diff --git a/.github/workflows/partitura_unittests.yml b/.github/workflows/partitura_unittests.yml
index a0c2dd08..93bb2043 100644
--- a/.github/workflows/partitura_unittests.yml
+++ b/.github/workflows/partitura_unittests.yml
@@ -28,7 +28,7 @@ jobs:
     - name: Install Optional dependencies
       run: |        
         pip install music21==8.3.0 Pillow==9.5.0 musescore==0.0.1      
-        pip install miditok==2.0.6 tokenizers==0.13.3 
+        pip install miditok==2.0.6 tokenizers==0.13.3 pandas==2.0.3
     - name: Run Tests
       run: |
         pip install coverage
diff --git a/.gitignore b/.gitignore
index 1b28c046..6a035d51 100644
--- a/.gitignore
+++ b/.gitignore
@@ -152,3 +152,6 @@ static
 
 # phdocs
 phdocs.txt
+
+# fluidsynth default soundfont
+partitura/assets/MuseScore_General.sf*
diff --git a/CHANGES.md b/CHANGES.md
index d6fe20eb..451d6134 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -1,6 +1,31 @@
 Release Notes
 =============
 
+Version 1.5.0 (Released on 2024-07-17)
+--------------------------------------
+
+## New Features    
+
+- Dcml annotation parser
+- New kern import for faster and more robust
+- Barebones Kern export 
+- MEI export
+- Mei export Updates 
+- Estimate symbolic durations 
+- New harmony classes and checks for Roman numerals, Chord Symbols, Cadences and Phrases in 
+- Intervals as partitura classes
+- transposition of parts
+- Export wav with fluidsynth 
+
+## Other Changes
+
+- improved documentation
+- improved typing
+- New tests
+- optional dependency of pandas
+
+
+
 Version 1.4.1 (Released on 2023-10-25)
 --------------------------------------
 
