3c97S0vLDM8729vbs3nzZpVjpUuXxsbGBhsbG9577z3q1atHYGAgnp6eVK1aNUPF68uXL0lISMjwDHaNGjWAV89Yp6SkMGbMGL766isMDAyUPk+fPmXdunVqs+AAR48e5fLly6xfvz7D9aSP//q116lTh+joaAAOHjzIv//+y6ZNmwBIS0sDoFKlSkyZMkXlhsW6desYNWoUGzdupGPHjmrnoy0k6C4BsnoO3NPTM0dbkWkzyXQLIYQQQghtZGxsnO2b6i1btuTy5csqx65cuYK1tbXG81JTU5Ub+05OTjx48IAzZ87QtGlT4FVgm5qaqrKtWGZjJCcnk5qaqhJ0b9y4kaSkJI2Z6MDAQJo2bao8V56uadOmGBsbc/nyZVq1agW8Wovgxo0byjVt3ryZ58+fK+ecOnWKESNGcPToUZUbeWvXrmXEiBGsW7eObt26afw8tIUE3SJHi7PpGk0Z51q1ahXY+/bp00dje0FuX6RJ9erVC2RcXXb69GmN7VlVJWiSfqc2M1l9jYicyUsFiBAi50piJjsrBZnJFiWPu7s7zs7OzJo1i379+vHXX3+xfPlyli9fDrzKOvv6+tKjRw8sLS25e/cuS5cuJSYmRllV3N7ens6dOzN69GiWLVtGcnIyY8eO5eOPP8bKygqA4OBgSpUqRaNGjTA2Nub06dN4enrSv3//DBUUgYGB9OrVS+2CwY8ePWLjxo3MmzcvQ1vZsmVxc3PDy8uLGjVqYG1tzZw5cwCU+b7578rdu3eV60jfp3vNmjUMHTqURYsW0aJFC+XZdFNTU8zNzXP8ORcWCbp1QHF65rqgSaZbCCGEEELouubNm7NlyxY8PT2ZMWMGtWvXZuHChQwaNAgAAwMDLl26RFBQEHfv3qVixYo0b96co0ePqjxOGBwczNixY+nQoQP6+vr07t2bH374QWk3NDTE39+fK1eukJaWhrW1NWPHjs1QBXv58mWOHTvG77//rnbO69atIy0tjQEDBmTaPmfOHAwNDRk8eDDPnz+nRYsWHDx4MEc3rJYvX87Lly/54osv+OKLL5TjQ4cOZdWqVdkep7BJ0K1FTExMlGcaXleSt/wSQgghhBCiJOrevTvdu3fPtM3ExISQkJAsx6hQoQJr1qxR296/f3/69++f5Ti2trbKM9bqjBkzhjFjxqhtL1WqFHPnzmXu3LlZvh9Au3btMrzn4cOHs3WutpGgW4v4+fkVm2ertUVBlpDnRbVq1Qps7IJahC2viqrUPyuPHz8usLE1lZBntfBXSSyXls9ECFGcFeTPZ2392S+EeEWCbi30Zjl5bvfZLoll6VIRIIQQQgghhNAmEnRroTfLyXOb/ZaydO1lYWFRYGOHhoaqbXNxcSmw982KtlYd3Lx5s0jeVzIPGclnIgqatlbciJKhIP+Nk38/hdBuEnTrgKz22VYntxnydLqYKZebDEIIIYQQQghtIkG3Dsjtll55fT5cMuVCCCGEEEIIkTcSdBdjuc2Qp8trplwbnD9/Xm3b69spFCdGRkZFPQWd8vbbbxf1FIQQhURKyIUQQhQFCbqLsdxmyNN5enrq3GrqkpkXQgghhBC6ztvbGx8fH5Vjtra2XLp0SXkdFhbGlClTOHnyJAYGBjg6OrJv3z5MTU0BOHv2LJMmTeLUqVMYGBjQu3dv5s+fj5mZmTLGgQMHmDZtGv/++y+lS5dm6NCh+Pr6YmiYMUyMjIykSZMmGBgY8ODBA+V4SEgIs2bNIjIykuTkZOrVq8dXX33F4MGDM702Nzc3fvrpJxYsWMCECROU476+vuzatYvw8HCMjIxU3iM389Um2j07UaTyGrRrg+Kazdakffv2attOnz6t8dxmzZrl93S0XlFd8759+zS2F+Wid6JkCA8PV9vm6OhYaPMQQuSdbBmWM8+fP1fblh6waoOGDRuyf/9+5fXrgWVYWBidO3fG09OTxYsXY2hoyLlz55S/79u3b9OxY0f69+/PkiVLePToERMmTGDYsGFs2rQJgHPnztG1a1emTJnCr7/+SkxMDG5ubqSkpGTYSzs5OZkBAwbQunVrjh8/rtJWoUIFpkyZgp2dHUZGRuzcuZPhw4dTuXLlDL/PbNmyhRMnTmBlZZXhel+8eEHfvn1xcnIiMDAwQ3tO5qttJOjWYrq4kFlRk0y3EEIIIYQoDgwNDalatWqmbe7u7owbN47Jkycrx2xtbZU/79y5k1KlSrF06VIlEF+2bBmNGzcmMjISGxsb1q9fT+PGjZk+fToANjY2fP/99/Tr1w8vLy/KlCmjjDd16lTs7Ozo0KFDhqC7Xbt2Kq/Hjx9PUFAQx44dUwm6Y2Ji+PLLL9m3bx/dunXLcE3pmf1Vq1Zles05ma+2kaBbi8lCZnl39OhRtW2tW7cuxJlohwMHDmhsL4mZ7qIimezCJZUFGUk2W4jiQ7LZOaNN2WxNrl69ipWVFSYmJjg5OeHn50fNmjWJj4/n5MmTDBo0CGdnZ6KiorCzs8PX15dWrVoBkJSUhJGRkcrXRvp1Hzt2DBsbG5KSkjAxMVF5T1NTUxITEzlz5owSTB88eJCNGzcSHh5OSEiIxjmnpaVx8OBBLl++jL+/v3I8NTWVwYMH4+HhketK1OzOVxvJd6iW8vT0LBYLmQkhhBBCCCFeBY2PHj1S+S8pKSnTvi1atGDVqlXs3buXgIAArl+/TuvWrXn8+DHXrl0DXj33PXr0aPbu3cs777xDhw4duHr1KgDvv/8+cXFxzJkzhxcvXnD//n0lKx4bGwu8uuF8/Phx1q5dS0pKCjExMcyYMUOlz7179xg2bBirVq2ibNmyaq/t4cOHmJmZYWRkRLdu3Vi8eDEffPCB0u7v74+hoSHjxo3L9eeXnflqK8l0axkTExOio6OpXLkylStXzvH5Jb0kXSoDhBBCCCGENvLz88uwOJqXlxfe3t4Z+nbp0kX5c+PGjWnRogXW1tZs2LABe3t7AD799FOGDx8OQJMmTThw4AArVqzAz8+Phg0bEhQUxMSJE/H09MTAwIBx48ZRpUoVJfvdqVMn5syZg5ubG4MHD8bY2Jhp06Zx9OhRpc/o0aMZOHAgbdq00XhtZcqUITw8nCdPnnDgwAEmTpxInTp1aNeuHWfOnGHRokWcPXsWPT29XH9+2ZmvttJLS0tLK+pJ5Bd3d3cWLFig/F9Xvb5ieE6vQ9evPb8V5MIiUVFRatvq1q2bp7ELyo4dOzS2u7q6FtJMhBBCCN2i6XcKkBLv4iI5OVlje6lSpXI9dlJSUobMtrGxMcbGxtk6v3nz5nTs2JExY8ZQp04dfvvtNz755BOlvX///hgaGhIcHKxy3p07dyhdujR6enqULVuWdevW0bdvX6U9LS2N2NhYypcvz40bN2jQoAF//fUXzZs3p1y5cjx58kSlb2pqKgYGBixfvpwRI0ZkOtdRo0bxv//9j3379rFw4UImTpyo8j2SkpKCvr4+NWrU4MaNGyrnrlq1igkTJmS6enlW89VWkunWYrnZZzs3JenFKTsuNxyEEEIIIYQ2ykmA/aYnT54QFRXF4MGDqVWrFlZWVly+fFmlz5UrV1Qy5OmqVKkCwIoVKzAxMVEp+wbQ09NTVhNfu3YtNWrU4J133gFerZKekpKi9N22bRv+/v4cP36catWqqZ1vamqqcoNh8ODBdOzYUaXdxcWFwYMHK5n6nNA0X20lQbcWy82WXbnZWzs+Pj7DHbHi4vV/JN6U1zvSuf1Hsyg1adKkqKcgRJE4dOiQxnZNW+0JIQRIJrukyEsmOz99/fXXuLq6Ym1tze3bt/Hy8sLAwIABAwagp6eHh4cHXl5eODg44OjoSFBQEJcuXVK2AwNYsmQJzs7OmJmZERoaioeHB7Nnz6ZcuXJKnzlz5tC5c2f09fUJCQlh9uzZbNiwAQMDAwCllD3d6dOn0dfX5+2331aO+fn50axZM+rWrUtSUhK7d+/mt99+IyAgAICKFStSsWJFlXFKlSpF1apVVVZcj46OJiEhgejoaFJSUpStLW1sbJS9xbOar7aSoLuYyU2gntMgXQghhBBCCFFwbt26xYABA7h37x4WFha0atWKEydOYGFhAcCECRNITEzE3d2dhIQEHBwcCA0NVXnE8a+//sLLy4snT55gZ2fHTz/9xODBg1XeZ8+ePfj6+pKUlISDgwPbtm3LNFuuydOnT/n888+5desWpqam2NnZsXr1avr375+jcaZPn05QUJDyOj1ZdOjQIWVl8vyYb1GQZ7q1UHoQXFjXUJzLyzU9l5PXO5m3bt1S21a9evU8jV1QNM0ZtHfeQuSVZLqFEEIIUVQk0y1ylR3XFXkJrN9c1OFNtWrVyvXYWd1QKajqAwmqtYemhfhAexfj01USVIu80nTjRr6+hBBCaCJBt44rTlnq/KDLFQ5CCCGEEEKI4keCbh2XmJgogWYBMTExKbCxbWxsCmzskuj1rSzelL7wRm79999/atvSn6vKjawy2ZquCfJ+XUJVQX4NieJBstlCCCFyS4JuoZYuZtHlBoQQQgghhBBCm0jQrYVMTEyIjo4u6mlIFl0IIYQQQggh8kiCbi3k5+eHu7t7tjLN8fHxWY6X24x1dsbWdnFxcWrbqlatqvHcrNrzIjQ0VGO7q6trgb13cVSQ5b95KSHPCylpLlyaPu+kpCSN5xobG6ttS01N1Xiu7PsrhBBCFH8SdGux7GSas7PKdW4z1pmNre0l55KZF0IIIYQQuq5WrVrcvHkzw/HPP/+cpUuX0q5dO44cOaLS9umnn7Js2TIA7t27x6BBg/jnn3+4d+8elStXpmfPnsyaNYuyZcsq5wQHB/P9999z9epVzM3N6dKlC3PmzKFixYoArFq1iuHDh6u8j7GxsRIPJCcnM3XqVHbv3s21a9cwNzenY8eOzJ49GysrK+WcHj16EB4eTnx8POXLl6djx474+/ur9NmwYQOzZs3iypUrWFhYMHbsWDw8PFTeOykpiRkzZrB69Wri4uKwtLRk+vTpjBgxIjcfc6GRoFuoZWJikiHwjo+PJzg4uIhmlHMFma3OizZt2hT1FITQOtq6j7ymTHZWJJMthBAiN06dOkVKSoryOiIigg8++IC+ffsqx0aPHs2MGTOU12+99ZbyZ319fXr27Ml3332HhYUFkZGRfPHFFyQkJLBmzRoA/vzzT4YMGcKCBQtwdXUlJiYGNzc3Ro8eTUhIiDJW2bJluXz5svJaT09P+fOzZ884e/Ys06ZNw8HBgfv37zN+/Hh69OjB6dOnlX7t27fn22+/xdLSkpiYGL7++mv69OnD8ePHAdizZw+DBg1i8eLFdOrUiYsXLzJ69GhMTU0ZO3asMk6/fv24c+cOgYGB2NjYEBsbm2VVmTaQoFuoldn+3Z6engW2h3R+kEy3EEIIIYTQdW8+3jZ79mzq1q1L27ZtlWNvvfWW2gRT+fLl+eyzz5TX1tbWfP7558yZM0c5FhYWRq1atRg3bhwAtWvX5tNPP8Xf319lLD09PbXvY25unuGxySVLlvDuu+8SHR1NzZo1AdUKWmtrayZPnkyvXr1ITk6mVKlS/Pbbb/Tq1Qs3NzcA6tSpg6enJ/7+/nzxxRfo6emxd+9ejhw5wrVr16hQoQLwqiJAF0jQreMyy0a/KT+fzc4sEBdCCCGEEEIUjBcvXrB69WomTpyokmUODg5m9erVVK1aFVdXV6ZNm6aS7X7d7du3CQkJUQnanZyc+Pbbb9m9ezddunQhPj6eTZs20bVrV5Vznzx5grW1NampqbzzzjvMmjWLhg0bqp3vw4cP0dPTo1y5cpm2JyQkEBwcjLOzM6VKlQJelY2/OXdTU1Nu3brFzZs3qVWrFtu3b6dZs2Z8//33/Pbbb5QuXZoePXowc+ZMTE1NNX6GRU2Cbh2XnSBYmzPTJVW1atWK5H01LSwH2luOL0qGoiof11X79u3T2O7i4lJIMxFCCJEdSUlJGRbnNDY2zvIxpq1bt/LgwQOGDRumHBs4cCDW1tZYWVnxzz//MGnSJC5fvqxSFg4wYMAAtm3bxvPnz3F1deWXX35R2lq2bElwcDD9+/cnMTGRly9f4urqytKlS5U+tra2rFixgsaNG/Pw4UPmzp2Ls7Mz58+fz/TndmJiIpMmTWLAgAEqz44DTJo0iSVLlvDs2TPee+89du7cqbS5uLjg7u7OsGHDaN++PZGRkcybNw+A2NhYatWqxbVr1zh27BgmJiZs2bKFu3fv8vnnn3Pv3j1Wrlyp8TMsahJ0l0DavhhaXkh5uRBCCCGE0EZ+fn74+PioHPPy8sLb21vjeYGBgXTp0kVl0bExY8Yof27UqBGWlpZ06NCBqKgo6tatq7QtWLAALy8vrly5gqenJxMnTuTHH38E4MKFC4wfP57p06fj4uJCbGwsHh4euLm5ERgYCLzKhjs5OSnjOTs7Y29vz08//cTMmTNV5pmcnEy/fv1IS0sjICAgw3V4eHgwcuRIbt68iY+PD0OGDGHnzp3o6ekxevRooqKi6N69O8nJyZQtW5bx48fj7e2trI+SmpqKnp4ewcHBmJubAzB//nz69OnDjz/+qNXZbgm6S4A3S9B1bTE0baVp0Yasthgqqpse169f19gumW4hdEdJzGRr62J7QgiRHelB7+uyynLfvHmT/fv3Z8hgv6lFixYAREZGqgTdVatWpWrVqtjZ2VGhQgVat27NtGnTsLS0xM/Pj5YtWyorhDdu3JjSpUvTunVrvvvuOywtLTO8T6lSpWjSpAmRkZEqx9MD7ps3b3Lw4MEMWW6ASpUqUalSJerXr4+9vT01atTgxIkTODk5oaenh7+/P7NmzSIuLg4LCwsOHDgAvHq+G8DS0pJq1aopATeAvb09aWlp3Lp1i3r16mn8jIqSBN0lwJsl6Nq+GFpeSKZbCCGEEEJoo+yUkr9p5cqVVK5cmW7dumnsFx4eDpBpoJwuPWGUnhx69uwZhoaq4aCBgQEAaWlpmY6RkpLCv//+q/Lcd3rAffXqVQ4dOqRsN6bJm3N5/f3TH8Ncu3YtTk5OyqJyLVu2ZOPGjTx58gQzMzMArly5gr6+vtbfdJWgW0uZmJgQHR1dIGOXpMXQ/vvvP7Vtb64K+aasnn9++fKl2rasvvHbt2+vsb2gvF4eJISuSd/iJDMDBw4sxJmIoqLtv1QJUVSSk5PVtqUvVCV0T2pqKitXrmTo0KEqwXFUVBRr1qyha9euVKxYkX/++Qd3d3fatGlD48aNAdi9ezd37tyhefPmmJmZcf78eTw8PGjZsqWy4rerqyujR48mICBAKS+fMGEC7777rlLKPmPGDN577z1sbGx48OABc+bM4ebNm4waNQp49bXXp08fzp49y86dO0lJSVF+h65QoQJGRkacPHmSU6dO0apVK8qXL09UVBTTpk2jbt26yu+md+/eZdOmTbRr147ExERWrlzJxo0bVfYiHzhwIDNnzmT48OH4+Phw9+5dPDw8GDFihFaXlgPIBqJays/Pj8qVKxf1NIQQQgghhBBFYP/+/URHRzNixAiV40ZGRuzfv59OnTphZ2fHV199Re/evdmxY4fSx9TUlJ9//plWrVphb2+Pu7s7PXr0UFm8bNiwYcyfP58lS5bw9ttv07dvX2xtbVVK2e/fv8/o0aOxt7ena9euPHr0iOPHj9OgQQMAYmJi2L59O7du3cLR0RFLS0vlv/Q9uN966y1CQkLo0KEDtra2jBw5ksaNG3PkyBGVzH9QUBDNmjWjZcuWnD9/nsOHD/Puu+8q7WZmZoSGhvLgwQOaNWvGoEGDcHV15YcffsjfD74A6KWpqx3QQe7u7ixYsED5v65Lv47ivPBZfnvz711bM91CiJyTTLcQQmROMt1CaDcpL9cBiYmJxeImQlHIKrDWJP2ZFnUePnyY67GLo7CwMI3teSltj4qK0tj++oIhImtZLfSX0+fNCkteAmtdvWYhhMgOCayF0G4SdOsgyXyrJzcnhBBCCCGEENpEgm4dJJnv/KFpyy/IOkt+9uxZtW22tra5mpMuK8hF2rQ1k52+Uqg6jo6OhTKPnLp48aLGdm2dd14UVSb7+fPnGtu1feEXIYTQVpp+j0vf11mI3DIwMCA2NjbDGlv37t2jcuXKpKSk5Gg8Cbp1wMWLFzPssy2EEEIIIYQQIv+pW/YsKSkJIyOjHI8nQbcOSE1NVclspwfgUmaekVQACCGEEEIIIXIjfSV0PT09fvnlF2U/cHi1R/kff/yBnZ1djseVoFsHmZiY4O7uTnx8PMHBwUU9HZ2V19KjihUr5vrcglx0TBSevJRhF2TZsaZV+6F4lo9rKykfF3l169Ytje2yW4YoqaSEXBSE9AReWloay5YtU1lY2cjIiFq1arFs2bIcjytBtxZLD67fXHXXz88PQKXkPCslJSsumW4hhBBCCKHratWqxc2bNzMc//zzz1m6dCmffvop+/fv5/bt25iZmeHs7Iy/v3+GLOyqVauYP38+V65coWzZsvTt25elS5cCr9aJcnNz48yZM1y8eJHu3buzdetWlfNDQkIICAggPDycpKQkGjZsiLe3Ny4uLir9YmJimDRpEnv27OHZs2fY2NiwcuVKmjVrBrzKHGfm+++/x8PDA4AePXoQHh5OfHw85cuXp2PHjvj7+2NlZQXA4cOHWbBgAX/99RePHj2iXr16eHh4MGjQoJx/wGpcv34dgPbt2xMSEkL58uXzZVwJurVYenDdqVOnTNvTg/LskKx4/ssqU6mJZLJFQWZAY2JiNLbnZSs9IYrS+fPnNbY3bNiwkGZSeCSTLUTJdOrUKZXFuiIiIvjggw/o27cvAE2bNmXQoEHUrFmThIQEvL296dSpE9evX1eys/Pnz2fevHnMmTOHFi1a8PTpU27cuKGMmZKSgqmpKePGjWPz5s2ZzuOPP/7ggw8+YNasWZQrV46VK1fi6urKyZMnadKkCQD379+nZcuWtG/fnj179mBhYcHVq1dVAtbY2FiVcffs2cPIkSPp3bu3cqx9+/Z8++23WFpaEhMTw9dff02fPn04fvw4AMePH6dx48ZMmjSJKlWqsHPnToYMGYK5uTndu3fPw6ed0aFDh1Rep6Sk8O+//2JtbZ2rQFwvTd1T4jrI3d2dBQsWKP8vLjp16sTvv/+epzEk053/jh49qratdevWhTYPId6kq6uqC5GVkhh0CyEEwIQJE9i5cydXr17NNGv8zz//4ODgQGRkJHXr1uX+/ftUq1aNHTt20KFDhyzHHzZsGA8ePMiQ6c5Mw4YN6d+/P9OnTwdg8uTJ/Pnnnxp/N35Tr169ePz4MQcOHFDbZ/v27fTq1YukpCS1e9F369aNKlWqsGLFimy/d3ZMmDCBRo0aMXLkSFJSUmjTpg1hYWG89dZb7Ny5k3bt2uVoPMl0lxDpWXMhhBBCCCGE7njx4gWrV69m4sSJmQbcT58+ZeXKldSuXZsaNWoAEBoaSmpqKjExMdjb2/P48WOcnZ2ZN2+e0ic3UlNTefz4MRUqVFCObd++HRcXF/r27cuRI0eoVq0an3/+OaNHj850jDt37rBr1y6CgoLUvk9CQgLBwcE4OzurDbgBHj58iL29fa6vR52NGzfyySefALBjxw5u3LjBpUuX+O2335gyZQp//vlnjsaToFsUawW5h2NxzGbv2LFDY7urq2shzUTkhWSyC9ebJWiva9++fSHOpPiTTLYQmZM9q3VDUlJShrWajI2NMTY21nje1q1befDgAcOGDVM5/uOPP/LNN9/w9OlTbG1tCQ0NVbazunbtGqmpqcyaNYtFixZhbm7O1KlT+eCDD/jnn39yte0VwNy5c3ny5An9+vVTjl27do2AgAAmTpzIt99+y6lTpxg3bhxGRkYMHTo0wxhBQUGUKVOGjz76KEPbpEmTWLJkCc+ePeO9995j586daueyYcMGTp06xU8//ZSra9Hk3r17VK1aFYDdu3fTt29f6tevz4gRI1i0aFGOx5OguwQoKaXlIAupCSGEEEII7eTn54ePj4/KMS8vL7y9vTWeFxgYSJcuXZQFxdINGjSIDz74gNjYWObOnUu/fv34888/MTExITU1leTkZH744Qdlfai1a9dStWpVDh06lGEhtOxYs2YNPj4+bNu2jcqVKyvHU1NTadasGbNmzQKgSZMmREREsGzZskyD7hUrVjBo0CBMTEwytHl4eDBy5Ehu3ryJj48PQ4YMYefOnRky/IcOHWL48OH8/PPPBXJDtkqVKly4cAFLS0v27t1LQEAAAM+ePVNZ0Ty7JOguARITE0tsMKqrd3eL6o51nTp1CmzsgpScnKy27cSJExrPLciKBU1/j7dv39Z4riyepD00/T2CZLOF7noz4/amrLJvQnvo6u87JY2npycTJ05UOZbV99nNmzfZv38/ISEhGdrMzc0xNzenXr16vPfee5QvX54tW7YwYMAALC0tAWjQoIHS38LCgkqVKhEdHZ3jua9bt45Ro0axceNGOnbsqNJmaWmp8j4A9vb2mS7OdvToUS5fvsz69eszfZ9KlSpRqVIl6tevj729PTVq1ODEiRMqixAfOXIEV1dXFixYwJAhQ3J8LdkxfPhw+vXrh6WlJXp6eso1nzx5UvbpLu5ym7GOj48v0PG1SUm9uSCEEEIIIbRbdkrJ37Ry5UoqV65Mt27dNPZLS0sjLS1NuZnWsmVLAC5fvqzcyE9ISODu3btYW1vnaA5r165lxIgRrFu3LtN5tGzZksuXL6scu3LlSqbvExgYSNOmTXFwcMjyfdNver9+g/Dw4cN0794df39/xowZk6PryAlvb2/efvtt/ve//9G3b1/l783AwIDJkyfneDwJunVA+vY+uc1Ye3p6ZmtrsZK2rdh///2nsb0ot1UqqjvWFy9e1Niurc9TalpgoyifvZfMQ/Egf4+iuJJMdvEhz3QXT6mpqaxcuZKhQ4diaPh/Ydu1a9dYv349nTp1wsLCglu3bjF79mxMTU3p2rUrAPXr16dnz56MHz+e5cuXU7ZsWTw9PbGzs1Op0Lpw4QIvXrwgISGBx48fKzugpK8Ps2bNGoYOHcqiRYto0aIFcXFxwKutT83NzYFXO0g5Ozsza9Ys+vXrx19//cXy5ctZvny5yvU8evSIjRs3Mm/evAzXevLkSU6dOkWrVq0oX748UVFRTJs2jbp16ypZ7kOHDtG9e3fGjx9P7969lbkYGRmpLOyWX/r06ZPhWGbl8tkhQbcOeP2ZidzI7srl2d3zWwghhBBCCFGw9u/fT3R0NCNGjFA5bmJiwtGjR1m4cCH379+nSpUqtGnThuPHj6vEDb/++ivu7u5069YNfX192rZty969e1WSFV27duXmzZvK6/S9t9N3lV6+fDkvX77kiy++4IsvvlD6DR06lFWrVgHQvHlztmzZgqenJzNmzKB27dosXLiQQYMGqcx73bp1pKWlMWDAgAzX+tZbbxESEoKXlxdPnz7F0tKSzp07M3XqVOUGYVBQEM+ePcPPz08lvmnbti2HDx/OyUebLUeOHGHu3LlKUqpBgwZ4eHjkKqEjQbdQmJiY6HzgLeXlQgghhBCiOOjUqZMS/L7OysqK3bt3Z3l+2bJlCQwMJDAwUG2fGzduaBwju8Fs9+7d6d69u8Y+Y8aMUVsS3qhRIw4ePKjx/FWrVimBfkFbvXo1w4cP56OPPmLcuHEA/Pnnn3To0IFVq1YxcODAHI0nQbdQlLS9vIuyfFxbZVZGo+uyWgSrqMruZKG0jLK6aabrNwVFyRUWFqax/fUFgoTIDSkhFyJ/+fr68v3336v87jFu3Djmz5/PzJkzJegWWSsOC6apI5luIYQQQgghRF5cu3YNV1fXDMd79OjBt99+m+PxJOgugUryFmK6QtM2LrLwTeGSxWkKz5vbjbzp0aNHatvKli2b39MRxVBRfQ1JJlsIIXRLjRo1OHDgADY2NirH9+/fT40aNXI8ngTdOiY/stTZ3UJMCCGEEEIIIUqar776inHjxhEeHo6zszPw6pnuVatWsWjRohyPJ0G3jsmPLHVOnovUtVJ0yeALIYQQQggh8uKzzz6jatWqzJs3jw0bNgBgb2/P+vXr6dmzZ47Hk6C7BMrJKuUlbe/u/HLr1i2N7VktoiUl5PknqxLwrFbtrFWrVv5NRmgk3xcC4L///lPbltcFMOUxBFFcyaNQQuS/Dz/8kA8//DBfxpKguwTKySrlnp6eOrVisGS6hRBCCCGErgsICCAgIEBJDjRs2JDp06fTpUsXpU9YWBhTpkzh5MmTGBgY4OjoyL59+zA1NQVeLfoVHh5OfHw85cuXp2PHjvj7+2NlZaWM8c8///DFF19w6tQpLCws+PLLL/nmm2+U9pCQEGbNmkVkZCTJycnUq1ePr776isGDB2c6bzc3N3766ScWLFjAhAkTlOO+vr7s2rWL8PBwjIyMePDggcp5q1atYvjw4ZmOeefOHZX9x+FVqXfbtm15++23CQ8Pz+rjzLXTp0+r7NPdtGnTXI0jQbfQSNe3EdN053fOnDkaz500aVKu3zev20HJHevCUxwz2Vn98HF0dCyUeeRUw4YNi3oK+e7QoUMa29u3b19IM9Edsp2jEDlXEn83KO6/K1WvXp3Zs2dTr1490tLSCAoKomfPnvz99980bNiQsLAwOnfujKenJ4sXL8bQ0JBz586pXHv79u359ttvsbS0JCYmhq+//po+ffpw/Phx4NXikp06daJjx44sW7aMf//9lxEjRlCuXDllP+0KFSowZcoU7OzsMDIyYufOnQwfPpzKlSvj4uKiMuctW7Zw4sQJlaA+3YsXL+jbty9OTk6Z7hvev39/OnfurHJs2LBhJCYmZgi4Hzx4wJAhQ+jQoQN37tzJ3QechVu3bjFgwAD+/PNPypUrp7yvs7Mz69aty/Hv+hJ064D0cvD4+PgMX3TpdO3Z64IimW4hhBBCCKHr3tyuytfXl4CAAE6cOEHDhg1xd3dn3LhxTJ48Welja2urcs7r1arW1tZMnjyZXr16kZycTKlSpQgODubFixesWLECIyMjGjZsSHh4OPPnz1eC7nbt2qmMOX78eIKCgjh27JhK0B0TE8OXX37Jvn376NatW4br8fHxAV5ltDNjamqqZOjh1aNGBw8ezDRAd3NzY+DAgRgYGLB169ZMx8urUaNGkZyczMWLF5XP9fLlywwfPpxRo0axd+/eHI0nQbcOSM82ayrzlm3AhBBCCCGEKH5SUlLYuHEjT58+xcnJifj4eE6ePMmgQYNwdnYmKioKOzs7fH19adWqVaZjJCQkEBwcjLOzM6VKlQJelae3adMGIyMjpZ+Liwv+/v7cv3+f8uXLq4yRlpbGwYMHuXz5Mv7+/srx1NRUBg8ejIeHR75Vrf3666+89dZb9OnTR+X4ypUruXbtGqtXr+a7777Ll/fKzJEjRzh+/LjKjQxbW1sWL15M69atczyeBN2ixHrzm/hNYWFhGtsLct/VgiqLioqK0thet27dAnlfUbi0tXy8IGlafAuKrmRZyseFEKJg6GIJeVJSEklJSSrHjI2N1S4U+u+//+Lk5ERiYiJmZmZs2bKFBg0acOLECQC8vb2ZO3cujo6O/Prrr3To0IGIiAjq1aunjDFp0iSWLFnCs2fPeO+999i5c6fSFhcXR+3atVXes0qVKkpbetD98OFDqlWrRlJSEgYGBvz444988MEHyjn+/v4YGhoybty4PHw6qgIDAxk4cKBK9vvq1atMnjyZo0ePYmhYsGFsjRo1SE5OznA8JSUl0/L5rEjQLRTFoURdsv1CCCGEEEIb+fn5KWXW6by8vPD29s60v62tLeHh4Tx8+JBNmzYxdOhQjhw5ojzP/umnnyqLjzVp0oQDBw6wYsUKlTWZPDw8GDlyJDdv3sTHx4chQ4awc+dO9PT0sj3vMmXKEB4ezpMnTzhw4AATJ06kTp06tGvXjjNnzrBo0SLOnj2bozE1CQsL4+LFi/z222/KsZSUFAYOHIiPjw/169fPl/fRZM6cOXz55ZcsXbqUZs2aAa8WVRs/fjxz587N8XgSdOuoNwPk+Pj4XJ33uuK4PdizZ8/Utp08eVLjuQ4ODvk9nSInmWxRXMniW7ojr1sqCiGErvL09GTixIkqxzRth2lkZISNjQ0ATZs25dSpUyxatEh5jrtBgwYq/e3t7YmOjlY5VqlSJSpVqkT9+vWxt7enRo0anDhxAicnJ6pWrZphIbL011WrVlWO6evrK/NwdHTk4sWL+Pn50a5dO44ePUp8fDw1a9ZU+qekpPDVV1+xcOHCLLdmzcwvv/yCo6Ojykrhjx8/5vTp0/z999+MHTsWeFXWnpaWhqGhIb///jvvv/9+jt9LnWHDhvHs2TNatGihZNVfvnyJoaEhI0aMYMSIEUrfhISELMeToFtHvfkMd3a39dL07LeubQ+WGcl0CyGEEEIIbaSplDw7UlNTSUpKolatWlhZWXH58mWV9itXrqhsKZbZ+YBS4u7k5MSUKVOUhdUAQkNDsbW1zfA8d2bzABg8eDAdO3ZUaXdxcWHw4MFqtwDT5MmTJ2zYsCHDDkply5bl33//VTn2448/cvDgQTZt2pShTD6vFi5cmK/jSdAtFLq+PZgQQgghhBDFgaenJ126dKFmzZo8fvyYNWvWcPjwYfbt24eenh4eHh54eXnh4OCAo6MjQUFBXLp0iU2bNgGvKjpPnTpFq1atKF++PFFRUUybNo26desq6xKll2uPHDmSSZMmERERwaJFi1SSWH5+fjRr1oy6deuSlJTE7t27+e233wgICACgYsWKVKxYUWXupUqVomrVqiqLkEVHR5OQkEB0dDQpKSnK9qY2NjaYmZkp/davX8/Lly/55JNPVMbU19fn7bffVjlWuXJlTExMMhzPD0OHDs1Wv9mzZ/PgwQNlWzF1JOgWxdrr38RvunTpksZzBw4cmN/TUWjaWxIKbnGQHTt2aGx/c3sKIYTIb1I+LoQQWYuPj2fIkCHExsZibm5O48aN2bdvn7KA2YQJE0hMTMTd3Z2EhAQcHBwIDQ1VHiV86623CAkJwcvLi6dPn2JpaUnnzp2ZOnWqkm03Nzfn999/54svvqBp06ZUqlSJ6dOnK9uFATx9+pTPP/+cW7duYWpqip2dHatXr6Z///45up7p06cTFBSkvG7SpAkAhw4dUtmWLDAwkI8++ijLIFZbzJo1i379+mU5X720tLS0wplSwXN3d2fBggXK/4ub9NLvzK4xu4ugpT9zoesLpqmTk7/36dOna2yfMWNGXqejlgTdQgghhBBC6LYyZcpw7tw56tSpo7GfZLqLieyWhru7u8ue3v9f8+bNi+y9i2qbCwmqhci59BI4dTRt0ZbVAjK1atXK8XyEEEIIoVsk6C7BisMWYW+SmwlCCCGEEEIIbSJBdwljYmKibCVQ0jPeeX1W5Pz582rbGjZsmKex80LTdjzyLKXQZtq6lZSmTHZWJJMthBBCiKKpcRW5YmJiku39uNXx8/OjcuXK+TQjIYQQQgghhBCaSKZbh/j5+WnVPtraWJ5ekjP3QgghhBBCiMLTunVrTE1Ns+wnQbfIdfAcHx9PcHBwAcyocDx79kxj+6NHjzS2Jycn5+d08o2UkAtdpa1fu8+fP9fYnp0ftkIIIYTQbln97v+6smXLArB79+5s9ZeguwQyMTHB3d1dKVXP7bPdnp6eWpV5B8l0CyGEEEII3eft7Y2Pj4/KMVtbWy5dusSNGzeoXbt2pudt2LCBvn37AnDgwAGmTZvGv//+S+nSpRk6dCi+vr4YGr4KAdWNExYWxnvvvQe8SjL5+fkRFBRETEwMtra2+Pv707lzZ6W/n58fISEhXLp0CVNTU5ydnfH398fW1jbDuFOmTOHkyZMYGBjg6OjIvn37lJvXvr6+7Nq1i/DwcIyMjHjw4EGGuWV1TXlRrlw59PT0stU3JSUlR2NL0F0CpW8vlteAObvblBUlTQszXbhwQeO5Li4uGtsLcrG0J0+eqG0zMzMrsPcVQmSUl0y2bBkmhCgMqampatuKaptSkXcNGzZk//79yuv0wLJGjRrExsaq9F2+fDlz5syhS5cuAJw7d46uXbsyZcoUfv31V2JiYnBzcyMlJYW5c+eqnLt//36V32srVqyo/Hnq1KmsXr2an3/+GTs7O/bt28eHH37I8ePHadKkCQBHjhzhiy++oHnz5rx8+ZJvv/2WTp06ceHCBUqXLg28Crg7d+6Mp6cnixcvxtDQkHPnzql8fb548YK+ffvi5OREYGBghs8jJ9eUG4cOHVL+fOPGDSZPnsywYcNwcnJSriEoKChXMZAE3UIIIYQQQgihZQwNDalatWqG4wYGBhmOb9myhX79+inJmfXr19O4cWOmT58OgI2NDd9//z39+vXDy8uLMmXKKOdWrFgx0/cB+O2335gyZQpdu3YF4LPPPmP//v3MmzeP1atXA7B3716Vc1atWkXlypU5c+YMbdq0AV4l+8aNG8fkyZOVfm9mwtMz+6tWrcp0Ljm5ptxo27at8ucZM2Ywf/58BgwYoBzr0aMHjRo1Yvny5QwdOjRHY0vQLdTSxoXSsiLl5UIIIYQQoji4evUqVlZWmJiY4OTkhJ+fHzVr1szQ78yZM4SHh7N06VLlWFJSEiYmJir9TE1NSUxM5MyZM7Rr10453qNHDxITE6lfvz7ffPMNPXr0yHKcY8eOqZ33w4cPAahQoQLwah2okydPMmjQIJydnYmKisLOzg5fX19atWqV7c8jJ9eUV2FhYSxbtizD8WbNmjFq1KgcjydBt1CrOOzjrWlhpvv37+dp7FKlSqlt07SHN2Rdmi4l5EIUD1I+LoQoDFJCrhuSkpJISkpSOWZsbIyxsXGGvi1atGDVqlXY2toSGxuLj48PrVu3JiIiIkNGNzAwEHt7e5ydnZVjLi4uLFy4kLVr19KvXz/i4uKYMWMGgFKabmZmxrx582jZsiX6+vps3ryZXr16sXXrViXwdnFxYf78+bRp04a6dety4MABQkJC1D7TnJqayoQJE2jZsiVvv/02ANeuXQNePac+d+5cHB0d+fXXX+nQoQMRERHUq1cvW59fdq4pv9SoUYOff/6Z77//XuX4L7/8Qo0aNXI8ngTdJdibC6qBanY7sz3BtT37res3CYQQQgghRPHk5+eXYXE0Ly8vvL29M/RNfzYboHHjxrRo0QJra2s2bNjAyJEjlbbnz5+zZs0apk2bpnJ+p06dmDNnDm5ubgwePBhjY2OmTZvG0aNHlZs0lSpVYuLEico5zZs35/bt28yZM0cJuhctWsTo0aOxs7NDT0+PunXrMnz4cFasWJHpNX7xxRdERESoZMLT1xz49NNPGT58OABNmjThwIEDrFixItvPSGfnmvLLggUL6N27N3v27KFFixYA/PXXX1y9epXNmzfneDwJukuwzBZUez27ndlCa8Uh+53OyMiowMYuyEXWhMiruLg4je3qnusSIt2bmZrXZZaxEUIULE3bmGqqzBOFy9PTUyXIhez/m1muXDnq169PZGSkyvFNmzbx7NkzhgwZkuGciRMn4u7uTmxsLOXLl+fGjRt4enpSp04dte/TokULQkNDldcWFhZs3bqVxMRE7t27h5WVFZMnT850jLFjx7Jz507++OMPlWpTS0tLABo0aKDS397enujo6Gxdf16uKTe6du3KlStXCAgI4NKlSwC4urri5uYmmW6Rv9Iz4a/LLPsN2pMBLy43BIQQQgghRPGirpQ8O548eUJUVBSDBw9WOR4YGEiPHj2wsLDI9Dw9PT2srKwAWLt2LTVq1OCdd95R+z7h4eFKkPw6ExMTqlWrRnJyMps3b6Zfv35KW1paGl9++SVbtmzh8OHDGbYhq1WrFlZWVly+fFnl+JUrV1Qy+tmV02vKrRo1ajBr1qx8GUuCbqFWZqUe6rYZK04ZcCGEEEIIIYrS119/jaurK9bW1ty+fRsvLy8MDAxUVtOOjIzkjz/+YPfu3ZmOMWfOHDp37oy+vj4hISHMnj2bDRs2YGBgAEBQUBBGRkbK1l8hISGsWLGCX375RRnj5MmTxMTE4OjoSExMDN7e3qSmpvLNN98ofb744gvWrFnDtm3bKFOmjFJRZ25ujqmpKXp6enh4eODl5YWDgwOOjo4EBQVx6dIlNm3apIwTHR1NQkIC0dHRpKSkEB4eDrxapTx9vaOsrimv/vnnn2z1a9y4cY7GlaBb5Ehm2W9QnwHXZun7BgpR0pTE8nHZLzt/SQm5ENpFSsiLn1u3bjFgwADu3buHhYUFrVq14sSJEyoZ7RUrVlC9enU6deqU6Rh79uzB19eXpKQkHBwc2LZtW4bM8syZM7l58yaGhobY2dmxfv16+vTpo7QnJiYydepUrl27hpmZGV27duW3336jXLlySp+AgACADKuHr1y5kmHDhgEwYcIEEhMTcXd3JyEhAQcHB0JDQ6lbt67Sf/r06QQFBSmv028GHDp0SBk7O9eUF46Ojujp6ZGWlqa2j56entqF5NSRoFvkiLqFDjw9PdVmwQuTZNuFEEIIIYSuW7duXZZ9Zs2apbH8+eDBgxrPHzp0aJb7Tbdt25YLFy5o7KMpQH3d5MmTVfbpftOqVavU7tGdLqtryqvr168XyLgSdAuV7HVuM9bZXXVQm5QvX76opyCEeE1UVJTG9tfvhr/p+fPnGs+VTHbOyGJ7QgghSiJra+sCGVeCbqESMKcH39qyMFpOSaZbCCGEEEIIkRe1atVixIgRDBs2jJo1a+Z5PAm6dUxme2sXBFkYTQghhBBCCFESTZgwgVWrVjFjxgzat2/PyJEj+fDDD3O9pokE3Toms721hXqaSiQdHR0LbyJaIn0VSHVK4mcitIem8vGsmJqa5uNMhJSPCyGEKMkmTJjAhAkTOHv2LKtWreLLL7/k888/Z+DAgYwYMSLHW5QVq6C7sLLAIncKo2RdsvNCCCGEEEKI/PDOO+/wzjvvMG/ePH788UcmTZpEQEAAjRo1Yty4cQwfPhw9Pb0sxylWQbdkgbVbUZSsa8rWPH36tBBnoh1kSxEhRLr79++rbZOFJoUQQghITk5my5YtrFy5ktDQUN577z1GjhzJrVu3+Pbbb9m/fz9r1qzJcpxiFXSLvMtutUBustavj1lQWW/JdAshhBBCCCHy4uzZs6xcuZK1a9eir6/PkCFDWLBgAXZ2dkqfDz/8kObNm2drPAm6hYrsVgvkJmv9+pjasFBbdHS0xva8PF+qrUqXLl3UUxBCaAnJZgshhPby9vbGx8dH5ZitrS2XLl0CXq1b5OHhQWhoKI8fP8bW1pYpU6bQu3dvpb+vry+7du0iPDwcIyMjHjx4oDLevXv3GDRoEP/88w/37t2jcuXK9OzZk1mzZlG2bFml3+HDh5k4cSLnz5+nRo0aTJ06lWHDhintjx8/Ztq0aWzZsoX4+HiaNGnCokWLVALSO3fuMGnSJH7//XcePHhAmzZtWLx4MfXq1QPgxo0b1K5dO9PPYsOGDfTt2xeAU6dOMXnyZM6cOYOenh7vvvsu33//PQ4ODgBcvnwZNzc3Lly4wMOHD7GysmLgwIF4eXnlqOKzefPmfPDBBwQEBNCrV69Mz61duzYff/xxtsaToFsUmvzYD1wIIYQQQoiSoGHDhuzfv195bWj4f6HbkCFDePDgAdu3b6dSpUqsWbOGfv36cfr0aZo0aQLAixcv6Nu3L05OTgQGBmYYX19fn549e/Ldd99hYWFBZGQkX3zxBQkJCUrJ9PXr1+nWrRtubm4EBwdz4MABRo0ahaWlJS4uLgCMGjWKiIgIfvvtN6ysrFi9ejUdO3bkwoULVKtWjbS0NCVw3bZtG2XLlmX+/PlKn9KlS1OjRg1iY2NV5rd8+XLmzJlDly5dAHjy5AmdO3emR48e/Pjjj7x8+RIvLy9cXFz43//+R6lSpShVqhRDhgzhnXfeoVy5cpw7d47Ro0eTmprKrFmzsv3ZX7t2Lcs9u0uXLs3KlSuzNZ4E3aLQZLYfeF5kVqJe1NlzIYQQQggh8oOhoaHa9YmOHz9OQEAA7777LgBTp05lwYIFnDlzRgm60zPlq1atynSM8uXL89lnnymvra2t+fzzz5kzZ45ybNmyZdSuXZt58+YBYG9vz7Fjx1iwYAEuLi48f/6czZs3s23bNtq0aQO8ytLv2LGDgIAAvvvuO65evcqJEyeIiIigYcOGAAQEBFC1alXWrl3LqFGjMDAwyHCtW7ZsoV+/fpiZmQFw6dIlEhISmDFjBjVq1ADAy8uLxo0bc/PmTWxsbKhTpw516tRRuabDhw9z9OjRbHzi/yergDunJOgWReL1rHduxcfHExwcnKc5CCGEEEIIURiSkpJISkpSOWZsbKx27+erV69iZWWFiYkJTk5O+Pn5UbNmTQCcnZ1Zv3493bp1o1y5cmzYsIHExETatWuX6/ndvn2bkJAQ2rZtqxwLCwujY8eOKv1cXFyYMGECAC9fviQlJSXD79WmpqYcO3ZMuW5Q/d1bX18fY2Njjh07xqhRozLM5cyZM4SHh7N06VLlmK2tLRUrViQwMJBvv/2WlJQUAgMDsbe3p1atWpleU2RkJHv37uWjjz7K8vrLly+frZXIARISErLVL50E3SJTWQXFeS0Pfz3rnVuenp4Z5iiZbiGEEEIIoY38/PwyPKft5eWFt7d3hr4tWrRg1apV2NraEhsbi4+PD61btyYiIoIyZcqwYcMG+vfvT8WKFTE0NOStt95iy5Yt2NjY5HheAwYMYNu2bTx//hxXV1d++eUXpS0uLo4qVaqo9K9SpQqPHj3i+fPnlClTBicnJ2bOnIm9vT1VqlRh7dq1hIWFKXOxs7OjZs2aeHp68tNPP1G6dGkWLFjArVu3MpSUp0sPpp2dnZVjZcqU4fDhw/Tq1YuZM2cCUK9ePfbt26dSeg+vbkqcPXuWpKQkxowZw4wZM7L8HBYuXKj8+d69e3z33Xe4uLjg5OQEvLoBsW/fPqZNm5blWG+SoFtkKqugWBu2Zctr4F65cuV8monuUHcXUNelpqaqbdPX1y/EmYi8uHHjhsb24vr1K4QoHpKTkzW2y7adwtPTk4kTJ6ocU5flTn+OGaBx48a0aNECa2trNmzYwMiRI5k2bRoPHjxg//79VKpUia1bt9KvXz+OHj1Ko0aNcjSvBQsW4OXlxZUrV5Q5/vjjj9k+/7fffmPEiBFUq1YNAwMD3nnnHQYMGMCZM2eAV1/7ISEhjBw5kgoVKmBgYEDHjh3p0qULaWlpGcZ7/vw5a9asyRDcPn/+nJEjR9KyZUvWrl1LSkoKc+fOpVu3bpw6dQpTU1Ol7/r163n8+DHnzp3Dw8ODuXPn8s0332i8jqFDhyp/7t27NzNmzGDs2LHKsXHjxrFkyRL279+f41hIgm6RLwpqC7Cckky3EEIIIYTQRppKybNSrlw56tevT2RkJFFRUSxZskTlGWkHBweOHj3K0qVLWbZsWY7Grlq1KlWrVsXOzo4KFSrQunVrpk2bhqWlJVWrVuXOnTsq/e/cuUPZsmWVILdu3bocOXKEp0+f8ujRIywtLenfv7/Ks9VNmzYlPDychw8f8uLFCywsLGjRogXNmjXLMJ9Nmzbx7NkzhgwZonJ8zZo13Lhxg7CwMCWpsmbNGsqXL8+2bdtUVhJPf+a7QYMGpKSkMGbMGL766isMDAyy9Zns27cPf3//DMc7d+7M5MmTszXG6yToFvlCG7YAE0IIIYQQojh68uQJUVFRDB48mGfPngEZq/kMDAw0Vv9lR/r56c9hOzk5sXv3bpU+oaGhSsn160qXLk3p0qW5f/8++/bt4/vvv8/Qx9zcHHj1vPrp06eVMvHXBQYG0qNHDywsLFSOP3v2DH19fZXnrtNfa7ru1NRUkpOTSU1NzXbQXbFiRbZt28ZXX32lcnzbtm1UrFgxW2O8ToJukStvPvOti1uA3b9/v6inIPKJlJAXD1I+LoTQZVI+LvLT119/jaurK9bW1ty+fRsvLy8MDAwYMGAA5cqVw8bGhk8//ZS5c+dSsWJFtm7dSmhoKDt37lTGiI6OJiEhgejoaFJSUggPDwfAxsYGMzMzdu/ezZ07d2jevDlmZmacP38eDw8PWrZsqfxMdnNzY8mSJXzzzTeMGDGCgwcPsmHDBnbt2qW8z759+0hLS8PW1pbIyEg8PDyws7Nj+PDhSp+NGzdiYWFBzZo1+ffffxk/fjy9evWiU6dOKtcdGRnJH3/8kSHQB/jggw/w8PDgiy++4MsvvyQ1NZXZs2djaGhI+/btAQgODqZUqVI0atQIY2NjTp8+jaenJ/3798/R96iPjw+jRo3i8OHDtGjRAoCTJ0+yd+9efv7552yPk06CbpErbz5Pnd3nGgq6DF2y7UIIIYQQQtfdunWLAQMGcO/ePSwsLGjVqhUnTpxQsr+7d+9m8uTJuLq68uTJE2xsbAgKCqJr167KGNOnTycoKEh5nb6V2KFDh2jXrh2mpqb8/PPPuLu7k5SURI0aNfjoo49Uyqdr167Nrl27cHd3Z9GiRVSvXp1ffvlF2aMb4OHDh3h6enLr1i0qVKhA79698fX1VQlyY2NjmThxInfu3MHS0pIhQ4ZkuiDZihUrqF69eoZgHF4tyLZjxw58fHxwcnJCX1+fJk2asHfvXiwtLYFX26z5+/tz5coV0tLSsLa2ZuzYsTl+BnvYsGHY29vzww8/EBISAvzfdmnpQXhO6KVl9vS6jnN3dy/2wZe2XWN2g+m8bvOVU5qy2Xv27NF47sCBAzW2v7nlw+ty+7yOEEIIIYQQoniRTLfIF9ldSTyzbb7ykzbdiBBCCCGEEELoppSUFLZu3crFixcBaNiwIT169Mj2c+Gvk6BbFKr82J9bCCGEEEIIIQpKZGQk3bp149atW9ja2gKv4pgaNWqwa9cu6tatm6PxJOgWxVr58uXVtmVVPp4VKSEvGR49eqS2rWzZsrkeV/akFiLnbt26pbG9evXquR573759Gttff35RCCFE8TZu3Djq1KlDWFgYFSpUAODevXt88sknjBs3TmUhueyQoFtHXbx4sUDLtHWVlJcLIYQQQggh8uLIkSOcOHFCCbjh1TZis2fPpmXLljkeT4JuHZWamioBZjZoylKm73GoTtWqVfN7OkVO0+cBecvcaqvTp09rbNdUDQHkuHwou4oyky1Z9vx1+fJltW3pJWkif+Qlk50VyWQXLk176so2kCI75GtIFCRjY2MeP36c4fiTJ08wMjLK8XgSdBdjBb09lzaSGxFCCCGEEEKIvOjevTtjxowhMDCQd999F3i1T7ebmxs9evTI8XgSdBdjiYmJJT4I1ZS5vXbtmsZzs1qZMH2fxMxouvsKRXcHtjhmsrNy7949je3NmjUrpJloD8lk5y/JZou80vSsekFm94uSZCJFXhX3ryFvb298fHxUjtna2nLp0iUA4uLi8PDwIDQ0lMePH2Nra8uUKVPo3bu30t/X15ddu3YRHh6OkZERDx48UBnv3r17DBo0iH/++Yd79+5RuXJlevbsyaxZs1R+Zzx8+DATJ07k/Pnz1KhRg6lTpzJs2DClPSUlBW9vb1avXk1cXBxWVlYMGzaMqVOnoqenp/S7ePEikyZN4siRI7x8+ZIGDRqwefNmatasSUJCAl5eXvz+++9ER0djYWFBr169mDlzJubm5soY48aN488//yQiIgJ7e3vCw8NVrunw4cMsWLCAv/76i0ePHlGvXj08PDwYNGhQjj7/H374gaFDh+Lk5KTsN56cnEzPnj1ZtGhRjsYCCbp1WlaZ7Pj4+EKcjRBCCCGEECK/NGzYkP379yuvDQ3/L3QbMmQIDx48YPv27VSqVIk1a9bQr18/Tp8+TZMmTQB48eIFffv2xcnJicDAwAzj6+vr07NnT7777jssLCyIjIzkiy++ICEhgTVr1gBw/fp1unXrhpubG8HBwRw4cIBRo0ZhaWmpPJbj7+9PQEAAQUFBNGzYkNOnTzN8+HDMzc0ZN24cAFFRUbRq1YqRI0fi4+ND2bJlOX/+PCYmJgDcvn2b27dvM3fuXBo0aMDNmzdxc3Pj9u3bbNq0SWXeI0aM4OTJk/zzzz8Zrun48eM0btyYSZMmUaVKFXbu3MmQIUMwNzene/fu2f7sy5Urx7Zt24iMjOTChQsANGjQABsbm2yP8ToJunVYVpnszBZaK+4l5yU9sy+EEEIIIYoHQ0NDtWsMHT9+nICAAKX0eerUqSxYsIAzZ84oQXd6pnzVqlWZjlG+fHk+++wz5bW1tTWff/45c+bMUY4tW7aM2rVrM2/ePADs7e05duwYCxYsUILu48eP07NnT7p16wa8qqhbu3Ytf/31lzLOlClT6Nq1K99//71y7PV1c95++202b96s0ubr68snn3zCy5cvlRsOP/zwAwD//fdfpkH3t99+q/J6/Pjx/P7774SEhOQo6AYIDAxkwYIFXL16FYB69eoxYcIERo0alaNxQIJunaWptDmdiYlJhsA7Pj6e4ODggpqWTnF0dCywsfNa8iSLg+SfKlWq5On8+/fvq23LahE2bfX8+XON7aampoU0EyF0h6YFCPP6yEZxLSEXhUd+b9ANSUlJJCUlqRwzNjZWuw3t1atXsbKywsTEBCcnJ/z8/KhZsyYAzs7OrF+/nm7dulGuXDk2bNhAYmIi7dq1y/X8bt++TUhICG3btlWOhYWF0bFjR5V+Li4uTJgwQXnt7OzM8uXLuXLlCvXr1+fcuXMcO3aM+fPnA6++Pnft2sU333yDi4sLf//9N7Vr18bT05NevXqpnc/Dhw8pW7asSoY/Nx4+fIi9vX2Ozpk+fTrz58/nyy+/xMnJCXj1Wbi7uxMdHc2MGTNyNJ4E3TqqcuXKWfbx8/PLcMzT07NYbzUmmW4hhBBCCKGN/Pz8Mjyn7eXlhbe3d4a+LVq0YNWqVdja2hIbG4uPjw+tW7cmIiKCMmXKsGHDBvr370/FihUxNDTkrbfeYsuWLbkqfx4wYADbtm3j+fPnuLq68ssvvyhtcXFxGRIYVapU4dGjRzx//hxTU1MmT57Mo0ePsLOzw8DAgJSUFHx9fZXnqOPj43ny5AmzZ8/mu+++w9/fn7179/LRRx9x6NAhlSA/3d27d5k5cyZjxozJ8fW8bsOGDZw6dYqffvopR+cFBATw888/M2DAAOVYjx49aNy4MV9++aUE3UKzzAJxoX3krnT+yWtFg65mszUpjpnsuLg4je3FcQvAgnTo0CGN7e3bty+kmWiPglyAULadE6Jk8PT0ZOLEiSrH1GW5u3Tpovy5cePGtGjRAmtrazZs2MDIkSOZNm0aDx48YP/+/VSqVImtW7fSr18/jh49SqNGjXI0rwULFuDl5cWVK1eUOf7444/ZPn/Dhg0EBwezZs0aGjZsSHh4OBMmTMDKyoqhQ4cqlRg9e/ZUkn+Ojo4cP36cZcuWZQi6Hz16RLdu3WjQoEGmNySy69ChQwwfPpyff/6Zhg0b5ujc5OTkTBfbbdq0KS9fvszxXCToFkIIIYQQQogCpqmUPCvlypWjfv36REZGEhUVxZIlS4iIiFCCSQcHB44ePcrSpUtZtmxZjsauWrUqVatWxc7OjgoVKtC6dWumTZuGpaUlVatW5c6dOyr979y5Q9myZZWb+B4eHkyePJmPP/4YgEaNGnHz5k38/PwYOnQolSpVwtDQkAYNGqiMk/58+OseP35M586dKVOmDFu2bFFWDs+pI0eO4OrqyoIFCxgyZEiOzx88eDABAQFKiXy65cuX53gldJCgW+cV94XRckrKy4UQQgghRHHz5MkToqKiGDx4MM+ePQMyVkYaGBhkuW1tVtLPT3/23MnJid27d6v0CQ0NVZ5zBnj27JnGuRgZGdG8efMMVT1XrlzB2tpaef3o0SNcXFwwNjZm+/btysrmOXX48GG6d++Ov79/jsrTX69C0NPT45dffuH333/nvffeA17t0x0dHZ2rIF6Cbh1lYmJCdHQ0lStXlkBT6ISCXOTl9OnTatvyug+3Li5OUxIXSstr+fib+3y+LquStNzehQc4f/68xvaclsPll5JYPl6UpIRcCPGmr7/+GldXV6ytrbl9+zZeXl4YGBgwYMAAypUrh42NDZ9++ilz586lYsWKbN26ldDQUHbu3KmMER0dTUJCAtHR0aSkpCg/62xsbDAzM2P37t3cuXOH5s2bY2Zmxvnz5/Hw8KBly5bKIzVubm4sWbKEb775hhEjRnDw4EE2bNjArl27lPdxdXXF19eXmjVr0rBhQ/7++2/mz5/PiBEjlD4eHh7079+fNm3a0L59e/bu3cuOHTs4fPgw8Crg7tSpE8+ePWP16tU8evSIR48eAa8WkDYwMAAgMjKSJ0+eEBcXx/Pnz5VratCgAUZGRhw6dIju3bszfvx4evfurTx+ZmRkRIUKFTR+5n///bfK66ZNmwKvtjsDqFSpEpUqVcryZ3dm9NLS0tJyfJaWc3d3LxGBaPozEequtSRmwUvC37uukqC78JTEoDuvJOgWQugyXfxZJTT7+OOP+eOPP7h37x4WFha0atUKX19fZZutq1evMnnyZI4dO8aTJ0+wsbHh66+/ZvDgwcoYw4YNIygoKMPYhw4dol27dhw6dIgpU6Zw4cIFkpKSqFGjBh999BGTJ0+mXLlySv/Dhw/j7u7OhQsXqF69OtOmTWPYsGFK++PHj5k2bRpbtmwhPj4eKysrBgwYwPTp0zEyMlL6rVixAj8/P27duoWtrS0+Pj707NlTeQ91N3yvX7+u3ARo164dR44cUdtH3TW3bdtWCfCLggTdOiw96DYxMck0uJbtwYrOm9tBvCmrX9KL6gekpu2xoHguKpYVTX+XuX0uS2ifgrxxJUeI2wABAABJREFUUxzJwl9CiKImNxqELpHy8mIgMTEx05sMxX17sMyUhJstQgghhBBCCN0hQXcxJtuDCSGEEEIIIUTRkqBbFGvpCzBkpmzZshrP1VRuCppLTnW17Lgklo9nRVf/LjXZt2+fxnYXF5dCmon2kBLynJESciFEUZMScqFLJOguwYrjQmtSXi6EEEIIIYTQJhJ0l2DqngUvTq5cuaK2LavMlmS+RHFVsWLFop6CEOI1t27dUttWvXr1QpyJEEKIgiBBtw57fa/uN2Unix0fH6+2TVez4MX9JoIQQgghhBBCt0jQrcP8/PzUrk6enSy2ppXNS0IWXAghhBBCCG0VExPDpEmT2LNnD8+ePcPGxoaVK1cq1Zje3t6sW7eO//3vfxgZGdG0aVN8fX1p0aKFMkZCQgJffvklO3bsQF9fn969e7No0SLMzMyAV1tAurm5ceHCBR4+fIiVlRUDBw7Ey8tLZYvbhQsXEhAQQHR0NJUqVaJPnz74+flhYmICwB9//MGcOXM4c+YMsbGxbNmyhV69emW4posXLzJp0iSOHDnCy5cvadCgAZs3b6ZmzZpKn7CwMKZMmcLJkycxMDDA0dGRffv2YWpqyo0bN5g5cyYHDx4kLi4OKysrPvnkE6ZMmaKyJ7i2kaC7BDMxMVEbeGvKgusSbV0YLCoqSmO7gYGB2rZatWrl+n017WkJsihJSaGt3xdClFRSQi7ySvasLn7u379Py5Ytad++PXv27MHCwoKrV6+q/AyvX78+S5YsoU6dOjx//pwFCxbQqVMnIiMjsbCwAGDQoEHExsYSGhpKcnIyw4cPZ8yYMaxZswaAUqVKMWTIEN555x3KlSvHuXPnGD16NKmpqcyaNQuANWvWMHnyZFasWIGzszNXrlxh2LBh6OnpMX/+fACePn2Kg4MDI0aM4KOPPsr0mqKiomjVqhUjR47Ex8eHsmXLcv78eSVwh1cBd+fOnfH09GTx4sUYGhpy7tw55ev40qVLpKam8tNPP2FjY0NERASjR4/m6dOnzJ07N///IvKJXlpaWlpRTyK/ubu7l5gsbXrQ/Ob15vUzKC7l5ZqC27p16xb0dNSSoFsUpay+/orye0MIIUTOSdBd/EyePJk///yTo0ePZvucR48eYW5uzv79++nQoQMXL16kQYMGnDp1SsmO7927l65du3Lr1i2srKwyHWfixImcOnVKee+xY8dy8eJFDhw4oPT56quvOHnyJMeOHctwvp6eXqaZ7o8//phSpUrx22+/qb2G9957jw8++ICZM2dm+7rnzJlDQEAA165dy/Y5hU0y3cVMerCc10x1cdnj+++//1bbVpSBRVG99/Xr1zW2S7BVMhTV37NsVSaEEAVDAmvdkJSURFJSksoxY2PjTLcn3b59Oy4uLvTt25cjR45QrVo1Pv/8c0aPHp3p2C9evGD58uWYm5vj4OAAvMoalytXTmVx4I4dO6Kvr8/Jkyf58MMPM4wTGRnJ3r17VbLVzs7OrF69mr/++ot3332Xa9eusXv3bgYPHpzta09NTWXXrl188803uLi48Pfff1O7dm08PT2V4Dw+Pp6TJ08yaNAgnJ2diYqKws7ODl9fX1q1aqV27IcPH1KhQoVsz6UoSNBdzKQ/i52eAdfVjHVulZQKByGEEEIIoVv8/Pzw8fFROebl5YW3t3eGvteuXSMgIICJEyfy7bffcurUKcaNG4eRkRFDhw5V+u3cuZOPP/6YZ8+eYWlpSWhoKJUqVQIgLi4uw4LLhoaGVKhQgbi4OJXjzs7OnD17lqSkJMaMGcOMGTOUtoEDB3L37l1atWpFWloaL1++xM3NjW+//Tbb1x4fH8+TJ0+YPXs23333Hf7+/kpwf+jQIdq2batkqr29vZk7dy6Ojo78+uuvdOjQgYiICOrVq5dh3MjISBYvXqzVpeUgQXexV9IXRLO3ty/qKWgVyWSLolSQmWwpmRcic6dPn1bbJltjClG4PD09mThxosqxzLLc8Coz3KxZM+W56iZNmhAREcGyZctUgu727dsTHh7O3bt3+fnnn+nXrx8nT57MdHcjTdavX8/jx485d+4cHh4ezJ07l2+++QaAw4cPM2vWLH788UdatGhBZGQk48ePZ+bMmUybNi1b46c/AtGzZ08lOejo6Mjx48dZtmwZbdu2Vfp8+umnDB8+XLnuAwcOsGLFigyVuDExMXTu3Jm+ffuqrQDQFhJ0CyGEEEIIIUQBU1dKnhlLS0saNGigcsze3p7NmzerHCtdujQ2NjbY2Njw3nvvUa9ePQIDA/H09KRq1aoZHjl9+fIlCQkJVK1aVeV4jRo1AGjQoAEpKSmMGTOGr776CgMDA6ZNm8bgwYMZNWoUAI0aNeLp06eMGTOGKVOmZOvxhkqVKmFoaJjpNaU/F25paanM4c0+0dHRKsdu375N+/btcXZ2Zvny5Vm+f1GToLuYSl+Z/M1vtOJebl6Ss/pCCCGEEKJ4aNmyJZcvX1Y5duXKFaytrTWel5qaqjw37uTkxIMHDzhz5gxNmzYF4ODBg6SmpqpsK5bZGMnJyaSmpmJgYMCzZ88yBNbpi/5md01uIyMjmjdvrvGaatWqhZWVVaZ9unTporyOiYmhffv2NG3alJUrV+rEmgYSdOs4ExOTDHd+4P8WQntzS7CSVm5uaKidX+KPHj3S2J6+d2JmdOEfFpG1rL4GypYtW0gzKR6kfFyIzEkJuarw8HCN7Y6OjoUyDyGy4u7ujrOzM7NmzaJfv3789ddfLF++XMnqPn36FF9fX3r06IGlpSV3795l6dKlxMTE0LdvX+BVhrhz586MHj2aZcuWkZyczNixY/n444+VlcuDg4MpVaoUjRo1wtjYmNOnT+Pp6Un//v2VfbpdXV2ZP38+TZo0UcrLp02bhqurqxJ8P3nyhMjISGX+169fJzw8nAoVKih7cHt4eNC/f3/atGlD+/bt2bt3Lzt27ODw4cPAq1XPPTw88PLywsHBAUdHR4KCgrh06RKbNm0CXgXc7dq1w9ramrlz5/Lff/8p7/lm9l6baGdEIrLNz89P7V7b2VWcst8l6YaCEEIIIYQonpo3b86WLVvw9PRkxowZ1K5dm4ULFzJo0CDgVab50qVLBAUFcffuXSpWrEjz5s05evQoDRs2VMYJDg5m7NixdOjQAX19fXr37s0PP/ygtBsaGuLv78+VK1dIS0vD2tqasWPHqsQXU6dORU9Pj6lTpxITE4OFhQWurq74+voqfU6fPk379u2V1+nPrg8dOpRVq1YB8OGHH7Js2TL8/PwYN24ctra2bN68WWVl8gkTJpCYmIi7uzsJCQk4ODgQGhqq3FwPDQ0lMjKSyMhIqlevrvKZafNO2LJPdzHw+vW+ee1vBtTx8fEEBwerPb+42bFjh9o2V1fXQpyJqvPnz2tsf/0fy/x0//59je3ly5cvkPcVQghtd+PGDY3ttWrVKpR5CCGEKH4k013MvbnKX16z4kIIIYQQQgghsk+CbqEoDmXmxTVjL4QQQgghhNBNEnQLRXFcZK1SpUpq24qy1Lp+/foFNrYm5ubmRfK+onDJntVC5JyUjwshhCgoEnSXMOlbib3uzW3FCkJhZdGL200DIYQQQgghhG6ToLuEefMZb8j5c965CaAzW8CtMMTExKhtc3JyKsSZqHr58qXG9idPnqhty0sGXrYbKxmy+voSQghRvKSmpqptk5/9QhQ9CbpFjuWmDN3T07NQFnGTTLcQQgghhBBCm0jQLQpFZhl2IYQQQgghREa1atXi5s2bGY5//vnnLF26FICwsDCmTJnCyZMnMTAwwNHRkX379mFqasrhw4dV9s1+3V9//UXz5s25ceMGtWvXztAeFhbGe++9p7zeuHEj06ZN48aNG9SrVw9/f3+6du0KQHJyMlOnTmX37t1cu3YNc3NzOnbsyOzZs7GyslIZd9euXcyYMYN//vkHExMT2rZty9atWwE4d+4cs2fP5tixY9y9e5datWrh5ubG+PHjlfOHDRtGUFBQhvk2aNAgy+14i5oE3UJ5zlvds92Z7fWtK5o0aVLUU8iUqampxvaHDx+qbXv06JHGc8uWLZurOYniw9bWtqinUOg0PZIBYGZmVkgzEUKIwicl5MXPqVOnSElJUV5HRETwwQcf0LdvX+BVYNy5c2c8PT1ZvHgxhoaGnDt3TvlacHZ2JjY2VmXMadOmceDAAZo1a6ZyfP/+/TRs2FB5XbFiReXPx48fZ8CAAfj5+dG9e3fWrFlDr169OHv2LG+//TbPnj3j7NmzTJs2DQcHB+7fv8/48ePp0aMHp0+fVsbZvHkzo0ePZtasWbz//vu8fPmSiIgIpf3MmTNUrlyZ1atXU6NGDY4fP86YMWMwMDBg7NixACxatIjZs2cr57x8+RIHBwflM9FmEnQLJQutrvz7zXJyd3d3rd1eTMrLhRBCCCGErrOwsFB5PXv2bOrWrUvbtm2BV7+Pjxs3jsmTJyt9Xr/pbmRkRNWqVZXXycnJbNu2jS+//BI9PT2VsStWrKjS93WLFi2ic+fOeHh4ADBz5kxCQ0NZsmQJy5Ytw9zcnNDQUJVzlixZwrvvvkt0dDQ1a9bk5cuXjB8/njlz5jBy5EilX4MGDZQ/jxgxQmWMOnXqEBYWRkhIiBJ0m5ubq+zEs3XrVu7fv8/w4cMznbs2kaBb5IqubC+W1bZg2krdP3xCiMxJJlt3FGW1zn///ae27c1fcIXQJbKQmm5ISkoiKSlJ5ZixsTHGxsYaz3vx4gWrV69m4sSJ6OnpER8fz8mTJxk0aBDOzs5ERUVhZ2eHr68vrVq1ynSM7du3c+/evUwD1B49epCYmEj9+vX55ptv6NGjh9IWFhbGxIkTVfq7uLgoZeGZefjwIXp6epQrVw6As2fPEhMTg76+Pk2aNCEuLg5HR0fmzJnD22+/rXGcChUqqG0PDAykY8eOWFtbq+2jLSToFjlmYmJCdHR0tvsXZlZcF24ECCGEEEKIksfPzw8fHx+VY15eXnh7e2s8b+vWrTx48IBhw4YBcO3aNQC8vb2ZO3cujo6O/Prrr3To0IGIiAjq1auXYYzAwEBcXFyoXr26cszMzIx58+bRsmVL9PX12bx5M7169WLr1q1K4B0XF0eVKlVUxqpSpQpxcXGZzjUxMZFJkyYxYMAA5Sbq6/OdP38+tWrVYt68ebRr144rV65kGlgfP36c9evXs2vXrkzf5/bt2+zZs4c1a9Zo+ui0hgTdIsf8/PxytBJ5UWbFT5w4obbtzedZhBBCFLxnz55pbC/ITLdks0VxJdls3eDp6Zkha5xVlhteBcxdunRRFiZLr2z49NNPlcx1kyZNOHDgACtWrMiwgPGtW7fYt28fGzZsUDleqVIllfk0b96c27dvM2fOHJVsd3YlJyfTr18/0tLSCAgIUI6nz3fKlCn07t0bgJUrV1K9enU2btzIp59+qjJOREQEPXv2xMvLi06dOmX6XkFBQZQrV45evXrleJ5FQYLuYiB9ITQovEXOXn/PrOjSwmtCCCGEEEIUhOyUkr/p5s2b7N+/n5CQEOWYpaUloPpMNIC9vX2m1agrV66kYsWK2QqkW7RoofKMdtWqVblz545Knzt37mR4FDI94L558yYHDx5UuYGa2XyNjY2pU6dOhvleuHCBDh06MGbMGKZOnZrpHNPS0lixYgWDBw/GyMgoy2vSBhJ0FwOv380qjL2w33zPrBTmwmtSXi6EEEIIIYqLlStXUrlyZbp166Ycq1WrFlZWVly+fFml75UrV+jSpYvKsbS0NFauXMmQIUMoVapUlu8XHh6uBMkATk5OHDhwgAkTJijHQkNDcXJyUl6nB9xXr17l0KFDKqufAzRt2hRjY2MuX76sPHOenJzMjRs3VJ7HPn/+PO+//z5Dhw7F19dX7RyPHDlCZGSkyqJs2k6CbqFQl73Oj0x1UZWYa1p8ISuaFiUBKeUSQojckIUihRAie1JTU1m5ciVDhw7F0PD/wjY9PT08PDzw8vLCwcEBR0dHgoKCuHTpEps2bVIZ4+DBg1y/fp1Ro0ZlGD8oKAgjIyNli92QkBBWrFjBL7/8ovQZP348bdu2Zd68eXTr1o1169Zx+vRpli9fDrwKnvv06cPZs2fZuXMnKSkpyvPeFSpUwMjIiLJly+Lm5oaXlxc1atTA2tqaOXPmACjbfUVERPD+++/j4uLCxIkTlTEMDAwyPBoUGBhIixYtNC7Cpm0k6BYKddnrvGbPc7rwGuR+8TXJdAshhBBCiOJg//79REdHZ9hOC2DChAkkJibi7u5OQkICDg4OhIaGUrduXZV+gYGBODs7Y2dnl+l7zJw5k5s3b2JoaIidnR3r16+nT58+SruzszNr1qxh6tSpfPvtt9SrV4+tW7cqAW9MTAzbt28HwNHRUWXsQ4cO0a5dOwDmzJmDoaEhgwcP5vnz57Ro0YKDBw9Svnx5ADZt2sR///3H6tWrWb16tTKGtbU1N27cUF4/fPiQzZs3s2jRoux9iFpCLy0tLa2oJ5Hf3N3dS2zwVRDXnh9j5jSIjo+PJzg4OE/vCa+2OVDn9bIYIYQQQgghhCgIkukWhSInz4BD4T2bLoQQQgghhBAFSYJuoZVysjr660pqhYMQQgghhBBCO0nQLbRSTjPj6hw4cEBtW0ksLz9//rzG9oYNGxbSTIQQQgghhCgZJOgWOVJYW3/llmS6hRBCCCGEENpEgm6RI0W19Vdude7cuainoFVMTEyKegoin6RvpZEZ2ZJJCCGEEEJ7SNAtsvT689Xq9uzWlgy4Lt0QEEIIIYQQQhR/EnSLLL3+fLW6xc10LQMuhBBCCCGEtvL29sbHx0flmK2tLZcuXQJeVbx5eHgQGhrK48ePsbW1ZcqUKfTu3Vvp7+vry65duwgPD8fIyIgHDx6ojHfu3Dlmz57NsWPHuHv3LrVq1cLNzY3x48er9Fu6dClLlizhxo0b1KxZkylTpjBkyBCVPgsXLiQgIIDo6GgqVapEnz598PPzU6os//jjD+bMmcOZM2eIjY1ly5Yt9OrVS2WMO3fuMGnSJH7//XcePHhAmzZtWLx4MfXq1QPgxo0b1K5dO9PPa8OGDfTt2xeA6OhoPvvsMw4dOoSZmRlDhw7Fz88PQ8OiC30l6BY5om5VcXUZ8KJWlN9c2qh06dJFPQWRT6SEXBSkW7duaWyvXr16rsd+8uSJxnYzM7Ncjy2EEMVJw4YN2b9/v/L69d9rhwwZwoMHD9i+fTuVKlVizZo19OvXj9OnT9OkSRMAXrx4Qd++fXFyciIwMDDD+GfOnKFy5cqsXr2aGjVqcPz4ccaMGYOBgQFjx44FICAgAE9PT37++WeaN2/OX3/9xejRoylfvjyurq4ArFmzhsmTJ7NixQqcnZ25cuUKw4YNQ09Pj/nz5wPw9OlTHBwcGDFiBB999FGGuaSlpdGrVy9KlSrFtm3bKFu2LPPnz6djx45cuHCB0qX/H3v3Htfz/T/+/5bOToWKGhUi9HYeKbY5rRzWeM/G8CbH+IzN6j0jlWqWEGFYmFNb2hyGHRhyfPMuoi3ExspaX4fkkEMhqX5/+PV8e3nVq9LpVd2vl8su83o+Hq/H8/F6dXrdn/f78/GoQ7Nmzbh+/brK89auXUtISAgDBw4EICcnh8GDB9OkSROio6O5fv06Y8eORV9fn/nz55fmy1EqEpGIEilsVXFvb2+t2Ftbsu1CCCGEEKI60NPTK/Qie3R0NGFhYXTv3h0AX19fli5dSlxcnBJ052fKN23aVOAYEyZMUHncokULYmJi2LFjhxJ0f/PNN0yZMoURI0YofU6dOsXChQuVoDs6OpqePXsyatQoAGxtbRk5ciQnT55Uxh44cKASGBfkzz//5MSJEyQkJCi76YSFhdGkSRO+/fZbJk2ahK6urtr7sXPnToYPH65csN2/fz8XLlzgwIEDNG7cmE6dOjFv3jxmzZpFQEAABgYGhc6hPEnQLcpEWW3xVdY6depUaNv9+/c1Prd+/fplPJv/KWrrruzs7ELbNL2mokh2VJRWZf7caPLo0SON7cbGxhU0k+qhNJnsotSuXbvcxk5OTtbYbmtrW27nFiI3N1dje61atSrl3OV5XlEyWVlZZGVlqRwzNDTE0NCwwP5//vknVlZWGBkZ4eTkRHBwMNbW1gA4OzuzZcsWBg8ejKmpKVu3buXx48f07t27VHO8d+8eDRs2VJnziwvxGhsbExsbS3Z2Nvr6+jg7OxMREUFsbCzdu3fn8uXL7NmzhzFjxhT7vPnvy/PnqlWrFoaGhhw/fpxJkyapPScuLo74+HhWrVqlHIuJiaF9+/Y0btxYOebq6sr//d//cf78eeWCREWToLua+f3337Ui41xZJNMthBBCCCG0UXBwsNp92v7+/gQEBKj1dXR0ZNOmTdjb23P9+nUCAwN57bXXSEhIoF69emzdupURI0bQqFEj9PT0qF27Njt37sTOzu6l5xcdHc2WLVvYvXu3cszV1ZV169YxdOhQunTpQlxcHOvWrSM7O5tbt25haWnJqFGjuHXrFr169SIvL4+nT58ydepU5syZU+xzt2nTBmtra7y9vVmzZg116tRh6dKlXLlyRa2kPN/69etp27Ytzs7OyrHU1FSVgBtQHmva+aW8SdBdzeTm5krgKYQQQgghhJbx9vbGy8tL5VhhWe7nS7E7dOiAo6MjNjY2bN26lYkTJ+Ln58fdu3c5cOAAZmZm7Nq1i+HDh3Ps2DHat29f4rklJCQwZMgQ/P39cXFxUY77+fmRmppKjx49yMvLo3Hjxri7u7No0SKliuLIkSPMnz+fL7/8EkdHRxITE5kxYwbz5s3Dz8+vWOfX19dnx44dTJw4kYYNG6Krq0v//v0ZOHAgeXl5av0fPXpEZGRkscevbBJ0ixqrtGWwpdknOf9eFSEKExMTo7HdycmpgmaiqrLKx4si5eNVx19//aWxvWXLli89tpSPi8pUmWXcUkJeNWgqJS+KqakprVu3JjExkaSkJFauXKly/3PHjh05duwYq1atYvXq1SUa+8KFC/Tr1w8PDw98fX1V2oyNjdmwYQNr1qzhxo0bWFpasnbtWurVq4e5uTnwLDAfM2aMUgLevn17MjMz8fDwwMfHp9jfn127diU+Pp579+7x5MkTzM3NcXR05NVXX1Xru337dh4+fKi2inqTJk2IjY1VOXbjxg2lrbJI0C1KTFv25C6IZPmFEEIIIUR1k5GRQVJSEmPGjOHhw4eA+sUWXV3dItcWeNH58+fp27cv7u7uBAUFFdpPX19fWe/ju+++46233lLO//DhwwLnAhSYpS6KiYkJ8Oye9tOnTzNv3jy1PuvXr+ftt99WAv98Tk5OBAUFkZaWhoWFBQBRUVHUr1+fdu3alXguZUWC7mqqPAPjtLQ0Nm/eXC5jVyVVcVGy9PR0je0NGjSooJmIoty6dauypyBEuShNJlsIIfJV98XjPvnkE9zc3LCxseHatWv4+/ujq6vLyJEjMTU1xc7OjilTprB48WIaNWrErl27iIqK4ueff1bGSElJ4c6dO6SkpJCTk0N8fDwAdnZ21K1bl4SEBPr27YurqyteXl5KFaeurq4SzF66dInY2FgcHR1JT08nNDSUhIQEwsPDlfO4ubkRGhpK586dlfJyPz8/3NzclOA7IyODxMRE5Tl//fUX8fHxNGzYUFkcbtu2bZibm2Ntbc25c+eYMWMGQ4cOVSl3B0hMTOQ///kPe/bsUXvfXFxcaNeuHWPGjGHRokWkpqbi6+vLtGnTXrrKoCxI0F3N5P+APH78uNyyvtqyPVhBJNMthBBCCCGquitXrjBy5Ehu376Nubk5vXr14sSJE8pn/T179jB79mzc3NzIyMjAzs6O8PBwBg0apIwxd+5cleA4f+Xuw4cP07t3b7Zv387NmzeJiIggIiJC6WdjY6PsBpGTk8OSJUu4ePEi+vr69OnTh+joaJXbeXx9fdHR0cHX15erV69ibm6Om5ubSub89OnT9OnTR3mcf2+7u7u7sqXZ9evX8fLyUsrYx44dW+A92xs2bKBp06ZqwTg8u2Dw888/83//9384OTlRp04d3N3d+eyzz4r71pcLnbyXyflrOU9PzxobfOW/9pr8HhSXZH3VVeZ2J5Xl5s2bGttfLFsSL6+oVUOrYvVIaV25ckVje3lu3VVZWaLK3HZO08+7/KyXraL+nty7d6/Qtpr491cIUb1Vv0/QQgghhBBCCCGElpDy8hpOmxdFexmS3RdCCCGEEEJoEwm6q7miguqavCialK+pq47l40Upz5LS6r7IS0nVxPLxopRn+XhRKut7sDK3ncvMzCy0TcrLy1ZR318JCQmFtr322mtlPZ0qT26JUyd/Y0VVIkF3NWNkZISnpydpaWlA0QuqafOiaC9DMt1CCCGEEEIIbSJBdzUTHBwMUOxAOr+/EAAxMTEa252cnCpoJlVDVlaWxvZvv/220LZx48aV8WyE0A6VuVBaUZ5fbVdULslml0xNzGQXRbLZoiqR71YhhBBCCCGEEKKcSKa7Bqpui6c9T8rLhRBCCCFEVRcQEEBgYKDKMXt7e/744w/g2TacM2fOJCoqigcPHmBvb4+Pjw/Dhg1T+gcFBbF7927i4+MxMDDg7t27KuOdOXOGBQsWcPz4cW7duoWtrS1Tp05lxowZKv1WrVrFypUrSU5OxtraGh8fH8aOHavSZ9myZYSFhZGSkoKZmRnvvvsuwcHBGBkZARAWFkZYWJiy/7eDgwNz585l4MCByhhr164lMjKSX3/9lQcPHpCeno6pqanKed5++23i4+NJS0ujQYMG9O/fn4ULF2JlZQXAxYsXmTp1KhcuXODevXtYWVkxatQo/P390dfXL9kXoQxJ0F0DFXWft6i5pHy8ZAwNDTW2/+Mf/6igmagqquy9qHmLquHixYsa2+3t7StoJqoqs3xcaI/U1FSN7bKwohBFc3Bw4MCBA8pjPb3/hW5jx47l7t27/Pjjj5iZmREZGcnw4cM5ffo0nTt3BuDJkye89957ODk5sX79erXx4+LisLCwICIigmbNmhEdHY2Hhwe6urpMnz4deBYse3t789VXX9GtWzdiY2OZPHkyDRo0wM3NDYDIyEhmz57Nhg0bcHZ25tKlS4wbNw4dHR1CQ0OBZwuHLliwgFatWpGXl0d4eDhDhgzht99+w8HBAYCHDx8yYMAABgwYgLe3d4HvSZ8+fZgzZw6WlpZcvXqVTz75hHfffZfo6GgA9PX1GTt2LF26dMHU1JQzZ84wefJkcnNzmT9/fmm/JC9Ngu5q7vfff1e7vzt/kTVNqmo2XC4mCCGEEEKI6kBPT6/QC1TR0dGEhYXRvXt3AHx9fVm6dClxcXFK0J2fKd+0aVOBY0yYMEHlcYsWLYiJiWHHjh1K0P3NN98wZcoURowYofQ5deoUCxcuVILu6OhoevbsyahRo4Bn62eMHDmSkydPKmPn980XFBREWFgYJ06cUILujz/+GIAjR44U+p48H9fY2Ngwe/Zshg4dSnZ2Nvr6+rRo0YIWLVqo9Dly5AjHjh0rdMyKIEF3NZebm6sWiBZnkTXJhgtRepq2xHn11Vc1Prc0i1FJJrtmqKxMthDFIZnskpHKgJohKytLrRrN0NCw0L/bf/75J1ZWVhgZGeHk5ERwcDDW1tYAODs7s2XLFgYPHoypqSlbt27l8ePH9O7du1RzvHfvHg0bNlSZc36JeD5jY2NiY2OVQNfZ2ZmIiAhiY2Pp3r07ly9fZs+ePYwZM6bAc+Tk5LBt2zYyMzNLVWV5584dNm/ejLOzc6Gl44mJiezdu5d33nnnpc9TFiTorqbytw4rqMw0v02T/Gx4Vct4y4UCIYQQQgihjYKDg9Xu0/b39ycgIECtr6OjI5s2bcLe3p7r168TGBjIa6+9RkJCAvXq1WPr1q2MGDGCRo0aoaenR+3atdm5cyd2dnYvPb/o6Gi2bNnC7t27lWOurq6sW7eOoUOH0qVLF+Li4li3bh3Z2dncunULS0tLRo0axa1bt+jVqxd5eXk8ffqUqVOnMmfOHJXxz507h5OTE48fP6Zu3brs3LmTdu3alXies2bNYuXKlTx8+JAePXrw888/q/Vxdnbm119/JSsrCw8PDz777LOSvyFlSILuaip/KzAXF5dC2zTJD8ol4y2EEEIIIUTpeXt74+XlpXKssCz38wuMdejQAUdHR2xsbNi6dSsTJ07Ez8+Pu3fvcuDAAczMzNi1axfDhw/n2LFjtG/fvsRzS0hIYMiQIfj7+6vED35+fqSmptKjRw/y8vJo3Lgx7u7uLFq0SNm27ciRI8yfP58vv/wSR0dHEhMTmTFjBvPmzcPPz08Zy97envj4eO7du8f27dtxd3fn6NGjJQ68Z86cycSJE/n7778JDAxk7Nix/Pzzz+jo6Ch9tmzZwoMHDzhz5gwzZ85k8eLFfPrppyV+X8qKBN1CVILz589