{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "\n",
    "import scipy.sparse as sp\n",
    "import scipy.io as spio\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "import isolearn.io as isoio\n",
    "import isolearn.keras as iso\n",
    "\n",
    "import scipy.optimize as spopt\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "from analyze_random_mpra_logistic_regression_helpers import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/johli/anaconda3/envs/aparent/lib/python3.6/site-packages/numpy/core/fromnumeric.py:56: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n = 602450\n"
     ]
    }
   ],
   "source": [
    "#Load plasmid data\n",
    "plasmid_dict = isoio.load('../data/random_mpra_legacy/combined_library/processed_data_lifted/apa_plasmid_data_legacy')\n",
    "df = plasmid_dict['plasmid_df']\n",
    "\n",
    "#Filter data on sublibrary Alien2\n",
    "keep_index = np.nonzero(df['library_index'] == 20)[0]\n",
    "df = df.iloc[keep_index].copy().reset_index(drop=True)\n",
    "\n",
    "#Filter on min read count\n",
    "keep_index = np.nonzero(df['total_count'] >= 1)[0]\n",
    "df = df.iloc[keep_index].copy().reset_index(drop=True)\n",
    "\n",
    "print('n = ' + str(len(df)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size = 594450\n",
      "Test set size = 8000\n"
     ]
    }
   ],
   "source": [
    "#Generate training and test set indexes\n",
    "test_set_size=8000\n",
    "\n",
    "plasmid_index = np.arange(len(df), dtype=np.int)\n",
    "\n",
    "train_index = plasmid_index[:-test_set_size]\n",
    "test_index = plasmid_index[train_index.shape[0]:]\n",
    "\n",
    "print('Training set size = ' + str(train_index.shape[0]))\n",
    "print('Test set size = ' + str(test_index.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = mask_constant_sequence_regions(df)\n",
    "df = align_on_cse(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initialize hexamer count data generator (separated by USE, CSE and DSE regions)\n",
    "\n",
    "hexamer_gens = {\n",
    "    gen_id : iso.DataGenerator(\n",
    "        idx,\n",
    "        {\n",
    "            'df' : df\n",
    "        },\n",
    "        batch_size=len(idx),\n",
    "        inputs = [\n",
    "            {\n",
    "                'id' : 'use',\n",
    "                'source_type' : 'dataframe',\n",
    "                'source' : 'df',\n",
    "                'extractor' : lambda row, index: row['seq_var_aligned'][:46],\n",
    "                'encoder' : iso.NMerEncoder(n_mer_len=6, count_n_mers=True),\n",
    "                'sparse' : True,\n",
    "                'sparse_mode' : 'col'\n",
    "            },\n",
    "            {\n",
    "                'id' : 'cse',\n",
    "                'source_type' : 'dataframe',\n",
    "                'source' : 'df',\n",
    "                'extractor' : lambda row, index: row['seq_var_aligned'][50:56],\n",
    "                'encoder' : iso.NMerEncoder(n_mer_len=6, count_n_mers=True),\n",
    "                'sparse' : True,\n",
    "                'sparse_mode' : 'col'\n",
    "            },\n",
    "            {\n",
    "                'id' : 'dse',\n",
    "                'source_type' : 'dataframe',\n",
    "                'source' : 'df',\n",
    "                'extractor' : lambda row, index: row['seq_var_aligned'][59:99],\n",
    "                'encoder' : iso.NMerEncoder(n_mer_len=6, count_n_mers=True),\n",
    "                'sparse' : True,\n",
    "                'sparse_mode' : 'col'\n",
    "            },\n",
    "            {\n",
    "                'id' : 'fdse',\n",
    "                'source_type' : 'dataframe',\n",
    "                'source' : 'df',\n",
    "                'extractor' : lambda row, index: row['seq_var_aligned'][99:],\n",
    "                'encoder' : iso.NMerEncoder(n_mer_len=6, count_n_mers=True),\n",
    "                'sparse' : True,\n",
    "                'sparse_mode' : 'col'\n",
    "            },\n",
    "            {\n",
    "                'id' : 'lib',\n",
    "                'source_type' : 'dataframe',\n",
    "                'source' : 'df',\n",
    "                'extractor' : lambda row, index: row['library_index'],\n",
    "                'encoder' : iso.CategoricalEncoder(n_categories=36, categories=np.arange(36, dtype=np.int).tolist()),\n",
    "                'sparsify' : True\n",
    "            },\n",
    "        ],\n",
    "        outputs = [\n",
    "            {\n",
    "                'id' : 'proximal_usage',\n",
    "                'source_type' : 'dataframe',\n",
    "                'source' : 'df',\n",
    "                'extractor' : lambda row, index: row['proximal_count'] / row['total_count'],\n",
    "                'transformer' : lambda t: t\n",
    "            }\n",
    "        ],\n",
    "        randomizers = [],\n",
    "        shuffle = False,\n",
    "    ) for gen_id, idx in [('train', train_index), ('test', test_index)]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate hexamer occurrence count matrices and corresponding isoform proportions\n",
    "\n",
    "[X_train_use, X_train_cse, X_train_dse, X_train_fdse, X_train_lib], y_train = hexamer_gens['train'][0]\n",
    "y_train = y_train[0]\n",
    "\n",
    "[X_test_use, X_test_cse, X_test_dse, X_test_fdse, X_test_lib], y_test = hexamer_gens['test'][0]\n",
    "y_test = y_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Concatenate hexamer count matrices\n",
    "\n",
    "X_train = sp.csc_matrix(sp.hstack([X_train_lib, X_train_use, X_train_cse, X_train_dse, X_train_fdse]))\n",
    "\n",
    "X_test = sp.csc_matrix(sp.hstack([X_test_lib, X_test_use, X_test_cse, X_test_dse, X_test_fdse]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting logistic n-mer regression...\n",
      "Regression finished.\n"
     ]
    }
   ],
   "source": [
    "print(\"Starting logistic n-mer regression...\")\n",
    "\n",
    "w_init = np.zeros(X_train.shape[1] + 1)\n",
    "lambda_penalty = 0\n",
    "\n",
    "(w_bundle, _, _) = spopt.fmin_l_bfgs_b(log_loss, w_init, fprime=log_loss_gradient, args=(X_train, y_train, lambda_penalty), maxiter = 200)\n",
    "\n",
    "print(\"Regression finished.\")\n",
    "\n",
    "#Collect weights\n",
    "w_0 = w_bundle[0]\n",
    "w_L = w_bundle[1:1 + 36]\n",
    "w = w_bundle[1 + 36:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Store weights\n",
    "data_version = 'doubledope'\n",
    "model_version = '6mer_v_pasaligned_margin'\n",
    "\n",
    "w_bundle_no_lib = np.concatenate([np.array([w_0]), w], axis=0)\n",
    "\n",
    "np.save('apa_regression_' + model_version + '_' + data_version + '_weights', w_bundle)\n",
    "\n",
    "stored_nmer_weights = {\n",
    "    'nmer' : [t[1] for t in sorted(hexamer_gens['train'].encoders['use'].encoder.decode_map.items(), key=lambda t: t[0])],\n",
    "    'use' : w[: 4096].tolist(),\n",
    "    'cse' : w[4096: 2 * 4096].tolist(),\n",
    "    'dse' : w[2 * 4096: 3 * 4096].tolist(),\n",
    "    'fdse' : w[3 * 4096: 4 * 4096].tolist(),\n",
    "}\n",
    "\n",
    "nmer_df = pd.DataFrame(stored_nmer_weights)\n",
    "nmer_df = nmer_df[['nmer', 'use', 'cse', 'dse', 'fdse']]\n",
    "\n",
    "nmer_df.to_csv('apa_regression_' + model_version + '_' + data_version + '_weights.csv', index=False, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load weights\n",
    "#data_version = 'doubledope'\n",
    "#model_version = '6mer_v_pasaligned_margin'\n",
    "#w_bundle = np.load('apa_regression_' + model_version + '_' + data_version + '_weights.npy')\n",
    "\n",
    "#Collect weights\n",
    "#w_0 = w_bundle[0]\n",
    "#w_L = w_bundle[1:1 + 36]\n",
    "#w = w_bundle[1 + 36:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set R^2 = 0.57, p = 0.0, n = 8000\n"
     ]
    },
    {
     "data": {
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2+WHShOp0OjCZTPB4PNzGaDTCZDJBCIFWq4V0Og2bzQatVgu3282T6DSRJAnlcpkn0v0ed68+T+qj3W7ztZjN5rfVZ6fTQakk5094PJ63dfy9pN1u37PvQqGAarUKh8MBn893TOHN6y+VSqHb7SISicDn8931Oz1Ds9mMZrOJcrmMdDoNu93OilOr1WI0GqHf76NYLGI8HsPtdiMSicDv9x+7N8ViEZ1OB5VKBXq9Hv1+H5FIBJIkIZPJoFKpwGg0wuFwwOPxwGg0Qq1WYzKZoNPpYHd3F1qtFhaLBbFYDEajERaLhc/xta99DblcDjqdDqurq5hMJtjZ2UG1WoXRaITb7cZ4PEa5XEar1cJwOESz2USj0YDFYoHNZoPX60U6neYxOhwOOJ1OaDQaeL1e2O121Go1JBIJGAwG+Hw+9Ho9vPHGGyiXy6jX6zAajax8P/rRj6LT6WBzcxPtdhterxeDwQBbW1vo9XpotVoYDAYIBoPodDro9/sYj8dYWFiA2WzG4uIibty4gVu3bqFcLmNpaQlWqxXRaJQXpXq9jlarxQvicDiESqWC1WpFKBRCMplEo9FAu93GwsICKpUKqtUqtFotVlZWUKvV4HA4IEkSkskkSqUSNBoNFhYWkMvl8Pjjj+PKlSv4xCc+gUajAbPZDJPJBIvF8l6kpd8l3+oKGDg93/u0Nm8bW2m32+j1ejAYDPzSG41GVCoVaLVa5HI5BAIBGI1GeDweCCFYYRaLRZjNZhgMBvT7fdhsNtTrdUQikftWokIIGI1GVgbvVvnOKhbgdIV8v31Sv06nE/1+HyaT6a527/Q8kiSd2jf16/P5YDAYUK1WkUqlYDQaoVKpjl0rCS2WOp2OLUvl2OglA4B8Po+DgwMYjUbuy+fzYTweYzAYoNFoQJIkXLhwARaLBaVSCd1uF+12GwDY4ut0Omi1WiiXy3A6nYhGo4hEIrh+/TqsViv0ej3OnTuHWq2GdruNSqWCQCCAZrPJ16bX69Hr9TAYDFAqlRCNRmGxWNDpdGA0GqHT6eB0OmEwGDCZTLC2tgaVSoVisYjRaIR8Po9gMIjBYIB6vY7JZIJKpYLhcIhOp4NOpwO73Q632w2dTseWea1Wg1arRbVahVqtRq1Wg8fjwdHREeLxOJaXl2Gz2dBoNFCr1WAwGHDx4kVYLBbcuXMH6XQaZrMZ7XYbOp0O58+fR6fTQTqdRq1Ww2AwgMlkgs1mw3A4hNPp5OvodrvQarVQq9XodDrQarVIpVIwmUyQJAm9Xg+TyQSlUglqtZoVKxkvw+GQxzYajdh6HwwGuHXrFpaWlmAymWC1Wnlx0uv1CIVCWFtbg8FggMlkwsHBAc+b99K4mJVvdQV8Ur43cO+c8PvOtwfeevF1Ot0x15JealIKtVqN25LCLZVKKJfLGI1GqFQqcDgcGAwGMBgM/KLfr0IymUxsSb8bIWtdp9Oh2+3CZDKh0+kcs9a73S70ej3/fq9zSpKEUqmEyWTC92jecfMUv7KP086jXIRIKdIxSot9Mpkgk8lgNBrBZDKhUqmwZTU7JiEEXy89E+qTXOlOp4N2u410Og29Xo87d+7gySefhMViQavVQq1Wg81mQ61Wg0ajwf7+PvR6PUqlEsxmM0ajEex2O3Q6HVKpFPr9Pm7fvg2PxwOVSgWPx4Ner8eKWaPRYG9vDzqdDv1+HzqdjpVZPp+H0WiE0+lEuVzGa6+9xkr2/PnzKBQK2N/fx9HREaxWKytbt9uNo6MjtgiPjo7QarUwHo/R6/WwtbWFwWCAVquFfr+PfD6PyWSCixcvolwuQ61WI5vNotvtolKpwOv1otvt4s6dO1haWoLFYsGNGzfgdrsxGAxQLBZRKpVgt9tRrVZxeHiIfD6PXq+H8XjMFrrFYoFKpUIqlUKlUoHBYIAkSWg2mzCbzTg6OoIQApFIBO12G5lMhhcSjUaDZDLJz4FgB5PJBI1Gg0gkgkajgXq9zgtbLpeDRqPBG2+8wQtWPp/HysoKdDodnn/+eQyHQySTScRiMWxsbCAYDEKv18NoNGJ5eRlOpxNCCH7PH5R8qyvglwH8vBDCIL21I8CLkNM5E4o2L0JOZ4SizduqnqV88WmFJctIaSElk0lWRKSIaaU/PDxkK4Rcs263y+3u5eqfpriUci8lRv1MJhMMBgMYjcZjCwyNZZ61Pdv3vHNJkgSTyQS32w2VSnWsDZ1Hq9Wi3W4fU4b3C3nQ/e50Okil5CJsbrcb5XIZk8kExWKRscNOpwOLxcKLI52HFlASpQXV6XSgUqkwHo/5+Qgh4HK5YDAYUK/Xsb6+jslkAq1Wi3w+z0per9czRjkajVAul5HL5VAsFrG6ugqz2YxyuYxutwuPx4NgMMj3uVwuQ6PRoNlsYmFhAVqtFvV6Hd1uF71ej2GBcDiMcrkMAKhUKkgkEphMJkilUuylbW5u4uDgAHa7Hc1mEw6HA1evXkUkEkGtVkM2m8X29jYODw/h9/tht9vR7XaRz+extrbG2KhOp0M+n0e/34fL5UK1WkUoFMJwOEQqlUKr1cLa2hqMRiN8Ph9UKhUODw9hNpv5/lYqFXzta1+D3W5Hp9PBYDBgGKZQKMDpdLKX2Gg04HK5MBgMYLPZ+P5ZrVak02kEg0G2xHU6HdrtNkajEUqlEvL5PGO0wWAQOp0OXq8XuVwOQgik02k888wziMViSCQSUKlUaDQaGAwGMJvN6Ha7GI1G2N3dRaFQwPLyMjY3N1Gr1bCysgKfz4fz58+z1UwL97s1hk6Th6qAhRAWyPUQAJkCFxNCXAJQkSQpJYT4OchZUX9h2ub/hJwb/3khxM9Azhv/bwB8VnqLvvErAP5ECPF3IKfR/iXIaZovvN3xmc1mGI1GbG1tsUu5sLDACkNpNZGiGQ6HbH34/X5otVqYTCa0221ks1m2YgjbO+mBzlOQ03t2rN29lLSyn8FgAJfLxRaNUiELIVjRKRWksu/Zv2mRKpfLrHzntRmNRtjc3OQX1Ofz8aJF7vn9TOx2u43JRK7ZQkqPlKjf70er1cLKygpbKpPJBHq9Hm+++SaMRiPjvaT42+02nE4ncrkcLBYLtra2MBzK9XQIX43FYmi1WshkMigUCuh0OhiNRrh16xbG4zHi8TgvXvl8HoFAgC0pgqjsdjsHyjKZDDQaDXtK3W4XXq8XtVqNlaDFYsHBwQFKpRKKxSIrqmaziVKphKOjI9RqNaytrWF/f5+t28lkgnK5DCEE7ty5A5fLheFwyLhoo9GA1+tFqVRCs9lky7FYLCIQCLDyDwQCKJfL2N7eht/vx3g8RrVahRACOzs7aDabOH/+PFv20WgUhUIBg8EAhUIB/X4f4XAY29vbmEwmWFhYYE/AYDAgk8lgMBhgNBrBZrNBr9cjGo2yZVuv11Gr1WC321EoFNBut2EwGGC1WrG4uIhcLofd3V3U63WGLgjiu337NsrlMmw2GywWC/r9PoQQiMVisFqtaDabSKfTaDabvKg3Gg00Gg20Wi1YLBao1Wrk83nY7XYcHR1haWmJ4wQPUvkCD98CfgpykRmSz04//wxy7noQch1eAFxb90XIhWyuQs7Z/iXI9WKpzTeEEN8LuYzdZyFXKvseSZJeeaeDLJfLaDab7NYCYIXh8XhYgUiSxMGUdDqN8+fPc2CuWCyyMr0fEULAYDBwAFAJF5DyB3CXkp6dIPNw5FmFrLQMlZZrp9Nhq53GoPyb+qLv5sEc5B63220Eg0FUKhXGywl3nIcZzwpdv9JzIBgBkIOlDocDFouF4R8KKHW7XcTjcV4oSIlWKhVIkgSHw4FqtQqz2Yx8XkaqwuEw34NutwubzYZKpYJ+v89KLhgMYnNzEysrK5hMJvB4PLhx4wZUKhUSiQScTifG4zGazSYrkHQ6DavVinw+zzBCv9/nBWFzcxMajQaj0Yitu2aziU6ng5dffhl6vR6tVou/LxQK0Ov18Hg8rFi63S5CoRC76GSl0uKytLQEm82Ga9euYTgcwuv1Yjgc4rHHHsPR0RF2dnbQ6/XgdrthMplQrVZx+/ZtHB0dwWazQafTYXNzE2q1GsPhELdv32bWwWQygd/vx2AwgNVqhVqtRiKRQDQaxdLSEsMQZH1bLBbodDpIkgS/349isQiTycT3jdgHOp0Ofr8fq6urvPgCMhNiMBjg4OCALVWykgeDATY3NwEAKpUKZrMZ/X4ffr8fo9EI4XAYGo0GxWIRRqORmTEqlQrNZhP7+/twuVxQq9XH3o0HKQ9VAUuS9FWcUqdWkqRPz/nuBu7eBmi2zW9B3jngnY7rGDWHrMZwOIxcLscTU/mh9jabDbdv3+aIMb3E5K5aLBa43e5jVu08UQYATSYTB/fIRSbFfD9ButkxKo85idpGyrHT6cDj8TDbg9x5wu7oHtG/1DfherVaDW63m4M9gUCAYQqTycSUq9n7rnwWwFsLTblchtFoRLvdZthgOBzyyw4ABoMBxWKRXeBer4dkMomLFy+iVCqh0+mgWq3CZrOh3+/D4/Gg1Wqh3W4zC4Hu12g0QrVahcFggMPh4ABUOp3GwcEB6vU6Q07ZbBZqtRpmsxkajQZut5thgX6/z1H0Wq3GeKrJZGLL99atW1CpVDCZTBgOh/D5fAgEArBYLPj93/996HQ65HI5OJ1OaLVaCCFgtVrR7/dx4cIFHB4ewuFwYDQasavfarXY7b5y5QqWlpawtraGL3zhC7h48SKKxSL6/T5arRauXr0Kl8sFr9eLVCrFga1MJsPKm7Ba8p5oHk4mE140+v0+7HY7nE4nbty4gdXVVV6AiaFy/fp1uFwu9Ho96HQ6HB0dod/vQ6/X83kIEgkEAjAYDDAYDEgkErDb7TCbzXA65fLRgUAADoeDsV5JkmC1WuFyuZDP53l+EE7s9XoxmUzg8/nQaDRgt9vRarXwwgsvoFqtIhKJoFQq4cKFC6jX63fR/h6kqF966aWHdrJvNfnc5z730qc+9SmObOt0OsZ9iLdoNBrh9/tRqVRQq9VQq9Wg0+mg1WohSRL6/T6GwyH6/T6cTic8Hg+v5IPBgF8oesmU5yKRJAmNRoOpNaSo+/0+W8UUrLHb7TCZTCdOkna7jUZDLsdKbXQ6HUfV5wm5yxQsIWuLKHUA2L2z2WxQqVQccKIXoNeTIfper4dSqYR4PI7z58/DapU3ABZCQKPR8LHz7oVy7GSpENbbbDbZKiXX0uVyQa/X8/0nbmkoFILdbofH48GdO3eg1WoxHA7RarWgVqtRLBaRy+UQiURgNpuxurrKrItkMgmn0wmXy4VAIAC9Xo/RaIRz586h1+vBYrEgm80ytEHP/9y5c+h2u7BarbxoAmAqGUEV5Aa7XC5oNBpe7C5cuIDJZIJwOAyv18uBI71eD7fbjV6vh3q9zpF9rVYLj8eDfr/PGDhxeS0WCwwGAzqdDnw+HywWC3NdR6PRMRf/woUL2N7e5mdM2DMgUwDX1tZgNpuxsLCAwWAAvV7PbAOtVguj0QitVsvPORwOYzQawel0wu/3s7IPhUIIhUIYj8c4OjpiXjJhvi6XC+PxmJU2XVutVuNAYygUwsLCAr9PTqcTPp8PGo0GdrudOc4Ug/H7/RxwpsWajBO73Y6LFy/iscceYwaIEILn7Ozc/OxnP5t96aWXPnc/euXtyLd6EO6ByzyX3mQywefzQafToVKpoNfrQZIkFAoF1Ot1lMtldqtbrRY6nQ5sNhsWFhaYL5zL5e5iQZwEHwghmLNJ0VclPkuu/72oaSed4150tFnrlxILyuUyMzqIEkZwgBAChUKB3fpwOHxXQKxSqXB7svDJilda+LTg0Ng7nQ7za8vlMo//4OCAAzrhcBgWi4WxZeKV+v1+VpTEee12u9BoNAgGgzg8PITFYmEa0tLSEuP6arUasVgMzWaTubsejwflchnXrl3D9vY21Go1vF4vEokEzGYz49FkYWk0GlitVuzt7aHRaMBkMkGr1XJQN5FIwOFwoFQqwefzwWq1YnV1lSGVdruNa9eucZDX7XazIqBF3OVyMR3Q4XBAp9Mx/5WsRb/fD5VKBSEE8vk8U7YikQgr8dXVVQgh4PV6cf36dTSbTfbWrFYrzp8/D41GA71ej3w+j1AohEwmwwr/befNAAAgAElEQVSXYJlCocDJF3q9HgsLCxwbaTabvLDTnAuFQjg8PEQ8Hkc4HMbi4iL6/T5KpRJjuM1mE1qtFnq9Hvv7+wgEAqxc4/E4rFYrHA4H9vbkvRGsVit7FkajEfF4nBcbSvLo9/sYDAZQq9VMLSQu8traGrRaLRYWFlCtVk+F+d5Led8r4HkuPSnhzc1NNBoNxGIx+Hw+xr7y+TwcDgf/32g0otlssgUmhODf2+02W4jEJZ2lQhHNyWq18kSlcUiSdFew7O1czzylPI+ONsu7tVgsCIfDHJ1XRvJJiVarVVgsFhSLRfR6PVbuxWKRIRg6jxJSKJVKKJVKUKlUzJOWJAlGo5GVKS0GLpeLsegpIR5Op5MtGgBMs9LpdPxCGgwGRKNRdDryRsFOpxN6vR5LS0uskMfjMdLpNHZ3d+F2u5kP63A4ONurUCggl8uh2WxCp9PB7XZz0On111+HxWLB9vY2+v0+4vE4VlZW4HQ6sb+/D4PBgFwuh4985COc1FEul3FwcACVSoVQKASn0wmbzcbKPJvN8uKm0+kQj8ehVqsxHo9RKpXY0iZa3Gg0QqPR4Iw0wrgpq6vf76PdbiOXyyEUCiGXy+Gpp55Cu93GU089hUajga9//euw2WxotVooFoucgXZ4eAitVssW8NHRERsIpAAdDgey2SwvGMFgENvb22g0GpxZZzQaYbPZ8KEPfQhf+tKXsL+/j7W1NRSLRTSbTVy4cAF6vZ5jCtVqFcvLy4zjEifZbDbD4/HAbrdDrVbDarWyZ1cul9k7jEQi8Hq92N3dRa/XOwbtuVwuGI1GNBoN3Lx5E36/n72o5eVlHu/9BorfrbzvFfBJys1gMDBVZnt7Gx6PB/F4nJVXo9GAwWCAzWZDNptFp9PB1atXmZBOSoSCT3q9ni0UJRWKIAYij5MlqczAI0V5miitXGWgbTYoBxy3NE0m07Hgn/I+WCwWmM1mdDodtkQJHycXsFqtIhwOs+VMC4Zer0e9Xkc4HOZroGBKs9lkXJQWIKX1QunY7XYbQgjY7Xbk83n2LpSWdbFYRK1Ww2QygRACpVIJVqsV2WwW8XgcAOBwOHiBoMW11Wqh2+1ib2+PAzIOh4O5tBTUo2h5rVbDcDiExWJh7qndbkcikeBFQq1WMy1xPB4jm83yorO0tIRCoYBWq8VQ12QyQaFQQKPR4EVtY2MD7XYb0WgUg8EA+XyeEyZovpGlvbW1xXQtp9OJwWAAjUaDdrsNj8fDVnqxWITH40EymcSFCxcYb7527dqxFONgMMjPglgz7XabU5M9Hg8bHBRMTCaT8Hg8KBQKcLlcODw8RLPZ5HtI84K48pPJBEtLSzg4OOB0462tLXg8HrbMCfO+dOkSkskkK1iKH/T7fXi9XmxvbzMXmZS9Wq1m5gN5FQ6HA1arlRcZn8+HQqEAjUbDMCN5r2+Rqx6OvO8VMHCcCUD/V6vV8Pv92NraQjgcxnA4RCwWY1damWwhSRKy2SzMZjOq1Sq8Xi+i0ShH57PZLAA5eFCr1RAIBJjCRKszIONnxCPVaDTcplQqHXP/Z8eqtHLntZ1dZMjSBMA4K3FiCf6gfgEwf7JarQIAK07C6ojmZjAYGJ+jhI1KpcKWDNG3KLOwVCoxZDAcDlEqleB0OnnxIGu92WwiGAzCbDZjZ2eHg5sGg4GDOe12G263G4uLi7h+/TrTosh9p4ARZSwWCgWoVCpmVZC7vL6+jmq1ikqlgr29PSwvL6NeryMej8NsNsNsNuPcuXPY3t7m+0V4bLFYZAy4VqthZ2eHKVu0UAeDQWQyGdjtdoxGI1itVg5+1et1xukJL8/lciiVSshkMlhfX+dFiXjY6XQaOp0OwWCQLWDCVwnqIQpaMBjkOMM3vvENtFothilcLhd8Ph9yuRxcLhcvOmRJUwCNLHWygre2tjhI22g0uP4CLQY0j1dXV5FMJrG0tIRSqcRp9tlsFv1+n/Ftn8+HYDCI8+fPc4CTWCiBQAArKyvQarXH0rV1Oh10Oh2fe39/H7FYDAaDgVlMsVgMm5ubCIfD0Ov1uHDhAlqtFmcC0j2bZQOdQRAPSWZxUkmSsLS0BI/Hw9xZegFJCZOSW19f54mptD6JnkYvx2Aw4ICX0+nEcDg81i896Mlkgnw+z9QxAMcmxCxFjaxccrfnTR7lJCIFTUkGfr8fqVQKDoeDrWLlOYjDGggE2BoiIXx6MBigXC7zImI0GtmiUSZkWCwWrKyscK0MwhjJ3e31egwdEA3NarWiVqsdy1SkRWI8HiOTyTAm73Q6ceXKFSQSCWYmaDQaCCGwvb0NlUqFarUKt9uNdruN559/Hjdv3sRwOES73cYbb7zBUIPNZoMkSVhfX4fFYsFkMkGj0cCrr77KFqzL5eJ6ERqNBtvb2zCZTMjlcky9omDdeDxGKpWC1+tla34wGMDv97Pyevnll/kZZLNZLrrjcDhgt9u5Rgil/QLAYDDgQN7t27fZgnQ6nchkMuzGRyIRbG5uolqt4saNGzCbzWi1WvB6vQyLXblyBZPJhDm5m5ubDEOQZ6FWqzEYDHD16lVUq1WMx2NOABmPx5AkCfF4HDabjefH9vY2AoEAL1YUCPR4PBwgpWDxxYsXEYlEYLPZOJbi9/vZkzEajdjb2+N3jbIMKZmEIAXCpOv1OvL5PIQQqFarGAwGsNvtuHTpEsMW9L4Q+4cgsDMI4iHILE6qpH3RS6gMWJG7TIraYrFgfX2dqzuREI56eHjIVK1IJMLBHQB3KUjqIxAIcMCFJsYshKBUssrssXsF7AhzJSVPC8EsJ5jOQZQggmSU+DUFEFOpFL8I9GInEgnodDrEYjF0Oh1WmlarFeFwmFkeZrMZDoeDz02pznq9npkRpHwsFgtb2YeHh+j1egiHw5wVJUkS0uk0AoEAR/4peBQKhZBOp9kqpT71ej3sdjv29vZw6dIleL1e1Ot1RKNRmEwm/peSH5LJJC94gUAAwWAQqVQKxWIR6XQasVgM4/GYkzWCwSD6/T4mkwlCoRBSqRRnXxELo1gscupwq9VifjlZdh/4wAcQCoVgMBiYniaEwP7+PpaXl9kapoJB4XCYueXD4ZCNiHK5jJ2dHdRqNXQ6HYTDYcTjcdTrdXi9XjQaDYRCIV4EKP5BQcBr165xhTCDwYDl5WVks1l25bvdLhYWFpg1IklydbbV1VUsLS1Bp9PhzTff5HeMFCAtmMR77vV6qFarnLxD3GDlHFhZWcFgMGBIy2q1QqPRcJCzUqkgk8lwe0rGICphMpnEM888w1AbAA6En1Tj5L2WMwWM40kQRN+hwiBKK1KZ/UYMAbVaza73PIVHD5FwJyUWedJYZnm78yCEeUp2lj1xkpyk5Oml63a7KJVK6PV6CIVC6Ha7cLlc6Pf7zMmltlTFi9gDBoMBrVYLhUIB4XAY1WqV+blKVgZZhEo8nLyQSqUCp9MJq9XKSlUZDKQEGKLIEaYJyHDLcDhELpeDx+PBysoKB3aOjo7gcrmY6kbRfQoExuNxXoyIfUCBRwrufPOb30S5XEa1WsWVK1fQ7/fRbDYZStBqtfjiF78IlUoFp9OJxx9/HDabDU6nExaLBa+99hp6vR4He4iJoFKpUKlUMB6PGUPP5XKw2+0c1d/c3GTqn8fjQaPRwKVLl7iKGiAnKhBmarVaYbFYkEwm2XUfDofodruMZROF6/HHH8fu7i7X2HC5XBBC8ELZaDSQTqfhcrlQqVRgt9thsVgwGAzw/PPPY319HW+88QZjzsRQoZoOpNRWV1fRaDRw69YtpvnpdDqGlfb393FwcMAwAl0/8ch7vR6Ojo44HTs+pTu++uqrbHkrjRJKhHE6nQgEAuj3+1wpTq/X3/X+KOuQ/LlLxPhWFWUSBFVtMhgMiMVidz0gomeRW0x0JqJFzQphjMViEZFIhIMSp8k8Jar8/72U7EnfK63Wk5Q8AJRKJY4u0/ipAhcFUwiW8Hg8jLOGQiHG7DweD1Ot6vU690EKnjBgwsP9fj8zRih6H4vFmO1AwcDDw0O2vgaDARwOBxqNBjqdDr75zW9Cq9Uik8lw6cFWq4VsNgu3241arcYpzH6/n+EGgo6ohKJGo2FGAC20tVoNi4uLzBix2WwwGAwIhUIMJXg8Hty+fZsVIHFaKcGiXC4jHo/jK1/5CgwGAwd9MpkMB76EEJxU0uv10O124Xa7USqVuHgP1TWQJAm5XA7PPfccDg4OWFFevnwZ9XoduVyOec3b29tsmZvNZr53Tz/9NFwuF5aWlmA0GnH79m2USiVUq1WcP38eNpsNg8EAKpWKvaJ2uw2r1Qq73Y5AIIB0Oo1XXnmFjZa1tTUAMiecgl20mF67do3pbqT8nU4nW/9utxtarRaTyQT1ep2z6bxeLx8XDAZRLpexsbHB+Pb6+jpisRh0Oh3q9TpDNwsLC1wHeGlpiRNfMpkMlwmgsRKk97CUL3C2Keex4jGUweb1ynWXyeVXCsEKRFciniO5W/P6DofDCIfDHKygOhPKdrMybwIo273dCdJut5nJAMgKze12H6snQX1SIRtlgIZcbkrRJphAq9Uy1kYptkajXOd2YWGBIRyHw8FKw2g0ctZgrVaD0+nkiDkxGWYz5gDw+VOpFDKZzDEMmbA7smyogE273Uar1cLR0RFX2yLGACDDIcRgoIpbvV4Pr732GkqlEhKJBKe6VioV+P1+hmAAeY5QUkIkEsHHPvYx6PV6rK2twefzsYXbaDSg0Wg44YXcauIs37x5kxcPq9XKtRg8Hg8nAi0sLGAymcBoNOLo6AjJZBLtdhu3bt1iHi0tiMQe0el0SCQSGA6HKBQKUKvVsNlsWF5ehs/nQyKR4BKPs7WwjUYju+eJRIKfazQa5UAgldIcj8fMz+52u1hcXITb7UYsFsNjjz3G3F4hBNxuNxwOBwcmX3/9dV58qSi71WrFxsYGQqEQnnnmGY5ZOJ1ORCIRrK6uIpvNMnskHo9Dp9Oh2WwikUjAZrNhZWUFgUAAkUiELed6vQ6Px4NIJMLlBGbhx4elfIEzC5iDKZTy6XK52ALudueXoqNJSe40VZwiXFcpSpeGuKr3SpKYJ/fbbp6clKAxO9GU54jFYgDeqslA6dHKLCqVSsWcaCqpSBYbFYqx2WyMm1NWEXF6yVpR1lwVQmB5eZk51bPBQMpGpCxEKlZOf+fzedhsNnZdr1+/Do/HwxW0CC4qFApYW1tDoVDgUoSNRgM2mw2JRAJarRaJRAJLS0sYj8c8Hyi9WpIkxpBtNhszHdbW1vD93//9HOghi9ZsNsPlcnGqrdFoRDgcZgYMZfnRojEcDjnFWUlljMfjbA0Se8BsNmNtbQ1qtRrnzp2D2WzG0tISstkslpeXYTQacXh4iPF4DJ/PB5PJhEajwVzjcrmMb37zmwDk7LeDgwNoNBrs7u7iypUrkCQJCwsLHFQkC1qv1+PGjRtssVOm3cbGBme11Wo1Zmro9XpotVqMx2OcO3cOu7u7nGlIBY1qtRo2NjbQ7/dx69YtTCYTpgA+/vjjDH98+MMf5oDl7u4uVlZWuB6Gz+fD4eEhw2OZTAadTgd37tzB5cuXue6Fknp5P4lOD0Le9wp4Hh5KVpoyS2v2wZDiHg6HzN0l3i5wXJkR5Wb2QZ+WHTc7xlnurrLdSTUVlAG+e9WQUJ5DyVpQQhMkRIGiXSKGw+ExLjEATlsmCp7VakWhUGDLz263H+NFKzm6swFHgisoGGi329FutxEKhZDP5+FyuViJE+2IdmvQ6XSw2WwcxKHdLPb399FoNBCNRrkMI2We5XI5dDodOJ1OBINBZilQDQgKDI5GI1y8eBGZTAaSJO+w4Pf7sba2xtDN66+/ztSoixcvQqfT8TNIp9NMD1taWkK1WkUsFkOpVILX64XVaoXRaES/32e2QTAYZEiHoIhyuYxsNstptYC88D/22GOcDlytVjllezweM0Ol0+mwi9/v99mzEEIw35lq/1KAtNlsMk2z3+9jaWkJW1tbzH7Z3d3FxYsXeZeNer3OwcDLly9jMplw0XaNRsPxE3pnkskk6vU6bDYbdnZ2OLNua2uLq9Xt7OwwuyEUCnHRJNqO6WMf+xgajQZnsxIrp9Fo8CKtnLPvxe4z70Te9wp4VjkR9anb7XJO/kmWJ9GDyI0m5TtPsQJ3Y7f3UoyzmC1xd5WMgnmW8bzv5mFb85Q0bd0D4K6te8hqJY6qcuyzVDoi65PVl0qluKZvLBaDRqPhF0eZsq0cJ73w2WyWoYNer8dMErvdDq1WywFCUt4AePufjY0NTpfe29uD1WrF9evXGYKggCC561QP1+Fw8ILaaDS4jGYymeS0Y4fDAZPJhN3dXRwcHMDv9yMWi6FSqQAAU7La7TZnSzocDkQiEej1ehQKBbbwIpEIHA4H71FGSpVqb1DCxe7uLux2O8bjMZaWlli5EPSzvb0NjUbDmPVoNOJgcTQaxdbWFqLRKK5fv47xeAyXy8VB2Fu3bvEzfeqpp7gokVqthtPpRDqdBiB7gEajkS1ogmaGwyEvdK+++iqzNzweD3uNBoMBN2/e5Mw1n8+H4XDIiynR8cgLIOaDzWZDJBLBysoKL3RUeIj2dqvVajCZTEwfpXoOHo8Her0emUyGC87H43HY7XYEg8Fj7+jDlve9AgaOb0lDuBq5Kd3uybtGUJ55MBiEzWbj709TrLNKdp6FSX0rlRtlwynHAhynpCndqZNqTpzUPwA+3ufzoVqtcpIFtaMkCqoDMG9BAd7CbOk+qNVqxONxLrc4yxwhYr8SGiGmRbcr16vd398HAMatyY1Xjk85nmKxiGq1CqfTyQyPdruNQqEAt9uNYrEIIQS7+vl8nq2nUCgEAMhms7BarUzZkySJK2eR1U+QDPWZSqW40LharYbb7WZrjiCCbrfLySe0KeaNGzc4qefSpUv40z/9U4Zv3G43ms0mALm4D20ZRGwGgjtqtRrvPEGLCsFFzz33HPr9PqxWKw4PD7GwsMBV/hwOB/Npj46OmO5GadvEiDEa5Q036dqMRiOefPJJ3vSyXC4zFEQ492g0QjQaxblz59But/Hmm2/i6tWrzI//+Mc/zgWBCJNNpVLw+XzY39/Hhz/8Yb6+XC6HfD6PF154AY1Gg+tgxGIxHBwccDEiShcnLjMFT/V6PY6OjjiTlSzre23q+iDlTAErhAIKZG0RX1OSJA6mkMxS0lQq1TEWBCU73I9bM6/27zxesiRJd211REqIss5IUd4v3KBU0iqVit1x5ZYsBH1Qrj9ZOhSkm72HpDyJBO9wODhYZrVaOdhD106MCrpnZMnS7hPD4fDY3nqUlEG1AGistKjo9XpUKhVYLBZOlSaqm9Pp5KLhdK1Ug4B2D+71emw1JZNJ6HQ6LCwsQAh5OxzyDFQqFTNhKpUKwuEw7ty5A5/Ph2azyTj06uoqszbIeifrmFKRiapHATe1Wn1Myfj9fq69Qc+dYBaPx4Nnn30Wer2e8VgKihIl0OVysSJ6/vnnUSqVsLu7i1KpxEHlYrHIKd/D4RCLi4tIp9NYX19HsVjkJJCdnR3eWPTNN9/ExYsXkc1mWUlTlh8tAE6nE5Ik4fDwkHm7fr+fg731eh0rKytoNpsM71DdC9pc8+bNm3C73bBarTCbzVytcH9/H61Wi8uHFgoFrjFM9ECqMKjX6xk2CYVCvLhSbRKv1/u24yvvVs4UMN7aKqfT6SAYDDJumcvlYDabmXg+67ITJc1msx3bR+7tBMxOUoZKXjIFsCjwQRYjuf1kmSqDYKe5VKdZ6JRerMzm63a7nJpK1h4p+9mdQorFItrtNi9cdrsd2WwWfr8f5XIZoVCIr0eZoELcV7oHdF6yeIlJsLe3B6fTyUkk29vb7OK7XC5EIhGOyicSCbZMq9UqK9hKpYJIJMIJHeSmtlotNJtN5pLWajXe6aLZbMLpdGJ5efnYZpypVIoLxSSTSZjNZk5hrlar2NjYgMvlYsZDJpOBXq/H3t4eDg8PGX4oFAq84zJdB6UACyGQTCY5kGg2mxmOIOuaFlBlaUqz2YybN29idXUV+Xwe9Xqd61VQbQqi521sbMDj8WB9fZ13Hk4mk4jH41yPgTLRqPJZIBBg7JR2Wib61wc/+EF+D7a2trC4uIhyuYyrV69CCMHWOOHy1WoVjUYDxWKR6X0GgwHxeBytVotLRFJhHofDgXw+jyeffBKA7Blsb2/D6XRib2+P2R40plAodGzeU6F+YsgoGUBnLIiHKKQAydrSaDRYWVlh5be1tXXMolLWSSB3lbKcqB7CPLbDSTJPGSp5yeSmk6WoZBsoXfZ5fcwuAsoF5DRMWMlTJtyuXC5z9B0AW8VUlGU2QYKoXsTfJSuPPIxut8sshlarxbUGaEykGJXwBGUQEt2K8D9iD1SrVej1egwGA1Z4arUam5ubvF1PKpXCysoKdnZ2mEJGHoXP58P6+jovomq1GqlUiq25nZ0dfP3rX4fBYEAgEIDNZuPiP1TysdlsYmVlhbm1vV4PTqeTk1l2d3ehVquRy+U4e67VamFxcZEXlna7jZ2dHUQiEWYMEGxjNpt59w5iEVDwjIr8kLFAi/Lu7i6WlpaYn63T6RgeofKcZEUSB3g4HOLw8BCZTIZrbVBB8/X1dYbqBoMBkskkfD4fL8ZUZ4EWLaojcuPGDUSjUeZS6/V6NJtNzljU6XTHUprX1taQSCTg9Xqxs7PDc4LOS+naCwsLzGO+c+cOSqUSl7ik+1+r1XihINooJRUB4ESWhy3vewWsVF7KnRFICa2vr3M7YgcQnkiVpOglIFI//X0areUkZai0iJU1D8hKp6j4PBobWSP3KkGptFpJlP3MKmYKwlANBbPZjFKpxAtFMBhk3Jd2Du73+zCbzexOm0wmTrAgSlmv12NlrVT6So/E6XSyoqcFkvBkiuQXCgU+Ny2GtEUO0ZFoE0a3283JAYQF6vV6LldoNpuxtbXFpRK9Xi+nxabTabZiXS4XcrkcGo0Gdnd3YTQa4fV6OeOKEkxo52SCOqhGRLfbZXghGo0CAGOpRIekAKTVakUgEGBeNAW+yGjodDpskdOOIVR4JxQKcSIHcbgJFiIeN9VOJkpgq9XC7du3sbGxgddff50TJWiPN5VKxeU7k8kk7HY7NBoN76WWSCQAgGt+tFot5PN5eL1eZktQIC8ajSKTyfB8oFoi4/GY4QXiQ5PXRHAB4fHJZJLnGT0vguroHSQFe3h4yB7ibPYqFZl6mPK+V8DAfGuQvqPKXsp0WZoE9XodTzzxBNPWiNCurHEwT+ZZp8pzK61Z4PiGlMo91U5iW8xaxMDxEpR0HCkcZT/EWZ3FsJXQBF3vZDLBeDxGvV5HJBIBIO+Qu7a2xjjf5uYmfD4fW4Amk4kDYJRYQLtokLIFwMqlUqlwsXWr1cpb5ej1et709Mknn0SlUsFgMIBOp+NSoQQfpdNpjrovLCwAAFfOIkxYuRMJKXytVsvBpv39fdy+fZu3Qm82m+j1emg0GrBarYjFYlzXl6ztVquFwWCAV155ha1/qpbmcDjg8/nYMotEIojFYrznIJXZBICdnR3Gnv1+P5LJJOOYxIAgxTsajTAej5nnPBqNcHBwgEKhgFqtxgk5sVgMWq2W6zxQxbmjoyOGL6rVKu8UQfgvVZCjfdTIq7h06RLXwF5eXmbYiM7z5ptvIp1Os+L0+XxYWFjgTUPb7TZXgqNA397eHkMOH/jAB3Dr1i2mplWrVfT7fS5NSVmOhUIBsViMn40kSbwtktJLIo6/Mgh+RkP7FhPCWY1GI9cjJeuMlM+dO3d4qxTKk1dar/OoZSexFOZZs8BbZRnJaqWUaZPp5Fq+J5WgpKAD9UGpyEajkbFSstpICZOSVtYYJiuY6jBQcIiKuBPh3mq1IpPJwO/3M5auvBfkOZCSVTI5CKejgi7FYpG3xqGtoZTV5WhbomAwyPjpZDLBwcEB85CpKLrP54Pb7WZIg66L7km324Xf70c4HGZWQTgchlqtxnPPPQez2YxsNst1IZrNJp555hmuPaFUeNlsFqlUCi6XC9FolEubarVabGxsYHt7m11+lUrFypWqq6nVaiwuLkIIwTtOEJTz2GOPIZ1Oo9vtsjdGhYdardaxmrd7e3scZCwWi8wWoCwzg8EAn8/HpSTj8Thj7x/60IeY02s2m5HJZHgnY6qbQYVsaEsuj8cDo9HIqcIEMySTSUQiEfj9fq50trm5iU6ng/39fa4PQV4m7RG3sbHB9DmtVss0QFrEiNpI85d2VFHu4E2LhHIO0rN/FHKmgHG6+02KhyxhAHC5XABk19zhcLAVogwenQQ/zLNO6TzzrFngOFWN3E0AnFp6krVNcARhqMTjJSVLhdaVilav16NWq8FoNB7b/ZiUoxJPpkAMKVbC2JRBM61Wi2g0ynhfOp3GaDTiJALKmHM6nceSX2aTV6gQuVqt5m3fifZEKc2Ej9JYySVdXFzk7LZOp4NYLIbhcMiQE1037eSh1WoRDocRi8WgUqng9XrZTZ9MJkgkEpy0sbGxwUWW9vb2eHGqVqvw+XzI5/OIRqOMI6+urnIBHUmScPv2bdy6dQsLCwsYj8dcMCYUCqFer+PixYtsiZ4/fx4WiwWVSoVpXJPJBJFIBDdu3GCYZ2NjgzmxlB5usVjw+OOPMx5P9XJp0TKZTJhMJqysfT4fLl26xAkotVoNLpcLWq2W06qJpaDTyXskLi4uYmdnB8vLy7h58ybX2L18+TJXPdvc3MTy8jLvqUixACpRSXMsHA4zF5hSpwmyopRms1neqYOSRIhK1+l0EI/HOVhHtU5opw8KuirfJWU25sOU970CViq+WSoU4aak4IjKMhgMEIvFOAsJeCt6ej/FPJSlI2mLH7K06XyzxyutYZNJ3hvs4OCAU4aV1jYpw1mog+hd4XAYANhiIqVHiwwtRIPBgOvtEs+ZFgIqJUi7eDmvlTYAACAASURBVMyOt9lscu1Y2laduJnZbJYxwF6vh0AgcCzbipSk8l7NuxdK+ISsJQqyCPHWnnV2ux0XLlzg7eiz2Sxb/VarlRcYSZK45gMF/4hX+tGPfhQ7OzvY3t5GKpXireEp4eNP/uRPEIlEsLe3h/X1dQ7IUvpzq9XC2toaW7KSJO8McnBwgFqthqOjIzz99NO8+zElAjWbTfh8Pi5/OhwOsbm5yVxjogJarVYOZlEBIIJvXnjhBYZmXn31VU5+IFgtl8vhmWeeYd632SzXD15dXcXW1hbu3LkDo9GImzdv8rG0FX0kEsHy8jJb0ouLi0yFIy55Lpc7xtDY3d1Fq9WCXq/H8vIyZzt6vV6GJQA5m7JSqXAQkjBiojfSvKDayjQvlDRHCoBS9h3h1QaDgYO3wFuFos4w4EcgZMECYPeFlCHlylMxHVJoVLxlHoZ7vw9x1uIlhaKkmc0KWdCVSoV3YFYqQFK689gYs8G+2drBpIRpkaHAG1F5lFxcst5nq8C1222GMkgpEOeS3EBl6jApG3pByDohURbimfVSlIsSlbZstVpcPP3o6Ai1Wg2JRAKXL19GLpfD4eEh0uk0FhYWeKPKdrvNVC6qN0s4Ml2TyWRCIBDA66+/zlY7pRV7vV489dRTvAVOKpXi7Dly8y9fvsxKnsZN8ArBOMRTDYVCXBSKam1QMkitVsNgMGA6G21/5XQ6kcvlsLS0dMx7UBa7B8DJEVQNjQrgd7vy9kxUknJhYYEtZ6qmRinkAJh21mw2sb+/zxY6pfiWSiWGPzQaDTKZDAqFAo6OjpDL5RCPx5FKpbhKnMfjwd7eHnubNHbieFPyBxVop3F0Op1j22Qlk0lODae07XA4jEqlwrvTUJlUJaz2KJQv8D5XwPQiEV5FEWUAjJHW63WuV0DR05OU7duppDSPOkbK914UNuWGmURVo/MrGRTz2BjKf+dlsikXGbISKdvrJIxZyQNWBrCoMDhtWa5U8OVyma/R5XIhlUpx6jEp91arxTADIEM/s9i0EIJfQKPRiJ2dHVbwer0eBwcHGI1GSKVSiEajXB+AivAcHR3xgkalCxuNBhdlr1QqzPIgq7bb7fLeatVqlTPSdDodzp8/j0QiwbV/6ZmSa6600igoZzQa0Wq1MBwOUSwW2cIzGAxYWVkBAB6HRqNBp9PhJAli4hB7olAocGU2eqbD4RA7OzsYDoccLyCXn8pK0r5qBAkQ7zuZTDJ2TEFUSkqhbYSy2SwCgQAfS4V3Ll68iHK5zIwUouAR73dxcZGTnqrVKl/H0dERtFotUqkUb1lFdYppB+pms8mBVOCt1PdcLsf74lksFk6DpvRwekdcLhcGgwFXdjvDgB+BUN660kIkC6vT6XAx8NMwXRJlKb9Zy/UkxXyv2hCniRJrJVEefz9sDDpm3pgIHiF61mw69kkwB9U2MBqNnOygPBf1DYDTaeneEneYqFCEFwNgehmVvlTS6wgOqdfrHJjrdDqIRqOsPIijq6RpUVEW4vIeHBzw/SoWi1zcnJJKAHB9EArm+Hw+LjhOuznQJqIrKysYDodc+5Z2PKbAIJXgfOKJJ9BqtXhsxMogtgk9v3K5DL/fz4kbi4uLHPCiCm+TyYSVOO0qvbe3h2q1isPDQ0SjUXi9Xq4lQdlnDocDyWQS1WqVA1tmsxn9fp83x4zH4/D7/WyIFItFZLNZuFwuxo7Jy+n1erwrMT0PjUaDarWKaDQKt9sNo9GIc+fOMWujUCgwx3c4HGI4HPKcpmy6arWKer2O1dVVDsbV63VO0SZMmLYJ6/f7TJujwC6xJyhx6lHK+1oBq1Qq3qpFqUyUpOz7WRkpm4bcopOYDSdBCko5CR+eJycpdWVm3CwbQ7kYKC1X+pdEaUkTVjt7n2ZhDlK+tPfXvHHT9SUSCdy5cweBQAAej4cDYrTwZDIZtvYAcK0DssQJplC+tFQljV46Kst47do15nm7XC6EQiEkEgkYDAZsb2/jwoUL8Hg8yGaz0Gq1KBaLbAETz5juBSBzw2mPMlLGFAikOsbEKKDymaVSibd/SqfT7FnQ5q7K+5NOp3nPM8LNqXgPFTIilsBgMMDq6ipjoP1+nzcqpS3o9Xo99Ho9vF4v7HY7e3vNZhOlUgkOhwOHh4dYX19n95x2MqYkBqphQbAEUe0IpiGogNg2xJeORqM4PDzEaDTiojvtdpuxbVpAaLGOx+N444030G63mXc+mUywt7fHsI/FYuFNEWhMVBjLarXC5/NBpVLxvoOAXEMkHA7zBp/3k6X6MOR9r4Bnkxe0Wi329vb45ZpNkZ1HK6MyeNls9ljNiFlmA1kI9yMnUdXu97rmsTGUiwGdg6xKqocAgOlDyoWDlPgszDGbMm2z2e4KzM16AGTJmEwmLsGoDLaVSiXuh1xwqilL5HqTyYRCoYBbt25x1JvgIqqh0G63oVKpEAwGcXh4iFarBbVajWw2y7S3xcVFpkO5XC4YDAbeeJSsOqI4pdNp+P1+AEAoFILf70e9XsfNmzchSRIrI7KoCYIhDJLSdM1mM6fzRqNRFItFLgpEiQ50bwKBAC9s5DHQvmdUirHT6WBjY4OVdCKR4OwzeoZEBzMajQiFQrh9+zYAsDJfXFzka1epVFhcXOT94cgir9frzL6o1Wqw2WwYDodoNps8NqfTyWnApVIJjUaDMw7JYqV7SBmLND8IRlhcXMTBwQHjz1TatNVqMSUxGo2i1WpxFTWqUUKp+p1O59hcJINGmQ4/jyb6sOV9rYCB41Yk4Xv00tGkmMeznRWlogJwbFKRolPWT7jXmO5FZ7uXzMIb5AYS5xYAb+7o9/uRy+U4lbXT6RzLCvz/2XvzKMmysz7wdzMyMjOW3LNyq8qllqyu7i5ZCOoYhAwSB2QLNIMxgkFmQLYZS4DENmyGYfHgI44PGsBgA8MID4eRPMxgFvsAFkaHxWKRkOimW0NXd5eqq6or932NjIjc4s4fkb9bN27e9959ES+2zPidU6cyIt5y3733ffe73/f7vk/vJzpHdFoat3I8Znx8XJkezPpxPI6OT9pWaQLi8VJK5eBjFjLmTuB39LIzgQxTJ1LY0KlJW2t/f79yXpLKxmsxNJdmBS40pDzNz88jFovh4cOHmJiYwObmJtbX1xUXmH1z7do1TExMYH5+HoODg6o9ZH1wQd3Z2cHg4KDSNre3t1XmNBbApC2bbdzY2EAmk8Ho6KjS+AqFgspFcnBwgMPDQwwMDKhFicJ8enpaUee4BacjdGRkRCWuWV5eVqH3TIXJclH5fB7Dw8MqQT0AJdQfPnyIoaEhTE5O4vbt27h7966i742NjeHhw4fI5/OKD86gH2Yso7Z7cnKC3t5ePPvssyrYIp1OK4oZTUT5fB77+/t4+eWX0dXVpapj6NGT7e3tKrKOId/kZZvJteqFCy+AgaKQIB+QZVQoNChU9e25Dp2VwMQppj1YNwm4arQudLYg6Ofq4apXrlxRPFzG6tORJaX0vS9fFpLgdQGrszh07jQrCVOjYd5cevWPj4/VYkPtmknHdXPH4OCgKjkOQO1Sbty4oSLcSN9iDgoWFmX13lgshvn5eYyNjWFgYABjY2PKxMCE/PS6x+NxvPzyy+q5ent7VS6M9fV11XbyV09OTrC+vo62tjYMDw+rBYe1BekQowDJZDIlW306K5k9bmpqSmnRqVQKn/vc51R1X9al+5u/+RulXU9PT2NxcRFTU1MqzJf0NZo8uDA+9dRTWF9fLyn3fv/+fcWF3tzcxFd+5VeqhZjcbDpLOV7kEt++fVtplKlUCsfHx1hcXFQl7/v7+5Wdno7HVCql+p45jx89eoShoSEcHx8jkShWDWFeYGYr5PP09/cr+mE2m1XsERbeZHsymQyy2SyWlpZUUv9GMD8ALQFc4rkHcCYnBICS3LJmknJSWbgF2tvbU3W9gCf11Vw1WjPjmt/vYZ+RhTZ1ypluA+ZE5TlmG6jFp9NplZybzAAyJXSbME0du7u7mJ+fx+TkpDI9pNNp9SJym0gtlmHDNImQNaG/+Pfu3VP2V+bNpSmFttpcLqcWQzr5+vr6kEqllEBmvgZWQ9azwDFBDdNY9vf3o6urSwmHra0tVZkjFothc3NT2UUHBwexuLioxj2VSuHRo0dq0aGmRsdgNlssEU8PPm3SR0dHePDgAUZGRiClxKuvvqocXXRIcqyYj6Gzs1P1BUPBk8mkEkBcwADgtddeU8EfU1NTePDgAVZXV9Hd3Y179+7h6aefVuwghvIyzJoRkDotUA9KGhgYUJppKpVSTlZdEZmamlK7LzqN7969q3Iy00yxsLCA+/fvAyg6ZN/whjdgf39fcXpjsZhKd8n5zLk9Pz+Pnp4eDA4OKh5wo6BxWlInmJ55k27FScfoJltVYz2Aghm4ZmdnFekecNNogxx25daFM5/RRqUjJU+n5tHZpQc8SFnMYcsENvl8HsfHx9jb24OUEqlUSm1LKVCZ53Z2dhZjY2NKiOuBIXweRkHp0YBsNzOmSVnMLctyOm9+85uxtraGkZERrK+vqyoHNJVQIDEpDQDcvXsXAHDt2jV0dHQozRcoXXyuX7+u2ssXnYEjbW1tSmCmUimV8S2Xy2Fra0vZrJnnYGVlBel0Gp/97GdVwnFGCrJcEBePdDqN4+NjzM3NIZ/Pq3PIo97Z2cHw8LCK/GPFbZpLmL60r68Py8vLqmgmqV0M6BgfH8e9e/cwPT2N5eVlzMzMKEciucW8J/MtzM7OYnJyUu0GKOB1Xi1Nd4ODg+jq6lJlnqSUeOWVVzAzM6O0ZTrH5ufnsbe3h4GBAaW0MGdzT0+Poqwxn4UQAqOjo9je3lYmIabZpBlmf38fJycnWF5eVou8ly+nxQOuEygcV1dXsbi4qBKlCCGU9kMN2Ja1Sw+dBYpkd5ZUt2m0tsH2yxHB35nk3Ey8E+YZbefwOajF8j60B7Nt1NLm5uZUCkRWKmBaSCGE2nLTtjo1NaWS1ugLh874oKkiFouVRDXp1EBqkqzFxyTn1GJJF1tcXAQA5XCiHZZCgo4cCjIGIuRyOVVVgRQ8AGpBAIqLVywWU8wNbouZNH1sbEwlY2cEGAXN8PCwSt24vLyMyclJ1c9HR0dYWlrC3Nwcuru7MTQ0pMKu7927h+vXr+PRo0cYGxtTBWTn5+cxNTWlIsVYNLW9vR0bGxsqreP169cVb5aJ0D/3uc/h4OAA9+7dw+3btxGPxzE1NYV8Pq/q0dFEMjc3pxaT9vZ2XL16VVU30Wsgkq+bzWYhRDHZEullXKh2dnaQzWaVMCe3m2khqUGTtUFGiZQSX/iFX6jswplMRtHMyPPlYnPz5k21o9HNY9y92uZ/OYpNFGgJ4FOQRiSEwEsvvYRr164pJ4KZpJzHU2joobN6kITN3OA12EGON24D6SAz00u6wCb0geJW/fj4WLWZAgl4oskwdwTJ9qQQpdNpTE1N4dVXX1XChFQwMhYAqCg6JrvRQW/56uoqbt26hUwmg5GREaVxk2XBRW5/fx/T09NKIFPY6dFPDGpgzmZqvrRv7u7uIpVKKZtoNpvF4eGhqmjBJOA0N3Dbn81msbCwoNIxUsvf2tpSEZJkgMzMzKg5wmQ8uumF+S12dnbQ2dmJl156CcfHxyqJDmu+3bp1S1XeYDJ8Ok91WhUT8TBAg6kxd3Z21M5hcnISyWSxjt0b3vAGFYXGtJyHh4cqx3VHR4dKgL+3t6eiQknpAqCCWshUYBHPvb09bG9v49atW4pSRpbCzMwMCoWC4ubSZ5BKpbC9vY2hoSEcHR1BSqm0XQpcOupmZ2fVwspUn7FYDDdv3sTo6KjaNfFd8dp9Bik+1UZLAJ+CXuWXXnpJZd7i1s7MGxoEW5AE4J8+EvA2U/A8UnLKceqZ19MFOO2ujLTSQ4FpSzNt4/RaJ5NJ5PN53L59W9kdFxcXVVTT5cuXS3YHNHHwxacjb2VlBfF4XFGaGILL2mkMB2cWNtqwh4aGFEVNCKEi3R48eIBsNoupqSnMz88DgKoY3NnZieHhYWWHnZubU0l2qFn39/cDKFZzZtWJtbU1LC4uIh6Po6OjA4VCQZkAWAiT5h4uFIuLiyr0Nx6PY3V1VWXsmpiYUE6keDyOoaEhpd339fWpKDBeZ/o0fJcViZeXl1VWuI2NDdUO2qkPDw8VO4KleZhFjSHjN27cQDweV1XBOQeYzCcWiyGVSqm8E1yAFhcXce3aNbS1tSkTAZMv3b17FycnJ6qM0v3791WI+djYGJLJpFqkyNWdnJzE9evX8dnPflbleaAQZfXj0dFRZZq5desW2tvblW0/mUxiZGQEx8fHyGazOD4+xvz8fIlpxvYeCPGk+ky5jKNKUN8wkAbD8PAwrl27hrGxMZVAht7q9fV1ZDIZdSztowx3NAfONpDUVGgjJTeR2y+X83Snnh5E4gedwcEXj4EB1CIvX76MeDxe4lDRFwbdqzw8PIyZmRnlzWfQAWlX3d3digtLmyYAFd22t7endg7MUDU6OqrCYo+Pj9Hd3a1yNVAAUosj9AQvtMmOj4+jp6cH09PTSpjQ9DA7O6u4qIlEMf3m/Py8ciR2d3djYGBA8Wbz+TyWl5eRTqeVltrV1YXd3V0sLS2pf/39/cqxxWxzh4eHePTokQpX3tnZQS6XU6kVmWuhp6dHJXJPpVKKhpVOpxWjg9v+QqGA0dFRjI2NobOzEzdu3MDnf/7nY2FhAYVCQQknOoBnZmaUcKPTrqenBw8fPoSUUlVL5oKXyWSwtLSEzs5OpRx0d3ejs7NThZeTzsWQ4kQigdHRUbXwkafc3t6Ow8NDxU7o6OhQ0XfMUMZgCubNWF5eVmN7dHSkxm1ubk7VimM+YVL1ksmkYvGcnJwouiMdmKQImqBmvbq6WpJwq9ZoacAaKFxodqD9aWNjA3t7eyrqSKfb2Gy1fgJRt8UyssdFizWzgtlyMdhgptrklpjlf3gNL61d7xvzs56siM9A88LVq1dV/7GNDBlmNjS9jFMy+SS3MbWp9vZ27Ozs4OTkRG3Z9ZSD7I9YLKZqsDHhTXd3t1ocAeCFF15AJpNBR0eHIuJzZ8PgAWrk/f39qhLF8vIylpaWVOisbnIgP5fPzZpzzJs7NDSEubk5lWqTgpR9zjpt8XhctZv2ZhbpHBwcVD4ILhypVAoTExNYWFhQjrxkspghb3R0FPl8HleuXEGhUFAmjenTMGKdk97R0aGS5fNzMplUXOlsNquckDdv3sT29jaeeuopZVKg2Y2l3rlLYFWUq1ev4ujoCJcuXcLw8LBKsckyQly0+vv7lU15YWEB09PT6OnpUXZ/RiWSwaLPJzrqWCiAv5HTTy1ahxl0xb6uR2BGSwAbMAUbULRR0qZJLq2eDIZwNebznLDBFnp6TAoPv3vaIvEIOg51u7aLJq07FKnFA1DOQT1hkU4BY5SXlMUK01JK3L9/H8fHxyq3LG3EQLG80N27d7G3t1eSCIZMC33BIseV7TCTrOhOvZ2dHdy4cUO1Rz9WD6fOZDLY3d3F8PCwst3SXMHELmtra0rbpylHCKG0yqOjI0xPT6tClgMDA4qfTGrjpUuXVBa17e1tpeXSWUW78/HxMRYWFlSejL6+PlXNg1S5RKJYl+/1119HPp/H9evXsbOzg7GxMbXgMvLx1q1bKtvY1tYWRkZGsLu7q0KK9TGnMCPdkjXXtre3sbS0hJOTE5VPmXXsbt26pRYMBj0UCgW89tprmJiYUMnxE4mEcqTt7+9jdHQUqVQKly5dUs9EEwkX3Ewmo6iezFTH95Hg+LKMkw597tL3oys3tURLAKM0JwJQGvUFQOUn5Xe20ORyjPlhgi14fdMe6ndP3bHHl5SajavpgsfYktaz/XoRUb0/yAnmRKcjTNcWWVX6/v37EEJgbGwMsVgM+/v7OD4+Vk6fnZ0dLC8vq5daZzQATxYnnSqojydzCjBIgsKeIdi5XLFMEPNB0MmmPw8rKzMIhf8zeowaMjXakZERnJycIJ1OK5YGq0YDUFQwlm+/fv26MhUwURTNAuzj119/XUWJ8X50oAHA66+/ruzvuVyxMsf6+roSgkxwRAcZqW+Dg4OYmppSQm99fR2rq6sqi1tHRwdGR0ext7eHZDKJhw8fKhsxk7pTc2XkX6FQUCyRTCaDg4MDxXnu7e0tiZ7k7oyCkOObTqcxMzOD2dlZbG9vqyIEsVhM1aNjQI5uPgtyvpFdk81mVR7leuDCC2A9AY+eC0HXgvXtv5lDlwhiMXiZCVy3PHQWUGAxlj1Ii7blGGZYKx1hQZxjbgXNpPV6tBlfeN6D/cEySGtrayW2tkKhoGzqjA4DoPK3MmKP92lvb8eLL76ImZkZ5biifY82W0Y5cVHQdwZ6Bi4yHxKJhHLAcQHr7OxUwm1ycrLEYUnHHO3KsVhMlf9h3gUGDczNzSGZTOL27dvK4QcUKYpbW1s4OTlRbIdcLqe0aC6OLATKkOG5uTnF3z04OFDbd5ZdIu+YQSlra2sAoKqW7O3tYXd3FwsLC8rcsbm5qZ6VdnwAyuexvLyMRCKBvb09jI+PIx6Po6enB/Pz84rfPDU1pWz4ZC5wZ/b48WM8fPhQhVxfvnxZ5YdmrguODwWnfj4Xdfo9WPttbGwMJycnmJqaws7ODl577bUzWrCLcmOas2ptfgBaAljZgpaWlpQnnYUpSSzXt/d+A+v1W1Q8QxvFLWii6ZqoHu7rVwnAps3bzA28FnO3mpoIXwpW8dWjnZgXQHf4UVNl7H8yWcxhUCgUcPfuXYyNjamACwqQ4eHhkoxt+svLBDHsAz1MWjcfMTEPqXZMNUm2Cbfg3MYznJkacjZbLB7KhD101NHWOjU1hWQyqaqE0Gk1OjqKF198EQCUXZn2+VQqhaeeegr3799XnON4PK4Kd+rafSwWQy6XKzGZ6EU9SRVk3zNqj7xcfeeyv7+P+/fvK7NNR0cH+vv7MT09jUSiWBuRuRX6+vpU2Sbuzsgjp6Z7dHRUEhiil4U3I0713ZnJfmFABkOwec9EIqFCmc257CJQowj5rwQXXgBTiOi5EDjxlpeXVaJp14F1oZ6FpYzpx+tVOLzuaf6mE9Ip2Lyi4vRzdM1aD5rQv9e1Ntt1qD2TB0wNNJfLYWdnR2maPF/fkup0rt7eXhwcHODatWtIp9NYX19Xnm9SjZingEEUzLFAYWxbJAYHB5XtkTmHKZAvXbqkno/CYmVlBa+//roSRkzszSTqrFLS0dGhsqkBUDkWOjo6VMLyTCajShYdHBwoZx0dfdvb27h27RpSqZTKLUx76/LyMnZ3d3Hjxg0MDg4qoc1Mcuvr60ilUujp6cHt27dVnmE+b09PDzo6OhCLxXDlyhVlZuOCWigUMD4+jmvXrqmacbTrM7JvbGwMS0tLGB8fV7Z/fcEjb51Jleh30B2+OqvFFIbc7dERp3PxeQ2GGicSCWVaCot6CV+gJYCtTjd9y8lVvdxBCjJN+MFLc/YKwnANp/SLijOPMc0suiDWq4nwO9MubmNr8P/r16+r2H09U5z+3Oy7K1euqCgtAJiYmFAmAr19tAtz0aN90y/AhVFjY2NjEEKoar+0PxOZTEYtGjRXPXjwAJcvX8alS5eURqz/Ttvma6+9psoHTU5OqppoAFRVYUaSsS/JUuBixIxsjx8/LsneBkA502iLllLi8PAQXV1d6OnpUQsTgywymQwePXqk8gNTmEkpFQWMSXE4fkxLefPmTaysrKi8DnoVkoGBAWXWSaVSeOtb34qVlRUIIRT9jPehgNZNBxxD7vbW1taUdmtzMre1tal5VC9HWiW48AIYOOt004WG6cnXEVbguQpfPUKNTixdiNk0ai9hzeNNx6FLW/zMGmbbzGe0VQjRt/z08A8NDZ0xkVD40N5MYaIzCICic3R/f19pQLoAZIQY+193GOoCZW5uDicnJ2rrzzJFBwcH6lh6+BnUwCAEltVZWFhALBZTmvPg4KAqCURvPb3tsVisRBtmcvPt7W20t7djd3dXmTQAqGxqFEgLCwvqOAZJzM3NIZFIKG2fNdcAqAVEz4Y3Pj6uKo8wURIzsK2vr6O3txexWExptPp8YAg0342VlRV0dHSojHaMkKOSwIognZ2dKukSn585JphNjtGGnAf7+/tYXl5WxUL1+cXjTk5OAODMIk+Hqy2plD6P66n9Ai0B7AkOpleNtrB2XdeBNrU/Mgd0nqKpUfuZOSrRwL3ap+fxpQ1Ut6ky4EKvpKzvMNhW2ij19tEhReHX19dXkiby8ePHmJ2dVS8Zo7/ImSW1ju0yFz72L9vD3LgMwmA0G52FbJMQAj09PXj8+DGuX78OKSWGh4eV1pnJZDA3N6ccjOQHk9NMnnJ7ezseP36MRCKB5eVl9PX1qT6bm5vD+Pi4WhD4DFyIuru7VQJ2bvn5/GRWMIsYMTAwoMbj5OREBV/ogvTg4AAvvPCCyvPR09OjhJ7OYqEN/PLly1hZWVGFLvf39zE3N4cbN26oRSyXy+H69evKDMOKxslkEgsLC0ilUiqCkUl99MWYXOPOzk689tpreNOb3lSys6JwZ9J4fedEzj5tw7y2/p7WM/+DjpYANmBuk23Cy8avtVW6CLvCmtcl99XUXk1tM0jI6vbUSqDnv6BQ6uvrK2kbX1SzMoY+4bu6ujA7O6uCOCYmJpSmy4xs1AIZVEBqFh1HzHA2MDCAhYUFpYVzHCg8+ZlMDDrV2E/T09OKeUFtsre3F4eHhyX2Smre7e3tqhLDyMiIil575ZVXlH2VAu7w8FAlBed1FhcXVVY2lixaXV3F3t6e4tZSsxdCKCrZ4eGhyuY1Pj6uHIkUrrSvMvAhmUziwYMH+MxnPgMAqnbd2NiYcmJOTExgaWkJL7zwAtLpNB4+fFgSPchSQFyMqGFnMhk8fPgQnZ2dKvCEC2UsFkNbW5sK0aaA0xdoCn/2M00kZMkcHByoGvnW2gAAIABJREFUZ3v48KFK08n3iQsktXUen8sVo/BYKWRhYUGVJ9IXY87feuV/0NESwBp0SprOfvDj13pVurDxZoNgClI6zLxobzq8ovLKSdoTBGqOJO/r5YeA0pL1tC2aC8vAwIBqH3P9UsgeHBwAKC4crLOXyWSwtraGnZ0djIyMqJwV6XQaly5dOmNWsWk4QpRW88hmi3XROjs7MT8/r5gSDLww7ZIbGxtKQ6OmzHOuXr2q0j/Sy89oLraru7tbsVh6enpUkplEIqE04enpacRiMXXP7e1tVUL9/v37qjTP2NgYgGIUIDO+caGVUmJpaQnPPfec6v+9vT3cunVL5bBYXFyEEEItCIeHh+jv71dZz8hIICOEY848E08//bRKjk7WUCKRUBGMk5OTJTZqfWxox+/q6lKmFvMeZJswsMP0w1AIU4DrObcZ8cfKIZxLuiJAe3YUu8JK0BLAp+CqGI/Hsby8rEI0bUKNWihpSuZKqgsckzcbBFvIsZeGbcIUQLS19vf3B670Ltq6EE8I7qSKUbsDSu2+1AbpqDMXFl3L0xknQDGHAcObmU+A9sv29nZ0d3eXVFw2+4aC3dw50FnIhECbm5sqqQ2TwFP70xMwcW5Q61pbW1PmCibr6erqwtjYmLJV615+vW/5G7/r6+vD7u4urly5otI96mae8fFxldBndnZW9ScX/tHRUWxsbCi2Dk0Zq6urGBwcxMLCAp599lmVHpUUwO7ubjx+/FilcLxy5QpGR0fR29urOMak3qVSqRLKGB1+TDN68+ZNZDIZ9Pf3Y21tTY3dU089VVIVW59HjICjtk6TFh2nfAe6u7vP+F/4frFoKY/lu8OkRqYNWH8vWbqpVRW5AWAa9I+Pj3Hv3j2MjIyUZAYztSquujb+oY0366oJ61orgNAarO54o2PIy0PsqiWbC48Ze09nGO2+1LK4vTQ5n7pmvL29rRgnbINeB42ming8DiEErly5ohLOePWfnrqT9ySfmRp3R0cHdnd3lZBcW1vDnTt3VC5evW/I6BgaGsK1a9dQKBRUiR1qxLlcTpkraJOkDZ/ziNpXLpdTidxZNHJhYUFVXZGymDyIeW4PDw8xNTWlSu4wQOPo6Ej1XWdnJz73uc+pxDXxeBxPP/00nn76aRWZRvs4qWhbW1u4fv06enp6VOJ0mhr0HYs+L1huiY60bDargmYWFxfVvCcVzpzzrArS29t7hm/PhU4vJe+3A7X9BjxZlL3O4z3qjQsvgE2D/ujoqLLbMVSVwiaM3ciLN+vaHr14JgVpOaYMnSJmlrl31ZJt1ZR1yhg1VNojn332WQDFfA5LS0vKlq0vArQVM8UmBZR+b5pgKIiSySRu3LhxRvjqGib7T0/daT4D+4OON9bzy+WKSWmWlpaUKYHb8KOjIxUEwQgtcluXlpaUOYYVMbgAM+BFz1WxuLiIo6MjFTnW1dWl7JWbm5sYHBxU84Y5Jfr6+pTT6+mnn1bhy9RQ2bdkeqTTaUxOTqrS7QDU7oQLEdN98lydujcwMKB2BczVwPHRKXPM4kaOLqtYM6jJxN7eHh49eqQCTwAoR50etZnNZkvMgOYOrdwACltkaD1x4QUwbUkkfXOyUyvTV1Iv9oGXhmtqjK7t0Vd3ACqcl2YMF3OByb81nYZ08JhasnldrwVBZy3wukKIkixytKf29/efMcXYntPGMCEdiVQn3UFqHqtfV2dqmLXqWHiV9yT9SYhiJYz29nYsLy/jqaeeUoKQ21WaEHhv2n1jsZgqO0S+qx7wAkAxPLLZLFZXVzE0NKQi8BKJBFZXV88IVdo0mQiH6TpPTk7UDkOfe7S9ctzYH/rz87eNjQ1ks1mVuUyfo0xkr9vLqTmyPNP6+roSZPSDdHd34+bNm2feB5oCyMt+7bXXcOPGDezs7CjzCdtJRxozu/E8c4dWju3Wj9lUD1x4AQycDfFNpZ5kYzLtd2EpXqaAKEd4UouiILZNRhts7dSdhqaWbGuX7Rl1W64eKJHNFkvRUMjyeZeWliClVOXCTTaHrp1T6FDg0wOfy+WUo8mLjaL/Rl4vtTw96bbeN8AT0wSPoRC4d+8eOjo6cP36dVX3jtFyQgiVzxeAcqIdHh6qGoLUkGlKicfj2N7eRk9PDyYmJtRCtbKyomqYcZwnJyfx1FNPqbZyp0GtXa9AQSeoHvhCDVLPnEcByn5hCSqmj+zs7EQ+n8fExETJzi2ZTGJjY0OFBsfjcRVAMz09XdJ+2s9104OZb6WjowNvetOb0N7erpxhyWRSOWGZ3IhBUHpYeViBab5vLu9sLdESwKfgy0ItwCSFm8EENpuoCVNAlCM8yYckad9P4/aD6TTUw2tdFgS97bp3W6e42Ujv1NxYOJGCS39+U4jqaT91fi9fbr2PbLuStbU1pf1Se6VQ0B2D/MzE7LSzzszM4MGDB0gmk5ibm1NJyo+OjtDd3Y29vT2Mjo7i1VdfRSaTwdDQEK5evYrj42NV4Zf3YIAGs4KNjIwopx3nDVkIeh9y7nEOAShJ9cn5NjY2hnQ6XeIA5SLG0vDj4+Po7u4uCcJhn21ubioGBPnLtF2zCsYb3/hGtUtMp9OYm5tTnGEuuouLi0rw68KX7adzmwVNaXKiCcyMYCTFkO/hxsbGmVSZQfDi+tY7/4OOlgA+hU1T9dKu/GyiOkyjf9itD4Ue65zRoVTu6k2NdW1tzSoIg9phllKybf+9np82ViZr0YWBLkRpT2Y+B2qbutDXYb5MFHTxeFxlAjs+PlaCRQ8QYV+sra3h1VdfRV9fn8rR29/fj83NTfT392NsbAwrKytK+FKrPjw8xOTkpKr2zPDiZDKpsr8x2TvLIcViMczMzKg6e6yewYWMASgbGxs4OTnB9va2ooLRoUdGx8nJCZaWlrC+vo6jo6OSwJfOzk5sbGyUZGyj7TyVSqmCo294wxuUGeTBgwclczqTyajaetPT02qXODIyosxCwJNSTyYnnOD40pZumsaY65hBJPrOLZPJKLaJaX4KmrN+PptGEL5ASwAD8B4svyCMeDzu5CTTBUSQ8PTaLgEooV25Cm+ve7AyhetC4Gf/tgWj6JowFzPaG9l+XfjrTiSaWcjX1W2hQUwN0suYde3KlSuqqCaAMwEi+rNx+z05OanoUYwGoyDVaXfk/j5+/BjJZLGiNmlox8fHqowSTQ6zs7MqGosMGtP8Qzs0+w9ACeOE35PXu729rSLKJiYmsLOzo0xBBwcHiqVBIcy2m6wf7rJ6e3sVdzmRSKgCmbw/d4kU/nNzc0pIDw8PKxYLn0E3YzCjXGdnZ8lukruFnp4eFcatL/IcM7IzbHROM3jKa842IoQ+2BcNd+7ckc899xwA7+2KTZCtrq6q1HipVKqEahSkUXoJxqDKFlFsvSgIyfjQObwuMNthC1wBUNIfyWRSMSRItB8fH1fhxaadXadusQ4d8KR8EyPUbPZF3psv3NTUFICzzj2S+nmeEEJF39EkoPeL7QWnTXZlZQXAkzzGQ0NDiiu8u7uLZ599VtVHo/bMar6689EcG57DHMbk1hLkM+sht6T7UdDxt8XFRfT29iIejyvWh876WV5eVo60Z555RqW3PD4+VnQ82mdpV+aYSCkxODiI7u5u9Tv7hrkemFdjf79YLfvmzZtIp9PqPWKeYc5XXkfvZ17Lb/x1/4PXnC0XQojnpZR3Kr6QgZYGfArdlqnDxgrg1pYrPrd8piZog5dW6rpdCppQtmvxGrpG7SV8/a5v0+5Nu3KhUDiTa1hnAzBcls5AM+pQ14b1+9kq1+q7kQcPHihebyKRUNFxZnt1TZtCimYCKWVJ8iWvcdDve3h4iEKhoKpj6EEfg4ODJcnY9XLvFBp6uC5t37FYDLOzsypkWQiB4+NjpYEK8SQKjHbRjY0NZXag9slEPHrEmBBCHUfWD3NckJUBQGVq0+vRLS8vo7u7G7OzswCK9mdW1DAZF8fHxyrKbXBwUPWr/p7QWbm/v6+SAVHg6o5c9qttN8rETQ8fPlS13UxHeSOjJYDhzVTQV2LgiXAyo7o4ObzCkoPgul2yZRgLupYZZOEVsswtcNA2Xz8eQAkTguYNtpHPYgpV9pPumNFzCptty2azislg0pD4jNzi9vT0nIkmNPtH16qSyaTiGEspFbWLv/FZdc2Kz7CysqIEIJ+TGvTa2hoODg5w7949JJNP8uDS9HB8fKxyGZBiSOG4ubmJdDqtzBnpdBrLy8uIxWKqaoVO22Lfk6HR39+vyh9tbm5ifHxc5VJgzmRq4qwSPT09XZK/2MZ0IReZNt/t7W0kEsWE6A8ePFDMioGBAczOzqKzs1Pxn/V5S2c3hTsDWejo425EN92Z85bzguPolTI2Kg24WrjwAtjGVKADghxPajG6cDKjunRN0MaBDJoEQZ5ZrwxjftcC7JWXbWYEVyehTUjTZkinFe2V1N5MB52+SFBY6Fm/aMoxA0XIj9WvpWtIq6urJdUuzH6nuY3jzfuyvE4qVUxgzmQ/8XgcDx8+LEniTrS1tWFkZESVRGe/0oSiVwCh1scFsFAoYHZ2VtW8u3z5shKmtIFywdGLXVLLpgbM0u4M1jg+PsaDBw+Qy+VUEctcLoelpSXMzMwgl8spKt3g4CAGBgawtLSEnp4eVfhUz52g0wP14BDOD2rYZKwwYTyDS7hIsdJ4KpVSyeNZWYPlivSsa14wzT+cFwzk8XOW1zvgwgv1j8WrMygMmLx6Y2MDu7u7eP311wEAd+/exaNHj7C+vl4ipE3hRE3BTPBBCg01gaC22EDNkg4klwTxusnBK+mIvvjwhWc/uJhKyCygfTKRSKgtLF/e1dXVkpeKxwPFrSmrTvAFZCkbcka5FeZW3dYufpdOp5XdOJPJqH7X20ltiVFrpMex/fF4XEUFPnz40Np2tm9lZQUnJydntDI6Eenx5y5Jt/XS8cVFj/1Px+P169eRSCRwdHSkchvw+Vksc21tTfFmj4+Plamgvb0dOzs7yrFGGp4+z9mnjFaksOJzALAqJv39/RgaGiqpJA0Uw5NJWwOgSh9NT0+rABoA2NzcLHmX+N6wph6phnoIuW0O6vNCP4eC1jZXGxE1F8BCiPcLIR4JIfJCiOeFEF/ic+yvCSGk5d++dszbPI655domrsQcvLa2NnR3d2NjY6OE/uInBGzXiWoSUJDG4/HQjjOzTbbrUkAzwimfz1sXDN32qG9Vda2Z9cu4Hd7a2lLXMvuD1wSKTiwAKthCX9B4Tb88FbQFUstbWFjA0dGRcoiRqrWwsACgaAOnsGWodCqVUnS5XC6ntuR6HTuyX4QQ6O7uxvz8PF555RXMzs4qLZnz5+bNm5icnFRmLb0faXeenJzE3t6eCpig7fjw8BBtbW3o7e1VhSypPZOzPDc3BwAYHR1FLBbDwMAA2tvbcXJygunpafWZwj+dTuPmzZsYHh5WSXCo+fK5uXDRJMSx1oMkqIBQEEopVRu5SLE9vb29mJmZUbXjaL7Q3yXuYMg08Zqv5hw054Vtl9UIGc/8UFMThBDiGwD8PID3A/iL0///QAjxjJRy1nLKdwP4IeO7vwTwZ5ZjnwWwqX1eC9k2AE8KAw4PDytbJb93pWzpf4elwniZKyohj/udYwZS+JkhWOxSt8Xano+lxO/fv68cPl7UPjo1WVZmYGBAlQj3e2bdPq9zh5k4hqYEVkrOZrPY2dlBb2+v2urrUWN6+4UQioM7OjqKy5cvq+01a9ExSEMP0WVwC8OcgeL2nDxXLp6cS5cuXVLRcFxUdXs3TQiMdCM3mnZvjh13FENDQ3jzm9+stFmde8t5ZdZj0x1cpOLpIdv63NCdzwDUjrGtrU1F+U1OTqrE7zrThWO2tLSktGh9XHUnmy27nb6jtPkDbGikgAsv1NoG/L0Afk1K+Sunn79TCPEOAN8O4IfNg6WUOwB2+FkI8RYA1wB8s+Xaq1LK9UobaIYBm0lIKrleEIJsVlFPJNv9vBYMPYxUt8V6PR9L7VA7ZRUL1g2z5diQUpaUKvdajHQa4KVLl1TqSQpbKSXa29tVIdBcrljUUY/m0+33/J7aGO2d/f392N3dxdbWFoaGhkrs6XyOeDyOnZ2dEjOD3r80NTCvgekI1SluQFEA00F35coVZV/mvfVQYWrtFJ5cNJnxjCYMcyxpq9bHnowMUtu88u+y/+j8krIYYr68vIyJiQmVlpL31bnhtBOzIobOSaad2RxvM4mSbQ76oZGFL1CGABZC9ALYlSGlkRCiA8AXAPhp46ePA/hix8u8F8BdKeUnLb89J4ToBPAygA9KKf80TPuMtp6ZoJUY9P1su7r90I+KFjVs5hFbqDFQyrW0bem87LJ6zl865vTk3VzcbE5MvT16f9OskUqlsLm5WZLtLZVKIRaLndmt6AwM86Vlzoh4PF5Sg44ZypjjgZqmriUnk0nk83nMzMycqcjL/h0YGADwJOOXfr6tf7PZrGJizM7OKlMBFyldW9YXDc5N4IlZyCvnrZ77mvdl7TpWbCatjH2va57JZBKvvvqqotelUillPujt7VWBKjafA4U757iNMuk1R/V+KMes0IiMCCcBLIRoB/DjAD4AoBfATQAPhRA/CeB1TaP1wxCAGIAV4/sVAF/h0IZeAF8P4H8xflpCUYP+awAdKGrHfyyEeJuU8oypQgjxPgDvA1CyVdRhi/KKWjh6ZfEqZ3KVM7H0+9kYEPq1g15or3boW3xGuAkhzmzJXdrD65K+xEKcZBfobTb7wivfsV5DjFm3+vr6sLS0hNHRUaWNm+Yp3ot5LrglNyOx+By0U9q0O3rzmRh9ZGQEm5ubkFKqmnE0bXCxoD9CNxXoFD6WEqLW6PXMZPkw+dDs7Cymp6dV0IxumtE1T/Z7PB7H8fEx+vv7VWId2wLDz6yEwbHWKW62OW97J1x3lF5BQ43GiHDVgH8EwD9G0Wb7q9r3LwL4PgAuApgwNWdh+c6Gb0JRgH+05GJS3gNwT/vqU0KIaQDfD4utWEr5YQAfBoqRcLYbceB1p0olK6+lDVaBHtZmxZek3InlEiZtvgRewtck0Ovn6wJSz8fAbaT+DBTwbI+NkaGXtLEtiPrmzKuvqW2SjtbT06MymUkpsbOzoxKht7W1KX63brvUt+TUEPUdEwMSbLlndcehntby+PgYly9fVhUnlpeXMTAwgEKhoConHx8fq7ZxnrJWHwNQmD9C59OaASQjIyPY3t5WzzsxMYF8Po/NzU1Vc4/2e/1ZWSqITurt7W2lVQP2iFApn2RqY98AKAkuMaGfE7TjMuei/k7UencZBq4C+JsBvE9K+SdCiH+vff+3AJ5yvMY6gBMAo8b3wzirFdvwXgC/LaXcDDwS+DSAdzu2ywlRGPS5Kvtpu37XN50RLsnUg6DbQr3OD3p2cn51Ar35LPzbTP0JPNkymzmDddusTajb+o88XG6PE4mEol1RmOvH8HcusslkUiUjPzw8RHd3NzKZjGI/6AsJ769X/+A9c7kcDg4OMDc3h8nJyZJgC+CJ45C2bAo2PdF7Pp9XBT6Xl5dVJjKWF6J9eH19Haurq9jZ2UEul8Pk5GRJ3g1dKzX517RPk5oHQGWvSySKOTI4Lvoir49PPB7H4uIiOjs7zwh9AGcyteXzeZXa0ovV47WgB8FL2EapQEUJVwF8GcADy/dtKG77AyGlPBRCPA/g7QB+U/vp7QB+2+9cIcTfBfBGAN/j1Frg81A0TZQFnWuoa36VDJy5KocV6DZnRFAy9bDwOz/MtU1t0ISZL5YaJFCa7F03Q5jat1dEH7nEerRbZ2enygsBPCkLRHumTuIHoHJC9PT0qPwN29vb6rdkMqkWnLW1NbS1tSnuLSs4M1lNKpVSEXP6wkLHYUdHBzo6OrC1tYWxsTGlGZKmRRMOI+6uXr2qcklwMafwZDFQM2pPX7ht4dg7OzuKBkYtmzxfMwk/r6uPEStbcB6awtcMINILINicabYF3UtDdjFbeM2XRoCrAH4FwN8D8Nj4/l0AXghxv58F8FEhxGdQpJN9G4BxAL8MAEKIjwCAlPI9xnnvA3AfwCfMCwohvgfA6wDuorgYfBOArzltmzNM+x0HsdzwYvPatlU5jKnByxnhSo+rBEE2ZiGeFOsMEpz6OeZnk3caNAa2a1BgJZPFEOONjQ2MjY2pIpW6Fx94UiTTXBDIyGAkJIUVn5H9ks/nMTw8rJKSU+CR40vBSJ4tx0p3HLa3tyOdTuPo6EjZWfXgDB3UjvUote7ubty+fRtbW1tnEhyZmqQ+lrzOlStXAEDZq00HJrV1AKrNHBtG7g0MDKjzdeaDmYFOCHFmF1TO3PWz6XoJ20YTvoC7AP4ggH8vhBhFUev9aiHEUwC+BcA/dL2ZlPI3hBCDAH4UwBiAlwB8lZSSgv2MV0wI0Y2iOeFfeTAvOlBkVlwGkENREL9TSvkx13bZBjMovDgMKt0CVeKMqBSuzgubPdll8dJ3G6aTzzYGfnZovR3c8jM/AcOG9R0IUJp7lhFz5NDqnFdqvfx7aGgIa2tr2N/fx4svvoiuri6Mj4+XLJAASoSbTSNjH+fzeezu7uLo6AjxeBzd3d0lixkddF5O4ZGREVXFRQ8v13MreDFL9OoZ5jhxsUgmkyVUN3O82Te6lsx5C5xN/mTugsx76g5P8xgXm24jClsbnASwlPJ3hBDHKDrj4igKvBcBfJ2U8r+GuaGU8pcA/JLHb2+zfLcHwDP0S0r5IQAfCtMG4/wzjAdOqiidb5UKzHKcEZUirPNCFy6ui1eQk89FE7ZdU5x63VOpYpax7u7uM7scHfv7+1hdXcX29jZGRkYAQCVKB3BGCA0MDKigiJOTE7S1tWFrawuTk5MlC6QeqOHVZrIQVldX0d/fj0ePHuHq1aslO52Ojg5lyvCrxu0VfXlycnLGrMbjmBDJyxxAIWy+B/qOwRTQLlt/m2C1LVDmDqyRbbph4cwDllL+LoDfBQAhhAjLA25UmOYGhnfqybOj0jRd7VheW/5aT7RKJnqYxcsWmGD+Xs5uxBw7r+PJT2aOhnw+j2vXrqnttVmvTneyAVDcWZtNWqdvkVED2AuP0pTB8HfSz6hFMumO7dUzr8c+SiaTePz4sXJmckz18dEDJ/QxMHc/tj7XSyfpIeo6XOaNbaelmz5MZkWj2nTDoqxIuPMifAm+4NyuASjRLsLYasM6q0y6TCW0sijaZMJvogddO8xLYnvhdegCwyUZkX5d1+M6Ozuxt7eHyclJ5PN5leWLLBOG5gJPssylUinMzMzg8PCwRAPkODJ4RdfezQCEk5MTZb+9dOmSCn/X55/+7AA8tVm9nUI8Sak6PDyM/f19lYPCHB9yeMlYcMmOZ+OI6wsWxzBonth2WgBU0iP+XesdYC3gGoiRg52rKwHkAbwG4P885dg2Jcwy4mGEThjBqYeemlvFXC6nSPlR8BWjIp/b2uCSm9jrXBtczB1+1DRXmNGHFHB0Pg0PD6OtrQ3Hx8eKZ2xLwtTV1YXZ2VmVA0F38AGl0Wh63lvTidrZ2YnFxUWk02kV8qwHp3g9u86B9tttsLgl6W4UvgQXvvX1dVWxYnR0VD0jhbGL+Yi2f/05vd4LL6e33n6X99G8VrPBVQP+IQA/BuC/oMixBYAvBPBVKNqDpwH8vBCiTUr5y1E3shbQSd+AXXDYBFoYPq55vq7VsFoA+aleRSjDPE8Y+20YhMlN7AoXcwcFUNjCpjzGr5iqaQah4BgaGjoj9DlXKASz2SwmJibOMCl0u7afE5U0N9p3vZ6Dz86oQt4H8N5tSCkxPDxcwk4wf9fTgJKWptcg9KoQYruvTVM3x8vL6W1eP+h9NMe0kSLcXOEqgL8UwI8awvWXhBDfCuDvSynfJYS4C+A7cEopaybYBtFcVb22SdRigvi4tvNNT7LJuaxkZa+Wo8KLWhQFgmzBQc9kare69sVtfzweRyaTQVtb2xmnkX5f/eXXk/Dw+elUYw5fRoyZAsVMam5uoff39xGLxUqqFtvAZyebgYyNfD6vhKU5X/12Zbr2r1PiksmkyhRnCn3alL00WT63vmCZ4+WnGIT1ezRyhJsrXAXwPwDwLyzf/zGAnzn9+w8A/FQUjaolbINom7heL78rH9frfP5PjVi3e1WKqBwVtu0igJIKwVFN/CBbcJAziG2z7UoKhQL+9m//VuVw4LPoWq/OmbVpdTpHlzbPWCxmZQDoob/6oq33K38/ODg4I0RN8NmTyWLZ+6OjI2X60NurB6HYdmVeQUGEvmjonGkXTdbcpej2aI5vVIpBtZSMWsJVAG8CeCeKuXx1vBNPcvCmAOxF1K6awRxEwL5tAvy3SS6D7xW9xc9tbW2+/EfzeC/40a1cr0EEbRej3gKWw/HUI8v0XQRz6+rt6urqUqWCGNKbyWSQy+Wwvr6u8vraxp33pnNNj/wyeb560puDg4OSIA19bJjkvL+/37M/9GfWtfOhoaEzNlr23/HxMebn57G5uanScvr1r9fvFKBra2sqOZCfJusiFNlnUQhMXottbzYh7CqAfxLALwoh3grgMyg63/4ugP8eRbMDALwDwKcib2ENYApGWwVeIuw2ye9YV1uY3/FhjwkjMG0anC5E9JfQb9EIg3K0GmqupG0BOHM+BVcqVcwhwSoStGd3d3crO7zfPZLJpCrt3tHRUUJR07VA9hvLAuVyuTOOSymfJDnf3t5W23zdv+BFwxJCnLHRUgtmdWW9hpxucrBxiPXrmvOf7WTinVQqpUoc2cap0jnsCj2NZy7nnQiokeEaiPF/CCHuAfguAO9BMYPZKwDeLqX8xOkxZQdDNAJ0zcU1434lCGsLc9EMvezUQdqPfr5po9Q1NJvGm0gklJDY398PVS7JC2Gob3z5WJyR53V1dZUwDygkb926pQSKac8eHx8vEWT6PfXcBJOTk0qI6fXreJ4u5OiE0yP+9L7Xt/jU2l1pWEwnbCd1AAAgAElEQVRPaY53Op1W2cqAcE5S2/xnf5r5G8IEWbAfbQt6OdCvtbS0hI6ODghxNhFQoyNMIMZ/A/DfqtaSBoBJnPcKlYwCYTU9m2YSdE3Tlu13Ty97nl6GRq+KwReI/2w5foPgt2V00Z7MRPGmWUTn4JoBNnp/SSkxPj6OtrY2JQxdOM5+NnvdXqsLVbPvdWedmW/Bj4ZlS5JvjrcZbs3nsi0E+m/xePzM/PfK3xB292cu6H4Imh+cy7TF2/qp0VFORYw+GBnQpJSrkbWoDjC3Z7Uy6oexH7tq5l7ee97Hyw7tYs/z4psmEokzOX6DnsmVR+zVRj34QM8hYQYH9Pf3Y3Nz01eTZOkgIYrFMk2PvxD23AR+NnuTTcHfvLb95Abb8i3wGFtf8NmFeFK2yIuxwOt4zXEKyKWlJXVNHUwE5JeLI2gM9QVdN2WZ93IxVZisGbOfmgGugRjdKPJ93w17XoaY5bumQKVpIiuFy33Caua6YPR60czjXe15tv4Jm92qHB6xrY36jsBWYy6RSKgqyzZNks47PbCT6SBNDdHLcWTrD3Nx8et7gsyDfD5fQu1z2enw2U1zhtc234vuZwsOCtolhYFtDG3RoIC3I9x2Ta9+aga4asA/BeAtKNp/fx3FNJITKJYC+sHqNK028PMINwr8BKTfNi3MYqILGJvn3WyPCb/sVjoq4RHr2/LZ2WIRbVsJc69Fw2w7BSuFFqmEej5jm5AwoQuw/f19zM/Po7e3F4BbBBcXgq6uLpXUvJwwb71/gmyhfs/lZ78135WwzAMz/NkU+NzlNTu9zBWuAvi/A/DNUspPCCFOAHxKSvlRIcQcikL5/6laC6uMZhlo2wvnopG4mjd0Gp6X5z0ILvei5gqcTVFog9f2lCYF3fkV1Bbbd6ZwpvDt7OxUGpmLNkbu7fr6Ok5OTrC0tISbN2969okt0Y1L2K3f84QRhl7Pped11gVskPYaZteon6tXYTYrZVQaDdoMcBXAAwAenf69e/oZAP4cwP8edaNqiVqbHCqBi902LEwqmZTS115aKXRbdpDw9cqQlcvlVHYxl/aF0WBzudyZcjlBi7Q5Fn19fWpHYINfVKTenjDQcwBzAfGbF167Kj2SjXmC9UVC13z5DH4256DnN3NAm0yL8w5Xa/ojAKzpcg/A153+/U4A21E3qtZoFOGr2yKDwBfIViY+DPTr8AVqa2tTyYnCXDeo/aYt2+94c2EoFArqu/7+fgwNDan8C6735DbXq63Uznd3d5UQkKd5H2ymDv28RCKBo6MjXLlyBd3d3ao4pt/x5tiVO4Zmv3Z1dXmmhtTPsT2XyWFub29XHOaNjQ2lpfMZmJKTAt9khNj623x+Cl/6EuLxeMPvSKOCcHnphRA/COBISvlvhBB/H8W8wAUAnQB+QEr5s9VtZnVw584d+dxzz9W7GQDczAm2LWZU0T+mM4Zwvbarg8bGfvB6Br14JiO/AJy5T1AfeNHXvK4RlqFB+Hn1/Y73amcYhElt6ncfCloAyjzQ19eHWCymNFbdNKDfh6kvbTRB/Xuv5w/6vlxEcT0hxPNSyjsRNUnBNRDjQ9rfHxdC3EYxEu6+lPKvo25UPVDPMEYXc0IYh0k5MJ1T5UTMBQWJMDSYxTBtz6WPA6lZhUJBBUGYOTfKoSvZ6Gy6LdPVoVgpojQp2UxpDM82nWZe99HZKbFYDEIIdHR04OjoCJ2dncrsY84VcqLN0lHUpFlxw7xfELPBBUFKSaNnSys3IftrKOYAPheo9yDZ7HGmRmp7mcqBy0ITVhj4sTSAsyk7NzY21LF6JBdtjzp9i6HDwFnnVBjPvP7S005aKBTUttrG/w3qN5vWTG0vyMZtizqs1CFsCl89PFt/ftt9vNgp1GA3NzchpVQLpwmTI+5XccOrD8LCKz2svphG9d5UC6484G8FsCKl/M+nn38RwD8H8CqAd50K5KZEVM6sSuGX4MbrZQoL14WmHGHg5czUFw864AAojUpP+m3L9evnnAryzJsaNfuATqqDgwNlY/bi/3r1m/6dlBILCwtIp9PIZDLo7u4uceIFjYNX0Ea50G3lDM/Wofep2ZdAKTslkfCuFGPC3Gl48Y1tfVDuM+p5SIBSWinnXqXvTTXh6oT7Ppw624QQbwHwTwC8F8ADFAM0mhZRObP84Opcs20R9VwH1M7KbYPNGeWFIMeTV/tN8AVYWFiAEELZctnf6XQag4ODim1gcx7ZhLqtneYz0p65v79/pg8ODg6Us2poaAiXLl0qaRfHwtZv5sufy+XQ3t6Oubk5tLe3Y29vr8SJp8PkvpptNDVE87m9xs3LyeUlLEmZ4331vtQXDTpjXR2zXAT5TDZzQ9i56HUf5vvgHNOfmwt6pe9NteFqgpgA8PD0768G8NtSyo8IIZ7HOcgPUU0qWtiV3tTqvCrghkU5Wm1UmhhLLPX19SGfz1tzxAL2JOam0wx44oQzA2f0Z6Qma2rUeh8w8lG/P/vcVuRS7zd+l0wmIaXE5uYmJicnVYFOWxkjL+6rbfdlcxx68bPDZtWTsjS5UJBd1qYx+znQvHaUJqe4kjmtFwNlLgidzsf78PtGhasA3gMwCGAWwNsB/Nzp93kA3lyXJoA+Kapx7XLMG9VaEOoRZk1BNTAwYLUDEvoLbXOSUdsEoEo32ey11IQ3Njawvr4OIURJPlyzD0wBySAEv6TituvoodipVMpaoULXvsl9pZAw7bFmH3hlRvObY1GMs/5+UOidnJwgFotZlQqvhT5KPwuf2asSTdCC3khwFcB/BOCXhRDPAbgF4GOn3z8D4HE1GlYL0AlTycD4ORIqcaxUwwGnX7dW8LMDEl618kxnDjUZv3wHfDlPTk4AAL29vWfu7aIVDgwMWLVYHfr3JnPCy15NLZYLh21R1B2F7AOvSLlydzZBSYTMcUkmiyWdDg8PMTs7i8nJyTNtIcxn8mKelDun9WcOqkQDuOeVqAdcBfB3oJgP4gaAd0sp10+//yIAv1mNhtUChUKhooEJS4EqB0EOuHrS51wRJPjMFyQoARCFkU3o0M5M4dLe3u7pHPTqN54bdmEOGgcKMlstOh22zHfJZFIJvSCBZ4P5vEGh7RS4+rgwJ/DQ0BD29vbO5PHw4kF7mdV0s5IrwjosK1GCagFXHvAmik438/sfibxFNURbW1vZzrcw5oVyBz3Im11r+lw1hL3XC2LTIPW/qSm72CxtAs7sN2qF+/v7qmCna2pNl37RbZamo89mqtBz8voV1/R6xqDnNc+xzWdzXPSsdybNLijowgwbL8c8YD6H61xs5HQDvgJYCPGMx087UsqFKrSnpmhrays74UctVlb9Hq4c2GqhmsK+nBfEzykFeCeq8es3XSi4jqtrBCPvqec+8FoIzDpxJt0qTD+FmSfmfLaNi2lqIYOBi4Yt6MKvj1zbVul8b0ThCwRrwC8BIEeETyABQAixjyIF7YNSymjK+NYBlQxMLVZWr3tUYwHgy2QTXtUW9mGFSlC5Hh1+NmZbvwaNq4swN69pbsH9gkj0pOUASmzHrJbhCpd5ovePqZB4PY95nlfQRZCT0HUON7opoVwECeCnPb7vA/AFAH4cxexoP+dx3LlHPSdC2AXAL1dBUAFIv8lfazu0budldJaukQXtFFz6zctebZaudxUKulmEbfQyvZjfc/vu5XQ0/3ax9/r1jwvM83SmiOnw9OsjW9u85lO1FZ56+FN8BbCU8p7Pz58WQqwD+FFcYAFcbbimUvQDBYeXjc5Fo/Sa/PUK49YDL/za4qI5udpwGbygl64PIxT0um9c5PxYBASFsB+1C4CvDdbPP1GOZul3nouQNa9FRDHfy0G95nF5xZ2e4K8BXI2iIS2cRRQRQ9Rs19fXVYpBVqXVTQ4u0U5epol4PH6mtE+1wdwFehpEr74yo+X0SC0zEs3rXn7jEGYR1BMLBTEx9HalUikMDAyULJq6bTibzXqObxDMqEdbBJ7LeX5w7aNK53s5qNd9gTKT8WgYAbAeeFQLZaFSu5dJWGeRSltAhF/OBb/2FQoFPHjwQFGX9Jc4rGkkDGx21SDbrpkUaG1tDfl8PrB8kH4vBnWUy111qXphMwv4VbjW8yB4ja/tHjat1SV1p/lctuuVg7DzPao5Vk/7ctkCWAgxAOAnAPxZdM1pwUQldi99YumEda/JWI6AF0Kgr68PQgi1LXehTRHlbv2kkewF8LcnmotRPp+HEAK9vb1Oten0a3ObHwbk9lJjdHE4eSWZYTts/Gh9fF0DX/S+0u+jRyH6ORmj3L67zvcw96wFX79cBNHQ/hpPWBA6egFMohgF9y1VaFcLGiqZFF6RVlFAd4YBKLFFVpNa5PdC+dkTzcWIC4VLbTpeuxxhoz9nmFI7uonIxd6qO76o7Zuh2jYh61VROmhXYbteFELMRfOtxhyrh0M9SAP+I4/vd1EsTfR7UsqjaJvUQtSo5sSigKeGF2Y7V87Wz/WFKifCLqp7V/qcelh0GOaG7Xxbsh22hXl+9cUkbB/VY/te7TlWSwSxIH64Vg1pobkRlLzGC3oGMhe4vlDmNl4XQOZxgJs90XZvVztkFFvcqIQH+1yvYOHSR37Xq/X2Pcw9mzYSroVSNEPehXqgEi3DdUuv930lL5QXB9rVrKCH1AbVljMRxrnJsOgwIbfm+X4BKlGlOdWzpdUaYZ2gjYiWAHZEvcsWNSrCJkcxz9VpbGGcPK52Qj14weYcDGNWCEpWE8b5aGtvkOYZRgGohdZXbiHZFp6gUh7whUA9eYK1QrkcY69qDi4go+DBgweq+rGtXeX0PbVyvcKG7Trmca7OHABO13eB2Y/kDHPxMLnLrvAbD3OBCjv+LuNSTpsvGloC2AGuL2m1UG2BX86LEsWipNPYGF1mC3Iw+76cAAO/MXQJKLCd73r9oD4w+5Fc5a2trYqFe5jnCWpnmPMvgtISBVomCEdUY0vnGgIb1kYatg218Oz7XcMWnOBl82X1YddtviloXaP8bDBTKvpd33U8zH4EngRU6JUeXPo6LD+5Us5tUH/qSeVbZgg7PAWwEOL9rheRUv5SNM2pL4JeGlcKkMtxYdMYVosIX4kgrXRR8gpO8LL5ZjIZLCwsoKenB4C9TL0Lk6GcxSpMRWk/55zt3rpgl1KWRLj5OR51gbu6uoqtrS309/djeHjY+blcbelec9DPXm4mlW/hLPw04B9zvIYE0PQCOKxmZUMYbdVFsLoIxyiI8GEyUtna6AW/a3gFJ3g9D3M/9PT0WCPXXPu+0kCKcoj/fomQvAqO2vJU6591gTs0NIStrS2kUilsbW1haGjIVxMOswCVs0DbxralAdvhKYCllGO1bEg9EaRZuSBsxI3rpK4VEb4cYeYHlxwCbHdXV1cJB9X2PPwewJnItUqCM6I2t3iZFGzJyvmbWXBU/92GQqFwRuD29/crgewnfMsZ27ALdFRz8iLgwtuAgzQrV4SddGEmdTnXKhdRaNSu12B2MkbQeUVj6cfbvnfte6/jXDRC1yTtPFYPLuE9bYlydHMDAKf509bWdkbgDg8PO2m+5Y5t2AW6kYMfGgnOAlgI0Y1iSfpJAB36b1LKD0XcrpqBL6WUEuPj4045AbwQdtJFqXWW64ArV5gFtcXlGhS+YUqqe33v2vfmcWH63c/eqV/D/OznnPNKqBMEm8ANcsBFMbaNnluh2eAkgIUQd1AsRR9DMRHPGoBhAFkASwCaVgATTF7S1tZWkdPARUOy/Vap1hkWfoInCu3F5RrlaqSVZnPT7+PX7y6asWsGMb+FxcWpZUPYjGxA5WNrG7NWsEX5cB3BnwHw2wAuAcgBeAuAKQAvAGjqysjkXUopSxJlR4kgni0nda14xi4czSja4HINcmm5BbcFJeiIktzv1+9B7fC6Bp1slY5lNXmzrm3yaoPOf24FW1QGVxPEGwG8T0pZEEKcAOiUUj4UQvwAgI8A+M2qtbDKEKI0paKf86MchLGH1spmVm0nSViNiCwB8kb1ig46M8WlL8Pe28sWby5QfgyZSrOsmWiEsHcXJ2o9dm7nDa4a8DEAVj5eRdEODADbACaiblStkUqlMDk5icnJ4mNFuaKH0W6jcqK5IEw5mTAIqxGZlKWTkxMsLy/j5OSkxD5MwerXl+VqY1528MPDQ7Uo6O1w2TEEmVD8ftOZEfWIIHONYqvFzu28R9C5asAvoFgF+T6KFTD+VyFEH4D3oFi6vulRzRW9VtptGM2pGna7cvrPpKPl83lVkt0WSeXVl1GPnX4fIYTq10oS7gD+1Sh4r0Si/DL0USDMDqmac7sRdgLVhqsG/OMANk7//lEAeRRND1MAvrUK7aoLqrmiN4Jdl6iW3a7c/qM2nk6nkUgkFFUrnU5btXQvR1bUtldd6NNO7drHXte2nW+OByPj+vv7y7pPFAizQ6qW5ltJXzcLnDRgKeWntL+XAXxZ1VpUZzQrfzEM9auadrty+89Lw63UnhsGLqWOyrWd6yYUk0FgGw8Ke69EN7XyFdQL1fZTNAoufCAG4F0httlQCfUrSlR6zUrOr0Tz9QqB9uPtusAU7F4lglxMLRdhW05EpQw1Mk3OlQfcgyLd7MtQ5P+WmC6klJO285oB521Cu1K/mlHLryaEOJu9y2tuhNV8g3YcfhF+Ya/VzPAKDKoEjf5+u2rA/xeAOwD+A4AV2CslNyUqmdB+Ga4aHc3W3koRNEZm9q6ohJ3rjsPl2ua1qomo57RL/0ctKJthwXIVwF8O4MullH9dzcbUA+VuxzlhbBmuWmgcMNDGfLl1geCVvSsqU02UOw5eK5vNYmNjA11dXaHD52stDIOuVy1B2Qx2ZFcWxGMUucDnDuXwYTlh4vE4tra2SgIH6o1GaEOjYH9/H+vr61hfXy/xpmcymTOllGwMilQqhYGBgUiEUFQvP8c3l8vh6OgICwsLyGQyzucHMWCiZh+4Rl26MFjKaUu1+O5RwVUA/88AfkoI8VQ1G1MP2LSBoIHmhDk6OrJmuKoXmjkstBxKV9DvuVyx5hkAHBwcIJFIYH9/HwsLCzg6OioRCLYXdX9/H5ubm5H0ZxQLI8c3m82iq6sLu7u76OnpQT6fd7p+lMLQFS7Xk1KeCUk3Ucncrvd76QdXE8RLADoBvCyEOAZwov8opYw2frdGKBQK2NjYKNkauZYaL6f8TDXRqPYul74x+7ySLbIZ0JDL5TA0NKRe/o2NDc/Uo/rfUfZnFFt6sz2Dg4O4fPlyqJI/7JOgUvRRO2n9rueVkN4MUmnEuR0FXDXgXwcwAuAHAfxPAN5r/HOGEOL9QohHQoi8EOJ5IcSX+Bz7NiGEtPy7ZRz3LiHEy0KIg9P//5FLWwqFwpkQU1ND8Ft5y+GqVgtRay5RwEVrMfvcNA8EHa9rceb9qFUBwObmJrLZLBKJBOLx+Jmk7iYq6U+9TVFt6W3t8QpUiQJRzx8vzVcPu85ms9Z+asS5HRVcNeAvAvBFUsr/r5KbCSG+AcDPA3g/gL84/f8PhBDPSClnfU59FsCm9nlNu+abAfwGgH8J4HcAfC2A3xRCvEVK+Wm/9rS1tZ0ZVNPL3EwrbyPRy1y1Fl1TNfMuhEm+7nc/U3N07aMoOL/VduaVQ4nTq27Uc67ofROUkL6R5naUcBXA9wBEYWb4XgC/JqX8ldPP3ymEeAeAbwfwwz7nrUop1z1++x4Afyql/MnTzz8phPiy0+//sV9j2traztTeMge60b2oJrw0jVpSitiOcvIJCCECz/ESRLog13cnlYxhOQLOXASiFB5+DirThOK6eNUTYRLSB7W3EUyBYeEqgH8AwE8LIX4IwN8CONJ/lFJmgy4ghOhAMaHPTxs/fRzAFwec/pwQohPAywA+KKX8U+23NwP4d8bxfwjgO4LadNou3++afeWtNaVIR5i+05kHrkl8bPdj6kgKvrDtqAR+Aq6a9w6qyKGjEedzFP3U6AEXXnC1AVNIfgJFU8Ce8c8FQyhW1Fgxvl8BMOpxzhKK2vG7UDQt3APwx0KIL9WOGQ1zTSHE+4QQzwkhnltbW7MdYjvH6bhGQy0oRdWgvXlp8V5t0v82U0ea13RlC5SLWtOezDEpFAoldlWX1JnNjmZO3OOqAX9lhPc0e0dYviseKOU9FIUu8SkhxDSA70cxLWY51/wwgA8DwJ07d5pnpMpA1FtO83pBqRmj0kq8rhPW3urSnijaXEsBZz4zg4Lqmc6yUoQ1JTSiacUVrtnQ/jCCe62jSF8zNdNhnNVg/fBpAO/WPi9HcM1zi2pRioBi4nq/empRODD9kuTo31P4VJIvuFnpTjY7ajKZbBhnWxiUuwA2omnFBZ4mCCHEM0KINu1vz38uN5JSHgJ4HsXKyjreDuCTIdr8eSiaJohPRXDNc41qUIqCqEFRUYe8rqN/XygUSgIm/NgWfu2Jqs312AKbdtRkMtl0tK1KTQnN8pw6/DTgl1DULFdP/5Yobu0JfpYo2nZd8LMAPiqE+AyAvwTwbQDGAfwyAAghPgIAUsr3nH7+HgCvA7gLoAPANwH4GhRtwsTPA/gzIcQPA/hPAP4Rilnb/p5jm+qKZvTcEkFaR1Raidd1UqkUEokENjc3nSobu7TnPNRzA+qjEVY6l5vZlFAu/ATw03jCt306iptJKX9DCDGIYlWNMRQF+1dJKR+fHmKmtexAkTVxGcVqzHcBvFNK+THtmp8UQrwbwAcB/ASABwC+IYgD3AholJe1EpTDVojyPrR52l5aW/+GYWSERaOZMFzvHYUSENVcblZTQrkQzeQxjBp37tyRzz33XF3uLaVUNtTDw8MzfOQWwsHGg61H/zbbohpVmPR5n8tCiOellHeivq4TDU0I8SEhxBltWQgxIIT4nagbdREQxt7YbIukX3ur9SzVskGHRaNn39JRzTDpFtzgSkP7egBfLoT4xlNqGIQQb0cxUfujajXuvMNlu3WeNKpaP0u9trPNIoCitLleNNNBVHANxHgjinzc50Uxmc6/AfBfAPwKgC/1PbMFXwRpvtUmmEd5Tb/21oss30xMhnogSo29JXzDw5UHvAvgG4UQHwTwCygmZ3+HlPJPqtm4i45qe4Wj1kiDQnHLeZZ6sESqpam75GuoBxqhDRcVzlWRhRAfQDEx+/8L4PMB/NtTk0RFGdJa8Ee1tnZRe+wpTPzaG+ZZpEcpoajgJfyqxWQIk6+hhYsDVyfc7wP4VwD+mZTyG1EUwJ8C8GkhxPdVsX0toDoaSpSOEzMPr6nlmfd1uZ6tlFBUCMrxHEUeYPN7r3wNzZa7oIVo4WoD7gbweVLK/wgUs59JKd+LYrrHf1GtxrVQXURh/wuTID3M9cxSQlEtQi626HL6JYxQJ3e5WVgDrQWienA1QbxNWkZBSvmfhRB/FXGbWqghKn35vWy75W7l9esNDQ1Fbn5xtUWH1XyDntU0vzQLa6BlKqkuXJ1wEgCEEF8M4BkUw49fllJ+Skq5XMX2tdAECEqQHlbLq7Zwcr2+q5OsXKHe6MK30SL7dDSKA7NSOAlgIcQIgN8C8BYAG6dfDwoh/gLA10kpV6vUvhaaBJU63WzXq+ZLFnTdsJpfs2i0YdCouRnOk1buagP+dwASAJ6RUl6SUl5CsU5bAsC/rVbjWmh+VJLUptwy5JWiHM7yedHITDRaZF8zJ1+3wVUA/wMA3y6lfJVfSClfAfABAO+oRsNaqD0aZTLX+yULy4Rwrf5cTVTz+o20sJy3sGdXJ1wbgEPL90dwF+ItNDAaaVtX7a2vi7Yaxk4cZCetdt820tjVAufJ3OMqPP8UwM8JIVTlidO/f+b0txaaGPXWOKOigrkgjGkjjAPOSyOrdt/We+zqhfMgfAF3AfxdKJb5eSyEuCeEeBXA49PvvqtajWuhNqjVts4mHIL4s1HfvxrCym+xqHbfnrct+UWDKw3tdSHEGwC8E8AtFCthvAzgY1LKQhXbd6FRS8dOtbd1tm1ytWlOZv9V07ThdS0pZcPQ6s4LzpPDM1AACyHiAP4IwHullL8H4Peq3qoGQL0HuR52vWpqvjZBW02B6NV/tRRWtRzDcp6n3nO8HJw3e3egCUJKeYRi8MWF0XTrSYECzp9dz2+bHGTrLefZg/qvFkKn0cew3nO8HDR6n5YDVxvw/w3gn1WzIY2CRhjk82jXC7KT2lCukPDrv1rmIW7UMWyEOV4OGrlPy4VzOkoA3yGE+AoAzwEoeSOklD8YaavqiEaJ/jmPdr2o8yv4wdZ/F6UiRxAaZY6Xg0bt03LhKoC/EEWnG1BMRamjOZbPEGiUQa73/euJKISEqfnWI69Bo45ho8zxctCMbfaCKwvizdVuSKPhPA1ysyJKIVEvrS9KR1fUTrPWHK8/XFgQ4wC+/PTYP5dSvlb1VrXQwimi9O4HCfQgARe2pFCUJo8w12pGdsNFha8APk0/+TEAPadfHQohvklK+VtVb1kLLZSBIEHl5/DzOy9sSaEoTR5hruXVrpZQbkwEsSA+COCvANwAcAXArwP46Wo3qoXKEXW142ZAud79oPNYny5MSaEoPfau1/J6jkZIFtSCHUEmiDcC+DIp5UMAEEJ8N4BtIUSflHK76q1roSzUa+tbb5Rr5w06L5vNqn9DQ0OqpFDQfaK0Ybtcy/YcjZAsqAVvBAngfgCq4oWUck8IkT39viWAGxD12vpWC35bZ9tv5Qo9r/PYB/39/Tg4OEAymQy8j96uWjvNzHYFLS6NMMYXGS4siJtCiCHtswAwI4RI8Asp5ctnT2uhHojC208BUm++qJ9m5vdbue20naf3ga3skmuba2mDDbMo1XuMLzpcBPAnjM8CwH9Fkf8rTv+PRdyuFipAJVtfU4DUiy/qp5nVWmurNDdwI2zx/drezJzgZkeQAH66Jq1oIXKUq/l6Jc2pNfw0s3pobS73KNcG2whoxDZdBPgKYCnlvVo1pIX6o9G2o36aWaNqbWFtsC1cbMVRFkIAACAASURBVITJBdHCOYVun2w0wRbk9W9EROUYbOH8o1XP7RQXlQdp44ieZ0FRr3GmOaKFFnS0BDCaMzdqEFxLqTdjWsJyUc9xPo9zrBY473PywgvgWhRNrDVcX/bzmF/VC2HHOepIwou00EWFi7BohRLAQoi0EOKNolim6FygmkKoHhMo7MtererDjYYw4xz1uDXyQteoi8FFWbScBLAQIiWE+AiAXQDPA5g4/f4XhBA/UsX21QTVEEL1mkDlvOw6v7ZcNMML4jLOtaic3Ch95bfQ1Hsn2MiLVpRw1YD/NYrVkL8YQF77/uMAvj7qRtUDUQ9wPSdQOQtKJVpfM20Vg8ahmuPGoIxG6Cu/hSbqNpZ7vYuwO3MVwP8QwHdKKf8KpRUwXgZwLfJWnRPUcwKFERyVaH3ncatYybj5PX8j9ZXXQhN1Gyu93nnVfAlXHvAlAKuW78/v0hQRmmECVRIscF4DDaII47ZdM6q+iiK3hI2fHPV4ntf5ERVcBfDzAL4KwC+efuYy9i0APhV1o1qIDq4vaiXBAq1AA/f8FGZflSNIo8wtod+bbYl6PMu53kVJIO8qgH8EwMeEELdOz/mAEOJZAG8D8NYqta2FCpHJZJDP551f1LATvlppF5sRYTQ9/laOIK1WbgmzLdXwiZTblvMMJxuwlPLPUBS0wwAWAHwtiqXp3yKl/Ez1mtdCuchkMlhYWMDR0VFV7I3lOlbOg43YC2Fsx+XaRnXbbVdXVySCspFs043UllrAOReElPJ5AN9Qxba0EBGklMjn8+jp6cHu7i4uX74cqUZTrhZWTc2mUbasrm2oxDZKKls+n1cmAxNh+qOR7LSN1JZawEkACyGSfr9LKbPRNKd5USgU0NbWGIGFnMQAcPnyZaTT6bKvZXuRy3lJqpmWsVG2rGEXgXJtrRS+Xn1ZTn80kh2/kdpSbbhqwBmU0s9MXOiE7Kurq9ja2kJ/fz+Gh4fr3RwA0Uxivxc57PWrpdk0Sr7dcheBctrq15eV9EcjCbxGaks14SqAv9L4HAfwJgD/HMCPRdqiJkOhUMDW1hZSqRS2trZU0cZGQKU0p6AXOej6pkZYDc2mEbas9VgEvPqyEfqjBXc4CWAp5R9avv59IcTnAHwTgI9E2qomQltbG/r7+5UG3CjCt1JU+iJ7aYTVEAj13rLWS+gxxWUr/3DzotKE7M8B+NUoGtLMGB4ebijNNypUYqOstUZYb2ETpm5cLWzf9e6PFtxQtsQQQnQA+ACKtLQLj/MmfIlKbJTnPZGKiaDnjDLHwkWja51XuLIg1lDqhBMA+gAcAnhPFdrVQpMjSCNsFNpYrRD1rqBl6z0fcDVB/KjxuQBgDcAnpZS2HBEttOApFBqFNlZLVENgtmy9zY9AASyEaAdwBOBjUsrl6jephfOMRqGN1QOVCEyvHcNF6bvzikDDpZTyGMAvAOisfnNaaGREYWe8qPZhotwsa+vr63XPIdxC9HA1QXwGwBsBPK5iW1poIJgaV5Rmg1QqhUQicW4dl1FCSon19XUUCgVks9kLtWO4CHAVwL8A4GeEEOMopqYsWYqllC9H3bAW6gdT2EZtNriINuAWWrDBVQD/x9P/f+n0f+5FxenfFzoU+TzBS9hGmUjcT5jTzNHS8ooQQmBoaKil/Z5TuArgp6vaihYaBl7CNiqPu58wp60TAIaGhlra8SlabIfzC18BLIT4VQDfLaW8V6P2tNAA8MszUK3rSymRzWZRKBQAoKXxGWj1w/lEkBfknwBIRHlDIcT7hRCPhBB5IcTzQogv8Tn2a4UQHxdCrAkh9oQQnxZCfLVxzD8VQkjLv64o233RUOvQYSEEkskk2tra0NbWdi6F70WOVqv2szdr3waZICJ9A4QQ3wDg5wG8H8BfnP7/B0KIZ6SUs5ZT3grgT1AMBNkE8D8C+E9CiLdJKf9cOy4L4Lp+opQyH2XbXVDv6K56379SUDMGzp/Gd5Edj9V+9mbuWxcbcJRLy/cC+DUp5a+cfv5OIcQ7AHw7gB8+c2Mpv9v46ieEEO8E8DUA/rz00PoGidR7EtT7/lHhvAle4GIHn1T72Zu9b12ImMtCiBO/fy43Ok3e8wUAPm789HEAXxyizd0AtozvEkKIx0KIeSHE7wsh3hTiehWj3olR6n3/84Rq9N1FDj6p9rM3e9+6aMDvA7Adwb2GUKSrrRjfrwD4CpcLCCE+AOAKgI9qX98D8C0APouicP5uAH8phHijlPK+5RrvQ/GZMDk5GXhPl219vROj1Pv+5wXV2EVUq9R7pbDN62qZsKr97I3Wt2HgIoB/L+KEO6aKISzfnYEQ4l0A/jcA75ZSqog8KeWnAHxKO+6TAF4E8J0AvuvMzaX8MIAPA8CdO3d87xvmhaz3JKj3/Zsd1djKVrPUeyXC0javq23COu/5oMtFkAkiyv3YOoATAKPG98M4qxWX4FT4fhTAe6SUv+t3rJTyBMVE8TPlN7W8bX29J4HL/fXnaJkqniDqrWw1zUKV5BW2tatlwqofggRwZBJFSnmIYhjz242f3g7gk54NEOJ/APAfAPxTKeVvBd1HFN+cvwNgqfzWNr9tyQb9xY0yOfh5QSqVwuDgYCQaYLXmT5CwDBKetnadx7neLPA1QUgpo86W8rMAPiqE+AyAvwTwbQDGAfwyAAghPnJ63/ecfn43iprv9wP4MyEEtedDKeXm6TH/EsBfAbgPoAdFs8PfQZFZURGqta2vB11Mf3Gz2SwAoLOzsyk9xyai7M8o+6HWRUhdzQi2drVMWPVBpTXhQkFK+RtCiEEUeb1jAF4C8FWaTdf0in0bim38udN/xCcAvO307z4UbbqjAHYAvADgS6WUn4mizVFPSL4kXV1dSKfTZ36vpnDmi0uu7Xlw2jU6/a5WTq2wNmzbb808D5oV4iLbe+7cuSOfe+65UOdUIiCllNjY2MDR0RF2d3dx+fLlEiFsy0IWxUuhX1d/MZs9cIP92dHRgcPDQwwODjbk89Sqnxt9MWpmCCGel1Leifq6NdWAmx2VTnAhBLq6urCxsYGenh7k83nlHbfZ9vL5fEUvExdXUzPS21MvRCGUGpV+pz9bLYViy4zQfGhlxHZEVJ7idDqNy5cvIx6PlwgN3RHS1dWFfD5f0b3oZMtmsw3nYInSARil4ywK6M9WD3ZBI4yvFy7ybtsLLQ3YEVFqW+l02soL1TUYIUTZ9zJf/MHBwbpoRl5k/6B8wGHb2ShCx/Zsjaih1wMt84gdLQEcAlFu8byuwe8ruVcjbM29XrgovPiNCtuztcwCzZ+voZpoCeCQqOXEqeReXi9+pbZXl/ODXrgovPhRPEs1YHu2RmtjrdEICkGjoiWAzzHMiV6phul6vssLZ34X9iVtZG25JWDOorUTsKMlgJsErpqn1zHlbgN5zbDn67l9XeH6kl6ULW2lY95oaJZ21hItFkQTwIU1EHRMOeGm+jXLOT+bzYZmO7hc9yKEzkYx5i00PloCuMHhQmVypTuFoWzZrlnp+VGi0ehnUSLKMW+hsdESwA2OIG2PW1BXjdBVW/S6Zjnnd3V1VUVLPY+aL+Cm4V+EXcBFQMsG3ATwso2ajigeE4VdkBpvJfZVhlPn83lFyWrBDS59X8n4eM2RZrIpnwe0NOAmQVBAA1+cKOyCpu23XFD4trbJ5cHVHh4WXnOkZVOuPVoCuMaISgjZtqBR2AWjtC1GuU1uCe9o4DW+LZtyfdAyQWio9vYrau6quQWNgvAeNWk+Cv5nI3N+mw1e49sKlqgPWgL4FNV+yavFXfXLJ1EuoibNV6r5XgTOby3hNb6tYInao2WCQG22X7X0WlczV0Wt4WVqaaEyBOUiaaE2aGnAiHb75WfGqLWGcV482nq/NZM5olH6v1Ha0cJZtATw/9/euUdJctX3/fObmX5M9/TO9mzvsNrV7go9YJEsC1tEibCR4ByBQYljW+JlHwJ7fEBRgMROTATGNlLiB1YsCxRsHgILAjEOr/gYE2wBsmVheZEQJJIXWdoVWrTPmZ3Xzmi6p2d6um/+uFU1NbVV3V3d1d3V0/d7Tp2ZvnXr1u/euvWt3/3d3/1dC92yVUYdHKcdWRohTi9uK8uhe4m4fCiilCNO/WGrwJggXIjSVtnMMLlTbj+tyOLNE0eXpKjNOJ0yZcTFo8CWI5FItC1HHPtDWMTRdGUIOCKEJYdml5t2QxbvyxUXAvFDVEuQO0kocVmlJiLUajWmpqao1Wot28/j3B+aRVw/IMYEESHCmDEa2Z3bHTq2G1kszi5JUWi+fnWOcogdB48CpRRDQ0Ps2rWLSqXC8vJyS/sMxr0/NEKcTVeGgCNGmAdbL2h6UIcJQxLtfAg6SSBREl2rWxh569wJm22vX3J3Pb37DIZ9tmE+6L2utxdx/oAYAu4x6gVa8XaYTk3sBL1cQR21nZcsyjq0U5Y3dobfztFxelFbRVT7DELj9ojLxKMf4jAi8YOxAccUXltnp+1wzXbMdmxpUdYhirK8q8Bsm20rcYzjDPeoplMhPPvBThw38gVDwLGGu8PEYWKn3ZcsyjpEvUDDJqdMJtMzIgm6V5QydKrfxKF/9iOMCaJNdNPm1ethVBS2tCjrEPUCDVumXtgLg+SP87Dei173z36EIeA20IuXo9edO4qXLMo6dGKBRi9WLAZ5ZcR19j4IcZcvbjAmiBbRTZtXHIbBbsTtJevE8LebdQyS3wzrtz6MBtwiuuXa0g0t2x3MvVQqkclkYjHcDWPeyWQyfaEhBsFEKBtMGAJuA51+OboxBLUJPp1OMzc3R61Wc0i4ly99mA9PP9lJ68FEKBs8GBNEm+jky9GJIajbxBDVDhpRI4xc/eD+FBW2ct0GFUYD7hCi8o6IUsv2aopuM4o9hA+j/XZK8wxj3onzKqcosVW0/G4gjqvxgmAIOASafbBRvyxRab5+5gwvwTdLvp02j4TZ5TmbzTI6OsrQ0MaArp9ewkboR2+IXqHfPlTGBNEkml0B1q0hcZSLIIL+b7W8qGBPDDZq92KxyPz8vJMnrpGvWoXxhmgO/WiOMhowjbWlMBpIN4bErXorRD1pGIdJSG+edDq9JbVF4w3RGP1ojhp4Am40ZLHJOcyD7XQksdnZWarValP2Wu/HJWqZujEJWa/d3XlqtRoLCwvUarW2tMW4mi/iKFPc0G8fqoEnYD9tqVarMTQ0dB45h3mwUXYALyGUSiWKxeKWcc9qdx892wY8Pz9PMplkbW2NiYmJTTbhZtEvbeaHuH44uo1+aoOBJ2CvhnX27FkWFhbYvn07w8PDXR/Kel8iP0LIZDKkUimGh4frltMPQ/FW9tHzw9DQ0KZn6SXfZsipX9rMD/384RhkDPwkXDabZWJigmw26wxhs9ks586dI5VKdXXiwzt5VKvVzptUEBEKhQK5XI5CodDQFh1G/m5PWkQ9aRIUbrHZSbkwbRaXyVX7mn6bfDLQGHgN2Ks55PN5FhYWyOfz5HI5xsbGmiavdkja7yUql8u+9sxmzSFhzCa9CiwU9aRJo0m6ZswZjfIEtVW7faDeM6hXdj9OPhloDDwBu1/O0dFRJicnKRQKzhC2W+Tlfonc28cE2TOjtEX3cujd6a1uWiGnVjxi2u0D9Z5BM2W3Ovlk7Ma9xcCbIOwhZ61Wc3xJw0zeRDn8s4fQY2Njm4bCQfJENdRsx880Chka3a9dv94od4Lwa6uodufwewZhyg5LpFvNX7ofMfAasD2DPjc3RzKZdFy7bDTq1FEP/5o1M0RtMmhFg+pWpLZmtfNGw/R2ZHBf722rqPqA3+q/TpkX+nnCcSth4AkYtBmiVCoxOztLJpNhZmbG0TZ27txJNptt21WqFdQjmk68PGFs3d16gZsloHY+BvWebVC53vxR9QE/c0Yn+pexG8cDA0/ANpFs376dlZUVxsfHmZqaYn19ndXVVSdPuVxu21WqHRm9iynisG1Ot2RoREDtfAwaTXyFKTeKNgi6Zyfat98WLWxFDLwN2CazSqXCxMQE6+vr5PN51tbWSKVSgNaQE4kEpVKp6y4+QXa6Zu2ajeSt1WpNXeNni+zkLrteNCK+VmzYjeyr7djGW0W372nIt7cwGrBFJF7bWzabdTSQYrHIs88+SzqdZnR0lLGxsabLjtI1zautNDN5VW9YPj09zblz58jn80xOTta9xiYG7/LnuLzAreyI0cxIohdaotFMBwcDrQHXajVHu/QSytjYGIVCwXkRxsfHqVarnDx5kuXl5fPKUkpt0qCimGFu1zuhnnb3/PPPc+zYMUSEhYUFqtVq3WvCaP5BGnSnYLd1qVQKfW0zWny3idC4hg0OBp6A67n32La3TCbD0NAQa2trjI+PUy6XzyPb48ePc/z4cYrFYmi3pHrnWx3m1yNvpRSrq6sUCgXm5+dJJBIsLCxQKpV8rykWi8zOzjI7O0sqlapbJ78Pj19aVIQclQtYXGBcwwYLA22CsEm1kXZpDwkLhYIzGef20yyVSo4t1R6iNztB5d6TLci0EcamWc9dyl3e6OgoADt37qRSqTgEtmPHjk3X2ASXSqUoFousrq4GDo/9TCZwfsCjUqkUymOh16vAuqWRRuVyZ9A/GHgCzufzTS28EBHGxsacrXxsTcvWkO3hb9BOE36wX7hKpcLc3Bx79uxhbGyspZerWXcpG7Zrnd9yZ7cWaRPc7Oys8389TxA/MnSngX8EurD18talUzbTbi7R7obLnUG8MNAEvL6+zuOPP87o6Cj79u2rq9nBhklieXmZubk5AAqFgkMAdh4bzSzisHcj3rZtG+VyGWCTy5vb77YZN6xm93Szyde73Dko+podga1cLjsfIT/4kaE3rVmNtZ5G2Ok4x43u3yl00uXOIH4YeAIeHh6mWCwyPT1NLpc7b5eJYrHIzMwMIjoKmVKKkydPUqlUGBsbC0V4fnnGxsbYs2cP5XJ5UwwIb0AeO9yin8bj1lJBm0HqBZd3X+MO31jPBzWTyTQ9zPc738g04tc+QRphtzTAXvlbN+NyZxZQbA0MNAGLCCdOnKBWq1Eul7nkkks2EY9SirNnzzI/P086nSadTjM0NMTIyAhnzpxheHiYycnJptzB3FsIecnGbdoQEcfmWi6XSSQSTE1NsWvXrroaj1tL9dMW/eyuYZbURh00x52nHqF67xtWA/STp9FoopV6dxOdkMnYlHuDgSbg4eFhDhw44MT+XVxcZPfu3U5HXF5edly07DjBo6OjVKtVXvziF5NIJDbFjfCDUoqZmRmHBINW1dn39NpmK5UK+XyeSqVSV+MJ0lJt8i+VSuTz+fNIy1tevZe7Xb/joPZpRKheU0PYCU63PPVkdJ+Lo6+zG1HKZGzKvcNAE7CtzdqLK9bX1zlz5oyz3c/p06fJ5XKsr6+TSqUcIty9e/d53hBBsHfsTaVSzuKOIC3V1rr9bLPNaChB2mIqlaJUKtX1YHCj3nnbHOKVGcJNrrnvFXZIHWaCs5FHhp9mbccECbvpaT/C2JR7i4EmYNAEuby8zMjICAsLCxSLRZ566ilyuRyTk5MMDQ1xwQUXMD8/z9TUFBdffDHpdNrRLG2PBz/YnfuCCy5gcXHR2cGikU3Ta5uF+oF53JN0Qdqie1FJq7C3a8rn887HxG2f9lsp1wxaGVI3Mis045Hhl9/2ZvH7SHqxFYbtxqbcWww0AVcqFe6//36OHDnCyMgIN9xwA4cPH2ZpaYmRkRGOHTtGoVBgenqaK6+8ktOnT/PEE08wOjpKPp933M+CXlJ780wR4cILL3T8fIM0L7edOCgOsNsjwybuepN0UdkL3ds1zc/PA5qk3PbpdDrd1j1aRdAQuhmPDDfsc257+SC4gsXRzj0oGGgCXltb4/Dhw0xNTZHNZvnOd77D5OSkM/G1tLTE/v37WV9f5/Tp0xw9epTJyUkymQznzp1jYmIicPGEUnr7eJsc672kXg+GIDvx8vKy44+7Y8eOpifp2nmxbC3P9pmen5939tBbWVlxzDK2B0cjzdFPa2w3lGTYIXQjL4NBcwXbCpp8v2KgCbhWq7Fnzx6OHj1KMpkkk8mQy+VIJBKOhruwsMB1111HIpEgnU5z6tQpAPbt20cul6NUKjEzM0MmkzmPjO3JL/dEnZtsbKJNp9OOB8PMzAzlcpnx8XGUUs7Lvby8zJEjR5zJNHu1XblcbmqSrhX4xaUFzotRa39k/Mwr9cqD9sms3hC6HWKv9/Fod9geJ8LbSpp8P6LrBCwi7wT+M3AB8APgV5VS366T/3rgbuAK4DTw35RSH2+nTBsjIyPs37+f17zmNSwuLpJMJsnlcoyOjrK0tMTevXu56qqruOKKKxxizOVyDiGOjIw4poDV1VVe9KIXbSJh7/bxbrJxEy1s2CZFxIlJbBOtPSxOJpMsLy+zurrqTBy6V+Z5J/TagZcYbfnS6fQms4v3BQ6zTLkVMvOrW5BfcZRaapgg6a0EeO8Ftpom34/oKgGLyJuAe4B3An9v/f0rEblcKXXcJ/8Lga8D9wFvAX4a+KiIzCilvtJKmW6kUikuu+wylpaWGBsbcwi1Vqs5Wunk5CQnTpwA4NJLL6VcLlMsFp0XaWVlBcBx8XL78xYKhfNsxPZEj4iwbds2lpaWnCXI2Wx2k8vYxMSEsyBjZWWF4eFh9u3bR6FQcIje6yoV1QvuJUbbxuw2ldhub828wO5JrjB2WTfq1c3P7BLV5FKYIOlRBnjvNMwEXO8h3QwwLiKPAE8opd7hSjsKfFkp9es++e8EblJKXeZK+xRwhVLq2lbKdOOqq65SuVyOWq3G3r172bt3L/l8HoADBw6Qz+fJ5/NUq1WGh4fJ5/Mkk0nm5uZYXl4mkUgwNDREpVKhUChsyl+tVhERhoeHUUptciWzfYPPnTvHtm3beMELXgDgaMrVapVisejYgd0LM3bu3OmUBzj/V6tVAObn50kmk06AeTdReN3G7PRareZLYHa6+/rZ2Vmn/B07dmyasPLGVHbfEzZ8ktPpNLlczleOehuQzszMkE6nnUhubln98rvNI/XQaMTgXsiSTqcDl2IrpZy9BdfW1tixY0ek9u5OIU4mkbhCRL6nlHpZ5OV2i4BFJAmUgF9USn3Jlf7HwI8ppa73ueYh4B+VUu9ypb0B+DyQASRsmZ7yQ1U+mUySSqWcSbVMJsPOnTu5/PLLSSQSJJNJdu/ezeLiIk8//TTj4+NcfPHFzjb3tknCJpGhoSGmp6epVqvs2rWLq6++mmw2y+OPP+4sjd6xYwcrKyuOrTeVSrG0tOTYrEWEarXK2bNnqdVqbN++nWw2uynIkE1WtseErbUXCgWKxSInT5507M2gTSd2gB53sHbY7Ipmp7sJyu2RYd/TJue5uTkWFxeZmZlhcnLS+eDYcnjLdePs2bOcOnWKWq1GoVBwPkz29d7l4428Q7x5g/J4bfbeGCBhy7PbyxBef6FTBNxNE0QBGAamPenTwA0B1+wCvuWTf8QqT8KWKSK3ALe4kqqNBLextrZWW1tbA2BxcVEB688880z10KFDa5Ysq5ZsNSBplV0DViw5K1a+GqDYaP9h69o165oUkLDy1YB1K+8qkLbyJay861Za1cpTsc6LdQ93HZV1nTt9xMpbAOY86RV0zOhVVzMMW9eLp+3s9ISrnnjyDlmy2vVZ99yv5nM/GynrfAIo+9SjkSxBz9mvPgVg1ue8/dvvnlsR7nYYdLy4E4X2wgvCq3WKT1qj/Ha61MnjW6ZS6l7gXgAReawTX7V+hGmLDZi20DDtsAEReawT5XaTgGfRGsMuT/ok52uwNqYC8q+jtTVbawlTpoGBgUEs0LUtiZRSa8D3gFd7Tr0a+IeAyw5xvinh1cBjSqlKi2UaGBgYxALdNkHcDXxORB4FHgZuBXYDHwcQkc8CKKXeauX/OPBuEfkw8Angp4CDwC82W2YD3NtmfbYSTFtswLSFhmmHDXSkLbrqhgbOoonb0IsmDgP/USn1kHXuQQCl1Ctd+a8HPsTGQow7AxZi+JZpYGBgEFd0nYANDAwMDDQGelt6AwMDg17CELCBgYFBj7ClCFhE3ikix0SkLCLfE5FXNMh/vZWvLCLPisit7ZYZB4SRWURuEpFviMiMiDwvIo+IyL/25DkoIsrn6E0A4BAI2RavDKjnAU++m0XkSRFZtf7+Qudr0j5CtsVnAtqi6MrTVHvFCSJynYh8VUROWbIebOKaK0Xk70RkxbruA+JZythyn1BKbYkDeBN61dM7gJcAHwGWgX0B+V8IFK18L7GuqwA3t1pmHI4W2uEe4H3ANcClwO1o3+pXuPIctNpql/vodV070BavRC/gudxT12FXnmvRfui/YZX5G9bvf97r+kbcFuPe5w38EPh0mPaK2wHcCPwe8Hp0GIODDfJvQ69H+CLwY8DNwPPAr0XRJ3reIBE27CPAJz1pR4EPBuS/EzjqSfsUcKjVMuNwRCEz8Cjwh67fB4HlXtetC33CJpRCnTK/AHzTk/Yt4M96Xd9O9gu0C6gCXh6mveJ8WB+ggw3y/DtgCRh1pf0mcIoNJ4aW+8SWMEFYgX6uBr7hOfUN4OUBl13rk/9+4GUikmixzJ4iQplzwIInbVREnhORkyLyNRH5iTZE7TjabIvHROSMiDwgIq/ynAvqN7HsExBZv3gH8AOllN8Cp3rt1e+4Fvi2UmrFlXY/eq3BRa48LfWJLUHA1A/0412mbGNXQH470E8rZfYabcssIu8CLgQ+50p+Gvhl4OfQi2DKwMMictn5JcQGrbTFGbTGczNwE7reD4jIda48Qf0mrn0C2uwXIjIOvAH4pOdUM+3V7wh63va5enkatu1W25Kop4F+YoSWZBaRm4E/AN6slHrOKUypQ+hl4Xa+fwD+H/Dvgf8QhcAdRJhATU+jScTGIRG5CHgP4F7Y0499AlqX+y1oAnd/lMO0V7+jHk/Uy9OwbbeKBtyJQD+tlNlrtCyzrtNX9AAACIlJREFURb6fA96qlPpqvbxKqSrwGBBnDTiq5/cIm+sZ1G/i2ieg/bZ4B/AVpdR8E3m97dXvCHresNF2LfeJLUHAygT6AVpuB0TkjcD/RE9IfLnRfSwXnB9HD0FjiQif30vZXM9DEZTZVbTTFiJyDXAV55sfguBtr37HIeAVHpfLV6PDIvzIlae1PtHrmcgIZzTfhA5E/na0K8g96FnO/db5zwKfdeW33dA+bOV/u3W91w0tsMw4Hi20w5vR7km/wmZXoglXntuBnwEuRr9g91nXXNPr+kbcFr8K/Dxag7sC+CB6GHmTK8/L0aOkXwcOWH8r9IcbWtNt4bruU8ARrBl/z7mG7RW3Axiz+vBL0W5oH7D+32ed/yDwgCv/OFrD/V9oN7Sb0F4Rbje0lvtEzxsk4sZ9J/qrtIr+4l/nOvcg8KAn//XA9638x4Bbw5QZ1yNMO1i/lc/hzvMh4DmrvLPoGd5re13PDrTFbcAz6B1M5oFvAzf6lPl64CmL0P4pzoTTaltYaTk0Sd8WUF5T7RWngw3XOe/xGev8Z4Afea65Em3TLqO1+9vxfJBa7RMmGI+BgYFBj7AlbMAGBgYG/QhDwAYGBgY9giFgAwMDgx7BELCBgYFBj2AI2MDAwKBHMARsYGBg0CMYAjboOETksIjc0Ws5/GAFm1/uwn0usgKAv6zT9zLoHxgCHkB4djuoWLuB3CUi2R7J491x44yIfFFEXtiF238BvcKv5xCRB0Xkj3zSu/KRMOg+DAEPLr4FXIAmn99Er5K6KyiziCQ6LE/Jkmc38Evo5aFfFZHhAHmSUdxUKbWilDobRVkGBmFhCHhwsaqUmlJKnVBKfR74U/S6fvdeXzeKyKMisoaOBYGI/Kxs7KN3TER+102GIjIpIn9h7Z/1nIj8cpPyKEueM0qpvwX+C3rt/aVWuUpE3iUi/9val+z3rPTrRO9jVxaRaRH5kC2PiOy0tOkPuOT7cSvv663fm7RLEbnDMpm8TUR+JCLLIvJpEUmK3lPthIjMicjdIjLkuu4tIvJd0fvqnRWRL4nInvCPpTGsPcoeEJEl636P24HQRWRYRP7EejYrInJURG7zyDpitdOCdXxIRD4mIg+68oh13Q+tcv5RRN7SifoMMrZaPGCD1rECeLXcO4FfQ6/3f15EfgZN1L+CXhu/D/g4kELHgAW9ln4/OtJcCR1H4qIW5cEj0+3A+617KYvg/godRvMgcAk6eEwNHSxlRvSmi38pIt9ExzD+M/RWMfWivl2EDj7/r4A9wFfQAYqmgNegA658EXjYOgeQtOR7Ch0A/U7rXp0ITv554HH0Pn7r6FgFZevcEHq7nDcCM1aee9EhVv/EyvMedHu9HTiMHv38EvB/Xff4HXR8g3ehY/5eC3xSRBaUUv+nA3UaTPQ6OIY5un+gSfJrrt/XoGPGfsH6/Up0gJKbPdc9BPyWJ+3n0QFbBHiRdd1Puc7vR8eivaOOPAdx7TmH3pHjEHACSFppCviI57rfRX8chjxlrQIZV9qHgWeBT1v5x+rc+w40+Y+70r6MJrOkK+1B4I/q1OmAJfOF1u+LrN8vq3ONb5k+Mi4BbwvxvH8f+Jbr9xngfa7fgv5wPGj9zlpt8ApPOR8Gvt7r/ruVDqMBDy5eaw29R9Ba5l+gd7hw4zHP76uBa0Tkva60IWAUrSG+BK19PmqfVEo9JyKnm5Ana8kjQAYdpe4mpWPZBsnzEvQmqjVX2t+jtdFLgSestPcCrwXeit5UstGE1nGl1KLr9zRwxCPLNBuBuRGRn0RrwC8FJtjYNWEfcLLB/cLibuBTIvI24AF0sPSnXLLcitZu96OfTQIdzc7eXmgXm5+REpHvAnutpMuBNPDXIuKO1pVgIwauQQQwBDy4eAi4BR239LRSquKTp+j5PYS2zX7JJ+8MG6TTCkpo8qoB00op77395Km37Ys7/SI0uSj0pOMjDWTxtoUKSBsGsLxH7kdPbP4bdMjOAjo8Y5jJwiV0/FkvtgPOB0EpdYeI/CnwOrRt/nYRuVUpdZ+IvAmtqb4HHRB8CW1G+AUf+YNg24t/FjjuOefXTwxahCHgwUVJKfVMyGu+DxwIuk5E/gn98v4zrN0ARGQf2rOhEVQL8jwJvFFEhlxa8E+jY7L+0Lp/Am23/iqaeD8mIg8rpbzE0g4OoAn3/UqpY9Z9b2qhnKeBG0VElDXmt/CTbN57DaXUUfS28v9dRD6G1njvQ9f/EaWU484mIpe4rlsUkSm02elvrfOCfmZTVrYn0Wac/Uqpv2mhHgZNwhCwQRj8V+BrIvIcehJqHe2pcI1S6jal1NMi8tfAJ0TkFrQd8W42JtSixkfRuzJ8VETuQWu3v4+2o5asPL+NNhXcgNYiXwt8TkRe5TFdtIPjaMJ6t4j8Mdo08tstlPMx4N3AR0Tkk+iJtRvRO1H/HICIjKLdBb+ENge8AIt0rTKOAAdF5HVoe/eb0RsPLLjucw9wm4gcQZPtv0W7AJ4BUEo9LyJ3AXdZ5PwQeieJfwHUlFL3tlA3Ax8YNzSDpqGUuh/4l8Cr0DbER4H3sXmYehC9u8jfAH+JnrH/UYfkOYUehv8E2sPhPrTnwfsBROR6tBfHW5VS5yyt8iCaIN/rV2aLcswAb0NPSD6JtgX/pxbKeRbtNXEZ8A10+74ZeINS6utWtiqQB/4HWiv+c/SEpX2/T6A/jp8Hvos2v/yh51Z3oT1HPg18x0r7czY8KQB+Cz0h+R7gB8A30dvPHwtbL4NgmB0xDAwMEJHvAw8rpbwTsQYdhDFBGBgMGERkP3ry7u/QHHALeufjW3op1yDCELCBweChhnbJ+wO0GfJJ4HVKKa+bn0GHYUwQBgYGBj2CmYQzMDAw6BEMARsYGBj0CIaADQwMDHoEQ8AGBgYGPYIhYAMDA4Me4f8DTr+XInjcWE0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Evaluate isoform proportion predictions on test set\n",
    "\n",
    "y_test_pred = get_y_pred(X_test, np.concatenate([w_L, w]), w_0)\n",
    "\n",
    "r_val, p_val = pearsonr(y_test_pred, y_test)\n",
    "\n",
    "print(\"Test set R^2 = \" + str(round(r_val * r_val, 2)) + \", p = \" + str(p_val) + \", n = \" + str(X_test.shape[0]))\n",
    "\n",
    "#Plot test set scatter\n",
    "f = plt.figure(figsize=(5, 5))\n",
    "\n",
    "plt.scatter(y_test_pred, y_test, color='black', s=5, alpha=0.05)\n",
    "\n",
    "plt.xticks([0.0, 0.25, 0.5, 0.75, 1.0], fontsize=14)\n",
    "plt.yticks([0.0, 0.25, 0.5, 0.75, 1.0], fontsize=14)\n",
    "plt.xlim(0, 1)\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel('Pred Proximal Usage', fontsize=14)\n",
    "plt.ylabel('True Proximal Usage', fontsize=14)\n",
    "plt.title(data_version + ' (R^2 = ' + str(round(r_val * r_val, 2)) + ', n = ' + str(X_test.shape[0]) + ')', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set R^2 = 0.64, p = 0.0, n = 8000\n"
     ]
    },
    {
     "data": {
      "image/png": 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MB75wODwSmCYFxeMUyKYVwU4rON4tDRp2ALZh4y7ArMC37KB4WIfb3RIcTwN2ALZh4y7BrMBnDcLWv4lFZF13UxGMOCtZmx2Abdh4BmBW1rqoZ8PdVAQDztZ32NYB27DxDMAsX4ZFPRt8Ph9CoRBcLhdCodDTPvs9S88KOwO2cSTcLZ1Ii+Kon/us79esrHX8PaUUarXayLWOX/9J87yneb/OMqO3A7CNhfFMWCpmEo76uY+z37KC0GFFuna7jWazCYfDnBRrrVGpVOD1euF2u0+lnZk47efrLGVt55qCUEqtK6V+VymVU0q1lVKPKqW+5qyv65mOZ6rN4FE/91H2G2+3LZfLx14i3ufz3SFP47n6/T78fj9arRZ6vZ40bLRarVNrZybO4vmadm9OGuc2A1ZKxQB8BMDfAHgpgByAqwAOzvK6bNx9RZh5cdTPfZT9rEGINo+xWGyujHDRrNR6Lp/Ph3K5DK01DMNANBqVYOzz+Uau/6TUEPNQIot+zlnbniU9dG4DMIAfB7CvtX615bUbZ3UxNp7C3dSJtCi01uj3+wtlS0e5X9YgxOA3T6A7yvR9POC53W5ordFqtdDv9+F2u+H3++F0Okeu/6QGYuv9Ukqh3+9Daz3yeRb5nMtUgCwb5zkAvxzAXyil/hDAPwawB+C3AfyG1lqf6ZXZeMaJ7flDdblcGAwGC++/6P2yBqF4PI5+vy/G5rMCXa1WQ6vVgt/vh8vlmisrtZ7L6/UiGAyKmY7WeqrSYTxQTtMYLwJrNhoOh1Gr1aC1vmPwmZR987/jmeysTP2sNc3nOQBfBfCDAH4VwL8D8DwAvzZ879fHN1ZKvRbAawHg4sWLp3SJNp4pOIsfqjVoMzDNyqDb7bYY8XS7Xfh8PqRSqZnnGA94brdbXMzcbvehMjO+1+l07shSF8WkbJRZNu0t6eY2iabodDoTM9lFFCCnTaed5wDsAPAJrfVPDv9+RCn1LAA/hAkBWGv9ZgBvBoAHH3zQzpBtLBVn/UOdJ4Pu9XoIBoPwer1otVrwer0yXZ+UGS5rFYtlDU6TjhMOh4UDt7q5jWffzWYTSil4vd47ruE02rSPyiOf5wC8D+DRsdceA/AjZ3Atdx3OWpf6dMPTgffmIOFyuRAIBBAKhSTI9no9VCoVBINBpNPpqdP4WRrfac/MUQanSceadpxJqgjrNXY6HSilkMvlUK/X4ff7D838rTgunTZpIJsX5zkAfwTAfWOvPRvA9hlcy12Fsy48PF1x3nnvSZwsJWaVSkVkbEoppFKpmYFzPEDOemYWGZyYzdKv2HqsScc5jFZhcNZaS3AeHwDmed6Pk5BM46PnwXkOwL8K4KNKqZ8C8IcAvgzADwN4w5le1V2Asy482DgZjK8D12q1UC6X0el04HA44Ha7EQwGYRiGTOmZVVoD56SAddgzM8/gxOO2Wi10Oh3xKJ5GF/C/k2gVgoNIuVxGr9dDMplEMBhcqNB23ITkOPTUuQ3AWuu/V0q9HMCbAPwMgJ3hf//zmV7YXYCz5jNtLB9WqiGfz0smp7VGvV6X4AsA3W4XDodjoqKD1ESv10O325VMehnPDAOh3+9Ht9tFq9VCIBAY0fkCGAmGvEYrrTJ+ve12W7apVqtoNBpYW1uTbQ679uMmJMehp85tAAYArfWfA/jzs76Ouw1PBz7TxmJg0KQSIJvNIhqNYjAYIJlMAsCIooGrY5RKJRiGgdXV1ZEgVa1W4Xa7hc6gNO04XWlWjtrn842s0kEFBSVnDIZOp1MGBSstYaULnE4notEoyuUyqtUqEonESLHO2modCAQAYKSxYxmDy1HpqXMdgG2cHM47n/l0xLI6sxbdnhKtSqUy0sHWarUQi8Xg9/vh9XoRCoVEVZDP59Fut9FoNMTrweVyoVarodfrwePxwOv1ynpyXq8XjUYDABAMBkcW3Jz3s1gH/lQqBZ/Pd4fOl1pn4KlgOC7HG6cLOGBwsGBQZiZrbbWu1+uo1+t38M+8rtNOSOwAbMPGEmANDDSxmdYttyjnOE8nF6fqlUpFgm4sFoPWWgx1OMVn9mkYBlKplHg+0IjH6XRKwYvbu1wuGIYBAHcUmxb5LOMD/6SllHjsScFwEl3gdDoRiUTQbrfhcDigtZ6qopj0GWYpP04adgA+x7ClYk8f8EfO4pdhGFBKAbgzIE3jHK1FNKfTKd+7tbut3++jWCwikUjcISVjI0UoFIJSCqFQSGgEq5qBfyul4HQ6R/Sz1um/1nqkC48B2tqRdxT+dNJzPR5wpx1jGl1gpROY7U+St036DGcJOwCfU9hSsdk4b4OT2+1GpVJBtVqFYRiIxWIj0+DxbceDSLlcRqlUEq4ykUgIXUAZVq1Ww2AwQDqdFvcx8r7BYBAOhwOBQAB+v188HaZN31m0cjqdQgfwfV4j3wcg2wF3BstF+NNpz/W4KmHadzuLLrBSG9P2mfYZzgp2AD6nsKVi03GeBye6l/X7fSilJgak8SACQILvwcEBPB4PGo0GfD4fut2uyLByuRz8fj9CoRDq9fqIQ1q/3z80uIw/U06nc6TTjNnzuAnOYDAYCZLH6SRbhiRsEl0wLl87bB8OOJNc1k4TdgA+p7ClYtNxHgcn6lWj0eihJjbAaECgJrder8Pr9YpMjN4HLJBFIhEAGKnwu1wuUSgsOn1n95i1KYKFumkmOFaMUyazYN12vMhm3aZYLE5tKT7s+Mvg1U97ZmUH4HMKWyo2HedxcLJe0zwmNuP7cp9ut4toNAqfz4d4PI5YLCZBwZrhxuNx1Ot1lEolOBwOhEKhkWxuUiCxPlPMcFutFrrd7khTBPCUsU8wGJx4j62642q1ikgkMlLkA4ByuSyrbJDOYJNIt9tFIBC4gx5RSqFarYq5O813DsOig/K07rXTnlnZAfgcw5aKTcZZDE6HZUbHuSZu6/V6EY/HRwpwfN8aqMavq9lsot1uI5lMinPYtCWEeCxmuH6/H51OZ6QpolKpSLdct9uVgp/1nMxUtdbiH2zlvMvlMrLZLNxuN0qlEhKJBEKhEMrlMgaDAVZXVyWTByDHc7lc6Ha7qNVq2NzcvMN8ZxoWHZQnbX8WMys7ANt4WuI0B6d5p7fHuaZZ+463GDudTpTLZQBmIHE4HKjX64hGo3C5XOIMZu1kswZwKxXgcrlGdML0i2AHncfjkUyXEjFrpurxeGTQsQa+ZrMpg4jf70ehUMBgMJioO1ZKCR1CJQn9jKcVMifdP2D+AXDa9qc9s7IDsA0bh+A4mdFxV1EeD7rU8Pp8PuTzeQSDQXg8HqETAoEAHA4HKpWKSNAYSA5TQQCQzJifmVPzVqsleuNQKCQcLc85vkR9IBAQ051WqwWfzwePx4NkMim6Y3alOZ1OeL1eCfSpVGokK583EC46AE4qzAGnO7OyA/BdgvMmy7qbMO/0dhEHsVmw8qu7u7vw+XxYXV2VY1Lvmk6n0Wq1pOWYWWyv14PWWhoTrMY2k5oYrOcFAKUU3G43PB7PSPuw0+lEo9GQ1uTBYACPxwOn0yl+E1aqw+FwoFarIZlMCj/MYh8zYMBUgDBT9vv9CAaDUohbhEtfBk6b9rMD8F2A8yzLuhswT2Y0j4MY23wPGyRrtZoUsLrdrkz5o9GocMTjvgiNRgO9Xm9k8UyqDcgLDwYDyaL5nsvlGuGNqabQWss1MtDyGtis4fV60e/3xcOBgRSAKDpisRhCoZB4PvT7ffh8vontzFx3jpK6RZ/hp2MSYgfguwCLTJGfjg/pecBhmdGk78C6nE6lUpFWW4fDMTXAUH1Qr9eRz+dFq8uMMBaLyXZUPbAF2e12o1arYXV1VVqNyaWSa2WwI2XA6202m9Jp12q1pFDGho5yuSyWlvF4XLLw/f19aK3R7XbR7/dx/fr1kWy20+kgm81Ca410Oo1gMCjnoMdENBoFYKomSKFQnzzL/2IZs42zhh2AzxGOGhwXmSI/HR/SpwPGs0v6L1gNblqtFjweD4CntL/joJ643W5L4NJaCx3AbjiryoHdd41GA+12G263GxsbG9JqzGyXXKvP57vD9CYQCEh3XaPRkIzV5XKJppk+CgSlcplMBo1GQ+gJv98Pj8eDdDqNWq2GQqGAdDqNSqUiA4zb7Uar1RI5XKVSQb/fB2A+p2w6mWclYypBjqIfHoetA36G4jjBcd7iwXlsYDiPOIpTGafWlUpFmiPYkEFTHB6XQXgSOJjSh4GBl1REMBhENpuF0+lELBYTE55bt27Jc8PiFhUR1Nvm83kYhiEZODDajVcsFqG1RjAYRKPREPkYAGn26Pf7d3hXaK1Fi0x6ggHWMAwkk0m5zm63i0gkIhI4HtMwDPmMvEZrhm59Vq3PMWcCPp8P1WpV7uGswt207/csEhQ7AJ8THDc4zlM8OI8NDKeFeYPqUX6E/O68Xq8UvMbtFSn14vkp6RoHAxtVCpzaM4CSHuDSQvzeaR3p9Xrh8XiEciCtwAJbLpdDoVDAxsYG7r33XsmqO50O/H4/crkcer2etFKPF8oY3MrlMra3t9Fut+F0OmEYBpxOJ9rttuiJAdODuNFooNvtQmuNzc3NkaYMBmC/34+DgwOZPQQCgakdc/TdYNbPgYjWmaRIJoGeG/ws1u/X1gE/g3EawfEsZDbnAYsE1aP8CGe5bVEK5nK5cPHiRWmyAHCHDwEHiV6vJ7IsNi4kk0k4HA5ks1nEYjEkk0npYnO73UilUuh2u2g2m6hWq/D5fHdwvLVaDdlsFt1uF48++ihKpRLW19fh8XikA49BbHV1FUop4aF5fVy9Ym9vD9lsFoPBAMFgEH6/H263G5FIZER7TMUEC2tra2vyOdn8QMvM1dVVed3pdIqiY9az6na7xadYKSWzi0kDbrvdRqlUEvP38e93Wb/B4WDnmGdbOwCfE5xWcDxtmc2iOAkObpGgusiP0Npqa63e8xx0MGO2xXtvlZnR78Hn88kgcXBwgP39fZnGsyNtY2MD0WhUONNqtYr19XXJfGmBmUgkoJRCo9EQuVggEMD29rYsbqm1RrFYhNvtRjQahWEYaLVa0phBzrrdbosCghIywzBw7do1VKtVeL1eFItFxGIxPPDAA3C73cjn8zIQMTsPBALo9XrI5XIIh8MjMwC6vZVKJXg8HinejW9n/T6tlEgmk4HWGuFwGE6nE3t7e/B4PHeYrlupE8Mw5N4Ty/gNWhzl1Dzb2wH4HOG8B8eTxjyZ6lEC9CJBdd4fIVttDcNAuVzG2tqaZLb05W21WndkW4DJtXIpdcq3rFItdrDt7u6KIQ4DYiKRkGDlcDhGMu9gMIhUKoVQKCSSLwZUFsxu3LghFAMzTF5Xt9sV0/J+v49qtSoddZ1OB4899hjW19fhdDqxu7sr2X2/35c26Hq9LgtuMjONx+PI5/PSyNHpdEQFwoDI+2aVqU0bKK3fJzvrAAgt4ff7UalU0O12ZTDhIMhtrQPf+Pd/1N+gtUUbgJ5nHzsAT4Et1zp9LMOqcBIWzWzm+RGy1bbf7wtd4Pf7JQuljtaabTGzU0qhWCyKVMuqt2XQ9vv9iEaj2N/fx9WrV4XjZHtwLBZDv99HrVZDo9HAxsaGaH+t9IcVm5ub0Frj4OAA3W4XyWQSHo8HgUBAOs/IJQOmITuzXCoWSqWSNF2QD6YuOZPJAADS6fQIP9vr9VCv1+FwOGQdOofDIQW+ZrMplEk2mxX+2rr8/LTv0+v1CsVBUyGfz4disYjBYDBSZLTuN4snPgrGzYRgUxBHhy3XOhuc5Oq1x81sxgdjttoahoFOpwOPxyOBk3TEeLbldDqF70wkEqjX6yJbszYqOBwOKZ4NBgNRMsRiMVEJAJCOMRrdWLNeBmuqC6LRqHTPxWIxHBwcyCDh9/tRKpUQCARQKpVkoc5cLgcAsl04HEalUpHOuFAohMFggJWVFXi9XnQ6HeF9+/0+YrHYiO643++LVC2VSiGfzwMw5XH0lTAM4w6VyDRnN2a1zKxp12kYBkKhkAxw1m6/k5plWguxi2DhAKyUigKoaq3nSrGfjrDlWmeDwzLVs1BxtNvtO+Rb1J1SccDgal1G3bq2GbMtamABc7odj8dH/B5o69hqtVAulxEOhyWjZmCjhrfRaMDhcGB9fV18GtyB6YyQAAAgAElEQVRut5z39u3bqFQqCIVCaDabKJVKIvsCnlptGMAItVGtVqUTj23BpVJJjuV0OuHxeBCNRsUcPhKJwOFwIBaLyWKgoVBIjud2uxGLxdBsNoWuCIVCKJVKACCNGLVaDel0WgaTeSwieV+bzaa0LddqNaFkJs0ETgLjdqQABvPsN1cAVkq5APwsgB8CEAXwbADXlVK/AOCm1vq/HuGazy2eyXKts8asDOUsVBx0B/N4PGi327h16xYMwxD+MxaLjXSnWa9t3Dpy0vXncjkYhiEZHpsuAoEA+v0+gsGgrO1WrValmYOKBXKmpDgAM/ju7e1Jg0a/30c4HMba2poU9yKRyEi2xkyS2WOz2USxWBQOOZlMSuDM5/NIJpOIx+MjmmOrTC2fz6NYLErA5oDj9/vlumk6T0piZWVFsvh5LSKpweaMATApkPHv4qQx/t1iyRzwTwH4DgA/COAtltf/PwCvB3BXBeBnqlzr6YBlTiHn5fk52dNaS3bH7ZvNpgRga1FpfKUJ6/tWWRQVAFQXUAZG3pi0Bs1xCoUClFKIRCKSNZNWcDqdyOVyyGazAMzBo9lsotVqYWtrC61WC71eD4VCAQcHB5K1Xrp0SWiCzc1NVCoVZLNZeL1eaK0RjUalKOb3+7G+vi76ZGb94XAYwWBQ7iU57nA4DK01ms0mIpGI6IJZBAQw0mGntRb6xmqbCTy1iodVvjctQJ9FQfso55w3AH83gNdqrd+vlPpty+ufBXDfQmd8muCZrki422GVglUqFQSDQaTT6Tu+83A4PBIc/X6/mMj0ej0pJI0fd3yliUlGPLlcTnhkytbIowYCAWll5nSeel+qKLgE+8HBgVAKDPzMvoPBINbW1iSjbTQaQqdkMhkUi0UYhoFIJCKuZZVKBRcvXgQAoVrYsOH3+/HsZz8b5XIZhUJBOudardYI58tpP60tA4GABEpKyMrlMvL5vPzWSqUSYrEYGo3GyGocbNywrlNnne7z847Lyp4OmDcAbwJ4csLrDgDT+yptLB2LqDPOu5LjLK+P8qtKpSIBk8WhcY4xlUqN8IqFQgEAsLq6egf9wEzSutKEtTmDwZ4rRdTrdRQKBTgcjpHMmIWj9fV18WngQp2JRAKGYQjfWq1W4XK5ZEFPr9crdMX6+jouXrwoAZOZK5slms0mPvOZzyCVSuGee+4RsyAqJG7fvg3AlHtFo1E4HA4UCgUUi0XU63WkUimUSiVEIhHUajUAJmfLQYP3yecbbYV2uVzIZDK4ffs2ut0unE4n7rvvvhHPifHFQ8fXqaPG2rpqxrwraJwXzBuAHwPw1QC2x15/JYBHlnpFNqZiEXWGddtKpQKv14twOHxuHsxlX9+iAxMdyqh9DQQCkolO0oaym63f70uzgDX4Wr+XSqUizQvkZRl8GeybzSY8Ho+oJ5jh9no9yZzD4bCoBgBI44TD4cDa2poEU07V2YLMot5znvMcNBoN4VMDgQAajQZ2d3fR7/elqYPT+kwmA8MwJJD1ej0x0aGaotvt4uDgQDre+FkbjQb29vYkI2a3HumJcT682WyK58VgMEC1WpUWYWux0VqDGdf/AmaQpuG7tXh3Gs/RMjBvAP43AH5bKbUGM+t9mVLqPgDfC+BbTuribIxiEXUGt2VGwCk0cD4kdcu8vqMMTDSN4XRZKSV2i5PAqTmDNpst6H/A43EVCY/HI9ItANjf35dgT6UE6QBqg69duyaUAzlfACgUCkgkEkgmk6jVaggGgxIAaetI4/XBYIBMJoNoNIqVlRUAEK3w9va2NHcYhoFKpQK/3y+DQafTEZ621+tJYwSAO7reuD05YMreCoUCPB4PVlZWsLKyMtIZSOqBrmuZTEaaPNg8EQqFkE6nkUql7qjBjOt/eS9Je1DSd5wW4nNpxqO1frtSqg+zGOcG8MswC3Cv0lr/xQlenw0LFlFncFursH/e9bVOA8u8vqMMTC6XC8lkUiRQAGZm4D6fT/hMTq/39/extrYmWa9hGKhWq0gmk/D7/dJGnE6nEY/H0el0pDmA3HEulxOfh263K4WyarWKRqOB9fV1uN1u7O3tjXxGTuOt18auNbfbjUwmI5IvwKQF6BmslEI4HJb9c7mcBMZmsylFr0QiIQOI1+vFwcGBnKvb7aJSqSAej0txrNPpiNa5XC6PcOr8vjOZDHZ3d0XCls/n0ev1UCqV0Gq14HQ6Rc87qQ3Zqv+1UhrjRvVHwbk249FavxPAOwFAKaXuZh3wecUi6gy+R6tAZl3nRVK3zOs7ysDEbRehPTjN5UKVbB92Op0oFAro9XpwOBzI5XLC11JqxQJVLpcbMdlhFhkIBBCPx2EYBvL5PDweD4rFIrxeL5LJJLrdLoLBIHw+n6xY7PP50Gg0sLKygkajgZ2dHZTLZQmyPAffZxGLVAWlWzRFDwQC2N/fB2AG+L29PVy9ehWAKW1rt9sIBoPwer1i/L61tYWDgwMZXNrt9sgS9dZ7Vy6Xcfv2bQwGAxSLRQQCAWxsbGB/f18KaByUJ8nNZlEDyyiaL0t+ur9fn3vbI3XC2cH37LDIg2aVRZ1HSd1Rr2/8x3iUgWne8/FczAoJBm5yuwAQiUREPuZwOGT5Hiog+KPu9/soFovi7+D1enHr1i1cuHBBNMCrq6vY2dkRYxkW1dbW1uBwOLC7uytaXsrBaNDT6/WwtraGYDCIbreL3d1dybpJK3g8Hty8eRO5XE7WlNvb2xPqg40Su7u7cLlcaDQaWFtbE+41EolIU4rT6cTKyopQL+FwWDTGlIwBEM0z1R27u7sIBoMwDENaq+nARm8NPh+Tlnua5FF8HBxXfmoYGh/5yC4++MFbc+8zbyNGC5OFxRpAG8A1AP9Na/3muc9s48QwKUAtkk2c1TXOu98knu6oA9O4JeSkc5ESiEQiMgWnPwIr+PQj4I/X4/EIBeF0OhGNRoWn7PV6SCaTMvUHgK2tLfR6PWxtbcHn80kRj7SCYRhoNpvIZrPCVTPrjEQiKJVKKBaLstwPM3CuVuFwOLC1tYVoNCqfh4G4UCigXC5jdXUVoVAI5XJZFB1aaykO7uzsiJ43GAzixo0bSKfTSCQS8Pv9SKfTMgjRUIgDVLvdliIeC4NKKcRiMeGjaeTOppB6vS7UwqTlnkj7WA3mD3vO5n02FkW53Mbb3/4EdnaqC+03bwb8fwH4GQB/DuDjw9deAOCbYPLBlwH8J6WUQ2v9Xxa6AhtLxWGFhPPgc3Gca1gWTzfrGsYlZeRVtdai2hgMBrJCBX+0hmGIiqDdbksAjsViogJg0Y9BPJPJYG1tDaFQSIKU1+tFoVBArVYTox+ttQSZ7e1tVKtVbGxsiA3lrVu3JIhxgkpzH8rS2u02Njc3kc/nkc1mEQ6HEY1Gce3aNTHHUUphb29P7m06nRZns3w+j8FggEQiIYty+nw+sbMkP87lgZihMiCn02lorUXGd+HCBbTbbVnzLpFIIJFIiKqC3hTxeHxEvzwt2M77HS8zAdFa49OfzuHd776BTqe/8P7zBuCHAPz0WHD9z0qp7wfwDVrrVyqlPg/gdQDsAHwGGA8a0wLUMgPYUR/i41zDPDzdPNc27RrGf7QsLDGzJb1A20M2QzDLpb7X4/HIPoZhSJGMf8fjcbjdbuFz8/m8KAJ2d3dF+8rVJFikbLVa0n5cLBaxvr4ufgxsJWY7M5UWzWZTmhs2NjbknlB7vLm5KUY4jUZD6AWuDcfgRwOdarWKRx99FFeuXJEiJAtzPp9PljSiH7HD4RAaxTAMbG5uylpxdErb3NxELBbDYDBAPp8XPTJXGeHgRGXIOAVRq9VEh0zZ28HBgTSvcB9gtrfEImg2e/izP3sSjz5akNccDoWHHtrCz/3cfMeYNwC/BMBPTHj9fQB+Zfj/7wbwi3Me767DaUzrp51jWtCY1Bm0jELDcbPo41zDYTzdPJktp7mTrsEqj2MxKJ1OY3V1VarsVBRQz9vr9XDhwgVEIhH5kTPo1et1WTKHPCd5YsBUGlSrVTz++OMi7Xr88cfhdruxubk50g3m8XhQqVREosWMm7wrmyFY/GPmyYLXwcEB/vZv/xapVAqJRAKDwUAy31KpJJ9lfX1dTIHYkUbag5luJBJBOp2Gw+FAtVpFOBxGt9uVQEmvB67tlkqlsLe3N7IoKP/LNeJI6VDbG4vFRnyVQ6GQrMbB66eem0sZsTOQ94zZNrNsWl0ed/HOJ58s4x3veAK1WldeSyb9eMUrnoWtrcnLTU3CvAG4COClAP7T2OsvHb4HAEEAtbnPvCCUUm8A8AsAfkNr/bqTOs9RcBrT+lnnsGZzXAxyWmfQpAC26OBx3Cz6uMWOWTzdrMyWsiWrPeO4dIncYrlcFhWCx+MRbpbn54+dwadcLktQYZAqFAqSubIYx4Uvn3zySZTLZSSTSeF36dHA5oZIJIJAICBNH+xy4+rLXq8Xly5dQiQSQbfbxd7eHlwuF5797GejWCyOFALZ+Xb9+nWRiD3vec+Dy+XCjRs3RlZeLpVKyGQyKJVKuPfee+H1eoVyqdVqCIfDkuEzaHJAqNVqYiAfDAZxzz33iJZ4Y2NDpG4ulwvRaBTxeFx8dEm1WAdMq9sbXc88Hg/6/b5cD7lmPr9s6GCjjLWJxuLXK8ZBi6DXG+C9793Gxz++P/L6gw+u4Ru+4TI8HueUPSdj3gD8CwB+Qyn1NQD+Dmbx7fkAvhkm7QAA3wjgYwudfU4opb4SwGsAfOYkjn9cHBaQlpEdzzrHeDbn8/mEhphUIbYGsKMMHsvIoo8rG5p2T6ddW61WQ6vVAgDRnIZCIaECrMU4TntZCOOSN9bKvM/nEylXvV4X0xjylPQ/4PFarRby+Txu3ryJg4MDlMtlaR9eWVnBlStX8Mgjj6BQKIj3QrvdFuOf/f193Lx5EysrK7j//vtxcHAgrcG7u7toNBrY2tpCqVSSrJF8MzPSSqUCp9OJWq2GarWKlZUVcRxjtsquPAbNZrOJeDyOSCQi+5Kq2N3dxfr6OgCTzohEIqhUKtjZ2ZGFObnah9ZaXMqsq14Ui0VEIhGUy2V0u11Uq1V0Oh3RL5fLZaFWuNad1eSdSo9OpyPcMzXNHGBXVlZGvJgBSEa9yDO4v1/H29/+BHK5prwWDLrxspfdi/vuS8zYczrmbcT4LaXU4wB+GMCrYa539BiAh7XWHxpu80tHuoJDMPQf/h8A/gVMS8xzh/EWSevqt8vKjmcFPWtGyUzHuh7YrAB5lGz2uBnscTHpnvJ6KHGyLsdOUFHQ7XbF1yGTyYjPLSkbTler1ap4D+RyOelQI6g2YEC/77774HQ6USqV0Gg05Dq11sJ5MrPudDq4cuUKDg4OxJicmS4ztnA4jO3tbVQqFWkJJt1BL4XPfe5zElToirazs4NeryfnXVlZkeBLT17SJcyeWTtoNpuygjOLbZFIRNQSbJWmHrhQKCAajcq9unnzpigdqtUqHA6HyNJIC7Co5vV64ff7RTrHbB146hm7ceOGuLF5PB5JLBh8rQtxBoNBuY+1Wg2VSgVra2tSBLUa+CwSfA1D46Mf3cUHPnALg8FTzm333ZfAy152D4LBo9vhLNKI8UEAHzzymY6ONwN429CJbWoAVkq9FsBrAYiT02mBXySLAHxAgOUVvQ4LevybraHMYg5rNDhqNrsM4Tswe3Yw6b12+6l1t6xaU6WUKBQAiCyM9Atdzdj4QV6ThS0qE1h1pw6Wi24GAgG0Wi3R1KZSKYTDYWmSCIVCUiCjjy/3I1fLRTzpZNZoNOB0OqXaH4lEEI1GUSqVZCXk3d1ddDodsXc0DAOPPPKITMHb7bYM+ADE/tHtdksBjoW2mzdvCodMjpZZO1ULlM9praG1RiQSweXLl+VzW53b/H4/ut2umOSUy2W57xzAcrmc+Ffcvn1blkWyLtPEohlnDVxElNfearWkc44dhlz2icVNBtparSbLQ5EmslJws2oHk57DcrmNP/7jJ7C9/ZS8zO124hu/8TK+/MtXpX3+qDjKihgxjDmgaa0PjnUV08/1GgD3wrTDnImhBvnNAPDggw+eeqMIp6Q0wCYPtkxz98OC3jgXzD75SbA+cCxunDYOK5hNynLH1t2SwMPPPe6kxQHP5/NJ8SocDos8i9ws7Q4B4ODgAOFwGFtbW7h9+zbcbjdyuRy+8IUvyNSdTmBOpxPr6+uyqjDPxx96sWiWSAqFArLZrOhzGSB5PVwSKJVKyedg1u1wOFAqldDpdGQ5IQ5EjUYD8XhcBhNO+yuVCrTWaDQa8Hg84pRGfwYW7KhW4H1nl1ypVJIBplAoSLZfqVSkKYMrd5Bi8Hq9oovm99HtdpHP5+H1ehGPx0ekcvydUIJHzpyUAmVx/Dw0qaf/BVuVueIIjYioX6apj/UZmKX3tj5rXq8Xn/lMDu9616i8bGsrjFe84llIJpfTUTpvI0YYpt732wGEJmyyGPM83znvA/AmAC/WWncP2/6swSmO1VzEam14GtP1eYP9+APHIo3T6ZTM/bjXeRwp2KT32E2mlBIjb/J4AMTWkAbi405a/EzMhq0dblpr7O/vC3+bTqdRrVZF/uXz+dBsNlEulyX4c3HMzc1N9Ho9ybaVUuKhS+8DANjd3QUA0btaM+IvfOELoqGl0Y/f7xfjHGqBNzc3hdulYsHn80lTg9PpxK1bt8QfdzAYiE754OBgxOyI56cBD6kUzg74vCaTSXlO8vk89vb2pHBWq9Vk8Gb2SpleIpGQTrlsNjuybh0Li/1+X5o+Op2OzDp4baSGDMPA2tqa8MdcDZocPu8/5WrWz0nFy6zncfxZq1Sa+NCHbk6Ulz300AU4HMfLeq2YNwP+RQAvgsn//k8APwDgAoB/CeDHl3Y1o3ghgBSAz1nSfCeAh5RSPwAgqLXuTNv5NMFshFpQZlfLFn0fhnEqZBrGHzjyfkelScY/47y896wBY5L1IBdvpISLmU8mk0E2mxWVAHnCWQOelbJhgYpNECwsMcCwaJRKpbCzs4NMJgOn04lQKCTSMno8xONxWX+t1WpJoYmUA7NprhLc7XZRKBRE8UDul8bk+Xwefr8fFy9eRDKZRD6fFwlcsVhENBpFLBZDt9tFNpsVLwXeL2bC/KxKKRiGgY2NDRiGgf39fTzxxBOyoCRtMalyqFariEajonTwer1wOBwol8uIx+NQSqFQKKBUKiGdTqPb7Yokj0oH0kXUU4+v3JzNZlEsFqXoSL64VCohmUxiY2MDoVAI+/v7Qu9wlQ0W3ehsx8/KDjrOkGY1ZFhX3bh2rYT3vS+LdvsprjeR8ONbv3Uxedm8mDcA/xMA3621/pBSagDgY1rr31dK3YIZlP9g6VcGvAPAJ8Ze+x0AT8DMjM9FVsxgw8yIPyROq86i6+ywbHY88HFqx78XoUmm9enPw3vPmh1Y37MGVZ6PPGa5XEYul5P2X2ZMk5y0rAMFACmG7e/vSxGo1Wohl8shHo9jc3NTil1OpxPJZFJWBU4kEsjlcqI6YFMG22yZ3bLYRDnb3t6eLDnUarWkTXdtbU2M1UulEra3t3FwcCCObcViUfhgZri0o2QRj5RGt9uVjjKfzyfcNwdI8quZTAaZTEbsOROJhGSTsVgMm5ub0mpcr9cRiUTQbrfRaDRQq9VEt8zrJsVBOoSNIO12G4FAANvb29LOzWeNBj1erxfXr19HIBCQIqPP55OGEipN3G73iEqEhThK99i8wUYWAEIx8fmhLrrf78t1au3A+9+/h0ceyctzCxxdXjYv5g3ACQA3hv9fHf4NAH8N4DeXfVEAoLUuAyhbX1NKNQAUtdafO4lzHgUMNpPkLdYvntuedADu9XryI6QiYlLBjtNYLnd+VMOeScF2Ed57Fq/N9+gNQKRSKclMqSktFoticB6Px+/weLAOFCzU9Xo9ZDIZCeKctVjd2QCTF93Y2EC1WpViT7FYxO3btxEMBkUyxcUhGSgajQYymYwYlFudx7LZLAzDgNfrlfXcqtWq0BN0MWOAHacYfD6fnNftNldDvnXrFgKBgFAKdGtjtri5uSk0S6vVkiWJkskker2erFLM/cLhMAKBgDxL8XhcstBkMinBlw0aNJxPpVI4ODhAIpFAt9uVoiKv6/r168J137x5U3hqOsvV63Wk02msra2N+AfzuWXHHX9zVi8OBmerWsW6bFG1WhVFBxtEMpkm3vWuHdRqhjzHx5WXzYt5A/ANAJcA7AB4HMCrYOqBX4qxIPlMgzXYjMtbllmAmxdcXYAGKtNada0ryfLhXpY8btm8t/V48Xh8ZOVc6lcphYrH46J/ts46rAOFtVjGpYNWV1dlxWHrKhXUt/J7pTkP22I5xe52u4jFYohEImg2m9jd3ZVuLmaVNKxhQHS5XEilUjAMA8ViUfTbNEq3ttlSstbpdERiZRiGFFzZ5hwKhaQxhINWMBhEPB6XqTt1v5FIROoWzWZTCmjkaHO5nOhoa7Uaut2umMLTC6JSqWBlZQUej0dUENRB89p5LbTx7HQ6yGazeOCBBxAMBpHP51Gv1+Hz+XDlyhVxeON3yOIa1RoM8qRT2LDBZ4TBd3wQ5iANmEE7ny/g05+u4uMfz8PpfKp9fxnysnkxbwD+fQAPwsx4fxHAO5VSrwPgBfBjJ3Rtd0Br/bWnda55Me80+rT0sjS0ZjGErbFWHEcaN68V5LJkasR48wjPR71rOBzG6uqq+DKMfzZrFkTj72aziWazidXVVdTrdVSrVQwGA/nhsqWbgSOXy0mBJxQKyYDDVSqom2WANgwD6+vrwjEzmwSAS5cuSSMHzdZpjsMA2e/3Jbixiw4AYrEYyuWydKWx4AY8Jb9bW1sDYM7IksmkFN7IrbJJg1I3Fr0Mw0ChUBDrSPoSP/HEEzKgUIHAgSkWi2FtbQ23bt2S56LT6eCxxx4TeqLRaODGjRuyP7dZW1sTa04uZ8RrZotxu93Gzs6O+EiQ+qDDHGsBfE6mUX+JRAL7+/tDBYYLH/hADdlsRwa7ZcrL5sW8jRi/ZPn/9yilnguzE+4JrfXfn9TFPV0wzzR6HCdVnHO73SNFBQYda/CiYmOeRo3xaz6uFeRRMS3wA5COJwr9J/HZ1iwonU5Ldsalfegl0Gw2R3S0lFhZ7RPdbrdkelxw09phFgwGZel22kcmEglRMJAmoTqBgYfLyG9sbCAajeL27dty/ZxaU15Fze5znvMcae7gKhfdbldW3mCzAwO802munvzoo48im81KowWfGQ445KcPDg5kBRCasG9ubsLr9eKBBx6QWQeAkZkHKRzeKwBynzlocBZz9epVRKNRlMtlGbS42CdnEmwWoTbZ6/WKCuIwVYNVhhaLxfCxj93E3/xNHv0+JPguW142L45qyH4NpgewjSPgpIpzDFLs6+fDZfVDYMFl3kYNK06j5XrSsdrttvT3c3ppzXh4LePeFpNmHVyNgby93++XaTkzykwmI1IyivsDgYBQB1QZWAtOAGR5HRqax2Ix1Ov1ESMYripMCRh1r1Q7pNNp8QEulUrSmstFN30+nxTJfD7Tm5hUAxUBgUBAPEFisZjI2Fh0unbtGm7cuCFBkveq3+8LtUK1Q7FYRKvVEk+Ier2O1dVVXLlyBcFgEKurqyNWlX6/H+VyGZFIRIyGuBTS5uamcLfRaFSWarI2k/h8PvnsHNCoGLF+9+OrV1sxjforler40z/dxqOPFqGUCx6PAx6P+0TkZfNiXh3w9wPIaq3fMfz7NwB8H4B/APDKYUC2MScWpQDmCWzWoM4f8PgyP/M0asw61yxOe5mDynjBjPyhVWUxLiNiEOR5J32ufD4/ItNii2u/35fCE