diff --git a/docs/source/Tutorial/notebook.ipynb b/docs/source/Tutorial/notebook.ipynb
index fc75b60c..31d98713 100644
--- a/docs/source/Tutorial/notebook.ipynb
+++ b/docs/source/Tutorial/notebook.ipynb
@@ -52,7 +52,6 @@
      "is_executing": true
     }
    },
-   "outputs": [],
    "source": [
     "# Install partitura\n",
     "! pip install partitura\n",
@@ -65,20 +64,21 @@
     "import sys, os\n",
     "sys.path.insert(0, os.path.join(os.getcwd(), \"partitura_tutorial\", \"content\"))\n",
     "sys.path.insert(0,'/content/partitura_tutorial/content')\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "impressed-principle",
    "metadata": {},
-   "outputs": [],
    "source": [
     "import glob\n",
     "import partitura as pt\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -103,20 +103,6 @@
    "execution_count": 3,
    "id": "photographic-profession",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "Output()",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "51b999065d4e4460b960ff64e7507006"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "# setup the dataset\n",
     "from load_data import init_dataset\n",
@@ -124,7 +110,8 @@
     "MUSICXML_DIR = os.path.join(DATASET_DIR, 'musicxml')\n",
     "MIDI_DIR = os.path.join(DATASET_DIR, 'midi')\n",
     "MATCH_DIR = os.path.join(DATASET_DIR, 'match')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -233,53 +220,12 @@
    "execution_count": 4,
    "id": "c9179e78",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Part id=\"P1\" name=\"Piano\"\n",
-      " │\n",
-      " ├─ TimePoint t=0 quarter=12\n",
-      " │   │\n",
-      " │   └─ starting objects\n",
-      " │       │\n",
-      " │       ├─ 0--48 Measure number=1\n",
-      " │       ├─ 0--48 Note id=n01 voice=1 staff=2 type=whole pitch=A4\n",
-      " │       ├─ 0--48 Page number=1\n",
-      " │       ├─ 0--24 Rest id=r01 voice=2 staff=1 type=half\n",
-      " │       ├─ 0--48 System number=1\n",
-      " │       └─ 0-- TimeSignature 4/4\n",
-      " │\n",
-      " ├─ TimePoint t=24 quarter=12\n",
-      " │   │\n",
-      " │   ├─ ending objects\n",
-      " │   │   │\n",
-      " │   │   └─ 0--24 Rest id=r01 voice=2 staff=1 type=half\n",
-      " │   │\n",
-      " │   └─ starting objects\n",
-      " │       │\n",
-      " │       ├─ 24--48 Note id=n02 voice=2 staff=1 type=half pitch=C5\n",
-      " │       └─ 24--48 Note id=n03 voice=2 staff=1 type=half pitch=E5\n",
-      " │\n",
-      " └─ TimePoint t=48 quarter=12\n",
-      "     │\n",
-      "     └─ ending objects\n",
-      "         │\n",
-      "         ├─ 0--48 Measure number=1\n",
-      "         ├─ 0--48 Note id=n01 voice=1 staff=2 type=whole pitch=A4\n",
-      "         ├─ 24--48 Note id=n02 voice=2 staff=1 type=half pitch=C5\n",
-      "         ├─ 24--48 Note id=n03 voice=2 staff=1 type=half pitch=E5\n",
-      "         ├─ 0--48 Page number=1\n",
-      "         └─ 0--48 System number=1\n"
-     ]
-    }
-   ],
    "source": [
     "path_to_musicxml = pt.EXAMPLE_MUSICXML\n",
     "part = pt.load_musicxml(path_to_musicxml)[0]\n",
     "print(part.pretty())"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -304,38 +250,20 @@
    "execution_count": 5,
    "id": "423aac6a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "[<partitura.score.Note at 0x7fb004c3bc70>,\n <partitura.score.Note at 0x7fb0048fb790>,\n <partitura.score.Note at 0x7fb0048fb7f0>]"
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "part.notes"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "id": "0a929369",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "['__class__',\n '__delattr__',\n '__dict__',\n '__dir__',\n '__doc__',\n '__eq__',\n '__format__',\n '__ge__',\n '__getattribute__',\n '__gt__',\n '__hash__',\n '__init__',\n '__init_subclass__',\n '__le__',\n '__lt__',\n '__module__',\n '__ne__',\n '__new__',\n '__reduce__',\n '__reduce_ex__',\n '__repr__',\n '__setattr__',\n '__sizeof__',\n '__str__',\n '__subclasshook__',\n '__weakref__',\n '_ref_attrs',\n '_sym_dur',\n 'alter',\n 'alter_sign',\n 'articulations',\n 'beam',\n 'doc_order',\n 'duration',\n 'duration_from_symbolic',\n 'duration_tied',\n 'end',\n 'end_tied',\n 'fermata',\n 'id',\n 'iter_chord',\n 'midi_pitch',\n 'octave',\n 'ornaments',\n 'replace_refs',\n 'slur_starts',\n 'slur_stops',\n 'staff',\n 'start',\n 'step',\n 'symbolic_duration',\n 'tie_next',\n 'tie_next_notes',\n 'tie_prev',\n 'tie_prev_notes',\n 'tuplet_starts',\n 'tuplet_stops',\n 'voice']"
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "dir(part.notes[0])"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -350,23 +278,23 @@
    "execution_count": 7,
    "id": "2a8293c9",
    "metadata": {},
-   "outputs": [],
    "source": [
     "a_new_note = pt.score.Note(id='n04', step='A', octave=4, voice=1)\n",
     "part.add(a_new_note, start=3, end=15)\n",
     "# print(part.pretty())"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "id": "eba2fa93",
    "metadata": {},
-   "outputs": [],
    "source": [
     "part.remove(a_new_note)\n",
     "# print(part.pretty())"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -385,19 +313,10 @@
    "execution_count": 9,
    "id": "e95eb0f7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "array(4.)"
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "part.beat_map(part.notes[0].end.t)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -412,19 +331,10 @@
    "execution_count": 10,
    "id": "05346a03",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "array([4., 4., 4.])"
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "part.time_signature_map(part.notes[0].end.t)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -446,39 +356,22 @@
    "execution_count": 11,
    "id": "74943a93",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0--48 Measure number=1\n"
-     ]
-    }
-   ],
    "source": [
     "for measure in part.iter_all(pt.score.Measure):\n",
     "    print(measure)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "id": "6cbfd044",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0--48 Note id=n01 voice=1 staff=2 type=whole pitch=A4\n",
-      "0--24 Rest id=r01 voice=2 staff=1 type=half\n"
-     ]
-    }
-   ],
    "source": [
     "for note in part.iter_all(pt.score.GenericNote, include_subclasses=True, start=0, end=24):\n",
     "    print(note)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -495,7 +388,6 @@
    "execution_count": 13,
    "id": "fe430921",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# figure out the last measure position, time signature and beat length in divs\n",
     "measures = [m for m in part.iter_all(pt.score.Measure)]\n",
@@ -513,17 +405,18 @@
     "# add a note\n",
     "a_new_note = pt.score.Note(id='n04', step='A', octave=4, voice=1)\n",
     "part.add(a_new_note, start=append_measure_start, end=append_measure_start+one_beat_in_divs_at_the_end)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "id": "f9d738a5",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# print(part.pretty())"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -543,30 +436,21 @@
    "execution_count": 15,
    "id": "5d82a340",
    "metadata": {},
-   "outputs": [],
    "source": [
     "path_to_midifile = pt.EXAMPLE_MIDI\n",
     "performedpart = pt.load_performance_midi(path_to_midifile)[0]"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "id": "4e3090d9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "[{'midi_pitch': 69,\n  'note_on': 0.0,\n  'note_off': 2.0,\n  'track': 0,\n  'channel': 1,\n  'velocity': 64,\n  'id': 'n0',\n  'sound_off': 2.0},\n {'midi_pitch': 72,\n  'note_on': 1.0,\n  'note_off': 2.0,\n  'track': 0,\n  'channel': 2,\n  'velocity': 64,\n  'id': 'n1',\n  'sound_off': 2.0},\n {'midi_pitch': 76,\n  'note_on': 1.0,\n  'note_off': 2.0,\n  'track': 0,\n  'channel': 2,\n  'velocity': 64,\n  'id': 'n2',\n  'sound_off': 2.0}]"
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "performedpart.notes"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -581,7 +465,6 @@
    "execution_count": 17,
    "id": "d6eb12f2",
    "metadata": {},
-   "outputs": [],
    "source": [
     "import numpy as np \n",
     "\n",
@@ -608,14 +491,14 @@
     "    part.add(pt.score.Note(id='n{}'.format(idx), step=step, \n",
     "                        octave=int(octave), alter=alter, voice=voice, staff=str((voice-1)%2+1)), \n",
     "                        start=start, end=end)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "id": "572e856c",
    "metadata": {},
-   "outputs": [],
    "source": [
     "l = 200\n",
     "p = pt.score.Part('CoK', 'Cat on Keyboard', quarter_duration=8)\n",
@@ -626,53 +509,54 @@
     "                      np.random.randint(40,60, size=(1,l+1)),\n",
     "                      np.random.randint(40,60, size=(1,l+1))\n",
     "                     ))"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "id": "f9f03a50",
    "metadata": {},
-   "outputs": [],
    "source": [
     "for k in range(l):\n",
     "    for j in range(4):\n",
     "        addnote(pitch[j,k], p, j+1, ons[j,k], ons[j,k]+dur[j,k+1], \"v\"+str(j)+\"n\"+str(k))"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "id": "09fb6b45",
    "metadata": {},
-   "outputs": [],
    "source": [
     "p.add(pt.score.TimeSignature(4, 4), start=0)\n",
     "p.add(pt.score.Clef(1, \"G\", line = 3, octave_change=0),start=0)\n",
     "p.add(pt.score.Clef(2, \"G\", line = 3, octave_change=0),start=0)\n",
     "pt.score.add_measures(p)\n",
     "pt.score.tie_notes(p)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "id": "834582d5",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# pt.save_score_midi(p, \"CatPerformance.mid\", part_voice_assign_mode=2)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "id": "006f02ed",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# pt.save_musicxml(p, \"CatScore.xml\")"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -718,7 +602,6 @@
    "execution_count": 23,
    "id": "first-basin",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# Note array from a score\n",
     "\n",
@@ -730,7 +613,8 @@
     "\n",
     "# Get note array.\n",
     "score_note_array = score_part.note_array()"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -745,28 +629,11 @@
    "execution_count": 24,
    "id": "alternate-coordinate",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(-4., 1., -2. , 0.5,  0,  8, 60, 4, 'n2', 16)\n",
-      " (-4., 1., -2. , 0.5,  0,  8, 72, 1, 'n1', 16)\n",
-      " (-3., 2., -1.5, 1. ,  8, 16, 60, 4, 'n4', 16)\n",
-      " (-3., 2., -1.5, 1. ,  8, 16, 72, 1, 'n3', 16)\n",
-      " (-1., 1., -0.5, 0.5, 24,  8, 60, 4, 'n6', 16)\n",
-      " (-1., 1., -0.5, 0.5, 24,  8, 72, 1, 'n5', 16)\n",
-      " ( 0., 2.,  0. , 1. , 32, 16, 60, 4, 'n8', 16)\n",
-      " ( 0., 2.,  0. , 1. , 32, 16, 72, 1, 'n7', 16)\n",
-      " ( 2., 1.,  1. , 0.5, 48,  8, 60, 4, 'n10', 16)\n",
-      " ( 2., 1.,  1. , 0.5, 48,  8, 72, 1, 'n9', 16)]\n"
-     ]
-    }
-   ],
    "source": [
     "# Lets see the first notes in this note array\n",
     "print(score_note_array[:10])"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -783,18 +650,10 @@
    "execution_count": 25,
    "id": "subtle-millennium",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('onset_beat', 'duration_beat', 'onset_quarter', 'duration_quarter', 'onset_div', 'duration_div', 'pitch', 'voice', 'id', 'divs_pq')\n"
-     ]
-    }
-   ],
    "source": [
     "print(score_note_array.dtype.names)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -827,7 +686,6 @@
    "execution_count": 26,
    "id": "passing-lending",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# Note array from a performance\n",
     "\n",
@@ -839,7 +697,8 @@
     "\n",
     "# Get note array!\n",
     "performance_note_array = performance_part.note_array()"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -854,18 +713,10 @@
    "execution_count": 27,
    "id": "pointed-stupid",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('onset_sec', 'duration_sec', 'pitch', 'velocity', 'track', 'channel', 'id')\n"
-     ]
-    }
-   ],
    "source": [
     "print(performance_note_array.dtype.names)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -886,22 +737,10 @@
    "execution_count": 28,
    "id": "subject-reducing",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(5.6075 , 5.5025 , 72, 37, 0, 0, 'n0')\n",
-      " (5.63375, 5.47625, 60, 27, 0, 0, 'n1')\n",
-      " (6.07   , 5.04   , 72, 45, 0, 0, 'n2')\n",
-      " (6.11125, 4.99875, 60, 26, 0, 0, 'n3')\n",
-      " (6.82625, 4.28375, 60, 39, 0, 0, 'n4')]\n"
-     ]
-    }
-   ],
    "source": [
     "print(performance_note_array[:5])"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -916,7 +755,6 @@
    "execution_count": 29,
    "id": "spread-performer",
    "metadata": {},
-   "outputs": [],
    "source": [
     "note_array = np.array(\n",
     "    [(60, 0, 2, 40),\n",
@@ -933,7 +771,8 @@
     "\n",
     "# Note array to `PerformedPart`\n",
     "performed_part = pt.performance.PerformedPart.from_note_array(note_array)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -948,11 +787,11 @@
    "execution_count": 30,
    "id": "changed-check",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# export as MIDI file\n",
     "pt.save_performance_midi(performed_part, \"example.mid\")"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -969,7 +808,6 @@
    "execution_count": 31,
    "id": "figured-coordinator",
    "metadata": {},
-   "outputs": [],
    "source": [
     "extended_score_note_array = pt.utils.music.ensure_notearray(\n",
     "    score_part,\n",
@@ -979,26 +817,18 @@
     "    # include_metrical_position=True, # adds 3 fields: is_downbeat, rel_onset_div, tot_measure_div\n",
     "    include_grace_notes=True # adds 2 fields: is_grace, grace_type\n",
     ")"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 32,
    "id": "vietnamese-pathology",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "('onset_beat',\n 'duration_beat',\n 'onset_quarter',\n 'duration_quarter',\n 'onset_div',\n 'duration_div',\n 'pitch',\n 'voice',\n 'id',\n 'step',\n 'alter',\n 'octave',\n 'is_grace',\n 'grace_type',\n 'ks_fifths',\n 'ks_mode',\n 'ts_beats',\n 'ts_beat_type',\n 'ts_mus_beats',\n 'divs_pq')"
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "extended_score_note_array.dtype.names"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -1007,19 +837,6 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[('n2', 'C', 0, 4, -1, 1) ('n1', 'C', 0, 5, -1, 1)\n",
-      " ('n4', 'C', 0, 4, -1, 1) ('n3', 'C', 0, 5, -1, 1)\n",
-      " ('n6', 'C', 0, 4, -1, 1) ('n5', 'C', 0, 5, -1, 1)\n",
-      " ('n8', 'C', 0, 4, -1, 1) ('n7', 'C', 0, 5, -1, 1)\n",
-      " ('n10', 'C', 0, 4, -1, 1) ('n9', 'C', 0, 5, -1, 1)]\n"
-     ]
-    }
-   ],
    "source": [
     "print(extended_score_note_array[['id', \n",
     "                                 'step', \n",
@@ -1028,7 +845,8 @@
     "                                 'ks_fifths', \n",
     "                                 'ks_mode', #'is_downbeat'\n",
     "                                 ]][:10])"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1061,15 +879,6 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0.25, 47, 1) (1.25, 47, 1) (2.25, 47, 1) (3.  , 68, 1) (3.25, 47, 1)]\n"
-     ]
-    }
-   ],
    "source": [
     "# Path to the MusicXML file\n",
     "score_fn = os.path.join(MUSICXML_DIR, 'Chopin_op10_no3.musicxml')\n",
@@ -1105,7 +914,8 @@
     "\n",
     "accented_note_idxs = np.where(accent_note_array['accent'])\n",
     "print(accent_note_array[accented_note_idxs][:5])"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1134,7 +944,6 @@
    "execution_count": 35,
    "id": "essential-academy",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# TODO: change the example\n",
     "# Path to the MusicXML file\n",
@@ -1144,7 +953,8 @@
     "score_part = pt.load_musicxml(score_fn)\n",
     "# compute piano roll\n",
     "pianoroll = pt.utils.compute_pianoroll(score_part)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1159,7 +969,6 @@
    "execution_count": 36,
    "id": "massive-monaco",
    "metadata": {},
-   "outputs": [],
    "source": [
     "piano_range = True\n",
     "time_unit = 'beat'\n",
@@ -1170,7 +979,8 @@
     "    time_div=time_div, # Number of cells per time unit\n",
     "    piano_range=piano_range # Use range of the piano (88 keys)\n",
     ")"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1197,25 +1007,14 @@
    "execution_count": 37,
    "id": "mature-dylan",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 1440x720 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "fig, ax = plt.subplots(1, figsize=(20, 10))\n",
     "ax.imshow(pianoroll.toarray(), origin=\"lower\", cmap='gray', interpolation='nearest', aspect='auto')\n",
     "ax.set_xlabel(f'Time ({time_unit}s/{time_div})')\n",
     "ax.set_ylabel('Piano key' if piano_range else 'MIDI pitch')\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1232,25 +1031,13 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[59  0  4]\n",
-      " [40  4 12]\n",
-      " [40  4  6]\n",
-      " [56  4  6]\n",
-      " [64  4  8]]\n"
-     ]
-    }
-   ],
    "source": [
     "pianoroll, note_indices = pt.utils.compute_pianoroll(score_part, return_idxs=True)\n",
     "\n",
     "# MIDI pitch, start, end\n",
     "print(note_indices[:5])"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1268,7 +1055,6 @@
    "execution_count": 39,
    "id": "parental-links",
    "metadata": {},
-   "outputs": [],
    "source": [
     "pianoroll = pt.utils.compute_pianoroll(score_part)\n",
     "\n",
@@ -1278,7 +1064,8 @@
     "ppart = pt.performance.PerformedPart.from_note_array(new_note_array)\n",
     "\n",
     "pt.save_performance_midi(ppart, \"newmidi.mid\")"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1325,13 +1112,13 @@
    "execution_count": 40,
    "id": "rolled-cloud",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# path to the match\n",
     "match_fn = os.path.join(MATCH_DIR, 'Chopin_op10_no3_p01.match')\n",
     "# loading a match file\n",
     "performed_part, alignment, score_part = pt.load_match(match_fn, create_part=True)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1353,7 +1140,6 @@
    "execution_count": 41,
    "id": "latest-smell",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# path to the match\n",
     "match_fn = os.path.join(MATCH_DIR, 'Chopin_op10_no3_p01.match')\n",
@@ -1364,7 +1150,8 @@
     "\n",
     "# loading a match file\n",
     "performed_part, alignment = pt.load_match(match_fn)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1388,19 +1175,10 @@
    "execution_count": 42,
    "id": "radio-interim",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "[{'label': 'match', 'score_id': 'n1', 'performance_id': 0},\n {'label': 'match', 'score_id': 'n2', 'performance_id': 2},\n {'label': 'match', 'score_id': 'n3', 'performance_id': 3},\n {'label': 'match', 'score_id': 'n4', 'performance_id': 1},\n {'label': 'match', 'score_id': 'n5', 'performance_id': 5},\n {'label': 'match', 'score_id': 'n6', 'performance_id': 4},\n {'label': 'match', 'score_id': 'n7', 'performance_id': 6},\n {'label': 'match', 'score_id': 'n8', 'performance_id': 7},\n {'label': 'match', 'score_id': 'n9', 'performance_id': 8},\n {'label': 'match', 'score_id': 'n10', 'performance_id': 9}]"
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "alignment[:10]"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1419,7 +1197,6 @@
    "execution_count": 43,
    "id": "published-understanding",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# note array of the score\n",
     "snote_array = score_part.note_array()\n",
@@ -1432,7 +1209,8 @@
     "matched_snote_array = snote_array[matched_note_idxs[:, 0]]\n",
     "# note array of the matched performed notes\n",
     "matched_pnote_array = pnote_array[matched_note_idxs[:, 1]]"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1449,7 +1227,6 @@
    "execution_count": 44,
    "id": "offshore-bridal",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# get all match files\n",
     "matchfiles = glob.glob(os.path.join(MATCH_DIR, 'Chopin_op10_no3_p*.match'))\n",
@@ -1476,7 +1253,8 @@
     "    # Compute naïve tempo curve\n",
     "    performance_time = stime_to_ptime_map(score_time_ending)\n",
     "    tempo_curves[i,:] = 60 * np.diff(score_time_ending) / np.diff(performance_time)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -1485,18 +1263,6 @@
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 1080x576 with 1 Axes>",
-      "image/png": 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uda1Y1lP598tyX0kC9/PNJX+v83g8nrvBvUiP/1Hgv68e//fAn78HYzgyQVQp7RZf1+7x3Me0M12v7dyVrJrxjlDaAUSEj5w1rKXK6wt+IuvxvJvpDJT2apaVH3MCjlXlmRsl/QO0a1vpK0noDDQHNGN3D9zvQqOt6uIfnzHMNoXnblnyE1Kz7/F4PA8ydzpoV+B3ReSbIvJXq21nVPVG9fgmcGbUiSLyV0XkaRF5+vbt23d4mAcnCEHDBPVBu8dzX9PONlyUdyVPySmgu7rjIRfGDWdawvNz5Ykxn/J4PHeXwipp4QLiDaX9eO8HC13l5duWtw6gdDvn+M33ulYsqEJ3n2r7agpWYbImfOxcQC934/B4PB7PneVOB+3fq6ofA34I+FkR+b7hneqKoUZ+46jqf6uqn1DVT5w6deoOD/PgmKBS2kt80O7x3KcUVunlut6veDf62TKLdoG0c3vXkpiPngtIC/xE1uN5l9IdMrdcr2kvjvc1Br3V5zr7v8+MDtrdz/3Wta9UHTIma8JsQ3hk0vDKvPVt4zwej+cOc0eDdlW9Vv2cA/4x8CngloicA6h+zt3JMdwpgqiqabdA6s3oPJ77kcHkej9Kez9bBiAv+9Dv7HjcdF24VE1kB72aPR7Pu4dO9Xd/J2vaB0HyXGd/qe1p4dT/4Xp22Oia0d6n9rDcV0RgrAr2P3zWoMBzt/wipcfj8dxJ7ljQLiJNERkbPAa+H3gB+ALw71WH/XvA/3KnxnAnWVfafXq8x3PfspYNJtd7B+1ptoqGIbnmu6bIA3zorEHVT2Q9nncjg1TzRiR3LD1+YB6Xl7DY2/v4Uc7xAI0IjOy/V/ty36n1gXH3zFYsPDlreGvJstD1i5Qej8dzp7iTSvsZ4Msi8izwDeC3VfV3gL8N/Gsi8h3gf1c9v+9wNe0+Pd7juetceQ7W5o/lUp3qT3cs2f24VFNs3kPHp8klR9vLux4/mMi+uWhZ7vuJrMfzbqKTOTW6HkFoBCOQHfP6XSdTxqoMobn23hdfqdq0bU2PNyI0Y9m30r7Sd6nxw7z/lCEJ4ZkbJ8Ai3+PxeB5Qwjt1YVV9E/jwiO0LwJ+5U697t/Du8R7PPaDfhqVr7vHY7JEv186U0EAt3F1p72oHk+fUa6fo1Nrk3QX2auv+/lOGNxYt375R8plH79it1uPxnDC6uVPZB20k4+D4a9o7uSvFCQRudZT373H8aurudaM6ZTSj/dW0Fyq0M+Wxqc33yzgQPnQm4I+vlVxdsVycuBeNiTwej+fBxt9ZD8l6n3avtHs8d4/OovvZXzuWy7VzXa/p3PVli1XCUqjH05TNFkV7dK/2YZJQ+MDpgOtrys19KGEej+fBoJ0pjaHgOA7kWGvaVZVO9RpnWsLtfdS1r1QmdCLb73etWNZLhXajW7hc/61KO8Dj08JETXjmpt13+ziPx+Px7B8ftB8SEwgSGqyEPmj3eO4Wg6A9beNcII9GO91wT96JUkvSfIVYYqK4iTTGydMVKPbukfTeGZd6+u0bFvUTWY/nXUE33+yTEQeQ2+P7++8Xru1aKxbONIXCsmc9+Wq6PTV+QCuGtGDPfuud0mUMbTWzA5dm/9FzhrVUeW3B3+s8Ho/nuPFB+xEIAiiDxLvHezx3i85i5QJpIe0e/XK50tpDae/SRfKMRBKIEsLW7L7M6AACI3z4rGGxp7y97CeyHs+DjqrSzZXG0GJgHByve3x7vaUcnGq6+9et9s73l7x0yvxWE7oBg3tgZ491yHYREpqdFzrPjxnOjQkv3ipJC3+/83g8nuPEB+1HIAihDGKvtHs8d4O0A3kKUxfc8yOmyKeFkpfsHbRrmzAviYggSogbs5SUFAPVfw8emRCm68JztyzlMaptHo/n5NGrVPDNSvvxpsdvtJQTktDdX251dr63rFVTlJ2U9vW2b+leSnvARG10iv2Aj54LyCy8dNuXBHk8nnuHiPyKiOxl97HTuT8iIn9zl/0fEZEf3mX/z4nI6yLyqoj8wND2XxWRORF54TDj8kH7EQhCKI0P2j2eu8KgjnzmYRA5ctC+NqRW7YSq0tEu9cIgCMQJtdo0GgZknf052EuVNtrJlO8s+qDd43mQ6Yy4rzgjuuP72+8OWlVWr3GmJcx3lWKHRcFBu7dRae2woZy391DaO0W44zUGTNZcyv5uyr/H4/HcaVT1p1X1pUOe+wVV3a272UeAkUF7tVDw48AHgB8EfklEquaffL7adih80H4ETAjWJD5o93juBp0liBKojUHSPHLQPnBLHttFaU9JKSmo55X7e5SQkGAbYxT7DNoBzrQM44lwyxvSeTwPNN0hFXxAFLiWb8fla9HOIAldOzmA002h3KWufaXvWtDt1NoyCV0/+d2U9n6h5GpGmtBtpREL/WN2y/d4PJ6tiMglEXlFRH5dRF4Wkd8UkUa170si8onq8S+LyNMi8qKIfG7o/LdF5HMi8i0ReV5Enqq2/6SI/L3q8V8UkRdE5FkR+QMRiYFfAH5MRL4tIj+2ZVg/CvyGqqaq+hbwOvApAFX9A2B/aZoj8H2IjkAQQmFiSFfu9VA8ngefzgI0Z1ixyzSSOlHvaEF7e4taNfIltQ1AvRAIIzABBgia0xRz10HVqf77oBVDb2/vOo/Hcx8zSF3f6h6vCrl1qvtxvMZwWc+ppiDi6trPtLYfv5q6xUmzy72qGcv62Eex3Hf7Jmt7j68WuiBfVXdNpfd4PA8OnxP+BDBzzJdd+M+Vr+5xzJPAT6nqV0TkV4GfAf7ulmN+XlUXK8X790TkQ6r6XLVvXlU/JiI/A/wN4Ke3nPtZ4AdU9ZqITKpqJiKfBT6hqn99xHguAF8ben612nZkvNJ+BIIQSvHp8R7PHaeqZy+b48zpLdoJkPWgPHwU3M7c5HKgVo2iqx1q1Ajy3Kn8FXFzhqLsY/vtfb9eI9p9UuzxeO5/OhkkAUTBZvd4OD4zuk62vWZ+Zpe69pW+Mr5HsN2KhbVdPHVX+u7nfpT2eihYPV7zPY/H49mBK6r6lerxrwHfO+KYvyQi3wKewaWtD9e6/1b185vApRHnfgX4vIj8FeAYll0Pj1faj4DxQbvHc3eoTN/SRhPIKGsNt73fhubUoS7ZzpTWDsZMAIUW9OkzI7OQX9kStM+SAXlnnqQ+tq/Xa0SurVJhddeFAo/Hc//inOM3/30nxx2058qF8c2vcbolvHLbbru/WFXamXJxYneNphXD9bXdlfZILLVwP0G7+9krXBq/x+N58NmHIn6n2Hrj2vRcRB7FKeifVNUlEfk8MLyMOViuLBkRF6vqXxORTwN/FvimiHx8j/FcAx4aen6x2nZkvNJ+BNaVdmuh8AVcHs8do70IUUI/CZhrK6tBFUAfoa69k0Er2l1lB2hIE7L+pqA9aZwGIO3M7fv1BhP5rk+R93geWDrZ5tR4cDXtsHcf9P3QL5TSblbaAc40nbp9e4va3k6dm/1OzvEDWrGri+/tkA200lea4f7mOfXq99/pWh6Px3OMPCwi31M9/svAl7fsHwc6wIqInAF+6CAXF5HHVfXrqvpZ4DYuIF8DdlJsvgD8uIgk1YLBE8A3DvKaO+GD9sOycIM4X6SQqiDWq+0ez52jswjNaS63e7y9bLmaRm7V7JBBu1Wt6kJ3eUk6hITUpOZazQ0F7WFYQ+ot8u7+/USa1US26yeyHs8DSzfXbQF1XKXKH4fS3t6h68WppmCEbSnyA+f4vYL2wfUGXh/DqCrLfaUZ7O8XSCo13pvReTyeu8CrwM+KyMvAFPDLwztV9VlcWvwrwD/EpbsfhL9TmdS9AHwVeBb4IvD+UUZ0qvoi8I+Al4DfAX5WVUsAEfkfgT8CnhSRqyLyUwcZiE9cOixvPUdtbZbCTLjnWQaNxr0dk8fzIJJ2Ie+T1aZ4bbnrNpWlc5E/ZNDeyZyHXHMH53hVpasdWjJWZdLkEG8uCg2bMxTt/TvINyKvtHs8DzJZqWTl9vvKcda0d9YNNDe/RmiEmYYw194paN/9uoOso3YGp5qb97UzKCy09qu0r6fH+wVKj8dzxylU9Se2blTVzww9/slRJ6rqpaHHTwOfqR5/HteeDVX9CyNOXQQ+udOAVPUXgV8csf3f2umc/eCV9sMSxgSSUZoYa9Ur7R7PnaLj+rM/szZOQUEcCKk9YtCeD9q9jd7fp4fF0pSmU9kBws0HR41ZtN8hL3r7es1Bymx3hJLl8XjufwYLcs0t6fGDoD09hvT4Ue70A840hYWekg29zkrqFgzjYH9Ke2fE/WmlCvwbwf6C9tAooVhSr7R7PB7PseGD9sMSRhjN0TDGWiDdxXbV4/Ecns4iy0XE66nhXEsYj2Ky0rqgvSyci/wBaVd/rjsp7R3tIAh1GhtB+xalPW7OApB2b+/rNQMjJCF0vNLu8TyQDALerUZ0UeBash2P0u6M7UYF4WdarrXccF37aqp7quzg7k+NSNbT74dZqdq9NcP9/QLF6nc4J6/7Fpcej+eOoqpvq+oH7/U47hY+aD8sYYzBBe1a4pV2j+cOUa4t8FJ3klY95fy4oU6DXCulHQ6ltrdzxchotQpc0F6jTiDBRtAebZ75xs1ZBCFt79+MrhmJr2n3eB5QdlLawant+TGlx++02DjTEAIDc1uC9ol9tGkDp7avjVDal/vOqC6U/d27bLZMU5bp5z5q93g8nuPCB+2HJYwIyLBR4pR2H7R7PMdP2uXqQo9FM817z2QkElMPQgqrZFHdHXOYoD11k1CR7ZPZXHMyUpcaDzsG7VJvEQY18s7+zegasa9p93juJ3q5stjbX7DaqRYDayPcgpJAjkdpz919ZBShEWYbwq2qrr2bK3m5twndgLFY6IyYyiz3lYk9+rwP0KKPaklslDJd2d9JHo/H49kTH7QfljDG2BwbxqgP2j2eO8LSwgK32sr5czPUkoyaJNQDVyDa1QDi+qGC9k6u29yXBwxavW0L2vMC/a3fhHZ7/diwNYPtLmLV7ut1vdLu8dxfPHfL8vtvFljd+++2k7mSm1GLgXHAplrzw9LJdNdWlWeawmJPSTtd2lffAfY2oRvQil2gX9qNcVpV1g6g1tvC3R9DA6ZY2t8Lezwej2dPfNB+WMIIIyUYsGp8TbvHc8xYVV59+zZBFPO+h5oU5CTUqIdV0F4c3oyunSqtnerZaRMREUs1081SMAHlOy9TvPnH2DdeWT82asxgum369Pf1uvXIpcgex+Td4/HceTqZc4Rf6O4jaM91x5KbKDh6TXtaKIXd2YsDXF07wOqrz2Nfe4ap+Vf3rbQPrjustq9Wfd4n9xm0a95GMATJNKZY3rQA4PF4PJ7D44P2wxJEmACMLbBB7JV2j+eYefm2pWwv8vCFWWzgFsUS2Qjae4OgPe2A3f9sOCuVtNze5xjAqqWnPZrS2tiYp2gUU155CQC9dW19V9w8hRQl/f7uKfJWLdfKK2jslCefIu/x3B8M/lavr+0dfHYztvVoHxAfJj0+6zuzzYr1Hu07LAyAq2uPbUrn1g26GnF64VXqK9f39XKtQa/2oWyg5cqEbr9Buy06SNggrE0TkNPrt/c+yePxeI4ZEfkVEXn/Ic/9ERH5m7vs/4iI/PAu+39ORF4XkVdF5AeqbQ+JyBdF5CUReVFE/qODjssH7YclihEDRjMftHs8x8xqX3n5WofTccqpUzOk6pTshIRG6G5b60q7qgvc98lARRoboVb1qlZvDRlqVJynWO3CfOUSf/PG+q6gOUVIQN7Z3UF+Qefp0kWjdjUGrz55PPcDg3KWG3sE7VaV7i5K+6HS41/8Krz6x+tPd+rRPowR4eH8Gms9y5WL342ZnIHvfBPW9vbeaI1Q2pf7rk5/Pyn2qooWbSRqEdcmAej3lvc+0ePxeI4ZVf1pVX3pkOd+QVX/9i6HfAQYGbRXCwU/DnwA+EHgl0QkAArg/6yq7we+G/jZgy4q+KD9sFRKu9ic0vig3eM5LlSVr18rGcsXuTRpoDVNSp+ImEAC6kGACPTKwznIt3eZ+Ha0jcFQp74xnrRDWSxj1goIAlhehn6VDt8cJ5KYvL2A7lDz2tEOy7pEQIAEKUrpWyF5PPcBWenS0ZMQVyde7Bx0rzvH7xBQx4dJj+93YOkWdN39rbP+Grufdq5zmaVwkjmZIHviU8774+WvQ9rd9bx65Nzn20OLiit9ZSwRzIg6/a1o6UzoTNikFidk0iTv+7p2j8dzZxCRSyLyioj8uoi8LCK/KSKNat+XROQT1eNfFpGnK4X7c0Pnvy0inxORb4nI8yLyVLX9J0Xk71WP/6KIvCAiz4rIH4hIDPwC8GMi8m0R+bEtw/pR4DdUNVXVt4DXgU+p6g1V/RaAqq4BLwMXDvL7jvA49eyLMHJKu/VKu8dzXFhVXpyz3O4on2mtEBFDbYx+OUfd3YcJJCAyQr+0kDTAmAMG7e5na8vEV1XpaIe6NDCysZ5Ztq+jZY4xExSPzqDvLMDt2/DQQxCEhLUJ6K6SkxGzWY4qtOCWvUlCwoyZ5VpwDYn6dPJd8ls9Hs+JYLC49uik4ZV5y422cmlydPDaXV8MHH2tOHA91LNSR/ZY30ZZbJT93HgDHv8InUyJd+jRvs7qIpPaZnXi/VC0GW9OwOnvhuf/AF76GnzXn4Rw5/tPc0uv9pW+S7nfD1qZ0EnYog70ZZIivYHaHDH+nufxPMj8+0/qnwBmjvmyC7/8qnx1j2OeBH5KVb8iIr8K/Azwd7cc8/Oqulgp3r8nIh9S1eeqffOq+jER+RngbwA/veXczwI/oKrXRGRSVTMR+SzwCVX96yPGcwH42tDzq2wJzkXkEvBR4Ot7/G6b8Er7YQljjAGjOWWQ+KDd4zkiV1cs//S1kudvWR6aMJw3S9CcptCCgoKkCogDAiID/bIAMZC0Dqy0JyMmvh06FOSMydj6Npt30O4CQV7DBDE8+QRQwq1b68dEzRmCboe+bjeju2VvYik5Y85Rp4ERIUn6Xmn3eO4DBqnxF8eFJICbu6TID1Twxo417e7nvnu1D7pWhBHMXYE8rYzu9gig596hWQuJJyxnyhcZjzJojMGTn4TeGrz2NK5P7WjGkg2lPS+VdqZM7rfdW95BMEhYpxZCX6bIrWKz5f1dwOPxeA7OFVX9SvX414DvHXHMXxKRbwHP4NLWh9PSf6v6+U3g0ohzvwJ8XkT+ChAcdbAi0gL+Z+D/qKqrBznXK+2HJYwwRjBU6fHePd7jORTzXeWZGyW3O8p4InzfpYCLtT680oPZR0krZ/aauJmjiBAHQppXE8/aGLTn9/16nUxHprAu2yVCIlpsBO3F8huICEFWB5PB6dPoRAvm5taPCZszhAsv0yvbjJuJTdfr0uGUnCapnOgTEuKkTyfVo9/5PR7PHWWQ8t6IhbNjhhvtXYL2gdK+S007uBT55uhDNjMI2i88Ae+8BLfeoZM9tntqfFnA7avI7AXOFGus9JQxFoHzMHkaHvswvPFtePsFeOxDIy/RjITbHXdvXamGcJB2bxI2EDEIIGGT1EbYdImgdmpf1/B4PPcn+1DE7xRbb8ybnovIozgF/ZOquiQinweGlyIHAVzJiLhYVf+aiHwa+LPAN0Xk43uM5xrw0NDzi9U2RCTCBey/rqq/NeLcXfFK+2Gp0stCMl/T/oAT5V3I99fSy7N/1lLly++U/O7rBWup8qkLAT/83oCL4wY6VR1ka5pU3f10OPU8NgGpHQra8xSK/f0NtjO2tXvra58eXSZlcr3Hsk2Xsd05TDyFtPswOYmENXR2wgXtVQ27NCeJCMm6GwsHqabM622a0mTSTK1vr0udKOrTyffX193j8dw7epXSXg/hbEvo5cpSbwfvihxqIQRmd6V932Z0eXU/mzgFk6fgxpt0snLHVpUAzF8FW6Izs8zWc5qRkNiFjf1nL8H5x+HGm+7/EbQS53KfFspK5Rw/sY+Wcc6EroNEG503apGhywQ2W97R88Pj8XiOyMMi8j3V478MfHnL/nGgA6yIyBnghw5ycRF5XFW/rqqfBW7jAvI1GFJ4NvMF4MdFJKkWDJ4AviFucvnfAS+r6v/jIGMY4IP2wyICYURockqJIc93TTnz3KeoZWb1Tbj1+r0eyQNDaZVvXi/57dcKrq9ZvuuM4c89GfKeGbNhdtRegDCGpEWfPnFlQjcgMQGZLd1EsL5/MzpVl+65Va1a1iUMhvGuhbyPqqVov41Yg4knYGUNZmddXebMmFukW1lxJzfGiYgpO4uUWmLVctPeICDgjJzd9Do1qROHStf6RSCP56TTzZ0JXWCEc2Pu3nSjPfp7vpuPzuAZMCjH2bcZ3UBpjxM49zh5v0eydH339Phb70BjDBtZZhqGD1+6iBZraDm0oHnpgzB9Ft56Hpbmtl2iVWUKtDPnHB+a7f4fo9Cyt25CN6AeQlsnUS3W6909Ho/nmHkV58T+MjAF/PLwTlV9FpcW/wrwD3Hp7gfh71QmdS8AXwWeBb4IvH+UEZ2qvgj8I+Al4HeAn1XVEvhXgH8H+Fer8769W9u4Ufj0+KMQxoSSUZoqyyLPIdlHXxTP/UN/DdESst1ddz37550V5dV5y+PThg+dMdRHTUI7i9CcBhFS2183oRuQBAbF0i+gPuwg39rdA6Wbg9XN7d4KzWnrGhMySfDOM2BC7PlL2LJLFJ1C+rchL2B2FkyEnZ1A7QJy6xZMTkKtQRjUXF07PTraISPlgrlIIJtvsXXqxIFQmj6Z3V/KqcfjuTf0io0a8kYkTNaEG2vK+0dkeney3dPIh9Pj98V6TXsMU2dIozEmb71BK35k9PGdFVhbgkc/SJkuYOJJTP009K5TpguEjXPuOBF47yfgmd9zBndTpzddppUM2r4py31lsibr2Ue7oYVruzmstNcjYT6dBASbLmGinYQpj8fjOTSFqv7E1o2q+pmhxz856kRVvTT0+GngM9XjzwOfrx7/hRGnLgKf3GlAqvqLwC9u2fZl4EgTP6+0H4UwIpBKaQefIv8gMkjT9kH7sdFOXZrkJy/sELCvLcLqIjSnhkzoNjsh1YIApKRXAGHi/t+H0t4Z4fC8rMsATJYNKAs062Jf/X0MCYGpO5U9CGFmxint4y2Io426dhGixgxBp8OiXWBFl5mSqc293isCCWmYGIIeqfVV7R7PSaabs6nv+rkx4XZHKez2VO9urrsq0tFhgvYgdP+L0J55lCRdoZXu0G997jKIwU5OozYlSGYwYQMTNLDpwuZjgxDqLSi2O2I215V2F7Tvu549bzsTumCjXWYthG4ZYKIxbOZbv3k8Hs9R8EH7UQhjAjKKQdDuzegePAZBe94H9eUPx4GbCO/Q91cVnv19ePtlKMohE7rNGSxJEIDY9ZpTavtzkG9Xc9RBXahVy4ou05QWUV4AUI6PQ5kR3p53bsur7aGgPQYRdHZqk4O8aU2SdFP69EmoMSM7my6Nh3UI+/RLr7R7PCeZbq7Uw42/07MtwSrc2mJIlxaun/vW1HVVRa27r8SBIHKQmvYUoo373vLEQ1gT0bw9olTLli5onzmLtauAYJJpAExtBpuvouWW+UkQQbk9aI8CIQlhvqekBUzuM2jXorNuQjegFgqlhTKcxBadzWn6Ho/Hc0RU9W1V/eC9HsfdwgftRyEMMVI4IzrwSvuDSGcJFXHBpDejOxY6udLYSZG68Qb21newxsKbL5ItXQPYprTXq6C9X1QbamPQb6+bw+1EO1VENpT2VV3BYpmSKch6qOYUYQ6PfRqDgXeehdU1GB+HOAbj0t11dhKWllxJDEBjgrgUgjTnrDm3azrpRFgHKUl3MKzyeDz3ntK6oHVYaT/dFEIDN7cE7YN2b03Tp+zPU6y9Tbb0Itn8N8he/x1s5uq54+AgSnsG0caNslMEdGYeIV6+Cb3O5mMXbjjV/PQlyv4CJp5AqnuVSVzJULlVoQ+jkUo7uPKhQXu7iX20extlQgdQr/7tMpkE8Gq7x+PxHAEftB+FMCYko1AftD+QpF0oUtJo3D3Pevd2PA8I3dy1FdqELeHGq/DHX0DLNvkHP0iha/DK16ivdDCy+VZVC8xmpb0+7q6xRxlDZ0jlV1WWdYkadWpSh6xL2V9EooRw9gNw6eOQduD6WzDpPgMyWKA7NeUWCG7fds+b4zSlxcO9KWLZ3bVpMmxgBLLQZ254PCeVXrUgOFzCExjhdNPVtQ8oezfpL77I+eJpGt1nyFdfo+jdAC0xfcW8+Sq66BYf40AOlh4/pLR3cqU8/RiIgZtbnN/n3oGkjm3W11PjB5iwgQmb2P6WtpjBzkF7MxaK6va0H6V9w4Ruc9Beqyw9UhqIiX2/do/H4zkCPmg/CmGEId9Ij/dB+4lloXuIdjNdpwp0a1XLLh+0HxlVrfqkD21cvgGv/SG88U1UDMUHPo6cfg/FpYuU+SJjrzzv6tyHiExAaJTuYGZZqyaLvdVdX38t26g77dAmJ99oyZb1UE0xzXNIkDgjvNY56PSgf9O1lBso7TPVQs4gRb4xjkEItypgI4gkJjYhebzf2bvH47nbDBYEh5V2gLNjhtXUdaFQteRrb5FlfXoyTX3yceKpD5Gc+jTx9IcIZcKdlLn7glPaD5ce38mg1qzD7HnnEj8IuPsdWL4NZy5hs0WGU+MHmGQGW6xtTpEPI7fQOaLrzeD+nIQuxX0vNkzoKh+PsoCyWC8t6BVg4qmq9ZtfrPR4PJ7D4IP2oxDGGGMpGTjM+KD9JHKrbfnfXi+4vHzAyUJnEYKIdOB4eweCdlV9V/Wv7RfOvb0ZiUtnf/MbcPnbIAGETezpC+jsGaKJJzDTT9F7/CJBuoJ9/kvQXl6/jsEQBUK3rOSwpOVckfu7txXqZKy3ZVq2S0REtHABv6ZtNIqRYCgftJ3C1HnnzvTWHyO2RCREI+Oc4wdmdGEEtYZzcN4HdalTeqXd4zmxdAeVL1uygs633POba4qWfUBZNRdZix6j3jqLiVobdd19952h6UbQnh82PT5Td98897gLiufecTvmLrufpx+qXOMnnGHmEBsp8kOGdGF1TLF93jLorrHfevZtJnRXvg2Xn1lX2nsFmGQS1RLN9/Ye8Xg8Hs92fNB+FMIIE7jaN8AH7SeUdvW2vDB30KB9CZpTLh0xrkN+/EH7NXuV27q9V+6DSicH1DKx9Ap85yvQW4Hz74fpS1Aq5akZN+kNauS1JunsE+h7P0iZ3sQ++7vrQXEgAbGB/kBpNwHETUh3nhAWVunlSiuCvvbo0WNCptbrzzVdgyjePOFdmHf18h/6Prcg8NbTgAGbw+nTm8zoaIzvO2hvSp3CKLmOTk/1vHt5c8ktMtp30WLeSWQQtNe3KO3jNaEZC9fbFi1cOU7b1kf2T5d+FwvrZTv7TY+XsnDGp5XSnpVKVlYLjmNTMD4DN950KvmtyzB1Bhu4RYRgi8oOYML69hT5QdA+WPgcYpCNdDATuqZbrFB1352dJZJAMQL9QjHRJCC+rt3j8dwVRORXROT9hzz3R0Tkb+6y/yO79VgXkZ8TkddF5FUR+YFqW01EviEiz4rIiyLyuYOOywftRyGMMQbEltgg9O7xJ5RBmuNyX7m6us/AvUhdPXOzSp2Oanek7VtKnzVdxb5LUga7mTLWu0Fr9R2YPAdPfh/MPAxXX0NrNWwrWU/t7Gsfjeo0znwP+tTHKfq3KL/129BdrZR26JdDM+A9HOS71eJNKxGWdQmDYWKQvqoKWQcN44269SKHpRVotuDMI3Dxg9BdRtIUtTmcOQP9PqxWKfmNcZeqOiLddCutoE6pQld9K0HPZuY7ykJXud3xQfu9pJcroXGB9lbOtoS5tlJWQftaUdtc8lORd5do0yHvu3tEHEC6j/T4YODqXgXtnereNWjHxvnHod+FN77tMsDOPILtLzAqNX6AS5Fvb6TIBzsr7eOJYARmGvtIjVdF8/aGCV3WI89WKPI16K1Rj4R+DmICTDSOTZf3vKbH4/EcFVX9aVV96ZDnfkFV//Yuh3wEGBm0VwsFPw58APhB4JdEJABS4F9V1Q9X5/+giHz3Qcblg/ajEEaIAWMzbBh7pf2E0ivcZKkVCy/tV23vLLufjSpoj+vH7h5faIGt/mvruyNlsJ0rcd4mDg1c/C7XX33hOnTXsGfOgggmdpPOlD4xCUFYJzr7CeSD30uZLVA8/Y+RXndE0D7mzANHKEfg6tkBkqigrW0mZGLD4C7vO/U8SmCgtGd95xx/umrfVncBviiozZzSDhsp8vWWC/7TvQPx8bCGqrByB7I3PEenXyh/+E7JzfbdX0xLq4/0lZVDBO22hBuvYKzP4Dgqg9aUozjXcor5SqeLBDU6udl+bJFT5h3XO72qaY/26R5vqjZx60F7tfA8KO1h+pwrx5m77FLop8661PhofGPRcQtBMgsMpcivp8dvv1/WI+HPPRnyyMQ+TeiwmNDVs2fta2T5AlmxDN0laiH0Cjd+k0xhy+729nMej8dzCETkkoi8IiK/LiIvi8hvikij2vclEflE9fiXReTprQq3iLwtIp8TkW+JyPMi8lS1/SdF5O9Vj/+iiLxQKeR/ICIx8AvAj4nIt0Xkx7YM60eB31DVVFXfAl4HPqWOQQ1nVP1/oC/68MD/Qp4NBkq75ljjg/aTSq9Q6pHw3hnDH19zE/GzrT3WqzqLYAw0KiU2akB+w6UsyvGsdeVsTKzXdJVxJo7luieZbgYN2yGsNV0NuipceQXqLcpmjJQlpjIzSjWl7u69iASEpz9C+aEa+q1/hrn8PNG585RqSQslCcUF7QBpGxqT2157oFbZaBmACZna2Jn1UFu6mvbBpDftwVobTp9xz8OYjlpiiwvwZ6YgDF3Q/p73DJnhtV0AvwuNSCjSiJXSB+0nkRtrypUVy5UVeHxa+eg5M1JxvRP0qwDn6qry8fO6a/vAbXQW4fZb1NPdvR08e9MtdFtq/IAzLddzfbndYWy6Qb/YSClfp7eGVYuG4VB6fNU9tFSiXT5PpswBs11pH7yGCJx7DN56AU49jLV9tOwR1s/teE0Jaxsp8o3zQ+nxOzvI74cNE7oWZdmnv/IqAWANaHeJWnhxvdTAxJMA2HSJoHF2X9f3eDz3B//Gny3/BDCz54EHY+Ef/3bw1T2OeRL4KVX9ioj8KvAzwN/dcszPq+pipXj/noh8SFWfq/bNq+rHRORngL8B/PSWcz8L/ICqXhORSVXNROSzwCdU9a+PGM8F4GtDz69W26he/5vAe4C/r6pf3+N324RX2o9CGCGBU9o1SnzQfkLp51AP4bEpoR4JL+5Hbe8uQX1yI0CP61UK9fGp7UUVtLdkjC5dCt2ueLyxaCnsg5Mm28mVlnYgqVyGK5VdLzyB5qvrqZ2FFhQU1Ib6s4sI4an3QZggWZfYsLlXe70K2ndIke/kijEl/WCFlrSIZGhGnvdACySsrfc3Zu6Wc8077SaXcyq8nGYsF4UzVBI217XXq99pDzM8cBPiMo3olimlehf5k8ZqqojA+04Z3lyy/NPXSq7tt7TmiKQFBAa6ubJw0DWdjuuyEJbHmxX0bqQ3qjVlRRIKM3VltdcnxS0sblPae20slmJsEq2+N5IqUN9LbTfb0uNdqv4mJ/czl1zgfuE92Eo93yk1fv266ynyfQiHyoCOwMCETk1Mu/c6pt8jal3ENlrY9hy1UNZL1EzYQExC6evaPR7P8XFFVb9SPf414HtHHPOXRORbwDO4tPXhWvffqn5+E7g04tyvAJ8Xkb8CA+fxw6Gqpap+BLgIfEpEPniQ873SfhTWlfaCMoidMuc5cfQKON0UAiM8NWt45kbJ7Y5yqrmDkmBL1zrs1GMb2+LKFTfvQtI4lnENTMimZYa2rrGmq0zJxqRrvqt8/WqJ1YAnZu6Oynen6aUl57QHtYc2qex2vImu2XUTpT7VJFeS7ReJYqTIiaqgvVcoEwhEdQhC6I0O2tcypd5wE+lJ2TK5zbqoLZwL/YC5Khg/69SrBWspg5CFvGSMaMOM7rnnXIpplDj1ah9t3xoRFP2IrIQ+PZrsrsx77i6rqXPQ/ui5gIcmDF+/WvIv3y55bMqp7sk+2mAdlrRULo6bSum3zDYOMEdou6A9KnzQfhRUnWllbYegHeB8I2Vx1bKSu4XF5lZVvreGFaUcH0eXViDPiAM35cr3WP8xZQEk64F1Z1SqfhDCYx8CwK5VqfHB6NT49VOSWYrOZcr+AmGtyiDaQWnfL1p0IGzS7b+FtRlN20LGTqEKdmmVBilpGaHqskZMMoXtzaFqN1z2PR7Pfc8+FPE7xVZla9NzEXkUp6B/UlWXROTzMKQIuVpzgJIRcbGq/jUR+TTwZ4FvisjH9xjPNeChoecXq23D11wWkS/iat5f2ON66/g75lEIQkwgrqbdp8efWHq5rreeeWJGSEJ46fYuUkd32QWUzaH06UHQfoxt33JyQkISSahRY0039xhf6bv7zvxhesyfULJumzjABceVys5DT2KzJURCpGqvl2ofQUg23VcrwgTyPnFgQMoNpV1kVzO6Tga1Wo+IiJpsuW7WgyBAwqHtc7cgCmHG1bQvliU2iFkunLO3DoJ2a2GhqhOtNfeltMeBQB6SldDTHqqWMl14V7X/O8mspsp49VGYbQg/+J6AD542vLVs+affKbm6cmdUd1UlLWAsdmZnV1cP8HkoC9eNQYxT2v1n6dAMWlM2dlmcOVt3CyNvr7kPSmNrOnm3TVFLsEnNLQjmfXfvA7Ji9/cmKHNXq27cFK2T6fb0+zKHK89he4vYsrve1m03XIp8yynzQegyyUYY0e2XgQldrj3yYpVGeIbQQtCYQesTWM1olSuosn6fNvEUisVm++u04fF4PHvwsIh8T/X4LwNf3rJ/HOgAKyJyBvihg1xcRB5X1a+r6meB27iAfA0Y2+GULwA/LiJJtWDwBPANETklIpPVNevAvwa8cpCx+KD9iEgcYzTHBj5oP4mkhWLVGesAhEZ4csZwbVVZ6u0wcapSTDfVRUc1FxQeY3p8rjkhTp4Zk3FSUlLdMOhZfsCC9qxUSDuEYcHVYIHu5W+h9RY6cx6bLWKSqXXlJaVPRLxhFDdMlECeUgsCp7QPC0XJ2C5Bu5KElnBUglHWRcNgc7u3+dswNbU+cV4qS6IwxlrLirUbQTsMpci3XE37PqiJJc8T+vki+eJz5CuvYrPFfZ3ruXNYVdZSZTzZCMICI3zobMAPvCekFsIfvFPyx9eOv6xhkDadhMJDE4a1dJf71Fa6Sy5QnziDqD3WBcZ3G70qwGzsUNMOMBF2CY1wtV1DZMSxvTWKWg0bJ4CiaW+9jn3P9HhbbKSvU/Vo37oosHQNlq5hF14HGNnqbeS1BynyRd95cowwotsvWvbIyzUy7ZBEsyRllRlVH8c0ZrCa08iWgY1/UxONu3OLvTOSPB6PZx+8CvysiLwMTAG/PLxTVZ/FpcW/AvxDXLr7Qfg7lUndC8BXgWeBLwLvH2VEp6ovAv8IeAn4HeBnVbUEzgFfFJHngD8G/rmq/q8HGYhPjz8iEkcYm1N6pf1EMpgo1Ic+6e+dNbwyb3lxzvK9j4xIPe0sQX18oyUOOEXimNu+FeTUxCn4YzLGvN5mTVdJxCm7A6V9LVX6hW6uZ7wP6eUQFR2CKKffWWCtc52VJz7CRD6P0XJTPWZfU5oyugxBogTtrBCbgCCw68ZdgHvfFq84N/hoQzXPrXN7jqOSYKvKDs6ILgg2TOhUYX4Bzrv3omctXVXOJDXmOilLZclpm7nuAmNjGw7ytRbcvupKLMzuac2xKYnaayhvUpqzCLiUe889pZ05lXU4aB8wXRd+4D0BT1+zfGfBMlkeqbxtGwM1Mgmd0i4CV1YsU/V9vE5n0d2npqusvP7asZXyvNvoVp0m6rukx1P2aNXqkBnqoWCGDQOthX6XfLKOxgEWt2AZV7YX6X5q2qt69rxU0nJE+v3CFQC0fRNz+lEkGFFKNIKgNkPReYcynScM4yMp7Xk6T5YtEjTfR6P2MNx+0+2ojRMWXcooIqmCdnefFsQEiARu0dNzLKz2lVsd5YkZr8N53pUUqvoTWzeq6meGHv/kqBNV9dLQ46eBz1SPPw98vnr8F0acugh8cqcBqeovAr+4ZdtzwEd3Omc/+L/wI2LiCLGZC9qtPdKqtef4GRjgDAftcSC8Z8ZwecWy2t+iYql16fGNKbYR1Z1h2TGgqhQURJXSHkhIQxqs6ep6ivRyf0PtexDU9namxEUHk8TUrl1mrHGO3uwMt3ovs6ZttOrzW2hOSTE6NR4grEGRYTDE4RalfeDgvkVt71sX9MRhSbDVR8SWaN5Fw3BDaV9ddR4Vs65N0lLVWm48qjFlhJXSUgxqQU+fHmr7NjCj211F0jLlUv0y9d4cZdxCp590262/f9xrVlP3tzYqaAcwInzXGYMI3Ozv8Bk9JGm1AFULnenYqYZwZb8p8u1F1+2iak24U8aJZ2/2o7TbsstEszKh25q63u+AWvq1mGuhQbFo2tlIj99P0B4P2r25bZuU9s4ipG3U5mhvGVPbv2GzBFtS5HdokbkX1uZ0O68hEtBqPuW6HPTXXClZGBGYOrbeIMkWQbfcp010Ihco3162vHz77rd5PCovz1v++Fq5Pt/xeDwPJj5oPyImcenx5WCV26vtI1FVbtmb9PTupmwOJl9bDYXeN2sIDby09Qu6t+ZU0uaIoD2uH1vKaUGOoutBO8CYTFBQ0KNHWij9Ah6dEow8GEF7N4c47xDanKDbo/HwR3kkeJSx3NKJDJf1Cit2eciEboeAKEpc0K5CFNr1HsDupNEO8r0ycP/eod0etOd9954Pt3tbWHCT2VMu/X3RWgRoxjVmUQoxzA8WcM6cgXYbOp2htm87B+1lf55s8VlaQY85+17S8YfoG4tgYEQHAc/dZSNo3/mYeiScawm30uRYfQjWlfYqjfqhCcNKX7cvLm5lUM/enIEgdMaoPmg/NN1cMcK6F8pWVC1apsyMuaC9tYNz/HwSctWIczlKO0TGVVnl5e7v57DS3sm29GgHp7IHIbZegzwl2Ec9+6br12axRcd1wDiE0q6qtHtvonmbWvNRgoEBXm/NZTsBQdDA1lsY7ZHka+sLUgAi0YlT2l+dt3z1cskzN0oW91uSckIYlNDMde6vcXs8R0VV31bVAzmw38/4oP2ImCTC2IxSqi8tH7SPpCBnVVe2ma3dafojlHZwNaOPTztjqXY29EXXrVrR7BS0DwK8IzLo0R4OtR1r0sRgWNOV9Xr26bowVRfmH4Av405mScoOASUGA1NnkaJHS2ucqT1FTMKc3uKWvVmZ0O0QNUU1QDF5ThxsUXDCyO3fqrSXzrQuCdketGc918JtOGifn3fv85DSPm4MYRgzhhJIsBG0D+ra5+Y2+rOPMKNTW5Cvfod89TUkqLGUn6cr59Aypk8PTIgew2fLczRWUxeU79WX/dEpQ2qDY50oD9KmB8HiQxNuDFf3ajc3qGdvuftWHtT2ZYjoGU0vd58BkdGfAS16gFKvNXlq1vDw5NagfY1SLW9HIbdtQDcM0KyLiBCbPZR2azG2XC/vWe/RPviqKDJYvQmTF7CmxFhB5GCVjoMg32r3UC3fsnyBolgjNmOEcVXWZEtI21BzC6eBqWHrTYScsXJlfQEdcBlNJyhof/m25ZvXSy6MC3EAz9+6f+7DVnV9vuCDdo/nwcYH7UfEVEZ05WCyn6a7n/AupV91VOjr3W1F1CsgClg3ABrmfacMApvT4TpLLjiPRqi8cVUfmh/9dxi0extW2o0YWjJGW9ss9d2YJmvCbENY7Cn2PneDTrs9EqMoIGEEYYRNFwGhVjvDxeAhzppzGAwJtdEmdIBU702YF4Thlpp2cJPGLQFL3wYkYUlgXCnCJrKuU7jDZMPHYG4OWg2oufd8sSyZCQIIIgSYNiHLeUquCjMzzqxubq5aNIhHmtHlK69Q9ucJGxeJpj5IMOgHX9Tc34UEXmk/AaymuqvKPuDCuBCK5a2lYwzaq89yUn00GpH7+98zRb694OrZq7KeIqxD1jmWBcZ3I91cqe+SGq+l8zaRsMHHzgdcHN9yr+q1WY0Mi8ZQUCMNzXrJTBzK7kH7QPleb/emBGZI9V++Dtaik6ewxiJhE9KDmbpJkGCCBlazQ6XHW5uCzQhNDRMNsouqBfn6BKmmZOQEySSlgbFiZVt6vNqTIXC8OOfU9UcmDX/ykYD3nXJGtQvHnN1m8zZl79axXhNgue88OAIDt9r39xzB4/Hsjg/aj0oYE0hGOQi+vNI+krQK1jPSu9rWqpdDfQcDt0YkPDpleHPJbtSCdZZGq+zgatrhWFLkCwoE2eZkPibjLq0ya5METu2ZbQiFdV/O9zODdm9W7foCiE0XXX9hs+Gi/4h5lAvm4s4Xqt4Hk6VEpiQtlcIOfaZqLaf46MZiTK8MaCTu+fb0eNdyjTDaqGm/dRPGxyCu0bGWVJWpIFifSM9KgGrOjaKAIHCK/LqD/Ni2oF1VsfkaYf0cYethRAxJ4MZT5nVKSgrxNe0ngbW+MpYoi3bBfVZ3IDTCqSTl8ordM915v/QLiAM2mZpdnBAWurqeJj2SzpKrZ6/MD/Og5pT31Kvth6Gb797uTYsuIEiwQwlPb42bcUSmijUJvTDA5i7Qj0zVSWMn8mrhf1DTnkFzWPVfuALNSUrJ0biGOUTQDlCagFLsodLjrRZIWYCIWzSA9eymMmlwzV7lpr1OGNQpk5hWsbxpcVVMhGrh7rv3kOduljx70/LolOF7HjIYEd47Y0jugNpe9m5SrL11rNeEjdT4S5OG1cq01uPxPJj4oP2ohBHGQDEIBHzQPpJBKzNFSbl72Qi9QnesSwR4/ymDVXhl3rqJT5GONqGDjV7tx2BGl5MREm5Lv6xTJyRk2a4yUXP7ZhuVGd19nvpW9DokIVhbIrUGWvSr/sKbWxUZMTuq7LChtJsirzIotprRjTlTyHTD6b9vA+rxDkF71nOtj8SARNDtQnsNJscgSlisTOimgwCq2s0JCalpwdW8euEzZ6qUelv1at8yibYZoJv6wCfGXbfMKpdoKb3Sfo/pF86pO6l1WNB52rp70Hs26VNY9m8Wtwdp6Up3hnmoUnF3fI0y36hnrygGwaRPkT8U3Vx3N6Erupigvt6icitFd5V34pCaBEybBmkYUVSdR+Jgj/T4rPp+HNS050pzYHTXdgZ0TD+M7S9g6lNuofEQizOLrNAxHXfPOmBGhmqB2ByRAAmq78XeKgQRS1GX6O1XkHdewZqQsl6jZjtkQ1mI62VI93CR8ts3S16Yszw2bfjui2Z9oSwKhPedMlxfU24f43eu2twZEh5zWcBiT4kDeGzKfRbnvNru8Tyw+KD9qISxy2odPPdB+0hSUuq4L/f0LqbI94vd2/aMJcLDE4Y3Fi3arnpk76i0Jy6wO4a2b7nmm1LjB4gIYzJORzuM1dxEqhkL9UjuazM6q0rZbxPGMVpkSNKgrHqSB8kO/947EbuAJMhSogCQLSnyWxzkVZV+aajFboIYjqppD1yPdhFxwXdZOKU9SlgqSwwwYYxLfwcEmDHKjTxzKfKnT7vOEYuLLmjP+pvSTrV0n3kxG0F7IC4Nup/FBBpwQ1PK8uTUeb4bGRi+JYkLMLrsHgxNRAXjifDW0vEohv1C11PjB4wlwmRNuLKyw2t0lzfVswMUQeJKNrwZ3YHJSqWwu39vaNlDwvoOF+iznPdZTEKmCXgsy8iikF7ahbIgDvZKj6+C2yqrp50pjcFYFp0BnbamscUapn7KLSaPUNqLskcvvTEys82qJTMWSzWQA9a1O6U9R8LmxsJzf5W83mClf4vWjdtEC3P0xWJrTeIg2/CLAeceD/fMjO6ZGyUvzVneM2349AWzbfH8iRlDEsILc8eotle/63GXBSz1lKm6MNOA0Pi6do9ngIj8ioi8/5Dn/oiI/M1d9n9ERH54l/0/JyKvi8irIvIDW/YFIvKMiByoRzv4oP3oBE5pL21107/HNe232pYvvVWcqPrnQgtKCpoyhsEcWGlXVa6sWK7tZcY0gl6u20zotnJ+zE2i2itLrqY5aY0+UIwL3LNjqGmn2GRCN0yQj1FYpdbYCBhmG8LCfeZoO0wvhyjvECV1l44ZN1xqfNjcOcV0B7SaLJsiJzICskVpT1rOorlSGXsFKEItsgiC2Ra0D9q9DTvH5zCxobRPBYFTYqqad7EwZQJUC64XhVPaYYsZ3cZEWkv3md/6uzYjoZsrGTHXtMeqvc9rIO5zVgciZ+QedLSzZznPpUnhVls3G1oekrSAZERL9ocmhNsdHd3SaUs9O+A+/0nr2IP2VbtC+YBngwzuJTsp7aolWvaRsLHDBdaYK3N6SZ33rCzx6OUXwSpdm0OWVkr7PtLjo4TCKmkBrRh331y5CVMXKPNlAEwy497nEUF7P7tBL71Omt/e/hLkqAkoAueEf+Cg3WZQBe2AK0Xqr7GclMRzNxmXceJ+QU9TtNYkNAVBb2W9jGm9DOkuB+2qlm+/M8crczlPzho+dTHYHLDbEkqXwfX+UwE31pTbneNZkBssUGh5fEG7VWWpr0zXBSPCqaYcOGhXVd5YPL4SH4/npKCqP62qLx3y3C+o6t/e5ZCPACOD9mqh4MeBDwA/CPySiAx/s/9HwMuHGZcP2o9KFGMCUKtO2bjHSvtby8r1NV2ffJ4E0qqFV00SEmoHUtqvrVp+5/WSP3yn5BvXDvblmVeKSRLppv7nWxmkn7cXq3r2HRyDAVeLnR9NabdqKYd6tG+ll8VQJgTJxoR7tiGs3cf1at3c9WiPosAZ0UUJNl/dlhq/F1aV30kL1qxF8qxS2sv1VlmAq+uNm5C6f79BLXASlRi2TNLKwk2Gw2Bd/WFhARo1iCI0jFmy1tWzw7r6hVVaRmhq6VLkWy1oNFxd+yBoH6prd0q7rKfXD6hHrn420wgrSmaze17n+W5mNVVCAwQpIREWS4/dy2EerdJS3z4GtX1Uejy41m8AV0elyG+pZ1+ndrxBe64Zc+kb3LLXj+2aJ5FutTCyKWgf+pt0zvEgweigfa2zzIItaYQB5xZuUJMAY4SezSHvE4d7pMfnKSAQxXQHzvGxwNI1N47ph7DpIhLUMWEDkqqmfej7TdWSFysIQq9/lbLc/J2bkaImQEMD6gLVg6Clc8JfN6FLO6S2TydRpudWCUxETWrk6RoahEg9ppYtbyyuDpR2vbtB+62F26wuvMrHmi/y0VNbvsc7i/DaH8Kb3wDgiRmhFsJzt47pfjxYoDhGpX01hdLCVN3dM043heW+bmqvtxc328rXr5ZcXrk/5xaedzcicklEXhGRXxeRl0XkN0WkUe37koh8onr8yyLytIi8KCKfGzr/bRH5nIh8S0SeF5Gnqu0/KSJ/r3r8F0XkBRF5VkT+QERi4BeAHxORb4vIj20Z1o8Cv6Gqqaq+BbwOfKq61kXgzwK/cpjf92B9QjzbCUJEQLMM4vieB+2DuuelnjJZ271l0d1iUM8ek1CThGVdRlV3bKcDcLtj+fZNy+2O0oqFc2PCjTWltEpg9vd7DVrMBFGPm/YGF01Ine0TrbFEaEhKt9OGRx7a/aJxHda2KxcHYdDuLdpBaV/uK+RjBOESmabEkjBbDXu+o1ycOBnv60HodDOMzYhDQw8w4t4cEx8saF9aWmLqC1+gKBYxD2dEBozZ0qsdqoDFuRmvVX+ScVRuT42v/AmsMZjhdm9jDQgj1oBclWlTrW+KcX/zqiDChQC+UxRkqsSnT7ugvVapT1uCdjHxthrYZiTc7li6GmJNQEru6jy3BPeeu8NqqrRqOVYs0zLLgt6mq20asoOqiguozrSEt5aVD5452uunhVIbEbRP1oTxRLi6anliZugzNKhnP/X49ovVxmDperUodfTPU1q2iVfeod/osDY2xZiMHfmaJ5FuFVutvw83vwMr1+Hx74YwqUzo2DE9fq69RBvhPYvXSUyI2IDABGS2pEh7xGYSq1BYJRz1XZZn2MBNzdrVAkIzAm5dgeYUGtewq6uEjQvu+KTpFOK8v+67kherqFqa9Uv0+lfp9N9irPHU+nduqilIiDUGqwXBAZR2VUXzLoJZV9q1u0Jb14j6k9RzAw+/l/jy8wS9LkVzDKlHJLdX6eeWsSRYV9rvdnp8t99GMZwfU7KlFwgb5wkaF5Dbb8HcG27hQ1JQS2gM7z8d8K3rJbfaljOtw2tcaksUWz0+vjnioJ/8dG0QtBvAMtfR9XaRezEI1nc1uvR49sH3/IfZnwBm9jzwYCz80X8Tf3WPY54EfkpVvyIivwr8DPB3txzz86q6WCnevyciH1LV56p986r6MRH5GeBvAD+95dzPAj+gqtdEZFJVMxH5LPAJVf3rI8ZzAfja0POr1TaA/xr4T4FDfYF6pf2ohJXSnuX3PGjvF8pq6m68g76dJ4GUlIiIQAISqaEo2Q4p8os95UtvFfzzN0raGXzyQsC//mTAI5Puo9o5wHf8IJU0Dp2sUbBzWuf5YIV2ys4mdAOiulNCjtBKqRgE7Tso7at9aOg4USCsqVPKXPobzN+nKfJp1/0eUTgYf4aYBBM193+Rdpv+P/knxCur6FIbk2cgkER2Uw9goHKQ74It1ycjUVCO7tGOoqFBgurvd3UVxpqunt26ida60g4uRb5K8zxvXFXojaKACxfcuSurbvK8JT1+VBlAI3aq21IJVgIKtegDnn58kllNoVF39/C61KnToKN7O3M/OmVYS4+WSpuVilVIdugPf3HCpeFvUtHW69lHLH7Vxt3PY1Lb89L9OyS9NebLm5T6YLaT621V2tM1dy9559ugtmr3Nto5vlRlrrNIUhbMpmuUZ99DEtYIAkEpWOl3iav3d0e1PU8pqzKcQY/2Vrbo1PRKZQfdyFJKNtTu9UsUSxgJiMNpGrVHKMou/ezG+v6MlOTWVcL2CtbmB0qPVy1g3YTO/Ru0ezcopGR6OcfEdTj/OJFExN2UvlFoJIgWZB23kComRDDHqjrvhzxrk0uT5uyHCWqnKFbeonjuf8JefxEmz8G5p1w2Q1Wi8J5p5yfz/FHVdpsz31FeX7DHmh6/1HOZQWNVi8qD1rVbVa5WXhld/7XjuX+5oqpfqR7/GvC9I475SyLyLeAZXNr6cK37b1U/vwlcGnHuV4DPi8hfga2TyP0jIv86MKeq3zzsNbzSflQq93gtMqjd26B9oLIH5oQF7donEfflnuB+9gfbihzat8mWbtG/cYXffaVLFMd85JzhvTNmXYloRRurwePJwZT2KHSBVaGFcxAbwWlWuGmFXjjGDvZCjoGDfNbbMDw7IIMe7eEOQftyX5lMXFbAmq4ywyyBEabrct86yKedDpEBEQUsIhkmOTfy2L5aClVaw+m+3S789m/T7vfJx8exV3KkalWURJb+1lrfWrWI2W/TyVokpkSlJJAtimPWdemhYdOpP4sDM8L6ej17CIybofXNIEKqYH5CLA0RruQ5jzz+OPzRH8Frr0GruVlptykm3r4g1IiEQixrJTTFUKi9p47K72YK6+rSzyQpghAT05Qmt3WOXDOirZ+dIR4aF5428OaScuoA61DDDEo8thrRbbyG4aU5y7U15bGp6ka2Xs8+uf2EYUPG1tHFj7xsIwgTNJlLl1ioz3NajphacALp5u49WM/oylO3UNdZhBuvYBvs6Bx/w2akq0uc6q8QjT0E0w8RLXYI+yFgXdBe3Zqycoe6+TxdV9o7uWIEaqtX3BgmzlKuvloteFbv7yCzJ23D2CyqSlasEIeTiAhxNElSzNBLbxAF44Rhi9T2aa0sQ7+H1fqBgnarBWiJGPdvUGpBp3eNyDSpr7ThoSchCCFp0Oxb5oxSq9cxrFG0l+FMdR800V1V2l3bzQ4SnsYEEcY2CeZXKPtt8lPjBFNTBLbmpgh5D+I6oRE+cMrw9PWSm23L2UOq7ao5S31lua/Y8vhqFxcrE7pBBoUR1yJ2v/3ab7Vdtwwj0PVKu+eI7EMRv1Ns/fBuei4ij+IU9E+q6pKIfB4YXnUd/FGWjIiLVfWvicincWnt3xSRj+8xnmvAcNruxWrbjwA/UhnY1YBxEfk1Vf2JPa63jlfaj0oQImHglPYkuadGdLe77gv+oXGz3rvzXlNqSU5OglsKjogIsoxi/g1XO/by78PlZ3nrym3yNOPDY8v8uadC3n8q2JQ62Izd484B1kQG9XNhULXW2kVpn9Yl0miC2709/iTW274d3jAsJ8dgCGX77FxVWUldacOYjJOT01OXjjnTEBZ7eqJMBvdL0WsThSG2LCDA2cGNqGfvaskfZqt8OV+lO1Dy+n347d/Gttu8/mf+DN0LF7C9DHL3YYjDUUr7IGhfYy1T6kFJyU5KO84V3kQuNR6gWYN4w4RuUylHGCPWuvRQzbkYRdwsCrIkgYcfhtdfr5R2F7SrlqjNkCDZ9vs2I+iZkqw0aBBR4JX2e0V7UEYRp8QkGDE0xQVEe6ntUeC6UFxesetmWwclrd72nVpUzjSEZrzFRX6nenaAqOYCvWNq+1aUPQICoqDFWD9lRZfp6dHbX540esWQWzs4N/fx0zB7CebfgcUrO5rQXcm6jM+9TSupkV94nJCQMK4TWUUiw2q/Q1y9v9lOdcd5ih1S2seCDFmdg6nzKIrNVgiSoUWYMHFBcqW0F+UqqiVRtLFIWK89hDExnf7bFDbDpqtExAhVEH6AmnZVV8IzuJ8t6ALSazPeC5wfzJlHYG0FFGr9Eg0iyhoQCsXahoO83O2gveyRl5YwqMHVF+DytzHN04Qf/jHMqfdSdK+Sd950JnvZxuf68WmhEcmRatvVZiyXJUuBkOXHI+yoKks9Z0I3zEHq2t9Zce3izo3JelmIx3Mf8rCIfE/1+C8DX96yfxzoACsicgb4oYNcXEQeV9Wvq+pngdu4gHyNnVPcvwD8uIgk1YLBE8A3VPXnVPWiql7CGdX9/kECdvBB+7EgUeSU9nucHn+7o5yqpZwOrtMvOBGmZYM0+ERqMP8O8p2vMPXa88iNV9xk6NQlLp/6NN8a/z5m4oz3NdfW0weHaURuNbg9yj15B/qFEhgIAvdlW7BDPmJZ0CpXyWpTe7dVi6vJ2hHavhXkO6rsa5kzlpmoyXrAMJgYzzaEwsLyfWgwnnc7BPUGmnXRwGAwSLQ5UyFVy9eyNWy1SPqtvINNU/in/xRWV1n+/u+nc/o0ydQUpQXWVjEYosBud9WOGy6QSdt0MqiZEovdHrTnPTR0s2gxsQva63UIBRsmLJel688+TBBDma0rRQ9FEZYqRf6JJ1xWwErXLSrk2ZBz/PagvREJfVNSlNA0ifuMeqUdcIaNi3aBTO/OPXXQ7i2MMhKpFhklJiahs0e/doBHp4S83MEsbh8MJtk7pccDXBwXbrbVOT2P6M++jdrYsaXHl2WX0CQEjfO0CkOUF8zZW3u6699vdPMtCnieuq4h555EG5PIzTeQfPvfaEdLVm+8SjPtEl14EpvUCCSAsEZUWIgjur22Mzpkl/T4bDhoV06l1ysDuoex2RKgmNqW93zIQT7LlxExRMHGfNJIQLN2idKmrKVvE3TbxJIgalHKg6fHa4kxCammrKVz1MuQaK0DU2cgacC3vwpvfoe42ycgISNHawnaXd64kInuqnu8Fh2KrODMwouudd7px+DxTyP1caLxJ4gmnkIDQ9m9gQ59vwdG+MBpw3xHubF2uMBdy5wVLbgdhfSy4xF21lIohkzoBpxpued79Zi3qlxbsVwYN4zFsm7A6PHch7wK/KyIvAxMAb88vFNVn8Wlxb8C/ENcuvtB+DuVSd0LwFeBZ4EvAu8fZUSnqi8C/wh4Cfgd4GdVj6eezKfHH5KVjgsIW3Xn8ipljg0jzD0K2gurLPaU7xqbY7y8QaTjLPXGODd2b03L+pUJXZKWcP0lqI/DuSdZakVM1b+LfgF//FrBTEto1UPoroy8johTmQ6qtNdCoayC9WInBbO3jAFqE1Pc3itoD6te7fnhFaZcc6IRKjvAShU4TCRCIIaQaH3hY+ByP9/Zvrp+0tH+GuHsNJrOo6HBmIThDhi5Wr6Rr5GifDoao68lz3SXuf7Pf4eLi6vw/d/PrdOnIU05PT2NNRHF0jIBAXFo6RduEmIGirgIJE3K7irdXNd7tI9S2hkO2hcWYGoSipxOEFGypZ4dnCpfZOtK0UwQ0DCGy3nOIw8/7DJurt6E007l1CpLZFQNbD2CNCgI2zmnvvEMvcfr6JQP2kstuWGv0aNHxl0K2lNFJSeOSmpDmXNNabKsS5RauiBsB0433T3qrSXLpcmDr4en1Vf6Tko7uDT8V+fhxprycLC8cz37gNoYLF+rDLYOf8+warG2T2DqBLVTlO13mE4tt6KUJV1kWo7be+je0c2VmXr1/hW5C5ir+75eeC+6+ALBjTdg4jGXzVBxNV1j/PKLmKjB+EPvoUeHkBCihNBCFhhM1qVbGZJlo+K/sgBbYo37EKz1LY/2rrqU8loLu3zVGVqGW0qzkia051FV8mKZKJxkXktuFj3eE9aoS0AUjlGLz7CUvkXQnieWmJ5VrLEHUtqtFpXSXmfezhH1u7TahftePPsodNvMrSwSFCUzC/OM5SHLBmjE2NWeW6wPE8RE2GJvv4jjwuZtpNcmtsAjn4SJzaUdQTIN449SBs+gvaVNlXSPTwsv3Xa17efGDv633csyCpSe1OkWK6jakeUVB2GxmitsnQtM14Wgqmu/OLHz+Ter1PiHJoR25hYA0kJHdq/weE44xSjFWlU/M/T4J0edWKneg8dPA5+pHn8e+Hz1+C+MOHUR+OROA1LVXwR+cZf9XwK+tNP+nfBK+yH52kuW1666m6aJI4zNscG9U9qXes7EaCrq0Igg0ZUTochm9F2KYLuqFX74o4SnnsAmCRkp37hWUih898WAImo45WiHllfN6GAOp73C9Wi3uocRXcel7I1PTbHU093TW0Ugrm1KnzsoOTkRo+tj14P2ai5Yk2Tdfb8ZO1OcPbMBThhpViBFn6jeRNMuEhrXqqiiVOXpvM2qLfl42GTahJzXgPf93h+yePM6i3/6++Dhh5kvSyaMYWxmBhuE6OoaRoUodJ+XdESKfNZxCmkSuklpsHWxpOrRDjjlZ3kZxp1CtRw6tWu00l6ABOtK0UNhyK2iIDMGHn8cbt6GooB+p2r3NjpoNyKUkeXUC6/QevMa4c0FigO2X3rQyDXnqr1Mnz4JCV3toNtK1o6f1RSa9QwjrCvt4IJ2Remxe3aNiPDopOtycRjVal1p3yVoP9V0baiururu9ewDai33WT3CIiNU5pllRhA0EBNiaqdI0g4trbOoC+Saoar3vQN1WfVFX1fai+pLNHSfB6XAnnsUUQOXv73+XWVVWbv6AkGvSzB9EWm6LKmAAKIaoYRIIIRZj5VqEXmk0l4ZoJVBRDtTTHeRcenBzMOoLbHZMiaZ3t55JWlCnlJkS1gtWDINvpGt8U6Z8qVslTeKPlaVenIea0KC1RsYFZAAq9Z1GNgnA6W9LyVdukz1I8zyols8mjwNc9e5VZRcq4/B3A1aPUVNhDQsuQU6y8BGevzdytSweZt+EbpMvuZow1kTT0IYo735zdtF+OBpw3xXubZ6cLW9l+XkYsgloW31WAz4lnpOOBrfksAVGFfXvpcZ3eVBanxLaFT3HJ8i7/GcbHzQfkjCUBhkyEkUITbDBgnkOdi732f5dkdBlfGgQxgIE8HqiTCjS9XVh7J226XwJY31+vbXV3pcW1U+dCZgvCbkUcO5su9Qg9mMhfZBgvYcahHrSvvImnZVWL4OzSlmx2KswsJeQXFUP3TQXmqBxe5iQuda0A3q+WMSMjI3scKp7fdb0N5tu/ezFseuB3lg1tslqSrPFB0WbMFHoiang9j9/fyLf8Ejtxbo/qnv41sXT9O3JfNlyWwYUmu1KKME225jCktUlT+MqmtP0z6mzKhFbuemlm9FDmXh+ghLiGQ5lCUk7r1ZkpBYhJbZcpusUldFZb0m82KVIn89z12KvBi4MQe9tmv3Jhttjjb922hJZFMmX3qVUAIkzcnuchuke8rSNbj91vrTvva5Yi9TUnLBXGTKTFNSYqM7f09dTZVGbWBCtzETrlEnINhnivzhe7b3C+f8PLINWIWIcLppXEZQZ3Hnevb1wW8YMh6FjByxOVHVmzxonEWxTKcgCHP2Fi/MWf7Jq8WJKMs6LIN7SH1Q015kzJcl38gtmSq26CJJCx76qFvsvf4yAAsr17HLN8hr04xPnKKQyhSWEKKaWyw0UC9SVir38JE17VXQboOIpZ4y3r1KoxbD+BlstoRiN9ezD6gc5LPuDRa15FkbMGVCvi8eZ0ZCXi66fDlfZVktGk0T9lNS08eYEEvuFhj3SWkzUFgxHWISWsttyFI49xiIUM5dZ6ne4O0nvoui16V27QYmqFE0DVlZQm/ZXcjEgMJd8PBQVbKsg7UhYQDWBCMXC8RESG0C7S5s2/folJCEhyt/6aY5ycoSF668Qscej4P8Yk+ZqslGdtkQp5vO/2anuvaBa/yFcUNghMbAM8inyHvuM1T1bVX94L0ex93CB+2HJApcOhGAJDFGc8pBb+X87k+6b3eVqTglNBaRkKlwlaXuvW3JY9WSkVHTCNqLaJXGGRFTlMKLC11ONYWnZqsWOAP1dbjubYhW7Ca2+zV66hdKIxTKKh3RYteD33XaC66dz/RDG+nn+6lrP2TQnlcLBzulxy/3dV1lhw3FbzhFvp3pfTUx7ldqdy0MsJq7Ra5KdX6+6HKzzPhA2ODCoOb7j/4ILl8m+FN/ive970Okavlq1iZX5XQQUDOGbHICbXcJi4JwELSPcJBPC0iKNkllRrgpPT6vlNMgcKnxnSpVM3LvzWIQblfZYb3n9SBoV9X1FPnX85zXp6a42WqxdG2OG2sr3EzbzGvI5Tynt2VBb9mWnHvjFYJeQYhBs5xC30VB+/zbMPc6qKWjba7ZKxiEi+Yh6tKgQRNBKJM7O7FXde0ya0lKTIwZSl0VERrSpKOdPVXBsUQ41RTeXDqE0l6yr9TUU02h189I1/aoZ4dNhoxHISt7oJYocMGhCZuYaBz688wwy3LZ5aXlZaxyYkxQD8O2dm9FynxZcg3DlzodsryDhHVk6jycehQWLsPtt1i7+hwrUYMgGmeqNUFJiSCunCKqEZoQFaiLspz2iIOdlHYXzNkgZLGnNNJFGjOnwQTYdNEtLkbj289Lmigwt3aVt4k5HSR8Ohpj3IR8Kh7j41GLTJWvZKtc7S0RhBNkjQi1peuecRAjuqJHISW5UU6ZU8itt9x34umHocjpz9/kzelJXjp/jrTWwLz5Co1gColyemGEVpltd7NXu5Y98qKkMJbFZJ437Ou8bl/j9fI13ixf563yDd4p3+Jy+Q6dWg1NV9e9SAYYESYSYSU9RNCepcS9NhOdJXpFfuRe7QMTuq317APONKu69h3mMjfbSlbCw1Uv92b1ee+9i756PJ77ER+0H5IoZF1pN1GE2JzSVEH7XXaQV1XmO8qZugs6gsZ56qGln65RHtLJ+DjIyFCUpNPB5muk+TXK7k1EhLfnY6xJ+e6LG87cZZC4gGiHoH3gIN/dx/ddYd2XUj0CS+kUDzZ6pK+zeNm95sRZklCYqO1DyY7rri7vEL3a8/Ue7dvT40urrGXK5FBLu0GLvEGK/HBd+52kVGXlmAzR0m4HEBoRaJmhcQ0J6rxSdLlcprwnrPFoWK1U9HrwyivwvvfBU08xYUKeChu8WaQsac6pIKAmQjY5jbb7mDxfD9r725T2Fmmh1Ms1wiqFPhi28Uhd0K6BuJT3bhXER4YSZSmImNqqssO60o4Kw0rRY1HEYlnyrX6f5x5+mNsLK7x96zpv9Nu8XAhf6/V4dsu9YSXvc+7Vl+nMPoRpjmHSnPwY+/ieaNRy+dYqr9/OWG1f4Ya9TkTMRfMwcbVYFUhAjTplfGeD9m7uFmGjOCWRhK61/GG3S1otsjSlSUlJyt41R49NGVZTPXBGTFrorvXsA2YbQi1bdllHu9Wzg3MVj+tHDtoL28EgBEMlHkH9LFr2GcuVm8sJZTKPSsnKPWigcqMoePMYStMG6cED93jN+vTUMpE0WLOWl7vLZKbqIHL2SRibJb/xMu2sy7XTT3GqzAjqY5QUG/eaMCGSEEGJpaTM+pSB7poeb4OI5U5BK8gJak1ULTZbwiQz21PjAZIGN8s2S71lpuNZPhG1CIaOOxfEfCae4FIQ0u0uckMSVprToKVLdz9IyzfbxwIiEfUUmL8Bpx5yZWMLt5gvMuZnz9A3sHThEszfYGzNYIwlS0KKjiuBW888uhtBe9GhXyoLYcpiAJZxZmSWSZmiJWM0pEkiCYplrRGDKrZza9t1JmrC2iEyGNt5SqhgTEDW7x85aG9nbtFnJ2+bmYara9/JjO7yskuNP1uZ1tVCZ/TrlXaP52Tjg/ZDEgZQlFVNexJhNMcO1NO7XNe+mjqVZjrquFZa9bM0IyG2K6zeuw50pOomuPHCFcr0NtSb2HyFNxYti+2Yh6ZyWltj18bEjmZ0g9Xg/TjIb/Q8ViyWpAqSNznI531YnYOpC+spprMN4XZHd1fUhnu1H5C8csKORqTHr2UuW3+itvFFHEmEwZBWSvt03X257mmYd0Qulylfzlbp7eAvcBCKbpsyqpPYDKsFEidcRni96PNwkPDUcPukl192Kerf9V3rmx4LEkQNK65QABHBzsxiu32Cfm+9pd82pT2q0deIce1AYBFkk4I6qPPVwLgJ5LrSHtCzShEmOyjtVXp8FdANlKL3Jwl/fmyMH221+JMf+hCPNlp8/MoVPhbBJ1uTnA9D5svNM/XspRcZy3MWnvoY1BrvrvT4tMNq37JQtFlYfZ2GNLhoHtrWCrEpTWxoye9gBoIzoStIwpKEGjeLghtFwa3q/Roo/nu1fgNn7BQYuLx8sL+dfgHJLpnuA6bq0MqXWMtk93r2AUMO8mpzynTxQOMCyMsOAQFiNsoGTDKNmJiVlZvcnJvl7LgSNefXfTnuJl+41eYf3Fg98nUGQXu9uj13sx4Fhkdrdf5kLSYvM75ZCF1rnb/JQx9hIWlwdeYRaqbGjBFotCi03CjFCWMik4BAhCXI+/SDknzUgvogaDchK+2u+86LathsGdUSM6JNpqryfNHjtuRMFSUfSc6OTJkORXgsDHg0KzBRizdtQDfPUFE0278BjpYpKjgz0Zuvu24XF590O+eucysQuhNTxCJcOf8IlAXNd64SmIiiackL6z6Pd1FpL/I279iMXEpMFDFXNpg2M8yaU5w2ZzhjznLWnGdKpinqLayYkUH7eCKk5cE78/SLjGba5nRviTJLycujGQ4Nsll2UtoDI8zUR/drtwpXVy0Xq9R4cNlE9Uj2JYh4PJ57hw/aD0kYbCjtUtXAlgO/0bsctA9WUyejDhK1EBPSqI9R0xWW7mFde0pK2F+FuVdh/AwmmaLXa/OtGyWzSY3TLbY7Q9cnIW2PTNdrHaBX+yDNK6lqYQfK3aa69sUrLkqefnh906mmkJXsvtgRDYL2g7d9KygICDYHjxUDD4LJ2uYv4oTa+gJIYITp+p2va1/WAgVWj0Ftz3sdTK1Ju79GWzNuivKiLTkbxHzXcMBuLbz0Ejz0EExOrm9WYFwjJoOAZ4oOpSo6PYsC4cIyKkoc6vaadmDNtBjTNmp0s8oObtEliFCxm9PjQ0NHDBoE253jwanygAzegqFJZyxCYgz1yUmiixeJ3nmHuMhohnXOhCFda92EH9CyhGefIzp/gf7MGWxYh35OcQwmRfcFvVX60qaMU1jKOCcXRv5dDFofdvcRMB+W1RQIUmqRkEjCcvUerVY/B4r/fura40AYT4S1A5qy7Tc93ohwWhdZYHz3evYBtTHXDsyWrHVe59bSV7AHNDssyi4B4aa2hSKGoH6GK/OLNFGeGpum3lhjOb37i05vlilXNd1WfnJQerkSGtbbjq7lKXkQk0tJattMhcJbGP6H9gL/Ml3hD22XZy59mFszl5jK+owbgYHSPrT4FMcNVAyiBY0ioxuU690CNpGnEIT0NcKmPaf4x3VsuoBIgIk37MALzXm7fIun80XeKfpMxIazNsaYndM1Us1o9Lu8t3WBc198muC5d1AsWhwgaLcpihCYBK686hz0zzwKquRz17gyNc3FsMa4GK42WzA9S3DtMo0somiUrl1hZ/mupcdbVb7Tn2eBhOnCMh03WNGShRGvG0uMjWvYMMb25p0HyxAD07eDLExZW9Ivc8b6K8z0lqGf0zti0L7QU4zA5HZv03VON4WlvpKVm8e6lMdklWv8MI2IE9f2rdSS8i54Hng89ws+aD8kUQB59aVrEjeJt4N/zrsctM93lSSw1OkioZvgNhoT1Oiwcg+LlLLuDerzlzEEhGc/joTjvLnQQ7TgU2ebIBtq/DrNSfdzhNpeCyEw7MuMbrASHldp0QPzu/W2b2ph8SqMzbq+shWnGvvocTpQ2vODf/Hmmo9U2cEF7UZgbIsbbCIJKem6+j/bkPVuAXeK1cpxf/UIrSX7arlS9LidLfJ2S3ihs8BaYMlNzHuiOh8Nm5tTPd9806Wof3Czp8iytSjCp6IWa7bk5aKLzJzGqiJLrj6yHiq9EerHGi1a1gXt4Yh2bxpVZkimSo+v1cAWrAYRdREao9Ljq5r2wRuw46TzfR9AV9fg+g0kqK2r9ouVehteu4Z0uyQf+xgAedwkSMt3TdBedFcpTMFq4wLlAsgOWR2xJEgptPcRMB+W1VSJ4z5R4BbJlqv3aHUoM6IpTVLSfXkONKL9LS4Os9/0eMqcadaYN9MuANqL2phbnEw7ZPkiBQVpOTqbaRRWLaXtuwyILWaKt/JTrKTwXZO3mQpb1ENYOUAAeFx0KMlVuZod7fvO9WjfuCd1sx4LgfBq2eW1/gJrFIzX6qxoyTNpH1XhtEREZcDFvIdBoN6ipNzknxFEdYwRCi2YLXN6YslGxSJ5ClHMWhkSln2asaBhgk2XMPH0pjZhy3aF7xSrXLPLPGmUyaROWMqO3VcAsqJNmGWEUidfWCRPc5d6bzPXZWBPFC1TrEDUz2F5Dk5dcPfE1SWW+m3mZk7zaFjjbBBxM0koTp2GImXq+jJElp6IK4GTEJA7mh5vVflW3qabrzHOFGO2YCJuEovw1ojAOSbBRnHVCaiH5puzNwZZcAfJYOymOSVKVBbURJB+ytoRuzks9ZTJHUzoBpxpCarb5zK308S5xm9pB9yM5MS5x8/pTW7YG/d6GJ77FBH5FRF5/yHP/RER+Zu77P+IiPzwLvt/TkReF5FXReQHhra/XfV8/7aIPH3Qcfmg/ZBEoRMGrVVMpbSvL/LfbaW9q5xt9FEsJnJGQWEy5SaOnf1Pzo6TonMdbb9NnBuCxllk/AzvtBuspsrHTveZjGMMhv7WGtF6pSSMqGsXEZrR/nq1D9Yq4rAKkCTEYDbavq3edkH3kMoOzkgqCfcwowsTMOZQSntORiijg/aVvjKebP8iTkhQlLzKSphtCIWFdrGfGf7Bsaq07eGDdqvKV7NV/kW6zIvdRTpa0KyN81hRMhMnPJZM81TY2FRzCcDzzzuF/eLFTZvnKmfjJ6Maj4U13i5T2rOzWCBYcKm+SVTS3zLhyEqlHbRITIGx2fYe21kPHaS6D9LjGw3IU9aCaLvKXhTw4rMuWDdmXWnfMWh/6gMgivnO20iQMGUMAbBQlmAt8dtvkJ6aZfbSIwCkUYMgKyg0vWttkO4laXuNNE7I66dZ7Zdoe7tj84AgDenR3W4keUyspkq9lhETIwgrW5R22FD895Mi7ybA+38PC6sUdn/p8XSWaCXQjadZ3I/pW819J2hvFZu7hY+s2H8qeU6OlDmBqW8KGq0q354LIZ7mfHybSEPqkZCT3tXWb6ktSavPxdtH/O7tFrqeGg+wlvZIo5CHgoRPm4APRmP8G81z/NXWKR6VBiuZUNeIWALO532IYjSMKSldj/YKieoEpZIHwlRZkBvLWjni3ppnECW0i5Co6NGIBdUM1QJT25wa/45dZE1LLgSWC9qHuE5garBLqnvZnSckZG2lQ1+UsrCAYm2xv6BdLGiJFUMydwvKFM5ecvvmrnFbS7pTpzkbRDwSxqzWGqyYAKZmmbi1jMlzViOgt4yIrLd9uxOoKs8WHW7na5yXgNhOEpMTBDUeCWrcLPP177kBRgyRqZEnDSTPKdOlTfsbkRAF7n6xX9pp5owJy4IeStLv0z5AOcIodjOhGzDTEIywqfVbaZX5LObiuNk2z2jETmk/Sd89ab5Kli+eqDF57h9U9adV9aVDnvsFVf3buxzyEWBk0F4tFPw48AHgB4FfEtk0Af3TqvoRVf3EQcflg/ZDElb//HkJQaW0l/bup8f3cmUtVU7FLoBMg4Ab9hqETepRQH/QXuUuUnSukbZfp4xbJDrGqpngy9cjvjlXY6ImPNzqISIkbPQgXyeI3CRzh3E34/2ZpfQKRYT1eueAgIBwIz1+8bJL6xs/te3cU03ZvWZcZL3tW6EFV8vL67Xqu6GqFBQ7Ku0r/c317AMSqczoqrr2mSobYLUYfZ2jsqYliqudXz2E2d6alizagkeChO8pYLpf4z21WabzDBMKJqhvP+nWLbh926nsWyYTt8uScWOoG8OTQZ1AhG4SUdZrmKVlAGrR9vT4dgZZ2KIWCGHZc8qXLdfrRsm7zqgLNtLjm03yrM9aEG2vZ7/yNrzwbbh5verVXrKrUjQ2gZ6dQt64jEiEEWEqCFzQ/sYbaK9H+yMfZiY0GIF+2MCUSp7lztH5ASftrJDVakxNz5Jbw/L8zkF7mIUoSnePXumHZTWFJElJpEZHlVyVughr1lJWE8ZYEiIiOuyt+DdiZxS1LyWcYQ+OvdPj6SzSig39aGL3jKABSRPEoN15bJVplJX7z1rIyRCbEwSNTdvfXFRW+spj584jlEi6xFgUQZDe1Xaj87Zg8M98VKW9N6S0qyoreRcTJzwS1IjLlCBsINXf8Z9qNFDghTSlLsJk2q1S40sU3aS0m6hBaC15aJgsM0IDS6PurXkKUcJaETJuUoK4RpktIhjXQ7yirz1u2i51SZg0ljRfIGjMYjCuvGwEqkrZWyIk5Ob8LYwElIUiarGa76tXu4hFbYFFiOZuQL0JrVkAVm9dYWFsgvGkxYQxXAoT8rjGPMDMLIlJGLt+m05inQlokVZB+52ZLz1fdLlWZrwXy2wQ0c0TQikIwoRHggSB0Wq7xGRxhBBis6Vt+8cTOVB6fKefo2XV2x5L0u/TLQ9vNtTO1HkY7RG0h0aYqTx6BtxsK4WabanxAI1QsDrC0PUeYdVC+wrh8pv0V19Dj8kY1/NgISKXROQVEfl1EXlZRH5TRBrVvi+JyCeqx78sIk+LyIsi8rmh898Wkc+JyLcqBfypavtPisjfqx7/RRF5QUSeFZE/EJEY+AXgxyrF/Me2DOtHgd9Q1VRV3wJeBz51HL/vnZHq3gVEoXOOLgqo16rarEFN+110jx8owtNxFyGgawra2mbCpCT1cUxnhV6uG31n7zBF5wpF5wpZPMZNDVm78ibX4vO0xfLeUwnvCWpopfYkUmNVV1DVzWnSjUlnEDeCZiQs9vZW23oF1ELBVu3eDAEhIYWWbsKwNg9nqn7aWzjVMFxdKekXSm2nSXRch7xHnz49eqzpGtOye/ulggJFRwbteam0M+Xx6e2vN1D/Uk0ZE+ei34iE63dIaR+o6+dMzLUyo1Al3CUNbysr1fmPBjWk5yZFSaOOZh3sWEQQNraf9PzzEMfw3vdu2mxVmS9LHg7d7xqIkCD0JKAYb2KXXSZJHNptRnSdTMmiFkkpRHnf1bS//SLMX4UP/itgLRqGQO6C9m6XuekW2m9TNme3K+03r7uf7TUIIsQWuytFxqCXLiBPP4dcvQ6PPMJMEPB6mmKfeYbVqQlajzyCMYZmbOmHTRIx2H7PmTvtUpt635P3SdM+aavJ+6fqvJ5MsrIwz9Rjow83WYDB0NE2LWkd61CyUukWOTNRQUKynhr/UBTxWpaxZi2T1WehKS1WdBmrdmT9/YBmdb/t5jCxD/U8XQ/a9ziwuwLLNwhbk0yU4f4MKcW4lmCd29gJCwh5sX83+UwzpMyJkub6trxUnrtVcqopXJyZJFtoUPZuMjE2A0GPlVS5sO9XOBq3y8IlvwA3D9BvfCuquum7cq0s6BU9WtF5pkxIWnQxydT68ZNBwGcaDb7c6/FoFCH9Nkyfo6zMTodr2k3UwKiQiaVVZNSMsIqlsEpohu6teQqtSdaKkKlwDY1q2HQRk0wxLNS8Uy6QqvKh8Dy2vELftmk0HgNuOf+CERTkmO4qaa1JeusqRgJsCWoL1BT76tUuYrE2J1zrEvRLmJiB+hj0uywtz7P26HsYI2Q8CGiK0BDhVpLwpCjmzEOMv/YVli+cpQSCzrIrt7gDSvuL+UZnkkfyVQqEXh4QBBYJ69TEcDFIuGozntQ68dDfckxCGhlMP6Qs+9iihwk3FponEuHmCIO3neikOcZmiCoax4RZn26RozbfcNA/AAMTur2CdnB17S/dtuSlEgXC5RUlFLstNb5Ml2hErj1kJ2dTtsm9wmX4pKgJyfs3MPka4djjBEN/g56TxaP/z86fAPboQ3pgFt76PzW/uscxTwI/papfEZFfBX4G+Ltbjvl5VV2sFO/fE5EPqepz1b55Vf2YiPwM8DeAn95y7meBH1DVayIyqaqZiHwW+ISq/vUR47kAfG3o+dVqGzibpt8VEQX+v6r63+7xu23CK+2HJBpS2k0cO8EtyyGK7rzSPrQifrujBAZapo2ELXJxX7xd7dBqTBLSZ6l7d2oMi+51equXeac7w+8tnOHWjWVsGfDeS2f48+8L+ei5gDhuYQs3qUioYbEjzOgm/v/s/Xm0ZdlZ3Yn+1lq7Pc3tb/QRGZG9MqVUqkcNQkamNWCDbGC4XAUPcDOMXSrbZeMqZPxsDOPVMzbmFWWebTDCUDzANDZYQoD6DnUpKRtlGxkZfdz+3nNPs7u11vf+2PvcJuJGl40k5JhjxIh7zz3NPvvss/f6vjm/Oev3WFzJqnWieoF7PQYrr4Q0AIfDC5y2JRpTR76tna3Z3Jkjez527kbm2reY9nrBcSOS2XHcXLiHPH4ckzQZX3khVkrVC4kdqoS5lmKzeumYdq3gQBNheLMS+Z63BErRVpp8NMDpiI6yeF8iUXgFY8dgAM89B/feC8HuqmXDeyoR5nbcHimN1gY70UE2aplvHHq81HPBW09bgtchYRwRVkXNfGWDWoL6VD1KJGPJjDLY4YAziedsuUERBszsnGcXgcVmtm6wWc9w2vK6TJEc3lez+U8/DcCMMSRnzjBcXeX8fXcz1cTHpQGMdIJWGinyOobpaxnZJoV3lHGLqSggmZpj0Nu8KtunqLPSXwozui0TukARq4QN71HURTtcKZEXhHztGTjzhfq42ANjtvZGI5TGHhyxucpC3Du49CQ8+6f17wfuZq6lWB3doJw16SLDVZxS+DDFusENS04rKTHe74p7e2LZk1t41cH6+2NaB/B2SNc7otDSy1+aMYa9sGQtCujakFVv8c9TSpvbevKl1TRqF4sRJcKhpIv4CpEKdVnDccIYvr3T4WWa+rzSmNABuzw0dJiitEaUR8qcOWMYKrc79k0EqpJcRZReM6FyRAsiFTreXgd78Zxx6ySqwwk9ibIjSkrCeL4e3bpK0V5QYrIhK1YTbw7pTO3D6QBfVHh/Y0w7+Ho/VA5tbc20pxOUSxfpeYvMHSZSirZSKKU4oEMW4hRGAzhxL4GNCFbX6mtZMUCpF18e/5TNeM7lnDAJ9wYtvB2igzZFVWDwhOvrIMIJE+NEOHMZ6x2rGBfFtWWJyBVs+0Rcy8gvN3i7GrKyJPYFgsalLcR5fF6QPU/vh7WsVhFey4RujK259pHgvHBh0zMXlbuk8b4aUPWeoCX1qNkVKSxfIZSSobzFJdPYqTtQKqDqPUG1+cwt1v0WLsc5EflE8/OvAW/Z4z7fq5T6PPAFatn6zln3323+fwg4vsdjPwG8Wyn11+Fyc6SbxltE5NXAtwE/qpR66808+GuYynlpMa4hKgcEEVo3RXsUvbRF+8YlOPtFuO1VMHmAlZEwkwi4ETo+tBWLNJIh+zq19Ls/WIfJPSTJLzLOLq9wei1lQZ9gZt9F7pIRdxxO4fDMluRZhW2kXEO8I1YxSG1GF6sd7mvjGKNsY5dJHGxntQ8rmLrGVyeztRmUx9MXx4IrOIKjSwXrF2Bify2P3wMzaW14tzISjk7C2VbARVdwaIdzcp3VXlK5DDQU5DhxV85N78D4swkuY9pFhN4oJ5YhEzjsoEBcXkfrSEU4cQ+xiXc5V8+2FLk3L4mKYtM7ykzzkUtCelzYFMvMTZwqeuKYVAalFOVwSBW0afkc7wt8lKBNe/cDHm9Gji4zoANYbtiffTtY7xhFXwnl5ARy8RxqlG0ZDuZ2m60clnUWrW61CZcapr0qaj+C1XMQh8ihYygxqLxkhEPiiCL3bMZCuFNdsL4KRbPI6m/C/D7IB6AnrskUSRyi9s/A6dNQFMyGIVOPPMJKt8PG0cNMNcdLO1KsB916ZjjLcb762u6o5n0K5yBpE6qQqflZNhafJttYIZ07tOdD2rQZ0CeXnETdwIr1BrFZCJiCNFQN014woTVTWqPYXbQnpGgv2IXHwaUkVznXtxuvwhuNUBo7iScBeDtCqj4m3V/fOFiFC4/VTcyZo3DwHjAhc6Xn5Frd8LvuAj7pIMUmTk+gTYqUG1gqQi7P3LwSlRth0Ftxb6NKeHLFc3xKbzU4dTyPGpwhyDdIw4j1QQFXGQN6sbFiLcZrpnzIiq/YcI6Z4OaXNuPxmlaz2WfyTRRwPO4itm4g68sbjlsPbgrltI57A3anVYQxRhmUcpRVzrzRZKqibz2tMQNgKxDPpo9QktPRBV40igQd1eyiF+GJco3ztiDxs7ynt8xtUY4ypnaNj9tXlceXRY/KFuRZzm1ErN1+J/65TyPOIa7cM7Hlcihdy+OVE4xIHX8ZtVhZOINNWwSdGRL0lnLucBDxTJwyGizSmpjCTxykff4U+V0xrSpHpe0XlWn3Ipy0GQdNxP1h/VmJHeCCWZTL6CxfIiw07H85E90Z5nXIaVdwu0m2PFYiIiSMcWqIJsQX69DaPidNxNtmdHNXORx2YlSWhK7Ao3BpG9YHqKJgYEe0ou5Nv8f1TJiM1VZc27Uwt2Ou3Us9sjMf725S+KpW3aSm/gIMv0rM6KqG3FEmpgpDwpkHcMPz2NEFfNkj6N6O2SMC8Ra+crgBRvylwuWdpl2/K6VOUDPorxORdaXUu4GdV83xl8KxR10sIn9LKfUG4C8ADymlXnOd7bkAHN3x+5HmNkRk/P+SUur3qGXzH73O823ha3pd+FJi6zrrqE2pTABVBXF8fXm8rWCwcfMvOlyH849s/Wy9sJYJ+9MMEFTQxlLVUmoKTBwRBBHZVXLPX2ysD0vCMOU77w25e75irhzW8247ih8VNKZIdkhEtCuDfAtJp44y2sOMbqtov47RUVbV0naHo5D6MZe8Q/WW8TaH2aNXfew4Vm15KCy5kpXE8Mzls29RfbV21QCFauZtr80CVoyL9u1zgj35fsonfxe3+jkO+CeI8pP1Ranq13OoLsdXPWJiHG6L2R8vlldfgui3TbHY3OArjXPqpubahZppn2qk3TYbUIYdUj9CfImEEUbvaCBZW2ezHz8OnStlz0vO0W3m2ceIlQaEamoS8Q69sUlk6sIq28W0C51IYZMWoS0JULVJ09wRiBJYPAeurCWawyEjcagoIvEREoU8s9Ot/NKF+v8Dh2p5fBCBK6/JFIl4JDKoo4egLODUKVoXLtBdW+PU/S8DpZhs9lMawlAnaGVQRUHxte4gn20y0iFhFBAQcmBuCq8CVpdWrvqQ1ksU/bZZCDoo6JgQowwb3jNNiVt/hAnsLgd5rTQTG31KOwAT0MmuzHIG0KYA5W6Yad8pj3fZIlX/WaTK4PxjcOoz9R9vfz0ceXnt+8ENJl00kKiN9wXKaeJgEuUdmb8xfwDr67g3mqblIwseAR44sP2dVNqgk32YvE/LOPou/7KZR617R+A1My7ECly4yTi7MUbNNWXcBL2Y9+koTSdqI7Z2+1bBVZrfWWPst4tp31m0J7VcXgmVtxxQ9TG1WO1gDBuvjZ6LCKWsm85SMAq6PF1ZPjYa8V/6fT5ULNLzMD8Q7vjD/8zw6TNs6qj2VYnbV2XaXbbCUDzRekY37HB+8QiFNXjr66Ld3kDRrhyCBQfKe0gmwFk2Vy4S7DtM6WFyx7n6tiAmT9qsOgvZgOq2+9Eji+2t4KoMdFhHzr1IzGmFIMBsM5ogNkfEUaoO3f5Z4sEmWoVbTZbbg4RCPBd3nG9DIiRKsGLRuoWvNndt38SWg/wNfO9EyGyJcRaNQnVnMF7wWc7QPz+mfTUTZlo31qgPtGKmpVgaCOd6QmxgOtx9bZGmaA+1w+ivHqa9cgM0itBM1CM6ShN0jhFNv6Jh3Z+k6t2adb8FAI4ppd7Y/PxXgY9f9vcJYAj0lFL7qVnuG4ZS6g4R+bSI/ASwTF2Q94Grdd1+H/h+pVTcNAzuAj6jlGorpbrNc7aBbwYeu5ltuVW0P0+MVbVbY2BhiJTljTHtF0/Cox+9qrRyTxRDOPP5mh1OOpBtsjqqu6dzUX0BkqCFw9GtjwlGMiRKJiny3pdlAeVdRRKHJJGFrEdo5QqjN91E0nk73CH7vuzipXQtkd+j2dBpiKHBNXaxFyG3teTY48gFAqUoRVOtXMBHCbSvPXYz31asZcKTTTRL37vdTrNN7JsrB6S0MJjrFhMVFQHhrllYWTmF6a1TrpYU6b3Es68hnn8D8dxriKbvR6kAsdmWEmHc4JhJQSE3NtN6E8jEU4qgqnobY2duSh6fGYUAkyoAW1BVFSrpYKoc5yrytM2ak63YM555pm5yveIVVzyXNPPs85fNlkdK4RCqyWm8s5jekDBsivYd685BWTd5XJyACDrL6oVxnMKhEzXjfvapLRO6IZY0CFFiOBRPckEyFsfH5sJF3PQs56Ym8cMhoMFVKBVclSkSVyBxipqahk5aS+S/8AXSbpeTJ45hBNpjpj1U2CgFAnReUL7E2cVfceQDBkFKbAxaaSZTg7Rn2Vy9etEeqICE5Iay0m8Gm4UQxwWJTii8Z+Q9M+Uy3g6ZcYNdTDviaa+sUKYtygMnCG0G/eX6LUnOil/mtDvFOfsMaXuFG03czG0d9xiZxthw0EOe/CCsn4f5E3DXW6Cz+5zVjRXJ9ZIuxpsdKgSPckJqJlFAeQOxb1txbxiUiVjLhFPrnrtnNZ1od+GgoymM0nS0xarimufoFwuFeAbOE4nhtjDCeVh4nkX7Tqa95yoGNmNWBxDGeDdCKbMrp34XRoP6fJK0cFg0evs8v7wMQYxRAaKEShzzvpb0L1Y7zq1N0b5qIyZVRonnSVfy6QoeLgoG3jMfKqaCnDfG+3h1NcToPu0nn+W5EhbcoF4b2HJPqXt/uESpNPtXc85tHKYctsh9iLdSNy9vqGivEBTaerR4SCdYX7lIaSsm9h0lF2FiR9F+RAdUSZs1byHro/efoAy76IUlyqq3PdP9YhXtzTpnrJLytj5X5KVmbvFLSJKgTQh5fa2e1yFdbTi1oymvlcaEHSwWTQwIvtzY+nsnakxab6BoH1Z148W4CgVMTB8iUIIUOaPnkdU+qoTCwswehrVXw75mLXN+03N4sjY93Ykx0453N5zO8+WAtUO0GLRuU+4gdnTYIZx5gKB9FFes4K7SOL2F/67wFLXU/AlgGviFnX8UkYepZfFPAr9OLXe/GfzLxqTuMeCTwMPAh4D79jKiE5EvAb8FPA68D/hREXHAfuDjSqmHgc8A7xGR993Mhtwq2p8nqg2osu3ZahWENy6PH/XrfLjqOoz8GLaE002c3/HXQXsG8s2txdpUMESpAGfqj7OlOgSEjBiSplOUtsKWL13GMYCIw3lHYCIKCoL+Rh1t1jjLjqFMhNIR0lxME5XsyiDfQmuyZi8uY3mTQBForhmnNHY/TUOFE0cO7FeG+aIiH/Uopw9e4VB+OeZaik1dcaGwHGxWczu78WNpvSsHhCqkpVqMZHTN5oi9LKNdqgKKDJXMUA1yDvVPoVSwK1ZJBS3EZURNzvx4rt1oRTewN7RgvxlsNosnKepiMnSmdpO/wabPMNAIwkJl+ez6Ms/ZkqdSxUfXVjgnlue84SNZzvuHQ54rS3jsMZibgwMHrniu8Tz7/GVS1xgNKPxEF8ETrPcJL2PaRYRhJXQicHHdYAnG6pYwqVddh++A9SXU2hrFsE+FRwf1fr4nnmWCgCd9n2ExgtVlPrzZ43v/3v/Kx86chtI2TTfVMEV7NDZcUee+6xAOH6wd8hcWCB58kHU80Y6ZyFYIYgw+TNF5QeW+SlZOLwW8w+cDBkFCy2x/tt3ZOUbDES67evOrrTrk5NgXcea/VziSyJKQ1FFv4pmo6jnWCT/a5SBPb5G48hTzhxlMTWJDw+bCFzjtnuOcP8OGrBMSkW6cZUbO3TjT7rad49fWzrB8/gkGVHDHG+HgvbXyaA/Mt/UV5wC3x3fVUyFao6wjMB0MAeUNmNGV1CZ0RhmUjvnCJUccwP37rlw66Gbee5J63OBmHLafL/riKLyQOM3BNMA4zcINFJ97YVjVjZMkgGdsgalK5nUAQYTYEepq0niArF8XzEphd2a0LyzA7/0eLC4RhAmqYdpTKWiLYcleWbSv2RgVZzyVZ1Qm4GVJl+/odPi2TofUDGhpxd1mlrObi4QuJ/Ga1pOn+Fy+xihslACXSeRFhM3RCsaFVI+XrHGIw/e0cSrEjVydKnAD+81gEQVBUaC0gXSC5YUzqCAgma7P4ZM7mqyJMbTSCdbFQ9YnDltsHrwdRiP8ysW6YQovmoN81ahig8YUWOwQUHDqKYyUVEeOoJPJraId4HaT0PeO5R2N0iDqYrEor1Bqt4u8VoruDTrI9wsBVRG6CqU0U1Pz6ChCZQX58yjaxxGP14t724n97doVvnJw22Wu8eIKxNfHnYijFV57bfXlhHNDFgYRn79Yex/tPOcrpQnaR2tS43nsx1v4moMVkb8mIi8TkXeIyAhARN4mIp9rfv5BEblbRN4uIt8jIu9ubj8uIivNz58Tkbc1P797bDLX3P8VIvJyEXmn1FgTkdc10W2/efkGichPicgdInKPiPxhc9spEXll8+9+Efmpm32jt4r254kzH4DNc7Clbguj+qJ3I0V71lxQb8T4xTs481CdKX7bq+sZ76QLzrK2MWQyUQR+iAo7W/LrkJC2ajOSEZ32BCLQf6nz2n2F80IQhBSSEwzWCVpztYz4MqigjVRjM7p4bzO61hSIh/zKhWUrVNdkccbsVhpAicVXBXNrX+SutQWsUlyYur776HxLsRLmVKXiQOaY0QGXLivavQJVZoSEtGhjsVdK/XegoiLc4ShMsYlauIDNPecn7meyWoNTnwa7/RzKpIgdYZQhJNz1/BNhxVomz9t4aS/0G1bdF/WpwVQaJ8LwBvOxR4Fi0TpOlpY8H2A9tNMJjviSiVhzIO7w1laL/UHAE88+y2h1dc9ZdtjOZ9+LaQeQKMG1YoL1Hkp7As1WVntuwXlqeXxcN1jMoPkORAmUIzhwB7RaqPOnGKwtIlpvpQlMp21erifRwDMLT2O953/7+X/Hpz70MX7qd/4LftTIZZvRC+9LLvqMszskx+Lq3GZlIjjYNK/SlNa991Lgd6W6jY3LfNhC5RXV17IRXd6ncFAkMYkJt4rM2flZnMDqytXZ9vaLLJH3IgxcThrUBlQb3pNUPVriUGg6PkOA/phtXz6FibsE3YOssc7KwS756CLRoM8+tZ8T+nYOMYfxkOic0Q3Wj4UVkubUUA7WqIzhiweO8mx0FWa3wVxL0S9q13MnwuN2xPuKddai3Zd2qQa4uIMpCgLTISCguoHYt624NxWROcPiQHjZnK4VAZdBmRilDJPagy7ZKF56M7q+dxQCt1WX2C9P0/KGS8/TQT6vtqXxz7qCCWfpRmk9pmRHV5jQ7UI2gFY93uPEbhfty8vNhvZRYQuNYMURuZy2aNadx47P31VJ7oRHceRJRVcJ96QdjiRtWlpTiueCrDOn2ywqB70VZpKU6M77OXD6HPlggY87VYvzL5PIX/KbqKxP+Kymv6LY96ZDnHiwVWfKj5qIyRtQKGhdIQJBVkAQUiYdRkvn6c4dYtAw7DuZdoDDQcxGlJCPNkkjRX/mBE4BG6vQKI1eLDM6y5hpr7fBVwP02ga+t0x/5iC63UGnnV1F+yEdESvNqR3GcJFpYY1CqgwdTeGLjV2N68lY0b8BzqVfgKJEW4sEIXE6QRjVqSqFzW/62r3emNBN34RF0VxLoRTEpjam24ktlh0FYmmF6obPWS8lvHi8y8hdSm8UspnLLrZ9DGXilywy8BZu4asRt4r254kwVohrZtoBFYZIdYPy+PEF43pMuwicewSGG3DkAWg3xWY6iQCDXo/5tD656aCztdAPCWipNh5P0vJUKmUw3Hi+b/WGYG2JEwiCkLLaJM5y9MT+Pe+rgzbeZYi47Qzyy/Pax2Z0e8y1d6Jrz7SP2dYkVAyl7nLH3jG9fhE7OcspyuteLDdUBbGnndV5rodMRN87+jsYVRdF6KpomPZrFxNePBa7y4RO8k2oKvLc028dRp94Vb3YevZTW875KkgRsYi3xCrZNUowFVQ4/+LOtW+KIxCFdY3BVGW2br8RLIYBG1a4Iwx5i/ZMu4RXtqc54Qraccx01OZAEPCGJGHmiSd4xhjK2y/L+fr8Z+CjH2DZOTpa07psEbgVzxMm2FaEWdvE40hDtSVxHTTHRzsCpwUbRKjxdyCMoMqRMMHddjsKTXXyYVSrRVmWJFoThRGJMtynJ/ALF3nvhdN89iMfA+BDn3+UU5ea+DcvDMTyebvEk9LnpAzoN74D4vM6pimdBO3hda+Dt7wFHRkU4NleQI0jdnzcQhUl1deyPD7bpHRCEUcsassnq3oeeN9sFwliNpavXrTHKiEgYMgAJ8KFF5B3DDAowOvGOb6Je5uu1ghNhE730fIZiNQS+cFKrf6ZP8GUmSGlRen3Mxse4dBqzqSewqigLhRQRLpkWN6Yu3th60U1gLc5JoiYw/GEHfH5arAnew71GA/AqWHFx8pNTtmcQCkutoJd5zhvB/j2JLoYEeiIQKd4l11XsVBJhXIVgWltKZgm9ki4cCI8aUd8SaC0BXEobBQv/THcF4f1wowM6egBbRew7iyFv/mGwagS0hDWxLLmLdPOkUYJ4kpE7NWLdt/EiKb1WJrDETTNWb+8Qm9Z8IMhKmqjAesdocvpiqHywmozKrRZjHg4K1gNNbcXfU4kMUFgtgwAT9o+TnLm9QSXJOdAf0TQ7XL4lW9jImhz5InHuGQqnqksNt9uyIgIZ7OL+JFgHxW6R1scfeM0okpMN8SXDmt9rfy6DjQOcQ7jPAQhl0ZDdJYxu/8YPe8JlbrifH00iNhM2mwMN0gDhdUdbBRCnqF8c4y+SOe7qmkuh2OmfbCCvnSJUWsf5dQkJoghae8q2o1SHDcxy77aur7HKsaHMbbso+NpRCrEDjhfVfS9ZyKGflk7sl8L/cKjVYW2FUQpUdwijRPCvCAvC0Y32AwfYy0TJmK1OybwOgiN4sSU5u45vcs1HupmnkKjgxbi7RbT/mISAc8HdbOwJPdtcBELA6GUPdbVOoJbRft/1xCR0yKyN/PzNYhbRfvzhInqdfh4JE1FUW1Ed72ivcy3Jd/VdU42C09Bb6GWR04d3L496ZBZUEWf+WQESMO0l2g0RgW0aKFQ6HCE1ZPk2SZykxeIm0FZNSx/EGH7S3Vx2t2eZ7civG8w4GxVocI2IIgd7TCju0ziFCb1v6uY0V2raM93MO1DsWgvtAc9cDnJ7BEyqXaz5nvgaZcxF2mkHyJSZ5YrdkvkbRiiyoKAkEAFxCRXLdrtDhXE9ob2wHvyJh5pYm4/nHhdrdh49lOQ9VCmbqmLy9AS0peCRZdzwRUUXc9GULAwePE+103viNwONYBtHLRvgPldtZYzScR+E/JgklCOBuSmTTuoGxQuCrYkpkm/z/2LiyzdfTefr3Ys2KyFU88gF8+zNhzsco0fIx4vyMIE24oJNjZxtiIJtk10xkqMTqhwWCqTwqCROCpApM5ojxO47X7cygJRWdIvcpIdiQWzKubwwhr/5wc+sr2JzvEr7/kjhjget5tcaKJp7lMTGBRna2UW4gowMSrt1Ezcq14FJ07QF09LKyrZaa7nmOmfwgYxqrTYr+WiPe+TiSGLA0Za6HnHqq8ItCKZmmO4vnpNv4+26jCSESftiC9UQ3ovYB62do7P6YQhRgVsVDlTbhOdzKGDDglC6PO6aF9+DsIYpg7RVRMcNkcI8wSz787a4X1YRyaJHaDRhKrCy/a4zrVQuHr0R8ThqwJjIu4Qz70m4aIr+Xi1yXCPxtlkIqzGGR8tNnEIb4i6vCpoU2rF2bHs1RWIL7HtKbT36KogCjooV15TGQT14tl4hzbJVpzi5Vnya77io+UmXyhHnPKaC+WAYVSw0viBvJQYiMM7RYeCSDu6PqB6nnPto6qOe7vgSjIvHPCCDmLENc7xVzOhy4f18ZrWTLvFbjnHrz++xtJpWH92hApbBOKoNKiqYEobnIcV5zhZljy02WNIyJ2+zW2uNjoEUDrCifCcX6WtDCtoWhhm+gOk0yZoT3P0ZW9i7tIys5vPsRgEPL25ttUsWqZgsLqCrAVM2Ix9bzqEF0fPniGcAO+EqvL46vrGhFrVee7aA0mL5ZULpFrT3X+UnnO7TOjGOBxEVGmbjWGPRHssKTZtIUUGrhnHkhfnfFduMe0KqUao00+ig4TlQ68ipkAHSV20V8XWawPcZmKMUluz7REREjVFezQNKFy+xqeyjKeLgomkjlLrX2cJNywtJvCYyiKx4ZJZpx23MM5TZoObjlNdy24sn/1yfN1Rwyv2X3kt9dUmKuzUI1zitox+b9SL46VCIQXKVeS2TaQDNjLFerkH066j+jp7C7fw3wluFe3PEyYEI5cx7bZxj/d+h0PdZdjR4b0m0756tl4kzt5WGxHthDb06BCXm8yG4wVFp4nwqYtCrTQpLUYMiZMpRpVHGkbrpUDVSP1NYFD9ZYKwzm8d44K1bHrPsnPosYN8dQ0zOqjZ9j2L9nqRe7Wc1GxrcenJxWOwDHqnsYFisj1PqoST13A3XnAlPe94eZxSOkXmDLHStUR+x6xxFTZMe7PPW6pF3kS/XbF/GkfhnRntMlgHHZBVkEodOUV7Gu74OkoFS898gi/0l3iiGvGh4SU+XRU8YzM+Xa3xhWrIYsew2cn5XH7tWfobRS2Dd5jGhC4NFUWl6GhzXQf5UoQPZUM0wluSFkYpylET96ZLxBb4KMSMF76PPUY3DDn6wAOcrSpOjhtdl86Ds4zEE166cMU8O4zd40HCCNeKwTno9UgDtVUgDXcw7VYcNkjrUYtmZhmAsH7u7MBt9DGM1hZIsz6H0h2M2maP3sUlPvz+T6CU4n//pz8BwO+870846QdU4jhAwitpcUAnHFIJS1KQi0NcXhtXpR3IR1uFaE9qd/1M6S1GQy0/x/7hSXAWU1Rf40X7JkOTMDKWxAQESm1lJU/NzVGWJf3e1c9VbdXGiec5X9/nRkc39sI47m0qSHAiuGKVlgKT7EOFHZRSTPqM4WAd+iv1+fjy+fKZo3U+9tKzAPhqiEYTUCLibihCqbBSF8OuwrsKFU8geG5X8IaoSy6ej5WbLO04//S85ZO2T94qibKIt0YTzOuQfSaibYWTNseJbElgbXsKjYa8T2i6ddG+13l3B0qpCJygdLz13RpnyVsRHquGfLLsM/AOaw2JnmRWArwueDpY44J9aRfU694SOkeqPZF2zDiN9cLF51W0C3EoXHQFoRgmxdYmdGPn+KvGvTWsdtpFRHDNTLu3jt5T6yyPemyeG6HiDoFzlAYoC1pGk4jmyaLg83nOnDj20eVAGBBL2bjdKtAhZ11OIQMiHVMpxctsXLP73Qm0DojufYCZ9hy3PfIItEJW8h5fLGqvmCc2h9gzPTpDz6GjDn3kMEO3ihePTiJAcJVQZdcu2kUEhUWcx5QVw2GJLF5gamofJCmb3m8V7Z/61Kf41Kc+BcC01uhkgjVXkdgBTlLKtI3kI5StzTxfNHn82IgOhT/zKGo4gLtex5CYkAI1Ztph11osUpojOuK8KyjEExLhwwRX9VE6QIddimIdDwxEtmPfrjPXPswrjAFTlUhsGOqcVqdDIOCzAf2beN9ZVY/B3Mw8+7Ug4usM+7BTfwZitxRfX+m5duuGeA+lb3PXrEZLxOn+lecqZaJaifgSElK3cAtfTXjJi3allFFKfUEp9d+a308opT6tlDqplPpNpdT1g2K/CmEiUE5tzbSrMESc3baVvxrbng0Qb/F2ePWifXMJLj4OE/vg0Mv2vMuqdOnKgFQNULqem63E7ioKW6pNSUncbjGqwJcv3Vz7uGhXpiIYbGC6u43FTjds6tD7ZvYxqPcBtRStoGjCWnagNQVltmvGG2r2FLiqy2lW1WyQqLpo7476qDIn67YIVMABrel7x+IeF0wR4SmX0VaG+9Jaltiz9T49ZCIG4rbM2mwUoJ3DNFLMtmojCBlXLn7GUW27mPbROpxbxZ5fZkYNt+S+n9bCB4/cyzmjmTz3OBNVyW1KuM9McczEPBCGvC2a5JXrBfeFCWdcwefL4QuWtPXFIYCy9WlhX1sxqoQJdW0HeRHh01nGmrccqCwHghCcpcwzyrBTzwX7ChfHaB3X342nn4Y77uCeqSkOBgFfzPPaUf7Mc7gkYT2JaV26cMU8O0C0xbSnuE6KWAe9DdJwu2EzKOumg9F17J/VSdNI81vHkxgDInzOlpxtTZGmLV5pc1rxDkbt0gX+P3/8AWxV8Zbv+lbe+vf/H7TShCeefJZzpxd5gAkmdYhqlAhHVAsBLkjWFO0JpO26UdCMPGx4xyET4qE2PitHsHKKUEMVhOjSYb9W2QMRyPqsBSlV4Dhm2hzVMZdcSS6e+X317P/yNaLfUlr0xJE3qpbsJtmqnegVjii0tE3CpvckxQpp2EGHbZRJUGimfI5aea5mPmePXfkk2sD8ceivIMP1mmlXAaFRGFVsRYldDc4Lpavl8YXLa+O3pB6FEjtiXod8fTRBS2k+Uw142mY8ZTM+Xm5SivDasEO7n275KwAcGlly8Zx1xZYEtmxNYZqi3QQtAi8Ucm02vJKcQOrZ0XGWfBzAkiv5SNnjtCs4oCNcFdDG4EyLKR1xr8QEWP40H/BQNaB4CRbVuXgy8SS2ItIKlGK/0XivWHQ3p74onWA9FKFlIJ42hq6rIIgRl9UGoeYqS5VRMxecdnDUOykgYPmxTX7vsU/y5l//MX7zg+9FVAuNwiuPKzMiA11vEOCVcczLNYwkZjoB4yrE6C2jtpOuh1GWSsUcVSmTgwyRCtdp1w3bICB48PXM9frsX+kR+02eyId8tLfJyZOW2WLEXMujA40/eICBq79fKolRIqA8/aVrHwsiDq08yoM+t0j24U/TOXeW6f1HyXydOjJpDKdPn+atb30rb3/72xmNRsRa02pPkomQZZsYnVIkHaTMoczq9/gizrQrwAw24NyXkOl51P47yC1ElGiT7Fm0A5wwCQKcdgVKKYKog/Ul2AIdTVHaAdqXDBt5PFzbQd4LZFWJNoKuKohiBCHoTtYmpHnOwN64GmVsQjeTXOeON4japE9QYRd0AN7SHq+tvsI948r1EVE41WIqURxoJSyOii21zxjj7wdfy8att3ALO/DlYNrfCTyx4/f/A/hZEbkTWAd++MuwDS86TAR6x0y7jqNa9T6Wh10tqz0f4m0fV67UF63L4R2cf7Q2mzv24FVdzpd9h0lT4PP1LebaNpFiY7RVzQy0WgVD6ZDtwVq/WKhsRby5TnL+CyjvCLuHtv429J7FRnkwbApcFbS3HeSp3UHFXLawa03W/18W/Xa9rPbMCmmT0Z7jaZX1hXEUawIvTChFS2lO7uE6utDMtd0VJEylmjiAXlXv08sl8lUU1IZDVd68jxSNZriHRL6iQqG2ZJP1Bm3CxXXsqdMM9Cp/Um7whWrIQBzH0ynuuesbuMskHCuF20S4M+wwo2NS5ehogxF4davFTJHwdFHwRfvCCvetwrwwxAFMxLW0t4shF095lYX3o0XBJWs5Ehk6zpMqA+WQ0kEZtEl9BlJhk7TeX6dO1aMkL385SilenyQkWvOpzR75xdOcO9Li1KGYyeXztC6bTRURPvShD9FfWMRHMb6V4F0FGz3SoB5XsV6ajPb6MVvyeFsBUjeClKLUcLKqeDLPmFQBJ+5+sI4KCrfNv5ZOP8OvfeCjAPwv7/x7rFQ9vvXPvw2A3/+Dj2LdNlO0Ij3O+wXmVcQF18d5WxftSZM/nw2pxDMUx5Ema3vVObj0JKAIDVQqQKOu6aD+ZxplBt5xPtQEGm43bW4zMQKcdQXtdkqYdq4Z/aaVZsMHRConVOqm50J3YqPKSYJ6Vr5X9InciE6r9uKoAIIWE+UGureAmz6ylZF+BWaOgQmRhSfq4iaewigwKr/uAni7GFbkNkN7j0lmANUsrKGlDG8OJzhsIp62Gc/YjEMm4huiCe5qRXiBtR3eFl3rmdUBJ12GrfqooI0LFIQtyAcoU3sDFNdwkHfi8C7HUEedFVZwyvO4H/KZaoBB8bqwy0oJohRvbLWoTEopnv1Gsd9pDlQpC02Bf7k53gtFXxyVQGxLxmP2E6EjdmaXE/iNYCwH7gUllUDXCS0lTdFeXD3qDWqmPU7BBNhGUaUxLD20yntOfxaAz59+hH4vrZNBlFAWGaGBAz7iOzod7oljXFHQl4jZqAQEbwxKR1zyFX3p4ZXQocPtqgODPqUb0W8V9F0deZUcuweZn+XoqfNMe8G7Nf744ia6dByaHhEPKuh0GLUtXhzdYB7SFPEeYzyD1esx7RUoj3LAMCOrSuZO1/nsveY8PaE1P/VTP0VVVYxGIz772fr972vNUIqwOdwgCQOKZKI2vxtu1FntL1LRXooQew/PPIRo4MR9KKUZVZZQyjod5CpFe0cb9uuwbnSJYKKJuglTZuh4mlKEpOox8h6tapPT3jV6q4XXKF8BFuUcJPUFyXS6RKII8pLVmxghWW+K9qkXiWkfK3B02EUpg4j7qmHaKztCfIAlIQ3gjskYh+OZtd3Hydjv4ZYZ3S3894KXtGhXSh0B/gLwi83vCvhG4Lebu/wK8Jdeym14qVAz7exi2hHwY4OQqzHt+QAJQyQMa4btcqydq13lD73sqjE/WSWs06UTCTJaQ4VtrFg8fheTG6mYkJAoGVKoSYbZ4JoXR3ElVf8UYq8tmdwL1lYkvVXiMw+jMZjutgnd2YZlPxaGDL1HRNBhG29HiPgtMzofXrb4TifrpsVlzYatrParvJXc1sZeQ6kN5xJnAUVpBGNLnLLcGaRseLtrcSciPO0yOspwuOngzrXUFtMeKc2sDrnUSC+rwNRFaNMUUErRUu0959rHGe1q3IRxFimGDK0nH65DscpBHfF1UZdvjCa5N2jRiVKIO6jK4l39GjHJLtO++bZirkqYyerZ18+/gMJ901sCpbCVohOpLUfzyNXHYW8PRvNcVfFkWXJ7GNI2irZrPsN8QOkEidvELkNciU3b9f66dAnSFOZrz4NYa96Ypqhzp3iiWiW77TAr+/ejVZ9s4cyu13vXu97F29/+dn7mb/0vuDCGwODjELWxQRzW7zuraqagE6ktuapXUeMl4aAcMTIRnx0N6DnPQVdxzETo4/fA4btg/kj9Ytbyi7/9O/T6A177utdx96tfwVQ+4pu/89sBeM97P8qFvF/PA/qKp04PeOKxgrZYxBX0pNqWxzf7ZKPZhweDiFCEfm8Jeouw7w6CwFCZCNBIdv04rj+TyDexCBfjgCkV0PbQ9hXzOuSMK/AidGdnKXprlHZvBn3NW0YSM6Mhwb6gor3vmqKdmGG2hAY66TwXXcEfFxt8VoRi5Rk2cZyeOnjVxhUmgLnj0LsAxQgTzaI1xKa47gK4aK4hSQBFk6oRxh20SfF2+xphlOJVYYcHwzavCzu8KuwQKc18q/6eXh79dk+QUnjPcrmBanKnVdKFvI8yCSEhzg3wV3lP287xdfE4Kiq6F/6Q1fWL3BWkvCns8nheMvCeN6cp+41BdEiOoascyli6ZcBbo0laynC6E/Jo9eI1o/re1YWUrc0jAdqBJaoMq9cZ57kco0pwCANTkYghcSWJ0nUDz1f1d/xqyPq7TOgAehcN2cUlPnPxGQAWh0v0llsobVDKY6uMyNSGn+NtH45ynImZMfW5XnQt/33WjshVn0SlvMxMYpTC99Yp/YBHTl5iefMc1hektMgffAU4y7Ezy6RnC0o8xw4NCQIh2MiRhmWPdZuWmUEnCd46dCj4MqO3dC3m2KKUQ1UOW1SQF0ysboIO6TVmemtnzvDud7976zGf+EQdh3wgTiiihI3hen2cRx28AunXWe0vZuTbzLlnIBvgjhxBJVNUTnCuqJtoJoUgrM1I8yuPxYMmohBPTxxh1MXhcOUQHbQpVURS9fBAJsJEfG2mfeQCFCWBy1FopEkxIW4RBSFhUbJmR1c1mbwca3kty98rueH5QKr6PKB01Lj4C6H2RAZGX+Ea2LkhXgIcIUmgmI0TJmPF0xvF7vWNGUcGfo0q027hBUEp9YtKqfue52O/Syn1j6/x9weVUt9+jb//b42i/Cml1LfsuH1KKfXbSqknlVJPKKXeeDPb9VIz7f8G+EfAeFUwC2yIbLlanQcO7/VApdTfUEp9Tin1ueVxbMpXEXTYGNE1ch0V1ycPL9cr2ke1WV0QIuVlFw3v6jn29kz97ypYHgpl2KVrSlSRoYPuttGZ2r24aKk2OszIVJeskqtK5H2xQbn2RVy2gCuuznJdDdZVaOtQozVM2Nky0QF4rqrYZwz7jMEDIxFU0KE2o8uIiFAoXHDZQkubWnEw2sCJbC2Y4yar/apMewVpoNhsCvLYFngT47WuGwUIB5QhUZpndsjTLu1g2cfF9Xy7nmnPm8/5kIkYiqPnLWU0Ltq3F9YtWnX022Vu+NVlGe2UI8gzClEEhLx5eZ1Xhm3m9I7CHqA1iSoLxOVNgyOm3JFrHweKmVQRDGPuD1osuJKHruE2fS1siqOrDMNS0Q7VVtfd2MZB/rKF8IZzfDbLmDWGB+KYgXe0xvK1YkjhFGEzu+hxSNyuZ2ovXoSDB3c9V1cLh5afpJcmrLXvoJy6lySKWbj4CFmzL3/6p3+an/7pnwbg/NMn6/xkpbFpjN4cEAf18TGqhGEptMPtRbSqV8DgHWtZn4dFI77kWJIyW5a0VQDtNhy/H6b21e934QL/4T1/DMA/+Pt/n+WyVoYcecMrODI7w8WFFd7/yU/hVUBR5iytFdhCsbQxpOsr1qlAxXXEnAkgG2yZpk0qw4RzcPGJOsZx7jhBGFKZAK0UkmW4F2Cw9lWLrM+qtwzCkHmVYvsnqTaf4riJKcSz6Ctm5udAHEtL63s+xWmXE6kW0yrEqILseRbtWSVUqqAdhBgMZb6MiacpdcijdsSkNszqFq3NVS62J/i4VPxxscGHih5fqAaccwVu5/p57ja8WPTaEkE0BUAaFNeNUBqfW2IDVTlAgDhsN2qkKwuLIyZm/w6pdhwoJmLF8mVF+4wO2S8Vy67AmRYej0omoBiiVESgApSrrjQBbVBRga8wBCgTk416xNWAVwxH3G0SPl8ULDvH69OUfUF93KZak5uUxDsSU5tHdbXhzWGX+dxxxhVsvEjH9UAcCIS+IN5RtCdlQO4dGzchkR9V0A9KjIZADFO4enEU1JFS6mpFe28Fhj3o1Kow1yxtLj1pePbMJxk25lmLoxXWL9XXOaUcpXPEVJTjU6r3jLIKF0RMB821Qys2lWFB1tHKcUTPMd1ME2a9M7z7Tx/nO//c3+Ktr/wufvU//xIKRTRzkNEdx5AnLnD40YL70jbCOmwMMT4iO9DBSUUnmCdQEUHSRkShlUebkkvPXH2fidj6fkWFsx6lhKg7BY89Vo+WKMW//OmfxlrLoUO10m5ctE8ZA2m3NqMLIA8nEa3wo42tpueLAesc3cUzyNxBpNtBBx1yC0YyjAY9zrG/zEF+jH269p1f8hVRVH+mVVl7Z4yCSRK7CeIZes9kotgsrp4OkTmDUBLaoh7oSsbkREQQxrSyjNwW9XF8A1gfuRdtnh1qpn2s0lTjKFrv6tg3+5Vj2l3jB+OkBUqRBLUx4P6OIvMFZ3vb2zaWx79YSo1b+NqCiPyIiDz+PB/7+yLy/7rGXR4E9izam0bB9wP3A98K/Ful1JiF/TngfSJyL/BKdivRr4uXrGhXSn0HsCQiDz2fx4vIvxeR14rIa+fn56//gC8zTASB3lbB66i+qG95o121aB8icV20X8G0b1yspdb77rjmay+PBB2EJIlCFSNU2N7KaN8pj4d6zlprIUwNQ2uuKNpFBDs4S9l7vJ6NV+Z5db2dLdBFhrY5gdqeCV62loH3HA9D2mNGwXtUUEvUxA5Qqo5buoJpB1zaJRsu8uHiLB8rF7du70SK4R4MloiQWyEJ2Yreim2FhAneRFSuvlCLctxhEta8Zc1XiAjPNCz7Ib29IN5isYb1ax1oLuoXXIYLDFpHW0w7XD1H2lLtbqiUI/xoSKUCXNCis3R+7x2bTqI8UNWFe6zqubidufb7O4qVkXBMx7w8bLHoKz73PAr3umjXDEvBRY7P2BEnTcYXs4JV53imzFm0lr73ZN7zySwjVIo3pSlDPAK0bPMZFkNGKqUTayTbQKIQMRGmP4ThEA5tj0+UYnkuf475lWUmjj7AhUqhdcShg/cRXVrklFziX/7sz/DjP/7jWw2NjYVLVGgkCHFphN7Y3GLaxxF4nVhtF+1eAZoNW/D0sEcYtXlVZCAIMMMRLQy0dhtN/cZv/wanLy1y9OhRvvO7v4d+NWTKaUxX851v/DoA3vO772UZxfLKAO9hamOWjRVHR0ZUeFbHp+kmG3hDHC2la+OjwQKuGFDsvweUJrIlYgJQBp0XFF+DCxGX9ThvIhKlmdEaX23i7Yh5ZUiV5rTLmZ6bJdCK9T2i3zLxXHIlt5k2qYoxKmPE85tpH5vQTQQxUvYoXEGc7uORaohDeFXQ4Z5RzmEdMD11mDt1i3uDlI7SrPiKh6shj07FfKEasOIrRAdIdwqdZZimGmvdCNPebH4SKGw5QJQhCVNU2EZ8eUML0rm2YmVYFxDlju/9nWJxCKebc69Kp0A8qhgRqKQxo9ubpSqlRLkmc1xHFPkAo6CVj3ikKDhbVTwQxxwLd/qoKIYqwVhLEkKvGR1SSnEos0RK8YS9tgz7RrEpDiOKxOeEYX3ebYWOrg+xAhfszZl8bQYVU0ZjRTHdNBbERIivtmdnd6LM4anP1gXg4buB2jl+2INsLeCR83+6dddLmxuQeVwVgHgqhMTn22aqtmRUClEcE7sMhUfCgDNe2FQbTBFzr5oF6vjQbO0M/+GP6qXVxfML/ND3/yjf9M1v5/xTF1h/2V30NkKOLj7Mtx+ZROdrFKsDtI4Y7AuIdEqia2VA2OritEacJ20VrJ+rqK7CHjtfAR5tPTLMUJ0O6oFXwpNP0s9z+s89x6/8yq9gjOFXf/VXAfjkJz+J954ZrVHJBHbURwLL0EzitUIGGzXTLu5FMROzvsIAvok4UGGb3ApGcowGYxq2+ypFe6Q0Uzqoi3bTRkyALRrDy3ASLZ7Y9hl4z0SscH47qeRyZM5gTIkuLQrQ46I9ilBxi3aWU/ryhtIv8nzE/tX3M6eXbnqf7AVxOeJLdNiYBTeqTpEm9u0ryLRXVChXUkkbpSAytUfEdKJpJxVPrWwfJ0oHtYrha9UD5hauC6XU8Ya1/r8b5vq3lapng5VSH1ZKvbb5+RcaIvhLSql/tuPxp5VS/0wp9Xml1KNKqXub239QKfXzzc9/RSn1mFLqYaXURxsvtn8OfJ9S6otKqe+7bLP+IvAbIlKIyHPASeD1SqlJ4K3ALwGISCkiGzfzfq+0Zn7x8Gbguxr5QAJMUHcYppRSQcO2HwEuvITb8JLBRGCoR3OhnmkH8L7phO41014VYKu6gKkCyHbMM4mH5VP1HHd37pqvvTwUZlsK8gA1tCgdUjVOytYblnzF0WYhlTbRb612zvr6BL7c2H5JV1JtPoOvephknqB7O9Xao8/L1EOKEbEfgYHAby/izlQVAXA4DLcWk0PvmQ8TlDJ4O8RQz5T6wDGQPoUUFBT1YjLuMaoWGBUzVHGXTPaTKk07qjOWL0fhagOYNFCsSkWoNKoq0GGKCTpUdgDM4LAcMy2ecRnP2JwjTQ77qxvH6DFmUoVCWB4JRybri/qcDrnoR9ymQUdt2DGXFqiwltrKkGlqtYQTh8PtZtqLIS7LKIIIE6YEi5f23rGtSdAhqhjVRbupF6c7XZ/n24onluti9XgnQaN4pBry2WrA68IO5iq+CDsxEocVIXQGL9AzlkJ5LML5ytKvPJfI2bTbfT4NfEOrRao1i40R4XbRPqBPm1aokKyHHxft5xrVTMO0l2I5xSWCc+eYlQnm7nw5C7qOmescPk5y/iy//HP/kX/y938SgB/71/9ffvZd/4BiOGB1c8CRIKBKIsLeiKjoA5NbjGNnB9NuKgtBzIb3dFzFvRNTeBlyXim6w4JA6Zppb5CJ5z/8x/8EwDvf+U6WPLhBhntikskHNvmGN76GX/hv7+WDf/RRTo2GdHsj2h3DHUmXxy7mVLMXUcpwjop9AEkLBj163jKtA7AFh/sXGRx6BWthi4OPfZz2xSeIg6N4FDovKH1Fi6vETP0ZxXq2Ti9o03WKjh9/bwR8yW0m5kmbMQwMrYkpNtZXasfqHcfvmcaH4riJyaSDchdr7wrxtZz5JrBReNAl0+EUw2yRCoOPuqz5ivuCFh1ANhahPc1EqEEUd+6I/Vr3lrWiNrS84EpawB3tmP2bMdHKGSQxRCZnOLpO0T52ZQ/AlhmBNqggQo9TEuwI1bB+V8N8S3FyTfj4Zs4lVdVxhkDHZXR0zCnluF0E054DnoXRBkHQxrjhVWPfKipC79GmhVKaKh/QtiM2Ntd5qiy5M4q4N949693SmqFO0H6Drvac8wXOC0YrjMDtJuVLdsRyMxLxQjAQhzhFSwpMdBAYkRrLpAtY8LVy6v4b/P6sV54ytMyalLXKMz1ulJhaNszlRbv38ORn6tiwl7+5bsJTn282LijageUTzz6ydffNPMcNVsiriDC0VOJJfIHz7Xr/VEU90tOJoeojSiFKc0rlxJTcbY4SNoXV0K3wp597hKfPLXHgwAF+/Md/nH/yE+/iA+//EB998Bv4yz/wA/yjO17F/fo04cYa5/MNhus51URA1Q6ZMfu2titKJxhpjbeepF1Cv2LpdMrhe67cR+IzUGAKC1mO2t+CN78V+a+/j3zpS/zmv/t3OOf4wR/8Qb7xG7+RI0eOcP78eZ544gnuv/9+2q0pqqVTaBlS0qqvCYN19Pg48BVcyzvgBuCsxaAa4kGhghbZEAw5gVbb6SVJG1Yu1J/jZTF1+3XIkzbDioYwwVa1wqpv2kxqQ2o3Gfp5Duwwo+vGV15jM2eIgwqfVWgElbTxgAsTTBQTZEKcD7ngLMeusxLfXHiOmXOPs7+4gEz9FVRn+gXtp/E8uwqb0a0x0y41076afeXc2McZ7aVvkwRq6/wfqZjDUxXPnBWWh8J8u9nnJr6V1f5VgvR31t9Erah+MbGavWP6k9e5zz3AD4vIJ5RS/xH428DPXHafHxeRtYbx/oBS6gERGZ+kV0Tk1Uqpvw38r8CPXPbYnwC+RUQuKKWmRKRUSv0E8FoR+Tt7bM9h4FM7fh+ryjNgGfhlpdQrgYeAd4pcJSt6D7xkTLuI/G8ickREjlPLBD4oIv8D8CHgLzd3+wHgv75U2/BSwoRgVH2dsU7QTWdXfLNA24tpb8ylbGDICZEqqy8aABuXanfpfXde83WtF9ZzYa6l8IGqWVhX1d1JMXwiy/nTLCNvnncc/ZYkI9bdBN7miM0JyCjXHkaqPmH3TsKJu1DKgIlummkX7/BlQehH+CAkKuvXtiKcs5ZDQcCGWE67jEwcA+9RStXyz2rbjA4Fl/xF1mWNSipSlbKaHMDS4WgeAo7VpqHQDtWeDNbYUCgNYCAVqSiUsxAkJMEEzmXgBSsWoxS3m4RlX/G4zehqw8HLFpJGK9qBo7cj2uWQiRhJSSYeE3V2Me1QjyTkZFuzouPRhWAX057hi5xSxahDh2F1DUaDK3du0q3novMhYjPCrVz77YX2vrZCKVhs1ADHTMwrwzarvuKRPeS1e2EsfTdVszDUjkNRwP3S4vWqzbe1O9wdh7w1TXlDmvKKOOatrdZWJNuGWGKliAQQj82GjHSbdghk/e2i/dJSLROcnqZqCnaH5+jZPvHUPNH0DH+u1eLrWy04dITf+MSn+Yl/8C8A+N9/7v/Ja7/3rzK1ry74FxcW0SbAxfV+DfvraLU929uO6oz2+n3V8U25MbS0JohaOF8y0IrJrKpHVnYwhu/92If53KOP02m1+JEf+REuFgNcT2j19jNXRBy//RAP3nGc4XDEe3/vIwyCIUfmEvadgPbaFMVGSRQYelT0pIK0S5UPyZxlUgWw8DSpdxRBB/uFD8JgHWMMAQ4I0EVO+TXGHkhVspr3sWGXWAkdP4RxEoDLOGZiFHVhPjE3i856rO0wrnAinHUF+01ISxniqmR6/STaDZ6XRH6jzDEKJoxhmK3QD6e4RMWMDjhhYlg7h3IVzB9nwmd1VvsOTOuAYyPLN0VTvCps0/UVSzgemTzAc6un8ZUj0QW5rR3ir4bcyhaj5KsRRoUQxKiwVn74G/gOm9jzRJDxyKgkVopzUYQXwVcDDiYzWLGseIsJ2/U4xmgDZRIiJ1dl2ispm7i3umBVw1VmemfpXzrJYa151WUFOxcvMvPwwwx0DCJMKhBd7MqyPmZiWkrzhH1hMZVZ02Q0VYVBEYQtFJrUOGIModcs3YQ8/qIviIwibXiMCW9BaUQ342+XNxhOPwr9Nbjr1dDajjbd3LDkvYCZifN86rnn0FoxN1//vXDPkVdJndWOEDVzuKWDKs/JrDDRSaDKEGMYimfDrNJWliN6zLJbBoNz/Kf31yz7D//wD/N3/s7f4bEnH+av/tA7sNby//vFX+Tb/vU/5Xcffxz53J8yVY0YLg34dLHOx/7kM/zyv/s1/tE/+kd83/d9H//59z6AGIM4j1YV3amKhVN77yNxOaIgKCt84dCdCThwkOLQIdY+/GHe8+u/jjGGf/JP/gkAb37zm4FtifxEexIRsOUGXmKqtIXPB/XwPi+OmZh1Zc20U6JNilKGzIL2eTPTvoNpF4Fi97V7Y1HgXH0MLItFh21cWRe4IzRhkNL2BcOdsW9XOU1n3hCbEqkqtAKaKFEfhZgkwjhFNx9wwV7/fZcrlwBN5HLcZ38Pls7u+vtIRiz5xb0fvAd81UehUUG9TUrvlMfXjUR7jXPWS4nSDkGEwrdIdjQzIhUx3amIDJex7bey2m+BcyLyiebnXwPessd9vlcp9XngC9Sy9Z2z7r/b/P8QcHyPx34CeLdS6q9T87XPFwHwauAXRORVwBC46tz81Z7gy40fA35DKfUvqHfeL30FtuEFw0QQKEXuhMqCaZh256Xu3O5VtDdyrMUyYH1T84C3telcGNcse9KF7rVHAXp5fa2ZjS0+DDAmhmyTKq14rhQ2moVl3/ttcx7VJokHZHqazILpn2TCLII+QDh5PzrYlgUrHe0yP7oh+AopSzQWPzlHMBriq5LHvOWMz3DGsNI45V+i4JRTvIIEFbTx2SIino7qEvcSjurbiKhZpqdtxkIQ8Zpwkk7hOK1CVnzJERLakaJ043zj7S73dka7MBLLftGIqyBMSYIpRnIG64ZYUy/objMxJ11OIZ77g90s+xgtY3ctPPfrELD0vK2L9tHC7vurNuuyxoghHbpbows7mXYpBrjSUeqA8I67YfFLcO4M3HP/7hfXBtWaQvfXmughRUS0a6EdGcV0olgabl9kj5qYTe847XLuDzzRdVjIsXO8VIYSi9GefSZiI1RkFezXAZFSpEZxQF952uiJY1IFddidq+oIJZPQMg4ph/iJNmiDubgABw82BfsCFsftwy7x6ga84lUApM1x+7vveS8/+G9/ERHhJ3/6X/CX/s7/yMdXLjF7YILF52D10gIyEWKT+piXzQ2SoG7maAWtcNs8Lygtth0z8p5YCRImjMocp6eYHBW7pPGVeP7Dv/pXAPzID/wAExMTLF04S9zTqLJLOJxmakLzra97NV989jTv/63/xp9/2zs5NBuQhorZmZBy0SBzgmA5JyMmkzaZtwTFiGkvsHwGM8o4fPEZep0Zjt73eoKlcwRlhY0SVF5Qyo0XHX8WsDxao8DTNlOsyICWH2LSY7h8GbEjoniGQybivC+5fW6W8ydPsry8xmwTH3nBl5QiHG8W3rrKCBCMGzESx/RNXs56NicJFFE5Ytk7TsVdTijFK8M2CoGV09CeRnXn6QwWGHiPE7lCuWKU4rCJOaBgFKQsHrqLi098jImNPvHkFFDPTHevQiIWti7YlVJIlaF1AEE9hqN0tOdc+xhOhC8VBU+5kkALt9uUV6aGR5XiTJFx0GV0kjnmtGZJqtpzpTUF/VXU7CyBCAPJr1A0AJSUJN6johgvQjRcpEA4lG9wdzVEqbHBYg6f+hQ8/TRT1uIOHqCMYVJ7MCXruWcqMVv76p4g5QvVkIu+5PDzZFb7TZNRuRwDhGEKLiDRFqWgdZMO8pekZMYYcl8vitquuS43pqO75PFL5+DSc3DoTpjbbcmzeM4SasOTz7yPyjnuf/AOWu2IleVNBtkZrEwg1ToVnm6jGikdjBrZ2EQnhl6GN4aeVDhtiVVEjiMCBm6F1bPn+f3PPY5Sir/+1/86AIf33cbP/ty/5q1v/B7+zc/+Hzz5+CN8/3/8Hf7pez9Gr8pZWN3c833/wR8kfMs/+5/q2ExXsu9YxVNPQW9ZmJzffTx4Vx8nejCiBFRzbG+84hW8+5//c7z3/PAP/zC33347UBftv/mbv8knPvEJ/sbf+Bt0O5P0URTlBs4fpUpbyPII1XyWL8ZcsrMVRinEF+hmZCKzQkhGqDVqnAyy00E+3VZYPft5KIYByXcqlnzJ7XEXN1itn8d7giClXQ1Z9Z44qOete3uME3gRcqcJTYkuK4xWVFH92t4EBGkb44XpbMSXbuB927VLVK1pwte8Gf/kx/BPfgLdW4HbHwAT0JdNNqXHrMxh1PVrCqn6qLBbpxlAY0TXyOObdJ5RxVa03ZcTlR9gMOSuNTbcB+q5dlGb3D4jPLXiGZaadqRQJsKXex/ft/DlxQ0w4i8VLv8S7vpdKXWCmkF/nYisK6XeTa0AH2O8oK5Zk8ufTORvKaXeQG2s/pBS6jXX2Z4LwNEdv49V5eeB8yLy6eb23+Ymi/YvR+QbIvJhEfmO5udTIvJ6EblTRP6KyFXa/F/lMFHDtNs6ZkqHCq9CpGhYuz2L9gGCpzAR1oSUztWS+c1FyAf1LPt1pMxj87W2GUGcNixsnzM2Y9Up7ojqs9xgByvUVm3SUGFDx8BG+GqTQtpE0w/sKtihWST68qbmy8SX+LJES8VoZh/nXcHH1s7wwWKTAsftQcRrww7fFE8xr0OecRlfrAZI0EbwiMvRShMUIYlK0EpzzhU8bTOOBDEHO/ME+YA2mpVGFj52kL88TmnMtLvAIzjagHIVKkzpBNMoBc5mW7LpUGnuNSkHTHgFyz5Gyzj6hWyxZZHSTCjoidRFu7NbizuAtIl+G0nd/Kj2ymgv+rjSYlWEueve+razp/fewekkqqqQqn6+WCVXmEeN59p3MnqBaDa85+INjDtsNrPWeVmz7JGB/UFAEtBktQdb97scToSBd0yNi3lbF+1Oh3TIwVf4tI3qD1CDEfbAPk6xQIXlBAdonbtYP+7Yia3nfO9738v3f//347znXd/9nbzrf34nk+V+jOTMHZ4CYPniAoQJPlAQBMj6+pZ5XjuqZXUOh0KhrSVThvUoYtGX/KkfseALvA5oj6pd0vhPnz3F+9/zR2it+J//4T9kxXpG60PaRYwJQqqNKWam2nzby+8kDAIe/uQnWB+uIc1ozP4TQtgLqPKEVJUsSUEWp4wQwmzI5DOfg7PPIFbg6Mt45u7XI2mXIAgwrsClHXReUn2VSP7s8Bx2eO4FP8/F0QoRGqUn6DLCoDHJfMOU1IzXcZNgRVjqtGgnAb2VbRPS51xOVxvmmu+psSUBGuNGz4tp77ucNAhQxToX0QzDlPvDFm1lYHO5VtDMnUCHbVLl0S6n76/+Or4aEOmQQ+k860HKqLREqr68XWuuvXD1PLuIIDbfNssCVNBCrtJEXXeO9w+HPFmWnAhD3hq3cSPDoSCg4z0ns426yAq6HDUBXoTTzkJrGmyBckJIAK6gvEwi75o0EuM9SseUDpJsBRulTGhDcOGx+o5PPw2/9Vtw8iQcP06kFEGWU6qASeVQSli7bFTskI6Y0IanbPa8ky76zeetqhIjiihO6yx1anlvywb0vKO6xuc1RmYUm95xUMWsOcekMWhXbpnQwY6ifdiDZ78Ak3Nw225T4sGa0N90zB8M+PBHPwzAa7/hfmYP1Y345Y0zeJNA7ig1RFtFu7DZFO0zEwlUOaI161KhDbR0zHlWKHzOwK3wX37tjyit48+//et5NDzImWF9Tl76wgFe9rIH+K+f+E/81M/9Y2YnOjy1sMTC6iZGK/YdPcg3vO1t/NAP/RA/+ZM/SbfbJcty1nKLcwKuYHZ/hQlgcQ+2XVxRR5flFYLGx7WC4PP9Pu976CECY3jXu961df/LmfappIUJYij6jFRIlaZQ5qhx4sgLLNqdCMo5jHeI9o3ZLeQVxLqszdbGiTxbRfu2um2wJow2wTmYHIUse4sOu3ixjIoBDoiCFqkvGTQqjolY7ekgPyhBUIS6QlUVBAE6aEYo8QSdiVqNkmesO0d2jbQDcRbVX8FPHMTM3Inc/SB2OkWWzsDDH4Zhj0qaGFpuZB96vB2hw+7WLVseWWJrdRy1z8NXAtYOMRiGPiHZQchEjQnj8Zl63z+92pwDttasX9mYulv4iuLYDhf2vwp8/LK/T1Cz2j2l1H7g227myZVSd4jIp0XkJ6jl7UeBPtC9ykN+H/h+pVTcNAzuAj4jIgvAOaXUeADp7cBNGeV9WYr2r0XUTHtt+G5tbQ4tOsRX5TWK9iGEMVY03gRY2zDtS89C3IbJA9d93UFTtKf0azYmnmB5c5VzVcahIOG+WGN1f9fiMlQRXRNioiEr+gSmexdDP4/aI1JODdbrIvQmLqDiS6QoqHA8OzXNOp6pwSaTEvFt8TSvjrocMBGx0rzStJgk5Lwr+ZwIlcgVTNKyr3ikGjKnQx4I2pBOEeQZHQ8jKcnFXzWrfezEXBkHeNrW1w2IsE0YdAgIEJdt5ekCHA8SXht292TZoS7agV1s+7SGSjS9ZpZxp0S+jn5rbeW1V1Ro9HYH3DukGGBLh9cx0YEDMDUB53dL3rY3YAowSL4B1PFUHo/X25/xvnZtirMz9um50rJiHeduIA5l01smdMCwhCryxFozqTWtUJFZaCuNUeoKB3mAnliE2hG9fn+W0oLXAS2fI77CJx3CSytopVk+1Kak4gQHaKukblbMzkOnPv998IMf5B3veAdVVfH3fvRv88+/93vg0gU2CkNSpuw/XEtFl89fQho2zE50kI31LTnddka7Q2MIXEUunl57gtQEeK047wvOCiz3N6jSuunqRPj5n/95nHO848+9jRMnTnC+cPj+iJmgS/cQlGsTRN0Jjk/GvO7BB/He87H3fpILTXE1c7Ak0OAuzRIrh6XkXKwZiuPYMw9hnnkIZg6zcujltG97GZVSTQMoIvQVNkgweUH1VeIe74t1fL76gp5jzVuyUY/ZqMUATVf6GBWiwgmUSZEmxWFaB0xqwxmxdKemKXurZJXQDzR977jd7GiOuxytFJEUNx375kXIfcEEkJcbPBF02G9Cbmtyf+ktQBDBxDwq6JAqReRGW3nUe0HsABV0uGAtEkRY7zGqApFrZrXnVogNlK5AuYog3G6k6qC1FY25c9sfLwo+MBxSivCWVovXpikH25rNQiiscLQsseUma86hwg6xEqZ1xBlfUrTq+XhV5AQEKF9dIZEvqcBbAjSYmKL0pNk66zOHMZ1ZeO5J+L3/DB/+MExOwvd8D7zxjXXRPhySm5TQOZJAsV7ubjAqpbjXpIzEc/Z5ylr7YglRKJsDAUkYgQ4QX9EKIbEBHrh0AxL5pcjgHBw2AT3vmTambqaH20U7OoSqhCc/DUEMd7/2ilnoc0+Ciizzhwwf+MxnAHj1W+5n6lB9XT974RzxRAtfeZyqVQJQM+2DYU5oNGmowVmc0WwoR6AUB5nF4ThtT+Kd51d+4w8AePv/8D+xmAsfWLJ8+gnL+sWQw/v3ESee7/mR7+ahX/vnvPdH/yIP/dhf5dQv/RT/16Of5hfe9z5+6Zd+iXe9610cP368fv/9HHEe8RbtC/bdBstnobrs2up9Xle0WUllFc8tTrKyIvzcT/0UXoQfetObOG7qa8CGlBx7xb20222effZZFhcXmW7M6Ew+YKgNRauNdxVk9XnzhTLtJYJ2FcaXYAJ0M6+dWSEhrzPax4iSuoDfYUa3eHr7z+lGiBNh1LD1/cbANwpSYgXWFVQitYN8fmWx2C8EhUdrh7EWonCrae/xRGkH0SHdogBvOXcN00TZXKSqLJuzc3g0wcQd+P37cLffXq/VHvkI0mx8xfUbvQElIKgdRTturHZwpE3U67XOWS8lrBtgVMDIhrvl8dSfXxBWHJvUPLvmqZw0We3yoiUQ3MKfSTwF/KhS6glgGviFnX8UkYepld1PAr9OLXe/GfzLxqTuMeCTwMPUo9737WVEJyJfAn6LuiB/H/CjIluM198F/m+l1CPUDvQ/fTMbcqtof57QIRjdFO2uLtq9jvBFBXG8txFdPkSShMpDZVpUzsH6Jcg2Yf7267LsUHdw4wCMH6FMyjCe4Nn+Em2teUXc4RKrqKDPmt+9UGrrDrQGfFQJnzbxFVqS+slXURdPojZXb2q+THyFKkZUgSZOp7ivu4/p/oCuCrg92m3g0zaGSSJeGbTZ1CHPuILeDnO8nrc8VA3oaMNrwjZaqTr2DM1kkSE41ny11Q2+3Lk1q2qp6Uh5wNOyFYJgwhSlNFHQxbsMKzd+gk+bon3nxbmrBUXAwrhoL3Y3Hlq0sVSUUtTO8bvi3jJwFa6ssHGbdmLgwAG4dHHb42DXBkzUTE/WQ3xFrBqZXbhdQM+P59oH29s49J5YDIuuYvMaBaAVYSSeCWUYlEIZOPYZUzcfwrrjrpSiqwybe0i2e00hPzlm2l1F4QTRIbHtg1hsq010aRUfhaxOGyZp1wX7Zg821uDYCbz3/MzP/Azf8i3fQp7n/M2/+Tf5V//nz6O6E3DhHMu50DE5E4dqE56VSwtIGKO9p+y2kV6PtOnMt5uFh8MSYNCuYqQMq5MHiKcP87ogYUobplTK4qDHZyPh0WrIw70V3vMffhmAv/93/y4Ap9dyWpll/nCHeAKKTY2Z2M+Etnzja14HwPt/56MsVyMycWhV0J2F3rkpeqWw4Hr8kdpkE2F28TTsPwFv/G6qpMNss8hddQ6ilFA5qjDBFCXVV8kiRMS+4HnTUy4jLQbMtGYY2ZK2HxDGs6gyR1nZYtqhZtv73mHmpojsgEvrGUuJIVJqK9lBpFboGAyxu/nYt1EpYEom3JCztmQ5nOLBoF037ryD/hJM7AdVz30myhC5EZtub0ZMZMxedThXVRAkNXupHJqqfr2roLA10565Au0sJtoxrhS0QTwb5ZCTZclnsoz3Doc8VhQcCUO+ud3mUOMrMTZmWh4Js84x7UeclxCUweE4bFp4EU6aAEyAKjJM89243IyukhLlKgwGZSKKjRWUlGxO7CNYKeFDn4ZnH4O3vAW+67tgZgba7bpoHwzIdYJxFamB/h6F+T4TMasDnnYZ9nkwZAPxxEpjXI4nacYLAvCWTqRIyvp8e+k6KiMrwmIU0HURJlBUIsxoXTfTx87xytQy4mcegiKHe19XF307kPWF5XPCxAHPYKPHF597jigMuO919zN3sC7az126QDrTQvm6J0DTqCodDIc5aRpBo6bKI0OuIVKKeTXJrLTo+WU+/tEvcvLceQ7PTnHwzd/KfZOGWaX4/ccta9OOE7fPkag2A9XHzhlec9ssx8TSvu025oIZnq2qLXXDkSNHAFgYVThfs9SuGnLgjvoytHR6975yLkNVHrICL0An5j1/8Dh/8lu/RRCG/Pg73gEPP4wT4VHf41md8YY3vAGoXeQTrQlbXZJ8SGYceTqJ4FGDXpNa88LOd5X4hmkv67GyRkWYVRBS1KrEndjhIO+9sHwW5o7Ua7lwNcQoxXrTJBw28us0bBFrReBzho2DfOmuZKUHJQTKIqb2XZCojljUaDyeME3wOiAtLJFUnL3GcZotnydXjjOzsyz4EhNPY+I5rBnh738DrjtF9OwTJM8+taXquxYCVa8Nx00NihHqc++H3gY07vHANc9ZLxWs2FrRoVKsaOIdRXtYt+koKblnTlM6OLUutcEUL44nwi38mYUVkb8mIi8TkXeI1DJXEXmbiHyu+fkHReRuEXm7iHyPiLy7uf24iKw0P39ORN7W/Pzusclcc/9XiMjLReSdUmNNRF4nIg+KyG9evkEi8lMicoeI3CMif7jj9i82yWgPiMhfEpG9s22vgltF+/PE2D1eGnm8Cajl8eU1mPZsgEQx1kMRTGKdQxafhSiF6UNX3n8PDCuhGym8HeBMiy/qmKQccEegqXAMyImVYoPtIrIUz3MWNqOCqsnJXY73mHtaPVO7iNoKuQkHeXElushxYUAStjATc6z3VpjXms5lbMT4964KeHM0iQRtnsxWOO1ySg2fqQYEKF4fdgnH81atKQyGdjbCKM+qt8SBIjJXMu1ZswDe9I5YCaaJHNJR3TGPg0mMLclkd1PjWmg18+87ZXCCY1olnA9DRCnIdkfptZrot6EM64z2y+PeqhJrwSVdWqGCw0dqxmF5mSsQd1BBAvmoybWPUSh8sF2oRKbOax/PtWfe44AJQjad5/w1LmhjyfukMiyXDjHCvqYQSAJVH69WmFBmT3l8bUKnt927XZ0/HMUhqtkvLm0TLqyyeXAKr2Cexg37zClQisUo4du//dv5h//wH2Kt5Z3vfCf/9t/+27qIOnQUli6xMuwTRTlTB2v347WLF5EwQonHtVrQ75PqevvGTLsVh8FgXMUwiNicOYQ/8nJ6fkiiNG+ULneriJnOJGddwb//5f/IoLfJG+++k6/7tm9n5ISlxSETTjNzvE08CeKA+BAug2+55zbak12eevwUzz36GI/bjKeKdRbnMh6dqTi7GhMqz4RyTM3OMHX0Lnjdd2y5TXe0JlaKNecgjAnEY3WELirsV0nRPlxbYri2iNxgnvDlGHjHgi3YX5XoZAJnN4iUJzBTuEc+gjz9WB351JxzDumIUClWJrtERnFxeZlepDlm4q158rrIFzSG2BcMb3LbBs7Whol2lUumxazpsn/cgOuv1AxWo3xSSmPC1p5mdGPUaiEhNy1WnCMIE7wVrDjagb0ma1U4IQ4gdznaOYIg5aK1PFYUfLLSfLEo+Hh/hc/nOQvWMq01b0pTvi5Nt/LJoU660KpOFwG4TRVs6hZnrcWKpUXIERNzRgpcOonKBihliBxXMO0VFdrXcW9KxxRrFxDvOfDQEwSnLsDRY/D1D8KxA9vNZq0J2m2S0YiRTgHPpFaMXLGnqdW9QYtShFPuxs/FUMd69sURURftSqcYrUCHW5FVQakxwMJ1mPYLvqBCM11FlKb+bKe0Alsz7fiq7tCfexLWF+sZ4u7MFc9z7glQoWVqP3zyDz+IiPDgA8fx6TyzB+u593OLi6SzHRRQFn5LXZJVQp4VpK2kjpEDsshQGiHF0CIhtjkhIb/8i78BwHf9+beQhCmvmTbcdjpgTjQXj1oe7wtBME1GTtkOUP0BzjnSYy/jrihi5D2XbL1PxkX74rDEe0HE46oRnWlFd2a3RF5E8C5HVR4Z5XgtqH057/7Vf4qI8N0/8AMce9vb4OJFLi2fo0IY4njjm94EbEvkk/YEpqpQJiNPJvFKYNB7UbLaLVIfs75ER50tyffIekJKdLC70bKzaF+/VDdS9p+oP95sXTGnApaCpDZ+zevrWBKkxEoRuKIp2uunulwi3y+EWFdUeEJrIY4ICLeL9qSFmIAgy5nEXjOecLB6kX4cobqT2x4t3eMopbHFBcr7XkO5/yDR4iXKag8z28sQqAJl0i1zxWpzlZPVgKIoEHEEWhEH9Uz7lxsVJdqV+Gb91M6W4cyX4OJJ1KVnaV9cwF98mrmNZzmeneLS0yehUVfKDSgKb+EW/qzjVtH+PGHCbXn8uGgXHSHVVWbaqxJs/TfrhCKYwBc59Jdh7gTcYFzRoIROWOFdwcMupB+1ORoqkiJjjQEhARMqYZPanXfRlXyk3GTJGw6amEOVYsIHXGwFu9mpcgS9RZQ2KFvdVNfSugqqHBcExFGbtbRLZSvusFcuxsZF+9B7utpwXzLHtC95rBzyxGSMQ3h92CHduT+COtc0ykZ0EVYb1ri9R1Z7ZoU0hE2xJApUVZ/I1TjHN5jCeM/Q33DCAkbVrzWWx9tm3vOAScmAQdyu1RI7EKqQiJiRDLHYy5j2EZXNEeux8UQ9h33sWM3uXNoj+k0pVHsOVQwRl6GVJiTcVbRDLZFfGQnWC6OGTQmVQkRxwRVXnR8ds+epaFadIwlgvmGAx133zMKENlQiZJcVSD3vmNppfmOrmj2MQyTrIWGEzy16c8jawTYdEtJGLcDZ5/jj0+d55de9kT/6oz9idnaW3//93+ff/Jt/gx4XJIeOUFqPWXoWCStmD98GwMbCAhLEKDxFp4WIoz2qP4dOtM20GxWgbUnPhMRKkaDpuwwFdDNHqg33Ts7x54Iuf/h//XsA/sH3fy9EMecLh+2NmO8aknaLZowTW+1nmMccige88bv+PAAf/80/4JIrWbYDOrHhTjfJ3Y8f4DXBNPuMpZIMDt1dx/jtwIwx20w7jiqI0JWjqm6umHmpsHb+Syyfexz/PKIgAU65nLgcMacMRdgl8WuExqDPn2G9XGbdrsOgj7iaZTRKcdTEXAhjOi3DQn8JEbhthzR+POetowlCXzPtNzPTOKwqjAwZSIGN55lWIVPj420sje9sF2cq6NC9RtHum8Xyxcbb5nDSwSlD4So6QX7VmXYRobDU8nhfop1lUSd8fDTiiaIgVxEzJuTBwPHtnQ7f1e3y5laLIzuSDsYwum7crYwETcW08kRhlyeKAiuWQAXM6QAvUKSTkPdRKiTynoJ81/4rKQm81OceE2M3FvCFpZV7eO1r4OveABOTsPDU7o3odmkPBrWDPDClAFPs6bA9rQMOmohTjRHojSKjNgQMvEf7kmDLAdtsMe1KNF0My9cZMTntClSpSH1AZTwBjXM8bM20q7UNeOYR2HcMDhy/4jnyobB0BuZvd5gQPvrHHwLg9V//MnrMMX249iO6sLxCkHYIozol1DdRoUtDQduSTjveig/NI43XUG0EfOljljPP9th8KOUD//VPMFrzjd/6du6farH2HPQXFX/lgYC7Zwx/ujHg03mfVE9j0hS92cehSA/fy6EgoKU1zzRrk8OH62bC0mYOVvAIvqyviwdur8f3N1eagkgcXip0WSF5hQ8CTq+t8JEP/y4mjPi+H/jH8LKX4YOA9YcfImhSIR58c820j4v2TnsSJYq0HNCL2ogChr1mLvmFMu2CshbtSlRUn6i9CIVzRFKh92Ta6/PI0mmIYpg6UH/tBxswp0KGWuN0SFX1UUBqImId1kx7I4+HKx3k+6WQhhXON/L4OCZgm2knSlFxG1/mHLYVS666quJkff08vfYcM6HZynRXOiLoHMdXm5TZAtnMPJUo/GDtuvspUMWuefZef5lNWWWpGtQmTVw9neelRiV1RruXer0286U/hM+/D557DJ57jPTMKfRzT8Bzj3FP/3HS84+ycrpOjb4ZoukWvnYgIqdF5OVf6e34cuFW0f4CEMU1026toI1C9DWY9rEMK4qwApVq10WYeJg5ckOvJyIMS6FrBpyzlkUVc//UPmIlVFmPAsd+ppilTUXFJ8p1PlsNiJTi66NJ7gwnIRhxqKqNjr60c5Z89Wy9QEsmUdbCTXQty6pC2QJpmPbT6QQaOJj1r7hvu1kYj43yorDDCR1wl64vfq8NO0zs4U5OOkmY53R0nc9biKcdwfDy3VxBGAi5WBI0VAWqcsQPfxKyIWHQJSDAugHuJti5iXi7mz6OcNuvUxSwEqVXFO0AbdUiI6uNZ3aZ0I2w3uIqT9CerEcAJmchTeD8VQy/2nOookCaRVWskl3yeKiLdi91Xvt4/x4KAvB1fNDV3JQ3fT076a1hoB0TgWby8qJ9pxndjrl2K8JA3LY0HraY9lYSQr4JUYRaWqPCkh2cZa5h2cuFi/zYv/slvuXHfpzFxUXe9ra38fDDD/Od3/mduzdwfj+bBHRWTiLGMHekXgRvLCzgwwQlnqrVwiN0G0ZkvJhy1Ey7uIp+EJIoRaI0fTci1fXseL1/23z2E5/k7LOnuG1+jr/0Pd8DwNMXHW03Yt/+NkppkqbeXj4VkOtZ2mqDv/g93wHA+377v/F6nfIGHXEimuCVh1q41YCJjWkmvSd3fdZ3uNSPMWcMm95ThTEBlsqE9bzsXhGAX2aICOJKVJGxUNx4o2uMQjznfcHRsiREMTATJLJB2i9g9RKbhw5RGYPdWMTbbYn8bSZGlKKYTKjsOsFI7Wrk1UW7wkTTGBziK/K9h372xMBalKwS6ICJaJ5EqTq14DJp/Bg6aNPCk9kMt8fiWuwApULOe82U1uyL66LdVhVpWFxxntraP81XKQ4UhR2hvGdkUlpa893dLt/U6XI8nWC/FFeolvbCfFuxlgmaEpTi9vYMm96z7EsMhrRprg3TetGuKkvgPB6/dV6DHXFv6JqR21ykICCNYjhwqN5PM7fBaAN6O6KmOh1aoxGDpik3qR0ox2q+9w64x6Q4EZ7Z8dlfD2Pn+NBVOIHQNMZ9KkDwtIL6711vWL1GIbjmLX3vCAea2EBuPFNjEzrYKtr1Rz8Jn3gITrxiz+e50PQt9t1VFzwf+fifAvDgN72KEQfZf/hYfb+1DTBtokjAO0b9ikhZloaCcQXtdgJlRoVQhJoyV7iNEJcuk/UMv/bzf4izljfddxdB+yCrfxJx8vMwtQ8O36X4un2O2elVzgwU+fA+XKuFzi12apo81iiluCMMWXKOnnNbTPtyb9iMNPutZtj8sVoMdPGZ+r2JWLyv0JWFvMQnEb/0q7+OiPB17/gf0dkxNkYhK/feTnjqFHf1wDvh3je8BqUUDz30EFmW0e1MEaGJ7ICeTnBhgAw26mPshRbtzUy7VoJqlHVZBShHKHaXwSNQF+3eUQ0yVi/C/G2gtaIzA+Kh068vflmQYssBqVJopQiDlMQXDLynFSpCsxfTDklYILZCe4+K0+2iXTyEKcQtxDkOlSNyfN24vQwrg1WqYgDtwxwMwi2mHUAn+9DhBNngFE9pw6VKUV6naPc2Q+N3Fe3DwRIgZFXBoCFrWtFXhmkv/QglHtsU7WG2Xo+bvvab4Q1/Aff6b2H99W/Ev/7bmP6Gv4CL2vR7GaBuZbXfwn8XuFW0vwBE8TbTDqDChmnfa6a9cSmtggALlH4IrkLSyW1H0+tgVIEXGNFjyTpuT6c41p7AaiizDRIVMU2HgJhlKTnlN7kzSPj6cIJJHTAfdUBXjArLgcyy4CoWXVlLQdfO1XLQ1iR4f1NdS2dzVFXgQsV6kPNcYJiMU8LBlaMakVKESjFsFr4q6IBS3CmeB9aLLWfoKxAmBNbSapSYq97SDtWWMd8YmRUkrE3oEqVRtkCVFuOB/ho6aBMT4uzohua/xtjpEls1JnapitlvQhbipI6VK3e7PLdUG2HMeF/GtIsglSdqNxfPpA1z03Xs2x5FgWpNopTBZ7UhWEyMaNnVeNg51z5qivY7o4gOhszLVSXym+LqefZC6CvH4Wi7AB+b0owqmGgW/DsXDr2GpZ/cwbSLqyi8phUZJNus5f0LS2QRBLP76JJy6tQpvv6bvpn/9x+8F2MMP/mTP8n73//+LfZnF7RmZWY/7dXTpKbF0UM1095bWKIwIRrBJjGCZ77a5DvvCZhMFF7qYsRgcN5ShBGJ1miEyhe0dArDphBtt3nve98LwF9+/WsxR47hRHj2omVfkDOxv1lEtOt0nHNPgJ2cIyhH/MVXv4LDx4+xuLjC5z/4EZTLUSZpFoEwPNXiYAFCybk9Du/xXHvfRATisDoEpfCj4VfeEVccOIfylgv9mxq9AuCcq+dfj5UFaM3QQ1Ju0F1apZqYYnDb7VTT85Qby7vm2tvKMK9D1jsJraqP7u0+R3o7QpsUY1oYFMaPrlCAXAubruD2pz/P4fWcEYap5jMYS+PtxDxr0t/a/ypsk2hFYEd7OshLNaQMamn80TCkE6WgApytSM3VmfaiIXWTAGw5xChNFkR0lCJoZOcqaF8z9m0n5lu1IWXpLQrN4bjLhIZLVYkRQ6tpRGwV7UVB6Hydi7xjrr2iIvRSz4GVI2S0ST9o0dYGpufqO8VdiDuw8HRd5QB0OqSjEUNRKB0zoR1awXq5dxO4ow1HTcwZd+MjDoOxRNgVddEe1MXYWO47LtrbLiQToXcVifwZl9fNys2AmRR6vi7aadRZY3m8WlkDFcLKlWaMthQWT9UkvEkdF89e5Nlz5+mmMSdecx9eurQ6M6RpzKgo6S+tY5KASHs2B0IqJdZDLAVpWme0D40hLw1VpZhtKw4/MOTlr5rlT/70FwF4x1tfzdydMYfudey7De56PZRYzrDIy7oB95uDPLWRci66AxtPoCZn6dVhnNwehhjg2araKtqX1jdxDgS/xbSbUHHgDlg5VysJvFQgFlM6fFnxxOaQD3zgQ0RxxF/78R9hOtQ88kXP0/feRqICip/9I9wfPw2TLV7xildQVRWf+9znmErbKG2YKEZsoPGtGBnW8vgXOpNciWBcidam9jegVoihKkJv92bagbVnh4iHfcfrm7vTzWe7ppnQhs0ghTIjboxflUlpS8mwOQ9MxIreDr8bL8KwEpKwQqxFe4dK0l0z7UQpQRLjK8/+YogAZ6vd79+LcHrlFOJhsnuMSW0a/5km7lAppHM7Z8sRcbmJjdvkg41rkhFS1UTKThO6criGUQrtPZea5lkr+Aox7W6IxlBIirE5QZXXC+1yCEFIFHYgCKgCQYcRQbtLOew3CSS3ivZb+NrHraL9BSCIFdpDNV4TBAHifO1QZ+1uU7FGhlVZCB59lAMf/23+/+z9d7St+X3WCX5+4U07n3TPzXUrJ0lVypZKckCWbQzCbuweoA1jAwYawwRWMz30DDTdNF5MD8yagWmGbgyysYltAzbIllCwJblKuaSSVLnqxnNPTvvsvd/9pl+YP973pBuqyiUsrTb1XatWnbvP2emNv+f7PN/nMVJjgvjmF75NTUpPhWPTDumGbd4c16zdOFHoYspJZlh2JS/ZEuEDTirPA7pVM7lAX3WItWCrSlnMLV2peNpMMbvLNXCfuwOCCOE8/nfg6lvlUzwWEwds+4KRXiMcJPjRrR2nO1Ie3PCETgBRM1Wv9CZBjPSCyBRoIdh2FZ3wcN4a6v9bdxj3FguJqHK8ELWRUDZCqJBYtpEmJ+W1szu9SFA1hjP78SoBAadkyDhq1+7VN7DtcRP9Bhxn2sspFR4KR9hrqNukA3MzkE5g+xbbrTUAGULagPaGyTq60A6VYC4RrKe+ySMXnFCqBqpOsWZLyhtkqPvzoX2hWSssFZ7z8SFAShr8nlUeLQQtIY/NtQ8b1mtwhGnP8gordC1Rz8eIuItZXSY7Oce86PP444/z6KOP8qWnn+H84gk+85nP8Ff/6l9Fqds3r1ZnZ1BmyuLIM9vq0Z7pYo1hfZyiELWbfjuG4ZBudMiyAyjrqfDkQUgiBJkwSFfRVUdAe5Lw67/+6wD8vne+BWbnWUkd+TBncQBBw9wIAU55hqug75pFlBXzJuUP/HitDvj5f/IL+Aa0B6Fg7ixsXIUT05AYxfWoZO+G+eEZpRDAUEdoHF4HgERlaX2cfCfLW3yzj0eTrWNRkq+lVl3JQGpaRQpRh3y6yWD5CkHYYXLvwyAE4dx5KlNihmvHnntBRZiky4yCanLDcWszhE4IVAcNSJv9jhzk02JInGd0N7Yod9aOSeMrpbjYKbjOFkPqZqvQLRIhCW7hIO+dxdmMjUYafy4IiMMEgcY4R6TKY9epo7WfdhE1oF1IzUTUMub9ErpdmwG+hkXpQlugJKTGIYIOUiruCzWZ92xaiJsUiKlSEHcRRVHPrTtzMNe+P/6jm7g30l2qsiBV7fpzzcw1H34KJ++FYgK7TWxjp0PkHG46xaqEwBpiLRiWtx/1uE8nSCF48TWy7WNviYSso0KdINyPLW2AWkvXN+Suqf+9bG9uzpa+jsI8SUhuFXHCERO6ejt4pfFlhphMIWrBlSs3vc76ldp4+/S99fXm879Zs+zvfvg8VTBPZQWrU83CYo0EVy9fw4cBrdgyST2RzRDO0FEOghDKjPUUykyhY8HCTIESAY9/6kmuXr3K4qkzfPDh+xic76PetM197xLIluUS9blzF6f47vmYNw8U21dh5LuUyQybtlFoScm5IOBqVbHYNEg3t3bxFSDBF4eN51P3AKJm252r8N4gSwNlwYe/8TIAH/qpH+fC+RaPPirZkAUX91oEs/cTPfnbzHzlm+xl02PRb4lSiKRDr8iYEpAlMT7dQwhdH+OvI7pxvyrqMQOh1AERklce5XOUFCh9C6Yd2Lmc0u5DZ6a+b8QdQRDBeAcWZcg4SPBlTiTq40jomMSXpM1x1Y/EMXn8pKj77oGu8FVFAPgoRqGRQuJxoCNUlGCtp5untKRg6Ybj9LItYHcF5SPimdMHSrd941fjPU+Unu14wD2+gCDETEavGPvmqjEegdhXp5Q5ZTkhQNBzsGcLJs7SCmuDxNJ+e+8/xkzQKDIXkeRbSOHqUaW0bhiH1GY1ZeOSH3TaVOkUIb71ps8b9Ub9b6HeAO3fQskAhKvd4wFEGNY4XTWb9ahEfjqG5Q3Ev/0IvRdf4sTSZaayRVm89gvNpKxd0Vs+5XQ8QAiB856dGJK8oudjXjQZJ1TAOd+joqI64vYdiIC2DtmzKQJ4s26Tecfa5ks1w96eAR0DEl+9dimsz1LwliqJyVxE27eY9jUb+TLT8mbZeEeIQ9AuZB1r9GrvpyMkEm8zBih2juSJ7jvI581XNdqihScQAkyJr/3D630AJHqAtiVjXtt3/EaScE3Wi85R4THUBk1SSGZlQBl3a3+AG8zopJAkol5QHsy0ewdlhjElzkHUaxxco1YN2k0Ba8fBC1DPwIVtmO7ivTuIPyluMNRbaAu2p56RdbRlLYk8pTXOCRz+psz21NfzoV2pWC4MWsLZ6LDBoBpTmqzZtj2pjznRj7whEZLoiJQ4K0qsDOjJomn+BFR7O7hTJ5ihw8/+7M8yHo/5kXe8lac+/rGDRd3tynvP0nyEkpJzqxNiNDOnauCwvDlECbBFSdXvwt7hPjgA7ZWlFAITRsRCkHpD4AyJSmA6hVaLq0tLPPPMM3SSmHu+5xEqHM9dtcQy5eSiQKvDHPfxpJ4eUXcmSBniR1v89E/8GAC/9qu/yvrG+oFT8cm7aiuL4nrJCdUjDxxf8VeZ+EOAEghBT0p2VYDCgVJ4L9HZlPxbWMT+xyjnDNjGaDDb5dKtDDZvUxNn2XOWUzKEfAxJH7X0NVRRIe97B9NIopDMz96PVwHZ9vFw6BMyYL49xx1hiK5yhvnhfK23OUK3GqZdotzvDLSbbBOBwKuIucvfoO8MOEsxus5yT2JEfdXY3QftQpEErVs6yO+b0C0TM6MUHSkJw5gQSWU9UXPtuJXc9JBpF/hqihSaVAUHY0RQx74BuNfAtkdacEcfcuswjZnTSQ2xEFwsLd57WkLWbF17BlFMEUBkoaD+nPuxUdq5+jhOdyhtSaUTQiGgN1ODomzcqLMGsP5SLTvrdgmlRKcphUzAFrSVvqWD/H7FQnKnili25cHM7ivVuFEGGZNR+ZBQ1wBNNI1DRR1blZh9B/mbN/ySLfBArwzxCFTcmNApVXuLQN2hm4yboO/klqB97eXauKwzK2rQ/skatL/r3fcx8gtESLTUzJ1srleXruLCiHZiKZ1DTEqUKWmHQBCRDzOubUIQWJKuRONJVJ9/+L/UXhs/+ME/wGInZK59ljEZm37IZdZwOO7kJJEIYGODdz3zed7zxa8ykXNcNx0e3834F9dTPr1RYXPFbuWwJ2pDz7XNLSqhcc7hi8ORnLgtmD8LaxehKjKcd8jS4ErDizv1dfa7/rM/TIuKuJsS3jul3FZcXrmAsiW95TXyF67x7vfW8cn7c+1Ru0enSDE+Ik1ifJEh9sHhtxBzWXlPYKraYOgI067IUBKUugG0RwlFDvnW5IBl36/ODEx26muQCToUtiKsDakRqjajK0yG955eBNPKHwDccaP+C7XBFxUaj4sTyrFitCGxOBACnbRxUqGnE3qyTnkpG2VP7h0v2YyZ4TY2GtBrJfRE3dzd8wbnPZ/LMrasYb43T193mNEldjKmegVjR1+NMT46iLe16R6lN6ggpOs8yltetlltjkszXvBtLOumKDSZi+iX2zVAafcOQPuBg3xDnESdLmVlwLg3jOjeqP8k6g3Q/i2UCkE6ccC0iyConaWb+ewD0L6yAh/5DXjmZUy3zejhu9BKkFURVTa95Wvfqialx4qMUBi6jSPWlh+SxwE9n7CWDZl6x90qZiA65N4z4vjrD1SbzE8x3jMrNXdnE3ayIeOZs3jvec4JKqFuijC7XXlnsEVRg/Y4ZGQ0D6uTnO7ei8NxdfwCK34be2RB3ZaSiTs0jhK68+ryzyBCopBVyYys5xqDoH7+voN8dpDR7tiHWKIqMELyzaJgOqkXGpHuExrDxL36tt8yhpGUrFKxJSpGRW2Wsg/CEyHRSpFGrVvOtc+IGWbFHHIf1FY5eEeZlzgRULXCuoGxz2AF6tZmdADt2dpB3uZooRFO3BTVtNip59rXcke7uTGf0nXTQnhxk0R+nzXvCcWaqefZb5ydbR0xpekJxdS7A9OcobP0bxjvmBYGJwM6fgzeUeylVBhaZ+6mLEo++9nPAvA//9k/zcxDt54TPVrDylMkU/ziKbqrG3xl5OierCW6qxs7KKnwVYkdtGE4PHjeAWgvK0opEGFIJCRDX9Lxvo7RS1Notfj3v/ERAL7nzffBmUVW2eHFZcupJKfdD1CNsVZVeCYTSPqGYhATqDZub4NH7r7Ae777rRR5wU/+zF/HNcdH/0RN6IyWJsxEi9wrT7PnK15ilUmvqucbqefad4VEKY0S4JDIfErxOh3b/2NVZQqE9ygBc+WYy1V1y5nuW9Vqc6yddrZunhUZ0dpLTBbO4efPsmynLJewRIgYnMTurNS5zU0JIXhz5wSnkpCWS1ke1dtq33Vb6BaBjBFSErv8dxb7lu8iEKR3PooyJfOXv8FodI1Nt4sZnOAeTjNHlwk5ZdP4VEGHnpveZEbnzITSeTZEzNkmdQGpCFSEcQ4t6nP0RtNMOJxpD6XHlVktqVfBwblbf892871f2zX5/pkc8Cyl9fOcsJwKNGMnWK4q2kWTa98aIJBQ5gTOHzDtpS/Bu3oWV0Uw2mQqAoSTREEAQVArg7IG4J28r76ubV+DTucw9k3FgGcgIfcFxt1eS3W3igmE4PlXYdu990y8oyMU1maUxIexUA1o965p6FaSlpCs3wDavfdctQWzUpPnzbUubEzoZO2DggrwWMT2DngBZ87BaAS7hyMiw3XPdNww0tT3hX2m/W3f9xbGzKK8QIuAmSbxYunKVZyOaMUGKz3Vdo6yBa1AUFSa6y8WVIkg6RUILdFo1pe3+MhHPoIKAv7kBx4j7LWZlbO0iVlllwrDhbxP8vSL8Mu/DL/6q/DSS1x4+D7e9ON/kLd0Yu6MLN0kYzV3PLvreWno+eWJptXpMM0y9irwxt004nX2/lqEt309x+MRhYHKsDyq933n3AMkUrFi12kvWE5OW6xcKQhP9UhGY4IXrvDm99axmJ/73OdwztFq91BVhqoCJnFUK0iKvNl3rx8lVjgCW4KSB87xWeWRrh6DkMEN8ngpGY0StJty4o7jv+rOwnQEXSvxQZsSQdSYTQoVEwmBsAW59/Si42Z0+4a1gaqQxiAlELVZeVax9Iw8kK8HcYxTIS5LmVeK3Du2mobgc2aKLzIG6YS0tUg/Figh6MjajO6LWcaaMbw1Ugy0RvfupZ20CItdsvE6tyrvDM5OMf5Q3Tkd7+CxjMwMvjDMC8myLVGNye2trlm/W2V8hTc5SsbkVtErd+tF9szpmhDx7sCEd59pT7odnIcyM/CGPP6NuqGEEP9ICPHQ63zuHxJC/JVX+P2jQogffoXf/zdCiJeFEC8IIX6weez+Jtd9/7+REOL//Dv5XG+A9m+hVAjSgtlfwwUN074P2nd2mHnySfjIR2pTqe95P5Pve4z0zAIiVLjcYor81tnct6i0hEDnhEIQ6gTrHWvsIOIeCTHX003aQnFSBszIiMrJm0D7QlDPWU8anHXPcB2pI77e7jG0lktZxq4FTIF/DV1v70pcWQKWLEkQBNwRBHS7p1gUs8yPK7YY8SLXGTWd6raUOCBrFv+ykX9Kbv9+l71iwzqEqeqFFVAE9d9PmhtLVoHHU0pLa38e1JSUSCoP2+kIrEEFHQKvyNzeq84MXzMGCZyLAlaCguu5wVChj8yo94RiHN/sIA+QiBZzcv7wgWKK8x5TFHgZ8nIET0ybfRS3od+5LWgX7QVEmeHLWjEgjaS8QWq90BIgPOu5PWDrFrVGAtorho350n6NvUUAHSRrlTmIejtadVZ7/fP+7PrIWyrvSBtp/dEqigqpA6JiCEA63MGHAYO5O/jc5z5HlmW85cJ5Tr7pzXCL97uxruYTnCzonbmX0c4O1V5G99QCAGurawiloSqx/U6dyNBsT9uALV1VlEIiwghBPd7Q9rqWyqQpvtXiVz/6awD8oUffRn/uAkvTMbtlxh0L2TGWfe1iLUaJz2aUUYdQdxCTFOsK/tb/+Ofpzw349Ge/xl/77/7Hep8JweKdkO+klK7N3WKGPvN4H5G3LS+yzMRndCvFzkhjpUAKh9MBMisov8Oxb2XjZi2EYK6YUHjPsnltbNiKK5mRmiRPwRqqq08z0gHPXbifj08ynqlSNirFC2VJsnAvvsiYjK4efxEhCdtd5sSI5VHDtJt9xquFEgpUROSKg1nP11KqGKLQbM+cZXj2AezwCsNLjxOomAvtB4lEwIBaBXMoke/QwjK5IRXDVxN2UDgZcO6Iq3sQJHjnoMlFnt5iTbkvj0d7lMnxQqHzKb19BzBqBlnI6OB7v1p15YS2sjy/166vNd4wKxVdGXL90jc5+fXPkpkSWjMgNDKfElow1NFwFRXC2brRZz1Mhox1jEQQJg1bmXRqph2gMwfdedi4CElECOg0ZSprcDCQHmTJxN5+uREIyT0qYdNVbL3CMT/lUBlkqgzrY0LVXOv3r0PeNMkiMCfVQdrIfm26iql3XFAxO1OIpWVCY0Inmri3A+f4nVoF9ZZH6icfYdtXL9bK3fnaF5Pnn32ezY1tTnRbnHzHmxGujfSgRED/zCkAlpau4cOQAItOoNzNULYkUfDyVxxTHL3TAqEhxBMR8Esf/pc453j7D/4o7+wKfKeNUjHnmKd3fYe7Pvk07X/2K/D5z9cNlfe/H/74H4fv/V7U6TPMBIp7hOSB2ZI/dj7ij5wP+eHZmN5og5kTdazh6rjAW1/L44/cEzuzgv4C7KzkOGuQRUFqLDtpShAEDE6dpqdmWbGbtBCcnEYMzAq7/bOE7Q5qaY2Z+RlOnTrFzs4OL774Ip3OAC0hmRom7Q6Vt4j9MaVv4Xpn8AS2YdqbRnJmIJZ53TRX4bG/996zs9um10kJk+MNpe5cvRnSoaAVtim9QB2A9oRISLTLmThH78BBvmHaC0+oQEqLqCpQEhnETEeAkRRZvd4LkhZOKuw05ZRUVDieznOeyqdctQX3T6c4UzFpnaTf9Bt6QvGNImfJGB6JIk6FzXhNOEvr1FvR1jJde/qW6xrffH7jD5sX6WSLXIRM0zb5nmGh3jCsq3pd8e1k2ksqhCvRuk1uPO1qp/aV6C/WKp5mfRWK8GDdk/Tq2fx8WuJxr2nN+kb9p1Pe+5/23j/7Op/777z3/49X+JNHgVuC9qZR8EeBh4EfAv5/QgjlvX+hyXV/FHg7MAX+7e/kc70B2r+FUgHHZtplGNR+PPvy+I9/nGh9HR59FL773XD/Axhr8EFFPhch0gxjzaEc71VqUnq0zImlRMiYbUaUlLSjWTIhKaZ73DvcQ3z243SEwNiYMdkxlvtE3AIvSQMPRUow3uLEwt0McTyf7nLyxa9gxnu1Sd5rkRu5ClfmIBzTqMWsDGumVmlke8DCxHEPp1AorrDOy34FI1M87lAi38SxKXH797voJTvWI6uStvAoIRhjidShg3xmPJXwCOVJpEc4B6bCNof5jrW46RihW4QiwJspObff9s57lqqKU2bCd+mahf5ykTH1JQGHYLMrFMO4Xe/H8lXmMsspFRaXGawMKFsxQ+dYMwaSNvQSyHMYDrHese1HmH3jmU7N1vhJneUuK0VJecDWAgRK0Etgr+BgLjYUggWtqaxAANeP7NeRM7SFYuwcqfWcDm6eK4+PmNL05D5oNweGdIMb3P7zojyIe3Pekw13ECdPEsmIT3ziEwB88OGH4Pydr7ytmrpstlDA+TvexK7xDNY26J1aBGBjZQ10rcCo+r3a+K+RyO8z7TYv65/CGCMs0lW0haqZ9umUDV3xxKceB+APvuMdLPTvYH1dEsxtcXqxRDWg3TnP6st12IObzfC+gw67yEmG8VPuODPLX/v7/1eUkvw//87/m3/9r/81ACfOG5TP2drq0BGaGUJyH9HbDpmO4ImLqzzz5C4bq5K0kARViQ1jZF5Q3kLa++2sytSe7E5qknxMW0ouvgaJ/MRZxs6yKAOujndZWrrExWyP585eIIsTToWK+8KA+3SbqXO0Fu5HCkm68fzNLxb3mBcjtqae3Hi8mdau5k0EnFQJYRP79loqqwxBMUIGMTtaMz07y3YvZPbSyyxEZ9GNoVkkAtpEBxJ5qdskQlJWk2NqA2cmrIuE2UYav19JmOCsx1HUc+a3YtoNhAoKV6Fsrb5p767Tuv5iPVfRlNCt1ySPh3px3g8sqQm5OvRYLEIIHhQKuXaZ0lhEOqIMY4SOoCwJmmtxSUFFSWip00TyKa4qGAUxoZOo/fSDpFvPtO+bvJ28D2wFe8sEUUSYpqQiAgSDxsBrKm/PtEPtYRAL+Yps+6RpOHacpbQGJ5JbMO0VraBWYJ2QAUNnMUca41dsQSQEJ2XAdubpBtWhCR0cZLR7V8HWNiJqw4kT9X8NaC8zz/YyLF4Apevv9dlP1Qqix+47w3hwEuUDnAWHYnC6Bu3LKys4HaNwRB2JqjKirGB4DbKxo3XeIlsSrwSB93jr+cf/6J8A8Cf/9E8TTvfwnTbSSMJPfYYLH32S1so2PPQQ/PiPw4/+KDz4YJ1gA5DU+2uQCaYUlN7Q0YL3tiWPvPQVZpvxrLVxWYN2mx3u06bO3A+myrGpQxQVS5P6/nHyzDmkUgjdocTR3S0odixvPrfJ3tw9pNE8wbBg+vJLx+bae60eSkArK5i2upTeIBrF4bcyl1x5T2ANSHnQwMmNJ5Z1cgL6OGjf24DcdpgZ3Hxe7ac9jrehFXawXtXz4N4jpCJSIYHNSZ2jE9Y8zVHQ3o0EVjh0aZFSIoKE6R4ILymm4LwjihO8DHBlwYwtORkoSu/5WD7iubxkurXG2HlM9yRB05haryzL1nBXoLk/ig5k4sHuBjNby0yTWezeCmZy+abv5EzdZDMcgvZ8sk2qOggRkGcQOM9ZqVkXBQZ3oGL8dlTlS4St0KpDXjmSYgRhUufwwZG59oiKCu893U6Cl5oira+Vb0jk/9MrIcQFIcTzQoh/JoR4TgjxK0LUc6lCiE8LId7R/PwPhBBfEUI8I4T47488/4oQ4r8XQnxVCPFNIcQDzeM/JYT4n5qf/3MhxNNCiK8LIT4rhAiBvwH8kYYx/yM3fKwfAf6l977w3l8GXgbedcPffAC4KRl/6wABAABJREFU6L2/gal45Xp1muuNum2pEIQF08wyyai+SZNENYN4xx1snjvHAw/dB19fgbiNmW4gvKGc6xO8lFJZW8vxwlc3pBuVDhkWJEJgpGKTPVoEBFKyEbRoT0ec/tVPwMoK806h3vEIuR8zFtMD1qgbKQKfMA4cbF8FITkxfxcLvuLS3jJ3CUFZVFCVtWxNt1/xM3lXYfMUqxVFEHFGHrkxdmdh/SotH3KvOM02I7YZM5K7THXJVd+n7WdIdAsQ6NsAaOc9Q+/pyghXFTgMs0Kz5SvaYXhwY8krsLqOL4sRSGfBWwwKqwKwFZuTISc654kIkCZnQk5CdMv3XbeW0lXc567jhxHfbwK+6GOuZCUn2ycOv6ZUrEb1wiXMmhvN7aqc1sxCUeJEiGyFeOCFsuRk3IFBF7Z3yVaucrU/S4lhmzF3+1Oo9mwda5RuATXT7vGUFMQcvme3DenQEx+x9jutNevGcEportuS+1Vt/jTylhmpWSoszsPZ6NZMe27q/ZAIRSAEI2exot7uR53jAcrCEHcDfLZNUeaQGvRD96FQh6D9LQ/DwuLtt1NTznu27S4d0abVP8Fa1KZ9fYXWyXr7b62ugXoAWVUU/XZt8jMcwqlTWAwCwbTM8ELgwwCDI3YWLSQChclSfuP5K+TTjEfvvovT99wDSrPxcp/ZM1dwcY5W9bmzvQxFBnc/6vnaakY46iC6PVS2RuZytLC87Z2P8l//3/4Ef+t/+Cf81E/9FA8++CAPXThDqw8ba21Oec8pF/OF7RGb35wnvn4asbhL584RbmOPYlsS24oqStB5erAo+06VqQpKHEWS0J+m3CUF3zSWkbX0XsE4cKVZfK+VjuDqs5yZjmndcy8qOcNDQYt7I8kaikTGXKEiC7vozizF1jXK+wzhUfVG0qOvClZNxvKowzk5RejkYC5TyhaBG5I5i/P+wHjzdrVjSuJyioh7rLBOV1d0Fu+j/eLLiK11uMseMHUDOiyzTeYL4qBNcsRBfqAUAktRZWzLee66QTWSBC382FN4Q1sbplV402cpbN0Uy22BtIYq6KHLnFBQy88bK2up25hyiPf2QPp7u3JmTKQU/Vjwwpbjrf3ag+OO3VUmzrKDJZyOmXpH2B4gt9cIGllu7vN6/Gd/rZ6lFKZiqiLmnIW4uU8ljRdHnkK7D0kfBqdh6wqildBOU6bUGe8dDEpIJq9CESghuE8nfKNKWbcli+rm7TXelxbbEus9joSw2RxCqFru7+usdudhxmsssOoM52RI5i0bruJenVCYGtiHoT00oYMatCcDyMa1CV08B50OXLgAX/oSpCnrV1t4dyiNB3j8N+uZ7fc8ehepmKEvFVtWEKAZLDagfW0dF0RIHGFHMqdyWmsV6RDOvdcyVI5QgVGS0Hs++9EvsbqyzKm77uVPfc87cR95BllY5L/+t/VozzvfCW95C9zuXIzr+0K3EYeMSJmnj1pdYsY5BoPaCHVjnONsWDeebVXnvTU1exqWL6fYYQFVxbW0BkYnzp1HAkMJgUiYLqW0y3UWz3rGnQfI1tbBCfLnL/HYY4/xK7/yKzzxxBP8qZ/6SbzS9PIx+elBPX4ymcB871uUx3u0KfHhkZn2CjrktSGsOh7dsXEFiNp0umXdIDvyncNYELU8k12I70ywMsSUUwwVASGRTlBVRtpcb7pHHOTHJSwkni3pCKoKpMKaBOdAOEmRgcMRtRIqnWDNlF42YbY94I5QkhrNrI8wu8sshzHPJYrFPEcDq5VjQSnONwkvFSUajdpegmyESuZIqyE2W6uz3NuHccKuGiNVC7+/zrKGMtslV7PErkNlJGZacteCZslasqRkWn37IELlc6QzaNXC5RNCV0B7EYK4XlNNh/W+ESHee0pKWkGIiToU0xwImmP32/aR36gbau6Lq+8F5v4jv+z29rtPfe5V/uZ+4E97758QQnwY+Bng79zwN/937/2OqG+gnxJCvMV7/43md1ve+7cJIX4G+MvAT9/w3P8W+EHv/bIQYuC9L4UQ/y3wDu/9X7zF5zkDfOHIv683jx2tPwr8i1f5XjfVG0z7t1D1TDuUzX1G7MvjowD+1J+CD3wAmyQHGe0+jCmdQ+Io5mbweHyaHkbMvEJV1jM2nkTkxCpiS4yxOPq+Re4923Gbu778BeTKCnTa9L7yJboXl6m8uEki35VtjLeYnSu1kVAQ86agBeMdRlisg6qqXlPupXcVvsgwgcbqiAV1A2h3FqYjhBDMiz73i7M8IE6hfcI2KS+zykusMlYeKW/eDt4WDKcbdKdX0T7HliUWy6zUjJ0lDP2BEV1mgLBmVGLh0aXB4zFIku4MSkg2RrsIodCqjTYFKbc3bblWVfTKHTrOotvnmIki7inWaO++zMrmc9hsDe8MPaGo4tpD4FYS+WNVTil0AFmBVSE7OkR7wboxDMMY304Yq5LV1afxeE4xQ0HJZdawSiOiDqR1Fqus6oVacYNEvp14nIfsCGF1qgEU2iuKJrO99I7MO3pCsVQYIiQL0c2XhBtNaXpCMfKWobe0hCQ8YkJnHFSmohUHuGyPcpoTohGnTzLc3uWrX/0qYRDw/kcfOWSDblG28kx2PN9YmjBNp7A0w29+3PLN3TP4b2yS9Gt5/M7aBgQKbSryVgun1cFce53RrsmKjFKHhEIwXPFsPGPBe0RescuE3/rSUwD8gbc8DPMn2Fn1bI0izrclIzJcw7wuvWzZvbBNdM8UrzzBbgu6fVRm8M6iXEXoLX/kp/8YP/Kf/34mkwk/+qM/yt7mCr15mJYdnv88XP5IxM5FyWTWcd/bJR94bI533bGICCW5cESupIxb6KKk+g7L/YzJ8MC426PwlgtViqSOjHqlWnUl1gs2JiPuWX2JU7NzdBfPYHWHtgqYUhCi6Tfbduo90fwF1HTMbrpy/MWSHpF0DBizPK6zpIU6zLtXOkFhEa6i4NXZ9s0yIy4yiramEAXnWGAhd4iz94J1cPVQTdenjQB2mSCEItYtApseOMgrSnaspVLtY9J4gHYQI50nd5aOrm7JtOfGE2ooXA3ajYppmQKJOIgJhZppB38wz3+78q7C2xzjIx6Yl+xknu2sQjmBXL3E7OwihdCIdNTMtc/UkX5FSkBAQUFJWcfAASIbk3tNqRUd62FfHt9qIqOyw8/IwoX6ei9KWmnK1DmkbqNcSaIVhXx11u6sDGkLxfM2u6XEd+wtiZAYm+MceFpE6kiTRmq8s7SaXdF3jRldo2a7agsEcF6FbGd1Gst6t874WNhvulQN0z7ZRuQGMZitm/AXLtTb+MoVVi/CYBGSbv3eZVXyxc98EYB3vP8tjG2XqGkeLcQB86dqtnB5fQsRJgjvkFqSdHLctKR3QjM4W5IDgQSrBKGHn/+HvwLAn/jTf5ZwMoJLlwm+8HTtgfIjPwJvfevtATsc7K8oN8QEh2uBpZfpKsFcv/bG2RhOsF7hXVWPGB0pIQS9hRyfVzAtudawmgvnz6OkZYJhZnKSbFpyavYiMtDMvP0eRKSx9DFbe7zj4QeAmmkXSmP783TTTVBtjJKU6bBuuHwroN0fyuPFEXl8QFHHAR7xXrGVZ2sJeufaSCkO1mhHqztbO8hXgNdtTJUfeMgo3aLtiwO1YD8SjAuwzpOWnnZgqIQgMBVCCUxe7wclGqYdhwzrrHZnDJ1m1OSqLbigIj4YJTxcjlDRHCeiNpfLkufLknuDiPOBPlC5lb4iLMzBuqMnBLmRVKqPSa9h81qV573HV+NjUW9MR6TGUNFjsNjBA9l2RRvPSRUyiQrG1bfPCLW0ExQaLxNkPiZwJbQH9S/bM0fM6A4d5IUQ6HaHatokPrzBtP+nWkve+yean/8p8L5b/M3/TgjxVeBr1LL1o7Pu/6b5/5PAhVs89wngF4QQfwZ4bRndr1ANU/+HgF/+nT73jZ7Ut1AqBCUO/eZUFOCcuFnuvr+wCSMK61FY8hOzROEyfjjGFumrHgVpBblwzIqKUAdsMmJAGykMW9bTG43pPvU03Pl2ePsDhE9fYvGJJ/AnvofRKXGMgZrTHbbSPYpQoucvAHUu8uwkxQtH6aCoCuLXIlVztcGU0QqrE+aPdrN7+xqzHegMDh7uioRZP0fXCM5qxw5jdrSl6E0pzARdTXHVCFeN8DYnNYZOZQjdFF9JDBVz+1LI0DAd73fVPU7XQFLg0FWJw1M5SRIlBJ0eK5MhpfdI3SasdknJa8nbDeyc8Z7lquJBs0PmQ3T7HNHMGfayE8TJC2wWhtnhS8yoy8ThAMI5pmFCLz/uIH9TFVNyKRB5AarPFTRzVhIEjuel4m5GZLOK3uoeA86ghSL0AVfZ4CrrnGvPwKieeZdOolDk5PSPvEUU+VquNxXs/6IjJT0pKSyEujak28+B7gjFclnSdZJ2eDNLGTdXiWkF7RB6QnPNFZTe0b9BGl8Y6AKtOKTY2caNcuJgjnR+ht/6N5/Ge8/7Hn6I1vzCbTfR5a97rjcq6efP7VD1BZ3NWXZaFn/fGU6sPs9Co+jYXVsHHaKynFyA63cPQLvxFo1iWmRUQUhbCHbH0JsYJruQ9XYpqHjit58E4IcffQvMn+D5lyxewb39gEyFrIghCzuLbKQlwYM5KzJHRyC3Y+gNkHkdVeR9SSQVqZjjb/+Dv87FF67x9Dee4X//5/4i//pv/VeonRbb12HurOBd9yU8PjOmd5dFCU3bxygdUHhB7ApGYYvZ7YLqOyyPN838dtadYbp6ndnpHmcHXa5UFW+OooNj6GiNnWXLGnYreGR3jRlbYs9foPABhYpo6Rq0d4gPfBdS55idP0907Wl2ty/i2+cOz8m4XmieCcY8O5rFRQUqSSic44Wy5JRsIfFIVxusJa/CRO9VI/plSdluE9guZ1QbRhu1DriwsHKxWSTm6DvuoifaDJlwys/SCrtE+XrtIB8EaFGw62K6YfeY4ztAFLUASWkrOrri+i3wdmGgE9WgXdnaF6S1L4PPDoHEMTO6oHP7/ZVeBwSVb3HHQPDUGlwbVbyVrTpr+sKbsHkO03HtAdAaIEWIzcdETjKVKR6Pdh5hPaKso7mmYUivGh8y7U1c1jHQnvTr3HZ/jdZUMnUOoRJ8sUNHDihfw2pDCsH9OuGr1YRlV3L2hmztsbd0hKI0U6wXQER0ZHcLocFVdJo545atjR3XXO24vWQLTsiARCiW0ooXdUaiPe9rter9Z+sUAXQIOzuIvIS7GlXVYAD9PuMnr1BED3HXo4fv++Unv8xkNOHO+T79h+7GuhZRo3Q6kwQsN+M8q9u7OF0frwhPbz4j6RcszIVsFxOsDgixeCnZW9/jiU/9NkEU8Zf/2I/Br/0aXLmG/7E/DD/4Y/X8+qtVFNcAP8/ocZINhpjdDfRoSHswx9zMAICNvUm9drEGa8ZIZo+9TNzNMZVBTCuWmhiE2TvOkcmCBTTm6VlUtEPLvwQLd2IXS9LFDmJXUJQp93cCWq0WL774Ipubm9jBCaLr61Spo2rFjMc7dOW93xrT7h3KWqywWFciRUBmLKEskfr4cbR1vY7qm7u7DUvUoP3IGgVqifzWdXClI4x6UK4wchkd1UWomNgbdkwJJPQiWBr5AzO6MChxCHRVQhRTTQIqPNECpA3TTpAgohZ+ZEmy8YEu7mHdgr1tqrLAdO/lsXabM13BtrUsKsWny/IAtFeUDPaG9RPbA3rjTdadY1P2OSuhGr1cj4HJAO8t8ghoLya7lKXBx/PMno1IX4RsaMBb7tVtHtc5S2Wd/vLtKGNSIhSljwmrdZQ1tY0/1M3F3RUopoSNKrXyJQgI2h2K4RI4+0bs23e4XgMj/rtVN3Z4j/1bCHEnNYP+Tu/9rhDiF4Cj8ub9bo/lFrjYe/9fCiHeDfwB4EkhxNtf5fMsA+eO/Pts89h+/X7gq977W7tGvkK9wbR/CyUD0A1Gd86jArA+PDaLCNQ3hDDGS0/h61gNNz+LiULUKCVPJ7d+gyM1KTy5cCSipAwcHs8iM0x8QTYec8eTTyKNgO/9XhjME7zlfkQc0//45/FZdiyTfC4OGIyGjMKojnoDUmOYScd0pMRaQ1m9tlxg7ypcWWFDidIxnaNO4lGrlv2Pd256XlsIcgezoss94jRn9DmksIy3v0A1fhlX7CBVC925wGb3IbLkFCiJqyqMtwyERglBGViMqxmr3NQZ7V2hapbVFNSJ7ZI4CJnvDFDZhKWqQukOobVUrrzlXPuKMQgzYZ6Swtc3ul4ssEoQRyewM2/lK9E95OE8stylaya3NaM7VuWUwntEYbHtFqUQVFZwOqh4PtwhxTAzOMX8VKMnNSvSF23OscCEnM1WAFV+YEYXEd/EtBd4+qFk+wbfqtNas2UtJ2TImi0PDJq8F6TG0/Wazi3I70Om/XCu3XrP1LubpPH7EVatWFPku+hxhVo8iZCKT33yUwB88E0PQa/PrSodelaXlpm762XueMzQum/I3EzEBz7QRT/kuPuti8y0AhYbN/fh6gYuiFCmjstxg+6xmXYlFHmR4VVQS+oyCLxle12wma2zvD7k0pVrzPb7vPveu3H9eV5as/TnPTOqZEYuMCbj5aspPrSIvmfdj+nKhGpPQneAcB6dC5yrSIRijw797iz/+F/+XQaDAf/uP/wmf+sX/g2PfFDyzj8ID75XcN9cggCWm+g3hSQIdO1S7EvKsIXOy3ph8h0sV+U4BKY7R4Vjkm5zdxBQec/127Dt123B5aqkh+ZN2U6tQIoUKQNQllhLDJYWMYmofRZS5xBxn6DVR22tHFcHSYVRMSfVGOGmjMoaxC4bw/NlyUhEKEC56WuKfZvmu0hnyeMOwmsG090aqA1OwoWHa0D6q78En/s0XL3EDB0MjjEZKujQ8oZxk+VtZcWIkDO3GG+KwxgIqExBS1VkxuNuYI8LC5ESFLYgsJZUxcT7Td99ozdqx2qBxL2CGZ0zKTZbQyWLWEK0FNw7K9nODSwtQatHZ+4UptVDN/J4kl7dfc5TItuACUA5hyjq7zh2kKuQrveHoF3pOgbtyGcEaqdnURFnKVlZ4lUCeAZK4LUlfw3M3SkZ0JeKF012zDugdo63dKWirKYUIiJAcWyiR9Z53+3mOlZUtefGhq1YcyWF91zQMbvW8tlsShzCI3nGyX2Wvdmv6AjGu3irjl+rLlxg/M0VIlUye0Ts+KlPfRKA9917hvHcKYSP0L4B7S1Np9Ol004oKsNwVOAFRMKjgpKTZwtkFJMWE2QQYDEgPFeevYL3nofvf4iFT38av7mOeeRe+OAHDwD7VmVvOqaOlRA1cM+m9KnVKdNrz4DWtO59E7OzNSjaGI0xXoOzuPI46+y9x/uCSJaQGa41M+2dO87glGVuL2G8rjjZiXDjLeziCdbkhPLuhGA0YW9wmvLSEu9656GLvJ89iZAQDcfQSsjTXYQM8P71gXbvPdZUSG/ZXB/xwpPX2N31eGEJvUGq4+fnxhWIO9A70zSfbsO0A+yMHJ2wj64MG03ijNAxkZAUzfnYiwXew+q4Pr7DoMR7W59HYUQ50iz1Kp5pGWwFRW4hjNFBhEUgshGnVcgDOqEjFX60SVmVTJKT9GNB2ES3SiHoN7GrxhsslmhvpzaDnLuDVhARmoLhZJugfz9SJVR7z2OzGhscZdqn4x0KB4nuE/VighbkewZbGvpSc0IFLIv8NaeFfCvl8Vib1iSEj4nKPaRS9RoS6iYqwHT3iIN8fRzG3S6FFVC8tjXrG/V7ss4LId7T/PxfAI/f8PsekAJ7QohFatD8mksIcbf3/ove+/8W2KQG5GNqjupW9e+APyqEiJqGwb3Al478/o/xOqTx8AZof93lvUeGro5nslA1Y5AOjatuuHDkKcRtvKuoTFV3B6OEfGEWOUnJprdfiO3XpIIcS1sWpNIwS5dIBKwWI04++zSDa1twz31wYlDfbXxJ9n3fi8sqep94nD132BiYNVvo3LDZmzkwMRuNdpDeo/sLoBRVUb6mrqU3OVQlRiuS8BYMUG/2lqC908S+7Vc7Pk3uuoy6C4SzjxLOv5Ng8AC6dZotEdPVCVIH+LLE+BIpBAOhKHTjIF9CahxO14s6i0OVJdY5vJckQUivM6BbZlzLc1TQRaPATJncQiJ/raqYK7fpKk3p6xt7NwRkRWU070na+KDDF9SJ2s3YGfbiTi2trG4jua9ycJZSeERpKVsdwJOqXUSwi5QhlVqg28yxHnWRnxEdzjDHuNsmpcCN6ptwJCJKimNmdKn3LEaSnamnsoc33FNa44DISTxwydZJBHvWUVg4EagDNUZeDlnbexJ3RGp6kNV+BKjfzLQ3Dt86xU3GxJnEnF5AesnHP/5xAD740P3Q6d1yE135hiPsbLJw9x75iU28nNL1MxTAyHrOxQGd2TYzQQshBKPNHXIv0c5ijMH1ujAeg7WNPF5RFhnogDKvFwe9lmFb5hTrE554ujbs+cF3vA3Vn2F3JWRNWO4/YwDLnDpBUAVcLXbYO5Xz9KTk65OUZ63i62PDTjLAAyot8QISYZmKCCVmOHPnCf7JP/0wQgj++v/3w/zmZz9K1BLNflN0c8+KrxdFQggiFZErRSAspYwQ3mHz1x4J+btRxtTfazaex4UBm+MNFrSmJ+VtJfJPllO8k7w7aZEM1/GBxocBE3oo5RCNTLpFhBSClpSk3tcRboMFwnRyk0S+0gkzjIlFxm7mkbp1cP0YE6GQSJu9JjM6O91GIpi0+nRlQDBaq5nV9mwNRjMJW5uwvQLXrtAlQSPZZVKb0UnBtKrBaq4MpW5z9hasZxzEIAKcKUl0ifc3uzEXxhNpqGxB4Bw5kmh/kXwESAghELr9irFvZny5Hv1pnz947L55SXuywd52CmfuJZYS1+4jjKHIJiAVoj2HyCYE7vBaoZxFFjlIxdhYjFd0lDwE7VCb0WU3NJwHp6DdIslG6MmEosnGHggLwrNT3P6ecrWqyJxDCMEDusXUO64dyXdPvcP52vizshkZIQGC4Cam3aClINL1fWFBarad4YotaAmJs/BbaUpaet4ftekeTW/ZB+1BDdqFU9A9XJdl8+eZDh1n2ku1rLqp3/zN3wLgsfvOsjezQOQDjIXQw9VnAKeZW6hR4MrVNZxShHgqfJ0tFkSkZUqsNUY4hPBcfGYJgDd32jA3h3vnI7i7zx/ETy6lhv/pyZx/8vWCtTWHMbcBV0kL8oxERESlo1i7BKfvoD2YYXGm/kybwxHGS4SzVOVxtZjH4n1FSIGvHNfGdaMxPr9Yj4Z9MyGI4HxYx8JN5hN2XElxrkcgS6b6JKO9KY+9pY73fOKJJwiiFkWnR7g3RiQJfrpHIdTrlsdXeKQ1YC1lFTAZD/nSJwu2lg3KVEh9eNzmqWe4QR3zpnS9r288jjkkeXfHniRqEzvBTnPe72e1V7a+fveb2LfrTcKFViXCOkJv8VFMsadxHY9PBA7PZM+BkOgoohIB5GPeGnS4W9fnix+ukQcJpe7Tu8FyZz92deoLZDZBl2V93nUX0HGXTpmTjrcQUhMMHgShsNkqQmikPvS+Scc7THSLk+0EZQxBLBCuZG+zvtHfpWIqPJfK248Q/scqLz3YEiUjcqeI811UGB2C9qhT76t9MzoRHvi9xN0uzkOVvcG0/ydcLwB/QQjxHDAD/IOjv/Tef51aFv888M+p5e6/k/rbjUnd08DngK8DvwU8dCsjOu/9M8D/CjwLfAz4C97X8hghRBv4IIeS/N9RvQHaX2etly+QxSsoIfAWjKmvKU6G+BsXJg1oxxuMK0FKAh1SLM7CJKOcvrorcFp6hMxpKYdTIT1aTF2FevlpFp+5jJ45B9/17prp7c6C97RbAavvfYzO2h7m8c8czAj2JlcpTMxOMsO0YbQm420AkhPncUpTViW+fHUFAPkE4x1lEtITt6BpO3W2ODdc+NtSknt/kPctVEhezTNJehgVHEhjjfeMnKOrY7TWOOtwzVznnAwotcXi2Ms9qbcEspZvOyyyKrHWYVVIHETQ6jKnFHvpHoVsI4UkvMVce+k961XOaTtEhXPMffZxeOoplBQkoSWvNC0peU+SMPaCy8bT9hXDuFXzVLdj24t6WxfWQmnJ223a0RCrJ/RcnwfEaTZUiyqUEEU3Rb/NiR6z7bsoMWyll/B4IhE3ZnSHx1zqHKcSifOwNT1cyM0pRSgEY1eb5xnv6QnNhrUoI5kJDi8Ho/wqab5M5aZEWqAkBw7yHaEOpHw3mdBZT6QEY7OJ3kmJwx7m1DzXXrrGtWvXmJud5a0Xzt+Sad/b8Ax39pg5ZVFasp1fxhuYDXpcbQKtz0ea7lyLpHR0Fmbx3rOyNyEAjKkwg06d1TMcYjEY4/GmwmlNPvUoZxmc3sO2PDwf8LFn6vnlH37Tg7BwgosvO2wMdy7Ux0SgO+jLcxTKsD4/pBVWDEKBCDXPYvnoqMPl1PHySsrUSbQvMTKicD2EkLz/g2/nb/yFP4H3np/4iZ/gE5/4BB/5yEf4uZ/7OT7693+ev/Mzf5kf/pEP8a53vYu/+sN/gC88dwnhLUZHIARuOr5pO307y9gcj6AT9gnimGm6Teotd4ch29aya4/HrF2pCi5WJfcHMXcIYLiGayUQxOz6NpESVMIhECTNbGJbiJppVwl+Zp62U1Tb1yiPsG6VTpCm4FS4x7CQCBVh1q9yx9c+wTivL77Ja4x9k3ntazGOE2a8rKXxvUUQEl54Fpavw7u+G2Y6cPVlRFnQp8OIKU4lxEJSVSmVLciVo3ULafxyYUllSCAVzlmUqK9Z0yNz7YWpvSciJbBVBgic84RS1MZLNwAJEdwetNt8E1eN0J07EEcaaYFy3DFaYtskFIPTAETtfr3IbYyd6CwgioywalIqEDUAyjMIO7XBn1W0lTicaYfajG46PhYRRtiC+UWifIROUzIRAoIZVQPjnfLWM6dT5/hilvFSM2u2IAPmpOZlmx3cIybNvm0jsSanJCFW4rjxoAzwTdRjOxCkleeECsi8Z8tVxF7x21kGVnBXmXCufcMoReMv442pjd7ChFHbsWyv471nbbyIixIW3JWDp2RZxheeqD2H3vXo/aSqx4kgYGRgsibYWgdZBcwu1v5My5eXcFoTCk/lHVQlhVbYKiNQAc47hIRLz9Wg/cGH3wQf+hC+ymrn+Aa0f+W6JZ96Xhwa/uk3Cz71KcdXvuS5esWTpkf2SZwcGJzMLG9TuBx77i5a7YSTszVoX9/ZpSIC5zDV8WuOdwbvKkRV4Y1jeVIfl607TnI6bbO3JjhzP4Tb2+jWgL1uyYvTiuuLPUTkoIwYEvLec/Xx98QTTxALwaS/QJhOEWGEKgt2yvJ1y+P3QbuwFi8182dh9s510j3D+Lph63qEaQxrNxuv5hMX9rdP+5ZMuw4FSReGqSOIEjoiZFqOqJyrs9qlRDcO8t0GWG9NPZECR4X0hsB5bBiT72p+5ef/Gj/7oe9lmk9JG0ZeRwmV0Mev887B3hpp3CWKOugbUhf277k7PiPY20aJoL5+qQDZPUEC+HS3uaZGBIMHEUIhgyONcu8Z7+xQhR0WWxnRpaeIZImiYm+jPs/OBAGJVTxb5q+s5viPUF47hC0JVJsyLwiqCSqMyQPF0E+w+GNz7SEhJSXee9q9mlSp8grsra8vb9Tv+TLe+z/uvX/Qe/9j3tf50t777/Xef6X5+ae89/d57z/gvf/D3vtfaB6/4L3fan7+ivf+e5uff2HfZK75+zd779/kvf8/+bp2vPfvbCLc/tWNH8h7/7Pe+7u99/d77z965PHUez/nvX+VWdpb1xug/XWWQCK0QwtwpmbalQYnAlx5eOMRztaANW7jbIlzJQhBrGOKk7PgPNXO5qu+37hwKF0QCo9XASEBy0vfpL20TH8k4NG3welzkI8OHIcH6R5bd96JevTt6OdfInv2KcgnqHSbPbnI1Cimvr5Z5XvbhFFSG3wphascefHqzQSbp1hvqeKEgbyFC/vRufYjtb/IPcq2R3l9M9o7Io3dc7Vgsx/EaB1hvMNXOc475qQmUjBVlq2pp5COQAm6iFrmWeUYBAhNEtagfVYpomzCigvr/WDMwVz7fi1VFVG5y6wSqKURg6UX4ct1Yy4OK/KiXhQvaM1boohNr3GmpAg7lN7dHrSXUyxQOosoHXmnTaAqlG0T2QEPhBF53GY9HcGpU7fMa58PThJFfcrJGmnPEDfO94WvQab3nqlznG4ppID1I4s3KQQntWbVGM42M+EdIdkyhqBSdKLDxUFVDev/N8YuiRYHLKESgo5UN5nQAZRGEAWeotgj2s4QrR7V/AyPf7IedfrAe74LKSV0b2baL38D4t4OgxMBabCAqYZEpWEh7HGtMMxrSUsJkvkW8bSkuzgPwPLOkACPqyy2ecyuXMfjyctmBEAHmCn04nXgEv1uSPpCxmdefBEhBD/08IP4mQUubTmSeVhQeePSHbH7QsyYBNeacL5tORFp3ndK8UNFyFsGc7RCSbG3y5aJWJ1mVITsWUEi++TZOn/lT/84f+j3/wDD4ZAf+IEf4EMf+hB/9s/+Wf7Zh3+Bj/zDX+Tj//7X+fKXv8zSSy/yic9/DeEM6ACHRKbTemH/HSpnCryUxLpN0OoS5GMumZw7gjr08NKR+LfSez6VTQiF4PuSDuxtQjnF9XvIsE9q6/ziEkNCeNCY60hZLzB1DEFA3J4h3N5gm8OFbNUwRCfZYWIThusbhJe+jjIV09EIrwJil7+qPD7zjqAYgwgYhyEns9GhNH55CZ76Mpy9g/yt389aukC+k8L1a8zQxuMZyYJYJwQmZT3foxCCheh4A2q5sHxyr+CbpSRC14kisj4/0yOYpOlDESmPq6Y4ocBDJCT05+v56iPNTqlaeG/xNyxMvbOYyVWk7iDjE8d+Z8ebLFQjdmbu5uKwfqzV6WM92Mle7efRngWhkNkQhSJ0CkxRZ0zrqGa/vSKR4mam3dmbYy5P3U1gClrb60wRSJXQpkIJz+5tTFf3mz/DI/eDB3SLwnsu2XobjBrQ3vWWylsMCZE+DmiEVPUNGeqs9hJON02MZWO5WjhmlOJhnxAimWvd4Mmwz7RPx5DliKhN1tVMSZnYCetXBNGDdxBuXq/BFTUILYqCh84v0LpwhtS0mdeS5V1HWWMMAq+ZXaz3zfLVFZxWhN5Reo/HM2yi7FSgcBgkkkvP1CqgR9/xNrAWPx01oD3Ge88zmxVvnVzmj7/F07rbsXWmIiscLzzneeKznsc/63jpxRo0kmfgPb1ry5iZGcbdEKU1c3MnCIKAcTplL3OAp8p2jx9DrsI7g6hK8rJiK0+RSjNz+iThMwk6hNN3e1hfJTx9DxOXUbmMvUGPoOtoDSdsDs7z6KC+5n/lK19BliXZzAnwEu8EGs/eZFwbKb4OgGi8RziLsBYnNVES0j+7w9kHStqxYfNawld+A557wfDrWynxgiPp7HtmtGti4RaVzHjGqScKW3RliKoK1lyKEJJQRXVWu/doKeg0fjDdSGBcAdYSeEclE3yl+fxH/zlLz3yTixefIZ00We1N7JvJ0vpcAkiHuCpjHAxoHznfqiZSbl/dtmczwr1tVOfEYZxdf5FIh7T21tk09XkgdZtw9lF07+7DL5ZNSKc5LujSdqu8UJZUeOJWxWjT4L0nCQSzVczIuoM0kN+tcsrVGe2qQzUdo0xFEEWsRRnX2ORZrrKcWNJiC1PlhESNyXBFNwkwOqGcvjZ16Bv1Rv1vud4A7a+zpJAIbY/L4zV4GR4D7Wp/EZB0KI0BX+GEZN0FZAsDvFaw8eqgfbv0JKIkkB4nQ8Rol+zqN+is7BHNnamjX5JezRR4W88vNl1J+/bHqM6fpnzi0/CNz4GQ5MkcedYibUB7NdpB9+Zox12kDnHWkJUp/hUWwd57qjzFe0MRR8zcCrS3+zWDNToO2vfzjKdHFmnK1uzbHofNgv3FXD+ICYIQ7xy+KrEYBkITSIEJTQPa69z2fewpTIEVEhB8cuKYBC0iqThZZVwz4FRMaEos7thc+7WqYqHaoqPbyKeeRpcZXHwOPx4ShYZpoQ8WFueDAKsipC3wSjMNW3Xj5FZVTinxOGuQpWPaTtB4hFfsWU9PKWbbXTaLHDM3A6MR3DA6IYSg1VqkOy3I25YtJijUgatt5j0O6Kl6Qbo6Pr4AOq01hfe00CRCor2k9J6wknQaha93JbZh9IyrF8xJANkR+eUDKuEh3eLGMtYRaocsM9TuBM5ewEnP45+sR4x+4B1vr42R2sdHKbaWPOMdw9wde+w+O8tXvtjCGEvLFrQDzY7x3NE4Tului3ZZ0V6szeyWt3bROGxlMN0Eul38ynUAikb14nSATQVBZ4RylpO9iidf/gKlMbz70UeY73UpohOsa8vpnkT5KVq12FkRrGeOse7RD6GlYECbrFsQITjtE07OtHmznDDX6iK8YWQD9qynrWYRRYqh4Bc//HP84A/+II888gg/9EM/xJ/8k3+Sn/iJn+Bv/r3/F3/9f/3H/NW/+Tfq432S4q3BB3UUoM5qD4TvVFlTYoxk+4UY0erQsxUr+R7gORcEXK1qLwGAp/KcDVfytjChKxWsX8Q5i5udQ0WzTE3VgHZH60jM4r7qxiIRMkL0Z2hnFXvTtYPzrGpk1jN+B194Jl//ImkQEY3HkE+pZETwGrLax84Q52OqMMKrmIXJVr3gLT18/rMwOw/vfh9Ll9vslTOsLyk2f/sSCRERAbuMaYUdAjvl8nQXj+B0fAjap9bzm5sFa5c8y1uKWGqc87imETktjzPtAGiPLjO80Ajnav1Bv24+HZtrD2pG6ca8djtdwrsS3b3zJkNNt/ISOlLEpy/wwnY9U98LI6qohZiOyXHQHtTu2pMteqJPx0eIbFIDYB2Te0/oZM1oH2XaW805fKO0+NSdKKXobiw1zZgW2BxtBXu3GR3abe4DR5UbM1JzUgVcsjmld0yatAplcyrvMT46iHs73Egaj8N7SyeslQ2ndIik9ts4GwR8T6vFOBNoCf0bb1mmACHx0yHkJcQtTLsGTlc3d6gKmPmuC7X77Eo9wvGpT9VeHe+7+zTp3ByCiBaCpRUYRILxKiirD7LaV66v4LSmDqMDA+x5g0LgFTjpEc5x6fl90P52GI9wziA6PaRQvLRl8btbvHP0TR6+9DW+ezHEnHDwFsP7vhseeEiQtASXL3qefqlFMcphc5UoK6jO3XnQGI/bfWbnawXA2k6GR+BvMFP1Lsd6gygNK8O6QdNZOEXPRuwtK87cB2q8DWVBeOoehs4T+zFWKey5Lr29XTZm70CIkIfvvYeyLHnxa1/DdfuUKsJZR4SnSMdU3oL/nSdmVHikMWAdXgS04lMYZ2lHm8zNO+57d0R7AF9+ybBjLPqOI4qcpFM3ntzNKp1gzmMNSN+iS0hYlaw2c+2xTtC2OFjD9Bt83QkFpS3BgXIVpWhjrWW4sQbAxtY1pvuz73GCkxprzGHjYLSDtzk78SKDuDHqHHq++Guws+KJhCQSkizdJKgsYub04QfuLRImHdrpkM0j7L1QUX2ON2UnQ6alIWrHDEcbGC8oBMSxxRrLeBvaAXSsJjCKl01+oHj53SgnLdIatG5jpmNCXyLDhDwKaBMxR59pu80uE16aPsMqu6Q+J/UZrQBM1KlBuzf416C2eqN+75T3/or3/k3f6c/x7ao3QPvrLIECbQ+YdrPPtMsAXxyCdr2/SInbVJXB+Zo53vUx09keLgwQGxuv+n7bxtKRBRpP4BS7z38RN06JS4185K3QakHcsJfZCHqztNM98J6pF6i3vQk3WYJPfBySEySBIMtaFK5inA1xxZRWd5auCiDpICpDXhbwSsYe3lAUOWBxSZuuuAVol6p2ZR3fGrRPbrgR9GkzpaizW4Eda4mFoK0igiBCeoepcgwGJQQzQuNCy15eg/aB0njRLNxNQYXEesFIBGxZIGlzuszInCeTEUFj+rQ/1546x7AYccIXyKUxbK5TnF0E53Bf/QyJBmsDGgNdYiFwMgJTIrwnjdswvY3qpZxSBBGUBcI48laEEgLlFXum/sx3dGewwPVOszi+BdtOe562cbRHhk320ATk/vDzQw2EzvUlu5lnXBxu45NaI4AtY/lANMB5WTtY+0Om3ZUjzP6CsmHaW4E4No+7qEJO3pCjnFX1YjMMPGo8QqYl4vwdFKbgc5+umfYPvuVN0O6Q+UNw4ZznyjehPTuk1fNsXOkzpGS00UE5T2rr7Xl+3yY6SWg7R3u+BjYrmzto73DG1gqLM2dwK8vgHHleooEyCKgyCKIcjUJLxxeXPwvAD7z1HRBGDMsue9pxoS8wdopWHZZf8LwUG+ZnJRdkwML6FRargCIpcdJRjBW02jAa0msvkAd9jCnZNY5QtggrT+lT+ifO8LGPfYynnnqKj370o3z4wx/mp3/6p/krf/Ev8X1/+EPc+/7apGk4GuNtw7R7UYP21xBj9rtV3hRUBOxeDKiiNi0ccppy2RbcFYYY6ibXtariuTJnTiseDJtjd/NKHevX7mHUgIoKHVhA0Dpi3No64iAvdAvf69AhRm2vHoALLxU+CNHTIec2X2CzgGHnNKeeeY5kc41MRAS+JG9cwm9XuyYlKDKKuI120Eu3IOjBb/9WPZLyvu+jNIqNy9A+1SE+P2D4zDrP/IeUTlH7SYRhQuBKTDlEek2nyXeuKsevfCPn4jc8akOyuuGJgjbeOowviNQNTPs+NtEeZTIcqmYKRcWVrsHhjzvINzF3RyXyzkwx01VUvHjMGRqon7u9THnyDPcsJGSV5+rQ05US2+oh0hGpd7XpWtTFpzvMywX6vl0DcRVhPZTeEbtmqXAj0w43g/YgQs4uMNhaZmptvU9dQcvAxKUUt4hl2gfrRaMU2q/7VQvjPS/b/MA53tmMykNF6ybQfgBMnKEVCIyD0AnerNq8K+zwWJKghWAn88wm4qYmB6YEHeInO2AFqBDbTRAINrdzgrkp/UfO1BFwV64A8MlPfgKA9957lvFgDuECNpcFZQGnrWR0CcxOwMyZGlytXq9n2rXw0LDtE2/oCEUuPUjJaHPIeDSmmyScvPdemIzwvkI0yrWvrlg6xYSzAwmbq7xp6wqPtgMuFZanbMW58/D2dwje9g5BTsyli57tLz6DCGOSxbsYM8V5T6vdZW6mMaPbGeOlxN/QdPY2w9kCSsPSqAbt3VNnqS4F6ABO3wus1Q0McfI02z4hkrsoMSI/22NQDBmpAduqx2P31sH2X//CFwi0YNxdwFWGEI+aTtlz9nVJ5Cvva4WSszgREMVdCtuhxzoSSXs24s3fK/ja5X/G//Aj5/n6xc8ePjlums+3YNvVoD4W7SRCq4ieqdj0Kd7X53bk8oN7bi/aZ9ohswWysvV6xbZZ313BN3+3tr5Enjus8YStFlbHWGvqRhHgR5sUQpLpGQZNCsLuWj2FsvJS/bn6QuH3VlEyhO4RdY0OEbOn6RQT9kZbt91eo50djHfMyZwdY+rGAZ4wMkhh2FmpU2OUECyamNRbPl+NyH+XVF9CVygUQkW4bFzbdwcxRgsSYk6LWe5tPcgJMcPC1OGQDJnwgr/GmOxY7Bvf4cSVN+qN+t2sN0D76ywpFELXRnTHZtpFgDsiGVX7csC4TWUqPBYEINsYoShn+rCz/YrvlVWe1DsSUSG0pPvS82wVKeFOSdDtoh59W/2H+4uofAzdWSJn6OyuwsUvMLu+xPh730Y1dw6euUZbW6haZJVne1Szku3BPC0hMe0+ytl6rv0V5EbelZTZFC9qV+GIW4B2qGfs0+GxTnYoBEEzy3q0+tRs0j7bvmstM0rVRl1hG+E9piwx1CveORlgA4ulBu3zWuEawCmqgkoq8BKnAsbWQ6vLfJkSCMGOiMEbQusP5tqXqop2ucmsVKhnrkI7IrvzHMwu4i4+R2c6BBcwaoCwEIJAx1Q4Ot4yilu14Zy5hQy0SMmCGDmd4p3CJAFawJwK2GtykWfbPTpScDmQOK1hbe2mlxGdeXCGzqT+zAJNSVFL4xvA0hKCc736hr+0d7iNQyGYV4qVRjq3YQyRk2gO5X1luYtrFrP2ALQfn8e9VY0Kj/KGQHu4vIYXMZw5y9e+9DXGozH33Xcf51sx67rDP79acnlS76f1yzWhuHjfDtl6wkYHZs5nDLf62J0uW9kqAyXo6X3gkNAOJL3G+Xh1fQcFiMpQeluD9jJHbe1SFCWxFExEDJUgUjlatdDBLL/1wjcAeOzEAzA7z9XdejudH9QL02LS4plth1vwvHVWcGpnjbNTOD0c4oXHnijJ94BeHz8ZEcTzhO0TFKZkt3HJTipd5xBza1mwEoLTIoaFmq0d7o2xxh7I41WWfUeZdlyFFRpVaTLfQ3nDqbLgis3pS8mMUrxYljyZ50jpOa0VJ2VQM1fDTdxgDiEUmYtAGpSqTfeOMe3NsZZ6j1QJXjrC7iLJ9g47HAIIrzXiykv0tOAbi++EvTFJlNDdXKvN//BIV5K9QpNjtxwTlBXTuM3JNEWWJTz3Un0Bf/8HIGmx8mKtfF64v8fiIx0WL4B5+SpXPtEhHXrGWhALQWhSYltLVXdWPP/qUxUvrjgemwm5o6cY5Z4wSOqsdpvTCsXxmfbmcuiFQZsCJxShc+zpnKvBOrsyOwaIhVQIFeOPOMjX5nMS3Tk0nzuolZexAsqTZzjbCenHghe2HD0psUkf8glZ07QUnXnIankytkDkE0RngTKfkgYRfWNrhUx4pFEXxqCDmx3kARbOkkwmmHQH0Yw2DHKL9XAlv/l+t2st3aZ5MzzCtnel4qwKuWJzJq42GfU2pxQS56PjGe0AookCPeIgP60E/1m3z3tbLaQQWOfZyfzN0ng4yGgX4228l4ikjdUCNelQjBSt+3YwwFNFwf/84Q/zkz/5k3z1q19DK8k7GxO6xIS8fNkTa4iXJIEW2JFmvpnpXlndwAcRitp0L23k/m0hyaTFC7j+Qn1PvvfkaUSnUzPt3iC7cxjjeG7XcE80JQwULJyE55/iEZfylpbmpdzyxUkNXOYXBI+8t0USVGw+dYVL5Z10RLc2QyMjbLc4MaivPaujKd4JfHG8CWNthihyfAVLaX0d6509S2esOX1vPfvN2grMzEEUs+YFofAkjNie69APMsKR4Xr/HI9dqFOQnvzc5+rv3l/EKI2sKjp5XkeZvR7Qvs+0O4eTmjDWZO4EkZgiXQU6pHCOf/tzf5d8MuIX/5cjPlX78YW3mGsX3fp8NbsSGbSYqQSlL9n1to59w5M2sZiHoF1Q2BJdGVCKoojZSK8evObayhJeONI9iJKESic4cwS0D9fI4jYlbfoNaN9reJ3hem2k1wP03jp0TtQLz6Of+eRdRM4id5cZu1tfC3dWdymTiFa5zaQzh497OA/SWzoDy85Ko+oLBFEZ8I6gw8Q7nihHjNxxJcTummdv41u7R0lZNqA9wWdjpFTYMMLhCfYDkaUiTOaYn1oelOc5xTzgGDIh7HQpKgFV+UZW+xv1e7reAO2vswQSpEXJ/Zl2j9w3onP+IPZNmxyCEHRAZSscDo8AGWOAYmEGvbtLWd1e0jMp67i3viqhrDC720xszMwkx77zbXXXH0AFtdtmNoK4hd5d5q6XH6csUqIzb2Xv0fcy+p73wPo681tLCB9QlCGT0QpeCHrdGUwOOukhnKesSqx9BedQV1HmdUZ7ENby0VtWb7ZeBafHGegbHeQBIhEcSOSrxoRuVtUX7TiI8VJjqwLTSKBmm7n2ia6wwjOrNbYB7ZiSEgleYHXA2NYRRyqfclYItokxWLoWRkxZ87tcKzMWzJDW9RFimsGdp6lafbj7QewoZWblIlh1kMcKEOmE0nt6WIbRvmT0FhL5cspUB+h0ilMhZRygEcypgLHxtRQ4bnNSB5hiyub8/C2ZdtGaBSFo7y/ohcDjKSiOMe3tUDDXEiyNbpbI7znH2Dm2raVl6+27H/dWlbtY3aIy8oBpj3XNWpX29jfncQHaG4SSuJfW2Bu2GMpZHv9k7Qfwwe//fhiP2E1qRchnNg2rqeXa09CbL0kGE65utJiey7n7jMF3NXsvn2M4HHNON3Oz3sMLTzFbjBg0oH1zcwchQJuKzBk4fRrnHWplg6woCIVkamMEjkCWhCLg4gt7XB+OWOgPuNMOMP15lsaWJIZ+817XX27xkrLce1ZyVmwSZiMi3aM12SaoCqr5nGIEdLpgSoJCMB8KYl+yUTqm1hOV4OOE1N6+MXdGJAxO1KqB0d4IaysUYMMImae1T8J3qLypaL1wncELXyTNejjhOV8UVE3m9V1BcHAOzwWCBRnWPgfDVSgy/Mw8QsVMSg/CoDRoFGEDrvD+QHVTM+0JzlrGbgF7qeKlp7Z49sUxeWDIVl7EpnuYNz3A3oyn2lmmigSDvW3GPkbiULZ8xbn2UTEiKCvG7TYnx7vw3Iu1pft7vwcGM9jKs/oyzJ+FaK4HgaZ3Z48Hz18hCTVb34x5ccURNY2GMA159rc9n/6c5VJg+O43aT74noCFQc20OhUjnSP3FW1tSI/0QPNGHm9FibQGiyLxjjKSeOEZRYJhdrxpJ3X7QB5v821ctYdqnz8mfQWQtoKNq1QLixDGKKF4YF6yk3nGU5CtPpX35PuqoPYCwlb4bAdfThBFDp0FinzCRMf0q+o