rbM+/t0WUv9zcXI3tpdkHtFOnTi/9XFmMSgghag4pH68ZNJWSF8XU1JTWrVuTmJhIUlISK1euJCEhQfnM2LFjR44dO8aqVatYvXp1ica+cOEC/fr1w8PDA19fX5U2Y2NjNmzYwJo1a7hx4waWlpasXbuWevXqYW5uDjwLzMeMGcOkSZMAaN++PZmZmXh4eODj46N8ljIwMFAy8V27duXUqVMsX76cNWvWlGi+ZmZmmJmZ0bp1a9q2bUuzZs04ceKESql6s2bNAGjXrh05OTl4eHjw73//G11d3RKdq6xI0C0KlF+CXtiia9padi5ZeSGEEEIIUd1kZGSQlJTEmDFjePjwIaCeGNDV1S0ymfCi8+fP07dvX9zd3QkKCiq0n76+Pk2bNgXgu+++46233lLO//DhwwLnApCXl1fomLm5uUXuuFKU/NeraZzc3Fyys7PJzc2VoFtol/wS9MLu/Zay89KRTHbZys7OLrStqD0ZMzIyNLaXJuNcr169l36uKFvlWdEghKg+Hj16VGibsbFxBc5E1HSffPIJbm5u2NjYcO3aNfz9/dHV1WXkyJGYmppiZ2fHlClTWLx4MY0aNWLXrl1ERUWp3N+ckpLCnTt3SElJIScnh/j4eADs7OyoW7cuCQkJ9O3bF1dXV7y8vJQF/XR1dZUs9qVLl4iNjcXR0ZH09HRCQ0NJSEggPDxcOY+bmxuhoaF07txZKS/38/PDzc1NCXK9vb0ZOHAg1tbWPHjwgMjISI4cOcK+ffuUcVJTU0lNTSUxMRF4dg94vXr1sLa2pmHDhpw8eZJTp07Rq1cvGjRoQFJSEn5+frRs2VLJcm/evBl9fX3at2+PoaEhp0+fxtvbmxEjRsg+3UJ7FbboWnG2HYOKz4jLhQAhhBBCCFHVXblyhZEjR3L79m3Mzc3p1asXJ06cUILhPXv2MHv2bNzc3MjIyMDOzo7w8HAGDRqkjDF37lyV4Dh/K7HDhw/Tu3dvtm/fzs2bN4mIiCAiIkLpZ2NjQ3JyMvBspfElS5Zw8eJF9PX16dOnD9HR0dja2ir9fX190dHRwdfXl6tXr2Jubo6bm5tK5jwtLY2xY8dy/fp1TExM6NChA/v27ePNN99U+qxevVplobnXX38dgI0bNzJu3Dhq167Njh078Pf3JzMzE0tLSwYMGICvr69Stq+np8fChQu5dOkSeXl52NjYMH369GLt3lSedPI05fyrKE9PTwm+/n8uLi7s37+/zMct7nssXwtREUqT6S7N1lxFSUpKKrStZcuWLz2uKDnJdAshikMy3UKI8iCZbvFSirPtGBQ/I15eShOMiaqjNF/L8lywTPbL1h6VFVSnp6drbG/QoEEFzUQUpTQX4OSiTvUhgbUQojxI0F3N5ZeglLXibDsGz8rLK7KcQ7LqQgghhBBCCG0iQXc1Z2FhUannL25wXl5SUlIKbSsqw1leFyxE1aJpNcyiMtn5q3yKmksy2VVH3bp1X/q5RWWy8xcvKkxpthcUQgih/SToFoD2bgFWUpLpFkIIIYQQQmgTCbpriKKC6rS0NDZv3lyBM6oYsliVKC25979k8rcbKUiTJk0qcCYVJ3+F14I8v7prQeReYO1Rnu+1/B4RNdXNmzcLbZOKQlGTSNBdQxS1r3ZlL6MvhBBCCCGEENWRBN0CKP5q5NpOysuFEEIIIUR18ODBA/z8/Ni5cydpaWl07tyZ5cuX061bN7W+U6dOZc2aNSxdupSPP/5YOX7nzh0+/PBDfvrpJ2rVqsWwYcNYvny5yjoWZ8+eZdq0aZw6dQpzc3M+/PBDPv30U5Xxly1bRlhYGCkpKZiZmfHuu+8SHByMkZERAGFhYYSFhSnVXw4ODsydO5eBAwcCz6rCmjdvXuDr3Lp1K++9957Ksdu3b9OxY0euXr1Keno6pqamStuqVatYuXIlycnJWFtb4+Pjw9ixY4v9vlYGCboFUPkLngmhrcqr5PTixYsa2+3t7cvlvOXtr7/+KrStPMvLy3O/9aIUVUKuiZSP1wwODg6VPQVRzWm6VaUyf89ICXnpTJo0iYSEBL755husrKyIiIigf//+XLhwgVdeeUXpt3PnTk6cOIGVlZXaGKNHj+b69etERUWRnZ3N+PHj8fDwIDIyEnj299PFxYX+/fuzevVqzp07x4QJEzA1NcXDwwOAyMhIZs+ezYYNG3B2dubSpUuMGzcOHR0dQkNDgWeLxy5YsIBWrVqRl5dHeHg4Q4YM4bfffsPBwYFmzZpx/fp1lbmtXbuWkJAQJTB/3sSJE+nQoQNXr15VOR4WFoa3tzdfffUV3bp1IzY2lsmTJ9OgQQPc3NxK94aXIwm6RZGq0iJrkukWQgghhBBV3aNHj/j+++/54YcfeP311wEICAjgp59+IiwsjM8//xyAq1ev8uGHH7Jv3z4GDx6sMsbvv//O3r17OXXqFK+++ioAK1asYNCgQSxevBgrKys2b97MkydP2LBhAwYGBjg4OBAfH09oaKgSdEdHR9OzZ09GjRoFPLvYPHLkSE6ePKmc68WANygoiLCwME6cOIGDgwO6urpqF9937tzJ8OHD1XaPCAsL4+7du8ydO5dffvlFpe2bb75hypQpjBgxAoAWLVpw6tQpFi5cKEG3qNqKuh+8qqrMBYw0LSwCcmW4LGnrQlVVNZOtrcozky2EENquvP6WaVocE6rvApnlJSsrS20rUkNDwwK3IH369Ck5OTlK+XY+Y2Njjh8/Djz7jDNmzBhmzpxZYEVNTEwMpqamSsAN0L9/f2rVqsXJkyf55z//SUxMDK+//joGBgZKH1dXVxYuXEh6ejoNGjTA2dmZiIgIYmNj6d69O5cvX2bPnj2MGTOmwNeZk5PDtm3byMzMxMnJqcA+cXFxxMfHs2rVKpXjFy5c4LPPPuPkyZNcvnxZ7XlZWVkFviexsbFkZ2dr7cKVEnTXIC+bsU5LSyuTcSpCdbw4IIQQQgghqr7g4GACAwNVjvn7+xMQEKDWt169ejg5OTFv3jzatm1L48aN+fbbb4mJicHOzg6AhQsXoqenx0cffVTg+VJTU7GwsFA5pqenR8OGDZULKqmpqWr3Wjdu3Fhpa9CgAaNGjeLWrVv06tWLvLw8nj59ytSpU5kzZ47K886dO4eTkxOPHz+mbt267Ny5k3bt2hU4t/Xr19O2bVucnZ2VY1lZWYwcOZKQkBCsra0LDLpdXV1Zt24dQ4cOpUuXLsTFxbFu3Tqys7O5desWlpaWBZ6vsknQXc3lL5CWlpaGhYXFSwWlLy6wVl0z30IIIYQQQpQXb29vvLy8VI4VlOXO98033zBhwgReeeUVdHV16dKlCyNHjiQuLo64uDiWL1/Or7/+io6OTrnO+8iRI8yfP58vv/wSR0dHEhMTmTFjBvPmzcPPz0/pZ29vT3x8PPfu3WP79u24u7tz9OhRtcD70aNHREZGqjwXnr0/bdu25V//+lehc/Hz8yM1NZUePXqQl5dH48aNcXd3Z9GiRVq9TooE3dVc/gJppVmZ/MWVzV/MfGuzpKSkQtsqcw9vKR+vONr8C7g6MjY2ruwpCCGEKCNSPl62CislL0zLli05evQomZmZ3L9/H0tLS0aMGEGLFi04duwYaWlpWFtbK/1zcnL497//zbJly0hOTqZJkyZqn9ufPn3KnTt3lK9tkyZNuHHjhkqf/Mf5ffz8/BgzZgyTJk0CoH379mRmZuLh4YGPj4/yWcvAwEDJwnft2pVTp06xfPly1qxZozL+9u3befjwodqK44cOHeLcuXNs374dgLy8PADMzMzw8fEhMDAQY2NjNmzYwJo1a7hx4waWlpasXbuWevXqafXnawm6RZFeXNnc29tba7cXkwy8EEIIIYSoTurUqUOdOnVIT09n3759LFq0iGHDhtG/f3+Vfq6urowZM4bx48cD4OTkxN27d4mLi6Nr167As8A2NzcXR0dHpY+Pj4/K/dBRUVHY29vToEEDAB4+fKiWxNDV1QX+FxgXJDc3V+0ednhWWv7222+rBcnff/89jx49Uh6fOnWKCRMmcOzYMbVkmb6+Pk2bNgXgu+++46233tLqRIsE3aLEqtL2Ypqy2fn7CBamqG2AtHV7Dk20dVExUXLaWsXRqVOnSju3EDWRpt8FULm/D4QoT1Xxc1hJ7du3j7y8POzt7UlMTGTmzJm0adOG8ePHo6+vT6NGjVT66+vr06RJE2Wx1rZt2zJgwAAmT57M6tWryc7OZvr06bz//vvK9mKjRo0iMDCQiRMnMmvWLBISEli+fLlKIsvNzY3Q0FA6d+6slJf7+fnh5uamBN/e3t4MHDgQa2trHjx4QGRkJEeOHGHfvn0qc0xMTOQ///kPe/bsUXu9L/6+unXrlvI68vfpvnTpErGxsTg6OpKenk5oaCgJCQmEh4eX4p0ufxJ013DavCjay5BMtxBCCCGEqA7u3buHt7c3V65coWHDhgwbNoygoKASrdC9efNmpk+fTr9+/ahVqxbDhg3jiy++UNpNTEzYv38/06ZNo2vXrpiZmTF37lxluzAAX19fdHR08PX15erVq5ibm+Pm5kZQUJDSJy0tjbFjx3L9+nVMTEzo0KED+/bt480331SZz4YNG2jatCkuLi4v9Z7k5OSwZMkSLl68iL6+Pn369CE6OrrIZFll08nTVBNQRXl6ekrw9YL8cvAX35ea/F5Jpludts5bqNPWTLcQomJJplvUVFXxc5iouSTTXUMYGRmRkpKiltmuSouilbWigur4+HiN7VWxjFb+CFUf1alCJZ8ED0KUnPxcCCGE9pOgu4YIDg7G09NTbbuvkiyIVhVK0Wtq1l4IIYQQQgihnSToruFe3A5Mk7S0NDZv3lzOM9IeNjY25TZ2enq6xvb8RSkKUr9+/bKejqgEGRkZGtvr1q2rsV1Pr/r9+paMnRBCiOKS6j1RlVS/T22iREqyErk2bxWWTzLdQgghhBBCCG0iQbcotqq0VVhxXLx4UWN7/nYLL6O0i7SJ6q+0me7SfH/evHlTY/uL+2YKIYQQQoiXJ3UZQgghhBBCCCFEOZFMt1BRFRZL00TKy4UQQgghRFWXk5NDQEAAERERpKamYmVlxbhx45Q9swF27NjB6tWriYuL486dO/z2229qu+v07t2bo0ePqhybMmUKq1evVh4fPHgQPz8/zp07R506dXB3dycoKEhZP+bIkSMsXbqU2NhY7t+/T6tWrZg5cyajR49WGffu3bv4+PiwY8cO7ty5g42NDcuWLWPQoEEAhIWFERYWplSEOjg4MHfuXAYOHAjAnTt38Pf3Z//+/aSkpGBubs7QoUOZN28eJiYmANy+fZvRo0dz9uxZbt++jYWFBUOGDGH+/Plave6RBN01iJGRESdOnNB4X3Z1WyxN04JlpSnPLcrdu3fLbWxRtkpb5v2yNC2WV96kfFx7yK0oQgghCrJw4ULCwsIIDw/HwcGB06dPM378eExMTPjoo48AyMzMpFevXgwfPpzJkycXOtbkyZP57LPPlMe1a9dW/n3mzBkGDRqEj48PX3/9NVevXmXq1Knk5OSwePFiAKKjo+nQoQOzZs2icePG/Pzzz4wdOxYTExPeeustAJ48ecKbb76JhYUF27dv55VXXuHvv//G1NRUOVfTpk1ZsGABrVq1Ii8vj/DwcIYMGcJvv/2Gg4MD165d49q1ayxevJh27drx999/M3XqVK5du8b27duBZwvoDRkyhM8//xxzc3MSExOZNm0ad+7cITIyssze/7Kmk5eXl1fZkyhrnp6ekvEshIuLC/v37y+0vbplujUF3Q0aNCi3eVTHPb6rq8oKuuW+agESdAshhCjYW2+9RePGjVm/fr1ybNiwYRgbGxMREaHSNzk5mebNmxea6e7UqRPLli0r8Dxz5swhKiqKU6dOKcd++uknhg8fTlpaGvXq1SvweYMHD6Zx48Zs2LABgNWrVxMSEsIff/yBvr5+sV9nw4YNCQkJYeLEiQW2b9u2jX/9619kZmYWunPLF198QUhICP/v//2/Yp+3okmmW6ioboullWdgrYkE1VVHeQXVRZGgWoAE1drkypUrGtubNm1aQTOpGs6fP6+x3cHBoYJmImqq3NzcQtuqw3Zizs7OrF27lkuXLtG6dWvOnDnD8ePHCQ0NLfFYmzdvJiIigiZNmuDm5oafn5+S7c7KysLIyEilv7GxMY8fPyYuLo7evXsXOOa9e/do27at8vjHH3/EycmJadOm8cMPP2Bubs6oUaOYNWtWgdV9OTk5bNu2jczMTJycnAqd+71796hfv36hAfe1a9fYsWMHb7zxRlFvQ6WSoFsIIYQQQgghyllWVhZZWVkqxwwNDTE0NFTrO3v2bO7fv0+bNm3Q1dUlJyeHoKAgtfuoizJq1ChsbGywsrLi7NmzzJo1i4sXL7Jjxw4AXF1dWbZsGd9++y3Dhw8nNTVVKUW/fv16gWNu3bqVU6dOsWbNGuXY5cuXOXToEKNHj2bPnj0kJibywQcfkJ2djb+/v9Lv3LlzODk58fjxY+rWrcvOnTtp165dgee5desW8+bNw8PDQ61t5MiR/PDDDzx69Ag3NzfWrVtXoveloknQLUpE28vP5bYCIYQQQgihjYKDgwkMDFQ55u/vT0BAgFrfrVu3snnzZiIjI3FwcCA+Pp6PP/4YKysr3N3di33O5wPW9u3bY2lpSb9+/UhKSqJly5a4uLgQEhLC1KlTGTNmDIaGhvj5+XHs2LECKwYOHz7M+PHj+eqrr1QqWnJzc7GwsGDt2rXo6urStWtXrl69SkhIiErQbW9vT3x8PPfu3WP79u24u7tz9OhRtcD7/v37DB48mHbt2hX4/ixduhR/f38uXbqEt7c3Xl5efPnll8V+Xyqa3NNdw7i4uNC1a9eXDpyr2kJr1b30qKxpugceSleun5SUVGhby5YtX3pcUXVo+nkE+ZkUoiqJiYnR2K6pXFSImqokme5mzZoxe/Zspk2bphz7/PPPiYiI4I8//lDpq+me7hdlZmZSt25d9u7di6urq3I8Ly+P69ev06BBA5KTk2nXrh2xsbF069ZN6XP06FEGDx5MaGioWvb5jTfeQF9fnwMHDijHfvnlFwYNGkRWVhYGBgYFzqd///60bNlSJWv+4MEDXF1dqV27Nj///LNa+fuLjh8/zmuvvca1a9ewtLTU2LeySKa7Bnr8+PFLX5Tw9vbWuPp5ZZOLLUIIIYQQQhsVFmAX5OHDh2oXo3V1dYu8gF2U/MV+XwxOdXR0sLKyAuDbb7+lWbNmdOnSRWk/cuQIb731FgsXLiyw3Ltnz55ERkaSm5urzPvSpUtYWloWGnDDswvyz1+IuH//Pq6urhgaGvLjjz8WGXDnjwGoXdDQJhJ01zClXbypqi20VlUzZ6mpqYW2NWnSpNzOW54Lzz148KDcxtZWkt1XVVV/HoUQ6iSTLUT5cnNzIygoCGtraxwcHPjtt98IDQ1lwoQJSp87d+6QkpLCtWvXALh48SLw7LNikyZNSEpKIjIykkGDBtGoUSPOnj2Lp6cnr7/+Oh06dFDGCQkJYcCAAdSqVYsdO3awYMECtm7dqiyAdvjwYd566y1mzJjBsGHDlM+pBgYGNGzYEID/+7//Y+XKlcyYMYMPP/yQP//8k/nz5yvbm8Gz5N3AgQOxtrbmwYMHREZGcuTIEfbt2wc8C7hdXFx4+PAhERER3L9/n/v37wPPYhhdXV327NnDjRs36NatG3Xr1uX8+fPMnDmTnj17avXipBJ01zAWFhYv/Vxtv58bJNMthBBCCCGqvhUrVuDn58cHH3xAWloaVlZWTJkyhblz5yp9fvzxR8aPH688fv/994H/3SduYGDAgQMHWLZsGZmZmTRr1oxhw4bh6+urcq5ffvmFoKAgsrKy6NixIz/88AMDBw5U2sPDw3n48CHBwcEqCbg33niDI0eOAM/K4fft24enpycdOnTglVdeYcaMGcyaNUvpn5aWxtixY7l+/TomJiZ06NCBffv28eabbwLw66+/cvLkSQDs7OxU5vjXX39ha2uLsbExX331FZ6enmRlZdGsWTPeeecdZs+eXZq3u9zJPd01TH5p+Mu8P/K+VpzKynSXJ017l1fXLdYk0y2EEEIIISTTLWosTQERVG5QVFUDa02MjY0rewoVTgJrUV1lZ2cX2qavr1+BMyk7stCfqMpk4VghtJsE3TXUy5SKp6WllXqM8iaZeCGEEEIIIYQ2kaC7hnqZFcxfXLW8NKuga4PyzEIWtXpicVeurE6ePn1a2VPQKhkZGRrb69atW0EzEaV18+bNQttKu3iltqqq2WxNJBtYdWiqtIDq+f1ZFPn+FUK7SdAtis3IyEgl8H4x8/2iysiEV+WLAEIIIYQQQojqR4LuGsbIyIiUlJSXWsX8xe3Citqvu6pnwoUQQgghhBCitCTormGCg4OLDJaL68XM94uKyoRXZ0WVvtXE8vLff/+90DYHB4cKnEnF0fR9IOXj1Ud1LSEXQlvVxPJxIUTVJkG3eGkvZr5f5O3tXWYBfnFJZl0IIYQQQlR1YWFhhIWFkZycDDxLUMydO1fZP3vKlCkcOHCAa9euUbduXZydnVm4cCFt2rQB4MyZMyxYsIDjx49z69YtbG1tmTp1KjNmzFDOcfz4cWbNmsUff/zBw4cPsbGxYcqUKSqf3wMCAggMDFSZm729PX/88YfyuKi53L59m9GjR3P27Flu376NhYUFQ4YMYf78+dSvX7/YcwG4evUqs2bN4pdffuHhw4fY2dmxceNGXn311TJ658uHBN2i3BQVlFdnksVU9+6775bb2IcPHy60rU+fPhqfW57bBOnq6r70c+/fv6+xPf+PlKjaquo2VVeuXCm0TU9P80eL6rglohBClLWmTZuyYMECWrVqRV5eHuHh4QwZMoTffvsNBwcHunbtyujRo7G2tubOnTsEBATg4uLCX3/9ha6uLnFxcVhYWBAREUGzZs2Ijo7Gw8MDXV1dpk+fDkCdOnWYPn06HTp0oE6dOhw/fpwpU6ZQp04dPDw8lLk4ODhw4MAB5fGLv+eLmkutWrUYMmQIn3/+Oebm5iQmJjJt2jTu3LlDZGRkseeSnp5Oz5496dOnD7/88gvm5ub8+eefNGjQoLy/HKWmk5eXl1fZkyhrnp6ekvHUIP+KUUHvkTZuA1YS8nWvmbQ16C7NvqkSdNcMEnQLIYQoroYNGxISEsLEiRPV2s6ePUvHjh1JTEwsdIeeadOm8fvvv3Po0KFCz/HOO+9Qp04dvvnmG+BZpnvXrl3Ex8cXe57FmcsXX3xBSEgI/+///b9iz2X27Nn897//5dixY8Wei7aQTLdQUd0WP7t48WKhbXXq1NH43KZNm5b1dEQ5qVev3ks/tzyDmtKMLUF1zaCtQXVR5PejEOqq6kU0of1ycnLYtm0bmZmZODk5qbVnZmayceNGmjdvTrNmzQod5969ezRs2LDQ9t9++43o6Gg+//xzleN//vknVlZWGBkZ4eTkRHBwMNbW1gWOUZy5XLt2jR07dvDGG2+UaC4//vgjrq6uvPfeexw9epRXXnmFDz74gMmTJxc6jraQoLuGezGzXZMXPxNCCCGEEKK8ZGVlkZWVpXLM0NCw0AV2z507h5OTE48fP6Zu3brs3LmTdu3aKe1ffvkln376KZmZmdjb2xMVFYWBgUGBY0VHR7NlyxZ2796t1ta0aVNu3rzJ06dPCQgIYNKkSUqbo6MjmzZtwt7enuvXrxMYGMhrr71GQkKCStKjOHMZOXIkP/zwA48ePcLNzY1169aVaC6XL18mLCwMLy8v5syZw6lTp/joo48wMDDA3d29wNetLSToruFezGyXZOEzbSxFr05ZeiGEEEIIUX0EBwerLUrm7+9PQEBAgf3t7e2Jj4/n3r17bN++HXd3d44ePaoE3qNHj+bNN9/k+vXrLF68mOHDh/Pf//4XIyMjlXESEhIYMmQI/v7+uLi4qJ3n2LFjZGRkcOLECWbPno2dnR0jR44EUBZuA+jQoQOOjo7Y2NiwdetWlTL34sxl6dKl+Pv7c+nSJby9vfHy8uLLL78s9lxyc3N59dVXmT9/PgCdO3cmISGB1atXS9Atqq+qUIpub29f2VMQFcDMzKxSziulhEJULY8ePSq0zdjYuAJnIqoj+Z0vipIfaD5P0zayBgYG2NnZAc8WKzt16hTLly9nzZo1AJiYmGBiYkKrVq3o0aMHDRo0YOfOnUqQCnDhwgX69euHh4cHvr6+BZ6nefPmALRv354bN24QEBCgMsbzTE1Nad26NYmJiSrHizOXJk2a0KRJE9q0aUPDhg157bXX8PPzw9LSslhzsbS0VMn0A7Rt25bvv/++0PdQW0jQLVQUtff2814sRdeGzLe2XwQQQgghhBA1k6ZS8uLIzc1VK0/Pl5eXR15enkr7+fPn6du3L+7u7gQFBZX6HAAZGRkkJSUxZsyYQvsUNJeCzgMU2ef59p49e6qt13Tp0iVsbGwKHUNbSNBdAxkZGZGSklJgW0m2+XoxOK8Kme/nFbUKY6dOnSpkHqL0XiyjqiiS1RCiapFstqiuSrNbhtBO3t7eDBw4EGtrax48eEBkZCRHjhxh3759XL58mS1btuDi4oK5uTlXrlxhwYIFGBsbM2jQIOBZSXnfvn1xdXXFy8uL1NRU4Nl2pubm5gCsWrUKa2trZT/t//znPyxevJiPPvpImccnn3yCm5sbNjY2XLt2DX9/f3R1dZXsc3HmsmfPHm7cuEG3bt2oW7cu58+fZ+bMmfTs2RNbW9tiz8XT0xNnZ2fmz5/P8OHDiY2NZe3ataxdu7YcvxJlQ4LuGig4OLhE924X5sWsuCzCJoQQQgghROmlpaUxduxYrl+/jomJCR06dGDfvn28+eabXLt2jWPHjrFs2TLS09Np3Lgxr7/+OtHR0VhYWACwfft2bt68SUREBBEREcq4NjY2JCcnA88u1nh7e/PXX3+hp6dHy5YtWbhwIVOmTFH6X7lyhZEjR3L79m3Mzc3p1asXJ06cUAJ3IyOjIudibGzMV199haenJ1lZWTRr1ox33nmH2bNnK+cpzly6devGzp078fb25rPPPqN58+YsW7aM0aNHl9vXoazIPt01VP57VJbvVUnHKo9y9JKcXzLd1Uf+1duCyJ7AQgghqjvJdAuh3STTLcpMSe4Hh2dX8DZv3lyOM9KsshbfEmWvstcSeBkZGRka2+vWrVtBMxFFka+VEELbSWAthHaToFuUmZLcDw7PMt1lUeb+PKlwEEIIIYQQQmgTCbpFpSlpkF7WmjZtWqrna1ptsTQrU4qSy1+EoyqR7GnVUZqvRf59c4Wpit+7RdH0uxHk96MQonrLzs4utE1fX78CZyK0iQTdoszIlmFCCCGEEEIIoUqCblFmqtqWYUIIIYQQQghR3iToFmWWoa5pW4ZJiaQoDVlVvWY4d+6cxvbqWF4uvxuFqHiyern2kBJyURAJukWZZajLY2G0kpJMuxBCCCGEqOrCwsIICwtT1gZxcHBg7ty5DBw4EICkpCQ++eQTjh8/TlZWFgMGDGDFihU0btxYbaysrCwcHR05c+YMv/32m7ItbkBAAIGBgWr9a9euTWZmJgCbNm1i/PjxKu2GhoYqCbtx48YRHh6u0sfV1ZW9e/cCcOTIEfr06VPg64yNjaVbt24A7Nu3D39/f86fP4+RkRGvv/46S5YsUS5Q79ixg7CwMOLj48nKysLBwYGAgABcXV01vZVaQYJuUWYqe2G0gty8ebPQNnNz83I7r6ZFNKDoq6Dp6emFtjVo0OCl5iSEqHiFfcgQ1cvhw4c1tsv3gShvks2ufpo2bcqCBQto1aoVeXl5hIeHM2TIEH777TdsbW1xcXGhY8eOHDp0CAA/Pz/c3Nw4ceKE2vfDp59+ipWVFWfOnFE5/sknnzB16lSVY/369VOC4Hz169fn4sWLymMdHR21+Q4YMICNGzcqj5+venJ2dub69esq/f38/Dh48CCvvvoqAH/99RdDhgzBy8uLzZs3c+/ePTw9PXnnnXf49ddfAfjPf/7Dm2++yfz58zE1NWXjxo24ublx8uRJOnfurPkNrWQSdIsKUVGLrEmmWwghhBBCVHVubm4qj4OCgggLC+PEiRNcvXqV5ORkfvvtN+rXrw9AeHg4DRo04NChQ/Tv31953i+//ML+/fv5/vvv+eWXX1TGrFu3rsoOHWfOnOHChQusXr1apZ+Ojk6Rt8UZGhoW2sfAwEClLTs7mx9++IEPP/xQCeDj4uLIycnh888/Vy4afPLJJwwZMoTs7Gz09fVZtmyZyrjz58/nhx9+4KeffpKgWwiovEXWTE1NX/q5mu6PAs1XlUt7P0/t2rVL9fyXdf/+fY3t+b/YRfHIPXbVQ2l+F8jWbzWDkZFRZU9BCFGN5eTksG3bNjIzM3FyciIpKQkdHR2VbLKRkRG1atXi+PHjStB948YNJk+ezK5du4r12XLdunW0bt2a1157TeV4RkYGNjY25Obm0qVLF+bPn4+Dg4NKnyNHjmBhYUGDBg3o27cvn3/+OY0aNSrwPD/++CO3b99WKVvv2rUrtWrVYuPGjYwbN46MjAy++eYb+vfvX+jn6tzcXB48eEDDhg2LfG2VTYLuGsrIyAhPT0/S0tK4fft2ud+LXdMWWRNCCCGEEOJ5WVlZZGVlqRwzNDQsdAHKc+fO4eTkxOPHj6lbty47d+6kXbt2mJubU6dOHWbNmsX8+fPJy8tj9uzZ5OTkKGXceXl5jBs3jqlTp/Lqq68q94YX5vHjx2zevJnZs2erHLe3t2fDhg106NCBe/fusXjxYpydnTl//jxNmzYFnpWWv/POOzRv3pykpCTmzJnDwIEDiYmJQVdXV+1c69evx9XVVXk+QPPmzdm/fz/Dhw9nypQp5OTk4OTkxJ49ewqd8+LFi8nIyGD48OEaX5s2kKC7hsq//9rT05ObN2+Wexb6xaC+vMrNpbxcCCGEEEJoo+DgYLWFy/z9/QkICCiwv729PfHx8dy7d4/t27fj7u7O0aNHadeuHdu2beP//u//+OKLL6hVqxYjR46kS5cuSvXVihUrePDgAd7e3sWa286dO3nw4AHu7u4qx52cnHByclIeOzs707ZtW9asWcO8efMAeP/995X29u3b06FDB1q2bMmRI0fo16+fynhXrlxh3759bN26VeV4amoqkydPxt3dnZEjR/LgwQPmzp3Lu+++S1RUlNp95JGRkQQGBvLDDz9gYWFRrNdYmSToFhUiP7OeLy0tjc2bN5f7eV+8mvi8okrAK7P8t7K23CmqfFzKpUtG3pPqoTRfx9KUpmszTb9ba+KWYVXhA5+o3uTvc9Xg7e2Nl5eXyjFNvzMNDAyws7MDnpVfnzp1iuXLl7NmzRpcXFxISkri1q1b6OnpYWpqSpMmTWjRogUAhw4dIiYmRm38V199ldGjR6utNr5u3TreeuutAlc/f56+vj6dO3cmMTGx0D4tWrTAzMyMxMREtaB748aNNGrUiLffflvl+KpVqzAxMWHRokXKsYiICJo1a8bJkyfp0aOHcvy7775j0qRJbNu2TeX+dW0mQbeoEC+ubF5e24tJplsIIYQQQmgjTaXkxZGbm6t20dPMzAx4FmSnpaUpwewXX3zB559/rvS7du0arq6ubNmyBUdHR5Ux/vrrLw4fPsyPP/5Y5BxycnI4d+4cgwYNKrTPlStXuH37NpaWlirH8/Ly2LhxI2PHjlVLfj18+FDtAlF+afrzF5W+/fZbJkyYwHfffcfgwYOLnK+2kKBbVIqK2l6sqi5ipK1bhlXFq+UZGRka20vzPZKUlKSxvWXLli89dmmU52sWJVMVf2aKoyZmszUp7c96ZW1vKaqPnJycQtuq6++h6s7b25uBAwdibW3NgwcPiIyM5MiRI+zbtw94ljFu27Yt5ubmxMTEMGPGDDw9PbG3twfA2tpaZbz8v/0tW7ZUuZcaYMOGDVhaWip7gD/vs88+o0ePHtjZ2XH37l1CQkL4+++/mTRpEvDsM0dgYCDDhg2jSZMmJCUl8emnn2JnZ6e2f/ahQ4f466+/lOc+b/DgwSxdupTPPvtMKS+fM2cONjY2ysrkkZGRuLu7s3z5chwdHUlNTQXA2NgYExOTEr/HFUl+CoUQQgghhBBCi6SlpTF27Fjs7e3p168fp06dYt++fbz55psAXLx4kaFDh9K2bVs+++wzfHx8WLx4cYnPk5uby6ZNmxg3blyBi56lp6czefJk2rZty6BBg7h//z7R0dG0a9cOeJaNPnv2LG+//TatW7dm4sSJdO3alWPHjqldoF2/fj3Ozs60adNG7Tx9+/YlMjKSXbt20blzZwYMGIChoSF79+7F2NgYgLVr1/L06VOmTZuGpaWl8t+MGTNK/Lormk5eXl5eZU+irHl6ekqZcTF5enpy/vx59u/frxyrqD21y0N1+bpra6a7KpJMtzrJdAuhXSTTLUorOzu70LbSbmMqhCg9KS8XaiprT+3yoGnf6aICj8osx6qswFrTBz+omh/+Hj16pLG9NAFoZQXVRamqQXVpFh2rjt+7ouaorO9PTX8joejFNYX2kMBaCO0mQbd4KdqaDa8uFwuEEEIIIYQQ1YME3ULN77//XuTK4hW15VdpyVX6kqmO2cDq+JqEOk1bl4B8HwhREPkbKYQQFUOCbqHIz14/evSoyIxxeW35VVqS6RZCCCGEEEJoEwm6hSL/Xm4XF5ci+1bUll9CCCGEEEIIUZVJ0C2qNU0LM8m+ldojPj5eY3unTp0qZB6icpXmZ9LJyakMZ1IzVNbvR1ldX4iyl5WVVWjbi9s2CSEqngTdokDaulBaUaS8XAghhBBCVHUBAQEEBgaqHLO3t+ePP/4AIDU1lZkzZxIVFcWDBw+wt7fHx8eHYcOGKf2DgoLYvXs38fHxGBgYcPfuXZXxzpw5w4IFCzh+/Di3bt3C1taWqVOnqu17vWrVKlauXElycjLW1tb4+PgwduxYlT7Lli0jLCyMlJQUzMzMePfddwkODsbIyEhlnJCQEFJTU+nYsSMrVqyge/fuKuPExMTg4+PDyZMn0dXVpVOnTuzbt0/Zq/vOnTt8+OGH/PTTT9SqVYthw4axfPlylQu2Z8+eZdq0aZw6dQpzc3M+/PBDPv300xJ+BcqWBN1Cjbm5ebXZNqw02ZqitlLRNLZkakpGmzPZVTF7UJqtt0TNUVnfB/L7UZTW+fPnNbY7ODhU0Ey0h7b+PRKl4+DgwIEDB5THenr/C93Gjh3L3bt3+fHHHzEzMyMyMpLhw4dz+vRpOnfuDMCTJ0947733cHJyYv369Wrjx8XFYWFhQUREBM2aNSM6OhoPDw90dXWZPn06AGFhYXh7e/PVV1/RrVs3YmNjmTx5Mg0aNMDNzQ2AyMhIZs+ezYYNG3B2dubSpUuMGzcOHR0dQkNDAdiyZQteXl6sXr0aR0dHli1bhqurKxcvXsTCwgJ4FnAPGDAAb29vVqxYgZ6eHmfOnFH5ezV69GiuX79OVFQU2dnZjB8/Hg8PDyIjI4Fnn99dXFzo378/q1ev5ty5c0yYMAFTU1M8PDzK8stTIhJ0CzX53/hVMdtdHS4UCCGEEEIIoaenR5MmTQpsi46OJiwsTMkU+/r6snTpUuLi4pSgOz9TvmnTpgLHmDBhgsrjFi1aEBMTw44dO5Sg+5tvvmHKlCmMGDFC6XPq1CkWLlyoBN3R0dH07NmTUaNGAWBra8vIkSM5efKkMnZoaCiTJ09m/PjxAKxevZrdu3ezYcMGZs+eDYCnpycfffSR8hieZffz/f777+zdu5dTp07x6quvArBixQoGDRrE4sWLsbKyYvPmzTx58oQNGzZgYGCAg4MD8fHxhIaGStAtKo+RkVGhmbzqku0WQgghhBCiqvnzzz+xsrLCyMgIJycngoODsba2BsDZ2ZktW7YwePBgTE1N2bp1K48fP6Z3796lOue9e/do2LCh8jgrK0ulRBzA2NiY2NhYsrOz0dfXx9nZmYiICGJjY+nevTuXL19mz549jBkzBniWcY+Li8Pb21sZo1atWvTv35+YmBjg2XbEJ0+eZPTo0Tg7O5OUlESbNm0ICgqiV69ewLNMuKmpqRJwA/Tv359atWpx8uRJ/vnPfxITE8Prr7+OgYGB0sfV1ZWFCxeSnp5OgwYNSvX+vCwJumu44ODgYq1WXhOVZv/S0pb33rx5s9A22W+4YpVXyV5Rty+U5vtPysfLVnp6usb2yvoDrq3k/RLlrSaWj4vqISsrSy3ZZWhoWOBnDUdHRzZt2oS9vT3Xr18nMDCQ1157jYSEBOrVq8fWrVsZMWIEjRo1Qk9Pj9q1a7Nz507s7Oxeen7R0dFs2bKF3bt3K8dcXV1Zt24dQ4cOpUuXLsTFxbFu3Tqys7O5desWlpaWjBo1ilu3btGrVy/y8vJ4+vQpU6dOZc6cOQDcunWLnJwcGjdurHK+xo0bK/eoX758GXh2L/vixYvp1KkTX3/9Nf369SMhIYFWrVqRmpqqVOTm09PTo2HDhqSmpgLP7nVv3ry52nny2yToFlrDyMiIlJQUtW/qgmhbCbpk5oUQQgghhDYKDg5WWxzN39+fgIAAtb4DBw5U/t2hQwccHR2xsbFh69atTJw4ET8/P+7evcuBAwcwMzNj165dDB8+nGPHjtG+ffsSzy0hIYEhQ4bg7++vkpDz8/MjNTWVHj16kJeXR+PGjXF3d2fRokXKRf4jR44wf/58vvzySxwdHUlMTGTGjBnMmzcPPz+/Yp0/P2E1ZcoUpQS9c+fOHDx4kA0bNlT57Yol6BZqgoOD8fT0LFZfKUEvWGkzjflX/Qoime7qoTSZbFGxJDNbMvJ+ifIm1RSiqvL29sbLy0vlWHEr6kxNTWndujWJiYkkJSWxcuVKEhISlMqPjh07cuzYMVatWsXq1atLNK8LFy7Qr18/PDw88PX1VWkzNjZmw4YNrFmzhhs3bmBpacnatWupV6+e8pnUz8+PMWPGMGnSJADat29PZmYmHh4e+Pj4YGZmhq6uLjdu3FAZ+8aNG8o965aWlgC0a9dOpU/btm1JSUkBoEmTJqSlpam0P336lDt37ijjNGnSpMDz5LdVFgm6RaUq60y5XAAQQgghhBDaqLBS8uLIyMggKSmJMWPG8PDhQ0A9yaOrq1vkLY4vOn/+PH379sXd3Z2goKBC++nr69O0aVMAvvvuO9566y3l/A8fPixwLgB5eXkYGBjQtWtXDh48yNChQ4Fnme2DBw8qC7bZ2tpiZWXFxYsXVca5dOmSkvV3cnLi7t27xMXF0bVrVwAOHTpEbm4ujo6OSh8fHx/lfnOAqKgo7O3tK/WCnATdQqOiguIXrzaV5Ln5z9+8efNLz680tPlK+Yv7KAohREXStFUeyPZEonLlf5AWNZum4LI6rG3yySef4Obmho2NDdeuXcPf3x9dXV1GjhyJqakpdnZ2TJkyhcWLF9OoUSN27dpFVFQUP//8szJGSkoKd+7cISUlhZycHOLj4wGws7Ojbt26JCQk0LdvX1xdXfHy8lLui9bV1VWy2JcuXSI2NhZHR0fS09MJDQ0lISGB8PBw5Txubm6EhobSuXNnpbzcz88PNzc3Jfj28vLC3d2dV199le7du7Ns2TIyMzOVUnIdHR1mzpyJv78/HTt2pFOnToSHh/PHH3+wfft24FnWe8CAAUyePJnVq1eTnZ3N9OnTef/997GysgJg1KhRBAYGMnHiRGbNmkVCQgLLly+v9MScBN1Co6LKxzWVoRen9Ly4ZexCCCGEEELUFFeuXGHkyJHcvn0bc3NzevXqxYkTJ5RgeM+ePcyePRs3NzcyMjKws7MjPDycQYMGKWPMnTtXJTjO30rs8OHD9O7dm+3bt3Pz5k0iIiKIiIhQ+tnY2JCcnAxATk4OS5Ys4eLFi+jr69OnTx+io6OxtbVV+vv6+qKjo4Ovry9Xr17F3NwcNzc3lcz5iBEjuHnzJnPnziU1NZVOnTqxd+9elcXVPv74Yx4/foynpyd37tyhY8eOREVF0bJlS6XP5s2bmT59Ov369aNWrVoMGzaML774Qmk3MTFh//79TJs2ja5du2JmZsbcuXMrdbswAJ28vLy8Sp1BOfD09Kz0qxlViYuLC/v371d537y9vZXF1IoKugtrL87XoTLLy7U50/3TTz8V2pa/J6IQQpQXyXQLbZaRkaGxvW7duhU0E1GZqnumW1QvkukWBSruYmpGRkaF9tNUev78ecpTdnZ2oW3avNBKs2bNKnsKQogq7sqVKxrb8+/NK4gE1UKbSVAtQAJrUbVI0C1KRVPQ/HwwXlFbi0mFgxBCCCGEEEKbSNAtKkRlbS1WVRdbqcwtDYQQ1YOmTHZVpal6Caru73xN7t+/r7Fdth8UQgjtJ0G3KDfPl54Xp9RcCCGEEEIIIaobCbpFuXm+9FzT/eFlWXou5eVCCCGEEEIIbSJBt6gQRS24Vll7dWure/fuFdompedCiJqqOpaPF0XKx4UQouqToFtUiOIuuPaikmbBJdMthBBCCCGqgwcPHuDn58fOnTtJS0ujc+fOLF++nG7dugGgo6NT4PMWLVrEzJkzAQgKCmL37t3Ex8djYGDA3bt31fp/9NFH/Pe//yUhIYG2bdsSHx+v0v748WOmTp1KXFwcv//+O2+99Ra7du1SG2fz5s0sWrSIP//8ExMTEwYOHEhISAiNGjUCYNOmTYwfP17lOYaGhiqf9TMyMpg9eza7du3i9u3bNG/enI8++oipU6cqfVJTU5k5cyZRUVE8ePAAe3t7fHx8GDZsWJHvaWWRoFsUysjIiJSUlAo5T3llwTXt5SlbjgghhBBCVE01YZ/uSZMmkZCQwDfffIOVlRURERH079+fCxcu8Morr3D9+nWV/r/88gsTJ05UCT6fPHnCe++9h5OTE+vXry/0XBMmTODkyZOcPXtWrS0nJwdjY2M++ugjvv/++wKf/9///pexY8eydOlS3NzcuHr1KlOnTmXy5Mns2LFD6Ve/fn0uXryoPH7xwoGXlxeHDh0iIiICW1tb9u/fzwcffICVlRVvv/02AGPHjuXu3bv8+OOPmJmZERkZyfDhwzl9+jSdO3fW8I5WHgm6RaGKu1d3WZynMN7e3iWag2S6hRBCCCFEVffo0SO+//57fvjhB15//XUAAgIC+OmnnwgLC+Pzzz9Xu+Xwhx9+oE+fPrRo0UI5FhgYCDzLMhfmiy++AODmzZsFBt116tQhLCwMeBZcF5Qtj4mJwdbWlo8++giA5s2bM2XKFBYuXKjST0dHR+OtktHR0bi7u9O7d28APDw8WLNmDbGxsUrQHR0dTVhYGN27dwfA19eXpUuXEhcXJ0G3EC9DU0AuhBBCCCFEdfT06VNycnIwMjJSOW5sbMzx48fV+t+4cYPdu3cTHh5eUVNU4eTkxJw5c9izZw8DBw4kLS2N7du3M2jQIJV+GRkZ2NjYkJubS5cuXZg/fz4ODg5Ku7OzMz/++CMTJkzAysqKI0eOcOnSJZXEmrOzM1u2bGHw4MGYmpqydetWHj9+rATq2kiCblGtVdUScnt7+8qeghA1SmnKFJOTkwtts7W1fckZifJw8+bNQtvMzc01PrcmlLIK7ZWamqqx3cLCotC26vr9WRVfV1ZWFllZWSrHDA0NMTQ0VOtbr149nJycmDdvHm3btqVx48Z8++23xMTEYGdnp9Y/PDycevXq8c4775Tb/DXp2bMnmzdvZsSIETx+/JinT5/i5ubGqlWrlD729vZs2LCBDh06cO/ePRYvXoyzszPnz5+nadOmAKxYsQIPDw+aNm2Knp4etWrV4quvvlKy/QBbt25lxIgRNGrUCD09PWrXrs3OnTsLfF+0hQTdosyV5RZgJSXl5UIIIYQQQhsFBwcr5d75/P39CQgIKLD/N998w4QJE3jllVfQ1dWlS5cujBw5kri4OLW+GzZsYPTo0WqZ8Ypy4cIFZsyYwdy5c3F1deX69evMnDmTqVOnKveSOzk54eTkpDzH2dmZtm3bsmbNGubNmwc8C7pPnDjBjz/+iI2NDf/5z3+YNm0aVlZW9O/fHwA/Pz/u3r3LgQMHMDMzY9euXQwfPpxjx47Rvn37in/xxSBBtyhSSYNobdoCTBZSE0IUR2kyJi9mLWqC7OzsQtu0eVuvorLZmlTFrJqoPmS70OrB29sbLy8vlWMFZbnztWzZkqNHj5KZmcn9+/extLRkxIgRKvdsAxw7doyLFy+yZcuWcpl3cQQHB9OzZ09l1fQOHTpQp04dXnvtNT7//HMsLS3VnqOvr0/nzp1JTEwEnt3HPmfOHHbu3MngwYOVceLj41m8eDH9+/cnKSmJlStXkpCQoJSld+zYkWPHjrFq1SpWr15dQa+4ZCToFkV6/PhxiTLIJV38rCxJplsIIYQQQmijwkrJi1KnTh3q1KlDeno6+/btY9GiRSrt69evp2vXrnTs2LGsplpiDx8+RE9PNbTU1dUFIC8vr8Dn5OTkcO7cOeW+7+zsbLKzs9Uucurq6iq3+Dx8+BBQvxD6fB9tJEG3KNWV/4LI4mdCCCGEEEKUzr59+8jLy8Pe3p7ExERmzpxJmzZtVPa6vn//Ptu2bWPJkiUFjpGSksKdO3dISUkhJydH2YPbzs5OqfpMTEwkIyOD1NRUHj16pPRp164dBgYGwLPy8SdPnnDnzh0ePHig9OnUqRMAbm5uTJ48mbCwMKW8/OOPP6Z79+5YWVkB8Nlnn9GjRw/s7Oy4e/cuISEh/P3330yaNAl4tp3YG2+8wcyZMzE2NsbGxoajR4/y9ddfExoaCkCbNm2ws7NjypQpLF68mEaNGrFr1y6ioqL4+eefy+y9L2sSdAuNi29UdZVVQl7UgiflWSZ2//79Qtvq169fbucVZUtT+S5odwlvTVMTFz6sjt9/6enpGtsbNGhQQTMRouQ0Zfhu376t8bllnXypKFX1NpeSuHfvHt7e3ly5coWGDRsybNgwgoKCVF7fd999R15eHiNHjixwjLlz56qsaJ6/pdbhw4eV1b4nTZrE0aNH1fr89ddfyoKggwYN4u+//1brk5/FHjduHA8ePGDlypX8+9//xtTUlL59+6psGZaens7kyZNJTU2lQYMGdO3alejoaNq1a6fyery9vRk9ejR37tzBxsaGoKAgpk6dCjz72u7Zs4fZs2fj5uZGRkYGdnZ2hIeHq62Urk108grL91dhnp6eUmZcAvnvV0Hvm7e3NykpKVhbW1fa4mgloS1fdwm6RWlJ0C1ExZKgW1RlEnSrkr+RQttIpltoFBwcjKenZ4nv667pKnPBEwmsq4fSfGBISkrS2N6yZcuXHluUzKNHjzS25+TkaGyXBR8rjgTVorqqqkF1USSwFlWJBN3ipVTmtmCayIUBIYQQQgghhDaRoFu8lKqS+dZULlieWQ1NW5WBZK9E+ZJMtvYwNjbW2H7lyhWN7fK7QghRHLKlnRDaTYJuUSQjIyNSUlKA/2W409LSKnlWQgghhBBCCKH9JOgWRcq/rxv+l+Euzj7clVGCXhWy70IIIYQQQoiaQ4Ju8VKMjIyKDLzT0tLYvHlzBc2oYJW1ME7t2rU1tlfm6uZCCHWaVv4tz7LNrKyschtbWxX1mg0NDStoJlWDpu9NkLJi8Uxl/Q4TQhSPBN3ipQQHBxfZx9vbu1gZ8bIkmW4hhBBCCFHVBQQEEBgYqHLM3t6eP/74A3iWwJk5cyZRUVE8ePAAe3t7fHx8GDZsmNI/KCiI3bt3Ex8fj4GBAXfv3lUZ78yZMyxYsIDjx49z69YtbG1tmTp1KjNmzFDpt2rVKlauXElycjLW1tb4+PgwduxYlT7Lli0jLCyMlJQUzMzMePfddwkODsbIyAiAsLAwwsLCSE5OBsDBwYG5c+cycOBAZYzevXur7BcOMGXKFFavXg3Apk2bGD9+fIHv140bN7CwsODIkSP06dNHrf369euVmtSSoFuUm+IE5tVVUVeVJZNdtjRty1TUQlZCXU3MmFTW66qJi97dv39fY3t13d7oZVXXnzmhqqjtBYv6W1ZZ3yeVWblXE/5WOTg4cODAAeWxnt7/QrexY8dy9+5dfvzxR8zMzIiMjGT48OGcPn2azp07A/DkyRPee+89nJycWL9+vdr4cXFxWFhYEBERQbNmzYiOjsbDwwNdXV2mT58OPAuWvb29+eqrr+jWrRuxsbFMnjyZBg0a4ObmBkBkZCSzZ89mw4YNODs7c+nSJcaNG4eOjg6hoaEANG3alAULFtCqVSvy8vIIDw9nyJAh/Pbbbzg4OChzmjx5Mp999pny+Pnq0REjRjBgwACV1zBu3DgeP36MhYWFyvGLFy+qbKP7YntFk6BbFEt+OXlaWprWbhcGkukWQgghhBDVg56eXqEXLqKjowkLC6N79+4A+Pr6snTpUuLi4pSgOz9TvmnTpgLHmDBhgsrjFi1aEBMTw44dO5Sg+5tvvmHKlCmMGDFC6XPq1CkWLlyoBN3R0dH07NmTUaNGAWBra8vIkSM5efKkMnZ+33xBQUGEhYVx4sQJlaC7du3ahb5mY2NjlQtQN2/e5NChQwVeULCwsMDU1LTAcSqDBN2iWPKz1p6enlVmuzAhhBBCCCGqqj///BMrKyuMjIxwcnIiODgYa2trAJydndmyZQuDBw/G1NSUrVu38vjxY3r37l2qc967d4+GDRsqj7OyspQS8XzGxsbExsaSnZ2Nvr4+zs7OREREEBsbS/fu3bl8+TJ79uxhzJgxBZ4jJyeHbdu2kZmZiZOTk0rb5s2biYiIoEmTJri5ueHn51foWklff/01tWvX5t1331Vr69SpE1lZWfzjH/8gICCAnj17lvStKFMSdIsaS9Me3lD0ImyaSqqKKmEpquxJ00JDssiQOikhL1uavj9v3ryp8bnaWhosi3dpD239HhHVR1UsO66qf8cq83Y5bf1aapKVlaX298jQ0LDAv0GOjo5s2rQJe3t7rl+/TmBgIK+99hoJCQnUq1ePrVu3MmLECBo1aoSenh61a9dm586d2NnZvfT8oqOj2bJlC7t371aOubq6sm7dOoYOHUqXLl2Ii4tj3bp1ZGdnc+vWLSwtLRk1ahS3bt2iV69e5OXl8fTpU6ZOncqcOXNUxj937hxOTk48fvyYunXrsnPnTtq1a6e0jxo1ChsbG6ysrDh79iyzZs3i4sWL7Nixo8D5rl+/nlGjRqn8/FhaWrJ69WpeffVVsrKyWLduHb179+bkyZN06dLlpd+b0pKgW5SLyipBlwy8EEIIIYTQRsHBwWqLo/n7+xMQEKDW9/kFxjp06ICjoyM2NjZs3bqViRMn4ufnx927dzlw4ABmZmbs2rWL4cOHc+zYMdq3b1/iuSUkJDBkyBD8/f1xcXFRjvv5+ZGamkqPHj3Iy8ujcePGuLu7s2jRIuXCx5EjR5g/fz5ffvkljo6OJCYmMmPGDObNm4efn58ylr29PfHx8dy7d4/t27fj7u7O0aNHlcDbw8ND6du+fXssLS3p168fSUlJamugxMTE8Pvvv/PNN9+oHLe3t8fe3l557OzsTFJSEkuXLlXrW5Ek6BblQltK0LOzswttK8/txEp79VWyblVDaRe+qYqqapZS0+8CkJ85UbmkuqlsVcUMqFBX2opEbeTt7Y2Xl5fKseL+jJuamtK6dWsSExNJSkpi5cqVJCQkKPdDd+zYkWPHjrFq1Splte/iunDhAv369cPDwwNfX1+VNmNjYzZs2MCaNWu4ceMGlpaWrF27lnr16imfCfz8/BgzZgyTJk0CngXMmZmZeHh44OPjo/xMGhgYKJn4rl27curUKZYvX86aNWsKnJejoyMAiYmJakH3unXr6NSpE127di3y9XXv3p3jx4+X4B0pexJ0ixIxMjLixIkTxdqjO19FZr21IdAXQgghhBDiRYWVkhdHRkYGSUlJjBkzhocPHwLqF5h0dXU13l5RkPPnz9O3b1/c3d0JCgoqtJ++vj5NmzYF4LvvvuOtt95Szv/w4cMC5wKQl5dX6Ji5ubkaLzrGx8cDz0rGn5eRkcHWrVuLvVNSfHy82hgVTYJuUSLBwcG4uLgUGdw+H5RrS9ZbCCGEEEKIquCTTz7Bzc0NGxsbrl27hr+/P7q6uowcORJTU1Ps7OyYMmUKixcvplGjRuzatYuoqCh+/vlnZYyUlBTu3LlDSkoKOTk5ShBrZ2dH3bp1SUhIoG/fvri6uuLl5aWsV6Srq6tksS9dukRsbCyOjo6kp6cTGhpKQkIC4eHhynnc3NwIDQ2lc+fOSnm5n58fbm5uSvDt7e3NwIEDsba25sGDB0RGRnLkyBH27dsHQFJSEpGRkQwaNIhGjRpx9uxZPD09ef311+nQoYPKe7NlyxaePn3Kv/71L7X3bdmyZTRv3hwHBwceP37MunXrOHToEPv37y+7L85LkKBblIv8LcZANetd0fT19ctt7MzMzHIbW/adrhrka1F11K1bt7KnUK1UxYWqtJmUkJctTbeTlOfnAlmwsWxVxfLxsnTlyhVGjhzJ7du3MTc3p1evXpw4cUIJhvfs2cPs2bNxc3MjIyMDOzs7wsPDGTRokDLG3LlzVYLj/K3EDh8+TO/evdm+fTs3b94kIiKCiIgIpZ+NjQ3JycnAs5XGlyxZwsWLF9HX16dPnz5ER0dja2ur9Pf19UVHRwdfX1+uXr2Kubk5bm5uKpnztLQ0xo4dy/Xr1zExMaFDhw7s27ePN998E3hWen7gwAGWLVtGZmYmzZo1Y9iwYWrl7vBsAbV33nmnwC3Bnjx5wr///W+uXr1K7dq16dChAwcOHKBPnz4l/yKUIZ08TTn/KsrT01MyqyWQ/34V931zcXEp0dWi6lpenpSUVGjbi/edlJQE3UIIbSZBt9BmEnQLIbSNZLpFuSvu/RbapqiFl0obWGsigXXZqapbXGmroj5UavpAK8FY9SFfS+2haftKqNwtnSpLeQbWmkhQrT1q4kKnQrtJ0C1KzNzcvNK2BCuKVDgIIYQQQgghtIkE3aLELCwsasTiaHfv3tXYLhnSqkG+TmVLyieF0C41MZMtqofyrESTTLbQNlIfJoQQQgghhBBClBPJdIsyV5ml59U9+y6EEEIIIYSoWiToFmWuupSe//333xrbpWxZ1ET169ev7CkIIYSoBuRzlKhJJOgWL+X3339X9uF+Uf6+3JWR8a4Owb4QQgghhKjZbG1tC0wAffDBB6xatQqAmJgYfHx8OHnyJLq6unTq1Il9+/ZhbGzMkSNHCt2bOjY2lm7dupGcnEzz5s3V2mNiYujRo4fyeNu2bfj5+ZGcnEyrVq1YuHChsh94dnY2vr6+7Nmzh8uXL2NiYkL//v1ZsGABVlZWKuPu3r2bzz77jLNnz2JkZMQbb7zBrl27lHYdHR21uXz77be8//77AIwbN05l3/F87dq14/z58wW+Vm0hQbcoMSMjIx49elRogJsfjFf1jHfr1q0rewpCVDnp6emFtjVo0KACZyLKU0ZGRqFtdevWrcCZCCFqqtzc3ELbqsO2hqdOnSInJ0d5nJCQwJtvvsl7770HPAuMBwwYgLe3NytWrEBPT48zZ84or93Z2Znr16+rjOnn58fBgwd59dVXVY4fOHAABwcH5XGjRo2Uf0dHRzNy5EiCg4N56623iIyMZOjQofz666/84x//4OHDh/z666/4+fnRsWNH0tPTmTFjBm+//TanT59Wxvn++++ZPHky8+fPp2/fvjx9+pSEhAS1171x40YGDBigPDY1NVX+vXz5chYsWKA8fvr0KR07dlTeE20mQbcoseDgYFxcXAptNzIywtPTU8l4CyGEEEIIIYrvxfL7BQsW0LJlS9544w3gWZLro48+Yvbs2Uofe3t75d8GBgYquxtkZ2fzww8/8OGHH6pllBs1alToTgjLly9nwIABzJw5E4B58+YRFRXFypUrWb16NSYmJkRFRak8Z+XKlXTv3p2UlBSsra15+vQpM2bMICQkhIkTJyr92rVrp3Y+U1PTQudiYmKCiYmJ8njXrl2kp6czfvz4AvtrEwm6RZkLDg4GKLD8vLxLzqtyZl0IIYQQQogXPXnyhIiICLy8vNDR0SEtLY2TJ08yevRonJ2dSUpKok2bNgQFBdGrV68Cx/jxxx+5fft2gQHq22+/zePHj2ndujWffvopb7/9ttIWExODl5eXSn9XV1eVsvAX3bt3Dx0dHSVL/euvv3L16lVq1apF586dSU1NpVOnToSEhPCPf/xD5bnTpk1j0qRJtGjRgqlTpzJ+/PgCy84B1q9fT//+/bGxsSl0LtpCgm5RbvIz3s9LS0tj8+bNFTaH7OzsQtv09fU1Pvf+/fsa20uzoJTsdSxKS9P3Z2Uudqarq1tp5xYVR0rIRXmSv5GitLT1eygrK0ttboaGhkXOZ9euXdy9e5dx48YBcPnyZQACAgJYvHgxnTp14uuvv6Zfv34kJCTQqlUrtTHWr1+Pq6srTZs2VY7VrVuXJUuW0LNnT2rVqsX333/P0KFD2bVrlxJ4p6am0rhxY5WxGjduTGpqaoFzffz4MbNmzWLkyJHK55Hn5xsaGoqtrS1Lliyhd+/eXLp0iYYNGwLw2Wef0bdvX2rXrs3+/fv54IMPyMjI4KOPPlI7z7Vr1/jll1+IjIzU+N5pCwm6RbnJz3g/z9vbu9AF2MqCZLqFEEIIIYQ2Cg4OJjAwUOWYv78/AQEBGp+3fv16Bg4cqCxMln8/+5QpU5TMdefOnTl48CAbNmxQ+wx+5coV9u3bx9atW1WOm5mZqWSxu3XrxrVr1wgJCVHJdhdXdnY2w4cPJy8vj7CwMOV4/nx9fHwYNmwY8Oze7aZNm7Jt2zamTJkCPLvnPF/nzp3JzMwkJCSkwKA7PDwcU1NThg4dWuJ5VgYJusVLedltHgoKxMtTUdlsTZ6/EljWtPUKbFWl6f2sru+ltm7dJRUgojxduXJFY3t5/t4WFUd+1kVxaFosTVu/h7y9vdVKtYua699//82BAwfYsWOHcszS0hJQvye6bdu2pKSkqI2xceNGGjVqVKxA2tHRUeUe7SZNmnDjxg2VPjdu3FC77zo/4P777785dOiQyueBguZraGhIixYtCpzv83OZN28eWVlZKu9TXl4eGzZsYMyYMRgYGBT5mrSBBN3ipVhYWACVsy2YJpLpFkIIIYQQ2qg4peQv2rhxIxYWFgwePFg5Zmtri5WVFRcvXlTpe+nSJQYOHKhyLC8vj40bNzJ27NhiJaPi4+OVIBnAycmJgwcP8vHHHyvHoqKicHJyUh7nB9x//vknhw8fVln9HKBr164YGhpy8eJF5Z7z7OxskpOTNd6PHR8fT4MGDdTes6NHj5KYmKiyKJu2k6BblEpV3xZMCCGEEEIIbZSbm8vGjRtxd3dHT+9/YZuOjg4zZ87E39+fjh070qlTJ8LDw/njjz/Yvn27yhiHDh3ir7/+YtKkSWrjh4eHY2BgQOfOnQHYsWMHGzZsYN26dUqfGTNm8MYbb7BkyRIGDx7Md999x+nTp1m7di3wLHh+9913+fXXX/n555/JyclR7vdu2LAhBgYG1K9fn6lTp+Lv70+zZs2wsbEhJCQEQNnu66effuLGjRv06NEDIyMjoqKimD9/Pp988onavNevX4+jo6PaImzaTIJuISpBZZUGHz58WGN7nz59KmgmZUtby8hEycjXURRFyseFEDXJgQMHSElJYcKECWptH3/8MY8fP8bT05M7d+7QsWNHoqKiaNmypUq/9evX4+zsTJs2bQo8x7x58/j777/R09OjTZs2bNmyhXfffVdpd3Z2JjIyEl9fX+bMmUOrVq3YtWuXEvBevXqVH3/8EYBOnTqpjH348GF69+4NQEhICHp6eowZM4ZHjx7h6OjIoUOHaNCgAfDsltBVq1bh6elJXl4ednZ2hIaGMnnyZJUx7927x/fff8/y5cuL/0ZqAQm6RYWSLcOEEEIIIYQomouLC3l5eYW2z549W2Wf7oJoWt3b3d0dd3f3Iufx3nvvKRnpF9na2mqcYz59fX0WL17M4sWLC2wfMGAAAwYMKHIcExMTHj58WGQ/bSNBt6hQUo5eudq2bVvZUxBCCCGEEKJGkaBbvJT8PbjT0tJKlL1OS0sr1XmLOpcE9EIIIYQQQghtIkG3UAmgiyt/6y9PT88SZa9Lu0e3ZMqFEEIIIYQQVYkE3UIlgC5v+QH+yyppprw0+zfn5uZqbNe0P6QQQrvIz7MQQgghKosE3aJC5Qf4L8vb21tj0C5ZcCGEEEIIIYQ2kaBblIqRkREpKSkVdr6SBu2astkZGRkan1u3bt0SnaskHj16pLHd2Ni4XM7bpEmTchlXCG0nmeySSU9P19iev8VLQTRVGEHlbg13//79QtsqaytHUX1U5ucKIYR2k6BblEpwcDADBgyokNL04pBMtxBCCCGEEEKbSNAtSi03N7dKBrtFXXEuzyvW5ZXJFmWvOmbGUlNTNbZLRYTQlMkuSmVmsotSVX9mRdUgmWxRlnJycggICCAiIoLU1FSsrKwYN24cvr6+6OjoKP1+//13Zs2axdGjR3n69Cnt2rXj+++/x9raGni2CPG///1vvvvuO7KysnB1deXLL7+kcePGKufbtGkToaGhXLp0ifr16/Pee++xatUqpf3s2bNMmzaNU6dOYW5uzocffsinn36qtPfu3ZujR4+qvY5Bgwaxe/duAMaNG0d4eLhKu6urK3v37lUe37lzhw8//JCffvqJWrVqMWzYMJYvX678fF28eJGpU6dy4cIF7t27h5WVFaNGjcLf3x99ff2XfbvLnQTdQgghhBBCCKFFFi5cSFhYGOHh4Tg4OHD69GnGjx+PiYkJH330EQBJSUn06tWLiRMnEhgYSP369Tl//jxGRkbKOJ6enuzevZtt27ZhYmLC9OnTeeedd/jvf/+r9AkNDWXJkiWEhITg6OhIZmYmycnJSvv9+/dxcXGhf//+rF69mnPnzjFhwgRMTU3x8PAAYMeOHTx58kR5zu3bt+nYsSPvvfeeyusaMGAAGzduVB6/eKF29OjRXL9+naioKLKzsxk/fjweHh5ERkYCoK+vz9ixY+nSpQumpqacOXOGyZMnk5uby/z580v5rpcfCbpFuSnJ/t1lpSpm3IUQQgghhHhedHQ0Q4YMYfDgwQDY2try7bffEhsbq/Tx8fFh0KBBLFq0SDnWsmVL5d/37t1j/fr1REZG0rdvXwA2btxI27ZtOXHiBD169CA9PR1fX19++ukn+vXrpzy3Q4cOyr83b97MkydP2LBhAwYGBjg4OBAfH09oaKgSdDds2FBl/t999x21a9dWC7oNDQ0Lraj7/fff2bt3L6dOneLVV18FYMWKFQwaNIjFixdjZWVFixYtaNGihfIcGxsbjhw5wrFjx4rxrlYeCbpFqZmbmxcYYKelpbF58+ZKmtUzpdkyrHbt2mU9HUV5LjR08+bNQtuKKmuX0jh11bEcVZvLf4UQQpScpm0RZSFJ7ZGVlaX2GdDQ0LDAv8vOzs6sXbuWS5cu0bp1a86cOcPx48cJDQ0Fnn3Nd+/ezaeffoqrqyu//fYbzZs3x9vbm6FDhwIQFxdHdnY2/fv3V8Zt06YN1tbWxMTE0KNHD6KiosjNzeXq1au0bduWBw8e4OzszJIlS2jWrBkAMTExvP766xgYGCjjuLq6snDhQtLT0wu8JWn9+vW8//771KlTR+X4kSNHsLCwoEGDBvTt25fPP/+cRo0aKecxNTVVAm6A/v37U6tWLU6ePMk///lPtfMkJiayd+9e3nnnHY3vfWWToFuUmoWFBY8fP1bLMhe1vVd5kEy3EEIIIYTQRsHBwQQGBqoc8/f3JyAgQK3v7NmzuX//Pm3atEFXV5ecnByCgoIYPXo08Cy5lZGRwYIFC/j8889ZuHChEnwePnyYN954g9TUVAwMDDA1NVUZu3Hjxsr6LpcvX1ZKs5cvX46JiQm+vr68+eabnD17FgMDA1JTU2nevLnaGPBsnZgXg+7Y2FgSEhJYv369yvEBAwbwzjvv0Lx5c5KSkpgzZw4DBw4kJiYGXV1dUlNTsbCwUHmOnp4eDRs2VFuPxtnZmV9//ZWsrCw8PDz47LPPNLzzlU+CblFuSrsnd1koTUavPK8Ml2em0dzcvNzGFmWnMrdVKs0iWUKdpkUXa2L1iGybJIpD04KOsphjyUk2u2rw9vbGy8tL5Vhhf++3bt3K5s2biYyMVMq5P/74Y6ysrHB3d1eqG4YMGaIkuTp16kR0dDSrV6/mjTfeKNaccnNzyc7O5osvvsDFxQWAb7/9liZNmnD48GFcXV1L/DrXr19P+/bt6d69u8rx999/X/l3+/bt6dChAy1btuTIkSMqpe3FsWXLFh48eMCZM2eYOXMmixcvVlnYTdtI0C2EEEIIIYQQ5aywUvKCzJw5k9mzZyuBavv27fn7778JDg7G3d0dMzMz9PT0aNeuncrz2rZty/Hjx4FnF7CePHnC3bt3VbLdN27cUC5uWVpaAqiMY25ujpmZGSkpKco4N27cUDlP/uMXL5JlZmby3XffFSvz3KJFC8zMzEhMTKRfv340adKEtLQ0lT5Pnz7lzp07aufJL31v164dOTk5eHh48O9//xtdXd0iz1sZJOgWpWZkZMSJEye0Yq9uKS8XQgghhBBV3cOHD9UqGHR1dZUMt4GBAd26dePixYsqfS5duoSNjQ0AXbt2RV9fn4MHDzJs2DDg2ZZbKSkpODk5AdCzZ0/leNOmTYFn23bdunVLGcfJyQkfHx+ys7OVbbmioqKwt7dXq57btm0bWVlZ/Otf/yryNV65coXbt28rgb+TkxN3794lLi6Orl27AnDo0CFyc3NxdHQsdJz8bH1ubq4E3aL6Cg4OxsXFRQLeCvLo0SON7ZW1B3hp9n7WtAAMVM+yOVnMrGxVZkmzlEurkvdDFEdllZBX1783spBa9ePm5kZQUBDW1tY4ODjw22+/ERoayoQJE5Q+M2fOZMSIEbz++uv06dOHvXv38tNPP3HkyBEATExMmDhxIl5eXjRs2JD69evz4Ycf4uTkRI8ePQBo3bo1Q4YMYcaMGaxdu5b69evj7e1NmzZt6NOnDwCjRo0iMDCQiRMnMmvWLBISEli+fHmBn/3Xr1/P0KFDlcXR8mVkZBAYGMiwYcNo0qQJSUlJfPrpp9jZ2Skl7G3btmXAgAFMnjyZ1atXk52dzfTp03n//fexsrICnq2krq+vT/v27TE0NOT06dN4e3szYsQI2adb1ByVsU3Y8yTwF0IIIYQQVd2KFSvw8/Pjgw8+IC0tDSsrK6ZMmcLcuXOVPv/85z9ZvXo1wcHBfPTRR9jb2/P999/Tq1cvpc/SpUupVasWw4YNIysrC1dXV7788kuVc3399dd4enoyePBgatWqxRtvvMHevXuVINbExIT9+/czbdo0unbtipmZGXPnzlW2C8t38eJFjh8/zv79+9Vej66uLmfPniU8PJy7d+9iZWWFi4sL8+bNU0lEbN68menTp9OvXz9l3l988YXSrqenx8KFC7l06RJ5eXnY2Ngwffp0rai41UQnLy8vr7InUdY8PT0l+HoJpXnfRo8ezebNm+W9F0IIIUroxfLQ59nb21fgTIQQQpQHyXSLMvHi8v4lUZbZcQn4hRBCCCGEENpEgm5R6Qra41sIIYQQQgghqgMJukW5KEn2+sWtAUT50dZF2IQQoibLX7lXCCFE9SRBtygTRkZGeHp6KgF0SbLX3t7eZbb4gWTMhRBCCCGEENpEgm5RJoKDgwFeKnjOf251UlRGuajtosprew/JZNcM9+/f19hev379lx775s2bGtvNzc1femwhaqrS/EwKIYTQfhJ0i3JV0VuISaZbCCGEEEIIoU0k6BblqrIXSdOUcS7PrO/ly5c1tjs4OJTbuYUoz6yZZLKFEEKI8hcWFkZYWBjJycnAs8+Oc+fOZeDAgQBMmTKFAwcOcO3aNerWrYuzszMLFy6kTZs2AJw5c4YFCxZw/Phxbt26ha2tLVOnTmXGjBkq5zly5AheXl6cP3+eZs2a4evry7hx41T6rFq1ipCQEFJTU+nYsSMrVqyge/fuSntRcwE4deoUs2fPJi4uDh0dHbp3786iRYvo2LGj0mfr1q3Mnz+fS5cuYW5uzvTp05k5c6baXFauXElycjLW1tb4+PgwduzYUr/f5a18aliFEEIIIYQQQryUpk2bsmDBAuLi4jh9+jR9+/ZlyJAhnD9/HoCuXbuyceNGfv/9d/bt20deXh4uLi7k5OQAEBcXh4WFBREREZw/fx4fHx+8vb1ZuXKlco6//vqLwYMH06dPH+Lj4/n444+ZNGkS+/btU/ps2bIFLy8v/P39+fXXX+nYsSOurq4qCyEXNZeMjAwGDBiAtbU1J0+e5Pjx49SrVw9XV1eys7MB+OWXXxg9ejRTp04lISGBL7/8kqVLl6rMNywsDG9vbwICAjh//jyBgYFMmzaNn376qfy+EGVEJy8vL6+yJ1HWPD09pcz4JZTF+5ZfTp6Wloa1tTUpKSls3ry5RM8tjRfnX1mZ7vxfiIWRTLcQQgghhCiJhg0bEhISwsSJE9Xazp49S8eOHUlMTKRly5YFPn/atGn8/vvvHDp0CIBZs2axe/duEhISlD7vv/8+d+/eZe/evQA4OjrSrVs3JfjNzc2lWbNmfPjhh8yePbvA87w4l9OnT9OtWzdSUlJo1qwZAOfOnaNDhw78+eef2NnZMWrUKLKzs9m2bZsyzooVK1i0aBEpKSno6Ojg7OxMz549CQkJUfr8+9//VgJ5bSbl5aJMPb+g2uPHj7GwsCj2c8ujFL2yFg6zsbGplPMKIYQQQgjtlJWVRVZWlsoxQ0PDIhfYzcnJYdu2bWRmZuLk5KTWnpmZycaNG2nevLkS1Bbk3r17NGzYUHkcExND//79Vfq4urry8ccfA/DkyRPi4uLw9vZW2mvVqkX//v2JiYkp8BwFzcXe3p5GjRqxfv165syZQ05ODuvXr6dt27bY2toCz96b2rVrq4xlbGzMlStX+Pvvv7G1tSUrKwsjIyO1PrGxsWRnZ6Ovr1/oa69sEnSLcpW/lVhxvLhf98tkvqXCQQghhBBCaKPg4GACAwNVjvn7+xMQEFBg/3PnzuHk5MTjx4+pW7cuO3fupF27dkr7l19+yaeffkpmZib29vZERUVhYGBQ4FjR0dFs2bKF3bt3K8dSU1Np3LixSr/GjRtz//59Hj16RHp6Ojk5OQX2+eOPP1SOaZpLvXr1OHLkCEOHDmXevHkAtGrVin379qGn9ywcdXV1xdPTk3HjxtGnTx8SExNZsmQJANevX8fW1hZXV1fWrVvH0KFD6dKlC3Fxcaxbt47s7Gxu3bqFpaVlga9dG0jQLcpVSbYDezE4L4vM94tXE59X1FXF0sjIyNDYXrdu3XI7txBCCCEqnqbPHFC+nztE1eDt7Y2Xl5fKMU3fF/b29sTHx3Pv3j22b9+Ou7s7R48eVQLv0aNH8+abb3L9+nUWL17M8OHD+e9//6uWDU5ISGDIkCH4+/vj4uJS9i+siLk8evSIiRMn0rNnT7799ltycnJYvHgxgwcP5tSpUxgbGzN58mSSkpJ46623yM7Opn79+syYMYOAgABlK10/Pz9SU1Pp0aMHeXl5NG7cGHd3dxYtWlRu2+2WFQm6hdZ4MSv+YuZbCCGEEEKIqqo4peTPMzAwwM7ODni2WNmpU6dYvnw5a9asAcDExAQTExNatWpFjx49aNCgATt37mTkyJHKGBcuXKBfv354eHjg6+urMn6TJk24ceOGyrEbN25Qv359jI2N0dXVRVdXt8A+TZo0UTmmaS6RkZEkJycTExOjBMeRkZE0aNCAH374gffffx8dHR0WLlzI/PnzSU1NxdzcnIMHDwLQokUL4Fkp+YYNG1izZg03btzA0tKStWvXUq9ePa3fXUWCbqE1XsyK5wfgJSkzl/JyIYQQQghRHeXm5hZaUZGXl0deXp5K+/nz5+nbty/u7u4EBQWpPcfJyYk9e/aoHIuKilLuGzcwMKBr164cPHiQoUOHKnM4ePAg06dPL3SeL87l4cOH1KpVCx0dHaVP/uPc3FyV5+rq6vLKK68A8O233+Lk5KQWUOvr69O0aVMAvvvuO9566y3JdAvxsvIz32lpacVeAf1Fmq4mvvhD/qLS/PA+ffr0pZ8rhKg5yvP3kBCiYkn5uChL3t7eDBw4EGtrax48eEBkZCRHjhxh3759XL58mS1btuDi4oK5uTlXrlxhwYIFGBsbM2jQIOBZSXnfvn1xdXXFy8uL1NRU4FlQmx/ETp06lZUrV/Lpp58yYcIEDh06xNatW1Xu+/by8sLd3Z1XX32V7t27s2zZMjIzMxk/fjxAseby5ptvMnPmTKZNm8aHH35Ibm4uCxYsQE9Pjz59+gBw69Yttm/fTu/evXn8+DEbN25k27ZtHD16VJnLpUuXiI2NxdHRkfT0dEJDQ0lISCA8PLz8vyClJEG3KBdGRkakpKSUaoz8zLe3t3exF2OTTLcQQgghhKjq0tLSGDt2LNevX8fExIQOHTqwb98+3nzzTa5du8axY8dYtmwZ6enpNG7cmNdff53o6Ghl56Dt27dz8+ZNIiIiiIiIUMa1sbEhOTkZgObNm7N79248PT1Zvnw5TZs2Zd26dbi6uir9R4wYwc2bN5k7dy6pqal06tSJvXv3KourGRkZFTmXNm3a8NNPPxEYGIiTkxO1atWic+fO7N27V2Xxs/DwcD755BPy8vJwcnLiyJEjdO/eXWnPyclhyZIlXLx4EX19ffr06UN0dLSyAro2k326haKs3zdt+DpkZ2cX2qbN2wpoqytXrhTall/mI4QQQlQmTRUkUj0ihKgMkukW5eb3338vdoa6rFR2kC+EEEIIIYQQz5OgW5Sb3NxcCYKFEEIIIYQQNZoE3aJakxLyslWaEnIp9Rei6sjIyNDYXrdu3ZceuzwXj7t586bGdm3fUkaUjZpYQi4l9UJoNwm6RbkryZZfpSWZdSGEEEIIIYQ2kaBblLvHjx9XWjAsV361h2SztUd5ZjFF9VCe3wPl+bu3qEz2/fv3C22rX79+WU9HiAojn2mE0G4SdItyU9iHn/LMfEumWwghhBBCCKFNJOgW5SZ/b74XVWbmWwghhBBCCG2Xk5NDQEAAERERpKamYmVlxbhx4/D19UVHR0et/9SpU1mzZg1Lly7l448/VmnbvXs3n332GWfPnsXIyIg33niDXbt2AXDmzBkWLFjA8ePHuXXrFra2tkydOpUZM2Yozx83bhzh4eFq52zXrh3nz59XHq9atYqQkBBSU1Pp2LEjK1asUNlnGyAmJgYfHx9OnjyJrq4unTp1Yt++fRgbG5OcnMy8efM4dOiQ8pr/9a9/4ePjg4GBgTLGvn378Pf35/z58xgZGfH666+zZMkSrd6vW4JuUa1JuZUQ6qpj+Xh6errG9gYNGlTQTIQ2kxJyIURVsXDhQsLCwggPD8fBwYHTp08zfvx4TExM+Oijj1T67ty5kxMnTmBlZaU2zvfff8/kyZOZP38+ffv25enTpyQkJCjtcXFxWFhYEBERQbNmzYiOjsbDwwNdXV2mT58OwPLly1mwYIHynKdPn9KxY0fee+895diWLVvw8vJi9erVODo6smzZMlxdXbl48aKSiIuJiWHAgAF4e3uzYsUK9PT0OHPmjPJ5/Y8//iA3N5c1a9ZgZ2dHQkICkydPJjMzk8WLFwPw119/MWTIELy8vNi8eTP37t3D09OTd955h19//bWM3v2yJ0G3qBDPl5SnpaWpHSsrkkEXQgghhBBVXXR0NEOGDGHw4MEA2Nra8u233xIbG6vS7+rVq3z44Yfs27dP6Zvv6dOnzJgxg5CQECZOnKgcb9eunfLvCRMmqDynRYsWxMTEsGPHDiXoNjExwcTEROmza9cu0tPTGT9+vHIsNDSUyZMnK8dWr17N7t272bBhA7NnzwbA09OTjz76SHkMYG9vr/x7wIABDBgwQGUuFy9eJCwsTAm64+LiyMnJ4fPPP1eC9U8++YQhQ4aQnZ2ttWsISdAtKsTzJeWenp5qxypDeWbGSrsljiwAJ0TJSCZbCCGEtsvKyiIrK0vlmKGhIYaGhmp9nZ2dWbt2LZcuXaJ169acOXOG48ePExoaqvTJzc1lzJgxzJw5EwcHB7Uxfv31V65evUqtWrXo3LkzqampdOrUiZCQEP7xj38UOs979+7RsGHDQtvXr19P//79sbGxAeDJkyfExcXh7e2t9KlVqxb9+/cnJiYGeJZ0O3nyJKNHj8bZ2ZmkpCTatGlDUFAQvXr1KvZcunbtSq1atdi4cSPjxo0jIyODb775hv79+2ttwA0SdItyZGRkhKenJ2lpaSr3dz9/vKyz3ZLpFkIIIYQQ2ig4OJjAwECVY/7+/gQEBKj1nT17Nvfv36dNmzbo6uqSk5NDUFAQo0ePVvosXLgQPT09tXLzfJcvXwYgICCA0NBQbG1tWbJkCb179+bSpUsFBtbR0dFs2bKF3bt3FzjmtWvX+OWXX4iMjFSO3bp1i5ycHBo3bqzSt3Hjxvzxxx9qc1m8eDGdOnXi66+/pl+/fiQkJNCqVSu1cyUmJrJixQolyw3QvHlz9u/fz/Dhw5kyZQo5OTk4OTmxZ8+eAuerLSToFuUmODgY+F9mu6DjlZntfr5MpiAvXol8UUFXJfOVNhst2WxVmrb5AblPUwghRM2WnZ1daJs2Z/9qGm9vb7y8vFSOFfZ5cuvWrWzevJnIyEgcHByIj4/n448/xsrKCnd3d+Li4li+fDm//vprgQurwf8qJ318fBg2bBgAGzdupGnTpmzbto0pU6ao9E9ISGDIkCH4+/vj4uJS4Jjh4eGYmpoydOjQkrx0ZS5TpkxRStA7d+7MwYMH2bBhgxIf5Lt69SoDBgzgvffeY/Lkycrx1NRUJk+ejLu7OyNHjuTBgwfMnTuXd999l6ioqELfi8omQbcQQgghhBBClLPCSskLMnPmTGbPns37778PQPv27fn7778JDg7G3d2dY8eOkZaWhrW1tfKcnJwc/v3vf7Ns2TKSk5OxtLQEVO/hNjQ0pEWLFqSkpKic78KFC/Tr1w8PDw98fX0LnFNeXh4bNmxgzJgxKquJm5mZoaury40bN1T637hxgyZNmgAUOBeAtm3bqs3l2rVr9OnTRymxf96qVaswMTFh0aJFyrH8ReBOnjxJjx49Cpx7ZZOgW1QaIyMjTpw4oZYJLw0pLxdCCCGEEFXdw4cP1SofdXV1lYzxmDFj6N+/v0q7q6srY8aMUTLJXbt2xdDQkIsXLyr3TWdnZ5OcnKzcjw1w/vx5+vbti7u7O0FBQYXO6ejRoyQmJqosygZgYGBA165dOXjwoJIBz83N5eDBg8pibLa2tlhZWXHx4kWV5166dImBAwcqj69evUqfPn3o2rUrGzduVHsPCntf8s+prSToFpUmODgYFxeXSguUiyrhLu6VyPKQkZFRaFt13O6pKFX1NT969KjQNmNj4wqciSpNZYggpYhClLXSLq4pRFHk93b14+bmRlBQENbW1jg4OPDbb78RGhqqrDbeqFEjGjVqpPIcfX19mjRpoqwIXr9+faZOnYq/vz/NmjXDxsaGkJAQAGW7r4SEBPr27YurqyteXl6kpqYCzwJZc3NzlfHXr1+Po6NjgYuweXl54e7uzquvvkr37t1ZtmwZmZmZygUAHR0dZs6cib+/Px07dqRTp06Eh4fzxx9/sH37duBZwN27d29sbGxYvHgxN2/eVMbPz5gPHjyYpUuX8tlnnynl5XPmzMHGxobOnTuX7k0vRxJ0i0r1/A9zWSyqJpluIYQQQghR1a1YsQI/Pz8++OAD0tLSsLKyYsqUKcydO7dE44SEhKCnp8eYMWN49OgRjo6OHDp0SNn1Y/v27dy8eZOIiAgiIiKU59nY2JCcnKw8vnfvHt9//z3Lly8v8DwjRozg5s2bzJ07V1klfe/evSqLq3388cc8fvwYT09P7ty5Q8eOHYmKiqJly5YAREVFkZiYSGJiIk2bNlUZPy8vD4C+ffsSGRnJokWLWLRoEbVr18bJyYm9e/dWakKjKDp5+a+gGvH09JTg6yWU1/uWXz5e0NjPn7Oqfd3KMxtdmrFLU1ojW5kJIYQQQghRtiTTLcqdkZGR2gIJz7flB+VpaWkVOS0hhBBCCCGEKHcSdItyFxwcXOhiac9vD5DfpzRl5lUpUy6EEEIIIYSo/iToFlojP+udlpbG5s2bK3s6RZZpl+eiJaUpTy/PMm8pIRdCCCG0j9z+JYR2k6BbaI38rHdhWfHiZMAl0y2EEEIIIYTQJhJ0C63z/H3ez3uZDHhprvxq85ZiL0tbt63R1nkJIYQQVYH8nRRCu0nQLbTO8/d5P8/b27vQLHg+yXQLIYQQQgghtIlcFhNVRnBwMEuXLtX4nxBCCCGEEFVdQEAAOjo6Kv+1adNGaU9NTWXMmDE0adKEOnXq0KVLF77//nuVMYKCgnB2dqZ27dqYmpqqnePMmTOMHDmSZs2aYWxsTNu2bQvch3vVqlW0bdsWY2Nj7O3t+frrr9X6LFu2DHt7e4yNjWnWrBmenp4qt4UGBwfTrVs36tWrh4WFBUOHDuXixYtKe3Jystrrzf9v27ZtKufatGkTHTp0wMjICAsLC6ZNm6bSfvbsWV577TWMjIxo1qwZixYt0vxmVwDJdAuhhcprD3BtLT/T1nmVp/v372tsr1+/fgXNRFSm9PR0je0NGjSooJkIIaoyWUitenJwcODAgQPKYz29/4VuY8eO5e7du/z444+YmZkRGRnJ8OHDOX36NJ07dwbgyZMnvPfeezg5ObF+/Xq18ePi4rCwsCAiIoJmzZoRHR2Nh4cHurq6TJ8+HYCwsDC8vb356quv6NatG7GxsUyePJkGDRrg5uYGQGRkJLNnz2bDhg04Oztz6dIlxo0bh46ODqGhoQAcPXqUadOm0a1bN54+fcqcOXNwcXHhwoUL1KlTh2bNmnH9+nWV+a1du5aQkBAGDhyoHAsNDWXJkiWEhITg6OhIZmYmycnJSvv9+/dxcXGhf//+rF69mnPnzjFhwgRMTU3x8PAo5Vfk5UnQLSpUabYDKw7JdgshhBBCiOpAT0+PJk2aFNgWHR1NWFgY3bt3B8DX15elS5cSFxenBN2BgYHAs8xwQSZMmKDyuEWLFsTExLBjxw4l6P7mm2+YMmUKI0aMUPqcOnWKhQsXKkF3dHQ0PXv2ZNSoUQDY2toycuRITp48qYy9d+9elXNt2rQJCwsL4uLieP3119