5f4YVcbLRmDwaAEF07NaRXpdrvRbDaFujAMA3t7ewgGg7h48SKUUnjssceEP2YDRSQSwe7urgR7ysPYwMEgur+/j2aziWg0KkY4GxsbACC8NVupadTj9/uxsbEhPhCsDRSLxREZHddl42BFnj0cDku7L5swtNZYX1/H6uoqIpGIdN1xuSPSC9ZMnTMHtoWTHiG3HIvFxCSJHDYHFCpP2EnncDiwuro6M/hanzvrIPzIIzt429seQ7sNOJ0OeDwurKyET0xeNi/mzYBfj2Gbr1LqRQD+OUxznJfDbNB4+Ylc3RFBYv40ONdZWNQwZtK+1EweFtjGgyu7jMadvmad97AgOovTnndQWbRBg1V7ejhw+s5rpVbVug7ZuP4aMKkHtrYmEgmhGqyeBJwVMNs0DEM6wiipsnLM1E+TL7S2yXJhyXw+LyY3DL7ZbFaoD6pmnE6nZMc8f7lcFh0vM91+v49UKoV4PI719XV84QtfELVLJpMRDXgkEkEsFpMWamsDB1UQ9IUIBoMIh8O4fPmy+Dx0Oh1Z4Zh88srKCi5fvizUFaV2DOaFQgH1eh2JRAKBQEA67OhRQUOjCxcuCG3EBhQ2UVBlQT0w7yttNhOJxIIqnQHe857H8b73XQOgh9+VC1/yJQm8/OUPHFtedtxZ37wB+AKA68P/fxmA/6W1/j2l1CdxNv4QM2Gdlp1VEJ4VzA4rzlmDSz6fF7kM95nV1ECOl1mMFYedd54gOo3vnXdQWbRBg5kPByPrtTmdTslAqWygEH9ccsYMkZptOp21222xqCRvXi6XJahwW/rV1mo13Lp1S7q3aCROKoEa36tXr8LpNBfrbDQa8Pv9uHnzpjh6cabCASIYDKJUKgmXnc1mxeSclXsWtADg8ccfR7lchtZaFgEFTBqCEjm2BFMxQT0tLR1zuRxWVlawtraGQCAg6olGo4HV1dWRgiVVDWwA4fnW1taws7MjPDI/G7lg8sPBYBDRaBSJRALpdBqFQgH5fF5M2Pnckv5JJpNS6KMXxMrKimi/rU0W1lmQtfEin+/gT/7kOvL5tvDTPp8TDz+8jhe96FlLCb7HnfXNG4BrAJIwZWgPA/iPvAYA52OZXQuomTwN3e00HBbMZslgVcUAACAASURBVBWuqOWlSJ+jv9vtFi7R+vBZO9C47bgP8Ph5ua115D5Jo/R57smkY6VSKbGStHLazMCAp4ItvQioLuC0HoBUzOneRe6XWtZyuSyr9NJYhllsKBSSrI9NErlcDoAZFLe2tsRTolAoIBaL4ebNm6L/rVaryGazMkDQQ6Lb7eLixYtoNptCs1DBwoHl6tWrqFQqwi8zkHDlCF6jw+GQ79PhcKBer+Oxxx4T3pguZMlkUgp1hmEgGo2K7Ov27dtoNpsIhULY29tDLBaT1mp2Cd6+fRudTkd4ba7uQW7Y6/WKj/K9996LcDiMZrOJtbU1pNNpaTTZ39+Xe8CMnE52VKLQTId0Ge9xo9FAMpmUAZMaas6CBgMDn/pUAR/5SBa9Hrdx4OrVOL7ma9LY2kovJS4cR01EzBuA3wvgvyilPgHgOQDeNXz9AQDbC53xFMCq9WnobqfhOMGMpuAsmLHjiYUSrfWI6xfPwwo3p7vTHohyuTyxqDWvbG7atOswNcSkezLN8Wz8NQ6q5BQ5QzAMA4FAQDJaFqxo1kOVApUR7G5jJmc1cKEPA71vmQFbC0MsoFE7y3MHAgG43W4kEgmhO9juy6XiWb0ntcIWYprdrKysSCbearUQiUSwuroqz065XMbu7i7y+TzSaTOIsKEDgHTJBYNBHBwcIB6PC59MfpUBmdl3pVLBs571LDgcDmxvb0vALRQKWFlZQTweh8fjQbPZRC6XQ6VSEXtKyuP42bieXDAYRCQSQTweRyAQwObmphjgk1c/ODiAy+XCrVu34HK5sL6+Lu5p5Njr9TqSyaR83/yOAoEAOp3OSGs0eed6vY93vvM6bt9uQCkHtDYQCvnx8MMX8dznxuTzLwPL0PnPG4BfB1P/ey+Ab9da54evfyWAP1r4rCcMGracJQdsDWa0E2TX2Tz7BoNBabUEIBIi+tNOcv0aDAaSmQGYSgOUSiWx6eO+8+p3jzPtGg/wwGiPPgMvg8iklT84xaYWlZmZ1b+XxuC5XA4XL16Ez+dDLpeTz85ON3KwPDaN2VdWVsSEhtIn8vCGYUh1v9Fo4Pbt26JDZisvAGmO6PV6kkEy27506RJ8PrOpo1gsyiKYyWRSApt1rTcuY8T/sjhWKBRGCq6BQECelWq1imq1ilKpJG3ChmFga2sLOzs7Qk9orbG3t4eVlRVpuy6VSuh2u4jH46JC4KBDqsUwDMnwmY1ev35d7me328Xu7i42NzdH6DO2A1N1ks/nsbGxAY/HI80sbGii/M3KiZOOoYabFEaxWMTBgRMf/WgJgHPYwmzgwoUY/uk/fQDBoJqoiT8O5k1YZmFeHXARZtFt/PWfWviMpwAuNnjW4FSfMpx8Po9arSbZyyyk02npUuKaXwBGuFEAI65f7XZbuEVmeOMBlcG20+lIsZJFrXmUFuNevItOu6zXM96jz78ZVK0SOu7DHyRXPLAK5t1uN8rlshSdeM+5Ui8A7O/vS9DgIMegw+nv6uoq+v0+9vb2pBmAWTElWoFAQAxpWIjidwZAsutQKCTNFWwZvueeewAAn/3sZ5HNZgGYScP29jYuX74s2lZ2twEQ3wnrQplOpxMXLlwQ7jMQCEhg4/3lM8GmiZWVFWlssK7kwX243l04HJZni4Mk9cxsd75w4QJWV1fRbDalmYTfGfXWXJ+NMwQOTFSZ8PtmcGTixKWJyIOz+MnlmzqdDtbX19FsNpHNFvDxj9exu9sb7u8S97IXv3gLvV73xDxZDktYDsPMAKyUemDKWxWt9e6Rz/oMQrPZlGm+lZ8EDn8IfD6fGIxYtyc/xkoyR18rb8bVC6rVKpLJpAR9a588gy8wWrTyer2iDx5XY4x78Vo56UUfROsUjnw1NcqtVktaYglybrFYTBQL1ml9NBqVRRmpg2ZzA70j6DFLBQRbf3n+crkssqzHH39c6Ap2sgEQvvnq1atS/Y9Go4hGo6KWACDHoVEPJV25XE663lhkYgGPhbpUKgWv1yvFKQYrq1aXbbybm5uSqTYaDWl0YHZsGAay2Sx2dnaglEK5XMZgMJDEwOv14sknn5SBnCbyxWIRsVgMq6ursuYdbTHvvfdepNNp9Ho97O3tiT0mBz/yyrTDJC3E7D2VSglvHY1Gxd8iGAyKiodUIiV+/MyGYSCdTiMUCuGRR7bx3vdmMBi44XQ6MBgYd7iXNZvLWRThJHBYBvw5mOu/AeYyRODfQ2OcXwbwb7TWxoR9bQAIBAKo1WqSuZFKGNeyjgewXm902W3rQ0M+lDaJfJ2rPhwcHEjBh9yZUmqiPzE7uHgeqgLooMWuK+pLrV683IcSp3g8PpNimdXGTI8HDijsdLLeExbefD6fNBhwZkDVC53o6NhFDSozZsAcyJi10gYxm82KYxmzPwZKpZS0sHIaHo1GhT/m9L5YLIqPcDwel8yNa8KVSiXU63UcHBzA6XRibW0N4XBYpFecStN3OJFI4NatW5KF8v6T/+YAcuvWLQl29KXgasX0lSiVSiJVo6wMMDN1usLRYCeZTCIej4vumG3bbADhKiAHBwdotVrCYQOm8TzpHQZNFsmy2SzW1tZEZUEumV2IzIxTqZR8ZnL7nHlwCSWXy4t3v/s6PvzhLAAH3G5zoPnSL03i277tS0XhcJzOz8OwjMajwwLw/VNejwH4CphrtFXxlCrCxhgYkMgBWz0EZvGp0wj+WZVXn8/sEKJmlkvhUA0waTrPgh6Nrpk1seDBB5+DBbNoaje5dDmX85k2JbNK66zB2ro9H2gem9Ninpv700PAGuypcWVDxfXr12V6vb6+LsY9V65ckQGGQSkajQpFQe734OAA3W4Xt27dQjAYlKkyV+jlsemby+yT66DVajVpdb506RL29/eRyWRGvHr5X/K8tLNklrq7uzvCedbr9RF3MmaxDodDzM95z6iSsHY/5nI54f7Zas0iYSAQkCl+o9HAlStXZPmgTCYz4q6XyWTETrPVao04u1mbLxwOhyymSZ5bay3FR8CUsfHeJxIJ+V4AyL1mw1AiYTqTdbtevOMd28hm60NbyQE8HuCbvukSHnro/pHge5zOz1lYVuPRzACstX58xtsfV0rlAfw07AA8E7FYbKLl4yy7ymkE/2GVV5/PJ9X3fD4v/KC1lZPHZRZNfo06W3q+0hjFSgdYKQ8AUqQiF1ssFieK5anKmBWsGWgzmYxYC3Y6HZGicQAgZ8iAwwzf6XQKl3vlyhUJQCxmMpsvFAqiZd3f3xf5U6/Xk+V/uHYcTXhoaEODHtIePp+5YvHBwYFIsDhAsND2+c9/Xrx3Gfy4wCfbj+v1upjKs5OOa8pxek+j9LW1NZFg8Xvd39+XZhLOegCM2GpSHcNATtrk5s2b6Pf7QpmQAydFweBPGoGzHwBiNdnr9ZBIJOR5AczGDSYBXPmY3YhcRom8ML2MOVNst9tS4AwEArj//vuRSCTxsY/t4UMf2oVhAG63C16vB5cvh/CSl1zA+vroczdP5+dRsQwJGnBELwgL/h7AlWMe48xxUsY44xgPOPME00k6XmB25dXn82Fra0u0rz6f746Rn+dm5xZXpGUmx2kkAJEwTboW2ha6XC4pflq9e3lf6ekwaa0ugioFZu/WzB2AVNiZxVE5QSqAMitev8PhEA1tLBaT1t6DgwMp8KTTaQAQJ7RKpSIcKWmMcDiMnZ0dmQnQ64Hro/Ec1rbcRCKBjY0NOJ1OWZiS94ADCDvJ6C9hGIb4TpC7tU7vQ6GQ+DxwQGOBi007DNJsoKBBOQfQTqcjfhOAuf4dawyGYSAUCuHChQti9hMKhcSFjQMHM1+qRNh+3Wq1ZAbCDr6rV69id3dX+O5KpYJQKITV1VVZ5ZkGUpTAURdMCsntdiOXq+HDH76GJ54ooN/vweVyw+t14eGHL+Ghh65MdC9bth2sNU4s69jHDcCrAPKHbnWOcVLGOPPgqDKWeSqvh23D95jpMVMiz0hulxnaJLTbbfG0LRQKI/taVyq2rt9l1R9bM3LeB2aZbGqIRqPCI1oHBPKqLMyw8EajF6oguC29fak/pWOX1bdgY2MD5XJZCmdsauEinlZZWCQSEXN066yB2TRd0Jg5M0sMBAISRAOBgNASWmuxxQwGg+h2u9Ja3Gw2EQ6HZfFPr9eLy5cvi1kNdbL0gKaWmJSAx+MRq0zy1wBQqVQk22dxktpeFgm5rDyXbCJtxABKrXGj0RAKCzCpBQar9fV19Ho9kbrxfrE4uLq6Kpw5C8sM4OFwGMWiD3/917fhcvmEV0+lPHjZyy7iWc/amGoduQyZmPVZt8YJyuSOe+wjB2ClVALAzwH48FGPcR6wrKkEcLRMehEZy7IzdZ673W4jl8tJgLDqSll0mwTeO0rSrBaDAO64r/zxTvsMzITIHfLBpvgfgCgZGKhZnWcxkdPaRCKBUqkkvg+9Xg/ZbFYW7PT5fJL9cyAhXdPv93Hx4kVorbG9vY1qtSrXbF0qJ5FIoFAoSBbItmkGDvoZcLFNFg7dbrdQUiw8MYuk/SWzTHKXDodDzN2ppuH1tNtPLcDJYM6ZD7v4UqkUUqmUcL9U53BqzkIlg1kikcDW1hYymQxcLhcuXLiAZrOJVCqFcDiMVColmTn1y61WSwa4jY0N4ZUTiYTQXSzY8vqorqlUKkKDcYWQSqWBT3/awN5eZUj5mAb0X/IlIbzgBStYW4scOxGZF5PixDL45MNkaH+Pp1QQVkQBXITZBfe9x7qCM8ayphInnUnPOv5xAzMLWOyMYnvqYSO79d4x2yRHDDylTBj35Z3nR0N+st1uSzWfpjEsxjGb4jnYpMBiJDNupRQymYxkLQwGwWBQskZ2XvX7fcmg77//fkSjUeRyOTSbTRlY6IJGiSD9FTjlJ8jhcip//fp1aYCgIiKZTMracewgo8KBemtm9rVaDYlEAoPBAIVCQRozyIfTf5f89NbWlhyP9wmArDbBAh+/bw4SrVYLt2/fRiAQEIMd0h5cfJOzBg5g/I5TqZQ0bUSjUVy4cEEGm9u3b2MwGKDTMVdYJkdMpQZpEnMQSOLatRrq9SqCQWP4bBt40YsiuHgxisGgd8cM6iSxbDqDOCwDfu+U16swlyb6U631cttLThnLmqYsM5M+7PhsiGBV+LiBn8GX+01b1HIc89y7Re6rVXpnbcdm80en05GOKVbZGSz39/clYJFLBSBtr5ySU+BPpQODe6lUQjweF9mY1QmN7cXlchkrKyt48sknsb+/j3g8Lg0v5Kk5PXU4HGKByWYFZu3kdznd9vl8Uqyq1+uS/TMTpocDaQsWHtmK7Xa7RcnBQcrtdo+oTrxeLzKZDK5evYqLFy8K/8xA7HK5xIaTa+RxgOC6dCzYkg9nwTKfz8Pn82Fzc1NMdwBgfX0d6+vraLVaMlhy9sBBbG1tTaiHQCCAwQD42MeKePzx1tAbIwrDMPDFXxzHffcBWvdHVDhH1aAvimXSGVYcpoL4yaWc5QywSFa4DBrgpEbI8eMzI4xEIqjX61KRHg/Mizwg/FEDkGr2vJh17xad/o1n1BTds7DHtdZYVWe1nMUgr9crzmDUQe/u7qJarYpkzel0SgNDJpOB3+/H7u6uZL7M9KxrkTFjJh/LTK7dHl32ifQJlycCIE0erVZLNMKkDvr9PrLZrBS0KJlyucxFRjnQuFwuxGIxXLp0Cd1uV/yBubIvLTtXVlZkSk+/YVpKWlUTpEmUUiLnY4MIOzeTyaRon6kDJp++vb2NlZUVkSGS52632+Ja5vF4EIvFZLVia0MFwUw9FosNB03gQx8qotMx6ZzBwEA47MXXfd0q7rsvIVl/NBoVE3i2LvN5G8cyabtl0RlWHLcIdy7BBoRl0wFWGmC8Y+ykRkiCxysWi9JWal2ccjwwW/eZ59ispK+urs7lV3ESGL+HNMOpVCqinKDMrFqtCvfMFlk2H/CzcGUQ8p5cTZct3gCws7ODcrks322r1cLm5qbodCkt4wrMdDkDIAHa6/VK9xtgTvFZ3OIqGoVCQQpL9GbglJ7FulKpJJQD1RBcTJP35datW6hUKvD5zFWR2ZJM4/VCoSB+GNYmCrZOl0olybbJLwPm4EGZGpswuMx8KBSC3++XNmWtNTY2NqQRg9mzYRi4ePGi6KO73S5CoZAsmUQP5lKphFgsJgY97XYHe3tufOpTZbTbPXi9TigF3HdfCq961f1wuUyaZWtrS+57r9eTTkHem/Hn/SwL7PPirgzA7Bw7ql/BNFi1s6zismgxHoT52jLh85lidHKV7KwD7gzM835mPqRsEGEmc9Ss4Sj7TuuQAyCNJZwm+/1+sTIE/n/23jxIkvPKD/tlnVlVXUd3XV3V3dPTcwAEQGhIgAAvkOAFCiRjF+aubXl1OeSwGPauZdmO2A2td+WQFHIorNDKlrRaK9YKhby2Ze9FLoNL7vJYMrhLEgsCEAkQGhyDGUzP9Fn3fVel/8j+vfkqJ+vqrp7pOV4EYtDdVZlfZn75vvf93u/9nil64/V6USqVRGs3FosJnYxOmCR8avsycqU4OvUwWOpM3JTNMFlgQGfMxpgUmuF9czgc4nzI9qCUJHvNsb8Z6Wh00nwGFP+hJgYhlc3NTXG4FIQnfAFACiu4Y2ALdjIcCNeQg+xyuZDP59FoNFCpVAQfJ7WNbZgSiYSIw4fDYUkuplIpgYtyuRzOnDmDYDAoTT8dDgfefPNNqQTlzuX06dPCvKhWe3jxxQZ2dkoS9VarJTz77AY+85kHoGmDm+YTE7pWPROrHTcsOA+7Kx0wgCG9gnnBASp31jAMSTqpZcXHveKOirTpmNniOx6PT4XjWieplT6mnnOScz3M9U+TXFxaWhIIgDsPlrmqcpEAhlS5gsGg4NmkSxEvzmQyyGQyACDOp9vtihC43+/HlStXZBHP5/NStUa62enTp0VbmBl9Oj6WzDJ5R/iBn6OAPB0eJR07nY4k5Pg5Mk24QLBopN1uIxKJCBOD2DATgvl8HhsbG0MFHRRK1zQNvV4Pi4uLyOVyaDQa0uae3aa73S7y+TwymQy2t7fh9/uF8dBut/Hud78b0WgU169fl+q/K1euCF5tGAYqlYrcz2w2i06ng1AoBL/fjx//eB8/+UkPmuZCp9NFvV5DLObG4497EQ6bxyIspM4NOz0Tu3l23LDgPOyudMB8UdjuZV5OcBR3dpoy4XmaHRal67ps9fx+v2xTR8EJqsgO8cFR9LFpF5fDXP+o76jnY4UeI0jyeIktqtvgvb09bG5uCvtgeXlZEnaMyprNplTbsWCDDhEwcdt8Pi/XubKyIi3p+fKTKkcMlGLtLN7gHGHUVy6XZfHmos0KQ3Kcz5w5I50w3G43KpUKGo2GLC6MdBcXF0WeslKpiNMlzYv3cHl5WWAQskTYnZkOjFQyXgvLjQlNkGvNxSQajcLv9wu3l2XILElmOXE8HpfnxoWCNLbFxQT+9E8zuHixeyCM1EWtVsX6eh/nzg3Q7bbQ6wWHKhI5Ls4Rl8sFr9c75HzHaY0cByw4D7srHTAn8HFoAqvc2VnLhO1snkkCdifg+KiXandOq3ObRB+bxrmqicJR20LrdVsXAOv51IIQOl9GjOFwWBJI7OKwu7srjpmwTDqdRrPZlGIIOknAZC2Ew2GcOnVKHEmr1cL29rYUVLADBB0enVcul0O9Xse5c+ekIpDVetyuU8CH9DSKo5M+RqyXUTKTaOQe8//JzmAX5lAohEqlIkI+hD4osUle7rVr10R5jKp49XodOzs70gqeTq7dbktBRafTESlOOjrCIuyRx0o2YueE/Xq9Hvb29hCLxSSq1zQNTz75JEolB770pUuoVDpYWAii1+vC6ezgqadiWFpy4Nq1a8LldjgcuHLlCjY3N+F0OvGud71rCCZRneyoAOE4EmfztJEOWNO0n5/2IIZh/MZ8hjMfOy5BdjtnqeK9s66484YspmUzWJ2p0+m8Ca44zOJCx29ti8Tj8b5NWgCY3GKZL196FjF0Oh0Zv9vtFvx2f39fjsFMP7+jaRoymYxgntFoFLVaDV6vF8lkEvF4HHt7ezAMA/v7+6jVakKnarfbQwk/JssIN2xtbaFSqYiqnK7rglNqmoZXX31V5g6r1NjVQe1Fp2oPkxrI/1iiS+lGVuuxrRI1FOjcSY/jYsHiDwDY2dmR87KgYG1tTcq0+QzolNWiAxaYUJZ0YWFBnicV0Or1OpaWlkS5Lp1OI5VawYsvlvC9712DpjnQ7/fQanXx3vcm8OCDgNvtkF0Oe+iRH82F+MqVK3jooYdsczt3At5rZ+Mi4L875TEMACfKAR+HILvVWbLqS3WeAMZGs1YHPu8qPKfTiXA4LFvNUfDDPDUorNfEJBZpViqmTPwWwFARBRcAq2NmVMXOCBSEUfVjudCykwX1E4AbymZkMHDrysQdsdGNjQ3p9KBGhFQj83q9wrn1er1YWloSNoCu68hkMtIxmVQ0TdNQr9eluIDMAM4dOiwmA1kCPRgMsLy8jHQ6jXa7LYwOwiWqkyILQn2mrGZk5K3rpl4Hq8t4b3Z2dgRmSKfTiMVion7GhYVMCy5mFOkplUqIx+NynalU6kAi0lx8iGMTK9e0EL75zbeQy7WEBrew4MUHPhDHu961JDoQZGBwPK+88oo8b7I2crmcXK86b0fN6Vul83JYG+mADcNI3cqBnGQjv1btBEFe5DTJKx4jl8sJZ5Oap4wSjpIsVB0XVcRGTTZOSDXqPIrTt0bw1msCcBNzhJGVVZ/VLjJnaS61YenQKMhN7Ht7exvZbFbwTafTKVt7Cs6QvdDpdFAoFIa0Her1OpaXl9Fut7GxsSHC6JVKBSsrK/B4PKJ4Rn7y7u4uarWaSE9GIhFhCBCHb7VaSKVSuHbtmlDAXC6XMBzYaYM4MJNjkUhEyqHp+InX8piMPikhSeEdwhSsZlSLWBwOBxqNhpT+qotmq9VCKBRCKpWSXnKrq6twOp3SHJO7CaqpsYU8GRik05kLkRNXrvTxjW+8jcXF6MEzdeDRR1N48skFdDpV2SFRWJ/j1zQN0WhUimQcDgfW1tbkmq3z1i5AGNX78CTZXYsBz6tCRo1WVGYFSfCAffKKXEWOgRlqj8cj2gucKHYTahabRI+zXosdhjbtfVCdrbVbMRchXdeHSoeJ45I5wuiYGfdRMAeZHdzqApCWQ2zW2Gq18M4776BcLgse6na7kUgkxDE6nU6srKyIg1PLdff29vDGG29IR9133nlHEpmrq6si0UjxcD5DOtlXXnlF2r6zeCOdTkvVGKNBFSNVS27D4bA4NDoyJurOnz8vx/R4PNjZ2UGtVpN7lkwmRXnM4XAgFAoJt5edl5kLoAOuVCrI5XJyT0OhkETVFAlSBZioitdsNiUS5jNZXl5GIBAQ6ITPzexJ18WPftTB1lYTPp//4Hw+fPjDCXzqU6cO+uP1pTuJ2l05n8+jUqkgHo8jFApJOfnZs2eFZkkaomqqE6ayHiEa0v5mLVI6bpvaAWuaFoTZkv4UAI/6N8Mw/vGcx3UkYyJjHrgqnRsjX5VZoSbigBvJK24LmQGmqXKK3FrzuCxnnUbgw7qtYtTJBUHtdDEvyMOOrkYMk5gkRWDIbSWflY5WZY6odfzq9VgVpnRdh9frRSaTkdLaUqkkUWS1WsX169eHyP7RaFTwTzoSRpmMshYWFvD666/j2rVrQ5ASy38DgQCSyaTwkBuNBlqtFiKRCPb39/H6669LIorzjY0q2cKoVCqJ9jB3T8Q0Q6GQJLsASOmw2omEes7pdFogD861H//4x3j99dcF3mDSikUeTMyxq4XL5cL29jYKhYIch9rGLOBg+yUA0smZuzoqwpVKJdGafte73iUcYC4kJkau4+WXG8hkagc85T6iUTeefTaG9fXo0CJNB8xiGarLsf3VysoKlpeX5XdqD0S794JBQqlUAgCRuGw2m0in0zMXKR23TeWANU17H8xW9E6YQjxZAAkADQC7AE6UA2b5JnB0MN5aHqsm9+wiSEY5KsZJx0riOKu8GLFms1mJSilAzu9Zo3hrJMqIEIAktFwul+0icBRepPW7wI0iCEa37XZbWvwwIlN50oQGuM1mNw4eizoKxIO5i4nH45KY8/v90HVd9AfIX93Z2RHMkWpn5LPSSSwuLsqiQXFwvpD5fB6dTgfr6+sAIMUSgJnczGQyMAwDV69exebmphR/MBoLhUJyjTs7O6hWqyKMzudCXJvYMyEgCsOT6cEIjqyDarUq/1J6kosEk5CsrHM6nYhGo7IgRCIRSeo1m03RSvZ6vYjH44K3cusfjUbRarUQDoeRz+elMo9UPc6pfD4vnF82HW02e/jJT1poNJzodAbw+fQDep2GRx7xYW3NhCHI/CBH2ul0YnV1dQhn5yLECJz3hIlAu3daDRIYILEoZWlpaeYipVth00bAvwbg9wH8AoAygA8D6AD4dwD+xfEM7fDGyGse5OtZmA10yIxmgBuOTtfNCi0rG8DauZhVT6PwZG77uS2sVCpDlU50wHaLgNvtlntzmEibx+ELwYIBJn1Y3VSr1aR9D194VW8YuNFTjpVMxIU7nY7AO91uV6AMJqPMLrhmJ2F2kGDyhslHFhEAEJWvRCIBXddx9epV6VrRarVkoaLD3dzcFM6w1+vFxsaGMBHa7TZeeOEF6RTBzr6Li4tyX9jvjW2eiPWS4kWny/JxzgO17Txhk0ajIfeNYux7e3syJ6hxzLJswgjsWxeLxQRHDQQCWFtbE/4x2RZ0vOVyWVoDMZrt9/vY3t6W3+m6LrsMTdPwxhtvIJ1OHyjG9fHDHzYA6IhEuuh02giH3fjoR6MIh28wNViZuL+/P4Txq0FKq9WSKJ3FLZzzmqaNLDAap843KXq+XTatA74A4AuGYQw0TesD8BqGcUXTtF8E8FsAfvfYRngI4yo4L/L1rFzCUU7behxdN0uL6YTULaYdnswImQR9JnTYQIs8kQAAIABJREFUh6xcLmNlZUUchroIMBHDF3qSjaPI8TjEG/lCsMKK+rd80ckNZQWX+qLQ8fV6PWQyGWkdRMyX+gOhUEhwU+4Q2JGY3S5uiLqYVDxqIdChb25uijgMaXqETSKRiNDeuFgmEgl5eVkVxoWtUqkIXYpRHK+LEXgul8PCwoJEsKrKGZNXLEfmIkfOL+UoSbUjpEDIiR00GCESo+ZCtrCwIG2RKNTDzhZcwFZWVoQpQ+dGh8Uol46LUT6dIROXPl8AP/pRA9euAQsLYQBmruTRR5fw6KNu+P0O0epgu3vAhNycTifi8fjQ/GLpNcfAHZXb7ZZ2TaMi2HHBkh1v/yTYtA64B4CdjzMwceDXAZQArB3DuI5kxJTmdaMPQ2WxksRHfZ8vu+o0AHs8mewCZuHdbvdQxMxMP7UdVDyVuOy00Iy6nVNV1tTfW/tsqdfRarUk4mFTx3g8flNnZpaa5nI59Pt96XjAayW9ia2SqEVADi/FYtxuN65cuSKFFazIIibKzs7RaBQ+nw87OzswDAMbGxtCuWq1Wsjn8+L8mHSigtr+/r6U9lKekdKTpKBxwWKhCJ8xHTEF71WhIADCDuHzZ580VRuCoux+v186YTCC5XcIbwSDQamOo95vMplEvV5HIpGQ0miPxyP3eWFhAbFYTNgM3EmwVTx1NFiUAQTx7W830O/74HQO4PP50es18LGPJXDhQko6XkQiEaHnqS2WmLj0+Xxy3exC7XQ6hVlhGAYKhYIssqN0gAlbAbjpM7MGUbfKpnXAP4LZBfkSzA4Yf0/TtAiAvw6zdf2JMmZqgaOD7bMUS9g52knf53fsIAErnqziWqypp1NhVpgOw+6Ys+C/o+Qv1Qi6Xq9LoQMnOM9HaUNm4dXafd4jUwmrJVEyo0hqPFA9q9VqSbUbJTjpMBj5sblmKBSSHQOryBYXF7GwsID19XVhFLBRKrfxTNBdvnxZtA3okE6fPo1EIoFarYbt7W1xQuS6MovPxY5lt8RzGcWyOwcpXWqiTO1IXa/XRQSejk/TNClIoToZnT6jYSrBUT+42zX1IhYXFwVy4EJAmIVdQhjVh8NhrKys4NSpU3C73bh27ZosRsRqr1/fwt4ecPWqE7GYA2632cnj3LkwnnrqNCIRvxTLnD59GlevXhXYgtesKr1ZmQlkjLjdbsGzWQXJoMLuPWITVuYj1MX+pNq0Dvh/AsDeNL8K4P+FCT1cAvDXjmFc0DTtlwH8DIAHAbQB/DmAXzYMY6LD58swD7B9WubAKEc77vvjnLMdnmzFtVR8q9vtolKpyJbYrriC559mG8a/W1XWyB1lpMHoVP0OAOkjxiaMVBijFKE1+UbqEx1WNBpFOp1Gq9XCzs6O3EMmZ1ZWVqQxJhkkKkWsVCqJU+10Orh06RIWFxdlcTpz5gwKhQLy+TzW19dx8eJFFItFibzIKKD2bjqdlvJmJqlYSkxIhM9wfX0di4uLwtwgu4E7AGLdlLoky4Db7mw2K9QyJjp5j+jUAQhuHAqF5P5yXvT7fezt7UnHDcIUAIYKQOj0ubDRGauVfOweEg6HUSw2celSFbWaC4HAAIOBgXA4iI9+NIlHHrnRv4+LHIXfyWLgAkJMWXW+qhNlZV+73UY0GpWedkzeWt8XJvDY5WMcVHGSbCoHbBjG88r/7wH4+LGN6IZ9DGaF3YsANAD/AMC3NE172DCMwrgvMhqYB9g+LXNglKMd9/1pnPs0jlPFtxhFjoI/ZsWyrfKXHAMd5aix67ouFUx8MWq1GmKxmNCdGOETyiDbgfedBQncytZqNRFZYsRIFgiTT2wsmU6nMRgMkM1mhQHx9ttvixIXEz9kKpDbur+/L7xeRt2kuJFdwudGrNrj8aDRaMi1kD7G3mt0BuTn1ut15PN5ofIxeca54vV6kU6nhUlB9ozL5UI4HBZRd36e0Ec4HJYCCSYeAciYB4OBFCcwQjVpYjdaxxN/3tvbk8acS0tLyGQyuH69i+98J4dm0wOv1416vYFwWMMnPuFHNGouRrlcTjQguAizIGYwGKDf70vTUWu/QY4RgETKxKCDweBYGI00PDWXMgqqOEl2YgsxDMP4i+rPmqb9NdxgYHxl3HdVCtRhbFT2f5wD5KpN8juFaEZ9X/2OtSLMapMcp93f6TSI542DTyZh1HbjVxcWKxRBI57H6DQej0vETDoWk158GYkFVqtVlEolcdJra2tSvUYohtVc+/v7ok8QCoUE/93e3sb+/r4IFNFZVyoVedE3Nzel+SXbyzPB5nA4pM1OoVBAJBIRLnev10M6nZYtMcuk6RBZdkx9CMItb7/9tnyfnHIuZlykCPUwwiVOy/vIZxEIBITfTKiCi47PZ3Y/djqdyGazonVBmIL4s67rstDxnrK4ptlsIp/PI5crY3s7hCtXyqhUmgfi71488IALTz4ZgqY10GqZiWNG1mRxLCwsSEGE2212SNZ1XRTs1N0TnSh3GWxiyoh/XDBjl0s56dEvMD0POATgV2BGvgkADvXvhmGcmv/QbrLgwXmLkz5INaXDrIDjVJXGfZ4VSUx6kONrxUat31GZBPOaMKVSSVS8ms2mFB7YRdjq9Vq7fNDsrl+lxQH2UITqvFWVtkKhIM6YHGI6JEa6VEkjXspIDIBEb1QZW1lZQa1WQyQSQTweF7pWtVqF1+sVji+TPsRuK5WK6ETQ2QGQKJ2QC5N4xFSJSfL5sgBD5a7SGXGcLOelcyb0ouu6RLmEDhjFM9G5srIi4zUMQ6rcXC6zWzGjXi4O1C5WxYTM/mohWfjJD6YjJnOCiwIThdlsHy++2EAg4BPti8VFLx57zI2zZ2OIx+O4dOkS6vW66EREoybfl+ycWCyGSqWCaDSKlZUVqfRT8xWcYyrPvlAoIJPJYHV1VRqbjmsFf1ITbeNs2gj4/wTwPgD/N4B92HdKPm77ZwB+DOB5uz9qmvYFAF8AIDXj41gIo6K+WavF1M97PB6ZKOMI3+OYBPMw8lzpCKrVqlC4rCXaHMukMmY7mxaKsKPe0en7/X40m00Ui0VJmnGryqiaztnn80lBBiMlRrLs/lsoFPD2229L+So1ggFTtH5zc1PYFwAkMi+VSoK1kneraZokiljMQP4q76vqdBl1koJHxTHODb/fL7szVaXM6XQKC8LhcEhhBRNvpCCSctZqtWSOs7VPPp9Ht2uqrZ0/fx66rotQe7lclu4YLKygYH08HpcFiu3mi8UiCoUSdnb8KBSCgncDGs6c8eDBBzU4HCYLh52aycbxeDzCk2bhCylxTOByV2TNVzBhm0gkkM/nce3aNSwsLEgvObvg4E63aR3wJwF80jCMF49zMKNM07R/CuApAE8ZhmFLYjUM4zcB/CYAvO997zPGJbqA0Z2EZ60WUz9POtAkwrf1HJqmIZvNAjj61olOVI0yWIrKrX+/35dmjBzLqC4f485Dx2DV851E21OdP/nJxWJRaFOhUEiq2xgJUpPWMAxkMhl0u11p0Mn2OZ1OR8qSS6USAoEAEomE0LcWFhZw9epVKTSoVCooFovCUKAYDx2+w+FAPB7HxsYGQqEQtra2UC6XpRiGpbqqhOLq6ip6vR5eeuklcT7UeaBTrNXMEl2K57CZ5mAwEAiLi7Ku66IT7PV6RWuBiUCKCxGySCQSshMgi4WfIQOD91Jt+El63NWrV9FsunD16iKKxS4WF90IhYJwuQycOdNCt5tFreYQiIG7Ez47llADN6h0pLuRDulyuZBMJgUnLxQKkvTku5DNZmXeEuJJpe4+fbBpHfAmTC7wLTdN0/5XAP8ZgI8bhnFl1u/bRbSAfdcH4GbnwM/zd1anom7FGbFNUhmznkOt9DksfYZOr9VqIRqNStsear1ms1nBVJkhV6PTUV0+Rp1rnJ6vqvpGAW27+0Y2BTPmXAwWFxext7cnhH+3241CoSD92DqdDl577TVxPNVqVSQrSf/iuQgDUIR9Y2MDg8EAly9fRq1Wg8PhkCRVMplEMpkcgg7ITCiVSlIIwZ5n5OkS7mK3Xmb9mexjRE9nl8lkhhwMI75isShFIvF4XDiwrVYLmqZJIo7zNpPJiJBPMpkU/Jw6v7qui8Ql4RVN0yRCpWoery+Xy+PaNQ25XASRiBPhsKk5fPZsBE8/HcPu7lVsbpo0u16vJ88ZgEBHpOCx27Pf78fS0pJ07mCxCBcRjqVYLGJpaQmxWAy1Wk3uHfUyTp8+feTAZFYu/62waR3wfw/gf9E07W8ZhvHmcQ5INU3T/hlM5/sxwzDeOMwxRkW0k/Rw+RlVr0BNsHH7za0kyeOsgpoEdagOiPXvwOHoM6TvDAYD0YAlV1TNNBPTVGlIKkZtFRcapShnXdRIowJu8H89Hg9KpRJqtRqSyeTIJCATL3TWdECdTkeoVZRQpKYDHV8mk0EqlZIuFqSpWQsMGLESeyWVi5Eb6WS8f/x8KBTC7u4u8vk80um0lFtTn4KcZV6X2g2CMAWrvxYWFpDNZgX6YIGNqtzHUnEmxjgX4vE49vf3xRHTcREbbjabaDabWFpaQjKZlOPzPnLH4Ha7RdGPUTnZCIah4+23wygWNQwGfZTLFayvr+Kv/JXHcP68T0qQWULN5CL5zLquC65NPehms4lIJIJUKoVcLicJZ3Kn1YWZmhiRSAQ+n0/Kpmu1Gk6fPo3l5eWp3we79+Okdkee1gG/BsAL4KKmaT0AQzCAYRj+eQ9M07R/CZNj/B8BKGqaxidQMwyjNu1xxrEYxjEbVCfDUmG+ILu7uwgGgzLhSI2xRtTTPHi32+zwwKTcuEqfUUaHQT0Ajke9NjoBRpykPVnv1ahx87pU+hNgv4CpTAFCCOp9sZ4zFoshm83KLoKRn6onsb6+LtEVYRVCDSzIIFUpl8vJdn1/f1/Km6lvwHEMBgPByyORiESDhUJhiG7G8RCaCYVC0ire5XIJ3YpJJy4mbFQZjUYRCoUkiifUQOiCVY3EVLmQsMCClWPkMRNH9vl8ortBtoPP58PKygq2traGStHJSFD1J2jFohcXL4ZRq3XQ75PG1cUzzwTxnvfEsLm5iStXrsgCTU5zIpGQ6JUQT7FYFDydxSKVSkV2S8ViURgwyWRyaEdKrq8qRkXWhNXGRbTWv82a17mVNq0D/ncAkgB+CbcuCceWSH9i+f3fB/D3ZjnQqCz+uIdgxXb5EnKbzu0gcAPzBWbvo8axsNqL8IN1Ek3aQjFxRqk/VbCE343FYhKBjcOareO2igONy0bTGdVqNbjdpm4yubbWHnEqjszxt1o3OntQHzeVSiGRSEhRBUtwCbkwmRSJRLCzs4O1tTUEg0G88847onzGLL3P58Py8rJEzmRE0DEDELZCqVSShBi32OQPM/nmdJpi5ozSWa3FeUNMXdWN4PjJ8CDmTSFzKsIxAiZGuri4KEwQcoTz+TyWlpZk50CFN/ZrI9RAoSSOx+wqMsDFiy4UCg3oegA+Xw2GMcDqahcf/nASut7H5cuXRdOY2O3y8jJisZjod1BrgjsrOuNgMCjBwNLSkojccyFzOp1IJpNoNBq2HVwmMY/sAptWq4WtrS3ZBa2urs6c17mVNq0D/gCADxiG8epxDkY1wzC0W3UuO7Niu9xS8f+BG46Xzm4cV3bUg+dEZLKGmqtquyOK1YyKpFWpSzvnq+K10yT5rOMGbsbMxx1H13WhlqlsEJWap45rb29PijWKxSJ8Pp9EUKurq4jFYmi1TIHttbU1eL1e6VTR6XSQy+Xg9/ulmKNQKGB3dxfdrqkdXK1WhQVA7nWz2UQqlcITTzwh+Cs1HuLxOMLhsKiS6bouz97v9yOfz0vVGAApVGFk6vf70Wq1ZJGiA/V4PMKcoFA9xwRAIAGyPQaDwYG4eUWcCXvRkR7n9Xolj8DdgdqlgpE+nT9bxHu9Kbz66gCDQRuBgJn8evDBNSSTWTgcFfT7PcH2mWgk75pYealUkmo5wi8UMAIg/HYmIKnH4ff7ZVfJhXMWGxfYZLNZURikXsja2pp87k4V43kTwNxhhuMyPtxZty52xiiHlCTiVsCNB8r/H5Wgsyb01M+QT8qIStf1m9odWX+2o3pZpS5p00Tho6rl1Ouz65RsZzwfk5Es1bVS86z0N8pQEg/d2NgYuteNRkMgAiZoWJnmcrkkQZbL5YSPncvlZLEg7svuEBT/ppAPnT5wo6NxKBRCvV4fagPPiJtRPQDBYZmgc7vdsiiyoISFGTwfMWvCJrwHbAFPHF1lqHAnQq5uIpEQ7YtGowG/3y/ReDgclnLolZUVbG5uHjhoDYVCDPW6AbfbC4cD8Hg8uHAhhscfX0A+r2N7e1t4zeQts5pN1XjmAsRrZF6BgkhWGEzV3mCwcBibVF1q1REGTi5HeFoH/IsA/ommaX8HwE8AdNU/GobRmPfAjmKjxHhmBePHJZv43UnHtCb07DDVxcXFod5VLAIAICLk6s92DnDUBJsUhY8rPLFLvk0qh2aSRr1WKpup53e73dI1gj3aisWisCYADDXqJD2JJcjtdhvZbFacQrPZxPb2NjRNEyiCLAftoA0R5wR5uf1+X0RvCHF4vV4sLy8LnJBOp8UhVqtViVrJqeb1stqtWCwKq4DYK3cC3DExs8/KtVgshlwuh3w+L5Gyrpu92vL5vLTrISYeCASkeIS7J6qf7e7uIhKJ4Ny5c5IwZIFNu+3B9esx9Pt+6HofbreBhQUdn/jEEt7//vWDkmizjJu6xoz4g8EgTp06Je2Uut2u3ItgMIhOp4P9/f2hFkLxeHzkTk1VfpvVxuV11NJ5NlA9yTatA/7Gwb/fHfH3w9X8HpMxarVGe7OC8bNACOOOOQ2mmkqlbDFfTjDrz8B00fy4yTrLPbFzyKrTZXSrZuq59QUgdDI1IuF2mb3AnE6z/Q5gRrxkQVBUp1qtYnd3V55HrVZDPB7H4uKiaO5SjWxnZ0dKcX0+n8hptlot2eJvbW2JiHuxWJSokcwLtgGq1WooFosIBAJYXFyUhBalP+v1urAhKAnZ7XaHeqWRZ8sCDdKzSEFkFZq6+LJwgzzh/f19PPLII7IL0DQNp06dQq/Xw1tvvSVthDKZDLa3txXFtAG2tlzY2tIRCHjgdptzb3VVx6OPOrG01EOxWEQ8HhdnSl2JUqkkFYO8l0yYkX1DqILtj7ibU1tisciCn7XOoXnRwwhnqDuCk2zTOuDPHOso5mzcOlkd5qxgPB0fH6bdBGGyhtGr3TEPg6laHZ7150lMhXHf5fdHFVJMY+r52X8LuMGAoEMhdMII0KpkxUo3XdclEdftdiVyUe8daUkul0tExTudDiqVCmq1mkhElkolhMNhefHL5bK0pCmXy9jf35dzMuKMx+MSWTNLT+ggl8uh0+kgnU6LkEy3a6rtsRCE+r6kABaLRaGBsaSaY+IWnQlKJtgYQXc6HZw7d04Ss5VKRQoYdnZ2pNkp2wnt7u5KxE2nzZ9drgVsbSVQKOjwek04IRxewOOPB3H+vC5ViFy0OFd8Ph+2trawubkpz1PXdSnaIKbvdDqlWzQXHrI4WI25v78vixY5vtFodKhgYxapV2B0IRUAkRq9E2xaNbSvH/dA5mlUqRrFHpgWjGfyi5KPVlyZfwfM+nvSZ6z82cNiquOig0lRtXpeu+OqiTkWN4xaZOyMDohNJfP5vJRiE//jeOwWHXaJpopYMBgUbQJKFPI+qoyLlZUV6Z9GWCObzSKdTsPn84n6ViKRwNbWlvBt2bqdSl9nz56Fx+NBs9nEysqKJPFI+2L0fv369SHNhkajIdg08calpSUEAgHB4nl/eG9YiAHcqJZkj7ZIJIILFy7g2rVr0kiUnYojkYjALKTBASactby8LNALizmuXbsm1+90OnH5cgNbW04EAp4DipcTZ8/G8dGPLqHdLghDhOXTTDTHYjF4PB688cYbwkMmfs4ditqlhfxtloOTJ09qJu8D76FauQdg4u7LGmyMon3eiTbSAWua9jCANwyzDdHD4w5iGMbFuY/sCMaEzCRhnUnbn0nbczohViORvWC3oh8GUx23yo+Kqnu9Gy3gp6GZqeIsdovMKOv3+6hUKoKtsmkjHcLq6urQNQI3d/mgDCMx2kAgILoMu7u7EiEzccWiDVKuyAjodru4dOmSJLRYTUUBn1wuJ/hxJpORIpG1tbWDSq+zwr++dOmS8L2JU3J8XIxDoZCURbPVEoXF0+k0crmcsCqo6cuuG7zH5EfTibK0mJ/v9Xrw+/3C9KDwUK1Ww+rqqhRcMDl59uxZodV5PAG89ZYX5bL/gM7Wx+JiBJ///Hvw0Y+uYG9vFy+/vC1FKMViEcvLy0OyoYZhDAnLc45xfnB3oYrq6LqOjY2NoXcPgHRpZtJS7doMTC7dt85XtajmpNHKZrVxEfBrAJZhtiB6DSb3V6WG8WcDJwwDpo1zoNMWSYyDLKwMBmZ3RznsUWyDWceujlV1cLlcTkqaqY06KTHHl4Lfs7ayH2VOp1P0Y/lyNhoNFItFkYdk1Zl10SKVj+I7VCrL5/PS4YEluMCN6J4voKZpSKVSUhzAUtdarYZoNCqshGAwKL3eFhYWsL29LRE3t/sf/vCHceHCBVy+fBnXrl0TfLPX62FlZQWtlikIT8bFmTNnhkR2GOV7PB7EYjEkk0mZV4zygsEgEomEVAYS/w6Hw0gmk6hWq4hEIkgkEqKJsbKygmg0KhFmo9EQqKLX6+HatWuiX0GVNLPd0gouX/ah2TQQiSwgGo3C7wd+6qfW8PTTa4KdkypHoR1GsPV6HZlMRkrCST/TdVPfmXq/uq4PJY8BSORrnZ+rq6uy21F3N9OW7nO+tlo3urNw53ani/OMc8APwWw/z/+/48xO9Ibb2mmST5MgC+skJD5lt6LPysAY5fyZjQfMrLKaSWaLeCYgmXiadF1qsolb8UljZETKSjEKjAPmFvnKlSvSUZjH4X+MmtVtJCP3/f19JJNJ6LoudEJCEIQCqOZFR8Z7RUhia2sL8XhcOLIqhLG0tCRiNPF4HOfOnUOhUMCbb74pz1EtM2blGhN8nDv8j9xbp9OJ69evo1AoCDuCvdAoWKNyvMkXjkajSCaTaDabwldOJBLY2NiQaj9Sv4hZE6ogFLG6uoq9vQy2tvzI5czdQiJhRofptIH3vtePeNwjTAtVGa1er+Ps2bNDHYl9Pp9E3A888AA2NzelACaXy8EwDKytrUn14KgFWw0yiB9zDo8q3bcz/s3anWXeKoK3w0Y6YFXz4VbqP8zDrDxgwgRqEoiiM8B4HHbS5LBOQp7Prux5FgaGnfNn1nmUcI/b7RYMs1wuY3l5eWRvPPW6WL5Mzuc0mByTLU6nE7FYbMiBs9MDubF20Xur1ZJ2POTldrtmhVmhUEAqlUKn05Eomttidp8gD7VSqSCTyUjnDJLvyUJgNR6pZtyyc3tfrVZx9epVbG1tCde1VCrB5/NJ4kgVxHG5XDh9+rSIjJOdQbWxQqEgSmOkj/G5cKfESJG7JTZZbTQaOH/+vETtFE8nPEInHAwGRXM3n8+jVOrh5ZddqFSccDhwUArtxdNPx7C42EWtVpUSYXb7YJfkUCgklD6yWNTqOeAGgyOXy0mike/XKEhPTV5bE2KzvgucM3bdWe50m1aQ/R8D+B8Nw+hZfr8E4F8bhvEzxzG4w5qVB8wtD8F7ADKZp03IsbeV3YSyYywwUmW7GLINiLlay3KtNip5OE64h7AAt7R0RKMiYeDmSBYYj8lZE3i8dsofUlvW4XCIuhcTU9Zj8HysTiPWS21ZCsYQe2U1GvUS6ERqtRrS6bRU1LGIgMUWTIKxHDccDiOVSmFpaWkoouT95MLCkmriwYRE6Ej5H2lruq5LDzcyMrhVrtVqwkzgfSULgo6xVqtJlF2pVKSkORQKCWxDDq3ZpTmDzU3g0qUaOp0bwcTCQgt/4S+4AAxQKJjR+WuvvSZRPYMRn88nxR71el1aQu3s7CCRSIjwO8V0iF3f0AeGJOK4Q2H3aC5e3BWp7wy5ybwW6/wYZbMm0e8Em5aG9p8A+KSmaX+Z0bCmac/AFGp/57gGd1iz4wHbbeknRbc0dpigWAqAsTQXOphutyuYFZM4jBzYPcBOqnEUXMEkFCNgFfbgNbIKq1KpoN1uS/fkcZEwoxUyR8ZN8FHRi67rWF1dlYw3i0fskoFcSMidJSzAZ0M6X6vVEoofx0YZTRYosKT3+vXrUqhACUafzydtiOhUU6kUzp49K/3bdnZ25FksLS0JdY7C8Kx683q94iio4eD3+yVqZ1VbtVqVyDsej4vcJavJqKug6ybtbn9/X54VITJi1tRbYMRKJ29CEy0UChvY32/D6WxA111wOAxEo3mcOeNBvV7A1atvCsd8MBigXC4jnU7D6XSiUqlIGTAXQ14HtT5CoRDi8bjMHS7kbrdbcHYAsuACEL0Oda6R0z0Pm/advVNsWgd8AcC/AvCypmm/BOA8gF8A8I9gNss8UWbHAz7K6mnVmJ00oYgPMgImRkiJRYqjM1K14q0qxYsRFifeuL5X1mvkdntchw4r1Y7HHWVqQsQayTMSJh2N0ph2x7A2UCTVicT+TqeDbDYrspAsnmDRAvUPKNXIljesHkun07IQ05GRnUHnXq/XsbW1Jeppg8FAOkxcunQJsZjZcuett96SlkH1eh3Xr19HPB4XzQVTW8Er7X0ILZw+fRrValWi9FarJXS1SCSCaDSKra0tEX4nNktGRTAYlJ0TGRNLS0vY39dw5UoE3W4LQPtAIKqBU6fK8PkGANyCTbPhJncX/X5fIDjuDJi0ZQQbjUZlnACk9Jml+P1+X3aUTA6yspAMBQYbKqdbfT/YCWZc55h7wablAVcA/GVN0/4hgF+HKc7+rGEY3z7OwR3WVB6waoddPYlrArCdUFYjRWswGCCTyaDf72NxcVEcIXCDzmOHg6kUL5XlMM01WBMfkzCzSXicHXOD8AoZqfZrAAAgAElEQVQjeRVvV8tN1WPw2DyO3UJCKILFEsQgSTtjrzhGiHSkpIlRglLtxswqLjocTdOQz+elxxu5uFwguWCurKzg1VdfFeEY0sgo/k5hccIEFI9nwo2ylsViEX6/XzBxRpi85+l0Gru7u1L6Gw6H8cADD0jXjnw+D7fbjevXr8Pt9iGfr6NWW4DL5YSmOaBpDjz0kBc+XwHhcEy6ixA/T6fTiEajWF9fh8fjwdbWljg95hNOnTolOwSWSjNHotIBVThP13VZlBqNBhYXF6VrRyKRGGI9sOEnn/84dtG8K+NOuk3dFVnTtF+AKcz+/wF4DMA/P4AkbplC2rTGrdq8TC1vtJPNsxqxWFKhSKC3Rqjcwlkr6FSKF0s/D2PTRP3qy2DtbjwKCqEEo10Ew4i7UCgAMEn2oyqe7KAJVpoFg0GhXdF430ll8/l82NnZwfnz53Hp0iUUCgWEw2HEYrEh1gv1ckOhkOwout0uFhYWRFmt0+lgc3NT8FfqC5MSxhZGhH4ASDNNNcKmDjATdGRm0FEzgUi5SUIXxK5TqRSi0ajIYLIEudn04aWXAMMoIxp1weNxw+Pp49lno9D1Jtpts+XP9vY2AEgxSiqVwurqqmhasPURq/sINxAC4nOn9gapdrzvdMaEvprNpiw4VhqaynpQ59EoOdNZmUJ3g02bhPtDAB8E8DcMw/gdTdP8MJtkvqBp2q8ahvFrxznIWW2cGtqsxhWZDoOZ4nHH5gRlpp4TlC8eAKHQkPajHk9NjBETPaxNS/Gx6248KjqeFMHQGZnty3tTVzzxuCx2IEZMJ0wOra7rosmwvLwszSszmQx0XRc4xefzIRqNCnuByTK3243t7W2cOnUKqVQKgUAAly5dkg4irD5jEUS9Xsfu7q78jnAFcKOohL3YWMRByMDn8+HixYvY2toS/NTr9UpFGZOq6XRaovROp4N2u41QKASHw4XXX+/j2rUQNK0Hh0OD2+3CxoYXDzxgYHU1hFLJZH/QyZ87dw6hUEh4xn6/KWTIZCL5s1SA63Q6AjPQkRaLRYlu+V0+B3XOsDEBGUV2ZjeP7Pi7h2FH3Ok2bQQcBPAewzCuA6J+9jc1TfsqzEaYJ8oBj1JDm9XUFZltiaapW1exM7s+a5xojJCsEe5h8Wrr9m3a7RwXFmt55zi8l/d41Evk8/mGdBWAm9kVk2QwVTlKnovj4FaYUaimaXjssccE/uC2P5VKSfEBO0/ousmnzWazOHXqFJaXl4V+RSiCEeQ777yDvb096XZNB0qNA8IYjPgpTE4cdHd3VwoZ2GuOuxtS/siBzmazWFxclOKPvb06/uzPStjddUiz0EDAjSee8CCddkgi0Ov1IplMyqKg/qv2t3O73Ugmk+j1emi327JYcJFfWFgY6hiiLmxcMCbNmUm7rHH0sVm1Wu4Gm9YBf8wgIKeYYRh/oGnan895TEc2KwvisLiSuiKPiuJGHVvFS62OdJqJZsVbJ43dun1jJDpr4YfqbCkHqeK9LCRg7zNuoVdXV4eiY2rBUueB90zlNE8rg0lhdC5Y1WpViiZY0RWPx4XTGolEhJFA2hojatICvV4v4vG4jH9tbU225YQZ2PmY2sDk/vZ6pnoYcIMKSM5vt2u20QkGg9IWKJFIyELL6J6VdarzZ3LS4XDg2jUNb75ZAeCWSr9IpIcnnvAjEukjHI6K8tjCwgLS6fQQ1Y3zqtFoSBFJv99HNBrFxsaG8LA5r6xFMSqXl0lPtQrOOo+t8JV1Llufv50dJVF+p9q0STgDADRN+xCAh2GWH180DON5wzD2jnF8hzKVBXEUXEmdYHZR3DTHtnMo0060WcZu3b5NEnG32qjkGvnSbMXDlzmTyaBcLksyi50HeBy+uCperp6j1WoNvfTjSqBV3ihhDeo5uFwupFIpKXzw+XyyvVUTgxQy5yJDZ8h7FQqFsLa2ht3dXSlHppYvABEQd7lcIsXI4zDxRiyfUAcr3VigomnaULkynXAwGJSuH6+88hZee62Bep1luhocDuBDH1rCww8vCLebvFtWPhKLX11dFfiAHTyIL7tcLoTDYbRaLek84nbfrD/NBbPT6UhFIe+BFe8HzC4UnKeslNN1fQhmmDYBfthE+Z1q02LASQC/B+DDAPIHv45qmvY9AP+xYRiZYxrfoUxlQTB6OgyupDpKviCqNOVRjz3ps7NgYtaoehoRd6vZJdesjIxwODwkuk5nTWK+WmShYrbq37iglMvlIbqatQSauweej6I0Xq8XhUJBOvyynFfVnlB3JnRUmqaJ0yFfl06W0fLi4qLo+FKSsVKpCN3qwQcfRCAQQLFYlCSa2+0WWITjJ3Xx1KlTqFaryOfzOHPmDB566CG888474iDZJj4cDuPixQJefNGFwcCEDZxOBx54YBnvfa8L/b45BiYIyaghbJDNZhGLxRAMBuH1erG7u4t4PC54OOEKl8uFvb09wZnD4bDsLKwBgd/vR6VSEQYIdzhWo/h5uVxGvV4Xdoi1UvO+3WzTQhD/AoAPwMPGQXt4TdMeAvBbAP45zNbxJ9KOiiupUIBVmvK4MatZjm8XVdvBH4c5n8rIoJMjD9Z0FM6hZpGsmFIjao6NCwqjNmKl1oUGuKH5ygq3YDCIbDYrUEAulxPRdmra8rpzuZxgoGQk8NkRu7x8+bIwZsiWUMt1C4WCODkuECzbppIaecBcjBOJhCiJUUuY93JlZUX+/tZbb8l9GAyc+MY3drG11YfL5YXL5YamAR/84DKee+7dqNdrKBQKUjTCSjbitP1+Xzprc9dBIR2yMLxer1SmMTHKxKvapYTPkBV84XBYOmrYOVMVimPnEHYXsUbM9+1mm9YB/0UAn6LzBQDDMF4/oKZ9Y/TXbo/ZJeGOiiuNyuTO49ijbNaxW6PqWbdzo85nx8hgxp8JLxVqYLqA/6qRrCpryM7HxBvpLNSiC5Y30wE7nU7BPknVarfbsjXXdR3ZbBa5XE6wS27Fu90uLl++LLoTdGSMxOlAqd9AWIOVY36/H7FYTCraqJrG6jti4+FwGIlEAplMRsqnmfRj141HHnkE5XIZxaKG738/g0KhB7fbgW63A10f4Kd/+gwefjiNYrEgrIVEIiEC7YQ/KOvY6/UQjUalmILSnAAEFuHiqOu6LDDNZhO1Wg2BQADlclkWz0ajIc+Hi8+oBBvnBHMH1CnhbuO+jbZpHbADQMfm992Dv50o48qr0seO6hxHRYfHjVndakzMzokDN+v6cjGyFryobelZGaZqRzDqo6wiEz5kPfAc7XZbdHwHg4G80Nzq0ymSCkXcv1QqiSoZoSIyCwqFAkqlEgqFgmDQyWQS3a4pOh4IBLC9vQ2n0wmHw4GlpSVsbm5ia2sLmqaJaHw8Hoff70exWEQ2mxUIxeczW94nk0mhoRHOiMfjqNfr0DQNyWQSKytr+PKX/wNeeGEP7bYDPp+OUqmMVMrAhz4UwZkzS0LDY9S9s7ODYrGIXq+HtbW1oa7NVIhTk2eUbaTaHfm8AETDmrkMKunV63UsLS3J99RFcdR8YfSbSqVkN8G5MC5Rfa8VXdjZtA74OwD+N03Tfo5JN03TlmHSz75zXIM7rLFyaZ6QwN2aoZ3mJVCdsop706mpQj+Eang8K1WJEax6brJW+J1qtSpJIzoYSjtyG9ztdrG+vj6UUKtWq7h27ZqIwVQqFdHqZUEDANF3aLfb6Ha7WFlZkVLgZDIpZcpkSrDSixG16qCz2azo8bJBJWUeuXPg58PhMJaXl5HJNPD7v/8K8vmWMC6czj7e8x4HNjb80h6JOwhi6sSMef+I8VOYvlQqiYCO0+kUofd2uy3qacCNiJiQCoX0uUACENUzsllGzY1SqSSMFOqfqMm3UYlkK0x0r2LF0zrg/xbAVwFsapp2FSYLYgPAJQB/43iGdnjjy3DYCrJRdpIztIeJJg7DEFHpakwIEe4plUq4evWqJLN4/0fJfvJc1o7RPEcwGJR+Y263SceiQ2ZCiOOv1+soFApCu2IFF2lq3BXl83kpTGBfNrfbjVKphGw2i3w+L8fvdrvw+/1IpVIoFotCd2u329L/jAsSk3mU02TUSWfDqP9P/3QTP/hBFi6X+4Dy5sCZMyE89pgXhtGSSjri0dzOc/Hxer0iLB8IBCSJx3NxJ5HNZuF0OoW9woQlI/N+v49CoSD0NE3TRL6UbJJJTpFsCoq2U+eYkTbvoV0iWc0VlMtl6bl3Ut+v47JpaWhXNU17FMDnALwLZieMiwC+ZhjGYOyXb4ORQD6uOudussNS7Q5TecS/W8Wxs9ksMpmMOCDAxIlTqdRN7JFJY2CEx4iPGCW313aFG8R5Q6GQNOoMBoNwuVxoNBrSu43iR+QMO51ObG9vC4RRr9dRLpexsrICn88nfeA4n3RdF4F0OkHDMBAMBrGysgKXyyVc6VbLdKhra2solVr48pe38Oab2YMoNgSn04GnnkrgE584J4wMahczamfkXC6XUSqVRKiHMAEhCRZksMR3MBig1+shHo8jGo1ib28PvV5P2jWRGkdcX13Yxi3k6kJvTcDRcasc/HGJZOosE66aR/HUnWYTHbCmaW4A3wLwNw3D+AqArxz7qI5oDodDXpJ7wQ5bwnlYFoeu3yyOzcw5cUu2tAEwtrEpoQG+1MvLy/I3qoeR6cACD76ovFZV4IUOkZQsYpyVSkWcuGEY0s6IHS/a7fZQZRqdAql36+vrUtW3s7MjrY1UDJxwBSPORqOBZDKJixcL+Pa3d9HraSiVSvD7fVhacuOppyI4d86Per0uVDGyNRjFAhD6HZNsS0tLSCaT2NvbQ7/fl5Jn9Xq4OyCOq+u68HnJHXa7TdU0qvuRmzzqvbEu9KTg8XkTarLmSPicVCiDok18LhREulWsiZOCP090wIZhdA+acp64SHeUsd7/XsnAHsWRAofDtdXvsvSWeKLD4cDGxgYikchYrjRf6EajId156VxrtdqQ7jH1e9XCDW5dDcNAIpEQUZlkMilVfKSnEZul0wmHw5KYIn0rn89L6W6v10O9XpeIlOLlbBHP/19ZWUEymRS93+vXr8tiNBg48fzzNbzxRgndLtDtmhzmCxdCuHAhiEDAFDff3d2F1+tFOBxGvV4X9gFpfCw2AYByuSy8XJY1q2L16r1kotPhcEjkz8iYjJJqtYqlpSVpGT9KIpXPTn2WdNSdTgfLy8u2XGIewzq3dF0XSKVcLsu8mVac/Sh2kkR/psWA/x+YWO+vHONY5maj5CjvVjuqIz3sfeJ3q9UqotGolP0Gg0GJZMctDtwq82VkyXej0bip8we/z3/L5TLy+Ty63S4ymQwajYb0UeO2mEkh9k4jVU7XdWQyGdTrdXQ6HcRiMSm9puYHE3RMRg0GA2lF1Gg00G63RfpxbW1NGk8Su75+vY4f/rABj6d5QHfrYmHBifX1Oh5+2IOFBRMuASAiOFwweL+oCkcMmEySUCgkynxUj6N+L7+bzWaRTCbFmasyqGQrcL6QjTJOItXuWaqdNabRkrabP7zXqnzpcdtJEv2ZWo4SwH+jadqnALwEoK7+wTCMX5rrqI5o85ajvB026xbpdiYI+WJSo5eqXBwXYL84UPuAkXMgEJAS3lwuJ1tiRrDqcTKZDEqlkpT9cutvTfJwm8sqP1VbIxwOSzNPwhOshMvlcuj1euh0OkgkEohGo9Ks8+WXX5ZKMmKqpF4tLkbx4otlvP56D4bhhsPhhMPhxNqaCxcueFGpNOByuaTEl46rVCrJ4kXHTkEjnisYDCKdTgt2SriCUMA777yDbrcresNMpAEYiqT5DNSdSLfbHaIG2u2krM+y273RDYS/n3UOdru3Xpz9JIn+TOuA3w8z6QaYWsCq3STSc9+OZidpi8TxjFsMVIqanakvrvqzug0NhULQdV0cLaNOdnNQv8PsOZkT3IpbFdsymQyKxaJoAAMQgRpW6FkpdNyWU3wnFAohGo1KHzSOcWdnB7FYDNFoVBJv+/sNfP3r28jlenA4nPB43PD73XjsMQf6/SJcLrO8lwkz6mqw8IEOkvSzdrstiwWdLJNfhH6ItfNvvDfURWZUzYTYqOdjdcajdlLWhf6ojux2OMOTRCmdlgXxweMeyHHbOCdyUgB52ry3SEe5vlkWA3Jkrdnsccewi9zJAyYmzNZK6osTjUZhGAb29/eFfsX7BZgvdrFYlISTruuyva/X69IiyOFwiCA6FdTUCjCHwyHYMVkHq6ur0maei8QPfrCN55/Potk0pSwdDgfSaS+eeSaNnZ13UK2aY4/FYohEIqKqVq/Xkc/nsbCwIJ2L/X6/iOZQSIf3iiwHTdOk6zRZKGyAytJrAEIT432fRpRp2jkyD0d2u5zhSaGUTsOCSAP45MFn/8wwjLePfVRzNpUsrkYBLI+dRbbxVtg8o4JpHegoJ223GPBf9bPjFo1ZFhSqjDGytXKNVWpTLBaDz+eTZFogEBgSk19cXES73RZM2e/3I5PJSA8z0hXpyChFSbocvwNA4AgVGzarxwb47d9+Czs7TeUq+vjIR5I4e9YNTesgHA6LOA8lIVmJV6/XxcmyFbzaYYIQhZ3KG3WJa7WaMCmoXZFIJIZoYmxSMKkb96w2D0d2Upzh7bCxDvhAfvJrAEIHv+pomvZXDcP4vWMf2ZxMJYu3WmZvsmq1KtEau+4yYhjlHG5llDzPqGAa5zfOSdslXugc1c+OWzSm1Y1VF0oA0pDT2lRUvT/sLceebOx8AZgiQt1uF/v7+ygWi6IcRt5sJBIRp6o21fT5fNKuJ5FIiN4DABlftVrF/r6GH/6wilLJkC7PkYiBT3wihuVln1Sz0SlSG5lJNyafGKmzgwVZGJyThmHcBJVwkQoEAoIHAxCWA++7Vadhlrl00naGd6NNioD/IYA/B/DzAFoHP/8TmNKUJ9bUlkR8aTnZSY6nU2KEoOuja95vByY7r6hgmmh6nJOeNvEybtGwYsQql1d1KupCSayVmXa7Kjo+OzVRxcST2+1GPp/H4uIiXC4XAoGAJOLIvGASjm3ju90uksmkOFhyW/P5vDhFM2rt4KWXmrh0qQqHw3kAVWh4/PElPPCAC81mXTitXq9XOK6M2AGTLcD29NRRVhtYqlWBKgbOuUgH3uv1pIUQo3LOezVxeRjne6vm/L3s6Cc54AsAPm4YxhUA0DTtbwMoaZoWMQyjdOyjM8/58wB+EUAKwH8A8N8ZhvFn476jqqGNigLUv6tCMHYT4CTRVlSzm7jW300TTU9y0tMmXsYtGnQKo1rY2C2U5L6OGzuz6BSToQMCgOXlZUnCkdZGTDcWiyEQCAztgDRNQ6FQQDAYlDmTy+VkC2/2hmvj+98vo9t1wOvV0e/3EAq58dnPnkE6bRZVGMaiVJix7NcwDCkNbrVaEh3ncjmkUimcPn166F6Nqh5ktE9pUHb0ADAEs3H8dgm2afIgt2rOn7SE8622SQ54EcAefzAMo6ppWuPg98fugDVN+0swm3/+PIDvHfz7R5qmPWwYxrVR31PV0MZFAdNu8e3a9RzF5rHi201cALaTeVI0PQvkcRR4RO1sYSXdj9sujzsHn43L5ZKuJYygyfttNpvweDwij0iOMgssWPlWLpdFNrLT6UghiUlNq+LFF8t4440mnE4XnE4TRz5/fhHPPnsasZhZrcfEGlsLsSiIeC0x693dXdRqNXi9XuTzeUQikZu6CFtF7UdF+wBEC3mcgx3l6Aj90Kzi8moHmHlHqic1uLlVNg0L4gFN09TyFA3AeU3TJPQxDOPizV+bi/0PAP6tYRj/x8HPf0vTtGcB/NcAfnnUl6xqaHYOaNZsryoubldSO63Na8UflRw7zg4dh/nsJFNfamb4x22XR0X4dvAGYC5IPLbT6ZSKrVqtBp/Ph3q9Ls8km81KKTMLMhwOB65fL+K7383j+vUSgkFTbCYSCeC5587j0UcTAMx7zcIO6kSsrKxIGTV3WWwQyio+Ysc7OztSHswol1E95924aH/Scxnl6Aj9UDnN5/OhWCwilUoNtY7nvZx23k7rrE8SJ/d22DQO+LuWnzUAfwyT/6sd/Dt30QVN0zwAHoeJOav2DQAfGvfd49CCsGvXMy/HeZjjjJq4J3kyW0n3ajKUhRzjKqnUxYtdGsgMYPTJ8mUuSIQe1CQrxXkMw0ClUhkSkSmVzI0dxXfeequFl14qo9vtw+8PQNe92NgI4Wd+5kGk09GhMTHSJu7L6jQrhBAIBLC8vIy33377oGR5IHAEAOl6AWBIw1eN9v1+/1DByyQbNV84/7ho1et1uFwuVKtVxOPxoQVuFibLtM56ngnnO9EmOeCHbsko7C0G07HvW36/D+BT1g9rmvYFAF8AgNXV1blrQcxrpZ7XccZN3HlP5nltPa3XDswWsXPxoqMdDAbioKy963j9DodDnDIA0XrIZrMi+5jP5xGPxxGJRLC7u3vQRSOM73yngr29DlwuNwIBHzRtgE9+cgVPPLGMYDAgY+p2zWalNEo82s0/3oNoNCoUNF4zVd9UXV5iuizIUKPSaXcJPA7Ha+12wuPu7OzIQkXITf3ctPN21iBjnjuqO83GOmDDMN68VQMZY9ZKO83mdzAM4zcB/CYAPPbYY8a8tSDmtVLPc8U/KrQyjR2VR2wdG3Bzdw1gusWITkBtsMnKNZWbq2maFG+QpkZjEpDlw+xWnMvlsLi4iFQqhbffruFHP2qh33dB1x3QdR/S6QV88pMJLC3diLo5bmvT0nEi5uo9WF9fl93Uzs6OjIXH0HVdaHMspphllzBN0Qt/JkbN3n+k801y4OOeE+/PSduJnSSbRQviVlsOQB/AsuX3CdwcFQ/ZcWlBzMu53Ukr/lF5xFazu3b1pR7nyPmzYRjSBy2Xy8HtdosoDQBpEUSVs3g8jlarhWw2K2pjTL6pnR96PeCVVzr4yU8aACAFG+9/fxxPPhlFt9u5iZXAMmg6LsJek67DykxYWVmRghCVelatVmfSWzgMxMXxqAL307Bhxh2P574XYYVZ7MQ6YMMwOpqmvQzgGQC/q/zpGQC/f3tGde/ZUXnEk8zqjCY5cn6emXue0+VywePxoFgsSvsgRpfADToZldSWl5el/Nds497CH//xNlotQNNM7DgaXcBP/dQGUil9qOxX3Zr3+/0hHjkjVlbwqXSwcddP+Uyr0x51/0c5+KNEn8e9O7tvN9uJdcAH9k8B/F+apv0QwPcB/FcA0gD+1W0d1T1k07yU89pyzuLImRTt9XoolUoolUoYDAZYXFyUFu3keHe7XUl0aZomOLDZMcODP/mTTbz0UhaG4YLDMYDXq+PChRiefXYDTqchzpftfVQGAUXpy+UyAoEAFhYW0Gw2Rfh9FpHxcRCBdZcwDmawfn4Wm9Zx3svFE/O0E+2ADcP4bU3TogB+FWYhxmsAPmsYxubtHdm9ZZNeynlFTnZlz0w+WY+pMgLU0mEKi3Pb3u2aeh+dTgfFYnGoSWcmU8cXv3gJe3t1gQ6CQRc+/ek1xOMDNBoVOBwOuFwulMvloQQfAJG8pLIZYLII2OSSybhxC9K02Lk1yTpuoTru6PNeL56Yp83kgDVNWwBwFsBFwzC6xzOkYTMM4zcA/MatONdR7V6MCtRrnkWM285UR84SZFXJy+pk+FkWI7DXGfms/H6/34fX68XS0hJcLhe8Xi9efjmH55/Pote7IQR+6lQAzz67Br/fOcQlHoXzAhDpSPU+MBHocrnGNgaYxpHNAkvcKlOZH6T83Svzfd42lQPWNC0A4H8H8FdhtiZ6AMAVTdN+HcCuYRj/8/EN8c6wezEqOI5rZvQ2TfLJGumpxTKEDUir0nX9oINwG1/+8mXkcgMFP3bgox9N4aGHAnC5HNjd3RUheHKW3W63JKeI61I8Xe3gQSfNhqBHSZiNur+3O8llR/m7b4ezaSPgfwSzG/KHYDbopH0DwD8AcM874HuxpPI4r/kwUZ5aLANgKDnmdrvx6qsZfOtbW2i3b8hMLi8H8LnPrcPpbIqKGEuAAQzRwpxOp0R8FG9nG/lgMIher4d8Pi/qZaOEnRjRTlKJG3d/b2eSa9yO4L7NZtM64OcA/KeGYbygaZrKwb0I4Mz8h3Xn2e3eFt4OO+w1H5YzPAoPthuPKrKkaW589auX8eqrOTgcHrjdpoj6U0+t4oMfjKPZbKDb1VCpVESshx02qIBG50yB+FarJS2K/H6/QB/sa2cX/VojWmLW1jJqOneyKoDxMp632rh4WHcE9212m9YBxwFkbH4fmONY7mi73dvC22GHuebDlKlSGjIQCIz9jt14NjfL+OIXLyGfr6Pb7RwInnvxcz/3CNbXQygUCtLEFbgh3BMIBETHlxEoW75TnF2VPGUSjt2WSX9TzS6iDQaD6HZvqMQRRolEIlIRx2PZyXjeDrsX5/px2bQO+GUAnwXwLw9+ZhT8XwB4ft6DulPtXuQ+znrN0xZ2kL3AsuNOpyPVWuOgDo6n1xvgm9+8ih/8YEcSZQDw0EMRfOITK4jFPKjVatA0M/IFzMiOSncUBVKbVLLnmsvlkrHV62Z/Wp/PJypqlL20CjaN2jGov7d2JnY6nXK/ThK8dS/O9eOwaR3wrwD4mqZp7zr4zi9omvYIgI8BePqYxnZoUwXZ79vJsknSnmqETK1edgduNpvw+/0ToQ6VXkbzeBz43OcexLvfHUev1xMpSrXrBBkL7JxiZWXoui7dM9ioE7Avb7bj/46KHNXfj+pMfLvgrXuR2XMrbdqmnH+qadrTAH4JwDaAnwHw7wF82DCMf3+M4zuUqYLs9yeNvd2uF4sObpS0pyq4wyKL5eVlcZbW7hDqNRiGgRde2MW3vrU5RC87ezaCZ59dg6Z1xLH5/f6h1j2q87XitCrnmMyKRCIx9HkAQj1rtVooFApYWVm5iZo3KnK0VgRaCy/Y2ojMA35nWjvM874XmT232qbmARuG8cjLBVsAACAASURBVDKAv3SMY5mbUeLwJGzVTqLd7hdrnLQnRdspuON2u9Hr9RCLxcZStCqVDv74j6/jypUbfQJcLgeeeeY0nnxyGZqm2To2azRqB5FwzGxtpCbYeJxGowGv1ytdoX0+n2js2t3bWbQi2IKoXq9PhYXbneswz/teZPbcapuWB+wf93fDMBrzGc58jOT7e4GJcBi73S+W6mT7/b50qAAgESY1erm9tzpfJs+8Xi9ef72Ib31rG4OBQz6zvBzAz/7sA4jH/UPHnlQxNqvOMpkSqrh7IBCAw+GAYRgoFApDDTX5nVqtJnS2xcVF6YZhNXVHQO4toY9pn9thnre18ef99+l4bNoIuAYbCUjFThQRkFnte3W1LpVK0k/M7sU+CZS5Vqsl4t9q2x0A0oyTNCe1JBmAJM+y2SK+9rVrePPNCjweD1wuBzRNwxNPxPGhDyWg645xQ5BxqJHouAy/3e+sPdoIFRiGMaTKBgxjvd1uF+12G4ZhSDsgdsSwJu7K5bLAEM1mc0jnYhqb9XnbNf5UJTjv2/xsWgf8GcvPbgDvBfBfAvi7cx3RHOy45CjvBCuVStjf3xcRbwA3OeHbTSOidu/S0hKAGz387JJS1pJkVsft73fwpS/toVLpKK2ngM9+dg3ptA+GMZiYBxhXaTap6o7fb7fb0qdN13XE43EAJmVN1/UhVTYVZmGE2e/34XQ6USwWEYlEbMV1uCMIh8NyL2YJMGZ93mrEbIVc7tt8bdok3Ndtfv2Hmqa9BbM8+bfmOqr7dmhrNBpDUVSj0bCNgm8njcjtdgtNi9VUVjI/x2ctSW61Ovjud6/jpZeyAlMAwPnzC/j0p0+h1TK39moLolHXeVQoptu179HWarUk6rSyGXhtPp8PhUIBPp9PxONHjUPdERx2dzfL8z4JO6R7xY6qhvYSgH8zj4Hct/mY3++XyLfb7UqUeZJM14c7VYzb3qrOYG+vhm99ax/7+3VomgMejwvBoI6Pf3wZ58+HDhxYT2hkkzpYH9XR2PVom7R9J+ThdDqRTqdlAQJg66x5v4Bbt2O53Tuke8kO7YAPmmb+Akxa2n07IcZot9FoYGlpaWRy53bbLBHZYDDAiy9m8P3v76PfB5xOB7xeD86ejeC5587B47lBO3S73dA0DaVSCQ6H4yZ82ToG4Gjaudbvq80rrdv3VquFXC6HwWAgkEUgEDjAr02tiXGtjG41XfC+4z1+m5YFkcVwEk4DEAHQAfDXj2Fc9+0IFolETqzjncVarRZ2dwv4+te38NZbeQCA2+0BMMCnP72Oj3xkfahrMB0hnSChgWw2O5Tgm8SEmMWs3x8XVavt5g3DEGjlfpR579q0EfCvWn4eAMgC+IFhGHYaEfftvh3ZXn11H1/72jtotw0pJ06lFvDZz64hlQoOOV/VEbLdPWD2h2s0GsJQaLfbN3GK521kQthBK2q7eV3Xj6yhPIvdiuKb+5Vzs9lEB6xpmgtAF8DXDMPYO/4h3bd52Ul7GaYdT6vVwx/90Tt4+eUddDpdaJoDDocD73tfDM88swHAGKvARZ3ewWAgHTA8Hg+AmxkX8zQV/6WK2ahx3Q7ne9zFN7e7wOdOtIkO2DCM3oHw+kO3YDx3rd1qZ3jSXoZpx7O5WcaXvvQ2SqWWsAIWFlz4/Ocfxfp6aKp7yCRft9tFKBQSicdRjItpxj7NeVVOsJ1okDqufr8/saRYPS+AI82fW1F8c7sLfO5EmxaC+CGACwDu92I7hN0OZ3jSXoZJ46nXG/iTP9nE88/vAtDgdJq92B5/PI3PfGYDum5+bxbuq8qltWNcTONYp312dpxgtkqyjguAcJopTGQtwlDPWy6XAWDmEmTVbgW17D59bXab1gH/OoBf0zQtDVOasq7+0TCMi/Me2N1kt8MZHvVloGAOYE8TmzWiH9de/cqVDL785SvI5dqiNhYIePDTP30ajz22MtO47WxUEcU0jnXaZzeKE2xn6jFV/V91HOpnBgNTWOgo8+dWUMvu09dmt2kd8O8c/MvmmGREaAf/f6JKkU+aHcUZHha6OMrLQLoUBXGsiSur87KL4CaNBwAymQxeeGEX3/3uLtrtLhwOM/JdX1/Ac8+dw9LSWAmSidcw7r5N61ineXasaiuXy/B4PNKiaJSN0//lONTPOBxmSfUonvBJsvv0tdlsWgd8H/89gh3WGR4Vujjsy9DtdqXJJHBz4mqaCG7ceEgv+/KX38aVK2U4nS5oGuBwAB/4QAwf+cgpeDzOmbBaK146aYGYdlGc9Oz4jKgxPBjc6Dc3ytRjjtL/VT9DKOMokeVJywncN9PGOmBN0/4NgL9tGMabt2g8d60dxhneLhyXpcKMgBmRqX+fFMGNs5/8JIOvfvUK6vUuDMOM7KJRD5577gw2NuJjo2m7yNbqXMivHbdAcCGgaNGkpN4kOKHT6UiJsM/nm3gf1GPayWLanfcoz/6k5QTum2mTIuD/HMDfAVC9BWO5bxa7XUkNJpBGYcCjIrhJjSNLpRq+9rUrePVVU8fB4XDA4dBw4UIYzzxzBj6fdyReq7YoskZxqmQjFw0VY7ZbIFQZSaso/CzGZ8RuyT6fbyxUoS4e6s/HTUm7nyA7mTbJAWsT/n7fjtFuZ1JjUsRujeCsnX35Gdqbb2bwu7/7OqrVHgzDdL7hsI5Pf/oMzpyxF2fnsa0tiqxCO5RsLJVK0kgzGAzC6XSO3OLPKyK0PqNR0bsdbm63mByX3U+QnUybBgMepwN8347Z7oSkBqNQu8aRpJd973tbUlDR6fTw4IMh/OzPPgJdv+Gw7SIzlVvLqFHX9ZvwUlafEX/tdrtSjm23xZ9nRDjNM7I6fPakm6Y5qV3EfNjF4qTPpXvNpnHAe2rJp50ZhnGfBXGX2GFfcjuHdv16Eb/zOxeRybSk/5rP58bHPx7F44+voNdrAVjAwsKCbWRmx61dXFy0Fa3RdV1oYByH+jc77u+o8x6HWe+P2pPObgG43RHzfbs1No0D/gKA0sRP3WF20sp0T4IdJVOubnF1XccrrxTxh3/4FjqdHhwOB9xuD9bWAnjmmRRCIc+QUPkoOUo7bu0okSG1zNfn89liqnYNN29VObAdBDAq+cbPzRox37c7z6ZxwF+52wR37jVKzrSLzVFxUROKcOD3fu8SLl8uwTBwgPdqePrpFJ5+egOapqFWq0l58DR6vare7rhzs8x3Gu4v+8pZ+7Udp9mxGkade9aI+b7dmTbJAd+V+O+9RMmZZbEZh4tO48QvXszhK1+5jGbTdBQulwvJZACf+9wppNOhm5J2un5zW3rV+Dsm+CbZJIyT19dqtVCpVEQnQj3XYW1eO6pxEMmors530y7ubrymcXZPsiDuJUrOLIvNqEz5JCdeLtfx1a9exsWLRTmPpmn40IfS+PjHT8HlGm6OOa4tPU2lnrEdz1GdJb9XKBQQCoVs+7XZjWGyett8dlRWwfZwODwEz1ix7LttF3c3XtMkG+uADcOY3Fb2DrR7iZIz62JjF0WOc+JWehkARKMBfP7z53H6dPhQY5qGenZY03UdS0tLUr02jrM7a+v4o+6oyPhgp2TDMEQ/eJZncqdGkffSzpR21J5wd6zdK5SceSw2dg6z3x/gO9+5jm9/+wr6fZPXOxgADz0Uwec+dxZOpzERWpgmAWVHPZvkYCb9fRpoo9sdbh2/t7c3MmE4zx0VCzp4/U6n09YRjRM3ulOjyHtpZ0q7Zx3wvWRHXWysDrNaHeCLX/wJdndrcDgc6PV68Hic+NSnTuHRR+Po9VowjPEOYNoElNvtlgIHvpDjHMwsDmgctMHW8YZhoNPpoN/vS5Wd9bPTLnKTFgaVyeHxeG7iO6s26px3chR5L+1Mafcd8F1ix73tJAzwwx/u4ZvfvIpe74ZE4sZGGM8+u4ZoNIButzvURn7e0olq00u7488iHzmOEUHOcbFYhMPhkAVgVEQ6akGZVEZtPYYq2D6NwtxxRuO3w+6VnSntRDpgTdOWAPx9AM8AWAeQA/CHAH7VMIz87RzbSbRbse2sVjv4gz8w6WU0l8uBT31qHe9/fwqapoksY7vdRiAQOJIDUJ2w+vMkBzOtA5qGERGJRKDrppg76XCzXNNhsOx571buJWd2J9qJdMD/f3tnHiXXVd/5z6/2rt5bLamtXbLkRbHZbIydsHiLdzKyAYfMQPAsMRxgDnMGAhmYTDyBMMkZD8tAgJgkkJDYhxi8b9iOMQbsYbNJbBbLsi2rZUvqbvVW1VVd650/7rtPr6tr79q6+n7P6SPVq/fe775X733v7/7u735/wCZgM/BR4JfO/78E3AJc0sZ2dSSaPewsTC8DGBvr5Zpr9rBhQy+wtB5aKpUqWZSyGpg0NUPkhSpmUJpgqiWgajMijM1yiyZKoVIsu1lYa17kakZHErBS6hngGs+mAyLyh8A9IjKglJpvU9M6Es0adqZSujjmz39+Yh1OqfQyL9n09va6pFMrDJEnk0nm5+dJp9PL5B0rEUyx74uFaCKRCNFolJmZGeBEvLnac1ZCuVi2JUgL6FACLoEBIAUk2t2QTkMzhp3e4pgGg4PhkulljSiBZFK+AoEAIuIKnJvrKrZ/NTHvUiEao69g/t9oYiyXVx2LxVZdmphF47EqCFhEhoBPAl9VSmVL7HM9WreCbdu2tbB1nYFqPbRKxGXSy374w5dR6sRCyFe9aj1XXLELUxyzmH2ovwSSlyBN6lc0GqW3t5dIJILf7y+5v9d+MZQK0ZjtJi6bSCQaPnwvPN9qThOrF6s1L7kVaCkBi8ingE9U2O0CpdSjnmN6gbuBl9Ex4aJQSt0E3ARw9tlnr+ol1M16YCu9/JOTCW677TmOHIm72yKRAFddtYszzlhf8fz1kldh+MJoHvh8Pncyz1uRo9aYt9c794rGGx3hZDJJMplk/fr1DVuaXM21VtP21Y612OHUglZ7wJ8D/qHCPofMf0SkD7jP+XiVUmqx+CFLkc/n665w0G4084Et9fIrpZallwHs2jXEvn27GRgI12Wv2o6kMHxhJu9KTXzVs7oPWCYab+5DLBYjk8m44YhmkuJqTxOrFWutw6kVLSVgpdQUOqWsIkSkH7gfrUdxmVIqXuEQr52mezLNQjMf2GIvfyyW5s47D3DgwIy7XyDg46KLtnPuuSdRSQu6FBYXFzl8+DDpdJpQKMSWLVsqZiQUkm0pj7qecIchVq9ofCKRwO/3E41GicViHD9+HBFxi2B6r6VRI5K1lia21jqcWtGRMWCHfB9ET7ztA3qdUATAtFIqXeH4kgnzjYA3ub5Ssnyx48rt38wHtvDlf+GF+LL0so0be3nb206kl1WLwmubnJxkenqaSCRCPB4nHA6zdevWssfU8ltVs3/h+YtJPM7MzBAKhejv78fn8xEOh5ses11LaWJrrcOpFR1JwMBZwLnO//cXfHcB8Gi5g5VSTettvSItJoHfxCfLPVzVvsjNfmAjkQgigarTy6pBsWvLZDKIiJvRYBZUlDumVfHuwns7MzPD4OAgwWCQQCCwJEOhFUPobp+kWksdTq3oSAJ2JuHqlsLM5/N156FWgnkh0+k0wWAQpVRV3natspDNemAPHZrnttueqzq9rBgKCcNbty0QCJDJZJYojoXDYUZGRpaco9L9WCkplTp/4b01q928y4WVUkvyd1c6Iil3LXaSam2jIwl4pTACMc2YiKulDHmx46A9sbBcLs+jj47zgx/Ull5WiGK1ygrrto2OjrqlfhKJBNFodJmUYyXx95WSUi3325ByLBZbpmNhrqNYLm81Yahi98u7v52kWtvoSgIuFQNuxFCvcBhbbQy41bEw77Vq9bL608u8KCSMRCKxrG6bubahoaGSGrrl7kcjSKncIohSz0Ap0i6Vy1tNGKpQ9CcWizE0NNRQD9ti9aIrCbhYDLiRQ716QwStioWZa/X5fDzxxGEef3wKOBHX3blzkKuv3lNXelmpWmWl6raVI7xS96MaUqqmM611EUS1nWQtYSjvtZgRWTUedi3XabF60ZUELCL09fU1zKtaqQB4q5HJZEgkcnznOy9x4MCsM7sfakh6WTGSKpWvW2+nV4kI6z1vNc9ANZ1kLWEo77UMDw+TzWaXVeIoZdPGh7sfXUnAPp9vCTGYVU/1DPUqvQTldAbaRcrPPx/jttueJZXKo1Qev18Xx7zmmj1s3FhbelkxFBJGKQJZSadXjgjrPW+xZ6BWTQmzr7dgZjW6vV5UK8hj48Pdj64kYFhOjH19fcuqzFaDSi9Bse+hfNWGYm1tBFl71cvyeR8+HwQCQd70pm1ceGHt6WUrRTPimyvRHC70rKH636nY82TCB9W02Ryby+WqDkXZ+HD3o2sJuBgx1qNPW48AeKFts9S1mWlIhellgUCAdetqSy9rNBo98dgIzWEv+VWqruHtGFfijdZ7rF3E0P3oWgJulPdQ6SUo9b1X/AVwScN7jDluJcPMUullZ565niuvrD69rBEopbnbKOIoFO1Zaa53LalwphpGsX1XYqcS7CKG7kbXEnAjvYdKL0GxmKixHQ6Hl+gPFBKstzSOKX9ebUhipepljUQ9nnyx65ydnXVzh02ebKmlxCsdkteSCuf3+9321Po8WU/WohS6koC9amjtetiN7WAw6AoDFSMNM2EXi8WIRLRuQjwed0vxFCbug06z+8lPjvLgg0vVy3buHGTfvj0MDlZOL2v0JGGtnnwxwl5cXOTYsWMEg0GmpqaIRqOsW7eu4lLilaCWVLiVPE/Wk7Uohq4k4E5SQ6uGNPx+P0NDQwQCAbc8TqnE/UzGt0y9zO/XxTG96WWtXv5aq3dajLBNBYxsNsv8/DyLi4sMDg66y5vLTZDV25mUOtZ6rRatQFcScLPV0GpFJe/HS14+n85U8C6lNiT1zDMTPPLIURKJE8I2xdLLvAQ7NzdHOBxeMlnVjPSmWgmrGGH7fD5mZ2cREaamptiwYQOzs7P09PQwOjrK7OwsMzMz7sjCoNxS33KoZmFGI56fTssTt+gcdCUBN1MNrRnwkpfRovUm7i8spHjooUM8+2zMJU0R4bzzNhVNLzMEm81mSSaT5PN51zNuRizVex215PmathrC7u3tZWxsjJmZGbZv387Q0NCSkkEzMzNLhHJMyl+5pb7l2tMqpTO7mMKiFLqSgIuthOt0lJrI279/kttvf45YLOMSxeBgmH37drNzZ3GdBUOwyWQSEXEXC3gVwaC24XUzvLjCaw4Gg66nPj8/73YWJn/bxMtNjH94eBhgSQzZjBgWFxeZnp5mZGSkZHtbkWdrF1NYlENXErBZCbeaUZheZl7gatLLvJN1Pp/PTaHyEkwt3mqlkEa1qETihR1DYSghl8uRyWTI5/OMjY0ty1owI4Z4PO6K5HjnAkoJwFfbEdXTCZUieRuWsIAuJeDVXBMOYGpKp5e98srS9LIrr9zFmWfq9DIz3AaKkqEhmFI6DbWgUkijGtQ6FC/VQUQikWUdrHdf4/kODAzQ19dHNpstuzqx2o6okboWNixhYdCVBJzNZpmammJ0dHRVPdgmveyhh14ik8m52wvTyxYXF5mamnJDDKlUquS1NmIiqVJIoxpUMxQvp52byWTcxReGVEtdr1cMvtTqxFpDAY3UtbBhCQuDriTgfD7vDpVXy4NdrDhmsfQywB2Gm8mpfD7f1Je4WEjDW969GrvVxFvLaefWshKtVGhhJfHeRsaLrcaDhUFXErDJAx4YGFiyvZPibt62vPjiArfd9izxeAq/XxNcYXpZobKbz+dzPdLCtKxmwBvSKCzvbjzVctUhqom3ltPOrXUlWqlJzXrDMY3MC7Y5xhYGXUnAxbIgOinuZtqSzcJDDz3HL34x48RVfeRyWc499yQuu2y3m15WTIlrdHS0bAy4WTAkaJZXe1fxVaoO4SWeSt8X085daTilMFZca2fcqLzgRp/LYvWiKwkYcGfqDTop7haLxThwYIpHHplgfj5LNpshEAgyNBTh8su3cvLJw0tyexul7NYoFPNUlVIVq0NU0wkWI8lGe4md1BlbrG10JQHncjn3xTLolLjbwkKCRx45xOOPHyGfVwSDAUKhEKeeOsill24nFJJl4YRWtL0Wj7CYp5rJZCpWh6i1E2yWl9hJnbHF2kZXErDP5yMejzM5OcnWrVuBzoi7HT48w803P82xY0lCoTDZbIbe3jDXXnsme/b0k8lk3FxXb5tLpTI1Kp5dzCM09srl7BbzVMstAy5VkaKZoZRi98nm5lp0CrqSgL1aEF60K+6mlOIHPzjE/fcfIJXKkkqlCIfDnHzyCO94x+ls3HhCMD2VSi1ZbuslYS/hNXIIXUxA3tzDaipFGNKqVCGisCMBqk6nqwel7lOtubmWmC2aha4kYKUUoVCIkZGRdjeFeFynlz3zzDHyeQiFQvj9wm/91kYuueSUJUP1aofGjR5CF3qEQFXnr6cj8BJgLBZbcTpdOXIsd5/K5eZ6lzFDbeWlmgHbAXQvupKAA4EA27ZtY2iouFZCraj3BfjVr45z993Pk0hk8Pt1hsO6dRGuuGI3u3atX3auamO9zRAmX1xcXCKEXk7D2GClHcFK0+kqdQC13CevML53GbNZBt6ueLGdMOxudCUBB4PBusm3kGzreQFSqSwPPHCQp5465m4LBAK84Q1jvPGNY/T0FF8gUm2cutHx7MXFRbLZLD09PWSzWYCqCpiutCOIRCIrSqer1AHUcp/Md4XLmE0aXL3XuFLYCcPuRlcScL0oRra1vgDj47o45szMorttYCDM1VeXVi/zoto4daPi2Wa4LSJuKKDaNLdGdAQruY5qOoBazh+JLF/GbOLappNoNTole8eiObAE7EExsi32AnjrlhlPO5fL873vHeb73z+8pDjmqacOctFFmxkY6DyvxXQ4IsL8/DygX/hyL3kxRbFWD8mNfdDx/nqqI5dCqQk6MynZ6kornZC9Y9E8WAL2oFQdMDjxAnjrlhmvKJsNFVUvu/jizezYEcHvp2NKJHlhOhzj+SqlyuootzseWSiLCdDb2+uGCBqFchN05nMrr9uumutedCUBZzIZZmdnS8aBa60D5n0Bpqen3eOUUjz++DhPPbVAJpNzhq15du0a4tpr9+Lzpclms1W9uN582GAwWDKfttADXMnsuLfDMcLn5c7TbiIyiz3S6TSJRIJIJNKSttgwgEWz0JUEnEqleO6559izZ88yEl5pHbBoNEosFmN6eoEHHjjE7GyASCRCKpVGqRwXX7yDs85aRzisgCBzc3Pk83lXRSwWiy0JXZg2mXzYdDpNLpdj/fr1yzQVSnmA9XqjtQ5v201EuVyO+fl58vk8MzMzDA4O0t/f3/S22DCARbPQlQScz+eZmJigt7d3GQGv1IuLRCIcPJjg3ntfRCSM358nkUgwMhLmoovG2L17nVvF15tSNTc3Ry6Xo6+vz/V0Tdu88pJm8UgxTQVv2/N5XY5+pR5grZNUxlatRNSIXFa/308oFCIejzM8PIyIEI/HW0KINgxg0Qx0JQFns1kmJyfZvHnzsu9W4sXNzS1w113P8uSTR8lmfQSDikwmwznnjHHxxdtZWIiRTCaJRqOuCLgRES8sjTMxMeG+1N58WDOBJyLL2leqenIrvdF6iKhRsWPTofX29rr6yCbE04kxdguLSuh4Ahb9pt0PXAq8Qyn1rUrHpNNppqen3VVNK6kDZjA+Ps/f//1TTE0tkMvlSKfTRKM+LrxwA6ecMkIkEiKX03FhQ6Le5P50Ou3Gpufm5hgbG1tCGt58WBMDNroQ3soQJj/XWz253mFxq1ZYxWK6Y+rp6XFHB/V668PDw25p+kLN4HrOW+89sKvTLBqBjidg4MNAzdPcmUyG48ePMzY2VrSYZLUvjUkv++53D7KwkCCb1eS7Z08/b33rboaH+9yVUiZ536Qr9fX1uXHfwcFB5ubmSCQSjI2NMTo6uqS0TmGbFhcX3TJA8/PzhEIhAIaHh4uGVaA278/EnU18ulnlm8x1pNNpUqkUPT09budRD4aGhohETgizF2oG19q2ejzzdmeDWHQPOpqAReRs4EPAWcCxCru7MN7i3Nxc1cUki3k03uKY2WyOYDBEb2+YN7xhEyefHGVsbNSN60YiEWKxmLt01Sxw8K6oGxgYIJ1OEwwGy5KGd3GEUop8Pk88Hqe3t5eZmRn3GnO5nEv4tRKB8UrD4TDJZNIVVW80TBjG2GlEmShvZ7USzeB65wPanQ1i0T3oWAIWkX7gFuC9SqkJb020SlBKEQ6H2bBhA7lcrmQxSfPyFhKZUopnnpnlwQdPFMf0+31s2hThd35nF729+uXz+/1LXnwTcpidnXXFXAx5hkIhfD4fg4ODrsdcjDQKF0eEQiESiQQ9PT34fD6UUszMzDA0NMTs7CyRSGTJCrZaiMDc01rurWljueF3YUpdLpcjEAgQjUbp6+uryVYlVBrNlGtrvfMB7c4GsegedCwBA18BHlBK3VfNziJyPXA9wLp16xgaGmJsbMytogu4hR3NggozjPQSWTye4Y47fsXhw0n33H6/j0su2c1rXztCNpstq5G7uLjIwsIC0WjUOdZPb28v2WzWLWJZLt+22OKIzZs3u96jN+7pjYPWSgT9/f2kUiny+TyRSKSilKRBucrFpj1eiclIJEJfX9+yzqoVqCblEGqPo9u0NItGoaUELCKfAj5RYbcLgK3Aq4Gzqz23Uuom4CaAvXv3qr1795LP58lmswwPD7OwsLBkyWosFnOHkYY4Dh1a5L77DpLL+dzh5YYNUa655hTGxnqraoff72fdunUuuS0uLnLSSScB1S2aKPSuTNx6aGhoWdzTLBuuh9zMxF+tE0mFso3eysXm+0KJSb/fXzXBNxLVhArqTS+zaWkWjUCrPeDPAf9QYZ9DwHXAXiBeMDz+pog8oZR6Y7kTmHX7JpsgEAi46WCF4QKAfN7HE0/MCkWegAAAEpZJREFU8/TT025VYoDzztvERRdtX1KfrRJM1WLQHtjw8PCSFXWVYPaZnJxkYWFhyYSeIbFG1Uqrh0TKVS42ZN7qis3VtHU1hwpsxkX3oqUErJSaAqYq7ScinwBuLNj8NPAR4M4qjicajdLf3++GHwpfQPMgv/jiDPfee4h4PEs4rDMNBgbC7Nu3m127ape0NOeNxWIVl/aWQzabJRQKsbioVdXKiYkbtOJF9Q6/S1UubkbF5nqrGJu2rtZQgc246G50ZAxYKfUy8LJ3m+MJjyulXqh0vIm7+v3+JSlL3gc3l8vzxBMTPPbYUvWyM84Y5cord9HTszKvbSXqWYZsvWGM4eHhsse08kWtlIXQ6OH5Sq5ttYcKbMZFd6MjCXilyOfzzM/P09PTU9QDm5pKcPvtB3j55RMar5FIgCuu2MWZZ47WnBVQiEZUiigVxmikzUZ4zK0guLVMQt0SRrEojlVDwEqpqlkxk8mQTCbx+/2F5+CnPz26JL0MYMeOQa6+eg+Dg+Gi56uVqBpRKcJcRzXkW4/NSl5lJ8UdW0VCnXTNBt0QRrEojVVDwLVAKcWRI0cYGhpyJ67i8TR33fU8+/dPu/v5/T4uvHAb5523CZ+vOL/XUy23ES9NrZ5lrTbLeZWVUs3MPsWuvRkkVuu11dOGTo61rvYwikVpdCUBm0m4dDoNwK9/fZy77tLFMQ2Gh0NceeU2tmwZLEm+UH+13Ha8NLXYLOdVVko1g+LXXiiX6V363ahrM+0ptwikHiJdy2EOi/ahKwlYKUUikQAC3HXXAZ58cukq5rPOGuX1rx8mHA5WnCTr1Gq5pVCt91fOqyxMNQPctDKjO1GMrAyJVbP0u95rq0Su9RKpjbVatANdScAigs83wh13HCWROJHhYNLL1q/3V12pwmx/5ZVXXFF1QzLtrJZbDIagjIJaMeEeL0p5zIXkPDc3h1JqCVkXIytDYqWWfq8U1ZBrvURabZijmg6uE2PJFp2JriTgdFq4555j9Pb2ui/Ab/zGKFddpdPLjEIXVP+ShkIhkskkc3Nzbq4rLC/e2IoXr5QdU7JncXHR1YxoxEovI7FpCNVLVl6v2Lu/6awa2TlVWwXZtK2e1YHl9q/GA+/kWLJF50G8ObDdApEBBedyyimn0tcXZu9eHzt39pBILLjykEZpLJ1OMz4+TiaTYWxsjL6+PsbHx1lYWGB0dJSBgQEOHjzoVrQwpYK8BJjL5fD7/YyMjDA4OMjRo0dd1TOv/q3P53OF1s22kZERV/thZGSEWCxGJpNh48aNbNiwgfn5eQ4ePMjU1BTRaJRgMIiIsGXLFoaHh5mYmGBycpJgMMiuXbvc9gSDuqPJ5XJs2bKFrVu3Mj09zUsvveSGFUKhEMFgkA0bNrgi8slkktnZWcLhMKOjo+TzeY4fP04ymeTo0aNEo1FOO+00du/ezdGjR9m/f7+rpDY2NkY4HCaRSLiCQuFwmOHhYRKJBMlkkmAw6OZoBwIBhoaG3PtpVtYtLCwQi8VIpVKMjIzg8/kIhUKuvoZRmevv73ePA010/f39zM7OMjExgc/nY+PGjeRyOfL5PNFolEgkwvj4OMlkklAoxMDAgLu92OIRb2eXyWSIx+PMzs6STCZZv349W7duXbaPGV2Zf9uxDNuivRCRnymlKkopdDUBwwwDA4cIhfLuyyoipNNpAoEA6XSaZDJJMpnE5/O5L6pR8DJlfyohFAqhlCIajS4JT2SzWXw+H36/HxFBRNxzmmN8Pp+rcWu0H8z/169fz+zsLMePHyef16WPjMqb2c8QttGg2LhxI2NjY8zPzxOLxdi0aRO5XI6hoSG3s4nH4ywsLDAyMkIoFGLjxo2MjIwQjUaZm5tDRAiFQogImzdv5siRIxw5coR4PM7IyAgnnXQSO3fuZHZ2liNHjjAxMUEoFCIcDrsayNPT0/T396OUctvp9/vJ5/OuTaMNHAgE8Pl8ZLNZEokEk5OTiAhzc3MMDg4SDAbZvn07fr/fLUuUSqVIJBL4/X4SiQRKKVeDw+gcm5h9JBJhw4YNZLNZFhYWmJ+fZ3Fxkbm5OU499VTXsw+Hw66nb0Y4xps1v+ehQ4eYn59HKcXQ0BBbt251w1JG9c0o63mlSi3WFtY4AUseMOyZBpTzt2S3gr9S8Dvn8u7jPZ94Phfa8Hm2S4ljBC04n3f297bdfB8BMp7zmf29bTLHZIAUEHS+N8eZ8/mdP6+tvLNfqbYNOOdUzr7GvnLs+D3nz3uOpcT/FZD12Pbev6Cnvea4HNAPTDvbss4+/oLz59BhNXNtPucv43wnzvm932U932U999Mkipv2jjr2vefHcz+811b4/0ZhlCqW8jcR7bbfCW2o1v52pdT6Sjt1JQE3EiLy02p6sm5uQ7vtd0Ib2m2/E9rQbvud0IZG269e5svCwsLCoqGwBGxhYWHRJlgCroyb2t0A2t+GdtuH9reh3fah/W1ot31ofxsaat/GgC0sLCzaBOsBW1hYWLQJloAtLCws2gRLwBYWFhZtgiXgOiAaD4iIEpG3t9DuV0XkeRFJisikiNwpIqe30P6IiHxBRH7ttGFcRL4sIuta2IbrReS7IjLr3P8dLbD5fhF5UUQWReRnIvKmZtv02H6ziNwlIi8713tdq2w79v+biPxEROadZ+5uETmjhfY/ICL/6tifF5EnROTKVtkv0p6PO7/DFxtxPkvA9eHDnFgp1Ur8FF0x+nTgUvRqq4dFpFVlhzcBm4GPAmcC7wLeDNzSIvsAUeBB4IZWGBOR3wU+D3waeC3wOHC/iGxrhX2gD3gG+BCQbJFNL84HvgT8JnAherXgwyIy0iL7h4GPAa8DzgYeAe4QkVe1yL4LETkX+APgXxt2UqN0Zf+q+0M/BOPABvQy07e3sS2vctpwahvbcAV6Oe5AG34HBexosp0fAV8t2PYc8L/acK/jwHXt+q2dNvShnY+3trEN08B7W2xzEHge3Qk9CnyxEee1HnANEJF+tLf3XqXURJvb0gv8e+AQcLCNTTE6EYk2tqEpEJEQcBba4/biQbRHuBbRjx45z7TasIj4ReSd6E7g8Rabvwn4llLqkUae1BJwbfgK8IBS6r52NcCJR8bR3tDlwEVKqVSFw5rVliHgk2gPMVtp/1WIUbTgz7GC7ceAsdY3pyPweeDnwBOtMigiZzrPfAr9Dl6tlHq6hfb/ANgN/HGjz73mCVhEPuUE1cv9nS8i7wZeDfxhO+x7DvlHdCzyLcB+4FYRiba4DcYDvxt4GR0Tbqn9FqOYkt6aW8EkIp8B3gi8TSnVyjmQZ4HXoDVmvwz8XasmAkXkVHT8/98ppdINP78T31izEJFRtKdTDofQExG/zwkZQjghVfmEUuqNzbSvlFo2xHeGyDPA+5RS36jHfj1tEJE+4D40EV2ulIrXa7se+84xZwM/AXYqpQ6uxH6ZdoXQoZXfU0rd6tn+l8AZSqm3NMNumfbEgQ8qpb7eSruO7c8C7wQuUEr9utX2C9ryMPCSUuo/tsDWdcDXWDrpbmRQ80DvSkagXVmSqBYopaaoQt9TRD4B3Fiw+WngI8CdzbZfqlnOX7he+7W2wYmD3+/YvWyl5Fur/VZCKZUWkZ8Bvw3c6vnqt4Fvt6dVrYeIfB5Nvue3m3wd+FjhM18D7kBnH3nxNfRE7KfReuN1Y80TcLVQSr2MHm67EF3xd1wp9UKz7YvIbuBtwMPAJLAF+CN0XOyeZtt32tCPnoAaAPYBvU4oAmC6GUO0Im0YQ8dfT3E27XVi0YeUUtNNMPkZ4Bsi8mPgh8D70Ol4X2mCrWVwRhu7nY8+YJuIvAZ9vw+1wP5fAu9G/94zzv0HiDei863C/p8D96Izj/qBf4tOjWtJLrBSahaYLWjTAvr+P9MIA/av/tSUlqWhAVvRnucEutcdR8eDT2vh9Z7P0uoV3r/zW9SGG0rYv66JNt+PzjRJAT8D3twB9/zrLbJf6ve+oUX2vw685Nz7CbQDcmmr7n+JNj1Kg9LQ1nwM2MLCwqJdWPNZEBYWFhbtgiVgCwsLizbBErCFhYVFm2AJ2MLCwqJNsARsYWFh0SZYArawsLBoEywBW7QEIvKMiNzQ7nYUg4hc5yzzbbadHY6uxdnNtmWxOmAJeI1CRL7uEbrJiMgLInKjZ2Vbq9tzXYH4zhER+ScR2dkC898EdrXATkWIyKPFqi20qpOwaC0sAa9tPAychCaf/45e8VWod+GiBZU3Ek57NqGXnL4GuEtE/CXaE2qEUaVUUrVZ39libcIS8NpGSil1VCk1rpS6Gb20eR+AI8GpROQKEfmxiKTRZZAQkbeKro22KLpW2p95yVBENoiuV5cUkZdE5D9U2R7ltOeIUuq7wP8EzsDRQnDa8wERuc1Zj/9pZ/ubReRHTnuOichnTXtEZL3jTf8PT/te5ez7dufzEu9SRG5wQibvEZGDIhIXka+JSEi0HvO4iBwXkc+IiM9z3LtE10+LiciEiNwqIptr/1kqQ7RG7j+LrpMWE5F/EZELnO/8IvI3zm+TFJHnROSjBW0NOPdpxvn7rOj6fo969hHnOFOH8GkReVczrmetworxWHiRBAq93L9A18A7AMRE5FI0UX8IeAzYhhamCaOV4UCv398OXIz2aj8L7KizPRS06U+Ajzu2lENw9wPfQNfLOxn4a7RU4IeVUpOiJQXvFpGH0GLitwC3KKW+Vcb2DuDfAFeh6+B9Gy0CdBS4BDgN+Ce0QI9RRgs57fs1Wl7zLxxbb67j2ivhZuBfgHPQddrOBBad73xo4ahr0cJN56ArOhwH/sbZ5yPo+/Wf0DXn3o8edTzlsfEp4O3AB9CavOcBXxWRGaXUvU24prWHdopa2L/2/aFJ8h7P53PQkpDfdD6fjxZdeVvBcY8Bf1ywbR+6QoegVcoU8Fue77ej9VRvKNOe69AKW+bzFnTVhXEg5GxTwBcKjvszdOfgKzhXCoh6tn0OeAEtJXgA6Ctj+wY0+Q96tn0LTWYhz7ZHKSPKgiZpBWxxPu9wPp9d5pii5yzSxnngPTX83n8OPOz5fAT4I89nQXccjzqfe5178KaC83wOuK/dz2+3/FkPeG3jMmfoHUB7mXcC/7lgn0It1LOAc0TkY55tPqAH7SGejvY+f2y+VEq9JCKvVNGeXqc9gq5+/CRwjVoqc1nYntPRgvheofwfoL3R3ZyoYPsx4DK0qP5vqspSioeUUnOez8eA/QVtOYYuzgqAiLwO7QG/BhhxrgP0KOFwBXu14jPAX4vIe4B/Br6tPFq9IvI+tHe7Hf3bBNGqYojIIPq38v5GSkR+glbdA9gLRIAHRMSr2BWkvTUIuwqWgNc2HgOuBzLAK0qpTJF9Fgo++9Cx2VuL7DvJCdKpBwk0eeWBY0qpQtvF2lOuPJB3+w40uSj0pOOPKrSl8F6oEtv84JZo+g56YvPdaOnEUeD76M6gWsyjK/AWYghwOwSl1A0i8o/ouoCXAn8iIu9TSv2tiPwu2lP9CLp45Tw6jHB1kfaXgokXvxVdEcaLYs+JRR2wBLy2kVBKHajxmCfRGsRFjxORX6Ff3tfjVK4VkW3ozIZKUHW055fAtSLi83jBb0RrJj/v2A+i49Z3oYn3yyLyQ9VYQfPT0IT7caXUi47da+o4z7PAFSIiyhnzO3id850LpdRz6MoM/1dEvoz2eP8Wff0/Ukq56WwicrLnuDkROYoOO33X+V7Qv9lRZ7dfosM421WDKwFbnIAlYIta8afAPSLyEnoSKovOVDhHKfVRpdSzIvIA8Fcicj06jvgZTkyoNRpfAv4L8CXRpXN2oeOdX1Qnash9Eh0quBjtRV6GrnJxQUHoYiU4hCasD4quInG6Y7dWfBn4IPAFEfkqemLtCuD30JOCiEgPOl3wVnQ4YCMO6Trn2A9cJyKXo+Pd70QXcfWWkv888FER2Y8m2/eiUwCPACilYiJyI3CjQ86PocvBnwvklVI31XFtFgWwaWgWNUEp9R10OZgL0DHEH6NLI3m9yeuAF4FH0JWTb6ZJcUOlS0Vdjq4U/XO0B3gLOlMCEXkLOovj95VSs45XeR2aID9W7Jx1tmMSeA96QvKX6Fjwf63jPC+gsyb2oMs//RhNoO9QSt3n7JYDhoG/Q3vFt6MnLI29v0J3jjejC5fuAP5Pgakb0ZkjXwP+n7Ptdk5kUoAuw34DOpTxC+AhdFmsF2u9LovisBUxLCwsABCRJ4EfKqUKJ2ItmgQbgrCwWIMQke3oybvvoXngeuDVzr8WLYIlYAuLtYk8OiXvf6NDkb8ELldKFab5WTQRNgRhYWFh0SbYSTgLCwuLNsESsIWFhUWbYAnYwsLCok2wBGxhYWHRJlgCtrCwsGgT/j84qywle7/a7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Evaluate isoform logodds predictions on test set\n",
    "\n",
    "def safe_log(x, minval=0.01):\n",
    "    return np.log(x.clip(min=minval))\n",
    "\n",
    "y_test_pred = get_y_pred(X_test, np.concatenate([w_L, w]), w_0)\n",
    "\n",
    "#Compute Log Odds values\n",
    "keep_index = (y_test < 0.99999)\n",
    "y_test_valid = y_test[keep_index]\n",
    "y_test_pred_valid = y_test_pred[keep_index]\n",
    "\n",
    "logodds_test = np.ravel(safe_log(y_test_valid / (1. - y_test_valid)))\n",
    "logodds_test_pred = np.ravel(safe_log(y_test_pred_valid / (1. - y_test_pred_valid)))\n",
    "\n",
    "r_val, p_val = pearsonr(logodds_test_pred, logodds_test)\n",
    "\n",
    "print(\"Test set R^2 = \" + str(round(r_val * r_val, 2)) + \", p = \" + str(p_val) + \", n = \" + str(X_test.shape[0]))\n",
    "\n",
    "#Plot test set scatter\n",
    "f = plt.figure(figsize=(5, 5))\n",
    "\n",
    "plt.scatter(logodds_test_pred, logodds_test, s = np.pi * (2 * np.ones(1))**2, alpha=0.05, color='black')\n",
    "min_x = max(np.min(logodds_test_pred), np.min(logodds_test))\n",
    "max_x = min(np.max(logodds_test_pred), np.max(logodds_test))\n",
    "min_y = max(np.min(logodds_test_pred), np.min(logodds_test))\n",
    "max_y = min(np.max(logodds_test_pred), np.max(logodds_test))\n",
    "plt.plot([min_x, max_x], [min_y, max_y], alpha=0.5, color='darkblue', linewidth=3)\n",
    "\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "plt.xlabel('Pred Proximal Usage', fontsize=14)\n",
    "plt.ylabel('True Proximal Usage', fontsize=14)\n",
    "\n",
    "plt.axis([np.min(logodds_test_pred), np.max(logodds_test_pred), np.min(logodds_test), np.max(logodds_test)])\n",
    "\n",
    "plt.title(data_version + ' (R^2 = ' + str(round(r_val * r_val, 2)) + ', n = ' + str(X_test.shape[0]) + ')', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:aparent]",
   "language": "python",
   "name": "conda-env-aparent-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}