4y75fSeeWztucOEOQl4jhClLVoGhWFVAM2ChHx2Iqp86Re8+FxoF/94Z7wn06wfs6eLcjFMZkFCICw/G4Nzg04XOGdhNVmZbHr1m7OTgP87cC7aaeRfamQFgPvQHjdMyv/dIn+aW/9/f5M3/ixxkMBrz1Z36GP//zP88v/uIv4pzjA+95mLjVYrszS7UaMjaeaAStliN9YEQ1UiycOQnA6toWNgiROLyUDL1BekcUxHgqkHDthfq6f9/5C/X1fbSHizQyavPMiiN3ngeTKbS78OZ31xFwX/8Cb00kb2ppXsgMXxo37tqDFue7m8z1Ki6Xd/HM52NMIdgjRXe6zDcO8uujKc6CLDLsERNIY1JEnuNzz/WGzZy/+wwLOuD0/dSjAjtbcPI0qasYq4xItun5nO2ZmE7fMxjucWVwhnfddS8AX/785/HeU/TnKGUIWPpZxtRbsldac9ymKu+RpsRbh5chOtCktofG420GKuSJZ5/l6je/CcDnPvYxpvujZ68A2gvpCRMwu7U55MB4oGTdlUiVEEpBXtX3ixNtQTcSzLcEpS3QVYWQkjJP2JosHbzm+tIyVhqmQ4jiBK8jnPP46V7tpVCMScMuVrbpRvUY4mgLZk7Wt7+NK9Cd7qBsRTY4fdNnVoM7UGFIf/saG+bWowbjrV0CXVJ6QzJ/gXbYwnoHxtKZNaR7kE88rQDSyrOoQt4bdHHA56oxm01jpZh6nnscLn/9d7zLDsp7j5QVGo2QIT6foKTEhHVjNzgam92egWwPnKUj2mhgSkHS7ZBbBdn0jbn2N+r3dL0B2l9nSSQORxQ1M+0N0+5FUPvTNAyGrvI6EBSoTD1vU7ujayrvmS7Oo6qKdP32i/pJCRmOGVHiypK9yjD70jXUwgBx333H/zjuwWQLlr4GRYoIe1y78z0EcxdoiZid+07BYMD8C88SSUeRt6hGG9DpIyrNlz8CxaSDRyHKgsmtGOOmvKso8xQTBARhTL6j2Vq6BajbDzwdHf+O7Vsw7XAokd+0BR6YaUB7oCKUUriyxFLfNGaEJtaCXV0QKtFkiFvwHleVOKHwNzDtAHeanEy2GTvDggmZocNVt8O4eomBL1HPrcD8HHQi9OYOaIUTIeFkxOxo9YBpB4hVTOE9XVcxjG7jIG9KsIapDlBZjtMBRRQQC8FAKwyQuloa2U/a9Ispl+bmYGcHiuP7QOg2PkpIqgkKiWu83AtfS/USIVANqDjXF2xP/bGF66mmyfN0nmOB2Cq0rIG5tyXGTthL22xdDyirQ6Ydbo6sOlqjwqOFwVpPtLVL2TvN00/Ab3+skcZ/9/vBWYZhh5aGlhb88tcNu7nn9MM7bI8qLsuA8/MhsanQCx2qZJbhqmZ22lANezuQjun5KTONA/3Wxg4C0GVJ7i0kCXa2D8trUFboMMSUAYEqCIVFE/Iffr2WtH7/Ox9GhkPW00WuTx2nEoVzU7wNefyaIJrzvP+EwG+/hAkT1Km3IYVkfm+Lcqag2APX7YE16GnJbBTQVxXbhWdoLLLMCOIFMjfC3WbWriU05/rz6CAgm2aM85IYi4mSZqb9OwnaS4LdKcn2dbJhjI0CutMxs1Jzyeac1ZpFrXkg1Dg8p/dHJtYvQhDjuh2EipmWgDRIdXyenccfJ/rlX4bNCZcvW57/QsLFr8ELLwwoViNae5s8t7dJvrLBeLRGOtdjMgO+M0QO18gDT2s8ZGIAHNLlt51r995T5rsI58hbXRa//hSMS3j7d8GpOnR77VLdbz3zANBqxo0W5omyLR79rjHnZjtsbHmyqwHdHc3q106zuuFYu6/kLY9Ift/5+vvPtuvc5tImaOvJvaWjqxvc40HLWs0iraWUIS1rKANJSECU9NnLNtjzRyTyuoW3Od6VmMkVpGqhksWbvmt7tA7OUZ46h2oWvXcM6hi057cccXeA91BNhvXrdmt/CD/ewE+2QGpEZ5ZpNiENW8wU5W1AexemtwDt/QGSELW7CkIjkCSqoMss41Kw4w/vBfvS+AWl6El5jGkHaAnFhSZnuycUpckwMsJbcfNMewPavTtk2tMbrlk7TaLGbHIb0F5WeGfBCHy3y5/50J/jL/0ffpJ//q/+P3zps18iTVPuuvNO/ot3vYu/+1/9JT716V/i7//FH6MY9EjzFtWmxDtoZZL++wzmfM4ehsWTtYx5Y3sPqwKktwgpyJ0j8BYXRghXIiRcfWEZgIfvq0GuG23ju22UjHhq1RKFcCFIa9AexTVwH+/BC9/g7Z2QhxLNc5nhi+OSqQqRwy0WLvR55LEOZQHXvplwdTclardZbBzk10YTnAVR5pgjTZXSjlFlBZnheloD6rN3neNtj2mCUMBmfayxeIrnzDbgyLNzGOuZzCiirmV+usfER/TueDOLgz6bm5usXryICELS1gzeeXpZDaK3zQ3Ghq+hKhzaFnVnJ6iVHXkl8CJGOIMRFf/yXx0aPBfTKf/+N36j/kcQ1Yu3W4D2zHu6bcF0pwbtcWVpC8+aLZqsdkFpm5GBSPCh+zVh4HGuIjAVKEkxTdgcXTt4zfWrq7hOxmQXCBOUCjBC1WB0vI2zJeNoQCtKkEKQ7tbXpBMXYLBYK9Nae8s4qZi0F276zEHQwQ/mGOytsGlvvueIqiTNpsQiowi73DlzCtWw2t47eoP6hNlZPT4W15ea94VdEiH5Ujnmmi24+k2w9vYTga+lKkoUFiUChDFYa1FSYqP6BD5g2gFaM/Wxlo0IRYj0UPiKoNfCeIXJ8jey2t+o39P1Bmh/nSWEwntXm3baozPtYa0Cb5h2VeUHndzCFuANQoUMpMZ4SXpyBnAU15du+16TwpFj6ckSUxmCS8vMVTB995sJboxZa8/UrsPtWbj33eiww6hx+O7RIpOG8p1vQ08mnFl9iTSN8ZMxqttm2Nx7q7SD1wEqzxib23e9vauwRY7Vijhqcemrguc+By9/xeOOZP4SxrAzgl/8JXj66YOH912jb3SL3ZfIrzTZ8jP7cUoyJFAKV5mDmWslBAtakytLoOoMX4tFVAXGOxyKvHK8+JUvMTLuALTPlRmxjtlDgJlyTixANUO72MJfu0yWpfDAPeAs4fYOpCNMFCNcwNmdFxhPD1eCiY4xHmJb4ZUmD6KbQXtZL0gypdBpjgsCykjzH/72P+Bjv/RPAdhrmDeRdLnDlmyeOMGetTdJ5IVO8HGb0KREXmFE3UTaZ9qPxk+d69U/X9s73MY9pehIyZIxCECV8kAa76oRFZas7CDKmCI7DtpfSSI/Lj0tSsrMoHcnzL7jDCYY8o0nv45Siu9966MA7CYB8/FF3t5fopqsc+nkFtNkhYsTaFUt3rYYMqoM1nfov11QZidIn0sp8glsrUEY0BKO+caBfmdtG68l2lQHXgjmzAlY3UbnU2QYYwpF2ClQ3qNFwEc/UZtH/cF3v5O4N+XlHRhljvMdibEpyysJV7zlu+5UzE6vYW3GZO5OQt2C7mn64118a0JhLSbqIJxDpTlaxdwRV2yXjr1p7Qgct07jvWNqh7fddmdlQnehNoRaH02JXUkVxui8oHidMUjfannv62uJEfSiCaaMyUWIz/a4W8Vk3rHlK76n1cIrjwAW96Xxu2v4uIsPdc20Vw6ha0ByFLRvf2mJy58Z0v25j3H96SFFkTBYhHve47nn/Qu8u2/4rqBk/tldxuN7CaIL3J/1WNjtYkYtbBwTZimi9Fgv0C6/bezbFIee7mKEQkhF7+oy3HUv3HM/UHsrLL8I/QXozHi+/OwL9TZoFB1y+QpvfrjDmbsFlQ8QKwGy65m8t6R/Cn7fIEI3zbLZZjRlWkYILzGuIlIlpa1VWVDL42MtKFxBaC1THZNUBWUgwQkGyQnivGTJrZM1mcRCN/FGo5fxrkB370LckOCANbRG6zB7iiqJ0c2iV0vB3bOS5ZEnitpUQlE1q23Rmgep8JNNSLcQKoKowzRPmQYJA1MeN6Hbr30TL1t7Ceyff3Q6iFYfOU0pRxsInaAoOZFo0nGfiR8f+HDsurrtOFCKgVI3Me0A9+uEtwZtekJSmYxCRgQIwhvk8eJAwWEIVQ3qb2Tat7PaGbsd3gDava+bq2VBfVOHKon42he+BsBf+j/+1/zSv/1FPr/8BM9ffJ5/9tf+Gn/u7Q/wyKP3oydTJjMDdjYCZq0k3/UsLgjCswYTeiaJpaUjet02xljWd6d4LIGs900fT6Zko8SDay/WoP3NDz9Uf7TRDr7TJstCLk4MD8xI5GTK6otdptvAwim44164+hJsrPDObsiDieb5zPDRa6usTHOW2nN0ZuG9jwnmdJvry47VseLkbH3d2RiO68SKqqRyh/f9yo2QRQmFZWnagPYL50j24z7XVkBpqrlZLrsddvKQ4fgEWZkgg5RikHCOPQoDy907eOy+eq790he/iFCSve4CKEEwGhIJxdaR8Y/XWsZ7AlvhPUhdX1+mxqFkiECR210+9iu/AsD7ft/vA+Bf/vIvH77AbWLfMucYtCWmhNzEBA56DsY+Y4Kukx5sRnHkmM1sifWOqKrwaJxNWN8+BO07K5tUrVEtPJQKFcRUQtZjJqMdEJahmqef1Of1XuNT3F+Ak3dBPrHYtXXy3gxTbpCaAEoo7PwpWvmY4S3m2v1uifMjROBg7k56WrAXTjDSY50j0DmtLuysQKsxc9xfoyVC8d6gy7wM+NLehK/uZMTtGrjn6e3XBq9UFRXK1yZ0LhtRWYeWkioK2XOWtSMGuLQH9f+nu4SEBCgcDtWROBlQpm84yL9RhyWE+EdCiIde53P/kBDir7zC7x8VQvzwK/z+vxFCvCyEeEEI8YNHHv9LQohnhBBPCyH+hRDiFt3w29cboP11lmwWQiqySH8k8k0GNWi3FViDslW9sIHaPR6PV1EdmeQ1w5NzOCmorl+/7XttlZ5YGSLpsJOM5OIKwZ13Up1eQN8oSZ87D/e9Hy68HWZP0yqmVGXRyLdrw5XRhRNUMzOcfPFr5Jsp1lpEL2a4Xr9EsddFKI0si1cE7dgSV2R4HaBlQjqsiZfVi/DMZ5qIEu/hy1+Gp57nhatXmXzuc/X8KLUBlaXuZh+tSATEhGz4CS0hSBoQKmSA1gpflZgjDrOnGyOoRApiUS98ZFVg8Tgk/+Y3PsXPfuj7+bWf+wfYqA1CIrIRZ3TEWARMm1zaYW44VWq6L2yxuRiz0p1iyxyrQvC2jr6JZmmLErn84sH7d5TGyhDVSMkncafumh+tcooHUi1R05ppX9/a5N/8zb/H/+XP/5esXXqZUTPXTtxmocqRc3OsGVOz7UdKCIVIBigqksKQUxIRU/icqfcHxl5Qd/9nE8HS6PhC+HTDts8qRVGJA1bKlXsYAdW0BzaiapIQ4iZaafqKTDu0REmZ5UjviU4OWCt+C2ctD9z7TiYvebyHNClIlGW6anmwNWTm/EWeSV/GFmPuP7FNKlaZGI+gTRo47rt/kTLVXH1ypQbtOiAUcKJXN2B21rfxSqCq6sC0rTpzAmMcyfo6MowwhSJq52hvmYxLnvjKk2gp+eE3v43WuXnG0QplVnJ+xmJtyeeXYmZ6gsdOFrC3RNaZR8SD2rhqcJ4EzYxZJWsX5LIN3iMnU5SMORNVeP//Z++/wy097/Je/PM8b1997b5nTx+VkUayqmW5O9jgArhgExsSSA6/5BAICTkpHEISYnLyI5yEFOxgfuFQnGCKbcBNlnuXZUtWl0Yzo+lt971Xf+tTzh/vmj0zmpGxHBKuH9H3uubS1t6rvOtdb3nu731/71twrlc2bvxoEk9GxGbzOffdIJP4k2MX50FMZHLyIMJNMwr7FwPasbrMq8bgtYc4qUNifWzSZ8YK6tLhuC7jEpd0zrT0Sml8b7k0dao2IQjG8niN5ymkcLZA+/knUzYPDxDXXsPcpOL29Xu5/e4R0zt96q0EOb2AH8csxEeo7A1gfhcby3UWHx1QjzeQxqGzsAsvzwgGI1LpEn4bpn1oNH7SpxAu9TTFtQK27976+/oZyGLYdr3l7/7iL3HXK17FP/jdP+FcllNMzsCZUzhCsq1dpXZ7nR2372F4Y07Xsbys7l/0XACmGgIBDPOgnLNWBY5TXh8unEOpsgQu5CbH0YrC8QlURj9zWDsp0GHIJA3cLOc0KyirkWPQbvIuTjCF9K+MTmTlNNJo2H4tGoUjLjJVE+MZWc+6ZFH9ItMuHai0YLgBw02IWpCXTcDMq9Aonptpz1Esxac5xBmOcJbCKqjVcIMqZIa8s4hwKziiYLoqyEctciXZLKNx6WhNQ0pcIWhLSWzMZSAIwBWCBSfA6pQCS2pDPCuukMdfZNrLnVz1y6z2S2sjtkxeGXxRmq5aC1mKRSKsw6HVDkVeMDOzwH/4tf+bd73lR5ianaJjO7BrF3rtPGwOsUnKejhFNvSY7goSB665TXCur+koGEqBVZrJsVP72eV1jABRqTGanKOpNbELwho0hrNHy/XAHbffCnmGzUbYWpXDix6ZsNzeThguGTbO1zn2KciHwPW3QL0JTz4IacJddZ+3TITcunmGNKrylAn40HrCV9OM6es8/FCwoS2zE+Vozlq3j7IOoijI9UX1hFIjZG4g1SzGJau8Z/cuogsM6MoizMyx6PQ4GhfEWYUdgUeWzyDJWG/6zNouDpITcpqX37AfgKMPPICQgm5tBu370FunicdQp89bXVRg8VWBQSAdj1xbtNX4VuM6NR586lHOHj5Ma2KC//zrvw7AZz/5SZJxfv1zgfbYWtrjaLjRKMLBpaUVloJVW+CPHeQvNdTNdIay5fYo42BtxMraRVLGWstK/yTDbgmGnbCKli42GWK7q6gwoK8rW8kGvbVSrBlUBJMLUJXrDDczdHOWAVcHynpmNz6WcPVk2fi/pNJugW82ULWInVN7SchwfB/jSIpxRHF7G3RXIRTlKEl6icreE5IXezXkMwFr7YT4ziEGS/wcoTl/VuU2L0G7UyUfDXCUwvED8sBjqVA8nORb8XWlaWYF4i6+8PFw0RQ4YUHhV1FpAS+A9hdqXNbav2Wtffq7fO7HrbW/8m0ecitwVdA+bhS8CzgAvAF4nxDCEUIsAH8fuHPseO+MH/cd1wug/busC+yGDA3yEqbdCh9rgSIvV4AAYQVrNLnVCDQ4ATvHoD2OIkwtwiyff873Wis0NSfHFQbnxDmscJF33gKAJ54F2oXckuNTnyAUgnDUZWAMofAJ8NikT++G/VTzEfWnHkAZD9EI6K6UXkM2q6Osi5fnxPlzd71NHqO1xvouOi/nDq+5A667C3rr8PinMtI/+RQ8+igf2Oix/7/8N37kve+Fp8tz6NJZ1mdXiypdm9JwxpFPmSXPfFzXh6KguGTubdt45dYcsxYajVcolLVoHJ45Vkbn3P+nH2RoBURViIfscEMKJ2Aj77OhCkhXmDu5zGTeon7nK8n6yyyLEYUvwPcwaLxhgZzZQbh2nGJULmyqUqJlACpDChiE1ZJ9UpfcPLKYC44GTlKgXZ+N9bKFbozhk//p39K7MC8eVZFacZ3v0qlUGGxcZXSiNolAU4ljNAYXl8xmjIym8ixH5B1NwfrIXsaSXwDt047DMLfUL2Hacy9EDz3QASrPsdZeIo+/+iLBWMswt0SyQKcZQghkpcGXvlgy2q9+1few9nSf02dAhwWhmqD3xLXsDG6g1QrYHNVZO7uHiW0OyqZ08yrCKUORrt/uMbNnjmSjQ/ep8jyRjstMJUC6LsPOgKEqCJQitwZjDfnsBAWS+vISuQwwSuJXMhxr+Op9T6G15hXXXENLQOOaAySeZto9R6s14tSaYTGu8D37HPzOMRCS3sR2wguLVDckqG+nWayTRX2y3MGGHs4wQcqAlqdoSMviGLQT1Kg6ExQmJTdXLgwBHl8y1JplE2KtN8LVGVkQ4WQ5xV8Q026MKrOP0WRBQiO0JCbAmByRxex1QvpGc1SXQHn+gjS+twwWTK0BQiCciJEucFyFxCEiYOWk5dx9G9TasPsd1yH/2g+U5pKf+ARimGNVApPjec1Kg+HCDrbfYKhtm2S41Gfw5AbC+KzO7kEIaG9ukAiPQD93Vnvfavy0jxIOzeEIiYCpi9Lyc4dBNg1fFz0+8Bvlwv53PvRRHj59hk/X5ziyvMa5tXUaVLGVkOWJCqdkwPWRy57wcvQYRpJICrqZjytL0C6dC1ntY6ZdlQ7Nuc6QRqNwcYUhwcXkkpgIF8muJKRAc5pVkH7ZtBMObm331b40WDxGHtSgPoFGb8njgS12WRYCVWmiRn30BcBRnYC0NzYynYIsZmgsuRdRKbLLQLuymjXb41g4YJUu/WSNiKBsTJJCrYYvHVKniuqvIvCQaKZCjcBBJ21GdkRiky2zUbg4CnU1th1KJ/PCWlIR4iEJniWPF8Ip/WbGTd2Kx2U+Apmy9DPL5NWk8UU2Bu1lg9UKh689WM6WH7ipVGO4wqUhmvRtj3R7C2sV8oln0MZy1pmiPgpRQ0FzD0zVBUtDTSQdiswjN4r2OPHi/LkVjCNxaw26u2+gKgSpBCkMG+s9hv0htTBi4frrt+LeqDV5fMVSrwn2ipjBEriTDYyCo58CpRy45aWlyu+pB8FaWumQffEme/fs4RW+5kDFpa8sD4w0RwWsyIyJ8WjIWreHtg5CGXJ10VxQ6RGyMOSpYjVJEY7Drt0LBEgYDmDQZzQzyX3dHqupx/6owksmPYSeRhmH9bahKXs0NKxoOHDLSwA4d+gQwnEogipFvQHJgFYO0iiWnifwKqzF1zlGCKTjkxaAVPgUeEGbP/7I5wF469vexi3797PvttuIRyM+/elPly9wIav9WQRCYgytukBKGAwj3GPHqJ9fpCIUK6YgdCu4lzjIQynXtlrhaYXSDtZWWF0umzD+OOp0bfk8qZuSjsANIwrrgi2wSY8krFJQpRUKrLX01kqWHUA6grnJJQapwAln6Nurp2XI9jzS92mun7lCIm+GCY6TUExtYyGskVHgeCHGkWijsUXK5DawBooyNfiKEZONszB9usqL5yO6tZyzrfi7lsgXNse1GtepUowGaOHiOZLC9ylsGdn7QJpuRYtSacOoZNqlELhWYN0MFdQokhyrXzCi+1+phBC7hRCHhRC/L4Q4JIT4YyFEZfy3Lwsh7hz//BtCiIfGLPcvXfL8U0KIXxJCPCKEeFIIsX/8+78phPjP459/eMyMPy6E+KoQwgf+FfBOIcRjQoh3Pmuz3gL8kbU2s9aeBI4Bd43/5gKRKGVhFWDx+XzeF0D7d1lDMyQjQ/oGaQSFKi+o1rmEab8Q1xNWwSpSrRFW47ghFSlpSZ++52KbVVhdec73Wi8MdZnhSktw7Bx2egYz3QK4kmm/tGotQschHG5umUXNM0FKQWdvk3DvLtqHHiYtAiweicqY2we+DclshKM1RTa8uKh7VvXTPsYYbBigBy5ClOPrs3sEL7ptk+Z9H+HcF85znzvN//6bvwvAPU89xRP33ANaf1vQHhGRWkPo5iwftzz0STj8gI/n+UhrSYqLzYRpx2NHXXJNrVycGjROkZdMu3VZWi1B76lHHuLE0nIpkY/7NKSP50VsqpyzaY9askbrmWXktgUmF/azMBAINyStaAY2QQUSb3kTb8+NgCA5VjYfqkKgpE+uM+rCoXfBjO7S6Jw8JnMDTJ7hFBrteXQuAeNf/+M/4tDRY+X/jJsuu3RO3mzSuwpoF2ELIS3BmCmwQpJhMORb+/VCXZDIn71EIj/lOOz3fRakhzLlYt7qHKtiEjfADD2sDjCForAKR5asVvIcEbrD8Xo3JEdlGY4Ewgpf/PyXAPirP/Z9bJvtsWYE3VUYHZvACEPvQJ+Wk9A8v5Nz6Q7W3J1Mejeynu6kkIZAwKwn2XZgmrrXp7/aIxmVYL1uNNXpcjWz2B0QqoLCWgwa7TsMpyapr6zRUz5OAU6YEWjDZ77wLQDedOBGsAYxvcCamKNtYzZH5zi8Bi2nyp3zG5BswsReUkfiXzJbJ9q7qXoufniSNBPYKED2+zgyQArBvkjRHQ4xjgeuTySbCCEZ6c4V+64fW46s9piYLL/3jUGMqzOSIMJJc4rnyLb+H12FUaXuUZc5ulPVgsKJSOICkiEL0icQkmdUgoCxa3wMow5IDxuVIE84ISOVI1xNhYjuouDot6AdbDC7F8TUFOHkJGff+MbSUOqz38CuLUEQwYteBQdejuOU87+T102xcM2AcLDBKJ7g1HAK5blMdjYZCofAZM8J2kc6wc0SMi+i1R8iXQ9aJYjaXLKcGmoO7sy454N/xHCc6DEcjvjmH3+I2b176WnLY4eOcu86nEos30i6NKzmxbWrX4ObgWCj8PGFN44hKoFIPMYjmQbPAa1z0BasQApDIj2MsPRVSbVFqWI7U4xIOc8GG/52etEexLMiF8sN7kCWMGrOYa1Fo3EvAe0XzCaFluiojlY58QWGsTo1XvDa0k0+HZEYg3UqBLqUxw9twmm7wiHOsMQmNoxoUeOapM4eZpEIRmQlaAdyInJrEHH5HnU3IXBhNGzi4LKk10it3QLrrQug/SqzuABWpWPQHuDAljy+sJr8giJFuuVMOmVm9qVM+2ZS/nxV53iVjaXxApRhsOHw9JlS1nzDjfu3HtYW5bjEZtDBtltwbok0h/XaFDNrPmbaUpuHhgvriSLAgW6FwhiaM2WTaPHMMsYRTGjNdmPwkCRO6d59YZ79mtl5RKsFgzLubSjanFea/dMOxZkRKoHpu+rs+z7IenD8s2AqTbjhNlhbhtPPwNnjZSd+YTf1IuOOms/bpyK+vx1QES49kVOb2YbjOPSGI4Z5yfYXeRcAbQusyRCF4fzmCAs0pudohkGpOlpZQlv4bOSzkgnafsiBSkAxBB1HxKZGry1xPcUO26dvFRP7Xlbug+PHEbL8HpL2DOQpURJTsYbl5xnbVWBxlcIAjueTKAtC4xuFdCt88qNfBOBH3/nDALz2bW8D4I/HkvlyjWbKZvu4jLWk1lJ1HKptGGwI2NwgWlqjIS0do3DdENdkjC45XjOdgSrwjCG3PmHgszpWUi7cdgcAa+fPk1djRp0StBvpoLUCkxEHdXJRpREK4l7Z+78A2tGKyfoqo0oLNiKMheFVxoE8r4putmgM1lm/xChS5RZPr6AqLq2pvQghSMlxfR/jOCijEaqgNqlxfcjWL4z5XDyHtLKcfBxqbXjJzog53yer6u+aaS90jNDlfULFfTQungNp4KCsZN51SYzhkXR8H6y2QeU4WYqDg4sgIcet1lGpwn47degL9T+0Dpw48bIDJ0784J/zv5d9B299PfA+a+0NQB/46as85p9Za+8EXgS8Wgjxokv+tm6tvR34DeAfX+W5vwi83lp7C/Bma20+/t0HrbW3Wms/+KzHLwCXzjyfAxasteeBXwXOAEtAz1r72e/g823VC6D9u6wBAzKbInyNKE03ARCed3Gm/cJiKKxhTSndFcYQjU11pmVA3/XRrQp2NIDhlYY+2li6WtOSBWx2cdb76Ouv35LMet8OtDsufrVFOOxuRX80RIVpmqQVjXrFjXhJF3FmgB5IVDRi23XgeYKcJlIbnCK5Ql51ofpxH4zFhgF5x6fWBscTcPw4ja9+jN03KFZe/Cre9Qv/hCRJmBjLmf/9Jz4BR45QEWMJ6VWaAol2IHdZPJpw9CFoHPk6wde/ijAe0kKep6XhHOVc+zunmry4VmoetS1Bu8JgrWR9rVyAW2u5555PQtSAdISroe5HZGg6/VPMHT+Np3y4806I+/ga5uQsUgt6FUCCE2fU0HQmryFfOw/9DQIpQQYlaAc6wXj289LWcz4i9UJIE0ShycKQwdgZXkqJ0Zr/9qtjJc7YAyHIEvx2m2Rz8woGQLgVtBvgD8s7pcUhNxZEfpk8HqARClqhuCz6TQrBi8IQq8rH1nwwRQ+FITEVnMJDmqAcaR7P00aueM6Z9v54FCIQBTYvkFJyttPn2DPHqDVq3H3X3Uy3B/jXCQbdGuk5n+TWPoU7YDZ2WTg3z007JY90NA9ulOxBgmVH4CCFQAjJ/LTBqeUsnm2CcajpjNrMGLRv9Am1orCgS9hOf3qKsNenF4OjLMJNkUrxmc/fB8Cb9pfs2WZzkpGdxE9aHDyf0R1EvG4viM1jENQxtW3kaIJL5wfdkKCxnbq3TJynUImQ/QFSluf2dRWFkw9ZHo+kSOEQygap6V/m4WCM5cGjBu0mNCZLQ6iN/gihM4pxfrBOrs7O/48uZTUiz7E44FiaQUzu10hHJWiXQrB3fC2bkR7eBWl8nkFQwwYlK6yFR2YLpFsgeiGH7y8XfPt2biBrVYgialJStFoMfvAHEX6E+OxXscvnyy6g5+OMjSdFbRpRMeyqLDE5P8WpboOh9Wl1u6TCQ5ic3BRXbTQOiwEiy8krVZqdLtRqpVmetXzumZwnmhnT03D/b78PgJ/8yZ8E4H0f/BjXmpg79+7gpZvn2e67bKYhWibcKIdbpo+X1lEzwG/mbOYeAQ7WWqzIEeIi054qi3TLmDqMRWARVjHyHDqVnGFGmTySDGiLGtM0OWf6fEa5fDqu8cSouMIPhEHZFMrDOmUQpsW5pNkUuOWct86BCw7yo854306VcaRCQnUa0pgY8JXElYIi8PjkI5+nk/eZpMm1LHCts4NaNIWTjrai/Eak4Lp4lQpCQepFyLEM36qYqYpgYyRoiwk2zBBLsuVb4gtBVUp6z8m0p+TCRVoPwUUjuiU2SiUC47n2sTql4gkKXTLscBG0X92ELoc0LhugA0tvzWFpHNW1f/91pGbASG+S6E0oRmykRxjsrJMUGQM3oChazCiXxh2lvwOFIDaGuufAoILJBK2F0oxu+ewq1nFo6YKbrSTHYByJFZpTh0uAd9327WXE3qCPQXE6blG4lltmHXqH+hAEtK/zqW+D3a+B4RKc+jLYHdfAzDY48gScOwEzC9BoQnKx0T3lObRcj0JqRKXG9Dir/XwnAWMxKkbbglT3ysZmYTm1Wd5rJhd2UBmr2tTieR5VguWKxw6vQd0H0fd58LClvyGJ9QSDiQqxGbDP6aMtxPX9hJ7H5sYG8ai8tg2n58FaxNoqbWHYMMXzSs0orMVVOUZIXM8vm8tC41vFE8+c48yxU7Sn2tzysv08bJd4w9vfCsAnPvEJ0jQt1XdwmUT+wtheJAT1CUjXMqwQeIMhVaGxQFd6eFjiS4BirkpjSccachvi+jn9jQ0cz2Xh5hK0r59dLkF7t3SQL5xS1WasYuTVsTKk7pcSdbgEtA9WcUOFmpygOFuuM3pXAe2+8Mmn5qgVMaPe8tZ1ons+p+qukUy02VYrG5YZBdILMI6DMRq0QmJoz8EF/9f4kmb9+SOliHTvrWUsXCQk1Mx/B2gfIa1A4KKyFCNcPAl910FYh52ex4Eg4ExRcLooSqYdSol82RIr1Yb1kEw72GSA/Qs0b32h/kLqrLX26+OfPwC84iqP+atCiEeARyll65fOuv/p+L8PA7uv8tyvA+8XQvxtwLnK37+jEkK0KVn4PcA2oCqE+OvP5zVeAO3fZTl45ex0oJFj93gA6blYK8cLgBFGOuXCyyiUKTBAZZxbO+cEGMclm6hjigK7unrF+4xySDFMOgpx9BRaSsT1N5bmHbjIZ5sQPXs7GxPU4z7DS6I/5mjj5pLFWka2rUV0Zp3ifIFsj4hqgvokZEULlMJVOZvP4SA/SgdIa9BhRNH1aEwa+OY34QtfgKkpnHe+jV/5yD/h/Mpxrt19Gx96968jpeQPvvUtzn3hC0hricZmdJeWtZbDJxRrz0SoLGffwkn2hQdxN9YwuYcwBl1kaC5+poZ0S9BAeQF3VIEGrBZsjmXoAJ/+yD1bZnROmtL0KwgMftJh8pmzyB17Ydu20hAGkMLDMR7D+fZYaiqpbS7Tm9xHTAgnnwRr8d2IzFoaxpA4LsoLLzejyxJiP0RkCSI3JNUK/fMlaP/pn/5pHMfhqx/6g5JtD6sgBKRDKpOTxEVxRUNHuhHKDZBpD9cIFIbcgiW/zIjuQu1oClZH9gp5+3DcQa/7ApP30QIyFeEWHtVGgC4gG7vjVrzndo/v5xZpFc7YT0BIh8899AgAd7/mJQSOSz5YQ80G1OZa2BuGyB05e1SBXfIga/HGm13mQsGRgaFvLKEDOy/Rv4aDlOqBaXQ4INl0qKmC2my5mjnf6eNdwrQntiCdnMAD0qUuvmNwTczhg+dZXVtn59QUN05NQBBx3q+iEsE2bw9LqUtdNblh5mzJvE1eRy7K4zN41rW60tyH41mUWilNuooUOQ7f3hMW1FTMcX1RUhzJBsZq8kvcwI8vWU4PNQvtlMZEC4CNwQipUoqgAlhEMnpOtcv/yMqzBJTGSgfplFFQXtUnLsRWzNfOsanmbnf8OXvLIBxwfGzobTnHWycDBUvfqhBW4cCrwOlucEopjhw5snXMjmo1xA++GTwPe8/HYKVUIDkiR0gfUZkkH44I8pjW7ROcCWHVb1LtbqBEgMLi6PyKuXZjLakaIvOcIqwRdfvQnmBgLH96NuXJoeL2eZfaE9/g6YNPMTc3x3ve8x5e++pX0h/F/Md//6vIXXuZzEa8Sg94V2uWO2shpplfAZxHVnHWJjiNgk7h4QoHoQ25TrfcmAtt0QZwDK5KMTi4xpLnmtz3sMqnn+Wl6ma8r2dpcWbkYbwuc2HBo6OCL/YysktNPzdX4Pwq0cnTmKeewH/6GO4zJ+HYMThxgvVTRxjYEwwKS73WRlnIx1Gcwqthgwo2qiK8CJUO6HshtaTAlfDpRx/iXXe+mf/j7T/LPG2iCyaoUW3LQb5KSEppxEWtRjWO6dZnEXEfoRVGx0xXBf3MEuoGqXVAdrYYdiiNR5+TadcJqQyRpgTdF2baMwqKC1Gf0sWO5fEXlAUX5L3rsaURXGlgB0CRQjpC47B5RiNrVc6vHgWgPl/l2MpJji2d49j5NTqLGcOB5ky4h+RIj1xJ5DDg+hskaVheu9Z6Giss18+6eI4Dg4DG9pJpXzq3inYdKBLIYjI0VloQcPJQCdpv2FcattlBDxX4HOv7tFqCeeOSnOlT3dNgjJ2ZuAYW7oLOcTj/IHDzXeW6QynYdQ2EEWTpZc3fhuuihcX6EbNjs8WznQSsxaYxhU3J9RChCtBwtlN+x9MLOwiFQ6E0Tx49y/npKgdqAYETYjLBmVMOngu+EqRJjX6tztDP2WY28SM4vWnZO1fuh7XTJ7FWMGxvw0qBXF+hakozvufyprhaFVicrAApcL2gvE9JhWsVv/+pLwPwpjd/L7FZJbeKPdfsZPeLXsRgMOCzn/3sVWPf4vG6pCIl9QmQSR9lI/x+n0CALwxr0iEUgvQS87yRyXGVQhpNZqr0kvL7rM/NMLljFwCrp5cRtYLOKMePIowbonWBDT36pk4zlAgh6K+VI9zheK6e7iLacwkX6tj1gHwEfXMVph0P1ZrEtxa/u7TVBFs7fgzcArN9O5EIsNaSUhro4rtorUEVWKuYXABRCIr4ItOeJ5Zzh2BqBzRnym0KhURWDMPe879HGWvQegRGIIqCwlgsEjcIiB2LVRIvhxt8nynH4ZE0ZehXwPVLMzrhI8bHtGh4pMZBZMlW0+6F+p9bB/fuvf/g3r2f+HP+d/938NbPPvgu+38hxB5KBv211toXAZ8ELjVpuQByNFzp7mit/TvAPwd2AA8LISb/jO05P37shdo+/t3rgJPW2jVrbUHZLPhOlARb9QJo/y7LlX4J2n2DuMQR2PFA442Z9iF6vJi1tkCZDIuk6pcM2owT4CAYTTURtiBdvtLpc5AbUmGYtBny2FnymUmC9iSFLS7Pr3yuqk8QWUN6SUa6EIJ6x8Md9ukfmCGVEeF9x/CnE7TVNKchK9qIQuGonH5xZQSLtYY8G+EYTeFVkbnP5JlvwhNPwIED8IY38Cu/8m+4555P0G7U+YOf/CmuPbnEG/ffgtKa//sP76F73zNUzOWxb3Hf8sQX4fBxQ9OpsudaRf30lwgqZQZ43ndxsagi23KQf3ZpNDbPUI5Dd31IkZfyL4CHvvZ5krHDsEyG+NKl4oXUT5yhpgO4447xjt8oXe+VRjhV8uk2hS9wlMFZXaEWeqxO3QjDLqydxXdDcmOpjRUQcVi7KI/XClRG7IV4WYpVBl2p0F8sAclrXvMa3vYjP4rRmn/9y79cyhmDCNIR9YmJMhrw2WZ0ToT2fFAZlSwnFwpDACK7YqYdYEezPNXPPSuzfTiOrquMmfbCiygygVN4tCfDcvE0Kq9nkSeeUx7fz6AiFcZIZFGQuYYP3v9VAK597Z18rXuck2adMzWX0WyBfyBjj1uhXsQMz7Ro7XYIIsHrZj0anqCQhlAK5i9QacM+Ik0Jd12H08pRSlNRmsZsee1c2ujja0WhNbnNSYxFNZt4rote2cSPCjyVct+XyvSC77/lFkSewdQ0i6mmMhAsXOdyZnAD21pN3Pgc1OchbJKNj7Nng/bQr6OiWaRZR0QhGIMcjpDCpW5jGlJxTEVboC6QdYSQJLo8F5Pc4dAZi9swTEQZE2OmvTsYYZRGexHGWtzRaMtg739mJSpGFBojHIQQmOGAoO5SSIdkvZx59YTkFX6DaemV9EvcAzcCIbG+LKXxhaUgYbTmEuqAA68GzzWojQ1e/nM/xx133MFwvbz2jYxBtGcwr385+A588pPw5S/TePIp5KNH4alDmCcOUzl7FvpdJjtrLIVt3HiILASF1TgmI3nW/oqtATVEFgWucHDTjM3GBJ/YTDl93nCH8vn+633+83veA5SNNH804t3/8l8C8J9+4zfZrNTKc/PMKULpsFtMonzDBpdTTEtjV/SgYrFCoEyI0JZUx1u5x+PeDta1uEWCFpLAWFKl6cY5a6fP0S/GJqZjIHEk0cRpm2vCgGvrCS+peSzmhns2U9azAg4ehD/5KBw5RePQIez9Xyf8+sP4X7kfvvhF+PznUZ/+DHs++wk6aUo7iIi94KIZnRti5vdi5vYgZECajBh6IbW8BO33j71IvvzJT/Gxj33s4geO6mVjwVoq43VQPJ5rr8Qxm40SoLlpji1GTFfKa9FGLCh0k1DkZFwES23HYWDMxRnWS8qqhFj4JTMnLpHHo9Hj87Rk2ssL1YUZ/gugYyO2V89nhy15/PJKDTtMmL6xzrEj5ciSE13L4sE9rBy8jvVDN5ItTaOHMwyjbQRZRmWjy56zx9h3u6RXWJpeaf5ZCWH3pMQNQXYqNLfNA7C8uIFxXawxkHTJpAMUIC0nD5WS/JtvuAEA099kKKosW5f90w6DowKZD6lff7kJ4dytMH0jrDwOq0cDuP0VcO1NMDEDUaX0O8guMsJNz8UCmRcyM85qP7c5Bp/JAGUyMtVFKg3acqZbfkdT27fjWckXj6wwzAdM75vm5nCSZ/o5cU8wV/W48zpJU0rMqMqACr12hWB0lmbksK4K9syWfhUrJ08ipSTxKthqBBtreNYijCZ9HqBdYRF5hhGS2ufuxfnG13FkgasVf3pv6avy197118hMilBDXMdw91veAsCHP/xh8Mtr1nMx7bUJcLNNis0+TmcDkaZMCsuqcHGFILskpi7RGa4xOMqgRZWNfqnWaM3PM7GwE4Clk4sEFctmFhNUKhhc0okZ9PQk3aJKc2wYeWGe3VrLoOhhB+vkjQnqEwLfBOhlh5698qbs4aOrVdxKlXp3idW0/F47ywcxvsCf21my8SgsFh8X6wcoARQFWE17rtwlZnDRPPPUE6Ux3Z5LhMWhkPgRpNaQxc8PuBcUSJ0jjIQiRenSL0qEIbHRbJx1OH1/2cB4yTi94sEkwUTNrbl2B1GqQZoeyrio+IWs9v8Fa6cQ4qXjn38UuO9Zf28AI6AnhJgF3vh8XlwIsc9a+4C19heBNUpAPgDqz/GUjwPvEkIE44bBtcCDlLL4u4UQFSGEAF4LHHo+2/ICaP8uy8UtQbuncWx5nYPSjE5bf4tpV165iFGqQJscKyVVrwTtTeERCEkcBVDxyJauZNrXc4sBJlfPYoYp+fZ5oqiOorgy7u1q1ZggEAL1rOgPxwgmemDrAYv7r8M/epa66ZEQ05gCKVoo7RDmMb2rgHaji5KJk5LCCXFyj+rK0zDqQ3+dz/2rf84//+V/gxCCD/z9n+bOl+5m5iXb+Sd3lLOB73/4Po7+wf2c+ho8/aTh1BOWzdM1Hv0MxH2o79fcsj9k+rGDJMNNxM03E9Yg3RD4SMyzHOQv2zZ0Ceodl+VzpTR+Yd8ett/8IvI05lOff6C8G8UDXFxmXJ9rnzmDs+v6kmWHMlO+PgHxCONHiMlpcqGRfgDLy9QDWI22Qb0Np5+mIj1yLNXxYnEYVksQo4utuLehF+ClOUIZ0mpE73wZ5bZ9+3b+6S/8M4SUfOgDv8fJkye3GLb22FG8t3759yecgMIJMDYnSnNScjLj4oviqid1KxQ0AsGZ3uULoWFRgnHH5qX01K+gEknoOdTb4+ic+GLsW1LYqxrfDDJLw1VoLRA6QwnBQ/eXs+N/5XWvZvswoWoNWbCL3bbOzbLJTmuI1xWqN8FUqVQncARv3uayvSbYFjhbEVqsl/sqmL8BEXkUQhNZS3uqZIgW1we4aKRWjEzJtLra4ky0YGWdoJrj6pSvfPkJAN50bRnnV0xMsdg3TBYOpg07bxLcNH8UpAMT+wDIx4qO4CpNMre+m8IpKAe/FQwHSBngp11anmBVVFhKy/0lhSSUNVJTekGcXG/gSKjPWloyZ2oM2juDIUYr8AKsFeOs9v/5THtaJAhtELIcwTGjAZVGiA0cRouDK5/QK5UjCBdbKUeChBPRHVj6/QTXONzyco+wKqDT4RvHjrG4tsZoNOLDf/AH+EIwshbhBIhaHf36V8LsLPbcOaLzi4hnTsNDDyGfOk7j+EnEw4/w4oe/yKYMKPoptViVoF1nV8S+9a1Gx6XRVkUbpNY8GbQJCsFNZ0Nu2+Ny6swJPvGJT+D7Pj/5utfBhz/MK7KC193xIgbDIf/x198Hcwtw5mRp8iVqeJlkmU7pmE7J6C/blM3lVaBkJlMd4ljITErFKxfAF9yYrWNwixSNQ2gtqZC8/+f+Hf/ijW/n6acfQ3tVyFOGWcYjw4Ltvsser0JGwf6KxxvaAdGZ0zz9X/+Qlc9/HhX4PL73rZx41ZvJfvxdDP7amxE/+qPwznfCO95B767b8IVlOOxQl5IkapBdiH1zQnA9hBsipEOcDIn9Cu2sNJY8vnjRL+fv/f2/z2gsbSaqlfPAabyVDDAihXqdaDhk4IZQaeGmKUYNaPspjoS12DLQFWrSZ8Nc9O1ojln3Z49lWaOwtiCRAfKSjHZrLYpSrqytKf0Uxt9HdTw9NizK6LdUPYc0HiBPGa3ndEdVmlFMvyLpdrpU6lU2FmaIbvV51ff4fO/r4cDNfQ7s3c6+6hLJ7ftYvvkAN42eQT5yP71MEwnBRqJoVKDte9TroPsB7fkxWF3exDgS0DDqEvs+vikosJw6VIK8W24tkZHtb7BqKhS+z/6Wy8ZTGZVGRjBTu+Ij7HgZNHfB2fuh253E7BsrQMPxyFZy8V7eGssUssBnapzVvtQdYhE4eUJuY3LVRxYaUxjODsrve3r7DpZGgvj8OWZrKfPbd3JqKeB8t2AqlLzsOo/JBjQcgRlGpCagO9kkXz/HrknBwFVsa5ck1PKJEzjSIZYhtlaDOMaLR0hbkH6HjUoDaGuReYbVAm80wHn6INsf+ApPPnqUs2cXmZid5WWv+R5WSDg9PEVmM146nmv/+Mc/TpbnEFYuB+1jMiGSkqgOrf7j6FGKTBLczVVajqEQLloIlEq27oupzpDlwYihynK3HG1tzs8xsa0E7edOnaceeAycEZ4TYHDJay1UtclQV2iGMOpZiqwE7RsMWOw/TWJT0tYEriuZ3uailhx6Sl2h9nGEU8ajRVWqWUq/u8z6ShebLZJU61SiGh4+2XjsqE4Ero8WFqEU1ihcX9CcAtUtx+IGm5aVU7D9+pL5V8ayNjIsrw2QDCmc5+8gn5MjTIHRHiIdkZftBrTvk+SWvO9SbArSkaUqJXeEIetac8KvQTbC14AAH4mIDIVXpRgmL2S1/69XR4C/K4Q4BLQpZ9O3ylr7OKUs/jDwB5Ry9+dT/25sUvcUcD/wOPAl4MarGdFZaw8CHwKeBj4N/F1rrbbWPgD8MfAI8CQlBv/N57MhL4D277I8fCwGPI0jIB9fI6QzZtrzDLIE7ZWLmEIVWDQISc0rb6AuZV527LvYqk+2ciXTvppppIDayeNoV5LPzVD1KyjUt59nv1BBhSCs4A46WzehC2UHI0xlmuUbr2XkO0wceYqRHVGbAE/UyYxPkMYk4wiTS6unM5wkxjguuXWpuR7ukacgSzgzHPEjv/7/YK3lF3/+53nT//Ur8OrvJXzLD/Dqm3fwqv3XMsxjvrj4KfbL0+TCcPqwpXO6Tnsebnq9wZm0TK2u0nr6NP2b9lDs2VlGAm+WEUY6z1BcKYEy1mCx6DxFCYel5ZKhnt25wB0/+H0AfOiD95Q36HiAg4v7zCqBmEC8uHS1JUvKf40StOsgxK/UKWoR2gE6HZrkDAqwO2+APKUxHKAQCJ3iCkE/GMvtkv4WaB94AV6eIrQhqUT0xkz7jh07uHX/dbzs7e9EKcUv//Ivb0XQNCoVbBQx6FxpYFbIClhNlGQYLJlx8EV5I7xaXZDIZ5fkng4zO55nL++2mRdihh6VakFVbSCNKGf+KGfa4eoS+X5maXgKpSWOznlqs09no8PCzgXuvu5Gpvqb1IRL6uxlt1thWgTkxSbDJQffbVJfuOTYMhYtYNel1tDry1CpIapN3EqERhIay+QYtC+v93GsRirFyGakxlJRiqw+gTcaUZED7HDAk0+eRAjBq+fnIfBZa06RjWA6d9A1S0utUVM9aO+FsdHXUCn6PTj8hOS+rxo2Ny/uv1a1zSBqYouslOQNBzgyxKQ9pnxJ36twcnTxvAtlE20VR5dHDDOf/bsFI5vRdCzTW6B9hFEK6/nlAjoZPe8IpD+PKlSKUJoch44M0cM+XhAhmh7xZgb5swx/estlbFiWYMNS2q9NwBMPWISXsWM+pDo2RWRjg3ufemrrqb/9279NRVxU3QgnwvoWvv/74V3voPvGlyN+4n+Dv/W32HjpHcSvvI0Tr3ktkS/pegFpapjq9smhZNqftb8GViNGXbRwqGcapTSj1hSz5z2qQrLtWnjve9+LtZa/9qM/ysypU+XM+/nz/NIdtwHwa7/2a2w2WuVs8Fp57tZ6HhZYpASdm+Tc9+nP86N77+Df/vUfx2LJighpDNpoHFcxyi85B0XJBhbCIcQSC4eTjx3CaM2n/+SDDFV5r3h4vQvA3fUyASRHYVZWmP7UJ/neB79Cy5M8/KLbeey6W+nIaXpxFR042EqEU2tBswkTEwxnJnAlFGmfwAiKSp0s7oPRpfO69BHShyIjVhmJF9IeywLOnCsN0rwg4tzZs/yTf/CvWDlp6Q5qZIml6PWRCCL8LTO6QGvyNEW35ktD9yyBfJ3JSHBupMksTMspMlKGtmwEXZhv7z77fjWOF4xFgDCC4BKW/UJpdBlfN26eBq7AlSXTvhF/GxM6IF3v0lsXRLMVam7C4Y1yn8/t20ktCDhhNPf2M5biTSyGmrcNcahLX3ucesWLqbz4dtTxo0x86yuYgaJwNPWKIMRhoiYo8JivzSMErG/0y2PUGtAFI9fFtwVr6136nQEVP2DfDddDEpMMEtbdiFo7oLXmoDsDavNA9cq4PyFh72vBbk/56rFVvtFfIrZFOb4DkF4E7ZNj0B57AZNjpn3lAmhP09J/w2Q4qUInmvOj8nyf3bWTJBfU+8fxZ9ro4RxfO6WIKoo75nx8R+I6grmaxCkc8sJnfWoSYxS7nBVETdOsluB15eRJHClJjINptaFIcXsdpFGk32GjUo+/TlnkYCTSdRhOzNI4d5KP/87HAXj129/AaXeNZccHlbBUdLj+2mu55uab6ff7fO5znyvvucnlTLsnBL4QiMXj1IszpM4kUiuijS4RGlcIUsfDMWXcqrGWQufl+IYGS8jiOO6tPb+NmfY0ju/TWe/gFxoV5AxGEtwAneWkCnJRpRmU0niAyqxihQ5+d43U98jCAA+Pub2CIHHpbFriqzQ43KCOCiOq1pL3ljh+8jiOThlNTBAJBxeXdLxWqBEhvAAtQY+ZdoCJbUAC3YHlsQcta8Kw2NDc+4ziwwcVnzuu2Vw6hT88RuHkz8tB3lhDx2zgaIXVPqQDYrdORSeoIKA7sPiFg0DQGfeDd3oeuz2Pp50KA2PwR+XayrEC6RWkfoMiTV9g2v/XK2Wt/evW2hustW+31sYA1trXWGsfGv/8N62111lrX2ut/SFr7fvHv99tbZk/aq19yFr7mvHP77fW/sz45x+y1t5srb3JWvuztqxNa+2Ln8OIDmvt/9dau89ae7219lOX/P5fWmv3j1/rx6y1zyvu4AXQ/l2WK8rFGn6BQ7lGUNqWTDs+jHpgLcotQXuuFMaWkUfVMZAXQtAUPonvY2ouxSi9YnZ5rTA0igHBuUXSbVPYShNXWiz2Mlfgb1d+Y5Jw2NlykAfAGrL+BrK2DTVqsfqSHUTnVsiWTuG4gma7TqF8vDRBmoLus1iPbpEiswRcj9wETAQJxCOyPft4x7/7T2x0u7zhDW/gF//1v774pFoDbr6Ff/zy2wF43ze+zO6NR9i+H170FsOOO1e58RWCkV92e2fuv5+wPkV81y2sRBZTtSViVA62KK7KtF+QSFqVo4TDykoJdnfs2MFLxqD981++h8KrwqBL9KVv4Dx5BHnjLTBfShcZjKXoYR2KHB2GBLiomSlSypm/dn8VbWDol6Cxlg5QMiBVKQ3h0A0vB+0KS+IGOFmGtTDEMFjbwHVdZjFIIfiRf/TzSCl5//vfz6n1ThkTpXKCiQmSqzjIa+FjXQd/vBCLKRcYmb26e+rOpsTayyXyo6J0WDZ5HyEcUtfFDDwa5jTeyUdxtSZLx0Z0F2LfnrXbM2XJFCXTbkCi+PLpEtS84nUvxxGSvLeIDlpop4JJ4cuPK7qdDvFim6n9pdTVWstKrnliVKoFtl+g0rSGzVWYniNLLUMdoYTELwzTk+XCdXWtjxyD9picxFoaWjOoT4OUNEeLnDp4iqJQ3HDdddRVuZA9X22hY5gwEivXmR0eJYjqJO48Z05bHv6W5cFHFUunJJ31clMee9gyHIwNrSoevcpU2T4S2RbTTjak6jrIMOT0SGOs4bTtMRIOWQFHV3q0Khley+DYlAaCujt2Mu8P0Urjui5GSpwk+QuRx2cqRShDjMNSWMOMBjgywG17FKkmWbvUsyEuj/WoBUW+5Rx/6smQTqxoTBdMVqsXH38JaJdScvDgQc4++ujloH3spWB0uZAWbgWEIM80WXsC3aoxH3jkns9QCSbWN1HSQ14l9m1gCrykjxYujTghCcIyjuyUZHY3pMWA3/md3wHgZ9/yFhgM4BWvgLe9jZcduIHv27ODwWDAv/+DD5ZyqjOnAHC0YJYWPWL6NuaTX/0i7/7hn6DIc5751iMIX5PkEa42KDSuk2Is9MbqC0OGqxUKB18rBsbQHY9JffNTn2Gpo1krDBuDPrfXPGqOJBhmVL94P+qjfwK9Ht6rX8XNP/4jXDtXZ2kzYXj4c8jTZ9EoJHLL98Ray7ASIi3YrIdUDqrSJDYK4hIwS6+J8BuQxiTWEnsRk2NPk7Onytnct//0ryKE4Dd/9z/w2T99mqe+VeXMQXji3iHfugf8LCAmw1ar+ELgDofEjbnSEi/J0ckqUxU4n2qMtczLJj4l227HPieREFfMtZdxb6CcEIzAH9/+iku8TTSmVHpgsGPgUfUFw8KykVikgHZ05bFutGXt4CbC8dh2vQBtObJSoqbZvTu5rhryfa0Aa+HB3gqnMkm8FqHuGzHcNUHY9Kndcjv9W15CbW2R5lc/h2tjWoHEEYKJhiAXLrU4pFmvY4zl/GYXbGkXmDkSYQwnnyn38c6JqS3n+MFAsVGtcd2UT/+QIHT7VCYp76fPqq5NOeiskr52HVsxrD4heGywThaMVXmXmNG1PA8hIPM9pseKrrXeEKMtMi8bJNZkOHlBnhacGwOk2Z07SOKE2uA8WW03B5/xMBXLrhnLxIVEA6OYqCn8QlLkIWsTLUDSjM9QqUGlUc52L584gZSSzICp1bEIZL9DaPV33KjU5c0DoQqssTiuYHXf9ay+aj8feeQwAG9+6+uZoU2uZhBWMNRDag7c/da3AmMX+asw7ZEQ0FmBY48iazU2a7diLISdDgUZ09JjKP0SDBtDhsHaAl9rrBE4QcSZcyVoX2gGXLv6AM3t28vX3yjvkatxjAjq6DwjVg6KkFYo6K2VE3rdageRDohGI4atFgU5vvBpTgumAof+GvSuNtcufPJqSOSEeKNNemtncaSPrkfURDB2ji/wcOivdrHSBSxKFVtNr4ltEAjB6WPwpUXN6qRhaWSJPLhxWvKqXQ7zkQKjqFbPPS+mfcOuk5HRNBHGuiVoJ8RHoXyP/tDSCjyiOmxckoh8WxjiVlqcUAob95FIXASea8miCjpOwLwQ+/ZC/eWsF0D7d1nOhTxYv8AVAjM2o3M8UMYr55hhSx5fqGIcvyPxvYuy9kkZkPs+uu6TFRrW1i57n41Cs+f8MTCGeH4SP2pQjBnmq8njV1ZW+OhHP3qZXCpsTOLmKcNLushunpAoRVifwJyconNgP8OKi3zwETKbUZuLUCpAZhmOya/Ize0UKW6eoaWHtj7tYhm05mc/+GG+9a1vsXv3bn7/938f+WxTtOsO8P0vvpnrt81zZn2dz3zpS1TOnCGRFr9S3ng2tab92GNUBwP8V38PvhvyFRSnhEKmCltIHKUZ2Ssp3wug3RQpyrqsrZcAfPv8dq6/9SZa2xbY6Czx5a8+A5//Ev7R08Qvvgn76ldffJH+RimZwMVoWDwxz9o5jZzeRhwCWUqjU8q1B9qBIKKSxWgZkKqEunDoOg5cMKPLYnLHRUgQWY5FsNYvW9Lb2i2c488AcOP11/PKH/qrJdv+vt8qtyUdUWm3yTc3rzAj09bDeB5ePMRiia0hEJKMq9+w2pGgfolE3ljLKLdbzvG4dVKtsCOPyBkgEHjSIc8vyOMvMO2Xb8dg3NSuOQXWGhyt+dLpctTjZd/zcpRJYNCnqJZNkfU1GCUdjh416FGLfI/mm4OcD28kfLqbsZQbDlRc/HEckNlco99RPLU0y1e+bFnpRyRCQGrYNlUuXNfWekCBozWxyUmNoa40vWgKG/g0u0scfuIUAHceOACDPszOsaihMYDJxnH89ScZJAGPnrqJr30FDj9tSVPLxLxh/z6X13yP4K6XCKQDjzxsyVJLVLdYU2Xk1MEm0NvEkQEiSwiCkJYv6CnLI1mPRQacEgPOLFVwgz67J/ss5wbfpmTHBUFSLua6/QG6KHAxKD/8juTxVqV/7vm0WpegPXEEZ6t1tFG4mUC2PYRQ9E5f0mC8II0fu8nb0Ke3Zlk7H1K/JiYMoVZGpwJw7vBhnjh3jmq1yk/91E8B8Jnf+z1GxmCtRbgR1uSlJHo8LyqcClm3QyFckmqVGhnbGhGONfS9kGCpg3I80MkVoD1WSQlE8PDjAXGlhupFOEawcD387u/+LoPBgFe/6lXcEscwMwM7d8LEBLz1rbz7beUI3Hve8x7WhQvnTpczwsA0TUI8Pv3Il/i7b34n2ViCnI5ihNpkkIc4BozRCKf8jjbHhKcVGY7RGCHxdM7ZzqCccwbSOOaP7vkkpzLFtIrZH5UotXLvF/BPnSO7/WZ417vghhsQjsN8p8/syEN7gmz9NPmof5lz/OqK5uTJiHhRIoZDMqUhqpNZSz7qAuA1r8Wr7y3j3qwmcavUihwtva3Yqn/8f76LV//1/w2tFb/1sZ/hptf6zF4XsGP3gDyF7jMhFktS8y6CdumSeTWkAmsypv0BQwyjHNquy4ScIidjSNk8aDnOlaBdJShAyQC02MpoL9AoW1DYDMXFcY6tuXavjNnbiC0TkUBexfPj5GNg+11a2wNcm4N0ODT+vHN7drKzHjDvO/xAy2GHG/NM3OBDnz1DIRXZgVmErTPlSja3X8u5216F2Ohy7VOfpdbpwjNPMFUrMNJBKkN7zGqfXVovFVIYjOsgUJw4Up5He6amoV5H9/p045xsos0O4dA7AxPbBghHXnQ8pwTrT9lVDrFOgeFar8333TTLjpUplh63PJqNUNZcxrRXpUQIB2U17dlxVnt/hNYaOVbRCGMQeUEyzFmJU4QQzG3fhlo/i1GW4/1raNVgYl5R9ywRbml2t/QY8+ogVSvI4wr9ZhXrV/DX1pieyYimd5fH5MmTOI4ksxJTqYLnIvo9qungO55p1wKE0TAGygM3ptPY4LHOIud7I2abDX5sWRH1MrqpS9LzGYxSXKu5YzzX/rGPfYxcjtdtRXmOxtZSy2J45iHAwdm2kyTahpYVvPU1CpMxKxyUE2BMylArEmuxuiAscrRxCSoRZ8+WHgX7lGbno4/Q3Fbu65XFcwTWYyOLccIqVimGuornCqq+oLcKwULCIF5m24njhG6NXmuCzJYicoDdOx3SkWB1eOVayMMjr1aoSge0RgxiRKOFiQJ8URJHGTkPff6b7Jzbzh/+zr2UHsrp1nhJVBfsbQtmUsGLZyQ/+nKHtx/w+Ct7XG6Zc1hoQMvXaCuJKkskve8sbm1kR3Rth5at4+MitAWjSbSHK2FoPJLcMDfhMjEPvdUyag7AE4KXVKsMgzonuqtlXjsWgUA0w3IkP70yiemF+stZ1tpT1tqb/qK3439WvQDav8sq0yEFxi1wRLk+KDQ4zphpH5ceg/a0KLAYQuGUyH5c09In933yRkhR5PCs2eWO0uw8dRTVrqGigCCsU4wjuJ4tj7fW8uY3v5m3ve1tfOITn9j6fdSYQABZ/yJb62fD8QJ5kqrxcPUs8e03kS+fJz3zDI1piZF1TJwRmZzNZy2gOkVMmGcU0gMimqNzvP+Zo/yXj3yUIAj4kz/5EybGc3KXVX0Cee01/KM3fi8A7/3iF2k99hijS0DJYHmZ2YMHcW+4ARYW6KYhQ5kTVwJ8J0NnHl6RXxW0G8qbt9GKwjisjw3cbLwLmwlufsMbAPjkf/19GMWYV7+M7Nb9W2C/3IDNclY9TVg/B8O0wfqSQoTbKRxL4UJts+yS9zOg0iBIhmVWu0qpSwdlLVlY32LaUy/CqAI3TjGuz3qvBO07JtpbC6mGK3jjP/g5pJT87h98kNPLq5CMqE9OIvKc7rNUGLmtQBBCtgm5RgtFVYSkz8G0A2xvCFaGpTx3OAbbVbecZ1d+lSIDp3Ap4gHPHLG4CJQqH1gZH27xs3Z7/4KZnVSAJU0yvrW4ihCC6V13sdEZ4A4zRtE0ubKMBhZR3eBQX/DxaYcvFxnHE8W05/DKhs87pyJur/mMhpYjhy2PfnaJs2cFG2aa3XsEbrWCcQVksK1e+oBsrHXAKhylGBqNAWpFwUAH5NvmaKwuc+jJcvF053XXQTwknVtglMXs7z5BvXaGc2aeh7q3o2XEtdcLXv5KwctfKZnarpmolWZsUUVw+x2CPIdHHi7j8ELrsulPYEIH1s4gjYvIU4LQp+0Lht6Qw8WANiFLXc1y5rEwrfCCnOVcE67lJKseVdEirFXQ2jAcDHBR5EGAl6Z/ZvxR3nsaNTz1bR/zfEsVGWiNFg6dSouRVrixQUQeflXTP3cpaF8uZyiL8lhJCoe10w6tWR85Oypn98fxd1jLp75amhS+7nWv4+/8nb8DwKc++EGSuGR4xThhw+oEq2IMDkI6jDbW0K7PoN5kmx4xMdmknqRstJrIc93SBVklJJfMtGtrGRYjnCwD4eMMhwzCGmq1wuQCBFXDe9/7XgB+9u1vL9VOd9558bM12rz0h97I6192N8M05T/8xm/C6TOwWjbuhBCMjmzyk2/8cUb9AW99xw9x3Y2lH0LaOU03D3Glg9AFUpaAoJOWrK+1OUIptPQhyTm3fiG1orw1f+Aj/5XUq3Cbk5e52N0ubndAfPdtxHfeBN4FgGrone9SNS43t10KDGe++SgOLsnQ8vR9lse+qTGOQyTqqOWYNaWoVZpkSNKxGd1WZTGJMSinQqQzDp/LUFlGo9ngQMXjh3/h3TQnJvnq177MvZ//Ixrb68xMj5jfB71nQvLUMqp7BELgjkbExqCcEGlKoNiWayRCY3KBKwQ1ari4jGx5TLUdh74xl41lWZ2SS780OVSXyuMVse0xMr0tpr08zC6a0Q1zy2ZiryqNTwcei0c1zdqQaK6FHfYQwuHw6XK2fNve3czVSpBjTZfdrsP8Y7NUhqdZvrbP4uQ1TOg5fFk26FZb21m9+XsJ3Iwdv/d+eOxBpuwaSBcMNFtl7NvZxVWsI8msxroOkHPiSOkbsGt+HqSkc6ZPIhTOVJPouAsCWtMDqNRACHqXgPUcw17a3Moss6JK1BC86A0eOzcnOXsITmf5ZfGRUohy1MIq2vNlw3Cj20cbgRjP+wljEVnO+fUhxloa7SmqQUi6sUE8ktTn5rj+Oiikpu4IKngwWoWsT0N0aAmFHUUkRtCfmsDb7LMw2SfauQNXOqyfP0+eZSAtRdTAeB7kBbXu2nc8066kQGiF1AZhIBEF2tb51kdKL6rve9tbcaxFf+yjJGe6qGFInOUcPljgNa/h+gMH6Ha7fOHBR8cHRLmPsiJj7uhD5czB/D7CmiStLFA4DbzhCJkOaUqL44bkaGKVkBqFtZpQ5SjjENYizp8tmfbtjRo+htZc+f2fOHmSllOhr1JEEJGqgE0zRTMQJIOyKWwnjjFx8mkaThNn70spfIcchT8mbOZ2CyrK4fTS1R3kdbWG9DzcRBIUAaLuY8IAHx9rLRkFn/nwJwF4/FuH0K6LztMteTzAwg7BXt/hZXdJGuGzIINRhC5sOvMUPtjs9J/9fVnFilkmIKBtysaTUKUnRWrKjPaVvodCEs1aRttSjIHu8sXXmHQctjWn6Y869AuBRuPhIhouuXGxz0en/0K9UP9/VC+A9u+yHJxy8eAqXFFe45QqlZPKjhdSrocZA/RM52ANgeujMFuLk5r0sWGAdiVJGFzGtKeFQXc2mOqtke2aQVtJGNXYWC1I+iDU5W7WH//4x3nwwQeBsdxrXKLWxncc8ktAO/mI3PUxwzp1F6yMaN3wYgqpGZ0/QWMKjGyhU0VVJ5exHrm1jLIBjjYUrofrBHgb5/lHDzwEwPve9z5uv/32y7ZtKVf89uAMjwYeBAE/9mM/wkyzwaOnTvH0gw+SjRdIGIP71a8SVatw991kxnJ8VPrk5zUP4WSo3EcqRXIVgzyNRqoCbTW5kmx0xjPt7R3km5KXHiiN8D594jDpHXfhTE2MnzeWWGpVjjY0JumdHNBZNXzp1Id48rFv0jk5j44iUlfjba4TCE0/tRDV8NIRUgbkVtEYLzRHYRWyIaQDEj9EKYUXJyjHZb1TfhfbJ9oQl4uEhiOZu/Z63v5Xy9n2f/N7fwrpkMZzmNEZfER1FpMPcEYJRhTUZUROdmV+87h2NiXGwvlBybID1EWpaSu8CkUKTuGhkyFFARIHY3JUbglcgRRckdU+yEoA4mUFwjU8dnKRwhhuvPE2JmaqrJ8bsnbS45yqMxhYToiU2Ouwrpp4jsOLfY93Tke8puEzXTgsnYEHvmH5+tcsZ05bJswK2180zStf63Pd9YKwXsUARksmPQ83DEnjlN4wxi/MOALHoaIK+trH7F4gSAY8/dgpAO6cnwNr2aj57Fx9hPooR7Vv4htr11APJXfdDXv2Cqo1MV7YXJ7R3mgKbr1NMBhYDh5URNIhsTWy2TkYruIcP4xQBWHoo90MWRswjH0WkjYbyz60Be26oPBzljct8mxOdSLCNx61cezbZqdX5vwGIX4ck12RaHKxrM7Ked8/Z6bdqAyrQEvJsDZFV2mcRIEQBFMe+eawdAvORmVzqjkPwy7Gr3LusAIZcc2LLalMCYRDOGZ3GAy49/HHAXjTm97ETTfdxF133cWw3+fBe+4pHeSdMWhXJWhXtlyk5psbZNJl0JplRg8JJxs04pTudINiZUBgJNoqCp1vKVOGVpOrGK/IcY2DKhR9UaeSV9lxI9x7770cP36c3bt38+ZWC+bmYCxhBaBSqjl+6Z/+YwDe+/X7WX/kcfjQHxGdPcuZQ4d4y/f9AN21TW5//cv5jf/6a+zaWxptDTdO0TUBAg+p83KEgvKc8V1QOkdojcHBZIbFzfK68MrrbyasVjj+5EOMNjap5WOwdeYMUkjEjh1bJlIAqt+l39U0qpZt21qMWntJj57g+CObPPKpcsE7c8Cw8wAs7Gzj9Uac7RRMuh6DqLrFtG9VOmLoeDgEuGnKoyfL6+jOdovw3Cmun5vmR36xHH36h//wH9JTFpIBOw+AKxx6pz1GocDzvBK0W4t2fIQxSKeBU2wivQKTlUsQIQQVUSG2MdZaWlJigf4lCi+rE/ILx5C+JO7NFihblF4maIR0LxzAQMm05xqUuboJXTbwcExCLYqhMYXo90C4HDpaxr3tu2Y37pi9z7IN1p+q4A9d7po5RrgwDXobc+PmfC+3qAxUa5riht34cQwHn6SSbRIFLsZAq1Uqjs4ubWAcSYZBSIm1BSePlOz+nu3lzPfm6R5xI2CuXmF4RNDcCZ7pQ63B0ijjI2fXWB3qy8D6pUoCvwa3vT5g13CClQ2PY4urlxmJVqSDRlGb34EUgs5gSJwLyDNc4SMtmFxzfjwK05zdjhASlcRI6XP3TR4bxqCkpuFIKtaBzglwfEIPFpwuNgnJtctaOyLsFDSDGDvrMt8o72urZ88DlqzSLFekwqHaXXse8niQWoHRgEVZibERX7znSwC8/cd/DN7yFvpSsuvTX6G9MqQSGqoTCWurcOCuHwLgjz42HjtNR1itqR99hDBLYP9dkGV49RDZniTWTeQowRn1sRTU3SoKS1rEZDpHWY2XFRjhENZDls+X3+n1YUE9XmZqfD8/ffIUE0GFPLGYCIamxWrWpBmWLDuVZ2h3n6DlzyD33U0lmMCgyCm2mHY/FMw2HRY3NcZcfo/whI+u1tDCMGluoKJ3ICsW64V4wqdAYbA8+LVvArCyuI51JFqb0qNlXNtvgJtefTHi7dKyJidyoaDKejCDwwZJr/uc35W1lhWzjEEzK+cRYxWVowqUAW0knhQsDhx8X7LpFZxuJOhQs7l0+Wvtbs7gWEsaJ2gUoXWxdY/MOJfH7b5QL9RfonoBtH+X5VLK442b48jyfqH0OPLNjEF7eFG+lqgMjCH0Qrq2w5JZxFhDDQ8TRBipyaoRevUiaF/NLJOnj1F1LKOFNgIXndY5frBg8aDLNz8ieOQzlqMPWRaPaf7ZP/sXW8+95557KC5Y2kuJU2ujL8xqAzYfkdba2FWH+UkBAoJ8AjPRIt9cxgsEftTCZppIJfSNoRjf7Lta4+Qx0kLqBlR9j7Xjx9nMMlqtFj/xEz+x9T7KWh4c5NzbG9G3iq+bgtgNCecm+Zm3vxWAD3zpS4jHHgMgfeQR7OYm8pWvBN/niVGBNj43RhF53cHaFGl9bA5ZcZE1uFDaakxezo3mymVzDNpnJhZofu6bfI8uqEYRRzbO8uSZGDcubxrqAmgfbIK15H6bsw+P+PSJB/ntP/xP/F8/9zNsnk4ZMkdKClozGW+ULHOlDkYTGUFuLdVxRujgwvevcmIvRBQKN83QnsfGWAGwY3JiKz+3OTZ6+6mf/wWEEPzOJ7/AmRPHiSYn8YVg8KzYNz8d4BQ+2AJvuIkVioYMMZjnNKObrJTSu7M9s8W0R/QRwiV3XfIUvNxix4ypxEGIguGwbNpcyJm+tPpZORdfDBXWgXOrpY/AzS+6kYWdQ+arGSaLeHSpygPnFDrYZKbv8PrhHAcin0MPSZ541PLlL1q+cZ/lyCGLUpbr9gte9dKUPVM9mvvmkVJgjGW9qKApQXuUX5LVvtElVIqyp+DgDTISx8fduY08Tzl+bBnHcdjj9bEmJtFr5LZGmr6YdT1NN7Hs3y1xLslwLiiNDZ8d9zY1LbjxgKDTK4gHLnkRMVzYCZUG4vEvIvOUwofU71EXHu6oxVeOWppJnW2zgkQ6DB3N2mHLZJgxf0tExXWpt1sAbHSHSKvIgggvjb/tAvaCiaD9c86mNUU+ZtpdRtVJRtoiRglWOvjTDq4alrOGvTEF0pyDUZfllRZ5mrLt+pBOAp1BQtxxOX3MYWnRsn7kPJ8/VKacvPGNpez8wjXjy2OJvHBCQJQsu07KRA4g21ynU29hwiYTxQhZrzMZp3SnJtB5jr+ZlQ7iOtuSyA+splAjZK4JFKhc01MNds8E1CcEv/ZrvwbA33vHO3DS9HKWHcrBUtfjJTfs441vfCPDNOVX1zfgyGHkJz/B617zGs6ePcstN+3nP/3K/8lGdo7t2+cA6K+eZSR9hHGQSmPIccd3XekZpClAGyQSXRiWVkvQvm9ygdu+71UAfOqTn9/KaufsWWi38erty0D7xqkOxkC7Zghn2mhxPaNRwPo3HsNb0NzxJmjtM0gpmN1bp5FknFwumMShH9auBO1ZTM8PCIwkOZ9yPC63a9f0FAwH7A1d7nrnX+fOl76UlZUV/sV7fwuKHE/mbN8P6WLI+iBF1GrURiNGxqCcEnA7To3MaKbdTYpUbDUYI6poNBkZ7bGD/KXNYqsSMqfMlhbmont8zBAHicWUTPsYtNtnxb7B1U3o8tgltJs40kBjCtsvvSfOnD2L47lcs3cHDh7apKw9E9NZryDvPkFdD7lj1628pV3j9lp5z+8pi4oFE+469eUzqJe+HIwhGKwROi7CEVugfencGpmEDIMrQVvFyUMlK7t37+7y+NlYo2hVaHQrqASm95vSO8ar8dUzI9IMBgenCEaVq8r+AdwQ7nhtxKScYvVkn0fOdrb2eUW6ZSpKrcFUs4G1lvO9FPKcSXc3wihsoTi/WY4tNObmwUpEmuCHEZ4rWMkNoasIpSAYrJbZ81PXg+OzEHQIc0maB6xNVPCVT3WYUpnP2NYoGefl06UCKglaWGvAjQh7G2MfoD/bjE4LgdS6XIRZgTZw+OnHWF9ZZ35+mte97OXQbHLota9l6DfZ/a2HqY4SGrtS9l8jeOkPlhL5j3z8Xs6cLch7I/JTTxH119H7boHGJAz7UGsxu9dhoCaxicLrd8nJmPbKpt5AxSQmRSqFyC0GBy0VyXCIV4nY3l2jtrLC7Pgaf+rkaSZrASiHZHwsmzyhGQoGZ07QFN+i0pgi2vdK8EIcIXGsKJn2S9Scu+YcUmNYPF9e76y1jLqWzRMeS8sRZ48aNk/kBI2YohKV2e34pBRsrK5z9HA5nrd0fhUciTYa8ouEiOcL2nNXP7asyfEdkMJnJZjB2IBk4/RzkgY92yVmxLSYwVMFangK6daQSpG7FVydYTNLVzhEFQctDAgY7szYXOSy1xXVNoEQ2FF5bfSQUPNROBSDF5j2F+ovZ70A2r/LkmPQjqO3jOgKfdE93lhbGpuMa2gypLVEXoQaZ2MWFIR4OEEFhCFphGSXmNGtJAVTp08Q7pghlwKBJN6og1+w6zqPHTeU68n1M/Bf3vNBDh58ktmpnezacS2dTof77rsYVeg1JjDDLlYVUGQYlROHTcSmZPt8eUHupiDbU+hOyehW2210bnGzIcLqLTO6Da1x8iFCWTIvpFl1OXn8OAB79uzZes/lXPPxzZRDiWJnaCET9E3OI24F09/gp37xl4h8n68dPMixRx6hcuoU6cMPM9yzh/qePfSU4XCiuDZ02eFWMDUfQ4aVHjKzFHmGfpYZncGg86SUWmWC9c0SQN545gnax0+wftNt3Pq9JVD42P1PYPuluc6WqV2/BMbHDk8Qd9b4t1/5UPn9DYZ86kv/Pzq9HcSORKUjJnorJWiPSol2JS/IjMU1OaGQdIKLTZuRF+DlOTLP0a7Hxlq5j7dPTJQzgGlC0ylPx5lrr+ed73wnhVL8yvv+H6hUqAQBwwtmdPEADn2TyaVDyPNnAAevt4LFEo2lodm3MaTc2RQsDUq5qCNL5kZ6DTKh0LFDzYu30hCEkUg0gzFojzxI1JVMez2AZJCDC6vdcoG3bfsshR0xLSR790lOzkUMrGFmNWF0JOBcXKFYhyPHLUfOWKanBQdeJHjla0pZ+u49gmBQjiEwNYvSlgcOW872QzIBVgmiLKMxXy7+zm/EhOPOfahBxYbE94kWGjzT6aGN4bob9tA4dwQVKE629zHybyXXIWfWDX4Ntj+LTbiY0X6l6eP2HYKFXYo4FQw3qsS6gJtfBes9RH+DtewcdekybVooBU9vam6aCZlwfTrSobPhEzBg5w0WPwip+y61dmlsuNEbIq0mD0KcJCM3V483BDD5mFX48wbtKkNoi/J9crfOAAc7HIL0kDWXyBuxcU5DdwmqbUAyXI5ZXqkzOZ/RmI44tVKgySEOOHFM8OTjlj98/xcZZhl799zE8uJ2zp+zvOtd7yIMQ5766lc5cuIEQkikE6HzTcCWiRzGoDob9BttZNQkRNMLS7fxYb1O7mqCMyOksOQq3Yp9G1qNLYY4ucEtFINMYtw2e24QPPnkk3zhC1+gWq3yE/v2lZGPF2IfL61KA+IB7373uwH4z1/5Gkdf9Wp+/Etf5ujqKgf27Oa//OTf4JYHTlD9gz9m76kTAHSWT5FLH1NIhDVkKtvyhpCuRZoCaww+grzQrKyW14VWe5KXvfaVAPz+x+8hTxMYDWBpCXbuJMAjo9haxHZOb+KGHhUn5+ixFslmlXz7DWzzevSiI4gQEjQBEqfZYJuX0rMF+TlJXqkT5+llaQBpMmToRwTaIVlMWdLldu2dmYbhgB2Bg+9Ifubf/RpSSn79v/4+jz5zApIRC9dDxQSsntcUtZBKHI/l8SVol0YSy4BZNvG1Q3f8tpWx50FsR1SlxBdi655jdY7FkMiwzGjnojx+ZEe4SARQWF3mtAPYy0G770AjuBJ8FIlLwyub5bYxCaMhzwzK+8L0ru1MVysIIVl6epPOZk5xa86sWWOKJt62XbRdSSDL5sN6bHDinO2L92OCEPGSvwJC4Bd9Is/Fc6HWLI+v1XMbdD1L4rr4Dqxt9ulv9qkGIdu2z1MUljTrYiohwfEqfh0akyOM1jy+UqPvJtw6WyF0Jd88ZIiz5wa4jg/X3tlitpAcPxXz6PES1NQdjwIDUcTMxDg+s5uWBqw2xRYjtLIsdso1SXNuAYtEFglRVMVay3KhqbmGyEhE7zSETahOQzTBhLtBLRfEaUh3sk4hLO2OpTKVMtcojTeXTp4CIHErWM8pYy6Nxs3i78iAUwsQOi8DxAFt4BufK1nzN3zvy6j6pQriaeXyzTteQuZAuNylYxJmaw63fN9+9u+/kcGww70PHuHUN09RLB6nN7sLb253eX8edKHWYG4vFOEkWSwJOz0ykzHrRQjHpacGDFWOpw0UBm1dNobl/au1MEswipFYdo9HLU6fOk2tJfDjCiM5ntdWCVPDY+S9h1DtCVp7/spl45QuAj2e375QO2YcPA8OP6N46iuWb34EHvkMnHjIIe66mFaFhe19Jma75NUI35TS+YycR+97aOt1eptdEqXQRmOL72w2HZODEFT9kMSHJNtJPhpi0rUrHprZjHW7RlXUaNiIoncYpIfX3I+nUzKvjlskxHFIHmicmsBDEAjJaCZjmBuGl4bouD6eF6DGRrkugtATpFGFIo63mnYv1Av1l6leAO3fZQkhkMLFOEUpj78w0+6CkT7WUGZtA9ZqEpsjjKHmRVtAsyDHwyXwKyAgrXllFNBYBt0/dQYvTQiumUXlCiEk/cUa0VTBxJTPrpsFN71acOcPKv7g3ncD8Pf+93/By28r80c/+tGPbm1v0JjEWksy7MKgQyYFhZ3AsYKFBXAldBOLMzGDHg4gy6jPTmCNg+yPcC6Za9/UmjBPEIUhC0ImTMrJ1dJ4bM+ePVvs+me6JXB8fSugoi3nFgW6LzlXjTg/HDE1N8NP/PA7AHj/5z5L88knGbouG3ffTctxeGiYIwXs0TG//7738+X7vkGiFK4PMrPj2LfLZ7k0GpNnGG3pdBOKoqBaq7ONIcOXvIpT19zMTa9/EwBfePJBRmdHW88DYLBJp19nY8XjTx77AIubm0zNlIuLP/zj/8xIt+kPPFJymp3lMqLFL8F5tVBk1mJ1Rl04DFwXxkkBA6+cTRaqANdnc6XcXzvG8nyShEAKQgE9ZfiFn//nCCH4rT+9l69/5gxBexLWV8mOPgKPfRF664waMwgEUtbwRptERpXuu0gyvt1ceymRP9011N0cazKE3ySjwAw8Im9APsZ/wjg4aEZjo5vIvZxpt9YyyC2NQJAlGTiw3Bvn+W5rYnKLPuHw8BnDM3GFfWsue7oJ2ajBrpsEr7xb8qq7BdUZ2LEPFhYE0aUS1vVlCALyqMU3nrasdCyu41M4pUlgmOY058dM++aIYOx03VSaLC4ziCsVzVO9spFw24E9OBsD+gvbWKou0B56LPUg9S1zc1B3nw3aL2S0X860X6ht2xXttsdwI6LTySh27UH7TUaDlHC4xq2dLokW+JlgRRu2T8ECDTbPBQx9wf5rOwQ1gZQRgedQbZYL583+EGEKVBiB0qj8ylGQC2WKcgFuMdiruAh/t2WLHKst2vVpGJ+4UqHfG2Gliw4E1Tak51ZQwwE058g2uqychmAqYnLBIpyQtTTDcTU37anw2u8TvOwVgsMnvwDAa17zJvLc8vRBi+c1eMc7ymvBh//bfwMozeh0eRxr60G/R1YUdBoTtOpNBhQcc4bUPEHiRejQ4p4ZlWMF6mLs26ZRBGkfY11knjKwAc1qRHNG8J73vAeAv/kDP0BLyitZ9gtVqUPc56677uJNb3oTo9GI2/4/P8nh1TWum5/jN//5z9B88w9Q+aEfRt20j+2VkglbP3+aQvjoQiKNpTAZoV8u0IVjcWyB1ZbAQl5o1pbKxW5rZoK7ZhfYtu86NjodPn7ft+D4M6X53Ri0GywFmji25Osdmm2fpGfpxk2m93aQN+xk1845wqce5f7N0lguxIFajSnf4Lo5S0cNRdBgaA2MLjR/DKNkSOJFqFWJl6csp+X16pqZGRgOcIVgV+DgXXeAn/l7fw9jDD/1q/8FM+rhuIK9e0OSIaznPpUL8njpU9qVx3T9SdpiSMNkrI3K/eEKl4CAuEzroSXllgHqhSSBRPi4tly2XGDaExsz7El6HVEqpraY9vJCdSGr/bmi3orEJWK97LhXajCMOdIt98X8vp1MBAHdU7B4folsh2J2T4UdSwVuvQmN5tbrjDR0RnDN+YeJnJjNW+4kqrbA8/HSPpFbgvb65Nj0bbHDSrPO8jV34Omco2Pn+D0z89Cokw4LUkYEQURxLmT6BhBxn7OrcF767Ji3XFevcPcNEm3gm09bCvXcwF3WKly7w7IjrHF0ccijB/s0PBcDFF645SC/2E1AKZROIYtR+uI1vTm7DYXFyzPCSoWOshQWKp6h1V8HlcHEvvE5M0nVV8yIIUUcsdGskoqCia7ArwrmZsr3O3/iVBnLJyrge4giw8EgdfEdxb5pIfBVVrL0QpAh+NZnyzntH3rj96CArw9SjmQ5ut3EBBBsDolNQSgVfa354R9+OwD3HX6AYpQwqEyxsf0GKkKUMXBaQ71JUBH4C9OkQ3BHCUW6SUU4BE5EqmL6OsXTCptrrHBYXC1B++T8JP4oQQD7xg6Kp0+eJqxbwqRCbn2sLJjsH0WsPM16tUW0/aX440bXhXIRICTpJSqbpvSIJuDYqmJp0RK0Lbtvtdz5Jth/u0/7xgrTzU0Km0G1glCCQV9wZjXnvnu/ddnrr2yOrmDav11ZU0CW0rKaDCjcCbKkhhqd2UpvgDLebdks4eAwY6dKwG41Xms/gjImNnVquCplNIiQTQVC4gnBzW6FqGlZqadsLl7+/m5QweRZmZJhLaELaRih4gT7goP8//IlhPgtIcSN3+Vz3yyE+Plv8/dbhRBv+jZ//6dCiGNCiCNCiNdf8vufFUI8JYQ4KIT4B893u14A7f8d5eJipMJ1xvJ4VUa+GeGV41UX5NFGkaKR1tJwoy2gWdgCDxc/CBGOoKhJUiO25tr10Wdwwgi2t9GFwpgaRSGoTunLTOh+7/d+j2PHj3LNNdfwc+/+G7zp9W8F4GMf/dgWExOOu9pxbwOGHTIhKbJJXA/qE4JmKOikFn9itmQ01peobW8hkOjBiBp6awHV0ZooH2GVpYgi2hvrnByUnfiZnbu22PX9kcubJ0LmfIeHFzMoXOK1AGeiwtlMsbG5xv/x7l9CCMEnH3uclX6fpbvuolapsJobHnzqaT7xz/4R1+7cwc//zD/h3/+rX+NkZ5PAAZmALvKLsvZxaTSmyNHasrpctmVnmpPUzz7I1L45TCLY96rX4jgODx19lMWjazhmvNgzhmx1g3OLk2TyHO+9t/QF+Ee/9PO89DUvpdfr8fnHP0CRV9hIFPXuKtoYnkwcrOtTzTO0cEoHeekwsBoTNjDA0Avw0gSUoXAcuitlY2a63SSz+qIZnSM4dc4Qn7qR1935Jgqt+I//4ZcZPj1i+pFvEi+fhrk9cMf30p/YBZ6PpAJ5RivpkIsCn+A5Y98ApioQeQJloO2Wi1PpN8gpMH0Pz45g3Mm3qYN0IU4vOMhfPtM+KtW9NAJBnuVIz7LSHzdC+lU2j/s88ljMM3FENPJ47TbNvjs1t7zLZcfLYoKFhGtvzLFhzgPHc1KjSnkzlAzH+gpZY5avHYTOwPLi6yXTLRfl+hgFfmFozpbH9uLmkHAM2uvKkMZgGh6eSTi4VCooXrxzO14i6M1twxcrOIuCnrZMbRcEgaD6LNCej89V/zlAu7aKXbtdKkWN7qbhhI3Y9AvS1KFSnWN2tMRk5zB6aPEjwaaB7imf08cCNiYlRW2Dx7qKPzwr+ZKEamsM2gcxFBlFGIG12NHwqlJRq0sTQemMVT1/nmy7yjEWtOczI3zSKKI36CJkgPIltRZUshPEPbDNOc58swcGdt8dIKRAuBGbeY7nGlpugOMIanXBFx9/AIC/8Te/n/mbLCvWcOzoRYn8J37v99Bab821gyiZ9s4GA21JmpNM1ZoMioKnnj6MIw2RMsT1kGCjizASo5Ot7OIVlVPJhmAleSejcEN27ayysrLGBz7wAQD+/u23l3Psc3MXP/6lIwlRHVQBebrFto9GI2ZmZvjg5+6lvm2OhfsfoDh1kOrMDDu2leqP1TNncQJJrn0cbbGmQHrj13UNUudYY/GsJbeW9cUSHC9MTXCNzbjrzW8F4Lc+8Xk4fgx8H2ZnCcbX/4ycpdMZjh4x0XaI+6CrbaZ290uAfN1d7CNn9OTjHMtzIuFAtUrdcWmHMTGa7qhBbDRbIct5Qmw0QyfEnNe02pbzG+MmY3MaMyxBzJ7ApbDwt3/hF5mfn+eBp5/ht99fNlx27PaIPIdzfY8gSYiLAitEmaiRx6y7baqOZNrZYC2+ZMZaVEhJMNbQdhx6uoyFU/F5BJKRjJDj3Re4kJuMnJy8H5LF5XVcXEh2ueCA7QkqXpkb/uzSyqJShwqbEFaxUiKGMYfGYwrb927HVyGHHlmlWFhnetcke/U0zuoKzC1c9lq9whKcOsp8vEhx4wFUq02AhGoNLxvgSo8gErSmS7+D1eUulgLj+jg65vjRcmj3url5VLXKmbUBViiCooEQDpPXw6njPTaHltZ1DpNVSYuQZlXw4uslw8TyrSP2itnmrQojhNXcfUvItmaFZzb6jNbK60UW+Ey3SxC91ElAaYqij85TrLEs9sv7e2t2G7HWBFkJ2lcKjcFQETn13jJUJiFsjc+ZCSoBzLMJmc/Q+vTaAUE3oRIEzG0bv9/J03gODEWEDXzIUhyjkUXxHc21Kwm+KlUnAvjmiTP0N9ZZ2DXHi248wCc2U56IRyBzdk7DsFkh7A7IjUE6pZbqjW8vQfunvnEfiahwauo2GEcPMhyfF7WyQdPaP4u2Pno1QcQdlFW0vTpCJ3RUgqd1aaLguJxdLFHm7GQdiUVKwTadENaqDAdDut0OLT/E5AHK00RmyFKtRTe6kZ2t1hWfVVqLg0t8SVPeFQLXOtgdGtuC9RQOnYCvf8Ny9KDLyW7I0pJmaUOz1I04e3ySB+63nDyX89jXS9DuBuW1drEbg9Goq/gFXa2sznFOn2LP8tOkykLD0h/uxpocHV9E2Ot2jZyMGTGLGZzAqBi3cR3SHfv+AIlbxwxjCny8JkjhIIAZ6bEjCBjOZqwsXX48+EEVkScI61JQ0HR9kmoFnabfuVrghfpLW9bav2Wtffq7fO7HrbW/8m0ecitwVdA+bhS8CzgAvAF4nxDCEULcBPxt4C7gFuAHhBDXPJ/tegG0/3eUg4u2Cs8vQXuhyyZ/4TXRE7ugPQuU3cjMalxj+X/Z++9wybKzvBv+rbVz5aqTU+cwqSfnGYWRBkmMQBIYIZJ5cZBljI2NwZ9FMggRLcAYDBgEkmwLvQghIQFCASuN4kRN7J7u6e7T53SffE7l2rXTWuv7Y1d3T9aAufx9xvNcV19dp8Ku2nuvvddzr+d+7tu3CxdEz1JSpBAE0kG7LoKYsFDLK+1RBEuLZPv2ooRCJSlJVEF4KYUqOOK8ME7MO97xDgDe8Y534DgOb/yeG2nUpllaXuLhkeBT2Q9IvQJxd4e0u0PXL2GaDtVJkFJQ8wXtyOCP5UnrsLlKMF5GCB/VH1Ano6kUodaEWhHEA1JjQ8EhWNm6ANr7U3lC8tqax01lF1sImm3N2WFK0bJJugFClJC25LH1DXbt289d3/ItpErxntNPcnZhgcc+9Sle/7rX8hMvu44Pvvu/0u/3cUc+s2c21/NeUGNhooRQP713WxmFSRNSI2nv5EBtplgCq8uMt4mDILaq3HjbbWQq4zNff5BsIyIjQ/c7bJzMyPwx3vuRtzNMEr79zjupz97C977tRwB49/t+g6hYo9tLGfb7PNHZ4nPtmG23SCHuj7zac9s3bWBYGiPxSyjbxYpihNKkjkN7Je8DdvdOsEKPreE2zVXD+gOSk6cNfhF+7mfy7/yrL7yXpLNMu+3xIDeiFo7kFXwhoDqOSDMiETDR3yEyMZ7wiF9AjE4Iwa5qDk7LIu9nV9IjShQidpC6T2YXsBxJGglsG6IRaD8P9hOVb7s3omSWXUijGGkbNkbU0sm9FboTPv5sj/FDNeanLG6+tY+p9OivPMpiusGTNDlt7+Ad2uFUYZNPtNa4l1UeNOusdM7S64fcuzPJMDbccrlkblxQKUKMjxEGUsHY+IjWudPHHk3U5UwxiAxW2UOqAUcX84rHLUEBaSStyf2URI9zK32KFRibH+3HM0B7hMJCYovnvlVqMoKSw0JQwEJw37lNmqUitT5YwQRWfQG3t8VC+AiTpYyPL6f896+mnMg8WoGHpIstJPtKDjg2pWrOvGgOBog4IQ0KGGOwh/3nFKM7388u/TwBNs+4Hv6XQqUYlYP2mcAiC8r0ul0s6aMcgVcwVMwyrcEE5570GG60Gd8f4JXPe637dLIU3wFvJCB2+tgxnlhdpVouc8stt/D4ULFdV6yvGq655uXM7tnD5rlzfPazn72gIC8sD5CkzW16QkCpwrgF3/sjv8wPfNe/4i8f/TqVfkxnrILba6ESG6EShiZXH+8kfZw4wYo1/UgiiwFz0wV+6id/nyiKeNmVt1EKq6zNXcegbzDG0DIJd+ttzuiRbkYhb4Eh7HLDDTfw1re+lYMHD/Kud70Leele2q96NcXDV6KffJzyZ77MnsbI93p5BctXRJmPZTRGJRdAu7Y0ThqRCQsZayIbds7l4/TgvM14FnLjt92JY3t8+r6HWHrwgXxhQUr8UU9rRML2YpNSEVxLEPYFxbkyQSUfB317itlDh9i3fJyz200GKVAqUbQExTjEXkhR7YAWDnowAifRgKHRNJOAYi9hfAZW1vL7lRWNsbwEqttjxpUEErbcIr/+678OwNt/5dfZ3t5GSsHCnE/Hs0k7AqvfJxUC3AIkIU1j4fl1Jp1ttvoXE/GCKOZ2cQypWRYK6AxW0Ukbq7SbUNhY5jzdXdBlNO+cLTFoy4ttJNJ+GuvkWw9bXDL+7Er7sEcuEKvbUKpihj3IFMdW8/3dd2iBrWVDuGuRxozDfu8g9vZ2Llg6O/+0bXW3d5hcfITK/CydvftyVgMQFjzksI+UEs+RFKenkULQbPYx8SD3GNcRp07koP3w/Dza8zi108URGW6nSm0vbIeGs2d6VOoehVnDGBf72Cdrgqv2SzbbhodPPw9oD/LrSUYRt11Zp+Q7DEfU4tSyaTTyxc+NzhCjNPFwC50l6NRwdnRPr01P000MXpoQlEpspBrPUtS767jaQH0fKoH1h6G56GAXasw7LcTQIUwdmvUCemuTUlBmdk/OkFo/vYglYWhcjOdBEmMZg8ziF2X7poTASRMY0cY/+eCjANz+mls4kVq0RAR2H9fWXOaUaRcaOJ0+ShkymYPTmUsv5fDhwzTbbT6zA5uhjQV4Uub2oAClvHe9emAc4bokGyn2oJuDUaeMZVKG2RBbaUSq0LbDmZHA7nwtQAqJZTtUeh3GFvIFn8XFRcoVidUMaNUbdOfnWAt2Ue+NU6o+fc7JTIYUAheXwVNsXbU2ZE2JN6W49mWaa68XXHKZYH5BENgufdtnp2XoJxpZ9qkGKUeuhomFHc6ceBzLcdh7w535udgZgFGkyYsDvEYniCShNmLCJCVNv1PB8sZQgxWMSuibHh3Tpi7quIMtdNLCKe3F8vJ5+7xo3MAqoTsR+B6iYABJWVpIIThoBQR1w6ksIhleHN+eW8zvodomIaGIT1YJyJSB4Ut97f83hBBijxDiCSHEHwkhjgkh/lSIvNdKCPF5IcT1o8e/K4S4f1ThfsdTPn9GCPEOIcSDQohHhRCXjJ7/ASHEfxk9fvOoOv6wEOJuIYQL/BzwFiHEQ0KItzzjZ70R+GNjTGyMWQROkgP1S4F7jDGhye1NvgB8+99kf18C7f8LYUsHjcLyDVKPfNptQNhkC9fkDecAJiMjwwaE5VygYp+3bvONjfI8fD1kuzoGW1uYkycZZgrnwG5SFCZJiLoVKvMp0uKCeugf/MEfsLy8zOWXX85b3vIWTkcZX9Ap33THtwLwoQ9+FIBAStJSjay7zbC7Q8+u4PYsavm6AnVfEGdgt1KMbZE018F2sP0ihCFFUvpas5FloFOcYUwqbIRn46xtsTjIk9t9e/dcqK6fjy8dVRhXccdeD1v5rLckY5NlTHeH+/spP/L2nIHyp1/+Gj905Ag/9h3fwcOf/wx+EPDP/tk/42sP3s23fXe+oHVuZxMjEixjQ5jQewZI0SN6fIpFa6TEPBMEiKJLEG5RKwq6A8UrvvlbAPjc0fsYLMVkJmX54VVCNeRM6et84I8/gGtbfP/bfgDhpxw+fBuvfOUr6XQ6/OVjd9PxJI9vdCjvbBBlMHCL+MMByvKIs4iKyPe/05ijd+BmAKw4973uuA79nRa2ZVHat5ssgYef2OLzx5sExjB20HD4VXDznbfw+luvZxgnfGh4Dj23m+WzMQ98Elrro4mrMo7Whki7lOIhSbKDTy5Glz6PGB3AQiVP9oqyh3SrpCIjGSnHy6xPZpcoVhzScATa43wSD0YEj/MU+e4odyhYkGUx2IaN/kgR9mCDQcHjMiuir2pUC4KK28esrVFZPsd1D57iKjXGFUxwU3Gcy+w6w+UqtbBCgM25lVM81O2wPOty2ZGIsTxnoloSZJZPJg0mg6mxfPLfaA5wRwJ6xVCRGI1V8Ri0VzmzuIVjWVzdH6Atm5X6AUTfJ6uscvASQwhIAcEzCuoJ2YXkO0kNi2smTwhGoUyKYzu4RY8Jx6boJwyTScJuGXlujaA2xVHrEJ7p8J3BY8wey7gusbnj8oBqJpkrRlxShdvGbaRlUa6MQHtvgIwjUr+AMWCH4XPavum0mzdEuPkxMM9hg/i3DaFSjAbtOEwVBLgV4n4fjYuyBKiEktdhqzfL0mMwVutS3VPFqCj3f1Y2QxHhS4k90gT4xEc+AsBrXvEKHMehkxoKDQO24fRJybd9//cD8Ifvec+FSvt5FsFwZ5udcp2GY/FTP/pv+cIX837MB1bOUekN6dZquFmHbODgqIimyugZRZwNCZIE3UmJgdJUhSSz+OhHfxeAf3LkZjbcBR5dneDLXzR87n8avng0YmvLcFL1WdKDCwryhHmbxe///u9z4sQJZnfvYsckTNtF5DU3EF53Cc4gZqIbUnJswt4AkTYJ0wCpFFpnYJ8HlgorSzDCxgwz1sIBKs2olYoUFibwjGGhZnHkyBswxvC+L34l944nV2i3kGx1UkynRbUhyAYpoalSm5FIR+N50G/bcOW17C447H38UR7pK0K/gGcLir0U5jIqymI9eYoYXRzSSzSdtMBEIcHzDFvreaV9tjHDYGA4ek8PDOz1bM4lije9+Tu546braHa6/I//8T8AmB73kdMe/U2D1esTj0B7HA+IjCEIpqg4KTppX3Cy8AkQCIZmQF1KbDUk7C0i3Tp2YYZIa6S+SI/vmx4Ci85SQDIQhFk+/oWwn8Y6saTILfOeEcMeeGaAY6VQqkMnX+g9upxT1fcdXGAnC6mWh8wHu3IV+dVzOZV+YurihtKE6J6vkNketRtvZmgUARZNhmwWJfGgi+uBI20KVZ9aMRd9669tM2UESkcX7N4uPXiIIYKNTp9iooEa3h7D/Sc0ddGjsddHYxgneNq+7J4SHJoXLG0YTpx7DrDrj94fDZFCUPZsrHRkm2cU9alcIG+zPQCjCcN1VBSjhimrg/yeXhufJooiHK0plYpsJIqGFVHobiC9aVYfK/PoB2DlHlj8HITDMepun3KoiVKHdqNMHHapaofxXdMIIdg+exatU0ItMIUiRiusNMNJwhdl+6YEWHEKGNI05bMP5oWKm159I7qgmK8kZH2B1y0yO6zQFJOkzT6DTsbQDJHkLgVvfvObAbjn3g+z0zcEcpQe99sQFMDJcy5RKOE3ArKWgXaP2CRMeGUsBFbax8pMvrzqOpw5b/dW8rCkC5USQTSgPpsf68XFRYp1sNoFwuldJAtVzHqJiYr/rP08Ly5bFsWnVdrbLagMXApVwz12l9VayPguzSWXCq64xGXhiiK7LlE0dvvsWXAZr0WMTWseu+8BjDHsvfo6JnYdBGBjuwto0vjFVdpJQtCGykhDZxgosgS0tSvXbeqfYVNv4OFTG2ao4Rp2MItVuMhoIupjhCRMBGqg8GdcUmHQiLz4oTUlabGv6rJVitlYvTgm/BGbVSeKjBTfOOhSiVRrzEug/X97vLr52K2vbj72rX/H/259EV99GPgdY8ylQBf4F8/xnp80xlwPXAm8Qghx5VNe2zbGXAv8LvBjz/HZ/wC81hhzFfAGY0wyeu6DxpirjTEffMb754CzT/n73Oi5x4CXCSHGRgsLdwELL2L/LsRLoP1/IaxRX6HlKiwzUo8faeCop7C2jc5QOkVio+38DQJBMupLKgoX4zp4eshKqQ5RxPChh+hU6lTHy0QqQUUCk5WpzF70aA/DkJ//+dx2553vfCc9I/hqN2E707zx+3LQ/pE//djF31seI0lihllKx9QoZvIiaA/AikP47N04UULSzJM0r1LBimJknH/vqTQFHWOHMdqykb6HvbbB4iBfib90/z7spyRHW5uGJ/oxtTIcqXrMj0naOy69QpHdasDJwZD9197A4ZtvZhDHrJ87x/j8Av/0Hb/Iyrlz/N7v/R57LptiZl9e1Ti3vUkUp7iuBaGib55Nj1fDhEzYtHZGYm+uiyj4mO4WE3VD6gzZ9fI7APj8sQdYGaxwtLfNZvMEzEW885d/FoB//rpXUCxdjt4pkG2N8RM/8R8A+K0P/L/sFATFeMg1q02SzND3Sng6HTEuIoojYaS+UYRGYTDIOO8VXB1VrWfqNTYHJQZLFUorksJlQ8Zf0SRoKDqZBq/Av/+F/wjA73zwT6hWM8bnmwjL8NgXYPN4lTQYI7FcSDVBajDDTRzyqmbM8/d0TRQFN8ykTPgxwqkQk+Ye7YkFST9PngoWOgXbkhgRE4eGwmh8n6fId2ODa4GIBYgEbTK2wzyhEJNzHERRiARbToXddYNSfcxOB88r47SaFB6+n7JxqAufW+eLjGVFVk+UqLXG0A8kWM4Uew/ZbBRaPMgai6ZNUEnJZICSGpEIZsZqAGy2ethRzBW+h9PWZJbEKTsce/BrGGO4bO8CXrdH5BUZ+GVaT8wRFFMmDm8zyAxF+9mJfYzCxaYbGr7wiObh05rTa/m+G2PQRmEJB6fsUOwG3NYwTE+MsZ1Ns/PAGlkUsZJMsjF+OcHZmCu6D3L7DRtkgaHak2hj6BDlHvCuRel8T3tvgJ0kDP280m6F/eesOpmki3ArCDlSE1Z/N6BdaYVUGVoLlO0yGUicQhVlNL2eQqMw2YCCnzIUk3h+xvRMD0q1HLRbAd2+RrsRBdvCHjGD/upTnwLgrje8gaEyxAosS9BYgO0tw7e86R8ihOCjf/ZntLpDBBLhFEFrktYOm+UaD7733fz2b//2hd96fH2NiTCi1WggZIbbTrDjmB2V0tEZOhsSJDFxzyAcQ2OqzN33Ps72zgr753bx/S87yNVvvYFbbxdcfkQwPSNoiYTBkoPY9DllBizbGmwHwqdbCXV9gQFmRECmY7Rr4LrrMdNz7Crm12HUWiLUPlIZDBopR9eHjLF0hhYSOcw4124DMFmvYc1OYgMN3eOmO74XgPd8/RHUU0TyPBzWmimBalGerhKu9EiDOtUpMFITBIJOU0JQILnicg7ubFBc3+DuzMK1BcV+RF8o9s5KdqwSrdVO3jMfDWj2DMoUmG7ErHZaZGnGWLWKbMxTrwt6q10eewT2ehYaWE40b/2evNjwsT/LF2aK+FQPFDBGES4NctDuFRkmEVJlVP0xyp5N0WxeoMhLIQkICE1IScDE4DQDI3Eq+0mMQQNiVEB3LUPfDDCxR9p3QAnC8CmV9mfMDc8VYRccE2HJDIpVTK9DpjSnz+QV0j37Z7GSHaaam3jtDMI+rJyFqRmwnyJO+dj9dJt9zu25mfEJjwiVi5GmLbysixl08DyBI2xKgaJazJkYGyubXKJsUpNw+vgKAFdecTlnhYfsD/CHGlOq8lhL49pwqN6jU3bxsKmct797Sly6SzA3Lji6ZFjZfkbFPRi10AzzubrgWwhlwFg5xXvk1b7Zztuj0uEOxDGbOwOUMYzXx7BsDxPlwn+q4BMZmOwuE20Zjn/6AGsPQHkWDn1rbjd35p4GBUewV3XoDj1ajTIxKbVuitUoM1muorKM5uo6sTaYQgnQoDKC9MVX2mWUgDA8cuYsnf6A+v7dXHloiumix5WyQrrtEkiH9o7Ars1iC0O23ufYzpCwC61MXdDU+PwX/oyNVg9Xj+aCXucCNT7WfZTQFGZryCwlbinSuIlrBxSFxFURdqLBgHQtzo5A+0LgYHlF9FgNVyU0JnOWweLiIsUquGGB3QXJlGvhrTaoTDx7P88XecqUSMhIR+N7cwPqmcvr6xV2WR5nVczn4g4PpwNSLLAsesWAqFwmEAKpJDEJD9yd2wMfvvk2FqZz3LCx0QYBWZrkGgHfIEzUB2FRkgY7S4i8/DPDgY8dzDCMzqHTPmOpQzZYwnIbWKXdT99I1CW1fDobMVKDN++QmFyd5we/6fVceeWV9Pt9jlQDpGd4vHkxrwlGulEqzuc9C4EslYgMmGc6YrwUf5/jrDHmy6PH7wduf473fKcQ4kHg6+S09af2un9k9P8DwJ7n+OyXgfcJId4Kz9Mr+SLCGHMM+BXg08AngYfgGcJc3yCeLYn8UrzosHEwaISfIZoe6UUNnKeB9jhNECbFFjZ65Pfj4RERoY2mKBwi1yPQHVYqVViFXrvL5p6ruNrKSKMBcWzj2mX88YQIG0tY/M7v/A7r6+tcd911fMsb38hftS4SaK991R0UCyWeOPkQD997hqtu3INbHSMyBksbulmDsi8pjCquVV/ghH2iTGAbgWpugTEUGzUGrYSol0BhJEInMmQYkdk2VuDBxibL3TyZPfQU9XitDceeMHQKCYfqghIul85oPnvUZ71e4aC3xlTU5ms9h3/xn3+TP/qFd/LavXt42U23ct2bvp1G4BLrPqEJqR3MxW1WWi2iQcx4SSLClP4zKouZUahhSmI5tLZzbYA512JQNPQ7y8TlDnZdE9bG2XPpQc4ce5Inv3qM8elL2dUx3L9xjq9/7etMjY/zz29+M+vRXqb9YwhlU7zyNi659WU88ZUv8sV77+cfHbyS4ZPrqNsFvWIRC4GfKmJHY5mUgrDoGkURSaYEbhqDgo2RRclksU47LrFrtsiefdCfnOARvU3H22FZC6Yoc/srXsktt9zCV7/6VT58zz3ccuWV7L9T033C4sTHAh76coWDdQeRKXwCsqSHVjFCCiITURaV5xy7Qgj2lfqkPYF0K8TEpBH4aYZSmlalwKYyjGudVxyshGEPglo+XobnHfJiQ9kThJ0MIzParS7KGCqlIvvqVebCJjtdw2C8xNXVkDTswTDBufxSsErw+ANw9AG44gYcW3D1AclXj2rufSzhUNxm37WX4LrTdIjZZMAGA8J6j7ggUYMctM/V84Rqq9lFRwmu0aTtlMRx8fwhxx58DIBrrroc4piwPsV6WGB+o0wjqJGVt+iFBUr2s5PgGEXadbn7mMaSUCsJTq4a9s0YkPnYk8LGqbmIUxaTwximwb71Etr3fJb7v7QDhRrNtM7y6euZ23+cwH+CylbAfNjHFh5tGZGZhJJvUarmyXyzN0CkGcp18kp7NCR5Bj3e6BStQmx//KL41t9RpX2oM4RSGG3QtsOYL7D9CgZNp5dSrih02sctuczNWEyNN7GWgWINk60inTKbbYWwM8quhYPNcDjks/fk/eyv+9ZvpZVc3J/itKG/KkjS3Rx55St55HOf44//+E/4wbf9I4TlYQ8foh0n3Pf4Uf7ox/8dAP/kx9/GH/7S73F6ZY1yNKRf34NyIGh1aU9W6WURy5aHq2PEQJFFKdZ8lZrj8tDR3Obo1l0LiL17YWKCElAqQ20uY11rsscLiCc8xiYNJ2WfIPCYOG+9NopOIFjApiRsBrqL3dzGHpvDzIUsFDyOtgf0t5cIZw4jU51TsZ2Ea2ctzjopIlMoYaGjhNVRlXe6UcWbGcc+dYrisMtVt38Tc+8eY7m7w2e+9CVe87rXAWBnDs1en0uCFlYwQ7S5zI5R/Ml/+mkePPog2C79NZt3/WpEL+4zWFshjf8TLOzl6u9+M40oYmfE8n5ks8TKuQ2m+j2SZsi28qgULCoy4tj5+2ijQVKsM1HbRJT7PLpmsGxJdUZwOsq469vfjPOjP8EXv/wVtr/+JcauuBF/poxbzRguD7AuccAtMDAGJw2p2XVMeYLS1ipb/ZQ9tfz6C0SBHbNN0l+kYiI2gkMcki7DkRCq0AJLQiYjMpURdzySxAYE/VBBI6+0nxcxfMFx3gPXjZBk4BUw65ucafdI04T69DR20WHi3EnqfgenrWH4NXjoQbjsCHzdh2I5FylbP8ujtcupTE+CNKTa4CJJt59AuAITDfAZEmmbaiGlUp6AjdOcW90izrpsbzfp7OTK8XsPHeAjieSKjSYYl/VKgSyFl12SIb8W0S2OPavKfj6EEFx7EKIEHnhSE7iSxmiOf2qlHUagXRqklmRSUZvNWRwbnS5GCFS/iZ0krK3lc/v49Gwu0B6HeEi2lMPg5BDdWqMpZpjfVWDmGghG2qr7Xg3H/7zMcNVj1mtzfzLO1nidmITiThvlV5ir19notlk/c449V19GVqzj6Ay0wc9i+i9CiC4TYMUxSMlmO6+uHpwoc+nRk0xNBQzWCgQ7PWbKZWQP9useRVswGXfpW2NsrqWstAzfO3OEI0eO8Oijj/IHP/5mLv/QR6AU5K4N4zMYY9hJz1CQdWrjYwTOGu22hdfbQkzsoSpsukbllqmA9CRnl0eg3XOwZBk1NYGztMn4WH6QzlfapbYoNRsElkNPWVSfA7QnJEgkVVFkiy4hMVVstjYNjTFB2bE5gs0BO+B0FrGkI86qjIKMcQ8foS0UkyJFZrmQ3YNfzPvZL7v1NpyVfN5YX2thJGRpDEbB87SFwch+LeojhI0tBVUdE45A+6AD1Yl54uGTuP11LB0h7QJ29eCzGS9Rj8wO6KyFTFoCM2mRkUKnzz1fuBuAX/mVX+Gd73wnu0oup5oxUerhOxa2V8ARoKKEXKZP4/seqWOT9tpPUX56Kf53xGcaV3zl/0df/cwbxdP+FkLsJa+g32CMaQkh3gc8lc5yfiVI8Ry42Bjzz4UQNwGvBx4QQlz3DX7PCk+voM+PnsMY84fAH45+1y+SV+FfdLxUaf9bRKfT4UMf+hB/+aHcVoQgQWrI9FMq7U9ZO+mmCZZJcaVDNrKp8UU+gWaklIRD4nn4OqVVLZJo6CnY3rWPCTsliSPSyGZ8ukwmUxxcut0uv/zLuUbCz//8z3N/P6OtDC+vjnodHY9vviu3Nnv/ez6G1oagVCMSkp60UL3ShSo75P2BVR3ST8DNDCoZontdCmNVrCwj7PUpjehiRZ0gohjtOTiDhNWtJqlSVCenmCxdtDlbXYGzPU15MmPSt/CwmC9ZeJ7LSlSjp1NuoU9mQO85yGt+6mco/cDb2BcPaDz+IAADtcOmydg36rc7u73NsJ/glhy8OKUbXlx1NcbkFl3DmMxxaG7lSfCsb5MVPYr9mF3Gp6rLDBfH+Y7X5yr7j371Puy+xB/r8hO/8V8B+A9v+xfoyGfuUAmnkvB1J+HLzZR/+vafBOA9n/gsFGP8bJvBmSHtkYJ8IVXERucK8tKipxUDo7Eyg6UStDFsdPLEYrLQIKsFiEoBK4uoCo8b5BSudjhFk0XTxgBvH7UP/N5nPgNbW7TQ7LlSMHGwQ9SHtt1ApClF4wKCOFrDw3tB2zfIVceFsBFWgZgM3XcoewOSBD748ffynW/+ByyunUFiYc6D9gv0+FFPewJlVxB3U5AZWxv5vjVqNcq+hW6GdEODnKxQtfpkO5sY6VKY3AO7DsCBy+DsaTjxCABT9Zziucva4uCswZvJKZQ14XNIjHEd01R8iyRwMNJACtPl/NhvNzuoGMZUBbWjUSUX1+xw7KHcjvD6G66GNGNbFoj6RaYKkmI4gxMIUtae1c+eGc1qS3H8tKQUCF55leTKvYIkhTMbBj2qdFjYuFUXlYDutQEYv/FyJqcc5Pppmk8Ilk9raoeKjL/yGjarB3GzkMuTE0z1E2Jj2FTbVGVMYVRiaXX7mEyD46JtC3v4bK/2C/3sTgUhZA5U/o6E6IYqHYF2EI5HxRMov4oD7HQS7H4fYwmEX2Hf5THFkaChKZTyPkfLZ22QIK2Mimfj4PD5z3+eKEm47uBBpqen6TxF0LCrYP8BQdwU3Pwd3wfAe97zHqRdQAgLp9/jvqWzfPCnfwqtNf/mp3+c7377W/E9h82dNnGvRVgeIzJQijvYESTpkDMqpqhi0p5BZgmyVqBiS44+8SQAR2amnqUYv2MSMIZL52OyBErLFSaEx3Jgs9O/aGXUMymRLZgR+dwfx03s/gBrbBeyWmd3Ib/HtzeX6eODklhakeiYg2MCTIJQGUKDSg2rI0vH6VqNYqWALhco9vtUKj3etOcaAP7g3b9/8fu3HaykR62agnD48rGTfPevfQ+/8Au/wCf+7BN84kMf44tf+jCf/OTH+fLnvsBDT5zk8cVFHr/7s3z06DHqUUysDL5lkNPlvP3p8Q7bJwf0fJ9a2cJPI06OQPtCYxzlFbBqZWbKffYdEKycNYg1i41UY83s4o5XvhKtNX/5p3+C+PpnqG7uEOyx8ft9tjeL4BUItaaeRdhCYAWTlFzo9y4e14IoIpM+UbiMXZhh286p5OeVxI0Wef++CVFohk2X1YKh7WmGQ5MLWUrnRVXahz3w3DhfZfeKiG6Hx3fysTy7dzc6M5SjiGFlDnHznTAxB2NTsLAnFyhbPA5nTjCoTHG8dohdNXGB0i0Ga9hRB1UIMDrDz9qkqc1kXVGu5PTg1dUtkqzLyRM5uNszOc1OqUyqBaWlPmHgkRZdrjskqdGjT0paLDJB4Tn3B/JWgBsvERQ8+MpRzbFlTZKaXMTQsmGYg/ZiIS8YOZkkNRnl2bwCutXpozTINMKKE1a28uNRm51HK4FMhziZxX1fcamvLOPVYeLWPex79UXADlCchNnrYbDRoNprw9CiZQUMyx7uzg6mXGOunn9gfekcCEXi1UDlC4Z+Fr9IerzAiiOMtGh28t9arpSoOwFWHNM/fpKplcfZf+xBLv/C73LlF/5f6mdWKHZ6FEqGw7sjMgn3PKn5sXf+CVNTM5x+8Iv8zD98C8OdrZx9Uq6SmVwnJjMJ1CYICgoGEK7vAJKSHTAlXOzIIAVoIVlbyfPxBdshcwLCxhSOzpiq5hoZi4uLOK7AK4C9XkWtF7BdKNaevZ+JSXBwCfAQCAbE9HqG4RAmJi++LxCSy50Cr3Jr7LNLdI1mW6YEEiwshJG0wjaP3/cIUkpuuvl6dh3Ir62NtSbYFjqJc6GmFwqdQjykJxI6hDRUTB+N7eYMFiFthoUaXqYR0sGpXooQz+w/G4LKiKXPYGdIqQw93yEzgubpxQtv+9Vf/VWWlpa4suGToHl8e5Tb2B6utEnjGIEgJaVmeQyDAmn/6ayol+LvdewSQtwyevw9wJee8XoFGAAdIcQU8M1/k40LIfYbY+4xxvwHYIsckPeA8vN85M+B7xJCeKMFg4PAvaNtTY7+30Xez/6Bv8lveQm0/y1ibW2N7/zO7+QXf/pX8ie8FKGe0tPO01mq7SRGaI0vLJRlkRkYZnlVISWliINyfFyjUDKlW59gbW4X0gsIrJjhIIPMYmpvmZQEV7j8xm/8Bjs7O9x+++0cfsWrORFlXFGw2eXZlC1BO9O86U1vBOCzX/pz1k5CSUp6jVnWgmm8UFKb5mnRWzvBm3/jXfz5E8vIQUjcXEeW69hSknabNKz8hutFISSKtOBRXt/hzEiEbmLXHsqjRQmlDCefNPSKmlJVMetYtLKzNNw+Yw1BPCiyhk+h3+S6kkNPwbJ0UVNzzF15JZw6jjp7ik21w1A53DiSDF7dbBL3QmTJwdGGfj+88Ps1ikgpRJKROQ5bWyMF4IKFLlcoGJuJbp9GxSJUcOstbwDg8498hSsmevzOR/+Cs6trXH311bzq4KvAD9h7UPKQWyQuaA4MXf7tt7yGl7/85bR6fd7/tQepNEKKS5s8vuSRGYtCmpIYMCrvaw+Noq8VdqaQKkVnsLzSBmD/fJ2wUGSjVUCN2gt8YTOvGjhpgXX6PM4Wr3v9XVx22WWc29nhi3/91+yMVoT8ak6X22IMjKAQZ2jpk0brOWgnel4xOsj9vaVbRQiR2731HVyrh8rg7s99nMEg5L6j92JwsOyYYQ9sKfAsCFPItGGQGCoeRJ0My07Z2hqB9kYdW0h6GyGpZVMcDyjJPnqnDaVqboUEcPAILOyDU8fgTF79vGy35JraBrZjQ/3pJQdHWBRw0AUXLQxa29SkxKuUydKMnXZIQUnSXoyuGjzV5PFHlgG44ZIDqGLAuazCuB8wEUBQdijZUygzwLcuTvKZMnz1ZMpaE+arNrdfIQg8QaMiGK8KTq4YUnW+0u7g1/JKXzZIwA2gXEVOTlHZXmKsJ+k5ho2qQhs4605ybvJ6sH2q3Q5TWxu0T3yFA1/+UxqZRkhJdzAkjSJsFKnnY4dD4mecS510R/TxnCKIdP7O1OOHJkPqvNIuHQfXEljFCrYR6DDC9Lso3wOvAMN+rjxuOxjHAgzCDtgZpjheSsMNsITNx//iLwC46+UvB6CdGowwFBxoJYa5eagGgoVrvoVyrcaDDz7IQw89BEDn3Fn+zX/+bZJBn+/6ru/ibT/zdizX4eCevDf0zPYmvhHEroebdHFTiKOYHaUoDiNML0I4Br9YwLJdnjhxDIArrrsOGo2n7fuOifF1B1k4R22my9IiHMrKlAp1trIBK1G+GLhmIgQwNQLt2c4ytvCgMY1ojLNQzEF7c/UMiQhQ2kJmCalKCI1G6hSRZdjakCjY2B6B9vExXD8gqxYpdPsUk7P8g0tuQQrBR//iL9kauYu0Vh3KuovlZ/zW+z7IW/77L7HRXOe2227jh37yh/i1P/w1fvLHP8Bv/9ZH+b1P/hn/9fN/yT/9llwb5OG1dUpxiEksMqmxaiV0GbaOdems98nqHi4SX0Wcaub7u6s2gVUqIMsV6Pc4cFCwa7fAnJVsbRgWI8Wbvj3X1Pnow6cgKFE9/SRe9yzzZo1Wx2eYBIRG01DxaMiWKPpFdLhGMuiDMbha4vbWSWwolHaTGkP/KaBdqNE9yAyQwmbYdJhVJ9m1cYIoMig0QlrwInyah10IvNEc4hYwvQ6PtfL7wPzBOaxIY6IhJ/qT3LvZIE0MHLwcbvsmeNld8E3/AF5+F0/M3QpCsKchGKIQOkM2T2LcErJSx5gMP2mTJTaNkqJYy9scVld3SLMOJ0fU+ANTMzzhlbDXHexen6zqsX/GZ3ZMQNijT4JXrBMIJ08ydk5C2HzWfnmO4NbLJRNVwfGzhk8/oDm6pEmd4EKl/Txod7UkFRmiUqdRKpJpzVYrwkpS7DRjZWekHD+/h8wI3GSANbToBSlXHtiEwxNUq8Vn/QaAqavAnRzH2VFUOkN6iU3YqMDOJrLWYG507a0tLoNQRG4FbAvSFC95kfR4CTJNMFLS7uaaE4WZSczhy+DWO3n04Bu454Y76VozVMsK101pbK5y2YP3YoVtSqUhh+fhwAHD1NxBfvo3P01pbJKHvvJZvv0t30WUJFCqkI0WwTMTQ30CNwBbWyQb3TwvswtYSEScYlmGnW5ElqaUaiWK2GyJgNN2BUsK5ir58VpczIFpqQaDNnS2oDrOc+ovJKPcTwhBAZeQiJGjHJOTz3o7vpBcZhe40q4yZ0mmrVzEDuCer91DlmXsuuJK5hsBu3flFZyN1W2MZWGi4Tdc9DI6gSQmkpqImLpJ6CpNoZJPB5GJSPwyfnEvTu1ShOU+eyNRfr56vRo6HlKpCfq2JEXSOnn64tuiiLe//e3smrCpJw5HOzHKGBACyyuQJUNcXBJi6rZP5Pukg5dA+/9FcRz4ISHEMaBO3pt+IYwxD5PT4p8gB8lfftYWXjjeNRKpewz4CvAw8DngsucSojPGPA78CXCUnAb/Q+aiB+KHhRBHgb8YPd/+m/yQl0D73yIWFnLWw8rZVbTW4CdIlSf6z9XT3s5ihNEUpEVqCf7Lr7+XV970Kra22yQmxcVBewGONPjZgFN3vI4nbrydAIklQuJBhiV8yrOCVCsevrfPu971awD8yNvfwVd7CRO25JqRIW3VEnQyzV133YVt2zx87As89rUmgZJs7z3CcvVygshQe8aN/j/95i/yyJPHeO8XH0L2eiTNLSjUcGwLETapjoac6PaxtCEt+pRXNznTG/ly79p9AbSfWYRhZGBCU/cTRLrOUHXQYpVGSWPw2dJV+v0NLvEks7YkRXBNwcW/+jpojBN97dNsD9qMb3TYF5SYGCuTZhmbaxtkgY1nIAqHF4CpQhOFCUZrMiHZ3Mot3+Z9m2R+F7awcTvbVAKBKGjqkzcyPtZgcW2Dk5/9NL/z/r8E4J0/9+uEW0PGdpcZCMMAyQ0ll2onT3J+5md+BoDf++xXEXKbOb3G0qbhyaMlknN55VSriLKwMOR97VaqIIrpdQTb7dHvmh2jabl0REBnfZhbnAFV20ImJQ7SoE9CU0b8u3+XU4L/6BOfYCfMk0zHV9gOrJsGwvGRYR9blEh1hBcPR2J0zwZxxmjS3iJGx0i3mjMUTILq2di6j0KyfOokAButNVTm4bgp/W6eQAWOIEwNvdFid8UTxP0Uy8vY3ByB9rEGNtBf6yPHKriWItA9dLePM/l01WUuuw6m5uDY12EtB9hsb0BjEuSzb1EeNhRcFKC0hRvFlKdzcL+63Sbq91HDCFEbkrQ6LJ1ex3EdjkyP0WlMcWbXVdywzyLrCoI6GN1AGR/HWkebXHX+7kcMq92UuTG4bq+DbV1Mog4vCKIEzu2M+uiEjVcVaBzSIeDlIPrMYB9yu8XrDrTYc4Xg1IbmwQdgNVZMej69oIGaOkw18Wnc/T8pqmX8OKE4ovv3m12k0qSeizcMSZ5RdTJpF+GUESMKo5Du312lXSfILO9p39lpsri4SMkPEJ6P2+2gBiFZYxwQOWjvt/Oe4BElWVg+rSTG91JqVhljDB//eO6dfNddOXBsJ4ZzKuNEkrCZKKQUHDwksZXPy9+Yz4Hvfe97GQ6H/Nhv/TabO032X38D733vewlReGgOHsjvxYubm5QHId1KDSfs4RvQg4R+lhA0I0SUkAU2hWIRHJ9TJ/OWictvvvlp+50ZTcuEFFSeTE7taZKlsHJWcLA4Q0lYnBmsc1aHbJiIcmxwhCTVEbK5ieVXoVRjO6oxX8yroVvLSyg3QCmJzFKESWiaNAftKkNmFkYo1tdzDY7ZqUkKVg1dLeNGEaWtU8xO7uZlV1xLmqa8//3vZ9A3DHZsiukm/+SXfpcf/uVfItWKt731X/HOP/k0d37nXXz3D3wXb3zjd3HddW/gmjtfzk2338Y3veY1ABxdWqKQDJCJQ1unVGybdLaAneyg7AjRCHBTGy+JODPqtV+oTpDWPHSxlFOGjeHwpXBg3iLZENyznPGGN+QLoZ/+7OcI91+HPHQTxnPY03qIveEjLH71HKLdZHLpKBz7Gjz0OSYWH2fhxOcJv/qXsHYa1TuNZyRheZK6nSf6LaWILni2C1xbERFB5jDs2IzLdcpJjyTWudCrsDHop3lFPzPi0KAU+E6YC8s5HvS6PD6aNxYOzFLYUVgiouilbGwkHH9gjWb5KVZvUkKxzGJL4jkwU5IMjaLUOkumhtjjl2Dq46Az/LSDMTZFV1ObyLexvjIC7Sdztfq9U9OsO2VKT/gYp4NdL3J4IZ/Xo16LWCjGgvG8CrrxKHSWYf0hWLkfBttP27+CJ7jpUskdV0sm64IT5wwPrXgsLQ2IE0WRNraJsZXMRS4LBaZq+b1nrRnmbicaVnbya6E2v+eCR7tOBbXCGsWiZFCdIXgeIrIQsHBHHd8W1Jb79FNJp1HFdFp4ZY+Z2siu88wyCE1EEeM6kGQ4WUxqTA7Oniey0WtWkqCNoDXKRWq1ClJIosRmNUooNTe4dOVBilfME938cpJGnaljT7L3sUeYPHc/k83jVN0NLpk31OYP8bY/+BiV2gSf/Nzn+Y5f/A1ix7sA2pVJMY0pBIJawWDCmE6zg2sHCJ0hkgzp2qxv53PhxFQdOwVlQznaRniSOT8f10tLS2itKdby6nTU5zn72bNM02+nOCb/XAGfITGbm5pqTeCKmGRky/jMCITHhBQEQuGI/PNfvTtnMR+6+TZqdsq8zLBdj0FvQCdJ4Tw9/gXC6BQTDUmKJVJhqOuY2GhEyRB280U1hKBYOoi0n4cZMgLtW9s1PDGk0AgIyVBG0jp9BoDbX/9GHN/nj//4j3nf5+5mPHBpdTVLo4U/1yui4hAbh8QkNByPOCiRJuFLtm//90RmjPk+Y8ylxph/YIwJAYwxrzTG3D96/APGmEPGmFcbY77dGPO+0fN7jDHbo8f3G2NeOXr8PmPMvxw9/nZjzBFjzBXGmH9t8mgaY254HiE6jDG/YIzZb4w5bIz5xFOef5kx5jJjzFXGmM/8TXf0JdD+t4hiscj4+DhpmrKz3sK4z660P5VZ1FMRGChKm8wS/NV/+wiLDz3Ipz75pXyFFhvcACkNQTZkJTM0NTRsizQLScKMoFwlkwnxEN79m79Nv9/lmmvu5Jh3M8eOQvFJh5PHYX3NUFCCrjJUazVe8YpXoLTiS/f9Fa1j+ekOu1CxU9zgIhD55Cc/yd335DfyUxvbSKWIdtah1MC1Lew4pNTR3B4EmO4AYQxZwaewvs2ZYX5jnN29BykEcWw4s2gQ42AXEhr2Ko7RVO1plMmYDrawSg4DMcZOOESEXV5R8ZkyKTcWXZASc/PL2FFtql95jH3r2zi2x665fDY7t7lFmGhKNuhhzHC0IqxQhL0EgaY7CMkyRaVQpOBaZLt2YQsXu9vEl1BuaLb6gtd9U94f+o9+7J0Mo4TXffu3MjX5Snw1YGJPkdVE4dox++spWQbhAO644w5uv/12Wv2QP7r/6+z3V7BnDaZRIlocsLHisLme276dj7SdEW0OUbh0R5WRsclp1o3mlOXT3DYwUmivWoKeMtQJsJGEpHzP93wP8zMznFxf50sf+Ugu1CJywePNrIAoVCHs4SubVErsOE8YYp4+aRmVkLaPooZrWMEM0p8kQ5HEBitxEKrPcrtHPFoY2G5tkCUujpsxiPJBXXBy9fgLdm+eIO4lWHbG9na+ul2fmKI/NMhuiDddxaWP39kmEzbe5K6nX1BSwlU3Q30cHrkHzp7KBZ8mZp7z+vOxMQUPJQzCEtj9OO8lBdZbPdqrPYTVxxQUpx8+A8Cew7vwwwFblSnO3vhKLpuSJH3w69BXEGXTuFJxtrPBFx7RDBPDFQcMEzWB9wzdkYmqoFEWLG0mGAMSG68CRrhkQ8Avsf4wPL65H78guX7PaSanBDOHDGs7imNnDJO2QMgMWZiktlYgswKk2qKWdCnVczG6dquFEIrY83Gjp1fajc7Q2QDpXtQsENL5O7N8i1WGTFNSIfiFf/8j3HTTTRQQJH7ARGuN2EjSWi0HO2E3/1esPQ20D2SILyWBCDh+/DhnlpcZKxa54Y5cBHI1zvDamzS2z/HEMCPRhulpqPsWl47E197//vfz/f/wH/Lo0lnqU1P8xz/+IL7v0yfDRXPo8B4AntxYp9QfMqjVod2hFNjIKEGGQ4JeDCpBuw6Vks9mJ6TV3qbk+ixcccXT9rtFCtkWJWFTssex/ZCx6Ygzpw3KrTArAsaHMU+aPimG6sh+KE67WJ0W9vgeVs7GDFeXWPDzCvzm0hL4PlkmsAxoldLUGdZIuFImEmMpNkYe7fOzM/iyiq5WcJSmfu4U/Yk9fM9rXgvAu9/9B5w7a9hcPctb/8m/5QN/9Xl8x+Nd3/Gj/H9+/dc5HVk0sbCxqVah2zUMdEYgLK646ur8eC0v4ZERDDJCZShJQbtUZNd8k+JuTRIUcJTAyyKWR7T9qfo4Z2ZjVot2ThkOBwghuPwKuKxu8+SaopVOc+ONNzIcDvn0pz9NML6X7tXXk5XL1Pwt4pP3Y61tUWquQBSC61NcuIT+xC4GaYLaPoVKmnjFfaS2gydSJNBWisgYbEArgeWOLKYGNirUFO0BjtFEmSY1CiFHIPIFqu3DHC/g2yFYF0H78fVRO8C+OSqDBDsbMF2OuaPwNSyhubczy6OLGjXyQ8+UYbWrqRQFVUeQxB383irD8iR1bxJTm8ToDDfuYoyFa8HE9Ej0baVJZIYX7N4mF/YQrtrIHYnv9hmfKl9YMOwOtlGFEmMigM3HIWrDxGUwfjhn2Gw8Aiv3QX/zwgIwQLUouPGw5FVXGRrVlOHyCY5+8YtsH32UCc5gZ4IMEJ7DZL2WX5s7IV6St/2dbecHqjG3QCwkQZqglaLudxBjcxjLpvAC8khOyWbi0jpzYY9uy6JTq5OQUlUxMyNb3NVTS1iWIqKUe7VnCU6ag7L4BSjy6eg1mcYYBK0RY61eq2Ah2W67hNEWtzzwKSYaFvbLX4/edx2tPQdJvBrJiRZtrakkLcTm40x3v0QlO83U4Uv5mV/6BI1qlY/f+yBv+Z7vZRD3LnyvqlZAWtQK+Zy4fXqTklPARBlSGyzXYa3ZBmBiooqtDLHn52KWns1YMqQ23iCOY9bX1ynULu7TU4spRsP2E/Don6VsPgLRms3GxgbH7nmUj3/gL/j99/4iv/lffpBX3HEHr73rLporTxWsHh1/HDJSFAoXFyUN939xJEJ3y+1Ut5cYe+JhGuMjC75mH9KYOH3h9jp0gk6GqKCI8jwqekgmDFmgyRLoJH18fGzxAtJZUR8tPdqtMpXCELsQEOoMYwRrT+Ztba+rVvhH//SfAfAL/+5HOVlKOZFq/mSrywO9GOkEOGmIxiUlpWBcdLFEojWm/2wWykvxUvyfHC+B9r9l7N6d93+tnt3AuCnSQJKCtARCPJ0eP1QRUoNvOWRCsbGcW7s88uATpCbFEhLhldDCMK4jltOUYQZjlqbbTSFJqY1XSU3K6rlt/vwjvwfA237xZygtGF457uIpydKS4eGHNCcfNsSZoa8Mb3rTmwC47+jH2D4lMCGkXUG1dDG5T5KEf/Nv/s2Fv7dbbcIoIdtZg2IVz7Nx0yHNZsyMbZP2B1hSkwQ+hc0WiyNl+V0jEbpTJ/OefjEdUfJOU7Qyxp09lOwJStYYVbeNXx6g5AzNMCPpbrLfdXh12KMxUuRtBikr1x5iammDwuISzB9m73w+wa80txkOMwquQcQp26O+do1iOEiQaLa22wBMVarowMUqFrGKDZxuh4CMQlUxlJqbb8372jv9ENd1+P6f/nFaO5qp6gCrUmYtTpkr7eBVToMzoN3OqWs/+7M/C8Dvf/5e6D1JqiPcy0vs3pWglMWZE0NOfV6QhjDsGTZOKLx0iD/u0BwlwfXJWYSMcacFvUjTWs0TjqotMUBPGYo4DEhxXZd/+y//JQAf/e3fpjmiyJca0I5MPtPHMX6cEAVVRNxHZgmxuQjaddIlaT2CSfs4lUM45b0IkYvSJBFYiYNM+xzbWLvwmY2dLZLYx7UVw0ihlSFwBMMsV44HCIQhVRGWrdlqjvofp6dpthOCdIiplwhED7mzhXYLlKrP6MuAfLXrupflwk6P5VZejD/H+wAfCyvwUTI/F1Y/oTaTZzobrR79tRY66KKKPse/nlPuD16+H9Nps+WVmfNd0m5+6wsaMMgM2hSwVZ3HV7dw3YhXXCkpVTQSgfPMPjzyanucZbR7dv4bXBCuTTaE7TNFTn7FMKxWKF8+hnP2FIEEZ8xQP2To9wydUxIhM+zFFdzNbdKbbsPIFJ8hpVHi3Gp3kDoj9TzsMHpa8vrUfvYL8XdIj0/SEKE0nVjT63bY2tpia/EYQ7/EeHONrlckcQT4RWit5yCueNHuLUltUq9PYNkEFPirv/orAF535AhWrUaiDcuxZv/Jh7nt0a+QbZzj+CBDCMGBOUl1/5VcdulVNJtN/vTDH6bguXzff/rPXD0/R2wUmVHYSrPnQG5V9OTGOuVwSFRrkMUpBRlRSDKyZkIxS5BJRFKuULMEjzyZJ7aHp2YQz6DGr6sdhAmZtGYoWxMIIZne2yTLYHnFQzoeB4eGceFSwKI4EtNL20tII4jdOTYfPYq0LearOeNiY3kVHTioVCK0QauUbZ3lSa8GItBCsTXyaN81twtH+JhaDac7wB906c7M8sY7X0WjUufYsaO861d/kR/+1zdy9MQSe3bP8af/+J28+ZvfxMrQAJoeEguLai1nD/QjQ4DFwSNHcCyLtY114iyi2I+JM0MgBU2/gF8DUzBEboCf2bhpzMqozag8VkGUfNaKTi5I1s9BjBCC11xlUy4J7j6RcfONeVvWxz72MSwhcSt10kqVk4cu4dzlL+eRyu0wewSueRVcdgv2oetJZq8glAa9/STSqRIU8rkkIqRqWbS1ZmgMvpTEmUE4IRJJvyuxBkM832CjyUzGoKdhBBTMiwDtrh2BtDBpQhYnLJ7L5+dds7vxVIgUCq9ap7T6OIcmu8xeMsWpVcMXHjZ0BoatNvSVYaoscARYOyfIpCBu7KKMhyw3MLaFM9zGGBujYdeIIbKx1ibRQ06fyL+zuucw1VMW/UJC4CvGpi9e3/3BNn6xhrtzCsJtGDsI5WmozMH8TTBxaV4t2HwMVu6F/kaeiPQ3YONRKttfZn91jX2lFkFjkrVehTiNsc/bvlkWY41cCHO1FeIpjdBwtp3T46szCygMQZpgyFleqjaJhcR7IWAGTB4aY64UopuKZbtBiqIy6DAxmR+H1cUlbKHoUQTXgSQZea/zghT59HzLRJYggHaYL+Y0GhUEedvGZY9/iIqOKN31bdDYizM5S1apwOQYWeyRPH6OnclLWB67HK/SYEwvYamQQ3uu4kM/9U7qlTIf+9jH+Kff9y9QWf5bMqGgVMVJhvgFl3Bzi6pVYn8isbTG9lw2dnLAONGoIKSNsh2U62O5FsVOm8mFvEVicXGR0siy3HagWM/XXFqn4fEPwdLdcGrrId721jex/8A009PTvPqWV/Lj3/eved8f/Ac+8IHf44tf/Rqfv/cBfuNXfulZx8jBYWRChys8IpPwyFdzzaArbryZ4IlHKdx9L1PVfKytNUOEVgyTF7Z9MyoegfYS2vUo6/z9sWfQMqM3jCiK0gtug7hPt18iMZJyECF9j4FR2MJi+WQO2q+cmuTXrriM2ZkZTj/0APorH+Zwu0BvYPjycMgZ7SG0JktGTByhoDBGioKw/cLf/1L8Hx/GmDPGmCu+8Tv/fsRLoP1vGbtGfrkbZ7fATbFHQF3rnCL/1Ep7bGKkNvhuwE6zybCfU7iOf/0o3RHFx3MLKGCCiE5miJVh0spo9RIspRibrJCQ8KkPf4I4DnnV6+7Cv/Zabpp1ePUVDjffKnjVnYJdNwzpjm/R6SnaSl+gK37pnk+SqYjOMYtiYlGoX1xF/a3f+i2OHz/O7ukpJkcKsqf6EXptFRA4QRlfhTT7Q0KlEWGIJSG2XYKdzgW7t3179zLoG86dNUwvpPTkGap+iu1MU5U1AMr2FDXHIXDWKVar9JRPs7mKEIKSvjg5n8zOkU7P0ChWYacLiWLfXA7i1nY2GcSCogeOStho5UBFGUUaRlgWbGzmFMfpQgnpSertc9hBgNPtE4gE4xi8Ouzb9xo8N6/KvPVt/whdWsD2utRrBh0U2Ep7FFF4nsRunKHZy8HSq171Km675mpa4ZA//9KXKYerHI8C6hXJxHRGY3fEsCvYfNhi5QQ4OqZYTMFz2N7IKzmFqXnG/U3G57fIKmssnztNpLpURldlV2mKuISkaGN467/8l9QLBY4+/DB/fXeuqlqoG2KhyewJEBKv2ybz62RC40cDwpwlRBaukbQfByFxGkew/PELxzoZ2b3ZiYBsyJObyxde29jZIQktHDtDWYpoAIENUWboxFB0BWkoQMY4UrHdHFEpp2aIdnrUCpJ2UKUqe5hWl3RymoJ8tko7kPvgXv/y3JqoVM4B/HOEj41TcFBCoqSBMGNsKt+fjVYPPdxB+DEqmOKJrz8OwMHL9tNvNWkHNfZ7NsPRAnxQh15mUNqweHISW1ocOdyiFAhispyK/xwxVReUihlrOxI9qrjZRZdwG1YeLhJPGKr7PYKDM+h+i8lOk3ZmkBOa2UnJYD1msKawH3sCpmcpHb4J4Qh8Qkq1/FppdjsYpch8HzkckjzlpmKSLiAu9rMzqrQb9aKser5RxGmEzDLayUXQ8+Sj9yEsh6DbpluepJcNoVDKK6YApSomGyKsgJ2uQnoRZdvDFvYF0H7XbbeBEDRjzZZSzCd9DgSCI49+la+s7JAZw2xD4pXhjlf9AABSSn7sLW9h8robGbMt1nsZa6sZK6ckgX8VAKfWNylGAwaNMQwCK25Ry1KCJKHiJ5hhjBlr4GrDQ8dzO69LF3Y/zbZLG00zXaEsCpTtCaSwCWQVEXSYmNYsnTFkbhk57HOlrHGTbCAYWf/tnMWyipw4HlIQTdyxMWrFgKJjM+gNQHXJjIPMFOiE2GgsoxDGQJTRSoYkw5hS4DMxPokQAieo4nZDnGRIZ3oa6dZ48ytfBcAfvuen6XZbvObWq/ngn/83DpUnKSzUODfUCKkYGgulLWo1SG3FMIRAWDjVGgdHHtFH19fw+0OyTOJahrBQpqcVodHEbgHPSETUZ6vZzPtoZyapWi5hqcCADPoXlfQLtuSmAxZmVnHkSG43+rGP/QVxnOKXx0Ao7DjBXBUgnCpbJxJUfHFsFcqT9OwAkyQ4wQKudHFxCU1IXcoL9HhfCBIF2CE+AWEfXNPjv33wj3jnr/4BKkvo9TPEeRuXF+jLDbv5WqEjYrAsTK/P2W5IfzCgUK4wXRvHi5sor4g3cwB6CquguXJyg1sukySZ4QsPa44uG2JpmC8L6K2i4w4bY7PUZBkpBFahjnFsZLiFMTZZBvsumcWSknZ7wMrKBq2dHoHn4QX7sLsWTrFPyc/wRgt4PTXEhD1qOoXeKtT2QPUp4sRCQnkmB++TlwMir8YvfTH/P+5CaRrmjuA29nDJkUOU6zWETnFSORr/F73aV5rDvM0sU6yM8pXKxALSNnhxgpIWrusRSUPwIkyInNIYMzOCsbjPYq9A5vo4W5v4E2PUyhXSOKG/vUkfH+O6YAxWPESMGBbPF+kIjMokQWDojOxGx+oFUILifR+mGK5z4rrXUtp3KUZDFo4T1Ru4aURz4Qr0IGTsvrvZskvosUM4vqAcblDxNAcqk/z1+95NtVrl4x/9ND/8Az9JlmW5GF21Ab0mxakxpNqhuV7FJHswRuD4Dusjy9npWgVj2QjHQjkewrNxVMb4VL7QvLi4iF/Mx2JlHLpnBcc+Aqf/Z35a978GPvnouzl16gniOKLRaHDttddy62u/mTf9w7fx87/yK/zz78495t/9gQ+SNp/eJuGKi73kLi7HTj5BHMUsHL6Ey60U1tewspTZUTvPudYAYRRRPHjBc2riAQqNCgpoz8dTIZaAvqtQwYBkmItKPv8GDMR9ms0yxtUUrAhZ8BigcZCceTJv0bvsumspBQG/NCpA/drP/STXK8OlLR/HTVnDQxtDFuWLPAkJbmGMDI15CbS/FH/P4iXQ/reM85X29eUtjJ1hCUYe3Tlj9HxPuwaUTrAweLbP8tLShW0sPfI4q2muSOq5JZQQ1PWQYWpIFYzLlOFgiHQcCoUKKQlHHzwKwJ47XkPdEtxQuthLZlkCZyyh6EE7C+lkhl27dnHttdcyGAxYan+G/Vs+l/Z8gpGI2fr6Ou94xzsA+Nff+Z3M7T8MwIleiOh1SVpbWMUagYroDiM2dxRe0kd4DlYUYQ8TFkc9j4f27eXJJ8FyUszsaRQZ1WAGVxZxhcWWiRlimPXn8KwYp9Eis8fZWtt42rFdydoMzIB9WzH2/v2w+xB8/QEOTeeV9nPbWwwHKYXAxtEpW93RzTpV6DhCWrC1kVeHZoMAfIkMCliWwuoP8VSELTWFCY0lS/zI9/8/3Hn9VfzgP/85oqFhbG4HKWDbDYAeRa2Z9A5S8H1aaplQtRFC8DM/nqu6f+AzX6PeWeTJrAgaRKrxagnXfbNm96xFsQozM7k3cV9Bv93FsSzk2AyiELFTKpFYHv32JuvDM0TqOJ5YpZl2CIyFxhCRUapU+KGR3dN//dVfBUDWRpU+MQGOj9PawUiLzCtTjGNiHdLrPELWX0S6Ndz6lUj76RNpTEo2FAQmJlNwavWiautms00WCixLo62EYRcKjsAY2BwYyu6oYiVjhNBstfJE3hvfhRv2qRUlbdelNtgiVQZ3cv45RXYuhF+A214LN9zx/G/BxivaGClQQqBTwdREXiHa7PSwkxDpWCi7xuMP55X2Q4f3Mej2GHoN5nzJsJW7pLll6KeajW2IE5tD00WENaoYoJ5FjX9qzI4rothhJR9q2CUXnUFpbxm1xzBRs3F278KgmVxbopNp1hLNlfMWC/NDig+fotny4cbbKNl1jOdSMAOK573auz0crUgCH6EUaRTxeBqSGYNOu0in/DQ13ouU4P/1ansSR4hM034KsDr28H34pIg0JfTH6WcK441spKQEvzTyaPdZ6SVYdsKYW6LX63H33XcjhOC1r341AMdDhU4TaqLD+sFJphyL2j1f4OF2SElKJqfg1jv/H970xu/jf7zzZzlw+eWkVoXH7xV89aGUfjelFlhMzlyHbUlWtlvodpvh+DhaC3Szy6USru0KLCtGpQq/VsLC4rHj+fi+4pLDT9vnjWwVRcqknYOhjokpWmMYo5nd3yLLYL1TvlCiPT+OUxUiWtts9cdww0Wm9k/QqlyGdjwWSvnxGTaXSPDQqcIZnR9LpQilMbHiXDdvZ5luVHELecXLFQEiHCIKNqomCdMSP/imVyKlRAjBz/3oD/PHv/yjGBFgMGTzVcIMDozWurYjiecLZFExHIKPBCm5fE8+dz22vkaQhMjUwhGGYZArTQ+kRNouFpLV9RW0NoyP1RHFCpfpCnahREtkeV/7U2J/waY0Ca9+yyXs2nWQVmuH3/vdLzGI60hhcJMBkWXYfbBIEsHifRdFROvlOh1/gdieQozUzQuiSMSQqhTExtDSOrcjFQnSShGZy7APnu7w0b/6BF++52E66+fodPRFC8RvUGkPymDpdNTm0efRzXxBdWZ2P64Dbq+FDkoUjAGrArsOwvZxppwt7rhaMlUX9EKD5UPdTqF5ipYfEJfGGBvZstleFeO7yLAF2kZlMDkdUBud5y9+4VEA5sYnccMGaUMwbrfxHZAjwc7t4Q5e2KWkwtwIvbHvuXdKCChNwdwNMHUE6ntg9lpYuDWn0dem8/dEQwolD2nAilOMgcyklGfysb/WGYDWrG0OyLRhvFLJ5w3b4A8TjCvwg4CQlMKLMdZyi5RKBRZkn46WbGXjuBsbiIbPdD0Hr1vLSwyUBUEBg8HK0hFb7PkXIc/3tEuVAOICaJ+o2yQPPYi9eYqHLp/BeloAAQAASURBVL0R59JrAWgtwvKnBM36XqxoSDG2Wbr2CGJ7lcYjD9CVDmm5wfhwi5LsEg4M1918K3/1qb+kXCnxsQ9/nHf++K+jTAy1ceh3KUw08O0+q6cihl0fy0qxHI/NnXwsTVVKGGyUbTEUPtK1cFXM5ES+0Ly4uIgQgoWDkK3CyU+CSmDPHXDZd+TrM5/5dN76+v53fZ6dnR3uued+fuzn3scP/dKP883f8i18/xtfz679B1jfafLh3/+dpx0jZ3R+BAIbm0cfzp1aDtx0GzPLj2HQ2NmAhZE1zFprgNAZSRzyghF2UULieBWM62GlQ3wbekJjygOy0MIX/vN/PhmSJZpWu0ih3gM0WeAQG02y1aHf7VLyA3YfPgSvex3fd801XH/gAKurq3zwE/+R2kpAWQhajstAGdI4QSBITELZrZI6DlFv54X34aV4Kf4Pi28I2oUQk0KIbxNC/JAQ4h8LIW4U4gXMG/8viaeD9hRLCHQGWQaWcxG0RwIMGY422NLn7PJFS76w0+GJ0+dISQksH2VJymZIpsFFYHUTtBpgey6BXyYxCccezif4PVdfxyuqHtYzANCAlFpVkIkhG1GeHL7xjTld8Yv3f5RKUTI+LpFWPtn9xE/8BL1ej9feeSc3HTnCwqHcD/14L0L2esTNDcQItKcyYulMipuG4Nl4W22UUqw2m0jLYrq2i83NmImDi7RUSqZ2UfJdCjh0TMqjusM9usmykJTsMkO5Rbk2Tq83pB/mlfHMaE6qFYJUM7vZhald8MrXgdJcNpq8l7a3MVGGkhYFmdIa5vvZ7KTYOkI4kp31/GY953vgO0jPQzgKOzX4/RaOpZFlg+PC9/3AL/CpP/sQre0JasLHCvIy7KrlYYs+hdTCEg7jzl7ifsBOvMxANbnzTd/OtYf20Q4jHvvLD9O1AnZCQZBkxMZgWTE3HvS45ZCPpSJQms1RojvdqNNyPaQAq1wmtavEa9NEG7sp2lUKok8nWyZMTmP0kMFIUO5ffdd34TkOX/nEJ1hcXEQXDNKCNKxAUMEe9HHiiCio4RmboHmawfAsdnEBp3rJxQrUU+K8cnzR7ZEkcGrpxIXXskzR2toBAdgxw/7Tbd8qnmDYBSFjjISdkeWOPbFA3YTYlmTgQam9TWI5+GMLOV3zzN3QXsqb9p4ZjnvRU/g5whKSoOChhMS4BpXA7FgOdNc7Pex0gGWV6PZanF1axwtc9k/UGSQKx52gEAiiFvi1PH89sWGIB3D5HkGjVCDVEdooYhTuC4D2SjGj6DqcOGcwxlA5VKO4b4zazTZhDAsTAumVyKYa1NfPkhnDQBumXcme+CHKaZsTxdtZ2ghwpY/2PXwdUazmlO1Wr4+VaKLAx9OwO1EsqojPx03aSefp1HjI6fHwdyJGlyYRQim68cVtPfzAffg6InZ8StpmYDKMN2JNFKsYAUbHCDtgPeojhGHGL/OZz3yGNE25ee9exvblYONomFGN+hQdTX96nPjmm2kMejS/+iUSBUFBML67yL/6of/G1TNXc45J0g0LncLUAcWhA5pd8wLLHmfP/DTGGNbOLKKKVZTrolpd9sxLbjqYkHR6KGFTrBSwkRw9ntu9XXn1kYv7qyO2sg2QZaasGp2sy7nNr9JPurgyAK/F1LTg3E6JLE4gvkgdTbvniDoxYTtCBx73tA/x2GaFoVtkdzE/Pr2tJYbSQ2QKaXK/dqlSMAYTKVa7bQBm6lV8V8HaQziDBJEpROBgSjGDqMRVh+b5H//1z/mLP7+Xn/7H34VTrJNuDrB8xXYtHw+XVAwWhvUonxu8qmEY5vR4gKsOHgDgsdVVyskAkdhY0pA5Lj3bpuv6eFoi0pQz27lA2sxEA5cqZd9iThboFn2ikb3h+djlWdjAujC85S1vAuDLX/kYT55o0G0KrBEYn5oq05iB5lLIxmI+D02ULDYKV9FTfi5qCBREAY2mYOVjMDUGywiwQ2wpiJoeSkF/5xRplk+4/eY6nY5GnKdrm+e/FsJuDtqlysBxML0Oj49A+/z4YTwrREQRolSH7VZ+s7j85fmNY+soXtbipksl1xwWTDRgOlwk1RlnG7P4WFTIz71r+aggQPQ7SMsmzWBqwqUx4kR/6XO5KOJMbRopG1TmYcJqIu0ctGtjiLZPUBm0kfV5GD/0vPt0IYSA4gTU91280UHOYgIYhhSKAZYQWDJGp5JUK2rzeVvCejvEKM25zXwRdmpigjQTCFsRDDMyF/ySR4p+caAd8GvjzKkBuqRouuNkqz2cQDBdGvVSnznFMFPglwGDzFKs9IVt3xLOOwqkCCTdkb7OnvYG5sQpjleO8MSRWzhQzX/jcAckgrY1jw485jdX2Znfy/ol85RPP0n45DHi+jSezvCHi6gMBrLMNddfyXs//JsAfOQDf0miohy0a42TWARliNub9HaG2JbCEg4bW6NKe6OCMYKudjgbVtCuhacSJhs1IAft3XOwda9AZIJdt8MVb8m7H4SAkydPcub0GSrVGlfM3Q7A9hbYkUdQVbQ2zlKvVPjH//pHAPjV9/x3TLd14RhZ2EgkLrny/GNfz3PIqy6/lPKwjalWkLbDHj8fI2vbPaTRJOk3oMdHXTIBjlfB8kqAokxMO9VY9RDTfoEqO0DUo9eElDJBKR9nkW+TGUN4Kmf77Z6YwC6VYHISeeed/Ma35e2Mv/e+dxGtrOH1LAg8BtqQJcNcjI6Ehlsg8QLC3ks97S/F3694XvAthLhDCPEp4OPknnYzwGXATwGPCiHeIYSoPN/n/77HeXr8+rlNjJ1hP6XSbtkX9W9iaTBoHC0wjs3y4rmnbef4Q0fZSCNK0iN1XDwzxEMQGEnajBHEBL4NfpFmp8m5M6ewHJdvu/EaqvbTT582htCk7CrZuMCJEX3xfF/7X/zFX3DFKxWX3Jq//7777uO9730vjuPwk29/OwrN7BX5SvuJVogcDEh3NiCo4AmNET22mgmBGaIdF2+7zbkwtxVrzM7T2TA4E4vUxzNWBgvMF4pEZAQ4LOoBMhXMEbBihuwEAW2TUJrPj9vy2dzy5qTuonSX/Zt5SwELh6FWh737uTTIqcBnt5vofkQkoCwUvTQjSwzbHYWTZRjXpjkC7TMFH1MOkLYP5TJOMsTrtJEipa8NMzOC1f44S/EBwgFcNhOgwh6RUaw6ULY0dpLTy2o1C9PagxqWaacr9GjxpjfkasynHn6UQgHOZUX8NCEZebU3pMMuU8BRA0SqWR/Z48026jSLuf5Bveijig7KG7K+VKYi5/DlYfp6AV94mGyb/qg3fXLvXr731vwE/tEHP8hAa/wi6JaEsTlE2CcYxkSOh7QLFESBQXUaVZh83gp3TIruOThiQBynnF06jRCC3QdysaSNzTUwEseP8kTXubidsicY9sH2EuJM0x4MkAIKkwtU0z6JU8C2I7x2k2R8Khfn2z6eZyPNU7D6QE7d/BtGOSiAJTC2QWeG+bFa/ltbfWSvjvQDTj78dQAOHjmACVO0gUIwhe/DsJWL0G11DE9uGuZrkv2zElfkSW2kB6QvUGk3xqDJ2DXh0AsNa00oHl5g/Jtv5Ny2QUqYHQNLeqi5CYrJkGAnZ5TM9JqIo4/AvjHKl+/j+DHDxqqFcT08HVGq5aC92e1jZ5rIL6CN4bLUcKtboZANWFIRjwrJ8CmLHuLvELTrNAJl6CUXt/X444+RqoyBV6KealKt6cnR8XmGcnxH97EsmPEqF6jxrz9yBMbGCJVhKVIcSAcoDKpcJtldY3jptdRWlznz4NcxxjCxx5Clmo3lHfqNCjfstbn95ZLSlKIkcyFGg8O+3fm9eHFtDSuKSCsNTKsLtsZ2FWmrR2Y7VKoBwsCJk08AcOUN113Yt062xgBNyZrCExZJfwV/0CRau5dSmJGZmIX9fWJRZmcbCC9WmbdOn6G90mVdlzjrXkq56FKt2gy8GruCHLh1VhcZSh+ZKjBgqwihUpS2sEg528qTy5l6Dd+KSPtN7Ifvx/g+pl7CtnbohyUMhu9505W8/luuh34LuzyG3uzhNCRLJmDME3iWoiQVa2E+PzhVhY4lKsmv2ysO56Dv6MY6xTRERw6ukDhCszI+z1pjmsBYWEnE4ohqO9toUFB1PA9mhI8qFmn1nk7DtYVgwbM4E2e8YbRQfO99H+PSIwWU9si2Y3a2DGW/RGMO6vWQUw/AoGPwbUGp4NKmBP0ccAQU8uqgGHL+jnMetPvCodcWaJWxtXGRGdTb3qbX/8aVdpUZ4hAKFZA6Bdsjbu/w5KgPeWF2PwXdhVQhG7OwtgK1BpQqeQXbKcDGYxB38UtQNR2q8QbD6hxd12JCBMjR/dbFIiuVMWEPx5KoDBpFQ6Oas4Pu+VI+HmfLM1hzDQ7tEnhZiG0LZFChM1yjdPYJXLsEe27IOdN/2zi/GBoNKZd9LCmRMoTMJjWK2lxejFhrDxCpYWkjp0hPzkyTZhLX0shhirTBqzij8/SN6fEAxXqDihaUTY+d8XHSRBGoiJlyDtrXzpwBoUi9ct6DrTIKWfqCPe2ZMaAUQuscGEd529/84gotd4KvXfFayoFgxs3n8PNtUZ14lqQUMLG+iMyKnLtyH4OZCdQD90AUge0T9E6DELTjMpmJuOm265ibm6PVbPPE8SegkTP/nGGGVxI41iY6C5G2QEiLjc3RtVMrYSyHTDp0rGlir4CnEybLeT5z8vgipz6dr61c9uZcW/Cpp/iTn/wkAC+78UoII4yBrU3wtEdsZbjtVcZnZvnBH/h+irU6Dzx5iq98+I8vfF4IgYePLwKUUjzxUN4ydutkFbdcAMdC2C577BFo3+wgtSKNXxi062EXLQSeX8FySxgUZR0xMAOsgiLbeeF+dh31aa9DYaKIY0c4EvqOTYqhdypnpB4Yq4Ma5Qd793Lb930fb7n+eqIo4t1/8hOIbYuaJxlaAa2wjydcEhMz5ngkfokkbD9NlPGleCn+T48XuvvfBbx1JGn/z4wxP2WM+TFjzBuAq8g9777pf8uv/P/DqO7Ke7/Wljcw1vPT41Np0Gg8JVCW5MzpHJy67sin88EnOJNElIVL5njIbMC+1Gc+8Ui6Q6SV4UmP1JU8dM9jGGNYOHwFl1efbaERkqJ1h5K9Sj1zWM8GpEZx5MgR9uzZw+bmJl9/+F4cT6C15od/+IcB+JEf+REmG+NokXH4ynwCPbWyCa6DOrsExSqOgAJdIm9IoGMyyybYanN21PM6sWs3hnWCoiYze4hUgblibsOmgO0sIf5EgeJ9ZW6SDebdAltWkTP1EOkqNtbXGdqGVbVJPTU0tjowPpdnVQClMjUvYGysQpoptlc2CYVFWWakTkp3G1phhk9CZllsr+UT5kLRQ5eLbPSLRPXdWHGE3+0RyISB0UzMGoyGJ08YqjXBgUaAPQjpOIaOiKhaEtKRlV4NQKLbuwmsKptqlX3X5kJYy4tnKRUz+laJrJeQGAMjvYIwNdjZEJMp1sM8CZqtNxiUbaQUlGwbq+yg3JAkhs0NqDoWHVWg4czjGENHjcTh6nV+9M47kZbF5//n/+TkmTP4BUjbAt2YA5VSaLeJRIZbv5zK2K3gVmib515xNsYQ67zSLlWPU9vbaKWY2TfLrv0jNsnOOXQqsUsJw16uHn8+Kl5OM3X8mM3tHsbAWBDgex7ecEAYVCgMN7DjhHR8ktLWacDA7PV58quSHLjvnHy6EMQ3iKLjkLkOSliYVLEwsknbanepVG1EzeX01x8G4JJrDpH1I2wtsL0JbAnpAGTZcN8TCmMbrt6dJyyOzJPaUI9o/s+TkGrycT9dtykFcOJcnhhobTi3bZhpCBxbIKVPNjVG4NhUzi3hq5Ta/V9Ge5LugUMcuQrqY4LHHjUkVoCnEwrn6fH9PjLNBR8zYzCDPg1pc73RTFsea5bPF5IOi1m+cHaRHv+/riCv0hgyTS+6uC2tNcfXtwmdAvVEAYZ1KfKWhtrU05XjxQDHuLjCvdjPfuWVUK9zOsoVy/erHgmasFTAOBEbey5l+sABgqMPMzx7DgLDzVd0acxmMF1goWZhjGFAho/m3JbFiW3B/HxeOT65sUlh0CeujmO1ewy1QpsU3eljfB/f89jYadHptSl7AbOXXpqfa9Um1D2GVo1xmd9X9WCTzA3oeAFyZxmvtQr+No1dVZo7EDY7LG8aHj1XZ/OxY2TCZebSA7z8hjq3Xi6Zm4CuP8GuIAcLO2dOoRwfkeSg3ckGiCxDpzaQcm4zvz7n6zX6keKhJY/lr5ykrR06uLR3tnmk77K0alha6tFr9iFNyMwkdqeLmi2xGRvmA0lGRlkohplFM9HYJYWdWXRyBj5XjBTzn1xdpZT0UVG+8BJYcHx6D+emduFpiRUNWWzmlec9tTGEVcTzwBMWpUqDbq/1LDuuvb5FYmDhmhuYmprizJkztNqPsutgnSpdtjahYLwcJFwSIm144is5iJ4qCTZFFd3LQbsUEh+fmCGVkfWjUAbsISVZpNPJKJg+Zzcvtlf1trcYJioX/UM+b097NGrHD0p6VGn32GlvcHInBwh79+7GjTpYSmHXZmF7E2ZGVm+WA9NX5gsD64/QD/tM958k8ALWa+MYDNNcrDI6SLJKBZNGBCImzSSBpRgfy2nh/V4OjnZVF6geKLJ/Bqx0iCgGyCQm3ngImWrcsYPgPT8D6UVFMPr8cIhf8HFtATrByqy8nW0+Z8Ksd/oM5SRnt/L5amx2nlRJXJMi4wzjO8jgPGh/cZX2cqNORTpUwz7rM3W0knhJn4VGThNfWcxt31K/BijIMgpp9MJCdBhEopEookwxTDNsKfDdgMUD16LrglIAk86o0t7MT5seFuk1Jii2t3CaNkoY1m++im6pTOner2CK08j+BrLg0upIMhNjS5fbb88r3V/78r2Y2gQIgdNuQ6lCrbaNlEMsGxKtae50EFIwVQyIhY2xXLpyhki42BbMlHLq+MnjizgFOHgX2M8h9/KpT30KgFfffAUFeZK4b9jaMkzXXcKdDo6JGZueZbJc4q5//FYA/tMfvg+eUm2fk/NMiEkeePhBwn7I7MI8e4seHNiL3esjLJu9o6lufb2FNBqVPD893hiDirooL8CXAY5XIkPTUAnaDTGOwHQKpPHzA+b2mR5x6jN/hQNZiiMFLccmQ7J96gwAlzRKkDzFyu7KK/mVf//v8WybT9/9AY594X4Cy2Acn52wjzNSkC85kDgV0iyCbyCo91L8/Q0hxB8IIS77W372DUKIt7/A61cLIe56ntfGhBCfE0L0hRD/5RmvXTfyfD8phPhN8YL9os+O5wXtxph/Z4xZfp7XMmPMR40xH/6bfNnfp4gW8mrYyvL6CLQbTAbZqNJ+HrQnEkDjIlCWxeLxHLRffW3e27l0/xMcW43wsMk8H2kSimhMTyLtEEtkOEEuQve1e3Ja0w3X3fCcVdMBKWk2pB8LDgQ+w0TzZNxDCHGh2v6xj30MgL/+67/ma1/7GtPT0/zUT/0Ug34P6Wj2Xp7T41bOrRAHHnrpDBRr2MKiaprEdh+PjMSxCbY7nBmpqc7s3kOmYnyrxNnQw5VQD/KDsG1S0pagvh2w8SiIps3tbo1aMk+ofPQU9KIVFss2luqysNnD1hIWDtPv5fZxjzxZYKfjsGc2r05sn1tlKHPQrp2YnU1Dd5jhihRjWWyPPI/nCw5Jochay+fLa7vJhE9xa5OAiEwoRBFK5fxYHjwMjrAohxnrvo0l+ow7JYQZVawcQbEEnbagbi+gZZm563bjWJKV7SZZcwmnVCbsJsTakKl8shgk4GQRZIq1fj4RztTHSQoaF0lNFnCrHpYOyQLN8lJu+5YayEyBkjXOQDWJdR/qdS6dmODl3/RNKKX4zf/4n6kVJUIL+kzkFYLmNhkKJSxsy6csKvRMj+w5Etj4vHJ86iDVgCfW8sWB3ZfuYXq0MLWxdQ6TSZxCzLAHngVyNPzKniDqg+1ErI3s3iaKBWxs3GGfnu9TbG2hbZti4CGjFjQOgBPk9M35m/Iezc5yrngcvjg6W8m2UK6LHt3BZgs52Npp9WjMZSSey4kH8+vlwFWHkf0Q3/XI3AomzFkpx7qGQQa7pwRVN9+QJWxs4eYes/C8lXY1OpaWcDg0L2n3DetNw0Yrt35cmMgPkCU9sC3k3Azj68v/X/b+O8iyLL/vAz/nXH+ff/nSZ3nbrqrt2MYYzgCDAQEsAgJILUGRlILQ7lIUN0LSH9rYWMUaKWIViqAisBIV5NI7CKIIAuQKhCEwBtPTMz3tTXX5ysxKb55/159z9o/7ynVXz/RghhQ16F9ERmXly3zvvvvuPed8z/f7+345f/k1zGhE9szjaOljWYKnnoZ6XTBMKlh5TG1qPnU4nGCnBVkYlOAomiKNfMS82+SzXouWsHmniHghHzGeDuc/DKbdFGnJtCcPxv68vdUlxyJIMhwpOFQpPPMTMLN4F7Qb4ZFZE3wT8tZbb7G5ucliu82TFy6AZXE1znEQdPIhXd9mIh0CW5DZMc6zn6Y206H20ov0ul2C/JAJhqJWoyolMQoNDPuK/Z6D58HM3HkAbm7vEKZj4kYHK82ZjEZkaYKepBStNm6heWe1vL7PzS0h6nW0UQyLbRJhg6wzIzwokjJpQcwzWDjHdnUOcxjRu/YaPZGyOnD4V18d8uo1jRwOmC92WX70FI8+eYLqNEZztiUYBgscmTLt+2u3UK6PKgRG59hFBEWByEBLw9ZuOV4da1RJ8Ij94zSLAn+5SagiKmmPgYStQcjt9TEvv3JAkhkG0QzeaMJ4LsAYWAklCkUNDQg2Io0IFa6R9Kdr+BNnzuG7Lgf9HqPDXYySWEriShgbRYHBVxZOnrA2ZdrPNBdQXoWbiSJRhk61gylydpP+A9fHsmvhCljLzV0T1N/8zd8krLdoqSG+UexuCnBDHCLOfaKUqd98DeYrgonbZDJJ7rYfhKJCSkJ9ehsak4LQVHTIOFJUijG39+6lXQwODyjQRENA2h/ItN8RSgRBjsCgbZfRcJ/V3fIknb94BKfXQzshrs6JdHEPtAPYPiw+CYDcehlPR/hzZ9gVKQLBLPc21YUQ6FoLU+SEekSRW4BiaenBSMvTJ8/z2AmLIhd4aoJVraH3LzG2DLa3iKy3Hvpevq/y/NJ/IonBcgk9B2FSHCXIUVitWeq+R1YoeqOEzf0+AM2jx0nQBCpF5Ap8B+HaU+f4D24hur+EtGjU27TiEUNfkAYzuFGflc40q/3WFsKKSJ06IBB5TlCkD0RdvrdyY3CzAoRhODXEbfoO8cIRxlWH2lxCy7axhYXKIBtD5zy4yqE7cwQ/j5jZ6GOMReSn3H70IiKJaacFZhzhV3P6PVOCduHx6U9/GoCXvvkqOvTB9WFwgKx0qFZ7uPYYx4Wtg0OMMTTbTWzXJ8lAeD5zsw0mdogtDQtOmTyy273NiS/lOA+JMk/TlK985SsAfOHHnsKVhxys9inyMjBG7fSo2BoR1uDmNX75l34Jy3H4Zy++zK2v/d69cy8EQgi+8vWvAnDx3Cn8epNstok1jsDxOOKXqH136xAjBTr5LkZ0pkCnEcoP8XBwpuqIpknAniCljzCSaPABf24M3dUxTr1Ccx7Ic2zHomsbLCRrV0sTuvPtGjgC8nsbCMd+/uf5z37hFwD4x7/ynxAPNfWwQpxMULp0ylciR7gNCp1jsu8RXfdR/ciWMeYvGmMu/RH/9p8bY/7f3+VXnqQktx9WCfB/A/6zhzz2PwC/DJyZfv3k93NcH6anvSmE+CtCiL863RX4FSHEr3w/L/KjWHMzHfwgYDwcMxoOsWyNVu/vac/R2DrHEjaTRLA9jXv7xS+UbMfmlXc57Oa89CIY28MUKZ7MUH0gnGDrgiBokJuM114pQcgXPvXcQ49pYjK6acy1kWGhriF1eWs4Rhl9t6/9N37jNxiNRvyNv/E3APiv/+v/mlqtRjwaQyg5PbxNe2kOlResIREHeyjLwxIWNTPCV2M8S5MIC284YTUv3+jy0eMoMhzbZW2iOVaRJCgSFGOjqK+FuK7E9mD9GwCCY04Ak1PMzXSo5QdMREYnK5A3+6yNlvj6S1W++Q3D1cuGQV5hNLE5MTvtXd7aZiIcPCEInYT1NU1iFXgiJ5eSgylztVjxKHwPIwK0W2etmEVu7OOJhEJoRkpz9jycOiNot8sFd32S0/UspIiYcxsPnONGQzAYGIQQFPYscvEUZxc7GAObr36bzmydLIMs1iRFCWKiTCNVDNqwPSyB7XxnEWPnhMrQ1i52zcXKItSsYdA3iKg8loHStK1FtLDZy9fJqxW6Y8FPfq50i/3df/R3mfTL9zqJaxDUcHvlQjuhZElbYpr7be7tvN+pjIIsnYL2bMS1vaks7bFzLK6UkTT7vS10LrC9jDyFIi8l8pYERxmKHGwnZuugBJUztQp+keAUGZFnUe3tkYcVWkmvzFir37f4lXZpkLT4NCBg53XYf7dk4L9LVR0b5fgYMozjEsSacKaF1oadbo/Edbn8eik7PXLhLOF4jB20ERLUBNZiw9gYTh0XBK6gat/bBHNkSKzLRcIHgXY97ZW1hMNKBwIPrmwYbu8bPAfmmtO3N3XKVytLPO4YHt1dQ507g+600Lpkf2xbcOFJKGQFWRRU6+Ufd0cTvCQlD0p3XBONMUahizHSrVMRFh93azzlVIjG27w73kEI64cE2jPMfUz78kKpurh0bZXY82ES4Vk2/SxCTxfVpogRwmaQFSiroC4qd1n2Lz/+OKLTYS9X7GSajrTwkgFRLSjzxC2b1I3oaYuTX/gCtpSMvvYV+ltbJLaFDAKqUjJBMYwM+weKVsWlVYfG/AUArm/vUokixs02AkF62GcSTxBRipmZwclz3rxRtiedP3aSt1YNL1zb4epmzhtbLVa3BG9fkrz25h6r2wXf+vosb3zD53cGHV7rPcW4H9GO/5BWx6aeDTla0zxrv0K1Imk++bl7fcNAzRZ0/WWW/XIlvrO+AZ5Pri3svGBWZ1haIyMDnmR7qgw62qiT4dAqYjpzHZoXztP2U06LHc4sGE4t1XjmyIQg7XJt12JvW+JrxX6jgmfBnCdQFLgamq5gI9ZkUtNw7btMu6zXeWR6b1+5dQ2MwSkcHHmvR9jVFr5KuD2Vix+tz5FUAl7sKt4dKhq1Ni6S3fdI5KUQHPMsbqeKn7lvznGrLTqTERdqgvU1g3YCyCJaC4Ijj8DOTaALqd9klJoH+toBQlmOpbmJAYHpByirIEwnbOzfY+JGByVonwwo+9o/ALTHU8VtEOQIo4kcwXh/n53uANtyOfPMHG53QOHXGEYHrNsFut158EmcEBYuEGtQ4SyEHbrE1PFw3iNhN40ZUAWBOiTPbLQpOLqycvdxz/U48+hpjsyWeNrTEVYlJMr7TGqz1BNKaf4Po/wA4un45odIkWHngtxolO8zXy9lzRvDmM2p8qC2cgIB+GmKyRVW6JI63z2f/WEVNDtU04LUiolrHZzxhKPzJQGydWsLKYckVg1cF5On+FnyXXvaczROUQCG/hS0NwKXSVJFuDZBY0TLuseyA9RXoNWQbDVPYaHprK3hKpdMxvTm5sirNVa2r5ErF8tPiccFcZbgCO8u0/6db75GIQqoNWFwiFXpILyCI2dGSFuwvldek7MzDYTtkmcK49d5dEkwcptYgD9J6LQX0Uaz23t/vjrACy+8wGQy4fy5o0TVM8TKJbp9EyFhVFVUDvs4M02Ky2/Dd77Jx999hS8+9xxaa37lV/57WL1exnFO6w//8A8BePL0Kewzj6GyAVZcIKptmo5NEIakSUo3zrDSCal++Lk304x2E1RxsfHsECMtKmoIVoaYKpaiD+h8O1g36MmEvFrlK79vUHGB9AIGOsPGZm0a9/bIbAt8D5L70L+U/Od/7a+x0Gxy+eYbfP3/+w9oVQLsIuNWVB5vRoZnVzGmICm+uwv+R/W/7RJCHBdCXBZC/CMhxLtCiP9ZiHLiEEJ8VQjx7PT7/0EI8bIQ4h0hxP/jvr9fnbZ7vzplwM9Pf/4X7rDkQohfFEK8LYR4QwjxdSGEC/w/gT8thHhdCPGn7z8mY8zEGPMNSvB+/7EuAnVjzLeMMQb4+8DPfT/v98M0R/0WcBx4C3jlvq8/1uVJi/lj5aS7vbmNsAtMAbkyWPfJ4xMJtipwpM3mmuBwtwTtX3r+ImFYp7u/hyO3uLxv6A9CiiyjYeWIgcJppoi8IPQbbOUJN6a9SJ/+1McfekwjMyLOSpdQ7aZ4aYX9sWKfiOeff552u83Vq1f583/+z9Ptdvn4xz/On/2zf5bCGLLxCMvXuFpx5GTJALxrRGlGF02QtkedCcePjrBRFBk4ccLqpGREVo6UvfBj6ZJpOF6xiMgZmALHWPhXA5pHNSufgMkuHF6BhUAwygNazTPMIZgf7DN+8YDtWw6r6iz1uuDRxwWf+bzg9MUqSjqcnJq33D7cJ801jjaETsJQF+SyICBntx+hCkUrCKn4Fpnt43kVPntBwtwJsp0IfXBAbgpGytDpCE6dvrfgrkY5I0/iOQWh9eBCqdGELIU4MgxNTj1c5vFT06zZN16lNVvFFYLROL8L2uO8wDI5xkj2+uXkMzO7BLLg5JvvMnf5EiIMqJuIUaCQFgy2pqC90FSEB2KW9X7EH1zZ4+akxpOteU587FPkScRv/8P/Ecs1jLrAzBL2cIgoMpKpeZ0jXCqiysD00e+RGqbkFAnYqUKrguu75Q73I49eYHa6qNw72KLIbGw3xWDuSuRrriAZT2XlVsLOXklfzdTq1PMBUkBka/zxGNtV+MKB2fMPv6GCJqx8rLTKHe/C2jdg4yXYvwyjbcgmD/SmBTgIz0eInMJ20MOM+kLZ2nF7/5Ctwx67W/uE1YDmiWWa4xEqaON5cHsDtjPDmeOCxnRPpnrf2tMVARkpGIXzQUz7VB4vRdnicHZZ0hsZtg4Nyx2BlHeY9lICqWYb+EGI02qjHy17io2+96JBAMqpgTDMT83LesMJVpKiHJvCsSGeYPIRYBDOvc2kRemwdONFzNp3fihZ7RoQKkModZdp/+yF0mn9nbffIgsrZIMxvuMgTMxuPs3HnTrHb4zHIAyzbv1uP+aXz5+HmRmuxwW5ghlLQtxl7FXpDaAmHIRVsJUnVOp13Oc/RzoasnH1GpN6i0BoqlKyn+Ss7RqqtuaRFZflOXCbTyGEYP2gizUc0Z/pYCPJDwdEgyEUGubncLKct66V4rEzp89xYycm5QCdNRkIiyB1KZTASvcZph55VqdypcpsFU48OcvSE0+xspjw9LFtlq0N1Po12uxiHT+FFd7Le4+7sPrPoVccYWlq/LW9uYP2ApSRyDxjUStEUUCikT7s3clon20z0Rbe4dtgO4gLn0c3qsxsXMGqR8R5jUBNeKIzILJbvPV2H9+x2A19lgOJFILCKIQWrASSzbSgMIaZisVwUBomUqnyxJHy3r66s4mVJti5TSAEegqQvEKiGLLT7ZfXWGOWfBoJtZsYqNZoCYdk1GP4HrO3E75NAZz99GepVqu89tpr7IxixETRqE/IUuiOQ8gTMJqjj5dRVxuvCqRXZ5hxt6/dw0ciaVs5510XiKHwyboWxlEEacTm7n1M+8EhSqqS4ZM25gPk8dEQvBAscgSaLim3NsoNiPn2GcJOjj2KMJUWRZaTV0J68iHP5dW50vw42cyjDEhJjKLN+yXsptXBGI2fHlAUNkrlnDh59O7jR2ZmOXu+jRCCOAZHRbihy4gMWwQEWfGBEZjfdwXhXdAeBgGuTBG5INMGxxXMTSMnbxQBG70SdQVHTmOEwc9SUAov9Ihs+aGl8Xeq2uoQKAs3GxC1ZlBGsuhp/KBGNIqIB7eJTYBxHIwqcIuEwpj3tWHcqQKDk2YgDINxuRZpBi6DyMcJPVx7SOc9oD1oQ2cJ+u48Jgxoba5hqxAhFGOZMDh1lmZvE5NZqMAjEJtEkcaWPk888QTVapW1WxtsbK1BYwbGQxy3icGQk5fZ9lMTurlmFRwPnWtmhocsv/z7ZH4LJDhZzLET5X1469at97857knjP/mxJxiINlvJMeJun/lGl9uTiPZogjU7Q751CxZXqH7yx/i5X/pzAPytr3+Lwd/76/Drvwpf+z3Mxjovfv2bADz29HNUlxYR6RjSnM2VuXLMbpYb/JuDGDuZEH3AeTd5jM5SZNBACIGLg/Z8rHyIlAalqtgOD2XajTFsvh2jUezH1dLsry8QXsDY5DhKsn5jyrQvzpfS1eTBJ6q2Wvy//sv/EoCv/+rfxwlCXCG4NSyvgcxkuH4FpcHkH4H2f1P1S+m3P/VL6bd/5of89akP8dLngL9mjHkEGAJ/6SG/8381xjwLXAA+K4S4cN9jB8aYpylZ8Iex4/8F8CVjzEXgZ40x2fRnv2aMedIY82sf8hQtA/cbm21Mf/ah68OAdt8Y858YY/6OMebv3fn6fl7kR7FcJPNHp6D99g62q8qe9oK7Oe3KGDKpsI1GxQ47+wmT4QDXdTm50ubMyccAiDcu4T6uUKZCPCxY7Gac83KsIAYsKkGDVzY36G5s4vsh58+/H/gYY9hORxhlYbDoZwlzoc9oaLNWjJCWxU//9E8D8M/+2T8D4Fd+5VeQUjIoDDKe4DqaEJsTJ8o89MtRDnlGvrMNnk+QxATZAKk1ehJjKc2t6aJuZalkbg6VhSthJRAckpAY6HRD7GTIbO0bzMyuUl0oldCzUzZi4J6gGfh0kkM6xSFHnl7hx77c4MmnBStHBL4v8DoBqfQ4NZ1UVg/2IS1Aa3wrI3ELhJcgMWxN2YHFagUCm9QOCIMQ1xGcefoEoWNT3d7hoB9xmL5nJznLKLIc7dnYtsQWDzaZlX3tcNDXRCjqBDz+WNnXvnHpXdIgYC6QRKOCaNoTlmQZjskwSrDbLRej4fwytqVojCIq27ewbQitlCxL8WY1vS2Dygw7E82NW5JLtz02DmsElR4nHvV5rj7iE/9OyWR945/+PcaWYdwFZlawVIHf65Jyj61uiRYazdA8OPml5KjIoipiUhJurN4EYOHIY5iw/Ex393bIMxfHUWhLEQ/hyQWLZ5ck8Ygynkck7ByWz92qNahnIzACnfQJighR8/BmHillpR9UQpZRRsvPla7HtgeTvZJ53/h2mTm8/Tr0buIXCum5YAqU62BGKc2Fskd04+CQdy6XDvinL5yFQlPPEmK3jRbw2qqm3RQ8fkIwLspFSe0+pt2VIWqqkJEf0G6k7jDt00Xr0Xnwp3G4R+fu/Y0QEikcNDl88cvwuR9HTT+X+0G7EALcOkJA0za4vkeaF6TjIcpA7rkQTdDTfHbp3FvAR2aCn8SYbIIS9g/MtBcSZJGDuse0f+psg8AP2FxfpZsZsvGE0HaxSdnKSi+CO6B9Jx2htUXdKL75zW8ipeQL58+Tt9rcShVNKXHzlPEkYl/X2N2xeHtd4CPZplRrnDqyzN4TTzHWBt2eQQKeFry8nuNqOLUIruWyPCcorHmOLM6itGH35g2iegXhemSjhEE/RyAw8/PYRc47V8rr++iZC9jhNicXHC6enePkEfj8aZ/PPqY4M9fFFm0+ddLlQhjg3HKIggm1yhEm86cwM3Vm/U2axTXsoMBavBcdVyRw/XcAJTCmSSWsEToW41FElqcYQGeGiS4wSiNTxSiLSUYTfM+lVa2yZ0N3/W3iuXncYIbx0RM4ccSceZ1RHIAqqKgBy0tt9KjPXioYVKosBOVYpigQWrAcSHI0g9zQqVqoabS68X0uHCnXCZe3t7CjMaQWVSkphMIRIJRkGO9xOBhh2RadoEU6ja/bTzUmrFDHwRlPuNGP2XkDrv0WXPqnMJNLAglbOHz5y18G4G/9zosMBgE7/QP8qmJzNyw34bIYKQXnP1ne/sWOwyG1u33tQghCUaEQMY95NhkptgkZ7QvcakIxHLJ3X097b78LfkE0mBozfgDTnoyndilFgRaacZqyttMH4Mjieex0H1koaC1RpBHa8zgw71f/bESabmEz51t0iSkwtB8ScyUbs6VpV7wPxibPC06dOH738RNzCywebQIQDydYpsANLFIU1eJOQsMPCbT7AUyd/KthiCczdGyhAWkKOjNlC9rOzj4bvXIjNlw4ibBz/DgDFE7NJ5fiQzvH36lmy8eiQTMfsDszixIWoRoxM1Nej4ebN5loD2wHigI3LzcNP4htz4zBL8rxrjeaMu2+Q2ZcwnYdV0R07HLjNe6C5YJbhfkVgdI2k/ocrf1NrMjFFYKJnDA6dgJXK6qTPmOvQ03eJorAFh62bfPJT34SgBde+GbpIJ8luLlBBRVyMoQq2Ngv58L52QZKOpAVtPs7+Hu7DOI22nXxTcTyUmlm971A+zPPXiC12qznC4wnAW3vOtH+NrOOi7Yc9kZDvhouw4lTPPpLf4FHP/0ZRlnG37pyA+bmYDzi8v/0j+geHDLTqOF+6gs07ByZTjCZYrK0hLIki/XSi2G7F+HkEZMPYNqJhyg0VlCux1xsjOshsj5SOgwSm6D+cKa9uwXj/SGxAb9Z4/GnwMoz9gcekSnQ24dkScJMvUZjpgVBC9L3o/+fnCp51tdvEUmPui0YxjGxskqm3a9gDCTpR/L4PwZ12xjzwvT7fwg8/5Df+VNCiFcp/dgeozRWv1O/Pv33FUqS+r31AvB3hRC/DN8lUujfQH0YbdM/mB7o/w+4e/Ub8wHOVn9MysNi9mg50ezc3kMcLWBS9rR7DqgcJlojhEJqTXZgsZ+U/eyzc7P4vuTcqUd54+0X2Xn7XY4+r/CPVOBAM9qZUK36GBMhpcuhHfDmK6UT9qOPPI1tv/9jS1D0swm5lFAx9KKYlbbg7Zshh8MJB+2In/u5n+Pv//2/D8BP/uRP8rGPfQyAXpxiihjP1fjC4dyJkmm/utfHtHzU+i1oVnCTQ9xkhDQG2Z+A47C6U0YCrSwtoTHsFA6nmyUY3zATfFzq1xO09zZB00B/laMfn+XSP6+QvCZwj8FO4fDczAnm1r5FY6ZB/dFz95qmKTc/vjbMcfwqJ6e5tWv7B5jMoDxDReYM3BTHTREGdu+A9iCAwKZwfNrTBadstWnNdFiKDghqPV68McvTvqEWTl9vMqZXFEjfwbMDxu9ZqFWrpdHgzqiAeWhIhyeeKlsdbl+7ySFwbqbGa4MRe1HBcV0QZzlCF2gF+1O5qbO4hKMy/IMDtjZj1GiC2LpB9a3fZz+YI98LmCjJ9YrHGeFxdrHLfEsyL4fYlW3Cyz0+9+x5/mVnhp1b7/LNS9/i8yc+iWodwQKqB/tEs8fuHrcvAnwC+qZHwzTveiLciXvznCGHoz12t3dxPJeaf5yjRfnZ7u3toXIPS4wxtiIe2Rw/IQDB6k2DsTMEBbsH5cTarLepZQOM0XjDQ1yTUMyfwKotfe8bC8CtlF9QLuzzqHSYv/PVW8XWGtf3MIXCuA56NKY9XQCt7R9yda88lmNPPkowGhMYRdedYXdsKCbwzBMCSwpGhcG3wL7venOET47B/i5xUdoUSGHdPY+WFDxxQrLbMzSrDwJ9S3oonUK1XHSrKJ4y8A/mXFtBHQEEKqHSbJHt7NA77COQZJ6DiSZlPrsdPhDdN84OcQzIPCURkuoPCtqFQKoyu3k8XfDMNx3OnHqSN995kcsb2zyzVMezAmpixGameNpojE7BmqWveujc5eqlb5ZRb088QatS4VqtRZFCQ1qMel324wKvPsPZWY/XezHj2z6D+ZjcKKpSUnvkETbcKs7iLM7GGq9fg6EoeGLZ4DoCSzjMtQSuKzl25AjrW3vcvrlKw7JQ1Rb2YMzYqhBYDlazihzvcPlGKbtcOP8k0pswGy6yP1VNzOBCtMd4qBiLFk+csKmcgf1vVFi/3ufk2QaWHTBePMbMbp8wGHIzDbA7x4EyvfDm75cmh5V5sLZrpE7I0UaFywdDhr0SXKpcl5sRhUEqzVpvmnQxU8c4AVGU403GXGvNcUF4qNl50kqNMN1E5j5aG6QUSKvNSfsGPavCfuoxExSAh6Jk2hcDAZailxlm6xa3gf1Bl7G7y2OnSsOxKztbVIsIlc1RERaWyHARjMjpbq2WxzU3i7F8lG8BmskErr8rqd+qku9mvP1EQnazSrUhSYdw+wXBiU/aXI4LvvzTP8s/+Sf/hN/8yh/wYx//E+jBiMrJIb3XAqIKhFkEXgUvFCyfM2y/LhjUmoy7e9zROIWEjBnRN/0yDrUIGfY1wcKA27uHaK2oOg7jPKd3OEA6htGhBvHdmfb544AqmfYkzri1X47N586cx/R2EUohZo+i195A1ec4MCnnuAecc2144bCg4QoeaUheI8bBofKQZZXthWjPxY0PMcYmywuWVpZwbJu8KDi/tIBslQAo7/UR0iBsSW47NO6YQVZ+iPL4/fJarFQCXKEwRVFmteuC1mw5jr7z7g1yrZmphGirhiDFSwoECrtRjs/frzzecwQ1b456vsOhZ5NWatS2bzHXXmRz4zK76xtMzilwbIzWOMUUtBtN5SG98zkGJy/Hu/6gHE8bgYvwAqxmDejSthKgQdwtE0MA2i2JcRz69QVqu5dxd0dYbUlPJmS2xJnpULl2iytmiWX3Ct2he3cD//nnn+f3fu/3ePGb3+LPXXgGigJvOEaFdax4gtQFG7vltTQ3UyfTLu1Rl5q3wuCKYXK6jqi6BAcJi+1yg+RhoH17e5s33ngD3/e5+PTjiHaVa7mknh9j2bnEzPYes0HA/jhnXcd8c85nOcpZ8Sx+/D/8y1x64ev8yh98g7/yV/4y9pd+lq///n8KwIUTK9gLKwgmyPEQZfmoZoMi9Fip+Hwb2OxNsLIPBu0q6qPRuFOFkRAC4QaI8TaOCOjlinN1i97W+ze9194x9PtjZhbh+DMVCttQdyfsTSyiwqCvl60Cp2dnyvYDvwHdw3JRbd3bJFpeXqYeBAwnQ65tDXlCCuwiYjer0bRSfM8lBdLv4YL/Uf3w6h95H//m/0ov/V5JyAP/F0KcoGTQnzPG9IQQfxe4f3f1DrZVPAQXG2P+j0KIjwN/EnhFCPHMe3/nQ9YmsHLf/1emP/vQ9WGY9gz4b4AXuSeNf/n7eZEftdLGsJUr6lO2Yvv2LiLIEeaee7zWMFIaIRU6FcjUIpKljG9xcR5LwmNnS5nsjTfexbdyxk6IU4HO0TFHzqeoLMaWHm8qi7VptuaTTz770GMamIyRmiBcl4rrkpPiehqkS9J12GTEj//4j9Nut2k2m/zyL//y3b8dDocYclxH4IuAR06UjOWNmxuYVh2zvooIm3hZipPGCAF2d8DIcuj2etiex2yzycRYaMvieMXigJixKTid5Mjbb+G2AuTRZ0FIAn2V+Sfg8IqgkUh2EoPXOIJJA4adOag82Ef+na6im8Ow1uS4Wy5c1g66iESRKU1oCrxzMbVKDkazu9cHSnMy7dpo26M6dWql3kAGbWpZxhPBFhNt+NqbmrVdw2BiKEZjBkWCE9QI7CpdHhzwpRQ0moLduFwM1nB4/OPl/bu+ucPeeEi1UaOhC/ZjjS5isjzDUilRmjMcTXBsi2J+lko8gcxQ2A2c2lGyeouw1kC3Qo7OKlZ0n9PBOp9cXOMxe5dKvE59nIKcUIw2WNo74Md//LMAfO0rf4ftMUxUG7yQ4PDgrjz+TrVki5yc8ZTNhCnTPnIw3g7Xt8qxY+nMCZpXV7n4+htYlqTX65NEGl0UOHVFfO/PiYbg11JMoTk4KM97vTlDJRuRehadvVXSTgdr9pGHXrffs4QoAXxtsex9X34OvBpkYzwvAKMxoY2OcmbvyOMPelx+50r5Xi6eozOJEQhG9gyTFGpG0Jqy4ZMCKrZ4z0tKlHCwvwv4VSbHEg+yTMsdwdNn3j+kSumj9b3WJqXju7L5+8sNW2Ag0AmVqUS11+/iGkPmuRTjMSYfI+7LZ9dGk8T7OEJgFRkT5A/MtCshsIoStE+mucftmRqPnCuv88ur66R5hpUZarKglyuiqUNvYVlEKkNqn6///h8A8KUnn4Rajeta0rAEwxF013sYV3HmSItHOx6nj0Adj+2+5usbYwIEgRQ889hZWvUKk6HHTk8zN69ZmBo25QOHra8L6iEsLpUg9ObaBg4FaaNF0O8TdHcpqnU8qdjd6zOcjGn4FVhcoRYIPFnh0GQ0cMo+5GifwdAit6t0lmyqC3DuVEi8I7i+EVGxZkh8SRHU0QYKz8erlCBn49sw2oRjnymFIp6sElkhR2vlhuFoaxUlJTrXjLVGZwYLxVqvZJWPNKvkVoDVO0QKwZX6DIkyyOYsynGI7Aq2MySbOsp1Ry2WgwFivoXJ4dp2jjIKjUZoiSUEDd/Qzwzt0MJ2oB+N0UbxyLmp4/72NmE6JMpgRjhUbcOy7XBoxfSn48Gx+TlSGZIaw63f3uDdb2S88i1NnNZYlhnt8zD7ZxIe+0VYegb6q9DatYhzQ3rsJ7Asm3cufRvLDPB7mqw1RPke3UMgvSddXT4LnUCw028yjlKIy8fu9LX3TY9CSdTYo5CKQI9Y2y7B57NzHSwhGI4m5DomzlRp+PYQpj2LDaqAoA5kCUYYkijn5k55Xi88fRZ5eIARNtb8MjJJCL0KKZrRfRt5r/QU49zwfMdmQkZMQYiL/xAixnEDlOcj4y5G2xSFolKvMNco7+VHlpa406uTjQelxNg2FG4FfxyV5nHB98i9/rAVBGWPl1LYXoBnCTydYTSlieFCuWn/2tulWml5pkWWCVxLI6ICSxRY7fKa/n7l8QDzzVmMAmmPSOsdnCxisV2uOXZWt4hlBo6H0BonK8ef9AMc5AtjcPMMBByOyuul4Xs0mx5dEWBLC9uUY1PcLaXxAIEU2DWHXm0B1xiC1dtU8HBkTiFzipkmsuJSdLtkXo6KDthWGRs64rlPfQIoHeRpdkBIZG8fqjOgFAbN1nTTeHamQZ5BGI3QIx89FPhLHtRC/CymGTaBh4P23/3d3wXgmYsXaC7OMFdzmCgYZQvsWgHH9y7jz85R2TrgoF2lcG2+GY1Zdi2e/Ikvc+TUKdZ29/mNf/EvYDzgqzdLldETS4scvXmJQkXYwzHKdsHpoUKPI9NEgM3uBDvPSgPGh1Se9EBIXL+FMWXbjXZdZJ7iCK/8vMLyMrvfQb6/a3j3ElRrY1ZOBAQ1lzSO8YMC1baJIti7WvrqnGnXod4Gb7oefA/bLoTg0WPHAbj85jUsx6FjEvZSm9hk2M60NS3/iGn/Y1BHhRCfnH7/Z4BvvOfxOjABBkKIecoY8w9dQohTxphvG2P+C2AfOELJunxf8idjzDYwFEJ8Yuoa/+eA3/x+nuPDgPb/FDhtjDlujDkx/Tr5/bzIj2Lt5AVyuZSR79zeBz9D6nvu8QDDXINR6MzQqllsH5T97EuL82D7PP1ouXB657VLLLuGHj5KSOpzYxqdFJOmaFy2pGDjtbKf/dlnH25C92bUJZcZnWGT8dseBZokTwnqkG9VGBcFaUXyyiuv8MYbb9Bu3+vBHI/G2DLDtQUHu1XOHisn7Y1rt8iW5xGbm5iggac1J6IYpI3dG7KuysF49sgxLFPQUzZpAoseXDMjKtGII2urJHGIfeLJUpfWPgVxj8VzOzgV0O8I+qlhXJ3FZDaHK0fIzb3or/VI885AUXMERbOJm0vanTpZUTDaPiCRNoHKsFoZnijl8ntTB+ClIEA5LrlXo16ZXuqeD2EdqQLm012OLBcEPrx2XfOV1zVf/daQtV5EP6mzv13lrYOIg7FPmt+beBoNOMwyPC1xhaR14hQrrRppXnDtte9AUGNe5sRKcbufYHSBlSZsj6dZ660Wg6ZHLY3QmcA2Aefm6ngzLRZWOgwWH6N64Rn2vE/xdvt55JnPU5z4LFsnnmO/9kWu+1/iVjLL9cMlPvfFLwHw7W/8Gre6Iw73DDTn8fpdFJr8PqapQhUHh74uWQBlNGlRYGJJYe1w9XZ53hZPn2J28watvQ3m2uWEubd3iM4VTq24a+IEpczUrcboQrHXLSfVRnMOLxkiJ4fILGVw+gJV67tntn5f5VYhn+A6HhiN8iVFActTF+LNgx6X374GwPGL5+mMIwyCkWyTxFCz7zEuo8I8YEIH5aackg7CZGUP8N0HNPRLVlRTID8kyySlhzYF2iiM0SidYlnv73v1Kk0w4OmESmua1d7rYStNFnioUReDRrr3QPuEMaQxIR5SaybaYEzx4HF/n5VLsHWG1oLJVEbbnqny+LmyLef6jevk2mDFmkAabJNxZTzCGEMiDbFWBKZyd9H5E2fOMGm22Ms1c1ry+m1NS3aphYLZ5iwtHEJX8NRZqFkub47GvPauIMsNShhuDwzjocfikqbTEPgYtIK133fp3YSWC+2pg/ytrV2CbMyo1iHIEsL9A/JGG19lXJ6a0J2aXSF3NbVQoHEZUTAjXNAKM+myP2nSbDlYU/XF8pOSWSfk+lqEGTcQrktq5eQmJQlaSGFzeBX23oK5x2HmbJm5XHclY7fNkWnMVn/1JrnjInJFagw6M0ijWJ/eN0fqIUN7hjDqEdZ88qrHu0OFG7YxrkALl6E9jx4doNEMui6h1UN12hypWdzuZVzeLEGl0OWxN3xDVkgmBTSbgvF0E2bx6DL1MGQQTRjs3macQVO4VC1BVWpioehtlkqb451ZlBsy6k04u/oiK/GbXDuqKJ6qMr8QsTRvs+vFGGOYvwDhLGx9VbK+Bod+lU8//zmUUnxr4wbBgSQpDOGplMHIIhvfc4W2HMHJRwVJ1uSgD0z6QOnJ4eCg0Zg8RI0kxi7wsxG3poajZ2dazE+d+oejPTKtiSc2Bo25bz6Be5LdoAZkMUZAnmTcPCjHv2d+7DR2t0vhV7EqLkJrZsNyHLwjkd9LynnpfN1iwZccEqMw+DgED2GELTdE+T4y6mG0pMghrHn8yWef5ZEjy3zysQswzRJXoz6WVChHknsV3CiCsFIC9x9GTc0RSWKwPQLXwpYpKpMoo6gvlb3269tl3N/ibIeskNiWQsYF0hXYoYeFxP2QzvH3V73dwtI2mglpOAdSc7Rejmk7azvkMgbbBaVw8qkvzAeMZzkGR+VIaegNpu7xgYsfBhwgcESN3IxJxgqV3QPtvgSv4RBX62irSnttDVE4dCyouhnX8h7bx+ZQg7fYrFoYurw+6vFGMWTm2cexLIu3Xr/EGAGuB7197Poik8UT4DhsTpn2zvIszvY+jlHkM4/iBRDWLXBdXEtT96fRvw8B7Xek8c9//CLhTJ2aI8mNYWwbtlWLVh6ByZG9MfuLs/i2Yl0ldPOCec/mp/7D/wiAv/ob/xJz9S2+/kJJgs584sdYvPwGvPpNrLQgtyRFLURVfZanDvIb3dIEOf6AuLQiHmAsG8+t8dVBxr8apBRueS1UtKCQhsIvP7P7+9pf/pphnMC502NqsyXeyaKSCKqesTEGrr6yCsD5Vh0GY3j1dRhP3tfXDvD4VDG0+vYVlOPT1gmFcdjLC6RrY4RAfwTa/zjUFeA/EkK8C7Qoe9PvljHmDUpZ/GXgH1PK3b+f+m+mJnVvA98E3gC+Ajz6MCM6KA3ugL8K/AUhxMZ90XN/CfibwHXgBvAvv58D+TCzwHXggwMb/xiWFIJnu106U5fVnfVdCHKkEnd72gEGucZkAqENS8s2a6tTJnN5Bdwqj59ewHV9Nm5tEEZdsCukCLJ0RKTHmLygZ4U4suDaq28D8IlPvp9pj0zBZbWLg8WZW4u4/QomUcRFSr0BOnbJ+jabjDh+/DhHjx594O/HoyG+jomVx95gkblaG79WZdIfsNNoIuKYIslBG2Q0pig0VpyxNp2w548eIy9yNiYO+zuCf/5qzvXNTY5urpJ16wyyJ2kcnzb81pbAq2MNrzP7dEa0Lbh0yfCrl9v8gfwJeqLGYKpUiQrDH+7ntD3B8zMS0aiQ55ojR0pma29tixSJXxQgMkSRI41mf6ecMJerHoVrU/h1wvuJzXodW1dwTU5LbfL0o/CZC5Jnz0rma2M8P8e3OxS9gPVBwfVhlbdu3Vs0NJsQ2wVWVH7Qll/lsROl6uLad74NYY2ma2OrlGv7MZaKkXnO7tTddr7ZJA5tKuMIHQkGOxUO/jBCphLfjLAEjEJNwxF0e4KDWHO45nDtquHltzNG6WnqNZuLrRuceuwpnnzkceJozJtv/s9cumlKM7o4Qk5Gdx3kodyZbooWCQmxicjIyVMwXoSVj7m+VgLShVOnWYy38dWE+Va5mNrd30PnJdOejLm7ux6PwAlTjNIcHJaL3lZ7Hn+8jzs+ZOzVyY6fpob7Pe+rD11uBYoU37HRUmCsMuf5aKdE4q+u7XKw16VSr7ByYpHaeELuBgzzAPKyf33aisfkIaA9R4FwsYDCTCf8PINX/hBe/Fdw+wbKFFjiw4F2a+ogr3WKmjLulnw/aA/rTUAQFDHhVCo7GA6xlSapVVBJDIMR8j4TurEZ4SRZmZWLYFKkgIHvIu3/XpULEEqhNUymhlXtVpVPXDwFwLUrl8iVhrjAkZrTrmY1nvBOVNA1BakypDsHXL16lUajwcc6HdarTdLMsHpDgoQTjR6qGjIjQ2whqeNg3IKlIKQ1p+gXKTdvwzs7Bdf3FE0r59jREnz5RtO9Jkh75fjT8QTN+ScAuLmzRxAP6dZnqWCo5Dlxe4ZQ57x7o2Rwji+dQtgT2pWQ7h1pvHAh7pL0FX2rTrtzv98AXHyyArbhrZcyPNNgzzJ0s4TEn2GyV9ot1JZgpSThpqAdYq/Jsl9+1gdrN1COB9mUNUzBsgtuT5VBRxoVDu0OYTSm0qqy5OVcGipsEaKbNfzokP3gLKmcIY0TRDIic3OSaosnZj1aMwVXdmK6QxBFObXXXI1tJJuxpt5UpHmKUgYdBjw2VYrdvP4OUW6oYVMRkr7JsLRkf6d0wD7ZmkE5AfE4wjaC58N16uGIjbjKu9ci+jcsumlBjxwhQTxqeONQU71tMbds+Lmp98ZXVm8SxAliUsFdHpJJj4PNB5cWi6ehWm2wsy/Qo3sdeKEowU2Rh6QD8NsKOR5xa78cs87NtFkIykF+ONgjM2UbD/A+tj2+E/c2Be25EDCMWZsqGB65cAx3OCQP22hd3v9Vv04Nm0OToo3hGwcFoQ3PtS20MfSICYyLRBA8hGn33BDlB4hkgjSKogC36vPvf/5P8Df/479IZ6n8LIwxmHiIjUIFLoVbwZ1EPzxpPNzLak9isH0qnsC1YlRmkemC+tFTD/z63OICcS7x7AI7ShCBDa71UEXBh6l2Q6KoIpgwDucwrsXpcMrw3tzEoMlcFwOIJMISgvQhPe3aGApjcFQB0tCbMu310MfyHfpCULUbGGMY9kt52B3QLoWgUndIWzWUruL3+sh+TMdyeFwLljOYffRpVlKLyl5BfRxyfiPj6gRu2DYXLj6OUooX33oL3AD6B7h4GAcEkttbpQKkdWSByuoGViVk3H4CywVfGzI3wPIks9N++/eCdqUUv/075abnn/zCU+igRjqycV3YrxeYQU7ohnC4RlQIhguzPB0qhIBvRGNWXIunfvGXaDabvHjpCr/26/+MrYMu9Wad+LM/hX/qLNY7b+G8vUpmSexqDV2vcsQrx42tgxGWVqT3KWEeOL5kiPJDXOEwGm4zHO5xYAlsLCo6phCaO1YMdzbJ1q9rrl6Fk+cMC+0I/HL9nE83hpOaxPdh63p5Ls7PNEuwvrEOL74G3/o6TO7J/PLccGShVKtuXL5M5vq4RUxDuuxmGik1RlofMe1/PKowxvxZY8wjxph/xxgTARhjPmeMeXn6/V8wxpw1xnzBGPPzxpi/O/35cWPMwfT7l40xn5t+/3eNMX95+v3PG2OeMMY8boz5P5uyusaY5z7IiG76vG1jTNUYs3Inem76Go8bY04ZY/6y+T4Zlg8D2ifA60KIv/5R5FtZxhgWXvoKzxXlALK1vgdufpdpv9NuurWrsbXCsQR+xWNjCtqPnDgOtk8tLDhxvDQwvPH2G9SdCimSPB8zLiborGBg1ah1tzjcO6BabfLEE6cfOJbCaL6T98l1zJKskm0GhJMQHUGWJUgHTBXYqjEhp2ceSCAg1oZ8MkLmKXkRELGAKDyWT5aLiEtKYYDioIswBooClRXIJGc9LxfRK8ePkRYp/czlaENQbW0wf3Cd/rDJb751gRvSZjcyZLmhP4FryRmurGVc27nB0DH0twxOaPAVbO1D3yQYY/jqfk5h4PNtTefgFY44G2TC4dj8lFG9vYm2BHZWABkySxFCsL9VMgQroU8eBNhu+GCufb1JkNho16c1WWVioF0TrMwKAvcQv+3zudMz/MzjFS6eFFTnIna6BqXLeytoaApLwbBcaFg4PPpoudBZfetNtF/Ft2xqpGRZjFtEmEKxO51wOu0ZlC8IoxjPjrFP+OjugMmNgIPrIxZdwVqsObMomYwNv/uCYvOyjeMKjj1R8JkvBswfWaE56fHs6Qq/+KUy5vH17/xtbm5BVFvBQuAf7LxPIl8XDSws+qZHQk4SK3QwwI0MNzfLXrIjZ87QHG3hmozF6cbU3v4OpgCrkqEUpFH5pTU4QUocJ4yGY2whqNdahJNNTFqws3yO0Alx/giMzAeWUx5T1QKEReFYCAqO1KYmhVfKSf/4xUdoWuCOInIvZJC5iBzqfknWJ8pQaN4H2lMUQng4SDITwagP3/w96O5BUMFcextdpO+Tx39QyakUXuvkPtD+fnl8rR6gpI2nUipT0H447OPkmsHxRQok1s3du/3syigmZkIltxBIbCGI1R2Tux8AtEuDpRVKGaLpgqrVqHD6eEizOcew32PtoAdRhsHwVKB4xleMtMXXhmMmxuLWd0p12heefx4pJe96DfpbgkLD6SWBnfUR1QZWnpNkE9rCJZeKQHu4nuD0YzENR/LWYYFjw5nmgEgoBDBeV0RbDsvPCoSEjgP1uXIz89beAfZ4TL81jyUEroCoPUOgNG9fL0H70so5wjCh4oZ0yXCR1IQD0QGTA5txGDLTeXBDpl1xWTnlcl2O+c1v17guKmwZj51slhu/W6Z/nfxiaaYG5fVV8wWJ02QpLAHnzvo6yvWhyDHGIFKQTsHmdJNxqV5jELTw4wl2s8qpSkGiYDsK0Y0azuAA09ZcOv5Z9iotqslNYtcgGg3mXJeVxZxmK+P2HoxH5YrZsg0VYbERayrN8tpLYlChxxMrU9C+eg1jwM5tjlo+LRzqacD2tO/5aK2F8kLiSYRjYKlhODN5l6eerjHbEGRrKdfW4bfXRnzt1oTLB5qTjwieGzoUQ8HFL5VRtt+4eQM3PoReE9cFsWDo7U5Q6t6axbIFjz1i0csb7Nzq3/15VdSwsMmikGIMQStGRBHru6Ua4FwjYCkoNwZ7B3tga+KxPb0X3g/aLat0jydPKCzJ3q0dcqWY6xzBkRFWnKLqS+RJef27fkhHeAwpeLmf08sMn+44uFIwIiNH4+MgAO8hyyrH9lB+gMkzfB1RFKAdiWtXMapKMNMsP5sErHSIZUtyCdqtYEUTCH9IJnRQ9rRDaUZneYS+jWel6MRCoaieeNDotrW0jMoNvm2w4wTbd0lsB//77Ge/U/UQBDV8IsbODLnn8ohXXgNbq5ulP6HjYASQRnjm4fL4Ytq26qgcY1sMpkZ09TBAO5LcEczYdaSwGY5Llja4JzDEc1xMxyWTHdzJBH+nDxgakwlN4dI6+RjNmSbB2i7SapEf7iC1xZ7O+dSnSyXuN178ZrmhMh7iKgHpmDhR9PoDHNumVq8SdLuwskiiWlgOhEVB6tUQjsVRJ8N2HHZ3d4nje6z2yy+/Qq97yNLiEhcungTbZfDuATMtzXZQsHxwgD1/Ag4PGJPjVjRNscVZx+VqFlO3DX6lyi/8B38RgL/01/42AI8+/zGkcKh+4uMURzrI9T2s27eRvo9s1FmslPPS1t4AqRX5BzDtOh4i/RqR0rQGN+iMr7NhLCROaTxraRJREljRENLE8PXfLfeLfuzzEcJo8KagPSo/t54nqXoWB9ulc/yJVguDRK8ch/NPwNpN+F9+Hf3KS9y+HvONrxvqlfJa3b/+LqPCR+YxJzyfSBu6ukBbEl18BNo/qh+d+jCg/TeA/4pSEvBR5BtT0w1iLlRASMnBziEZY6TmAaZ9bUPjWgWeBcqy2Fwr5ZnHT50CJ8B3Cs6cvgjAm6+9wfFKjUzYpMmYRE1I0wLLq7H7VmlCd/7cM9j2vY9MG8PbeshqmlFXimWaZGNYWXRxcpd8HDMuDE7bUGz7yNxikwftPHuFphgP0crg2wFBu43JXI6fLE3DLg8mGEugdvdAWKBy9CRD5IqNadzb0eNH6CeQa5ez9V2OineY7VRpnrpII3VRdcMr1zS/9ZLmq29o3tmpEnvLnGts8QtfnvC0Z5Psw7H2BDP2uNZPeGOg2I4Nn2pqmodv4BYTwtCQCIuTMyWgWd/aQUkbkeeAwctTlGVzsFXGrSw36xSujee/h9VsNPEKSJwaftEnntxjdEbDfaxKhYZdxREWLeHjziQUCqY+a6RegeOA6k2ZduHw+MWyZ3vj8jWGgY8rBDWhQSQ4KsLkmp1RCdrbM3NIqyCIYozt4J/2WXo6pd2wibcSxt9WHOxAewGqgcCbNzz/aYtzx22CTo6UAqe9gN3NScKCP/OLf4IwCLl85ZtsrL7Lpd4c0vbwD/cZEVHcJw+VQtIQTcZmTGQiIjXGzXJEHHB9fRqzcuwYbjpCCsFSrZRS7ne30LnE9ssJMB6V0ngA20vZm7rldkKfQIKdjEmEz2hxhRoPOvD/wOVOs5ttDcLCuBKEZjloPvBrR598nDo5znhC6tYYpQJ3DM2VEqTfc45/8OlTFAgHBxu1fbNk11UBH/s8XPgYJp1g3V5Hfsh+zjtMu9IpSpX99fIhoD0MBcr2cYv0bk97dzgkyDVZ6JMuziNW90GVn+fEjDEYwsQghY2DIFN5GY/0A4D2DJBaM4rL9oBq4CLDKlV3zLmzZXvO2xtb6Ek6fV8xi1bOhXqAVgWHWHzrha8A8BNPP81BqnktbjFnbM6cFNiOwZoMsOt1br26w5tf28A/LAGX5ytIfCI34enjgoU5OLVU3ktjCuyRxcGVgkrTYeGpEhz7RuCFiyzMtskKRffGKkmtRuF4aANZZw43z3nryioAc6fPUw3AIeTQZCXLbjRmcshBt4XfEQTywYviINVs4bG6lLC9bVObOc/uI8/w0rsr5Jnh9JceDEYwJqfWUWROiwVvmtV+exPj+gijUKlGxKDtnN2dki1eajQYej5eluC3Z6jbOfO+4NrIRTdq2Colqm6y5npsCqg6bzAoNJ3ZFgEuSmhOHI3xpcetgyZpocnQLLs227HGq0x7e2PQFe9u7NvNtbLXNcskHhKhBdXYYevgzjjaIXUCdBQTCEH19Cnq++uMjWa5I/ji6YhHGx5r3gFv6wOOHCn48p8ULLYtiiuCcWOJZ599liTPeePtF5iMLFrUsJYMOh+zs/0g0fDoeUEaNNi62rsb8xiKkBPyJKOBjWWgGvTRuWB7p5xTz8y1WJgCjt7+LiLURHeY9veY0ZU+HNPEhiyhsC3Wbpata6dPnyc9WENojZg5QppOcJAIP6AjXCaF5qVxzMmq5GhYzsWHxEgEFjY+1oMbxNNypE1Ra6CVolIMKQowpsCr1DEqoDKd05IY7HyIdAWZ7RBkptwZrf4QQfv9TLsQhIGPb2WQW+QUWIvHCe8zu62vHENrgysFTpri+C6xbf+RQbsQgqrfwDE5E22IZjucECm26zPo9hj1cxLHQgAmywhUTvwQ0J7fAe1FDkLQj8rru1apkNgGZUkWHJdA1onSEXZFY983FQXSQdYMeWUGkUrc7UM0kslolwkJeaWK+9gpnOGAPKkwicbUkwljZXjmU1PQ/o0X7jrIVyJNJYW9w3JcnGnXCQ8GWIXCOXmMIrWRoUeY58RBHVxJoxgyN18qH1dXV+8e2//066U0/ieef4YirGNvHOL81m9wRFwh1QUzewcwdxxGGVFVU9MZRhQ8HWi0gbfTCaEUfPHf/z9gWRa96drj3Kc+QdUoDBnmzDJi+Sjy8JD6m1exqnUWp94/O3u9sj8/nZA9jAhMxki/xjAeYe/u0tm+TS/xOIglVhzheDBMDWEDxn3Dt75RRtJ+8jPgi6m6Zsq0F0mEkRZj2+ArQXer3HRvmg4bB4bv7LfIn/wUfOaT9CsL3Pztywz+4a8zd/Amp0+XoH1r9Qq93EUYw/xUBbehUoy00er9qQ8f1Y9OGWNWjTGP/699HP+m6nuC9mm8269S9gO8CvzqR5FvICtNalnM7FLZ136jv464k9Nug8YQU+B5GY4WKNti+3a5MDh59hzYPq4HZ0+WbQ5vvvYWJ12XwvGYpCmjZEyuDMfrbV5/+VUAnnjiwX7262ZMl4w8gqo0VIelbPbEKYdKxUMlEWkKVlMjjMA7qDIiY2ju7Tx2M8V4Z4jrwfxiA8+RxHmFMyfL93X96jpqbhazvQOuj8hSzGBCFgRsTJ3Qjx5ZYaygocfM5G+SexWSpUeojgNOhpKf+bzkMxck54+URl1f/pjkyedOMd/x6VhXOXcGtnYNdgwLvsfVbs6Lhwmng4Iz4zfLHKXaEkHVxziC47UmALe2d9DSRuQKoRV2kZEhOdydyrxbFQrbRzgBv7mZMbzTl15vYAuJ0BWwFGpYKiAKY8gnhwTVGeSUGW4TEAQJxovY7pZ/PzQ5QSjIu/dA+2PPlYqJ9Vvr7AlwXR87K5gJUhw9AaXZmrrb1uYWcKTGm8QUVojVqWFkzMoxTWc5plov6L4LL/0hPHPUwl/WVGuCCg6TO8x5q4XdzRFCMHO6xc98/nMAvPXy3+HaTkhs12gc9hmTcJnbbJlDsqlkuiGaCAT7Zo9cJ9QmNjt7IwajPn6tzhMNH/IUKWB52ie6d7DzPtB+R2Zquym7d4x3Ah9P5FjRmIlbJV+Yo/7DlMZDiY4sh9DWCGGBbVDS0BI+0ro3pJ179in8KMFWhonTYDgo3UhaU/XnHdD+XiO6lAIB1G5uwesvlg62n/oJaHWgPYfuzOHevIVVfEAcznvqbuybTtE6RkrvoQt7z4PCCfGKhMo0W7A7HOHmmlxI4hNHkYWA6eJuZEY4OFhZwWuOZKhzhCqIjf7BmHbAUgXDqFzsNCs+OqjiORHnzpaM9jvbO6TjFIRAqRijYixH0tEgJxZvvfA1AD65tMJLcZ3crfLTj9goy2CrAULn2LJBNEpQMmPzX0jSfYHt5mRxgMGg7JSZusB1BIHWDPOCwcs2IshZuuggBHh1yEeCTsPhyHIJQjevXCf3LbKwhpIWeaeNleZcXi2VJCsXH6MWQiwdFIaO8CAZkPYyhrpJ2IFgCkiGueGrezm/uZkTxy5PHLX52GKK++2jtG8eYZB4JB9XDzB4AKPoCv7CFrnVZm7KKG1t76NdD2NATXJEJilcwf60d3i21kGIBMcIxsMFlE650LSJcot+pYUipWCHsFellxwnl5soO2O57uPhlu0qIuLcfEBWWLxxuwSrR32bTENPx3iuTTxxKEKXx+/I4zfWwRgmGZyVNVaKGnaWsTM1yFtudojcABHFVDybxmMXMZbNeKNULshkSGshZmFWM79csHgkQ1qC45+D+YnNtSuan/zZUiL/++++Cv0BtayB1/QRtQkbNx9UfwWepH2sxXBc0Lt1L2EhU+WY4wqBb3WJI0V3fwtLCE6cPsJStdwcGezvInxFNLTBmPfdC/FoGvcGmDRGa83qRtkKcP7R8xS76xgE7vxJ8niCiwTfp4rN6ghyO+MTM3fULpouES18UqMfKo2/W/UmRmv8+0B7pV7BtwT+FLRHkcbKJ1ieJHYDgjs9/z9MebwflD0f09YX3w/wrQydle9HOA7ztXumd7VjpzBC42pwigSnYlPYLt4fEbQDNKsNBIKIiHhmFi+PaC8cAWBrbY/U9TACjMoJs5T0IcCxmP5M6gI0DKZ+DfVKhdi1cAS0LZfAapBNNO7Cg2kdgWVjKhaqXoHIxuoOCRIQ4yFdO+Fdf5+9cxYqMLB/QKwtZrtrNIpDjj1VTiLf/ta3GbkZWXRA3LtEUMDmQQmQZ1t1vMMhuV+h0m6gMpC1gCDPKZwA5bkEImG282BWe6HM3X72n/38s+ReBX1lB22gPXiTTnKINQQ0mKDNqOWxMill7GN7kyVLcimNmXUNxfwSv/in/tTd93z845+gjqJQY0SRwvIp4pNL2Dd2sAtDYNs0qiFFoTgYTvCS0fsc5It0jFY5VtBiMjrA6+5S6W/j7jtsT2y2bo65va54413D3sDw7ruwehmOr8Dpi6UaAbjLtBdJUhptmoLR6h6qKFjqzGDlDlsDj4Gp8IeXa9za8bjEWfae/hlWnlnkMd7gYv9d6l7AZDJge3uEMoYij1mwffZ0hpI2Jv8ItH9UPzr1PUG7EOKnKJvlfwX474DrQojvy3nvR7FE0MTOcpaPloz01b01tDZ35fGJpak3C6RtsLVgkuf0DrpYlsXKibPgBDg2PHq23Cl857VLVG0DbkBaJPSjMUa4nGw0eP3lN4AHneM3dMSGiWkUPqZIqTkCsdNAWNDoSGZmA3w7p7dTkDmGShXS2yEO1t0sZIC3Vw1uMqBRl1C02PgqDA5rPDrNar9x5SZ6ZQl92C1ZzSLFDGKUH3B76tx7dHmJsTKsyC3yio1YuIiUDtm6gxNCZVbQrgnOH5UcnRN4jihPUvs0pCMuntzG9mH93TYrsc9qZBiNxnw6f6sE7PMXobaIV/EwjuDEdLC/uX+Aq0Frg1dkOEXO/mCCVpqZwMdxbXLPZ5j5vP41wwtrU8alXm5uVCaaqFJBj3egSNiKRlhJRLMxe/f8tPFpyi6Nzhrbh2Uf99AUtH2bLBYkicESDsuPn6XmuXRHE65fv4pbqSPygvlqyplGArlme1CqHKoLSzhS4UcRhRMiGxUKkWGpgpbIaX8u54nzkhsDxegrkq0dQ6INFRwSCpTR0GohC00YuYxDh//Tv1ca0r3w4j8kmyi29Dy1OOdsVKdBhUOGXGaDdbNHjqIm6kzMBBM71CKbyxvrACydPcXyuAcqx1I5R6dusjt7W6hcgJ1i2fdAu2WBkCm7B+V76wQBoUwgihk0ZnFq1R8+0w7gVPCtHImF0DmFayNGOfXZe+jp4x+7iD2aII2mL9rkE2j6kuY0CW88vRzeJ4/PE2ZefZXg1jrp4iz6uc/ek5QCxelzUBTY00z7D1Nl7Fspj39YPzuUDJRxQ+wipdoqo4C6wwky1yhLEs3OIJqz8O67FKYgJqJGjfXxkP2qTU4BRUH6A4L2QhuEUXdBe6Pio/06jq14/HzZO3759ib5YIyRDoWKMDojtQxRCuM3rhIPhyycOMXOfsb1mWW+cNyhU5f0ckOY9zAYipGHkLD4NARzGfF3XEaHBapwcJVLKkvmzBcCBGxd01gDi85jOa43ZebrZQrgQluwsFyaEt2+voblKvonz9A/dQLL99hd32QUxzT8KjNn5mkGHj00AmjhQLTP5EAy8esEbXCxePGw4J9uZKxFmotNiz91xOepShX/uQSdeVQuz7LcmXCjooiKe6DijtmgU49RziwNx8d3bYbjmHwyQgmL1fFpLA2DImfcG+DYFn5lAV+M0BOb/bUOh1c1y56m6dhsTDOga+MRrYM6lf3TqExhBX1mA4WHg6IgJ6dJwFwt4vpBwTg2HPPLDY79NKLiBURjGxW493rad7dR0YhxbpgVHkHuMenu0R9PcB2Htt8g8gPsZMxydRs72sQcO026v4NWBRvjTRSas6JN4Wj6lOApnIGnz1lM9uDcsz8NwO9cehN5uE00sam7s7itlMmoT6/7IChbPtskswwbr/fumiqmU9Beq4NI+tzcGWKM4WitirO8yNIUaI72dsHTFIVNnvIA066VIZlM+9kBXSToVLF6UG5AP/74I5jDHZTrUe/MkycRtpDg+VwaarLEYbmumbb+sk9EjmaeKgkKX3zwkkpUy8QBX41QRRkbeXy2xrNt+65zfJJmWMkUtHs+wVTN9kPLaIcSsHt+ybQDth1SdVJ0JsmUwbPMXVd7gHDpNNIqsJTCLVLsmoex/uhMO8B8o4ENpERMOosISzHXKh3kd2/fJnMDECCKDL9IHprTnmHQ2uCYHCPkPdAeVhjbktAWBNg4pkIxtrA6DxqZhZYkEzb20RCV1NDjlOr+kLmxxVztGPM0wdaMz8xRjK+wX0uYTy7Rzrdx25qjxxeYjCPe2N7C5Clm2MU23l3QvlDxUNqiqNdx/PJ8WvWAsEjJnRDtu3ikLM08mNX+xpU+7779LSzL4k986kkyP8Rc30FZNiLr8sTmJUaRhChi7NbpzrdYmoxZNvMYnXC0NiYl4oCEwsC/95f/CgDVRo2lx56kbhQq6SGQKCWZrCxweKgoYgFI5lrltbbVneAkw/eB9jQqNxmdoEk+2ARlCL2MLy+nhO0QqxLRaBmSAg5HhsMuBAIeeVpgu2LqXhvclaTqOCLzXBQF+1fLdcjZpVkCX9KduByMq3zrdZe1Q59TR0Z84ktNmj/zJ+CLP4VXsThTL+fKm5dvk6OJ0zHH3ABETixtzEdM+0f1I1QfRh7/V4HPTxv6Pwt8Hvhv//Ue1r/9JSttrKxgcaUccA92ttlpxeQ5VJvQOGWYXykQ0uAaWN0ud/Jn5uaoev5dLeXF8yewLJtbV27Rn/QJ/Ao6z0jSmIrlIgOPt14pTeg+9rGSae+ajKtmzIxwKSYB0hrRtB2yrYBKRyN0ymy9SljXqG7GXqJpzcOgK6hmHgNSDIaDfcP1rZSO6ONVXHbeauFakExqPHq0nEDXr95CHT+GSVNMVECu0FFB7odsbJTSxJXlBYaZTbs6ogiatCwPoyFbdWgc44OrOg9Bm066ytLplC42L7wsEHsFJ669wfoLCauXL7L1TpPeRgVXS1TF56jfBGD1oIub5eRa84gUVPKcve40oz0MMY6Fdj1GkwCVwxtXNYephiAExyUcK0a1JqnJYLjF7mAbAcw15u8eogW4ShGGfdIceiMYkTMflhPOoA8SG1mp8MjR0nX/zRdfxKvUsdMMhcYqIsgVO/1y0VCbX8ERBU6couwqblChqDrodEItyRmJgguPQ+dZ8OuCg8tw87aiMmWsJ+SlGx5QHUq07fLspx7l7IkTDMd7XPrW/8KeXCQaJ/jDIUfELOc5QocGQyKuscWAnByFddjCVjGXN8vP8uj5k9T399A6QziaY045ROzubGMKi7zI8GvmLmj3a5CnE3YOSxajBO0RIsk5WFihLi28H2Y/+51yK9hWhmMsUBodOuhhRnOh9Duotuo8fnwZazTG0oZ900FFguWT9zwnxoXBluBb94H2yQjvW18hPDhAPPoc2WOPkcsHJ31Vr1AsLiDXVu8ufL9XSelPQfvDnePv/p5XwcozGnci30ZjZFbQd5vsu6cQjz4OW1uM+5sYDL2J4aDIyIMAJSW2Lkj4weTxBQKpCwZxqapoVAKUbGNQfOLJqYP8+jpJf1jKD/Pyuk6kYZLarL70IgD/u09+ApNCfOwoH5stN38GucFNu0gNSeJQmZM4ARz78YTFtsNkS7O/qnCzEKQmExlVKeltB0T7cOR8CdTv+Al4dVApzLdgbmpKtLa6ge1oes0jDOaO49iCq1Ofg6MzR2m0Uny7woFJaeJgC4mZHDDYb2OvaBwpuTkSXBoozlQlv7ji8mzbxpWCeSrYgaH+ExELT8Ezxw9QBl7q3gOGampeZlcSpN/BGMHKNIVhuLVTZnCPhrg6Zb1XnrvlTo2hNUuY9hCphQ5nmOzC7W+nnG1C3xJklZC5KKKwDY6WqKyKW3HYTq5jIVHkdLcUt/6pR3jVxUjN7X1DaCxmvdI4rxYG6FSQKs3s3DyzjQZxmtLfvMp4KsBKC8PO7TJ9YXlhDq0lAzekmR9SrWjor7FYHxMVOYeDDeJxj9O0OCUb2NjsE6OnQPvUM5IFX9JNznL82HEOJ2Ne/Mo/ZTgxzLjzBCGY2i7raw9eg7OdBn7TYrDf57AcmkgyQzKGdgf0sMfN3XKsP9WoEdkzzE7bY4Z7exhHY4xNGj3Y0/6ACR2g8hiTGNa6JWi/ePE8cnBAWq0T+DYmTbD9gLGCl3sFxx2fji/okaGNYZsxVVwqOOSY72rOJqt1FODlQ/JclhnylakrfK08oGQS4RURheeRuSHeJC7dyd0f8sZnEJZ9EpSgPbRzTG7INLiyoDP11GgFPkVlBtcqEGmBjcJqlpsjPwhoP9YMKISHTUy3tYCQmpVaeY/s314ltsvXMEWBn6coY8jfI5HPTRnd55gcEPfGq2qVkZTULQtXWGQjgRg3oDlC3/ccgQQtHMTJAFSIGRvMzg75qItX6zAvWsxTIzz2HDKqIHaHSHJcZbjlnecTny5dJ1+/PsGrLFBNQir2Ahv7fQCWA5dhtYUXOGirBO2yHuBnMblXJQ98pC44O9sE4MrVm2S54df/+e+jleJTz16g0VlETcaovTG7F59EhQEXNl/lQM3A7g7b7Rm6jRYNWaExGtMxIcddj7qTsGH2UBTMPvE0f+cf/z3+77/232NbAQ2jUEkXS9gkWUHXlaS+JMk0wvVYmH6+W70JTvZ+pj2Py3vF8Zqow03iIkAVktakz3KzhicmOAuK4ycNz34GHj0FK/OCpbPTJ0jHd1l2AJUmxIGLwbA/zWg/t9ChWpNYno3rVbl4TiDbdYTpI6epHszM4ix2OFstz9/GrZvkBWTJhFnbp2oZYsuC4geLQP2oPqp/m+rDgPaRMeb6ff+/SZlP98e6hF/B0pLFxZLZG2/u0m0kjLRCA+3HDJaVgzE4+h5o7ywuMZExb4hDjOXSrmuOHXsErTWvv/EataCOpQqcLKblBFzZvM2wN6DVmuP06RVyo3k56RNi8Sg1bo41dXdCVdRQh/t06t+GjW/RNi6NNvh2wta2weuUA2924KHQREjeekuDPWTGi8lTn6TfJgwgGtc5tTiHtC0Ob28znutgLAvTizDax+SabT8gnkQEtTq1MCBKDJUgpeE2KVBkfYEVW3dZzQ+szlmk0TwR3iR6boL9bMwvNm/SbMe8o59gMmyy/Rrc/AOb4XUXUXWpGotWu06aFwx3DlBaYxc5Is/ZuTNhhj7KczG+R38cUDWCfB++tTnt7643qIwytOPQdwPMcIPJ4Tq+dHCnkwCA1hmMLaRIkTLhVrcgxzAfOghZgnYhBFLYPP5IKZm7/MrLaL+KiyBLU1SRgeIuaK8fOU5QTJC5QjkVKtV5dL2BykZUlEGmKaFf4PtQe8bghfDqNzUclEBlQg7TRZU9jAmsBpOKyy//uz8BwO/81t9G12fpRwJzUK54HWGzJNqc5wgLtCiEoapmsQ9nsIoJlzbLFoETj58m2NmjcCGp+Rx3ywlyd3sHFBRZjlfXxCOIRuDXDCqJ2e3dYdpDqmqERtCfnWHeen/v9g+l3CpCClwMQmtUxUGPElrTrPYzT57HluAMR8jA52DYwgWWzt8D6OOHOMfz8tdRWULy3PM4x0tWOdcPOlxrU5CfOoU0wI13PtThWtK7Gz31MBO6u+VXkXnOzJTt6A4nWFlplHFoanDuHAhB9u7rFNrh6mhCKA2NSpXMsfHzlFhYGP1HZxeU1lhZwSAtj7fqB7zxWhtjNEcWXJYWT5FmGVeuryJxUDpDmYLcshinguvf+kMAvnj6LDOiyp88e5SGLYmVIdIKLxmiIwsCwdxyC4FAWykXPukyuyjoDzLWv+bha4tYJjgjSXe9it+GlXPlOHYHtLtT8NVwBAvLZVvbrY0dHB2z2nyG24tfwNc5V26Ui8HlhZPUQsVY2EQo5oUP6Yi0lxBFs7grBT427wwUs57g+VmH8L5rJBQONTwmCxOWnjVUpOZC0+LGWLMdl8d2x3HcDgt8t0IuHZZbJSDp3r6NtODxfkZFZKxPx6ulVo2BNUc16qFNjZlzVeorsLMWsbnVQ4qAJGgRDA8x9YKwOmRg6tSby8j+OteKHv1rhtGBob3sovoO9m3NuCu4tSlY8rPyek+3WJi8Q5ykUKny+NFSkrx55XUm+T1G+3B7Gpm4sECuBH3Lo1n0CKoezF+g4duIdkzR22Jpp09HhNSx8XGIjWI8Ta0QEp59yqKv4XOf+DMA/Obv/yqDCThulZqsUG332DpMSZJ7bPtsVSI6DYTosfZ2af7aPTQYA52ZHDWecHOvlO+fbtToDtq03XIu7u8eYGwNWGTJg1ntd0D7HV83kyfkiWatVwKRR86ewhqNSBsz5EIg4xjHD3nhoHyOH28HSMroty4xCQXL1IhR0+vju4B2v4KWFm42whRQFDlcuABf+tLdOLd8NMARGYXnk3sh3mTyw2XZ75Qf3Me0B3gehLogL8qs9vY0PnOlUWMkKri2RkwKHFEg6yESUbYN/BGrFdgou4InI4ayga77HJ26/++urpJZPtq2IUvx8ztZ7Q+qMQoMugCbHKU04yxHAGFQJbUk7SmTG3dBjhs4FU2q7y1dQynQOMjjAb6BOA5xdg/IigSq5f1amJRWe5akfpbKepcsc2jolD2V3wXt33jlNXB86B9AkXJ7v7yW5psV+p0FAgcKuwTtdiPEz1Nyt0LuBxhpODfNvL905RbXtgwvv1i6xn/p+acxfhOxvkEWC3bOHqU4eZKFwQbjfo6KEzZnQ5Tr0WyewBv3kdqwpKs8JueIiUmCTdazlJ/+d3+OM5/5BLZ2qKIwaR9tV9iID0ldl8IOKRKFDHwWqiVo3+hFeOmE6L3nPeoDAsey0JMxv/Z73+Yf/M4lnMEhc5U6jSJhT2kSNCNXc6kd45wqcH1RelSk47v97KgCnaXEldLEcef6KgBnOy1y4dJacPjsFwN+6rOSRqfJ1n7G/v69+dibb3Nu2i65f/MakfLI0gmucJl1JJklUfmD7Tcf1Uf1v+X6MKPuy0KI3xJC/AUhxJ8H/gXwHSHEzwshfv5f8/H921uuh5Qei/PlYDzY2MUTmjUnJcsNY60RJkUojSsdbq6X/eyd5SN0RcyqGVPYLqGbcOrk0wC89vprBG6NQOdUVUEQNHjplZcBOHv2WSpVwa1Rxtvrmny1yn4qiIqMthnQXtugZr2N3zCgFa00xQ8tFmZitncNmWuoVAXjrZKtXe9WGRaG+fYQN0nJkwqzTzRoLUGShDjSZ+7YMsYYbg6G6GoVNUooam1MrlibygDnjx5jlOZgpQQuNLwWEQV618GyBbXl73EenRCax1hUB7R0l2d5k8dmBPqxs8SnQ9pfNDz1H8D8BWASIiseJslZPlbK9zfWdpHakMYRWuu7oH0pCFCugwkDDoc286HgjLF457pmJ9HQaBIMx1jSZ78+w1ApmttvUxUuVO7tAsf9lNF6hd6moV4ZsDYsd21blkO9LuiXL4clHJ6YmtHdevsSSVjBli46iSjynFFcMIwTHNsmnF+mEg9BgfKruG4d0ZpFWwV+nODECUORczSUbBWGR58UDELF+r+0IJJE5OB5aM+DXo+q1SEP6vzpn/sxHNvmzXd/m8O9CSO3Re/m6r3MFcAWFnOiWYL34RLBqIZUY65tllTXycdO423fJqt5xLWQptSEgUccxQyHETpTuA1FMoF0Am5NYdKM3cPyNdp+QJCPKGybuN5k1vrXII2HMvZNSnw0aI32LMwoYW65VEmce/pR0Ap3MMSq1DgcNGj6UF+59xTvA+15BtGY4YkTOO0FLGFjCYfMPMimK1MgKjXEkVOwcQuiMd+rpLx3Hj5IHg9gh3VEoek0y9/vjyJ0kmNJGKkCwpDi6DLFlcvciC28LGHJEVh+QOG4WHlMJKz3OWZ/P2VM2Rs/TEvgVa36XMIiMxY1b8zZqRnd62tryMSg0WQmI8fmsDfh1tuvYds2T/szxPNHOTlXApleZkhlQTgZkhceYU3SdDK8eEKuEyrS5tgpi9qZnN0dUC+EZEXK8CWBqmnmzwmcKTiypiaAXmUKkDUsHnkKgJu7+7jjAcoR5J5DYDLevVpe3ysnT1MN4TaGChYLwodon/hAkDODnC0YpRbD3PBY4+EAbIEKCcXdaMqLDYuKLXjxsEAbg556hjgheKGLtl2WpgvL7uoq2oKKHoOdsnnQB2CxVWdiVwnHA6TTpjLjUTuuSZ7ZZbQJ81sdJpUaxWjA8sWYylIXELSOXaQzNqy/uk2viKgtSU7/hGD2MwdU2hpnw+Klrxs8lVKPDtHRHsIYivEQE4Y8cbS8IW5ff4dxVi7OkwL2p2ZQpxbmyWTAKIO27uO22lDp0Dt2nhunH8GxLRrvfAdGewRYhDgk6LvnBmN4ZCbl+PwOn774cZphhTdXr/DSt78NQlBzZ2h7CXF98ADb7tkCr9HCr/SJBpr9deiWqlza1S5KwfpO2Z51qtIgtxt06uWc0D/okmuFVzGksV0y2tN6L9Ou84S9fswoS6nX6rTsMUIr8tY8qdDINKWLz0akebZlU3ckbeFyYFK2GBHg0MK/K9/+bky74wYox0YWMVIrsryAahWOHLn7O8WohyMKkrCCsZxpRnv1A5/zj1xBcLenXdgBoSsIRVa62uuCxlw5ji63Gox0gGsrzCTHtQp0tYKP/VBfju+nhFslZMIkC8maNc445bnbWVsjFQ64LqbIcfNpJBjvZdo1qgDLFPQnJTBr+A7CcckcyaxTjhFxF0RawatYxPreXBhKiRYORWgIOzXikYMuBBkKag2UKdBGMVP1GK6cQ+ox9nZCJctJyLn4iY8D8I2XXsI4HkyGEI25vVsaONaXZkmdCoENOVN5fCPANRolnFKl5Nscn2ajr95a5fqm5q1X7oD2Z1B+HXNjmzho0J+tsLDUxg8Etc1rjCLBbsfBsSqEzdNYWLjRkMxM+GQ4ix/NMSZl195iW42ItGDGdhBCo9IhG1JSpBPsvMKhqZPGCisI7kYnbvYmeFn0/p72pI+0HaI05nBzh//PP/h7/F/++v9If2MD2/aZo8AyGTfI+A4x1ccL+qem40E2jZyZMu0qjigMTCoODoKtG+W4c3ZujjQHwiozTYEQgsfONfEdePtKj2FUjlXebIOzjXKzbn/tMmPto9IIFxdfinLjR33EtP9xLSHE37wvH/37/dufFUL859/l8Sen7eMPe2xGCPEVIcRYCPHfveex/0oIcVsI8b0Xjg+pDwPafWAX+CzwOWAfCICfAX76j/KiPwo1xiUnZGm+ZDz3tnY4EtmMUNzMCsZa45BjaYUjbFanzvGzR0+wa2J6JmNs2wROzOlT5WLz9VdfJ/BreMbgZwWBX+Pll0vQfu7sc/g+vHOQIhAc7Fi8cGPESvQqRw7WsHqSUX4e9/wnQNp4yRBX+CyuxEQKbtzQLCzA+MCiu2UxwmLutMFOR1jjFKvSZulZm0pLkCYBOnc4drJc0F1a30I3GqjuAJNkGKXZyMqBcOn4MQ6jAtdJqTqCltMkIkdtOtRXQH4YZXTzGJ1qhSf1LR4JFcHCM9Tm6tj1lLdvaYwwVBfA1hWM64LKWFkpF2hrazsIIUgmY4Qx7ExdzJcrPrntoOo1xhPJ4qzguZMWxQ68tF1ArYGb5bi5RDmKd2sruJMJnbh/z10XePeNmB3b4cBAtjegq3KSFCrYNJowHJZ9dZZweezpkulbvXqTge/hWA4qjlF5zt40J3am1cBy6lSSAcKIEvwJC2fmOMpWEI1opwVdk3GiIkkVOK7Ef9KQC0PvJZdBUp77olaDXg9Xhnh2nXBlhp/67KcwRvNPfv03KWrz7B3m9N698r5TLoXAGoRIpRmNx9zeKSfLMydXkIcHZK0aSa2GrQrm200AdncP0VmBXSuBkzHg1BU6ze62Jcz4FdxsQu7YUGlS/9fRzw7glGyAZwmEMujAwuQ5v/AX/yyf/oUv8r//j/8iaI0zHFF4VQbjkMVl8cD1+L6M9nhCgSYPA9zp4tuVIdl7mHZFXma0n3qs7A+9+tb3PNw7bvGlc/wHnxO7UsfSmlDmVOo1jDGMuj1cC2JT8MK6YnBuhd4kQtw64Ck0Riik66MsF1EkZMIiU3/0mBuBAqUZpuV1VqmGjAJBd+QTuAPOnfsYAG+vbyAThUKRS8Mosbnx0ktorfnUM8+gEgt54jjVoDzHg9yQWjnO/gjtOSyGB7j7N/EPN1BxudBtC5faYkHlaU24UaX9poeX5fz/2fvvaMuy/L4P++y9Tz433/tSvVS5OsfpxvQMgCGJzGwmkCK1xGSRki1boklaXrJoUXKgZVkysWQGk7KpJUgUTZAiQJAgBYAEBpOnezqH6q5cr15+99184t7bf5xb9arj9ISGBXB+a71Vr256556wz+/7+31/36/36JiGqzBzIUYlXBjcJBh+ESkSZA6dzjk6jRqzvGB47QZWlVhZEuicN9+5AcDGw5fIPU0qFOdlDSkETA8YHrSI1h1KR7M9kUQOnIk/+PbYIcRFskcl/uRIwXNdxXFueXNkKnYOVZe51rKUfsTSvLO0v7WFUAIrphgn585cwHG52aIIA9zRFOV38LuwK1LitZRH4x61awET2WZWlCjbZzw5xpXgh+cZv9hice8IZyPFLmistTi1kt5zmkfOKNIduPqru3QHOxx7PQQOTCfoOOSR1Wqk5/a1y2Ql5NqSlZbD3YqZcHZpkVRGJOmMJgmi3WLbjum7CdPGw+xe/GFIUnj5f0QcvEGnMPizMdngKuy+Are+QLj9NZ5uXqG5kvCTT/8gAH/vb/0/yDOL8mN6VuIvTbm5m73L/q3ebWNcTRyNuPkaHB1Uo7Ce7lNquD1nB202FzEyptVeQwoYDkYUOsONIJ2+u9M+G4EfgnIF6BKtc67vVMKlF85fINm9XgnW99ZJrEalKQcmpO4KHmpU50MPnwEpR6SsUkMIQTJn0XyUEJ3yQrQfIvMpypTkuX7fa+x0iINmFtZAa5ws/2RAexBCls4X8YA4kMQyI88lpdU88dwP8NvPrvNHfvA5ylQSeBqmOZ4oSaLoOxKhuxt+2EAqS5GVTDtNLtSr++7OrRtk1sX6ARQF7txnO30vPZ6KHq8oOZ5UwL4ZuGjlUDqKnnMC2sOmIHQapGZ0jyIfSoHGITeW8GwLMYS8dCgwUGtSzu1xfSfAnloia4QEt8eEWYkipXHhEs12gzt3trk1zSHLYDbi1lbVoHEeu4CflgSOILdNkijjoKVxpcVYB2EFWRyyOb8H7W5f587Ny+zcuUmv2+apRy+hnQB7c5/+wjpBCMvFBO/cBu39y+ybgImjqcs2+HWEVyOYTcnMhMgRPKp6JLM2M1FyRydMC0XPlViVMygnJNajmUsyG5F4LYrSgOuzElaCjrf7U4Li/aDdJEOkH5EOd/jKa9U6Yazly69cx80TAilYFSnXvRlbueaBrscxhoOyPLGcma+H2VyHYBb7CCT7V68CcLG7QFFYVLN+jw7vBDXOrLqEdshX3zRkhUW1WjwwLzDdufEW49KnzCaAwhUK60iM0WDef619L37zh7X2T9/1R/823vtz1tq/8hEveQL4QNAOpMB/CPz5D3juHwPPfjvbBB9PPf5PfMTPn/x2//Bv5LDW8vldxVHqc2qhAu27W3ss5IKoULySpQy1RpgcZQyB9NiaK8evnD1PYku0hSNHEjgp58/dtX17lciNMEKhspwwbPKNr1d2b4899gzTFO6kOedrgvPhNfTNr9HJd5g2FxlOfxBqp3AH2/D2bZgdUZMRUTMl9uHV65r52sZoy8PrpfgLluT2iFDntB5aYpxanj80TIqAInc5f7ZK6N64fAVW1zFpCls7FEF4T1Rt/fQGx5kldBI6QYRRiulUIwffZJ79/hCSaOlBTrngrz6BG3aIhcvyWsE0hWs7lrAL2BjrBqAMGwvVzP2N29sIpchn0wq0zymTq3GIVYLMb2BKWOxI1p4QXCgd3rlu2fEauAjCWYlBc0ckTJ02oafg6G0A9rcsLwxmCN+QNGOEGnJ0WFKMFVIImq3qXjAZgxIO5555GCUEW3uHHOQpnuNh84KiyNk7rlo83U4bx3UJZlMwEidqIYTAjZYwjQZm2qedaFIM3dDiSkgLixNC80cM3tTlxmsFeWopazXutvprzgJ51OSP/+HfBsAv/sv/GmEjaLa59cZt9rffLcIDcyVmJrxxe4+iyOidWmFVF5BMGa+ukHTaCK1ZblVtqb39A0xZ4tZOkmC3rqHIOJiD9oUgwCkzsigg9CNC8Z0ndx8YygXHx1cWUVhs5FAKw2Nr5/mjf+2v4fSWMbMpqtD0pzFZEXDmwsnbS2NJNdTu37zZhBKDjiL8u6BdhGhboO8Xs7JFZfcWhHD6IuzcgmGfj4q7tm9SBh/ZoQpqDRAQ6xm1uYL88cEBdU+w1jHcHBj+iRMwCGs8ev02nTIj9xyGb4dMcx9RpGjpkH6b9PjCGpQpEKVhlN4F7TEFLvsTF09MefiBihn0+q0t7CzHYMgkTGYuV74y92d/9DEmuaL9wAm1YVBYtMpxtye0wj5te0jiC0qTIvdeJc+PaeMSODBuFzz24y415RJ/KkO3NLFw0LZACoXMJnB8DakgCMcUE8Fiy2Nj3rXceeMdjCywsiQqc964UQG8B567wKGQdPDoCh+KGVl/ynTaIzxXkmg4mEku1atr/INCCsESNfokTIMKZG7GitVQ8o3jklmR3huBqHczcrfO6pxxsntnB+O55FKjnZL9veq86TXaCB/cSQZhj9vNPpmUrNqAS8949FYdKDtkM83xwR6Dw2Nc0eDtf+aTcpqLZzMWbMZAlBzMiwmp1Jx+xOFHfjSly5scbjf46uBBhNdEzCaY0OORuVf71s1r6LmCfKbhYG6lttHqoN0Ib3aAJwz9xiG30iv0iDgvm1zZeAqaK7A9hOkBK3deYnnvHcTxdXQxg2gBFh4k3Pw0r2+c5k/9sd+NFIIvf/l/4J/81C47b8T4h9DrSEbhgN3dk/3cXmhjgNbyMekE+keWqAZi1ieXPrt3qkLjZvcUzXMxJuqx6FdA7fh4DzeALHEx93XakgmEdzXWdEmhS27uDgC4dOkixcFtjJKo7gopGpVljJyIBV/cu24rv/aUzFq6VAAnQaMQuB8hROe6IToIEOkMRxeUxbvZMFlqUdkxjrCMaw2CrEAiqvnz73aEUQXY0wScAN+VNLyUPK1Ae7B5jr/5238rDz35BHlWidPJyRRXGGZReM9d4TuJWtRAKYuaFoy6XdZrDko59A92GadgVKX+796jx79/pl2lFmU1g3EF2luhR+66uL53TwMm6Vf+7KFqYu0JRT6UYIVLYS3hhTrN2YQ900P7PrnrUN5lzIiAoGYZra/gjCHYOibSYw6s5FOffgKAL9zchTzFWsvt/aoAaZ56mFqR40rIbZPp4pTtoOSQGS4SEOShS9dkRFHEdDLgja/9fQB+9AeeRYYtzO4ORaKZrK7juRAf7+M8/ghROebgeIAWsORWOSj1FbyixORjSpvxWMOlkdcZ5jV2Ew+ha9QdS7+RooFztodODFMZYJstSlNiHJ+V2l0huglunryLHq+tgXSC8CLK4T4vvHnr3nNffu0mXjJBCYkqJwjXMptJnvJ9fCG4nOfvU47PplM0lixy0ElOf2sLpSRn2h3yosRrxZWFKYAQ+HGDh5fHJDl87S2Lrjc51W3TCiKmkyGHR1PyrGCaZ4TCxzgKa01l2fq9+E0ZQojTQoi3hBD/rRDiTSHEzwghovlzvyKE+NT8978uhHheCPG6EOIv3/f+G0KIvyyE+IYQ4lUhxAPzx//43S65EOIPCiFeE0K8LIT4vBDCA/5j4CeFEC8JIX7y/m2y1k6ttV8A3jebYa39irV259v9vt905RVCnAX+KvBpwAJfBv49a+3Hl07+TRZCCMRyRv+1kqWlquO7e2cPoUpWxzGpTnAs1E2ONIZAKba2qmO0cf4ct9KCO7nmYSU468LFcw8ghODt195GWo8kqFeur26d116sROieeuoZLm9rtMz4VPo2MjB80V/kSlxjpe7S2QlpezfgC5+HWR8Wa7QXVjiUmqWVnNtXBdm2oFaHAo+kl3Dlek44OSaoK2ytzZdeN6QCpsBsFvPQuQq0v/PWVeznPof54q8iZjPyIGJ7t0o2T59eZWA0scrphgvMKEmOIM4dmhvfwk4NWxz6ZyCoZslaBCS1CQtty+XbsP6kxagIZAQObNarG9WVrV0Cx2GSlxVon2/XcquBEJCq6ga01C3xGx5PrEuubQu+dqrG7xGS2rjABhJrCwLRQCzEMNnFOCE/+7U1VJizUc/YDuroU/uYqwmH79RhqdKCC8wh6a1dogsL+N0WZ5YWuLK7z6tf/go/ttBEjPuURcHeoAK1rV6P0DV4RzO0cfBq1c3LUSEsrGBeeJPmHCwNyFkNXXZSjQgso5rm4ve5fPmy5fVfKsiiBuQZTCYEtToq6vEDn3uIld4SO/vX+NVXb/D7PnuKQZlx5auX0Z97hpXOCRBJxuCqEa/fqG68aw9coHPjBhjN0dkzFLtDhC451ay62oeH+5iixIlPqtYq1og8Z78/7xiGPrLMmHUadOR32ertveHGFWgvLSb00FJTOy6or3kcppLT4+NKhG7SwAjJxn2gfTxX+35Xp31Wddp1eALaXVklzLmZEc7FhLQt8e92y888ALeuwjuvwqc+96GbKoRESR9HfXQCHjZqZCiiMiVqteD2bY4Hh6wYQ7tuOOPm/NPpFLHwCI/dvIldCpi4LuyGZHUfkWcYoUjL6be4M6vIsShTIoy+R48P63U0ioPcodA5n3ryElIqru/tMziYUF/w0UoxmCiufLUC7c/1Nkh6pzizdOJlf5CXNI72WZhcRcV18jhGN9poX2J3XmR255dxuudY0po92uiGwyPP+WwzoXzTUqMC7coIOHi9EvMsEoJ4SjaC3qpkde00L73+Jttv3eDUH8gxuUv/5hbTLKMVNmg8XMfIgPNy3r2cHjA7hNws4K2X7KQG1zo8oBJ48VV4+OkPFAFbpc6AlHdalsQWhMLluZ7DP9zKuTJOeKQVA5aok3LsNlmb+17f2T1CKYuIDMaUHOxXXd52s4fvpKgCktMRuUhZk21ioxECVh93GLzTICtcrlzepnXTkh516DwDZ39kieJQsTEYMap1ucw+qbJ4QKgNa93XMHnJS7eeYutNxVm/zdrGDUzg8fBd0H7nNhNdMs0d0tJysFeh5/Vak0wF1Mp9fKfgoJ4QZwec8R6iCBQ3gxZZ7xT+JAd3FRVbjp2CURjSUadoi6p4sWYspetR/+Fn+V0Pnudn33iHn3/xb/JA60+QX7cwCpjVZ9w5zFhdrd7T69S4pVwMA+pdKLah0QY7PaavPQZHO7hSsrG2SueBkMkvtVn2PXbTgv7RLnIRjHHIJgl+JTBNMoKFu/elsqAoCm4eVIXehx9+AHu0SxbXaEQRwzwhKA1j5XPOO1knKktIg7LuvS5giv5ouzfA8UOs78NkhtI5xXtA+yyx+Hkf6brMohprszlbJvgEQPtdN4wkgTBCKZ9WmJLnitKWlFGDo0sPMVhYpswtDuBlMxzHkkTxvfXxO4levcZMCUSRMW606Xmw0O2xu7/LnTuHaMfB1RqZpzhCkH7ATLvMUxCW4bha75qhR+55+J5LiIMpK3eJ7kXwRQ0pKop8qJpVsVy65Bbc03Vix/CSPM/C02dZRqNNVq3bwsUJJ4yWlqB2SO35yzw+HNI/81meeeZRfvkXPs8X377GHz3bor+3T1IUxIEPiz3qV3ZxlUtehlBPsEHAmBwrxpSuTxH6GFNwZn2N1y+/zc/+zH8FwI99/5MQtrBX3yE1imx9lfrkGK8sMAs9qDUpRndIzDNs+nONlNoS6ijAmfTJoimLQYdNGbFXzuhLl6YO2FYjfDulIzxaJubmtKB0Y+Jui/KyQBvFyryqdedwjFOkFNaSW4snBJnNkWmC59eZJBNeefv6vePx5cu3cZJjxlqTZxMe8z22RpJ3kpLznsfrWcZ0NiS+Tzm+HA/JlSQLPPrXtrHWcmZtBZUZcuuw7Uh2bxzzw2fmrjBBi3pyjafOGp6/Inl5r8ZmPeBCo8vX0xlbN7dIFlok+ZQg9DCOxKKhLMH9hFh/34t78b+xv/QZoPtd/tij/7v44S99k9dcAv6UtfaLQoj/N/BvA//Ze17zH1hr+0IIBfyyEOIxa+0r8+cOrbVPCSH+baru+J9+z3v/EvBj1to7QoiWtTYXQvwl4FPW2v/ld/b1vrX4OPT4/w74/wIrwCng71P5tv8rHUFLoTEYVaPdrlPkBaPZLmGuWJtXeIUtsRZCKdnergbyNjc32E9KBjO4pit27VLHZX3zHEVR8OaV61gVIlXA5ds7zKYzFpfXWVrpcXmQsxJMaArNO9GDhGfP4wWaO7dr6NtXaG1/HprtaphyMKSTFUgh2FzMGMaGWy9ZnnwKvv/xAArBOwczlu0BTivgta022sBCV5AC40mNR+e2bzffvo5c6GE8DzOekEcxO3vz77NxiqksaZPT8CtqfHIE7aaLe//oblnAr/0CbL9HJvhDooGPxXL6TIEx8OZti9eKQFQd9DPzGdFrO/v4jsTqFsq47N8F7Y0axnOZ5T6xO8WNXqXUCatPCi6miqt7IYelpDZJEHM58XYOLJ6F2jJXXr/KdLLDUxualrJEqsm0UbC4MObodYfD6zPC4assyVfJjw9x0gqUP3jxDACvfOUruFEdlRXIImd37tHeXFjC9Q3+LMFYnyA+ESVzFk5jTY4/7BMg6ZOzEUkyLfCEYC83LPQ8Fh6Awazg8Oo6xgBz5eOaWkA32vyhH/+tAPz8l3+Jfl+wsRHSze/w4itDdo5OEp9kAuNkwo3dCrSvP3ie9s1bWCUZrK2SNRpYAWtz2uL+4QG2MBjH4AXgeICnmQ4nTGfVvH7bdyHPmNUbdD+CBv5dCa+G5xhUYShjDy0LxHHOQiCZ5Q7Z8TFKW3aTDmFUUIvvF6Gr/n03PX5C4TpY171H/3RF9d0Le0KRN7ZE3mUQuB6cexAOduFo7yM3txZdJAzWPvI1YbMFCHydEd9VkB8d4xYwNSU33D4bDUm8/ineOoL+K28wzny83EPrCINBWUtm9bc1115Yi2s0lIbJXI056sSEGxlT5TAYlSy3DZtnHsVYy0svvgWAVR7vXL7NYOcO3U6HTbeD2TxNq1bt36Ico5NXeObmLxKoEbMzG8SLn6ZVe4xW59OY3gN4hSHMDR1yatzmYPQa3uQqWTlGO1ATDpqC4OgO6BwWHwY3JIgq0B7XYG29kii+/c4NpJ/juCXX3qgol2sLa9hawaJsUrt7/GaHjA/q1FYDErdgPzWc9wTRK1+E3duwfeMD95MUgot0EQYu06e0hqYreKSp2E9TjksXKX3CVkbptTk1TxhvHwzxypTGmkGj2d2tQPtCYwFXJohCUK7X8VD0ZBNt8oruLhwWz3mE3SZyd5/i9oiVM20u/k5wY8mstUiY56wnloyCYQ2whsbB2+hiTHj+NGun24wiuHHUocihUCmNKGSt26UoC27dvsI4h6S0HB5U3cL1RpuBjOgUhyhPokOP0EKW77DoC3QQMmosAgJuXCVsnsFEPVIpGXEyouFJQdcWXA3X+Hd/9DMA/Owv/g02f6eicxHaiYvuC96+ciIU5jkSt9FienzMmcfBqUG7UWDSCVf2qtetRzWicwvIIEK16iwH1b23f7gHXqUgn86q6yBPLWVxX6e9LChzzfacrXTxgUvIcZ+s0cRTPnkywxiJ9gLa3kmqtM2YpnAROGRzWnxiP9ruDcB1I4znggA3m1Dq8p6dHUCaFHjpEBWHTHyfIJ2zZcL4Qz7xO4i7I2BzMTrphNT8FJMqCltivJACSeaGKDkX2csSRKAwrv8dKcffjY7vkbsRAQkzFWAaLqdaVTH+zu0dSuUgjMUWMwLk+2bac2vxshQh4HhUrc+t0KP0HRzPx0cxFzon7FTNlkBWFHk779r7yiM3FtuMabcEZjtnGNTJKCltiiuq69bxpwilOP7s72ey8RCBLYlfe5EfnB//L7zwImjLrVcrJm5noYtFUisy3CikSCWEhnrQpEUAekLmu+jAI7clZ+YCqncdeX70+5+GoEN+7SbT7hKm5rAwrHIumaeU509XTi63RvTUvDCqPJx4GW82ItNVPvJYwyXOQg5NxpE7wpWWU9MpvteANGeWFzi1JvXFOsaX5IVkNYwRQrB7PKl0D6y9R5HP8hHClCgpGQ2HXLvP9vTrV64zGB0zKTSLOuXxyKOJ5CvDglPKwQF2psN3KcfPjo8ZRXVw4eid6rtfWF8hS0E7Lv3c5/aNku2j+bUwb+qs1Uc8sC64dSiYRW0uzZs4B7dvk5aGJJ0QC7+aabcGU37742Lfi98Qcdta+8X57z8NfP8HvOYPCSG+AbwIPAzcP+v+D+f/vgCc/oD3fhH4O0KI/zl8FyqW30F8nJU3stb+N/f9/6eFEH/hk9qg3yjR80eMxJRh7nJqdYHj4zH7/Zs0mw9xBp/EE+zbHFuCsRn7h32ElKysn+L5/DZCC64nigxNLUw4f+Fxbt24wouvv8GPP1rDEYpfeLVSpr5w8Un6CcxUwYNhghpLrtomF1opmW/p//MjZlfeQf2uFfihH4Kf/4cwOiLKUhxf0AsTnE6Nmzslm3dcOudcZtdiZhspi1vHTIzPOO/w3KOS4z58XVpG4zoPPlTdSHau3MC2WhSdDmrnDtPVBfbmc1uLS8scypymVDhenVFaUA4lvbX3nNe3r8JkVIH2U9+cN9/AQyAog5yzKz5Xti3naz5iGoILZ6Mq87qxf4ijHKQ1CJNztNtHAEvNmCT0GechC1GCdEDrGVEv5KFlyfVtyWXR4NRoipWLYDW93EBc4054kRd3xjwbv8P5RZ+r1464sG55S8LC6THRS3fY/tU7tJ5T2PZ5ZqMbqDyDQPDgYw/zjz//VS6/9AriT/wbaOtQWpfdUUULayyu4jgZbpqgVYQbnHSj3e5pCs+l3LtBWzzDvs04HwqEAF0KjjA4SGpdif/pgr0vLXB0+Qq9Z48QGxuEssm4tsgf+f2f4a/+9H/Pv/j8z7H3R/8tav0Z505BOn2Hr19+iqcvSlY61XjZeDrh5kFFHT57YZ3o4AamUWfSbNGKx2jlsjFv9Owd7EKhKWxJ3AJroLQFh/tVZtRt1FFSQJYxbTTpqU+IGn83vBjlScJRwbHvYR2N6Bf0PMFN65D0B9hccKjb1OoZ/kl9hMmHdNrzMEQhK29mQAqJKwNyUyW32pZY7D0hNAA2zsONt6vZ9udO7ALfG+pjMA9cP8JKhV+kRPMEdjAY4OaWgVeCmPFY2OTcoz3eeGmN/ov/lFsbD1HLXEwRU2KIjCGxFmuKewWpjxsFFscUWG2ZpFWi47cahF1DthcwOD5m8cyMSxee5frVl3jp1cs8V/tR8Np8/fP/AIAfevpTJJmk+eAm2mRMZlcoJ7usHF3D9AWj5gp2/SlCr9pXCg/dXsWWDvFkyuLyg3whm/Cw8GlxgEz3KJ1KR2I4uoOTjGHx6crvzY3xgxn5FHoxrK9XuhI3r2/RqVsakeWX36xA++qZdXxPcF7OGwFlRt4fMpmcZeExeCnJsVrx+ParMB5WHfad23D60gfuK18oFgaClJKrHHOJLo83DP2R5ZWh4nM9HzeeUAZdekrh+R6DSYo+GjBZ9LFJxsHRCKUkrbBFqRNU6cBqjIdCyuocMzbHEQ5SKhYvtJg8f4twcYnTP9LmXu0hrlE7rtEeHNCP6swCaPZv4qdj8s46Mih56nTMr92By7bJp0pBMU9kH11fY+voiNtXXuX4yYc5HPaZzmaEvkfTr3MsAtp5HydQGM/FUSFZcUQjWsCRDn2/zkLNhfGQaHcbdynCIBmSAs17+6tpNYeqxo9+//fz0H/3C7yxv8c//Gf/nD/29Cnqn0q59U7I29dm7LxtWblYXZe1TpvhtSvEbc3GE6BGIzSGq1vVmnM2rhM92AUvQnWaLAXVRd4/3MMogxUO+Ry0J3MNsugePb6gKEq25yyoM0tN7G5O2ungCIc861NqQekFdOad9sQW9Ek4J1ps2YIjm3NKhKRounz09e34Adb30QiCYkKaW6zViPlBTNIMNx0hFxsUjkOQZFVV/5PutAPKCYndMTKXpKUh9n20gZkT4xqNlZYgmyFChVHudwe0K4fCj6ipKWMTY2suq/UG3wC2buyg1wKwYLMJvhDvp8dj8bIEpGUwrEB7M/QwvkfgRJXWQEWiIJw3akPVZKaPScyQSLUJpUduLaYZ0u5KgsGYwXGbbEmjbIY/Z+TkJESO4Mg2cBYfYnx6gddqp/md11bw/i9/m9eu32Tw8uvcPq46/q2lHtZCo8yQUY0yAUKN64V0nTrN3CUJHLxCUkjF6e7JdfLEow+yvNiDRJMcjEjOPoQNJZ07e5Xtwf4R+umzjG5PWXz9JsGz9zG8aiu4o7eZTnew7iZnapLNo5Dn8xmrNcGTqs2WTrBui29cHjKTmt1OjW0b8YgjyVJFTQrazZj+YMJ+f4woUmYmoq0UeTpCGI0sMr78xjbaaB5YXWM0S9g+PuLrV26zdmGTFV2QuJaz0mFSFLwxKzntugxmI9LWEgFw7ajgYHeA2FxBiYLdd6qGzsX1VfKkxLgeiVtdzy++OWPlsx7Cb1TXRDrkgY0uV3c0SbPHpbkdxNHWdfLiYfJ0SiA7aMfFYinL7Jtcnd+L70Z8jI74JxX2o/4vhDhD1UF/xlp7LIT4O1R6bXfjblVH8wG42Fr7Z4UQ3wf8DuAFIcTT360N/1bjQ0vDQoiOEKID/IIQ4t+fzw1sCiH+IvBPf/028X+a4cmcyCkYzhxOrVXgdv/oFkaD0PBYEFRJs4btvW2MsbQWFpFeNTPolC5HRrAzNdSClNOb1ZzoN155lVi4xLh87eWXAbhw/lPsjSx+u2DJ5hxqn1K4LIYTGjdv8/Bbr3EUr/D6hd8KjgPdHkxLVDauLIr8FC+GvbZm90W48zXI+z7O6gx1PCR3anzqoRqdhmBlGVwHDsd16lFIY6FHkWTcnkwplldI1k8zXFjgYA7ag24XYQsWXAe8GoeHBX72nnl2reH6XAytv1/9/5uEEpI6HkMyLq0LPBduFQJZ1sDzaGhDu10nK0qOdvooW3J4PMIYQy8McF0XIp/+NKRbq6q0xlTjJStPCi5MFbtJndnhCMft0iuXCQSkfsQ/flmzNXuER067eF/7FVo3rvLgzTeRZcHS/qs8unmb3aTHldeexe1sMM1q2Hny89jTTwBw7fXLpHGMcWIS2mwP79LjN3HUBJnlWDfGDU7An2otgx9hDm/TFR4aS6E0i74gLcAAh4UlwkWtF9QeSxmP6+z/StWtE0IQ+6usPbDOpx99ijRN+dXLzzMaQUrEw/EWPW/C1y8bvvBLllFmEcWEm3tVp/3BtQbuYEzRbXOsY4b7TbTncDqYC+Xs7mELSVZmXPo0XHoO8iLl4LCixnfrNXwytICk2aSj7gO2n0R4NaTnEuQF1nfAM5jjDFcKFh2HYjgiNQEpPrVGhuPc32m3SAHR/bWl2YQsCt9H/fRERGFnWGsx89l2ef+svnLg/MMwOIK9re/oK0k/AOHglSnR3NavPxgiMw0iZdW1rKkWgSN4/DPnCMqUyc6QbdfBmpjSGAJtqgT32/Bqz63BMQVCGyZzkSCv2SKOQQWK41TiiAkPXqoU5F954x102CV0mrz+lV8B4Ps3LpC2FllZi8mLYxjeQh1PuFqeZn90ETo+UXxS3BBC4MiQtFuxEFaProN0uWMbxMEa0uT4MsXJxjj9LYi60JgrbnsRnj8Da3AtnD47V5Df3sNS0AwEl9+oHEvXHj3NgmwSzMEws0NmB5CbHs1Ny7Uk5/TBLu39GxV7YvNCdUzTdwsR3h9BLtigQZ+EbTtGkHM2lvQLj1uJhxMZRNxECFherHQ4jrd2SIuEvaMKUfR6bTQ+QT5DOnXoyTlor7p8xuSoeR4RdhZYamrOroOYax4Ya8hFiWydJsgSarMxvjkmGu/iNjfJ4wZSSNa7Pg8sSm5KybSsUc7G4Af3bN/23n6Zo8RyZ6fqnm0sLlJqwUxCUCTYegBSorwWUjhk2RYLvuDQjcELoNZAXHmdmhZgJVMKCnuy1tfnyv/jjYf4M08/BsBf/Wt/o8qusimXHoqQNc2rX8rv6UY1u22sNfQPh+Qa4mJAYTQ3b1ddx3PNGs5yC5wQd7HJ0ryDPOwfkJYGP3LIE4O1mtl7lOMpcnQ6ZWtcFVTXI4vBUnSXwEpsmlJokGF4r7h3hzESwRka+EgOychsZfP6zejxeD7K9dAWXF1giuLdyvZphp9NoNkBAX6SVeBafvvWah8ad+fk5+d2BdozlJbMcosNgkrVW4Y4osQYgV/OMLGHEPKeUOd3Em1HUQQRnirIC5+kFbE6ZxftXL9F7nkgBXY2JhDyfUJ0pbVVsVzYe532ZuhjQofQmWsN9EE6J9aQvqjhSp+JrpgkkVJk1sEIQ+t0k/rsmOGRILUZ2hbMSo/Xhpo3hxN2tcsv9Q27qkEwyckaPv0nn+bxTz2KtZYvjzNuzwtAjZUFrIFankFUI59ZbGCqWfYwZClxKFUdJTSzQLBRPynM/NgPPANBC7bukGWWZHkVK3Mak2NAonVBca7Flc2HaW8fwuHhyU6JOjhODTk5oLQpSggebnqcSpr8oOxyMEgZaPjiuMYXr44oQ8VmJ0T6NawjybXFeC4LraqIsNsf4WQTpnNGSJ4c42hNURZ87dVKhO6ZzVM8c/4sAC+9fotVq/GKGcIxhEKyIh2upJpekYE13FABNwaGl64OaPqW+loTheXwapWHXFhbo5imZMrFhjHNuuQgS7jz1gzyHIyC420Y9onFLkmzwwNzx5+dG5fJpMNoNEEiYQ7aTfluB5jvxW+62BBCPDf//V8DvvCe5xtUk79DIcQS8BPfyocLIc5Za79qrf1LVGLs61TW55+AH+dHx0fdDV4Angf+EPBngH8J/ArwbwE/+eFv+1cjXOnjhhI9K1haOwXAfv82VkMxTzi0yREF3LhTzQe2T60zKTQa6KgAHMH1qcRXCefPVIWbl15+hZ5o0VItXnjhJQBW1r4PrQyNrqZRZNzWEW1P4F99mcZL7+D4Zwi/77dxZ6B465aB3gJkFmZjmoXEExmxaxiuG9IB7L0Col6SJ2P8PKG+3GOxXSUlUSBo1+BoVinIr52rBgBfvXED2WyRn1phVxt0UdBcWGDkgqdLVkKXQmimw13qfk7Quu8Gu32jUqrdPF8B9sF9N5mPiCY+U3JQhnMrgjEWdIh2fZw0YWW9ou/fuLGFYwv2jivwuBJHGCGxocdwFtBp5PPjURXTGqtwvqPIJi36xzMWsHzaFVgLXxj7HNyAz/Vc4nyM3D/CUwW126+xuX+bwmjczQc5Wn2IwYHP5DLkok4ySBDAk5/5FABXb24x9F08IRBFzvaw2rbO8jmkmKCyEuPU3wXaUQrZWcIe7dOc776+zdmMJWUpyLVlrzDEuMwoiM9OaD7RZfpWn62vVK+PVBvZXeAP/njFDvof/slPU4Y9dg8MSgqeCd9mdge+9qZl2rGIss+d/S2UUjzc9lBJSdpqMuoHaN2kcD02wypB29ndQ1hJkmW4vsALBLNZxsEcfHTiGr4tMAhmjQ5t5xNmEbkR0ncIshQrBGXgYoYV0NwMQA7HjG2ICQMawbsB7LS0xM6JuBTGQDoji/z3gXZXhhhrKG2Gvl+9/P5YPQO1Orzz2nf2nTwfoVzcMiduzX2nh0NqhWXVFSwrh5qoEhS12MHzPFbv7JO14DCJ0RYcU2KwJPpb96ctrEVRwH2ddrfdouE6BE1FgmQ6nfHII3MF+WvXkUiSseTaN6qT8FOtVYq1DboNsONt3NEh/WCTb9hLLPpDTDOmJlvv+ruu8CkU0L1AlE04neyyo3M8pwPCo2mHlHsvYaUDCw9W3RYAN8YJDFKkqBIWFi9RDwNGScrhtZtgDG9evgHA2cfPsyrbJ390dsj4MCRcrrGHoRj1efTGG9BdhAuPwvK8MLD70YWYU6JOl5BbjBiaEV1f0vB8biUOygMadTCw3K6O5/6dXWRZsL1XFduWlrqUuPjJBOF3oK7noL3qDRmT4czPSdXqEkiN41poVIl1RorF4jQ2UCqidXSLpdk+s7CO6pynsAmuCBFC8MSGRCrYT1qUyRDCkEfWq2LJ/tuvMc4s+9tVkeP04gJlCaWT4Rc5phFihUBgCbwVCj1hyRnR92royQQuPgbTMUtvvoZFYuHE+g1ozAH84eJpfvLJJ2jGNZ5//nm+8sZ1yGcsxxGNFTgsZuxW+qt0FtsI4PjgmExDmA1JlcfW1QownF/tVUrUUuEttlieg/bB/i5JbgjrDnkCmJJkDEqBP8dHZT5lf++QXBsW2h3cSR8rwHaWKASoNCUvBY35eFBmSw5JWCTGFYqe8OnbnOm8GBF8hEd7dfAcZDC/Rsscm2f3ioAARTrFLRKKRuWR7iXpJ9Nlh6oQ4Af3Ou2OivA9S2g0swKcVszexYsc9DZQUqNLRU1PKYJqffwwkcZvJXwUKoxwHIspXZI4ZL1T0SD2bt+icENAQDojwL7f8g2LkyUIyX2g3aMMfOL5bF7Sh6B9slwIIYhVj8KkZGZCqAQZDtrkyIUuSww47EteGI75al/zi7uKLx8WFHrGYhhTF4JdL8adSgI95cAInn6uEhL+wrTg1rzAUl9bQpWWoMzRXh0tDMKzuCgIQsKyRMgmqjRM2x7L8cko2Y995jEI23DrFuOgjuw0CMd9QixkObmrGfea3Fh9BFl68Np99xwhcRqbqGREVlT35QfqiiXp87V9+MbePjmCwO2xPJzQCX2ePVWnXouwrqK0BitclhoVFtk6HBFkg3v0eJ30ccqSQmteePMGAM9e2OTcXAD0lTd28PIEN08ppCZwYBmHQMDVwYiOUryQOXzhdskKA851BPtRjMVyeNfubW2DfJaTBxH24C2WXv+nXHz75zn++b+P+aWfgxdfgi//S0Zf/MfE13+OCSMebLQAuHX1LQoRMBxOkUis8rBY8vx7Xu2/yeMy8L8QQrwJtIG/fv+T1tqXqWjxb1GNfH/xfZ/w0fF/m4vUvQZ8CXiZChM/9EFCdFAJ3AH/OfDHhRBbd63nhBD/qRBiC4jmj/9H38qGfChot9aesdaenf/73p+z3+yDhRCBEOJrc7W9e2p9QogzQoivCiGuCCH+3lyF7zdcOHPQ7uU