workspace.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "62912f35-2f40-4d93-872e-439919754adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib qt\n",
    "\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "# import pandas as pd\n",
    "# pd.options.display.width = 1000\n",
    "\n",
    "from vrAnalysis import analysis\n",
    "from vrAnalysis import helpers\n",
    "from vrAnalysis import database\n",
    "from vrAnalysis import tracking\n",
    "from vrAnalysis import session\n",
    "\n",
    "# from vrAnalysis.uiDatabase import addEntryGUI\n",
    "from vrAnalysis.redgui import redCellGUI as rgui\n",
    "\n",
    "sessiondb = database.vrDatabase('vrSessions')\n",
    "mousedb = database.vrDatabase('vrMice')\n",
    "\n",
    "# pd.set_option('display.max_rows', 100)\n",
    "\n",
    "# device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "# print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "247fd85e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analyses and work to do:\n",
    "\n",
    "# ROICaT Figure:\n",
    "# - add a \"print pair data\" button to the interactive viewer (and maybe even a \"save figure\" button?)\n",
    "# - build an example figure with the ROICaT data (can be simple, just make it soon)\n",
    "\n",
    "# LBM-s3d:\n",
    "# - get started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07bbdd66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Database Management:\n",
    "# I need a way to report how many sessions the mouse has experienced each environment, independent of \n",
    "# which environments are represented in imaging sessions (which is how I'm doing it now...)\n",
    "\n",
    "# Required Updates: \n",
    "# need to update the placeCellMultiSession object to reflect changes to spkmap code\n",
    "# anything that uses pcss.get_place_field (pcmm make_snake_data and make_paired_snake)\n",
    "\n",
    "# Compare cvPCA analyses with eigenspectrum of spontaneous data unrelated to SVCA\n",
    "# And I want to start with the rastermap on projected place field data\n",
    "\n",
    "# Compare cvPCA to SVCA (do a hybrid: use cvPCA to get the spatial PCs, then apply those to the SVCA split)\n",
    "\n",
    "# Buzsaki Data:\n",
    "# https://crcns.org/data-sets/hc/hc-3 -- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4097350/\n",
    "# https://app.globus.org/file-manager?origin_id=188a6110-96db-11eb-b7a9-f57b2d55370d&origin_path=%2FVargaV%2F&two_pane=false - https://buzsakilab.com/wp/animals/?frm_search&project=67125&frm-page-14333=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "471f554b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Post Dataclub 240429: \n",
    "# -- need to consolidate all my figures (especially for the last few slides in a script)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1b225b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Post Meeting with Kenneth Plan:\n",
    "# - Relate to Kernel Matrices:\n",
    "#   - https://people.eecs.berkeley.edu/~jordan/kernels/0521813972c09_p291-326.pdf\n",
    "#   - First, look at the kernel matrices (the position x position covariance matrices for each environment/session)\n",
    "#   - Study the structure, and how it changes over time. \n",
    "#   - Compare the cross-validated kernel and the non-cv kernel matrix and compare their changes over time\n",
    "#            - notes about ^^, this will tell us how much changes in eigenspectrum relate to reliability across trials vs the shape of the kernel matrix etc...\n",
    "# - Studies of non-place cells:\n",
    "#   - Look at the eigenspectrum from non-place cells, suppose as a function of the reliability...\n",
    "#   - Do cross-validated decoding from non-place cells\n",
    "# - Discussion of SVCA results\n",
    "#   - SVCA dimensionality could have issues with noise estimation... the same way the trial expanded cvPCA plots did...\n",
    "#   - Predict cell2 group from cell1 group, and predict cell2 group from their cross-validated place field, compare variance explained and overlap in variance explained\n",
    "# - Rastermap: \n",
    "#   - need to find a way to remove expected spatial activity from full spike trace data (then maybe do rastermap again?)\n",
    "# - Signal to Noise\n",
    "#   - For each ROI, measure activity in center of place field, outside of place field on a linear track, and outside the track (or in other environments)\n",
    "# - Measure spontaneous periods of activity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cdee9cf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from figures.roicat_figure.pair_of_pairs import PairPairDatahandler, PairPairInteractivePlot\n",
    "\n",
    "# for choosing a pair of pairs to highlight spatial feature discrimination\n",
    "mouse_name = \"ATL022\"\n",
    "pp_handler = PairPairDatahandler(mouse_name, fake_data=True)\n",
    "pp_window = PairPairInteractivePlot(pp_handler)\n",
    "pp_window.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9010f0a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from vrAnalysis import analysis, tracking\n",
    "\n",
    "mouse_name = \"ATL027\"\n",
    "roistat = analysis.RoicatStats(tracking.tracker(mouse_name)) # only make this once so data stays loaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e21aef79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATL027 Env (most): 3, Sessions (-7): [7, 8, 9, 10, 11, 12, 13]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Andrew\\Documents\\GitHub\\vrAnalysis\\vrAnalysis\\analysis\\placeCellMultiSession.py:566: RuntimeWarning: Mean of empty slice\n",
      "  c_data = np.nanmean(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROICaT_Stats is saving a ATL027 figure for tracked_snake_env3_ses_7_8_9_10_11_12_13\n"
     ]
    },
    {
     "data": {
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LafHixXT88cfTH//xH9OmTZsGO+hAILBgEMHIgUAGvvSlL9HjHvc42rp1K73nPe+hr371q3TRRRfR4x//eLrsssvmdWzXXHMNveENb5jXMXB87nOfo2uuuabrD8/ohS984TyPLhAIBPIQ9N+PD3/4w/TiF7+YDjnkELr00kvpk5/8JB1zzDF07rnn0vvf//75Hl4gEAhkIei/H3/3d39Hr371q+m0006jL3zhC/Q3f/M3dPXVV9MTn/hEuv/+++d7eIFAIJBE0Ho/vve979HnP/95WrlyJT31qU8161566aX0ile8gk4//XT68pe/TBdccAF99KMfpXPOOWdAow0EAgEbQf/9yKH/l1xyCf34xz+mM844g4455pgBjTAQCAT8CPrvh5f+f//736ePf/zj9NSnPpU+/vGP0+WXX05nnXUW/e7v/i795m/+5gBHHAgEAmkE/fcjR/7fsWMH/dZv/RZ94hOfoC996Uv0hje8gf75n/+ZzjrrLJqZmRnQiAOBwELCUKfT6cz3IAKBfQVnnXUW3XnnnXTjjTfS6Oho129zc3M0PBzx/Rbe/va309ve9ja68sora4WWQCAQWEgI+u/HE57wBLrjjjvolltu2ftcOp0OnXTSSTQ+Pk4//OEP53mEgUAg4EfQfx927dpFBx98MD3pSU+iL37xi3u/v+aaa+hxj3scvfWtb6V3vOMd8zjCQCAQSCNovR/8eXzmM5+hl7zkJfSNb3yDzj777K56e/bsobVr19LJJ59MX/nKV/Z+/4lPfIJe9apX0X/+53/Ss571rEEOPRAIBCoI+u+Hl/7Lus997nPphhtuoPXr1w9wtIFAIGAj6L8fXvp///330+TkJI2NjXV9/3u/93v0D//wD3TbbbfR2rVrBzXsQCAQUBH0348c+V/DxRdfTG984xvp61//Oj3lKU/p40gDgcBCRFDTQCADmzdvptWrV1eEEyJShZPLLrts79EFk5OT9IxnPIOuvfbarjq33HILvfzlL6dDDz1071EQT33qU+m6667bW+eqq66is88+m1atWkWLFy+mI444gl70ohfRzp0799bRjm644YYb6AUveAGtWLGCJiYm6NRTT6WPfexjXXWuvvpqGhoaon//93+nP//zP6dDDz2Upqam6GlPexr97Gc/a/CUdHQ6HfrIRz5CRx99dAgcgUBgn0PQfz/GxsZocnKy67kMDQ3R1NQUTUxMNG43EAgE5gNB/3244YYb6IEHHqBnP/vZXd+feeaZtHLlSrr88ssbtRsIBAKDQNB6P7yOuW9961u0YcMGeu1rX9v1/Ute8hKanJykz33uc43HEAgEAm0h6L8fOYEZEcQRCAQWOoL+++Gl6StWrKgEIhMRnXHGGUREdMcddzQeQyAQCLSFoP9+9CrTH3jggURE6rMOBAIPfYRVIBDIwJlnnknf/va36U1vehN9+9vfpt27dyfrvvOd76RXvOIVdNJJJ9GnPvUpuuSSS2jbtm30xCc+kX7yk5/srffsZz+bvv/979N73vMe+trXvkYXX3wxPfKRj6QtW7YQEdH69evpOc95Do2Pj9OHP/xhuuKKK+jd7343LV261DzW4Gc/+xk97nGPox//+Mf0gQ98gD772c/SSSedROeddx695z3vqdR/61vfSrfeeiv967/+K/3zP/8z3XzzzfS85z2P9uzZs7cOhBkpCHlw5ZVX0q233kqve93raGhoKPv6QCAQmE8E/ffT/9///d+nn/70p/SOd7yD7r33Xtq0aRO9973vpe9///v0R3/0R7XXBwKBwEJC0H8f/ce4Fi1aVPlt0aJFdPPNN9P09LTZRiAQCMwXgtY3t/WkcMMNNxAR0SmnnNL1/djYGJ1wwgl7fw8EAoH5RND/9ul/IBAI7AsI+j84+n/VVVfR6OgoHX/88X3vKxAIBOoQ9L+/9H92dpZ27NhB//3f/01/+Zd/SU94whPo8Y9/fF/6CgQCCxydQCDgxqZNmzpPeMITOkTUIaLO2NhY53GPe1znXe96V2fbtm176912222d0dHRzu///u93Xb9t27bOmjVrOi996Uv3tkdEnQsvvDDZ52c+85kOEXWuu+46c2xE1Lngggv2fn75y1/eWbRoUee2227rqvesZz2rs2TJks6WLVs6nU6n841vfKNDRJ1nP/vZXfU+9alPdYioc8011+z97uqrr+6MjIx03v72t5tj0fCyl72sMzIy0rnjjjuyrw0EAoH5RtD/PPr/+c9/vnPAAQfsfV6LFy/u/Nu//Zvr2kAgEFhICPrvo/+bN2/uDA8Pd17/+td3ff/zn/9877O76667zDYCgUBgvhC0vpmt59Of/nSHiDrf+MY3Kr+94x3v6BBRZ8OGDZXffv3Xf71z/PHHZ/UVCAQC/UDQ//bpv8RznvOczrp167LaDwQCgX4j6H//6X+n0+l85Stf6QwPD3fe/OY3Z/UTCAQC/ULQ//7R/2uuuWbvc8V4tm7dmtVPIBB46CAyIwcCGVi1ahX93//7f+m73/0uvfvd76YXvOAFdNNNN9Fb3vIWOvnkk2nTpk1ERPSVr3yFZmdn6dxzz6XZ2dm9fxMTE3TWWWfR1VdfTUREK1eupGOOOYb+9m//lt73vvfRtddeS3Nzc119nnrqqTQ+Pk6/9Vu/RR/72MfolltucY31qquuoqc+9am0du3aru/PO+882rlzJ11zzTVd3z//+c/v+ozsNbfeeuve78466yyanZ2l888/3zUG4L777qPPf/7z9MxnPpMOO+ywrGsDgUBgISDov5/+X3HFFfTqV7+azjnnHPryl79MX/va1+gNb3gDnXfeefSRj3zEdQ+BQCCwUBD030f/V65cSa961avo4x//OH3wgx+k++67j66//np61ateRSMjI0QUxzUHAoGFi6D1zWw9HqROxooTswKBwEJA0P/+0f9AIBBYyAj633/6/4Mf/IBe+tKX0mMf+1h617ve1bd+AoFAIAdB//tH/08++WT67ne/S9/85jfpoosuomuvvZae/vSn086dO1vvKxAILHyENzAQaIDTTjuN/vRP/5Q+/elP01133UVvfvObaf369XuPRNi4cSMREZ1++uk0NjbW9XfZZZftFWSGhobo61//Oj3jGc+g97znPfSoRz2KDjzwQHrTm95E27ZtIyKiY445hq688ko66KCD6H/9r/9FxxxzDB1zzDF00UUXmWPcvHkzHXLIIZXvDz300L2/c6xatarrM45YfvDBB3MfTwX/9m//Rrt27aI3vOENPbcVCAQC84mg/zY6nQ697nWvoyc96Un04Q9/mJ75zGfS0572NPrABz5Ar3zlK+n3f//3aceOHdntBgKBwHwj6H89Lr74YnrZy15Gb3zjG2nVqlX0yEc+kk444QR6znOeQ4sWLar0FwgEAgsNQevbA/qV4yEqNqyvXLmyr/0HAoFADoL+BwKBwP6JoP/9AQLQjjvuOPrP//zPvWMIBAKBhYKg/+1j6dKldNppp9GTnvQketOb3kSf+9zn6Nvf/jZ98IMfHEj/gUBgYWF0vgcQCOzrGBsbowsuuIDe//730w033EBERKtXryYios985jO0bt068/p169bRhz70ISIiuummm+hTn/oUve1tb6OZmRn6p3/6JyIieuITn0hPfOITac+ePfS9732P/u7v/o7+8A//kA4++GB6+ctfrra7atUq2rBhQ+X7u+66q2uMg8CHPvQhOvjgg+m5z33uwPoMBAKBfiPofxUbN26kDRs20G//9m9Xfjv99NPp4x//OK1fv54e/vCH920MgUAg0G8E/dexdOlSuuSSS+gDH/gA3X777XTooYfS6tWr6YQTTqDHPe5xNDoa5odAILDvIGh9bzj55JOJiOhHP/oRnXTSSXu/n52dpRtvvJFe8YpXzNfQAoFAwETQ/0AgENg/EfS/HVx77bX0tKc9jdatW0df/epX6YADDpjvIQUCgYCJoP/9wWmnnUbDw8N00003zfdQAoHAPCC8gYFABjZs2KDuQPrpT39KROVOpGc84xk0OjpKv/jFL+hFL3qRu/3jjz+e/uIv/oIuv/xy+sEPflD5fWRkhB7zmMfQCSecQJdeein94Ac/SAooT33qU+lzn/sc3XXXXXvHRUT08Y9/nJYsWUKPfexj3ePqBd/73vfo+uuvpz/5kz+JAIRAILDPIui/DytWrKCJiQn61re+VfntmmuuoeHhYfU5BgKBwEJF0P98rFixglasWEFERF/84hfpZz/7Gf3N3/zNQPoOBAKBJgha3z4e85jH0CGHHEIf/ehH6WUve9ne7z/zmc/Q9u3b6ZxzzpnH0QUCgUCBoP+BQCCwfyLof39w3XXX0dOe9jQ6/PDD6Wtf+9pe21AgEAgsFAT9Hxy++c1v0tzcHB177LHzPZRAIDAPiMjAQCADz3jGM+jwww+n5z3veXTCCSfQ3NwcXXfddfR//s//ocnJSfqDP/gDIiI68sgj6a/+6q/oz//8z+mWW26hZz7zmbRixQrauHEjfec736GlS5fS29/+drr++uvp937v9+glL3kJHXfccTQ+Pk5XXXUVXX/99fRnf/ZnRET0T//0T3TVVVfRc57zHDriiCNoenqaPvzhDxMR0dOe9rTkWC+44AL6j//4D3ryk59M559/Pq1cuZIuvfRS+tKXvkTvec97Gu1G/eY3v0lPfepT6fzzz6fzzz/fdQ12gr3+9a/P7i8QCAQWCoL+++j/okWL6I1vfCO9733vo3PPPZde9rKX0cjICH3+85+nT3ziE/T6178+jmQOBAL7FIL+++X/yy+/nO666y468cQTaXp6mq6++mq66KKL6Hd+53foBS94QXbfgUAgMCgErffT+p07d9J//ud/EhHt3YD4zW9+kzZt2kRLly6lZz3rWURUONje85730Gte8xr67d/+bXrFK15BN998M/3Jn/wJPf3pT6dnPvOZ2eMMBAKBthH0v336T0T0k5/8hH7yk58QEdHdd99NO3fupM985jNERHTSSSd1ZcwPBAKB+UDQ//bp/89+9rO99/GOd7yDbr75Zrr55pv3tnPMMcfQgQcemD3WQCAQaBNB/9un///xH/9B//Iv/0LPf/7zad26dbR792763ve+RxdeeCEde+yx9IY3vCF7nIFA4CGATiAQcOOyyy7rvPKVr+wcd9xxncnJyc7Y2FjniCOO6LzmNa/p/OQnP6nU//znP9958pOf3JmamuosWrSos27dus6LX/zizpVXXtnpdDqdjRs3ds4777zOCSec0Fm6dGlncnKyc8opp3Te//73d2ZnZzudTqdzzTXXdF74whd21q1b11m0aFFn1apVnbPOOqvzxS9+sasvIupccMEFXd/96Ec/6jzvec/rHHDAAZ3x8fHOr/3ar3U+8pGPdNX5xje+0SGizqc//emu73/5y192iKirPurKflLYuXNn54ADDug86UlPctUPBAKBhYqg/376v2fPns6//Mu/dE477bTO8uXLO1NTU51HPvKRnb//+7/vzMzM1F4fCAQCCwlB//30/3Of+1zn1FNP7SxdurSzePHizmmnndb50Ic+1Jmbm6u9NhAIBOYTQev9tB7Xa3/r1q2r1P/EJz7ROeWUUzrj4+OdNWvWdN70pjd1tm3bVttPIBAIDAJB//tD/y+44IJkXa9fIRAIBPqJoP/t0/+PfOQjyXqy/0AgEJgvBP1vn/7/9Kc/7bz4xS/urFu3rjMxMdGZmJjonHDCCZ0//uM/7mzevLm2n0Ag8NDEUKfT6fQc0RwIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgE9jsMz/cAAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgcC+iQhGDgQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKNEMHIgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQKARIhg5EAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCDRCBCMHAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUaIYORAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAg0Aij8z2AhYK5uTm66667aNmyZTQ0NDTfwwkEAoH9Hp1Oh7Zt20aHHnooDQ/3b+9M0P9AIBBYWAj6HwgEAvsngv4HAoHA/omg/4FAILB/Iuh/IBAI7J8I+h8IBAL7J/pJ/6enp2lmZqbVNnvB+Pg4TUxMzPcw5g0RjPwr3HXXXbR27dr5HkYgEAgEBG6//XY6/PDD+9Z+0P9AIBBYmAj6HwgEAvsngv4HAoHA/omg/4FAILB/Iuh/IBAI7J8I+h8IBAL7J9qm/9PT03TU4sV0d2st9o41a9bQL3/5y/02IDmCkX+FZcuWERHR7R//OE1t3lx8uWsX0fR08f/u3UU5NlaU4+NFuXQp0QEHFP+jnJoqyiVLinLRIqKRkeJ/RPePikfPo/5nZ4tybq783Ol018fuLYxjdDTdJu8bdfBbquT9W5C7FZpc0w94xtFPeO6xj2O89tpr+9Z2NubmXM/jkb/2a2X9nLYlcuYX6nr65HXwP1+jRER79pTtYt1hjWrjkv3KOrnrUaLNeSjvFSVok7WjVKMv1r3+Clt37KC1xxyzlz73C3vp/5e/TFPXXFN8uX59Qd+JiCAgLF5clPydgqbK982vwf+SRnPg+eE5oJ3Fi9NzCNeMjFTpPec/6Fu+hxQ/0NA2vRoeXjj8YqHDek7Kb9f+8Id9HIwTObSVGP3vFzz0V0Mdjc7pT7aXs6Yc9DKJubn21q/VToqO5MxfJi9svf9+WnviiYOj/1deSVPr1xdfPvAA0X33Ff9v2lSUd/9Kfb3zzrIOwOk1UakHrF5NtGpV8T90hMnJ7roTEyUtlrL9smUlHwKvlTt6lywp28azwjVcRwBy3lNTntAG3ebt5873OvQ6Pm3e1vXRFi+rub95lf+b6IRE9MhHP9quoLWD73qVc5vyhpx7zZHtpX7B67cl08s+NB5hyesA1wd27Sr+l3YTYGysSo8k3WP9bN2xY/D0/xe/KL7cuJHo/vt/NZCtRSnvZ/FiouXLi/9BbzFWlKtWEa1YUfwPWRx2JMjqo6OlvC51jNFR/V3xksOy6XhkkjrkrqOG9CCrX6vPuvXS1nhyn3Oq3xboVCvyvzUOS+f30CdlTjzy5JOL/6X9k9N4Dw2se/Z1zzfVh6bPpNqcnS1tQfI3bkO27AES0v6ija3uWg38fq21Ku9fs4Gl+vHYlxXb+NZt22jtcccNjv5ffz1Nff3rxZd33020bVvx/86dRbl9e1E++GBRctsmxi3l/8nJ8jvQf9iDwDMWLSrpPUrMk9HRtG6B6xcvTttyNLl/oaJXe27TPgckl3ehbX2GwSX/9yI7N5Vzjesb2X+aykGO8WTZfZroD5ZNJkfur6PbbSBHntLQUL7cev/9tPZhDxsc/b/qKpo6//ziy40bie65p/if23lSAL0+8MCihI1n6dKS7uO71auLEgFwRxxR2oik/L94cfkddATwD9ThcpjUqyRf0LDQ+QJHP3jEAqHbbbVXkf+b6ndNZPmGSNJ/jcb38lw1esnbTfEUj+/S4gMWD7Vou+e7FDw6jzYe2Veu7S3H7mn8tnXnTlp7xBGDo/+3305Tf//3xZe/9mulnV3TuQDpr4VMzuOBQL9Bi/Hs0P7u3dXnCX4yOVnVG3r11/Zin8iVdTztNLVL9Uvu0dZ+W+vOo6t7nplhM772+uu76yu21dbQkj06S/5P0SXLNuKxtWj036NbaL831IOyodldPPxIQ686kwcZtGfrAw/0xf4zMzNDdxPR7UNDNNVqy82wlYjW3n03zczMRDDy/g4czTB11VU0dcopxZd33VX8EZVCAwSNgw8uyiOPJDrooOL/Y48tSiiaXHDIYchtG8YsZ2OOEcdqR17bdKzaGHKYiTUO697rhHavI6QXpTZH2aoZwySEVxnEngNu4MhhrB4BDHUwvrk5mkoF01vGFEtRbbLm+FpNldqcbCIUe+eKR6GsE0Y8wrTHeKp9bmqolE5Hre1fOYP6fXTOXvp/+uk0BacoURmEgGC0LVu6L1yypNvphO+IujeleAyCOQZ/wHLyNb2+rv+c9Z2LHCNGG/15x7EvgD2DSczFFCy+oK01Wd8zBzwKMGtnCkE9qfa88NApj4EwNY42jCjedrzIpd0peIy0TdtO1dfej9jQMSj6v+y002kKDqjZ2TJYDCWC7cAXZmbKcR9ySFHCqPKoRxXliScSHXZY8T94gmdNcP6Yksl7XSOpwB+tbY8xPvV5ISN3re4DSNL/tnVLC5nG2ymMuZcxWs4kDZ453kSWt4zGnutynTc5sqOH9jRZz5xO1dXjpfye41dBYAOT/5/2NJ9xEs6ll7yE6PTTi/+PP74oYf+BHqDZDuSGxaaBidZvKRrPxyFLvmExoKNt21dOn/ydyvfKgk8mp8QsZnaWvWjiqFLsNsn2ZN/ab6xOUv730tIcWA4nbU3IceT0Yf3usdvk8J8cR1rORmStrSbO2dz6v+pjYPR/0yaawsaRVatK+//GjUWJdYW5evTRhQ+AqPQDrFxZlJBnPDRV+70f9CSH96autaDN215sXnX+idTctXR9z9poS8/qFR45TnlmFfm/X3J/i+1OYd3Ivjw0ulda1NRvk+MXsdpJ1R3EvMvxnRClx9SUP2ryKf7/lZw8MPr/qEfR1Mc/XnzJ15AcK98MDj4KWV4GCufI9Sn06rtJtdd0LeTSaa0cFFJjtXyHQJ2vcFCwnrfiN63Q/1QyMy9yeHjDayvyv6zjmase+2Vb9DsXHt9Ck7oWcu3MnnbkfPPIU54xad//qq+B0f+pKZo66qjiy9WrSzs/Nh/CD4C5etBB5f9Yc3JToZYEKmwsOpraLa3re7Fp9oomtKPpfTJMIlmO1ba05UgMDfU2Fq3d1DoeHqYp2E1z+0nVsfS+FJ9vulab2L15ELFlj021kwsp8+U+z5z10utaEzbNftH/qeFhmuozb3Gh0ymTF+yniGBkiU9+kuijH62vt2ZNUZ5xRhmghqwJRx9dlDBOTk2lM1u2KZzkEIAcgiYJpqW8NVWK5WdLKWhKTD1GyRxG5BmPZvBv8q49DJUDxhJZhzuRUgoqH5/lWJK/S8bJf+MZg+tgZcaRu70l+HNJPZ+m79symmnzODU/LMevZSDJUeI8xoumwkUv69CzwzR3bG3ikEOq2c/qgE0oT3lKUZ5xRlE+7GFFuW5d6cSSBkqPYaSp0NjrM2wy3watbNc9Gw890GDx0rafq7ZWPQZUi3em5rBG/y364hHU5TyV7ezenQ5m5mOA4SeH96bGYn1nzYO2DI+eNrkskNqI45G1LOWalx6jcerZa+vIMj7K33j2zLp3NzxcZiJDhs0BYWjj3USvfKX/gtNOIzrppOL/444rymOOKUoYV3bvLnUEPAcEqml6Af7Hb03hMew17aPJOumHUT81FzktqjP4c5lErkdrHUpZpm794BpclzI85egcsn+UKXrLDR5176PTSesPXFeQeoPWV92mSE7/Afk8mmY2SLVHlNaTrH5z+UeOXOeRBTy0PUcv12hPE6ccn/918ovVF68DPjAonHYa0fe+V1/vZS8rynPOKYOQefAxx/BwueZhIwI/QLl1a8nrsJ6w8d3KrAkes3x5Vbew5LpebEWch+faPlJta4Z6WUejl3WyuLV+tM91vEIbK8+eZI3Var/uN62OlB342qujt3v2VGmf9pzQpidg2WNMl9mCuR6AUygAPDMtqCdH/pfXaHNCsyvh+Wg0KGU38tg5rOw92vjrMhx65pRmj9Jka3kfmt3IOtVPIocPab9Zz7EfOP54one/u/h/506iHTuK/zFvQXcROHn77cUJWriWqLQH4VhRbv9P0SKuz1u0tS27imU7aAKP3GONo87GmeqriQ1S9p1L/1O/abJnzlrlderGYdmIiKon5+TYcbT2PLIaYPGenDFbfbRh08u9XmvL4kMpHTJn7B7ZjSPVhyXje+l3aq165Lq6tlNjhLw7SMB/qwEbzh/72KJ81KNKu8+hhxYl6D5kcz4XPPDohU3oioZcmbNfqLu/XtvT2m4jSDx3HB5erunzFp/U+rACzIi6f/fQISnbc7ldti3vS7P/y7rW/QCWDdtrS6nrwwIfax3t9MpDdfZL7bsc/bUJn9TaSX3Hv/fKbnV8kNeBvWSQ+Kd/Ksqf/azkP4j3QaJC0P/h4Wrwca92+xzkyM3W9R702odnLvbK13J00Zw6lkyuXS9Pj+axI6mTCwDL9mWNR6MD8n7gF7b4ED7v2ZM+3cmjMwF8PVt8wLLF5/bJ62j0SXsvKFP0LMd+qeln2vjq7IJ8PKlxaHxNe//S75S6B16nqWyvjU2253m+g5A9AwsCEYwscc45RDfdVPx//fVpAwmOa/7JT8oFA6M1jOoQYKamSiNmypA7Pp4+vlRTmjAuGEu3bSuNyykD7vBwKVwh7bnczcsdPNIZYO300gTKOkOyh7l4BO1e4TUKNRHcLILbZPzea5qkevcY6+RnHrAgr9eU09R7Hhrq3omuwcvgU2PV6gxiTnnGYdVtey61Ne80AawugERbR1KA59+l6G+/8MtfEn3gA8X/hx9epX0IKkY5MVHOc37kMn4jKvgC7kP+hgCG0dGyDrIwo5yerj5rmVmNC7YYB44J4hl66hRDfiRoagPN3Fw5VvAcyXump6sBhegLY56aSh8758mGrv2ufZbBGdYzsBSYJoKxx6jUlhDO28URTznGMiA3a3IKlsNGy6wmg3naMrgDHsMNl1s8hmRrrL2Mt6mjqlf+lTPmHONS02chAxT7DZx4kgIcTU9/elE++clEJ5xQ/I/NhzJD/vi4bx1aDsQUNONH3dyU/dbVkSXn06lAGU/AmqZHeMajfbYCZCw6pF2rQVvPHqOzdc+p+9HkKI+R1VqHkAm076ys99p1so4cG8YOHqRBM0qmMuOk+tU+a0eSW88lJ7DKA2vtNnGOWWvE6l/73voNpWU7yNnEmLNZ1MrOjkz0g8JrX1vS78WLy4w4GCOC0MAH+P1Ius+B66E3IFBBM4Kn7C4cGm3l2fq1OprjTEOKf/BSvjvI+lrGOC0LNNqp60sD7zt1r7zvlB5jtW3RVLkO8L75+pElwNeR7FPW88Kib6nnotF1TSZPjcsKTsb1vD3ZP67j44D85NGPUuB9pd6FtvlI470pG5pGw+Qzm5jw8XCZaVHLdl3XjseexemL9TxTdhutTev7JjKopY8PCuPjRP/n/xT/L16c1gXxfJYsKem+tPFb96w5Q1Pz3pO0QHu/2jUpnqshNbc1WcCyYXjmi9VXkzmQY+Pt1f7ShK/U1a2rp/ERjjrdxHt/vdoTiLptRlIn4O2nxpz7/lPPulc+a13neU5em6a3Twse25XVrkcOkrKNNm81mmTpBvL/+TiyGP7biYk8udRC3VzMpUFaex6anurD836s/q22c65tS//mny2dlsgv66TGbfGaHBu2Zsvz8EWtzTo7Pf9d2oE878fTZ+677MXOoT07q/1e13NqPFrf1m8p25lVZ19GDk0jGrz/l6gMRl6+PG3D4LaMOr3Mwzs1cFk9ZW9ByeV/AGOGfWJy0k6yIftNfW/NY+1ZNNEFte89ukqObtxW/1odyVua6NO9XAfk8B8JbpvRbDqpcUgb0chI2o/AkZP0QyLHbsj7krEBVv9aMG/q+fLvPXNTgstynjg62a/GHyUN8bSTI5dqckvduOT18v829E8Lw8P5p0T0A5EZOYKRK3jgAaLnP7/4/wUvKIkvGDoM5kccUZSHH146qHhgGZFOkCwsdKFTIxYWofQ4epsQnSaEUusjxSTq+m1S1zNWD0P0Cmc5fUnhgX8vhYimzKHu2XkUteHhekOjx5GhCe4AD9hMjVFjurKOBx5Du1UnZ+3wNnKEPk3wsp5PzvMYlMCRg/FxovPPT/9uKVsWPPMf74XzFv67rO8do9W3RRckjdfmEBxx2hHrufSVX6Mp4k2NZHXQrrGyV0l4aFeOUbOp84UDgm1KwOW0NDUODjwPLZuycfROLfjOW25Y0a7nwSfWztaUcqwFqGibJ+qMS7I/rY7HqK8pmk3WTN1Yc9CEFufIJLkK54AzI1f6zFkjnvkif7PWWiqgSbtekxMsGT1nrBKe4DitbU0fyHE8yPvRjqrX+pbPygqksJxAOXw11Z4cm/ZZG4/3RIkUJN3OXec5Rl5P2205HjS5qO49WTTI+1zqgnEsPaQXWYW3nSsX1QUBaLzTA64XyPmq0b2c54GMwIPC//pf5f/LlxMdeWTx/xOeUJSPeUxR4tSTNWvKAGM8Xytjeiq4QqNTHCn+wT9rQdD9ggzYxDMYBHDv4+M+XdbDw3P4s9W+pAsWr0z1lWPD0tohKu02KKUzqdNJBxTz7GkpedQTuMDHZfUBpHRcLWBT60OOM7XmLJ6u1Us5WPjYZFA8p7dWMLy8H0/gvEX3U2P2ymX4TqNhnvFoMppsR/bbq8O1bfz0p0W5cWOZ7AO6KTJLIanH+HhV15abwdesKTNqosRmFC0JiISla3jsnd7f0V7dM/fak+p02VwbWhN4dNMcnsHh0QHr+LZWV7PNaO22xbNSmxEtmtFERrHQ6dQHDXLa0YvdXbbJSw80+7/FI7TrecnrW77DXu2gHt6Zo5PKd2E9F08f2ljnIxjtne8sStBvojKIHjI2Ns6uXl3ScsjAclMi35gE9ErvtGfoCfJO/eax/2h6ngUPDfP02VQeBjx22CY6usemY9Epy4acok/aWtPGmMpoCWgJRzR9IDV+7fnINjEGzT+g2aOkrTeHTuTY13h9zf6ew4eseSJtIJpNRNL93FNTUnVzYNFvre2cE9xSn7V2U2MaNK6/vij/v/+v/A6nVK1aVZSg9YsXd59gxX9DXNDKleV3qRMRrWep2RDAf/hzSvnEAI1mDCI7PO8/Bc9ab9pXai7yMrWevYlKvL9Z9D+Xp6bmjvfZ1fEGbaxaX6mTEWV7RPZJ6xY/lP/n2DeaIsV7OSy5xWOLqXt3nNd4bDIp/qydKuaRC7U1kqprtZ0jK83NlXL/5s3pMbYBPn/nE3Un2e0HiGBkiYsvJvqf/yn+54tFOl248wULJyWs1SlUqGMRL4+B0EM8JZooVF5hvK5dT191DKAt5lRnMPUIEV7i3kTA9hgG+T3AaI7vtKC0OqMDUd5uV7SnBazJ9iwjF2AdoZAaj1dB8gg+KVjruel7Tt2HpmRo16TWoTaGVABcqr4ch2yHK9ughcgohnloBQ2hDsqZmfI+8N2g8OY3E911V/H/N77RW1sIJv5//p8yiAFHumFTC8+w7Mnu2LbSJtGrIsPR5Gg67T7busf5MGxofdfxD0vwB+poUOpYNX59SgjXnpOk6fjM1yfGA2csN0Z65m8qmMZDn7T2rL6aGLhz3g9XqFLteudjjjwn27bmncYrZB3LgGohFSTE+YglN6GOPLq73zj+eKJ77in+X7GinMswNMLxBNp+1FElTV+7tigPO6woOY1PnYzCgefa9Ji31HttC5Ys3k++lDKseORtq73U59z2tLasNZbTX0o+rTPgp9a65z4sOqkFkVnX59Tx6JJ1z8NjMLSMz1r/ubpXHXKNmqk6lq5hzQ/52ZOdUZtT8vqdO8vsvPI0DE7bpHGVn6aBEv+DFg8Kr3wl0Sc+Ufy/ZQvRddcV/6P8+78vSpx6dfbZRE95SvE/jvDEZkI4jPg959g3+iEDzwc889ZDky1DvXVd3bg88iXf+CLBgypTjloP3dUy9kp49YgHHuhuB8E0KD2ZarSxtaWfYaxcB6lrRwuazemLX+t5vh7ajt9kwLN3fE1ke+vaJvxIq6tlbMb/sgTNv/NOol/8ovj/hz8syl/+sru9tWuJTjqp+P/EE4sSsjTk3tnZMvh30Nlidu4sdY7/+Z/ixEGikh9h/eAElaOPJjr22OJ/bFyR9J9n2GwLObI458849jplA+Q0WdbRskHLBCwc/bpnjW43tQPL+hbd9tj7PfbpOvsP/7/OL5Eac0o+z+mDv9OUrYj3k2qHb2qR65lfk2Mv7FXf9MgkHh3Sq49psE489dhbrL6tzauWHJ8zNzW+KmkHTjbZsqUs8V1KN921q6S7Rx+dvo9+4bnPLceTuld++k9qLmpyYVNY81X2a31O6Xe58NClpm00GUcOTe51LBZd8GQ8rbOFe3i7xz9AlJbzF4o+ycc3H1nQAf7MU/ZXjyzA349sx5qbkm5yGTs132RCFaJSTgW9xQnet91GdMstxf+33lqUGzYU5caNRbljR9kX5Fy8E07nrOy6qSQ4eM+LFpU2dOgB2smCCPDlG0IGhTPOKMonPSlN9/nJtp7TXOtos9cm6JFJctZRrj3Ye32/2q27TqvTi33fI4fltOuVL1PwynryRDmPvGzZyTG3tWBi6AByzaMu9/9aaCL/e+ypTXldznuy3rNVJ6cdjTfge3md5rfF//JUa8ja999f2ttR8pNpQVeg+2NDNmj18uXVDXmwgaCcnNT5lrw/jBH8q18YHo5g5AWCCEaW+OAHiS69tPj/rrvydubCGAnhClkQDj64FLT4oiTqDlJIHc/Jf4OQCELAj4vQsvzxcng476jVttEW89V+y3He1NXTvve2I8fjMVZZ3zd1zGN+pOpyQcNCardrql3POLV2iNJrzXr2TQVEKzjWcwRhzlxOKR656yHlZPC206sBVwYBeZQejwNZU+zxP5xSg8KznkX0rW8V/y9b1i2oEZVKvubUAb2GcIaAhTvuKHd44ZlxAY6oEOJklmG+8QX/pwwlGv+QRwp5d3k2mZ9eOs3B20/t6q3bKS7XqBXQ3UTBzXVG1aFXR7imhGm0vc6Ju2dP9T5A6zXhPOf9ejYQyDF3Ot2KjxcWP/A4GLW6HqUxdb12XQ6P1OQG+Z01vqaKs+f6lHzJxyXXrfbZs1abGNbawD33lFkwZ2fLwATwgTvuKEooyT/5STUTAug+Si17jlTIlywpHfvIBgoewX+zHF91dMVrCPO8nxRyaWKOPNdE7s4dg+e5NKH7vI2mRqlUXe1eJf0HTbYyUaIdvmGwSTCQZ4xanVQWdEsHs+ilZ1weaHMq55hF+dnzLrU+5fyzZBvt3dbNX0s2T9UnKukEl1PrrrH64J8RVDko8JNRtm2rZpjCZx7UedNNxf/IqAlwhx53XvEStH7x4qoBF/R/0SLbpiNhvV95vaXr5shN8rM2Nz3z3UITumndT07f1gahXPkrh6/KZ8fXsKXPL1tWlCkjtyb/NwE/XtB6P3XG9rm5Ute37ivH+dREJ9X4s+xLczRp7fQyt1NtanVz9VrZjrYZRc63iYnqZlHZ9kknET396d3X96ozw+4yKNx8cxkkgc0lHBrd5IEWRFXb1cxMes175JZcpGQjD3/W2vF8n3v6ShPk2GNzeYN1nad/L7zvualtCfCcVpVzHx5naVM7Sep66x1asrBn3nn6yrEl5tBgax61fSy3pTN5xmP13eSePeDtDnoz+jXXlJtkND6v0Vb5/PjmD6KC/tcFrNU9S0teSUFuIMuVU1PIvbYXemm9A+07ja966H5dNlzvb6n32uuzz7Ud1PXB7TpW3V7ovrQn1UH6+nKek0Wbc2Bdm2Ov8fjaLHusdWqINSel7xAxKY99bPV6bV1ZiapS31m00MOPrN/gZx0kYLf32Gpzda8m7dS1hetT7zVX3uzlnrU+PfbYnDVr6eHWfKubm1Y7njF66K7WRy/8IPUd6Luk36DFns3o3LaT8gN4aLuVGZk/Z7kRz+J5Hjs5oNG0Op6ltSPbqEOdDGzJJrm6bhPdNlcm91yf06anbr/9v8PDEYy8QBDByBIPexjRK15R/H/zzUTf+U7x/223pa+B80gexYZdvWvXEh16aPdv2PEFJZUvdu0ocskMILgjOG7nztKZmwoGWrSodH7BWSED4PjOhSZKpIdIexQqwAqy8KCp8W1fhxQ2JMG1hJG2GFmukGUdp4nPdczbOx4ru16dQNDrvWvt5jjZAn1D57nPK4PKJifLdYOdhlCOQX9nZ0snHa7TAs88cwhtItAN5X33lXQeawT8BKWWfSdHWOQ0Wdt5J9vgR9ARVdfTzEz5rMCX0B4PwOObaXh7HiVDjjuFNpxICw11tBWBLDltNA1G9ih2HmDdWMdNeZS1Qb5vi243cSA07bvuHVgG+CY8UBuHZVzl7XqUdAB0b1DYsoXod3+3+H9srAw043I2UZkF+dRTiR796OL/RzyiKCH3YwOilvXegsdI1UQ2Gh5ulq0eJei4lsHRypZhjadt40WvclPO+HuF5UDrFeCn2pFrRIX8nzLcaoYRORc4r5Dj1gyO8jrMId6XlCEsnSMnezja0eZtjmHaOkLZQx81vpaaA1r/KDX93GPATbUn29DqatDsFfJ+tODXOvrG6cIgj5EkInrVq4i+//1yHJDBkT3o3nuLEu9wxYoyMz4yZB5/fFFy2RyQz17bjKLppp41kZJFeFn3XHkdj0FcljkyBf/fMxc8czGHR3iOzfW0Z+koTexhOY7B1DikDdBDX+RnK6ONNg7YlEDTc7IvDw+XNshUX5YTibeTgvVcPe+Qz5tU1li+ITj1XHl7qU171uY9i6Y2gVfX9rTThuzC341n00+bOP30sv/t24k2bSr+h/wvbRjLl5d2H54JmcjmzxLas8+RKTx6p9dG4pEh6trm7Xh1R630Ppde7D98juXYuXP0GY+uztvtdR2lnJu5jte6cfB7aZKF09I56uwEqXHk8OwmcoPHhtEUTdea57cmm4tzxuFts6lMMwiceWZpt56aqt/8N+jxWaibO5q8oZUpGZ7LITJhVhOdwYPR0bz6Wl9yjWs6dx2PsOiLh0fwdur4qzUej86vtanZgdAW6HbKfsOv15DDU6x26k4X0Z6ddcpe6tkND9vrOYc+pfqyrufzLzU3tXcp+9Dk/xzZT6vb79gPDQuJhhJ10//UM+ef62hfrg0B4HNB+mT5KWJEenZUbltCmVpj3Oak2Uv5eCz5xyMLa21a36d0As+zbyJPacih8d6+cmRPCe2UKE4LpSyeozt57D8WmtCBTqc6Rs/zyaG7npPHPLD4Ry5ybLZyrVrtWe8gZ+zafJVxIbxOkyQX1jhSpyUHHnKIYGSJb36T6EMfqq+HnWbPeU5hwCQqj2desaIoeaBWimlqwnOOcdhi2paAnmpPM0ZqfVoCdh0s44nFQFJ1Um17x2NdZ/XlMdp6mH+O8dcrVMiMUvIoBc7kUrultCxqnufJM6sBcocVnGUo5+bKdSMzvHLG1svcBLigrBnoNGOFvJ9U29r3nvastesx2HuMZR76YN2HbNNzLE0TQZLfM5z/A8LQlV8j+vrXiw8XX+y7CEHHj3tcUSIoDRnyjz223G0Lx5UWeABnLILYeGaeXuZiDvhc8giCUkDl7ciNLlpf8n/NyJS6r14VAUtxluPgDuicNd804LNXJRDrRhoceR18Zx3BI4PGtB2ykt7Lz3NzVfqPcfCgtrvu6r6uVzkodY12HCWvm+I/msHGY7i06tTRYK/hx4MmRnCPrMbnugymkOAZ3OsyLVvt9AtbthBdfnnxv3Uqyu23F+Vhh5VZ77HmQDfxXKamyu+ksRf3t3VrNeAZ109MVDNeaE6gOoPc3Fz1/Uj6pMlGmjFEvnu5q92SCazvmsrvnnXooSspul2XoZ/Idgpo8pwHHtlM4zE7dujjsNrW+kplVvDor8DsbNUZJnkFUXXeWzRIBsZr8kIdndH6sOYEdwDgfzlW3pd8DpKveGVj+Z6x1iz6bznpJLzzsclalfQh96STVLbsfgGZPb044ohStsc94oQHbAibmCjptty8x+eYfL8cqXlqybC9yscWctrul/xutZVjQ9HkOW3e1cldGu3I0dNSNgkNdTIk2pL2Fh4oDPnfChpOJTbQZDTMW9nX6GiZNTYV+MDfQZ28y+GhXVq7Fn+WdTT5v65/vp49QF/aptwcOuvZyJnaOOaRmXjbUubT+tDk1JTsmBNY2y9861tEH/5w8f+nPuW7Bptozz67KJ/0pKI89dSiPPzwkv6n9IBcyPnSFPKdz87qcm2/4bGpNNFJPfZLbeNAzhhlu3W/9Wrb8bQjabq0t2g0TNp4eBIc9IXTHjhN1OxGvF1uM9LkfjkOD++Wny1eLtuvQ8p+5NGlct9TnVznbc+zflK/aX1p9+N5P3KM1pq1yl7pWlP84AdET3lK3jXYfAi6f9pp3d+vWZOm/5wP5OjqFlJzaHjYF4jSZB1KeGQfL01M/WatdSCXd1m2UVlHfrbmuAVrPeesa+t5ykyZc3OlnC7nC7fxWM8hBSuY2QpclnaNHPqt6cGWjJ+yH1mw+pDzzLt2UvzMSpYl++C6qqVDpexqHt7Jv+uV/nvaw3cD9v8SEdEf/mFR/sd/1NddsaL08z784UX5qEcV5cknF+VRR9WfbFtnF8bzkydsWfYfqw9Z8nasrNxyPFof2v9eWHKHpQc30bW1fq0M+TkyWg40Giufg8dPw3k75HTASgIiaTLXB1KnXqFvbkdKgV9rbZKUJzkAHruAh/7j+dT5fz0yeZ3tQns/2n1Z61BeY8nLdTIJH09qk4FFg7Q1o/kHPbYqCYvP97q26jA8HJmRFwgiGFniy18m+ru/Kz+DGEPgw64pZEz47neJ/t//t/gfwTTIppPDIIeGSqIOgQOZFg48sPwfTEbukFq8mOjgg4v/Dz+8u+SBcNoR0LwcH7cNySilsKw5P1PEuEUjZ4cWACFpiCFqiQBpDBWZt1N19+ypBpZZhF8SS87sPEZaj+MLmaRSguHcXL0xpanBkY/Vo0h52skZUw4TzzEUWkKa1U6qT+s7bcwpg4DVB79m0DujTjyx3Gjy5jeXGZER3AP6j4CDTZvKrGkoQf9vvLEot2wpA83AN3D8nBXwZgF0HDR/xYpyzSNwDYHPPHMPz/pMVFVkeOAEnGzyGs4jmmYQI2puqOsn5JiswKX5RN2zu/NO/XvLGSXrEOlBzClIYyb/3jIMEhW8qC5o3aPseIwyFiyDnPb+c/iQpkSm5pSnD48R0VLg20Juux4js1T2B4VOp6Tbs7OlfI0gOtB/niEfvGD9+qL8n/8pSvAB8A6iUu6GPL9qVVGuXl3+hvUHHrF+fTmmW27pHg90BX6iCb5D2zCSnnpqmbUZ/ctNMZr8r2XiSxnfrbm9kOhnCi3qJmq7FiwHU65RRs6lttDEKUqUNvjw8ckjxDWa1ouczNvJccoCWA+Tk73zGImc+eF5B03nr/V8c959L0ZE3ido66Dwgx9003jI6ygxHq4XbNtW/H/DDUWJbPqcHsNug/kOvoJNwxs2lPwDJeoMDZU2nAMPLEo8I86f8MxB/1EXm+QPP7wMkDjppKLEMa58k2SdrMbhoemDpPdN11ZdnVz6nXPscxP7gvf6lN2F221k8LBGb1P02+u8BCzDf52jVKsrP3vkZg059NKTmSa3f6sdea/ejVF17VkOK208msONqConpq6X4wBkYBbHoE9Gue8+ore+tfj/rW+tbqKBTM7tOHITFX67+uqiHBqq+hHAP0CrYc8hqj7nRYuqJxnKZzU0VA0UxQYAfD83V44VJdYjbEcHHVS192CMfCNNW/KxnENNrx8EcvrKcerWXePt22PH5YlHQO+ljgFeoQVpa3SXBy9odTSHr+RHIyP1c8p6Ph5fW242sxTN4gH7Erm2IQ9/99hLmqyDJjanXGhjr2ub05dBByU/5SlE73538f+aNVW/L+R/8KUtW0qe8ItfFOXPf16UmPOdTtWOBPDgMskLON2VNBi0neukoNvwCaCEHX/JkvTpCtq771UuTbXXpk2yiU2pqS0mBzkbCntdzx76YPlfLV6Vsv9riapS4PeSCkrWZE/tGabu1eML1dpuki3esy4sn0cOvDS57rlwPS01Lq/vJIev9WozAA0bJP76r4uSy/+w8aDkiUPkPYJH/N//W5RXX12VzUG3+Trgm8CIymewZElJ22Ejhc6Ocdx/f/XUXlyPa9esKW1BONlR2v/5dSkewXVBbUMqrkllz7cw376BHHrQq7xuraNeeXDK7u+x5/I6qU2N1ncaHc+xWVv3XkfntAzhcly5J0lZcnIvCfms5+KxyeSgjViLuja4LthExuLX9ip/eaHppvOBCEaOYOQKvva10omzc2fJdMHooXhC4Ni5s8qQU9nHUt8BuJ4fAUdUCAySsWMBcaUUv0HAgODCjYqeILIc5UIaE61rc4w6Wl8K9gb0NhEMPIx1PoJ5vIRYcybI6y1DoRQ0PMfz4HoI19oxzRZSWRSI9CNQiLqN8ymHCOAVAuWz8ii1moBgvbteBA3et5XtRrbpMTZpfcg68v64gCDpnLZGmhiJNMyHcoL7ue++cmcuAo3vuKMosfFk/fryf/AIGB61gGMZKIwgg8MPL42GoPuoc9BBRQY2Xl/utB0dLQ2VWkbxOrT1vnKQ6xTIacNah5bA3Pb8rRtb3fcp+qatR3596phmDfJeNT6Qw0M9kH2OjlZ3ezeBxiNkn3V9SFqqXVOnFPeq/GmKWYrXWDKKNo+tdjxrRfJOyxGoracceWcQWbk4brmlDBSYna06oUDTuTFSBkyDDiMIeOXKKm3HKRD4vGxZ1WjHDZdNaHpb8Mj0g6CbCwVtGUpSa9Ujg1r8jbernVKSus4DzZlVt547nXSGZe6wTV3vMfhp6PU9We/HQ/+bOGzlZ+97y7letqPNpSZ6Ve7z9vQxaPp/wAHdWZPwv8yAAflqeprowQeL/2WAGexIa9YUp6MQlXI8Tj/hGZPbvtde5t8g2utXm7nX5I41Vd+S1ay+pBw1M5Om6dbRvJYTSQsC8Mj98npAawdoQnc7HV+gQZ2cocnolk1Pwrqe6xUe2pWjA1jPMHXPXn0G7aXsNvjM6Y9Gi3ICw5rwDw2Dpv9PelJJ84eHqxnS4ejn8jhsoQgikwHDY2Plb6D3kPst27y2+c+y66V00ByZZHg43cd82eJybPJtrUsJTzs5vg+rjiXbe0/qgr6qZTIm6k5+A3jovyWDy3Fw3UPymFxZ3lvXY/fztifnvadtSzbXaHzOGFN9cX6Uw0+08fWix3vmhsazFpKtYMuW7oBh2H8g20PW107JkRvWOV8FnZe2ffAFHozPN5gTdW8Qb4uv9nJ923r1oNqx+GIvJ7B5ZM7cNVZnT7Zkv9FR+5RbCStBlEeu9KAu2GZoqDdZL4f2WPY1bstN3Ztlw/boLNaYNeT4i1Iym0dOtPri13veUxM+pNUbdDISotJGw/V5Sdt5/E/dxmNN/pc6gkUXtPlm6YA58yUH/Pq6Dci8rkVb2/KX5dhbrN/akEGJetMx+LzLkQd5Oyk5m8v42gZ1L3Lvz8OPPPKg5z034Vkem7r2Wxu2L+36tu0/nndbpx81mYta/yl+rI0x9yTFXPC1Mp+IYOQIRq6ALwieqQ9KJAQVDTnCaw4R40YLj0IljVR8Ny9+wy4sMAl+nCMyuaHkijdRQSAglCGzD+ri87Zt5XdSgefZGdAOgjtwPT/ikWeIJiozvnFjMXasoU8tWByAAMWDtvGuZYm+V6+u9i+zSk9MVLPTyWM1xser12m701LH7GoBKhYTTwmoc3NVAiiPZNCcLqm6ROnj2SzGyoUmzAWLcdbtXPMwTG1zgGWwRx8eJUwzGvVyJDqvL8fDP0vHktxdqe0Gs5wVlsDmEY6aKBmW8jUorFhR9n3ssWXmsJQDb8eOKs1BwNpttxXlHXeUv+F60F04C7TsB5gT09NltgVkX5MZGjZtqmbo0RRGjBt0dvPm7jHfc08zAwCUbZ7BDQZXBOKhRCDGkUeWmTrxnczGwx1xHmXFMtBYsATiVLvWNU2cEznjrLvOc0yPbEsGLGj3pW0mqVvjvA5KeSTo3Fwx93i/kjZ6d7SmHDyeaz1zi9NJj2PHGl9KIdNoqRVknUPbtbES6fMod25b/XvaBnCv2nHp/cSiRUR/+7fF/z//eUkf77+//32DPiJrAWjpunUlncRpJ8iUBrq7aFE1GELSUh5MkpIXZmbqjavj493HiPOS123DiLJQ0KuRSUMTB5i1jvhak7I3PnuPTAakTG8ZN2W7kHE0WqbpBjwIiJfaPXs2nFjodd55ZBGJHBpo0e2cvjxOMQvWWs1xwHl0BG09QCccFC67rMxsfNVVpQzfFo46qiiPOaYoQeNXry5PBuLfERV0XNoKeBAzkR6cCmgySY7jV16j2ZpkXQvWfLGO/vOsn37yE8uW4oW1njW7gLVmU+uPB9HLsbdteB8erm581OxAdbLn0FD6uebOlxQ8tg0L2tqx2kyNjb8n/J/KMOWxheTMDev6mZnyf49dTF5vjTVHL+J9DVr+v+02os9+tvj/nnuIbr+9+H/DhqJE1mPOl0CLQbf5SYT4nduRifTNKHJ+IdHIAQekN+riPe3aVa5DzCVpW4cdnqgaHCFtTrwvmc2N255Tp6fwADoLdXTO0m097XrXei98hLdXt0assWvPPqd/3jb0wxT4EcypdvjvVkZL+Z3GY+R9S55BVJX/69rgdXNooIdW8//bmi/aPJZte7Lee+a2xUesteaRyT3j8fAbQJMh59MucOWVRXn33aWdHSX8jJi/w8Ml7cRpRMgyzwOP8b+U7XlSEUu+zJE5+21v4fNEwmOf6HUMubbEVB3+WW606oe9R/bpeaeeNaatdUnbNXjkuToewX3I0m/gkcU5UsfHW2O02s1ZI4Bnw13ufeW049F5tGvq1uPcnM8mk7regnUfWl0PH2tiq2oLSCJy2GGlLI1xgI5Dfj/ooJKWIy4EJeoOD5ftQH9AAivup5exIignJ3W/aAp1OthCQVM6brXh4TGDeA45a0pb63X2kty+tKSCdWs7V27IsTVY9ihPO6lrtL5yUUd7PO/Je71Ekw3A/F32Mv+9diavDpvbv4bUacltgSfTnE/0S97chxDByBJ33FEKDHv2VDPjyAw5MzNlYBeyaELQQBbNjRtLJVYepcCP2YFiCkEHQQnr1pWOKqnEAlzYhBFTHu2mZV9IGRM5chhg7qKqI55eQmH128R51BYxt/puopxbfXDBVnP2889aJjJNmUwxW9SVQW8peJ45BO1+E2Z+X9pxlnXCotewJ435EpZQoymhUhjwOIosZVRCuy/5u3U9H0cOffAY3waF73+/3HQwN1euI2RGkPSbZ0bgxzsTlU6grVu7s63hOvxGVAQDyyOhPcFKoO2rV5e8QR6zCX5y4IGlggyFGcZR8JeVK9M8oalR0xLYm6CJ0lM3riYKUNN+U+PIHY/1m+bsISrnMw8QljSd0xkZWOAxHGnOKeuYOEDKNADv23P8tcdR5LmfnGODtLKOBnropXZfGj+Qcqls18OzrE0I/Nl7DCUWX9Talu2iHmSCQeERjyiPaebjwbORtH7z5jJQGcH0KKEP8I15AO4LdPjQQ0s6Len4IYeUm2JwbB1oe45B10sL21LOcwxYqc9We9oaa2JY79WBwa/1OHhyDU3WZ60Poiq99cjpMuAg5yhOC5xmY4zauDxrHePw1LV4RWreaIa9VBva51xdLtVXznr00F0NnrnV1DlQV/K2UXJHIL7rJWNVE7zsZURf+ELx/9BQGYx2551FCZke8tWOHd1BXnW4++6ixNoCrzjooGrgNWTyo48u5fYmQcQ5crvXNiLXn/Wec8bj0Q856uTBJusgBY9MI20x1ryX12g8wgOtr9T1XJ5P2Ygkjdau55D8wrKXSKDOyEj1XcrPWobYXNk+dY3nd8veYmWqlYHG/LnIoE4PnfSM2SMH5eqYObJNzji0cclnNygsXUp00knF/wcdVB5rDPs97DWQ7e++u9vOQ1TahuAHmJws6fchhxQl7En4zDecYC5w+71MWJFjm/HQEkuP8LSTI0t4bNhePpQaW468ao1HW89WH01kNe1z3fjr5NTc58c/c/tH3eaVphmdNFt2KuEDryP1cKwjYGwsfYoQvy9p49GywzflHyloQbdyjDLBjPaePfOuCQ3g7fKNZ3XjsGxY1tryfIZvdZD46leLcsuWqk1H24zSBhYvLm3xsPWg5HZ7HgRH1H3iFk+KxEsr+yaQa/9O2ZZ6tR3l6MGeuTVfaMJzc66pQ8rOzul1k3elvWePXU32hfFp/COlj2jt1PWb+r4X2s7peOrdabJwEzrO66U2/mrruulz8thrPPfskSc9tp1B23+IiL785aJcv76U5ZGUBCX40oMPEt16a/H/LbcUJWQUyCadTilHgSZDtscGlomJMm4Htn2+OV1mUtZOP5dyTw6006clpMzEv7OQquPRJ/iG3Rw7icfe4hlLU7+IXLP8+aZsB1wW9IzH4pWw7aAdKcePjKR5hEXbPRn2PXYj7R3UrXXt3pvoOdpvvMxp2+IjdXxde84eHdNCiu42te17eJWW7M7qK2du93szeq6tNdA3RDCyxMMeVmaJnJsrhQcZ3MOPv4IQAcFCKvI7d5ZCjcxYCgFiaqp7lyyHZiSwMsgAPGAan3OItyVQ9mLsstrRhOE6o5nWdu6YU320xezQjjf7ZcqZpRlrNYaK+SoDzPgxbZ77yFHaUoLL3Fw1GELrXzpkZLu5zo7UuLy/9YtJ5SrXdfAo2b20mfpNjt2TudQaq/Zc4OgZFJ74xHRQZB2stZr6jW9ySSkpHmeJZmCXn633w8cFAVA62bgDIMUPkRViaiqtHFv3I4MqtfvQIPkpfwbyWUtavGRJ1QmgoYmRy2Mc8hrxcwCZZF9CW7sgU8Y7/rucZ1zpkfKXZeRpYozZB5WfDs3PUTYD7/eWW8rTT7ZtKw2KoIGYL5Dnd+0q5xscRiiPP74oZ2bKYAbMM9BLGBWRqYyodHhhM+P115d6CAKUZV8TE2WbWPvylI6lS0sjqAx84HJqKqBdo5GSbltyu8YXc4yAchza5koLTYyiOchtp4ms5pXRPVnu68DbbXKUJ663rtXaaet9NDHKcyOiVacOXr3RM//bRqrtppn5eLu9vLt+yEG5eOABoqc/vfj/cY8r6Tw2k8iAy61by6CzjRu76wJcBsVvPLMaUUGPIf/A/nTFFUX5qU+V/AcAvwA9HxnpDpAmKh0SPLgNwXXgceAnwKZNZWbon/ykKHF/uOexsdLWJTP28M2RPDMov1e+WRN8A9ejLu7rgAOqJ6ugDz7fUgEYHrQ5x6Sukut4xvc5Y9LoXNubuCxdvS20Jf/X2e6ajr2pYzJl1+rHM8xxpFn8scm81ZCj92rjGXRw0ego0dlnl317HKSgYfIoZx6MkAo6B13fvr26mZ2/A4zDCrSsc2jyscoN89A5Nm4sN9fgOu10P3kyCu5VnoLIr5clzxiNUmaA1t4/l/9TdI7rGqkgHusUOy0AwrLHpWDJ601s623aqz39yw0r2okIeC6Q87XsyfJ/jRbK9S95KFE5P7S2tc9e5DrLc1Dn42qjDyDXzij7zuk/J3DCku0tf958BKO9+tVFOTWVtj3zdwi5W2amx2d+MgTak4E7XOaT5dhYaduR9F/LVo/fZOD+7KwvaYG8x9TJnry+TE7EeYQMks7xf9XxtSa6eI6M1LavToM2Ps0PnhqX9p30z+TQBW0zugXPc9FOVORoSvcs+p/TZhM7PkcTWu6dTxadlJ9TNlerXUuvsZ5nr7ERKfB1PR++EpyGtWpVaY+ALQMbRLgdKOXbBebmqifTSVrKT8xGn7CxLF5c1Rdgk+HJseQJ5vA58Ozm0LFxP9oGltSmFu0ULgvyHWo2mrp2vDbcfs2TXP6S8uHn2KK9dgZLTpW/SXo7NJQ+7SRX50/RAS1rPqD5BOqS9nmfhUfO9fyWw3u1dupk2NwYm5y5IGl7rz4L63oZ02i1bfn1tP5y7LiBfRoRjCwxOVnuOrWMQtYO/hxBKlegk85/jVhZRt5Ulg5+X6l2cgximnGpKZoIwbxuSli0CGMqoE8bhzUeb/CwF70+U4zDOjrTCmDIme+awSZ1LRdOLCGriQHMMtxLpu0RELzPIMfo3FTg8XwHNBWIvXU84/Eqt6g36Mw4GzaUAtD0tM8g50FqzXOjojVf5XUa/dY2n/BrtOM1tXkrlUbJD/l45P09lJG6x5SDhSOHz3vnllUvlfWM91VnIOz1SGdLGW2CpmtO+x5rrNcAiByHonVNL7JiW7zCaKfr7bW91o33OjTnOG6wTRx6aPeRmh4ennoe1m54ibm5qvMIxsQdO8rfsI60oOLUaSeejRac9qQcb/x6yaOkk1i73oIl83muy3Ewpa4lystoqfHkHP0w1Q6H1PcQ8LJzZ/kd31RDVPBtzKG6o+KtcXmfpedIUA9S7zBXh0rJlV7DnhxHEwcP1zHq6lptWvSFX5vS57V2czaHad814UOeZ68ZbXN5fq844IByM8fISPfJVdq4liwpaTLWHwK8kDF/y5aqYwiyNTbvrVhRZslEhjT0uWxZ9ehnvgmFqD27gObI0Oqk5oJGS7UAJqJizJqjq26MvD05DnmCGZf1cpy48h64fpWyr1ntaTwih2dpdM5abyl7C7ez1AUE8Lkg+9L0i6b2I1k3lSGTw5MhWfI6bd5o+nhqDnrodQ6sQCttHfUCzYaQ8374901sQznXcshNGP3GwQeXjn4uY8lNqzK5CFF18x/4CL9nbBRB8C63b8lNhGh7y5byOpk9jQcVpBzfOfqaJsNqgWt16887XyxfBX5P0fi5ufJ9yHYsWaIp3ZU+F82vkJL7+X2l3o9Gg3LWLNcRkLkbweHgB6i7aFF1I1EOPdAyCOPe5YYtnhlQgvMMrCn5mydjnDV2rbR4Q6p/3m7de/Y8S34/nkB3bb4RdSeySOksHJocJuGZk7Ku1oc8EVBLiKTpppIGDhLnn1+UO3dWs90j+EvL2CY3oT/84UV54omlbI86CDTT9HGpz09NVdeqZsdfCDZ4je6n5qRXlu31vur0d76ec8bg+c7iOSmarvE8S4/X2pGyhyXrWzpcHfox55rQeI/9x0JTeb7pnE717xmHNldTckKujO/hH7JP7XqrrmeMwHwEo0GO2rixav8B/eZ0V/pdoRtD1x4fL9cjNpFL+s3t99rzybEZA9ZcSvHgujYXAizbZNP2POuv7rl47D+yXw2eWCOP/Z7Itr3V2Qw5z0jZ+C2/Lv8tFfhsIec9WzRR++zhMU1ooFVH8y3VycL8/yayhIYcvVfT81KlReOtcVhzWuqEbWOhyM2BCEZWAYUzlzB5nPjyN/7ZwwgtQ3ndGLUdTTlKoPc+6urw9uqIVp3zpsl7aZo1J+fZyb41o0MKHkHDq7DmtKkhVYc7o6QgYGVCk0INF3g8gZ51xmvejgzg0NqxBP/Uc+XGeMBynKWeITfMpYwhmhGwSQYZ/p4sQ2oq80qvzjePsaANB1+vWLy4vHconhaaKi/W2pV8I8foRZTOwkNUnWcyUI0HLNeNXfuOBwOk5hmfx6n74PfuMYJbBhbLqUbUbQiQbecIqjkKp3Wdt0+LZ0Lps45Dq8t8wJ1ITQR2bghN9ZXTfq7BMWe+aApiii5ZfWlzOyfARkOTuZhqg/eptTsfNNiiZYMez003EZ1wQvH/6Kju+CPqdk6lHFSgo2Nj5TpEO7gGx75t21b+JrPMEKWfA/pYvrz7yE6i6tHOmtwi56gm72pZHuWmRmsu5fB3jVdoa8tzXep67RrOC6x2tXbqvq9rz1NfZh6tawfzTWbkwBhHRqptSNl8aChPPwO0TS290DDrfctsIHNz1XmibbzqxQFnGRG19jx8oJc5xWHJ+B4Zx6Orp3Qf7fpe1j7R4Dcjjo2V4xgfr2ayAd1HkMKmTdWjm1EH6+DAA4s/our9YF2PjZVHf6Iv0O/du8tgNPQhjxXXNqPgfYGPLF1azYgvwXVAqfthjW3aVGYPQtAT7hXXzsxU9W7NsYeNjgiqkzrL8HB3tmVeTk2lA6k8gacempTrDJXygmawr6MHdbZAIOdIRKmfcUgZnPOIVDt435yWYjxYMzwoTbYp+9yzJ73WuTMndR/8mdbpi6Oj+slE/P5Qj5f8Gcj3K+tOTNTTbq0dbb7UBapZtl/enrXRSrYj56jXQWrp4Sg96w/ftZUt24uZme4TTeQ9ehyKFtrSZ+Qz04LjLNojYQULyj6Hh6vBhjwLKKBl/5SlnF8euyO/hxQNzcn6ben8mhyX+qxljE71qbXDr6mjhbyuRosQ7Igy1Y42NtBtK2EJDy5IrWN5GiLvQ25m6XSqgeUoLRnQkyE854RQPhdy5AU5XzW6zflY6r60OZbqF585r5GlNcc1pPqweJllw7Y2LXjW+KA3oxCVJ5LwcQCafpPadMffc10A+J49JZ2Uz0zLIKmd9idlGbl5jtskrfdZZy/RZNgmPiqLtvI6OfZXD33V5nid3UdrV6ONknZYdgJrPXrkhJQMPDxcn/zJ0vlz3p02Tiupidyowr/nNhxtrNoc8cig1prVMoVrWcd5yWl7itdY49c219TReA3WvPHMLdm2N+Ot1b/n+pQsrbXb72A0DTgZxeKrgMVXc3mn1Y78LkcP0WRF+Rv3+6fmh9ZeE3ufJT956ufMUQ/aao/rtr22A3hooAape1kn5abelfdEwzbg1Ys9G8Wb9OtJeMJ/q9uMqMlavSaMaHveL5T+rXff9glvEpZNJDBQRDCyxPBwaQDNFSK0tmQdafTmwoG1KD2CTkoQ5UoqBP5UhgZuTJS7uVAuWpTe4cWJcd3ufg8h8Ag+Wv19mcBou1E84O9dChJ1RhXtuz17SqU2dezzyIjvHcqjf7Qd8zKbWyowVhuztp48hjCtvbqMTl6lWMISLFPrW8v+pBmC6oKjefue5yqhBWbJUjNSaW1bTliU0pg5IHSWryj73LnTp2x5ssOkoAmv8notaN3KHpV6Bxq91fiRZ9577tUahxyPtR7l/9basIx/Vjbofim8uUb8nOtT7WnQsiCjvhaEhr49CinasQKOPUb9HEgF0ZKDPMYPjxyVYySweISGJgHLHn6mQT47rwwqr8+9R3m95/cB03865ZT0sdocCIjKlU+BHIOcx8CujUMLuNEcVPyzZSC37jU1bnmd/GwZeVN1c6DxTs3ZkaInOYYo79z30ELP+7b4B+R22ZcWNKLJ9Phcl8nAO/9loL12vZybnnknZVmtjkfv1N6zHBcvU85lbTwycIf3mcNHPPQBdXKOxNX4mlZHc4DzayydX4NHNho0liwp+16ypNzYkdIztbWq6UUSKTqj9TEzU82abwVPIgjIOgoxVZfr4bANgVZgbk9NER17bPd3WvZ9/C8zhmrHdMq5xe0DmtyPOrKPVJ91qJMZvbxH9t8EbegeMhBMOqe4/J8qtc2IKDkdR5uyT65fYA7JDJ2S5/DvNDpex6c12q5BrgMe9FpHezSdVGJ2tp6mDw+ngxr4/aVkEc0+AHh0f3kPnJZ5dCjrfmTb1ndW24PejMLphRaMAPDnkwpstzKXogSN3b07HSy+Z0+pW6NtbEq5777ys7Sp8o2KRN2bN/CepO7PTwOQMo12lDOOlJb6BP9OwpK1ZB1tc4xWT0J7P6m5rPFXgN97nS5q2Ui5PVO+gxw7rAZtbskgfk+2ei1QTNJ7WWdurn5Tu2b/kZ+HhopTiZrCmktN7Wu98GEPj/DyKk8fvM1+IFcez6HxbfbbBjQdzrIHeFBnp7au4bDWkZR1rYz2Hv2sF8zN6TyBj4fLG1pQKCDH2nQzegpcVmvDfsnbafKcPe+9bpw5gWSeuil/sofuaickIraA8yXIEql2cuGhQU3ei5efNKUVuePSrmtyvdce04QWNrXjzkdmfK6TaRtL+OfZ2fI76LaQ6bGRZsuW8n9+2iFR1dbCwWMu5KkpkL/5iYRcxiTS7SUp2xSP9UnxGG63ga6PEn2iHW3ju5w3fAO9xZcs3umxn9atiaZ0QRtPygau2Qs1+ylK/G9tVpbXac8stfmDf+abD4m6fQSol9pYmho/ke47sHQFudbb5sXW9ZpsYV0n7TZ1MUNW397vLPknByk+Vve869ZaTl9Wu9pv/UKvOligNUQwssR115XGu507q5lwYPy7556ivPfe8jhOZLaRgoa2SxFEmQshqZ2xY2NV4V0yhZGR6tGfEO5hmFqypHrcBOrw4z9Rpy5okPevEZS63f3a0RR1/aWQUtZy222bMFkE3+PwyhkPr6tlJeAY5K4nDVrGBk8mWom2GfVDBf14Hnzd8rLt9vk8HvAxPUP33kN036+yVU5Pl/Qfxx3KDGVbt5bHMsuSK6Ooj+vRHtrfvVs/+paom/5LpZjzFo2ncIyMlAoi1hoyk/Fjn2XQAK7hfATjSfGaRYuqCrTcpcmVrlRmBa2ORkNzsg56eEKvRnAP3W6b1+QafFJBBL302/Saubn0EbiWMd1qO+e98zopmaZXp6FlUO5lvs2H4bLtsSwkYP5xw1HKKDk9XZ23WrCeNNyi5Ju9ZEZk65hO7d2ljEKewMT5MAQMAk3lB8uR0Qttb+oYz8WyZfbvnnF4aNLQUNVQmdMmZBYe4CvXj8WTc3Q4bdOFXDP8ZAhJ77ncm6OTpgIic53NvQQ1cFom27b4gMZ7uQMkVd8Dz3zvd2YEie3bS4fRgw9Wg77uuKMof/azovz+94m++93i/7qgnDYAOfuww4oS8vfQUHWjAfgInuHSpSVdwFrFNVhzS5eWbR9+eHeffA1jLUHXkJvah4fLeSKzuqPUguNksN3cXDXwjdP0usyy3GljBU5baysF2RcPapPXWxlTLKRon8fJpoHXlbZErU4v4OOTc1FDG7q+ZV9r0kYv7QR6x6Dp/9atpd1mdraU6WHbRyZ48IFbbiFav777O2SNh49gEOAnoyDLvLTJTE6W9B8lt+kQddttpB2IByPgf2lD4Gte0j5+PcpUoILHj8Bpu2eNpgIeJibStudeHeJtX59rn/IEIcu6lhwv62ibSOYDHnvQIJGrL/baR7+R29e+yjNnZkp6zm07/CQUopK2b9xItGFD8T+uu+WW7jrzBb55kKig8aDzK1YUJege/HGTk1U5Hb/xBFRyo4pMXMUTVsmgNL7Bw+NLkn3x71N2NW0jUCoLv5bMR0Mbum3ToBxv+97f28Ig/Mht2dvbGsdCoYULhf7vq7Q+BR7rA1tQyse7bVv3KYn8t40bi/Luu4s/olJ/GESQNeg+NnkdckhJ7yH/493xTZH4DtfLE6lWrix5A2xNoAOgb3v2lL5u6QfmpdQtLB+vpit46Gyd3VO7Tns/KR3FM/+b2jbaon3zHe/jwYBjPWrxUKNrQD/8vv3qq99j9PhOAgNBRO5JnHpqyYS50yXlIJX/E+kLUDI5mcWAOzK09lJZCrQjP+Q4+GdpINTuoU7pys2A5Gm3n8JujhKZs1uk14AOy7BY91y198WdYhCI4WyEMALDgCejMd/ZlBOgYB3T08s86cc8aDqeOofVIOZs0/WUCtT3BDRopdW/bNMbUAIM+pieAw6o0laiZgqIh1fI9rW2c4NWUgY6ra2m89Y6Djw1Vsl7LP7Ix87pWmqsOfyo6RFHHt7gUVibPHNt7VljlhnIJEZHbUdVm/Dy39xsdt4+PI44/n0vAekWX/YgR2YbNOrux7OOcp10A86M0KGhUjbnThfITdKRQWQHQhF1Z09IZVTVgos43cOYYPCUBjqeuQu/abqLR1epo2HaWAHtnq35IrPJSRo/Pl7NAsGDqyVvsALBrdMj6pz+Wh35DryBZhavQzvaPOPX1AW+wYguM59yYzEMybLkQfGpYBPt6E1A4yupOcD7ksEquUHIss8caHQbz1p7F5rRPHcc3jnSxL4gx6nJsrJd6/hqbWwWZB98rnvkQT6mQWLDBqJrrin+v+ceop/8pPgf5c9/7m8Lc3vt2tKmhPuBfs55DZxGp55alI97XFE+4hGlI0g69j0bpKxA2EEaYnNsK3zeWNlGPHMoxaM8/XscX5wPSHrtcXzlwEMXRkertBzgmXJSJ6JosALt67Lna2iqA+XoYCk7R6rtVJs5Y/W8Z0uHs+R/T18efdwal4c+tGXbqrt3ovmh/+hzZKQqt0t9YHKy3NANB/2RR3a3yWUbbPR4xCOKEhnmly/XTxeUkPTOOllLo3spHYW/71433S5UeOh9E5umhV7l0ab2ubrgA+s+Oa33yLeg/7KuJ9OyJsNaND6VgczKPAdYGw49G5S0+0h95rTUguee6+aCxSPmGykdl0O7r15scL1gfJzoyiuL/ycnqyeRwB/BA84QdIbvANB8mW3d6puo1APAR448kuiII4r/UWKjIDaeLF+etlFpNibLXr5Q59J8o43n0uazbWJ7wDWaPG+dWtXruDw+LY88mNN3rk9Afp+Slax25nvteN69p24OcnWNHPkfgcGDBOjv8HB3ciUOvuEb8j/GDz6AwOMdO6o8HLSeb8yW9m0enInr8JsMFOb6g6TxTe3TvUCz7VuymjwVW9NrLL1E2hk1m5F89pb9VNOP6vQiTVfNSbZo+Yvk2PmpYhqdQiB4iqZ7TytJQbMjyTnuSVKS6ptIP3Eo5fPQNrgCli9Hm1v9WiM5dNKa4x4+pH2f4mcenunx+XE/pfbMZfspWwbfpNBvLGTdaT9DBCNr0Jz3KcMGUfqIScsZry0AmR2GK8BQgtG2zMb24IPVXUrY0aodr5bKjKk5VKwxA70K6jkE0jLmewRcqy+Po8FztITn3ntVtgD5vsbH6zOjeY5Q4J89jiue5Yxfw69NCSrDw/WOFI1xWEquxbxzHFXatak5pN2D7J/TC7kpQZY7d1aPdkF7/FgN/IYs7SjR/tgY0YEHFv8fckhRwpDFFRqZKcVzRCJfB3XZ1LmgXdcev25Q2Latu/+6TFdE9YG5/J4RTCZ30W7ZUtYBvQZN5kdHy124UNZ37kw77XlmYijVMiO+lj1B7mTlGW5k1uReoT3XfsFyVjcxYLWNNhxiOYpgk7F4HFa5/ci15jEGakaDJgZ3T9uWY5zTa6JuxTl1/BD/Hv8jAyOya917b0n/0R54L+S7gw8mOu644n+sZ+k80WiubGdyssxmghL8QDvFosla9Tjp+O8DDsAeoo4d7CqVbQ650RDZ7zX5R5Mh0abMNslle2mUlMfxEunHjPJrtXG0Rdt4u23xBkv+B1IZdtoKnNHGIcHfobwmdxypdvi8wbvXZGJ58oiU7YeHqwZ2KVfyMaRObdB4pzzuTRuj/H5oKC1r8TJldPbI27xdS16XjgIpL2htSmi6ijZ263hatJNax5qcbI2rzmDP+7KyWKXepaYXSXplXa+NddDy/8gI0atfXX6GnI3MNghGRhbM6enymXHHEsf27SVfh7wu9b49e8r1iKxloP+33Vb2J58Ln6vy/WjPUAumI+q2EUlnmEfm1NZqqg6HpetjvJa8kerXKyfLcXjoi2zbo/9qkO+J2wWsTUv8s9YOb0sGhHE+kDp6M0cH0pxREp1O1SbEj6AlKuwTkFXlRg9rHst3MTeXPorWO48t+ibHkeKHvB1Lj/DINnXj5841yy5l8RiUVnBeahweO6qlg+XSjH5i3brSLkakO0RlCTlfZofntnnY55At86qrivIf/7EooeMRlXIT5xF4L9DvMEas3W3bymd1/PFFecopRXnCCUV5yCFlJv1U4PPwcHd2e60O/22QaGKb0XwE8hrLLqD1nZKx+vFMPP4Mz/WewGDtWtmftrlFnq6gBVLIzPj4nCPf8fGksqVy2d56djkyjfa5zu6vzTtL566j3xpyabvH7qIFBvQyVo+dISVzper3E9PTRG94Qzkuaf+RdiAu2yMoGeWddxbl7beXdB9rBXR4zZqiXL261Btkqdnbod9r2QTlmLkNMKWXcZnf49/sNzzznsNaq730n9tXHa/pdTx1/RAV60eexixpvUbzpRyv2S1l/zMzpdwDHgOZBHNzbKz+/oeGqnPTY5PXUHdajna9RZesurIvTsPk9aAZ8PnNzKT5Gbfvchs8798zpzQ9OiW35+gcsr78nGqb8yHLVov6gz4ZhYjoP/+zKKem0j5VyN+c9+L9YsyIwdixg+jGG4v/v/WtooRdyQL89IcdVvYHfoFNjCjXrKlmMNYSFgIe2ciCR6bOyXSbIwdaeqI2tzw0ud+8LUcunJioH49FG9EGUVq21/y3nncgxzUyUtJ7qWPwWDXpN0M7PAAfckrq3vmcStEe7nf1vFMpz8n+iPJ8ZPxZpmiHFmifsjeOjvroft071MZj+U6k3WP37tI+ndo0oQURa3KDbNvyz3joZOAhgQhGlrjwwnJn1K5dpaCdyoywZEn1GGS5e/a++6qZcKTAz49plkferFjRHZhGVBIAfH/oodUjc7Sj0zB+MBAYSbHoZ2aqx6SjHSjAS5akBQ1NGEk5t+T/8vo2YLVTx9DroAnc8npPQFRK0SPy7SzXxpPKKOtRrq26mlEzpw76kkFyc3OlwSbHweNxGmpjsJ69lbGOyD5e0MqYKDcn1AWU4L5Sc8rKii6fC3dwajsmZTs5RlpudNYyE8prU8Idf6c5AnKbWLWqfC7bt6fHaL1DKWDyoHPQbyiXyHRgZZvX3os1bxHwgJIH+dTR1dHR6u5byd84pMOXI0eh8hg2NNQ9l5x5zL/z8IZc3uL5LaeOde/yPjSaLDeLSKWg06kGlvEASaIiWBZ0G0cYgvegT54ZissQRN0GS2ygQB0tsEPKElqwNeSV1E5xvtasDIMpQyFXliT4ca+DdqRY8MoNEjnOrIcCNm8unUnT0z4lX64NyNJa4DKeIa7BOrr//lJHgEwO8COYMb+gK/B1LTca4hppzObjkMaq3bvLccvNjXw9QMeQ65mv6yayhFUnJVPUtdtEpq9rU7uGy/+yHYtP9upc03in3Dyo1ZXXaTwC32G+ajxDCyzjY9YcTZoekONwSzlMLVjvyZpb0lBp0X8P+DqqywLt1deaGE61PlIyDne6eMbT9D4kBp0Zc2amzIJ8zz3VTX8IGuPrS24QvOuuouTGeFyPbDk8aw5RsWYgb8PhhL62bSsdW6C3WGPoc8OGalCclMNWr65m8UQ73OaEvqyANakjSJ2HI/We+ZzSZDRecnh0g6Y0X+r6QK/yj2c82sakVDt1mZHl+CV9sZxRnObLuY25BSxaVM4Fa+OjzLCv1U2djqDJ5BY/BTxBNU3ouMXnU/W1cXFYskVdH6OjzZy52hz3OLOk7S3HTsGv9/DeQdt/vvvdIiCZqHseStsK6DcPApYBA6C1WoDXi1/c/VlzFvLnU2ffQz9EVR3Z2iCh2VM1h6hsL2UXy6GRFk3VaLJXT5XjqOtDszfKZ+CxjXp5X879yGvqvgMQIJnS8zTI+xsaqm5ilO+ZtwuaLh3SXEewTrqD/Sj1LnNkfC80e19K98qVqT3v2dtWXTspP4RGm1OyAd8cKQNZLdrOn2FdYg6N92r3gzHyTRqDwE03lTb5G2+s0gMAtHDp0tLOgmzF2rvI0VctXa7JXOJ8IRWQAvmO66SpDIMavdTkd8u/I9sBtE131qb6Onhou6dNi5801QtS7XDakQpo0sDvB3JKiu7zvqUtEZ9HR6tZ7zUbEd6P3JQi4wgszM5WgyflZkktoFaTX6SOwq8n6vbfanPc4+fBd3VxEBpAJ9riOZzuar8R2RsWZV0iW+aTz1yTU1Pj559Tp43zdzBo+k9UnlqiBULn2LUsWijryv9529o80eSW1Fi5/bKuD03+98gb8nuvvFpnt9T0Tn6tZ52k1rPFV625LXm5Nm+tdVA3hzzv1LpueLgq/4MGaicapjaT79lTTXqJ9Qhf1f33Ez3wQPE/ZAjYNLG+eRxd6hTFRYvKwHrQR8hhSKK3fHlpm0wFQI+MdCdXItJPukZ9jX6n5pS2HiwbnCcZT8reaPWRGi//P2c95OruKTuQNU5NR5C/ATxeRm6qahu92nUDrSGCkSWe+9zSKDIzUxLazZuLEkQYu1537CiJLgiK3AU+MVEaKlPHQ3GDpVQQp6fLceA3BBxwgUMyFSxkjGfx4qrB0nL6AB4DnyVEyCxA/Bl4CK3HsN1EQdQIZE57vSjJHJYwniL8vD9Nga/bUTg8XL9rSjOCy+PV+PuTmZE9RwHxOlgjlqDblnOiFwakGRo97XoEVA9Dl9do11vjshT6JoZyPoY6JYWvZ4+hsq3sil7ccgvRSScV/09O5hugeQlYActeobFunvHP6AubavqBHMNGqm7TjB4anbTmfU6Ag0fx9dD9HDplPTvrPVv3DnkB0DKk1Rkq5+bKOuDZ2CAFWr12bdWYqd2HbFvL3IxMTnK3quXk095J6l3yso7fN4XH6GAZ46WRlPM8TQaQ/aToC28nZbjUniF/B/JZp2RJfr3FRzy/ITB4UFi1iujHPy7+n5hIP3u+q1cGFsMQhHLbtrKOlIl5ZnxpsOEbHyHv45hnZE0Af5yZ6Q6i5oBxZuXKMnhMGvy5AQf8QwalaZAGZX7cWmp+WHqEhhQtbUp3LUNjSqbO0T34b9paTfXloXOWrKb1D2iyOOcFqXYkvYYuCXAekuLTnU69/DI0VH+8WmqM8nvrmaFMGcQs4xR/3x5nS50x0zJea/eljbUO2hz1yJxaUEFqrFbbKec3r2s56FOnqPQLP/5xmZn4ttuI1q8v/v/FL4pyw4aiRKbkjRv1tTUfkAHGoOmg48uWdW/+IqpmY167lujoo4v/wWvAe9AO34wus5lom1/xm8zCxrNty3kHZ3qd/pmS7T06tiaHpRxOHl7D14gca86a7VUW5XI75mZO/8D4eDmH4BDywKJhVv98I1/qevmdhz56bDIeDMJpYemd/e4L/aVQlxm/H7Ds0v3AgQeWtP3uu7v/Jyo3mtx+e1HeeWf526B5VQpY+8i+Cdp+4IFlgDRsQwichj4/OZnOms+d7yk5DHNkyZL0u+Mypdw4rOm/qYy7vK3UZ/5dXWnBY7dsulY1+73Wv4RlN0rZ//mzx/+WH8ATxJxC7rNAUGeTZ9jUL9CUluXopG2028b1nrWy0DBo+j83V8r8U1OlD1YmmIKPeMuWahCO3Myt6Zty4+74eFUm5/5kfCfldy1BlGXvljK5zHI3MZGmR7x92bZ2KkvKzsHtHXXB6/yzx0ctP1vBRk3hWaMePb7XtW7ZKVKydFN6XmcTaQOnntpeW3Xoxe7YVvttIcfmWfdbatxt3o/Hvob/c7LrtgX0bcWn8Dp1tjuekT6VFZRvbpeburzJ/2S/Hr+i5yRBj9zgoW85+qal39ddW1fPsttYsn3dRiLO51MZd62NSdoY6niodl+zs6WtLnU/XP6X/XJ/gDzlTdMZUgHGFrR38qhH2XWtecfvXcoS2kktOW3KcWj+Y81+mPIPeeZ0zvg4fQE0GTDlD+Ht5sg2aFtLVuflN3WAvN0v9NO+FshCBCNLHHFEGRTTJuoWoLYoPEKiRvDwHRRoGFA3by4DJrSsySh5EAQvueLpcdY3cYDkoo22h4fTwl4bwpCn/zbbI/Idg1bXn5XpxmrDc52GugBqbuCYT1gMzOt4k3Xr+tPaa/tZtG2YravvUXy14zP6iM7Rx/gU4Jx7tgRKrrR4giebjIPPHxkQkCOMe/pvOhcsh5OFugy3nJ81yU7hoc1WoI3WbpPshbmAIxLQFM1UcJHccJK6HmWKLuUYPoeHq/KG7NtS0q36stR2qi8EvvJQQq/0ERj0MW333kt09tnF/9xwJI2JPOtlXUY/7SQFabSyMDdXdYZhswHkeaLyWcERgRJBBdopLgCnYTLAGPBsXNQgM0RzuuMxzKU2Dmh6CKDNKfnuNF4jDSz8XlN9aEEa1m7xVFCcxXc1vpYylhGl3yF/vtKIKDNgDw1VM+PjMzdqyo0lHoM2xsWN8HgvlnE/ZcznzzD17DVnqhyfZqjU3lPKkCvlGd6ODLSxjuPT3nPqqGA+HiubbR00Y6TFcyUt0+RUy9nskTNllqN+4yUv0R258j549ifQZpSgzXyjCeg06vCNKkTFWsS9ygz509PVDBGgTwgSXbOmDCiT9hpLRpIZ1EdGyutwUgXGjnEtXVrly5JOWAG+4EfLl5fODjlvefsWTU2tP489qtdAl0HIkClewfuT97PQDOxeJ4Xn+ibw8GBOYyUNs5wusj2Pvtj0t17hsUH30p6lC2pIJVrgDsVBH9N50klVB2wuchxx/XjfC2nt7+vICaBYSGgriUOOfdHTRq+0r23+0aQ96/oc+78XOe3k1G06t3t9Zgt5/Zx6akn/vfeZqqfJqRZy5qkmb9Qlycj1N80Hms6thTan2qCbbfTpyYzcC9q8h369Q0snbNu/n+M7y+2zF7prBS9yW5a0a/XDJ5xDL62TFPoFZGS19FbrPctnqP1mfd903tWdFqTZNvn1OX2lYPltPXbhJnXrUHdvlo3fasOy00tbl2d8njmg2YG0McikIRLaiSY5cTvW2mgSnDwyUk1MYPWZ+k3bQNDUF5nqz2s3TNEIy06XsktpY7TsmJ74hZz1ZGXW5/PXs56tLNAS/d6MODS0MGTHhZLQZB4RwcgSV1xRGp137iwztMgdsnBGcSeRPA6E736tc1IPD3enrCcqDUqLF5cOMgQZySw4Y2N21mUioqOOqu6E9QglmoF5fwnmeajd1yCdHbmGsTohoi0njldJaGJQaGrMSwVAaNf1y6HRtB2P0tZGP/OBXgRb7ZrU+9UC3+Q1/H/PWvDMM097DdChHjKq7GsYHesuFwAqz39YKJq5+0V6vd6B5JxB33IMKeTWD7SPlp79wOnIgQfagVBaEGRqNzwPUpMBazIIedGiUn9AYBcU1Olp/Xh0ou7sCVLv0O5B9p+TIZwbO1ObEbmBpC5b5aCDpnjWtoc6kBlhgGiyVoeIGWF6DQJaYMDz6LrHXjDojRkLAbC/DAqzs2XQ7fR0ScsRGAx7D9+MIjc3oITNaPv2sg6+QxAyb0ceb4isZ2vWVAOMZfDvffcR3XNP8T/mCdYTgpRXriz/R2ZM2JX4ZpmULsr5GsaIOqA3yMI5NVUfCK/pjVopN8cPOlvefKLtAOr5Rtu2ijZsFhwPZRrbtqzVqw1YzmVtE/ig5/vMTJm8Y24ufUwtaPX27VXfAErwrm3byg0dMosmvp+ertprQNt37Kj6IeQpg/z0RWQ7ho+AB9NjfuOEI9BrlEuXpp856O/UVMlbUKIv7aRHy+ZUt441uzDQawCHB22123bQ06DQxtgWcsBar+0t5HdXh7Z5Zy48tnRvUoo2ARo7PJy27XCaL38DbQev2Ly5/B90PGWHIarS36Gh0t4Dug1aDB2BXyP9vzzJQ+qEIw2SNnPaLrPn47NHj7D68Pg1BkH3LSzUNc/H1TQh1EMRbfmNe+1robVtye8LhS/Oh62W0/OU/Z/Xwf+Q5VGC1t93X/VkFWTWRx2+4VyerM75EOw+MqkUz3Qrg0vBDxYv7j4li6iauGTx4uq7k/aXqamyPvrXTkGsS4jAZXstoROu8SQ+tLDQZNhe4B3HIOn/fNp0Fsp7aQsL9X76aYex7rnf9p9B+yBTiGDkCEau4JBDiA4+uPh/errbocRLrnjKbGXIepMKPODfcQMm2sb1cC7x7KBg9lIJ1AQWqXguXVoKIatWFaU8gvPgg8udYYcfXpQwckIhXrSoKnzIQGiMicje1WsdKRToHb0G6Xra8ex0bIImYx8erg8gtYJDrR1E3t1xTerk9JkjtPW647HXIG3rPjx9DVhQGJrbQ3TTTcWH3btLIUEGGGiKJkrwA9SdmSnpvAxcgJFyy5byOp7tsg2ANvNMZDxAAb8RFU4p8AYcm4ijPfF59eqyvsxs9it0hSWlHOoLRRB8CKJRAFSv9Bpo8k7n5mhodrddx6JFObzLy8tSbTfdsGWhLYfrIDfQ5KDhOh+a21NfqW384AdFOTpaNTDKYzq3bq3qBLLcurWU6WXWS8jma9aUAWKgpdzgibZk9gNueNSCmInK8e3ZUz12fd26ojzyyKI89NAyMAE6AXQNHnggN85Yu6T7tOElkIGc9asZRhzZDSo15vt9N6VZddd5aPLcXPV59MqjcrIm1I2vCfolHzTRLfuFL36xPBlrdraU83mWY6Iigz4R0c03l/zihhsGNUobkNNB09euLUvYdKCPyOBmvvE9Va5ZU80cP19BW3UZ41LXNRlXnYymBVfLz5rtoQl/9D6nXtbPQqTfObJqE93A044l2/O+Upl/Ae3oTEuP8Ny75vDV/tc+W31YyQL493UBqBa0sQ46Mxq31c/MlJsA8e4gb/PgK/wPOVsGGuzeXcri4B/r1xflnXeWddAOgokPO6wo164txwU9AiX6nJsrx4pnJmn06GhJw2H3gUwPvWDp0mqgG/QAvvFRBp8BHv2Z24Hq5oVGx9vWV73t5ciF1nisNdpr2/gsTzyQdefm6h2gPGuUh955kGpnaKg+8FTLzqXRmV7mh+U/sN6hBx6a2DTRSC92fn5/TWxe1rPP0WsWCkCbNWjBaTJAjW8wASS9lLLBnj1Vvy23v6SSSPHx4Dvpm0UA8/LlZZtWoFjdaVNWgFg/ZEZrfuQki7Hkwib3Ycn9veolTXyqOXJ/p6OfgCj7knykiX7lkTOHhny0J+Wn7UVGrkOO37ep3invXTuFy9IR6pAr42jBoZYch89N5AVtXXp0t37hO98pSp7gQ4tvISrGh2ckN6rzmCHwBrQD+/vRRxflypWl3A//KxIPLllin/zKx8IhT/TRkqzI92TF3/D33WuAMK6Rc6hX9CseQ7tOu+e6ee+1IzWxkfJ25Ulqsi6X/aVtvykNS7XDdQ2sFS17cl1m717BZTZLFshJomPRKc97Ts17rd0cntPU1pPiQ1oMo0dmaqIj8O/6Tf+9+mK/EcHIEYxcwQEHlAtg8eJSkYPwIIkqz4wGyKCA2dlqhgXrWHoIHjAUcmFEKppcSa1TEDUhT9YZHU1nWJ6vRdtEMO11rG311a9nxpWUunpEujCQUka1dlPGTY465ZaPB+BE2MNs62AxXU0A15i2tm7qxqEpkSnhNccImWuwqHMAWNf3aljWhDQNOYaWQSujw8NExx9f7Vt7v562eGnV8dZPCdHWfOs1+Ddn/su+td8CCxO9OHGsOl5jWRODRBODbi6/zlnrXmOxrJsaU+56tYwhoF2QQflmCVwrFXEt04nlyEi9e68y3KROi+jQUHFUJ/que/favctgZC3rjZTntbmP66anq5tZpOHGayCUv2lGzpRBGt9v356na6QMN7Oz5dxLzZvx8eqz0vpIoVeep/F7jxFGG8d8gDs7eckB5yfGqMnxltwujZD4jfflcXylvvM4wDwOK6svq18JTidTMrQ1X+T3VjsarGPOBqFv9tKHtmabODj7hRe/uLStcCdSKlBmdLT8DUFo2JTIs96k6BvWXqdTBpjdcktRIihi69bynSOIDBvGUa5YUW5skZk5OX+XQQ3YaILPq1eXwcwyKxHuYWam5EMpO5L8n9fln+X1ktbzDJsacpw2vSCXj/R7XN72pN0HNNkr26Rou0VzLOeWBZltW4Pkx5Zc6aG7FprwDys4v4kezO3LWtYolHW02WuPSfFOjxOp1zmuXT/ozGizsyX94zqTzH7JS/yGQF9cj40f/GQTrD+N7qUCDjikjsE3x8vs/TIYYfnykn8g4Qr8GvyIXBlcITfk83Gkkolwf4S8BvDMWw0eXbIf9Naj41vX87IpLOc07wNyhazDN682lY+1PjVwPuC57zr7j3YEc1Pk2Jqa2i6aQJOZcuxP3u+9aNp3ExughvlITsQzCdeBB8gD1juUfmAELu/eXeUJ/KQrmekQfAR8ZWam6nfGb2hny5YiSzPGxEtgdDSd7dgKOtTom+RjOQFslh6Ra7/tF3J8Qh79iLeb4jVeHqTJ6SlImxAfF9dPU/2nZMZcf7Al32pjq2vPquPRH7x98nbaajdlX7L6rqvjoVPyN77ZrG782ru0bNDys1Z30CdjERE94QlFyWXhVMkTTWkZ7YkKeopAY6k/8IBlbGpHgirI37OzZdugzXKD+NRUlV9BXgcf4JsiMX6MEZtUeKwR1j5ioPjpJ9J+JU9qnJioZubXNrDn2O8968djF7b6SMEjd1v9W3PcoumSx3B7pOSr3D8i6bZlY7XsNW3I27w99KXxp5S9v6nupMk2vOwFHvk21Q9PRCpPOpI6u2YXaDIuz1rj9VOna2h9WbbAXvXe+fIDBAaOCEaWGB4uM0ESdTtgeMkDBZARmR/Lwz9zg2XK+TI2VjqT8B2EnKmpUlhAtgLJwGZmdEcOUTfzTymIvGzLcNYWPONpy4Bm1UkpGl5Df11d/k7rHOKyvuwDc0cKjRZDy+kT0DKmeRxEqJNrsGzLGOnpc5BG55QQm2sITfXvaccrpHmM2DlzyGqr7fftAc82I5VPaUzcvr2aLRP0H99v21bNqHb//d19cacj+AAcRocfXiqzcHRBCeUKIug9eITMem85vDg/8MyhhWogBHLnTS8G/5y63jXSSx+etr078dpwKmhBado4pAGrVwelZcht41166zal5alrLJnCM++0nfK9IGWMlv3KOjmy0YAwNLen+5hOaQwC/Ydsv21bNVs+PqPOrl159wraDAMfz1aG58cNjADqyKz32vH2aEcet6kFNS9UeIyIvbRNpB8d3haazH8vnQPNBb2VAccWcG0qu4IXGh23gp3q2uFIPatc/qo5kep4HndAYK1jPQJLl5abiSWdtYx42tqrk+W57GgF96eesSX78dLDW5o6ZuvqDgrf+hbRaacV//ON2Sn5/4EHShkGcjsyEWMd8cz2gNxUzrPNWzoGeAyeL65furQMLIOOIOef5thpYvDvVbfbF7DQ7tHj7OYA7Zb0lh/PjfmpZT3BtdJhpR3z7Qk64G1qGB6u57WW40qjpR4nprautQzG/DrtCFqtb4uma/ekgdtsUYIu4Mjf224r6QKeIWgATttYvbq6mStXv2oirzS19eD/QWdGXr68fM48CACQRxZ7HMiWfqYFeGk+B7mO5fPZsqVq84UdCBnWJierJxmmMhzz8cv74n306jDeF9HkHjX50nK+e2hXk3FY+jfgsQ2B5vPsyf2G1z7Qln05xx/iWQca/WxbBm7bn9GPcUjk+M8GAfBOovT6095zylfAN13LoDKUXP6xTpu19Cspb2hJA/YH+twLPDJOju1Ca1/ycAs5/jztNxnEziEDwjR+lLo33m7O2ADNLmWdsiA/N7EjWfYWq1/re4se9OIDsviI53nn8Ldc2a2OJlpo00fcL/DAQBlLI5ErP6TmNKcLKZ1S/s8/59pP9xf0Ki/za3uRt3PWoXY90DSQVvKBpmu9V8j2+Lja9rV46K/3mqb1NHD/K2I02kYTX7OFpnzI85tWV9pA+oWFIhNnZka++OKL6eKLL6b1v0pY8vCHP5zOP/98etazntWHwQ0GEYwsccQRpROHqN6BR5QW5nOERM1IJa+xMDeXVoZx/a5d1aPcZJA1N3zCUAmCCePm4sVpgYnvqEgd5bkAgk6IyBfE472+l7oaUa+bS8PDtiIjnUheJ5YcD+qDWMLJxTN84Df0xXd0y/Zkuxwpx0M/mUUTo5lXuZCGIw9D1zKxpBgyV+jlrng5Vt5O3cYIPg7uWJHXezJAypLv8reyRGJMyDI2KNx/f+m80bLEyzFPTpZBXzgWGc+MrxVtlypRmc1mx47SoShLHsx8zz3ld7zcvr1sG89X7qJdvbrcaLNiRVHiSCC+2aXumE5NSc8xxgwCvRrlex17L4K5l15adAVI0YNOp5qlqVdInuO5Dy6ES2eqZWjMMRhq18v6mrMwpx1rjB4DlFVKuSnH0MhRJ4dZ49Gg8QhNTpGlx6CG/wcdjDA93Z2NAfcjM8uCnu/ZkzYioq62YQUlsmFOT1eDP0HH9+wpg3+gm4Dn8OOVZZA56DiuWb68mhENvIE7yzybGoFB0Pgm9KkpvbTWlmdt1BloNNnR06f2WY6DXy+PQ5O0ntdNGUK0jYZaXUnvc3gv/4w1kkODmvByrjtZR0N76Da3FRB10wB5NDunGQA2HHiyh8gxa6czpeiv9l2vRy16aHuvzlxs2hsUHvWoUm7msgDGqL0f8AvQcpTcxoIMaCjlqQQ8A7DclDUyUrYFfejOO7v72rq1au/BfUDWP/BAosMOK/5HkCJ0HfCBI48ssy1jblsb1j3wyGqWTOKxc+TMKW0cdWPUxppDp6y2m6BOBpWbT2S20927y7kIHVTLTAa5Q8oW+J7PBcljcnhFriybkrlS3/F2tPfNZaa692npIZaOoWV1Tp0+kRo37wMy4EEH1Y95bq4+87RX75Hj4Qk6Um1YWRU1PoR2Bp0Zc+vW7ndSt9b5OwT9lfaxnTurNjcpG2zZUuUffFN7amMY7wtty6z3XGfAnJF8jNu15HdoB3zhyCPLNnNoX65u3i/krFnLR+C100h46EudbsBlUG08KTuLzNDNIXkGb0/TBYAmdiQ59qEhW96W1/XK7+vak/Xk9znyQl17HE3XQS/zRdMpLTks9Z4s3dQzdm2sbdkmcwB6rmXllLx8ZqaU5UHLQbehu9xzT7FhiIjoxhuL8qabuq9pA5DzsRkS9PqEE4ry2GPLpCag3zIoemqqe/MiUZU/88yYg6TbGupsoZ65napfd41nbnr0mSa0zPvcUyfaeseequc50Rbw8JFOp9RxU3a6XDrlHZ81Xu0zR2rDlsfu4enbO3b5PHLWp2YDs2i6Z8N5E1lPs18O2v5DRHTHHUXJ6ZwE1xGkHxzzmJ9QBV4Ceg+egWt276766iDzT0+XujoSH3K9gaj4HXZ62HK4fojPMqMy6vBT2JEJGbZJuXFxcrI+cHR42Ocjlmg6p3LQVP5vQrdzbPxNZNFU/9JflWOT4d/L+tY4PAGV8jlw/wRsm3IcOYkyuT5eNwYvPO9Zy5jeC53s1RZvnV5rQY6Ht2NtkkhdX/c9/42/N3wHetcvaD6L+UBmMPLhhx9O7373u+nYY48lIqKPfexj9IIXvICuvfZaevjDH96PEfYdEYwssW2bbgDNYRiaszBlUObEIxUskmssSx2PNjpaChoQRrQxp46m4042mf0NJercfXcpKEnmwNuXBlzpiBsfT2dv0K7XnrMMzoDSDkfc4sVVZwueEzfaaoYIolJQ3LMnLbDwIy/kcX6yXL68O+Mp+tc+p5DKxsWP7ZQBrIA8SoFfD/D5W3dMszfDWiro1iO88vGkFFVNSMkRjLW1bwmZqXdk9akFu+YIwZ77qTPgpNqpY9rakaIa3cwJggCdGRS4UdwSarmQJo13QO67SL0f/lxlMDMPBJd0iR8Hgrqg03BAb9zY/Xn37nLdSmUU49mxo6TtMBZAaAQt3LlTD5hOQaM5AOglDKkoV6zQsz8TlbR+bKwa/C6PwNq+vRw/PxqJj2tsrLo5hyvwRMU8SL1znlFJbu5ByTNiNDnSTuP//FhOjrm56sYSjQ+gzdT7sZx11lg1o6R8Lxo/aLLGLIe/lfUstR4tx4zl4En1qfXBx1qnsHKel6MwemRYrR3LWFDnUOR0SrsP+R3oyaDw4IPdmXFS4O8ndYSbh8/ytafRa5Qpgydo6s6d3XIoUdUAeuedRD/+cfE/6iIomjve5PuVfc7Nle9cyskIUpqbK6/DeEDnEAh92GGlMZRnZpZjkMfFge5qjjNJO7RATU1HkBnv5FGlw8Np+V8eZ83b5nKcHLNnrcr2vHXxHjAmK0OO1VedMdIybmqwnCZ1RmvNWaG90xyDpdRxt26tBg+jHTxTHvgvg4iB0dHueSrHXwdLv9fmvDW35fUWP/E8e9meVsfiuSlbiva+eAbWQYAbP62gW76+Ic/hvYCmcRokabs8pnnXrvI7GczMAdp59NFFCbl3bq7kBTKoDaex3HMP0S23FP9D3gdAo7dtK9cC7gd2Er6BETxS2lI0WTjlSNu6tXyOuA6OMwRLH3FE9XnyoGu5JrRsoCl7GN8gKm0U0h40Pl5dW/JIxYmJtC6oIUc/1K5Nrb+5OfvoZaKCH2CseIce+4ZsV9ORrc2IclM7h2fjmSWTA3U0zHp2XF7w0Cd5vQWtndSGdc4LrfeMNqT9VOOvVsZElLlyBi8tWPzIkv/rHIttg9N8zeGu8VB8BxomsybzujnrGeD+g1TwixYUIW3ifJ5o/RPp+i/6hK3orruqWTzl+5qZsU+TRCl9C+AN+J7LhVz+IirkKvCLww8vSmy2BP3nASWp+c+Rshnw+tbmTMDSCeV45Py3bCoWLeJjlTqJNu+k7C5tXlo/0o+gjdWzGV3+NjSUPtVDs7tI9EqbrTVqyb4ePi+v0drW6DXKuj5ybXA54/Fcb+kVlu0s1Q6HPFFkEECffGNSCnNzJQ3KeWZaO5b9X7ZtbSKxbCGp8fBxpWSJ1Li19ng7UufRaKrUp2EvmZhI00lrbWi6cWrM2nU5co9nzXj0Z17X0uNT4LwmFcSjnZBljbtuozp/B5Lucz4ifcLaBhYZjKzRRI99InU/Vh1PH4BmU/T4GDT5ISUDe2iJ5iNIzUUu/8s+LLqd6i/Vh7xek3VS8hhvV+pggwT8i1xWk+ByLmhXak5wWVieosg3rIPvSB/v7Gz5HUrUhR1nerq6VuVJuStXlv8Dt95alNhAMzdXlbdBk/nGdUmntazmKXuJlrAQfUmfM+drWnBznX2O/y/5IfezSJ4nN2IuWZLewAlo+rM2Hl5fq+ORc+vWhawnk5NYa57XqZPVeMByDp+Vc3VkJB3bxN9bHQ3S4ugkmsoEnE5pG7R4OTGRHqOnL6u+h+5abXhsENozlLaDHHnHmv8aH2iiXzWBR78aBDKDkZ/3vOd1fX7HO95BF198MX3rW9+KYOSHDK68smSId99dGuCwawOOHZ61gAdgEXUb0oi6jWbaTiSiQkgAs5e/jY9XmTXqQLgYG6saIQGu6EnnqWSw3ImUIkxcWPQYi+vaQ5spaAFDdX1IxzbvQzq1uJE2Reg1xUETkuocB3VGB3xOKTeasqMduSedPXJcY2P1jt6mRNqjSGmAU7dX5lDXfxvMxxIIcsaTY2i0rmn6zD1jSPVhfU4Z0T3ten/rB/7rv8pjmqenqw5+HI16++1liZ20MogAiuKDD1Yz0WtAsBUP6CLqzoyJNc43LuB7qUSCtoNncGc5+kIwA5w4y5ZVsyXIY+Ny36UUNjG+sbF6oVObU5zuWjwF8BiOUuPg7TfZ4Ztj3Neul/AYYmdnq8Y/GSA2PFzOi4UCzO39ETlG5/0Jg86Mtngx0dVXF/8PD3dnrieqZsF84IFS/oc8CZ7Bsx/IjFRSBt2+vWy7ydFEixd3Z7cnKuk2/55vwCOqBlBMTKQz4oP+LVpUDVDjegiQyrTF+QCuk8ZMbsi1nBN12b49OgZvJ1VXCwjBZ77ZJZVVmo9ZZjCV0IxlVlZqjWelgtFy+ZB0MGnOLAlLLpTB0bz9FA/XaGOTI908tPXQQ9uRpZu20eQ6i0Z67tky3PM+6mQkrU3LaejBoIPRrryyzB68aZOepZKXmzd3ZycmKk8vaSvr2dRU+j3ydy/XqLTxTEyUMj3sUDKobPXqamCXzKZzwAHVzSNSpt61q2pglRk7lyypOiIlD+XZSbVjrD2btaXzSTqetE0kciOC5qTG9XxDUJ2tSrM1afYbXl+Dxgd43ynbDnc4WUFjgKyj0ZrU5pNcO0VOILcHvdhd+oEcm9Eg+s8dj6wvN+C0eT/oS/bRb3zhC6WsPjdX2vuRiR4ZLn91PObecr7A5UyZCZ+f8EVU0ATI9DIzGkqeCEPSI83uIum+tVEk5XMgqta1km5oMgr6h9yLTZZaUJAmo6f0CC1JS2rdWJvIeSmflbwv63orkFrTi1KJQjhy6Hdb0J7hoNe6F14bfM71dVgodiAux+cE2nhg3WOvelRTbN1aBnLyDeJShuR6gDzlRNN9JM0CneKbhOUGWS77yU3NMnslfz+pQEAu/6OUm5anp6sypwwY5nK31Dl4MJkMWGviE+IBSDk00MroZ/WX+qz1kfps1bHmRF39HICfW1hoNmdPEojA/GEQfnUJLTt9vwHaDrpFZPsFU89F6vVEZRyQ5bfUZL6c5ESAh3dq7fWbHvTqC7UCK1NyM4e0Q2lta7Qxx1dtBaJK2cYz5lxImR58mdt/5Fi1fjW/McfcnC+Q0kpQgnZTPCtnHlvXNW0nZzy5/n6PnJsz31LXaok1ep1nbdnXrBg5uXmibVhxFIPEr9bQVsHvFi1aRItq4jT27NlDn/70p2nHjh105pln9m2I/UYEI0s85SlEGzYU/69ZU92xD0URytfSpelMtzy4MrXblCtNKcedZqTSDHt1i8ojcHgY/b6K1LPzwMuAmggY/TBWYX7KXUtcGNEEE6JuB1SdMMJRxxCt73gAdipokAeESAMJNzjUCahanZx34HWy1bXJ54tnPNbzrZtnmjHREk56DSKQGLRRsSlOOqmcU8uXl+PGkWcnnVSU3ECWOs6eO6t5NjAiPTgN18vsYPxYHI+RS/6WA+/67rdg65kvWtCWRI4yWddXG/DSjracHSklsKkioV2Xuasv2V6TzPjWPMx5Z70qqk3eoSVredrLled6Qa/yS05gCv/eky2vTTzwQHfQE8Ytsw1jXS1bVmarxAZBvkGEqNs5LJ1KPNArdRqG9uws2SiVeZobNVMbKzTl3Hp3vc6vHDpnjcMKFEjRfe1ILcByeMnxaPJ/aj1rfWnG59RYudzB2yTqDgSB3io3JOVkPeOQwc2p4z+Jqtn4x8aqbctAwD170s5cXqbeh0UvtTmSmuOW3K1twLX0vDp9nmdM0ewB6EtuMsZGU7xTvsnYc3JOE6ewl7+l3h2/RjM+EulreND0/5Zbuh31sOUgMBfzlR/ByTc1E1Xf5chIaVTlPIGXS5aUgWLaiSQIeECQldz4ODdXti2P58Q9LFtWBjFop4MQFcF1Mnsy7hnjOuSQsm25gcwzX7juXpfFpCn4PJQnmuzP0OiczFKm8QEZlOx5X1r2s9R4tP6tLDiDdKTIeavJSE3kIE1/tT5bMn7d6Q9EdkYn2b52vfzOklua8BTZLlGzjXm94ClP6T6NRQaIydPeeHZfGbjGTxSB3oAS7SBgmB+hLDcI8ucKGgx+wjeMe2yA2lyWpfZerXYHjV5ttf1EE5+J5Yy14Fljso4VFCDlfs+Jhjn3Z4HL/znQ6LDnHeTYGy35NLX5yLLHWnS2iR3JYwfS2m76XHpZf177XMqW0W/wxFFcV0kFCmubpT22EVzPM5hjbWobflO0HX3s2lXdFK+d7MOzyvNSyzZp6XBt09Uc+2WO7cnS1S1Y+nOKtmrw2K57ta9p805udNE2Hko5X37WbEWowzMcp+R8XDM0VL+JfXi41EHrNqVrffF34rFv5Ng4U+Otuz4HvdhRvfICMB9ynIePaHXmIzO+tik2pfPwjRmyrpYZ2co2b8kSKZrsoR18PKmTc7TNG1afKR0y5z1r9nJL77SeoeTPPDFkSm/VTrqTOrJ2Wm3d80l9J+GhL9bzsXiETIgJQN7YvbsqJ0ibvvUutdN05fMBzd+zp+xL6hRaghvpo9JOW7P4iPa/dg/8f0vuzqGXcj1p11t8TKLuPafGp9GgFM/KvU9rHBYdyGmvybN/CGDt2rVdny+44AJ629veptb90Y9+RGeeeSZNT0/T5OQkfe5zn6OTEJ+0DyKCkSWuvLJcwDffXM2IDIWPp/NP7fznQWoyW6XMJLZoUfU4Yhgaly4t68lFahFWMAyeJUVTJmQ7XNDiJQyhy5aVhtNU9kzZJv9sGT41NBHUc4S0ubm0AVY7UsVCjhLpYaip9uqYbipYIJUxoRe0wTD4MQ24V+uYRA9ymHdbRlVPm20zWK/BsG6tccEnlSmcyN6l2bYQ0YuS3gQPPFDSNu5oQjAxggFQ3nNPmT1fHjHJjTqS7lvrUBqHdu8u1zPa3rat2pdsSzrOuFIgszNyegE6L7Oo8ew5MGLKwAd8XrKkmoVBC5hJZTQbdEbUOvRTKO61be16nnGjTbT9HPg4m2S7tNBEiRxEX231ua/34blu0Nmyp6fLTSBzc1VeBzqOo9x4pkMYwJAZUx75hfZ5yZ1RMtgR62H58pInySAyYHi4pP8y4JMfOSaPkAbPgiNgYqKk5bhHTdaX45ABGNyxrGW0xLikzmQ5l3Ocfdr1EpzXyCBBfj2Rbci15B+trJv33AnqMdhrAYE4uQHwZDS2dCZLZkwFKuPa0dGqo1XjT5hf1nv2HB0LeAxjWka8VH2uD6cM3JZeJtu19JscOdpj3Mx1IFvPLHXvHJ4gAku3RZuDpv+PfGSZGXjnztL+gwyY8qSse+6pblSXjpHp6VJex9rgjlp830vgnWZ3kbI1HxM2Q4JXoC7fXCPpP2T+Zcu6j+wkKvUarGseRI++AH66F9qUdiSLpvIsbHKNyyNOOQ+X9jXu5JAbSCWfJirtaLhebma3Nhd4kgZodh8J/k5T2Tx5MEIqiHhoqDoOPB/etxaY4IVF6zVInW+h6YAWmtgvPbaVfqBXR1EvyOVDg86W+rnPdcvLkg5YDnFJZ1BOT5dByFLehpzGT9WQmyeWLy/pK2iPtulIHt0MWqiNVWbmtJKiaCcJSmiOeulH4MHVEvLkR9SZmameOKllDrV0Avkd2sazXL683AiUskMND1fpmAzenZurZvGUPGPRoupmI7nmOe+UtnCuL+UEqGhjljRdfh4Zac+270Hb9p/5gGV319DL863TzZr0tdCc/4OWAZYvJ/rRj4r/V6zQZXmikibt3FndJJra3MUhg0X4ZnKtvrSTgK5weTulc3GZEWPEZkZ5Xzt2lHxD6iO8nVQCFkvn1miY9C3LTZt1mfE981XSctlm7ppN1dGCcVIn3Grjy7ERaePg99WL3KTFAgwCWqbS+cQg772XvhYazdbQdIyD3oxIVG7e1uRLDZZtNHWtJau1Zafz6KSSTlntaHRGwqLXOVjIczpln+bP0JPJOofH5D6PlN10EPq0Fatk4dhj2x+LNo425lZb8V2DnOdN30vbfad8Jtp4+q0T5spa/cKv9PLbb7+dpmATJzKzIj/sYQ+j6667jrZs2UKXX345/cZv/AZ985vf3GcDkvcha++A8KxndTtFU8oEL6UjBAbH++8vynvuIbrrruJ/lDjuDUbK7durxj6UO3aUziwYMaWDZ2SkKghIw9jISKn0wdAJhxMCEJYvLxmZdCBzIy2CzuC4QsmPeE5leuPEKHV0WlMC0QuT2BfgFexkZuTc64l8z2loyNemJ3tmL4qHxyHPkWKAWvCL1ZdsL/U59V1dwLR2zFROoICVDc6CVDy8QTTedvkYe22zTTz60dWALf6/JyMl6Dho+44d5fyXwhXm/AMPlE4k8I9bby3KO+8sjZ9yEwmweHHpUJEZOvEMt26t8iEtgJo78nlf+H5igujII4v/UYKPoG9tZ6B0JnGHOr7TDIdtKe49okMtbuBgGKIeMgt7YWWyTAUaWDTbk2HHQ6d4Xcg2lnEnpchYdCrXuFMXAOhtr64di4/kGHW891xnRNGM0JqBPCfwIscgpsnUgzZGTkyUz2Pp0lIuTjkyZmdL2gwHD8qUrE5UXSMjI6XMJoObd+6sZoiQR40tX146vOGoQtucHyFQWrbH7w/9gzfgeeAz14sAzTklDV+awTPl8PLIHW3Rfo+hRMuA0U+kAlm14Gh5jRY4IXk4ry8D1jhtTjlY5bWor9Xh3wFaYAdkkrrM0byO1HHHx6tZLlKym9YHlzdS12t0UnPGpmwGmmwv3ze/NkUnrTEDdboM70sDv/cUbffI9JoD2co0ImnXoHDmmWWA2NRUKUNLAx9o+q5dpZyPDYoIWIY8PzdXpR1ybvD1oNl6+LHQROW63Ly5KH/5y+q9pI6D14C6999f2q1uuqkoEZwNZ8Ehh1RtQuiDB/zi2cnNjFw/SdmIcvROL9pyRlhyVJ186sm4Zq01b7BqE6eTJpNLvcGTIVnKbnv2pOkQb0ceyds0aDZ1naaza+1YcilRcT8yiB/Pm2e1rQvQ0ZxSkhZb9iiN/ltyQx0v4PZY2Z7H6c3ptufdpfQh/v2gM2P+1m+V75JvNJRBaQ88UJT331/SZHwHug/5fXa2uu4x1+EHuPvusp0m4HIPeAPP8AzIjeFyU/n4eLnmcT34EL5fvry09+DEAHw+7LCiPPDAsm0pj4GGjI1V143UI7jc7Qna2hds+3Vrw3MPdfTOEwCsyfuyj5TdiF/jyYCf6oPrD6n5r+k8qU0BfMxaoD1R97zx0CktO7pMLqRttE3xbI/cbNFRjTbnzJmmfLUOVpCFxodSdfl3Of23gS1bStrMgyPls+M2Gu7D5b/x00yweRE6AmR8nmEffAMlTziC79AXeI1lo4X8jVMdjzyy3GgvN7fzjTTYaIigPBnYpMlPWGt8k6LMugxoumpKt+X/azaZhZa1H5Djact2ZMlzvE/pw7HmScqmMzxc3Twrodl/QC+5zSnFR3ifkv57fLOaP85KpARYp5l6gvrlptlUVls5fjmuunlr0UtLDhoEmsjrmh8hx58wCEAmn5ionnYCaDpPKiEHT2ol5yvqPPhgNWustvEYkOMaHk7H2QB807XsA74DnviQZ7YlKsc3O1uVd+S7nJiobiix3nPKXsLvI0cmamrrsXTtJm1rtuNUUhMLcr7MzaU32YyOprMc4x3OzFR5BMbBN9PKe7VOb5DzxFq7mh4A2UbKfpbdPsdeqL1bbW7VzTeuk3rmXcoOZNmhNFg2HsumgzHI/tuirRr9T/FpjfdpzwffSZtg25gv3inxq/UwNTXVFYxsYXx8nI79lU3+tNNOo+9+97t00UUX0Qc/+MG+DbOfiGBkCZ49jCO1gDlzQYmgMBjqjjqqNOxhccmABW1nrGU8l8ZrLoRru3fRF/4HM1m1qrxv9I0xyuwqEEB27SqVYyjZeGY8OALKLPqAAoznwwU5ywGdw/wXAmHpJ7z3l6qnZb8BrMDlVHtaRlarXY8hykIvwqbHYeVhTl4m3sZc9Dj6OaSywY0gdUJa7m9a3SZrNWWIJRp4MFoy4NRyBOI3PHuZ2WZuzs4qQ1Q4/CUf4Ub1lHOQO0ihBICngNZz46jM4iaNiCtWlP9zpxxvlxtskTHuxhurfYIu4HmgXQhbq1aV38msntzJKx1oWhZ+QFNqWwpCGJpzOF2AOuWgKTwGfyLf0WlAylCpZU/LQU525rm58r2mZC0reMtDw5ryzpbmjwtNlGPruXCk7qMfQZaWvJwal3YPAzZGdg45lGjJ4vr+tecMp48MAuN0F3RT8jWuR4DO4voHHyyDziRNxzpeurSUq0FTtWyVUifgG0OIChoLhxmC0mRWzwMOKGV69AXwTHJ4dgjck1nmJiZKHQnPDjoDz5SpHVtNVLwD7RQa/AZ45lAvazvXuZDTp2WktWDsqM7CIDMTwlH6UIBF5xYauPMih1fm3E/Tex90tqQbbyQ6+eTif26f4LSY48EHq1kMQRsRmLVmTTm35WY90OG77iqdYJCpecANaDvaloG93EAuN5PzgAfwIQRcw36DOsuXl2NFX2iPn7CF98mdWETdgWdyEws2wgB79pTXIZgN9B/tcDkV/Af86P77q45nHiSOcaBtlJp9LyWD8GxuKQeEJ9DYw49kn4OGXKMjI93v3AvIG7nXeozwlrMwVTclz3Nomz8AT5Yjre+FQO+t55L6nGqnzl7j0cGsjUCD1LNS4MEomoNfZndEEg7UJ9Lt+DzogKi0n8MPwDNsyiNwR0erp0wB3LYD3QJ0FptiuLwNvgG5HffDNyDyU7+Iys9oZ+XK6mZ00G3Q6/vuK21CuEdcjz6XLSvl1FTSl5GRtP1ocrL79Ei0iXsl0jM9a+u7LtMn/64JPeBo2y6stefRQ+RzkBtNRkbSa5rbdprY4DWbk9MJ24V+0orUvWu03aKpFl2sm0NePdYjt9f1pY1Zm2cW703ZqnL11/kE7PaQzTkk/5qYKGmNzKiJ8oQTyvrcXs/R6ZR2H9BdbM7dtKka6CzHMzpaTUKi2Uvk5kFpl9q+vbQ1YTOi9CcQlXQSMh5K9Ll4cZVur1hRlJxGS5oq54u2GYXz4NSJWtwXriXu4u3x55LaXKbxEQmuLwLWOrJQJ5NZfvG5Od+pJJbdv+4ay46VmhPyOnkt5m0OtOfUq72vTrdo04fTVpse1Mn9dTYrj1+kbfo+H7oAaCqnd9aGopT/l/MBeWKTbFdLdsFle2m3R6ldn6KlRFUbvORnXP9JZXPnepG0j3H+BBrKT3gh6vZLgO/IzTrQkxYvLhPCoNToCdcpeB/j42n9ksskltyPfjw0WcJ6dp52ms5/SfdlEHGvdn1N5pN2H4tHSF2DqF7+97wDC17fuec9p4JsNfuGtbklNRdy/AfexFWejSI5sMYodQVtXXnsPk10whz0GuPQFlrwc3c6HdqlJb7aR7APaIYDhjyOVwb4QjHTFLRUkBQ3XsgsMfKIH6LuI73wWSpdmiOkjunKMaVQJ3R6F2+Ow8CCx3jivSbVjpaRRLaTei69EpImRqa6+0sFo3kYiLePVHvyO+toOOs6y8BmPZc6Z4k1Zg+z9D6Xunmh1dHGmFpHuZkeNCeJRM695ji1ctuzxthHDM3tIbrlluLD5GTaOa3RXUB7DhCqPMZiCU9AojVvtXYseOZA2wKcZfj3wHoHKdquXWu9A4/DQEJT7FP3pK3n1H3VPZfUuuF0ru4dalmytQ0rvRhX+bV8l7cGK2tBDiynCzdMyzFa9NLTn7WGZTt8DKnxaH2kskJ4+EndOs81Xua0zdvj7Q54M8rQnMjil+KrHl7HAwgQCJVCjvEhF7lye129XHnX4yhtijrdwDvWXvhP7hqRv+XoRdZ9aUZnq906Z9TwsG40zIUlk/MxYMweGdHzHJrIspw/51yfM2YLObKWRtM9Ord2fV07nnF434XnOUgnx6DwmMd0G1BTAUhcP9GyM/HSul/YdY49luj447vr8+xrCOiC0+bmm4vyf/6nKK++Ws+EWYejjirKhz+8KI8+usyEfMghRYmgatislixJbwKRpYZcfuSBhy6kxtEUvdpLPLwh9bnOsSKdnlpdtCmz3wP8tAbr3qStCTRdOsA4ND2i7mQU7TftudTJlVriB25TqdvwmEO/ml6n6QYpR792OpPsy7ovbU7IDXV3310GR+E9oS5owcqVpZwLWiGdnrnPTgsI6yfm5nQ9NGV354HGMlsrD0CQfgQeREZUOOpTG+p27aomM5EJR7QNj5IG7N5dZtTEmly7tijBe1aurG76lp/5PGxCQz32lkEjd14SVU/l0OpadLxXu4mURXhdKTfVJQzh49GyYXrk95SuoOkckmfMzZXztkmwt1bXY3exrtdkvbrr+efU+C0bVq6PQaLO7sf/t55Hjq2Hl557zrEfDJr+E1WzXXNoz65O97KeC2/XIydYOmndfNf60ILR5Mlc0ga3ZEnpz5AZ7bWxpsau1bG+z7FpWtdp46mz83lsv1y3zbWRSdTp8XU8R9J/1Nc2keTYrKyxgm5L2Z6fjCLB+YDc1Cvb13intR5SfMiyQWhzykP3c+zcWt1UH3Nz6bYtm5XWd91Y655Lju/FQ4M8z3XQ9h8ioh/8oChPPLGMwUklxLACASUdJSplcpmUZHS0bFturNMy9Gu2JnkihDw9ccmS6ppP6SX83qxTH3Lodw56pZ9Wmym6Z9W1ZGELTeQ4j+2gbgwyKFFb6zlJBCW4ragX/sF1jbZjPXLkfut6WVfj86lkmql+cY01z+T1KRrvRd11dXpAHY/xyMLa9db76Xf8j2YvnA9kjuGtb30rPetZz6K1a9fStm3b6JOf/CRdffXVdMUVV/RpgP1HBCNLcKMtPhOVjBiMXctaLAkTN1Lyo5KJSoMujIPbtlUDnHnJs1KmSn50XL8hsyXL3a+rVlV3RGnHdMo60vA5NtYdlE3UnYXHk9EA8Dh1rbp1woxF2CzF2SJETXYackEjNVYuTGi7XbU2U9AMk7K91PFsWtbkVKp/D7R34FEULQO7x0CYw3z7gTqDiwZPBqZcAa5tDDoz2txc4ZC3fufl7Gx1t6nMorZrV3VOY/1BadiypTtLDm+HHwXH+QVRmWFnerr7CBbeF8CPxQRNxTrEePiRQqlAQH7P8r60zJjyeE5u7IXCLY8NRVa2devKYAhk0eSGUJmRXzs+qM74MTFRDRbPcQpo0OhK27CUQLk7WtJobiDMcfZIcONmDv+wFCzrvjwGtRxofaX6zzGme8bUJh0dxHybDww4S2BneCTvGWpynTRCaoECUn7furXqeOOybepILk7bZPCBlgFGjg3j4X3LTJgymMg751FP6k5cH5BHOcs1xjMjyzraWtWOS6yjPdzQLte6dfyXh37KueQxgGr0LvU59Zt8Z2jTE1xmjc0DzRklDZ+pZ0mUlv+152Jt1Mjh5Tn6Yi56aUeb41YfHtqVmrfc8SXbtfrU2raQww8HmZmbqJBbQW/m5qoOeRkExjNJIlAYQXv4vHlzaZORdfF5vvDLX3aX/QDeIWxDoP8HHFD9DiXk/0MPTWeV5khlNpuYqK5/S6dNBVV7+YhsT/Ypkx3wOvwe6k660OyOvH+pA8pA46Gh6jPzZDLWnkHdhhXNqSXHNTdX3Yyi8WDrBDXZtqzjyWBd17YXGi3dF4E1d9BBRKecMr9jGQTGx30nI/L1JW37sOOg3Lq12xZEVJW/t24t/wdPuPPOoty8uRqUIQOWd+9OZ+/fl7F6dUn/YQfipznCJ6BlTSYqfoe/QGaX5oF0oEMySBbPl9uaZFC2FRTkCVrJcZbzz5adHPehZTuW7eTIbJbDWvoRLH5oHfecckAPD9cHKlvPUDv6O1W37nqr/qCgyfFNfFMLhU9p9zPgZCREVF3X8n/+mft2JU2HbWfTpjJL/b33dpfQC7ZsKX288vp7731o0XQNoMnSRzw5WQ0OR7l4cek/wHXITs3bSSWU4SfJaElmeMnXj4deW35pD+3xBPVYgC1QbgzkfCBlC8rxc3JgrVp+YAsW3Sfy+Wi13+q+55gv+/lCocHzMQ7LjgpaOEi8+MVFqQXXaaceysBgaWPfsqWU5XEiFU7Bgh3ovvuqvGG+bUNtQfP7EhW0W254lHyAnyqGja7IkMw3Tkp6zeVLGcOllSk7ssenmUMvLfk5Vw/wrFW5+U8LQE7F72jIqetph0NuRvdsSkytTw7LRqT5dpvoGDn+EW1svdZpoy9LNiGq541N/QEamsR+NYF3HfUbmWPYuHEjveY1r6ENGzbQAQccQKeccgpdccUV9PSnP71PA+w/HmKRCy0AQgJR97HGCP6CUsgVUBBRHG8DBZMbJaXTnweR4bM0OGqOBOlk7lVZB3MCoz/44FKhk7te+c4tGP9SRwKNj5eChTSc8mN2uNGQqFQuLScQR+o3z7HlGeiQI7OAheER/f9MDJFTCJD3bR3Xs1CgOQH2FbTJiNvqvxfkBCHnBiB46g9KGJEYHS0zI8/NlYqldBRxeg76jABfecRwp1PNmiODd++7r1RMUd5xR/mbPIYGJdZ1p9MdUIyxEVWP/eT98yOPc4DjOR/5yO7PcPxwoR6Qx5Dy8QNSAViypHyuGDM/dlq+Hy0YIbWrmfOjlMKxkI+VtQxynmPaFhr2ZfofeEhgiDpEn/1s8WFysmpolLR1587q5hGtTDmpuRNcbgLh18gj4CB3gw+ARvJ+tY2PcHBJXQN0m28QRNuQzbnxTgYq4zc4kaamynZkFh3tCOUcw55Gk7UgZCBlLOaZzbRNLLxPHrCcGmuvhqU2+YgnsCxnM8oggHeQ46xrUqfuuhTabq+tdiyneU6b/ZwHTdsetPz/t39LdM45xf+LFpX9S7sN7EBbtpTBx9AbUEJXGB8v6euBBxYlHCxof8eOsm0ZjDA9XdJQOGnkxuzly0u7jdwwDzvUbbcR/exnxf+WvA8aCJkewV+g9TMz3TI4UTWIa3KyzLCMdvCZH8crTxrD9aDfPCs0xoVnd9BB1cAwLQisaTCpxEKhkx5gXjQBv3cZzADI4LY2gPn7UMC+NFcWGrSNSYPsG7TeCkaADMnpdoqWLVpU0i5cB1kYmzE6HdtBKpM3SLsH38yYsvtMT6d1Fe7fkLoGSoxheroaXI2+uK9Ayv2gSSi1pC/aRgQZxMCTk0h7j+STa9Z0Z/TnfWnBTSlH+EKGxqsgH6TsWlq2ylTAMP+uLXqvjRlyyyAd9BJeWdpaq7z09mWhiby+L8xbCS0AaNDyP1Fpf7fAg5Fl8Bn4AepMTJSJNOA3PeKIouR0FP+DFkP+v+++8n/JY7jNKEXTew0c6idAr5EA5sQTi/Kkk4py3bqSR0qfB+erMuiX+5jrdISFEpDSJjDP2kYv9gV+vVanl8RD82Gb2R/QT/o73/6zFNavL8rR0ermQ3lC+s6dJd1Gid9g/9m0qbr5BHUgN09MlKeUIBYHv+3eXfIUmcTEQ+N5YK7mC+4FoM0yaYAWBAw70sMeVpQnnFDacmQcEWT8pUuregin56lNJAtlTs3XOLg/SANf1zkJSlIbD7Xrc09V5HZB7zg83zfx01jQrs9pU9u82qTfnPfF/Vfyel62rXv1avd/qCXXagkf+tCH5nsIrSPetAR3cBDVG2y8u2JyiE7q2Le6duRYcB1XYD1EPBV4wRVfOKNgjNywoSghyK1fX36HXcGe3b3S4cSDI7AjChkS1q0rlXrU1wKpU4ZGTRlNvE93EHC/kBPASVS+Hyk8cKdhnUN/ZCRtzMzNplwnmMzNlfPUQmqnWa872FLtp/qs27msMXZP2xZ9yZ0DdcilYXW0z7tjrG6MvN1B74zdsqW6iYKopDOARyDVDJY5QdZa8JVmsK3r33qHso/t26uGTqnAzs2VCgyCnuTGmT17ynuWDivO12TgHDI/o+6mTWVwhzzadHKymglH29wilVgtQ45UajXIQHLJn633zAOf5XisLGyp98WhfSezpFp0O5XRRgtu9qx5CS0IXls/MLCkaLzGp63x5ChvfB3kGFw97ye3f+2zBi6/5GTEbHp/dffY671rMijowaCwfTvRs59dfvYEPVrfpSDv2dIVtOz7VmBsat7PzZX0CWvtnnuKEvRX0zU0uiADFhBgACPY7Gzp1INOgD4QvLd+ffnbrbcWJa7hugL6ksFtxx9fOrFgwIWuoBksZaAxR8roDN7V6VT5h3Zstaaz8bpLllT5jxyX18nscZYDlrwus2ZqbXiM3R5Y7fAgII6mBq0UvaqjUzk0vVfnXKodDx+znPc57Wlt59yXZVy1+LVnvqdO5+gX3vKW7szIdeBGXku2Sd0rt7XI+Y9yerqk06CPoN+aLC1lUMjCa9YQPfaxxf9SXuD0StJLeQ/cwZ/KbLNkSbUPjI8HWWCM0hHNnyXGhufDswnJ8aPkG0RT2TN5pn0tuI/3uWNH9RhU8DztpBa5kZ/bniwbiITHHiBlxdnZUm6SdB+fZ2fL/+XGRS7rp+RrfD80lL4fjS9YfKhO15+bq6cZ1jPU5Et5P5YsrdFWK/OpdpSubCcVhOmhk5YuqM0JbZNbXZ9Wtm7tvuR12mc5NikzcR0MzvZBYWZGtwunTkhcudKW5WU7ErlrPgXOh6T/QJuvnpME5XhSnzn4XEhl7uXfy7FqwdKwZYAu8VO0JJ3VMlum7lF7Pqlnb9FdDbm2Vc/3qXFo18uASAspmXxoKH2iIc9AVzc/tXY0pGxnmv1HwqNHA5YdybLt59jWZX/WZ962Ze+x7l3eh8ZXUvfj0Q08OpBmO9PG43ke+E5umh4ENPnfmpOSJ2j310Sntd5PDm/Q2syZ/x5bl5SxtBNArMQelhxkyT+WDEHUnbDEemaebIiyL4t35/BXDXU8RpsTvC/ITVLul585pI+Wy+/yeu1+5IZUbYyajgFAL5RInWjJr8/w5XfJHVrbqXv0rAPeRurdecaq2VSa2IisdjSalMoQq+kGGjQZj5e8Dw8t88QEtA0tKF5udkMsivV+NBom4ZFXtDa155yi7Z7TwnPpUwoW/Zfj04KJtXWQmvfT06UNCPNE+oj56emeUwqt5wtIHcPjO9bkdg9dS70Xyz4wPFyV/1N8gEOepMlle9B2aV/TNjXK7NTaPcjfhoerPijr5JcUjdbmi/aeNPoor0/NW42GeaDpwVYfqfZzdEoP3c7RAzg8matle1yOkiUHvsM87hcs2jxILIQxzDMiGFlidFR38Gg7UVEH/8tdq1hIu3dXA3yxQwp1ZmaqwcPcaSMzM8NJLjM19xOc2SIIDQ4eBAEjKPi444ie+MTifwhucveT5piXjqI65pLaGZuz63VfIASeMfI6dZnRvIynqQGuCXKcwICltLclaOdc37ZQPwh4+soVVDx1c/sdBHhG9+npKk3WMmOmMmKCRu/eXQ1ykjzj3ntLGs+z5eMzz25DVCph2HG7ZUv/MiBoRyljlysCxBAMBgf92FhaYOe8Cmsewd4ym+bUVDqzJj9eJ/cIXqI8Q4BmqMmB18GUui61q5HX4TIJ5ifqgR9YAXn9hGcd4z1L7Av8eaFi0PQTaEumGPS7n5joHl/Kwc+d5zIjGc+agM+Q5XF6isxswIOdAK7Ay7YRbITrt26d30w4kP+R+fOww8r/wSuQoQzlU55S8hbUBf/gmb2kUUqT/1PZbyx4HF5Ar+uo1+ul8czqg+ukgBZEUAeuQ6T67XTSbWmGT8vRI4MfJXJl0JSBz3u9B23RMo8ek9NuP3S6Np5Zbt+DzoywYUNJr/g6kkGqkM137UrzBu30LKlH8Cw6OAnlF78oyn35aOYVK6pZoCHjQ1fgRzBD58JvfDM65p3kA+Pj1WBoj0PIctKh3ybw6BNt0iB5Paef/cqM1issPoJ36AkCWKjQnJaWk1p+10a/TX6vG0MOv/VCnrinYT6OaQad2bIl7Tjj9nd5eiJs+9hAsmlTmSUN8r+04+/e3Z0Jjag7Gxr4hdwEjnL37vnlF5jr0ANWrChpkNy8h88rVqR1A5TLl6dl++HhatINGQw2MdHMaSrvq+66FJo4h7XrNae1dX1qTeVuHOyFbmjBCBb9T21+yoXHltfk2tzr2uJjdYGJvbSdg154gOcemug+/cCPflSUixZVbTtSD9i0qdwcB3ovs1dy/61MEqFtFhr0BkwJ2GEhr0s7zuRk2m/LT27k/gJe8k3Y0n6jbczuVS7ZH4H3MJ/IlQdT9v9+YCGcwtiWzO/1Y3n4UY6d0TM2T18LDaDtMzPdQZf8N9D8u+4qk+1B7tdOUwcvwHXQB3iwtTz9pC3Axr52bRmLA7lW+qyJSjotE/txW4tMAiU3o09NlTRIbgLn+sBCzWi8rwN+FLkOm84tK56oTqew+uTjq0sakysfpmhZnQ7m8QGl/BjeseWgLR9u3XgG5SvOGUdqg1JbiGDkBYMIRpb45S9LwrtzZ+n0x6KQwb/T09XsjjA4QjjZsqW8Hu2hHS64LLTjdHgGSqJuoyCeEb7DZzyLe+4pA+XwG89oQFQYHGF0xHdgRNzYmsqywZVZaZzldfEdsrbJnTIaQbI+p3av9Gq49CrgbcDb/iCJZFvBww8FzMdzHzRydl8NCkuWlEZFHpgms3OB1vNMwvhNBiPs2FH+BmOkzASzZUtVQeXXz+cmFPAx62jnJpiYqBoqZXDaoYeW34F/wCG3Z0+VD6GEMqZlRtZ242Iuom0tkEo6Aq0A6JQCpGXq9Mxxy4GtZbJbaMEIuZtpAu1gIT7TnDENmv7PzhJddVXx//h4d7AwUZXG82zocsMA3ySgbVAkKp/FokVVow6XZTk9IyppIg9gAC+QvMHKrK/tzpcOIo3GyiOTJQ1asqQcM2R80CTUmZ4udQQ8Z+nc4o4v6azmO8txH1pQmqyjva+ULM/LlHyau6u+rk8N1vvS2vE49j0Bc9Z3XniPdm5rrQ/SULjQ2gF6dUr12keqz9y+Bk3/r7ii3ERNlN5EyGkJaDs2CCLzOxxPW7dW6T9oNJ7h8uVlICxKHtQAfQPyv0aT8Wx5gBtRdyC0lXmvDYBuH3xwuVER9BvP48Ybq/VR94QTihLPmQcjS51p5876TPSTk/VBEZqeZ9FZGRyn6QFW8I0MUtWcJvtyQK4F6z4eCkcy9mPDS26/TX4fxBgWSpsW+AaHycl0Bj1epk4QRMnlwlTCka1by75kwNrISNVBi/a4HwEyNPwPfKM6xiODqXkyFCJdzsU1aFfzU6AOaDzKpkAgxJFHlrwBgRTgGcPDpWwJPYRnrEaJ/+VmB24/qUtqwuuksgdyeGRQD5rKTSmZm+uL+J8f401U3t/IiF9255A8j2dPQ6nNoVQGU0tn0tDLM28acNtP38W+5BdJjbHp2OcjaPAxjynKmZm07YDrBZL+y42H27ZV7fag8VyvSAU+c/+xTErFeQ4/xYOouT8Z/d5wQ7Pr28TUVLlBUW5UXLOm5Amwi2ETO/iBFhQNHmFlqLXsPzmbWyT2hTXcFnLvdX96Nm1gEDwn97dBjqNfAM1fsqQqt8gMyYccUt18AhrNN6PAtwu5FrI06D+n1eAVXJ7vZaNhv/y3/QBoM2j8YYcVQdT8O84PpE4gfb3WZkTLtt/UJtPLRjjeRlub2a06vW5QbgNcv8A76xe89uq2Ewk0CfJtu699yf/Ox9Fvm2AEIy8YPASsvy3juOO6j2mQwohmlJSKqpYhTSqaUBh5kAP+lwHQWmYF1OXH27cdzMwzRHBMTlZ3y0pH2sqVVaXPMvDJ4y15hmTrGOM6h75VlxsX645w8Djo+Wdrh3jquvkyOlnMkh/ryaGNNbVDqk0BrC20ZSxuGws5a+R+gA4NlUe/W9DofyrYSQtSkpnyN2+uZs3kfEQGmsmg5t27u4N0icq1yze8QMGVR41x3oXrU7sh+T3LwGlgcrJUFOEMkvxAo+34DX0vW9adJZm3wzOryefL+YpUULV1k3JG8XGldvG2TbcXSoYQDulE0pR0T9YdKaPwduqO0xvEM8hVHPs0pg5lZDB6CGPQz6EzsZjo2c9OV9CCEeR3Muvlzp3dm1d4yb+XBkc4VDidlPSSb4QEnZfBaDzjstysJzE+nqaXnOZj/JLXcd6B6+EEOuyw7s/8mOVUZhyi6prMPYIN/8tNiMDwcG8OJm0u1Mn4Vh+aLCzb1TY+8mvkkZkSlhGGX5uTWaEuQ7IGfl+Yy5ZB2KOXSWjPvt9Bmb3Cmic5PEqbm6m+rDlhXW+NTVsHOWtr0O/pyU8uAqCI9PkCGosNi+vXl4FXN91UlHfeWZSQtTkwx2HjgYMI1+4rSBmJwc927iyDs2ETOuqoojz++KKcnEzTbwR033dfNXgXn6emyuxtsEPJDS9aoIEWVCBpTUpf49dpPKNubns2CeQ6kOZbNwgEHiqQQUeS9kgbDw/wBb1HEDBs9vfdV5X3QRt5FjYZsMY3sMiAh4WWuKQpEJyBgGPoBqDfIyPVAGPYlTj/kDZ9HlAOHUOezOhJRqLRVrxvT8BC3feDBmyEniDjnA1tdb95+pOBp4PK1JWLhfIuA/2BpmNL2wFoAD+dT9p/uP8U8uyGDUUJ3sA3p8tTWKBX9Ds73HzhoINKuR3BxJJ+L1vWnZGZqPQDLF/enUGf1+F2Jdi8QIO0E1ZSMj337ViByvwaDs/GhaAngcDCAYJdrSQenNbLZFRyw/n27aW8D/oPGxFPQIV2oAdoWYsltNNlpB7RD8DWC9oMcL1IZvgHjYYd6JhjqnFE0h8wOVn1/6KdZctKe48m06eg2YFy5HZro3kK1jUaP2jbl6z5bWUdDR4frLT7S910drZ66qdsb2ioOl88drCmGwObBBxbz6mJX8KCplP2EiTtnavWPTeZ93Xtp75roy/veBaC/LUQxjDPiGBkidnZUhmcna0KBLL0HNPM68hdU/jMd9jKoOatW7t3yfL+5wNccPrlL9P1oKRB0ZRHsq1alVY0ecCZzGzMDZUyYA7EC8xvZKSqNGrtpYI9OCOQmSvQB2eoMtjDo6DmCE68r5SBb3S0NH5IRZx/rgtMGRoqBQ3rmIY68Pv1BiZY42oDdQzeuqafCKY0rxjacj/ROMvyXpcZYWamO4Mxke5MkpmNQTu48oY61vUymAH8YPPmMkBiIWDTpiJQgwNZC6BA8mM6uYLJP4+NVbMAgRdyR5MMauCGBPmsNWVUBpsAnmBkDanNS/z6VBaeNoC5KI/KBB/olc40yZjDx6O1I4MEc9DUcdZL3T5iiAbobO5V4evjMxuaSxgx+trfr+aktdFEM7rJLJpcR4Ccj1ILTpaByig3by4DHOCg4se77WtAAMLq1dUNKzxQmajIrgwaD/7BZXVtgyMvR0fTtIpnuIcugD4kjbeMXjm0x8qwA2jteQLfuPyMeSZPjOH8oG7ziKZfaWhLhu53ZgTvOBY65oOfzdfzGnS21EMP7d7glsp6huexZk1Jqx7xiKKUegB3WEn7D4KRN24sHVXQ3cEjFiLqgsRnZ0t9BOV113XXWb68mxfwktuI8D8y7YMf8A04eGaS/nObjDxFCzTfMpBbJ3RZG5NkcJwWdC03gnL7H+p5NuvIMQ8Pl3omoNldpG7AbUsSqU2EFl3g7XiCtAe5QbyXje8LOSB8kBv6H4qYm+uWrTV7D1G3bR6BBih5EDLqyMz4kv5v2aIf7/xQB54VStA98IEVK8rnADsOaNuyZVX5VtJZnula0mIe+MZ9Crw9nmEttVHdQtPAs9R1vOxlAyVRPb31tte2Iz7VTq+bEhcKFmoilEBB3/mJJvzkQqJSlr3jjvIzkohop90SFTRenoy7L2Sp7BWgT7Dp8Gz1RN32f9B7GVQ8NaUHGKNMycV8bcmM7/JkEw4ZEKXdj4RFlwDNbtNk/c83TQ4EBon52JDEk4lAXpenjqC8776qLV/G6tx3X/W0Esi7vcbxSL1kUIA9APchMTVV0nZ5ai3o8dat1fgUgNN1maiQ+wn56TP8N22jOaDJldKWo9HoXpJQNbWR9xoLI69r6rftBd4+e/H/zjcWemxQG/NnkDLEoDZqRTDygkEEI0vIAATN+EjUnRFSBh/ACAnF88EH7WxlREU/aFs7Ghr/z2cQMpjFypXVYxHwmR+hgF1PhxxSlFLhXLKkFDrkUWGAprxxY2IqoMvjxGybAPSDoDRVKjH3Uu01yXDGr8sJwBgaysvkoWUj8sK7G8p6rqkgE82IWCfo9gNNA9ybKHaW4bTXAMCFyIB5Vnyu4KWO6dyzp7wPBDJpkEGg2i5AeYwkD8KVWfO1zTEyi7nMsHDPPeX1OetRbuIYGyvpLp4XMtug5IZGBJytWFGUCCqYna06zQE+PmlE1IyRsuR8oO6YHo6cjSEymGBmpkqztHWZom98reUErGnKseSrVkZi2Y4WRNDEye2hD3wdyWzd2jV1Tjqt/34ED3ieR4p/WONoOq7UvWqBMrKOZbiR7Whj1eZtr0bE+XLYDQ+X8vvcnC6LE3WvXXmvkPH5BhR8Jx003MCGdYfvIEsfd1y5fmXWHT5uTe4iqtJGPkYAtH3JkpKPpWgO15FkFn1+jJ3MaKAFBcixaU4ly+meoumW/K+tkTpZT5uHMlB9dra6xnL0Ek8fvF0rQyfml5TXUcr3z8GD1FLXW8FtAK9Td2rK8HBa3tf4ZQ7dt2hXzjzRvvcYzdo2rHmyBmh9pcbh4REaX9PqtC33Dzoz8u23l8dC8uy+KOVJIJwWyucCeYZnxpHBV5DRh4dLeg/6CwfW1q1lm5Jecn6L/mRmHb6G23ZaQd6HbYfbhaSNSGbx4YEGMuMceGenUwYNoy6yIR9xRBnYgDa1DPkeepDaINg0wMuzGTE3o5qElLV4n5JuS9leW6uWvCppR9tOrVyngPVOc2TqumfAv+Mb0lJ0SdM7czav5v6GdgflvHmoY+vWUnaam6tuCtDWiDzlCjQdgWs8M7Kkv5xeyQ3R2qlTMnMk33wtN85gHCi5ryIV+MwzPUvZnvM1GQSAupArPXx7aIjo4IOL/2EbkhnSeDACNrHDfmQFqmm2EBkUh2snJurpgGVHsq6zbMYeOpUT6MBLyACpPvg4ZVIVre0cWLqFvHeuF8iM09oY6mwq3mfYlv3HcxoeYG0oTfEIy9egzS2Pvb6J/pDLn1Ow+KsGmRyi3+ABMUuWlJ8hy0LePfXUopyZsZNQ4Xu5GQUl9AK+GUUGmPGgOKxV0F9unxp0QFodsBakzoNnefTRpRwp6Tf3l4CW4RlyG5PMpI/feHuegOU69FOuylmHGi3UAulkfAFosmaTacLPtPF5bMfyej4eKVPk2DA0vuax0Ws6VC++jjq/TKqdpjad1G8evSanfQ1t2fgtzMcJZqATBx1U3SgM2RFy69at1dNqJY2/995yszR8sdKvsGdPeb08BXf79tIWtK+ciML1KJyIKLMgL1tW0nnO67zQ1pqUo7QYoflYG5YN2kNXNDnTsmGn5Ca+Macuo3EuLfKcjCvB+0z5w/vhv02hKU1rIlPk9tXknnP4ioeXW21rvFPjfTn2ucB+gwhGlhgfL5WlthTwXuElWm0xwLp2+/FMNGWiDr0Kw1ZAiedZWr9ZSpPlDPb0h98t5Q/CbipII9eZhPHIoPHhYd97qBNUhofbyYLlEVwshZOjl/E0XbPW+uzVWZdylGpossa9gga+8yiaA1ZGO6Nj3Q7/XmiMta5zgjb6oQg1QQ594nU0h7z8LO+V06t+Ca+5xqVe+s81wte1w+muZoyEYUMaHDk0hxBHp5M+XgfYsydPCYXyKdvlJxjkwEMf+mH8S7WXmutaexrvbOookv97jMba/MkxoOaMORdoaz423+FY+VwF3kLO/PA86345C7W+6vrOvd5z77yPVLBfqj6R7ZjzGEi0Mdc5c7Wd/bLuzExa/vJkc9DuV3vOoK+pOTwyknfPKWjvwqMjyL6Hhupplydzsjae1GcN2nPV1q7Gc+X1devZa/zLuQ9rjta9S0+7vJ41F1Nrls9ta/7x9TJInHhi94bEo4/u/t2jVwFN5oQXTflIW7J0ap5Z71mub04LU2O37CWzs9XNGppDWzqhUp85msqVdXV40GBOe9ZzaaqPpGiXlhhAbnTVNqVrNB3fo76la8g5bc3VXuV3Sad436lxaNlWm9Bmz2/8/mRwkrb2UwE3vB0ZiIrP/BQ5mYVKyyJo0bvUGtPuDfcjN2ps2VI6pR/72Or1/Qa/V48cJp8HNpUce2z6OiB3/uY8+ybQ5IUc20XO5i5L/vHQNGtcFl3ytCNhHeXM26l7v5YeYT0PzzvgcwzvIRVwYNFSj36lIZVQxgLnI3X0n8OiMym5XbveQqqOJncAmg7XqzxWp3dq804L7KiTU1P9o706Hc76LUdO5Z8HeVoOgCAqlBw5Ni4Pv9c2MmvPLiXv83VgbU6W9T26Vwq5dMrzW6q91Hdoz2NXy9ElLT5t0RMvrPXN36WkbxoNsOxikPE8dE4myrF8BgC343voNeqkklmNjNjzX17TZC55dErrO+s+rTE20dO0vnJoj3VNSk601r5HJvA+QzlvtfWE3zZu1MfTT2gbSmETwqZrqV/J/3kdL3J0xyZzwdN3jqxfd11dO9o6TLXfFHxOS7tLjn1Am/+a7GnZqlLteOREgM/NFH0ZHk7LTRr99dBvD1KB8hpdkfagHFlSg5ZAVEvYxusSVU9J4wkyU5uXPLY3S4/mpWWHwmf8L5OyaaetpaCtT4+NxuIfsh1Lt7XGofFp1Ov3ZkQPDRoEFsIY5hkRjCzxm79JtG5d+VnuuNSye6WOCNYM71LwR9DQ7t3VLMz82EZAKgwgpsPDVeKpOYVT4JkuZSYzz0LRnkEqi0NbRNDzm0ZstM9SwNAIvMeIWHcNry+fmcdA6Hk+w8OlMyEFK8gsJ7hMM4Sl6mmw5kBbyDGE9TqO3HbbEMY9yoLmTMppR2sz593lGKlyjQ4tYmh2N9H11xcfeFACoNEJfCd3L/JMrzJzjBSM5+aq9JIfYYPrUWrCqzzGEhlx+O5bKdgCvG+ZrUxm1+WZobQd97hGZsvU6FxKEePP18qemSP0eoIQUtdrCkgOr+IGUfnuUZcrF5YihPYsY5t8ZzJbDX92qbF6jnbm9QFtLqTAlWM4oGV7loNHwqtkWwZPy8iL8VgGCXxuQruszNpNFTuUqfnLaYmV8c3j7EiNzXIW1N3DoPFf/1WU2rMHbeXBnvI5yMAonhkHJY7pRKaEu+8uv5MZ0bhxCVnBYDxBXzt2VLM3y0zj2juUBo5ly8oMBnDG4TdNbpFZx3i5bFn3mKXhZ/Hi8jeZ2dJrhE/J4paOYc1biw+l6D4fX2ptaHXlSQj8njyOEEs3SBlx8A488BpqPLK9h173khlBo3NWOxbvS7Wdm9W67tl5+UNqrJ4+c99fE6eH9Vw9a62pftYP/Pu/l/Rv586qnC1PuJqeLtca6C/PMolrpfwvdYTp6fLUEnk06LZteRljegVkzyOPLEqcbML1Ibwz2JZAx3n2STjuUkcwa/K7nD9a9nwrMNKS+7Vs0kR6Jkd5tDOXyeU4uLMLc0HqWVy2R//SacLvPXXPfF1YazV1Ahtk8dHRatCYJadLe6OVoUm+C571CZA2Sksn1OChLx5bhKTtGv1vK5O17JNfZ9FtucnJIy9ofWr2DNlOqo82IWVHbP5DyfuGDWNQGB0tZXKuC0qHJqff4Akopf2e038Z1I92du8u14jkJzt2VE9klLxm167yfwR1D5JneICM/8cdV5THH1+eQoDTE8FruA8C8wTzF+0sWlQvV2rPXtIy7sOxZBIJjU5oNi45Hs22hOu1a7XxaPo8l59l4IW8Z74ZMRU83Jbt1UNLRkaqemaO3J/bXy9oI2lKDubbHiLRi19kX8Bb31qU3O/KZRmOsbFy/cgM8liD3DYjM2Jy2U/Kg+ADmzeXtH1fwdRUmQETm3OQTZSfmAKaLk9RgY1o6dKqP8KaS6BzuJ7bvCT4PMY75LyFl031aIs+WXJlSh/yyp7gBbgvWXIfgdy4Ysn2Um634DkZi6PtjQeDpDnW+96XkavXtH3/WoKHfgP0f/fucv1LPRx84MEHqzK51HX5BmTYeGDr37KlKLmPYF8C6CVoPU4OWLOmtPegDvcxEBU0X25+RYlrFi9On37O51uOTCbXKvdVe3xbsh1Am/8eeTV1uoY2Ds3+o/GIe+/V+wP9n5iojwvz2AM8vl0PTbCencZXUzoU1xd5UlGJ1PXe8dah1/Zyr/H4YJrI7dLGqfXJ518v/iIObv/tJ3gSnPlEk6ziDzFEMLLEP/xDyRD5LgsZKMwZhjT0ABohSGW54EFKlsNLGkchzNx6a2lElYIO2iOq37XOlTcQdRxNAYFjYiJ9fC9vRxoRccwahBHNCCgdPpOT5fXa0dYpeHY0aYEGlqKoOS44+mEY8zDonL440ctxlqd2tPLfmkALRkjNKY/T2uPAqmsz9ZulcHqMBk0E3KYGWQup+m0KYZZBWdaVigQfx6Azo01Pl8cA83FIhSEneFE7QlMaHrkyKgOJRkaqx9nII5l37SqvTwU17NhRdXyDloK2Tk2V70PyITyPJUvKAAPpSIThceVK25GOMcjnknrO/Dq0t2hR1Vho0X1pUAbdmpwsnWDSIJazJjxz3XO9tx1ZX7t3zBuPIXe+AeXRE2CSQ+cH6bBqypcBaxOU9b0mf3n79u5sTdF2TfGW92FlH7SCBUHLBolHPaooueM4tdZmZkpHEUoZjDA5WQ0wBv1E4NvBB5cZ4kBvEeDFZWA8X3ms2fR0N30mqjpWhoer10FHwJhHRko5HX1Cr+Gl1HVwX7iHu+4qx4P7gNzPN8rJOQR6hTFPTpbXgUfx8aUCzDQDScr5ox3BDOQEJ/BgfivgIEdmTcEKmiUq310TI0fueOqOe7Ou4Q5cj+xoGWclPA5Bi/ak5o02NiujgTXm1PySdMbbjkSO/sDpvzaPU+vHMlQCudmkpL1kUHjmM0satHNnVT6VsiNRVWaVAWs8IAoyNXgF2tGCVMH77ruvdC7guE55tProaHWzB54hAtk2bizbQcAatw0RdQcF4Z5hVwJNGR2tbqgDbcbYR0dLui1tVvyzdM7x7KgAZHPoGHyzDHgLnoMWNMg32hNVN9sPDVWDBPFctKQDcl2Dn01N6euW1/Vk2OwVc3PVzege/pUKGJb/8/YshxU+80QLcr5JZ6/VTi5ydC5Nbq2jzd6+UrKjdl0TXcXiI7ntpNAkOLMNtBUQ6QUPkNL612i9FnzMoa150H/YZnbu1H0C+A10EbT4nnuKEvRzerqUi6FbYDy4dtOm3rI6gm6uXFnSZNh7pON3eLjKw6EboPz5z4nuuKP7etB4bIQ5+uiyL77xkqjgEaAnUk6wZCOpi2qbcLUkNFIP0mTPuo0L3M+Tmi/j4yX9xlzUNuJbtAO6n4XURhWtPUCuB0/AmZb4RKvrsWd5aDGQ0g3q+FqdHS5Xh8uRzXOu97SVS5Ob2Jq0vlLyQq/jGATe+Mai5CcjpjalcD3PSkaCOrC7gAZCzr3vvpKmgybeeWfZLuRc6ZvlMizkN/QB2siTguC9gI5A1+HyrwyURh3QohUrSrp/2GFFCZkcdIv7mWV2UegFq1ZVN7HLDdrcvibtCnzzhyw9ujHg2SDC5X/AkoFzgtr6gQ0bihJjs2wykhZrtNlDw1J9eNvLkWEt+0vqXea+C0+9Xt9vip/l8rUmfqomdrY2kOrfw2MGgQsvLMq7765/H9ZzAf3furWUwUH3pew3OlrSRxnfMjxc9dtKXjM8nLZv4Jp77iG67bbif4wH1/NN5eAXUm7jdo5UYifu85Xyv6bfgFfBRgV9CLFGixZVZV++sUJu9taSRdbZjAeht+bYDGZmqpvaJd/m96UBeiLgke37CbnRBXOA21NlNn9Nbk/RDM4XUvem2XgkPPEcWptW203k5Vzk8KomqPMNo30rmWeT8fV746dHPhwEFsIY5hkRjCzxwAMl49cM05rRTS5AzZiSInA88w76BUPWdk3x73jJd3HJDDS8DzB9tCMVzomJ6o5JuaPp0ENLA6HMesN3uMrdTtoOp5xslQsN/SQgvTpk8D4tRmoZFvF7W46hFHi2Co+Q2ItTxOM0b6qY9Wp888AjHFno5V16np3XaJrjEEkdadsvcKWQqEqnQcP4vcvsaZLWT076DOO4DkI5P75UBipbDgzwAYA7bKQSKzMuLF5c0nDJR3DNXXcRrV9f/I/yRz+qtgfeAMcSgrzBO1atqmZPkDzUCoYZhGMfaGvNeeo2NZbxzTlyDgwCbTlEBmHAtQLVekE/xlynXHvg5Zse+SvHYWXRO8+uVzg5Bgk+9tRGGcsRIUtNkdey4IC2Q0bnzvtUNnWUs7OlTA+D4/e+V5Q33liUMEC2AegIcEaBfiOY4LDDSnqPrGd4l9jcuHp1NfuytTGpLYN/k3Y81ywk3aUu21mnk17HngCDHAe5tUa0QAWPLpiStz1GM2vzqTV+SwbW+qgbuza3rXZ61YtSdbVxSDnXchTlrlGPM2q+7AHLlpW0dmqqpE+p7Cxzc2V9aVvhmXKkbK+VWmYUoiLIDIFmyDwDfQDByVu2lMEMCDhGMENOQDe3EfWalfTUU4vyjDOK8vjji5LbjvCd3LDCg7BSBm6PfcIKLsoJWPBiPuattvYl3Zb8oNNJ17FORJF1reQGPGA+FSxiHdOZQ0u0zx45VfblCUbTrrNkvrprtbFptDD12eJ5Gm1O8REvD8mxKeY4frV2B70ZZXi4tH8Qpe31njnCZX0p74O+QSbmjlbwBB6AzDMoE1UzOW7bVvIbOKIh9/eSLIKD8wXYfQDI+Mh6fPTRpd0H8j7uGbaJsbGSHkibE7dfyA3VMmiDKL0JcGIibyP2Q8UpmFrbHtrOr7GSkMh+NP+BrOeh9/PpC/LoD9p3lkzcq63J46PQ+u2lvSaBGB7e65G1+DsYNP2/7jqiU04p/udyuKRTnqATzUfAA9R4uX17d5Z7ou7P4Ak8eJmo2GBIVGzmk8E8MkBrZKRco+hDO00L9UFf5akna9aUpwfD7gM7Psply8rrpOzHfb51p60Ngh575Z754A05titr/fWaec9jz81px0NXtLoe36FnjJb9SPalzUkPDfPIiqmT6npFUz926r1YGygtePi8Boxj0P5foiod5Uj5eomqOgL3saIt0FC5uYUH5sqTTe67r7TpQ+6+666iBP3mgbmg7djg3Y9nCBkcsr20/y9fXtp5eLZkovL75cvTdjXOu+qSWsn/FwJ6oZP8npuizv7P17lHP7T4UF0yEssvyOld3T1ze1+On0iO3eIhXCZJvTPtfuTaT42trh1tbrflV5ft5cY0pOZ07jpcSGu1Tdtvr+PYz7GAvKcLBBMT3RldZCYxCCo8I5kMIpDZXbZsqWY2gDABBxLPBLPQwY/OlAovD0CGoRRKqawzPl7NNiCFE36EgmREHiVjeLhklqljCSxCkOskqWMc3DHjuQ/PEZEAbxfCq7VzDAZfueNMYy7SiMiFHQ+zrcPcnJ4NNTWeur5zkcuY6+AdV46xodd77eX63GsthbMuCI0/k0EbI/kGCT4OfKdlwEtlRuPH90gDI0rwjHvuKYMIsBmFZzqW2Qokz9mypbyuLefTICCdUCihsK5dS3TUUcX/UGqhAC9Zkj7Clh/XlnKAa8d0WkcCpdCUdniEe9mHZgTR2sNckL9xup3aLWspmh7I+acdg20ZejywFM7Uu2pKk3PG0bTdfikjuXMzJwDD+723j6YBKW1ierqbpqTWHS9TjnB+VHrqCE4eeAwdQMpBu3aV6xnBZ+AHvK50QsH4B7o5O1vdxMgzKxAV6x1jlCes4D4nJ4vsOERV2R51772X6Lvf7R4rngcMmWvWlAHLGCM2sMgMORx8kyTqaboFUfepAoDmrNOypRHZdAW/8fFI/iFhObt5WRcwxJ0dGuCslGOXx9FxgG4jiKzpUZwep4fVdorHaYYj7XMTmmFl1pF98WeXypZkfae9Z9mHJQM0CW5oarhvEtyQW7cXXaFt7NxZypdcJ5WZ7XkQMWgebDyw6YC2T09XsyVDH+Ab0Lm9SF6P58aPB+XXL0Rcd1136QH4Co50XrOm/I4fvYwyFeigQdqBQAuXLCn5GD/Gnqh7s4S0j2g6A37jvJ9jfDw9Vt4u6qQ29A8P23QKgeiaPoQ+5L0CHpouf9d+4zynjkZoOquHzml9eoKAe5VhPehFls5Brtzd5N49+lXOWLzXtn10dx24/M+DiKX8Dvq7Y0dVpuYnUREVdFz+BtkawWSbN1c3IcIetC/Yc5CJEeV//Vc77Q4NdR/rTFTqD4sXV2mY9ln6KrQAIGnnkSe0aBvYNL+CdfoKxiDpvuQRWsY3ufmD96XZ9m+5pfs3GVi4aFHZVi+np3AZtm4DDIe2CbGXrN0amvgK+uGcbmIfbIvGNuVBvQbFpfq19CttTIO2/xx7rG3/0fzAKd8wz/oog48lrd+ypapHyKC0hzLgK5anYPHsyVJG51kzpa8ZdHvx4nIuSx6hBUVL3wCfhym7hrVhWCtTfmxNR5d1OY1N6SPDw6XsoJ1qhmstWR6QskddNn2O3LWbCsjqBw2o2zSXe73E8HBvtguPvJ1rS/FgEHpRzjjmY0MSZH1+0lHKD6AleAJwzdhY9WQs0HrY8zdurNrbuY0ea11mPUbde+6p8guZVJDPSWlf56fg8mzNvC+ui+F+oA/JIGnNNs+zLxMVcjxoO+z+sly+vPtELiI98FNuVEQfixdXT0/UbOp1Nl8eC6bZ/fE5Rbf5O+CyMy95MoLUyVq8tAJypV3Qk51ek+M1vzEfswZLD0jdO/+/LfpvBd3Ktj12e3mtNbbcDU5WwHSqD01fyuFrbfMIaxweaNf0O/6nH/pe03Hs54hgZImlS6vGHaLSQWXBElz4kWtEugFTHjnGAxZkfZlFeceOamYeGQDdllI7MlJ9RlAi4UQ6/PBCsScqsiTgO6Luo91SwV4a05XGt1jAaXz/+/r31o4oj1FSE+RSsIyRWt22Hc+eQN/5MnqlkBOc3GvbGnLer6evnMByS2gcFEZHbUFQOr85UkEn/BiwFI+Ynq4GKkDh48qS3FHLs+hDsZVBESi5wovr0RdoAed5WuAFH0NTQAE98siSR6xdW5TIsANldOXKatZ97pSqCxbm/MOj5PTL0TpogB+nMDdXzXqTE+zEFde656LxgZy17nUieaEpwFY9TUmuo125tNZy3niur7uu6TO0dgHnjMeDtjcENYE3k5YWFGoFl1q8gaigrfIIUJQ7dnQH+RKVTiyeDRO/4TsYBtt2Zm3fXj0GzAPQ4ZNOKsoVK0q6f8wxRYlNKDwzpjSG8jIVYLC/o05f9dDfNtejx2iHOpaMVUdj2toYpLWZY7DMdRR5DKfaJjl87oUm58rdvcyLhS5fLVnSTUOkk0OCP4vUpkR+6pUVuCZtQzwzvvxN2ni2bKlu+pAbGBdyUBv0AGTWPOGEojz55DLDpsyiz3lDKihZcxBZ8MzttoNMm6y1ujE88IC/bc/xyqk6PMOyNaacuSczkjVFk+ubyu2pktfXHGB1dhKvvSS1IY7rLnV6HQ8s9PSvXW89B287ss1BQmZih0O9iX7H62oOZ/5ZS2oCGq+dmgL5m59Qdccdxf8oFxpAm+EPWLOm+yRFonLjCTJsrl5dyrL4Ddmkly+vBqtrp5N5NnZYjv1+ox92TwRjLHRYPCjlE+pH//1AL76FQdA9y0bk0XVy5CmN9ywEeV9icrK6sTkXmv83Fcxm+Yi5zJ/KiMyD2WD/kbrBrbcWpUcGW7y4zHqsydtEuo1FbgIZHi6fH/y9OAUF8vzq1dVAMc1Wn7Lfc/k/FdRjyWEPZWBeLDQsFP1/oc+Btn0eAT+4zJ+SQbSEBChlgg8O0HvYaFDOzZUbKGBrgv2Gb1SHbR+8gduY5CYLLamD3FzvQU5dbAohKnkB5HdkTcbnI44oeUPKx8vlf3zH5XZJ91NBwPsC2tx468ky7EHbcqh1olZb8T9t+T697TeFJQt77HZW/IKnjmyvVzTVYy1dI3jcfoN9iFIPCLOz3U6/OmcAD0aQ2W+QPWHPnqpgwIUQIn2nIldg0ZYUQkDUly+vOqBlNs5Nm8qMKTBi4rccPPhg+jq0e+ONRNdfX/yPAAMESPGdURAw8BuMkBBSVq8ujZA4qhTCIs9okDImLnTjx6CR+wx6McLn9pkyKDRFL8FtdePwBFd4MKg56b2XuoCzNsfhETgG7Izq0JBO/6UxkaNOkOSOwFR7vC+5i3337vTRJ1BSN24sDVAIcAAfwv0ccEDZJj+WjajkJ/y45xR4tnooEHLMw8OlQoTAYjiRUK5ZU/IGnsmAqHtXbcqJxOdG6hhg/uwlBrH2Fqoxnveds2mkH/1b3w2q717qaXXbupem7eTQVG/g3P4iP1lBxIDmLEk5nHiGNcjNkP+1zSgysGT37pKugpZCbsYGv927q0FoKBGYxI9ylvoEPy4UdcBzZAawHTvKTS0W5HHKuB4867rrykxqP/xh9zXgAwccUM2Mg3LJkipvkJn2p6bsjGhE+uaChzoWirOjbedUP8a80OZG2+PJbW+Qz2PQzoT77+925mgnoRB1003QTnnMMrfV8Mwz+I6oO/ODlGFBC8fHqydoycwny5ZVnTYymGDPnuoGNLkBkgeXSpkc7YyNlfeGwDdsfMH4du+ublSHrA/5f2Kimt1LOlFuuaXM8iizZCxZkg5GwDtcubKadV8662Zmyu9wz5zHEHUHqQxyTja1YfSbvufq5XjPHjtEW8HjvdoVeh1rnZztkTs0e4kGKYNKfVOT460xN9FXPe1xmTqlM/HnAjozKMzMlPRXO4JZgt9PajMKr4MN5ihxn0NDZb+gU7B7dzpVnUJLToJnxW1CRCVfmp2t0kl5NPSmTdVnDrrHs7JJHQP3yu+nLvht+/aqPwS8h/sspI+A24g4T+Il52/SriZpO28npSNwX4Pk07xMbWS1NimkPu8PaOqQn094+UoOj+zV91Hnj8i1t+UEFvRD1hj0JhQOaYfh0Gw8Mnu+LHkd2SZoyp491WyTPBmItOUjaQf8pUccUdJiyPL8ZEWiwv4j+0dd+Aymp8s+QGchE4M279lTtf/L+bttWxk4Dd4CHYHrJSmbGaDJ+DzDJt+0zseoBUXLgDfuT0gFXGt+HzmXrQz7+wOaBH952um1vUGiqR6yP2Jfes91m0g4pDzITxIEUrqXFkfEbUQp/zHnNXITi7Ttb91an0jK0km1QGwJtP/gg93JtPhvKHfsKE/whh0JbWunVklbP09GIpMjahngZcmvSWVP5vecsv9YG14WajD0fK01i056Yk88tqF+39t806lB+m6a+sCb0Hjtt36vn4Xi+1sIY5hnLFBKOY/gR79bmR40oggmqQkVMsW/ZFbj43kTUiq8PHsCN4LKPqFYogR4Nh4okdLgyY+NkMxfHiOzeHGp/MmSBylIJdDKbNM0iCDF5HolAL2216/dH1YQjYacrDWe4x7QN9rdvTu9buQxoPz/QQTEtq04N0UTx1tbO4q819Q5kbxIzRftc9tH99VgiJjjR5uTcveexSM4Usc38vY9c8oyOqeOlPY4QFDyjS8wVMpje3gQGA+YkO3nOJWbOKK9qDPU79xZKuyA5G8TE83G1o/7aQkdKmjvEPUpY14v83l/RY5M0famnQX4LjBHBwqeEceziQTwGKAsWpR6957j6LkxUwZFaBteZGYGrk+kMvTwQC9J7zlvICocPnzToIbh4WrWMq3dJgaJfdg54JnzfaHZg6TXUkf4/9l70xjLsuwsdMWNyMioyKFyqCG7pq6q7upu9+DGNNiPlgUPtzHPwtJDIPkHfmKWLGEskLFAGARuZNnCiEkGWzJCBoHAP56wgAdCNk8eHvDgWdWAMe1qd3V3dQ1dVVlDZmVlRkZGZt54P259vdf9zlprr33OuUNUxSeFTtx793TO2XvNe+1sHS8LORBlQu0r96/5fHlXImOYXgRef70EgUXQ9gpsnI4c2jUbhJ5joL/6KGdPpkcfZ892j/AEdN+8ftAv+MDbb5esm9i4bmXch40I7SHbJYIk3v/+EgSMIGn0hcCHt98u94ox41mirj49yzqWnp/1e3GtjmUHaEHfdoc6kVocVlxnaHsZOSRqLxus21Lf+63vemDbm6YXNdtBpl2NiCZq3XyZ4AQXLXPSy05tIWNDswKhMvMlE/AQBc7V1oTWEbx71nYy2HgRIKAzjzKvsuxAHv1v9ZmsEovUsa33NcQODBwedtu2TlPkIEFgqF14GRhbn1lE2x4imhF9v27BDJH9fxVzwjqZZ1F6iKbHUdZklkEivQFX3nhuba5kWAEl3Ld1Ionls2ObkGfj4f/XHUNkT60LDuUJ0Tv06H/rPPaS4Oi+suPqi9b2hvC8vu2t2/yNZOqhGMsnsW7PDIBtQ/v8AKa/kW0HODgo9htOAqXrWvK+B5aJ9YkuoM1sd9na6m7as05bYx8Bn9iu+Yg1DhE7UUhE99nfamU/5vFZ9iygVYdbFiw9urV+5jsEgnO8Dr4fiha6n73fGu2IfGRj6VcZ3p59B95vLWPNxJfoNeKN0Xp2UTs81mjc1nr0aJiuy7TDeoacgGJsWPe8CqzDGFaM42Bkxt5eYYjZQDOPIGpHiWcEhML45pvdMvpITp25TGR+t5PIbPerPvJTpJv97O23S0azPscsA+fPl6N84Dzi65kzfgCFlUnAC0quEcgM1s0hM1b9qN1aAJ6GFWCc6SP728ZGMVR6wQfLUNK98bXU9eovI5AjWgf87GqCR2tfIvGOwEz9Pn2PdXRHC/TRN6wssTK6teXziJpgptvRgpzuX2ReIOPnquugHpRIy3HAQBnNl6CwcPZOPQbco86oLFIUeZ3NAUCmBWS40Yoiv2et+HoZg3TWTL1bVl91H57Cur1dsrYBrORa7yAyDB8BNAW0ZZxRjHV7HkNpdOT04/pWO2MbNSPlOGon2uW/jLnd0NbCAuU9fPWrJaAqcvBn5ol1TCfA2dP0ZkLrKGetC4gUOqvlf/yGK4LIkHUA368aeL4f/rDIN3zD7P+PfnR2/cAHZld9fBsbM6NMNKukxSPJQ4Pn/HTazUox1EmQcULV6lj19OdMME/mPXvyVuv7WYZjpeW59glY6xuwELXbovu19GG1wRmAFw3QH8CT+zP8VW9G5yw1oMU6yBj1WP6+dm0+c6Wux04uPTb0r21GyESz7Ofq4bHHZlcEM0O2h46wu1v0BmTIgR7w2GPdoz85U9r2dj2I1DLmtyAjF9b6t66t9VucJDrQLGqXdcax+WrmmbU6RGpO3Vp73jiGbozOnKoUfe/R9qwju8Y/rI081vMZQv9bbW96Y+CyEfFOa4OfV286tYN+vXasLPqg77Dp46r1Asj7L7wwu4LW4/urV8vpWYt28kWAreXhh0UefHD2P+w4oP/Inn/hQjfjP66nTxfegKsVhO8FPFhJTWr0pS8WqY+0rlGGd2/6BLQWZHQOyw8wVC6NvquNaxG+nD5+iLHG0RIcEf3WoosNbQfQ8toqMwtm5SnvXi3ZJnouzBMgo2v5n/UGbQ8CbQdvgI0Imwj16YmcfKMvYMt5/PH568MPl5NsIbcz/T57tsjyfOKXZb8HVm1X7mOj7VN3yDgWZZNf5CmKLT7rsZ7jqufSorAI3tK331b01aPHAmwIVuAo81dt2/eSgIh0M7eDr+mNIvgfcTyg1Vevluz2oPeI8dG2In2aiEiR/2sn3S4bTzwxu374w8X+A74BOxC+f+CBwhMi+z+w7r7Y1nFl7MPWdx6dzsiHfW37fMpZtp7X7yre4Zg+Wo7JybQZBQi3BHtznRZ7Uit0vVq8zhhxSGNCP99VYh3GsGIcByMzdnfHU4AXMcGG7j7x2rGM+V5wqGW4t67eEae6DI+bFXJNLDKZkfs4hjIESe8G65MBwzIU1oIYNWqZVRkwaHP/LcJAFKRsBadlgppRBvX0sbEZ51ENGcU12iFuzYWxDJ4RPCF6Mum2aWUlqWWM0+2w0pQJutJ9cLCrzuQFxQcOEGx+gPFMpAitLDBruov5i2ClJeFQNuTw9MwRng4K8t7dMgKphyruQxyLUZ/rJFz1GWOLDNDKc8ZSAiJFo48jJNOGlcm+1me2Ly9rn26nz7PKBHHpPrgeDPc49vbWreIkZ0dpX0Wc7zkzp7JyGMCbwaJd/qDpug92klinfXjH1GrZyQuG1ru2OZhyWXj/++eP7KrNGcuRDUSyyFj0MUNzItk+kmk9eeH69e4RnOws08EO4OW8OVKkOKoAnpvTaXdzjtYHvOep57YnM0fPJYLXThRs1HICzpgGG34+kYxuBQhYx562YjrNZUDmZ+W1pa+Rbto6xiyG9hHpvxENydxrRq8HajqDLmMdA8kZ1K9eLesfax3zhvVX3bZF/1Fv2RsotrcLL7eQkSX4yEcEWo0Jaxz8Hejmyy/Pri+8UPQz0GvINij79tvFCYayeAeg4ydOFLkHwWP6WEyR2fHRH/7w7H8EeCPAGDRfZ/OJgr29ea83s7Pc0SqHtdh/uL41t1uCAbg9fSxrZBPx7GK6H7TD8pTOkpQBt4NxHR52dYKWDJmW7JmRtbh+5r1HdjtrvgHWuCJ7TaYP/hzRZk/u1+/fyua4LOjnw7qBFXzrvWfLyQyda1mo6Zia9ojk5UXOAMztZWWKiP/0kV1b+tR1+9hH+HOL7uLVZzoZ6b28NjNJAqI1ao2H2/TuXbdtJW0Zoh9mxpyFl2ENODy05TeReZ3B0x80X/FOD430gog2R4h8U5G/ifvlPvW1xUdQK2v1GcGyI9U2aU6nXbqtN0vz++HEQboPXGH3RzDV7dtlbN6x5zdvFh/DL/5ifJ+LQMZXZj1XHTysP4uUe9WnworYSZcyejCPg8ckYr9L/s6i7VwGuhwC4Pb2upkxIUtiU4j2o6MPvFOcynv6dDd7JsZhyQtWUhQPlizcx6+XkeeW4evoo7Mss72h47A2R7bYOaK2ub0xkaEVPJ6Ipmfm2dDnEo2rxQfPdMqylzMtuX69BNvyyasaoBX/z/+Tu4cxwSeca/D7HkvfytgfdL/W3M7M74zt2bP7a/ufxz8g55044fubMjEs1jzmuRTZ9oHMM9EyXwtNz5RtsdFGNq8IQ2X8DFr6sMrqOB8L1n22JCexPo/9XCIbkaXXWDI4X/vYr7w+rd+i55qxzUT8je3M1693EyLxJoqXXio2HcwJyMS4avvPojdSZOnmorEOY1gxjoORGVohz5bn/6Ng0giR0Nli2IjgBR9YAqmXxeHOHd/YrGE5Sbhv3BeUWDi39A7ZjHPA63ssaMdXhBZmOzZ0356jKQoq0IohrpagJFKMkxsbdmAxw6uvf2fG1+eZRQKD/t7bUZdF7T1bTqQWRIb/oQpQRgG2xjH2HM48n7F28CexMb0rGwcUyC/iOz2srJeARb/70LIMLEOANdbahgpdxjNMa6MmysJgqZ3eoEE6+EBkXjnlbDVWhuPImVRTWCxHrZXdmtFHCYwEdt2u9cz7IFJq8R5wHy3Z73W7Hk33sm9bfekyTNt1WU/uyshBGbQYBjQgi1hBRZ7xLVrzUV+MVoeEpzzqeceGJD3/WGbT/fM6spRj756tYGTP6ah/05tYloUvf3l2PX3azrSuoTPSe0exifi0x0JfJwcbBzAOnRmT3z3GpZ1BWNvagCAi8rWvle85MzOAtXLffSKPPDL7H6eoICMasuCcO1fKcwY8HXjszUkd2BvxVfzvBVNOJv7JLC3HnVuG3Fp577PHwwEdsODxRwsRveaNglZ5DjjIBDfr5xIFJWPeeoa+iH5b2d09aL0zqpORi713qO85MjjW5D+L52U2xVr6Dc+XSI+w1lONbltjtZ5TSzvXron80A/54xwb0+n8PKw9e83PGPp5e3NAz1+Ws7WMbW0c1e1EsjA+7+52MxFjLmBd37pVaDsy6oPu62zMCFjGZlPQTdD4Bx6YZUfT34HG4/5eeaXwGIwdMhZ4x333dQMVLLm9hVf2kSGtLDxjwbJ7eDqC/j4aD9PpSM/zeKc1RqtvpmGRbsBlMc67d8tpbeiLeTDGLeLbHbe27MynfC/euj448Nu26HcmmJ5pRtSmda3NO02zuE8LES3ydDBN/3n9ZTJVaVmrRqf290s9yI7LRCQL1OQyD7V5q9+ztbHCk4Ei+1zk5OYy+rPnPLXKeMjQ2Dt3fJkccuWpU91s97zZh8dWG2tGbomQkbPHQsY+Yb3T2mbe7Ni909X0s+Ojvy2bEXhCNI892UjbLWq2s8zatWRha7MA20v09zU7biSTR3KL9b4z8y3idbguWn7RyMxbLymKfj+f/rTIT/zEOOPMAuNptQuDTvEpeyL9aKqVkCmqz+uH6WW0KXKIXZXHgWsf2pWR1bL9e31m6reMI0uTo/JcJ9NXRPtAbz27iz7REFeLZ0TPIwvL/sPP4PCw7uuLTkKL0EKfLNrOZaL1aNni+bNFz2syincf2TIamb6sOhHtwbVmY8Im5iw+8Ynl2n809Km3jNa1Gj0zkdlaZruPbt+TV7Su683FiCZHNhWWVyw+kpE3WtbqUGTov6f/9u2rpUzWLsz1M/weuHOnS/8tMC3GZ9S9fbv8BtkeG4nwbg8P/b70+LwkVtp/gPpR8hiWyTN8PqLNVqB8n/mQmWct7URrlmEFEVv3bPnP9DVCK48ZC9euifzkTy6u/Vb5cpHjeI9jrYKRf+zHfkx+6Id+SP7Mn/kz8nf+zt8REZHDw0P57Gc/Kz/90z8tV65ckW/5lm+Rv//3/7587GMf+3q9W7duyQ/+4A/KP//n/1xu3rwpn/nMZ+Qnf/In5RE4NFrw2mslg0tkBMwwIl3XM/xHbWb6sLKjZjOA6LLaMMDBDNjJhqveGeUFJU8mxYnExkTdN/rCLjlAMz0mnnqXMQcKcVCDxfwZk0mXMLPBM0ss+igDmd+je7DmDTuEPKVU/2YpjLUdTVYZfNZZ1fAOEQxtGTXh7NQCur5GjNUyLLcYIvoYo60+WoSj2nfoK1IYgJa136I8WfcTjbUleIfHZWU8X3Yw2nPPiXzoQ+Uz32sUEOUheq5AC1+wvrMyCFgZtFHGow+6vHckqM6AjQxr4A1o7557yjFt3hHKu7vlfx3orMseHPjC8nTaDeDI0ArmJ7u7bbt3+9CVjMJpZSEBrCC5KKga33mBwXfv+nRf18nIPRFvwe/eOtL8IGsUq32uGZAig6OldLUoiBFvYGTobWRA0p8tGhqNTcMKLPEyqHj9o4wlT+rrnTvdsUZrLpNRdWzg6PdI/ge2tro7ey1DY7T5rw8ixyJkYMjGOrMB6DSCwBBUhsAzHQgCYIxo7/btLr3FeCCH33dfccpx9nC94YTpriV39zGeW+uYN7ro+6vRacsYb71nb6ycJciqb9WzxsrjsQxzrE8xLbt926b3NVjPx6PJVkAUy/36s7dGLJ6ekWGjd+rN8YjeRjS1xZAcPTuLNtbu1RpzFPjmtWOVtdZhFETQJ/tJ1L/Oor4MHByUsesgYIyDbSMHB8WJC3kXNBD8QCSmGfiM73iDiOVoYtn+4KD8r50JukyUWVA7Y+OzBwABAABJREFUotnWAEeEzoKGe0U98BMElH7+8yK/8iuz/2FPQzt4Ph/6UNG17r9/vozOzsyOAz12T5bQzwVjxKZI1kMiWToKus2s1cyGAabNet5ZwbYis2eRcdrw+7U2iLDMpmUufve4ap6RsRFx/5b9h3mdJ3+LdNeIrsOZCi0a68mwli2wjzPJ0sczAazRvVrB2Shba9uSZa3nYrXNZfl+WuQxndTB0xW0Xv/5z/ttLgI666PmeZ7+apWxrhkezvM2klcim2Bk4/dkLGt8/F4gU9661eWHrA/ovrD2QUu0jd3b9Af6cOVKtw/NezlAAklNdHZ23pgB3wLqnj1b34QYBYvo9enJlRaf9oL79XPpYwuZTktGKkaLf8LSvT37va5n8Rrun09cEfFtvZpetcjmnv4Q6XvZ51PT4zMydXajeUaP6Ou/8Opa9MHjG1Y70XOp9T+dduW4ZUL3ndFFa/Y5/V3Ls/c+Z2G9A+9+rERT7CPoK794NPH27S5tZrlOJwRoWaOt8Oyn0ZzOrHGvblQns46sMWuw/pBJGsKwTj1hZOZmhk5F96fLWIGaXruRHaqP3abl3UX0geta44jojHXN8MUWXhfplF4ZLp9FVBYn7C4Teo5FMpb1ew3Rs/f0O6s+oDehtsxbK8EUrtZ3ImWzwOXLZcM66wHoS9uIIJvjevHi7Hr//fNJB0Vsu0ukz3MSK964nn0/NZ4S6dj6ndbofwYZWmiV05+tDXQi87ZBltMBnd2a9RA8A7z/g4P58iLzdkLUrZ2epTem6u/0NXou1qkyrfQN9+PpyJl2uD2rbIbPZ+z3rToL+2K9dvU4Mv1bdTJrpDZekeWfjHiMlWFtgpF/9Vd/VX76p39avvEbv3Hu+x//8R+Xv/W3/pb8o3/0j+RDH/qQ/MiP/Ij8nt/ze+QLX/iCnHnHyfFn/+yflX/9r/+1/OzP/qxcvHhR/tyf+3PyXd/1XfL000/LJhO9Cg4fvCRy9sxo97U0RAbPdxv6KudD2rR+t4SBWtaP6DcrO4tlgNVlRbpMaXu7BJeASWMd6IyZ+A1CA2dIO3Gim0k5ynrpZV3OBjtkjrOtCdqREaSFCbbW61M227/nMOjjJBsTY9GbaGzsbFw0nnyy9Dl0Lmi0KLF93nNWoGw1Ho2BzL16zydS8Cxhfuh4vDKRAyIyCmV4hBW4yXSeFUcd1Gk9w9/8zdn/oMmg9XDEbW4WusyBwfqamQu1Izin065jijM3ixRnlBcYEiky0Vxn+hnNKY1MGW8u6bngZTW0Nlpl5IaMUdFynnj1sxkGvd/68tWonb7GvqG4c6cte8My6GlGye87FmvNRpn8UKeFbnvG1cy6yqzraBy6n4he1gxQev1ERjK+Z1w1DWC6kJHpmUfoDSs8jq0tkeefn/3PGTS00Zd1AyvQDIgyrfFYrVNPMmD5fywD7lCZrQVj9ZVpxzv6vW/fEY/Q//cxzvbFsm0aOzvzz4OPV7acJYBnULay0OIaZeTTdAL6Px+DpwONMUYOttJ0hWkFG/PPnStZibHhH0FoOtM+ZDUcuffVr86uX/rS7PrCC+V/0Bfw1UuXyhgQdPzoo7Mr+sZGlvvvt4/x8+4HaFn7Wbkyk+knkgcxXq7H7epgNI9XWIFbul28K3b+aJqfuWdPbucgEu5f17FsRFxmMsllGa45+/Rc9+QOK2g8khv6bEzNOl1aZOeabqnbs54TyxAcQKp/4+BM64Q6q++aXJi1qaAcnJ7Lwu5ucZBnMKb838cuEfUTybneZlMdgIf6vLlxMokDybPIOI4jOV7rzy1Z2Cy9prZ5Sm/+yOgIXvuWrp3RwTgQem+vGwii+4L8D1kcMgGCtU+c8OU5y54Deo9gBsDaRM6Bxltb85vg9VVvTuH30kem134NT9+zYK0RL/NoNljI4knR2K0xWhulrDq8Zq2+hsjSGdtmto+WcSzb/q+RtYsx+tBxq/2Mbcd670NlijES21g2VqtOZk5xny1o7WsZ+uuQcVi6OtfRNDDKehjJrjyGlg3rFjJJJSBfZGzqtbHr/yN6Ha2f7GaRbJm+c7tVl/X6qo211v6Q+n1tecu2/4gU3aev78HTLWvfDfGvWG22jF/TetYNIGeizK1btv7PfXM7Oq5EZHZfsGPxKY5apsUmcgQx603+KIckK5Bvo7XLOsvdu/6m68j2YPkpLVu8yHywbE0Hs2wH1mePTk2nvsyI+5xOu/J6rU+NKEYiqu+1ubEx/14tDOXX2fp9NqFn+sr0nWmzZZ1HtLSvrjDWvWXug3XbsaH111ViqHz1LsBaBCNfv35dvud7vkf+wT/4B/IjP/IjX//+8PBQ/s7f+Tvyl/7SX5I/8Af+gIiI/ON//I/lwQcflH/2z/6ZfO/3fq+89dZb8g//4T+Uf/JP/ol8+7d/u4iI/NN/+k/l0UcflX//7/+9/N7f+3ubxrJx/W2ROyrtvOdE0kIfGx2sz/iOg0v17iMvANXarQSmrZ1T+I6zYkG4OnWqbsTf2ys7oHgnEoxLp04VZsTOBSgdW1slIw6czJyZ5ty54pjysqjt7HQZun4HYxESaudQxiUOG7LkDH+NQfhHBi0C0xjtLxstAlwGfQWHPsERfZFRxJcJ0D8rK1Y0nsyues/Qt78/n3lYZP5YZFb++PNkUtrioy+1ogelDbT9xo3ZFU6/GzeKwsIZCbRRncuAtuugJ/AInS1Tfz57ttTTSpKInUkVZbTCid+8zMaWwypjZBobrcaYjOEnMlKdOYIbqjA/3qWATLFUWcAyRo9lZFtk3XfKH26f7N9nH2xtze/QrgVTZO5L7/pmYxvo761bfjZEK1sNB4dmdIxbt7rGO2SyfOml2VXvROagNh0ghfFwRnzQ9ve9rxyxjUzT4AfauMkBaqivaTVny8FVBwh4We91trJV0H0LGVliKJ5+etz2limH9Vljixhf5Njp0190X6vUO7LOrr584yjhpZdEHnxw9r+Wqdn+cuVKuUJ2Zrld1+HNxKDj0NN3dopdBDIxrrot9IEgYNDr27e7MjlvQMbvIt3xoK/77+9ml+FsKK+/XjIv6vHrdh59tNB2zrCGvl95pfA/ZED61V+VOZw5U7ImI2AZTqkTJ7rPBfeDZ3nfffNBdLqMNnRz0Dmumud4QU4ZZI5C1PAcX9ngHGQl6gPrvnijIpDJuJZ9TpnN6DVknnMrHWPZLwq0WST6OI8sZxRvTMr01YohdjpdtuagXCSiALzIZsby9/XrduZgkWKr1ycVAbosB4OC3mmew7I9Z8bXcrunz+hNLbAhgJaAtsP+gn5FCm9A33fvduk+Y3+/3D/mImg7eMd99xW7ADvA9/fL/1j3VtZ7gNeqtid5GcE1oo193EcLMvbXTB0N/Y6OCuADWhRWbds/Rjs4i9sygHmiN3x5diC9TiP/LfMNPoF1Oi3lWZ/Qp9VCXubTCnVGfx5blHEd8hxo/fZ2dwOEZUdi/wPLh/v7ZazaJ6zvWfsI+IRb2J5OnOie9IJ2zp4t/AI8CrKn5n24V5btNf325BWtA3kZ7fW7zNiYlkGHtK63LAz1GS5Tlh4LLXJ3rc6YGNuPPLR+37qrsNlacTue/zbK3K/rMC3l+rdvF1nY8gNYflGReb0CMh9oImip1vM4wJj5yf6+vykMvOaVV4r9h2k76M6pU4Wm84mIoL8nTnSDgCH3R7TZej48T7wESfo7iw95Np4xY42YDy4CJx2/2Vh8QduBhuoqXrmxYlBWwVcywbyZUz+iYPhVYUExd2a7i1wj6HPVzxPjeI9jLbyz3/d93ye/7/f9Pvn2b//2uWDkr3zlK/LKK6/Id3zHd3z9u5MnT8rv+l2/S/7Tf/pP8r3f+73y9NNPy+3bt+fKPPTQQ/Lxj39c/tN/+k9uMPKtW7fklgravQbFbzKZz4zQxwHHTsxot6kVTMBXHYzsHRe6t1cECtwXGwovXxb52tfK//o3bTgdIzX6xkYJMHj44dkVziRcJ5NuEBuA8Wxt2Ud6MdhpkzkmVgcsUPmNsQh/ZudQhnG17kCCMs6/1Y7dOca7C0MFuGVg7N3wQzCddp3x+N4r721GyQRZW+ubnRynT/vBaFoxw3fsSLeyqYA3IKgCtPD69RLogOwqr78+u2rjKOgI6DeUSB1ojHFAScZ9QVHa3u46djBmHZDB7cBwqgMEAA6y0G17iqZ15DCgFUd+rpmjnFuyS+hrSzaUaOf1MZ1fGyx9Q5KHIzQnlv3MDmVjfu22BCOzDKqdFaBBoJNMH/TxUBHYKKlpO8v/+IzrtWtF3odRitu5fbsEKH/lK/XxePhv/y1XDjQdOgF0BVwfekjk8cdn/yNr5vnzs+uZM10jIh/venDgB3bpz33k/ZYd6i2BQxleYZXXV96QGgWN9bn3zE7uKBuOVd+b/9ZzGSvow+uL/29tO7tJwWunb+Bvn00TkfxgfRfJLS19ZLDsY5rPnJmXKS2HvoidPZ8znliZkTlgQQcTQL6G/UVvCkc97TQSmV9j0A2YD6C9K1eKLI/rojNP1IDnCvr/1FOzK+i/SDfQTdNtdnDxxvfd3a7cbgWsevNMv0u8Dx673gDjye3WXAAyjq+MHUgDTkYrSyW+77P54AjJji7GtImsm4PL+74vbV5GAEOLHLVo3LlTaPLBgT+WSB7jjSt37nSzgmEdQv49PPSDmW/dKjSdgxl0Rnt8B3pn8RPQUmSkxxWbQW7cGD8AEPI6/AHY7HP+fKHX2HCCwGM8l8mkm60bdc6cKbYk7zQla8Oi9e4y87TPXM6gTx0rUEXf11hraB3o/buVD4mshtb10THGfN6tiRmWCR2AnNlkoO2wHOSk/cjeKUa6He4LNFDTf9A+DiqzNrzw5pRXXy10HpvP8Rm84vbt7rHr2l4vMpOjMTYtZ+t7397ufoexQ3e6cKE8I/2dSOFh29vdLL+annt+FMu24yFzUsZkktuMWDupxWrbKtuiW1vw+l8kLV0UTW7xb6wLX1hk3MAi+mvpM1Mu4/ts7XdZ0PJv5pRAIPL5MS1ke9Dt2769VPsGuA+9QQT2Hth2nntudoWMf+OGz3f0PXu0Anzh5Mkir+vgY5H5AGim29ZmPmvTtx6DvleLttfifbQPh2H5WxmR/xawYrgi+l0bT4ZnWf1rwBbIv+k55tFQ7RdAGZ6b+lRdzxeAOnrzkj7ZV/c9mdinfVnj874DMu87kudaZLQIHm+KZAGrr1r71v1Y4xvaV+RvisZmXWtAm2PEIkaI5sIysQ5jWDFWHoz8sz/7s/K5z31OfpUzoojIK+84xR+E8eodPPjgg/LVd46FfOWVV2R7e1vOw+ClyqC+hR/7sR+Tz372s90f9C7UySSXUt/LuNgSiLS1FQe1RQoZ98eKrz7aEYubM5ppZo5+wUA4CFhnevOcdTs7XYXVyigARuYFgWlnUpQ+3ws00GP0BMvMUVgWhjpfMw7jlp2Bur1aZmRdlp+9fv81xp7JwBwxRn1sGwzlnkBnvacWJpxFzfGXfe+egUT/nhEM+hgkMs5L6zl7wr0ea4sRJTPG6PtFCyMWQGcsp4z1DGqCV+Rc5qyPGn0NUKxg6oAHviceq3a648oBvtFx8rhagcbW2mWazvxAZwVlJ58ODmD+Y2XPjAKo2FnPV61csyFAj8HL0AlYu6QtmcLKAqqh5110pBDoq87ABMCQgKv1fGrHq2m6oI/czLajdzBnHOdjwKK7QMZBOZbzPjKyWjQ2MnZ544gcr9njRvG5jxKb4T/WBj0OKl0SNuRQZKKyPXrvMwousmhsbV7orCpsDDw46G5CZGPOhQtdHYGfr2XU5Pdz925Zq+zwQjaEq1fLJhbwFtAVOK6m01KP+YjOxuMFiulnAcMajKsvvljqwHkF/Q/GUB3MAGjepPuYTru8jem3pv+eDGqtEcB63pEMy+Wt9xbRMM/4p2k0y/D6+DweN2fm48zcup42VNagaT7mdiZ4xDPOa/QxBmZoqi7j0SedkTviI7X+M/KlVd+yIbQYBqPnEPEfj4bpOp7cYt3PsjOjvfJKLE9Zx9JbRzHqz9ZcsDIx6tOpRLq0Xtfn8dy6VQJQI9uM3iyoYZ3QxRsXoYu9+qrICy/M/seGRdDmd+xy8vLL0gEyHz722Ox6//3z/EKPAxth7rlnPnhNpNiT7t4t5TnrGTKl7e52n7mVBafP2mil6bj2yZ5m6Y8RXeDN/ZnNI5bjyatn2Y8i+Z03vnBfmgdzHxnabI3Ls6/p0xai+i2I5GxuW9PEjIPHS2Rh2Rd4rusr6nPmdi3zsD1C0zTvfvT4WhxVGXgOykVhb29+s7FnU9FX1t8tG0CGh3u+Bp3dkel+dLIin2xo2R6s5CZoh/UJ9HntWgl4AP1HchPoCFeu2KcIiIj8xm9IE0DTEQAB2n7PPYWfgUdwNriNjSLXQlfApkbUufde3/6v1wMHlGSyKS/aluHBklVrYDo+nfpBCBGfbDlyVrfv0f8WW3LWNtOXzkdt8uca/bc2O1iyQca+7Mn/mjZlZNAMIp7l+dii+WKNHeXgE1oW/u2/FfnDf3j2f8u80/9bV37WUduZ98vvWSca8fQHK7sv22iuXSu+YdiMoVfoDZBY45xgBOO6dq3YaZ55ZnZ99ln/nkHjP/KR2RWbE++/v5tZE/R8c7M8I5ZXtN0GbXPgM3jF6dPdUyCjdR3ZXZaVybWVfkXlOct1SzuTSZfeW/5ktiNZfEXHXWT6zo7V0wO8Ot7vLTQsoxNG9TKxI9GYrTI1+701xy0dw7sHq9/onluyDC8TGLPFn6P3G5286tkpLRnH4tOsr7KsrwM+vbmkdQR+5nqzO+i+xyP0phZ8xyfu6gy8fAqYtjtzEDM2I8LWc/JkN7mKTrTBJ3NBN4Bsf+lS1zeAPrX9x/P7RnS/JWYokgszNidrDKxv6vb4WeNdavndy5Js0R5uz/JDeP5fi3ZEcm702dMJIjpjfZ9JCsrttPYRyQki83YBL7Yto09oG0Tk38vINt5vlt3eQo13WmOL9IgW2aQPIr62TKzDGFaMlQYjv/DCC/Jn/syfkZ//+Z+XnSAd9wYRt8PDw853jFqZv/gX/6L8wA/8wNc/X7t2TR599NEZU9NjiYLFRFY3iVoM9BnHiv7cYvSPri2OHc+5PJl0BTjLiQRYjDQisCLzAegRIsZTg0WMWRC0slzzDiftWIRQpTOFogzuh4WIiCnwc9rcrBsWs4qQVU9kPnCB11iL0c0aT6Q8RUwzU9/6n+EZoIbSjD6OHs3gvc0K+jhhdmi8/XZ3IwNnadG7O/EdyuJ69WrZqQnnBBwb+B5zX0Tku77Lvp9FggOSRNoM42OV0ajRGGveWkf7enNAAxlsMn3p76xrKyJaERlTagb72phaDVZWGeu5WAosB+BFY9E8yhuXFbADvgHAWKzpOQT9yAjEz5GdUTqoraZ8cf8aOjOU9y4iupuhyS1GQZGuw5eDKkXmd4uL2Ef2eXMiSye8da3bazG8ek5rLQfxEbu6D6se9+0ZnTUf8pzvN2+W57qKzSiZo4FaAi0zhgn+X2N7u9CMPvM+M0ZrTLzu9HrAe+GTVfTcwnph/q7nGGdvZoPWmTPFQIlgNn00JxxMfPRmi+HIglU2ytBQa2eRfC2qh+dobWbhtck8Q9NkblfrFbXTVnQbHPCg28kEsnqwgssyjgSrTO1Za5ocbZCtwdJnLP3XGyu3FY3Z4hGW7uTpBlbmdm7n+vUSlARHNIJWsfb39rqB7Va2XswFOD2WhQ9+sNAU/cxYR4+cR+wQbzV4gpa1IMq+rDNkfvGLs//xPjjwwLpn6Gko+/rrIr/+67P/owADgPUQBJV96lMiH/3o7H/oHBwsfeFCeR4tPDeC5VDx6vfV07z1ODRYQY/Zc0BMJmVtefL2ZNLltS12G8DiEV4gkkiXx+gyGYeVN46M08aSx5iG6XWUcd7wd/pa04U1/ffGOpl0s0VF/Xvt6EBh3iRsPReW0SNbaxTwZrXH8r52qIvM5hTKLzsY7ezZOJiAN/pp1N6BLtPCnyNbdoRo3kZyqVcmM0/0mNmRaG1a8AI49HPizT16bvWx2zCi963b5eCMzLuInp1u26uXkfut3zx/RiSL9wXq1zaua/CGxY2NunypEdHmiLbje+bHWvfI8I/aODKw1mOfgKzonqO+2G5j2XiszLsITgW9tk5VZV+BzuzONibeBLuxUWiHtaltkfju7y50Rtv5PBknY5O0bF6WzBYFQtUC9/S85ZP7LB0O70Bn1heZP7IeeoPF83i+sU63sVECip98cnaFTA97zqVLJTAYv8GnqU9T5M0fVmZkD5HtDWihv7r/jHyc6cP63Nd/4sHalJ7ZWJKRE4BIzvXkQgsef9TtRDQ5Oy4LUZBcpD9kYL1vb561HE/fKlNEdCpjM8uMI7LxZ94d6n35y/V+xwb6tk7Isp5PzUab4REWovlm6XBM29k+sLvrn9ACRIGNgLUOAWsDppegydJ/rTpRssea3KR5RAuds9qt2XAiuTDq07L1ttgconHw+9Z8gIPX2WegoZMGch+1RFPWGuG4JJEuzbNiFSLegGvEh4BMILm3Nlt5TWYucNt6blj+VW4vY1+s9a/fE4/n4KCblIJP8NOnkOoNYyJl3rz9dvERoD6SxuL6+uvlNx0LdIx3NVYajPz000/L5cuX5VOf+tTXv7t79678yq/8ivy9v/f35Atf+IKIzLIfv+997/t6mcuXL389W/KlS5fk4OBArly5Mpcd+fLly/LpT3/a7fvkyZNyEgEkGlE2NI1IgIuQaXusMhZDrRm5WpWgFiUlUt4yDCTD0K0rG19w1U4yK1tODWMrjGMC9+O9n8PD7ju3hBAwkyjrZQ2bm936nFF5MmlTwMZ+9kPbG6KUZhGt1cz4axsraqit1UXg2rViKFtWf9EOV0bGyGT9NvYztGh8Rmj2MkRpWO1llGpPwdP02NqIoa83bswHSuixW8ZaAL+dPFk2a+gj5DS2tmKDp75695r5jeEpnJZhOvOeIrQoUJHRGWUtpdRrdyxnV+a3rKPIa1MbL5hewnA+9H5qY9CwaHyLEdwCyzgWP2gxmvWVlaJ6+O3NN0V+6Ifq7Y8FSyHvi3WWD1vA715nJIajCYjmaCSbW5neROaDl1hmvHu3u9kJRhAdEGhlKhWJNxtljDvrDOYXnNFYpCv383zd2Cj3nzkBxUOWzrXI/8d4b2DZ8r+mSZaR19KrawZyjcipFTnpIlmR22a+jmDeRx7JOVQyzkKvHcuI7jkmdaYegOnMtWvFCM59Wk7uTMCC9b5aHA99HL8tMmjWQRLRS/BBHkcUgNbnZBORLh/xsujo74C+jlpur9VpzvX1JqZMm7V3l3mHk4m9SchDizzZ1yGescMOGVdr+WVvRjw4yMkhGd02Y6+2yltz0msnCliIAuYsW4zIvL0l2rDutQecOFECyix5G/cVnTKCOi1Zy1qeL8A83xprVL9Ft7V4FmMM+4KX9Wws20UE7sPiHVYgnEcL+2RRaxlfn3oZ3t0yxkjmasnAF7WbkesABIfqZz/kGUfPJZIvr10rwavLwJUr/ml0FlZt41nEeq7JYVlZojbPWueEtfkp0wfbcy2bfo0mt9ovM/DoQ7QeLERzgIOQLfsPQ8v/kOU9+0/rM4jsSJ7cFT2PTP+ta2SMNZ2RqbP1WzBk7LV5twzZgfvFZuVlIvK5ZdZmhpbV2vD6ja74X2+m9/rp67Ph8XibayKd2fJzAqDtCHC8caO7sUmfoMcnYnGiJ2szC49HP7vIntTy7NYFnt3GkqmtjMaLgjVPPB1skb7vCH14buscqdmOdWzCKoDx7e4WuvLQQ3bZMW1EwKLt/1o+XCXWYQwrxko9rZ/5zGfkf/yP/zH33R/7Y39MPvKRj8hf+At/QZ588km5dOmS/MIv/IJ80zd9k4iIHBwcyC//8i/LX//rf11ERD71qU/JiRMn5Bd+4Rfku7/7u0VE5OWXX5Zf//Vflx//8R9vH5RneMsYHT3FLFM3G+zkTVrriDxGZJDQ10wGVd55z4E7u7vd4C/LSOoZISNEu6YiA24G3nOJFOdlolVR5bkZHcHZothZGRMzxjdPOLp7N3bc6b75fw9jCdyZ+i2Ce7ROrfXA7zB6zi2CVyud8bKXa0RHL/cZ15KP7jk8dz5HQ/Qz8HaQZjLOZo72yRrBvQwyrUqltxNP9+Upo1aGKYYOCuMj1KJsQJyRwHouzKv0kT5w0DBf0s+Hg+KgCB8czGe9tdqJFFZLAY/ei5eVF9BHN1lOOmuzh0YrH/HqTSb9NqhER7n1cfZlaFnk1LU+8zzTxx7iOy/AvTYmhjcnWvmTx1ssJ2hfA3tNhrT6sHhURtZecjDo4WSz9K8DpWpGA+87rp9xzFjIOBKZTvYZh1WWP2sewcHDViYBb1yW/M5GRWsuWPS2VaZieEZV3UbL2ozK1Ay5uq/M8a7WvXtHXurNhZZOwO1yRv4Wx7geT+24rcmkm3GOr5pHtLyn1jnPfXCmkcmkq+9ylkm90QrIBMMzonmX4Q0RTY7KRr+1HGWYQYYHLwt680IEPSdbdBxvXaMt65qFlTlSQ89J7kNv1EbwL66Y/9Z4WN7m/kTmj4AWKev87t1yogQC1/BZ23NwX/gN2dN0wBXTMKzLc+eKbhE5pTxZxHqWET/jMhH66MbZOhxEwO/OovmejBMhkv2t+cLltZ4SZZzzvs+MOcMzM9n3I7TYO1qdNmPYw1qyTNfK1XTcljaj9kWWvxlse7ttg7bGujuUsu8ko4vU1o8u40HL/16fY9rd1/39AGOMk+k+Zy69fdvPdoZ3cupUN8NVBn1s81qOOirvadUYyz+xCLT6Fjwsm/6fP3+05uFRGOMx2tD6TjMy45DN5KvGUeT778Yxv1doTRT859lWNPro0ZZ9wWrjvfIOMuirq0f2xqiOZyOIbEMZ+73VV+TH89oaU1daNKzxDZH1+ugcQ/rK6MiLsgEuE8uYU8u28WtY/pVVjeM9jpUGI585c0Y+/vGPz3136tQpuXjx4te//7N/9s/Kj/7oj8pTTz0lTz31lPzoj/6o7O7uyh/6Q39IRETuvfde+RN/4k/In/tzf04uXrwoFy5ckB/8wR+UT3ziE/Lt3/7t7YO6cmX+eB6gdnSBiB+IZzGXyIlqOYo4uIoNSFFgFjtT9VjZGbS97Wev1QYtdt5Yx5gyY4aQBwPXiRNdYxk7rbXj17rWjlTMwDJ+93UwAS0OldqYWvuIfhuqlEZKbgtTYWeYdlJlAnaWQbwb+jiUwCk3eecZbQ999on6QZkNoWfe+gyH7lDrgyUbIzcObom8+c4Rxru7ccAnrrzZwnKqAZbzH99ngvu8LI+TSSnPgWGRosu8Qh+j5PXtjVGPZ3e3nvWmT1AYj8NT/lqdutwvH5Vt8bMMz+lLp/g9txzdnaGTfce1KLq7uTmMviTGZdJoppfqc4derkvmTuNeQ/4zev/LM2wf3qkEMY6MjTu3RX7jN2YfTp6cPzpaQ29I8E7TiOimlS2yT6BwdPw1y9InTthHnXGfNYMaj1ukq2PojMbQMaysBR4N1fTPG7Ou59FmjYhGMD/NoEXetfpepDzV9/SLVWLdxzyWgz2BwfS8hUaPSc/7tBXUOdy/5f62EPzGb4jce+/s/0zAsKax0IUtfsByP9NLyzajaVnGfgR4AccihXayHUnXAe32NilsbHSP38PVOnIS36EO1vnmZjewmDPbnzjRPcY7Om4dwPF+ekMFH1/dYsPTm/94ret3yrqfxY+szSd8zRwF6n03nXaTBVg6qXdMs7bFePpihKG6hddnFOwaBYe2ygItiByUkbNwDIzV3iKez1A77Kpw7Vo3KFPDss3XAtkt2461mS8TEO/NeysTfbSumV7jqoNU+2z4sugdHxWsaaO3RqzsjBiXzvbIY2Nfg9bPeGyarlgbHXVZfc/MB6xM/R6sIG2LTtVsS7V2wN+5vuaz/Jt3Qgr362GoDybz7Lw+34tY53tf57FFeOklkXdO3ZWtrX42GUCvhxr9Pzgo9A1rNNrsqem1yKyuRcv1NTrm3Bu3htZLvM3FoNXWRnPLP1KzqdfWvGeriuxHGXt9JLv1oY9j+I5bymj7m1VPP/ujHKh8jGOMDcSwbG3VE3BpOcw7AermTf+0Ed0+96XlXS/pk7a/cH0r6zAnD+GTaO/cKXIgbDOw22sazXYk1Ef24hs3uqcbWv4I9m8iCyo2kO/udumTlVCP45hgwzt7tmv3ATSt5wRTmVNpLLuNx+et+CwPUeCz1kEiXon3wPoU62DWfVnooyu0oo9tybKL1vSrjK7ROj5rXMsMkO6DVQUBL9ou1oK+c2ER43iPY6XByBn8+T//5+XmzZvyp/7Un5IrV67It3zLt8jP//zPyxkcvS4if/tv/23Z2tqS7/7u75abN2/KZz7zGflH/+gfyWYfIfvee+3juCPjt/ebvrKAYh1Lz4KCzlCD/7kemM7Vq7NAapHZ0dYiJRMN6l69KvL663zHi8MDD8yuSOuO45bwfC9enP2JdIUQfD59upt9CtfNTdvAqT+3LvJMIEbNMWjVzzilrHFE30f36Cmjum7t2VhZbzKKPN+PJTy2ZtTB94sm2j3b7wSuWRgavLIMDBVQMuOu9bFq4eSxx8rnoU5PD6Bpmi94iuqdO93gBdB/ZO69erXQe9D/r31tdn3lldn18uVSHjwBdbTzmtextZllDGxsiLz//bP/H3lkdr3//tn10qXZ9dFHy2/4Dnzk7NlutjOLftcyRUfO/1VjKB9blcIhEh/tbNH/BY/V5Dh9HW8taOljkfynD5apUBsy9cLuy8PWlsjHPjasjchA4hmyRLqGK9D/vb15GV5E5K23Zlct/4PuQ8YH/X/++dn1y18en4YvGk88IfLUU7P/n3xydn344dn1/vuLLgG9AVfoh/fcEweY4XNtDusyEU3OOKM8Y4zldKyNyYMn/2vj5LEBpA1LfF6LpHuZQOel010HSx/HN3xDoSGt8E6U0qeE4DfIpDoYmWX7N97ZFPn224Xus00Hn69e9Y+zRGDuW2+VMrAVHSXAJqTtSvgfugH0B5Q9d644pvDd+fOzK3iElv09m4ze+B5tsmQa4Z0upuuxXUsHwXhzSo/DusL26dGsvvR/FTwjCphZNdZFb1yX53HUcfasvfG3xa5l2Yw8m7GmNxygduPG7HrzZjdbPeR/TfMh/7/00uz6wguz64svzq5f/Wqsk68rQLc/9KH56yOPzNuCRHKbtq3TFPmEI+v0Cobnc4ig50KkB3hO9wi6vhd00OJQ71tmUTimccdYNB5+uE3+jzaHRfYf3vygdQRrozfkfNB96Aag+W++WXgBfsMVdqCXXlo/uR++Fth28Bk2nkcfLf5jPuFEn74bBbhYAYQa1qYYbifi4bqsZyPSY2jxf3l2/GyQNsDBaMc4xjFsaJriJYywYns82d6i7Rzro08bh/0Hsv6bb87TeZFC66Ej3LhRvrt8eXaF3xffHyWAXn3ykyIf/ODsf9h4YPPXp15ZiU5EZu8CzxXvEkHWuFobViz9zHvPWg+oJcWyZHuL93g+66yMjnuLyjBWvSlliD1qGbpBSx9HQVc5CmNcNI6DkdcGa2JJLfilX/qluc8bGxvywz/8w/LDP/zDbp2dnR35iZ/4CfmJn/iJ4QNggd4zHEVBqhlkdtxGSokV3MxZdizhBkotHFRWhgQvCzS+v3pV5LXXZv/D8cUZ0bTRk4+3h8Bw924RuAD0hTHrgGM8c9TXQoSVKRTteTvX8P32dmmThRkIRYeH9ezYejxg7NEuZ75nvaMZz88SeHD1DI137oi8/HJ3bCJFSDl5sk4A797tlqkdt2whCjzW88Q7KhXQ63FsRMF7Q3bHW320GK+ttluCYPq2uyjmmNkhuMIAysOtE/NzgedFJhi/xXGlnR5RZsCaU8EyllmObM+oZRnvcAWv0Lt7ea3yGr97t5vN38qGzk7zKBsEftPHP4OfeUq+rgdajF20OghA8xQN9K15JwwA1ukEGDf3pflSlH0fn5mvWYHhnAFJzyUYovm45mhuoj1r80gEnvfoq3X3rEf/MxsAWgy4lkxgvcOxj6NnWOPI8HlrDH3odcbArVGjbxZtt3inF4Ct211FFn6R2dj06R5DYM1JXte1seCa2QDn1fc+i9gOMA6KA70DHb95s8j5PB5Nfzn7psVPmF5zsLSW+VAfQRavvlroLMYDGq3fIWReBJ8xn93fL+WZH4KWnTrVDXjGVTt4dBYikfKe9eZKpukW76sFOW1vd4PY9JzCOwMsWqwDk0VsYyTrIRkwr8nKQ5zN0ysXYVWBa32CR6L6uh2PL2b0CGuDRx85Vf/PNCMaU2ugDvdpnVK1aFjZfQFLtvACDawMvJHTm3+znFnRZpZatvpIt0VZvWEdtBRX2HxeeEHkS1+a/f+Vr8yucHxlgOw1Z87MZ0AWKXQSm9QvXLCDEERmtBROKFzRnpZ74YzDGDGncM9a5uPxWOPmviy50JMvNW3ngDf9mXkloPWJiH4/88z82PSJNfjs6QaW7czjA5NJ6Z9tX5bO4ckv0ak72q7p0VVrY5FHe/R9WcjIuVFACq5eH7pMH2cat6v1eo8+ZE41itA6Vk9eHaqzLAN97XJcf2g7EY+x5HYvmE3bRnhTIwc3vP12N9EJb2587rmywXEZwNj+v/9v/ipSeANn2Nfw9Hntl9A8RaToCnpTiz49Ul/12vJOp9G0kE+lRF9WYB3+R1lL1+ATB06fLnyZ+Zn2R+B/nc1UZN7mU5P/Nf33YNF/blfL/56vbV3phYdW+jpGO33ab0VmPEftXfVFxh+QQWQfy/ABnVmf6b72yeI72Af0ZnaR+cRXuCKAGTbl117rBkOz3eXGjdzGF/AR5ic6uMyTky26bflxWU7nwFwd1My+aq1fWL5lfbVkeyvTfk2P3t4uYwPPsbJ6Rhn68V4suo+rR7db7P/vlXV+jPceongELeN7J2ZGdDtqM2qHx2bJ/y36HfODaOMj+MDly8WWArofxf1w8C9k4v39Yt9gmwy3gX5FSt/aLsZxKVqmtWxCDM/XZ/mkoBNwcPT2dtd3wn5+vdk1OoWFwXq9tv9bgdTYkMr3jDonT3afg9Vvxv8LZJJPZeTcjK8r4//12h8TY8n4UbuL5rGtNKnl+bbEzeg6bPtbFCy7+yqwDmNYMdYuGHnlePttexdjzbCs/7cMwZFhXMQ2KLcQpCgwVytWYJzM/HVWTSimnFkT1xdfLNkWlgkIAR//+Oz65JNdgYCV0d3drqKKsjrDsgdtROTjHliYGCtLihWwxu+/RkRhZPAEhMjJzeV0/0MNUWyMBDJCT0Y5yI6jT5kWhtHq6O/T5ljC1SKz+0QCSgZLzuS4Mb0777z3NmRoZdBzRkWZF1sM7NqA5ZW3xsHj2dmp0xWdhRk8gh08k0m3DAdAWPwM0Iqjd1yo7gsGOSiz2tDImz74uB8d8AzwUZ7RUT4AMrBp9Fl/YwmdNQM1nll01DaAd8BzYnPT5419jCpWPe2U0kdFWdCBuRkFvs+z1muVjcatqAUsWEHnVt1MkHZNgY9kilb+FskELYYE/hwFVi0L167Nn4wSOYaszyL2c20xEGbeS6a9aDOKF7i2t9el6fisg9IQmIYrZ+F5/vlSflF44AGRj3xk9v/jj8+uyKyjM6Z5m1AiHcEKDGg5Oi3ixRkjZB+Dmp4TbCCPDLGM1s0oKM9lrSAED5ubXRo4JEBr1YjWrPWdJ99FtoOoXWscLbRL98+wgmy98bXoLNY6WHbm0atX2/l95rlm5memHa+OpatwmYzcvbNTTiLRR4CKzPMILwvPq6/Orq+9VhwiuPJGeA609QBaBhqPzMY7O/MBBSK2rqBP4hIpsjx4xAMPFPqPucdzQK9DL5hgOu3a3pif6Pk8VBfweE2WHrTQ9qjt2n1Mp3WZZmPDl2nYjmmNJ5KHWp+PJ59a9ljv3i09whqzR/d1+7Xnm6GRLWPnMYrENLFFxteIToZbFQ4O5pN3RBtvGJHMl9G52Makjzn27KXa6c7zjcdz4sR8cK1Ioak66YanI1ibWpheIoDh9ddLQgxOXKLvi4Pi8Bn3u71daDnoN4J3T54scx/0m09fPH06ziCPe/A2UgCad1qBXSIzWs92JH4+NT8P6tYyrFn1NKCPAXie1mlrXEbzhT42dD2XME6WJax7h+7o+VWstWLNVW/jVpRASJfxAgktWctLcKPb9GRgTf+5HR3E7s1b7sf6TtOEmj1K/xbdD9usWnwdmfmvx7rMk1x1/9b/y+6rRabRPMabH9NpWZuahooUuqVPYWE7CU4dQaZKkS6PAP1/442iE4APRJk6mY7rU1DwP58m8/bbJQgO36E+NqlculR4AvQGnJSin5OXZAn6hKbtbBuyZLWMXKjtRno80XrKymwYq7eeDw5KW5Ysob/X/Ua+nKGINgEzxvKjLBJj0Y6x7zXShzJye0vbQ+tmEhSMjVa7o6cb9PVJZem9iC0TeO9X8172q2Dtb293dQTE2OgT2/lEXc7mfOVKCV7mzeB6IwsnduLxnDzp+8ZEur5c65Qp7c9EmyLzvAb6Ap+0iPZ1kkaA+cHurk/DtUzp6Wlaj/B4TCZJkb4PgIOBs0HGfeJ0onYiflGb41nUaFCLXyLb7iL8+pl+s+g7Ps2Ta74tjYxdLjO2o8DnjzEKjoORGffeW5hwKxHILDivjLe7amz0CYrw2rDas46d46AGHezgCVWWoZB3r0ZGu1bjXQuh9froK2j2KWMFuOgrhETOQKCVd2SuA3gX7YkT3ewJnAXHEk4iASYKhrB2eHm/92FSfRl9X6Vi7HH0YehjzUmrfAstae13VUKIpjfauVyr04qxjZzZMdT61TsuwQeturX+WhydtfH1WRtRVjke4/5+4VHeRhMrUKyVx2R+b4HnENGKM/qzAkpAp0HjOYvO9nZR3D36r7/L7IyNjonj+cbQhp8Wx+97GUPkPK+daP2O/eyXHYx29mwsi0eywBDDRm0zSSaYobXPRdZblhE7ema1cbAjegx4jufWurX7ai1jGQH5N34eejMKO6oscDBD670PeQ8RL25dnxm9tw8ifXEsvbBv/9Znr/8hNL61zrLpv3Y6Z4I8+sKakxl9KJqbY8iWVtBKFCziHT967dp8tk2R+cz6uKIey914B2fPFrnQyn4Je4YX5DSddm1MHBisaU+U0YxhOYpqa2oZOq/Oesk0Xp9WgN88e81k4tP/llNPssd/1oK3MmWt8hwwM53G9sba+DLQa2SIvRBt1T63yIWR83yInBm9p75Y9IY2hp4b2qGbkSMjDJEL+7YXybde/y02niw853tkm7HqW59r89SSK63x9HFgW/aklmc2VB70MJ0W/gl+bAV9Mx2w7De82Z+zzFnBuxxIofWISJ7DRtK+dJLh2fta6y0Tfdfqovsa0uaQdhFIukxY9OXdikXw7GOsBvpdcjISPvVQy4wsy1tyA9uPUCfauKLHk9mw3qLrR75vHn8mNmARWNd1NBZ/zbQ9tO6y4mIsZG3HYz/PiFf2/c3D2H6EVvvGkH4tXSWSaYb4Z7Jjr+llGT9apINl9RC2uXDZyFerN8R5wcPaLuTdM+oeHnY3vFjvi09GWQbdHqKvLQKr4Blj9ZlZj+tm/9/YWA8+3TfI/12E42BkxmTSlhlnLAOHJUS3YlHOqEz7kQMLBAW7jXTQUR+jt1WmZVel1U7Gsd8iaPTpMzIoW8/VcwjeuVOM+CxMaKefJ+hr5RRtwonFx3FsbHSDmK1jOlnxtZBhXC1zs+9cyqBvkAl/zqz5RTPL1vaHOFZanTqZYOCxoWlJiyE4MpAw+tA9q7732QPTDGuMNYOyDnZtyVxqKRk1J7BlrLKeWYuzNEMruA8rAIPbzSByfOnn482PaOz6+WDdsNKJgI4LF0pb0bHK4B+g+xz8ocdkZXRCu5yVlJ1aIiULipV9HP3wvfJOaL3zGLDmesv668OX9BzhPlo21WTmS7QOMvQ2a1yqGWhag0a8elGfC8bhZNNf5xrRe46OZvTor64fKeAtgSQ8Vv1/NA6gZaMfj+/goLuOMzzTas8rY2WNzfDeqJ3o2fP7sTJDRQZC1PWeX2YDjdWm9V48B5Ou6z079L23182AAT0AfOXUqZJpFLScN0Bq46inI1h6pzXXW+Qlby7U7r1G56z1bMnx3HYfetkqL0e0tCXbnnWtyT/WvVt9rpsBWGNrK+cAy/ClSLdr0Ven01gmAmr0ejqN62fGgbo8Hq574cL8CQNcH58txwWXxf8IYgYN2trqHj9pHensyV/6OobRe5H2hr5t8r3rQO5We9WqMFQ35mC5ZaCVztXaEvHpSaYv/Xuf55CRyce851Xhxo0SyCnSdj8ZG7JXNtLzonpZ3hKV98bh8Qotn0aoyTs647N3X1YiC03TavqupRdZV09u0mUyp4p5djVtI+HvrAzL1oaZFrBNHplQ9Qlf6D86PRHg/lucp610gd9Fix4Q2UIyNCwjD7W207KuM3raGDSJ+8h8560RS57jdlrHnrFNLQrI7hihhQZFtN2aJ5FtyOqfv+tjT2vlI1zGkvG9ZBmRvYQzqFuneWWy1Gv+wRsprMzrNd0p855XjWgusH6Tod9D72+Rz6cPLdTIyFHHKJhOc/ar1jY9eLRsGUCyPK0naZuoSOERBwe+LyqyKUf0EsjYJyz7nGXfFpmnYX3lSsA6JcVDNPaaP8LyNWPMOzu2/0+X4f+HoGbztfrs23ct23bNdsu6Usvm8VUBmaqPcTSxznPLw7rYrNZhDCvGcTAy484dW1nizxkhQDNUOHP1UQf6ajnvdRZD/M+MWQcA8VFn/Hl/vzsObndvr1tvkUdlg0kiSApGMxzJduFCOSYCVxwXdO5c2QHKAQL66AQEQXtHMVvG+T4G3YzhM0LGYWvNO0uwxPOLjA9W8Bn3tW5EMnr2RxFHddwaYzvdjgIiZdla85aSxfWZj2j6DyUYwUHIOobrjRvdzGP4DDp+40ap/9Zb82V1X6D/fKwm358ea0twMujVpUsiDz00+x/0Xx+vLDKfGQ10XNNzBLmCD4DG6/bQlnWsjogdwOoF3ogMo/EWLLkh42yJ2mvZUR61CUNunx3qLYakyWRxWVBanO8twTnLwDIMuqvq3+tDf152ZkwRO3gl0gM8gxropuXgZ7qp6bfFK1heBz/Q8js7a9CePpEE+gZ4Amev3N8vz58DRjHWq1dnR6yJFH6C+tGOewuQ//loOOgBmv7jiiC3c+cKvWdZXtN6NsCiLHjEvfeWdjxnlLVBpMV5CEROOqs+jz0KcLeClbyNJpYzio+xO3Wq6BEeIvrQqkN597wMrKLPdcRQWeaoYzqdd7R49hauo6/W8fb8HWiyzhLMG341zQfdRj2d4Rbgd8a2EC3D8cYx6AOvvFKO03zttdkVfUeyEdOynZ0ip/PR9dqxh/vBdxiztgehPmR8HVDL98h0Up/u5Mn4m5ulP9YfcKTzuXOF/3gb6g4P/Tmgj11HfdZn8P10amfz1VcN3JeWW/BcvbI6IwjzBM3DM8cwe46uVvl/HbBu8r/Guugh6/I8FoGLF22naDSXPfqv17Anq+k6XN+i8V6ZW7cKL2HZXvMO5iMoq4OCmf/o0/TwuRbAqnkn6yyaB3oBGPoz6CLL+jorL+g15FVd1gr21e1sb3flZA5Y29mxN7rwZ84AxvqIDjRmaLsfn9SV2Yhv/cZyfs3mv0osWvZ+r9CwdxOWnRlfpNCOKIjXonMRjc/oCKCTzD/u3rV1Ar7yd6DtWteo2ZEmky4Ns05B5ZP2cB9ah+FTT9iuBDuDrsfy5vZ2qQ/5X9uG2KfLMqzWvQDmA2fOdG1M7FfW4/cC4La344BnIAoWj77TY5hM4oBCPvWWn/1Q+mfZmo4Sjuq4V4XIXjdmm2P30QeWfMYJ9bSOUKPtWs6tbVazvtMZar2TqKLYHstHENkT8B14DmiJ1h0837BOOsF0EvRe00Iv2Z2m2V6w985OaRNXSxb27GDRZhTrtA+P7uu6XrB3JNtm+IHVjuc307E9XtljHOMYRzYY+cd+7MfkX/yLfyHPPPOM3HPPPfLpT39a/vpf/+vy4Q9/eEEDXDze4543A5y5ahUTtUVZaXEeLuNeIqGKx5FxZEdCcKavTL+tz6VFeOjbh4doR1Q0HijnVmZLzlqG68ZGTumsKc7rgkywyKL6W1af64ZobmbmTUuW+hFwKBvtwYu1NaIVonfrHDiKjucILXR2qAN7bF7RKmDz+PXRbpy5iDNXiHT5BzvAdJCG58Tc2PADKAArE+rYWLe5qemL/k4kF8RoGTH6brRaJlroyYjY2L8pskOGLf5ff47K6HuoZVO3Nn7pPrKbEKLvW+VUr0/LuWbxQEtOj8bpoYU+9p2nXh+ZdxgZCK17ZUNn5h1YzzB69gDoNNfXx6txPSv4hcuAxut52/JeWxxwmXajuR0934zj2EKGTtbkf72uW9ZI33uNxlrrq/VZRM4Wr56lG9RkgrGh34mWN+C4jt5pHxtE1n7j8XwrmI3r6L5b+veC66bTnFOL28S7xAkYr79eHHZ6A4RIkSX1ZkJ26FiyEQd/bW0Vhxk20OAznEr33Veci+xgGooWGU0jYzuL5gw7VFmOj/RwtKvleJbbrfsB37A2ukT18bt3utkQ2UfEtiF4esRkUl9Hfe1IGdkg05fVzlhyu0e3rbUW6RgRWnjeivQAt+/MmNmhLTJcf+rDhzNlI7kyaqfmbM/Yi62NedZ9oi3rxBluM6NXRWPy0Ho/gJWFv2Wt9tHBplM/IEUHhHunp+hTDzkg0Xr2HFzIm4Y4MEL3AbQeUzu2bX2VdEZkmL1ukcj0NZZtSOuaLHuuAhlbSF/0fYfrZp9cJmp+lqOA7HvP6oNeWb1hUwN0N7PJcDq17f0ihV6fOGHrC33QQtM9GSWj10Q8eB0xhN4fpftcF/AmCpE2nbTVduch42NYBMaQhS09Imqnrw+0j920jz4Q3Y9FV4bQLq2ztLSnr0yv+URzkfom8rt3fTnd0r3Y12D5eqNTeNnWm5n/WfupHtcYOKar8Xoastayv42BrG910Wgcwy//8i/L933f98lv/+2/Xe7cuSN/6S/9JfmO7/gO+fznPy+nYEM/YjgORmbozGAtTiWNyHCYEaJbGJk1Vlbk9e5bDkbl4yfeeqtkxEGGHFx1NjTO0InfXnhhdm3NkNaC8+dn10uXSpZkZEvDMWS4XrjQzajA193dOGsmrtERPlzGg2VEZFhOQ/2bVZ4/QxllwcAyDFpjZLBwk3WStKB2NMUYfQyt57XjXVv7yjh1x1KMItpllWFa02IA6CuU8M7LBWPjq8+JvP/9sw+WoGKtfY+mZ45a13X6BI7o762MPLr/mgNDZD7DDnbCgtbjev16ofvgFW+8MbuCD7z+epd/jP0uz58v2fKR0QCZzPD9I4+U7MvIvAteoTOSsbMFzwP80sr0YB0N5O3i1WXQNp+EEK19K4AtWo8Yt9ee158us7VVP95HtxHx/MwxQS3Zl9fVMJ1d6x6y/F0kF5jdMp5WntjH4FKrx+WXnRlna6uf47+GWps1uTAjD3qykeZHHt3XOgIy6lgZ0XDV2fZFurzizTdFXnpp9v9zz82u0A0uX54vu0hsbYk8+eTs/w99aHb94Adn10cemV3vu6/wBCv7msg8bYduwFl5dIb91gCdGlplPuZjkaMoQ0vBoziobTIZP9sOo4VnWd9xncjBbcl8LXRgqKwflevzfCwDd9QP95WR//XvGTpVk4/152UHI2j5LNJxrWfZchxlC8+N9Kus3UdkZuNBBmQrIz7KcrY0DmS6fr0EFL/44uz61a/OrpD1F2n/aQVneoOu8NRTs+snPiHy+OOz/5F1jQOrdIZmKyOaiL1ZLjoS2uMV1jpqkQ0imSBj/8lgaDtW/SG8MqJtLZuLa21lfh/ST5+2x9SBWuw1Y/W1TrrcdNput6nZ5C0bUa1Nvnona1kbRJhe65OydCZ+XUZnQWa5H7/pk7b4ZC5cIds/99zSbXdVgL5A7n/ssdn14YdnfyLFRgQeYQWU46oDskTms6exjqB1hQw98jJr6rnFc0qX4ZPGMsjM0ZY1uoh1XZMHl0m3loVFO+QBi06NFeyR8TVY36+CJ/TVOTL3Uytbazs6YdGTEbWNR2fSx3ci89mUOaMyZ9O/fr3Y+9nuDz7w+utFN1hlQHkE+JHPneuejALfAOxCly51fcvMK3TWe6bb2h/gbaqJfEpAdj2OsW42N/2g5kWgZcwt9rCjjnfTvY0VE7BIZE53XQRPrtnvre+i3wBtDwKdh/zPGfcPDrono7P/V/t22Z4EfnD5crER9XmHoMNnzhTaybRI3yfKQ7aHzf+xxwqdtmRxkRkfQBwRvrPehWcbjewt/L1lX7bqDqGFIsul2y2I+Bqe/aJwFOloZDsGsnZ3q23vc6adjO+jr+8E/y/ahhCtx2XinTFcI5/oyZMn5aQ+QeQd/Lt/9+/mPv/Mz/yMPPDAA/L000/L7/ydv3Nx41wgjoORGdqglMHYBpxWZByR1udIcc0wVEYU+OAdS6yFFI/g1rIn8M5NFhR2duaPSBPJHaHDjjwdpO7V39vr3jNgOaMYkcPJGpe+Rz2uyaQIkqhnPSfP0aXfM98jBzVkDe0oE2XG9NqxDO5sREFdfXRfFLQVzSlvnmfWj+7TcxK2OLut9R05JL3vM8bAyLE9Fizmz33oz15Q5YJw+P7HRc6eqRe06BPA63JdBJ4xkQmmeI8YjA6lLSPAhrxDA1ud5B6s5+wFYmka7wWRaVrNGfWtvjnYoy8WNT+GKklDDQPWkVr43jNsWJmUvHFovghE2VejoCUejxUg4/FgbcTznq/mz97GLy0fwSmwLGSzZrQo+a3GAqat+ng1zh4YyTbWMW0cWMybzLRRCAY6S76M5BS+D+/7O3e6GzIyGdL0vXubSPS986YcXhs6iLjlODWGlgW8+aHv2RuXdmrxmtNrz9MxJpPibIwChT3eoHUFzqjgHeGq6/FRqbqdSA+AQ9Nz0lmyOb93Pce1TsBlOWAHz3J726dHGYNyNG8tHTfjQPA2LekMjLVs21bwihV0ZWV15PK6TY0MXbDKWHYBnourhhegpnVk/R1/zsyXiGdy2xkeFdlSWvTWlj4iJxnWHIIbrl71g9pwvXat1Gc7jhXYxY607e3ijMKmRAShIahAO0GYd+q1x4EFGbuCZTOqGfEjedXSy62ANc6AxutJ95HJesx0X8+RzEZDq22NrE2R34tl72O6naG7kT5rycneEeT6fXs8PLJtWvOGnaeAHrNng4gC3Lkd/t+DJ//rMUT6jHeUuYVl2wz29ubt/xmHWyaIvqaPW7DeHSOity18SNf19Af9nnme8uZr3U8UdNVHj4iemeVzYPmphb9Fm0gAvR4y9K2mC1q6OjtlozJ37hRZGuWijGSej+DWrfI/y/+aL+C5oG2sa60jWH4D3ffhob15iu+dbQfRhmxLf0D7lm4MWCcw6DL6PfMx4wjy0bINfsNGJx3MyJtfLV3BkiH052gdRXptRtfOnPrTYkuzfmu1zy0L1hrh9x75YqI132oHYp2vRUbEvNW6Ch9rr98zymPeAhbt4Hdo2ZUiGyevYx7DzZvdzTGRvmpda6dSap4H3YT9irpPtOetXT1Wluv29rqyY5TxPyOrWX0hSQw/e/R18mTXXt/Ce7XOwH2A1ns+Yw98HzyuSHeKnlkLMrJwa/st5SOaWpMB+/aRoWERLYwQ3Xukg2HuILh1mdB6lnev0XyLZJGx0PIOdJ0W+bQWp9I6rsgmGT1njwZpuh3FFnlyoIZnT7B0MOu0E+7LG3OUEEbD05mi+9N1vOSB+P727e69so5gxUVF66BlLvJcuHu3a4v3kqrpsUVrzZPVtBxktcP6h2V3jxISeOPi8UVopRee7JfZWK1lg8h/4G2E4zFY9fUc9WRYiwYtOkB9zfDoo4/Off6rf/Wvyg//8A9X6731zqaQC9g8dwRxHIycRcTIAM8IaAkBLcQmKmsF9GbaiQQNL7OOZgoe0dPZGKDMMhEDQ7x50866qdvR98fBK2fOlF2pCJxA8IqViRKGIu14RnuRYrlOyDD86bTcGyvycNbdvdt1ruG5YleVViJbjMVcRx/3FmVMygj1NVjKZIvypZll5hhtr/9WBdFrM+tAaFEMM8+1RRm26kUOES7DtGh7u/y25GOaN/Zvipx+Z5d81llRmx9DaUmG52Qw5jgy/Gwso0V2TF5ftTKWwtswnq9TyWguRHQpgjf2yAFhwTISe+V14EGtbc0jeKyZ7HiZjJ2177JlMnQ7+s1yDnj1IyVZK1iecdXagMNlwdOvXy+yDZ65FYDnnYphKZHW1TPCaOO858jT7VtGHF12f3/+/2XiypXiNLROw/AMHCLdspkgBSsAL+K93L92NHFZXCHb6mfvjdEyOrDTw9IReMyRHKWvUSYCtM+OIV3fGpsuc/asbyiy1nOLoYjL6iANz7Gys1N0lGhO4Z4tB57IfBCepa8hGC86XhNyPsv72umI/62gdV1Hg+n+5mZph4MZ9DPAc/EMaNEGPyCzmVjrR/wueEx6PHpcNcO0npuRPs3BENowiPa8wAtrwwpDz0MOwOZ1deJEcVJbxlY2iFv0qqZr6SA9zyipadKS5f+5uYHP/Dtfaxs2I/tPRm6O+HOtXw8Z+0/UrheoZmXP540I1lrjU0tQ9+rVbvZmXN98s7SNDMZYzzgZ5cKF+Qxouk8d+OzRVz1XI9kI9+c5I1qdE958wfdWFn4N3hACsMPJ6kv36fFl/blmGz08rOs8k4k/7/S84YD06Nl57UU2lUj20tc+NpkWHYP7FukG/Wo67AXKR4kWIkcVB6RevVoyXekT8kTKWj11an7d6fHoaxRUvWrUdMfMZjXrfmpOwUifz8qkQ+w7esysp3rvq6VN6/soYCBaa1FAHsD0W7fJ49AY23ZnoYX2tEDXhw7LAcb6ZBOMA/I/ZD8t02dsOBl7C5+oEm0yizbW9ZF7rPmDOT30mUeycLSpcgxEAfeAZS/0Arot6LVaWxtZO3VG12ZdY5mwMmPyO+ybSCJ6hpG8UNvw6NWzPnvf1WDpIYxWG2ttPUfPJ0JGd8rw17GeHY8hi4z931q/HJRiPQ+mwbyZPLIptow1gkULorWVkaOs/7nOkPqtdM4rY5WN7s+TLzUvj8bjvUOU2d/3g9g2N4uOzac+ZALwMvdhAbr7MpGdQ1loXTBTv4/tINNHpCNYdJb5oJ6jkW7tjduyh3pBxCh7+3ZXBgV9OnmynoRB3wPTS/25xhP0843kudpmf0vPY0S2Ay1/tOiHgGX/yYD7smKDhsq1bLfvw0/66sF67WAcsCW2yFMtwb/WBtdo3mUCjLkdizZHfCna9BrpOl7/PDe0z8O7574yXx8MtZuMOQ4ReeGFF+SsSsBlZUVmHB4eyg/8wA/It37rt8rHP/7xhQ1x0TgORmZEgp2IvYCWYUStCc9ZI41nqI+MBczwo/HpbBm1o5z39sr/ON7ta1+bXbGz8+23Sxkv8EGPlY9O00wbACO+997Z9ezZckwPO9810+YdM+zw11m1cGVmoI++YQEKfe3ulrFBGEewtT4SCOCMQ3fuiHzlK/PlcH9wFljPhZGd1y2CDZe1lKWMYyYSqmvGyDGYT0sbtfEsaywtilBfgS7jtGlpGwrvsqAF4qzxrI9hrgURr+lrHIoEyj5Gv+h77kvTUXZo8vFxtSA9BvOsbCYyT+DXV+aZnE3hzTeLwxbf4X70cUheBhiM4cSJQv/5aDhcz50rc5Wf661bIi+9NPsf6wd8Q1/hsOKgtHVBy1p7LwNz4t2IZcjWGufP2xvShjpfavehN+BEffQxsFvlPQOS1R7TK02TvV3s16+XYC8c1/nss7PrM8/Mrr/5m7PjnEWGB517p2Hs7HQ3GgIoc+FCyZoJeZs38e3uloya+gg4kUJHNze7Tn+GzgzoBeNPJl2HMT5nnWxsdGSd48SJ8WmpJ5tk5uXmZl3PtMY7lD4skp9Yc3EMtDh1a/JsC4YYecdud1HQtC1CJoggsqlY7dUMufr/jGGYx9rqdGH5FPRcZ/cCODvizk5pG7Yd0Prf+I3Z9ZlnipyKgGMARthbt8pvnLWsFXCmIvsEbCEPPVRoOfgADMK4LytDuZVF2cvqBTvM2bOFX3iG5smk/MYZ7PBudNZKAH2cOyfy8suz/2H3wb2DD/S1GXA9yylloXZ6SiboTY+nZivKttNSVtfxNtQNsZ8MQUZ2BPrYZnD87bKROTJ5THiZe3gORO83swlxLJtgRiaK5rpVlnmLZff2AgRAk/b3C72GTQRHO0MfeOWVwhtg70cmPPCat9/O0QbIjAjCAr3b2Sk0EM/KOpEENPnixdkVdhK8y1u3usdWc4bLra1CX9Ee2sFmm/vuK2MEj+FjqE+dKr+hfibwR3+3aJuOnjeZDJteWQ3WcTLoYyMakzZHepnX3zJ4Q99n2IcuLfJ+xjo9rgWZbGytdpdMO4xWHaOGrI+49p3WEVgf0ZuXQO/h02Xf7muviTz//Ox/XF99dXZFcgWRwltAJ0FDdPCkh/PnCw0FH0A7oMlPPCHyDd8w+/+DH5xdIe+gzOnTdmCNyHyyiD7ZjiP0XVtMZ4eeWpiBl1gkew+rWOsWWmTpzG9DxtDi37PQUn9VdpiIZ65iM4reAN/Cuz3bypj++T7jyvAqTce9oMfIrsXJCK9dK3Qesjxke8j/zz9f5H/QfZSFrK39pX1w+rQIAvQef3x2hb8Mcvjp00XehpzNftOzZ7uyePSeIz/NMtbZKuj/0PtahZy8ir5aoH1B68Ifx0ZE9xadjGTNgpHPnj07F4ycwZ/+039afu3Xfk3+w3/4D4sY2dJwHIzMmExsASgjuC3D8eYxOStDcp/AtcgAGwmNuGpmDkXOa0cbPjNBYJYjDsFeMILyEUOHh/VdxVaAAN+XzujEV62AsgN6qHMiMkJE74eNZAiugIHglVdEvvrV2f8wBOC3K1dm14OD0g6MtHDaPfzw7PrAA0WogwGWjzk+ccI/ps0yuHhzQSv7GaNsZsf/UPRpJ2NIahVevTKRIsIK72QSK8EZR3hfRczDsp1R02npMzqmxFqH3trn9q0r/6+RNbi3BMy1OKxa6mfuR9NP0A6Pr1k8IlKcrcC5TOCGdkxpYFwnThRjJngc8wztyMwcTdqCiPdac+pzn5v9z88HfPH69RIIAucajAXaaYigBmTDArTz7sEHZ/9DyedTCnZ3i3MQzw6fEWyxtVWcl14mVX2PHvo4pzQy6yDjcKq1lR1Hpo3MmFsdEdb67uPks2RQLmetT/wP49SycO2a7YzK0H8PUaCZbr8PTc0YwCwdwRojt4F6VjbejG4A/LbfZo9DB3hFWbu9e7ayL1tBvLVnbz3DsdaUhRY+n+HT/F61roJADp1NVGRm7IXDD8F+bDTWWUnxfjjo48KFYqTlDZT6lBoO8MFVB5rAMG3xVZGYd7boqPr/bHkPkXG/NlYrqN8aQy3zTCufiWhZi0Olj33Bqhe9O8gmy8KdO/N9WnYA/t7TiwBtX4icFBm6wOPpa3OKdDlP3obstrtbvvNOz7p1q5QBfYDDH06hb/u2Qlf0hj6R+azFoF1YB7ArPfJICR7WQQMi87yK9TJ+F3re88YB671Fa95Di37V6ry0rqDt4Kf6eYrMaC1k+3eO+OucRqazsoNOoz18f/FiN6u1Zf/he7I2yUSyEcNaY0CmfkRLW+S5aFw12SijqwydC1FbLTJOXxtU9O68serPy5b/t7bm7TlDdPUWm8gYiOgBrrVMZJa8gc/soNf1dRIS1EV90APQfU2HuS/wA9gprl0rPAFBzZATr10r/etsabpPHWgA+sQbs8+e7dKsFhpi6SMZPh3NraHzDu8BQR0I9oBtR/NVDvbWJ2foAGmR8nw0v8d3sP+AJ8MOdOqUfdqi7ns67cp6LT6UKAPYUBk/g4xcp9fYspGVzVvqZ+hcZP8Z+x2MBU3LMjZ1736yzwNXj/dneKg3Nq6TkV0zfIT1B+YjevM26MJHPzpfdm+vyP2wM3DiquvXuydCACdOFLoNWoMNhvBXnjnT9ddywqhMYiYLVqKcjD2stt6ydSM9GjI9aCp8vUgI8Oyzxf4Pvoo64ME7O0W2x7vEVZ+AgWcN/sq8YmvLPnVLX0XafH3eWhmDlvTxF7Tod2ONIUPTrbgQrm/pwX2RsVcyIlv4KoKRQScODvrpghG9bHk+Gf2wRabIzBeLR2Ts5SwDRkmgmGe09sXtWeUz9Swfg/c8I34f3Yc1ntocsNZhtEasdjhhInQm0P/f+A2RX//12f9f+EJ3/AzoVZ/85Oz6iU/Mrk88Ufy/4APa7ysyf8JuBi125aMEvq8o8UUkF+r6aDczf2s8chl6UkZe1Z8zG7uHoK+NZxHj6IHv//7vl3/1r/6V/Mqv/Io8sqqEASPhOBiZcCgbcrhdT429TGxIMmtIButK6CMlOSOY9jkie9VocQa01BEpCiHXQ8DAo4+KfPM3231EQDswIt6+3Q0Ix29QLu/eLcZIKKPRPIx2Ag1xFFn9WQJdTWmz6vVdR7VAycmkK2BkBIRFGPqWtbbUM1g2LT6cbNoZ7IYaG1oMUJGCmTGODoW3+SNzPIjnzLHqRUZWix5g3mlneR9lPJOtIBLmx9po0gLrPqM5hWBfIOIjXpajCFa2osjRxLCemZWRlj/XnnVfg9oiHMZDxhHxnBYDX8sczdL1mrMiUzfb75KNkYdn751/Bx6f73s0XYQhBpvW9iJHRs1AknFG6f/hTGI5UW8UsTaRiMzkS8iM0UY0/M9ODx0IyAFQepyoz44rzc9YZrTmPY9xaGacvuCAeugDMBhG0PczhIZpHoH3wnxDP0MYMT3a1Wo46kuPosDg6DurTvRbK72N2uO5aK1LltGiTT9W3xm9iH+z2qvRF6//ZWB3t+00FuvkjoyzxaIP3r1aGY0Z1nuyxsVBY3BaWKdO4X8rGK2WpWN/vwRAwemNoDL0NZkUuoC+QLdwn2fPdgOE8dvBgcjnPz/fJuqDzj34YHfDoxU4gTLcR3QyCr/D6dQO7uNrRoa1/tftWG1rcNZl0H84jj784XgMDPBsy+nBvNfSNVgXYB6hn13LZl7+PkJWT7ecldxORgf0dC59zcyFDP3n/yN9z7OnRqcQTSZdvuGd7OCNreU3QAe+LgPb2/GmO/4+evbR/G05atWSPbk9C5n1Y73n2n3otYornpnOGhbpGCIzmg3+g+AznRFZZOZMB22HfIiABz0nOaMZxqwzq2G+Yk7pzRO8niOd1qNPeq32sdlyWxrW+8/Y5UD3cZrLBz5Q2rPkdJH5gDEviUjL2HX7/Fw0z+CMUC2yX/S+rN+4XLThsUWui+w2mfdljSeTgMWD7tvrL2P7inTcSD+K+vCeix4rgmmWiRb/U3RfQ+3DQ+tnxspyf4afWJtIan23Iruea3RW605D1k80Dos213hfNB6rndY1pjfjiJQNIp/+dPc++IQVq32W7XVf3skFnj7gwdMpW2QcS3636HVEu8bQ5zNjtfqyvrdkTR67RysydscM/R8LWX2mz1odC8jgrrOhe8/V0vmtd+rRoFadNLKd1XQ/PVb+TaNml7xzp7tBhE+rytopMmuNE4xwEkAem9Wu911tjJEOZtmJuR1PJrD69Na51U5mToiUpFG4Qv7/vb+3lOENIhm6rXUHz6ajbUa153J4WHRA7iOznjL0V783nidWQsloHdX0+EyGcd1vhn5H4OcR1Y/WnKd3an0x0o+8uaj74LFa6xlrftH+X0vfWwUax3B4eCjf//3fLz/3cz8nv/RLvyRPPPHEgga2PByBiM3lYmN6Vzb238kq0jpJM4JcxOyi9loU+EjojIQZkfnd/dy3dmBx5hXOSHzrVimD76zj6ZGFC44rfIYxUhupYDzELtj77y9KFxgo+tSOMOwKhaET94GghHPnSqZfLGoYPPUxl172ZHw+ebKU59232snF9S0BjNu2+s6854zQ682Jw0M/ME0fAz3k+ACtyGYYcaSAe99F997C/KP1HfUZCY19AntrtCLz2fvO68NyLnA71pzsM1ZVZ9SNGAlsyOH8Du0+9zMU1pwYS2Bq4VGegGuVHQu8VrQzKZrDnNXLGjsHvunjp3GkEPgOfkM7OtM/O0ihRL31VjGee5k+0ZbI/NGd+nsrC7+lxDFv0PVr72WZAnj2iKAxNjkMNfwPaWMMBH0fTpZw1FIGGMcSxnO4taBjZrPwDFAZo1DGIJeV/71x6bIZBwj/1pKlINJHuL29vUJDdaYtkflNa/jOy37jtS0yC26DTI+xQv5Gdt7Tp319RtNo7WjTVx2UbB3dhj4wBtZx0K7OypMJAgU8J5u+D+s9Q+fietFGkxYDuYY3T3UAsufwioya1lqpGQqjPizDYcaYGRlDPUT8JCOLRwZzC1zecqjX5HerjxZnVkQfMmNfRRYcCy3zn4PXxkTG2QBk5tv2tn8EdZSJ3uJrNfp/505xgHzoQ7MrZGOdgR0ZWxCojOxcsAtsbxc6iz4go3/ta93svgzLhgG6r21NsCkh8wvGrGVznelFj1EH0YLvIOgXvALvZG+vGxxnZeVsCVSIgkIzGwsz0PaeVuh78Oprh1XmBAfP6ZLVxTM8d6x1vQ7OjnVC7V2t8nlFzrionFUn+q1V141kIi7jXXVZntuafvOGwEzwJJfd2ytZGdk2o68sJ1t2DugGsLOAj2g/BMpYvAv8BsdDP/fc7AofweFhkc85qxfGev16uQ/LgY0rv1fU10GV4AnIFP3UU7Mrsk4++GDhR5x1HnV3dvwTRLRMsqi1lJUZGS38o2XsQ+8zcz9HIcENY114TzSOiM+u29HUfYNFxvYR9IHWnzOyY8SfM3yR7e1M/w8O/M3oepMKdAPY6CG3X79e6Cr4APQAlHnzTb8MPmdw9qzIxz42+x+02bIrgG8gczCu2jbPG+ZRBzT+0qXif/Y2SfL/DN5wH6EPjVgmXWnlA0eZXq8C68IjLKxibAjcH4rIpp+x7bTcuxUgabXH/JTprc4G7cnk+/vdxCJ8H/pUJQ4s1H4BltstezX6YD+Cvk8tp+vrtWulPMYBGR+ytH4mrOvozfvsq9B2NbQHed2j3zq+ip9PlLgkSrZo6dNWQjWNKLYnM+8sm46nO1tz3LInebZJC0PWjdaPWuQyS+bxTlKO/IK6bB/61hq3U6sfbbqL/BB9ZWFvHNn+x8TGxnrwv0Y77fd93/fJP/tn/0z+5b/8l3LmzBl55Z0Tj+699165hxPRHREcS2sMi2Hj+1WMZV2RcWzW6k6n9WPjan3UAjCyhLvmiJ9M/J1ZLU78VidzH1hMrg/TXVT51nb6Gj7HRt+++gZ59Omj7+8erB1N3Gbr/bW8wyUb8Q5lI2cA1YofK1IZI8gi5/HYwu9YyNBmdqzoLHWtQRlZWDslLdScfK10N+I1NVgG4b7KAGctQ7DH7dt+21p41oYDazybm13ls+88XCQfW3Mse2PGsnEoIwXODMDG9O48/faMZBmnwDo4oNYdtWekaTMbIa3MypbBEn2A9iBrZBREwO/d2iAS7UL3DLle+Vo7DB28ZZXp44yK3oWX/aa1D4Y2RoLX91k3GVqf1ev5eWSCi1oQ9blMeMbjTNlsvRbDdiZIe9F47bU48wrQousfHPjBUmhHBxdl5LooYLiPjOSdSlFDLQDOG6PIfDAa6DYHsOnNiMwHHn+8BBqg3pUrsysCF155pXv8O5xQjz46u+pj5MEbIAMDt26VZwMHE4y+qDOZlHt8xzj89QA4PN9z54ozjN93a7ZUnZGZf/OM25bzcCi9jXQD63evvZre3Kr/ZYJds3ZGrpetk603FhalIy8C6zIOjb42rAhjBsgPaSfTfiazVBYt5SN/ghfU1krDvAxVVuCEFRjg8TbL/pNx2A5xRNd4xKLX1jqu3SEYej99eMWqMFYA1DHyGPu5ZuSMobrpEJoa+XZ1HS8YWdN6tt9oPZ43OWMzOsqePRtvdEd7oP+oj6u2+eB/yPta7seV6T5kYegX1ik3nLhqe7vcM+tDVl+WH9rbjA5o+xHgnWyI8jXU2rOg+/BOpKp9l8WYtoRF2YZWiZrOvigfczSWVdjQx/JNWc8u46sby/7ZYhO1TgMbI8Yn+k1nWPauesMKaDrGatmPANhm7r+/69+0+ABvCAdN5dO+RQpdA/3XiUaYN1in0HlzIfPMoozhkW6B+wBf2N/v8k72zd6969NkXdbLrKzrZDYh1pJRjCnzRDpcpq7Xb7Tmx8JQOa/FJr9IWDLJMvs/wvipn/opERH5X//X/3Xu+5/5mZ+RP/pH/+jyBzQCjoORGd6OhiHGBqtNq71IYIkWbqZ/rw7/ZqWX5zHosUBogPMGzE6ka9hEGc5+LFKUNeu5QDHEjlQ4g86e9R07fZ1q3vf6HUIAQsYF7MLS987CiKXUsjOpBa1MlwWyqK3W8XjClO6TnWCW4hod9cHtR+uxdj/6XWaO8OF2PRrBZT1jgQUej9V+i+Jr0Zvae7UMJfoaKVvZcXnfee0tWRnduHN7PgNKzaFpGf6to3Banl1Ew7x2LP7B7VnrJ8PrormeCeJlZJRRHYwA+ooMCThG6cUXi9MfZaxxcJugRVCQ9G5VKKV8lOf58+V/HAGNMvooHM6Ez8qoDnyLjjvMOLMY1jv0gsgiPnDvvX4fQ4FnbwVFMP3PON6szzVnrG4vynQ7ROlrfU9RvaHGnyFlgcmkHiwSOW4T79IyXW94GQ8Xhf19ewNEjQ/UfqvJpYswlGfajuZ45t3V+tbfWZlxdHZ6kZKtDNcXXyz/I5MNjF1nznSzHOCz1gugW8BAyYY5Dc5kj+exs9M9CtoKYPAClPtsUBpStvZ7RIMstOzcbglcxvOeTHx9M9LBLF3V469W9uPo2MKMwbKFznE7VpuWHG/p6LU+av169aP74LncshlXt1cbv8Wfl4X775/PmuHNt8zz0c/FcvZ4YPmjr56V+Y2Red6R/qufF2j666/PrnwylR4b+ACCiFFWpNBtlq2n01IO2S5xRZ83bpRMyHgvkPsfeWR2/eAHSyZjyL7oC3X290sW5//yX+bHirlx7lzJbolAZ5y4pTNueroBcOdOCUJAn9Bv8LxOnSr2MLaB6XIeND1nR1HLvLOQdegweO61BB1mdKesfcGrr8eXoV3cHpeJdJZWG0CN/0yndSdojWdl7AsZftjCN/QpVcvASy+VsVrZp/rohJHdJbIj6Tre84zmfV+HXs3+E83xzBqLZAFLrrPkdJH50zS8gGWRQuvABzhD/9WrRbdAe6DR4NsXLhT7j85yqds/edKXjaysxRm7cGa+WXIhB6NZddgnwKdc7u11T/oCtF8DvBJ6lrXxPAp0wxjYhmfJ37U53uoXsa7eXNLvsnZyQYYmR9B91ezL1jis3/voGpmxWzJwXxlAZP75YnPZMgH51KLJfe7Lej4t7WXazsgmur+h4+C+WniEdborZyoEjT04KNkmWdfXvgGmU5bsyL5YTatBw/RJuHyfoKnoQ9uGcNWBaVaf1sZ33YcH3oips4pypk0rK6b1XjgYDb/pE1+sYDwNnVnQSziSDUrmUxIstNi1vO9qNp6MjpHpo4V3D6W7Xh+WjtGiu0Sw+Enm/Vg0i9vVeveyEZ2MW/teJK9nRt+39OeVaaHJLf1EfWTa0TSf/a5Mt/VmFL5av1k2ZJbVIp3UG6su4yUusWKn+H60bOPpCBGdsGQTq7wOOhYp9iTYrF57rbu2uP977im2JdjFwBdB40+caKP70bzwshVb9pIIEb23Pnvfta7xvuMZu30L1jqw2on4kGVz4PYyfq4arYj489jI8KxloHEMh0OTA60hjoORGTUi79XRyBC7ViFgyKLMGIutbGMZwuTd1/XrXWcUgsn0Z53BRmT+eB2RWRvLcEiCiMLBA+cUjlu4eLEEPPCuWQQenzpl73LV7W9t+cerWYTcM9xHRHQyEfniF+fLo08YDLe3u8IDM5vNza5gEc31yOmihRcLGxttwU7LZCBDDGut7bFAptdan/ZaymQRCTMt32Vo4JIFhcOtE3K4e6qpzrs9Y6kJj/5b89ZSMPGZHfGswGh+pI/3wW88P6B06aPdWo5jQ/+g7aBXt28X5a1FEER7mmfo4GWRwldw1bwG4+CAjHPnfOfYdCryzDPz4wD91RkgmH+xM0kjMtB52Y7x3rTT0FNUW5WCSCHKOGYAa0OQJ/dEvLfF8DgWskpon9/69LcOSt1AHO6esuWQFgOCZRhr1SmyfVnzPnI2Wv97ZbjdTHl8PjiYP+JMXy1dA7+BBsEBdfp0Oc7YyqIJOo+gLZTRx8jBeYSrzrqAK8qDtrORVDvdQZuhG+jj3jjgDTQeZS9dKvQ6sxmxxbGojZp4Ll6g2WTiy/RW1vshyMrPNblQf89yckT/I0eRNbbISeLVaXVUefWt71k/s+hTi45Sc7Zl2xs6NzJOQs54smiwPsuOAqt8H+dlC1pliSH96brRfdUcm9vbhc7hqGJLjkcwAWxFnJHm2WdFfvM3Z//DpvGVr6RuxQVk41/8xWHttEDzE+gC4A38eWenay9hO87ubtEJUF/PUdjTIONjA+X586UvL4jMov+ewykjF2UyrE0mhYezPczKKgR4pyZ4fXjttOgRltPR0hFqdr6I9+h2PRurdT8ZmS+y3UYyZEZmrNG8DJ/W7S17M4rOkpvxBbTqYH1oc/QOo/KRzyHjs+CyLXwoa//hTJiw3+B69WpXxgdte+WV+Y3pIuXzsoPYxwDoubb/sK1I6yMoj0ABBA6cPl2eGcqjjG6H6RHbZDY3S5t6477+bB1za9mDvMBnHRDH2T8z9DIKVGgJArBoKZ8W0dc+PdTuU+uj79q14K1V3UafE0FbZVgOvlkmakec18Cy0Zg2OK985jRHjSEBMZEfOdOetgdB/ueEI6D1X/vaPL3XV52MZF3BMroVsMzZmE+cKDZ5poE64Jg3M+p24UdnHqH5Cn7z7P6Wbd+z33M9jYy/RPORFnuYJccPoZdR2y1tZNd1H9vQUN9CX7tECz/1+mztd9nyv0gbn8+W9Z5dX5nCancM/1K2jSg2iMtkNvDzb+ARN24U/y3r+AcHXb5hbXxn3yc+6z7wP2dfht3knnu6Y2Mdw9q8CjoJ+nvfffPJFHVfwN7evI9bt6dlMNTjdra2upsrwWvgS/nAB2xZXveh63v6YmTbiU7B5frTaV3Wi3wE1gYnj59Y8stY9u6Ifmd07hZ+ZsVaZPuO2uuLVvtC5GdvkSuHICMvLAPrMIYV4zgYmaGJ4tCJmiEWGhHR8YRwiwhnBAU29EcKeEYIjQwUEWHpozi0OA5qbWXrLhOWIdfKAOY5DiaTknWad78Cm5t+oELr8TqMaA7oADWNjY22YIQloOnY+Mn8sxwcoDrWPevnXnMm1RxfHn0BsoZL/J/JvL6Ctblxh3a+R4h2bgI1Jx+XGYohvCaLPvMzUli9cS1DWV+kQJoxrC3CyXHKCaiP6Haf45UjZPiI7sM7djoaT6TkZOpHY8o4ujxDy2QSbySqoe8a7vPOFu0sW9e2DWyIkW3LGkf07PvSp0xfGeNJH0U+WkfR95GRlZ0lcITo5+TJuTDG3bjRzXqjj+vEGkM9BDFox5Xe2Ig2RQot3NjoOtt5E8i995YAaWxUhKESDv577ulm3bGyoNUM21aZyFFjtffSS3YfFrLOor7tWGO2gtsyDtUx5IVo7Wi6PRb68qFaOxq159K3X8txkC3ft+yqDYQ6ADrSsXFlfSbDP4DMJnBdP5IpvIwtVrZIbk/XRXkOFMD306m/uTjS/fVpJ1wezhJkKwaN/S2/RTpA/atXS9ACnFD6tC0eH8YBm4h2RunsxiIiH/rQ7PrUU7Prww8Xp48XILC72z2hi/vm/zXGtHM8/XS9jOUQ0v1rmq+zpenPGl72y+m026YVAIegdaAPvVk3W14ros2RY/GRDCwaxshkdm2BJeMMDQxrxcWL83JIzZY+nXYDavVmOVzZWY7f8AzPny/Z1LUDXGT+GHnO2L4IW4hXr4W3a37k0cSh86bGM9GH10/EZyM+linbB5n6WRtIhv7X6H7r/Qy1O2LD5jHGQ6vPrTWotdZfX98f0451RcYWYv3G30d6luU/GIpIbmoJ0PHatQJ++Hpw0A3sQp/6ZMJv/Ea7j4OD8j9v/gafFJnf2CJSArV0kBoHBiN4DJv3Ll7sJv3oy89qdDKr/0btgP7XeJ/X/5jI2hAysl4tFiHyHeqy0enPfTZbZGCNPdNHRn7xfBatPgfe3KkDQFnv5Q1kV692NxRhXelT5fiEoygZ2yr0udb37vG4VnkoY/Mdsm5b/WcRav4vPZesExFRBjZ9JAOx7PdIWIhTEjAntR0KdJvn26VLxQbEdh9tv8HYIPvyaYr6ZBQvmFj7I/CsrZNxvfk1hO8ywD89ZHW5KKB4CLi9zc3xdf1I9hzbdrEKjM2fsvShT399/RGL3oyyal+DHsd7HMfByIyXXy6OEOuIJCBSuriMSLtQyPU9odsiqi1CkSV4Z+rz/fAuIUsYaRFyLKGGM2Pu7XUNwZxp5/Llki0Bjisc+/zyy7Priy+OR/RgUMNxndiJhOulSyWoAUZnDmrQWSs5mEF/jhQOZuzsiLt7txuozMcPWQEIbMD0vstCjxnvNWNYjtalZ/yw2rHG885vnbtvMaxllAZrffcNKqgZRrJG+cya9fqyFHnrXbQElCw5M9rGwS2RnXd2pWeyq+jnmsng4X1vPRfLIRjxgei9RP3Wylrj6aN4R+uS71Vn1eQgBu3sg3MPNB3X55+fXbUyiyA0dhr2zaID+gjF7+TJopiCxsPB/r73za5PPSXykY/M/ofzEQZPTfMzR3m2GLAy74cDDDR/6OO8RP3pdD7wz6q/uZlzZHg8L5pL0T1EtMdqm8tkjnditMga0XuP7tl6lvxdyzGxWfmyxnu14TTqE/WWnBlnbgPS1labc81DpCPovrw1m5FpROprPjK0R9mgM/qIvg+068kAmsaz/M5G8LffLgFmkOkh49+6VRxLCPaAERMy/yuvlO9QH33B0Hj//YVOQxbnU1CuXBH56ldn/yNrjXaYicyMkzCGes/XOuIXtF5nOsD/rAdE70uDnz3zvJs3i7wPwyuMq9oBbG0a1O1roK8oUA31WGfQJw94yOrOnmyj28nI2VbbXnmL7tYcQ3oc3nj0mvXGk6Ev0TgydoaMDGnRdosGeDzK6mPZmZH39+fXZ5/nyu8rk0lVHxXcJxg+mnc6wLGmJ4qU9Q96EPXBNN16tx7PsfRN5gPXr4u89dbsf/ABHeTH/AOyPuT/L3+5ZM2MAHvNxz42u4Luot3nniv2GTwXvp4713W0evSb/7c+jwmmzZpmeHK/Pq0K9Bu8b2j/gJVZJ1ortWyZWT00Y28Z+j4ieQ7XzIZFT7a39JChY/Z0hEzbGRo5dDyLhpbDM3YXPSf55AwdXF+7j5rNiOeOduijfo2/W3abyE6YmZMZvaQFVl/eOtJ8lZ+HJRsxb8FnLQtndGEvc/vmZvfURA64mU67m4OiZ8bJYqws8VZ9lq/ZJmPdV0YeG/p+o42PGVkvmgu4ZuTulj64r0xdy95uyUw1eXvMjZE1+3CkB0S0KNNnhq8uUv5pgd4o4s2Z6L54/mk+75WtIWPf88ZmjdHiDS1jqOlwmi8yDdR9e3xN2yvYp6s39OA79hHgiiA3kW7yJc0HwLvBs+GvhV5weFj6YLqvbfV8j1EyHkakw0Ww1iNnns/YC/E5Y/f32rDGFbWj+RTeM7edsUFEsJ4l/rcC4GprInvPNVj2BetdZvTEGm+zdH9vTCJdPTYC7J9W/xFv8fhj1u6yKOC9t/LePqdncTmRHD/O1Pd+r6GFP9dskxGsMhZfy/Be/s2SsSKelZG3IpruISNH9YGua9lL4OsGmPdNp0Xn4fq4nxMn4lOzdFn9f+a9Wn4F3pBkteE9sxb+WhtfjfZkdeSsvlEba23DrZZTvYQUmXFky1rJLbheLQ4o2/9xMPJ7BsfByIztbVsYzTBAwDOGWGWizDi6rLeTTyt8LccBMnS70W4noCYgW0Q5IpSRIKaPpuS2refIV97tZx3twBl2YJCwdmHBIYOAAR2M4O3k1s8Q/aJtBFToYABPIdKBCzB6o552ZkLQQDtsnDw87M4zVkan064y6811CywAWX1ZxnRuWxuaeRzWnK85qqxsttbYsp91O5n29PeeANciqFpCmkV7am1qwZ/ncUZxtgQnHk/0fKwxYzfkssCBoLX3YhnUIkRleJ5YO4Zb5mJGSeIymUDjaN5mMJaBva9yzbRVZ2gAb2Cjps7CwO3qK/MIyyllPXORwnMixUi/N4/fb20VIy2USfQfbTCJkFEQo00p0VFAuq2o70ydjIzS16jZUnbo3LR4lVc3I5O2yK2137yxWrTQojMZIzNo8CqO27XG2oKM/pCRIaz30bLhJXr23lgz79CiPUOV6aH1W+Qnq69If+D2PH1G6xpcxsqM0DK+FqOmltUAL0u1BStgjcfWQk9qY8W1lgVLjyfKbJMxGnvPU8vAmTULRGP37rm1DCMj20e02Won0lX4/7EMaH1kyEVhe3t+Ddeeq/7fk981vYQcZs0/bWvgMi38w0N2/mf0vJrOs7VV1gSvWb2ZEDIvZw597rnZ9ZlnRL70pdn/nPX4zp0SqIxnB3kXtpCzZ0W+9Vtn/2PzHwINnnhidn3f+2YbUlBepC3QIIIVQOq9Q+v9WPwZnyMehaBszmCtnUug82zj0e1wMDLGaOkRmfuJ4PEsy77AcmELLaqtHe/9WPM/Ot2pxkc0fYmyqgOR/TRzxGnNNmMFX1nP3rsfPT7U01nMRWbrG2se75AD3XU7H/xgfF9jA2MDmGbhPkB3bt0q98qbymAXPn260P0WmcKCR5MPDuonjUV9ZWxF1udIn8j4Efg7trvs73c3I8Im+Oab3QBW0H08+/PnyyYSDugFLTx1qht8w5tHLJ0noztxcJ1Fw/i96UydPA7dl7eR586dsgETfgAEJ+BZ3r5dxggegc2ZCKi+556yJvmZafrAzwF9AidOzAdsc32RGS8asknbslvXeCj/z5+99WPRUqust6aswGnuM8oWyYiCZoGMzJbVmTPyi9d2VnYElm3/eemlkjhCxB9rRka3kNHjW2xEGp5d2eIRnryh3w9vXNCnY3gJPbS9XGdVtcZ+506Xr2IT4a/92uz63/97zj4NIBnUk0/Ork88IfL+98/+R2Ax9AC85/vuK3K/FzgdyebR3I4CGyP6FPFMIOLvTIP5GWo7B8vyeO+3b3eDmnnst2+XtsEbeANNVsbh+R+tFaAWqKXbs2iz1UdGtvfoc4ttR/fljdniNdGYI13Dkumjdr17yMiVLevHShqD35a9GV33ackSFlr4IMOiHdZvtfqR7JR5B1HbnOjv9deLvYYTBOpTtJjXQLdCUo8vfUnkhRdm//fx829szE6sEunadjTNf+ih2f9aJxAp2e917JRHg/Q6BFjmzMhhmblhvZMMfdPzle2MWs4GmIZlTsa1+Aj/b62H2n3r+e9t1ozu3UqCk6HxfRAF4epYM8g/PEZrvnA7lk0mE/jvbcrN+pFrti/+32urJYbQ8iFaJ4Qd412N42Bkhj6OJbMQsgu4hkUuukhZsoRYz4mkDYQIoIWRS2erEZn97jk5dLtoE/U4a/Gbb84b0ETmjYisPFrGRDBZGMK0siUyH2jGDEM7tZDtkhVXK1sBQ2fP4CBiPv5HpPvsrSAHfk+a+fGR1vpo6yz6Ck4ZWJkyORPTWP1lDDctaFWuh2DIODWGPoO+95JRzrNK8BIwlxkzI0SvIzJjXtR9eQqJ/k1/9uaC5jls8MwYcLWAy8o0B0Bcu+YbPaz7AG3V/AOfmc5axio+CsjKtAOeAMUZDjXtzOGMTNqBBVq66PkbbThpRSYYzcNRXKcW1uw+DrvnA3SwIQ3Oglas4nlYxoYMz4oMwV59SwFvgaaXLdltWpxakdMlCt71NgHqq+ewArRThZ0dk0n3SGwEibAzRqTI/Sx7njjRvWfLcITv4LQHTQatPnmyG1CjdRV89ui+hvcOIoeIvvL9871bbWaMkaw7WeWsdiInOa5svMsgWjMZAz1/tgz2UQa7jCOiZhy1kHEW6PZrememrZrM1uL4iNrxyq4D79vayumhLcjIq/y/V8/7bNXPBEdYY43sNVzGC2rY3+9u6NbOLFwRhMCB2Ji3H/lIcTBxRsu9vW62fM6Idvt26Q8bzn/1V+fHKlJsOZC3EZysbTWcrZ4D2O7cmdcpRIp8DGfZU08VBxr4APOjkyd9/cHa4GRlz9TZqixsbJT++6BVFq/pCdNpXf6JAhwBK2i8xY7VItdF7UTOHy6rwXbMdaCJLeB74pOCrODi6DlhfS8LW1tlzCLl+WM9PfLIcsfD8J5VRLetuhGfztT3+tIOZfgGQIvZyaxtOyy/o47OWswBcHt7hd6zfIs+rl8vNBkbNHDlTIzLBug9eAOycl66VILqMO/Ah/QV/2O+avsP7h90BGV1UDDTs76nnvD9AJG8a7VjJZvxxnOU8F6wWy0Kyw5GePjh8eX/CFn9rqWdWtCJLtPii9Flavqptv94my+sk7E4uO2NN+Y3n4jMn5QC+YCzJmMz43/5L23BzIsC5J9HHikyBOg9ftO0nRNM8ebIkyeL/cnaSAOeCXmS/cB6LlgbC/ugDy3T80fLXWO1n+k3i2NavXys4plb8j/DoqX8OSNvt6JFJo+CQrWcLjJ/6qz2Z4p06ac+SZChNy14p3Poq7bTiHR5htWeDnwG3WdbE+p/+csiX/zifFtsR9L8jG1V6Ov27a4sjSu+39zsbt4AbYY8/9BD85tUdX3tc/BO1tKfM3OT6b5l/7fqMTwekfHzW/w3sn30ybbdur4yMlKfthdBr2qyVmRD1t+3yJF9YxnHvv+WeLU+WJcYn3UYw4pxHIzM0E7eDFodRlG/LcwhGofVNv8fKaM1Q+PeXtl5jyMwX3ppdkUw8euvF8WQBQYdDLaMnc9stMP7hcJ38WJxPkHIwmdcL1wo7SAYgQMPtCOEhQdLiIich30ciRoQghaNZRPRPv2titCvK4NZ9TNsMTaPeUxdAgsNrFs1IsVVf18LwtGGRjYiakUPiiYUs8gYyUqgNjLCCIlgAjiTrl4t9bzj5C2HO+4H49KZWwD8BiVua8vPmqMFeA4QRh0Eo507V5RRDjDW4MA95mGnT8/zYz1mkZJhgvmQ5llsqFx3LJCeZoJuh+Io0pZVjRnvYxnvJYWWoEPr81hONSv4Rl+tMlYQcRRwxsY6y0DIhj3OjHD1atdRBKcSghT29sr/uFqZ0dA2Z/dadTBBC0CHL10qmRrghAIfuHhxdn3gAd9RpQ2Y3lHQW1vl+XnOqIjmRwHLjA3avOXBm/96PJkjIZeJFkdxX129Vq+vQ6MlmNnqy0JLUN6Qfqw+jzIyxuIx6w2BZZzlAGH9nZepdmvLzxKvN3izjoFAA9B6K8MO+MFrrxU7FHgC+tLOF9ihlskvcK+g9ZD/794ttBljZNvTxYulPGet0SeuWLoF2oMdDtl/LMdXy+aEjIOJy7bO9Vp5fWrR2BhrfXGA+HsNY9uWlmz/kd3d9eY5GQf0WPA2o1hZ2Xnj4c2bJWEJb/4GbXr11eI3gG4AGg36/8YbhcZz1uqjDtBQ2GrAw3RyFOZnwMFBeVag7dAjzp0r7wP8Au0h6OPu3a4uYGWjGirrtdRd9lpfFtaZnhxjtRhb7si2mQk28ei/FUSMq05SxaedwH7/xhuz65Ur/mZC0HptR+KAs6MABKHBtnP6dHn2sKFBttfyPGg55HcrKJn9EZYf2rP7rAtNWoWOu+y+jnG00CrzLFomz9pV+9jV2Dazvd0N6GWb/I0bdR+BzgzLG0wg27/11jwvELE3DoJ/rBtgm9exQqDz+M46JYc3S3Piw+1tfxO6fu7WqYtAJrFIH79vnxg3y0dg9V3LhttXPxgSV7cOGEMHa+0r+/1Rx3Ew8trgXar9D4B22PP3Gn0UvlqZjILYZ6dW37F5ZR96SOSjH833FSGTPcRzrGYUmenUPkJIZF6A4myX2R3Cur39fVu4E7GDkb2jrvUxSCwI6kwSbLiFcjuZFAMn7+aCAHXPPV2BgJnexsb8//o3fgZefZGZ0bVlt3b0Lj1kdmgtAn12EEVrdawxR8+jj7PQqm+txyPOVA9loykA7kgFGK7K8HOMGBlDcCYrEOi5DlTAbzDk6iBtDlBGfU3rOeCax6dpu3Z06et06vMGYDIphgiPL0b0ZaDCtjG9Wy/UggyvOl6DLkBXl01fN+RwvPeyiMwIGdmoZbOP1U6fsS5K1qrJdZmAqlrmCh2AzRkRrCN1eTOLBmdvxme9KQZ0Ept1ONOsld0RwDgvXy5GWhhuoQdMJmX8OIpUO8MwPozJO6ZT95+R3/l5eLReIxP4nJmPiwjwbZHt++rqmba9MmNliqi94+x4sv1naBg7wFeF2sa8ycQ3krc8V0snjOZU1kHVCmscVjZaHgefzhE9F90+2vTsHAcHXXsJPh8cdOl0C63QJ6Yg4IGPVdY2GXYecfbamzeLUw2b9DmQ7tatQvOsE1FEZkHaCGLgDJmw31y40A1Q0M/7v/23+Xvm56Lvx6PT0TzKZFNroYlZtARQj4VF3MeQvsa2uVp8t1VmrCFrq+X2lh14pDe/7ex014g19pq8MsZc8WSjjK+i9r1G9J5qmau4j5rcnYGl82dsm3360m238FnNJ2u+Bi0js/3dOr0EYHqrs7BBn9C8EwHfGD9vXNnZ6Qam8Xim03mfgkjRGbStiOtbgQZesgD9nK3j2vWV/+f6+hqhRmMzuk4fRGs3I/MtGtm1MkT2zK5nYBXyv5Yl191G15e+Zb7n71jujcqOaQ/KrBuAN0fqzPi4YtOHzr6v/xfpZurUfVl+X7THNnDQQgQXnz9fklnhiqRY+hQWPGu2+0OPmE67mzt1hnyMyeIbIvNzm2lxRraPeBTT4uxc4NNxAEvuaKGlGX9n629DbKStJ20tQp7MYiwbU2sffeW3MbBIPt8HrXy5pT9eWzs79ZMBLJt6ZlwRWmyr0QldVjIT6zuR+Y003A7T3Zs3C82zYnpE5u+TN+frgGE8a5Rh/qTt/5xUS/sVvBMWt7aKHSrajMLyf3QyCrAM2jN2H+sqw40lu72bsWj6vy4y/jqMYcU4DkZm6MnZ12nZB9lF5xljMuPJjrPFGJNxLEbwBLdIULfKsNFCBxGwYME7bff2uru38JvO4sZHH/N4tKERgoKVvYyVNZTVGZc9QUM7njjAuCVD89aWbXzU49LgADMLnjE1Um51X57joSX4JIMxldIMoiCC2lobi1laToahhvq+/S+jXk9svBOOPDrGChBY9DhajeFD6H8UfBI5IixjDhsIYXB8++1ugJmVzRMZ2XAF/beO8kH/rCju7BTDIGg5jJCWIY2P5NVKLWizF9x2zz0liJjLiHSNoawwb211eVT0npeRPbl2tPQisQwFOBNMtm5KSV9j5FhK5JKNkYeysX7vIIOMgXDRgUC1vjJGZ86wMJ126b2m8XAesfxubdrwZPw33yxBYwjshazvOepFuhn/9/e7eggHsJ05I/Lgg7P/ESiMz/rYN5bXOdP+zo7Ik0/O/v/IR+Z/294WeeaZ+XHjqnnGumXEH5IZrWVur6MsukyZuqW9Vh7l6e+ZsVo6yrKz5Wk5T4+Hs3YM1UkzMnXLc84GLmcC1bwgtFo9RiZAmJ81y8STyfwGDJGyCePaNTsjM9dn/sH0/623uqeLsI68t9fN8Am9Qt8D+oVDD5mRQaPPnu3OJfAa8LLr14sziZ8vdI67dwu/4OOat7e7GwyjQIOjJO/UHL1Zm+2QPociGmMrneyDdXX2WfRq2aclZJzxQ9BXn+kj3/ThwVE5K+iWnfc6syXoNOgl6Kc+PYWzXfJVB3gB2lEPWzPKw7aPPvXpWaw/LOJURsj5CPrlrPcPPeTL/ahz6lTXJoQr6PfmZpdnIhjtgQfKvfHJYBbt4SAErTPU5oK29XvBaK3w6h8lPpXBu+1+FoFVPKOjlKF7Fc/HsjVZn2sbRKKkS9pmw3xDf4bczkHA2g7j+Vkz71k/X6bFzGf3933eou02oK8Yow4wxlXbgkSKTKL9ulZGTEAngtJt47O2/eB/9mNb2fOtDSc8BzNZObms1p36wLIdrBvYFngUsKrgw6EyxDpgqH+nr729j082smdZgbpsp4/GwzqCvmYChUHj4ZuFneTWrUKrsJkE8TqwFb36aikP2slxM+fOFdrjbWCYTPxMxDq4WNuURAq91f5gyOnsIwaN16fzsP0GfZ082fUJaH6G77xEIzoAm09G0TTZ22Co+YpnW8rYHVeFlhi3RdRvLftewCr84cfByGuDI6R1LQnXrtk7Xfow+KGEKSLmixJqMu2MyVhqCqsVcKwNod5zsYK/9M5REZFHHumOx8rGiKuXsdHqv8WpMNbOOX2FcBbBM0JawkVm/ntHd2aPcsZ7WReMIXC0tDtG24uCFjqt3zL1s7+t8hm0ButaRjcuGzkfrV2eFu3xnonFI6J52xLoEPXlITPmqD3LUOMFKEeByqD199/frc985Pbt+Sy+tb6s94Or5yzXNJWVP+YnFu+K+rKucMYBuD991JLn5NNZb6zvPPA4dFnmDTzHNjeL0SFzz0PeE//vYSy+7H0+SgbUZdUDVmS82Ni7IbL7jpHIcroAy34uQwLZxzKQtI49E/jm0eTMkdCHh921Df5hbZpjwykMdg8/LPLN3zxf3zpKjY11bKDb2vKDCDTdygSNs+OMHTU6SJv5xu5uMXBmTjTh7/SzQ32+dyujsR6/13ef9bQKI9VAtJyuEWHlJ280POdD2ZCNNX8vVezvF9nRolPRuukTGGnZFywwncxkV8rIjNb4rKCBLCyZhtvTAQjRJhKRmVMHgbkIbkPA2f5+d4yoB/n38uWywQTtIKgB4zt1qhyHrDeEixS6a23+Q4CZDhiAo4szSELWvnat2DngfEL2Y3w+e7Y7Dot+456x8VLLz8iMyTqGztTPWftbggjWBUdhjIwMr8vIv331mb46U2Yc2T6j+qt8p57938NYYx1Tn/Dotw4iiALEolMAcLWycYnM0zDLvu5BB3SJzCcOAb3moOLJpCtvWxvsPHuLDi7gzX/cru43CsDw/AdD11grwOuOEo5SEOgxloNl84L/6/8S+dZvLX17NKPVjp7RezP+lRY/SqtuEI1Bt5fxeRwc2MFnIvP8iIORQfch096+7ds57ruvu1mbZevd3e4JIpE9uM975jaWgdqcgB4C9Blb5POL+oZtCPrERiLBg2UXY99JK5bxPtbJdykyvt18EXb4VT+jGjjLboSM/J2x+WYR0aOaLd0qY/kcI7kfV67n0U9rfH11Deue+Tur3yjBlQemPTqJoFdWv2cvWNuSKax36ulDepMml9W6Cs9dDirmTYoaoN9373Y3+UQyhZV9mccYtQP7nte+nnfcbjSujF5hzUmeN7odXmPROKxEknp8ul5mffMYdBxcRq6rlfX65+dirb2MPOnZJ/TzRr1F67EZ2WQZyJxG8S7HsfbP0JlLNIY6+mvO4MjJlSmfUVItJTIj8Ftte8cZ6KwHcAjBGcTXN94o/yMz2RtvzK5QRrVjHIzRC3r1gIXODiftMEJWAmRnhIMIBtXHHiu/cdCspVwDnKnnzJluRhs2fEaCnCWkWTu12MG0KII7mZSxLbqvLDLBHpn6GeNQts8h6GMQiMbT9/1kDGp9nkNEG5cdlFZ7NuvkOFsEIh4RBYhFwjgr9+ARb77ZzTKGnaU6ww3+54z2b79d+AR2xGIXLO9MXRZAk8ErcNWGUM6eg6um5/ju4sXZFbt49RU8CvwEQTQ7O+VZcxAZeNe9965fZsyWzSiLdORxH6uqv0JYwXRLDYxbxvs1cLh7qnyIMmf1HV/EQ1vkjYyxIgOL9zK9t2h7LchEt8t0XxvWvGz1oOM3b3bLgLZfvVroPju1NM9AfQD0F/Rmc7PQQnwH2gp6e/p0uSeUAd1F2UuXiizPWW/0JhnwBNRHe5bRrc/810EajGiOZAyVVjstfKTv/UT9e8gEXUWG7gEIaeUyZfMx2k72vREF3BwV7O6uZ1BMi2F9rL5aYK01z1Bv2aNQFvTypZdm1+eeE/nSl2b/v/ji7ArarjN3od5zz823l8G1ayIvv5wvnwEyIX/TN82uH/jA7Hr3brF1gY/hHiC/nz1b+M5jj82usEOBV2hHET/nnZ0SxAwbkxWsd9TW5lFHSyDuIt7NKmjyUeMDZ88uPxuzSF7Gz2BRvKLFLhfpCtaphZwRDXZ/HSiMMnBav/562XTB/gNtR8Jv4BvrBtB9ZNH/0IdEPvjB+e84U6bOjAmeAJvRmTMiX/va/G/RkdLHOMYxZviu71psZvxjFEynXT8C+5GvXy8+AtB96AHPPivy/POz/+FHhj8A9P/atfWg+8iG/+EPF5mebUw6gJozIrONX2+Ot06/hW6QseV4WGYAtmX/PAo8at3GOPZ41u3+lgE+aWKRaNlcYqE1bidTtubnj4IWMxtWdFk+rRxX0H9r45/2DfBGRbQHPeCNN7pJjiwZ2AuM1JsssfmcfbpcV/8G+Vvbc/A/fuOTsvRpABg7ftO+B86Wr+8Hczejf2cCIZdBB/BcMnEmwNhB/uuIZcXADH0G2TikFj6/DBp8jLXAGnpd1gCrcPisYhdf7TdPmLACFkA0wKihcEV93bljH8ujr/v7/lEON250j2nWgQq44n8oqvgMgWVdsgjA4fTBD5b/9VES+nr6dDfTM64nTxanHJRRKKo6wyVn1sFzwfO4dq0bQKKziYrM3gUCQlCWAwT1kRDevJ9Ou0ENGDuMrPfe230O58/PrgjaO3eu+1xYsT97tltGC6i13UXRWm0NKPGE+ZYAXysIwBrPohGNowW1HVrH6IesUDsG/8v25W2q0UEKUWaF6Hg3kRlNYhpmbZwB3wDtYufY3l6hZ6CT+Kz7RnkcE4SrBiuqVjZN/AZ+agUjgwaCroGmnTlTHHCcWVMbJzUNF+nyUq3s416t4G/s0uWNSbo9Dg7hbEUnTnQzEnLm5s3NLk/gI490IAZvBNLBHvp/kfln6WXSaDnWT28WivhIHx7DfVmIlMCoj3e+2+ijAA91nq8BNkRl2209Tm/oMxsapKhlqSyiTY08T1oDJnRwlEhZY7odL+ux5gOew0rTFab7MOa9/np3E6R1fDTrDaBzUVZQnYkY98n0hAOOt7cLDWM6BUwmhT5ypjYEl+mMP8w/dnZEXnjB/g19inTpLD/DW7e6fJXp//Xr5X/mhxH0cXEis+cMmo7nw8fXnT7d5Xk8R7e2uvfKx7JqPmI58vjYO6YHOztxlgMPVkaMFtoX8aGa4T/TV+umwowNo2XTtIVFHKVeQ1/jb+Zdthjal4kMbY82pXAdLf+w3K7pN2gP6vG6PnNmlrleZD4ITWRGz1leR9Av2r1xo+vogn0jQ6f64gtfmF2/+tXZVfNAbasQKXROb7ZBMNmv/dp8u6Cbm5v+ZvSdnfKM0C9vfNSyDdrk4OabN8s7gyzP/ODWrW4mNLw7LeOzLK4D50RmzwL8I9qUX8tUqMFzU9fx6lm2B6uM99tYQaWRjG6tOY83RDR9EZtiau1k+lw1LWzFGLrSIu55KO/lejUZg+t4Dn7ND1h+t65s/4Fcd999RQa+//7ZlTez66QovJkdMv+VK4vjBSy/682ICDSGrV8HO7Bsj8/6aGd8pwPUGFYmNZEZzcfzZNuODujj4BBcwQ/u3OlmO0O76FOfPOZlRjs87CZQYRuPts1oW5dI8QPozQReJtTt7a7NTPsD+Dla9Jb1