HV1VV7rTt37mT48OFKwik9PR1fX19++ukn+vXrp/Tr0KGD8u/Nmzfz5MkTNmzYgIGBAQ4ODsTHxxMaGipBt6j+Kms7sKp6dbc02WxtVV7Ze22mKfOQk5NTgTMR2koy2UJol+zs7ELbynOr0NKqqp93apqsrCyysrJUjhkaGha6OO+ff/6JlZUVRkZGODk5ERwcjLW1NQDOzs5s2bKFwYMHY2pqytatW3n8+DG9e/cu1Rzv3btHw4YNVeZsZGSk0sfY2JjY2Fiys7PR19fH2dmZiIgIYmNj6d69O5cvX2bPnj2MGTNG43kAlXM9Ly4ujvj4eFatWqUci4qKIjc3l6tXr9K2bVsePHiAs7MzS5YsoVmzZgDExMTw+uuvY2BgoDzP1dWVhQsXkp6eXml/dyXoFhUif3G04iyGVhqS6RZCCCGEENooODhYyT7n8/f3JyAgQK2vo6MjmzZtwt7enuvXrxMYGMhrr71GQkIC9erVY+vWrYwYMYJGjRqhp6dH7dq12blzJ3Z2di89v+joaLZs2cLu3buVY66urqxbt46hQ4fSpUsX4uLiWLduHdnZ2dy6dQtLS0tGjRrFrVu36NWrF3l5eTx9+pSpU6cyZ86cAs+Tm5vLxx9/TM+ePfnHP/5RYJ/169fTtm1bnJ2dlWOXL18mNzeX+fPns3z5ckxMTPD19eXNN9/k7NmzGBgYkJqaSvPmzVXGaty4MfDsPngJukWNUNjK5EIIIYQQQlRn3t7eeHl5qRwrLMv9/H3MHTp0wNHRERsbG7Zu3crEiRPx8/Pj7t27HDhwADMzM3bt2sXw4cM5duwY7du3L/HcEhISGDJkCP7+/ri4uCjH/fz8SE1NpUePHuTl5dG4cWPc3d1ZtGiRUmFx5MgR5s+fz5dffomjoyOJiYnMmDGDefPm4efnp3auadOmkZCQwPHjxwucy6NHj4iMjFR7bm5uLtnZ2XzxxRfKHL/99luaNGnC4cOHcXV1LfHrrigSdIsa6+bNmxrbzc3NX3rsovadPnr0qMb2Pn36vPS5tVV1LSHXRFO5n5QVl63nV0AtiL29fQXNRAhRlWlzCbkmspBa1aCplLwopqamtG7dmsTERJKSkli5ciUJCQk4ODgA0LFjR44dO8aqVatYvXp1ica+cOEC/fr1w8PDA19fX5U2Y2NjNmzYwJo1a7hx4waWlpasXbuWevXqKZ+V/fz8GDNmDJMmTQKgffv2ZGZm4uHhgY+Pj8r34PTp0/n555/5z3/+Q9OmTQucz/bt23n48CFjx45VOW5paQlAu3btlGPm5uaYmZmRkpICQJMmTbhx44bK8/IfF3Z/fEWQoFuoKe/FzsqTlJcLIYQQQojqJiMjg6SkJMaMGcPDhw8B9Qsqurq6RSZ+XnT+/Hn69u2Lu7s7QUFBhfbT19dXguTvvvuOt956Szn/w4cPC5wLQF5envL/Dz/8kJ07d3LkyBG1EvDnrV+/nrffflstAdazZ0/g2YX2/LncuXOHW7duYWNjA4CTkxM+Pj7K/ebw7F5we3v7Sk14SNAt1Dx+/LjaBK+afvGUJpNdlKKuKrdq1arczp2amlpoW6NGjTQ+t6pe4ReiJmayr1y5orG9sAyCEKL6kWx29fPJJ5/g5uaGjY0N165dw9/fH11dXUaOHImpqSl2dnZMmTKFxYsX06hRI3bt2kVUVBQ///yzMkZKSgp37twhJSWFnJwc4uPjAbCzs6Nu3bokJCTQt29fXF1d8fLyUj5D6urqKp+TL126RGxsLI6OjqSnpxMaGkpCQgLh4eHKedzc3AgNDaVz585Kebmfnx9ubm5K8D1t2jQiIyP54YcfqFevnnIuExMTjI2NlbESExP5z3/+w549e9Tek9atWzNkyBBmzJjB2rVrqV+/Pt7e3rRp00apEh01ahSBgYFMnDiRWbNmkZCQwPLlyys9tpGgW7w0bcyIV/YPlBBCCCGEEKV15coVRo4cye3btzE3N6dXr16cOHFCCYb37NnD7NmzcXNzIyMjAzs7O8LDwxk0aJAyxty5c1WC4/ytxA4fPkzv3r3Zvn07N2/eJCIigoiICKWfjY2Nsvd1Tk4OS5Ys4eLFi+jr69OnTx+io6OxtbVV+vv6+qKjo4Ovry9Xr17F3NwcNzc3lcx5WFgYgNrq6hs3bmTcuHHK4w0bNtC0aVOV+8qf9/XXX+Pp6cngwYOpVasWb7zxBnv37lUSRyYmJuzfv59p06bRtWtXzMzMmDt3bqVuFwagk5ef869GPD09Jfh6CflB9IvbehUWXFf09l/aJikpqdC2li1banzu+fPnNbbn35/zMjTdq16e2X0hKlNR5XSSBRJCCCFEZZFMt1Dkryz+4pZehZWbl+fWX0IIIYQQQghRHUjQLV6akZGR1gXeUuEghBBCCCGE0CYSdIuXVtP33C6qhFwTPb3y+9GTEvKqQRbBKltSPl51aFrsESp3SxchhBCiPEjQLdS8mMFOS0sr0fMrc4E1yXQLIYQQQgghtIkE3ULNixns5wPw4gTU2rTA2v379wttq1+/fgXORJWJiUmlnbs6Sk9PL7StMvdk1MTQ0LCypyBEpZBMthBCiJpGgm5RIsXZw9vb27vS7vWWTLcQQgghhBBCm0jQLcpcTb/XWwghhBBCiNIICAggMDBQ5Zi9vT1//PEH8Gx9jJkzZxIVFcWDBw+wt7fHx8eHYcOGKf2DgoLYvXs38fHxGBgYcPfuXZXxzpw5w4IFCzh+/Di3bt3C1taWqVOnMmPGDJV+q1atYuXKlSQnJ2NtbY2Pjw9jx45V6bNs2TLCwsJISUnBzMyMd999l+DgYIyMjFTGCQkJITU1lY4dO7JixQq6d+8OQHJyMs2bNy/wvdi6dSvvvfceAAcPHsTPz49z585Rp04d3N3dCQoKUlkvaevWrcyfP59Lly5hbm7O9OnTmTlzZnHe9nIjQbco0vP3eJf0/u7KVpkl5Jrcvn1bY7uUX5aMtpaQayIL3gmhXZKTk/+/9s47LKrjffv3Lrt0BKR3VFTAiqgIqFjAggV7rNhbNLbErrEbjTHGiCWxYOwl0diJDQvYIoqKgoIgqCB2FJC6z/uHL+fHCpgvZ3cUcD7Xda5k9nHvfZgz9ZyZeT5qd3R0/CR+cFQnNze3RJtcLv+EnnA4qlGrVi2cPHlSSBeeWAYGBuL169c4ePAgTE1NsWPHDvTq1QtXr16Fm5sbACAnJwc9e/aEp6cnNm7cWEQ/IiIC5ubm2LZtG+zs7HDhwgWMGDECGhoaGDt2LABg7dq1mD59OtavX49GjRrhypUrGD58OIyNjdGpUycAwI4dOzBt2jRs2rQJXl5euHfvHgYNGgSJRIKff/4ZALB7925MmjQJ69atg4eHB3755Re0bdsWd+/ehbm5Oezs7JCSkqLk3++//45ly5ahffv2AN4/JPD398fMmTOxZcsWPH78GKNGjUJ+fj5++uknAMCxY8fQr18/rFq1Cm3atEF0dDSGDx8OHR0d4W/6HEiIiD7brzNi4sSJfJkxIwrn7ec8MK0kyst9v3379kfttWrV+kSecDgcDgfgk+6KBJ90cyoCc+fOxd9//43IyMhi7fr6+li7di0GDBggfGZiYoKlS5di2LBhSv928+bNmDBhQpE33cUxZswYREdH4/Tp0wAALy8veHt7Y9myZcK/+fbbb3H58mWEhYUBAMaOHYvo6GicOnWqxH/j4eGBRo0aISgoCACgUChgZ2eHb775BtOmTSvWFzc3NzRo0EB4YDBjxgycOHEC//77r/BvDh06hF69euHp06cwMDBA3759kZubi7179wr/ZtWqVfjxxx+RlJQEiUTyn3nAAv6mmyOa/2V/95fKxw72AqC01IbD+dR8bEAK8EEp58uET6orDrwN45RVsrOzkZ2drfSZlpZWiYerxsbGwtraGtra2vD09MQPP/wAe3t7AO8nw7t370aHDh1gZGSEPXv2ICsrCy1atFDJx7S0NFSuXFnJ5w/HrTo6Orhy5Qpyc3Mhl8vh5eWFbdu24cqVK2jcuDHi4+Nx9OhR4YFATk4OIiIiMH36dEFDKpXC19cXFy9eLNaPiIgIREZGYvXq1f/pS1ZWFiIiItCiRQtkZ2dDV1e3yL959OgREhMTP1tbzyfdnFIhdqn5p3orzh8CcDgcDofD4XDKIj/88EORfdpz5szB3Llzi/xbDw8PbN68GTVr1kRKSgrmzZuHZs2aISoqCgYGBtizZw+++uormJiYQCaTQVdXF/v374eTk5No/y5cuIDdu3fjyJEjwmdt27bFhg0b0KVLFzRo0AARERHYsGEDcnNz8fz5c1hZWaFv3754/vw5mjZtCiJCXl4eRo0ahRkzZgAAnj9/jvz8fFhYWCj9noWFhbBH/UM2btwIFxcXeHl5Kfnyyy+/YOfOnejVqxeePHmC+fPnA4CwNL1t27aYOHEiBg0ahJYtWyIuLg7Lly8X/g2fdHPKBYUPSSvNCeX8rTiHw+FwOBwO50tm+vTpmDRpktJnJb3lLtjHDAB169aFh4cHHBwcsGfPHgwdOhSzZ8/G69evcfLkSZiamuLvv/9Gr169cP78edSpU6fUvkVFRSEgIABz5sxBmzZthM9nz56NJ0+eoEmTJiAiWFhYYODAgfjxxx8hlUoBAGfOnMHixYuxZs0aeHh4IC4uDuPHj8eCBQswe/bsUvvy7t077Nixo8h327Rpg2XLlmHUqFEYMGAAtLS0MHv2bJw/f17wZfjw4bh//z46duyI3NxcVKpUCePHj8fcuXOFf/M54JNujmgKv/X+L8riAWwfLu/5EFXiKKelpYn+LvB+n0tJfM4G40vk2bNnJdrK62FofOklBwDevHlToq2sHkLJ4XA4BZTHsdLHlpL/F0ZGRqhRowbi4uJw//59BAUFISoqSjgHqF69ejh//jxWr16NdevWlUr7zp07aN26NUaMGIFZs2Yp2XR0dLBp0yb89ttvSE1NhZWVFX7//XcYGBgI46DZs2djwIABwl7yOnXqICMjAyNGjMDMmTNhamoKDQ0NpKamKmmnpqYWe3jwn3/+iczMzCInpAPApEmTMHHiRKSkpMDY2BgPHjzA9OnTUbVqVQCARCLB0qVLsXjxYjx58gRmZmbCXvOCf/M54JNujmhKExrsU8Xu5m/TORwOh8PhcDgVjfT0dNy/fx8DBgxAZmYmgKIPFzQ0ND76MKI4bt++jVatWgmht0pCLpfD1tYWALBr1y507NhR+P3MzMxifQEAIoKmpibc3d1x6tQpdOnSBcD7hyanTp0q9kTxjRs3onPnziW+3JBIJLC2tgYA7Ny5E3Z2dmjQoEGR37exsRH+jaen52d9WcIn3ZxPQlmM3a3Km2zg4yeQq3r6eGkbTE7JLF269KP2qVOnftQ+bty4Em07d+4U5ROneN69e/dRu46Ozify5MuAv83mcDjlmbL6NltdfPfdd+jUqRMcHByQnJyMOXPmQENDA3369IGRkRGcnJwwcuRI/PTTTzAxMcHff/+NEydO4PDhw4JGUlISXr58iaSkJOTn5wsnoTs5OUFfXx9RUVFo1aoV2rZti0mTJuHJkycA3k9YCyao9+7dw5UrV+Dh4YFXr17h559/RlRUFP744w/hdzp16oSff/4Zbm5uwvLy2bNno1OnTsLke9KkSRg4cCAaNmyIxo0b45dffkFGRgYGDx6s9HfHxcXh3LlzOHr0aLH5smzZMrRr1w5SqRT79u3DkiVLsGfPHuF3nj9/jj///BMtWrRAVlYWgoODsXfvXpw9e1Y9N0YkX8SkuyyGtuKwgb/p5nA4HA6Hw+GUdx49eoQ+ffrgxYsXMDMzQ9OmTXHp0iVhMnz06FFMmzYNnTp1Qnp6OpycnPDHH3/A399f0Pj++++VJscF8btDQ0PRokUL/Pnnn3j27Bm2bduGbdu2Cf/OwcFBCKOYn5+P5cuX4+7du5DL5WjZsiUuXLigdCDZrFmzIJFIMGvWLDx+/BhmZmbo1KmT0pvzr776Cs+ePcP333+PJ0+eoH79+ggJCSlyuNqmTZtga2urtK+8MMeOHcOiRYuQnZ2NevXq4cCBA0r73wHgjz/+wHfffQcigqenJ86cOYPGjRuXIvfVzxcRp5vH7eaI4UsMq/Rf9eRTbBEoa3wsT8aPH//R737sKfzvv//+0e+OGDHi445xygybN28u0TZo0KBP5kdpKSlMC/Dfq3X4W3IOh8PhcP53KuSb7g8P+CqLh3hxOBwOh8PhcDgcDqfiUyEn3R/uH1b17Rxfnl5+4CsaOBwOh8PhcDgcTlmiQk66P6Q0oa2K4+nTp9i+fbsaPeKUBQoOkyiJ+vXrfxI/OGUbVtsI+PJx9aLqdpBDhw6VaOvUqdNHv1uWl5B/DE9Pz8/tAofD4XA4XwRfxKRb1ZOzP1W4K47q8DfdHA6Hw+FwOBwOp0xBHM4XQlZWFs2ZM4eysrLKhS7X5tqfW7s8+sy1ufbn1OXaXPtza5dHn7k250ugQp5ezuEUx5s3b2BoaIi0tDS1nrzLSpdrc+3PrV0efebaXPtz6nJtrv25tcujz1yb8yVQsaPKczgcDofD4XA4HA6H8xnhk24Oh8PhcDgcDofD4XAYwSfdHA6Hw+FwOBwOh8PhMIJPujlfDFpaWpgzZw60tLTKhS7X5tqfW7s8+sy1ufbn1OXaXPtza5dHn7k250uAH6TG4XA4HA6Hw+FwOBwOI/ibbg6Hw+FwOBwOh8PhcBjBJ90cDofD4XA4HA6Hw+Ewgk+6ORwOh8PhcDgcDofDYQSfdHM4HA6Hw+FwOBwOh8MIPunmcDgcDofD4XA4HA6HEXzSzeFwOBwOh8PhFENeXt7ndoHD4VQA+KSbU6HJz89XSl+5cgWXLl1Cdnb2Z/JIHAkJCbzj//9kZ2fj/v37zO7hvHnz8Pz5cyba6mLNmjXw9fVFr169cOrUKSXb8+fPUbVq1c/k2X+TlpaGu3fv4u7du0hLS1OLZnnOj/LI0aNHMWzYMEyZMgUxMTFKtlevXqFVq1afybOyBREV6YNUJTs7u9z1XwWoM0Iti749JCQEt27dAgAoFAosWLAANjY20NLSgq2tLZYsWaLWv4ElLPtJ1n0wh1NR4ZNuToUkMTERDRs2hJaWFtq3b483b97Az88PTZo0gZeXF1xdXXHv3j1R2gYGBhg6dCguXLigZq9LpmbNmoiNjWX6Gzdu3ICGhkapv/f06VOldGRkJAYOHAhvb2/06NEDZ86cEe3T5s2bcfHiRQBAVlYWhg4dCj09PdSoUQP6+voYNWqU6I7/zZs3Ra60tDQsWrQI8fHxwmdiuHLlitKg8PDhw/Dx8YGNjQ0aNmyILVu2iNIFgF9//RWTJ0+Gs7MztLS04O/vjx9++EGw5+fnIzExUbR+STx8+BBDhgwR/f0NGzbA1dUVlStXhqurq9L/b9y4UbTup8iPDRs2YODAgQgODgYA7N69Gy4uLqhatSrmzJkjWvfEiROYM2cOTp8+DQA4d+4c2rdvj1atWgm/xQJV7uWOHTvQuXNnPHnyBBcvXoSbmxu2b98u2HNycnD27FnRvqWkpGDbtm04evQocnJylGwZGRmYP39+mdIF3r8JnTVrFnx8fITysGzZMujr60NXVxcDBw4s8pul4cSJE/D394exsTF0dXWhq6sLY2Nj+Pv74+TJk6J1AXZluzi0tLQQHR2tkgbLvn3ChAl4/fo1AGDp0qVYuXIlvvvuOxw5cgSTJ0/GL7/8gh9//FEl/0vi/v37oh9WsewnWWr/F9HR0aIfmLIcl9y5cwdff/013NzcYGVlBSsrK7i5ueHrr7/GnTt3ROtyvgCIw6mAdO/enXx8fOjQoUPUq1cv8vb2phYtWtCjR48oOTmZ2rZtS126dBGlLZFIqFatWiSRSMjZ2Zl++uknevr0qVr87tq1a7GXVColX19fIc2CyMhIkkgkpf6eVCql1NRUIiIKDw8nuVxOPj4+NHnyZPLz8yOZTEZnz54V5VOVKlXo0qVLRET03XffkaOjI+3bt4+io6Pp77//pho1atDkyZNFaUul0mIviUSi9F+x2gV5cvDgQZJKpRQYGEirV6+mYcOGkUwmo3379onSdnV1pe3btwvp8PBwMjMzo9mzZxMR0ZMnT0T7/TEiIyNF6/7444+kq6tL06ZNo9DQULpz5w7duXOHQkNDafr06aSnp0fLli0Tpc06P1asWEF6enrUrVs3srKyooULF5KJiQktXLiQ5s2bR5UqVaLffvut1Lpbt24lmUxGDRo0IH19fQoODiYjIyMaNmwYDRkyhDQ1NWnv3r2i/f4YqtzL+vXr08qVK4X07t27SU9PjzZs2EBEquX3lStXyMjIiCpVqkQ6Ojrk5OREUVFRgl2sNivdAmbNmkUWFhY0adIkcnV1pVGjRpGdnR1t27aN/vjjD7KxsaGlS5eK0t68eTPJZDLq3bs3BQcH09GjR+no0aMUHBxMffr0IblcTlu2bBGlzapsT5w4sdiroB0sSIuBZd+upaVFiYmJRERUu3Zt2rNnj5L98OHD5OTkJEr7v1ClTrLsJ1lq/xeq5AmrccnRo0dJU1OTmjRpQnPmzKE1a9bQmjVraM6cOeTl5UVaWloUEhIiymdOxYdPujkVEjMzM7p+/ToREb1+/ZokEgmdP39esEdERJCFhYUobYlEQqmpqRQZGUljx46lypUrk6amJnXr1o2OHj1KCoVCtN8SiYR8fHxo0KBBSpdUKqUuXboIaTGUNKEvuFq1aiWqgyvIDyIiPz8/GjJkiJJ9/Pjx1KpVK1E+Fx4E1ahRg44dO6ZkP3v2LNnb24vStrGxoQ4dOtDp06fpzJkzdObMGQoNDSUNDQ0KDg4WPhND4Txp2rQpTZs2Tcm+aNEiatKkiShtHR0dSkhIUPrs1q1bZGFhQdOmTRM9gThw4MBHrxUrVogeANnb29Pu3btLtO/atYvs7OxEabPKjwKcnZ2FSf21a9dIJpMJE0wiog0bNpC7u3updQtPXk+ePEk6Ojr0888/C/affvqJvL29RfnM8l7q6elRfHy80menT58mfX19Wrt2rUr57evrS4MHD6b8/Hx68+YNjR49mkxMTOjatWtEJH5yzEq3gKpVq9KhQ4eIiCg2NpakUint2rVLsO/evZtq164tSrt69eoUFBRUon316tWiJ4KsyrZEIqH69etTixYtlC6JREKNGjWiFi1aUMuWLUX5zLJvt7KyoosXLxIRkYWFhVA+Crh37x7p6OiI0l65cuVHrylTpogugyz7SZbaJT2cKbj69+8vOk9YjUvq1q0rPNAtjjlz5lCdOnVKrcv5MuCTbk6FxMDAQBgY5ufnk0wmo8jISMEeGxtLBgYGorQLN+ZERFlZWbRjxw5q3bo1SaVSsrW1/Wij/DF27txJtra2tGnTJqXPZTIZ3b59W5RmYY327dsXmdAXXJ07d1Z50l140FJAVFQUmZqaivLZwcGBTp8+TUTvJ8n//vuvkv3OnTukp6cnSvvFixfUpUsXatmyJT169Ej4XB15XThPzM3N6erVq0r2mJgYMjIyEqVtZ2dH586dK/L57du3ycLCggIDA0Xfx4I3/CVdYgdA2tradOfOnRLtt2/fFj2YZZUfBejo6AiDTqL3g9DCb0ljY2NF3csPJ69yuZxu3LghpKOjo8nExESUzyzvZXF1nIjozJkzpK+vTzNnzhStbWxsTHfv3lX67IcffiBjY2O6cuWK6MkxK90CtLW1KSkpSSkdHR0tpOPj40X3N1paWhQTE1OiPSYmhrS1tUVpsyrbP/zwA1WpUoVOnTql9Lk62laWffvXX39NHTt2pLy8PBoxYgQNGzZM6SH6N998Q56enqK0JRIJWVtbk6OjY7GXtbW16DLIsp9kqS2VSqlBgwZFHs4UXA0bNlTLpFud4xJtbW1m9ZFT8eGTbk6FpEmTJjRr1iwiItq0aZPw1quA+fPni3qCT6S8bOlDEhISaNasWaLf2hVoeHt7U7du3ejly5dEpJ7BSp06dZTeYnzI9evXRU/W4uLiKC0tjapUqVLk7UBcXBzp6uqWWpeIaMaMGeTp6UmvXr2iadOmUadOnejt27dERJSRkUG9evWiNm3aiNIuYM2aNWRtbU07duwgIvVNukNDQ+nGjRvk4OBAV65cUbLHxMSQvr6+KO0+ffrQhAkTirVFRUWRmZmZqPtobW1Nf//9d4l2seWDiKhZs2YUGBhIubm5RWx5eXkUGBhIzZs3F6XNKj8KMDExUXpgYGtrSw8ePBDSsbGxou6lkZGR0uBNX1+f7t+/L6Tj4+NF1xuW9zIgIIC+//77Ym2hoaGkp6en0qS78IOHApYtW0ZGRka0b98+0ZNuFroFWFhY0M2bN4W0l5eX0oO86OhoqlSpkijtBg0afHT57pQpU6hBgwaitFmVbaL3S/pr1KhB3377LeXk5BCRetpWln3769evqWHDhuTk5EQDBgwgbW1tcnBwID8/P6pSpQoZGhoKS61Li6Oj40dX+6hSJ1n2kyy1a9SoQVu3bi3RrkqesBqXODs70/Lly0u0L1++nGrWrFlqXc6XAZ90cyokISEhpK2tTZqamqStrU1nz56lGjVqUOPGjalJkyakoaHx0Q7wY3z4prs4VFliTvT+Cf73339PdnZ2FBISQnK5XOXByqBBg+jrr78u0X7nzh1ydHQstW7BW7OCN2u///67kv3AgQOilz9mZ2dT586dydjYmPz8/EhbW5t0dXWpevXqpKenR/b29kXeYInh9u3bVK9ePerTp4/aJt2F3zSuWLFCyb5z505ydXUVpX3jxo0iKyEKc+vWLZo7d26pdTt16vTRFRpi9/wTvffZ0tKSTExMqGvXrjRq1CgaNWoUde3alUxMTMjKyopu3bolWptFfhTg7e2ttFT4Qw4dOiRq6XDDhg2VJsZpaWlK7caJEyeoRo0apdYlYnsvz5w5Q4sXLy7Rfvr0adFbYJo1a0Zr164t1rZ06VLS0tISNQhnpVtAy5YtafPmzSXa9+zZI3oiWPAgo06dOjRx4kRasmQJLVmyhCZOnEh169YlfX190WdmsCrbBbx9+5YCAwOpbt26dOvWLbX0Yyz7diKinJwcWrt2Lfn7+5OzszPVqFGDfHx8aMaMGfTw4UPRut27d6cpU6aUaFelTrLsJ1lq9+3bt8QHpkSq5QmrccmePXtIJpNRp06daOXKlbRr1y7atWsXrVy5kjp37kyampr0559/ivKZU/GREJWT+AccTil58OABIiIi4O7uDkdHR6SmpmL16tXIzMxEhw4d0LJlS1G68+bNw+TJk6Grq6tmj4sSFhaGwMBAJCYm4tatW3B1dRWtlZ2djfz8fLX7/eFJxVZWVqhRo4aQXrlyJXJycjB58mTRvxESEoJDhw4hPj4eCoUCVlZW8Pb2Rt++faGnpydatzA5OTmYNm0aQkNDsW/fPlSpUkW01oenZevr68PExERIF5xeHhgYKPo31M358+eRkZGBdu3aFWvPyMjA1atX4ePjI0r/7du32LZtGy5duoQnT54AACwtLeHp6Ym+ffuiUqVKon1nSXh4OPT09FC/fv1i7WvWrIFCocDYsWNLpbt//36YmJigefPmxdqXLFmCjIwMLFiwoLQuM7+XrNiwYQPOnj2LrVu3FmtfunQp1q1bh4SEhDKhW8C9e/cgl8tLbDN27NgBmUyGXr16idJ/8OAB1q5dW2zdGTVqFBwdHUXpsirbH7Jr1y5MmDABz549U7kfA9j17Sy5c+cOMjMz0bBhw2Ltubm5SE5OhoODg+jfYNlPstB+8uQJsrOzVfqbS4LluOTChQv49ddfcfHixSL1cfz48fD09FTNeU6FhU+6OZwyTnp6Ou7fvw8XFxdoamp+bnc4ZZyMjAxERESUOJmr6Lx+/Rp79+5FUlISHBwc0LNnTxgaGn5utyoMBZMdDqc0PHr0CBEREfD19VXbg9JPxebNm9G1a1fejnA4HJXgk27OF0leXh6Sk5Nhb2+vFr3c3Fw8ePAA5ubmZbpjLohZ7OjoCKlUiuzsbBw4cAAKhQItW7aEhYWFWn4nNjZWmPQ4OTmpRZMVCoUCUqm02M8fPXokuox8qrz+kBs3bqBBgwZKccLLGnl5eQgNDUVSUhIcHR3RokULUTHiAaBbt27o27cvevTogdu3b6NFixaQSCSoWrUqHjx4AIlEgtOnT8PFxUWU/vPnz2Fqairqu/8r+fn5eP78OaRSKczMzJj+lqpIpVJUrVoVQ4YMwaBBg2Btbf25XSpzFC7fDg4OaNmypejyXRzlqX39FLDufzU1NXHjxg3RbcjHSE1NBRHB0tJS7dosyc3NhVwuV7su67LNym8O53/iMy5t53A+G6rEf1y6dCllZmYS0ftDoL799lvS1NQkqVRKMpmMBg8eLBwcU1r09fVpyJAhFB4eLur7H+PGjRtkZWVFUqmUateuTUlJSVS7dm3S09MjfX194STf0rJ48WI6efIkERG9fPmSWrdurXRCcrt27ejVq1dq/mveo8p9TEtLo549e5K2tjaZm5vT7NmzKS8vT7CrcqLx/5LXly9fFqX9X4jNkw/PKbh+/ToFBgaSl5cXde/enUJDQ0X7NHbsWCGk0sOHD8nZ2Zk0NDTIwsKCNDQ0qE6dOkoHT5UGY2Nj4aTo9u3bU9++fSk7O5uI3u/PHDp0qEqH7UmlUmrVqhVt376dsrKyROsUx+HDh6lZs2bCnmKpVEqGhobUv39/pVOlS0vt2rVp/vz5SidqqwuJRELDhw8nc3Nzkslk1KFDB9q/f79S3WHFnTt3qEqVKqX+Hst2lYht+f6wfW3VqlW5aF8jIyNpwYIFtHr1anr27JmSLS0tjQYPHixKl2X/a2xsXOwlkUjI0NBQSIvhxYsX1L17d7Kzs6NRo0ZRXl4eDR06VLiPnp6elJycLEr7v1DlPu7evVtoT4mIVq1aRfb29iSVSsnExITmzZsn2i+WYwdWfl++fFmprTt06BA1b96crK2tyd3dnf744w9RupwvAz7p5nyRqNIJFT69fNmyZWRsbEybNm2i27dv07Zt28jc3JyWLl0qSlsikVCtWrVIIpGQs7Mz/fTTT/T06VNRWh/Stm1b6tGjB926dYvGjx9PLi4u1LNnT8rJyaHc3Fzq378/+fr6llrX1tZWOBl02LBh5ObmRteuXaN3795RZGQkNWnShIYOHaqWv+FDVDloZdy4cVSjRg3au3cvrV+/nhwcHKhDhw5CR/3kyRPR2qzymqjkgWHBValSJVFlu3C5Dg8PJ7lcTj4+PjR58mTy8/MjmUwm+sAmCwsL4aC0Xr16ka+vrzAQf/HiBXXs2JF69OghSltHR4fi4uKI6H1omA9Pqb179y4ZGhqK0iZ6XyfbtWtHmpqaZGxsTGPHjhXiBKvCli1byMDAgL799luaOXMmWVpa0rRp02jt2rXk4+NDpqamdO/ePdE+m5iYkIaGBrVt25b+/PPPYk+OF6udmppKubm59Oeff5K/v78wwZwyZYpaDjYsCbHtNst2lYht+S6P7es///xDmpqaVKtWLbK3tycTExMh7BSRag80Wfa/+vr61KFDB9q8ebNwBQcHk4aGBi1atEj4TAxDhgyh2rVr06pVq8jHx4cCAgKobt26FBYWRhcuXKBGjRpRYGCgKO3/QpV+snB+b9q0ibS1ten777+nI0eO0MKFC0lPT4/Wr18vSptl2Wbld2HdgwcPklQqpcDAQFq9ejUNGzaMZDIZ7du3T5TPnIoPn3RzKiRubm4fvZydndUS/9HNzY1+++03Jfu2bduoVq1aKmlHRkbS2LFjqXLlyqSpqUndunWjo0ePqnQqurGxsRAeJjMzkzQ0NJTetkZFRYmKC6ylpSWEmXF0dCwyMbt69SpZWVmJ8rlr164fvVq1aiX6Ptrb2yu9vX327Bk1btyY2rRpQ1lZWSoNDFnlNRGRrq4uffvtt0oDw8LXvHnzVI637ufnR0OGDFGyjx8/nlq1aiXKZ21tbSG2rq2tbZG3/Ldu3RIdy93Dw0M4mdbNzY3279+vZD9+/DhZWlqK0ib6v3x59uwZ/fTTT+Tq6irEl12zZg2lpaWJ0nV2dlY6Ofrff/8lW1tboY5/9dVX1LVrV9E+P378mPbv30+dOnUimUxGZmZm9O233340Xvr/qv3hqohHjx7R/PnzqWrVqiSVSqlZs2aitCdOnPjRq3///iqVbRbtKhHb8l0e21dPT0+aMWMGEb2P5LF06VLS19enY8eOEZFqk26W/W9sbKww+S0Ii0WknlBnVlZWwkqLgge6x48fF+xhYWFkY2MjSptlP1k4vxs3bkw//vijkn3NmjXk5uYmSptl2Wbld2Hdpk2bKoWrIyJatGgRNWnSRJTPnIpP0Y2MHE4F4M6dO6hbty4CAgKKvVQ9tVcikQAAkpKS4OXlpWTz8vISfQpuAfXq1cOqVauQnJyMzZs3Iy0tDR07doS9vT2+//57UZpEBJlMBgBF/gsAGhoaUCgUpdZ1cHBAVFQUgPf5UlizQDcjI0OUz4cOHUJWVhYMDQ2LvfT19UXpAsCzZ8+UTk01NTXFyZMn8fbtW/j7+yMzM1O0Nqu8BoD69evDzs4OAwcOLPYKCAgQ7XcBUVFRGD58uNJnw4cPx82bN0Xp1ahRA1euXAEAGBgY4M2bN0r2t2/fis6P2bNnY9q0adi8eTPGjRuHiRMnYuPGjbhw4QKCg4MxdOhQDBgwQJR2YUxNTfHtt9/i9u3bCAsLQ/369TF16lRYWVmJOoU+MTERHh4eQrphw4Z48uQJUlJSAACTJk1CaGioaH9lMhm6dOmCgwcPIikpCRMnTsTBgwdRu3ZteHl5YdOmTaJ0C9q+wtjY2GD27Nm4f/8+jh8/Djs7O1HaK1euxNmzZ3H9+vVir5iYGFG6BbBoVwG25bs8tq+3b9/GkCFDBJ+nTJmC3377DT169MDhw4dFaRaGVf/r5OSECxcuwNLSEvXr10d4eLjKvhaQlpYGGxsbAICFhQVkMhmsrKwEu7W1NV6/fi1Km2U/CfxffsfHx6NNmzZKtjZt2iAuLk6ULsuyXaAJqN/vAu7du4cePXoofda9e3eV2ylOBeZzz/o5HBa4u7vTmjVrSrRfv35dpSe/ixYtopUrV5KVlVWRp7M3btwQve+r8NKlD0lISKBZs2aRnZ2dKO3WrVvT0KFD6dGjRzRv3jxycnJS2lf39ddfi3pDtWzZMnJxcaHY2Fhavnw5eXp6Cst94+PjqUWLFqKXVtapU4c2bNhQol2V+1izZk06cuRIkc/fvn1Lnp6eVK9ePdHarPKa6P2T9I/FnU5KShIVJ1kikVBcXBylpaVRlSpViizTjouLI11d3VLrEhEFBweTra0thYaG0pYtW8jFxYVOnjxJjx8/ptOnT1OdOnVo2LBhorSJiP7880+ytbVVio0ukUhIW1ubJkyYoNJ+44/VyfT0dNqwYQN5eXmVWtfFxYX27t0rpCMiIkhTU1PwNTY2lvT09NTuc2hoKPXv31+0dnFvutVFjRo1aOvWrSXaxdZ3lu0qEdvyXR7bVzMzM7p69WqRz3fu3Em6urq0du3aMtn/FubUqVNkb29P06dPV0t88Xr16lFQUBARER09epQMDAxo+fLlgn3t2rWiY6Kz7CclEglt2bKFDhw4QLa2tnThwgUle1RUFFWqVEmUNsuyzcpviURCoaGhdOPGDXJwcChyDk5MTAzp6+uL8plT8eGTbk6FZNy4cTR+/PgS7XFxcdSiRQtR2g4ODuTo6ChcK1asULL/8ssvopcX/S8DWrFLIa9cuUImJiYklUrJzMyMoqKiyMPDgywtLcna2pp0dHSEQ01KyzfffENyuZycnZ1JW1ubpFKpcLhNw4YNKSUlRZTuoEGD6Ouvvy7RfufOHXJ0dBTtc0kd+ps3b8jDw0P0QIVlXrOi4PCagolrwZLtAg4cOEBOTk6i9ZcvX066urqko6MjlI2Cq0uXLkrLOcWQl5dHV65coV27dtGOHTsoNDSU3rx5o5ImEbtJZlBQEBkaGtKUKVPo+++/J2tra6X9i9u2bRO9bPN/8VnssvgzZ86obX/4h/Tt25cmTJhQol3s3lSW7WoBLMt3eWtf/fz8aNmyZcXaduzYQXK5XHTbyrL//ZDnz59T165dycjIiGJiYlTS2rZtG2loaJCTkxNpaWnR3r17ydramnr16kW9e/cmTU1NYVJeWlj2k4UfYkokElq4cKGSfcOGDaLbKSJ2ZZuV3wX9ZIHuh+Vv586d5OrqKspnTsWHhwzjcNTMpUuXoKWlBTc3t1J/d968eZg8eTJ0dXUZePY+hnNMTAxq1qwJfX19ZGVlYfv27Xj37h38/PxQs2ZN0drR0dE4fPgw4uPjoVAoYGVlBW9vb/j6+ha7JPV/ITs7G/n5+Uzy49WrV0hOTkatWrWKtb99+xbXrl0TvRWBZV6z4OzZs0ppKysr1KhRQ0ivXLkSOTk5mDx5sujfeP36NU6cOFGkjFSvXl20Jmv++OMP9O7dG1paWmrXXrt2LbZt24bs7Gy0bdsWs2fPhra2NoD3oXPy8/Ph7Oxcat3Bgwfj119/hYGBgbpdZsqTJ0+QnZ2ttO1DHbBuVwtgWb7LU/u6f/9+nDt3DitWrCjWvmPHDqxfv16l7RMloUr/y5rw8HBcunQJnp6e8PLywp07d7BkyRJkZmaiU6dOGDhwoChdlv3kf3H48GHI5XK0bdtWtAaLsv1fiPU7MTFRKa2vrw8TExMhvWXLFgAQteWIU/Hhk24Oh8OpAFStWhX//PNPmZ7AsuLFixe4efMm6tWrh8qVK+P58+fYuHEjsrOz0bNnTybxdb9kcnJy8Pfff+PixYt48uQJAMDS0hJeXl4ICAiApqbmZ/aQw1EPrGOAczicLwc+6eZUWJ4+fYqoqCi4u7vD0NAQqamp+OOPP6BQKNChQwfUqVNHJf3Tp08jLCwMKSkpkEqlqFq1Kjp37lzmJz3x8fFKflerVg2+vr6oVKmSWn9n8ODBWLRoEaytrUVrREREwN3dXY1e/R+PHj2CtrY2TE1NAQDnz5/HunXrkJSUBAcHB4wZMwaenp6itP/66y+0b9+eyZuHX3/9tdjPJ02ahClTpsDS0hIAMG7cOFH6eXl5uH37ttJkytXVFXK5XJzDhVAoFJBKi57fqVAo8OjRI9jb25da88qVK2jTpg3evHkDIyMjnDhxAj179oRMJoNCoUBycjLCwsLQoEEDUT6zvJcA2/z+kNzcXJV14+Li0LZtWyQnJ8PDwwMWFhYAgNTUVFy+fBm2trY4duwYnJycSq39/PlzoT6y4MmTJ7h8+bJSXnt4eAh1RiysywjAzvdPybx58zBmzBi13GMW/e+PP/6Ib775Bjo6OsjPz8fUqVOxatUq5OXlQSqVYsCAAfjtt9+Y1E118CnKyOvXr7F3716hn+zZs6fKDyPy8/OhoaEhpC9fvozs7Gx4enqqlNdEhAcPHsDOzg4ymQw5OTnYv38/srOz4e/vr9a2Rh3jHc4XwGdc2s7hMCM0NJT09PRIIpGQpaUlRUZGkq2tLVWvXp1q1qxJWlpa9M8//4jSTk1NpcaNG5NUKiWZTEZSqZTc3d3J0tKSNDQ0aPLkyaL9zsnJocmTJ1O1atWoUaNGtHHjRiW7KqFW0tPTqUePHsJeJKlUKvisr68vej/ZjRs3ir3kcjnt379fSItBIpFQtWrVaNGiRfT48WNRGiXRuHFjOnToEBER/f333ySVSqlz5840depU6tq1K8nlcsFeWiQSCVWqVImGDx9Oly5dUqfbJJFIyNbWVmlfo6OjI0kkErKxsSFHR0eqUqVKqXXz8/Np5syZZGRkVGQ/nJGREc2aNYvy8/NF+ZyWlkY9e/YkbW1tMjc3p9mzZysdbqZKufb19aVhw4bRmzdvaNmyZWRra6t0aNXgwYOpS5cuorSJ2N1Llvm9e/duId48EdGqVavI3t6epFIpmZiY0Lx580T77evrSwEBAcXuCU9LS6OAgABq06aNKG2pVEqtWrWi7du3U1ZWlmgfPyQ9PZ369etHGhoaJJPJyNzcnMzNzUkmk5GGhgb179+fMjIyROuzrO8sfWfV36SlpRW5Xr9+TXK5nC5fvix8JgaW/S/LGOBERKtXr6bWrVtTz549i5zp8ezZM1HtNhHbMtK1a1fhwMeoqCgyNTUlMzMz8vDwIAsLC7K0tBQdhjA5OZm8vb1JQ0ODmjdvTi9fvqQOHToI7WCNGjUoOTlZlHZMTAw5ODiQVColJycnio+PJ3d3d9LT0yNdXV0yNTWle/fulVqX5XiHU/Hhk25OhaRp06Y0ZswYevv2LS1btoxsbGxozJgxgv27774TdeIw0fv4uV26dKG0tDTKysqisWPHUmBgIBG9P/HUxMSEfvnlF1Hac+bMIQsLC1q2bBnNnDmTDA0NacSIEYK9IL6nGEaMGEHe3t5069Ytio2NpR49etCUKVMoIyODNm7cSLq6urR9+/ZS6354sEjhq+BzVU5OHT58uDCA6NChA+3fv1+l06gL0NPTE2Lrenh40JIlS5Tsq1atUukgq/nz55ObmxtJJBKqVasWrVixgp4/f66y3yNHjqT69esXGeioGkt28uTJZGZmRuvWraOEhATKzMykzMxMSkhIoN9++43Mzc1pypQporTHjRtHNWrUoL1799L69evJwcGBOnToIEwMVSnXhWOi5+TkkFQqVYqTHBERITr+LRG7e8kyvwtPHjZt2kTa2tr0/fff05EjR2jhwoWkp6dH69evF6Wto6NDt27dKtF+8+ZN0tHREaUtkUioXbt2pKmpScbGxjR27Fi6fv26KK3CDB06lKpXr04hISFKbUdeXh79888/VKNGDZVOz2dZ31n6zqq/KXyIXOFLHX0Cy/6XZQzwlStXkq6uLo0ZM4b69+9PmpqatHjxYsGuyoNHlmXE2NiYoqOjiYioffv21LdvX6HdzsnJoaFDh4p+yDZgwADy8vKigwcP0ldffUVeXl7UrFkzevToESUmJpK3t7fSuK00BAQEUOfOnenmzZs0YcIEcnFxoYCAAMrJyaGsrCzq1KkT9e/fv9S6LMc7nIoPn3RzKiSVKlUSQk/k5uaSTCZTGrzdu3ePDA0NRWtHRUUJ6fT0dJLL5cKT+61bt1LNmjVFaTs5OSm9XY2NjSUnJycaNGgQKRQKlTpmU1NTpTAuL1++JG1tbeEJeFBQENWvX7/UuvXq1aMOHTpQdHQ0PXjwgB48eEAJCQkkk8noxIkTwmdiKBgE5ebm0p9//kn+/v6koaFBFhYWNGXKFLp7964oXSIiQ0ND4Ym0ubl5kafTqoTIKjx4u3r1Ko0ePZqMjIxIS0uLevbsScePHxftNxHRvn37yM7OjlatWiV8puqk28LCgkJCQkq0h4SEkLm5uShte3t7Cg0NFdLPnj2jxo0bU5s2bSgrK0ulcq2np0cJCQlCWl9fn+7fvy+kExMTSVtbW5Q2Ebt7yTK/C/vcuHFj+vHHH5Xsa9asEf1AycrK6qMrQA4ePEhWVlaitAv8fvbsGf3000/k6upKUqmUGjRoQGvWrBH9dtTIyIjCw8NLtIeFhZGRkZEobSK29Z2l76z6GxsbG+rQoQOdPn2azpw5Q2fOnKHQ0FDS0NCg4OBg4TMxsOx/JRIJPX36lIiITExMijxcio+PF90nuLq6Kj3UDg8PJzMzM5o9ezYRqTbpZllGdHR0hLGUlZVVkVCSd+/eFT2WsrKyoosXLxIR0YsXL0gikSitADh16hRVrVpVlLaZmZkw5ktPTyeJRELnz58X7OHh4WRvb19qXZbjHU7Fp+jmOg6nAqCpqYmsrCwA7w/9USgUQhoA3r17J3qvkJaWltKJmlKpFPn5+cjLywMAeHl54cGDB6K0Hz9+jNq1awtpJycnnDlzBhcuXMCAAQOQn58vShd4v3e08L5tfX195OXlISMjAwDQpk0bxMTElFr3ypUrcHJyQvfu3fHy5Us4ODjA0dERAGBtbQ0HBweVTyOWyWTo3r07jhw5gsTERIwZMwZ//vknXFxc0Lx5c1GaPj4+2LlzJwDAzc0NZ86cUbKHhobCxsZGJb8BwN3dHWvWrEFKSgrWr1+PZ8+eoV27dqhSpYpoza5du+LixYvYv38/2rdvL+zhU4W3b99+dD+alZWVUFZKy7Nnz5TKgKmpKU6ePIm3b9/C398fmZmZonQBwM7ODvHx8UJ6165dsLKyEtIpKSlq27unznvJMr8BCG1UfHw82rRpo2Rr06YN4uLiROkOGzYMgYGBWLFiBW7evInU1FSkpqbi5s2bWLFiBQYNGoQRI0aI9ht4Xz6+/fZb3L59G2FhYahfvz6mTp0KKysrUacCKxSKjx7upqmpCYVCoYrLAuqu7yx9Z9Xf3Lx5E3K5HAsWLICTkxN8fHzQokULSCQSNG7cGD4+PqKjQrDsfwFg/fr1+PXXX6GpqYmXL18q2d6+fSs6ikFCQgK8vLyEtJeXF06fPo3ff/8d06dPF+0vwLaM1K1bF6dPnwbwfo/4h6d3JyYmQkdHR5T2q1evhD62cuXK0NXVVeonnJyckJKSIko7PT0dlStXBgDo6elBT09PqV+ws7NDampqqXU/xXiHU4H53LN+DocFAQEB1LFjRwoLC6MRI0ZQw4YNqUOHDpSenk4ZGRnUo0cPateunSjtrl27Uvfu3Sk9PZ1ycnJowoQJSvGLL126RJaWlqK0q1SpUmz85sePH1ONGjXIz89P9NNwPz8/paVay5YtU3ojde3aNTI1NRWlTUR09OhRsrW1pcWLF1N+fr7Kb16JlJfJFsfJkyepb9++orTv3LlDJiYmFBgYSAsWLCB9fX3q378/LVq0iAIDA0lLS4uCg4OZ+B0bG0szZswQpV0YhUJBixcvFvYzqpLf/v7+1KZNG3r27FkR27Nnz6hdu3bUoUMHUdo1a9akI0eOFPn87du35OnpSfXq1RNdrufOnUs7d+4s0T5jxgzq1q2bKG0idveSZX5LJBLasmULHThwgGxtbenChQtK9qioKKpUqZIobSKiJUuWkJWVVZHY7lZWVirtef1YXqenp9OGDRtEbQvq27cvubm5FXlLR/S+3XN3d6d+/fqVWrcAlvWdpe8s+xui9ysqrK2taceOHUSk+mocIrb9L8sY4HZ2dnTu3Lkin9++fZssLCwoMDBQdF6zLCOHDx+mypUrU3BwMAUHB5OjoyNt2LCBwsPDadOmTWRnZyd6H729vb3SVqCpU6fSixcvhHRkZKToMUm1atWU3myvWbOG3rx5I6QjIiJElxMiNuMdTsWHT7o5FZJ79+5R9erVSSKRkIuLCz169Ig6d+5MMpmMZDIZmZmZUUREhCjt+/fvU7Vq1Ugmk5FcLicjIyM6ceKEYA8ODqZp06aJ0h46dCgNGTKkWNujR4/IyclJdMccERFBlStXJktLS7K3tydNTU2lyUpQUJCwN04sT548ofbt21OzZs3U0gkVXrbJgri4OOrduzcZGBgI+7Lkcjl5eXnR/v37Reuy9vtDrl69Sr/88gu9fPlStEZSUhLVrl2bZDIZubm5Ubt27ahdu3bk5uZGMpmM6tatS0lJSaK0v/nmG+rRo0extjdv3pCHhwezfXAZGRkqHcrF6l6yzO8P9xouXLhQyb5hwwbRy8sLEx8fTxcuXKALFy4I5yOoAqu8fvnyJbVr144kEglVrlyZnJ2dydnZmSpXrkxSqZTat29Pr169Eq3Psr6z9J1lf1PA7du3qV69etSnTx+19Aks+9//4uLFi8VObP8X+vTpQxMmTCjWFhUVRWZmZqLzmnX5/vPPP8nW1rbIXmZtbW2aMGGC6DNWOnfu/NH990FBQdSqVStR2iNHjvzouRU//PAD+fv7i9IuQN3jHU7Fh4cM41RoXrx4ARMTEyF96tQpvHv3Dp6enkqfl5bMzEyEhYUhJycHTZo0Udvy1cTERMTExKBt27bF2pOTk3HixAkMHDhQlH5KSgoOHz6M7OxstGrVCq6urqq4WyK//vorQkNDsWrVKtja2orWOXv2LLy9vSGTydToXVGICE+fPoVCoYCpqanKIWESExNhb2+vtAySNQkJCUJoFLEoFAr8888/uHTpklLYGU9PT7Rp06bYcF//C69evUJycjJq1apVrP3t27e4du2a6CWnLGF5L1nl939x+PBhyOXyEtuZz8Uff/yB3r17i17C+1/ExMQUiS3u6ekJZ2dnlXQ/RX2Pjo4utpyo4jvr/qaAnJwcTJs2DaGhodi3b59KW2sAdv0vS27evImIiAgMHjy4WHtUVBT++usvzJkzR/RvsCrfwPuwXteuXUN8fDwUCgWsrKzg7u4OAwMDlbVL4sqVK9DV1VXaAqEuEhISoK2trbTkXCzqGu9wKj580s3hcDgVAE1NTdy4cQMuLi6f25XPSnJyMn777TfExcXBysoKw4YNU8ugk/N/pKSkYO3atUXiJHfp0gWDBg1SirnL4ZQ3Hj16BCMjI+jr6yt9npubi4sXL4o+R4TD4XzZ8IPUOF8UVatWRWxsrMo6hw8fxvfff4/w8HAAwOnTp+Hv74927drh999/V1n/Q9Tl98d49eoVtmzZIvr7p0+fxvz58zF69GiMGTMGP//8MzOf1ZEf165dQ0JCgpDeunUrvL29YWdnh6ZNm2LXrl0q6UdHRyM4OFg4nC4mJgajR4/GkCFDhINpxNCtW7dir/z8fIwbN05Iq4tWrVoVOTyntHzzzTc4f/68mjxSRldXF8+ePQMA3LlzB66urtixYwdyc3Nx5MgRuLu74+bNm6L1Hz16hOfPnwvp8+fPo1+/fmjWrBn69++PixcvitL966+/VDpATgzquJdXr16Fi4sLjh49itzcXMTGxsLd3R16enr47rvv0Lx5c7x9+1ZNHr9HVb+zs7ORm5srpO/fv4+ZM2diwIABmDVrllI7IBZW9b2AR48eIT09vcjnubm5OHfunMr6hWHV36ij/AHs+t+UlBQ0btwYDg4OMDIyQmBgoFKev3z5Ei1bthSl/SnrOxEhNDQU69evx+HDh5XKflnTfvHiBUJDQ4VD654/f46lS5di/vz5iI6OFq3Lqt1evny5Wsow58uEv+nmVEh+/fXXYj+fNGkSpkyZAktLSwDAuHHjSq3922+/YezYsahXrx5iY2OxevVqfP311/jqq6+goaGBLVu24IcffsD48ePLlN//xY0bN9CgQYNSn1j79OlTdOrUCVevXoVUKoVCoYCbmxseP36MZ8+eYdKkSfjxxx9F+cQyP+rVq4fly5fD19cXGzZswLhx4zB8+HC4uLjg7t272LBhA1auXIkhQ4aUWjskJAQBAQHQ19dHZmYm9u/fj8DAQNSrVw8KhQJnz57F8ePH0apVq1JrS6VSNG/evMgSzS1btqBz584wMjICAAQHB5dK9+DBg8V+3q1bN6xcuRJ2dnYAgM6dO4vyWSKRoFq1ahg6dCgGDhwo3DtVkUqlePLkCczNzdGlSxcoFArs27cPMpkMCoUC/fr1Q3p6Og4dOiRK38PDA7Nnz0bHjh1x4MABdOvWDR07doSLiwvu3buHw4cPY9++fejYsWOp/TYwMMBXX32FoUOHwsPDQ5R/xcHyXjZt2hR+fn7CMtht27YhKCgIly5dwqtXr9CqVSs0b94cK1euLDN+t2jRAmPHjkWPHj0QHh6O1q1bo2bNmsI9vHv3Lk6ePAlPT89S+wywre8pKSkICAhAREQEJBIJ+vbtizVr1ghvYVNTU2FtbS3qpHFW7SvL8sey/x04cCDu3r2LoKAgvH79GtOmTYNEIsHx48dhbGyM1NRUWFlZiToJnGV99/f3x86dO2FoaIiXL1/C398fV65cgampKV68eIEaNWrg3LlzMDMzK1PaV65cQZs2bfDmzRsYGRnhxIkT6Nmzp9B2JycnIywsDA0aNCi1Nst2WyqVomXLlhg2bBi6du360ZPjORwlPttucg6HIRKJhGxtbZVOInV0dCSJREI2Njbk6OhIVapUEaXt6upKv//+OxERnT59mrS1tWn16tWCPTg4mFxcXMqc32lpaR+9zp8/L+ogl6+++oq6dOlCaWlplJWVRWPHjhUOZDt16hSZmJh89LCUj8EyP3R0dIR4mm5ubsI9LWD79u3k6uoqStvT05NmzpxJREQ7d+4kY2NjpdOLp02bRn5+fqK0d+7cSba2trRp0yalz1U9yKXgJOoPD+EqfIk96Kcg/ur48ePJ1NSU5HI5de7cmQ4dOkT5+fmifS7QLjjEqrgTgq9duyY6bjTR+zjgBYeEeXh40JIlS5Tsq1atEnUomUQiofnz55ObmxtJJBKqVasWrVixgp4/fy7a18LarO6ljo6OUhz0/Px8ksvl9OTJEyIiOn78OFlbW5cpvytVqkT37t0jIiIfHx+aOHGikn3WrFnk7e0tymcitvU9MDCQPDw86N9//6UTJ06Qu7s7NWzYUDg08cmTJySRSERps2pfWZY/lv2vtbW10mnaWVlZ1KlTJ6pfvz69ePFCpVjarOt7QRs4evRocnV1Fdqshw8fkru7O40aNarMafv6+tKwYcPozZs3tGzZMrK1taVhw4YJ9sGDB1OXLl1EabNst4ODgykgIIDkcjmZmJjQ+PHji8R053CKg0+6ORWSkSNHUv369enOnTtKn6vjhEkdHR1KTEwU0nK5XKnBTUhIIF1dXVHaLP0uHN6nuEuVAW1UVJSQTk9PJ7lcTmlpaUREtHXrVqpZs6Yon1nmh4mJCV29epWIiMzNzSkyMlLJHhcXRzo6OqK0K1WqRLGxsUREQjiRwqfe3rp1iywsLER6/r6MeXt7U7du3YTBt6p5UhCi6sNTmNV9Cn1OTg7t3r2b2rZtSxoaGmRtbU0zZswQ8qu0SKVSevr0KRG9D/lz48YNJXt8fDxpa2uL9t3Q0FDQNDc3L6IfFxcnqr4XzpOrV6/S6NGjycjIiLS0tKhnz550/Phx0T6zvJcODg4UFhYmpJOTk0kikVBmZiYRvS+bYvObld96enoUHR1NREQWFhbF1nV9fX3R+izrO8uJIKv2lWX5Y9n/6unpCQ9nCsjNzaUuXbpQ3bp16ebNmypNulnV98LaNWvWpAMHDijZT548KfrhNEttY2Njoezl5OSQVCpVKusRERFkY2MjSvtTtNupqam0dOlScnZ2JqlUSo0aNaLff/9dKTQZh1MYvqebUyFZt24dvv/+e7Rt2xZBQUFq1TYxMRH29CQnJyMvLw9JSUmCPTExEZUrVxalzdJvAwMD/PDDDzh9+nSxl9i9cFpaWkqn9kqlUuTn5yMvLw8A4OXlhQcPHojSZpkf7du3x9q1awEAPj4++PPPP5Xse/bsgZOTk2j9gjyRSqXQ1taGoaGhYDMwMEBaWppobUdHR5w7dw61a9dGvXr18M8//6h8cvKxY8fQunVrNGzYEIcPH1ZJ62PI5XL06tULISEhiI+Px/Dhw7F9+3bUrFlTlB4RoUaNGqhcuTKSk5OL7N+Oi4tTaSm7j48Pdu7cCQBwc3PDmTNnlOyhoaGwsbERrQ8A7u7uWLNmDVJSUrB+/Xo8e/YM7dq1E33KM8t72aVLF4waNQohISEIDQ1Fv3794OPjAx0dHQDA3bt3RecHK789PDyE7QXVqlXDjRs3lOyRkZGi2+wCWNX3tLQ0GBsbC2ktLS3s27cPjo6OaNmyJZ4+fSraZ1btK8vyx7L/rVq1apH2QyaTYe/evahatWqplyKXhLrrO/B/5e/Vq1eoVq2aks3JyQnJycllTjsnJ0doN+RyOXR1dZVOoi9Ywi6GT9Fum5ubY8qUKYiOjsaZM2fg6uqKiRMnquVEdE4F5XPP+jkcljx69IhatWpF7dq1o5SUFLU8aR8zZgxVr16dFi5cSI0bN6aBAweSs7MzHTt2jEJCQqhOnTolxj79nH63aNGCli5dWqI9MjJS1DLFrl27Uvfu3Sk9PZ1ycnJowoQJ5OTkJNgvXbpElpaWonwugEV+PH78mBwdHal58+Y0adIk0tHRoaZNm9Lw4cOpefPmpKmpSUeOHBGlXbduXTp27JiQvnXrFuXm5grpc+fOiX478CHnz5+nKlWqkFQqVUuc0OvXr5OrqyuNGDGCMjIyPkm8dYVCIfpNz+bNm5WuixcvKtnnz59fZDlxabhz5w6ZmJhQYGAgLViwgPT19al///60aNEiCgwMJC0tLQoODi61rlQq/WiexMbGKi1RFgOLe/n27Vvq1asXyWQykkgk5OXlpRSj+59//qE9e/aUKb8vXLhAhoaGNGfOHFq1ahWZmprSrFmzaPv27fT999+TkZHRR9vG/4Jlfa9Tpw79+eefRT4veANrb2+vcixtFu0rEZvyx7L/nTJlCrVp06ZYW25uLnXu3Fl0XrOs7xKJhPz9/alr165kbGxMhw4dUrJfunRJ9EoLltrOzs506tQpIX348GFhxUyBtq2trSjtz9Vup6WlFdmqxuEUwCfdnAqPQqGgxYsXk6WlJWloaKjc6aenp9Pw4cOpdu3aNGLECMrOzqZly5aRpqYmSSQSatGixUcb5c/l9++//04rV64s0f7kyROaO3duqXXv379P1apVI5lMRnK5nIyMjOjEiROCPTg4mKZNmybK58KoOz+IiF69ekVTp04lV1dX0tbWJk1NTXJwcKC+ffvSv//+K1p37dq1dPjw4RLt06dPp6FDh4rW/5C3b99SZGQkZWVlqUUvMzOTRo4cSdWrV1dLXjs6Oqpl7+LnIi4ujnr37k0GBgbCnlS5XE5eXl60f/9+UZr/9SBCXaj7Xhbw7t07evv2rVq0ikPdfl+4cIGaNGlSZG+xjY2N6DMnCmBZ31lOBAvDon0lUv99ZNn/5ubmCtuiSrIXnANSWljW90GDBildu3fvVrJPnjyZ2rZtW+a0586dSzt37izRPmPGDOrWrZsobaLy3W5zKib89HLOF0NERATCwsIQGBiotFxPXWRlZSE3NxcGBgZq1WXttzrIzMxEWFgYcnJy0KRJE6UlYuqmPOTH54CIoFAo1Boj+eDBgwgNDcX06dNhbm6uNt1PQWpqKohIbaekA+/z+OnTp1AoFDA1NYVcLhetlZiYCHt7e5W3BfyvlNd7qW6/nz17hvj4eCgUClhZWcHR0VF1JxmSl5eHzMxMVKpUqUT748eP4eDgoJbfY9W+si5/rPpfdfGp63thMjIyoKGhAW1t7XKlnZmZCQ0NDWhpaamko852m8NRBT7p5nA4HLyP5QtA5Q6eNXl5eZg7dy7Onz+PFi1aYN68eVi2bBnmzp2LvLw89O7dG+vXr/9iwpi8fPkSI0aMwJUrV9ChQwcEBQVh5MiR2LRpEyQSCTw8PPDXX3/xfXZqok6dOujVqxcGDRokhH7ifHoSEhJgZ2cHmUz2uV2pUKSkpGDt2rUICwtDSkoKpFIpqlatii5dumDQoEFqfajJ4XC+LPhBapwKy4kTJzBnzhycPn0aAHDu3Dm0b98erVq1KnUM49Lw8OFDUbGdC0hJScH333+PVq1awcXFBbVq1UKnTp2wceNGUbFYSyIjIwPBwcGYOXMmgoKCRB9YAgDR0dEIDg5GTEwMACAmJgajR4/GkCFDhPxngap5feLECfj7+8PY2Bi6urrQ1dWFsbEx/P39cfLkSdG6Hx5uFBkZiYEDB8Lb2xs9evQocqhLaZg3bx42bNiAhg0b4s8//8To0aOxatUq/P7771i/fj1OnTqFX375pdS633zzDc6fPy/aL1W4f/++qBjGADB58mTcvXtXONCme/fu+Pfff3H+/HmEhYUhLy8P06ZNU8m/O3fu4Ouvv4abmxusrKxgZWUFNzc3fP3117hz545oXZb1hpX27du3sXLlSlSpUgXt2rXDX3/9JRyaqA7evXuHsLCwYvM1KysLW7ZsUfk31Nn2AWzre0nUrFkTsbGxKuuw6idZlb9OnTph69atePfunWiNkrh69SpcXFxw9OhR5ObmIjY2Fu7u7tDT08N3332H5s2b4+3bt6L1WeXJtWvXkJCQIKS3bt0Kb29v2NnZoWnTpti1a5dobQAICgpCYGCgoLN161a4urrC2dkZM2bMUKn+s9T+GKqMHVj1B5wvgM+3sp3DYcfWrVtJJpNRgwYNSF9fn4KDg8nIyIiGDRtGQ4YMIU1NTdq7dy+T346MjBS9x+7ff/8lQ0NDcnd3p6ZNm5KGhgYNGDCAvvrqKzIyMiIvLy/R4ShcXFzoxYsXRESUlJREjo6OZGhoSI0aNaLKlSuTubm50oFI/yvHjh0jTU1Nqly5Mmlra9OxY8fIzMyMfH19qVWrVqShoaF0WIo6USWvN2/eTDKZjHr37k3BwcF09OhROnr0KAUHB1OfPn1ILpfTli1bRGkXPmwlPDyc5HI5+fj40OTJk8nPz49kMhmdPXtWlHbVqlWFw2xiY2NJKpXSrl27BPvu3bupdu3apdYtCBlXvXp1WrJkCaWkpIjyTwyq3EcrKysKDw8nov+LWVz4ULawsDDRYWeIiI4ePUqamprUpEkTmjNnDq1Zs4bWrFlDc+bMIS8vL9LS0qKQkJBS67KsNyy1JRIJPX78mPbv30+dOnUimUxGZmZm9O233xYJPVVa7t69Sw4ODkJZbN68OSUnJwt2seGxPmz7HBwc1NL2FcCyvnft2rXYSyqVkq+vr5AWA6t+knX5k8lkZGhoSKNGjRLCPqoDb29vpXNNtm7dSh4eHkRE9PLlS6pfvz6NGzdOlDbLPKlbt65wjsr69etJR0eHxo0bR2vXrqUJEyaQvr4+bdy4UZT2ggULyMDAgLp3706Wlpa0ZMkSMjExoYULF9LixYvJzMyMvv/++zKn/V+I7XNY9QecLwM+6eZUSOrXry8cGnby5EnS0dGhn3/+WbD/9NNP5O3tLUr7wIEDH71WrFghegLBstMvfABIv379yMvLi16/fk1E7w/i8vX1pT59+pRa19PTk2bOnElERDt37iRjY2OlU1inTZtGfn5+onxmmdfVq1enoKCgEu2rV69WOoW9NBTOaz8/vyKn6Y4fP55atWolSltbW5uSkpKU0gUxiInex6U2MDAQ5fPJkydp/PjxZGpqSnK5nDp37kyHDh2i/Px8Ub4WsHLlyo9eU6ZMEX0fdXV1lQ43+jBub3x8POnp6Yn2vW7dujR79uwS7XPmzKE6deqUWpdlvWGp/eFBQsnJybR48WKqXr06SaVS8vT0FD3A79KlC3Xo0IGePXtGsbGx1KFDB6pSpYoQl1nspJtV21ecvrrru0QiIR8fnyIHWkmlUurSpYuQFgOrfpJ1+bt9+zatWLGC6tSpQ1KplOrVq0erVq2ily9fitIsQEdHh+7fvy+k8/PzSS6X05MnT4iI6Pjx42RtbS1Km2We6OjoCG2gm5tbkdOzt2/fTq6urqK0q1WrRn/99RcRvZ+oamho0LZt2wT7vn37RPeTLLVZjR1Y9QecLwM+6eZUSPT09JTeXMjlcrpx44aQjo6OJhMTE1HaBW9hPjwFt/AldgLBstMvPDCsWrVqkRBN4eHhZGdnV2rdSpUqUWxsrOCvTCaja9euCfZbt26pFK6EVV5raWlRTExMifaYmBjS1tYW7XdBXltZWRUJYxUVFUWmpqaitC0sLOjmzZtC2svLix49eiSko6OjqVKlSir5nJOTQ7t376a2bduShoYGWVtb04wZM4T7LEbb2tqaHB0di72sra1F38d69eoJD0+OHj1KBgYGtHz5csG+du1aUW/+C9DW1mZSTljWG5baHwuZExoaSv379xf9kMPc3FypbCsUCho1ahTZ29vT/fv31TLpVmfbV5y+uuv7zp07ydbWljZt2qT0uTrCb7HqJ1n3CYXL3+XLl2nEiBFkaGhIOjo61KdPH9FvjB0cHCgsLExIJycnk0QiEcJYJSQkiO4TWOaJiYmJ8Mbf3NycIiMjlexxcXGko6MjSltHR0d46EX0voxERUUJ6QcPHpCurm6Z02Y1dmDVH3C+DPiebk6FRC6XIycnR0hraWlBX19fKS12T5iVlRX27dsHhUJR7HXt2jXRfpubmyMlJUVIp6amIi8vTzi5tnr16nj58qVo/YKTU7OysoocLGVjY4Nnz56ppCuVSqGtrQ1DQ0PBZmBggLS0NFG6LPO6Vq1a2LhxY4n2TZs2wdXVVbT+27dv8ebNG2hraxc5nE1bWxuZmZmidF1dXZX+7vDwcNjY2AjpW7duoXr16uKc/v/I5XL06tULISEhiI+Px/Dhw7F9+3bUrFlTlJ6DgwNWrFiBhISEYq8jR46I9nXy5MkYP348qlevjq5du2LTpk1Yvnw5vvrqK/Tp0wfjx4/HqFGjROs7Ojp+1L8jR46IPjmaVb1hqU0fOXu1RYsW2Lp1K5KTk0Vpv3v3TulgMIlEgrVr16JTp07w8fHBvXv3ROkWaAHqb/sKYFXfe/fujfPnz2Pjxo3o3r07Xr16pZKfhWHZT7Is24Vp3LgxfvvtNyQnJ2PNmjV4+PAh/Pz8RGl16dIFo0aNQkhICEJDQ9GvXz/4+PhAR0cHAHD37l2ltra0sMqT9u3bY+3atQAAHx8f/Pnnn0r2PXv2wMnJSZS2paWlsE85NjYW+fn5SvuWb9++Lfo0epbarMYOLPsDTsWHH3vJqZA4OTkhJiZGmCQ8fvxYKZTI/fv3YWtrK0rb3d0dERERCAgIKNYukUg+OjD9GAWd/rJly6ClpYUFCxaotdNv3bo1ZDIZ3rx5g7t376J27dqCLTExESYmJqXWdHR0RGxsLKpVqwYAuHjxIuzt7QV7UlKS6JOjWeb18uXL0bFjR4SEhMDX1xcWFhYA3j/oOHXqFOLj41WaDNaoUQPA+0nK1atX4ebmJthu374Na2trUbrr1q37aMiT3NxcTJkyRZR2cdjb22Pu3LmYM2eO6MPlCu5jr169irWrch/79esHR0dHXLp0CZ6envDy8oKrqyuWLFmCzMxM/P777xg4cKAobQCYP38++vbtizNnzhRbTkJCQrBjx45S67KsNyy1Bw4cKLRHJVFSeKv/wtnZWTjMqjBBQUEAgM6dO4vSBdi0fYVhVd+B9/fz3LlzmDdvHurVq4f169erJfQUq36SZfkrCV1dXQwaNAiDBg0S/XBm4cKFSElJQadOnZCfnw9PT09s27ZNsEskEvzwww+itFnmydKlS+Ht7Q0fHx80bNgQy5cvx5kzZ+Di4oK7d+/i0qVL2L9/vyjtfv36ITAwEAEBATh16hSmTJmC7777Di9evIBEIsGiRYvQo0ePMqfNauzAqj/gfBnwSTenQjJjxgylGKMfDgKvXr1a4gTgv5g8eTIyMjJKtDs5OSE0NFSUNstOf86cOUrpwm80AODQoUNo1qxZqXVHjx6tdKp64cEsABw7dkylk6lZ5XWLFi0QFRWFtWvX4tKlS3jy5AmA90/f27dvj1GjRomO4fuhTx8OphISEjBixAhR2gWD+5Lo27evKF0HB4ePhsORSCSi3yDNnz//o2/6XF1dlU7fLS3e3t7w9vZW0lPHKdcA0LNnT9jY2ODXX3/F8uXLlcqJp6cnzpw5A09Pz1Lrsqw3LLVZRn7o2rUrdu7ciQEDBhSxBQUFQaFQYN26daXWZdX2FcCyvhcglUoxb948+Pn5ITAwUC2RLFj1kyzLn4+Pz3+GQ/yvNrIk9PX1sXv3bmRlZSEvL69IOWnTpo0oXYBtnlhbW+P69etYsmQJDh06BCLClStX8PDhQ3h7eyM8PBwNGzYUpT1v3jzo6Ojg4sWLGD58OKZNm4Z69ephypQpyMzMRKdOnbBgwYIyp81q7MCqP+B8GfA43RxOGaSkTp/DAYD8/HwkJibC0dERUqkU2dnZOHDgABQKBVq2bCk8ff+SmTdvHsaMGQNTU9PP7coXAc/vT0d6ejru378PZ2fnIkvZORwOh1M24ZNuzhdDdnY2Hj16BFtbWz5Qwfv8AKD2vGCly1q7gNTUVGRnZyst/StL3Lx5E+3atUNqaipcXV1x9OhR+Pv7IyEhARKJBHK5HCEhIWjcuPHndvU/SU1NBRHB0tJStMabN2+KfEZEMDMzQ1hYGJydnQGIX/JcEVBnvfmU+V0e26jySn5+Pp4/fw6pVAozM7PP7U6pUUdbAgA3btzA8uXLERYWhpSUFEilUlStWhVdunTB5MmT1daOlNcy+Cn8zsvLUzrbgcOpKPCD1DgVks2bN+PixYsA3r81Hjp0KPT09FCjRg3o6+tj1KhRQudRWp4+faqUjoyMxMCBA+Ht7Y0ePXrgzJkzov1mqQ0AJ06cgL+/P4yNjaGrqwtdXV0YGxvD399f9H5dlrostd++fYv+/fvDwcEBAwcORE5ODsaMGQMrKytUqVIFPj4+xU4w/lfWrFkDX19f9OrVC6dOnVKyPX/+HFWrVhWlO2XKFHh7e+PGjRto3bo12rZtCxcXF7x69QqvXr1Chw4dMHPmTFHaGzZswMCBA4UlxLt374aLiwuqVq1aZIluaXj58iV69OgBe3t7YZnlsGHDYGVlBRsbG3h5eSkdIFgajI2Ni1yVK1dGXl4ePD09YWRkpLSEVgxHjx7FsGHDMGXKFERHRyvZXr16JXpZ6I0bNxAYGIiqVatCR0cHenp6qFOnDmbPnq1S2QPY1RvW+V0e2yiAXX2/cuWK0rLkw4cPw8fHBzY2NmjYsKHK2yiOHDmC5s2bQ09PD9bW1rC0tISRkREGDBiApKQkUZos+zGWbck///wDT09PZGZmwtvbG1KpFEOGDEGHDh2wa9cuNGjQQFhOLAaWZfDGjRtYuHAh1qxZg+fPnyvZ3rx5gyFDhpQ5v0NCQnDr1i0AgEKhwIIFC2BjYwMtLS3Y2tpiyZIlos/6YFkGWfUHnC+AT35eOofzCahSpQpdunSJiIi+++47cnR0pH379lF0dDT9/fffVKNGDZo8ebIo7cIhc8LDw0kul5OPjw9NnjyZ/Pz8SCaT0dmzZ8uc9ubNm0kmk1Hv3r0pODiYjh49SkePHqXg4GDq06cPyeVy2rJlS5nRZa09duxYcnZ2pl9//ZVatGhBAQEBVLt2bQoLC6OzZ8+Sq6urUhzV0rBy5UrS1dWlMWPGUP/+/UlTU5MWL14s2MWGPiIiMjY2pjt37hARUWZmJmloaNDly5cFe1RUlKgwPytWrCA9PT3q1q0bWVlZ0cKFC8nExIQWLlxI8+bNo0qVKtFvv/0myuchQ4ZQ7dq1adWqVeTj40MBAQFUt25dCgsLowsXLlCjRo0oMDBQlLaNjQ116NCBTp8+TWfOnKEzZ85QaGgoaWhoUHBwsPCZWLZv304aGhrUoUMHatq0KWlrayvFkhV7L0NCQkhHR4e6d+9O/fv3J11dXRo7dixNnTqVnJycqFq1apSSkiLKZ5b1hmV+l8c2iohtfS/cJxw8eJCkUikFBgbS6tWradiwYSSTyWjfvn2itLds2UIGBgb07bff0syZM8nS0pKmTZtGa9euJR8fHzI1NaV79+6p5LO6+zGWbUn9+vVp7dq1Qvr48ePk7OxMRO/DKLZu3Vp0THSWZfCff/4hTU1NqlWrFtnb25OJiQmdPn1asKtS/lj6XbNmTTp37hwRES1evJhMTEzo559/pmPHjtEvv/xCFhYWtGTJElHarMogq/6A82XAJ92cComWlpYQ/7FGjRp07NgxJfvZs2fJ3t5elHbhOKF+fn40ZMgQJfv48eOpVatWZU67evXqQjzj4li9ejU5OTmVGV3W2nZ2dsLA5PHjxySRSOjQoUOC/fDhw1SzZk1R2q6urrR9+3YhHR4eTmZmZjR79mwiUq1jNjIyEgbCOTk5pKGhQREREYI9OjqajI2NS63r7Ows+Hzt2jWSyWS0YcMGwb5hwwZyd3cX5bOVlRWFh4cT0fu/XSKRKMVKDgsLIxsbG1HaL168oC5dulDLli2V4pWrI44x0fuB+MqVK4X07t27SU9PT8gbsfeS5QCfZb1hmd/lsY0iYlvfC/cJTZs2pWnTpinZFy1aRE2aNBGl7ezsTLt27RLS//77L9na2pJCoSAioq+++oq6du2qks/q7sdYtiXa2tqUkJAgpBUKBcnlckpOTiYionPnzpGZmZkobZZl0NPTU3hArFAoaOnSpaSvry+Me1Qpfyz9LjxOq127Nu3Zs0fJfvjwYdHarMogq/6A82XAJ92cComDg4MwobKxsaF///1XyX7nzh3S09MTpV24MbeysqKLFy8q2aOiosjU1LTMaWtpaVFMTEyJ9piYGNLW1i4zup9COykpSUjr6urS3bt3hfSDBw9IV1dXlLaOjo7S4I2I6NatW2RhYUHTpk1TqWNu3bo1DR06lB49ekTz5s0jJycnGjx4sGD/+uuvqVmzZqJ8LhgAEb3Pn6ioKCEdGxtLRkZGonzW1dWlBw8eCGm5XE63bt0S0vHx8aLrYwFr1qwha2tr2rFjBxGpb9Ktp6dH8fHxSp+dPn2a9PX1ae3ataLvJcsBPst6UwCL/C6PbRQR2/peuE8wNzenq1evKtljYmJE18vi/JbJZPT48WMiIrp8+bIobZb9GMu2pFq1ahQSEiKkY2NjSUNDg7KzswVtHR0dUdosy2ClSpUoLi5O6bPt27eTnp4eHTp0SKXyx9LvwmXDwsKCrl27pmS/d++e6PxmVQZZ9QecLwO+p5tTIenXrx9mzpyJ169fY8CAAZg/fz7S09MBAJmZmZg7d65SiKHS8vbtW7x58wba2tpFDhTR1tb+aHikz6Vdq1YtbNy4sUT7pk2b4OrqWmZ0WWubmJjg2bNnQjogIABGRkZCOj09XfRhMaampnj48KHSZ7Vr18bp06cRHBysUhztH374AX///Tfs7e0RFBSEv//+G3fu3BH2NAYHB4vaf62rq6sUYsXMzKzI6fl5eXmifK5evToOHz4M4H1oHG1tbRw/flyw//PPP6hSpYoo7QJGjx6NEydOYOnSpaLDphVHpUqVkJqaqvRZy5YtcfjwYUyePBmrVq0SpWtjY4O7d+8K6fv370OhUAjxom1tbYU2q7SwrDcFsMjv8thGAWzrOwDcuXMHN2/ehI6ODhQKRRG72Hrp6OiIq1evCulr165BKpUK0Q8qV66M3NxcUdqs+jGWbUlgYCCGDRuGdevWITg4GF27dkXnzp2FEGWRkZGitVmWQS0tLbx+/Vrps759+2LDhg346quvRMfoBtj63bVrVyxatAj5+fkICAjAmjVrlPZwr1q1CvXr1xelDbApg6z6A86XAT8ekFMhmTNnDqKiolC1alU0bNgQ58+fh4WFBWxsbJCcnAwTExOcOHFCtH5BHFAiwtWrV+Hm5ibYbt++DWtr6zKnvXz5cnTs2BEhISHw9fUVBlapqak4deoU4uPjceTIkTKjy1q7bt26+Pfff9GgQQMAwI4dO5Ts//77L1xcXERpN23aFPv27SsS+9fV1RWnTp1Cy5YtRekCQKNGjZCYmIiYmBjUrFkT+vr6OHPmDLZv3453797Bz88PNWvWLLWus7Mzbt68KfzNH04iYmJiRMctnzx5MgYOHIhffvkFDx8+xLZt2zB+/HhcvnwZUqkU+/btw88//yxKuzCurq64cuUKpk2bhtq1a0NHR0dlzcaNG+PYsWNo0qSJ0uc+Pj44dOgQOnbsKEq3YIA/c+ZMaGlp4eeff1bbAJ9lvSmMuvO7PLZRANv6DgCtW7cWJiPh4eFo1KiRYEaNCYEAACy/SURBVLt+/broSAtjxozBsGHD8O+//0JbWxsbNmzAgAEDoKGhAQC4fPmy6JjXrPoxlm3JjBkzkJGRgQULFiA7Oxtt27bFypUrBbuNjQ3Wrl0rSptlGaxfvz5CQ0Ph7u6u9Hnv3r1BRBg4cKAoXdZ+L168GL6+vnB2doanpyf27t2LEydOoEaNGoiLi8PLly/xzz//iPadRRlk1R9wvgx4yDBOhSYkJASHDh1CfHw8FAoFrKys4O3tjb59+0JPT0+U5tmzZ5XSVlZWSgOTlStXIicnB5MnTy5T2gDw4MEDrF27FpcuXRJOYbW0tISnpydGjRolelLFSpel9suXLyGVSpXebhfm2LFj0NHRQYsWLUqtffPmTURERGDw4MHF2qOiovDXX3+pdCK4ugkPD4eenl6JbxbWrFkDhUKBsWPHita/dOkSPD094eXlhTt37mDJkiXIzMxEp06dVBoYsuTs2bO4cOECpk+fXqw9NDQUW7ZsEU58/1/Jy8vDzJkzsW3bNqUBfkGc6ytXriArKwvNmzcX5TfLOsmS8thGsazviYmJSml9fX1hNQQA4fTywMDAUmsDwNq1a5XK4OzZs6GtrQ0AiI2NRX5+vhAG7n+FdT9WXtsSVmVw//79OHfuHFasWFGsfceOHVi/fj1CQ0PLlN8AkJubi40bNxY7Ths9ejRsbW1F6bIqg6z6A86XAZ90czgcTgWgVatWCA4OhoODw+d25ZPy119/oX379tDV1f3crnwxEBEePHgAOzs7yGQy5OTkYP/+/cjOzoa/v7/w4IDDKa+kpaUpTTANDQ0/s0ccDqe8wyfdHI6ayM3NhVwuV4tWXl4ebt++rdTpu7q6qkX/Q20rKyu4uLiozfcCUlNTkZ2dLXrp46fQfvr0KaKiouDu7g5DQ0Okpqbijz/+gEKhQIcOHVCnTh2V9OPj4xEWFoaUlBRIpVJUrVoVfn5+qFSpkmjNgwcPFvt5t27dsHLlStjZ2QEAOnfuLPo3CjNv3jyMGTOmzE6kpFIpDAwM8NVXX2Ho0KHw8PBQ+2/k5+cjMTERjo6OkEqlyM7OxoEDB6BQKNCyZUthyaVYPtUAXx315u7du2jbti0ePnyIqlWr4vjx4+jZsydiYmJARNDV1cWFCxdQvXr1MuX3p9BlUd8LyMjIQEREhJJ2gwYNIJFIVNZm2d8UhmVboq7+d8OGDfj555+VzlsAgJo1a+Lbb7/F0KFDVf6NwqgzTz7VfQTUO975ECKCQqEQtjmI4fnz55+kz8rLy0NoaCiSkpLg4OCAli1bquQ3p4Lz6c9u43A+DatXr6bWrVtTz5496eTJk0q2Z8+eUZUqVUTp7t69WzjNlIho1apVZG9vT1KplExMTGjevHmifc7Pz6eZM2eSkZERSSQSpcvIyIhmzZpF+fn5ZUr7zZs31K9fP7K3t6fAwEDKzs6mr7/+miQSCUmlUmrevDmlpaWJ8pmldmhoKOnp6ZFEIiFLS0uKjIwkW1tbql69OtWsWZO0tLTon3/+EaWdnp5OPXr0EPJXKpWSpaUlaWhokL6+/kdDsPwXBXof3sPCl5jTU9PS0opcr1+/JrlcTpcvXxY+Ewur+iiRSGj+/Pnk5uZGEomEatWqRStWrKDnz5+L9rUwN27cIEtLS5JKpVS7dm1KSkqi2rVrk56eHunr65OxsbFSnPTSsH79enJxcSGpVKp0ubi4KIVrKy0s601AQAB17tyZbt68SRMmTCAXFxcKCAignJwcysrKok6dOlH//v3LlN8s84OIbX3Py8ujyZMnk46OjlA+Cn7HwcGBDh48KFqbVZ/Asi1h2f/++OOPpKurS9OmTaPQ0FC6c+cO3blzh0JDQ2n69Omkp6dHy5YtE6XNMk9YjhtY5ndubi7NnDmTmjdvTt9//z0R/d890NTUFOqqGKRSKbVs2ZK2b99OWVlZon38kLFjxwqhRR8+fEjOzs6koaFBFhYWpKGhQXXq1FEKpcjhFIZPujkVkpUrV5Kuri6NGTOG+vfvT5qamrR48WLBrkpYB6lUKoSi2LRpE2lra9P3339PR44coYULF5Kenh6tX79elPbkyZPJzMyM1q1bRwkJCZSZmUmZmZmUkJBAv/32G5mbm9OUKVPKlPbYsWPJ2dmZfv31V2rRogUFBARQ7dq1KSwsjM6ePUuurq5CDNGypN20aVMaM2YMvX37lpYtW0Y2NjY0ZswYwf7dd9+Rl5eXKO0RI0aQt7c33bp1i2JjY6lHjx40ZcoUysjIoI0bN5Kurq5SXN/S0K5dO+rQoYNQBgtQNWTThxO/wgP8wv8VA8v6WDg0zNWrV2n06NFkZGREWlpa1LNnT6UYvmJo27Yt9ejRg27dukXjx48nFxcX6tmzJ+Xk5FBubi7179+ffH19S63LcoDPst6YmZnR9evXiej9ZFMikdD58+cFe3h4ONnb25cpv1nmBxHb+j516lRycXGhQ4cO0YkTJ6h58+a0dOlSio6OptmzZ6v0cJBVn8CyLWHZ/9rb29Pu3btLtO/atYvs7OxE+80qT1iOG1jm96xZs8jCwoImTZpErq6uNGrUKLKzs6Nt27bRH3/8QTY2NrR06VJR2hKJhNq1a0eamppkbGxMY8eOFdotVbCwsBBC1PXq1Yt8fX3p2bNnRET04sUL6tixI/Xo0UPl3+FUTPikm1MhcXV1VRrkhIeHk5mZGc2ePZuI1DfIb9y4Mf34449K9jVr1pCbm5sobQsLC6U4oR8SEhJC5ubmZUrbzs5OiIn++PFjkkgkwpNgIqLDhw9TzZo1S+8wY+3CsU1zc3NJJpMpdcr37t0jQ0NDUdqmpqZK8XRfvnxJ2tralJGRQUREQUFBVL9+fVHaREQ///wz2dnZKeWFqpNuGxsb6tChA50+fZrOnDlDZ86codDQUNLQ0KDg4GDhMzF8qvpYwLt372jLli3UokULkkql5OjoKEqbiMjY2Jju3LlDRESZmZmkoaGh9GY7KiqKTExMSq3LcoDPst58GM9dX19fKUZwUlISaWlplSm/WeYHEdv6bmVlRefOnRPSjx49In19feHt3fz588nT01OUNqs+gWVbwrL/1dbWFup6cdy+fVt03GiWecJy3MAyv6tWrSrUw9jYWJJKpbRr1y7Bvnv3bqpdu7ZKfj979ox++ukncnV1JalUSg0aNKA1a9aIXlWgra0txOm2tbUtssrp1q1bomPQcyo+fNLNqZDo6OhQQkKC0me3bt0iCwsLmjZtmsqD/KdPnxLR+8FWZGSkkj0uLo4MDAxEaevq6tLNmzdLtN+4cYP09PTKlLaWlhYlJSUp/c7du3eF9IMHD0hXV7fUuqy1TU1NKSoqioiIMjIySCqV0sWLFwX7jRs3RHeeRkZGdO/ePSGdk5NDMplMKDf37t0jbW1tUdoFXL9+nVxdXWnEiBGUkZGh8qT7xYsX1KVLF2rZsqXS8jhVdYnY1sfCb2KKIzY2VqW3mIXvZU5ODmloaFBERIRgj46OJmNj41Lrshzgs6w31apVU3qzvWbNGnrz5o2QjoiIIEtLS1HarPxmmR9EbOu7gYEB3b9/X0jn5+eTTCajlJQUInpfTsT6zqpPYNmWsOx/