59VPzpOfoNtLAXX0ZbQuUtlzfqmZd2+tnGGQFxkLX8YldyaFSDCZTLpyrOu2vvfw6RjoUjs/rL1f0+Oappwm6JQ3f4GUZeybm4TuvIl5+kWx1k0Hjh3j4ksvGouCt25Y79MCLYDCilWkEBYuR5SjSuB2L17AcRAo5ndBwchrL76b0LvegPwvRheLCueq7vfTmm6hOD21hay76srKxybEt8HNDJ44ZptuQ79NduMlg/BLj6WWS9A7llRexjWaVCAsBhx89+3s3mnP2yoiMla7AjWGSxWjHRZUlK3Pbtys3tjnjao7mXuGnohA8l8ILKMuAVn3eabcnCeTyE7BSdklGguPDEdlszK3E8OadkEcTw6PBr2GP9smefJZ0bQ2bTynKgJvtdUzbJeyCe6kSRxof1RgNDE6hWT6zWgns5AVvXL2OpxQyL9iZz7QvLFxElUNkVqLdGC96N/hTCxuI0RhbHNPA4chmbEQKRwhKDXu5JsZFYygVdD/dodUZsvdiyc43Kjp3rXWOH/mhJ+m1u7z88sv82rUXyDLL4Szi4OtbbOQTLjwAQS3l4OAmxho2zmzSSFKUFRyrFgIfv6yR+yGrYdXl2989RBea7L4bYJal7B9VBYleXMO3OVoIinoH75slsd9pSIWKYlxdILRAhwoxLrAaVh2NP5oy8GrI2CcM3j3fPS7tu5Xj08o/NgkDvPewo7xKiJTCJveSbPVeVXwpYWWzolV/J0q1rotULm5RUJ8nsIPRCJUZlh2HSMT3/rZJZwx7i6xnYy5EEyZZHWvAzu3ept+G7VuOxTElGH2v0+62O9QdB6+hMJ6gfzjjkYcv4fsB20d9ansRn//ll8lnU85uniV22zQePI0QAjvbQzghL0zPYYxiITpC1xvU5LvndB3ho22BqS0ia0ucne4yzSsKdO71WBvvM8v2yXobOM59QMaNcAJwxJRiIojCkDNrlYDm9otvUWYzLt+uREAfe/pxgrt/1xqKo2Om4x7tM/Da8ZSzb77AQhTCY5+u1qlaA+rNarb9m8Q52gQ43DQHlFazMelzfJxhrMVdbGCNYGWeWG7vHOEWGfsHAwAWOy1KP8AfjXHbLYw0c2B0AtqVcBAIbKuDKxT9WJHPgUg6X9cCESFbm/hlwcxzOVw4gwUKm+HOBeE2OwLHg52sSVEYcOGRu0Xn65dJreHw9jvVd1parE5lJ0dpg2k6CKEQCJTbRMmAltwmD2uMMw21Jpx/iPqdLZo3bqCx7wLtPpZQwn6jRb27wh96rmqM/NR/+7OQz3CFw0LHo1yZsfsSpAPwopgw9Bn3j8m1JUyPmQY17sxnaS+eXq40XAB/pcZSVB3f4f4uaakJGy5lDkVWkIwqfH+3UFdmE25vVwXkc6dPo492yMMYrx6SAyJJyLSiNe+C7lB15E9RFc16wsMAO7YCvt+00w7IuFbxL4sS8uxdnXY73kcCWaMqLLlJ8snMs9+N4MSr3XVjlAstMnJduZZceepZUtnBczS6UNTtjDQMvivUeKiOgx/WEZ5BlQ4FiuWN6p6+v3WD3KmKRLbI8MscYysm0N0oMDjlvNM+t3xrhD46jAjn4013lePvj0hWTJFxeUAoweCR6BzabRblmGRi6GcJbVfwXDfmJ04JzsWapxfqrDuKoYzRmUecTtnXhmc/U7F7vvj2dW5PqlyjtrGEWxj8okA7dbQyKI95pz0iyPPKe9BKsihmMa72aRxHfPbpR0BG6Ds7DHrLqFASjo4IBNjRiGQxZuSFGDdm2n2A4q137hVfAFR9DYVDOa4617EjeLbj8EhT8QONEUuyIMx6NJIxXjPAFwE9z6UIA4TQFDgs1eZidEdjavmIqTEYa7DJCCUsSaZ58+2KPXn69BqPblTNneffvo4qNeF0TG4Laj4kOTxV85jMxiQpvDoJKaOSZ5ojCt9j6jlYKzi6NredPbWGTjKyeptwukMax9x46GHeXr/IjnkSHnuO2YWz3HjkDCr0SALBYhzQqTWZjMcc9idMp3c77dUaWhbfm2n/TR6ltfaPWWsftNb+fmsrESJr7W+x1j4///2PW2svWmt/yFr7+6y1f2f++Glr7eH89+ettb9l/vvfuSsyN3/9o9baR6y1/2tbRd9a+4y19glr7d977wbNP7djra1Za9fuWs9Za//i/P9y/u9/9K180Y+ix3/QIP/9zzeEEI98xEsy4LdZax+n8rP7cSHEp4H/K/BfWGvPA8fAn/pWNvh/KuEJD+UrIpMTrFZc8P2Dm0hjKTUYo7G2QJWWG3cqkLqwvsEg10gJl6LKDkvXHPqzGV7YYXl1lWSW8PbRhFe3++R5zqmNs/hxi85ygVckJLkgvHGLlXe+zmx9ifEjP4hAES/B4+cE3Ybg+YMOr952uHltxmg3I00NDS9jai2NH7bopywHpc9adozrWES7ulFOSsvX+iWtriAvfaaJy4NzBfk333oHt7OANtzrGq9srjG1mkZmCaI6g+OUoqjR6ZzH9xaxGLLbL5MNrjFaglF+Dd2of1PBrrsR46KQDMhoRIJGDUYyxDgxqkxYXa0KCldu7FATJXfu82g3vkvixaAFjfrdTvsJ2GydgaVeB/dY0T8YcKc/4ObQUN91+az9VXzdRz/6GPrsRfZql0hOP0mQSoRS9PWI0Id8ydI+I8j26gyOLCLL0bbgwpnqBvb6V76KG9VJRxNGWY7nOpxaPoOajbFGYr3o/aB9cQ1pBcXeVdrCY0RJ7FianqAsBQeFuWe3k7tAt0v7jGVh5Zjt52H/NYicHm5nmb/4J/81AP6P/+lfJnF8Lr9uGB0LHlx7m9/xw5KHFidszanx586u4Y5nlDNBv75It12yMBmTejGBMLRbTbTW9A8PKfMSbQ3WWop0wt5cOX4hCHEoKTwXETX59QhVq+OaElWCrknELOf8aBNvMsItDP2ghhN5uO67uzST93q0z6YUGPIoIHpPUuoIHyEkuZmhbdVpl3wA9T+eM6Vmk2//CwmBDCOcMqfRaQFwPJyg55XAujhhY422p0xPLVJrhawdXCYtYowRmHmCMvs2Ou1paXBNiSksk6z6HNXt4UlBUPOwkcN0ZAiClNOnHwTgq1/4NX7xl34RgM898AiZX2fxbLeylJr1EVGPG4cFDWFw7RDT6J7Qa60Fre+BysKm0L1Iw404NXibO/mMKDXUk4TjSGGCOvJ+loMX43jgOjOyEdRqks3N0wBsvXKFm1eukhYF3XqdzaUlvLugPZ8yPTQUpoFYs5SvfZWFMkU9/pmTeV+ApTU4Prwn2PVhoYTkEh2sKdgvxqy/81WaW1cZl4Kw62KUy+qcl31nf0A0HbJ9NC/kNVsYX+JNMliuAJuHQgiBkj7aVuuXwgHXJ+70SDs13rJVkTIjxcHBEQ5OfZ2kvcaNxiqFgtJmWGtwZfWduoGkFgl28yaFNqAMD64sIYTgYOsmgyzleKtKns8uLjHTLoGaIAsHE4qTQgIFUbBGXeXIKK/cGCZjOP8I/tI6rctvEB303wXaAbqOpG8E/to6f/TJZ1FK8TP/7F9w51Y1z7oURjhrGSklt+YEx6jTJh0M0HmOW0wYlTX2d+ddubOrJ6B9IWAprtadwf4+WWEIG9V5NhuWzMb3idAB+fiIm/PCyflzZ7DDQ7JaA9dxKKxAJxmlE9DxJIXV7DGlR4g/L5q1q6PE/t2iyccB7X6Edh2U1lDk7wLtaryLlIqk2QJtcNLsk7F7uxu1Ooyrc8hxQpQLbVlQlmCNpTvtUE5cPEdTpJbIZqRh+F0D7QB1x6eIPILUodDQPbOCFIKj/S0m0kVICUVBOLd9u0uR19ai79LjJQymd33afcowIFKKIoEyeT9oF0JSU10yMyEQOUY4ZCaHTofzHclzBxkXnZJLdYeL9QA9FyHthjEP9SS7acSkUMTTnAmGR7+v6rR/9fIVrm5XuU3j9DJeqXGLnELWKR2DcjnptBcpxg3BCgqvzqNxk3/9z/4kf+X/9BfwghAOR6RTw2xlCRlIglGfQEjKMiFf6XIkI+oCJouPM50ZePPNky/o1VBBGzO+c0/o8OGm4vu6Dg07IBM+O3sBTT1CNX18FbLoOeRxCFJTCMXi/F621Z8QZxVoT8kppiOysuBf3DwkmY7pttpc7HX5PWfP4rkeb29v0T9OCGZTjM4IfMs4s5wPFP5swotjlwXPp9nTyOkx47hOhiGZJAz29vE9l9XWAmWhycKYttwnWhZMzzzGyw9v8ny6ykH3AXYXQvxGE7/exroC4SjOdapcdW9vlzKbUZS6EooFyux7nfbvxW+O+Ch6/O8XQnxJCPGXhBC/QwjxrBDiB4UQf1II8d8APw+EH/bmeSXibvbqzn8s8NuAn5k//l8Dv/c7/hb/fwgrFNqVtEkRy5Vi+P7eNgJLUUJuM7QFVRpu71Tzd4ubGwyLAldITvsVzczUPKyTcTDLufhgNef34lDz9e2K2rR27gkaNXAaBZ08Zzyc0rl5FXV6hcNnH0b2qyQlXgQlBZ9+UPDoBZd4uUMx1IwOpgyPJIP+lBsHhp99s+DGgcVpF6wnAywW2Vzl5UHJP9jKeXWgOa5rpFX0pzGPzUH7zctXiZfPEMkGd44GACxsnKI0hq4GvJjRMMUIj6XlHlGwRiN+kOa+xG+dxV19BG0S8qZTeVrn37zyKYWgiX8v8VvpCoYqpHRqCFGyvlxt27WtfazJ2JonYMtxBFIydWMCW1KrgSNDrDWY+ZyvELD4bI2FiYfdHfHa7hB7aPmB3S/RPjWBp3+AcmkBKRSljBivnSaezKhnloNswEIbDoaW5U9Z6mFEnkjSfgXazz3yMADvfP0b9GpNVL/arm67RScKcGYDtHVQoY9y3gP+FlaQ0sPs3qQ9l9J43hzj1RImpiA1hllZdbtyF+h0EALWLx7ROg23vwSDdxzc+ll+1w8/zaefeZq9vT3+i5/++5SpgYWYrreNk01Yi2ZcuVN1ES9sLMHUUEwsk80lVg9vsLL9ChkhUmuW2tV5dnBwgM5LSjQlBptm7M092pdDD8eUFJ6He59C7CcZMm7hUaBKIFKU5YxiJEkO+7i5JolrjESA657M1hprmZUfoByPRkcx4XsAuRACT4T3Ou1CSOQHKUVH8+/8nYB2QIY1nLKgNRcnOh6OKTKDRBKLk/16tD1EN5rED52hu/U2vvHIcCjKBCldEvOtdxemucE1BZMsx1hL5LuUUR2JwAtdTOig84I0m3D+/LzD9KUv8dXP/zIAP7i0SX5qg4UWmLQPpiBJF+mTcy4Yoa1GNRYreq0u4Su/DF//FZy5r3lpM1Au8eLDBDplf/dl2v2bzGgyjFyMTt8tAigdcHz8ue1brQbrpx8A4PYb13llzlY6vbxG6HknDIl8QnIIfrfGnZuXqR/dRjz0AME88bsXdynyH0OrIBQup4yHuLNDqaa42YxB6VFrC4wbsOpVt8vbh2NK1+P2UXWeLNdbOGiUBnmqBXCvqCGlh5kfRwdVzX9/7nNETzzGoc3ZMQmpTQnmRY9QhBStVcpIUpCTmIoVddcFQQnBch2GpcfUBFhVEAY+5xcW0Fpz++bb9LcqAH1mocfEODg6QWkXE4EzL3qUNsN1moRek2Z9yqjQMJlUXsqPPYeu1Wm99CLlZEB6HzDtOJKBttjFBU4FXX7sc5+lLEv++n//s1AWrNRDpAL5SMJ4G47egUanhZuNCWZ9pLLc3BFMJocESrF2do27NiXCkSy1Kn2Z44NDNBoVzEH7cUE2rXS87kZxvM+Ng6pwcmbzFHY2I293kUKRAXqWYryQtifYZYrBcuq+EUYpBB3hYQEPifoYlHHHDTBKIqxAmYy8qO5HRWHx0gOk45HVYoI0r5KzT7LTXm/AdFwVztwQV0naXkZZStKyOmZpqRHCECZTPMeSfRc77QB15ZCGPpHRGC3QvZiFegNrDNf3x1gk6IKgqEB5NteXKrCYElyTgYDBrAJljThEuy6xlO9Sjn9vxKqDEBLsEQaX3BTYVouwJugMRhRJBsJFCMGkmFPv3ZhPX5KsxB630hh/nJOJErfd5OIDF0jznK+/UzFAums9vLTAM4ZS1tFK39dpD/F1gZ6ftzYSBGnEf/Ln/zg/9j97lmkQw+0tJoVLutDEeoLGuI+TF+Q2QS8tsu8EdEqHQnUYtNfg9dffpRWk6puIYkae7r/re5t0wFHWoBhBTUxwmiFSePQ8SR7UUE5JjmK5Me+096cE2YSxMfyz2ZDZ4JDCGJ5/rdI0+tTaBovdHm3f56lHHgXga5d38NIZMp/gBZashFtDi+gnpH6Nh7sOWuf0JwP2ojpYTf9alYec21ylmBnAkEqFT06t4fA7FxxqbcWXTw351ZsjhBNyLhPU6k2kmVHWG1xoVAf66M4WBZbhMEMqDyssRfk90P6bNay1N6y1H9VA/k0VH0WP//eA3wnsAH8Q+E+APwdcAP6mtfYHrbVf/6gPF0IoIcRLwD7wi8BVYGBPystbwOp3+iV+vcNYw4EYkXiWBin1tarju7u7h5AlpYGZSbFAWObc2q8AeHdtkxkGF0XbUTQcyVT5xE1NqTPWNirQ/vwLz/PVr30VgOX1p9hctcyEpplnTPsTGr7H+OIqRvqw7xO0wZmPRLmO4OyK5OzDi5xvFjy6LniolbPeK1hqWHatZu0UhPWc3mRI4Tj83KDO833NaihZ8AV939J04eC4xkNnK/Gk7XduwNnTZH/oT7C7M5/RX1tCZiVtx8U4AUlREgQB7l3hnMNdxGiIc+EponANVzXJm3PBxqN330w+LJr4ZJSktmSlIzBRRGLqWErONKtF+vqdfUxnma3DCjyuNGooCQOnzoKvERJcpwJA+j4g07koqNdatHam+Hu7nL1znc6qxvmB3wa9ZYzJ7olBHS8v4QuH5cEYK2boMMcYOC4h9hXaxMwOK/Gai0/P59pffo20t8jRpErMmt0erVjgTkdo6+DEwbsFzQDaXaQbIY/6ROWUS6KOh8QEKWk04Zo85qvFkNQaEs9CowGOgxj0OfNDUF+FG78KarbJru/z7/y5P4GUkr/383+bUdCnkIYkBbYukx2PeXu76rSfPbNMfkdjQhd7oU3tsE9U89DawwjJqUbV8Tk82MMUJQUlJSWkOftz0L7i+0ijyQMP/4MypU8igiaOkARpjokctE7JjktmN/s4VlPETQaOehdo/0C7t9mUXFhM8P5OO4AnIwqTUtoTUbD3xV3QPv3OQLsXxYhS02tXCXt/NKFMHU7Ls6j5yIEuLZP+GKcd4j30KHGRsNjfIiWgKGYE0if5Nujxs9LgGM1oMtd/iAMKL8BF4igXWfNRoiRLZ1y49GkA/vkv/QuuvfEyjuPy8MJ5ogdOo6RAT6t1YutWl9TXnPaPMIDbmI/jvP5CJfR2fIgaThBCUs7ZMEv1RQ6iVfLZHlYo+uI0uTCU5fhEh+BuuDFBNCMfV7Zvp09XRbOtK7e4/mKVRJ89fZpacAKA9HRMNlFE7RnJa9/AObWIOXcB970jHfVm1ZH8mAKDkdYsbA+YipyaHdLPXeJmTh7VWVdV1+f24YgwTbkz77Qv1ZqossCxINZOOu0AUvgnoF04lGhCv0tdljSt4LIZMbMp/nyUSAnJKotILcgYs2MPEULiiJOZ2c2OJEFxlMWUsgRreHi1ug3fuPIag53KTeL0QpfcGNAZUvjoQOLIuDpO8+5/5K9Rq/tkeoSdVOuAcD3yp74fIyStb3ydYXFi49Z1q1n3UauD4wT84Z/43QD8zZ/5BdLREb2oun8k3YR4Eba+DPVmB4mlObyFKTWXr1RaAJuNOu7yArgnx3VxeRkBDPsDSpOD4yEVDPZ0Zb8677Rba9HjPtf71bV6ermBMYai20WgyLHYWU7phzQ9yy4T2oRE79GyuKsY/3Go8QDKC0AKsBbH5uR5tRjNphovHSC8kFnoEtz1aP+kQbsx1XolHZR06UYJOlckc2EeoUuMgHqW4LiQ+dF3FbS3lEMRBjS8nMyGFHWPtU5177hy+wghBZQl7pw9dLfTXlqLLsG1OdbCcD7K04xCtHIJhSJ9j3L8/SGFQyTbWIYYIckNmHqI9BQNPaJIC+z8WE+LGVI4hI6H6wh+/AFF5sfo44KZLhlp+PRnKmHOu53t7qk24SzHUZLC1tDOffT4IEQKqrXcCgigMCGrR1P8Mue6D/mtGwzqq/iRwkxmxFZjZjPShZhS9cg9Qy/ziBDsrD1a0eOvndivefVNQFKOTnzUKRKKIuVg1qaX5kgnw22FCOHQ9SRlECGFofRdVubn3Z3+FD+vruvI5ixmU1pK8crrcxG606uIM9W07Gcer0D7Fy/vEaQzVDrFc6v98cWbJUskPH6qyWEJ9dmI7aJkL4xwgePr1Zpz8dwZ0oMBWrkUTokjwYkjGt6MH1+IkWcHvFjkuP1VnGxM1GjgFAlZo84D84rc/u2bGGk53J3gSIWR8nuWb9+L3zTxkbKkc87+35rPAvyYtfb3Wmv/d9ba9yrzfdj7tbX2CWANeBZ44ONumBDi3xRCPC+EeP7g4ODjvu3XJaSQLIsFSk/imAHNXg8/8BmPp+TpHkUJM51grCUsc27PvXhbixsUqqQjE1L9Dm2lmNkAJ9L0ajOibtW5+saLL/D8C88DsLb5LEsbc0pukpJPS5qeZFwTCBlg7rjUPshlqrcAwsOZpvR0zmJcsLFgaPagtmgpXHCOhhyFNVJl+ZFllx9ecjlXU8xcSxTA4ajOcruGF4aM9/vs9fdIJBzcrhLY+moPOYV2INDKJS8MgXufN/e1Nyuq6anTALhOHV0LMAo42v1Y+7pJ9XlDMto1iFseE6eF0IZzzSoDu7l7SNlcYHuvuksvN2KkA0eqzkJc7TtnDtrNfRR5qaD+UItzN3Z58KUX8YKYxh/4oUq9l0q1+S4ddOgp3N4pOkcDlCw5NiNcB/YGFjcG36tTjDLK0vLwsxWYeefqDYbdRa6XVULX6i0R+BlemmCNgxtHVbX//ggjZNTA6Y/Jyz6rMuQp1eZ3eD3Wyjqi8NjROQe24HZb8oIZoNstODpCKjj3oxAvwOsvKbbyHsuLTX73D/+baKP5z//B38BRJVv9GvZgi3x/n3fmoH1x/Rz+3ghnOSBtxNSPBkRtH7TCCslavQIFe/tH6ELPO+0a8pT9fpWUr3kCIQx5GNDwf51ENaMmUkE0LSgjF+0W5IcJ6dYAEwZ4bo2RY5HOCT3+g+3eJqShjyuc9wM3qk6lxZLZ6ftF6O69yKtswr7DTrtbayBLQzt2cByHWZozm4zuAXaA/jZYOyLoRngbZ3DrMeuD6yQ2QBcJvvLJ5x7f30pMco1jS4ZJBcoacYh1nMqCUSqcGPAkNply5tIPAvDy669jreXBcw+g/BoLl6oZaTvbpyDkypFP0LB00z5GuHiNdmWRd+cGnL4IUiK2b1RidLa6Pn0lmNbOshsuM124hF94aCci1zNK/R41dy/CD6ZkI0utBmub1Tp669Y2t1+Zi6pdukDtPlGv7HCCLlyynS+RugHqmScIP+y4Lq9D/6AS0/yIsNbA3jbNwoVml9COOcpcvHpO7tfZmNvvbR0M6TiCrYMqGV6M63hFhmsVbFbr1Emn3cfYEms1CgdNSTQX09u0GkPOjk3xOVl3DQpn6OELhy2zQ4l4V6Fjc7ES3+rnMblyoJjxyFoF2m+/8zrHO5UGwHqzXYH6wuAHktJ3cGWAIzzKeSFBqZBGvIT1S8bHJwKjcdTg4Mln8JKU8qUvVuCQqtMOcNTqEnqCc6uf4smHznF4POLv/t2/ixCCrh/Rz2ds/KClzGD/rTaRJ4gme0wnAVs71f3nTLOG12uDG5Jk20yS63idDguBi7WW48EeeabwQpgOKnB8VznelhPy6Yybc9C+3qiKk3S7lTK+tTDN8OOQvkgoMazyfvZQb14M+WYe7XfD8UJQEmFKpNbk8xGUZDbByaYoPyYN3MruDT550A73KPLSCWnFKWWmSOaddmHndm9pguMZsiD6WGMAHzeayqnAomcx2kU7hqXFypLx1tYhxvWhKHCzqtN+F7TnVKBdmQxrNcP5/mrUIqzr4VB12p3gHhHjfVFzurgCFBMKa7FCQ6tFww7QeYmeF9RTPcNXJ2vHhaZi8VQNkVqyWc5+Ds889+TJ57ZaOIFHnKZI5VHYAO1plCNw5jPtAJ6VGKHAE2TWx5nMWKGGmWl2Z/vs1xeJQtDHA8I8oygTyqUFBkUdLTSnpEek4SBeRTea8Oqr97ZBOiHEC+jJncqbFbDpgGFSMiljuqMEGZXIuIEQglAJbFgH12Jch0Wv2sY7xzOCdMjvq9d5SOTU8oxSuLz6+lsAPHtuAzUcwK0bPPfgfFzqnVu4WhOM93G96v7TUQkP9uChbiUsKYYj+rbkIGqgjWUwF5a8cP4c+X6fAhcbFCgpcOKQmR6BN+NsT4L1+edv1ZgmKYEf4ijIahEPzsea7ly9CgIOBhMc6aGVwn4MZuf34nvxGyE+AS+R94e1dkCltPcc0BLiXntxDbjzIe/5f1lrP2Wt/dTCwsKvx2Z+/LCGznhMZKDQM+pBTu9UhZzHwzcptWWqU4TWuEXGzkEFJhuddQqnpC5KhCjoqYxS+aTGcnZzSnepUpD/xvMv8ubrbyKV4uHzT2NqBdJa8ukUMSuo9WIyqSmzADV2iT8QtC9WqHSSU8sKAkoiX5NYy+ePcnakSzAaIxptPrts2IiqU2EtkigfiAzTrE6aCVbOVjP733jzNVJdcrhVHbLGyiJ+AvVAkucOWhjCaJ48Do6qbvqZS/csaxynAVJStupw+PFAeyhcPBRDUoQQrK8KRnQwBjpS0mrEpHnBrb0DdueiQiv1GOv7jHXMQrNAIHFUjEC8q9MO0FzNWd5+A11qdn7sSZLmyR3+bqfdCJih8U6dJcygnY4Y5GPaLdg9trg1iyfrCGOZ9HMuPf4Igeuwezzk1sEu17erJLPRW0F6I2RWYKyHE31AQhaEEMY4k5wyG93bXlcqLvkh3jQmTho8KTrUCkufjO1ODP2KC6hcOPfjlhtLBQeHPTAFf+H//O/S7Xb51S98kS+88zxpbhgMBQfbO+wfHxL6Pk7rIo1kgF1pYNOCoLR4dYnUHqWQbNQqQLN3cIgtSwo0BRqbZSegXYEVgrwW03I+dHLmuxthAyUFXlJgAg/jlcxuT9HHx+haQMuN0EIwcE6WuumHgPbkA+bZ74YnqyTGWvPumer3RlT7zunxQYzUhlgW1OaFqcHRIbk5YQvsX9c4zoy4U11TtFp0mJHqEF2kBNLD6oLZexSXv1mkukQazXBagfZ6HCIcRRMfKRyCSKJDiZpN6a2cpRmfAJnvWz1LurTGUreitNq0z2TU4DCyNBoF8WyEDmKi8QzeegmWVuGBJ6q58e2bOMaluE8sclEFXKtt4kddvAKsG1MISZq9Z+1wY5xAw9yLt73wEKHn0p+MefOlNwA499jD+PeJ3xXHE9yDm/T1jPSxz+BF6p5WxPtieb2iEH+TbrsxOer2bWS9BWtnCZTF5pbCN5RBjWWjcX2f49GMwTRld26V2Gl2cJIcFdQxscBF3lPnVnMFeW1yHJyK8i08PBmhzZBVoUjQHNxXnEmtRlrJGbuMtjmHcsbATu89v9KRuDgcFBGpCiBP7inIb7/wa+RpSiMMqLsBWpUUpUcQFhjfxZcRSnj3Ou0AS/EpiihmdHTz3mN1HNJ2l/yhJymO7mDfquTga0riCzhotAgDSTF1+TN/9A8A8FN/429hrWU5CimMYeqlLD0KB1cCPEIwluG0zsHBjeqYdlqIqAZOwHHR56g4QrZbLAXVPjs63iHNFF6oEKIq3obz09WkRxR5wa3BHLSHkkJ5yGaEwiUVBj3NqcUhx6REuNTvYyvcDU9ILooaa+LjzZ47bohVEjBYDeWsOi75uI/Mc2SzTSFtZfcGn5x6PNxzH2BUnYdShQRBhsgVqZlbW1qNFRCnM5QLWRDifxc77R3HIfciRGhwcg85y2jNc6m9/WO05yNKjSjGuPd5tRfWYApwbIbWMMyq8zGux+CcgPaPInw5widUTXw5JTMGY3LodqnpfTCQTBXWWjKdENwH2n0lWGw3iDyHIMm4NtY88elH7z3fXFpGIKjNZqBcitJHRJXdmxDinmZGlJdozwNfkBkXpjNcFXB2R5MWhq0LEU5kUMMRoS6Y0ce2I7YLF1fCiusSlQJjYbx2sbJ+y0+uS9VYx+gEM61YjdmszzgRSAJkP0H5JSo+EXlw4wbGkzgSOkENKSX7o5RseIgUgmJ6iJOn3JxoDvbuEHoRD26u4iYppDM+c/48AC9eu4pNNbXhPtKxfHZD8VtPJbiyKuY9ErswGtD3AwYoZlYzvj73aL/0IGV/gHYchFMilSCuL3Lb7DO1Oc86Czx5TtLXMV+7rimsxVUwCwMeiqv9euWtywglGEwmuMrFSIEuT/bL9+J78Rs5PjHQLoRYEEK05r+HwI8Ab1KB9z8wf9m/AfzsJ7UNn1wIxKxPM8txtCZQfTqnKjG3wfHbFCUkZYbSmuHRgFIbGr0FjAiQriGcY4WWmoFwmFqHuDlhvbdBo9NlPB6jtebUmfNsdJvM/IJOaRhlmjBNoRORo2EUo4z84E57rV5Zusw0bp4R2YLI0yAtV9Kcpk05pRMavUWm4mRBa7qCViTIQ0NehMwSh7PnKtD+/JtvMEkShrv7CCmpLywQ5oZaFJOMSqy0RHetS66+UXUe106MBpT0UdKjaM1Va+d0ym8Wd+farbVsbgq0bZALBy+ZcWqlou+//NZVjvaPEcBKIyL3A3IT0q7nKOkjhKgA+H2ddnZuo7bfpt2u4/QeoLxQ52umz2UzJtVZNe8vfZJ5jhItreMpn+bxAIcJaVDpF8xcsEWdwFfMjhJqLpybKzK/+qUvc3MO2pu9TawzQuQFWkW4/gcYJzgONNoo7SCGw8rneh6bkSQUkqPc4mqfWm7pCIc7nYhZOqtmSoHbaMLHLefVKkrDbJjyl/+DvwLAf/TX/gaIMXeGDV6Z+6KePbVOqTzqfkbRaaKGE1wUQoAvFaXjsjk3NN/dPYCyJDclmSmZDgekeYEfRXStxihBWq/Tcb57id1HhueD4+FOUmzsYd2S4mCGzIeYyKflBXjAoTrZnvEctMf3baJNJsxC733z7HdDCfdeh/19yvH3R1TjniH0txkVaLfVPF+zSqxH/UOmc8pqkVlG2zPcpsa7y2gIAloqI9URNsvxlIOyJZP7gP7HiawokBhG8057rRahlKQ+7+QGgU9eF9TKlNxYzq+duffeZ06dJ7xwGtcRkBxT5gWD3R7pek5TZXjJBOvH1F56vuKxP/psJS6xdgaKHO/wGGNL9Hx6asXxSTTEtjr3AiSpWycvj9H3i+y5MU4ASk4ppoJaHHNupSr07hxVhbzzTzx2QhEvEvJBSlEes3PqHGdXu+ToD6f91lvVevpNVOT1YBc5GiE2LqHCGp5jUJlmbCw0G0hdsLBc3Se+fGOM1obOQgflurhZit9tkaPf5V5w4tWe4cwf12hi1aG0OaFNaIiA6yRM5vstQSOABTwCfBwRcYt9+rY6L7uxIHRdpkYwlnWQBY/MLZt2Xq0m3ja7XYpCU7qgcxcvyDG+hydjHOGh7QmLo+37pLU18sEBRTn3qZ5fK+XaOcabp8lvXiaas6u6ruQISbDUxh0e88O/64+x0Krx0mtv8oUvfIHVVpV8b08SVp4GrwZ6t42fCLQXcbA7n7lf6UFQByG4nve5kh/jdJssz9fV/tEOWQZ+7CAo8QJwvOrmq7MjdvoJ4ywn9Dy6IieLO3i+AyjKLEMXhrgWMiGnwfsB+91YkxHNjyrk3ReOF2A8D+sIZFpg8oo1Us6OkXkO7eq89ZMMfL/yqPukwg+q9XPeaVdOiCsz/FyRa4PFIuYe9K1yinEkKP+7Yvd2NxrSQbs+oiZwSgeZZNTOVPfOw+1DSscDrTHZpPJqnxchy3mn3bEl46Ryaai5CukH4HoIxDcF7QA11cNVgsLOMLaAU6dwnQn+8YTJyCHXM3Jrid13F9iXG3U816VjcibaMg6WWFyscpF4bQUQ1JMEpENhAoh1JUIHMLclDApN6fpIXZB6MXaWQ9imtrWHH2yStGAYzQiGx7iqoNQTrGeZpZfpyJx24BDMl8G+06p+GQzubaMXrWKUQzm6AcDB4RGZDGhlAmvGKE/jRK17rw/iOoWSlTif69Ode7Xv39kntyViuIcsCj7/dsV8fWTlLHKlg0xzwHAqDNlY32CczHhje0JtdMyMks2WxC/nRUM/5uHIIU6OOQzqSCNIrWZw/W6n/SLleIbxPHBzhB8y9gOmpKxbjwdEm15H8uAFn2lf8bXdAiEEU99nMfDotnpMp1OOxhNSPUFpB6sU+nvq8d+L3yTxSXbaV4B/KYR4Bfg68IvW2p8H/rfAnxNCXAG6wH/1CW7DJxNCIHsP4EhBM8mITEl3o/JWPe5fp9CQ6BSpDXv7VQe0s7pGkpdIZYlElew4NiV2S2aEaDVhva3YvPTwvT9z/qHHqTfvzrPnTCYJdSnJGx6FcJCHAcoH/36hbmtPlI67CzDNUULRyBIcmbNWg7Wm4ZF0H9dY3OYyMwrK+6xU1mLJpG5BB0ynigfPVzfR19+8zO3bt7DW0jq1DMajYUrCWo3ZOMNKS1zzK9ur/W3YvADvEVpzVIO8FWCxH7vb3iLAZAccJVfoNcD1IzLl4yVTVtcr4agvfPEFrLX04gjPdck9j1wHtJr5vcRXSv+k0751DV7+MiyvEj99iQsPRjxWP8WaCNm2CV/Tu/RthhAuqVslKXUnwl1cI+4PaagJAzKUhCGWZFKjUVOYWYLNCjYuXQDgytde4M5eVeluLZ/FmhEyLzCyhuN/SKLX6SELjTspKMoT0H4qlDSVpJ9bRkV16faEi+x02LUJ5ugIYy0vTQt6keAnfl+PeiskKfd5NvyTPHbxGbZ3dvn//I8/g7Gal69X+39z5SJNf4xXZmSLPYKj46qD5YdEriJTHhv1KhHe3TvAMZppppnlmuO9uVput4tncoyUZHGNzift0X43HBfh+vhZhg19jFvgzA5BpdgowHVCTinJES65qa67aWmJHE6Eo4qcskgpo5DoQ0A7gDdX4P7QmXaoQHsye5co0Lf8lcIG0lh8k52A9sER47S6Rg9vA3aMX7O4wfziDwLabkpRxuisQAqDsJqR+fgdBmMtmdZIqxnN5qC9HqGUQ30+u+tIF+oKpQ2hl3B64xIAjUaT870N2g9XzgkkfaZ9w8S2EIuGtshQo5RwdwfXAk/9wD1lX7pLEIS429X5WM677YuuopM06OgIhCXEIfNbFFjS/L61w4tw57Zv2QhqscOZOQgFWGk06LaXTiji+ZjiaEbaOqbRPKAp3kAlOzj5kFJPK5r7e2NpraLIfxTN8sZbWKWQaxdxwzpWWNqmZJhbRKcGxrLUqxL7r75RrQkLiwtYIQjTBGehOwftJ7dlOS80GJvfKxaVlASyiRSSiT7kjGjjIHjDjDDWkqBxNcTGIhC0RI86IVsccmCHtFxBo6YoLBykEThwYbGDq1R1/wBO97qks4TU9TG5gxNprKPwRYQjfCwWTXFyCNqbJJllNryKtZYQNf8WgsmlB5l02zTvXIH+QSVGV1qc5R7R+JCxf44/83s+B8BP/dRPUY8cQuuzn1Td3fXPgsnatPcleqHG/lzd/tzGCriVwOjObMp+mqN6DZaC6hoeDnYr0B4phCiJ5k1FU0wwRca1nbnYa7cD0ylls1vN6wtBOk2RVuDVK3vNu+f/dxrKC5Geh5EgihKTVWDGpgOEhqJWFeG8ND2hBXySUW9U92sq0O44mthq8txyIbZEaMrSoc2UQiqk/HjFiY8bHopACvJ6QFBKVFIQna6ukeODA3LhIUqLLaZV0c6eCNFpXdHjB+Mq32kFLqXro1RAPgZTfnPQ7skIV9TRdlp12ldXEUFJvHvIaCQZzUXo6u67GQ/rjRgvUNQyjRtqjjLLw09UFobN1QUcYwmyHJRLXgbYeae9+qM+SElYFhReiJen5HFIKZYhXof9fYraeVp5WAlJpn2EzLCeS1h/iNRMOW1u4jVvI40mcuGw6o/B8Um+4MkaOu5SznYxecKwf4wKI9Sxh2WEcgxOrXXv9XGjSakUvirJHYeF+b1nb6fPHiO8cR+lNb/2RjU+89TyKrIVM5lKpsaF/hHPfabaB19+Z59wOiEp5mA9nYAXgnJw8pSOSEnCJlo75Lbk6GbF3rywcZYySUmDGgFTyjjkSBpaBDSsxROSJREQPpqzYdpkeykz6VHGLkLAmcXq/rO3fYAVU0yi0FJRfm+m/V/JEEL87bv+6N/Ge3+3EOLf/4jnnxBC/PYPea4rhPiXQoiJEOK/vO/xSAjxT4QQb82t0P/Kt7pd3xS0CyFcIcT/SgjxM/Off0eIb15Wtta+Yq190lr72Nzb7j+eP37NWvustfa8tfYPWmt/Q15NIuxQtnp46ZhuIlncrCrkB4fbUKakZYZblGwfDgBonloDR4OECFvdLBC0nClTG6LNlDC2XHzwhGb10ENPQbPqnthpihpPqfsBScOjkC5mr5pnf1fh+/Xnsb/6j6HIq7n2tEQWUMtSarKkdEqso1maHSKBsLWOxTLhJLlfCwUqEqTCJ81cLp2pukPX3nqHWzfn6qjra+gEGmjcWswsybDCIfJVNcuuVAXa3xOuU8cGHibwPjZob1gPlfcZlQcIAZ1OTKJi3NmY5c2qoPDlL74EwHItQihF6nhY4xFFOXKuTK1kJepkb7wNr369Ags/+BNVdxvw4joXZZ1nZYeWtRzYnBeYMgwEIQpPSPxT5xBasDA7YqYnBHVLv7RYK4lrdVyTMjkqWX3yqWqfvfAS20dV4aa7ep6ynECuq65G8CGXUa0OUuJNNKWe3bOqc6XgTKSYlXCYg1NCSsm53gY5hjuHW7ydlIy15enYw/EEraUevVNH1L8P/uIf/n8ihOC//Omf4eDwGm/NiwlrDz1OMz1EWcNoZYn4qI/o9Sg8S+grcsdnvVYlrTvbByirmebVz+Cg6mQ22l0cU1K6ChOEn7xH+90QAhnW8IoMXA/radRkB+nn6CBGOSGboUQDN6cVGBuXlli9mxqfY9BR/KH0eDhR4P5Ievxd27dk+uGv+Sbh+DFCSkKTEzdbAIyGfSZ5VQjYvwl+PMTxwPdPQHtTpZR5hNGWmSlxEd+SV3uJResSZTWjuRBWXK8hZMad2QgPhZIusi7B0bTklAce/CxSSD778JPIxXWWlyuQWY4OGI8DWHcoVE7bZnjXbyOtwXnsMyf7CaoFbPU0ztExIk0p5ud7yxN4xmVcCpC6oq9Ln9xtkBVHJwU45aEiDyVm9xTkN+5be86urhD7J52ycjxBj8ckrqTZW0ELB1lOIN1hNH2LwfglRtM3maW3TgD8XYr87odQ5LMUu3MTs7KK9CP8oPp+XZExKiVioYa1hlOtal73V77xGgCLvR7kOb42eMudD+i0OwjkvNOu5sepRAqJLxtkZkyExwOywYSS63ZKajWutkgKXBRTITnNEk1iduhzII5ZaBoy7bCXRRCGeCbj0tIJZetsb4F8ljBTIcoIRF2CUPjzmXY4Ka4AtDpNJqZBPjxCmwQpBDUcEmsJpMfhE49Tej68+EV6RYIBxq0usU0YH2v+7L/+B1FS8o/+0T9id3eXnh8xTHMKW9LahOD8Gpm7hj5dY+dWBdrPn1sDNyItE1Jj0FhMM2YpnOugHO+RJuDXXKQo782zm+yIIhHcmBfUN3tNTFaiOz0Q1VqRTzOklahadSxq3yXQjuchvACjS6SWkM/XiXyI1IpsPl7mpdknS42/G/XGfbZvUWX7ZjW5hiUBUpaUhaTBlCIMcMx3t88jhSCWiqwWEEkwuaC2VjVAjo4OybQCa7D5DP9d9HiLLiyOLRiO5iJ0gUvpuig3+Ejl+PdGoBYogFQPII7RrYjG0ZDJVDMuplgkjfeMe0WuIo5qBLlGKEvY0Dz3wz+Jclwu/pZP4WhTebFLl7wIINQnoF0I8APCvKRwA5wsI28EFMMM5pZxh+E6y25AfJxSqoJUjJH1NlNWuOmcoeYtIeJD1NJrNN0DDkyMFeJdnXYpJLK+SmkzDm9eQ5ucZrdJfujjN4ZYRxHcpz3TajSxSuGoHDyHxbmw6u7BkIPiEG94CMrh+derHPCpjQ2oBdzah8PUh6MjnnuuAu1fu7KDW2TYyVxwOB1DUH2eHR+DNNQbC2SFYNg/YjqaUIsjml4DmWekUYOwnFDUImoiZEl0yEzFFFoXEcID95GA+njGyIRo12I9l/Nz++L9rW0kU/REoB2F+R49/l/JsNb+6bv+6N/Ge3/OWvtRoPoJ4ANBO5AC/yHw5z/guf/MWvsA8CTwWSHET3wr2/VxVuC/DjwN/LX5z1Pzx/6Vj7K3jsWweLDD6kYFHneOdnDMiKzM8POSO/O5xebKJtIpUcIQSUEg63gipC5n5IToIkM0ch556PF7n3/pwqcoa5X1SzqdQZITuTlJMwITIfa9d8+z37pCcf0lZrNbmMMdmCchMpGE6ZRYFOSyIBMly+NjkA71+goCweg+P91TocT1oXQFs6LGuY1qIdx++xq3bt4AoLt2CicrCIWLG9ZI0xTpObhJBju3YP1cVVF+TziqukmU7RocH9wTKPqoECbBs5DYHGNSllZiCjemHE04NfdEf+XVSnBqpRbhKJg4IR0PEBZ1r9MeIG9cw77x9Wqe9qnvr7p9tXkLZj6fGwuHC/isixAlPDJHUJ93uYKFNfBimsf7+HOKfOnARIMVdWpOQf+44PQz1c3rnTffZvd47mt/5iJFOsEUFuUGHw7ag7Cay5vmCARZfqK0vxFJXCO4npS4BUwp6Pp1avUO+0c7PD9LWHQlq36V4HfiJTyTkp095g//75/hj/zeP0VRFvwf/vrf4tqVyg7r3A89Sf3gEHyHme/jpzk0fVJxjO+X5CpkIXJxHIfj4xFFMiXJS5K85OiwEllst5o4RlO6Dtb/BMWTPiBkVEPlBq/UmIbC944QqsTGNYwNWA4VkTBcnVTn2qSEuvse5Xg0Ooo+lB4P4Mvq/HA+YLb1XnwXbN9UECKUi1dkxHOK4uj4iGmuSaeW0SE0eiMQkuD/x96fR9uWpmWd6O9rZj9Xv9vTnxNtRk9mZEuCNFogKAqlUFVYiqKVilp1r4XeutcqRC/XjpJhg5aAA9O2dJQ6lBJbEITMxIRsIjMjM/o4/Tm7XX0zu+/77h9z7X36E+ckEYEm8cbYI/bZe8215p5rzTm/532e93n85Wc3ighDgbcIMBbm1YJQSKb3kdVeOIczFmkN4+VMbdJMgBmfGV0iRGOFxk88St+wypy1E1/F3/kjf5zf876vJ3n4DKEvoJgxuTgjsynZGYtXFWy++gpiPCF/1xPI9dsEhhw9jUTibW1TLc3oOksp8/mZAVWnbgQEzPwmAkFWbB8ywzKICeIZ+dJB/ujppw6f+vTJkzea0O1OKcocQo925yQ2Pk7VeJiV9GnS6AyBvw5IsuKa3Jtmp35v7zTXfvE1nClxJ+pxIF/5WD+gbWeUBFSrKc4Jji2Zrc+/UgPPtW4PvchQ2sM/1qPE3sC0w9KMzuaHCo+D8QElI8DhXM6qCNgUIefdnCkVnoHCLkhEwkQUOOAEq3RI2WVEcvoqwcaEHV1QxD6umPLE0Wvvy+mVHiZfsKd7tGyOS8QStPuHn39z3Vx7t9uiImA+nlOZ+rPfEB5Tqnq8yYP904+Dtax+/uPIqmS/3SXyIb+8w5FHn+W3fOAxqqrib//tv81mElNVsL2c+T7+DQnNb3yWseszm45JPI+1M8fBi9maLnDC4YCJ77G5nG0d7Fwhz0H7HuunKzbrkVtMvs94GHFpaXB7stekQiFXWjgEpRMU8zmeEFSpqtngu43E3E/5AcoPMKZEGAXlAlcVkI0ReBSRj3Tg5/lba0J3UI1mrQwqSzxdG7h2REHpHOf3cyqvQhSShBlFGKGrN37K+61UamZBiAoCdO5Ij7YBGAz2yK0CK3DFvJbHO4tzjhKHqBxSVNeY9sjHeP4NoD3svPHrx6pJ4QJmVd2ALo50aQxHLGY5k3yGESEtfetSuZc2iExFZSQ2KPmvf8dv4q//+xGPfcO78SpBUBY4P6YqJDawh+aSAEQxQVVivRDhLEUqqEYzOHuWUoeMvRWSpiHdzwkRLKoZttnjcl5SScFm4wTNxmO4IibWlwj8l5kHwQ2gHUCoHruFx/aly/heSSPtUo09/GiIjQMCca0ZsZIkGO0hlYFQs7JsrF7pT8lHr+NNhoyN4rVXX0UIybtPHWNYxVgLmfAxwxEfev/7AfiV1y+i8wKmuzhrIJ/BMgZ2PNmlQPC+1XU6QuKWzvEPnTnFbJQh8oysneKXGSZJidCEqkFh68jVVGjaeEwe8GkqR555tcNOGPJws37DL71+Ee1ZpsMSIzWYknfqK7OEEKeWzPU/EEK8sCSX4+Xvfl4I8ezy+/9jaW7+RSHEn75u+3NCiD8thPiMEOILQohHlz//ngOWXAjxO4UQzwshPieE+AUhhA/8GeC7hBDPCSG+6/p9cs7Nlobt2U0/nzvnfm75fQF8htrb7Z7rXu5E73XOPX3dv/+DEOJz9/MiX6ml4jZFmBKN93hkOVt9dX8H7QbktqBVllzarQFbunECFS5BOxIlfJqySyz3GauYonK4xpQzp2qG1g98Th1/ijwp2cRjOpuSFBVl4qh8n2IWkeTXOcf3d7Ff+jR5S8NAUO6+SvCurwUhUAuHTnJ6ruRlWVJhaU+HoEO8RpuEEePrmHZPCjYTweeUIy9Tjm32EEKwf+4yr7/2MgCrm0cJbUWiFfgJeVEgA4/g7Ct1J/n0I7c9ZlJ6aBlRtiKCqwMY7kF37a7HuawGhGimFBi74MiJFvt+SjGsOH28XmhWVc1CrrdStIShn7ASmeVr1otM/dprmNdew558FvnMhw4N8mi169ib8HoTuoJUhrxPrbA9spwRycEfgNw4gTr3HBtHR7yOJfYF48pRFCmN0HAxW3Ds8Q8ggFcvb2GdI9CazY0V7HyEcBKpQ7zoTtFhCSgPWRr8MiRnj9DfQEqf47GkKSVXc8OZSpJTYZxlY+UI57YvcEXO+Mb4GkBJ0w0CCTuzLVSrx1/58T/Hv/75f8rHX/zUoVz4yFMP0/jpX8ZGAVXpiBDQbeJ2QzxhKXWMNIK1Xocr27vsbu+ycjpDqIC93Xp11Gk0UdUOVZAgw7cno/2gZNxAmV0CY3GJpPGgYbwQ+HHMdBaQdmFzknMls8wrx6xynEquW4TN64x2GTXQd3GB9mXMRvCuN55ph1/dXLsfIJXGL0sanaUR3WDIPMvZvVAvpMJ0jKhilFwuBOf7yHxCW3oYJ5iXBV0hWZh63vNe5lCLA6bdGqZLpj1sNVHSUdqSRV7hpCANNJNUsjKdI5MWcZjgck33idr7woz3me6UFKdbLGKPtcvnWHn5FcpWB/PYM7d/8aQBnRWCK+fJz9T3OV8Knmgpnh8ZPOfxMNAWCQNRcEx3qS59Cfv6J5Ff8y3gJ4TRDsMxrKRw9IF3Hz71iYcfoRFdO7eLwYSiyjHtlI4fsUNGgEKrAFSA73VwzjKcPIcxM/Da9YYbx+Hsi7VE/vqGpLVw4VVMt4Vs1NRegMZEER0zxbJGvhbhhODkkoE3pm4grXW6iMriS2Czvdz2RpWKkj7GFigUAlFHLQJGCJQIapCs4SGRMnAFGRbfOEq3oCkaTLHMKGiIgOOssuJaXGTCluuTR1OuGE3D9Tm93j58zTO9LqbMGehVTlW7mETUaSRIhFDLeL6cg11d6TR4TWgW45zK1EC7ieYyDh+NwTJPI3jkPcSf+o8ce+GT7D3zIR4MBGqwx1wf5yO/7TfwLz7xPD/xEz/BH/2jfwK5p7g6W3AsaeFFsPKU4dxP182O080GaqUDXsz2eATLj/fI06wn9fVvsLPNIjcgNN1NQ9AW2HKGMxn9vRWu7NQNx1OdFCM1utPCCEEFmGlOqiSTUL1p0ngA/BClNKVwSCswZU45G0CxQHoxWegT5aZ2/L6dUembXdc5yHuNGKGgrQqc1VyalhTSEhpLIHLGURPvLQDtTanZCQK8OCAYGoJmTKA9snzB3qLihFAwnxA6gaO+TlXOIfIKJSqG42ugvQp8Au2z6EPQrI1Z36hiJShoszD7ZGZCeWSFUJ8j7O8wmjpU1MaTt14/15pN0m3AOMZVyZEjOQ+KiKEyBDl4ZYnxUoy1KN9dm2kHCCOi0QATRDigDB1VBZw/z7T3IAhBlFbMXpmQhBHeoGC32eR8kRFpQU/6xE0Pu/8wYT5AiAsMvBnxoI8AZgvHq1cdF/ZiGrrD0WBGqwPltI1zYzxvTKVDPBVe2yWhsHGMHY1RgaYXH2S1z4l2LqBmMz5xoY8xhgfXHiRabbE3gG5QJwBkkwFPP/ggYRjy2vYW/VFJON6myGcEztb+E0B/vIdJGjweJuxXEz7+8msAPPzQg8z3xpAVVC2NEGDSxnKEImDCDrmdEakWx2XM82HGkVWH97rFaUMe+Dy6vP9eeOV1lAfzfIFRum6MvVNvef2I+2cfoh57fjNr/4+J7/jEGzzmEeB7nXMfF0L8JPB9wP9+02P+pHOuL4RQwM8KIZ5yzn1++bs959y7hRDfR82O//6btv0B4Jucc5eFEG3nXCGE+AHgWefcH/ly/qil59tvBf7K/Wx3L0y7EUI8cN0LnQG+/IHNr5A6V2XM/YjSD3FO8VC7PpRbW/vI9BLGFPhlwaVlFm/YO4kXGbSzhFKhhEdDdfGlQAUFxkiIpqx2HuL7/+wf53/9y38GpSJMWJEYQZYvaOQzilaEU03MUKGNIl4Fsjk893Eqr6R44jHo9DA7Z8HzoNWBaYkSmrV8TEXNpDXnE0SQgh/QJGBKgb3OgfhkU5L7jkXRQDnHyvFjWGP4wsfrtL+V9aP4lSEJFJUJMeRoLN6l83Dk5F2lfVo3KVvBPc21O+coyiGhbmMFLMyUdFXhgh62rDi1fiPg32imCAETndJL6o+pkiG88Fnk2dcwR45gHn/iGmAHeOhReOrdNzzPgXO8EIJm7oivA2r66GkqJzg6uYyhoogdUwPZokEaK3wxRuYdjq/2Do9pr92mHYOcjzFolB/iR3dh2oMQqpJwUT9msZzhTbTgVCTZzx0zs4ykocJ2eri9BQ1RkHvXVBM66JJIn2qxw8I6VlZW+KEf+qHDY9vr9QhXmiR7e9hmiphlSM9DpD4uDFCuAqGwWrHRrm+6e7s7VFVBXhbs9usZuvUkQlpLHofo8Joj7dtRXiMBY4lKi4k006ykCn0iTzPPA4IAjqg6z/dLY4N10LjeOX4xJfMU8T043t8VsMPSGM/71TnI+z5K++iyIG23ARiORmRFwe55aPZAMEUfSBvnQxieh+k+XaUwRjCvFgRCIm3F/HYz2rep0lmcqZCmZJLVi5yw1UQuP8P7iwwjFD4C09KIxYy03aI0UKQrbCzjykYv9SmdpjwuEBPDyRc/Q6gjZkeP4DXuQn0dO42el9jB9uGP3tdVPJhKLjmP7QzaIqbCYmUD9dKLlFmd844X44UlxTgnjmFt80l8VZ/jRx99L/EyHQNTUI4yrFtgGynNICCjvMWETgiJkiGVvS5ebuPY7V3kty9DnlEe2zhU9fhIbBRDNiP1IqpEYjyP4+rGz9h6u40qSyJfYDrd5bY3gvYDpl0IgUJhlqA9Y0EkOxhXktsZWkgek00EEJgK6wxtuWz6XKekioTPybhNub1KeaVFKR4gdI4HN65piU+3UxYyZEyDuFpgIoES/mGjT9/kIK99jzBNmI/NDUx7XfXfkwXAyjo89h7WhjtUr36RcK1NMNpjXEb8V1/9LCePrHL27Fk+/vGfIbExe9ni8BpaYjj/yjkATnVb6CQFL2I3mxOwIGLGRAs2mvXCvb+9TWks1imcrZk2m+8Dgv1+zOXl2NKpbkoZxPipjxCSEoGdFQQeZIE6NGF8U8rzUUgqrVAWnLFkg8uIokAFMVnsEy3PvbdHHr8cr5mOUTpEImnFBcJIpmRYHJEx+KLAxAn6LVj5tZRm7gWoVoA/s3hlyUq7/txeGi+wTkCeESxN6DJnKXB4ZQlUDCcHGe0+WZQSCnVPJnQHlUhJKdpYaxlVVzCbK0RJQLJ3gcmiINS3fx/SOKXrewRFycJa+lWB1QYlSxoORGkwKsVocy2j/aCiiLAocV4ATmBiR1kCzjFonEBI8KMKMRzhBRUJIWWry2U3o6kFDTTKryPt/EUH444yCxMm23v88hdL/v1nLOe3HEc6McfPdNlYy1BxRL4bIiQoN0ZE4WGkLUCAxMUxpbEEoaIXHzDtM+LxAFGW/OIrterv3WunGXU7yGnGiVMNimaXYprhVxXPPvssAJ95eRtvMaEYLdd4Qd3AmE72SRsraCF4MpXYs7Xc/qGHH6HY6WMBlwqkFJC08JB4IkYKSbaUyK/gE0qfxfEODRYY58g9j8cb9bn/2iuvgbA4FhipcLaC+zRlfaf+i6qLzrmPL7//+8CHb/OY7xRCfAb4LPA4cP2s+z9b/v/TwKnbbPtx4KNCiD8A/KpnP5cJav8n8Fedc6/fz7b3wrT/cWpDudep+9kngd9733v5FVTWOV4zGQVwRFryoMN6a4yUksFun9KMkEYSVhWX92t5Zbx2ChVUeM4R2Bnz+au0k6fwREwUZpR9hVQzrNvgv//D38tiAZe+JGgEYCYZoqqIzBjXPkEuI9jTxD1Q0sCnPoGtShZPnsILe3jrD1F96eNU8z66twrnX0eJmDQbESaQlHO8LEds1HP4DXyuLOfaD1xyT7br6I8JEbYSHD9zit0LF7nyQi1DXz+ySVCWxM02i6HFqopGfxdlLZx+9K7Hz9MNMq0wzQS9v3PXx1ZmhnUlqXeUbTdkbqa0EvAb6whraDlJO4kZzurF9WYzxTqYBw0eTCoEEjnow7mXEacexRwTN8g6AVjbqL9ueI8LlLo90xG1N1nEDTr9LZJkyjDooJUjn6e0VzWtaMJi33H6wVNc2K3ZnG6nR8/PKYuMynmoKEB7dwDtUVQv2IxBjSf4vR5FUbPtSvo8kmp+cVLQrw5Au+H1uIkyimcXBeeaM1ZdQCI0SEUadYnzfS5mFQ/HHh/5yEf4iZ/4CZ577jkef+IJmAwJFguq4yuI4Ri7soJwJS4MEFLgVZZSexxJQz4D7GzvcrqosAh2lp/vY6GGiaOMAoKD4dG3qVToYY0mLBzzSDHIclzLJ/AjrJOEITSkofQFL4zrG3dyHWh38ylZHN7VhO6+KmnUyo0vt4IQpXz8qqS5lMcPR2Mm44x4BKff7TD9CWF3pQaRl78IgQ9VwWoAW0YxyTNCL0bakokzpPdwnylwiLJAVIZJVoO8oNNCLhfLo2xOsxmjEciWppQTjncfZR8QR06RhAJbGGbnh5SbPmWsaH3pFaQx+CcfppzskOrwzjuwcRypA+Sli5i1EiU8hBB8zarmhddzXp4omp6GGPJXnqdhfCo7wxvuIjtn0BF1A9MGpGmT73z/07xy8QoPPfnBegEIUEwpBxll4FDNJgLJguq27uBKJTcYQdLq1kqKrUv1+M9BnX8ZG0XYbudQ1SOEQIUJZqfPShByYSGo0pgTN/khrDfbqJ0RwWab0qvfo9uBdofF2hKNpnL1ZzhzOQ3Vxbkhc9MnkAlt4fO1cpVfWEZ2RTIhxTEi5/h1z7nSFvhSY5xlJzvJQ4R8zcaJw98fbwa8NglYGJ/It9iwbmIclBY+5U3xmUm7ye64jzE5xhbEooYpC2dIhE8WLJvCJx5A7uyjXn+JQDuCUcGgjNkMm/yBb/ta/te/+U/5sR/7Mf6XH/p7XJhNmLGgQUxFxcVXl0z7Sgc/aWCFx7SaEyUOYyxT37G+VKf0d/aw0lKUGg+HswaT7yO9JuOR4dL+EICTnYQs7tBQFoFiYhx6UaBjDUK8efPs9ZuJ1j5OK6jAWKhGl3GFwwtCFpFPY5Yzh8M877e0lsZ3TMa1P4gOaUQZrmiRJTlGOJLKIF1OFafot8CBqK0UTkpsr0mUWdS8oNttcXlvn0ujGaatEHlGcPi5tzXTXlVI7CFob0cBZRzScJJsBJ0zd3vVaxVJgcUHIkqbIbQmOHacxgsX2S9X6N4BtOOnrPiSqHRkkaVfLpiUOYSOJhJXWioXUGm7BO03Mu1+meP8uonpEkNVAkKwFxyj6QnK6QhZ5WgV4omAedJhKgpO6vRQOeU3oJhAcyVlIFOKrW0GV0Y8/HCXM5uC0BfsFy1m3Q2c0sy/GOC1CtxijNi8zpyTutEo4hhTVvg9xdpSHn9pMCeYznHO8IkXayn7u9ePs99sc3o24rkoYGct4ehrGUynfPCDH+RjH/sYn37pCk9WJXb/XP0CQcLefICrSnrNeu05Ycb2shH38Lsep9wdILQPgUFWErVk2oUQBDIlt/V9VQjBMRHx6vENtH8WPT3PLAjZCBesdNfZ62/T394nWN/EKI11FkxVxyC/U29Z3QMj/laVu9u/hRCnqRn09zrnBkKIjwLXL0YOrmyG2+Bi59wfFEK8H/hW4NNCiPf8Kvf3x4FXnHN/+X43fEOm3Tn3s8BDwP8I/FHgkQNN/q/XkkLwdX6Lh70m1lNsWcuVRpPV1RbOOa6enyCqAq8yh0Z0zfXjiMDiOYdHXs8/upJItfACizUOXyxYSEteQJlpirjC14LFdEE4HaI8R9A9xUIY2F7ms7/wGRjuUzz6ACaJiIJNvLWHAEG58wr0VqAqkaVPmM844TseFHN0niOWF86DBev1c+3rXUkqBEMXYUvNqZPHbzgG7SObJKbCj1MWowIrHelsjOisXpsRv0NplSIQlO2kznMv7yxdKqvBMvNUogezw4t2Z30T5QzVYMHR9WtqnKOtmMopZlGTlbSqF9FLACXOvOuQtbpbOefqzGVx+8VaKnyyY6dgf4+TeshIWnLfMRl5CB3RChfoyrL++LW52tbKOm1vjsgznNOoOOSOfo5hVI8Y+DGM+oRB7ZB/4Jh9YimRv1L6yOmMoS34Uthk1RM8PS6RCF60k8NIpkayRlrOuZQto32U4id+4ic4ceIEv+O//e8IxgO8vKBoNVDzBabbRiIgDEBLAiGoPO/QjG53dxdbGGxRsDOon/N4KME5yjgkOnA0f5tKBgFCKvzcYCKPihwX+3h+3XQJl5fmB1JJsSSd0+suy+V8TBnHdzWhu6/61Wa1+z5Seeiqotmrj+VgNCbLCqy0NI4skFWJHzZh/wIsxtBeAVPS80tsqSiKjEBqpKuYuHtjGErnkCZDWHvItHu9NgJJrBTWZExKgcChGprCM5yIFDsnfgfH31s3tvtfHGJLS35aIK1GXXgNtXkEVUqqtHHnWDUA7SE3T6G3t6gOIoKor7cP+VMa2uNjOw63NaK69Cr6gWewzRQ72AIvPox9KyaQxgF/4gf+OH/3R3+QdrJy7W8cTXCTGUUg8du1W7vF3dbLQKsY68w1wztYusjvHF6z9GIKgz3sseM16LnumqGjlMpWrKuaNcqbIeulwfOvPWZlOduqVzrkSwHb7UA71I1ELTSGisqVGCpCERPJNgs7wh7MugvBASWqRUhrqaS6PiGkGwi8QGMFXMh7IAUPdGK+9pFH+C1PPkloDRPZIjAlYVBhfIl3A2gPboh9A+h2mjCv2MkdxkwPzegmrqJNQO6BWe5D8NgzTLvrFMNtmrMtJoMSNh7h933jU2it+Kmf+inErI8pBTuLuiFbYrj8Wg3aT2728NMWoxlUcoGvFE5I5p5ls9cGYDgYULmCsqw/c7Yc48wCGfQYjgbsjGdoKTnaDKnaPaxwCDRD4/CzAploJILkzWrmLUt59aiEWPq5FIsZrgLtKcooIDzIaH87Ztq1rmX4B1ntOiIJMygVmXUYAZ2qwtkCkTTr+8KbXJ2lEazppvgI1KykvVqD2a3JDFOpmmlfJmHkOEosXlUinGE0q49XK/IooxQ1V+DubZ4dlqBdeFgXI7AIBMHxB4nzfcrJglTf4X1QPr0ooGMsJYLd2ZxpVTcQmggoLZUIMep2THvdCPBFgHMgfUvhFGZ1ndHCo9OBfLCHpEAriRYhs7BLqQuOXNfsD5qQj+H4aoDqdVhrF3zjqRGPnZS1vwi1F4uJWxgvIbsa4HcW2GKBim+8T2shkWkK1hKElm6nXlddGsyJR2MKJ/jsi+cAeNf6abKVLqtywfnAZ3cjpZxlMJsdmtF9+rVLWGsoq+zQOX53vIsSgk5zhYGbMmLOpddq5dJDDz9Ctb+P1R4iLLDCw0uiw2ZHIBsYVx4alW6KEBcEFBsniPI5WSiRJufMZt2tuXx+G+cWVNLDYannD96pr9A6IYT44PL7/w742E2/bwIzYCSEWAfuy/xNCPGAc+6TzrkfAHaB48AEuG+GSgjxQ0AL+H/c77Zwb+7xIfCHgR8E/hTwh5Y/+3VdSgiO6QbNMOaYsfQ7R9nYrPVYL10ZoReC4f6MoiyJmy2CNMVpg2fBNxViNsPYnFi28T2QyoEpyCNDudCUcx/TrGjhMZpOaRYDhNLI3gnymcObeTTda3DxdeyZR8g6El+30CpBtlZQfpNq5yyuVwNzmWlUmfEAJSfNApFVqGYtLddCEuMxuW6u3U/giJBsmRCJ4vTSaA9A+z6NTovEgY4TsnGJU5awKt4QsAMIodAqoVrm8bK3fdvH1dL4AZ4J8T/zCdovniW3GdZVtDd6IDVuf8KxI9fc+I62U0onyIOUNCpruWo2Wzq2RigZHrqx36msKw8z2m9XgdBUR05gpOb48CyBD9PQsT8A6bdIvAWpLmk+/oHDbRobx0jkFFGUOOshkgAp7zLTDhBEMB6gnML3VyiKPYwt6AWSNU/SPHuOzr/6d7y8tU3RaHIsDvD7Qx4SKSNKLrt61s+L1kmkY7TYwSwX2c8++yznz5/nO77newn3dvBMSR6FSKGg12JaOV4qA4yGUECpPY436uOxtbVby6jzBdv9Wq52UoPDUQU+reQeV0tvVmkfIRQyl4hAgcqQsQeulsoFy6vVmeQaGDqUx1tLsZi8oQndfVWc1O7x92CyeNvyfITUKGvo9up7wmA0QdkSfazEUTdKfB3C9suQ9tj1HybPIRVjjAsxeY6TmsiZewbtubNIkyMqyyRfgtLVDtIJNqM2CsmwWFBJRRwGmNTgDRd89+97hA88G2INDF/oI9uOWROCy2NENaP1wGNUsxGmkRK+AeOvjj0ClaG6evaGn0tpeW83IlWW2a98gb7vwUNP45pN3GgPpI+O9A0O8nvpu9hZe4LYv9GELi8KpA9xq8diKTW/XTPhQGljzHVJAJvH6/d1KZFP9q6AUpgjdWNNXXfN0FGDCss6Fi0leTtBzyesHKmvpY1Om9A6wrLAW6+d4xUSdZOvwkHz0CzN6CoqsqW/TShCEtXFOcd8cA5e+QL8p5+ldfkVgsEU6aBFHbF5fVPWl4LY95EShpWm9BpIm/Mf/9gf4//+w99HOZsx0D3SYkEYGEpfo2V8wz7dHPu2ttKkWRkujg15VV8XGsJjQlXPhQsOvVN6vubqY+9nsrJBp/8a5dmzsPEIm8dO8W0ffhpjDP/yX34UnYdszerrWO5Krr5eKwlPndxE+Qm7Y4tQGb5Xu9uX5ATNJr3Qx1rLcLxNXtTntV3U95npuMNWv56jPdFMkZ6HbbXr6xcwN4IgL7CpR4L/puaSA6ggAl2PrjgnKMscW0pc7OM8j2CRY5V3S1zqW1bN1qGDvNQRob9A5Iq8slRWsCYXVEKg3yKD0URofAlFK6wB6qSgtV7fQ3Ym0xprVRad15+pzFkK41BVhhCO4WzJtIc+VZQgJvX5c6/y+EiCw6NAEosmgUxRJ04gdYW31Seu7nzNSuIGGxgKoeiXGROmCAFN53CVo3QhRlukdxNoX96U4gqM1qgqo/+u9zB55L04C82Owfb7OA2JlUg/YqRinDQcvR60N6CYwpGe4NGnNmlEJXoZ4Xf4mKWBajX2wCiC5hjnDCq+FW8ESQMnBZ5d0Ox10UoxmOWUwylfujpgOpuz2lonjNpEKxFWWGZxxKzXIcORb+0egvbnzl+myCy5MxCmlM4yGe/R1iFVFHOFfWLn89rrdUb7sZMPocdDSu3h6ZIyaOArcXjcDv6OA+KmXrcqeOAUWI1ignSWM2s1wXT5wjbKZVRK19J4+w5o/wqul4A/LIR4Aehwk1m6c+5z1LL4F4F/SC1q8LuMAAEAAElEQVR3v5/64aVJ3fPAJ4DPAT8HPHY7IzqoDe6AHwG+RwhxSQjxmBDiGPAnqaX5n1lue/P8/F3rXmba/y61/v+vAT+6/P7v3c+LfKWWEBrheaR5zmrQ5PjJ2gTw8vYVZG65slVLKztHjtGM6u5wgCW8eAXvVz6FLeeEwsfXEc4zyKygapao/hrFfhsXV/jGw+ZDwnyCF3aYxwHFGNLBmGT/M7C6QX5yDesMYbAE1lKiVk8iBnuUkYMgRC/jrh7KMo7MplgLKr0GrhoETOyc4fSLlNUYIeFkIpiYgAqPU8euAeP25hECkaOtxksTskmB9Ct0ae85W1brJmWisUrC/u3n2s1SGu+/fBbynKBwFLbCmAV+N4YwRuyPOXK8lrZLITjWSsiUj44jpCtr4L2YH7LXB0z79QzRzXXAxEt5Z1lk2Fhh0WzT2DlHGhoWkaM/deC3UVXOkZWS6PQHDx+fHj2Nq6a4ssKqEHUbZ/1rT75sZnhhDRCunCNUdfMly+uM1MdjSXfrItPC4n7lP/FwpIlWV6DfZ1NGdPF51U1ZOIMKu6TKJyz32CpuBJITY0h2t5CephQKISWiFdMvHFdkh0KAdmC8kCOHoH0f6UqUydjp14u9IxqMUjglWUnucbX0ZpXnIZVG5BraEVk3RnYSKlMfxwOmveEJ1kJBoLhmLJTNKakwUVxHir0ZlTRq2Xo2f+PH3q6EQAYxojSsdpamWuMJqirRJwrybAQI/GGdvmA23sULZxMmmSYwI3AxNs8xUpBimdzjLF9eWXxTgrGMl0y7v9JBF4aW9en4mnmeUwhJqD1c0zLfn9JOJEoK+q+ALPoUD3m1YeO5K7gk5mjvKFW+wDYaBG9wjFVvE5IEd+nVw59ZZ0FaIuXzLfNXSWYZnzzxLraQuEYTW2UwG6Ob8WFWe5JCaVdYiFWS+Noit+hPyYoKmj6tKCZbgvbbvfdKRrXxm7nufWx1awZ06xIUOdFgB46cwqqli/1tQLsuchLPI2/GkM9YX4L2tSOblHlF4Az+RocCc4sJ3fXPWce+aQyGzC0QDvzBGO/ll2h8/JPYT/xreO0FMIaov0306efgP/wLGp//LOH2NqPrmw/Uxl0IqKqSabAKtqgXtvmM3MC+t0JULgjCEuNrfHENtB04yF8f+8bKGicShbs64uJ0CdrRGFwdV+euqbmaSiC1x6UP/ia0B53P/gxFbuD403zkN78XgI9+9G8Rlj6jRUnmCmZ5xdWL5wE4/eAJ8GKuTEp8XaBlPQpkXQZJwvoyOq0/uEJe1MfUFAOk12Tvis/usP58nW6nlNJDNBuAxAiYVZJGmZOHmuabKY1flvIikAKhHdZIisIijaKKDuLeMszd7g9vdqXNG0C78gqiQmIF2FKxqiZUSuG/RaA9QBFIwdAP8PyQaLSgdaRmeXeHQ4yRuMrgygm+EGTOklWO0BYgHcOlaWY79CnDCDlUCAn3KvgSQuApj9JBrDr0/FPQ7SIaCm9niOrfZZnspxyXFcZ5ZK5gKqZI60itrUG7DXGRQUqBvmmmHSApDKXvo6qM0ZEzDIJ6LZO0DQy2KRsNwlmGbHQYlA5fWZrX+aoEzdqzsJyBDlrYxMMO9m/YRS0CpNAU/RAE+MkYnEEn7VvfiyTFKYUrC7wkoLf0PLg8lfzS6/XzPn7kYaaNDseSnFEhyJME20xYhJrZ5T02NjY4ffo0s7zg9VeukrsKgpQtW+JNhzSbPS6JPRwOfTljNl/QbKR4YRc1HFE140PneFFOoBwu/w4fLfzD6DeAWCjCox4jbwPPTnDC8mCvbqBeOHcJXeYY5QEGdxdF5zv1X3xVzrnf5Zx7l3Puv3bOzQGcc1/nnPvU8vvvcc497Jz7RufcdzjnPrr8+Snn3N7y+085575u+f1HD0zmlo9/chlf/j+5uvrOufc6555xzv3jm3do+bxd51zqnDvmnPuSc+6Sc04s9/OZ5dffup8/9F5A+xPOue91zv3c8usPUAP3X9/1qV9Ann0VEQSIfA5INh6o3ZOrs5dJZ4Yr2/XFpblxjHZcg/YYqHbmjM8ZzHifEI1UDZQvkdkMIsN4EDIrNX4A2UIS2z2ioiDsnWBKidktWN37JF47xj71PvJyZ8myX2NC9Oop5DynGF+C7gpyvEBIH7Jd3GQXIzzkdVnJTXwodlmYKeWSJTmeKKSVzFzEAyevgfbukWNENse3mjBNKGYlmllt0hXfG2j3VJ1FbtqNO5rRFdUAdfESuj+Gdg8PnypfUJk54UqMCmL0ZML66dpBvpfGhJ5i4QXEoUawNKHL5oemPkrWrJO9ea79urJLGd6dmHaABI/JkePI0Zgjbg8TO6YVmKK+yR3vjAibx+ksnYwbZx7EFTNs5RAiQPt3YVG0rqPowrCW0T3/KdTP/2vCly9TXn0JUy14craFX+S8uHKccLjP01deg14P9usb66OygUDwmpuCF9HwG6TFPhfyG6NPpqYi2t1FRR5Vbqh6HTSWuQ0oghaZAM9A6fscbdQLja2r+2hRMB9uU1aWKE1p2worJU5rorfZPR7PR+gAsRCoQHPlA0+iQp+qiupFynXr7g/2NB9euQ6gzacUGHTcvIXh/LLroHE1uw8HeVPB85+CvGaNZBCjLDQiQRxHVMYiR2PMakGRjwnyArEYwOppBrOUwsZUysevRiibIMuCiYXIGWbOHCos7laL0uG5AmfcIdMe91psvvASrc/+Jzb8FGkyBlYQAaQBpTditlMvHHeeywiaU4arHuk4oxru4586RTCztTt/2ro31vLIaVx/+3DEwLr6M6tmGfH5F3n8oQcZd1b5d/05ZXMF56pakRIneP41pp3pBm5w5poJtzWU4zlG5FRpQtMPWdRhX/jiVrAshECp+EamHWpDuv1tOPsSwlk4+SDWFkjhIa77DPlLt+RyPqblh+TtGIvhaK9emK9tbOBnc7Snkd3eMqP9dvshkULXIzso1P4+5Rc/SfvnP4b85M/D+VfwG5ss3vUQ2dd+HcOnN9l99l3wVV8Nq5vInStsfPY51M/+FHz244fX25XUwymHrUoGerVuNJVzmI8pnGaoV4nMAi8qsb6Pf10T8zCr3V53HV1dp9NpsLq9y8XZjNJUh1GZcyxBeQ20CyHoepKdtI184t0Ewx0Wv/wJ6J3hN374vZw5usaFCxd47bmPM5/BhAXnL14hyzLagU/75FGcjthdLPA8Q7I/oXNltzbpS1I2onr/+sMr5MXB+e6QQY/9Ldjt10qOM52UAo1opUsTOsvMSFKzwITBmzvPfnDs/LBWfuk6l9w6kEZTRHV30VtkGO9tFDM2W1CVsJgfZrU3nMEPHaJQNOWYSiq8t8gYTwlJLCV7MiSIQ8JxRuNYrQLc3+/XM8mZwWbTw9i3vLR4NqvVG0vQ3owC8ihB9CVhB+7nch5In8K6a4aFGMqjXYK9PuJutjt+woYniK2klAVCTvGMJaksFkXlQogtHvKG+fGDxnxsLMbzkVVG5Sr29+pbvhIL5HSAa3fR8wLR6DGsHKESeNeNKPjLJVw+rsd5XCvF9m/cYSEEPe8E9vwGcQ9k7ZiAdxvQrpIGwlO4okQ1fFaXbPxgN+OTSxO6x1rHYKND280ZVCAbKbaZsog0+VYdm3fAtj//hdeZRhE01rhUzknmU6pGxJSMI/Q4/6U6dvb4saNMpgY5GVN2YvwiwyVNdNGnyK8ckiyhbJC7OW45ZpOgKQNDFZ9a3uNmPNKo/67zr59HugojRK2iMW+BIcM79U69zXUvl7XPCCEOdb7LYfxPvXW79F9IZXPEeIr0fGS+AKHYXOaZ7+3u4BVwYQnaW5vHaCSW0jpiDKZfYHLILu/XJhsyxgsDRDkj9EqGlMz9gsgXzKZzQjEiziWqu8HM5az+ynNEaQnv/mpyN7yRZV+W6K2jVILZO4/tdmAyRqgGbraDm00w+Ie55ACJBVkOaydye5DRLGiXkj3bYL0VELdqQLpy5CihqfBFiBcGFFmBsgVaqHuOqVEqqRdJ7bhmwm8COM45ysEFgtcvIVePwkNPEEgfmRVkZkbYCyBsEC/GHHtXHS93erWHNhXzIKEb1Rf1Q6Z9OUN2PWt1p7JL9kjeYaYdIMVnfvQUlbAc3X8VEse4spRlPQPcicYEyuNDX/NhWnHMifd8iDKbYCuLkgE6eANWN4ygMvC13wrPfi2sHyXoL/A+9zmqf/f3Wf/EvyI1OS88+j7UsXX85z9Tg/w8h9mMUCjWRcC+q1MBvGiVjl1wpbhx1npmctK9PWwUQF5SrvbQtmBmQowMycIQVRlsFHCsWS8kL1/dQ9qC8U5tStNe6REUOZWvqDz/xsXJ21HaQ3kKMo+QitAr8DxFUUb4PtdMyICVQHLqOpl8DdotfnyPtMy91JeT1b6/DRdfg71aSSHDBOUsgbY0mvXCaa07IFOWLBsQjvsQN2HtAXa2oT8Z0a8EXjXGtzGqLNkxlsgaHDC7B4n8onRIVzKbZRjniHwPFXp4WU4wm3ByNEbh2KsqfFchWhEmmDC5AoPXwU77iFMleeCjz2/jlGX19KOI6YQSi14upt6oxNEzGFfC5RpYGUpwDu9Lz4PStJ58lmebEVOR8ZmygZG29sbwYvwwpxiVxDH1qt0p4gPD9mJKMSwxukA1E7QKyKjuqrDQKqay8xuVORtLifzrL1CkbWi0l2kTN14vAi/CaU2ZTegFEa6bUErHQ+36Wn1s8yjeYo4X+sjOyh1BO4CsBLz+IsEv/CzJr/wK8vJ5VHcDnvkgfMNvw3/vN2OOHWfstiiqEZ6/wK504OkPwDf+dsSzv4Hx0U3K/g586hegyOm1NEIKlCzZKldqm9lyAfMxc2KmXovIZYhUgNT41zUx1bJBYa5n2oWAUw9wej7FzBa8NJqQLPnFsSvxS3E4jgDQ05J+aQkePM0iWCXf2obLF5EbD/AHfuvXAvAv//nfwi08drMZL79Us+On2i10EjMuIgoWpNmIYz/3S5z62KdxosQEAetL1nq4v0WWXWuQqqDLYAd2+7Uk90wrJQtSdCQBSe4sRVYSigobhW9u3NvBe+mHdYNQWXITM9cr+EAeBWgkajF/e5n262LftI6RGhqupCpBlIpETrFSEfhv3Yx9qjRjpQjShHi0IDlRg/a9nX2M9rGFxZUTAiQzZ6kq8FyOkILR0jSzGUeUXoDry3uWxh9UojwKJw6b+cYsmG+uoU1J8Xr/zhv6KR2taDmJUxZP53jGElcGKxSlCSExN5rQweF6JKos1tfIPANZ0u87Oh1BObiCcRWysYqYzZlEPQocLSkPvS+gZtqhBu1KJrhWA9ffqRtw15V2CfMrIekmiGK+3PVbx9hU3EZoiS0q/FSysox9u7g/4pdfqM+ZJ9ZO0jrTo5rMmVtHt5cS+B3yNCBbGu8egPYvful1Lpx6kkXSZjId0BSwnUKTmK4NePm5XwLg6LETTIcZMs8oWwGyqptv2lZYV2FsPSITyBTnLEVNpJKgcUBzo8tUxJSi5PFWvU459/pZTOVwtgbttnwHtH8llnPunHPuiV/r/Xi76l5A+3uATywD6M8BvwS8d6nv//zdN/0KrjBC5lltglVkgGRjOfe9vTdkWK1ycZnR3tg8SRIbjLXEwmBHdTc3u1wz7UII/KCNEgWKjEVQsAhKer5mMr9Cmi3wdQfbajPf2yPcHSOfeAabNsiK7VtY9vpF23hRFzHoU7TrRYvKFNYWuPkM47wbALYptvFQZMo7BK1BA1YKwdU8JVYF66drg4+VI5sEVYXvx5RTgVM5ni1RyHtm2mtJWoOyvWQUbmLbTTlGPv85VNCCJ98HUVI3OErHwk4REkSjQ1RlrJ1+gB//fd/Fn/zu34qYl8yjhE64jHsTPuQLCOu/9WDm1NwNtC8X4HcDnwk+NkkpOiv09l8nSmEqoN8PkMrHFWM22oJv+MG/yp/72Z/jyMY6WT7DVRbtBXjBG8wrRlHdbJASVjfhqfcjf+PvQD779eSxgNde5GTR5/Ff+hlWAonZ34LXX6y3XbLtXeFjcIwp8cI1mq6kKof0y2sS+Ww6IpxOsWGAkR5lt42yhokJMPjMoxhdGoTSxFFAI47Is4L5oM9gexeAdqeDNhWVVhjvbVxsHpTn42moKp/QWNphgZaKRREShndvINj5lEJCFLyJ6oAwAqXuD7SPlgvDbMm0hynaOnwM6RK05/t9LBa3fR6NgM134YTks8+9yu/7/Y/wHf/o78B8QqpSVGHoG0voLDjH9B5Ae1Y5lMmZTOsFUiOJEBp0WeFJRXL2PG0pmFQVxlmCIEI2powvWq5+FtL2PtOOIEJhLl/Fbayzkq7DZEwpLH7avqdD4SUdqm4Xc+lVcI7KFcS7e8jhAB59GoKQk17E6ZalX/mMoxjGQ/ATdATlZI5SgmR5eTsw4S6GU5jNyAOJ32wgRcDiDUC7kgnO2RubfO3e4ZPOevU137j8FmWOj8KEEeViQi+McJ0UIy3f8/R/xff+z3+Cb//W7yLIFwSxD607MO372/DcL+F/7GOIV76IDBMWTz3F+Bu+AfnMV8PmCfB8pFDEss283MZIgXUeRbZFXuyDlDRWTjB5/EnGTz1TL+jHAza6dVND6IqBa1DqGBZjbD5j5lIyHdE0GSYVgMQXN/59N8e+AXDqAdp+wMb2FV4ejzCuXlhPXIWuwGApl5/FrpZUQLG2An7ERHfh3MsQr/B7v+tb0Frxsz/704wuDNmd57z2Sg3aT/Y6qDBibxESlZfY3L6AnhX4eQnCUAbhNXn83hbZkmmXOkWogOGeY3e/bjiebqfMwwaeBwjJ1EKwKPGUxQsSvNsoMH7V5Qcoz8disGXAxNvAq0ryOCAsDBiD8d9Gpv36rHZdZ7U3VUFVgFc5ArnAKY8gfOtAe1NqLAav0yUsS5LNWh6/t9fHRR42s7h8SiQkc2cwFYdM+2g5ytNIY5zycGNF2L6/14+kIHMedsm0Gztntr6G9jyys+dvxsDXyk9ACE4JMFJihCOwAl1UWKEoqgAX2Rvn2aG+rwchUWmo/BBZFSAzcNBqQ7Z/jgqI/AZYxyXdxuLoevpwpOfg5RGQT0BKjej0MOUcZjeqg+a74Aw0NkFWSwAc3XqQvKiJ8BSyqBANxVpSN7M/+eolLu/0iaKIU5trpA92yIYLiiikE3psyohFq0U+7OOs5UMf+hAAn3/5PNPJnCsmx5sOcXKBS1sctW3Y+hwvvVp7VBw7fpzFzghpSmhpnFOQROiDuMdq6eMiaxPjg+i3ZHl+rp5oMAo6TK3kiDdmfeUIeZazvb2DQ+KAqvwyx9XeqXfqP6O6F9D+zcBp4Dcsv04vf/ZbqIPhf31WGCPyDOGHtQtsWbK+ZNp39vpkLuDibj3T3tw8jvIrcAa/AJlVYH3KnT4eEokgjLsIHLGdMAsLcq8iEAqv2qIxL9E6Yd5uUF0a4ZWK+F0b5MUO7jYsO1DPxa4cRY9m5IkDIVBzUbtoZjlGBIcSrcrMKco+sVzB++znscsZcz+FI1YyLWKcEDy1zDJ/6KseJy4rVNggG4L1M7QtUF5Qy7rvsbRuYkJVs7w3gfbqS/8JOZ2hnvm62rQljPFQyMKS2RnOOXR7Fc+ViErw+LFNeqttKEpmcZNeYhBIRGFqVuyQafcRSOxdzOjMXZzjDyoQCg/F/OgJ4vmAVdVnpi17++D8BuQTjvYkuUyZNtp0dUFW5phKoD0f704Z7QcVRpAtbvyZlARHn4akRfnACUZPf4B49RQUOaW28Llfgk9/HH75F+HqRTpFfcPruwIZdWiogLja5WJxDcBVg12CPMN4HpVSiG4LEMxshMFnFsWookBJQRmEbLRqADnc3ma0U0vhOu0WwoLVEqd+bUC70mCMT1BYRFUiUcyL8HCe/U5VzEeYKCJ+g/f7vitp3J88frSMFsvrhYUOE4R1eM7QaC2ztnf28co5we5lRHMNWusM+pY//+e/l9lsxEu7Vxlu75AEMaq0DExJIATKVYzvAbTnlUXZksm0PjcaSQxCoEuDbnWRiwUn9yZYVzCsDKEX45oV85052cASntphGjdJL17FVAXx6VP4IsZMRpRJTCjvzVhLi5DqyCZ2Pob9HUw2oXnxIrK7AcfqxmGLgK4vKLVkFMbY4S6oCB2Cnc9wFtJULPsndeMm251SFTkuFPjtBk5ocqq7OtofNEOrmyXyx85Ao0XW6l5Lm7gNaLdRglnM8FWIXunglKE1rfhr/78/T6pSomyOv5oeNrt8VD0qcfZF+IV/Bb/887C3hTj+APn73oN43zdSHjkCShHe5AcbihRrFpTCUhQtfK/NLDtHXuwR4+EhGbSWsoPRgJVUIIWHkyVzkTLTbVjMqIwlsxGFH5FWC0wsEELdAbTf1PxMG4j1I5zY28OZKV8aGxpCM6HCW34ED9j2nlcvP/qdHlEAA7kKRQ79KesnTvLtv/HDWGv52E/9A2Yzx4WXavO4U2sdZKPN5Oo2x8bPY3wfq2N0BbrKyL2AzSXT3t+5RJYJpN9BxUcoCsd0atnaqU3pTrdi5lEDz5M4IZlWgiAvQRmi8C2KrvR9pOdjTYlvSmSR42tNFvlEiyXT+3Yy7XECSsNkjL+MN2voksV2SjJWIAqs1IRvZmPzpmpJjZUG0+sSmYqw00NKwWA0pvAF5A6TzQlEDcBMBb7NcQJGy5GvtJniCBAI7lcUEClBjoex1zHtUQO31kP2L5IN7rChkODFnMLglMYKR2RAFhUOSWFCCM2toB0gjAiKEuP7OEAcJON0IOtfJktbNIoMKX1eURFtX9EU3g1qFSHrdVqxvNXIzno93jcc3vBS0+XyKt0AUS4QYYj2Im4uX4WQRoiygPQaaP9nv/ISAA+ePI3f81GrIYvhAtNI6GjFhvZYdLq4Ysq4n/HUU08RRyHndwaMX3uVi6YgWexgPc0RbxNv+4v89L/5GX707/2L+nkffJByexfnHKapwXnIRKGWowDVErRLIfFlfGhGd5D4Eh5JIIjYJSWoBpzePAXApfNXsAfPUd7dgPideqf+S6h7iXw7D4ypLep7B1/OufPL3/36rDCGPF9e+AyqKFk5Xku6BntXeE08wdWlVGj1xEkKYfCsQ49zMI6oGcB4Qj6t6oVa1EAJRWRGGGnxPMiKjNBMaJQBaI9pI8ReHuPrkPBIuGTZ27ey7AfVW8crNSYfYdIIPc5xyoPCYZYdYoBFfhkpFJ19gxyOMLtbWFviN6AnBWUVkTvFH/wj/wM/9k9f4vH3PYGuRO0cPwTrFfimQMZv7Bx/fXmqXhRV7bSOUTqIJNq5gj33RTj1CHKtNvdDKVQQ4eeCwpVYlxOur6OcxV+UVJtdRCfBGUeWNEi9Ou5N5Itr79eypAzugWl/40VTisd48yi+hCPzc+SRY6/vEH4TV0xZT6EsGxT7K3S9girLMHgoHeKHb8S0x7BY3PJjKT2CyyOKTojcWGP7wffz2ld/A+Pf+d3w+FfV7PzLL8Bzn8D7Dz/F5osv0ncF+A1CFbJiBlxczisb56C/h1fkGK0pO12khsoIKkJWgoAijus8XOMooogjrfo4jrf2GWzXK5n1VoJwDqsEVr/5ctI3LO2hNRjnIZzCK+ZIFZDl6tA5/k5VzEeYOH7zMtoPKk5h8eUw7fV7rsIUVVl8YWguQfv23g7rW2exRYbefAyAv/SX/hrPf/EXD5/mlYtXiYMIYQWzqkAIQersPTHts8qiXMl46cacpgmKCuEE3rFT0O5x8vIWiordsiD2QlQMVTAiaY3JOgtc1MGcO0uVRmxunkYIQTEZLp3j783oT4sAs7aG0cDl1xEvfA5hHOKJ9x0+JsVHC0EQCAZRWjNkixwdKRRzihk8/Cg88+5rSotyf8KiNMhIkqYNclFfb+7GtEsZIpAYexNL8+Dj8OFvBiEPJbXyJlArhUBGCSabomRI0mngfMF8r08UCOajOZ6pCDe7lMt98VHw+gvw4ufAD+Cp98PX/1Z47D24NEU4g1j+59+cLW/zWrauYjAeafQAnm4yy85TlPu0CBl5rjbSGw8IlSDwPZysyHXCxKYgBaUVTGUbqxUNMcMEEic8gpsiKuvYt/JWU8/TD9IoDMeml/nCsCJytRndwcMOQEdLCRSw2+oS+TCdWGxnBa5eAWv5yPf+bgD+7b/8KNOh5fKSaT99ZJXAGdSVz1E1Asa9ddABWmr8PGMR+KynNSAZ7Fwhz0E3H0WFKwy2oXIFWzu1Suj46ip5lOBrAQimFsJ8gZKWJLi/+9k9lx+ghaLyFH5VIvMM7Smy0CNaxr29rTPtQtR57eMRWmic8mlHGYvdNp3S4FwGYYRWb5JR522qrRVOOObdNr4T+KWgm9ZNgq1iAZWlnM4JlmsWU4J2BdNFQWktoZboOAa39AW4T9AeS4FFky1B+6KaUxDjzmygppeZXbmL87ifsuEyPBmitKMtgAqcFRhCXGhvP/YSRURVhfFDLBZhJ7WdTTTDDPeZNzeIpvvkVnM50az6gp4IbmDa4VrsG4DqbeBchd2/ca59fKmOwNMhqGIOUYS4jYrElx40YmRZYQPB5tKIbn9c38ueXD9JeayFCAry0QKvHaOrAQ3vKkWnhzVzRttTtNa896ueBuDcxz/J0M2JZ7tEySqt3XP8X//sX/Db/+APkOc5f+gP/SG+6j0fXCoEHbQkOB9ihUbi6SaVmR7OsQcypbQZxpUoIQiQTOOANInZJ8YZyyMrtfT/0vnLICQOR1m8I49/p/7Lr3uJfPv/Ap8H/irwl5Zf//tbvF//+deSpdbCQziLziuCRkyj2aIsch4WW1zZqS+cm6dOk7sKjcUbZEgpSc5sIMuc0dkJIZpCSQLdQBRjfN8QBpJpMSA1JXEmod1hYirk9oT0aJe83F2y7Jt33sfuGkrFqMGQsu2j+mOK9lGMijHLGKSyGlNWY0J/k+bFyyA01XyCtTlBA0JPEBQxs1KT+nN6m+uELkNVHjpKWAwtMi5RWYG+TYTI3UqpCCm8eq69qvBnE8gzzOd/EZOE6Ec/cOMGUUJY1KZWxiyIjm0ggMZoxO63fjVqvUlZQdVpoewy7m2Z8XuokaU2o7sT0+6cxbryrs7xB5XgM49SbBSxvtjBpdAfgQzaOFfRMDMaHQU6pCUWmDzDoBE6wovvgWk3FRQ3yU93tvAyiT11glYwwheSWQmLRgK/+Tvg2Kn6hveBb4TNE6ycP8ditEspBDro0TMLhtWMuXHMjCPe20JJQYXErqwhbE5lFJaAk6lHFdQmaKKylHHE0bReFI2nW4xGNdO+0aizZpES1K9BGqTnozVY4yGMIsznGBVSVXXU/N2qXEwwUXLPgPKeK27AfHbLbOFtazGrGUY4BO0iCBBO4buSxlLdsL29RTq4ShY3WTQ6vPzyy/ylH/n/ANBer68Dr+z2CbRFGUlpSgpjaWAZv4GDvHGO3Lh6ITxZelo0YpSpzc/wA3joSaIS1rb2GVULIunhRUBvTPuJHTIs3VxT9fcQx4+R6jYA1WRIld47aFdCo3RAsbEKVy/C1kUmG8dq9cKypBA0CQgiwTBsUpkSxgN0GqOWDvJRJGg2l6DdOcrRjEqUmDSi4YWH4PFuUX9CiHqu/Wam/bq6W9qEilKqKkcaQSf0MUnAfLSPs5BNp3imRK/U0nionbQZ9aHZrs/ho6dA6cMmonP1+xEQ3jK+U5QDIt2t96PSCCFvAO5JMaPAkDUbMF6qwHwPo0t0lDLOfPA9SjzmsgmmJPZyjK+QMrjFRPBa7NtN16hjJ1F+yqndSxi74OK4XmaUom42HIAOKQQdLdmXmqDXwhvsMTv6GBQljOZ8/Qee4sHTJ9na3eZLP/1vufz6OQDOHGkj964y0l2GJ06i5wVKB3hCoRc580Cz3qxRW393C9y1y+jeVdhfnKeqDBuhj240MLUBAhaYWUfT1OdgGr1VoD1EUatEdFWgigzlK6okIljOZ1dvJ9MOtRnddFy/xzokDev744pfYIsc8VYdi2W1VW0cO+y08SyE4wW9bv2aV2dTsFDNF0TLJaupwBc5g359XrYDj0r7YOrj5t2nZ168zGrPTYFzllk1x5IgzxzF6Yrsxat33thPiUxJWwe0AmrQbgSuEpS+h/TdrTPtUDPteY7xAxwCzYRWR5CNL2HKknljlXAxZWB9JqniXWFEA/9W0N64DrSnPVzgYwbXYnSvfhYmV6D30PIxRXaDCfENzyUULk5Qro5IW19bu+H3H944TtbrMRZTxLzAa8YoOyZQQ8JVhcOwd7k+Vh/8QD3X/tpnPk9mdmlnBV3j+Lt//x/y3/xPP0RVVXz/938/f/2v/3Vy46P2+ghtcJFPJUO071AIAm91aSRXywlCuRwXW7LtCZppENBph0hZMbQdHu/VH4BL5y/XSiilqN6ZaX+nvgLqXuTx3wk8sLTO//rl1ze81Tv2n30tmVuNBmfRZYXBsblZS9VfeP7zzLOMIIrora+QC4PGoMYL/EDhnzqO0DA9t0dALdNMwx6yLDm6MefBtYJ5NqYpQsQ8h3aXnd2ceDQnPt18Y5YdIG0igwR/XFI0FaIsETZAVhoTxDjnapZd+gQzgTcZ45Rfz7zbHC+BQEOURSxKRaAzMjUjcRnaaLw0JRsXyLBC5iXqPpl2AE83KFsBDgimA/jCJ6myEebxJ/DCm9xkwpgwdxRYSjMnOLaGlIpoUN+xksWC0glEq3kt7i2b3fB+AcvYt+K2sW8HTtU3s2a3Pbz4OKkoOi26sx1IHHsTi/Pr45CaMd2e4NiDENsMUxa1E7Qfor17mGmHWyXyZ19F+hHe6SfxvAUrXs6iVPWNvNuDx5+E82dhkcHj7yEJElovfpEBBTpcpVVlKDfhUmGYGEeys4VSksLzMKsrCJtTuPpYnYwVRZBSCtC5oUrjQ6b98uUdtnZrdng9jXDS4eTdY/LesjoA7VYiTADOkYll3NutCsBrVeSUVYaOG296FjNxUo9l3Evs2wHL3mhde7wfIKRPYMpDefz2Xh8lJFnaZV8Jfvfv/r3k+YJv/u3fzfu//TsBeLk/JBI52kisqZiUhtRZ5s7e1UG+xFFVoG3JeF4v2JNGiiqrGrR7Pqyso3pHOXppC2sWzCqH9CNW3jOmaG1RBQ147SJOGdKTtTSexZyqyu+LaYdaIl9sroFzmCRieqC4ua5aBASBpIiCOrl+3Ec3r4H2G//AOeWkwlCg0wCtgkPQfruYtetLqQRj5re9XsA10H6zPB5ARykVFpEvWIlDqjSkygcMBg45nuALi2h2KLiOaZ+MIL3RGPHgvLK2oCO6dOSNJlLWllRmSuIfJVZtKOrHXw/cdXYVWQyZNJPab6EsaIUeRlUo6ZGLgOyZ9zM69RSVH+EXC8LQUvkKLW5txl2LfbsJtGuNPPkIydY2p70Rr4/AWEfuCSJunMnteZL9yhIe6eGP9hmGG9DswPY+shjzP3zkIwD8zP/1Ua5eqefQH15PmPubXOo9SeBX+PMcqQKUUOisYBEo1hs1S9sf1Iz60iqC/hYMxkvGPg0pghizdJovESyq2jleCvnm+lxcX35tOIdWqEpiJ23wLDYM8Rc5aI17uzLaDyptwHQC1iKvB+1hjikr9D1GuX65FaIJlGA3ahJIRTKZ0e7V58CV4RQpFXaW4S3vz6YCLQpGg/r+3gk1ZRChl/F+9y2PlwKLR+EslZmSWUslYvyjG8iWoHzp0p039mu/nTWncE7TdhIqMNbDBALlc3t5fBQjihKCEOscveacBx405IMLVGhsq4E3n3MhSGkEggdU3VzOMdjrrkVBE6psqT44dJCvQfv+y3DlV6D7IKzXxDcyXyDvQLD4SEyUoK2BqmL9yDVSSErJ1595mEVvhWk2xCHxOj4aR9tr4ToNfLVgtF0b1n3oqz8MwMtfeJHGYMrqLOfH//E/5/f88b+AtZYf/MEf5C/+xb+IEIJFoVGDPi6SeNqRRw18auLF000E4nCuXYsQKTTZAWgXipm0bBxpILTjStHlkXYbgEvnL4FxWCkwxa3KxXfqK7uEEH9LCPHYl7nttwkh/pe7/P4ZIcS33OF3PSHEzwkhpkKIH73pd/9GCPE5IcQXhRB/U9xO8nKXuhfQ/jzQvp8n/XVRh6BdgrOIoqTCHoL2T3/64wC01jaIAkflLKoEb17gRR5y7QR+AsX2Hp5RVFgaUQNVOTaUoS0ydFnQrSQ4jel0GG+NCY1DHDFvzLIfVG8Nb1JgOg2MXZDslwSFwoQhZTWkMnMi/wji4usI7VGdeBC3mGPNAqkgTAXdXDM3CZ7M8ZMJgc1RLiFMNPm0RMkFAom+Q/f2bqVVE6vANlOS3cuwu0XxwDFUexMpblrkRzFRVmKFJjNTvF4H4ft4wwmNCOJ5hhEC3U1RTtRxb4s5aO+GWXslgzvGvt1LRvtBJUuGLuutkpg5PT1mbByzRROEJKpGh48NzYKyskgLMgoR4h7k8XBNKQA1XXTxPJw4RRjXIGZd7zDP5bU5t/d+oGZF/+N/AKkIH3qGcNBnfPV1RNgmlh5t2+dSbphZR7S3jcJShDFmdQVpcjITESloegKiBoUU6MxgGxGb7Xo1dOnKLltLo8XNOMQ5cFqhfy1m2qVEBRphLY4V5n6HqaxBRnC33VnGvXlfRrPpDevAkHF2DxL50aBWKaxs1oy7tRAECOnhVRXN9hK07w+Zt1cJhceP/uTf55Of/ATd7ibf+xd/mM0HHgTglcGQQC7wjMZVJdPKkCxlhXeTyBfOUlUW7SomsyXT3mqgqjoSjeXMtXj4KdpW0ty6xOX5gsBvkGcD5vmApt9lev5VqrUmq40jNRM8GVNiUI32fTVGPBFQtCLcw0+yePIxuA3gT/Bp+BIjNYMoglEf3UhQMiMf3chG5f0pbpGx8BV+K0KJmrEK0G8Y9adVXDPK9vaLPmPzWrB+m3PaCxsYHHY+JfYSRCvGFmOubjmiyR7K14h2zbRLBLqq6kbdTU77UvgIBNbmtGWHVNx4rS2qmjkPvRU63nGEvbYOOADukdchzHYYRMvr3mRIJ/UQEsqipAwShnaNkWmT+TFBsSAMDFUg8eTtQPttYt8O9vfMI0gLjw5fp7SCvbkk04IQfcNMbldLSgduo0eQjblyJYMz74IK2Nvle77rt+P7Ph//7KcoypK1MMDf2GC78W5EZFGqxJtkaFkz7V5ekEWS1VZ9/g2GA8rcHiQp0t92DOb1bPyZJGDRaKE9kEIzcYbSSpJqjh8kCPkmRUDeXJ6PQuK0opNUPLAZ1+aBYUiwyG5oML9t1WjWqqDpBKUjkjDjq/oBD6QFlbF4/lu7TwGKUMJIKbwgJp7Maa/VTfurwynSU9hpjl4CL1cZtCgZDuv7YzvwyMMIlUukBnWfveNICozwKGytPlxYhxERLT+kPL2KvXAJeyeFvJ8igaNojoebdISGCioRUGmLvBNoDyOkA99JEIJQ5+h0iBj2ybyYMAooR1OuJgmn/IBUaCI0Dkd+vRnd8lJQTEAIheisYPd3GV2Ec/8RGkfh1NfVUxCVs+g8QyW3T0vxkVRxXLcx85z0yArhsoF05sgxWn6KaK2ymI8JlYDEwxeSdX+DaftppAA1/SJZlfGBr6kTIF586RwnvnCFH/1HP833/dkfB+CHf/iH+VN/6k8dqoWyUuENh5StEJ3nuLiJtAW+jBBConV6GEUshCCUKbmd4JwjQWMBr9Ek9AR7peJ09wQAly9ewRQGpxRl9U5O+6+3cs79fufcl77MbX/KOffn7/KQZ4DbgnYgA/434Ptv87vvdM49DTwBrAK/8372617uSn8O+KwQ4t8KIX7q4Ot+XuQrsg7k8U4icKiiDuLYPFKD9s9//pMAdDY3sBIqLDIDr8zxYoVor+C3QlS+j9mpF1hBFKCNZl4sGBZTdOnTXMzBC5g1G9irIxoNSR4Vb8yyH1R3DVWCVD6lLEivTAhVAxMELPLLKBnhuwS2LsLRU6hWF+sEZlYvAv0GxE5iTQNrczY2FvhFhRApyoKzBVrMa4D9ZXTjPX1trl3Y2oimPLqOr2+NIyFK8CxgJAs7A+0jkhQ1HNNMQE8XOKUIOwEKuWTa5zdI46GeUwVuK5G/BtrfGHz6QuGjmHe6hLJk3e0xU469PQ/rRYTlNbovqCZUpUEIDxXpWxsSt/yttwHtF87WFMOZh5DSoypjmnKArKBfVTUTuL4OJ8/A1hX40ueRx8/gN7uYFz8HOsETAet2yNUiZzSdEY6GICVls42IfZRzTGxIw6tvpkHSpJTg5RW2kbDRrvfrypV9tvYPQHuAtQbnacSvBWgHpO/jiQLjUrQMGC+zWe/GtJt5HUUWRm9i3NtBHTSw7sVBftSvmdU4rRfORQZ+gJQKXVW0O0uZ6LTAeD5bF/b4az9UTyj9v/7fP0bWavH0I7X28dXBEN9N0ZVGm4pJZUmWYH1wx1Xnkmk3Bok5BO1xu4EqTQ1qD5penRX89VN0L2+zN9sn8pqwzL+N9yuKYow6vk7srdSPn4wpsHj36Bx/UFqGOGcpT5/CpgmYW29V9VkOke8xCCKYjhHaR4dQjm4E2NnOBFvMqEJJ0Gog5Rs7xx+UUnWjypjbqyasLWr/jNs0JXTUqOcpF1M8FaFaISofcfasIZ33UYFGdlbID5zjJ8tGX3pjI0kIgZT+jRFr11VZDlAyRN1hPOUAuCe6wzQYYZ2B8YCVpo+UkNsKE6SMt8dUswWFHxOaBUFoML5Gy1tPpNvGvh3U6jqy2SO+8DJnUsnWTDLV15j2A9VCT9fv61anhdfN2Tl/lWF6FFqrcOUqq6nkO77jOw6f9nSnyWL1BLuzhLiZ4XDo+QLZaKOEj5eV5KEk9Hw6UYCxlr2dbbIMjHGMBzCY1KD9gThg2lpFa4vCY2wMFkFYLQiDt84pHc9HCQVSoWTBSjihCH0QAm+R1Z4Db3c1l9fAyRipY0Rg+WDP0uguwFi8t9A5HsATikhJprbCb7ZJ5jOaa/U15PJgivYFsirIhxmCGrQrSobL87wVeuRRjM7lfUvjASIJDk3hHGU1IrOCWAWEaMqH19GzAbPzd1BN6RCkpllVRLJdX5VKiyHEKLNk2m8/0w4QllB4mipbMDV76NGEcaNDTMVgnDFPE54J6j/qQK10fePr+tg3ANldoxyMOfuvMqIuPPCbrmXWZ9kIYS36DqBdCQFhgtQCf55RdmPWl94C7zlxgkXUJfYjzGRG5IFJFD4aTwZEjXUKv4c37nN+8iqtXsKDxzbIipIf+N/+At//0X8FwN/4G3+D7//+G7HMotQE8yGmEyPnJSJNELbAWyp8PNXE2MWhu38gU6wzlC4jXhKVNklJAphI8INNjq6uUlUV2xe2sEpQVe/I478SSwhxSgjxohDiHwghXhBC/BMhRLz83c8LIZ5dfv9/CCE+tWS4//R1258TQvxpIcRnlqlojy5//j0HLLkQ4ncKIZ5fMuS/IITwgT8DfJcQ4jkhxHddv0/OuZlz7mPU4J2bfncADDTgA/cwQ3mt7kWv+HeAvwB8AbBv8NhfP6U90B7SgkKgshyI2DxWg/aXX/0CAN3NdawwWGcJM4tf5Xi9BKTE3+iid/pkFzVsQqF9YhkyyAuMCumi0PMcpGI7T/CmYxoti0kiAn/l3vazt16bFo0NVTvA7lxBCoVLaoYojR9EXDhXs3vHH0RVuxgpcZM+rNTzUrICaRpURcG4sPQqQyWS+uOoSrRdoIS+57i360tKHyVDinVN1lolf+QBBEM8r33rg6MYf+kgX8g5zhlEs01weY+JS2CUU/ghjYZDGXFtpv0m5uL62LebuTFjCwS1q/K9VILPrNFEBh5Hym2+4D3A/q7H+pkIbzGGEISz2GKBsQ6lQpSnEG/A7h2izevl8WdfhVYHuvV7b6qIVAtClzGpFLk2hM0m9FZqvdyLz8OJ0/iPvpvsl3+GxeWzeLpBt5rhvDmXd0as5xnOUyzW1pE2RyMZVRGrUQ1AwmaLUknCRYEMFGvd+mZ/4eIOe4O6873aSmq5ntLYX4uZdgDtEciSue0RoMmEjw0Kgrs40eXzGiCFb2ZG+0EFUc2ez+/BQX7Uh83jN77nvo8Q3g2gfbs/xM1n/Ik/95MUWc7Xf9Pv4v3f9s2co+TrnngUgLOjMZ6ZoqxHUJUMrSDC0Jaa103GSXV7cFk6hzMV0homSwfrsNXEK8satF83Y+s/9F78C79MevkFivY3AZDIkMGF2tchWV2vpfEAg31KT+Gn93eMvaX0+iDaB3vr+XJg7pQEHpMooJzkeJlBR7CYzoBrbHTZn5KVFt10hM0EKX0ypjR44xW+kgFSKCozI+DW6651t2a0H1QYJEylpFyM0bKHajWw7iKvnd/l6XKKjn1ke4WCopbpT5egvXHr8ZLSr52hb359W1KaKdEbKK+EkKTeGsNqn8JXhKMBa5vHURJmVYnXSJmdv4gxkOmIsFjg+yVV4OPdwfn/trFvB693+lHEZ3+Bp+WA503AtvMBuWQKDSEapSr6esIvdSTPphWd536arcsnaZ90cLUPF17kIx/5CP/oH/0jAE6sr1GFCfMqIEn20Vj0NEcd3UCdO4uXF+SpQuBYSyMGi5yd3Svk+REm4zqGfrdfx0ydjkPyVg+pLFJ4TMwUKzSNoiQMe3c9lr/a0n6IK+cYDOzvUkR+fW/LFtBZgel9mFi+GXV9Vns7ofAdq+9aEHQX5JXDC+42Z/Qm7YJU7DmDanSIpruk6/V7cHVvjAoUsiyZDxf4vRRRWiQlw0l9f2xHfs20z9WXBdqFEPjKp3RgbMaUkJaWBCj6j6zheIXsi5doPPDw7Z/AT0mKBarK8UQN2itCjGcJvDsz7QBhYZn7HjbPMMUcPa+YHF3jSD5jvzSEzRbHdX0fOwDt14+YBMvLXH5wqQw3GF+xBK0dHvzNJ25QHeTTehTLi9t3PhZxA+EpgsWC/orP8XaH88MBHz66ySxexQiBP5sjYon1HbFMEELQDSRFcxW3D/uFo1Ge5f1PP8arl7b455/+FFJKfvInf5Lf83t+zy2vuSg03nxI2Y6xlUYmIbiMYNks1LoJ+WXKakzg9wgO59onxKq+JpdRSpwq3ChnkklOb57m8u4uW6+ew71/BfOOEd1bXv8g/+iHqM3K38za/+7gez7xBo95BPhe59zHhRA/CXwft3qv/UnnXH8pR/9ZIcRTzrmD2PI959y7hRDfR82O//6btv0B4Jucc5eFEG3nXCGE+AHgWefcH7nfP0gI8W+B9wH/Gvgn97PtvTDtc+fcX3XO/Zxz7j8efN3vTn5FVhghihLh+chyAQg2jtULp6qqO4LdIxto5ShweAuLLwvEsmstWl0ib8DiXL34zD2fhqeZLyL6+QrrIodFBc0223uGZDEh3KzBgJL3eGeK0xrsjktst40xM5yziEaFp1J81YSLr0F3FRotdNLBComdLJn2FFQFXpWCEZjFHF0pjE4xMwGqQJoFSgVvMEB85/J0g8q3DE49SqnmaN24PRMdJXU8Xi4plmZ0frdLUC3wB8dxA0MVJYSqRAlVy1Vvy7TXDNFtmXb3xhnt11eKRyYlptXmSLVF5Tn29kU9VGcLmjbDqxYUxkBlECJA+/fQEPDqptChg/xwAP09OPPQ4UOc9Yi0osGcaWlryZwQ0O3Cynq9/X/6RdrNNbL1DRavPY8SDZomwxcz5HCPYDbFBh7Z+lGkyZEIxiY4ZNqbjRZWacSiwHeOxsYqUgh2doZY62g1Ejzp158p7WHvRf3xVpTn44mSsmrioalsRN5YHMZ93a6K+QgbBMTqLViQClGfe2/EtM+nUJXQ7F4H2ucQhEipUdbS6x6Yau3xk3/zH/OpL75Cd3OT7/x//hDThiGRgidPHMMLQvqLOYvBDpIQvyiZAUVZ8IAKmTvL1SVTcXMVOIwxSFcxXTpYB702uqjPpevHS+L2BmZzk/bVs+zMBF18elVMf28PebRJ6nUOzx/T3yPrtO7b6O9ghjo/BO23MlVSCHwUcaAp05RhYWA+R4cSe1NOcTmcUskSEwWkgcIIjcHeuzmeSm51kF+WuUvaRIDGhBFVNkXJAN1topVBDy8SlzNEO0F68bWM9smwPm9vw7ZKEdyYF7+sA2n8bdVJN1Uo6+fNGiGMBzR8n9CHOQV6GWxvl6A9sQtEAk55h02Um0uL4NbYt2Wp0/UoYXrxSzwcefStx7Sq+/67LPi8HfIZN0TqCk+3Md/6zVTvOcO4nzN/5Sp86QL85P/Jb+jv8sjx4wCc3Oyx0N2alQ4WqLJE5xWy1UOGEV5uwKul52uN+nwa9i+TZTDs16KQrZ2zAJxuNyjTBKUtQihmxqIV+EVB+FbFvR0cGy9ESEmFhfmMPAoISwdl8WUp1n7V5Qd1tOpkhNYxCFh7aoR1M5xQ+MFbf11vLGPfbLODn1c012sTtJ39EU5KVFWwGC0IhUAag6SiP6nv4e3Ip4gS1ELet3P8QQXSo7A16TW1IQ0lCdDY9TZ0AsoX7zbXnpKWObrK8BFQWkoXIOI67UHfYaYdICotle9hsxw1GmFcyKLZRIwHTJGcaK8ejhZpIfFQNzDtOqzHAYpJ3ZS68NwGODjx9NVbGhjFtL5W3E35JOMG6Bq0z2PN//w138BHfsd/w7c//i4m0TpzaYmmGUUjQLiCRNaf17YnKBpdosmE7fkRtPB55t0P1PsoFT/yvX+Z//533QrY89JhZgZdzXGpprIeKlE12bQE7UpGSKEpTU1SKqHxZEhup/hC4iGYxxFxGhGqnOHCcrR7FIDB1R2MVNjq9ve+d+oroi465z6+/P7vAx++zWO+UwjxGeCzwOPA9bPu/2z5/08Dp26z7ceBjwoh/gC8gQnOPZRz7puATSAA7ssj7l5WLL8ohPhzwE8Bh3dn59xn7ueFviIripHZGOH7yGwOSDaO38h29I5sIKShrAxBURGIEhHVCwLZ7OFHBfSnmLlg4UkaWmCLFqXosCJfglmOW+2wPyp5iDk2iZFCI+8x8xioJfI7V5BnjlC+eBaHw4VdovBYnY8+n8LDTwLgeyGTMMbORlhX4Tc0vgRZxDiniPMR0mhUlJANQIUloiyRvwq2UqsmGbsob46xOaG/cfsHLhnzqBBMsRi7IFxbQbkSd9YgpgvMag9hSzzZQFhTzwjfRtqnZHjb2Lda6nrvA3EJPk4GZO0G7at7eGLO/qSJUQ0s27TNjH3jKKsatEsV3htoh1o+dyCPf/2Vmrk9efq6Bwg81WBVjflSlZBhaEEN2s+ehW/6TfCLP0v8iV+keOYx5ru/SPfKFv4KrIsxeX+HYLHApOvYjaMYO8I4H4emqetFQjuKGYYBbjRHOkvZbLPaiNke16Bopd1AulqK7nz9a5PTDqA9fDHH5hucWH8/Z69oFs0M59wdGzDFYoyNEoI32zn+oO4FtB+Y0LU61xQh2aI+Z4VGWUe3FSGlZDoa85f/2j8E4A/+4F/BOxbTV5ZHA83VvmTz2ANceO2LPP/q6zQe/BBBOSCXjmlWsiE9UqF4zSw4cpuBz8JZnLVIZxgvQXvUa6GKCukHh/GQUEtZ84ceJ7l4gd3XXuE9D6+wc26IETnJsS6RXjbaraUc7VM9uHbfoF0KhRIexcEc+W2YdliayAUeLgwYCsnqeIhKIjBzqqxe0Loyo5wWVKJENbooISiXkso3MqE7KCVj8mK7bk7doJKxOGdQdwC1PgoTxVSLaT2W02nhKUMyukJULnC9EwihrwPto9uy7LA00HRVrTC6zrvmmjT+jZtP8RK0L1Kf9sUJ0jgagWRblkvTvHpCY65iVk2GiQUIeShTvbm08Mnc+LbnmWqsYNfXsGdf4v2PfQ3/N/C5aYlqLtjB0iHitEjwkFwuHK1jp9naWOHKWgfnG97/zCn4D/8EsXeZH/62b+MH/uk/5Zvf+xgT1cHTFqkLvP0ZEoVodiBK8LMBThps4LOW1OdTv3+VPIf9MUjhuHLxAgAnOy3OJxFKzBBCM7OOUEGYlajorQWpIgjRhcZQf77zKCBaOsf/msjjoWbbxyO8ZVZ7lQ0wZQ7Ke8vl8QAtpbAyo2w2CbKK9IF6LbW9P1y6q1dk0xlhJQkri3CGwZJpbyUBWdTA35H43bu9yp0rUZqiUBTWkYuIphKEy+uDfXCT6oVL9clxu/uJnxI7eKaAUGioDJWLILF4yNvfg5YqsLgwWF9TDTK80ZxCSKo0ZHphhsHjiZUbHdzDm8wcoR5jXPTh1X8DeblG47hC5dvcXNWiVvIE8Z0bfCpuYJXCryqKWPP05iob6SniwJI115joOafnFdPVOrLRXxJIbV9yvtWjfe4FtkvDineG7/z2b+GLz32Jb3//b+Bk57vZfwlWb7IFm8zBG84QqsA1fJz1UHGd0X4w7iOEQOvmYV471C7y02oP60ztIB8FrMUhiZ6xX/k0l2uRbDKpQXv+zkz7W133wIi/VXWzxPyGfwshTlMz6O91zg2EEB8Frr+pHYABw21wsXPuDwoh3g98K/BpIcR7ftU77FwmhPgXwG8D/v29bncvTPtXAR8A/izvRL7dWGGMyAqE7yOKDIdk4+SNoL21sYmUBpNbAgu+KJFL0C4aK/gJKLuL2dVkWtP0BJ5ZkNg5qS2gtEyqNoWZ0fYLTBrc0+LshuqtQVngR6s4W1F4BcbFaJXAhVdrt6712tgsQFElTdxsdhj75ntQlDEhHkk+rudlozqj3WvkiCxD3Gfc2/Wll3Pt2q9ZNW8ZFXVLeT5ojzi3FIKlg/w6yhnmV3dqp/hminbVHePeDqp2kL8daM/vyTn+oFI8kJqs3SJWFZvsMcwdM9vEYmmZGb7JqMoSKovvBeg3ymg/qHAJ2o2B86/D0RPcHDyudZOWqiiqkunSWZdeD/K8Xvy/76thZ4vN519k5/hx7O4eel6xasckuxeRwlKkTVSrg7OLeqEBh0x7xw+pkghXGERVsWi2OdK6toBbbSZYVcceCs8H9Ta7Hh+U5+OLgqKQJOkG3jhCBJbJzXFU11U1H6PeCuf4g0oaNWi/W+zbqF83Yxrtmu2Ssgbtno+QHtJYIl/QWLphF2XF7/ud38HT7/oWFs2KuanoX5B86mXL6kZtRvfFc5cItcYrDIWGWZ4jhOABHTKyht3bsO25tWAN0lnGi/r3YbeJLk0N2m8qr32MarNLevUFdsOH2b06wKwlJGFAeHD+jkeUpqTqtL+sSL0DdlcIgXB3Au2aQihanmAvjGszujRGX+cgn+9PcUVFIS1hK0JKn0LUc/73nB2vktua0QlZP8+dmPbDrPbFFCk8RK+Dryzp6DJBkcHqKiUWh6tB+3R8i3P8QallM9FcJ5E/kMb73huz7FDnMEvpk6U+OIeYjEgCjVEVlW7ge6AljIhoiTkmkggkgbh9I/OOsW8s37czD2OnA9rDHbrWcL7KKbCsCo8PyR6nZcKq1uQOAqtRSrB2tOLq1Gf4zDfCE4/CmS6/9Ud+hE/+47/D8QeOMy0T0pZF2hI9y5BCopq9GrQXBiFKTBCwFh8w7ZfIM8f+Nphqn/lsRtNTtDptXCiR0mGRLJwhsiWRERC+xWM+XoASggqHdZDFPuHyvLvd/eptqWYLJhN8GeCkwmZ9qjyrP3d3GTN6s6qtNE5Yps0mXuVortay573+iNJYNCDMjBP9hJMjhRCG4XTJtMcBeZjg51+ePB5qM7qF02TWUXGNaQewj27gphnlxf3bb7y0qw8XIxD/f/b+PMaybj3vw37vWmuPZ6yhx2/qb+Kd73cveS9ni7REU6CkyBEVSYAGgIZoQNCAGJIROHHMgIr/MCJHcAAjTCDHIgybQRLHhokAomI5IgSSkqjLe3nn+X5Tfz3XcMY9rrXyx9qnqrq7qquqv+6q7171AzTqdJ19ztl1zj57r+d93vd5DNQNrU/xuT28NR72uuny1uHiiMrGDHYts6yH0pZiviDXGePx/eeD7AEzRwgt8rMbsLwHL/+cJrq8jtu5P6sdwM4nKC/oIwqDACbt08aGpG2xMSS5YV1to6Kc3d4Ip2s2ypYqN3gccZfUsh4L5doGsXXY3W0mteeFy5/g//R3/gr/5h95heTKiFtfBP/AkO1sCen2Dkpb6KV4b1A9hUbdl8gR6SHOt7RdJGOi+mHUxi3IRTPLUpI8pa9LZlFE7sJapJjOsdrg7TPS/gOMF0XkJ7rbfxH4nQfuHwILYCIil4BfOM2Ti8ir3vt/6b3/FeAu8AIw4+AM3smepy8iV7rbhlAE+MZpnuNY0n4g5u3gv2eRbwBJFlTmKMaUJU6E8aUx0cFW0svP4VSL1JbUNkTa7SnteriJMpDnWzS3NZU4ojhjQ1e8ZJaoyQxMyp1iSFxNGQw1bR6jDzEFeiTWQ6U2KsGPh/heRlsNQz703Zvw/CuBLNCR9sEYv5zjXEXcD7FvZZmS+4TYNfh2SJpryl1QgwpV1Oj3QdqVaIzuITgiM0CpRyyks5ysqPEqprBz1OYlIiXU77yF2Aa/NsC4dt+EDg51410p7QdjnEJGe3sqpT0STUbEcjwkjeGCv8du41mUCdZEjN2Sni2o65YGg4kM0UlJe5YHAvfeO6Fj4EBr/N7r6yEDo8hsyZ3mAGkH2NoKpnSf+izj927CdEGR9YjevsnYlozvXSfylsWVK0RKEFfvxb0NO9Iemwh6PWxjUbWlGQz2Yt8ALo16tEmGsk2IOXtaBPg4RDFGGto2GE7pWUJshC2OiHlxjqacEz/N/OG8Hwou1SOiZibbIeJq5VSdZFAtQQSV5mjrMcox7LLan7+4wf/mb/1vyYuIiba8daehmgijnnDpSjg+vvXebeJYEVUWbxTzOhznV1VMKorvtA/vT9l4lHdo1zLtFIne5gBTt5j44fNNrkeUrz5PxJK7v/vb7BYV2bURscpDagPA9j1m1Ki1zcci7aZ7Hv2IHPUETa0UQwPTNKeaTjD9BC0F1SSsDsvbc6gXlElMOkrRElMdzEU/AXQ39vFgXrtIlzn+iHOGTnvYeok4h75wkUgs/dlNYlujL17cy2iPyzq0Rz9CaQfuKzaepjV+hUhlFL3u85juMEgiVNRwe57x4kXF+sAwVwnjaI5NDIgmPqIosRf7dsisPYB+/jWc9rjvfYvnZi1Z0eM1N2IsEabrWNiIws/SChFC/0JDZOCbNzS88lG49Q4UM2pfU0cxteuRDx3iG9SiRCGo/hiyPpH1KN/QRBEXe+G4ney8zXIOk3ue3cW3AXi5l9AO1jFGEBEmLVixDJu26954yiQ1jokctEZjcbgsIy26ca3zcI+HoLRXBXFtsSbB2oq2qc6MtA9UhBaY5DlaNL3EMMhSWmu5XdYYJWjm7F4XItUgzrLTmWaO0pja9Ejs4xnRAWRaqIlYOk8rGUMje74Z/qOXACi/ckSL/CpjztbgwnerbVPI3OEmdHsvmtFrLTaOKFxMPC2YDEZ4V+BnJZdGF/evDauHYGiwtAfYb9KdMl78aRi/BLJ+Ebdz76GYSruc4ZWB+OjzVaxi2l5O0lqUqzFJnxdjTTu8xD08mV2wpjRNHmFF740y9o3QbGyiRdHbuctbywpJesQqp04Nmz/cUM9g53v3v96s8PSmu0hsIYlo4gFa1WgV3ddRtGda3KntseQhCs4v6WFotUKNx/SoaXJN3H0Oy9mcVhm8bYN30zP8IOKbwN8Qka8Da8CvHbzTe/9FQlv8N4DfILS7nwZ/rzOp+wrwe8AXgX8KfPQwIzoIBnfA3wd+SUSud9FzPeA3ReRLwB8Cd4D/y2l25FjSLiKXROT/JiL/qPv/R0Xkr57mRX5gkeWdaZnCVCUg+Ag2LgS1PUliovEFaixSOQZShJzuNBAFlQzwSUK/v4W9bShaCybjxwYNn+kXMF+CirhR9hiZCal4XD8/+Tz73n72oDdAbd1F/9QvYH7s5/HehFl2gBde3ds0RtMORri2wRZT4j5EGlyl6PsNPjm4QtsOiTU0S4+OZuDlfZF2gEiHxx+7+ExzkrLC64TSLWB0AR0phtshe1ev9dG+M5srugX2EUo73L8APo1z/EEMiFlqgxvmXHVbVHhmC0MbxbzEgp/KHMvWIwAmPR1pL4pgQJf34NLDRlNapwyjiNyW3FvNbK13/YHbXev1hz5K9pE3yN9+m5kDvWjJ33yLF6Y7GIH5cy9gXB3yzW2OUWEBs/cagyG+bdCNpRqMuXJQae/n2CRGW4sePN050EciiohowHuqCtpSMSZhm+LQfG27nNHiiJ9G3NsKK2PGo1rkvYfpTmiNXyHNWOVT6aSPWEeiPS+99DzGGH7tf/3XKNxFbk8V1yfCMHH8zBuK158TrjwXlPbv3LpHHAlRYUELi7YFb9EivKxTtlz7kJN80XjEW5RtmVbhOMo3eiGn/ZAFeyYZLh/AcyO2JzOaXsxws0cs2d73Z7lzm6XxXBxceaxuhlVLtj5C5YWgtHsRBklM08+YNJ5YGsBTd3FQ9faMsm2RRJOOUpRKKAkK2HFxbytoFaPEPOQgf5zSDmCyYZhdLpekwzEqi3jZbmNiITpA2pOV+dhRpL0jyAejKk/TGr9ConLq2OGjGKY7jHsxcdRye5mQ5xpJc6xz9PSSJtaAJj7iM9iLfTvCjM4kY9wLV3HvfpvnyoJ+m1E097f3rhlF/9Z12n/yW1y4s81MNbx2Vbi57ZleeSOsUr75edrpNoVO8ZKTDFpwFWZRIL0BYgzkPaLao6ynTSMuZeH4mc5uUC2gXsCkCdeJV9KIcrSOMYDT3GtbEMewaUOB6Wmr3XFC1LTUWUyDxWZZiHtT6kwI8qHoEgvMYo43CS0O2zQYFR2TnflkkKBJtbATJRhlyKqCjVHYp3fnBRpPbAq2b3pEagTHzjKcK0dZQmFy0lY/9kx7roRaeuz6AVoUfSV7vhlcjbGDDeqj5tqVgaj7Dtpwam98hk/d0Uo7QJqR1WGco6HF49kZ9mkpMAt4bv3h9dBhZnSXPgGv/wm48JHwf71+CZnOcM395yu/mGLjRx9fscTYXkbUNiS2ZJ720SIUoyvc8441NyUWjfQSGhXdd32NL1wEEYZbO7xb1pBkxKqH7w8xV7dJ1+DmF+5vPpsVkM8n+ARQCU3aR0u152uy9xZ3psWruXYRhZaY1ld7DvLt+jo9V0NfEZlwICxnCxqj8d6GMPtn+EFE673/y977j3jv/6z3fgngvf9Z7/3nutu/5L3/Ie/9H/Pe/6L3/te731/z3t/rbn/Oe/+z3e1fX5nMddt/wnv/ce/9/9IHbHvvP+u9/5T3/v/x4A51z7vuve9775/33n/Ne3+7e8wnu+f6W977o2N9DsFJViy/Dvxj4Gr3/28B/95pXuQHFqusdhUjdYUSxaJpWV8Pb9WV5y5inaLyLVJ7BrpCiUa6mTWR0BabJ1voWrPYdbQmJnIlpllA0VK5HjOlWNMLJA4V0lO3x0NQ23fukW28Rrb5euhRuv49uHDlvhk6LQrpj3Gi8POtcC3KBeNh2WY4q2noIw3BOV4tQRTmfZL2ONrA2YTouDbPvEdclKBSal9jI4PkOWuzd8P+X8rRSJgfLYNiySHOtys10B4wo7PdwvNRJOEw9IlpVUQ96nHB72Jdye4kwsU52IK0mVHVDdoL6JQ4O0V7vG1DfNvLrx2pYvfiEQNKtlekPY6h3w9KewfzxmeQl1+j3LoLdybk//ffQk+n2PUhxZWrmM5MammzPZV9hWg0xjUtqrH40ZDLa/tGSReHOXWUINZiTukQ/kQRxWgD4hqmnQH3BZVRYw9tkS+Wq9m+8dPbp2NIu6kKaFsYHRjCTPO9sQ6d9RDrMAp++d/5c/zD3/g/8zOf/BS/+52U69qyFhs+/rJjkEP9PdgYBtL+5t0tdASR9VgclbNUdXgPXtIJkQjftQ9EojUe8S2qaZhU4RqSbfQxjSU6xIQqlxh0Bq9eAuORVy6SiifWg72Z752d93Bra1yUx1tFr1Rc/Ygkh5USlsURDFImjUfVFTqGdhbex2BCZ3HGkOcapWIqWuJTqv9a9x5S2pVqg8eIHK2mhVlxj1/OyeIcO0jJ2h18ZIg3Luwr7bPuODnie6SU6Qw0w3f1tK3xKyQqp/UWOxzAdIeNYUQctexWnkr3mUlGXJekqcPGGlRyuJEWx8S+EcYK7IvP4+oFF7dvkmqYl4qKFusdTHYx/+yf8Nq/+mfUd26z/s4NKhyXL1siA9/Y2YQrV+H6t2iXMwqVkMQ5JA3GW8y8QK2KHPmAqHUo56gTw5U8nON3JrdoFlAvYavoSHueUPYHmMiD1Wy1DU55Nhob3L+fNnGOEjRCG2lqHC7PiMoqXIvPq1tpFfs2naBMRkmLa7u4sjMoJMRdVvtuEqFVTL5csrY2BuD6bIm0jiQqadsWpWvEe3aX4bgbZimVyd+X0p4rodCXuC4vMdCyN4eeYKiVRa49T/PmrXDOPvQP6M73rcK10KoEn9pjlPYcU1tcnGCdxQK3+hnYhtHSk48eLiofRtqjHIbP72+j14MvULt9c+933nv8Yo47pHPqIBIV0/b7mKYlthWTrotgkl1hHlk2mgXWW1SeIiph+dZX4J0gAOWXL9CguDSZcLupcUmOyobozZcp3A6XPuUod2Dyzv7rTReeXrkLuQ7RnnkP7ysi9fAxF5khbTvDd10GpiPtve49saM1YleT9IQkDwfCfLYMSjs2rKee4Rm+j3Ekae/67QE2vff/T7q4t64qYM9g3z74WJF2FKqpsbXnzm7LxY1A2q8+fxknQlU6jLf0VRkczQ+0v8lgncjvkHpFsR1i37B1sAJd1MyKdcqsZl0W2H54nD7kZHYsNi4Gl+ppaKdMd7fC3POLrz20qe6NsGrfQT4ZgLJCUWe0DTTSQypA1xi/ANGY9xmbpXVKXW4cn1+e9lBtS+Li4CCvHaafEzUFSkE7zPYz2otlIL7q4cN8T7V6Qkq7VzHFeMA4a1lr7rEz01QqxWHBtTRti3Lg4xO6x8NejisQSPsRMGbASHmKpqTuMrnZ2LiPtAPEn/kplh7s738NNVniL2S40YDi4mVUV7yYNdmeCd0K6XgN8SCLCpNHXFrf/6w3RkNao1HWoodPUbU+DlGMMaBszbSbZb4QpSgOb5EvO0OeLBs/vX1K83DsLQ4n7VHROaMPH1Taw/6qJENZUFqIlWZEyte+q5i1OVc/5PjYWkrPCPdmDTtfFNZUaI9/e3uHVju0VYh1eLHsdItbI8I1nXLLNszd/mm8bB3aVlTLktZ7ksgQZTGmaYgOU9oxoHskPc3tn/pJ1l7eIJJo79xUuoZi9y7D8ZUTq9kPwkgaDIgeUUTbM4pSEeOeYktlsCwwGbSzBb5taecFrQ7Zv7ESlCRd5NjpTGCNzrGuDIpNBxF77PkiTvez2hOdY3sZQo2PDdEoKO2CYGbTQI4O8RBY4aCDfN3uAhCdojUeINszo0tgPmWcatLEMq0ts9c+y/VLb5C0BWliaRONlsNjAldYLZwPg4hCX7iK7UVkt97jUqrYKTVS1zSf/xfwj38Ttu5hP/1Z7q1fZLQTvpdz3fLqFeHGrmFx+cPQFFhbM9UDLo8Slq5Ei6DmS9RgTOs8ddLHOIVuGurMcKkzotua3KVegKvg1k4gF6/kCcV4vEfaJ7ZBK1ivOgnwac+0xwkGwUcRFS0uzYiWxfmZ0AH0B6FgMJsiJhQ8cRCZhNCS8HQRo0m0sBPFGB2TLwuGndL87nSBtJ7E1IiqUVIj3rHbmWYO84RWehjUXqf6aZGpcIwXDoZ6/5yVoqmwxB96nnrq8DduHv4Ee6TdY2to4xiV+GOVdl01odDuLaUxbGcWVVouYcJn8uBDDslqfxBq/RIguJ1be7+rfIsqlrjoGNIuEXaQo5qG1NXs5gNQwjvpVeK0Za0uaMUhvT4RKeV3vwRvhrHcwbhPFSWszyYsKs+ucvDDP0e+8TrOO7JrE+IB3PpCeK268cynkNldfG7wrcL0UnDtoaTdmCEeT2vDNdWohNbXJMGOkro/wBhFnjrSztNiPi/wAg73jLT/AMJ7/5b3/uPnvR9nhUetpn6/+7kQkQ06Nz4R+XFg8rR37PsCXUSTRoN37Fxv8dry8uUwQ37lxat4JdSlxzjoqSYoMgei0WS4gXcNm6OCYhvKqCN0TQ1Fw858Db1e0a9KbC+0Bx2b8X0Yurl2toI5SW/rRlACNx92ao/SPlZH+Pn9sW+LKqPuSLsvQaIQ9+aVIUrPKKZmFZNSqz3SHo8HaFdjtND2M7SokLNePpzRvoJSQR07qBA5VyOo0znzEwiMUhmLYU4vd2w2W2wXETOX4bC0LqQHKFGYJDm+MLHC6ji5fBV6R7+/kR4y0BppF0zcgbn2ySTMVAN4z4XPfwW1u6B4/YdQVy9g5lPatTFtOkBsBSph2eo9E7q93VjfAAHmJSYR1jf3leG1tTW8WJT1qMH4hO/YU4CJOtLeMOtIe54qxqShRf4Bc9FmuYsoTfI0j1ulwiL8CKU9Ws5B6/uV1TQLn1lTQxyjrBBpASeUOynORbz+YkS84fhQR+6+892gol/pXSU2CbtFwb3FJLjvNp5W2T1zOYCXdTDB+s4Btb1sPeIrZpOgTg+zDPGh1fiwjOYYjagERPHjF5esx2C8Dh0uwK3ZTVRrWV97/qHHnvjtE8WF6DV6+uFs9IP7AdCIZhxZdtMh5c4OOstwywXl3TlYT+NrsnH4rEUCUTptaoDWgQ20B1rkRdljPTDitA8IdTkjUTlu1EN0jevnRMlgzzle5tNgSPiofVDJXurFqjXenLLzauUgX/ZjcA61WDJIhKVvmPeuckMukvmCOLHYJCY6pigRYt+ONnkyuk/zwkXi6S5X6gnJt7/H+P/zW7Tf+Rq88kPwJ/8M2Yc/ynS0CZMpifXsUPPqVSEy8GZxCTZGNFXBLBqzMYLKlUGZbi1muME/32r5Z0Uo2JqqoU00l/vhfdma7LC468gi4frNoLS/3Muo1sYYE9S9qavQCsZld5546jPtCRqFHQ2ZXRiTmASpHo4nPVMoFa4zsynGZHjAtx5zBhntEEwbB8rQGCAbkC6rPTO696ZzsI5IdaRdlXjv2O38N/LhAG0NOg6d6o+DFWkHGBwoXCeEdIfkw5dwGOqvvXv4E+yR9qC7NFmEivbPUYe/aIZqg111q4V7gyGtW5DNW9aT9FDSrkRIDnGQPwgZj9Eqxm7tO8hXbolaVseS9hiFzXp4EfKy4GvPv4H8ub/MW21OmlpG5ZI6CQlGF+aeZbMM17i6Yi2LafIBvekuzhFa5IFE9TCSsPRbXH4DFneCcd68gGbuyf0OvpfQuISo89uID/FuivQgzLF3c+1GErwPRpg5mnmWEmUp42qXdDMcO/NFgRUd5tmfkfZn+D7Ho9jf6qz1twlxb6+KyO8C/xXwt572jn1fwAT3T42mWniaRctoveV/8dmP80d+7A3+5C//FZyCunIkypE0FRKnoPevKmq4gceyOZjiGpjMOsI4m9O0CTu6zyibk3ih7T1mazyERUh/CNt3YDYhXkzCLPshCkoihqo/gMUU7y3xAFQDE3eZbfURWunhF5AMG3yxxOcDtH76lXhgT4nol45aaRq3JNkcE9GiU4NNDEZlQRkqHr0IUiq9L6vduepUJnQriAgDlVNpIb+Ys17dY7dQLGqN04aqdVhr8SYhMoKctCgwHIHS8PpHHrmZUhGjqEfql9ysu4vS+noYHNvZgaaB//F/pPeFL1F99MNc/1/9bdTrHyZuNPVzzxPrmNYVOEmxnoeU9v7GBiiBeUHqHP3nru7dN17bwPkK8R7pjU/ztj1ZRDFGB6V9MgkL7zSFDYJiVD3wljfLKTofPlJBfCJ4ROxbtJwFlf3gPqxa0cslxEkg7TgiI+Q59LMcLjsQ4bU0pt8art+tGT4Po1ixOX4RgG+99SbaKlQDylimxT6pikXxokp4z9YUnWpcWo9yNbNpIKT9Xo40DXghPoTAKBFileAQvPfB6bdT2htv2d2+Tp+YeP3C+3r7IpWiHlGk1KKIUDTKMIqEetBnMpmhkxhfLVnenOGbkjLSZJ2BolMRDn9iE7oVTOclspprDzFn9ti0iURFuDTFFjNi3cOOBggtdjxAq9AxFHsF86Pj3lZQKsa5umuNn526NR4gUxmIYtkPXwoznTNIhVo3THZhVkHmOtIeacwx50QjMdY3h3pHQCDt7YtXQDle+J3f4uqX/4BJf8Tk538ePvPjkKSsG0U5XmfZWjYmc3Z8jdHw6hXh3fkay0svsrN5hWV+gf7IgW8ws0XIa++vca/2bEuKVgZdtrS5JjWaYR6MzCa7W2QxvPPuWwC8POhj+xlRpKmtoVAVsRh6lQvXykO6s54oohiNor56mbt/9I+QWELn23kq7RCOv9kUbcI6Q6zHnKEx3lBpnFj8YI2orBhdDKaqN3fnYV9oiJIlka4oypqitURKMIM+unr81niATO0veg8q7Qkajye6DM34OeqvvHX4E+QbsPkhkCQo7VmMjnm00p6kiGjSsuGd117lq69cQ1zJeNGSmziYAx62r4c4yN8HY5DhOn777t73smoXSN3gjyHtEQrb6+G1plcsmUUR9cZHuVV7LvcterGg6kUoybi4s6DG0mBhss1aLFSjMfFil7Q2e6QdoKfXqV3B4PXQCXXzC3Bzx2MnBbFaIv2ExhnUHml/+MMUUeF8coC0QzDC7IlhlqfEWUq+3Ca9GtYps3mBQwE2dJs+wzN8H+NRV6YLIvK3gZ8F/nvg/wD8I+AfAD/39Hft+wRpxnziqEvPOKm5sLzFa5fW+c/+wf+Vj3z2s3jlaRtPL/KoqoYHlD3V3wCEQW8LZRX3djp2MZ2x3IlZrA0YRlNir7C95PQmdAexcQl27sLb38aLgudfPnSzBEPdH+AXc6yrQ+ybBtsYbs8vEydQT4V4VENZIk/TzOtBdAuIvKjwKqGwC9T4Iptj6K+noH0wsPK+U9qPXgSpA6oVBIOnxyHtEFrkK6Vx6z0u1rs01rIsNTZKWdQttB1pj/XJlfYshz/7F+Hq8WrlhXRM4gru1F0RYuUg/9Zb8D/8D/D228hP/RT6Z36GrUTDH/t51Gd/lOknPkHmw2x/80Dc2wpqtIlSClmWRK7BXL7KlXGPjUFGevEKcbFARM5XaY9ilBIiqWmbUOuIImFMaJFfPrBOaZZTouwMukPyPixnD//eOaJifv88O+wrfGUBcYxYMGLRJsyd1+QshpY1LYyNQr6XsIhaLv2448IFYXMcvtPffutdEgTKFmU8s/J+JfQVExY732srWu+x1mOoWaxIe7+HaRsEfWh7PEAiGRYN3pKs5s91xi0W6J0dxjrfn5N9iggzp5qeBjXImLQeoxq0LFm8O6NxFcQR+ThDEOoDs6qngeri0loX5tqdD4Z3+rj2eDQ2y2iKGUoZ/HiI4HFra4iE1tt0WQYlqP/oc6lSCR5H1dwFTt8aD6HQYVRKmRCKzrM5/RTQLbfvORa1Z2gLJPbYKHrkeEJ4vqNj3yCQdtKU6vI6/UHOnc/8DG/+xM9SjPe/f+tG0a5t8N3S4u9OafAssLx6VXDxgJvzlDvrVyDqozOLuBozXyAozGidWeNpo4RGYuKyxfZi8I4Lw/Aa/egWo3TB3Tt3ibXi0qCPzSPiyDBtEryuyFQgTwc74Z4akhSDQjqviWxVVDsv5/gVBsNOae/TJD2UT5AzNMYbGYNTFtcfElcN44vh/HhrawpKI1XDYKPAxEum09ApNE4MTZoH0v4+ah4iQtqtiIf6fqUdwI8s7ZVrVLfmD42ehSdQMHwOqhprNTbRwVvkmJl2hSJpLJPNdW7kfUzbsNF0tdwjOuwOy2p/EGrjEkymezGV9WI33GEefYwpEch6OK3JigKlG751xzPHc3XgkMWcqheRSM5oe4JLUpa0sLtFzwjteANfFawvW7bqhqorDOd6jIiiZJtLn4Q33/R85RueS9WcSJf4LMV6jemFDyE9IiXJmEEQGVyzT9p9RY6myDJMlpAsJ/Q2L6CVoqob6tYCjrYpD33OZ3iG7xc8irRroE/IoesROng0kHPKbLofZJRk3LkBkRYu6l36W7cpskvU0Zjaa2yrsDiGcYuuG1R6/1unoh4+z1HlFv2eYWdHQn/XsmExzWhfihhUC1Aen2WPr7RDmGu3Ft79LuX4wpGzkzEa2x/higLXzPdi35oS5jNPmgjVFKJBA0WF5GfUGg97Kkha1nidULklfrQRLrZpCmLDLFTVLYIfobRrleBcvWdqEpT2x3PJHRDjdcJiFDOIIFpuMV8aqmzARI/QbYvomMicsihwQsUnj8ekSthpVnPSwzCH+PnPw3wOv/AL8PGPs05MhaMYbcKP/QizzQtkvqXF7ZH2B5V23RuHOfxFTWRrZqNNfvNv/il+82//WerROvF8DqLQ/fHp/rYnCROKXYnuMsa7daYRxYiUZer3FIfWO/xyTvQ+fRhOhLwfjIuqBxYLiyni3cOkfUUYyiUkKUrFRE0TRuNrSxFllLnjpdTgLLRfiUnXoFyref5VWB+FJIi33ruFJsT0eSOUbUnV7iuhmWiu6ph3XMXCW2wLmpp5R9p7wz66tWH054h4oFSlWFGMzGVSwmrZS8Rt5qztLInXLjx9xZJV7JsOrsXrPSa1J+rcpd3uPVodFo39UdJltIfv+2ln2iGo7Sul/aQeGFoUkoasdgC/Fto2XVdYq7Gk887g7pjC1+q1qvruY7XGrxCpnNotYbSGTGcMciGOW7ZnnmXrGVHgcoUTITqmk+C42DelgrHY7OOvof7UL5K/dI1Fre5TCiMl/LGr66gk4Xvv3uO7RcMdVxEZ4dWrmlvFmEnp6fdylliUa9CLJUobmnRE48DGCQ0RUd1i8wi8Y7OLSqzrG+xUbwLw4iBD4giXx8QmYm4jnGoYqRRVlU9/nh06pV0wXUxnuiqqnbfSPhyBbYnLlp2rH8M4fSbO8SuMtMaJox6OScqG0ZXwXbm9PcMLSN1y5fWC9SslO5MVaY+okxxVPn5G+wp5d756UGkHqGlJPv4S5URCMfwolCWtpPjUBSPfY2balQhJ4zA0vFe29Kxlo/Vh7XKEl0CGweL2PWwOgVq/hExm2Dacd5rFDoIEg9xjIFkfqxVZWWB0y+fuhNe5nJaosqTuZaQqI9nZwW9eZt7PYBIKGebCBk1Z83xTsSxhuxudUWLI1Iil2yV+seW7pYPbwov1AiUVPs7wymAyQszkEcXCyITCZmNnaDEoUcGMTgw+ivD9Ab1iilnr0+9M96azAg+49hlpf4bvbzxqRXXTe/93vfe/eti/M9vDDzCs9Xzn3RzxnmFfGL/3PbzSTIYv41ponEIqQYmll3ikbB4iuFoluH4PP9tivK6ZF5ZW1mhnsKzX0M819KYLXC8FEcxpM9oPYjXXDiw2Ho4PWyFB0wzGeDxuvkM8CKTddqK09sF8PkoXeGtR79M5/lQQgTQnLiro3MHtaAhxj7ZrJUtU/siM9hVW87fOVThvcd6ij1mgHoU+MaiExTBlmHrGi3ssWsPUDLievULUlIhKT25Cd0pEuk9PNEWzG34hAlevwngMf+bPwPNBrV/vLoRbSUaLp4oSUttgCXFvSqD3wDpBogyVxKiqAdsyH25woZdxJY+Z9ddIyjliNDo9XyM6oIv7giTdLzxskGE1zDslcFnNEGeJszMi7fBwi/xup9Q8RNq747Uqw0y7ijC2QbQCZbm7nqC18HKi2fkuRDuG9eeELV+z+YpwaTOY0V1/7zZeLFHZhFZJqdgu7m9ffk1nWO/5ti2wLRgq5vOwEO4Nh0jbBNIeHb54yiTH4/FovK9RYtiSmsZbNnYLGK8f+rgnjQRDJQ4lERs9mMc9GhsWZ0JLoyy2NyTRYf58pVA9ctb0CGjdw7oK59sDpP34QpzO+rhyCc7hXnqRcnMde+0VWu9Cq/6ec/yjv0OrZAvn28dqjV8hVRmNq/CDEWo2ZZhBGjfslp6ihfV4jo0NiCI6Rmnfj3073IwOgtquVPhuXkqFZaWZP5B0sxlp3njhMh8uZmw3wj+aLrhRW165ItTxOq2H8aDPEkvkW2RR4Ps9FjacU22cUGKIq5Y6TwJp75T2e5O3ub0IJnTXBhkuy1CZwqicHSWIsqypNHS4nIWyrA1oTdyNM+1ltJ/nTDvszVAnXREpqdqz6TzokBMRa898OCBqHGsXwzF+e3tGC8G0jSXiGnZ3wjV+nBrKNEMXj29Ct/f6SjBAfkBpD+UVocLSv5ZSxpdovv7W0U9SlrQugbwzmHzUMrszm01qi8Gyu6zpiWdQ+UPn2Vc4zEH+Qej1iygH7ST4GLXLKVoM7gTHt86HOK1IygqjG96cOBJgQ2bBMK+fMZgH7xWVO8qsht0QMRtfuEBbVlxu5zSl4o7dPy/09DpNa/ncuzusvQgvLoV6d4qhxic9mqSHUSVKJUfGhGqVo0Tf1yIfHOS72LfRmLitQTv6nR/DYjLHA7Z92JT2GX5wISL/RZeP/jiP/dMi8h884v5PicifOOK+DRH5pyIyF5H//IhtfrPLfT8VTjLT/gxH4OtfC27bVy9BWsxIZhN2Lj+PTzXeQuM0vhW0tqQqRGbJA0RBRCGDMX4xYX0N2qhld/kxlrcSqt4Ic7Elny2wvRQl+rHbt4Gw+B5vwHCNpnf04jBBY/tDrAhuvk3cB6WELpKYbu1FpCZY1PvOaD81shxVLkl1To3F9WLob9CMx91+pXuxWccp7UBYhJ9iAX7oc4kiVTmlEfK1HuP5PebWMCsaFssF4jw6iomSp0PaRTR5NED8nPlKUf35n4c//+dhtH/MpaLJ0dyLE8rBJcp8A+MrnAilTegZeehiKSKYfh9V1bimpR6t0yqNR1gORiTlEiKDOSszwsNgIhAh0YGYpwdqL2uk4NlzkV85x6dnobT3uu/Ggy3y0x28NvukfgWlgrJVFhAniIpC1JEGcNwYJKwbYWgUt78E+bpwdSNm29fkm/Dicx8G4N0bt2m1EFcNzii0Krm3vJ+0D5Tmkoq4ZRvaFrSrmc3Ce5StDVFNi0Ef2ZGTd6M6hSuwrkSplJvMGc5rstbD2sb7eutOihiNw+NVxHpUU/TXmC/mrFLYGl+RjMZdYSE4x8fox3K1N3p/rj2cMwR1gohIk/VpaKEqiF94kXf/7C/gP/TagYz2WTgWjvEGOajqP05r/AqJ6uHw1P1gODiiJIlaJnWntJsFbRKa6+JjOwlWsW+PNqMTsVhXcTlVKGfYbizNA0qhXt/k1XrOzyQxjWr4/+6WfL5syJ67xJeTN7i43mfpWxLXwrzAD/rMm/BB6ySh8BFJbWnTCK9gcxC+f1vTd7i5800gmNC1eQ8TCZFKmCSCEViTDMry7IhznOyR9riow3f/vNvju3GWZLZAmobIcqZKe4h9E3ayFCRi3E+JjWFR1mxVJVK3OLvAtw27u+Eav5ZEFHFO1L6/mXaAV1LNR/P7v4OrrPaKlsFVqDeuUX5vC2aHjD0BVBUtKeSWCPVo35SuczCpHXEjRKWjr4Vo0Rw5zw4nc5BnPEapGLsVHOTdfILCYE/weZoopc0SoqIkk4qyhEuRgmKK8442H9DfmQSD12FK2VP4poLFjPziJq1o8uVd0jrivare63IzZLx9M6WRbX7ujyrSWHD1bXwiQEST5GgqzCMSkkQEo4f3mdG1viJFI0CztkZsK5wv6XUeMcVsgQea+ujC4jP84MF7/8ve+6895mN/03v/nzxik08Bh5J2oAT+I+DfP+xOEflF4HCzo2PwqFXLH3ucJ/zXBe9d99y47rn6ak6/L8SzXWyUsRiNwQjOQuUMDiEyjl5VIggqO4TgDtbxvmFNLVCJZ/s7OxR3LO3zQxKWpK3vnOOfQMX70z8Fn/kjj9wkEo3rDXCi8bNtdAQmBe26i08N4NFuihM5W6UdQgthsSCVHrV4rAE+8yOULz+PQkLLaNm1m55IaS9xXWvn47bHA/TUkIqW9FKftWKHRaNYVJa6mOJRaGMw6dMh7QDjZETsC26uVJsjWpPXJWZXWqYbr9LGGcqVIbKu1Q+1xq9gBn10WeMaiwwHzNIh82TAfDAmXhaIMehD8rzPFFFMrFZK+/6vjSiyKpB27z3VcoJCSM6CtK9ylx9U2ic7NFn/8EzmpOsUSRK0MigLsfYo1XC7F3MtMUyvQ7ENlz4JG93Iw5KWj33qYwC8e+surYakstRekUc1t+cPG4W92hlONdYTSctsERY1vfURumkwcrTSnktwjy99gXMlCyWUtFzd6cjb2lkp7YG0WYnIVAPDdWaLEmUEb1saHNl4hPPtgYz206vscNBBfoH1Fd7rE5kZmmwQstqLBYNeRv+TFxkNc6qOtEez+bEmdBCKvEoitEoeuzUeIO0KLst++GyTZcFabtlpPK2FYbQIGe2iiE/QfbRaOB8FrUNxqm3nXEwE4zXTxj+sFK5tgPe8uij4WM/wcgbfLFq+22u49MKIi33FwjfEroXlEukPmVmFCDyXa2ZRjunmgb1RbHSkvUzf4fYixFJd6yXYLEMZEGLKxAfn+MYEd+mzaI8HiFOijrQnRQFJdn4Z7StkOZiIeLbgI9UgdJCd4Ux7iibVsJvEiERkvt37DN/dnSN1C86CrdidrpT2iDLuEbfvb6Yd4Fpq+HT/4fNdgqbEkq2Dfe4a1YSjW+TLksal+Nw9ujV+hTQjqy2R9/R8xQiHaR6ttIfsAXn0XPt4jJIEv3MP342EaWVwJyDtkcS0eYppWvo+rCee7yn8fIL1Le1wnWx7C5fG6CShHua0toTdLQbjPjZO0JO75HXMtHRMu/388lue3Z01XrxcMVov2PwwiL2NTwzeachznK+PjTWOzBDnG6wtMJJgfQs4cjTL9RGJbYlcQd6tScrdGU7JM9L+AwgRuSYi3xCR/0ZEvi4i/62I5N19vy0in+lu/5qIfE5Evioiv3rg8W+JyK+KyOdF5Msi8uHu97+0UslF5M+JyFdE5Isi8s9EJAb+LvAXROQPReQvHNwn7/3Ce/87BPL+4P72CQbv//Hj/L1HlvW999uP84T/OsBaz7e/5VnfEF58OYffuoWgqPprofIYCcN3rvHWRotTniRypGWJEoMcYn6lhhs415LMFmTrA+afu0syAT7SJ5rdIfaKWR4R6ydAik7Y6pbohCbP8Z15SdwHfS/cJyWYfovUcxBNdJZGdBCIeFXSc4qpMjTaksUxrSnQokPcW7EM82BHEA4gdC6ICWZ03WLp/XQyjFSPHVE0lyP6zqHnSxalx5e7AEgUE6dPz2V/IwmL/tvVDq8Pjv6c1yXmui+42V2MsSXeJCwb4cXsKNI+RNc1rmmIBjmLqI/gsIOcqCghjlBnFA10JExE3LXAP7juzkuhxjKnoV5Owjxzfgbzoyv17CBptxZmu9SHFfAgLJKroLQrMShXkhgY9jzbOuOlJKjsUQ7rr0EpMa133PYlP/yjz5HomN3Fgu3FAhMNmFnD1bjk+sLTOo85EG20rgzryvAtW2NcxbQj7fnmEFW3KG2OVH+1KLRKKO2MgVfcU5YUw3hnEf7u8eMrwafBSnVqlUa1DaPNS0ze8oyNpfYFbRQxWMuBCUollNQMeLzvuRIdYtfsEuebsNA8AaJ0QIWnWc6IR2PixBDrflDarcUsFnDlcGPQB5ElV0+eQHEEeipch5YprBmDni1Z71t2Wk9kK9JeTRtnwElJe0zjjm491SrFo2jtnCTe4IKJuNeR9gEHnn89dGeMt6fo9Qu81IMPJwm/O6tpc0+iHM7XJMsKvEcGI2aNp2eEjURYRDlrtceL4CPFhX74O+9uv8eiCsThpV4PmycYbVi2MT5xRCKMy+B1cGYkNY5JqyUxGnOWCv9xGAxhNmFU+9BveYakPUaTamE3Cf4TWVGwNhpzc2eH9yZzPl1b8BZv7d5M+yiLKZIeyeL9z7QfhQTNtLu29F4dsvjaOptvvQWf+MTDG5clrV8p7Sc4P2Q5aXMXIy0js6RfNyiiR5J2ESH1x5jRpSkqGyKTGXU7h+UclfVP5DMSS0zb6yF37jBWBQI83xPcbAcXaXw8IN35HnZzjEFhezmV3CKabDHceIl7cYrdvsu6xNyuhC+7CaOtAW/eNLx6dczG8A4Lu8VzP5rj/tUdinsaJCLuJVhK0mNIu9mba59idNi29cFBfr62xlgpTD0hWyntkxlOr1E9M6J7qvj/bf9nPwk86Ra7rT+6/u/93jHbfAj4q9773xWR/xL468B/+sA2/6H3fltENPA/icgnvfdf6u67573/YRH56wR1/JcfeOyvAH/ce/+eiIy997WI/ArwGe/93zzl3/O/B/6PwPK4DQ/D03cJ+gGE1sKP/rjwiTdAqglSLXDDdcR5BLBJC8uY7ZkD5UkiS1ZXiEShmv4AVG8NpyCeTcnWgeIeXjT+Qxm92RIRh+vnT0ZpPyESNHV/iJ9P8N4RD0JWO4AvIFmv8UWBSzIifXbtc8Ae2cqrFq8Slp3RVKsdRpKgfD0io/0glEr2lPagYj0+qe4T41VMeSkh9xBNdilqjysLNOB1QpQ/PaV9oMfEWjFZzbUfgTERAkxoSJ3F+oZGEqzVDI/avf6AqGlwVUucae5kF9gx69R5QlyWwWH4DEzHHokoJpKjSDsIwjYF7XKKSfJjW5GfGHoDWBwg7fMJOEdzlIFjmu+5x2sVIdZRjUZsvfQhIh0zuydMr8PFjwfD4gRFfOcP2brz+1z7kOdifhmAG3fuotqGxhqiqMU7+1CLPMDHTM5wFhP5hukykPb++gDdtKj40YunWFJqu6D0LQuluEIf2d2G0dqZHQ97We1dQPP6Ro/aQfxCirw8AhEGa+GcIRKFzOXHVNohqO2tXeBcUNpPtI9dgaYp58Qqww9eJdI9aizRYhEaO09o5JjEm8Tm/XWJpBKBiih9AYMxejpn1LeIhcyXJLGjjWNEDMkJzolGYlpfHxn7JiI4G9PY0NL6XBoxa2HOAxFMeQ/SjGhnmx6aXd9wKdb86fWUP7mWoI1HXINZLPGAGa4xbTxDI6zFijrtQ21BBBcLl/rhGnDr1m2+++ZbAFzLM1weEUWaWROh0pZUKbKq2/ezIs9Rwnqj+CQXoVicvwndCp2DPFWnSp5he7wWRU9pqjTG64hsWTBeHwNwfbZALGAbfFuzs3KPz2LKOCe273+m/SgkGCpanPcMnoNl7xrNm7fCOMVBeI8vqqC0pydX2qOqJdNwOZqTFBaF2h+tOuphx8W+AWr9IjKZUbTbqKLEnLArMlYx7SCHqmWDgp/WEaOBhfmEtp8TzWui1tKOciKJ8Saj6kcw2SEf9SFJaLa22Owp1u+MWRbwT3Z3UBcLPvaSIVdjCjcBqdHNPUSEUuekvRiLP9a7SasYrRKadnafg3wPw3RtRKSEpNglT8IBsZgs8Fo9U9p/cPGu9/53u9v/NfDTh2zz50Xk88AXgI8BB2fd/7vu5x8A1w557O8Cvy4i/y48/uJBRD4FvOq9/+8f9znOaNX6g4c8F2hr2H4b4hylI6SqUHhc1FK2sJg5JPbE2pHWDUr0oUq31iltv4eZ7ZB86DlUcxdGY+xaS//NBS6NQev35xx/SsRoqv4AuXsHZyuSQUbiwPSBm5Bcq/HLBbY3PNmF6UliL/atwQ8SKlPgvKVVbj9+6ZiM9hW0SsNslOhj85aP3S0xaJVR9wtiM2Qw26Xwl5CqCO9QlGCip0faUzGoKKdcTroM6SNUc1GMiNilIXctFkdBgnLqyPZ41R+jrUMVBUos24MLmNZilSOqavzaByBQIopJTQ0W8gc+euWFMQl3WdAvzsg5foW8BzcPNC5Nwu3mqAVUmkFdgTZoMYh1KK2oB5tsaMXXvuLZNLD5ke7p6i2Gy10cQo9vcGV0lXdn73D91h1efPFlWmdQiUNLw+15xOUHagV9p7m4m6AH7V6ee39jgKrdsd0TiUqZsssuJUalXCCHnW14/sXHeqseB0YU2iuaroPgYs9zMxsws5YiSfFRTD9XFBVYMXj8qePe7ns9nVM34TN07mTPk+gEF8U0xZQeMRGGjJhtFqSzBQo51oTuSUJEiFUWHOSHa+h33qF/sWWt5xgvC+LYMksjREVHGkIdxGrhbH29d/tBuDbFuZq2nXM5yZC55lbd8vKDm69twM42axJzwxc47zEibEaa664KcW+zBTWCHmww2/Zc6ynWY+EbSR/mFnFCm0ZczkJHxbs3bnN7excReKmfU+cpkTFsNzE6sfQlwVRdAeHMlPYE1dQoL6FI90FS2t95E5bdiNkZKu0AfaWxxmOzIabcYW0jdOzcmBZ466AuEduyMwuEeZTFLKMBV1qNeUpLpFXSRI1lcNXw3uY1yp3PE73zDvzQD+1vWNfY2uOiBJ+WJ1Pa04yobhnHgLdkpQ+Nf49Q2iE4yO9QPvJarzYuoL7+debtNmpZoU8Yy5pIjO3lSNMSuyW5CHHewnxKM+yT70xRCM0wJdUDnFZU/Qh3dwsVRZh+j3pRsmEW3Fj2qL4+Yn0wI31lwVdoeV2NWdhtlvMb2GqJQlFECbqnwvlZjv8gIzOkqrfQq04rX5GrFDsYInFEr1qQd9+pxTSQ9rZ5RtqfJk6giD8tPFgtvu//IvIyQUH/rPd+R0R+HTh4YlsdGJZDeLH3/q+JyI8BfxL4AxH5kcfcz58APiMib3Wvc1FEftt7/7MnfYJnSvv7wY2vh/axzZdQojFVMCaykWXResrCIWmYVUqqCjHJoe3aSqX4Xh+ZbpPP54z6t+n92CVqbDCh64dj66yV9nIwBGuxxS5xH/qx8OmPKmiFeNhAUdD2Bie7MD1JdIpEUhSIymiMwkWKNtb7FdpjMtpXUCrB+QbnimPzlk+CXPepVUXSX2cw3aV0DtWUYGKiyKDeZ1vro6BFkUUDvC/ZfjBi7AGsXORTH5zjKxIExTA6YoE+XEMhRPMFeM/u1Y+xdfUT0NaYukE+CAtOEzFIG376Z4T+4OG/Y4OMBodeLomzMxzpyPvQ1OEfwHQH4gR7lIq9KuzVJSrJUdYSeUtMy1UM37vu2PwwdFHr7E6/RzQtuKd7sLzD65fHAFy/uY3yNW0dRgM20obbi4eV0LYGp1oULZMiEJfeRh/TtOjo0d+JVDIcnqVYLskYvViEgsMZmdCtkKCpVDgP9VSNH60x29qi2N0hGo3xvgkZ7Z36/zhxbytotX9eOWl7fILGZTltMScSw0fkBVKJ9+Pe1PHK2pNGIO0FjNZQ1jNWBb3UMpSSKLbY5PiM9hX0CRzkbZuGz6Dd4XKq0M5wZ/WdOIj1DZjustYKjtARtMISi/Etar7ERRqV9CktDCPoG8HnPXxlUR5cqrncxRXeuLuNtY5Loz5pHGHzhCSK2akVUdSSSYKslOUzm2lPQhzkanTmg6S0A9wLruNnTdqH2uDFYvtjorLZc5C/OSvw1iJNhXctO90ozyhPKHWPnlaop7QUibt1fEVLOgJ1aZNl1X94rr0ssTWhSyU6odKeZUS1BWeJxRMvbXjPj4jaXCElFCBXvhiHYjxG19AsJ6iiOnGxOpGYZtAH74nLYLgXZw2ymNH2xgx2dnF5DxtDYgaI7lEP+zhbwGyXaGODpqxY77y2xCp+8cU1PmwGbPmaP6TESsxyeh2qAqcjrNJEuQo+GicYUzR6iMdhXYGWaE9pd0mCyzLyckbedWUud+c4JbTtM9L+A4oXReQnutt/EfidB+4fAgtgIiKXgF84zZOLyKve+3/pvf8V4C7wAjDjlPHn3vtf895f9d5fI3QDfOs0hB2ekfbHx+wu7N6Ai6/AoCM0VZiNdlFLjaexDpVYYu/QVXXoPDsE4uj7PXy1YO3zXyK+Kuh/6zWkaciKCpsnaJUij+F2/LhI0LT9EQ6Hm90j7g7N6bvhZ5QXuLrE9gbnprRLWZB2c6HlR99gcXEzOMfbNhCHEyrtANbV78+Zv0Nf9WlwqIt98rpFyhLVFHiTYIyEEYmniHEyxivHVjF95HYb3SI7tjWtUtRdbNLgKKV9uIZWimQ2o3WW4evP0f+hFzG2QtUtHHFsnymiGNo6dMEcgjWyoFpXJUk+Prv92nOQ7xbnu1swfMS892qsoyzCXKIVelg+1HqGt4R74rjYjVIWtsQubjL4+nvot+4xH13hI8+HRfc717dQNLjKYYH1vGR76Wns/cS9raFVLdq2TMpAkPK1HqZpMcco7dmqSKZiLks/qOxwZiZ0KyQYavF7HhXDjQ3mi4Jmd5tsPMa5CqUS6q6d9P0q7St4f7Ln0aKQLMeuDDI71NgQr9UbnPl4SaJyWt/S9vooFEM7xeiWfluAsdjInJi077eoHu0gD4rIjKibbWIFY2W417YPt9Svh2zutd1AFnb9Pmlf+JbUWfyiwA57tDbs36pDqNfLaGsQBzaL6DtPP98nnS9sjkGEtheTRTm3m5pIO3ok+4kjZ0VSVwWxrvPmg0PaO2K3dReUhqfYHXYYRtrgtMUOx8RVzfhiOJfc2F1g2xbaBqxjdxkK0/1eRqMT+unTEw9W4zRlR5AHV2GqXoLr10PhZYWyxDbQphFi/MkML9McJYaorIiVJ1raY1V2OIWDvMT43QmqKIl74+P3B4jF0A76eBFMOQexRH4Hbxvq/oh8Zwc3DtcZo3tEuk81GmBtAbtbxBc2oKyImgmX1oTPfkgxyIUXVc4PqzUcnu+KZnv7BlJVtDrBmogosXgVn+h9Mya8R62dEUlC62vyVezbcES+mJJ3nhbLWYHTCtc+6vz0DN/H+CbwN0Tk68Aa8GsH7/Tef5HQFv8N4DcI7e6nwd/rTOq+Avwe8EXgnwIfPcyIDoLBHfD3gV8SkeuPGz33IJ61xz8OnIX3vgppHy68Au/cQBB0HdQcZ1pq5XDaoSJHJi26bh/KaF9BiYbBGDf7Nr3b22z9+GdxiSbanhGjmT8p5/hTIMZgB2OcCH6+TRLWUUyvd/ebCSUayQcnap98olAqLKyKBblcZaaEkikoRaSy/cXXCWfaD7v9uBioIbeA+oWY9B3BzOeYpsJHKZEOzs9PE+txjzeVYqeaApeO3k+J+Kgakvi73FAxdavIDfeZlB2EGm2gRUjmS9q2xYmlwpHZEt00Z64SHooohqY58m4jivXColFEZ5l4sCpoLGahBXo+hUvPwWLr8O1XRLkzo5OFoBHSyhLdXqAv5BSJI0GxvXgLvVwysH2i+YJ7a8/zyssh9u2d926incWW0HrPhWSG8xe4u/RcPdCJ0FRQq4bI1uyuSPt6H7O9jT6GwGTBpJWxGhGJhp2tYOo4OhsTuhWCUVTVeVRUrF/Y4OZXw3399XWcr7uM9rDoflz3eAgO7lplWFecWGkHUNkAe/ft+35XYUNG+/rZjROskKoeHk+ZKVIdk5QzhsayZmb4OMYpTqG0G0TUMaQd4midut2ltTMuRRFfbx0lLRkHzotdwcfs7DDYuMy2r3mZTjHDMvYtLJa4S0OWbVdsjPZJe+01prbYPMbYgo21IfOO4D23sRaOz2FGS86Mmli1DCSFarYXwXUmWCmpHzjS3int89nZmHU+gAxDpDzFYEC/qFm/HBYft3YW+LZGtR68Y2c1ytPPaH1K/4hi7ZPAflZ7IMiDq3BjcI168lXi69fh2rWwYae0N4MYFfkTK+0imjXbYIyglzVcOv76lJ0gq30V++bvbKOcQvcHUB1x3TmACMHlPbxW5PWSF15v8NU9BGhVQtzNswNonZNQU/dG2MjDZIt8c4NF27KczPiJH7//PRhJxGfVOl+rWtw3v43NUmyjaNI+hgKv4hN1b+6bghaYKKNyOygRMjT1aES+9Sa9YXfemC2wSuHKZ6T9BxSt9/4vP/jLgyq29/6XDntgp3qvbn8O+Nnu9q8Dv97d/sVDHroNfPaoHTr4vEfc/xbw8UdtcxieKe2PA6Xhykfg+U+E21mOeFDOQV0jylFEDh9bjGlJ8KiqQY5yiwZkuI68/Q6Rh8kPvcKcmv50geBpe/GZzrND186ZJNgoxs92iDveMb8FKgLdTnCizj7ubYWsB+WSnIhGxVQuLMpilYbWeDih0n6AtJ9wgfooDFQPRFNfcBibsVZNGEgVTOhMdKJ4qPeDFANRxryeHGkKtcIlEnAVjYqoGnWkyg4gwxGiDElR4psWK47SO9K6QLUOOesEgcNgInAudFocgZeWhsv0Hs5Hf5pYvdZyDtNd8P4Ypf3AiEcco51CgGpHM8q2GT4Ht0qP9Y7l9B3ymaMXDYiWSya+5NpHgwfLO7dv02/muNKzNBm53EMJD0W/tTU0yqLbhkk315uNe+jGEh83064zrtDniuqU9Z1tGI5DcsMZIkFjcSAx1ldcvLCv9A/XxthOaa9oSTDvu9BodK8zrTz5JVSnPaxrQhcQYL3DNRVxWZ7YhO5JIlvFvlEgww3UdMYbA8+HkgKXRFhCjvlJcVzsG0BkRogo6maHq0mE9XCzfqDQluXh3/Y91iRmSoP1ntY7KhxxE0xQGfRYtGFxv1Lah72UVmJM2dLkEcrWbHRGZgBXN9cR73CDjIXNaUyNwTEmD6ZiJ0xXeSJYjcdMd8LP885oXyGK9t+HM26Nh/BdTpRQDjKwivULYwBu7c5xbYNyHm89u0Vnmpln2DYm7z+9a+t+Vvu+0t6MrlAu4vtb5KsqtMenBjHuhDPtKUo0a21LpjSmqE6ktEei0ahHk/Z+HxX3ULfvhfNVdrLjW0Qg6+O0Ji4XvPRaQzu7i0ehakeM0I4ytAoJJwmaRqe0gwy3c4f+qI/zwmJ3cujzx6J44zs3WGtS7KWLuFaFjHap8So6cfdmKJ6WGJXgvcP6hp5olqMh2WKxR9oXsyWNNnj7jLQ/w/c3npH2x8XoEqxabJMMvKA9qLpFtGMZWXTfocWSujb8/hEtxGZS4Bdz5OpVXGSYUNGfLXFGdXnNZ620d21G/SF+vouOQcfgHaRj8Msp9jwy2lfIelAsyYjwOgktYiLEkgUnXjjRIkhWEXE8GaVdiZCoHNtfoKOL9CYTUmnxUUxsnn6bYYrBRD0Wtqaxj06UcK7C46l1RNU+Yp4doN9HTERSFPgmKO21twwWEwRQozM0djsKK7+Iw+ZkOyRFEQobZ0nadSjssZzvq2qjR7SPR8F4MjjIJ6gWlLKY+YjhhSUb60tulY6t6i6qnjOoekRojPXMyilXX3yB1MTsLgv8vVv0y3vM9JjGzriQVtx5YK69qYPSbosljfPExpBEBvEQJY/+DmkVs5m9RhZfDL/Y2Trz1njYb19tlcG5mn6ikeEYBMbjPt5btASl/f2o7CtkyXMM8h86fsMDiLIwOrM6P9VYzHyGRp0oo/1JI5ccRKjcEjXcQM1mfPTHW154eY5LIjxCdIpCppEYe4zSLqKIzZi63eH5OBR23qsPIR3rm7CzxZpEeGCXmiUWvCObL7ACfjBg3pr7OoT6vRSvIqRsabME5WrW1sZ7T/v8+hhw+NGAWRPR6hIDjCXbG0c5M8Td9Wa6c7YK/0kw7I7HM3SOXyHDkGiYpylOGS6MeigRtmYFVdOg2xh8xG6nmua9HO/Spxb3tkJ6gLQnA4hHiln8ErzzTigWw57Sbocxojixe7ygUA2oiuAcPzhZETzjmNg3ERiN8Xd20Cp62KH1EdBpH2cMel7S+oZ2thUifpcFpjeiNRajO38hNLVJcYMBdr5FksWoOKa6c+/wJ18ukG9+jUuv/AgvRFewKMhiEIeoBHPCUVCtA2nXXadO6ytyDIv1EcZ5Rnk4x8znBa3RONvsf1bP8AMB7/1b3vtTK9bfr/gAXSW+/+BWSmaaIVGMah1RVeG1pTSWZNAi3pM1FeIF9QjzK/3Vb+GGPfRwDIDHdyZ0gazrJ5HRfgqsKst1fwDzoNqu5trTMfjFFBvFRPE5ZXOvlHav8SqQdi8Rseh9pf2EysVKbddPYKYdQgayixdE8SZ+EVO0IySKSJIn8/yPQoohjftYcdw7Zq7duuDM3Epoj3+U0k6SIHFKXFZQW1paKloG0ykiwV3+3HEC0s5yHgjxWStIeT+0x0+2w2sfd2zuxb4lKCsoaZFlj9HzmgvpNjdLx3T2PWKJyJeC1oZYFOVsl3iY8fwwkOi3bk15oXyLahlT03Al2Wa78NTdXHtbe65/A1xsKSdh5n6QptA2iBfiE7xPSbwZDBaXC6jKMzehg/0Z9baLfXOuYvz886SXnkO6SMiV0v5+TOhWUMqcuvvJZEN8l9UO+6TdnBNpj5VBVELplkhnRqdlgvILmkSHBfopEjWOi33be91oHe8tfZakSnG7PuT7urYOsynjOni97vqGpbeIb0jmi0Cd+n2mjb7vvDXupziJkKLF5RHKtYzX94tIL4zHEClUmjGpI3RcYpwnkSR8387yvLAi7dZ+cFrjV1iRxnNR2g2pEqZpAhKTK89aP7w/N6clTb3ELitmTYsSiLKcyCVPnbQHpX2fIA+uwkRdwxcl3L4dflmW2Fbh1zR4wnf7OKQZCkH7IaaOAmk/gdIOJ4t9213LaNWAobl4KtJudEybp6jFkpYaN9/B5gPi6Q5qbR3nG/QB0u5VSjsa41wJtsFkKc29e6Gz7EF8+QuAR334kyQl1CYm7kVYfPAlOiFWYpZ0rxHM6DTVeA0lwka3JJjNizDq6R/difcMz/BBxzPS/hhw3vOb79X8wU7n2pn1EBOjmwZTN6i4pV5riQce4x1ZVaHEHN0e/+5bqMkc+5Efwiym4STnHNlsge0liOgnRihPg1VWO3WFqxck3e4n4xYpljS9/Oyd41fIcnCOpG5RKqPGdrNQKsy0p9mJlQutM5REiDyZvyXXA1TUUoyHJFbT2AgVGeIzIu29JKKRqJtrPxrWFVgcSx+hnH600p4kEKeYsiRqWxocrViGHQGRDwRp7zoZjpprty3cfAfWL5zdPq2Q96GYw3T70Sr7Cmm21x6vWsFEluErDZsX1xlFcxZ2QjO9TS+5BNMJPPdCaFFcTNGjlJfGgbR/82ZN6z2b996l9cLI3MV79tT2b/8rqBYweKGlmITPsp9nqKYBr4lPo7Sdkwkd7Cvtq6x26yo+9iNv8Jl/62dxriOFnVv7+zGhez/Yy2rvimk1FjObYXR8bqQtVnlwkB+uoUTht+/i6wIbRwhCciqlPSy2G188ejs9RImhbnfY0IY77SHf186MTu9uMyRi29csaENG+7zA4lH9MbMWRgfOW0meoU2Erz02jxHfsnGgC+j50QgSjYpitpsIYyqMFSJMaI8/yxSMg0ky5zA7/kisikjnQNqVCH1lKLIY0RFpU7M+CN+d96ZL2nLBpDMpHMeGKs1JGiF6ym9hgqHG7ok1g6tQ9J6nLvR+i3xZYkkhd2jHycbhlELSDCkr1HyBQp+YtGddfrz1h6vHzntujQ2pxPRUcirSHklEm6XoZUntlshihtcJqmnRo30TOujc9UWw65exroS2JM4z6mWxHx24ws42vPVdeP0joA00FXUck+YRFkd0gri3FfY6UH0bPDVcRS6GZmMdUXBRh/d/Ni/wSnDegj3a9+YZnuGDjmek/TGgRMi08J15dwJPMiRO0K0lqhrQlt6nW3TiMDiyukLEHH4BdA6+/Ieo0Sbtq9fQbY0ulpjFgtgHl9uzbo1fIcZQ9sPF28239ubak1GNXy6p83Nwjl9hpVQWCzKV40UQnaBFdXFvJ784pclzDHofemK7NlADlIHlOiTE4Dwqioiyp98eb0TRV4rWZMzrWagsHwFrC5yKqZxC/DFKexwjcYYpg2GZxeGw9BdzQNCDszUeOxSrRfBRDrFvfyfME7/6sbPbpxWyPlRVMHc6CWlPsj0jOq0jtFgufqqgbzYZRYqx+xp16xjVw1Dke/FlUjSynONGhmvjUJh46+6Ee36NqgLftCRql0hq7sw9733Lc+86XPsk+LhlsRuU9l6eo9oWUJjTtAvvdAZH47Mn7ZHokBvcZT65A7PVznUz5N19T0JpfxwkUYbXhqYM73OFRc9n6HMseCUqo3EFvj9CVITcu4XD4rpiTXwK1StR4QJRutkjtxMRIjOmaXa5ZEJ77279wHlqvevW2NlmTSJmtEx9Q+4szBfYPEWbjGW7b0IXdjghMRFULS6K8eLYGAaCMeznDJXBpwYd5ew2gtINkfWYhlDUO8v2eJF9tf0E8aRnipXSfpbvx8GXV4Yqj/A6xhTt3ojDjUmBK5dMtzvSnhiKOCO25kza44H75trREcvkuftIe+MSyB3qNF3YaY5akfYoOXGxZOUgf1Ts2x0WlOM+a2Shw+yYGLmDiCWm6WeoombZbKMWBd4JBsGO82C+3PlirIqmLhlgeym2mpHmKZQ1i90HxIMv/UE47j/6CVguoa4oTELci2iFE8W9raBUgiDBjE4Smk5pb9bW8KIYK4cSYVnWNK3F80xpf4bvbzwj7Y+JXuK5WzuuF75rj0/RTdtltTtmNHjxGCxp3XQmIIdcmN/8LswmyCc/gx8MgJZstmA4XaIQbC86cxO6FRI0y8EQPPfFvsXDGsoC2+ufr9IOoUVeYpreNfRqrrZYnkq5WrmQPrFd0z0ipWjX5lxIL3HVeDAJUXo20TkJBpNkzBpLaxdHbmddgdMxhfMof4zSrhTSG2CqmtyWe2NhefEBIu0rz4BD2uPFWXjzG7B5GdY2z3jHgN6BGfpHmdCtkOah1TyOw8KkbWnbJVoM42jEZv0Wpe+ju6xiLl4myfqoxZxmGHFtLXwX3r67S1TWbNHDWail5Wqyw1s3HW9+ETaeg+c/LDRtzWK66HY1R+oGLXqfVJwEO9tBoTvjiKgVEgyVhO+zdQdIu68QUVSyv9157Z/NMtoikPYaRzqbo7qRqHPZJ5XT4mh8hfRHyN1bOFpsHIW85FOkXWgxxCqjOoa0Q9cij+OyWuKU5Wb5AMNJ0qA+72yx1qn9OzSkWPx8RtvPsS7s2/BgsTGOSdIESosTBbHiUpdk8NILl4mWBSQGa3o00hDrlqhRSGcOeObK8opEfdDa40fj8POcOgDG2uC0o81G6KpmfT2IBzcmS3xVM9lZkfaIMkqJWkV8Bko7sNciH/cgGcHUXIPZDLa2oKpoXAqZRZ+CtKusF+Jh5wtU/+SjMo+KfXPe8x4zsvEFcjndPDsEpd32e0jd0Cx2kaqB1qP6Q1pj0Trf6yTY80AyOX44xC63SPMMVVdMtg+Y0d26Ef599JPh2rJcYquKOopJMqFV0anWlCKCUmkwo+uMMI0oTK9PG0f0m4K8+04X0wK8fUban+H7Gs9I+2PAe8/bbcs3m5p/eHPB75cw0XHIgK5rxHtK1+LxaO9IqxpR5uEFsLXw1T+EjQuoF16B/hDnWq7MLJdnDU4cLk0w56S0h6z2AU4p/HybjdfhhZ+CKJrgnKPtDYnP6xBaLXKWC3IiUDGRREF1PKXS/qShVEwqCX48p05fpV2/glIGk5wNmUkxmCShsDAvb1JUN1iW11kUbzMv3mS+/C6z5bewrsKqmNJCgiLVj27lk+EYaVoGzYI1UaxrTbpcICLIapF3ntibaX+4/S2/dzOo7K+dg8oO9xvfnUhpT0MXjkgYraltyMAFytbRtyXbegz37sBojUYVxFmOWSwocnhx/TIAb29vM1yUbMuAZdVSiGdD7fCNr4NKPD/0o+Hl6qZiPg3P3x/0kLZFo+9v4T0O52RCt0KCpsbuxb6tYF2NkmTfROqcCo1GFGQ92iIQjqacEzUtnGKR/qSx5yDv5shoA982OG9p0xjEhBi/UyBVQ2pXYP2jF8ZG91ESMfILIoH3DptrX9+E7XuMDvRzpc4ii5J2kNHajrQ/UGxMe310bbFiIFF8+LkX+Dv/zr/N3/w7f5VkucRlEY0ZUOmaSBxxLQdiQs/4WrvKaj/LtvyToNeHn/+fwQvXzuXl13SEF0fdH2LKmo3NUHi5OVkidcvOJBQYx2lEYTJipzFP+aNLHlDaIajtO+4lvAPeegs7L7EqxaXuVKRd0hxVnJ60Pyr27TYLaixXR8+Hro4TOsevEKsY2+uBdai7d0Lbfl0h6xexdrnXGg8HPJCUgfEmrpmT5QmqbVmulHbv4Q8/F46t17rOxjt3qKuaZX9AYhqsMqc2Cg1mdEVnhNngvCM3CU2WkVQlvY60z2fLoLQfNo7zDD+QEJH/4nHz0UXkT4vIf/CI+z8lIn/iiPs2ROSfishcRP7zB+77bRH5Zpfv/ocicvE0+/WMtD8GRIT/+UbGHx0nFA18ddHwh6TcqjzTRcXCeQSH8w6DJakq5DCX9W9/IywWPvFpAHTcw6URm/OG0azE9sJc9lmb0K0Qo0EpbK+Pn+1iUrj4MfDzHRwemw/OT2k3USAU5WLvwhV1FxWcO/dFUK5yVFYwMZe48/JHMFqeekb7CimGLBYq+uxWU4rqJlV9l6bdxdoF1lV474jMEGsGVBZG0fGfowzGSOsZlAuWFrT3xMtlmEs75YLgqeAopd229O+8e34qO+yT9iw/mZq3On6dRSuDtA5rS7z3LBc3SEyPeaRZ3nqP5aBmtvwOEtVk84JdKl6+8gIAb9+7R14WVNWI1inutpb6xi6+bdn4lMfEgfD4tmA2C7GJvXEfXTcY9P57ehzKIpzLzsGEboWkM4p6kLQ7V6E7EzpBnoh7/ONCpz1sGQiHne+em3P8CvleS/sChuGz895j4+jEGe0HsWqRr9z8kduJCHG0hrILRsZx48HYNwgt8vMZqqkZE/YlKQpcY2kHGbUNv3uQtOeDPrp2wXgqUpiy4i/+pX+b53/005i6wqcRU9/HRQ1GLGkpYZ4dzp60rwr5HzSlHUIB7pwc7QcSoQWK0ZCorNi4EI7NG5MlqqrY2Q0FxnEasYhzcqV4ymmqe1ntBwny8DmwklHllwNpn5b4KMUnFm1PvkOrmXazLJETOscDaFHE6IdIu/WOG8wYkjAyOYzHMDhd0k8iMe2gF8Sn23dQpcWaCD0a4XF7JnQrrCLx9NpVnCsxymG0oph0Svub34HJDrzxI6FVv67hG9+g2linyIcYqfAqPrUQpFWGczW6WwdaX9MTQzkakFQFvS62dDF7prT/6wbv/S9777/2mI/9Te/9f/KITT4FHEragRL4j4B//4j7/5L3/lPdvzun2a9npP0xkSnh39yIeT2O+XSa8NG1AZlWtEXNdusQXHCO9y2qsUj6QMRUXcM3vgKXr8KlK0CYz3F5AvNdmO3iemFxr08xV/gksWq9sr0Bbr6793u/nOJEde3x53gIZTkUIasduniVU2S0P03kakAUVUxsTdvWRFqdIWnX9IywzUtM1SdZG/wwa8NPMx68waj/cUb9jzLsfZhB/jqNjqhbdX+L6RGQ0Rixnn41x+NRWOKiCsTuFLNyTw1KhXzwByvp73wHZZvzU9khvEdpBqMTktpVPrpfkXaPawt23QI1v8No8Dr5/Ba7s+/QjBK0SpA8Ji0qprbi4sXL5HHGdLmk2t1mNu8xUCm3J455W/HKtR1mB7mrrZnMA3HJRz2ktUiccuKV8Dma0K2QYEKkmsRdYSoYRjlXo1SIe0vQJzOHekrQ2QDblNA2+Plu5xw/Prf9ySQB0ZSuQHWkHaOxRoeIqFMikgwl+sQt8pFXbKglM9eyaB9wmV4VgHa2GXfnzmQ2wQq4fo+ijUk1xOr+z7OXp+CEFoVPDclixrs/+dNM1zbRtoZeym7TJ4orDJ6oUWEUBc5+hjv+gCrt54wMQ6KEapChK8vapXBeuTFZIK1j+4DSvtQ9evHTX4eICH1iJuwXBPth6cY8vQZbW7hpQRtHqNifrj0+7YP3KCcnNqFb4TAH+TssqbG8QFcA+ON/HH7iJ071vLHE2H4fvGDu3EMvK2yaokZh/8wDpH1VNDXj53BKcLYkUYpiMoW2DY7xGxf2uze++U2oa4pLF2iTGKMdyOna44G98dGDDvI5mmY8xrQNva4QN58sEBz2Uekyz/B9BxG5JiLfEJH/RkS+LiL/rYjk3X2/LSKf6W7/moh8TkS+KiK/euDxb4nIr4rI50XkyyLy4e73v7RSyUXkz4nIV0TkiyLyz0QkBv4u8Bc6tfwvHNwn7/3Ce/87BPL+RHE+w30/IFiPFRcS4Xtzx6eGA2qteQnLNBIyhLuNI/IOXTXI+AHS/s2vhoXCJ35471daEppeir81RRDa3igsxp+Qq/lpsVKkmv6I9M5NvLWI1vjlBKc0Ps1OFmnytJDmUCyIRTP0CUNiKLb37ztH5LqPiRyTbEFTtphEkMdYBD8OEgyRgix13KqENx5BUiraEPcWn4DI9AeIF9JigXKCwWPKMnQ8nNMc80OI4vuVdtvC975BNVg7P5V9hR/+6ZPPzK4UP+8QpVGtp7Ul2/O3iFzLOI9Y/957FK7l4os/QRM1uCzBeCiXE8ww48WNy3zj5pvcunsHV9RMlhuo8ga7FxyvRNvcXoSuLO89tCWzxb7SruoGFXUK8I3rYZTnhZeO3t+VCd05K+0AdhX75ivEGzxuL+7tvObZVzBZnxaHW85hNkHH2el8A54wtCi0zqjcEj18AQR8kuK8JT5F3NsKIkKiBpRuhvf+kQUSo3tEOmXDzXlTtdwqHa/2D1zrVmZ02/e4evESzjvS2YwSwQ/6LJroUPNMnaXETrBe42NFujuj8eCWNcbVuF7GTpOih1MiUWir9tvjz3qmfeNSOF/pZ0uxg4jRpFooegneai5eCNfzW5MFSML2LJDUcRJR6uxMSDvAOilvM6HyLYkYogyydZj4l9jkX2BraJMYHXMqIzrJcwROFfe2Qophh/3EBusd7zFjRMpw9R0enly9XyGRmHbYBzzm3i6qMdjxGlHkUdY85AOUYNihJDIb1MMB9t3bpFrRTmf4b3wFKQv4yZ8JGzsHX/4yXLxA2RZ40Yj4Tmk/JWlfxb65MLbQ+oqeytgdjUicpdcV4ubzAge4tjjHXqsfbHz1e7/6k8CTXgRsfeyV/93vHbPNh4C/6r3/XRH5L4G/DvynD2zzH3rvtyUQqv9JRD7pvf9Sd9897/0Pi8hfJ6jjv/zAY38F+OPe+/dEZOy9r0XkV4DPeO//5mP8Tf9QRCzw/wb+Y39cTuoBPFPa3ydeH2h2as+OShERTFmTKOFKDNASO4sqG0gPnDTLAr719VBxXN8/vpVKcf0e3oeTj+2Zc3OOhzCDqVHUgyF4h1vuhjsWU9pej0jOV7Ui60ERKu4fkwtckN4Bpf182w0jnZFpTT2cIr7FGI06o+LLalzguZ7j+tKxVR29ephZC+5kSjv9PuIgqQq0F4z16KJA4iS0u30QYKL7Sfs7wTF+fvHF89unFUbrJy8mrVTurpVPrNDYArf9dRJXYo0inQzYTdZpelFYQPV6JN7BYo4dRLw4CqT8vXv3uLzV8PnrI/pNi9s0WL3FtGgpW08DRE3JZBHet956jjQWHXcE5it/CL/32/Cdbx69vzvbYbF5jh0Xe0VG2c9qX7nIK4mpOqX9PBFlAxyeophiZjP0OarsK8SSUbsCZTJcf4BL0y566fEKcaka4Lw9NvoNIDZrDH2JVhW3ygfWLXESjqntLWJRvIRBFgtaBeQ5s/YI88w4IfNC4zQuMZi6oGka/LRE+xaXZ5QuQpkydF7YTmlP0rNvB7/yInz6p872Nb8PoEToiWGRxXgdcXk9ENlb0yW6EXa6UZ5hnlCplGFyNt/rNcI5ceeAgDa4CtPZCD9aw9bQpIG068MN3Q9HmiEolKh95/4TIus6jNouKeY2Cxosz3M68v8gIqVpB0MQQ1wbsI5qfRPtioda4yEUTR0eKwoZX8S3M1IluLqh/PKX4LkX4cKlsPFbb8F8Dq+9SmVBpxqL34/tPQW0ihFReF+hJeqy2g3NcIRuWwYdaV9Ol4CnbZ+4+PkM5493vfe/293+r4GfPmSbPy8inwe+AHwMODjr/t91P/8AuHbIY38X+HUR+XfhfS8i/pL3/hPAv9H9+yunefCz8u77xKt9xb/cgjfbhE+YGLMowDs8Du8saVMhHlR24ET83W+FxfjHP3XfcymV4Hs9nL+DiKbNY7Jzco5fIUFT9sO+u9kWerCBX8xoztM5foUsD21XTb1vmFUsA4E8jYHWU4BSGXlkIFqipCU66WzwE0AkGuWFFwae93bgC7uWn7t0+IVw5izKJ/fHJh2FTmk3iwVrtkfqBCnrcy+Q3Ico3m+Pt+2eY3y9OHEh84MBpQJpqSuIYpTTSLFLbC3Z5hsMeh9jY/Etvju6xFZ9h8vxc/g8I/UetZhjhzEvDUNnwdv3tvnZxYLtyUuo5w3Kewrd0LLFnfkVNoYQ2wWT5Yq091Ctxaxa9JfLsD9/8C+CQvJDH3l4f3e29rK1zwsrFb1RmoSQ1a6kWzlLRMP8/El7GhbSy2IXPZ+hzsno6yASnTNt7oHz1J/8JA6HY4F5DKUd7p9rj9Wji1RxtE5UadbNLrfLyw9vsLYB2/eAMKsq8wV1P0crzbw1vNo/5LyVJKRe8K3HZjHGNdSLknRWoLGU/SFWPFq1ZGiU7WbazyGT/BmOxkBp7mYJVgwDoxikCbOyotgtmU5DMW6UJ9xVCf3sbIotmURkPmKHksuE43xwFe58BYrRNWyzQ5tHqIhTtceT5fSljzbJqUclDjrI595wgxnjgyr7+4CJMmwSkSxKqn6fem0NZStM9PC5flU0rbDo9avYCBLt0XXNrE3I3viR/Y2/9KWg/l/YoHKeKNM4AVHRY3VvapVhbYmJYlpfEYvCra3hBYZxWHstpgucUpRVyQfAgecHEidQxJ8WHlzg3fd/EXmZoKB/1nu/IyK/Dhw84a9mXiyH8GLv/V8TkR8D/iTwByLyIw9uc+Id9f697udMRH4D+FHgvzrp458p7e8TsRKu9RRv2gQfxURFhcZRuxp8S16VKNGog8Tmzq2QZTy834BIqwSf53gsLo3BmL0czPNCgqbqjJL8bAvnLRQLml7//JzjV1hl2xYHYs3K5QdiPlCrmF4co1WNSEt8xgpkhsGK5eMjzdsLx71D1PbWO5bWo9wxcW8rJAnoGLUsuBR5NrVHlRVyzqMI9+Fge/w73wnZ6K8+lnno+SPrha6cOEY5g/IQJxcZXfgxVNmw1hbMRi+zVS9paSBLiQFZzGgHhpe6GeW3t3ZJswmfdGuspxlN1eA1LPVtrs8dpXOkbcmkCMWObHNEVLeYOAskvSrgQx+D51+CL/w+fOOr9+9nXcFifq7z7AAxCkFolEJE4VyFdeFYaPcy2s+3Tp0kPRCh2rmJOEvUP/+oxFT1cHhqv0T6I1xnKhmdIi/5IFbRb8fltUOYR010zlimbNWWyj6w9lrfCMdWVeJcjcyXVIMc6zTea0aHdQglKamJiOqWNo3RroaqRk9KFJY6G9KoFqNbMkxQ2svig2Gm+Qx7GKuINk+wOkJZz4VBuN6/e3uLnUW45vf7Ga1K6OdntxZZI2VCtadsr+bap9nrNPGY5mJYL52KtKcZmWTE/dN3Fh90kL/FggbH85y+Hf4waDG0eYqqKto0QQ0HIPLQPDvc766vN16EJCKlJLUVv7/2Kr9TZZTWw61bcOcOfPzj8M53KL0QZZpWGSLUY3VvarVykA+xbwDJcA2nNWsmHBvLyRKrFVV9fAfQM3zf4UURWZk2/EXgdx64fwgsgImIXAJ+4TRPLiKveu//pff+V4C7wAvADE7XziIiRkQ2u9sR8KeAr5zmOZ6R9ieA1weaZZQzJ0IXNcZaatcSeU9aV4hE+22xzsHWPdh82OVfqQhRBjccYMfhpHteGe0rxBjKOIY4wc238cUUsZY6752/0r7KkF21xENQ2tMPhvLbN31MFNS+JDrbudUEQ4nlYyNNrOALOw/36jVYCusxKHon+SiTBIlT1GzJD48b3ugV0Fgk7x//2LOCiYLSvlLZNy7B+oXz3qvHQ5KGYztO0GTY5AKD0YdAGdi6S08LxdorTBrNwm6jdIJKU5LFkmYQ8eIo/N1v3dnhyisTdKl5JbrCZrNgVw8YyC6fWyyZO0fSlOyWgeBmG31U44LSvpr17Q3gJ/5IGOn54ufg61/e3889E7rzm2eHbp565WAsCdZVOFehxBzIaD/fc1YiBptmtHdvABANzrfQAZCpcL5c2qCue8KblbwPpS5RA2q3xB0T/QaQmQ36UoAsufVgXvsBMzpny05pz7Bd3NuhHUJJQh4nqMZ2pL1BLWr0ZIlEQhn1ieIGhaVHgiCBtD9T2j9QGOmINk2wJkYax0ZngPbuzi67yy6est+jJaZ3hpegNVI8nt2uRd4kkG/CZDpm+uN/Hr+ZI8ipZtoxJhScTznPDuFaLwgLam52KvvgMZIfDkOkYmwWzgPl2hqr1NrDEo0O5tib3gXcoA/NnI/+zE+y8aOf5dszx//r3Zo3//kXcHEMVLh7N3n32qdJE4eV08e9raB1hvMtyiucd1jfkmQ5NokYd34Hi+kCpxX1IZGwz/B9j28Cf0NEvg6sAb928E7v/RcJbfHfAH6D0O5+Gvy9zqTuK8DvAV8E/inw0cOM6CAY3AF/H/glEbneRc8lwD8WkS8Bfwi8B/yD0+zIs/b4J4CrqZDkOds+4lJriauaKzrhli9Iyiq4hq+MpXa3A6G4cHg0n1YJ9afeQOsEsTuoJ3TyfVwkaFrx+N4wRL0td8FD1evRP++az6oQsjyotC9gOD6X3XkQsc7JzBZV05KkZ/s5pitTGIGPjzSf37HcrRwXkv3PrMZSWs9An9CbII5RcYpelDS+RKo5Yh3yAehs2MNKaV+p7J86R8f494s0g607kAzozRWehP7wWrjv7h2UMYwvXmC7Ekq3TS4KlSVkiyXFc5oXx+Ec89adbYyZUmvP9PoGL7/wXbYiTYPjTrPF15cXSZpqX2lf66NvbBOlB0h7nocW+R//N8Ks/Zc+HwqQH3vjgAnd+RPQ/di3FOsKUB6lYpadu/J5G9FFovF5D1vcRSEfiJn2XHIQofRLrsTXsO494C6RPD6JTdWAGXco3Zxcjx+5bT/aJNXfIdPb3CpHvHSw5roi7dv3cEbhndAOc1p3eNwbAElKFkWYpqZJYnq+wewMiCZLJNYUuk+cNCjv6KuEKYT2+LOOe3uGR2JdGxCh7vVJiy0214OC/e7OjN0yqKl5P6f1Mf0zJO0Dwtz1DiWbhGvfqkU+GQIvWUzX9XMqfPSTMD59540SIfGaWyzw+CemsgNEErodYM7iylUSV6JVgpKHz6NGFNorKixKNHLledw//ypJnvMTFxM+Ujs+9/YO733te9x44Qof/u63Sa69xmTwMlfvfplGmccuqq68n9QBB/kszlgmCetdN858usRpoXmmtP8govXe/+UHf+m9/9kDt3/psAd6768duP054Ge7278O/Hp3+xcPeeg28Nmjdujg8z6Ax26th2dK+xOBiPDaWsKOyvF1i9QNM1+jaEnLGhGzH+F0t4vkO0Rphy72TVqsr9AqO1+jN/aVqbY/hPkufr6Lw9P0zjGjfW/nOuOgldJu20DUPiDt2lpl5AoER5adPWn3eOpObU/0w2p7jaO0MDqpc3GSQJKjFiXWl9hqFkh7/sHobAA6pf0HQGUHSPLQNaANiRmxsfkpSDolZusubGxyOddsV2NaL9S+hCwiXRSUfc16NqCf95kWFbu37rD2uuXu99ZIXMQrcctmFKHabT4/L4iaJbtVIO2DQYYAUZzuk/ZVYWZF3K+9GgzqvvwF2N4KXS8fAKUywYRFY5fVHpT2JPwOOd+Iyg66i/9UWT8cr+eMRAxITGWXKDG0qwLH+ygYnyb6LVM9RGesmx3eKx6QJ+M4GHNtb+GnW3hRNP2U2kYhIUMfbkQnOiKpXTAF8y3MCpL5LkSKqRqi4waDJyNBbBuuHWcd9/YMj8SaNghQDoaYqmZtfQzA9Z3pHmnPhgOcT4h7Z7dOEhHWyNihxHUkcXAVvINyF3zmHu888+GPhQjgx8Dqer/2BFV2gEhiml6K85751ecxtjy0NX6FBE1NWGfoiy9BW+G6ou44VvzcnW/wsbQh8zP+gDV+a+3jKBpi8bRKP/bI5SoWWQjnj9ZXpGkOabxP2mcFXilsWx35PM/wDB90nP8K5gcEr/c1dTagrFpM1bBwgbQndYWkvf2843t3oNc/cu5aqRTnKlpXnKtz/AqrdiXbH+HqAr97JziE5sPzn2mHzkG+IxZlV0H9gCi/Wqes64S1RU50xjPt6d6cmyVWwidGmneXjjsH2k8bLKXzjM0Jiy9JgooTVFlj6wK3mEHrkN77c6l9olgZEFbV+eayPwnsKX8eHDB8Lvy3bYO6vXGBy6nCY1i0OVYckqfEVUWbaHzseelqiGl7+823GX2kpWl7NFs5eT3n4xtrvGDnfHdew3JJbT2x/v+3d+9xlt1lne8/33Xde9e9qq9Jd9K5hySEAB2QixpBQIgig0IEUXIEvCAzR8945mSGYwTHGVE8M154yQzDMFEBBwdBURQvSAYJEsydhCQkgU660/dbXfd1ref8sVZ1V1eqL9Wpqr2r+nm/XvXqVXvttfdTVav33s96fr/nF5JGAVhAUqkVTeigqLTPkuBFL4OLL4NvPgBPP9X1ofGzkvJDo5RgWNGMLkholMu9dfsiKEBYLZJ29Q93N5BSIBEHxbJvAG1rgkKSBappZ6pY+q2fRj7F6VazCSSieJj+aIaJVp2ZhdZrP3wQmzhKroCsL6XZiU/ePDNNIYyodjI6SUJIhupT9M0cwZKIZjwCUYdYRlUpQavsgeGV9p6Slmu11wdrhM02Y+uLkTxPHZ1mvPybVfoGUB4RrfD1lhGKFRYmy/5V/ZvgWGG9mp31MO+zNTuvfSmr7ACJYupbN9HcNEZz03kkli/YOf7Y/cvpSQDhyBaskpDt3VHsbDbhwQcZCaa57oIRtrz05dRzEdIkVk47iM66EBQEMYEiLGsjiY41iZIUS1NGgzJpn2qQhwEdX6d9TTGzHWZ2TbfjWCk9kHWtDQOx6BseodHIiJst2pYR0yZudFB1ztitA/tPWmWHshkdhlm24LyhlXasI/PAEGDY/p3klSpEZ/8Cu6TmLPt2LHnvkUp7EFQZPT/k4muTYrTFCqqUf5tGWTW7ajCkEhad5GdN5h0s16KSdpIUOkY+dYR8ZgLlRti/tB8UnpXZpH21V9nhxKS91YTZ5OfQgWJ7/UbWpyIK4EgrJcMI+vqJ85w8b6MkZ+v6LQDs3rWXetKhfzNMPrWBqD5JXO3nuRWxrj5Nc3wCgIFqtag6mormifWZYg3p+WuJS7D9JXDJFcUw+dHeSNpnz/vZtdqBco327i/3NiuqFhe5gh4YGj8rCaq0rYGZ0cnbBEFC8CwvcBRLv3Vo2+mXWEqjUSqxMcqj7Bp/jGa7bHoKMDKGzUzDgf10opi8mlBvJws3oYNyBFZImokOwqKAcGacav0olkbUowEIWqSERMSErbLy5kl7T1G57FtzoAKNnHXri6Hjj+4/TG4wEIdkcVr0DFphQ6QEiMPlvPYwgb7y7cYqZ1lpfxY2088VjNG/xNMp4yBh+srLePrH34ziYqTSqSvtEa3yM0c0ugXSlHz/zmLnQw8VI+C2nkfwgpdz1fo+3rQ14RXrM8Lg7NZonysMquTWLJrR5U3itApJhaG0eC+YnKqTB2CZJ+1u9fKkfQltWDdE3snI6m2yvEVqRtjuQPkhjanJohPz7FqVCwiC4x+Oe6PSXszNmu0gn00fpVN2femNpL1WzGOH4//2yBJkYZCgIEBhcSV4JSWECB1L2uNAXDMUsmsmZ19ZbT/ayZCFZ7bcGxRDVZOUIAObHofGFMo5q+Y5y6bWXySUq73KDscvPpkVX7MVgoPlFJux9QQSG9KAg80YggirJcSWY50WeZKzdbQYbrlzzz72jc+w8bnQnFiHjefUlbO5GvGifJL2keL/zmC1StDOgJCkmhZJe+0kF8Ek2P5d8PJXwOW90aE/Obbs2/GkPQwSmmWlvRfE5ftB2COVdoBKUKNtGVnepF2ud/xspUHxc57ZEPkqNnABWTDK4cY00/UdjE/ez+TM4zQHRJa3CPYdoD3Qj4CZdnLy160oKirtCjECLAlJxveTdBrkSUJeSQmCNlVCIsUEs0l7j4zQcsf1K6JRSzFC1pfD4x89cASAkTSiEVdIFtPwbYmEChii8oz12gHyNFvxz0apIka19J8XUyUEYRX1nU+Y14kVnnJFo5SiV0pmOYoTNLoeO7C3uLD7t38JtRRe9koYKkZNVEIxGrfJBSh6Vhc7wrBKltUJKdZqj8OYPE0ZKpd8m5xugCD34fFuFfOkfQltWD9C2MloTjXJrU0qI2i0Ubk2Lwf2Ff+eptJ+bLvLneOhuNqdENKsDWBBAORkZbfwnhgeX6kVw66yTs9V2uH4XKtghasBkqgQcYg6B2yG3Ox4tb2c2z6RZQR5wOCZ5jJlpV05MHUEZhrIdLyLfy8YWQff/y9Wf5Udjlf+8rLiODuM9+ABGBopLqIAmyricDMlI6JTS4gsJ52ZIetL2DJcrH29c+9h9k/sY/ACg9oI7QMRreZhBvtHWK9pmhNl0t5XhU4HERAmlaLJ4+mSmfO3FolSD5itprekY42gcmI65Meq8N0WjWxg8qprCDZu7XYox1SCPgyjnk+TWYtoCSp2oSLiM1z6rUJEHqb017bwnc5zGOy7kjTZQJ7Xma5O0mztQ52M1lAfeR6QWcTgySrtUHSQz0NMIk8iqgf3k1iLTlIhrgR0aFMlIiYimn3f6KWLjw6AgSCmXknIg4gNI8Xfp9EpXg+H05h63EeVLmTtFEPkm3SYtuJ1eegCyIKcqGY90TtjKSTlso9T1iLKGqRh3ymnGB17/S2HyGvD+XDkEPmXvgC7d8L3vhI2X3DCMXneJKNoOvhsK+1GTkhAZm1Cg7y/n6G4eG+ammmQ5RmWdY6PWnNulVkbryw9IhoYpBKHZBN12jlUOi2UG0GtHD58cH8xzHTe+uxzSTFChEFCoN74kJkS0gqKrscAnb7+ovOxeuD0ma2qN+pFQ7rZ5nQ9Yna0RDeG8F3IEAIe5zB3s4fdmuCKIePpes7eRs5k3iFYTKU9DKFWI+gYmplB01PF7zrpsQZOPdDca0mEUfmzlB9KZ4fIHzoA645flNhUCcgtYbIDVCuEEsnMDO3+iPP7i/s9ue8I4cw+dk9Nsf6aCu0jg2RHjlLpHyQKYGqySNoH+vsIs4yAsJhqUJ9ZVcOGZ0eYtJQfG7XULl8PemV4fE0J9Qu2UQ1XdhnIU6mVy7418mkyaxOd5Rrt81WC/nLpt2cuOTlXleL/7Fg1ZyYTE1mVWmULQ/3PZXDouUQj5xOGNVoD/WS5yC069etWWqGqECSyNKLSmiLJW7QrFYKKUHkRJyImmpkuLjz2yIUnd9xwGNGp1cjCiNFahXTOVK7hNKIZV0i7lLSPUrzvzVbb+zfBc96WE/f1yCjEJTDbjNKsRZI3Tzk0Ho73QJqd1x5s3oayjM5f/QnZ5s3wPd//jGOyvEmn/Kz7bH5vs0UumWEYOS2sb4BIUEtSzGBmpklmWVHkcW4V6p3sZi2o1KhVUpKpaToG1WYDTASzw+MP7Iex9ceb0i1AEmFYPWWzj5WWEtJSjvqLiw2tWv+KN1o5qdkqYH26SDB6ZGj8rDRZTzU9rysNsEZU4fnaxFWsZ4iUPUzRHDxIs+8Qd0xMMZVn1IKQcDGx9Q8iE5ppwPQMCmPvurycKlXI5lTax48Ww+TnjNbZUCkuoE20E3KBqinJ9DSd/oQLynXAv7P/COsOPsmBHXcxdnmOtdeT7ZuknlQYTAMmJotq48BAH0G7U0znCILi/9RKLoL8LAUq5l026RAGFQLFtFRUVXpleHxNMS9gE4PPYh30pVYNKqCQejZBx3LiJYrt+BD5qVPeb7Zx5lC1ONd3149XwqKwRrrxMirJOhoDVfJcGNHCy70de+KUOBORQZZErFOTOO/QqfahihFjpMSECotK+0AP9eVwxwwHMZ1qlU4UgYn1/cdH/QxVEppRSlXdqZrGCuknOTavHcDS4vxdK5X2KIgJEEE2TQKn/Vyazkvao03birnmnSaTr7iG6cYOsvzE4el53qRT9vx5VsPjZzvI57Md5FuEfQMEMvrLlU2mp+qY5ZD5Wu3nAkkfLddHP5tjXy/pllPsv07S606yb0zSlyRNSfrQvH2JpI9I+pakRyT9yGLi6o1PMWtFpUaSJFTrDTCj2myCBcVyb80GTI7DRZec9mH6q5edMrFfabMdmekfBnbR6u/vnTelypykvTEDPdTcCSAK+057dXq5DSlliJSWZRzQDAf7J3hs5jBRCzaHi/w7VipIIcFMHU2VTcp6YKmvNatSg2Yxh5Nmo+iLASck7aHEulSMtxPyGKJqTDJTp9VXZSiFwYEBxicnmc5H6Duwg041YGjzJRwaN2amxxnt62N8uvjgOThYI8g6kAwUz5fnq6rSDrPNkDIq6XmYtTlYfoDslUo7lMus9ZBEIQpS6p1xDCNeooZWiWoECk47RD5VSJ/FTITTDMQJT9dzrhma8/caXQdPfptGX0I7iwgV0HeqP2eSFiOCgoAsjoitg/IOWdpHlhgROdXyZyyS9pOPfnPdMxYV7y/NOKbaydg4UGPX0eJcGq7EtMKUJDz1KI7lNEqVpxinaRmpQtrla03PFDWWQKiYvDNNrPi0n2XicqRTs+ylw6bziM67jGDDRvJrXkyzfZhm+zBpPEYl3YwIMHLaCokJnlXzSyks+ghZBqJYNjmpYGFEf5qyfxImJ+vkaV4spdo710zdMjGzdz6LYz8HfO4Ud7kO2A781QL7GsAvA9eUX3O9F9hvZpdLCoDRxcTVI5nXGlGpojhhsNUiIKPabBdJe6VazEOFU85nnxUEUc8MjYfiQ7BhZBvOIx8dpdk/2DvDvyq14gJHfbr48mZCJ5Uo5HwN8LrqZtY1RwmaNTYstnlNtVYk7fUGqtdRFHvSvpwq1eIDBhSV9oP7i4uA8+bfjiYB462UXIZqVaqTU+R9CeTTXHThxQAcbSbsG7qCg0f3sL7/YZLpFkd2HmTj0CiT00X1Y2Con7DVIYjnLvfWW6NXTiclpEFGFFaJo0EadIo12nvoNbUXJUGVuhXnwVJV2oul3wbOqBndVoZo0GGwv8HeRk42d97ptkvInvdCmiM12nnRhO6Uo5fSCmEGQRSTxRHKm8iMdlojTyDGqFA0WlSn45X2HjUSRYQmGv39hJ0O64aPj/oZrCS0kgqVsDvD46GY1w5whGK52XY5VL9nihpLICqn9sWKT+i5tJCgnJc+O6ed/n7YeD7B9d9Fre9ChvqvoZJsoNU+zMTUQ0w3dgCUSfuzf30Ogip53iJQVDTUTFMsCBkom9FNT9QxOj48fg2RtK2sWH9C0sOSPi2pVu67XdL2cvvDku6S9JCk9885foek90u6R9I3JF1Z3n7zbJVc0pskPSjpfklflpQAvwrcJOk+STfNjcnMps3sKzBnGM5xPwX8enm/3MwOLubn7a3L/atdnKC0ymBzkjGFDHcymrOV9oOPFsNNR9d1O8pFm61QtYfHCJ7/fNpxzECvJO1BeVFk4khZFfSk/XTiIGD7QB93HkoZPWW5agHVGgQRQb1JMN1EYeQXSpZTpVo0orNy2beD+2H9My/8rUvFY1MpTYtJaxXi1mGohFhnhgsvuIT7H7yfgzsfZuyym3msv8KG1mFGOwfZ/1ROfuHFHJ4uPsQMDtdQOyeoVo83dqyutkp7yCEyzAxJNMmODb92J5cENSY5BEB6mg/ni5EG/dSzcSw89QflEVUYsJSJ6hTtIyn7GsZ51TIxTxJaV1wJrbtptuOTL/d27ElTAiumjVgSYHkGoaDSR6acmkGiBCaLpQ5P1WfGdU8UiAoxMwN9RLv3sH7k+MWV4UpCM6wynHSv0l5TTMUijtBgE/20yREiWkNJexzE1HNIwzNr1Dh3rXYqFXjzm2GgXOYyiKlVtlJJNtJo7aXZKvKVtsIlGZ0QBlU6nQlCBulYkyCt0IkCBsumrVOT9eK1wJP2ZXH4zl94KbDU678eGn3xb3/1NPe5AniHmd0h6WPAu4Hfmnef95rZYUkh8EVJ15rZA+W+g2b2AknvBn4JmF+hvxV4jZk9LWnYzFqSbgW2m9l7zvQHkTRcbv57STcATwDvMbN9Z/oYa+eVpVf0DRLXm1xbqZI2m2RRUjTwOrAPRsaK7VVmNmm3aIhquoV2EPVG5/hZlRocKT5segJ5Zq4cCLhyMGTbWSTtgYlgukXQ7qDIh8cvq7RajCSRivns01NFX4x5RhOR5SkzeQR9xRDBhIxOmLF162UAfPv+u7gogMPtfvZvu4xk8xaGJp7k8Nfv4mg5PH5gpB+1M8J4TtK+6irtxcig2WpPLy331suqc5ZySpZwvn2lnNdOfPr1kS9gkL7EaKbT7K6fWEFtkWHWoZHFp2+emVYIgoTIjDyOIM/Io5CgUqFjbSoEREQwWY4A8M7xPWt2rfagmTM2evziymA1oRVUqXQxaYdiiPw4TTqW0yYjIuhKD5vlMltpr4Zn1tsknZu0AwwOPmO6ZxAk1CoXMNh/DQO1S2mHz265t1lhWMUwQlTMaS8r7cNJ2UF+og6y46PX3Fqx08zuKLc/Drx8gfu8WdI9wL3A1cDcue6fKf+9G9i2wLF3ALdJehc8q6tLEbAF+KqZvQD4J555ceG0D+CWkPoGCHY3sLxF2OqQRRXodIqkskfWMl6s2SugrUAEyTpgT+8Mj4ciUT9SjjDxSvsZiQLxsnVn8d+/VgNEkBlqW9HdPPXJYctm7nzyvbuLfxeotI8kQkqYziKslhLkGYlyZqKci8+/HIA/+8ev83//5J18u+9qnq43uPb5L+bg+H7ae3YzPlMm7aM1gnZGnFSL5d6kVTin/XgzpJSIJhmDPoHxtCplB3kUHOsavRRCxcRBBeLTf1AeVMo6qiS1aXbO9LF9zkeUlnXoWEaWJaduQgdFpT2KSDPoJBGhMhSHKEnp0KZCVCQjk+PFUqarqNniuWYgiDjYX4Fmxti64WO39/dVORzVSOPuJu0jVNjNJOM0aJGvqaHxAKlSBNTOsNI+f6TTqRSrJMVkwdL0ATi2Wk9u5OoQxDF5kjKYlmu1T86AZZB7pX05nEFFfLnM70Z5wveSLqKooF9vZkck3QbMrTbNdkfMWCAvNrOflfRi4EbgbkkvPMs4DwEzHL9I8L+AdyzmAdbWq0sPUP8wtDtYa5qg0S6S9iOHiqHbZzCfvReFCsqOzNmxRis99cZUmVMJ7LHu8WtOkiCFSBVkMYoTiNfIEmu9KC0TZlnRNT4IixE784QSQ3HAVCelU6sRWU6UZRAb3/38V7BlyxYefGIHf/O1O7joqYcZP9xiogLR5k0cHRjmaL34ENM/2k/YyYjTyvHl3lZZ1WjuWsFty8jIfXj8GThWaVdEtMTLeVaCAYg7p136DYpq+2ACu5ikmR3/7NWkSTs3yJNTr9EOxdKqCggzsEqCclCSkFdSQjISQmJimJwgq9ZW3Tl+LhkMYpqVKrki1s9J2gf6q+RRStyl7vGz+kmICThCg3Z5bq0lG8MBLg76SaMzrbTPjnQ6s14DHXLQ0iyTNzvnXlY8t5IQSxKGZue0TzWBnHa7ebKHcKvTBZJeUm6/FfjKvP2DwDQwLmkj8NrFPLikS8zsTjO7FTgAbAUmgUUN0TIzA/4CuKG86ZXANxfzGD2Uea0N6h8k6OQwM0XYyujE1WIeKpywtvJqkxDRpDOn0UoPvTHNJuphWHxYc8snTQmSFLXbKMtQXPGkfTnNjhyZbco1tq7o47CAdamY6CTkaYSigMAyghA67Tbvfe97AXjfH36WkaTJ4IOPc7g1Tm39EGFS50i9GLrcN1g8X5LWiqR9lQ2Nh+MVmwadY8M0e6lzfK+qKcGCmGgJq+yziqXf7LRLvxVxxFwc9dGMZ3iyfrw637AmWS7MzqTSXhRRQosgCTFiOpUanUpArOKic0wEk+N0fEpVTxsOI1rVGu04ZMPw8Tnt1f7+Iml/RpFtZQUSw1Q4QoMWWW8VNJZAJdnAWP9zCc5wxYtjIzM5s2r27GfKpZhyKQWEQaXoIA8oDbBKylBSxDQ1OQMYzZYn7WvMo8DPS3oYGAE+PHenmd1PMSz+EeCTFMPdF+ODZZO6B4GvAvcDXwKuWqgRHRQN7oD/BNwsadecpef+H+B9kh4AfgL414sJxMsPS21gEClkeLpC2DGysFqszz4wtKrn/hYdmTvH5on21Jz22Q9d/uFr+aVpsZxSq1N8eaV9eSVpkaRLxYCvU4zWGU0CdkxXaJmoVmPCVoswhOnGFD/1Uz/Fr//6r/PNRx7l73fu4brBDdg3HqJzyXnUqg0mZooPMdWBKhxuEqd9Rff4oeGV+TmXUKiA2Ip5lbNLD3nSfnqhAsJ0I+EyfCxIVAMT9XycSjB42mGzVyeD3BFM8c3OOJdTXOxuWZNOLlBM/+lCLKfsRHmIAnH0Rc9hbOoozTQiNiMhIrQApqc8ae9xI0FEVq3SCUNGB4YJBLlBdaCfIEkJNNPtEBmhygGKOHqqoLEEJBEuosfF3OlJZ1KGbB0bvbk0v7cwqNLJplEoiMHSlMFkdnh8nSwM6HTqS/Jcrmd0zOxt8280sxvmbN+80IFmtm3O9l2UVXAzuw24rdx+4wKHHgauP1lAcx933u1PAt9zsuNOp4cyrzViYJiAAB0+AogsSk/a8Xk1me0I2l7iF9glMVtpr6y+quCqkySQJATtDGV58f0qbK64akjFxb7ZYcWnSNrHEpFbSsNiqKaErToKjGZjhiiK+eVf/mUAfuP3fpfG+c/Ddk+T79lFZajDeFlprw71gUGSVKBRX7UXwtJy2aHjlXa/Pn0mRuNNDMdL/14lCZop9Wycg+1v08pP/aG5ppjz1cfOfIa6FdX2Fm2yXNTC+PTrOZcXyGMLCQRWjYmikEYcF2u0kxZNHfOcziocTXIuGYoiskqNdhwQKOE1l53PC9cPUhsaIUp6oxAyTEpAcU6utUr7Ys1N2s/EUq9tH4ZVcmsREKIkgCRlZLYR3WQDC6DZ9KTdrU7n9qvLchgoOmUGh48ghZBlxVzUVTqffVYxEzCnToeI4PQfmlaSV9pXzmylvZ0V00Bq/jtfdmkVsCKBP9kUm6OHGGtPklvKTB5CX0rUmCFOIvJ8mlYL3v72t3PRRRfx6KOP8tWH/on9yfPg4BE6u56i1cmIw4AkDTETaRQWr1ur9O8728G4UXZzXuo52mvVNg2zVcuzZrlm+hmJt9CxFgdaj3O0vfuUc9yviAdpduDRrFiWrW0t2lnAYHQGI3uCAOKEOAsIBM2gQhSEtJKYVCJWDBPjAF5p73EDsVDURyspehP85x98MZ9/7fPoJNWi90YPCBUwVDa77KmCRheECojKHkhnorXEa9vPNqMLTVgiCCMGK8WUn4nJOhaIVvv0K1m41cHMdpjZNd2OY6X4J5mlNjgMBARHjhAoIqyXV/TWb+xmVM/a7NXTKVq996YURrD1Eti4pduRrH1pMVy7Qkqq1HsIrIRKDdatg+991cK/76kJuPMfSO75Mv1hyFRWKZpvdVpESYB16jQaEMcxt956KwAf/p1/z5GBq5ioXcT4kWJY52BagSAgt4CkbOSzWi+EpWUPDl/urbfUwhE2JpfTF44xnR1iX+tRZrIjmD1zXvKF1ZhKq4/vtGeYshZta9HqRKdf7m1WmhJmAgVsqqTUkphmElMxiqS9XKO9481Le1olgDSPmB7sJzJDgRBGJ4pIKr3z/jNCkSye65V2mB3pdGZz2ltkKC+S/aUQhmUHeQyLgTBgqJwuMzXdRIJWq7Ekz+XcSvNXl6XWN4CCiGBiEikknpkuKmWrfB3Y2Q++DTq9NZ991jXbYf3mbkex9iUJBCGhQkKTJ+0roVKFrAMbFzi/8xzu/1pRha/PcMH+x5noVKGvgvIOaWhYZ5pGs0iK3va2t3HppZfyxBOPc89XPsnO5CUcSoq1j4eqFfI8g1ykWdkAbNUm7SE5xhQtn8/eYwKFDMfnsT65lEgpR9q7ONj+Nu15Q+aHYjGa9zPRgu9wlFbeppMnDJ1x0l4hygMIA8K8QUZOK45JULlG+zikFcx7cvQ0SQwqpt5XIWhk1AeHaFYqdIKIgWpvVNoB1lHlAoZ8eUk4ttTmmWiTEZ5Zo/kzEihBBEUH+TgiCEMGqsXfZGKqDoGRefd4t0r1YPa1ygVB8UG304GoQjw5uerns8OJjZwi/xB87krTYtmxgQEY6Pc12ldCpXp8ms18TzwEE0fg2hfD+k1s3v0IMzMi66sSWE4UGFGrwdFm8QEqiqJj1fY/+sivMTmRsHvoagAGqxXIc8xCgk5ZJVml831n50d2fLm3npUEVdbFFzMcn0/HmuxvPc74vCHzF1RDmlN9TNKinXXAEgbO9M+ZpESZIIjI2g3aAsVh0Tl+ttK+yi+mnyuGg5hGrQrtjGb/EI2kSitK6Ut65yNsqIDzNdBbUwe7ZLYH0plokS9p0i6JMKwUa7FLBJXK8XXapxoYRqfjlXa3OvXOK95aUhuATgeFCWGjvurnswPFPPay0UpPVtrdyohjiGIYHio6i/fInMI1bXbZt8a85jlHD8ETD8P522DTVrjiefSTMfzUU0xX+wnyNnEIYbPJeOf4UMW3vOUtXHHFFTz15Lf533/zRzz97eL/9VBfFXU6ZGFULPcGxQWDVWhuou6V9t4lib5wtBwyP8pUOWR+OjuMmXFeNSBo1mi1A9qWFWu0L2Z4fAcsCqDToBWKIAqJCInKNdoZHFreH9AtiZEwotVXRe2M6ZGNTPeN0opThir+f7sXzfZA6tjps/E2GeGZ5fdnLAyqWF5c5A5qFZIwohJFZLlRb7TI2u3TPIJzvcmzr2WgvkHUyQha5Ty9NVBpl3SsetVzc9rdykpTaHeK0SReaV9+aZk4z+14m3WKYfGVKjzn+cVtA8NUt13M0J5dtJo5SkKUt0k6TSbax5P2KIr4lV/5FQD+9A//I0/uOALAUF8Ntdrkiovl3tIKRKuzSj23E7HPae99gSKG4/OPDZk/2n6ag+0nWJ82EUIzVTp5Dnm6iKS9QtyhGP3WadIJRBioqLS38uIi2MDyNN1zS2sojGlUa+RBwOSFWxm/dCutOGUw9s8ivSg9tlb76bPxFhlhvrSjE8KgCpZjlhEkCYQB/WWBYWZqhrzjw+PPBZI+Omd99MUe+3pJt5xi/3WSXneSfWOSviRpStKH5tw+UK7rPvt1UNJvLyYuT9qXQdg/TKIhoqZhYQDDo90OaUnMfvj1Svs5rlItk/bM57SvhNlqd2POesSP3AczU8Ww+Dg5dnPtiucSBAnh409htYS806Ca5UzOG1r/5je/mec85znseXoHn//T3wNgcKCKOm2yICkq7au0yg4QKSAsX6e80r56zA6ZL7rMt5nInmBDdS+TM2KgNUg1SAnPdPhxmhLmkIcxQadFFgYoDIkJCafK/0sDXmlfDYaikCztox2HxPUmQatNK6kwEPgFuV6UlJ8VTzdEvmM5Obb0lfawigiwvIXSCAtDBsqmhZOTdfKsDQs0v3Rri5m908y+eZbHfs7MPnCKu1wHLJi0Aw3gl4FfmveYk2Z23ewX8CTwmcXE5dnXcugfLJbDmp6mPTBUXOlfA1KvtDuAahXa7aLa68Pjl99spX12ePyBPfDUE3DRFTA6bxRPpUq+7Tlo31GUZ1jWIglEvT55wt3CMOR973sfADu/8w0AhgZqqJNhs0n7Kp3PPqtSvk550r66SDrWZb4/HGM0PUo9/w7Tbeg/k+XeZiUVAgkFEVhOJxBJGJIoRlPl/wevtK8KfZHoVAZoRiHxTJOg06YVV+jz/9o96fha7afuID+7RvtSzmkHCIMKUgDWQXGEhQEDsx3kp+p0LCs+v7hVT9I2SY9I+oSkhyV9WlKt3He7pO3l9ocl3SXpIUnvn3P8Dknvl3SPpG9IurK8/ebZKrmkN0l6UNL9kr4sKQF+FbiprJjfNDcmM5s2s69QJO8ni/tyYAPwj4v5eZftMqWkrcAfAhsBAz5iZr8jaRT4FLAN2AG82cyOLFccXdE3AK0W1Ou0Boe7Hc2SOT48fm1chHBnqVKFVruotHvSvvzCsBjR0KhDqwnf+HpRIbzsuQvePb74SqZ2fY31h58m7yQkgdFszJCbndAk6Ud/9Ee55pprePDBBwEYHKoRtDLyMCqGx4+s7hFCKVHR5MjXaF+VAoUMxedxQWWYJ6d2kjPDeYuZjlPeV8TkGFkYkgRRMZ994mCx4oI3olsV+iORJwO04oC+mRZBp0MrrlEJ/f92L4oJEDptpX12jfalrrQHQUKgEPIMpTEE4bG12ienmkWzy6xd9OdxS+dPf+KlwNgSP+ohfuSPvnqa+1wBvMPM7pD0MeDdwG/Nu897zeywpBD4oqRrzeyBct9BM3uBpHdTVMffOe/YW4HXmNnTkobNrCXpVmC7mb3nLH+uHwM+ZQutd3oKy/mK1wH+tZldBXwX8PPl3IJbgC+a2WXAF8vv15aRsWLt8GofraGRbkezZAZISAh9jui5rlqFZgtyW9VDqFeVSrUYHv/QXUUX+WtfXCTzCxjrSziw9WrUahPMTJHmbVRvMDmvshAEAf/u373v2PeDw1WCdgZExfz5Vbrc26zzGOAifPjzandetUo738ZU6woG50wFOa0yaQ8UA0Yex0SWF53jpyahr/+k/4dcb+mPIAirNNKUgekmcSujE1fwPnS9SRLpGXSQX65KO0AQVMEyFIeEZvRXi88qk1MNcnLoeDO6NWSnmd1Rbn8cePkC93mzpHuAe4Grgblz3WeHqN9NUVCe7w7gNknvgiUbuvdjwB8v9qBly77MbA+wp9yelPQwcD7ww8AN5d3+ALgd+H+WK46u6B+EK64FidYaGn43rAovxNdCP+dV+6BRjvpJFvEh2p29tAqH9hXrsl/xPBg8+cXA0UQc3nAZzaFhKjseJ6tuJGo2OdLqMBTFtNvG3j2wZzeMDP4wl1x8HU98+z42bBgizyHKrLicW13dw+MHlAB+fq52ocTGNODpOgxGi2hYVY4CioOELAzI0goBFGu0T4wX79NuVQgl+gip9/UTHjhAbpClNdLAl1frVSkhrdMMjz/IDCEB0TKMVA/DYkSg0ohAAX3lnPapyTpmWbGMqltap6+IL5f51eoTvpd0EUUF/XozOyLpNmDuMNHZzoRl1WLeg5n9rKQXAzcCd0t64bMJVtLzgMjM7l7ssSsytkjSNuD5wJ3AxjKhB9hLMXx+bZkdwjc0gvnwG7fWVCrHXxJrq7sau2pUqkXCPrKumMt+CkOxUFDl8OVXoLxF9eh+omaTHQc63Hevcfs/GA8/ZLTbxhVXhvz1Fz7Hf337j3H9cy8CRNQpyx7+t3U94vxakZydced4OPY+HOYhh7ddQGv9xqJzvEW+3NsqNBzENGsVmGmQBQEWp6Q+Or5nnW6t9glrcoQGWxg4tpzwUgqDKgIsFkQhA9UiR5uYbJRJu1fa15ALJL2k3H4r8JV5+weBaWBc0kbgtYt5cEmXmNmdZnYrcADYCkwCZzu/6i2cRZUdlrHSPktSP/CnwC+Y2YTmzKk0M5O04Hh+ST8N/DTABRdcsNxhLq3Zeb7rNsBk/dT3dW61mdugLPE57StiYLjoEn/ti4u5uKcQSAzFFQ6dt5Xz+qskj++mNtnkiX1tLpkytl4gzjsPBodmH2crF7z4+TwStCBTsUxWwqofHu/WjisHQtJAjC0mSytXtggzMVipkMUJRkjU6BRNqNbQKLhzwViQ8HStSt4x2nGIkoTIK+09q+gp8sxeKrOeZJyEkI308a1leP4gSBABiijmtNfK7vFTs0m7N6JbQx6lmIL9MeCbwIfn7jSz+yXdCzwC7KQY7r4YH5R0GSCKad33A08Bt0i6D/h1M/vU3AMk7aC4WJBIegPw6jmd7N/MyTvPn9KyJu2SYoqE/RNmNjtnYJ+kzWa2R9JmYP9Cx5rZR4CPAGzfvn11rc0w+2F3wyaY/E53Y3Fuqc1N5nxO+8q48DLYclHRK+MMjKUhBxv9NC/YTPrYw1xZ/w6PXf58vnsAwnDeB6is+ABT1ERE0i6zdq+0ux4RB+LygUVOJZQgrRBlHQzIwoAUEU/OLvfmSftqMhKFPF7tY4b17A/qRFW/YNzL5q7VXpmXahyyGaZocQkjy9YoNFBEoJBOEkAYMpSWjegmG4R06HRa3p1p7eiY2dvm32hmN8zZvnmhA81s25ztuyinb5vZbcBt5fYbFzj0MHD9yQKa+7gL7Lv4ZPtOZ9kGF6koqf934GEz+09zdn0OeHu5/Xbgz5crhq4ZGoZX3QhbL+x2JM4tvTQtGjhJx6eCuOV3hgk7wFgS0Mj6aY0N0u6rcN74U/THdRrBAh1/2m1yy8mUg4mkkxXPlfjf1q1ySUqYCcywMCRSSORJ+6o0EgU00z5aKXSIiSv++tTLji/7duIQ+dyMp5igRsx6lu/CsBQhhNIIhWKwOrtOe4PQOrTaPjzerT7LOSPoZcBPAK8o17G7T9LrgA8Ar5L0GPD95fdrz+i6bkfg3PJIU1AAQQCx92zoRWOJaFs/7UpCe6BG0mxS272LyYUaA7Xb5GSYAoyApNP2KrtbG9KUuAN5IBTFhERocqK4KFVb3Y0WzzWDsehU+unEOXlgxKmP8uplSVnHnt+Mbj/TNOhwAUPoNFO9no1AMSKEKEAKGCgv8kxM1gksI+v48Pi1wMx2mNk13Y5jpSxn9/ivwEm7S7xyuZ7XObfM0rRI2E3ePb5HjSYip0qz2o+FIEKqh/YzeUmbTZo3rLSstOcmICTO2j6f3a0NaYVoImPfxVcwmIwQ403oVqu+SHSSAfLYwCCJPGnvZQtV2jPL2cUkg6SMzH8fWmJSQKAIkgiFIYO14vkmp5ogo9X2flNu9fHem865xUlTCMLiK/JZYb0oCkR/VKFeHcAi6Chg4Mg4E+3GM+/cahWVdiALU5JW05N2tzakKVGrjVVqBEFIpBimJnxo/CrUH4msMggDENYgTf2CcS8LJOJ5HeR3M0WbjAtYmYtmgRLyWIRhyGClOF8mphsQiPZC74XO9ThP2p1zi5MkUBuAar9X2nvYSJIylQ5CZHQMqjm0Du/BbF5fz7LSbpaTRwlRu+lDh93akFYImm22MMAAMXEWwPQU9J/tSj2uWyqhSIIK9TiiE0XUQn/v6XXFWu1F0t62jN1MMkqVAa3M3y4MEgggiGOG02Iq38RMCwQdT9rdKuRJu3NucZIERjfDhq3FMHnXk9YlAVPpGHkU0FFOJRfJwf1Mz2sMZLOVdjMCiyA3r7S7tSFJkUHYzpAgmmqAmQ+PX6UGFDGTVmjHCf2hv/f0unROpX0XkxisWJUdIFSKWYaqKf2BiMOIViej2WpTb3rS7lYff9Vzzi2OyrnsXmXvaaNJQDMdI4tD8tYMaf8o6cH9TNqJXXOt3cAAWU6Ql21IPGl3a0GaIkTQagGQTJXzWH14/Ko0FIZMVgdopxWqfsG456VENOlQtw77mGYDfVS1clPqgiAuGmtVU8KsQ1+16IMwOd2k6ZX2NU/SRyVddZbHvl7SLafYf13ZXH2hfWOSviRpStKH5u17i6RvSHpA0hckLapruU9Idc4tni/11vPGUtFmgHZ/jfzgNGn/KMn+fUwf3gvNBA4dgoMHYfdOcnJyQZyXH4S9e7xbC9JKsexTswX9fUST08Xt/Z60r0bDkfjaVS8gsDbXBcvXedwtjYSQHOM7HEHAFlZ2WooUIcuLpL2TUatWOTo1ydT0DO1Oa0VjcSvPzN75LI79HMUS5SdzHbAd+KsF9jWAXwauKb8AkBQBvwNcZWYHJf0m8B7gfWcal1+qdM4tXpr6cm89rhqKKOijOdiPNabQYzsY/dLXqX7kv8HnPw9f+xrs3YuNDlO/vrgYHXml3a0laUqAULMJQDhZh0rVRwmtUsNRQIt1NIJNVENP2nvdbAf5cZqcxwCJwhV9/kARIsAqCUGW0VdejJ4en/Y57WuEpG2SHpH0CUkPS/q0pFq573ZJ28vtD0u6S9JDkt4/5/gdkt4v6Z6yAn5lefvNs1VySW+S9KCk+yV9WVIC/CpwU7mc+U1zYzKz6XIFtfknmcqvPhXrHQ4Cuxfz83ql3Tm3eP39kGWnv5/rqpGkxszoCARPkMUxOv88pjeMkn/v6wjWrYNKhbwzyczE19Dux4kzK95SKr6cklsD0goiQK0WIRHBpHeOX81GogAsBIM+T9p7XlIm7TEB59G/4s8vxUgBVo2IOhm1vqLB6vTEDJlX2pfef77xpcDYEj/qIX7x8189zX2uAN5hZndI+hjwbuC35t3nvWZ2WFIIfFHStWb2QLnvoJm9QNK7gV8C5lfobwVeY2ZPSxo2s5akW4HtZvaeM/1BzKwt6eeAbwDTwGPAz5/p8eCVdufc2bjhBnjFK7odhTuN0SRlemCM6RdeTP31r0OvejXZ2ADTm4qEHcAsI+u0yBFRLrIkLfoWOLfaJemx4fExEUxNehO6VWwkPv6R1RvR9b4qEREBWxki1Mr/vRQUlXYlIUFu9PUXSfvUxAzWaRVNKd1asNPM7ii3Pw68fIH7vFnSPcC9wNXA3Lnunyn/vRvYtsCxdwC3SXoXcNbDRSTFwM8BzwfOAx4A/u1iHsMr7c65xfPhpavCWCIOVkfJszaNqaP0rd+Cnvwm00f2MbD+QgBya5G1GuQEpFlOlla6HLVzSyRJUBCiZouolUOz4fPZV7HRaG7S3sVA3BkJFbDdNqMuXQQOFBEoIIuFDPoGyqR9sk4nzyDrQOTT/JbM6Sviy2X+1ZcTvpd0EUUF/XozOyLpNmDuB51m+W/GAnmxmf2spBcDNwJ3S3rhWcZ5Xfl4T5Rx/Qlw0mZ3C/FLlc45t0aNpQHNdANmxt7pnYTDYwTWor77G0zNPM7RyQeYaewka7UwIO5YUWl3bo0I0gphq006WX4u80r7qjUQiwgRAok3olsVupWwF88dIwJIQwgC+vqKOe0Tk02wdpG0u7XgAkkvKbffCnxl3v5BiuHo45I2Aq9dzINLusTM7jSzW4EDwFZgEhbdWfFp4CpJ68vvXwU8vJgH8Eq7c86tUQMRtAY2EhARPnUfTwy1SGs5tvcxsksuIo4GCcMaU8EBICDpZEz4ygBuLUkrrG+lhFNljcLntK9agUQtEB3rbjLoVoeiEV2I4hDCgIFaUVydmGxA3oGsDXj/ljXgUeDny/ns3wQ+PHenmd0v6V7gEWAnxXD3xfigpMsoOv58EbgfeAq4RdJ9wK+b2afmHiBpB8XFgkTSG4BXm9k3yyZ4X5bUBp4Ebl5MIJ60O+fcGiWJWmU9U+s2sWnPFDuuH6Hv/CuJHv82A+FFBGWX+E6nQ9jJSRWQJT483q0haUrSymFqCoIA+la+IZZbOgOhmM67HYVbDaSAQCHEEQQBw2kxFH5iuqy0d7zSvkZ0zOxt8280sxvmbN+80IFmtm3O9l3ADeX2bcBt5fYbFzj0MHD9yQKa+7jzbv8vwH852XGn48PjnXNuDRup9LN701XUWmJsqsrBTRvoYMwcehoAM8OaDYJWRhTH5F5pd2tJWoFmEyYnoG+gSNzdqvWivgrX1/w1yp2ZUCmWhARhwEiZtI9PN1HewbJ2l6NzbnH83cs559awdUnEodFrqVubTU/uojq4jqOpMXVgJwAZhrWbqJ2RhJE3onNrS5JCq0zafWj8qnfDaMoPrvchze7MBEGCxQFBEDKcFt0LJ2ZahJbRbnvSvtqZ2Q4zu6bbcawUT9qdc24NG01EM9rA1Oh5tHc+yJU2zMy6MQ4e+g6Y0SFHrSZmAYnkjejc2pKmRdd4T9qdO+eESsnjgCAMGI2KPgjjMy2iTot2x5N2t7p40u6cc2vYWCKGk4DHB6+ByUns8LcZHNvGdHOKfUd30iEvKpFZ0ZE58+Hxbi2ZHTmSZzDgneOdO5eEQYIlAQpCRqOi0j5eb5O0G7Q8aXerjCftzjm3hkli+0jI7tFLmOn00XrqEcbGNhIQsvfg4xylQdBuEeSgSgUCXwDZrSFzL0INeqXduXOJFKE4JogCRmYr7fUWcbtFo9XqcnTOLY4n7c45t8Zd2BcyMlTjicpFxE8fgHCSeHA98cED7GSCoNUiyICym7xza8bcHg0+PN65c0qgGCEsiRkIA4IgoN7OyBtNGl5pd6uMJ+3OOXcOeOFIxMH1FzB9SKQTR2mP1Rg9WketJuq0iDJ50u7WntkeDXECFW9g5ty5RIoQAVaNifOcarnk4/R0nUa73uXo3HKS9FFJV53lsa+XdMsp9l8n6XUn2Tcm6UuSpiR9aN6+myQ9IOkhSb+x2Lg8aXfOuXPAllpAbduFPD0T0797hvZwRCfvcNGhBkEzI+rkUPOk3a0xs8PjB30+u3PnGgURgQJIQsJ2Tq2/SNonJ+u0Wo0uR+eWk5m908y+eZbHfs7MPnCKu1wHLJi0Aw3gl4FfmnujpDHgg8ArzexqYJOkVy4mLk/anXPuHHHd+UNMDowx/u0poqFRZjTF2L6DhO2AMPdKu1uDZofH9w90Nw7n3IoLyko71Ziwk1Hr6wNganKGTtuT9tVO0jZJj0j6hKSHJX1aUq3cd7uk7eX2hyXdVVa43z/n+B2S3i/pHknfkHRlefvNs1VySW+S9KCk+yV9WVIC/Cpwk6T7JN00NyYzmzazr1Ak73NdDDxmZgfK7/8e+JHF/LzRYu7snHNu9dpUCfjOtgvZ9417GGpfzfRoH/U9j6JWmyCKy6T9SLfDdG7pRBFs2ASbz+92JM65FSbFBAqxSkzQ6VDrL5L2yckGrazZ5ejWmH/1kpcCY0v8qIf43X/66mnucwXwDjO7Q9LHgHcDvzXvPu81s8OSQuCLkq41swfKfQfN7AWS3k1RHX/nvGNvBV5jZk9LGjazlqRbge1m9p5F/CyPA1dI2gbsAt4AJIs43ivtzjl3Lrn8ORfRyWHi8Sma6zYx0xmHVhtFMdT6uh2ec0vv+14DF17c7SiccyusqLQLVWKCdofaQDE8fmK6SVaf6nJ0bonsNLM7yu2PAy9f4D5vlnQPcC9wNTB3rvtnyn/vBrYtcOwdwG2S3gWc9fI6ZnYE+DngU8A/AjuAbDGP4ZV255w7h4ytG2Zo/Sj7HtsJr3sOLR4maLcIw2Fv1OWcc27NkAKkmLyWEGQZ/f3FFLDxeptg8ihkHQg9FVoSp6+ILxc71feSLqKooF9vZkck3QbMWVaE2SEXGQvkxWb2s5JeDNwI3C3phWcdqNlfAH9RxvXTLDJp90q7c86dY7Zdvo3q4YPsa6TM9K0jaHUIo8Qr7c4559aUUDFUUoI8o7+vSNqPtjJoTMPk4S5H55bABZJeUm6/FfjKvP2DwDQwLmkj8NrFPLikS8zsTjO7FTgAbAUmgUU3SpG0ofx3hGIY/0cXc7wn7c45d44ZuGgb61PR+vZe9o5to9GsEqZVSBY1vco555zraaHSYk47MFAtCqzjzTZqNmDSe7isAY8CPy/pYWAE+PDcnWZ2P8Ww+EeAT1IMd1+MD5ZN6h4EvgrcD3wJuGqhRnRQNLgD/hNws6Rdc5ae+x1J3yxj+ICZfWsxgfiYEOecO9cMj7B1wzCjT+/m3pc8j4s3bGXrkC+J5Zxzbm0JgpS8EhEpYKhWXJgen2nRUY6NH0Rbr+hyhO5Z6pjZ2+bfaGY3zNm+eaEDzWzbnO27gBvK7duA28rtNy5w6GHg+pMFNPdx593+lpMdcya80u6cc+eg6rZtXDp5mOlMzPQNEvnQeOecc2tMqASLQ4IwYCgtk/Z6i3Ygjo4fAps/Jdq53uRJu3POnYu2XMBFacDo/t0k9TqJJ+3OOefWmCCIIYlQEDBUnU3a25jE7kYdZia6HKE7W2a2w8yu6XYcK8WTduecOxeNrqM2MMAVB/eStBqkff3djsg555xbUoFiSGOCIGA0KdKeo/U2UZ6zp5NhE4e6HKFzZ8aTduecO1dtuZDLjx7gwgqkfV5pd845t7ZIESQVCGA0LpbZnmi06avXmW62OXr0YJcjdO7MeNLunHPnqi0X0EdIEoio6pV255xza0sQRChJUABjUZG0j9dbJHnO0M4dHH3sG12O0Lkz40m7c86dq9ZtYLgyyMXqI/RKu3POuTVGilAYYXHIaCAkMTXT4uC2bUyPnU/nsW+S//Pt0G51O1TnTsmTduecO1dJaMuFxAqgWut2NM4559ySkmICBVCJSNotKn3Fe12rPsmTl30P+zdsZuo7j8BX/w7GD3c5WrdUJH10zvroiz329ZJuOcX+6yS97iT7XiXp7nJt97slvWLOvheWtz8u6XclaTFxedLunHPnsqueCy94sSftzjnn1pxAESLAKjFRvUWtv5gKVp+ZYGwmY//689mz7VLIM/jaF2HHt7ocsVsKZvZOM/vmWR77OTP7wCnuch2wYNIOHAR+yMyeC7wd+KM5+z4MvAu4rPz6gcXE5Um7c86dy2p9cNmV3Y7COeecW3JSQKAI0piw2aQ2UCTtR5stNkw/TSMdZn/WJnvpq2FsIzx8L9zzFdRpdzlydzqStkl6RNInJD0s6dOSauW+2yVtL7c/LOkuSQ9Jev+c43dIer+ke8oK+JXl7TdL+lC5/SZJD0q6X9KXJSXArwI3SbpP0k1zYzKze81sd/ntQ0BVUippMzBoZl8zMwP+EHjDYn7e6Gx+Sc4555xzzjnX6wIlUEkJOuPHkvaD9YztwV522MW0p55inxnnbf8e+M4j8K1vMHhgvMtRrzI3XvFSYGyJH/UQn3/0q6e5zxXAO8zsDkkfA94N/Na8+7zXzA5LCoEvSrrWzB4o9x00sxdIejfwS8A75x17K/AaM3ta0rCZtSTdCmw3s/ecJrYfAe4xs6ak84Fdc/btAs4/zfEn8Eq7c84555xzbk0KlUIaErY7x5L23TNQq0wTNkU7y9kzXq7XftGV8F2vZGLThV2M2C3CTjO7o9z+OPDyBe7zZkn3APcCVwNz57p/pvz3bmDbAsfeAdwm6V1AeKZBSboa+A3gZ870mNPxSrtzzjnnnHNuTQqDhHY1Jex06BsskvYD4xmdapvh8SYZcPTIfjobzieSYGgUi+LuBr3anL4ivlzsVN9Luoiign69mR2RdBtQmXOXZvlvxgJ5sZn9rKQXAzcCd0t64ekCkrQF+Czwk2b2RHnz08CWOXfbUt52xrzS7pxzzjnnnFuTAqXklYiwk1Erlzc9fLjJZLXCpvgw9XYNmzzCnk6ny5G6s3CBpJeU228FvjJv/yAwDYxL2gi8djEPLukSM7vTzG4FDgBbgUlg4CT3HwY+D9wyZwQAZrYHmJD0XWXX+J8E/nwxsXjS7pxzzjnnnFuTwiDBKgkBMFAu+TZzqMk+9THWN0WnHRJMHGZny9dqX4UeBX5e0sPACEWH9mPM7H6KYfGPAJ+kGO6+GB8sm9Q9CHwVuB/4EnDVQo3ogPcAlwK3lvvvk7Sh3Pdu4KPA48ATwF8vJhAfHu+cc84555xbkwLFUK0QYAzUipHR+eQUu7MRntu3k0po2FSHg1NHaddqxItbPtt1V8fM3jb/RjO7Yc72zQsdaGbb5mzfBdxQbt8G3FZuv3GBQw8D15/kMX8N+LWT7LsLuGahfWfCK+3OOeecc865NUmKsGqVwHIGy6Q9q09wuD7MeBowknZoTOXEU0fY7UPkXY/ypN0555xzzjm3JgVBhPXVCDCGqgkAjfo405Oj7Lcq60cz1GoTHT3Mzravz75amNkOMzvryvVq40m7c84555xzbk2SYtRXAzOG02Jm8OTkBIONDRywCkMDTYIYwn3j7O10aNn8huTOdZ8n7c4555xzzrk1SYpQWgEZY0mxlNvkxCRXjVY51BxgOoa+akB+cAq1mzzt1XbXgzxpd84555xzzq1JgSJUqaBAjIRFpX16cornXxIx0RxmbxYwPJxgzWn6Dh9hp89rdz3Ik3bnnHPOOefcmiQFKEkhEqNl5jM9Nc3AujrW3MChLGZ4Q0SFaYKnJ9jX6eC1dtdrPGl3zjnnnHPOrVmhEkhiap0WlWqVPM/ZM76bLbUxJvOYPIlIayLfcwgz42Dkq2KvZpI+Kumqszz29ZJuOcX+6yS97iT7XiXp7nJt97slvWLOvv8gaaekqbOJy5N255xzzjnn3JoVKMGSmLjRZMOW8wH4wt9/iedvTTmaDbK30aEyXKM6tZvalPCV2lc3M3unmX3zLI/9nJl94BR3uQ5YMGkHDgI/ZGbPBd4O/NGcfX8BvOhsYgJP2p1zzjnnnHNrWKgUKjFxs8n1r/geAD798T9n64Y2M50xDuTG4FgfERNc9GSTTT6vvedJ2ibpEUmfkPSwpE9LqpX7bpe0vdz+sKS7JD0k6f1zjt8h6f2S7ikr41eWt98s6UPl9pskPSjpfklflpQAvwrcJOk+STfNjcnM7jWz3eW3DwFVSWm572tmtudsf14f++Gcc84555xbs8IgoZ3GRDMNnn/jG/jT//ox7vz723l87z76wo3U+RZZ3xCD0XfYs3sfGvFa+6Jcse6lwNgSP+ohHj341dM9M/AOM7tD0seAdwO/Ne8+7zWzw5JC4IuSrjWzB8p9B83sBZLeDfwS8M55x94KvMbMnpY0bGYtSbcC283sPaeJ7UeAe8ysebof9Ex4pd0555xzzjm3ZoVBhTyNSJpNhi67gisvv4Jmvc6ffPYvuGxskClSjmYBYV9CUN/BxHil2yG7M7PTzO4otz8OvHyB+7xZ0j3AvcDVwNy57p8p/70b2LbAsXcAt0l6FxCeaVCSrgZ+A/iZMz3mdLzS7pxzzjnnnFuzAsVQTYkb4xDBK77vtTzyrUe5/S//gle/5qeYZoiD7SNcPDzE4J6dHMk3dDvk1eX0FfHlYqf6XtJFFBX0683siKTbgLlXZGar4BkL5MVm9rOSXgzcCNwt6YWnC0jSFuCzwE+a2RNn+oOcjlfanXPOOeecc2uWggiqKWGziSnnNa/+MSRx/5e/zO4De2jZOmaiDnllHSPVo4wNHux2yO7MXCDpJeX2W4GvzNs/CEwD45I2Aq9dzINLusTM7jSzW4EDwFZgEhg4yf2Hgc8Dt8wZAbAkPGl3zjnnnHPOrVmBIqxSI7CMIKszev5zuO7aq2k1W9zzT5+j3dpIW8YkA2A5wcyhbofszsyjwM9LehgYAT48d6eZ3U8xLP4R4JMUw90X44Nlk7oHga8C9wNfAq5aqBEd8B7gUuDWcv99kjYASPpNSbuAmqRdkt63mEB8eLxzzjnnnHNuzVI5PF5mxM1pWgZveP2N3Hv/g9z5d3/BW7e/i/FajcPAYJhg7cluh+zOTMfM3jb/RjO7Yc72zQsdaGbb5mzfBdxQbt8G3FZuv3GBQw8D15/kMX8N+LWT7Ps3wL9ZaN+Z8Eq7c84555xzbs2SIqxWI8CImg0a7YyfeMtbCYKAu26/g8F4N4dbwzSrEzSf+1NMrb+m2yE7dwJP2p1zzjnnnHNrVqAIahVkOX2dBjOdjA2bnsOLXnAtnXaHHfd/lpnmeqajDtHBNn3k3Q7ZnYaZ7TCzc+bqiiftzjnnnHPOuTVLCqDaBxj9eZ12nHFod8Qbfuh1AHzhs3/JxnSEpgJ2Te/GMl+n3fUWT9qdc84555xza1rQNwQY1WadaCjj4C74ibf8GGEY8vUvfY2La/uYUj+HKgdo7Eu7Ha5zJ/Ck3TnnnHPOObemqa+fwCCaqZOsz5g+CiPrL+dlL3oBeZ7ztS9+ljwcJa9NcviI9+p2vcWTduecc84559yaFvQXlfZKvUEw3KYVtzm0N+GNP1Qs3f0Xf/I5RqojBH05uuJId4N1bh5P2p1zzjnnnHNrWlgZgDCgNtNCCeTnzXBwF/z4m3+EJI659467icfHiZKQ2thEt8N1z4Kkj0q66iyPfb2kW06x/zpJrzvJvldJurtc2/1uSa8ob69J+rykRyQ9JOkDi43Lk3bnnHPOOefcmhZGVYhC4nqdKjFsrTNxyBhYdynf/ZLtmBn//Ld/zn7bzvT0+m6H654FM3unmX3zLI/9nJmdKqm+DlgwaQcOAj9kZs8F3g780Zx9v2VmVwLPB14m6bWLicuTduecc84559yaFirFKjHRTJ0+UpLRjEalxcF9Vd70Qz8AwBc+8zmGm+tpTVS6HK07HUnbysr1JyQ9LOnTkmrlvtslbS+3PyzprrLC/f45x++Q9H5J95SV8SvL22+W9KFy+02SHpR0v6QvS0qAXwVuknSfpJvmxmRm95rZ7vLbh4CqpNTMZszsS+V9WsA9wJbF/LzeZcE555xzzjm3pimIIE2IGnUqxASVDtMbZzi0K+WmN/4LfuGX/yMP/fMDDLUeJotb3Q53dZFeCowt8aMewuyrp7nPFcA7zOwOSR8D3g381rz7vNfMDksKgS9KutbMHij3HTSzF0h6N/BLwDvnHXsr8Boze1rSsJm1JN0KbDez95wmth8B7jGz5twbJQ0DPwT8zmmOP4FX2p1zzjnnnHNrWqAIq1SIZmbomDFKBZ1f5+ihnNrYNl7xshcD8JUv/C8G406Xo3VnaKeZ3VFufxx4+QL3ebOke4B7gauBuXPdP1P+ezewbYFj7wBuk/QuIDzToCRdDfwG8DPzbo+APwZ+18y+faaPB15pd84555xzzq1xUgxpTDjRoE7GpfTz9OgMM9UGhw7286YbX8VfffHLfPZTn+NVL3t1t8NdXU5fEV+2Zz7V95IuoqigX29mRyTdBsyd+zBbBc9YIC82s5+V9GLgRuBuSS88XUCStgCfBX7SzJ6Yt/sjwGNm9tune5z5vNLunHPOOeecW9OkCCoV0kaTNkYH0V8Laa2vc2gX/OgPv56+vhqP3v8wO/Z9p9vhujNzgaSXlNtvBb4yb/8gMA2MS9oILKr5m6RLzOxOM7sVOABsBSaBgZPcfxj4PHDLnBEAs/t+DRgCfmExMczypN0555xzzjm3pgWKsGqFqNUmzo091mQdNYLzGhw6mJGOXcCrvue7ALj9b77c5WjdGXoU+HlJDwMjwIfn7jSz+ymGxT8CfJJiuPtifLBsUvcg8FXgfuBLwFULNaID3gNcCtxa7r9P0oay+v5eiqH595S3z58/f0o+PN4555xzzjm3pkkBVKuQddjUErsqTbZqiL6RSaZqdQ4fHuKmG7+fP/vrf+Af/u7LmBmSuh22O7WOmb1t/o1mdsOc7ZsXOtDMts3Zvgu4ody+Dbit3H7jAoceBq4/yWP+GvBrJ4n1WZ1MnrQ755xzzjnn1jxV+oqkvQ07KzBhGWN9MUfWzXBoVz9vuPF1vO4vvsj5L3qlJ+2up/jweOecc84559yap75+yHIqjQaDROyxOuuoEW1os+9Qm3jofP7Xr/9f/B8vfQ5B4GlSLzOzHWZ2TbfjWCl+NjrnnHPOOefWPNUGACOfGuc8VZkmIyFmYAQma3UOT45QS1OSrN7tUJ07gSftzjnnnHPOuTVPff2AYTPTbFBKABywFhsHUhojMxzYJbj8e5js29ztUJ07wbIl7ZI+Jml/2W1v9rZRSX8n6bHy35Hlen7nnHPOOeecmxX0DYHl5DOTRArYoAr7rckYNdL1GXsOt8i85ZfrQctZab8N+IF5t90CfNHMLgO+WH7vnHPOOeecc8sq7BsGwKbGAThPFTKMjonBYTFRneHo3i4G6NxJLFvSbmZfpmiJP9cPA39Qbv8B8Iblen7nnHPOOeecm6W+AQgC8plJAIaVUCVkrzXZMlilMVxn/668y1E690wrPad9o5ntKbf3AhtX+Pmdc84555xz56AgrkIUkk9PHrttsyqM02ZAKdURY9fRJuZ5+zlLUtjtGBbStUZ0ZmaAnWy/pJ+WdJekuw4cOLCCkTnnnHPOOefWGsUViCKoTx27bbMqAExZzshwwHhlhvp40q0Q3SJJ+jNJd0t6qMwff1bSB+fsv1nSh8rtt0n6uqT7JP3X2QRd0pSk/0/S/cBLJN0q6Z8lPSjpI5JU3u96SQ+Ux39wtnebpLD8/p/L/T+z1D/nSift+yRtBij/3X+yO5rZR8xsu5ltX79+/YoF6JxzzjnnnFt7gqQKUYzNSdpThaxTwl5rsnWwSnOgweTRtItRrj6SbDm+zvDpf8rMXghsB/4V8FngX8zZfxPwPyU9p9x+mZldB2TAj5f36QPuNLPnmdlXgA+Z2fXlOvBV4AfL+/0P4GfmHD/rHcC4mV0PXA+8S9JFi/olnsZKJ+2fA95ebr8d+PMVfn7nnHPOOefcOUhxFcUx+cwU7bxx7PbNqtIiJwpjtlwJ1edMdDFKt0j/qqyQfw3YClwEfFvSd0kaA64E7gBeCbwQ+GdJ95XfX1w+Rgb86ZzH/D5Jd0r6BvAK4GpJw8CAmf1TeZ9Pzrn/q4GfLB/3TmAMuGwpf8hlW9NA0h8DNwDrJO0CfgX4APAnkt4BPAm8ebme3znnnHPOOedmKYoJK/0Ek9NMtvcxml4IwBgJMeKoddhcqzJ1msdxJzIzdeN5Jd0AfD/wEjObkXQ7UAH+J0We+QjwWTOzcoj7H5jZv13goRpmlpWPWQF+H9huZjslva98zFOGAvxLM/ubZ/9TLWw5u8e/xcw2m1lsZlvM7L+b2SEze6WZXWZm329m87vLO+ecc84559zy2LyRuJ7ReupbdKwJQCCxWVUOWosLGaav0ZUc1C3eEHCkTNivBL6rvP2zFKuWvYUigYdiufEflbQBQNKopAsXeMzZBP2gpH7gRwHM7CgwKenF5f4fm3PM3wA/JykuH/tySX1L8QPO6lojOuecc84555xbSbZpI2GYEn17J5Od482uZxvS7bXGyQ51vecLQCTpYYoR3V8DMLMjwMPAhWb29fK2bwL/L/C3kh4A/g7YPP8By+T8vwEPUiTj/zxn9zuA/1YOg+8DxsvbPwp8E7inbE73X1niEe3LNjzeOeecc84553qJ0iq2fpTaU0c42jzIYLSRUDF9ihgiZo8n7auGmTWB155k3w8ucNungE8tcHv/vO//X4oEf76HzOxaAEm3AHeV98+Bf1d+LQuvtDvnnHPOOefOCWE6QLZhgLgTEjz5NFPzqu0zZMzEXQzQ9bIby+XeHgS+G/i1lXpiT9qdc84555xz54SoOgaDA2RV6PvOYabzI2TWAWCDUkLERMVTJPdMZvYpM7vOzK4xsxvN7MDpj1oafkY655xzzjnnzglBUiFuJzQvGKGy6wg2M8N0dgiASAEvCIbZOJl3OUrnTuRJu3POOeecc+7ccP5W4k4Mg33k2Qz9Tx5lOjtIXqz4xYBivHe86zWetDvnnHPOOefODedtJUiqJDPQHgipPnGA3PJj1XbnepEn7c4555xzzrlzQxTBBdtI906RXbgJ9u6iMmVMZYfIzYfFu97kSbtzzjnnnHPu3HHRpQQmor4ROtkk/d8+Sm4dZrIj3Y7MuQV50u6cc84555w7d4yth4Eh0sNtsk1j6LFHSIIaU9kBzKzb0Tn3DJ60O+ecc845584tF11CdGQCnbeVzqGn6T9oZNamnh/tdmTOPYMn7c4555xzzrlzy4UXg0SajGDKAxVULAAAEEZJREFUiR5/nDioMNk5gOHVdtdbPGl3zjnnnHPOnVtqfbBxM9Hew3DBVjrfup9+jdGxJiStbkfn3Ak8aXfOOeecc86dey66FNWniTZsI5+eINq5lzioQOBd5F1v8aTdOeecc845d+45byvECUkWozSl8+jdrI8vRY1qtyNz7gSetDvnnHPOOefOPeWa7dq7m3DbVeTffoysNdXtqJx7Bk/anXPOOeecc+embZdA1iEe2oCynNZjd3U7IueewZN255xzzjnn3Llp3QYYGCSYniIa3Ej26DdAWbejcu4EnrQ755xzzjnnzl0XXQqHDhBdeCXB0/tJ8kPdjsi5E3jS7pxzzjnnnDt3XXgxAGEck8RjRE9Odjkg507kSbtzzjnnnHPu3FXrg03nwZGDxJc9F4sr3Y7IuRN40u6cc84555w7t227BGam4blXUz///G5H49wJPGl3zjnnnHPOndvOvwDiBHY80e1InHsGT9qdc84555xz57ZyzXZ2PomyTrejce4EnrQ755xzzjnnXLlme+XAvm5H4twJPGl3zjnnnHPOuXUbYN0GlOfdjsS5E3jS7pxzzjnnnHMAr3wtM+dt7XYUzp3Ak3bnnHPOOeecc65HedLunHPOOeecc871KE/anXPOOeecc865HuVJu3POOeecc84516M8aXfOOeecc84553qUJ+3OOeecc84551yP8qTdOeecc84555zrUZ60O+ecc84555xzPcqTduecc84555xzrkd50u6cc84555xzzvUoT9qdc84555xzzrke5Um7c84555xzzjnXozxpd84555xzzjnnepQn7c4555xzzjnnXI/ypN0555xzzjnnnOtRnrQ755xzzjnnnHM9ypN255xzzjnnnHOuR3nS7pxzzjnnnHPO9SiZWbdjOC1JB4Anux3HWVoHHOx2EAvoxbh6MSbwuBajF2OC3oyrF2OC3oyrF2MCj2sxejEm6M24ejEm6M24ejEm8LgWoxdjgt6N60IzW9/tINzKWxVJ+2om6S4z297tOObrxbh6MSbwuBajF2OC3oyrF2OC3oyrF2MCj2sxejEm6M24ejEm6M24ejEm8LgWoxdjgt6Ny527fHi8c84555xzzjnXozxpd84555xzzjnnepQn7cvvI90O4CR6Ma5ejAk8rsXoxZigN+PqxZigN+PqxZjA41qMXowJejOuXowJejOuXowJPK7F6MWYoHfjcucon9PunHPOOeecc871KK+0O+ecc84555xzPcqT9mUk6QckPSrpcUm39EA8WyV9SdI3JT0k6f/sdkxzSQol3SvpL7sdyyxJw5I+LekRSQ9LekkPxPSL5d/vQUl/LKnSpTg+Jmm/pAfn3DYq6e8kPVb+O9IDMX2w/Ps9IOmzkoZXMqaTxTVn37+WZJLW9Upckv5l+Tt7SNJvdjsmSddJ+pqk+yTdJelFKxzTgq+dPXC+nyyurp7zp3uv6cY5f6qYuny+n+xv2LVzXlJF0tcl3V/G9P7y9osk3Vl+pvmUpGSlYjpNXJ8oP2s9WL5+xL0Q15z9vytpqhdiUuE/SPqWis80/6pH4nqlpHvK8/0rki5dybjKGE74DNrt8925ZzAz/1qGLyAEngAuBhLgfuCqLse0GXhBuT0AfKvbMc2L7/8CPgn8ZbdjmRPTHwDvLLcTYLjL8ZwPfAeolt//CXBzl2L5HuAFwINzbvtN4JZy+xbgN3ogplcDUbn9Gysd08niKm/fCvwN8CSwrhfiAr4P+HsgLb/f0AMx/S3w2nL7dcDtKxzTgq+dPXC+nyyurp7zp3qv6dY5f4rfVbfP95PF1bVzHhDQX27HwJ3Ad5XvNz9W3v5fgJ9b4d/VyeJ6XblPwB/3Slzl99uBPwKmeiEm4P8A/hAIyn0rfb6fLK5vAc8pb383cNtKxlU+7wmfQbt9vvuXf83/8kr78nkR8LiZfdvMWsD/BH64mwGZ2R4zu6fcngQepkgCu07SFuBG4KPdjmWWpCGKBOK/A5hZy8yOdjWoQgRUJUVADdjdjSDM7MvA4Xk3/zDFhQ7Kf9/Q7ZjM7G/NrFN++zVgy0rGdLK4Sv8Z+DdAV5qLnCSunwM+YGbN8j77eyAmAwbL7SFW+Jw/xWtnt8/3BePq9jl/mvearpzzp4ip2+f7yeLq2jlvhdnKcFx+GfAK4NPl7d043xeMy8z+qtxnwNdZ+fN9wbgkhcAHKc73FXWKv+HPAb9qZnl5v5U+308WV1df4+d/BpUkuny+OzefJ+3L53xg55zvd9EjCTKApG3A8ymucvaC36Z4Y8u7HMdcFwEHgP9RDpn6qKS+bgZkZk8DvwU8BewBxs3sb7sZ0zwbzWxPub0X2NjNYBbwU8BfdzsIAEk/DDxtZvd3O5Z5Lge+uxwW+L8lXd/tgIBfAD4oaSfF+f9vuxXIvNfOnjnfT/Ga3tVzfm5cvXLOz/td9cz5Pi+uX6CL53w5VPg+YD/wdxQjB4/OuRjUlc808+Myszvn7IuBnwC+0CNxvQf43JzXiF6I6RLgpnLKxV9LuqxH4non8FeSdlH8DT+wwmH9Nid+Bh2jB8535+bypP0cJKkf+FPgF8xsogfi+UFgv5nd3e1Y5okohul+2MyeD0xTDIHtGhVzZn+Y4oLCeUCfpLd1M6aTKasePbM8haT3Ah3gEz0QSw34d8Ct3Y5lAREwSjFk8f8G/qSsOnTTzwG/aGZbgV+kHP2y0k712tnN8/1kcXX7nJ8bVxlH18/5BX5XPXG+LxBXV895M8vM7DqKqvWLgCtX8vlPZn5ckq6Zs/v3gS+b2T/2QFzfA7wJ+L2VjuUUMV0DpEDDzLYD/w34WI/E9YvA68xsC/A/gP+0UvH08GdQ507gSfvyeZpi7t6sLeVtXVVeif5T4BNm9plux1N6GfB6STsophG8QtLHuxsSUFxZ3TXnSv6nKZL4bvp+4DtmdsDM2sBngJd2Oaa59knaDFD+u6JD705G0s3ADwI/XiZX3XYJxYWX+8vzfgtwj6RNXY2qsAv4TDmM8esUlYcVb5I3z9spznWA/0WRRKyok7x2dv18P9lrerfP+QXi6vo5f5LfVdfP95PE1fVzHqCcEvYl4CXAcDktC7r8mWZOXD8AIOlXgPUU85K7Zk5c3wdcCjxenu81SY93OaYfoDzfy12fBa7tRkxwQlyvBZ4357PWp1jZzzXP+AwK/A49dL47B560L6d/Bi4ru08mwI8Bn+tmQGX14L8DD5vZil3FPB0z+7dmtsXMtlH8nv7BzLpePTazvcBOSVeUN70S+GYXQ4JiWPx3SaqVf89XUsyB7BWfo/iwSfnvn3cxFqBYxYFi2NvrzWym2/EAmNk3zGyDmW0rz/tdFM2o9nY5NIA/o/jAiaTLKRowHuxmQBTzG7+33H4F8NhKPvkpXju7er6fLK5un/MLxdXtc/4Uf8M/o4vn+yni6to5L2m9yhUHJFWBV1G8z3wJ+NHybt043xeK6xFJ7wReA7xldq52D8R1t5ltmnO+z5jZinVEP9nvijnnO8X59a2ViukUcT0MDJX//5hz24o4yWfQH6fL57tzz2A90A1vrX5RdDT9FsVcsPf2QDwvpxi++QBwX/n1um7HNS/GG+it7vHXAXeVv7M/A0Z6IKb3U7z5PkjRlTbtUhx/TDGvvk3xAfwdFPPAvkjxAfPvgdEeiOlxiv4Ss+f8f+mF39W8/TvoTvf4hX5fCfDx8vy6B3hFD8T0cuBuilU47gReuMIxLfja2QPn+8ni6uo5fybvNSt9zp/id9Xt8/1kcXXtnKeovt5bxvQgcGt5+8UUjd4ep6j+r+h7zyni6lB8zpr9/d3aC3HNu89Kd48/2e9qGPg88A3gnygq3L0Q178oY7ofuB24eCXjmhPfDRzvHt/V892//Gv+l8x6YaSoc84555xzzjnn5vPh8c4555xzzjnnXI/ypN0555xzzjnnnOtRnrQ755xzzjnnnHM9ypN255xzzjnnnHOuR3nS7pxzzjnnnHPO9ShP2p1zzjnnnHPOuR7lSbtzzrkVJem9kh6S9ICk+yS9eIWed5ukt875fruk312m5/oFST9Zbt8uafsSPOYJ8Z/kPomkL0uKnu3zOeecc643eNLunHNuxUh6CfCDwAvM7Frg+4Gdz/IxzzRB3QYcS3rN7C4z+1fP5rlPEc9PAZ9c4ofexpz4F2JmLeCLwE1L/NzOOeec6xJP2p1zzq2kzcBBM2sCmNlBM9sNIOl6SV+VdL+kr0sakFSR9D8kfUPSvZK+r7zvzZI+J+kfgC9K6pP0sfK4eyX98ALP/QHgu8vq/i9KukHSX5aP9z5JfyDpHyU9KemNkn6zfN4vSIrL+71Q0v+WdLekv5G0eYHneQVwj5l15tz2E+XzPijpReVjLRhzWVH/R0n3lF8vPUn8V5fH3leOWrisvN+fAT9+tn8g55xzzvUWT9qdc86tpL8Ftkr6lqTfl/S9UAzrBj4F/J9m9jyKCnwd+HnAzOy5wFuAP5BUKR/rBcCPmtn3Au8F/sHMXgR8H/BBSX3znvsW4B/N7Doz+88LxHYJRcL9euDjwJfK560DN5aJ+++Vz/lC4GPAf1jgcV4G3D3vtpqZXQe8uzyOU8S8H3iVmb2AomI+O4R/fvw/C/xO+bjbgV3l/R4Erl8gLuecc86tQj7nzTnn3IoxsylJLwS+myJR/ZSkWyiS3D1m9s/l/SYAJL2cIlHGzB6R9CRweflwf2dmh8vtVwOvl/RL5fcV4ALg4UWE99dm1pb0DSAEvlDe/g2KoelXANcAfyeJ8j57FniczQs87x+XP8OXJQ1KGj5FzLuBD0m6Dsjm/Lzz/RPwXklbgM+Y2WPlc2SSWpIGzGxyET+/c84553qQJ+3OOedWlJllwO3A7WWC/HaeWZk+E9NztgX8iJk9+ixCmx2yn0tqm5mVt+cU75cCHjKzl5zmceoUCfhctsD3C8Ys6X3APuB5FCPiGgs9iZl9UtKdwI3AX0n6GTP7h3J3erLjnHPOObe6+PB455xzK0bSFXPmXgNcBzwJPApslnR9eb+BsqHbP1LOz5Z0OUUleqHE/G+Af6myBC7p+QvcZxIYeBbhPwqsL5vpISmWdPUC93sYuHTebTeVx7wcGDez8VPEPEQx6iAHfoKiov+M+CVdDHzbzH4X+HPg2vL2MYq+Ae1n8bM655xzrkd4pd0559xK6gd+rxwe3gEeB37azFqSbir3VSmq1d8P/D7w4bIi3wFuNrNmmefO9e+B3wYekBQA36HoUj/XA0Am6X7gNuDexQRexvijwO9KGqJ4D/1t4KF5d/1r4I/m3daQdC8QU3SWP1XMvw/8qYol477A8REF8+NPKRrctYG9wH8s7/d9wOcX87M555xzrnfp+Og/55xzzi0FSZ8F/s3sPPMVfu7PALeY2bdW+rmdc845t/R8eLxzzjm39G6haEi3osou/H/mCbtzzjm3dnil3TnnnHPOOeec61FeaXfOOeecc84553qUJ+3OOeecc84551yP8qTdOeecc84555zrUZ60O+ecc84555xzPcqTduecc84555xzrkf9/4seGtHlWd6LAAAAAElFTkSuQmCC\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "fig, ax = plt.subplots(1, figsize=(15, 8))\n",
     "color = plt.cm.rainbow(np.linspace(0, 1, len(tempo_curves)))\n",
@@ -1518,7 +1284,8 @@
     "plt.legend(frameon=False, bbox_to_anchor = (1.15, .9))\n",
     "plt.grid(axis='x')\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 0b4820cc..9b1bd0b3 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -29,9 +29,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = "1.4.1"  # pkg_resources.get_distribution("partitura").version
+version = "1.5.0"  # pkg_resources.get_distribution("partitura").version
 # The full version, including alpha/beta/rc tags.
-release = "1.4.1"
+release = "1.5.0"
 