hj76wFENwHu3I3MSiOfP8pCxzfSx27Ty8GhjINfzykTJSDSNZ1sMy+FvvlnkVNjJQau1bxZ0GvIp+tD2iUUnHdG2HR00LFLo1LlzRaaHjA9ajzonT3ZPPcFn7cdl+sQ2Cf2/lXkR75Ble531spZQ7Pr1rk+AefHt212eba0N9MW2IrSztSXy+OOz/9nnoTfv43mw/qDpMPNcyw4/xO5+jOVhWYF4y+67db5FAb0taIkl8H6zMi2z7Kf9tp78f3AwLyNy22iXx6TpJmiEB21LZ4A26syyfIKUptVMt9mGrDeIeO9V28WYbqPOffcNf9/eSYaorzezs92HsyHr//Gb5u3gz0zTdX0vM7O2FT399Ox/9o+DF545478Di9doW5nIvA3KsyNp21Cf0xEWgaPCkwJ/chNaNj0PRasPZpHjeI/jOBiZcf26vRiYoeoAHjBU3k2jDUresb3RAm51PnqOoWi3kpV6vbYwrAABXDUjswQLfb19uxvIhDr6KGQOdNaKhxcgZB3B5gkKmUBsfc+sfOlnxwqmdUyzdzSFNWYvk4H1DnR7GJvnIBIpzBrPF7t3tdPe2zVlKbUs8HDZGn7Lb5kfK2A571n41Qpwi8MKsNZRJrB3SOBPazCZh8joZCkOGQdcZoxen/y/biej7FjzeFm4cqWsPes9ZxwzLQ44XScyjETKTQ1W8BfPD/2Z7y0zXyx+Eh1nias3frS3v18MYlp5xJWNbPgNdPO++7o7Ltn5cu1aN/smO8muX/ez9mO+XLxY+oKjCZ91MDB4AwyXnClTK0t95tJ0KvK5z9m/Wc+bFdbWvvAcmP6jr42NrkJoZdH/1Kfy/bbAmustQZgR3c+sEasPRib4YKxgAq+9aF1bsmMLnerLz5ZtjNRGNOueeVyZe9flW5xBum5GJunDq60NHrW5rX/zDFrWRhGrPR34JFL0Af0Z/3PQrB4rB+/is94sx/Qa9FaPB8YtDhR7+OFZVhuREvCGIDmd4QBtsSHNkik8PU8H1Hpyk5ZzrffD9D+iHZHc7q0/Tdv7OAJZr5hMShbRrLzkfc7yrWyZFrk9Khfpmd73kTwWjcOaUzXabPUVlc+U5fFoOhUZe1dlANbBT9F4WnQmS+fP0P1MoGVkq8q8J/5Nz5No8xXzD97IoGkzQ2evR6ACBzDpAFem7fpEEHY4cNCTzr7PGykwvhMn/I0hWtdA/wgag/McG+/PnfM3sGU2O7SuOT5xAM/y1i2RL35x9r91bLXI7Hkxz7aOFOWxaQcT2slk3eT3gr7R7uFhySadOeqVoZ83r1HL5unZ3rQzyuvb0h88vZrHpq+6XHS0rhf4ptvgdcjvUttKPbqi2+GrtaFU6+i44n+0zYEh2hYOoA7m7/XrRSfGBrJl4erV+Xv2eH8UMM9lM/S7xkNbjjPOBNp749H/e/blVpsTYNnP2d7CvPfUqWJDwTPX9grL1uDBk1M1f/budWur8AhcI3qXeUZMF7SPKJI3rPHz2HGyAMB0ezqdD6LT0LaZ2n3o590yLzgo+fBQ5JOfnP8uQkb2jZ6dp3tpeDTZWiuZMl7f1m/6M5e3eE3mhDrvuWp60RKIYdEw73hyPcdr70fz12XT///7/xb5Pb/H/93i13xstTW3xtJnWnQuj2dE42i1GzLt0UFlaIsTYkB+1vU8uUPbkXQQMvrqY0/j7/W850AovUmBk1RYmwC9zQl6PdXsChbdjd5hZJ9m247mgTw2zuLZ1/aamW/WeED/uU7Lc5lOY7rCdaL1lOEjNX3EQjSOFpmt1f5u6UHeOFpsBxotOm2GJq4iURz0aeuZZRA9K0/f05t6AYuueM/X0kkz8gVvRLh6tZtt2JKFLd1RlxXp+gaYft+61aX3vCFub69Lm3Vgq2dvh93+1Ck74QTuA/flxWcB+r6sk8j11UIkC0SbnzL0wPqOfbocoyNSfB24RifTZrIne9A8z8PhYbH/A33szLVx6KvXF8vyGTs1t2OhRa/JlNVjzsyTiO574+C60Thq/dfa0WXwzPlU07GR0XGXgXUYw4pxHIzM2N3tGmeyiIhnbRG2TsYoAwYDxEdniWTDst6Bb+3CHAOW4uDBUkD6LNgWgptty2uzRQHO9s8MRGcVYkFMH/fMc9jKSACjahRwXFO2omOAWgSYyaQ4GReNjBKof2ODuUjdaGe1Z62nlrmdUTZWiRYDfA2412UfmXn+fHtGzBXiUMoac48G18987HuLDBwZwbO2UaBGU2v1LOXCctRysBiX1QHLTDMsXhVlTqmt+YhHWMpKRBeijDQYE2g47yD2dhQ70HNRpHJUfYTIiJgx/kVOAbTjOe+1YcJ7T9p4vUoaPNSYmUGiPr93u522ufT1tnvW6w1NCzIYaiDJYN34fIa2Z5T87LPzykXBk5HhNGo3Y3Thq+6Lj1yzxpVxlmTkyQgeLdebRTnzfCbIYpGA/pLRE8dy7vZB5KTQ/NkLcImyMBxjHst+Lltb8xnco+Ak/Vkkzu6u27fqt9o5Mk5U67PnFNNj5nFY8lBmjLXnYQVR8uZizY/xHYIWL18uJ4AgkzAywmPj4JtvlrY5MBif335b5Etfmv3PwS8PPji7PvJImRe4H9T5zd+cXSeTsgnxiSdm1/e/f76vyaToERzEYgU4evNuZ8feTAPUZJjptOuEyswlaxNhBpnjovvYfzI8vIWGLJKf9OVrNafc1lb39C9ur5VORM/B0/l1AFILX4v6wlpfFs6dK88SAdEi/nNscTDy/x5anK8t8zXjI9DBX946svRftpVGAZKoc+FC1+5izS0OYtYZHPm0Kw5umE6L7Av5Fu0gG+ezz5aTBJG1E8B6euCBEkTnZdXVmXsj+wDovHXaIcbsyf/RCX5a92jRYVvmJAe3WfY5PO+WuTmdlvUW2eSz4+xbTvfJwSt9+vHajr7zoGUi/TljFxvqdI/4rF6rnsxo2T8zyGQpHhOf+UxZh9m+a3K7tfmptb0hvCDybUXfR3aTWmZkfc/RZhbPBqJ91nxqoc7cC3rNWX3xWdtzAQ580wlv2AaiE4pxUByfznrqVPfkPuYV29td34DFMzLrOYI+ActDnznFdXRwm7XBvAW1LKdHARZNHmLLy6zHvsi8/z50i/8fglXYxbBWW0/l6uN7Geo/6Gv/8crqMjW900Ikb1jtcVyLdRIHn0irT6YFb3jxxfl6+jQUDmgEvQX93ty0T+rT96M383JyRJzK8r73dbO680l++uQNLzPyGGuH22j05VbROm8z9p8oGHoV8OZ95HfNyG4ZOWiormAB/Y4dU5OhD/q5cLloU8yi5f+hetGY43iP49gDxtAOktYJklmMY4EJWsYorwONIwKScSJlHG9eJmFLmeQrjNA3bnSPitHH/kAYQdvWkQO84wxKExjkdNrN2OJlGLHawTO9997uLmD9m8hMcYWDC84XCDDWLlwWnCyhBs8cz2drq3v8Kd+zrhfdnzdvteLZJ6Mmt2tl0ub2MoK3LtdinIlgGSUzxswWh+1QjEFfIqOm7qNF6eqrzALLNka2IkN3RervJ/v+qO1UuH92jN5vFo/ps8ajLMysBGqeAWUUO2p1FmMvsw6+v3Fj/kh5q4w+yhNBDKijnVyeYx64e7cYOFuyRYLuI5DhgQe6DioOaj51qhhBofhqBRjKubdjWGcoGQm9g49rGDvwbDKJHU1HBYsw/vXAwt77gtt2MVYwwFiG5L4Gyz6BEyJtRlQvqM3KjGzJ/+w8YqfS5cslyxeuCBi7ccM+Elj3tW7ywwMPlIyamt6LzAcn6OPrRexjP71MPVtb5f45MICPSeuLRTpJPPkykmP6yqBD60XOEm/jV8boNpZMnWmvpUwNQwJJ1gFvv90W4GgFlGeC+Vvk7758yAoG8NqKgqd4bup2Ij7A2X9Am0Grtf0HxzVDbkVw2LPPlmBfBI8tE1/5Sr7s1lahyaDXsPVA/t7e7p5E9eSTs+ujj86up093s5WxjWg67dop9TvkTJgcRGy955bN4331+iiYIZP9tTaeTGCWLt/XYTxGEGhWL6/Jo5ngnmgcgF7n1jvw6rP8kYUXrKTf4aIz4zCuXy/3aQUjtASWtNhNojKWDNsScBDVs+pAJ44CznhDhdUny+RWFjZsJgGN/+//fXb9n/9zdl0FzfcA+oigZNB20PGHH+6eiBUFLOtAN772CcLR68c77cR6/7wp0YI33yznMp+UFcHyF/FvrXJUi/N/qG7tBSPw/9mxZvrKbCDLjKcl4CFDXzSP8O7doqkRf1s2/Rcp/sXW5DFD/L+a51k0tU+gWyTLR6eneOvHKuMldTg4sP21IsVGfvVqsbdD7n/++dkV/IA3hywaoOU4BetDH5pd9WYUXLUtRiQ+0VDL6ByMxu89WkeZeReV8fiCB9YjonnH9N5KgBK149F/77PXnkdPMrSwRS+vjcOr1+pHWxSs59Ty7DL8o69+5dnFloGW+B+LbuvfGC26bF9k9WR9jWxE1jut8fnp1I//AU2/fLnQfdD7z39+dn3mmdkV/GCR2NkppzKB3rON/qGH7Dgd1Mc1kxl/DH9mbY54v/c5xTDbD/MYq0zkQ/eC/yNZNiO/WH1H87fP+otoqddnJFNbz6wml1lo7WvIvVtjarGXALrMogPUI7vWMrEOY1gxjoORI7QKnxHRaCFMLWNrFdZaDfvW97o+B8T2JTreGCIGkjEEZ4hNpow2+LQQ7Ayj6KvktJTnDJmtyCi83m8ZZae1j2iOekY7HfBoZdDwxspHiPNR7hraIY3/sUsZger6M+6RBXc9VqwxbdAWmc/wxM/KciCw4cjKNAJDER+tou8NgSy4Hy2ke0Z0jM8S2NeJEV+9Wp5zXxpmfJfKHppBn0yhy84uukSMEqw4ndaPuRXxj+dp5eE1JSLLjyLDMtYmB6OhzN27vqGxb/azaKxsBO3bl0fbrSAYbtOizWhPG871MV0i9lHXTOfYOB85kC1Hnpc9JHJM6nZq49HHzwPW8bCZo+HRDh8Pfu1a95hmBFMi4DLiEdyPjEg3s9By3lBkHHi1sURtDkHG6N4in/O82d4u62WI8S1yjun/vav+PxNc4WVa0/SSg6p1xnzOqscOq7Nnff4B6PXsyXU62FtnAUK7MPh6QWibm91j7zwarWHJ6JlAN6++/sy0o2W+tOgVFu/UdNKSi0WKbHz3bpd/AXiGev4jsJyzFVlzm/U0K6sW5pgOdMnI0jV5JQpG0995fWTfe4sev2xn3fnz3eyiWXi8XCTWifl3qyzTJdYTo03XyNJ1+nRO98rQVLb/RPcROQ4yGdZY3tD6q5c1Ezq7BvR3OMA+97lyRTA0ng82+j388Oz60Y+KfPM3z/5HoAICjvX9YRyQiVif3trq6vG8qcSS+diRN5l0g9r0+s7I1S1rqw8txnzUwW6a/zA8GdaydfLcGiuDliU3sI1Gz8koKNOjc5nM+JGtinWMjY3CYzC3rGfJ64j1HB1MzxlrrXtk/WFnp86HIplWj7mPrXUM7OzYDjCPhmk5jN+Lfl+sD1obWKz5gd+8jPoRomc9RI/QemsLWuzcWdtvjVdZiVOiTfF9nnOUBdzyi3hto+z16/6zsmhHxhbPNFTbfvgoZ1xv354/SUVfQb83Nrr3zydtnTjh36uWWWqBpxYt8uz4urxFv9lGxLTM6kM/C87ox6emaTu3pd+hfaYZ/N7vuafIbx6/17SD37emTTzH+YQIaz1ZfrSM/N+nTEbfXAaiTTUtY7RkiQiRD7Wv7YThye26jqfnZXwdkW2G5Y+9vS7NAfQc7XPKoCWrefzZ2oxhyacef47ovgVvjenfa379qMxkUugR03addMsLFkRdncgFZTlj9OnThT7x+4G9I5JztR2lj0yTsSXU/C1emUiG9eaUbqemo1hrjX1M0f1kaHPrMx1KbyObQbYvLV+2ZiceA3iX0brW/J/HaL33lvdjyWzRc/Tq82frfliOuX692C5Apy29j2UIlk20n4c35GEzx8c/bp8GtSxEczHzXrzfdZk+8782Dq+uLlvbdJKhC9HmdLSv1yq/Q10/0x/HkjEyc16Pp+b34XoYQ8tpzdFvnmxk2bH489ATpVrKRDKbbqfFdpDhudHzXdbJw/q+Vol1GMOKcRyMzNDHqGQQGa+BvhMtK4jW6mcYYERELQW21Wjo9VVTLrTAbhnlPSNORGDZsBYZlPW4PEUK2NwsSqx3FIM2dnu7qMYgkN4xzcB02hVYODu0PsqZr9YOK8tQKZK/F+yCY+EmY5yJBHc2uuk1bimT/M6s914zguh5awV9cd3IqM4CSsZ4wc9ge7vMO89waT0768hYgOtrgyfX19c+Qt6ygOA5kZhONTpWOmK9Vd9zyFfadn/LKFhR24tCq9GaYTmjWhDV8TLlRjTZWj+Z+eIZ0vSa57K6PXxnjZkzX4IW68CzzE5uL9uBNY4IXtCW5j2cBcmiex691TJB5LQXmT0bjz+fO1eyGkVHqnuGdj0XUJ6zW1t0fyi8dlqzlkXKKMCZnFqP16vds+prY9pz81RPHE42296JXvNRlrCa0dya2xnnj4Vo/F5GHMuw7ekzUXvROPsYVGuGH15/2pEiMpNXWQ6z5G5eo7jCMLu3V2gngsgee2y+7IkT3c0fTMO07MlyoH4n3nPVzqBIBkDGOYDp7eFh92g6lv8tnZB1Ba1H6ABajZMnCz8CDbay5jz77Px4rPfG78cKgq8Fcus+2bnW6lj0kJWhPMNpCw2xaIdFxz25P2vDqNGnzJqPxmjJdavIjDYUGZuM9y74f5TRmxk0rOebma+RsThjkwG8jV/RODL8SdMAlOeTqO65J95sJzKjTV7wwB/+w37/7JjXRzmjT4xRb1bzNixaNMmb/5Z9IXr2PObptPA/jJWfUwTNIwDPlmPZkbiMFWgQyQve/GiVQ6J5nJEvuB3NY3iDeIvNN/MOWoKbuVytL2wMZOi1ZvElS1YUibM+WXM7I1euEtY6BKx7X7fjZRlDdUxLbm+Z7y1lrD4jXlXTq6z6UXsesrpYRg+qzakxAHpfW7MRrNMP+dlFyU3QlxXUYAX7sR0r8277YGg7fddTn35b5GerL23zX1d6u27QQTGsy2aSP2V0J6tsTUbyvvN+a7FBR/4Ia3w1GmbxCLa7nD3btduwH3Zvr2wi5FMH9/fLbwDsDFr+9my0WvfPZLT0/JFj8b5W/TkCAoHZhoJnlz0Fhek+n3Kwv+9vlNWbUjw+oTewwP7vBS5Pp92kMVYgaO2kIr0eWzcS4Rptwq2hRUbpS7P7yGdW/631ubxlS661pfWQ1tNWxoT24QBaB9RXkWFydvRc9PNokXO93/X/fB/a/x3ReG/jsLXhi8ejabuXhMeyl0e2smi+155LJKNn5GV97944rHG28HILmbnAcTv6/Vg+Ae7bm5P6BEo+lZ5t+3fvdvk8b+Le2BB5+unZ/7BxWhur2UfhzR/9G+qAH+3udu2pOts1zzdLJqjRVy07ZuI4WnTSzNy01mMNWmbL8EevDeszj0cj418/xrsex8HIhEPZyBFj6/cWA3dG8bTKRwIL/x8pn1H7rWOzymbqWAILE60o4FiP23MC6axnvOPLy26r29WfefcXFDo4fl57TeTKlfm2ray2YIAQ/DgQQjO7TPbNoUYmbts6wrlFSbKye3lHN2iDJXbM1TLHWWgx2tUEfw+REygzh6KyXqB7dO+6vZqgHRmSgKGKnzYmZ+ZJ5r2uYsekxiIcBV57nK1Vow9PseZfHwdMZg5mfrME0hZnsPXZE7ojJ27UjtfenTtdRcjiH97GA8uo6dF2rcigD2R6w3Vvbz5AQmQ+UOvLX57/DpmztNEVdD4KJgCPYyOmpueeY0oHMHjBELoMz/+I5/B3HKQWIdoQYfXLGTv1/xnjFBvDW2iy1Y6lYNaUx2xffehcK02pycnWulwFMs9ilcZSD9G4xxivfieLvv8arYdMD0fGq6/OrpDJdfZM0E59TCiutWOndZYw3tygaSxvOOBNICdOlDJwGOlsEmjPC6DTdMbaZIf6eB7exsco808r+FmxMdIKarOyKOCoPG7Xov8ZXs5lNW+o6cFevx7Glg/fLRj6XI6SMXKIrpBx0FnXqI1F6S4ZaBlWfydi6/UZ+cfKYl7LLGjVB91/883Z9fr1biZ8rnvrVuEfnCkdeN/7ygYV2HjQHjIvX75ceBY7AEH/9Vg507LmWRgHO1t2dkq/2Cj2yCOzKzI9X7zYzWg2lP6zXoTPk0kZmyef3rrVzS5pybQ1m4p2CHpX77t1QqvdY4z7iOxiQ9rU13WHzvr1bsHQdZ0JAmixWUVlWVfe2+ueQGKdxANYejjKgN6//PLs+tpr5XfQa9BJZL0H3drfL/QeugUA+r2762+6tnR2L3Ocdoh7GwS3tuZ1CpH5EzNwbyznW7K4dtJzHfTnnX6ig/24joWIHvC6ixzpHm1vtR1YMtOiaFWLPqHH0+e3VaHPPa4b9JxaJS9Yl2c4VtCUJdex3TbKzsvBrnt75bu33ppd33hjdsXpJy++WPgH+uWT7s6ds08bFYnptre5XZdnvqaTAvGJKDqjMHjKvffO/6afnTfGyaTQfz7FdKisz8i8/8NDX2/Va403yQ2JQ7DqZ9rJ8K6o7RZEOvwy6Y6+l3WzZ6/C/rPumwsXgT5+YQtWAjfPdmXF//Dn69e7JxHievVq0QVA/7V/VGRmV6gFrt5zT6GT3ilcJ06IPPjg7H/EqXBigL290j/kdvShT9rCaYlM/y27GCdW4s03GloPwP1HJx3VEM0J7TO27PwibckIp1OR3/pb5/vl96Rlc483WAnBmKZGMr71fYYPWXwtkwhqXWS8sTD2/SyaHy1S32sdx3sc7zKr23BsyOHwiZElvi1oKe/tWhqrL+v+IiOEx0AmE9+QZQVaZhxw3I5IV/m0dul4GZG1MRDGzK99bb4P7eyGM4qFAH31hJDoPmqBhq1orc+ZFqK2rGDmDLQzrhVjGe1an0uLs+Xd7ugZW1lfAQ4leVxUn3blHf5SQ1+BONNui2DtBRNkg98tx4VV1oLuKzqmNnJc4/somAFXdnSB1sP5/8YbReHl4FTQ83PnikENCiuCgAGdqY2D0HR2Rs7YCN5l8UIrgwXGypki8Nl69laAWp8yfTCdzu/KtpCZL7VyPdC0dhcBvp+xFLSs4bWGvs87U28VzqDW++nj9GxpI+NgjQw1Q9Eqk9fGo+tGRyd7ZXSwEzvpQW8ht7/2msgXvjD7/9d/fXbFRg3Q/K2tbhABAoVBd0+dKsFbaBuGR8zRmzfns3aKFJr20EPlikAHDgbD2M+c6W7MsLL6RCeswGi5DDlsLN1kUWs9EyyRkbnei8jo3u+25zQGPR+z37Fou3b+REf8euu5dQy14CJ9ioXnvI/o/9tvF6c7gg8QaIaghNdemw/g1Vdkjwcdr+Hxx2fX3/bbZlfQdIznP/yHMh7wjyeemF0/8YlSFwFvoN9M4yeT+cAyETsTfPQekGVmLGRkRdwzrlE77Lg6caKfExh9t54AMhZqevAi+hrLvpzVv3G1nIPWtdb/u41fMFiGjY5Z1nYLz04S6bZZnbgPor6YFrdm8kZZrq8DDXCFvQVyN+jL+fOFjmCzxwsvzF+fe678D3oPGo2++gK6wUc/Ort+/OPF/q8DlPWYz50r30H+R8CZtgNxYEJ0wpw1B5AUhdFqJ8mUabEJRfSS5/06bwqorWf+32vjKNFCL6gIWBfH/qrgPR/9nDLJhfr0YZXp04flk/Xaj8Yl4tvbM6dYWHIivoPd5vLl4osFbQff0JsKcf+w7T/33HxZnaETdqD7759dP/CB2fUTnyjyPzb4ceZGnbAEtJ0TT2kZl2VXtGed7tQ6bzz6PzaycyvDI2ptjUVbxvIVL3Ic64xl6jxHEavSfcai2wDGGtlPM7Yh7561zIc2Lbs3+AbsNHozOfy0sPdouw/KYjML+3bR7u5u8dciaBjyOuzoV66UeuAtoOngCx/5SPmfT0wCH7t2rftcrVgh/I8+Wuk/7n8Ixpb9a8gkkqphGfafYzr37sG66CzrMIYVY421/RXh4KAt6LUVfQW5FsNiH2W0T/vZPj2hxtrFEgk+1m9s+OWdtXr3LBs+dVAyCyq4IhvCzZtFMOFdT9oRwlkKoJRCONFBDXxUAASPe+4pwhAHM2vBpWVO8rPTu1WjOt7xOuh7Y6MISosKVDtGO1blUH+vQNMdLyBWPd/RAxkXaZjx6PXYsGi7FYzG47KUFi9gQSuzXsDb9euF3iOjJD5DqXvjjW4WHtBifWQNBzogwCzaoME8U2dGQOAaGzO3t7tBDDqYAYo7ZysDNjfnMzHguxGA4N0mTDblcDKg/yF1k+h1X+uMJTyzoVj2M0/T6bENjutiINdocRxn66I+rmw8ioLRmN5puR0GR8jroN+3bhUa/A3fMLtiw4jOpgwjppb3h+CDH5xdH310dkX7zzzj01l8f+HCfPCySHGAaZ0hMt4NmTMtQU9HAWMFS7/bUVuz74XnFm20EynPQGeh4t/6Itq0wcjSVw2dTavFzmKNy7vXliAJ7XT36uuTrThL8Ftvle9ACxFEAJl2Mill4BCCTNwKBDHgmsEv/dLsCmfXY48Vmg7HF8aMMvff382MxhvYd3bmT9vCdyL9AxSXofcdJRqSsdX2edbRc26x1VqBwl57Fv22aFhUPzuuvuXWcW70saO1BMhb75B1davtaA55Y9Z9ZIIQvD6t+W/xFc+mEwE0DDL62bPzmZBFio3l1VeL3M9Z09Dn5mah/62bT2rAeP7zf56/ipQNJ5qmi8z0AdB/ZEjjzMjnzpXfvIydOqton5MERYptnzdiWvCynmmMFdy8rmjlnRna0UILazJXK1rr1QKP+r5bS96M6NOiMdYmDus5Zegvt1NrswUZmaQ2z6L3o+t6AbXWqbeQ8a0s+Ezb9dU6tQ71ROZPvfJ0Hs2XQOdgG8L1//1/pYpLl4rdB3QfdhvI82fPlv9hl8LGdZ1pGfTf4wPb2+PNiWO8+zHUXq3pwrt9nrXoVfx/tu2hyMgHLbqLxaMYtY3s3F6Nj+jN8Ry3oxNFoTzH3Uynhc6/9NLsyptSbt8uvlcO7MUGxldfLfVagDrPPDO7/rt/V34DTYfcD/p9+nT5DbYd6Aof/vDs+rGPdTc1WvL/otbhImNHViHPLQN972ss2tHnfWQ2GSyqrywyNoMhWBe/xjqMYcU4DkZmHBwUhqgnasYQ0OLgqbWlP1tGRIAVvajdKEOD1SYra9FRmHDwQGDQQd1cHmXu3u0ewQAhQn/mQDEIEfqoSrwzffTmsgBB47HHivLIWQ8gVJw/XwIUYJSEMAIDrD6mGeireOLZszExmo8WU+J2dJCyxzD0MQ1WMDQDgiX3baGP00bP3xaDvWdU0WWs8dWMdS0Gxyw8Y1f0vi0jdsZZYSk/rUY267PGMtexUDDa889357/lUGdFgTc9aKWr5d6t+gz9LmrOH8vhFdF2Pv7X4jG8JrTjjTPQs8J5/bq/wQO04557uo54fZQO5gecUmhPZzRGlgD0y7zq9u0uPbKeCzsZMS4olydPlnEjGAIOJp31kjeWcLv62CD8hrHq96MzMuuyW1vl/gHr+NAWGsHQZenZbbQczwNMJrJxh4JEWmiP1VeL47XVuFQzbFuyFq+VvoqQtc69zIL6/qzsSrqMVV9/7wX8675qfVi8N3Cob0SO+UXgzh37GPjouVg8QWSeL2QMhV6ggR6T9czRZ8tJG/wu9dXbtKf7Rr8IoMVVv3cOxuCABWuNREeVaQOl/l6kyNtseDxxovs+omM1EYyM4GGdPRnlMWYej8h8QIFI4Q14Pnfvlv/5+ejnxM/MO97eA8vr1vz1aCjKWHwRY4/oLR8tPZnUAx4OD4v8H9EwpqHRaQvcjnX6TYYWRpmcPF1Br8cW8Fj397uboFjW0rJfpKMzfbLu2eNHd+505TemzVoWZplY31fNqaHnP+uEi4YeE+5TJKd/e7Q1gsWfW2SZFrllOrVPF/HGxOPRZVvsEJFdy8uwaclRXPbmzTI/eMMhxnX27OxoTJGuTA4arfUIPp5TZ0pDO+9/f2lbpMyTl14qmxdRH33pYGS0AzsQrvj+7NkuXWIeZmVP06jZWyz6wBvPo3fLtF7Dkv9Zr+PvLdtBiz6i50ZNpqg5Xmtz2tK1rXXZYjv2vov0iL46i5fNtq+OYNE75jXRe+Y6Wl5umQNj4Pr1cnLG6dP152FtqAAi+30k20R2xj72Rqtu1v5rfc4EGkR9WN978tzubqGzlvxhnZairwcHXdrOQc06qI3pGujmrVsliBk2JrYjiXQ3xbCN/9y5rrzPJ2SdPm2fqKjrWHNOv2e2F/F93b1ryxDWZ5EuTcdnfaoM6xx6XJl1zO9gKKJ5nOFtkc7P9SM7R+Z+vHdgrTXLNlmzNWVsELW1O7bDPCNDLtn+LyKx3G7xPk/3ip5nJMe3INKNLZ3FmwMZ+4Blb/T0zagd7RdgWw4/S8vuouk3n1YI+ga7i9YxELDWN2swaPD73je7Pvjg/OdLlwq9hyyPYGStD8NGxb5hKzNyy2mSFiI7UEYniOw9Xh0LGR5T2xyXsb9YCc6i8UT2tD6+Oj2+iKZzvQwNyegxNdktGo81Zo3aZqcsn6/ZUnQ7sMMuEy06XN+2M7ruUD+RJf/X5ptV37JBtKyNsZHRqzSiecbt8KYYbZPmU2o5M340/1t4sDVmzzdkQcv/Hp/e3IxPyRWZyfWLkvnebeh7XzV62Wrj8drPyPORXawvDfL6ymLRp+MsYo73Hcd7HMfByAy941zDYyDaMN3nGHqrjMVwakSr1XjQYihpQZ+xa/RhzFZ9/TnjpKuhlWD1EdYyylb2vSE4D0SOg0WiLJiZDJlWMPEQgjqZFOFuCLIGR6t8C2qGf6uPIY7b1vHUvtdttwjwNWSU4pbnsApBAX0/9JCvrLU6BDM0qOXZRQp8zfApYhvPo/I1ZOhd3+eTcQq0GGoyayzDR1rmgKX4eu1knH2R4mt9n3EQRe+QaXvfLMqRQ4TnYmZOeJ+jvrNzfYjDKrtz2asfzSnraCuP/1jzhQ0UrXzJUxAjeUyPtea81/8vemcsQ8vzOzvdYApdLmpjTFiBjbW164FpTdRuH/rfssayGePYEQ85McvzuIx1fChvisRVrxUrM6fI/CYXDs7iE05Ony7j90492d0t9Thrjg7+Zt6nnxMCJzhYvGaA1GU1MvON+2g9dg1G3rHAY8zMNwurOPqN57pIPz3a+sz8NSO/bG93Nz+18kwRe05Ya1jT4GVCB1hn++bnycFGGZnP+4778D5bv1l9Ms2x+s7oft54IudWH5ltOvUDlZ98Mj4RS2RmBwEt5MAHPVbmCQhUwwb4V14p/3/+8/NlrKOhGcim/OyzZf3gig3sOpABwQwIakPgAzas60BJfl9bW0Xer2XhF8m9B7TH9iTdzljOCm/+Re1zYJ+FLP0covdabURlvL4impyRs4eij3ybkR1b73nZwWinT5d1KFJ/nq2240XavsZ690PQV3+I2gGwtkEvRdps+578nw34r/EvbSMaystZR85sHNB9gS4zvY5O6MqgD43P2oqWLev1ReuaXzSGjqfvOl2UjV6PZxm6F4ODXzUiG8Yi33kfOud9zraTKef1lXkWFl+07KqerTXSP3V7KOfpEVrniQIuPdqO9nXyAt4cqW00nFyF27l+vfscrU1REa/BRhmPr+lNJPwbEM2FRcz1WuBRX3loFT7ECPq5DuF5fen2EL9E3/aislEfOD1omYhs2FxGo8Um3+pfz9pKImiZEbD4q9dO383okb7oBd9H9D/SpzK8wYK3KR517twpdn/opNYz8JK8aN2DfVpsuzh1qtj9+f1YyV4s3sD2GfgT9HNhm46FdZN1301oXdND7WstcqHVV80Xm/WXt/CtRSejimItlol1GMOKcRyMzIgyHWgMnTx9lbV1gDa61cq1lrECBzxBQQszfNUZALyMfnoXFB/3jOwHcGTt7XUzI1gBXjxGzCUIF2fOFGMqO6PgjD91qmTShDOasyfo3WmWUIRMcXpsIvPPxcue0GJInUy6gg/qtWbIHGOOt7Yx9rqKjBh96y8KPfs6lEB4nfQMUvT62u15pG7f/mRjfv4vemcW0KqUWkoXG674+6ywyN9Zn7m8lUG0ZtCw6LdFpzgDpRaKue2WbGEos7k5n7lSxKb1NX52cNANagOP0fyAM7PxUTxWhkEri6b3PieTMg48Dw5Ci4IRrI0mVh996kc8ptVA817HUF4zFC3Gt544vGNk3lsgDiebtoFu0c+11XjWtw9LvtZXnW3MC/C6e7ebLZ+DZU+c8MdvOVSiTNoM/GYdVWnJ+JDtv/a12fVLX5pdX3hhdn3ttZIRGfI/7h1Onf390g6OZ+OsrVtbJRMOMmHyBruDg+49g/4jQ+ZTT5WAGMj9/Bx0xjiUwb1vb3cNpozJpMtbrLI1h5U1Nq+/dca6j09kffSIZT6rZW9G0UGeq0aL0z1jVLUMwfzZyizFsrnmEZ7TvBYshc81fmSd+qBtM+wg4oBji26D/uN4za98pZt9fGyg/Rs3Cp2HjYfH98wzXT6CYGRcL1yYP/pZZN5WBH4GPg2+jGd4eBhnu9R1loVF05V1s0ONeb/roDstwl69ar5c63/o+CI7Tis8mbk1aKUWlBS1Fznduf1MUIHeKMibP3RmTN5wgu/ffFPk5Zdn/7/44uyKjSH4vGxA3v7Yx2bXD3xgdsXGk4sXC50Hr7h4cXYFz9C6D+i13swIvSVzkkkG1omIQEsSklWv5zGwbvewDF+kBst8+vPYvHOVAeqrtq+1QMvSlpzNZRiZwE2LR0V2Gq4X+XajMmz/11fOYMnXq1fL/5CJLT6C31iP0PcFus2brRHotbFRfmN/LT5rvy3oNsog8PLBB7uyPerrLKVeH1tb5X7QF+sB+jfObM+/H1Ws6/iPgmy8TH0i6mMV77Dl3nUszKrnW4vcHtl4PFuMRi1LtoZ3kmY2Poj9EbDR37hRaDtORMfJVJD/r10rtJDpP05Yf/XVXLZ8PvUWn7W/EzwBtBxyO+jvbRXPwHQfmfLvuaebjISz6Gv7D5++OJnMNr3rPjgBig5YPsZqsGzaumw9YYy6yz4Z9xgrw5p4XdYMWADZhZzZkZQtW2tjFQJPy5gjgScq7zm+NDggK6oXHdsIaKcjCzx8nOnBgX98ta7DAhwcZ9qBhnpwPsEoqtuJAudwRXkIVQi2uHatGA0R1PAN3zB/vXSpGDYtRbWGaE7j3rWQxsYOdoRNJl3G0zLXW3f51AR3XSYzf/mz5fi1HMi1djIYGjTbuGtpI0MLvSBTq/1g1/fGwa3qeEYHgq4mk64iZr3DlkyhLeBnqb/jPvQY+vRrGSwtYzM+ZwKNmYZCEdNHhvH4rQApps2gpdoxEmV+9HindT9waqEdnWGNnwOMf1AGT56sOwQPDrrKMOg2lOy7d/3gOr3ZBmOEUooAhrNnC08BjYcyi00q29tF4eX1t4pNJAHCzQ89sCGJYOujhowzYWwsqi+1dlbyrqz7ijaysb7AmW51ZmPOaq0Dfq3NFmjX2oyg+8zIPxn+fudO18HPWQJ2d8v/nElSPxf8DzCN3t0thjgcdQlaZgXIWhtg0AfGDGMkjJO4B5FCCxEE8C3fUvriTTXgVah/9Wq5R4wJWSohY1+4UH7zjh/d3OxmPeA6e3vlmeNZ8ebEyEk7mRSe4s0P7ThuofdRO1EdDmJo5THrjr6OvJrcn7FBROvaqt/ipPDa8upzWctpUXOIayzbGHn9eqHbrc/OQ/b9WPW8fvo48DJrNTr+PdN3FGjA9aPTI6x5aNmK2AbDG2hu3ixy9ZNPzq7/y/9S6ovM3jkHNUA2f/752fXZZ4u9BrQMNBkOp0uXSvAw5G08T9R5442yCQZBcXCS4UhazbMigAd88zfPrr/1t5b7hI6EYAaWSU6dKv97AWu3b+fmbc1WFaGV5yxB5lx4UIYlj3n2Er1mI5tVZoze0d+6PS+zn1Wfabtlg4joZYsje1nQMqVFLyN63+dkxAhWGS9oS/fX8uxbAy9wrdk2M0GEOtCAbUVafmZZGtjdLc+c1w9sIRcvFloMXQPyut6AyKedQNZH3ydPdoO+cK+YL9eulXoRcB//9b/OX6EXPPzwfJZ8kS5f2d0tzxg0Hnzo/vsL32J7mvYfwP4DHSMKKubAi1Z7e59TE/vQDj2nojnaYtu37IYZuaXmd4roi+YLLX4EhvWbR0NaZH7rt0x967lEdG7ZmfFFyvrc2qrb1Vrl9z7tZJDhJ5YfIaNvRvMsY3f05ome29pupD9bdjHNI5hv3CJ/kQ7aBUADYQs/d87fjK43pevN3lxfpNBTDfCMRx6ZXR9/fN5eJDIfqIyrd+qJtiN6ctzWVmmTabuVKIS/84KTNTL08ygiYyO11rVls82s9T7yV6aM5+uKytT69Oi2tp16eoweB9uwrbnO/q9lQtMb756t9xbxwxqvjXhn1g7lJeaLgoq9+WuV0Vl2a3KLdUog2/+1/YfpuPY58Clx1mlxsNfr5CG6Pat/Sw/HmFFWB++yr4NPTzx5svAE9qWCN2hfkOcTyqw5PX5LH4f9CsDYrc2EDMs/z/K7pQd4SQkzMtJ02h0zQ9sUI/riyS3WOKIyEY/L+Dy8MlYyTW43spu3yOuRDGqNL7JhtPAq7jNjr7DqLRoZm/iyxvEex3EwMkMzP41W44tX1xMSIwY01jhq9bN1M0QwMqIvYuG1CHlZwY/rWIYi/pwRXmtMUjMpdq5BqNje7u6a1UZSGDh5HIAVKAzo8WQyo3ll9PeZd76KI7mAvgxxCIOu9e99P/b6aaUpfY2XfbAKJm0dhTJEABuKZawLi0dFho1aAPZQQwe3Z7UZGY4y66ZlfbeipjRpeIbHaBy1Z/e5z82uGfrttbNIhYCV2du3i3Kvsz9IcvODhb7vboiTpmXe66OfEERpBaLAIMInKGjaVFuzmfFksWiavELFzA1+ZoMPZC04vVuBe7QMbECGPrbM/4iWAhcuFGfJkLVlOb76rmPuUxts+Ojm979/vn8dsBYFrnnZd4CzZ0vAtHeE2vXr9azS02kx1nE72jiJewSd5LFrHmwZlDlAnrPLvfVWcbyhLHiFPjYOwQ+sa7TKJByYxsbf6dQPPO1LD1Zp4Mnq82Poxq0GtUjHzejsQ5+rJ8tYfSxbJ6xlRl6GLWNRyNptamUisDFd92tdeaOLReM93Lnjb9KBzKQdGKCBHHC8t1c27+GKzSwIUjh9WuSjH539D4cTnFpo98qVErCMQGXeXHNwUGg6yiD7JYKln3yy8DPUs4IJIDMgUE1n4/+1Xyv9iRQ6qwMeakFo+sSYPvM8snu2YBlrLOuEahlPi8PZSrQwpC8LtbYmk+6JPEMxlF6ugr72sflEGJNfruJ5RD6KSC/JzElPr8o45LW9nYMY9JWzJSMLGmj1l79cMomB/oPugVdcvpwLNB4C8IPd3cJjEGAMHgHd7L77uhtNtP4Ae4KV9ENkFhyRCUJmcMCBSHkHUaAy+gUfsvoci4YxDe3j78liiD/PWiOWL6YlC2Hkf4r8Tdn2LLSMq1VXWVZQgobWQz27WuYedN3aM6/J5pHOlkWWj9TazOq2Xv9a5uFAM263xnPY9sEnE+pN5KCJyJR/+XIpw5sHQVMxrlOnuicZcrbK06e7gcUccCbS1W0s3Ye/s+ZLNBdB2wHQ5j6bQiz0le1baE3L3LJsCF57mu7y2LXtLUPbgRY7xdg0zVrX0TsZaof12uGgf6tMqyy7ilOq9KaDRdt7ss87E99Ss5kMnXctdN/i89aza/G1WTw0OjWd+/BOSNcbUPlEdMirWjeG3I3TS7Qd37NHRZv8oxghL5lbJM+JFPsRfovofuYkXA8ZfqJPFWZdAfU3N+0kEPpzJDv2lVMX5dutfVf7LTvWGiJ625eWZWiQB2vTg2f7FemePjo2WvWRRY7jPY7jYGTGf/7PZTf65mbOSRIRTw9YwNGRxbqdyKHjjSODjCLk1dH9M5Gxjvu06rf0ZaEPgW5hQFH9aE5khCwP2qAGAQgKb2TsAuGeTruG04zQwPezsdHGgD1j5GTS7R9Cng5GgHEgI4y0HP3K49TKaCZQgJ+5Vb+v8lcrazFLHrO11vg5WTupM8pB1K+FmsGz1TC85MxoG1O1c1AbfCKDMv8/tmBx5854bdbuo3UeZ5SDPkam2nOP+qvVs8q2KNc1Y5X+3woYs9av1V5tPB60ks7lrZ2xEX/2yljlI8WXFV5u78SJrkGP7yGz1jKKvDXmCH1ooSUjWeOAAv7EE7l2NSynSct9ZZC5D2vMmQD7TL/Lzoxw7ZpvFBHJO0mszxay77v2fnU70bNvkUcj2h4Z/1A3k4mvRnuy88bTQ3T/fCympXt57WbpRG3ziZYpPGR4DZcXmaef0BswDg56R6aeqD0et/U50j81D9fH04nY/KB2MoolC3OA4ZtvlkBCfId3ogNeYFDGVTsUa7RL6xh6/AyPTka6dvSeOcu2Hk9N3o6MgNZnS/fJyGRD5TD+ftHGSIbevKCR0V+8z2i31k7UXm1+RDKs7rMW8J+h27rNPnYTS4/uA70OMQ4OppxOC43wssKcPl02sfBGHFz397tHQMOBBdrx5JOFBoPecca17W2Rxx6b/Q85A/Iyyvzbfzs7OlSkBKWBXmNDzL33lvrIgolM/ZcudW0pGCsCMl57rbv5D8EVaOfBB0s2UR6rZZuMYJ2Kw+DxZGhiRG8yclnUR8bGE9Fddn5yX9vbuUCz2vPtu45aZa1aeT0OLxuXRV9aNn8sGltb8xvRIj0T5VvQYl+JeHem3ZY5YfH+iD/X+Jl+z5EdNdLxeTw8X3Z2+s17651m5JY+vM5Cpp0Wu5pVFoHWETJ2Hy6r7fW1epENENB6ANZd5p1GdLNlTvSR5zSd4zWqN43qjNn4TqTw0gce6G464nFZ9kIeV6S/Zpz/WbTIri16TaQ7rSIzJt7XuXNtgeB9EM1fre+12Bv5O+sdRO+F6bRF6zPj8U5QsBDRosjWxfWgz0OGtepldNKojD5RRWQ+Uyavcaxv2F2sU3295yzS5X2R3KHr8brJZLRn6OceZcb0MitH7Vjzj22KFj9gW1e0cSyyR3nzLaNrR4jWiGX/8eRH62Rnb8xWAHW0ZvrYFq22Wvwr1vP1MNQuMBRa52dY9+PZ+XSdlmdujWfoO2vtszaeTN9D1k+2LCUuMsvUdBUdG8F0NiPD1sbo9c20zBozl82eNOc9+xY+YNF2QNNx9BXFGHnj0ZsaWzZ0tNouRHK8XSTnj6/piy3jySJjq4pQk6Vb5UPrc43nRbrKKpDhR8sax3scx8HIjG/8xpLtLDK0twr1fYiV1d9YQlpEUGrGSAvMJPXRx7W+LUQGCv15CFMCrOxpXqYFka4AZGUo5L4iwyGX0UfTMWPUzjYOWNZtI8tCVujwxubBUjBrgouGFYwAQ4IHSzCMFHjvGm0k8Prz+m8xLPcRcK2xDmVckZOB+/I+Z3/z+sxiyTtjDyebbX1mhZm+RqAx2l2EoDOEL7YYqKO1MtRwxOOJxpFFjedpvugpyVoZ5eARTfOjXbfWkXFjwLo/L+tCtOMWSqguMwYvt+qPTTczCtoYbba2wf97yBirWgwcLWhtY9mZEU6fjseYCdDKwKIBkQFqSLY2a3704cd6HdUy52UNli2w5BbLsCgyfnY7/X6iAGyWxaN15G0Oi+QwPZ4IQzJatkAbLBFwxvehMyPwJkRgY6OUiRxvPH/xnnUgXgu9z+iZXt81eOPo61DAvWZkI6+u1XbGmRT1ETkgrL5qPE9/v+zMyJOJ7TDO1tV1MvNFP+cW5z0j4/wUKU7xqG2PzmXWinU/3nyz2s7OxRqYv+r6yGQDe8Prr5fgLWxk4MD/ra1SHrQL9A7BCL/5m6UdZF1GgDHk8fe9T+TDH579/w3fMLsiIzKCmu/eLXMQ2Y4RjAzb5M5OLN8+/bSkMZRHeFnvozIWrNMhhqCvzWGI/m3ZCyM7VJ+13md8llM2cnByff37u9lxMpmMNw9bZAseg7629sHtZOSEVnmdZRGrzpD5kuERmUBFy5YSyfEZW5B30sreXglA5bHpTJu8URB19Kksnt0fQW6nT5fsl+AtOhsnb8CrBa/pz+hT23OYfuvPtY3muk3v88ZGLhv72PbOGs3Tnz29bUyM1dcyxtoXLXa+VYwfG9LGwmRS9/9q+T3jj6l9r6FpiSeLR+vAouO1fseyOUX19P1keETNLxj1pb9nGxPk9unUn9vMO6z+W05krvk7M5mR102OY/q/KDnMWo9R+RZEMk/L8/ZOStFokS+zfbfYpKwgytpaz6w13TZko2WiFofAGGJLj9qJ5HaLNi/KB5pBH5tkVCYjh0+n9bgdy24PORu2mVu3fN8L+trfLxvLkTUZWZSBw8OyQeWFF+avepOKtlWLlM+w7Zw5U+J3eHO91gNAH/GdlpVqJ+Jm7DFjAs/R8vt6yMiJ0Zrpi0y/NZu+tQ5a5N6xYdGHzPO05Kc+GOO9LAJD6eaY43iPYw011RVjd9cOAGxRqLiOZTSzjgzg8hYRw29swNJGVJ3lSaT/7t6hRsSMwlpT0vXnsQ0T2qFdY2rWWC30cTxHfQ9tL1OupU2+asUuc0wbt6eDE9gRyA5B7eDkY5C0su8ZqSODrF6PXhafjJFIl/Geq17f/BwAGBO0w4rflz4yzwte1OPkehB4M0dI6bFGxhxuJ2MUjWjrkjMjbLx2WWTn0fJFn7URlfFgGdZ03T7CnMVPxqBhkQAXKaV9FIeacuvN18wOUuu3zBijsjXj4Z07hXezoQXr8p57Cu+G88k6vjeSL6wMNjwepjmWgljLqK+fYZQ9wRuPnlOgXRljsRcoo4/l9gwMljxlyWGMPrKfSLl3PA89du9e9bMEL+DgcytIj8eh10HNcG/Vj9YI9xFletPvhzdaAVY7q8iMg7FbWYkyNB7zGOtbzyl+Zpq3ox6MZDB6aec05gJ4NwxYFy7kAvda+HGtjWyZIQajSNbKHH2W7at2T5NJzkHQ2q5Xx6PtGXkk+l0/j5bj2bz29FjBv/iZb2zYxzvrMeg6ltyPq0dfLBknylKtjcy6T/28OXBaH9/I9AkBiTgK/PbtUgbODWRJwmeLXmZk65rOrL/TZT1aavVlPRePhmldLKOjZ05oAZZ8MopMJvNyWWYdeWX1c7U2DHN7kTzn9R/Jx5k5ZL1Tfq9W+y1OpIwRnOUwfeUxajmoRg+sfnWGdJFZoK+3sU8/J+/9aLklQ+893tSqR3s6/2Qyf7yoSLxh3MsYHUFnxmH7D5CxQ2k513OERLJ5JAt7wYeWjmBlIYxkCHzHGf/xLC9fFnn22dn/cEgCyML9wQ+WwCfMRc6kvbVV+AacltGGAksfAjzeovUuLfvqq9ZVWjKnW3Qmc4ww/l92MML+fu7EDCDSTS0a5q15zTszJ5vw54wO10cW1W230LYoo6suW8uMrOckXy2abAUIQya7cmV2ZVlNBxWwD0XzQLSN8rhqPY9lPrRjQWfI1XV2dtpkJAugOUybMzQZ0JsI+yCiT2wP0oEcXjvWXGBaz+U1LPqv5wk+eye06HeiA8d1/5ofQNfxNsZOJqUMAsotfwbAyWe0Pso+goiHRbqTJWPhM3/X4gfoiyHzb9WI5AYgsqlbOlz0PDJyds3OredABi020cz3kU3d+s2zM+o+vHuN7APW3I74ENqz7Agi87yd6UHtHiz0tf9YiPqrZdKcTOrJqDJ93r3bzSDPtFXPhUgW5fcLPg2Z+urVwmv4tJtr18r/nu1hOi31v/a1+SvqXLgg8vjjs/8/+MHZFSfiwGZrncIFWPo4Q5dh3sABipNJ3S9i6dPWHI3oTMa+7N1H62/rgj7r15KpW+hknzJW/xEtjOwTNZ3C4meLQMsGtj7jafUjABlePAQ1OX6I3dIqW8t+b0HX4efIfOXwMHcKXiZRQm1uHxx07SyaH0A/xHjAP+B70ycrsl0XV13Gi8OzZAr2MemTmjAuXKGzbG4WfyCf+BjplIDlI7DsYjV538ow3nLK2THe0zgORiYcikNkjxdTDi3PZ6hxtG8968oOcEvp8YTOiCGy0evgoBvsCmjCzUzJYlJssNKGNThC0D+MXvffP7vqzAqZ97CMnVRj70gHMga543W9WPRVCJZsjDy8/wE53O6ZVXYyWyMb0mCUSbX7HpybFm2Odr1yRho9bzznqaaXTP9BZ3UmGi8wGNBODk8R0hnWeAe2LusFLEeOUq1AvPjifHlkg9MZM0H/ceUj1JYNPN8Mlp0xcYVwZdIERqdFQ7G97b5n6z4PD24bJReHw8lmGcfWibiwh913jlY/d36cQS0RG3I4/vof0l7kTLLabtHTNI/xjDD6MztHLLkbBizOogCcPdsNdLDkeA58gM4Aen7+fJHlrUDH556bXfWmNr4y/e+bTdnRDVrp1uHZe9v6Pca7HkN4X2+0yCERMsEIGi20axUyWsZx1hfeZkLtgOa+owBUffU29+Pz/n5xSiBg7atfnV0R1PXaayWACvQbmW3gXN7eLv1iYzUyJeN69WoZD2dYhkPk1VcLT0FfyKYMx/aDD4qcOjV/H7jec89svLo+eAV0Dk2z8Rz72ni8udDaHrfDNq+x1uUxjuFhZ6f/POsTiNG3D+/zu6XPvsg4p60AZg5K1QHGIjMaDVkcMj0H1O7vF0cx6P2Xvzw/vsmkqzdYgVEs96MO6Pnjj5dgJ2TNh/38gQcK/Ydsj3ZwXydOiNz7jrwN/cGi18vMoNaakVAjE5RyjKOHdaY1NYwVnD2m3N3HPtK3vcxmlEz7rbYcEXsDmxcYM50WWszBqZDj33yzmyxJJw8BUA/BqbDDY+PL5mbX7oJxwP5+5kyh8+gD9Bs859SpmQ4gUja3QR85e7b0z0FfWpZmuh8957H5ALd34oTvFznGMY4SxuJZY7WT2WzR2k6GttcCeXXf0cYQ7pNpvG7LitGJeIPIvL3d899ev94NAgW91qfQsj8asj3q6sQysDmBpoPuPfRQkeV14go9LmvjGPTWs2dF3nhj9j94E8bYQsfHlL0y/WLT9Vho2bh1jPVBC50ZimVtmsiM4z2O42BkwtoFb7ybkBE0orItO84yQQQQGK5e7e46sZRSKKMQMGAwjLJQchDAzk43+wKYMD6fP1/+5ysMiKdP20IIAKcawFkZdYZCb0ertfuJhb6NjeHBDIvG8UaC/mjJTBkZ347Qs2/iAZazPLP7MPNchgpiR+GZe/doBRNzEIEVRMabP/b3C71mPqAVRtB5lGVlUu+YB4/Ab5qv8C5K0Gh8vvfeEryAAIGLF2dXfewO73C3HPMoz1mUd3dLH5kdrMdYa7xXZFLrPt8r996Kd/1zyex015lo2LEPGv3GG8Ux//rr81fI85cvl6Czl14q340J0OETJwoNB90Grcb1/Hn/mDbI+PfdV+4RugGC03Z3i07iZVO3NrNEWXCirDfOJpbWOcrlVxKIeoxjjIWjIH8vAy16jGW/YRqPzzdvlsAwls1B269eLbYQXBEoBof988+X79YBDzxQAgsQcAAarx1foOXabiQy4x3YjMJ6A+i4zobMdB/zdtlHeR7jGMcoaLGXR1hkUAP/pm0rTE84WNsKRrA2BXKgMGT8t9/uZrbiwN7Ll0tAGK6Q8V9+2b+fVQDy/5NPinzoQ+V/kZJVUR/pDB0BV20/wnPgdw9fw8bG0ZNPatngMvVa61r1h7S1Sozt2D9K936MdrC9XaPm84gCSrSMz3zDylju2eTffrvI9JD3YdtBMJbl29W2Id33IqHt8LDTgN4/8cTs+oEPzK6PPFLkf9B7PlVpd9dPVLWzU57Huvpkj3GMdYXlX4wCamvZrXX9VW8iH6udTDZdb/O3pvlRgLDITL7nTYDa5gM7PW/6hs3/xo0u3+DrW291k4YsAwgUhv8Xdpzd3eLLhU0HG060PoCAZfAGLf8f0//35j2/G7DIpBdWX+swT9ZhDCvGcTAyIe0AzWRbjTDUIGLt9m8xNrQctZQZV8su2ghWevjoeFlWZjmIeG+va+Bkg6XOjMl96d1K+khske6u1evXi+DDxzei7NZWN8AsOnaLj/GyjvvJZOLAc4LjaXOzW69vRo8+AW+RgSMToAhY87Y2z7KB7uvAILKBrX2cE5m2W+ZW9rchhuRVIVJGR5gnJt+ZdJ3BRykIrcZLwyyci+BDXmb7bL+86zXKxsxKtnbEcRAxH9umeV6UhZOPH9PH7UIZBcBjdHZnpgOcqe327e4RzuBHOrsDb0YZCn6Xi9zQsChHkxUozxk6omOAW41X68CrNFbhLB8JG9PGY8vHHn9fnudlANMY49ik1vFFdLdF1uP2tCFYb/YTmTfsIaDL27Cyt9el5dbGRXaK4TcdXAyaDpoI+mnpF0yTtazF/XP2nBMnCv2HQ05nUYPDDRmaMUYELpw61c0mZukstcC0w8Mu37AClzlgme/HQGcdZo47j06wsfTIFv1hbF7Rd523bGDrqxeNpQ+1tGN8v3J5d6wMMDV6W3unnv0ner9RgECfzZFWW5msN4AVcOY5+zQtRFtsJ9ndra91vWGF6ZM+/hBBDKCbqK/5pdW2bsfKaG9tVMd3uHKQ9Z073U0oqP/FL5Y6LE/qTYm4D9THJnYEN1y8WHgB03aLXjKf1Uczs46D9rQjzNMxdHC0twFH28VYZ/FOMtBlgEhu0L/VbKLRcdyaD3lOam98Xp+eXJfZKG7Rh9q4rL76lrGepfU8ub1V2X2uXJmXi7w5MLa9MNp8l4XXf0buyYxdrz1vjJhbVnbDiB+xLVzPcaw1fUqVyEzG5yPdrWDm6GQtXL1gaGBjo9jgOUuZpt/ehg5Lfsaz4qQi99xT+kI7POZr14oeAvlf6wbgE6CvLH/v7HSf+cngRDiM35Pj9dgsupuQ9zvPnOlcFBwU2fStue5t0rTuJ0KLjGWVqfUx1DG/SDrax/e3zECDY4wD6z2N4cPRfATrkWXJ6bRLvzWPYPs0239u3Cj02UsaNZn4flY9b5n/sP1E6zMeXz046J52yBvNd3ZKPU6Ope33kQ//S1+avx/0gbGfOVN+i05EbKFPEbgdy0YU+fn79lvDouz/xzj6mEzGO3Fh3eZSq89+EX1Fuqk+7Y9P99Pt4jvE5nz0o/N93r3b5RGWLsc2GNBEneCD+0cAM2w9+hQuLYvr+9G0nW1EkZ4Guq9jkEDLAfCI114rm9HRNsaDOqdO+SfiRjpLlLCEn2uUzLAFLXaBoTbOvuu01V+r62SxCl9dS1vrRuMysOxRqxrHexzHwciEtPNrkQEyUX/e51Uheg4143l0rL02HHqZjbVxygua0gwbfeA7BBVbTM5S8KzsYiJlh5M+ZoCFK238qr07LdQwoMju73fHofvAMwOszMie4qyFE8/4qJkvyni7sLTRgJVQXZfHw8gyygzTXNf1xMiOq8/4x7rnRT67FbyXbGDwKnAUswUuNKDEW+uWU6nFuWDRf/4NCmJklLfoJvgXdvWyk+zOna4ya/FZVhYthy8fP4fPW1vdYzp1gAHGinbAW9ghp8cTnRDgOaMiQ2Nm7WeUwFUExmqHF64BOut6TehNb4w0/mXTu42rV0ROv2OAWpVM0LfflvUCtJx8kGlXy/GWM5mvVvCASAlWunWrBAHrIzNF5jcaWk4flIHRjw1pkKVv3Ci0i2kqxnH1aqGhKPPww7Mrjkne2upudAS9BR1/8MFiUNTOJ429veKEYl6jr0yDoQecPNltmwO7Tpzo0uQoGMGT2zc2YuOlVVfEpttRIK0eew1eO0MdC0PpQRSk0VIvQoteYwVS9AlSahnPUUNt/Mu4v74G02XoZS19sPN8a6uNR1gZMr0TTfTVo8lst9C/YYMfaNf29nywmEih6ejr+edLtmXUZ2eUDiIAOCDjzJlu1ktc9clYvKkRZe65R+TZZ+fbxNhZ1tf3b9mBau93TGc+soGuO3RCgUwAXgtqPPDdCKwHay5hzS4L58/n5Iyx38tRes9DHbaa5nu0Hddr17oBWboMn4jCGfLfeKME63JmzLfeml1v3Khnx9zeLoEJkJM5GFlvEOGgOH36IbeD+a95BcqgHfwG3rOz062H9u65p2SGA08A/ecABJHuphjL/o/7ijacZ2QC9tNwXeu7aD22zMXWebsomWuRAQuMvglmjnGMPog21Vib+PiUQk4mdfXq/CknIuXzq6+W7MasB+grfsPG7OiUpwhYS8hajCtovD7REDI5MluC/t97b/lfBxajPrfj2W9qfmT2/1qbLtgGM5YMa4H7sALVVkGrjpLcdYzlYqicO7Qdr72+bWc3eQ6Jv7ACR6PNKBzTw0mXbt/u0mvIwIeH834CkULj+TRca6w6GBj3BrkbdhadgMqz22i7O+Rt2N9548rGRtdPyvZpnRnZ2zQXYTIp/NSKxdHlIljvzbKZeb4BHlNfLDvWrg+i+LeWOmPD4+19xrGK57/IzZQiRzYY+Vd+5Vfkb/yNvyFPP/20vPzyy/JzP/dz8vt//+9fzNiWhGNN9ahhGYtz7La8NrPOZQ/RbviWMiK+EzZylvfZOW/1Gf1mCXm4RsIeB17oAG6RmeADJTraGZvBWAFhnqAV7YyyDB5eJgwrWM8ylNR28llZqXHVRu0oWFxfh2KIglH7bdVYNL0jbMjh6rOxrRsyuyCz9VrRGgzCgVkRIt7QZ+x6LN64Lbod0XY21uoMQF4Ax9ZWUdKhuPMu2sPDroPJymjGAWseX6qBd+hav8PI7NHvO3e6GdX4OVn0n3cgi3Tv1dosYxmJcWVabhmNrYBt/vzO/xvWb8dYOi0+PHf+vfUOxnaetmAM/lBrw6K3UYZO77i4/X1/U6Smw55zTTvHkIWT29MGWIY+WUVkRtdhMEVwMzJA339/aSPSsTyaDkyndcfdxkY3+xrT5rt3y//cnv4egXxeRvnbt7tH9oFuaz7FGy8j/gpouUFnjxApPFTL8cwbogyb3EcrFrX+xhpfBi1G8FXjKI3VwroFNEb6dC3gqCajt2S9tPpHHS+oWdN/3hTz8suz6wsvzK7PPivyhS/M/m859pMDhu+7bz54TaTQIlxFCu2DAwybYp54oowfPIKDyLa2SmCaRa9xZR2BT0FZBs2wghejDJkRD0cZ66QYBuuSGdqu66zbBoJ1RxSQuCy02nj1Bp5VIHKCenKv/s6yd9fs9n3ldmuNsG0UTvxW+wLfo+YLXsAzyty925WvrefCm4vxG9rRgW8c+Kyv3ola+hmALmkepXH6dAmGg/wPXnH6tMgzz8z+580wVjByBPAGDvrAPW9u2nYjDcsuFvXP7zLyeUSnknllJhM/yC+a/1n7Xg2LDKBmLMN+vogxroqmXr9e9L+sP2tRdDKDvvMtqjdEf4jaZfoJGmfVj3T1CNZpJZ6sqE8AtE4eFJnXC6yNjrodETsTpsi8PwJ6BJ+spctwMhLejH769PzGcpH5LJ6Yw96JJhloe3lmvkS2HSBKXMX2MH7OBwd+UitAy908by37jbehX8PiA8u0nRxFjMV3luz/FZHh73KsIOYx287WH8uvm/UrYL1ZvID7s2w9bA+A3QU0WW94YdqODeM3b3ZPVsFGxRdfnF2vXi2BzmxzBg3Z3OzaUjhgWW8iZN1Hb2SBzRk0nTcj7u52N6hrWRabdFg2t05l5XfVwiOGznVNxzlGKdLFM3FV3tismC5LnvPsP1HbXj+rxjqNpQWLHncm4cIy0LLmROTGjRvyyU9+Uv7YH/tj8gf/4B9c0KCWi+NgZMb/+X+KfPCDs/8nE9/IpRUcjzDq70HQvCxSd+92BewobT/X18FFkXLMzNsKcvKMmZpI87E2ug+0x8FAgFaeeOe+FcDjBRHoXVORw8sKWNKwjlTUBkZcIeigT70jSmQmKHCWD+9q3VdkgGVH0YkT8VGTcOzzGC0l0COEmlBzX2jHCmrjvvS4ef5qIynmgndk3p07XWWU56bOWmMZAtAOr+eM8UP3XVvz+si+CJ7ggzUiUgRrDqrGc9ZzAdBrRI/d6tvKksRzynoumfvjfvWxrp5DRAv1EKqXhc99rhhy9va6GVN0YAyu7Exgw5E+Xpbb4fekYa0fPBfP+S6SE6482qEzHfJxwpqea2VPpJuRYH+/m22SHcBnzxYHCr6z+COPIzqCE7wG46sFMwD8PKxMktERbl7bGZoe8SfvSF3tBGVj5mRSAiYYVuAZB2/pvqNAZYCPXrYyJHtyi36neHceTdSwduiiPXynswjp+9PPLtoYwrRLvyfmG5oe4HuPPmrjBeC9A69/fZ1Ou0YdDq7Q/CiSlSy6791PFDzgyTbWZh/LGIK58Oqr9lgXhI03Xhf5H782+6BPa8gENnrrUQdIAjWZVGSe5jPvtxwY3CZnkrl2rSvDstNja8tfW97Y9Pi1nIo2OXNXJksk3v+tW/Z8F5mtNeZDWj5GX8zPrGdem7ebm35WeGsjm9fO7dvdoGOmjSdPFtrFfer3jjYR3Izrzk555/wO9PpmYyj61/PZo/+6Xa0LiHR5sCV7Wtk4eU5bm/fwPNh4benjHp3R98UnD+h6uHJgobUuWEZ5++1Sz+KruFr8mMeK9cjyjzZ0s67DfGlzs0s7eF3qOcHjyMgverOQdzSipWtYcibTsmXhN35D5H3vm/2vbQeAtUEqI/NFsgyXYX388LD0y0H4gDXvrTnF/N1zVuDeRLrZgm/d8nVBPRd43nOfZ86U4DOsZ0veRTugKxiHDhAALCczO6p0ff29SEzjUQ9XvIP77y/Xb/3W+fsAmCaKdE/o0kEw/OzYYaXb5ACxl14qmTHBR9jOpjP0ABZt9/RWQM8pj59o2qPraWxsFP5lyZ4i9lpjWN9ztjpL7tDr2dOtM0E9THdFynOw9DSvPcuOGq1vT2+NMumxjKI3XPE9WxswAf3sOICEM1Ppdmq8TySWQReBn//5+RPzao5Jy3bq6Y26HtfXNkV+Hpa90rOti5RnFgXmRkFBrMsyndTvlDcnaFiylYgttzDNQN3p1OY/GAefpGLZItguba0/b7OFvgeer7wx7tYt2+mvr1b/vA4vX55fdyKFdoF+37kj8sors/9femn+3qfTYv/XvgndnqbtvNa1ruA59mt02Kpj1dfgdWPZjgHv+WrZnqHrc8b1jA0vsvexjGKtZ0sn5Hu09DTv2VvrmmV8faKZd9y5RiTv1+aAFRxk8VAvQFK/H9RDANCy8NM/LfJ//B/lc5/AFv6s3w/zcqsdRtb34tETHYTFfNma216ApvUsPL+41uEi2YTfPeqD1usgMIxZ00b2tbD/Vj/76Jl7G1bwvU5AxH4M/bzRL2dj1rYvtIO2wV/1u/DmXSR3ACdPllMXWe7XNh62AXIfm5td+5F1Uq63CR3XaEO77hu+IA8W7cj4YCy6xRsU9W+1DY9RMqqIHlhrBd95upj+jd+XlTTG69uyZUR+lhZdx9LPMj786JQErDHW75eB55+fXbUcBlg83aPdWo6uxXjcvdv1m2nZ1pMrI77Bv1mbETlQ9+CgG5gLYM6fPt2lCzyXptOubmLpGjX5Uv9Wi6HS9wO6e+NGd42zbK8TUACwSyHBh6Vrg6Zr+6kXd2HVZ7obldXxICKze8LawGZ4vcYwh1GfE2to2ZPpJMZlbY6x+A+/F76fO3e6tIvrbGyUNV/zxegylh/XmovcnkcLrWQmrGMeHnbfHc9tbWOy7LkAP3tLX/T0B77qdqL2oo38NRty1Ie+50hu5vvgtXLnTmkHPsxjzOE7v/M75Tu/8ztX1v90OpX9/X3ZjTaRNOI4GJnx+3//PGPyBLAMQ9V1aoJgBF03cnxllEerTUZGsM3A68Oq7zGOGoGMnnl2PFH9qJ2MMcYitJFxC59Z4PIyZIp0g29PnBD54hft37QSxUySs07qYOSMEhzBC8bR987HdEZrzYOeL54zNNOO7j/DoIHIiNh3HDye1t9qiAyQLfX5fwsWLYvaW/IxnYe/9VMiZ8/UC2aeVcu7HROR4bLPuDO8KkMno75a+GHUZwaZ+7AUIU8J0GOP+BjqeAZcPT5+Hkz/b97sOgdBv0+e7AaA64yaIjPjJAesWgFEWlnke9Xf6+/64lOfGlZ/DGScMMfwUVuTkTyn+b1eL9/3feOP08HhxftE3v/+8kXGCeTRDP27N4es4NAWQzCgHSGRw6umG2TKWMayvmukRbbiDZR7e/POIpHusWiRHGaNgzfXaAeYtckIv2F8KA+5hQPXdFCzF0B6cFDq8Ua0KKgNzpyzZ0U+//nZ/54TSSOS7TNHsHHZiJ9xO/r7b/qm2bWFr2cMlRaiue3NxZb1qb/z5KnaGIHI+IhxRZtqamgxPI6JzNpftjHywx/uOpk1WmThVhtRrT3rO0uu9PrUhvoWup0xWmdgrYMWfT5yTnj8UcvtnjFf1/UCsG/f9p0cOgBBb/zRVy2bc2AZ+oJT6UtfKs408AjwDJS9erWMFe0gU+Zjj5UxIlD6oYdmV32kdC2j5bKBDJ8ZtOiW3uchyMz7Gn2L6IM1Vs+hXZMdl4lI78Vn714tPXrJ9h/53/63bmCThaHybgt0X0NtgC32uQwyNk6vTU2bo/YjnlmTjTT/8By0lr2Fx65le75qfYR1r8wJK9A1dLA123u+/OXZFUEGzz9f2nnggdn1Yx+bXT/5ycJjEFSBYAR9jDTmOXQE7ySnvtDv3Qt806jZf1rnZmRfrvm4xuyjj8wXwbLXD5XDxkIkh3n9RWO3jllfJH7gB3KZkYHItnOM4Yh0jWiTjqa73mlVVrZ6tsUgqPe554p8jgQ5qAMZ/b77Ci2+eHF2Bd3VtAAyOGRyXEGrJ5PumNnXcPp0oeW4avvPb/7m/P1wgihrg+Cq8ZGPrHoEx1g3LFv+F5npz/p6jPVEtDk52tRu8QoOYubkYKdPz9tONCw9PBODxePSOoMnE1k6C+sae3siv/qrs/9Z7gYfOHVqfHnfw3RqB1prbG2JfPzj7ePpI0NrmQKwbA+ZfqN2hsCySXqb47X932unb8zRqrFo+p9934vGO2O4Rvd78uRJOckJX1eA/f19+dmf/Vn5N//m38h//I//US5fviyHh4dy8uRJ+ehHPyrf9m3fJt/zPd8jn/zkJ3v3cRyMzNC73o6xfKwDYRgDQ4zGmYCdmpMOBkY2huL56uPV9Hf6Opn4O6Kso3d4J1D0LjMZT6yseZ6DH9DMpY9jfp2xCOfI2G1EjgdcPYOlJTAt2xiZxTrTqUUam7N9Zvrta4zPtu/114c2R0bRVkcAj9tTklHewuZmCSzGVWfgQlAEBxODbu/t1XcMTyZdHpE5XtPCOq8XjaMyznVF7fm1Pt8lGyM39m/Ob0ZZl/lQG8f2dnzU2SrRQm8jRyt+w33u7Pg0WQcFcIAZBwNYQQ1Mi3VWIT7mGdje7gZDWwHLXuAEYJ3ugSuOZNOnLegANZHZmoHDDM4w1NcyvrdzH8gG3XK5vtkMK4a8QzHGuTWfMWJDgiw8LWNYVr0MhgQaZ7DIgOOh/bLhf9GYTocF6tbqDQ0m4z44G22mTivG0icsudkKHtPXgwP/eOSDg252Sqssgr0gS4Bug1Y/8EAJhMVzRBkc1/nGG936/A70qTJe9pHNzcK/+FQYOOP/9/+9jI2z7+j3zZtZ9Hv5r/91/jsr4Bhj4wz9WtYfyzHUspYyZZdBs1qC2bxyY2LV9qyM3ZKBZ6DpeCY4b6htoC80/Y/Gw8FB/H+mH75G9glvvrfwGut+Mn15V5E6/c4iWs9RW55sZI0jsqXXnu/WVgkWA92O+uD6Ecay62rd6XOfm//NGitvNGR9RusF3qbEyJ6lM2Vy1kwOTjg8LIF/lp6nv6/dF9CyVjJtt/A3Ldt442id64ClN3oBvpHd3arLMk0rfWP9PXOvUbvLPhlFZFhg99D2rPLrYoMaC618PuJDfBpl1EeUtbtm04/a0/OYx8O0y5K/LXAf1gmUnJgKJ4tcvVoy4+sTwkSKPejUqfrJBRqe/T+b9XgMmS8r6yxTZ1k0+toLFqWzt7Zp1c+seZRZV/9v3/k2tC8Pk8k4fGvM++qjww21nWm7ET7XxrG93aXbFo9gess0vabDMbzEhdo2HwU3e7LV1lbZoBKdfAaM/Z6ssouylyyrjtfGota+bpdPOenbzjG6yM7NZYxDRB599NG5r//qX/2r8sM//MMrGNAMN2/elB//8R+Xv/t3/6689dZb8pGPfEQ+85nPyAMPPCA7Ozvy5ptvype//GX5B//gH8jf/Jt/Uz796U/Lj//4j8vv+B2/o7mvd1m03gjY27N3jPQxBi4TWYYyhvKcNaJkjB8t46q121omA0t5ZEXROnYlMtJyljN9zBuu+E4f+66vIt1scDCWbm8XJxqX1cooByisy1wG+ihikVC+TMdVZOD26mis27vIwht3ZIRZo2CETjBLbayLoDNHEYukyUMMWVl+VzNG6t2HQwNKUAb0GtC8g+k905LoOJqDg3KEJ4yFbJTUwWyswIMP6UAOTzne3PSzJlv3zDjq8/4YX4cZLLiGbYb97dzz9T4HBzZGyDhPs/U89JWpa07C1jUb8cya83JZ9MGj/9kADhFb9owyt3kGVJ2pgZ2xFo+wnGKg4aDN+E3L/LW5dHBQ9A/OFArDrHUMtrWpkY845YzN+l6dObDQ9bgiMH0b7R7fjbrFMhBlJbGg9TzrN1w92hE5F/oGpgCrCF4cyp/43vXGDOsY3yh4BvA2mOhnx0HdaEdn48cGj9dem10RPIVNIG++WTLrgPbBuQUbzdZWoaW/9muzKwIHMK73vU/kiSdm/+Pesbnw1VfLGBB8wBnaLl0q9BXZkhFsff58Kcv3ClqMunrDuncsa+vJKJE+XavfV2bKtGO12cduE8kNFl9r6WMZTu9Itqn13zq+jB1oLPtKFpNJHKQEZN5Pxkkd6fO1/rzfI5uBVy+S+Vk+tY6FtewD3nzQMqy3aUPX9WwPrfDk5Wwd60hrfdUbFlEP9BMbxq3jcq3x8FHmnHF5a6vI26DJOoDnf/7P+f4vXJhdkblze7u+0Ty7ni1bGbfHwcgWMMaIJnprzJKZPJ0uCuSw2m7xbVm6LbfD89Dqs9a2N4Zozffhr9YY+9jyMnx+mQFeEcaWm4faS95tGOv+hsobln5WK+vV53a8zMaa53GAsXXiF+suvKFepPgRQD8vXOjqHbxJPtow3nczeYQMzRgyL/rWXfe1lqWpq+i/b/2WNpe9GT2LobSnpb1sX2PMhzHn1BhtZWQckWLLaLXdidgZ9i2dB99BJodtHPRbZ9jnU1NgI7p8WeTFF2f/v/TS/G/aZgM5nU9hxOf77is2HT7JYTIpNiX8hrgffVKKtxnl3YKxbOBj29LXRc49Rs6utaxxiMgLL7wgZ1VcyKqzIj/11FNy6tQp+ct/+S/L93zP98iDDz5oljs8PJRf/MVflJ/5mZ+R3/27f7f8vb/39+RP/sk/2dTXcTAywzq+XMQ3Ji7L6TdGEFyrUNNiiLXK9CH4LcYYC4sMYuA2rV25mftgtDgesmUhMGXqZ37LjIePabYCMHQQm8i8wdLbBRk5b1qcW633PvQd1oJXor5a1kNGuBkz0CnjLG8x8kY0dcnOqE7w29gOOEbLHBmjv0UhM66h8yYquwpD1jqDhcbombPDDNdlPhfLYWvR9kXy9xoWoURmHPKrRAP93cjKbg33uIoAxI07lCkwQl+HbWt9Lt/KN7y++vDeyCncdxx95JNF8MwoWNBrz3pmUQCSd2woAn0PD4tcDFrMxtaDg66jGH1sb3cdqpDN0ffhYTcw2Lof8AY+5jnawBjJ0lFm/YwTOHIoen23HJmXmf8j0ujO0+ir/7asxz73NXSt9aFXfcoPxe5uLnuIpZ9F76CPM2rdZIEMWmltH33VmksZmZGzzFtteraL7W2Rhx+e/Y8rO6z0kdBwVIG2Yk4dHBR6DycWxqcDBzijGmdH0ZsJrew5Tz9t35+FsRwzGQy1Qwx1/vQpn6GtHr/P1vMwpt3GK5+lYWNjTPl6TIwVjJahTxre3GmV24fK4n3aacGQTE9ZRDJo5nhi6/nCzt/nHfLvtbFmEMnCuEfOXKk3Adbs49lxeVmTre+iQLeWdZexJWbQ554nk7ax9rHl6zrM5/vI6dq+1jK+RdpYI12B1+pRwlCbzDEWg2XKNhm0+NdZ19AnG3L57e2SzAr3iLLQS3Z2uhvCVynrRfRpkePqa3/v4+fP9N+CVc/foVi2bWcIsjEvHlptj95vfeT1vmNvkbGssmPZ9DN0ocUmz3UseYM/a/rknTa+u9u1I3Fw83TajYFBwCH60pvAvfuzftNj/vznZ//zBnP0oeXXsddhX3vSEFuINe/GshWNResBPe9aZIBofQ+hD/r3PusxevZDn1mUbGMMaP1qlXhnDGfPnp0LRl41/tpf+2vyR/7IH5HNyga1jY0N+bZv+zb5tm/7NvnsZz8rzz//fHNfx8HIFo6CY2gZ4xnb+BAhMtaOzVz6/JZhDvpzJjMyCy76Mws+UC71caScyVJnWv7iF2f/s8CD7EKPPVZ2SUUZP1nIzCj03GcWqzxmfOh6WgV9GIvhc3uLKp+tr79fQXavZWbjDAP4jiBSz26ygJ33AzF60GNLIJ9VJhNkxdABFMikxhl20M7mZlFQ+QhojE87maIgsjHmsFYKgvb6rM3R3u0i1iq1uexMwFWMtVZ7trOK53G4daJeqBVj0zy0N7Rdrr9k2ty0Noc6k7yAM8soZDmA8Z1nILGCD1m239oqcq6XCcwyCkV9WfA2I2bA2TD71n8H2TV8mJl7DfO1F93PON9bjPMDMffsVkmLx6Izje2k5sQyMJT2vMvk/FHQ95mMZVuy2uEM7lE7uFpBai0BRF4/tbGOFXTYgnW3Oy6jvTH7GEt3Wnafx6hjrEC+WtuLaH9VsJz5LEPrqydna8c8O+CtdlAf9XjjytBszN5njTHXcZ+MSqueQ336H3sdLMHGs9B+Mzb1dcdRGquFoz7+dys8WmydaMunNOmTEfn9WjailjVv8beaznLuXKxbvPxyvf91wqqCglYpt4/ZzlHEUbr3ZfDwIYGEfdsdu+911EUzwcwtQaHeb9lgWe+3yB9hxd94NqatrZIZeRkbTYZgLB15kfR4Uc9uVWuupa1Vz5tFx/+sWTDyuuGP//E//vX/H3/8cfnsZz8rf+SP/JGwzpNPPilPPvlkc18rD0b+qZ/6Kfmpn/opee6550RE5GMf+5j8lb/yV+Q7v/M7RWSW/vmzn/2s/PRP/7RcuXJFvuVbvkX+/t//+/Kxj33s623cunVLfvAHf1D++T//53Lz5k35zGc+Iz/5kz8pjzzyyCpuqYslOi8Hj0MjM6a+wb61OvooG318jcj80Qn4n4+60cwbBI0NjNrQ6GXPsgIjI+Oml1knE1SmAwjg0EdfOF5TZ7xkAUVnyMERoBxMDGgizMcqH2P1GGuHYEsfNScotz2UrvXdlbxAbNy5XYJYoiPqrPUcCXLeOrRgPQePfmR2xLXMpaithKA+F3bEfbTQwqidqIzlzPKOONNH6TBv4SDeO3fKRhBksMTxOrju7c0HnYl0nVk6M9pbb82uONoZfW9uitx77+x/HMWDKzaQnD5dNpbwEZznz5e2OLhC0/pMUDQDdXRwMpfjTDmZAGyR1C7IVFhbja5Z42kxIus2W9ZRRD/f+T+8v4iGtI6/htY1WkNE04IyGwe3xuk/iY1rb8nG6XeywGZp9dBAI6vNIRgid1voY6CIDGqZtvquwxZeF/FuAHTUqhcFNUfyP/gO+ACu+mQQzgjFvObNN0s99IHg4VOnRF5/vfwvUnjDo4/OrvffX37zshzr58M0HbA2qVDZ7HYCNyN5hnZEWOfgtsp9hPJUZv72KVvDogyWxhr++pxYN/TlixHP855j5Jz22rL6jDa56SvLElbm3bGRGQ+gx5eZ9x5NzhydDFn/9m3fjoT29/ZE3nhjvh5+06do8dGb6BM0+5VXZsd5ipT23rGPyv/4H6UsbEK/5bfMrr/rd82uH/2oyJUrs/+hIyCjvc64HG0wZICmZ/Re/GbxDO5Tv8uMLltDVu7wkJXnamPNrtmaDBwFuFj6wBB7rHXvmQAZa0yZMXDgD9bc/n5ZEw899P+z9+bRll1HeXjd+4Z+/XpQS91St1pqDbbkCdnBg0xkwAMYOWTBD2yCiUmMIcGxTeKFl52Q2CQxGDyJxDZh2VkmgO1AwCQr2ISVAHGyjLMSG+IIG094woOmbqml7larh9dvur8/rj7tunWq6tTeZ7j3td631lvn3vv22Xufc/apXfVV7dp+fV0AXIDnrI7oJHyMWPeV64d1SSqsdmUbUl5r/Y6MW3lORP5r9XvcFX7DGODcvjxHXhe/PivQWKvb6xf6LX0Om5vVepD8A9kmte2ewe1Abz97NpWXMpXvQoIs+QDakucQpXkEHNGhQ2negLz3ZL3c0RDfB4NkU0gbAe1vbFQXPkp/Ah+b/Dz+fTRK9kyEm4nYfTnj1OM2Zd/5u+pxVtZ7w8+V/dbeMchCayccr8848t1yLFm0ulrlRrkMk/JEuz/W9fCMg3Xz2bSBa5+ft+e6toJF2uLAI5xmG/3IQYSHLelPRCfhcyieJ3RyyFLNj2zNP6urSYYj0OvYsfERiT82N5NuL3V87jO23iPO30BvxxH14dzl5cT748izeKKvgNyhRZPJEt54icwnORgM6vXLUt4xghxfnaePlfoRvN/r3htPT9XKeOhaBkfsRg5rt+Q+0CZnNqtoW+5P6z55+qD8TdqbJ04kvuXee8dH6OhcnkOX5hmM+XE4rPpZAcw5Z85U7RDMNdCJd+xIMl36fbV5RLvn27E8PuRY4PM915mJuuU9t7GNApw5c4a++tWvPvL961//On3605+myy67jK655ppO2rzjjjvos5/9LP23//bf6MSJE3T11VfTs5/9bBq29F5MPRj56quvpre97W10ww03EBHRBz7wAfqBH/gB+tSnPkXf8i3fQrfddhu94x3voPe///30uMc9jn7xF3+Rvud7voe+9KUv0Z6HCfbXvOY19Ad/8Af0wQ9+kPbv30+ve93r6Pu+7/vo9ttvr00vXcG5c3pWFCBCSlplNXR5fkl9kfOiZHSTvmFymJ9PxJyWObfO4NaME20VbJ1RsLmZFGMQjJJQ45+lwsEJVBiGcBBJw5XXYzkGPYKbl5ftW9dWhzpHp1aPNxY8wyNS1qrXM8xynShNjFHtfyXvX+n91ZDjuM0ZE14/SrdZsN6jrsG32o32vem7YdURIUhySAutbvm8NadwW3OUDLDibXmIXLPnpIvULx2AGjlZR3zyz/J87lCQ40oaO5qzA3MNN3xln0GO3nUX0de+Nv4MXQbBzZhzdu6sZsCU2wV5987LmmwFHHSBnPeBj+fI+9eW/hV5D+sI0GgbkTHqEaiWvqH1K+fey8xTWj+6cIjkYu9eXe7nOO+boi09HdDGVtM+N9VliOIOAO/eW0G/PLgMpKN1PHs2LTSBnJY6wNpaVd/HuwF5t7FRDWLgwWyoV/4PsnTnzvHx8suJrrpq/BnBwwjKeexjx8dLLrEXVy4tVbdpA3LllReEFq2PlynJsB9ZFMbr8III5Pk580DpO+PZptrn0vrr6mlrHrZke137FjzddxbmBA5tzqube+t0+xzk6Lk5enuu4y1iG+f0w7M1ItdsPQPuXJC8Hj/fyowmt9nUztf0J2kHyHmFqKpbXX75+PgwFzpRRsvQaS2yJEpzm1xUiaOmA3tAH7WF623JlTo7OtcO9fQhr646vZSPKeucXLvTeh95mUiWmJzrquubPDfCc0Tatt51OF55fTKopg/wRQZN9ATP1pH2VRcoGZMecvQV/o5EuPmIjPbuOd++nmhSx0cZviBEHqV8LdEDOSfj8UmaLczLnD3rL3hH/eBr4B/h907aQ+B4cOR6uBbgLL9b/gS+e4q2CF1+9uZ5PI+ccRvRzZvyDJr9IYNs69rk7fIgh8i1ev5Iqw35Xtx/f0p+gOekjXnJ1XKbMsIHR/snP8vvXXKGdeDyP0ff9nTQOm5G+43rkjn8f5N52oN3PRH9WMKb+5pyr1pfPVkh5bV2xLuERRNYPIhAtlOn0v+wGBC+YgQsr6wkWX7XXeMjFhpqgZdYcPjkJ4+PSML22MeOd7clSgvNeeAy6pLBcTn33Bv/QC5vLstrfJAlr6NcqyVTvefO352c9yTyHnjwOKq6+rTryuXj5O+R9iP6j5d4SOq+nh2dM/e1Bc/nPA0+qqksrNOhm7Rbcg4ftzncmRxb3Fcv5z4vW72sZ26uuvgPejbk6IkT1bakPbG0lOqEvEdwM+YBrR9SR7/00iTTDx2abANl+c7okh/Yu5fo+PH0mYPzP5C90g9sJSfhQJujkb/AXJa36iGqLoCTaMt+1WSR5/OO2BjWOdHfIrpwJGFmpP4SGa+VKQ3SzuFSSuOHcvoyTVuD9yMD/+///T963vOe98j31772tURE9LKXvYze//73t9mzCbzzne+kd77znTQajWgwGNCBAwfop3/6p+kNb3hD47qnHoz8/d///RPf3/zmN9O//bf/lv70T/+UnvSkJ9G73vUu+tmf/Vl60YteRETjYOWDBw/Sb//2b9MrXvEKevDBB+nXf/3X6Td/8zfp+c9/PhER/dZv/RYdOXKE/sf/+B/0ghe8IK9Dy8t5RneO4p2LHKGeU19umYjBERFoOfeDG01d3mPU7zkDiPSsB17AGvovjdrTpyczchLpQRJQZmDwwshF/bt3V7NmYjXV7t1p9S6UGAShQbnZs6e6ekpmSCCKERvW++IFHmiLBCxirxSW4V36Dk1bHkTQhIiKOkZy7l9TArPvYOTduycN4bbki/ZZogmRoP2v6b1vagR3hRzjIOJE3dysOoh4lgEcPQcTjjLrJYxQBLudOVPN4o9ANRiKKyvJcQDiEufzRTqaw4BonFkB5ffvHx8hbz3ngnRgzM3pq3+7QtdbsmyjHFMy2EarU8iMKd99DZrssKARYla5JrDeZw7LYZVDwmgy1WvT6qfWlvbdcmQPh/oCF9l3TZeX361gZp4FjQcU86PWVwQTQA5zclNmutLK4HxkwsROJ9CpueMfMp1nP8a8wzNhEiUZu2NHNdMDyqANnn0Bxy7ngRzHbwSenmBlJ4o6Vdu4/jbvoXxHtPe8LUdEJLijLeK4bZsw0q7n3MuRm9p3T/42uWfaYjlvDGhbO+bAGgOR98j6zs/T5lePk5Fl+Dsrg35lFuTVVfu+aDusWH3mur08Qo8/fryaNVnTv/EZnA4WpWCRynCYOB4EM4Az4u3CGQU7AMfLLqtm30Fwm4ZpEva5dnHbfY2MW8B6v/n/tO85DqY+EOl/W3307kvfNmH0Gj2evC1+LNf5WcfJb26W9THavlWvd0/rxpA2d3qcDMATfMg5ArIR3Pq5c1VOXh7PnEmf5fzBd9WS/L/msOejmSAAAQAASURBVEV5BKMhwxG+X7iQZDF0egSeYbfPnTsnAwuIJnfKAn8EHR8LHeEHuPzy5AvADik8sNiCN24kp69x/CX8Z0Qnj4zr6Ji33smmfoNIfU0DHiTw/A8d6j7LfG4gh3Vfpj33AaVBFhH9oBSefRc910PEV+HZKrx/lj0SQanNrbVlnaftjGLNVYuL1SzHCGDDe8VtMIubX1ysPkPJNZ0/X59dmLclbZUTJ1JmfBkwh++7dqU5RgajRe6dB9SXywu0xf9441b+35M9Xem3GmbF99EWR5SzSGfWgPdIu4bSALy29e2mc0xE1/LejTpbI9IW/5/HzcuyXA5bi735oj1tl1te5sQJojvuGH/GEbYBMhovLCQuBbJc2gHr61UuHvo6OJalpeocJRe28XEndx3hcw9kukzWOD9P9OlPT7YBoAzX9b2EJfJ8zEvaM7XifYbDWKZm+b6VjKm+5Evd+B8Oq/NzqYxvck3es+yqzTbR9bzYlHdvsx8ZeO5zn0ujkh0oGuKKK66gt73tbfTEJz6R7rrrLvrQhz5Eb3zjG+kTn/gE/f7v/z41yZI8IxrQGBsbG/Sf/tN/orNnz9Itt9xCX//61+nYsWN06623PlJmx44d9JznPIc+/vGP0yte8Qq6/fbbaW1tbaLM4cOH6aabbqKPf/zjZjDyhQsX6MKFtAX0aRnsSTSeXOqEXlsDOUrKNW2vq/5GFP0IPAdaiUKnBYtEJjntaK1Mwe880EAqR5y4lEHIkvhcWUl9hHIDpYQHtaE8VuZy5QR1g3yEwYzvCwu2oEdbXNhBmYDCoZEOOeRSqfLaBrqsv+26mxgW0TJcOc4hhEv6MeMY0aB9BahPR+lWqbsNRB2+FsHIDV5p/EqC0MuMzOuVWzfjCLl99izR0aPjz1i9ClkNfWRjo3pt0pF25kw1kzza5qthEaAAchJ6zuHDyXkltx1CfTwYuW67Tnke/16KLoKcthqmOU/OAAbUr8E1IkaoRInzSMBczvzZNOjDk0+zQnpbiLzznsNM0/W9bDd14DJNZkaWevvqanULZtnP1dUkkyH/kUUHAQRra7YuzRfNQN+HHOdl0CcZcIyghN27UxCC3AIUR43skIF9bcqgtoOdSmz0rSpTmzj0vaC4krbbwLSeg2cDRRFx4nhyq+RZaln2NFgkeuR99gLDtD5bbTW1VyPkvuTx+G+yXzzrvWwDZVdWku6MwGLo4lwPhyxFRjM4pYDTpxNfI4PbcDx5MrWBQOOPf3yy7aWl5OCCMwrXsLZWXbwI3ocvSsRcMOs6wVZACT+y1eaaJnxuTr3TvC9cbrUR2FgHj+/ORZM5OxqEEG0nV4/3ZLQ3Z1nzEM8+KxeReAvNZVke3AD5j8UkkPv4zhONoD7IWwR+ra8nLgayHfo/B+qRQRLadTzmMePj9dePj0eOpH5Dxwf4dz5fEFV3etGecx/vZlccaW4Aj2eP1AW85fQren6OfTQrc0uOPVEStDpLsPrv2Ve5vjFA09naeOaReajUFxQZr9p9sdqLcKOezQLweykDzPiichYvQEQ6F67tliv7bN0H9GPnzskduYhS29KfTORntER5zWay5HxfMQ1doa7dvvvVdXtNYy5KMStzTNvQdqiWyOU/62Smxpfwc9u415q+XVdO+54bm2Odz2VRZK7RApblzlFy/uD6PGQpeBvw8PfdlzgZ6PiQtzhnba3K16AMl8XoB3R8+HqxM+2BA4mvl4tBgI2NxL3L7MU8GBmLyaXPmS+ukT5ZvjNKUx4OaEtf62oeiuiVufZ3ZNx79m5Jm03RhT8+t63csdK1LTAczsY8Ngt9COC2226jl770pURE9MxnPpNe9KIX0ate9Sp6wQteQP/m3/wbes1rXlNc90yw0J/97GfplltuoZWVFdq9ezd96EMfoic96Un08YcJ+IMHD06UP3jwIH3zm98kIqJjx47R4uIiXQryn5U5hm3LFbz1rW+ln//5n6/+484704TEHRRy1QomkoWFqmCSW/yur1cnNy2TixYkBVirZ/hWS9JAlQ4WDdKA4fV4BrO1ktQjESU8Jx3PFukpadq95vWsr1czT2qBZ/JeybKcsLTa0shRrc9cwZHXCuDe8GwHvN6NjSrRyTMcIGsOlJq77x4fQWoOh9UxBEWFj3FrtRMfj3XbO/CgNgubm0lh8wI65L3XynIiWp5PNJnlTnsPZXAero+/D/J91MgUi2DxCCgZBKk5SjWlpm4y096VnO1ohsPqePOu2Ro3c3P1BCU3zHrepnNw9B6irz3syF1YqF6rBu86rP+XBhlGyGp55GPcGotc5tfJOb7VpOVE0t4RbysUL1Oh7I82N3iZDeSWkhjH3BljZbvk41kahFwHkG0hQEBmUbjyyrTlmiQE+DWgb1bwL5cJCCrg/fniFyfLA/wdxmfITnntCwvp2lBGyvHhsJqtM+I0QRmcs7FRfdc13SJinFhjQiNgNLlrjSU+juVYlrofX5iEZ4V7ybfO49mwiarz2WCQnqucl/izsFbs8+uzthznQYhSLmjvPJ+b+DFqOMosq4AW/Nj1Nj0CAxpViSmiaqCm5sDgmU2IJnXIyPwswYkkKQek3ObPzpP/1vVYOpNWn9XfuvNKztH0qMh41bbXlAsEI/cDqOsH2pLbYWrZDhA0IGU83vP5+SrhiTElHWIc/H368pfHR/mOAly2A+gjnyfleRoJL3U+7bt89ppuj2wU1vjVFgTl6NaanaaNG4vIlXKYnydl4mhk61Gol2dJyoE231ptcXh6C46WPe/JJ82Ot+aG4TC1C3vVyzaDAP2+cO+9k7sQSZtJ0xekw1dmw+KciqUDR20Eb0GFloWLH7X+lziRvLLe4j3LZtDAZay8Z1w2WkHEgKb3aLwY3iWZ7ZLvbALZazmKhsPUFrbnhDMK53L5LeUt+rdzZ6rz8svHx5tumixz4UJVLsEOWFwc85j8f+CRwLEsLFSdc3KhycaGzoPxe8Dhbdcp5wapzwwG6b2TC/r5uKnj8riuJd8H3h8ZdIIjfz6SD5P2o+ybPFr8K99hAZ9Rt8x2tHu3nTGIzzmoR8vWCqBuLFaV2fJ43dr9QZ0I0JGZWZeWUt04ysBPPr9KfZnvEIF2+w6U/9znUpC/pl9q+mbdrho8Yy4gnehLSzaXqPGenk4j29BsFk2PAyw7T9M3uC7Dj7wN+Y5p2/Fa+pxXD9+qWN4XLm8hV3CEvON2gcyopunL8n2RfNAVV+hbN/Oyo1Hqq9w9Bb/zOQ9HGTjMs/lDVmDByfJy4lLgF0MWZJ6hDUD7UkZrclvC0wu1ucLTKfBcPB20zpb0xnM0E6rGifLvuba21x+pm3jny2vjPIoW0MIxN+fLA7SpJWHAd2m/yPvCeTHL96I9Q08v65n/of/yX4i+67vGnzX7zHruGjxZJjmJlRX73mtjUrbB+WnAG0vWe+j5ZK1z+XlyQQNRdcdVPi/Ka/UgdT/+/nh2r+U/4LLUmkci94W3ieuWtrXGo1rvIVHVb4D5hPdBzr1cP4X9IbN4Ahsbk3yTvEbUY/nzLK5fni/r9WxIayx4vKNWX91c5ckg7X+a7me9h7wtT5biuxy3Gjdp8Y382dTtasC5Heu6tNgP7RotzpV/jnC9dbo1kb5Qq2t87nPj4/x8ddzL7Lqa3q3pxHW7NHM/f0Sn8mS6/J/UZYnseAyN55b1a/qT1nZOFumcxCC8H5KTkWNbi4uSbS0vVzMYSxk9N1fliLTFKfIdxXvJfbRyByrJc1jnoQ2i8TOVO2Pxe4HfcB5sA277S9vE4maIbG6fg/tyifQxIZ8Tz1qNPnv6tzX3RuQmhzXGI3OVpwtE7OqIbs/LWvOIxqlLzorXV1dPpF9aeZ68Ul6/FQdEVPWV8eeF/yGovytEg/K7hnefZgRLS0uVWFwiomc961n0+te/nt73vvdt/WDkxz/+8fTpT3+aTp06Rf/5P/9netnLXkYf+9jHHvn/QDyo0WhU+U2irszrX/96eu1rX/vI99OnT9ORI0eIrr02CeyoEtzVYI4odKWIkEs57ecYBVZ7TVDyDDwiV/tuGUkRhx43wL0tnHGsC7ImSgIbCgvG7e7dk9kxOXCfFhaqTkstC2ZdMJznlNImbUlMcIcGsghxJy4/8nqkMqLdH0upafruRgio0vMjKJU3OQRu1Dnu/W4hp/8gqHvC6MrDNHr43ek7K2cEyNw5i33b0sgZwx4ZWfeOeESY5yzxjGwtWANZNy2nIV9ogv9Jh5dGkEijRzM05TVzp5a1YIVvg9REn4rM5bwNyyi16iaqd2YR1RN7XSFC1m4l8IDgvgA9RGY15MghyGfpGeSQxE3rbeu6I3pHhDiqq6+uLY94tdqSBNC5c5MBbkTVIGktw7LU1fliB2k/rK9PZtAkSk4tBKXt2qUHI/E2eQCeXKyD3zkRJmU8ft+xww7o4qSV1NelvaUFB0WClTT933uGdWS8Nu5y5pFI2dx3x3OuyTIl9zDSt6YyQDtfOmG7xlVXpXdFQ6nt5pHUFroqm4scfcrrj6c3W/VptnrkGXjOQqtfWh8hU0+fTjIVBDl4HO4IkXwP7Fecc/z4ZLA7UTVY9tQpIiRSgB4v29IWVEP+Li2lQP8rrxwfH/vY8fG668bHq65K+o1cFABwmaztiEWkByxLcJnqLVzEYhQpr3ngHCCDnjSOiLdfV9aT6ZF5PtehY52nyeiSOSYHkf7Jz/w777MlB3L1MKBn/oduumlyzrHGQA4HmCsvvbnbq2eW7AwN2liv49CHQ5+/rRuTEfmvnaf93+KcuY5vBV5L/dnqK1D3rvPFPlp9X/va+LNcTM51dE0Hj0JbaIvzZUAeh/duyIUL8hzvuVv1a4iMO16XF3hWog9677rnxyjRB6Oyva4ez46QZevayjm/52Qk9P/9f1V9TMOsy1qi5n3MkZtemzn2Yiki87PHt1vvnxc4qi3akckloL/whTBYYIvMnAgcBi909qydgIjXCw4GAWdy4QlRSmKFRY3Q+fftS3NChKe27AAvmEfOOdr/+DPBvYr0o1T2oKzHF1p1a3IrMj9bWVu1vmn9kZ9LfBZRNLmvEXj6mNbWNHbt+dZv7b/NbZShTv+PniNluuTWz5yp7mTOd6klGusq0Fcg4/muKfgdZRBoL3faWl9PMhOyXS7y3b17csEyP+7bl/qPIGTMFTwhkpYciR81aLxNnf0QjRECN+XN5RHbYFbRdh8j/q+tjq79v54u0CdmoQ81uPHGG+kjH/kI3XrrrZX/fdu3fRv9wi/8QqP6ZyIYeXFxkW644QYiInrGM55Bn/zkJ+mXf/mX6Z/+039KROPsx1eCVCei++6775EI7UOHDtHq6iqdPHlyIjvyfffdR8961rPMNnfs2EE75AoVosmJiah7IthDqUOypKzn/CmpT97HUkQdSZYxqZF2OY4zHkSgOf1lW5J0lGV4EIHM0ADwbMNQJmBUyowjHDzLBoIO+LacRElhqctUnIMIIRGpQwZQe8SG50jPIRojyDHQSuqrQw5pWILouZ4BDzQhiWcAg80NPdB3Rq5rSwcha3NExBmVc+9zgj60gAXPuI2sBkYZme0JSjW+E01mMiPSs61amRq07Dlc/suVtRGCfdpoo29dGhf83tdlRpgWZqUfLQGLL3rDuXOTwS8l5GzEoRipJ6KL5+o4VnAFh9TbIyuoI7upRIIscuwsLfumdvSyFxJNBhFIXZwvzJCZ4gC+wM5asMjtCPzGswIQTWZHt1aN47h3b7IJHve48ZE7mv7yL8efZaBwU0d6CTQyEuAEJq4HaOrol2h6vTnOH09v0frjBWlE3llZNvL+SQcnkT3e+PXkZHhoir7GKPDAA5P6WBv8Dw/YacuOKAlgaoqcsaXJZq2eHC5Gm4fq9Hb+WXIzPLBXc47z47lzKVhW7pyAMnv3pkAAuZMVyp47V90pQy6+OHaM6OGd3x5xVMnAAb4zFoInkdVn374U4CDvAeyBPXtSEDL6inrqsmF2hcc8Jl42Ry70cR05DjSAj/8mgU+5Dn6pX3pyXNNBUb5poMAs2ip33z1OSEI0ybFaASkR/butxT4cfc+LVtse9yuvQ2ZeL4Wmw1rvn/aOyWfn2RG8Pktvgow+fjwtOsFvKIM2l5aqXIyc33ggmszGhgCGhx5K8xfqg/xeXk7BEPAfINANMn952d8NB7Cy3XM0eZ7cD+HtnBaF1pcc/0Ru2037GvFnNIH3PkbulXVublsaIrJT83N1jVIfaASenmC12cUcYyH6LlvyUvP55uj4Hsek1ZPDp1lBxDwrdeT5WBnGuW8Xv8EPyxMtyeRPWoCXttOAvBZtpwD8D1km5e4gPIs+4h8ii1Akh+PZCPKeRXZRIUr3ykNJTELOOxeVW3IMafVJGWbpH1qfcuW21kerPgt1z7ItnTOHy+47M34uSu9JztxLZMvQkvchF9r4s+rSxkvkuiJ6h3bN1vzDf6/j8HjcjpalmGgsP+Wut5Lj8fQ5noQD+j0ChGEr4Hj+fNLTZbwPT0AluVp+DyT/j3r47ukW5I5HRNVEI1xvt56dx/tr59aNgahPSZ7n/e7NDSV6C//ds0llfRGbJMcX6aEpV2W1r11zW33extTxd//u36Wf/dmfpSc+8Yn09/7e35v43//9v/+XlhsmjpmJYGSJ0WhEFy5coOuvv54OHTpEH/nIR+ipT30qERGtrq7Sxz72MXr7299ORERPf/rTaWFhgT7ykY/Qi1/8YiIiOnr0KH3uc5+j2267Lb/x4bDf1VjRCTtHgePnzRI0QdWWsOr6WqNKec4E5tVXQubwsQRSFL8hgIJn2oHiJVdhya0hiKpb0mmGLJQPGawxHNY7uLwxDuQq002ds5F3U7bVJeqcuxoiTpM20TaR3DNGQ2f7Gg7PkSHPiZJmTZCj1LeJCAkov2sKfy4ZFG1Lg0Z0l9ybHEIhSq7WIbrVJMhHyGQ4o2B0b2wkmcy3IiKa/N3apoo7zKSBKq+DZ0/AcVoBD4BFfkeIyq2oj3Uxh5XI68w2BpvGVtcdYbS8K98B1rVM39zUtzaWbUfIl0gZaf9YW65q/fHqbos8AbTn1Oc7VkKEeWWavqP8fJCNMsBBZlzmZTW7VzrKtAxrdfNYdCusromrtuwAj3Tj97CJQz7XuSoXuOLI5Ybc+hU6Aurj28nL7caXl/1A5Ui/S9B3Zpz9+/VMrE3Qxf1pMraa9kPLEibrbbozRO49K3HQWu1Zv0fsGfk/bWtq+Y7J4LTV1RRYgADoO+4YH5Ex+cyZdH/l+efPp4w8crtRlLlwIf0PGdqw7SfOXVmp2kpy++m5udS+XPjId7gq0fe1+93VHJGbkdKyG/j470sXiXBnbbd3seKqqybfJ2ueLw3SkAshtPFm7XjE4Tk/rTGp/U9r2wuA4ketjKYjyfdIu1/WFuTePODxap4NZwUwWO1a7UOPwgKUJz3JblOTC14/5HlcR8PRWqhORPSZz4yPVnbKjY0k52ViEs7jw16QvA9+58/S2qZ5bi75C+Q8woM0EIwRGQs5XGtTeSWfk3fveRChZd/xRDUR572UA03tzpKyno4TkU9au579670TXaOLwF5vvPJjTp0cmgyzvtfVW2KrTwvyfnjBaNKmbesavLHu8cs5HLI2B3vB1bAb8D/wAVikwtuXW7trclZLmEU0uahRLnyPBiGjTUuelD6ntuU/r9cKFs+pLzJuPD0kRwfl70GpPd3W/czBNDIjc+T4iQCpx0d0SQ88WNbSOzQ9oXR+L/GlavxnDnJ40C7GX+T9sZDTx1y70bK9uO6ojTe5GBKL4cEf8cXsUv+Hbn/+fJov+CJI3uYVV4xtZqIUnK3tmivnXi05Vs4zqLNj6+qx5pgI36eV71KXsOz53Da9sSTb1q7Pesdz/QCaPSV/x2eur3SBvrkzrx8zjte+9rX0uc99jn7yJ3+S3vGOd9Df/Jt/kw4fPkx/+Zd/Se9///vph3/4hxvVP/Vg5De84Q30vd/7vXTkyBF66KGH6IMf/CD9yZ/8Cf3RH/0RDQYDes1rXkNvectb6MYbb6Qbb7yR3vKWt9Dy8jL96I/+KBERXXLJJfT3//7fp9e97nW0f/9+uuyyy+gf/+N/TE9+8pPp+c9/flmncoz9HGGqIWLIRwxyTRDkGPK5k2TJyxNRVJo6lSLlPcPOyp6zspLIO2S3lI7fBx+sbqeJspzUg4KAiR2KAg/ileQdDD6sHr3ssurWO1AGdu8eZ/ggSooGHEXaVj5to6tAs74Fdh9kRaTtOuWj9H1ti9TM7VOO4dEzGTlYvUC0ucsuUKKEljquZJul50fO9a4jQhrntOE5pSL1RYgarbyljG9u1mfPXF+vOma0rTgtgpBnSpYZ9gHuhJErYaUhwB08kO2YG/btS3MSDyYiSplytoDy2ynaImzaIk5L0Jbcbkr2domex2lvmecjpCZHXYBcxI7gcs6bg+vGdMShUkqUyO+5Y1xzRtWRL9qOJpDROPIgMs+ekKQx5DACvu6/P9Upr4OTMtD3pbxGNoWNjSTvDx8eH6+5Znw8dCiRkTwgjGhyEeGjfQ6IoksnTEnwgNQFNjfT/C6zS5e00wRdcQd9olQvtcZJlOC2wB218ryInIwQ7REimes8VlYWrf46/kXWHe1PRNfX7ou2FSeX8/yIsoNBNZsM6uYOHzh7eNAwUZLDV1yROBjYE+BqcDxxItWNNuHYBzn/wAPVeQTv/qWXproxf+zfPz7yeUXaDV3tnlJqs/apW3eRvXZWkcNZbOXrLEGUq9GcdJHzrCCW6H2Wz4V/l79FnKkSWoCkhOfAK7UNcvR/rR4v8MML2sJ3aRNAz8bxzJmqTSC5/bNnE6ePI/7HA8ylLo96INsfeij5BHhGfd53fl+g+yH72WWXJd4fPoKrrx4fkfX70KE0F1iLyDlKnP65sHRXD034UKueukCF0oAbnJ+z2M2TR6U6ktdWST1d8lDTyIyco//n8v/W+d699+rUAjgi7Vpjqa332pPNOWOp7hpKrlnjg6zdwCB3z5zRg2aI9MUFlj3DF+bJhX38Pll95eNF8z+gDHZGgf4PXf/hHaVp797EBbW5Sy76ZsF79tMOPLVQ+k5E9Kicejx7aNZ87Tnz8qzaGNo1eDsxW3omkc3FRHd2lrpDU9u4redbwtNH5H9u/EAODwZwHsjSt/libtx7qZPgOfM5wnq+fBdGOU74+JF99ha1Q35D1u/bl/gnuTAcPNC+ffZY6iIeRKKt8aLVm2OryHeX62GevmKNz4hemMuR5+hmOTy1TIZQ148IIuVzZGDX8LiMPjELfajB3NwcfeADH6AXvOAF9N73vpf+9b/+1zR6mJd+9rOfTe9617sa1T917e/ee++ll770pXT06FG65JJL6ClPeQr90R/9EX3P93wPERH9zM/8DJ0/f55+6qd+ik6ePEnf9m3fRv/9v/932gPBS0TvfOc7aX5+nl784hfT+fPn6bu/+7vp/e9/P82VKtmlhlmEoIsYtRFnluZY14yaunpyrlUrU/ISlQqanPM0g9Wb/C3lSssAifsLEhDfQQbKuokmDUZrsvSEspZRA0QnAg/4tm0wRtFnGJ48SAGTkDSKvW175HiZm6uSmfLeDYf1WwGNRvYW1/x7EyW8b4EfMSLrzu2qD9F+lLQlP8s2c0iHvgmKJu01fWalMli23aSeCLxzu3wfS+4vlxkWUciNPyjoHtnrBbfJgGXNSK5zvGmLY+TCl1OnkhNL294TgQ1yRSrmKL5dHM9OQ5TegcGg+puEJv8lNNmvzTV33TVZRpsnrbnS0208aIa4J7tkPzzDNfKu5pTtav7yDN8ujcKmhFoX2Ny0t96T5fhRgzZeJLQFEV5d3rivgzb3lugimrNEayvyfOvuo2f78M9SXkb0f66nQnbK+iCfFher/0MwAQ9GwG/yvsAZdOCAvRhF20KN183PWVkhuu++8ec77xwfv/jF8XHfvhQMgUAzyH3YzZozKqL/A1w+IFBOXrOWlU3KF75wUW5pnUMCRv+fc54n2yPvv1WP/L2uX02u2ZuzIg7tiAzT+hp5nyPXP43MaB4n03SOKp3zZmFuzH12FnB/I4EmEZuS/08LFJDOehmMdvp0CgSDzNK2YAZ3grJy4Tkf/7g/kLsYx1//OtH/+3/jz1iggl2scDx+vJqBBvVAnu/eXc3whus5fjz1A7/BVuCOMCn35XzEM5t5Tg6ZAc2TpTIbD+8DHIGWs8KTv1rWVi/DZol84m15clI7t64trQ3+3Wsr2m6Oo2oW5M00cPr05HtVIq+7vHezaDNNE5Yc0MoAuGdWcIFW3+pqkvNS7+fcDGQYfkOGSnAbf/VX4zmAKGWixzlNgeu44gqiSy4Zf5ZB0XyLaJSR/A+HDJzTykpdXu60pdkI6AefO6T8l/Dex4iOrnEpOTZGRBeJONYj/E9EjkdsDz731cmKUllyscmgPnwlTTFr/WkLOfqLl0QK8vf0aXtBB1+MyHl1nMe/339/0tfxm5Stw2F1ETnmGJRZWUmfrZ0GFhaqiUZQL08uIm0CtLlrV+KGYKsgiz+/dtQpM6pyndzigrjclsmnPB6J+yg4BoOYTr6V0HW/c4Pa+sRWfWYcUb7D4uY5ura9ItyIh7a4nbag9dm7h5F4G7njDMD9PZC7Gudg2eQ84FfOR5D1kKmc28d8IpMR7tpVTUaFNnlyEbnDHS8L20LuqMl3zJU6uBf7pM0NRL6M5z5wy0bQzo+8K7JfHPKe5dRT4nPQzo/WU9fHCMfjvbul73Kdf8/qT5f+4i4QsYv66scWwY/+6I/Sj/7oj9KpU6fozjvvpH379tGRI0ca1zv1YORf//Vfd/8/GAzo537u5+jnfu7nzDJLS0v0K7/yK/Qrv/IrjfszIqYU5zoLPZKgri4t81YO8alt/+UFLFuOoNyX0+prrjDWspVpfdOOGrT7YrWtZUbQrsvKrMAVHzh9eEYFfs65c0lR+MY3xkcEE8C41YKjZcDz/v1plSsyot1ww/h42WXJMJVBaDBAd+ywV8SiTc/g9M5DvVzJyRlTTR0ikffRaqt0cvLaylGmvMk/x1jXHGldT3hRg2aGjejRcC5P/nJYzstIWV6+9Nq9bTdQrxzvOe9GNDBAlok4BUqD7HIMDlkmZ45YX68adlpwA+S9zKKPcx98MC0UQcAwtsDhcwXk9oED4yOyxnCyE/9DZnwc9+wh+spXxp/l6kcYw578nxaQvWcb+UZdV/KyTzmsyQcZINMHuG5c9xwieq4XUJjjLMxFhKC09F7rN8CaPzQ9ytPbc+Yj7RpkwJFcTKLVqc2zVhlObsqsZ1JP3rcvyVTMCSiLeWBhIcltkIlwOPFFgVZmZXzXrh2Yn0/l0K5clEjkB2Pw6+LXKseot9BEnlsHi8yPEHIRtEmUoX0vaCyi0+QECFjvoxekoSHyzubqrvi9bd2+bzvg9Gn9npeQ1fz9jIzbiJ7soS3SvK7eiGzlgQKSw/AcPd58JDE/72fEJBrLtrqMvzyjPeSmXDCyspLkNeS35H/OnUtyWm5ryXfPwm+wEY4eHR/BB3FAp3/KU8bHG28cH5eWUlAE7AjwSseOpfbBAz3zmeMjOKKbbqpuq+nBeg6jUf2z0uS3Nm/gOVlcIJ+n5LPkzjtrLEXex81Nfytceb4EvxclfI3Gz3oZt9CfugQUvD/W/RkObYctL2fBk425cy/6hnHcF5aX0/3VnrOn23hceBP5r3F3ETnu2SHyyMeCpx/ItryyOUlRSq89h//xfB/WfeQZMjEnyDb4Am/MEZgTIL8f85jx8VnPqrYpneenTqXMZggqQ30ou3NnkpfItA/+5Lrr0sIWea2Q9UtLVf7HG9tywYkGOZYQXMF3YUH/ZQIUoslkLrJ9fJfBJpKT4xnsZJCGFqQQsXs9OWnVzfsKeO+ox93KcWvZfVYbs4Ycv0ybNlsUfJ615Jv3DErmvCj/49kIkbFUMrYjc1eE49GupY6H0t4jrqNIfUUm39i3r7oYEXVzbrzOjtjcrCaxknol74/0Ecg+aNfKr09ejwwY5nXJrMnz80R/8RfjzzmLSaaJCFcZwbSuxZITTevRxr9sg7+zXaGpHJ6FMVaCOlnucSGApu/m8Bwe/65xd1q7si5PLlm7sdbJSKttzX62UOfD5r9x3kbaXjxextLpudxEGchFGb9z7lzV7pZxLjxuRy7I42XA+/BFLERJNvOFj3K8oN4DB9IiFOxyDv2Zz0Py3nuJRrSA6RxEuFIvjqjuOUX0Re89iOgkWjuebLdspmgSjRK53bYsreNYS+rJmS/amvtLENW7u8Ys9CET+/bto32QQS1g6sHIs4YBjfKdZW04ivhLX6p85/TbE7g5jqq6ejlkvR5hryk+OcRrhPDkk51laGqZ67QsxQCc/QgaQ8AwJ99kP+Rq0bm5qlLlPQtJ0J05Mxn8RpQcV1ddNT7yzGiWgjAYJCUKRyhQwOamTVRGApg5rHIR8pv/P0dZjhDl0X7y/2nvk4Y6IyfyXnYxoXpGQEQ+RZ6l9ZvmNOkJAxo1N+wjMjD3f3UYDu0tQLV+WY4zrYyXKdSTTxHjWL4v8t5p2Q+4898iGvk1WLKUOy00so/XpxkXcDx5Tm5tzrHmGn605gZgY8N2mm9sVO8DD0ImimW/3Mb0sAWNk2x4jmwgZ0vTFjAa1jhoJTyiMFpHBE10Ee9/TUn0EtLBIy08Hd/TEzzHr0Vy8bY1HZp/59nz5cJAvpUngn9RNwg+jGOehQd1SyczPx9twrmGelZX0/9ACCC4bN++cQY2oqrziWcmAOGKOUYGCnCHVZOFK9Ex1fauFF28hxJNMpXnooldrtXTtExb502TjJTYuzfpdaUovR7r+UZJXkum8//X6aceJxN5Fl4wjdVfoqrtErlmLeBZm0/ktXL7gWgcgI4gYpmthjuB8K5Dfwenws+VWY5l9uOzZ9M1Xn75+HjTTePj937v+LhjR8qoiUBj9ANt3ngj0VOfOv6MIDQ+bj/3ufERshxzBNenEBQts7BF5l5gc1PPcpyLzc16rqsPx4Hm2Nc4QcumbNo/j/fxdGGpB2lzXwk/0JQXLr0f6CMCLfvC4mJM/lv8ifVbtD5+fsTpaX3XEH1/2nrHIjZLHf+j9Ueb56z7ofFHkPt8oaDcOhltQKZtbKTzrOOpU2mhOeQ9FojwoGLp9AenjuPSUpobkL0YujqC89fXiQ4fHn9+7GPHR4xbnsUT7w8WpWg8Vw5XC2jPydoh0dMpeMKSuh2JNBkmA5g9ROxOorygG62Psp7S8y14734kICQHET0sVwbm9G0aPJgXaAzwse0l/+BH+dkqY7WllfP6GPGTRs7JraOuTE4b/P5qi7LkYgSvfRkojHPvuy/J6W9+c3yEns3lk+SGZMZlbccMKdvX1xMHY2U/5rvWQm/HAkj8zu+LlIUHDqR5SC50x5Ev0tQWhqANoM7Hy8+X98BbyBLZhXcroK2+avKhr91ao1yyFU+hlW9z/ukL2mJEz/71bAH5u+d39fz78nz5XdOxtLbk4o1S/cm7H1bZyFiQupcWcMzrlbas5PFPnaom5pA+1eEwySHIKi0YmPNFRJOJQXjf+fmSI1pdTeXkYjk+L8is9/jOd6pFf8AVcX8C2rUWoSwspHssZTDfJVJmTY7oFFLe88WIXkyQlXjIiwWzvjdBk7rbknu5aKKTac+2qe3T1vV3vcimD04x2o9HObaDkTV4WXn7QFtGeluKaA5y6/FWrWvfJSKOMy0gjGgyqNgKQubGJFY0gcSEUsKPUBCg+MgteVZWqmSorI+vwsL5cvvQhYWkzPCtl/Ed58PAhVIDUvSSS6pb/0iFRQtA0AxLqWBoCkdkXKBv3oQun2UumSOhKR51DlsPEWIucl8ibfJ+1t1frZ62jEitbN1qXg1af3rOjDkippjnBgFEUFpPnVzMdS60PZ81vT9WmbqtnOuIgMi52vnyerhTy1qx6ZXhZbVtbIgm5yOZmU0zyK0+b26mLGuQ4XL7uB070r2Vcp/LPdTpkZHSmNaehTR4NZkI4z4iL+vkpvzMv2sOBM0BYclgjaSKzFnyO5eRXjBN3cp0/pxKjNKII84jxHLbiJ5DVO+gbBkDGqU2I/qHdu813aTtoBXZn8j/ovZA5Hqa6F0lTrLcNqJygEgPapPZtfj/5GJEPNvl5SoZKrd05lmLcUTgArcd5CJCmU2B6/8yUHlpKdkSCFCQNsLevSlQAWVkRoTRKMltToLy75xo9OYKy+nExwII27rxxz/nOMKjMiziMC6RZznvSpvvSF3bWv2ebZBTd47DalpELgd3fvC+RJxR1oKIukys2mcL8l2z6rJgjbO2nYaacywHWr/kWJqfr7+e4bB6HvReyD1kr+RltIWP1iJ0fo4MWJALT86cSbL5+PHJ42c/Oz4eO5YyY+L8toB54IlPJHrSk8afsbMW5gY+D3hyH5D3PLLQUda7uJgC8HK4DwktIARleSCet4ik7l3g+oLVhpZV19MdZT1ecgrNHqkLoIrIc/6uaEH+dfqcphdqeq/Fx2qycBpOvWhWI5SvQ46OUteONRaivGPJHFH3u4WIjV5ShycftPdJLvaDnozFe54N59kI+A4d/8KFaoCynEcuXEi+ARnUBp3/7NnqbzifB6PBNsC88Zd/OT4OBuk83D/w/0iOcsUV6TdpP3A+KGeBiQyy1uYKQJszcM0R/t2SM1Z5+bt2Hq9PO09DnYzXylptR8/TzmkyZ/L/W21pPgbpR9N29JTX6unCWv965n8qqJNVXL7knmuVjdyXOr6ayF6YFO1HE3i2dlQXwdHyDWt1em1JuY3j8nIK9sXCQFmWZ6u05gG+M66U8eBvzp6t2gayzAMPJA7lrrsm2+D+AHBBUu4uLaX2oe/DxkEWzSuuqO62CPkf8f/i2nniEy0IjWhSdsg5gT93zGcRXsB6lly2e7qoNd608aLJtBx7vkQmR/RfjV/wUPce1vUzwuda/ITn6/C4A+gEfSLy3Ern2Ry+xdN7Im159US4VQnt/ZNlIwt4vL5pR1kn/1+dT3ZxMcl2S6/k/lbIeDmn792b6tGSB+J3/M/iiLQysB94ffK+oizk+qlTRF/72uQ1cz8EYmkg08F1Qebv2ZPmC8kdcH1dynSZjFADrg9lNzaqC3A8G8GTrZ5Oz4/yMwfXKXLex4htkDNH8PGb09cIIjJMOydHV2tLznnnt6WPbqMIN910E/3CL/wCvfCFLwyVP3r0KL31rW+lw4cP0z/7Z/8sq63tYGQJbfLbqmjbwdQ2MewZ2blkdK5iLctKR5MkFU+fTsaS3HoZBiLPsICJGN95IDNWMsHhhFWsqE+D3CJ0ebkajADDc9++VJdUQq68cny89NLJwDQifYK2Asz4/dUMVAlJamrBaTCUtwKm5SwpRYQA6qvdnPYxtnsEApIHOQ7KrlFihPaJafWnjpSNGut1RjEnI2UWHV5WkmTad5mRUnN88Ww7/Ih5hCjJbch4HJeXk2zHkf+PaDx3SFkcWWiibe2ZY/hYRspgkPrWtt6SK4PaGsvWHKE5o5q02fe7V2q4l6BuMULbWF3VszMCGonXJNCY67sRR4p1frStuvKew7e03SbnNtX/+X21MiwQVZ+htAdWVqqEp5wHtMUoGomI7GYgDEG4c0cT5gi5ABLYs6e6pRzAdzSRGTAQ8LVvXzUIIbL1LMDnDk+nl+d6WXKk/PfIvxynvzw36kyy2tYQISo9ePpdxNbImXutczV9t6msjzwn6xzrvC7Bt8iNgF9DyVzVlKydFfRpn0TGRGQe03bGsjJk8u0+JUfEM9JYW8RDxs/PVxd/gCtCFmSezUdCc6jL+U0rDztg//7x8YorUkblI0cmy/CgBJ5JjR+1nb4AT8Zb8/RolDgqa6GgtoONvHbu9JbzmTevzdr71JQ36RNN5rk69B2MwPX5WUTJsy9xTOa27dlKTYIb6tqK8D0avyJ/t/6nAbIGMhrczOnTSV+Hvi8Tlpw7Z9sIWqZ+aSMAPHsmAs0wr+zdWw0wQ+Ax1/8lR4Qynh4jefyo7ifLa34F+DY8HV/Wk6O/R3X0Eqd/ybm5sOZOTV71MUfUZdpvs41pYavwcl3ymE3RVj9K38s6PjiSRGRlpbozCo7g8YfD6k5YOAeymSej0pJZ4QhORwYac5sBcwzqw3XxuQGLT7DgEDvj7t+f+iR16mllK4b+v41tANOQYZHdGNv2UXltRPXuOh9Brl1TYoN6C0vludFgRs8nK+0Gz4do7aI1HFbltZYZWUIGFfOkhpaeqvl2ZQJDoirvIuX/zp1Jb9cWjWAukZn1IWMXF6uLfGSSrMHAXzwiv3vzhowRktDs7gin2LZ8aMKjRxHhjD2b3bM55HgrvZ5pyF1NZuQsDC9BxCfaB2ahDwpe/OIX04/92I/RpZdeSn/n7/wdeu5zn0tPe9rT6MCBAzQYDOj8+fP0V3/1V/Snf/qn9Pu///v0x3/8x3TzzTfTK1/5yuy2Zph1mxJmdFBMBV3di9zVGpHfvIlL/g8TAA909IIZcJSC0SsjM1ri+8qKvTIWx3PnqgaqVgYBCghmxkopoqS0gHzEVm4gLvfsSYQloK3qQsCbNFi1sSEN54sZW01OTKu/OUrRDGBAIxpsKsH0FqKBExGHzFZBmwp7k2CgtvrhKcGaQ0XKQi4vpfzXtvaRq3DxnTu3YKjKrAk8U7h0IiHQ4PLLJ7eOI0qGHuT52lr6LLOURTKbAZyAbQopK9oK1Gnq4CxpU/utJNBNc9i2hT4N79K+d22MSiwuThIEuWMneo7ELM4DJQHQmuM4cn5Tm8CDJGM1h5OU+1qGG7kwBAEDXDe3MmLyIAVJWCIYDUHK99+f2vDGPwjUJz5xfPxrfy19hwznwQdEk4tScrKe9YGIE6Br9HEvNHvRGtPa7zLTX5uOEStwh5PFOYHK0Xat+qYRjBDJKNbFOOkzsCWnHznIke1d6B9y3GrZ+rRgBKmDe5wMZDOyT/JsMdzpRJTOwcLzO+9Mcl4uOEHgmYeIPrRvX7IFsAjlkkvGRzilrr6a6Nprx59vvHF8xJbOufc8p7xnK+RsGSvbLp07ZmXuk5iVfkU4DU/Hl7ZcRD/cRrvo4z7nBirL33P62Nb1aHqYlu1VloGskXbF8nI1QBlt8C2iLU4HZR58MP0P50MP4v2Cjo//QX7v3Zv6iHkAQWiHD6cyfEEJb4sD/5O2QoTzKeWFmuiXXc5dXZzfpK1t+bmNaaMPDjGnH9rR4vQ12S79tRpvLxch8t+lHSF3P7nvvsQb8UUsvG2iJNvlIhIEkx04kJI3PeEJ4yNk/SWXJFsCPm65M6IWmFi3e2HXmBX7dxuPbkR2RtS+N/X/egusIlx824so+9C/mvr66urVdHsrjof/T5P/ktOXvt37709yFwkHtZ0NZYKRnN2voNcfOkR03XXjz9dfPz6Cz7nsslSnnOswtvfuTb4AGXDM/dvebrcl8GyCWVgEPCtzTlsc+6xcTwSaLOya/98ORnbxL//lv6SXv/zl9K53vYt+7dd+jd7+9rfTYDCgwWBACwsLtPqwPBmNRvSd3/md9MEPfpBe9KIXFbU1A2//bAFZMbfRIXICPNpctdE0mM3KmMcVH77tJAc3fOXKWu0oswvD4cUVGbmiiq/Qveee8WeZGQdG6epqdfUVgtM0YGWT7FdO4Fod6p5HqaLr1VNiQNSdV3duLkqCgmYNW6SvIxrQaDhWmAfkZHgCGl7XlpxvhlWDInSvQnX3HAAyS9AI1LpACu239XWi228ff8a8IeU3AiKIkoGLeUBbVII2tK3Y5JY7vB/oA9qVgT58SzjMZzkZOqOLlXib3ADRjD4rIEsLTPfqaXsBgnQaRhZlRerj0MaSLF9yXYXXPJqfwsImOAu8jEPSEc0ReS6lZbxz6gK/I3qPN35zggc0AtVbvGc5IjSywHtXteuSQZRtBb16pHFEr7SIQi04GoFrPDgapKi2bRwWJuI8kKI4Z9euqryWcnw4rF6PluEAv8EW0Ba31GVG4H207g+/L/L90wJzvSAYT35HxnbkN6AkiMCrT9qLWt05YzPyzhDZgZCaXPAcizmyI4eobwPchu8bJXNEH/0ASoOU29aDNEi5wLP2Rca9DITVdED53sm5S9s9RS5K54vIcYQzC8HNZ89WAx5wPt/6Eu1CboPj2bs36TAog/MwtldWEkcEeY17wLMEyV1TtHup6fK8X3weQT24d9yegANP1uttr+z1p2SxRpTzibwjnm0gy3vvVo490xSR/liIcLWebavNEX0vRuT94ehK7pbyj7OOpvcwOv5K+AA57jS5re2MImWydrSC2iATd+2qymvIZug6e/emuQE8jQxuPns2+QJQBnJ75850ry+9dHx8zGPGRyQlOXQoLVCxMmPy7aKljNeCFHLGslZW6v+lDvm6OcKT0V57OTqsbCeKtm2FUnQVHJTblmbjbGN20IVMj7aVW1+JXMHiDa63SP3fW1CscTxWeS6frDa4/o8y4Hi4ji4DnTHXcK4IMhztarvX1um53i4ofI6QcwsQ0bdznnNk/tf4/0h/vDY01PGxXt25fFITm52X8Xgb+VvTOSLCz00DeNcWF6v98LihtudhXl9d0gJvTPMy2uc20fQe5Ojv2kJzwOOMoe8iGHd+3udNcbT0SK+Mdp+tpA68z97uuwDkPo7ct/rVr07WDdsA8T+Li/WLBfl7qO12LhGJBbLmi8Eg5i9qMr40vqT0PajTRfqUXyXcEUfU55fjP5D1dcFVtQFPx+m7HzOKK6+8kt7+9rfTL/7iL9Kf/dmf0Sc+8Qm655576Pz583TgwAF6whOeQM997nPpauy0V4jtYGSBRkFVXZACRP28LG07mrq6F03g9bVO0fAy7PAy1tZrPGBYrqwCCQln1KlTk5+JklHJHVl86zfeJjd4oXDBqH7c48bH665LWXJQBooKyFJNSdOEZp2hORzGhC3ui0Ua5CiqHqLKSBNHiKc4tT0x5yocbdXddrues64nDDY32gusjbTXY1uPKngES8TIiRiaOXMEnxek/Jffz5+vBp/hu5Y9WWZqW1lJ8wQWmMhtmvk2nQhCwHdk2FlermYB0pxRVjCCZ4hrzizcB4+Qk3Lby9aYYyDmyDJv/sgh/zzSLSILNcLDI0GaOMe6cIoFyNqsLPVtAXrRNqaH3LFUJ5vrzrWy5nAd0CNFiSZluxWMpjmjpKNpba16PgIPIOMfeCDJeK0t2AaQxSBgYQdcdx3RlVeOPyP7DrLo8C3hIF9loDJfuCgD1iRGo2qgsrQZhkN/iz3Aer5tkouRutrWS5s4dWeBTOsKfQej8XfYGxN92Cql9lUJaczrKxlPbd2PiO6XW5+s08t6iWfPMyLj97qdsTjgvIFM521BdkLPhvyFzD5+PAUTox7If55pB/Kfb+9JNJapsBdk1ky0deFC+g06PuoD5ubsnbEArsNy3ogftW2wAZRZWpp0AvO2+L2rew+jbXk7fWmBiGgf/bOy02ttR2RGxJ6RugTvj1xQqj0nL+srzuFB/NG+a989u6juPdbexz4R4Rm7CsTILdNEb4iipI222qpr27Ih+ViPJP+wOBmeIQ3yEbIYZTknL7OnQQ5DjmtjW+60cvp02bjHrof79iV5AHmP75KjIarKHr6IRJbXFhqWcCAAtxnkgqBcmSNREmhWipxrbrsfXGebNb+bx0dFMCs7o7RlC+aM36aBJDn9KEVTHd/iW7hstuT2hQt6kiaitLDt/vuTXi0DvLg+Zc0fkPGnTlV3qUUZcBhLS1U5C7nPA4WtBYs8GzJ4enAinBsCwNcj6z2CQQ4dSv1AJmXMA/Dx7thh69Kco6kLHm46jiJ6YRdy2+q3x8lHwOWw14Z1XoRn0Opp8hx4n7vg+XMxP99cj2wCy4Yi6rY/no0RmQebzENtn6ONTU3WW//jXA34DenvPHkyyXnsPIXMxNgt/OTJSb8oUZWnXl9P8weOfS6CktyMliwFchsZ8q+6KmXGh4zHfMAz46NOjB/Ow1s74Xo+VIk2ZXOf+nrbKOUm26jb+39TeV6qX24VtBWD1UY/ZhwLCwv0Hd/xHfQd3/EdndS/HYws0ChTpUcIzPpg64IgKUETsqnuPC9goS6YQSPzva18LIcGz7BgHVdXqyQmlAkoF3v2pMACudJr9+7UJxB8OO/gwXS+dGBIBwtXUqQCx0mmuq0ceD2ewiFXHVoBBxq0gEBZbym5meN0iZCC2vVEzmuqdNT1R2vf66v1vHLa4/V5bfWE0XBukoycdbm9FVEatFh6TlvP0JobOLkpnVHScUxUXaUqV6hqq1BlRjGNpAWGw5QZE20hqACOJk0mW4FiGvg5VhAyrnk0mvzMEQlG4/fZ22IJ53rZMq3ztHlewiMIS8eYtSV8bnBcDqxxbLVv/aY9pxyn/awG1YHg4qSohJatz+p/rk6hPZc6cnZ+3h73OXqD5iDSzqmr23vncp1sOfqXhjqngDf+I8Q0l78y0EfK/6WlySAtoqSrg2S9777q1p1SN9q3L+n00vG1sZGyXsqgBATa799fdVDJa+X3Rc4/nDS29G3NLpCBy3yOkVlwNXkjx502VuvkG3e6aEdr/tHsRc95VCffcjNy1GUR1+rRnGPyGXJ93rueHBvFk4U580/fmdF4UGTkvvLPdc+y7rece6a1nSP3PeTo55E+y3q9AHNpW9bpbnXcAP9dPlfOd3g6J9HkFswYk5C3PEBLjlu+eJBoHHCGoDPUpwVCy2A0Kcfn5qr9wPVcdlnqBxxdKAP9f//+lDUT8h/3HP168MHUN9k+X8Ao5QLa4tcgbQTIfT5XWNupegG+8vv8fJXHQr9wXSdOVG0sHkhdZz9w26vu3efta/OkbEPyhZHdADgPY8lLnm1JBtNo904LSq6bF3ldniy0Mgpq50wjM3LT7GcRuZvLXZe00cY5s9hGpF2MW3AKGvh9tjKQ5fgI1terWzfjXUOQ3IUL9qIA7iOQwXWoF3Jrx47JDPa8vrW18YIWlCOqyngiP5M9wG0KDu2d1wKdAY/3wXe56ENDjr0p/5f7znkckaeHEfm7KkX8GXV9y+lrFHV2fYlcmoWtt9tA1zLZswfa5BvbQhs2QrT+nPGfM24jsl3j2y07nO8AKPU47uOVvgEt+zHq0Ra8EI3nB8hUzAPoz4kTKTjvrrvGR/iG4f+94gp7oYq20ETq9hqfbz2DSIZl9Juf73EQFjxbmddhyUtuR1v2dHSukP3wuCaP4+T95v/LeUei72ATuZIrI3P4kj6B95Pz/xE7D/BkUc7z1s7LKSPrjNjPGkqeB3/HIrqRB4v/1GI9vL56Mt5aeMwXMMt+S72dy3acD50aevj8fHWhi5wHOKzduDwufDhMshzlYU94vKp8Xty363H6cm7w4OnNuB8l3HGO7KnjSi2eHNB4m4jeofW5ztestRVBhFvXynpzZkQGlsgVbz6DHb2Nix4XiaU4Y5i2obiVkSPMcurxEFFgvP9pypFFbnKngCQssYLr/PmqEQplgmdckCtr+RbOqBNHKB8gJffts4Pg+IpfvvUbUbdju6tV8CXk5qMJOcZ+FJ5SM+sE5TT61xaZ0DdyjepcaMRR5F7kEviR67CMFG0LNmvhyrlzVdmsGaVoyyIueaY2jFeQi1dckeYW/MaNaqKxrJXZLgHuIKojX5puc8LvswwekPeb9yNi5EhDL0roYB4qJWGi6FvO5DjyIvc5p41ImVwHRAcY0WA7M/LFAG0sWUEwXN5K8o9npsdnZKeRgcIbG0mHht6OoIA77xwfjx6tBqMBkMejUZobkOEA9cD5s3t3NesNAi/geOKfpR5/6aXJMSV3RJkWcD2PFmxuVnfQkST44uLkZw11QQS5fSLSiViLgO1SN50GGcnfnzZQGkwwK4FdpXVY9TS18z3i3yO45fjlZSw9G995JmIsVkKAMc+kLeuW7/fp02lMYx6AbH/ggfQ7ysisbhHwDG2YEyDrH/OY9P2GG8afkWFHy4ZrZc3h95VnfdPA6/HGVsNt/kzkBn/NAmSW0DYAubat39qYBR5mK43TrQiN67H8DprjWOpIfPcSzsHzI7j9s2erOyKePDl5PHWqytvLeWljI8l4ZNGE/rx3b5rrIEeQPROyft++qr7vBZHJheZaZmV+XglK9IKSINE6vrsLDnOa57eFWelH18jh5TzOKkfv32r3tuv+5vqkvIBjK4hTk+1eELHcpVDLoi95Ixwh2x94oLrbbSQIjWdfJhpzNtJOxfUsLqbzIP9lUqrl5ckdsPhR09XxPy+4qS6LsnU+gL41ed6avyjSds549nxSXfhSgRI/2DbyoS3yzPFJafKmbrx4wZD8N+s8bdy1NT5K68nRo9riLb22LW6TLzSRC7Khhx87Zu+awrPny+zNMjh5fT3VybP387LcBwnIDPuDQQpwxpzAj7g22PrXXDN5PHiwmjVZk/9S39cSVsnxLvkgHqTszRHymtuKBdPQNKbAm3+i9T3aEblXXfBgsg+z8KxmoQ9TxoxHhfWPwcp5oqUd1X9YhLbmgChRej1CrK3gK+88b/JuWndOWS3Qy8taYN0jLdAAkM5dPgnKIDBpgPK2pBHHV7GgTrnNPf+frAeClysj3gocfNa2tfzkJ8efETiB/2Erh0gwWgR9Bw3VKQGRsk3b0sp4Rro1Nj0jpe636P9zjKe6duocvlpQRI7s0u5Lz+NrcO8xot0PB+d4K0k56pRmb2xEA/Dq7odmzMr/lSpeOfNZU6XKy7wo52CeicyaPzRZKtvytoLT6pHGH0jBCHmhObUAfl0aIcLrWVmpGrr8/M98ZvI3aSjOzVWN0IgckZkR+HmSlIyMBV5m1hcpXCzoymBu0+k2JaN+sL5mZ6uOIsdBGtVbInNzZI5ooks0RURX0s6JEI050PR+WbcMJjtzJgUUIChNynZOukGmIkAAQWE33pjKRDLdoo8IGEbgwb59qU5tW7cvfGGyH1LWLyzYmeynhbosiF049XPHYrQvml6njX/U421RnWPzRPoUeQ/rAqC9+rW6c+XdNnREZGGJTl4im+swDR5BswdktkrNQQSeRy7I5sHEMhMx3pErrhgfl5ZS+7JNnokGC0JwnnzH1taSnH7sY8dHGXB2+DDRddelz7ye1VWiL395/Bn1XHLJ+AhbZXm5GrCsZbS0oD3bnPM9eGPRshE87kDjJLpyfvI2Z8me0fQoYFoOtMg733dm5Ah3Y8EaE9p409rxzo/0z/JRaGj7WUe4q8jcH9GVAC35h1e/58OR+r5cIK7Voy00kcEIsBX4AkbMP1iMgiPqW1pK2zHLgAMEEx86lOS2zMK/upoWuGCOQVACX5wo5X7bMtFbpKJxRRF52cTHpvVTmyNybO2IDefxl5GsZ9a15nLkdeVyOO4otPOsZA5a/6axGNHjf9rmuzXkjPFpBTHU+YK0sh6s6/HsYO091LINW0kpuLxBGchrLAzEDlPHjqWEUNCppW/3wQeJ7r578jzMDTiH72gFXZ4Hs+E7ZDP0fvhtwRMsLyeZjjkBdsBllxH91V+NP0O2S58F9/8CkbHUVMdvS8aU1NPmuzKN966p725asqIJptFnjU/dimjK5Vgy3au31Mb27IDIXCPnAR44DN1bZvnluhfOgy5+773jI+aBU6equ5NAJkOOHzqUrhl6CzLTYz5YWUk7UmHxNfR2Xi/kPhaRc7mPo0wwAo5naSnxP/JaNf4fKJUvMiMydHq540od+uRLSq7Vs8MvNnj2zMWENrjAtvrxKMcMsaWzgdHSzvLMLdMcUH04jLYSco3iOoVHI4slmXLuXJV81ILSeCYFohTorG3TY23lw4PatG0esNoKChNISWRI2LOnulUy+gylYG7ODiyTwW0avC0dtG3brK0vIyh1ZkZIb+/3EhLRu76uAsTaVPQihJxFwGrnefcjJyNUG/jv/53opS8df+ZbszR5LtFzuyCD6xBxYLXVdg4pyefgrhxnPEhZbnWmORvk/+Tq2c3NajAzZKrmmPGMfSnbZVnu1JJtbm4SffOb489ye2UYznx7ZUC+hxHSoTQLzqygiVNrFpHzjm2jgtH8QvO5MhLQ0JK8HdH4/RtM26jWZJm1iBDQHNCl29taMisa5FGHxcWkS0sHj9RbeRuSCF1cTAEGmBNAgCJ78smTqW44nHDkmRuwFafMkL+0lIIREMQsA5d5X2FrRBaRaEEEEvLatfNLgxEs8PnaItI0olx7V+u2j/ccpFqZpu9lV3aQ9s5YjvjImIgETWlBQ9N2rHPcfffk4t6cwK4m/efjNursIWrPrpv2M2i7Xb7gBAFY4EA8fdvLkGztZMKdL5Cl8npQ9qGHqllzIJOf8ITx8fGPn8ykifOIksw/dozoz/5s/Blt8gUnOE9mz0TgwpEj6X7IeU0bC9LBpG3lDEiOR7N5AJy7sFDdOlQLWsL5crEEt9PbsNk1eA5SQJsbuuwHjnXyOirL6jirNhGps++A7nPnkrzI1ambckRNHaTTxKz0oxSWjaAtWLeCKfnCF8hrLHLxsl9KHokvfJH94rwYZDI4HdgIw2EKUMAR8wDOGY2qPoocXlfbkhl1yy2d29xxZdp6Ctruglf32sv5fdbhLQaW6PsaV1e7z8ZWh6ZjvI9AkghXXHduBNEAeuk38Hh2S34TJb4ERwSMbW4mPy2OWuIS7sMlqvpxL1xIerG24JFobBfILJwawBsh8O0b3xgfl5bS/6DLSP5/796k9/MduYjykolwlOiOEdsgp62687pGJMZA+972vKa9n6U+ciBnAYLVH61vHl/et//3YkJbvGOO3ejxfFr9dT5Irw3v/dF2//HmAXyGnMQOUvxc65o8vgG/aTFCsA0kn8Tr4bE4RGleOX06xfhgjoGuv7hI9PWvjz9jHpO7KO7Y0Y6saTM2oG5hX6mNPKtzxSyi6Rxh1Rd5Z6fxLKbtN+X9eJRjOxhZYLCpkCxdounLP6uDOHpdkcCNttAkAMQzmoB9+2xDl2//A+UDhiLfCghlZKAytvmBAvLQQ8l4lKueduyoriiXGdYWFqpZ0xAkoQWYlRiGnkIpMRrZY8FT8jzk9KsL4mdW3822kBOw0FYbHWP00h8j2runvwanPUb6bH9a1xohNEvkvxZspzmxiCYdTjKoAYSLttBEbgnEgyWsQOe5udSGzHqMc86cSXVJ5wDmgYWFSeeVvFb8LuU0DGdvoYpWD/osjX4eeGAZLqVjK4f0yAkSmhVsG+JZGNCoPBtbl/faOH9AgYCfFtrJPp8HyHjvqnRmRe69R2zI71q2euu7dr4W1CbPj+ziot0LlEWgMIhCvt0bZDQcTfh+5oyd8WdxsboYEQ5WZE9YW0tyXmZI0HZ6KXFQ5QQhaM+iZBy3uZBAjs3IuCtdDNIWDxCx0yIOgJL+RN5Hrw2tzb4zY151VSwzjnetJWgjqKUN7mQ4jAcA5CInOLPUSV1XlwW5rT3O4byNXLAo+8EXmoO3gRMIgb+PeUw1wBjZd3C8//5JpxMR0de+Nj4i442G668fH2++mejaa8efr7xyfMTcInfzIqrySJjz+LwgAxVkUDL/H6C965FsmXLuLn3eTYMjLHB7URsn+N0LmI62H5EzEdmxvh6zmR7tNsH6up6N09MF6gLTveDFkmAuDfx8L9Oo1re6Mjn/4/cishgxoj/l2BFaGasNzzEq31X+3PBZ6yvkrMX/86Bmuc0z+I8HH7QzLGNschsB/cDcxQMxIP+xCIVDLpSUQXIbG1VOR9tNEWUkVyS/875q8wAC6CxEbNzomLLGJN9tzRrjmo5k2Xt19Vj98tDUxvBsUznevd3eIrxYTp+18ngn+gK3NyI26VaYL9v2qebqDTm8S067behx1nkat2PVid9XVtJ4hZyWHP+5c0n/xzyAsjjef3/ib2ATQP7zBXvQK2XA8b59yf4A34PANz4PoU+SB+LHOlk4N2dn2IT8L9Wpp8VddxWDkDN+S7mYnLkmFzk+sib18d+nkaUY74iXGCMXXca1tImmfrRSnSbSrmxD0wc93V7T9WQZj7+HvJW+Wb7jFl90QpR0bO6/5Rw+P/I5Qy50l37cwaCaoZ/Lf6lnyzlncbG6WyLmAY3/iaCJjBkOu1vY59nYEd310YSSa895v2dptzAi3V6cVj8e5Wh1ZPzZn/0ZfepTn6LnPOc59MQnPrHNqvvDykq54Wx95ygZdJEXJpfE8Jz/Hqlq9SeihESMDK++kjIatH55ZFK0LR6AIQO8YDDytqwMXPyzNZHywActe+bnPz/+DGWGB5jhdyv7sra9gpYJgdfvIfo+yIkqh0TUzvEI8zoyXetjhAyNwHuPPHhypqQ/OQEMkX5FCFjvfO3ZymxJHWOwuaGvmAc0udDGmPTO0xw7EhES3apb1pvzzK2AqCgZb/XZu2eRuaZ0rooQ7tbz5rIdkFshIyiByN6mUJsjtCwM2pxANNZhvvCF8WdcqzRmh8MkuyHbYSTz7G7SoaRtyWMFnVlzBgc3mmVWRil3z52rdxpqc7k3j5TM8955UYNX/q8uiJF/tu6BVm/k/Y6Solb7GnIcV96cCzK/J4xo4DubgRwZq9WlfY/cT2+cRfrRBJp8KnEctEnoltgUnm7tvdco5+1WYt0XLr/lnKm985gvrDZ5Nn85XldXie67b/wZQWyQxdj++cYbU4ACFihKEnJ+vqrfR8i7kjGqlW+boInKwLrxGpHnGuR9yb2+Ev0/Rycv1VM1RDOjWO3nvNdtg9sclszo0xka1YUjzzdHXpfYE9b33Hq4XuSNbUvWaPdMts/lt7a7CC+jLf6T4Is3sAiEZ9mWfZNBaXBKnT+f2pJ6IX5fW/MXNSIzztGj4yMWs8NhhSAFfj2oh/fVWxiIowxQkwHPmlOLO9Vk3R4i+nLd+NXsq8g75tmNMniL2+OyrKZHAV4QZBO0Ia+sdywig0rlQd/Yu3eSqwUi3I4HayxEubdI+3IsanZiToB/jg2n1Vcy/r36LL7D6hPKeJmMiSaz3vNgX/6dL/6TznvcU54ZWQYl4HjmTKpbZr/HufPzKbAYslNy9dxPBZmOOWf37ur1yKCG3buT/SAXS+TudtXWe4zriKDE/s2xEflnb/7w+lOnP3njv65u2U7OnJdTP6+vbi4q1S/l79PQ+QHY3UQxmdYmcmRhl6i7/1H+py07wuLA+cIOBNtCznI5LLMVoywWP5w4kcpDzspAr6Wl9BvK8PPRFsqDU0G2Sg7IW5RFGegdhw/ru90SpfF52WVpoSPO5xw/eB/ZBr4vLFSfR24Sqq4wbb1w2u1LzFp/LHTRz2nMBdCdIr4Pz4epoYQv57B02Kj9GtETtM/8u7aLV65uhXOsRXs8szz+J2X8iRNEd901/vyVr4yP4D2wmIMnXeKL7Pjx3LlqBm7IScjk+fnJ3a14f/g5kkPHPADe/aqrEu8usxXj++HDVT5BLjjhdirmBM7JIP5H7nYFRMafxnN551n+Xn6OlX1/OKz3i9S1j3rqOIg6nrsPjtdrvw6R+5Lj3+DnRM5rIsO8Nvg5GPddx/9446VPzEIfanD77bfT05/+9M7qL2Y6f/Inf5LW19fp/e9/PxERffCDH6S/83f+Do1GI1pcXKSPfvSjdMstt7TVz/4QXYmV4wTSkKNkRQnLOmhkgratTQlynEEe0ag5CTwy0TuPSN9eTfsut+eUK5s0o1gzkmWWY41AlRkRtG3aQCLKrXgi25YsLSVFBUrQddeNj9gK9PDhpChZpKQW4AjlRjNcPUXDOw/nIoCiCcGhkWa5708OiZKjsEsHU6Svw2EZOSShySsvYEBzaJQ4u3Puryafeg5Gps1NPStEaV1A28pGqTE7S2iDaKhzmK2u2nODtgWnJdvX1qrbWsp6NjaSQSbnKh5EYGXG4QSqFfiANldW7OwLRGkuQdDB4cPjIwzhSy9Nhi3kvybrtWwJRJPvhjTuNZRk1tSMFu3ZyTISkTkiYthp84Elyzz9R0NTp5Q1x8g+aJjFYIIckqtt8KCcyPO1UCqjI45OWY/3DCOOfX6/PTIzCu19avLuyz4S6QvytEzy1kIePkegvCd363QbTqhJ2Y7fuf5rzRVEetCXPFr9WV5OWTIh/2EPIGBheblKVErdnG8pmkM2l2SA1MjIHEQIS96fCCFWQthH+4h6Zd1akF2d877uf1Y/POLRm/NyxkLOO6+VjWzl3CZOn9YDSIGILNPeyxwSPec517XrtWfVERkfOYS//F/kPE8398h4GXBw+nQK/oKD6vjx8RFOJZ6tBmVlmfX1qlxDWyhz9myS6TLggAesIfgMfZRzVptAlswbbpg8HjmSykDue1nw5XPh/BZfvEg0mVmfHzkkHzQcJq5KludjAf3Qgn9RTwkXos1ZXsCznJ/lOTxLEl9ARDQZ4CLfI22sWzooP1oBqBq897tEXmvjpAn/M4sOoqY6cNP2p4Emfo3InMfLWwFnZ88m+Qr5jeOJEzYHAhl78iTRvfemz0QpCJgvvrZ2mapbgDILgG4P2X711am/0PdxHTxwTS4w1wIXpL4j54bcwGUPFtcb4Vs0WLpa9L3M4SA0+evZ2DjmzFWWXRWRl9F32eMe6nRfrR+R+nL6czFj2vIeiPQjUsbS6fl3+T9tHpBBaOC2H3ooyXm5+AL6+/Hj6TfIQIDLO0ueYY44fjzNO5g/ID8hfy+7LHHq0obEdW5spHYRRIYj6llYqC7gkzL6nntSGzgfC1j27k2LECXvgzLLy7EMmOi3Nf/x+1XCb/N6ZTKSknoj+uY28jEtf+M0nhkPQJUo7U9k7oxAJp7QZKmskx9z3i0Pdefl6jba+VY9Gv9vzR8XLlR5dq1fKI95RAYw84WGkoPgMWPSBwouBOecO5fmM/wPQciQzZofAfMc+KnhMM01SHDF5x5k1AdkcPTycp7uDjktn5km/7U5Q44BK7kVr0fTJSNJm6x6PD63qf9AKxt5R7x+yHYlTxJBzi5JWqbwaaNv/n8bJm6++Wb6tm/7NvpH/+gf0Ytf/GJayNn5NIDiaKuPfvSj9MY3vvGR729+85vpBS94Ab3tbW+j17zmNfSWt7yF/uAP/qCVTvaJ0XCunxcxt42I87Sknhx4bXmCPnJ+n4gYMNo5VjCbpjh5ShIUH0mgels5yLLcSAdZi2DeEycS8QojlG/hgOOll44/w5jlWZOJ/AleK+MFGltjgdcDpUzCm6Ct701QMm6nPbZn4V3rwIk0ohbJ7kh78y1OsBebkd422riGrXYfPAOEH62gNr61j9zeDYbqhQtjspIoGcqYB/hRrqyVGc0Gg37vb05mHAvTIs0udhTeu2L5PRyTJ63K4yh4dhwgR29uKxDPQ9P5vsRxkIOcLIcamr6rfbzrkYC7SHAejjygWur9WsCyFUg1P0/0pS+NP8tAM55pDf+TC02A6DZtcrznbu8GlBBPEdugbXRZfx/kmySN5e/R89vGLAQe7N3bfGtQL0jPWtDMnUk4jy88iIyLWXC65gQpaBmJI/dFC1q0CPv5+WpggOwjXyAu2+L1WW3lBEbJz/w7P0qOSG7pubqaHDlw7HCb4dOfHn+GbYBADozt/fuJrrxy/BlHuSixS2jvOhbMb2Mb00Rb4//RYG9ac7a3Ww9Hk4Bn7fy2bYqcwFHrPKLJed/TAYj0Re3g+hGUcPp0NRCb2wx33DH+jLkP3DoWfFx+eQpMkLy/tgDR4/3bQttbsjcN/Gm7/a0gD9ruc9P6toMR8hGx6ywZFrXBIs+1xB6JtB8NYitp0/PxSp4msnBMLt47c6bK6cg+c/7HOp4+nYKrIb8RzHbZZWluwP/A+9clhbJQyukAkTFQkvxnK8rYrYhH033tYs5p26aQ9c3aPNlBLMDMImJz5PI/2rzBj3yxM7gr+HzX14m++c3J9rEIET7WjQ09o34bkPVE5446+T8ctrdTFK+TH3PPa6PtturT0Pb9utgwK3JqFvpQg/e///307ne/m1760pfS6173Onr5y19Or3jFK+jqq69upf7ikXrs2DG69tpriYjonnvuoc9//vP0nve8h57ylKfQT//0T9MrX/nKVjrYNwb3HiPaFJk+5Oe6/3mTizyHH3PakODGkoXSgIeIgdfUKMg5fxpOS20i5NluiCafs7fVd90EyOuxVnxpz5vX+5nPjD9bK5CWlqpZeyKZcQBvZVPk+cgsyqORvU1DBKXjJTLutPcgsnrKe751/arrk6yvyUqtpvcu93wrGEIpOzjzkF13BxisXiBaV7bWKkHpqrscB4t3vldPpB/ebzn9sRAJ5rJ+836Ptp+DUpI08gw8eSADKXg2BLl4gwdpfOELk78B2vZHcHDJrTx5hgbrfg4G1TZKsuVo8l9DE7I6Z2zV1ddkfEXew9y22hjvXl+ijmRRLmskKPdlcO5sTg2NMSBnNTeHpxt5ekxbem1Er8wZEzl6S1N4MlD2oUvCJteuqdNXInqYF9TAA+GwUATyXo4pLciOA8Sk1OW5PLcCC3LHj5T3MqCP/2ZhNKpmRtPqiejCVpuRMZUbMNPU0drEceydY+nYHqYVDJw773SJgE2SDW57edlitfFe178mfcpFW/Y412VnwYk2P58XCOXZI3X3lWfz8eSTzIQvF+qdOZMWnyNjG5xS8/MpWw52REFbkP979iT7YceO6nVIeJlBS/T9pnN5jv3aFCVBlF3o+FZZb17x+hHZwSDSvgdvfovI2ia7JZQiIodlWe+8kjm9CUq5F6tMid4StZlLnm+OXsmRc82R6/F2sZDzPOYXTf/HkQcny/95iU/kro5ERF/96viIgAO5lfPCQuqrDEIGcjNmRnRZD1ZmzAhy3tloXSX8YmSuiOh3/L2ouzauX+Zw/G3PS6Xw/CvTkP98ZywLJRxLFDnPio+NnD6V+IL6QNP2c+6Bx7dr/cnxw3m6vaW/aVk4odtD18fv8/OTwcdEScZz/y9kO3R8Pi/VLSzZ3EzyHYsZNTugboctr42tjiZzRfSelPA/2yhHl3OOZ/N4ekIbOn2ubuJxiCX2S4kfreSaZP2z4guW//P0Oshp+AG0bN2eTgC939Lp55Skm951yQWKmv6v7Yiu9c36HtkFu06Wem16srpUn6vTy7Q2cusvGW/a9TThtjW50AdP3bX+34V8KO3HjOPHfuzH6Md+7Mfok5/8JP3Kr/wK/at/9a/o7W9/O33/938/vfrVr6bnPve5jeovDkZeWFiglYdJkP/zf/4PLS0t0V//63+diIguvfRSOoUsfVsNBw8mJZ8j4jDziKyuyHcgsqXErJEP0+pHqaC0DMyc7Ty6JKZ5PTBipdLAnVIy+BiQgcIeRqP2tmrLcUw2JT6tejR4CnuOsyWCkvERbacJSVXSXt35kbbazpZRg9HijotnNVkJoasZxyVBQREMh9XMqcOGK/BnDBPBjV2DO7W+8Y3xZ5nxjWdKsLZpm5ZifLG8d3Xo4f4WZSQezvU7XiOYhjMKiD6nHGejhUggU24bOYGOTZ2oOf3Q6oyQdzn1RZDruGrrftT1B+3xo/a7dR/W1yczIBOlRSh8HsBcULfgxIMXjLOhLOr1UEfSRvSPNpyZEdLOI+qtftSdU9dWDpraHqU2et39aOv6ugJ/r7ogKnPk3Kxh1vvXFzznfYSns/RcTeZb88DycsokfM011bqg21vnc/kPeAHHEqWcj1zEzuvJCRaU0ORLqZ5QN89rz7mpI7kJSpy8HF3K5JL7rAV19olSPdya4zROpS8e2GorootYfW1zPM/anJIzXuXY9GSPNwZg40Jmz89Xg4+956Xt5IPgNXkeX4yO/0lZbv1OVPUjcP7fOy8Ci69sqvfn6rLe/F53fluO+lK5l8OtR+zqpjKppB/cBpsG/8PfpybX36Wfs2QeacO+yzm/Sf0RaHpYl/NZSVsS2kITTUfH+49FhQg45u+FfEcQqHzhQtolUcoD+NMWF+v5Hy7HPVkh74NWn5a0SsIKRvPGeBt8j1ZvG6jTB9uyETwZOSt2gIaSebYP5PDCpejiGtu2lUrkm6bnWt83N+2F2ZF+8N2q6vrVFE3ny5y51JN3WqZ+eVxfTzupyN0PpR3A25N6/OamvkuK7KM8D7I+J1HhcFiv62ncfA4i7zWfV+V51neOHBkaGavz874PCEcreaV2T+VCWc615Ngv3hzcls+ia36g792fvX5sEdx888307//9v6d3vOMd9Ku/+qv03ve+l777u7+bnvjEJ9KrX/1qetnLXkZLBXFbxbPXE57wBPrN3/xNetaznkW//uu/Tt/+7d9OCw8rt3fddRddju2gthrOnYuvPpGwhFbU2W3VE6lDW0ldorBrk5zXxxzHbVuKQ0RQRu5hxDj36suZGCMOJq1vmqKB73LC4fUdOzY+QgmBcOBZcCJju22DwXJ4DQbJGG3ynDTkGpx1z6cLR03O+1RCGkYd66Vjuu5/OYRyLmnWIgabGzG5GXU81NVjnVN3XqlxUzK+uhoTRDSQTsccUr8tJTiCEmO5tAzfvhrBxDjy7TpBPkoSb36e6OtfH3+G3JcZcnjAGhBxUMqyUbRxj7pos2kbM4qpBRW37aTom5g8elR37jYldZvUp0HLIpWji1vQHDxafSWkhXZuk3q8slyW1emwnlMrogtHkPOceH8g2yHrkcX+woXqNs98S7cvf3myXWRWOHAgHSVRCUSynWnnes6sCOrGgqdzafXktFl6fgkiuvA0iLk22rJsLc5PNH0+XYJnpYrIJ82uKu2zZc/n2uOevK0rEyWkJSK7AXi6veVYqZtzcmyVHDtMjluuk0eIfmnXaBmxodNjS+UTJ8bHU6eq23IiuADHCxeS7JVtbm6mzPgIcIPcP3Qofb/kkvHnXbvGx8iixKb6mLXwndftjfGIY0fTAawybSHH5i/lDrpyvGp2n/Y+N+G3eT3aZ+07UZLFfeH0aV3/byJnNCdqDgec+x5G9Hcp3/iOe3X98IIeNN1e/s/6novIfYnMnV6AGJf1Uu6Di0HWyNXVqgNeyygm65Ycz+pqeh6YG44fnzyeOpXaghxHEp3l5TRPQP6D90fZw4fTXIDz5HPV5jfMEQhkiwQsR31C1vjS7l3EppT/0+r0yjbhy/sIos3pnxZkodVT8k5GbKjIM9T6N42dM/DuacGROXyFPEc7vzSQUDvP0nty5hqtPu33HK7JO9bNi7n8S+Rac3wgXOeXc6W2W5XkReQ5p0+nRFH33js+Stl+/Hgqg/oOHhwfL710fNyxI3H7PMCYaCz/H3xw/Hn//vER+j/3A3gyvG1Iv6/2nOr0/6ZySmsroreXtlVnY3CdOjIfdQ1t7gQ020B7j3JsBOuaeb2RbKldYX09xpfnoKkfoW27NUeHjowBXm/OtUb8v5ZuLsvVnSd/9/7HdX5Zj9ZXK9BTuxfyHvK5ArYA+P4HHhgf+W5Y0PFhI+CcwSDZJnv2jI/Q+zkPhN22rB2S5GJ1C9b8oT0DK7i5jufDd+s5R96VyHjM0es0cNulyRyVYztF9aIIL+D9ZvUjAj7GrYW2HH0tQs+VU132Y4thcXGRlpeXaXFxkUajEZ07d45e9apX0S/+4i/Sf/pP/+mR5MRRFD/x173udfS3//bfpt/5nd8hIqIPf/jDj/zvf/7P/0lPecpTSqueLpaWEhnZ1LGhoaky0dagjRATTeppo+5IGxHluaTetpDTD360FBWusEhHFRSVEyeIvvCFVI4oOZyuvHJ8vPrqZNhivEP5iCohbaPnLLjbaAm5hFFOfT1nxhkN52j0cHZeN6CvKTHSJWatPxcDvHvax/3OmSP43PDQQ+MjDFYYqXv3JgdVH/K+qSHYVZsRlBi1WxxFmZWJqO3M5qO+M6VfeWVZkAWHR656v+UQ0W2R5tNG2/ZD03s4DURIUgQTeyQwnwdQHgBhyBcl1m3TSeRv1dw26p5Hm88rxxk1DfSpU0QRkWuybFNZ2jeWlqrvDlH748ST/14ARlfO06aQfS7lX3LHS1vX2sTpWBq0BN4FTqHDh8dHHvgsF6PInU74Z/zvzBmiv/iL8WdkwJFZ0Obn0zPLueddjq06W790TMzKO6K13STgLRc5Dqau7k/utWuBwV1i9+5+OKcufAtdoXT8NQ28iJSNBC7V1cPnrib3Pmrn1QXMccexFbB89mwKggang+PKCtGXvjT+DB1fzjF8ZyzrmnN1f/RZBuR5CxY5rPeui/eha72hiz6X2MptvoORIKWIDZfTp2kEo+G9mEYg9DbaRdtBNV4bXG5HgqG9wCMr8I4nKdEWsaDsV74y/iwDlXGtg0GM/+kK2tzStf+378Dei6kt3kbXcrHPtrz2o/ZwVxxiaWBkk5iYunZzykQWfeQEl+Zw+1q5tq7Lu791vLKn/3gBm1ddlf6Ho+R9wBGdO0f0uc+NP0OnRvwPP8L/a80Dbdqosrwm/5vY+tq4aVs3bxq/lvM+t83FRcad9pt2Tsn1AIuLeTIdY7srbAcjZ+Mzn/kMvfvd76bf/u3fptXVVfrhH/5h+u3f/m26+eab6TOf+Qz9g3/wD+gVr3gF/QU46CCKWbcf/uEfpiNHjtDHP/5xuvnmm+k7v/M7H/nf1VdfTT/0Qz9UWvVUMRrO2RlaOdom6LyXvo12c/vTBmRfPUe6tTpHK++taNLS1NeRKDwTAX7TMhxYBiK+r61VV/5oWY6sAGMtC4MU/LhP6+tp9SuCkfF9dTWtsuWZkIlSUNo996RANayegqLCV8yiXUksalvxyNVPHPIZ4P7yc7AaWCqPfGxY41XLDA7kOsabOCa1OnKVhihylAjNaNLGZkkQAW+zyfkzgAGNaHDu4dWI3niLymv83jSQyjLSSp04kcUo1rNsutDDI+8iZJ53X0rGktcPD1Jur6xUgwjgMAIWFia34+RHXMvaWnIsyQUnPGOaRdSvr6fMyJDlyIgAwm/fviT/rcUoc3NVOa8ZrhFDSp6nGb7S8NDGm/V8IjIoMv61vmn1WNkLPVkqodWj9cH6H8bN4qJNJGg6jpbRA/U+PDYHEXkl++Odo+3oYJ3P6hnceyzejxYwosFk1uHItUX03JJ6vHHf1Zw5HPrbD+N7ZHu1iC6Qoy9oiy+kk0ZmEOZ6vHxvtPeHvy+8TV5e00txrsxoJmX0xkbqE/6HenhmMwDXg/pwXXNzqRyOmFfm5pIslZnMuH2CfktdHN/X1ia3debXqs0DKKvdO7QPe0Q6fL3tyADNLgLQD00WRsCfoWWfcRuRjx30n9ejye+cOYeX9XbAwbmRjByWrpQrS5rOrxJe2b63aV5dzdNvI06TqL3YlICO6EYl9cp66n6L1q3p3dq7JvvP66sbHzzTNTIPI2vw3XePjydPpjbk+6z1FYBM5DoyykC3ht6O79xGkPXxdxh6Pna44hmRZfsyUNm7J9ddNz4+9alE3/qt48833jg+ImBNcxbIrTe1rTglN6TxQXI+4ufILLiRLScBTdeqk9H8PD7uLD2Z11snbzW+RXsPLb3H04fkPKDxl9o9sDhWza7R7nmEMwAima5yuJW+0Ffb07zGXJT0VZOXXhnrXdEyEnOOxdpBijvmIUvxG7gV6KtaP7R6JD8BGcaDqGRGM6n/c54DOr3k79fWkly1dhJcXEz2wtVXj49XXDE+7t2brhkLq/A/YN++ZDdABkvfxXBoy2ueDc3KesnHDcrLeZXXU6f/R3XKHH6/RFfzyjV9r7X3oYSLieh81rm5dUf8EKX3ZRpyUtt1qgQ5XE/TunNsyy4QsQ00u9OS7fy7xfGcO5fkLGS6lKlnztjPE/WdOZPkpeTredCU1HmlzjU3l+YCnCfnp83NJEvhb4UcloHDWptoa36+Wp7rpEhMhbaQERMZlvftS3ODDEoDIruWDAa2TOfziWYLyHOsuItcG7OpHS2RY+u3Jf+t7xq6lFO5KPW/cXi+jz4AnkDjEj0OW37XOMWc68mdO635mOvSUmaV8Peabl/CSXK+POJPiMwfOKLNpaUkJy2On3MyOF/y3rxfqFuTl7IfmE9w5H5bj6fAbxZvPhxWfc38GqStgvY5X1O30IvH/2hyH7B8Atp7zH0LHMNhmoPlfZHteIiOH49LqvNPegG1XhsRWRJ5LzU/mHUe77u1Sxsv48mcOn3OK+NBe6/xGTbxNqaO3/3d36V3v/vd9H/+z/+hyy+/nF772tfSq171KjqEHZaI6ClPeQq95S1voRe84AXZ9TdKAfDX//pfV1Mx//zP/3yTaqeKAY1iCocHzzlnvZyawpJjYGrOrKbC2yrjESUREiUi6Dw0TYUfgXbN0nEREbzeM8kxmryyWmDUn//55P9grMMptrpazWCgKVeoUyoa1nYLWtnhMLYV0GWX1ZexwJUD6z2KGDmeU9h7vpFJPPI+55CIETnltY33SFMCIga4pdRo55eSq33j3Lkqca8hIsO6uE7PkRg9p+0+9NFmLkqMfg/eVp7eu5+jS+ToC1oZXvazn50sC+OPG9nSiMT/uCFb54yan6/+hno5kSrvlba4pa1V8G3pPR5kX9vWtTR52YT47DLDWEQfzMn2yL9jm8GeMFg5T7S8s/qPUv1Unp8zTjQ9UxJXuXK2pB9a2Sbvaunc4GXfLHUq5yAyv9fdXy24p1SH9HQ9EOqQ6dD/4aRbX09jCQtV5MJF2R7/zheuyOwLciGnR2ryei1ylOub1rjX5s5SeSn1W9TnyVCPUNP6iHasPnIZEnnXLH0h8h54NmlEb891tDS1cbpEDmfh/Z5bxkOpnMshliNbXjblxTTI87fS7kjavZfvgrao3JrH+FEuasFCbb5NJwKVIevR1vJy9TfUA3J/fZ3ozjvHnzE3IBgZ8wAPeJCLGaU9wD/j+rSdVyQPxJ9/11lwNdks9SktK/qsQY6ftt4Z/iya2mKWbbq52R0vPC3kOOma2v4e52bV2dYcEeG368rX9QPwFtp6/pWSPkS5ybbGYl09XD7JYDYeWA25LYPjVlZSZjSch7LImDw/X9X7Iad5AAZ4fshtlIF84MEe0u5FAMTa2qQjH+fxtpeWUpBhyUKGXOe5HEP8WLfgcX6+md4XGbcel95WUIMHrY1IX4HIAqLIPZxGZuQ+MuOX8jZSr5T/19qIzBVem14b3pisu49tzu1N5zqrPv7dkgelcyegBWLLAGqpp3rY3CT65CfHnxGsDbvha19Lv6Nuru/zvvJniIRVl1wyPkJPXlrSk5jwvnsByxyaP7IOEe4sB16b05ALXfIfOT6mXO4sB9450/AjIg4hMk8Txeb5yPiM6Cse6hZmD4e2reiNhVJu3bpmfr61C4FnQ0X6xudJqUNImbpvX+z51D0DTUeKXocsa83dfK7gi3KIkm2wuppkOnR5zANHj46PKyvV+B+U5b4C1Ak7ADYDuKJ9+9JnzA1ykc3OndVFOpLr0ca4lD18Dvbm4pIgZq1d67vHg3ocSttcRpdzXdN+eHphZI7T/CBdQOvPNDALfajBS17yEnrqU59Kv/Ebv0EveclLaNHQRa+77jr6u3/372bX34N2tYWhGSBAhJjwyJOthD76XiIYm04ykTJ8kokIyDqnMCe7pDLByUWLfIBycOJEcjDhf5jwL7ssOaOgKEBBwPeFhX63YI4QWW2Ns1l716bZH43UlOhiwm/LqO753o2WdyXiZmkpFoAh76skjrgBaDmp68jENoylhhhRYEFBQwzIWDmfi7buhUbMWWU0YymXzAGs7KQ8qw7mAm3Ouvfe8WeZiQDG5J491RXDbYMHIdShhOxpA9r7V0dwRp6zphc2cWD1gdJnkPE/VYYMbT1kNH/B/F8XGC3t1DNvac+XfyeqOhV4WUm6efXk6Jl8HmlChHm7T+To5hFim99Lea05O6RodUpEr6fk3fTur6W/c/1fZnPjgQYgBiFDZRbk+fnJzD5Ek/bA5z8//owyCEqG/L/yyhTob8lpHkRsbeWmjX8Nsh65mJGTkdaR32cvcNk6r9RBH3EgeGPKu2cRR0YdGapdsweN5LXa4r+XBGlo74GlA2u2dt/BCBF93zon595HkNNmU72h1A6PyHtZr9dna2xo5b3zPX2uxN7l4997D6y2eFkZaKD1C7o55D0cpIcPj4+nTlUDDL7whfHx058muuOO8WdkRPtrf218/K7vGh+PHEkBBjl6egS4Vm57eEHIfaGtd4UjonOVBgNtZXQhl/oCt7XrZJj1G/+uvfsavHtmya7cHeFy7qenP8m2IrseehnKLXh2jdaeN0fIucVb7KnxL9Z5mp0n+8E5f3Dzd901PiJAAMG7i4sp2AtH1Muzg3rzkFxEIu2AffvGcwBRmltkUAKvF//DfdF8BzhPy1aH++HttGXxUZrvJOKnKZn3Oc8XCUKI2BSaHLDqLJF3mnzxgoGt++L5Mvm9t8DPbRoQArSVoCAHkSyNHNZ49eS49V2rJ1f/t7gHrW6tvojd2gZK62vbBmvKy/L7LDPqg5vhCT6gbyN4S2ZGlnXy76hH28GH3wPJWyLBzjXXjI+7dukLCnk9Hkr9GhE0tdPk54hs7vKam9yrXK4KKB3THv9jnd/mezxLKOXpSvhlzn8CXtK9CKfZBu9hIWecROBdu9UG5xLleV3a3Dl6ofa7lO1y99vTp5Ocx3VIe2BpKc0b2CGFA3q+toO51UctwaDU070yVlubm9VdE3F9fIxacrLp+NXsR41zxrErvWfW+J+ITsNRt2Mqr3PWrtVCLifSZT9mHG9605vop3/6p2kPeGOGM2fO0J//+Z/Ts5/9bHrMYx5D73vf+7Lrz3q7n/KUp4TLDgYD+ou/+IvsDk0bI1JW8U0LniPmYkOfCq2nTESUTvmbRpZpqfSJxpOxNFi1YAQEFsgyfPWszEAG4/LYsUR0ymA0kDu7diXFBsqMdE7x7SJkJkt+n6xABfl/DfwcKGOeom0pChHC3XvOpUSjLJuLNmRNqXGcU1eEWNMcB3X11vWj722aiWh06EoiajEwlqMLw7AndHI/Zh05pE4XTm6LUF5erjqo+FFuEyS3dD5zxh6DmnPCI9TwP7m1KM+qbGXf5/dObtNsETDW+bLP8rwoOWmV05xKlqOUO+Dq+uP1KXfOqqsvt4zXnry+wPVMPH2PEED5TaHj9AEZ+DmL2GpGfxuI6CASnk4iy0Xq42UjbWly3NpejfdByuvjx8dHLFhcWakGISPr5UMPpayZGMPQ7XE8fbq69RtkNA8mkxnR5DkLC+l/1jaf3E6xsmcOBlX9PwJNRlvbkUXRxrvl2SE8GDFH7lvzkkbKa/V4v3m/59RjzbV8bPMtFHk9OaR+V6gjJ5vIB+98b+7Uxm9Tud8HwV7SBs6ZRhBKHaTt5i0oteYTzRml1SPLgP+BjLzvviTjsfAQ79PNNxM97nHjz1LuQxYfP56y5Uj5zd9HS1/n1yo5Jo8P4nOLLIsgPQleT52c4++vbD8SfNiWXa712Qow4eVz+CyOOjnp9SfCr3lt5M6XdXbYNB1EvE9t6f9dzmElOnEdrP6WyuSmgQttc2VtzS2Wrq+BBxXwpCFERDfcMD5yzl8uVJSJR3j2TO43wDnIes/5fqI0pldW0oIVzCNyUYrH8Us5zq9RO8osbFJmDAa2/OcokUER+ZQjY7XAIYfDMNuP6s+eLo2jrKskKEdrKwf8vsg+RjgzIDLndAkrGN1Cib2q3RevvhK/zKxyQ20+0yY2Rtv18p1EtAAxoslrl/o/l99SpoP3AZ9//vwkFyTP/8pXxp+h78PXe/Dg+Hj55em3yPxqvaOaj1jz91rjnd/nHP6/T7mg8S+RhdlyYZIn7yOZ5L3+1fkavL5adbaJiJ7q2SizBq9/2nVFeOocaL6Xunvm+aRyUcIhtoUcjihnLs1tP9JWpB7o5Pi+tGQnKoGufu5cNZkh/AgbG0R33z3+jMWIPEsxvvPEJrx9yPHRKMkji+Phvl15vrZgUfoEeP3Y/UvybF7CHomov0e+BznJeKL154yznLLyPdcWJkXq167TsmMiCfGic02Oz6Pr+J9pck2yHzOON77xjXTrrbfSM5/5zMr/vvSlL9Hznvc82pAxiRnIYnkuu+wyGniBhRcBBjSaXMUo4ZEO3gtYImz495LgEs0hUkeI5CqvEcHUlEzPUaA8hSyHFPVIIatfw2FehjePXKrLwsnJSC3T2p//+eR5l146PkIB0RQ5qXDw9gB5PYOBfp48V8u2IOvLydRpPWdt3OWukrPeiVJls2tE5Uzkfc4xWOU5nizMNQqkkd4TBjSiwfpa6oMn79tEiTLZRfsRooWo/p3qyiHWRl0570hb81gOecHLyZWkMDxPn07BBJC//JmgHAxCBCOAJF1etoPISp9d02fWhvM3R0cpRY6xm/u/tlAq20uIltyghhz0bKQN7j1GtLhQ33YkcyOHR35I5BKddfB0WK0eOffm9IMfm8yZbTketOeTQ4JHdL/Sa47osN52rPiMueGBB8bHkyeJPvWp8WfMH5DxcEZdc01ZZnzvPlh2AG8f0NpE9p7IvY4gIrNykKs31L1ruU4Kyy7R6inRcerelSbkauS9iGTw6hOezR+BJ9sjdo3GAXjnl9xzWa/8rKGUwC3RrXPeD/49V9+u+59Wj7atsHw+kM1aEFmE/8H/kFkGZZaXUzAb5DjfUhmLV6BTIxiNZ8xcCOg5fQLXs5URGdN97ESVU9aT8dp75I1fS7/V2piVccfBM/FHsvJr1+XJIE8niOinEhHbi5f1FkAA8tlFM8rK75HnnMOXRLh97frkXCmva3GxmrVZa7Pu3mmyXWJ9vZpgRHI7a2sp6EyWBZaWEpeDbPl8cbrMiMzPIxrvioKdUTA3aNcunYsyKJkHHHi+JEDLrIbvuJ4SzjXHZvDsPe98bSxadXvjlp9blxBAQ4k/gusm1v3NvS8aIs8qIt+mOUcg8N+bFz3+Jmd+5ZDPJ8oD4HvdmCzlZKLcRxRt8cuebNe+R+YIOe75gnHZvty9hPtULd8sDyJDgJnMnry6mvR96OiQ0cimvG9fag8ymQcqI9AYfcOOuDzhVE4sR1MZDHgcEc8MnduP3PHsBf92nV1Vu0/WjgraeZreIYF6eL0l73wpL+DpoHX18LnPi8XpCpYc5f8DovfU4z7qyua2Z9khw2HZvF46F3fN3TW1q7x6NF3Wmz9wtOxfXo+VqBDf5+erc4rcmWT37mpb/L353OfGR+k/xnyyd2/iWyzZE9k5PfKORM+/7rr4eRGeL9JuhKOV7xNfgCPrmQZfzefNEm4zF54si8wRObumdJ20r5TL7qIfmXjPe95Dv/RLv0RHjx6lb/mWb6F3vetd9J3f+Z0ddG6MkaO7ra2t0bDhfcx60n/yJ3/SqLEtgfX1mJPWIwFzyEhP8Wn6kuQI6kj73rXm9MMjanLvQ4kRrCkBlmLgbYMN8LLSecQNRHyXBupDD00euXEht0fgmRIQfIAjtu188MFk8MJ4xZZsyG5+1VVJGalbIcWB/3FSMmIg5ijRkbI5k1SOIcAVdu1/UWgkSF2/6vqW04+mSpFn6Fr9aGpEaZhGBuGmbUbGm3fvSmSqBo9s9t7ZuhWn0bHd1nuU8/5EkDPX5LzH/Joj749l3PLMxta2b6dPV7f3QWaZlZX0GxahwPEEUnLfvvQZ8r/pds1N5UHduODGvlemBNqYaIP4jLTZZj0ljpBc2WOhLSdD9H9d4JJLfGKYExL4XkK+RK45Rz/wnrMmn6z+cH1OzgO581JEn4uQF9pnnNtkrtWehXd/vXmDyN8ym8t6Luf5eZEymmMRgE5+4EAKOgbwLCHrNzZS3daiFA7PiSQR3QmFw3uWHllcSpSXjMnI++iNJa2vbcvbJs4GDaVzg/VcNB3Jq79vZ9S5cz7/owUF1F0HJ2ClA0GDxpeUyDnvd+sZaPI28gw9/Qn/0xbWgfNA0BM4B/AWe/cm2VVq8zTR17Txr+nr/Dtv38oCzuvTjrINyGjcp+Xl6n2APXDnnSkzGngnuSjxiitSENuhQ5N9BLgcj+iOsiz+p80d2hzh3Q9Zd1P5L9v0/ifHLw9MkePe2/Jeu66c6ym5P5GMP94z1Prj6YWW3cv7MwvOHwt854pIXyPzmad3R+9Ljp4cKScXO3gLgaTu6S2S8a5Z60PdmIzu+iDLeONe67vcpURrS9pFXkCTfL7cboSchQyUwQQrK0neI6gMgF9hbS31Fbw/fA0XLqTMyKgTPBBflG7pOfxeets8E41lO8pbWZE8+cQzrFnBaBG907Pl5M4zpXJX+03WLftj1WnVLa/Dky/8nMg8WDeHe8/J6lukLqtspM+RBSFtQ2Yq9JBrv5Xae7Iv2sIBKY+0ICdr0YU3XiPjt8TujLYVGfeybMRvxe0KbWGhLGPp/eDatTa8PntBr9LfC/7/5MnxcffuNDdg4QmO+/ZVA80gWyPZiz1Eni/6zucqqSfLcTgYlOnyEV1YYjhM7Wu6bM4YKulzU/3X0vHrUPfs6jiFtneNseaI1dXp2ggRni+Xw2syh2s6jca3aHXWQSsTsfVL5jHtHnj31Ws/2havM/KuWmNSmx8hU3PvizXnRfy/fMEK3kfoK9hlcNeuJP+lns13q61bjB7h/NvSZ7y6NXmcw1tGOB6tHmthKu9LxDa1nvdwaJ+fy3HWIVde5ZTV+lHChWjoWv9vyg+32Y8M/O7v/i695jWvofe85z307d/+7fTe976Xvvd7v5e+8IUv0DXXXNNat06fPk2nmH577NgxugM7Kj2M8+fP0wc+8AE6BB65EFtvr/iOMZpfoNHijlbrfFRub98DRmQ7vbf0PfeUmzqDAfi//3d8RPZMuaUPPw8KCpQabZuGSPr1nAyb2nV0tTVrD5ONNxZpGFhh1jaatjmNPisYzTcMkMxtb5wbuejcR2RODpmXU2YWETCGrfsZltEeqdQn2iDrSozVOsggj9VVos9+drI9/I/PA9gWB6QmjFnugJKOJs2QkE61COEp5xPeVpPnzcpkvcdbUUZn1mO+b9OWPUr7eHajnp/LaGmn36b8Xxf9a7vOGZnLW0cH1zVo612IkEneuZKAk44RLZCDE5jIMgY5C+KRO2G4I4j3i/cdhKQVYMwJS1lPDjRSqKCeUt1tst0t/L7Met8z+zdaL9/2qwjLy93ZoVFMez4GPKK8hBDHfZUBVqX9iLbbNaKcTEl9ch7gur50iPBn8ZnPjD8jQBm6Po4nTqQMgEePjo94LnhOCwvVYAbNgYX2uaOLl9HmDjn38Gv0duapc5Tm3vccZ26OXIgEoF4s0MZCJChtFu/D7t2xDH1AxAHHy5Ve8yzeq220Cy/oRB41Jy2fIy6/fPxZ6u+c2wEnhKAxyd/MzfkyHPVEMqg1QdNAL09u5zjmt9/BMfqcx6ahi+cE3XV5D3LH/TQSt7Rgs2e3ESnbx72IBDlJvX11NQWKWdkVI37T1dXqris80Pfee/W6cM7mZnVukMHR8/M2p8MDjjF/yAWleHd37erOB9b2+JuV4CQNXferj+v2gvRmBZrsaBr0ezFgKzy7aaKpzc/5j7qATZ6wRO6wwhfUyiQkmj4luZPIInQPbfFh8t71Nafn+J/bmG/6RlN7qsvzJKbNxc8o3vGOd9Df//t/n37yJ3+SiIje9a530R//8R/Tv/23/5be+ta3ttbOO9/5TnrTm95ERESDwYBe+MIXquVGoxG94Q1vaNRW0ds9Go3owx/+MH3kIx+hO+64gwaDAR05coRuvfVW+oEf+AEa5Gz/MYPY0oGsRGWrVbagclH8nDzC3FupHiHaJVkojdHNzWq2ZJ7tkmi80hafsSIKR2QSWllJBqE0IhcXU5ZMuU0nz4wpHUIy4IwTHF7WzDaMSN5uZJV1RMlrGjyYcV3uWMxZCZSDWXfSloJd11aSxa0EwjyKsNXuV2gs1r13POjWcl5yY9RaHbq+XjVCeTb+b3wjfSZK8ptv84bgYx58wK9hMKiujm0ScMaB6+H1S1IVKFzV+cjo6oMwL0Sf78BWe9+mhcHmw3pQJOOV9j+J6Dzdlt7TBJqea/XB60fpoovI4jt+ft39yFkVz3/zdFHZFtf5octDT4f85RkyZRYelMHCwXPnkkyHTg45ye0Ja9X46irRX/3V5G+Q7ciQf+21yRaA/NcWHOaQqLKvwGBQzbAmAxiiQT01GOSeH3mPS965SGCJ5vjSMkrWOStz7X0t6NzqswdPTlgEctTZ93Cdj8jibfSPHFJ+Gv3oEto7G5kPJf8juR2+own4H2Q7O3ZsfLz//sT3WIG5m5tVWcEdT+B/IPeREfPAgfHxyJFkC0QIf1wP+qzNFfJd58EO+B+ytyHgEzaIlhlNZitdX58MuOD18MAO+Qz4Ik2i8f+1wAucL6/DChrhKOFctfM9mWr9z9ODSvXFnDnTk/+RwJrSfswaZtjOnEmU6i2l7VhcCpelFlfrZa7WdHJtFwAcH3xw/Bk2AuYGyOxjx1Jm47vvHh/vuWd8xC6IPHsydHpkJjpyJMlFZL+/6qrJsnzRFeqRi0m4ji719kgAssbt4Ddt/sC86GUM1Z4dLxO1Ca16ZF28H1Zd8nyJnGDDUngLx6JlicoCIz20xRNuBXTl59nGJHLHNBAZy1IuRMryoxZ8TDS5My7mAplVXZtrLH8y75+2+8qXvzz+DJ8AgDKXXJLmAitDJg/MspJR8QWLAGQ8+r62Vv0N4POKtZuG9V32FceInSbP88q3FZDV9sIXOW6i5XPQlJd7NOnE0/DHe5ydZyda/Zp2TEEuX29Be3et+8PlttQH+bHOttbq0XZSk/+Tu6ifOpUWjcM2kDFDJ06kMrAxIMd37Up2AvR0LDS/8srx8TGPSfaCx//gfG3xeA5KbH2PV4jU4/nZPT23rg1vjuF1RGIB6hD1x9UFavfxXkf7KnUbj2vVdp1oE1F/RNd4uA+n8b4/jB07dtCOHZPJcVdXV+n222+nf/bP/tnE77feeit9/OMfb7Vbt956K+3evZtGoxH9zM/8DL361a+uZF7esWMHPfnJT6bnPOc5jdrKDkb+yle+Qj/0Qz9En//852kknI7vfe976aabbqL//J//M91www2NOjYtDFbOEy2ywMu6yTs6kPsc8LPwcs0y2iaFmipQmiFclyXGOg/fkRlHBnhBYVlYqAYayxWyHFaGtDbHWteBNl6b025DPsPcLYK2yjufaxD3TEYOzp2lwZAFXEbI2Tryo+mz2SrPtiM0DaKcuYB2zYiV36Xc9ghLyGucs2vXeBtmXl46jzY3k4ELY1iW5UEEnhNK9lEGznkrfXlbkfdlGnOEhjqDN+IUoxkcmzOGvu/PgEaTu0cAFmnXlrMtlyxoq32trTbqLA1+ybWvmvy/CxmCrDeR5yqhkTFeIIWnMyLoDL+BxIQj7J57iO66a/yZ2wREk/JczjEyiIxnT/OCGeQ14hzYE1Eiqw59zwvWvJZDbnKUZH9oy1k2K3Mq0fTm+a9/nejGG1PbdYGIUWdQnSNjG7OFps8HchIZ6hEU5sGTfx5hr82df/7n46PMWgZ5u7Bg6/Rcnvc5PmUQWk6GWg2lmbhL0JaNPyvyIKcfXfa57/vx1a/q+o888jGKcSq3Htf4uQif1BVmZWwBffXHutelmc7lkcstJP+QbWlciNTbeQAbysggMHznWzlrY+rzn5/8TS4CmZ+v7mCljXGpj6IsOJ7IVs6oi58P8PsEOe0Fdcr3rwS8vqaZt2blnbL60ads9vSWnH5MOwAZNjJ/5yPyu47X3UY70O5n2z6WErmg2YKeTRj5n+UH1MYdPwfcPiAXg29uprkEi+BlWc1G0Ma99A1zGyMHbWS+1PhLbz7pM5t4F1xtSflS+Rrhi6y6m/LcfQK28/p6ddGr1PH5DnORTOfePJ3DVTflGaPtNEFbz86zlUrGm/Z8ZIKQnH55sGJ0+GeZ4fiyy4iuu07vK+rhmfGhr/BF11/5yviz3PUcfNTevdWdcHMShFjl6pCzgCHn/kbOjwQ39+FjyPUNlXAGpYHcOfXJenN9h15ZPs47RJOd0NvuBxHRkSNHJn5/4xvfSD/3cz838dv9999PGxsbdPDgwYnfDx48SMeQ0KIl3HLLLXTLLbcQEdHZs2fp5S9/OR2OcNkFyNLETpw4Qc9//vPp9OnT9OY3v5l+8Ad/kK6//noiIvr6179OH/rQh+i2226j5z//+fSpT32KLr300k463Sk4ubiN6cNbhdKFUVqC0oAH67oWF5PSG1k9IklJzYCFooJsDMNhMhZBVPIsNQAMVji1cORtyAwLMriZQwYhAPPzaRWYN+layoM2wVuTPidZNWM0EmhmwXNAe2SdBCdd6+rhwYtWfbweS8H1zteg3Z8c5S4SyDENY9QixS1ECI2cMRQ5f1bQA8lckSI571EUlsxpSpBo724JMaPVwwkRWR+yXgIy670H7f3DUWZa8J4/3xo0J/ua18dI/7VsjPxYhxzHToQokU4JT942dVKXvI8R+R8lvdqWBx0boxIjGtBoedcj33sLhu5Zb7UM8KkFx28Fh13JHJPj7Ig6qoh8Gb25SQTb29IdeRCBDEbwtmnTnlMbOyHx/lhjwdMztTIS0TE26/bmRYKKDBpWg9d7Jwr3789zRLftDJoF5OqIuefW1dEXIlyBp9d5C8YtPY47kSJ6rlxMgiCD9fUqJwPMzxN97Wvjz9D7ZTbmtbUkt6VTClhYSE4syRFpQFvoI8/uj3eK78xCNPmuiawgFeeuxnlJ3ZpzOxHuQXMW1gWCzM/bgadAdFcNj9+zztPqsWwe3o6V9YmfI52y/P5GnGKW8zXnung5bWFgl7jhhsln29Qes5DjLO8TbTkxPY5Gu3brPRoO63Xh3D6WXI/maPX6Zdn+i4vpN/mucR7Hy9BGpG/TDBm/spIyo8mF5tghcc+eJNOl7OBtoQ0ZuMz1dRmwgyOfTyI7bEn+x5IhvM9eFmXteqz/eXO4t7jAgsbzeTI1J/t+qY6Vo9d5vKXVr1JdWHuP8BvGdJ+AzqOh5N5HZKGXfCY6D3myoq6PXdq2Odx6Dko5/5LzPNkeSbrh+RvrsmnyerheJHfNhW5PlHZZwXly98TFxequIjI51cZG1acsA48XFqr2g/QV8PviyTvrXfd8oR6nLmV69D3wZCCOEZ08ottrspNoUjeoG69en3k7sp46nzHvh6f/e/Niju9Dg/fMusLTnlZfRtORPFsnMr9byPVB5si3iG6t9aOpn6mujuh9yvHVlc4XkX7UteXJf3mONo/IMpxH0vRmyFIr4Hl1tarTy51yOXD9MuEIT1jlQY4BuYvi5maV/9H6YM0fsh2t73XlIv9DfXXvjScvNdkh++rNR6V917J0W332EOEuvN9z3qWOFwtFptc+gD7ceeedtBcLBogqWZE5BsLvNhqNKr+1iTe+8Y2d1U2UGYz8S7/0S3TmzBn65Cc/Wcl8/IQnPIFe//rX0w/90A/Rs571LPqlX/olestb3tJqZ/tA386vWYjKn2kM51Qn5UUBXJdyfY2DQu69d3z0JK1FEPL/y4lHO0dmzywFE8JTxbSdpHVou38z7FAfzWeurm7a3vKuJJPblDtN67oYZGBb1zDte9Gk/eEcDbp+xzY3E9EIeS0DBE6fTsaJ3EKZk4tW1hwu6zXHkva9DpbhweegCNnbtTOX98MLiuiKhG8Lpfcph1SKtO+haeaihni06edb/Xo7DaYuJb5K6m8qIyD/QfpJ4pJveY+jzGgzHNYHGvMykmAE5ubsDDuyrrq2+sAU5PNWf+8uGuzd22+2pFlEWzJs1tFU3k5ZN3EBHR4yGQELcFI9+GDaAhFBA5D7mDt27aoGmGmZ8T2ZnoNZ4X/awKyNjeGwnz5JnaKpLO15MSIR9ZOMpGsbtRRtB6C1Ufes28/W92kCiz7w/siAtbW1akZmyWEMh9XgA8gQBGwuLlavWwsg8nbWAkq2Go68R5FghLbf+S7Ggnetlp+nLZsyGqhQWrf1fdZ0gpJ7EJGFbczNsyR/+kCXvOOsQQtalb5Zzu1Ap5d+W8wDDz2UOCHUiTGI7zwxm9ztqot7KHcwiQQ9eTJxGjq4tphFoum9swKYc8+X6HLOKm1j1uwogI+7WeOLtqJ86wOR+9Kl/RM9p43xdPLk+Ai5Lxeur61NBgJz8N1YrKBqlOHZ7+XOihyR2CCZQKtLRIPutxq2ct+ngFkLRt67d+9EMLKGAwcO0NzcXCUL8n333VfJltwUd9xxB1155ZW0sLBAd9xxR235a665pritLKn3+7//+/RP/sk/qQQiczzucY+j173udfSbv/mbWzIYOduZ3XAkh1yQW1zAbDtadbQWOKGt1MXWO1A+YHgiKG00Soq+XBGlbbPsGRUdrsboDJah662AzqlPM0q1e2jd16bOih5kxsX6Xg9WH35Hch0qTVaARuu2vmvn56xyi6DL+krm0aZBlE3baKrFWnVzDTlnFSIvC/nPCUp+jrdrBeS+lmVMtull0czFrBJPElxuW1mFLma0RaZ6q7+nqPMOVh522PaRGY1jmnp+hESPnl9SR9vvjUbYtz1uvTm5Tq/T/lfaH+13EI2oG8EDOHq7lrQNrZ99yMmcDAIRcrLDAKLB5ob+j7bGbN1vsq1I4L1VtkX0nql9fT0FbGr3QzviszWmooxnzj3WsvBIncQbtzmO0hLbo6585Lxof0rR5ZjOqdsaP9pvWhY27fkgy4zcEQU69sGDRFddNf6M8cJ3MkG/SoIPmuo2dVmBovU11WOaBHh1ZRtqbXjyJSeATbM7PX3Fky8lslC7r30HI587p+80ZEFbmCrhBfJF5+cc/doqywMnPJmjnSePbbzPvE7v94geF+Fqm+pxOW1Y37V3TNsRKmI/eP0A3w87wOP0pYzXFpdb90qzIxZEAgm+ONEKgBgOy3SkSObEps9Z62uO3uLp0jltNQlw0eqWKJ2zupAF8t3oE17GUE8GRDJkRmRqUz6gbgcE3oaWWTwik/viqup0vqa2SUk/SvyB/JlI3Uqz26yxoOlR3jXLIDDoNpddZst9DU19P3X6RkTP1M4vmZM15Mg+7T3IOT8iV3K4K9kn7Xu0nqawxm0pPzANfwoC9iP3kO9QYcl/zVb33nkJ7oeI7PoT0R1L+B/NzrFkmYbIGM8ZNxE5GemH1l7TOaOEF/DmmIgdz/+3IfhkuWt5F5C7qMukJBxakivpo5YonV+0cWe9jxHdnnOtsv0ISucqjxdrIic1uZBrb8rz29LLpiH/ZxyLi4v09Kc/nT7ykY/QC1/4wkd+/8hHPkI/8AM/0Gpb119/PX3iE5+gZz7zmXTdddfVZl7ekHInA1nByN/4xjfolltuqS13yy230C/8wi8Ud2qaGNEgL8gumCVxatsfzwAezddegTZxRCZQmcWAb9PAsx2gLLbpRBm5pc/6ejJM9+wZH5FNgW/fY026fIsHy5mlrfDyrv3ECb2tiDKdQ+RGiQVpAETgKTWe80cidxLOMQbq6qg7/+FjRUq2RVaxfg7W15yC7WNAo7xtXjlKxksUTZTfnHqnVV8f5GbTNurkdkSubG7a27FFDFV+rgw05jL+C18Yf5bBCDAQudIoMyPzbTZxHsrjCFnPtyyW95cT3ZIUlUbgwkKS/962oYCU5fz+WKQQ74+1zacWgKrJ8TrC3wtkLSVhSsZbhOSN1lM3x0SN7Mh9wecpbNM5Wtr5yOfOdNe+nDkGKjbOtDP3t53t3qmv+Jk2cRBxWe+R1kRjuS5lu1aW2wJESddfWSH60pcm25cZznbs0LdTJtJluzxfc2BZgQYc8jxer9ySPSIDrXnAK0tky38PkTY85FxPW/Dkvwdvzm0bjnOg9wWP8/PVDE1RdKWjTxtNr2tWbIym9VjBHry+nO04cZRyfHW1yvdw2Y4jdCNkOMb3c+eIvvKV8WfIZJzHz8H/sDDxwIHJ73v21C9YHw6r90E6pbitgfNwf/giGej/dfo3USzgxpLx/Dlp84flNNVkoeUkjjh1PacYv4Y6B3/fNneEs2pLFva9QJVnA4wix/Z6NKLUwRqpt+t7nBPUoDmpPd1eZi3WgpIBJBEB13/6dFpwgoXnKLO6SnT33ePPkLe7do2PnOORfD+OXMbX2fqbm3bQAQ84kPyTtgsLrsfTzeu2NeZ98+Zp7bMFbT7KmSMkP4br8caL136k7xovZvWV1y/1lpz7o5W3dg7TzteucxrByLOW6fLRjLrxVyL/c2x2Pn7rgg6j70GdH4FzO9ChpU+Vz32om9sGX/3qZJ1y99qFhSq3o2WvlO+kt8NV5L5KPwIwN1eV/zl+34j+zuHJnDr/X8RG0PjyHD0oIifryncNz59dN8brME0ZDJt0G2PIsbVVkgY1gSc7JHL0yQh3zH2YkiPic4/lP1hfJ/rGN8af10T8BMb23r3psxWgzN9nueuhp/9LX8HcXJ58snRFb66JyPgob18iSyPcPhCJL6mrI3p+U/D2pyGTpU+oZWjq2DSQ24fXvva19NKXvpSe8Yxn0C233EK/+qu/SnfccQe98pWvbLVfv/Ebv0GPfexjH/lcF4zcBFmja3Fxkc6DDHFw4cIFWuhyBcYWxFbJIrodONwxmhKo2oSqKSMw7KQT6qGHxscLF9LkAqISR741j7aCiWjydymgrLJ1wDYNbd0f2Z9pQ/Rj1HYADtW8v03vQ1PnV247RDSa73ceGdEg9lw6eHbbmB2o79EsBJtwmW+Ro+vrycElnVj4vrFRJQQByGtvmzYvY5qcM6LZk2UQkEbo1pEEkaBXjSjU4FkIVgC21ZeSNqz6ZmU+k2jYr0fkLpOto917GtXZFF3r7dPSt0vaxb3YthEE+ngvI04XXhYBZSAKrQUwRFXyUAsiACynlAYvMwLA2+pje/QSRAjBvgPCukafc43T1ras2cZUoHEqViCU51yIOLVk0FXEGcr1f+m8mZ8n+vSnJ/8He+D++8fHBx9MXBD+ByBgbXGx6rCSzrHBIAUq4zxvS2ePK7ICobzsUZFAYQ1eELBsV/7u1ad992yVnICFWcFW6GOf2L4f5egzmDgSpCTB30tLLjTtmxY8K/V1GYywuqrLfaKx3P7858efwfFAxkPmnz2bfAIPPjjZLx5EC50c3AxkPGQ+DzTgixj5kd87T06iDU8W8kBeq4wXRGZhGu9wl23Kezit4K7twN5tzBpy3jsub6TskfB0Po230WQ6vsvztSBeGaAM+bl373jnE15eLlw8ezb9D/VA1nM/sJTlcjH64mKaC2TMh1yAws/z4kMigbnyfyXBblZ9dfOHdr5nK1h91GwWr12rTJvzSEQ38vwqOf6ZyP+2dettTANt2+ORuBSNC8F5MgC8Tl6A58FCxQceGB8RpPzgg2nekbwTdPzl5STb5TzAF7JIHwD6xgNJLb8BPxflvZ0p6hZ9l8qQiGz3xkQJDxWNhK2bj5rMHaVtN8EsRP8+jK0ajPwjP/Ij9MADD9Cb3vQmOnr0KN1000303/7bf6Nrr7221X697GUve+Tzj//4j7dat0SWpfikJz2J/ut//a/0N/7G33DL/cEf/AF9y7d8S6OOTQuDh3Mjb2MbMwlvsuUGIhQLGJjIfrx///i4sVGdQGVGm7W1qhLh9ccKQosGYMvtcUuCubS22grCjbQfOf/h762FOUUDsGfVsHPuobl9dUcYnH6QBnvbDYDbKgtRtpHQ1jNrXZfwnPcckPMWtBWtpdC29dTa8zAc1q+4btGxIZ9vKPi87vcSzKpMFni0yLDBubM0WN5ZX5A7kL3sGDjWEdKaA1rLspGzTZvWZkQ3Mq5noJFEFomjXbNG6ljvtEcORUgh77cIiR7RK636tLa15+z1OdKGd4/4Lin8yOtDnSiLhSo8+4F0gskFLIuL1SBmnI96eeCE9m4QjccBMrVFro9fBz9q52vP2xubdYETEcKRZ8BDeUnkLi1Vz7OCT2T/ZZ+tMh4itmROu96W2do9jYz7vjPjnz49GQyaMz+X2qslaJtB3SJ6SG/gXAo/ltbTxf31+sR3riJKgQoY2xsb1QA1vL/cUST5H5nZWJsXcxYj8nFsOdw0PcpDxBFulSm1MXKd7NuYTdx9t56R1dKpu3RWa+hzjtkKqHvXPdtn2rB00AjqOF9kRgawODHSH9S9tpb0fUvGe+C7cEm7QeOMIsHikffOC3huA1G70zov9xqavM9eMEJOcHZk7tTOaxJsMS3kjBcezWA9pwj/E0XJvcqxo0vb2EqI+sv477NyT6IBrMePT5bnXAy+a1kuObQdBK3FhNr53L/gLbzBEba+5El4dvRIsGvd7jRasDc/35qPZd9Rnpfl52jlvb5r9Xg2D6/fCuDLkS+anNLa9nger+66/mj/kwtlp4VZ06lz7yNReZ+bzlkWPFmWq6807VPdvYnqaiX3OHLtWpue3iF3O4H+/3CWU7XPOVlPc3ZB9DIja/xPTlKS0nsX+b/H/3fNB+TUk8u7l7TB0fRdy+HnOt4ZZasGIxMR/dRP/RT91E/9VPudMfC5z32Obrrpps7qz2JeX/rSl9JP//RP03Oe8xz6W3/rb6ll/uN//I/07/7dv6Nf/uVfbqWD29jGRY+IJNImRrk9DxT39XWie+6Z/J/MgrCxkSb9vXvHR5n9YGHBzoTJf7cM1Qj4BIR+tIUScnNaRkYTA2LahlFTOP3vInu0iw626MkNSN3OQPkoRY6xrRmjPJAJK2ERYIYVsnxLT+kgkgbA2lo16wKy12PuGA7tuUF7r62VsXNz1S1ZtIzPdUEEi4u2LGcEX2Wm8gLNNERIv5I5pcTZxn/LMc4zDNQJWVRiOeWQS+z3vhejjJZ36YQyIK+jjy3L2iQSIpgFfaJUH/OCKLtor6ReOWZyZLsmC6XcXlkhuuOO8Wfo+5D7kLHcUSS36+RyXMppmSFncTGdLzPk4Li0VK1b23Hl0KHJNprIz9xykSDZLnXxWXfaN5VBufd3Wte6d2/a4paPbRnMqc0ReLe07datrccjWXW1d12b562537uXnlyxHK1tPhvLRo/qU3VOTs+Zy/sQCdiw+hgJOonUoz1vjCVwO3yHK+x2IjPKEBF9+cvjI/R/BBzj/LW1JJOlEwgL13ftSjJdggcl4zPeDTmfEKX5xsuMjN28cL4MBtCyUwPa2LS4nfl5PzjI2nEGZRcXY1s5l6ArudfUYfpowZEj1R16+kCbz+TR9HxnSSfSdBxtK2W54AzyG/L87NmUwRIykXP7gJz7cP7580n+A+BtLrssfcdvUt7yhSyS28/ldOQ5no/AmsujchvHuuBQXo9ElIOzuKWm0PrcZIx757YVnNRW2VmBzBiooS2+QAvAaBtd+rYse2SWEAk+yvHBajJd1uMF30q9Ui4GP3MmyXvMDdKPe+ZM+ow5AhzP2lrKjAnZDt0cMn/v3hSgduDA+Ai9H+dsbqY5Rd4fvjhRLlSUZeRnomRX8PkA/YjAGm+eHe2NVc2Ga4omtkHEDgZ40HhJWxFoc18p15qDDvyxtYgEW3s+mJI5PPdeeuO86TPwgjDroM1ndf64Jm1Frrmurgg/trpq8wG8nbodTTY3q3wa+geZf+pUku3eXGPteruwQHTffePPkOkytmd5Ob1bUjbDL8zvm+T0NT0eHI+X/VgC/+P+CGvHrwi/FuHyPD1+OOxuNw/Pz2PBkx3a2PfsIqsffcx9Ef5Ju46O/atR1a9rzEIf6vCUpzyFnvGMZ9BP/MRP0Ete8hLaB12yJWS9dS9/+cvpd3/3d+lHfuRH6G/8jb9BP/ADP0DXX389ERF9/etfpw9/+MP0x3/8x/Sc5zyHXv7yl7fa0amj6WhpMiG2ibbaybkfbRP2OejCOdxVwEKkTd42Jk0ZVEyUlJEmWF9PiolcCSUD2Xh/NEVlGmRFDgkjlT2vPq/O0nEzy2SOhR4CKPoORpuAZ3BGUHjtZqBktI22jdESbMXxPG20JR82NxPRKOU2iM+zZ5OhLMlEzCee04bDCz7OQd2K2FLZWhdQWne+BA8W8bbutjIreL/VEThaWx6iQT0RtKHDRsjVyDzbJSIOSY8MjNSTMwY56ZbTx5J+tElINEFknHTZVtdtem208V4iCFnaFjzDsSQd5djWMmNG+lai/29uJnK0bd1aq8d7RyJ9tcqW6sSR+atEL2vT/i2pS+t75P7KYMw+8YQn1JfRrkdmI8mB5+zTCNmIThOB1FGakr+5QY9dBHWUnFv3Htc5MKx6NFgBCzhyvdvb/tfTHW+/ffwZ78+JE+PjsWPj4/HjyYkF3ujyy8dHBKzt3p3GstyeuRTeuL3iimZ117XF77PnkJGZ2UrQpd5QIptnrT+zDMvZzNHF9XWlh7apN9XVk2OXRPpRp6tZ52vzSlvXGoGcu0u46NJyXP4DchcsTwYCXhBBF+O/DXnbpR4xDVtw2rjYZHsEXSy6i2DW7muJ3TkN/3IUkSCYCHiwLj96dbbJw0poWYYh/8HFRBaVeH2V6OI5R4LAStqdtfeqz/50FVx2McNbjOLdBxlc6qEL38uj5Vnn6vRNrkfTBSIB0F4fIvx2Ca/MIfV/AHaAtqgx53pKEg96/dH6UeoXaWrndRX/0NTPE2kjUjZHD2qLO5hRmbIdjBzHu9/9bvrABz5A//Af/kN63eteRz/4gz9IP/ETP0Hf8z3f00r9WcHI8/Pz9Id/+If0mte8ht73vvfRH/7hH9LgYaE0Go1ofn6eXv7yl9M73vEOmvOIjFmGHJ11grotYba+3p6x5KEtRamNPmlBFoA34fdNBJfU06S+UkKXl8FKWpkZExmTv/lNorvuGn++887xEQHMyMqwsTHOjkOUspZdc834ePjw+Lh/f9WJhXN4AIMMeJPbiA6HVSfANIy2aTpFtxouRnLWk0n4v4T1zuc65q2yuQ6MnG2UctryHOFef3JQ4njiv+UE4nloOzgo1xita5NvA6+RkUePjj/LzAonT46Pp08nOY/fEMCG75ubKVAB8h4BA1hxu7SUgsjwG87hWQQt+cqNWmsVMJ8XrKyZns7WtgzxMhF4mPbc0JVD2yvTtgHeMQbra2mFuvbO5siFHAdEbjCJHPebm7ockOfktJPzHrUld3NQOr9q59eVaSq/I3qDlwHeexaaLJRZM5EZEzL/oYfSOJdZk3kghQywhEzH9127kr6PowxcW1tLcxXsERkcMT+fAuZQd04gB79fEeLeK1v3HkdtUqsfJdtcam1oc19bQara+XV6D+dOIu+WNZfzNrvKUmGB286R99nL7hLZ0UG7Vu8dl23Itqy6te9Wf6z3LcJ15erG1rPX+qC9l034Ft5G3b3x3jGeCbvuOXv3F9AyYct66s6HvMeCE/At0NHPnk18zxe/OD5CNqPMoUNpUSMgM7bNz09mUiaqZkjm2dOsMTk3R3Tw4Piz3H2lVAZI+ejdu7pxxo/a+drvuXa7VV9EtyqR+9r73bYeJ+UTbyPyzve9TfPqasoiNQ3uaZbrzdEFSnl7z2bJuQ6tf9a401Byz0rvc8475+mZXN+Q2TKRKRPcDn+vIJsxV/AFYMikJjMrc/5HBrhJrn84tIPg0PfRqLqjhNSxotyrZ1fhd27j8GNEF8hFzjsya2jaxxL9rG+d34Kmh2kypCQraVfcZJuYZh9n+b4AObxEl9ejBUFC9iL5CLgVzAsrK9XxCbmNrMW7dyddHvXJ6+HZ80tiPrT7Inf8RZvz81VOSKtvK4ydiwFtxZLMGmB3njkTs1EA612Pcr+5ep/8vY7/jz4vuRg3wjPW9S36/xybNGKza/aD5jux2m86B0aegXbNUmeN9IdfF3Rp6P/33js+fuMb4yN2ziVKuvz+/eMj4nn27Uv6P+aEyALFyIIXDdPQ+9p6rtr/I9xO1/KxC/4fkGOT21VW+1EOAG3J3ZK3MTW86lWvole96lX0l3/5l/S+972P/sN/+A/0wQ9+kK6++mr68R//cXrZy15Gj33sY4vrz377d+7cSe9973vpTW96E330ox+lOx7eDvaaa66h5z3veXTwYUJ5NBo9Eqi8lTAazuW9wKWk2zbGiChZ0+xHaT3THAObm0mJ4A4h/I9orHBfffX487d+6/gIw5VnPZbZMrWtHfCey+1CocAsLqZ6UDfO4dt3IlBCKsGcjLTua04glDbBes7cWXimXaJpUE+T9vpqM4jRcG48BxDRgEY1pR/GLPQ/Qoi1WWeTa44Yf6WYhWfRB+q2rzp+fHzEvYZs50FqdeNiNPIDjyQgpyHj4fDiGTYtx9WOHVV5L0lWbdxcbLK5hJCq+18buFjubw1G8ws02r1n2t2IY8j0sEVjS/W++2EgPJ9G0bfe0gY0wqbNugEpQ3nwMNFY7mIOueSS8VEuBtyxI9kRPPiAaHJrZ21hIcfcXDrPC7JGoIP8Xy6M80bVPSceGbetj02nH1uiHc+J2ue7Ng0ewMuGwst5dRB1l+lJQ2mAoueYkc4tXq9VN3dktf3sIo5AgNvzkftQ5/Thz1LeK66nWoFQEecYl9V1eq0XiM0XCMpFiLALjh9PDivwNAhGBg9DlPgZyOarrhof+YJDuWBFZlOOBimAj2obuTZmiTNZ45HkHBxpq87h4/XB6qvVphZY1YdukiNL+96mOVdudanPtY2og1Q7zyrTJBCvrlykzhL/Qe7zrUNbnFVk0YTXLs8sjOBhyGksIgcPtLpa3d1QcvKbm6m8hQsX7GA07Tq8sVSSGbmU/7HmZ2+Mlba1VeRDF9iK/Fxbfe7ympsuYOiKv9/GJKZlK8vYC8hm6OZzc0m24yj7urKS5ghrZxSun6KMPEYC2LiPQC4QaYocvSFaX9uLsHjdTc7vE7Par6bAM8CixC7g8QuyH9pvmvy36tL0sJJn18d85vkVPTS5nlLdsaTuiD7fVCcmqsbtgL8Hf7O8nBasQ/4jgRUWqe/cmXgfBCgjYJnPI9K3GwFfhAhMMxlhLnJ4lllFrm3r2ana91JwHqpkl8EMRCiRPjALfYjiiU98It122230tre9jf7wD/+QPvCBD9Btt91Gb3nLW2i9NNEOFQQjAwcPHqS//bf/tvq///Af/gP9wi/8An0RGTe2EDpxSuaiZDWN9Vtd3U3hkel1CvusC2sLbd3DyIqvUrJV3mMoJ1BKrr02GZp1hCOHtu1PnUMl2u8DB+L9aAvSAI5iVleFlvRLcw7kBHfntu+dP0MY0WA25oOmmLX73GJ/1ACjFtFJAJ1c9aplbsF7ZGU/JrKdJJubRHffPf4sM+QDe/Ykh1VdtoG665Hta04t2WfPcPUC1mZtLEeQMx/2cH1dvzNtYhp9nZrMb7JIp1TnKj1vFtBH/9q2Z7rsM68bAWbQ+0Gsg1TkkDofl9v4LBcqYj7a2KhmQtO2hOZOJ37UkOPoycBFocv1hVl/97tE7vacs4CmTlDNBvQcKiVzU9O5piQwywucLn2mszAWtD5wOwJyGmT+ddeNjwj43dhIMl0uIue/y51VUIZn2EdfZHAb5oO9e5NTC8Fx+F/biHKlTRdEd8VhTsEe6AXRAEuJaXppZonj8xYHRVFqP3d5TlOfRd17PCsBhZGxpJXxFsmUzJk8KI0nAtGgLUaX/M1WlU+lvP9WRtMAVuu83LJbdczMEtqSc5FFjF77JbZ6REeLzvt9yue2+lDyHua+c9DFpU4O+c/nFZkRnycRkYHFksdZWEg2hsyeiTmC96EuQ34XKH0+nu+9Kxn2aJKNkWfOd2noC9o7N43nksPp5L4/Td+3ru3eWUHpfbIWK3M+Ctfs7YhW0hYH9Fv4eiHHDxyo6v3aziiQ7VZyw8Eg2RGS9+eJByP6yqzsiNE2OvJnNGpbQ67NXvJuzJi/0cu31nc/thqGwyHdcMMN9JjHPIb27dtHx5HoohDZb/+DDz5IH/7wh+nee++lxz/+8fT93//9NHx4EP3e7/0e/ct/+S/pC1/4Al177bWNOjYtjGigBkD06shsydD0MkF1Bl5/121NC21dl1NPa+NNCzB4pP0aQo5L6pyMLZpklUboVic1Z7W/bZHvW9lR2xK2g1dmH1vuGQ2H7a/81wDFUC44wbY9Dz1EdOxY6hPvjyQciaqLUPjRc2YRxQyQwSAFPFjbQ62uVoOztYBsrY+8TFPwgHK5tSgwP98e2dAyQu/MjCwq6fv9Hmwy4rzUSu3SQTJLARJEZfeoaQBC07pL6uMBtRKajtx1UFFdgIYMGpbHjY2qo0oLGJYZdSS47Lfk/XBoE5/8iC1EZX9K5XfkGfThzOzTgaDNhzn1NJEvkaxyuW1to1904Widpt5zMQe/yPvKnTkyEz30VOjYZ88S3X//+DPfuplj166UNR8ZcXhGfMByrsFhtWNHfzxPm0GQfcLKQk4Um/9yAv5n7do9TLOvfTsLvfa20jNrGxfDtZcEDrcB8D4IMMMikjNn0v8gmxFMxjPcQ/+3+Hpv1ys+tqHnaxnRcC76lqP3e/puzv0s5Ttmwbke1dm7tkmtdvtucxtjRBawdGkT57TZdx+m2V7b74TU/dbXq7w05C2SkqyspN/kTiY84IwHnVmo6792LuYD2BwbGykjp6yX8/g5XFAJF8K/a9vQy7qBEo7K43c9e0DCs3G70Pm7CtzX/jfNxUKlnFXbescsysuLFW3rc33qWJreLbPf439ra1X+HzIMgcfc/ysXpaOthYVqgLK0GUajqv6v2QFnzuhltGtty6fqzRFeQHlOPRJtczV9+AS9pDxeLJim67R9bQ2g5aqYBmahD1E89NBD9Lu/+7v0vve9j/70T/+UduzYQS984QvpJ37iJxrVmxWM/NWvfpW+8zu/k+677z4ajUY0GAzoOc95Dn34wx+ml7zkJfRHf/RHtG/fPrrtttvo1a9+daOOTQuDlfM02P3wVralL0vJi9+GY1K0Oyjtf5NAlFwhbJ3vCT+vzyWTQvR5RSYRIKcfkTa1714/5NY7MniKZ0O2yMThsLrKVYNUamS9ka0cNjbsLBDW9zYRGVN99CPSny6IU/w/sgLbU848B2lOX4GeV8YOVi+kALjIfeaBiZEVcBZpwe99U+WyLdmTA28FqKWseopptO9192rWghHW1/MIpxwijZeVxieOMDR37tTnWq0+DVyOl4wzOQ+MRul8650fDlOGz5z7Uve7VY9XVgvKiJwXRVt6ZbRu2YYcG5p8igS1eTqW9wzxG5yoPWE0sZguSF63pSe0RTpqzgn5v5L+8Hqbyq66tvj3SD11soyjhFTKJdFLELGPeFlrIcRwWF2sIRfAbG5Wr60kYCwScMDb4n0kmiQ8kTUTiARoef2RbUWQawvK83LmI003l0HjvJxcXFOK0rFcR85qY8qr13pnuXyRAeqzhui8GCHPSxhJjzyXv0dkqscvaOe3zaKWyp4mcl+zEUrekbYdWW1Ayn84jHbuzNuJSi404Vk1IcP5blm87ShK7JCLBZ5Mt+zF0vEW0ckfzYBeNRxW7z2csVz3krqVXEzLF6ZK9Hm/c+WlhVnjVKJocs1RfjjCV9XZxh4vFvWLIOgMchryBRnSsEOih+Gwmt0SvBLeh8EgBTFj3GM+wO8a568lLFlerl4HB3+PIvclxy4v1Ttm4T3oqw+P5vkZyOHA2uRz6trzZFCu3KvrU6ntErHNo5xbXX38mHPvcuDpYxE5JctqviFrvHE/gtYm53mIkt7PF5VL+ajtaCjLyJ2tonMfXwTPj8DcXJL/Hrfj+ZKsPnj3znsemh6H7/J8jYvznqvVV3kd2nP2dJESjkibO+XY1vph9Tnalsdr8eufFs6d8zkQwNI3+LFODnhltHtg+Wm0/mg7rsrzce/5O1aX+Ef7jb8zdeOWl4nw7tp1WQsGvD56SYpy+E+5wHt1tXqvIG8j/kJt/JfqG9D/IWdlf3btSmWlbo/vo1HqN/yu3o6GAPrI/QHSN6BBLoaX9Wnjt3RxSoneof3uzfMe3+i1a8EbC957Y9XjwZNhHm8feZ+lXNKO+HzyZH1fG4B3eZqYhT7U4aMf/Si9733vo9/7vd+jc+fO0c0330zvfve76SUveQldgqQVDZDl1foX/+Jf0OnTp+nnfu7n6BnPeAZ97Wtfoze/+c30rGc9i77whS/QT/7kT9Jtt91G+0B4bEUsLeUJiKaGeHQU5jhLIu15QrStdPkeqVSHUoGdg9x6p0G6lD53uQ2mJAi5ggAlRDqctMlFczRZCgovEzEqIoGnbRI8ded7k678rB0jpE7Ju1ZyD6LjJqL0Rtusazei7PU9S2sGgYfSZ6jVsxXhEWKAt7q56XVvtfs2P1+Vqdo7JxV2SQzUGSIPPTT+LGU6l+MeCUk0SRhKEpIHHkiD18qCz6FlxpEEainBLY0bbYxKI5afI8t7gUQ5RJwHb46R46TPd0YzIpvUxxF5viCoe8KARlVCqQ7eeyzLWHXWOWpKdBqP0LPOLdW768hWr82cenP/39Y5RPX9beu9bEPv4Ho+B5e7MjuNd32coJSwgs/47948hO8Wsay9VyibkyGhqV2glWlbJmMO1JyG1neNXI2Q2KX3o2TOy+nHcJjugwxQnzVo975JXV2cnytXcvozTf27TY4ox8603nlNR+rz/nCbQS5GBCCHNzZSGZ4tmSgtAFtZqQYNaJkxIasQ4AadDbbC2lrVWS+zsO3YkcrIIAB+fW3oFXU8RdvPzNO56uS0169cZ+o0kOtE1b73De6YR194tlgOra+ek84q4z3DLuaXkjojvJxma0z7eQJN+jGLc7xXF/R/AM8C8n9trZo1Ter2fPxDXkvZnsvvRvRz/v7V1RnRbbR3r0S38Xh/79yIThCxn716mvDkUd6+rXcgp6/TCEbji1EASyZHFhGVQuMAJXJkepdyOOd9ilyXV3eO3crf9RzuLdIf6ddo83lbbQKLizH5JAPmoNufOZN8BPgfT1CFNqW+j++Q/wsLKVANAW4yQyb3Ncj/8XnEes45Mj4aoCX9Kxzyne5SFlrg8sXSHb13XyuTo4tbNljfaCsWJQe4P6W+hxw5ELm/nv/Ue6b4jdsskXnkYkDuHFMCGairtZ8TZ6IhZ07gwDOXHD84njNn0txgBQoPh5Nynh/5wkOP95dlLLtB08mlLNJ24/KSd3g6m+X/1WKechYJ8XOlDNWeV8RXXTcPtsX/ePyax3Pza9cWsXDwe4+jpkf1xB1EKcWuMQt9qMN3f/d30xVXXEGvfOUr6e/9vb9HT3rSk1qtP2um/9jHPkb//J//c3r961//yG833HADfe/3fi+98pWvpPe85z2tdm5qyHU21gmmLhBRtK1ztDIe0djWhKodI/dOTgp8crDaz30GEYPVErCeAZQzkUXAx6M1BobDqhMJfT5/fnw8dozonnvGn+++e3y8887x8fjxVBbtYeXD4cPj47XXjo/791cDH3Dk2z5AYZNbQPBnCuXIW+1kjU3+u7XyrYuJLYeM0hQOb9Wh9j0K2bYX6B11hJTIM2+sR8jVvo3RBx6YNEIjsrTkmUWyHJUqmTn90cZoxEHqyTd5jtWfOuW3CTzjPKLM58yBWj25fZPn477CqIzIQv47AgMwD4CExErDo0eJvvnN8WccIf8xLzzwQKrzyivHx8c9bny87rrx8dAhossvH39G4BDeH8j6HTuSEStl85ySibYtg0oGcgNcpsjxm7sYrS00lfs5zrGcukv1Qu9dk8/Zq7fnzMhEFCN5vfuioe651LUZufcRojHHkZODnHvg6cuyLF8lre2c4enAsg2JUhLQ0ltK57PIs8ydj5AZAeBBCERjmQiCUgb28gx/kNsyyxkPLuZZMnnfIxkycR0bGzESsA3ZXPfu1rXRZn9K3jUpN0sZtRw7lLchF0ZxG0YSx5pjL4cvwdZ9feENbyD6wR8cf+byBvYrFtpzG0HqGbgO6G779qXzLX2uZBxKSL0ncn81vqKJHubNjzljlPehRH57ffMcGHX9IfKDpSS8rB8WV6T1J8cO5s8Q8h5OKOj0999P9OCD488ISgDv89nPjo+f+pTdBgIPbr6Z6KlPHX9+whPGx6uumizDs3BacycParbgjQUPkbmzbeeHZkd7z9vrY50OurnZHm8kUSqXct75NmRfW9jcnHxX6zIv5c691nNeXa3Oq8BwWN8P7361rfNr16yN7UhQj9UPTbex7AAOjfPFZ3m+db+teuq4Z40X0+S2NU9zWPYMb1PyGrwsZDr4H9gD4P8feojo1Knx56NHJ4+YF3btStnzwfFAlvMj53mI9GuWNoLMoj83V/Ye5fDK/B5aQQCeTRnJXMfrs+wYfk5dMILWvqZ/eFwg0djWgy5tBR8SVYNNeGY/z8aW7clM8jx7IMakl8QG7X7ta3qZLuEFf0l4ugRHRN/3xr8nB4BI4HYOj52bNCq3Hc9G8PR/PsYivFodx6S1r/XV0rE8/4Gn10U4Uq0fsh6tLF90SJR2GALX/+UvE33pS+PP3/jG+AjfAMb/tdcmnf6JT0y/ESU7eHFxksPR+jo3Z++wqy1yl9fDA6rlwnkZELi4mDdutXe9zl4otTutPtShVG+K1svrz9l9RWvbynbptevptla21C6h+SVy7qeGpmOork1en5QrOXLOm9MjemoO6mRY5H9enfI36zrW15OMkRyexovJfvHxWyd7tOfk2Tw544b/T+pd4H3g473zzjEHxNu47LLxETE+hw9X+U6N07eST/EFJ9InIJOTECX/saXr8M/ShuO2nKe343e5i1FuRvm2+NtIu23xsJH6S3Yl4Mix+SO2zzQz429jAh/60Ifo+77v+2gukh29AFmRXsePH6dv//Zvn/jtO77jO4iI6Ed+5Efa69U0YQn/EkOxVGnVlOG6uiLk9epqTEjVTYQawaf12SL98H1trRqUqk0OEfIjQvhLtK0QRlCq4OYQdVZ7ROn+XnPN+I+34cFSOLhxqSkYgFRQNFgZ1ZuQ/bztJnVYdXrKoiwjSUTvndX63IT40d6DnDY8aEq0R9zktNG3MXrJJfljqM75rz1naYB7W3laddWVzf0f+lNnYPLn7DlErHckcg283sh86pGIOYZ3ztiMkJvRdnPhzYVEydGAciD3Dx0aHw8cIMLKNmvrNA7pRMpZGev1m6/KlURjhJjWvktDMxdNdCTtXW8it73ybRE4Wv0l98CTm55T1iOQ2tLRghjRIDlpefuSNMghoIjqx4tGQPGyVvteG5K4idowctxq9o11HZ6DRpOXso/e+5xji/E26gh2Tyfx9DmPTPEId2uRnKdHSXuLB69IApUv7JN6Ot+u89JL9fY9dPU+bm7WB6OV1Kl9Hw51QlrCGv+8roiN7KHEdszRBXk7JQv7+FiXwev4Hw9csN5V7x3pWlfKwVveMpnpJMK7lLw3pXO/dT+1fmhzRtf3s07nmSbQp6b6YUmbfSyq1cYC+BaZ0XLXrhSgjEC1G28cH2++eXwcDFKAGQLO5HXMzU1mNyaq7nCytpY+a7tv4Xc4x7zF5HL8a4iMwYhuE5nfrbnck7e8/jrb1LOhct41T8eOlo9wVHU2yizKBQ5pP9bp1HxRl+RCeCBfJJgY5XPm95zEHF6dnt4egTe+PJvH0/vl71JuR8YfbwPwtgW36rH6pP3f6geOkpPRrj3C03m/yax+fFtmrX9aPd795efLQGOJ9fU0x1gZ8nl/S3SIXF5U6gLyd17e4jp5eas+q258z9ELIlyT1a/l5RRskoNcTkj2AwEmnp6qAfdFyzrYNXKet3e+9ltkXo/0rfR8q2zu9UTq92zjNtCH/r6VwN8xmcEY+vvBg+Pj0lIKLMYCFU3PhXwGT3/s2PjIF6xAdkvfPfqwuprakDu28DGBhQdy3PBr0LJkEk0uWrZ0thxunsPjC2U/ctuo41q1uj27xJIzPMNy5L3UeIY63ZEvQJNyOyL/I3pqH9B0UusZeveeI6Kbe+hKtmuos+Ga1N01Zq0/08DmZjXzPGwNJBVcW0ufAchWyPP19eQLw2IWDXKuQVs8C762E64E5hYp17jPWdpM2nslbQwJzVbO9ecBlkzV4tZkvV7iN349Fh/mycsSrkaTzZ6NIaHJywiP5J3f8fscNUe6xiz0QcMdd9xBV155JS0sLNBTn/pUuhuJSw1cg5jCAmQx5BsbG7QkHIb4vodnv9jKOHpUJ7s84dcW4dr1RLptvE0fpWRv7tiQK1FhBGKF1D33pM/IoiDJl4WFSUKd18tX0MtsxzLgeDjUt+UhShPswkLKvpmT2TjilPVIwIjDNsdgLSV5m6ApMeXB27K+D4WhZ+NitLij/aAYDVp2dYtImDEDa0SG06FNLO6oL8MwIGX7+BxECJIZfR4qMIZhBMIZhOPKSnWbHm2hiBWgzOW5J/dxtLJmciMVTl1JOLbpIK2DZqg+WtDweruQC6NVJXtPxxhdtr/9Sodzk8cuYLXBZGljOUk0+Y70WU/DgK7Wx2fmHFWLh5/bwLsnkUC6zc30f8hUkIkg/I4fT79JGQ85vriYAsw8O4AHOBMlewDf5+f1rdv4d6LJRQD8f/yYQ4hZ9XB4tkKOzd8HpjEv8SALPHsEGOSAjdec93DUpby0kGuX9/E86ojtbWyDjxHIV7lN54kT4+OxYykjGjIioyx3SsldThDUhuPu3el/CDgAuHMKc4S37bNcrI1dMXI4Fc7fWFm6ePYczdawgvo1XliW5XxJHY/UluwodWg3bTvH0bRVYc250nG7jUlsbladwprD1XJaRgMeLR2t1E8TeZe20hgH3yODCi5cSNyLxc0PBvY947y9XIQieaDBINkCklvFnLO2lvom2+DzgpfRGGUjY8J6r2f52U7T5phW+9PQc3GdffgBcpHji4ogsrhL60OdLcoXXWyjH/BngvcG8hUymmcQ3v8wzykDxQDu/5XZ772sl6XP/aabys7bxsULLwiyK3g71kh4Yz3yHsyijOyzT3UB7lrZ0sWV8n9bSdfPWQDG5b/c6eKKK9JRxvJIjobXLXl7Xrate3T11e3Us42LBx3r4NvByD6uv/56+sQnPkHPfOYz6brrrqOBteD4YWx4Ce1qkG3pfelLX6J5Nlmj8S9+8YuVsk972tOKOzY1XHnlo4tg9FZzPVr60ZYkmJV7yD/LrJdQSg4eTFutIVBZCpLRKNXDt3fmZc+fT4SnDJTgK6OslT+o9/x5ovvuG3/WtovG75FATWurAC87HcBnpxxlNRIo4I1xi4z3lOlclJBU2nepzD8aSP22sYWDCVoJaJs1WGNxK47Rzc1EMIJkhpxFUMLZs8nZjzKeockD1IgmHVDSmNWCmiOkjtS7JFG+umqT5hoR67UVWegisS3TXHQhFwab5cZNcZsdXIcVgHdRytIZxaPqXksHE2QzsmPxgFKZ2UyTiZZuPxxWsxxoAceyjLaLiqWT5wQDR53nHiH9KJbhfSDnPZzKO7vVx0SOnbeVrmsa6HMslHJg3vOGvIb85YvTYQtAl5e69Y4d+nbtHGfOVBcRoj4ZEMH7qgX8IuuGtVsazzDr7S4gd2IDUGZ52d6JLRdbyW5s4lhvo3wOZtU7s40YhsPZDOarwyy+t7ngHDYWiGAR4pe/PD7ee++kPCRKgWrwC+zdm/4nZTrnZuQiFIDLanzWgtmIxvZEXRbc+fnqgpCtFEy8jW30gZx3oKv3Zfs9jENbrJOTnVTLmIvFJ0g0hUWI+H1lJdkCkMmQ9VhouLCQyvBFI7xNrY+RHRa3sY2tgG051i1K5qq2nslWerYR+c+/S78vvmM+4Dq7XFjLF6DU7cg5GlV34QWaLk7ZxjY6xnYwso/f+I3foMc+9rGPfK4LRm6CbAb2x3/8x9XfX/rSlz7yeTQa0WAwaBQlPTU82gRnziTX5b3J6Uf0vCZtl0iHnJT2pXVrzhytLTj75SpvBBFcckkyGq3AUx6MbK2e9e4TJxzl1j2yn0REN9ww2edIoG9J4HC0btmG9t1qfzjMCzD2+iqRs4pdQ9P3Wl5HbvZY757NAvj9nSbZPY0FGm3K2lkLiuh6HpkV8OAvyF2epZJoTDjKhSoSa2vJaMU2aMiihnu5Y0ciL6WjifdBrtTVFoFgMYq1rerSUjUookSWlj5r7TxvyzSgboteDo1YtuSkFkBRF6yttcW/R1aUW0dvxTov22SO6APaPdQC4/nvRFXCho1R04Qrvb7S+bXtoHsrM6Bn5Xu7UOT2q66vnmMnEqzqBbd6bVvPwLsvkXfPa0v7LjMbIwiZby9+9uz4KBclDodJlmO8oywWIHL9XVuEgv7IeQj94MFtcmcWXDMvI8ddyQK7qLPPku2anMT/cA38WixCl5PGltyO6Nbr66k9uWU8yu7cmYJMeHYk2YY3lqx+5LxPXnmtraa2TglyZaTU07eSLmktEpafu2hTQ1vzk4cSvieqP+Wc79VTcu+1cyAPoLcj4PeBB5KMgP4ugwdXV1NmqjvuSOcRJafW+nqSZ5AvyLiG465d1UBlubhxYSHVCfkks2kuLZXp/TmLUqJjy3r2ViC01g9uF1nOvlxY/fdkvFfe4xk9HSki22eR/+F6EVHeHGeB6xt1uh8vw20OafN5NqXFFUfn8raC2doOfs+BNz/32Y8+oN1nyNd9+8ZHyNTHP358XFuzA8y4Tl+nn87PJ05HAvd1YaE6br0F69bz8GRqqd5Sqm/UyVlP3no2srwePkdYfY7Il1yOXitT588YDuu5rui9nJZ3ft8+ovvvH3+OJKXK5R9L54/Ib1a7Oe1H73vO882pO/e5W3OcJrfkHBrhJNuyhyK2gdafnLmcXxcyGeM3fL/kkvHxwoWkq4LLAbdz773p+5rYmQ5zA/cZQJajDdgBOO7alfR+a2eU4bC68EVbKOg9Q6tMDqK6LMYVErpI3sWbq3jfZRt1i234bxGd3murdO6M6LBbXacimnyGFv+/vm77qzyeW+NdIjtC58j9HLQ133bhz+i6Pm9+5Xa95Ke9Bc1NnpOmF8rxt7KSfLIyQeCOHUQnT44/S54FyUg2Nuw5EzzM8eNJruF/0PU5hyyvQya14jaCTFjF5wFud2vXnsNFe+dpvHudnSPPt+4d5gOiem5Hs7WteonsxABR+cLb5cemu8LnoCn32zI8d1zf/ZhFvOxlL3vksxX72xaytLX3ve99XfVjdnDunO78lPAUuRKDrA3irq6O0gmyjiSq+19TNCVX5f+a9sP6vQkxW9IXzyjg20Vq7W9sJCMUSgACz/jv+J8MVOCZN+UqLIBvDeRlzUT/oATJLM5QfHimBsvQ9CZ4bkhAaYCzDSuGuQGOerStSqEQ4H/yyAPncoLRtD7XjXvPkGnLyRZR4FZXq8EqmgJfR2RtbqZ7BydqTxjQKPWP3/sS51rOPBBx+vHPJc6xiIMm1xnlIYf4rCO6S+q3yndFfpfet1LnsFdWym1857IeclYaNzxgWMowmb1mbS2988i+AzkOGTA3l+TigQPjI4xi1Me3hPOMpbrsmRyeMdr2fKwRoJYTyjsfkIanB95O0/tjzTUR3cbTtTySwHs+cv7oAx4BjPtbunuK5XyM9iMHXdXtPWeZOYt/Lu2PN1fJMjltevYDEFkU4Dk/NFLTgpcBXtM/pA4L2T4cEn3zm+PPl146PsIxxIOTERAss+hbCw+JqgsYNaeUte2zBk5GInAiByVy3Hu/eZ2ROTeiF5Yg9/2sm2s2N+33J6LjR3S19fU058OWxJjkYxUkuiS68W6cO5fKWIEuXeHUqUmHYIl+3VS/1J6TZ5fJ8+rsK60/Xj0lNodG2Gv115HodTaUtlBAInI9dZyBV588R0PEWeI9U+27ZeNvbqb3D79BtmIeWFlJ8wZsbCwGhB7/wANVOx7vLN7PM2dSGQQW4P3hfMmhQ+PPT3jC+PikJ42PR46kMs98pn2tOcgZQ5q8sxYW8nk+h4/FUXNeymeI+xt5H3k7lr6u6SSWHezNEd51eZDvEdej5JjCHHHuXPqflXW1K0jnaskY9GSQFyDTRCePzAN9cPTWd+u3Ov+KV8ZD5PyccRyxNXLeh2gbkLMIEIOsPnMmvVMoA8zPE9155/gz+AO5YHxhIf0GeSS5+dHI1vf5deA8ufAxov/zexeRc0BT/jPymya7JKzAofl52wbVguskcsZ/lM/K0eNy9FV+nyzdHuAcngzi0e5L3/zPqVPJLs615Tx9ro6f8GxS7X85gZaeLqshRwewfo+MyegYj8wtsh7Om9bp2bnyP+f9ybEJcurjeqrGFUkfpsx6T2Qno+L11NlnHiLPlyenuv56v2y0Xatsjk7Ny2vXjM+RnVUs3U/T2725Jsd+ledo11HKVVn98K4n97mhfN/6P1Gaw7w+8zmsbpdNz56PgC9GlHVq8kBCG38lnEOuTKu7H94cEZGJnBezxpt2XV48htUuf86ybm7HelxX3fXIczi064vElUjdnOv4sq9yt0Lt2cgyo1GS4RF9XwtClufU6UpeG5qfRoPc6TFnTMry/Ai/tjb+S/XuiP5vneO1KXn31dWy8arVWWIT8yP6BC6yI0RNp64xC32ow//6X/+Lnva0p9FuZQejM2fO0J//+Z/Ts5/97OL6s4KReZT0RYvFxfa3F2tKrE0bURKHw5voc9tqi6S16sutv23J4SlOpX2F8mG1tbFRVQxw5CubrKAbrozI/ktyaWlpkgTVyszPJ2dURKkHpvU+WURj24iQThFDIoq+risKOEx7wogG6drllrQcs3J/cjBrfa4zNKfR7jRQaqB6kHIbGRGAzc1EWELxRxAx3+KNO4h5GRxXVtJ7AhIfgQ+XXz4+7tpVzXYm556NjWq2WTkPLC5W30m5IpVnapMBvTzYuWkGslnCcBjLiNx0bmgLGe2O1qewu8pWlz1N22ijj23aPCXyse377OkCHqScKQ1izwEnKuUiQhDtGxvpf/gNcp9nXEAZGbDMg061LLxEkxlyMDcgAAL3gS9SlMSTXIyoZWGIOJG0bMWAFyA2a3pCDkrH64xhdPohoje/ub8G9+1LQZlE1eBL6Bl8LElHgdxpgi+0kgtbcdScHTxIT+pGUufau1d3dvKj9j8tGNM6j78X1i4AOYE2ESdq1NEaQQ5vYzmQS9rtG5ubaVxJ5xHGJndKwSEks9afPFnd5hnfIccPHKiOTfzvyivHxyNHiA4fHn+GbQC5j+C6Y8dsvR3yf/fu6oJFIJIJz3uWWpb4iNMI/AS3P3CU400uZFtaqvZJ3gMOL0i6ZMF7H2jKlcpAyz7QdO7MeQZagEtJfdN+7iX6d3RsNBlDXXBMfdrRkHdYxB3B5ibR7bdP/ga5zwMnoF8gixp0fciyBx5I8k0Gt/HslTIIWdN15KJ2bfcsa2cU/j2iCzR9HluFG2or0NE7P6fs/HxVh2iKadoxpUEjszZ+vH5pwVwyu6vm16sbH55Ogu9NgyE1WDbLxQjLV7e5mbgXKfd5wil8hrxHUpEHHxwfT55MnBD4HrQF/X1pKcl5tMn1Wxzl3CD9wYNBmmvkeOOBY5HMwdu4eNCz/5eIqgl3mqIL/lsLls3BNPz8uQsYmtT3aJcL4FXA/0B+c/7SShzBF5JJeY9xxmU+uCa5+3ruM2g77m5WwTkia0dDjO2lparv/NE0ttuyI7bRGM973vPoE5/4BD1TJo0goi996Uv0vOc9jzasXbYDmDGraQYwHDYnUCMr8rpEWwaZFxRaUmdk60IN3v3MQdvKUMn5GvmsZbGSnzXSwHMawnkk+8UdnXAsgYxE9gU4Y0+d0p2vRJMOI01BIUoBcDyTsKXU7Nw56QTmfdUck5HtCi1CLhoob407vnLHCqDTjAOvHxZh07fiYTn5vL502ceelwwNNjemZ+TMWiB4n+jqOUfvZcncMI3lbNEVhzLDlgwm49v8gHzEfKAZrDKYgWfT0RxdRH7gnZY9E+UtwpHLf/ksSrJqNoEVzOMF0UT0GG2+t1Z9e9t9ApHtnzUng7alUN3Y0xwQDWXZgJwsrV2hVF8vmTf6kPm5sq0kaCvSbunCwK6CETRYbUWCRrR223quTRfiIZgAuv65c1XdHt8xLxw/Pg5IIEpzgwxKIEryGkFocuHL4mI6T67G52SnDFiwdvmogywnAyU1mcqdHpYOz/sh9X8tyyPPHorfeJue/s8dcdquK/zoyV0uzy3bKcfJ3DOmIv8ROBkN9iix+XMRaaNOT+D1aOMDxyZ6bu71ReY/L9uGFzAt6/XmmhyZ7pVtElDo1SO/a1vI8jIILJByH1uQ3313yp6JRSD4H3iYe+6J9VVuAQr5D3l37FgKPoDj6uDB8REBy5ddVh2vPNM/0WRWRUvecYeWx19K5OphKMcXA/C+a2Wb8rCewznCJeZwTNr4iyxSmAZH1CakLdVEF849N0f31I51WYk0+1kra+kiXegJXXPypVxe6ZxXZyM3vXe550IvhT8A+vzZs0m+SgcilyVy1yzwNTzgWGZYllhZSWNTZiSDP2A0SmWkbI8stODyu04niCIyz1u61upqdSyUcCOejqJxRIDkb1ZX7WzFfNGnXJQjA1N53fJZavI/Yt976JtjPXlyMgCiTg9rm6+IwtNFm7TPdayILlsiy0r7XDIXR2Tz5mY785l2XyIyK6JreeNOKwt9H9wDdHzsmKVxO9IPcOZMdadTtAG5fcklSYe/6qrxEQtnkIxk//4UUCU5fi5LpL4vuSJtZ0TNHsiZNyLImQe0xYhWfyI6tWbzRLBV9O1ZBB9vdfJe0ze097HteVEi93xv/sjhs0rKaLLLskeifYn4GmT7feoiWptt9Vk7D2MYi0nA5dx99/j4wANJtlvgMgyyB7E9+/ePj/v2Jf1fxvhwnpovOuH18UWJmg+1DVjciFaG29Fa5mCiSd+J3BWe70QseRupN3PeXvsf+sPlEZHO/9fds+35QAXPpTntfsw6Rk4n19bWaNhwjG0HIwuMhnP6P+qCCThKVsaWkmZaHbkkh9V+W0pHW2R8DjxyddqwnOXa8yslTO+6a7IMJjQYp2traZyCfIHCgMAuTLhEyYjE//hqHb7VDu+zDDjjwG/ow2CQ6pSTv7c1hZzMNQMPgQYwvi9cSNcDwxn3gBOq3r1/tKzimvYq/57f3dFwzp4Dusa02p0FTPvaS9qfRp+DbY6WdvoFLiGig8270yZG+y6ddhfiePg5DBYDgQIzhhEN6gvNCPru64BG+TpXDiFWMp9FyIa6870+Wb/nOFJyrj33WjxbpY4sznU0RQLOcuwrrT/Wvcpx8nHSzKsHOj13HkmgHplF0xs3KDsY2Nu78TI8k5qsG6hbUOIRsV5QjbQVZDmtvrr/edfh9VfW1wbBzT+X2IsaERshvyMyo6ntDgdln2jb5sl13pfIHv5727pIifzcamir333VU/eM+XbMRClQGFsh33TT5O4mRNUs+mtr1QUqcpEjUXpf0AY4FRx5BmArQGswILr5Zv+a2kKbY1TWNS07oG0HXtvO8q0CGVDXZ+Doo+Uec8zqNW/lOY+PYfDUWGCFowepvxPp2ylH6+G7sMidVnhwggxunXa294huP21+/GJH38/+UsFBzvo8qPVn1voYwVbsc1N0ec0IFsPx6qvHx2/91vFxdbWq2+OIIDWePV8mo0IA2v79Se/HXCPnirm5qg9YLoCZm0v8jzUPTAvb88CjCxjzS0v1dpXHN2rlmvoIcmJ8ItD47pJFH/JcLUgb8BZSy+vjAZva/Y3w9VZfNbTF++e0ofkwrOuKPhvITiwIgV7z+MePjysrSf5L/kfG5nDgf5Dfm5vV3bZksOzi4uQOiES+XuXxy7KMBqvuqIzOeT45cYEaZknvqbvfkYVRdT4+b6GVtnij48z4TV0wbfZjFnH69Gk6hQUNRHTs2DG64447JsqcP3+ePvCBD9ChQ4catbWtQWnokYh6JNiijcCmmjrcLEN9BgqXouSNnSVhD7S1esUrD4NQKgj4vrxc3ZYBAQN8mxKQhlA4UBbbQPBAA7ldg6Zg4DeZIXlhwV4dpF2nzEjAM61J5UM6x/gMJLPwrK7aAc/yWjRExqi3QktCU+q1Cb/tcV4aiFQacDRDGKyz7H/W82zLYRVBZKFIn/15NCJ37pnyM3hkrke/YWjyDI5ysQdkPV/dKZ1HcvHHYFBvkHnL/tg8NdixOPkbwB1XmDes7cH5anYrU5RmTHpEhxbUZl2zXE1rnY/vloGsZdiR52sBcYFAsUp4byRwlJeTK4e1wJRINgeLyGJ4RBb3hBEpK7TlZ4kmgYC5cqIJUVjSXrTuPuUdJ+okaRchsnJ0E+898Mp7/y+5V7nnQE4CPDAA33nQGVGSWfw7MuLgf1oWNRlogLKa3JakJHdSYbs4T4bIPsrr5ZlkZBu875IU5eNIyluZHW5x0bYJtCy0dVmL+fnyXI2YtuY3r82686y2tP9FnC7yu2creOhbj8qxzbqC5yDSnDUWtHteN394OklTeCR/W/OJ56yLLAIprVuWKbGRc51snk4BWYfxLHcvOXs2Zc2RQckIRjh1Ku2WhbKQ0XxHKNkPjB84wC6/PHFKuC65o9TyMtHnPz/+LPkaHHfvTnVbWWs2N6sZZKxsbLx9fpSB0hGZGnneJcF1OeM49/y2+JtIH+vKb2Mb22gG/j5LvR1OXWRIPnu2+o5Cp+Y7plhZwlDfmTNpTkCb0Pv57ofgdFBGZtpcWhovkCEiuuKK8RFzhqaPQN/HohueeVPuIKJxRIC8B1y3t+SUx/9o80hkvs/hmZty6978EeCPsvph2RpavbMS0Nc2hzDLiPgWgK3uY4hwZx4nWccxleo8Wj2WvaftTqCdD10eclYGHK+sJJtAHlHf3Fw12yXmCNgIfAdelIVMlj5nojQ34De+wy6yN1t6N5fN1rMYDqvBzADnP7wxbunrOdwrfz6WrNCes9afOjTlpgFvt2Wvvdx5LFov+tQ3eGb8ttDWfNGV3O3CLzANn8Cs1BNpo+Sd0PRLjZeSsT3IlI+4nZMnE+8jM+NDpp88ae+MyPVvvC+I/9F28IN+LndRwTywsED0ta+NP2NRCuYRlOHyic8bRJP6v5WJWMueX8qFlPibNEj5VsKte+fncjNefXXnRe0IWbdnb3Us/zX6chqYhT5oeOc730lvetObiIhoMBjQC1/4QrXcaDSiN7zhDY3amhHr79GLqWxDulWxFQ3gEmiKhhYA5Cn+IAg9ZxSUDygoR4+OjzAKjx9PSowMMOAKEBQTKBpQIqBU7NtXVTCkMjIcJoJTM7yJxooHFB0EQOPoOfS0Sc+bAOUqYAl+7z3HvjUh8zatQAVOzFmKRanCkTPz5QQPtEWOMkw1MyZXWqcJrQ+z0K9HE2blfkfe3c3NZDSivCQaiVIZuT0b5PCpU+mzNFy5AwpzAu4RZDSycF56qZ5RnyjNA5g7UJ6X8WS1R4zVyc263zxY8h/1RDLnr69XgzK84IiITLeO3lbtfJ6t2wZbq5vX6y0SwjGXIO0bOQHURHlOCa+ekjIaIg5FWbaLfnQNrmM1IV9yr6/kHvYBLRhBbiPGA87wGcEHkO0IPDt5MhGT+B+cUNr2vbADIP/5PAD5jzlB2gO7dqWgMyszzuJizCGeI1fkYsT19er5KANHnqb/8y3ccJS/4XzMnXyRjrV99Y4dVVnMCVyiSbkNoAxfKCoXi1pkLW8L2Ny0ZbFG8lrPQCMsI//rC01kXRNHXN15JXXMWsBERA9rq43I+MkNfulyzi6pz3OoSPkE+Q15ft99aetOZLrAFs74ft99zfquAfLo4MPbsiDw7NChJJ9k4MT994+PXM7JQGPuAJOZebzF5BH92LrPWp18XpFzjATnoSI6ZyQ42uqP1W/+uyfjo/2YNZnTFqap40XtPm2rcF5G4z29sWPxlp6duI3pgT9TuRhRLtAjSro8OB4ehEw0fv4yUA12Bb4vLKTMmNDtweVovILcIQUyeseOlLXT4yA87gL1WnLbQ07AweZm1f7RdnOUMhj3LLg4n4jG98cKpuZ9lf2Q11wXgIdjJJjBO1/+ZpX12pimLFlZScE1jwaZ9mjyLTSxoUrPL4Hm240E9Wh6LeQBOB3o+EePTtoCRGnhIfzALCNeFqDHg8ffsyfx/vgfeB+Uueyy1FfpK8A5nAuRi9u5TNQ4Z37UAoUBzilaehR/PhYXqHH71o6/vP+SB1peLpOP2vwY4c5kf7Q2+5QPs7JARcLjqiLPqy3evdQu6eMZlvRNsz2sd63UP1/3vGYJ3j2McAbg/SHL7757fLzrriT3EXAMPzDsgo2NyeSFRNWEhXv2JHmNLPzIxozve/dWF6hInnthgegZz5jsu3Zswi9otrLG40u/iNwdgJfBvQK/hnt56lTVBwNoMlnKfx7fhHsGvRT2Fu67ZyNo3IyEdn81+V93z2f5PTKwHYzs49Zbb6Xdu3fTaDSin/mZn6FXv/rVdM0110yU2bFjBz35yU+m5zznOY3amvpM/9a3vpV+7/d+j774xS/Szp076VnPeha9/e1vp8cjnTyNo65//ud/nn71V3+VTp48Sd/2bd9G7373u+lbvuVbHilz4cIF+sf/+B/T7/zO79D58+fpu7/7u+k973kPXY3tSYLYDg7ehoqI1IoY9lGpY5Fude2hDUxg0tnOJ1qQYpjcoESAFJybq06OcrsdrY/algwoI7Ne8mt46lP9enk9nmMlEgRs4WJxqGylvjt9nYo89u5dzuq0aJ2RturajbaZo/VEHOFN0KUG1vTaI/VM4x2LyjDIYHnNPDskZDvkPwxMBIWdO5cMMpkRDXJ8aSk5n6TzXwZCE1WzVfItgXCeDEYAuBGIunEOD0awnDbawoqIw6uEDIy2lVOPRI6caZO0spx0vLx3z3Lm5Wlse537fs+SMy23/Yic1O7HrFrPOWSm/H/db3VtRtrySP2c51bnsJI7mchg4nPnqoEFACcXJRkJUhHfd+5MtkFJYAq3J57whPh522gPmvzuCrlOrbqFoW1jczPpPBzWHB6ts41+ed9z5UoTZ5hmY3t2kNWfiJ0Vta+889tajFLCC5TMR1F43JJ1/yCr9+9PTv4jR8bHm28eH/miCR50RlRdTDg3V91RBUfYCDt3VnfxQL9Q7969RM9+9vgz5hbtuiznjZe1WEL7ndssJeO1S8dxCTzeUCKXk8xBW073aSPC43Y1BqLjrg1bKcp91V1rW2O9zbGRG9DVFyLzc+59kDJLytTl5apzG3oPAhfuuWcyMQlRsiNQ7549KegAuhrKAHwOxvwDuY85YmOD6Bvf0K9FC6aXmdZkkhMiO0B3587k6wDXhXuh8UddBtxPk0vcCuh4m+YKIkkEcjHLAWMRbOUx2lSeT5vjL21fZiVGPZCP+/end4snHyFKQconTiRbAFyR3JFqx47EF0GGyizK584lOwL94omqcMT5kO082yXOlcFsmm5ep69zHV/qyTlJoLZx8aNuAY/8HEWXuoTXRp9+2610X2bBXmnTVpaJQaCj84QjCJzlCQqJJmV+hH+FTg8dHPYEjjwYGUfIei7bESiN+lAGxz17qv4HKaM3N+v191ld4LCNbcwYbrnlFrrllluIiOjs2bP08pe/nA4fPtxJW1N/Kz/2sY/RP/yH/5BuvvlmWl9fp5/92Z+lW2+9lb7whS/QroeJhttuu43e8Y530Pvf/3563OMeR7/4i79I3/M930Nf+tKXaM/Dwuk1r3kN/cEf/AF98IMfpP3799PrXvc6+r7v+z66/fbbaU4LoDTQdybOWcNUMpFuBZQGppTWYyHicOLkHxxFMsPB6moiH2UwggwY5uDBY/w7UXWyh0I0HFYJDfyPr5CCMRwhAS3nrLZqNWKw5jqc6hxWUVK/iRLcBmmR04e2DIWIgTcl1Mq/PomJtp6vVee00XdfpnkPc4NKcxzpGiDTIctlBoHFxarc9/olA4t5pmWUkwHGOfdpMCB6zGMmz5MOwogDOErARBYVWGgalJCDPsZsrmO9JCgnWneTsi1hNKy3F3rVW0v1hbbnqj6DFnPhyYg68skLps+9Tq8f0XO0LFY5K92JqosHtYWC+Cy3dMYRgcy8DBxXXNZb8h71cyeSXKjIbQ0EI+DaeXYYfLdsAj5X8PvI6wO07GmAJwtzArvaClaK6A1N5XApOd+mPJHfpxUcND9fDdyZBcySXlEne9oAH5slgVmzMj/lvPNcblkZ1/n2y3D2S3m9ukr0uc+NP8P5hB2uEGiwtpa4Ich2fJcL2IkmdzkhmgzaqcvIyp+fzGyMYLKdO1P7mLu0LThxvpVtZnGxGqCM9nn2e/xPcl2jUbWPOOJZrq5WOSarbB08Hktem6yTjxErC7O2xbWcz7R5wOMUgRJdyZtrNFnPx2Bf8BanefO69Sw1GeYh537mBorV9UPjT7X+1cnkLoMKtGzf3tgs5Xi1eq3f6sqUchfymvnRys67vp4yYOJ/mD+Q/ez8+VQe8l9mACNKQbsIApALxS9cSEENCGZAtnuce+mlk1st8yPk7/JyOo/vCkKk86BybtCCib3nLcugP6dPx8a2lKGez0Kb3/E9woV78ymRPudFxrynd2htoo66LGxaG5YtZvUN6HsxIlG+zdGGzy8qZ/rwnfTp4+gaffc9x1aRZT25oJW1sjuurxPdeef4s5XB8fTp5G/lC9SJJnduku80ZDPn/3GezHYPmXzVVVWdXia5WlxMgclytxMvGNmT9SU8vcYf5TxDj0uM9KNkvLbpl434nTxdOGIj1PV3c3M6SUgAPufMAtfQpn++rq42r1eOE23cWHpKKe8uEeWqSngab/yXvNce54nftKzqUl6dOzfOfExUlf8oe8UVSad/7GPHR3D74IoeeijVKfVtjf8BJG+/e3eaC8AfoR4eP4QEpHKhicwWz68L4ONG9sfT3+U1aDtDaTu0W+dbfeJH/ixzdn6VC/A9vkb+HuXzvfcvIrfrkMOfEHXO/3hTXp+YhT7U4Q1veAOtGvbY2bNnaXFxkRbkjkwZmHow8h/90R9NfH/f+95HV1xxBd1+++307Gc/m0ajEb3rXe+in/3Zn6UXvehFRET0gQ98gA4ePEi//du/Ta94xSvowQcfpF//9V+n3/zN36TnP//5RET0W7/1W3TkyBH6H//jf9ALXvCCSrsXLlygCyBhiOg0X4Ert/loE6VOvjb64gU1PFx/OBS55DoiZJsnrCKKStOgIu/8OiIgVxmsI1L554gixCEDzeD0gVNJq0tuHzYY+Eab1UeJ6NhFlgILXj3a9pieoWjdz/V1e/tqJi8qxhLO4auM0Q8ri4/mIJJK3+7dVQVQZiAdDm2HlUbgy/9xeMZBruMjF13XfzEhIgdy5K83N5SiTi7mOulkvbnIaSviTPJg3XvPSRGBR/hHniHaX1iobuVZAj5nyO0oESyxsZF+87YsxrZB0vDmGXvwWTqzYFhr8jKiT3H5LbctlfMJX8gjs0qAyOUZI/gKYaL0+xVXTG61Q1R9vjww0ZPtlsOLOxzxrLwVwxbZ623zBkTli0QpAdURBptMd7L6z79bDjyAZ/2WiBLW0vnZJXKeQw4xrf3edEzJd0N7R3Kcy1obWr+1754zV9uy2NKbeX+kLNSI8oiuJh07+/enMtyxRDSZ6dKyDXgwssyMKevjAcvSmYXrWVtLhCkcVBKagx7IGVO8vPY/LbiPtzkc2sFnOXN6xHEVfa/k+SULOpv+b3PTJ3Xr+qq1oWUpnia859vUsdhE756RubMxIvNJW7o4hzd34xh5x0t0GVmWBxdBXkOHjAJOH8CS40RJh5Zy/OzZpNfKYGjovcePpwBnOLEgLyHj5+aS7osMPZCbkPnf/CbRZz4z/ow2Uea668bHJz85fT54cHyUuvXiYrpWuRsYl9VyPuQOniaO0Yhu0tR+lUESfHFNH1k8JdpqS6uni0yVHrRAc/655Fq1eT7nvByOpw6Wzpmjv6+v+5xOXb+ajhd+fp091HQOjuhoWvmmuoA8LydzI1EKOpbyXrNnJF8DeXnqVJLpcitnnLO8XM1SDJmK71yfl8ER4Es2NqqBz5gr4LPgi1EsG2plJc1VMiMy5zTkghOP59CQIwu7lpul8iW37iZtlAaX5QRrtIUIj+XNETnIkbscbckXDXVzQ5vzUcSn2rVtw3W1Jvdek63ec/KuXdsm3TpHuz/IaC9lOtfJrSRt3FaQu53gyO186xq5jMWcAP5HZsYfjdKOvJF719WY0O59V+B8icVRavaIVVdJ+9Z32VbOzsxtoC0fZAkic1XkXc9FW7pjiQzrAn36dZq0EZmDNXs+x9bO5a6s8hE99cCBtJMJzofchj2wuTn5mSjNDTw5CfR0zB8yM/7CQtphy5Pxkv/XeG7sjOj5NUrejab3XrahxRjJvm5uVjkz2Wcv07+GpmM84geL1lWK3GfYMf+j3ZZpYBb6UIeXv/zldOHCBfqd3/mdyv/+wT/4B7Rz5076tV/7teL6px6MLPHgw2THZQ+TzF//+tfp2LFjdOuttz5SZseOHfSc5zyHPv7xj9MrXvEKuv3222ltbW2izOHDh+mmm26ij3/842ow8lvf+lb6+Z//+crvg3Nnic6xVSR1gQYRw1UrrwUWSrI5QmxrmbK8l72OzNHezshK8xyFjJf1HJKyPNBUKS91IrUhzKP1l5DW/BypUGuKh2Vo8uyX8pqlw2plZTKgQMNgUG2fO1KIxoSlJAblSlsevOsF+EYmsCbOQg1dziYWERaBvM9evdp5vCx+yyEItXo8w3vKGNCoPeNSgxfIkXuOBY+4kmVy5F4pCRn5X5f3PNKPkrI5ZA4v0/Z41+qr2w1iNErvsSWbNVjzgQdOfEI2a9n3EZgLeGM0x2lYWo9EH2RoBCXyv6366tpo4njTnmHPVtqE/Neg6eh1+vLmZjWgUZ6jZUPkZSy9na8a1xYp8WMXKGmjqfM0p83Sa9fOq6trmtk8JOoWmszNJT0f1yUXjCwsVH/T9H9rZxXNNsX5cmcUohSMYGWk4XOn5azzoMkp7Yi6EeCWoytNC6V2L1F7Tq7hsGqzNXWs9B2MYLGTERtMzg2arI8Q7RGeJbJ42wtgaoqu3oGcd6zNoIgcB3RXcqCPeVrjf2TQLpfncDThfZaBvbt3E11zjd4m6uEckXxmCGTbv5/o2mvHnyFD5HbNe/emzyiD9+Dee8fHtbWqbhTR/3kCCuv5arxlxMZoCx6XMw1osrCt93da4NmKeGCyZSdqQQkyYP+hh5KD13JiLi1VuUzAG29aPyyOdGmp+v50OUfkoC2uqm3k2PxcP/Xq6QOavCfSF4hL/h86+tJS0snBzchxvL5erQcBzAiI4G3x8U6UApb37EkLTFAGbWGxy5kzto0L8IzRQMS3JPUqDk9Xi6DrZ9+n3iA/99WPPsG5Gkvf5t+lvo8AHr7tuVygJe1fHpjijVeZJZD7xiLZYoEcvv5iea4Wct9nr54uy+ecI3eewpH7aqUsl3bAyortG8Z7MBqluq0dsjwbFe/Bzp36Tljy6GVhl3VHdPNpjm3Ol7RRVx/n9IVp9u3LXx4fteyv2BECuv3996fFs9BTsHCLZx73AhKJxuOfL7oiSrr6vn1J/8JRJtzh8l+bG4j0RBjbKOP6S9toWm80VkPy9jhyeY7PWBAok/CdO5d+QxmZYXluLi02lGOLz0GSk5RjdGGhWg++g4PSkvfJeodDu0yEh+V1WYjKbLyjTeDp3Z4fLRKz0TTmIjIWc+qZgkzaDkaO46Mf/Si97W1vU//3/d///fT617++Uf0zFYw8Go3ota99LX3Hd3wH3XTTTUREdOzhFX4HQVQ8jIMHD9I3H96K6tixY7S4uEiXimwgBw8efOR8ide//vX02te+9pHvp0+fpiNHjowFESb86MthlSsdYW05ByPneGRmpD9WGe0tzwl+9H6PECNeHU3vb87z1gRtJACqrg1+f7X6oDTI1U58q0rZN6kE7NhRNf6k4Tk/rweWWfAM1ac8JXbOLKLNCd2qu22FdtqE6gzOwCNysoG3gRwnnScX+nTMFLQ1UnLsD8gJXG2ixMrPsr427lHuvNCWMzYnWJaXAekI2aw9QxCBcntmvhhFkpEA5oO5uarDS4KTbjyjJu/P2loiepAhGRnpUfbQobSVZ+52yBYePv+R8TqsCeKeIh55f9qS/7lbBJXqgdrvUfQ895vyX/7WxYrdKRrlWYjq39a84enCXj1eP+Rvnt0Q0bdznoGmk3uLVyOBb5H+WMGPRIlQlHKWBw7IRYTy3nGCG0EDUtfnjjxty3vUK+uWhCVRku19oO13rM3AyLq6S8nzaev7JejbRrC2hS7RuyO2eoSL4EFO/N20+pPDb2jfva265TmWk0HTF7R6rLojAY4eh8EdEjnEvPVda0N+579Z1+PtUMTLWPJScyZqbUJ3xniWGY65Pg87QOrmREmGS92eb8WMMsiaibbQtrYFs8zGvLlJ9DDnWwlKwKKQ3bv9cQ80lRlNZJ82tmW9pfyj51yTYyii/+TOWZYMbKrb57yXXWP37qoTv64fddcfkf/RcRwpX6JT83Mi25PntNkW55UDbz7U3qMcnqUt/0yTse09/+GwGmgmd/dbX68uRkEZyO+HHqoGUWrzrZw/RoKn4IlG8BvqBefDE5/Ia0MgxIULqYycIxCwcMklab7AUWZqGw6r9jsfE20t5rWeb5+6tWcja7Ko6dyXM6ab2NxdguuQlo2NMtr1YrcG/o5YNr+nC/Px1/a98rYyt56hNm5ydsDR9OVom7nI0Quj8tyST5qcyNG1Sq5Rs2t4PfD/4vkgeBLc+n33pd/kEXr8gw8mewH6OuQsz4bPg8WIqlkwd+9OshhZ7yG34Wvm2ZOthVJawgWNb5PvqraTSE7g/lZEW3pzji4Zeedyud9p4HGPs//nzacWcm3UtmSXh6b3vom8zuWscur2ylo6Pp9fS3h/jhJbJ6KHRew1DvwG3RmLtb/xjfHxjjvSXAB9H3IfiwkvXEiyXO52wmU+PkO2S1l/ySWTC8p5fZDDCwtEhw9P/qYF09f5gjhX2leG+y7hjafS/7WFi2CuzDVZuuzHrOPee++lK6+8Uv3foUOHzFjbKGbqbf1H/+gf0Wc+8xn63//7f1f+NxCOztFoVPlNwiuzY8cO2qFtB1tHQLZFluUoI7kjtWRiz3Fwagaa/O4RsLz+EudepB8eSg37OoUwYnBG73Mdac2NUQ2YyFEGCgN3/miZkIkSUfjQQ9WtBngwM+qRTiwYoXi/uPEnM4KjzNxcMoa967KMR02xLZmsm5IfpQpzzvsYKaudU0e4e8ZBZGx7bURkqXYd09imbRrINcy6Qkvknxt43Bai96zJ3BCpX2urLfLRm6u8OUWu3tTKyNWzkPuQ+WfOVFd9Q0bzrTDxGc4smRnnxIlqcATAMyNcffX486FD4yMCGCDzV1er7QMwbg8cSNl8oDijPixW27Vrsl0iGvAsbNrWzfJYNx5m2VDjpGxfbZViGlbatJ7drMj9krHdNCDF64+lAzTV+SJ6SyToQxujFhGm2TylgSVWtmyiJMMsHWtuLslAa5vmBx5I8h9yF1k/IONPnqxmAgEBirb27KkSldiCmcvkz39+/BnPWwtKk4EGMqhgZaU6XiwilMgngnOyiHT57s7yXNI1+r72pSU/YNT6zqG93yULvKPt5dQpbUjNppSBxfLctmV9KSJzVuS+RctYY8AL3vMCMTwZ3/Rew2kEWQddmuv6ktOBTg35fe7cZGACP+KcM2fG2aGIkvwHIHe5M0pmSAY/u2MH0ac/Pf4MHgmZomAXXHFFNahZymi+bbTMrBzJPMgdXiXOrD7fkRy+JuKkm0W9dxp9yuH4I96siL1onRftR93/SmDpuXwsAd4iiZK25O9150XgvSM5zv+IPyNSb+618jJ1c4YMwMVczpOTQBZDlsss+IuLRHAwgstBUAPmCL5gXQYz8Cz6kOVYcHjgwORx376UGVkGIcgACKLkN0C9XNajHK4d90rbNpkvqsFR8j9A7vs1C3p7m7Z6aXtRlAR6dQFNhtUFE1vnyzKeX68p9+z1qaTOPrm70sChNrh9rz5uH+XMEbKM54cr4a7q3mvp74W85Dw5ApYhy6X8PnYscTvg73GEzn/8eCrvAfJZylaeGR/BaNJHzP3KKA+OH9w+5hFNfku/tBaMzL9bgcqzjByfcA6a1jNrun1TlMynuXNYhPstua+RuJ1oGxGbpw5cpkZiezwZGOG1Irq41VZurIZVlpe35H50bo70FbIMC6Qg0w4eTLIc3D4WBuJ44kQKWMbcgLJHj6ay0P+tfvOF7OgP/AHgc/bvJ7rhhvFn2Ag4Yu7gOpKl//Ms4hZHVJdV3+LsIs+5VKbVxZa1gTbe2S7g9avj+J8IfdMHZqEPddi3bx999atfpec+97mV/331q1+lPXinCzEzwcivfvWr6b/8l/9C/+t//S+6GuQzjSOuicbZj3lU9n333fdItuRDhw7R6uoqnTx5ciI78n333UfPetaz8jqyujpJQpQYMCXgk4s32bU1apsoGl49lnKhlSk1sjXB35ZxXRJgkFN/BDmBIRbkNs2okwcjy6w3Mhjh7NmqwoKgNBwffDBlW5DZc2AE8hWtOEJR4AoDtkeRQQh82xJJVEqFYTBI124Fa/CV6p6i0TZho42tOsXUOz+icPOylsJeeu1ePdb7z2f/iAI2zVna27JPQ0T2ReopveaIkVT3XPk8dLGgTyW7D5KXSCdHc9tCIIC1vc6OHdVMCJgH+FaEMF5llhoQh5dckoxZnCfnjI0NorvuGn/+2tfGR5ndeWkpGaGAlvVAmzeIkgG8Z4++FRaO0pgF0NfRKN0zOZ/xec6aW/h9tra242X5tRHpc1eEELPktXaulRWRl5dErtcPLfDT6g8f2xhbfaIN/SunjTr0Ib+aOrxyycyc/nR1/aV2SFfIsTmi48fTSeQWnlrWepwvMy3AAXX0aApQkBntgR07qvKR775CNJYhCEbQtiBEv2QWNn4+zvF2cUFZuTiSO7CsreR4mbrMOsNhVU5HthaV8NiyiP4eKdNmMGTbjpm+FyMePUp07bXjz3z8lzw7YHOz/jr4eClpIxcR3saS8V0Ez3poGlAcsXly6s+x3TybrMvMLVYf+U4p0kmPc6DnrqykPkKnx/8Q2HXqVApUk4ELGPM8IYR8DzQdluv7/LhjR9LNwRHjiGCExUVb7nOdXcp0ns1HBrFpuxxYARPAxoadTZFvP2rxWbxt2b6cnxYX7ffP0981WO+4tkW27Gs06KqurUjgZ1eok9Ul8q0NPsfi7krbyLmOHGd5288uwrdZ7cvfPFns2d8lbVmInpNzzzVY5bnMxzi3dHMuJyH/8b977hkf778/cTuw1WWSk7NnE++D8wA+v8B3JxcYeln85fddu6qLUOT1LS9Xs7lJm4ND8i/Ly9U6ZfA3P88aQ9qcJ30XuZwKeDVtJzPLHpFzD5HO/9S9R1yHlXpzRD/UykyD/5GJDjhy5ZGFLuR1W5xSSdBKE524ro0mNkXULqgbnzxLdhOU+toArw+abiB1YXDyc3NJlkM2y91K1tbsRCXAwkJ6xz29SQa6tQW0Dbm5a1eS6VjwzjN0Ek3q3XLO44sRcc/kIh0+L8qAZ2BurioD+W6SVhlvzrA4eU2nBnjblvznbckxoEHOVfwaLJ+HZs9oO7qhfsxf05D/1q52HG3xHN6z0373OLu6ejyZ2sdikBx4fa2LH7Dq8X7rKlCziZ3VpLymRxJNyjS5YB0xPXj3z51LchG/gf9HWWsnOQtyRxQENRMRySSkkK08iYj0MUv5vXt3Vd5ri9It+4rHKqENHCNcMNeppbwHNP+KJZt5TJXcncaLI5K/DwZVX3NuDJQ1N0VsZC7/Ja8m5e1wmH6DT2kbU8fznvc8eutb30ovetGL6DIsbiCiEydO0Nve9jb6ru/6rkb1Tz0YeTQa0atf/Wr60Ic+RH/yJ39C119//cT/r7/+ejp06BB95CMfoac+9alERLS6ukof+9jH6O1vfzsRET396U+nhYUF+shHPkIvfvGLiYjo6NGj9LnPfY5uu+22vA4Nh0kI5k602osn/2cJCy6YvJfa2/5Xq1M7Wm1EEVUC2lDYokEOJUpRbv8iwX459USce15bnpEvt17W6pcGGSZxzZiV4EHNgJwseVvcCcaPvOzTnjb5m0eIAZphJtvICQrQ+liCqAM1MqbqHDtN37Ucp4fWD60uj9zP6WuustsmSomjpnKv6fmek6+Jk8NzaDY1ENss3xdKSFai+utpY4yV1CEdPMvLyfnfNTY3iZ7+9Mnf2iJp/3/23j7Gkuy6DzvvTfdMT0/P7OzM7Ow3uVxySYqfEklJoJDAEiTbMqQAQQRLhmEjTOhEiGwYgqQYCQzbS9imE8exHBCJESe0KNuwBchBHMj2H7IdJ3KsOJEoK44kiApJkbvL3dnP+dj56Onpfp0/3v54z/vVOafOrapX7/Vs/4BGvX7v1r23qm6d73PuGOunr8N51TQDWMb72wcjB6NN5DBXGVOjjRZadDfjpMP5e3tNp0BG/o/m7Bm2ZzM72MUbIzKCZIyrGRrqycuWLNHFyVZ776I5e+Nba4Hb8jPWbaLrsoxKnuHKktuZ7mt9AJVn+Jq1YYtle8bBQbMKPzs7JhORj3xk8TvrPluGKz1ny9DI6KoH16ytrvzIctBkHFWegyrSEaw5t/VjzTXSX7k/Tcu4reU4iwIDloGHHloMSGnT1TM6QmRT0v9neN1QazA6N7JRtZ0fJXDW6NXrJod0tRXVPK9M2762ItiFtFMY4O2WL1zwbTq6yhgnvnOl5b295v3AGDrw7ZOfXPyN7TSW3NBFn7X4iD6/y3qtwTJ1lYwePvT16PvUZp+L1q81L13FdAxoWi3SLsMOZQ+yxqkJEIjk7b527pp+ovsyBr0eWg/uqtd7/XS9L0yrNQ3jfrT879nvJ5NmIBfoP6pO6nlnYOkWmE9bP9OpyCc+0fzOQ0Y/i9bxum/hXLvu2vROq8+h3tEa/Tc7D6/tGID8n70XmfXmnRN9N5RNMHrO0Twyvp8aOb0LH7R8ZFZbT2aL3vOu+tEQ7SwMaQP2+rKqDCMJBIl9Hh3X/WqbT8bmxQWvuHCViMhb8R2pIKmMPMmV7a1zeJ1EdhLL3pLBUfFf1baJdK+h6DbvtjMGLNpRY/ON7Gzcj9ZDI/7hraFM4LQe2+MxGRqtf8vIT33WvXXPanRby+7JNiqrv4jmtN3r7L3zYM2n6zvK1YpB9xHMe+pUof+oQAx4QbQivj6hx4runS7oxP1++7e398PoYieM7OaRTNF3fEtm8saP7AHRO+vZ3SP6kuGzGt59sfwHUT/8m3VfuCDYwDg8XI2KYc1j3fHss8/Kt3/7t8szzzwjP/IjPyKPP/64vPDCC/LzP//zcu/ePfnMZz7Tq/+Va+R//I//cfm7f/fvyv/yv/wvcvbsWbny1rZQDzzwgJw+fVomk4n8+I//uHz2s5+VZ555Rp555hn57Gc/K9vb2/KH//Af/mbbT3/60/KTP/mTcvHiRblw4YL81E/9lHz4wx+W7/u+76ubkGaytcQwUoSGUvL7VnK1+l4Wagx6yzT41yjO3nwyxoPZrBsDrWF6kaCi23hBKxAw9vaa27ThiOz6O3ea2/MgIwrb9LzxRsly4i2BkIl06lRzuziuOnbqlMgXv7g4Vw5gOHnS33pT98uV0bi/6bS5dbPe9pmrI3RZm0M5KWrR5b0eyhgYKU0RMoLKSJjsq4oS6/J8NDIKz7KMH+u4pldp6OniCB8SmfeGDYtWcAG3gSKrKyOAN+A3VFO4fbu5/SX605WXcW901QWRwmsODwv956ozmlcw3bbaekGUfbeSjdC1nyHoZdbZl+lnzQynhxIYP5YFNmJbGJomtRnVMSes/9o5eVjF8+5qZMrI5MvUI1Y5Vu3YXOWYafvBQZH/QYP5/zt3imyPivhwDqAy8rVrJXsdVc9QIRP9bW6WdQsDKOi3rl7w1FPls4i9lSboPZJktNwuskjDogrHnIhp6Q8ef1/mO5OhPW8njG0pZCdLn2eecSoMuaYygSC15/Yd25pHTeXRrvOqcVxZ53n/R3PqKlda98VzOGRtRN5164o5rBNwoMDdu026DzuQDmrGd6iQD9sQvt/dLfoC+AnaREBwBLbvfMc75n/4LCKC3fJ0FTSudmlVsR/Kmbosx2RXjMGjuozflY6vwlPEu+1YGDqYsS+dy+puy+YFa6Y/dkYXn4FI+7qo7S9yIGfWIHs5dbIb7DWgyZDpYeN/5ZVC01H9HlWQQduhK+i+ud/a6oYs20NnuHixJEdCV8AuFo8/Xs6B/wHnoT/ND7ydTTKI5IYoqIH/H4NHLMPWFY0x9DzWhZ54Pru27zz0PT/bp4eh7Bo1OkdX+1RGJ+jjQ7KCaLroIX1llYhm1I7lyfagzXt7izZ8kUL3Yb955ZVi22G6D73glVeaO2H1BWgy6Dbk+fPni70Iv4EfPPbY/Pjgg0UXQJAddtzC99vb7TtbaSzjXV0HLNOfN9T9WUXSUF8ZuQttzd6vTOzIUHykr1/eo6Vd++ui27YFeYvk19jQsj2f19X+z/dlNityP+g972x+7VqRy1HtGG2gB7z+evmO6T7bhYYEaLNF4/EZtB0VWvUOVyzv611v0QZ2f2snj3WIo6iFJ7+MKVOuof+4DRF5GHse6473ve998i/+xb+Qn/iJn5D/4X/4H+Tg4EBOnDghv+f3/B75q3/1r8r73ve+Xv2vPBj5r//1vy4iIt/93d+98P3P/MzPyKc+9SkREflTf+pPyZ07d+THfuzH5OrVq/Kd3/md8ou/+ItyFkKqiPz0T/+0bGxsyA//8A/LnTt35Hu/93vlC1/4gpywsjsCHMpk0RiZqaLqOTAyAkJGcDliL3gDzvwPZTKvRLfEMQbDkP0vy9ir1xKqHHBgGHBw0NzKAUIABBi9jTx+g4EP1RPu3SsKL2dfoe2ZM2UsLwXkxAmRj350cSxe/9ZWkTXbgAGaaVrHdX/fIiH/KMy9LXtK09SRr+dwY7O9UR+8dT0rCbI7qpjW8fD7ESafrHFmcPVLC5z8sb9fFFa8j1A+odTeuNE0akJxxfe6GiIbR9H26tVmUDNoO7ZgfuKJooxCsUQbTqgRKYoqDJUIbrh8uRnEpvkJ7hWcWlYCTZsxs6tcFxkmag2mLTg8fq8aOJRJjjYPfe+G6O9+ep6Zaxnjese8p8FYvfUk0AfQtM3NQvs8x9WtW83tmSG/g7bfu1ccVTjy9s8PPFCq76A//Ka3VAOd1oZF/X+G350+3aTbHJit5UuL53CAck3AQuQcyDi0O+J+lSeP9HWNEQhSO49VYkx7VtexVnGvaoMrMv14waCg3/v7hd5zUjpo+9275TzQRJbbX3ut0H04sXQQMgD6D/kdth7NX558cvE7BA9Ajt/cbDrZvvSl+RHOMT3G00/PjwhUQyDD1laOpmu7k8hykxkz6BL4vC7oOsdVBCOse9VUjaNA344yhr5fYz0vz++l7UCcpMfbE585U2gwFyFBkPLrr5ffuJgJ5PZLl5p6BN4xrQdgV1QEEXBVLL3FL+4LeNULL5RzcB6uz9pVyEtqPHeuOT7bf3TAApCRN46Cx/kY9fa1Y5raxNj3pM/zOYrPL3tNOPIW7dNp8Qkzr7BKOEO3AAEAAElEQVR0BKahoN8XLxbZHn2jLXwH2YA19AM5nenuiROlb9htQPfBj86eLYFqsCfBf6DpOvqEP0T7ubkyKPzYmh+wLpCx13fxVR9jfIwp/6/zGhhzbut8H7JYp2tgGzbbwLe2mgnikNG1jR7yPngF7D/gA2fPlvcFtnycD95x82axEYFO8q4sW1tN+g87DvSCd76z2IL4HcX/upgg766H4yuvNGOe8Owmk6KjgN6zHmAVGV01TV+ntXeEcByMXIePfvSj8s/+2T+TO3fuyNWrV+XChQuy9db79uqrr8pDelelSqzc6naYqE89mUzk2WeflWeffdZts7W1JZ/73Ofkc5/7XP9JaWJTsw0CwAJqFFyXMV4MWc3A67OGmA5Ukazawb6uFTL7YqiKaLVOEg5Q47Wht1dmIURXU+OKOtzm9dcXM3J1Wxw3NkqGljWPNlh0hAUOXTHBqoiGMVnh1dtmA5grjtbWpKzAc5W5N98s53Ownn42mDegq4mKzJVsCGkwqrKSra9Rb9Wh2+rKc1DAIWDqADze1k+vtcwWzG3JG1pCgJFh3dCTFg2WhAFEQd4WPHp/FOjo0BUxR+QrtcE1g60TK+BMVzfzjkyXQA9AHzSP4LZawQPYOKoDxz70ofln0EvQxmhr6ZpgAox5cFDo9PPPz48wmOpKP57Ceu9eM8nHqsKD8zFn9M33S4/FW0Tu75f7wbsdaNrKuwHgWjXdBd1+655PLKMbX7MVgM3yrbVePFjVPyxj7Vv3YTL2Ns1S+c4ts9JYTaWUShzFIL/Beeb9DKb3LING1cqsNa3lQJGS2CHSpOnamYU5cEV+zEcHA8OJxIHKOFpVgzE25qXlXcyDdY39fb96vp6bRWcBrxKBFbCWkcusCg3cD9NddS8nbKdg2nrypJ8wo+l4tIVhW3DedBrLEjX9vIXJLKH7HeNoYYl87b7BUPeF6a1O2uPdrlAFDcdr1xYTCkWaVXTefLNpJ+FK+wcHRS6FvA96Ddr+yCPNBEHI5Ag4eO21Mh/0w9UzH3igXCMq9WOuWqZmpxgfRZpz1kF6bD+yZFlPTga03N5Wjd/6Th+94HNtg+LEG2s+3m4yVjVRtm/t7fn6g74XnDjKPELbMqCfHWP1GMpOMpS9+xg+dCCAiMhbO45+83jjhr8zoi7Gw3oDaDK+394uQQRsZ4e9QSfAs69AByWj2j3oLAfQbW0V3YADhrXuwXQJ0LYMb/ctfR6Olg3bo9dad2DdhIMkLNsO08Lp1LdDRT67KPExCqT2+Ij2i3hzjubWNVhjXe3/fbHsCvXHeHsD6wv0FnI76PfLL5dAM1S9xxHy/wsviLz00vwz5O4MQItBxx95pBnQqwtV4ft3v3vxPE4q13Sb7e0IYNOBxtbOWvjfCzjV9DZj0+HdALQMa/ENnkcUzIwxhwh0q412sny0Xh+ZJM2u0VaW7/1+wtuBD7wdrnHdALoPeg+5H7T9tdea8Sgci7KxUWwgXIEY/+/uFjs76DZ+Q2DiuXNNeg/aru31zzyz+JveyRDfW35NEduO30X21PQ/SlgfqhJx27thJT52xRKLofSCFw8ksljQYElDr0Mg8DrMoQanT5+W06dPy+Hhofzjf/yP5fOf/7z8w3/4D+Wujp+oxMqDkdcNEzlsGiGyiJT8ZaMtCKQNNQFeOrg6On9oDGXwbFMAljEfjZpxrUDNtrFnM9/5rw2PcE7oSji6ra56jPUFwUMHY+FzRqGCMMKB0CdPlu04vSAly3kD6CCsjEEs2o7XctJraEXTO1rZUwxt4GNYTi0vmIA/R/PSba1gsii4IuMoa/vNoh0RuDrGkjH54z8m8uf//PyfnZ325A3r+Vhto/vhfVdTiSkaA9DrLUqyyRiShzCMZHlOFLzeh5aPwZ9pXpPMNQ8xn7YqWpubzesHDdU0n3kCB9RaCSvMK/RvOB/z05mxHJBrKYVelqpFpzgw16L/Fo/xjIj4XgdtRYFrnmPfQo0xsm8wfvSuZN7Nvg4mr99oPiNhsntHBEGvEW3WaAs2tHhvV14anZd5Dm+N3whFHlq27oqKazDPqzGi18xjKN4XoWuQnnV9h7SGuRLZdNoMTsJRJ9jhMycRWtVzAKsKPycLcjDCqVMlwADfsUFVVz3AdTBNPXHCp9vZRAjuE+dpw5i3PqIt3mvophcw5vWTCWZjeT8KOM5Uh7H0IkZkLLbmyP32tWvUQgeIaNTQA+u+9jUEZ2T7mnMinTTznddPDS+PaPMy6O2q7D1Doq/sB5p67145DwHBoNdw8N+86QdJaXsSyzToB/L3/n6zaiagHU9PPWVfF+bzxBPlO/AB3pJTJ10wDdNJgZz0jXtgJRHyPbD4gKWz4DMHPut7ib74uVoBGJyMo+fnOe31fHT1T2tMfT3Wu8pz5euytr9mzGbtSSha54FjcyzoIgKRPFZL57r0k4HF37ugNmCgCz+qwTrQWsYqgqi76lqga6CLSB4Evbxzp2n/5x2t3nyzjIH+dJV6kUWaxImPGX0GbQ4OmttE866MoFsiTdqhaYq2Kem2lowMfqj79ewt7EOxrsviPVyZTbf1bAeap/L4kc3Kk/F1+0gPqaFdvLOOdX5mLOsesN4wJvCcsnQ7uo42DG1bt8aveT618O5Lxk6m/Whef5EfTesP0TNos7lFc822b8MY/ExfO+9ygoTD11+fH69dK9UqIZPjXiO55NSpwjfQhpNJplM/QAzY22uej0A4PU9dtVP3o5NauKASH0+fLrK8l3BivWtW0l2UsJ6x43o2Js0/vHdV03Gm5ZniXJHPoybITl8L8xjLP96mz+sxM/L/yPZ/EWnyaQu1tpWh7Otd5lPbvmb8PrxGz8Gz82Vt4tH72Ed3i87py2c9u2GWH2UA2gfZWcuiOqFQj6vtHNAJwCu4QNRk0iyExDskXrvmFzLS+NrX5kcueqeLk3jJiJpW83eWbBzZY9muwTzHKszE52rZJLPuo7iMNr9BpkhXVr5k2h61tQLCeQ11kYl1wcLjYOTe+It/8S/KP/pH/0h+/dd/XU6ePCnXsjtVBPjKV74if/Nv/k352Z/9WXnppZfk5MmT8kM/9EO9+jwORrYAAj42MkEifeERT/1/F0GwaxCAR2BrFcTs97VtxkRfIVafw9sygLEjGHg2a1YM4+rHOmAZRwgnMBxeu7b4WaQII1A4r19vGjo58GEZwHuMbXpgSL14sWwBgS080ebcuaYSy8KNFkZYcZ4YFQctxRDnJKqDtTrktaKZMZh4SnL2fMASXIZwtuhzxs6M/W/+m2Kw74q+tDoyrA3lvPKeU5YHRYqYd17NmoqQMQpl0PXcPk4p7VxYJtquaTJpOpqYpp09a/MEkWJc3N1tKgxcTefOnaZxlCt0Xr8u8g/+wfwzDKc4RlU8uwJ0GteqK8GjygKMsg8/PD+CR5w/3wyC4OA4Tdu9Y5RcExlMLWeUF0BXu9Y83qBpexce0fYd94fPY1dGzm7hreG1qw1CiAwkkdEDx8iQ3AaLFmaec4buR8+7Rl6xxsxca+b5eL/V6By1bSIMLUNwtXotwzJ04DAnI3JA1u5ukfcRRICKOzjqAB/I+9AntJETlRW4khiwt1fmj7ZcqT+qwgZsbxf6DZ0A/58/36y2yZV6Tp3yq0Ry4IBu41U207Dob9tait4HK9HQM5JGY2Xeqwzt0PJPFDjB8x8Lmm+L1Dkn+LchHVBtfWXlykw/PKca3lXD88a2v6ybvacLujodmaYeHDRpupbpReZ2Hbbp4Aj6feVKcSJ95SvzY1+bDugvKmViu+Vz55o7PrENS4N3X8G164AF0H/Ygy5fbjrnLHnQCkzTY+iqQh6sxHkrAKLN0RRVeqsJqrHsN1ZfGSebh2xwWRcaOgT0FtxdMeach7INDT3n+4HWMlb1XPvAC/Da2WkmiINug8a/9lqpqMZ8QBcsAQ1EG1TThM2/KziB8syZZqAZ7C9ajme9wdqFi58l6NLOTlP+jyp08i6DgK7QzMEN+lyeh1Wdui2hbzbLJQa2ralIj9Cw+E8bIv3B0uFXGSEwcgGUTqh5PrX9ZvTNtiDgKJgmW6gn853V59sV+l4yfbp4cX5Ewp9ONOcdUmC3uXKlVCbHETQebQYIbOkE0Gj4b598cn68fLlpm+fdBkWavg7cp3PnmjYhPkfbCKLAXIDpnKbjbTrx9nbTR+D5ATR4Ppb9J5voDnjzsNCHfutzEVswJtoSKodADf3O8MNa3+xQNmxv/Xa1U0S2/pp5RH3U6MT6nGXbs2p8IG2AnYSvC3LqhQuF7jNNx/e7u007O9roXbTQfmhbLd5D2GYuXiy+Waa/eqdFzIN9vLqKPj5zbJDeZZz5AMbSSfps/9GB1HgGXlGUvb1mUnxkk+mSoKfPiXzMmFckz2Vs1GzHit69jI/gfq+MPwL29vbkD/7BPyif/OQn5fOf/3znfnZ3d+Xnf/7n5fOf/7z8i3/xL+Tw8FAmk4n8xE/8hPxn/9l/JhchY3bEsQTPuHmzCLEZ1AYV1PZV20ctujLxLgYJqx/vXmWv1yOQUZBGFETpBXVmDDWWMzYz50ylqsgxkhkDTA/M8/btZnYqO+/v3m0yA4yPAK0nn2wK7xhbV+Hhbe1568vtbZHv+I75Zza64f7cvt1k/sygrXVoPVNvex2dWRUlJWSMKG3BQZbjN2rj9ZsVrmvXTBtq1njmvK5K04A43NjMXdcqaPUY6Ep31/V+ZIyr3H7o8dsw9P3JKA4izcrBOpBrqHkwomv9+Mfz/XTBUX4vRbrdB4vHZIJWawKQIhwVOqGhZa02I9mynEIeamTPKMA4Qhej2zJ591A6QkZu4jGs77wgyoxzwQr4zFxHNHfrN8je6Ftvh9Y2Jvo5PCyfOWFRy8YwFkZrE/I5z0MHkX3iE/Nj5HTPPDtGbdUBzyGjn3+bMVIHGkS0o21nlOm0LtAsU/3MQ1daFhksLV07Q8OAsSujtQX+We9a5h53oT26v0xCao1OymNF88hgVbzbo8UR7+ti07HoQl94ayG7pjzoHU0A7mdvr1n1BomCCDTWaw50BfYa9H/qVNEbYIyG/QTbLn/gA+Xz5cvzI84BP9ndLcHHoK06cE5k7hzSFZD1dUWV7RkZm0rUXtNtXmeW7JhBDU3MnB85k7h6f9fdr4YCz0tj7GQUnThVKzevGkPcq4iP1KDWeXkU4L1jmXVSq7tn7l0NrOIknIgNPPqoPw7zztmsuTMi78ooUtp4CYInTjTlfyuIrAY1cpn13bJooUWTh7K3RGO22a7bZJwMj/ESCyM5LNo1BQUBjrGIZdHNrH2oy/g16/co8YVloK/fQNtw9P+a7nKiCmRrBClvbZUx0A/kdsjxTz1VAoM5MRz/nz8v8tBD88/QEbjt1lax/3s6nUhTZrUCc2sKM1noK4sPjRqdnWk8Jze2oc811voDIt7H8x8TYyQ21N7ntvZDyiYZW0i2rz7zaOsjy4c8G14NtN2yCx9bpv01A6xpvTsS+uQdcfUOt5yoyHYlvRMhQ7/fnAxv7VoC+Z/Pj2DRxqHtdNaYbWuh7zxq11qbHlFjm7bOjxJedFsvPq3NX6Wxvz/azigZM8UYWOYcPvOZz4iIyBe+8IVO5//Kr/yKfP7zn5ef+7mfkzfffFPOnDkjn/rUp+SHfuiH5Ad/8Afl3/l3/p3egcgix8HITfzLfynynd85/2wxu8jIC0CI1oaXqPoPxuIxrD4jxyoDLzSUjtOnm0Gg1nbnlpGaj10IbCR0AhlnewYZAmtdQ5szsm3MPopMxkkSbQOsx4aTnLdHBrQTKaqQDOGDt2m2AoTh3EKFNGRcvfFGCXyOjFXIRMLa5AqVDz7YzHZlRfrChcUqZ/o3XeFYZ8L2QUaA7JNBmhVQPRoUGcqttZURrvj8iBZZTjoG96O3nVhm5WwDk5deFLl5tsyLr4mDUG7fbjpv+b7u7TUrjHM/N240twfSdJsz3bHu8Q6fOtV8F7D+scZ3dppKgAWL3usjf14HRO/JUE66trG63hPPyN8HTPetMTy6r+k/zvOy4nWFZcDqj8di4+i9eyUYgrf51Nt06gxWkeZ6ztApXc0Z3+H8rS27T91PBkMFLOgxNc8VKTx1b6+53SjoguZ9XRPGMOc2xTa6P12N62NXRnj99VJtQ69/j7ZHzkx9jifHWeezo1ZvT2hV1/bGj55hl6DFSOexZHtvHn0dLZl+orlm5lNjGLR4g3e+Vel7KP6q+/GCfiNDI+gK2k4mzUqUgHZygV5D/kc/1nXxfcF8Dg9FXnhh/llXsNTHU6fsKsUicbV5Tdsxn+j+ttG36bQpj3nXl0XEY6LgAW+OmUppNe9sNA89Rtu9s7YdjWwqCJQcC6++uigb6Argeo6AvmdcYVZvW87yP3i31gd4W1o4bM+da8o7NbssWNUxIj6yLJtMjQ0kY2vS6wQ0iO1juq/MNrW6b92PHsu7P7X0O+K9fB0sE0f9bWyUtQc6zf1omRHn6cAAkcVtOjlwGf2cPy/ywQ/OP0N+h/0HW0L/b/+byP/5f84/w6aDta0DkVABGfOA/gpD9yOPNIMa8F7oe8c8tvb51Nh8M2uz7eid39bGspdEuob37gN7e833SPfX5/3R686THa3++lZXrcWXv1yC4i17mGX/YR6hrxVtcB2QtZifbG0VOw0CfVDw4eGHmzvvdJFfrN+6PNPsmF776N2JZOGIDzEsuh3ZnjMynzfXCBYdb+NHWd4R0ROuWInftPzPNhBd7Vhk0YaE89nWf/NmkXcgy7AdR6TI6aDXnIyytyfyS780/8xbS+Mc7b/y7tn2dmmvAy/wm4hdGTlav7z+srpZm51Qrztem5HtgPvJBAro77x1VntdXfhi9Jv1Xq+iMhrb6C3o55SxEfE7lvGZ4B3R9p+oSmob/bBoaiSD8txq/DzRPDQ8W0xkv7HOjfppu1aLlkYFoiK7lufvsnSzjK6deY+stqDTbHfXdiDe9RBrDPLHgw+KvOc988+ww+KodzaEToDqyb/924v/1+5siPEhh6Hq8SOPFJnI2tFKZFFXtvy+OPbVf/v4v7raiLzza+WyzG+ZcTOw4lXa5DH9HXZmGBOoEJ7lq967GtGM6B5YNKOPPSGypUTyBqOWFvJnS97oQue8caz/LVjPIpKlavrOjOnxiJrrjOYznTZtIOx3nc2aNiLeKUVXz+c4IK3zQl5CW05qv3OnnId+eE2cOiXy9NPzz7DtWPELsBvhyLR+Z6ddV7bouGUn8dDmP/AQPcPIJtLF7s46i1UZ34L3HmTe59msqVNqO4nIYjVogH0402lTF10SInI3JjCHG+TvPnXqlJziXXdGxEc+8hH5zd/8TRER+eQnPyn/4X/4H8qP/MiPyJkzZ+T6wL6Z42Bkxr/9b8cBijXGAn0OGyj5Jb1xoxBzMAddGQSGegjqUdVWICLCHiLC1nbeEMgY/TLGyIix1xgWLWLOv1mMo6tRNzu/NoGFq1zymLrqGQeu6YqZbcLr7m5zmwfebln3gbYwZupMK7wTYEpwZmmFk7dMwDkY48KFosRCkcb/gHZU8ns5m/lOG2t7nRoDsoU2RaaL4GD1E50PWFlI0fn4Laps0MXQYq2XsfDoo4uV8dvoSxaesNm1vzFh0T1en12cNdoImEG0vroY9jJjW2P2yZzuOmYtQMOjoGS+L1wp4eCgabDkwDWtjHIyCrbrfPHFYuBBf6AZWrnUFXlEmkFkOzvNbXV4u51Ll5oByxY9h4xXs/4yz92qJpcx/Gegq26KlGseA9n7NDQdG7ky2uHFS3awRhf5tob3LoP+d5lzX+N1V8dKpr+Ih64D/xzyHWmT/9r6YPkf/2f0Rg2urMMJK7u7xdkEnoP3BrK5dliBf+B8GC7ffFPkt35rcY7slHrqKXvLZX2Ovj7MmXVuvc2m5STso//21Qf6IjLOjylzdnWQMWp2qRoCDz5Y5IesA2gIHWE2ix3gup03VpvxPUObu8jx3hg194XbWsHE1hzx2auimNFfrblZY3m6hn73+8h41r2zEiky/UJGRD9wAGj66CWqa9oO+owgYNBvnQgHsANEAzoCdAMEKnzta6W/556bf4Ye8Mwzi33cvFkSVlju1zYidkZZWyrzs9OBo2ybZfuWdhhk1htXeMacn3ii8LjM1vAZx2sf/qOT9KOxa96pzLqNdPjMfRkSH/jAom7oBQlqeE7lvnbqqD0j8qpFcnLGTl3j4Nf91tjCM/OqcdZnbKLW9UXz8Ma3eITXnzcuo825HJ0/nZY1zMkoVoAxJyqCxr/5ZpHbYYsH/dZJ3JDJOTAMNPXatXIetncGdIVMPDP0jbYISNK+KdB0TkrU646rwWmbEdu6reeEeYDev/rq/HjnTjkfgRPgWRjD2hnFCoDj35aJDP/wftP3p09Su9enh7GT0UWa8oOI/z6ug91BxPbbtrXXyJzb1R42hL2j9vzIzt3Fv2rR3Rr+YfERPt+KKYiShaJ75QWw6DHbipHs7xc5H9+BjuO9fPnlIheDR0B+1oVyuKImAPr51FMi3/7t88/w2z711PyI2IcLF5qBxdGzYPlf3ztOCtCwdAJ9XQcHhefxGDpZ2EtA0G3ZDtVFf7RsEBmZK0pYt+YRyZ7WZ5GcfS3CGFWKGZYNMZJTveux7l2NTFwL77wooZplJSvw2aJTbXOMrjmiYUPRf722+Zqtd61tbUc0vmZ+tfcOsGyD0f0FXfFsPPo3i+6jH8jyXlESXWiTi3DqYpyYt7dD+smTIt/6reWzSC7hiu/T3l7hTd5zt+QYHf/DMjkXgMjodJH+a52f0VejhEVvLGvdenZii490tRnjPKtYQNQ3gwv0DYzIbDImMIcnkXj1Fv7cn/tz8uyzz44/obfwG7/xGzKZTOQHfuAH5L/4L/4L+cAHPrC0sY6DkRnb27YwwuiqhPZd+W2CTzRWZJTsizGV8lWMtYrtQmqh7wsHIwBaKGBjpK7kJDIXJtiRD+VUB6VxRhQ7rHRmFG/TpistwDE1BvCOI9ABW01fuNCszAyBDI6jhx5qVubhapqbm3bVNv7fy0Cqde5mlFnPCBIZgiKFitt4c2N4wpWFsSuj3b69GAAxFK3x7j3QV9lZJmqed9++u57fpZ9V3OexxuRxdIKJyJz+s4MKRxgOb91qZrly1ZC7d5uByqDxcAJtbBQnDRz7OL74Yvkf7zor0Ojn/PlSNQ1HbfAUWax6gHvATrbd3XLtoNug8bqyvlVRB9cDeO/zqVNljlypR18DK7rMD/b3m7sS4Jq1ss7ySY1RPqNUtn3nwZJBa4K0l6yMMib79+xdBSLe69Fyy3Ckz+Mx+LcaaK3aW5OWLNCFftZq8EM5yTIOoqFRM3drLdQgkr+y3zM4GEE7nljeB63XVQ+4WgJXQXjzzWaVV8sYiXcesjX4EK5ZbxuN7xCMAOPib/1Wk48BWpbEuHpLapFCd3X1NKv6MjvyLGOm5+jS7x7a8C4sOkkms9NRm4NHOxDaZHzdhvu3quUBJ0/6vMqik57DS+sRUUUQfLYqiS8TemvaCNqI7SXMWPcnordovyy7TZY2LUuWzowf8c5onWTmFTkWMzpOH10jg2XY57w5HxwsVsARadptbtwoSYSgxZDXdQCCtXWnHlNX4edq/JlkYy3bggbBXgObDMa6dq1cD8v2+ghazLuD3bjRDLTG9Wj+gc9ehbYzZ5qBtOgPwXVf/apdOUZfz3Ta3M2Oec/2dhkLbTj5SPPLKKCcd1fSOhDTJ96p5fbt5k4zvJPTdNrUF7n4hq5cOvLOWHLzpp2w5dmBM47JjFyXDXbi8zM0SZ8fVQf25uaNHX2n+XwGfXTKNmRoe5cAAT43M6/IjhrNJ1OpU/fHVe9xBJ3Tdhuu7K2Ty1kngPyN+dy82ayer6smYywO6LJ2kbAqMoss0jlre2cNfV84uR40W9vdcV90kiMHDVuJjriPoM0c8Ly93dRVWO7VNiJdBVe31fafSM7Uu+jpeeF/rZvyWtKVytAP34PJpDwXr7qvDrTha9W0HWPwWFbQCQIcx8Lrr5d1q/l3jf2mRo/X59T4ZaJ++9h2svQJiOYW8Zi2c/Qxsr21PZeoTWZ8/t6bR7Y/yxYI1AZeRu25GIlOMMRRJ4KLFPs77C3XrpXPvOuVliFZR8f1gZbpxAyW5/B+v/66yBe/6F/PmOAK0UgeBI3XQcTgCaAVWv7H9Xv2GysYmenu5mbTl2/tghgFmLVB8z7PxrO/7/Nny9bDdigOVMS89ViWru4FsS8TWPNaD6mxQXiym3c+n9clINCyAUbne4iKv3g6kIc2v4jV91B+iC6790TzqfWdeNdu+Qgyaypzz/mdE2nK2aD5t2415X3e2UfH/7DMp/3CLPNZNmhOzOCKy7NZiamBPAqeo/kLkhe54BTOuXWrvL+sa+iEfPbNQk4+e7a5owpoIniqDq7GtbKNX9uY2J6g7ykXG+Cxp9OmnYV3PdB0m33V1trida9lAk9+17TQs/FnYlj0WvB289rYKN/Bzrgk1LoylzkPEZHnn39ezqliuF5V5GeffVY+85nPhH3+yq/8inziE5/oNa+/9tf+mvzMz/yM/MN/+A/lH/2jfyTf8R3fIZ/+9KflR37kR3r1a+E4GJmxt7dIxLwXL9peAagxImYNgJ6QZ83VEkp4/kyoNaygFxyZWFnza9uayFLArfviOX4tRbOLIKeRqU7k9aPvi2dQ0NfjKZMW4/DWH4+PIxiXtwXDwYEdfCxSBI3r15sCAQsnOqiBgfk89FCprMNz1VvpIDPK217NUt6AmucUCX2R0blGsLUy4KzxM1sW8/lWBZa27b/0uqsxCtVsBRoJ7LpfXsOW4IPnDKFxLGxvl+pLVlWeSPHoolBxH21jRIY575za6qJtykWU3WnRLv7fov/RWvLeoyiLV78PnhFFKwIeH7MCTzNb7XkGfytTNXrnMT4HVul3xOIjMPJl+Bjm5FVIECnvAt5HvSb4PHZyWdv/ctuDg3mlMJHiSOFkkHPn7C3X9HwyBmrLGaWvp40W6zGigKo2OmetX0s+44pB1nvlBe1aAXTeWNpQHt0XhiWrRYYJHt9SajlAcizcvr1YGa1NU40qa+j/u9DtjHIfjRGdE8k4bXxMywKRvNGm42R4lnVdURBm1HeEyHgyNPo4unTbSM9jg5VFmzkIgQPFrl9vJo/gfUR/k0mzSgwMhtpBA0MjG+h0kNT739+co/5fn8dbcGq60/aubWz41Q60819XS9Cwzo90ui46qT5612MZe701ofvxdO6NjXI/rfdBV7oTKYGKcN7PZk0nnWU74EC3iIeOXRnn5k27ik+kq9cEqHmyeCR3WFVeIrtCJP/oda7Pj5DRm3l++j3iMaxk3IzObV1zDc/z5or56vMjftJlzEh3iuR/b4w2O4W3TSdovLbboB9rZxSAA3vh8HnttVIxErYi0BDI8w89VPQGXBvkVZ3IAh7RlsStgd/gnHrmGZF3vWv+GfQF6xCOjVdfLfcBfT744Px48WL7Np8aGZ0/83y5rfXOsixtnefNL9M2mqslh/GayNh/ojGs+Y1dGXM69emW1z6zBlheztgQLH01Kl6Q0c/akNHhLH7k/Z8ZL/rf+65tvOj9sfrIyFgZG1wmKCija2TejWgeXGCEdzgRaQbZsq6gA4RZFrf4CG8FrcfkimiwY+mgOVTExHeAPhe8wXO+6/fDKv6BY00lyogPZHi2R7cjmS9CZs4WuO8aGdB6RzLXHlV25bbW/0veprmBf/APRP6T/6TMo0a2y34vkqtKCujgjEg34HVm2d68e22NX8PDuyCidxlZIhM4l6k4aH2XlcH52IfXeONY89L9WGPChsPn6cQ6BGBB9sW7pmVyyPSclKKDybgIFfMIK0mBE0R2dua7UuCzyGJgF46c0M08LPIJWYhkam7TRf6O+usL6/3x6Io1P6bJVvxCNFZmbjX8zDp/FZXxeUdNEX9NWXYOzwanYfXHzzAKkrX+z5zvwXpenp0jG//jnW/Ry8gGGMkdkT0M/XoySM1YGrX+dG/OQI0cZ9mRLDkOFexB7yFTc3KwSFN+10nqVoCyPurgXySzsC1Nx+1w0p32a7/73fPP4EuYqz6iH/aHa32A5X1rbXtrICPzRc/Qa8/jt/VjrUku1mKd5/GB6Lq0Lz1616w+9fnWefy/1pGtMbhvxAAsCbom5yoBFffcuXMLwcge/sSf+BPyh/7QHwrbPIWdJXrgT/7JPyl/8k/+SfnVX/1V+fznPy8/93M/J//xf/wfy4//+I/LD/zAD8hkMpEJ6+sdcRyMzLh1qwi/+iWrESyAjIAboUYAzBi7jhIyAt0xfGS2W/S2cNNGSTiNoHBypuybbzYrInCQw+5ucWCjDYLl9Lk66ECkMC1d/ZKVTwguWmF9+OH5ZyjZHBxx5kxhcsh6xXFnJ65kzMgY9Lw1vLVVv222Hsuq4Bhl0HmCv+43o0TWGBprOL0l+IxcGflQJqVa9n2MQxlGgBgaEwm2lVlzDHVPB7kHcOzXgJ1IVmAWB6xp2o7gJP7/+vWiHHNlBNCMg4NCC7limw6s4MA3KziNaaBVrQvBC6D72ArukUcWq9uI2Nv1cMCCRQPblK7pNK4yOTDW9Z2PcDiyMfLw3ANyuOHsKnEfo4rmrHiXEGsdH2W+sRTUbG8eyWgcxMCyp3ZG4V3lrTzv3BH5+tfnn8EbINdpPgI5nWm8rsIG3oA2HGyxtdWsUgYar6sp4P5YQc2cKMlHXSWAA6ctvZXvnVVtgHmF5RTyDO+WUTNKIPYwmzWNz5aBUO8iYI0VITKKWk7zsS2Fem0c4/5EXxmry/mZ4Igh4dkVouRevGt61yq27XCgwLlzhR5wFWVUOrt6tQQ4ZCohR2B5nauX/at/5QeqYe46qAFtYOe6erVZ5YYDIKzEvmPcH9C7JhzjGEcVXeR/nWAoMpfrQfdB22G3h2x/40bTpg/ZHnqBTkaH/A4+4BUy0QB/0U5P8DFUyMf2tu98Z7F9sT6hk224IpquQBUF4TDgm8BYOpCpLRlLV5u3Ake98S1/hBcspavfWYE6fK4nb1uJv5lgzL6yFnw4Y+HTn459Mpkg1bbvj7Ea1AQQHXV4cr628XMhMt5tSv/GSSk498aNZsI66D/a3rvXDJC0iqFxMPRQ0FUvRezKxnrnDtw7+JHBY5Bcefly4UkcFG0VnQHf4mr1Z882k7YtZIKhM+e2JUrNZn4ifjZwjgPUMtcHWNc19s5YIsfy/7qjhge/XfHSS4v/gy5pesuyN/t/d3eLLZ/jd3RlZNBr1gP07lkc27NMIAgTcRzQB1AQ8dw537Z//nzTvs1VmKfTZlIxoGVrL24o8hFb8Oh11wS/7Dht4PfQKlgYnRfxIQ7APsYCLl26JJdg8xwBn/jEJ+QTn/iE/PRP/7T8/M//vHz+85+Xv//3/74cHh7Kpz/9afnRH/1R+dSnPiUXu8SdvIVjiyrjwQeHMzTXZtxE8OZUm6WXCZxuU9rGYPzHwsVykFFodDYVG9t4KwXtOMYRyhf+P3GiqehabT/2sfa5DWHsiqoWR2Nm2/dp2xddM1GXha73iSsWjYTJ7GAxyHvd6VBG6DPwTbP+Khzza4wxAjZXEjjXJahHOyA4K1NX1X/88flnrmgWBWRZAP0HutDdbNaqNx/LsOYF4onUVdgZykHTEUtfd0vgPat4Vyb7tMUtf+4C795kDWs1yYvRuzb02ot0gzadx3K4Mgy9JKTQXY0wXe5LdD3e817mu6/H5IpSTLe1g8ijb7dvN41tLNvrMUH3ERgAfqCDCLROwP9jZ5TM7h4ZeEa7NhrfhY5FfUeIqiZwP5FRs+3dtypoWeAdByKwUTHqN6p0nuGZY2Fv7+2ZCL1ueuNRRZucu0ydMuI/nHiud4zxkv/u3FkMKBBpOqtFipPG207yzJlmJTTYlbRD7NFH50fwBt5FZjIp/cAuwIkmly6VzwgizmxZCWgnklf1Xp/vVUka03Zw7Jw9xrqhaxJRpKvXnneMAo8faOAeQm7f2Ch2f9h4vC3aRfydtTSN5EptWv7+0IcWv+PAJquisSXbR3Iy/3+/rhtclw62eDvKtV0Q8dOae9fRNm+2WbYPdlU0tsYvXnMPMteTqTya6dN6TjUBpNZ11dgLrffaCzTT/4OWMy1FIsvmZqEf8PtykOq9eyWhXFdUFllMNLeqJWucPFnsRptUDEInh0OmB1+CPqELhXAgLFfY1Hyki6/Zku27xHd0tRVl0NXfw0VeVo1VJH0i8HJry+eZfZ9vxlYPRIUEMrpfZp1ZtoO+MUtsT85Usrfkusj/xu0jWy/PS//f5g+MbM81tD0zlv4tOnrjbmwUGsy2FNBfLcdjXKa7W1slEQO/MT+7d6/YeRCgzLsnbm4WmgzdgscSKZXxeZ3oYoRtxZuse2IVk+pS/Enfe893Amge4fmRM7bwaP3WyCo6TovbRLZ5Pa+MDNx2X/UYmWsdOjEpmM4qscw5PPfcc/LGG2/Ic889JwcHB/Lrv/7rIiLynve8R3YqE2+2trbkj/7RPyp/9I/+UfnKV74in//85+Vv/a2/Jf/pf/qfyp/5M39Gbns7NCdwHIzMiLag99BFUOnTf/a3rmPcb8aKIQIDhjSujgld5UZkMXsJn9l4qKuhMdOFMKOVS3ZQcaaUrp6ANhCStOETTiS+Z7qSAG+PzNs/nDjRZG6sjO7sFCUWR11FRws/1ljW1h9dqgRHAm4UMMHIVD2L+ql1ZnVZ05EDcI1wOD3xzcqYR6La4hi84T7DqivE1ow/2BrEWtAZslbwgchiMIFV5V4f9TY9oPEw5iBD9pVXShvQf9BY0NTDwzJunyxarUDjiIAFnfEKXgMjK6ogXLrUbM/bdkXBCJbxIqLf6/qO1tDoLo4aA9Z7sZJ3dRkGUO/eBPes+tqnJxaPb2FlfGzZDrRVY52vj4O9ItmT22h9API+O7fQ5s03SwUE0H3QcR0QzZWMre0+4WDS20TrtpofYa64TtDvJ54oFWxQPROBa6DnDzywuJOKyGKFG10lZ9kYWn/v219kIO+Dt/h8LU0bvUq9rlZ91NFFz7tfrn1VyLyHy3rHIhueVYGSt+dkp+Ht20WGv3JlfkTVY8tJACfUN74xPz73XH7uXcHOc6uyPeg/eMTly83KyNAHLl8un9nxpXfhgt7AldG0rM/Op4zzpQuO39ljrBuGXJPH67s7WKbXcjd+05XQRIrMrysjo0IaZP2XXy7fc/AB6KUuQII+OWDtnkr8BX2t8aeB7kJ30JXNYNu3Kp3xzii62AoHulnBzTUJK0PT+65Y9fhHBWPw5XV7Fquaz7LGPer3t4O9cAFsb2Hf7M2bhfYyH9CJ62yLYZn28LCZhAL9QfucwSN4frie27dFXnyxtBcp9iRdjdmr5gx6fv58s5Il+4wnk2ZSDdpcvlwqasJ/4CU16s+Znaj6rsma89dt/WdgzXkV1wF54e2Eo7hejuED9Ax0jun4nTuLvECk6X+9e9f3H+B77f9FP7Dp6wR2TmKZkB1Y2/Z513TgxIkmnYX9X+sBvCMW6Lf2C+MdZ/l9a6v85vmB9e55XvXjmgB1C8v0FaNv3mnlbYi3QzDyn/2zf1Z+9md/9pv/f9u3fZuIiPzzf/7P5bu/+7s79/vud79bPvvZz8pf+At/Qf7xP/7H8jf/5t/sNc/jYGTG7dvtwX/6t0wW7dBZb9FYUZvMOJYRveYaawN8M1lCUWYJl8vn7SCvX1/cUkCkufWHFTjEY2pmyYzVur8c/Kud7rwNMTNmnUkEZquVLRzxGY4RzTRx/TpIV7c5d86vchBl+vJcDw9tBZWB87xrFhH56EfteVjz8WC1tbYRzmQk1hhFM5lMmTEjeNduZRzWZD1lxtCZUZkthTJjeO+1NjrA6D0SJq+/JpMNMpS0wctUq0nwsJ6NtV68Z5gNNIgy1vogc34me1D/lumn7Z0y2k6irEZe43p+NWvbQ5RVWWuMiZzd4HlcgUafy057OGEiGmbRdm7D/+v3maHH+shH7H60cdPjwVYmNcsGWsnGdbDCqgOVdSWFWuzvN4OqM0EokTxVw4+sfvm+8P3Ra5PXqFUN2jpmknOcdTIx3scJnKAjYSKHcQZ/Bl2MBsazHKx6fQ10MmZb1jZ/ZmSC4bx+Muu+LZO7bY5D8VVvbvqYWP8L/1sOHZFFh7/eWkxkMSAKxkLQf06wm81s55PIIh/w+IcOZGOnlgU+3+oXlZH5nkW0PZKboq1B8Rn6Iu6zHoMD3cAPtrZyiSZd0MWImZXr+8ovNUGUbTTe6sd4Z0dPpNjdjbfpjJ53xl7itdW0jNfmzZvFdoHfsP4gt+zsNA3jVhWRZfOSSGeJ3t3InuTdu729Zl8WzWi7ZqtNzdquQe25GR0B0PcC8j9vUa/vC77TQbZ6DJ3Y7dl4Dg6a18Q2J83PrMo8aAv7D9b6G2/Mj3obaDi+OFABa/2xx0Sefnr+Gc4nvb0mwOtG6wh6ZyQRm/632T8jHaqrjdTDEHqpNe5YsHSMMcdnaL2txr4AZOTkSJfL9qlhye2WXaAP/a/l90PQ3a5rPzqvi33ZsttY53jXk6EL+hzPRhqdp6+HfRyQt3UQrne+ps28hlhut/Qa1ic0ouf7lpM0dc8YkU7I/WTtOV1smvp7j3dm9Feg7zvb1UbE59fYsrNtvLZj2jss6Pd86LnU8n7rc/Y8S95mXTh6Hz0b4GzmJykD2nbAhScgT1mJv7X3uw9ftd5DT0bTNKPGnpTRCTPrP3O+RYvxDCDj437Dxr0MWHP1fM3aBgX7T80YbfKZRqR36qrQXlXgjJygaX7GVuWd3/Ydf9+FTll+KO9+TqftOw5oeNda288qkfEDZGh0JCPp7zNyT8bemKFzbXO2xrT+7yKr1SBLCzPn8fk1tMOSBTJjRG2Wue69IGLQtlOn/KB7fV/YR8BJJNY1cMKIZSMCdD8e/dfnerZNPXafqsXW3CxfZg1tb7P9ZtFlvURjRfclGtPTazKwdAyejx6zT2GyBDIq2BhY5hy+8IUvyBe+8IWl9T+dTuUHf/AH5Qd/8Ad79XMcjEw4PPeAyLmzq57GyrDqqpHHiJF2zrYFcWpBAwyVBQ+RdiOkJYxzP1bAstUvb51TY0TMKGY6wCNT0dLrr6vBZF2VrkqMSSPGrox2ePGSHG7PK2IcicrIx1g5xngfqtciqrp4iKRfTb+5ehpX0dfBaF7w1717cYVOkbmSe/ny/DNnneqAYQTfZPhQjRNIK5xQgNhgbzl1PYW1a4XFMXjE2zgTthV7e3byUg1qHMcanpxQu1tLjZMjcrp4iObe9p13Pn9nBabUGBiteXR1Si8bXYz6beshs940fRYpAQyatnPVTN6Sc28vTlDh8fne691HdLCXPmrDY5tjRsvv7PzUbaNt3jxjJo57e76OkjHQDWXMt94L77lntqRt06Harq2rw9RyjGecLsuADjSMUEPvumIZFXraHDGWbFODmnOzfMTDquSYo6DHe/K/phlsr+HqOdYWnFz95s03S6IMjggmRsCKboOxYPfR9B8VyR56aPGo7UKg5dz2gQfKNUJ+R6U1Dtw/d66ZaBjZf4ZC1/dpFettzDEjeQzB52NhNssFHrX14aFrAEcbxpRlu15fF3Tt7yjQ6BrUXk9bQNXhYVPGsmQu/s7zGVhjaD7Cuy5a/gRUXWY/gD7qXRL1UcsCq3j2nl3rGO2IdNUlByOYGJqWZgLWIvSVyd/OyPgF70e02d4sPZzp9t5ekeG9XRRv3mwW3NIVMUXmugOSB9GWd7ja2Cg7WiGJEDI+vr9wodB5zJF9zdNpk0fwLiiWbV7LoEO8/7r/dbEzMo6CH2AVkWJ6TfJ3kS+JbWx6TXqVUy07TFQIKbofbYGRWV0ysg3xHL1z2+bB84n4W581ENkU9XPT1df1b7oghVewUM+zze4e8fLIl2mNxYh4neXHtRLCRYpsrn/Hb2wrunev6e+1+rV8WrrtwUHhEd6uI1aQKhdn0H6EjP8pWlv8XluyaI1M4fmKrTEtHazGdhC9u95vFn2I/OqMjP0+WrcWP1oy73w7BCMfFayplLQ6TGYHsQLcNTCxhlh0RY3z38Gk7zyGeqtqnX1dnPZDKac1xoaxlGNvHtqYyFv4wIkEx9Obb5bt2V59dX7EFgrXr8+PV68ufhYpDisosvv7TYaDTF0IDqdOlQrP+A5t4Fjb2SmKpbV1gsg8C5szsXX1KHzvMfjJpHm+txUbfxapE8ZrkQkyyjj/M/8HAUON0EtLke9Di1SbyczJvF8SJr/yf8vk3e+e/7OzUxf8UoMMH9HCcJuCmDGiZARLLZBGzzJSzhnRum27vxGGlCS78nVpCRSuEcZrAziivr1qldoZ1HbN+vlsUlKApyTr31gBxrjWmLOZyHvfu3h+NEY090jZ8s73zrWQmY9em32UN+u76J3tgowhKHrXau6v1XfNWEvG4clTdVUVs/LlUHK1t6as9Zah7V3mN53GVXMArzq2XutdZBJ9DX2eT1eDDxun9LV7mfs1MkJm7Uf9zWZNeoL/LZrMQbs6UADnWTuZAJ4BK1NhTffx8Y/7Y3QB38fIWK/XlBfIYbXPjL0sPS/iQ3y/h7QvZGheRu7p49BYFnSgucjwz65W5xpiHjX9ZmWJjMOqrZ/pNLdOamTozDwy53dZd5H8lJFPx4AeiyvXwd6i0ZZoMps1Ew2tpHa00TYhPn7wg4vnc4Xk114rtqavfnV+/J3fWbyujB588qTPn6PtlXGfzpxZ3BYU34ksbg3dpttGzu4h6bbulz/zvKI5d9Flx1zbQyGST2vQ1RbYl0e02eCyyDzvtrkM1a7t3DHlhHVd09Y6YVpsyeZdxsD5d+82d6viYLSDg2bwAVdPOzz0kwB1IDPGgo+Bt4je2yuBE1zRHj6Dhx4qPgG00TvHsN3f4hVtbTLIygt9UPte1NiqhoKns4isJmAO8sb2dp19uq/MmLmvvCOcHtuTW2psGxGG5FVenxnbatR/32vM9NMFXcceiodbfIC/46Iid+82vwM0vWTazjae6bTQWSQPMvb3S7VmyP3/5t/Mj7/2a6UN73Jo2QRB7yGTI1FR7wqgfcEihWdsbjYTXbRMj3OY7kf6Y/QM++idlh008kfzeTU8K6o4qo8ezamx2eq5rULWwvrTY3s7RkZ2Lc3XsF7b7o+F2czfAQrQ/3MyVw2ytCfy3fB30U6wvG6jnTSta27zeejzIl7ONl/uT6//LvwoCjxFG2sHg4i/8r2yxuC5gs5puUoHBOt5aLmd/QZ6l1nI5F7BKV2wimON9E72/9f/tTgf637ifNiBeIcskfIsQf9Z/n/kEZFLl+afmbZvbTXpPic8RvfZKpwS0Uu+RquoVeQ/wzEjR3l2x2g+eo1a7wT3G+3K1zaG9Z733aW2BX1NPEPO4+2O42BkxnTa36BQ024ox1utouidN+RbMbShMSNw8P9jCLPraJxse65aEGQBBQ6Wixfn216KNCvkIMP11q3yGQICBAxdKQdZsyzUa+MiHEtXruSusQ0cuAxl99Klkn375JOLxwsXmoZKrtBmKaPRlrhtQTka1hpvW8uao3rM23I09TEEdYXl1IrGHPvduny5ZGKL+AJcZl41c7f4QDSW9VvX8draRG2HUny9sbO/jWE87DJG5h3r2l/UF2/PDNRUsdSBBpahUmSxegL/pitushOJecb162Ut6W2Z9fHWrTI3nbEsUgyf9+6V9hgLQNvLlwsvYIXzzJmSFIM2qNgMBfb8eV9RBaxqtswHtraagYBshNO03XsnskEBnvEiCorIjsdz9RDJ19a5/AzHRkYZruHPmftbA8uQkOFZbMyP+E9kIPQwnea2NbbO88Bz3dvLGXXbaG7mHbP64y1Ga8dqa5fpT7fVsJz9GjoYmKsd6H4teq//19WT8RveWU3rueoy97O7W+guHF24v+BhOtAMdJLv0/Z2s+ol857d3TIGdAMcH364mejIz1nfF4DfkciI2CXJy/suC013Iz2E9Qh9ZN7Lz0LrGJ7hMqrQbN2PsSujnTy5eH/b6H/07ut74FXN0ed2lYd5bK+tHqNWB/W+7zPnqJ/ovva17Qwhx/c5bwhdJbq3GVug3jqTZXzYaqyKZrD/4PtXXpn/iZREdSSl63O97bwz0DIyO4r4ePZsqbbMwdbaSQVeA5keMv7OTlOW5wCIGzfKfYBTDePj+9Onm05Gri53927TgYe5wgZ37pwtZ+j/I/ptyYcRnYrG8PSHaD1bVU7b6ItG2w5vywCeT+172lWubWsb0XbrntXocFGCViQnMJ+3+s/Mo80+YsnLej6Z873f2r5fBjLzyfqEMvPmCsT6mXgBAtrGz/Z+tslcv15keMjXoP+w7dy54wdRahmS6W4UlMC0DDrCqVOFFoO2w9b/4IPz49mzhaZifM0rWL61nOUYQ/sU9P/b23FlN/SXsem0BZhFvjJNq713Q8vm0XNiW1VkP+K5RbzKokVohyI4Y8IKhOtCO2psGW12gTa53fKreM8y018Ea66WbJHpsy3Aq00vsubmzWNoW3wNljFWRDtYrrRkfcsHK1Jo++uvN4tQWRWOWX9gO9Drrxce0RewzTC91QWnOPATwD3Qxah4Ny5tP+KAZStAjHmWtk0yLLt7W0CwJbdHMk4XZGyNkW3H4jEZum+NgWNbld9lAmPrANVM4HYfGT9CZAOssVdb32VkqxpbkX5ubX6ErjQ6uvZM3zU8L7J5dXmGWR6fSSqIdEFrlyuRxTgepvs46uKC0AlwBN3Xth58x8mIzA8s6B0SETzMcTfah822HNB92Eu0T5V1A8zjypXC1ziJXPMGnaCi+9vaKuOC/0C3QH+aRwAYX1fhZrsYJ2uKxPZ2gN8/wFqjUX+ezqOD8b3EiGhHqYzcr4HfsBaPcd/jOBjZwlCKQ8Qsrf+HVljGVLaWiVUqkRpDCcY1QlBX8PlWEBoLDda2zWyM1MFj+B5CDDto4NS6fbsoozwvGAEfeEDkPe+Zf0aFHq5acOJEcTSxwGJlvbIyqY1pHEymDWxtQVtRBWBLUO1TqaSrstG1r9o+u6CrIjYCDt/51KjbKh2izvRUCdzTE3bjAZGqgL8s5bov7he+tkx490gbKfFueZmxh4dNI6bluMJnDmYDDg8L3QYthzL38MNlPtiWjWHRXab76P/s2SZtt2h0VMH0qKyvvo7KI4DDWccKTn0wdDWe++RZAIfNvRFMhBXj1wlj864x9DzQQ6bx+F9vnRwlkUCmRwUyVI1CwMGNG4tJhyLN/+/caSasYB46iEAne1jHs2ebxrLI0I624DUIODtzplTL4YDj7e1mYkhU3ToKuG9zHmkenKna7Bn1rTb8vdXmKNClsZ1Rs1lcxSTzvXV/j8KWqMBRWh/Lwrpfe63Oz06Ovb1259H+fjNBHfYaTcNYJof9RgcFg1/UAP0xjdbXAb4CHnHxYgk+w3nsVLp0qSTZI/EYvEePwTzGqnrTB9lgpTYaWEvjo2C2dcPYdHM6XRxzne8No2adROcMYZ+O5LIxsW7Pr9bu1nX+3nlWEiLv8KIrGnvJ62h7+3YzQJkD15YB7TcQKfT/wQdFHn98/pkLjUD+P3euWajE2g1FBx+ILPoYvAQRq7qdJ9tbfISfgebTnj9CB1B4Y2qHfxRUHO2YlEmEsM6r+a1P26HQts23h3W/rmPcX/DW0HTaLEbCSemHh77/V7fFGJzUosdiWRNjDLVDgwbzmgig7d/yLfPj+98/P0KXEWn6AU6datp/LHtQ27VZcmuG/kf3ykvo1v6MSO7P+KqjOXvVoO83WsaFDjSsYOCosAf682yImj9HgcZtAdx6Tda8d7VrgM9jaPtsm92ydkxLjmqze2o5zJNfMtenr8cbezptH0Pr85mg7zYbsjcW7CI4j4N4L17M6QiMKEGQeUV0vjVm286IGVtwZC/pu9YtWbhGJo7m5M1D98e6QXbdZsfUY/A1652+atYiAq/1Wo/8IdwffElLQsa8NgbWYQ6rxnEwchvGDEBd1thd5zH0+OsqtA55nUNc4xABqG2/TyaLgQB6XJ1NBaMdMn8QYAwmcfNm+YyABStwgYMYkJmEYIdlAMIvnFPYIuihh0plHK6CqSsjQ5nVWbcic+VCb1Okx7KUgy4OIqA24CD6zhtL95sRVDJCfKb6m/e/ht5+4z7EkQnYOsbRBt4xKIonTjSrJgM1zkgdsBy1sbYAElk0hD799PwzVxDTzjIeiw1zBweF/3iOlY2NQrd5Kx9d0ca7D9aWZUybtYMoo7zxHDNYR3mqi5M6wNj0cSLBehbp7rz3zh/yGdYYCaI+WtpMrLGsOXSRq7vK4jVG/S7z6NrfUO96zXysRBM+79SpQgN5DItus25g0X+uxqCrH3vV1/D//n4JIvD4kp6/d9zcLNfFW3DqpBskUIKOWwGo7IzSx7aqx9oZlak+6AU3WIb3jIGbz7HmyuhK9yL9IdIVMvMa20qXoWuZ749xjHUA0wxN53g7e0DTeg5Q1lV9Reb2IK6swwEDr702/xMRefHFxWNUeRxjaRrJgUKg6ej/5k2R556bf/Yq7Zw9W37DUSdLepVjdJI7bEK8pbOuyt9m/9H00nMkW9t9Woh0FaCGFnfhI1n+0SUoYkysyzxq0TbvoWRR3f6o3qu3K7T8Dxs2kkdgA9f0n+00LOPPZs2K+rD1Iyj5jTfKd/AV8E4r0ANECt/gIOn9/WbCJDu933xT5IUX5p9hB4KvAfb/8+dLEDPoP/7XlTFB97lgyY0buXWPOYJHwfamC6p4wUk46jZedUYt/3Mby3aW2W3E0jXaAjCywU9eeyuoZ8nbNIfI8rM2f0bEO9eRfmZ8UcscZ+xx1/EZDA3I+qBBJ08WGRbghMVI/teV8rkSPhewunFjMXldpLlD4ptvDrcLEvgZ8LWvLY710kslCRFJibrCMtN75gM7O009xAvsFim0mHUEi85l1mZGj8jYZiwaFNmY2qoVT6d1AbFD22yHgrYJ8pwsOyGeJ6PGj8af29pY965G/s/44DMxBG322Gw/Xr/6s7ULb5vsEe3a0GV+0XnZOAzvmjPPIGsjha3CG2M2a9ryLRu9VVgEv+Fo7fgkskj/Qfd5hy3Qy8nErlIvUuj52bPNWBz8ppNuOPmc/QHajoSjLlgCPQExT1ZymkenM4Xsop1GWefR43iVkTXdjezv0a7p1mfrf56T/l/ToC42ora5LQHHwcjrg+NgZEKj6leflyEjaGQwlCNcv3kZIXHZwuEQb+AQc8z2MXCgzdL7sWAZGj2D4+5uEUJY4NDCCAQULxv/2rW5AihSnEZDGZmgXKPC5qVLpRLCO985PyIYWQcgc/VMCCynT5ffrCBkRleBdABkKxQCvQO7hqpSU3M/PEVviai9r8c4xtiofpe70CBtjORtfvTRq7Cm2zAf4S2B7twp9BZt9VbQInOHFvgO+gHv0YAy++ij8yPoP3iEtf0WjJGPPVbOQ3AclFPwj6gyjoUhncAO1oZmDVzVfezrOpTJ4jbNGWMboybZpsaRZ/VVY+zNjrNsOTYTmFKLPg5SC10DI4e4nsx6abuH3rjaAGkFFvMYXIGSnVK7u+U3L3B5b6/QdBglebvPvb359mkiTYeGdgqhqg1oOXiGrmbCFWTwm66wjH6snVEAfg7a4LdsHTkyytec3/ad7ncZ1xIZI8ekReuOyFYUrYFaJ9Ix3p7AmgBNnk6b9J6TSN58swRy4YigAgSX6UAzyPK8JfPubpHhawIOYHdBRbMnnyx0GvwH1wA+8MgjJdAAziTQevR34UKzImbk2M9UyF9XjEkLjunOarEK/n6MowErGdwLNNM2fh10JlICjPU2zZ5NxtopiwMe8P+JE4VOg94i0EBvpawrIIuUIGK03dwsQQj4jdvqYDIOPI5s/JovtFWJPGoBqOuKVfDaofSSjL2gVh/K9F1jmxrRX7WycdZl3DHBNh22idy926TXoPWQ5197zU4sESn6wJUr3XY9qQEq2j/2WLHl44gdTUDbz55tBrHpXVNE5vK/t2uuFVB7lOT9+w2reFc5SP8Yy8PbgRYzhg7Ct87ngh6g+1pW9wqEaBs9Bw+z3H7vXrH34DfeIfH69fIZ89BFsXANvAMI2kCe394utBx0XyeR4H/wBiSa4H+03dho6jw64ZwTzaNK8EOv36O0g14X1N4va8eaAXEcjLw+OJayCJOXXhTZeLL5Q59sVyurjI9RJlAEy4geGT28Uu/WPIbeZiWj+EZBAG3bXunzMwyjxhCQHasGmXlls7P1/9NpMQTyGHq7HfzGW/qA+W9uFmbA1Q+gzL35ZlFGceQggEcfLUIEB6Xp6mmYD9pwpR0NjM9Kqa56gGvFfDD3V14pyjUMnTrwGMIYV+/B/dXGSA5mxr3LrC0dEMLQQpqDSbT+mIboNjXOtWjbOc7isvq0MmW9eVvvLNbASJhcuyqTnTPNH/oGIg2djNJXenk7KoFAl3v3dr1fmerJoGGcGYv/b99u0nvQINDv6bQE/3JlBc0zPGUW9GUyKfPRVRd0P7NZUUzZUXX3bqmk8Pzz8yNn6urqOd7x9OlmZbYoIzaqjID2nD2r2jZ4QSQLRpnYHrSRNkO/o4Bc7zd9D95aLxOPPy4TfRVgfl6Z+2JV/dY8PKMT1ARBZ/vQ8+hrhLF0H0//sPSaqM8aA5+ng3njZ/pEP5l3q01viMbpy4eiyvhWMDK+g2OHAxj29ppBDGyU3N8v/WBMTjK7caPI+Qhqw7uvd3CJ5EocQUcwH+YZZ84UfQQGSwSwPfFE4UnQMbgih75mpsV6HvwdB1dH1TOtLZiZ/uuxvXWr+7eqbuqj7pPvnfWOWGvdC+CrCbDXbZft6LQAh2u09WUtXWCdlpNxtU7JFZky9EDzD2/Xhsz2spqGRTvhZGwfbTzL0ikZmvfxWNYOFdb8onfCmpN1jH6LAv+tsaOt0L15RbDuAWgnaJ5F2wGu5oXfTp8uid2giVbAwssvl88ixW4CG8vBQQk0w/rngGULaPurvzo/fvnLTdrM+OpXC23ngGPYZs6cae5+hX4ffbTI7fwe4Lnoyjpowzxvb6/pXGNHk14LrGNYyTHeu6JpB9NmICu/W++YyKLzMqqw08VmbLVdVUAA1qxIbFfLyKcR3czI8V1tPN4aqLVhR/Sui029j2wdzccaoyZJp9ZXkAno47Z9dacImf7Ysa9t+xywpoMUQLdhL0FgmA5g44AFLlhy/XqxqfNOiLg/d+6U38AbdBCCyKIszHRTV63HXEHbz5A9986dZtV9XYAE47E/RMu2GBd6EY76WXBFTIyhE2A4IEQn5zP42rWtiau/4Qg5QO+Qk9mphX1C0frV76M1nsji+vN0OM1v8Xz+v/+vOcdlA2t1f7+dZkTvnr7PkW0H/0dyco1M7vF5LS9HemvbtUX0P2u38caKdMsudDdCjV4zpE0men94Ppashz4yshbTfU03Qdu5KJW234MOsXyrK2R61S519Xr0zRWRdVI6FzHB/2j767/uXydfr0ihiToJEUfY/cEbMOZrr5XPoOnQDd797vnx6afLd6xjAPv7zXeM7VJ7e/57rYPjmG+wjUXb8ViX0+9Tm649mZR1wrYIrWNEsmsbDdLjsx1L3wN892u/5o+1LGBnhXPn2u3TkX0iIy8DkfydhUcPhpY3s/DidTLxO3qtZeKgIl0lsofhmOHn3lgRMvqV93/b+TXgBL0zZ/xihFxURINpmN71EOeDf4Af3LxZ6Dwnt2ibP9v7ta6C/jHuN76xeESbra3FKsl6XvheJzVCn8H/29uF5sEGAT7J/YosFjG02urxuTq0jnkCjedCWNpPwu9TJJMzLdjfb7dBa57F/egdWvg90jwXzyEjO3qy3mxWrjtjJ+wBSzRfBdZhDqvGcTAy49FHm4Zm/qyRUbpEchkP0RhDYDqNM8DHRuTEsRAJwzVv81AMflnIzitqB6aQARueAL3dDRuXNGqEM3aKaXz84/Oj5xCxOIf1fxejtWawzMg5COD27cLQ8RsEFC0UtRnjrW1gMvCYOfet/9fI0BnuJ3NfLVqYeQbWnLXjb0Qcnn9QDt+q7jnZNyp7RMgoF5nzI6OBpUjp7zPzapvbsuhjZEiN5tJlPhnjYY3B0xKiGRl5IcKQfH4I6IA1NlLBiPfgg/47r2k9f8dtDw5EPvKRxe8yjry2rcOs39rolNdXxgGgZRTP0J5xxtbQ7axzoAZjrt9grMObt9zfloGJHNrKfg1t99ZW7XmANlpH6GKEtNp452l+lNmyL1rv/H9Gbojkj5p3K0JGdvTGtPqJxvD6y/YTgbcxziB6PjqJUWSR3nFgAIx4mn5H7wSACpjsnND/swMjkh3Bu6DHgDZPJr4eYsn/1jXDeBjR9owDwpLhNbQxnWVivX1cG/3POFx1QKslV3bZetNCjaOYt1xdNmazYpjWiGS+Nif5dLoYLLMMaJtIH9uOXkt9K0K10Tn9rnXBMipWZeSlGv03wlAWaOs+eEmE1vdYL14AkYYV1MwBSGwTuXev6fjie7i/X+g/HE5w3ugAX3zmCm1wfO3tlYAuBEejLc7d2Gg6o97xjvnxiSdK0AKuHw4R2Hxu37btPfp45kzRkbjCjk5gZ+dNxtEfBTV7fFY7o9iuoG3CXhUgnYBZIxvVwJK3MeexcPHi6iujratdetVYgn6Z7veoP5NIt2D7PxzsZ8+WXaGiXVSi6psic5rMCeq6CAmOoMUcuKCd554MjP9ns+YuLHD+68RKDibT1dC8JDAdrIFrxHeQU1EU5dKlMjem6ZH+atHfNj1axLetajtxl6Ae/T3b4yz9qM8Y1lhjy/8iTd0uiz669jGGwdAyybqhqy0xsl8y/dfJDkzvdWV9HNtke61XcMAzaPPNm4UmczCyDlL2ksDZd6avAzI2+NrWVpN/aP2c6TbT5u3tZvVMi8a3yfSRXUxfR4b+8/nWWNbnLDL+L2selg3P4k3e+U89VT/XvtD30Evgt9qzXmX5lGoK6Vl8tc3OZ42RGdP6P7Lb83tnFUhg8HfRvdS7U/YNsm57Dy0bnkVXuA3HI0X2H92ft06s/y37dtQ35gp7iPecdHKxt7Oh3mXEu65Tp8pvsJeAXuPcc+eKnP2e95TzRBaT+j72scXv0K/WK9i2hLlrnQHfwf4CmVzvlMU2lEgmZRv63p5doFDP/eRJv7Kyfk6sM3ERkN1du2/dnwV+d2ezwuNwPyL6YK23zPvnFSrU95fXdFSkC/LBMe57HAcjM1591XbyjaHI3A/KUg26Xm9NAIXGUM6fodAlwGSoMQ9V0A2URd7KTVcAg2EPlarg8NGVEWA0BCNCv3fvNs/n7d6sDBh28Jw+bW+5I7JYhRLZX3p7H33OmTNNBVUro1YFM5HFZ4Fr5GvWYOMdQzt4ujgxI8E/QteqLJFy0oaMkmMJabpSzcg43KgI6B8SJ08N29/UcUyPjVXdTw+4L0PdH93PwPd8IkEyyNDQ9AVKLZQ90Dt8f+dOod2clQ+n0LVrhe7jO1aADw+L44G3j8N8trb8qmegJadPF/qvq93r/8+dK5+ZFurtQjGGVdGGlUeuCqqNKMzHLGUyMqrWGCM74FCMoJMxaca60CeZ34vDoenv/Yi37tGodGkgmOv9iCB9v2uSEWv0EDakamcUBwEgmOCNNwr/8GRW0E+RUu0AgWZ6pxXI8A8/PD9y9WJdtcxLstzcLIZJ6Ag4XrjQ5C2sB2iDMtP/iG5blW8tgzbGQH/sgOPgZCv4NAqy9oKtoyBWPQ8OatNVfLiPo2bTqA0oPWrXNybu13szlBw25v1h244en51J+hyu3Ag6Dtr8wgulkhS+4+rHV6+KvPTS4ncZgEajSswTTxRn1vveNz9+13fNj7rCGeaoeYrG5maR8bVOIDKn/exEsuT2+3Vtrxt4B4VjHKMWx++qDZY1eYerW7eau1TB7g8a+9prJenjlVcW22r/Au9WBdsOeM7OTglGg3zKBT8uXy70HzuZ8DlWMBoHLIs07fXax+AlkfD90mOw3KsDJzydx5LJuT8d+KPP00dtw4pkEw70wdEKmgKiRJWuhTBq7FmQAY4CjunMcpEJvr3fn0HXoNIocIiTRzT9h9wPPsDV7/XOuPDVgUdoe4XFW0QWE0aG2gUPdAp+X2v3XNh4oAfoHW25Wj6Omh9YyYcixT61s2MH02EMgJNreCzLV2sFrrH/IEpGYR9DVOwL0OfXBMNx8bvMORqrkP+RmHqMPEYuGnZkoWW+ZYJ3dwJ0UgnTEd6J/MqVItNzJWO92yzvesLJJAcHhbdYO34woBPA7gMafefOfE56DAC0/amnSgIl/MDwK+P7J58sO36xj/fUqTKeV5FepLmDINvmb95s7r5iJTYwTeX3yPIRM22NaKqm7V5lY6C2QjjPiz9bqLWdLblo6uHheoQFRrU+3y44DkZmXL1aBEqNLtlpltCZCeizMhbaMt4yWTnRb10Vm5rAxC6KYibDqi+GVGBriHEUlNmWPW6dF83DqkjJ5+stnNE/O/TBLMHotcESSigLJ5ubpW8+H0EFt2+XNroSjr6Wg4Oi+PL2vbpiJwcqc1Da6dNNZ5QWSrxghihbyBI42mjFlSvtmZeWMbJGaY+2WbPm5xlFNTxl1qrgaNEHLwCjq6FlQEyuviGTrYpgNIvu1hpnM2O0YVUS1f1u/Hs7gdfQ5mahj1DoOJtWb68JZRYKp94eGZ85UEEHMoMneBm7eo4IfGAjp1a6eCtNHcgMZR1tNe/zFETt1PIq81iBZhwwra/F29YS0FunWdWX9ZH75jEdfjSxkkAiGdaDrpLqJZq0fScy7+Ot3yYryIytCrCNZLVMAk5XrDKRzfrO+i3jrKzAsoKI1y6gOqPv8ec+iPRGr4KwrpgPGqaDUkUK3XvooWZ1SWtrTxgIEWBm0WYAY4LX6CA3zBm/oS1o9cWLxTCJOYPuvfFG0V+4so4GbyHNQW1WwgpXD7l5M1dZxAtm1tXg+D209BIORrDeXeYlVnAHnm+0U1HkgOM2liMNn1eRjIjgmtksrvCAYxRsLrJodPaq5FnOAStwNKqSwffYmnNbVZZM0Dr3GR29MTD3SIZhRAE2nk5qrd+aCt8ZJ75+57zrsaq7eLYDC9E7YunzbCPgimbaCq8T1PX/BwfFoeRVpD97tjj9kdiNdxY2Gk3b+d5hPnt7zUpo+A0OsVu3RL7+9fnnf/Wv5kcrYIzvC1c/O3u22H/YWa4DsjmJRdM/L5gB0FWcAeaPp0/7lZG1rO85nfS9ZDrF77m2u/BRr7voffb0D/3u8TrhoD/9jrCTT7+X+Mx2vmVDV5C2aGoXGd/izxX6mTteNC+r7ZB2qbb7oseL7I0119M1GC0zV6+/Wnj+FIt3RmP3tedlzvd2qQIiWwho2OFhsw3kZy2bcxACBw/fvi3ypS8tnmclk/zTf7p4PttfdEUy2Hh462TN+9ivcepUkz5yUNrOjp/ojjE3Nwsfw31mPebgwN+NQNvt2+QWLYNGshs+R7KSR59OnCj8PZKf2sbQ82Cea/kqoC+OhS9+UeTd755/tgLhuu7ux7BoksdbIp9h9HytedTQtRrfI4+dDVBpm49e/xFfjfptG8PyhQ7NR6z5eGsoS/ujObP8b43BtCdaU9yP7h+0UwfiiizaOWDnwZigjXjHTpwofXrbyGeBcb/2tcUjYCVvaD4AGq7ldJEiq5840UxYYfqrfR5c3EQnu7etza2tJo/jpFHNpyN5qk020cFoPMbmZvtcLf8Bj6H71QHgIou6KWTwr3wlHnMZgL65vZ2TT733t0Ym5s/cxnsPLXtADU3MzCPSeTJom3stsjqXN15GV+gyt778dSj9bDYr9NWD5qugZbxz0wMPLMbn6KMuRsVFSBAHpJNUYBMCnYVsr2VjXD/oN5LcM4DNFscIly6VBBUOXN7aKvSeabyW/9n+Cujn5CViWPa+yA/AdizENWHuZ8+2y36zWVw4hc+1bD38fLgKsrYve/PQ+hn7MfR7iTZYU0tCJEKOiXWYw6pxHIzMeO977W3aotWScQJ5bbsYHq2+I4VKE7824Wpvr0lsrPlwP5YRPaMEtv2WVSq7Gg/b5pRVqjGHaCuILmNGba3txAEI9vyb5diHQMBto2eoHVg1Tq1ou7cPf9geQ4MFSauiGJ9nOYm7GMjHRCQUZ96Nvsb0zG8WLWubR837tAIcPnhhcU3U0PaME98zRNUa7DzDQldYzpJMQEnUX00m9bqgz5q0aE/0jnQxgLa1r4FHryO6zbzi9OnyfGFk43Vz714zM5azZ2czkaefXjyPlafd3WbfMFwiOPnNN8vcMB8ob/hfV6TnZJRz54oSqqvti9hVYroEXGYcx0cAY1SYXVl1eqDtfdb/17zPfZ935nyLjnsyvUX/PUROS/0+4LeIH1bgm0HDtTS6i8Nwle9jrVGzBpq2R85PtPV4hHZkwakApwsMe7oaJ9NyveW9yJwPfMu3zD+zcQptrl8vQcw4ovqy3tYPNB3OMTjRMU8dBMlBBWfOFGMkJ5ysaseksTCkrDFU9Y8lV0YwgSobY0DTTy8o1VpvmfU35r1jXqO3MsQcrSSqGj5m6Up8H3SAPo4Zu403ZtQ+EzwMWEkBXcaM+LT+jZ3J+E3T7YwDWwcLizQT+x59dHEHLJEi44Nu37lTHFSoev/cc/Mj+MDGRqmCCDrNuod+R3jHLvAB7cgATQdwDVeuFH0BPAuOnfPnF5Mo+VpF5vSBt2m2EhCOCo8Y0g6bcYTXYIwqUjyeVeWrxsGfwVC2rzHXVhc5uusY+hgFuLTZUix6GQVyeOdbtn3rWjPX3xbsURuExTZFLW9zwBknWFjjazkZ9JGTvnVCG/N+Liayv99MasH/2kb4oQ8tXg/6QXDQjRvNimTsWNf039ulQ+ummAd4l9YfIAdCjwAfOHOmPRl9a2vwZNwU+tKVZdP27PzQ55KDERr4+MeL3JC9F54NX6+3oeiiF3Ri+QU9Gw1/9ubah96PteaH4KORDU/TNu85WwWIMojkwxo/UUbOxG864c9L/gPP2Nho2j4gJ6Pt3btNmzzkf72LCuRzTh7XPAPzgG2H9UaRZoVm5jXTadFREEiHKvpIiHzkkWKjYj/A5culPei/lfC4Ctq+bhhDHrxxQ+SP//F+fdQC/qJlxgT0vXfL8p1H9CUTzwREtp2M/aQvuj67Ps+59pn2nVckj7GcD3qtab5VHEOk0OibN31/Lej2iRPNoFvmBw8+KPLOd84/Q584RQXf7t4tsrNVqITBY+gEZ03LRUqyvK6Mj3ecCxZub9u2HJE4nuPtgKFtOxks+f5mzMNjYB3msGocByMzbt8uRChS8KwKFN5vUcUIMAmdachCuBU8aQWVMpO3jm1V9XQb7tdCH2IxRL+ZYL62c6Px+zKemuDO2n4yyiyjph683l6tZj6Arp6GNRWND+HBU8C1QSCqgMRz461Go2DkTKCBpYxGArs+j39jQ5FnJGr7zjNO1ay/ru9yn3ewDV2zoztiIqrSx2zWvCddHPzRNWScJlxdKOqnrW9vbpbzJ5q3V9HbUjCj+Xdd713Q5gTyvsv2m6Ed0RhDBdnNZotBviLN5BSdieodNzfH3Sbr4x8fb6xV4j5RYMeoKDt21drJ7h2RnTN+A352UXBRhIi2Wry8xhCQkVs8HpOpbhHxKkDrEZ5RJ7pvXY39XfjHUHwg2zeQ4X1d58HnWVVwIscbA/omV3K0zrf6ZT7ExtGDg3kisEgz4EAbFz3+inNu3CgObDi8oIfA8HjpUvlsBS54/DCzTmod822yc3Y9D8VTvOtZph7LY2mMLP+LSO56amiG9ZyjdcP3YX+/XT+0aHKEjJObYcm5NXpANI++v3vrNAoQyNyzTKCA7qPPc45Quya5GiPotq7g6/WpEw4xR9BQOPp1copH0/UR7/FHPzo/clWV3d0SLMAOL11FmStZcjDYdNrkY/hfVy3DdcAphv+3t/1KNlag8VDweKf+zUOtrZT/r11bGbmlbexs27G9NDdvLgYj18iOfWzQtfe3y3nRXGvWn0VT8b/Fr/vSOY+XRPQ7M4+aYIjZzN61gvsfgqZneET0LKfTprOfoZNVWDfQ9JZ/Y0f9bFbagP7rqsn4Hn169v+Dg2L/Z2Db9Gj3CKbVEXR1Lq56bAUR9/UFDWVnzyBj44wwtB+tRk623ueM7Xto1NjbLZtvjVyYvd9ddZLaPqw1npGBu+gc3rhtv1n9ZPqOaGnbmrR0Hquftu+0f8WD5Xey7GH8DHS/HMDF8QJ6xz2cb1XK5+JRlqzPxcsQ6At6/swzdoESkWKjuXWrnMdVLhHUfPNmMwmFk1sOD/0q/nqnWySWoEIm5nrp0mLREpGmPrCO9vs+cnIXX1WXvrpgbPnfG3soG1dXnSvTlnXqKEYiY9v02lo7WtXY9vWc28a02mXOG8pu3tf/UGvvzJyXOZfpNp+3sVHa4NkhUBh07/x5P/6Hdw0XsWPZ0Ab8h5+P5jnvf//idxwrt7tb6D4noWBMazcXS9bHZyuZ/CgVT2tDrcxX865Zbdrev9r3csn2/0j0GxPrMIdV4zgYmXHnjr2tduRs8Qi+pcgwtHBSo6zVKG/Z36w5LQO1jjPrPOt//V3G8Br11WVeXQX3rAG4C1gQ1VsqIBOKFTqtaHrbdEKJ3Ntb/CzSVBTv3m0KDdY2nVCOreoLmA8rzCxwnD7drFLA2y2cORNXvfQEbL01HIQZGH2tLcxqjCgeLIcgjl0zCzNria852r5UwxNerXMz80BG9UhYqPbZ17FinZMxiPGasLaVGmJeIrng6i48I3KSWefVjDmUkhDxjwxqjfA16HON02lz22BW/mazJm0HHdeOHiuwQJ9z61Yzk9Xa0ofbsDPp8LBJ/7nC2u3b/hYzwGTiO7xAw86cKYo3qt7AKPnYYyWTVhsoRUpQnOYRbPjUVZW9LdktmS+SP9r4kQVLXuUxLGNVtM1zW+BR5j2vNUC99JLffhnI7OjB6MsTloVap8/bFWM6h8c636Mn2kjJQQTsINL0n6HleNBprlqsqypYuoU+7u0VmVpvASdS9JQbN3w+guvd2WlWaIZzCXT8oYdK1RzQeJxz5kxzm2cEqkVVL2v0Nf1bmzFfJ2Dy+ZH+aG2hxv1Y28hFumzGGddH7qkxai4LmgfW6ElW+2jumerHXr/LRF/rbMaWUdNP7bV3nXuXe77K59QGrzKOlu/YzmLJ7dbuJPp4716z2j10dlS0fPnlElAAmr5KbG01AxWQnHL5cvkM+m/ZdsAjWDdAv9rZx4Hh6E8HvunKPiJ2koy3rWakK1hFK7wdi7RDu4v9MdvG41967vg8djCa3jo7ooW1wU9tvKHNFlFDayJ791ABNcuidVmbfrafjO2tdl5tvpy288cA3nvQHpYH9/Z8m45lb+Gjlu0hi0NvYD3i7t0it/NWzujn4KDMGeej35oCKhZAU1F5/4EHyne8C8rZs4XuQ2+AHmFVT7P8ByKLybhM3/T/uGbegQDY2Ch2Mebpeo23BQdZthXWCUWa91rzj0zf3hiar3jvCeZx+nRpf+WK3XaZgNxiFWKwnqV3H7K+Xa+fZcjAmXl451hrqGauXWTzNv46FE3vgtpAH/yfSa6pgXXvOdAYNP3q1fnxjTcKfWXfrg4Y9uzsoBOHh37Ve6sKM1dP1jZ+3BfwDaaJd+82g+zYXr231/Q/M06fLrSdafz584u7JIo0d1Y8f765MwrTZstHasm7GRt6m40pIydOpzYt9vqJxqihHax/WO+s9T/GAC0eE1ZwP1Bj3+jq57fGaqOvmXlZvJf5QWYNdNUFrXswVPyP95v1DKxrZr4+FNr0urbzusYB6cr1IsU2AzlcB/ayTK/lduYNmI8VIGzRdLSxvhNZ5BGgFZliaEMB9h9N27lask5UFFmMMeIAbiuo2YsR0vTfg5YXMvI2Q9uBuJAM96P9PZYegPM9njWZNGOTLJm4jX9o3WnJ9v/jYOT1wXEwMmNz066M0IUQaKbXtj1yVG1jNvOznSLFLJorG6eiuXm/R99FSrqeS5uhUVMLy1DeZoTx5hbNi8fPokZ4rOnLQ0TUdQasSGEkWNtguFl0CRrU57RlaomUqjkRY23LStZriteW9T5ailnbO7+35wtVunKPdkxZ/UVj1RqU2OjAgdzTaXPLJSvpom1M7lMjqxBx++h+j71Nm4Z2BGacTwy9xjLKKCMaywp09JStKLAFyBhXM0ZA6/2sUahqFOgMrPtirbcuSmhX2u49n5pzrfP1fPS2k/wb/uft67nqmUgzo5X74e2g9fna2eEFNev5fOu3zj9ntsWJ1jq/I1ZlVktJwtEzdOo5tD37SB7LvEfW3PrCqxSn136mKtCysUrN7PbtEkxz8qRvmLTkdm/d7O8vZm7r86N+gIi2a0ei3l4XffIxksUx10g2Qj/shLXkl8j4B/TRr/T7k5Ezuoyp9SQew9I1PF6n71Mb/Y90F0umsK4HcqDXz8FBc1yunjmbNeV2615xoJsVPOBt84xzNjdFvu3b5p+jygRt98ySX/hZaucY93PyZDPQIJK1vDWUNbyznB7puFFwA8/D26pXt2GdQX9X8z5zv9YcM++KBThMx8L+fr0smJHta2wpUf/L5sORbNTVOdBnzpG8q/uu4R8ZubuGD2lYQV/6KNIMXPLsBEPCsgdZVY5F7IRiL2gqams55HksYDot9B/gKpiWA00n14vMg57xmR1ougobzmN9ZDottrHHH58fkbiC57O7W5x7HIitq/nDwcV2Fn1dHp/WyfoIjtMJkyKlf50ciWtle7H1zujdBETmci/bW+CIO3u2BGzwetXrNqPbenIy7oWmwWMHr9++Xexjlu0BiGyJ1i6KGZ00kmnaHJIWf+a5WufXBC1qWtbGC6JAm2iMDE2u0Ulr+ms7rwtdruFHtTzH02v0OV5lfB1oyTRQ03Scj2fINN2qsIzxdQAaByrzO3JwIPLudy+OzxXSbt5s2rctOZd5LmgaZMmDg0LDOCjt5Em72pq+ByLlXuM3tq3rIGJddV/3Y+1CxPpiplKzph2ahnK/3Le1S5Knh2i7GsOSD1m20TzQq+Co1y3ORwD5mECC6o0b7desP0f2H6aXlm2mZleRiG5mdGPPbhPpZ5l1oseMbM/chqH7rdlZ0hsrQle6O3TgWgYZuUqkKYMjaArHd7zD1/N0hUu2/+Bo7YzC7zNgrW30Azp+61aRt/GdTk4QmdNP0FCWky35P5O8B+j3wqqWr+eu7ajMF3XiIebt9WPNzXuf9Bxr7GJWm4zsZ6GLLyAjp0Y0rK241TKgdSBe05w0urFhy8ci9vVEdjmA5Q69kwP77jMFaSJ0tTV4PC8jN+v/a2weFj/K2IM9RG0yukomdoTbajuQZyPNFB+LMJ36NibQpFu3ytyYhmlbD8vr7LfVu55D3mWf8J07JQga8imv8VOniv2Hd6uK5CLrPkd+K5HFgkNse7h9u/n+oq2WZT2bvJ4XPwNAzzWSgfka+X/Ns2p0BIDta9rHZfXnFaOK3iNL1veei3WNbB87xn2L42BkwuG5BxaDkTNoYxgZJ0Yb02Qhvss8rDlx9u+ynV1tiBxOyx5To6vCGRmCvTG6wlMms20yFQeYcfDz0QqrVVnNmxegM6xgLEQVHSgF7LDXYMfKtWvlfJzHbfX7BAEKRq/Ll5tb93CVmp2dkjWFtst0JNYARtX7BAuVilc55vTE4jGDkxVtKzGRntVCGGPSqey4Q4w9tsGwC/3vamSKro0rI0f3EQphlzUwm/kBBrp/3l7Z6s/bplMrb6Dp4A1QcnV1fq6MwArWzk6z+oGel3Z+iTS3i9vdbRo6rUppHLAZKV2eQSJ6xrWO1kwV9L7IOFqzRs9M26Gxs1OCUSzjUo1zWSMjq3ltx0R27Ezgg3eNGWeU12cWGadaxlDJxrw21Oh6GUend642glvGKs6CZ2xuNo2GVlAazueq+boaDs8DtB56we3bzepp1pagv/Vbi2OAxr/44vz48sulT9BtBGY98UT5n7fZZMPhzk7svOTvLEMy+mTdwAou8p5B7btWc/4y6Ytn+M+8+7Xz6PLu94GujGmh1vnP33VFmzMrW0Gbv8vIx0wLM/Ns+y763hpbf844ijJ91jhurd+i75lvRDzPchIsa01Z186BC1EQMdtNptNmwiKg+YiutixSZGvQ8+lU5Lnn5p+xxsErgBMnms4jDko7caIZAAWnFoJptc0Twa660j6eHQI3HnpoftSyPfOWjJw4JiwHGL8jejtWEZGnn179vBljJ6Nr+h/JkID1Plv/ZwIw+Pzad555BH+fpaUe+L2ykJlr7bUPLT9lgmC6PKdssQyPtluokZEs2360bkG32Wmuz8X5XN1dX7uVkC6yuE44qI0Djvf3RZ56av6Z7Teg9TqIAN+hcu4LL8yPr7xS7DTsyIbO8NBDIu985/wzaDxsTxcuNIPHrKrHnp3GC0DwkNGRs20x/6gtt+8Cj85YY3ShM3rueGZjAs/Oskdm5H+gr5xq9e/RDiuAKaPbRoj4Wmb+GbrbRtt1cnCXsS16G9HfoWTqGhrf174VgflADV04caKZhMjn68rIVrVLnMv2IysIi6tnog3mtb3drJgI+fD11+fHq1dLMQn0g3cDcu7ly0WWh24AfUb7GKCj4Dz4iC9dKudzcPTQ8nPt2hhT/+iybmsSLbqO0Rd4ttmxa+xz6/ZcMr4KC0Pou7XnZGjoGPfXs4fpe+kVr9nerrP/1PCqSGcCfeNK8NY8tMxv0fu28zgeaHOzWUHYAmgDbDHYFRU0Xicmg27jOsEr3nij6ATgA6Dn2v/LVY+1PQfPVfMLfb5VOMjyB7TJNkOt1ay/kb/jXeXb+vT6jsDJG7VYMv23xMNVYB3msGocByMTJrt3RHbOlC+6ODmy32tEb0XWwFezopfFtPsSsVUY84cca5XG/Jr7K5JbLxzUxo6rw8PFDD6RUsmlBrOZyMc+1vzOOvJnRtsams3sbYxxjnc+2uzuinzjG/PPuqKOyKIx0qtQmHFwaiMTt7eqpLNRyhqzTYGI7q/+v+8azzhv3sLgQbcJVI2p15JXSTWqEtMVNcalWrrQZR7e/1HfGeOo5UTNoNbA2NZ39v60re1adOWHQ1xzdhw2BFjKaZtzbDYrTiQ+X/fLyh+UasshmJGnIh4DJQ2BbjhGPEKjLcA4Wv+1clTbe1hLv2uM58uQO1eknR3KxK6IHzneMo7rmspmjOx60+2t+Xlz0+dk1oluM0ayx9DyxjKxSvlfg+X2GkCe39wsz9pzCB4cNJ1Q7LjSwb9ssNTH979/sR9LruLKmIAOBtByukjdO6O3GvPaaMMexogSTryKAlFyChAZpi2nrhUUbZ1rISOLWe01bezi+I3m5m21uizs7hYDtUhs8AcysmPf+9/HLhKNXeM0z6Dm/CFoZZ/5L0Om7oKhdYbsmG2OJksmYTn+8LBJp61gBK5whWtEf/fuNZPP8T+CAvb2ynfYvhdHXRGSt3nma9A7O3DA8tmzzS2YMVd9PVxx7uLF+VFXD2Y6z9Ws9FwB3t1ia6sZHAdESTJM/6MqPBFN1nSHeQsH3uk1FVX8abOF6LbQCceCR0/62N01f/b6G+KdZxkggy5BSquyaTCyPHUV9NWax7LGtt7ZKLFEtxXJFSUBeBcUvf45CEFX3vIKlejvkfQNWo53XxcnAS9AwAInyVy6JPLoo/PPfO24TydOlDl//evz4/PPz48nTza3VWZeYe2ewhUL9c6I1vmAZ1eoWS9d/SIZWVa36WJztuwevF69ivIiJSBlTNTYnjKytWVT6eoHiHRAD7W6SgbL8ilY8+si46+SxteOPwZPaOMHIouJISI5u73uk+VbKxkF9B40HsVEbt5cLCgi0pTjb9zw1wLk5kceKRWWeTdgHXjm7X61tdVMMGef93Ta3Amljx9AI7IlR7YIz/di9Z3hLZEdJ3of2+iTlhcivyDaIMh8TFj2rJp7x+dG32VoRq3taFm0T88jU2jOkwWsPvv6kGquuda35v0WPcuaBJoxfCig46BbN282E7m57WzWTEK0dgnBdx5t1/YJprvaT4H2GBM2Gb3DlEWL9dj37hXdAMHLuD7Q81OnmlWX9RrlasfWc+ZiA5EthGH5FL14JOs8QNthIv+DvhZrfF0QkttbAdjefLzxrP4iWHRmSciYb8bAOsxh1TgORmYwExmzsmIfISKjcGb7byOiNX219esJexrLVixrhHL9nXVezb33mF2WyA8lgHnGyMmknUpGznvdPz9nDka4d69kMnkMaH+/CDoQqnj7lMghohmiZyCcTtuDB7TCClgOFmb6Xn+6Dc/Z2tYDRysLK1NJ09tOxrp2i65ktmOKlLY2pWuFnHkhEDlL74YONM4gY0hu+97qr/a8TFuPXmonXWZuNYpidm7e7336GxI18xiKHwCaL2S2y/LeX00LIwOU56TQ9IorrEGBhSPrlVfK5+vX50cOkrP4nTZYcrYsGzMfeaQEJqMSjcVP+siOtQb3tvbLpEm1625dnQlv4fCt6vcTOVzpPCx02ilAVfOfDBn0cETg3bNVJDodOXjPWgcst2W9a6MmO561AwrOfxgoWVc4PFzcAUWkBKOhGsK1a4sVGUQK3QatPn++BJzBKInjuXPNYLTIiM7QssoQ8qAKXFjFLiGrxuHevfZGQ6KtMrKFNednR41eu7DsJKu4tqEDuFcBTZMZ2uHDSS0ZO1LkSPccGgcHIh/84PwzVz2GU0knvnCFNfCRa9dEXn11/hk2IoxhOWy4atrmZrPSm66oIzKX/7EjFniKVSH/KK6LodC1Eg5jbFvQ1lZdBfghgwD62u274Cit0XWhu12DGZaNrgFwuB6W0Q8NW6hOHhGZ22F0sog1xmRSzuMxwGu2t5uVaC2eYdmE9Px02+g3vUuKPmrHPtt/dMDBKmy+b0es4r7W7BYyNrwgj/sF99v1rBpd7qfeKZHpvd4RC2046Bfvj9YjmJZbfuAPfWjxN4vfe9djJcdYQV8idjJJVCDk7bgm347XDNT4bTJ+T43I/5Xps8s8apEJOhxK7vHoQlefbBe/bW1cVGTL4CBtq0Iyn5+hNzXJCSKFrsIugiRubRuHzYOfJei5ptug5TU2HmvHlEjH+LZvs68L2N/3E9esYGKAd1i5fbu5Q6PmRzifd83NFA+Jfov0V07wjtb/ULEJ6Kd2x6O+qIlHWXL85XEw8vrgOBjZQt/ArKHGzWRHWOd6/VhvXk3GV1djV992XYyQXZ9XTaaXdU6NIhM9yy7XqP+HMOJtoaYRCRqew0ozb4vZi5SAsVu3mlvv8JY8t24Vg6A+T5+jBTveYhJHHUwGYQLbrmjHEQIfEISgGTI7lsZQzKItE4YCCx9dUVN5pQZroAAvBHyse5ABgxU6IEPTIuda12Dmsdt27WMZwSdjoM88MuceHCxWSbCO9+75FTGB/f2mgx8OLF397P/+v+efoSjiaPERnIcEFgQgX7tW2vC8gJMnm/wDjjAdaIDvODhtf39xW1CRwk+wK4CuvpDhI54cFhkvMnJqRqmtCbaz+vH+z84j6ntkLW0yO4iDVPvOpyftcOe2Lg7yjFFwZBwHHXdEFESsj5GjCt+zjsAy/q1bIl/84vwzV0TD8Y03mvQeR60b8NabXLnnzJmiE+jtmUUWA5VB7/Gb5gccqMA6g66MmaFzCdo+6Ut7hnKycD99HSkW3upzMkskQa0SkTXTeu5rRBddtD3Dt3sgxlF4hm3IyGqHh83qZFz18t695hbMsO3g/9u3y3doi/91EiHA/EP3w1VvkHCot/CsAXgFKhqfPFn4BWR6Tma5fFnkoYfmn7F1M3gG9Ilz5wpvwL1mJ5dVoY11hmxFmjb+oemUV9XK0jWsMT17bHS+NY8M3VxyZRwTXZPilyX71gY8ADXzsJ5vFDiRWZNDzaemzRi0ue8Yqwg6zwB+AL2zFK+Fe5QcpquVcYUtzTvwG5LHLTn+F35h/hnyPtpyFWTMTaTYgWoqqJ88Wew0ViEVXCt+A2+AznDpUtOnAN3gscfmx0ceKXSe9QAd1NwWnDSdNuXtKCnLGkN/r8/nIil6rC4BMpYsnPErcn8aeL5jAmt1SLtwW181vt62fse0Ba2aZh2jDlq254Bgtt/cvWvL+/qcu3cLfYZNn5MJd3ebvmHQdlS+fe21cj5o+SpkPxGRd7xjfnz66fkRhUdA8y9eLPI/6D/bnLa3i02Ij1rG5/fHks092d6i1TX0FrDodybYMJpH7bgM9IX1sq6opfkebY5iSPpiTJ9Fl2BI67tl8JVIJ82cm/Ejr0tsBGRXpjk6xofpPcvbu7vNZG/QdLS9caNpy+d+bt4sNP3q1fkRdptV0XgG7DiPPCLyzDPzz+997/wImR60/ty5YhMC3Yc+Ye1iZQWkow0XPMnY5bz+ImTplOc/7mv/sXgUQ/erd9VZIo6DkdcHx8HIhEOZxNs08PdZQbBGGe/C/C2ibhGEPgEkNUbRiPhkCBOgt6llQT0S4GqzWAAeSxNFz4ivs1qiLdlr59AVegwvEyljyJ1M/EBlHXjGWxZgy0rdL/qJ7sO3fqt5OSlGmNkCJBPENZs1qy9Y50UOHRzbAg2WIXBHys6yDEfoN3pOGWXLui/rzqWzSqTHR4ZcC230OqNQ6fPaeF/bHDK8xmszppGz1tnXtn6t82qNDV34hKZzmfeGE004ceXECZ9uc4CXPt96pm1JMYeHIh/7WNwm4+yokS2icUTi7SMZ1jwy66QLutCZDNoCFtqMkRGsdyNzf8Z2dsxmxUG6seHTQr022mQBHVAStWFZMXLy8fl6vUXbX2cCUqKtvnH0qk5ZY2eMztZcvXMAfV89PqLbeFW1tO5Us4Uif6/H9a4nkgt12zbaE+mds1lx8lgVCETm9Ljt/dNtPD1gf7+ZhMJbgkbVZjXP+PCHF/uJ1qZXrcC6dxH9jsZgh76GN4bVd5s8ZcGq1BOd37Y2rTXq0RtrHvo+c2KpXtseL9D3KSOnoh8YttcVWZn6KOF+u55j1EE//7Zdr/S7y4mKFs3h/nQgBJz/CFRDcAKwvV2cPczTdcKhV80Z5+jql0wLo6qXln0jY9tch/fJkg9XPY8MVjHXoZ5XTT9WIEjU59DPcpk66Tr0FyGrK69iXXSBXkt9bE0W7be2n+atnC14FdW0XvLxj8+PGbrL/1u6svc+7e83gyr0ls7gJbwltQ484EQTwNLnmdfo6/J8Wpa+6L3rs1lzpwAO/rPmw9cwm8UVp72ken1/ve2iu655JPeMiWUFFEXI2MY1anjEMcZBRKcydgJNC/hdxf+W3dGyJ3lBnFoOZ5kcY509a59rQV+nV7jKay9iV0ZmWPSSg5U03fKqk2pdgxNntK+XC4tYRUWgY3i6hp6HLrgiUp7l9nYz8Z35a/Ydbmtnrc0a21AkZ3alM5nzllmcy8Nv/ub8+OCDzeu3ZJOMb7dPXEjGHmyB5x75z3g8PVYfP5g3hofIZ53x9Vl+Gs9HYe1infHPt8ls3J7RJp9GNoTIr6Kvs81uOp02fbg4Iug2mrvuB99xcLO2AzG9ZN1iOi3xP558ubfn776i6VOb3V3Ly9aRA7C5mNXWVnP3XNBxHYwc+eFEFv1obfza+g793L7dvC/e7i5R31l5M+ub031G/gxrtzPcj8yOzD2gc7JWiYzodL/jOBiZMJHD5RhAu6z4GgU1IyD0xSqUXP0slmUksO7dUNsMrgrM7Pl/65qZIlpt0A/WwMFBs+qldWQHNjuu7t4V+bVfm3/WldBESobVwcGiQ0mkWeFSVy3DkQUH7YzifrTDK9qmeWh0CVjQqHFOeH0P8V5n+o6CcHBkhWhdUWssWCbtrBEuGZmAqo5Y5fbiqaqY0XUOZWjp2E907yYZR3gNPEOhBgeaWe97VD2ZFVZdKeGrX51/5goLoAEbG02+jDb6HOYfMAZirmfOiDz++Pwzqh6gGvLFi8UYyzxCyx9c/QeAAfP8+WaWbCTPeUfrvbQcjUwvo0CxyBCG9hlZqwt9ydJ29L3kzFjG4camyPZbTsf9/fb3uDa4ogbWu9VF/s+cX4tMQOHQY/bFULtPZAy6qwYSAjProsYSYhl7vcqY2gnE232CN+g2eutnfdRV9F99df4ZR7TRlSDYAMdBEpubTR1BO/9Yp0BFHF3pjOVtdljt7zcdihbanBRWMi5QY6C2HBFRPxY/6krv9dEa1/p/HSyEGm30TqS/3Lxu17yOdO0YywfWoa5oyfK7lvH5N9695Nq1IpPjO7YHvflmOZ9pO9po2wZ+Q6WdZQJyPPjAI4+USjp6JxR9tHbowv86qIGDTazkGpb3wWvQ386OvwuLDtoAf44SaXiLbQRmaP3Bs8HpSm+RI9ujc5pXoP3v/q7ddpnAfbJ4nmez4s8eaoIIrP89fjqbtdPrDD0fSo9YFe9Y1rjL6HdZdlhrvXSV8dmWY/EDy5av22r7P3QDyPT6CLsLV9HXegXbdnDsWk0TNBRj60Bjhg48gG6A81ApE/akJ55Y3IlRpEk/T5woAc6sI1jBNFFQDfMPS+dg5z8HMOhg5Ki4ER/12DUBTFagAua+Svs/1qS+h5H830Yza+w4bed3CUKLKqha15XxQXk8KnNdfelbVz2py7g15+jALD4vSmyuQZTsr8E0zPLtWkG6IuX7u3fr5H/uR/uFdSVNfdSVMnEd+I13Pbl5c1x6gGr3OOpdeLUuIFKqZ+qK+RxozHYpnaRvBVUDbTYikfbARqtwA9MAvSYyQaE8v+i9tOYX0RnMYwz9jvHkk/Ojfociud9D5n7o/6MxWPfriwz9r4l7yPg7PdrYhui8Pv6Qrnxo2XFItbFc1rqBXcIrPqjfdd6tSiey8Xe865W2KzANQ7+6H26L7998s4xRs8vJUNA2HtBw0HT4ih99tLRh+R9HbYdhus+wbDsWjffeVXwPPmO1ASxfJsvYlm1Gzysq1IN+cL4XNG7ZVCy/Bt4xFIY6xn2P42BkBiv/NcF1HrLKZB8nd8SkugoBbxdnlFYigb4GBA99HY7RHDLrTCuIEPRRRQ1CAJRAnSXE1QJ0phMcFviOt3vb3Gw67VkxO3GiVEZjWEY3XtuR04WhM5IgDOHaZ7PmNp1sSNjasrfTzKLN6c9tM2sTiKo5t2EIZ3iXcaNzVlGl4O2ADB8aiP6nAoKPYWKp944dVToIAUfPqaCzXtkZxUHMJ07Y1TJFijPm4sVmZizTVk0LPQXYCnbl//f3Cx/kqj4nTjTpvaVg4TcOONZ8gLeds5xKnhNJfx8ZjtCP5yDK0GPL6BW1aTN8tiHidfz9KpLC2pTusdBV/h8jqC1jfFxX1PC3dQsQrAXTDu1w8nZL0f97wQxWFTSm2xp8PgdNbW6W7dG86pm6D66Mo5Mw2BDGtFXzI8bGRrNymMWHPHqr5+dVPYgMfNF7FdH4PoGwkaMomofX1mqTsZdY/YxdGYfXTxenexf6op0DDOv5WjJBFxtGjYNnDNToussIfFjF+GPYhHiHLCvQmAOR9HrkADPQ5MmkWfUGMjG2t3/qKX9eevvn971vsQ1oO+xRV6/a1ZL18fTpxQBaDat6mq5SJrJY0Yb1GfSnr9mroh9VWI7of5ed1ET62Y7XEbgebJk95rhRlSVG7X32KoZa/Wec05avItLP2taXpUdkeA2Ad/b27fZ7pvmaN6+24Lg2+2mtHb+GFvflpX0CKSJMp36AmKaBmSIioPuR05yrQ1rBxAhG4wRxHfiPPuFcR1AAHP/b24W2w27EfoSNjUVbjkYmQEbL/9y31gM8ud96fyy7TxaW/d9CWxU2q62F2gAqD22Vo/Xn6H5gnYwJyC9vBxxFOWGd57yKuUV8h2m8ThCE/5f9wC+/PD++9lqxXeBd58qWmoeDPrLfVMvL+A3J8gjw4s8ihYaAH+gCH+jHsuOwr9mqAB8lS6BPThTR9N+j+5ZNhtvUYJ3XOmMZAaCroMWZ4L6utvkajPHsx7Tjj3kfjtJ7A/Sx4XI/kPuRtH3lyvz4/PPl/xdfnH9+5ZX5EXwAsvrt280EFd4tZGenKYvrHURE5nQTsjzzAa1HQNfnGCH0c/p0s8AgFw7c3m6XhaO4vqxNxhujBkdxjY4NTYuXgGgpjIl1mMOqcRyMzNDZvSJ+pm+UTcDGh729Ypixgl8Ay+mKfnhLFq6Osb1tO031cTY72kEEQGS4zaCLgbB2Pn3n0bc/r28dOIxsHmQCWsbraOs1RlR9IWNYh9OqJgGAr1MHWXhGVv1+sxK5tWUrnzzmUXmPMoFmx/gmBgn+7EIPlvlsaudT4yRZxZoaWnLr2l9X40vbPasNDrX6b5ubptUebdaOmciR0jbH6Hp0cDMHHkWOSW+rSW5vwZqvJc/haCmnmeAxD5FSbP3vOVyHDpbNamZ9rjn6ju/3CjCRw6aszZ+zWKaG2cWxeYw5hr5nR+lZ6ArFHKjGOqmWhXUgFsNLRuFgZ30+07LJRORd75p/xrysajFcWQeGUxhN9TvnOYx0MiEHGuhrzgSItQXDHAXouXtrORMIlEFbcBH3PbaVTlf37Bu0ZOmZ3v9t4/G4GdmqC59e1fqt0fmXiVXo9V3lp+g3/o4TxHUQmU4srMXh4WKAM74TWfxeV8ARKUFpuoo+aLhl0xGZ03ych4op4BGRc4yrF588adN9kXnAA9N/KzjNS1jrul76BhMzvewbRD80MoGsFjixaNnwnkNGD+/yDDW/zZzfhVbU0g5vrIxexk7ivsjanDLySu043ljZc2v7ifqsHYOfFW9LrxPkOOgL8r9OWMF5OglFZM5DmPZwZbRbtwqvQVusD/x/5ozIBz+4OHe+5hMnmoFuFh32Emdw7s5OM9A4kjdZJrb0Im6rsUqdoJYPROuuq7xf20b/v4rKyDpAntFXJ4j66cI/Mv2sM/rawmvOj86tseH0ub/Zd6cL/ddtOd4A9BdBwAgOs+ZUQxf0Puee/3c2a9qG2A40mZRiJFHMhU5e1GNawcSQ+7lQyGxW3nFvF0WRkgSD6scWza9JIozuqye3Rzb5jG5cQ68s20wt/e5jg7DGHFv+F4mr+Xex9Xbl/6vW0xjrNp8IXXWnvuiiC3TVzzIADYRtHQG/kU1eg+39VuEq/VkfdUIk+uZihppugv5HOjjTYIveevcM3+uCo+xL1bE9Q8u5Q5xT20/f2I+MbbEGEY+x6C4KESwJWZf3srEOc1g1joORLWjDxrICI4YidEMrwF67Nqz6beoijANDGS6z460DIkVcMwdPwNCVFjjYlxVF3ae1zQ+OL7xgj4F+b95sBiPAqKqNVmgPBRMZYMgO0wonhDUonE8+WbZlwHdQ4HWGLj5jKzb8b23thftqVVzrE9Sm0WbIspwd3rkWsu+Rp6ytSjmoxa/8ih2MA0O2zvYTmT8/FqZYqNICrue8bBOmuc9McIj1DGoMnF0MCREyhg5rHfN8oqqx1rOwKn1xf/weWk4O7tuac5vTMrPtur4uT/myrkvPg7e3su61rm5gQSuamXOiQDNWYi0DJldmq10nIjZNtRSstnfWOl8f294j6322kmTwnXbuiSwGy3nV4fXc+V5F18zzsq7L6sd7R/T1WbRPt7XeNZ6rfi9GlisPZTKczL9M/raOvHMV6OLAq8H9cp+1I10fI8xmPt3WcjtvzxxtA88BytZOLZDPmR/cu9es1GDJBp4BVQdi8DbROOfs2Wa1TQROaOcUtmXGEW2iyjgWhl6/yzp/qPcg40BbxrhZ7O0tBlL1cfLVzj3ivW3zGOo+DaULWn1G53Wxh2Xk7r73JTPGGOi6ljzMZr4sn7EJ6mcA2gk6y8FgugonxgTd1DrCt3zL/DNv14nA4729wmsgE4Omw7bz2mvlPL3dqMgiX9H0Xs9DbwWNNrD/IGn/wx8Wef/755+RQA9bEXjE1laTf1i7p9S8G5l3va9deWgHnDXnmjHGTky0qt6NhWXwWI2sLrVOMm/XuQxNL5d17rL65J2oYL8EfdJrgZNH9PbNHERg+QG46jLoP443b5YKbXzU1ZO/+tX5Z66sDJw61dRfQKMxhxs3mgmKXLjn9OlmcDQSWB56qNj0NS0XWbTf4zfY/dG3ttd5VfMtWwijLx2O+EoN/1iFPmLZnMaEHjNj5+sq71t9dW2blVPXibZn5I82PatvYAzG6HPvl4G+7yHovQeL/rOdWiejaDot0kz0sPoG/b1zp9B7nI//Qetv3hT5+39//hkBSJDjQc9v3CgyPKo34/wIoO3atg76DVqPqsxPPSXy9NOL54Nu43rOnFlMbBSJbenRs+yiv98P8QcWLBq2isr4fW0qfWNyhhqrL+/1/EVd52ON4b0b+n3qQuOtJKYx3g1vrkPrMW39QV4HbWe5XSd/R8HEHIMDW4reVYsTzXnXk93dZkwP36f9/SJnc9FA7VPU9iJ9nZq2g/fp3RL12JubRW5/4on5ETT/iSeaMT2cYLuz0/TLR3ELGayb/rksZOkD2i15Z9zjYOT1wXEwMuPq1VhYjJTAKKODv6sJMK41KHj9ZFd8JrCxZh5dxoxgjVkTaGk9g7Z7FSkX0XljCqY14+qKNl4mYCaA7dSpophxf1qQYcMi+oZQMZk0hSKuuHPyZFEiIQxAONKZwPjtve9dnKs1Pv92924RhhAc8dJLi2Pcu9dUqnHUwhrfP1bcNzfLvXvwwfnx4YfnR2QuX7jQrOJgHfk7K3CMv8N90tusRmuwbUvpKOCtRknRbXFfx8K3f3u533jGbei7xapI+/0ZItjXomFd59hGb7MG8ozBxvuur7FgqECDGqfuWIrDN74xTD9DSMm6egJXYdPByOy0idZztP6gfLJxVffD52tlmQ2ufP7+fvkNCjiOOhACz5ortIGHnT9ftrKGogtajLFu3y48RdNyvuY2OjubNQP4oor/1nvJY1gBgaykZ/hQhFVUxskgoh0WD4yqLXA/XavtDaUTZBxwNVimDLwseto3UGfo8YceO+sIRDuW+3WQAle7YaOm1Tf61UZOGAFZ/tLBaZAHEUTMWztbiTz4DcetrWYQg7VrCsCJIpiLSFM+xXXpXQWiHVbYUAp+qKtxcjIKV6vTc+ZAak1LPH0v4otWxWh+ltro691Hqx+Pz+vkGk6qWjZ+67dEPvSh8j/TZIt3eXyxq5xpOWS8Nm3faVjz8Pi0bh+tE8sJi6PHz9rmZv1vjVXbT8ZG5Mk/WSedZ9+rsRMOYV+r4bmg7UzrRdqTCA8OfNnGev4c1KbP4YAHvV2oSKGRIoV2aFlaROQ972k6oXC+pk2g6VxpUyczWnQf1wCH21e+Mj9iq1Nth2FHGaBpCW9J6vEK/qwxnTadcxF9iI41toMMzWBEsrAO3kOfY9N/keF1jgwN0uhjo7HGyNAe/b9HZ9fR6VljN1u2czij2y0DXXQFq43l2GcbPAceW8HIbK+wxsUYoMlbWyXpA/1w4NmVKyVQDbzh9dfnx2hXxgiwuyA55uLFkqAC2wzzmIsXy2fYdKziBRyMzEHNIot6lEjhR9qO772HGdu6xT+62qsz68yTC6MkzUh2hJ41JrDmdaGR6N5kZL4Muthas/I6t+1ib9dom+sQMmwbhpKNI34frfFM277Xvgr/MWippql651iRQr+1bZ/1X9Cy7e1ie7H4B/5HESi2v2t9gP2sgKafbFfgfrQewecfHhbfCXjM1742P2o6zj5ZpvHaboN7xTu2RHqxppsevddytLeGI32cj1qvz+wADlh2oYhXeOt0KH9cX9QUptGfmc5G/Dlj47HQR8a07DaZ/mr0ka6yp4eu/seahKKuyNrQVwEeF/YBq7BR5n3k4iHaVgNbPGgy+0R3d5u7LwLo5/btIktD5uOq9deuNRMM8Zx1UjrGBa+wEii1jCdS5P/HHy86QWQL4er7iNfB8ZFHSqAzEtXBA8EPtJ2N363I/mmd08VWgP60zcnz02jaEcX/eD5zDeY71prE+dDvloSIrY2JdZjDqnEcjMx44IHFaHzv5fR+74plKG9dlck2YSpDKPvOxxq7ZozIwWr97wnhOrCWDV5W5nvkMMAxwzjajDqacVj9tVU0s4x3kWE6aus5qSOwU2s2K9s0dBGCrXn1RWTcyVRb9aqa6rY1Bqy+19bHwJ59D7s6UDxA0B0JC5Uxh9pqMoO2+7UOjqBVz2EoiW3oNbrq+zIUmEfMZk0+wRV3stfOji7rWUJZi+bn0d2IL2bQdW31lXuWee4xqjGRw8W11Wa8s/hiJpCDob+PKq9ze8t4Ec3DO1+jpgq/N2ZXQ13mPP195v3wnpklk0fPNyOXZnQNhnUvM86+yIjI23R2cWqx4VBD8wXmEWy41MY/z/h+6pTI5cuLv+GY2Umkr2G4Zt1Zc7T6G2KOmfc6Ok8fdYCzPqfLOtbQ+m+bzKz78Rxo2pHQVuFpaHzgA7YcEtHSLsG2FrrIKzXn1MoSGSdFZLdhZ3BmvdXom9NpXeKOlcTljZF5D9vmp/vpKsdl7C81sJxC0fy9CjTWucwHdMVkTvqwqvLAIQQHD7+HOukaYHtW5NDma7H60eCtRK0EkSiga2g9s4/+G8kxfefRlbZF52Wc5ctCxAe73LOudsvaa8/Qij7PPpI7Vu1Nixy1/F2X/rIY6r629df27gz1PNhx3Fce4/da0+2PfnTxNyBasxlfDoIk9G4o+A5V3UD/p9NmEgrLzWfOlIBlTqzTfKFNLtRzbTtqWDoD+4ciP1iXnccs8Dx0ck2NXBaNj6DvMYFryPAnrcP01fcytqGhkHm+NXSpRl72zsnOqysiP5w3frQ2oyIXfIx2amRdLtqpMboufU6UECIyp3Ft9qzNTb8yYVfdieV0/T+SgDmZUP+vd1sRae7Ke/euH/Csv8f9QcCa1kcwf/AE9ANsbJT2uOec0HnvXvN+cj8nT7b7zvf3m/1w4PNs1p5oHu2eaCUBcvB5tkL90PLlyP7fhTEztDnSN3WbNt6rbRlWslDb+JbMV0PjM/KGZS/3xorsltGcPTkm27fVT2R34uvx9Bnr/elq9+nis7D6jebsFVJjuivS3NFWg5PG+f/NzVJID0f2H2Rlv498pPmd/t96j7ggUgTdD/OEKHg24uEcK4KCKrp6crSWveBdy58dXQ/T9Nr3CHOI3qc22r6/X645s+tzxh68DNlXYUhVue883u44DkZmWA4O/n2dsG7zGQp9384uBr5VYRnz4aAxhna+RAZCbj/UfbUCHTxlKdM/mN/ubntlS0uYtZzLXrWZjLMiUmQiob7GcZHpp8YoH409lNGs7d5nx1oXZAyOVrt1v651gkdzuhrRu/STcRzoz9E74ikKte9Y3zXE18jBBNNprBgyaqrTWPSfKxozLEMuGyfv3Glu88mV9i2jpr4uDkyIAvo8452uAMltcdRt4PiyKrW1VaTMaFRars0og4zMtWd4nteX1/fIOJSJHG5stjdMYCIdqzXd74gMuBmDqrXO+ugLGYOy9VsXQ2NWXuiLPtt0WuD7bG3lxtV9gc3N5rbIbKjUY8MZADleB5xpOi9SaLxlTGzbycO6Pu1QZ5psba/sZffv7TWrykGP0MENXBHTm5cey/qtj7NkGRhK3h27Mr4OeB8SQ9/7VeoPfWXQjCM7khf0/5HzVWTxPfScsFtb7TTCcgpEDoyM/B+hi0xm6R5MX3WAsOcwiMZg2q4rI7NMrR1FevclkaZNRe82ArqPypivvjo/vv66yGuvzT8jiIAryZ88aW83qtvqgAOuTKwDnjnATFedRxtUO7PsbOyMipxanjNKV5XDkauyZRxFQ9KLLjarTD8WlrxNp4kx7E+RY36oMWv6iWhy1LdnD7DsJVYfGbnFC06y3h/AktU8+r2/3wxqimw7EdqCKzY3izwZ2WzbqtdacqH+DTQvur81iYoZ244XsKDP9dbE4WGpgMZ00oL3LC1+z9jYKEEDzJesHU2ssXiO0bph4DdthwKwNpCQs7PT5Ec173VGll21LdrSAYfQ6/vCsn/q3/QxamOhzdfFbdvuR196G81x6LZAFOClj5ng+Whe3nbtljwIsH3aqgBs2ZAhB3IVXMueGwX6Yj6ZpDfrfE4IZ5u2Zf/BfdL3xeMRWn6Pktbb5qrlf1TC5F109Vha5tVHzGFjw9dr9DvkBTNHu0xpuZv7ZPu/lThvBZvyvakJRK1Bhj5o9PUNdJFlM/rnGNAJMF3oZc2ch+K92biMNnt79FuX9aM/Z8by7Di6H36feDzul/uyrqNNp43eQ+v6vHsV+eGsOXtoC1Ll5Aie6927vg1dj8H2bbbjHxz4NiI9B+Yjlo3mxRfnnzl+R/NXjMtysi6woXcw19elZXXuW9NvBBbjaO164r1HON686dN9zSO4MAj31/Ydn1tjW6z9bQjU+r/WdWfcYwyO42DkNcahtAj59yG+GcDRlyiu2sByFMBBBBE4MNcyTkaCBhsCIDDg+4ODpmHOEvK4b2vrHd7qjI0HWhhh58/mZrlGrqygs2nxGdsIXLkyP8KBNpsVQQMV3x57bPH/8+fLNbJSjO/PnSvKETujtIDI18xGjGi7H604ZgI4IgfywO/d4dQJpl8iQHdTwWTZ6103etRFyV/FNdQG01TMcQz+ujYBidY7690rrURG9L5mbHZMWQG/Hv235uwdT58uVWsihx4bKBAA99prJeABW4ICukqAdZ5ICZLY3Cx0/h3vmB+xhY+m320Gjum0aURhRfruXT9ZSGfuYnzexg6IFEU914iPtCmPkXPYwCpk4FW8s6uW9Ue95hpjiPo/vEd95IQxZYyeYw32nKxn4CUuWm10YC07vnSVGpFFpyPrAaCXb75ZtmSHkZSDea0AOtBS0Lbz5wt904F3Ios0n2mq5kfebzBOaoMlV47RBsg2eqnhGSN3d5uV3dhZd+pUM6iOtwIdUHZL0aqB3qlV08VjLAFjBPstE5YzbJXzsP5vc66J5Og9g+X4zU0/GMEKQuMAYQ7iElkMxBVZdA5jzuAboIm6wpmV0CcSJztizAsXmg4idsjpgGXP1jSbNZ1PbMfRCSheoIvmnTwPvf5Yn8kEE0TyvxVMkAlMuB+Q4dNd74Fne7OQGaPGoR7hfn+mbzd0qWBsBROzvQZtrF1PrCqVInPaDLs4tlBGf7rwx5e+NP+MtghOfuWV8j9XsgT9RyADztV9f+AD8yMqrz32WDPh8OLF+fHSpWYQghWkwQELnDCi7SVeMIxIk8fwe2gF3FtBOV0CldcN1txXaXNegj/jmxi63yHnepTX0DHmQJBWBizTa7uLF8QcBSyzjL+31+QJHHB2716x/3iVjXXiI4C1qhPH8R3O5+R2kUIvcZ/gM9C7ngAcqDybFbrPtiZN61lujwLOvN90Px4yNvWjRMvG6nvoMVepH/Wd85B9HmP14B2sGFtbIg88YP+mabwX0wOaePt2kclxRFKJprtoj++gB8C2PZuJPPnk/DPb1kF3r10rflb0A7qH5J8zZ3z6Dz3iypWiJ4BuQ/5/4gmRd71r/vnpp+fHd797fkQCoyXb439djIp3E8NccS8sum3REE/+t+xR6/7Ortn8MnH/Y83j7Y7jYGTCoUzWJkhp1YFMq3AELnPMVd/PtcKJE4VZZrbiYegABM5E4q0zp1N/uxfdBk4gDnjQQcRWBqtIYfizWVPxhaKpg8E8YcLKBmNDnxW0GxkDraBq/N+mwETKqBUEHGXLebCycK3+uA/LMFAT/JBos8p39r4OhFhBkHenZ7lE4bV2Pl3Ww6rWUGPct5531TX3vfeWQ5u/w/86oCFjmGMFTTvE9HacIkXh1cHOOvlEZLGSxVNPzT97lc108C8AXgUF/9w5v9plxilVU81zJHhraoGWnOy5jes6wNriKhPcESEyRIvUU4mhtNculRFE2pMCusKTlaTlHvUJ8qg11Edta55LNOeW9dI6D68ymqazXoJJTVa+1Q9oKY4HByXAGIZKHGEgvHatzBVBW9AZdDVkDvDiwK7bt0vfGBPz09UsoRPA8Kmr3rDcb1Uma1sv+n7VVHjj73X1ZCS3tJ1bi8yaVWNNZgd+u6giCPUTrvG3Pk9277TP7RjHWBb6BgsuE5lKKUMjE9Rq/c4BClwtfmurmWwNIIlby/9MT3RyIILYwAdYntvbK20QxPbyy/Pj175Wxmc+phM9wD+Yj7AjDdcvUvQQzPXkSb/qpXZceUHUuq12gunzI/QJXr2fscyggqH7Pn5mx+gCtl9oaPu7VeVMpNhNDg4KTeeEDMAKarYqdn7wg/Mj6xO6EIoXvBslaFgVtT1aGu1EBVQmVDcq6x+/s+uNdX4+6zy3Y6w/IlkddJarxYv4AcdWMDKgA9gsO71ICUrb3S12H/ZLWzKtlwCzt9fkCQg4hm1eBxzX2Nn1fMZKRF1n/fN+x/G9X09YSbPA20WnzfoDOHlEo62ivTUOzgH9PHGifOY4m0xVW12cEMG/0TNl2m4dMwG+nh9ZJ5GjT9ilOEkxQq1/buh12vZeHBUsOUr3OBh5fXAcjEyY7N0tATurXiG1Duiatt61qe9TARJ9HfM1wZNBoEJvLCNTeQjUBItkfqupcKmDZaPAFN66AUe9FXPbMzs8FPnWb51/ZgFBV4JEgAFXRtBHfIYz6jd/c35EVtWNG8UxBIUZ17C11azI88gj86OuaMxOJ67CNpksbrWmj9po623PqYWrtoCfsQQNHscLtmtDW8DQCgWnyVvpKEcGkWLGWGZ29NsADblgzHsZBYgxnZzNZPLmW5mfnJ3J2yOPgcmkmWBiAfQRyMyxr4yUkSWi5903aHBoLHFtDkIXLXplOQvf+m4CY/VImMwORLbO5k/I0t+M0axGrh06mKFWts7I7Qkdo3U+2esc4x3zgof1cQiZZjZrVoln6IpZgHawcCWDCFpO15hOmzuiMCJ9Qju3uDokqsVrevWJT9jzsNZUtLbYUcWVhW/fLt+98MJiG5FyH8GPMGd95Mo4Neu21snhXXMUvF47blsffY2rmftiGZ29RNllIXudXW0Q3jO0rn0ZNoSaNbAsmroqGSlDm7skk3Sde3R+9K4DGTtAZo595Y42WUAHkUXyP4KOM2DdR9NIz36kbSzssNI8A3yDA6Z1UFlb8K+eB4+vk1y8Z4g2qMajz2es6t0Zgz6MbYN/9dVFntO2tqP7Uvuu9L3WIfTeWptyhs/X2PhrkdFb2q7Vsot1WdsRH6l5f/RaqJEVdVur4rzIov2HA8u4+vHhYVM3YDuSlu09WO+zFXD8zDOL30VBBPx/X5qRed596R3ra9acmXfpcb1A7KiNN57+P6tHtK1p6zll6KO1jvFdmx68DODeLyPgsFb3G6LvZcoHa+jDWQqW+dyGRlaW6DJv9ie0zSM7h9lM5OMfz7fvMsZRxFGe+1HH8b1fTyzTRnVU6PxYfpEuu2d1RYb+HxWseq0MpatoPcSzJVrFDIfCku/jEGaXoebxdsdxMDJjNitBi1GWRa0Bv6+T0uqzD7zzh3z5+/SVdVoOgb6O1r59eo6VtrGjoAjeDo0rHERChjZKshMJR72ljzdvPVcv4EFDb9mAvjU2NkQefHDxO87C0k4gvZWcSHPbH5HmFgp6e+WoMppnbNPGQC/7Vh+7KNcZw3uNkVbPhw1wtWu9y7sZzVlvuzcGbt8umXBHAZofjUHT1xVDGaUyxtUhg3Zq51Pr/ORKNNoJxDQ5cgp49Hs2851Zmnd4QdD6ekD/M84X63y0aQvAsJx9tfSyJmCoJmgkajMmrHWX4R/8XeZ9sCpQIyFobC3t5ZdL0KJ+zlawichcpuD1Zu2W0HbvtNyReef5/Ki91S+vd0uGy6x/T7bR1+PNT68pK6jIm4Mlq3n6mZYHeSxLVqvhEZG8HjlcPcdzzftjyWp6TSIYQcu6IotyPM+NE+r295u0ndvq9gzNM5j+c1WG/f3y3vH1WOslopeQ86EDYftPVMPUDm6cp7c5Q9/gR7iXr746P25uNnUDDk7W1Sq52n0UBGDRG08fivic935b7fW6wb2xKv/zHJluW+9jhnd776dIeXZjAvfA0s+i99nDMp0nESK6lP0+22+mfY3cvIz71EVWy6CrAyvzW+beWfOokSFq5tU2roidAMN2pGhcj3ZZlXb4HM37OAhYH72dTTTd5ncd31uV1SxwRcwa1Ly7Y2DMsVdFL0Xm27XWJNlH71yNzT/ryFu1Xgh00csyenDtOEP5Vdrm0vd9rAlsHMK+BLs2aCaKeOhKlMxbmEZPJv5cdFCaFaCM8wH2QzBNPjwsxUO8eVkyMSca3rhR+oHtGHK/rpjPVeqBjY1CryHTwx6MZMSdneaOKnyOlWgSyTienGxdMwea634z+nikM0d2gTb6FsnE1m+RbINxdZLoWOgbhNzVh+Ot+65jjMEr1oUfDYVIbh7iWqPnHo3T93lbNhrQ5EyFzD6I6ELG3pixW+rEP66WzPqAtolxouHJk02azv5fncwI3G/vwbKQtS+3tVkmICdE9jn+XiOas2fr0r42q2+PV2Z2Ndb8PdKlrXH12BlaaNlqo3NYtuMjz18f9bvOY2kfTNtuo5Efwppr27Vb87DG8mAVGonGtvqDrZpjYDQf8AqVaFrIhQajsdm2r+fc9q5PJr6sp5+7Lkwo0twpXb9H3vOOdhuP/EXRe6ArRePYZmvVPhzPl8Pt9VjW/Y3eOe89smwQkWzPazOiJRk9wPoNfb7+ut/3ADg8XA2LsebxdsdxMDJDbxc4No4F2tVgVY4vbjtkJja2pKkBU2VNqaOAM8ybA9+08nf37vwzhAcYRyGA3L0r8vWvL/6GfnSFMxgawaT4/ytXytabmW0ihgYMlqdPFyX24sX58aGH5kdUWn7ooVL5BjQHW13g+ektgaLAB25jBQp4hkqg63ugzjvM1FOfnoj/Vzg812Ed98Dh9plvXkO4HTUwIO1I3Tt3HiNmMK4jhrr+vv0M/Rxq+jt56psfD8+2yzArqQCunU/6/4ODRSODSNMhoave6yqX3IaDyPh/rXDiNxigXnut8BSPf+igLSjeXCntVHkWje3vYOQ8ebKp8GJL6Mcfnx+feKJU5Ge6r4MjuOo+B0JEVdgsWEZjT+HVhh925FsKcKQM83ldAjl64PDRx+TwKCWjHONtD5OOQw5dFjQN8Lbw1IZQrjbJRvF79/wgME2rb92af2b9AUEWb75Z6Pcrr8yPV67Mj5iDiK9r7ewUGob26Af9ijRlcdApvaUb2oCmI5ESx/PnC5/AWOBruObt7UL/kXgJHUHzGKahlkOtLThCnx+1rQmw7Csfj0z/ZTqt3/FlXe02y5pXbb/ren+GwjKvbwCdfFBY9iC2CYHuaz7Abbh6/c2bIs8/P/8M2g6+om0YsCOBD4Amg27u7TUdcMx79G8oPPHVr86Pv/M77qX3BngDtiN9z3sKTQdwfZjf/n6hR+DpsCPheO5cM9GFHVdbW+V82JwsfcLji5aj6n5EH9rW5b4c1Xs51ryHGGcVfaya93LxEMiKNXrBbBYHD+t2Gpb87wUP6LZcbZMTV+7cKXYayOT4//r1+fHq1XlCsUjxCegAbBwxNw7AvnevnNfXQ8x0G34AHDc3y/Vz8qh+XthFBjsHgG6jzdmz/u6L+h6iPesu2mbE9qOxdyZhLFuPXQYy72hf+lBz/rolNq0zugQE9uk/298y+I8XhWPRds9uf3ho23JEFn2+bK+B/K79wf/sn80/Q6bXySM4h5PrQT/R/7VrTR6xasB+gyPsN1tb5TPvwvvkk/Pj44+X7yC3a/+BSC7YL0oUjxLGu671TKDxGHSyC/RuNKvEEPTmqKAmIrBrks7b4T4CsA0wLLs9/6ZpO9N9ltu1/Qe0mWn8rVslMRByOn7TO5RjzhgTbVCQ4uWXC50HMB+0ffPNMtYqdtUALl0q1wN5n2n92bOFlqPNo4/Oj+AV5875u29ZAfdeEPHWll1UkRHJP0Pb9jN4O72zS8DXvvY1+fN//s/L//q//q9y5coVeeyxx+SP/JE/In/6T/9pOdl1Z/sl4TgYuRZjMru+SmQUUML9DSmI1sDLlMxcuzaQ30+wnlMmq76tT0Ybc5nNmhXNYDCMnhMEF+2UgmLKTijtwIICBgcRgghgTHz99aKY4nzMTyt8cPbAKMn9aIczZ1qJlOAxBATDiXTmzPx49mxh7PgNCowOPGZjH/7XAcc4H0KJJVTwPdaZazinTRjp6zSxYKyBSZ/qCcb8xg6YnMhhGXNk2vLNcdel6sEY6JKWdr9c+xLR+t7o4N/ona2pmpBxXHkVlicT3yimHSJoA1pqZbGyMZSTW3RGNXiCNnhCQfYq6+vx8B3O0VUVoXQi4AA0XjsKQcNxX8BjcM758+U8pqHa0Qi+zM4jHYhtJajoY5QZq6+ZEcmQbFCrfXd1AOFIqOY5GcPr0BjKUPl24jV9EAXRA5bBv3YMRo1TaUx9TdN6i86LLNI50DAYLjmobDZrrw62uVkCewEOujpzpvAGyORPPbU4pp4bV7/Z3y/GTxwho4PXbG01g5A5GUVX2GEjpE5YxLUxH9NBxex84mvXCaH4DnwIQXo6kJoDDbSxM6r4w+vTqhjBjq4I60hXVpHAeoxjrCMODsr7ANrO8vvJk37iguYRHHSFI2jRzo7I+943/wx5HU4lHZwM+g/bDGgp5Nxr15qJipgfZH3t0ILz573vnR9/4AcKT0JS+5e/PD9ysrxImQ8cT+A5p0/7wWzgHSLl/uI7XA/0gIceKrYt/MYJGnp3Dss2JLJoR+KqoBbf1U4s9Oc5sTK6gmXPylSjQoWnMVFrk+jDx8Ycqyv6+jy66EdW8iqfN5T9XycnexXJrJ1e8L+eK/eD36yiENY89FGPESHSuxmW3M794KhtPx6Nt5JRrPnpyvN8Pr4HrYHsygELu7vlN7TlQK/9/aZNBvxAB6nBJwAaj//39wtNhz8CNB334+CgaRvCfNBWBzRhfPg18P/OTjOxhHUEK6kRvEHL77D/IOBY60w48vPFM9CVQ3HvOJlxyEI5x+iPNv+gSC6w1kIXmh75J9dRz+uDZdvehuJpGmyb159ZTs3Y/DUN8YKktK0GY3BRKtD4N94oSdqgOaDpLNOKFDoFmg76/fWvl3GRvMF2bn19XCBka6s5HlcQ3dxs2v85iWRnZ5GGi5R7pn3goOGg/9BHdOAxxoIszLarnZ1io2Kbjn5O3nqz5I2+a9A7vza2YRWV8QFrB7Ux+eC60k3LFs6wfEkRhrrWMW3gkf5a4xOLkgH6wkoex/fROyoyp2mcYNIm64ssFskQmY/DCeGQgXVwMuwqXFgPfOCRR5rB0NZOW+wLRRu8z6++WmgpEg9xzuXLhW+ABmvfAMYGj2GbjLats02HfQXR7od6TXGsEccVWf1EWFe6sgJEbHHseSwDv/3bvy2z2Uz++//+v5f3vOc98hu/8RvyH/1H/5HcunVL/spf+SvLGbQjjjVMRu2q0O1rtj7IMOoMI4vARgfLeDdGwEQNaoS+iJKsi9G2a/C455DvC8tBFMGbv7V+WYm1nBfRmvj4x9vn4/WTHaOtP21Q5kBlq19WSnUFCJ0ZJlICAr7xjfnx//1/y3dcvVMbCFlQwW849/r1ZlAdhD08gwsXmoEc7GDc2moGYOsqOixcccXnS5ea1XYyArsljKPdV74io4PnbKGvsa2W7h8xpKs8O5V/V1K1dwnI3IelXqtnlBRpl0GygcgsZ3ACi9WXNSaUNfSTqYpoGUXagga13BBt/cz9ab7ADi+rCjO3gTMKxxs3ynkcjAyeceVKMeBCcUaSDdqcPFnotM6oFYkNHVyN8+bNZrC3rqbGyjCMDnpNsSFBO8PwuxUooTGdlnu9Ltmb66bkHyXj3aoxhPNoqMCHLDLGXu+cmv5rYQUcg957lRb0XPE+QxbVTvuPfWzxPJbDNU0FdHUAHsu7h3obPCuZhc+zdnzhZBautLa93ZwTB1ncvdvcCprlZ+t6eO76eiK9CG1A77mtfpYc5K2D0bx5eOfxkZ1y0Vof2xnVFiiUQc27GbWN7AF9gsAiZGlcW99jBdm1ndc36GJoDHVfsvffo8m6b0/On0ya1Spr5oJ+NV0BHbB2n/iWb7H7y8j2Fq/he6fplcdjbtxoVlbjSps64BkB0xzofPt2s9olZHudpMLJKDhqOszVfwDwikuXmrQZOoNOwPF4pd5dAHPFc4c9aGenGaCWqbBj/XYUA9tqgiui8/t6nbrIeBlbVVdekwlw6TNWm9yduR81gXs1VaP4++m0bm1nAjk89JX/dR+e/DWdNnkDy4c60Djj0/Lk08mk2P+7PFPr+4wd1+IDnMDHx1dfLbwAwcw4gme8+mpJQoRN5v3vXxzTCuQDH9E7W8GWwnZ39K/5iOc3svQI9jXs7jZ3lcH/e3s+DdN+JA4EYTvS+fPNBHzYkaygw1XwCtyPWlnYW2e1ScpD6fE1fLmrn9L7LZPUmZHr2niXp0NaNJmvPeODsebIbfreO8ve3Gb/afuN7SSZdZKx96ONVWDEe1cnkzIG0zLg3e8W+dZvXZxrZMfPrGP2zVpJF2yjv3atWXWfd6vSfXEVZ73TCvrmhEXsevjYY81KmDjqQDhv3VpFRHh+QBT7Eb1jXXlxJCvV6Oxj74zlwaNr02lsc7P+j/rnz94YFobyQ7fJVtHz7iqXZmR0C2PZxTL0s1ZPYfpk9clt9THSH6D3g0ZYPtm2uVqBvhbaklmy7zzov2WT1/+3oU1Ps/y/Wv6HDQdH3Est50ZFqEQW7dya7+A3kcUkQn4+OrmEE0t4Dvv7OdlmXe2dEc1YcnGQrPlm2VjWHL7/+79fvv/7v/+b/z/99NPypS99Sf76X//rx8HIRwJReXeLIVsKkHXUyBhILAcpQ48xhDCSUYRqx+qj+Lad12ZczQhHfZlchL5OzJr+omu1MmNrxuX+dL9eBUkwca386e9EFh02v/mb889w8MDA99xz5X9kNLGgoA1qnL3FhoWNjaYhzKoAA0GBq1U+9VSptsZb6KCf2azp2IES+oEPzI86YCGqaOPBevePoqMnwnveM+54r7/efAYiORqWMZDzWqw1eK4bnPmbAbZDO+IsSTJjxOyqPEbzcDAZwmlkoc1Iy4kmGUdTBlZwQ5ekFt5uTcSXu6J7aMlKnjIbKZw6uAD8B9/x1qK7u6VqJioy4H/eZllk0WkkYtMXAMZN9Lu5WXgKzgf/0IkjHCAMnqWzaDmwl/n2bFbuNfM+vYUzH7UDdygnBz7jWYyEiRwWOeTUqeZ7w9eunYVsNKh99zMOiL4GhT7nd3V8de1zaNTI/TXPsPYa2nQ5b9whxugL5hl6K7cMmH+Ad0wm/vrnBIk2WDxB/x85b27fLjT4hRfmx699bX5EZcyXXmpWOUOFB/ATTUvZIa+dUvj88MOLbUB39e4pTON1ggfzElyzVQ2aA54jnUE/C+bPuD7LYaSNst5YGflr7G2i9/fL87cc2VbwiifLW45RPQ735617qyopB5a/+ebi2tPna5nAqoat+7t2bTEAxQM7Uvi+3L3blAu5IreudI41xJUBtcwylLNtlRhqPlkHpRdMpqs7eudY6zfaut7b9UTPNRNA4cntmqZ6/FknrHjv5d5eU85G0vNv/VZJFkcb3Cf8//zz5b3rg0cfFXnmmfln2IZgT9LvOWg4y+868Y9/46Sf/f3C4yBTs33q7Fm/+o61FriyslU9tgtWaevQNoWIfkcBMtb/UWCNd743di2yc+T2fWzXGfo0hGyduR6Wb7oER2TPa1v/GbtUZmwtL0T3ILL7A5H8551v8QOPnxwe+snoWv7n3RItm5WHzBoHvbp9uwQRcDDB7m4zsRz0EjL/tWvlOw7oRTDxq6+WNpgbZBpUXH788eJT0MHHIuW+vPhi0UPwnLj68vnzzeIhXJ1OpFmdGmPpnbuWWaWPkXmfhuCzteAK3RqRnYPfXy0je7qoZfNi35gubMO7RuhdIPg7DpCxbKM4Yr1tbjaTizkYR8+Z7WG6+AAHpGOd6V3fuHosJzrp4gVD+ZEtupLxr4+JjA+e2+r/OZDM2tGWaTv/35XetrXPtuly73VlfD7qKp/eTi1af8A7h+BkHF98sfik8R3Ox3tw9myx7YA2s267uVl4DIKZAfDSBx6Id7LCMRNw30Uey9z7od5D67dV7FTFek0thrYPW8UTLN24ze5p0W3PrqXBcp2W+fgdsxKTOJ4COunJk03+Aeh4Bo5tiILwM7BintrQdfdxKwalC03nNm26NieRR0npDM0Pauw+PEd8r6856sfjOzrROqNH81rmKvy3bjV3z4L9/tVXi70fNB52f/h49ZyYJkN+OXeu2PB5JxPtD0DyCWQi/l/Do+3Re2DJl9yfha6xEUPJaCPFM0Wv45go7u5Ff/epU6fklKXL9cD169flgi4CtCa4zyLXBsDNm3E2VpsxPIsaZ/eQ1eHGUqpqjK1DoKbvLobTocbui9qxuH0m28kSRtixqA0TvAUEjz2d+mtYV8/xgg3w/RNPFIMLjly14OWXS9UCL5BJC9x6y82xcfp0M0ABwogOYGNBh5Xac+fKbywARQorB5NZbYCsQyMSmPSY+rtIsISQOBIOL17KOT9rDTaegmi1jVDDNzLPqQsyiky0JrqiC922fscziIR5SwjOOH/aMIRDKwOm89ksV5F4jlZwQ5uiO5sVgwba8nY7h4fF2WMF/IgsVhuzKsjjaFX8FSn8QG8JxMHDt24VpZWN+zo4WQd56evSSS5tVYV0EIEX6LuzUxxMvD2bvi9ssOe5RwZL7UDgren0Nbe9xxnl1gqssngNrg1V6cbCbFYCRLLtsV6tahT8OUO/h9KOuzj7Nc/riyGuYwiekTH+AZ6xqzaIYGznFWNZ47M+YMn/3Mba1pi3f9P9sBFR02gOssVR03N8Bh2HDIlAgZs3y3cwMOL/e/dsozdfF9N7yNuQ2/V50F/wG+R5XRkfOgsHg+mERW8da4ctO3PBM06f9mV7XIOm/1ZFe66yoX/ja8Z3MPbq98mjc9b3Y1dGvn3bNgQzLCdKZBDma6upwGg9d9YJoTdaqHEy7uzM9ey+sOhupJ8M7dDsa3/qaoxvOye65lrdoKszAb/XVD22aCCOliyv296716T7LHfv7RWZlR2cOhiGnbAcBHb7tl/RBrRE03/eUer118t3y3aEv/RSSa73eM6JE82tpOHM1VWLOUABbcBzzp9vBvzwWJPJYrC6yCKN5zWF3+AsOX26mdyvk/xxPQDLC/qIsbwAx2Xh1VftndC6OMQtnsfI0Oa+ASGWHSqim974GRnYG7dtXt5YloM+crBG81kHdOVLNfYpDS3bZWHZ//k3Tev5uVj0n3cXiXb3YLpvFTXRtFxk0S/ANB0yPgK+btxoJppzwvkywDtj/et/XXc+03S97TPrGKxrnDzZDIZAf7oyMdNtnTiGNeTZMDQt9II1tB8I64NlAU0va+yWQwFryeJ5kQwb6WdAJPfXIGNnt+wSnn3D4nVd7HxRu672oGiuNX3WyMtDPZ8adOXzFm/w6H30PvGcLf8vy2yHh015idvu7pZ3G+866KwuCvKlL80/4/2DTQT0+9o125YjUr6/dauZKDImcF1Xr5Z5/87vzI+/9Ev5fnQAMz6DpiNxHYmLDz5YdAHYoXTAJ45Mk6NdqzTvFpk/S6Y5lh2JE17YLqWLmnj+X70O8ZzHhCUD8NxqqrVqvpixEUXn8/0cg04NFcQY9Zs5L1onXVBjf7KeTxdZvja4suvzbeO9kd8W32u5nX/TNh+0YbndSqbio24DOse7zWoaD3qA87kA4u5u8/xVJDTUAjQZlfKRsPjIIyXOR9NQfdQ+AtBfyPSQ9Xd2fNnT2uHQig3y7P5RAiPzDKsAGuuL+/ulzyUXo4rI7pjAHJ7Ec38Lf+7P/Tl59tlnBxvnK1/5inzuc5+T//q//q8H63MoHAcjW9BCbA2T8rZ+1f1EirzuUx+trWMZVgaR/o3bWON631lCQOSsr0GbY1L3XUP89L1jZmTNuY0iTad+FhcQPSf93NuEcKvqgXUPIoGDt3RkWFQ4Uz2Zt+mxHEQ4ok20TSe+P3FC5CMfmX/mCi4wKt6+3cxyBfD9zZvNiso4osLAa6+VPjOAYodt1p55piiCUP440Gxnpzi1rUoGInFlZC0EtCkyFjKCcrTma5yxkXGor9NiZKfCRA5Ftt96plZFA4Z+n71gP+88kZh+1ipLmT51+z5ocwJZTrqMcXQo1PRXa1Dg9hllp+u7W2MIqDHSRo4mzQcsg1PUp9WP/o4NlqD1d+6UKmW8fvV7pZUrPR/tROfqIbqigcicR4D+s1Pq5s3yHcbnJJDNzaazh+mEdhBBecM1Y666UiGURxgVEVykjZFcrQzQvJznHBkBGZFjfSjUGnDG3qZtOh02+a8GQzlWasboknnclVZ3lT+8dhljZjRGtBZrjKOZNlke0zavLCwa3AUevddyO9aQdlyIlDnrzG5rm098/6EPLZ7HmM38qvfAdOo7DnQgAwICwHPAG77xjVIZE3oDEiLAI86cKbI8ByGjwuW73lXoNubBldbu3Zs7kkSKEZIrI2s+wlWktHGRK+XycX+/zAd6CfqJtnKOMIZsPnJlfDl3rn815shpnnHw1zhdaqvEeNVy9NGzVdU+7zbeot9nq3o5jl0CBaJgLcv47l1jxiaj6XabszIKHrFsOxmHrWV/gdMG52t6bV2nhh6DA4zZtiPSlMFxHRbvsAKMccRuU4Cu/IujV81d01jI9py4rrdSRqADghh0ZTPMDcEE4BWwH/32bzevi/Ed3yHyR/5I+SxSZHoOBtbz7+IcFsntqFVjR7Xm5emL1rs6FG8Ym/4//HDhk8vEGtm81gJt9qR1QNs7Gtk2Mzwn4uU1MoGFyCaTgTdXawwORtOBccyHdD8f+9hiP1aFYnzmpBbwu9deK/Ye2IEwD81zoKugDWj8c88V+h7RHsjtSFzGblXvfe/8eOFCs1oxfAaQ43UVT75XoOc7O01+MWb14lVjbPovUp6bflcz/jxGX79IW59ZWDJnRi4dej41/Ua+Av1bRp8aem5dZLQaW1XGJ9/VHgTo4FLPbm/5NSwfONt0uLK21qdZJ9BJKu961/wz5Hctr+N70APQe05uuXmz0H+27aCfvvjQh0S+67vmn7/1W+fHD394fkQw0c5OuUfMx7RuyMl28DWDZ2xv+zL1Mf1fDqz7PZTdpsamMrSP1tLhaq410gWZXvetaGrR/678w6PFuiiVFyBvnddFx63xJ+g514ypz+EiDjjPssl4a/LECZ9HWIVGPFvTqVPFns0FC0Ebr18vcjHoN+g1+tXF+3hHW23ntnZ70/1GO76JFNn+4sX5EYUCIb8/+aTIO94x//zYY/MjZHTY1C9fLnZ2yJMcDDybLcYtidgB1KxzcYyQtWvt/YK3WTDy888/L+eU/cmrivzss8/KZz7zmbDPX/mVX5FPfOIT3/z/xRdflO///u+XP/gH/6D8sT/2x/pPemDcZyt3AHAGQR+hoZZpHQWD3DHq0OaEHHIMDSg1R2ntDFGRKYKlJFhb+bQFPGh4Dp7M1seW0MnVgfS2QRAwOZBOVwNiwVCPiQB1KPmcnXbv3mJwoB7j5s1mpjFnaWphEUcIdDoQm4W0KNNrFdu04d4NuUX0UXoPl42uhs91wZhGoa4Kb+a8VVQdiQBn1P0Ai7ZHyATBtDk0+DyRwjt0NSDemjSqBKB5D9NizpSN5hgFLDD911nbbUlVQ0MrvzqwkREFDunzlwUrCJOR/a4WXR0hQwTZ6n6y50YGRq/vvm26zCHzW/RMrfO9YOJobXvt29C3+r63M0p0Xtc15dFJXVkBtIerLx8c+AZCbdRmJx1XCT550q9SadFLnAf5XdNvNrByIJ9Iod8IqkM/2nDNhmR9f7jiKKDvJVes00ZiXDMn1/Dz0gHYCNI+SugbkLdMvrFOxurptH/iTxf5mI35RwGRbGE9U12hvRbL1Aui5DIke3cBaND2drEvsNyhg9pA+3CEo+prXyvBC3rLZZESLH3uXOmL6aymaX/v780//4//42IbnRQCmxcHqsEJYVUe1bumoD/YV9imgrazWaHFfO1a5sX4nEi/tdXUCSKHrVXtEvNhXsk8WOtO2DL1fsNRtHssE0fhfvQJQhgqOKNW54kwNJ237AEMphk6yMFL4NEFTEDXuKIx6OX58yWojQModFAYZHvItNomwjunAZo+4TfQYAQsQF49d64p0zNNPn++aWNmWqh3DPACVCxY68XTnTRdZz+CDqLnIHMrwJ7BO+Loe8eV9bVcxsUKVoGh3ln+PFSf91M/mf7XhUesyzxqcBQCWZGMztB0ytsZxbJTe7vovvFG+YxkRO3vZP8q2ztERH7t1+bHX/7l+RH8BHRre7sEtbFPFIF5OzuLu9vqNrqICMvLrGtYyT6ADv7jXU+YxusxokRQL3ixqy08+m0VVU31fKIiVCKxn0ffFy95Vq/nmiBmKyGJ7+PQtp7I32TJFn3tYG3nZvpdRxvcGGMOUcSnq34Q+RW84iizWUns4H6sd4TlZN3G24VX8wjQa7TRsifzFkvu1jsOihQZFrR9MmnuTsvIxBTqQHnmA9quxXPk4iTa/4vf0A/Wit5hncfQcVHMc3Ub/o2r8evYHo8+bGyU82t29LkPcO7cuYVgZA9/4k/8CflDf+gPhW2eUsUdXnzxRfme7/ke+eQnPyl/42/8jb7TXArWyDOxJnj99ZLdFmUAWf/3caxHIfoRcY8Y/pgKSGSY7uO8j75bR4WKMcYc+wadeM8n2oJNKzTWVgn4Df+zg5+3W9jdFfmf/qfF8zn49tq18tnLjNVGM64CpB3t/N5oYYSVPTAHXa2MA6gglOgMKXa68zaZ29tNAyWgDZbM0Hnu+vlFCQ1ta8IKFIsUEH1eH0Tnj5wZeygTm271VXyGDvw5xjGOEcNT0KwtQS1jJlfPxFFX1tfbO+vjjRtNPgaepRVy3l6HM4e3t0ugArJx8b9O8OCqmZFMwPxEb9vJ27tp/hQFTHtjRNsaR0f0PVQ1iyQOzz0gh2/x7cmeYYQGIrk/wzOt3z1noXXPsslOHsbkMd664c/euQPJ/YdTMq6p/ydSUUF4WcbVvqjVRVcpZ1hrwqPXUWIGvw/aYcX6SLRtHOj4q6/O/0RKsBbTek3/MQ/oBtrhhDmBpqLqvXZO8Rac6MdKauQqpQh2PnXKptcii/cQ9wrjg39YiRU8vrWFJ7+72ukSOaXa6L8GnsFYeP31RT2rxqHSxc7RtZ8x390ufKT2ftVc85h2rIwOuGp9LbK9rXpufZBJPGFaZNF/dqLcvFnoPidNiBT7DL5DhWRUzIQeMAZOnxZ55zvnnyH3c0X7ra3CY7SNSh9PnSoOM5wHR4/etQz3jOmuxRssGd+T1XT1bDwHz7G+uVl43PveJ6ND74zjXY9Fizw5NwpY8M7l39po39iyfh9fRS3GoGE1z6Uvfx6Cv2f0yWVg6Geh6UJbcpAVjGDpDPiNdyIBbbt1q9B0JKEgQe/LXxb5nd9Z/G2ZwDXDtoMkEB3kgM/QEcCftO2IaTn+1997BVN4ZwP9mw4i4HHZ1iPSfC5WgHqUeIk+8Kxw7W8n9H1310E30Da8GkT0tut1ZWxMbYH6UR+6jRUcxW2sJADdVqSpz7Ne33Z/EWQb7YyF975mx2FGJP9ktmTX8CpWRv443gEQNg3rPG3rB2/gwhx7e0U+5WR0PJ979+ydvfCbyJzngO8g0BlH3Odz55q7VHGFzXPnCt1n27x+bp5Mru9FTSxL9F2XNrW0wPItjAV9Pbif3vyHSliJZJ/ofcz6/r3zM20z7YbyfdcgQ5MtjGGDz1zzMvUI9iNmzuHfIt4RBcxzGx1HhHnx/wcHTbsK735y505zlytOHLl1q5kAzT7e3d1FW77u57XXSjJ6WwXlLEDLeWf0y5ebO+zyzijb28VHYPEGHHnXXBy1HMG+YZbjNTghU8dFecGy02m3Cs2RjAU9bUmIwi7HRO0cLl26JJcgO7TgG9/4hnzP93yPfPzjH5ef+Zmfkema2oSPg5EZZ8/GW1ABGSOk97/1W8YZK+IrDNrZmEEmQCDDCL22WUfesl6MjLBmKTdtxue2MduUWP2cagzb3nh90Hb+ZNKslMXQxiIWNGqRqYyZcWB7sN41/m13t5kRxZWU9NrmgAcot3fvFsEiEhAigQCwMrv0//r8tmCAbBseu/Y8D+vA+dugjaJAmyJvtalVGCMFr8YRHgl3QxgXMhKcdT1DoasS12ftdTWuRmN7z1s71CN+4r3zIkWB48AlawtlNhDqMTyFFOdPp7mtn2vQ5T3KjJ0JrNKVcZ5+2j5fw8sW1f2wsdmS55g36GfKhkY2TOsgAJYdrWA0D13f2dp3rcaQlamWOjAms7fkmDErJ0b3XtNbbzt7/mz9z31aaKvQwHNtk5ut7yyD3VCyRNDPNwOOo/Myupt3zVn+0HZ/LT3CGjujR3hjZ9DXGFmDiOdZNMxzoOk51MhakfzP87h5sxhF0RY8UAccY25oC2MrDJ+7u6XNww/Pj48/Pj/qCpdd5O6Mruu19fprcxBl1qT1fCLZZmzD2cWLtgPKq9qnv4scVm3X05cWdr1P1jOo4SOZNt5a1Od4xus23Wno9RFde0SvcWyTS4fQvzLt29aZdhChLQcPWNceBRNEYwFegnjUZw1fs3gEAGeQRaf0+83zgPyut3/WAasiJQhYBwODN7DDC+dcuFAqI1++PD9GyYSMzLqLaNMQ9pwhYM2ReeeYwDOI5LCMfqOPXWwHWbuRPoo0db9oy9cavmwl8wKso2Z0yVo7UhcHp9dnFpENMDN+jWxjrZcM/4jmFdHrLrKIF+SQRbSO2+6vfv6ebq7XlA5M82DtjMjvBlcZ291tVpfEbwgCvHu3uZNIRIM5eBhtNzZKwBpXzdT3AHNlG5G2J0XBhhnU6Mbe/7VYBf23UPPuDyWLZ8Zu0z30fDJ6SGaMGr5kjRX95o0pYutgntxjFT+IxmB6a/Ecy1as57W/X87z7LnWnGsQ8Wv9HewQ3CbDD7RtH+0gp0fnR7oP+uTEB90f31++1pMnbVlEH7Xd3ds9bjpdTLLQY+7tlSBktEcREcj/urIy6D/kfr2jCHaFhY+cqx/v7DQrmFrXzrKVlxRSizFoWnR+hmYNuTttFpF9eigdqQv95c9ePxnbXxRHlEGNfroKvXIoG3gtMtecGT+jO3FbfQ7H6/C89vebtJx9xdNp028MWIkmGX0iqoyMZOsIGV7uJQJZ89F2f5E5bUdiIpISv/rV+RGy/alTi4HAIoUW60RI3omW57GxUXgBrh0+ZxwvX24GGmfk+Fr7kTU/hsdza+mLx+f1OX15WxIZE8QYWNYcXnzxRfnu7/5uecc73iF/5a/8FXkVhXZE5JHM+zYijoORGS+/3KxAqsHCsM6k8wIFTp6Mq0nwWBaRYKLJ87Becov4tWUlW99ZxLzN2VijcHrnWe00tKCeOT9DfGvmUaOk6+fVplDNZk2jHztx9LO01pa3vTnG0EIB5sPBxNphlcmwZbBwE7WZzYoQEN0XnZ0qUgx+UA6vXBF56aX5Z2TVQBjAu3Pjxvw9FxF5/vn5saYC44MPlurpCB4AYUdQweXLxRjKlZF1xWQOVGYjhkU7IgUto1xkDEjRd9xPxjhVY5S3zhsJCxUSxwxGixwA2fMYmefZZX1kHE1DoW1tta2h7PppMwRnDe+RgwnHGj4SXV/0DHh7E972cHe3Pet8Y6PQbna+6K1wOGGF53XvXrMyPpwMukL+P//n889475huRoZhveWM3uJMQzueuFqBrl6M7wDLwcPOI32tInMHlHZe6d9guHzoocI/OAiBZQQ9D1ZOLVnLq6hh9a3l1bYKC2Nj5PEnL71YeHjWoc7I0MbIQWPRA8+wEtHkjCzO/Wn5NJqz5zCwdBZvLGs+0e+RjsH3LAqcADIOK2suEU9p0zEyulOG/uvrsow7oKtt12LNXf/fFnymDZaZsSKd0tvOTCd8gIbDwQ+eY1UM8t6V/f1C71FhAdUQvvrVsjU8DDdcWfnq1aIveHqDrkQAHoEqCHBSvfOdIthKC0ZJfi5bW2W7T/AK6BU6YJkDaLnyz/5+sxK+5TDNGDgj2hX9lu1PY2z+8+UvF0OwpguRvcajqTUG8o2NnL4R0TlGxpYROfj5fP0suM+oIkZEC73riWRhDW+9ZXYOiJCZa828rLUQ8TPup0b+n818+48V/MuyvRU8zJUXQfMtOxLmpQMP+DtLHwHdtd4NHC0+qM+xdj3hZJCvfrXQe4tPYm4cbAJa///8P7mKLZ/85Pz47nfPjwhUQOAxApFFyn2wAs4wPutMeF4izeAK1oEs+Z8r/2TtqV0Q6fGZ92AszGbNys34nttFffD/bbK5dX+iRNIaPmu16ULL9HtYY+fz+tayWpTsE6GPvUZ/37buM3a2jM9DV31lZIrgZOeBNQxE9ntGVNGS2xwelmfHO3cAerdCniv4ysZGU/5nOrW35xf/gKz+0kuFtkO2Z5+ZXnfYyQo+g5dfLrQdPoUafNu3zY8f/KDIY4/NP0Nex/+ognzhQjOwjNdhVBwF/W5ttdPOIWiqR3MiG3KGvkQYqjpdDSKZ2kLGLpC57kgWbqO3mf4sWdqS2z35v+bZZWR7C5bvL7I9YE6ZQg0RbW+jsxE/4WJFun2UEFQzFp/bpn+CLkbBX975ug3PxbIHRcVMPPAOuzoxG7YdlsN1EjgCg3/3d+dH6A5f+1qh5V3ody3e+9758bu+a/H/97632Hm83a6m0+b64OIk1o4k2o6F/7u8o11o0hB9dmk7JqDn6eB3y8YKeO+zZZuP3v2MnYTHjOKHusjUEa/JtKnlgX3llj72xkwleH2ssfNF84zobduco/Z63SFw1pNNdGyP/k63PThoxgTx2ta2HZ5HlLjIsv3t280qx3y8fr3I9JD3YdP5xjfmx5deGp7ug6/DbvPUU8VGzFWLtazPO9iijeWriBJomN7jnulz+bmw/1f302Z/4c9AlACaRaatvi+19oBKZMjdGFjWHH7xF39RvvzlL8uXv/xleULbG0XksMYmMAJWHoz8S7/0S/Jf/Vf/lXzxi1+Ul156Sf7n//l/ln/33/13v/n74eGhfOYzn5G/8Tf+hly9elW+8zu/U/7b//a/lQ9+8IPfbHP37l35qZ/6Kfl7f+/vyZ07d+R7v/d75b/77/67xs1P4Z3vtJUKhhYsOZCkr5BlOVTaVms0ZkaA6tp3hDZDROTg0ef0MZBrIjwUapXFbD81BtQI02lRgDJj1wQaZ9YZw+rfCor2MjX0GvGYt2W8gFILpw0cdHfuLAaf6eO1a0XhhUCnnT4icwMqAgs4wEBnvbKzH8KI3oINa5sdTjVCvaXs1BjIs+jrAPHO8YxWIkvfpsEEDB06mDFjiO1rjMw8u4imZpRIbz617/UQTqBMm0iBtcbtQp8yiN6jGppvGVeje5YJxsk4zhBEwNv5LgOZZ2Gd8/GP5/v2/tffRdUT9JbNIovb9EDhBS22lGSuTs8OuRMn/AABvUU0kmIiw4gOtNZHzV+8xCR9ZF5uORjhHLRokTcGYBljLMNYxhiE9jAyjITDRx8T2X7rHbGU4i50LnJqRYE2Fm+JgpIzGdNtMrllILdorOc89fr10NbO0hEyzyByEFnn1OgYfeX2ZRvhZ7NC36PK9l6AQQYZnaF2HXqVzDLyhycftX1n8QoOeMD/OljPq3avM/rxHSe+RPSAK7VNp3M9QV8jzgcf0e8IxtcJj/paNLhy540bpW/wFh0ch3fE46/Z94DfUSsBZlWOqgcfXAzs5ntu0USPHlnOKP4NmM3at6m1oJ2XHv+wqkYxNE/md8nSy5hOWsb0NnodrQ0rUSSjc1ljZsZn+m9tc+uNGT1nCxEfsj7rcywdzlpb3vbmQ71XkS5ZA8zz9OmSmJEZ01tbOmANR5abz58vY0HWh87/8svF0YVANdggQCetQG/wPNiDtreL7Ar9AbT0oYfmx4sXi90Igcq8bfPWVlN386pfijQTZ4DZzHcOgpdHCQ1bW019xnr326rnTKd2gDTPlXnnWLh+3U7292hi5M2yaElGxgeixJdIH4jsSOwEts6J6Io3jwwiu5alS1qfs2PU/saIZHy2bwxlG+oLPQZXXDwK4O1/rWfA6xd0TgcKaFu+yGJFe5F50ZPnnpt/RtUz0Oq+9uZ//a8XjxEuXSo6D/Q18Ah91DxFpOlP0LJ5ZCf27C7o7+zZMha+09toewETWn73Kprpsbl4AQex6wqzYzvMr161ix8MhQwt82ii1SbSbbvaZLqg5rq8/5eB7Bht8x9qrn30g5q+Vkn/M/O3Apjf//7md/q4v1/oPdN4yOqvvFLoPJJS8L8+4rNlF6kBqmfi+IUv1J3PgW5IWIEvXBe14iR07QdgOYODmrX8jqNVRAb3WtN9kfn3XqGSyB8HWDJS5l3wkmqXCdZvLGQSBaPr7OIjs87P2MAzcqrut82+bdnnutjNM4h0/giZa/Xa6/8ztqZMvzXXXruTmgXIcZm5eX1mZS/Pr1j7vD7xCfs8LeuzTR5+WB2zA5rBthBdEIvjf3ThKK+ivp4H9Acc2a9+9mzZnQDyJOz4aLuz07TTs99qY8O3qQDb2+V8yzesjyJN24FOdmT+YRVF9HQNTTsi+49nz9Xn4jN0tyXBislfBZal5nzqU5+ST33qU8vpfGCsPBj51q1b8tGPflT+g//gP5Af+qEfavz+l//yX5a/+lf/qnzhC1+Q9773vfIX/sJfkN/7e3+vfOlLX5KzbxHcH//xH5df+IVfkJ/7uZ+Tixcvyk/+5E/KD/7gD8oXv/hFOcHl6tswm9VXxMwwjBrCbPXrjdGF2WUR9dk2n6OGTKADIxIYvKAc6/xMf0NDKx5RZWMWLKzqOXxtVhVNZk6cGTubFWbNAgYUzmvXymcYDbnS5rVrzewprpA8Rrb91lZRMGFYZCVwc7N8xrWjrd7uhwUW/n97uynosFBiKazWMWMwyjhT+btMP1a/2eoEA+FQJkXptxzr0T1rE+697zxY96zvVpVHlT4PgXW49rFo+7LGqVm/aGvxCv5tNisKoedwun27ScOtLFFWNDkITLdB30hc2d1tbssGPqL7Ad9C8C4Hb50/31RiwaP0tj1crRJVMBGwcP58eedxHnggrnljo7ltEGfhnjzZVFiZR+h1kzF2dQ0i8xzp+vmgn8xWq0MjYzhdlSOlpnpmps91oIkRlknLumKEe3goizL5wo4Nbeh7z6J33pLZ2HFhJRoCrBuA7u7tNdvzmHrrYzY0gnfcuFHoN/rGfHRyIvMNzEsnKjKYTokUeZtlfCt4iwMErar3aMt0XKRcF64Z/esgDn4++jpZhuUkF81rrOftObr02Na21/ocHbCQ2Wp77MpoZ8/awciMjGwf6UNRm2iMLrSH19Yxjh5q6XmNHBat8TYr+WzW3PmDbT0HB6Uf3llF25PQxnM03bhR6D9oIX7DHCaT4hDCuucqznfulPMRlIDKal/5SreANPSNOZ84UXgSHFYA5PCHHip8A44r0FT97lu6hcii4wtgvoZne+JE4T9Mv7UeoBPlRWxnFK8X3aZtK+nZrN25Zo0xFh580A5G9vShKAg4g6ht1wCOoZDR9WtsgdG50VjLqtadGRuI7Hx9x1jG9QzhD9KeWsuWY42r21j0n3UD0P+Dg0JzOHgANP7mzaZ8CZke9PyNN0pgGr7DmFalfuxwCHuL1g3YfgToAAFOXM8ANp6traYeg7nDL/DAAyVxBsWVYBPB/b59uynLRwkfuA+wXeGcU6fKfbFoste3NYYXHBcFNVjvl6eLLQsPPljGXObOiBk5XvNMzz9Za4/N0Osa326X4DOtR7dV74x8UkPpRxlEY9UGvrVhSH7Qxd9knWNVzcT/bbRdF/9ge7vWA2Bvh0+XfbtXrxbZ3CsYcv16UzfQu0OtEqAn2ufLtJN9Hno3MO030G0vXiz8C35j6AgWDfNoiCXjWEnBNbt6W6iRpbFOxoSlg1kBud5v1vfefR0D0XX09c/zb5ZNwQs+tMBtdJxKJCf0pZk8p6hC7Zi6mBfcqcfPFKGJ+vZo+8GB/6w0/bd2VNdtdBCxtWMfjkjoYJqOJPBXX13cSVfE3tmW7eR87/SzzCSjgB7omBzQcJyP+WDsw8NyHmwqvIuuSJGp2e+rixt6dlv0e+HC4i4pIk36cvJkcz68q6QVBG8lmnt+BMtG5CUn6r6Zn+mxMkH1PTCUqjzEPN7uWHkw8h/4A39A/sAf+APmb4eHh/LX/tpfkz/9p/+0/Hv/3r8nIiI/+7M/Kw8//LD83b/7d+VHf/RH5fr16/L5z39e/vbf/tvyfd/3fSIi8nf+zt+RJ598Uv7pP/2n8vt//++vmxBXj8kwZK9NxDQtgSwj1HiC3HTqCzi6Mk5NkK03L/0GR0G3XC0QRx0IxPcKBFsbw71t4KdTuwKgSN0WDBqewJHZ7sfqJ4K3biwKmTEEWAKcl6Wkq6SgHyvDyZuHrn7mZbhbiQDsGMLznkxKNihfF9aR3spBO6j08dq1IqBg/GhNsGNmZ6eZpcROpeeeK+NhDK4ooBkqC0W6epkWcDC+npeuVmkFM+v+NKwKOVa2LB+jtb0sYbyPgW1gTPbviUxpWyULWoBiB2DNu2udU0Nfoq3mI9oX0XivImYGlsLpKVQRX7PuhwXPoG3dl2ge0fn6f+8a8b8lWPN8mVdl3qPoeVnPB0acqG9WInksa0tna5s2b44aXvasDk4DTcW944qSN28Wesa8Rt93fIYMgSOexc2bvvFke7sohO98Z5mbPlrB1Rwo/Mor5TOux3LisDLKSuX+fmkPHoPABRgcH3ywuRVcJHt61ZMtGsLnamToXNQXV9q0MKbRTkD/Ew4WTTva7kN0X7OIZEUPFs3oUs00up62YzRXaz6W0c2T8S1YfKTNAWddjwXvnumxWBZgJ/HJk+36lYi01ihuW6OgjzU7m2TGYN534oQt54vECYsWP8HWZ15Fg42NQvu86pA6UACOfVSfBz3e32/SXdDSs2eb8r/eJlRk7ghD8Npv//b8+G/+zfz4m79ZxgJNfvzx+RHBBKh+s7PT1Hd11QSRuX6Az0x7tM6MzyzTe1XsdT+6X8/QaDmjxjDGW4Fhy8Tjj4v8wi/MP1++vFgZWs/HqoCdkZs9uTKiqZqueBW5a62qHo3X18O/6bWUsRFl9BGGpTNH9JbnZt3fmnWrbWUii7thgH54Sc4nTxZ9Hlux63cdY3uyZ42TzeKdwMZGkVWj8wGWa63K9pFcyHJ2JA95ie/Tqb8zSqTrRDKFNw9LNomcJZxM+NprzSBm8AEcr11rBn3pADOROW0BTwDv48CzS5fKusX4qPj29a/Pjy+/XK5DbxOq+7l8uRkgHAUTMA/WCYJehbSNjaa83pdH1AT5DY0oSFrrLm2yeGT/B9rkVI+WRjS/xr5tnZfhZxmblzdWxjafuYbaMXistnZo690PTUMse1EbMnS/xm+jz81UKeM2Wm7XAbwii/b6Lojm85735Pvx7KcRr8g+G89eqAPFwPN1RTVuC7sVAhUQQKf1GcgFXP1Sy5tMZwEruMKzY1pJgFY18sz9jGxMNXaJjGwydjKiSH3SXpttJks3I/rflpBh9RPp75m5RjQ1Qy+tzxraFp6l022/1ehAtTZs7/yuOlhm7Ayds87jICvLDpSx33rXw3xBpEmfdPI16xY85mwm8rGP2WP2tcdadNzTEfSaZBtKRl7RPIID5/iZ6PcaY0KH0vZ8nqvlx828R21ru6usXntejSwzsv1fROwAxT50Jjo/oh2a/mbovxV0qY9RDEtG3rauJaOz9Nl1MJpjJpklU8XZkl/4/4iHZ/q27llGX8zA6s/bBSnzfEGjLRkfbTXd52QU65mwDxXA2jh9utjM2C+okzG4uAXTaF0936twvLXlB81a8T8A+rtzp2kLhGyvZXycr3dL534xV/SH/0H/H3642BS5CKEuThhVQRfJyzpdeED23agB5rFK+88xRsXKg5Ej/O7v/q5cuXJFft/v+33f/O7UqVPye37P75Ff/uVflh/90R+VL37xi3Lv3r2FNo899ph86EMfkl/+5V92g5Hv3r0rd1XW1Y23Fv3h1umcs7sWfY2yHjLGl+i8GoI0xrWPoUwu61lkkCXcXYRw67pYaWSj4pDgYHGNjOAFx4mneGeFvxpDbuQUyATKAOxEtQLtma7cu9cMCORzdPVkr6KNzkji64qMF9a1syKj74UXaKOVDs9wGs2N56Wf4cjVvA43Nn2BzsLJk+tfcaxG+RcZLwCklp+skm5byBjcV4HZLEffPSXU+33ZAP33kDGCaESGd+/8NqMQ2nCAAldq2NuzHdfe3D36v7FRvoPji6s6X79eMml5Kzdd9Zj5GvrTNN8LlJ/NmlWpa56BNgTrbYH0XLUjroYGD4np1DZmZIxuEV/tOhd9bPvNMtbx95bxXR+98aLvLcxm7YaR6F3TcoRXrVUbIz3DU7R1rJ6HVzXFChCIngsMRNb5jIxcmpGFrTHY6GeNyVXOMvS/C921YBk62Qlk9demR+i2MAK+613NcyMjNr7zqvBvbDSDDf+tf2t+1JWa4YTCfebKBKdONYOHWUfY2yuVIbgKKOZz7VqzDe4vEmueeKLs1MKJj5pXRI4DL7nSopMcwIHAjIOD5m4wVoVO5htj4dVXm9uFrxrTaXOd3K+oqeKs+QevO0A7oyK+yHxQOyc8WPykRreNkHGKRYkGkS0Gv6MvtLUCBrrIX7W70bXB4sERH/CCByw+YgWaMX1lGf/FFwu9RVsknOB4+nShb+DF4Af4f3NT5NFH5585CJkr7YsUuoS547k/+WSzSj47c377t5tJkbybys5Oc0cVtNUBHF6AWM1z4vYafR1ZQyGqflajn9Q4rcdCTRBBm/Mc5/VBZi1EPovovNoxvbZD6UX8uaszN2NTZ7t/1C/rA1qHA6xElS6w+vFkz+j+eHbvCJaN39IpPR68v7+4k5ZIs0jK3p5d2V+PqYMIwBtY7p1Om8EH0TV7iSKWzQpz17qyV/U4E7AT8d6ad1djFcFoeq7L8ifW8Lga/bdtLH4OEf/x6FNkP7XmE8lh1hyz88ig61jR/czw7MgGmLkfbWh7Xmz/t+wtLPdzP9lxvXkAun/WMax3LdqdicfgcyJYVR5Zpt3bW0w612309/gNNBT0FraVS5ea8jV+s5L3rKR6fX0a2e+GROadWcZ4q6hmHdlYamThZTyTdfOBdoF+tm32bcu/ot957d+yxqhFJONH/NCDxW/HeIZM/znORAcr4zPmVRsj1HbvreRG61xORvd07giRbNKFb3u/ZXQEDxbPgq7ASe6vvFL4Doo1wkajd7ptuzd6/Waun239uhAYzrd22B26UMmS35WMKDsG1mEOq8ZaByNfeevFfPjhhxe+f/jhh+Xrb1WFuHLlipw8eVIeRIUj1QbnW/hLf+kvyWc+85nG91Xb4YosdzV3NZYNpUAPdV0ZwtwFGaI6pIPIOs8asxaRU6utbRu8rCWRuEoN/+5tz2ZVRmOB5+BgcTtm3UYbz8B02RCWYXCRkbdGubaUYuucPut2aHpROxdv/BoD0xDzyGBk5Wuy7wTxrBKRM6v2vFWii3KxzogMgn2MKH3pS+353jo5OKgLUEY/zHP29pqVOnXld0BXjNFHa/yhDLhdobe2F/lmcNrhWzVNq+XIo4BVBGlltlEaEIdTtYWrVQGSYTk2rWBt7x3Tsk2NIm/JOJ7RLkOndIChF9ir74tVFa8N1rx0RrueV2buPCf9f+0WgpnKExl+yoGrfNRzzVTI5+cUVfPXQMATGxajavf8vQVrd5o27O83K9lzMMLmpm9YrkH0nPD97dulajJv6Xz6dAna9ba8tNZUjTNXo+1dnc3aq5lb1T+4/wiRfFnjrO7rsLLGWEUwwjGOBiw516pmEjm6ROp3vYrmw7DsLp6zXe+uwcFNnIA2nZYENAS06kBU0DUOrNU7OcGZYfGGdUdfhy/LDZngc0DTZN5WGf1ubdXxM16ber3gfF0t/6hg1XpaX/S1iXEbz3nZpkewXMvH2awZ/Mjv/mRSEqG4WpPux7KJ8nWMBT0m08vseR6se+/B4hEZWLwnM/+2gIc2fw/vRsk2/cw1LJMf1Mh6Vts+8m5f9A3Ws9DFR1Y7Vg2Pi1CzfvvSjlXYsJepc2Rs4MwHLHtHja2or4y0agw9j0xQTnSeh4hXWbzL47PRmJGurd+1Nt9uhGXQ/cx7nEki7IOoyNwxYqyar46J6Foz/i8c9S5Wnu1BH7lvvSM0YiNwPgck6kDADPo+T+9Z1fg9s3ytCz1YZixCZg1Ev3v+FauPTByQZTuAPZv1AOue847b+N6K+Yl2RImCkPk7KxmFUWNTz9iwddsaW7U1x6F5Fd5n2HY+9KFu/USoeW+62MVEustWK8JxMPL64Eh4eiZEAA8PDxvfMdra/Of/+X8uP/ETP/HN/2/cuCFPPvnk/B8Y9bLIGI6ibd/191E/UfsMA4t+00Q+sz2bJzxkhIhoLGteGcaT2TrK6jvbJqJa1jXXKJqW8dlymHljWbC2hNH9bGy0K6z6er1g5AhauPGyrPQcwJA956F1/y2nUFvlxv39ZuU4zM8KQOKtcbVywc4oOBHfeGOxSoJIMws2CmoA8z9/fu44FCmOBAgIUbazVSHBW0uWEmNlDntbZOggjTYjmbXtLkP/PnYw2sZmTO8Aq80yjNR9zq8xbvUde6h+Vo0MjY/Qx2Gl/+8jnWZ4X5aeZwLVvAqboKnZDNuM8z5y2nuI+rOq3nv8wzIcEQ2dWPfeq96q5R/QOcieVtChtYUbX19GAceReYSusF+zjjMySSRrRbLSyFraRA7vH6P1utNkPfa63vNa4wrLRhb63PPsM4XMOCa8uYFutSFjTM84BNt0r3PnSpWBGkSORIuHt9FCyzDN/evrqeGP1v8ZnbIGfWScLFaxTbNGX3nwGP3Ql/9H9MB6NyJHUcam48iFKaDtuXOlUm5kA7EcmgAqogMZndZCn/XfV/7oen7GbuSdH+n1kS0Q/eiAcrZt8tapWm/ybCrWzg7WWvUS97Wdqi3JzNqFImPX1WizL2SchutEW4eeS4aG1FSH19D0IwteS9HatmSSMTCUw7etXwsWj+hCEzNre6jrms3aA8pq7Di1yNgdLfsT78pXM1aNnFDLCyMaZlXWFFncUprXjeYD0I04uEjT/IzOk0ENP4z8aNH6H5qGr5IXDGV7snhn5lnU6sNt9yrrI65BDQ3N+JOHkgUy11cTOJehaV2DDr3+uszJ+82bVxvabLVRW0DTeo+WTqftQXq6vXd/+0IXY/AKCli7p6DYFq5VV23VlSNFbFunJ+NYO9bVxALU6q8eMnqR1c4as4ZmrML+YyUBerB4XhfdXwPXjvWys1MKJGTOqxkjQ5O9/y3opMaaOI5lFX7Lom2OGbvyUPcw6jPDO0WKj7DLuBgjCiaO5lSjR6DtwUFud8roOw+1fLXNLh7Rwkz7LC3lOUS8sw1RMm1WH+qrY7X10ZdudoBl7lgF1mEOq8ZaByM/8pbD8MqVK/Ioqn+IyCuvvPLNasmPPPKI7O3tydWrVxeqI7/yyivyXd/1XW7fp06dklOWk/T55+fb3oksrlRvWw8tvLKxWQcGthE4bXSIjMSesVgH0VjBk/jeC4zRxnC9bZ9I0xg+lAMha+DugzHf8rENJ9G1ecEQWlCwsqVEbAboCRh9hUX9zrAxMlpvmBc7cSxDvMXEI2bJgoH1zuMz3g0ECuuqNZ7zCffuxIn2pISoapOmBfxbjTBtPW98Z22dXWM4suZcYzQbeZueye4dkZ23BPlax8EyaZgVyOiNnTHQe8+s6zWs0mg8JNqMeEP3b31nOSBqjAZeuzaws+TgoEkPINNEFXayCrN3Xo2SxYY9i3ZEtJAzQGucj9YcrXN4LAswWPJWzJGzQfMl8CMk2WQMAggSt/gIEF1HlPDCiNY9P29tBFwFXdFVgtvkfy1TR/ce700kSzNft4LEM0mMNaihE30NaV37qe2/bYwaw1HUZw3d7TqfaMyMDNsXNVWTGdbW0CwDa7TJOLWIjOERf/WCCPT3nISIbTtxvHatOKhAZ9k5tbPTDDDmsUQKHfC2z7Pkf4s2t+04o6/dkrs9Z5g+tlWM0HMdmpaNiYyMYq0pb21Z77UVIGbpojifn3n0fLpuB+tdY+acGttQpj8rsTWjk1p9d1l7WVk8i4z+HM0jWpNDXR//ZgUaYCxN47uMb+mxbY6d6F2L5qLfK9B0LQfqfiz+gT7xXt682axQi35wX06dauodt27Nj7qyERKLUM0WR22n5X7YFqzvC/+GuetqzoB+ppg3nrnFq7z3F7zr1KnC/zwapPWIVSaj1Ky7CH1payTPWXy1Zox157lt1555DkPIwtlx2tpFzynaRp771W0seeH55xfb453Fe6jfdciTlk2nZn3gfLZ7Z87RY9XoUF10ubZn1GZ7s3QEq6BMtFMSAJsBeAXaQHe4d6+dj21slGcInybaon9ty/MSX/QulxmbfmbnyshGFMlq60CXsrKs9f7x0dNt9RiR7tTFHlyzs2hXdOGLVoDlGDahzFy9edQGGnfRnSJE/VprgWkw21uisaPgYe639rll9TGNjBxWy+M925AVR2HFU/D7DLqri4tgd5o7d8p3uj+R5rupfcNAm05r2ejRL/o5c6bEc3Bch+YDkU0mQ7eHqhiK9rUJeUOA+WS2vUfjIztFhIiuDEU3lxX4tw78uwZt63VIf0LNvWGZOMtX2uz2mX66JiPWnNfXRgToddwm92s9jd/dqFie9T57NpXd3WY/lp2jbWfR2cyX5zJ+21r9OOqHr9Uq6Mg8Bcdorvz//v5oAckZlX2sebzdsdbByO9617vkkUcekX/yT/6JfNu3fZuIiOzt7cn//r//7/Jf/pf/pYiIfPzjH5fNzU35J//kn8gP//APi4jISy+9JL/xG78hf/kv/+X6QZ98crEk+RhKUg36zKfvHGqNmzVGn2W+jTXGrZo+ou8iZSdihNmxrK2PtTD8la/MjzBSIVhWKyK8hQOYS/QsuPKuJXiwYmUxIFa6tKMUyDDUSDn37mtWsGSFSG+DmjE61yBjOO1iBLHOz9zPGgdKzTufabNKo6RVGTuLIeiMd34fZ+6YqFGaxsaYEl8X47GlLEUOp4iPfOMbi31z0O3mZtPxAOB7yyCWUTSjqjwefcpk3FpGL6t/nBdVJ+2LzFpax3fgGDH0c33ttfKZDQKQCS5c8A24XflYrbzTdr7FnzOG/+g3z3EcyfRdDGvZOWaC/KLxomRTb1xLHrMMK7q/O3eKo9lLGrXuM987S4/Qjt+vfrV8Fim0EE6Tra1FXiBi022m91aV+6Fk4REz4pcGi4d7iNZmV+N3ps1QfCmja0TnZa5x1Va6tnsVGXL1dd4Pa9tCF8fKUBhjO23L0cjrd9XPdllyZm2/zCuioIauwRptdHI2a09ysGiz/l9XtxQpW+TqI37D8cqV+fG558r/aI/5vOMd8+N73jM/nj5dxsKOWr/7u4vHu3cL70ZQ8oUL8yOqZT399PxPpCTD4xzIsK+9VgLeAC46cfduac+7A548WWReruKJeZw/X34bak2OHYx844adeM+wAqr6IFrrR0mPrHHMDxnI5I1RY6O3xsIxG6SEvryA3M1Nu/q5Hssa35o7B7lqBzBXxkcglA6M4jF4u+Ys+P5FPoEIQ6/3rjJ0TUXatmN2fpl1m+mnbxBYZpw+foCMvHwUYN3zVcuBq8RQNp0hfCW1v1m2dEZUaME6N6ry2PYeZX3NVt99YJ1fywtqoftfl/enjXbt7jblfyQRgu/v7Ig89tj8s+fXFmnSDk5YzxT46OoftObDfb8dsUy/0bLQ1U62TH9tRgYAuspqnh+iKzw+kLW5Zmg7F7Kwkp75uqzkYtYfdMIX9Hj0w3FA1s7o1ty7xDR0tT0vS4+29M4aXhPFGPWxa1lBzdxPrbxQ87ws/9UyE9e6YMn2/4ybZgyswxxWjZVLfzdv3pQvf/nL3/z/d3/3d+XXf/3X5cKFC/KOd7xDfvzHf1w++9nPyjPPPCPPPPOMfPazn5Xt7W35w3/4D4uIyAMPPCCf/vSn5Sd/8ifl4sWLcuHCBfmpn/op+fCHPyzf933f12VCiwbVGmbdx6AQjWExh67GjzGrEbURT31/M/OxrjlDfD1ibo3V5X6sgpK0zdMzhurgL1Y0IajUjs/XbwWWtQWxRQEYNdCCnFXV1xpPJBcIkglsiZh+DaJgGstRWnPPPJpmCcE1gQPWdxlD/7oIPiLzueiKQTU0PSNo91XQIvrvPVdLsI0y8TI02bsvNcqGZQSMrssK2vLmqrMH2645I/DrOVnn81g14HtX2481LyijnHASvat6yxyReXIKPnNVRq6QrOdvoYvRoIuzYjZrrhPLMGxlqfIY3pyz19DVMdXWn3XuUI6d6Fpr5pihk20OV5HRK+MfTtVuBU880Wxg3R9+fy0aFiWaibTvntJHhtDHiDdkx86uCY+mR/TbenfZ6a7lO8685mp7Vsa89Sx4jlYWunevIh6ccVRa70zmvEi/wn3x+KMlj0fVFDw+YlXTYn6ix/PazGYlICtDb/n+6OfeZijMOBYt6Ll7a5rb8mfvN+898M7jObddc6SvZd79jMEzM1dLpuZzascaGggijGwIGaNt33tWKwOyk4PppqUbW/Syi16W1V/aEMluXas4e31G1VCsqoa4VwjC5KpNFu2xkmTabAeWnQ7IrKms3TJjt2mDpj1e1TSLR2SAttbObtzGWr+AldDp8Rj9PfrE8+aAAT3+Bz4wPyI44caNxerGuq1+5zA33lnrox8t82FZhKvObG42g6L52s+fL/Isgpn5uix5TF/nshxVnpy4Clg7q1nQ71cXm85QumGNfpblR5G9pW1u1hwygVkRop0YMu8x/5ax9fI5Ik1eyYH3lh0p6q+G39foWSIl6RAArUBV9YjWW7zD4xURP9M6Rmadt71HbfSpBjXzsY5t605X2Adt9uQykcXK8SKLNF8HpmlY+kgkW3gybE3VMv1dRg7L2BeOYWOZfDBDNzO6sPe/XpMe/dd2MdZdNI3mokuAVckV0Gvbk1ssmdyrPmglRWXWdg2Nj+597Xtk7UolYttkvDEPD3O6OttyLF8D29XYnzCZ1NH/NnuH/hzJJJEuiCPT7b299vuyt1fsCLgfuFYtR/BuI0gY1HYt1jP5XbFkFIvWe5XSI/qvr2toeSXCOvCJSIbMnJeRlzP01qJPXflpG93PyDbWmBaNbfN5RDJJ2xz5N68tj2f9b/EqvtZIttfvavR8cQ7v4p3RfyP6b7VnO4B3nzQs+40XsGz1xUURLR0h2lGXd2ri/i3azPOK6FS0NqOq99xPZo1mdEHr/MjuH8nvbFfIIMMX9fW00R6tj2f48lHhA8cYBSsPRv7VX/1V+Z7v+Z5v/v8TP/ETIiLy7//7/7584QtfkD/1p/6U3LlzR37sx35Mrl69Kt/5nd8pv/iLvyhnUSVCRH76p39aNjY25Id/+Iflzp078r3f+73yhS98QU50yerb2YkJbg2GepEySopFINsYo/ddzXy8sboiowBHxshMP13Hb5uX9ZxqhKPMHCKmq8/zgpEhDERBZH0Mh9w3I7NOap5pl/70vWMl2XqGlsLqVdLThkduw8/k8LCcByUU2zxDkYXCKWJvqyMyN2DCiInrsir88bVyFq51zVoYwRgPPLB4nnZs89wswaVGSHv1Vf+3JeBQJmXuq4ZFO2qMxZm+rXOHoOVd6e8yhM8hjLp9ZYLMNjJd52PNDYYvq5LlumKIyghaaerSn2WMYVqogyK8iqx7e4UXIMgORwSK37jRNHRyv3fvlh0LuC2+P3HCDlDAbyLtxgv0q/vU/U2nzWCcM2fmRwRSaNrBzjXNa/Ad5sgBhZNJqez00kv2XJeIw43mOzORDoE6xzgaWKY+MzSyRlaruj2Dd0QBfbF0B97JBO/s4WHTWRhVWo6AQIlVIuLzmp5rGi5iB8kxTWb5fXu7fKe399RH3S/fZ7S5caPMA7yBA+nOnWvqOLhW0O8zZ5r6A46a/rNRUvM8izfpaz950g+Ete49V+gcA3pnrDZkZbnIyVeDaDy+r31RI6daOnbGuewh6+DJOFlwfpSc7CGSs62x+btM8r/l5Musi0h/bjtfO4iiNm3QgcYcADcUauT5jCMvciZZfeB/zZ9A52CvYT5w4kSRj1lHAG28cUPk5Zfnn7GTDfgKKq0dHhbei/5g+8Z9v3WrGfiMuT766Pz44IOFL7dVeBVp2ois9xn2H1RGvnSptMfuD7gO2LP29poJrjgHQdIXL5Zr5K1Al43bt4ueFlVIrnEwWkE0UUAsw7I3Wm2YjkTOQ6aJmhd774vm9yyvRNfjzSNyLmt498y6ryy7njjRXMtRRWJv291I59djgQaybGPBk3v087YCV3Eun6/n98ILi+1ZL9/c9Ncb6I3Vhu/zwUFzTbLt6cSJIrPifebjwcGcRuHarKNed/zcrYCqDL1n2mfxcMDqD+eh4vTVq/PjK680kyv5ndvZaQakoJ+ILmB8Xe3OW296HaFvHC0fA1e9x/z0Tjo48j3UAdgcvKnlOS9x2PKjdd0qvA9wDVb1+zHtAkP7VPX7k0kwyfiarSActOlyz46SLWZoLOM6+/bZZsMZsnLyUDpyBpa/F/97sprFI0Dnrl+fH19/fX585ZWmL5f5pJYp0J9FE8EjYQvRuxuIzGkk25rASzX95mfF9vc7d0rf2u6DI98r3lntzJnCP3iXBbSZzZryv+UP9hJ+x4CljwNHnRYd9fkfNdTc71r7T9SW3/VlVrjn8buO1Wa3XGZchqb1ng4IenvzZrHTIOj7xRfnR7YHiTQTmvDbtWvNCvusT08mTX8K7EBIXH/kkULneSzYX7QdBbSZaezW1qLuJ7JI46HD8U5hsPXoObLOrcfybKL6HPQJ+9iSEKl7Y2Id5rBqrDwY+bu/+7vlMMjSnkwm8uyzz8qzzz7rttna2pLPfe5z8rnPfa7/hPb3C2GIAugsDB3gFY3fxVE0VICYFkwz2xszsbHmE2XreQY9q8KJNXfPoGYZNaN+GDXXESFyYGUccZYwnxEI2taFtc0mjjXVj/Uz4LF09lRbRlL0LPX/Nf1EzkzPGKPPZwMdnECZinx6bbddl9WPDkZghRdHLeRwoAMM1nCEXbtWngeEB2wt+tRTxdDO/ei1zgHK1hrj+8BBFvv7pdJQTbXuATCZKWfG2BJCxuEVrVde0xHP8X6LDP7R3LzjEPDm0ddwafXrzd8yUnW5L7VzXAUyDlKvvYXs9S7rfYtoaZegD6v6mmXAhCz0yCP2mJFsg3Nv3lxUXkWKwvvKK/Pj8883gxm0oVJknkACAyWSSaDAgmdduFACAjBHK7mGZRvNA9Enggk4cUU7tTK0YgVbpk12Ew7ByEhrra22Soc6kC+qBh05WjPVvr3xIwe/tbYj2Yj79p6zlqM83SByoE2n7U7/2azOOZ0JFol0sbZrztCOiF5lwe+/lrN5XmhjyVp8HSzjWzoCoCvkeOtFnwtak9FXIz7Lz9B6r6J1E61XkTmNhCFQz79t7lYVKNB0GAtxPzOVPvW1gIbDmQSn2HPPzY9vvLEYdCBSaDLOvXx5Hgimv9Pb+gFsONVH7z7oe+rde+ueQf8YE5btgpF5H4eS8yyZnNdAhk5pREEJGT2k5jfv+0yCXnSfNV2JdJYa3SRqk5ljG53L9DOEnY6rk1nVynhuNYE/oFOTSenHs+Nm+Nl02gxgysgm+nwROwgm4tPWWJlnCEDeZae5lm28eViyjeX4Ao9gGw/6ee01ka9+df75N35jfvyX/3J+BP3P4B3vEPnYx8pnfV1nzog8/PD8MyosQ6+BznDuXNNhaMkbNfKPduqNgY2N8kytZxihi30ikpczY0R9d0FEr6xgddgJa2hrNBaP2damjcfovrz/M7DogkUnujyDzJrw/vfAldFqxh/qHOt9xrqBQ123xbb2fcbsCosmR+N7wfigl489VuiittOLFHr+9a8XOR3BbAgeQNvr10tBDrRFG80vwbsht8Pugu/v3SvzR5unnpof3//+5pwZd+8WnxJkCU4039kpehEH/mvgXkeJl/jszWeZqLU51dCTGn2iln9kxuySsJiRYaP/I5tIm63Jmketj6GtXfb5Lcs+baFtnbT5LHiulmzuPTPdtq2o1XRaPoO2Z/QIq4qmFXfB/9fw+8yasvrxkiMs2z7mj+AtyEP7+016y7rYvXtNGsgFS+7dK/Q+Cm4GzWK7O2z+Fy4UWw4HoYHX3L7dvC5t4+fdFayK/dqejfNEFoPUuGAKjhadGvOdA/rKGV30gOx8utwP6/zIfsTgcyL7GP7XRQeGup8R7YtQyy94DEYm5slLuLL6tuy73Cazi8VRR9va1rZ9z19jVTa2/JS1PjGNc+eKXIo4FQ40fuGF4p9l+w1o/Z07zV2zuGr2uXPNueJ88Ijf+i2R/+P/WBwf52N+73lPsds888z8+OST8yPo+dZWrtAIy+uZxDYdaOzt9KKhdYQ/9sf8dj1hqfWrwDrMYdVYeTDy2mF/f3GLW8uQLZITEDQTbDPARv1447Z9nxEwujJ4hlW9pI3pZg1aUXBG2z0bymmo5xApO6x4R0KEJyRZY1ljR0oWFBjeFkFv39O2LadVPblr9co2hfDwMH7OmENmDWWMD5FDkAWcqIKM52ja3fUzYnVlBFYMrWoXXFmTgwpOnixGQM6Q4mvTR6saG8bloOLIyZdBRF+i/sZ2Rk2n7esw00fbdxnjY8bx1NVxlUGX5zv0mFkjrTd+pLRZ9zDjTMtca0TT2xDJBNlnguCmrucDrCx1gWfQZHhzzTgmI9S+j22GYG2M9CoXRbxK9wMDLPpBRiiO2jABox2CAnD88IebVQ6w3uDIevnlplLMY+s1CmUbiqq+ZswHvAG8Ynu78DMc2elx+3YZH+D3UPMuVBoaE2MGQOtr13qHPnrfeW0ADh73zmNkZLwap1qmj8w7PrSROKL7kRPV6ycjn2acbH0xm/nJiJkkRcwj41TSbby+o+r8+rmzAyJat9GzZJ0rep+7GCVnsyYt7JLApY1/nmFbO4i87Z51EDD6A03+lm+ZH0+fLn1yUISu+MzV3HA8daoEKkPXAI9AUNrWVk7WqtEjxpb/9/bKvbP0+YweHMmnfWVq7ezRyMiwup+uu0bg2KarWO9jhIhO1thwulxXxpFttecxrTlbFdNr52Qhy2e5H4tGc5sM3c/QTdiXdFuusA8cHjb7tJIVaniwZ++IsLHh2380jfUSvUCbtZyLBBFONNnba+6IxQEZ58+XcdkOhOONG6XNu941P6JasbYZcSVjBP/poGIdWCxSeKe1Jng3Ll3pDbCcUZkkuVXBsxd49NuyF1rneH4EHttqk7VPeLKI1cb7Xvs8oqrubc8wO882Ota2RmqCKiK5I7MWPXtERk/TfXSxR1lj1oxhtQU9YvtMtJUyX1+UjAhoWh9du1etMjo3et5d1wLL5JbTXcvwIkUe08nclr2H+/H0Bs172DakE4FE7OrfbH/Rdm2rGjSgK17zvPh88BZAJ0XqAAeRRXph3SsGxh1b/rfmECF6n62+utrHM3TF+qz/78pva2Ri73/r/MguGxVkqkXGr5KRt71+IvtPdO+855KxgU2ncd/s283YpzO6AWM2aya6W8/X60c/50yAfJtum9VNo34gw0d027tW65nCBo/qmej/zJnF6pYiIh/60PwI+VsHzuGIeeiqlxwoxkHNe3vNgllIOMTY0+liEqRI0V0ODpr3kYOJT55crNYvYu+Kwe84/6/XxLrT/wwy8lx2HjWyERDFkwBRUYYaZOT+trl4/XThY5F+Zj2XzBg19uXsHDUyvgctg1pzbUsiiRDFBXFxEz2nmmf//7f37vGWXUWBf91zu2/ffqbT6XS6m7wDGSSJUQiYoEJAZQggoIMDyk9QA370AygDjqCjw2N0EBiRGSWKIyCKDs5vBhwU0AkGGBlA8gvhDUkwj86jO49O+nH79u17+579++NQvevUqapda+3HOae7vp/P/ex7ztl7rbVftWpV1aqVqsvzfTRdZM0avR/BfaWkGXSfKnvR4mJpr8GEfnx7772jkwbRtoOrWFG5zccTqL+feWbp50V7DQYVf+/3DrZXXz06ZuGJq6ht5rbbBts77xxsaYZ6aTUagIEc5hNUsK04sfSMM3T7kbTSnedd47FODVMUzYn5uu041YlgZA6d0ZbqpLCo2t/bMVuDnZS2eToHz2+WcbPuAJzXnWNE9Owr3WdvmZwUhSvn+kiDUYmq5UFzlWCrTo9E1RQXmmFHO8YyrlvvgdT58UEBVUA0Yz49vmqpS2pElBQmgIFSgg4drgTQJWXxfzpopFspe5r0OUUJQKyBUM674n138P/c4PdcDhywl+dMUaolx0GuLNSeV+udkKiqX3pOtLZI5Untsq5ZzuDa01ZrpqRlnKr7jtAyvft7DJX8/mv1Itpg1NNWyajpGdzWMV54jIgeWeTRo6r0Om585JltDh4sB5TaLFG6ZCUOQnFwSpe1QfmPGQ14EPDWraUMxEGj5MDC+8Mzn5x33mD7mMeUwQh8oGg969Z19ch26blNYRJGirmkyGF67Rrq8woYwxKnvcwluhKOm4HMkXtTTsM6wTR1HWveOqwAYE7KWLAuWBd3YFGs59/jHOP7po5ftT6XjhHwN25oxD6CZrTE7JTf+MZgi8bAo0dLnR6zk1100WCLBsgtW0pjH+5rZaTRght6veExBYBstO3i+UygcxmmLS+ccl2sfpDj1eeqxiHecrTychw9APrz5hmHU+rqGYg05s6VA/xYTR5o+wP4sn9rZUjHSddXOk6biITHrK6OBjnhvigX1q2zV7vS2qq1veo3nhmZ12Ut/S3p7zn2UBogqWV+kSYjSmMGHCNgRkvMVopZMKWlM3kwF0D5DPHfqANLmwQoZavDcQTWSfsTfpyk69TpIyQ5NUl9Dr0GVgIKxNLb27AhWP0IlzlWmZbdXmuTZWtqw07SVHn8fZaO99hPq2xvnj7Yslml6B20nBwkLyyWV2Xz0dCeLXrdtcko0nGWXSwFqVztXvb7um2f6vqo56NMRx0fA84OHCjlPE6oxt9wm5r0AjMZ42QSHBds3lzajXbtGmwxcAE/79gxmtGSJxzp9XS5L+kWPAAOQA+GTkXTn7oAzyd1wmHOPhRLLnjGD4gnUx3/bOmVVhk5NpVcHVgq13qPq9qjfZbql+S29HykZAvMbYfWHnru2sQF3NeS7ZLOXzX+pPD+wyu/uf5v1Zly3yV9XpPthw+Xdhuut99112B7xx3lCiQYfNYF+LxhRkvMcHnhhWVgMcp9lPX4mWaqRdksBY5ZwroQUAAA4p1JREFU96dKd2xDj9fGhG3CV2ij/0s6E5cHHh3UYzvgx2rHaXVKPkSPLUUb/6asomXh6Ws8thrvCo0eNJ3VY0eynhPpHuS0R+p7rLJ5EGeKjd9j36J2fH6uXLendVlla0mK6GeP3Off0cQeuOW+WZx0cfRoKf/vv3+wRbmPsn7fvnIf7GexPLT5eHT7888vJ5+gLD/zzMGWZrjH7PYo0/E3mshQs9t4MmnXtbl68NwnWleq7lyjWR1VVdmOU50IRuZs2jSaoYz/r/1Wx7hkYT2pqeVNkuE3laaNmScr/f5oMIKldGqkBDQ0wRiWZE/CY+BIUY5ooCR3aqFyJM0MkpThKqWeOulwi8YCaWYrImW14IMvGuSgZZWgZXgcXlVZMlqi2Hq6vURQioPI+3vQPJlGcAx+yQ546wrrne/30zJg0uPo96kDZ609RTFq4PQYe6va2wbWdZWWEbUMwlXGZo/clnTHFH1Qag9ucSBu1WvdE4/BxvMseQzJHTGzcBiAvvvaeVgrD3CkZ5vfS6oLSH0mr1dbTon8PyPpJJ53KMVokeKUk+5zlSGXPrcphlzr/eF1UKOmZrSTJnzxfVKx5HfVPlXvU5UzyhoPYJDU6mp1hh2Asq+hM+x5HXgcd1DR8rk84vddykjpOZ8UGWI9b9ZzywPFaDmPf/xg+5znjJbH22Y5OHngs8cobzlI+LWbm/Nd3xT9gF9LqW1SG1GGLZOJO11BAzm1PtfqV/l1oc8tp65taNykBIuE/Sb/fFPeNYBykhv/DWX02rWj9hZJ1nM5nZotH+vUsqch1C6g2VFz7aAeR5ykh2l1SH0ED8zy9jlVcpvaVBBLD/LokPw4amOSbFQAg/PD4/G54ZNaJD01577QY6QxSpvQNrWZnVEKcOf9B72GKcklrHFnipPR0rs1HaDp/ix1/DeN/UlKv+j1SWlIkwB5XdYEc09brWAEKbMa0kTgqTVGtZ5N6f2p2krfSQFv/P2RbAhaHfTdtwINEK0/kiZW8IAMOjkG7yHth7jdHuvFoGb6HV+9hWLp0rjF9o5jZSyarS5nzN+mDLIm/2ntoFuPzEiZbJ0yxq7qw7x1aZ81cvwzHpuVJENwvy6C6C29VpPbNAhZO57aiqpsb73e6AQTLv9nZ339mTZx03pGpc917JdU9njeMa0cyd7Bf5OeKUnv9gS2Vb0L9D6hj5cG5wHYSa28qxtZ/getTM/Ely7Bfo5mUEUsu5i1MgqiZYOWjpP6Q76PRMp4WdJf+DvvsbVa7bJ8H1X2bfpMeeS0Ne71PMuWfSInCYl0Lz39mEfv5lDZcYzZTVEmo12IymjeN1jPjzSJheuKlu/ZetfpinR4HvxzjuzALQbvVrUnxeYryWt+rCbbjx8fvZ547WhSKZ5gyjNBnOv4y8t5/Rgtv0queO3c/Dtpy69ZcNITwciMoUw8k+IomkYDWxvEdfBhGVH4ficbqQ5PSzHUfqNZj7mxjwboVhlf1qwZnbWFy+pgVoXZ2VJJ40seSEsfaIpcr1cunUODj+mWtgO3qNTOzZUzaKUl4HBrKRjYHs2RR8F9cHZyh5zoA3KzPAZTz1iyiqbQm4UZS67lTCTxBDA3eRynKQOuJ/hA21faX5JXmtNfGrCiLMUlc1DGP/RQ2X/wJdSojMU68DjM1IDl9XploPSjHz3Y4mfqKOJLsOEWv19ZkZ1Q2A6eWZNnwaGZFVKcNdZ97zozwvx82c91AV4Len3rlMP/D6qpc93rkOPolJDkHcp/j+zTluYFGA1ixnc+VebTIDiNLt87D1zu0yUwMTMCZkLAa0czXPJMZClQYxzKQJTX/L5t2DAsgwHkyXxVzjVpQoQ0nvE4+7jRGNsnGTUl55/HodIGvV695WIDnbhm7dJ0gExTer23zCr9P/f5SbGFeR2VXE7ywCwrQAzHA0tLpUznuj3NtIlBYpg1/4orBlvMhrZ2bbkPl9vYjuXl4az9tB00aI62jR6/bVu5PChm5kSojUmz7VD9gWfmRGg78Hi8Hl2xf//whHrNzicF0WhLnKZm8KJjAtxqgfq0PVVBNFJQhOTg1BIkeAINkNRAA3qcVJ63Pbwc2g5N76HXl18X6fy0wAep3dRmjMfwc7ScsR6nu3R+2qp8eNzsrE++a2MEej74HdfxcUvr0uSrJFMlGavpjvSZ8NggkJTrSpEy4dPt4uKoLMV9UUffsaPMTsmzseOxCwujx+OYDANLqL5qvbs8M6bmVwBIC0Cy3g1p5QD8n056pcdSv4hn8lPTTNo4lFLHViH1Z3jN6SoJ48hG7aHL8UObumYXTHpiJ4T2QU3b4Tx+gJTARCrb+HvEbeoLC6O+VL4vlcl83IB+VzpBBPvT004bbNG+tGbNaFAb9g30vZYmD9Kt5JuVdFiu00h2H35edPUVfh+szzxbahdwm1WTeOxZ45S/47KFTwqeAG7P8Z6gTmvs5KnbaitdWUlrxyTR748+9/xZ9Lw79Lz4mM47XuTZ8vnEuuPHS7mMW/TF4pb6CLj8p/IT5byWfHBurpSdaHdB3+727YMtHbNz2YXbNWvKennbJfmtjbMARlfUwr5m7drRmCJqf8JyrHE4PQeA0i7WEpaZoksmoQ3jZkJHHUEQBAZWEHGVolU1O00bhErOcuw4sbN94IHB9sAB2+mDW56dgBu4Z2ZGg4d5nXTgyxUePHbjxlJpwKwUfLA8O1sOdPkSPnQpCN4OqpxgHbj1GDX5taT7YAD1KcDEZ+NtkYkP/p1ArGs2jdezTptnpGy2HlIH+QDDAztrBqc2mKVOFzQi4qBr797hMqixVnPSLS2VA99vfGP4OE9QIspdGtTGjaPLy6N9FD0OYDgAD5cQ5Q4zT1AbNdjgpJyuoAN7CY+Bu8r5LR1n9YseUgxlXlIMc1Ywguc6WHqCpqs1ZVDzTlbwBJSMw8iX++zw9n9HVlA5fNLqJNqzLTmauBPpwIFSz6fGR4Ay4Ovhh8v/0eCIMhnL27+/nGyH+yKSvES5hO1A2bi6WurgqK+j/n7GGYPt1q2j2VK5/J6dLduGxlGaVQKNoVg2X0Hq+PHRQAnsT3Bpuc2bR4MfrLHBOJbpPFlJNeYHwakClWGSIx5gOECMBx+g/KcTDVE+86zSdEKO1tdQ2wzug0tD41YKWECOHBlssX85cKAsB20quCQo6uqnnVY6uHBcgv3AsWOlTMdlQ/F42i94JqzzoAge3LC0VP7PMzy1zWmnyQHlk0obwQPcrhdMH/hOTwuzs909b1LgvhXUzyeRHDo0Gnzw4IOjW5S9OGERs/zi8s+PPDKalVjKGD1poGzHySnYZ2zYUMp//A77Btof8EA1qc/AZ0FaGTLIY82a0SCVIDhZsSY0W4HGWrZL3B4+XOr7KMvRbo/yf2mplPvYD/CJhnfd1Z28X7NmtH9NCfTdtKnU9885Z7DFoLhduwbbbdtK/zGXM1S2Syvq0u2aNaUf4VS0/3iDUU8Fur4WKWVb9rQ6AeXeNqRMPm/aZ9I00sRURJrsrPmUUn1ckqxHuYg2eZT1VOdHOY/yH+0+NACZ22XQpkJ1fW73kSYQc3sJlkNXueExNGhvR/s/nfiNMpXb6ukzy3Vz6hvmPmLqq8CysH6ekGXTpuEAaTxHuqVjMCkTd4NEMPLkEMHIEm06bXIc9N76PYESnvalvBl1OzlPUIP32Kr9tX26Diao6kCbCCzhv/Pl2Xq90axnVhkpy3TyY2hZuJUGYWiY054Bz7tCA8Ws4z0BLjzQjA5KpcBirB9g0Iljx4zOe6yDZlhDhYcvcYQd/Lp1+rJ3dACtZWimM1X5oI/OVsXy8Hi+zDfNGEkVFLqlS4p6Ag088CCNDpjps+VKJhHvLD8N7b5QhT+HzGt24ppzcttS97nzMMFGg1pBZJLcH6nAWMrTU7awJPTM0tHh8jzPZs7zURUoyR1UUgYALaCWOq6kbAkApZFyYWF09iwGKqAzZ8uWsh08AA5l9MaNAOedN/gftyjjab+oLfND9+F9BDUQ8uOkJUHxGvGsydKsVw8dy/9Cy4bvmUzDoc+LZ5k1qxzJacqP1Sb1WBOtJDSdPEWmWsYl2paqiV9Vx1v183bkZCKQ9MGqe2HVVVWvdrzUZikrGMJ16QS9/cSe9Nrx41P7OW08Qn/XsrnROus+F9rv9Hy4XMOgMpTVR46UOjka21Buo7zbulWftIGf6SQS7BOwHCx39+7R7Gk8OPr48bJMPI72H3h+0mopdCsZNS35bz2jKcHwEtjGMej/tcnVSRDPRJUcpHKkCU7a85Fil/LI/3HTZXB2itySHOIIHV9b90PKcu+tn9qKuJzmSzJLfUSX9z31uuLWyiJbZT+iurR2DxYWAL71rcH/3/zmYIsOKyl7Pspr/IzlLy4OsiMDlPo/1kXtMRgosHPnYHvWWYMt9h1bt+rBv9TJhkHM2Mdge846qww+wO/4xJetW0dtQ9bSoh66lv9NPrsp756k20y6/PRi6XNNob3PUn1N24bq+oJy67Js2E14N6ns1/R/bz2eoKuq58QKKvOUKyUB4VsaQIBtxuNQNh4+XMrye+4ZbO+8c7DFQLP77y/3R7Bs1PkPHmw+GA3v0+WXD7Y4cQSgrIsHA9NgAjqxHKDsl3btKvsYPgkRt3Nz5XE8CIGOI6r0W/p5nPp/F/phXT9w09Q95wm2iXeCpw/39Ic55Vj+TQvuG07tO/hzyscB3vK0WIJcuY9IuoF1PJ/0zVc03Lev1MlR7t9++2CL8v+hh0o538VS8zjpg/uKaYZkvC8YKPaoRw22l1022FJbE44jqB2pSqeXnj9JtqfItWm0/1icSrKwCcZ1vXJ856lle3w5Wj+Qal/z9NmWLqr5D4qiuj+bmal3Hz12ZU9sj2T/sVblQ/iE8ZWVUq6i3EW7PZZ3zz3l5BO+Aq0E2k0e+9jB9sILB9tdu0qZzDPi08klPHgX20ftMVKAMYCdKIo+h1X+A8kmmXvfsU10/NICVapAV0xCG8ZNBCMzZqAoHbkAvoGzx0ldJbCrjEyeAYzW1hRDcO6ARlI+U4IR+DW06k5po9RpW4IyJ9hJKscaUHkCL/i9k8q12rrKAiq5E0lrt1ZXHfp93zLNKcsZNy296fPH32MpwyX/jc+mkrIVY/AYbmlQM89yQwMzeGCA5FTimRUkJzOWyWds0XuDA1VUWGjmFXyGMLMOD0amWQ/4gJVu6fJ90j4UKou7ggZ2a4MBKlOtpQdxmxNsZSl5vA4PHqNOXVkm0ZRRVWoHf1elfbV6Uwc5KfIypx/RysrZt45RuU0DgFa2lBnHGqS3iXbt6LOmZTSjWy2TMXUKoYOHZ99BQ+bevfpylsjCQpkRGbd82eaZmXKAivKbZy3u9Ub7aSqbNdlO5RSdREO3loyXJrBgn9JxZuSZ/Q8B7CP9I5ddKUHVHt2e7isZAHBrGa5wq+n5KfJachZKdUuGCO18LLR+UZLNuYYOy2lf1R4L6dyr+hHrXiLegH3+TFK4ns3bVddA6MG6htxhNTubluWvjv4vjaclQyWC8pIuVY36PgaI8Ykihw6V+2BQAm7pGIH/xnXz+fnSCMmNiPR9x+N4Nh78fu3a0oiJfY5k1ORBGdgfzM8PZzemv1EjNh8b8D5mw4ZyH9xKsgP3l7J+tsmBA7LRWJNTkvGaHyN95zHkpsggS97S59lyElaVI8lGzW5jOSY9jhFavmW/qpKlnn7EIlfO5BxHn4Uqm11V0Aef3GwdP80OUk/bpX6aP6fUnmOtQAIwkNsow3n2HPy8vFzKyfPPH2xR3tJM+Sj3ucOKjgNQpneRRU3rY9asKeU1z35GM+tz2w5C2y7ZhACG30vUD7oORti/fzTRAP2fT9ZfEcYKfJw0N1dtt5FsRJKcssbsmkyX7FAe30AdO7kln6S+xnqPLRlYNX6Q6hi3vNPukwdPv9Lv65NImj73JsvL8Ud4nk1J1+IBuVTGowxG2cwd/aur5fEYKIBl08zA9947+P/uuwdbzJ7ZZrZfvO9f+lJ7daTAA+Dm50f9GPgZ+w465knJ3tkUX/nKYLthw+izJPVZHh2Yw+Vvlf5fVaZHzuXaiaXjq95VSTZ76rT6CA/W+EOry0Jqh+WTzWmPx3eeKmerdEUaXFY1VqiiyofkbXuVP8I7ZrHG6rjVflteHtXBeaKnmZnhCYUAZfAulkOTP6EtBlcZaQO0P+Xw4Q831w58N3BsgGMGvBYbN5Z9Ll2Rhe67du3oyrxdgivPSHV79Esk5Z2l+0o6uSbb6TuhBVhaNhKrP5PahlS9Y1V2mxRS7WDS91Xleo5LsYt5/Or8N6tOS4ZJdfCMslYykRR7nHSMJ9GJd9ySSsqzQX13WmzP4uJoJmS6uhR+z1dE5MfQ1Qq531bK9o52ji98YXjbJXRyvJWMCuU0Zl1GWb1xY7k/2v0lfzL3oUo+VTwOx1AtIb1G42AS2jBuIhiZUcAMFJs2Zx17IhthG0unTTpVzpHUcrzGSc/+dZDq8pBjUJaOt9pllWMF/XYFPQccHGPHnJtprYsgu6pgNNqDaY4rGtTAA83oIJcHI+P1oQMIbgTBuqlzjGdhswYCWgA03R+VATQi0iACVDSksrFePB7Phzp4uDLGy6EBy+OYGYuD5ZPFYds0XV6LKqMiH2ikDCI95B5b5RisU3ZO/Z7AmHFTJds9BmaAeucj1aEZdwBGHSqIFLCMzi2Uu0eP6lkpaJ/BJ5HQrHR4DF9KjhtSV1ZGJyhJy2TyjMY8szGtgy8t1OuVcpsHodHrxJ1RlhMFs7F1RHHGdoAtefp/EKjQZ7qJ8WGVjJsE/b/L/q3usbn3RAtq9lq5tIA3aRzBJ6XQfkiToTjOohkstGVIqSEWt1jnunWl3OZjNzrJhQeqSUtvc0M5PqtShoaOJ6MMZfcMJp9T0dYGUF+2TorOPy6kADUAWV5JaAEldKvJdGkfrr9T2w6f6IK6Pd8XYDSIGO0n990H8M//PPj/jjsGW8zqSfsRHmSNbNlSZlLDbDX4Ges6fryU7TT4AKDsF846azRrP/YVNMMyfre4CPAXfwGdceaZpUNOQgpi17LyeibxSUGQ/Hmhjn268gJAeQ3rLAscnHzkBpadDKQEtXH9YcsWn++FIwUC8UQlPIDhyJHRFSf5fVtaKmUxt+lwOYH10t+k4CDU11E24/ksL4/6IWj/SIOxaR0IDcCjK7sAAJx99mC7ZUt1kFKvV57TN78J8Oxny/u1xXd/d7f1BUHTpMh/lBFSsgvNnkB/rwrSk8gJJvYgBXVKupEVKK/5dKm+jbKYr5pFV7pCuc8TjOAxS0tlgDL2DZhxGbMw4zEA5bVHuU2TQeF54MRFtNtIfQQHr8+2bWVQNWb8RF19x47R1Vt4Zk26ei62kSc1odn3uY2I9gv43YEDAG9+s972Nrj00m7rGwcpwbInC3UmpVjkTJZpox2UpvT/lAk7/NyamjydGqlZ1WbaZ6HsojYI1Fm11cloHfzcUV7v2VNmy8cMyVgOXbUW+wjMsP/lLw+2OJnQuoYXXDDYnnfeaPJArAv7lX37yv4L5TfKeDzfXbtK2w5dvRFgoBtg/A+fKCKtjK71N9QvgtcOxyPYV2zZUp7HsWMAr3+9XFYDRDDy5BAWLMbM8RWYWT42+kPKbFfpmJSOMKUOaiyw6q8id5ZQzkyknPPz7t8mTdTlDUarE8hN//coBDlZEzz3sI17U1WmNOs1tYycYOa6vUlTgYGaYih9N44BiHfSAn7XcTDyTJ8ESqTKZg0aUM6R7leKnGw6+FagAMfMx7bozcr/TysNncOJiU/ueh2yK1f+N/W8facc7XkbOucuJh8hp2rAyyTQtfz/xtcBvud7Bh+qJnwhVYH/nj4vdbLaNJHTZ07iNagzEe5kpal+INUqNCnXmbcjN7uzZ/JQW+ecO1Zoqj3Wfe+6750Eq6DljO1Q72+drhwzQdA0KXZKdHRxpOdfCoCgQalSXZ5+hAa1oQMMf6PBBDxjqJQZRwuolSbp83OUVlixrp2URahN6GpYHrt3BAEHwXTRhk+JyzQKOu+lIActmyFtC88gJslfTe7SbVX/QbOCSu3gE+95MJrHXuLVr3G/lNUyg5ObSUuecbLQlr821Z+gBTDR9jXlg7f0dy7DJVmqjdGlyWUYcMwnkR8/PjqxBHVzPKYoRic1YltpMDK2mQeLo/9fig/BzzQLprSqB/6mTSKhurDWV0lY45hJsMNYWO1Lsf+3GSthUTeWISXGYlznCDB5z1EXNtO6++e2sW7MUlW5TT4/VpwMn8QiJRzR9G2UpY9+9OCP/la1ipu0L01qhX0Dfqb9gKSL060km60VnKzrY9HE+9brjQZ3Byc9YUnjPPhg+WJ7OtTUgLE2nUmagpASUJETfCG1oeo7b5mpDltvmzx15zAJwdNNDdqq6phEmgzq0QYVktOFZ6mhs14x2wDPjLywUO7DZ7LSrZYRgWbc5Msr82ycS0ujmdV4Vk6aGaFgWd43bixnVOEWZ79S5U3KtglQfr9+fXmctBQEbvHedayMFL3ZYaMXV/xyFGOqXCH8+eH759TV0js5EvjadVDWpAUtTGPQlEVOG1s8r+RA60nAMlha2fNRvmhGzZWVUZnOl4ZeWLADmACGgwC0JZSXl4eNoADDxkie7V5rM/2OG07pUkC8j6D9AB5///3QKWefXV5n2g5Np6a6VkrAv/S5roFTapu0rSo/d1KC1IZcUicueupr2zDpuYZ1x1dV9Vrf5ZRdRVPldTFmkbCunSbfqNzWsufQLJHcCUWzYPJVSmjWfPwsjRvoljq++GonqP8fPTp6nFUOZ2amzFzAl17D+0Yzo1F9H6BcYUUyePJs/PPzZdldZ0bu6hlMNcJ77CN8LJESMNkFlo7ESe1fPfVqdQQBIjnEJd2L/2bZf3CL8pu+pzzQADOb0WPwN6yDl3vw4MBuDVBmO8bMajSrJs/+zJ1u1GGF7cL+4/BhO7sagseff/5giwF4qPPPzZX9IfZRvFy6MlbX7yjtn1Lpwi4w6RPiUvX2puprqq9r+vqmjNvq1iXV0/az4DkfKSFKTgKU1Hpz/EWeCRWS/Jf8AHTpZ4BS3tHrgcfjPty28/DDZWZ8zJqJmdbw+wMHyvpRbuIWy19aKuUuZqnHbMWYDXPz5rJtfDxRFKVsx7LRjk8z2kvZMukx69aVYwLuP6ABHVg/Zk3rEryHVkbVFFKf8Vz5YukrKXXnBJTn6ugen/UkyDCJ3ICdOnWdrPR6eRO76vaviJTR3mO350HEVH6jDMfvHnmk/A1g2G/L/bfUN4v2Fi3b6erqqE2e+3hnZuSMygClHZ6uCsVXROz1dLsP0usNr5JFj8f+fu3a8jveV9FgZ7zWt9win3ObeHRYS7/y6KKWjmHFDbWFp66qNlaREqRd1RarjNT2pPhdKDljDm9769xvb2yZ1ha+cl0qfEKE1Q6rD82R7Z7ztOyOdIIIynRpRVvtGqEev3//4A+glPdSHE/VOa2ujvYRKIfRprJtW2lfR7nLA49pTA3WyWN9qI2fHoeftcnotF/gtnxsI+1PUvyRaA9riSq3a1dMQhvGTQQjc846K21/64WyBCPf5ioeTT3FTSk+0uDYqlMTTCkO9iqqBo25htOc69SmMfJkH7C2gaXwaIZGmkGAO4rolgcR8GCCQ4dGl/Lhg9rFRT3bAR0Qo8KDTil0VEnBxKi4oEJlZZ/EOmZnRwPVUEmjg0ssC5eHkIyR+B0qTjiApYPuquUe2gTvgZeqoC/6WXveqgKZ6gQ3SVjBi1imtZxeVfAjPa4pGZYyYLH6s9xrVqccq19MMcR6BlvRD+Tj1TtynFFSFgTeN/B9aDAC1iENhPE77tSiARA8UI1PtJiZGXUcUsOCZoSk/RGvgwec0WBkHpSMxtHNm8s+as+e0XNtk9tuA/iu7xr8b6024plEUrUcqVaOVF5KH8PxyGhpfw85OrVE3T5iEhxCXRqPtXrHUX8TUKtQU+Nfj1Hdagd3QlFdX5LT9PPhw6UhDfVJSf6iDOb9AF1ukxtH+XZxUc8gKclmHgyGevfcXKnL82sk9Tl8Is3S0qjxkGbmARgYJXG5T9zypeVwXKHV2yZ3310GawCMynLsx6ixVtMFpIyhlvzmwYF0H23SklSeppt7ZCTt89oKZqbtqcqGRQMkeftphhJEMphz+4/Uh2vXRipHQzqfFF0gaJa6OoGm+9H7rE0Qp7IQgxBwYgXKXRr4wHVxZHm5lPM0iIHWdeRI+R3aVDDQjMpmrAN1eh7ccORIGeiGth4K19MROubA6/Ltbw9vaRkY/HbGGYMt6vq0PXhuGFTdFYuLcjISxJIdfH/pnU/R02k/osk5GsBTZee27FApeIISaL8o1Z9bn/S5SerK55S2ttW/Nl3WJNSj1et5FlLHs1VjWikYDeUvbg8cKG3gGJSA/QCVragfowyVjsHvUP7jNjdrPLYDl4YeNxiogNtjx0pbEwZcd8XddwM85jH6757nPScoSbIPS3q7x5bD+x0ryLMJW4/ULo/cozq1JxDV47PQPqfiaX+urM+xb4zDxt/vdyffc6OCUgLUrPdI8ttqvt2jR0cnGEr+Wy6vcYs67YEDwxPBAUZtM9KEddzXs+rxJENtQgDDGalRBx9HH+WZjFL3vUgJyAMYtSukxA15/M9Vx1UhlVslszx+DamOVJ0vR6506bNOuQZ1qXu/vT6kJur36Pi5Y0Mr3gzlEJdLKHfn5kZlOd8+/PCwnAco+wguzwF0WX78+PCYgm4lG40GlWVtxtSgfZxOcAEA2LFjsD3zzNFJiAjeg9nZsh9sWf/IVTvaaMepTgQjc/r98oVKUXRT92/Kod6UIzql8/YG6Ka0o2kBmXpdmhgg5pbnqSv1NxzMaHjbp81y4ll1pfakXgNN4dY+a/tojlqvwssHn3xQSo2RWrYyKaMN7ktnzPJz5vvec0+ZEeHuuwfbNgeheL/POWewpQ4jnu0AnUhnnjnYnn02wIUXlv8DlMoIzYIsOfQB5PuysADw67+efTqpzEBhZ8YP0vHIhaYG+ZOGZ/BWtw/PHUTWIVd/sWRXlQ6Qep45TlnJAGQFGvNAYRqAxDNjYoAwBiUcPTp6PJf/hw6Vg1p0zGCA7u23Dx/TNqefPtg+6lGDLQaT4aB927Zy0InyHz9jP3LGGWUmHRyw8qWFaADS0hLAv//3zZ6HxROeULYLoJ4uLJWRYrhqynnfRJlNtmNSZLz3vkwjKfK+6XPP6cvrBso0dQ60HK6X0kBdKTASQM56rI0VpOw5GHiA5R84UNaPcgnlMLKyMpzBEmDUuUX1bpz8J2U646uVoC5Mj8fvqNwHGPQHqO/T4wCGdX2u91uO6EOHAP77f9d/b5pzzinPizJu+ZCTNepkJ/WatO3soQHlKZysY59xYzmcrGOqnIU00JLbiq2ANZ7ZeGVltI/gbV1cHHVi8YAFOh7hE9dp34PjiHvvHWwxKA37o3XrSrsPb8fSUtnH4P4o6y+5ZLCliTywPfw6bdsGsGvX4H+sC8cRdAI6Hn/jjYO/rtiwYfgd1p4FKQhY++xFes7aoqlgraZs6hZtBu02zTS1tQ6pvrGmyk65vjnjCCsACWWrFESJ+i7awulxXP+nGdO4TOWyfnl51C6Gn3FfmmETy5Mmt2OZONZAv4KVjETi3HMH28c/frBFW8+GDeWEEpwUwwONN20aHRtgcAJdMRHHFMePl+V3wTnnDAfFtcWkyIlpkk9tXbOmfOhtXMtx3p/Uurm8pRkXm8CaBGXZ/a2gfl4etXNYcD1b8hXg/3zVKxrAjLKXbzGp1P795QRB/I5Pbjx8OD2Bkhe0OW3bVsp0lPE8++aGDaUuz+3/uELKjh3lPjxrJl73Xq/sIxYWyr6gK6z735ZtPdXPkDs2yPE9pgTUWuVapAQa59ZbNcksFc85jiuuCMuw/K1cLkv1asfTY6vq8vpkqX7Nf0uh6j5bSQioDY/rgZbPW/IRa5NapFUPebIJmniKrlxLj8ftwkJpC+Kr6OIxNKkJXyEdOXq07FNwHEHbhfo52nkuuGCwveiiwXbHjlHZidcM5fm2baMxQdJqKngPDh0C+NM/hbYoiskIBJ72eUVNEB4ODld4tdmYniAjKbCFLwFiCUhqxOBL/PEtwKhzkSuNVNHmS/WldKySQzHF8C85hbRZEt4yrbKryqHOZc991uqiRqqqY7R2SPtJn7XvELz3PGg4NQCDd1jWc5/j7JPeEf5byvVNrVO6ztzAaCkhkhMKP2sZjenWep+xbj7glgLX+GCWL8FA4e+sNCtMui48GJnLkPn5cmCp3SdqFOUGXWm50EnQFAB87fBMHLCUcc975DG0pGAZajyyj/cDUju7CLJLIaf+fr+9oC3rN+1+0+yMfNCzugrwz/88+J+/qzQ4KCeIOfdeNjEoT3WA5RgLpOP54HR+Xg4+5m3luh4a1egSnCiv8XhrKTTt2tOMltxRRYMctGWaaX8i9blYN78OfEv1Qq2PoG1CIyu/hhs3lgF3Hcv/ApiBRtNrU94dSceR+nLrN/rMAIy+8ysrupGYOv1wApHVz3O4kWjDhrKf15ZukvozhF4Xqz/k5eRSFVxk7Ssd5znGGkdo19wju7x9BtfbsTy6PDJvG9cVLV1fcjB5rqvH6mLpuVx35HKGnhcPBuPvDD0On+cNG/SxrWccQLNGaveQnpdmD0CWl0vDoLasvTRG4OdOsytyYyQNIJBkOm0L3fLzpMZb/F+acJLzbHfF3XeXxlbah3NjMR0j0meP7iNlRkb4PaRjHml8prWDlsszbvB3jfbP0jJ8AINz0e6PJZOnKaihqfGIVbbHTsjrpbKgqo1VDh6Udfw4Kv+l8T/9TPehx9F9ikKfROhxhNFroQXkIp4xMn3XtOWWqS2D60H0+Kqs3VY7JF2YZtbB8qscZ9SpxW3AWN7Bg6PnyvvHNWtGzwflnCTLOL1e2bfxgAP83OuNJkHAAAa6NDSfoMK3lGc+E+D3f19uUxvs3z8qzykeG6dk9/PYdrQxpceWuLg4GtDI++AtW8r7oPU1qbIwx07uKadKvmnlpejSbZBiJ5T0BNxq+iBC9QXpnGnWLQkqm3mGdFpOVSCZpROnrGpx/Hj5/HJZRql6XukkEH4Nqa6jyX9pXCT1Q1y2W30VbRtAGaBF/Xp8jEL7QnxnUZaibUiyBXKwnXRFKi7j6XiEr8JCJ5VwmSHZFqWJ5fRzKqmB0k1A38sUG6JnXKO9+1R+43iPZofj+jrVD3Ar2YRoOQCjweK0bwAYPCO8j7DwjAO0fVLtudOAJtMRyy5Gy6i6NlXXRZO9XO/W6geQ9fdUu09VHSl4s2dX3QNrTGatDCTtz8fj0hgOJ39jMBcG5ko+GCmomY/r6coqWrtwi+/7unWj14/LaEun8MpBTd6nJpvDc24ryDoXjw3Qg/RMpujQ2nhAKjtFF/bo1E3Y5MdBlV0LoF3bEC+zbv9n2Q6sFU6ryqXlVOn/nj5idrbcT7Nj0TGPh1S5VHWM5cPhnz19ON1fe6Y2bix9wpZNkI9n8PpqK9XSfelWsoMBDK9sjv0F6pCof9P+jG/peIAnKuGBxrSPoTYhWk6HssWjanXVjlOdCEbm9PvDA/uqDkwSolLAAn/xpWA7qy7N0UWVYe5Y8gwMOR4jIHWOccFGtynCRRN0HkMsxatMSduqcjy/8baldFr0c5UBNlVgW04b7zEUVC5WV0fLwQ4Nt5bymqIopw5otPssZT6h75Xl8MXPVpnaPtLzWxV0Kw0QJTwDbm5MtOSE992i+9JnylLqq86HHqsZWdvEs6yZ5/pYwQiWDJKcoZpBL8Vw4sV67quOoe8D/67OINm7j9Su3OO8dede3zqG7vn54QyunDrvDT13KsPbpulMUCnXeRx4BsDSd9Y+KQZuqQ/L1S+8eAxulI6dUTPHV9L1jBR5ya8vdeRZfQsasD3Ucf5IePRu65rl6MDW8ZbxRWpjlR4o9b3WuWrvmtSfSTpgVR2SvpwyRrH0bdTbadCQNlbhQQpSnZ7xojR+5e2hY0rrHalqD/2OLydv4dHnqF6mvet0H80ZRvVvzfGbon9bwYv0GU8Zh/PPnv7DaqP0/E8iZ545HBTnyZKEWA5obSKPJHukzymTxy1bCsdjF7DkpPbZ2rfqey8eWcrbotVvyTXrOHqsZfPy9Ceeuuk+3KZI6+QBS/xdpQ4jKxgN4at8SPvz4zwZ0aj8t94f/D7FjuSxtVrPi2YfoW2VJobw//m7b9k2ed1btlRP+KXl0BVVAMpsyPv2jR6PQU/I3FwZsIYBxrg988wy6yfPbGn1A55xs6XrdJGlkoJBWgDDAYEp8tYKJE0phx5T1b/Pzw8HhUtbqY1SALT2TKeMEyXnpdR2yzbKy8/p86R9pLGCZL+iW889sOyonvOR+hGtHOs+AVSPYT2Zzaz+UdsvFak/4oG+tB4tYJ/eC2llC4q0siFtj5Qcg9ZB67V8BVV91aZNpQ0vxcbj0S/5s768PLwiC92HjrP4MssUS2YADNsc+dhLetesc5bK7ArsOzdsqLarAej+X0lH0GTI3Fwpv3fuHP7Na4vTZKBVP4fKOS1ToVQnl+OWLEwlRT+WZKpHF9f0ZM95eMY69HfPM6XhtYFh4Crq65aNCfV/KVCZ1yOtiMvr8AYx8/JT7K5WP8/LtvRLjwyy6uc2Ks9EQ/qucBkhTTzkegbXv6mPWGun1Gapn9Oef+maSbpNlY4k/WaRYn9pCuwf6djDeoaq5LRlo0SsftFTZq5+ljM+87wj0j5Wv2Q9dylyUnomq66VRyeh5aRcj9x+0DO2sI6tWtGc2v899jELvp80jqjKsDw7m3efpQQ+VTKIyjleHv9fKscKzKXwQFyeIGRpaXTSH++T168vy+Exf6inLiyMroxFs9UDDE8UtBIJ4f+YtIiuesJt0DkTmT2xNfT/VF9sIvTVHyeT0IZxE8HInKWlciDuFY4eRyJidWjaIIV+1mYne5VoDY+xgLbHEub4WRMkluFGUl7bClLKvWZaZyedb65imaJASfAMJXwQSZUDPgi1nEjc6DA7O+rMkpZ/8GRY1gx7qcqvZeTl+3ueRX7OEpKBju/PnWLSbCW+vMLx4/oSDKjIHDo0mhWFKjz4Pe6DSzHgMg/Yno0bS2fUaacNtlJmS20wOz9fOkFxNjA6d2iGHD4jCn+jy0djmbjsdJfQZyLlWeLkZBag5TXlAMiZcSh9l/IeULTzkfoaLTuodLw1QJTq0BwYWtkaVr+YoqB79AvPNZeokrdSedY14P2JdVxqEAJuuePBcw88BkOPrLeW55Se0aoBKx3wWoZC7dmU+gjuQMNgArpMp9aHHj9eHkf7DYBSxtJlQ/E4fI6KYnRGLl994fjx0edOytbLl+fhfcyGDeWM4Sc9CTpHmqyk4TGW5eiAqcelGFMsJL3FMrClyE1P35liOJX6D8+7zj+nBuFb+1Yd7xk70e8sp4BVV5UMpjq7FHSs4cl2xs9ndVU/Zzoe8Tg/PdfO81td3UrTW+iW30P8DZ0dDz1ULpWMGc95Hyg5mnh/sLBQ6vR0dQS6D3VqYbtQ1uOy0ffdB3DPPXI7AEonOW75cptbt5YBa+edN9g+6lGDLWbh3LRp9Hx4IMU4V0ah/ZO3fo8DTytPktseh0zK+LVp+0kukq6D36XozZKtCrGCyLRMiZYzV6pXwyubtWdAupe5mUtRR0vBOj+tj5CcTHidpeyb3MZEbU88kCoFSaewgpv5PtRRpOk70sQOzWko7cODimnQnMcuzPeVzk9bFWPDhtKujbId98WMbWecUerkeD1uv32w/drXRp9Pbo+iiQnw+UOnFrULaQ5W6oDD384/HzqFZob3Os40uW/dH771Yj0n2r4SKf2QhWX78ozR6f4aOf2Y1Edo7ZH2kdqgTQpIwdtH1EXL2JsCfZ+5nKcJR7headWpZWzr9UbbLJXDJ3NJ+jfaOTS5Se101oQKyQYplUW31nhTeg55EAGXGTQzGrXTAAA88shg+9BD5SpTDz442KJtH8cFBw6U8prrBtiGffvKsQCCfQadjIL6PvYb6CugGae167phw6hPgL9Xl11WPl8PPACdg+e8vKzbXz02EI9MQyy7S6ps13STNsfKUturbNfWhBVat9bneWWpFixu2cxS9W0sw3M+Gim2J29Zlm3e8gnz/bneXtU2+pleF14nlXdol9ZkIf1fe8atQDN6rDVWt1aowO89dkeeyZjb7xcWysmCaIvnGfKpbxf3ufvuwfbOOwfbvXvBBbXTAIz2pRs2lL9h8otduwbbM84YnaiIchLHDCsrZb+D5459De67bVv5v2cyI58w2QV4nhTtPlu2bOn7FHs3YslAD1Yb6+j49Pic/tGqw6u38rJSkjx6f9faZPXL2jnn6vwpY7LUMrXvi2JUbktZj6vaNDury3/8TANzEUlGa7Zjen20d1WaHObpBzz6gvRZm7xHMxyjDQhlIQ80vuOO0gZzyy2DLer4KFvPPHMgnwFKvymP41mzZtiXK7WHjnGl1av4KpLSu4bf0Yz89Hs64VFbgZJ+1/Vk9GBsTIiXYoLQli+vEnASkmHaGnB6ytTwOmYQLegr13AqKSMpS37UMZpZx0t1NaUMeZSsqnq041MHwRzeUeDgBgc/AHrHgx0INSjwdlHnlJZRIcUYTo2R2j6pRhmLnOM8yoikuFiKaYpzIeUdkdpTZZSXoHKrSmZ4rs/KSqkU4fE4gEXj6uJiOYDfs6e6jQ1SwEz6wCHnvU/tP6zvquryyJXcAXOKDPSUYznfU55bq+y28BiUJUOWZFjLWVUA6fXKQaO2PA7dh36nlesJbrDui5RRoeoY/M2Teahu3+vBqp8vK3bgQCnXsD9FIxwdgGrtlgJ2tOef6lpWZr6U90eS8Sm6L20b3UqBvnziDJ2Ag31CVywslAN52j9baM+gJFOt51+T25b897w/qXqTp2xeh0VdB4ymb9D7o+mOkt4i1V01RrEMulJdKf201K6qfloyulG0TAQpz4Sl/0tOLSkIDUGdj/dL1CipZXqjMpavyOPJ7kLPB7ea7NqwYVSGchlPA9b4GAplu0d2XHyxLtvptupZ8o5nsf18K8l4dCbhlmaJ5EZE2vdhUDX2g/feO7zdsmXUmIngM0LHllXLjTcNv29V99CS31IZueNObelxy17j6Y+ktnrGpB6dRDoPuqW6sJXpkD8nPMMIwKgNw5JzVddJwtOXW3Y+qSzPPeDHWFlXU85ndXV0krPUjqpJLZ59+v3RpYW5c2vNGn0yojUW1D7z/ek+VKZypwfN6Gjp5lXjPIpn/KIFQEiTWnjGZiq3uWzHunfsGPxJ7aDl80z93IElnSutE4Pf7r9/sMX+AKEBa9ISntgOLPub34TOkexzFtq9pzZYbexErz1+x7NaHzpU7od9J45R6FiyiYnQXn1ZOo5uLTsHfee0d8PrI7Bsq962SnYbSfbk2ppTkc4z13Yh6esaXG+n76FVPs1uRutMkcnSPnVt9ClYdm5p3xwdi+sxVWM4hAex0ayZvGw+HpF8dx49VdIzczKX8s/Su2ZNIEuZKNsUmk0BwKfXefblddFnysIqsyv5lIo2VqATVjhe+c/7U6ssj48hZ8ykHVtVXgre64GgHNDsQBKePiIFzfcAUL7X9Bx4RntJNlbZSyTdXJvMTaHPAs9SjAGxOIahE7t5tknUy6gdidchfdZkh9VW6XNK3+mRV5LctoK1q7LE9/ujwdVSW9HuwwMUu4Cv0AZQbe+g30n3x2NXtr7T6pLaU2U75v9Ln6X2pMggj17n0dGt76iekGIb9dTluS+p7cfvPffH0x4PUtIn/B7H9ijX8J3DY2g2Xc0mnmrb1PS6NWtKe0DK2I0nX7KOoXKLyymqP2vPpzUZvYlno+o3jiX3pTFYSvyOpCtVPbeSbi/JKcvWi595csaWkJo8DiahDeMmgpEZxYaNUMwNgkdmIEGRb5EC8gYJk9J+F+MeSHdVfxv1WIM9HGDiDBnctkGVYkiRjExWBkntO6nz8xj4tHKkTJTaAIv+xrMXHDkymqWYO9sXF8ssB3yLGQoOHCj3xwEvZhSgAYJYB126AaDMmPD1r+uGCVyK4dGPLv8/55zBFmfI0udGMioBDJRXDLjD2bM4U4vOotUmN0j37dAhgFe8Qm53S+TKW5MeXwo3zciaJMtzZEzqoMLByHVMPOcR6h7fNr3Z1tpo3v9cg0YVqWXkyn0kZ0mulEFf7jNuDah4YAw1ZKGcxuNQNuPM1pUVPRsofk8nb2AdPIiE9nko9zEIAPuTI0fKwF78TgoCxnbwrDdbt5bBDNxogNt160pjLGbCxCya2A+sWTM625Xfd3otOjZGFltPb+65b0Gmuhi3Ll2HaWh7iuGpCyz93/t9E0jLfKYgZSSZJGigmmeMUZVhB0APjKTjEY/BlQehcdkuZfzEz3Qf3lbsY44cKfsNLfPcmjXlPcRxxEUXDbY0mxrKex4wQQ3feB2wz+wSa5UEad8UR2KKI7yqTbQuj1VVWt1m3LIrBcuBZp1HHV3PKq+uw8rTDv7Zel4s5zbfh65oxfUxqRw+4QR1Upopn2bLpFvvNeDBCByvPUdzttD75nHaoFyUykuZdITl8KxhVD5wnR7l3r59ZbZ6nNBBdXqA4dWjUF/n15Lq/1gebpH5eYDv+Z7B/yi/aTY1tAWde+5gizYeKrcxQw9m0fQ45qUsOvibN+tbU9D2SU5L7Rjre+o0t8rgQdp4vZvC00f0etV2ObpP3X4kxQvX1GTynIAdKdBGKjflOqQEUPDvqgIoeGAZn/whyXg+UdBqoxS4ZgU+IHxlFZqYAsf61mQhzaYjyXYP0nXU6s8NouG/WU5//j1AKQ884yPpXa2izcAJT1CSdRyfzNI1HnnJ/0+59laAmFRHym/WuJMfn6LTplKnj6i6/inPoKUvp7wnqf1GE6QGKXn6Cy1BCC/LgvYD1r4eu1OK/9eizvNKdSSUt6ljL3qM9Z3kq7bGuLwuaTxvHef5jWPZQrqwg47b/pMqw+ln6Z5KY1FveRIpMqmuvcKSaZJeVtUmr9zRzlF6f/i4vN9PezdS2yZ95vVrZafYbzyTEihc3+c2maIo40fQDourGHmQ6rYms1Qls+r3qxMJSHqzJ3Ou9T569CdrcqKW9fjAgdJ2gfYZbjOjK2+g7QPvgeULRxsRXSmFT6Dg/ul+v5RrPDYHr6E1uZHaMNCOhfXTgGE+KYfXtWmTPlGyDd2zAo9JpAsmoQ3jJoKRBcTAn5yBYkNMXFBxlSEsmBy6vC91jQ1VSpqkRDStaNLfNUVjeXk0wFjK6sY7W9wHja6Li2Vd2OlzhXB2tnQ+4ZLO+BkHagcPlgpCzkwidEo99NCoQogOLymQGRUPDDg7++zSiYXKBxpScbtp0+hSElgOzcqD12wcM2MnkFYCpGvg6ZMmrd9q+xpO2vme8tQNQrH6I56djMt46rzkA0MajID/0wEqwHAWHJptGWB0STerHNxnYaFc8hKDoenEGYDh4GiELimNchoHmCi/Meh63bryutBl2fgxuD8aRvgknbm5MpiZOy+DYJKpY1UY1xiqqbHtyTAG9I5tq5wAtBxu7KNbbZlmOhFSM87SAGR+PF0KFD/z4DopwzH2LdjXUIMjnwyDshkNqevXj06GkZZk430mzyq9ZUs5Fjj9dJgIUgICxjHmnsR3LyUow0MX13lSrmfdc20iKE76bRyZCpFUh2vKMukIdYppwdHeIAKUxTzIVMpEj7Ic5S5+3rmzDErlthmUtffcA3DrrYP/cfI56vQoo+k4Atvx3d892OIkw927hycN0nbs3w/w7W8P/seMvfgblrd+fRkMjWVeeOFge/75g+22baW81/qq5eVyPNN1MDK9J22vpNQ1qYGabeMJagCwHfvacVaQX0pwnpUNqylSA820NuT6ZZo+H571ki73jFu+UsrataNOaStLvLZqZ6rMb/rcpWe1TnBSbr+fe14pgVC8TiuwW9rfU2ZOgoImqZI9UrBTU7pXXdrqv1IjOLRz7EI2WQFenjpy65+kfhagfqBw06TIlbrXso26qsrMffe6iEpq8p3S8OhcHn1uHP5fT0BuSmB6k/WPE887Qsez3CYplScFPXrrkMbhUjyIVT9vR1v6YFfHIV3J9BTZWrVPV+Mrb/wPb48VxMzt9nNzpQ1E0/+PHh3113L7O9pGAEp7FNpocOL30lIZw4P2H+5XkJ5xK9ia23+onovvKtqlaKA7t+nz91pa6UsKSuYByy0RwciTw0lmbavPTH/VNlYhltGBU/dJsxySnnosxSllH0sJsMpJESiWwajKAUBJSS/fBk11qJ6OVKqLB/FYmQwstNlOKcv/eMqjeAzLlhGvblA04jFwclLbURVMbfWWKYHXXgeato93Zh53rtFgCNzyoAiugB0/XiojVBnrgJnjK4M+AGBwfk3IiNTnsQvNRJPbnv6sCwOhB0//TP6f4b/Vvc7jdEo1Ub9WnyXb6y7lxsum5VX1dZ77nWvglo5P0ac8M3Q51uCW/la1bKjHqSv1I5IM0NohGZdwS5d34jOxpSxbVUvO033aXMlBYKa/mj4LPUeOWH1xinPE89x6xgxVz5hWHq9f0vk1eWv1NVYd3rZpWPJfq6vuOE+SGZqxyzO+qroW+E5a7aj6zftcc5lujc+ssVjKRLqcvs9jVKS/e84j55phX0GXhLPQdHHPe53y3FK5y3V0gLyVa6RMzdb4A8H9b7xRrrNN6PKhXJ5Zzg5reXstaMb7HGtL7NF20PqkrVUfLbfKiTQ3l3YeWtulJXV5eyS9hTqwqtrRhX2O4nFMavKF3i+PXLHq4hMG+OeZmVFHoAWW7bEjSfvkyLkcmyd91yy094dee35fpGfSsnFSOS+1VaofHTy0XOt9xmMtXRrRjreeUbS/PPxw6ejik+LRVrNjRzmZHldWwUBl6pTiQci0DoDBpEkMeL744tG2tck//RPA933f4H/aP/P+jD4/VfdHst/zlcwWF8vj8HritZOWy5Uci3gdaZm0zrm50QQA0jK8nnFiFV47kkefTbFFSs92jpM7Zazi2cdqg2cMaI1ZpO9Qzmu2HMuOY7UxJ5vmzMxolnxJXtYJPM29tyl9C62rq/7Mo5tQ+cLlEw2S0PQoqa+RzkFbDYDeS2tlFu/50Lq6Dkb+xjcAnvSksh0equRjimzTaMLWLNkVUt5j6VnQ9pXkrufZTpHfqfcnRd+W2qO1n25T+jOpDv455Vmi+zQ5pqFI2e95Fk7aDrqVoO2Uspry41Pseyl4+wRvHV7bjKXT8Hqq9EwvHruWpHNVvb/S8++xQUjl4HeaHbNNeGAhQNo1zzlnCa8OjVtLXgMM6wAeWajJbY9OTROKaUjvvrRPyvhBkh0ePazKjuQpR7ILeN4Dz7NhvatWW7VjJLiOT+GrpnhkCG5XV6v9xtL11dpHy9bqtr6rugZcr83R9b371pHl0nPn0edy+pHUsWmOrkeRZHALWOKlSyahDeMmgpE5OcYrSWnmZdah7uyAFONHE4PeOlj15wzMPHW1KQlSBb12vGUg9JSjDRgtrH1TyvHep6r7YXWIKR2hpexJHbvkTOVGPzS2UWMcN8QdPDjYohPnkUdKhwHNYAlQOmPuv79cXhO/279/sM0NCM8Fs5RhJmTcnnNO+RlnbfGZUfh569ZyCU/cYhZNKYCJLkHaAcWatVD0yKzCXvUMw8Yz4nYhg9vqq5pqx6TUnapE16Guccn6zhqk8X3bkO2cuga/HGOvpw+l3/HfqINHk/u4XVkZzZrPsx4vLIzKfeqQBxj0FXyyBm5x3wMHhidytAWX/5i9GL/fuLF0oOOsXdyi83vjxvI7dIzzAAYaHHPnnU213kXRm03Xt8etM3NyHLNSwEydPiLFeJZaTko7TlUwW62G5z7z/SSkY3OzMvCgOEtvtwyxdaAGQq1+yXBqZT2Wst0DDGQ+ynaU/3x76FD5Px8/0Mz2fHyH+jY+B2vWlP0RrqKC4wksb3W1lMV89RKarZhn+kR5fv75ALt2lfvTdmA5VgCpZKi/4ALolOXl8ty9z1LORKRUeJ/UVp1tlMuvY8rSilY5OXWfKuCymHwpzkmmrpO9LayxlGYjko7ngVp0gh8PFqfBqiifsR+QAr3wfx6QSldTQcc+nygoOauxb0Hb0+Ji+R32HxLY/6BN6NxzB1vU/2dmymcS+wi0A+Exj3oUwCWXDP7HjD9dcdVVZbsksM+bRLDdOD47Wemiv500GZRK1bLIqfDr0Ubmta4ykVtBQhTLD6FNQKO2Hi7vUTajHD18eFT/52MEqY/A8lAO05URcYyAn6l/AY/n5eCWjiOaAscDKNvphBWeRZ+upoj/d50Z83GP61YPaaoubzClJxBl2mVf0P49bEr+03JSJkU2Rc440/IRU78AleH4Hd0uLpYrmKCefv/9gy3aZg4eLOU1T/6Bxz74IMC99w7+x4mCbfoBUMdHOY5jy/n5UgfF39A2hCsjnn566S9AmxBdERdgcP1x/6b1GA9WErKcWJHUiDePjJbK48H8vLwNG9JiRjxt9PxuBcZqwbspdVn0+/WTIXrqaLK8JmjbVmbJf349iqJ6UqTX7+SJ2Wr6GbdsOtqkdjpG4L5hmqgPxwIo06W2o/xHnZ6vlEv1dm21qYMHh/cHkO1IWBeC8le63yj3UcZv21bKdNyiHo/bXbtKew/6e3EcQFdI7EgHj2DkySGCkTl33w1w3nmD/2lnSdOVAwxnFbFm5eB2nEu+NfVCp7wxnpkUTZLzNk+K8yMnOKNKAfIElPF9rKwHnjZq5a0K2cala+8JfNAC/y2FPVXB1a6xZPDRtvR/roTQgWvVM7i8PBrUwB1XVPFBpYZnxp6dHc1OIckyHjyMA0UpAMOaQWYNQLTj6BadVx330jNLR8vgYs9z45m8IhkvrPfYCs6oEyBWd0KDRcrgIKUuen2l36T/6WfrnbWQHNB17oGnHVWTmqRjtH1QZmiTUKzZrx6kvkJz2s/O+gx8VVnQJfkiXcs6/amn76P7cMOV5KzWMgpTuc3lNYUPNKWt1sfQdqJsxwC19esHW/quaAPoo0fLvoU7zGjWG/4M42AW6/YYF6m+/KhHVe/fIDPf/AbA5ZeX7UC6CIxM6RO8Bkr+veY8pfdNq4s+J1VBOFRfsM5FOx9avnZdPDJZyuhhXcsUvVDSh3Jm00tYugSAnVW01yuNXFRHpFD5z99Z7AdydH5atiWTpePQWMbb5eljUt41693x6j9adnepTG2fubnRoN+qrLQUyaag9RHSOELKboVjCzRYYnADDZzADJko/7GcublhxxRA2R/i+Z1xRhmggA4ndGDRTJA4Vuk6+OummwCe+MTB/3Q8lGO/aTLAoOkyU5/3JupEPO8sbV9Ov5giM3L76ZT+o67DKmXMQh0Ynv21zPb0e94n8Ew50nHSfeN10SUeeWb8ujo+tzPQcjU9V6pDck7jfigTEWo/kbLOcvg+fFIYDQJeJSs2AZT3YO3a0eUw8Xga+IDyGmU6d0pZ13d5eXSiDDrH8PjDh8uJ8rzNeH6bN5eBCthm3Of22wfbr351NGCuK+izAeAbt3JS7Y1aOZ79PHKljX4ohbr2jDrtTxknWb/VvYYe26jUZ1ljSqmvw89c/lsyXSq7Ckmme55FboOmpKz4oX3vuT70N2nc6bHL8b6B29KlyT9SuZa+jp95MDK/3nTcqS13TrMe43HS5BieiYyWwwMd+OT4Q4dG+1MeFEH7MxxH8Ann559fnscXvwidg231rFyTa/NNtf9bZee0w6rLem+s76vq4t+n9p0pNjBLZ6yr/2t1SqS2EY/RxjOecVGvV+p2Vps99jmt7dJnfhztXzzjIs3+b9kCrbGY5l/0PKNVbeXf8fObnx/tAyx7EP/NWj3IajPvP1BHP3p0NDhaClzjwXH4HB09KgfBUbZuLSfCYYAan8AOMPp84H2idiD8zjOObRP+nPFxoke+ULT+nu6vrYKi1YvHeuRZXR8Fok3GovqGxxZe1ddIZaeQa9tJKctTB71OmsxK7Y80udDvD6+ApZXJxwTcF0l9klxvT5Htko2IHzszY8tyTspv/J2p8p3g8VXB/QC2XRiP11Yg27ZNb4c1EYLLCYDRlZYwmRVOTrnvvvI3LBtjbKjdnddFJzDSvkCq6+GHS10e24gTTzAA+eGHywnquAoYBiVju5aWSjmLk3OCk54IRubcdBPAl740+H/9+vKF5TO+MJp/w4byJeYGipTOpUopbsphkVJeTrlS+fx4y9HqUZZyBxNaJ1VVXxW57bHKSxkoSkoJNyrxfeiS6rxsSdFIGRDxY+n+2j3o9WwjBe6j3R9J2bPalWOISHE60jZ4shKlGEA91H0OU4wf3uMBhjPG8YGmFCCJ2T+74v/9fwFuuWXw/9e+Vn7PMz1jf7B7N8DZZw/+R8UL+wbsOzZtKo/HdwsDAqnzkg8+6bXzDFy0ZzpXqT8ZmJTzy21HqpxFcICRIwdSA5Vzl+7k5LTZY6hIMSJaZaXWUYXVLgtPmz3GnRTDcko51nFSPyJtsT9vIwOTxcICwItfPPh/48bRrG0o69Houn27rv9Lk3wsAx0/PhXPPesiqxhSx9CPnz0OQbo/39LjrWtf1dbU9ynFwCidu3Y+9NmyysZnMkWX9kxYsajS8aXfKKgvcaxxRI6zz2qH1wGXMz7ztCP3uUnRya1jtHqtZ7Juv2g5CTFwris+97lywsyGDeUziYHT+Jnq6NqSxVTedKmbe8aS45wc76GOA6pOfRrePsjzjqXIDu1YOia0ykIkfVs7PmXfVL3XWtK5qs+S2uGRzfwYOjFPChioug+0Dj5ZwpKpUp2acxqP2bq1nLDCoVnY+EQTam/h7ULnD9og6LvGbQ/oeDp8eHQyIjqe0OG1vFyeI598SFeA2bt38D+3qeCW2kuqVnhomkOHymu/vKzr9PQZydFPJay+QZOHXn2lqi6JKjll4dWxU97nnL4gNUNg7rlpSPe96v6k7Ku1S9vPsuOk2EQknc0aP+B+fNI1HkPH+bwuKzDLsoMidXUd6/g6tnDa12g+AkvvlvbR6qYZljnYhtnZ0ckwtB/B/dD2jVsMLuBlAYxORpeClKRnC/dZWChtLl2BQXf9/mggCe8HqsbhnJxxQNc0oQtPCjnj4tT9UuxJXn1Bq0PqG6S6UmQet/N4JqOn2nw1UD70eqMTKfjxXntHHXuhZavyjOdz69KOSSm3Co+PgI8XqE6g2VFpudqEIusZlWxe+H/X/l+A4bEUb5s2DmibFP2fH9MmnmDMHFLHJ9q7kFuO9lnC0pctXZSX7ZmMR8vXbMW93qguLundXO7zZ9pKHCRdM8+5ejIqW0HAVX2M517S7z3+OI70XFvHexJa1NEJAEYDi9E/eeml1cd7+lWv3mL5D3Dr6bfwWuGklpawhk5d0mYbnvvc58KXvvQleOCBB+D000+HH/7hH4a3vvWtsBuDwSeECfdIjIGrr5YHvynGxBRFVaJuB5hAAbpxyswQWgfLcCTBOylpNpl17dtWGFMHJB5nVN1nKCUAlmdPs7Km5barKqjIE/jgGbxZg0na+fJnyqoj5dzxN5qtUptFaD0L1AiG5eDADGem0uUVuBOKGzk3bRoN9uPLNFCDJXWGAQyO5cvp4HODQcX0XPhsK5o5lC7ZTH+Tnn+eeahtXvjCMvCgC6wBlfQsWwYFyYBA6WLg7DWWeY7P2cfzruZcB/qu8veGvtfcaMEd0J5ZzlL7c+Vul4ZjlPH8fKyJL9J31oCTl80HeJZhmMpj/o6kOhc8TrEqY4Pnvkt1asFPVd8hOdlbLZ1LqjPHqEP3xf1x+bmueOITAf7iLwb/p15Xj9yt+z7y++FxSHjud4rc9hgmPM6KXq/6HaHH1AkmlvZNuS/Ssy2NR3KM1W3I6DrLG+L5ebIfUDQdf3bWZyRuIlCe9sG8rqbeQ5qtjL8rNNtY1TjPyh5Cy+UZzDBDAY6ZaHAdgkFkOFZ45JHSyY56Ow9Ge/jhMssBBgHTOvCZwgnZPLMxHaug3o6Ba1jHmjXl2AANqBgch9sNG8rzwcC1rnjNa8rz6Zoc/d/TR3WpA1Jy7Vj8+7qOZw9NlGPdC49dIrUOS75oQWddPAue/lnKxlzltPeO+zzjCMvx50kmweU9yjuUt4uLcsZhgOEEFzz4F8HA3wcfBLjnnsH/d9012GL2YZ55hx6HMh7b8MADoysP8LFpr1fK/UceKc+Dnic9jme937q1XG0FZTkf965ZM5o1H+Ut9idnnFH2MSsrAO95D3TGli3Dq3Ll6JWSTdGjk3icpx4su6dWl9U2STdJaRMvx2vz8pZnYTn/JXLugbev8hxf1R7Jhm3ZRFLkPbfnUrsNloM6IA0i5pPRLVs+HyN4JhtTeVx1fXN1fI9vwbO/515KiVO05255udT3tUz2a9eOjves8RZfWVHaj2bd58fj/zzgWOp7+YQTyb7Gr9k4J8odP17K/0OHRjONSvB7KI0NJTsjgE9XkuRtrg1bs097VrdMSfhjtZVeJ81nQdvJJyvRoFWpT+F1Vdkiq/RB6zgKHc/nPMP0Wmj2BWk1PFo/wOD6oKzgy6xbmemldmiByhTcR8tiWxTDE+f4eSA46c6TuIFj2QmtLJPSZ08fo5VtHZvzbNBnQWoH7qOdjzWRh5dD2yW1kV9Xqe+qykZN7Vmaz5r2EQ89JLe5TfjqXnWh90eTM1WZhHPkvceHVEeOe39Lsd+k6PZ1dT3pnbfGPFr9dZPHeJDGSR6dIEf/x3JXV0cnj2P/kSJTV1dLuY/2CSwXy5ub01dBsmw70lbTCaT+VYrN4deMZ5Cnk7+1SeAApfzgk/eorZ7HCPFnanm5zFaPfTra8dHmtLQ0uiILt7vQMQL+hnYbrHNhobQtof0Hz29+vrTPaIk55ubsSdu4bWpc1gBF0XoV7na0xdOe9jT49V//ddi1axfce++98Cu/8ivwghe8AD772c+2V2kGEYzMwRdUo2nHptXJpRyX2YYTAceeOuj3KcpC0w4jackPC8ug59236rsqPM9LarkpA2dL2qW0x8qs4MHTDksR1O5dqvKagqQI8qXqEaoMaEsMezpm6d7ibCePYqrVaUGVNGkZau03Woc2YKXLttFlPekWlauFhfK6fvnL1e1ukGKu40w8TfUjHUAnrqj9xgSfjzXxppIecZrgM7LltHoNqoHZb4+btu576kCGHgfQnHGJlpmyjxVsl2KwSUFykFuGKMtIWxX4TfeVjM1VbaNbHLi3PDNWbJNH50vVC6ucjhKWLJWM4FrZ2N96jDn0/xSDvXR9NEeKhKc9Wt1eNOea1A7pe+2ceTYprWyt/FxDrnW8R/9P0eV5oEHqs8Dhy8hJ5XADsfSbZ1++T9WYO2dsy4/Rsjzz+j1ygRsaUTZay8nzzJKbN+srkuA+l1wyumQaQicqcmcSXQoUDaXoWETDJRpCN24cXW1KcjIjp41Px8rGcnZYeN4bjxMpRZa2iWdsmzOOp+961XtP+0XtXbOcyylYcqXp615lg6tj8KcyXrs/uJX6EN5XSFjZl5u6Vpp+2e/rThPPs9nrjcosulKSdKxUjtRWzmMe47seXC7w4I8HHignmvAJIlTu4m/o+ELnIX1H0F7z4IODLQ1y5quESO3Ea459xIUXDrZ47ajdrOvMaIuLw+dQ9Ux67Peecav1THh1Gq3/sM4hR5e2HL5WG3m7rGvnHZvm3B+Lqmto1eFpj9Um6dicYJFebzSjuKeflQJaub5vlaP1nVTWa33CzEzeO+Kh7jNg/Z7TJks/lH7jyTGkfasy7Eu6kvS8aeUsL+s+D0R6FqSMwlV+nsXFclLMpz8t19UWa9aU7aDXPUeWWuNN7RiJ1Gesaf3J+i2lrpT+x3NdLPsN3Tah01Nd2DNO09pl7Z9r15LA1SosPM+nZ7IIHm/tyyewcXo9gPPPr25XzrWXaOpa87bWteUhkt/V6iO08atlQ0hJppD6HkkZ5HHL/SDWPe06Kz6Az3fhQcqk7Vn9x7rPKc+Q5/1J/b1qP8u+LfWPVfJfsrt76pWumdVm3sYU2ZOiz3nGlBbSOy/9xieIWDYZPuHQmjTh6TM4s7OlXRknK0tYq4MgVbpj1fG8HK6nWv00XaEq5zrk0O8D7NgxXJcUm6NhPWs0JgfLw3EjxkHSunA/nCCCn+m105KvWUnYeNvovtpYoyH6/fohbU21AwDgEEu+uG7dOlhXc3Wwf/Nv/s2J/8877zx4/etfD89//vNhZWUF1tZJWtQwEYzMuf/+tI7Da1DggkQa2HOFPWUGqNRWacvL8WQblhRTTRhbirJFzj4eRcU7AE5Riqq2UnlSJ+dRSnJ+k5SSup0nV2akbLh8Fq5n8EKPr1L+qfKaojRKdSHWjHAr+JZm+pXqpJnRtGfBUnys7BaWUu+5Lp7njjvbpHI4nntBB9d8VpmUufQxj+k0M84MFMMymrefz5J76KHSyYeOM6rcAQxfFy1j7saNZTYgfs1XV8vgHU2JXreuVPi5Yo3toDPfcIuORRqgwsv+zjM+g0EkmzaVASW45ecjzZJDPLKo7qBYqG+Gfa41KOwKrX9NdYjwcqRBqVaXVIdUJ2r1KUEIdF9reZ4cUvo6j56Q4oDw1O8ZZHuMm1Sm8t+k54S/j1K2DWmgSpdRBpD1VF4Xf9etDA207dimrjPj00G31Qdbk3SwX8MgvWPHRjPMSRmAPdlqOFTG8uwnvK1LS3pWbclYrG2pQ1GT8Za+a+2DeA172vH0+xwnEn9GPUH1VWVK5Wr7ePQ563h89hB8JjxLcCKSTqx9pvCMW1IGLgktMwJtjzX+5e1KGbPTz1z/5/tSGcF1PWTt2lKf4yuKaG3hdfDz0MblVAZpq7DMzpb7o66HW9QBacAxHofBaMeOlc8UH/vRsRDPzMB12SNHyoBldJhiOTRgGa8ZXsOuoEEXlv3H827Qe6llfJdsM1Zm1hRdL8VYbf2WoidbOpKkT1l6At1K7fDoT9J31j4pdgVPuSn2iVRSHTBVbUA5zTPkAOgZ1ei4EX/T9BBapmSXqsqMw9sk7WPta9l6rH4Df7NWkLImVFjlaOdBScnap8mZ7dtLp5ZWJ70H2DaaadnSv/B7TW/BZ4pmW0UnCzoocTs/P7r6Vlds2uQLGGjaduCx10n70602XpX0HU9fY7UtB0leToLtJbV/rOrv6f+ec7bq0GSRpcP2enqWyhQ9WaqPPyfS6inW9bTGH1V2bglPnbl6C0eqQ7tP9Dcu99FOfO+9pR2W2wewvE2bAM48c/A/TsyTxtrc/oqTY6wgAOl8cH8pqBnbhG3lKzUuL49OnJTsxFamWzwWAz82bgT4jd8YbWebSH5XDr12nlWqEE3Oesf8HhtKXVtild1Tui5Wm1PeTWtc5ClPKjfFJqPdA0u2p9gcLBsG3bdKz5XKpnhs6VxPz0nP1++n9eFc/8f+gNowrOuTs7qg5YeW+vKq90ayEXHbL8DoCiDSRD3tOaNyuKqPk2QR/+y5vquro+eDtprDh+XVbHid2G58plDHp/0S9gXcRkTlbp0M43XR3h0An/6Fn2mfzjOpSmNAHv9DbWnWKsJaeyw5rvUVXtnahJ1b2j/XXmLJu5wxhmVDS5Evnn1SoPdJs09bthRJ/8YxNpZH9+H7e3QI7XMV2vPqCYpOqUvqXz39iKSTWHZD/p00wU9aXRlg2BbHj0e5gnb7Y8fKfdBeg79h4DCdjI56O48BoRmfUe5jeQCj9hqU8djW+fkyiBnHLDjBHCcaPepR5W+o4/OV0jdtKscvtP4W6PcnKxj5nHPOGfr+DW94A7zxjW9srJ6HH34Y/uIv/gKe/OQnT1QgMkAEI4+yeXNaZoRULCGYMnjUykvFk62P1+GZMeYxNkl1pWAN2lLqkO6zZwDchQFXG0BIgyZJGdGu+cxM9eDTcgRYThfeHgnp2lU979JAXjOWVpXnaTcPDrLg7aIBO7xOz3NjDSItZZgH+tIsOLi/ZQzUArA9WQQPHSoVHb6UHD0HVGJQqZGyE40rGA1gdOBHwTaiIjWOmbsUa1AscfHFg+04nUBN9anWgMyqL8e4KZE7MMsx3knfawaEfl/OPEmRvreW0EwJME7R7qUA5lxjfB08zwAN1OXylQ/+JaeLlCWg6pmkOg6/33RwSpeHpvvygGHaVjwGZeyxY8MBYQDDEyTwOC6TaT/E+yRrGTvN2NDvjwZDdMXcnLxMM78/dBa3R554ni9rn6rjLQO3tI+1r2WE8WIZyFPkreWMsoJ5LGeW1ynnRVpq3XMPmhojSPdHM37m3kOtr6FyG2W5tZSndrwU1JCrJ1SNEaqedW7Y5s/v3NzoUmdcb6a6CcpZlGUY1EuzRGrBu4uLZZZKDDDBOvDzsWOjS7dx2b60VJ4PGvqw7dTxxYMiaPu05U+x7evXl/0FD0rGfmjTpvJ/bTILvd8062YX0HvL2yIh6WHW+CzXxqMZtGmdVXLbY5Px2jK0dtN3TGsrbbOWmYRuNVkqBdpIk22q5Ai9vimOPE030I7T8FzfOrqBBHVyaHae2dmyHVowsidzGsV6V7Q+K9dGqunLvEwNawyn2b+oTsKfe2lSO7fJWMu4W2MEvkwoP2fJ0cRl/bFjZR/F275mTdlv8OW0kcXFMlsybmn/AzDIeIn9GXfI06VcsY/83Oegc6SxlyYPvLpk1fFeHYfvbyUNsNpUB29bq/wGHpuIp/+1Pku/SbLHMw6vqpPqApZthpPSL3uvPY7jc+ScNXbyoJ27VKdX39CQninNZyLZKfhv0nFSOfy54EFGUl/D5dy6deVqh9rE4+PHy/337Rv+TdLNuW0H7fF33z0IfgYYJFyi+9BgApTtp58+2OIYgWa040ERVMbz64F1YFDE0tLo2AkzEOPntWvLceGHPwydQ1c68thQuOytK2O7sM17zstzrDbm0b5LrUuqN0WntmwyKbYvSWZIVOntlk1RKid3bIHyX7KvcLQg037fzmRf1a6U+2Stl249W1p59P9UHaGqHVLZ/Jjl5TLIC3VZviIJ7V9RfvM+YmGhlK88iAxlK/U98ImkuM+dd5bH8XuK44GFhbIdGDB21lmDLU1AhPIat5L/GOFJu2hgLZ+ogv3C7GxZ7z/9E3QOHfNwcuVknXIsUt4JT7+UI+O0fareu9TrI+2fUqZVdtXx1nW2ApY95PQDdAUH6RjU1zzJCLGvqPtM1tGpAaozwFMbkdZPe5IdSe2QxnBa+6hOzv2/lo2Hy3r6jqBNhPoGEJTpaJvhdVK9m4+9sJ/evHnU1oq2ehpkTJPjAZQTIDdvLuU07o/PFk1chX0JguXhM7Z2bdl+9F9g/4btmpkpz7nlgNl+f7KCke+++27YgnY2gNpZkZHXve518Ad/8AewuLgIV155Jfzt3/5tI+U2SQQjc2h2nybRBiAew01dZ4fUjpwZhvR7rTOwjHZWm7sYgLeNRxnxGku1Qb50T6R7iEIsJRNaDk0YtxGqUGhlV2Xy8Lan7qCiyfOmcCdD6gAcIR2aur/03PCBomdWMW5xFpRFqpzrOBihgBkoMIduL9HRajADGbPOPXiej0wK0LW01s4nBclZkmKwTMS6HiPwZ+c7n5OvW64zKDVIoA3ou64ZRen5NTB7OOkeOTlxz6jjF+XjJLBpk571DPEaWXMMNU0cy/fB/9GJ1REFzNjXQ+oztWzDdZ26LcLfk8bkuSV/m74GbRh7jePakC1tUazzj2GT7n2b/Yq2hOc0QuVtyhg7NehEqlerq6puT7lWOd52VbWRBpTUXSozB2/AEP++LRkvOS27JKdfX7Mmr628/5iby7uuufcwtcwcUuxiub95zglleR2Df5N2S2lSD9237vslBa/n1sFlmGSz4nZk7N+sZ9OTqZa3gZZt7Vtl+6Vyt6o+DcyAo8lvOtmDL+Up1XXoUPXYpkmoM3LCdPZG6PKcLOd0W3VYaJNjPOV4+vlpeF6syX+cVH+Gx8ZjHd9Uv6gFh3rvT4qdHfdFexDa3a1gGsl2YJ27Rx7l+DM8OlJKP+TVBbTAFKmcX/91gHPP1cttg7ryfxrkQJ02juv8mgrwysEKUuqyHV3U26aNxyr7ZLL/AOTZy62gvWnWO1LHzrj/ox8N8KpXNd8eCymD9aQj+XdS+vcm26HVLbWljr4hHTfp5NqQpLELtxlQ2wG/Hh6ZXte+Zd0DT/2T5FOVaDrmx7KFS/ZyhL8jVrILrUxt27SOw21O0uqL0rnjPnyFz5OcLVu2DAUja7zxjW+EN73pTeY+N954I1xxxRUAAPBv/+2/hWuvvRbuuusueNOb3gQveclL4G//9m9hZhIisb9DBCNz9u6VM24hUnBCVQdMHTM5Qaqpx/H2UOHlyT6iCT/pM/4vGbqrjEtSZkCPgu419FQhnbNmuEoxpljl0O+0Z0KqK9WghveBz4zC2Sh0uUQsU8t+Q4+XMpzx/S1jpCX8eDZd6b5Lz7SG5z5ZHXtVeVZbPUjvkXRe/B2T6tacY1ZbPY5++oxqz6v1HvGttDQFIp17x0GVM1DAzPKx0XZYeGSzFXiBW099nsFbanCItAWAmZT3x0NOuwB8TiTruliDNtxWvb/9vn49JFIG/blGA6t+vkyntG9V1gTPsm39/uh+vF+ZmdHvE83EpU08kO6lcj2HzsTqRyx529SgM2dgl2toqZpVLGE9U1LWMu09pEHafLlQKUOyZ1DM9+2ImXvvAVhQlkOl7ZF0av4sWfdSygZtZZKskmESxjMwk+NkpHqDQ16O6BnSeeH/fCY0zaiqZdmWjG5SnZ7lH/n1FTJ1zvD7al0XXpd1v3Kff2McMXNkQS7Tk+GePhtVxgqa0UYbp9EMm3wfOp7g8j9l3CftY+mpdeV/jt7vqdMjv+saDFP6CKssS7bzd956H63xcM7SsXXYuxfgjDPK9ljyGmD4fKSsGIh2zTyGZCpX+Mo3PCMqhcsyGgho6c2eMYY2lkzR36v0MKt+LMejS2jPt9WHc6T+TCqvKtOz9LxINh7tulA5Ki2ljlvMgqWNWdasKcfW2oR1qtvjeAK3NAMXf5b5RHi6P7cf4fcrK2U2FmyHpEtqz5vnnZNWPPL0vVa2ba2fp22ltg9pS9vK6wYYzZqf0q/RfapW2AIYzbojOT+lZxpgeGUszJCDv9EVV7SMaLjP7Gx5HGbx7BLpGbDkXNW7avXXlr08dcxfpRdYupGnrbQNVTqf1VdY+0vvFb/29P3RnmkqO6qyadGyOR5dPNUGVrW/9fx59MHjx8vM5Pg+4mfM1tjvl9cIs+FipkOaMauqj5Dg+9As/HwcQs+HZ9VC6q4sYbXP0ik88OeOf5b29YwXafssvRC3PBMxynG8//fcU/72yCPDW6o/0BVMAMpsxVu2lP9riUp6vfI80K7Hs7kdP17Ke3zuMIkJPn90Es84Jt8hUr+aW442HvKMSb06saYXpMgTj3yidaXYNVKQ9PcUf5U3QKeqfglLBmmrYdB9q/Q4y2bl6V97vfJ948d5fLySrUizA1nPCy2vykfQ61XfX+s+SvdLG/vwennZmm6fgjRmz7XtpIwFNfvL8rJ+PahNAbNU3nffYHvnnYPtvn2lLo5jS3zGqK0Wx3DYV2CGZZT1O3aUqwHgb7iVViS3YnEmBes5459z7HzeOBlPHR5biIcUmczL1uz5vK24leRbVbuld9jyA1TZ+VLHXdp1lOQ2b5/lX5H6GiluBvU+PuHcyoLMz3V1dVRPx+NQflPbvpZhX2s/34efh1WOVp51L6yxnPWucSR5K7XZsgfzuiRdGrfcziI9x5pNh9q8PDobh56fVj+1Y1rXFWDYn8fvi8f21jD9/mRlRvbyyle+El70oheZ+5yPiQEAYPv27bB9+3a4+OKL4bu+67vgnHPOgc9//vNw1VVXZbS2HSIYmbNr1/AMvTqD0DrHNonHoCaR0n7LkFpHqZfKnJTr6sEzSErpFLygwPcYEXnZnoAFi9znpmqpwXHfd8sQwPG+c1UDefq/x4GA+0rX0mME54oBVai4g0pyDvDlonGLy1AcPlxmu0RjKM5+oueMMrjLrDiI9RxKjhFtaSKqCHqMqk04O7zUlaVdtrULmjZYNllPTjtaXt6kMXCwvG5dacBqC6/xL+V4DynGzdzn0DOATqmTZ6mzBoq0r8C+AI0hfJnOhx4qv6PLw9Ht8nLZb3zhC3q72+C008o+J1cva8JhqpWdYshuqh1Nk3sOKe9fiv4k/VZVpvQ516FRVz+U9qHL6kr7SHVaxsQ6z45nzFAUpZPbo1uk6M3WPtIz4dGTNdoYy1VhGZbpZ/4ecGMpnXAiGTe5XiwF4fLl63CL8n/PHoBbbx38/61vDbbf/vbwPuPIhozs2mWvKjNpSLK0rtyvej676Efa0pVOFXBJdkTrA7uABiPw55VOiLzwwsH/KROtquqVyvGWmzM2kDICW5OpPH1dnffR44im5aT0q3xJ6cVFgLPPHm4T14OOHy8D1NDug3IfAyDuvrv8/+ab7TY3zcJCqYf0eqP3rMpGmYqWDZwi2eU49N7VCaaxgvrp5xR7lsfpqH2mx/OM2tb7Y5WZsiokRTvnFDlF960j56rOG4N/0KaCspUHE1h42kUnI/LfPEEjFC0zmuQQ166r9B545L/UZo9s1t5f+lkLFPPqbNr5SCu4WvZy6z3Ez1ZQPj+On8/i4qi9B2U89hW9Xvn/vn2DLcp62p/gs3DPPfr5tE1TuhIdO3n6jxSZqH3Wvsshd7w7jf7auvD7i+9oyjVsQseoWt1Z6jvRFo/blInr1j5WQiGrjro2OX58aoB6lby0yqH9kKXj5Y5N+OeUZ0aT30tLpe0D9QYMaFpaKvV87tvF7b59pW0H9Xa08UwT9L7n2CI99jirvLrU9Wlp44cu5HjdOqQ243ee9zDHdpZzv2l7tM91QP0/BUtO54wlPT5oWmdVn5UKl3MeXcXajz4jde5V1Vhe2pfW75nIJrXPitsBGMh3jNOhScJwH7Tlo46PE1xxcsrCQvkbTmJB2z7X8SeEfn86g5ExuDiH4js61zG8xxNCBCMzCpgZzTRC/5c6aq3THqcDgJLqdK9bR9VvTTm5Dca9pPKJpY+bMhKkHpOSUTbXmKrtmzpoQ7qcBW8ZqC1DJ4CvnZaCKmWb4cdZS27w8nLbIbXLuof8Ox50e/z4qOMNf6Mz89DQiMuvUWMmwECpwf/HOZi9887q+7NmjZxxCaBUvBYX7VltCB5PZxrTOieJSWyTQGo/kLRk/aRcg7rGh7bq6hKPsdfb9qYNR3UDDqrqowYXz0BXk/+Ws49+x4MQ8JiVldFAAwSdZGefXZ4PHs+DkZeWyrquvBLg9a/Xz6Vhik2b054XyaifG0STYghrWldK0dm85eToJyl1pow5mqCOoVK6Lp7AEq0c6Z2ncCcPzWRZ1Vbpc8px3uBjb3n0+xRDHmI53fkYfW7O9/7XeedT3hE6HtF0yOPHRw2LfGUEmsEC4dlOJUMhzYTKrz3vB44cGX3OcB8q29FQftllg+2jHz1cDkD5DG3fDvD7vz/arpONXJtIGw7/qvFmB/abZF2tSf2vK3IC6aTvrfOjQWMA5fsJUL0iCg1M4nWkWvBxf81B1e/rE+Cke+wJRkvRkXJsV9I+nqCj1Los+ymAb+lLWo72vFjOd8lWxTNXb92qO7po9hz+fOGYAcvbtWswKRBgYCv6/OflNrXBli3jycbZhi47bnlXpS830Y/kBAs0dX+bGjNZeOzmEjyjcerxAP6gDc3XkDr+85xrU5MBLHmZE5hijc94fyQtA6+9Gx79n8p/7bxotlXPSkW4RX2B6v8Iz8y3uFjq+Wj/wWeD3jdu98EABgxqW1wsgyI++1noHDyvNvuBlPGr910dty5OyW3PuOzTHhtt1b5W/U3dG0sHpp9x3K7JMjoOsM69Ss+n+pyFVo5VvjUxytLxLf+mto+Ex8bjse1Y+jafEC6NeapW3pMmgdPEHgADOYyymK5GQ+tes6b0OfJkUPPzZX1YF8p4LGdpaTgDPpYJMBzkrGXWl/qhlRWACy6AsWD53nPRxrRV474UXVor0zO29P7WBF5ZnxL/kDJ+9sQ4eEhZedEi1e5sXRcq3+lx/HsKz5pP30NNxlP5zdsjHaM9972eHHenHa+VJx2f6s/Typb6D48PR/IBacdyWS/1b/gd1eP5KmtS36llJKbPL65qhlt6DjzOhW/7/dF+iE5CBBj0I1UrQ9B+sSgALrpo9Fwaot+fzmBkL1/4whfgC1/4AvzAD/wAnH766XD77bfDv//3/x4uuuiiicqKDBDByCPMHDpYPhlSynYp62WV0LGWXUEkxUMysPM66NZKB6+VUxdLmdLINYQllJMUVDYuUhza46Kp+1p1P7s+d82x0kVdkoKhyQ7tu6q6LAeepkhZzigq5yzjKA+kxSXY6EAWjY40WyYtZ8OGsq7HPU5uT0sUMFMqUrt311NsJ4VUTWeazq2CqegHumTM99YKDue/jS0wvCuHorUPfWfrZIqkMrrKYSXVT4ML0OjIA47xM50ZKzmYcEuDzgCGs2MjuD/OqO2IGRAyPQH4jH+IZ+KJhLZUEy0jZfwg9fdVx/d69jJqvD2eJQitOquyg0kOUut9qAq4kfa1kMZgUhuxXVWZuzzLgElBYPyz5KSmdeI7Zi2zzA2N+H7TbJW8XslyoxksLSuPZBTlckG6TlX3wPuMo77vkX2WEdFyjlW9s/Q4q62Ila1ew3oPJX2eBxjQYDL8TrtPa9cO9wX0N7zeR4+Wzxn2H9hXYLkzM2XQImZf6Ir77x82lkrZnynSZMQUGYSk6hwp47uUpTTp+Vh1as9SynvQ71efd9W7P43jMaSN5wLh1zplcnqdYzQ8zjBknGPuphzQdR2TFpLtStPVPLKd2nB5MDW99vw95suq0j4C9X6+CsrSUvk/2n327h1sUdY/9FC5JPTBg/J5tQmX+V6atqlbdXh09Eln2mS2Z+w1adRpW9NyapJo672RxlfWPlXl0H1Tsupa4xrPPjSRBsBAbqNuz4PYpP4D243lUHsQyn/eDjq5Be/P5s0Af/zH4qm2RhcydZLfjSbIPb+m9N026x3nvfPYQgCGbalVeCaTdHHOTb13Wlvrll8zJqHxej2+Yik+wxqPaxNWpAmPNMEUbtHvy/sRHA/cc0850USyMyK8r+mSVLsaYo1T2x7DeuVCU+TEJHi/r1Oupdu0bVewbM9SG3Lq9eqOKNOxPVLW+6qJih77T78/uuoV/Y23mbeD1ulZZYS30fLLWb4TDY8Ph7ZVq8vz/GnfcbRnGo+dny/lrnWsVg5dvcFz7Tk0iJzr/zw4+aGH9Ak4SK9Xng+unt4S/f7JHYy8fv16+NCHPgRveMMb4MiRI7Br1y545jOfCR/84AdhXYqO2AFTbMlqiS1byllcXoHioa1gxyaNFbw8Tzna57qkCOnUsjxKvPR9lVKV6oBIcSqnPHf9vs/pYx1Pt1744IJ23lWKDx0IeYxmGtSZVBW4ILXD2s8TYCOVp5XjUbJSszDnKNhWcIW0nzYLizq9abZMgOHnEcvGWbdnnTXYUgUVj+84GGHm058C+JEfGXyggUPTTJcGklRynlOP/KbPcdPnn3Pu0/gc0YEmx7NMmyBXrADjyuBjj9xuUifI0UE8g2EPHqMFrYsHHPF2SUFGnsExlfFV/bHUP/J20fdRmyFLefhhgL/9W/33hilgJr0P5fqfFCgwje8/R7rvKfqJR1bUDeJJ1eP4MXXuUxvOFI98kTJtrV9v11EUo+fKdfTcsaCVEUEru9eTz8NLap/MDaUphkLLsJf7HPP2SIZcT3+SY/inbUbdngdr03PmYxP63PNMyDxDDy2HT26h2RToWPATn9Db3jRnnjl8zniufJWS1KyrTY3x+fPaZb/SlPOIPkdVuo30rtFjtMks0vFae1L7eY0Uu5JVruU41vQqAHtij6cducsyV8l/eg+s8URVRkLPJCQJSUZbzwR/Tqv6Lqsu6ThJpls2r7o2HTyWT5rwLCFu2bx4ezZsKOUkZj/DLPjolFpZ0bM2Se/sgQMAz3ym3r42wLb2evo5076vCX0l9X7n2mg16sr2XFtplVPXW2fb/aB1PlK/5CkHqWMn0Oqo49+hx+ZkvZTkv9YOemyKTZ3jeQ9ydbCU++sh1e/D95NWRuF6QarPxPMbym2ewQ63R48O7DUApS6PQWiHDw+2KyujbUP9nwauYcDaXXf5z6MprAx902jHqWtTabN+zzGTds099k8kV7bXkYWp5XsmkefcQ1puSob+pp5XTaf22vpz+gtpjOLxW2lbSf7zumhmTNRdUVajT5XqqVZmSisYGWU6n2CI3x8+XMp5lPvcDnTsWPlc8Mz4dJIklnPHHfK5t4nV36eMxerqG9Z3TeuMqUyaTEb4/UnVC3N8A7l9VUqSgJzyrf0t+W9dM6mvQNmuBS/TY7TA5dXV0cm/0rlXnQ+Vl54xNv+N2nw9tlaPbPe01WqPtzxaDn5Hk4lI4wbteMkuJfUJdEtXRkH5zfuDgwdL3Z7HBmE5x46lTaYKVC677DK44YYbxt0MFxGMbCEpgk05lVJIrbNpo0kKKUpAE4N9j4Cucw+bdvhKZTf9vNDy0NCDnYG1TCcqEzjTUVralxsRe73yfykIWaqHQq9LzrKaHnKvr+YE9b5rVc9dU8YuryKk1U+3nvq1WVi0LIuUd/bss6vLa5DiqVeXTrpAZxyD0XEEYFjtyGEc+oOGFkSA30t9hba0VmpdTRn/rN+lARnAsPNGkoFahkMpuLQto1Cq7sVnFefoLxapk+maMijXyQqdwQwIQZqppBgmcknpZ+saTus+Q5oe5akr1QGdE6RBnfdtj908BjXPvahqpyeIoOoY74RGTb+Xzsc696YDbHg7KCnvhPRseMYqOf2ZZ6zqeUekcjzt4bqv53mj5eLYYOvW6rq0c6V1oaOrS6zxTQ7ecVVT5LxHbdiPtPI85ddtwzjGClY/mSuDtPOo0seqyu73h5dgp23EY+nSvil9NsebYbnuOSFVzihJxx8X/JrnTGiynq3U87OC2fh30jhJc3RJzj7eVnosjtUwa3KXYCB1m7TlZB4XOedDdRKetZs+P3X0wl6v1Gm0rN9W2VK/WOfae/SounaQpvSNQlklSGuLFpTgzbDG/5ec35pOju+sZbttyubU1Lvnef7ofp4+IiUAo+r71Dr6/dGgOMkepd1f+s5jOd/4BsDf/31a++oyzdnmJdroK6reG0luWsdY9uFJ7+uqvm+yDg/esT7iyYysIbWTT1qw+hHad2iTHHLh5eTa+OqMfbx0GWci6eRanSl9prT6Ih8HLC+XK2JZkxIxGG1uDuBf/Av9XNqA+lJyfPie3+rqep575ynLsj963hfrva6Sk5L+I11vSx5ouoTULs2vJ8HbY2XMlVae56SMI6TrYgWOco4fH7WbUpuOBpZN/cDcJ8x1/bVrR7+Tniksh/YJ/Hw0X5+lJ+f6pjy6iPQMAAwH9iI8MYdnkrLVT9NnRFsFyRoz8XbNzeXZXWnfaT2L+FnytdPPlk+J/oY2+HvuAfjTP61udyb9/smdGXmaOMlGXQ2R6sjWOvScTlyj7QGZJNisfZCmB2YpnURuvZZClnLPcq+HxwlrOe09daKEReOcZ6ZJiiKI5VtZ0KTz8gYmN0nOQE/aP1XxrzrOUvzplg+mJcWjaoBJHTx8hitmMzhwYDSTGVUm+NI5fGbrvn1lWTQTAj2fjRsBzjhj8D9mzcEMydu3D7ZbtpRK1C23QNcUMHhGKzO1TiM5z3LKgLvuQCCl7mmkjX4xF60tfFJKVzT97CA8I5i3Dr6ly9Nzx1DuoJjXmZLZhrZP66etNvD+4OGHAfbvH/yPBkOsY2VldHlO7A9wpuuBA6NOKH4ec3PlQBPvC9cl+v3yGdy8WW9/C6DsV0lx9o1bdqXU32Zb67zXHuObVNe4r30OTbbZGwDWBnjPUvuRlAkPdceAKWOCXOe0xxipZbSXVhuRxgZSmV6kYA9uTER5fvhw2Tfcffdge//9gy1OdAUYXYYPyz54cLDdt69c7QR/27VrsL3oosH27LPLsQD+1hXTKDc4KTJQeg+qDP7jukYp9pbUcuqU52HSnivaHrp0Jwe/s5wInlVSuiRHB6D6qsdBxL/LRbPtSG3zlJPiYJXefd4n4NhgcbH8TnsWVlYGy3ACDOQ8QPkZ+5H5+XKiCrZ1z57B9nOfG2w///nWl+dUoQGPqdTROyQ9d1J02Vy56+lH+DjaCiZN0dlS3lHJh1O3X8k5nsuCXDwyjO8vMTNTPY5o6tmkzwJ/JiRyxouSvT3nWlvvaurxOftLtiLN5ygF03DdHu34hw6V8p7/9vDDpSxHXR73QVvRwYPl5JFvfWt4nxRmZgDOP3/w/zgyY3ZBU7rsuKgj35oqz6JLe3lTePoPSz+02ifp9qi3a9eKHmPJF/4dtwF40eS9JzYB8d6jlL7Wo3dY/YnHXsO31A5j+XRxi7Z59Lui3KYB3rx+tN/jMQ88UOrt99wz2KLN5/Dh0SzH2B+g/1cCJwldeOFge+65pf6PfgO0B2F5WD4AwNOfrpfdNlZwaVO6Xu7+4+wrvO9jE2204mbo72g7zkkk5h1f1+lTUsZLUj2WbVwqOyehQt0+0xP7ZE188azUlBNv5rGpVD1nVXVJMlqT257YHirjpQm6Wju1foT6b7kcp3E8+D8eT1e6wvrR3o/2f/QHPPBA+f+ddw62aCvCe7u0NJoB2+Lcc/37ZuCdb9s2ObmDTjYiGJkxtEyzh9wON9WYV2XYq0uTBnZwBHXk0GvHwe4OOqxSCK0OSDpOM7pZnZ33PuXcw6YCGHLLqRogNmn49GAN8usYq1OULUlJyh34VkEzhmoDX6sO6sxCoyYPWN6/f3SZBv68UMU/x5hZk5MyCBlpwsFF5VxK5tJcJ5vlPNXqoJ/rDgSr6mpTB8jZl5LiyE4pRzJU8vJSpxx6dJym+gCPDObZhqXjm5LF3KjiCY5IDZLj7cDjccCJAWAUaVCc4siTBtvcGYZGUbpcJ/YfX/5ydR1Nk7J8Vt0sOp77LDlqJ5RsvZ/p9q4+OEdupR5/ssCzXwL45HQXhnYA+flv03k5CcZ9qufysQY1Pqc4JusGRVh1cbmNRkn8vLJS6vJ8AhAe+/DDpfMJgxhwH3RcbdpUHo/GzaAdJuE98NL1+P9kJ65Dybj1qy4CdVLOEfsGlMmeY+iEdy0zDrURYR1PfvJg+4IXDLarq+X48t57Aa65prrupvBmELJIsRt2+bxZurDXlpJCmz6KJstPLUe7v03aaKrKkoLA6LGSbVarS6MLWwvFkxmNy7CUa9ewj6sRNDtS6ruKcHs5nuf8vC/YT/ttaaksm8pyfrzmy6L9guYbwPHEgQPl/v/7fwN86lN6+6YFKRAlaI9pvL4pduKc7+owzsntbcrtLvRuThs2SR58ZtnSuE4urRgpBTxz+w8PkqNB0WjL53360lKZsArlPbYNxxpbtpQBy0tLAH/2Z+Ipj5VplC9N0cb7OM5+0Vtn220bt27QRb0e2deFjG9a7jdlM8gpt8p3qJWT4temdhtuv5ESp/CyaZ+Dej9PiogTYR5+uGzvpz9dTlZvgUkIRAaYnHaMkwhGZsz0V8sZWtYLbAURSEFXniUCPIFQmgHMCrqy2qplZqqqi8N+E93bKcFkUsZBTzCbZdSp+8bX6aSsY1MMhV7jG89ewgMOJlmZnpS21XXs83IsQ6oW3Cm9s5YM4gNN+pkPIvl2cbFUFDD7Ac6aPXCgHDzyGVV0X26wxMEpOpfoYNQzQ8qT0btBZqAoz3N+Xn9/677PKQqyNMnBW6Z3X8u47zk+p1+kS5l4Bgc576OlqHvw9K9Svdpn6bc2HZRdOOxSl+HUyu9qsM//TzkuhZxAcKt/1wyFnjZIg0lpq2XGXFgo5SJOJsFgMpo9Df/HfXCL/cjRo9UZ9g8eLJ+pjpdpHgqCpffG0pur9FLp2iNUz03JDNsmmpGByitFTz+hZU6jDldFSrBsU5NAmiI3W01X0GdKC9A9GdHkf8okL1qOZUPgDitpq2VfWF0t5TXK6QceGGzRWHjPPeVv9DiAUv7v31/ug9kvU8YDXULlNvZr/L7Mz5eTeeo+p9ZSk1of07RjyLKXWM+m1K6q9lDnZ51ypDZKZVr7VMn2NnTAFN0vtS6Pvt0VtC08BcgkrJF4MpJjS5TsDFQW8f6C24EWF8vfUMfHMQKOHWiGTew/eIa1AwdGMyx3xec/D3DppYP/e73yOvDMvaire+zuXuoGIiIpNmfreO3zuMjRv+kzneLPsOjCWc6x+izP8eO8h966cyb2TsqzmYtlM9Dgk+TpM46/cV1yYUGXL1TW84zINGsyynCeGQ0nF+7fP5g8AgBw++2DLcrxaeF1rwP4vd8b/O+ZEOplUp7TFB/zpLS5S9oIFA1OPur4lDz6nZT1XlrJSjuO+2Hpyra4pXZ7gIFcp/IeoJTf1G7Dsy/TcvjKitOMFQsTNEOdIMxg+jhZ76vHliLp3ynxJSk2Hak/QDmNshm3NP6H23hwn0ceAbjvvsH/aJOhwcMAg36B+4TpyooAE5cCeFKCgCelHeMkgpElcFYWQL0A1KB7uhxIp0gQj6OpbpY9yqQGHUwabQV3pgbB5wStSwFO+AzxZ8kKaqaBZ1Qxwe8ABkFlGFiGg1JUQnC7sDCa5QwNeljOAw+USzjgFpcEkpzc4+ilse0A7U1AQLzPSVWZLQadmtkuW8pWXxtsl7N9rWXDbrg/su7FRGf0PhV1oiZ0x1SHK9/SfoD3CVpQclV93OCJfQUNNMA+ghs3FxbKfoRPmKLBbujUwr6hIwqYgWLN2tEf6si53izAHGYMbWZyTWPvuqWXYt89KUHS46bpCQw5wbZNBejWDXxrk0lpB6dNR2WK/i/Jbd42Pg6gGR+5Tk4nFmiB4DQwa8eOwRaXUv6e7xls6WRGPjZAmX/s2GjQMT93Oinm4MEyOKAL+CSTLmUgn4DdtH3Bgye4ualr0cY1zQ3MakvmdDHpTTq+qcB0dB5g0HDYQ5ul6UlLHueuNuGEyl0p8IFPPkcdn650hYEOKPdxMiHuu7paBvTu2jXYXnxxWQfAIMgNM+J/9rNlWV3wpCcN2/9zmGTdatKx9NscWeo9xjN5o4nACW+fPqkB4ZxpeNZTg/snKRgz9T3gYwQ6cURLVMJXvKL7oNy+7z6Au+4a/I92eww8w0CDlZXyu2kLQkbe+ta8pd6nhbb9B9NOXIfAgzZx3toXkVZcTLHtYHlUTml9Fsr2AwdKPVqzFVG/Jz+v+XmAM84Y/K9lSF5aGk46AlD6Bqg9p+MEI8lI90XznQOkyVQtTsDyA9MJj23Lp6p2pFBnkpxHZ2va3lGXuvbhcU2EwXp5oGiubaLplRYlurhWdWJPpEnKKeMQ6xmX+grNl0t1ffSvot+VT7CmZaPcphnu8b7iPnyFxIcfHpX/E86kBAFPSjvGSQQjc7rs+E8Gmpoh6JlFglCnXZUy4zGWWYZG7Zg6dSFW8I+VJQnL4c8pLRs7Hq0d0rVHZcRStFPut2d2D1WAsFPjx+cGd1r75ChakpPYel6tDAS41Yzv9LM2UOX3GABg7Vq5PHrOqIzgVlrahw9QN2wonTQYjMAzWj788HB2G4By9hT9Hv9H5WbnzuF2LC+Xjurl5XL/LqDZIbyZezltOqC15zZl1px3/++Qle1SKr+p4Aq6zZH/OaQ6NBCPQSJBxs54B+C8DP7O00CDE4U7BpHSPlXPhVfWejIjWkYhTx3S//SzFNhrtUHbl+oU1j4pzy8NGuPnIAUWAPizXgIMJjDxPo4OZnm/g8fRfgn7C9ziPhiw8OCDo0EMKG9x4HvWWWVfs2bNICChI2YG4cj1CvHogblyyWO80+6zVU5T+q3Wplz6/bzle6V9Uuutg9YOj04sfZfax0i6IS2nKHzBFXUyV6bq62gA88jLnPua+x54xqRWXZpspn2NJtulfkRaXpO/Iyh3sY+X3iGe9ffw4VIGI7Q9WD/PmkMzJPCJJvz8ZmfL1U6wbNyXTnzENn3lK6PtbpPjx8v+yWMXkO6hp5+WPvPjpOP5cdbSeNK7kvMs83ZZeMYYtO1aXyXp+Jb+77muWjs8+1I0w79HV5POx2M7SLWBoFNAezZnZ30Bxtw2hP2KdK44AZ6XR/sQ617wyRH8mBybDT3OY9ei77MWvJX6zmrvHF2tii5RD1DaVuiYiOvWW7YMtlu3lg4lfl6Sw4pfR/peV9kdAQDWrx9s0dZ01lllOVLWToDhjJt4jjTrGkAp8wEAtm0bbJ/2NID/+T9H29AWlr5JsWyB/LPX7mLZO1PG2Ck2RU+7+D6e8a/1rqXq7ZLehFvtXD1jFm8fw9HaY60YIH3W+jwJq9+W3mecMMDPQ5LFXMdHWb+6Wq0n0GuH8l8rT4LuizICqWPrkfa3rm+KjkTt9trkDVqmliDEmvwhyW0+eVvrb2mbud2PHodbug/vj3Db65VyfvPmwZavgrW0VP6PgW+Y/R6zJz/yyOh9njTwntD71aTvybtvyljUkoWetluyUKrbknn8WI/+5jkv6X2uWnGY9udWX6W9q/QZ8MijujILIO1+afvje2hdO01+WG3mPmK6L5+46KmL7ltl/6l6FnDrOR9LxletINjvD9vMpfNaWZHt4/gbwHC/iNcBv6M2Hm7vkc69Sv+h74E2nqHJqLiN59ChUqajvs6fsTVrynEIjhHQV4xjhdnZ0ez9PIszZccOgP/xP0a/bxNsjzRZ2tIpquQm/d8qR0okhmjjM/pu8HKseBlNl5XOgx5jHWcdy+H7WfZPRLKf5vSnqfp2lR1JKjtlPEGvadV1rbq+PMaC6+geej3d/i/FCvHfLCTbE5dvnokZtK28PM/9ThlXSd9bNlrtXaVyT0sERXU5ze4qtQ/LkbIf80z40sqIWt9w7FhZFp7r6acPtignd+6s1n/Wrh19BvkYZcOGso1nnAHw4Q+Pnmdw0hHByJzPf75cdnN+3s40ituqDkMS6qicSZmLEEkIc6ElDfS40QKhwSaWEb2qLmkQyPehWEqS5pCRFA6pHZ7OOUcJsfAMrrU6aZstpTMnIxNtl3ZdpWeSdw7W+XmVzBzQuRLI5Bg2pGMtJUtTBGkwm2bcocfzWbOe5YLosj+0rt/4Dd85NsHx46UBdXm52llCZZjWV1jODlpvysBKomoAJA1Gc95n61nyGH6sfXm7aF+TEtzhed5TjbRV+3jkpXQPUtpj6R10H9QBtGfBG2TWprznRAbWNDzvoWcgbxks8f5TWYhwg8TiopwtAX/DLdbB32vsD5aWSsNn11lq7r67DJabm9P7Oqq3awEYiGQ0S7k/VAbyZaIlQ4XVDn5/0TAuGdU1vZ2eM2+HRzdGJKeupOtrdUlBNNyYQtvk6UesIByt75QMuNr1SO07+W9Uj7dkszaxgxoBLWe2Vr81YdHj3OLlSEj6k9bOFJ1JGuNa5Wh9Zx09nJJqHEXQPkHLqXJGWfKl6jj8rBlVqT6POjzPkEDlHNf3eeDDwYOlnDvvvFIf74IbbhhkxwSQbTKS80S79jTjnHZfJcM2yjnpua8ag2nHSZ+1farkQa4MS0E6H/7ZskNJbdTeKWmMIbVHc04jc3Plc2vZC7XjERrU6XFmSXAZgaROLlnLVomw9DHNTueh35/ujISevlv6nPKuauWmjnU9beXyRTpe0mFR7+CZ8VG2HzlS9gm4L+rbqBPQuroOXPvWt8rJ8QB5wU5cjs/N6fo6vYbSJCPch98HKWjKkoG8HK08qhtptkDJTm1dJ61d0jMl6VhWW637Ugd+vGSfk66lFszmuS5S/bnngQGjnC7tOalMs/yfFKpsPNI+ls2WyqKqwGmaGZ9nz6RjBdwH+wE+Kb3XK//fu7fblVEAytW4tmzRx/Mee7C1ryXnpM+5eqCGNc7jdfLPtK/hNltLXlmBO1Y7mz5X/r0lk73yuoq6tovUOjAYNKU940Ybs5wsNHWfrfIsGaL9ZgV3Srq99tyuro5OfucBxwcPlrYhPvFSmnQzO9t9MDLK/yrfI35OsS9zJJmaYrvz2EbxunrGcB67t0SObLf2tybZ02M1WzHFY+vlx6f4VeixVf2z1b9qZWrH8+9o3Rs32nVQO5AnIzIvx3O8p3+VJkda90CzwXtka+qz6bGlNI1kQ2vDpur5TL+T7BzW+2BN5OG+Oh4kTSfFLCy0GozcdJecy6S0Y5xEMDLnyiuHl6vIoUtFP+UpbqpdqW9Ol8K76frbLr+qnJz6aaCBlqVgZqa63XU7b29GBCQlI0kdmlJKUn+zDB05vZHl4PRkGeUGQ2oolO4FN1BKQQVUoaBbOkMLA6BotkyAMsPaoUNlHbgkXEcU8+uHs3IiKc5BD+OSxRqTYpzynI+1jzSQ8QQRTcr5V+E1RvMgAgSvgSSbpYwG/DitPV2QOpCXjpM+0/I8RmNqtLPKSXEc5Dz3HmcFfR+4/OaBwpLDlfYj3NnO36u1a0uZjn0CbunSQNzIgPo2ZkNbWiqdHDfeCF1SnHMuwBbFkRvk4TH+TYv8zSW3n865Lv3+qGNf0kWrsiTQYzwTFjV9n06CtTiZnFGS8U7CE4TmeUc8hkIut62gVykYh/cbdPIgfsZ2oA69a9dwXYuLpb5/772DLV8RZ+PG8vndsKHbPuBZz5InxXocx9r9Odllm0SuU2vcZU8CHiczRXp/UzLgeNqiraIiOZrwGJ4dne7Dj5/2++cJTvLAncP0O+1aWRnLU8YVy8ulTOf2GymzJrc1Pfxwaa+5/fbB9q67BttHHinr4hPqsB/43Oeq29o2l14aSRHq4BkjU5oOsmuTKnuu51iv/lD3enic9p5Ah6Z8J55VuKpsKl36YlLx+AGkZ8ATtKIhPS/ahEGabU9aXhlgkAVz797B/yivsT/o98vxGc3ID1D2PwcOjMp/3N5662Dr8UuMmwsvHHcLglOBNvu8Se5HTwUk/Senf7XKkfy/fPIH1eP5BHH09dIVcnnWTArKefQt4Rb9uQcOANx//+B/nECOAce0bu4boCuiAAzGF5dcMty2Lrn44u7rDIIm0fy/HurajnKP96y8hkxKjF2ufwa3VZMCvOMAbSIDLVcrk44HcCzAJ4ocOlSutsMnD+JYjvqcsE/AzPo4DrjtttL+j7L9tNMGW8yiPzcH8NjHDv6/4w71tJtgUoKAJ6Ud4ySCkTmHDg07RauMOpZhIXU2ZIpjMoUUwealaUNcU3VL90kT0CnBPFId3ElQ1TYAu1PwBDJ5jb1cwZecQCnLKXDoDEieLU2qi5+HNJuq6vmwZgnR71OCgD3kOprq3kOtfGvmmIeUa5iqiHGlimdTWFwcXZIUP1NHGB536BDAr/6q3oaGmYHCztDqCajyGKYlPM4Bz7E5cjbXAZ5aJv0+VdHONd5JcroNrPe5bkCSRc55SXLdUw6X9dRYxOW+RJ17kNLvW8dVBV1VPS+eQGNJ76hqV9U+nrqsZRN5EBoGAXuu6/KyPnCmhk9tmR/JqIgGE17/8eNlX/ADPwDwkpfo7WuYGShsXaIpvb8NXdhj/LaOT6FO+63rI3223p+cvkG6B9o7T/t0fpyUzVkL1LeyYXqWBpV0WmtlFW4okgLFqpb9pUZFvoykVKeG534XRfnOa3h1L4+89Yzhct4NTzmSrPaMTauyt/Lj6L7SGAyxsh/TfbhOj/ugEXLHDj0jAhoyV1bKZwj7IZyEgm1dXCwdZWi47BK6KkBK4J9GrqyU5JOU5QbRxiEp/RnN0GPpP1ZbNVL6SU+50rsuyckch68HLsulVXK0+ngbvdB7a012QIcBRwoithy+0v4AZd9QFLq+T4/V9qF1a7orldFaf0qPrcpO6sWjF2vbXk+3I1jvpfWbJf/5dbH0BX49aCZfPiGItodn7uWTGefmSofSlVcOtvy+Hz1aBix86UuDLS77jOORKj2gbax7oAX7AYzKTTrZxxq75baJkyKvc/RlT9l1y7WSIFTVXbWPxz7nuU+WTu7pjzQsu7DHPkevPTqDrX1Srqukr3NSsu7zlbuq6uftsO5B3XetCqnv5eMIy0ZkZaKUtlyeSNnIeV3Sss+4Pw8mPvfc0fOSVkXFsq0M7vjc8YQjNPEI6vQY+IC/Udsi9imzswDvfjecNFTdb4r0/GgyzGMn9LQrdZ8cm5O1r+ecLTkp2YNSrofWfq9dQfsuVc5VtUf6TbJv8Ey1ls+XHwMwqv9zG5FX5nt8zVrmXckWon3OHctJZWryW7rOUgZIqU+gW5ogJAXJ7od9Aurv/J1ZXh69vtK105Ja0cnoPJMlBjdv3lxOPsd9sZ+n9x/rR/8vbmkgHH73mMcAfPzj0Ck4/pibq9bfrH6+KaznP+V4z7thrbAi6cB19H9epobWHut4T3+YIlupHmaVZ+mluNX6cOk+59jMvP2K5sul91aT9x55noonGWEKnmuXguf5l+pLGadaCUKkduBnS0fix2jt2b4d4Oyz5TpoEinNFiJld8d9UKYeOFDG+/CJM/hszc6WPoVduwC+67vk9jZAU0PCukxKO8ZJBCNzeFYEz2CnLZp6QnMHqqmdtad+z/dV5VlwYZg6GE2pI6dcy5Eg1aF9lqDloDDXBqNSppOc82kqA8/srB0EejJgGU5zDMNS1gOu0En7cAWFDjxxEIkDTHxOpMwKPAsyXYKTL72MM2MPHx6ddYVbOhsXjZlnnTVy+m1SQIVxJdUBknN8zr5NHNd0OVWDwBTHk6fcXJqqs42+hpPbL0rH8QElvutaAAL9LUXuFyTA0wp80IxlHsNyU3pNr1cv4Mhj+JH0sZz2S3Vp71a/P+pEQmiQghZMubhYZsvhRkQMHKPnSyefAJQzZO+/vww+wD4BwRmya9aURtW774YuKWBGvob8/ljPSFNGEOv+WuOQFF28rg6e4jiWyq0yalb9Vqd9k0KuI9DqX/H5RHnL5e7atfqyyJLhypJlWpZLaQk2Cy0QSvss1WnhGc9a+rbnHUtxglLnj+WU1tojfeZt9ZRrGfepLOQZ1VC2o66+ulr2LTSjAoCcGYdPSsQ+4oEHyu8wa2ZXUKcbHQ/hfcL3imb39DgOcmQYvYdVz7nHaH38uOwQpZ9TbUWaYVqSIZ7gUPxOcuryfaiultOP1XVYWaSM53P7KCvYFZdp5g4nCZ5FJ1Vua2Cd3vK0yTXSfUoJME6xY1bVz9uRo4vUbbMEl09SeTQwoWofaTyhZcZHWf3AA2WgGX6Huv5995VbzJ6JyyFPEv1+2a95dHxp8oYlU2k92m+8jnHRlocsRdeRjqOf6zjtJSyHeI7vx2qH1R6PDcJ6hlCm8zqsIIKmnkXpvLi9SLIfVdUh9QNWH8HbQ4+pO7bV7o/1bFjPljZBBEC37XhWXpH0d76iIZazujrqL6I2f9wfs16i/Ea9/eDBcnVDlPGYaXmawHHJ/HwzMjhVf8iRK5LtzWMH8vRRVruatidVlZ9bjlRWik0lty5rrK5NLKWfU/oBCZSzTflp26SO/7cpW6XnOGts26Rug1tN7luTWvA3tM0fPjy66okk4/E3hI7HeSZj3OJE83vvLTNgYp/AfcSpmfFxonqX8IBuCen+Wvdes5N4dAJpNUlPe6R2VT2X0rPtkfue85Go6j+suqn+b5FiR0a8cTq8jJT+I2dFI+99llalwjYCDGwyvG+ok025CXKT6zVBbv+h9fOSryClT7DeJ8/zzG09NJkU34eOC7SVsRYWSpmurZ6ysKAfjxMOaT+EfRPGDI2BtkwcqUxKO8ZJBCNzHnywnN0lBWd0ybiNkeM0lNYtf9zXLoWmB8BSmdMwGD3Zaep94vvmDuK9ii1utQEvQg2eVQNfANt5gwPCI0cAvvd7feczRVQGPXfMDLQw01ED7/OkTD5ooa+oc3/Ne5Hb1nH3p57+p6vnoQ3dICc4wYPHSCo5rjxlSQYy7rijwVfcMKgFLtD/pUEt/o+DUWuVhNVVgA9/WD+nNsBz14I1u25H6jOVYqBLMcKn0tY70RSegJC6ZSMpTrW6AWu9HsC6df796yAZr3OpMkQ3WRenKUOoFdiL0PcC+7y6zjStHVZwqKdcSf/fvl0uWwpY4xNgJKMoXgNqFMd9HnoI4ClP0dvXNHQizPy87x5WITmBcuWu9luvN1omf6Yl/apuu+r2lZYTFstP6c+kfbmj2NMOqT1V91waI3Ok88l99z39hzax0JJT1GGFv/GgZkmeaOX0enoGtRR5aWHpoClI98crH7V25AQXpR7D3+0Uh1VKu6TjrUnxUh18UjuOA3C7uFgGOHz1qwC//uvV7WyKXq/M0Nx2PZOE9N7kPJMeJnlcUKdN3ne+DXu/1RaA/CBkzzgmpby61JWT3mPaoMtgi7bGE/R/S9fh77jVP1t91fHj5XijK3hCqmnA8v01UWbXdC0/27IFdSF7UuxFbdpQtLqsvoeOC1L09To6unRcHf+r9zeLpu2wOQFwVjkevO30jJnw/0ceAbjgAn8bJhXu80y18XoCYat+a+PZ9NThOd4TzGm983We06bsc9YY27oullyqqrMKfpyVyT5lzJArVyw7FF2RLhfpmfDIGauNqb9r+1m2Ia4nUzuKtRKJhuf5RZst6tfUhizZ9vlkFAwwxmDixUV7ZS78nv+G7cAtvW7z863a/+t2j00xKe0YJxGMzDn9dHtWsmZ4T8USjFLHXkeh8Dgeqr7TGOeA1et4sBx4nrL5d5biUqVESE7Duu2SsDJQIlwxsYKCtLZ5lU+PAsdnTbb1bEnn5ak3VWmscrT2+3IGS4Dh2ac8oFdy+HNFBcHZbuvWldmS0OmPx2AWm0OHRmeyUuUClQV01qDTHpd727p1VKHg2bBmZso2acuOHj9eXgecjdUhkxYo3AWn4jlPKk3ci6bvZ6fB6jUY93Pc+HVK7QMzDKXFXEeBixkUmKWmS+rqHblBMHXrmBa9fVLo0mnedf05WM4jPlZIGY9634cqh2BXjjwPdQLnrONyDYzavp4xMt3X2kcLNMPt8rK+agrNkIBGTNyHG6Gl5d7GQa/XTABJV+95Tj25ASlV+3iCIyyjfBvkyCo+jq1bvqdOb9l1nOIzM7qzKXVllCaeoV6vGSdUqkNc+j5FtvPv2gw44+2SJhFaMroqQMFyRmll0u8XF0eXXObbffvKLMmYPfOf/3mwxUw74yRVV7FoSrZ6bMZWfZ5AqzaC2aaJJvS5Jq9b3TFk0wF7/J1PWaZZCoDwBGekBI/l+kyswBTPM5GiE1nnx8/L40fw1EF9BqiDo4zmmY2XlkZlPPUrcL8D6vFY3oMPAnzrW8NlBwEyyf3KJLetSZqwM/LyqvaxAuBoOSlj/jp2Estfz9ukfa7y7VIfpqet3LdLP3OfLvr2aRZ7HrAmTfTjq1ZRvy/AQJ7jd5jhnq50wo/jWZRPFurabscRkF7n2CrGfTySog+m0LSuPC5o/Z7kTzlyG+toKrkhrYPbTnJtwB4bnhUUXacu+r32m5QcQbOt9HplvA22mfcVCwu6vZ3KeJTzWvbiQ4fKSeC8H7j33nJFlJNV7gdjJYKROXv3Ds/EypklkRIoIAncXMNjldHCY2hvarDSRQffpfGP/l61RFcVKYqlZ0DjKTfHmNjv+/cDKAdJuGwvfr92bZmpjS8NQduc4sipGgxax1Q5QauWGrGus3d5WYDB+fKMUniMlJ0lx1ibEuRAAw5S6kKWl0tFB51PqADhM7F+/XDwMoC8fC+W7TFkNMjM4hGY6a+W7dHOn15fTXbWfY8l40eOMzfFSZFr5PEY2q3jtOe2rpPaMtzT9mkDoabxOhaRnGve78PM4pHhffmyPUWhBx1I7x5fvpHuy2U5L1c6Z94P9fulrMgdGALIocjS/dbeVWpEpPsD2LPapTo9WcK0pcu9eqX2m+ZEFOoYumZWv5HipNOQZJo0yeY7v83cdlt1mQ0yA8Vwe7Rz9jgSrXspXWeP01F7biUnaso4RKpTMuJ4jgMYzpJn9aFYNr5bUl1WkIXmfKXfa0vgehy/0vc5fTBtFz7nHp1C+s3TVjROcTyBxriPJf+p3OZ6P27xOs/OlvcXJ6JJ+j9fNhiRrovR940cxz/TYFdr5SPt2ZJW95DeZ08GA8sIiVT1ER79zqNLWvq/p+z5+XI8g1n0ueF1eXl0mWg+CXXNmvEtGbiwUE6ylPCMuZrGcw+q7h1+pvcBoLwHqHvRCal473bvHt5Ky1fnvIee91k6jtdJ/7fGD5bNoGqs3u/n2QKbwnNd6W8og3OgYwWu09NJxVIbeDsABn0F7zd4X1EUpePDknNcbuesXFf13HE5L/XXVbqetKqepZNLbcw5Hy5vpazFWr9GAx/4PjSYja9oQmUJ3kMMNMbgNNzed9+oM6vOs9o0i4vDyzQ3ZS+x5LanLI4kU6uOt+pK6dc877wkU7Xyvb+1KV9z7rOnj/LYvlKw+gH6nScZiVY2vo9Hj5bvKsp7tOOjnjc3p/evFtIy0jlO7hRdWGqntQxyjp1O0imkvgE/e8YxWtKOqvrpdn6+lGuom59//mgZXD+kNhmut9MgNoBBf4D7Y+DC7bcPtvfeO9hKyzTjWBDP7/hxgMc+dvD/3/5t98EPeF5zc7peWPWdhmesYD1LOXV4xodS/dr3qXK7qlxPnVodTY29mupbrDGGtq/VZ1i2JgtNp+L6vAS1EVU9/1I/49EtJKre86aeY3q8pct4bCGWjclTrjaeoeV5fAQpdktrwiJftYQmvNLqkJL38aBoDHI7erSU+5IOgKB+ceaZAD/yI3K9bVMVB8DJke2WjTBFDtCximUXsOoYJ7nvs6dP8pTL74P0jlh6HO7D28jfXUuHs1Yotfp0SV/A9463h9rdeUCx9NxwfwG+s5b9x0KyDdHypH0tPUiy/+ToBJ53TdqXQ+9bExPT6bPJ7VDYjq1by1ga3kbpuauyh0q/0e+wHXw88OCDg/hJgOEJjpQtW8q24jgEtzg+mZsrz/meewCuvnq0LQ1RFHnD8DbacaoTwcicc86xnVESXGhaxhlr4NHlgMrzBk6KouIhxyCWsm9qx1JHSWrimcDOnUs52vnzTls6JqdjzgkgpQ4rq66qQWBuz2I5DiQljQ+2qNEPt5KyyrcpRhjeHkkJ1owf3ufIcppoTjr8TA0TmzcPb6XeVgtKWFoqr+vhw752N0SxYSPAls3NFmoplFRZzHGsCmB22MaztOa+W9PUjzRF1fsIYA+a6hpZuQ6Dxkn6/PGJIVaggTaIlPA4VqRgtKaXR+SGBQmP89DrfJW+p8d7+qic++5xVkj3kstz6nDi12x+vpwgg4GFkjHTY6TVdCnJKNrFksmEAoheRCcKeQzsnoAD/p2lJ3qegdTnxfOc5BgG+fnMzY0G76c4sbxt0IxTFpZhL4UUIy9fRj23XM9+vV4ZNMDvixWkYDmquNGQyn/eX0iynbeDBjPjZ63fkAx8mq5EA421LX1uNH3MQhqHSM+vFhhGQRnj0f2062IFVEiZ0fCc+bNAJyhxVldHMxjjM02Dt3ifIvUxCF9ZhU5IwDp4oHLbbNpkPwMpzvZUW0SK48E63tNWfu2r2gYwbISuWo5PeletFXAs54s1+ZvXL41R8X9uoMfnTeqrJCwdj7YplZT+VpJh/Lzos8D3oe+3Vi9OBPA8v6ur1Y5S2jYE+wi87quro2MWfl70O6vv5rJPWu1JegawLq7/SctH8vZIdWurXmmfaXmW84ifn2X/oYHCGGTFA81wmc2jR0ez5iBUF8ZyMMMxZk9bXS33O+OMwRaX2HzOcwbbuTl9chgfGwIAXHghwCWXQGd85jNlmzdsGH0+UmRsio29axuJ9txKDklJ9nj0n5OJ3PGZREo/kntdtcxltE5uw8GyUf6vXVvaZHi9kp5YdwIZyn/t+qT60/hxXn0sZ7yagmUvSdH56O9Sn8DRZBg9FuW+pitJbcT7vrJSlrVjx2B75ZWDLQ004BNSEapfYj/0xCcCvO51o+fSJnQ8g7Qlz7w6QEo5KXYoCe34uu/DuPqEOr7YVCahH6S2fW5nkZ4TLsvpZ20sYNn/+Xu9dq3v2fPYyFLG1HV1K81uL+nb0jGWDRypsnnR/7lOLp2f5mPwZGqmY220u3vs75ZulDJeo5+xHdgPdAnWSSdaIU2939azIO2bI5OnCY8Noa1z7fXS7PNtkZL05fhx/Vns9UZ1cSnhCLfpS/egKo4oRR5Lx6eS8gxYttK6aH1DSvs8tgP6bPJnNPc8LNu8tjI6ha+Ohs/tox41iJ/EdlOsvkqyA+F3Hvt0Dfr9zvMdikQwcgQjy0iKIMejgGlleurVyvEeq6EJUU+9qc61OjTRYSCa8aXuPh66uA5Wu3jHYS2vIM1Samo5Bl4HYl3n3PcoB6tO6dnAjpMbUPl+2m8A+QHuKUtwWvvwYOJ+Xw+apcEM2vbQodHAYlRuMKj48GGA/fsH/2NGBT6Le3a2bAfOqO2Imf5qmdWBBiZweSllz+RQJUsy6nKqDBxV333nOFO3sp4P3FYFGlhZnzx1prSv6rcqmWwZaT0GXKt+6XOVEu7tQ5ruN7iDnbZV08atNlgavKev4XW0YWjAMj3vnPZZ+47XYTkQ2nz+q6Ay3mOM1ALC+/3RCTi8DjqY1YKapeeGD2rpNbzjDrk9LTGzeARg+TuZG6gx0vMeezKhIjlGdel4S27zd8uS25ZRVJKbVnYNRAuwtJ7DVGOOR6fh10E6d56ZWWqz5TCg+0nHS87lqvdROl5qn3Q8Zh/hx9NAYTxOMzQCjI4frGBkT/u5HoXHrq6mZYlHUuS39K5I76xkHKsqk2Nl2Kfnw41+0jEpOoT1PuBx6GCyniUraw6vA7+nRkQEf0Ndf3W13AeD2nBJOGzrsWPl84YrrEwDTTmqLPngcUo19d7w8qygTKs9XcIN997JTJ5xhNav8v26qkvrV3u9UR1ckitVbfXewyo9n9aD/QZ/Rteurb5X3jEa1/sl/T/FrpuCNQ7IqSN1vKj1S9u3j04IQVsNyubDh0dlMm4XFkbLxEzp27cPtouLpd6PmZE/9anB9pFHyn3weTnttMEW7w+dnH7//YP/Mai5K575zOYmxU5ToECqfcJD3TFOTrltXfMm5DrfbxzPh0cn4IFsdcr0Qvs8pM5KifQ7q305dr9cPOM9KVCATx6RxrPamIXq6Cj3Uc7yZZsPHSr1bZxogp8XFkYnM+LxmAXtgQdOjqWcc/0ynC715NS62tJ/JpE2/Oip5abgbYNnP7SveCYYWjYv3hd4JjVKZfP6UxJfecq3kMbROXVSeasd7/Ej0M9cN+d2TLq/lAyK+mC1Lcpy1Om5/3VpqfwN5ThuccWTUwFP/IGnD/fs64k7sJ5t+gx4VjX2rK6GWHbzKpu+116e4l+x6tDK08ri5Vb1h9I5W/5k7dwte67HR+CxBQKU7zS3zUi2GGklW2l/+tmTjMryI3Adiya78MDlr3Tttc8pdlH6XcqzKdn/rfo9fiNrX+2caL+CfQTKfZyETjPV8wn8NDiZ242wb6HjCoztwQzJGL+DyXG2bBmNmeKrpC0vl+OP9evl82qIfj+CkSeFCEbmWB0T3w+xOiX8ve1BXm75bRjGU/dNxVJq2qrDU+ckDeS1tljPdkpQgQeP4kTr5Aa0nPcoVdHw7Ff33tdpk6RkeQYpuffJGlxXKchUAdOyJ6+sVC8hSOu6/36AZz/bdx5N8P73A/zszw7+t7IVowPNyp5J6UJWdGn8bMtoN0kydJqpuo5WAAEdTPKs55JRkg8wm57A0ibjeC+b7kMtqHyyyubfefo8Luup48zTh1s6CpaJg9iumJsD+Pa3B/+vWVPWv3Fj+Tv+BiAH9fDzk/oRboxJNYrnBrvkPIvS+Un9XzBe6H1KXd2nqjykbvYzROojeDBaTtCJ9z3qQs/IdXR1jdeQqxmArcAJKyhUC47r9dImdjTJe94D8NSnlnXz7M9Wtnft3ln70HKqJkRgm7T6tfuSgidgod/X+3CPA66qXulz1XF1Ag4spxb93NW72UVdHvtlXd1TwhobNFFH6nNTF+05byqgydsHenRqrrNh0K3XWWzZfXDL7T7SeICPJbn8p46vtWsBfvRH5XNqg+PHAb71rcH/q6ulvsNlM5XRWgZ4KdOPhschzv+fdDznjPC+ytNndonVL7bRnhS7MEcak1rBZBpeL62nr8+pP0WHsPa15JrnuJz7mzN2AfAlreB1SLZ5qS5N3ksBb5Jsx++4TZ8mJeFBDDz7/spKeRwGQeAW91ldLcvcswfgL/9y9NzbhE6krwqS8tpaqnzEVc96VTtSmaZ+pEsm4bq0ocOmxjQAyBEyktyuIydTg5qrsMYsUvl1ZXsd35YkHyS7MJfFOWPsfn80mQgGf0n+WKkufjwPnD56tJThUjAb7ssnxdAJLwCD/gCTVx06BHDrrf7zbALaz/H74rH7dJll15pweyrgeQfGKdNz5K7X/mPpsPx54JPAJSTZzveX7DdSEsNUZmfrPcMpMjlXN7eOl3x+KWVyrFWveDuk8R6Hrv5m2X20lQyXl+VYHoCy/6Cr0PHAZ9wuLIyODXhfs7IC8PjHD/5fXAT46lftc6tBvx/ByJNCBCNzFheHHbmW86grJGdhymDWcmpJ+zbl4GlqsKTdA+u6tBkYqO3vcf41aVhONcABDEs9LTsYdi7SPaSBarweq05+nLS0M86w5wMy+jxWDQ487amL9Wx7HTu49TgCtXfd4zSU2qE5jJaXRweRtFwsG5Uann2JHs+dUFgnXS6UZksGKL8/dqx8TnCGVFf87M/KgRPT4CjqwknSRdle6g4qTha4HFheLt8pOtsQoPz+yJFSBuPzzg0b69aVzlhN3jZpTKwjryWDaNsBBxK5g9kULH0uRU6l6IUWUkBqU32vN7NhQxRr1gI86Umd1jlCl3LNGkeMY6yTgqVjpR4/qeeYQtPn3vUYt8qpJU12s4JWreAp7fjFxdJwhnotzwjb65Xf8b5TOh/cF+0KKcvxWcZIxBPs5MUaG/P7wwMnPP2SdJ8tOYOZerri2muby4zpoa4Mk44bpyybBjnqecbr4tHDuiRHL6W2Im0CsUf/lwJZpeA0Tf43+Y5opDr2tbF2bh/sseloti6rTloedxTxQIGjR8u+j2dWo1mLtMCJ5eUyoOCeewbb224bbO+9d7B9+OHRDEg0Iz62B+X+pZfq59YGa9YAPPax3dYJMGxf40F/nmfBGxQ3SXjG6h69sGu6aEdVHXV/95LSj9W1P9Zpc6otQwvkSLUfNdXPe1YassbqVf4vKl94XTTITAsiOHSo7AsweAyzn6FNcWGhlNuY2R7lPmbYnAaamMjL0Z4zj93S+2x3JR+9AVZV+3ltkimk+MWr2lS1T10ffJUPMbVs+u7z1Uar6qwqG7GiZrQ6c+qRSPV3a/unxiZI72MT904KAuM+WTrBmweBUXB8JgX/AgzLZpTfXLdfWhqV8Q8+ONju21fK8pQMptOIJf8te1aK75+XR/+35L9H/7fkk2Yv9dhPc98Zz1glxSdVZQ9tC+0+5/ocrfZrNmJv34uTAhAecdnrjcbg5NDv66ud03KlFRX5MVLMCd167eaesZH1jkj+AvrZ835TmzyPC/P42Dz6Aj1WayuN50H7Ct4DadI0n7COWyn7Pp+UsrhY9jG4D7YDZeru3aXPhMYE0bYuLZX9T1PJbxT6/QhGnhQiGJmzZ09pSJ2fLx2S+FJIHWtdYQ4w7GjlwpcKtpQlbKVBSkpHniIoEcvJ7TnOo4hZ5XkcGTkzDOl9zmmH1Z4UPI4zz2C01yufpboCvykjyI4d6cfUNYC2WUfOvU5xMtSt22PclGZo8YEzDUDGQbGkqAAMByOjEsKXBD14sPz/61+vPo8m+dCHAM4+e/D/rl2jWV+lrGVV8kB6Z6UBjTXQrOvs5G1JGfxJeAbFdQyDtO/yyGkrmxyvX3qftQkQHrwOA15uyrueavjkgcWbNw+2dNlbrE8aqLYNb79HG/foOm3QdNlttLUpY76lt2jPnJRZh3+W9FzJGYZbblTtiJlf/zWAK64o28UH556MxjxIT8qMrBmf6T64nZsbLdOTfRz3sQyWnrok2SotJwsg6/+876S/V8l2Kpsl3V6Tr9LYSRsbePsYbQzGf7eOleDvg7TEl1W3ZFhDJzIiLZOG36X09xZNGYe1cujzo2VRsPSPXL09xxhPj5Pub5NYsiMXj9zm+ywv6+dKgxy4vo9GUskBh0ENXfGrvwpw2WWD/9et0+UCnbCFS8nxd5O+c5a8xX2l68DrxvcX9Tk07G7YUL0EZ78/2tfyOmhACn9Oab+A9fJgfEsP52PBxcVRxyYP8t+0aTQrNX3GtMmvnrGTxxmM+6xZMzqpgOsE1NmnXV/aHm1M6emnpcwr9DgePNyE40miKEb7kRR7mGefLsYjqePgqjZ5nGJSeTnnajmSKfjMUJlRdZxnPE/lvzTBHaCUbSsr5buB8h+X9kSdhWbYeeihVjPjjPA//sdwZkzej+F7RScqoRzgOokkg/i7TpcoxTr4vpYMpNe+ajUBadKSpBdKfgOOtkIAXxaenqMngxa3t83NjfpgaLk0cQbFep+ssRcf7/F+jR7PnwUJaRIHz