mzVrRoGBgZSbm1vElpeXR4GBgdS8eXNR2izzhOW4gWV+a2trK9VLbW1tio6OFtLx8fGitYt7GHvhwgUaMmQIGRgYkK6uLg0YMKDUunXr1hUeDLi4uNCJEyeK/EblypVF+cyp+Eg/955yDocFpqamReIM165dG6dPn0ZwcDCmTJmikn5ISAgOHjwIbW1tZGZmKtmysrJEH27TokULfPfdd3j+/HkR2/PnzzF16lRRIaxYapuYmODZs2dCOiAgQCkMV3p6OrS0tMS4zFTb29sb06ZNQ3h4OCZOnIgGDRpg4cKFyMjIQGZmJhYsWICGDRuK0m7UqJFSfNeVK1fCzMwMZmZmgt/6+vqitAuoX78+rl69ColEgvr16wtxfMVSuXJl7N+/Hz179kTjxo2xc+dOlfQKw7I+/tff7eTkhEWLFonWd3d3x9KlS/H48WP88MMPqFKlCoKCggT7qlWrULt27VLrNmrUCEuWLEFeXl4RW35+PpYuXaoUj7k0sKw3vr6+iImJEdKjR4+GgYGBkD5+/DgaNGggSpuV3yzzA2Bb3+vUqaNUF/fs2QN9fX1YWloCABQKhWjfWfUJLNsSgF3/GxQUhOPHj8PS0hLdunXD6NGjMXr0aHTr1g0WFhY4ceIEVq9eLUqbZZ6wHDcA7PLb0NAQr1+/FtINGjRQakuys7NFaxf3PU9PT2zcuBEpKSn49ddfcf/+/VLrTpw4Ed999x3OnDmD6dOnY9y4cTh16hSSk5MRGhqKkSNHolu3bqJ85nwBfO5ZP4fDgj59+tCECROKtUVFRZGZmZlKb7oLXwsXLlSyb9iwQfRyq4JDmmQyGbm5uVG7du2oXbt25ObmRjKZjOrWrav0ZLgsaLdr147WrVtXoj04OFj03miW2vfu3aPq1auTRCIhFxcXevToEXXu3JlkMhnJZDIyMzNTeqNZGiIiIqhy5cpkaWlJ9vb2pKmpSTt37hTsQUFBFBgYKEq7OA4cOEATJkz46Bvf0nD79m2qV68e9enTRy1vYljWxwcPHpBCoVDFvY9y5coVMjExIalUSmZmZhQVFUUeHh5kaWlJ1tbWpKOjU+RguP+FggPaTExMqGvXrjRq1CgaNWoUde3alUxMTMjKykrYO1haWNab/yI+Pp6Sk5NFfZeV36zzg2V9P3nyJGlpaVHjxo2pefPmJJPJaMWKFYJ92bJl1KpVK1HaLPubAtTdlrDsf4neH7q3Zs0aCgwMpDZt2lCbNm0oMDCQ1q5dq9Jhe4VRd56wvI8s87tly5a0efPmEu179uwhd3d3UdrFvelWF8uXLyddXV3S0dEhTU1NpT36Xbp0obdv3zL5XU75h4cM41RIbt68iYiICAwePLhYe1RUFP766y/MmTNH7b99+PBhyOVytG3bVtT3FQoF/vnnH1y6dEkp9IenpyfatGkDqVT8AhUW2i9fvoRUKlV6c1SYY8eOQUdHR9STdpbaBbx48QImJiZC+tSpU3j37h08PT2VPi8tKSkpOHz4MLKzs9GqVSu4urqK1voc5OTkYNq0aQgNDcW+fftQpUoV0Vqfsz6qg4yMDMTExKBmzZrQ19dHVlYWtm/fjnfv3sHPzw81a9YUpfv27Vts27at2PrYt29f0aGmPkW9YQErvz9FfrCs7zdu3MCePXuQnZ2Ntm3bws/PT23aLPubAtTZlvwXqva/nwp158mnuI/FoUp+37t3D3K5vMS/fceOHZDJZOjVq1eptf/44w/07t1bpRUsH+P169c4ceIE4uPjoVAoYGVlBW9vb7WGSuRUPPikm8PhcMoZ7969w86dO4vEBO7SpQtat279ud375PD8+LTw/OZ8ieTm5iIlJUXt8eI5HM6XAZ90c74oWrVqheDgYDg4OKhVNyEhAXFxcbCyshK1v/O/UJffRIQHDx7Azs4OMpkMOTk52L9/P7Kzs+Hv7w9TU1M1efx/ZGRkICIiAs2bNxetER8fX2SA7+fnJ/pNYAE3btxAREQEWrRogapVq+L27dtYvXo1FAoFunbtqtLbkgLtli1bokqVKmrTjouLg6+vL969ewctLS08evQI/v7+eP78Oa5evYpu3boJbwhKw19//YX27dtDV1dXlF//CwqFotg3LgqFAo8ePRI1mGWVH5/CdwB48uQJLl++LLydsrKyQuPGjYU9uyxQpU5+qvxWt9+sdVnV9wKuXLmCixcvFnmL2bhxY9Gan6LOF6CuPvJT+vwhN27cQIMGDZCfn1/q77L0+3O23USEhw8fqvQg4sM20NLSEh4eHmppA1m126x0ORWcz7eyncNhx4EDB4q9NDQ0KCgoSEiLYfTo0cKenczMTOrevbtSnM2WLVuK3tPD0u+YmBhycHAgqVRKTk5OFB8fT+7u7qSnp0e6urpkamqqdAKvuoiMjBS9Xzc9PZ169Ogh7CeTSqVkaWlJGhoapK+vT0FBQaL9+uuvv0hDQ4NMTExIX1+fTpw4QUZGRuTr60tt27YlDQ0N0bF1WWq3b9+eRo4cKexjXrJkCbVv356I3u9Td3R0pDlz5pRaVyKRUKVKlWj48OF06dIlUb6VRFpaGvXs2ZO0tbXJ3NycZs+eTXl5eYJdldPLWeUHa9/T09OpX79+pKGhQTKZjMzNzcnc3JxkMhlpaGhQ//79hZBT6kaVOsk6vz+GKn6z1GVZ31NTU8nb25skEgk5ODhQ48aNqXHjxuTg4EASiYSaNm0qeu8qqzpfXB9ZuA1XpY9k2U79F6qUE5Z+l9e2m2UbyMpvlvnBqfjwSTenQlLQuX94CEjhSx0hiqZPn062trZ0+vRpysjIoLCwMKpWrRpNmzatzPkdEBBAnTt3pps3b9KECRPIxcWFAgICKCcnh7KysqhTp07Uv39/UdofQ5WByogRI8jb25tu3bpFsbGx1KNHD5oyZQplZGTQxo0bSVdXV/RgtkGDBsKhMDt37iQjIyOaP3++YP/pp59Ex9Zlqa2rq6v0cCQ7O5vkcjk9f/6ciIj+/vtvUXGpJRIJzZ8/n9zc3EgikVCtWrVoxYoVgq4qjBs3jmrUqEF79+6l9evXk4ODA3Xo0IGys7OJ6P1ARSKRiNJmlR+sfR86dChVr16dQkJClAZteXl59M8//1CNGjVo2LBhov3+GKrUSdb5/THK6qSbZX3v3r07eXp6UkxMTBFbTEwMeXl5UY8ePURps6rzrPtIVu2Um5vbRy9nZ2eVJt2s/C6vbTfLNpCV3yzzg1Px4ZNuToWkXbt21KFDhyJvANR1emqBbu3atWnHjh1K9gMHDlCNGjVEabP028zMjK5fv05E758wSyQSpXi74eHhZG9vX2pdY2Pjj16VKlUSPVAxNTWlq1evCumXL1+Stra28PQ7KChI9GBWT09PiB2tUChILpcrxTq9f/8+6evrlzlta2trpVPVX716RRKJRIiVHB8fT1paWqXWLVyur169SqNHjyYjIyPS0tKinj170vHjx0X5S0Rkb29PoaGhQvrZs2fUuHFjatOmDWVlZan0doBVfrD23cjIiMLDw0u0h4WFkZGRkRiXmdZJlvnNym+W+UHEtr7r6+vTtWvXSrRfvXpVtDarOs+yj2TZTmlpadHAgQNp7ty5xV4jR45UadLNyu/y2nazbANZ+c0yPzgVH/VvuuJwygDHjh3DihUr0LBhQ6xZswYdO3ZUq35BDMgnT56gbt26SrZ69eoViUn8v8LS7/T0dFSuXBkAoKenBz09PVhZWQl2Ozs7pKamllo3Ozsbo0ePRp06dYq1JyYmYt68eaJ8zsvLU9q3ra+vj7y8PGRkZEBXVxdt2rTBd999J0rbwMAAL168gKOjI16/fo28vDy8ePFCsL948UJ0bF2W2n5+fpg0aRLWrVsHLS0tTJ8+HfXr1xfimyYlJcHc3FyUdgHu7u5wd3fHzz//jL1792LTpk1o164d7O3tkZCQUGq9Z8+eKZ1HYGpqipMnT6Jt27bw9/fHhg0bRPvKOj9Y+a5QKKCpqVmiXVNTEwqFQpQ2yzrJMr9Z+c0yPwC29V1LSwtv3rwp0f727Vu1nNCs7jrPqo9k6XPt2rXh4eGB0aNHF2uPjIzE+vXrVXVb7X6z1GbZdrNsA1n5zTI/OF8An3vWz+Gw5Pr16+Tq6kojRoygjIwMtb3pHjlyJE2cOJHMzc2LPEWOiIggU1NTlX6Dhd/VqlVTerO9Zs0a4e0U0Xu/LS0tS63r5eVFv/zyS4l2VZZu+vn50ZgxY4T0smXLyMrKSkhfu3ZNdF7379+fPDw8aNu2bdSpUydq27YtNWnShKKjoykmJoZ8fHxEL9tkqZ2amkpNmjQRtho4ODgovQnbu3cv/frrr6XWLbwktDhiY2NpxowZonyuWbMmHTlypMjnb9++JU9PT6pXr57oMsIqP1j73rdvX3Jzcyv2Lea1a9fI3d2d+vXrJ8pnlnWSZX6z8ptlfhCxre9ff/01OTg40L59+5TiRKelpdG+ffvI0dGRxo4dK0qbVZ1n2UeybKfGjRtH48ePL9EeFxdHLVq0EKXN0u/y2nazbANZ+c0yPzgVHz7p5lR4MjMzaeTIkVS9enXS0NBQefLq4+NDLVq0EK7169cr2RcsWEA+Pj4q/QaR+v0eOXJkEV8L88MPP5C/v3+pdRctWkRz584t0Z6UlESDBg0qtS7R+8FZ5cqVydLSkuzt7UlTU5N27twp2IOCgigwMFCU9pMnT8jPz4/09fWpbdu29Pr1axo7dqwwmahevTrFxcWVOe0C7t27R7du3aLc3FyVdAoovERR3YwdO7bEScebN2/Iw8ND5YGKuvOjgG+++YaJ7y9fvqR27dqRRCKhypUrk7OzMzk7O1PlypVJKpVS+/bt6dWrV6J8ZlknC2CR36z8Zp0fLOt7VlYWjRo1ijQ1NUkqlZK2tjZpa2uTVColTU1NGj16NGVlZYnSZlXnWfaRLNsplrD0m6U2q/aPiG0byMrvT9GXcSouPGQY54vh0KFDOH36NKZPn67y8tuPER8fD01NTdja2qpF7+DBgwgNDWXud0JCArS1tZWWnJcFUlJScPjwYWRnZ6NVq1ZwdXVl+nvx8fHIzMyEs7Oz2sMesdRWlcTERNjb2wvLQtXJq1evkJycjFq1ahVrf/v2La5duwYfHx+1/7aqsPY9Jiam2FBQzs7Oon3mlA3UWd/fvHmDiIgIpXLi7u6uUthElnX+Y6jSR34un1WFpd/lve1m0Qay8rs892Wczw+fdHO+CDIyMrBnzx4hTmifPn1gYmIiSuubb75Br1690KxZMzV7yfmUlNf7eO3aNRgbG6NKlSoAgK1bt2LdunVISkqCg4MDxo4di969e6v8O+Wlznyq/OC8h+c3578or21rASxion+IOttXzqejvJdtzmfm875o53DY4OLiQi9evCCi90sHHRwcyNDQkBo1akSVK1cmc3Nzio+PF6VdeMngkiVLKCUlRW1+P3z4kJ49eyakz507R3379qWmTZtSv3796MKFC2VSOzIykjZu3Ej3798nIqKoqCgaPXo0jRw5kkJCQkTr/vTTT/TgwQPR3/8YLO8jEdGhQ4do9uzZFBYWRkREp06dovbt21Pbtm3pt99+E61bt25dOnHiBBERrV+/nnR0dGjcuHG0du1amjBhAunr69PGjRtLrfthnXF0dCwXdYZVfhSGxb1kWbaJ2NVJ1vnNym9WugWwqu/lsU9g3bZmZmbSxo0bafDgwdSuXTvy9/ensWPH0smTJ1XSTU1NpaZNmzKJic6yfWVZRojYlW3WbSALv1mXbU7Fhk+6ORWSwnuc+vXrR15eXvT69Wsien/gha+vL/Xp00e09smTJ2n8+PFkampKcrmcOnfuTIcOHaL8/HyV/G7cuDEdOnSIiN7Hu5VKpdS5c2eaOnUqde3aleRyuWAvK9p//fUXaWhokImJCenr69OJEyfIyMiIfH19qW3btqShoSE6lrZEIiENDQ3y9fWlXbt2CbEw1QHL+7hu3TqSyWTk7u5OlSpVoq1bt5KBgQENGzaMRo4cSTo6Oh892Olj6OjoCAMVNzc3+v3335Xs27dvJ1dX11Lrltc6wyo/CmB1L1mWbZZ1kmV+s/KbZX4Qsa3v5bFPYFnfY2NjycHBgczNzcnOzo4kEgl16NCBPDw8SENDg3r27Cn6nAHWMdFZta8sywjLss2yDWTZbrMq25yKD590cyokhTu4qlWrFjk9NTw8nOzs7FTWzsnJod27dwsDN2tra5oxYwbFxsaK0tbT0xOednt4eNCSJUuU7KtWrSI3N7cypd2gQQNauHAhERHt3LmTjIyMaP78+YL9p59+Eh1LWyKRUHBwMAUEBJBcLicTExMaP3483bp1S5Teh9qs7qOrq6swGTl9+jRpa2vT6tWrBXtwcDC5uLiI0jYxMRFil5ubm1NkZKSSPS4ujnR0dEqtW17rDKv8KIDVvWRZtlnWSZb5zcpvlvlBxLa+l8c+gWV9b9++PY0cOZIUCgURES1ZsoTat29PRO8P93N0dKQ5c+aI0v5UMdHV3b6yLCMsyzbLNpBlu82qbHMqPnzSzamQSCQSevr0KRERWVtbF2nEHzx4QNra2qK1i1tilpiYSHPmzCEHBwfRp1caGhrSjRs3iOj9gLbg/wuIi4sjXV3dMqWtp6dHCQkJRESkUChILpfTzZs3Bfv9+/fVMlBJTU2lpUuXkrOzM0mlUmrUqBH9/vvvSmHPxGoXRh33UUdHhxITE4W0XC5XKoMJCQmi72P//v1p6NChRETUs2dPmjVrlpJ98eLFVKdOnVLrltc6wyo/CmB1L1mWbZZ1kmV+s/KbZX4Qsa3v5bFPYFnfdXV16d69e0I6Ozub5HI5PX/+nIjev+l1dHQUpW1iYkJnzpwp0R4aGkomJiaitFm2ryzLCMuyzbIN/BTtdmHUUbY5FR/p595TzuGwonXr1mjQoAHevHmDu3fvKtkSExPVfmiJvb095s6di4SEBISEhIjS8PHxwc6dOwEAbm5uOHPmjJI9NDQUNjY2ZUrbwMAAL168AAC8fv0aeXl5QhoAXrx4AX19fVE+F8bc3BxTpkxBdHQ0zpw5A1dXV0ycOFHtp62r4z6amJggMTERAJCcnIy8vDwkJSUJ9sTERFSuXFmU9tKlS3Hq1Cn4+PjAzs4Oy5cvR7NmzTBixAj4+Phg7ty5WLJkiSjt8lhnWOYHwPZeFqDuss2yTrLMb1Z+s26jWJaR8tgnlIQ66ruRkRHevn0rpDMzM5GXlwdNTU0AQN26dZGSkiJK+6uvvsLAgQOxf/9+vHnzRvj8zZs32L9/PwYPHow+ffqI0gbYta8s7+OnaP8A9beBn8rvAtRRtjkVn7IVs4bDURNz5sxRSn84oDp06JDo0ycdHBygoaFRol0ikcDPz0+U9pIlS9CsWTMkJyejadOmmDlzJv7991+4uLjg7t272L17N9atW1emtH19fTFmzBh888032L17N9q0aYPp06cjODgYEokEkydPRtOmTUX5XFIIlGbNmqFZs2b49ddfsXv3blHaLO9jQEAAhg4dioEDB+LgwYMIDAzEt99+C6lUKuRJmzZtRGlbW1vj+vXrWLJkCQ4dOgQiwpUrV/Dw4UN4e3sjPDwcDRs2LLVuea0zrPKjAFb3kmXZZlknWeY3K79Z5gfAtr6Xxz6BZX338/PDpEmTsG7dOmhpaWH69OmoX78+DAwMAABJSUmiQ2v+/PPPUCgU6N27t9JEPicnBzKZDEOHDsVPP/0kSptl+8qyjLAs2yzbQFZ+syzbnC+Az/ymncPhfEBcXBz17t2bDAwMSCKRkEQiIblcTl5eXrR///4yp/3kyRPy8/MjfX19atu2Lb1+/ZrGjh2rdMpnXFycKO2SlnKVddLT02n48OFUu3ZtGjFiBGVnZ9OyZctIU1OTJBIJtWjRolz+XV8irO4ly7LNsk6yhJXfrPODdX0vb30CS1JTU6lJkybCvXNwcFDah71371769ddfVfqNtLQ0On36NO3YsYN27NhBp0+fprS0NFVdZwqr+8iybLNsA3kfzCmL8DjdHE4ZhYjw9OlTKBQKmJqaQi6XlwvtAuLj45GZmQlnZ2fIZHxRDQBkZWUhNzdXeCvDKb+Ux3tZXuskK79Z54e6y0h57xPUSWxsLLKzs8tdWWbNp7qP5bH9A8qv35yKAd/TzeGUUSQSCSwsLGBlZSV0nA8fPsSQIUPKtHZGRgaCg4OxceNGnD17FmlpaSprloS6fP5UaGtrw8DAoNz5zSkK63upTt1PWSfVCSu/P1V+qLuMlNc+gQXVq1dH7dq1i0y4VfX53bt3CAsLw507d4rYsrKysGXLFtHan4JPdR8/RV9WXv3mcErkM75l53A4pSQyMpLZyZhitV1cXOjFixdERJSUlEQODg5kaGhIjRo1osqVK5O5ubkQzkTdsMwPlpRXvzlFYXUvVdH9nHVSFVj5/bnzoyy2259bmxWq+Hz37l1ycHAQlq43b96cHj9+LNifPHlS7vKDqPyWkfKqzeGUBF+Tw+GUIQ4ePPhRe3x8fJnTjomJQV5eHgBg+vTpsLGxwY0bN2BoaIj09HR07doVM2fOxI4dO8qMz6wpr35zisLqXrIsIyzrJEtY+c06P8pju81amxUsfZ46dSpq166Nq1ev4vXr15gwYQKaNm2KM2fOwN7eXrQua8prGSmv2hyOWPiebg6nDFFwsubHqqVEIkF+fn6Z0ZZKpXjy5AnMzc1RrVo1rFu3Tun0zgsXLqB3795K4To+t8+sKa9+c4rCst6wrOus6iRLWPnNOj/KY7vNWpsVLH22sLDAyZMnUadOHQDv90d//fXXOHr0KEJDQ6Gnpwdra+sylR9A+S0j5VWbwxEL39PN4ZQhrKyssG/fPigUimKva9eulUntgtAfWVlZReJq2tjY4NmzZ2XOZ5aUV785RWF1L1mXEVZ1kjWs/GaZH+W13S6P7RRLn9+9e6e0R1wikWDt2rXo1KkTfHx8cO/ePXX8CWqnvJaR8qrN4YiFT7o5nDKEu7s7IiIiSrT/15Pbz6XdunVrNGjQAG/evMHdu3eVbImJiTAxMRGly9JnlpRXvzlFYXUvWZcRVnWSNaz8Zpkf5bXdLo/tFEufnZ2dcfXq1SKfBwUFISAgAJ07dxaly5ryWkbKqzaHIxa+p5vDKUNMnjwZGRkZJdqdnJwQGhpaprTnzJmjlNbX11dKHzp0CM2aNSu1LsA2P1hSXv3mFIXVvWRZRljWSZaw8pt1fpTHdpu1NitY+ty1a1fs3LkTAwYMKGILCgqCQqHAunXrRGmzpLyWkfKqzeGIhe/p5nA4HA6Hw+FwOBwOhxF8eTmHw+FwOBwOh8PhcDiM4JNuDofD4XA4HA6Hw+FwGMEn3RwOh8PhcDgcDofD4TCCT7o5HA6Hw+FwOBwOh8NhBJ90czgcDodThmjRogUmTJigVs3NmzfDyMhIrZpnzpyBRCLB69ev1arL4XA4HE5Fg0+6ORwOh8PhKPHgwQNIJBJERkZ+blc4HA6Hwyn38Ek3h8PhcDicMkdOTs7ndoHD4XA4HLXAJ90cDofD4ZQx8vLyMHbsWBgaGsLU1BSzZ88GEQEAXr16hcDAQBgbG0NXVxft27dHbGys0vc3b94Me3t76OrqomvXrnjx4oVge/DgAaRSKa5evar0nV9++QUODg5QKBTF+nT06FHUqFEDOjo6aNmyJR48ePA//z0Fy9v//vtvVK9eHdra2mjbti0ePnwo/Ju5c+eifv362LBhA6pUqQJtbW0AwOvXrzFs2DCYmZmhUqVKaNWqFW7cuPE//zaHw+FwOJ8bPunmcDgcDqeM8ccff0Amk+HKlStYuXIlfv75Z2zYsAEAMGjQIFy9ehUHDx7ExYsXQUTw9/dHbm4uAODy5csYOnQoxo4di8jISLRs2RILFy4UtB0dHeHr64vg4GCl3wwODsagQYMglRYdGjx8+BDdunVDp06dEBkZiWHDhmHatGml+psyMzOxaNEibNmyBeHh4Xj9+jV69+6t9G/i4uLw119/Yd++fcLS9p49e+Lp06c4duwYIiIi0KBBA7Ru3RovX74s1e9zOBwOh/PZIA6Hw+FwOGUGHx8fcnFxIYVCIXw2depUcnFxoXv37hEACg8PF2zPnz8nHR0d2rNnDxER9enTh/z9/ZU0v/rqKzI0NBTSu3fvJmNjY8rKyiIiooiICJJIJJSQkEBERAkJCQSArl+/TkRE06dPJ1dXVyXNqVOnEgB69erVf/5NwcHBBIAuXbokfBYdHU0A6PLly0RENGfOHJLL5fT06VPh35w/f54qVaok+FlAtWrV6LfffvvP3+VwOBwOpyzA33RzOBwOh1PGaNKkCSQSiZD29PREbGws7ty5A5lMBg8PD8FmYmKCmjVrIjo6GgAQHR2tZC/4fmG6dOkCDQ0N7N+/H8D75d8tW7aEo6Njsf78L5r/hUwmQ6NGjYS0s7MzjIyMBL8BwMHBAWZmZkL6xo0bSE9Ph4mJCfT19YUrISEB9+/fL9XvczgcDofzuZB9bgc4HA6Hw+F8WjQ1NREYGIjg4GB069YNO3bswMqVKz+3W9DT01NKp6enw8rKCmfOnCnyb9UdAo3D4XA4HFbwSTeHw+FwOGWMy5cvK6UvXbqE6tWrw9XVFXl5ebh8+TK8vLwAAC9evMDdu3fh6uoKAHBxcSn2+x8ybNgw1K5dG2vWrEFeXh66detWoj8uLi44ePDgf2p+jLy8PFy9ehWNGzcGANy9exevX7+Gi4tLid9p0KABnjx5AplMVuJbeA6Hw+Fwyjp8eTmHw+FwOGWMpKQkTJo0CXfv3sXOnTuxatUqjB8/HtWrV0dAQACGDx+OsLAw3LhxA/3794eNjQ0CAgIAAOPGjUNISAh++uknxMbGIigoCCEhIUV+w8XFBU2aNMHUqVPRp08f6OjolOjPqFGjEBsbi8mTJ+Pu3bvYsWMHNm/eXKq/SS6X45tvvsHly5cRERGBQYMGoUmTJsIkvDh8fX3h6emJLl264Pjx43jw4AEuXLiAmTNnFjl9ncPhcDicsgqfdHM4HA6HU8YIDAzEu3fv0LhxY4wZMwbjx4/HiBEjALw/Zdzd3R0dO3aEp6cniAhHjx6FXC4H8H4/+Pr167Fy5UrUq1cPx48fx6xZs4r9naFDhyInJwdDhgz5qD/29vb466+/8Pfff6NevXpYt24dFi9eXKq/SVdXF1OnTkXfvn3h7e0NfX197N69+6PfkUgkOHr0KJo3b47BgwejRo0a6N27NxITE2FhYVGq3+dwOBwO53MhIfr/gT85HA6Hw+F8USxYsAB79+7FzZs3mf7O5s2bMWHCBLx+/Zrp73A4HA6HUxbhb7o5HA6Hw/nCSE9PR1RUFIKCgvDNN998bnc4HA6Hw6nQ8Ek3h8PhcDhfGGPHjoW7uztatGjxn0vL/xfat2+vFNKr8FXaZegcDofD4VQ0+PJyDofD4XA4KvH48WO8e/euWFvlypVRuXLlT+wRh8PhcDhlBz7p5nA4HA6Hw+FwOBwOhxF8eTmHw+FwOBwOh8PhcDiM4JNuDofD4XA4HA6Hw+FwGMEn3RwOh8PhcDgcDofD4TCCT7o5HA6Hw+FwOBwOh8NhBJ90czgcDofD4XA4HA6Hwwg+6eZwOBwOh8PhcDgcDocRfNLN4XA4HA6Hw+FwOBwOI/ikm8PhcDgcDofD4XA4HEb8P+LALsu0MpmdAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -71088,7 +71184,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": { "cell_id": "00052-e5884ed0-47e9-4a33-a2d1-cc6943d6839f", "deepnote_cell_type": "code", @@ -71426,7 +71522,7 @@ "[249 rows x 13 columns]" ] }, - "execution_count": 35, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -71442,7 +71538,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": { "cell_id": "00053-e8b80f8c-6abc-4af8-8bf5-bbc6fcc49c9e", "deepnote_cell_type": "code", @@ -71801,7 +71897,7 @@ "[249 rows x 18 columns]" ] }, - "execution_count": 36, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -71828,7 +71924,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": { "cell_id": "00055-5ad07d25-ef97-45e9-9bb1-0f8a41592072", "deepnote_cell_type": "code", @@ -71871,30 +71967,30 @@ " \n", " 0\n", " 0\n", - " 697145036\n", - " M_vPNml83\n", + " 1447201088\n", + " DC4_vPN\n", " 0\n", " \n", " \n", " 1\n", " 0\n", - " 328861282\n", - " LHCENT1\n", - " 32\n", + " 423416887\n", + " SLP073\n", + " 11\n", " \n", " \n", " 2\n", " 0\n", - " 361312808\n", - " SMP503\n", - " 12\n", + " 298262663\n", + " SMP553\n", + " 10\n", " \n", " \n", " 3\n", " 0\n", " 518930199\n", " MBON35\n", - " 12\n", + " 44\n", " \n", " \n", " 4\n", @@ -71906,30 +72002,30 @@ " \n", " 5\n", " 1\n", - " 697145036\n", - " M_vPNml83\n", + " 1447201088\n", + " DC4_vPN\n", " 0\n", " \n", " \n", " 6\n", " 1\n", - " 328861282\n", - " LHCENT1\n", - " 32\n", + " 423416887\n", + " SLP073\n", + " 11\n", " \n", " \n", " 7\n", " 1\n", - " 421641859\n", - " LHPD4c1\n", - " 65\n", + " 329566197\n", + " SMP549\n", + " 10\n", " \n", " \n", " 8\n", " 1\n", " 518930199\n", " MBON35\n", - " 11\n", + " 84\n", " \n", " \n", " 9\n", @@ -71943,20 +72039,20 @@ "" ], "text/plain": [ - " path bodyId type weight\n", - "0 0 697145036 M_vPNml83 0\n", - "1 0 328861282 LHCENT1 32\n", - "2 0 361312808 SMP503 12\n", - "3 0 518930199 MBON35 12\n", - "4 0 2097928626 LAL107 14\n", - "5 1 697145036 M_vPNml83 0\n", - "6 1 328861282 LHCENT1 32\n", - "7 1 421641859 LHPD4c1 65\n", - "8 1 518930199 MBON35 11\n", - "9 1 2097928626 LAL107 14" + " path bodyId type weight\n", + "0 0 1447201088 DC4_vPN 0\n", + "1 0 423416887 SLP073 11\n", + "2 0 298262663 SMP553 10\n", + "3 0 518930199 MBON35 44\n", + "4 0 2097928626 LAL107 14\n", + "5 1 1447201088 DC4_vPN 0\n", + "6 1 423416887 SLP073 11\n", + "7 1 329566197 SMP549 10\n", + "8 1 518930199 MBON35 84\n", + "9 1 2097928626 LAL107 14" ] }, - "execution_count": 37, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -71985,7 +72081,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": { "cell_id": "00057-4f89cd10-03e3-4c31-992f-4d81fd2d353b", "deepnote_cell_type": "code", @@ -72007,7 +72103,7 @@ { "data": { "text/html": [ - "<class 'navis.core.neuronlist.NeuronList'> containing 5 neurons (4.1MiB)
\n", + "<class 'navis.core.neuronlist.NeuronList'> containing 5 neurons (2.5MiB)
\n", "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
IDNameDescriptionURLRelated TermsParentsLicenseCross References
0VFB_00010001fru-F-500075OutAge: Adult 5~15 dayshttps://n2t.net/vfb:VFB_00010001[Rel(relation=MinimalEdgeInfo(label=expresses,...[expression pattern fragment, adult SMPpv1 lin...FlyCircuit License[http://flycircuit.tw/modules.php?name=clearpa...
1VFB_00000001fru-M-200266OutAge: Adult 5~15 dayshttps://n2t.net/vfb:VFB_00000001[Rel(relation=MinimalEdgeInfo(label=expresses,...[adult DM6 lineage neuron, expression pattern ...FlyCircuit License[http://flycircuit.tw/modules.php?name=clearpa...
\n", + "
" + ], "text/plain": [ - "dict_keys(['label', 'symbol', 'id', 'tags', 'description', 'parents_label', 'parents_id', 'data_source', 'accession', 'xrefs', 'templates', 'dataset', 'license'])" + " ID Name Description \\\n", + "0 VFB_00010001 fru-F-500075 OutAge: Adult 5~15 days \n", + "1 VFB_00000001 fru-M-200266 OutAge: Adult 5~15 days \n", + "\n", + " URL \\\n", + "0 https://n2t.net/vfb:VFB_00010001 \n", + "1 https://n2t.net/vfb:VFB_00000001 \n", + "\n", + " Related Terms \\\n", + "0 [Rel(relation=MinimalEdgeInfo(label=expresses,... \n", + "1 [Rel(relation=MinimalEdgeInfo(label=expresses,... \n", + "\n", + " Parents License \\\n", + "0 [expression pattern fragment, adult SMPpv1 lin... FlyCircuit License \n", + "1 [adult DM6 lineage neuron, expression pattern ... FlyCircuit License \n", + "\n", + " Cross References \n", + "0 [http://flycircuit.tw/modules.php?name=clearpa... \n", + "1 [http://flycircuit.tw/modules.php?name=clearpa... " ] }, "execution_count": 3, @@ -145,32 +203,69 @@ } ], "source": [ - "vfb.neo_query_wrapper.get_TermInfo(['VFB_00010001'], return_dataframe=False)[0].keys()" + "vfb.terms(['VFB_00010001','VFB_00000001']).get_summaries() # this will produce a summary of the VFBterms in a DataFrame" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['channel_images', 'children', 'datasets', 'description', 'downstream_partners', 'has_image', 'has_neuron_connectivity', 'has_region_connectivity', 'has_scRNAseq', 'has_tag', 'id', 'instances', 'is_dataset', 'is_instance', 'is_neuron', 'is_template', 'is_type', 'license', 'load_mesh', 'load_skeleton', 'load_volume', 'name', 'open', 'parents', 'plot3d', 'plot_partners', 'plot_similar', 'potential_drivers_nblast', 'potential_drivers_neuronbridge', 'related_terms', 'show', 'similar_neurons_nblast', 'subparts', 'subtypes', 'summary', 'synonyms', 'term', 'thumbnail', 'upstream_partners', 'url', 'vfb', 'xrefs']\n" + ] + } + ], + "source": [ + "print(dir(vfb.terms(['VFB_00010001'])[0])) # This will return the properties available for this term you can use to query if. This is specific to the term type\n", + "\n", + "# dir is a standard python function that returns the properties and methods of an object.\n", + "# print is simply so it returns the output on a single line here." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['get', 'get_ids', 'get_names', 'get_summaries', 'load_meshes', 'load_skeletons', 'load_volumes', 'open', 'plot3d', 'plot3d_by_type', 'terms', 'tqdm_with_threshold', 'vfb']\n" + ] + } + ], + "source": [ + "print(dir(vfb.terms(['VFB_00010001']))) # Note this isn't just a List of VFBterm objects its a VFBTerms object that has usefull methods for querying the terms" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['FlyCircuit:fru-F-500075']" + "[Xref(link_text=fru-F-500075 on FlyCircuit 1.0, link=http://flycircuit.tw/modules.php?name=clearpage&op=detail_table&neuron=fru-F-500075, accession=fru-F-500075)]" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "vfb.neo_query_wrapper.get_TermInfo(['VFB_00010001'], return_dataframe=False)[0]['xrefs']" + "vfb.term(['VFB_00010001']).xrefs # This will return the xrefs for the term" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -194,58 +289,45 @@ " \n", " \n", " \n", - " label\n", - " symbol\n", - " id\n", - " tags\n", - " description\n", - " data_source\n", - " accession\n", - " xrefs\n", - " miniref\n", - " FlyBase\n", - " PMID\n", - " DOI\n", + " ID\n", + " Name\n", + " Description\n", + " URL\n", + " Counts\n", + " Publications\n", + " License\n", " \n", " \n", " \n", " \n", " 0\n", - " Ito lab adult brain lineage clone image set\n", - " \n", " Ito2013\n", - " [Entity, DataSet, Individual, has_image]\n", + " Ito lab adult brain lineage clone image set\n", " An exhaustive set of lineage clones covering t...