 # # The full version, including alpha/beta/rc tags
 # release = pkg_resources.get_distribution("partitura").version
diff --git a/partitura/__init__.py b/partitura/__init__.py
index c624b578..c9efb295 100644
--- a/partitura/__init__.py
+++ b/partitura/__init__.py
@@ -15,6 +15,7 @@
 from .io.importmei import load_mei
 from .io.importkern import load_kern
 from .io.importmusic21 import load_music21
+from .io.importdcml import load_dcml
 from .io.importmidi import load_score_midi, load_performance_midi, midi_to_notearray
 from .io.exportmidi import save_score_midi, save_performance_midi
 from .io.importmatch import load_match
@@ -22,7 +23,8 @@
 from .io.importnakamura import load_nakamuramatch, load_nakamuracorresp
 from .io.importparangonada import load_parangonada_csv
 from .io.exportparangonada import save_parangonada_csv, save_csv_for_parangonada
-from .io.exportaudio import save_wav
+from .io.exportaudio import save_wav, save_wav_fluidsynth
+from .io.exportmei import save_mei
 from .display import render
 from . import musicanalysis
 from .musicanalysis import make_note_features, compute_note_array, full_note_array
@@ -56,9 +58,12 @@
     "save_performance_midi",
     "load_match",
     "save_match",
+    "load_dcml",
     "load_nakamuramatch",
     "load_nakamuracorresp",
     "load_parangonada_csv",
     "save_parangonada_csv",
+    "save_wav",
+    "save_wav_fluidsynth",
     "render",
 ]
diff --git a/partitura/directions.py b/partitura/directions.py
index b52024f6..d0428911 100644
--- a/partitura/directions.py
+++ b/partitura/directions.py
@@ -13,6 +13,7 @@
 