7adsiKCJwCi1yvfJSnph1aHdV/qyO9x0VabU8dLnnGIZVeoeq8t+6MEX4HS6geoTUYKlKP0eqP6Dy7TTBOO8KA4DHij4wLsE77ylerzaZLnPAfgx3988P/WraPvDa6URYM1NPsGlTNaFlIqN7XED1Sv1GTp7Oyo3UaS0VWBNtTeom0le4llC9H6nLVr5TEKxQrcsWxD9PgqvUV6f6x3VNvS47V31hqPSGhjBamflo7r0sZfd0XEqjZNWv8iId0TT7v5Ckyp/tuq95rKEM1GQ98Vqq/jPvgdyjCpDq1s6ivmfl+eGZnaIAAGOnmXfOADg+3iok+mIppMlIL8pL6C65UUrrdJtgOue3vs0ZI9ISU6j+vrFI8v1ON31foWy24vjb00LD+CdH20+yyN87T6LHq9UbuYdA0s2xBPEOTJepxLjt1fqrvpxEMeu31b+n+bSM+89txR3b5q/CC9T9T+yVc53LlzsKU6vzbRnX7Pg4/xM44RFhfL/3HCY3DSE8HInN27y5cMoDsDea6js+16gukEjSb4nFjBXjnLLKRmzfGQmlkawBekJ+1Tx1BJneXUcQEwPOjQMoZ5jAe0Lg2vE6rqeGspNqsOruzNz5f/n3baYCsNkPA4vHY8OOHIkdKYvmcPwH/8j/5zqsvznz+aCY//T/EMdpwUMAFTtIKJZCb1GUP5XwevLPEM9j3HWsZV/L5L/cUToNhUHTnHpMr6quNosBIPWur3Rx20/F7QYATJIMeP0QyXMzPlILqJ5ziFX/3VUv+v+6ylBOWkBr7UfSZTzs3jTK06JrXOoB4bNw622nNSFGlLJ3vGER593zreo8vX6Wu870xT8l5ztKbIb6vPS3E00WAlvjwyNXyjnJacJlWZNakxkk+GRTm+aRPAmWcO/j///MEWg3klo/4jjwC8/e0jp94ab3tbuhPHk7EjaIetW+XvT/W+pm5gAOIJYtDkdqre4nHSVQWW0Hq5LQOdHnT5XSn40GPz1ew1KYFhkhPUgyTrrQkiAAPnDmZLkpZXBhj0B9pEiOPHy/24bJcCs7htBz+vrMgBpwDD1xTrv+oq8RK0xoUXAnzP95TtSkn+YT33Hr0/5Vlo2sEanFyg/s+RnjHPRPAuHPBN11FHNlvlSHjGLrntqOpraL9oBXVaQWj4mQePSeMHzbZD68DvUD9Df4A38BP7qHvuAXjZy0Z/b4sPfGA0yEIipT+wSOkrqo6vY9Px/qbV02af01SwUxfkXLs24BN3cmwr1rl4+oym/L+p9z8lqLmuD7ULNLuRJ4hMgvpr6Wd6nDRpGX/j/n46iaLKFmIFUUosL3cfjPzDPzz6HR8vepJdpNCEPt+EbMntszzljCv4tQldz9v2qoDP3P46pS4An+z12PSbRpMhALrvEMl9R1KTf6To8lY7mg50rnq26P9SsiDNRkSPoZOjAYZtRXxMgPeQTq5Fvy1uUf+Xsi9bCf6QpSWA//k/xdNugn4/MiNPCuFF4VADpEcweDtmjxEdsQI4tH2r6q+ql372LOWgdRSSQ3HSjaMep0DVAIbv53FW1KHKCMLrsZZJ0BxOtAwtcCH33ralrFpBV/S9SlF0JPgsodQ2elmzRt/fYxSSroXHmMhn1vV6PuWhajAsvUfWQPqhh7oNRu71Rs+9ioYMUDMwoRqJ9h5NC96gH2lfjwM6ty2e61oVoFtVT1sGEqmuOuXQgCpPuSkBA1p90mfP/UkdcHp0Lu3ZatJ4oeExyFr70MEsn6TDt/Q+43c8U/7CQjkI/v/+P/95NECx9XQ966oX69rx97INHblt+WyNA+j3OWOeVOdeV32Rt+6c94aXlzp2SnGopFo9tP3pmKGOE5SWz8tJyS5h3R9Lv+Q6sNQOi5TxnUdfTmmr1HY6eYTus2mTnWEcYFh+axliAXT5uLhYLse8Z89gi6tM7ds32N5/f5kJDdvKM87OzpbPxZ13ynW1SdMZOlKRnoE2+4vU9lCabg83ptM6pcBAT708iF5yeqRkqdTa7N0/hRQbHN0HnQmecTj+z+1A3oBmTZ5IWUu1ujz9vuRUst4LLh9plhjr2kn/a/tZOo5mR5Uc2Rr0d545F8ulmZC0DJs7d5b/82w19PpiHTxQuN8fXaYTA40feqj8jL/xLc2Mj1lvcMI5zb6G4ESZt70NOuXSS0v577GveX/zHNPl+C6lzpzjLTssPbaqfmmZcvpe8XfcEzzu6cdydcBxjkc8v1N9G2Vw3YzI3jZVlVulS9e9tt7j67zHHnu759n06Fy5OopktwEYXpFK6teslawABnL9kUcG/6Pe/61vDbbf+Eb5Pe7PV3nCPgwnKQIA3Hdf9bk1ydato7ogpUqmUVLHlJ4yPXqSp66m9tHa4CWlHZ4xf047Us+zjm2d9ou8vNTxldWv8mAvj/1KSyplldPvj9qIJLldFfFTFKPZQHOp0u0t3YTqJJr9SbqHkmz3+Cg0WUx1Hu0+U/lflfiKriIkZSLGLX6HOv3tt5ef8TjcYvZ63KLtpwmwL9i2rbkyvezenba/dn88erNlu6X78OerLVkotVH6XLevykE6Z+06WH7bVB9DCp53vuk6pfJSJhjiNiU2SILbdqR9eRySdO7aChOW/Yd+TrGppPgRPHjus9XfW8+4R2ZYdWorAqHM37cP4N57B/+jbQdtNXv2APzTPw3+v+MOuS4LXPX+jDNKfR/7DZzkjklJ6EqCOJ5oiX4/gpEnhZMqGPm6666Dt7/97bB371645JJL4J3vfCf84A/+YFohc3M+43eqA8Lq2LUyaDuqOiyPQUwSSNJSS5qxQzp361rx9ljKQIqhxqpD+2zh6cCs+211DtbAVeucqgZLvDzpHqCA58vwScdzSSgNSnl7cB9LgZEcTx4FV1MQ6HvArys1RuP/2lLiS0v6M+659hJSe7Rn2VKGrKVvquq26pLeWa1OepzkDNOcu0tLpYOJZtuhnw8cGHZeAYw6rKgD4od+CDpl//5RRxwlxRgpUUdOSe1IqctzvEfuWs+kZYzncsoaTNL2WEZ3/px6Ahakdmj1S3V5sIwG2jNE257iVJPazLNXWX0Wb6sk03kdklMLy+F9zeqqzxjpoWpAZxkKrey8uYYjvk/Kb7StVUtxWuVJz5Slk2iZcaT2SEZRHkxMjZo80AdlOjVkcrmPv+HnHTvGY4QEgJmFwwBrjKVskdz3UnuWqvR3j9HEM0ZIMcilvBueZ9KqS5u1DeCbBMXLkz5bfZ6kB9J2LS+X33H9gB6ryVnpvLQxT6rRN2VfKoc9K6KkZFjQKIrR8SGv27PMn/Sd59ni5AYeS2Xn6oFa3Sn6unSclO0Gj+EZjXkw2JEjo3o73iea9QCDtTDrATpver2yrZKhE2AQjIwGTh7MgPL/oYdKx9a//bcAr3jF6Pm2CbZdep8tOUe/o1vpeMuI7pHRHjxy3DMeScmyQXWtlKyivM1SfyhdZ6v/4cfQDK4aOdfesh1Y45kcvLort9NI4wGup0uOJ34d+D5FkScDeaByUZSygss3S2/n0Mwrmt1SsiNRqsaUlmymx+bcZ653WO2RJpHzYDL6PmlLDtN6PHqtNmFlYWFYhgOUk1K+/e3B9oEHSocXTkrh9qBxQoM2mkhakEOODLL0bWv86jk/j85l2QtT+h8JLYO253jPOMQ7LvLo9h59oQqPfJHKltpuTSZMfb5oeV6bGbaLP4Mo/2k/gLKDHke3lk3dsmFI19Aae6VMivf0wYj0Pmj3wNK1EMleqLXLGnfSfbiuh33yww+XOjzKcgwmQN1iYQHgllsG/998s3xeFB58iGOPlgMQKuG2K4lc+zI/3juOtGyI/PcUu4Kk23vGDdqEWM9EK+nZthJgeeS2NdbQ2pFiR5J+41vJT6/ZinB/qS5JXlrtyu27NJ+9VV6uPqTZkWj/xJPA5Zy7dd+betesMlOeKeldk/oI7dmmtnlud7faivcCbey0biwHJ4RcccVgu7QEcPDg4H+032DfgP324uLoOIT+huVYK2thuWhjetKTAK67bvRc2gQDsOm9tALTEW2cVnUcftaeJamPkeSmdTxuLV1aK8fSW3ArTeauku20PZZc0dpD3x/LrqaVY8kB6dp5+lWtX/e0mV67qnFDVf+kTSqjaAHB+P3x46P2I8luzyeV4ZaWL9l7KDMzpR3A0vG1mB6EPlOWTmDpFJ7npOo+V+kt2D5JL+Boupq0j9V3au8jtuHss8tVC/mqiaurAD/1U8PHcbv9wkLpU8Dj8TP2GYcOlc/SpZcOttyfcNppZWDyzAzAa187er4N0e9HMPKkcNIEI//VX/0VvPrVr4brrrsOvv/7vx/e/e53wzXXXAPf+MY34Nxzz00rjHcO9LtcchTgNgyfeE40S0hQTRv3ok3DtrZMWy5NLftpkeK8qeroU8tLqUOrL6e8lAEML8cyCHvaLqEp81S5QgUFZQl+3rChlCuaw2rbtnIZND5gRWcUdWp9+tN555FJccb2sq1f+9pocAe/P5LyKxmgUp6TlEBUKzAlJYirS7xGIQB5IB5Ug8uiTxMn+3Ln/Dke1/lqfYVloJbkP8+Qc/ToaJYz/q6vrpaGCW6UpOXhb8eOAfzZn/nPrS5/+ZcAP//zg//pkt1av9iVbOpSBp5s8tajK3bZhrbq9TjgxoVn/OBps2cfS74B2A7+Xk8PZktph2X49Bg3JXIMjXRf/hs3Gm/cOJqljJaD8hmDEnjW+6Ul/Vxp+9BAiVt0cmEwwvHjAFu2DP6/8UboHClDk2Z0pmhjObraluVk9jjdU2iqj+KOJmvcyeu2aFNGecZOuWNkXk5q/XXLRiwHKS6diFvPaj/S5JQqp5i0jyebDp8kXxS6TdK6h9b+ms5G7bqeZ8FTl+cYqx+pcqpadVMHGn5nTTS0Aic8AS3ac7dpU3ltUX6jrQcdT0tLo5MZ0e5Ds/HQjDwf/KDepqaZnx9+VzxO7klg0toDMBnj+S76mLaP0ZB016p6rL7b8+4jqf4ArnNK5dRZkUJ6T3NXOKq6R6ljOWs8UtX/WLqfFETGt1Tv5slD+D5WANvKynDQAkCZ7YyuhoW/PfGJg61k48H9ecAC2vwPHhwNeOsSvN4ns480xd/UNFbm6WnEq+t5j2+ij6iSz9LvWlAaxYrcsSJqPNE2Vc9k2zEYVft7xiFUJqfIds2+sLxcykK8hh65zZOF0T6C9wN0xRUeH4JbqhefccbwcVYfg+eHx87NlXXwycHSs/XII90HI9Nz1mw6OYkxpP0tu19d+0TTuq/H7uOpP/WcrUBLfl+aHqd5AlgtPMdY9uGU86HHaUkpUtrVlLyldWkJUHo9gO3b7XKs3+h7agUzV5UH4Auir3ruLHuuR/+3AqitFYL4vlTGc/uXJNO0d432DdazVbWyYr9f+oh5gLp0LTZvjmDkU4QJsBY1wzve8Q649tpr4WUvexkAALzzne+Ev//7v4c//MM/hLe85S3+guiMHsnJh9AX29MRVimd9AXMXY5yGmljIBaUpChQHseT53jPoFZCe8csuDJSV4H3kGvQ9ZQpdciW80irV7oeVcpR1T5aVnXJKMpn0dIsmJj1DGfC8czI/X6pqJx+utyeNsF2PO5x+rK9OQ5KLx6lGdvjcTKn1ElpczCL5ec4TVKpYxisW55Vbp3zsnQLq2wqi+v0DVbAgqd8Piu33y/fOz4QswZmOTTZR1iOJo5k2MpxZlV9X7WPpXumBPHwLc2Aio4mlOM0AAEzIaP8x+A2/Ly0VAYmYBa1rvh//p8ykEIixXBk0Yae0pRcqipXMrTknE/dcU3dPpjqm5qO1VRAXxvjGk3WUIOPVr+UITmlTq3cOlRl6rHq88hbum9dR6jH0eRpD++7c53DOe+jlRVLKp/3G3TCCMAgqAB1eZotAWA4iz5mVMOlnG+9dXhrZSTrgjpBMV7GFdzWhH4rBT821S9Kx6fsJ13XlHejKZ28KaqcFbnkWr/5/bYs+R57Rcq4yHI0SVj3XXJipZaXiuTgqbJ5eXQkyalljZ20dtGswLw91vE0uAjLQhnO7Sarq6MBaqjr33ffYPvww2UfgXakrjh0qLSpeDKx8v+lfS28zuW27IzW9x5/hqcuDWs877F5Sr97ZLt1rtY2R+ez2pF6nFa3dDwfd3smmvAVEWld1rLKWI61ygnC20PvMw9gsq6Tdt+t1ROl/Xl50ooO/Bhp/Gu10WMT8gQ1aBMN6T48WIrWY63og+Vrgcq0PqsdKNPRlsNXvTpwoLT3YNZ8HKvi87Nu3SA7GgDAt74FU0MbNh2t7C70Fa2uLsYqXh09x/bsIeWc6/bFHM0OkoImy736BgeP4+MGrw2uagxWFPIkYF5Ojo/ac38sew3Ha1PX3lm65d9Z5Vj9SFVdWtn8M7cJ0RUMeDAz2nZwu38/wF13Df6/887BFmX83XcPtvv2TX7kFU6grPtsed41S8f3lJliy2hKbte17VjHNmVvb6rvo3IzR0+37ldT58rL6/d9drkqmmprrvz2+CIlHdtzjlZ/qOnQKdDnxTOOsewMXE7zFQS8djYuc3g/sLw8Kv9RNz92bHQlXNTpMZ7ngQdK3R5t+5hl/rbbBttJl/3B2DgpgpGXl5fhpptugte//vVD3z/jGc+Az372s+Ixx44dg2PoRAOAg98xeh565JFBNL4GFyyS4yxFsfXSpkOnLZpos1dZG+fg0fu7RsoA0XleC3zJszZJ6fwRofM95GmzxzmXM2BNpeq+tGGY9hg166IpKta+qGDMzpayk8tQGsiMGdBQdkrLsn7HYHqoKADe+14oWlZisPzDjzwMM9h2mkEPaUoOd2HQC05ZFrhTuUsyjSCHNGOkVLbHedn0YH8cePsubT9Jf7K2KUZs3kfMzJQZubUB8xlnlNlzOHzZcAA4tH8/wNVXdyb/Dx5fLQfUHj3e0js0Y4TEND+jpwIpYw/r+I7u8wIZ24rtsJaBl6gbMOHgEM+C1dR4r020fig1YMFTvkcX51salOIZv/Lng2axl/R0gOEAFwzkxewy3Ki5vKwHiwhL/B5auxbgKU/pTP4f2r9/NCiGtpW3WXIo5pArX+j9SulnrDbXcfCnOEo9SE4g653Icb547qH3/nRhe7Dq/w6q/t90exoMijnUVJur9k99Ty27C98n9X2ysu/gtioLV78/GkSGclsKKvMg9R+8DJT7mH17dlZ//ukyrzhGwGWiMfDsoovK475T16F9+wBuuKE7+d/rjV7LKjz9/Djeu3GSOm6d9PMBSNPVvMe0xALaWDW8bc15bnHskdg3HGprElqK3lI3ELCuHiehBQhIQWR47flk8F5vdDUCrvPMzPiCorndn9bJM3LixG5MKvLoR48GQ+OWtgvt/4cOATzxid3J/y79ZkHQEiPyX7MPADQXaFaTQ00EYXeBt0/UdGGvTcfbDsvGL9l/NBtArzea3IjKevz/zDOH97EmONWxNwDAoSNHAC64oFv5TydKecaSOfcwVVdsOv4nxV+U0h5PAGlu+VaZObYvCc84Pud+p+p+NWXxCflf99lMGfPU7AtG7P8peOzuVp+XYpPnZVWRYxOUEoTwCZzUTsEnn0rQiSW0HD5BFEDOXszbvX79YHveeYPtOeeU+/AsymgjohO8+XhEaOshAICrrmpN/hfFoQmJj45xx0kRjPzQQw/B6uoqnHXWWUPfn3XWWbAPAwsYb3nLW+BNb3rTyPfnXHBBK20MgiAI8ti/fz+chk6rlsoHCPkfBEEwaXQl/88995zW6giCIAjS6Uz/pwFxQRAEwdjpTP6fe25rdQRBEATphPwPgiA4NelM/p8T9v8gCIJJomn5Pzc3Bzt37oR9+yZH3u/cuRPm6q7YOcWcFMHIyAxbMrAoipHvkF/7tV+D17zmNSc+HzhwAM477zzYs2dPq0pPkxw6dAjOOeccuPvuu2GLtbT0hDBt7QWINnfFtLV52toLMJ1tPnjwIJx77rmwDbPotASWH/K/PaatvQDR5q6YtjZPW3sBprPNIf91pvF+Tlubp629ANHmrpi2Nk9bewFC/ltM4/2ctjZPW3sBos1dMW1tnrb2AoT8t5jG+xltbp9pay9AtLkrpq3NIf91pu1eAkSbu2Da2gsQbe6KaWtzyH+dabuXANHmLpi29gJEm7ti2trclvyfn5+HO+64A5a9q191wNzcHMzzlQlOIU6KYOTt27fD7OzsSBbkBx54YCRbMrJu3TpYh8vFEU477bSpeEkpW7Zsmao2T1t7AaLNXTFtbZ629gJMZ5t7LS93iOWH/G+faWsvQLS5K6atzdPWXoDpbHPIf51pvJ/T1uZpay9AtLkrpq3N09ZegJD/FtN4P6etzdPWXoBoc1dMW5unrb0AIf8tpvF+RpvbZ9raCxBt7oppa3PIf51pu5cA0eYumLb2AkSbu2La2hzyX2fa7iVAtLkLpq29ANHmrpi2Nrch/+fn50/p4N9Jo90eviPm5ubgCU94Alx//fVD319//fXw5Cc/eUytCoIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIKTm5MiMzIAwGte8xr46Z/+abjiiivgqquugj/+4z+GPXv2wC/8wi+Mu2lBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBcFJy0gQjv/CFL4T9+/fDm9/8Zti7dy9ceuml8LGPfQzOO+881/Hr1q2DN7zhDbBu3bqWW9oc09bmaWsvQLS5K6atzdPWXoBo8yTU0yTT1uZpay9AtLkrpq3N09ZegGjzJNTTJNHm9pm29gJEm7ti2to8be0FCPlvEW1un2lrL0C0uSumrc3T1l6AkP8W0eZumLY2T1t7AaLNXTFtbQ75rxNt7oZpa/O0tRcg2twV09bmkP860eZumLY2T1t7AaLNXTFtbZ629gb5zBRFUYy7EUEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQTB+9cTcgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCILpJIKRgyAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiDIIoKRgyAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiDIIoKRgyAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiDIIoKRAeC6666DCy64AObn5+EJT3gC/OM//uO4m3SCt7zlLfDEJz4RNm/eDDt27IDnP//5cMsttwzt8zM/8zMwMzMz9HfllVeOqcUAb3zjG0fas3PnzhO/F0UBb3zjG2H37t2wfv16uPrqq+HrX//62Np7/vnnj7R3ZmYGXvGKVwDAZFzf//N//g/86I/+KOzevRtmZmbgr//6r4d+91zTY8eOwate9SrYvn07bNy4EZ773OfCPffcM5Y2r6yswOte9zq47LLLYOPGjbB79254yUteAvfdd99QGVdfffXItX/Ri140ljYD+J6FLq9zVXul53pmZgbe/va3n9in62vskWldPs8h/5sl5H/zhPwP+Z/T3pD/Pia1Dwj53w2T3geE/A/5n9vmSesDQv77CfnfDSH/u21zyP/u2hzyv5qQ/80R8r8dpq0PCPkf8l8i5L+fkP/dEPK/2/aG/O+uzSH/qwn53xwh/9sh5H/I/5w2h/wPpoFTPhj5r/7qr+DVr341/Lt/9+/g5ptvhh/8wR+Ea665Bvbs2TPupgEAwKc//Wl4xSteAZ///Ofh+uuvh+PHj8MznvEMOHLkyNB+z3zmM2Hv3r0n/j72sY+NqcUDLrnkkqH2fPWrXz3x29ve9jZ4xzveAX/wB38AN954I+zcuRN+5Ed+BA4fPjyWtt54441Dbb3++usBAOAnfuInTuwz7ut75MgRuPzyy+EP/uAPxN891/TVr341fPjDH4YPfvCD8JnPfAYWFhbgOc95Dqyurnbe5sXFRfjiF78Iv/mbvwlf/OIX4UMf+hDceuut8NznPndk35e//OVD1/7d7353K+2tajNS9Sx0eZ2r2kvbuXfvXnjve98LMzMz8K/+1b8a2q/La+yRaV09zyH/2yHkf7OE/A/5n9PekP/VTHIfEPK/Gya9Dwj5H/I/t82T1geE/PcT8r8bQv532+aQ/921OeS/Tcj/5gn53zzT1geE/A/5LxHy30/I/24I+R/yv6rNSMj/eoT89xPyvxtC/of8r2ozEvK/HpMm/4MJoTjFedKTnlT8wi/8wtB3j33sY4vXv/71Y2qRzQMPPFAAQPHpT3/6xHcvfelLi+c973njaxTjDW94Q3H55ZeLv/X7/WLnzp3F7/zO75z4bmlpqTjttNOKP/qjP+qohTa//Mu/XFx00UVFv98vimLyri8AFB/+8IdPfPZc0wMHDhRr164tPvjBD57Y59577y16vV7xd3/3d523WeILX/hCAQDFXXfddeK7pz71qcUv//Ivt9s4BanNVc/COK+z5xo/73nPK57+9KcPfTfOa1wUozKty+c55H/zhPxvl5D/3RDyvxvGKf+LYrr6gJD/3TDJfUDI/26YNvlfFNPZB4T89xPyvxtC/rfbZomQ//UJ+R/yf9yE/G+faesDQv6H/NcI+e8n5H83hPxvt70SIf/rE/I/5P+4CfnfPiH/2yfkfzeMW/4Hk8EpnRl5eXkZbrrpJnjGM54x9P0znvEM+OxnPzumVtkcPHgQAAC2bds29P2nPvUp2LFjB1x88cXw8pe/HB544IFxNO8Et912G+zevRsuuOACeNGLXgS33347AADccccdsG/fvqFrvm7dOnjqU586Edd8eXkZPvCBD8DP/dzPwczMzInvJ+36UjzX9KabboKVlZWhfXbv3g2XXnrpRFx3gMGzPTMzA1u3bh36/i/+4i9g+/btcMkll8Cv/MqvjHUGHYD9LEzydb7//vvhox/9KFx77bUjv43zGnOZ1tXzHPK/PUL+d0fI/24J+d8s45L/ANPXB4T8b59p6wNC/nfLtMp/gMnsA0L++wn53z4h/8dDyP/2Cfk/TMj/dgj53y0nQx8Q8r99Qv4PE/K/HUL+d0vI/+4I+d8sIf/9hPxvn5D/4yHkf/uE/A8mlTXjbsA4eeihh2B1dRXOOuusoe/POuss2Ldv35hapVMUBbzmNa+BH/iBH4BLL730xPfXXHMN/MRP/AScd955cMcdd8Bv/uZvwtOf/nS46aabYN26dZ238/u+7/vgz/7sz+Diiy+G+++/H37rt34LnvzkJ8PXv/71E9dVuuZ33XVX523l/PVf/zUcOHAAfuZnfubEd5N2fTmea7pv3z6Ym5uD008/fWSfSXjWl5aW4PWvfz381E/9FGzZsuXE9y9+8YvhggsugJ07d8LXvvY1+LVf+zX48pe/fGIZja6pehYm+Tq///3vh82bN8OP//iPD30/zmssybSunueQ/+0Q8r9bQv53R8j/Zhmn/AeYrj4g5H83TFsfEPK/O6ZZ/gNMXh8Q8t9PyP9uCPnfPSH/uyHk/zAh/5sn5H/3THsfEPK/G0L+DxPyv3lC/ndPyP9uCPnfLCH//YT874aQ/90T8r8bQv4Hk8opHYyM0NkvAIMXhH83Cbzyla+Er3zlK/CZz3xm6PsXvvCFJ/6/9NJL4YorroDzzjsPPvrRj44InS645pprTvx/2WWXwVVXXQUXXXQRvP/974crr7wSACb3mr/nPe+Ba665Bnbv3n3iu0m7vho513QSrvvKygq86EUvgn6/D9ddd93Qby9/+ctP/H/ppZfCYx7zGLjiiivgi1/8Ijz+8Y/vuqnZz8IkXOf3vve98OIXvxjm5+eHvh/nNdZkGkB3z/OkyiJOyP/2CfnfPSH/uyHkv86kyiNKyP9umNY+IOR/+0yz/AeYvD4g5L+fkP/dEPK/W0L+d0fIf5lJlkdIyP9umFb5DzCdfUDI/+4I+S8zyfIICfnfDSH/uyXkf3eE/JeZZHmEhPzvhpD/3RLyvztC/geTSm/cDRgn27dvh9nZ2ZFI+gceeGAkKn/cvOpVr4KPfOQj8MlPfhLOPvtsc99du3bBeeedB7fddltHrbPZuHEjXHbZZXDbbbfBzp07AQAm8prfdddd8IlPfAJe9rKXmftN2vX1XNOdO3fC8vIyPPLII+o+42BlZQX+9b/+13DHHXfA9ddfPzQrSuLxj388rF27dmKuPX8WJvU6/+M//iPccsstlc82QHfXWJNpXT3PIf+7IeR/u4T8Hx8h//MZt/wHmJ4+IOR/N0xjHxDyf3xMi/wHmLw+IOS/n5D/3RDyv1tC/ndHyP9RQv63T8j/9pnWPiDkf3eE/B8l5H/7hPxvn5D/4yHkfz4h//2E/O+GkP/dEvK/O0L+B5PMKR2MPDc3B094whNGUpFff/318OQnP3lMrRqmKAp45StfCR/60IfghhtugAsuuKDymP3798Pdd98Nu3bt6qCF1Rw7dgy++c1vwq5du06kgqfXfHl5GT796U+P/Zq/733vgx07dsCzn/1sc79Ju76ea/qEJzwB1q5dO7TP3r174Wtf+9rYrjsqIrfddht84hOfgDPOOKPymK9//euwsrIyMdeePwuTeJ0BBrP9nvCEJ8Dll19euW/b17hKpnX1PIf874aQ/+0S8n98hPxPZ1LkP8Dk9wEh/7tlGvuAkP/jY1rkP8Dk9AEh//2E/O+WkP/dEfK/W0L+jxLyv31C/rfPNPYBIf+7JeT/KCH/2yfkf/uE/B8PIf/TCfnvJ+R/t4T8746Q/90S8j+YaIpTnA9+8IPF2rVri/e85z3FN77xjeLVr351sXHjxuLOO+8cd9OKoiiKX/zFXyxOO+204lOf+lSxd+/eE3+Li4tFURTF4cOHi9e+9rXFZz/72eKOO+4oPvnJTxZXXXVV8ahHPao4dOjQWNr82te+tvjUpz5V3H777cXnP//54jnPeU6xefPmE9f0d37nd4rTTjut+NCHPlR89atfLX7yJ3+y2LVr19jaWxRFsbq6Wpx77rnF6173uqHvJ+X6Hj58uLj55puLm2++uQCA4h3veEdx8803F3fddVdRFL5r+gu/8AvF2WefXXziE58ovvjFLxZPf/rTi8svv7w4fvx4521eWVkpnvvc5xZnn3128aUvfWno2T527FhRFEXx7W9/u3jTm95U3HjjjcUdd9xRfPSjHy0e+9jHFt/7vd87ljZ7n4Uur3PVc1EURXHw4MFiw4YNxR/+4R+OHD+Oa1wl04qiu+c55H/zhPxvnpD/If9T24uE/LeZ5D4g5H93THIfEPI/5H9Om5FJ6gNC/vsJ+d8dIf+7a3PI/27ajIT81wn53ywh/9th2vqAkP8h/yVC/vsJ+d8dIf+7a2/I/27ajIT81wn53ywh/9sh5H/I/9Q2IyH/g0nnlA9GLoqieNe73lWcd955xdzcXPH4xz+++PSnPz3uJp0AAMS/973vfUVRFMXi4mLxjGc8ozjzzDOLtWvXFueee27x0pe+tNizZ8/Y2vzCF76w2LVrV7F27dpi9+7dxY//+I8XX//610/83u/3ize84Q3Fzp07i3Xr1hVPecpTiq9+9atja29RFMXf//3fFwBQ3HLLLUPfT8r1/eQnPyk+By996UuLovBd06NHjxavfOUri23bthXr168vnvOc57R6Hlab77jjDvXZ/uQnP1kURVHs2bOneMpTnlJs27atmJubKy666KLil37pl4r9+/ePpc3eZ6HL61z1XBRFUbz73e8u1q9fXxw4cGDk+HFc4yqZVhTdPs8h/5sl5H/zhPwP+Z/aXiTkfzWT2geE/O+OSe4DQv6H/M9pMzJJfUDIfz8h/7sj5H93bQ75302bkZD/NiH/myPkfztMWx8Q8j/kv0TIfz8h/7sj5H937Q35302bkZD/NiH/myPkfzuE/A/5n9pmJOR/MOnMFEVRQBAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQSK9cTcgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCILpJIKRgyAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiDIIoKRgyAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiDIIoKRgyAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiDIIoKRgyAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiDIIoKRgyAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiDIIoKRgyAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiDIIoKRgyAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiDIIoKRg6CCH/uxH4P169fDgQMH1H1e/OIXw9q1a+H+++83y7rzzjthZmbmxF+v14MzzjgDnvWsZ8HnPve5hltezc/93M/BM5/5zM7rfcpTngKvfvWrO683CIIghZD/zRPyPwiCaSDkf/OE/A+CYBoI+d88If+DIJgGQv43T8j/IAimgZD/zRPyPwiCaSDkf/OE/A+CgBLByEFQwbXXXgtLS0vwl3/5l+LvBw8ehA9/+MPwnOc8B8466yxXma961avgc5/7HPzjP/4jvOUtb4Evf/nL8LSnPQ1uvvnmJptucvPNN8P73/9++K3f+q3O6kT+w3/4D3DdddfBLbfc0nndQRAEXkL+N0/I/yAIpoGQ/80T8j8Igmkg5H/zhPwPgmAaCPnfPCH/gyCYBkL+N0/I/yAIpoGQ/80T8j8IAkoEIwdBBddccw3s3r0b3vve94q//7f/9t/g6NGjcO2116plrK6uwrFjx058Pvfcc+HKK6+E7//+74ef//mfhz//8z+HY8eOwXXXXdd4+zV+53d+B570pCfBFVdc0VmdyFOf+lT4F//iX8Dv/u7vdl53EASBl5D/zRPyPwiCaSDkf/OE/A+CYBoI+d88If+DIJgGQv43T8j/IAimgZD/zRPyPwiCaSDkf/OE/A+CgBLByEFQwezsLLz0pS+Fm266Cb761a+O/P6+970Pdu3aBddccw0AlEsxvO1tb4Pf+q3fggsuuADWrVsHn/zkJ9U6rrzySgAAuOuuu0589973vhcuv/xymJ+fh23btsGP/diPwTe/+c2h426//XZ40YteBLt374Z169bBWWedBT/0Qz8EX/rSl8xzuv/+++HDH/4w/PRP//TIbwcOHIDXvva1cOGFF8K6detgx44d8KxnPQu+9a1vDZ3f29/+dnjrW98K559/Pqxfvx6uvvpquPXWW2FlZQVe//rXw+7du+G0006DH/uxH4MHHnhgpJ6f/umfhr/8y7+Ew4cPm20NgiAYFyH/Q/4HQXBqEvI/5H8QBKcmIf9D/gdBcGoS8j/kfxAEpyYh/0P+B0FwahLyP+R/EATtEsHIQeDg537u52BmZmZkdtQ3vvEN+MIXvgAvfelLYXZ2dui3//Jf/gvccMMN8J/+03+Cj3/84/DYxz5WLf/b3/42AACceeaZAADwlre8Ba699lq45JJL4EMf+hD85//8n+ErX/kKXHXVVXDbbbedOO5Zz3oW3HTTTfC2t70Nrr/+evjDP/xD+N7v/V44cOCAeT7/+3//b1hZWYGnPe1pQ98fPnwYfuAHfgDe/e53w8/+7M/C3/zN38Af/dEfwcUXXwx79+4d2vdd73oX/N//+3/hXe96F/zJn/wJfOtb34If/dEfhWuvvRYefPBBeO973wtve9vb4BOf+AS87GUvG2nD1VdfDUeOHIFPfepTZluDIAjGScj/kP9BEJyahPwP+R8EwalJyP+Q/0EQnJqE/A/5HwTBqUnI/5D/QRCcmoT8D/kfBEGLFEEQuHjqU59abN++vVheXj7x3Wtf+9oCAIpbb731xHd33HFHAQDFRRddNLQv/e2tb31rsbKyUiwtLRU33XRT8cQnPrEAgOKjH/1o8cgjjxTr168vnvWsZw0du2fPnmLdunXFT/3UTxVFURQPPfRQAQDFO9/5zuRz+cVf/MVi/fr1Rb/fH/r+zW9+cwEAxfXXX68ei+dw+eWXF6urqye+f+c731kAQPHc5z53aP9Xv/rVBQAUBw8eHPp+eXm5mJmZKV73utcltz8IgqBLQv4Pn0PI/yAIThVC/g+fQ8j/IAhOFUL+D59DyP8gCE4VQv4Pn0PI/yAIThVC/g+fQ8j/IAhOFUL+D59DyP8gCJoiMiMHgZNrr70WHnroIfjIRz4CAADHjx+HD3zgA/CDP/iD8JjHPGZk/+c+97mwdu1asazXve51sHbtWpifn4cnPOEJsGfPHnj3u98Nz3rWs+Bzn/scHD16FH7mZ35m6JhzzjkHnv70p8M//MM/AADAtm3b4KKLLoK3v/3t8I53vANuvvlm6Pf7rnO577774Mwzz4SZmZmh7z/+8Y/DxRdfDD/8wz9cWcaznvUs6PVKEfJd3/VdAADw7Gc/e2g//H7Pnj1D369duxa2bt0K9957r6vNQRAE4yLk/zAh/4MgOFUI+T9MyP8gCE4VQv4PE/I/CIJThZD/w4T8D4LgVCHk/zAh/4MgOFUI+T9MyP8gCJoigpGDwMkLXvACOO200+B973sfAAB87GMfg/vvvx+uvfZacf9du3apZf3yL/8y3HjjjXDTTTfBP//zP8PevXvh53/+5wEAYP/+/erxu3fvPvH7zMwM/MM//AP8y3/5L+Ftb3sbPP7xj4czzzwTfumXfgkOHz5snsvRo0dhfn5+5PsHH3wQzj77bPNYZNu2bUOf5+bmzO+XlpZGypifn4ejR4+66guCIBgXIf+HCfkfBMGpQsj/YUL+B0FwqhDyf5iQ/0EQnCqE/B8m5H8QBKcKIf+HCfkfBMGpQsj/YUL+B0HQFGvG3YAgmBbWr18PP/mTPwn/9b/+V9i7dy+8973vhc2bN8NP/MRPiPvzWUeUs88+G6644grxtzPOOAMAAPbu3Tvy23333Qfbt28/8fm8886D97znPQAAcOutt8J//+//Hd74xjfC8vIy/NEf/ZFa//bt2+GLX/ziyPdnnnkm3HPPPepxTfPII48MnU8QBMEkEvK/eUL+B0EwDYT8b56Q/0EQTAMh/5sn5H8QBNNAyP/mCfkfBME0EPK/eUL+B0EwDYT8b56Q/0EQAERm5CBI4tprr4XV1VV4+9vfDh/72MfgRS96EWzYsKHROq666ipYv349fOADHxj6/p577oEbbrgBfuiHfkg87uKLL4bf+I3fgMsuu0xUNCiPfexjYf/+/XDw4MGh76+55hq49dZb4YYbbqh3Eg7uu+8+WFpagsc97nGt1xUEQVCXkP/NEfI/CIJpIuR/c4T8D4Jgmgj53xwh/4MgmCZC/jdHyP8gCKaJkP/NEfI/CIJpIuR/c4T8D4IAiWDkIEjgiiuugO/+7u+Gd77znbCysqIu0VCHrVu3wm/+5m/CRz7yEXjJS14CH//4x+EDH/gAPO1pT4P5+Xl4wxveAAAAX/nKV+ApT3kK/P7v/z783d/9Hdxwww3wG7/xG/CVr3wFfuRHfsSs4+qrr4aiKOCf/umfhr5/9atfDZdccgk873nPg9/+7d+G66+/Hj7ykY/Aa1/7WvjkJz/Z6Hl+/vOfBwCApz3taY2WGwRB0AYh/5sj5H8QBNNEyP/mCPkfBME0EfK/OUL+B0EwTYT8b46Q/0EQTBMh/5sj5H8QBNNEyP/mCPkfBAESwchBkMi1114LRVHA4x73OPi+7/u+Vur4tV/7NfiTP/kT+PKXvwzPf/7z4ZWvfCVccskl8NnPfhYe85jHAADAzp074aKLLoLrrrsOXvCCF8Dznvc8+Ju/+Rv43d/9XXjzm99slv/93//9cP7558P/+l//a+j7zZs3w2c+8xm49tpr4Y//+I/h2c9+Nrz85S+HW265BXbv3t3oOf71X/81XHbZZXDZZZc1Wm4QBEFbhPxvhpD/QRBMGyH/myHkfxAE00bI/2YI+R8EwbQR8r8ZQv4HQTBthPxvhpD/QRBMGyH/myHkfxAEyExRFMW4GxEEQff87u/+Lvz2b/823HvvvbB+/fpO6z506BDs3r0bfu/3fg9e/vKXd1p3EATBqU7I/yAIglOTkP9BEASnJiH/gyAITk1C/gdBEJyahPwPgiA4NQn5HwTBpBCZkYPgFOUVr3gFnHbaafCud72r87p/7/d+D84991z42Z/92c7rDoIgONUJ+R8EQXBqEvI/CILg1CTkfxAEwalJyP8gCIJTk5D/QRAEpyYh/4MgmBQiGDkITlHm5+fhz//8z2HdunWd171lyxb40z/9U1izZk3ndQdBEJzqhPwPgiA4NQn5HwRBcGoS8j8IguDUJOR/EATBqUnI/yAIglOTkP9BEEwKM0VRFONuRBAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAE00dkRg6CIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIIsIRg6CIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIIsIRg6CIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIIsIRg6CIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIIsIRg6CIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIIsIRg6CIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIIsIRg6CIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIIsIRg6CIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIIv/HwXf8a7+bLYxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2840x400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make snake plot of all tracked cells that are reliable in the \"sortby\" session\n",
    "envnum, idx_ses = roistat.env_idx_ses_selector(envmethod=\"most\", sesmethod=-7, verbose=True)\n",
    "outs = roistat.plot_snake(envnum, idx_ses=idx_ses, sortby=idx_ses[-4], cutoffs=(0.4, 0.7), normalize=-80, rewzone=True, sortby_blue=False, height_multiplier=2.0, withSave=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "220d7e4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f6c7131",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "527aeae2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbcd6e1c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ade5989",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2201088",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1f5de59",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ef16283",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d948eca5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb3c4bf1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "992a772f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a5db1a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c86d460f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26a1db0f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0eec13c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "009d26a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROICaT Figure -- Example\n",
    "\n",
    "# Goal: show FOV from two sessions, with matched nearby ROIs color-coded the same way and non-tracked but nearby ROIs color-coded differently\n",
    "# Pick ROIs that have a good place field\n",
    "# Show trial by position heat maps stacked by session color-coded the same way as the ROI to show that tracked has the same place field and non-tracked (but close) doesn't\n",
    "\n",
    "mouse_name = 'ATL027'\n",
    "track = tracking.tracker(mouse_name)\n",
    "roistat = analysis.RoicatStats(track)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77b2a872",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: 3 idx_ses: [8, 9, 10, 11]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4/4 [00:38<00:00,  9.58s/it]\n",
      "getting roi data: 100%|██████████| 4/4 [00:02<00:00,  1.62it/s]\n",
      "c:\\Users\\Andrew\\Documents\\GitHub\\vrAnalysis\\vrAnalysis\\analysis\\roicat_stats.py:159: RuntimeWarning: All-NaN slice encountered\n",
      "  nn_dist = [np.nanmin(pwd, axis=1) for pwd in pwdists]\n",
      "getting roi data: 100%|██████████| 4/4 [00:02<00:00,  1.56it/s]\n"
     ]
    }
   ],
   "source": [
    "# processing methods for preloaded roistat object\n",
    "envnum = roistat.env_selector(envmethod=\"most\")\n",
    "\n",
    "# get all sessions with chosen environment\n",
    "idx_ses = roistat.idx_ses_selector(envnum, sesmethod=\"all\")\n",
    "\n",
    "# pick 4 environments, the later the better, but if there's lots of sessions don't do the last one (imaging quality dropped off in last couple sessions usually)\n",
    "if len(idx_ses) > 7:\n",
    "    idx_ses = idx_ses[-6:-2]\n",
    "elif len(idx_ses) > 4:\n",
    "    idx_ses = idx_ses[-4:]\n",
    "\n",
    "# report which environment and sessions are being used\n",
    "print(\"env:\", envnum, \"idx_ses:\", idx_ses)\n",
    "\n",
    "# get data for all roicat plots\n",
    "\n",
    "kwargs = dict(\n",
    "    sim_name=\"sConj\",\n",
    "    cutoffs=(0.6, 0.85),\n",
    "    both_reliable=False,\n",
    ")\n",
    "\n",
    "sim, corr, tracked, pwdist, nnpair, pwind, prms = roistat.make_roicat_comparison(envnum, idx_ses=idx_ses, **kwargs)\n",
    "centroids = roistat.track.get_centroids(idx_ses=prms['idx_ses'], cat_planes=True, combine=True, keep_planes=roistat.keep_planes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "186033dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(2, 70), (2, 63), (2, 52), (2, 88), (2, 85), (2, 169)]\n",
      "[(2, 70), (2, 63), (2, 52), (2, 88), (2, 85), (2, 169)]\n",
      "[(70, 2), (63, 2), (52, 2), (88, 2), (85, 2), (169, 2)]\n",
      "[(70, 2), (63, 2), (52, 2), (88, 2), (85, 2), (169, 2)]\n"
     ]
    }
   ],
   "source": [
    "def check_no_duplicates(roi_idx_match, roi_idx_nomatch):\n",
    "    no_duplicates = []\n",
    "    for im, inm in zip(roi_idx_match, roi_idx_nomatch):\n",
    "        c_duplicates = im.reshape(2, -1, 1) == inm.reshape(2, 1, -1)\n",
    "        no_duplicates.append(~np.any(c_duplicates, axis=0))\n",
    "    return no_duplicates\n",
    "\n",
    "def get_centroids_match(centroids, idx, prms):\n",
    "    assert len(prms['idx_ses_pairs'])==len(idx), \"idx doesn't match number of session pairs\" \n",
    "    assert len(centroids)==len(prms['idx_ses']), \"centroids doesn't match number of sessions\"\n",
    "    \n",
    "    # prepare lookup table for going from the absolute session index to the relative index in centroids\n",
    "    session_lookup = lambda ises: {val: idx for idx, val in enumerate(prms['idx_ses'])}[ises]\n",
    "    \n",
    "    # prepare list for centroids of matched pairs\n",
    "    centroids_matched = []\n",
    "    for ipair, imatch in zip(prms['idx_ses_pairs'], idx):\n",
    "        i1 = session_lookup(ipair[0]) # idx to first session in pair\n",
    "        i2 = session_lookup(ipair[1]) # idx to second session in pair\n",
    "        c1 = centroids[i1][imatch[0]] # centroids in first session\n",
    "        c2 = centroids[i2][imatch[1]] # centroids in second session\n",
    "        centroids_matched.append(np.stack((c1, c2)))\n",
    "\n",
    "    return centroids_matched\n",
    "\n",
    "def get_plane_of_pair(roistat, idx, prms):\n",
    "    assert len(idx)==len(prms['idx_ses_pairs']), \"idx and prms['idx_ses_pairs'] don't have same number of elements\"\n",
    "\n",
    "    # prepare lookup table for going from the absolute session index to the relative index in centroids\n",
    "    session_lookup = lambda ises: {val: idx for idx, val in enumerate(prms['idx_ses'])}[ises]\n",
    "\n",
    "    # get plane index for each roi from sessions\n",
    "    roiPlaneIdx = roistat.get_from_pcss('roiPlaneIdx', prms['idx_ses'])\n",
    "\n",
    "    # prepare list of roi plane index for each pair\n",
    "    pair_plane = []\n",
    "    for ipair, ii in zip(prms['idx_ses_pairs'], idx):\n",
    "        i1 = session_lookup(ipair[0])\n",
    "        i2 = session_lookup(ipair[1])\n",
    "        pp1 = roiPlaneIdx[i1][ii[0]]\n",
    "        pp2 = roiPlaneIdx[i2][ii[1]]\n",
    "        if not np.all(pp1==pp2): \n",
    "            raise ValueError(\"Planes of pair don't all match\")\n",
    "        pair_plane.append(pp1)\n",
    "    \n",
    "    return pair_plane\n",
    "\n",
    "\n",
    "# set parameters for possible pairs\n",
    "mincorr = 0.8 # minimum correlation for matched pair\n",
    "maxcorr = 0.2 # maximum correlation for non-matched pair\n",
    "maxdist = 6 # maximum distance for any example pair\n",
    "\n",
    "# set parameters for pair of pairs (distance apart etc.)\n",
    "maxdistpp = 12 # maximum distance for pair of pairs\n",
    "mindistpp = 6 # minimum distance for pair of pairs\n",
    "\n",
    "# get possible matches and not-matches\n",
    "idx_possible_match = [np.where(t & (c>mincorr) & (pwd<maxdist))[0] for t, c, pwd in zip(tracked, corr, pwdist)] \n",
    "idx_possible_nomatch = [np.where(~t & (c<maxcorr) & (pwd<maxdist))[0] for t, c, pwd in zip(tracked, corr, pwdist)]\n",
    "\n",
    "# get possible match/nomatch pair indices\n",
    "roi_idx_match = [pwi[:, i] for pwi, i in zip(pwind, idx_possible_match)]\n",
    "roi_idx_nomatch = [pwi[:, i] for pwi, i in zip(pwind, idx_possible_nomatch)]\n",
    "\n",
    "# get average centroid of pairs\n",
    "centroids_match = get_centroids_match(centroids, roi_idx_match, prms)\n",
    "centroids_nomatch = get_centroids_match(centroids, roi_idx_nomatch, prms)\n",
    "acentroid_match = [np.mean(c, axis=0) for c in centroids_match]\n",
    "acentroid_nomatch = [np.mean(c, axis=0) for c in centroids_nomatch]\n",
    "\n",
    "# get distance between pairs of possible match/nomatch for each pair of sessions\n",
    "dist_match_nomatch = [sp.spatial.distance.cdist(am, anm) for am, anm in zip(acentroid_match, acentroid_nomatch)]\n",
    "good_distance = [(dmn<maxdistpp) & (dmn>mindistpp) for dmn in dist_match_nomatch]\n",
    "\n",
    "# check if pairs include any of the same ROIs\n",
    "no_duplicates = check_no_duplicates(roi_idx_match, roi_idx_nomatch)\n",
    "\n",
    "# get plane indices for each pair\n",
    "plane_pair_match = get_plane_of_pair(roistat, roi_idx_match, prms)\n",
    "plane_pair_nomatch = get_plane_of_pair(roistat, roi_idx_nomatch, prms)\n",
    "same_plane = [ppm.reshape(-1, 1) == ppn.reshape(1, -1) for ppm, ppn in zip(plane_pair_match, plane_pair_nomatch)]\n",
    "\n",
    "# possible pair of pairs boolean array\n",
    "ipair_match, ipair_nomatch = helpers.named_transpose([np.where(gd & nd & smp) for gd, nd, smp in zip(good_distance, no_duplicates, same_plane)])\n",
    "\n",
    "# indices to ROI pairs for good pairs of pairs\n",
    "pp_roi_match = [rim[:, ipm] for rim, ipm in zip(roi_idx_match, ipair_match)]\n",
    "pp_roi_nomatch = [rinm[:, ipnm] for rinm, ipnm in zip(roi_idx_nomatch, ipair_nomatch)] \n",
    "pp_centroid_match = [acm[ipm] for acm, ipm in zip(acentroid_match, ipair_match)]\n",
    "pp_centroid_nomatch = [acnm[ipnm] for acnm, ipnm in zip(acentroid_nomatch, ipair_nomatch)]\n",
    "\n",
    "# get plane indices for each pair\n",
    "plane_pair = get_plane_of_pair(roistat, pp_roi_match, prms)\n",
    "\n",
    "# check that match and no-match pairs are in same plane\n",
    "assert all([np.all(pp==ppnm) for pp, ppnm in zip(plane_pair, get_plane_of_pair(roistat, pp_roi_nomatch, prms))])\n",
    "\n",
    "# this lists the shapes of pairs of ROI indices across the session pairs for pairs of pairs =<\n",
    "print([i.shape for i in pp_roi_match])\n",
    "print([i.shape for i in pp_roi_nomatch])\n",
    "print([i.shape for i in pp_centroid_match])\n",
    "print([i.shape for i in pp_centroid_nomatch])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "417606c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session Pair: [8 9] ROI Pair: 39\n",
      "Match Idx: [10633  7025] NoMatch Idx: [9804 7771]\n"
     ]
    }
   ],
   "source": [
    "# Now make figures\n",
    "def get_masks(roistat, isespair, idxroipair, prms, pp_roi_match, pp_roi_nomatch):\n",
    "    ROIs = roistat.track.get_ROIs(as_coo=False, idx_ses=prms['idx_ses_pairs'][isespair], keep_planes=roistat.keep_planes)\n",
    "    ROIs = [sp.sparse.vstack(rois, format='csr') for rois in ROIs] # concatenate across planes\n",
    "\n",
    "    roi_match = [ROIs[0][pp_roi_match[isespair][0, idxroipair]], ROIs[1][pp_roi_match[isespair][1, idxroipair]]]\n",
    "    roi_nomatch = [ROIs[0][pp_roi_nomatch[isespair][0, idxroipair]], ROIs[1][pp_roi_nomatch[isespair][1, idxroipair]]]\n",
    "\n",
    "    num_pixels = roi_match[0].shape[1]\n",
    "    hw = int(np.sqrt(num_pixels))\n",
    "    roi_match = [r.toarray().reshape(hw, hw) for r in roi_match]\n",
    "    roi_nomatch = [r.toarray().reshape(hw, hw) for r in roi_nomatch]\n",
    "\n",
    "    return roi_match, roi_nomatch\n",
    "\n",
    "def get_spkmaps(roistat, isespair, idxroipair, prms, pp_roi_match, pp_roi_nomatch):\n",
    "    spkmaps = roistat.get_spkmaps(envnum, trials=\"full\", average=False, tracked=False, idx_ses=prms['idx_ses_pairs'][isespair], by_plane=False)[0]\n",
    "    spkmap_match = [spkmaps[0][pp_roi_match[isespair][0, idxroipair]], spkmaps[1][pp_roi_match[isespair][1, idxroipair]]]\n",
    "    spkmap_nomatch = [spkmaps[0][pp_roi_nomatch[isespair][0, idxroipair]], spkmaps[1][pp_roi_nomatch[isespair][1, idxroipair]]]\n",
    "    spkmap_match = [sp.stats.zscore(sm, axis=None) for sm in spkmap_match]\n",
    "    spkmap_nomatch = [sp.stats.zscore(sm, axis=None) for sm in spkmap_nomatch]\n",
    "    distedges = roistat.pcss[prms['idx_ses_pairs'][isespair][0]].distedges\n",
    "    return spkmap_match, spkmap_nomatch, distedges\n",
    "\n",
    "# pick random session pair and random index\n",
    "isespair = 0\n",
    "idxroipair = np.random.choice(pp_roi_match[isespair].shape[1])\n",
    "roi_scale = 2.5\n",
    "lim = 3 # zscore limit of colormap\n",
    "print('Session Pair:', prms['idx_ses_pairs'][isespair], \"ROI Pair:\", idxroipair)\n",
    "print(\"Match Idx:\", pp_roi_match[isespair][:, idxroipair], \"NoMatch Idx:\", pp_roi_nomatch[isespair][:, idxroipair])\n",
    "\n",
    "ccpair = np.mean(np.stack((pp_centroid_match[isespair][idxroipair], pp_centroid_nomatch[isespair][idxroipair])), axis=0)\n",
    "\n",
    "spkmaps_match, spkmaps_nomatch, distedges = get_spkmaps(roistat, isespair, idxroipair, prms, pp_roi_match, pp_roi_nomatch)\n",
    "extents = [[0, sm.shape[0], distedges[0], distedges[-1]] for sm in spkmaps_match]\n",
    "\n",
    "# make heatmaps of FOV\n",
    "FOVs = [roistat.track.rundata[plane_pair[isespair][idxroipair]][\"aligner\"][\"ims_registered_nonrigid\"][i] for i in prms['idx_ses_pairs'][isespair]]\n",
    "roi_match, roi_nomatch = get_masks(roistat, isespair, idxroipair, prms, pp_roi_match, pp_roi_nomatch)\n",
    "normalize = lambda data: [((d-d.min())/(d.max()-d.min())).astype(np.float32) for d in data]\n",
    "FOVs = normalize(FOVs)\n",
    "roi_match = normalize(roi_match)\n",
    "roi_nomatch = normalize(roi_nomatch)\n",
    "\n",
    "fov_plot = [np.tile(fov.reshape(fov.shape[0], fov.shape[1], 1), (1, 1, 3)) for fov in FOVs]\n",
    "fov_plot[0][:, :, 0] += roi_match[0]*roi_scale\n",
    "fov_plot[1][:, :, 0] += roi_match[1]*roi_scale\n",
    "fov_plot[0][:, :, 1] += roi_nomatch[0]*roi_scale\n",
    "fov_plot[1][:, :, 2] += roi_nomatch[1]*roi_scale\n",
    "fov_plot = normalize(fov_plot)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(6, 3), layout='constrained', sharex=True, sharey=True)\n",
    "ax[0].imshow(fov_plot[0])\n",
    "ax[1].imshow(fov_plot[1])\n",
    "ax[0].set_xlim(ccpair[0] + [-20, 20])\n",
    "ax[0].set_ylim(ccpair[1] + [-20, 20])\n",
    "ax[0].set_title('Session 1')\n",
    "ax[1].set_title('Session 2')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(2, 2, figsize=(6, 6), layout='constrained', sharey=True)\n",
    "ax[0, 0].imshow(spkmaps_match[0].T, cmap='Reds', aspect='auto', extent=extents[0], vmin=0, vmax=lim)\n",
    "ax[0, 1].imshow(spkmaps_match[1].T, cmap='Reds', aspect='auto', extent=extents[1], vmin=0, vmax=lim)\n",
    "ax[1, 0].imshow(spkmaps_nomatch[0].T, cmap='Greens', aspect='auto', extent=extents[0], vmin=0, vmax=lim)\n",
    "ax[1, 1].imshow(spkmaps_nomatch[1].T, cmap='Blues', aspect='auto', extent=extents[1], vmin=0, vmax=lim)\n",
    "ax[0, 0].set_ylabel(\"Virtual Position (cm)\")\n",
    "ax[1, 0].set_ylabel(\"Virtual Position (cm)\")\n",
    "ax[1, 0].set_xlabel('Trials')\n",
    "ax[1, 1].set_xlabel('Trials')\n",
    "ax[0, 0].set_title('Session 1')\n",
    "ax[0, 1].set_title('Session 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15999ec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 18)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp_roi_match[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13615e56",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8afe013",
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse_name = 'ATL027'\n",
    "track = tracking.tracker(mouse_name)\n",
    "pcm = analysis.placeCellMultiSession(track)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ed21cc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATL027 Env (most): 3, Sessions (-6): [8, 9, 10, 11, 12, 13]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 3/6 [00:20<00:19,  6.47s/it]c:\\Users\\Andrew\\Documents\\GitHub\\vrAnalysis\\vrAnalysis\\functions.py:208: RuntimeWarning: overflow encountered in divide\n",
      "  relmse[idxHasActivity] += 1 - numerator[idxHasActivity] / denominator[idxHasActivity]\n",
      "100%|██████████| 6/6 [00:41<00:00,  6.92s/it]\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'placeCellMultiSession' object has no attribute 'smooth'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m env, idx_ses \u001b[38;5;241m=\u001b[39m pcm\u001b[38;5;241m.\u001b[39menv_idx_ses_selector(envmethod\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmost\u001b[39m\u001b[38;5;124m'\u001b[39m, sesmethod\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m6\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      3\u001b[0m distStep \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m----> 4\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mpcm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_spkmaps\u001b[49m\u001b[43m(\u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfull\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maverage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtracked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpf_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmax\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mby_plane\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx_ses\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43midx_ses\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistStep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdistStep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfull_trial_flexibility\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2.5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m spkmaps, relmse, relcor, pfloc, pfidx, idx_red, roi_idx \u001b[38;5;241m=\u001b[39m out\n\u001b[0;32m      6\u001b[0m idx_with_nan \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39many(np\u001b[38;5;241m.\u001b[39misnan(s), axis\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m)) \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m spkmaps]\n",
      "File \u001b[1;32mc:\\Users\\Andrew\\Documents\\GitHub\\vrAnalysis\\vrAnalysis\\analysis\\placeCellMultiSession.py:35\u001b[0m, in \u001b[0;36mhandle_idx_ses.<locals>.wrapper\u001b[1;34m(pcm_instance, envnum, idx_ses, *args, **kwargs)\u001b[0m\n\u001b[0;32m     32\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mall\u001b[39m(in_session), \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequested environment only in following sessions: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m[idx\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39midx,\u001b[38;5;250m \u001b[39minses\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mzip\u001b[39m(idx_ses,\u001b[38;5;250m \u001b[39min_session)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mif\u001b[39;00m\u001b[38;5;250m \u001b[39minses]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     34\u001b[0m \u001b[38;5;66;03m# return function with processed arguments\u001b[39;00m\n\u001b[1;32m---> 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(pcm_instance, envnum, \u001b[38;5;241m*\u001b[39margs, idx_ses\u001b[38;5;241m=\u001b[39midx_ses, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\Andrew\\Documents\\GitHub\\vrAnalysis\\vrAnalysis\\analysis\\placeCellMultiSession.py:323\u001b[0m, in \u001b[0;36mget_spkmaps\u001b[1;34m(self, envnum, trials, average, tracked, pf_method, by_plane, idx_ses, **kwargs)\u001b[0m\n\u001b[0;32m    320\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mload_pcss_data(idx_ses\u001b[38;5;241m=\u001b[39midx_ses, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    322\u001b[0m \u001b[38;5;66;03m# get track index if requested\u001b[39;00m\n\u001b[1;32m--> 323\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tracked:\n\u001b[0;32m    324\u001b[0m     idx_tracked \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrack\u001b[38;5;241m.\u001b[39mget_tracked_idx(idx_ses\u001b[38;5;241m=\u001b[39midx_ses, keep_planes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeep_planes)\n\u001b[0;32m    326\u001b[0m \u001b[38;5;66;03m# get spkmaps (#ROI, #Trials, #SpatialBins)\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Andrew\\Documents\\GitHub\\vrAnalysis\\vrAnalysis\\analysis\\placeCellMultiSession.py:323\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    320\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mload_pcss_data(idx_ses\u001b[38;5;241m=\u001b[39midx_ses, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    322\u001b[0m \u001b[38;5;66;03m# get track index if requested\u001b[39;00m\n\u001b[1;32m--> 323\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tracked:\n\u001b[0;32m    324\u001b[0m     idx_tracked \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrack\u001b[38;5;241m.\u001b[39mget_tracked_idx(idx_ses\u001b[38;5;241m=\u001b[39midx_ses, keep_planes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeep_planes)\n\u001b[0;32m    326\u001b[0m \u001b[38;5;66;03m# get spkmaps (#ROI, #Trials, #SpatialBins)\u001b[39;00m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'placeCellMultiSession' object has no attribute 'smooth'"
     ]
    }
   ],
   "source": [
    "env, idx_ses = pcm.env_idx_ses_selector(envmethod='most', sesmethod=-6, verbose=True)\n",
    "\n",
    "distStep = 1\n",
    "out = pcm.get_spkmaps(env, trials='full', average=False, tracked=False, pf_method='max', by_plane=False, idx_ses=idx_ses, distStep=distStep, full_trial_flexibility=2.5)\n",
    "spkmaps, relmse, relcor, pfloc, pfidx, idx_red, roi_idx = out\n",
    "idx_with_nan = [np.any(np.isnan(s), axis=(0, 1)) for s in spkmaps]\n",
    "idx_with_nan_across_spkmaps = np.any(np.stack(idx_with_nan), axis=0)\n",
    "spkmaps = [spkmap[:, :, ~idx_with_nan_across_spkmaps] for spkmap in spkmaps]\n",
    "\n",
    "cutoffs = (0.2, 0.5)\n",
    "idx_reliable = [[(mse>cutoffs[0]) & (cor>cutoffs[1]) for mse, cor in zip(rmse, rcor)] for rmse, rcor in zip(relmse, relcor)]\n",
    "spkmaps = [spkmap[idx] for spkmap, idx in zip(spkmaps, idx_reliable)]\n",
    "print('spkmap shape', [s.shape for s in spkmaps])\n",
    "\n",
    "max_trials = int(min([sm.shape[1] for sm in spkmaps])/2)\n",
    "max_neurons = int(min([sm.shape[0] for sm in spkmaps])/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9527045",
   "metadata": {},
   "outputs": [],
   "source": [
    "cvmethods = [cvPCA_from_MouseLandGithub, cvPCA_paper_stimuli, cvPCA_paper_neurons]\n",
    "w_git, w_stim, w_neur = helpers.named_transpose([cvpca(spkmap, by_trial=False, max_trials=max_trials, max_neurons=max_neurons, cvmethods=cvmethods) for spkmap in spkmaps])\n",
    "wns_git, wns_stim, wns_neur = helpers.named_transpose([cvpca(spkmap, by_trial=False, max_trials=max_trials, max_neurons=max_neurons, nshuff=1, cvmethods=cvmethods) for spkmap in spkmaps])\n",
    "\n",
    "wt_git, wt_stim, wt_neur = helpers.named_transpose([cvpca(spkmap, by_trial=True, noise_corr=False, max_trials=max_trials, max_neurons=max_neurons, cvmethods=cvmethods) for spkmap in spkmaps])\n",
    "wtnc_git, wtnc_stim, wtnc_neur = helpers.named_transpose([cvpca(spkmap, by_trial=True, noise_corr=True, max_trials=max_trials, max_neurons=max_neurons, cvmethods=cvmethods) for spkmap in spkmaps])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dca7e774",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To compare shuffled with no shuffled\n",
    "num_sessions = len(w_git)\n",
    "fig, ax = plt.subplots(1, num_sessions, figsize=(num_sessions*2, 2), layout='constrained')\n",
    "for ii, (w, wns) in enumerate(zip(w_neur, wns_neur)):\n",
    "    ax[ii].plot(w / np.sum(w), color='k', label='ShuffledAverage')\n",
    "    ax[ii].plot(wns / np.sum(wns), color='r', label='No Shuffle')\n",
    "    ax[ii].set_xscale('log')\n",
    "ax[-1].legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fdcba57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To compare no average with and without noise-correlation\n",
    "num_sessions = len(w_git)\n",
    "fig, ax = plt.subplots(1, num_sessions, figsize=(num_sessions*2, 2), layout='constrained', sharey=True)\n",
    "for ii, (w, wns) in enumerate(zip(wt_neur, wtnc_neur)):\n",
    "    ax[ii].plot(w / np.sum(w), color='k', label='No Noise-Corr')\n",
    "    ax[ii].plot(wns / np.sum(wns), color='r', label='Only Noise-Corr')\n",
    "    ax[ii].set_xscale('log')\n",
    "ax[-1].legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9379884d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To compare three different cvPCA methods\n",
    "num_sessions = len(w_git)\n",
    "fig, ax = plt.subplots(2, num_sessions, figsize=(num_sessions*2, 4), layout='constrained')\n",
    "for ii, (wg, ws, wn) in enumerate(zip(w_git, w_stim, w_neur)):\n",
    "    ax[0, ii].plot(wg, color='k', label='FromGithub')\n",
    "    ax[0, ii].plot(ws + 0.01, color='r', label='FromPaper-Stimuli')\n",
    "    ax[0, ii].plot(wn + 0.02, color='b', label='FromPaper-Neurons')\n",
    "    ax[0, ii].set_xscale('log')\n",
    "\n",
    "    ax[1, ii].plot(wg / np.sum(wg), color='k', label='FromGithub')\n",
    "    ax[1, ii].plot(ws / np.sum(ws), color='r', label='FromPaper-Stimuli')\n",
    "    ax[1, ii].plot(wn / np.sum(wn), color='b', label='FromPaper-Neurons')\n",
    "    ax[1, ii].set_xscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78e5eafa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To compare averaged vs all trials\n",
    "cmap = mpl.colormaps['bwr'].resampled(len(spkmaps))\n",
    "\n",
    "plt.close('all')\n",
    "fig, ax = plt.subplots(1, 3, figsize=(9, 3), layout='constrained')\n",
    "for ii, (wa, wt) in enumerate(zip(w_neur, wt_neur)):\n",
    "    ax[0].plot(np.cumsum(wa / np.sum(wa)), color=cmap(ii))\n",
    "    ax[1].plot(np.cumsum(wt / np.sum(wt)), color=cmap(ii))\n",
    "    if ii==0:\n",
    "        ax[2].plot(np.cumsum(wa / np.sum(wa)), color='k', label='Averaged')\n",
    "        ax[2].plot(np.cumsum(wt / np.sum(wt)), color='b', label='Expanded')\n",
    "    else:\n",
    "        ax[2].plot(np.cumsum(wa / np.sum(wa)), color='k')\n",
    "        ax[2].plot(np.cumsum(wt / np.sum(wt)), color='b')\n",
    "    \n",
    "ax[0].set_xscale('log')\n",
    "ax[1].set_xscale('log')\n",
    "ax[2].set_xscale('log')\n",
    "# ax[0].set_yscale('log')\n",
    "# ax[1].set_yscale('log')\n",
    "# ax[2].set_yscale('log')\n",
    "ax[0].set_title('Average Across Trials')\n",
    "ax[1].set_title('Expanded Across Trials')\n",
    "ax[2].set_title('Comparison')\n",
    "ax[2].legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "286e9476",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2efb296e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\\\zaru.cortexlab.net\\Subjects\\ATL045\\2024-02-02\\702\n",
      "C:\\Users\\andrew\\Documents\\localData\\ATL045\\2024-02-02\\702\n"
     ]
    }
   ],
   "source": [
    "fm.s2pTargets('ATL045', '2024-02-02', '702')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "092350a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Performing vrExperiment preprocessing for session: ATL045/2024-01-24/701\n",
      "Cleared oneData from session: ATL045/2024-01-24/701\n",
      "Self.value['numTrials'] set by trialInfo.trialIdx>0, but this might not be right. There might be smarter ways to determine which trials are 'good' trials...\n",
      "Performing oasis...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18509/18509 [07:30<00:00, 41.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing red cell features for ATL045/2024-01-24/701... (usually takes 10-20 seconds)\n",
      "Facecam preprocessing has not been coded yet!\n",
      "Saving params...\n",
      "Session ATL045/2024-01-24/701 registered with 4.75 GB oneData.\n",
      "Accumulated oneData registered: 4.75 GB. Averaging: 4.75 GB / session. Estimate remaining: 9.49 GB\n",
      "\n",
      "Performing vrExperiment preprocessing for session: ATL045/2024-01-26/701\n",
      "Cleared oneData from session: ATL045/2024-01-26/701\n",
      "Self.value['numTrials'] set by trialInfo.trialIdx>0, but this might not be right. There might be smarter ways to determine which trials are 'good' trials...\n",
      "frame2time had 2 more than suite2p output. This happens sometimes. I don't like it. I think it's because scanimage sends a TTL before starting the frame\n",
      "Performing oasis...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13986/13986 [06:08<00:00, 37.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing red cell features for ATL045/2024-01-26/701... (usually takes 10-20 seconds)\n",
      "Facecam preprocessing has not been coded yet!\n",
      "Saving params...\n",
      "Session ATL045/2024-01-26/701 registered with 3.88 GB oneData.\n",
      "Accumulated oneData registered: 8.63 GB. Averaging: 4.32 GB / session. Estimate remaining: 4.32 GB\n"
     ]
    }
   ],
   "source": [
    "sessiondb.registerSessions(maxData=50e9, skipErrors=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b30fa3ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uSessionID</th>\n",
       "      <th>mouseName</th>\n",
       "      <th>sessionDate</th>\n",
       "      <th>sessionID</th>\n",
       "      <th>experimentType</th>\n",
       "      <th>experimentID</th>\n",
       "      <th>variableGain</th>\n",
       "      <th>behavior</th>\n",
       "      <th>imaging</th>\n",
       "      <th>faceCamera</th>\n",
       "      <th>...</th>\n",
       "      <th>scratchJustification</th>\n",
       "      <th>logtime</th>\n",
       "      <th>sessionNotes</th>\n",
       "      <th>suite2pDate</th>\n",
       "      <th>vrRegistrationDate</th>\n",
       "      <th>vrRegistrationError</th>\n",
       "      <th>vrRegistrationException</th>\n",
       "      <th>redCellQCDate</th>\n",
       "      <th>vrBehaviorVersion</th>\n",
       "      <th>dontTrack</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [uSessionID, mouseName, sessionDate, sessionID, experimentType, experimentID, variableGain, behavior, imaging, faceCamera, vrEnvironments, headPlateRotation, numPlanes, planeSeparation, pockelsPercentage, objectiveRotation, vrRegistration, suite2p, suite2pQC, redCellQC, sessionQC, scratchJustification, logtime, sessionNotes, suite2pDate, vrRegistrationDate, vrRegistrationError, vrRegistrationException, redCellQCDate, vrBehaviorVersion, dontTrack]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 31 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sessiondb.needsS2P()\n",
    "# sessiondb.checkS2P(withDatabaseUpdate=True, returnCheck=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc7ddf32",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ccd2ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This could be coded smarter with already calling checkValidity()\n",
      "Successfully added new record\n"
     ]
    }
   ],
   "source": [
    "# session identifiers\n",
    "mouseName = 'ATL045'\n",
    "sessionDate = '2024-02-02'\n",
    "sessionID = '702'\n",
    "\n",
    "# create session object \n",
    "# (at the moment, this only provides mouseName, sessionDate, and sessionID to the GUI, but will increase later)\n",
    "ses = registration.vrRegistration(mouseName, sessionDate, sessionID)\n",
    "\n",
    "# preloaded data\n",
    "preloaded_data = {\n",
    "    'experimentType': 'Blender VR',\n",
    "    'experimentID': 2,\n",
    "    'variableGain': True,\n",
    "    'behavior': True,\n",
    "    'imaging': True, \n",
    "    'faceCamera': True, \n",
    "    'vrEnvironments': 12,\n",
    "    'numPlanes': 5,\n",
    "    'planeSeparation': 25,\n",
    "    'suite2p': False,\n",
    "    'vrBehaviorVersion': 1,\n",
    "    'headPlateRotation': 5.9,\n",
    "    'objectiveRotation': 7.7,\n",
    "    'pockelsPercentage': 4,\n",
    "    'dontTrack': False,\n",
    "    'sessionNotes': (\n",
    "        \"FOV is different from yesterday.\"\n",
    "    )\n",
    "}\n",
    "\n",
    "# then open the GUI\n",
    "gui = addEntryGUI.newEntryGUI(sessiondb, ses=ses, **preloaded_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a06923cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "date = datetime.datetime(2024, 1, 24, 0, 0)\n",
    "print(isinstance(date, datetime.date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15c1404a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1119caff-5d69-484e-bb16-cc6b820c7d88",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Database Requirements: \n",
    "# ---------------------\n",
    "# GUI: db manager\n",
    "# - click on entry and do things:\n",
    "#                --> open file explorer to that session\n",
    "#                --> do suite2p\n",
    "#                --> do red cell management\n",
    "# - update table data? \n",
    "# ---------------------\n",
    "# Operational Commands: \n",
    "# - Automatically do suite2p \n",
    "# - Check if registration was done before a suite2p update\n",
    "\n",
    "# Further Requirements:\n",
    "# ---------------------\n",
    "# ROICaT Alignment Tools \n",
    "# Track Red Cell Consistency across days \n",
    "# Now that I've refactored the database code, need to update some things in documentation and probably elsewhere too\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f846af3-d4a6-47e3-962b-680643416cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Notes from meeting with Kenneth:\n",
    "\n",
    "# - skewness (violin plot) of Control & Red -- \n",
    "#     - all - \n",
    "#     - just reliable -- for each session - \n",
    "\n",
    "# Subsample control data for scatter plot\n",
    "# Fisher z transformation (but label by original correlation...)\n",
    "# Question:\n",
    "# -- if reliable on 1 day, is it reliable on other days? \n",
    "# -- make a matrix with source and target, color by fraction of reliable on target out of those reliable on source\n",
    "# -- also do this with your session kernels for control and red\n",
    "# -- also do this for different reliability cutoffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plan for attack:\n",
    "# Make a suite of summary figures on a session by session basis and a multisession basis. \n",
    "# I just want to be able to look through a mouse's data and evaluate the behavior, the imaging data, and how well the tracking did.\n",
    "\n",
    "# Inclusions:\n",
    "# 1. Behavioral data (running speed and number of trials across any environments it was in -- also metadata about day in environment...)\n",
    "# 2. Imaging data (example snakes from all environments, both train/test comparisons and remapping comparisons)\n",
    "# 3. Red cell data (number of red cells per plane -- and some examples of red cells?)\n",
    "# 4. Tracking data (number of tracked cells per combination (full matrix!), number of tracked red cells, number of tracked reliable cells per environment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0c852b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROICaT Analysis\n",
    "# 1. Example data figures (from roicat_stats)\n",
    "#    - show two post-alignment FOVs, highlight a few tracked neurons and a few (nearby) un-tracked neurons with colors\n",
    "#    - below that, show the place field tuning in each session, color-coded the same way as the neurons ROI plot\n",
    "\n",
    "# 2. Analysis of ROICaT agreement with functional data\n",
    "#    - scatter plot of sConj & place field correlation (with \"labels\" pairs colored differently)\n",
    "#    - mouse by mouse, session by session mean lines comparing average place field correlation of same pairs with different pairs\n",
    "#        -- (imagining lines from 0->1 for mouse 1 of each sessions mean same/diff pfCorr, then also in 2->3 for mouse 2, and 4->5 for mouse 3, etc.)\n",
    "#        -- can also have a supplemental plot showing distribution of same/diff pfCorr across each session pair? \n",
    "\n",
    "# 3. Control analysis with null model test (empirical version with subsampled null distribution)\n",
    "\n",
    "# 4. Control analysis with bayesian model\n",
    "# -- get pfCorr_withinSession (this is pairs of ROIs within a session, and should be representative of pfCorr_diff_acrossSession)\n",
    "# -- normalize pfCorr_all_acrossSession by number of pairs, subtract density of pfCorr_withinSession\n",
    "# -- remaining pfCorr_remain_acrossSession = pfCorr_same_acrossSession\n",
    "\n",
    "\n",
    "# ---- note ----\n",
    "# - should probably include target reliable pairs not represented in the source only reliable category..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c54a6e1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse_name = 'ATL027'\n",
    "track = tracking.tracker(mouse_name)\n",
    "roistat = analysis.RoicatStats(track)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cd4c8c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],\n",
       " 3: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],\n",
       " 4: [11, 12, 13]}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roistat.env_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa13bec7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]\n"
     ]
    }
   ],
   "source": [
    "env, idx_ses = roistat.env_idx_ses_selector(envmethod='most', sesmethod='all')\n",
    "print(env, idx_ses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93b4c867",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4/4 [00:43<00:00, 10.75s/it]\n",
      "getting roi data: 100%|██████████| 4/4 [00:02<00:00,  1.46it/s]\n",
      "c:\\Users\\Andrew\\Documents\\GitHub\\vrAnalysis\\vrAnalysis\\analysis\\roicat_stats.py:100: RuntimeWarning: All-NaN slice encountered\n",
      "  nn_dist = [np.nanmin(pwd, axis=1) for pwd in pwdists]\n"
     ]
    }
   ],
   "source": [
    "# get roicat comparison data\n",
    "sim, corr, tracked, pwdist, nnpair, pwind, prms = roistat.make_roicat_comparison(env, idx_ses=idx_ses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daf138e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')\n",
    "# out = roistat.plot_pwdist_vs_pfcorr(pwdist, corr, tracked, prms, max_dist=50, color_mode='tracked', with_show=True, with_save=False)\n",
    "# roistat.plot_pfcorr_by_samediff(corr, tracked, nnpair, prms, with_show=True, with_save=False)\n",
    "roistat.plot_pfcorrmean_by_samediff(corr, tracked, nnpair, pwdist, prms, dist_limit=10, return_data=False, with_show=True, with_save=False)\n",
    "# roistat.plot_pfcorr_vs_pwdist_by_group(corr, tracked, pwdist, nnpair, prms, with_show=True, with_save=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02158c70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\\\zaru.cortexlab.net\\Subjects\\ATL043\\2024-02-02\\701\n",
      "C:\\Users\\andrew\\Documents\\localData\\ATL043\\2024-02-02\\701\n"
     ]
    }
   ],
   "source": [
    "fm.s2pTargets('ATL043', '2024-02-02', '701')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3573c5c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on CR_Hippocannula6...\n",
      "env: 2 idx_ses: [3, 4, 5, 6]\n",
      "\n",
      "Working on CR_Hippocannula7...\n",
      "env: 2 idx_ses: [3, 4, 5, 6]\n",
      "\n",
      "Working on ATL022...\n",
      "env: 3 idx_ses: [11, 12, 13, 14]\n",
      "\n",
      "Working on ATL027...\n",
      "env: 3 idx_ses: [8, 9, 10, 11]\n",
      "\n",
      "Working on ATL020...\n",
      "env: 3 idx_ses: [3, 4, 5, 6]\n",
      "\n",
      "Working on ATL012...\n",
      "env: 1 idx_ses: [18, 19, 20, 21]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "TRACKED_MICE = mousedb.getTable(tracked=True)['mouseName'].tolist() # list of mice with tracked sessions\n",
    "\n",
    "keep_planes = [1, 2, 3, 4]\n",
    "\n",
    "kwargs = dict(\n",
    "    sim_name='sConj',\n",
    "    cutoffs=(0.4, 0.7),\n",
    "    both_reliable=False,\n",
    ")\n",
    "\n",
    "# save means/serrors from pfcorr by classification test for across mouse plot of results\n",
    "all_means = []\n",
    "all_serrors = []\n",
    "\n",
    "for mouse_name in TRACKED_MICE:\n",
    "    print(f\"Working on {mouse_name}...\")\n",
    "    track = tracking.tracker(mouse_name)\n",
    "    roistat = analysis.RoicatStats(track, keep_planes=keep_planes)\n",
    "\n",
    "    # select environment (CR mice had different structure and should use environment 2)\n",
    "    if 'CR' in mouse_name:\n",
    "        envnum = 2\n",
    "    else:\n",
    "        envnum = roistat.env_selector(envmethod='most')\n",
    "\n",
    "    idx_ses = roistat.idx_ses_selector(envnum, sesmethod='all')\n",
    "    \n",
    "    if len(idx_ses)>7:\n",
    "        idx_ses = idx_ses[-6:-2]\n",
    "    elif len(idx_ses)>4:\n",
    "        idx_ses = idx_ses[-4:]\n",
    "    else:\n",
    "        print(f'skipping {mouse_name}, because of the small sessions: {idx_ses}')\n",
    "        continue\n",
    "\n",
    "    print('env:', envnum, 'idx_ses:', idx_ses)\n",
    "    \n",
    "    # sim, corr, tracked, pwdist, nnpair, pwind, prms = roistat.make_roicat_comparison(envnum, idx_ses=idx_ses, **kwargs)\n",
    "    # # roistat.plot_sim_vs_pfcorr(sim, corr, prms, color_mode='tracked', with_show=False, with_save=True)\n",
    "    # # roistat.plot_pwdist_vs_pfcorr(pwdist, corr, tracked, prms, max_dist=50, color_mode='tracked', with_show=True, with_save=False)\n",
    "    # # roistat.plot_pfcorr_by_samediff(corr, tracked, nnpair, prms, with_show=False, with_save=True)\n",
    "    # c_means, c_serrors = roistat.plot_pfcorrmean_by_samediff(corr, tracked, nnpair, pwdist, prms, dist_limit=10, return_data=True, with_show=False, with_save=True)\n",
    "    # # roistat.plot_similarity_histograms(sim, prms, with_show=False, with_save=True)\n",
    "    # # roistat.plot_pfcorr_vs_pwdist_by_group(corr, tracked, pwdist, nnpair, prms, with_show=True, with_save=False)\n",
    "\n",
    "    # all_means.append(c_means)\n",
    "    # all_serrors.append(c_serrors)\n",
    "\n",
    "    print('')\n",
    "\n",
    "    plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0237631",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1f5d862",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATL012/2023-02-28/701\n"
     ]
    }
   ],
   "source": [
    "# Check ROICaT within session matching\n",
    "import pickle\n",
    "\n",
    "mouseName = 'ATL012'\n",
    "sessionDate = '2023-02-28'\n",
    "sessionID = '701'\n",
    "\n",
    "ses = session.vrExperiment(mouseName, sessionDate, sessionID)\n",
    "print(ses.sessionPrint())\n",
    "\n",
    "data = ses.loadone('mpci.roiActivityDeconvolvedOasis')\n",
    "\n",
    "filepath = ses.sessionPath() / f\"{mouseName}.within_session.ROICaT.tracking.results.pkl\"\n",
    "with open(filepath, 'rb') as f:\n",
    "    roicat = pickle.load(f)\n",
    "\n",
    "num_clusters = np.max(roicat['clusters']['labels'])+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scc = analysis.sameCellCandidates(ses, keepPlanes=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explorer = analysis.clusterExplorerROICaT(scc, roicat['clusters']['labels'], keepPlanes=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_number = np.zeros(num_clusters)\n",
    "cc_within = np.zeros(num_clusters)\n",
    "for ic in range(num_clusters):\n",
    "    cidx = roicat['clusters']['labels']==ic\n",
    "    cdata = data[:, cidx]\n",
    "    ccorr = np.corrcoef(cdata.T)\n",
    "    idx_offdiag = np.triu(np.ones_like(ccorr, dtype=bool), k=1)\n",
    "    cvals = ccorr[idx_offdiag]\n",
    "    cc_within[ic] = np.mean(cvals)\n",
    "    cc_number[ic] = np.sum(cidx)\n",
    "\n",
    "plt.close('all')\n",
    "plt.scatter(cc_number, cc_within)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i_clus = np.random.randint(0, num_clusters)\n",
    "\n",
    "plt.close('all')\n",
    "plt.plot(range(data.shape[0]), data[:, roicat['clusters']['labels']==i_clus])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "815"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(roicat['clusters']['labels']>-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'clusters': {'labels': array([-1,  0, -1, ..., -1, -1, -1], dtype=int64),\n",
       "  'labels_bySession': [array([-1,  0, -1, ..., -1, -1, -1], dtype=int64),\n",
       "   array([60, 20,  3, ..., -1, -1, -1], dtype=int64),\n",
       "   array([ 91, 246,   3, ...,  -1,  -1,  -1], dtype=int64),\n",
       "   array([  0, 175,  -1, ...,  -1,  -1,  -1], dtype=int64),\n",
       "   array([111,  23,  -1, ...,  -1,  -1,  -1], dtype=int64)],\n",
       "  'labels_bool': <7594x381 sparse matrix of type '<class 'numpy.bool_'>'\n",
       "  \twith 7594 stored elements in Compressed Sparse Row format>,\n",
       "  'labels_bool_bySession': [<1458x381 sparse matrix of type '<class 'numpy.bool_'>'\n",
       "   \twith 1458 stored elements in Compressed Sparse Row format>,\n",
       "   <1131x381 sparse matrix of type '<class 'numpy.bool_'>'\n",
       "   \twith 1131 stored elements in Compressed Sparse Row format>,\n",
       "   <1410x381 sparse matrix of type '<class 'numpy.bool_'>'\n",
       "   \twith 1410 stored elements in Compressed Sparse Row format>,\n",
       "   <1579x381 sparse matrix of type '<class 'numpy.bool_'>'\n",
       "   \twith 1579 stored elements in Compressed Sparse Row format>,\n",
       "   <2016x381 sparse matrix of type '<class 'numpy.bool_'>'\n",
       "   \twith 2016 stored elements in Compressed Sparse Row format>],\n",
       "  'labels_dict': {-1: array([   0,    2,    4, ..., 7591, 7592, 7593], dtype=int64),\n",
       "   0: array([   1, 3999], dtype=int64),\n",
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       "   107: array([ 232, 2603], dtype=int64),\n",
       "   108: array([ 234, 2642], dtype=int64),\n",
       "   109: array([ 237, 2621], dtype=int64),\n",
       "   110: array([ 238, 4162], dtype=int64),\n",
       "   111: array([ 239, 5578], dtype=int64),\n",
       "   112: array([ 242, 2652, 4222], dtype=int64),\n",
       "   113: array([ 247, 2635], dtype=int64),\n",
       "   114: array([ 248, 5980], dtype=int64),\n",
       "   115: array([ 249, 6181], dtype=int64),\n",
       "   116: array([ 251, 4216], dtype=int64),\n",
       "   117: array([ 252, 1480], dtype=int64),\n",
       "   118: array([ 254, 1488], dtype=int64),\n",
       "   119: array([ 255, 4105], dtype=int64),\n",
       "   120: array([ 256, 5644], dtype=int64),\n",
       "   121: array([ 258, 5736], dtype=int64),\n",
       "   122: array([ 259, 6004], dtype=int64),\n",
       "   123: array([ 261, 2599], dtype=int64),\n",
       "   124: array([ 262, 6100], dtype=int64),\n",
       "   125: array([ 263, 2656], dtype=int64),\n",
       "   126: array([ 268, 6103], dtype=int64),\n",
       "   127: array([ 271, 2645, 4157], dtype=int64),\n",
       "   128: array([ 272, 5958], dtype=int64),\n",
       "   129: array([ 274, 4026], dtype=int64),\n",
       "   130: array([ 278, 1550, 2679], dtype=int64),\n",
       "   131: array([ 279, 4194, 5624], dtype=int64),\n",
       "   132: array([ 282, 5630], dtype=int64),\n",
       "   133: array([ 284, 5617], dtype=int64),\n",
       "   134: array([ 287, 2596], dtype=int64),\n",
       "   135: array([ 288, 6136], dtype=int64),\n",
       "   136: array([ 290, 5640], dtype=int64),\n",
       "   137: array([ 291, 4084], dtype=int64),\n",
       "   138: array([ 292, 5978], dtype=int64),\n",
       "   139: array([ 296, 5657], dtype=int64),\n",
       "   140: array([ 298, 1468, 2675], dtype=int64),\n",
       "   141: array([ 300, 2601], dtype=int64),\n",
       "   142: array([ 302, 5587], dtype=int64),\n",
       "   143: array([ 307, 1505], dtype=int64),\n",
       "   144: array([ 310, 5720], dtype=int64),\n",
       "   145: array([ 315, 5621], dtype=int64),\n",
       "   146: array([ 320, 5663], dtype=int64),\n",
       "   147: array([ 321, 2690], dtype=int64),\n",
       "   148: array([ 322, 4230], dtype=int64),\n",
       "   149: array([ 324, 1496], dtype=int64),\n",
       "   150: array([ 329, 5722], dtype=int64),\n",
       "   151: array([ 331, 5628], dtype=int64),\n",
       "   152: array([ 341, 4254], dtype=int64),\n",
       "   153: array([ 342, 5799], dtype=int64),\n",
       "   154: array([ 344, 5811], dtype=int64),\n",
       "   155: array([ 347, 6013], dtype=int64),\n",
       "   156: array([ 349, 4285], dtype=int64),\n",
       "   157: array([ 350, 4279], dtype=int64),\n",
       "   158: array([ 356, 4290], dtype=int64),\n",
       "   159: array([ 358, 1537, 2734], dtype=int64),\n",
       "   160: array([ 363, 5629], dtype=int64),\n",
       "   161: array([ 369, 2680], dtype=int64),\n",
       "   162: array([ 372, 4158], dtype=int64),\n",
       "   163: array([ 388, 5585], dtype=int64),\n",
       "   164: array([ 390, 2731], dtype=int64),\n",
       "   165: array([ 391, 4033], dtype=int64),\n",
       "   166: array([ 393, 4304], dtype=int64),\n",
       "   167: array([ 395, 4078], dtype=int64),\n",