\n", - " []\n", - " []\n", - " []\n", + " http://flybase.org/reports/FBrf0221438.html\n", + " {'images': 96, 'types': 91}\n", " [Ito et al., 2013, Curr. Biol. 23(8): 644--655]\n", - " [FBrf0221438]\n", - " [23541729]\n", - " [10.1016/j.cub.2013.03.015]\n", + " CC-BY-NC-SA_4.0\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " label symbol id \\\n", - "0 Ito lab adult brain lineage clone image set Ito2013 \n", - "\n", - " tags \\\n", - "0 [Entity, DataSet, Individual, has_image] \n", + " ID Name \\\n", + "0 Ito2013 Ito lab adult brain lineage clone image set \n", "\n", - " description data_source accession \\\n", - "0 An exhaustive set of lineage clones covering t... [] [] \n", + " Description \\\n", + "0 An exhaustive set of lineage clones covering t... \n", "\n", - " xrefs miniref FlyBase \\\n", - "0 [] [Ito et al., 2013, Curr. Biol. 23(8): 644--655] [FBrf0221438] \n", + " URL Counts \\\n", + "0 http://flybase.org/reports/FBrf0221438.html {'images': 96, 'types': 91} \n", "\n", - " PMID DOI \n", - "0 [23541729] [10.1016/j.cub.2013.03.015] " + " Publications License \n", + "0 [Ito et al., 2013, Curr. Biol. 23(8): 644--655] CC-BY-NC-SA_4.0 " ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -253,78 +335,56 @@ "source": [ "# TermInfo query can cope with different types of IDs. returning cached data if available:\n", "\n", - "vfb.get_TermInfo(['Ito2013'])" + "vfb.terms(['Ito2013']).get_summaries()" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "['vnc1_catmaid_api',\n", - " 'vnc_harvard_catmaid_api',\n", - " 'flywire_supervoxel',\n", - " 'l1em_catmaid_api',\n", - " 'fafb_catmaid_api',\n", - " 'jrc_slide_code_api',\n", - " 'InsectBrainDB',\n", - " 'FlyBrain_NDB',\n", - " 'catmaid_fafb',\n", - " 'catmaid_fanc',\n", - " 'scExpressionAtlas',\n", - " 'Konstantinides_et_al_2018_OpticLobe',\n", - " 'FlyCircuit',\n", - " 'QuickGO',\n", - " 'catmaid_l1em',\n", - " 'FlyLightSplit',\n", - " 'FlyLightRaw',\n", - " 'AmiGO',\n", - " 'flywire783',\n", - " 'flywire630',\n", - " 'VDRC',\n", - " 'FlyBase',\n", - " 'catmaid_leg40',\n", - " 'larvalbrain_neuropil',\n", - " 'BrainTrap',\n", - " 'catmaid_fanc_JRC2018VF',\n", - " 'neuprint_JRC_Manc',\n", - " 'FlyBase_vocabularies',\n", - " 'FlyLightSplitSlideCode',\n", - " 'FlyLightGen1MCFO',\n", - " 'FlyCircuit1v2',\n", - " 'FlyLightRawSlideCode',\n", - " 'neuronbridge',\n", - " 'FlyPNS',\n", - " 'ExpressionAtlas',\n", - " 'larvalbrain_axon_tract',\n", - " 'FlyLightGen1MCFOSlideCode',\n", - " 'FlyLight',\n", - " 'DoOR',\n", - " 'neuprint_JRC_Hemibrain_1point0point1',\n", - " 'GEO',\n", - " 'lmb_cluster_pages_v3',\n", - " 'lmb_cluster_pages_v2',\n", - " 'neuprint_JRC_Hemibrain_1point1']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "['cache_file', 'generate_lab_colors', 'get_TermInfo', 'get_cache_file_path', 'get_connected_neurons_by_type', 'get_datasets', 'get_dbs', 'get_gene_function_filters', 'get_images', 'get_images_by_filename', 'get_images_by_type', 'get_instances', 'get_instances_by_dataset', 'get_neurons_downstream_of', 'get_neurons_upstream_of', 'get_potential_drivers', 'get_similar_neurons', 'get_subclasses', 'get_superclasses', 'get_templates', 'get_terms_by_region', 'get_terms_by_xref', 'get_transcriptomic_profile', 'get_vfb_link', 'lookup', 'lookup_id', 'nc', 'neo_query_wrapper', 'oc', 'reload_lookup_cache', 'term', 'terms', 'vfb_base', 'vfb_id_2_xrefs', 'xref_2_vfb_id']\n" + ] + } + ], + "source": [ + "# vfb. has many useful methods for querying the VFB database. You can see the available methods by using the dir function\n", + "\n", + "print(dir(vfb)) # This will return the methods available for the vfb object\n", + "\n", + "# You can also use the help function to get more information on the methods\n", + "# print is simply so it returns the output on a single line here." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['vnc1_catmaid_api', 'vnc_harvard_catmaid_api', 'flywire_supervoxel', 'l1em_catmaid_api', 'fafb_catmaid_api', 'jrc_slide_code_api', 'InsectBrainDB', 'FlyBrain_NDB', 'catmaid_fafb', 'catmaid_fanc', 'scExpressionAtlas', 'Konstantinides_et_al_2018_OpticLobe', 'FlyCircuit', 'QuickGO', 'catmaid_l1em', 'FlyLightSplit', 'FlyLightRaw', 'AmiGO', 'flywire783', 'flywire630', 'VDRC', 'FlyBase', 'catmaid_leg40', 'larvalbrain_neuropil', 'BrainTrap', 'catmaid_fanc_JRC2018VF', 'neuprint_JRC_Manc', 'FlyBase_vocabularies', 'FlyLightSplitSlideCode', 'FlyLightGen1MCFO', 'FlyCircuit1v2', 'FlyLightRawSlideCode', 'neuronbridge', 'FlyPNS', 'ExpressionAtlas', 'larvalbrain_axon_tract', 'FlyLightGen1MCFOSlideCode', 'FlyLight', 'DoOR', 'neuprint_JRC_Hemibrain_1point0point1', 'GEO', 'lmb_cluster_pages_v3', 'lmb_cluster_pages_v2', 'neuprint_JRC_Hemibrain_1point1']\n" + ] } ], "source": [ "# Query with an external ID.\n", "# To find what sources (DBs) are supported: \n", "\n", - "vfb.neo_query_wrapper.get_dbs()" + "print(vfb.get_dbs())\n", + "\n", + "# print is simply so it returns the output on a single line here." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -402,19 +462,19 @@ "0 [https://creativecommons.org/licenses/by-sa/4.... " ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Querying with an ID from catmaid_l1em\n", - "vfb.neo_query_wrapper.get_terms_by_xref(['17545695'], db='catmaid_l1em')" + "vfb.get_terms_by_xref(['17545695'], db='catmaid_l1em')" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -446,7 +506,7 @@ " {'db': 'catmaid_fafb', 'vfb_id': 'VFB_00102fxv'}])]" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -459,7 +519,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -468,7 +528,7 @@ "250" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -485,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -505,7 +565,7 @@ " 'FBbt_00111438')]" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -517,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -526,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -540,7 +600,7 @@ " 'template': 'L1 larval CNS ssTEM - Cardona/Janelia'}])]" ] }, - "execution_count": 12, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -555,7 +615,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -576,7 +636,7 @@ " 'template': 'JRC_FlyEM_Hemibrain'}])]" ] }, - "execution_count": 13, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -589,7 +649,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -627,7 +687,7 @@ " 'template': 'JRC2018Unisex'}])]" ] }, - "execution_count": 14, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -642,7 +702,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -687,7 +747,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -706,7 +766,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [ { diff --git a/src/vfb_connect.egg-info/PKG-INFO b/src/vfb_connect.egg-info/PKG-INFO index 5cf9eb3f..8c9d63ad 100644 --- a/src/vfb_connect.egg-info/PKG-INFO +++ b/src/vfb_connect.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.1 Name: vfb_connect -Version: 2.1.0.dev1+7ce18d3.dirty +Version: 2.1.1+dirty Summary: Wrapper for querying VirtualFlyBrain servers. Home-page: https://github.com/VirtualFlyBrain/VFB_connect Author: David Osumi-Sutherland diff --git a/src/vfb_connect/cross_server_tools.py b/src/vfb_connect/cross_server_tools.py index 853a5ae7..31077606 100644 --- a/src/vfb_connect/cross_server_tools.py +++ b/src/vfb_connect/cross_server_tools.py @@ -89,6 +89,10 @@ def __init__(self, neo_endpoint=get_default_servers()['neo_endpoint'], print("") print("\033[33mType \033[35mvfb. \033[33mand press \033[35mtab\033[33m to see available queries. You can run help against any query e.g. \033[35mhelp(vfb.get_TermInfo)\033[0m") + def __dir__(self): + return [attr for attr in list(self.__dict__.keys()) if not attr.startswith('_')] + [attr for attr in dir(self.__class__) if not attr.startswith('_') and not attr.startswith('add_')] + + def get_cache_file_path(self): """Determine a safe place to save the pickle file in the same directory as the module.""" # Get the directory where this script/module is located @@ -134,7 +138,7 @@ def lookup_id(self, key, return_curie=False, allow_subsitutions=True, subsitutio return key if not return_curie else key.replace('_', ':') # CARO lookup: Check if the key is a CARO/BFO/UBERON/FBbt(obsolete) term; though not in the lookup they need to be handled if explicitly called - prefixes = ('CARO_', 'BFO_', 'UBERON_', 'GENO_', 'CL_', 'FBbt_', 'VFB_', 'GO_') + prefixes = ('CARO_', 'BFO_', 'UBERON_', 'GENO_', 'CL_', 'FB', 'VFB_', 'GO_') if isinstance(key,str) and key.startswith(prefixes): return key if not return_curie else key.replace('_', ':') @@ -254,7 +258,7 @@ def get_superclasses(self, class_expression, query_by_label=True, direct=False, return pd.DataFrame.from_records(results) return results - def get_instances(self, class_expression, query_by_label=True, summary=True, return_dataframe=True, limit=None, return_id_only=False): + def get_instances(self, class_expression, query_by_label=True, summary=True, return_dataframe=True, limit=None, return_id_only=False, verbose=False): """Generate JSON report of all instances of a given class expression. Instances are specific examples of a type/class, e.g., a neuron of type DA1 adPN from the FAFB_catmaid database. @@ -270,12 +274,12 @@ def get_instances(self, class_expression, query_by_label=True, summary=True, ret if query_by_label: class_expression = self.lookup[class_expression] out = self.neo_query_wrapper._get_anatomical_individual_TermInfo_by_type(class_expression, - summary=summary, return_dataframe=False, limit=limit) + summary=summary, return_dataframe=False, limit=limit, verbose=verbose) else: terms = self.oc.get_instances("%s" % class_expression, query_by_label=query_by_label) if return_id_only: return terms - out = self.get_TermInfo(terms, summary=summary, return_dataframe=False, limit=limit) + out = self.get_TermInfo(terms, summary=summary, return_dataframe=False, limit=limit, verbose=verbose) if return_dataframe and summary: return pd.DataFrame.from_records(out) return out @@ -664,7 +668,7 @@ def get_images_by_filename(self, filenames: iter, dataset=None, summary=True, re return_dataframe=False) @batch_query - def get_TermInfo(self, short_forms: iter, summary=True, cache=True, return_dataframe=True, query_by_label=True, limit=None): + def get_TermInfo(self, short_forms: iter, summary=True, cache=True, return_dataframe=True, query_by_label=True, limit=None, verbose=False): """ Generate a JSON report or summary for terms specified by a list of VFB IDs. @@ -687,11 +691,13 @@ def get_TermInfo(self, short_forms: iter, summary=True, cache=True, return_dataf short_forms = [short_forms.id] if isinstance(short_forms, VFBTerms): short_forms = short_forms.get_ids() + print(short_forms) if verbose else None # Convert labels to IDs if use_labels is True if query_by_label: short_forms = [self.lookup_id(sf) for sf in short_forms] - return self.neo_query_wrapper.get_TermInfo(short_forms, summary=summary, cache=cache, return_dataframe=False, limit=limit) - + print(short_forms) if verbose else None + return self.neo_query_wrapper.get_TermInfo(short_forms, summary=summary, cache=cache, return_dataframe=False, limit=limit, verbose=verbose) + @batch_query def vfb_id_2_xrefs(self, vfb_id: iter, db='', id_type='', reverse_return=False): """Map a list of short_form IDs in VFB to external DB IDs @@ -706,7 +712,15 @@ def vfb_id_2_xrefs(self, vfb_id: iter, db='', id_type='', reverse_return=False): dict { acc : [{ db: : vfb_id : } """ return self.neo_query_wrapper.vfb_id_2_xrefs(vfb_id=vfb_id, db=db, id_type=id_type, reverse_return=reverse_return) - + + def get_dbs(self): + """Get all external databases in the database. + + :return: List of external databases in the database. + :rtype: list + """ + return self.neo_query_wrapper.get_dbs() + def term(self, term, verbose=False): """Get a VFBTerm object for a given term id, name, symbol or synonym. diff --git a/src/vfb_connect/neo/query_wrapper.py b/src/vfb_connect/neo/query_wrapper.py index 439c4ff5..0e0b5017 100644 --- a/src/vfb_connect/neo/query_wrapper.py +++ b/src/vfb_connect/neo/query_wrapper.py @@ -500,7 +500,7 @@ def get_images_by_filename(self, filenames, dataset=None, summary=True, return_d for d in dc], summary=summary, return_dataframe=return_dataframe) @batch_query - def get_TermInfo(self, short_forms: iter, summary=True, cache=True, return_dataframe=True, limit=None): + def get_TermInfo(self, short_forms: iter, summary=True, cache=True, return_dataframe=True, limit=None, verbose=False): """ Generate a JSON report or summary for terms specified by a list of VFB IDs. @@ -515,9 +515,22 @@ def get_TermInfo(self, short_forms: iter, summary=True, cache=True, return_dataf :return: A list of term metadata as VFB_json or summary_report_json, or a pandas DataFrame if `return_dataframe` is `True`. :rtype: list of dicts or pandas.DataFrame """ + from vfb_connect import vfb if cache: - result = self._get_Cached_TermInfo(short_forms, summary=summary, return_dataframe=False) - if len(result) == len(short_forms): + result = self._get_Cached_TermInfo(short_forms, summary=summary, return_dataframe=False, verbose=verbose) + cn = len(set(short_forms)) + rn = len(result) + if rn != cn: + print(f"\033[33mWarning:\033[0m Cache didn't return all results. Got {rn} out of {cn}") if verbose else None + missing = set(short_forms) - set([r['term']['core']['short_form'] for r in result]) + print(f"Missing: {missing}") if verbose else None + for i in missing: + print(f"Checking: {i}") if verbose else None + if not i in vfb.lookup.values(): + print(f"\033[33mWarning:\033[0m called a non existant id:{i}") + cn -= 1 + if rn == cn: + print("Using cached results.") if verbose else None result = result[:limit] if limit else result if summary: results = [] @@ -527,7 +540,9 @@ def get_TermInfo(self, short_forms: iter, summary=True, cache=True, return_dataf else: return result else: + print(f"\033[33mWarning:\033[0m Cache didn't return all results. Got {rn} out of {cn}. Falling back to slower query.") return self.get_TermInfo(short_forms, summary=summary, cache=False, return_dataframe=return_dataframe, limit=limit) + print("Pulling results from VFB PDB (Neo4j): http://pdb.virtualflybrain.org") if verbose else None pre_query = "MATCH (e:Entity) " \ "WHERE e.short_form in %s " \ "RETURN e.short_form as short_form, labels(e) as labs " % str(short_forms) @@ -535,32 +550,47 @@ def get_TermInfo(self, short_forms: iter, summary=True, cache=True, return_dataf out = [] for e in r: if 'class' in e['labs'] and 'Neuron' in e['labs']: + print(f"Getting Neuron: {e['short_form']}") if verbose else None out.extend(self.get_neuron_class_TermInfo([e['short_form']], summary=summary, return_dataframe=False)) elif 'class' in e['labs'] and 'Split' in e['labs']: + print(f"Getting Split: {e['short_form']}") if verbose else None out.extend(self.get_split_class_TermInfo([e['short_form']], summary=summary, return_dataframe=False)) if 'Class' in e['labs']: + print out.extend(self.get_type_TermInfo([e['short_form']], summary=summary, return_dataframe=False)) elif 'DataSet' in e['labs']: + print(f"Getting DataSet: {e['short_form']}") if verbose else None out.extend(self.get_DataSet_TermInfo([e['short_form']], summary=summary, return_dataframe=False)) elif 'License' in e['labs']: + print(f"Getting License: {e['short_form']}") if verbose else None out.extend(self.get_License_TermInfo([e['short_form']], summary=summary, return_dataframe=False)) elif 'Template' in e['labs']: + print(f"Getting Template: {e['short_form']}") if verbose else None out.extend(self.get_template_TermInfo([e['short_form']], summary=summary, return_dataframe=False)) elif 'pub' in e['labs']: + print(f"Getting Pub: {e['short_form']}") if verbose else None out.extend(self.get_pub_TermInfo([e['short_form']], summary=summary, return_dataframe=False)) - elif 'Individual' in e['labs'] and 'Anatomy' in e['labs']: + elif 'Individual' in e['labs']: + print(f"Getting Individual: {e['short_form']}") if verbose else None out.extend(self.get_anatomical_individual_TermInfo([e['short_form']], summary=summary, return_dataframe=False)) + print(f"Got {len(out)} results.") if verbose else None return out[:limit] if limit else out @batch_query - def _get_Cached_TermInfo(self, short_forms: iter, summary=True, return_dataframe=True): + def _get_Cached_TermInfo(self, short_forms: iter, summary=True, return_dataframe=True, verbose=False): # Flatten the list of short_forms in case it's nested if isinstance(short_forms, str): short_forms = [short_forms] if isinstance(short_forms, list): short_forms = list(chain.from_iterable(short_forms)) if any(isinstance(i, list) for i in short_forms) else short_forms - + print(f"Checking cache for results: short_forms={short_forms}") if verbose else None + print(f"Looking for {len(short_forms)} results.") if verbose else None results = self._serialize_solr_output(vfb_solr.search('*', **{'fl': 'term_info','df': 'id', 'defType': 'edismax', 'q.op': 'OR','rows': len(short_forms)+10,'fq':'{!terms f=id}'+ ','.join(short_forms)})) + print(f"Got {len(results)} results.") if verbose else None + if len(short_forms) != len(results): + print(f"Warning: Cache didn't return all results. Got {len(results)} out of {len(short_forms)}") if verbose else None + missing = set(short_forms) - set([r['term']['core']['short_form'] for r in results]) + print(f"Missing: {missing}") if verbose else None return results @@ -577,16 +607,16 @@ def _get_TermInfo(self, short_forms: iter, typ, show_query=False, summary=True, else: return self._query(qs) - def _get_anatomical_individual_TermInfo_by_type(self, classification, summary=True, return_dataframe=True, limit=None): + def _get_anatomical_individual_TermInfo_by_type(self, classification, summary=True, return_dataframe=True, limit=None, verbose=False): # TODO use the limit parameter typ = 'Get JSON for Individual:Anatomy_by_type' qs = Template(self.queries[typ]).substitute(ID=classification) if summary: - return self._termInfo_2_summary(self._query(qs), typ='Get JSON for Individual') + return self._termInfo_2_summary(self._query(qs), typ='Get JSON for Individual', verbose=verbose) else: return self._query(qs) - def _termInfo_2_summary(self, TermInfo, typ): + def _termInfo_2_summary(self, TermInfo, typ, verbose=False): # type_2_summary = { # 'Get JSON for Individual': '_populate_instance_summary_tab', # 'Get JSON for Class': '_populate_anatomical_entity_summary', @@ -594,11 +624,15 @@ def _termInfo_2_summary(self, TermInfo, typ): dc = [] for r in TermInfo: if 'Class' in typ: + print(f"Getting Class: {r['short_form']}") if verbose else None dc.append(_populate_anatomical_entity_summary(r)) elif typ == 'Get JSON for DataSet': + print(f"Getting DataSet: {r['short_form']}") if verbose else None dc.append(_populate_dataset_summary_tab(r)) else: + print(f"Getting Individual: {r['short_form']}") if verbose else None dc.append(_populate_instance_summary_tab(r)) + print(f"Got {len(dc)} results.") if verbose else None return dc def _serialize_solr_output(self, results): diff --git a/src/vfb_connect/schema/vfb_term.py b/src/vfb_connect/schema/vfb_term.py index 951d7e2b..01767020 100644 --- a/src/vfb_connect/schema/vfb_term.py +++ b/src/vfb_connect/schema/vfb_term.py @@ -820,6 +820,9 @@ def __sub__(self, other, verbose=False): return remaining_terms raise TypeError("Unsupported operand type(s) for -: 'VFBTerms' and '{}'".format(type(other).__name__)) + def __dir__(self): + return [attr for attr in list(self.__dict__.keys()) if not attr.startswith('_')] + [attr for attr in dir(self.__class__) if not attr.startswith('_') and not attr.startswith('get') and not attr.startswith('add_')] + def downstream_partners(self, weight=0, classification=None, verbose=False): """ Get neurons downstream of this neuron. @@ -1211,6 +1214,8 @@ def __sub__(self, other, verbose=False): return remaining_terms raise TypeError("Unsupported operand type(s) for -: 'VFBTerms' and '{}'".format(type(other).__name__)) + def __dir__(self): + return [attr for attr in list(self.__dict__.keys()) if not attr.startswith('_')] + [attr for attr in dir(self.__class__) if not attr.startswith('_') and not attr.startswith('add_')] def load_skeletons(self, template=None, verbose=False, query_by_label=True, force_reload=False): """