 import re
 import warnings
+from partitura.utils.globals import UNABBREVS
 
 try:
     from lark import Lark
@@ -51,29 +52,6 @@ def join_items(items):
     )
 
 
-UNABBREVS = [
-    (re.compile(r"(crescendo|cresc\.?)"), "crescendo"),
-    (re.compile(r"(smorzando|smorz\.?)"), "smorzando"),
-    (re.compile(r"(decrescendo|(decresc|decr|dimin|dim)\.?)"), "diminuendo"),
-    (re.compile(r"((acceler|accel|acc)\.?)"), "accelerando"),
-    (re.compile(r"(ritenente|riten\.?)"), "ritenuto"),
-    (re.compile(r"((ritard|rit)\.?)"), "ritardando"),
-    (re.compile(r"((rallent|rall)\.?)"), "rallentando"),
-    (re.compile(r"(dolciss\.?)"), "dolcissimo"),
-    (re.compile(r"((sosten|sost)\.?)"), "sostenuto"),
-    (re.compile(r"(delicatiss\.?)"), "delicatissimo"),
-    (re.compile(r"(leggieramente|leggiermente|leggiero|legg\.?)"), "leggiero"),
-    (re.compile(r"(leggierissimo|(leggieriss\.?))"), "leggierissimo"),
-    (re.compile(r"(scherz\.?)"), "scherzando"),
-    (re.compile(r"(tenute|ten\.?)"), "tenuto"),
-    (re.compile(r"(allegretto)"), "allegro"),
-    (re.compile(r"(espress\.?)"), "espressivo"),
-    (re.compile(r"(ligato)"), "legato"),
-    (re.compile(r"(ligatissimo)"), "legatissimo"),
-    (re.compile(r"((rinforz|rinf|rfz|rf)\.?)"), "rinforzando"),
-]
-
-
 def unabbreviate(s):
     for p, v in UNABBREVS:
         if p.match(s):
diff --git a/partitura/io/__init__.py b/partitura/io/__init__.py
index 4ad2cd47..9d227909 100644
--- a/partitura/io/__init__.py
+++ b/partitura/io/__init__.py
@@ -12,10 +12,12 @@
 from .importmatch import load_match
 from .importmei import load_mei
 from .importkern import load_kern
+from .exportkern import save_kern
 from .importparangonada import load_parangonada_csv
 from .exportparangonada import save_parangonada_csv
 from .importmusic21 import load_music21
-
+from .exportmei import save_mei
+from .importdcml import load_dcml
 from partitura.utils.misc import (
     deprecated_alias,
     deprecated_parameter,
diff --git a/partitura/io/exportaudio.py b/partitura/io/exportaudio.py
index 563b3009..cdf1a11c 100644
--- a/partitura/io/exportaudio.py
+++ b/partitura/io/exportaudio.py
@@ -1,8 +1,7 @@
 #!/usr/bin/python
 # -*- coding: utf-8 -*-
 """
-This module contains methods to synthesize Partitura object to wav using
-additive synthesis
+This module contains methods to synthesize a Partitura ScoreLike object to wav.
 """
 from typing import Union, Optional, Callable, Dict, Any
 import numpy as np
@@ -13,10 +12,18 @@
 from partitura.performance import PerformanceLike
 
 from partitura.utils.synth import synthesize, SAMPLE_RATE, A4
+from partitura.utils.fluidsynth import (
+    synthesize_fluidsynth,
+    DEFAULT_SOUNDFONT,
+    HAS_FLUIDSYNTH,
+)
 
 from partitura.utils.misc import PathLike
 
-__all__ = ["save_wav"]
+__all__ = [
+    "save_wav",
+    "save_wav_fluidsynth",
+]
 
 
 def save_wav(
@@ -89,8 +96,76 @@ def save_wav(
         bpm=bpm,
     )
 
+    if out is not None:
+        # convert to 16bit integers (save as PCM 16 bit)
+        # (some DAWs cannot load audio files that are float64,
+        # e.g., Logic)
+        amplitude = np.iinfo(np.int16).max
+        if abs(audio_signal).max() <= 1:
+            # convert to 16bit integers (save as PCM 16 bit)
+            amplitude = np.iinfo(np.int16).max
+            audio_signal *= amplitude
+        wavfile.write(out, samplerate, audio_signal.astype(np.int16))
+
+    else:
+        return audio_signal
+
+
+def save_wav_fluidsynth(
+    input_data: Union[ScoreLike, PerformanceLike, np.ndarray],
+    out: Optional[PathLike] = None,
+    samplerate: int = SAMPLE_RATE,
+    soundfont: PathLike = DEFAULT_SOUNDFONT,
+    bpm: Union[float, np.ndarray, Callable] = 60,
+) -> Optional[np.ndarray]:
+    """
+    Export a score (a `Score`, `Part`, `PartGroup` or list of `Part` instances),
+    a performance (`Performance`, `PerformedPart` or list of `PerformedPart` instances)
+    as a WAV file using fluidsynth
+
+    Parameters
+    ----------
+    input_data : ScoreLike, PerformanceLike or np.ndarray
+        A partitura object with note information.
+    out : PathLike or None
+        Path of the output Wave file. If None, the method outputs
+        the audio signal as an array (see `audio_signal` below).
+    samplerate: int
+        The sample rate of the audio file in Hz. The default is 44100Hz.
+    soundfont : PathLike
+        Path to the soundfont in SF2/SF3 format for fluidsynth.
+    bpm : float, np.ndarray, callable
+        The bpm to render the output (if the input is a score-like object).
+        See `partitura.utils.music.performance_notearray_from_score_notearray`
+        for more information on this parameter.
+
+    Returns
+    -------
+    audio_signal : np.ndarray
+       Audio signal as a 1D array. Only returned if `out` is None.
+    """
+
+    if not HAS_FLUIDSYNTH:
+        raise ImportError("Fluidsynth is not installed!")
+
+    audio_signal = synthesize_fluidsynth(
+        note_info=input_data,
+        samplerate=samplerate,
+        soundfont=soundfont,
+        bpm=bpm,
+    )
+
     if out is not None:
         # Write audio signal
-        wavfile.write(out, samplerate, audio_signal)
+
+        # convert to 16bit integers (save as PCM 16 bit)
+        # (some DAWs cannot load audio files that are float64,
+        # e.g., Logic)
+        amplitude = np.iinfo(np.int16).max
+        if abs(audio_signal).max() <= 1:
+            # convert to 16bit integers (save as PCM 16 bit)
+            amplitude = np.iinfo(np.int16).max
+            audio_signal *= amplitude
+        wavfile.write(out, samplerate, audio_signal.astype(np.int16))
     else:
         return audio_signal
diff --git a/partitura/io/exportkern.py b/partitura/io/exportkern.py
new file mode 100644
index 00000000..5c6500b2
--- /dev/null
+++ b/partitura/io/exportkern.py
@@ -0,0 +1,344 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+This module contains methods for exporting Kern files.
+"""
+import math
+from collections import defaultdict
+
+import numpy
+
+import partitura.score as spt
+from operator import itemgetter
+from typing import Optional
+import numpy as np
+import warnings
+from partitura.utils import partition, iter_current_next, to_quarter_tempo
+from partitura.utils.misc import deprecated_alias, PathLike
+
+__all__ = ["save_kern"]
+
+
+ACC_TO_SIGN = {
+    0: "n",
+    -1: "-",
+    1: "#",
+    -2: "--",
+    2: "##",
+}
+
+# Kern notes encoding has a dedicated octave for each note.
+KERN_NOTES = {
+    ("C", 3): "C",
+    ("D", 3): "D",
+    ("E", 3): "E",
+    ("F", 3): "F",
+    ("G", 3): "G",
+    ("A", 3): "A",
+    ("B", 3): "B",
+    ("C", 4): "c",
+    ("D", 4): "d",
+    ("E", 4): "e",
+    ("F", 4): "f",
+    ("G", 4): "g",
+    ("A", 4): "a",
+    ("B", 4): "b",
+}
+
+KERN_DURS = {
+    "maxima": "000",
+    "long": "00",
+    "breve": "0",
+    "whole": "1",
+    "half": "2",
+    "quarter": "4",
+    "eighth": "8",
+    "16th": "16",
+    "32nd": "32",
+    "64th": "64",
+    "128th": "128",
+    "256th": "256",
+}
+
+KEYS = ["f", "c", "g", "d", "a", "e", "b"]
+
+
+class KernExporter(object):
+    """
+    Class for exporting a partitura score to Kern format.
+
+    Parameters
+    ----------
+    part: spt.Part
+        Part to export to Kern format.
+    """
+
+    def __init__(self, part):
+        self.part = part
+        note_array = part.note_array(include_staff=True)
+        num_measures = len(part.measures)
+        num_notes = len(part.notes)
+        num_rests = len(part.rests)
+        self.unique_voc_staff = np.unique(note_array[["voice", "staff"]], axis=0)
+        self.vocstaff_map_dict = {
+            f"{self.unique_voc_staff[i][0]}-{self.unique_voc_staff[i][1]}": i
+            for i in range(self.unique_voc_staff.shape[0])
+        }
+        # Part elements is really the maximum number of lines we could have in the kern file
+        # we add some to account for the **kern and the *- encoding at beginning and end of file and also tandem elements
+        # that might be added. We also add the number of measures to account for the measure encoding
+        total_elements_ish = num_measures + num_notes + num_rests + 2 + 10
+        self.out_data = np.empty(
+            (total_elements_ish, len(self.unique_voc_staff)), dtype=object
+        )
+        self.unique_times = np.array([p.t for p in part._points])
+        # Fill all values with the "." character to filter afterwards
+        self.out_data.fill(".")
+        self.out_data[0] = "**kern"
+        self.out_data[-1] = "*-"
+        # Add the staff element to the second line
+        for i in range(self.unique_voc_staff.shape[0]):
+            self.out_data[1, i] = f"*staff{self.unique_voc_staff[i][1]}"
+        self.prev_note_time = None
+        self.prev_note_col_idx = None
+        self.prev_note_row_idx = None
+
+    def parse(self):
+        """
+        Parse the partitura score to Kern format.
+
+        This method iterates over all elements in the partitura score and converts them to Kern format.
+        To better process the elements, the method first groups them by start time and then processes them in order.
+        It first finds notes and then processes structural elements (clefs, time signatures, etc.) and finally measures.
+
+        Returns
+        -------
+        self.out_data: np.ndarray
+            Kern file as a numpy array of strings.
+        """
+        row_idx = 2
+        for start_time in self.unique_times:
+            end_time = start_time + 1
+            # Get all elements starting at this time
+            elements_starting = np.array(
+                list(self.part.iter_all(start=start_time, end=end_time)), dtype=object
+            )
+            # Find notes
+            note_mask = np.array(
+                [isinstance(el, spt.GenericNote) for el in elements_starting]
+            )
+            if np.any(~note_mask):
+                bar_mask = np.array(
+                    [
+                        isinstance(el, spt.Measure)
+                        for el in elements_starting[~note_mask]
+                    ]
+                )
+                tandem_mask = ~bar_mask
+                structural_elements = elements_starting[~note_mask]
+                structural_elements = np.hstack(
+                    (structural_elements[tandem_mask], structural_elements[bar_mask])
+                )
+            else:
+                structural_elements = elements_starting[~note_mask]
+            # Put structural elements first (start with tandem elements, then measure elements, then notes and rests)
+            elements_starting = np.hstack(
+                (structural_elements, elements_starting[note_mask])
+            )
+            for el in elements_starting:
+                add_row = True
+                if isinstance(el, spt.GenericNote):
+                    self._handle_note(el, row_idx)
+                elif isinstance(el, spt.Clef):
+                    # Apply clef to all voices of the same staff
+                    currect_staff = el.staff
+                    for staff_idx in range(self.unique_voc_staff.shape[0]):
+                        if self.unique_voc_staff[staff_idx][1] == currect_staff:
+                            kern_el = f"*clef{el.sign.upper()}{el.line}"
+                            self.out_data[row_idx, staff_idx] = kern_el
+                elif isinstance(el, spt.Tempo):
+                    # Apply tempo to all splines
+                    kern_el = f"*MM{to_quarter_tempo(el.qpm)}"
+                    self.out_data[row_idx] = kern_el
+                elif isinstance(el, spt.Measure):
+                    # Apply measure to all splines
+                    kern_el = f"={el.number}"
+                    self.out_data[row_idx] = kern_el
+                elif isinstance(el, spt.TimeSignature):
+                    # Apply element to all splines
+                    kern_el = f"*M{el.beats}/{el.beat_type}"
+                    self.out_data[row_idx] = kern_el
+                elif isinstance(el, spt.KeySignature):
+                    # Apply element to all splines
+                    if el.fifths < 0:
+                        alters = "-".join(KEYS[: el.fifths])
+                    elif el.fifths > 0:
+                        alters = "#".join(KEYS[: el.fifths])
+                    else:
+                        alters = ""
+                    kern_el = f"*k[{alters}]"
+                    self.out_data[row_idx] = kern_el
+                else:
+                    add_row = False
+                    warnings.warn(f"Element {el} is not supported for kern export yet.")
+                if add_row:
+                    row_idx += 1
+        return self.out_data
+
+    def trim(self, data):
+        # if an entire row is filled with "." elements remove it.
+        out_data = data[~np.all(data == ".", axis=1)]
+        return out_data
+
+    def sym_dur_to_kern(self, symbolic_duration: dict) -> str:
+        kern_base = KERN_DURS[symbolic_duration["type"]]
+        dots = (
+            "." * symbolic_duration["dots"]
+            if "dots" in symbolic_duration.keys()
+            else ""
+        )
+        if "actual_notes" in symbolic_duration.keys() and "normal_notes":
+            kern_base = (
+                int(kern_base)
+                * symbolic_duration["actual_notes"]
+                / symbolic_duration["normal_notes"]
+            )
+            kern_base = str(kern_base)
+        return kern_base + dots
+
+    def duration_to_kern(self, element: spt.GenericNote) -> str:
+        if isinstance(element, spt.GraceNote):
+            if element.grace_type == "acciaccatura":
+                return "p"
+            else:
+                return "q"
+        else:
+            if "type" not in element.symbolic_duration.keys():
+                warnings.warn(f"Element {element} has no symbolic duration type")
+                return "4"
+            return self.sym_dur_to_kern(element.symbolic_duration)
+
+    def pitch_to_kern(self, element: spt.GenericNote) -> str:
+        """
+        Transform a Partitura Note object to a kern note string (only pitch).
+
+        To encode pitch correctly in kern we need to take into account that the octave
+        duplication of the step in kern can either move the note up or down an octave
+
+        """
+        if isinstance(element, spt.Rest):
+            return "r"
+        step, alter, octave = element.step, element.alter, element.octave
+        # Check if we need to have duplication of the step character
+        if octave > 4:
+            multiply_character = octave - 3
+            octave = 4
+        elif octave < 3:
+            multiply_character = 4 - octave
+            octave = 3
+        else:
+            multiply_character = 1
+        # Fetch the correct string for the step and multiply it if needed
+        kern_step = KERN_NOTES[(step, octave)] * multiply_character
+        kern_alter = ACC_TO_SIGN[alter] if alter is not None else ""
+        return kern_step + kern_alter
+
+    def markings_to_kern(self, element: spt.GenericNote) -> str:
+        symbols = ""
+        if not isinstance(element, spt.Rest):
+            if element.tie_next and element.tie_prev:
+                symbols += "-"
+            elif element.tie_next:
+                symbols += "["
+            elif element.tie_prev:
+                symbols += "]"
+            if element.slur_starts:
+                symbols += "("
+            if element.slur_stops:
+                symbols += ")"
+        if isinstance(element, spt.Note):
+            if element.beam is not None:
+                symbols += (
+                    "L"
+                    if element.beam == "begin"
+                    else "J" if element.beam == "end" else "K"
+                )
+        return symbols
+
+    def _handle_note(self, el: spt.GenericNote, row_idx) -> str:
+        voice = el.voice
+        staff = el.staff
+        duration = self.duration_to_kern(el)
+        pitch = self.pitch_to_kern(el)
+        col_idx = self.vocstaff_map_dict[f"{voice}-{staff}"]
+        markings = self.markings_to_kern(el)
+        kern_el = duration + pitch + markings
+        if self.prev_note_time == el.start.t:
+            if self.prev_note_col_idx == col_idx:
+                # Chords in Kern
+                self.out_data[self.prev_note_row_idx, self.prev_note_col_idx] = (
+                    self.out_data[self.prev_note_row_idx, self.prev_note_col_idx]
+                    + " "
+                    + kern_el
+                )
+            else:
+                # Same row (start.t) other spline
+                self.out_data[self.prev_note_row_idx, col_idx] = kern_el
+        else:
+            # New line
+            self.out_data[row_idx, col_idx] = kern_el
+            self.prev_note_row_idx = row_idx
+        self.prev_note_col_idx = col_idx
+        self.prev_note_time = el.start.t
+
+
+def save_kern(
+    score_data: spt.ScoreLike,
+    out: Optional[PathLike] = None,
+) -> Optional[np.ndarray]:
+    """
+    Save a score in Kern format.
+
+    Parameters
+    ----------
+    score_data: spt.ScoreLike
+        Score to save in Kern format
+
+    out: Optional[PathLike]
+        Path to save the Kern file. If None, the function returns the Kern file as a numpy array.
+
+    Returns
+    -------
+    Optional[np.ndarray]
+        If out is None, the Kern file is returned as a numpy array.
+    """
+    # Header extracts meta information about the score
+    header = "Here is some random piece"
+    # Kern can output only from part so first let's merge parts (we need a timewise representation)
+    if isinstance(score_data, spt.Score):
+        # TODO check that divisions are the same
+        part = spt.merge_parts(score_data.parts)
+    else:
+        part = score_data
+    if not part.measures:
+        spt.add_measures(part)
+    spt.fill_rests(part, measurewise=False)
+    exporter = KernExporter(part)
+    out_data = exporter.parse()
+    out_data = exporter.trim(out_data)
+    # Use numpy savetxt to save the file
+    footer = "Encoded using the Partitura Python package, version 1.5.0"
+    if out is not None:
+        np.savetxt(
+            fname=out,
+            X=out_data,
+            fmt="%1.26s",
+            delimiter="\t",
+            newline="\n",
+            header=header,
+            footer=footer,
+            comments="!!!",
+            encoding="utf-8",
+        )
+    else:
+        return out_data
diff --git a/partitura/io/exportmatch.py b/partitura/io/exportmatch.py
index 3311c75b..72435956 100644
--- a/partitura/io/exportmatch.py
+++ b/partitura/io/exportmatch.py
@@ -395,7 +395,7 @@ def matchfile_from_alignment(
             onset=onset,
             offset=offset,
             velocity=pnote["velocity"],
-            channel=pnote.get("channel", 1),
+            channel=pnote.get("channel", 0),
             track=pnote.get("track", 0),
         )
         pnote_sort_info[pnote["id"]] = (
diff --git a/partitura/io/exportmei.py b/partitura/io/exportmei.py
index 9de25a7d..3a918858 100644
--- a/partitura/io/exportmei.py
+++ b/partitura/io/exportmei.py
@@ -1,2096 +1,618 @@
-# import partitura
-# import partitura.score as score
-# from lxml import etree
-# from partitura.utils.generic import partition
-# from partitura.utils.music import estimate_symbolic_duration
-# from copy import copy
-
-
-# name_space = "http://www.music-encoding.org/ns/mei"
-
-# xml_id_string = "{http://www.w3.org/XML/1998/namespace}id"
-
-
-# def extend_key(dict_of_lists, key, value):
-#     """extend or create a list at the given key in the given dictionary
-
-#     Parameters
-#     ----------
-#     dict_of_lists:    dictionary
-#         where all values are lists
-#     key:            self explanatory
-#     value:          self explanatory
-
-#     """
-
-#     if key in dict_of_lists.keys():
-#         if isinstance(value, list):
-#             dict_of_lists[key].extend(value)
-#         else:
-#             dict_of_lists[key].append(value)
-#     else:
-#         dict_of_lists[key] = value if isinstance(value, list) else [value]
-
-
-# def calc_dur_dots_split_notes_first_temp_dur(note, measure, num_to_numbase_ratio=1):
-#     """
-#     Notes have to be represented as a string of elemental notes (there is no notation for arbitrary durations)
-#     This function calculates this string (the durations of the elemental notes and their dot counts),
-#     whether the note crosses the measure and the temporal duration of the first elemental note
-
-#     Parameters
-#     ----------
-#     note:               score.GenericNote
-#         The note whose representation as a string of elemental notes is calculated
-#     measure:            score.Measure
-#         The measure which contains note
-#     num_to_numbase_ratio: float, optional
-#         scales the duration of note according to whether or not it belongs to a tuplet and which one
-
-
-#     Returns
-#     -------
-#     dur_dots:       list of int pairs
-#         this describes the string of elemental notes that represent the note notationally
-#         every pair in the list contains the duration and the dot count of an elemental note and
-#         the list is ordered by duration in decreasing order
-#     split_notes:     list or None
-#         an empty list if note crosses measure
-#         None if it doesn't
-#     first_temp_dur:   int or None
-#         duration of first elemental note in partitura time
-#     """
-
-#     if measure == "pad":
-#         return [], None, None
-
-#     if isinstance(note, score.GraceNote):
-#         main_note = note.main_note
-#         # HACK: main note should actually be always not None for a proper GraceNote
-#         if main_note != None:
-#             dur_dots, _, _ = calc_dur_dots_split_notes_first_temp_dur(
-#                 main_note, measure
-#             )
-#             dur_dots = [(2 * dur_dots[0][0], dur_dots[0][1])]
-#         else:
-#             dur_dots = [(8, 0)]
-#             note.id += "_missing_main_note"
-#         return dur_dots, None, None
-
-#     note_duration = note.duration
-
-#     split_notes = None
-
-#     if note.start.t + note.duration > measure.end.t:
-#         note_duration = measure.end.t - note.start.t
-#         split_notes = []
-
-#     quarter_dur = measure.start.quarter
-#     fraction = num_to_numbase_ratio * note_duration / quarter_dur
-
-#     int_part = int(fraction)
-#     frac_part = fraction - int_part
-
-#     # calc digits of fraction in base2
-#     untied_durations = []
-#     pow_of_2 = 1
-
-#     while int_part > 0:
-#         bit = int_part % 2
-#         untied_durations.insert(0, bit * pow_of_2)
-#         int_part = int_part // 2
-#         pow_of_2 *= 2
-
-#     pow_of_2 = 1 / 2
-
-#     while frac_part > 0:
-#         frac_part *= 2
-#         bit = int(frac_part)
-#         frac_part -= bit
-#         untied_durations.append(bit * pow_of_2)
-#         pow_of_2 /= 2
-
-#     dur_dots = []
-
-#     curr_dur = 0
-#     curr_dots = 0
-
-#     def add_dd(dur_dots, dur, dots):
-#         dur_dots.append((int(4 / dur), dots))
-
-#     for untied_dur in untied_durations:
-#         if curr_dur != 0:
-#             if untied_dur == 0:
-#                 add_dd(dur_dots, curr_dur, curr_dots)
-#                 curr_dots = 0
-#                 curr_dur = 0
-#             else:
-#                 curr_dots += 1
-#         else:
-#             curr_dur = untied_dur
-
-#     if curr_dur != 0:
-#         add_dd(dur_dots, curr_dur, curr_dots)
-
-#     first_temp_dur = int(untied_durations[0] * quarter_dur)
-
-#     return dur_dots, split_notes, first_temp_dur
-
-
-# def insert_elem_check(t, inbetween_notes_elems):
-#     """Check if something like a clef etc appears before time t
-
-#     Parameters
-#     ----------
-#     t:                      int
-#         time from a Timepoint
-#     inbetween_notes_elems:    list of InbetweenNotesElements
-#         a list of objects describing things like clefs etc
-
-#     Returns
-#     -------
-#     True if something like a clef etc appears before time t
-#     """
-
-#     for ine in inbetween_notes_elems:
-#         if ine.elem != None and ine.elem.start.t <= t:
-#             return True
-
-#     return False
-
-
-# def partition_handle_none(func, iter, partition_attrib):
-#     p = partition(func, iter)
-#     newKey = None
-
-#     if None in p.keys():
-#         raise KeyError(
-#             'PARTITION ERROR: some elements of set do not have partition attribute "'
-#             + partition_attrib
-#             + '"'
-#         )
-
-#     return p
-
-
-# def add_child(parent, child_name):
-#     return etree.SubElement(parent, child_name)
-
-
-# def set_attributes(elem, *list_attrib_val):
-#     for attrib_val in list_attrib_val:
-#         elem.set(attrib_val[0], str(attrib_val[1]))
-
-
-# def attribs_of_key_sig(ks):
-#     """
-#     Returns values of a score.KeySignature object necessary for a MEI document
-
-#     Parameters
-#     ----------
-#     ks: score.KeySignature
-
-#     Returns
-#     -------
-#     fifths: string
-#         describes the circle of fifths
-#     mode:   string
-#         "major" or "minor"
-#     pname:  string
-#         pitch letter
-#     """
-
-#     key = ks.name
-#     pname = key[0].lower()
-#     mode = "major"
-
-#     if len(key) == 2:
-#         mode = "minor"
-
-#     fifths = str(abs(ks.fifths))
-
-#     if ks.fifths < 0:
-#         fifths += "f"
-#     elif ks.fifths > 0:
-#         fifths += "s"
-
-#     return fifths, mode, pname
-
-
-# def first_instances_per_part(
-#     cls, parts, start=score.TimePoint(0), end=score.TimePoint(1)
-# ):
-#     """
-#     Returns the first instances of a class (multiple objects with same start time are possible) in each part
-
-#     Parameters
-#     ----------
-#     cls:    class
-#     parts:  list of score.Part
-#     start:  score.TimePoint, optional
-#         start of the range to search in
-#     end:    score.TimePoint, optional
-#         end of the range to search in
-
-#     Returns
-#     -------
-#     instances_per_part: list of list of instances of cls
-#         sublists might be empty
-#         if all sublists are empty, instances_per_part is empty
-#     """
-#     if not isinstance(start, list):
-#         start = [start] * len(parts)
-#     elif not len(parts) == len(start):
-#         raise ValueError(
-#             "ERROR at first_instances_per_part: start times are given as list with different size to parts list"
-#         )
-
-#     if not isinstance(end, list):
-#         end = [end] * len(parts)
-#     elif not len(parts) == len(end):
-#         raise ValueError(
-#             "ERROR at first_instances_per_part: end times are given as list with different size to parts list"
-#         )
-
-#     for i in range(len(parts)):
-#         if start[i] == None and end[i] != None or start[i] != None and end[i] == None:
-#             raise ValueError(
-#                 "ERROR at first_instances_per_part: (start==None) != (end==None) (None elements in start have to be at same position as in end and vice versa)"
-#             )
-
-#     instances_per_part = []
-
-#     non_empty = False
-
-#     for i, p in enumerate(parts):
-#         s = start[i]
-#         e = end[i]
-
-#         if s == None:
-#             instances_per_part.append([])
-#             continue
-
-#         instances = list(p.iter_all(cls, s, e))
-
-#         if len(instances) == 0:
-#             instances_per_part.append([])
-#             continue
-
-#         non_empty = True
-#         t = min(instances, key=lambda i: i.start.t).start.t
-#         instances_per_part.append([i for i in instances if t == i.start.t])
-
-#     if non_empty:
-#         return instances_per_part
-
-#     return []
-
-
-# def first_instance_per_part(
-#     cls, parts, start=score.TimePoint(0), end=score.TimePoint(1)
-# ):
-#     """
-#     Reduce the result of first_instances_per_part, a 2D list, to a 1D list
-#     If there are multiple first instances then program aborts with error message
-
-#     Parameters
-#     ----------
-#     cls:    class
-#     parts:  list of score.Part
-#     start:  score.TimePoint, optional
-#         start of the range to search in
-#     end:    score.TimePoint, optional
-#         end of the range to search in
-
-#     Returns
-#     -------
-#     fipp: list of instances of cls
-#         elements might be None
-#     """
-#     fispp = first_instances_per_part(cls, parts, start, end)
-
-#     fipp = []
-
-#     for i, fis in enumerate(fispp):
-#         if len(fis) == 0:
-#             fipp.append(None)
-#         elif len(fis) == 1:
-#             fipp.append(fis[0])
-#         else:
-#             raise ValueError(
-#                 "Part " + parts[i].name,
-#                 "ID " + parts[i].id,
-#                 "has more than one instance of "
-#                 + str(cls)
-#                 + " at beginning t=0, but there should only be a single one",
-#             )
-
-#     return fipp
-
-
-# def first_instances(cls, part, start=score.TimePoint(0), end=score.TimePoint(1)):
-#     """
-#     Returns the first instances of a class (multiple objects with same start time are possible) in the part
-
-#     Parameters
-#     ----------
-#     cls:    class
-#     part:   score.Part
-#     start:  score.TimePoint, optional
-#         start of the range to search in
-#     end:    score.TimePoint, optional
-#         end of the range to search in
-
-#     Returns
-#     -------
-#     fis: list of instances of cls
-#         might be empty
-#     """
-#     fis = first_instances_per_part(cls, [part], start, end)
-
-#     if len(fis) == 0:
-#         return []
-
-#     return fis[0]
-
-
-# def first_instance(cls, part, start=score.TimePoint(0), end=score.TimePoint(1)):
-#     """
-#     Reduce the result of first_instance_per_part, a 1D list, to an element
-#     If there are multiple first instances then program aborts with error message
-
-#     Parameters
-#     ----------
-#     cls:    class
-#     part:   score.Part
-#     start:  score.TimePoint, optional
-#         start of the range to search in
-#     end:    score.TimePoint, optional
-#         end of the range to search in
-
-#     Returns
-#     -------
-#     fi: instance of cls or None
-#     """
-#     fi = first_instance_per_part(cls, [part], start, end)
-
-#     if len(fi) == 0:
-#         return None
-
-#     return fi[0]
-
-
-# def common_signature(cls, sig_eql, parts, current_measures=None):
-#     """
-#     Calculate whether a list of parts has a common signature (as in key or time signature)
-
-#     Parameters
-#     ----------
-#     cls:                score.KeySignature or score.TimeSignature
-#     sig_eql:            function
-#         takes 2 signature objects as input and returns whether they are equivalent (in some sense)
-#     parts:              list of score.Part
-#     current_measures:    list of score.Measure, optional
-#         current as in the measures of the parts that are played at the same time and are processed
-
-#     Returns
-#     -------
-#     common_sig:  instance of cls
-#         might be None if there is no commonality between parts
-#     """
-#     sigs = None
-#     if current_measures != None:
-#         # HACK:  measures should probably not contain "pad" at this point, but an actual dummy measure with start and end times?
-#         sigs = first_instance_per_part(
-#             cls,
-#             parts,
-#             start=[cm.start if cm != "pad" else None for cm in current_measures],
-#             end=[cm.end if cm != "pad" else None for cm in current_measures],
-#         )
-#     else:
-#         sigs = first_instance_per_part(cls, parts)
-
-#     if sigs == None or len(sigs) == 0 or None in sigs:
-#         return None
-
-#     common_sig = sigs.pop()
-
-#     for sig in sigs:
-#         if sig.start.t != common_sig.start.t or not sig_eql(sig, common_sig):
-#             return None
-
-#     return common_sig
-
-
-# def vertical_slice(list_2d, index):
-#     """
-#     Returns elements of the sublists at index in a 1D list
-#     all sublists of list_2d have to have len > index
-#     """
-#     vslice = []
-
-#     for list_1d in list_2d:
-#         vslice.append(list_1d[index])
-
-#     return vslice
-
-
-# def time_sig_eql(ts1, ts2):
-#     """
-#     equivalence function for score.TimeSignature objects
-#     """
-#     return ts1.beats == ts2.beats and ts1.beat_type == ts2.beat_type
-
-
-# def key_sig_eql(ks1, ks2):
-#     """
-#     equivalence function for score.KeySignature objects
-#     """
-#     return ks1.name == ks2.name and ks1.fifths == ks2.fifths
-
-
-# def idx(len_obj):
-#     return range(len(len_obj))
-
-
-# def attribs_of_clef(clef):
-#     """
-#     Returns values of a score.Clef object necessary for a MEI document
-
-#     Parameters
-#     ----------
-#     clef: score.Clef
-
-#     Returns
-#     -------
-#     sign: string
-#         shape of clef (F,G, etc)
-#     line:
-#         which line to place clef on
-#     """
-#     sign = clef.sign
-
-#     if sign == "percussion":
-#         sign = "perc"
-
-#     if clef.octave_change != None and clef.octave_change != 0:
-#         place = "above"
-
-#         if clef.octave_change < 0:
-#             place = "below"
-
-#         return sign, clef.line, 1 + 7 * abs(clef.octave_change), place
-
-#     return sign, clef.line
-
-
-# def create_staff_def(staff_grp, clef):
-#     """
-
-#     Parameters
-#     ----------
-#     staff_grp:   etree.SubElement
-#     clef:       score.Clef
-#     """
-#     staff_def = add_child(staff_grp, "staffDef")
-
-#     attribs = attribs_of_clef(clef)
-#     set_attributes(
-#         staff_def,
-#         ("n", clef.number),
-#         ("lines", 5),
-#         ("clef.shape", attribs[0]),
-#         ("clef.line", attribs[1]),
-#     )
-#     if len(attribs) == 4:
-#         set_attributes(
-#             staff_def, ("clef.dis", attribs[2]), ("clef.dis.place", attribs[3])
-#         )
-
-
-# def pad_measure(s, measure_per_staff, notes_within_measure_per_staff, auto_rest_count):
-#     """
-#     Adds a fake measure ("pad") to the measures of the staff s and a score.Rest object to the notes
-
-#     Parameters
-#     ----------
-#     s:                              int
-#         staff number
-#     measure_per_staff:               dict of score.Measure objects
-#     notes_within_measure_per_staff:   dict of lists of score.GenericNote objects
-#     auto_rest_count:                  int
-#         a counter for all the score.Rest objects that are created automatically
-
-#     Returns
-#     -------
-#     incremented auto rest counter
-#     """
-
-#     measure_per_staff[s] = "pad"
-#     r = score.Rest(id="pR" + str(auto_rest_count), voice=1)
-#     r.start = score.TimePoint(0)
-#     r.end = r.start
-
-#     extend_key(notes_within_measure_per_staff, s, r)
-#     return auto_rest_count + 1
-
-
-# class InbetweenNotesElement:
-#     """
-#     InbetweenNotesElements contain information on objects like clefs, keysignatures, etc
-#     within the score and how to process them
-
-#     Parameters
-#     ----------
-#     name:           string
-#         name of the element used in MEI
-#     attrib_names:    list of strings
-#         names of the attributes of the MEI element
-#     attrib_vals_of:   function
-#         a function that returns the attribute values of elem
-#     container_dict: dict of lists of partitura objects
-#         the container containing the required elements is at staff
-#     staff:          int
-#         staff number
-#     skip_index:      int
-#         init value for the cursor i (might skip 0)
-
-#     Attributes
-#     ----------
-#     name:           string
-#         name of the element used in MEI
-#     attrib_names:    list of strings
-#         names of the attributes of the MEI element
-#     elem:           instance of partitura object
-#     attrib_vals_of:   function
-#         a function that returns the attribute values of elem
-#     container:      list of partitura objects
-#         the container where elem gets its values from
-#     i:              int
-#         cursor that keeps track of position in container
-#     """
-
-#     __slots__ = ["name", "attrib_names", "attrib_vals_of", "container", "i", "elem"]
-
-#     def __init__(
-#         self, name, attrib_names, attrib_vals_of, container_dict, staff, skip_index
-#     ):
-#         self.name = name
-#         self.attrib_names = attrib_names
-#         self.attrib_vals_of = attrib_vals_of
-
-#         self.i = 0
-#         self.elem = None
-
-#         if staff in container_dict.keys():
-#             self.container = container_dict[staff]
-#             if len(self.container) > skip_index:
-#                 self.elem = self.container[skip_index]
-#                 self.i = skip_index
-#         else:
-#             self.container = []
-
-
-# def chord_rep(chords, chord_i):
-#     return chords[chord_i][0]
-
-
-# def handle_beam(open_up, parents):
-#     """
-#     Using a stack of MEI elements, opens and closes beams
-
-#     Parameters
-#     ----------
-#     open_up:     boolean
-#         flag that indicates whether to open or close recent beam
-#     parents:    list of etree.SubElement
-#         stack of MEI elements that contain the beam element
-
-#     Returns
-#     -------
-#     unchanged open_up value
-#     """
-#     if open_up:
-#         parents.append(add_child(parents[-1], "beam"))
-#     else:
-#         parents.pop()
-
-#     return open_up
-
-
-# def is_chord_in_tuplet(chord_i, tuplet_indices):
-#     """
-#     check if chord falls in the range of a tuplet
-
-#     Parameters
-#     ----------
-#     chord_i:        int
-#         index of chord within chords array
-#     tuplet_indices:  list of int pairs
-#         contains the index ranges of all the tuplets in a measure of a staff
-
-#     Returns
-#     -------
-#     whether chord falls in the range of a tuplet
-#     """
-#     for start, stop in tuplet_indices:
-#         if start <= chord_i and chord_i <= stop:
-#             return True
-
-#     return False
-
-
-# def calc_num_to_numbase_ratio(chord_i, chords, tuplet_indices):
-#     """
-#     calculates how to scale a notes duration with regard to the tuplet it is in
-
-#     Parameters
-#     ----------
-#     chord_i:        int
-#         index of chord within chords array
-#     chords:         list of list of score.GenericNote
-#         array of chords (which are lists of notes)
-#     tuplet_indices:  list of int pairs
-#         contains the index ranges of all the tuplets in a measure of a staff
-
-#     Returns
-#     -------
-#     the num to numbase ratio of a tuplet (eg. 3 in 2 tuplet is 1.5)
-#     """
-#     rep = chords[chord_i][0]
-#     if not isinstance(rep, score.GraceNote) and is_chord_in_tuplet(
-#         chord_i, tuplet_indices
-#     ):
-#         return (
-#             rep.symbolic_duration["actual_notes"]
-#             / rep.symbolic_duration["normal_notes"]
-#         )
-#     return 1
-
-
-# def process_chord(
-#     chord_i,
-#     chords,
-#     inbetween_notes_elements,
-#     open_beam,
-#     auto_beaming,
-#     parents,
-#     dur_dots,
-#     split_notes,
-#     first_temp_dur,
-#     tuplet_indices,
-#     ties,
-#     measure,
-#     layer,
-#     tuplet_id_counter,
-#     open_tuplet,
-#     last_key_sig,
-#     note_alterations,
-#     notes_next_measure_per_staff,
-#     next_dur_dots=None,
-# ):
-#     """
-#     creates <note>, <chord>, <rest>, etc elements from chords
-#     also creates <beam>, <tuplet>, etc elements if necessary for chords objects
-#     also creates <clef>, <keySig>, etc elements before chord objects from inbetween_notes_elements
-
-#     Parameters
-#     ----------
-#     chord_i:                    int
-#         index of chord within chords array
-#     chords:                     list of list of score.GenericNote
-#         chord array
-#     inbetween_notes_elements:     list of InbetweenNotesElements
-#         check this to see if something like clef needs to get inserted before chord
-#     open_beam:                   boolean
-#         flag that indicates whether a beam is currently open
-#     auto_beaming:                boolean
-#         flag that determines if automatic beams should be created or if it is kept manual
-#     parents:                    list of etree.SubElement
-#         stack of MEI elements that contain the most recent beam element
-#     dur_dots:                   list of int pairs
-#         describes how the chord actually gets notated via tied notes, each pair contains the duration of the notated note and its dot count
-#     split_notes:                 list
-#         this is either empty or None
-#         if None, nothing is done with this
-#         if an empty list, that means this chord crosses into the next measure and a chord is created for the next measure which is tied to this one
-#     first_temp_dur:               int
-#         amount of ticks (as in partitura) of the first notated note
-#     tuplet_indices:              list of int pairs
-#         the ranges of tuplets within the chords array
-#     ties:                       dict
-#         out parameter, contains pairs of IDs which need to be connected via ties
-#         this function also adds to that
-#     measure:                    score.Measure
-
-#     layer:                      etree.SubElement
-#         the parent element of the elements created here
-#     tuplet_id_counter:           int
-
-#     open_tuplet:                 boolean
-#         describes if a tuplet is open or not
-#     last_key_sig:                 score.KeySignature
-#         the key signature this chord should be interpeted in
-#     note_alterations:            dict
-#         contains the alterations of staff positions (notes) that are relevant for this chord
-#     notes_next_measure_per_staff: dict of lists of score.GenericNote
-#         out parameter, add the result of split_notes into this
-#     next_dur_dots:              list of int pairs, optional
-#         needed for proper beaming
-
-#     Returns
-#     -------
-#     tuplet_id_counter:    int
-#         incremented if tuplet created
-#     open_beam:           boolean
-#         eventually modified if beam opened or closed
-#     open_tuplet:         boolean
-#         eventually modified if tuplet opened or closed
-#     """
-
-#     chord_notes = chords[chord_i]
-#     rep = chord_notes[0]
-
-#     for ine in inbetween_notes_elements:
-#         if insert_elem_check(rep.start.t, [ine]):
-#             # note should maybe be split according to keysig or clef etc insertion time, right now only beaming is disrupted
-#             if open_beam and auto_beaming:
-#                 open_beam = handle_beam(False, parents)
-
-#             xml_elem = add_child(parents[-1], ine.name)
-#             attrib_vals = ine.attrib_vals_of(ine.elem)
-
-#             if ine.name == "keySig":
-#                 last_key_sig = ine.elem
-
-#             if len(ine.attrib_names) < len(attrib_vals):
-#                 raise ValueError(
-#                     "ERROR at insertion of inbetween_notes_elements: there are more attribute values than there are attribute names for xml element "
-#                     + ine.name
-#                 )
-
-#             for nv in zip(ine.attrib_names[: len(attrib_vals)], attrib_vals):
-#                 set_attributes(xml_elem, nv)
-
-#             if ine.i + 1 >= len(ine.container):
-#                 ine.elem = None
-#             else:
-#                 ine.i += 1
-#                 ine.elem = ine.container[ine.i]
-
-#     if is_chord_in_tuplet(chord_i, tuplet_indices):
-#         if not open_tuplet:
-#             parents.append(add_child(parents[-1], "tuplet"))
-#             num = rep.symbolic_duration["actual_notes"]
-#             numbase = rep.symbolic_duration["normal_notes"]
-#             set_attributes(
-#                 parents[-1],
-#                 (xml_id_string, "t" + str(tuplet_id_counter)),
-#                 ("num", num),
-#                 ("numbase", numbase),
-#             )
-#             tuplet_id_counter += 1
-#             open_tuplet = True
-#     elif open_tuplet:
-#         parents.pop()
-#         open_tuplet = False
-
-#     def set_dur_dots(elem, dur_dots):
-#         dur, dots = dur_dots
-#         set_attributes(elem, ("dur", dur))
-
-#         if dots > 0:
-#             set_attributes(elem, ("dots", dots))
-
-#     if isinstance(rep, score.Note):
-#         if auto_beaming:
-#             # for now all notes are beamed, however some rules should be obeyed there, see Note Beaming and Grouping
-
-#             # check to close beam
-#             if open_beam and (
-#                 dur_dots[0][0] < 8
-#                 or chord_i - 1 >= 0
-#                 and type(rep) != type(chord_rep(chords, chord_i - 1))
-#             ):
-#                 open_beam = handle_beam(False, parents)
-
-#             # check to open beam (maybe again)
-#             if not open_beam and dur_dots[0][0] >= 8:
-#                 # open beam if there are multiple "consecutive notes" which don't get interrupted by some element
-#                 if len(dur_dots) > 1 and not insert_elem_check(
-#                     rep.start.t + first_temp_dur, inbetween_notes_elements
-#                 ):
-#                     open_beam = handle_beam(True, parents)
-
-#                 # open beam if there is just a single note that is not the last one in measure and next note in measure is of same type and fits in beam as well, without getting interrupted by some element
-#                 elif (
-#                     len(dur_dots) <= 1
-#                     and chord_i + 1 < len(chords)
-#                     and next_dur_dots[0][0] >= 8
-#                     and type(rep) == type(chord_rep(chords, chord_i + 1))
-#                     and not insert_elem_check(
-#                         chord_rep(chords, chord_i + 1).start.t, inbetween_notes_elements
-#                     )
-#                 ):
-#                     open_beam = handle_beam(True, parents)
-#         elif (
-#             open_beam
-#             and chord_i > 0
-#             and rep.beam != chord_rep(chords, chord_i - 1).beam
-#         ):
-#             open_beam = handle_beam(False, parents)
-
-#         if not auto_beaming and not open_beam and rep.beam != None:
-#             open_beam = handle_beam(True, parents)
-
-#         def conditional_gracify(elem, rep, chord_i, chords):
-#             if isinstance(rep, score.GraceNote):
-#                 grace = "unacc"
-
-#                 if rep.grace_type == "appoggiatura":
-#                     grace = "acc"
-
-#                 set_attributes(elem, ("grace", grace))
-
-#                 if rep.steal_proportion != None:
-#                     set_attributes(
-#                         elem, ("grace.time", str(rep.steal_proportion * 100) + "%")
-#                     )
-
-#                 if chord_i == 0 or not isinstance(
-#                     chord_rep(chords, chord_i - 1), score.GraceNote
-#                 ):
-#                     chords[chord_i] = [copy(n) for n in chords[chord_i]]
-
-#                     for n in chords[chord_i]:
-#                         n.tie_next = n.main_note
-
-#         def create_note(parent, n, id, last_key_sig, note_alterations):
-#             note = add_child(parent, "note")
-
-#             step = n.step.lower()
-#             set_attributes(
-#                 note, (xml_id_string, id), ("pname", step), ("oct", n.octave)
-#             )
-
-#             if n.articulations != None and len(n.articulations) > 0:
-#                 artics = []
-
-#                 translation = {
-#                     "accent": "acc",
-#                     "staccato": "stacc",
-#                     "tenuto": "ten",
-#                     "staccatissimo": "stacciss",
-#                     "spiccato": "spicc",
-#                     "scoop": "scoop",
-#                     "plop": "plop",
-#                     "doit": "doit",
-#                 }
-
-#                 for a in n.articulations:
-#                     if a in translation.keys():
-#                         artics.append(translation[a])
-#                 set_attributes(note, ("artic", " ".join(artics)))
-
-#             sharps = ["f", "c", "g", "d", "a", "e", "b"]
-#             flats = list(reversed(sharps))
-
-#             staff_pos = step + str(n.octave)
-
-#             alter = n.alter or 0
-
-#             def set_accid(note, acc, note_alterations, staff_pos, alter):
-#                 if (
-#                     staff_pos in note_alterations.keys()
-#                     and alter == note_alterations[staff_pos]
-#                 ):
-#                     return
-#                 set_attributes(note, ("accid", acc))
-#                 note_alterations[staff_pos] = alter
-
-#             # sharpen note if: is sharp, is not sharpened by key or prev alt
-#             # flatten note if: is flat, is not flattened by key or prev alt
-#             # neutralize note if: is neutral, is sharpened/flattened by key or prev alt
-
-#             # check if note is sharpened/flattened by prev alt or key
-#             if (
-#                 staff_pos in note_alterations.keys()
-#                 and note_alterations[staff_pos] != 0
-#                 or last_key_sig.fifths > 0
-#                 and step in sharps[: last_key_sig.fifths]
-#                 or last_key_sig.fifths < 0
-#                 and step in flats[: -last_key_sig.fifths]
-#             ):
-#                 if alter == 0:
-#                     set_accid(note, "n", note_alterations, staff_pos, alter)
-#             elif alter > 0:
-#                 set_accid(note, "s", note_alterations, staff_pos, alter)
-#             elif alter < 0:
-#                 set_accid(note, "f", note_alterations, staff_pos, alter)
-
-#             return note
-
-#         if len(chord_notes) > 1:
-#             chord = add_child(parents[-1], "chord")
-
-#             set_dur_dots(chord, dur_dots[0])
-
-#             conditional_gracify(chord, rep, chord_i, chords)
-
-#             for n in chord_notes:
-#                 create_note(chord, n, n.id, last_key_sig, note_alterations)
-
-#         else:
-#             note = create_note(parents[-1], rep, rep.id, last_key_sig, note_alterations)
-#             set_dur_dots(note, dur_dots[0])
-
-#             conditional_gracify(note, rep, chord_i, chords)
-
-#         if len(dur_dots) > 1:
-#             for n in chord_notes:
-#                 ties[n.id] = [n.id]
-
-#             def create_split_up_notes(chord_notes, i, parents, dur_dots, ties, rep):
-#                 if len(chord_notes) > 1:
-#                     chord = add_child(parents[-1], "chord")
-#                     set_dur_dots(chord, dur_dots[i])
-
-#                     for n in chord_notes:
-#                         id = n.id + "-" + str(i)
-
-#                         ties[n.id].append(id)
-#                         create_note(chord, n, id, last_key_sig, note_alterations)
-#                 else:
-#                     id = rep.id + "-" + str(i)
-
-#                     ties[rep.id].append(id)
-
-#                     note = create_note(
-#                         parents[-1], rep, id, last_key_sig, note_alterations
-#                     )
-
-#                     set_dur_dots(note, dur_dots[i])
-
-#             for i in range(1, len(dur_dots) - 1):
-#                 if not open_beam and dur_dots[i][0] >= 8:
-#                     open_beam = handle_beam(True, parents)
-
-#                 create_split_up_notes(chord_notes, i, parents, dur_dots, ties, rep)
-
-#             create_split_up_notes(
-#                 chord_notes, len(dur_dots) - 1, parents, dur_dots, ties, rep
-#             )
-
-#         if split_notes != None:
-
-#             for n in chord_notes:
-#                 split_notes.append(score.Note(n.step, n.octave, id=n.id + "s"))
-
-#             if len(dur_dots) > 1:
-#                 for n in chord_notes:
-#                     ties[n.id].append(n.id + "s")
-#             else:
-#                 for n in chord_notes:
-#                     ties[n.id] = [n.id, n.id + "s"]
-
-#         for n in chord_notes:
-#             if n.tie_next != None:
-#                 if n.id in ties.keys():
-#                     ties[n.id].append(n.tie_next.id)
-#                 else:
-#                     ties[n.id] = [n.id, n.tie_next.id]
-
-#     elif isinstance(rep, score.Rest):
-#         if split_notes != None:
-#             split_notes.append(score.Rest(id=rep.id + "s"))
-
-#         if (
-#             measure == "pad"
-#             or measure.start.t == rep.start.t
-#             and measure.end.t == rep.end.t
-#         ):
-#             rest = add_child(layer, "mRest")
-
-#             set_attributes(rest, (xml_id_string, rep.id))
-#         else:
-#             rest = add_child(layer, "rest")
-
-#             set_attributes(rest, (xml_id_string, rep.id))
-
-#             set_dur_dots(rest, dur_dots[0])
-
-#             for i in range(1, len(dur_dots)):
-#                 rest = add_child(layer, "rest")
-
-#                 id = rep.id + str(i)
-
-#                 set_attributes(rest, (xml_id_string, id))
-#                 set_dur_dots(rest, dur_dots[i])
-
-#     if split_notes != None:
-#         for sn in split_notes:
-#             sn.voice = rep.voice
-#             sn.start = measure.end
-#             sn.end = score.TimePoint(rep.start.t + rep.duration)
-
-#             extend_key(notes_next_measure_per_staff, s, sn)
-
-#     return tuplet_id_counter, open_beam, open_tuplet
-
-
-# def create_score_def(measures, measure_i, parts, parent):
-#     """
-#     creates <score_def>
-
-#     Parameters
-#     ----------
-#     measures:   list of score.Measure
-#     measure_i:  int
-#         index of measure currently processed within measures
-#     parts:      list of score.Part
-#     parent:     etree.SubElement
-#         parent of <score_def>
-#     """
-#     reference_measures = vertical_slice(measures, measure_i)
-
-#     common_key_sig = common_signature(
-#         score.KeySignature, key_sig_eql, parts, reference_measures
-#     )
-#     common_time_sig = common_signature(
-#         score.TimeSignature, time_sig_eql, parts, reference_measures
-#     )
-
-#     score_def = None
-
-#     if common_key_sig != None or common_time_sig != None:
-#         score_def = add_child(parent, "scoreDef")
-
-#     if common_key_sig != None:
-#         fifths, mode, pname = attribs_of_key_sig(common_key_sig)
-
-#         set_attributes(
-#             score_def, ("key.sig", fifths), ("key.mode", mode), ("key.pname", pname)
-#         )
-
-#     if common_time_sig != None:
-#         set_attributes(
-#             score_def,
-#             ("meter.count", common_time_sig.beats),
-#             ("meter.unit", common_time_sig.beat_type),
-#         )
-
-#     return score_def
-
-
-# class MeasureContent:
-#     """
-#     Simply a bundle for all the data of a measure that needs to be processed for a MEI document
-
-#     Attributes
-#     ----------
-#     ties_per_staff:      dict of lists
-#     clefs_per_staff:     dict of lists
-#     key_sigs_per_staff:   dict of lists
-#     time_sigs_per_staff:  dict of lists
-#     measure_per_staff:   dict of lists
-#     tuplets_per_staff:   dict of lists
-#     slurs:              list
-#     dirs:               list
-#     dynams:             list
-#     tempii:             list
-#     fermatas:           list
-#     """
-
-#     __slots__ = [
-#         "ties_per_staff",
-#         "clefs_per_staff",
-#         "key_sigs_per_staff",
-#         "time_sigs_per_staff",
-#         "measure_per_staff",
-#         "tuplets_per_staff",
-#         "slurs",
-#         "dirs",
-#         "dynams",
-#         "tempii",
-#         "fermatas",
-#     ]
-
-#     def __init__(self):
-#         self.ties_per_staff = {}
-#         self.clefs_per_staff = {}
-#         self.key_sigs_per_staff = {}
-#         self.time_sigs_per_staff = {}
-#         self.measure_per_staff = {}
-#         self.tuplets_per_staff = {}
-
-#         self.slurs = []
-#         self.dirs = []
-#         self.dynams = []
-#         self.tempii = []
-#         self.fermatas = []
-
-
-# def extract_from_measures(
-#     parts,
-#     measures,
-#     measure_i,
-#     staves_per_part,
-#     auto_rest_count,
-#     notes_within_measure_per_staff,
-# ):
-#     """
-#     Returns a bundle of data regarding the measure currently processed, things like notes, key signatures, etc
-#     Also creates padding measures, necessary for example, for staves of instruments which do not play in the current measure
-
-#     Parameters
-#     ----------
-#     parts:                          list of score.Part
-#     measures:                       list of score.Measure
-#     measure_i:                      int
-#         index of current measure within measures
-#     staves_per_part:                 dict of list of ints
-#         staff enumeration partitioned by part
-#     auto_rest_count:                  int
-#         counter for the IDs of automatically generated rests
-#     notes_within_measure_per_staff:   dict of lists of score.GenericNote
-#         in and out parameter, might contain note objects that have crossed from previous measure into current one
-
-#     Returns
-#     -------
-#     auto_rest_count:                  int
-#         incremented if score.Rest created
-#     current_measure_content:          MeasureContent
-#         bundle for all the data that is extracted from the currently processed measure
-#     """
-#     current_measure_content = MeasureContent()
-
-#     for part_i, part in enumerate(parts):
-#         m = measures[part_i][measure_i]
-
-#         if m == "pad":
-#             for s in staves_per_part[part_i]:
-#                 auto_rest_count = pad_measure(
-#                     s,
-#                     current_measure_content.measure_per_staff,
-#                     notes_within_measure_per_staff,
-#                     auto_rest_count,
-#                 )
-
-#             continue
-
-#         def cls_within_measure(part, cls, measure, incl_subcls=False):
-#             return part.iter_all(
-#                 cls, measure.start, measure.end, include_subclasses=incl_subcls
-#             )
-
-#         def cls_within_measure_list(part, cls, measure, incl_subcls=False):
-#             return list(cls_within_measure(part, cls, measure, incl_subcls))
-
-#         clefs_within_measure_per_staff_per_part = partition_handle_none(
-#             lambda c: c.number, cls_within_measure(part, score.Clef, m), "number"
-#         )
-#         key_sigs_within_measure = cls_within_measure_list(part, score.KeySignature, m)
-#         time_sigs_within_measure = cls_within_measure_list(part, score.TimeSignature, m)
-#         current_measure_content.slurs.extend(cls_within_measure(part, score.Slur, m))
-#         tuplets_within_measure = cls_within_measure_list(part, score.Tuplet, m)
-
-#         beat_map = part.beat_map
-
-#         def calc_tstamp(beat_map, t, measure):
-#             return beat_map(t) - beat_map(measure.start.t) + 1
-
-#         for w in cls_within_measure(part, score.Words, m):
-#             tstamp = calc_tstamp(beat_map, w.start.t, m)
-#             current_measure_content.dirs.append((tstamp, w))
-
-#         for tempo in cls_within_measure(part, score.Tempo, m):
-#             tstamp = calc_tstamp(beat_map, tempo.start.t, m)
-#             current_measure_content.tempii.append(
-#                 (tstamp, staves_per_part[part_i][0], tempo)
-#             )
-
-#         for fermata in cls_within_measure(part, score.Fermata, m):
-#             tstamp = calc_tstamp(beat_map, fermata.start.t, m)
-#             current_measure_content.fermatas.append((tstamp, fermata.ref.staff))
-
-#         for dynam in cls_within_measure(part, score.Direction, m, True):
-#             tstamp = calc_tstamp(beat_map, dynam.start.t, m)
-#             tstamp2 = None
-
-#             if dynam.end != None:
-#                 measure_counter = measure_i
-#                 while True:
-#                     if dynam.end.t <= measures[part_i][measure_counter].end.t:
-#                         tstamp2 = calc_tstamp(
-#                             beat_map, dynam.end.t, measures[part_i][measure_counter]
-#                         )
-
-#                         tstamp2 = str(measure_counter - measure_i) + "m+" + str(tstamp2)
-
-#                         break
-#                     elif (
-#                         measure_counter + 1 >= len(measures[part_i])
-#                         or measures[part_i][measure_counter + 1] == "pad"
-#                     ):
-#                         raise ValueError(
-#                             "A score.Direction instance has an end time that exceeds actual non-padded measures"
-#                         )
-#                     else:
-#                         measure_counter += 1
-
-#             current_measure_content.dynams.append((tstamp, tstamp2, dynam))
-
-#         notes_within_measure_per_staff_per_part = partition_handle_none(
-#             lambda n: n.staff,
-#             cls_within_measure(part, score.GenericNote, m, True),
-#             "staff",
-#         )
-
-#         for s in staves_per_part[part_i]:
-#             current_measure_content.key_sigs_per_staff[s] = key_sigs_within_measure
-#             current_measure_content.time_sigs_per_staff[s] = time_sigs_within_measure
-#             current_measure_content.tuplets_per_staff[s] = tuplets_within_measure
-
-#             if s not in notes_within_measure_per_staff_per_part.keys():
-#                 auto_rest_count = pad_measure(
-#                     s,
-#                     current_measure_content.measure_per_staff,
-#                     notes_within_measure_per_staff,
-#                     auto_rest_count,
-#                 )
-
-#         for s, nwp in notes_within_measure_per_staff_per_part.items():
-#             extend_key(notes_within_measure_per_staff, s, nwp)
-#             current_measure_content.measure_per_staff[s] = m
-
-#         for s, cwp in clefs_within_measure_per_staff_per_part.items():
-#             current_measure_content.clefs_per_staff[s] = cwp
-
-#     return auto_rest_count, current_measure_content
-
-
-# def create_measure(
-#     section,
-#     measure_i,
-#     staves_sorted,
-#     notes_within_measure_per_staff,
-#     score_def,
-#     tuplet_id_counter,
-#     auto_beaming,
-#     last_key_sig_per_staff,
-#     current_measure_content,
-# ):
-#     """
-#     creates a <measure> element within <section>
-#     also returns an updated id counter for tuplets and a dictionary of notes that cross into the next measure
-
-#     Parameters
-#     ----------
-#     section:                        etree.SubElement
-#     measure_i:                      int
-#         index of the measure created
-#     staves_sorted:                  list of ints
-#         a sorted list of the proper staff enumeration of the score
-#     notes_within_measure_per_staff:   dict of lists of score.GenericNote
-#         contains score.Note, score.Rest, etc objects of the current measure, partitioned by staff enumeration
-#         will be further partitioned and sorted by voice, time and type (score.GraceNote) and eventually gathered into
-#         a list of equivalence classes called chords
-#     score_def:                       etree.SubElement
-#     tuplet_id_counter:               int
-#         tuplets usually don't come with IDs, so an automatic counter takes care of that
-#     auto_beaming:                    boolean
-#         enables automatic beaming
-#     last_key_sig_per_staff:            dict of score.KeySignature
-#         keeps track of the keysignature each staff is currently in
-#     current_measure_content:          MeasureContent
-#         contains all sorts of data for the measure like tuplets, slurs, etc
-
-#     Returns
-#     -------
-#     tuplet_id_counter:               int
-#         incremented if tuplet created
-#     notes_next_measure_per_staff:     dict of lists of score.GenericNote
-#         score.GenericNote objects that cross into the next measure
-#     """
-#     measure = add_child(section, "measure")
-#     set_attributes(measure, ("n", measure_i + 1))
-
-#     ties_per_staff = {}
-
-#     for s in staves_sorted:
-#         note_alterations = {}
-
-#         staff = add_child(measure, "staff")
-
-#         set_attributes(staff, ("n", s))
-
-#         notes_within_measure_per_staff_per_voice = partition_handle_none(
-#             lambda n: n.voice, notes_within_measure_per_staff[s], "voice"
-#         )
-
-#         ties_per_staff_per_voice = {}
-
-#         m = current_measure_content.measure_per_staff[s]
-
-#         tuplets = []
-#         if s in current_measure_content.tuplets_per_staff.keys():
-#             tuplets = current_measure_content.tuplets_per_staff[s]
-
-#         last_key_sig = last_key_sig_per_staff[s]
-
-#         for voice, notes in notes_within_measure_per_staff_per_voice.items():
-#             layer = add_child(staff, "layer")
-
-#             set_attributes(layer, ("n", voice))
-
-#             ties = {}
-
-#             notes_partition = partition_handle_none(
-#                 lambda n: n.start.t, notes, "start.t"
-#             )
-
-#             chords = []
-
-#             for t in sorted(notes_partition.keys()):
-#                 ns = notes_partition[t]
-
-#                 if len(ns) > 1:
-#                     type_partition = partition_handle_none(
-#                         lambda n: isinstance(n, score.GraceNote), ns, "isGraceNote"
-#                     )
-
-#                     if True in type_partition.keys():
-#                         gns = type_partition[True]
-
-#                         gn_chords = []
-
-#                         def scan_backwards(gns):
-#                             start = gns[0]
-
-#                             while isinstance(start.grace_prev, score.GraceNote):
-#                                 start = start.grace_prev
-
-#                             return start
-
-#                         start = scan_backwards(gns)
-
-#                         def process_grace_note(n, gns):
-#                             if not n in gns:
-#                                 raise ValueError(
-#                                     "Error at forward scan of GraceNotes: a grace_next has either different staff, voice or starting time than GraceNote chain"
-#                                 )
-#                             gns.remove(n)
-#                             return n.grace_next
-
-#                         while isinstance(start, score.GraceNote):
-#                             gn_chords.append([start])
-#                             start = process_grace_note(start, gns)
-
-#                         while len(gns) > 0:
-#                             start = scan_backwards(gns)
-
-#                             i = 0
-#                             while isinstance(start, score.GraceNote):
-#                                 if i >= len(gn_chords):
-#                                     raise IndexError(
-#                                         "ERROR at GraceNote-forward scanning: Difference in lengths of grace note sequences for different chord notes"
-#                                     )
-#                                 gn_chords[i].append(start)
-#                                 start = process_grace_note(start, gns)
-#                                 i += 1
-
-#                             if not i == len(gn_chords):
-#                                 raise IndexError(
-#                                     "ERROR at GraceNote-forward scanning: Difference in lengths of grace note sequences for different chord notes"
-#                                 )
-
-#                         for gnc in gn_chords:
-#                             chords.append(gnc)
-
-#                     if not False in type_partition.keys():
-#                         raise KeyError(
-#                             "ERROR at ChordNotes-grouping: GraceNotes detected without additional regular Notes at same time; staff "
-#                             + str(s)
-#                         )
-
-#                     reg_notes = type_partition[False]
-
-#                     rep = reg_notes[0]
-
-#                     for i in range(1, len(reg_notes)):
-#                         n = reg_notes[i]
-
-#                         if n.duration != rep.duration:
-#                             raise ValueError(
-#                                 "In staff " + str(s) + ",",
-#                                 "in measure " + str(m.number) + ",",
-#                                 "for voice " + str(voice) + ",",
-#                                 "2 notes start at time " + str(n.start.t) + ",",
-#                                 "but have different durations, namely "
-#                                 + n.id
-#                                 + " has duration "
-#                                 + str(n.duration)
-#                                 + " and "
-#                                 + rep.id
-#                                 + " has duration "
-#                                 + str(rep.duration),
-#                                 "change to same duration for a chord or change voice of one of the notes for something else",
-#                             )
-#                         # HACK: unpitched notes are treated as Rests right now
-#                         elif not isinstance(rep, score.Rest) and not isinstance(
-#                             n, score.Rest
-#                         ):
-#                             if rep.beam != n.beam:
-#                                 print(
-#                                     "WARNING: notes within chords don't share the same beam",
-#                                     "specifically note "
-#                                     + str(rep)
-#                                     + " has beam "
-#                                     + str(rep.beam),
-#                                     "and note " + str(n) + " has beam " + str(n.beam),
-#                                     "export still continues though",
-#                                 )
-#                             elif set(rep.tuplet_starts) != set(n.tuplet_starts) and set(
-#                                 rep.tuplet_stops
-#                             ) != set(n.tuplet_stops):
-#                                 print(
-#                                     "WARNING: notes within chords don't share same tuplets, export still continues though"
-#                                 )
-#                     chords.append(reg_notes)
-#                 else:
-#                     chords.append(ns)
-
-#             tuplet_indices = []
-#             for tuplet in tuplets:
-#                 ci = 0
-#                 start = -1
-#                 stop = -1
-#                 while ci < len(chords):
-#                     for n in chords[ci]:
-#                         if tuplet in n.tuplet_starts:
-#                             start = ci
-#                             break
-#                     for n in chords[ci]:
-#                         if tuplet in n.tuplet_stops:
-#                             stop = ci
-#                             break
-
-#                     if start >= 0 and stop >= 0:
-#                         if not start <= stop:
-#                             raise ValueError(
-#                                 "In measure " + str(measure_i + 1) + ",",
-#                                 "in staff " + str(s) + ",",
-#                                 "[" + str(tuplet) + "] stops before it starts?",
-#                                 "start=" + str(start + 1) + "; stop=" + str(stop + 1),
-#                             )
-#                         tuplet_indices.append((start, stop))
-#                         break
-
-#                     ci += 1
-
-#             parents = [layer]
-#             open_beam = False
-
-#             (
-#                 next_dur_dots,
-#                 next_split_notes,
-#                 next_first_temp_dur,
-#             ) = calc_dur_dots_split_notes_first_temp_dur(
-#                 chords[0][0], m, calc_num_to_numbase_ratio(0, chords, tuplet_indices)
-#             )
-
-#             inbetween_notes_elements = [
-#                 InbetweenNotesElement(
-#                     "clef",
-#                     ["shape", "line", "dis", "dis.place"],
-#                     attribs_of_clef,
-#                     current_measure_content.clefs_per_staff,
-#                     s,
-#                     int(measure_i == 0),
-#                 ),
-#                 InbetweenNotesElement(
-#                     "keySig",
-#                     ["sig", "mode", "pname", "sig.showchange"],
-#                     (lambda ks: attribs_of_key_sig(ks) + ("true",)),
-#                     current_measure_content.key_sigs_per_staff,
-#                     s,
-#                     int(score_def != None),
-#                 ),
-#                 InbetweenNotesElement(
-#                     "meterSig",
-#                     ["count", "unit"],
-#                     lambda ts: (ts.beats, ts.beat_type),
-#                     current_measure_content.time_sigs_per_staff,
-#                     s,
-#                     int(score_def != None),
-#                 ),
-#             ]
-
-#             open_tuplet = False
-
-#             notes_next_measure_per_staff = {}
-
-#             for chord_i in range(len(chords) - 1):
-#                 dur_dots, split_notes, first_temp_dur = (
-#                     next_dur_dots,
-#                     next_split_notes,
-#                     next_first_temp_dur,
-#                 )
-#                 (
-#                     next_dur_dots,
-#                     next_split_notes,
-#                     next_first_temp_dur,
-#                 ) = calc_dur_dots_split_notes_first_temp_dur(
-#                     chord_rep(chords, chord_i + 1),
-#                     m,
-#                     calc_num_to_numbase_ratio(chord_i + 1, chords, tuplet_indices),
-#                 )
-#                 tuplet_id_counter, open_beam, open_tuplet = process_chord(
-#                     chord_i,
-#                     chords,
-#                     inbetween_notes_elements,
-#                     open_beam,
-#                     auto_beaming,
-#                     parents,
-#                     dur_dots,
-#                     split_notes,
-#                     first_temp_dur,
-#                     tuplet_indices,
-#                     ties,
-#                     m,
-#                     layer,
-#                     tuplet_id_counter,
-#                     open_tuplet,
-#                     last_key_sig,
-#                     note_alterations,
-#                     notes_next_measure_per_staff,
-#                     next_dur_dots,
-#                 )
-
-#             tuplet_id_counter, _, _ = process_chord(
-#                 len(chords) - 1,
-#                 chords,
-#                 inbetween_notes_elements,
-#                 open_beam,
-#                 auto_beaming,
-#                 parents,
-#                 next_dur_dots,
-#                 next_split_notes,
-#                 next_first_temp_dur,
-#                 tuplet_indices,
-#                 ties,
-#                 m,
-#                 layer,
-#                 tuplet_id_counter,
-#                 open_tuplet,
-#                 last_key_sig,
-#                 note_alterations,
-#                 notes_next_measure_per_staff,
-#             )
-
-#             ties_per_staff_per_voice[voice] = ties
-
-#         ties_per_staff[s] = ties_per_staff_per_voice
-
-#     for fermata in current_measure_content.fermatas:
-#         tstamp = fermata[0]
-#         fermata_staff = fermata[1]
-
-#         f = add_child(measure, "fermata")
-#         set_attributes(f, ("staff", fermata_staff), ("tstamp", tstamp))
-
-#     for slur in current_measure_content.slurs:
-#         s = add_child(measure, "slur")
-#         if slur.start_note == None or slur.end_note == None:
-#             raise ValueError("Slur is missing start or end")
-#         set_attributes(
-#             s,
-#             ("staff", slur.start_note.staff),
-#             ("startid", "#" + slur.start_note.id),
-#             ("endid", "#" + slur.end_note.id),
-#         )
-
-#     for tstamp, word in current_measure_content.dirs:
-#         d = add_child(measure, "dir")
-#         set_attributes(d, ("staff", word.staff), ("tstamp", tstamp))
-#         d.text = word.text
-
-#     # smufl individual notes start with E1
-#     # these are the last 2 digits of the codes
-#     metronome_codes = {
-#         "breve": "D0",
-#         "whole": "D2",
-#         "half": "D3",
-#         "h": "D3",
-#         "quarter": "D5",
-#         "q": "D5",
-#         "eighth": "D7",
-#         "e": "D5",
-#         "16th": "D9",
-#         "32nd": "DB",
-#         "64th": "DD",
-#         "128th": "DF",
-#         "256th": "E1",
-#     }
-
-#     for tstamp, staff, tempo in current_measure_content.tempii:
-#         t = add_child(measure, "tempo")
-#         set_attributes(t, ("staff", staff), ("tstamp", tstamp))
-
-#         unit = str(tempo.unit)
-
-#         dots = unit.count(".")
-
-#         unit = unit[:-dots]
-
-#         string_to_build = [
-#             ' <rend fontname="VerovioText">&#xE1',
-#             metronome_codes[unit or "q"],
-#             ";",
-#         ]
-
-#         for i in range(dots):
-#             string_to_build.append("&#xE1E7;")
-
-#         string_to_build.append("</rend> = ")
-#         string_to_build.append(str(tempo.bpm))
-
-#         t.text = "".join(string_to_build)
-
-#     for tstamp, tstamp2, dynam in current_measure_content.dynams:
-#         if isinstance(dynam, score.DynamicLoudnessDirection):
-#             d = add_child(measure, "hairpin")
-#             form = (
-#                 "cres"
-#                 if isinstance(dynam, score.IncreasingLoudnessDirection)
-#                 else "dim"
-#             )
-#             set_attributes(d, ("form", form))
-
-#             # duration can also matter for other dynamics, might want to move this out of branch
-#             if tstamp2 != None:
-#                 set_attributes(d, ("tstamp2", tstamp2))
-#         else:
-#             d = add_child(measure, "dynam")
-#             d.text = dynam.text
-
-#         set_attributes(d, ("staff", dynam.staff), ("tstamp", tstamp))
-
-#     for s, tps in ties_per_staff.items():
-
-#         for v, tpspv in tps.items():
-
-#             for ties in tpspv.values():
-
-#                 for i in range(len(ties) - 1):
-#                     tie = add_child(measure, "tie")
-
-#                     set_attributes(
-#                         tie,
-#                         ("staff", s),
-#                         ("startid", "#" + ties[i]),
-#                         ("endid", "#" + ties[i + 1]),
-#                     )
-
-#     for s, k in current_measure_content.key_sigs_per_staff.items():
-#         if len(k) > 0:
-#             last_key_sig_per_staff[s] = max(k, key=lambda k: k.start.t)
-
-#     return tuplet_id_counter, notes_next_measure_per_staff
-
-
-# def unpack_part_group(part_grp, parts=[]):
-#     """
-#     Recursively gather individual parts into a list, flattening the tree of parts so to say
-
-#     Parameters
-#     ----------
-#     part_grp:    score.PartGroup
-#     parts:      list of score.Part, optional
-
-#     Returns
-#     -------
-#     parts:      list of score.Part
-#     """
-#     for c in part_grp.children:
-#         if isinstance(c, score.PartGroup):
-#             unpack_part_group(c, parts)
-#         else:
-#             parts.append(c)
-
-#     return parts
-
-
-# def save_mei(
-#     parts,
-#     auto_beaming=True,
-#     file_name="testResult",
-#     title_text=None,
-#     proper_staff_grp=False,
-# ):
-#     """
-#     creates an MEI document based on the parts provided
-#     So far only <score> is used and not <part> which means all the parts are gathered in one whole score and
-#     no individual scores are defined for individual parts
-
-#     Parameters
-#     ----------
-#     parts:              score.Part, score.PartGroup or list of score.Part
-#     auto_beaming:       boolean, optional
-#         if all beaming has been done manually then set to False
-#         otherwise this flag can be used to enable automatic beaming (beaming rules are still in progess)
-#     file_name:          string, optional
-#         should not contain file extension, .mei will be added automatically
-#     title_text:         string, optional
-#         name of the piece, e.g. "Klaviersonate Nr. 14" or "WAP"
-#         if not provided, a title will be derived from file_name
-#     proper_staff_grp:   boolean, optional
-#         if true,    group staves per part
-#         else        group all staves together
-#         default is false because Verovio doesn't seem to render multiple staff groups correctly (but that just might be because multiple staff groups are not generated correctly in this function)
-#     """
-
-#     if isinstance(parts, score.PartGroup):
-#         parts = unpack_part_group(parts)
-#     elif isinstance(parts, score.Part):
-#         parts = [parts]
-
-#     for p in parts:
-#         score.sanitize_part(p)
-
-#     mei = etree.Element("mei")
-
-#     mei_head = add_child(mei, "meiHead")
-#     music = add_child(mei, "music")
-
-#     mei_head.set("xmlns", name_space)
-#     file_desc = add_child(mei_head, "fileDesc")
-#     title_stmt = add_child(file_desc, "titleStmt")
-#     pub_stmt = add_child(file_desc, "pubStmt")
-#     title = add_child(title_stmt, "title")
-#     title.set("type", "main")
-
-#     # derive a title for the piece from the file_name
-#     if title_text == None:
-#         cursor = len(file_name) - 1
-#         while cursor >= 0 and file_name[cursor] != "/":
-#             cursor -= 1
-
-#         tmp = file_name[cursor + 1 :].split("_")
-#         tmp = [s[:1].upper() + s[1:] for s in tmp]
-#         title.text = " ".join(tmp)
-#     else:
-#         title.text = title_text
-
-#     body = add_child(music, "body")
-#     mdiv = add_child(body, "mdiv")
-#     mei_score = add_child(mdiv, "score")
-
-#     classes_with_staff = [score.GenericNote, score.Words, score.Direction]
-
-#     staves_per_part = []
-
-#     staves_are_valid = True
-
-#     for p in parts:
-#         tmp = {
-#             staffed_obj.staff
-#             for cls in classes_with_staff
-#             for staffed_obj in p.iter_all(cls, include_subclasses=True)
-#         }
-#         tmp = tmp.union({clef.number for clef in p.iter_all(score.Clef)})
-#         staves_per_part.append(list(tmp))
-
-#         if None in staves_per_part[-1]:
-#             staves_are_valid = False
-#             staves_per_part[-1].remove(None)
-
-#             staves_per_part[-1].append(
-#                 (max(staves_per_part[-1]) if len(staves_per_part[-1]) > 0 else 0) + 1
-#             )
-
-#         staves_per_part[-1].sort()
-
-#     if staves_are_valid:
-#         staves_sorted = sorted([s for staves in staves_per_part for s in staves])
-
-#         i = 0
-
-#         while i + 1 < len(staves_sorted):
-#             if staves_sorted[i] == staves_sorted[i + 1]:
-#                 staves_are_valid = False
-#                 break
-
-#             i += 1
-
-#     if not staves_are_valid:
-#         staves_per_part_backup = staves_per_part
-
-#         staves_sorted = []
-#         staves_per_part = []
-
-#         # ASSUMPTION: staves are >0
-#         max_staff = 0
-#         for staves in staves_per_part_backup:
-#             if len(staves) == 0:
-#                 staves_per_part.append([])
-#             else:
-#                 shift = [s + max_staff for s in staves]
-
-#                 max_staff += max(staves)
-
-#                 staves_sorted.extend(shift)
-#                 staves_per_part.append(shift)
-
-#         # staves_sorted.sort()
-
-#         max_staff = 0
-#         for i, p in enumerate(parts):
-#             for cls in classes_with_staff:
-#                 for staff_obj in p.iter_all(cls, include_subclasses=True):
-#                     staff_obj.staff = max_staff + (
-#                         staff_obj.staff
-#                         if staff_obj.staff != None
-#                         else max(staves_per_part_backup[i])
-#                     )
-
-#             for clef in p.iter_all(score.Clef):
-#                 clef.number = max_staff + (
-#                     clef.number
-#                     if clef.number != None
-#                     else max(staves_per_part_backup[i])
-#                 )
-
-#             max_staff += (
-#                 max(staves_per_part_backup[i])
-#                 if len(staves_per_part_backup[i]) > 0
-#                 else 0
-#             )
-
-#     measures = [list(parts[0].iter_all(score.Measure))]
-#     padding_required = False
-#     max_length = len(measures[0])
-#     for i in range(1, len(parts)):
-#         m = list(parts[i].iter_all(score.Measure))
-
-#         if len(m) > max_length:
-#             max_length = len(m)
-
-#         if not padding_required:
-#             padding_required = len(m) != len(measures[0])
-
-#         measures.append(m)
-
-#     score_def = create_score_def(measures, 0, parts, mei_score)
-
-#     score_def_setup = score_def
-
-#     if score_def == None:
-#         score_def_setup = add_child(mei_score, "scoreDef")
-
-#     clefs_per_part = first_instances_per_part(score.Clef, parts)
-
-#     for i in idx(clefs_per_part):
-#         clefs_per_part[i] = partition_handle_none(
-#             lambda c: c.number, clefs_per_part[i], "number"
-#         )
-
-#     if len(clefs_per_part) == 0:
-#         create_staff_def(
-#             staff_grp, score.Clef(sign="G", line=2, number=1, octave_change=0)
-#         )
-#     else:
-#         staff_grp = add_child(score_def_setup, "staffGrp")
-#         for staves in staves_per_part:
-#             if proper_staff_grp:
-#                 staff_grp = add_child(score_def_setup, "staffGrp")
-
-#             for s in staves:
-#                 clefs = None
-
-#                 for clefs_per_staff in clefs_per_part:
-#                     if s in clefs_per_staff.keys():
-#                         clefs = clefs_per_staff[s]
-#                         break
-
-#                 if clefs != None:
-#                     clef = clefs[0]
-#                     if len(clefs) != 1:
-#                         raise ValueError(
-#                             "ERROR at staff_def creation: Staff "
-#                             + str(clef.number)
-#                             + " starts with more than 1 clef at t=0"
-#                         )
-#                     create_staff_def(staff_grp, clef)
-#                 else:
-#                     create_staff_def(
-#                         staff_grp,
-#                         score.Clef(sign="G", line=2, number=s, octave_change=0),
-#                     )
-
-#     section = add_child(mei_score, "section")
-
-#     measures_are_aligned = True
-#     if padding_required:
-#         cursors = [0] * len(measures)
-#         tempii = [None] * len(measures)
-
-#         while measures_are_aligned:
-#             compare_measures = {}
-#             for i, m in enumerate(measures):
-#                 if cursors[i] < len(m):
-#                     compare_measures[i] = m[cursors[i]]
-#                     cursors[i] += 1
-
-#             if len(compare_measures) == 0:
-#                 break
-
-#             compm_keys = list(compare_measures.keys())
-
-#             new_tempii = first_instance_per_part(
-#                 score.Tempo,
-#                 [p for i, p in enumerate(parts) if i in compm_keys],
-#                 start=[cm.start for cm in compare_measures.values()],
-#                 end=[cm.end for cm in compare_measures.values()],
-#             )
-
-#             if len(new_tempii) == 0:
-#                 for k in compm_keys:
-#                     new_tempii.append(tempii[k])
-#             else:
-#                 for i, nt in enumerate(new_tempii):
-#                     if nt == None:
-#                         new_tempii[i] = tempii[compm_keys[i]]
-#                     else:
-#                         tempii[compm_keys[i]] = nt
-
-#             def norm_dur(m):
-#                 return (m.end.t - m.start.t) // m.start.quarter
-
-#             rep_i = 0
-#             while rep_i < len(new_tempii) and new_tempii[rep_i] == None:
-#                 rep_i += 1
-
-#             if rep_i == len(new_tempii):
-#                 continue
-
-#             rep_dur = (
-#                 norm_dur(compare_measures[compm_keys[rep_i]]) * new_tempii[rep_i].bpm
-#             )
-
-#             for i in range(rep_i + 1, len(compm_keys)):
-#                 nt = new_tempii[i]
-
-#                 if nt == None:
-#                     continue
-
-#                 m = compare_measures[compm_keys[i]]
-#                 dur = norm_dur(m) * new_tempii[i].bpm
-
-#                 if dur != rep_dur:
-#                     measures_are_aligned = False
-#                     break
-
-#     tuplet_id_counter = 0
-
-#     if measures_are_aligned:
-#         time_offset = [0] * len(measures)
-
-#         if padding_required:
-#             for i, mp in enumerate(measures):
-#                 ii = len(mp)
-#                 time_offset[i] = mp[ii - 1].end.t
-#                 while ii < max_length:
-#                     mp.append("pad")
-#                     ii += 1
-
-#         notes_last_measure_per_staff = {}
-#         auto_rest_count = 0
-
-#         notes_within_measure_per_staff = notes_last_measure_per_staff
-
-#         auto_rest_count, current_measure_content = extract_from_measures(
-#             parts,
-#             measures,
-#             0,
-#             staves_per_part,
-#             auto_rest_count,
-#             notes_within_measure_per_staff,
-#         )
-
-#         last_key_sig_per_staff = {}
-
-#         for s, k in current_measure_content.key_sigs_per_staff.items():
-#             last_key_sig_per_staff[s] = (
-#                 min(k, key=lambda k: k.start.t) if len(k) > 0 else None
-#             )
-
-#         tuplet_id_counter, notes_last_measure_per_staff = create_measure(
-#             section,
-#             0,
-#             staves_sorted,
-#             notes_within_measure_per_staff,
-#             score_def,
-#             tuplet_id_counter,
-#             auto_beaming,
-#             last_key_sig_per_staff,
-#             current_measure_content,
-#         )
-
-#         for measure_i in range(1, len(measures[0])):
-#             notes_within_measure_per_staff = notes_last_measure_per_staff
-
-#             auto_rest_count, current_measure_content = extract_from_measures(
-#                 parts,
-#                 measures,
-#                 measure_i,
-#                 staves_per_part,
-#                 auto_rest_count,
-#                 notes_within_measure_per_staff,
-#             )
-
-#             score_def = create_score_def(measures, measure_i, parts, section)
-
-#             tuplet_id_counter, notes_last_measure_per_staff = create_measure(
-#                 section,
-#                 measure_i,
-#                 staves_sorted,
-#                 notes_within_measure_per_staff,
-#                 score_def,
-#                 tuplet_id_counter,
-#                 auto_beaming,
-#                 last_key_sig_per_staff,
-#                 current_measure_content,
-#             )
-
-#     (etree.ElementTree(mei)).write(file_name + ".mei", pretty_print=True)
-
-#     # post processing step necessary
-#     # etree won't write <,> and & into an element's text
-#     with open(file_name + ".mei") as result:
-#         text = list(result.read())
-#         new_text = []
-
-#         i = 0
-#         while i < len(text):
-#             ch = text[i]
-#             if ch == "&":
-#                 if text[i + 1 : i + 4] == ["l", "t", ";"]:
-#                     ch = "<"
-#                     i += 4
-#                 elif text[i + 1 : i + 4] == ["g", "t", ";"]:
-#                     ch = ">"
-#                     i += 4
-#                 elif text[i + 1 : i + 5] == ["a", "m", "p", ";"]:
-#                     i += 5
-#                 else:
-#                     i += 1
-#             else:
-#                 i += 1
-
-#             new_text.append(ch)
-
-#         new_text = "".join(new_text)
-
-#     with open(file_name + ".mei", "w") as result:
-#         result.write(new_text)
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+This module contains methods for exporting MEI files.
+"""
+import math
+from collections import defaultdict
+from lxml import etree
+import partitura.score as spt
+from operator import itemgetter
+from itertools import groupby
+from typing import Optional
+from partitura.utils import (
+    partition,
+    iter_current_next,
+    to_quarter_tempo,
+    fifths_mode_to_key_name,
+)
+import numpy as np
+import warnings
+from partitura.utils.misc import deprecated_alias, PathLike
+from partitura.utils.music import MEI_DURS_TO_SYMBOLIC, estimate_symbolic_duration
+
+
+__all__ = ["save_mei"]
+
+XMLNS_ID = "{http://www.w3.org/XML/1998/namespace}id"
+
+ALTER_TO_MEI = {
+    -2: "ff",
+    -1: "f",
+    0: "n",
+    1: "s",
+    2: "ss",
+}
+
+SYMBOLIC_TYPES_TO_MEI_DURS = {v: k for k, v in MEI_DURS_TO_SYMBOLIC.items()}
+SYMBOLIC_TYPES_TO_MEI_DURS["h"] = "2"
+SYMBOLIC_TYPES_TO_MEI_DURS["e"] = "8"
+SYMBOLIC_TYPES_TO_MEI_DURS["q"] = "4"
+
+DOCTYPE = '<?xml-model href="https://music-encoding.org/schema/4.0.1/mei-CMN.rng" type="application/xml" schematypens="http://relaxng.org/ns/structure/1.0"?>\n<?xml-model href="https://music-encoding.org/schema/4.0.1/mei-CMN.rng" type="application/xml" schematypens="http://purl.oclc.org/dsdl/schematron"?>'
+
+
+class MEIExporter:
+    def __init__(self, part, title=None):
+        self.part = part
+        self.qdivs = part._quarter_durations[0]
+        self.num_staves = part.number_of_staves
+        self.title = title
+        self.element_counter = 0
+        self.current_key_signature = []
+        self.flats = ["bf", "ef", "af", "df", "gf", "cf", "ff"]
+        self.sharps = ["fs", "cs", "gs", "ds", "as", "es", "bs"]
+
+    def elc_id(self):
+        # transforms an integer number to 8-digit string
+        # The number is right aligned and padded with zeros
+        self.element_counter += 1
+        out = str(self.element_counter).zfill(10)
+        return out
+
+    def export_to_mei(self):
+        # Create root MEI element
+        etree.register_namespace("xml", "http://www.w3.org/XML/1998/namespace")
+        etree.register_namespace("mei", "http://www.music-encoding.org/ns/mei")
+        mei = etree.Element(
+            "mei",
+            nsmap={
+                "xml": "http://www.w3.org/XML/1998/namespace",
+                None: "http://www.music-encoding.org/ns/mei",
+            },
+        )
+        # mei.set('xmlns', "http://www.music-encoding.org/ns/mei")
+        mei.set("meiversion", "4.0.1")
+        # Create child elements
+        mei_head = etree.SubElement(mei, "meiHead")
+        file_desc = etree.SubElement(mei_head, "fileDesc")
+        # write the title
+        title_stmt = etree.SubElement(file_desc, "titleStmt")
+        title = etree.SubElement(title_stmt, "title")
+        if self.title is not None:
+            title.text = self.title
+        else:
+            title.text = self.part.id if self.part.id is not None else "Untitled"
+        music = etree.SubElement(mei, "music")
+        body = etree.SubElement(music, "body")
+        mdiv = etree.SubElement(body, "mdiv")
+        score = etree.SubElement(mdiv, "score")
+        score.set(XMLNS_ID, "score-" + self.elc_id())
+        score_def = etree.SubElement(score, "scoreDef")
+        score_def.set(XMLNS_ID, "scoredef-" + self.elc_id())
+        staff_grp = etree.SubElement(score_def, "staffGrp")
+        staff_grp.set(XMLNS_ID, "staffgrp-" + self.elc_id())
+        staff_grp.set("bar.thru", "true")
+        self._handle_staffs(staff_grp)
+        section = etree.SubElement(score, "section")
+        section.set(XMLNS_ID, "section-" + self.elc_id())
+        # Iterate over part's timeline
+        for measure in self.part.measures:
+            # Create measure element
+            xml_el = etree.SubElement(section, "measure")
+            self._handle_measure(measure, xml_el)
+
+        return mei
+
+    def _handle_staffs(self, xml_el):
+        clefs = self.part.iter_all(spt.Clef, start=0, end=1)
+        clefs = {c.staff: c for c in clefs}
+        key_sigs = list(self.part.iter_all(spt.KeySignature, start=0, end=1))
+        keys_sig = key_sigs[0] if len(key_sigs) > 0 else None
+        time_sigs = list(self.part.iter_all(spt.TimeSignature, start=0, end=1))
+        time_sig = time_sigs[0] if len(time_sigs) > 0 else None
+        for staff_num in range(self.part.number_of_staves):
+            staff_num += 1
+            staff_def = etree.SubElement(xml_el, "staffDef")
+            staff_def.set("n", str(staff_num))
+            staff_def.set(XMLNS_ID, "staffdef-" + self.elc_id())
+            staff_def.set("lines", "5")
+            # Get clef for this staff If no cleff is available for this staff, default to "G2"
+            clef_def = etree.SubElement(staff_def, "clef")
+            clef_def.set(XMLNS_ID, "clef-" + self.elc_id())
+            clef_shape = clefs[staff_num].sign if staff_num in clefs.keys() else "G"
+            clef_def.set("shape", str(clef_shape))
+            (
+                clef_def.set("line", str(clefs[staff_num].line))
+                if staff_num in clefs.keys()
+                else clef_def.set("line", "2")
+            )
+            # Get key signature for this staff
+            if keys_sig is not None:
+                ks_def = etree.SubElement(staff_def, "keySig")
+                ks_def.set(XMLNS_ID, "keysig-" + self.elc_id())
+                (
+                    ks_def.set("mode", keys_sig.mode)
+                    if keys_sig.mode is not None
+                    else ks_def.set("mode", "major")
+                )
+                if keys_sig.fifths == 0:
+                    ks_def.set("sig", "0")
+                elif keys_sig.fifths > 0:
+                    ks_def.set("sig", str(keys_sig.fifths) + "s")
+                    self.current_key_signature = self.sharps[: keys_sig.fifths]
+                else:
+                    ks_def.set("sig", str(abs(keys_sig.fifths)) + "f")
+                    self.current_key_signature = self.flats[: abs(keys_sig.fifths)]
+                # Find the pname from the number of sharps or flats and the mode
+                ks_def.set(
+                    "pname",
+                    fifths_mode_to_key_name(keys_sig.fifths, keys_sig.mode).lower()[
+                        0
+                    ],  # only the first letter
+                )
+
+            if time_sig is not None:
+                ts_def = etree.SubElement(staff_def, "meterSig")
+                ts_def.set(XMLNS_ID, "msig-" + self.elc_id())
+                ts_def.set("count", str(time_sig.beats))
+                ts_def.set("unit", str(time_sig.beat_type))
+
+    def _handle_measure(self, measure, measure_el):
+        # Add measure number
+        measure_el.set("n", str(measure.number))
+        measure_el.set(XMLNS_ID, "measure-" + self.elc_id())
+        note_or_rest_elements = np.array(
+            list(
+                self.part.iter_all(
+                    spt.GenericNote,
+                    start=measure.start.t,
+                    end=measure.end.t,
+                    include_subclasses=True,
+                )
+            )
+        )
+        # Separate by staff
+        staffs = np.vectorize(lambda x: x.staff)(note_or_rest_elements)
+        voices = np.vectorize(lambda x: x.voice)(note_or_rest_elements)
+        unique_staffs, staff_inverse_map = np.unique(staffs, return_inverse=True)
+        unique_voices_par = np.unique(voices)
+        voice_staff_map = {
+            v: {
+                "mask": voices == v,
+                "staff": np.bincount(
+                    staffs[voices == v], minlength=self.num_staves
+                ).argmax(),
+            }
+            for v in unique_voices_par
+        }
+        for i in range(self.num_staves):
+            staff = i + 1
+            staff_el = etree.SubElement(measure_el, "staff")
+            # Add staff number
+            staff_el.set("n", str(staff))
+            staff_el.set(XMLNS_ID, "staff-" + self.elc_id())
+            if staff not in unique_staffs:
+                continue
+            staff_notes = note_or_rest_elements[staff_inverse_map == i]
+            # Separate by voice
+            voices = np.vectorize(lambda x: x.voice)(staff_notes)
+            unique_voices, voice_inverse_map = np.unique(voices, return_inverse=True)
+            for j, voice in enumerate(unique_voices):
+                voice_el = etree.SubElement(staff_el, "layer")
+                voice_el.set("n", str(voice))
+                voice_el.set(XMLNS_ID, "voice-" + self.elc_id())
+                # try to handle cross-staff beaming
+                if voice_staff_map[voice]["staff"] != staff:
+                    continue
+                voice_notes = note_or_rest_elements[voice_staff_map[voice]["mask"]]
+                # Sort by onset
+                note_start_times = np.vectorize(lambda x: x.start.t)(voice_notes)
+                unique_onsets = np.unique(note_start_times)
+                for onset in unique_onsets:
+                    # group by start time
+                    notes = voice_notes[note_start_times == onset]
+                    if len(notes) > 1:
+                        self._handle_chord(notes, voice_el)
+                    else:
+                        self._handle_note_or_rest(notes[0], voice_el)
+
+        self._handle_tuplets(measure_el, start=measure.start.t, end=measure.end.t)
+        self._handle_beams(measure_el, start=measure.start.t, end=measure.end.t)
+        self._handle_clef_changes(measure_el, start=measure.start.t, end=measure.end.t)
+        self._handle_ks_changes(measure_el, start=measure.start.t, end=measure.end.t)
+        self._handle_ts_changes(measure_el, start=measure.start.t, end=measure.end.t)
+        self._handle_harmony(measure_el, start=measure.start.t, end=measure.end.t)
+        self._handle_fermata(measure_el, start=measure.start.t, end=measure.end.t)
+        self._handle_barline(measure_el, start=measure.start.t, end=measure.end.t)
+        return measure_el
+
+    def _handle_chord(self, chord, xml_voice_el):
+        chord_el = etree.SubElement(xml_voice_el, "chord")
+        chord_el.set(XMLNS_ID, "chord-" + self.elc_id())
+        for note in chord:
+            duration = self._handle_note_or_rest(note, chord_el)
+        chord_el.set("dur", duration)
+
+    def _handle_note_or_rest(self, note, xml_voice_el):
+        if isinstance(note, spt.Rest):
+            duration = self._handle_rest(note, xml_voice_el)
+        else:
+            duration = self._handle_note(note, xml_voice_el)
+        return duration
+
+    def _handle_rest(self, rest, xml_voice_el):
+        rest_el = etree.SubElement(xml_voice_el, "rest")
+        if "type" not in rest.symbolic_duration.keys():
+            rest.symbolic_duration = estimate_symbolic_duration(
+                rest.end.t - rest.start.t, div=self.qdivs
+            )
+        duration = SYMBOLIC_TYPES_TO_MEI_DURS[rest.symbolic_duration["type"]]
+        rest_el.set("dur", duration)
+        if "dots" in rest.symbolic_duration:
+            rest_el.set("dots", str(rest.symbolic_duration["dots"]))
+        if rest.id is None:
+            rest.id = "rest-" + self.elc_id()
+        rest_el.set(XMLNS_ID, rest.id)
+        return duration
+
+    def _handle_note(self, note, xml_voice_el):
+        note_el = etree.SubElement(xml_voice_el, "note")
+        duration = SYMBOLIC_TYPES_TO_MEI_DURS[note.symbolic_duration["type"]]
+        note_el.set("dur", duration)
+        (
+            note_el.set(XMLNS_ID, "note-" + self.elc_id())
+            if note.id is None
+            else note_el.set(XMLNS_ID, note.id)
+        )
+        if "dots" in note.symbolic_duration:
+            note_el.set("dots", str(note.symbolic_duration["dots"]))
+        note_el.set("oct", str(note.octave))
+        note_el.set("pname", note.step.lower())
+        note_el.set("staff", str(note.staff))
+        if note.tie_next is not None and note.tie_prev is not None:
+            note_el.set("tie", "m")
+        elif note.tie_next is not None:
+            note_el.set("tie", "i")
+        elif note.tie_prev is not None:
+            note_el.set("tie", "t")
+
+        if note.alter is not None:
+            if (
+                note.step.lower() + ALTER_TO_MEI[note.alter]
+                in self.current_key_signature
+            ):
+                note_el.set("accid.ges", ALTER_TO_MEI[note.alter])
+            else:
+                accidental = etree.SubElement(note_el, "accid")
+                accidental.set(XMLNS_ID, "accid-" + self.elc_id())
+                accidental.set("accid", ALTER_TO_MEI[note.alter])
+
+        if isinstance(note, spt.GraceNote):
+            note_el.set("grace", "acc")
+        return duration
+
+    def _handle_tuplets(self, measure_el, start, end):
+        for tuplet in self.part.iter_all(spt.Tuplet, start=start, end=end):
+            start_note = tuplet.start_note
+            end_note = tuplet.end_note
+            if start_note.start.t < start or end_note.end.t > end:
+                warnings.warn(
+                    "Tuplet start or end note is outside of the measure. Skipping tuplet element."
+                )
+                continue
+            if start_note.start.t > end_note.start.t:
+                warnings.warn(
+                    "Tuplet start note is after end note. Skipping tuplet element."
+                )
+                continue
+            # Skip if start and end notes are in different voices or staves
+            if start_note.voice != end_note.voice or start_note.staff != end_note.staff:
+                warnings.warn(
+                    "Tuplet start and end notes are in different voices or staves. Skipping tuplet element."
+                )
+                continue
+            # Find the note element corresponding to the start note i.e. has the same id value
+            start_note_el = measure_el.xpath(f".//*[@xml:id='{start_note.id}']")[0]
+            # Find the note element corresponding to the end note i.e. has the same id value
+            end_note_el = measure_el.xpath(f".//*[@xml:id='{end_note.id}']")[0]
+            # if start or note element parents are chords, tuplet element should be added as parent of the chord element
+            start_note_el = (
+                start_note_el.getparent()
+                if start_note_el.getparent().tag == "chord"
+                else start_note_el
+            )
+            end_note_el = (
+                end_note_el.getparent()
+                if end_note_el.getparent().tag == "chord"
+                else end_note_el
+            )
+            # Create the tuplet element as parent of the start and end note elements
+            # Make it start at the same index as the start note element
+            tuplet_el = etree.Element("tuplet")
+            layer_el = start_note_el.getparent()
+            layer_el.insert(layer_el.index(start_note_el), tuplet_el)
+            tuplet_el.set(XMLNS_ID, "tuplet-" + self.elc_id())
+            tuplet_el.set("num", str(start_note.symbolic_duration["actual_notes"]))
+            tuplet_el.set("numbase", str(start_note.symbolic_duration["normal_notes"]))
+            # Add all elements between the start and end note elements to the tuplet element as childen
+            # Find them from the xml tree
+            start_note_index = start_note_el.getparent().index(start_note_el)
+            end_note_index = end_note_el.getparent().index(end_note_el)
+            # If the start and end note elements are not in order skip (it a weird bug that happens sometimes)
+            if start_note_index > end_note_index:
+                continue
+            xml_el_within_tuplet = [
+                start_note_el.getparent()[i]
+                for i in range(start_note_index, end_note_index + 1)
+            ]
+            for el in xml_el_within_tuplet:
+                tuplet_el.append(el)
+
+    def _handle_beams(self, measure_el, start, end):
+        for beam in self.part.iter_all(spt.Beam, start=start, end=end):
+            # If the beam has only one note, skip it
+            if len(beam.notes) < 2:
+                continue
+            start_note = beam.notes[np.argmin([n.start.t for n in beam.notes])]
+            # Beam element is parent of the note element
+            note_el = measure_el.xpath(f".//*[@xml:id='{start_note.id}']")[0]
+            layer_el = note_el.getparent()
+            insert_index = layer_el.index(note_el)
+            # If the parent is a tuplet, the beam element should be added as parent of the tuplet element
+            if layer_el.tag == "tuplet":
+                parent_el = layer_el.getparent()
+                insert_index = parent_el.index(layer_el)
+                layer_el = parent_el
+            # If the parent is a chord, the beam element should be added as parent of the chord element
+            if layer_el.tag == "chord":
+                parent_el = layer_el.getparent()
+                if parent_el.tag == "tuplet":
+                    parent_el = parent_el.getparent()
+                    insert_index = parent_el.index(layer_el.getparent())
+                    layer_el = parent_el
+                else:
+                    insert_index = parent_el.index(layer_el)
+                    layer_el = parent_el
+
+            # Create the beam element
+            beam_el = etree.Element("beam")
+            layer_el.insert(insert_index, beam_el)
+            beam_el.set(XMLNS_ID, "beam-" + self.elc_id())
+            for note in beam.notes:
+                # Find the note element corresponding to the start note i.e. has the same id value
+                note_el = measure_el.xpath(f".//*[@xml:id='{note.id}']")
+                if len(note_el) > 0:
+                    note_el = note_el[0]
+                    # Add the note element to the beam element but if the parent is a tuplet, the note element should be added as child of the tuplet element
+                    if note_el.getparent().tag == "tuplet":
+                        beam_el.append(note_el.getparent())
+                    elif note_el.getparent().tag == "chord":
+                        if note_el.getparent().getparent().tag == "tuplet":
+                            beam_el.append(note_el.getparent().getparent())
+                        else:
+                            beam_el.append(note_el.getparent())
+                    else:
+                        # verify that the note element is not already a child of the beam element
+                        if note_el.getparent() != beam_el:
+                            beam_el.append(note_el)
+
+    def _handle_clef_changes(self, measure_el, start, end):
+        for clef in self.part.iter_all(spt.Clef, start=start, end=end):
+            # Clef element is parent of the note element
+            if clef.start.t == 0:
+                continue
+            # Find the note element corresponding to the start note i.e. has the same id value
+            for note in self.part.iter_all(
+                spt.GenericNote, start=clef.start.t, end=clef.start.t
+            ):
+                note_el = measure_el.xpath(f".//*[@xml:id='{note.id}']")
+                if len(note_el) > 0:
+                    note_el = note_el[0]
+                    layer_el = note_el.getparent()
+                    insert_index = layer_el.index(note_el)
+                    # Create the clef element
+                    clef_el = etree.Element("clef")
+                    layer_el.insert(insert_index, clef_el)
+                    clef_el.set(XMLNS_ID, "clef-" + self.elc_id())
+                    clef_el.set("shape", str(clef.sign))
+                    clef_el.set("line", str(clef.line))
+
+    def _handle_ks_changes(self, measure_el, start, end):
+        # For key signature changes, we add a new scoreDef element at the beginning of the measure
+        # and add the key signature element as attributes of the scoreDef element
+        for key_sig in self.part.iter_all(spt.KeySignature, start=start, end=end):
+            if key_sig.start.t == 0:
+                continue
+            # Create the scoreDef element
+            score_def_el = etree.Element("scoreDef")
+            score_def_el.set(XMLNS_ID, "scoredef-" + self.elc_id())
+            (
+                score_def_el.set("mode", key_sig.mode)
+                if key_sig.mode is not None
+                else score_def_el.set("mode", "major")
+            )
+            if key_sig.fifths == 0:
+                score_def_el.set("sig", "0")
+            elif key_sig.fifths > 0:
+                score_def_el.set("sig", str(key_sig.fifths) + "s")
+                self.current_key_signature = self.sharps[: key_sig.fifths]
+            else:
+                score_def_el.set("sig", str(abs(key_sig.fifths)) + "f")
+                self.current_key_signature = self.flats[: abs(key_sig.fifths)]
+            # Find the pname from the number of sharps or flats and the mode
+            score_def_el.set(
+                "pname", fifths_mode_to_key_name(key_sig.fifths, key_sig.mode).lower()
+            )
+            # Add the scoreDef element at before the measure element starts
+            parent = measure_el.getparent()
+            parent.insert(parent.index(measure_el), score_def_el)
+
+    def _handle_ts_changes(self, measure_el, start, end):
+        # For key signature changes, we add a new scoreDef element at the beginning of the measure
+        # and add the key signature element as attributes of the scoreDef element
+        for time_sig in self.part.iter_all(spt.TimeSignature, start=start, end=end):
+            if time_sig.start.t == 0:
+                continue
+            # Create the scoreDef element
+            score_def_el = etree.Element("scoreDef")
+            score_def_el.set(XMLNS_ID, "scoredef-" + self.elc_id())
+
+            # Add the scoreDef element at before the measure element starts
+            parent = measure_el.getparent()
+            parent.insert(parent.index(measure_el), score_def_el)
+            score_def_el.set("count", str(time_sig.beats))
+            score_def_el.set("unit", str(time_sig.beat_type))
+
+    def _handle_harmony(self, measure_el, start, end):
+        """
+        For harmonies we add a new harm element at the beginning of the measure.
+        The position doesn't really matter since the tstamp attribute will place it correctly
+        The harmonies will be displayed below the lowest staff.
+        """
+        for harmony in self.part.iter_all(spt.RomanNumeral, start=start, end=end):
+            harm_el = etree.SubElement(measure_el, "harm")
+            harm_el.set(XMLNS_ID, "harm-" + self.elc_id())
+            harm_el.set("staff", str(self.part.number_of_staves))
+            harm_el.set(
+                "tstamp",
+                str(np.diff(self.part.quarter_map([start, harmony.start.t]))[0] + 1),
+            )
+            harm_el.set("place", "below")
+            # text is a child element of harmony but not a xml element
+            harm_el.text = harmony.text
+
+        for harmony in self.part.iter_all(spt.Cadence, start=start, end=end):
+            # if there is already a harmony at the same position, add the cadence to the text of the harmony
+            harm_els = measure_el.xpath(
+                f".//harm[@tstamp='{np.diff(self.part.quarter_map([start, harmony.start.t]))[0] + 1}']"
+            )
+            if len(harm_els) > 0:
+                harm_el = harm_els[0]
+                harm_el.text += " |" + harmony.text
+            else:
+
+                harm_el = etree.SubElement(measure_el, "harm")
+                harm_el.set(XMLNS_ID, "harm-" + self.elc_id())
+                harm_el.set("staff", str(self.part.number_of_staves))
+                harm_el.set(
+                    "tstamp",
+                    str(
+                        np.diff(self.part.quarter_map([start, harmony.start.t]))[0] + 1
+                    ),
+                )
+                harm_el.set("place", "below")
+                # text is a child element of harmony but not a xml element
+                harm_el.text = "|" + harmony.text
+
+    def _handle_fermata(self, measure_el, start, end):
+        for fermata in self.part.iter_all(spt.Fermata, start=start, end=end):
+            if fermata.ref is not None:
+                note = fermata.ref
+                note_el = measure_el.xpath(f".//*[@xml:id='{note.id}']")
+                if len(note_el) > 0:
+                    note_el[0].set("fermata", "above")
+            else:
+                fermata_el = etree.SubElement(measure_el, "fermata")
+                fermata_el.set(XMLNS_ID, "fermata-" + self.elc_id())
+                fermata_el.set(
+                    "tstamp",
+                    str(
+                        np.diff(self.part.quarter_map([start, fermata.start.t]))[0] + 1
+                    ),
+                )
+                # Set the fermata to be above the staff (the highest staff)
+                fermata_el.set("staff", "1")
+
+    def _handle_barline(self, measure_el, start, end):
+        for end_barline in self.part.iter_all(
+            spt.Ending, start=end, end=end + 1, mode="ending"
+        ):
+            measure_el.set("right", "end")
+        for end_barline in self.part.iter_all(
+            spt.Barline, start=end, end=end + 1, mode="starting"
+        ):
+            if end_barline.style == "light-heavy":
+                measure_el.set("right", "end")
+        for end_repeat in self.part.iter_all(
+            spt.Repeat, start=end, end=end + 1, mode="ending"
+        ):
+            measure_el.set("right", "rptend")
+        for start_repeat in self.part.iter_all(
+            spt.Repeat, start=start, end=start + 1, mode="starting"
+        ):
+            measure_el.set("left", "rptstart")
+
+
+@deprecated_alias(parts="score_data")
+def save_mei(
+    score_data: spt.ScoreLike,
+    out: Optional[PathLike] = None,
+    title: Optional[str] = None,
+) -> Optional[str]:
+    """
+    Save a one or more Part or PartGroup instances in MEI format.
+
+    Parameters
+    ----------
+    score_data : Score, list, Part, or PartGroup
+        The musical score to be saved. A :class:`partitura.score.Score` object,
+        a :class:`partitura.score.Part`, a :class:`partitura.score.PartGroup` or
+        a list of these.
+    out: str, file-like object, or None, optional
+        Output file
+
+    Returns
+    -------
+    None or str
+        If no output file is specified using `out` the function returns the
+        MEI data as a string. Otherwise the function returns None.
+    """
+
+    if isinstance(score_data, spt.Score):
+        parts = score_data.parts
+    elif isinstance(score_data, list):
+        parts = score_data
+    else:
+        parts = [score_data]
+
+    if len(parts) > 1:
+        raise ValueError("Partitura supports only one part or PartGroup per MEI file.")
+
+    score_data = parts[0]
+
+    exporter = MEIExporter(score_data, title=title)
+    root = exporter.export_to_mei()
+
+    if out:
+        if hasattr(out, "write"):
+            out.write(
+                etree.tostring(
+                    root.getroottree(),
+                    encoding="UTF-8",
+                    xml_declaration=True,
+                    pretty_print=True,
+                    doctype=DOCTYPE,
+                )
+            )
+
+        else:
+            with open(out, "wb") as f:
+                f.write(
+                    etree.tostring(
+                        root.getroottree(),
+                        encoding="UTF-8",
+                        xml_declaration=True,
+                        pretty_print=True,
+                        doctype=DOCTYPE,
+                    )
+                )
+
+    else:
+        return etree.tostring(
+            root.getroottree(),
+            encoding="UTF-8",
+            xml_declaration=True,
+            pretty_print=True,
+            doctype=DOCTYPE,
+        )
diff --git a/partitura/io/exportmidi.py b/partitura/io/exportmidi.py
index c1639bb1..fda24f74 100644
--- a/partitura/io/exportmidi.py
+++ b/partitura/io/exportmidi.py
@@ -13,7 +13,7 @@
 import partitura.score as score
 from partitura.score import Score, Part, PartGroup, ScoreLike
 from partitura.performance import Performance, PerformedPart, PerformanceLike
-from partitura.utils import partition
+from partitura.utils import partition, fifths_mode_to_key_name
 
 from partitura.utils.misc import deprecated_alias, PathLike
 
@@ -138,6 +138,43 @@ def save_performance_midi(
 
     track_events = defaultdict(lambda: defaultdict(list))
     for performed_part in performed_parts:
+
+        for c in performed_part.meta_other:
+            track = c.get("track", 0)
+            t = int(np.round(10**6 * ppq * c["time"] / mpq))
+            msg_info = dict(
+                [
+                    (key, val)
+                    for key, val in c.items()
+                    if key not in ("time", "time_tick", "track")
+                ]
+            )
+            track_events[track][t].append(MetaMessage(**msg_info))
+
+        for c in performed_part.key_signatures:
+            track = c.get("track", 0)
+            t = int(np.round(10**6 * ppq * c["time"] / mpq))
+            track_events[track][t].append(
+                MetaMessage(
+                    type="key_signature",
+                    key=fifths_mode_to_key_name(
+                        fifths=c.get("fifths", 0),
+                        mode=c.get("mode", None),
+                    ),
+                )
+            )
+
+        for c in performed_part.time_signatures:
+            track = c.get("track", 0)
+            t = int(np.round(10**6 * ppq * c["time"] / mpq))
+            track_events[track][t].append(
+                MetaMessage(
+                    type="time_signature",
+                    numerator=c.get("beats", 4),
+                    denominator=c.get("beat_type", 4),
+                ),
+            )
+
         for c in performed_part.controls:
             track = c.get("track", 0)
             ch = c.get("channel", 1)
@@ -364,6 +401,9 @@ def to_ppq(t):
             tempos[to_ppq(tp.start.t)] = MetaMessage(
                 "set_tempo", tempo=tp.microseconds_per_quarter
             )
+        # default tempo
+        if not tempos:
+            tempos[0] = MetaMessage("set_tempo", tempo=500000)
 
         if anacrusis_behavior == "time_sig_change":
             # Change time signature to match the duration of the measure
diff --git a/partitura/io/exportmusicxml.py b/partitura/io/exportmusicxml.py
index 0abe508b..168fd430 100644
--- a/partitura/io/exportmusicxml.py
+++ b/partitura/io/exportmusicxml.py
@@ -834,6 +834,17 @@ def do_harmony(part, start, end):
             bass_step_e = etree.SubElement(bass_e, "bass-step")
             bass_step_e.text = h.bass
         result.append((h.start.t, None, harmony_e))
+
+    # Does harmony annotation for cadences
+    # TODO: Merge with existing Roman Numeral and ChordSymbol annotations if they exist.
+    harmony = part.iter_all(score.Cadence, start, end)
+    for h in harmony:
+        harmony_e = etree.Element("harmony", print_frame="no")
+        function = etree.SubElement(harmony_e, "function")
+        function.text = "|" + h.text
+        kind_e = etree.SubElement(harmony_e, "kind", text="")
+        kind_e.text = "none"
+        result.append((h.start.t, None, harmony_e))
     return result
 
 
diff --git a/partitura/io/importdcml.py b/partitura/io/importdcml.py
new file mode 100644
index 00000000..5feee486
--- /dev/null
+++ b/partitura/io/importdcml.py
@@ -0,0 +1,353 @@
+import warnings
+import numpy as np
+from math import ceil
+import partitura.score as spt
+from partitura.score import process_local_key
+from partitura.utils.music import estimate_symbolic_duration
+
+try:
+    import pandas as pd
+except ImportError:
+    pd = None
+
+
+def read_note_tsv(note_tsv_path, metadata=None):
+    data = pd.read_csv(note_tsv_path, sep="\t")
+    # Hack for empty values in quarterbeats, to investigate.
+    # (It happens with voltas when the second volta has a different number of measures)
+    if not np.all(data["quarterbeats"].isna() == False):
+        data = data[~data["quarterbeats"].isna()]
+    data["quarterbeats"] = (
+        data["quarterbeats"].apply(eval)
+        if data.dtypes["quarterbeats"] == str or data.dtypes["quarterbeats"] == object
+        else data["quarterbeats"]
+    )
+    unique_durations = data["duration"].unique()
+    denominators = [int(qb.split("/")[1]) for qb in unique_durations if "/" in qb]
+    # transform quarter_beats to quarter_divs
+    qdivs = np.lcm.reduce(denominators) if len(denominators) > 0 else 4
+    quarter_durations = data["duration_qb"]
+    duration_div = np.array([ceil(qd * qdivs) for qd in quarter_durations])
+    onset_div = np.array([ceil(qd * qdivs) for qd in data["quarterbeats"]])
+    data["alter"] = data["name"].str.count("#") - data["name"].str.count("b")
+    data["step"] = data["name"].apply(lambda x: x[0])
+    data["onset_div"] = onset_div
+    data["duration_div"] = duration_div
+    data["pitch"] = data["midi"]
+    grace_mask = (
+        ~data["gracenote"].isna().to_numpy()
+        if "gracenote" in data.columns
+        else np.zeros(len(data), dtype=bool)
+    )
+    data["id"] = np.arange(len(data))
+    # Rewrite Voices for correct export
+    # taking the maximum voice number for the entire staff, and having the second staff starting from that number.
+    staffs = data["staff"].unique()
+    re_index_voice_value = 0
+    for staff in staffs:
+        staff_mask = data["staff"] == staff
+        # add re_index_voice_value to the voice values of the staff
+        data.loc[staff_mask, "voice"] += re_index_voice_value
+        # update re_index_voice_value
+        re_index_voice_value = data.loc[staff_mask, "voice"].max()
+
+    note_array = data[
+        [
+            "onset_div",
+            "duration_div",
+            "pitch",
+            "step",
+            "alter",
+            "octave",
+            "id",
+            "staff",
+            "voice",
+        ]
+    ].to_records(index=False)
+    part = spt.Part("P0", "Metadata", quarter_duration=qdivs)
+
+    # Add notes and grace notes
+    for n_idx, note in enumerate(note_array):
+        if grace_mask[n_idx]:
+            # verify that staff and voice are the same for the grace note and the main note
+            note_el = spt.GraceNote(
+                grace_type="grace",
+                id="n-{}".format(note["id"]),
+                step=note["step"],
+                octave=note["octave"],
+                alter=note["alter"],
+                staff=note["staff"],
+                voice=note["voice"],
+                symbolic_duration={"type": "eighth"},
+            )
+
+            if grace_mask[n_idx - 1]:
+                prev_note = note_array[n_idx - 1]
+                for note_prev in part.iter_all(
+                    spt.GraceNote, note["onset_div"], note["onset_div"] + 1
+                ):
+                    if note_prev.id == "n-{}".format(prev_note["id"]):
+                        note_el.grace_prev = note_prev
+                        note_prev.grace_next = note_el
+                        break
+        else:
+            symbolic_duration = estimate_symbolic_duration(note["duration_div"], qdivs)
+            note_el = spt.Note(
+                id="n-{}".format(note["id"]),
+                step=note["step"],
+                octave=note["octave"],
+                alter=note["alter"],
+                staff=note["staff"],
+                voice=note["voice"],
+                symbolic_duration=symbolic_duration,
+            )
+        part.add(
+            note_el,
+            start=note["onset_div"],
+            end=(note["onset_div"] + note["duration_div"]),
+        )
+
+    # Curate grace notes
+    grace_note_idxs = np.where(grace_mask)[0]
+    for i, grace_el in enumerate(part.iter_all(spt.GraceNote)):
+        grace_idx = grace_note_idxs[i]
+        note = note_array[grace_idx]
+        # Find the next note in the same staff and voice
+        if not grace_mask[grace_idx + 1]:
+            i = 1
+            next_note = note_array[grace_idx + i]
+            while (
+                note["staff"] != next_note["staff"]
+                or note["voice"] != next_note["voice"]
+            ):
+                i += 1
+                next_note = note_array[grace_idx + i]
+                if i > 10:
+                    warnings.warn(
+                        "Grace note ignored, no matching main note found within 10 notes."
+                    )
+                    break
+            assert (
+                note["staff"] == next_note["staff"]
+            ), "Grace note and main note must be in the same staff"
+            assert (
+                note["voice"] == next_note["voice"]
+            ), "Grace note and main note must be in the same voice"
+            assert (
+                note["onset_div"] == next_note["onset_div"]
+            ), "Grace note and main note must have the same onset"
+            for note in part.iter_all(
+                spt.Note, note["onset_div"], note["onset_div"] + 1
+            ):
+                if note.id == "n-{}".format(next_note["id"]):
+                    grace_el.grace_next = note
+                    break
+
+    # Find time signatures
+    time_signatures_changes = data["timesig"][
+        data["timesig"].shift(1) != data["timesig"]
+    ].index
+    time_signatures = data["timesig"][time_signatures_changes]
+    start_divs = np.array(
+        [int(qd * qdivs) for qd in data["quarterbeats"][time_signatures_changes]]
+    )
+    end_of_piece = (note_array["onset_div"] + note_array["duration_div"]).max()
+    end_divs = np.r_[start_divs[1:], end_of_piece]
+    for ts, start, end in zip(time_signatures, start_divs, end_divs):
+        part.add(
+            spt.TimeSignature(
+                beats=int(ts.split("/")[0]), beat_type=int(ts.split("/")[1])
+            ),
+            start=start,
+            end=end,
+        )
+
+    # Add default clefs for piano pieces (Naive)
+    part.add(spt.Clef(staff=1, sign="G", line=2, octave_change=0), start=0)
+    part.add(spt.Clef(staff=2, sign="F", line=4, octave_change=0), start=0)
+
+    # Add Ties
+    tied_note_mask = data["tied"] == 1
+    for tied_note in note_array[tied_note_mask]:
+        for note in part.iter_all(
+            spt.Note, tied_note["onset_div"], tied_note["onset_div"] + 1
+        ):
+            if note.id == "n-{}".format(tied_note["id"]):
+                found_next = False
+                for note_next in part.iter_all(
+                    spt.Note, note.end.t, note.end.t + 1, mode="starting"
+                ):
+                    condition = (
+                        note_next.alter == note.alter
+                        and note_next.step == note.step
+                        and note_next.octave == note.octave
+                        and note.voice == note_next.voice
+                        and note.staff == note_next.staff
+                    )
+                    if condition:
+                        note.tie_next = note_next
+                        note_next.tie_prev = note
+                        found_next = condition
+                        break
+                if not found_next:
+                    warnings.warn("Opening tie, but no matching note found.")
+
+    return part
+
+
+def read_measure_tsv(measure_tsv_path, part):
+    qdivs = part._quarter_durations[0]
+    data = pd.read_csv(measure_tsv_path, sep="\t")
+    # Hack for empty values in quarterbeats, to investigate.
+    # (It happens with voltas when the second volta has a different number of measures)
+    if not np.all(data["quarterbeats"].isna() == False):
+        data = data[~data["quarterbeats"].isna()]
+    data["quarterbeats"] = (
+        data["quarterbeats"].apply(eval)
+        if data.dtypes["quarterbeats"] == str or data.dtypes["quarterbeats"] == object
+        else data["quarterbeats"]
+    )
+    data["onset_div"] = np.array([int(qd * qdivs) for qd in data["quarterbeats"]])
+    data["duration_div"] = np.array([int(qd * qdivs) for qd in data["duration_qb"]])
+    # Get first index
+    repeat_index, _ = next(data.iterrows())
+
+    for idx, row in data.iterrows():
+        part.add(
+            spt.Measure(number=row["mc"], name=row["mn"]),
+            start=row["onset_div"],
+            end=row["onset_div"] + row["duration_div"],
+        )
+
+        if row["repeats"] == "start":
+            repeat_index = idx
+        elif row["repeats"] == "end":
+            # Find the previous repeat start
+            start_times = data.iloc[repeat_index]["onset_div"]
+            part.add(spt.Repeat(), start=start_times, end=row["onset_div"])
+
+    part.add(spt.Fine(), start=part.last_point.t)
+    return
+
+
+def read_harmony_tsv(beat_tsv_path, part):
+    qdivs = part._quarter_durations[0]
+    data = pd.read_csv(beat_tsv_path, sep="\t")
+    # Hack for empty values in quarterbeats, to investigate.
+    # (It happens with voltas when the second volta has a different number of measures)
+    if not np.all(data["quarterbeats"].isna() == False):
+        data = data[~data["quarterbeats"].isna()]
+    data["quarterbeats"] = (
+        data["quarterbeats"].apply(eval)
+        if data.dtypes["quarterbeats"] == str or data.dtypes["quarterbeats"] == object
+        else data["quarterbeats"]
+    )
+    data["onset_div"] = np.array([int(qd * qdivs) for qd in data["quarterbeats"]])
+    data["duration_div"] = np.array([int(qd * qdivs) for qd in data["duration_qb"]])
+    is_na_cad = data["cadence"].isna()
+    is_na_roman = data["chord"].isna()
+    # Find Phrase Starts where data["phraseend"] == "{"
+    for idx, row in data[~is_na_roman].iterrows():
+        # row["chord_type"] contains the quality of the chord but it is encoded differently than for other formats
+        # and datasets. For example, a minor chord is encoded as "m" instead of "min" or "minor"
+        # Therefore we do not add the quality to the RomanNumeral object. Then it is extracted from the text.
+        # Local key is in relation to the global key.
+        if "/" in row["localkey"]:
+            # if the local key has a secondary degree (e.g. "V/IV") we need to process it differently
+            inter_key = process_local_key(
+                row["localkey"].split("/")[-1], row["globalkey"]
+            )
+            local_key = process_local_key(row["localkey"].split("/")[0], inter_key)
+        else:
+            local_key = process_local_key(row["localkey"], row["globalkey"])
+
+        part.add(
+            spt.RomanNumeral(
+                text=row["chord"],
+                local_key=local_key,
+                # quality=row["chord_type"],
+            ),
+            start=row["onset_div"],
+            end=row["onset_div"] + row["duration_div"],
+        )
+
+    for idx, row in data[~is_na_cad].iterrows():
+        if "/" in row["localkey"]:
+            # if the local key has a secondary degree (e.g. "V/IV") we need to process it differently
+            inter_key = process_local_key(
+                row["localkey"].split("/")[-1], row["globalkey"]
+            )
+            local_key = process_local_key(row["localkey"].split("/")[0], inter_key)
+        else:
+            local_key = process_local_key(row["localkey"], row["globalkey"])
+        # key_step = re.search(r"[a-gA-G]", row["globalkey"]).group(0)
+        # key_alter = re.search(r"[#b]", row["globalkey"]).group(0) if re.search(r"[#b]", row["globalkey"]) else ""
+        # key_alter = key_alter.replace("b", "-")
+        # key_alter = ALT_TO_INT[key_alter]
+        # key_step, key_alter = transpose_note(key_step, key_alter, transposition_interval)
+        # local_key = key_step + INT_TO_ALT[key_alter]
+        part.add(
+            spt.Cadence(
+                text=row["cadence"],
+                local_key=local_key,
+            ),
+            start=row["onset_div"],
+            end=row["onset_div"] + row["duration_div"],
+        )
+
+    # Check if phrase information is available.
+    if np.all(data["phraseend"].isna()):
+        return
+    # search if character "{, }" in present in values of column phraseend
+    phrase_starts = data[data["phraseend"].str.contains("{") == True]
+    phrase_ends = data[data["phraseend"].str.contains("}") == True]
+    # Check that the number of phrase starts and ends match
+    if len(phrase_starts) == len(phrase_ends):
+        for start, end in zip(phrase_starts.iterrows(), phrase_ends.iterrows()):
+            part.add(spt.Phrase(), start=start[1]["onset_div"], end=end[1]["onset_div"])
+    else:
+        # TODO: account for unfoldings and repeats.
+        warnings.warn(
+            "Number of phrase starts and ends do not match, skipping parsing phrases"
+        )
+    return
+
+
+def load_dcml(
+    note_tsv_path, measure_tsv_path=None, harmony_tsv_path=None, metadata=None
+):
+    """
+    Load a score from tsv files containing the notes, measures and harmony annotations.
+
+    These files are provided by the DCML datasets.
+    ATTENTION: This functionality requires pandas to be installed, which is not a requirement for partitura.
+
+    Parameters
+    ----------
+    note_tsv_path: str
+        Path to the tsv file containing the notes
+    measure_tsv_path: str
+        Path to the tsv file containing the measures
+    harmony_tsv_path:
+        Path to the tsv file containing the harmony annotations
+    metadata: dict
+        Metadata to add to the score. This is useful to add the composer, title, etc.
+
+    Returns
+    -------
+    score: :class:`partitura.score.Score`
+        A `Score` instance.
+
+    """
+    if pd is None:
+        raise ImportError("This functionality requires pandas to be installed")
+
+    part = read_note_tsv(note_tsv_path, metadata=metadata)
+    if measure_tsv_path is not None:
+        read_measure_tsv(measure_tsv_path, part)
+    else:
+        spt.add_measures(part)
+    if harmony_tsv_path is not None:
+        read_harmony_tsv(harmony_tsv_path, part)
+    score = spt.Score([part])
+    return score
diff --git a/partitura/io/importkern.py b/partitura/io/importkern.py
index da0fecea..b7012569 100644
--- a/partitura/io/importkern.py
+++ b/partitura/io/importkern.py
@@ -3,624 +3,795 @@
 """
 This module contains methods for importing Humdrum Kern files.
 """
-import re
+import copy
+import re, sys
 import warnings
-
 from typing import Union, Optional
-
 import numpy as np
+from math import inf, ceil
+import partitura.score as spt
+from partitura.utils import PathLike, get_document_name, symbolic_to_numeric_duration
+
+
+SIGN_TO_ACC = {
+    "nn": 0,
+    "n": 0,
+    "#": 1,
+    "s": 1,
+    "ss": 2,
+    "x": 2,
+    "n#": 1,
+    "#n": 1,
+    "##": 2,
+    "###": 3,
+    "b": -1,
+    "f": -1,
+    "bb": -2,
+    "ff": -2,
+    "bbb": -3,
+    "-": -1,
+    "n-": -1,
+    "-n": -1,
+    "--": -2,
+}
+
+KERN_NOTES = {
+    "C": ("C", 3),
+    "D": ("D", 3),
+    "E": ("E", 3),
+    "F": ("F", 3),
+    "G": ("G", 3),
+    "A": ("A", 3),
+    "B": ("B", 3),
+    "c": ("C", 4),
+    "d": ("D", 4),
+    "e": ("E", 4),
+    "f": ("F", 4),
+    "g": ("G", 4),
+    "a": ("A", 4),
+    "b": ("B", 4),
+}
+
+KERN_DURS = {
+    "3%2": {"type": "whole", "dots": 0, "actual_notes": 3, "normal_notes": 2},
+    "2%3": {"type": "whole", "dots": 1},
+    "000": {"type": "maxima"},
+    "00": {"type": "long"},
+    "0": {"type": "breve"},
+    "1": {"type": "whole"},
+    "2": {"type": "half"},
+    "4": {"type": "quarter"},
+    "8": {"type": "eighth"},
+    "16": {"type": "16th"},
+    "32": {"type": "32nd"},
+    "64": {"type": "64th"},
+    "128": {"type": "128th"},
+    "256": {"type": "256th"},
+}
+
+
+def add_durations(a, b):
+    return a * b / (a + b)
+
+
+def dot_function(duration: int, dots: int):
+    if dots == 0:
+        return duration
+    elif duration == 0:
+        return 0
+    else:
+        return add_durations((2**dots) * duration, dot_function(duration, dots - 1))
+
+
+def parse_by_voice(file: list, dtype=np.object_):
+    indices_to_remove = []
+    voices = 1
+    for i, line in enumerate(file):
+        for v in range(voices):
+            indices_to_remove.append([i, v])
+        if any([line[v] == "*^" for v in range(voices)]):
+            voices += 1
+        elif sum([(line[v] == "*v") for v in range(voices)]):
+            sum_vred = sum([line[v] == "*v" for v in range(voices)]) // 2
+            voices = voices - sum_vred
+
+    voice_indices = np.array(indices_to_remove)
+    num_voices = voice_indices[:, 1].max() + 1
+    data = np.empty((len(file), num_voices), dtype=dtype)
+    for line, voice in voice_indices:
+        data[line, voice] = file[line][voice]
+    data = data.T
+    if num_voices > 1:
+        # Copy global lines from the first voice to all other voices
+        cp_idx = np.char.startswith(data[0], "*")
+        for i in range(1, num_voices):
+            data[i][cp_idx] = data[0][cp_idx]
+        # Copy Measure Lines from the first voice to all other voices
+        cp_idx = np.char.startswith(data[0], "=")
+        for i in range(1, num_voices):
+            data[i][cp_idx] = data[0][cp_idx]
+    return data, voice_indices, num_voices
+
+
+def _handle_kern_with_spine_splitting(kern_path: PathLike):
+    """
+    Parse a kern file with spine splitting.
+
+    A special case of kern files is when the file contains multiple spines that are split by voice. In this case, this
+    function will restructure the data in a way that it can be parsed by the kern parser.
+
+    Parameters
+    ----------
+    kern_path: str
 
-import partitura.score as score
-from partitura.utils import PathLike, get_document_name
-from partitura.utils.misc import deprecated_alias, deprecated_parameter
-
-
-__all__ = ["load_kern"]
-
-
-class KernGlobalPart(object):
-    def __init__(self, doc_name, part_id, qdivs):
-        qdivs = int(1) if int(qdivs) == 0 else int(qdivs)
-        # super(KernGlobalPart, self).__init__()
-        self.part = score.Part(doc_name, part_id, quarter_duration=qdivs)
-        self.default_clef_lines = {"G": 2, "F": 4, "C": 3}
-        self.SIGN_TO_ACC = {
-            "n": 0,
-            "#": 1,
-            "s": 1,
-            "ss": 2,
-            "x": 2,
-            "##": 2,
-            "###": 3,
-            "b": -1,
-            "f": -1,
-            "bb": -2,
-            "ff": -2,
-            "bbb": -3,
-            "-": None,
-        }
-
-        self.KERN_NOTES = {
-            "C": ("C", 3),
-            "D": ("D", 3),
-            "E": ("E", 3),
-            "F": ("F", 3),
-            "G": ("G", 3),
-            "A": ("A", 3),
-            "B": ("B", 3),
-            "c": ("C", 4),
-            "d": ("D", 4),
-            "e": ("E", 4),
-            "f": ("F", 4),
-            "g": ("G", 4),
-            "a": ("A", 4),
-            "b": ("B", 4),
-        }
-
-        self.KERN_DURS = {
-            # "long": "long",
-            # "breve": "breve",
-            0: "breve",
-            1: "whole",
-            2: "half",
-            4: "quarter",
-            8: "eighth",
-            16: "16th",
-            32: "32nd",
-            64: "64th",
-            128: "128th",
-            256: "256th",
-        }
-
-
-class KernParserPart(KernGlobalPart):
+    Returns
+    -------
+    data: np.array
+        The data to be parsed.
+    parsing_idxs: np.array
+        The indices of the data that are being parsed indicating the assignment of voices.
     """
-    Class for parsing kern file syntax.
+    # org_file = np.loadtxt(kern_path, dtype="U", delimiter="\n", comments="!!!", encoding="cp437")
+    org_file = np.genfromtxt(
+        kern_path, dtype="U", delimiter="\n", comments="!!!", encoding="cp437"
+    )
+    # Get Main Number of parts and Spline Types
+    spline_types = org_file[0].split("\t")
+    parsing_idxs = []
+    dtype = org_file.dtype
+    data = []
+    file = org_file.tolist()
+    file = [line.split("\t") for line in file if not line.startswith("!")]
+    continue_parsing = True
+    for i in range(len(spline_types)):
+        # Parse by voice
+        d, voice_indices, num_voices = parse_by_voice(file, dtype=dtype)
+        data.append(d)
+        parsing_idxs.append([i for _ in range(num_voices)])
+        # Remove all parsed cells from the file
+        voice_indices = voice_indices[
+            np.lexsort((voice_indices[:, 1] * -1, voice_indices[:, 0]))
+        ]
+        for line, voice in voice_indices:
+            if voice < len(file[line]):
+                file[line].pop(voice)
+            else:
+                print(
+                    "Line {} does not have a voice {} from original line {}".format(
+                        line, voice, org_file[line]
+                    )
+                )
+    data = np.vstack(data).T
+    parsing_idxs = np.hstack(parsing_idxs).T
+    return data, parsing_idxs
+
+
+def element_parsing(
+    part: spt.Part, elements: np.array, total_duration_values: np.array, same_part: bool
+):
+    divs_pq = part._quarter_durations[0]
+    current_tl_pos = 0
+    measure_mapping = {m.number: m.start.t for m in part.iter_all(spt.Measure)}
+    for i in range(elements.shape[0]):
+        element = elements[i]
+        if element is None:
+            continue
+        if isinstance(element, spt.GenericNote):
+            if total_duration_values[i] == 0:
+                duration_divs = symbolic_to_numeric_duration(
+                    element.symbolic_duration, divs_pq
+                )
+            else:
+                quarter_duration = 4 / total_duration_values[i]
+                duration_divs = ceil(quarter_duration * divs_pq)
+            el_end = current_tl_pos + duration_divs
+            part.add(element, start=current_tl_pos, end=el_end)
+            current_tl_pos = el_end
+        elif isinstance(element, tuple):
+            # Chord
+            quarter_duration = 4 / total_duration_values[i]
+            duration_divs = ceil(quarter_duration * divs_pq)
+            el_end = current_tl_pos + duration_divs
+            for note in element[1]:
+                part.add(note, start=current_tl_pos, end=el_end)
+            current_tl_pos = el_end
+        elif isinstance(element, spt.Slur):
+            start_sl = element.start_note.start.t
+            end_sl = element.end_note.start.t
+            part.add(element, start=start_sl, end=end_sl)
+
+        else:
+            # Do not repeat structural elements if they are being added to the same part.
+            if not same_part:
+                part.add(element, start=current_tl_pos)
+            else:
+                if isinstance(element, spt.Measure):
+                    current_tl_pos = measure_mapping[element.number]
+
+
+# functions to initialize the kern parser
+def load_kern(
+    filename: PathLike,
+    force_note_ids: Optional[Union[bool, str]] = None,
+) -> spt.Score:
     """
+    Parses an KERN file from path to Part.
 
-    def __init__(self, stream, init_pos, doc_name, part_id, qdivs, barline_dict=None):
-        super(KernParserPart, self).__init__(doc_name, part_id, qdivs)
-        self.position = int(init_pos)
-        self.parsing = "full"
-        self.stream = stream
-        self.prev_measure_pos = init_pos
-        # Check if part has pickup measure.
-        self.measure_count = (
-            0 if np.all(np.char.startswith(stream, "=1-") == False) else 1
+    Parameters
+    ----------
+    filename : PathLike
+        The path of the KERN document.
+    force_note_ids : (None, bool or "keep")
+        When True each Note in the returned Part(s) will have a newly assigned unique id attribute.
+    Returns
+    -------
+    score : partitura.score.Score
+        The score object containing the parts.
+    """
+    try:
+        # This version of the parser is faster but does not support spine splitting.
+        file = np.loadtxt(
+            filename, dtype="U", delimiter="\t", comments="!!", encoding="cp437"
         )
-        self.last_repeat_pos = None
-        self.mode = None
-        self.barline_dict = dict() if not barline_dict else barline_dict
-        self.slur_dict = {"open": [], "close": []}
-        self.tie_dict = {"open": [], "close": []}
-        self.process()
-
-    def process(self):
-        self.staff = None
-        for index, el in enumerate(self.stream):
-            self.current_index = index
-            if el.startswith("*staff"):
-                self.staff = eval(el[len("*staff") :])
-            # elif el.startswith("!!!"):
-            #     self._handle_fileinfo(el)
-            elif el.startswith("*"):
-                if self.staff == None:
-                    self.staff = 1
-                self._handle_glob_attr(el)
-            elif el.startswith("="):
-                self.select_parsing(el)
-                self._handle_barline(el)
-            elif " " in el:
-                self._handle_chord(el, index)
-            elif "r" in el:
-                self._handle_rest(el, "r-" + str(index))
-            else:
-                self._handle_note(el, "n-" + str(index))
-        self.nid_dict = dict(
-            [(n.id, n) for n in self.part.iter_all(cls=score.Note)]
-            + [(n.id, n) for n in self.part.iter_all(cls=score.GraceNote)]
+        parsing_idxs = np.arange(file.shape[0])
+        # Decide Parts
+
+    except ValueError:
+        # This version of the parser supports spine splitting but is slower.
+        file, parsing_idxs = _handle_kern_with_spine_splitting(filename)
+
+    partlist = []
+    # Get Main Number of parts and Spline Types
+    spline_types = file[0]
+
+    # Find parsable parts if they start with "**kern" or "**notes"
+    note_parts = np.char.startswith(spline_types, "**kern") | np.char.startswith(
+        spline_types, "**notes"
+    )
+    # Get Splines
+    splines = file[1:].T[note_parts]
+    # Inverse Order
+    splines = splines[::-1]
+    parsing_idxs = parsing_idxs[::-1]
+    prev_staff = 1
+    has_instrument = np.char.startswith(splines, "*I")
+    # if all parts have the same instrument, then they are the same part.
+    p_same_part = (
+        np.all(splines[has_instrument] == splines[has_instrument][0], axis=0)
+        if np.any(has_instrument)
+        else False
+    )
+    total_durations_list = list()
+    elements_list = list()
+    part_assignments = list()
+    copy_partlist = list()
+    for j, spline in enumerate(splines):
+        parser = SplineParser(
+            size=spline.shape[-1],
+            id="P{}".format(parsing_idxs[j]) if not p_same_part else "P{}".format(j),
+            staff=prev_staff,
         )
-        self._handle_slurs()
-        self._handle_ties()
-
-    # Account for parsing priorities.
-    def select_parsing(self, el):
-        if self.parsing == "full":
-            return el
-        elif self.parsing == "right":
-            return el.split()[-1]
-        else:
-            return el.split()[0]
-
-    # TODO handle !!!info
-    def _handle_fileinfo(self, el):
-        pass
-
-    def _handle_ties(self):
-        try:
-            if len(self.tie_dict["open"]) < len(self.tie_dict["close"]):
-                for index, oid in enumerate(self.tie_dict["open"]):
-                    if (
-                        self.nid_dict[oid].midi_pitch
-                        != self.nid_dict[self.tie_dict["close"][index]].midi_pitch
-                    ):
-                        dnote = self.nid_dict[self.tie_dict["close"][index]]
-                        m_num = [
-                            m
-                            for m in self.part.iter_all(score.Measure)
-                            if m.start.t == self.part.measure_map(dnote.start.t)[0]
-                        ][0].number
-                        warnings.warn(
-                            "Dropping Closing Tie of note {} at position {} measure {}".format(
-                                dnote.midi_pitch, dnote.start.t, m_num
-                            )
-                        )
-                        self.tie_dict["close"].pop(index)
-                        self._handle_ties()
-            elif len(self.tie_dict["open"]) > len(self.tie_dict["close"]):
-                for index, cid in enumerate(self.tie_dict["close"]):
-                    if (
-                        self.nid_dict[cid].midi_pitch
-                        != self.nid_dict[self.tie_dict["open"][index]].midi_pitch
-                    ):
-                        dnote = self.nid_dict[self.tie_dict["open"][index]]
-                        m_num = [
-                            m
-                            for m in self.part.iter_all(score.Measure)
-                            if m.start.t == self.part.measure_map(dnote.start.t)[0]
-                        ][0].number
-                        warnings.warn(
-                            "Dropping Opening Tie of note {} at position {} measure {}".format(
-                                dnote.midi_pitch, dnote.start.t, m_num
-                            )
-                        )
-                        self.tie_dict["open"].pop(index)
-                        self._handle_ties()
+        same_part = False
+        if parser.id in [p.id for p in copy_partlist]:
+            same_part = True
+            warnings.warn(
+                "Part {} already exists. Adding to previous Part.".format(parser.id)
+            )
+            part = [p for p in copy_partlist if p.id == parser.id][0]
+            has_staff = np.char.startswith(spline, "*staff")
+            staff = int(spline[has_staff][0][6:]) if np.count_nonzero(has_staff) else 1
+            if parser.staff != staff:
+                parser.staff = staff
             else:
-                for oid, cid in list(
-                    zip(self.tie_dict["open"], self.tie_dict["close"])
-                ):
-                    self.nid_dict[oid].tie_next = self.nid_dict[cid]
-                    self.nid_dict[cid].tie_prev = self.nid_dict[oid]
-        except Exception:
-            raise ValueError(
-                "Tie Mismatch! Uneven amount of closing to open tie brackets."
+                parser.voice += 1
+            elements = parser.parse(spline)
+            unique_durs = np.unique(parser.total_duration_values).astype(int)
+            divs_pq = np.lcm.reduce(unique_durs)
+            divs_pq = divs_pq if divs_pq > 4 else 4
+            # compare divs_pq to the divs_pq of the part
+            divs_pq = np.lcm.reduce([divs_pq, part._quarter_durations[0]])
+            part.set_quarter_duration(0, divs_pq)
+        else:
+            has_staff = np.char.startswith(spline, "*staff")
+            staff = int(spline[has_staff][0][6:]) if np.count_nonzero(has_staff) else 1
+            # Correction for currating multiple staffs.
+            if parser.staff != staff:
+                parser.staff = staff
+                prev_staff = staff
+            elements = parser.parse(spline)
+            # Routine to filter out non integer durations
+            unique_durs = np.unique(parser.total_duration_values)
+            # Remove all infinite values
+            unique_durs = unique_durs[np.isfinite(unique_durs)]
+            d_mul = 2
+            while not np.all(np.isclose(unique_durs % 1, 0.0)):
+                unique_durs = unique_durs * d_mul
+                d_mul += 1
+            unique_durs = unique_durs.astype(int)
+
+            divs_pq = np.lcm.reduce(unique_durs)
+            divs_pq = divs_pq if divs_pq > 4 else 4
+            # Initialize Part
+            part = spt.Part(
+                id=parser.id, quarter_duration=divs_pq, part_name=parser.name
             )
 
-    def _handle_slurs(self):
-        if len(self.slur_dict["open"]) != len(self.slur_dict["close"]):
+        part_assignments.append(same_part)
+        total_durations_list.append(parser.total_duration_values)
+        elements_list.append(elements)
+        copy_partlist.append(part)
+
+    # Currate parts to the same divs per quarter
+    divs_pq = np.lcm.reduce([p._quarter_durations[0] for p in copy_partlist])
+    for part in copy_partlist:
+        part.set_quarter_duration(0, divs_pq)
+
+    for part, elements, total_duration_values, same_part in zip(
+        copy_partlist, elements_list, total_durations_list, part_assignments
+    ):
+        element_parsing(part, elements, total_duration_values, same_part)
+
+    for i, part in enumerate(copy_partlist):
+        if part_assignments[i]:
+            continue
+        # For all measures add end time as beginning time of next measure
+        measures = part.measures
+        for i in range(len(measures) - 1):
+            measures[i].end = measures[i + 1].start
+        measures[-1].end = part.last_point
+        # find and add pickup measure
+        if part.measures[0].start.t != 0:
+            part.add(spt.Measure(number=0), start=0, end=part.measures[0].start.t)
+
+        if parser.id not in [p.id for p in partlist]:
+            partlist.append(part)
+
+    spt.assign_note_ids(
+        partlist, keep=(force_note_ids is True or force_note_ids == "keep")
+    )
+
+    doc_name = get_document_name(filename)
+    score = spt.Score(partlist=partlist, id=doc_name)
+    return score
+
+
+class SplineParser(object):
+    def __init__(self, id="P1", staff=1, voice=1, size=1, name=""):
+        self.id = id
+        self.name = name
+        self.staff = staff
+        self.voice = voice
+        self.total_duration_values = []
+        self.alters = []
+        self.size = size
+        self.total_parsed_elements = 0
+        self.tie_prev = None
+        self.tie_next = None
+        self.slurs_start = []
+        self.slurs_end = []
+
+    def parse(self, spline: np.array):
+        """
+        Parse a spline line and return the elements.
+
+        Parameters
+        ----------
+        spline: np.array
+            The spline line to parse. It is a numpy array of strings.
+
+        Returns
+        -------
+        elements: np.array
+            The parsed elements of the spline line.
+        """
+        # Remove "-" lines
+        spline = spline[spline != "-"]
+        # Remove "." lines
+        spline = spline[spline != "."]
+        # Remove Empty lines
+        spline = spline[spline != ""]
+        # Remove None lines
+        spline = spline[spline != None]
+        # Remove lines that start with "!"
+        spline = spline[np.char.startswith(spline, "!") == False]
+        # Empty Numpy array with objects
+        elements = np.empty(len(spline), dtype=object)
+        self.total_duration_values = np.ones(len(spline))
+        # Find Global indices, i.e. where spline cells start with "*" and process
+        tandem_mask = np.char.find(spline, "*") != -1
+        elements[tandem_mask] = np.vectorize(self.meta_tandem_line, otypes=[object])(
+            spline[tandem_mask]
+        )
+        # Find Barline indices, i.e. where spline cells start with "="
+        bar_mask = np.char.find(spline, "=") != -1
+        elements[bar_mask] = np.vectorize(self.meta_barline_line, otypes=[object])(
+            spline[bar_mask]
+        )
+        # Find Chord indices, i.e. where spline cells contain " "
+        chord_mask = np.char.find(spline, " ") != -1
+        chord_mask = np.logical_and(chord_mask, np.logical_and(~tandem_mask, ~bar_mask))
+        self.total_parsed_elements = -1
+        self.note_duration_values = np.ones(len(spline[chord_mask]))
+        chord_num = np.count_nonzero(chord_mask)
+        self.tie_next = np.zeros(chord_num, dtype=bool)
+        self.tie_prev = np.zeros(chord_num, dtype=bool)
+        elements[chord_mask] = np.vectorize(self.meta_chord_line, otypes=[object])(
+            spline[chord_mask]
+        )
+        self.total_duration_values[chord_mask] = self.note_duration_values
+        # TODO: figure out slurs for chords
+
+        # All the rest are note indices
+        note_mask = np.logical_and(~tandem_mask, np.logical_and(~bar_mask, ~chord_mask))
+        self.total_parsed_elements = -1
+        self.note_duration_values = np.ones(len(spline[note_mask]))
+        note_num = np.count_nonzero(note_mask)
+        self.tie_next = np.zeros(note_num, dtype=bool)
+        self.tie_prev = np.zeros(note_num, dtype=bool)
+        notes = np.vectorize(self.meta_note_line, otypes=[object])(spline[note_mask])
+        self.total_duration_values[note_mask] = self.note_duration_values
+        # shift tie_next by one to the right
+        for note, to_tie in np.c_[
+            notes[self.tie_next], notes[np.roll(self.tie_next, -1)]
+        ]:
+            to_tie.tie_next = note
+            # note.tie_prev = to_tie
+        for note, to_tie in np.c_[
+            notes[self.tie_prev], notes[np.roll(self.tie_prev, 1)]
+        ]:
+            note.tie_prev = to_tie
+            # to_tie.tie_next = note
+        elements[note_mask] = notes
+
+        # Find Slur indices, i.e. where spline cells contain "(" or ")"
+        open_slur_mask = np.char.find(spline[note_mask], "(") != -1
+        close_slur_mask = np.char.find(spline[note_mask], ")") != -1
+        self.slurs_start = np.where(open_slur_mask)[0]
+        self.slurs_end = np.where(close_slur_mask)[0]
+        # Only add slur if there is a start and end
+        if len(self.slurs_start) == len(self.slurs_end):
+            slurs = np.empty(len(self.slurs_start), dtype=object)
+            for i, (start, end) in enumerate(zip(self.slurs_start, self.slurs_end)):
+                slurs[i] = spt.Slur(notes[start], notes[end])
+            # Add slurs to elements
+            elements = np.append(elements, slurs)
+        else:
             warnings.warn(
-                "Slur Mismatch! Uneven amount of closing to open slur brackets. Skipping slur parsing.",
-                ImportWarning,
+                "Slurs openings and closings do not match. Skipping parsing slurs for this part {}.".format(
+                    self.id
+                )
             )
-            # raise ValueError(
-            #     "Slur Mismatch! Uneven amount of closing to open slur brackets."
-            # )
-        else:
-            for oid, cid in list(zip(self.slur_dict["open"], self.slur_dict["close"])):
-                self.part.add(score.Slur(self.nid_dict[oid], self.nid_dict[cid]))
-
-    def _handle_metersig(self, metersig):
-        m = metersig[2:]
-        numerator, denominator = map(eval, m.split("/"))
-        new_time_signature = score.TimeSignature(numerator, denominator)
-        self.part.add(new_time_signature, self.position)
-
-    def _handle_barline(self, element):
-        if self.position > self.prev_measure_pos:
-            indicated_measure = re.findall("=([0-9]+)", element)
-            if indicated_measure != []:
-                m = eval(indicated_measure[0]) - 1
-                barline = score.Barline(style="normal")
-                self.part.add(barline, self.position)
-                self.measure_count = m
-                self.barline_dict[m] = self.position
+
+        return elements
+
+    def meta_tandem_line(self, line: str):
+        """
+        Find all tandem lines
+        """
+        # find number and keep its index.
+        self.total_parsed_elements += 1
+        if line.startswith("*MM"):
+            rest = line[3:]
+            return self.process_tempo_line(rest)
+        elif line.startswith("*I"):
+            rest = line[2:]
+            return self.process_istrument_line(rest)
+        elif line.startswith("*clef"):
+            rest = line[5:]
+            return self.process_clef_line(rest)
+        elif line.startswith("*M"):
+            rest = line[2:]
+            return self.process_meter_line(rest)
+        elif line.startswith("*k"):
+            rest = line[2:]
+            return self.process_key_signature_line(rest)
+        elif line.startswith("*IC"):
+            rest = line[3:]
+            return self.process_istrument_class_line(rest)
+        elif line.startswith("*IG"):
+            rest = line[3:]
+            return self.process_istrument_group_line(rest)
+        elif line.startswith("*tb"):
+            rest = line[3:]
+            return self.process_timebase_line(rest)
+        elif line.startswith("*ITr"):
+            rest = line[4:]
+            return self.process_istrument_transpose_line(rest)
+        elif line.startswith("*staff"):
+            rest = line[6:]
+            return self.process_staff_line(rest)
+        elif line.endswith(":"):
+            rest = line[1:]
+            return self.process_key_line(rest)
+        elif line.startswith("*-"):
+            return self.process_fine()
+
+    def process_tempo_line(self, line: str):
+        return spt.Tempo(float(line))
+
+    def process_fine(self):
+        return spt.Fine()
+
+    def process_istrument_line(self, line: str):
+        # TODO: add support for instrument lines
+        return
+
+    def process_istrument_class_line(self, line: str):
+        # TODO: add support for instrument class lines
+        return
+
+    def process_istrument_group_line(self, line: str):
+        # TODO: add support for instrument group lines
+        return
+
+    def process_timebase_line(self, line: str):
+        # TODO: add support for timebase lines
+        return
+
+    def process_istrument_transpose_line(self, line: str):
+        # TODO: add support for instrument transpose lines
+        return
+
+    def process_key_line(self, line: str):
+        find = re.search(r"([a-gA-G])", line).group(0)
+        # check if the key is major or minor by checking if the key is in lower or upper case.
+        self.mode = "minor" if find.islower() else "major"
+        return
+
+    def process_staff_line(self, line: str):
+        self.staff = int(line)
+        return spt.Staff(self.staff)
+
+    def process_clef_line(self, line: str):
+        # if the cleff line does not contain any of the following characters, ["G", "F", "C"], raise a ValueError.
+        if not any(c in line for c in ["G", "F", "C"]):
+            raise ValueError("Unrecognized clef: {}".format(line))
+        # find the clef
+        clef = re.search(r"([GFC])", line).group(0)
+        # find the octave
+        has_line = re.search(r"([0-9])", line)
+        octave_change = "v" in line
+        if has_line is None:
+            if clef == "G":
+                clef_line = 2
+            elif clef == "F":
+                clef_line = 4
+            elif clef == "C":
+                clef_line = 3
             else:
-                m = self.measure_count - 1
-            self.part.add(score.Measure(m), self.prev_measure_pos, self.position)
-            self.prev_measure_pos = self.position
-            self.measure_count += 1
-        if len(element.split()) > 1:
-            element = element.split()[0]
-        if element.endswith("!") or element == "==":
-            barline = score.Fine()
-            self.part.add(barline, self.position)
-        if ":|" in element:
-            barline = score.Repeat()
-            self.part.add(
-                barline,
-                self.position,
-                self.last_repeat_pos if self.last_repeat_pos else None,
-            )
-        # update position for backward repeat signs
-        if "|:" in element:
-            self.last_repeat_pos = self.position
-
-    # TODO maybe also append position for verification.
-    def _handle_mode(self, element):
-        if element[1].isupper():
-            self.mode = "major"
-        else:
-            self.mode = "minor"
-
-    def _handle_keysig(self, element):
-        keysig_el = element[2:]
-        fifths = 0
-        for c in keysig_el:
-            if c == "#":
-                fifths += 1
-            if c == "b":
-                fifths -= 1
-        # TODO retrieve the key mode
-        mode = self.mode if self.mode else "major"
-        new_key_signature = score.KeySignature(fifths, mode)
-        self.part.add(new_key_signature, self.position)
-
-    def _compute_clef_octave(self, dis, dis_place):
-        if dis is not None:
-            sign = -1 if dis_place == "below" else 1
-            octave = sign * int(int(dis) / 8)
+                raise ValueError("Unrecognized clef line: {}".format(line))
         else:
+            clef_line = has_line.group(0)
+        if octave_change and clef_line == 2 and clef == "G":
+            octave = -1
+        elif octave_change:
+            warnings.warn("Octave change not supported for clef: {}".format(line))
             octave = 0
-        return octave
-
-    def _handle_clef(self, element):
-        # handle the case where we have clef information
-        # TODO Compute Clef Octave
-        if element[5] not in ["G", "F", "C"]:
-            raise ValueError("Unknown Clef", element[5])
-        if len(element) < 7:
-            line = self.default_clef_lines[element[5]]
         else:
-            line = int(element[6]) if element[6] != "v" else int(element[7])
-        new_clef = score.Clef(
-            staff=self.staff, sign=element[5], line=line, octave_change=0
-        )
-        self.part.add(new_clef, self.position)
-
-    def _handle_rest(self, el, rest_id):
-        # find duration info
-        duration, symbolic_duration, rtype = self._handle_duration(el)
-        # create rest
-        rest = score.Rest(
-            id=rest_id,
-            voice=1,
-            staff=1,
-            symbolic_duration=symbolic_duration,
-            articulations=None,
+            octave = 0
+
+        return spt.Clef(
+            sign=clef, staff=self.staff, line=int(clef_line), octave_change=octave
         )
-        # add rest to the part
-        self.part.add(rest, self.position, self.position + duration)
-        # return duration to update the position in the layer
-        self.position += duration
-
-    def _handle_fermata(self, note_instance):
-        self.part.add(note_instance, self.position)
-
-    def _search_slurs_and_ties(self, note, note_id):
-        if ")" in note:
-            x = note.count(")")
-            if len(self.slur_dict["open"]) == len(self.slur_dict["close"]) + x:
-                # for _ in range(x):
-                self.slur_dict["close"].append(note_id)
-        if note.startswith("("):
-            # acount for multiple opening brackets
-            n = note.count("(")
-            # for _ in range(n):
-            self.slur_dict["open"].append(note_id)
-            # Re-order for correct parsing
-            if len(self.slur_dict["open"]) > len(self.slur_dict["close"]) + 1:
-                warnings.warn(
-                    "Cannot deal with nested slurs. Dropping Opening slur for note id {}".format(
-                        self.slur_dict["open"][len(self.slur_dict["open"]) - 2]
-                    )
-                )
-                self.slur_dict["open"].pop(len(self.slur_dict["open"]) - 2)
-                # x = note_id
-                # lenc = len(self.slur_dict["open"]) - len(self.slur_dict["close"])
-                # self.slur_dict["open"][:lenc - 1] = self.slur_dict["open"][1:lenc]
-                # self.slur_dict["open"][lenc] = x
-            note = note[n:]
-        if "]" in note:
-            self.tie_dict["close"].append(note_id)
-        elif "_" in note:
-            self.tie_dict["open"].append(note_id)
-            self.tie_dict["close"].append(note_id)
-        if note.startswith("["):
-            self.tie_dict["open"].append(note_id)
-            note = note[1:]
-        return note
 
-    def _handle_duration(self, note, isgrace=False):
-        if isgrace:
-            _, dur, ntype = re.split("(\d+)", note)
-            ntype = _ + ntype
-        else:
-            _, dur, ntype = re.split("(\d+)", note)
-        dur = eval(dur)
-        if dur in self.KERN_DURS.keys():
-            symbolic_duration = {"type": self.KERN_DURS[dur]}
+    def process_key_signature_line(self, line: str):
+        fifths = line.count("#") - line.count("-")
+        alters = re.findall(r"([a-gA-G#\-]+)", line)
+        alters = "".join(alters)
+        # split alters by two characters
+        self.alters = [alters[i : i + 2] for i in range(0, len(alters), 2)]
+        # TODO retrieve the key mode
+        mode = "major"
+        return spt.KeySignature(fifths, mode)
+
+    def process_meter_line(self, line: str):
+        if " " in line:
+            line = line.split(" ")[0]
+        numerator, denominator = line.split("/")
+        # Find digits in numerator and denominator and convert to int
+        numerator = int(re.search(r"([0-9]+)", numerator).group(0))
+        denominator = int(re.search(r"([0-9]+)", denominator).group(0))
+        return spt.TimeSignature(numerator, denominator)
+
+    def _process_kern_pitch(self, pitch: str):
+        # find accidentals
+        alter = re.search(r"([n#-]+)", pitch)
+        # remove alter from pitch
+        pitch = pitch.replace(alter.group(0), "") if alter else pitch
+        step, octave = KERN_NOTES[pitch[0]]
+        # do_alt = (step + alter.group(0)).lower() not in self.alters if alter else False
+        if octave == 4:
+            octave = octave + pitch.count(pitch[0]) - 1
+        elif octave == 3:
+            octave = octave - pitch.count(pitch[0]) + 1
+        alter = SIGN_TO_ACC[alter.group(0)] if alter is not None else None
+        return step, octave, alter
+
+    def _process_kern_duration(self, duration: str, is_grace=False):
+        """
+        Process the duration of a note.
+
+        Parameters
+        ----------
+        duration: str
+            The duration of the note.
+        is_grace: bool(default=False)
+            If the note is a grace note.
+        Returns
+        -------
+        symbolic_duration: dict
+            A dictionary containing the symbolic duration of the note.
+        """
+        dots = duration.count(".")
+        dur = duration.replace(".", "")
+        if dur in KERN_DURS.keys():
+            symbolic_duration = copy.deepcopy(KERN_DURS[dur])
         else:
+            dur = float(dur)
+            key_loolup = [2**i for i in range(0, 9)]
             diff = dict(
                 (
                     map(
-                        lambda x: (dur - x, x) if dur > x else (dur + x, x),
-                        self.KERN_DURS.keys(),
+                        lambda x: (dur - x, str(x)) if dur > x else (dur + x, str(x)),
+                        key_loolup,
                     )
                 )
             )
-            symbolic_duration = {
-                "type": self.KERN_DURS[diff[min(list(diff.keys()))]],
-                "actual_notes": dur / 4,
-                "normal_notes": diff[min(list(diff.keys()))] / 4,
-            }
-
-        # calculate duration to divs.
-        qdivs = self.part._quarter_durations[0]
-        duration = qdivs * 4 / dur if dur != 0 else qdivs * 8
-        if "." in note:
-            symbolic_duration["dots"] = note.count(".")
-            ntype = ntype[note.count(".") :]
-            d = duration
-            for i in range(symbolic_duration["dots"]):
-                d = d / 2
-                duration += d
+
+            symbolic_duration = copy.deepcopy(KERN_DURS[diff[min(list(diff.keys()))]])
+            symbolic_duration["actual_notes"] = int(dur // 4)
+            symbolic_duration["normal_notes"] = int(diff[min(list(diff.keys()))]) // 4
+        if dots:
+            symbolic_duration["dots"] = dots
+        self.note_duration_values[self.total_parsed_elements] = (
+            dot_function((float(dur) if isinstance(dur, str) else dur), dots)
+            if not is_grace
+            else inf
+        )
+        return symbolic_duration
+
+    def process_symbol(self, note: spt.Note, symbols: list):
+        """
+        Process the symbols of a note.
+
+        Parameters
+        ----------
+        note: spt.Note
+            The note to add the symbols to.
+        symbols: list
+            List of symbols to process.
+        """
+        if "[" in symbols:
+            self.tie_prev[self.total_parsed_elements] = True
+            # pop symbol and call again
+            symbols.pop(symbols.index("["))
+            self.process_symbol(note, symbols)
+        if "]" in symbols:
+            self.tie_next[self.total_parsed_elements] = True
+            symbols.pop(symbols.index("]"))
+            self.process_symbol(note, symbols)
+        if "_" in symbols:
+            # continuing tie
+            self.tie_prev[self.total_parsed_elements] = True
+            self.tie_next[self.total_parsed_elements] = True
+            symbols.pop(symbols.index("_"))
+            self.process_symbol(note, symbols)
+        return
+
+    def meta_note_line(self, line: str, voice=None, add=True):
+        """
+        Grammar Defining a note line.
+
+        A note line is specified by the following grammar:
+        note_line = symbol | duration | pitch | symbol
+
+        Parameters
+        ----------
+        line: str
+            The line to parse containing a note element.
+        voice: int
+            The voice of the note.
+        add: bool
+            If True, the element is added to the number of parsed elements.
+
+        Returns
+        -------
+        spt.Note object
+        """
+        self.total_parsed_elements += 1 if add else 0
+        voice = self.voice if voice is None else voice
+        # extract first occurence of one of the following: a-g A-G r # - n
+        find_pitch = re.search(r"([a-gA-Gr\-n#]+)", line)
+        if find_pitch is None:
+            warnings.warn(
+                "No pitch found in line: {}, transforming to a rest".format(line)
+            )
+            pitch = "r"
         else:
-            symbolic_duration["dots"] = 0
-        if isinstance(duration, float):
-            if not duration.is_integer():
-                raise ValueError("Duration divs is not an integer, {}".format(duration))
-        # Check that duration is same as int
-        assert int(duration) == duration
-        return int(duration), symbolic_duration, ntype
-
-    # TODO Handle beams and tuplets.
-
-    def _handle_note(self, note, note_id, voice=1):
-        if note == ".":
-            return
-        has_fermata = ";" in note
-        note = self._search_slurs_and_ties(note, note_id)
-        grace_attr = "q" in note  # or "p" in note # for appoggiatura not sure yet.
-        duration, symbolic_duration, ntype = self._handle_duration(note, grace_attr)
-        # Remove editorial symbols from string, i.e. "x"
-        ntype = ntype.replace("x", "")
-        step, octave = self.KERN_NOTES[ntype[0]]
-        if octave == 4:
-            octave = octave + ntype.count(ntype[0]) - 1
-        elif octave == 3:
-            octave = octave - ntype.count(ntype[0]) + 1
-        alter = ntype.count("#") - ntype.count("-")
-        # find if it's grace
-        if not grace_attr:
-            # create normal note
-            note = score.Note(
-                step=step,
-                octave=octave,
-                alter=alter,
-                id=note_id,
-                voice=int(voice),
-                staff=self.staff,
+            pitch = find_pitch.group(0)
+        # extract duration can be any of the following: 0-9 .
+        dur_search = re.search(r"([0-9.%]+)", line)
+        # if no duration is found, then the duration is 8 by default (for grace notes with no duration)
+        duration = dur_search.group(0) if dur_search else "8"
+        # extract symbol can be any of the following: _()[]{}<>|:
+        symbols = re.findall(r"([_()\[\]{}<>|:])", line)
+        symbolic_duration = self._process_kern_duration(duration, is_grace="q" in line)
+        el_id = "{}-s{}-v{}-el{}".format(
+            self.id, self.staff, voice, self.total_parsed_elements
+        )
+        if pitch.startswith("r"):
+            return spt.Rest(
                 symbolic_duration=symbolic_duration,
-                articulations=None,  # TODO : add articulation
+                staff=self.staff,
+                voice=voice,
+                id=el_id,
             )
-            if has_fermata:
-                self._handle_fermata(note)
-        else:
-            # create grace note
-            if "p" in ntype:
-                grace_type = "acciaccatura"
-            elif "q" in ntype:
-                grace_type = "appoggiatura"
-            note = score.GraceNote(
-                grace_type=grace_type,
+        step, octave, alter = self._process_kern_pitch(pitch)
+        # check if the note is a grace note
+        if "q" in line:
+            note = spt.GraceNote(
+                grace_type="grace",
                 step=step,
                 octave=octave,
                 alter=alter,
-                id=note_id,
-                voice=1,
-                staff=self.staff,
                 symbolic_duration=symbolic_duration,
-                articulations=None,  # TODO : add articulation
-            )
-            duration = 0
-
-        self.part.add(note, self.position, self.position + duration)
-        self.position += duration
-
-    def _handle_chord(self, chord, id):
-        notes = chord.split()
-        position_history = list()
-        pos = self.position
-        for i, note_el in enumerate(notes):
-            id_new = "c-" + str(i) + "-" + str(id)
-            self.position = pos
-            if "r" in note_el:
-                self._handle_rest(note_el, id_new)
-            else:
-                self._handle_note(note_el, id_new, voice=int(i))
-            if note_el != ".":
-                position_history.append(self.position)
-        # To account for Voice changes and alternate voice order.
-        self.position = min(position_history) if position_history else self.position
-
-    def _handle_glob_attr(self, el):
-        if el.startswith("*clef"):
-            self._handle_clef(el)
-        elif el.startswith("*k"):
-            self._handle_keysig(el)
-        elif el.startswith("*MM"):
-            pass
-        elif el.startswith("*M"):
-            self._handle_metersig(el)
-        elif el.endswith(":"):
-            self._handle_mode(el)
-        elif el.startswith("*S/sic"):
-            self.parsing = "left"
-        elif el.startswith("*S/ossia"):
-            self.parsing = "right"
-        elif el.startswith("Xstrophe"):
-            self.parsing = "full"
-
-
-class KernParser:
-    def __init__(self, document, doc_name):
-        self.document = document
-        self.doc_name = doc_name
-        # TODO review this code
-        self.DIVS2Q = {
-            1: 0.25,
-            2: 0.5,
-            4: 1,
-            6: 1.5,
-            8: 2,
-            16: 4,
-            24: 6,
-            32: 8,
-            48: 12,
-            64: 16,
-            128: 32,
-            256: 64,
-        }
-        # self.qdivs =
-        self.parts = self.process()
-
-    def __getitem__(self, item):
-        return self.parts[item]
-
-    def process(self):
-        # TODO handle pickup
-        # has_pickup = not np.all(np.char.startswith(self.document, "=1-") == False)
-        # if not has_pickup:
-        #     position = 0
-        # else:
-        #     position = self._handle_pickup_position()
-        position = 0
-        # Add for parallel processing
-        parts = [
-            self.collect(self.document[i], position, str(i), self.doc_name)
-            for i in reversed(range(self.document.shape[0]))
-        ]
-        return [p for p in parts if p]
-
-    def add2part(self, part, unprocessed):
-        flatten = [item for sublist in unprocessed for item in sublist]
-        if unprocessed:
-            new_part = KernParserPart(
-                flatten, 0, self.doc_name, "x", self.qdivs, part.barline_dict
+                staff=self.staff,
+                voice=voice,
+                id=el_id,
             )
-            self.parts.append(new_part)
-
-    def collect(self, doc, pos, id, doc_name):
-        if doc[0] == "**kern":
-            qdivs = self.find_lcm(doc)
-            x = KernParserPart(doc, pos, id, doc_name, qdivs).part
-            return x
-
-    # TODO handle position of pick-up measure?
-    def _handle_pickup_position(self):
-        return 0
-
-    def find_lcm(self, doc):
-        kern_string = "-".join([row for row in doc])
-        match = re.findall(r"([0-9]+)([a-g]|[A-G]|r|\.)", kern_string)
-        durs, _ = zip(*match)
-        x = np.array(list(map(lambda x: int(x), durs)))
-        divs = np.lcm.reduce(np.unique(x[x != 0]))
-        return float(divs) / 4.00
-
-
-# functions to initialize the kern parser
-def parse_kern(kern_path: PathLike) -> np.ndarray:
-    """
-    Parses an KERN file from path to an regular expression.
-
-    Parameters
-    ----------
-    kern_path : PathLike
-        The path of the KERN document.
-    Returns
-    -------
-    continuous_parts : numpy character array
-    non_continuous_parts : list
-    """
-    with open(kern_path, encoding="cp437") as file:
-        lines = file.read().splitlines()
-    d = [line.split("\t") for line in lines if not line.startswith("!")]
-    striped_parts = list()
-    merge_index = []
-    for x in d:
-        if merge_index:
-            for midx in merge_index:
-                x[midx] = x[midx] + " " + x[midx + 1]
-            y = [el for i, el in enumerate(x) if i - 1 not in merge_index]
-            striped_parts.append(y)
         else:
-            striped_parts.append(x)
-        if "*^" in x or "*+":
-            # Accounting for multiple voice ups at the same time.
-            for i, el in enumerate(x):
-                # Some faulty kerns create an extra part half way through the score.
-                # We choose for the moment to add it to the closest column part.
-                if el == "*^" or el == "*+":
-                    k = i
-                    if merge_index:
-                        if k < min(merge_index):
-                            merge_index = [midx + 1 for midx in merge_index]
-                    merge_index.append(k)
-        if "*v *v" in x:
-            k = x.index("*v *v")
-            temp = list()
-            for i in merge_index:
-                if i > k:
-                    temp.append(i - 1)
-                elif i < k:
-                    temp.append(i)
-            merge_index = temp
-    # Final filter for mistabs and inconsistent tabs that would create
-    # extra empty voice and would mess the parsing.
-    striped_parts = [[el for el in part if el != ""] for part in striped_parts]
-    numpy_parts = np.array(list(zip(striped_parts))).squeeze(1).T
-    return numpy_parts
-
-
-@deprecated_alias(kern_path="filename")
-@deprecated_parameter("ensure_list")
-def load_kern(
-    filename: PathLike,
-    force_note_ids: Optional[Union[bool, str]] = None,
-    parallel: bool = False,
-) -> score.Score:
-    """Parse a Kern file and build a composite score ontology
-    structure from it (see also scoreontology.py).
-
-    Parameters
-    ----------
-    filename : PathLike
-        Path to the Kern file to be parsed
-    force_note_ids : (bool, 'keep') optional.
-        When True each Note in the returned Part(s) will have a newly
-        assigned unique id attribute. Existing note id attributes in
-        the Kern will be discarded. If 'keep', only notes without
-        a note id will be assigned one.
-
-    Returns
-    -------
-    scr: :class:`partitura.score.Score`
-        A `Score` object
-    """
-    # parse kern file
-    numpy_parts = parse_kern(filename)
-    # doc_name = os.path.basename(filename[:-4])
-    doc_name = get_document_name(filename)
-    parser = KernParser(numpy_parts, doc_name)
-    partlist = parser.parts
-
-    score.assign_note_ids(
-        partlist, keep=(force_note_ids is True or force_note_ids == "keep")
-    )
-
-    # TODO: Parse score info (composer, lyricist, etc.)
-    scr = score.Score(id=doc_name, partlist=partlist)
+            note = spt.Note(
+                step,
+                octave,
+                alter,
+                symbolic_duration=symbolic_duration,
+                staff=self.staff,
+                voice=voice,
+                id=el_id,
+            )
+        if symbols:
+            self.process_symbol(note, symbols)
+        return note
 
-    return scr
+    def meta_barline_line(self, line: str):
+        """
+        Grammar Defining a barline line.
+
+        A barline line is specified by the following grammar:
+        barline_line = repeat | barline | number | repeat
+
+        Parameters
+        ----------
+        line: str
+            The line to parse containing a barline.
+
+        Returns
+        -------
+        spt.Measure object
+        """
+        # find number and keep its index.
+        self.total_parsed_elements += 1
+        number = re.findall(r"([0-9]+)", line)
+        number_index = line.index(number[0]) if number else line.index("=")
+        closing_repeat = re.findall(r"[:|]", line[:number_index])
+        opening_repeat = re.findall(r"[|:]", line[number_index:])
+        return spt.Measure(number=int(number[0]) if number else None)
+
+    def meta_chord_line(self, line: str):
+        """
+        Grammar Defining a chord line.
+
+        A chord line is specified by the following grammar:
+        chord_line = note | chord
+
+        Parameters
+        ----------
+        line
+
+        Returns
+        -------
+
+        """
+        self.total_parsed_elements += 1
+        chord = ("c", [self.meta_note_line(n, add=False) for n in line.split(" ")])
+        return chord
diff --git a/partitura/io/importmatch.py b/partitura/io/importmatch.py
index dcb95723..bb1f061a 100644
--- a/partitura/io/importmatch.py
+++ b/partitura/io/importmatch.py
@@ -394,7 +394,7 @@ def performed_part_from_match(
                 sound_off=midi_ticks_to_seconds(note.Offset, mpq, ppq),
                 velocity=note.Velocity,
                 track=getattr(note, "Track", 0),
-                channel=getattr(note, "Channel", 1),
+                channel=getattr(note, "Channel", 0),
             )
         )
     # Set first note_on to zero in ticks and seconds if first_note_at_zero
diff --git a/partitura/io/importmidi.py b/partitura/io/importmidi.py
index 5843b781..96ee2756 100644
--- a/partitura/io/importmidi.py
+++ b/partitura/io/importmidi.py
@@ -6,7 +6,7 @@
 import warnings
 
 from collections import defaultdict
-from typing import Union, Optional
+from typing import Union, Optional, List, Tuple, Dict
 import numpy as np
 
 
@@ -127,6 +127,15 @@ def load_performance_midi(
         notes = []
         controls = []
         programs = []
+        # This information is just for completeness,
+        # but loading a MIDI file as a performance
+        # assumes that key and time signature information
+        # is not reliable (e.g., a performance recorded with
+        # a MIDI keyboard, without metronome)
+        key_signatures = []
+        time_signatures = []
+        # other MetaMessages (not including key and time_signature)
+        meta_other = []
 
         t = 0
         ttick = 0
@@ -138,9 +147,59 @@ def load_performance_midi(
             t = t + msg.time * time_conversion_factor
             ttick = ttick + msg.time
 
-            if msg.type == "set_tempo":
-                mpq = msg.tempo
-                time_conversion_factor = mpq / (ppq * 10**6)
+            if isinstance(msg, mido.MetaMessage):
+                # Meta Messages apply to all channels in the track
+
+                # The tempo is set globally in PerformedParts,
+                # i.e., the tempo_conversion_factor is adjusted
+                # with every tempo change, rather than creating new
+                # tempo events.
+                if msg.type == "set_tempo":
+                    mpq = msg.tempo
+                    time_conversion_factor = mpq / (ppq * 10**6)
+
+                elif msg.type == "time_signature":
+                    time_signatures.append(
+                        dict(
+                            time=t,
+                            time_tick=ttick,
+                            beats=int(msg.numerator),
+                            beat_type=int(msg.denominator),
+                            track=i,
+                        )
+                    )
+                elif msg.type == "key_signature":
+                    key_name = str(msg.key)
+                    fifths, mode = key_name_to_fifths_mode(key_name)
+                    key_signatures.append(
+                        dict(
+                            time=t,
+                            time_tick=ttick,
+                            key_name=str(msg.key),
+                            fifths=fifths,
+                            mode=mode,
+                            track=i,
+                        )
+                    )
+
+                else:
+                    # Other MetaMessages
+                    # For more info, see
+                    # https://mido.readthedocs.io/en/latest/meta_message_types.html
+                    msg_dict = dict(
+                        [
+                            ("time", t),
+                            ("time_tick", ttick),
+                            ("track", i),
+                        ]
+                        + [
+                            (key, val)
+                            for key, val in msg.__dict__.items()
+                            if key not in ("time", "track", "time_tick")
+                        ]
+                    )
+
+                    meta_other.append(msg_dict)
 
             elif msg.type == "control_change":
                 controls.append(
@@ -221,7 +280,15 @@ def load_performance_midi(
 
         if len(notes) > 0 or len(controls) > 0 or len(programs) > 0:
             pp = performance.PerformedPart(
-                notes, controls=controls, programs=programs, ppq=ppq, mpq=mpq, track=i
+                notes,
+                controls=controls,
+                programs=programs,
+                key_signatures=key_signatures,
+                time_signatures=time_signatures,
+                meta_other=meta_other,
+                ppq=ppq,
+                mpq=mpq,
+                track=i,
             )
 
             pps.append(pp)
@@ -342,7 +409,13 @@ def load_score_midi(
     track_names_by_track = {}
     # notes are indexed by (track, channel) tuples
     notes_by_track_ch = {}
-    relevant = {"time_signature", "key_signature", "set_tempo", "note_on", "note_off"}
+    relevant = {
+        "time_signature",
+        "key_signature",
+        "set_tempo",
+        "note_on",
+        "note_off",
+    }
     for track_nr, track in enumerate(mid.tracks):
         time_sigs = []
         key_sigs = []
@@ -582,7 +655,11 @@ def make_track_to_part_mapping(tr_ch_keys, group_part_voice_keys):
     return track_to_part_keys
 
 
-def assign_group_part_voice(mode, track_ch_combis, track_names):
+def assign_group_part_voice(
+    mode: int,
+    track_ch_combis: Dict[Tuple[int, int], List],
+    track_names: Dict[int, str],
+) -> Tuple[List[Tuple], Dict, Dict]:
     """
     0: return one Part per track, with voices assigned by channel
     1. return one PartGroup per track, with Parts assigned by channel (no voices)
@@ -647,16 +724,39 @@ def assign_group_part_voice(mode, track_ch_combis, track_names):
 
 
 def create_part(
-    ticks,
-    notes,
-    spellings,
-    voices,
-    note_ids,
-    time_sigs,
-    key_sigs,
-    part_id=None,
-    part_name=None,
+    ticks: int,
+    notes: List[Tuple[int, int, int]],
+    spellings: List[Tuple[str, str, int]],
+    voices: List[int],
+    note_ids: List[str],
+    time_sigs: List[Tuple[int, int, int]],
+    key_sigs: List[Tuple[int, str]],
+    part_id: Optional[str] = None,
+    part_name: Optional[str] = None,
 ) -> score.Part:
+    """
+    Create score part object
+
+    Parameters
+    ----------
+    ticks: int
+        Integer unit to represent onset and duration information
+        in the score in a lossless way.
+    notes: List[Tuple[int, int, int]]
+        Note information (onset, pitch, duration)
+    spellings: List[Tuple[str, str, int]]
+    voices: List[str]
+    note_ids: List[str]
+    time_sigs: List[Tuple[int, int, int]]
+    key_sigs:
+    part_id
+    part_name
+
+    Returns
+    -------
+    part: partitura.score.Part
+        An object representing a Part in the score
+    """
     warnings.warn("create_part", stacklevel=2)
 
     part = score.Part(part_id, part_name=part_name)
@@ -747,7 +847,10 @@ def create_part(
     return part
 
 
-def quantize(v, unit):
+def quantize(
+    v: Union[np.ndarray, float, int],
+    unit: Union[float, int],
+) -> Union[np.ndarray, float, int]:
     """Quantize value `v` to a multiple of `unit`. When `unit` is an integer,
     the return value will be integer as well, otherwise the function will
     return a float.
diff --git a/partitura/io/importmusic21.py b/partitura/io/importmusic21.py
index f0ad2038..363dc9fe 100644
--- a/partitura/io/importmusic21.py
+++ b/partitura/io/importmusic21.py
@@ -100,14 +100,18 @@ def fill_part_notes(self, m21_part, pt_part, part_idx):
             for i_pitch, pitch in enumerate(generic_note.pitches):
                 if generic_note.duration.isGrace:
                     note = pt.score.GraceNote(
-                        grace_type="acciaccatura"
-                        if generic_note.duration.slash
-                        else "appoggiatura",
+                        grace_type=(
+                            "acciaccatura"
+                            if generic_note.duration.slash
+                            else "appoggiatura"
+                        ),
                         step=pitch.step,
                         octave=pitch.octave,
-                        alter=pitch.accidental.alter
-                        if pitch.accidental is not None
-                        else None,
+                        alter=(
+                            pitch.accidental.alter
+                            if pitch.accidental is not None
+                            else None
+                        ),
                         # id="{}_{}".format(generic_note.id, i_pitch),
                         id=generic_note.id,
                         voice=self.find_voice(generic_note),
@@ -119,9 +123,11 @@ def fill_part_notes(self, m21_part, pt_part, part_idx):
                     note = pt.score.Note(
                         step=pitch.step,
                         octave=pitch.octave,
-                        alter=pitch.accidental.alter
-                        if pitch.accidental is not None
-                        else None,
+                        alter=(
+                            pitch.accidental.alter
+                            if pitch.accidental is not None
+                            else None
+                        ),
                         # id="{}_{}".format(generic_note.id, i_pitch),
                         id=generic_note.id,
                         voice=self.find_voice(generic_note),
diff --git a/partitura/io/importmusicxml.py b/partitura/io/importmusicxml.py
index b586b9f6..a359fbe3 100644
--- a/partitura/io/importmusicxml.py
+++ b/partitura/io/importmusicxml.py
@@ -78,6 +78,14 @@
 
 OCTAVE_SHIFTS = {8: 1, 15: 2, 22: 3}
 
+ACCIDENTAL_MAP = {
+    "sharp": 1,
+    "natural": 0,
+    "flat": -1,
+    "double-sharp": 2,
+    "double-flat": -2,
+}
+
 
 def validate_musicxml(xml, debug=False):
     """
@@ -517,6 +525,8 @@ def _handle_measure(measure_el, position, part, ongoing, doc_order, measure_coun
     # add the start of the measure to the time line
     part.add(measure, position)
 
+    # Initialize Beams in Measure
+    prev_beam = None
     # keep track of the position within the measure
     # measure_pos = 0
     measure_start = position
@@ -575,8 +585,8 @@ def _handle_measure(measure_el, position, part, ongoing, doc_order, measure_coun
             _handle_sound(e, position, part)
 
         elif e.tag == "note":
-            (position, prev_note) = _handle_note(
-                e, position, part, ongoing, prev_note, doc_order
+            (position, prev_note, prev_beam) = _handle_note(
+                e, position, part, ongoing, prev_note, doc_order, prev_beam
             )
             doc_order += 1
             measure_maxtime = max(measure_maxtime, position)
@@ -645,6 +655,9 @@ def _handle_harmony(e, position, part):
     if e.find("function") is not None:
         text = e.find("function").text
         if text is not None:
+            if "|" in text:
+                text, cadence_annotation = text.split("|")
+                part.add(score.Cadence(cadence_annotation), position)
             part.add(score.RomanNumeral(text), position)
     elif e.find("kind") is not None and e.find("root") is not None:
         # TODO: handle kind text which is other kind of annotation also root
@@ -1175,13 +1188,15 @@ def _handle_sound(e, position, part):
         (position, part, tempo)
 
 
-def _handle_note(e, position, part, ongoing, prev_note, doc_order):
+def _handle_note(e, position, part, ongoing, prev_note, doc_order, prev_beam=None):
     # get some common features of element if available
     duration = get_value_from_tag(e, "duration", int) or 0
     # elements may have an explicit temporal offset
     # offset = get_value_from_tag(e, 'offset', int) or 0
     staff = get_value_from_tag(e, "staff", int) or 1
     voice = get_value_from_tag(e, "voice", int) or 1
+    # initialize beam to None
+    beam = None
 
     # add support of uppercase "ID" tags
     note_id = (
@@ -1232,6 +1247,11 @@ def _handle_note(e, position, part, ongoing, prev_note, doc_order):
         step = get_value_from_tag(pitch, "step", str)
         alter = get_value_from_tag(pitch, "alter", int)
         octave = get_value_from_tag(pitch, "octave", int)
+        # When step is none check for accidental attribute
+        if alter is None:
+            alter = get_value_from_tag(e, "accidental", str)
+            if alter is not None:
+                alter = ACCIDENTAL_MAP[alter]
 
         grace = e.find("grace")
 
@@ -1254,6 +1274,27 @@ def _handle_note(e, position, part, ongoing, prev_note, doc_order):
             if isinstance(prev_note, score.GraceNote) and prev_note.voice == voice:
                 note.grace_prev = prev_note
         else:
+            beam = e.find("beam")
+            if beam is not None:
+                if "number" in beam.attrib.keys():
+                    beam_num = beam.attrib["number"]
+                    beam = beam.text if beam_num == "1" else None
+                else:
+                    beam = beam.text
+
+            if beam == "begin":
+                prev_beam = score.Beam()
+                part.add(prev_beam, position)
+                beam = prev_beam
+            elif beam == "continue":
+                beam = prev_beam
+            elif beam == "end":
+                beam = prev_beam
+                prev_beam = None
+            else:
+                beam = None
+                prev_beam = None
+
             note = score.Note(
                 step=step,
                 octave=octave,
@@ -1309,6 +1350,10 @@ def _handle_note(e, position, part, ongoing, prev_note, doc_order):
 
     part.add(note, position, position + duration)
 
+    # After note is assigned to part we can assign the beam to the note if it exists
+    if isinstance(beam, score.Beam):
+        note.assign_beam(beam)
+
     ties = e.findall("tie")
     if len(ties) > 0:
         tie_key = ("tie", getattr(note, "midi_pitch", "rest"))
@@ -1353,7 +1398,7 @@ def _handle_note(e, position, part, ongoing, prev_note, doc_order):
 
     new_position = position + duration
 
-    return new_position, note
+    return new_position, note, prev_beam
 
 
 def handle_tuplets(notations, ongoing, note):
@@ -1405,6 +1450,11 @@ def handle_tuplets(notations, ongoing, note):
 
             stopping_tuplets.append(tuplet)
 
+    # assert that starting tuplet times are before stopping tuplet times
+    for start_tuplet, stop_tuplet in zip(starting_tuplets, stopping_tuplets):
+        assert (
+            start_tuplet.start_note.start.t < stop_tuplet.end_note.start.t
+        ), "Tuplet start time is after tuplet stop time"
     return starting_tuplets, stopping_tuplets
 
 
diff --git a/partitura/io/matchlines_v1.py b/partitura/io/matchlines_v1.py
index 1fdcfe69..a794ee33 100644
--- a/partitura/io/matchlines_v1.py
+++ b/partitura/io/matchlines_v1.py
@@ -1150,9 +1150,11 @@ def from_instance(
             version=version,
             anchor=instance.Anchor,
             note=MatchNote.from_instance(instance.note, version=version),
-            ornament_type=["trill"]
-            if instance.version < Version(1, 0, 0)
-            else instance.OrnamentType,
+            ornament_type=(
+                ["trill"]
+                if instance.version < Version(1, 0, 0)
+                else instance.OrnamentType
+            ),
         )
 
 
@@ -1347,9 +1349,9 @@ def make_section(
         start_in_beats_original=start_in_beats_original,
         end_in_beats_unfolded=end_in_beats_unfolded,
         end_in_beats_original=end_in_beats_original,
-        repeat_end_type=[repeat_end_type]
-        if isinstance(repeat_end_type, str)
-        else repeat_end_type,
+        repeat_end_type=(
+            [repeat_end_type] if isinstance(repeat_end_type, str) else repeat_end_type
+        ),
     )
     return ml
 
diff --git a/partitura/musicanalysis/key_identification.py b/partitura/musicanalysis/key_identification.py
index 1fc5fa35..e66e4624 100644
--- a/partitura/musicanalysis/key_identification.py
+++ b/partitura/musicanalysis/key_identification.py
@@ -11,6 +11,16 @@
 import numpy as np
 from scipy.linalg import circulant
 from partitura.utils.music import ensure_notearray
+from partitura.utils.globals import (
+    KEYS,
+    key_prof_maj_kk,
+    key_prof_min_kk,
+    key_prof_maj_cbms,
+    key_prof_min_cbms,
+    key_prof_maj_kp,
+    key_prof_min_kp,
+    VALID_KEY_PROFILES,
+)
 
 __all__ = ["estimate_key"]
 
@@ -18,73 +28,6 @@
 # Each tuple is (key root name, mode, fifths)
 # The key root name is equal to that with the smallest fifths in
 # the circle of fifths.
-KEYS = [
-    ("C", "major", 0),
-    ("Db", "major", -5),
-    ("D", "major", 2),
-    ("Eb", "major", -3),
-    ("E", "major", 4),
-    ("F", "major", -1),
-    ("F#", "major", 6),
-    ("G", "major", 1),
-    ("Ab", "major", -4),
-    ("A", "major", 3),
-    ("Bb", "major", -2),
-    ("B", "major", 5),
-    ("C", "minor", -3),
-    ("C#", "minor", 4),
-    ("D", "minor", -1),
-    ("D#", "minor", 6),
-    ("E", "minor", 1),
-    ("F", "minor", -4),
-    ("F#", "minor", 3),
-    ("G", "minor", -2),
-    ("G#", "minor", 5),
-    ("A", "minor", 0),
-    ("Bb", "minor", -5),
-    ("B", "minor", 2),
-]
-
-VALID_KEY_PROFILES = [
-    "krumhansl_kessler",
-    "kk",
-    "temperley",
-    "tp",
-    "kostka_payne",
-    "kp",
-]
-
-
-# Krumhansl--Kessler Key Profiles
-
-# From Krumhansl's "Cognitive Foundations of Musical Pitch" pp.30
-key_prof_maj_kk = np.array(
-    [6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88]
-)
-
-key_prof_min_kk = np.array(
-    [6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17]
-)
-
-# Temperley Key Profiles
-
-# CBMS (from "Music and Probability" Table 6.1, pp. 86)
-key_prof_maj_cbms = np.array(
-    [5.0, 2.0, 3.5, 2.0, 4.5, 4.0, 2.0, 4.5, 2.0, 3.5, 1.5, 4.0]
-)
-
-key_prof_min_cbms = np.array(
-    [5.0, 2.0, 3.5, 4.5, 2.0, 4.0, 2.0, 4.5, 3.5, 2.0, 1.5, 4.0]
-)
-
-# Kostka-Payne (from "Music and Probability" Table 6.1, pp. 86)
-key_prof_maj_kp = np.array(
-    [0.748, 0.060, 0.488, 0.082, 0.670, 0.460, 0.096, 0.715, 0.104, 0.366, 0.057, 0.400]
-)
-
-key_prof_min_kp = np.array(
-    [0.712, 0.048, 0.474, 0.618, 0.049, 0.460, 0.105, 0.747, 0.404, 0.067, 0.133, 0.330]
-)
 
 
 def build_key_profile_matrix(key_prof_maj, key_prof_min):
diff --git a/partitura/musicanalysis/meter.py b/partitura/musicanalysis/meter.py
index db70be27..7dfd2689 100644
--- a/partitura/musicanalysis/meter.py
+++ b/partitura/musicanalysis/meter.py
@@ -21,22 +21,20 @@
 # from scipy.interpolate import interp1d
 
 from partitura.utils import get_time_units_from_note_array, ensure_notearray, add_field
-
-
-# Scaling factors
-MAX = 9999999999999
-MIN_INTERVAL = 0.01
-MAX_INTERVAL = 2  # in seconds
-CLUSTER_WIDTH = 1 / 12  # in seconds
-N_CLUSTERS = 100
-INIT_DURATION = 10  # in seconds
-TIMEOUT = 10  # in seconds
-TOLERANCE_POST = 0.4  # propotion of beat_interval
-TOLERANCE_PRE = 0.2  # proportion of beat_interval
-TOLERANCE_INNER = 1 / 12
-CORRECTION_FACTOR = 1 / 4  # higher => more correction (speed changes)
-MAX_AGENTS = 100  # delete low-scoring agents when there are more than MAX_AGENTS
-CHORD_SPREAD_TIME = 1 / 12  # for onset aggregation
+from partitura.utils.globals import (
+    CHORD_SPREAD_TIME,
+    MIN_INTERVAL,
+    MAX_INTERVAL,
+    CLUSTER_WIDTH,
+    N_CLUSTERS,
+    INIT_DURATION,
+    TIMEOUT,
+    TOLERANCE_PRE,
+    TOLERANCE_POST,
+    TOLERANCE_INNER,
+    CORRECTION_FACTOR,
+    MAX_AGENTS,
+)
 
 
 class MultipleAgents:
diff --git a/partitura/musicanalysis/performance_codec.py b/partitura/musicanalysis/performance_codec.py
index 9da86785..2eac40c2 100644
--- a/partitura/musicanalysis/performance_codec.py
+++ b/partitura/musicanalysis/performance_codec.py
@@ -819,12 +819,13 @@ def get_matched_notes(spart_note_array, ppart_note_array, alignment):
             else:
                 p_id = al["performance_id"]
 
-            p_idx = int(np.where(ppart_note_array["id"] == p_id)[0])
+            p_idx = np.where(ppart_note_array["id"] == p_id)[0]
 
             s_idx = np.where(spart_note_array["id"] == al["score_id"])[0]
 
-            if len(s_idx) > 0:
+            if len(s_idx) > 0 and len(p_idx) > 0:
                 s_idx = int(s_idx)
+                p_idx = int(p_idx)
                 matched_idxs.append((s_idx, p_idx))
 
     return np.array(matched_idxs)
diff --git a/partitura/performance.py b/partitura/performance.py
index d0818d7f..8957491b 100644
--- a/partitura/performance.py
+++ b/partitura/performance.py
@@ -77,6 +77,9 @@ def __init__(
         part_name: str = None,
         controls: List[dict] = None,
         programs: List[dict] = None,
+        key_signatures: List[dict] = None,
+        time_signatures: List[dict] = None,
+        meta_other: List[dict] = None,
         sustain_pedal_threshold: int = 64,
         ppq: int = 480,
         mpq: int = 500000,
@@ -92,6 +95,9 @@ def __init__(
         )
         self.controls = controls or []
         self.programs = programs or []
+        self.time_signatures = time_signatures or []
+        self.key_signatures = key_signatures or []
+        self.meta_other = meta_other or []
         self.ppq = ppq
         self.mpq = mpq
         self.track = track
diff --git a/partitura/score.py b/partitura/score.py
index cbc0f445..e882151a 100644
--- a/partitura/score.py
+++ b/partitura/score.py
@@ -13,14 +13,17 @@
 from collections import defaultdict
 from collections.abc import Iterable
 from numbers import Number
-
-# import copy
-from partitura.utils.music import MUSICAL_BEATS, INTERVALCLASSES
+from partitura.utils.globals import (
+    MUSICAL_BEATS,
+    INTERVALCLASSES,
+    INTERVAL_TO_SEMITONES,
+)
 import warnings, sys
 import numpy as np
+import re
 from scipy.interpolate import PPoly
 from typing import Union, List, Optional, Iterator, Iterable as Itertype
-
+import difflib
 from partitura.utils import (
     ComparableMixin,
     ReplaceRefMixin,
@@ -44,8 +47,14 @@
     _OrderedSet,
     update_note_ids_after_unfolding,
 )
-
 from partitura.utils.generic import interp1d
+from partitura.utils.music import transpose_note, step2pc
+from partitura.utils.globals import (
+    INT_TO_ALT,
+    ALT_TO_INT,
+    ACCEPTED_ROMANS,
+    LOCAL_KEY_TRASPOSITIONS_DCML,
+)
 
 
 class Part(object):
@@ -567,6 +576,18 @@ def rests(self):
         """
         return [e for e in self.iter_all(Rest, include_subclasses=False)]
 
+    @property
+    def cadences(self):
+        """Return a list of all cadence objects in the part
+
+        Returns
+        -------
+        list
+            List of Cadence objects
+
+        """
+        return [e for e in self.iter_all(Cadence)]
+
     @property
     def repeats(self):
         """Return a list of all Repeat objects in the part
@@ -627,6 +648,41 @@ def tempo_directions(self):
         """
         return [e for e in self.iter_all(TempoDirection, include_subclasses=True)]
 
+    @property
+    def cadences(self):
+        """Return a list of all cadences in the part
+
+        Returns
+        -------
+        list
+            List of Cadence objects
+
+        """
+        return [e for e in self.iter_all(Cadence, include_subclasses=False)]
+
+    @property
+    def harmony(self):
+        """Return a list of all harmony in the part
+
+        Returns
+        -------
+        list
+            List of Harmony objects
+
+        """
+        return [e for e in self.iter_all(Harmony, include_subclasses=True)]
+
+    @property
+    def phrases(self):
+        """Return a list of all phrases in the part
+
+        Returns
+        -------
+        list
+            List of Phrase objects
+        """
+        return [e for e in self.iter_all(Phrase, include_subclasses=False)]
+
     @property
     def articulations(self):
         """Return a list of all Articulation markings in the part
@@ -1227,7 +1283,6 @@ def use_notated_beat(self):
 
 
 class TimePoint(ComparableMixin):
-
     """A TimePoint represents a temporal position within a
     :class:`Part`.
 
@@ -1583,9 +1638,10 @@ def __init__(
         articulations=None,
         ornaments=None,
         doc_order=None,
+        **kwargs,
     ):
         self._sym_dur = None
-        super().__init__()
+        super().__init__(**kwargs)
         self.voice = voice
         self.id = id
         self.staff = staff
@@ -1872,13 +1928,15 @@ class Note(GenericNote):
 
     """
 
-    def __init__(self, step, octave, alter=None, beam=None, **kwargs):
+    def __init__(self, step, octave, alter=None, **kwargs):
         super().__init__(**kwargs)
         self.step = step.upper()
         self.octave = octave
         self.alter = alter
-        self.beam = beam
+        self.beam = None
 
+    def assign_beam(self, beam):
+        self.beam = beam
         if self.beam is not None:
             self.beam.append(self)
 
@@ -2697,6 +2755,30 @@ def __str__(self):
         return f'{super().__str__()} "{self.shift_type}"'
 
 
+class Cadence(TimedObject):
+    """A cadence element in the score usually for Cadences."""
+
+    def __init__(self, text, local_key=None):
+        super().__init__()
+        self.text = text
+        self._filter_cadence_type()
+        self.local_key = local_key
+
+    def _filter_cadence_type(self):
+        """Cadence should be one of PAC, IAC, HC, DC, EC, PC, or None"""
+        # capitalize text
+        self.text = self.text.upper()
+        # Filter alphabet characters only.
+        self.text = re.findall(r"[A-Z]+", self.text)[0]
+        self.text = "IAC" if "IAC" in self.text else self.text
+        if self.text not in ["PAC", "IAC", "HC", "DC", "EC", "PC"]:
+            warnings.warn(f"Cadence type {self.text} not found. Setting to None")
+            self.text = None
+
+    def __str__(self):
+        return f'{super().__str__()} "{self.text}"'
+
+
 class Harmony(TimedObject):
     """A harmony element in the score not currently used.
 
@@ -2720,7 +2802,7 @@ def __str__(self):
         return f'{super().__str__()} "{self.text}"'
 
 
-class RomanNumeral(TimedObject):
+class RomanNumeral(Harmony):
     """A harmony element in the score usually for Roman Numerals.
 
     Parameters
@@ -2734,20 +2816,293 @@ class RomanNumeral(TimedObject):
         See parameters
     """
 
-    def __init__(self, text):
+    def __init__(
+        self,
+        text,
+        inversion=None,
+        local_key=None,
+        primary_degree=None,
+        secondary_degree=None,
+        quality=None,
+    ):
+        super().__init__(text)
+        self.text = text
+        self.accepted_qualities = (
+            "7",
+            "aug",
+            "aug6",
+            "aug7",
+            "dim",
+            "dim7",
+            "hdim7",
+            "maj",
+            "maj7",
+            "min",
+            "min7",
+        )
+        # The key of an inversion is text from RN string, and the value is a tuple (has_seven,inversion)
+        self.accepted_inversions = {
+            "2": (3, True),
+            "43": (2, True),
+            "64": (2, False),
+            "6": (1, False),
+            "65": (1, True),
+            "7": (0, True),
+        }
+        self.has_seven = "7" in text
+        self.inversion = (
+            inversion if inversion is not None else self._process_inversion()
+        )
+        self.local_key = (
+            local_key if local_key is not None else self._process_local_key()
+        )
+        self.primary_degree = (
+            primary_degree
+            if primary_degree is not None
+            else self._process_primary_degree()
+        )
+        self.secondary_degree = (
+            secondary_degree
+            if secondary_degree is not None
+            else self._process_secondary_degree()
+        )
+        self.quality = (
+            quality
+            if quality is not None and quality in self.accepted_qualities
+            else self._process_quality()
+        )
+        # only process the root note if the roman numeral is valid
+        if (
+            self.local_key
+            and self.primary_degree
+            and self.secondary_degree
+            and self.quality
+            and self.inversion
+        ):
+            self.root = self.find_root_note()
+            self.bass_note = self.find_bass_note()
+
+    def _process_inversion(self):
+        """Find the inversion of the roman numeral from the text"""
+        # The inversion should be right after the roman numeral.
+        # If there is no inversion, return 0
+        numeric_indications_in_text = re.findall(r"\d+", self.text)
+        if len(numeric_indications_in_text) > 0:
+            inversion, has_seven = self.accepted_inversions.get(
+                numeric_indications_in_text[0], (0, False)
+            )
+            self.has_seven = has_seven
+            return inversion
+        return 0
+
+    def _process_local_key(self):
+        """Find the local key of the roman numeral from the text"""
+        # The local key should be before the roman numeral.
+        # If there is no local key, return None
+        local_key = self.text.split(":")
+        if len(local_key) > 1:
+            return local_key[0]
+        return None
+
+    def _process_primary_degree(self):
+        """Find the primary degree of the roman numeral from the text
+
+        The primary degree should be a roman numeral between 1 and 7.
+        """
+        # The primary degree should be a roman numeral between 1 and 7.
+        # If there is no primary degree, return None
+        # Remove any key information
+        roman_text = self.text.split(":")[-1]
+        roman_text = roman_text.split(".")[-1] if "." in roman_text else roman_text
+        primary_degree = re.search(r"[a-zA-Z+]+", roman_text)
+        if primary_degree:
+            prim_d = primary_degree.group(0)
+            # if the primary degree is not in accepted values, return the closest one
+            if prim_d in ACCEPTED_ROMANS:
+                return prim_d
+            else:
+                matches = difflib.get_close_matches(
+                    prim_d, ACCEPTED_ROMANS, n=1, cutoff=0.5
+                )
+                if matches:
+                    return matches[0]
+        return None
+
+    def _process_secondary_degree(self):
+        """Find the secondary degree of the roman numeral from the text
+
+        The secondary degree should be a roman numeral between 1 and 7.
+        If it is not specified in the text, return I (the tonic) when the primary degree is not none.
+        """
+        # The secondary degree should be a roman numeral between 1 and 7.
+        # If it is not specified in the text, return I (the tonic) when the primary degree is not none.
+        roman_text = self.text.split(":")[-1]
+        split_pr_sec = roman_text.split("/")
+        if len(split_pr_sec) > 1:
+            secondary_degree = re.search(r"[a-zA-Z+]+", split_pr_sec[-1])
+            return secondary_degree.group(0)
+        elif self.primary_degree is not None and self.local_key is not None:
+            secondary_degree = "I" if self.local_key.isupper() else "i"
+            return secondary_degree
+        return None
+
+    def _process_quality(self):
+        """Find the quality of the roman numeral from the text
+
+        Accepted quality values are 7, aug, aug6, aug7, dim, dim7, hdim7, maj, maj7, min, min7.
+        This format follows the standards from the latest version of the AugmentedNet model.
+        Found out more here: github.com/napulen/AugmentedNet
+        """
+        # The quality should be M, m, +, o, or None.
+        aug_cond = "aug" in self.text.lower() or "+" in self.text.lower()
+        minor_cond = (
+            self.primary_degree.islower() if self.primary_degree is not None else False
+        )
+        major_cond = (
+            self.primary_degree.isupper() if self.primary_degree is not None else False
+        )
+        dim_cond = "dim" in self.text or "o" in self.text
+        aug6_cond = (
+            "ger" in self.text.lower()
+            or "it" in self.text.lower()
+            or "fr" in self.text.lower()
+        )
+        hdim_cond = "0" in self.text or "%" in self.text or "ø" in self.text
+        if aug6_cond:
+            quality = "aug6"
+        elif "maj7" in self.text.lower():
+            quality = "maj7"
+        elif dim_cond and self.has_seven:
+            quality = "dim7"
+        elif dim_cond:
+            quality = "dim"
+        elif aug_cond and self.has_seven:
+            quality = "aug7"
+        elif aug_cond:
+            quality = "aug"
+        elif hdim_cond:
+            quality = "hdim7"
+        elif minor_cond and self.has_seven:
+            quality = "min7"
+        elif minor_cond:
+            quality = "min"
+        elif major_cond and self.has_seven:
+            quality = "7"
+        elif major_cond:
+            quality = "maj"
+        else:
+            warnings.warn(
+                f"Quality for {self.text} was not found, could be a special case. Setting to None."
+            )
+            quality = None
+        return quality
+
+    def find_root_note(self):
+        """
+        Find the root note of a chord.
+
+        Returns
+        -------
+        number: int
+            The number of the chord.
+        """
+        # Corrected step after degree2
+        key_step = re.search(r"[a-gA-G]", self.local_key).group(0)
+        key_alter = (
+            re.search(r"[#b]", self.local_key).group(0)
+            if re.search(r"[#b]", self.local_key)
+            else ""
+        )
+        key_alter = ALT_TO_INT[key_alter]
+        try:
+            interval = (
+                Roman2Interval_Min[self.secondary_degree]
+                if self.local_key.islower()
+                else Roman2Interval_Maj[self.secondary_degree]
+            )
+            step, alter = transpose_note(key_step, key_alter, interval)
+        except KeyError:
+            loc_k = self.secondary_degree
+            glob_k = self.local_key
+            step, alter = process_local_key(loc_k, glob_k, return_step_alter=True)
+        # Corrected step after degree1
+        # TODO add support for diminished and augmented chords
+        try:
+            interval = (
+                Roman2Interval_Min[self.primary_degree]
+                if self.secondary_degree.islower()
+                else Roman2Interval_Maj[self.primary_degree]
+            )
+            step, alter = transpose_note(step, alter, interval)
+            root = step + INT_TO_ALT[alter]
+        except KeyError:
+            loc_k = self.primary_degree
+            glob_k = step.lower() if self.secondary_degree.islower() else step.upper()
+            root = step + INT_TO_ALT[alter]
+            root = process_local_key(loc_k, glob_k)
+
+        return root
+
+    def find_bass_note(self):
+        # TODO add support for diminished and augmented chords
+        step = re.search(r"[a-gA-G]", self.root).group(0)
+        alter = re.search(r"[#b]", self.root)
+        alter = ALT_TO_INT[alter.group(0)] if alter else 0
+
+        if self.inversion == 1:
+            if self.primary_degree.islower():
+                step, alter = transpose_note(step, alter, Interval(3, "m"))
+            else:
+                step, alter = transpose_note(step, alter, Interval(3, "M"))
+        elif self.inversion == 2:
+            step, alter = transpose_note(step, alter, Interval(5, "P"))
+        elif self.inversion == 3:
+            step, alter = transpose_note(step, alter, Interval(7, "m"))
+
+        bass_note_name = step + INT_TO_ALT[alter]
+        return bass_note_name
+
+    def __str__(self):
+        return f'{super().__str__()} "{self.text}"'
+
+
+class Cadence(TimedObject):
+    """A cadence element in the score usually for Cadences."""
+
+    def __init__(self, text, local_key=None):
         super().__init__()
         self.text = text
-        # assert issubclass(note, GenericNote)
+        self._filter_cadence_type()
+        self.local_key = local_key
+
+    def _filter_cadence_type(self):
+        """Cadence should be one of PAC, IAC, HC, DC, EC, PC, or None"""
+        # capitalize text
+        self.text = self.text.upper()
+        # Filter alphabet characters only.
+        self.text = re.findall(r"[A-Z]+", self.text)[0]
+        self.text = "IAC" if "IAC" in self.text else self.text
+        if self.text not in ["PAC", "IAC", "HC", "DC", "EC", "PC"]:
+            warnings.warn(f"Cadence type {self.text} not found. Setting to None")
+            self.text = None
 
     def __str__(self):
         return f'{super().__str__()} "{self.text}"'
 
 
-class ChordSymbol(TimedObject):
+class Phrase(TimedObject):
+    def __init__(self):
+        super().__init__()
+
+    def __str__(self):
+        return f"{super().__str__()}"
+
+
+class ChordSymbol(Harmony):
     """A harmony element in the score usually for Chord Symbols."""
 
     def __init__(self, root, kind, bass=None):
-        super().__init__()
+        super().__init__(text=root + kind + (f"/{bass}" if bass else ""))
         self.kind = kind
         self.root = root
         self.bass = bass
@@ -2787,6 +3142,50 @@ def validate(self):
             "down",
         ], f"Interval direction {self.direction} not found"
 
+    @property
+    def semitones(self):
+        return INTERVAL_TO_SEMITONES[self.quality + str(self.number)]
+
+    def change_quality(self, num):
+        """
+        Change the quality of the interval by a given number of semitones.
+
+        The Interval Number is not changed, only the quality.
+
+        Examples:
+        - M3 -> m3, num=-1
+        - M3 -> A3, num=1
+        - A4 -> d4, num=-2
+
+        Parameters
+        ----------
+        num: int
+            The number of semitones to change the quality by.
+
+        Returns
+        -------
+        Interval
+            The interval with the new quality, but the same number and direction.
+        """
+        change_direction_c = ["dd", "d", "P", "A", "AA"]
+        change_direction_d = ["dd", "d", "m", "M", "A", "AA"]
+
+        prev_quality = self.quality
+        if num == 0:
+            pass
+        else:
+            change_dir = (
+                change_direction_c
+                if self.number in [1, 4, 5, 8]
+                else change_direction_d
+            )
+            cur_index = change_dir.index(prev_quality)
+            new_index = cur_index + num
+            if new_index >= len(change_dir) or new_index < 0:
+                raise ValueError("Interval quality cannot be changed to that extent")
+            self.quality = change_dir[new_index]
+        return self
+
     def __str__(self):
         return f'{super().__str__()} "{self.number}{self.quality}"'
 
@@ -3807,6 +4206,9 @@ def find_tuplets(part):
                             start_note = note_tuplet[0]
                             stop_note = note_tuplet[-1]
                             tuplet = Tuplet(start_note, stop_note)
+                            assert (
+                                start_note.start.t <= stop_note.start.t
+                            ), "The start note of a Tuplet should be before the stop note"
                             part.add(tuplet, start_note.start.t, stop_note.end.t)
                             tup_start += actual_notes
 
@@ -4802,6 +5204,139 @@ def make_score_variants(part):
     return svs
 
 
+def _fill_rests_within_measure(measure: Measure, part: Part) -> None:
+    start_time = measure.start.t
+    end_time = measure.end.t
+    notes = np.array(list(part.iter_all(GenericNote, start_time, end_time)))
+    voc_staff = np.array([[n.voice, n.staff] for n in notes])
+    un_voc_staff, inverse_map = np.unique(voc_staff, axis=0, return_inverse=True)
+    for i in range(un_voc_staff):
+        note_mask = inverse_map == i
+        notes_per_vocstaff = notes[note_mask]
+        # get note with min start.t
+        min_start_note = notes_per_vocstaff[np.argmin(notes_per_vocstaff.start.t)]
+        if min_start_note.start.t > start_time:
+            sym_dur = estimate_symbolic_duration(
+                min_start_note.start.t - start_time, part._quarter_durations[0]
+            )
+            rest = Rest(
+                symbolic_duration=sym_dur,
+                staff=min_start_note.staff,
+                voice=min_start_note.voice,
+            )
+            part.add(rest, start_time, min_start_note.start.t)
+
+        min_end_note = notes_per_vocstaff[
+            np.argmin(np.vectorize(lambda x: x.end.t)(notes_per_vocstaff))
+        ]
+        if min_end_note.end.t < end_time:
+            sym_dur = estimate_symbolic_duration(
+                end_time - min_end_note.end.t, part._quarter_durations[0]
+            )
+            rest = Rest(
+                symbolic_duration=sym_dur,
+                staff=min_end_note.staff,
+                voice=min_end_note.voice,
+            )
+            part.add(rest, min_end_note.end.t, end_time)
+
+
+def _fill_rests_global(
+    measure: Measure, part: Part, unique_voc_staff: np.ndarray
+) -> None:
+    start_time = measure.start.t
+    end_time = measure.end.t
+    if end_time - start_time == 0:
+        return
+    notes = np.array(
+        list(part.iter_all(GenericNote, start_time, end_time, include_subclasses=True))
+    )
+    voc_staff = np.array([[n.voice, n.staff] for n in notes])
+    un_voc_staff, inverse_map = np.unique(voc_staff, axis=0, return_inverse=True)
+    for i in range(un_voc_staff.shape[0]):
+        note_mask = inverse_map == i
+        notes_per_vocstaff = notes[note_mask]
+        # get note with min start.t
+        min_start_note = notes_per_vocstaff[
+            np.argmin(np.vectorize(lambda x: x.start.t)(notes_per_vocstaff))
+        ]
+        if min_start_note.start.t > start_time:
+            sym_dur = estimate_symbolic_duration(
+                min_start_note.start.t - start_time, part._quarter_durations[0]
+            )
+            rest = Rest(
+                symbolic_duration=sym_dur,
+                staff=min_start_note.staff,
+                voice=min_start_note.voice,
+            )
+            part.add(rest, start_time, min_start_note.start.t)
+
+        min_end_note = notes_per_vocstaff[
+            np.argmax(np.vectorize(lambda x: x.end.t)(notes_per_vocstaff))
+        ]
+        if min_end_note.end.t < end_time:
+            sym_dur = estimate_symbolic_duration(
+                end_time - min_end_note.end.t, part._quarter_durations[0]
+            )
+            rest = Rest(
+                symbolic_duration=sym_dur,
+                staff=min_end_note.staff,
+                voice=min_end_note.voice,
+            )
+            part.add(rest, min_end_note.end.t, end_time)
+
+    if un_voc_staff.shape[0] != unique_voc_staff.shape[0]:
+        if un_voc_staff.shape[0] == 0:
+            diff = unique_voc_staff
+        else:
+            # View `un_voc_staff` and `unique_voc_staff` as 1-D structured arrays
+            x_sa = un_voc_staff.view([("", un_voc_staff.dtype)] * un_voc_staff.shape[1])
+            y_sa = unique_voc_staff.view(
+                [("", unique_voc_staff.dtype)] * unique_voc_staff.shape[1]
+            )
+            # Find rows in `unique_voc_staff` that are not in `un_voc_staff`
+            diff = np.setdiff1d(y_sa, x_sa)
+        for voice, staff in diff:
+            sym_dur = estimate_symbolic_duration(
+                end_time - start_time, part._quarter_durations[0]
+            )
+            rest = Rest(symbolic_duration=sym_dur, staff=staff, voice=voice)
+            part.add(rest, start_time, end_time)
+
+
+def fill_rests(score_data: ScoreLike, measurewise=True) -> None:
+    """
+    Fill rests in a score when a voice starts in a middle of a measure and no rest precedes.
+
+    When measurewise is True, the voices are searched within a measure.
+    When measurewise is False, the rests are filled globally in the score for all voices and staffs.
+
+    Parameters
+    ----------
+    score_data: ScoreLike
+        The score to fill rests
+    measurewise: bool
+        If True, fill rests within a measure. If False, fill rests globally in the score.
+    """
+    if isinstance(score_data, Score):
+        partlist = score_data.parts
+    else:
+        partlist = [score_data]
+    for part in partlist:
+        measures = part.measures
+        if measurewise:
+            for measure in measures:
+                _fill_rests_within_measure(measure, part)
+        else:
+            note_array = part.note_array(include_staff=True)
+            unique_vocstaff = np.unique(
+                np.array([note_array["voice"], note_array["staff"]], dtype=np.int64),
+                axis=1,
+            )
+            for measure in measures:
+                _fill_rests_global(measure, part, unique_vocstaff.T)
+
+
 def merge_parts(parts, reassign="voice"):
     """Merge list of parts or PartGroup into a single part.
      All parts are expected to have the same time signature
@@ -4872,16 +5407,20 @@ def merge_parts(parts, reassign="voice"):
     note_arrays = [part.note_array(include_staff=True) for part in parts]
     # find the maximum number of voices for each part (voice number start from 1)
     maximum_voices = [
-        max(note_array["voice"], default=0)
-        if max(note_array["voice"], default=0) != 0
-        else 1
+        (
+            max(note_array["voice"], default=0)
+            if max(note_array["voice"], default=0) != 0
+            else 1
+        )
         for note_array in note_arrays
     ]
     # find the maximum number of staves for each part (staff number start from 0 but we force them to 1)
     maximum_staves = [
-        max(note_array["staff"], default=0)
-        if max(note_array["staff"], default=0) != 0
-        else 1
+        (
+            max(note_array["staff"], default=0)
+            if max(note_array["staff"], default=0) != 0
+            else 1
+        )
         for note_array in note_arrays
     ]
 
@@ -4951,6 +5490,349 @@ def merge_parts(parts, reassign="voice"):
     return new_part
 
 
+def _fill_rests_within_measure(measure: Measure, part: Part) -> None:
+    start_time = measure.start.t
+    end_time = measure.end.t
+    notes = np.array(
+        list(part.iter_all(GenericNote, start_time, end_time, include_subclasses=True))
+    )
+
+    # voc_staff is now transformed to only voice
+    voc_staff = np.array([[n.voice, n.staff] for n in notes])
+    un_voice, inverse_map = np.unique(voc_staff[:, 0], axis=0, return_inverse=True)
+    # Check if a staff is empty and fill it with rests
+    unique_staff = np.unique(voc_staff[:, 1])
+    if len(unique_staff) < part.number_of_staves:
+        for staff in range(1, part.number_of_staves + 1):
+            if staff not in unique_staff:
+                # solution when estimation returns composite durations.
+                sym_dur = estimate_symbolic_duration(
+                    end_time - start_time,
+                    part._quarter_durations[0],
+                    return_com_durations=True,
+                )
+                if isinstance(sym_dur, tuple):
+                    st = start_time
+                    for i, sd in enumerate(sym_dur):
+                        et = start_time + symbolic_to_numeric_duration(
+                            sd, part._quarter_durations[0]
+                        )
+                        rest = Rest(
+                            symbolic_duration=sd, staff=staff, voice=un_voice.max() + 1
+                        )
+                        part.add(rest, st, et)
+                        st = et
+                else:
+                    rest = Rest(
+                        symbolic_duration=sym_dur, staff=staff, voice=un_voice.max() + 1
+                    )
+                    part.add(rest, start_time, end_time)
+    # Now we fill the rests for each voice
+    for i in range(len(un_voice)):
+        note_mask = inverse_map == i
+        notes_per_vocstaff = notes[note_mask]
+        sort_note_start = np.argsort(
+            np.vectorize(lambda x: x.start.t)(notes_per_vocstaff)
+        )
+        sort_note_end = np.argsort(np.vectorize(lambda x: x.end.t)(notes_per_vocstaff))
+        # get note with min start.t and fill the rest before it if needed
+        min_start_note = notes_per_vocstaff[sort_note_start[0]]
+        if min_start_note.start.t > start_time:
+            sym_dur = estimate_symbolic_duration(
+                min_start_note.start.t - start_time,
+                part._quarter_durations[0],
+                return_com_durations=True,
+            )
+            # solution when estimation returns composite durations.
+            if isinstance(sym_dur, tuple):
+                st = start_time
+                for i, sd in enumerate(sym_dur):
+                    et = st + symbolic_to_numeric_duration(
+                        sd, part._quarter_durations[0]
+                    )
+                    rest = Rest(
+                        symbolic_duration=sd,
+                        staff=min_start_note.staff,
+                        voice=min_start_note.voice,
+                    )
+                    part.add(rest, st, et)
+                    st = et
+            else:
+                rest = Rest(
+                    symbolic_duration=sym_dur,
+                    staff=min_start_note.staff,
+                    voice=min_start_note.voice,
+                )
+                part.add(rest, start_time, min_start_note.start.t)
+
+        # get note with max end.t and fill the rest after it if needed
+        min_end_note = notes_per_vocstaff[sort_note_end[-1]]
+        if min_end_note.end.t < end_time:
+            sym_dur = estimate_symbolic_duration(
+                end_time - min_end_note.end.t,
+                part._quarter_durations[0],
+                return_com_durations=True,
+            )
+            # solution when estimation returns composite durations.
+            if isinstance(sym_dur, tuple):
+                st = min_end_note.end.t
+                for i, sd in enumerate(sym_dur):
+                    et = st + symbolic_to_numeric_duration(
+                        sd, part._quarter_durations[0]
+                    )
+                    rest = Rest(
+                        symbolic_duration=sd,
+                        staff=min_end_note.staff,
+                        voice=min_end_note.voice,
+                    )
+                    part.add(rest, st, et)
+                    st = et
+            else:
+                rest = Rest(
+                    symbolic_duration=sym_dur,
+                    staff=min_end_note.staff,
+                    voice=min_end_note.voice,
+                )
+                part.add(rest, min_end_note.end.t, end_time)
+
+        if len(sort_note_start) <= 1:
+            continue
+        # fill the rests between notes if needed (i.e. if there is a gap between notes)
+        for i in range(1, len(sort_note_start)):
+            if (
+                notes_per_vocstaff[sort_note_start[i]].start.t
+                > notes_per_vocstaff[sort_note_end[i - 1]].end.t
+            ):
+                sym_dur = estimate_symbolic_duration(
+                    notes_per_vocstaff[sort_note_start[i]].start.t
+                    - notes_per_vocstaff[sort_note_end[i - 1]].end.t,
+                    part._quarter_durations[0],
+                    return_com_durations=True,
+                )
+                if isinstance(sym_dur, tuple):
+                    st = notes_per_vocstaff[sort_note_end[i - 1]].end.t
+                    for i, sd in enumerate(sym_dur):
+                        et = st + symbolic_to_numeric_duration(
+                            sd, part._quarter_durations[0]
+                        )
+                        rest = Rest(
+                            symbolic_duration=sd,
+                            staff=notes_per_vocstaff[sort_note_end[i - 1]].staff,
+                            voice=notes_per_vocstaff[sort_note_end[i - 1]].voice,
+                        )
+                        part.add(rest, st, et)
+                        st = et
+                else:
+                    rest = Rest(
+                        symbolic_duration=sym_dur,
+                        staff=notes_per_vocstaff[sort_note_end[i - 1]].staff,
+                        voice=notes_per_vocstaff[sort_note_end[i - 1]].voice,
+                    )
+                    part.add(
+                        rest,
+                        notes_per_vocstaff[sort_note_end[i - 1]].end.t,
+                        notes_per_vocstaff[sort_note_start[i]].start.t,
+                    )
+
+
+def _fill_rests_global(
+    measure: Measure, part: Part, unique_voc_staff: np.ndarray
+) -> None:
+    start_time = measure.start.t
+    end_time = measure.end.t
+    if end_time - start_time == 0:
+        return
+    notes = np.array(
+        list(part.iter_all(GenericNote, start_time, end_time, include_subclasses=True))
+    )
+    voc_staff = np.array([[n.voice, n.staff] for n in notes])
+    un_voc_staff, inverse_map = np.unique(voc_staff, axis=0, return_inverse=True)
+    for i in range(un_voc_staff.shape[0]):
+        note_mask = inverse_map == i
+        notes_per_vocstaff = notes[note_mask]
+        # get note with min start.t
+        min_start_note = notes_per_vocstaff[
+            np.argmin(np.vectorize(lambda x: x.start.t)(notes_per_vocstaff))
+        ]
+        if min_start_note.start.t > start_time:
+            sym_dur = estimate_symbolic_duration(
+                min_start_note.start.t - start_time, part._quarter_durations[0]
+            )
+            rest = Rest(
+                symbolic_duration=sym_dur,
+                staff=min_start_note.staff,
+                voice=min_start_note.voice,
+            )
+            part.add(rest, start_time, min_start_note.start.t)
+
+        min_end_note = notes_per_vocstaff[
+            np.argmax(np.vectorize(lambda x: x.end.t)(notes_per_vocstaff))
+        ]
+        if min_end_note.end.t < end_time:
+            sym_dur = estimate_symbolic_duration(
+                end_time - min_end_note.end.t, part._quarter_durations[0]
+            )
+            rest = Rest(
+                symbolic_duration=sym_dur,
+                staff=min_end_note.staff,
+                voice=min_end_note.voice,
+            )
+            part.add(rest, min_end_note.end.t, end_time)
+
+    if un_voc_staff.shape[0] != unique_voc_staff.shape[0]:
+        if un_voc_staff.shape[0] == 0:
+            diff = unique_voc_staff
+        else:
+            # View `un_voc_staff` and `unique_voc_staff` as 1-D structured arrays
+            x_sa = un_voc_staff.view([("", un_voc_staff.dtype)] * un_voc_staff.shape[1])
+            y_sa = unique_voc_staff.view(
+                [("", unique_voc_staff.dtype)] * unique_voc_staff.shape[1]
+            )
+            # Find rows in `unique_voc_staff` that are not in `un_voc_staff`
+            diff = np.setdiff1d(y_sa, x_sa)
+        for voice, staff in diff:
+            sym_dur = estimate_symbolic_duration(
+                end_time - start_time, part._quarter_durations[0]
+            )
+            rest = Rest(symbolic_duration=sym_dur, staff=staff, voice=voice)
+            part.add(rest, start_time, end_time)
+
+
+def fill_rests(score_data: ScoreLike, measurewise=True) -> None:
+    """
+    Fill rests in a score when a voice starts in a middle of a measure and no rest precedes.
+
+    When measurewise is True, the voices are searched within a measure.
+    When measurewise is False, the rests are filled globally in the score for all voices and staffs.
+
+    Parameters
+    ----------
+    score_data: ScoreLike
+        The score to fill rests
+    measurewise: bool
+        If True, fill rests within a measure. If False, fill rests globally in the score.
+    """
+    if isinstance(score_data, Score):
+        partlist = score_data.parts
+    else:
+        partlist = [score_data]
+    for part in partlist:
+        measures = part.measures
+        if measurewise:
+            for measure in measures:
+                _fill_rests_within_measure(measure, part)
+        else:
+            note_array = part.note_array(include_staff=True)
+            unique_vocstaff = np.unique(
+                np.array([note_array["voice"], note_array["staff"]], dtype=np.int64),
+                axis=1,
+            )
+            for measure in measures:
+                _fill_rests_global(measure, part, unique_vocstaff.T)
+
+
+def infer_beaming(part: ScoreLike):
+    """
+    Infer beaming from the metrical position of notes in a part.
+
+    This function infers the beaming based on the time signature for all notes.
+    It separates the notes into groups based on their staff and voice.
+
+    Parameters
+    ----------
+    part: ScoreLike
+        The part to infer beaming for. This can be a part or a score.
+        If a score is given, the function will infer beaming for all parts in the score.
+
+    """
+
+    if isinstance(part, Score):
+        for p in part.parts:
+            infer_beaming(p)
+    else:
+        note_array = part.note_array(
+            include_metrical_position=True,
+            include_staff=True,
+            include_time_signature=True,
+        )
+        beat_ends = note_array["onset_beat"] + note_array["duration_beat"]
+        # split note_array into groups based on staff and voice
+        # unique_vocstaff = np.unique(note_array[['voice', 'staff']], axis=0)
+        # for v, s in unique_vocstaff:
+        #     mask = (note_array['voice'] == v) & (note_array['staff'] == s)
+        unique_vocstaff = np.unique(note_array["voice"])
+        for v in unique_vocstaff:
+            mask = note_array["voice"] == v
+            # get the metrical position of the notes
+            na_vocstaff = note_array[mask]
+            # get the beat ends of the notes
+            beat_end = beat_ends[mask]
+            # get notes
+            beat_multiplier = 4 / na_vocstaff["ts_beat_type"]
+            mus_beats = (
+                na_vocstaff["ts_beats"]
+                / na_vocstaff["ts_mus_beats"]
+                * (na_vocstaff["ts_beat_type"] > 4)
+            )
+            mus_beats = np.where(mus_beats == 0, 1, mus_beats)
+            max_mus_beat = mus_beats.max()
+            beam_start_mask = np.isclose(
+                np.mod(na_vocstaff["onset_beat"], mus_beats), 0.0
+            ) & (na_vocstaff["duration_beat"] * beat_multiplier <= 0.5)
+            beam_end_mask = np.isclose(np.mod(beat_end, mus_beats), 0.0) & (
+                na_vocstaff["duration_beat"] * beat_multiplier <= 0.5
+            )
+            beam_between = (
+                (na_vocstaff["duration_beat"] * beat_multiplier <= 0.5)
+                & ~beam_start_mask
+                & ~beam_end_mask
+            )
+            id_beam_start = na_vocstaff["id"][beam_start_mask]
+            id_beam_end = na_vocstaff["id"][beam_end_mask]
+            id_beam_between = na_vocstaff["id"][beam_between]
+            start_time = na_vocstaff["onset_div"].min()
+            end_time = na_vocstaff["onset_div"].max() + 1
+            prev_beam = None
+            notes_in_beam = []
+            notes_in_vs = list(part.iter_all(Note, start_time, end_time))
+            notes_in_vs.sort(key=lambda x: x.start.t)
+            prev_start = 0
+            for note in notes_in_vs:
+                # if note.voice != v or note.staff != s:
+                if note.voice != v:
+                    continue
+                if note.beam is not None:
+                    continue
+
+                if (
+                    part.beat_map(note.start.t) - part.beat_map(prev_start)
+                    > max_mus_beat
+                ):
+                    prev_beam = None
+                    notes_in_beam = []
+                    prev_start = note.start.t
+                if note.id in id_beam_start:
+                    notes_in_beam = []
+                    prev_beam = Beam()
+                    prev_start = note.start.t
+                    notes_in_beam.append(note)
+                elif note.id in id_beam_end:
+                    if prev_beam is not None:
+                        notes_in_beam.append(note)
+                        if len(notes_in_beam) > 1:
+                            for n in notes_in_beam:
+                                n.assign_beam(prev_beam)
+                            part.add(prev_beam, prev_beam.start.t, prev_beam.end.t)
+                    notes_in_beam = []
+                    prev_beam = None
+                elif note.id in id_beam_between:
+                    if prev_beam is None:
+                        prev_start = note.start.t
+                        notes_in_beam = []
+                        prev_beam = Beam()
+                    notes_in_beam.append(note)
+
+
 def is_a_within_b(a, b, wholly=False):
     """
     Returns a boolean indicating whether a is (wholly) within b.
@@ -4983,6 +5865,129 @@ def is_a_within_b(a, b, wholly=False):
     return contained
 
 
+def process_local_key(loc_k_text, glob_k_text, return_step_alter=False):
+    local_key_sharps = loc_k_text.count("#")
+    local_key_flats = loc_k_text.count("b")
+    local_key = loc_k_text.replace("#", "").replace("b", "")
+    local_key_is_minor = local_key.islower()
+    local_key = local_key.lower()
+    global_key_is_minor = glob_k_text.islower()
+    if (
+        local_key_is_minor == global_key_is_minor
+        and local_key == "i"
+        and local_key_sharps - local_key_flats == 0
+        and (not return_step_alter)
+    ):
+        return glob_k_text
+    g_key = "minor" if glob_k_text.islower() else "major"
+    num, qual = LOCAL_KEY_TRASPOSITIONS_DCML[g_key][local_key]
+    transposition_interval = Interval(num, qual)
+    transposition_interval = transposition_interval.change_quality(
+        local_key_sharps - local_key_flats
+    )
+    key_step = re.search(r"[a-gA-G]", glob_k_text).group(0)
+    key_alter = (
+        re.search(r"[#b]", glob_k_text).group(0)
+        if re.search(r"[#b]", glob_k_text)
+        else ""
+    )
+    key_alter = key_alter.replace("b", "-")
+    key_alter = ALT_TO_INT[key_alter]
+    key_step, key_alter = transpose_note(key_step, key_alter, transposition_interval)
+    if return_step_alter:
+        return key_step, key_alter
+    local_key = (
+        key_step.lower() if local_key_is_minor else key_step.upper()
+    ) + INT_TO_ALT[key_alter]
+    return local_key
+
+
+Roman2Interval_Maj = {
+    "I": Interval(1, "P"),
+    "II": Interval(2, "M"),
+    "III": Interval(3, "M"),
+    "III+": Interval(3, "M"),
+    "IV": Interval(4, "P"),
+    "V": Interval(5, "P"),
+    "VI": Interval(6, "M"),
+    "VII": Interval(7, "M"),
+    "i": Interval(1, "P"),
+    "ii": Interval(2, "M"),
+    "iii": Interval(3, "m"),
+    "iv": Interval(4, "P"),
+    "v": Interval(5, "P"),
+    "vi": Interval(6, "M"),
+    "vii": Interval(7, "M"),
+    "viio": Interval(7, "M"),
+    "N": Interval(2, "m"),
+    "iio": Interval(2, "M"),
+    "Ger7": Interval(4, "A"),
+    "Fr7": Interval(4, "A"),
+    "It": Interval(4, "A"),
+}
+
+Roman2Interval_Min = {
+    "I": Interval(1, "P"),
+    "II": Interval(2, "M"),
+    "III": Interval(3, "m"),
+    "III+": Interval(3, "m"),
+    "IV": Interval(4, "P"),
+    "V": Interval(5, "P"),
+    "VI": Interval(6, "m"),
+    "VII": Interval(7, "m"),
+    "i": Interval(1, "P"),
+    "ii": Interval(2, "M"),
+    "iii": Interval(3, "m"),
+    "iv": Interval(4, "P"),
+    "v": Interval(5, "P"),
+    "vi": Interval(6, "m"),
+    "vii": Interval(7, "m"),
+    "viio": Interval(7, "M"),
+    "N": Interval(2, "m"),
+    "iio": Interval(2, "M"),
+    "Ger7": Interval(4, "A"),
+    "Fr7": Interval(4, "A"),
+    "It": Interval(4, "A"),
+}
+
+
+def process_local_key(loc_k, glob_k, return_step_alter=False):
+    local_key_sharps = loc_k.count("#")
+    local_key_flats = loc_k.count("b")
+    local_key = loc_k.replace("#", "").replace("b", "")
+    local_key_is_minor = local_key.islower()
+    local_key = local_key.lower()
+    global_key_is_minor = glob_k.islower()
+    if (
+        local_key_is_minor == global_key_is_minor
+        and local_key == "i"
+        and local_key_sharps - local_key_flats == 0
+        and (not return_step_alter)
+    ):
+        return glob_k
+    g_key = "minor" if glob_k.islower() else "major"
+    # keep only letters in local_key
+    local_key = re.sub(r"[^a-zA-Z]", "", local_key)
+    num, qual = LOCAL_KEY_TRASPOSITIONS_DCML[g_key][local_key]
+    transposition_interval = Interval(num, qual)
+    transposition_interval = transposition_interval.change_quality(
+        local_key_sharps - local_key_flats
+    )
+    key_step = re.search(r"[a-gA-G]", glob_k).group(0)
+    key_alter = (
+        re.search(r"[#b]", glob_k).group(0) if re.search(r"[#b]", glob_k) else ""
+    )
+    key_alter = key_alter.replace("b", "-")
+    key_alter = ALT_TO_INT[key_alter]
+    key_step, key_alter = transpose_note(key_step, key_alter, transposition_interval)
+    if return_step_alter:
+        return key_step, key_alter
+    local_key = (
+        key_step.lower() if local_key_is_minor else key_step.upper()
+    ) + INT_TO_ALT[key_alter]
+    return local_key
+
+
 class InvalidTimePointException(Exception):
     """Raised when a time point is instantiated with an invalid number."""
 
diff --git a/partitura/utils/fluidsynth.py b/partitura/utils/fluidsynth.py
new file mode 100644
index 00000000..7f8d9f2c
--- /dev/null
+++ b/partitura/utils/fluidsynth.py
@@ -0,0 +1,326 @@
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+"""
+This module contains methods for synthesizing score- or performance-like
+objects using fluidsynth. Fluidsynth is an optional dependency.
+"""
+
+import os
+from collections import defaultdict
+from typing import Callable, Optional, Union
+
+import numpy as np
+import partitura as pt
+
+try:
+    from fluidsynth import Synth
+
+    HAS_FLUIDSYNTH = True
+except ImportError:  # pragma: no cover
+    Synth = None  # pragma: no cover
+    HAS_FLUIDSYNTH = False  # pragma: no cover
+
+from partitura.utils.synth import SAMPLE_RATE
+from partitura.performance import PerformanceLike
+from partitura.score import ScoreLike
+from partitura.utils.misc import PathLike, download_file
+from partitura.utils.music import (
+    ensure_notearray,
+    get_time_units_from_note_array,
+    performance_notearray_from_score_notearray,
+)
+
+# MuseScore's soundfont distributed under the MIT License.
+# https://ftp.osuosl.org/pub/musescore/soundfont/MuseScore_General/MuseScore_General_License.md
+DEFAULT_SOUNDFONT_URL = "ftp://ftp.osuosl.org/pub/musescore/soundfont/MuseScore_General/MuseScore_General.sf3"
+
+DEFAULT_SOUNDFONT = os.path.join(
+    pt.__path__[0],
+    "assets",
+    "MuseScore_General.sf3",
+)
+
+if not os.path.exists(DEFAULT_SOUNDFONT) and HAS_FLUIDSYNTH:  # pragma: no cover
+    print(f"Downloading soundfont from {DEFAULT_SOUNDFONT_URL}...")  # pragma: no cover
+    download_file(
+        url=DEFAULT_SOUNDFONT_URL,
+        out=DEFAULT_SOUNDFONT,
+    )  # pragma: no cover
+
+
+def synthesize_fluidsynth(
+    note_info: Union[ScoreLike, PerformanceLike, np.ndarray],
+    samplerate: int = SAMPLE_RATE,
+    soundfont: PathLike = DEFAULT_SOUNDFONT,
+    bpm: Union[float, np.ndarray, Callable] = 60,
+) -> np.ndarray:
+    """
+    Synthesize partitura object with note information using
+    fluidsynth.
+
+    Parameters
+    ----------
+    note_info : ScoreLike, PerformanceLike or np.ndarray
+        A partitura object with note information.
+
+    samplerate: int
+        The sample rate of the audio file in Hz.
+
+    soundfont: PathLike
+        The path to the soundfont (in SF2/SF3 format).
+
+    bpm : float, np.ndarray or callable
+        The bpm to render the output (if the input is a score-like object).
+        See `partitura.utils.music.performance_notearray_from_score_notearray`
+        for more information on this parameter.
+
+    Returns
+    -------
+    output_audio_signal : np.ndarray
+       Audio signal as a 1D array.
+    """
+
+    if not HAS_FLUIDSYNTH:
+        raise ImportError("Fluidsynth is not installed!")  # pragma: no cover
+
+    if isinstance(note_info, pt.performance.Performance):
+        for ppart in note_info:
+            ppart.sustain_pedal_threshold = 127
+
+    if isinstance(note_info, pt.performance.PerformedPart):
+        note_info.sustain_pedal_threshold = 127
+    note_array = ensure_notearray(note_info)
+
+    onset_unit, _ = get_time_units_from_note_array(note_array)
+    if np.min(note_array[onset_unit]) <= 0:
+        note_array[onset_unit] = note_array[onset_unit] + np.min(note_array[onset_unit])
+
+    pitch = note_array["pitch"]
+    # If the input is a score, convert score time to seconds
+    if onset_unit != "onset_sec":
+        pnote_array = performance_notearray_from_score_notearray(
+            snote_array=note_array,
+            bpm=bpm,
+        )
+        onsets = pnote_array["onset_sec"]
+        offsets = pnote_array["onset_sec"] + pnote_array["duration_sec"]
+        # duration = pnote_array["duration_sec"]
+        channel = pnote_array["channel"]
+        track = pnote_array["track"]
+        velocity = pnote_array["velocity"]
+    else:
+        onsets = note_array["onset_sec"]
+        offsets = note_array["onset_sec"] + note_array["duration_sec"]
+
+        if "velocity" in note_array.dtype.names:
+            velocity = note_array["velocity"]
+        else:
+            velocity = np.ones(len(onsets), dtype=int) * 64
+        if "channel" in note_array.dtype.names:
+            channel = note_array["channel"]
+        else:
+            channel = np.zeros(len(onsets), dtype=int)
+
+        if "track" in note_array.dtype.names:
+            track = note_array["track"]
+        else:
+            track = np.zeros(len(onsets), dtype=int)
+
+    controls = []
+    if isinstance(note_info, pt.performance.Performance):
+
+        for ppart in note_info:
+            controls += ppart.controls
+
+    unique_tracks = list(
+        set(list(np.unique(track)) + list(set([c["track"] for c in controls])))
+    )
+
+    track_dict = defaultdict(lambda: defaultdict(list))
+
+    for tn in unique_tracks:
+        track_idxs = np.where(track == tn)[0]
+
+        track_channels = channel[track_idxs]
+        track_pitch = pitch[track_idxs]
+        track_onsets = onsets[track_idxs]
+        track_offsets = offsets[track_idxs]
+        track_velocity = velocity[track_idxs]
+
+        unique_channels = np.unique(track_channels)
+
+        track_controls = [c for c in controls if c["track"] == tn]
+
+        for chn in unique_channels:
+
+            channel_idxs = np.where(track_channels == chn)[0]
+
+            channel_pitch = track_pitch[channel_idxs]
+            channel_onset = track_onsets[channel_idxs]
+            channel_offset = track_offsets[channel_idxs]
+            channel_velocity = track_velocity[channel_idxs]
+
+            channel_controls = [c for c in track_controls if c["channel"] == chn]
+
+            track_dict[tn][chn] = [
+                channel_pitch,
+                channel_onset,
+                channel_offset,
+                channel_velocity,
+                channel_controls,
+            ]
+
+    # set to mono
+    synthesizer = Synth(samplerate=SAMPLE_RATE)
+    sf_id = synthesizer.sfload(soundfont)
+
+    audio_signals = []
+    for tn, channel_info in track_dict.items():
+
+        for chn, (pi, on, off, vel, ctrls) in channel_info.items():
+
+            audio_signal = synth_note_info(
+                pitch=pi,
+                onsets=on,
+                offsets=off,
+                velocities=vel,
+                controls=ctrls,
+                program=None,
+                synthesizer=synthesizer,
+                sf_id=sf_id,
+                channel=chn,
+                samplerate=samplerate,
+            )
+            audio_signals.append(audio_signal)
+
+    # pad audio signals:
+
+    signal_lengths = [len(signal) for signal in audio_signals]
+    max_len = np.max(signal_lengths)
+
+    output_audio_signal = np.zeros(max_len)
+
+    for sl, audio_signal in zip(signal_lengths, audio_signals):
+
+        output_audio_signal[:sl] += audio_signal
+
+    # normalization term
+    norm_term = max(audio_signal.max(), abs(audio_signal.min()))
+    output_audio_signal /= norm_term
+
+    return output_audio_signal
+
+
+def synth_note_info(
+    pitch: np.ndarray,
+    onsets: np.ndarray,
+    offsets: np.ndarray,
+    velocities: np.ndarray,
+    controls: Optional[list],
+    program: Optional[int],
+    synthesizer: Synth,
+    sf_id: int,
+    channel: int,
+    samplerate: int = SAMPLE_RATE,
+) -> np.ndarray:
+    """
+    Synthesize note information with Fluidsynth.
+    This method is designed to synthesize the notes in a
+    single track and channel.
+
+    Parameters
+    ----------
+    pitch : np.ndarray
+        An array with pitch information for each note.
+    onsets : np.ndarray
+        An array with onset time in seconds for each note.
+    offsets : np.ndarray
+        An array with offset times in seconds for each note.
+    velocities : np.ndarray
+        An array with MIDI velocities for each note.
+    controls : Optional[list]
+        A list of MIDI controls (e.g., pedals).
+        (as the `controls` attribute in `PerformedPart` objects)
+    program : Optional[int]
+        A list of MIDI programs as dictionaries
+        (as the `program` attribute in `PerformedPart` objects).
+    synthesizer : Synth
+        An instance of a fluidsynth Synth object.
+    sf_id : int
+        The id of the synthesizer object
+    channel : int
+        Channel for the the notes.
+    samplerate : int, optional
+        Sample rate, by default SAMPLE_RATE
+
+    Returns
+    -------
+    audio_signal : np.ndarray
+        A 1D array with the synthesized audio signal.
+    """
+
+    # set program
+    synthesizer.program_select(channel, sf_id, 0, program or 0)
+
+    if len(controls) > 0 and len(offsets) > 0:
+        piece_duration = max(offsets.max(), np.max([c["time"] for c in controls]))
+    elif len(controls) > 0 and len(offsets) == 0:
+        piece_duration = np.max([c["time"] for c in controls])
+    elif len(controls) == 0 and len(offsets) > 0:
+        piece_duration = offsets.max()
+    else:
+        # return a single zero
+        audio_signal = np.zeros(1)
+        return audio_signal
+
+    num_frames = int(np.round(piece_duration * samplerate))
+
+    # Initialize array containing audio
+    audio_signal = np.zeros(num_frames, dtype="float")
+
+    # Initialize the time axis
+    x = np.linspace(0, piece_duration, num=num_frames)
+
+    # onsets in frames (i.e., indices of the `audio_signal` array)
+    onsets_in_frames = np.searchsorted(x, onsets, side="left")
+    offsets_in_frames = np.searchsorted(x, offsets, side="left")
+
+    messages = []
+    for ctrl in controls or []:
+
+        messages.append(
+            (
+                "cc",
+                channel,
+                ctrl["number"],
+                ctrl["value"],
+                np.searchsorted(x, ctrl["time"], side="left"),
+            )
+        )
+
+    for pi, vel, oif, ofif in zip(
+        pitch, velocities, onsets_in_frames, offsets_in_frames
+    ):
+
+        messages += [
+            ("noteon", channel, pi, vel, oif),
+            ("noteoff", channel, pi, ofif),
+        ]
+
+    # sort messages
+    messages.sort(key=lambda x: x[-1])
+
+    delta_times = [
+        int(nm[-1] - cm[-1]) for nm, cm in zip(messages[1:], messages[:-1])
+    ] + [0]
+
+    for dt, msg in zip(delta_times, messages):
+
+        msg_type = msg[0]
+        msg_time = msg[-1]
+        getattr(synthesizer, msg_type)(*msg[1:-1])
+
+        samples = synthesizer.get_samples(dt)[::2]
+        audio_signal[msg_time : msg_time + dt] = samples
+
+    return audio_signal
diff --git a/partitura/utils/globals.py b/partitura/utils/globals.py
new file mode 100644
index 00000000..3739e0d4
--- /dev/null
+++ b/partitura/utils/globals.py
@@ -0,0 +1,545 @@
+import re
+import numpy as np
+
+
+MIDI_BASE_CLASS = {"c": 0, "d": 2, "e": 4, "f": 5, "g": 7, "a": 9, "b": 11}
+# _MORPHETIC_BASE_CLASS = {'c': 0, 'd': 1, 'e': 2, 'f': 3, 'g': 4, 'a': 5, 'b': 6}
+# _MORPHETIC_OCTAVE = {0: 32, 1: 39, 2: 46, 3: 53, 4: 60, 5: 67, 6: 74, 7: 81, 8: 89}
+ALTER_SIGNS = {None: "", 0: "", 1: "#", 2: "x", -1: "b", -2: "bb"}
+
+DUMMY_PS_BASE_CLASS = {
+    0: ("c", 0),
+    1: ("c", 1),
+    2: ("d", 0),
+    3: ("d", 1),
+    4: ("e", 0),
+    5: ("f", 0),
+    6: ("f", 1),
+    7: ("g", 0),
+    8: ("g", 1),
+    9: ("a", 0),
+    10: ("a", 1),
+    11: ("b", 0),
+}
+
+MEI_DURS_TO_SYMBOLIC = {
+    "long": "long",
+    "0": "breve",
+    "breve": "breve",
+    "1": "whole",
+    "2": "half",
+    "4": "quarter",
+    "8": "eighth",
+    "16": "16th",
+    "32": "32nd",
+    "64": "64th",
+    "128": "128th",
+    "256": "256th",
+}
+
+SYMBOLIC_TO_INT_DURS = {
+    "long": 0.25,
+    "breve": 0.5,
+    "whole": 1,
+    "half": 2,
+    "quarter": 4,
+    "eighth": 8,
+    "16th": 16,
+    "32nd": 32,
+    "64th": 64,
+    "128th": 128,
+    "256th": 256,
+}
+
+LABEL_DURS = {
+    "long": 16,
+    "breve": 8,
+    "whole": 4,
+    "half": 2,
+    "h": 2,
+    "quarter": 1,
+    "q": 1,
+    "eighth": 1 / 2,
+    "e": 1 / 2,
+    "16th": 1 / 4,
+    "32nd": 1 / 8.0,
+    "64th": 1 / 16,
+    "128th": 1 / 32,
+    "256th": 1 / 64,
+}
+DOT_MULTIPLIERS = (1, 1 + 1 / 2, 1 + 3 / 4, 1 + 7 / 8)
+# DURS and SYM_DURS encode the same information as _LABEL_DURS and
+# _DOT_MULTIPLIERS, but they allow for faster estimation of symbolic duration
+# (estimate_symbolic duration). At some point we will probably do away with
+# _LABEL_DURS and _DOT_MULTIPLIERS.
+DURS = np.array(
+    [
+        1.5625000e-02,
+        2.3437500e-02,
+        2.7343750e-02,
+        2.9296875e-02,
+        3.1250000e-02,
+        4.6875000e-02,
+        5.4687500e-02,
+        5.8593750e-02,
+        6.2500000e-02,
+        9.3750000e-02,
+        1.0937500e-01,
+        1.1718750e-01,
+        1.2500000e-01,
+        1.8750000e-01,
+        2.1875000e-01,
+        2.3437500e-01,
+        2.5000000e-01,
+        3.7500000e-01,
+        4.3750000e-01,
+        4.6875000e-01,
+        5.0000000e-01,
+        5.0000000e-01,
+        7.5000000e-01,
+        7.5000000e-01,
+        8.7500000e-01,
+        8.7500000e-01,
+        9.3750000e-01,
+        9.3750000e-01,
+        1.0000000e00,
+        1.0000000e00,
+        1.5000000e00,
+        1.5000000e00,
+        1.7500000e00,
+        1.7500000e00,
+        1.8750000e00,
+        1.8750000e00,
+        2.0000000e00,
+        2.0000000e00,
+        3.0000000e00,
+        3.0000000e00,
+        3.5000000e00,
+        3.5000000e00,
+        3.7500000e00,
+        3.7500000e00,
+        4.0000000e00,
+        6.0000000e00,
+        7.0000000e00,
+        7.5000000e00,
+        8.0000000e00,
+        1.2000000e01,
+        1.4000000e01,
+        1.5000000e01,
+        1.6000000e01,
+        2.4000000e01,
+        2.8000000e01,
+        3.0000000e01,
+    ]
+)
+
+SYM_DURS = [
+    {"type": "256th", "dots": 0},
+    {"type": "256th", "dots": 1},
+    {"type": "256th", "dots": 2},
+    {"type": "256th", "dots": 3},
+    {"type": "128th", "dots": 0},
+    {"type": "128th", "dots": 1},
+    {"type": "128th", "dots": 2},
+    {"type": "128th", "dots": 3},
+    {"type": "64th", "dots": 0},
+    {"type": "64th", "dots": 1},
+    {"type": "64th", "dots": 2},
+    {"type": "64th", "dots": 3},
+    {"type": "32nd", "dots": 0},
+    {"type": "32nd", "dots": 1},
+    {"type": "32nd", "dots": 2},
+    {"type": "32nd", "dots": 3},
+    {"type": "16th", "dots": 0},
+    {"type": "16th", "dots": 1},
+    {"type": "16th", "dots": 2},
+    {"type": "16th", "dots": 3},
+    {"type": "eighth", "dots": 0},
+    {"type": "e", "dots": 0},
+    {"type": "eighth", "dots": 1},
+    {"type": "e", "dots": 1},
+    {"type": "eighth", "dots": 2},
+    {"type": "e", "dots": 2},
+    {"type": "eighth", "dots": 3},
+    {"type": "e", "dots": 3},
+    {"type": "quarter", "dots": 0},
+    {"type": "q", "dots": 0},
+    {"type": "quarter", "dots": 1},
+    {"type": "q", "dots": 1},
+    {"type": "quarter", "dots": 2},
+    {"type": "q", "dots": 2},
+    {"type": "quarter", "dots": 3},
+    {"type": "q", "dots": 3},
+    {"type": "half", "dots": 0},
+    {"type": "h", "dots": 0},
+    {"type": "half", "dots": 1},
+    {"type": "h", "dots": 1},
+    {"type": "half", "dots": 2},
+    {"type": "h", "dots": 2},
+    {"type": "half", "dots": 3},
+    {"type": "h", "dots": 3},
+    {"type": "whole", "dots": 0},
+    {"type": "whole", "dots": 1},
+    {"type": "whole", "dots": 2},
+    {"type": "whole", "dots": 3},
+    {"type": "breve", "dots": 0},
+    {"type": "breve", "dots": 1},
+    {"type": "breve", "dots": 2},
+    {"type": "breve", "dots": 3},
+    {"type": "long", "dots": 0},
+    {"type": "long", "dots": 1},
+    {"type": "long", "dots": 2},
+    {"type": "long", "dots": 3},
+]
+
+MAJOR_KEYS = [
+    "Cb",
+    "Gb",
+    "Db",
+    "Ab",
+    "Eb",
+    "Bb",
+    "F",
+    "C",
+    "G",
+    "D",
+    "A",
+    "E",
+    "B",
+    "F#",
+    "C#",
+]
+MINOR_KEYS = [
+    "Ab",
+    "Eb",
+    "Bb",
+    "F",
+    "C",
+    "G",
+    "D",
+    "A",
+    "E",
+    "B",
+    "F#",
+    "C#",
+    "G#",
+    "D#",
+    "A#",
+]
+
+TIME_UNITS = ["beat", "quarter", "sec", "div"]
+
+NOTE_NAME_PATT = re.compile(r"([A-G]{1})([xb\#]*)(\d+)")
+
+INTERVALCLASSES = [
+    f"{specific}{generic}"
+    for generic in [2, 3, 6, 7]
+    for specific in ["dd", "d", "m", "M", "A", "AA"]
+] + [
+    f"{specific}{generic}"
+    for generic in [1, 4, 5]
+    for specific in ["dd", "d", "P", "A", "AA"]
+]
+
+INTERVAL_TO_SEMITONES = dict(
+    zip(
+        INTERVALCLASSES,
+        [
+            generic + specific
+            for generic in [1, 3, 8, 10]
+            for specific in [-2, -1, 0, 1, 2, 3]
+        ]
+        + [
+            generic + specific
+            for generic in [0, 5, 7]
+            for specific in [-2, -1, 0, 1, 2]
+        ],
+    )
+)
+
+
+STEPS = {
+    "C": 0,
+    "D": 1,
+    "E": 2,
+    "F": 3,
+    "G": 4,
+    "A": 5,
+    "B": 6,
+    0: "C",
+    1: "D",
+    2: "E",
+    3: "F",
+    4: "G",
+    5: "A",
+    6: "B",
+}
+
+
+MUSICAL_BEATS = {6: 2, 9: 3, 12: 4}
+
+# Standard tuning frequency of A4 in Hz
+A4 = 440.0
+
+COMPOSITE_DURS = np.array([1 + 4 / 32, 1 + 4 / 16, 2 + 4 / 32, 2 + 4 / 16, 2 + 4 / 8])
+
+SYM_COMPOSITE_DURS = [
+    ({"type": "quarter", "dots": 0}, {"type": "32nd", "dots": 0}),
+    ({"type": "quarter", "dots": 0}, {"type": "16th", "dots": 0}),
+    ({"type": "half", "dots": 0}, {"type": "32nd", "dots": 0}),
+    ({"type": "half", "dots": 0}, {"type": "16th", "dots": 0}),
+    ({"type": "half", "dots": 0}, {"type": "eighth", "dots": 0}),
+]
+
+
+UNABBREVS = [
+    (re.compile(r"(crescendo|cresc\.?)"), "crescendo"),
+    (re.compile(r"(smorzando|smorz\.?)"), "smorzando"),
+    (re.compile(r"(decrescendo|(decresc|decr|dimin|dim)\.?)"), "diminuendo"),
+    (re.compile(r"((acceler|accel|acc)\.?)"), "accelerando"),
+    (re.compile(r"(ritenente|riten\.?)"), "ritenuto"),
+    (re.compile(r"((ritard|rit)\.?)"), "ritardando"),
+    (re.compile(r"((rallent|rall)\.?)"), "rallentando"),
+    (re.compile(r"(dolciss\.?)"), "dolcissimo"),
+    (re.compile(r"((sosten|sost)\.?)"), "sostenuto"),
+    (re.compile(r"(delicatiss\.?)"), "delicatissimo"),
+    (re.compile(r"(leggieramente|leggiermente|leggiero|legg\.?)"), "leggiero"),
+    (re.compile(r"(leggierissimo|(leggieriss\.?))"), "leggierissimo"),
+    (re.compile(r"(scherz\.?)"), "scherzando"),
+    (re.compile(r"(tenute|ten\.?)"), "tenuto"),
+    (re.compile(r"(allegretto)"), "allegro"),
+    (re.compile(r"(espress\.?)"), "espressivo"),
+    (re.compile(r"(ligato)"), "legato"),
+    (re.compile(r"(ligatissimo)"), "legatissimo"),
+    (re.compile(r"((rinforz|rinf|rfz|rf)\.?)"), "rinforzando"),
+]
+
+
+TWO_PI = 2 * np.pi
+SAMPLE_RATE = 44100
+DTYPE = float
+
+NATURAL_INTERVAL_RATIOS = {
+    0: 1,
+    1: 16 / 15,  # 15/14, 11/10
+    2: 8 / 7,  # 9/8, 10/9, 12/11, 13/14
+    3: 6 / 5,  # 7/6,
+    4: 5 / 4,
+    5: 4 / 3,
+    6: 7 / 5,  # 13/9,
+    7: 3 / 2,
+    8: 8 / 5,
+    9: 5 / 3,
+    10: 7 / 4,  # 13/7
+    11: 15 / 8,
+    12: 2,
+}
+
+# symmetric five limit temperament with supertonic = 10:9
+FIVE_LIMIT_INTERVAL_RATIOS = {
+    0: 1,
+    1: 16 / 15,
+    2: 10 / 9,
+    3: 6 / 5,
+    4: 5 / 4,
+    5: 4 / 3,
+    6: 7 / 5,
+    7: 3 / 2,
+    8: 8 / 5,
+    9: 5 / 3,
+    10: 9 / 5,
+    11: 15 / 8,
+    12: 2,
+}
+
+
+EPSILON = 0.0001
+
+KEYS = [
+    ("C", "major", 0),
+    ("Db", "major", -5),
+    ("D", "major", 2),
+    ("Eb", "major", -3),
+    ("E", "major", 4),
+    ("F", "major", -1),
+    ("F#", "major", 6),
+    ("G", "major", 1),
+    ("Ab", "major", -4),
+    ("A", "major", 3),
+    ("Bb", "major", -2),
+    ("B", "major", 5),
+    ("C", "minor", -3),
+    ("C#", "minor", 4),
+    ("D", "minor", -1),
+    ("D#", "minor", 6),
+    ("E", "minor", 1),
+    ("F", "minor", -4),
+    ("F#", "minor", 3),
+    ("G", "minor", -2),
+    ("G#", "minor", 5),
+    ("A", "minor", 0),
+    ("Bb", "minor", -5),
+    ("B", "minor", 2),
+]
+
+VALID_KEY_PROFILES = [
+    "krumhansl_kessler",
+    "kk",
+    "temperley",
+    "tp",
+    "kostka_payne",
+    "kp",
+]
+
+
+# Krumhansl--Kessler Key Profiles
+
+# From Krumhansl's "Cognitive Foundations of Musical Pitch" pp.30
+key_prof_maj_kk = np.array(
+    [6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88]
+)
+
+key_prof_min_kk = np.array(
+    [6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17]
+)
+
+# Temperley Key Profiles
+
+# CBMS (from "Music and Probability" Table 6.1, pp. 86)
+key_prof_maj_cbms = np.array(
+    [5.0, 2.0, 3.5, 2.0, 4.5, 4.0, 2.0, 4.5, 2.0, 3.5, 1.5, 4.0]
+)
+
+key_prof_min_cbms = np.array(
+    [5.0, 2.0, 3.5, 4.5, 2.0, 4.0, 2.0, 4.5, 3.5, 2.0, 1.5, 4.0]
+)
+
+# Kostka-Payne (from "Music and Probability" Table 6.1, pp. 86)
+key_prof_maj_kp = np.array(
+    [0.748, 0.060, 0.488, 0.082, 0.670, 0.460, 0.096, 0.715, 0.104, 0.366, 0.057, 0.400]
+)
+
+key_prof_min_kp = np.array(
+    [0.712, 0.048, 0.474, 0.618, 0.049, 0.460, 0.105, 0.747, 0.404, 0.067, 0.133, 0.330]
+)
+
+
+# Scaling factors
+MAX = 9999999999999
+MIN_INTERVAL = 0.01
+MAX_INTERVAL = 2  # in seconds
+CLUSTER_WIDTH = 1 / 12  # in seconds
+N_CLUSTERS = 100
+INIT_DURATION = 10  # in seconds
+TIMEOUT = 10  # in seconds
+TOLERANCE_POST = 0.4  # propotion of beat_interval
+TOLERANCE_PRE = 0.2  # proportion of beat_interval
+TOLERANCE_INNER = 1 / 12
+CORRECTION_FACTOR = 1 / 4  # higher => more correction (speed changes)
+MAX_AGENTS = 100  # delete low-scoring agents when there are more than MAX_AGENTS
+CHORD_SPREAD_TIME = 1 / 12  # for onset aggregation
+
+
+Voc_majmin = ["Cad64", "V", "viio", "V7", "N", "It", "Fr7", "Ger7", "v"]
+
+Voc_maj_only = [
+    "I",
+    "ii",
+    "iii",
+    "IV",
+    "vi",
+    "I7",
+    "ii7",
+    "iii7",
+    "IV7",
+    "vi7",
+    "viio7",
+    "V+",
+]
+
+Voc_min_only = [
+    "i",
+    "iio",
+    "III+",
+    "iv",
+    "VI",
+    "i7",
+    "iio7",
+    "III+7",
+    "iv7",
+    "VI7",
+    "viio7",
+]
+
+Voc_maj = Voc_majmin + Voc_maj_only
+Voc_min = Voc_majmin + Voc_min_only
+
+ACCEPTED_ROMANS = list(set(Voc_maj + Voc_min))
+
+Voc_T_degree = [
+    "I",
+    "II",
+    "III",
+    "IV",
+    "V",
+    "VI",
+    "VII",
+    "i",
+    "ii",
+    "iii",
+    "iv",
+    "v",
+    "vi",
+    "vii",
+]
+
+
+BASE_PC = {
+    "C": 0,
+    "D": 2,
+    "E": 4,
+    "F": 5,
+    "G": 7,
+    "A": 9,
+    "B": 11,
+}
+
+ALT_TO_INT = {
+    "--": -2,
+    "-": -1,
+    "b": -1,
+    "bb": -2,
+    "": 0,
+    "#": 1,
+    "##": 2,
+}
+
+INT_TO_ALT = {
+    -2: "--",
+    -1: "-",
+    0: "",
+    1: "#",
+    2: "##",
+}
+
+
+LOCAL_KEY_TRASPOSITIONS_DCML = {
+    "minor": {
+        "i": (1, "P"),
+        "ii": (2, "M"),
+        "iii": (3, "m"),
+        "iv": (4, "P"),
+        "v": (5, "P"),
+        "vi": (6, "m"),
+        "vii": (7, "m"),
+    },
+    "major": {
+        "i": (1, "P"),
+        "ii": (2, "M"),
+        "iii": (3, "M"),
+        "iv": (4, "P"),
+        "v": (5, "P"),
+        "vi": (6, "M"),
+        "vii": (7, "M"),
+    },
+}
diff --git a/partitura/utils/misc.py b/partitura/utils/misc.py
index 843f8c11..3695e189 100644
--- a/partitura/utils/misc.py
+++ b/partitura/utils/misc.py
@@ -6,6 +6,8 @@
 import functools
 import os
 import warnings
+from urllib.request import urlopen
+from shutil import copyfileobj
 
 from typing import Union, Callable, Dict, Any, Iterable, Optional
 
@@ -254,3 +256,27 @@ def concatenate_images(
 
     else:
         return new_image
+
+
+def download_file(
+    url: str,
+    out: str,
+) -> None:
+    """
+    Download a file from a specified URL and save it to a local file path.
+
+    Parameters
+    ----------
+    url : str
+        The URL of the file to download.
+    out : str
+        The local file path where the downloaded file should be saved.
+
+    Notes
+    -----
+    This method was adapted from a Stack Overflow answer
+    (https://stackoverflow.com/a/15035466), and is distributed under the
+    CC BY-SA 4.0 license (https://creativecommons.org/licenses/by-sa/4.0/).
+    """
+    with urlopen(url) as in_stream, open(out, "wb") as out_file:
+        copyfileobj(in_stream, out_file)
diff --git a/partitura/utils/music.py b/partitura/utils/music.py
index c6fd9b02..f9678228 100644
--- a/partitura/utils/music.py
+++ b/partitura/utils/music.py
@@ -11,11 +11,13 @@
 import numpy as np
 from scipy.interpolate import interp1d
 from scipy.sparse import csc_matrix
-from typing import Union, Callable, Optional, TYPE_CHECKING
+from typing import Union, Callable, Optional, TYPE_CHECKING, Tuple, Dict, Any, List
 from partitura.utils.generic import find_nearest, search, iter_current_next
+from partitura.utils.globals import *
 import partitura
 from tempfile import TemporaryDirectory
-import os
+import os, math
+
 
 try:
     import miditok
@@ -40,286 +42,6 @@ class MIDITokenizer(object):
     from partitura.performance import PerformanceLike, Performance, PerformedPart
 
 
-MIDI_BASE_CLASS = {"c": 0, "d": 2, "e": 4, "f": 5, "g": 7, "a": 9, "b": 11}
-# _MORPHETIC_BASE_CLASS = {'c': 0, 'd': 1, 'e': 2, 'f': 3, 'g': 4, 'a': 5, 'b': 6}
-# _MORPHETIC_OCTAVE = {0: 32, 1: 39, 2: 46, 3: 53, 4: 60, 5: 67, 6: 74, 7: 81, 8: 89}
-ALTER_SIGNS = {None: "", 0: "", 1: "#", 2: "x", -1: "b", -2: "bb"}
-
-DUMMY_PS_BASE_CLASS = {
-    0: ("c", 0),
-    1: ("c", 1),
-    2: ("d", 0),
-    3: ("d", 1),
-    4: ("e", 0),
-    5: ("f", 0),
-    6: ("f", 1),
-    7: ("g", 0),
-    8: ("g", 1),
-    9: ("a", 0),
-    10: ("a", 1),
-    11: ("b", 0),
-}
-
-MEI_DURS_TO_SYMBOLIC = {
-    "long": "long",
-    "0": "breve",
-    "breve": "breve",
-    "1": "whole",
-    "2": "half",
-    "4": "quarter",
-    "8": "eighth",
-    "16": "16th",
-    "32": "32nd",
-    "64": "64th",
-    "128": "128th",
-    "256": "256th",
-}
-
-SYMBOLIC_TO_INT_DURS = {
-    "long": 0.25,
-    "breve": 0.5,
-    "whole": 1,
-    "half": 2,
-    "quarter": 4,
-    "eighth": 8,
-    "16th": 16,
-    "32nd": 32,
-    "64th": 64,
-    "128th": 128,
-    "256th": 256,
-}
-
-LABEL_DURS = {
-    "long": 16,
-    "breve": 8,
-    "whole": 4,
-    "half": 2,
-    "h": 2,
-    "quarter": 1,
-    "q": 1,
-    "eighth": 1 / 2,
-    "e": 1 / 2,
-    "16th": 1 / 4,
-    "32nd": 1 / 8.0,
-    "64th": 1 / 16,
-    "128th": 1 / 32,
-    "256th": 1 / 64,
-}
-DOT_MULTIPLIERS = (1, 1 + 1 / 2, 1 + 3 / 4, 1 + 7 / 8)
-# DURS and SYM_DURS encode the same information as _LABEL_DURS and
-# _DOT_MULTIPLIERS, but they allow for faster estimation of symbolic duration
-# (estimate_symbolic duration). At some point we will probably do away with
-# _LABEL_DURS and _DOT_MULTIPLIERS.
-DURS = np.array(
-    [
-        1.5625000e-02,
-        2.3437500e-02,
-        2.7343750e-02,
-        2.9296875e-02,
-        3.1250000e-02,
-        4.6875000e-02,
-        5.4687500e-02,
-        5.8593750e-02,
-        6.2500000e-02,
-        9.3750000e-02,
-        1.0937500e-01,
-        1.1718750e-01,
-        1.2500000e-01,
-        1.8750000e-01,
-        2.1875000e-01,
-        2.3437500e-01,
-        2.5000000e-01,
-        3.7500000e-01,
-        4.3750000e-01,
-        4.6875000e-01,
-        5.0000000e-01,
-        5.0000000e-01,
-        7.5000000e-01,
-        7.5000000e-01,
-        8.7500000e-01,
-        8.7500000e-01,
-        9.3750000e-01,
-        9.3750000e-01,
-        1.0000000e00,
-        1.0000000e00,
-        1.5000000e00,
-        1.5000000e00,
-        1.7500000e00,
-        1.7500000e00,
-        1.8750000e00,
-        1.8750000e00,
-        2.0000000e00,
-        2.0000000e00,
-        3.0000000e00,
-        3.0000000e00,
-        3.5000000e00,
-        3.5000000e00,
-        3.7500000e00,
-        3.7500000e00,
-        4.0000000e00,
-        6.0000000e00,
-        7.0000000e00,
-        7.5000000e00,
-        8.0000000e00,
-        1.2000000e01,
-        1.4000000e01,
-        1.5000000e01,
-        1.6000000e01,
-        2.4000000e01,
-        2.8000000e01,
-        3.0000000e01,
-    ]
-)
-
-SYM_DURS = [
-    {"type": "256th", "dots": 0},
-    {"type": "256th", "dots": 1},
-    {"type": "256th", "dots": 2},
-    {"type": "256th", "dots": 3},
-    {"type": "128th", "dots": 0},
-    {"type": "128th", "dots": 1},
-    {"type": "128th", "dots": 2},
-    {"type": "128th", "dots": 3},
-    {"type": "64th", "dots": 0},
-    {"type": "64th", "dots": 1},
-    {"type": "64th", "dots": 2},
-    {"type": "64th", "dots": 3},
-    {"type": "32nd", "dots": 0},
-    {"type": "32nd", "dots": 1},
-    {"type": "32nd", "dots": 2},
-    {"type": "32nd", "dots": 3},
-    {"type": "16th", "dots": 0},
-    {"type": "16th", "dots": 1},
-    {"type": "16th", "dots": 2},
-    {"type": "16th", "dots": 3},
-    {"type": "eighth", "dots": 0},
-    {"type": "e", "dots": 0},
-    {"type": "eighth", "dots": 1},
-    {"type": "e", "dots": 1},
-    {"type": "eighth", "dots": 2},
-    {"type": "e", "dots": 2},
-    {"type": "eighth", "dots": 3},
-    {"type": "e", "dots": 3},
-    {"type": "quarter", "dots": 0},
-    {"type": "q", "dots": 0},
-    {"type": "quarter", "dots": 1},
-    {"type": "q", "dots": 1},
-    {"type": "quarter", "dots": 2},
-    {"type": "q", "dots": 2},
-    {"type": "quarter", "dots": 3},
-    {"type": "q", "dots": 3},
-    {"type": "half", "dots": 0},
-    {"type": "h", "dots": 0},
-    {"type": "half", "dots": 1},
-    {"type": "h", "dots": 1},
-    {"type": "half", "dots": 2},
-    {"type": "h", "dots": 2},
-    {"type": "half", "dots": 3},
-    {"type": "h", "dots": 3},
-    {"type": "whole", "dots": 0},
-    {"type": "whole", "dots": 1},
-    {"type": "whole", "dots": 2},
-    {"type": "whole", "dots": 3},
-    {"type": "breve", "dots": 0},
-    {"type": "breve", "dots": 1},
-    {"type": "breve", "dots": 2},
-    {"type": "breve", "dots": 3},
-    {"type": "long", "dots": 0},
-    {"type": "long", "dots": 1},
-    {"type": "long", "dots": 2},
-    {"type": "long", "dots": 3},
-]
-
-MAJOR_KEYS = [
-    "Cb",
-    "Gb",
-    "Db",
-    "Ab",
-    "Eb",
-    "Bb",
-    "F",
-    "C",
-    "G",
-    "D",
-    "A",
-    "E",
-    "B",
-    "F#",
-    "C#",
-]
-MINOR_KEYS = [
-    "Ab",
-    "Eb",
-    "Bb",
-    "F",
-    "C",
-    "G",
-    "D",
-    "A",
-    "E",
-    "B",
-    "F#",
-    "C#",
-    "G#",
-    "D#",
-    "A#",
-]
-
-TIME_UNITS = ["beat", "quarter", "sec", "div"]
-
-NOTE_NAME_PATT = re.compile(r"([A-G]{1})([xb\#]*)(\d+)")
-
-INTERVALCLASSES = [
-    f"{specific}{generic}"
-    for generic in [2, 3, 6, 7]
-    for specific in ["dd", "d", "m", "M", "A", "AA"]
-] + [
-    f"{specific}{generic}"
-    for generic in [1, 4, 5]
-    for specific in ["dd", "d", "P", "A", "AA"]
-]
-
-INTERVAL_TO_SEMITONES = dict(
-    zip(
-        INTERVALCLASSES,
-        [
-            generic + specific
-            for generic in [1, 3, 8, 10]
-            for specific in [-2, -1, 0, 1, 2, 3]
-        ]
-        + [
-            generic + specific
-            for generic in [0, 5, 7]
-            for specific in [-2, -1, 0, 1, 2]
-        ],
-    )
-)
-
-
-STEPS = {
-    "C": 0,
-    "D": 1,
-    "E": 2,
-    "F": 3,
-    "G": 4,
-    "A": 5,
-    "B": 6,
-    0: "C",
-    1: "D",
-    2: "E",
-    3: "F",
-    4: "G",
-    5: "A",
-    6: "B",
-}
-
-
-MUSICAL_BEATS = {6: 2, 9: 3, 12: 4}
-
-# Standard tuning frequency of A4 in Hz
-A4 = 440.0
-
-
 def ensure_notearray(notearray_or_part, *args, **kwargs):
     """
     Ensures to get a structured note array from the input.
@@ -421,7 +143,7 @@ def ensure_rest_array(restarray_or_part, *args, **kwargs):
         )
 
 
-def transpose_step(step, interval, direction):
+def _transpose_step(step, interval, direction):
     """
     Transpose a note by a given interval.
     Parameters
@@ -438,7 +160,7 @@ def transpose_step(step, interval, direction):
     return step
 
 
-def _transpose_note(note, interval):
+def _transpose_note_inplace(note, interval):
     """
     Transpose a note by a given interval.
     Parameters
@@ -452,7 +174,7 @@ def _transpose_note(note, interval):
     else:
         # TODO work for arbitrary octave.
         prev_step = note.step.capitalize()
-        note.step = transpose_step(prev_step, interval.number, interval.direction)
+        note.step = _transpose_step(prev_step, interval.number, interval.direction)
         if STEPS[note.step] - STEPS[prev_step] < 0 and interval.direction == "up":
             note.octave += 1
         elif STEPS[note.step] - STEPS[prev_step] > 0 and interval.direction == "down":
@@ -471,6 +193,90 @@ def _transpose_note(note, interval):
         )
 
 
+def transpose_note_old(step, alter, interval):
+    """
+    Transpose a note by a given interval without changing the octave or creating a Note Object.
+
+
+    Parameters
+    ----------
+    step: str
+        The step of the pitch, e.g. C, D, E, etc.
+    alter: int
+        The alteration of the pitch, e.g. -2, -1, 0, 1, 2 etc.
+    interval: Interval
+        The interval to transpose by.
+
+    Returns
+    -------
+    new_step: str
+        The new step of the pitch, e.g. C, D, E, etc.
+    new_alter: int
+        The new alteration of the pitch, e.g. -2, -1, 0, 1, 2 etc.
+    """
+    if interval.quality + str(interval.number) == "P1":
+        new_step = step
+        new_alter = alter
+    else:
+        prev_step = step.capitalize()
+        new_step = _transpose_step(prev_step, interval.number, interval.direction)
+        prev_alter = alter if alter is not None else 0
+        prev_pc = MIDI_BASE_CLASS[prev_step.lower()] + prev_alter
+        tmp_pc = MIDI_BASE_CLASS[new_step.lower()]
+        if interval.direction == "up":
+            diff_sm = tmp_pc - prev_pc if tmp_pc >= prev_pc else tmp_pc + 12 - prev_pc
+        else:
+            diff_sm = prev_pc - tmp_pc if prev_pc >= tmp_pc else prev_pc + 12 - tmp_pc
+        new_alter = (
+            INTERVAL_TO_SEMITONES[interval.quality + str(interval.number)] - diff_sm
+        )
+    return new_step, new_alter
+
+
+def transpose_note(step, alter, interval):
+    """
+    Transpose a note by a given interval without considering the octave.
+
+    This function does not create a new Note object, but returns the new step and alteration of the note.
+
+
+    Parameters
+    ----------
+    step: str
+        The step of the pitch, e.g. C, D, E, etc.
+    alter: int
+        The alteration of the pitch, e.g. -2, -1, 0, 1, 2 etc.
+    interval: Interval
+        The interval to transpose by. Only interval direction "up" is supported.
+
+    Returns
+    -------
+    new_step: str
+        The new step of the pitch, e.g. C, D, E, etc.
+    new_alter: int
+        The new alteration of the pitch, e.g. -2, -1, 0, 1, 2 etc.
+    """
+    prev_step = step.capitalize()
+    assert interval.direction == "up", "Only interval direction 'up' is supported."
+    assert -3 < alter < 3, f"Input Alteration {alter} is not in the range -2 to 2."
+    assert (
+        interval.number < 8
+    ), f"Input Interval {interval.number} is not in the range 1 to 7."
+    assert (
+        prev_step in BASE_PC.keys()
+    ), f"Input Step {prev_step} is must be one of: {BASE_PC.keys()}."
+    new_step = STEPS[(STEPS[prev_step] + interval.number - 1) % 7]
+    prev_alter = alter if alter is not None else 0
+    pc_prev = step2pc(prev_step, prev_alter)
+    pc_new = step2pc(new_step, prev_alter)
+    new_alter = interval.semitones - (pc_new - pc_prev) % 12 + prev_alter
+    # add test to check if the new alteration is correct (i.e. accept maximum of 2 flats or sharps)
+    assert (
+        -3 < new_alter < 3
+    ), f"New alteration {new_alter} is not in the range -2 to 2."
+    return new_step, new_alter
+
+
 def transpose(score: ScoreLike, interval: Interval) -> ScoreLike:
     """
     Transpose a score by a given interval.
@@ -502,7 +308,7 @@ def transpose(score: ScoreLike, interval: Interval) -> ScoreLike:
             transpose(part, interval)
     elif isinstance(score, s.Part):
         for note in score.notes_tied:
-            _transpose_note(note, interval)
+            _transpose_note_inplace(note, interval)
     return new_score
 
 
@@ -866,7 +672,7 @@ def key_mode_to_int(mode):
     """
     if mode in ("minor", -1):
         return -1
-    elif mode in ("major", None, 1):
+    elif mode in ("major", None, "none", 1):
         return 1
     else:
         raise ValueError("Unknown mode {}".format(mode))
@@ -888,21 +694,22 @@ def key_int_to_mode(mode):
     """
     if mode in ("minor", -1):
         return "minor"
-    elif mode in ("major", None, 1):
+    elif mode in ("major", None, "none", 1):
         return "major"
     else:
         raise ValueError("Unknown mode {}".format(mode))
 
 
-def estimate_symbolic_duration(dur, div, eps=10**-3):
+def estimate_symbolic_duration(
+    dur, div, eps=10**-3, return_com_durations=False
+) -> Union[Dict[str, Any], Tuple[Dict[str, Any]]]:
     """Given a numeric duration, a divisions value (specifiying the
     number of units per quarter note) and optionally a tolerance `eps`
     for numerical imprecisions, estimate corresponding the symbolic
     duration. If a matching symbolic duration is found, it is returned
     as a tuple (type, dots), where type is a string such as 'quarter',
     or '16th', and dots is an integer specifying the number of dots.
-    If no matching symbolic duration is found the function returns
-    None.
+
 
     NOTE : this function does not estimate composite durations, nor
     time-modifications such as triplets.
@@ -915,10 +722,14 @@ def estimate_symbolic_duration(dur, div, eps=10**-3):
         Number of units per quarter note
     eps : float, optional (default: 10**-3)
         Tolerance in case of imprecise matches
+    return_com_durations : bool, optional (default: False)
+        If True, return composite durations as well.
 
     Returns
     -------
-
+    out: Union[Dict[str, Any], Tuple[Dict[str, Any]]]
+        Symbolic duration as a dictionary. When a composite duration is found, then it returns a tuple of symbolic durations.
+        The returned tuple should be tied notes.
 
     Examples
     --------
@@ -928,18 +739,40 @@ def estimate_symbolic_duration(dur, div, eps=10**-3):
     >>> estimate_symbolic_duration(15, 10)
     {'type': 'quarter', 'dots': 1}
 
-    The following example returns None:
+    >>> estimate_symbolic_duration(15, 16)
+    {'type': 'eighth', 'dots': 3}
 
-    >>> estimate_symbolic_duration(23, 16)
+    >>> estimate_symbolic_duration(4, 6)
+    {'type': 'eighth', 'actual_notes': 3, 'normal_notes': 2}
 
+    It can also return composite durations:
+    >>> estimate_symbolic_duration(34, 16, return_com_durations=True)
+    ({'type': 'half', 'dots': 0}, {'type': '32nd', 'dots': 0})
     """
     global DURS, SYM_DURS
     qdur = dur / div
+    if qdur == 0:
+        return {}
     i = find_nearest(DURS, qdur)
     if np.abs(qdur - DURS[i]) < eps:
         return SYM_DURS[i].copy()
     else:
-        return None
+        # Note when the duration is not found, the we are left with two solutions:
+        # 1. The duration is a tuplet
+        # 2. The duration is a composite duration
+        # For composite duration. We can use the following approach:
+        j = find_nearest(COMPOSITE_DURS, qdur)
+        if np.abs(qdur - COMPOSITE_DURS[j]) < eps and return_com_durations:
+            return copy.copy(SYM_COMPOSITE_DURS[j])
+        else:
+            # NOTE: Guess tuplets (Naive) it doesn't cover composite durations from tied notes.
+            type = SYM_DURS[i + 3]["type"]
+            normal_notes = 2
+            return {
+                "type": type,
+                "actual_notes": math.ceil(normal_notes / qdur),
+                "normal_notes": normal_notes,
+            }
 
 
 def to_quarter_tempo(unit, tempo):
@@ -3256,7 +3089,7 @@ def slice_ppart_by_time(
         raise ValueError("Input is not an instance of PerformedPart!")
 
     if start_time > end_time:
-        raise ValueError("Start time not less than end time!")
+        raise ValueError("Start time must be smaller than end time!")
 