       "   168: array([ 400, 5817], dtype=int64),\n",
       "   169: array([ 402, 6142], dtype=int64),\n",
       "   170: array([ 408, 6135], dtype=int64),\n",
       "   171: array([ 416, 5642], dtype=int64),\n",
       "   172: array([ 417, 4233], dtype=int64),\n",
       "   173: array([ 419, 1516], dtype=int64),\n",
       "   174: array([ 421, 4335], dtype=int64),\n",
       "   175: array([ 424, 4000], dtype=int64),\n",
       "   176: array([ 437, 2751], dtype=int64),\n",
       "   177: array([ 452, 2661], dtype=int64),\n",
       "   178: array([ 465, 4274], dtype=int64),\n",
       "   179: array([ 469, 5719], dtype=int64),\n",
       "   180: array([ 477, 6003], dtype=int64),\n",
       "   181: array([ 478, 1526], dtype=int64),\n",
       "   182: array([ 483, 4045], dtype=int64),\n",
       "   183: array([ 485, 2677], dtype=int64),\n",
       "   184: array([ 487, 6129], dtype=int64),\n",
       "   185: array([ 492, 6066], dtype=int64),\n",
       "   186: array([ 498, 5699], dtype=int64),\n",
       "   187: array([ 507, 6087], dtype=int64),\n",
       "   188: array([ 513, 5984], dtype=int64),\n",
       "   189: array([ 518, 1517], dtype=int64),\n",
       "   190: array([ 519, 5901], dtype=int64),\n",
       "   191: array([ 523, 6154], dtype=int64),\n",
       "   192: array([ 527, 6024], dtype=int64),\n",
       "   193: array([ 542, 6109], dtype=int64),\n",
       "   194: array([ 549, 2643], dtype=int64),\n",
       "   195: array([ 554, 5662], dtype=int64),\n",
       "   196: array([ 562, 6560], dtype=int64),\n",
       "   197: array([ 563, 5651], dtype=int64),\n",
       "   198: array([ 584, 4038], dtype=int64),\n",
       "   199: array([ 585, 6499], dtype=int64),\n",
       "   200: array([ 586, 5866], dtype=int64),\n",
       "   201: array([ 603, 2768], dtype=int64),\n",
       "   202: array([ 610, 4035], dtype=int64),\n",
       "   203: array([ 620, 6235], dtype=int64),\n",
       "   204: array([ 627, 4088], dtype=int64),\n",
       "   205: array([ 643, 2806], dtype=int64),\n",
       "   206: array([ 651, 4517], dtype=int64),\n",
       "   207: array([ 652, 5676], dtype=int64),\n",
       "   208: array([ 660, 6639], dtype=int64),\n",
       "   209: array([ 663, 6151], dtype=int64),\n",
       "   210: array([ 664, 5908], dtype=int64),\n",
       "   211: array([ 667, 6487], dtype=int64),\n",
       "   212: array([ 668, 5806], dtype=int64),\n",
       "   213: array([ 670, 2828], dtype=int64),\n",
       "   214: array([ 671, 5931], dtype=int64),\n",
       "   215: array([ 672, 4063], dtype=int64),\n",
       "   216: array([ 678, 1529], dtype=int64),\n",
       "   217: array([ 680, 5841], dtype=int64),\n",
       "   218: array([ 682, 5821], dtype=int64),\n",
       "   219: array([ 687, 6253], dtype=int64),\n",
       "   220: array([ 691, 5967], dtype=int64),\n",
       "   221: array([ 712, 6137], dtype=int64),\n",
       "   222: array([ 718, 4202], dtype=int64),\n",
       "   223: array([ 746, 4587], dtype=int64),\n",
       "   224: array([ 756, 5832], dtype=int64),\n",
       "   225: array([ 760, 6595], dtype=int64),\n",
       "   226: array([ 762, 4134], dtype=int64),\n",
       "   227: array([ 792, 1625], dtype=int64),\n",
       "   228: array([ 801, 1644], dtype=int64),\n",
       "   229: array([ 804, 1562], dtype=int64),\n",
       "   230: array([ 823, 5827], dtype=int64),\n",
       "   231: array([ 825, 2697], dtype=int64),\n",
       "   232: array([ 826, 6527], dtype=int64),\n",
       "   233: array([ 865, 1564, 2686, 4223], dtype=int64),\n",
       "   234: array([ 887, 6573], dtype=int64),\n",
       "   235: array([ 902, 5836], dtype=int64),\n",
       "   236: array([ 904, 6509], dtype=int64),\n",
       "   237: array([ 922, 1581], dtype=int64),\n",
       "   238: array([ 931, 5675], dtype=int64),\n",
       "   239: array([1162, 5831], dtype=int64),\n",
       "   240: array([1463, 2592], dtype=int64),\n",
       "   241: array([1465, 2676, 4396], dtype=int64),\n",
       "   242: array([1466, 2726], dtype=int64),\n",
       "   243: array([1467, 2633, 4234], dtype=int64),\n",
       "   244: array([1469, 2625, 4133], dtype=int64),\n",
       "   245: array([1471, 2691], dtype=int64),\n",
       "   246: array([1472, 2590], dtype=int64),\n",
       "   247: array([1475, 2638], dtype=int64),\n",
       "   248: array([1478, 2667, 4405], dtype=int64),\n",
       "   249: array([1485, 2699, 4406], dtype=int64),\n",
       "   250: array([1491, 2698, 4417], dtype=int64),\n",
       "   251: array([1492, 2776], dtype=int64),\n",
       "   252: array([1495, 2687], dtype=int64),\n",
       "   253: array([1499, 2606], dtype=int64),\n",
       "   254: array([1500, 2720], dtype=int64),\n",
       "   255: array([1501, 2671, 4144, 6240], dtype=int64),\n",
       "   256: array([1502, 2649, 4239], dtype=int64),\n",
       "   257: array([1510, 2835], dtype=int64),\n",
       "   258: array([1512, 2682, 4203], dtype=int64),\n",
       "   259: array([1515, 2696, 4245], dtype=int64),\n",
       "   260: array([1520, 2605], dtype=int64),\n",
       "   261: array([1527, 2732, 4236], dtype=int64),\n",
       "   262: array([1528, 2753], dtype=int64),\n",
       "   263: array([1534, 2607], dtype=int64),\n",
       "   264: array([1543, 2639, 4024], dtype=int64),\n",
       "   265: array([1548, 2618, 4017], dtype=int64),\n",
       "   266: array([1551, 2829, 4547], dtype=int64),\n",
       "   267: array([1553, 2658, 4180], dtype=int64),\n",
       "   268: array([1555, 2614], dtype=int64),\n",
       "   269: array([1556, 2775, 4426], dtype=int64),\n",
       "   270: array([1560, 2778, 4543], dtype=int64),\n",
       "   271: array([1570, 2654], dtype=int64),\n",
       "   272: array([1576, 4047], dtype=int64),\n",
       "   273: array([1579, 2794], dtype=int64),\n",
       "   274: array([1583, 2659, 4056], dtype=int64),\n",
       "   275: array([1589, 2769, 4166, 5632], dtype=int64),\n",
       "   276: array([1590, 2651], dtype=int64),\n",
       "   277: array([1606, 2836, 4465], dtype=int64),\n",
       "   278: array([1609, 2797, 4489], dtype=int64),\n",
       "   279: array([1636, 2763, 4327], dtype=int64),\n",
       "   280: array([1645, 2736, 4141], dtype=int64),\n",
       "   281: array([1704, 2684, 4104], dtype=int64),\n",
       "   282: array([1797, 2722, 4263], dtype=int64),\n",
       "   283: array([1866, 4292], dtype=int64),\n",
       "   284: array([1972, 3611], dtype=int64),\n",
       "   285: array([2291, 2888, 4494], dtype=int64),\n",
       "   286: array([2345, 2855, 4198], dtype=int64),\n",
       "   287: array([2348, 3249], dtype=int64),\n",
       "   288: array([2423, 2689], dtype=int64),\n",
       "   289: array([2657, 4081], dtype=int64),\n",
       "   290: array([2660, 4025], dtype=int64),\n",
       "   291: array([2668, 4082], dtype=int64),\n",
       "   292: array([2669, 4027], dtype=int64),\n",
       "   293: array([2674, 4163], dtype=int64),\n",
       "   294: array([2705, 4036], dtype=int64),\n",
       "   295: array([2706, 4009], dtype=int64),\n",
       "   296: array([2709, 4028], dtype=int64),\n",
       "   297: array([2712, 4301], dtype=int64),\n",
       "   298: array([2715, 4032], dtype=int64),\n",
       "   299: array([2723, 4227], dtype=int64),\n",
       "   300: array([2727, 4098], dtype=int64),\n",
       "   301: array([2740, 4007], dtype=int64),\n",
       "   302: array([2741, 4030], dtype=int64),\n",
       "   303: array([2742, 4031], dtype=int64),\n",
       "   304: array([2748, 4267], dtype=int64),\n",
       "   305: array([2757, 4069], dtype=int64),\n",
       "   306: array([2758, 4170], dtype=int64),\n",
       "   307: array([2765, 4110], dtype=int64),\n",
       "   308: array([2766, 6010], dtype=int64),\n",
       "   309: array([2771, 4012], dtype=int64),\n",
       "   310: array([2777, 4022], dtype=int64),\n",
       "   311: array([2783, 4041], dtype=int64),\n",
       "   312: array([2787, 4010], dtype=int64),\n",
       "   313: array([2790, 4018], dtype=int64),\n",
       "   314: array([2809, 4347], dtype=int64),\n",
       "   315: array([2810, 4095], dtype=int64),\n",
       "   316: array([2813, 4476], dtype=int64),\n",
       "   317: array([2814, 4294], dtype=int64),\n",
       "   318: array([2822, 4240], dtype=int64),\n",
       "   319: array([2825, 4311], dtype=int64),\n",
       "   320: array([2827, 4278], dtype=int64),\n",
       "   321: array([2830, 4107], dtype=int64),\n",
       "   322: array([2837, 4249], dtype=int64),\n",
       "   323: array([2841, 4185], dtype=int64),\n",
       "   324: array([2845, 4190, 5950], dtype=int64),\n",
       "   325: array([2846, 4358], dtype=int64),\n",
       "   326: array([2852, 4482], dtype=int64),\n",
       "   327: array([2869, 4314], dtype=int64),\n",
       "   328: array([2873, 4142], dtype=int64),\n",
       "   329: array([2877, 4456], dtype=int64),\n",
       "   330: array([2897, 4159], dtype=int64),\n",
       "   331: array([2899, 4403], dtype=int64),\n",
       "   332: array([2927, 4277, 5754], dtype=int64),\n",
       "   333: array([2937, 4284], dtype=int64),\n",
       "   334: array([2973, 4418], dtype=int64),\n",
       "   335: array([2988, 4073], dtype=int64),\n",
       "   336: array([3012, 4196], dtype=int64),\n",
       "   337: array([3078, 4328], dtype=int64),\n",
       "   338: array([3105, 4459], dtype=int64),\n",
       "   339: array([3137, 4411, 6088], dtype=int64),\n",
       "   340: array([3272, 4949], dtype=int64),\n",
       "   341: array([3277, 5584], dtype=int64),\n",
       "   342: array([3560, 4557], dtype=int64),\n",
       "   343: array([4173, 5953], dtype=int64),\n",
       "   344: array([4193, 5581], dtype=int64),\n",
       "   345: array([4220, 5655], dtype=int64),\n",
       "   346: array([4244, 5610], dtype=int64),\n",
       "   347: array([4287, 5724], dtype=int64),\n",
       "   348: array([4298, 5607], dtype=int64),\n",
       "   349: array([4315, 5620], dtype=int64),\n",
       "   350: array([4334, 5691], dtype=int64),\n",
       "   351: array([4337, 5780], dtype=int64),\n",
       "   352: array([4359, 5766], dtype=int64),\n",
       "   353: array([4374, 5623], dtype=int64),\n",
       "   354: array([4401, 5635], dtype=int64),\n",
       "   355: array([4409, 5664], dtype=int64),\n",
       "   356: array([4412, 5704], dtype=int64),\n",
       "   357: array([4414, 5636], dtype=int64),\n",
       "   358: array([4429, 5614], dtype=int64),\n",
       "   359: array([4460, 5809], dtype=int64),\n",
       "   360: array([4509, 5764], dtype=int64),\n",
       "   361: array([4550, 5822], dtype=int64),\n",
       "   362: array([4554, 5925], dtype=int64),\n",
       "   363: array([4597, 6263], dtype=int64),\n",
       "   364: array([4600, 5653], dtype=int64),\n",
       "   365: array([4611, 5586], dtype=int64),\n",
       "   366: array([4613, 5706], dtype=int64),\n",
       "   367: array([4623, 5684], dtype=int64),\n",
       "   368: array([4635, 5686], dtype=int64),\n",
       "   369: array([4650, 5633], dtype=int64),\n",
       "   370: array([4662, 5808], dtype=int64),\n",
       "   371: array([4681, 5804], dtype=int64),\n",
       "   372: array([4690, 5726], dtype=int64),\n",
       "   373: array([4715, 5605], dtype=int64),\n",
       "   374: array([4751, 5729], dtype=int64),\n",
       "   375: array([4821, 5987], dtype=int64),\n",
       "   376: array([4950, 5789], dtype=int64),\n",
       "   377: array([4954, 5962], dtype=int64),\n",
       "   378: array([5193, 5672], dtype=int64),\n",
       "   379: array([5199, 5875], dtype=int64)}},\n",
       " 'ROIs': {'ROIs_aligned': [<1458x262144 sparse matrix of type '<class 'numpy.float32'>'\n",
       "   \twith 103827 stored elements in Compressed Sparse Row format>,\n",
       "   <1131x262144 sparse matrix of type '<class 'numpy.float32'>'\n",
       "   \twith 49519 stored elements in Compressed Sparse Row format>,\n",
       "   <1410x262144 sparse matrix of type '<class 'numpy.float32'>'\n",
       "   \twith 96264 stored elements in Compressed Sparse Row format>,\n",
       "   <1579x262144 sparse matrix of type '<class 'numpy.float32'>'\n",
       "   \twith 111993 stored elements in Compressed Sparse Row format>,\n",
       "   <2016x262144 sparse matrix of type '<class 'numpy.float32'>'\n",
       "   \twith 126894 stored elements in Compressed Sparse Row format>],\n",
       "  'ROIs_raw': [<1458x262144 sparse matrix of type '<class 'numpy.float32'>'\n",
       "   \twith 102808 stored elements in Compressed Sparse Row format>,\n",
       "   <1131x262144 sparse matrix of type '<class 'numpy.float32'>'\n",
       "   \twith 49081 stored elements in Compressed Sparse Row format>,\n",
       "   <1410x262144 sparse matrix of type '<class 'numpy.float32'>'\n",
       "   \twith 81502 stored elements in Compressed Sparse Row format>,\n",
       "   <1579x262144 sparse matrix of type '<class 'numpy.float32'>'\n",
       "   \twith 107110 stored elements in Compressed Sparse Row format>,\n",
       "   <2016x262144 sparse matrix of type '<class 'numpy.float32'>'\n",
       "   \twith 128675 stored elements in Compressed Sparse Row format>],\n",
       "  'frame_height': 512,\n",
       "  'frame_width': 512,\n",
       "  'idx_roi_session': array([0, 0, 0, ..., 4, 4, 4], dtype=int64),\n",
       "  'n_sessions': 5},\n",
       " 'input_data': {'paths_stat': ['C:\\\\Users\\\\Andrew\\\\Documents\\\\localData\\\\ATL012\\\\2023-02-28\\\\701\\\\suite2p\\\\plane0\\\\stat.npy',\n",
       "   'C:\\\\Users\\\\Andrew\\\\Documents\\\\localData\\\\ATL012\\\\2023-02-28\\\\701\\\\suite2p\\\\plane1\\\\stat.npy',\n",
       "   'C:\\\\Users\\\\Andrew\\\\Documents\\\\localData\\\\ATL012\\\\2023-02-28\\\\701\\\\suite2p\\\\plane2\\\\stat.npy',\n",
       "   'C:\\\\Users\\\\Andrew\\\\Documents\\\\localData\\\\ATL012\\\\2023-02-28\\\\701\\\\suite2p\\\\plane3\\\\stat.npy',\n",
       "   'C:\\\\Users\\\\Andrew\\\\Documents\\\\localData\\\\ATL012\\\\2023-02-28\\\\701\\\\suite2p\\\\plane4\\\\stat.npy'],\n",
       "  'paths_ops': ['C:\\\\Users\\\\Andrew\\\\Documents\\\\localData\\\\ATL012\\\\2023-02-28\\\\701\\\\suite2p\\\\plane0\\\\ops.npy',\n",
       "   'C:\\\\Users\\\\Andrew\\\\Documents\\\\localData\\\\ATL012\\\\2023-02-28\\\\701\\\\suite2p\\\\plane1\\\\ops.npy',\n",
       "   'C:\\\\Users\\\\Andrew\\\\Documents\\\\localData\\\\ATL012\\\\2023-02-28\\\\701\\\\suite2p\\\\plane2\\\\ops.npy',\n",
       "   'C:\\\\Users\\\\Andrew\\\\Documents\\\\localData\\\\ATL012\\\\2023-02-28\\\\701\\\\suite2p\\\\plane3\\\\ops.npy',\n",
       "   'C:\\\\Users\\\\Andrew\\\\Documents\\\\localData\\\\ATL012\\\\2023-02-28\\\\701\\\\suite2p\\\\plane4\\\\ops.npy']},\n",
       " 'quality_metrics': {'cluster_labels_unique': array([ -1,   0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,\n",
       "          12,  13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,\n",
       "          25,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,\n",
       "          38,  39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,\n",
       "          51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,\n",
       "          64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,\n",
       "          77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,\n",
       "          90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,\n",
       "         103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,\n",
       "         116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,\n",
       "         129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,\n",
       "         142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,\n",
       "         155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "         168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,\n",
       "         181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,\n",
       "         194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,\n",
       "         207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
       "         220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,\n",
       "         233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245,\n",
       "         246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258,\n",
       "         259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271,\n",
       "         272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284,\n",
       "         285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297,\n",
       "         298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310,\n",
       "         311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323,\n",
       "         324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336,\n",
       "         337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349,\n",
       "         350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362,\n",
       "         363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375,\n",
       "         376, 377, 378, 379]),\n",
       "  'cluster_intra_means': array([5.52396802e-05, 9.58243370e-01, 8.20263982e-01, 6.40487194e-01,\n",
       "         6.94655418e-01, 8.12980533e-01, 7.99521208e-01, 6.98906660e-01,\n",
       "         5.87702751e-01, 9.75719452e-01, 7.86498070e-01, 7.94003963e-01,\n",
       "         8.12245369e-01, 6.31103277e-01, 8.12827349e-01, 7.77465105e-01,\n",
       "         9.42956686e-01, 7.06353903e-01, 7.63837258e-01, 8.59559894e-01,\n",
       "         7.53590107e-01, 6.45302296e-01, 9.11596179e-01, 7.96594620e-01,\n",
       "         6.63563013e-01, 6.94920778e-01, 6.88266873e-01, 6.74161315e-01,\n",
       "         6.07180357e-01, 6.34296060e-01, 8.25376987e-01, 5.68917751e-01,\n",
       "         8.88250828e-01, 5.73653936e-01, 5.86911082e-01, 7.32513785e-01,\n",
       "         7.95756817e-01, 8.22203159e-01, 4.83608723e-01, 9.78756905e-01,\n",
       "         3.88605833e-01, 8.37081075e-01, 2.95101881e-01, 5.10960340e-01,\n",
       "         7.72884607e-01, 3.14509630e-01, 8.52878888e-01, 6.74432993e-01,\n",
       "         5.93565822e-01, 7.06951141e-01, 5.43287992e-01, 8.49986792e-01,\n",
       "         5.54878712e-01, 9.60731506e-01, 7.79202223e-01, 8.13810706e-01,\n",
       "         4.51797843e-01, 7.54872680e-01, 7.71179914e-01, 9.06235695e-01,\n",
       "         8.85812998e-01, 4.96959329e-01, 7.81420231e-01, 5.50565362e-01,\n",
       "         8.45823447e-01, 8.14610004e-01, 8.55686665e-01, 2.40103483e-01,\n",
       "         8.87849331e-01, 6.06923223e-01, 8.70522976e-01, 8.80014181e-01,\n",
       "         9.63416815e-01, 5.16850233e-01, 8.69997621e-01, 7.76059389e-01,\n",
       "         8.60627413e-01, 8.47680688e-01, 5.64888477e-01, 6.68813944e-01,\n",
       "         7.69350767e-01, 4.43923712e-01, 8.18110943e-01, 7.19713688e-01,\n",
       "         5.88819822e-01, 7.18378782e-01, 6.02378011e-01, 3.34097505e-01,\n",
       "         5.49233913e-01, 8.48193645e-01, 7.58438110e-01, 5.34375191e-01,\n",
       "         4.33945974e-01, 7.70589471e-01, 7.11263418e-01, 6.04982734e-01,\n",
       "         9.34794188e-01, 8.40188026e-01, 7.09691286e-01, 8.99802446e-01,\n",
       "         5.50617099e-01, 3.83065224e-01, 6.51713848e-01, 6.39668584e-01,\n",
       "         6.30035480e-01, 5.48628330e-01, 5.30699730e-01, 7.23446727e-01,\n",
       "         5.98370910e-01, 9.06234741e-01, 7.30951905e-01, 8.52563381e-01,\n",
       "         3.27332497e-01, 7.21359412e-01, 6.95624590e-01, 6.85088873e-01,\n",
       "         4.76666927e-01, 7.46599913e-01, 6.81604862e-01, 7.58505821e-01,\n",
       "         4.34456468e-01, 6.36550188e-01, 3.34384441e-01, 3.60499263e-01,\n",
       "         4.78653789e-01, 5.16495824e-01, 7.80562162e-01, 7.33322740e-01,\n",
       "         8.04068247e-01, 6.85458660e-01, 4.10407782e-01, 4.35224215e-01,\n",
       "         3.36862882e-01, 2.90981889e-01, 3.42666268e-01, 2.96585321e-01,\n",
       "         5.65835238e-01, 2.75524855e-01, 4.54566002e-01, 6.66894317e-01,\n",
       "         4.56489205e-01, 7.08374182e-01, 3.55758429e-01, 2.22820520e-01,\n",
       "         5.43587804e-01, 2.50871062e-01, 3.39439392e-01, 3.39943171e-01,\n",
       "         5.57348132e-01, 5.94563723e-01, 8.23027730e-01, 2.49079585e-01,\n",
       "         2.99715757e-01, 7.57050037e-01, 3.22499275e-01, 3.35746646e-01,\n",
       "         8.52198362e-01, 9.14574146e-01, 9.38132524e-01, 5.99947095e-01,\n",
       "         5.74109713e-01, 2.40823030e-01, 6.25139713e-01, 2.98324943e-01,\n",
       "         2.35181928e-01, 4.55616236e-01, 3.16275716e-01, 6.47206903e-01,\n",
       "         3.50121498e-01, 4.43307877e-01, 4.62279081e-01, 4.76022243e-01,\n",
       "         3.88764620e-01, 3.32224369e-01, 7.07462192e-01, 8.72831464e-01,\n",
       "         2.81299829e-01, 5.35408497e-01, 3.30535054e-01, 9.25093770e-01,\n",
       "         2.16957569e-01, 3.18247795e-01, 7.73357034e-01, 3.08694482e-01,\n",
       "         5.96052408e-01, 5.42701483e-01, 6.29970789e-01, 3.39091778e-01,\n",
       "         5.43933749e-01, 4.30191278e-01, 6.24283671e-01, 2.71863580e-01,\n",
       "         4.16530013e-01, 5.02254725e-01, 6.14763737e-01, 2.76154637e-01,\n",
       "         2.08091497e-01, 4.52265024e-01, 2.59306073e-01, 2.79569626e-01,\n",
       "         5.85698485e-01, 2.57231355e-01, 5.86370707e-01, 3.06961060e-01,\n",
       "         4.54117298e-01, 4.04231787e-01, 3.06785583e-01, 6.60486937e-01,\n",
       "         2.56800294e-01, 4.61557627e-01, 4.02224779e-01, 2.84001112e-01,\n",
       "         4.64536190e-01, 3.30162048e-01, 4.39427376e-01, 2.52177238e-01,\n",
       "         3.54511976e-01, 4.02773142e-01, 2.59989500e-01, 2.87836075e-01,\n",
       "         4.00251150e-01, 2.98170924e-01, 2.76293755e-01, 3.24440718e-01,\n",
       "         4.54188704e-01, 3.16601276e-01, 4.36646938e-01, 2.24137306e-01,\n",
       "         5.96981168e-01, 4.34150815e-01, 4.22879934e-01, 2.36496568e-01,\n",
       "         2.22692490e-01, 4.28916454e-01, 4.90158796e-01, 4.87282276e-01,\n",
       "         2.63620615e-01, 4.83272910e-01, 5.32081604e-01, 3.25079799e-01,\n",
       "         2.51331568e-01, 4.73991394e-01, 4.88118966e-01, 6.49986029e-01,\n",
       "         6.97370132e-01, 7.73255030e-01, 8.67677927e-01, 4.85341311e-01,\n",
       "         8.40888262e-01, 5.96145948e-01, 7.10102240e-01, 5.99994024e-01,\n",
       "         4.96151686e-01, 3.63221169e-01, 3.99583697e-01, 5.05404472e-01,\n",
       "         6.25988722e-01, 6.55851841e-01, 4.66573477e-01, 6.88245535e-01,\n",
       "         6.98142211e-01, 3.33803892e-01, 6.28664335e-01, 4.14903164e-01,\n",
       "         2.96812415e-01, 3.80284071e-01, 4.32553530e-01, 6.33200169e-01,\n",
       "         4.07270511e-01, 3.53846192e-01, 4.91772254e-01, 6.11548424e-01,\n",
       "         3.35390091e-01, 2.89890766e-01, 3.62734318e-01, 3.70682001e-01,\n",
       "         3.85620991e-01, 2.46806979e-01, 4.59664027e-01, 3.95616531e-01,\n",
       "         4.63559945e-01, 4.00904338e-01, 3.64723365e-01, 4.18100357e-01,\n",
       "         2.45129824e-01, 4.68506575e-01, 6.20698293e-01, 4.63465452e-01,\n",
       "         4.00287390e-01, 3.22438478e-01, 4.57969666e-01, 4.41870213e-01,\n",
       "         3.30927014e-01, 3.89163256e-01, 4.80200529e-01, 3.40961218e-01,\n",
       "         3.31210494e-01, 3.84840012e-01, 3.67805243e-01, 4.15633321e-01,\n",
       "         4.78249550e-01, 4.89041090e-01, 2.80489922e-01, 3.89821887e-01,\n",
       "         3.00480127e-01, 4.64263678e-01, 2.79123664e-01, 5.74540377e-01,\n",
       "         5.51926970e-01, 4.48164701e-01, 2.52032042e-01, 2.74328470e-01,\n",
       "         3.96921873e-01, 2.10660934e-01, 3.08116436e-01, 5.27294993e-01,\n",
       "         2.20539808e-01, 5.03001928e-01, 4.79427099e-01, 3.14901590e-01,\n",
       "         3.19134831e-01, 3.74436378e-01, 4.55377102e-01, 3.99871349e-01,\n",
       "         3.81376505e-01, 2.79832522e-01, 3.16972017e-01, 3.62189889e-01,\n",
       "         2.99236059e-01, 3.39402199e-01, 3.74498606e-01, 2.36661911e-01,\n",
       "         5.72711945e-01, 4.39819336e-01, 3.72413754e-01, 3.45098257e-01,\n",
       "         2.43788958e-01, 2.57763147e-01, 4.25313473e-01, 2.91200399e-01,\n",
       "         2.81842470e-01, 4.52847600e-01, 2.20952868e-01, 2.70962834e-01,\n",
       "         3.45280409e-01, 3.49844456e-01, 3.72441173e-01, 3.67483377e-01,\n",
       "         2.98358917e-01, 2.94769883e-01, 2.53041983e-01, 2.49030828e-01,\n",
       "         2.31749535e-01, 3.11392307e-01, 3.64292860e-01, 2.72283077e-01,\n",
       "         3.36116195e-01, 3.61553669e-01, 3.36958647e-01, 2.42615938e-01,\n",
       "         2.35768914e-01, 3.36468935e-01, 2.53440142e-01, 2.88172603e-01,\n",
       "         3.11512947e-01, 2.07809091e-01, 3.42053294e-01, 2.26682186e-01,\n",
       "         2.15130329e-01, 2.62931228e-01, 2.44322062e-01, 3.06121588e-01,\n",
       "         2.15252280e-01, 2.19667196e-01, 2.40788817e-01, 2.55156040e-01,\n",
       "         3.63815188e-01, 2.26393580e-01, 2.37153411e-01, 2.59270787e-01,\n",
       "         2.12556839e-01]),\n",
       "  'cluster_intra_mins': array([0.00000000e+00, 9.53705549e-01, 8.17141652e-01, 6.17216825e-01,\n",
       "         4.96265769e-01, 8.05383205e-01, 7.72445202e-01, 6.17191195e-01,\n",
       "         5.66425800e-01, 9.75564837e-01, 7.71685481e-01, 7.78133869e-01,\n",
       "         7.39863634e-01, 4.64227676e-01, 8.08463931e-01, 7.07264423e-01,\n",
       "         9.37548876e-01, 7.02986956e-01, 6.01145625e-01, 8.43860388e-01,\n",
       "         7.47364998e-01, 1.64957881e-01, 9.10637379e-01, 7.03363180e-01,\n",
       "         6.32175446e-01, 5.21336555e-01, 6.54315710e-01, 6.39033675e-01,\n",
       "         5.96539021e-01, 6.34276509e-01, 8.19847822e-01, 4.35568571e-01,\n",
       "         8.75516891e-01, 5.14148951e-01, 4.87927675e-01, 6.79584026e-01,\n",
       "         7.83608437e-01, 8.07287455e-01, 4.47438002e-01, 9.76198435e-01,\n",
       "         3.37747693e-01, 8.32083106e-01, 1.73542976e-01, 4.34901237e-01,\n",
       "         5.43410063e-01, 1.04873657e-01, 8.09155464e-01, 5.78399539e-01,\n",
       "         4.85284209e-01, 6.44002199e-01, 4.67025280e-01, 8.45846653e-01,\n",
       "         4.35192108e-01, 9.58733082e-01, 7.78485060e-01, 8.12934041e-01,\n",
       "         1.70832634e-01, 7.29435086e-01, 7.69095898e-01, 8.96824837e-01,\n",
       "         8.85323048e-01, 4.45684195e-01, 7.77337313e-01, 5.36775827e-01,\n",
       "         6.98506117e-01, 7.72818685e-01, 8.48201036e-01, 2.32244730e-02,\n",
       "         8.85510445e-01, 5.84337950e-01, 8.17095518e-01, 8.70664597e-01,\n",
       "         9.59132791e-01, 4.79985714e-01, 8.42316985e-01, 6.96778059e-01,\n",
       "         8.04875255e-01, 8.16189170e-01, 5.40464401e-01, 6.57376289e-01,\n",
       "         7.63838530e-01, 4.35830951e-01, 8.02895308e-01, 5.14740705e-01,\n",
       "         8.31182003e-02, 6.97004318e-01, 5.77013969e-01, 2.63590932e-01,\n",
       "         5.08774281e-01, 8.46837282e-01, 6.52290463e-01, 5.24455547e-01,\n",
       "         1.32444859e-01, 7.51623631e-01, 7.05882788e-01, 5.24423122e-01,\n",
       "         9.32090521e-01, 8.29288721e-01, 6.73125744e-01, 8.75621676e-01,\n",
       "         3.73390913e-01, 1.35395288e-01, 3.13994765e-01, 6.01524591e-01,\n",
       "         2.53202796e-01, 5.19534588e-01, 3.38062167e-01, 7.03146935e-01,\n",
       "         3.19865823e-01, 9.06085253e-01, 6.00345850e-01, 8.33939791e-01,\n",
       "         5.65052032e-05, 4.75327730e-01, 6.06323242e-01, 6.56107187e-01,\n",
       "         4.63006616e-01, 7.10169554e-01, 6.70399427e-01, 7.19486713e-01,\n",
       "         4.18046355e-01, 5.32001257e-01, 7.80738592e-02, 3.08445692e-01,\n",
       "         2.40311623e-01, 4.80927944e-01, 6.90248728e-01, 7.10772157e-01,\n",
       "         6.20970249e-01, 6.00962639e-01, 5.04451990e-02, 3.06689262e-01,\n",
       "         2.33292580e-03, 3.97882462e-02, 4.57545519e-02, 3.87021303e-02,\n",
       "         5.51408291e-01, 1.87813044e-02, 2.95965672e-01, 6.60758734e-01,\n",
       "         2.82497644e-01, 3.92252088e-01, 4.31785583e-02, 1.35447979e-02,\n",
       "         5.39519310e-01, 4.27626371e-02, 4.86993790e-03, 1.75474882e-02,\n",
       "         5.45661688e-01, 5.41050315e-01, 8.07208657e-01, 1.22916698e-03,\n",
       "         7.54559040e-03, 7.34434366e-01, 1.95807219e-01, 2.07590222e-01,\n",
       "         8.13726425e-01, 9.08373117e-01, 9.34471726e-01, 5.01131296e-01,\n",
       "         3.85870218e-01, 4.01008129e-03, 5.54229736e-01, 1.86486840e-01,\n",
       "         3.59189510e-03, 4.21804190e-01, 2.14780569e-02, 6.42094135e-01,\n",
       "         6.79005384e-02, 1.88581586e-01, 4.60223436e-01, 4.58744764e-01,\n",
       "         3.99589539e-02, 2.17261910e-01, 6.93476915e-01, 8.67094159e-01,\n",
       "         1.05414391e-02, 4.83532190e-01, 2.56429434e-01, 9.20301676e-01,\n",
       "         1.89208984e-02, 1.90341234e-01, 7.67881155e-01, 2.36129761e-03,\n",
       "         5.74036837e-01, 5.23442030e-01, 5.49898863e-01, 1.49792433e-02,\n",
       "         5.31990051e-01, 3.41332912e-01, 5.53743005e-01, 1.40951872e-02,\n",
       "         3.96523237e-01, 4.13189888e-01, 4.85639095e-01, 7.54323006e-02,\n",
       "         1.95097923e-03, 4.09765840e-01, 8.42809677e-05, 2.48742104e-03,\n",
       "         5.47389746e-01, 5.86876869e-02, 5.70769310e-01, 7.09438324e-03,\n",
       "         4.09031868e-01, 3.17263603e-03, 1.18405461e-01, 6.57325506e-01,\n",
       "         8.34465027e-07, 4.24564600e-01, 2.33097672e-01, 5.54323196e-04,\n",
       "         4.56308484e-01, 1.98547840e-02, 4.34969425e-01, 1.08873844e-02,\n",
       "         3.09324265e-03, 3.65102887e-01, 4.26054001e-03, 5.26142120e-03,\n",
       "         3.47845197e-01, 1.16656780e-01, 1.11423969e-01, 8.12867880e-02,\n",
       "         3.66710186e-01, 5.42867184e-03, 4.19503212e-01, 5.40971756e-03,\n",
       "         5.34413576e-01, 4.27630424e-01, 3.91973019e-01, 3.82792950e-03,\n",
       "         3.60364914e-02, 4.26262856e-01, 1.74643755e-01, 4.73423481e-01,\n",
       "         3.16619873e-04, 4.79712248e-01, 4.05222535e-01, 0.00000000e+00,\n",
       "         4.07934189e-04, 3.32988739e-01, 2.34793425e-02, 6.01317406e-01,\n",
       "         5.31667709e-01, 4.36951160e-01, 8.60814095e-01, 1.31744862e-01,\n",
       "         8.06622028e-01, 2.34059811e-01, 2.58154154e-01, 3.48487854e-01,\n",
       "         4.89337444e-01, 2.77376652e-01, 5.31077385e-04, 4.84470725e-01,\n",
       "         3.93652320e-01, 5.18069983e-01, 4.30932879e-01, 3.29237223e-01,\n",
       "         5.28699040e-01, 2.27928162e-04, 3.43258977e-01, 3.38411689e-01,\n",
       "         1.77359581e-03, 2.59971619e-03, 2.38418579e-07, 4.62865233e-01,\n",
       "         1.33043528e-02, 3.12149525e-03, 3.99110079e-01, 4.21113253e-01,\n",
       "         4.82797623e-05, 4.17232513e-06, 2.74525404e-01, 8.08238983e-05,\n",
       "         7.24196434e-04, 3.80516052e-02, 3.12649012e-01, 1.74373865e-01,\n",
       "         1.04422927e-01, 2.79688835e-03, 1.90734863e-06, 9.51272249e-02,\n",
       "         1.42449141e-02, 4.21124220e-01, 3.32615495e-01, 9.13202763e-03,\n",
       "         3.13109398e-01, 3.38673592e-04, 2.46348977e-01, 1.20815039e-01,\n",
       "         8.69667530e-02, 1.77447438e-01, 4.05859470e-01, 7.69613981e-02,\n",
       "         6.09123707e-03, 9.38303471e-02, 2.62159944e-01, 5.24483919e-02,\n",
       "         3.63911152e-01, 2.79082417e-01, 7.17282295e-02, 8.61072540e-03,\n",
       "         6.00469112e-03, 3.70952725e-01, 1.87350512e-02, 2.45738983e-01,\n",
       "         2.08520651e-01, 2.42884040e-01, 4.96578217e-03, 2.69412994e-03,\n",
       "         5.61714172e-03, 2.58076191e-03, 4.50539589e-03, 4.68092322e-01,\n",
       "         2.71329880e-02, 4.36857462e-01, 3.48011136e-01, 1.78773642e-01,\n",
       "         1.77423596e-01, 2.48199224e-01, 1.91276789e-01, 1.27884984e-01,\n",
       "         1.21121168e-01, 1.03080273e-03, 1.75735354e-01, 3.19403172e-01,\n",
       "         4.43305969e-02, 9.27197933e-03, 3.15738320e-01, 2.02101469e-02,\n",
       "         4.41442490e-01, 1.19447708e-04, 1.13850355e-01, 1.85758591e-01,\n",
       "         2.26974487e-04, 1.12256408e-01, 1.52650952e-01, 8.52378607e-02,\n",
       "         2.83341408e-02, 4.29986238e-01, 5.96046448e-07, 2.08179951e-02,\n",
       "         1.48513436e-01, 3.41427326e-03, 1.83558464e-03, 1.76341891e-01,\n",
       "         3.32884789e-02, 1.69277191e-05, 3.72743607e-03, 8.79843235e-02,\n",
       "         1.16407871e-03, 9.06503201e-03, 3.03983688e-05, 1.07288361e-06,\n",
       "         2.64644623e-05, 7.01665878e-04, 1.41036510e-03, 7.01355934e-03,\n",
       "         3.03363800e-03, 1.96723938e-02, 7.27939606e-03, 2.62989998e-02,\n",
       "         1.70743465e-01, 1.31130219e-06, 1.24692917e-04, 1.66893005e-06,\n",
       "         6.65187836e-05, 2.41994858e-05, 9.89437103e-06, 1.11341476e-04,\n",
       "         1.64568424e-03, 1.05977058e-04, 1.78813934e-06, 9.82761383e-04,\n",
       "         5.50913811e-03, 1.93119049e-05, 1.73306465e-03, 4.76837158e-07,\n",
       "         1.63197517e-04], dtype=float32),\n",
       "  'cluster_intra_maxs': array([0.80880159, 0.96278113, 0.82338631, 0.66375744, 0.92537338,\n",
       "         0.82057774, 0.82659709, 0.78062224, 0.60897988, 0.97587407,\n",
       "         0.80131072, 0.80987394, 0.8846271 , 0.79797888, 0.81719089,\n",
       "         0.84766579, 0.94836468, 0.70972073, 0.86570513, 0.87525928,\n",
       "         0.75981522, 0.89088118, 0.91255492, 0.90215921, 0.69495058,\n",
       "         0.86850494, 0.72221804, 0.70928895, 0.61782169, 0.63431555,\n",
       "         0.83090615, 0.70226675, 0.9009847 , 0.63315874, 0.68589449,\n",
       "         0.7854436 , 0.80790532, 0.83711898, 0.5197795 , 0.98131549,\n",
       "         0.43946412, 0.84207904, 0.41666067, 0.58701932, 0.91664118,\n",
       "         0.52414572, 0.92650777, 0.81512463, 0.70184749, 0.7699002 ,\n",
       "         0.61955065, 0.85412693, 0.67456532, 0.96273005, 0.77991939,\n",
       "         0.81468743, 0.73276293, 0.78031033, 0.77326405, 0.91564643,\n",
       "         0.88630289, 0.54823452, 0.78550297, 0.5643549 , 0.95055252,\n",
       "         0.85640132, 0.86317229, 0.45698249, 0.8901881 , 0.6295085 ,\n",
       "         0.92395037, 0.88936377, 0.96770072, 0.55371475, 0.8976782 ,\n",
       "         0.85534066, 0.91637957, 0.87917227, 0.58931255, 0.68025166,\n",
       "         0.77486306, 0.45201641, 0.83332664, 0.93845755, 0.83721262,\n",
       "         0.73975319, 0.62774217, 0.40460402, 0.58969367, 0.84955007,\n",
       "         0.86458576, 0.54429501, 0.72301513, 0.78955531, 0.71664387,\n",
       "         0.68554235, 0.93749803, 0.85108739, 0.74625689, 0.92398316,\n",
       "         0.72784317, 0.63717645, 0.84737706, 0.67781258, 0.83893132,\n",
       "         0.57772225, 0.72333735, 0.74374658, 0.87687594, 0.90638411,\n",
       "         0.86155796, 0.87118691, 0.65460867, 0.95540756, 0.78492594,\n",
       "         0.7140705 , 0.49032709, 0.78303033, 0.69281042, 0.79752493,\n",
       "         0.45086655, 0.74109912, 0.59069502, 0.41255283, 0.71699607,\n",
       "         0.55206382, 0.87087548, 0.75587338, 0.94181323, 0.76995468,\n",
       "         0.77037042, 0.63067842, 0.65782821, 0.54217565, 0.63957804,\n",
       "         0.55446863, 0.58026206, 0.53226829, 0.61316645, 0.67302996,\n",
       "         0.63048077, 0.95801246, 0.66833824, 0.43209612, 0.54765636,\n",
       "         0.45897943, 0.67400879, 0.66233897, 0.56903464, 0.64807707,\n",
       "         0.83884686, 0.49693   , 0.59188592, 0.77966583, 0.44919145,\n",
       "         0.46390313, 0.8906703 , 0.92077523, 0.94179326, 0.69876301,\n",
       "         0.72587442, 0.47763583, 0.69604957, 0.41016304, 0.46677202,\n",
       "         0.48942822, 0.61107337, 0.65231973, 0.63234246, 0.69803417,\n",
       "         0.46433485, 0.49329975, 0.73757023, 0.44718686, 0.72144741,\n",
       "         0.87856883, 0.5520584 , 0.58728492, 0.40464073, 0.92988598,\n",
       "         0.41499406, 0.44615418, 0.77883291, 0.61502773, 0.61806786,\n",
       "         0.56196094, 0.71004266, 0.66320431, 0.55587739, 0.51904964,\n",
       "         0.69482434, 0.52963209, 0.43653685, 0.59131956, 0.74388826,\n",
       "         0.47687685, 0.41423199, 0.49476409, 0.51852781, 0.55665189,\n",
       "         0.62400711, 0.45577502, 0.60197216, 0.60682762, 0.49920276,\n",
       "         0.80529106, 0.49516571, 0.66364837, 0.51359975, 0.49855074,\n",
       "         0.57135201, 0.56744802, 0.47276384, 0.64046937, 0.44388548,\n",
       "         0.49346712, 0.70593089, 0.44044334, 0.51571828, 0.57041073,\n",
       "         0.45265698, 0.47968495, 0.44116342, 0.56759453, 0.54166716,\n",
       "         0.62777394, 0.45379049, 0.44286475, 0.65954882, 0.44067112,\n",
       "         0.45378691, 0.46916518, 0.40934864, 0.43157002, 0.84627134,\n",
       "         0.50114119, 0.52692461, 0.48683357, 0.65894079, 0.65015948,\n",
       "         0.50225514, 0.61499405, 0.78140092, 0.69865447, 0.88186908,\n",
       "         0.92327619, 0.87454176, 0.8389377 , 0.87515444, 0.85530853,\n",
       "         0.92792916, 0.84513068, 0.50296575, 0.4490656 , 0.79863632,\n",
       "         0.52633822, 0.89263332, 0.84796077, 0.50221407, 0.8594631 ,\n",
       "         0.80735612, 0.6673798 , 0.80774289, 0.49139479, 0.59185123,\n",
       "         0.62989306, 0.86030626, 0.73611516, 0.75111407, 0.70457101,\n",
       "         0.63858598, 0.80919558, 0.67073184, 0.57977748, 0.45094341,\n",
       "         0.82763833, 0.90163898, 0.45556238, 0.58679616, 0.56071895,\n",
       "         0.68895531, 0.88974547, 0.82986176, 0.82951593, 0.47601482,\n",
       "         0.51588893, 0.81381428, 0.91801018, 0.48746529, 0.64453816,\n",
       "         0.66959035, 0.76292533, 0.57488739, 0.60087895, 0.55454141,\n",
       "         0.60496116, 0.65632981, 0.67584968, 0.47345057, 0.77881831,\n",
       "         0.59258801, 0.69899988, 0.48925146, 0.77103299, 0.59495568,\n",
       "         0.55757451, 0.53951228, 0.90334183, 0.89533323, 0.6534453 ,\n",
       "         0.49909839, 0.54596269, 0.7882266 , 0.41874126, 0.6117276 ,\n",
       "         0.58649766, 0.41394648, 0.56914645, 0.61084324, 0.45102951,\n",
       "         0.46084613, 0.50067341, 0.71947742, 0.67185771, 0.6416319 ,\n",
       "         0.55304521, 0.45820865, 0.40497655, 0.55414146, 0.66953248,\n",
       "         0.43325886, 0.45311356, 0.7039814 , 0.9054091 , 0.63097709,\n",
       "         0.50443786, 0.48735091, 0.40326998, 0.69797581, 0.49716288,\n",
       "         0.45811954, 0.47570896, 0.44190514, 0.52110773, 0.54204756,\n",
       "         0.69627476, 0.74304676, 0.55862498, 0.5634293 , 0.58952284,\n",
       "         0.50235653, 0.41007724, 0.46233514, 0.61371964, 0.72855526,\n",
       "         0.54456496, 0.67220604, 0.72240567, 0.67250687, 0.47821823,\n",
       "         0.46850416, 0.65326548, 0.49960089, 0.55004615, 0.45228255,\n",
       "         0.41561696, 0.6839819 , 0.4533627 , 0.43019411, 0.52583814,\n",
       "         0.48863435, 0.61213195, 0.42885894, 0.43922833, 0.48157591,\n",
       "         0.5093292 , 0.72212118, 0.45276788, 0.47257382, 0.5185411 ,\n",
       "         0.42495057]),\n",
       "  'cluster_silhouette': array([-0.99994182,  0.11635181,  0.06505359,  0.24126875,  0.54395878,\n",
       "          0.29953129,  0.17457771,  0.31242122, -0.05433562,  0.11128846,\n",
       "          0.36348942,  0.7629001 ,  0.73735811,  0.44007413,  0.61040751,\n",
       "          0.0463873 ,  0.28968335,  0.17920828,  0.30337797,  0.75712867,\n",
       "          0.60447196,  0.62677576,  0.29258935,  0.77481629, -0.14219956,\n",
       "          0.24180218,  0.49253685,  0.69009951,  0.3992401 ,  0.00555324,\n",
       "          0.61736077,  0.70057631,  0.27348974, -0.22836391,  0.42279156,\n",
       "          0.39250739,  0.02959151,  0.44213217,  0.47427304,  0.56609787,\n",
       "          0.66281099,  0.46681358, -0.15295395,  0.55730694,  0.21798652,\n",
       "         -0.37643306,  0.47545444,  0.66827252,  0.76224192,  0.29004604,\n",
       "         -0.26426932,  0.40339473,  0.55635602,  0.53374206,  0.77048114,\n",
       "         -0.00840895,  0.14773671, -0.03751394,  0.42156838,  0.32440691,\n",
       "          0.3496576 ,  0.60553566,  0.21395774,  0.81542284,  0.80064154,\n",
       "          0.75001189,  0.79396707, -0.19080886,  0.63518963,  0.57792786,\n",
       "          0.57853038,  0.18432507,  0.75685567,  0.36750632,  0.67656468,\n",
       "          0.77508257,  0.34769718,  0.16617369,  0.5417977 ,  0.65038445,\n",
       "          0.60364783,  0.67378921,  0.2678829 ,  0.69393185,  0.33189441,\n",
       "         -0.08318241,  0.77372909,  0.33327951,  0.5913725 ,  0.22100088,\n",
       "          0.15089277, -0.05083787,  0.15887382,  0.82920872,  0.18473974,\n",
       "          0.66561911,  0.57353447,  0.62939099,  0.77220265,  0.22689025,\n",
       "          0.49588215, -0.18478038, -0.17175022,  0.19176437,  0.80508472,\n",
       "          0.87750134, -0.26563704,  0.85561237, -0.26285077,  0.30068858,\n",
       "          0.14930649,  0.45996918, -0.4801945 , -0.11897076,  0.07116223,\n",
       "          0.3658038 ,  0.78699332,  0.87882547,  0.78026748, -0.12759389,\n",
       "         -0.05720338,  0.4875225 ,  0.01425355,  0.5792273 , -0.25922398,\n",
       "          0.88404811,  0.0676673 ,  0.89179447, -0.10921653,  0.92528543,\n",
       "         -0.13753215, -0.08281424,  0.09869324, -0.21989436, -0.35499366,\n",
       "         -0.17322237,  0.78634389, -0.24836675,  0.22314516,  0.85454629,\n",
       "          0.6089613 ,  0.01367824, -0.3450482 ,  0.13700202,  0.58420118,\n",
       "          0.06099062,  0.19520697,  0.80986571,  0.31707868,  0.07052394,\n",
       "          0.71446846,  0.32877109,  0.42643042,  0.78741017,  0.41058696,\n",
       "          0.56211461,  0.859878  ,  0.12671787,  0.74817799,  0.5500145 ,\n",
       "          0.78269512, -0.48545196,  0.08923853,  0.36776463, -0.42496862,\n",
       "          0.26026528, -0.26410889,  0.3264543 , -0.15907804,  0.55989777,\n",
       "          0.37405129,  0.87331809, -0.09476945,  0.13478605,  0.44455937,\n",
       "          0.50408354, -0.48774359,  0.20221757, -0.19662911,  0.29989856,\n",
       "          0.64707163,  0.74473055,  0.82493034, -0.40351618, -0.09778049,\n",
       "          0.76566743,  0.60610407,  0.78863517,  0.42286658,  0.72815226,\n",
       "          0.65618093,  0.67611788,  0.63389237,  0.54429818,  0.54289756,\n",
       "         -0.19818472, -0.33214749,  0.67884872, -0.21250803, -0.42931175,\n",
       "          0.84957956,  0.8814603 ,  0.44689783, -0.48621471,  0.72450227,\n",
       "          0.47780152, -0.22087866,  0.46362736,  0.63980398,  0.57529923,\n",
       "         -0.2424536 ,  0.61863524,  0.474056  ,  0.7279059 ,  0.25179352,\n",
       "          0.86680621, -0.48263093, -0.07874643,  0.79604195,  0.87447634,\n",
       "          0.37672894,  0.70204024,  0.26258473, -0.22823295,  0.78815753,\n",
       "          0.64565596,  0.69481638, -0.42714938,  0.11304612,  1.        ,\n",
       "          0.02357617,  0.27074442, -0.34495297,  0.78139532, -0.1057173 ,\n",
       "          0.61539744,  0.42545175,  0.68227309,  0.02576789,  0.82533709,\n",
       "          0.47355201, -0.14131797,  0.49686644,  0.17881677,  0.50964118,\n",
       "         -0.0062958 ,  0.09638949, -0.41193968,  0.10708026,  0.0409494 ,\n",
       "          0.6867974 ,  0.5049771 , -0.21299597,  0.047357  ,  0.76643582,\n",
       "          0.16770221,  0.83006837,  0.21436836,  0.81468626,  0.031316  ,\n",
       "          0.739804  ,  0.14803037,  0.12982057, -0.12833201, -0.17030415,\n",
       "         -0.19734095,  0.19952497,  1.        ,  0.65860215,  0.45550344,\n",
       "          0.37351087,  0.75092784, -0.48693454, -0.31766941, -0.04729445,\n",
       "         -0.08344226,  0.08589938,  0.50981447,  0.32528453,  0.78589698,\n",
       "          0.15888734, -0.47859392,  0.51675259, -0.45280641, -0.59819664,\n",
       "          1.        ,  0.88447092,  0.34367198,  0.81012968, -0.32219228,\n",
       "          0.53195089, -0.41613151,  0.56851885,  0.55255472,  0.43731409,\n",
       "          0.40871406,  0.37458843,  0.11061901, -0.20924951, -0.32157827,\n",
       "          0.33934791,  0.57992994,  0.07921434, -0.18879517, -0.48172473,\n",
       "          0.17278945,  0.3755487 ,  0.52276105, -0.02859538, -0.29602319,\n",
       "         -0.17121427,  0.69756338,  0.68202637,  0.26584117, -0.46815539,\n",
       "          0.62346519, -0.41548061,  0.31844495,  0.14091705,  0.08042299,\n",
       "         -0.25667306,  0.26267654,  0.2802514 ,  0.00447246,  0.37060877,\n",
       "         -0.3280745 , -0.03642828,  0.1864876 ,  0.56460698,  0.25806106,\n",
       "          0.19734988,  0.57861964,  0.03762601,  0.02040339,  0.34484251,\n",
       "         -0.16817604, -0.03640804,  0.58780085,  0.43623286,  0.24685408,\n",
       "          0.21973786,  0.65510985, -0.09221825,  0.38207424, -0.29290094,\n",
       "         -0.34439755, -0.23924095,  0.0482212 ,  0.12293433,  0.01301265,\n",
       "          0.14223996,  0.69385438, -0.07041849,  0.77737642,  0.48868173,\n",
       "          0.40502456, -0.46587263,  0.46132262,  0.76462633, -0.04870157,\n",
       "          0.50046944,  0.30556494,  0.74735911,  0.84466285,  0.13394233,\n",
       "          0.76956296,  0.72210689, -0.25878853,  0.27740028,  0.57635966,\n",
       "          0.55440747,  0.74639007,  0.69931087,  0.53741452, -0.07945189,\n",
       "         -0.42374747,  0.97862143, -0.26015125,  0.76727109,  0.69190171,\n",
       "          0.6200057 ]),\n",
       "  'sample_silhouette': array([-0.89853963,  0.93793244,  0.00426   , ..., -0.89807139,\n",
       "         -0.89711925, -0.89778852]),\n",
       "  'hdbscan': None,\n",
       "  'sequentialHungarian': {'performance_recall': 0.0798611111111111,\n",
       "   'performance_precision': 0.03035444947209653,\n",
       "   'performance_f1': 0.043989071038251365,\n",
       "   'performance_accuracy': 0.0224891744657075}}}"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roicat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ucids in list of lists for requested sessions\n",
    "ucids = [[[] for _ in range(num_ses)] for _ in range(num_planes)]\n",
    "for planeidx, results in enumerate([self.results[p] for p in keepPlanes]):\n",
    "    for sesidx, idx in enumerate(idx_ses):\n",
    "        ucids[planeidx][sesidx] = results['clusters']['labels_bySession'][idx]\n",
    "\n",
    "# this is the number of unique IDs per plane\n",
    "num_ucids = [max([np.max(u) for u in ucid])+1 for ucid in ucids]\n",
    "\n",
    "# this is a boolean array of size (number unique IDs x num sessions) where there is a 1 if a unique ROI is found in each session\n",
    "roicat_index = [np.zeros((nucids, num_ses), dtype=bool) for nucids in num_ucids]\n",
    "for planeidx, ucid in enumerate(ucids):\n",
    "    for sesidx, uc in enumerate(ucid):\n",
    "        cindex = uc[uc >= 0] # index of ROIs found in this session\n",
    "        roicat_index[planeidx][cindex, sesidx] = True # label found ROI with True\n",
    "\n",
    "return ucids, roicat_index\n",
    "\n",
    "    # get ucids and 1s index for requested sessions\n",
    "    ucids, roicat_index = self.prepare_tracking_idx(idx_ses=idx_ses, keepPlanes=keepPlanes)\n",
    "    \n",
    "    # list of UCIDs in all requested sessions (a list of the UCIDs...)\n",
    "    idx_in_ses = [np.where(np.all(rindex, axis=1))[0] for rindex in roicat_index]\n",
    "    \n",
    "    # For each plane & session, a sorted index to the suite2p ROI to recreate the list of UCIDs\n",
    "    idx_to_ucid = [[helpers.index_in_target(iis, uc)[1] for uc in ucid] for (iis, ucid) in zip(idx_in_ses, ucids)]\n",
    "    \n",
    "    # cumulative number of ROIs before eacg plane (in numeric order of planes using sorted(self.plane_names))\n",
    "    roi_per_plane = self.roi_per_plane[keepPlanes][:, idx_ses]\n",
    "    roi_plane_offset = np.cumsum(np.vstack((np.zeros((1,num_ses),dtype=int), roi_per_plane[:-1])), axis=0)\n",
    "\n",
    "    # A straightforward numpy array of (numSessions, numROIs) containing the indices to retrieve tracked and sorted ROIs\n",
    "    return np.concatenate([np.stack([offset+ucid for offset, ucid in zip(offsets, ucids)], axis=1) for offsets, ucids in zip(roi_plane_offset, idx_to_ucid)], axis=0).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: remove multipage tiff from: C:\\Users\\Andrew\\Documents\\localData\\ATL012\\2023-02-09\\701\\suite2p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATL012/2023-02-07/701\n"
     ]
    }
   ],
   "source": [
    "ises = sessiondb.iterSessions(imaging=True, experimentID=2, mouseName='ATL012')\n",
    "ses = np.random.choice(ises)\n",
    "print(ses.sessionPrint())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATL012/2023-02-15/701\n"
     ]
    }
   ],
   "source": [
    "ses = session.vrExperiment('ATL012', '2023-02-15', '701')\n",
    "print(ses.sessionPrint())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "def save_ref_as_tiff(ses, image='meanImg_chan2'):\n",
    "    ops = ses.loadS2P('ops')\n",
    "    expected_shape = [(op['Ly'], op['Lx']) for op in ops]\n",
    "    reference = [op[image] for op in ops]\n",
    "    assert all([ref.shape==shape for ref, shape in zip(reference, expected_shape)]), f\"shapes don't match, are you sure that {image} is an image?\"\n",
    "    ref_image = [Image.fromarray(ref) for ref in reference]\n",
    "    for plane_name, im in zip(ses.value['planeNames'], ref_image):\n",
    "        im.save(ses.suite2pPath() / f\"{image}_{plane_name}.tif\")\n",
    "    return reference\n",
    "\n",
    "reference = save_ref_as_tiff(ses)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Develop some code for behavioral analysis here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b6eb31f-c429-4410-9da7-382532f37c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "track = tracking.tracker('ATL012') # get tracker object for mouse\n",
    "pcm = analysis.placeCellMultiSession(track, autoload=False) # open up place cell multi session analysis object (don't autoload!!!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 3 4]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Andrew\\AppData\\Local\\miniforge3\\envs\\vrAnalysis\\lib\\site-packages\\scipy\\stats\\_stats_py.py:9205: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  z = (s - expected) / np.sqrt(n1*n2*(n1+n2+1)/12.0)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (0,40) (0,80) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Andrew\\Documents\\GitHub\\vrAnalysis\\workspace.ipynb Cell 5\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Andrew/Documents/GitHub/vrAnalysis/workspace.ipynb#W4sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m \u001b[39mfor\u001b[39;00m sb \u001b[39min\u001b[39;00m sortby:\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Andrew/Documents/GitHub/vrAnalysis/workspace.ipynb#W4sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m     \u001b[39m# pcm.plot_rel_comparison(envnum, idx_ses=None, sortby=sb, rel_method='pc', withShow=False, withSave=True)\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Andrew/Documents/GitHub/vrAnalysis/workspace.ipynb#W4sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m     \u001b[39m# pcm.plot_snake(envnum, idx_ses=None, sortby=sb, cutoffs=cutoffs, method='max', normalize=-80, rewzone=True, withShow=False, withSave=True)\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Andrew/Documents/GitHub/vrAnalysis/workspace.ipynb#W4sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m     \u001b[39mpass\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Andrew/Documents/GitHub/vrAnalysis/workspace.ipynb#W4sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m pcm\u001b[39m.\u001b[39;49mplot_pfplasticity(envnum, idx_ses\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m, cutoffs\u001b[39m=\u001b[39;49mcutoffs, both_reliable\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m, withShow\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m, withSave\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Andrew/Documents/GitHub/vrAnalysis/workspace.ipynb#W4sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m pcm\u001b[39m.\u001b[39mplot_pfplasticity(envnum, idx_ses\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, cutoffs\u001b[39m=\u001b[39mcutoffs, both_reliable\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, withShow\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, withSave\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Andrew/Documents/GitHub/vrAnalysis/workspace.ipynb#W4sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m pcm\u001b[39m.\u001b[39mplot_pfreliability(envnum, idx_ses\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, cutoffs\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, reduction\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mmedian\u001b[39m\u001b[39m'\u001b[39m, withShow\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, withSave\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\Andrew\\Documents\\GitHub\\vrAnalysis\\vrAnalysis\\analysis\\placeCellMultiSession.py:1005\u001b[0m, in \u001b[0;36mplaceCellMultiSession.plot_pfplasticity\u001b[1;34m(self, envnum, idx_ses, cutoffs, both_reliable, reduction, min_mse, withShow, withSave)\u001b[0m\n\u001b[0;32m   1003\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mplot_pfplasticity\u001b[39m(\u001b[39mself\u001b[39m, envnum, idx_ses\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, cutoffs\u001b[39m=\u001b[39m(\u001b[39m0.5\u001b[39m, \u001b[39m0.8\u001b[39m), both_reliable\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, reduction\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mmean\u001b[39m\u001b[39m'\u001b[39m, min_mse\u001b[39m=\u001b[39m\u001b[39m-\u001b[39m\u001b[39m8\u001b[39m, withShow\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, withSave\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m   1004\u001b[0m     idx_ses \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39midx_ses_with_env(envnum) \u001b[39mif\u001b[39;00m idx_ses \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m idx_ses\n\u001b[1;32m-> 1005\u001b[0m     r2, pc, r2_stat, pc_stat, target_red, sortby_red \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmeasure_pfplasticity(envnum, idx_ses\u001b[39m=\u001b[39;49midx_ses, cutoffs\u001b[39m=\u001b[39;49mcutoffs, both_reliable\u001b[39m=\u001b[39;49mboth_reliable)\n\u001b[0;32m   1006\u001b[0m     num_ses \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(idx_ses)\n\u001b[0;32m   1008\u001b[0m     \u001b[39m# put the difference and pval in a numpy array\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Andrew\\Documents\\GitHub\\vrAnalysis\\vrAnalysis\\analysis\\placeCellMultiSession.py:461\u001b[0m, in \u001b[0;36mplaceCellMultiSession.measure_pfplasticity\u001b[1;34m(self, envnum, idx_ses, cutoffs, both_reliable)\u001b[0m\n\u001b[0;32m    459\u001b[0m st \u001b[39m=\u001b[39m snake_target \u001b[39m/\u001b[39m (dv_target \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m\u001b[39m*\u001b[39m(dv_target\u001b[39m==\u001b[39m\u001b[39m0\u001b[39m))\n\u001b[0;32m    460\u001b[0m ss \u001b[39m=\u001b[39m snake_sortby \u001b[39m/\u001b[39m (dv_sortby \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m\u001b[39m*\u001b[39m(dv_sortby\u001b[39m==\u001b[39m\u001b[39m0\u001b[39m))\n\u001b[1;32m--> 461\u001b[0m cc_r2 \u001b[39m=\u001b[39m helpers\u001b[39m.\u001b[39;49mvectorRSquared(ss, st, axis\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m)\n\u001b[0;32m    462\u001b[0m cc_r2[cc_r2\u001b[39m==\u001b[39m\u001b[39m-\u001b[39mnp\u001b[39m.\u001b[39minf] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mnan\n\u001b[0;32m    463\u001b[0m \u001b[39m# also get correlation\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Andrew\\Documents\\GitHub\\vrAnalysis\\vrAnalysis\\helpers.py:186\u001b[0m, in \u001b[0;36mvectorRSquared\u001b[1;34m(x, y, axis)\u001b[0m\n\u001b[0;32m    180\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mvectorRSquared\u001b[39m(x,y,axis\u001b[39m=\u001b[39m\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m):\n\u001b[0;32m    181\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    182\u001b[0m \u001b[39m    get r squared between x and y across a particular axis\u001b[39;00m\n\u001b[0;32m    183\u001b[0m \u001b[39m    treats x as the predictor and y as the data (e.g. SS_total comes from y)\u001b[39;00m\n\u001b[0;32m    184\u001b[0m \u001b[39m    broadcasting rules apply\u001b[39;00m\n\u001b[0;32m    185\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 186\u001b[0m     ss_residual \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msum((y\u001b[39m-\u001b[39;49mx)\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39m2\u001b[39m, axis\u001b[39m=\u001b[39maxis)\n\u001b[0;32m    187\u001b[0m     ss_total \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msum((y \u001b[39m-\u001b[39m np\u001b[39m.\u001b[39mmean(y, axis\u001b[39m=\u001b[39maxis, keepdims\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m))\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39m2\u001b[39m, axis\u001b[39m=\u001b[39maxis)\n\u001b[0;32m    188\u001b[0m     ss_total[ss_total\u001b[39m==\u001b[39m\u001b[39m0\u001b[39m] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mnan\n",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (0,40) (0,80) "
     ]
    }
   ],
   "source": [
    "cutoffs = (0.2, 0.5)\n",
    "maxcutoffs = None\n",
    "\n",
    "envs = pcm.environments[pcm.environments > 0]\n",
    "print(envs)\n",
    "\n",
    "for envnum in pcm.environments[pcm.environments > 0]:\n",
    "    idx_ses = pcm.idx_ses_with_env(envnum)\n",
    "    sortby = [idx_ses[i] for i in [1, int(len(idx_ses)*0.5), -1]]\n",
    "    for sb in sortby:\n",
    "        # pcm.plot_rel_comparison(envnum, idx_ses=None, sortby=sb, rel_method='pc', withShow=False, withSave=True)\n",
    "        # pcm.plot_snake(envnum, idx_ses=None, sortby=sb, cutoffs=cutoffs, method='max', normalize=-80, rewzone=True, withShow=False, withSave=True)\n",
    "        pass\n",
    "    pcm.plot_pfplasticity(envnum, idx_ses=None, cutoffs=cutoffs, both_reliable=False, withShow=False, withSave=True)\n",
    "    pcm.plot_pfplasticity(envnum, idx_ses=None, cutoffs=cutoffs, both_reliable=True, withShow=False, withSave=True)\n",
    "    pcm.plot_pfreliability(envnum, idx_ses=None, cutoffs=None, reduction='median', withShow=False, withSave=True)\n",
    "    pcm.compare_pfplasticity(envnum, idx_ses=None, cutoffs=cutoffs, both_reliable=False, withShow=False, withSave=True) \n",
    "    pcm.compare_pfplasticity(envnum, idx_ses=None, cutoffs=cutoffs, both_reliable=True, withShow=False, withSave=True) \n",
    "    pcm.plot_rel_plasticity(envnum, idx_ses=None, cutoffs=cutoffs, maxcutoffs=maxcutoffs, withShow=False, withSave=True)\n",
    "    # for present in ['r2', 'pc']:\n",
    "    #     pcm.hist_pfplasticity(envnum, idx_ses=None, cutoffs=cutoffs, present=present, split_red=True, withShow=False, withSave=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:45<00:00, 15.10s/it]\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "reward positions are not consistent: ([112.5, 162.5])",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Andrew\\Documents\\GitHub\\vrAnalysis\\workspace.ipynb Cell 5\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Andrew/Documents/GitHub/vrAnalysis/workspace.ipynb#X54sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m pcm\u001b[39m.\u001b[39;49mplot_snake(\u001b[39m3\u001b[39;49m, cutoffs\u001b[39m=\u001b[39;49m(\u001b[39m0.2\u001b[39;49m, \u001b[39m0.5\u001b[39;49m), withShow\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m);\n",
      "File \u001b[1;32mc:\\Users\\Andrew\\Documents\\GitHub\\vrAnalysis\\vrAnalysis\\analysis\\placeCellMultiSession.py:546\u001b[0m, in \u001b[0;36mplaceCellMultiSession.plot_snake\u001b[1;34m(self, envnum, idx_ses, sortby, cutoffs, maxcutoffs, sort_by_red, method, normalize, rewzone, interpolation, withShow, withSave)\u001b[0m\n\u001b[0;32m    544\u001b[0m rewPos \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(\u001b[39mset\u001b[39m([rp[ei] \u001b[39mfor\u001b[39;00m rp, ei \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(rewPos, envidx)]))\n\u001b[0;32m    545\u001b[0m rewHW \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(\u001b[39mset\u001b[39m([rh[ei] \u001b[39mfor\u001b[39;00m rh, ei \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(rewHW, envidx)]))\n\u001b[1;32m--> 546\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mlen\u001b[39m(rewPos)\u001b[39m==\u001b[39m\u001b[39m1\u001b[39m, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mreward positions are not consistent: (\u001b[39m\u001b[39m{\u001b[39;00mrewPos\u001b[39m}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    547\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mlen\u001b[39m(rewHW)\u001b[39m==\u001b[39m\u001b[39m1\u001b[39m, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mreward halfwidths are not consistent: (\u001b[39m\u001b[39m{\u001b[39;00mrewHW\u001b[39m}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    548\u001b[0m rect \u001b[39m=\u001b[39m [mpl\u001b[39m.\u001b[39mpatches\u001b[39m.\u001b[39mRectangle((rewPos[\u001b[39m0\u001b[39m]\u001b[39m-\u001b[39mrewHW[\u001b[39m0\u001b[39m], \u001b[39m0\u001b[39m), rewHW[\u001b[39m0\u001b[39m]\u001b[39m*\u001b[39m\u001b[39m2\u001b[39m, sd\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m], edgecolor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mnone\u001b[39m\u001b[39m'\u001b[39m, facecolor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mk\u001b[39m\u001b[39m'\u001b[39m, alpha\u001b[39m=\u001b[39m\u001b[39m0.2\u001b[39m) \u001b[39mfor\u001b[39;00m sd \u001b[39min\u001b[39;00m snake_data]\n",
      "\u001b[1;31mAssertionError\u001b[0m: reward positions are not consistent: ([112.5, 162.5])"
     ]
    }
   ],
   "source": [
    "pcm.plot_snake(3, cutoffs=(0.2, 0.5), withShow=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')\n",
    "pcm.plot_rel_comparison(1, idx_ses=None, sortby=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]\n",
      "[0, 13, 7]\n"
     ]
    }
   ],
   "source": [
    "idx_ses = pcm.idx_ses_with_env(3)\n",
    "print(idx_ses)\n",
    "sortby = [idx_ses[i] for i in [0, int(len(idx_ses)*0.5), -1]]\n",
    "print(sortby)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_ses = len(skew)\n",
    "\n",
    "offset = 0.2\n",
    "ctl_positions = np.linspace(0, num_ses-1, num_ses) - offset\n",
    "red_positions = np.linspace(0, num_ses-1, num_ses) + offset\n",
    "\n",
    "plt.close('all')\n",
    "cvp = plt.violinplot([s[~ir] for s, ir in zip(skew, idx_red)], ctl_positions)\n",
    "rvp = plt.violinplot([s[ir] for s, ir in zip(skew, idx_red)], red_positions)\n",
    "# for pc in cvp['bodies']: pc.set_color('k')\n",
    "# for pc in rvp['bodies']: pc.set_color('r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x0 = np.random.normal(0,1,(10000,))\n",
    "y0 = np.random.normal(0,1,(10000,)) + x0\n",
    "x1 = np.random.normal(0,1,(10000,))-10\n",
    "y1 = np.random.normal(0,1,(10000,)) - x1\n",
    "\n",
    "all_x = np.concatenate((x0, x1))\n",
    "all_y = np.concatenate((y0, y1))\n",
    "extent = [all_x.min(), all_x.max(), all_y.min(), all_y.max()]\n",
    "range = [[all_x.min(), all_x.max()], [all_y.min(), all_y.max()]]\n",
    "\n",
    "num_bins = 25\n",
    "H1, xe, ye = np.histogram2d(x1, y1, bins=num_bins, range=range)\n",
    "H0, _, _ = np.histogram2d(x0, y0, bins=[xe,ye])\n",
    "H_diff = H1.T - H0.T\n",
    "\n",
    "extent = [xe.min(), xe.max(), ye.min(), ye.max()]\n",
    "xc, yc = helpers.edge2center(xe), helpers.edge2center(ye)\n",
    "\n",
    "# H_test = np.zeros((num_bins, num_bins))\n",
    "# H_test[0,0]=1\n",
    "# H_test[-1,0]=2\n",
    "# H_test[0,-1]=3\n",
    "# H_test[-1,-1]=4\n",
    "# H_test = H_test.T\n",
    "# print(H_test)\n",
    "\n",
    "plt.close('all')\n",
    "fig, ax = plt.subplots(1, 3, figsize=(9,3), layout='constrained', sharex=True, sharey=True)\n",
    "sns.histplot(x=x0, y=y0, bins=[xe, ye], pthresh=0, cmap=\"Blues\", ax=ax[0])\n",
    "sns.histplot(x=x1, y=y1, bins=[xe, ye], pthresh=0, cmap=\"Blues\", ax=ax[1])\n",
    "plt.imshow(H_diff, interpolation=None, aspect='auto', origin='lower', extent=extent, cmap=sns.color_palette(\"icefire\", as_cmap=True))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c59a688-ab1d-4ce9-a394-da4082a10ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add methods to analysis documentation for pcm!!! (include explanation of idx_ses handling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ae9a905-4e1d-4457-9ac0-8aee2100afc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now just need the skew plots and the reliability plots relative to other sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0c3e67b-9d75-4364-8389-c13469085b4e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eacda761-db40-4231-967f-207eec20c6bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "213362b2-0df1-4c94-98fc-c116f3311181",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f9c9c5f-e121-42c9-a515-0c9beaafc53c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a664d70-de8c-44fd-a1c4-4d4bf6388542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx_red = track.check_red_cell_consistency(idx_ses=idx_ses, keepPlanes=None, use_s2p=True)\n",
    "idx_has_red = idx_red[:, np.any(idx_red, axis=0)]\n",
    "idx_sort = np.argsort(-np.sum(idx_has_red,axis=0))\n",
    "idx_plot = idx_has_red[:, idx_sort]\n",
    "plt.close('all')\n",
    "plt.imshow(idx_plot, aspect='auto', interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71efb65e-f390-403d-b8c8-89f816683ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx_red = track.check_red_cell_consistency(idx_ses=idx_ses, keepPlanes=None)\n",
    "idx_has_red = idx_red[:, np.sum(idx_red, axis=0)>0]\n",
    "idx_sort = np.argsort(-np.sum(idx_has_red,axis=0))\n",
    "idx_plot = idx_has_red[:, idx_sort]\n",
    "plt.close('all')\n",
    "plt.imshow(idx_plot, aspect='auto', interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35c15844-230b-4428-940c-3ba768fa60c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a8c29ad-d229-468b-994d-60daa446d163",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a82e92fd-45f5-4d4c-bf2b-cc36e0fecfe4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aae9c31e-793c-475d-b9f6-1131dcd8678b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dcd1a9c-0bc0-4da3-b608-d7d9342894d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbf37659-8093-414a-83ab-38613aee581b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "105147fb-abb0-4e3a-b342-7e0a49278a1b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "548e7090-cccc-4b6b-934c-ae6305fe1f4a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83dbe85a-f153-4621-a834-f69f3fb03b69",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b5934ad-507d-47b7-af57-c99acd431200",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1fcb8db-9aeb-425e-87b0-e890fe107f13",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "971ce9b1-d5aa-46f7-98ec-9eff1bd68bf3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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