     # create a new (empty) instance of a PerformedPart
     # single dummy note added to be able to set sustain_pedal_threshold in __init__
@@ -3265,31 +3098,68 @@ def slice_ppart_by_time(
 
     # get ppq if PerformedPart contains it,
     # else skip time_tick info when e.g. created with 'load_performance_midi'
-    try:
-        ppq = ppart.ppq
-    except AttributeError:
-        ppq = None
+    # try:
+    #     ppq = ppart.ppq
+    # except AttributeError:
+    #     ppq = None
+    ppq = getattr(ppart, "ppq", None)
+    mpq = getattr(ppart, "mpq", None)
+
+    def add_info_to_list(input_list: List[dict], output_list: List[dict]) -> None:
+
+        for elem in input_list:
+            if elem["time"] >= start_time and elem["time"] <= end_time:
+                new_elem = elem.copy()
+                new_elem["time"] -= start_time
+                if ppq is not None and mpq is not None:
+                    new_elem["time_tick"] = seconds_to_midi_ticks(
+                        time_in_seconds=new_elem["time"],
+                        mpq=mpq,
+                        ppq=ppq,
+                    )
+                output_list.append(new_elem)
 
     controls_slice = []
     if ppart.controls:
+        # TODO
+        # * Keep previous pedal value
         for cc in ppart.controls:
             if cc["time"] >= start_time and cc["time"] <= end_time:
                 new_cc = cc.copy()
                 new_cc["time"] -= start_time
-                if ppq:
-                    new_cc["time_tick"] = int(2 * ppq * cc["time"])
+                if ppq is not None and mpq is not None:
+                    new_cc["time_tick"] = seconds_to_midi_ticks(
+                        time_in_seconds=new_cc["time"],
+                        mpq=mpq,
+                        ppq=ppq,
+                    )
                 controls_slice.append(new_cc)
 
     programs_slice = []
     if ppart.programs:
+        # TODO
+        # * Keep previous programs
         for pr in ppart.programs:
             if pr["time"] >= start_time and pr["time"] <= end_time:
                 new_pr = pr.copy()
                 new_pr["time"] -= start_time
-                if ppq:
-                    new_pr["time_tick"] = int(2 * ppq * pr["time"])
+                if ppq is not None and mpq is not None:
+                    new_pr["time_tick"] = seconds_to_midi_ticks(
+                        time_in_seconds=new_pr["time"],
+                        mpq=mpq,
+                        ppq=ppq,
+                    )
                 programs_slice.append(new_pr)
 
+    time_signatures = []
+    if ppart.time_signatures:
+        for ts in ppart.time_signatures:
+            if ts["time"] >= start_time and ts["time"] <= end_time:
+                new_ts = ts.copy()
+                new_ts["time"] -= start_time
+    key_signatures = []
+    meta_other = []
+
     notes_slice = []
     note_id = 0
     for note in ppart.notes:
@@ -3303,9 +3173,13 @@ def slice_ppart_by_time(
                 )
             else:
                 new_note["note_off"] = note["note_off"] - start_time
-            if ppq:
+            if ppq is not None and mpq is not None:
                 new_note["note_on_tick"] = 0
-                new_note["note_off_tick"] = int(2 * ppq * new_note["note_off"])
+                new_note["note_off_tick"] = seconds_to_midi_ticks(
+                    time_in_seconds=new_note["note_off"],
+                    mpq=mpq,
+                    ppq=ppq,
+                )
             if reindex_notes:
                 new_note["id"] = f"n{note_id}"
                 note_id += 1
@@ -3321,11 +3195,19 @@ def slice_ppart_by_time(
                     )
                 else:
                     new_note["note_off"] = note["note_off"] - start_time
-                if ppq:
-                    new_note["note_on_tick"] = int(2 * ppq * new_note["note_on"])
-                    new_note["note_off_tick"] = int(2 * ppq * new_note["note_off"])
+                if ppq is not None and mpq is not None:
+                    new_note["note_on_tick"] = seconds_to_midi_ticks(
+                        time_in_seconds=new_note["note_on"],
+                        mpq=mpq,
+                        ppq=ppq,
+                    )
+                    new_note["note_off_tick"] = seconds_to_midi_ticks(
+                        time_in_seconds=new_note["note_off"],
+                        mpq=mpq,
+                        ppq=ppq,
+                    )
                 if reindex_notes:
-                    new_note["id"] = "n" + str(note_id)
+                    new_note["id"] = f"n{note_id}"
                     note_id += 1
                 notes_slice.append(new_note)
             # assumes notes in list are sorted by onset time
@@ -3334,14 +3216,21 @@ def slice_ppart_by_time(
 
     # Create slice PerformedPart
     ppart_slice = PerformedPart(
-        notes=notes_slice, programs=programs_slice, controls=controls_slice, ppq=ppq
+        notes=notes_slice,
+        programs=programs_slice,
+        controls=controls_slice,
+        ppq=ppq,
+        mpq=mpq,
+        key_signatures=key_signatures,
+        time_signatures=time_signatures,
+        meta_other=meta_other,
     )
 
     # set threshold property after creating notes list to update 'sound_offset' values
     ppart_slice.sustain_pedal_threshold = ppart.sustain_pedal_threshold
 
     if ppart.id:
-        ppart_slice.id = ppart.id + "_slice_{}s_to_{}s".format(start_time, end_time)
+        ppart_slice.id = f"{ppart.id}_slice_{start_time}s_to_{end_time}s"
     if ppart.part_name:
         ppart_slice.part_name = ppart.part_name
 
@@ -3392,6 +3281,27 @@ def tokenize(
     return tokens
 
 
+def step2pc(step, alter):
+    """
+    Convert a tonal pitch class (i.e. step + alter) to a pitch class (i.e. integer in [0, 11]).
+
+    Parameters
+    ----------
+    step: str
+        The step of the pitch, e.g. C, D, E, etc.
+    alter: int
+        The alteration of the pitch, e.g. -2, -1, 0, 1, 2 etc.
+
+    Returns
+    -------
+    pc: int
+        The pitch class of the step.
+    """
+    base_pc = BASE_PC[step]
+    pc = (base_pc + alter) % 12
+    return pc
+
+
 if __name__ == "__main__":
     import doctest
 
diff --git a/partitura/utils/normalize.py b/partitura/utils/normalize.py
index 5da9e188..438b2219 100644
--- a/partitura/utils/normalize.py
+++ b/partitura/utils/normalize.py
@@ -4,9 +4,7 @@
 This module contains normalization utilities
 """
 import numpy as np
-
-
-EPSILON = 0.0001
+from partitura.utils.globals import EPSILON
 
 
 def range_normalize(
diff --git a/partitura/utils/synth.py b/partitura/utils/synth.py
index 507280c7..7585f09d 100644
--- a/partitura/utils/synth.py
+++ b/partitura/utils/synth.py
@@ -8,7 +8,8 @@
 ----
 * Add other tuning systems?
 """
-from typing import Union, Tuple, Dict, Optional, Any, Callable
+from __future__ import annotations
+from typing import Union, Tuple, Dict, Optional, Any, Callable, TYPE_CHECKING
 
 import numpy as np
 
@@ -22,43 +23,22 @@
     midi_pitch_to_frequency,
     performance_notearray_from_score_notearray,
 )
+from partitura.utils.globals import (
+    DTYPE,
+    SAMPLE_RATE,
+    TWO_PI,
+    FIVE_LIMIT_INTERVAL_RATIOS,
+    A4,
+    NATURAL_INTERVAL_RATIOS,
+)
 
-TWO_PI = 2 * np.pi
-SAMPLE_RATE = 44100
-DTYPE = float
-
-NATURAL_INTERVAL_RATIOS = {
-    0: 1,
-    1: 16 / 15,  # 15/14, 11/10
-    2: 8 / 7,  # 9/8, 10/9, 12/11, 13/14
-    3: 6 / 5,  # 7/6,
-    4: 5 / 4,
-    5: 4 / 3,
-    6: 7 / 5,  # 13/9,
-    7: 3 / 2,
-    8: 8 / 5,
-    9: 5 / 3,
-    10: 7 / 4,  # 13/7
-    11: 15 / 8,
-    12: 2,
-}
-
-# symmetric five limit temperament with supertonic = 10:9
-FIVE_LIMIT_INTERVAL_RATIOS = {
-    0: 1,
-    1: 16 / 15,
-    2: 10 / 9,
-    3: 6 / 5,
-    4: 5 / 4,
-    5: 4 / 3,
-    6: 7 / 5,
-    7: 3 / 2,
-    8: 8 / 5,
-    9: 5 / 3,
-    10: 9 / 5,
-    11: 15 / 8,
-    12: 2,
-}
+if TYPE_CHECKING:
+    # Import typing info for typing annotations.
+    # For this to work we need to import annotations from __future__
+    # Solution from
+    # https://medium.com/quick-code/python-type-hinting-eliminating-importerror-due-to-circular-imports-265dfb0580f8
+    from partitura.score import ScoreLike, Interval
+    from partitura.performance import PerformanceLike, Performance, PerformedPart
 
 
 def midi_pitch_to_natural_frequency(
@@ -374,7 +354,7 @@ def max_f(self, freq: Union[float, np.ndarray]) -> Union[float, np.ndarray]:
 
 
 def synthesize(
-    note_info,
+    note_info: Union[ScoreLike, PerformanceLike, np.ndarray],
     samplerate: int = SAMPLE_RATE,
     envelope_fun: str = "linear",
     tuning: Union[str, Callable] = "equal_temperament",
diff --git a/setup.py b/setup.py
index 4f9432f0..11f9a34f 100644
--- a/setup.py
+++ b/setup.py
@@ -16,7 +16,7 @@
 EMAIL = "partitura-users@googlegroups.com"
 AUTHOR = "Maarten Grachten, Carlos Cancino-Chacón, Silvan Peter, Emmanouil Karystinaios, Francesco Foscarin, Thassilo Gadermaier, Patricia Hu"
 REQUIRES_PYTHON = ">=3.7"
-VERSION = "1.4.1"
+VERSION = "1.5.0"
 
 # What packages are required for this module to be executed?
 REQUIRED = ["numpy", "scipy", "lxml", "lark-parser", "xmlschema", "mido"]
diff --git a/tests/__init__.py b/tests/__init__.py
index 712e6a11..32824bcc 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -19,6 +19,7 @@
 PARANGONADA_PATH = os.path.join(DATA_PATH, "parangonada")
 WAV_PATH = os.path.join(DATA_PATH, "wav")
 PNG_PATH = os.path.join(DATA_PATH, "png")
+TSV_PATH = os.path.join(DATA_PATH, "tsv")
 
 # this is a list of files for which importing and subsequent exporting should
 # yield identical MusicXML
@@ -183,7 +184,7 @@
         "double_repeat_example.krn",
         "fine_with_repeat.krn",
         "tuple_durations.krn",
-        "voice_dublifications.krn",
+        "voice_duplication.krn",
         "variable_length_pr_bug.krn",
         "chor228.krn",
     ]
diff --git a/tests/data/kern/chor228.krn b/tests/data/kern/chor228.krn
index 31a5e2bb..5c237046 100644
--- a/tests/data/kern/chor228.krn
+++ b/tests/data/kern/chor228.krn
@@ -7,9 +7,12 @@
 !!!AGN: chorale
 **kern	**kern	**kern	**kern
 *ICvox	*ICvox	*ICvox	*ICvox
+*IGrand	*IGrand	*IGrand	*IGrand
 *Ibass	*Itenor	*Ialto	*Isoprn
+*tb	*tb	*tb	*tb
+*ITr	*ITr	*ITr	*ITr
 *I"Bass	*I"Tenor	*I"Alto	*I"Soprano
-*clefF4	*clefGv2	*clefG2	*clefG2
+*clefF	*clefGv2	*clefG2	*clefG
 *k[]	*k[]	*k[]	*k[]
 *a:	*a:	*a:	*a:
 *M4/4	*M4/4	*M4/4	*M4/4
@@ -18,8 +21,8 @@
 4A	4c	4e	4a
 =1	=1	=1	=1
 4.A	4e	4a	4cc
-.	4e	[4b	4b
-8G#	.	.	.
+.	4e	[8b	4b
+8G#	.	8b_	.
 8AL	4e	8bL]	4cc
 8AAJ	.	[8aJ	.
 4BB	[4d	8aL]	4dd
diff --git a/tests/data/kern/spline_splitting.krn b/tests/data/kern/spline_splitting.krn
new file mode 100644
index 00000000..ee163585
--- /dev/null
+++ b/tests/data/kern/spline_splitting.krn
@@ -0,0 +1,39 @@
+**kern	**kern	**kern	**kern
+*staff2	*staff2	*staff1	*staff1
+*clefF4	*clefF4	*clefG2	*clefG2
+*k[f#c#g#]	*k[f#c#g#]	*k[f#c#g#]	*k[f#c#g#]
+*M4/4	*M4/4	*M4/4	*M4/4
+4AA	4c#	4a	4ee
+=1	=1	=1	=1
+*	*	*	*^
+8AL	4c#	4a	4ee	8eee
+8BJ	.	.	.	8eee
+8c#L	4c#	4a	4ee	8ee
+8AJ	.	.	.	.
+8DL	4d	4a	4ff#	8fff#
+8EJ	.	.	.	8fff#
+8F#L	4d	4a	4ff#	4fff#
+8DJ	.	.	.	.
+=2	=2	=2	=2	=2
+*	*	*	*v	*v
+2A;	2c#;	2a;	2ee;
+4r	4r	4r	4r
+4A	4e	4a	4cc#
+=3	=3	=3	=3
+4G#	4e	4b	4dd
+4A	4e	4a	4cc#
+8EL	4e	4g#	4b
+8DJ	.	.	.
+8C#L	4e	[4a	8.cc#L
+8AAJ	.	.	.
+.	.	.	16ddJ
+=4	=4	=4	=4
+*^	*	*	*
+2.EE	2E	8eL	8a]L	2b
+.	.	16d	8f#J	.
+.	.	16c#J	.	.
+.	.	4d	4g#	.
+.	4AA;	4c#;	4e;	4a;
+=:|!	=:|!	=:|	=:|	=:|!
+*v	*v	*	*	*
+*-	*-	*-	*-
\ No newline at end of file
diff --git a/tests/data/kern/voice_dublifications.krn b/tests/data/kern/voice_dublifications.krn
deleted file mode 100644
index 59386a7c..00000000
--- a/tests/data/kern/voice_dublifications.krn
+++ /dev/null
@@ -1,47 +0,0 @@
-**kern		**kern	**kern	**kern
-*staff2		*staff2	*staff1	*staff1
-*clefF4		*clefF4	*clefG2	*clefG2
-*k[f#c#]		*k[f#c#]	*k[f#c#]	*k[f#c#]
-*M4/4		*M4/4	*M4/4	*M4/4
-*b:		*b:	*b:	*b:
-*MM58		*MM58	*MM58	*MM58
-=1-		=1-	=1-	=1-
-[1F#		16A#L	8r	2dd
-.		16F#	.	.
-.		16G#	8f#	.
-.		16A#J	.	.
-.		16BL	8dL	.
-.		16c#	.	.
-.		16d	8BJ	.
-.		[16BJ	.	.
-.		16B]L	8gL	[2cc#
-.		16B	.	.
-.		16A#	8f#J	.
-.		16BJ	.	.
-.		16c#L	8bL	.
-.		16d	.	.
-.		16e	8a#J	.
-.		16c#J	.	.
-=2		=2	=2	=2
-*^		*	*	*
-2r	16F#]L	8eL	4f#	4cc#]
-.	16AA#	.	.	.
-.	16BB	8d#J	.	.
-.	16C#J	.	.	.
-.	16D#L	8r	8r	16ccnL
-.	16E	.	.	16a#
-.	16FF#	8dn	8g#	[8bJ
-.	16EE#J	.	.	.
-16r	2FF#	2c#	8f#L	4b]
-16BBL	.	.	.	.
-16C#	.	.	8e#J	.
-16DnJ	.	.	.	.
-16EnL	.	.	8.f#L	4a#
-16C#	.	.	.	.
-[8F#J	.	.	.	.
-.	.	.	16enJ	.
-=76	=76	=76	=76	=76
-1F#]	1BB	1B	1d#	1b
-*v	*v	*	*	*
-==	==	==	==
-*-	*-	*-	*-
\ No newline at end of file
diff --git a/tests/data/kern/voice_duplication.krn b/tests/data/kern/voice_duplication.krn
new file mode 100644
index 00000000..1a33e233
--- /dev/null
+++ b/tests/data/kern/voice_duplication.krn
@@ -0,0 +1,47 @@
+**kern  **kern	**kern	**kern
+*staff2	*staff2	*staff1	*staff1
+*clefF4	*clefF4	*clefG2	*clefG2
+*k[f#c#]	*k[f#c#]	*k[f#c#]	*k[f#c#]
+*M4/4	*M4/4	*M4/4	*M4/4
+*b:	*b:	*b:	*b:
+*MM58	*MM58	*MM58	*MM58
+=1-	=1-	=1-	=1-
+[1F#	16A#L	8r	2dd
+.	16F#	.	.
+.	16G#	8f#	.
+.	16A#J	.	.
+.	16BL	8dL	.
+.	16c#	.	.
+.	16d	8BJ	.
+.	[16BJ	.	.
+.	16B]L	8gL	[2cc#
+.	16B	.	.
+.	16A#	8f#J	.
+.	16BJ	.	.
+.	16c#L	8bL	.
+.	16d	.	.
+.	16e	8a#J	.
+.	16c#J	.	.
+=2	=2	=2	=2
+*^	*	*	*
+2r	16F#]L	8eL	4f#	4cc#]
+.	16AA#	.	.	.
+.	16BB	8d#J	.	.
+.	16C#J	.	.	.
+.	16D#L	8r	8r	16ccnL
+.	16E	.	.	16a#
+.	16FF#	8dn	8g#	[8bJ
+.	16EE#J	.	.	.
+16r	2FF#	2c#	8f#L	4b]
+16BBL	.	.	.	.
+16C#	.	.	8e#J	.
+16DnJ	.	.	.	.
+16EnL	.	.	8.f#L	4a#
+16C#	.	.	.	.
+[8F#J	.	.	.	.
+.	.	.	16enJ	.
+=76	=76	=76	=76	=76
+1F#]	1BB	1B	1d#	1b
+*v	*v	*	*	*
+==	==	==	==
+*-	*-	*-	*-
\ No newline at end of file
diff --git a/tests/data/tsv/test_harmonies.tsv b/tests/data/tsv/test_harmonies.tsv
new file mode 100644
index 00000000..ff56539c
--- /dev/null
+++ b/tests/data/tsv/test_harmonies.tsv
@@ -0,0 +1,242 @@
+mc	mn	quarterbeats	quarterbeats_all_endings	duration_qb	mc_onset	mn_onset	timesig	staff	voice	label	alt_label	globalkey	localkey	pedal	chord	special	numeral	form	figbass	changes	relativeroot	cadence	phraseend	chord_type	globalkey_is_minor	localkey_is_minor	chord_tones	added_tones	root	bass_note
+1	0	0	0	9.0	0	3/4	2/2	2	1	f.i{		f	i		i		i						{	m	1	1	0, -3, 1		0	0
+4	3	9	9	8.0	0	0	2/2	2	1	V65		f	i		V65		V		65					Mm7	1	1	5, 2, -1, 1		1	5
+6	5	17	17	4.0	0	0	2/2	2	1	i		f	i		i		i							m	1	1	0, -3, 1		0	0
+7	6	21	21	4.0	0	0	2/2	2	1	#viio6		f	i		#viio6		#vii	o	6					o	1	1	2, -1, 5		5	2
+8	7	25	25	2.0	0	0	2/2	2	1	i6		f	i		i6		i		6					m	1	1	-3, 1, 0		0	-3
+8	7	27	27	2.0	1/2	1/2	2/2	2	1	iio6		f	i		iio6		ii	o	6					o	1	1	-1, -4, 2		2	-1
+9	8	29	29	1.0	0	0	2/2	2	1	V(4)}		f	i		V(4)		V			4			}	M	1	1	1, 0, 2		1	1
+9	8	30	30	2.0	1/4	1/4	2/2	2	1	V|HC		f	i		V		V					HC		M	1	1	1, 5, 2		1	1
+9	8	32	32	9.0	3/4	3/4	2/2	2	1	v{		f	i		v		v						{	m	1	1	1, -2, 2		1	1
+12	11	41	41	4.0	0	0	2/2	2	1	III.IVM2	ii7(2)	f	III		IVM2		IV	M	2					MM7	1	0	4, -1, 3, 0		-1	4
+13	12	45	45	4.0	0	0	2/2	2	1	ii7		f	III		ii7		ii		7					mm7	1	0	2, -1, 3, 0		2	2
+14	13	49	49	4.0	0	0	2/2	2	1	V43		f	III		V43		V		43					Mm7	1	0	2, -1, 1, 5		1	2
+15	14	53	53	4.0	0	0	2/2	2	1	I		f	III		I		I							M	1	0	0, 4, 1		0	0
+16	15	57	57	1.0	0	0	2/2	2	1	ii6(2)		f	III		ii6(2)		ii		6	2				m	1	0	-1, 3, 4		2	-1
+16	15	58	58	1.0	1/4	1/4	2/2	2	1	ii6		f	III		ii6		ii		6					m	1	0	-1, 3, 2		2	-1
+16	15	59	59	2.0	1/2	1/2	2/2	2	1	V65/V		f	III		V65/V		V		65		V			Mm7	1	0	6, 3, 0, 2		2	6
+17	16	61	61	3.0	0	0	2/2	2	1	V|HC}		f	III		V		V					HC	}	M	1	0	1, 5, 2		1	1
+17	16	64	64	1.0	3/4	3/4	2/2	2	1	I6{		f	III		I6		I		6				{	M	1	0	4, 1, 0		0	4
+18	17	65	65	1.0	0	0	2/2	2	1	ii6(2)		f	III		ii6(2)		ii		6	2				m	1	0	-1, 3, 4		2	-1
+18	17	66	66	1.0	1/4	1/4	2/2	2	1	ii6		f	III		ii6		ii		6					m	1	0	-1, 3, 2		2	-1
+18	17	67	67	2.0	1/2	1/2	2/2	2	1	V65/V		f	III		V65/V		V		65		V			Mm7	1	0	6, 3, 0, 2		2	6
+19	18	69	69	3.0	0	0	2/2	2	1	V|HC}		f	III		V		V					HC	}	M	1	0	1, 5, 2		1	1
+19	18	72	72	1.0	3/4	3/4	2/2	2	1	I6{		f	III		I6		I		6				{	M	1	0	4, 1, 0		0	4
+20	19	73	73	1.0	0	0	2/2	2	1	ii6(2)		f	III		ii6(2)		ii		6	2				m	1	0	-1, 3, 4		2	-1
+20	19	74	74	1.0	1/4	1/4	2/2	2	1	ii6		f	III		ii6		ii		6					m	1	0	-1, 3, 2		2	-1
+20	19	75	75	2.0	1/2	1/2	2/2	2	1	V65/V		f	III		V65/V		V		65		V			Mm7	1	0	6, 3, 0, 2		2	6
+21	20	77	77	3.0	0	0	2/2	2	1	V[V|HC}{		f	III	V	V		V					HC	}{	M	1	0	1, 5, 2		1	1
+21	20	80	80	1.0	3/4	3/4	2/2	2	1	V7(+b9)		f	III	V	V7(+b9)		V		7	+b9				Mm7	1	0	1, 5, 2, -1	-4	1	1
+22	21	81	81	4.0	0	0	2/2	2	1	V7		f	III	V	V7		V		7					Mm7	1	0	1, 5, 2, -1		1	1
+23	22	85	85	1.5	0	0	2/2	2	2	V7(b9)		f	III	V	V7(b9)		V		7	b9				Mm7	1	0	1, 5, 2, -1	-4	1	1
+23	22	173/2	173/2	0.5	3/8	3/8	2/2	2	2	V7		f	III	V	V7		V		7					Mm7	1	0	1, 5, 2, -1		1	1
+23	22	87	87	1.0	1/2	1/2	2/2	2	1	I64		f	III	V	I64		I		64					M	1	0	1, 0, 4		0	1
+23	22	88	88	1.0	3/4	3/4	2/2	2	1	V7(+b9)		f	III	V	V7(+b9)		V		7	+b9				Mm7	1	0	1, 5, 2, -1	-4	1	1
+24	23	89	89	4.0	0	0	2/2	2	1	V7		f	III	V	V7		V		7					Mm7	1	0	1, 5, 2, -1		1	1
+25	24	93	93	1.5	0	0	2/2	2	1	V7(b9)		f	III	V	V7(b9)		V		7	b9				Mm7	1	0	1, 5, 2, -1	-4	1	1
+25	24	189/2	189/2	0.5	3/8	3/8	2/2	2	1	V7		f	III	V	V7		V		7					Mm7	1	0	1, 5, 2, -1		1	1
+25	24	95	95	1.0	1/2	1/2	2/2	2	1	I64		f	III	V	I64		I		64					M	1	0	1, 0, 4		0	1
+25	24	96	96	1.0	3/4	3/4	2/2	2	1	V7(+b9)		f	III	V	V7(+b9)		V		7	+b9				Mm7	1	0	1, 5, 2, -1	-4	1	1
+26	25	97	97	2.0	0	0	2/2	2	1	V7]		f	III	V	V7		V		7					Mm7	1	0	1, 5, 2, -1		1	1
+26	25	99	99	2.0	1/2	1/2	2/2	2	1	V2		f	III		V2		V		2					Mm7	1	0	-1, 1, 5, 2		1	-1
+27	26	101	101	2.0	0	0	2/2	2	1	I6		f	III		I6		I		6					M	1	0	4, 1, 0		0	4
+27	26	103	103	2.0	1/2	1/2	2/2	2	1	V6		f	III		V6		V		6					M	1	0	5, 2, 1		1	5
+28	27	105	105	2.0	0	0	2/2	2	1	I		f	III		I		I							M	1	0	0, 4, 1		0	0
+28	27	107	107	2.0	1/2	1/2	2/2	2	1	V43/V	viio6/V	f	III		V43/V		V		43		V			Mm7	1	0	3, 0, 2, 6		2	3
+29	28	109	109	2.0	0	0	2/2	2	1	V		f	III		V		V							M	1	0	1, 5, 2		1	1
+29	28	111	111	2.0	1/2	1/2	2/2	2	1	V43/V	viio6/V	f	III		V43/V		V		43		V			Mm7	1	0	3, 0, 2, 6		2	3
+30	29	113	113	2.0	0	0	2/2	2	1	V		f	III		V		V							M	1	0	1, 5, 2		1	1
+30	29	115	115	2.0	1/2	1/2	2/2	2	1	viio43		f	III		viio43		vii	o	43					o7	1	0	-1, -4, 5, 2		5	-1
+31	30	117	117	2.0	0	0	2/2	2	1	I6		f	III		I6		I		6					M	1	0	4, 1, 0		0	4
+31	30	119	119	2.0	1/2	1/2	2/2	2	1	viio43		f	III		viio43		vii	o	43					o7	1	0	-1, -4, 5, 2		5	-1
+32	31	121	121	2.0	0	0	2/2	2	1	I6		f	III		I6		I		6					M	1	0	4, 1, 0		0	4
+32	31	123	123	2.0	1/2	1/2	2/2	2	1	V6		f	III		V6		V		6					M	1	0	5, 2, 1		1	5
+33	32	125	125	2.0	0	0	2/2	2	1	I		f	III		I		I							M	1	0	0, 4, 1		0	0
+33	32	127	127	2.0	1/2	1/2	2/2	2	1	V43		f	III		V43		V		43					Mm7	1	0	2, -1, 1, 5		1	2
+34	33	129	129	4.0	0	0	2/2	2	1	I6		f	III		I6		I		6					M	1	0	4, 1, 0		0	4
+35	34	133	133	4.0	0	0	2/2	2	1	ii6		f	III		ii6		ii		6					m	1	0	-1, 3, 2		2	-1
+36	35	137	137	4.0	0	0	2/2	2	1	V(64)		f	III		V(64)		V			64				M	1	0	1, 0, 4		1	1
+37	36	141	141	4.0	0	0	2/2	2	1	V2		f	III		V2		V		2					Mm7	1	0	-1, 1, 5, 2		1	-1
+38	37	145	145	4.0	0	0	2/2	2	1	I6		f	III		I6		I		6					M	1	0	4, 1, 0		0	4
+39	38	149	149	4.0	0	0	2/2	2	1	ii6		f	III		ii6		ii		6					m	1	0	-1, 3, 2		2	-1
+40	39	153	153	4.0	0	0	2/2	2	1	V(64)		f	III		V(64)		V			64				M	1	0	1, 0, 4		1	1
+41	40	157	157	4.0	0	0	2/2	2	1	V7		f	III		V7		V		7					Mm7	1	0	1, 5, 2, -1		1	1
+42	41	161	161	2.0	0	0	2/2	2	1	I|PAC}		f	III		I		I					PAC	}	M	1	0	0, 4, 1		0	0
+42	41	163	163	2.0	1/2	1/2	2/2	2	1	viio7/V{		f	III		viio7/V		vii	o	7		V		{	o7	1	0	6, 3, 0, -3		6	6
+43	42	165	165	2.0	0	0	2/2	2	1	V(64)		f	III		V(64)		V			64				M	1	0	1, 0, 4		1	1
+43	42	167	167	2.0	1/2	1/2	2/2	2	1	V7		f	III		V7		V		7					Mm7	1	0	1, 5, 2, -1		1	1
+44	43	169	169	2.0	0	0	2/2	2	1	I|PAC		f	III		I		I					PAC		M	1	0	0, 4, 1		0	0
+44	43	171	171	2.0	1/2	1/2	2/2	2	1	viio7/V		f	III		viio7/V		vii	o	7		V			o7	1	0	6, 3, 0, -3		6	6
+45	44	173	173	2.0	0	0	2/2	2	1	V(64)		f	III		V(64)		V			64				M	1	0	1, 0, 4		1	1
+45	44	175	175	2.0	1/2	1/2	2/2	2	1	V7		f	III		V7		V		7					Mm7	1	0	1, 5, 2, -1		1	1
+46	45	177	177	2.0	0	0	2/2	2	1	I|PAC		f	III		I		I					PAC		M	1	0	0, 4, 1		0	0
+46	45	179	179	2.0	1/2	1/2	2/2	2	1	viio7/V		f	III		viio7/V		vii	o	7		V			o7	1	0	6, 3, 0, -3		6	6
+47	46	181	181	2.0	0	0	2/2	2	1	V(64)		f	III		V(64)		V			64				M	1	0	1, 0, 4		1	1
+47	46	183	183	2.0	1/2	1/2	2/2	2	1	V7		f	III		V7		V		7					Mm7	1	0	1, 5, 2, -1		1	1
+48	47	185	185	4.0	0	0	2/2	2	1	I(974)}		f	III		I(974)		I			974			}	M	1	0	0, -1, 1	2, 5	0	0
+49	48	189	189	12.0	0	0	2/2	2	1	I|PAC		f	III		I		I					PAC		M	1	0	0, 4, 1		0	0
+50	48	192	192	0.0	0	3/4	2/2	2	1	{		f	III										{		1	0				
+53	51	201	201	8.0	0	0	2/2	2	1	V65		f	III		V65		V		65					Mm7	1	0	5, 2, -1, 1		1	5
+55	53	209	209	4.0	0	0	2/2	2	1	iv.viio65/V		f	iv		viio65/V		vii	o	65		V			o7	1	1	3, 0, -3, 6		6	3
+56	54	213	213	4.0	0	0	2/2	2	1	Ger6		f	iv		Ger6	Ger	vii	o	65	b3	V			Ger	1	1	-4, 0, -3, 6		6	-4
+57	55	217	217	3.0	0	0	2/2	2	1	V[V|HC}{		f	iv	V	V		V					HC	}{	M	1	1	1, 5, 2		1	1
+57	55	220	220	1.0	3/4	3/4	2/2	2	1	V7(+b9)		f	iv	V	V7(+b9)		V		7	+b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+58	56	221	221	4.0	0	0	2/2	2	1	V7		f	iv	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+59	57	225	225	1.5	0	0	2/2	2	1	V7(b9)		f	iv	V	V7(b9)		V		7	b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+59	57	453/2	453/2	0.5	3/8	3/8	2/2	2	1	V7		f	iv	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+59	57	227	227	1.0	1/2	1/2	2/2	2	1	i64		f	iv	V	i64		i		64					m	1	1	1, 0, -3		0	1
+59	57	228	228	1.0	3/4	3/4	2/2	2	1	V7(+b9)		f	iv	V	V7(+b9)		V		7	+b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+60	58	229	229	4.0	0	0	2/2	2	1	V7		f	iv	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+61	59	233	233	1.5	0	0	2/2	2	1	V7(b9)		f	iv	V	V7(b9)		V		7	b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+61	59	469/2	469/2	0.5	3/8	3/8	2/2	2	1	V7		f	iv	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+61	59	235	235	1.0	1/2	1/2	2/2	2	1	i64		f	iv	V	i64		i		64					m	1	1	1, 0, -3		0	1
+61	59	236	236	3.0	3/4	3/4	2/2	2	1	V7(+b9)]		f	iv	V	V7(+b9)		V		7	+b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+62	60	239	239	2.0	1/2	1/2	2/2	2	1	V2		f	iv		V2		V		2					Mm7	1	1	-1, 1, 5, 2		1	-1
+63	61	241	241	2.0	0	0	2/2	2	1	i6		f	iv		i6		i		6					m	1	1	-3, 1, 0		0	-3
+63	61	243	243	2.0	1/2	1/2	2/2	2	1	V64		f	iv		V64		V		64					M	1	1	2, 1, 5		1	2
+64	62	245	245	2.0	0	0	2/2	2	1	i		f	iv		i		i							m	1	1	0, -3, 1		0	0
+64	62	247	247	2.0	1/2	1/2	2/2	2	1	v.It6		f	v		It6	It	vii	o	6	b3	V			It	1	1	-4, 0, 6		6	-4
+65	63	249	249	3.0	0	0	2/2	2	1	V[V|HC}{		f	v	V	V		V					HC	}{	M	1	1	1, 5, 2		1	1
+65	63	252	252	1.0	3/4	3/4	2/2	2	1	V7(+b9)		f	v	V	V7(+b9)		V		7	+b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+66	64	253	253	4.0	0	0	2/2	2	1	V7		f	v	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+67	65	257	257	1.5	0	0	2/2	2	1	V7(b9)		f	v	V	V7(b9)		V		7	b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+67	65	517/2	517/2	0.5	3/8	3/8	2/2	2	1	V7		f	v	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+67	65	259	259	1.0	1/2	1/2	2/2	2	1	i64		f	v	V	i64		i		64					m	1	1	1, 0, -3		0	1
+67	65	260	260	1.0	3/4	3/4	2/2	2	1	V7(+b9)		f	v	V	V7(+b9)		V		7	+b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+68	66	261	261	4.0	0	0	2/2	2	1	V7		f	v	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+69	67	265	265	1.5	0	0	2/2	2	1	V7(b9)		f	v	V	V7(b9)		V		7	b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+69	67	533/2	533/2	0.5	3/8	3/8	2/2	2	1	V7		f	v	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+69	67	267	267	1.0	1/2	1/2	2/2	2	1	i64]		f	v	V	i64		i		64					m	1	1	1, 0, -3		0	1
+69	67	268	268	1.0	3/4	3/4	2/2	2	1	iio64		f	v		iio64		ii	o	64					o	1	1	-4, 2, -1		2	-4
+70	68	269	269	4.0	0	0	2/2	2	1	V7		f	v		V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+71	69	273	273	1.5	0	0	2/2	2	1	V7(2)		f	v		V7(2)		V		7	2				Mm7	1	1	-4, 5, 2, -1		1	-4
+71	69	549/2	549/2	0.5	3/8	3/8	2/2	2	1	V7		f	v		V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+71	69	275	275	1.0	1/2	1/2	2/2	2	1	i		f	v		i		i							m	1	1	0, -3, 1		0	0
+71	69	276	276	1.0	3/4	3/4	2/2	2	1	iv.iio64		f	iv		iio64		ii	o	64					o	1	1	-4, 2, -1		2	-4
+72	70	277	277	4.0	0	0	2/2	2	1	V7		f	iv		V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+73	71	281	281	1.5	0	0	2/2	2	1	V7(2)		f	iv		V7(2)		V		7	2				Mm7	1	1	-4, 5, 2, -1		1	-4
+73	71	565/2	565/2	0.5	3/8	3/8	2/2	2	1	V7		f	iv		V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+73	71	283	283	1.0	1/2	1/2	2/2	2	1	i		f	iv		i		i							m	1	1	0, -3, 1		0	0
+73	71	284	284	1.0	3/4	3/4	2/2	2	1	III.iio64		f	III		iio64		ii	o	64					o	1	0	-4, 2, -1		2	-4
+74	72	285	285	4.0	0	0	2/2	2	1	V7		f	III		V7		V		7					Mm7	1	0	1, 5, 2, -1		1	1
+75	73	289	289	1.0	0	0	2/2	2	1	V7(b2)		f	III		V7(b2)		V		7	b2				Mm7	1	0	-4, 5, 2, -1		1	-4
+75	73	290	290	3.0	1/4	1/4	2/2	2	1	V7		f	III		V7		V		7					Mm7	1	0	1, 5, 2, -1		1	1
+76	74	293	293	1.0	0	0	2/2	2	1	I64		f	III		I64		I		64					M	1	0	1, 0, 4		0	1
+76	74	294	294	3.0	1/4	1/4	2/2	2	1	I6		f	III		I6		I		6					M	1	0	4, 1, 0		0	4
+77	75	297	297	1.0	0	0	2/2	2	1	IV(0)		f	III		IV(0)		IV			0				M	1	0	-1, 3, 0		-1	-1
+77	75	298	298	3.0	1/4	1/4	2/2	2	1	IV		f	III		IV		IV							M	1	0	-1, 3, 0		-1	-1
+78	76	301	301	1.0	0	0	2/2	2	1	viio64		f	III		viio64		vii	o	64					o	1	0	-1, 5, 2		5	-1
+78	76	302	302	3.0	1/4	1/4	2/2	2	1	viio6		f	III		viio6		vii	o	6					o	1	0	2, -1, 5		5	2
+79	77	305	305	1.0	0	0	2/2	2	1	iii(0)		f	III		iii(0)		iii			0				m	1	0	4, 1, 5		4	4
+79	77	306	306	1.0	1/4	1/4	2/2	2	1	iii		f	III		iii		iii							m	1	0	4, 1, 5		4	4
+79	77	307	307	2.0	1/2	1/2	2/2	2	1	i.V		f	i		V		V							M	1	1	1, 5, 2		1	1
+80	78	309	309	1.0	0	0	2/2	2	1	i64		f	i		i64		i		64					m	1	1	1, 0, -3		0	1
+80	78	310	310	3.0	1/4	1/4	2/2	2	1	i6		f	i		i6		i		6					m	1	1	-3, 1, 0		0	-3
+81	79	313	313	4.0	0	0	2/2	2	1	iv		f	i		iv		iv							m	1	1	-1, -4, 0		-1	-1
+82	80	317	317	4.0	0	0	2/2	2	1	viio65/V		f	i		viio65/V		vii	o	65		V			o7	1	1	3, 0, -3, 6		6	3
+83	81	321	321	4.0	0	0	2/2	2	1	V[V|HC}{		f	i	V	V		V					HC	}{	M	1	1	1, 5, 2		1	1
+84	82	325	325	4.0	0	0	2/2	2	1	i64		f	i	V	i64		i		64					m	1	1	1, 0, -3		0	1
+85	83	329	329	4.0	0	0	2/2	2	1	V7		f	i	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+86	84	333	333	2.0	0	0	2/2	2	1	i64		f	i	V	i64		i		64					m	1	1	1, 0, -3		0	1
+86	84	335	335	2.0	1/2	1/2	2/2	2	1	viio/V		f	i	V	viio/V		vii	o			V			o	1	1	6, 3, 0		6	6
+87	85	337	337	4.0	0	0	2/2	2	1	V		f	i	V	V		V							M	1	1	1, 5, 2		1	1
+88	86	341	341	4.0	0	0	2/2	2	1	i64		f	i	V	i64		i		64					m	1	1	1, 0, -3		0	1
+89	87	345	345	4.0	0	0	2/2	2	1	V7		f	i	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+90	88	349	349	2.0	0	0	2/2	2	1	i64		f	i	V	i64		i		64					m	1	1	1, 0, -3		0	1
+90	88	351	351	2.0	1/2	1/2	2/2	2	1	viio/V		f	i	V	viio/V		vii	o			V			o	1	1	6, 3, 0		6	6
+91	89	353	353	2.0	0	0	2/2	2	1	V		f	i	V	V		V							M	1	1	1, 5, 2		1	1
+91	89	355	355	2.0	1/2	1/2	2/2	2	1	V7(+b9)		f	i	V	V7(+b9)		V		7	+b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+92	90	357	357	2.0	0	0	2/2	2	1	i64		f	i	V	i64		i		64					m	1	1	1, 0, -3		0	1
+92	90	359	359	2.0	1/2	1/2	2/2	2	1	viio/V		f	i	V	viio/V		vii	o			V			o	1	1	6, 3, 0		6	6
+93	91	361	361	2.0	0	0	2/2	2	1	V		f	i	V	V		V							M	1	1	1, 5, 2		1	1
+93	91	363	363	2.0	1/2	1/2	2/2	2	1	V7(+b9)		f	i	V	V7(+b9)		V		7	+b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+94	92	365	365	2.0	0	0	2/2	2	1	i64		f	i	V	i64		i		64					m	1	1	1, 0, -3		0	1
+94	92	367	367	2.0	1/2	1/2	2/2	2	1	viio/V		f	i	V	viio/V		vii	o			V			o	1	1	6, 3, 0		6	6
+95	93	369	369	8.0	0	0	2/2	2	1	V|HC}		f	i	V	V		V					HC	}	M	1	1	1, 5, 2		1	1
+97	95	377	377	4.0	0	0	2/2	2	1	bII6(4)]{		f	i	V	bII6(4)		bII		6	4			{	M	1	1	1, -4, -5		-5	1
+98	96	381	381	4.0	0	0	2/2	2	1	bII6		f	i		bII6		bII		6					M	1	1	-1, -4, -5		-5	-1
+99	97	385	385	4.0	0	0	2/2	2	1	V2		f	i		V2		V		2					Mm7	1	1	-1, 1, 5, 2		1	-1
+100	98	389	389	4.0	0	0	2/2	2	1	i6		f	i		i6		i		6					m	1	1	-3, 1, 0		0	-3
+101	99	393	393	4.0	0	0	2/2	2	1	V2/VII		f	i		V2/VII		V		2		VII			Mm7	1	1	-3, -1, 3, 0		-1	-3
+102	100	397	397	4.0	0	0	2/2	2	1	#viio6		f	i		#viio6		#vii	o	6					o	1	1	2, -1, 5		5	2
+103	101	401	401	8.0	0	0	2/2	2	1	i|IAC}{		f	i		i		i					IAC	}{	m	1	1	0, -3, 1		0	0
+105	103	409	409	8.0	0	0	2/2	2	1	V65		f	i		V65		V		65					Mm7	1	1	5, 2, -1, 1		1	5
+107	105	417	417	4.0	0	0	2/2	2	1	i		f	i		i		i							m	1	1	0, -3, 1		0	0
+108	106	421	421	4.0	0	0	2/2	2	1	#viio6		f	i		#viio6		#vii	o	6					o	1	1	2, -1, 5		5	2
+109	107	425	425	2.0	0	0	2/2	2	1	i6		f	i		i6		i		6					m	1	1	-3, 1, 0		0	-3
+109	107	427	427	2.0	1/2	1/2	2/2	2	1	iio6		f	i		iio6		ii	o	6					o	1	1	-1, -4, 2		2	-1
+110	108	429	429	1.0	0	0	2/2	2	1	V(4)}		f	i		V(4)		V			4			}	M	1	1	1, 0, 2		1	1
+110	108	430	430	2.0	1/4	1/4	2/2	2	1	V|HC		f	i		V		V					HC		M	1	1	1, 5, 2		1	1
+110	108	432	432	9.0	3/4	3/4	2/2	2	1	i{		f	i		i		i						{	m	1	1	0, -3, 1		0	0
+113	111	441	441	4.0	0	0	2/2	2	1	iio6(4)/iv	bIIM2	f	i		iio6(4)/iv		ii	o	6	4	iv			o	1	1	0, -5, 1		1	0
+114	112	445	445	4.0	0	0	2/2	2	1	iio6/iv		f	i		iio6/iv		ii	o	6		iv			o	1	1	-2, -5, 1		1	-2
+115	113	449	449	2.0	0	0	2/2	2	1	V2(b2)/iv		f	i		V2(b2)/iv		V		2	b2	iv			Mm7	1	1	-2, -12, 4, 1		0	-2
+115	113	451	451	2.0	1/2	1/2	2/2	2	1	V2/iv		f	i		V2/iv		V		2		iv			Mm7	1	1	-2, 0, 4, 1		0	-2
+116	114	453	453	2.0	0	0	2/2	2	1	iv6(2)		f	i		iv6(2)		iv		6	2				m	1	1	-4, 0, 1		-1	-4
+116	114	455	455	2.0	1/2	1/2	2/2	2	1	iv6		f	i		iv6		iv		6					m	1	1	-4, 0, -1		-1	-4
+117	115	457	457	4.0	0	0	2/2	2	1	viio6/V		f	i		viio6/V		vii	o	6		V			o	1	1	3, 0, 6		6	3
+118	116	461	461	4.0	0	0	2/2	2	1	viio65/V		f	i		viio65/V		vii	o	65		V			o7	1	1	3, 0, -3, 6		6	3
+119	117	465	465	4.0	0	0	2/2	2	1	V|HC}		f	i		V		V					HC	}	M	1	1	1, 5, 2		1	1
+120	118	469	469	1.0	0	0	2/2	2	1	V(64)		f	i		V(64)		V			64				M	1	1	1, 0, -3		1	1
+120	118	470	470	1.0	1/4	1/4	2/2	2	1	V7		f	i		V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+120	118	471	471	1.0	1/2	1/2	2/2	2	1	VI		f	i		VI		VI							M	1	1	-4, 0, -3		-4	-4
+120	118	472	472	1.0	3/4	3/4	2/2	2	1	It6		f	i		It6	It	vii	o	6	b3	V			It	1	1	-4, 0, 6		6	-4
+121	119	473	473	3.0	0	0	2/2	2	1	V[V|HC{		f	i	V	V		V					HC	{	M	1	1	1, 5, 2		1	1
+121	119	476	476	1.0	3/4	3/4	2/2	2	1	V7(+b9)		f	i	V	V7(+b9)		V		7	+b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+122	120	477	477	4.0	0	0	2/2	2	1	V7		f	i	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+123	121	481	481	1.5	0	0	2/2	2	2	V7(b9)		f	i	V	V7(b9)		V		7	b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+123	121	965/2	965/2	0.5	3/8	3/8	2/2	2	2	V7		f	i	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+123	121	483	483	1.0	1/2	1/2	2/2	2	1	i64		f	i	V	i64		i		64					m	1	1	1, 0, -3		0	1
+123	121	484	484	1.0	3/4	3/4	2/2	2	1	V7(+b9)		f	i	V	V7(+b9)		V		7	+b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+124	122	485	485	4.0	0	0	2/2	2	2	V7		f	i	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+125	123	489	489	1.5	0	0	2/2	2	2	V7(b9)		f	i	V	V7(b9)		V		7	b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+125	123	981/2	981/2	0.5	3/8	3/8	2/2	2	2	V7		f	i	V	V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+125	123	491	491	1.0	1/2	1/2	2/2	2	1	i64		f	i	V	i64		i		64					m	1	1	1, 0, -3		0	1
+125	123	492	492	3.0	3/4	3/4	2/2	2	1	V7(+b9)]		f	i	V	V7(+b9)		V		7	+b9				Mm7	1	1	1, 5, 2, -1	-11	1	1
+126	124	495	495	2.0	1/2	1/2	2/2	2	1	V2		f	i		V2		V		2					Mm7	1	1	-1, 1, 5, 2		1	-1
+127	125	497	497	2.0	0	0	2/2	2	1	i6		f	i		i6		i		6					m	1	1	-3, 1, 0		0	-3
+127	125	499	499	2.0	1/2	1/2	2/2	2	1	#viio6		f	i		#viio6		#vii	o	6					o	1	1	2, -1, 5		5	2
+128	126	501	501	2.0	0	0	2/2	2	1	i		f	i		i		i							m	1	1	0, -3, 1		0	0
+128	126	503	503	2.0	1/2	1/2	2/2	2	1	It6		f	i		It6	It	vii	o	6	b3	V			It	1	1	-4, 0, 6		6	-4
+129	127	505	505	2.0	0	0	2/2	2	1	V		f	i		V		V							M	1	1	1, 5, 2		1	1
+129	127	507	507	2.0	1/2	1/2	2/2	2	1	It6		f	i		It6	It	vii	o	6	b3	V			It	1	1	-4, 0, 6		6	-4
+130	128	509	509	2.0	0	0	2/2	2	1	V		f	i		V		V							M	1	1	1, 5, 2		1	1
+130	128	511	511	2.0	1/2	1/2	2/2	2	1	#viio43		f	i		#viio43		#vii	o	43					o7	1	1	-1, -4, 5, 2		5	-1
+131	129	513	513	2.0	0	0	2/2	2	1	i6		f	i		i6		i		6					m	1	1	-3, 1, 0		0	-3
+131	129	515	515	2.0	1/2	1/2	2/2	2	1	#viio43		f	i		#viio43		#vii	o	43					o7	1	1	-1, -4, 5, 2		5	-1
+132	130	517	517	2.0	0	0	2/2	2	1	i6		f	i		i6		i		6					m	1	1	-3, 1, 0		0	-3
+132	130	519	519	2.0	1/2	1/2	2/2	2	1	V6		f	i		V6		V		6					M	1	1	5, 2, 1		1	5
+133	131	521	521	2.0	0	0	2/2	2	1	i		f	i		i		i							m	1	1	0, -3, 1		0	0
+133	131	523	523	2.0	1/2	1/2	2/2	2	1	V43		f	i		V43		V		43					Mm7	1	1	2, -1, 1, 5		1	2
+134	132	525	525	4.0	0	0	2/2	2	1	i6		f	i		i6		i		6					m	1	1	-3, 1, 0		0	-3
+135	133	529	529	4.0	0	0	2/2	2	1	iio6		f	i		iio6		ii	o	6					o	1	1	-1, -4, 2		2	-1
+136	134	533	533	4.0	0	0	2/2	2	1	V(64)		f	i		V(64)		V			64				M	1	1	1, 0, -3		1	1
+137	135	537	537	4.0	0	0	2/2	2	1	V2		f	i		V2		V		2					Mm7	1	1	-1, 1, 5, 2		1	-1
+138	136	541	541	4.0	0	0	2/2	2	1	i6		f	i		i6		i		6					m	1	1	-3, 1, 0		0	-3
+139	137	545	545	4.0	0	0	2/2	2	1	iio6		f	i		iio6		ii	o	6					o	1	1	-1, -4, 2		2	-1
+140	138	549	549	4.0	0	0	2/2	2	1	V(64)		f	i		V(64)		V			64				M	1	1	1, 0, -3		1	1
+141	139	553	553	4.0	0	0	2/2	2	1	V		f	i		V		V							M	1	1	1, 5, 2		1	1
+142	140	557	557	2.0	0	0	2/2	2	1	i|PAC}		f	i		i		i					PAC	}	m	1	1	0, -3, 1		0	0
+142	140	559	559	2.0	1/2	1/2	2/2	2	1	Ger6{		f	i		Ger6	Ger	vii	o	65	b3	V		{	Ger	1	1	-4, 0, -3, 6		6	-4
+143	141	561	561	2.0	0	0	2/2	2	1	V(64)		f	i		V(64)		V			64				M	1	1	1, 0, -3		1	1
+143	141	563	563	2.0	1/2	1/2	2/2	2	1	V7		f	i		V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+144	142	565	565	2.0	0	0	2/2	2	1	i|PAC		f	i		i		i					PAC		m	1	1	0, -3, 1		0	0
+144	142	567	567	2.0	1/2	1/2	2/2	2	1	Ger6		f	i		Ger6	Ger	vii	o	65	b3	V			Ger	1	1	-4, 0, -3, 6		6	-4
+145	143	569	569	2.0	0	0	2/2	2	1	V(64)		f	i		V(64)		V			64				M	1	1	1, 0, -3		1	1
+145	143	571	571	2.0	1/2	1/2	2/2	2	1	V7		f	i		V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+146	144	573	573	2.0	0	0	2/2	2	1	i|PAC		f	i		i		i					PAC		m	1	1	0, -3, 1		0	0
+146	144	575	575	2.0	1/2	1/2	2/2	2	1	Ger6		f	i		Ger6	Ger	vii	o	65	b3	V			Ger	1	1	-4, 0, -3, 6		6	-4
+147	145	577	577	2.0	0	0	2/2	2	1	V(64)		f	i		V(64)		V			64				M	1	1	1, 0, -3		1	1
+147	145	579	579	2.0	1/2	1/2	2/2	2	1	V2		f	i		V2		V		2					Mm7	1	1	-1, 1, 5, 2		1	-1
+148	146	581	581	4.0	0	0	2/2	2	1	V65/iv		f	i		V65/iv		V		65		iv			Mm7	1	1	4, 1, -2, 0		0	4
+149	147	585	585	4.0	0	0	2/2	2	1	iv		f	i		iv		iv							m	1	1	-1, -4, 0		-1	-1
+150	148	589	589	4.0	0	0	2/2	2	1	V65/III		f	i		V65/III		V		65		III			Mm7	1	1	2, -1, -4, -2		-2	2
+151	149	593	593	3.0	0	0	2/2	2	1	III		f	i		III		III							M	1	1	-3, 1, -2		-3	-3
+151	149	596	596	1.0	3/4	3/4	2/2	2	1	VIM65		f	i		VIM65		VI	M	65					MM7	1	1	0, -3, 1, -4		-4	0
+152	150	597	597	1.0	0	0	2/2	2	1	iio		f	i		iio		ii	o						o	1	1	2, -1, -4		2	2
+152	150	598	598	1.0	1/4	1/4	2/2	2	1	V65		f	i		V65		V		65					Mm7	1	1	5, 2, -1, 1		1	5
+152	150	599	599	1.0	1/2	1/2	2/2	2	1	i		f	i		i		i							m	1	1	0, -3, 1		0	0
+152	150	600	600	1.0	3/4	3/4	2/2	2	1	VI		f	i		VI		VI							M	1	1	-4, 0, -3		-4	-4
+153	151	601	601	2.0	0	0	2/2	2	1	ii%65		f	i		ii%65		ii	%	65					%7	1	1	-1, -4, 0, 2		2	-1
+153	151	603	603	2.0	1/2	1/2	2/2	2	1	V7		f	i		V7		V		7					Mm7	1	1	1, 5, 2, -1		1	1
+154	152	605	605	3.0	0	0	2/2	2	1	i|PAC}		f	i		i		i					PAC	}	m	1	1	0, -3, 1		0	0
diff --git a/tests/data/tsv/test_measures.tsv b/tests/data/tsv/test_measures.tsv
new file mode 100644
index 00000000..2d70eb74
--- /dev/null
+++ b/tests/data/tsv/test_measures.tsv
@@ -0,0 +1,155 @@
+mc	mn	quarterbeats	duration_qb	keysig	timesig	act_dur	mc_offset	numbering_offset	dont_count	barline	breaks	repeats	next
+1	0	0	1.0	-4	2/2	1/4	3/4		1			firstMeasure	2
+2	1	1	4.0	-4	2/2	1	0						3
+3	2	5	4.0	-4	2/2	1	0						4
+4	3	9	4.0	-4	2/2	1	0						5
+5	4	13	4.0	-4	2/2	1	0						6
+6	5	17	4.0	-4	2/2	1	0						7
+7	6	21	4.0	-4	2/2	1	0						8
+8	7	25	4.0	-4	2/2	1	0						9
+9	8	29	4.0	-4	2/2	1	0						10
+10	9	33	4.0	-4	2/2	1	0						11
+11	10	37	4.0	-4	2/2	1	0						12
+12	11	41	4.0	-4	2/2	1	0						13
+13	12	45	4.0	-4	2/2	1	0						14
+14	13	49	4.0	-4	2/2	1	0						15
+15	14	53	4.0	-4	2/2	1	0						16
+16	15	57	4.0	-4	2/2	1	0						17
+17	16	61	4.0	-4	2/2	1	0						18
+18	17	65	4.0	-4	2/2	1	0						19
+19	18	69	4.0	-4	2/2	1	0						20
+20	19	73	4.0	-4	2/2	1	0						21
+21	20	77	4.0	-4	2/2	1	0						22
+22	21	81	4.0	-4	2/2	1	0						23
+23	22	85	4.0	-4	2/2	1	0						24
+24	23	89	4.0	-4	2/2	1	0						25
+25	24	93	4.0	-4	2/2	1	0						26
+26	25	97	4.0	-4	2/2	1	0						27
+27	26	101	4.0	-4	2/2	1	0						28
+28	27	105	4.0	-4	2/2	1	0						29
+29	28	109	4.0	-4	2/2	1	0						30
+30	29	113	4.0	-4	2/2	1	0						31
+31	30	117	4.0	-4	2/2	1	0						32
+32	31	121	4.0	-4	2/2	1	0						33
+33	32	125	4.0	-4	2/2	1	0						34
+34	33	129	4.0	-4	2/2	1	0						35
+35	34	133	4.0	-4	2/2	1	0						36
+36	35	137	4.0	-4	2/2	1	0						37
+37	36	141	4.0	-4	2/2	1	0						38
+38	37	145	4.0	-4	2/2	1	0						39
+39	38	149	4.0	-4	2/2	1	0						40
+40	39	153	4.0	-4	2/2	1	0						41
+41	40	157	4.0	-4	2/2	1	0						42
+42	41	161	4.0	-4	2/2	1	0						43
+43	42	165	4.0	-4	2/2	1	0						44
+44	43	169	4.0	-4	2/2	1	0						45
+45	44	173	4.0	-4	2/2	1	0						46
+46	45	177	4.0	-4	2/2	1	0						47
+47	46	181	4.0	-4	2/2	1	0						48
+48	47	185	4.0	-4	2/2	1	0						49
+49	48	189	3.0	-4	2/2	3/4	0				line	end	1, 50
+50	48	192	1.0	-4	2/2	1/4	3/4		1	start-repeat		start	51
+51	49	193	4.0	-4	2/2	1	0						52
+52	50	197	4.0	-4	2/2	1	0						53
+53	51	201	4.0	-4	2/2	1	0						54
+54	52	205	4.0	-4	2/2	1	0						55
+55	53	209	4.0	-4	2/2	1	0						56
+56	54	213	4.0	-4	2/2	1	0						57
+57	55	217	4.0	-4	2/2	1	0						58
+58	56	221	4.0	-4	2/2	1	0						59
+59	57	225	4.0	-4	2/2	1	0						60
+60	58	229	4.0	-4	2/2	1	0						61
+61	59	233	4.0	-4	2/2	1	0						62
+62	60	237	4.0	-4	2/2	1	0						63
+63	61	241	4.0	-4	2/2	1	0						64
+64	62	245	4.0	-4	2/2	1	0						65
+65	63	249	4.0	-4	2/2	1	0						66
+66	64	253	4.0	-4	2/2	1	0						67
+67	65	257	4.0	-4	2/2	1	0						68
+68	66	261	4.0	-4	2/2	1	0						69
+69	67	265	4.0	-4	2/2	1	0						70
+70	68	269	4.0	-4	2/2	1	0						71
+71	69	273	4.0	-4	2/2	1	0						72
+72	70	277	4.0	-4	2/2	1	0						73
+73	71	281	4.0	-4	2/2	1	0						74
+74	72	285	4.0	-4	2/2	1	0						75
+75	73	289	4.0	-4	2/2	1	0						76
+76	74	293	4.0	-4	2/2	1	0						77
+77	75	297	4.0	-4	2/2	1	0						78
+78	76	301	4.0	-4	2/2	1	0						79
+79	77	305	4.0	-4	2/2	1	0						80
+80	78	309	4.0	-4	2/2	1	0						81
+81	79	313	4.0	-4	2/2	1	0						82
+82	80	317	4.0	-4	2/2	1	0						83
+83	81	321	4.0	-4	2/2	1	0						84
+84	82	325	4.0	-4	2/2	1	0						85
+85	83	329	4.0	-4	2/2	1	0						86
+86	84	333	4.0	-4	2/2	1	0						87
+87	85	337	4.0	-4	2/2	1	0						88
+88	86	341	4.0	-4	2/2	1	0						89
+89	87	345	4.0	-4	2/2	1	0						90
+90	88	349	4.0	-4	2/2	1	0						91
+91	89	353	4.0	-4	2/2	1	0						92
+92	90	357	4.0	-4	2/2	1	0						93
+93	91	361	4.0	-4	2/2	1	0						94
+94	92	365	4.0	-4	2/2	1	0						95
+95	93	369	4.0	-4	2/2	1	0						96
+96	94	373	4.0	-4	2/2	1	0						97
+97	95	377	4.0	-4	2/2	1	0						98
+98	96	381	4.0	-4	2/2	1	0						99
+99	97	385	4.0	-4	2/2	1	0						100
+100	98	389	4.0	-4	2/2	1	0						101
+101	99	393	4.0	-4	2/2	1	0						102
+102	100	397	4.0	-4	2/2	1	0						103
+103	101	401	4.0	-4	2/2	1	0						104
+104	102	405	4.0	-4	2/2	1	0						105
+105	103	409	4.0	-4	2/2	1	0						106
+106	104	413	4.0	-4	2/2	1	0						107
+107	105	417	4.0	-4	2/2	1	0						108
+108	106	421	4.0	-4	2/2	1	0						109
+109	107	425	4.0	-4	2/2	1	0						110
+110	108	429	4.0	-4	2/2	1	0						111
+111	109	433	4.0	-4	2/2	1	0						112
+112	110	437	4.0	-4	2/2	1	0						113
+113	111	441	4.0	-4	2/2	1	0						114
+114	112	445	4.0	-4	2/2	1	0						115
+115	113	449	4.0	-4	2/2	1	0						116
+116	114	453	4.0	-4	2/2	1	0						117
+117	115	457	4.0	-4	2/2	1	0						118
+118	116	461	4.0	-4	2/2	1	0						119
+119	117	465	4.0	-4	2/2	1	0						120
+120	118	469	4.0	-4	2/2	1	0						121
+121	119	473	4.0	-4	2/2	1	0						122
+122	120	477	4.0	-4	2/2	1	0						123
+123	121	481	4.0	-4	2/2	1	0						124
+124	122	485	4.0	-4	2/2	1	0						125
+125	123	489	4.0	-4	2/2	1	0						126
+126	124	493	4.0	-4	2/2	1	0						127
+127	125	497	4.0	-4	2/2	1	0						128
+128	126	501	4.0	-4	2/2	1	0						129
+129	127	505	4.0	-4	2/2	1	0						130
+130	128	509	4.0	-4	2/2	1	0						131
+131	129	513	4.0	-4	2/2	1	0						132
+132	130	517	4.0	-4	2/2	1	0						133
+133	131	521	4.0	-4	2/2	1	0						134
+134	132	525	4.0	-4	2/2	1	0						135
+135	133	529	4.0	-4	2/2	1	0						136
+136	134	533	4.0	-4	2/2	1	0						137
+137	135	537	4.0	-4	2/2	1	0						138
+138	136	541	4.0	-4	2/2	1	0						139
+139	137	545	4.0	-4	2/2	1	0						140
+140	138	549	4.0	-4	2/2	1	0						141
+141	139	553	4.0	-4	2/2	1	0						142
+142	140	557	4.0	-4	2/2	1	0						143
+143	141	561	4.0	-4	2/2	1	0						144
+144	142	565	4.0	-4	2/2	1	0						145
+145	143	569	4.0	-4	2/2	1	0						146
+146	144	573	4.0	-4	2/2	1	0						147
+147	145	577	4.0	-4	2/2	1	0						148
+148	146	581	4.0	-4	2/2	1	0						149
+149	147	585	4.0	-4	2/2	1	0						150
+150	148	589	4.0	-4	2/2	1	0						151
+151	149	593	4.0	-4	2/2	1	0				line		152
+152	150	597	4.0	-4	2/2	1	0						153
+153	151	601	4.0	-4	2/2	1	0						154
+154	152	605	3.0	-4	2/2	3/4	0					end	50, -1
diff --git a/tests/data/tsv/test_notes.tsv b/tests/data/tsv/test_notes.tsv
new file mode 100644
index 00000000..089174c6
--- /dev/null
+++ b/tests/data/tsv/test_notes.tsv
@@ -0,0 +1,1694 @@
+mc	mn	quarterbeats	quarterbeats_all_endings	duration_qb	mc_onset	mn_onset	timesig	staff	voice	duration	gracenote	nominal_duration	scalar	tied	tpc	midi	name	octave	chord_id
+1	0	0	0	1.0	0	3/4	2/2	1	1	1/4		1/4	1		0	60	C4	4	0
+2	1	1	1	1.0	0	0	2/2	1	1	1/4		1/4	1		-1	65	F4	4	1
+2	1	2	2	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	2
+2	1	3	3	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		0	72	C5	5	3
+2	1	4	4	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-1	77	F5	5	4
+3	2	5	5	1.5	0	0	2/2	1	1	3/8		1/4	3/2		-4	80	Ab5	5	5
+3	2	6	6	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	10
+3	2	6	6	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	10
+3	2	6	6	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	10
+3	2	13/2	13/2	0.16666666666666666	3/8	3/8	2/2	1	1	1/24		1/16	2/3		1	79	G5	5	6
+3	2	20/3	20/3	0.16666666666666666	5/12	5/12	2/2	1	1	1/24		1/16	2/3		-1	77	F5	5	7
+3	2	41/6	41/6	0.16666666666666666	11/24	11/24	2/2	1	1	1/24		1/16	2/3		4	76	E5	5	8
+3	2	7	7	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-1	53	F3	3	11
+3	2	7	7	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	11
+3	2	7	7	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		0	60	C4	4	11
+3	2	7	7	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-1	77	F5	5	9
+3	2	8	8	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	12
+3	2	8	8	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	12
+3	2	8	8	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	12
+4	3	9	9	1.0	0	0	2/2	2	1	1/4		1/4	1		4	52	E3	3	17
+4	3	9	9	1.0	0	0	2/2	2	1	1/4		1/4	1		1	55	G3	3	17
+4	3	9	9	1.0	0	0	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	17
+4	3	9	9	1.0	0	0	2/2	2	1	1/4		1/4	1		0	60	C4	4	17
+4	3	9	9	1.0	0	0	2/2	1	1	1/4		1/4	1		1	67	G4	4	13
+4	3	10	10	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		0	72	C5	5	14
+4	3	11	11	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		4	76	E5	5	15
+4	3	12	12	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		1	79	G5	5	16
+5	4	13	13	1.5	0	0	2/2	1	1	3/8		1/4	3/2		-2	82	Bb5	5	18
+5	4	14	14	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		4	52	E3	3	23
+5	4	14	14	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		1	55	G3	3	23
+5	4	14	14	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	23
+5	4	14	14	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	23
+5	4	29/2	29/2	0.16666666666666666	3/8	3/8	2/2	1	1	1/24		1/16	2/3		-4	80	Ab5	5	19
+5	4	44/3	44/3	0.16666666666666666	5/12	5/12	2/2	1	1	1/24		1/16	2/3		1	79	G5	5	20
+5	4	89/6	89/6	0.16666666666666666	11/24	11/24	2/2	1	1	1/24		1/16	2/3		-1	77	F5	5	21
+5	4	15	15	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		4	52	E3	3	24
+5	4	15	15	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		1	55	G3	3	24
+5	4	15	15	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	24
+5	4	15	15	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		0	60	C4	4	24
+5	4	15	15	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		1	79	G5	5	22
+5	4	16	16	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		4	52	E3	3	25
+5	4	16	16	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		1	55	G3	3	25
+5	4	16	16	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	25
+5	4	16	16	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	25
+6	5	17	17	0.0	0	0	2/2	1	1	0	acciaccatura	1/8	1		0	72	C5	5	26
+6	5	17	17	1.5	0	0	2/2	1	1	3/8		1/4	3/2		-4	80	Ab5	5	27
+6	5	18	18	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	32
+6	5	18	18	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	32
+6	5	18	18	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	32
+6	5	37/2	37/2	0.16666666666666666	3/8	3/8	2/2	1	1	1/24		1/16	2/3		1	79	G5	5	28
+6	5	56/3	56/3	0.16666666666666666	5/12	5/12	2/2	1	1	1/24		1/16	2/3		-1	77	F5	5	29
+6	5	113/6	113/6	0.16666666666666666	11/24	11/24	2/2	1	1	1/24		1/16	2/3		4	76	E5	5	30
+6	5	19	19	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-1	53	F3	3	33
+6	5	19	19	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	33
+6	5	19	19	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		0	60	C4	4	33
+6	5	19	19	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-1	77	F5	5	31
+6	5	20	20	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	34
+6	5	20	20	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	34
+6	5	20	20	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	34
+7	6	21	21	0.0	0	0	2/2	1	1	0	acciaccatura	1/8	1		0	72	C5	5	35
+7	6	21	21	1.5	0	0	2/2	1	1	3/8		1/4	3/2		-2	82	Bb5	5	36
+7	6	22	22	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		1	55	G3	3	41
+7	6	22	22	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	41
+7	6	22	22	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		4	64	E4	4	41
+7	6	45/2	45/2	0.16666666666666666	3/8	3/8	2/2	1	1	1/24		1/16	2/3		-4	80	Ab5	5	37
+7	6	68/3	68/3	0.16666666666666666	5/12	5/12	2/2	1	1	1/24		1/16	2/3		1	79	G5	5	38
+7	6	137/6	137/6	0.16666666666666666	11/24	11/24	2/2	1	1	1/24		1/16	2/3		-1	77	F5	5	39
+7	6	23	23	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		1	55	G3	3	42
+7	6	23	23	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	42
+7	6	23	23	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		4	64	E4	4	42
+7	6	23	23	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		1	79	G5	5	40
+7	6	24	24	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		1	55	G3	3	43
+7	6	24	24	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	43
+7	6	24	24	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		4	64	E4	4	43
+8	7	25	25	2.0	0	0	2/2	1	1	1/2		1/2	1		0	72	C5	5	44
+8	7	25	25	2.0	0	0	2/2	1	1	1/2		1/2	1		-1	77	F5	5	44
+8	7	25	25	2.0	0	0	2/2	1	1	1/2		1/2	1		-4	80	Ab5	5	44
+8	7	25	25	2.0	0	0	2/2	1	1	1/2		1/2	1		0	84	C6	6	44
+8	7	26	26	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	49
+8	7	26	26	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	49
+8	7	26	26	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-1	65	F4	4	49
+8	7	27	27	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		-2	82	Bb5	5	45
+8	7	55/2	55/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		-4	80	Ab5	5	46
+8	7	28	28	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	50
+8	7	28	28	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	50
+8	7	28	28	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		1	67	G4	4	50
+8	7	28	28	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		1	79	G5	5	47
+8	7	57/2	57/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-1	77	F5	5	48
+9	8	29	29	0.0	0	0	2/2	1	1	0	grace16	1/16	1		4	76	E5	5	51
+9	8	29	29	0.0	0	0	2/2	1	1	0	grace16	1/16	1		-1	77	F5	5	52
+9	8	29	29	0.0	0	0	2/2	1	1	0	grace16	1/16	1		1	79	G5	5	53
+9	8	29	29	1.0	0	0	2/2	1	1	1/4		1/4	1		-1	77	F5	5	54
+9	8	30	30	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	56
+9	8	30	30	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		1	67	G4	4	56
+9	8	30	30	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		4	76	E5	5	55
+9	8	32	32	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		1	43	G2	2	57
+10	9	33	33	1.0	0	0	2/2	2	1	1/4		1/4	1		0	48	C3	3	58
+10	9	34	34	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-3	51	Eb3	3	59
+10	9	35	35	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		1	55	G3	3	60
+10	9	36	36	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	61
+11	10	37	37	1.5	0	0	2/2	2	1	3/8		1/4	3/2		-3	63	Eb4	4	62
+11	10	77/2	77/2	0.16666666666666666	3/8	3/8	2/2	2	1	1/24		1/16	2/3		2	62	D4	4	63
+11	10	116/3	116/3	0.16666666666666666	5/12	5/12	2/2	2	1	1/24		1/16	2/3		0	60	C4	4	64
+11	10	233/6	233/6	0.16666666666666666	11/24	11/24	2/2	2	1	1/24		1/16	2/3		5	59	B3	3	65
+11	10	39	39	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		0	60	C4	4	66
+11	10	40	40	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	67
+12	11	41	41	4.0	0	0	2/2	2	1	1		1	1		0	60	C4	4	74
+12	11	41	41	4.0	0	0	2/2	2	1	1		1	1		-1	65	F4	4	74
+12	11	41	41	4.0	0	0	2/2	1	2	1		1	1	1	-4	68	Ab4	4	73
+12	11	85/2	85/2	0.16666666666666666	3/8	3/8	2/2	1	1	1/24		1/16	2/3		-3	75	Eb5	5	68
+12	11	128/3	128/3	0.16666666666666666	5/12	5/12	2/2	1	1	1/24		1/16	2/3		-5	73	Db5	5	69
+12	11	257/6	257/6	0.16666666666666666	11/24	11/24	2/2	1	1	1/24		1/16	2/3		0	72	C5	5	70
+12	11	43	43	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-5	73	Db5	5	71
+12	11	44	44	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-5	73	Db5	5	72
+13	12	45	45	4.0	0	0	2/2	2	1	1		1	1		-2	58	Bb3	3	82
+13	12	45	45	4.0	0	0	2/2	2	1	1		1	1		-1	65	F4	4	82
+13	12	45	45	1.5	0	0	2/2	1	2	3/8		1/4	3/2	-1	-4	68	Ab4	4	76
+13	12	45	45	4.0	0	0	2/2	1	1	1		1	1	1	-5	73	Db5	5	75
+13	12	93/2	93/2	0.16666666666666666	3/8	3/8	2/2	1	2	1/24		1/16	2/3		-2	70	Bb4	4	77
+13	12	140/3	140/3	0.16666666666666666	5/12	5/12	2/2	1	2	1/24		1/16	2/3		-4	68	Ab4	4	78
+13	12	281/6	281/6	0.16666666666666666	11/24	11/24	2/2	1	2	1/24		1/16	2/3		1	67	G4	4	79
+13	12	47	47	1.0	1/2	1/2	2/2	1	2	1/4		1/4	1		-4	68	Ab4	4	80
+13	12	48	48	1.0	3/4	3/4	2/2	1	2	1/4		1/4	1		-4	68	Ab4	4	81
+14	13	49	49	4.0	0	0	2/2	2	1	1		1	1		-2	58	Bb3	3	90
+14	13	49	49	4.0	0	0	2/2	2	1	1		1	1		-3	63	Eb4	4	90
+14	13	49	49	4.0	0	0	2/2	1	2	1		1	1		1	67	G4	4	89
+14	13	49	49	1.5	0	0	2/2	1	1	3/8		1/4	3/2	-1	-5	73	Db5	5	83
+14	13	101/2	101/2	0.16666666666666666	3/8	3/8	2/2	1	1	1/24		1/16	2/3		-3	75	Eb5	5	84
+14	13	152/3	152/3	0.16666666666666666	5/12	5/12	2/2	1	1	1/24		1/16	2/3		-5	73	Db5	5	85
+14	13	305/6	305/6	0.16666666666666666	11/24	11/24	2/2	1	1	1/24		1/16	2/3		0	72	C5	5	86
+14	13	51	51	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-5	73	Db5	5	87
+14	13	52	52	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-5	73	Db5	5	88
+15	14	53	53	4.0	0	0	2/2	2	1	1		1	1		-4	56	Ab3	3	97
+15	14	53	53	4.0	0	0	2/2	2	1	1		1	1		-3	63	Eb4	4	97
+15	14	53	53	4.0	0	0	2/2	1	2	1		1	1		-4	68	Ab4	4	96
+15	14	109/2	109/2	0.16666666666666666	3/8	3/8	2/2	1	1	1/24		1/16	2/3		-5	73	Db5	5	91
+15	14	164/3	164/3	0.16666666666666666	5/12	5/12	2/2	1	1	1/24		1/16	2/3		0	72	C5	5	92
+15	14	329/6	329/6	0.16666666666666666	11/24	11/24	2/2	1	1	1/24		1/16	2/3		5	71	B4	4	93
+15	14	55	55	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		0	72	C5	5	94
+15	14	56	56	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		0	72	C5	5	95
+16	15	57	57	2.0	0	0	2/2	2	1	1/2		1/2	1		-5	61	Db4	4	102
+16	15	57	57	4.0	0	0	2/2	1	2	1		1	1		-1	65	F4	4	101
+16	15	57	57	1.0	0	0	2/2	1	1	1/4		1/4	1		0	72	C5	5	98
+16	15	58	58	2.0	1/4	1/4	2/2	1	1	1/2		1/2	1		-2	70	Bb4	4	99
+16	15	59	59	2.0	1/2	1/2	2/2	2	1	1/2		1/2	1		2	62	D4	4	103
+16	15	60	60	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	100
+17	16	61	61	1.0	0	0	2/2	2	1	1/4		1/4	1		-3	63	Eb4	4	108
+17	16	61	61	1.0	0	0	2/2	1	1	1/4		1/4	1		1	67	G4	4	104
+17	16	62	62	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	105
+17	16	63	63	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-5	73	Db5	5	106
+17	16	64	64	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	109
+17	16	64	64	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-3	63	Eb4	4	109
+17	16	64	64	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1	1	0	72	C5	5	107
+18	17	65	65	2.0	0	0	2/2	2	1	1/2		1/2	1		-5	61	Db4	4	113
+18	17	65	65	2.0	0	0	2/2	2	1	1/2		1/2	1		-1	65	F4	4	113
+18	17	65	65	1.0	0	0	2/2	1	1	1/4		1/4	1	-1	0	72	C5	5	110
+18	17	66	66	2.0	1/4	1/4	2/2	1	1	1/2		1/2	1		-2	70	Bb4	4	111
+18	17	67	67	2.0	1/2	1/2	2/2	2	1	1/2		1/2	1		2	62	D4	4	114
+18	17	67	67	2.0	1/2	1/2	2/2	2	1	1/2		1/2	1		-1	65	F4	4	114
+18	17	68	68	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	112
+19	18	69	69	1.0	0	0	2/2	2	1	1/4		1/4	1		-3	63	Eb4	4	119
+19	18	69	69	1.0	0	0	2/2	1	1	1/4		1/4	1		1	67	G4	4	115
+19	18	70	70	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-3	63	Eb4	4	116
+19	18	70	70	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	116
+19	18	71	71	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-5	61	Db4	4	117
+19	18	71	71	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-5	73	Db5	5	117
+19	18	72	72	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	48	C3	3	120
+19	18	72	72	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-3	51	Eb3	3	120
+19	18	72	72	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1	1	0	60	C4	4	118
+19	18	72	72	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1	1	0	72	C5	5	118
+20	19	73	73	2.0	0	0	2/2	2	1	1/2		1/2	1		-5	49	Db3	3	124
+20	19	73	73	2.0	0	0	2/2	2	1	1/2		1/2	1		-1	53	F3	3	124
+20	19	73	73	1.0	0	0	2/2	1	1	1/4		1/4	1	-1	0	60	C4	4	121
+20	19	73	73	1.0	0	0	2/2	1	1	1/4		1/4	1	-1	0	72	C5	5	121
+20	19	74	74	2.0	1/4	1/4	2/2	1	1	1/2		1/2	1		-2	58	Bb3	3	122
+20	19	74	74	2.0	1/4	1/4	2/2	1	1	1/2		1/2	1		-2	70	Bb4	4	122
+20	19	75	75	2.0	1/2	1/2	2/2	2	1	1/2		1/2	1		2	50	D3	3	125
+20	19	75	75	2.0	1/2	1/2	2/2	2	1	1/2		1/2	1		-1	53	F3	3	125
+20	19	76	76	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-4	56	Ab3	3	123
+20	19	76	76	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	123
+21	20	77	77	0.5	0	0	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	128
+21	20	77	77	1.0	0	0	2/2	1	1	1/4		1/4	1		1	55	G3	3	126
+21	20	77	77	1.0	0	0	2/2	1	1	1/4		1/4	1		1	67	G4	4	126
+21	20	155/2	155/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	129
+21	20	78	78	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	130
+21	20	157/2	157/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	131
+21	20	79	79	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	132
+21	20	159/2	159/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	133
+21	20	80	80	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	134
+21	20	80	80	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-8	76	Fb5	5	127
+21	20	161/2	161/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	135
+22	21	81	81	0.5	0	0	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	140
+22	21	81	81	1.0	0	0	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	136
+22	21	163/2	163/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	141
+22	21	82	82	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	142
+22	21	82	82	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-5	73	Db5	5	137
+22	21	165/2	165/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	143
+22	21	83	83	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	144
+22	21	83	83	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-2	70	Bb4	4	138
+22	21	167/2	167/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	145
+22	21	84	84	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	146
+22	21	84	84	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		1	67	G4	4	139
+22	21	169/2	169/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	147
+23	22	85	85	1.0	0	0	2/2	2	2	1/4		1/4	1		-3	51	Eb3	3	154
+23	22	85	85	2.0	0	0	2/2	2	1	1/2		1/2	1		-5	61	Db4	4	152
+23	22	85	85	1.5	0	0	2/2	1	1	3/8		1/4	3/2		-8	64	Fb4	4	148
+23	22	86	86	1.0	1/4	1/4	2/2	2	2	1/4		1/4	1		-3	51	Eb3	3	155
+23	22	173/2	173/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-3	63	Eb4	4	149
+23	22	87	87	0.5	1/2	1/2	2/2	2	2	1/8		1/8	1		-3	51	Eb3	3	156
+23	22	87	87	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		0	60	C4	4	153
+23	22	87	87	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	150
+23	22	175/2	175/2	0.5	5/8	5/8	2/2	2	2	1/8		1/8	1		-3	63	Eb4	4	157
+23	22	88	88	0.5	3/4	3/4	2/2	2	2	1/8		1/8	1		-3	51	Eb3	3	158
+23	22	88	88	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-8	76	Fb5	5	151
+23	22	177/2	177/2	0.5	7/8	7/8	2/2	2	2	1/8		1/8	1		-3	63	Eb4	4	159
+24	23	89	89	0.5	0	0	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	164
+24	23	89	89	1.0	0	0	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	160
+24	23	179/2	179/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	165
+24	23	90	90	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	166
+24	23	90	90	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-5	73	Db5	5	161
+24	23	181/2	181/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	167
+24	23	91	91	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	168
+24	23	91	91	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-2	70	Bb4	4	162
+24	23	183/2	183/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	169
+24	23	92	92	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	170
+24	23	92	92	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		1	67	G4	4	163
+24	23	185/2	185/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	171
+25	24	93	93	1.0	0	0	2/2	2	2	1/4		1/4	1		-3	51	Eb3	3	178
+25	24	93	93	2.0	0	0	2/2	2	1	1/2		1/2	1		-5	61	Db4	4	176
+25	24	93	93	1.5	0	0	2/2	1	1	3/8		1/4	3/2		-8	64	Fb4	4	172
+25	24	94	94	1.0	1/4	1/4	2/2	2	2	1/4		1/4	1		-3	51	Eb3	3	179
+25	24	189/2	189/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-3	63	Eb4	4	173
+25	24	95	95	0.5	1/2	1/2	2/2	2	2	1/8		1/8	1		-3	51	Eb3	3	180
+25	24	95	95	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		0	60	C4	4	177
+25	24	95	95	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	174
+25	24	191/2	191/2	0.5	5/8	5/8	2/2	2	2	1/8		1/8	1		-3	63	Eb4	4	181
+25	24	96	96	0.5	3/4	3/4	2/2	2	2	1/8		1/8	1		-3	51	Eb3	3	182
+25	24	96	96	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-8	76	Fb5	5	175
+25	24	193/2	193/2	0.5	7/8	7/8	2/2	2	2	1/8		1/8	1		-3	63	Eb4	4	183
+26	25	97	97	0.5	0	0	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	188
+26	25	97	97	1.0	0	0	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	184
+26	25	195/2	195/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	189
+26	25	98	98	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	190
+26	25	98	98	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-5	73	Db5	5	185
+26	25	197/2	197/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	191
+26	25	99	99	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-5	61	Db4	4	192
+26	25	99	99	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-2	70	Bb4	4	186
+26	25	199/2	199/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	193
+26	25	100	100	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-5	61	Db4	4	194
+26	25	100	100	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		1	67	G4	4	187
+26	25	201/2	201/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	195
+27	26	101	101	0.5	0	0	2/2	2	1	1/8		1/8	1		0	60	C4	4	202
+27	26	203/2	203/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	203
+27	26	203/2	203/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		1	67	G4	4	196
+27	26	102	102	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		0	60	C4	4	204
+27	26	102	102	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-2	70	Bb4	4	197
+27	26	205/2	205/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	205
+27	26	205/2	205/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-4	68	Ab4	4	198
+27	26	103	103	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		1	55	G3	3	206
+27	26	207/2	207/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	207
+27	26	207/2	207/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		3	69	A4	4	199
+27	26	104	104	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		1	55	G3	3	208
+27	26	104	104	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		0	72	C5	5	200
+27	26	209/2	209/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	209
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+28	27	105	105	0.5	0	0	2/2	2	1	1/8		1/8	1		-4	56	Ab3	3	216
+28	27	211/2	211/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	217
+28	27	211/2	211/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		5	71	B4	4	210
+28	27	106	106	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-4	56	Ab3	3	218
+28	27	106	106	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-5	73	Db5	5	211
+28	27	213/2	213/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	219
+28	27	213/2	213/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		0	72	C5	5	212
+28	27	107	107	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-1	53	F3	3	220
+28	27	215/2	215/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		2	62	D4	4	221
+28	27	215/2	215/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		2	74	D5	5	213
+28	27	108	108	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-4	56	Ab3	3	222
+28	27	108	108	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-4	80	Ab5	5	214
+28	27	217/2	217/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		2	62	D4	4	223
+28	27	217/2	217/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		2	74	D5	5	215
+29	28	109	109	0.5	0	0	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	228
+29	28	109	109	1.0	0	0	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	224
+29	28	219/2	219/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	229
+29	28	110	110	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		1	55	G3	3	230
+29	28	221/2	221/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	231
+29	28	111	111	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-1	53	F3	3	232
+29	28	223/2	223/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		2	62	D4	4	233
+29	28	223/2	223/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		2	74	D5	5	225
+29	28	112	112	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-4	56	Ab3	3	234
+29	28	112	112	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-4	80	Ab5	5	226
+29	28	225/2	225/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		2	62	D4	4	235
+29	28	225/2	225/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		2	74	D5	5	227
+30	29	113	113	0.5	0	0	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	240
+30	29	113	113	1.0	0	0	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	236
+30	29	227/2	227/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	241
+30	29	114	114	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		1	55	G3	3	242
+30	29	229/2	229/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-3	63	Eb4	4	243
+30	29	115	115	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-5	49	Db3	3	244
+30	29	231/2	231/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		-2	58	Bb3	3	245
+30	29	231/2	231/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		1	79	G5	5	237
+30	29	116	116	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-8	52	Fb3	3	246
+30	29	116	116	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-8	88	Fb6	6	238
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+31	30	117	117	1.0	0	0	2/2	1	1	1/4		1/4	1		-4	80	Ab5	5	248
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+31	30	118	118	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	254
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+31	30	119	119	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-5	49	Db3	3	256
+31	30	239/2	239/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		-2	58	Bb3	3	257
+31	30	239/2	239/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		1	79	G5	5	249
+31	30	120	120	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-8	52	Fb3	3	258
+31	30	120	120	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-8	88	Fb6	6	250
+31	30	241/2	241/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		-2	58	Bb3	3	259
+31	30	241/2	241/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		1	79	G5	5	251
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+32	31	243/2	243/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-4	56	Ab3	3	267
+32	31	243/2	243/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		1	79	G5	5	260
+32	31	122	122	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	268
+32	31	122	122	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-2	82	Bb5	5	261
+32	31	245/2	245/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-4	56	Ab3	3	269
+32	31	245/2	245/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-4	80	Ab5	5	262
+32	31	123	123	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		1	43	G2	2	270
+32	31	247/2	247/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		1	55	G3	3	271
+32	31	247/2	247/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		3	81	A5	5	263
+32	31	124	124	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	272
+32	31	124	124	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		0	84	C6	6	264
+32	31	249/2	249/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		1	55	G3	3	273
+32	31	249/2	249/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-2	82	Bb5	5	265
+33	32	125	125	0.5	0	0	2/2	2	1	1/8		1/8	1		-4	44	Ab2	2	280
+33	32	251/2	251/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-4	56	Ab3	3	281
+33	32	251/2	251/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		5	83	B5	5	274
+33	32	126	126	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	282
+33	32	126	126	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-5	85	Db6	6	275
+33	32	253/2	253/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-4	56	Ab3	3	283
+33	32	253/2	253/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		0	84	C6	6	276
+33	32	127	127	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-2	46	Bb2	2	284
+33	32	255/2	255/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		-2	58	Bb3	3	285
+33	32	255/2	255/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		0	84	C6	6	277
+33	32	128	128	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-3	51	Eb3	3	286
+33	32	128	128	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-3	87	Eb6	6	278
+33	32	257/2	257/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		-2	58	Bb3	3	287
+33	32	257/2	257/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-5	85	Db6	6	279
+34	33	129	129	1.0	0	0	2/2	2	1	1/4		1/4	1		0	48	C3	3	295
+34	33	259/2	259/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		2	86	D6	6	288
+34	33	130	130	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-3	51	Eb3	3	296
+34	33	130	130	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-1	89	F6	6	289
+34	33	261/2	261/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-3	87	Eb6	6	290
+34	33	131	131	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		-5	85	Db6	6	291
+34	33	263/2	263/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		0	84	C6	6	292
+34	33	132	132	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	297
+34	33	132	132	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-2	82	Bb5	5	293
+34	33	265/2	265/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-4	80	Ab5	5	294
+35	34	133	133	1.0	0	0	2/2	2	1	1/4		1/4	1		-5	49	Db3	3	306
+35	34	133	133	0.5	0	0	2/2	1	1	1/8		1/8	1		1	79	G5	5	298
+35	34	267/2	267/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		-1	77	F5	5	299
+35	34	134	134	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-1	53	F3	3	307
+35	34	134	134	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-3	75	Eb5	5	300
+35	34	269/2	269/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-5	73	Db5	5	301
+35	34	135	135	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		0	72	C5	5	302
+35	34	271/2	271/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		-2	70	Bb4	4	303
+35	34	136	136	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	308
+35	34	136	136	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-4	68	Ab4	4	304
+35	34	273/2	273/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		1	67	G4	4	305
+36	35	137	137	1.0	0	0	2/2	2	1	1/4		1/4	1		-3	51	Eb3	3	317
+36	35	137	137	0.5	0	0	2/2	1	1	1/8		1/8	1		-1	65	F4	4	309
+36	35	275/2	275/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		-3	63	Eb4	4	310
+36	35	138	138	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-4	56	Ab3	3	318
+36	35	138	138	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		2	62	D4	4	311
+36	35	277/2	277/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-3	63	Eb4	4	312
+36	35	139	139	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		2	62	D4	4	313
+36	35	279/2	279/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		-3	63	Eb4	4	314
+36	35	140	140	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	319
+36	35	140	140	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		2	62	D4	4	315
+36	35	281/2	281/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-3	63	Eb4	4	316
+37	36	141	141	1.0	0	0	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	328
+37	36	141	141	0.5	0	0	2/2	1	1	1/8		1/8	1		2	62	D4	4	320
+37	36	283/2	283/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		-3	63	Eb4	4	321
+37	36	142	142	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	329
+37	36	142	142	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-1	65	F4	4	322
+37	36	285/2	285/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-3	63	Eb4	4	323
+37	36	143	143	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		1	55	G3	3	330
+37	36	143	143	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		2	62	D4	4	324
+37	36	287/2	287/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		-3	63	Eb4	4	325
+37	36	144	144	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	49	Db3	3	331
+37	36	144	144	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-1	65	F4	4	326
+37	36	289/2	289/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-3	63	Eb4	4	327
+38	37	145	145	1.0	0	0	2/2	2	1	1/4		1/4	1		0	36	C2	2	339
+38	37	291/2	291/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		-4	80	Ab5	5	332
+38	37	146	146	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-3	39	Eb2	2	340
+38	37	146	146	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-1	89	F6	6	333
+38	37	293/2	293/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-3	87	Eb6	6	334
+38	37	147	147	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		-5	85	Db6	6	335
+38	37	295/2	295/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		0	84	C6	6	336
+38	37	148	148	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	44	Ab2	2	341
+38	37	148	148	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-2	82	Bb5	5	337
+38	37	297/2	297/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-4	80	Ab5	5	338
+39	38	149	149	1.0	0	0	2/2	2	1	1/4		1/4	1		-5	37	Db2	2	350
+39	38	149	149	0.5	0	0	2/2	1	1	1/8		1/8	1		1	79	G5	5	342
+39	38	299/2	299/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		-1	77	F5	5	343
+39	38	150	150	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-1	41	F2	2	351
+39	38	150	150	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-3	75	Eb5	5	344
+39	38	301/2	301/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-5	73	Db5	5	345
+39	38	151	151	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		0	72	C5	5	346
+39	38	303/2	303/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		-2	70	Bb4	4	347
+39	38	152	152	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	46	Bb2	2	352
+39	38	152	152	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-4	68	Ab4	4	348
+39	38	305/2	305/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		1	67	G4	4	349
+40	39	153	153	1.0	0	0	2/2	2	1	1/4		1/4	1		-3	39	Eb2	2	361
+40	39	153	153	0.5	0	0	2/2	1	1	1/8		1/8	1		-1	65	F4	4	353
+40	39	307/2	307/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		-3	63	Eb4	4	354
+40	39	154	154	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-4	44	Ab2	2	362
+40	39	154	154	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-5	61	Db4	4	355
+40	39	309/2	309/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	356
+40	39	155	155	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		-2	58	Bb3	3	357
+40	39	311/2	311/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		-4	56	Ab3	3	358
+40	39	156	156	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	48	C3	3	363
+40	39	156	156	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		1	55	G3	3	359
+40	39	313/2	313/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-1	53	F3	3	360
+41	40	157	157	1.0	0	0	2/2	2	1	1/4		1/4	1		-5	49	Db3	3	372
+41	40	157	157	0.5	0	0	2/2	1	1	1/8		1/8	1		-3	51	Eb3	3	364
+41	40	315/2	315/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		-1	53	F3	3	365
+41	40	158	158	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-2	46	Bb2	2	373
+41	40	158	158	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		1	55	G3	3	366
+41	40	317/2	317/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-4	56	Ab3	3	367
+41	40	159	159	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		1	43	G2	2	374
+41	40	159	159	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		-2	58	Bb3	3	368
+41	40	319/2	319/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	369
+41	40	160	160	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-3	39	Eb2	2	375
+41	40	160	160	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-5	61	Db4	4	370
+41	40	321/2	321/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-2	58	Bb3	3	371
+42	41	161	161	1.0	0	0	2/2	2	1	1/4		1/4	1		-4	44	Ab2	2	380
+42	41	161	161	1.0	0	0	2/2	1	1	1/4		1/4	1		-4	56	Ab3	3	376
+42	41	162	162	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	381
+42	41	163	163	0.0	1/2	1/2	2/2	1	1	0	acciaccatura	1/8	1		2	62	D4	4	377
+42	41	163	163	1.5	1/2	1/2	2/2	1	1	3/8		1/4	3/2		-7	71	Cb5	5	378
+42	41	164	164	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		2	50	D3	3	382
+42	41	164	164	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	382
+42	41	164	164	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	382
+42	41	164	164	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-7	59	Cb4	4	382
+42	41	329/2	329/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-2	70	Bb4	4	379
+43	42	165	165	2.0	0	0	2/2	1	1	1/2		1/2	1		-4	68	Ab4	4	383
+43	42	166	166	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-3	51	Eb3	3	386
+43	42	166	166	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	386
+43	42	166	166	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	386
+43	42	167	167	1.5	1/2	1/2	2/2	1	1	3/8		1/4	3/2		1	67	G4	4	384
+43	42	168	168	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-3	51	Eb3	3	387
+43	42	168	168	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	387
+43	42	168	168	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	387
+43	42	337/2	337/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-3	75	Eb5	5	385
+44	43	169	169	1.0	0	0	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	388
+44	43	170	170	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	393
+44	43	170	170	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	393
+44	43	170	170	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	389
+44	43	171	171	0.0	1/2	1/2	2/2	1	1	0	acciaccatura	1/8	1		2	62	D4	4	390
+44	43	171	171	1.5	1/2	1/2	2/2	1	1	3/8		1/4	3/2		-7	71	Cb5	5	391
+44	43	172	172	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		2	50	D3	3	394
+44	43	172	172	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	394
+44	43	172	172	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	394
+44	43	172	172	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-7	59	Cb4	4	394
+44	43	345/2	345/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-2	70	Bb4	4	392
+45	44	173	173	2.0	0	0	2/2	1	1	1/2		1/2	1		-4	68	Ab4	4	395
+45	44	174	174	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-3	51	Eb3	3	398
+45	44	174	174	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	398
+45	44	174	174	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	398
+45	44	175	175	1.5	1/2	1/2	2/2	1	1	3/8		1/4	3/2		1	67	G4	4	396
+45	44	176	176	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-3	51	Eb3	3	399
+45	44	176	176	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	399
+45	44	176	176	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	399
+45	44	353/2	353/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-3	75	Eb5	5	397
+46	45	177	177	1.0	0	0	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	400
+46	45	178	178	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	405
+46	45	178	178	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	405
+46	45	178	178	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	401
+46	45	179	179	0.0	1/2	1/2	2/2	1	1	0	acciaccatura	1/8	1		2	74	D5	5	402
+46	45	179	179	1.5	1/2	1/2	2/2	1	1	3/8		1/4	3/2		-7	83	Cb6	6	403
+46	45	180	180	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		2	62	D4	4	406
+46	45	180	180	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-1	65	F4	4	406
+46	45	180	180	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	68	Ab4	4	406
+46	45	180	180	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-7	71	Cb5	5	406
+46	45	361/2	361/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-2	82	Bb5	5	404
+47	46	181	181	2.0	0	0	2/2	1	1	1/2		1/2	1		-4	80	Ab5	5	407
+47	46	182	182	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-3	63	Eb4	4	410
+47	46	182	182	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	68	Ab4	4	410
+47	46	182	182	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	72	C5	5	410
+47	46	183	183	1.5	1/2	1/2	2/2	1	1	3/8		1/4	3/2		1	79	G5	5	408
+47	46	184	184	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-3	63	Eb4	4	411
+47	46	184	184	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	70	Bb4	4	411
+47	46	184	184	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	73	Db5	5	411
+47	46	369/2	369/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-3	87	Eb6	6	409
+48	47	185	185	4.0	0	0	2/2	2	1	1		1	1		-4	56	Ab3	3	413
+48	47	185	185	4.0	0	0	2/2	2	1	1		1	1		-2	58	Bb3	3	413
+48	47	185	185	4.0	0	0	2/2	2	1	1		1	1		-5	61	Db4	4	413
+48	47	185	185	4.0	0	0	2/2	2	1	1		1	1		-3	63	Eb4	4	413
+48	47	185	185	4.0	0	0	2/2	2	1	1		1	1		1	67	G4	4	413
+48	47	185	185	4.0	0	0	2/2	1	1	1		1	1		-5	73	Db5	5	412
+48	47	185	185	4.0	0	0	2/2	1	1	1		1	1		1	79	G5	5	412
+48	47	185	185	4.0	0	0	2/2	1	1	1		1	1		-3	87	Eb6	6	412
+49	48	189	189	1.0	0	0	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	415
+49	48	189	189	1.0	0	0	2/2	2	1	1/4		1/4	1		0	60	C4	4	415
+49	48	189	189	1.0	0	0	2/2	2	1	1/4		1/4	1		-3	63	Eb4	4	415
+49	48	189	189	1.0	0	0	2/2	2	1	1/4		1/4	1		-4	68	Ab4	4	415
+49	48	189	189	1.0	0	0	2/2	1	1	1/4		1/4	1		0	72	C5	5	414
+49	48	189	189	1.0	0	0	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	414
+49	48	189	189	1.0	0	0	2/2	1	1	1/4		1/4	1		-4	80	Ab5	5	414
+50	48	192	192	1.0	0	3/4	2/2	1	1	1/4		1/4	1		-3	63	Eb4	4	416
+51	49	193	193	1.0	0	0	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	417
+51	49	194	194	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		0	72	C5	5	418
+51	49	195	195	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	419
+51	49	196	196	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-4	80	Ab5	5	420
+52	50	197	197	1.5	0	0	2/2	1	1	3/8		1/4	3/2		0	84	C6	6	421
+52	50	198	198	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	426
+52	50	198	198	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	426
+52	50	198	198	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-3	63	Eb4	4	426
+52	50	397/2	397/2	0.16666666666666666	3/8	3/8	2/2	1	1	1/24		1/16	2/3		-2	82	Bb5	5	422
+52	50	596/3	596/3	0.16666666666666666	5/12	5/12	2/2	1	1	1/24		1/16	2/3		-4	80	Ab5	5	423
+52	50	1193/6	1193/6	0.16666666666666666	11/24	11/24	2/2	1	1	1/24		1/16	2/3		1	79	G5	5	424
+52	50	199	199	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	427
+52	50	199	199	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		0	60	C4	4	427
+52	50	199	199	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-3	63	Eb4	4	427
+52	50	199	199	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-4	80	Ab5	5	425
+52	50	200	200	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	428
+52	50	200	200	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	428
+52	50	200	200	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-3	63	Eb4	4	428
+53	51	201	201	0.0	0	0	2/2	1	1	0	acciaccatura	1/8	1		-5	73	Db5	5	429
+53	51	201	201	1.5	0	0	2/2	1	1	3/8		1/4	3/2		-5	85	Db6	6	430
+53	51	202	202	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		1	55	G3	3	435
+53	51	202	202	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	435
+53	51	202	202	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	435
+53	51	202	202	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-3	63	Eb4	4	435
+53	51	405/2	405/2	0.16666666666666666	3/8	3/8	2/2	1	1	1/24		1/16	2/3		0	84	C6	6	431
+53	51	608/3	608/3	0.16666666666666666	5/12	5/12	2/2	1	1	1/24		1/16	2/3		-2	82	Bb5	5	432
+53	51	1217/6	1217/6	0.16666666666666666	11/24	11/24	2/2	1	1	1/24		1/16	2/3		3	81	A5	5	433
+53	51	203	203	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		1	55	G3	3	436
+53	51	203	203	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	436
+53	51	203	203	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	436
+53	51	203	203	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-3	63	Eb4	4	436
+53	51	203	203	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-2	82	Bb5	5	434
+53	51	204	204	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		1	55	G3	3	437
+53	51	204	204	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	437
+53	51	204	204	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	437
+53	51	204	204	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-3	63	Eb4	4	437
+54	52	205	205	1.0	0	0	2/2	1	1	1/4		1/4	1		-2	70	Bb4	4	438
+54	52	206	206	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-5	73	Db5	5	439
+54	52	207	207	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		1	79	G5	5	440
+54	52	208	208	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-2	82	Bb5	5	441
+55	53	209	209	0.0	0	0	2/2	1	1	0	acciaccatura	1/8	1		-5	73	Db5	5	442
+55	53	209	209	1.5	0	0	2/2	1	1	3/8		1/4	3/2		-5	85	Db6	6	443
+55	53	210	210	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		1	55	G3	3	448
+55	53	210	210	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	448
+55	53	210	210	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	448
+55	53	210	210	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		4	64	E4	4	448
+55	53	421/2	421/2	0.16666666666666666	3/8	3/8	2/2	1	1	1/24		1/16	2/3		0	84	C6	6	444
+55	53	632/3	632/3	0.16666666666666666	5/12	5/12	2/2	1	1	1/24		1/16	2/3		-2	82	Bb5	5	445
+55	53	1265/6	1265/6	0.16666666666666666	11/24	11/24	2/2	1	1	1/24		1/16	2/3		3	81	A5	5	446
+55	53	211	211	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		1	55	G3	3	449
+55	53	211	211	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	449
+55	53	211	211	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	449
+55	53	211	211	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		4	64	E4	4	449
+55	53	211	211	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-2	82	Bb5	5	447
+55	53	212	212	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		1	55	G3	3	450
+55	53	212	212	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	450
+55	53	212	212	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	450
+55	53	212	212	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		4	64	E4	4	450
+56	54	213	213	0.0	0	0	2/2	1	1	0	acciaccatura	1/8	1		-5	73	Db5	5	451
+56	54	213	213	1.5	0	0	2/2	1	1	3/8		1/4	3/2		-5	85	Db6	6	452
+56	54	214	214	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-6	54	Gb3	3	457
+56	54	214	214	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	457
+56	54	214	214	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	457
+56	54	214	214	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		4	64	E4	4	457
+56	54	429/2	429/2	0.16666666666666666	3/8	3/8	2/2	1	1	1/24		1/16	2/3		0	84	C6	6	453
+56	54	644/3	644/3	0.16666666666666666	5/12	5/12	2/2	1	1	1/24		1/16	2/3		-2	82	Bb5	5	454
+56	54	1289/6	1289/6	0.16666666666666666	11/24	11/24	2/2	1	1	1/24		1/16	2/3		3	81	A5	5	455
+56	54	215	215	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-6	54	Gb3	3	458
+56	54	215	215	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	458
+56	54	215	215	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	458
+56	54	215	215	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		4	64	E4	4	458
+56	54	215	215	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-2	82	Bb5	5	456
+56	54	216	216	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-6	54	Gb3	3	459
+56	54	216	216	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	459
+56	54	216	216	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	459
+56	54	216	216	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		4	64	E4	4	459
+57	55	217	217	0.5	0	0	2/2	2	1	1/8		1/8	1		-1	53	F3	3	462
+57	55	217	217	1.0	0	0	2/2	1	1	1/4		1/4	1		3	81	A5	5	460
+57	55	435/2	435/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	463
+57	55	218	218	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-1	53	F3	3	464
+57	55	437/2	437/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	465
+57	55	219	219	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-1	53	F3	3	466
+57	55	439/2	439/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	467
+57	55	220	220	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-1	53	F3	3	468
+57	55	220	220	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-6	78	Gb5	5	461
+57	55	441/2	441/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	469
+58	56	221	221	0.5	0	0	2/2	2	1	1/8		1/8	1		-1	53	F3	3	474
+58	56	221	221	1.0	0	0	2/2	1	1	1/4		1/4	1		-1	77	F5	5	470
+58	56	443/2	443/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	475
+58	56	222	222	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-1	53	F3	3	476
+58	56	222	222	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	471
+58	56	445/2	445/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	477
+58	56	223	223	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-1	53	F3	3	478
+58	56	223	223	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		0	72	C5	5	472
+58	56	447/2	447/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	479
+58	56	224	224	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-1	53	F3	3	480
+58	56	224	224	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		3	69	A4	4	473
+58	56	449/2	449/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	481
+59	57	225	225	1.0	0	0	2/2	2	2	1/4		1/4	1		-1	53	F3	3	488
+59	57	225	225	2.0	0	0	2/2	2	1	1/2		1/2	1		-3	63	Eb4	4	486
+59	57	225	225	1.5	0	0	2/2	1	1	3/8		1/4	3/2		-6	66	Gb4	4	482
+59	57	226	226	1.0	1/4	1/4	2/2	2	2	1/4		1/4	1		-1	53	F3	3	489
+59	57	453/2	453/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-1	65	F4	4	483
+59	57	227	227	0.5	1/2	1/2	2/2	2	2	1/8		1/8	1		-1	53	F3	3	490
+59	57	227	227	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	487
+59	57	227	227	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-2	70	Bb4	4	484
+59	57	455/2	455/2	0.5	5/8	5/8	2/2	2	2	1/8		1/8	1		-1	65	F4	4	491
+59	57	228	228	0.5	3/4	3/4	2/2	2	2	1/8		1/8	1		-1	53	F3	3	492
+59	57	228	228	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-6	78	Gb5	5	485
+59	57	457/2	457/2	0.5	7/8	7/8	2/2	2	2	1/8		1/8	1		-1	65	F4	4	493
+60	58	229	229	0.5	0	0	2/2	2	1	1/8		1/8	1		-1	53	F3	3	498
+60	58	229	229	1.0	0	0	2/2	1	1	1/4		1/4	1		-1	77	F5	5	494
+60	58	459/2	459/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	499
+60	58	230	230	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-1	53	F3	3	500
+60	58	230	230	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	495
+60	58	461/2	461/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	501
+60	58	231	231	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-1	53	F3	3	502
+60	58	231	231	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		0	72	C5	5	496
+60	58	463/2	463/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	503
+60	58	232	232	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-1	53	F3	3	504
+60	58	232	232	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		3	69	A4	4	497
+60	58	465/2	465/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	505
+61	59	233	233	1.0	0	0	2/2	2	2	1/4		1/4	1		-1	53	F3	3	512
+61	59	233	233	2.0	0	0	2/2	2	1	1/2		1/2	1		-3	63	Eb4	4	510
+61	59	233	233	1.5	0	0	2/2	1	1	3/8		1/4	3/2		-6	66	Gb4	4	506
+61	59	234	234	1.0	1/4	1/4	2/2	2	2	1/4		1/4	1		-1	53	F3	3	513
+61	59	469/2	469/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-1	65	F4	4	507
+61	59	235	235	0.5	1/2	1/2	2/2	2	2	1/8		1/8	1		-1	53	F3	3	514
+61	59	235	235	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	511
+61	59	235	235	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-2	70	Bb4	4	508
+61	59	471/2	471/2	0.5	5/8	5/8	2/2	2	2	1/8		1/8	1		-1	65	F4	4	515
+61	59	236	236	0.5	3/4	3/4	2/2	2	2	1/8		1/8	1		-1	53	F3	3	516
+61	59	236	236	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-6	78	Gb5	5	509
+61	59	473/2	473/2	0.5	7/8	7/8	2/2	2	2	1/8		1/8	1		-1	65	F4	4	517
+62	60	237	237	0.5	0	0	2/2	2	1	1/8		1/8	1		-1	53	F3	3	522
+62	60	237	237	1.0	0	0	2/2	1	1	1/4		1/4	1		-1	77	F5	5	518
+62	60	475/2	475/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	523
+62	60	238	238	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-1	53	F3	3	524
+62	60	238	238	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	519
+62	60	477/2	477/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	525
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+63	61	242	242	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-5	61	Db4	4	538
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+63	61	485/2	485/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	539
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+64	62	495/2	495/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		6	66	F#4	4	555
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+67	65	517/2	517/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		1	67	G4	4	581
+67	65	259	259	0.5	1/2	1/2	2/2	2	2	1/8		1/8	1		1	55	G3	3	588
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+67	65	519/2	519/2	0.5	5/8	5/8	2/2	2	2	1/8		1/8	1		1	67	G4	4	589
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+67	65	260	260	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-4	80	Ab5	5	583
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+69	67	268	268	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	611
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+72	70	279	279	0.5	1/2	1/2	2/2	1	2	1/8		1/8	1		-3	63	Eb4	4	645
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+73	71	565/2	565/2	0.5	3/8	3/8	2/2	1	2	1/8		1/8	1		0	60	C4	4	658
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+73	71	283	283	0.5	1/2	1/2	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	659
+73	71	283	283	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-2	70	Bb4	4	654
+73	71	567/2	567/2	0.5	5/8	5/8	2/2	1	2	1/8		1/8	1		-2	58	Bb3	3	660
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+73	71	284	284	0.5	3/4	3/4	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	661
+73	71	569/2	569/2	0.5	7/8	7/8	2/2	1	2	1/8		1/8	1		-2	58	Bb3	3	662
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+74	72	285	285	0.5	0	0	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	667
+74	72	571/2	571/2	0.5	1/8	1/8	2/2	1	2	1/8		1/8	1		-2	58	Bb3	3	668
+74	72	286	286	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-5	49	Db3	3	676
+74	72	286	286	0.5	1/4	1/4	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	669
+74	72	573/2	573/2	0.5	3/8	3/8	2/2	1	2	1/8		1/8	1		-2	58	Bb3	3	670
+74	72	287	287	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-2	46	Bb2	2	677
+74	72	287	287	0.5	1/2	1/2	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	671
+74	72	575/2	575/2	0.5	5/8	5/8	2/2	1	2	1/8		1/8	1		-2	58	Bb3	3	672
+74	72	288	288	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		1	43	G2	2	678
+74	72	288	288	0.5	3/4	3/4	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	673
+74	72	577/2	577/2	0.5	7/8	7/8	2/2	1	2	1/8		1/8	1		-2	58	Bb3	3	674
+75	73	289	289	1.0	0	0	2/2	2	1	1/4		1/4	1		-8	40	Fb2	2	688
+75	73	289	289	0.5	0	0	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	680
+75	73	579/2	579/2	0.5	1/8	1/8	2/2	1	2	1/8		1/8	1		-2	58	Bb3	3	681
+75	73	290	290	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-3	39	Eb2	2	689
+75	73	290	290	0.5	1/4	1/4	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	682
+75	73	581/2	581/2	0.5	3/8	3/8	2/2	1	2	1/8		1/8	1		-2	58	Bb3	3	683
+75	73	291	291	0.5	1/2	1/2	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	684
+75	73	291	291	2.0	1/2	1/2	2/2	1	1	1/2		1/2	1		1	67	G4	4	679
+75	73	583/2	583/2	0.5	5/8	5/8	2/2	1	2	1/8		1/8	1		-2	58	Bb3	3	685
+75	73	292	292	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1	1	-3	51	Eb3	3	690
+75	73	292	292	0.5	3/4	3/4	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	686
+75	73	585/2	585/2	0.5	7/8	7/8	2/2	1	2	1/8		1/8	1		-2	58	Bb3	3	687
+76	74	293	293	1.0	0	0	2/2	2	1	1/4		1/4	1	-1	-3	51	Eb3	3	700
+76	74	293	293	0.5	0	0	2/2	1	2	1/8		1/8	1		0	60	C4	4	692
+76	74	587/2	587/2	0.5	1/8	1/8	2/2	1	2	1/8		1/8	1		-4	56	Ab3	3	693
+76	74	294	294	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		0	48	C3	3	701
+76	74	294	294	0.5	1/4	1/4	2/2	1	2	1/8		1/8	1		-3	63	Eb4	4	694
+76	74	589/2	589/2	0.5	3/8	3/8	2/2	1	2	1/8		1/8	1		-4	56	Ab3	3	695
+76	74	295	295	0.5	1/2	1/2	2/2	1	2	1/8		1/8	1		-3	63	Eb4	4	696
+76	74	295	295	2.0	1/2	1/2	2/2	1	1	1/2		1/2	1		-4	68	Ab4	4	691
+76	74	591/2	591/2	0.5	5/8	5/8	2/2	1	2	1/8		1/8	1		-4	56	Ab3	3	697
+76	74	296	296	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1	1	0	36	C2	2	702
+76	74	296	296	0.5	3/4	3/4	2/2	1	2	1/8		1/8	1		-3	63	Eb4	4	698
+76	74	593/2	593/2	0.5	7/8	7/8	2/2	1	2	1/8		1/8	1		-4	56	Ab3	3	699
+77	75	297	297	1.0	0	0	2/2	2	1	1/4		1/4	1	-1	0	36	C2	2	712
+77	75	297	297	0.5	0	0	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	704
+77	75	595/2	595/2	0.5	1/8	1/8	2/2	1	2	1/8		1/8	1		-4	56	Ab3	3	705
+77	75	298	298	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-5	37	Db2	2	713
+77	75	298	298	0.5	1/4	1/4	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	706
+77	75	597/2	597/2	0.5	3/8	3/8	2/2	1	2	1/8		1/8	1		-4	56	Ab3	3	707
+77	75	299	299	0.5	1/2	1/2	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	708
+77	75	299	299	2.0	1/2	1/2	2/2	1	1	1/2		1/2	1		-1	65	F4	4	703
+77	75	599/2	599/2	0.5	5/8	5/8	2/2	1	2	1/8		1/8	1		-4	56	Ab3	3	709
+77	75	300	300	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1	1	-5	49	Db3	3	714
+77	75	300	300	0.5	3/4	3/4	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	710
+77	75	601/2	601/2	0.5	7/8	7/8	2/2	1	2	1/8		1/8	1		-4	56	Ab3	3	711
+78	76	301	301	1.0	0	0	2/2	2	1	1/4		1/4	1	-1	-5	49	Db3	3	724
+78	76	301	301	0.5	0	0	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	716
+78	76	603/2	603/2	0.5	1/8	1/8	2/2	1	2	1/8		1/8	1		1	55	G3	3	717
+78	76	302	302	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-2	46	Bb2	2	725
+78	76	302	302	0.5	1/4	1/4	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	718
+78	76	605/2	605/2	0.5	3/8	3/8	2/2	1	2	1/8		1/8	1		1	55	G3	3	719
+78	76	303	303	0.5	1/2	1/2	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	720
+78	76	303	303	2.0	1/2	1/2	2/2	1	1	1/2		1/2	1		1	67	G4	4	715
+78	76	607/2	607/2	0.5	5/8	5/8	2/2	1	2	1/8		1/8	1		1	55	G3	3	721
+78	76	304	304	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1	1	-2	34	Bb1	1	726
+78	76	304	304	0.5	3/4	3/4	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	722
+78	76	609/2	609/2	0.5	7/8	7/8	2/2	1	2	1/8		1/8	1		1	55	G3	3	723
+79	77	305	305	1.0	0	0	2/2	2	1	1/4		1/4	1	-1	-2	34	Bb1	1	736
+79	77	305	305	0.5	0	0	2/2	1	2	1/8		1/8	1		0	60	C4	4	728
+79	77	611/2	611/2	0.5	1/8	1/8	2/2	1	2	1/8		1/8	1		1	55	G3	3	729
+79	77	306	306	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		0	36	C2	2	737
+79	77	306	306	0.5	1/4	1/4	2/2	1	2	1/8		1/8	1		0	60	C4	4	730
+79	77	613/2	613/2	0.5	3/8	3/8	2/2	1	2	1/8		1/8	1		1	55	G3	3	731
+79	77	307	307	0.5	1/2	1/2	2/2	1	2	1/8		1/8	1		0	60	C4	4	732
+79	77	307	307	2.0	1/2	1/2	2/2	1	1	1/2		1/2	1		4	64	E4	4	727
+79	77	615/2	615/2	0.5	5/8	5/8	2/2	1	2	1/8		1/8	1		1	55	G3	3	733
+79	77	308	308	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1	1	0	48	C3	3	738
+79	77	308	308	0.5	3/4	3/4	2/2	1	2	1/8		1/8	1		0	60	C4	4	734
+79	77	617/2	617/2	0.5	7/8	7/8	2/2	1	2	1/8		1/8	1		1	55	G3	3	735
+80	78	309	309	1.0	0	0	2/2	2	1	1/4		1/4	1	-1	0	48	C3	3	748
+80	78	309	309	0.5	0	0	2/2	1	2	1/8		1/8	1		-4	56	Ab3	3	740
+80	78	619/2	619/2	0.5	1/8	1/8	2/2	1	2	1/8		1/8	1		-1	53	F3	3	741
+80	78	310	310	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-4	44	Ab2	2	749
+80	78	310	310	0.5	1/4	1/4	2/2	1	2	1/8		1/8	1		0	60	C4	4	742
+80	78	621/2	621/2	0.5	3/8	3/8	2/2	1	2	1/8		1/8	1		-1	53	F3	3	743
+80	78	311	311	0.5	1/2	1/2	2/2	1	2	1/8		1/8	1		0	60	C4	4	744
+80	78	311	311	2.0	1/2	1/2	2/2	1	1	1/2		1/2	1		-1	65	F4	4	739
+80	78	623/2	623/2	0.5	5/8	5/8	2/2	1	2	1/8		1/8	1		-1	53	F3	3	745
+80	78	312	312	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	32	Ab1	1	750
+80	78	312	312	0.5	3/4	3/4	2/2	1	2	1/8		1/8	1		0	60	C4	4	746
+80	78	625/2	625/2	0.5	7/8	7/8	2/2	1	2	1/8		1/8	1		-1	53	F3	3	747
+81	79	313	313	0.5	0	0	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	752
+81	79	627/2	627/2	0.5	1/8	1/8	2/2	1	2	1/8		1/8	1		-1	53	F3	3	753
+81	79	314	314	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-2	34	Bb1	1	760
+81	79	314	314	0.5	1/4	1/4	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	754
+81	79	629/2	629/2	0.5	3/8	3/8	2/2	1	2	1/8		1/8	1		-1	53	F3	3	755
+81	79	315	315	0.5	1/2	1/2	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	756
+81	79	315	315	2.0	1/2	1/2	2/2	1	1	1/2		1/2	1		-1	65	F4	4	751
+81	79	631/2	631/2	0.5	5/8	5/8	2/2	1	2	1/8		1/8	1		-1	53	F3	3	757
+81	79	316	316	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	46	Bb2	2	761
+81	79	316	316	0.5	3/4	3/4	2/2	1	2	1/8		1/8	1		-5	61	Db4	4	758
+81	79	633/2	633/2	0.5	7/8	7/8	2/2	1	2	1/8		1/8	1		-1	53	F3	3	759
+82	80	317	317	0.5	0	0	2/2	1	1	1/8		1/8	1		2	62	D4	4	762
+82	80	635/2	635/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		-1	53	F3	3	763
+82	80	318	318	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		5	35	B1	1	770
+82	80	318	318	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-4	56	Ab3	3	764
+82	80	318	318	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		2	62	D4	4	764
+82	80	318	318	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-1	65	F4	4	764
+82	80	637/2	637/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-1	53	F3	3	765
+82	80	319	319	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		-4	56	Ab3	3	766
+82	80	319	319	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		2	62	D4	4	766
+82	80	319	319	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		-1	65	F4	4	766
+82	80	639/2	639/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		-1	53	F3	3	767
+82	80	320	320	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		5	47	B2	2	771
+82	80	320	320	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-4	56	Ab3	3	768
+82	80	320	320	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		2	62	D4	4	768
+82	80	320	320	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-1	65	F4	4	768
+82	80	641/2	641/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-1	53	F3	3	769
+83	81	321	321	0.5	0	0	2/2	2	1	1/8		1/8	1		0	36	C2	2	774
+83	81	321	321	1.0	0	0	2/2	1	1	1/4		1/4	1		1	55	G3	3	772
+83	81	321	321	1.0	0	0	2/2	1	1	1/4		1/4	1		0	60	C4	4	772
+83	81	321	321	1.0	0	0	2/2	1	1	1/4		1/4	1		4	64	E4	4	772
+83	81	643/2	643/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	775
+83	81	322	322	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		4	52	E3	3	776
+83	81	645/2	645/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	777
+83	81	323	323	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		4	52	E3	3	778
+83	81	647/2	647/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	779
+83	81	324	324	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		4	52	E3	3	780
+83	81	324	324	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		0	72	C5	5	773
+83	81	649/2	649/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	781
+84	82	325	325	0.5	0	0	2/2	2	1	1/8		1/8	1		-1	53	F3	3	784
+84	82	325	325	1.0	0	0	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	782
+84	82	651/2	651/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	785
+84	82	326	326	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-1	53	F3	3	786
+84	82	653/2	653/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	787
+84	82	327	327	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-1	53	F3	3	788
+84	82	655/2	655/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	789
+84	82	328	328	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-4	56	Ab3	3	790
+84	82	328	328	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-1	77	F5	5	783
+84	82	657/2	657/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	791
+85	83	329	329	0.5	0	0	2/2	2	1	1/8		1/8	1		1	55	G3	3	794
+85	83	329	329	1.0	0	0	2/2	1	1	1/4		1/4	1		4	76	E5	5	792
+85	83	659/2	659/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	795
+85	83	330	330	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		1	55	G3	3	796
+85	83	661/2	661/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	797
+85	83	331	331	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		1	55	G3	3	798
+85	83	663/2	663/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	799
+85	83	332	332	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-2	58	Bb3	3	800
+85	83	332	332	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		0	84	C6	6	793
+85	83	665/2	665/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	801
+86	84	333	333	0.5	0	0	2/2	2	1	1/8		1/8	1		-4	56	Ab3	3	804
+86	84	333	333	1.0	0	0	2/2	1	1	1/4		1/4	1		-1	77	F5	5	802
+86	84	667/2	667/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	805
+86	84	334	334	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		0	60	C4	4	806
+86	84	669/2	669/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	807
+86	84	335	335	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		5	59	B3	3	808
+86	84	335	335	2.0	1/2	1/2	2/2	1	1	1/2		1/2	1		-1	77	F5	5	803
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+89	87	693/2	693/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	845
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+89	87	695/2	695/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		0	48	C3	3	847
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+97	95	378	378	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	919
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+97	95	379	379	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	920
+97	95	379	379	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-6	66	Gb4	4	917
+97	95	380	380	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	921
+97	95	380	380	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	921
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+98	96	382	382	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	927
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+98	96	383	383	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	928
+98	96	383	383	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-5	85	Db6	6	925
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+98	96	384	384	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	929
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+109	107	425	425	2.0	0	0	2/2	1	1	1/2		1/2	1		0	84	C6	6	1002
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+113	111	441	441	4.0	0	0	2/2	2	1	1		1	1		-6	66	Gb4	4	1032
+113	111	441	441	1.5	0	0	2/2	1	1	3/8		1/4	3/2		-2	82	Bb5	5	1026
+113	111	885/2	885/2	0.16666666666666666	3/8	3/8	2/2	1	1	1/24		1/16	2/3		-4	80	Ab5	5	1027
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+113	111	2657/6	2657/6	0.16666666666666666	11/24	11/24	2/2	1	1	1/24		1/16	2/3		-1	77	F5	5	1029
+113	111	443	443	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-6	78	Gb5	5	1030
+113	111	444	444	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-6	78	Gb5	5	1031
+114	112	445	445	4.0	0	0	2/2	2	1	1		1	1		-3	63	Eb4	4	1039
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+114	112	445	445	4.0	0	0	2/2	1	1	1		1	1	1	-6	78	Gb5	5	1033
+114	112	893/2	893/2	0.16666666666666666	3/8	3/8	2/2	1	2	1/24		1/16	2/3		-5	73	Db5	5	1034
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+114	112	2681/6	2681/6	0.16666666666666666	11/24	11/24	2/2	1	2	1/24		1/16	2/3		5	71	B4	4	1036
+114	112	447	447	1.0	1/2	1/2	2/2	1	2	1/4		1/4	1		0	72	C5	5	1037
+114	112	448	448	1.0	3/4	3/4	2/2	1	2	1/4		1/4	1		0	72	C5	5	1038
+115	113	449	449	4.0	0	0	2/2	2	1	1		1	1		-3	63	Eb4	4	1047
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+115	113	449	449	4.0	0	0	2/2	1	2	1		1	1	1	0	72	C5	5	1046
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+116	114	453	453	4.0	0	0	2/2	1	1	1		1	1		-1	77	F5	5	1048
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+117	115	1376/3	1376/3	0.16666666666666666	5/12	5/12	2/2	1	1	1/24		1/16	2/3		-1	77	F5	5	1057
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+121	119	953/2	953/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		0	60	C4	4	1102
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+123	121	965/2	965/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	1116
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+127	125	498	498	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		0	60	C4	4	1171
+127	125	498	498	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		1	79	G5	5	1164
+127	125	997/2	997/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	1172
+127	125	997/2	997/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-1	77	F5	5	1165
+127	125	499	499	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		1	55	G3	3	1173
+127	125	999/2	999/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		4	64	E4	4	1174
+127	125	999/2	999/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		-1	77	F5	5	1166
+127	125	500	500	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-2	58	Bb3	3	1175
+127	125	500	500	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-4	80	Ab5	5	1167
+127	125	1001/2	1001/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		4	64	E4	4	1176
+127	125	1001/2	1001/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		1	79	G5	5	1168
+128	126	501	501	0.5	0	0	2/2	2	1	1/8		1/8	1		-1	53	F3	3	1183
+128	126	1003/2	1003/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		0	60	C4	4	1184
+128	126	1003/2	1003/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		1	79	G5	5	1177
+128	126	502	502	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		-4	56	Ab3	3	1185
+128	126	502	502	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-2	82	Bb5	5	1178
+128	126	1005/2	1005/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		0	60	C4	4	1186
+128	126	1005/2	1005/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-4	80	Ab5	5	1179
+128	126	503	503	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-5	49	Db3	3	1187
+128	126	1007/2	1007/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		5	59	B3	3	1188
+128	126	1007/2	1007/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		5	83	B5	5	1180
+128	126	504	504	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-1	53	F3	3	1189
+128	126	504	504	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-1	89	F6	6	1181
+128	126	1009/2	1009/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		5	59	B3	3	1190
+128	126	1009/2	1009/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		5	83	B5	5	1182
+129	127	505	505	0.5	0	0	2/2	2	1	1/8		1/8	1		0	48	C3	3	1195
+129	127	505	505	1.0	0	0	2/2	1	1	1/4		1/4	1		0	84	C6	6	1191
+129	127	1011/2	1011/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		0	60	C4	4	1196
+129	127	506	506	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		4	52	E3	3	1197
+129	127	1013/2	1013/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		0	60	C4	4	1198
+129	127	507	507	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-5	49	Db3	3	1199
+129	127	1015/2	1015/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		5	59	B3	3	1200
+129	127	1015/2	1015/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		5	83	B5	5	1192
+129	127	508	508	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-1	53	F3	3	1201
+129	127	508	508	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-1	89	F6	6	1193
+129	127	1017/2	1017/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		5	59	B3	3	1202
+129	127	1017/2	1017/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		5	83	B5	5	1194
+130	128	509	509	0.5	0	0	2/2	2	1	1/8		1/8	1		0	48	C3	3	1207
+130	128	509	509	1.0	0	0	2/2	1	1	1/4		1/4	1		0	84	C6	6	1203
+130	128	1019/2	1019/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		0	60	C4	4	1208
+130	128	510	510	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		4	52	E3	3	1209
+130	128	1021/2	1021/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		0	60	C4	4	1210
+130	128	511	511	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-2	46	Bb2	2	1211
+130	128	1023/2	1023/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		1	55	G3	3	1212
+130	128	1023/2	1023/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		4	76	E5	5	1204
+130	128	512	512	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-5	49	Db3	3	1213
+130	128	512	512	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-5	85	Db6	6	1205
+130	128	1025/2	1025/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		1	55	G3	3	1214
+130	128	1025/2	1025/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		4	76	E5	5	1206
+131	129	513	513	0.5	0	0	2/2	2	1	1/8		1/8	1		-4	44	Ab2	2	1219
+131	129	513	513	1.0	0	0	2/2	1	1	1/4		1/4	1		-1	77	F5	5	1215
+131	129	1027/2	1027/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-1	53	F3	3	1220
+131	129	514	514	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		0	48	C3	3	1221
+131	129	1029/2	1029/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-1	53	F3	3	1222
+131	129	515	515	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		-2	46	Bb2	2	1223
+131	129	1031/2	1031/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		1	55	G3	3	1224
+131	129	1031/2	1031/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		4	76	E5	5	1216
+131	129	516	516	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		-5	49	Db3	3	1225
+131	129	516	516	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-5	85	Db6	6	1217
+131	129	1033/2	1033/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		1	55	G3	3	1226
+131	129	1033/2	1033/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		4	76	E5	5	1218
+132	130	517	517	0.5	0	0	2/2	2	1	1/8		1/8	1		-4	44	Ab2	2	1233
+132	130	1035/2	1035/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-1	53	F3	3	1234
+132	130	1035/2	1035/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		4	76	E5	5	1227
+132	130	518	518	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		0	48	C3	3	1235
+132	130	518	518	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		1	79	G5	5	1228
+132	130	1037/2	1037/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-1	53	F3	3	1236
+132	130	1037/2	1037/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-1	77	F5	5	1229
+132	130	519	519	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		4	40	E2	2	1237
+132	130	1039/2	1039/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		4	52	E3	3	1238
+132	130	1039/2	1039/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		-1	77	F5	5	1230
+132	130	520	520	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		0	48	C3	3	1239
+132	130	520	520	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-4	80	Ab5	5	1231
+132	130	1041/2	1041/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		4	52	E3	3	1240
+132	130	1041/2	1041/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		1	79	G5	5	1232
+133	131	521	521	0.5	0	0	2/2	2	1	1/8		1/8	1		-1	41	F2	2	1247
+133	131	1043/2	1043/2	0.5	1/8	1/8	2/2	2	1	1/8		1/8	1		-1	53	F3	3	1248
+133	131	1043/2	1043/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		1	79	G5	5	1241
+133	131	522	522	0.5	1/4	1/4	2/2	2	1	1/8		1/8	1		0	48	C3	3	1249
+133	131	522	522	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-2	82	Bb5	5	1242
+133	131	1045/2	1045/2	0.5	3/8	3/8	2/2	2	1	1/8		1/8	1		-1	53	F3	3	1250
+133	131	1045/2	1045/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-4	80	Ab5	5	1243
+133	131	523	523	0.5	1/2	1/2	2/2	2	1	1/8		1/8	1		1	43	G2	2	1251
+133	131	1047/2	1047/2	0.5	5/8	5/8	2/2	2	1	1/8		1/8	1		1	55	G3	3	1252
+133	131	1047/2	1047/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		3	81	A5	5	1244
+133	131	524	524	0.5	3/4	3/4	2/2	2	1	1/8		1/8	1		0	48	C3	3	1253
+133	131	524	524	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		0	84	C6	6	1245
+133	131	1049/2	1049/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		1	55	G3	3	1254
+133	131	1049/2	1049/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-2	82	Bb5	5	1246
+134	132	525	525	1.0	0	0	2/2	2	1	1/4		1/4	1		-4	44	Ab2	2	1262
+134	132	1051/2	1051/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		0	84	C6	6	1255
+134	132	526	526	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		0	48	C3	3	1263
+134	132	526	526	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-1	89	F6	6	1256
+134	132	1053/2	1053/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-3	87	Eb6	6	1257
+134	132	527	527	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		-5	85	Db6	6	1258
+134	132	1055/2	1055/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		0	84	C6	6	1259
+134	132	528	528	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	1264
+134	132	528	528	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-2	82	Bb5	5	1260
+134	132	1057/2	1057/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-4	80	Ab5	5	1261
+135	133	529	529	1.0	0	0	2/2	2	1	1/4		1/4	1		-2	46	Bb2	2	1273
+135	133	529	529	0.5	0	0	2/2	1	1	1/8		1/8	1		1	79	G5	5	1265
+135	133	1059/2	1059/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		-1	77	F5	5	1266
+135	133	530	530	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-5	49	Db3	3	1274
+135	133	530	530	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-3	75	Eb5	5	1267
+135	133	1061/2	1061/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-5	73	Db5	5	1268
+135	133	531	531	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		0	72	C5	5	1269
+135	133	1063/2	1063/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		-2	70	Bb4	4	1270
+135	133	532	532	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		1	55	G3	3	1275
+135	133	532	532	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-4	68	Ab4	4	1271
+135	133	1065/2	1065/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		1	67	G4	4	1272
+136	134	533	533	1.0	0	0	2/2	2	1	1/4		1/4	1		0	48	C3	3	1284
+136	134	533	533	0.5	0	0	2/2	1	1	1/8		1/8	1		-1	65	F4	4	1276
+136	134	1067/2	1067/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		4	64	E4	4	1277
+136	134	534	534	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-1	53	F3	3	1285
+136	134	534	534	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-5	61	Db4	4	1278
+136	134	1069/2	1069/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	1279
+136	134	535	535	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		5	59	B3	3	1280
+136	134	1071/2	1071/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	1281
+136	134	536	536	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	1286
+136	134	536	536	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		5	59	B3	3	1282
+136	134	1073/2	1073/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	1283
+137	135	537	537	1.0	0	0	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	1295
+137	135	537	537	0.5	0	0	2/2	1	1	1/8		1/8	1		5	59	B3	3	1287
+137	135	1075/2	1075/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	1288
+137	135	538	538	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		1	55	G3	3	1296
+137	135	538	538	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-5	61	Db4	4	1289
+137	135	1077/2	1077/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	1290
+137	135	539	539	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		4	52	E3	3	1297
+137	135	539	539	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		5	59	B3	3	1291
+137	135	1079/2	1079/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	1292
+137	135	540	540	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	46	Bb2	2	1298
+137	135	540	540	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-5	61	Db4	4	1293
+137	135	1081/2	1081/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	1294
+138	136	541	541	1.0	0	0	2/2	2	1	1/4		1/4	1		-4	44	Ab2	2	1306
+138	136	1083/2	1083/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		0	84	C6	6	1299
+138	136	542	542	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		0	48	C3	3	1307
+138	136	542	542	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-1	89	F6	6	1300
+138	136	1085/2	1085/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-3	87	Eb6	6	1301
+138	136	543	543	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		-5	85	Db6	6	1302
+138	136	1087/2	1087/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		0	84	C6	6	1303
+138	136	544	544	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	1308
+138	136	544	544	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-2	82	Bb5	5	1304
+138	136	1089/2	1089/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-4	80	Ab5	5	1305
+139	137	545	545	1.0	0	0	2/2	2	1	1/4		1/4	1		-2	46	Bb2	2	1317
+139	137	545	545	0.5	0	0	2/2	1	1	1/8		1/8	1		1	79	G5	5	1309
+139	137	1091/2	1091/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		-1	77	F5	5	1310
+139	137	546	546	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-5	49	Db3	3	1318
+139	137	546	546	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-3	75	Eb5	5	1311
+139	137	1093/2	1093/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		-5	73	Db5	5	1312
+139	137	547	547	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		0	72	C5	5	1313
+139	137	1095/2	1095/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		-2	70	Bb4	4	1314
+139	137	548	548	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		1	55	G3	3	1319
+139	137	548	548	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-4	68	Ab4	4	1315
+139	137	1097/2	1097/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		1	67	G4	4	1316
+140	138	549	549	1.0	0	0	2/2	2	1	1/4		1/4	1		0	48	C3	3	1328
+140	138	549	549	0.5	0	0	2/2	1	1	1/8		1/8	1		-1	65	F4	4	1320
+140	138	1099/2	1099/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		4	64	E4	4	1321
+140	138	550	550	2.0	1/4	1/4	2/2	2	1	1/2		1/2	1		-1	53	F3	3	1329
+140	138	550	550	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-5	61	Db4	4	1322
+140	138	1101/2	1101/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	1323
+140	138	551	551	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		5	59	B3	3	1324
+140	138	1103/2	1103/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	1325
+140	138	552	552	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	1330
+140	138	552	552	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		5	59	B3	3	1326
+140	138	1105/2	1105/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	1327
+141	139	553	553	0.5	0	0	2/2	1	1	1/8		1/8	1		5	59	B3	3	1331
+141	139	1107/2	1107/2	0.5	1/8	1/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	1332
+141	139	554	554	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	36	C2	2	1339
+141	139	554	554	0.5	1/4	1/4	2/2	1	1	1/8		1/8	1		-5	61	Db4	4	1333
+141	139	1109/2	1109/2	0.5	3/8	3/8	2/2	1	1	1/8		1/8	1		0	60	C4	4	1334
+141	139	555	555	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		0	36	C2	2	1340
+141	139	555	555	0.5	1/2	1/2	2/2	1	1	1/8		1/8	1		0	60	C4	4	1335
+141	139	1111/2	1111/2	0.5	5/8	5/8	2/2	1	1	1/8		1/8	1		-2	58	Bb3	3	1336
+141	139	556	556	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	36	C2	2	1341
+141	139	556	556	0.5	3/4	3/4	2/2	1	1	1/8		1/8	1		-4	56	Ab3	3	1337
+141	139	1113/2	1113/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		1	55	G3	3	1338
+142	140	557	557	1.0	0	0	2/2	2	1	1/4		1/4	1		-1	41	F2	2	1345
+142	140	557	557	1.0	0	0	2/2	1	1	1/4		1/4	1		-1	53	F3	3	1342
+142	140	558	558	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	1346
+142	140	559	559	1.5	1/2	1/2	2/2	1	1	3/8		1/4	3/2		-4	68	Ab4	4	1343
+142	140	560	560	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	49	Db3	3	1347
+142	140	560	560	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	1347
+142	140	560	560	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	1347
+142	140	560	560	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		5	59	B3	3	1347
+142	140	1121/2	1121/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		1	67	G4	4	1344
+143	141	561	561	2.0	0	0	2/2	1	1	1/2		1/2	1		-1	65	F4	4	1348
+143	141	562	562	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	48	C3	3	1351
+143	141	562	562	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	1351
+143	141	562	562	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	1351
+143	141	562	562	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	1351
+143	141	563	563	1.5	1/2	1/2	2/2	1	1	3/8		1/4	3/2		4	64	E4	4	1349
+143	141	564	564	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	48	C3	3	1352
+143	141	564	564	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		1	55	G3	3	1352
+143	141	564	564	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	1352
+143	141	1129/2	1129/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		0	72	C5	5	1350
+144	142	565	565	1.0	0	0	2/2	1	1	1/4		1/4	1		0	72	C5	5	1353
+144	142	566	566	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	1357
+144	142	566	566	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	1357
+144	142	566	566	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-1	65	F4	4	1354
+144	142	567	567	1.5	1/2	1/2	2/2	1	1	3/8		1/4	3/2		-4	68	Ab4	4	1355
+144	142	568	568	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	49	Db3	3	1358
+144	142	568	568	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	1358
+144	142	568	568	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	1358
+144	142	568	568	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		5	59	B3	3	1358
+144	142	1137/2	1137/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		1	67	G4	4	1356
+145	143	569	569	2.0	0	0	2/2	1	1	1/2		1/2	1		-1	65	F4	4	1359
+145	143	570	570	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	48	C3	3	1362
+145	143	570	570	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	1362
+145	143	570	570	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	1362
+145	143	570	570	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	1362
+145	143	571	571	1.5	1/2	1/2	2/2	1	1	3/8		1/4	3/2		4	64	E4	4	1360
+145	143	572	572	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	48	C3	3	1363
+145	143	572	572	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		1	55	G3	3	1363
+145	143	572	572	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	1363
+145	143	1145/2	1145/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		0	72	C5	5	1361
+146	144	573	573	1.0	0	0	2/2	1	1	1/4		1/4	1		0	72	C5	5	1364
+146	144	574	574	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	1369
+146	144	574	574	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	1369
+146	144	574	574	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-1	65	F4	4	1365
+146	144	575	575	0.0	1/2	1/2	2/2	1	1	0	acciaccatura	1/8	1		5	71	B4	4	1366
+146	144	575	575	1.5	1/2	1/2	2/2	1	1	3/8		1/4	3/2		-4	80	Ab5	5	1367
+146	144	576	576	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	1370
+146	144	576	576	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-1	65	F4	4	1370
+146	144	576	576	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-4	68	Ab4	4	1370
+146	144	576	576	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		5	71	B4	4	1370
+146	144	1153/2	1153/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		1	79	G5	5	1368
+147	145	577	577	2.0	0	0	2/2	1	1	1/2		1/2	1		-1	77	F5	5	1371
+147	145	578	578	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	1374
+147	145	578	578	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-1	65	F4	4	1374
+147	145	578	578	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		-4	68	Ab4	4	1374
+147	145	578	578	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	72	C5	5	1374
+147	145	579	579	1.5	1/2	1/2	2/2	1	1	3/8		1/4	3/2		4	76	E5	5	1372
+147	145	580	580	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	1375
+147	145	580	580	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	1375
+147	145	580	580	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		1	67	G4	4	1375
+147	145	1161/2	1161/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		0	84	C6	6	1373
+148	146	581	581	4.0	0	0	2/2	2	1	1		1	1		3	57	A3	3	1377
+148	146	581	581	4.0	0	0	2/2	2	1	1		1	1		0	60	C4	4	1377
+148	146	581	581	4.0	0	0	2/2	2	1	1		1	1		-1	65	F4	4	1377
+148	146	581	581	4.0	0	0	2/2	1	1	1		1	1		0	72	C5	5	1376
+148	146	581	581	4.0	0	0	2/2	1	1	1		1	1		-3	75	Eb5	5	1376
+148	146	581	581	4.0	0	0	2/2	1	1	1		1	1		0	84	C6	6	1376
+149	147	585	585	1.0	0	0	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	1380
+149	147	585	585	1.0	0	0	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	1380
+149	147	585	585	1.0	0	0	2/2	2	1	1/4		1/4	1		-1	65	F4	4	1380
+149	147	585	585	1.0	0	0	2/2	1	1	1/4		1/4	1		-2	70	Bb4	4	1378
+149	147	585	585	1.0	0	0	2/2	1	1	1/4		1/4	1		-5	73	Db5	5	1378
+149	147	585	585	1.0	0	0	2/2	1	1	1/4		1/4	1		-1	77	F5	5	1378
+149	147	1177/2	1177/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		-2	58	Bb3	3	1381
+149	147	1177/2	1177/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		-5	61	Db4	4	1381
+149	147	1177/2	1177/2	0.5	7/8	7/8	2/2	2	1	1/8		1/8	1		-1	65	F4	4	1381
+149	147	1177/2	1177/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-2	70	Bb4	4	1379
+149	147	1177/2	1177/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-5	73	Db5	5	1379
+149	147	1177/2	1177/2	0.5	7/8	7/8	2/2	1	1	1/8		1/8	1		-2	82	Bb5	5	1379
+150	148	589	589	4.0	0	0	2/2	2	1	1		1	1		1	55	G3	3	1383
+150	148	589	589	4.0	0	0	2/2	2	1	1		1	1		-2	58	Bb3	3	1383
+150	148	589	589	4.0	0	0	2/2	2	1	1		1	1		-3	63	Eb4	4	1383
+150	148	589	589	4.0	0	0	2/2	1	1	1		1	1		-2	70	Bb4	4	1382
+150	148	589	589	4.0	0	0	2/2	1	1	1		1	1		-5	73	Db5	5	1382
+150	148	589	589	4.0	0	0	2/2	1	1	1		1	1		-2	82	Bb5	5	1382
+151	149	593	593	1.0	0	0	2/2	2	1	1/4		1/4	1		-4	56	Ab3	3	1386
+151	149	593	593	1.0	0	0	2/2	2	1	1/4		1/4	1		0	60	C4	4	1386
+151	149	593	593	1.0	0	0	2/2	2	1	1/4		1/4	1		-3	63	Eb4	4	1386
+151	149	593	593	1.0	0	0	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	1384
+151	149	593	593	1.0	0	0	2/2	1	1	1/4		1/4	1		0	72	C5	5	1384
+151	149	593	593	1.0	0	0	2/2	1	1	1/4		1/4	1		-3	75	Eb5	5	1384
+151	149	596	596	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-1	53	F3	3	1387
+151	149	596	596	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	1387
+151	149	596	596	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	1385
+151	149	596	596	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		0	72	C5	5	1385
+151	149	596	596	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-4	80	Ab5	5	1385
+152	150	597	597	1.0	0	0	2/2	2	1	1/4		1/4	1		1	55	G3	3	1392
+152	150	597	597	1.0	0	0	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	1392
+152	150	597	597	1.0	0	0	2/2	1	1	1/4		1/4	1		1	67	G4	4	1388
+152	150	597	597	1.0	0	0	2/2	1	1	1/4		1/4	1		-2	70	Bb4	4	1388
+152	150	597	597	1.0	0	0	2/2	1	1	1/4		1/4	1		-5	73	Db5	5	1388
+152	150	598	598	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		4	52	E3	3	1393
+152	150	598	598	1.0	1/4	1/4	2/2	2	1	1/4		1/4	1		0	60	C4	4	1393
+152	150	598	598	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		1	67	G4	4	1389
+152	150	598	598	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		-2	70	Bb4	4	1389
+152	150	598	598	1.0	1/4	1/4	2/2	1	1	1/4		1/4	1		1	79	G5	5	1389
+152	150	599	599	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		-1	53	F3	3	1394
+152	150	599	599	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		0	60	C4	4	1394
+152	150	599	599	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-1	65	F4	4	1390
+152	150	599	599	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	1390
+152	150	599	599	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		0	72	C5	5	1390
+152	150	600	600	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	49	Db3	3	1395
+152	150	600	600	1.0	3/4	3/4	2/2	2	1	1/4		1/4	1		-5	61	Db4	4	1395
+152	150	600	600	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-1	65	F4	4	1391
+152	150	600	600	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	1391
+152	150	600	600	1.0	3/4	3/4	2/2	1	1	1/4		1/4	1		-1	77	F5	5	1391
+153	151	601	601	1.0	0	0	2/2	2	1	1/4		1/4	1		-2	46	Bb2	2	1398
+153	151	601	601	1.0	0	0	2/2	2	1	1/4		1/4	1		-5	49	Db3	3	1398
+153	151	601	601	1.0	0	0	2/2	2	1	1/4		1/4	1		1	55	G3	3	1398
+153	151	601	601	1.0	0	0	2/2	2	1	1/4		1/4	1		-2	58	Bb3	3	1398
+153	151	601	601	1.0	0	0	2/2	1	1	1/4		1/4	1		-1	65	F4	4	1396
+153	151	601	601	1.0	0	0	2/2	1	1	1/4		1/4	1		1	67	G4	4	1396
+153	151	601	601	1.0	0	0	2/2	1	1	1/4		1/4	1		-5	73	Db5	5	1396
+153	151	601	601	1.0	0	0	2/2	1	1	1/4		1/4	1		-1	77	F5	5	1396
+153	151	603	603	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		0	48	C3	3	1399
+153	151	603	603	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		4	52	E3	3	1399
+153	151	603	603	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		1	55	G3	3	1399
+153	151	603	603	1.0	1/2	1/2	2/2	2	1	1/4		1/4	1		0	60	C4	4	1399
+153	151	603	603	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		4	64	E4	4	1397
+153	151	603	603	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		1	67	G4	4	1397
+153	151	603	603	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		-2	70	Bb4	4	1397
+153	151	603	603	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		0	72	C5	5	1397
+153	151	603	603	1.0	1/2	1/2	2/2	1	1	1/4		1/4	1		4	76	E5	5	1397
+154	152	605	605	1.0	0	0	2/2	2	1	1/4		1/4	1		-1	41	F2	2	1401
+154	152	605	605	1.0	0	0	2/2	2	1	1/4		1/4	1		-4	44	Ab2	2	1401
+154	152	605	605	1.0	0	0	2/2	2	1	1/4		1/4	1		0	48	C3	3	1401
+154	152	605	605	1.0	0	0	2/2	2	1	1/4		1/4	1		-1	53	F3	3	1401
+154	152	605	605	1.0	0	0	2/2	1	1	1/4		1/4	1		-1	65	F4	4	1400
+154	152	605	605	1.0	0	0	2/2	1	1	1/4		1/4	1		-4	68	Ab4	4	1400
+154	152	605	605	1.0	0	0	2/2	1	1	1/4		1/4	1		0	72	C5	5	1400
+154	152	605	605	1.0	0	0	2/2	1	1	1/4		1/4	1		-1	77	F5	5	1400
diff --git a/tests/test_dcml_import.py b/tests/test_dcml_import.py
new file mode 100644
index 00000000..b3f358bd
--- /dev/null
+++ b/tests/test_dcml_import.py
@@ -0,0 +1,20 @@
+import unittest
+from partitura import load_dcml
+from tests import TSV_PATH
+import os
+import pandas as pd
+
+
+class ImportDCMLAnnotations(unittest.TestCase):
+    def test_tsv_import_from_dcml(self):
+        note_path = os.path.join(TSV_PATH, "test_notes.tsv")
+        measure_path = os.path.join(TSV_PATH, "test_measures.tsv")
+        harmony_path = os.path.join(TSV_PATH, "test_harmonies.tsv")
+        score = load_dcml(note_path, measure_path, harmony_path)
+        note_lines = pd.read_csv(note_path, sep="\t", header=None)
+        self.assertEqual(len(score.parts), 1)
+        self.assertEqual(len(score[0].notes), len(note_lines)-1, "Number of notes do not match")
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/test_fluidsynth.py b/tests/test_fluidsynth.py
new file mode 100644
index 00000000..c6923ff9
--- /dev/null
+++ b/tests/test_fluidsynth.py
@@ -0,0 +1,97 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+This module contains tests for the fluidsynth methods.
+"""
+import unittest
+
+import numpy as np
+from scipy.io import wavfile
+import tempfile
+
+from partitura.utils.fluidsynth import (
+    synthesize_fluidsynth,
+    HAS_FLUIDSYNTH,
+    SAMPLE_RATE,
+)
+
+from partitura import EXAMPLE_MUSICXML, load_score, load_performance_midi
+
+from partitura.io.exportaudio import save_wav_fluidsynth
+
+from tests import MOZART_VARIATION_FILES
+
+RNG = np.random.RandomState(1984)
+
+if HAS_FLUIDSYNTH:
+
+    class TestSynthesize(unittest.TestCase):
+
+        score = load_score(EXAMPLE_MUSICXML)
+
+        def test_synthesize(self):
+
+            score_na = self.score.note_array()
+
+            duration_beats = (
+                score_na["onset_beat"] + score_na["duration_beat"]
+            ).max() - score_na["onset_beat"].min()
+
+            for bpm in RNG.randint(30, 200, size=10):
+
+                for samplerate in [12000, 16000, 22000, SAMPLE_RATE]:
+
+                    duration_sec = duration_beats * 60 / bpm
+                    y = synthesize_fluidsynth(
+                        note_info=self.score,
+                        samplerate=samplerate,
+                        bpm=bpm,
+                    )
+
+                    expected_length = np.round(duration_sec * samplerate)
+
+                    self.assertTrue(len(y) == expected_length)
+
+                    self.assertTrue(isinstance(y, np.ndarray))
+
+    class TestSynthExport(unittest.TestCase):
+
+        test_files = [
+            load_score(MOZART_VARIATION_FILES["musicxml"]),
+            load_performance_midi(MOZART_VARIATION_FILES["midi"]),
+        ]
+
+        def export(self, note_info):
+
+            y = synthesize_fluidsynth(
+                note_info=note_info,
+                samplerate=SAMPLE_RATE,
+                bpm=60,
+            )
+
+            with tempfile.TemporaryFile(suffix=".wav") as filename:
+
+                save_wav_fluidsynth(
+                    input_data=note_info,
+                    out=filename,
+                    samplerate=SAMPLE_RATE,
+                    bpm=60,
+                )
+
+                sr_rec, rec_audio = wavfile.read(filename)
+
+                self.assertTrue(sr_rec == SAMPLE_RATE)
+                self.assertTrue(len(rec_audio) == len(y))
+                self.assertTrue(
+                    np.allclose(
+                        rec_audio / rec_audio.max(),
+                        y / y.max(),
+                        atol=1e-4,
+                    )
+                )
+
+        def test_export(self):
+
+            for note_info in self.test_files:
+
+                self.export(note_info)
diff --git a/tests/test_kern.py b/tests/test_kern.py
index 86cfb3f1..f29ed3b4 100644
--- a/tests/test_kern.py
+++ b/tests/test_kern.py
@@ -6,10 +6,13 @@
 import unittest
 
 import partitura
-from tests import KERN_TESTFILES, KERN_TIES
+import os
+from tests import KERN_TESTFILES, KERN_TIES, KERN_PATH
+from tempfile import TemporaryDirectory
 from partitura.score import merge_parts
 from partitura.utils import ensure_notearray
 from partitura.io.importkern import load_kern
+from partitura.io.exportkern import save_kern
 from partitura import load_musicxml
 import numpy as np
 
@@ -32,17 +35,50 @@ def test_example_kern(self):
 
     def test_examples(self):
         for fn in KERN_TESTFILES:
-            part = merge_parts(load_kern(fn))
-            ka = ensure_notearray(part)
+            score = load_kern(fn)
             self.assertTrue(True)
 
+    def test_chorale_import(self):
+        file_path = os.path.join(KERN_PATH, "chor228.krn")
+        score = load_kern(file_path)
+        num_measures = 8
+        num_parts = 4
+        num_notes = 102
+        self.assertTrue(len(score.parts) == num_parts)
+        self.assertTrue(all([len(part.measures) == num_measures for part in score.parts]))
+        self.assertTrue(len(score.note_array()) == num_notes)
+
     def test_tie_mismatch(self):
 
         fn = KERN_TIES[0]
-        part = merge_parts(load_kern(fn))
+        score = load_kern(fn)
 
         self.assertTrue(True)
 
+    def test_spline_splitting(self):
+        file_path = os.path.join(KERN_PATH, "spline_splitting.krn")
+        score = load_kern(file_path)
+        num_parts = 4
+        voices_per_part = [2, 1, 1, 2]
+        self.assertTrue(num_parts == len(score.parts))
+        for i, part in enumerate(score.parts):
+            vn = part.note_array()["voice"].max()
+            self.assertTrue(voices_per_part[i] == vn)
+
+    def test_import_export(self):
+        imported_score = load_kern(partitura.EXAMPLE_KERN)
+        with TemporaryDirectory() as tmpdir:
+            out = os.path.join(tmpdir, "test.match")
+            save_kern(imported_score, out)
+            exported_score = load_kern(out)
+        im_na = imported_score.note_array(include_staff=True)
+        ex_na = exported_score.note_array(include_staff=True)
+        self.assertTrue(np.all(im_na["onset_beat"] == ex_na["onset_beat"]))
+        self.assertTrue(np.all(im_na["duration_beat"] == ex_na["duration_beat"]))
+        self.assertTrue(np.all(im_na["pitch"] == ex_na["pitch"]))
+        self.assertTrue(np.all(im_na["staff"] == ex_na["staff"]))
+        # NOTE: Voices are not the same because of the way voices are assigned in merge_parts
+
 
 # if __name__ == "__main__":
 #     unittest.main()
diff --git a/tests/test_mei.py b/tests/test_mei.py
index 782c7976..e5f26b4c 100644
--- a/tests/test_mei.py
+++ b/tests/test_mei.py
@@ -6,16 +6,16 @@
 
 import unittest
 
-from tests import MEI_TESTFILES
-from partitura import load_musicxml, load_mei, EXAMPLE_MEI
+from tests import MEI_TESTFILES, MUSICXML_PATH
+from partitura import load_musicxml, load_mei, EXAMPLE_MEI, save_mei
 import partitura.score as score
 from partitura.io.importmei import MeiParser
 from partitura.utils import compute_pianoroll
 from lxml import etree
+from tempfile import TemporaryDirectory
 from xmlschema.names import XML_NAMESPACE
-
+import os
 import numpy as np
-from pathlib import Path
 
 
 # class TestSaveMEI(unittest.TestCase):
@@ -30,6 +30,40 @@
 
 #         self.assertTrue(mei.decode('utf-8') == target_mei, msg)
 
+class TestExportMEI(unittest.TestCase):
+    def test_export_mei_simple(self):
+        import_score = load_mei(EXAMPLE_MEI)
+        ina = import_score.note_array()
+        with TemporaryDirectory() as tmpdir:
+            tmp_mei = os.path.join(tmpdir, "test.mei")
+            save_mei(import_score, tmp_mei)
+            export_score = load_mei(tmp_mei)
+            ena = export_score.note_array()
+            self.assertTrue(np.all(ina["onset_beat"] == ena["onset_beat"]))
+            self.assertTrue(np.all(ina["duration_beat"] == ena["duration_beat"]))
+            self.assertTrue(np.all(ina["pitch"] == ena["pitch"]))
+            self.assertTrue(np.all(ina["voice"] == ena["voice"]))
+            self.assertTrue(np.all(ina["id"] == ena["id"]))
+
+    def test_export_mei(self):
+        import_score = load_musicxml(os.path.join(MUSICXML_PATH, "test_chew_vosa_example.xml"), force_note_ids=True)
+        ina = import_score.note_array()
+        with TemporaryDirectory() as tmpdir:
+            tmp_mei = os.path.join(tmpdir, "test.mei")
+            save_mei(import_score, tmp_mei)
+            export_score = load_mei(tmp_mei)
+            ena = export_score.note_array()
+            self.assertTrue(np.all(ina["onset_beat"] == ena["onset_beat"]))
+            self.assertTrue(np.all(ina["duration_beat"] == ena["duration_beat"]))
+            self.assertTrue(np.all(ina["pitch"] == ena["pitch"]))
+
+    def test_export_with_harmony(self):
+        score_fn = os.path.join(MUSICXML_PATH, "test_harmony.musicxml")
+        import_score = load_musicxml(score_fn)
+        with TemporaryDirectory() as tmpdir:
+            tmp_mei = os.path.join(tmpdir, "test.mei")
+            save_mei(import_score, tmp_mei)
+
 
 class TestImportMEI(unittest.TestCase):
     def test_main_part_group1(self):
diff --git a/tests/test_note_array.py b/tests/test_note_array.py
index 3f6489d0..bb439be9 100644
--- a/tests/test_note_array.py
+++ b/tests/test_note_array.py
@@ -170,14 +170,12 @@ def test_notearray_ts_beats(self):
     def test_ensure_na_different_divs(self):
         # check if divs are correctly rescaled when producing a note array from
         # parts with different divs values
-        # parts = list(score.iter_parts(load_kern(KERN_TESTFILES[7])))
         parts = load_kern(KERN_TESTFILES[7]).parts
-        # note_arrays = [p.note_array(include_divs_per_quarter= True) for p in parts]
         merged_note_array = ensure_notearray(parts)
         for note in merged_note_array[-4:]:
-            self.assertTrue(note["onset_div"] == 92)
-            self.assertTrue(note["duration_div"] == 4)
-            self.assertTrue(note["divs_pq"] == 4)
+            self.assertTrue(note["onset_div"] == 2208)
+            self.assertTrue(note["duration_div"] == 96)
+            self.assertTrue(note["divs_pq"] == 96)
 
     def test_score_notearray_method(self):
         """
diff --git a/tests/test_synth.py b/tests/test_synth.py
index b4ea8522..9cc26c9c 100644
--- a/tests/test_synth.py
+++ b/tests/test_synth.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 """
-This module contains tests for the synthesis methods.
+This module contains tests for the additive synthesis methods.
 """
 import unittest
 
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 13b5cc80..87e95bdd 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -9,13 +9,24 @@
 
 from partitura.utils import music
 from partitura.musicanalysis import performance_codec
-from tests import MATCH_IMPORT_EXPORT_TESTFILES, VOSA_TESTFILES, MOZART_VARIATION_FILES, TOKENIZER_TESTFILES
+from tests import (
+    MATCH_IMPORT_EXPORT_TESTFILES,
+    VOSA_TESTFILES,
+    MOZART_VARIATION_FILES,
+    TOKENIZER_TESTFILES,
+)
 
 from scipy.interpolate import interp1d as scinterp1d
 from partitura.utils.generic import interp1d as pinterp1d
 from partitura.utils.music import tokenize
-import miditok
-import miditoolkit
+
+try:
+    import miditok
+    import miditoolkit
+
+    HAS_MIDITOK = True
+except ImportError:
+    HAS_MIDITOK = False
 
 RNG = np.random.RandomState(1984)
 
@@ -601,45 +612,34 @@ def test_interp1d(self):
 
         self.assertTrue(np.all(sinterp(x) == pinterp(x)))
 
-class TestTokenizer(unittest.TestCase):
-    def test_tokenize1(self):
-        """ Test the partitura tokenizer"""
-        tokenizer = miditok.MIDILike()
-        # produce tokens from the score with partitura
-        pt_score = partitura.load_score(TOKENIZER_TESTFILES[0]["score"])
-        pt_tokens = tokenize(pt_score, tokenizer)[0].tokens
-        # produce tokens from the manually created MIDI file
-        mtok_midi = miditoolkit.MidiFile(TOKENIZER_TESTFILES[0]["midi"])
-        mtok_tokens = tokenizer(mtok_midi)[0].tokens
-        # filter out velocity tokens
-        pt_tokens = [tok for tok in pt_tokens if not tok.startswith("Velocity")]
-        mtok_tokens = [tok for tok in mtok_tokens if not tok.startswith("Velocity")]
-        self.assertTrue(pt_tokens == mtok_tokens)
-
-    def test_tokenize2(self):
-        """ Test the partitura tokenizer"""
-        tokenizer = miditok.REMI()
-        # produce tokens from the score with partitura
-        pt_score = partitura.load_score(TOKENIZER_TESTFILES[0]["score"])
-        pt_tokens = tokenize(pt_score, tokenizer)[0].tokens
-        # produce tokens from the manually created MIDI file
-        mtok_midi = miditoolkit.MidiFile(TOKENIZER_TESTFILES[0]["midi"])
-        mtok_tokens = tokenizer(mtok_midi)[0].tokens
-        # filter out velocity tokens
-        pt_tokens = [tok for tok in pt_tokens if not tok.startswith("Velocity")]
-        mtok_tokens = [tok for tok in mtok_tokens if not tok.startswith("Velocity")]
-        self.assertTrue(pt_tokens == mtok_tokens)
-
-    def test_tokenize1(self):
-        """ Test the partitura tokenizer"""
-        tokenizer = miditok.MIDILike()
-        # produce tokens from the score with partitura
-        pt_score = partitura.load_score(TOKENIZER_TESTFILES[0]["score"])
-        pt_tokens = tokenize(pt_score, tokenizer)[0].tokens
-        # produce tokens from the manually created MIDI file
-        mtok_midi = miditoolkit.MidiFile(TOKENIZER_TESTFILES[0]["midi"])
-        mtok_tokens = tokenizer(mtok_midi)[0].tokens
-        # filter out velocity tokens
-        pt_tokens = [tok for tok in pt_tokens if not tok.startswith("Velocity")]
-        mtok_tokens = [tok for tok in mtok_tokens if not tok.startswith("Velocity")]
-        self.assertTrue(pt_tokens == mtok_tokens)
\ No newline at end of file
+
+if HAS_MIDITOK:
+    # Only run these tests if miditok is installed
+    class TestTokenizer(unittest.TestCase):
+        def test_tokenize1(self):
+            """Test the partitura tokenizer"""
+            tokenizer = miditok.MIDILike()
+            # produce tokens from the score with partitura
+            pt_score = partitura.load_score(TOKENIZER_TESTFILES[0]["score"])
+            pt_tokens = tokenize(pt_score, tokenizer)[0].tokens
+            # produce tokens from the manually created MIDI file
+            mtok_midi = miditoolkit.MidiFile(TOKENIZER_TESTFILES[0]["midi"])
+            mtok_tokens = tokenizer(mtok_midi)[0].tokens
+            # filter out velocity tokens
+            pt_tokens = [tok for tok in pt_tokens if not tok.startswith("Velocity")]
+            mtok_tokens = [tok for tok in mtok_tokens if not tok.startswith("Velocity")]
+            self.assertTrue(pt_tokens == mtok_tokens)
+
+        def test_tokenize2(self):
+            """Test the partitura tokenizer"""
+            tokenizer = miditok.REMI()
+            # produce tokens from the score with partitura
+            pt_score = partitura.load_score(TOKENIZER_TESTFILES[0]["score"])
+            pt_tokens = tokenize(pt_score, tokenizer)[0].tokens
+            # produce tokens from the manually created MIDI file
+            mtok_midi = miditoolkit.MidiFile(TOKENIZER_TESTFILES[0]["midi"])
+            mtok_tokens = tokenizer(mtok_midi)[0].tokens
+            # filter out velocity tokens
+            pt_tokens = [tok for tok in pt_tokens if not tok.startswith("Velocity")]
+            mtok_tokens = [tok for tok in mtok_tokens if not tok.startswith("Velocity")]
+            self.assertTrue(pt_tokens == mtok_tokens)