classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e9bd2db-cfdc-4b02-a7ee-b99304edcc8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import pearsonr\n",
    "from scipy.spatial.distance import squareform\n",
    "import pickle\n",
    "\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Descriptors\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.model_selection import cross_validate, KFold\n",
    "\n",
    "from rogi import RoughnessIndex, MODI\n",
    "\n",
    "from tdc.single_pred import ADME, Tox\n",
    "from tdc.utils import retrieve_label_name_list\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as pe\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "sns.set(style='ticks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ffcf1c0a-0e09-4ff9-b16a-3cc8245ad4bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_rdkit_descriptors(smi):\n",
    "    m = Chem.MolFromSmiles(smi)\n",
    "    descriptor_names = [desc[0] for desc in Descriptors.descList]\n",
    "    descriptor_funcs = [desc[1] for desc in Descriptors.descList]\n",
    "    descriptors = [desc(m) for desc in descriptor_funcs]\n",
    "    return descriptors\n",
    "\n",
    "\n",
    "def smiles_to_descriptors_df(smiles, descriptors=None):\n",
    "    Xdict = {}\n",
    "    mols = [Chem.MolFromSmiles(smi) for smi in smiles]\n",
    "    for desc in Descriptors.descList:\n",
    "        name = desc[0]\n",
    "        f = desc[1]\n",
    "\n",
    "        if descriptors is not None and name in descriptors:\n",
    "            Xdict[name] = [f(m) for m in mols]\n",
    "    return pd.DataFrame(Xdict)\n",
    "        \n",
    "\n",
    "def plot_scatter(ax, x, y, title='', marker='X', s=100, \n",
    "                 fit=False, fit_outline=True, fit_lw=1, fit_zorder=0,\n",
    "                 color='b', edgecolor='k', linewidth=0.5, alpha_face=1, alpha_edge=1,\n",
    "                 annotate=True, text_color='k', x_text=0.55, y_text=0.9, fontsize=10,\n",
    "                 zorder_scatter=5, label=''):\n",
    "\n",
    "    if fit:\n",
    "        coef = np.polyfit(x, y, 1)\n",
    "        poly1d_fn = np.poly1d( coef)\n",
    "        if fit_outline:\n",
    "            ax.plot([min(x), max(x)], [poly1d_fn(min(x)), poly1d_fn(max(x))], linestyle='-', color='white', zorder=fit_zorder-1, linewidth=fit_lw+1)\n",
    "        ax.plot([min(x), max(x)], [poly1d_fn(min(x)), poly1d_fn(max(x))], linestyle='--', color=color, zorder=fit_zorder, linewidth=fit_lw)\n",
    "\n",
    "    # face\n",
    "    ax.scatter(x, y, marker=marker, s=s, color=color, edgecolor='none', linewidth=0, alpha=alpha_face, zorder=zorder_scatter, label=label)\n",
    "    # edges\n",
    "    ax.scatter(x, y, marker=marker, s=s, facecolor=\"none\", edgecolor=edgecolor, linewidth=linewidth, alpha=alpha_edge, zorder=zorder_scatter)\n",
    "\n",
    "    ax.set_title(title)\n",
    "\n",
    "    if annotate:\n",
    "        pears = pearsonr(x, y)[0]\n",
    "        _ = ax.annotate(f'$r = {pears:.2f}$', xy=(x_text, y_text), xycoords='axes fraction', ha='right', color=text_color, fontsize=fontsize,\n",
    "                        zorder=10, path_effects=[pe.withStroke(linewidth=2, foreground=\"white\")])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7ab64471-2d5e-455d-b39b-487df207cebe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> 50 classificatoin tasks\n"
     ]
    }
   ],
   "source": [
    "# ---------------------------\n",
    "# TDC classification datasets\n",
    "# ---------------------------\n",
    "adme_props = [('HIA_Hou', None), \n",
    "              ('Pgp_Broccatelli', None), \n",
    "              ('Bioavailability_Ma', None), \n",
    "              ('BBB_Martins', None), \n",
    "              ('CYP2C19_Veith', None), \n",
    "              ('CYP2D6_Veith', None), \n",
    "              ('CYP3A4_Veith', None), \n",
    "              ('CYP1A2_Veith', None), \n",
    "              ('CYP2C9_Veith', None), \n",
    "              ('CYP2C9_Substrate_CarbonMangels', None), \n",
    "              ('CYP2D6_Substrate_CarbonMangels', None), \n",
    "              ('CYP3A4_Substrate_CarbonMangels', None)]\n",
    "\n",
    "# pick 12 at random from ToxCast to be appended to Tox list\n",
    "np.random.seed(42)\n",
    "toxcast_list = np.random.choice(retrieve_label_name_list('Toxcast'), size=19, replace=False)\n",
    "\n",
    "tox_props = ([('hERG', None), \n",
    "             ('AMES', None), \n",
    "             ('DILI', None) ,\n",
    "             ('Skin Reaction', None), \n",
    "             ('Carcinogens_Lagunin', None), \n",
    "             ('ClinTox', None),\n",
    "             ('herg_central', 'hERG_inhib')] + \n",
    "             # 12 tasks from Tox21\n",
    "             [('Tox21', l) for l in retrieve_label_name_list('Tox21')] +\n",
    "             # 19 tasks from ToxCast\n",
    "             [('Toxcast', l) for l in toxcast_list])\n",
    "\n",
    "# combine\n",
    "props = []\n",
    "props.extend(adme_props)\n",
    "props.extend(tox_props)\n",
    "\n",
    "print(f\"--> {len(props)} classificatoin tasks\")\n",
    "\n",
    "# def func to get TDC dataset\n",
    "def get_tdc_dataset(name, label_name):\n",
    "        \n",
    "    if (name, label_name) in adme_props:\n",
    "        df = ADME(name=name, label_name=label_name).get_data()\n",
    "    elif (name, label_name) in tox_props:\n",
    "        df = Tox(name=name, label_name=label_name).get_data()\n",
    "    else:\n",
    "        raise ValueError(f'Cannot understand {name}, {label_name}')\n",
    "        \n",
    "    # subsample\n",
    "    if len(df) > 10000:\n",
    "        df = df.sample(n=10000, random_state=42)\n",
    "    \n",
    "    return df\n",
    "\n",
    "# -----------------\n",
    "# ML models to use\n",
    "# -----------------\n",
    "models = {'KNN': KNeighborsClassifier(), # neirest neighbor \n",
    "          'LR': LogisticRegression(random_state=42), # linear model\n",
    "          'RF': RandomForestClassifier(n_estimators=50, n_jobs=12, random_state=42), # tree-based\n",
    "          'SVC': SVC(random_state=42), # kernel method\n",
    "          'MLP': MLPClassifier(random_state=42) # neural network\n",
    "          }\n",
    "\n",
    "# --------------------------------------------\n",
    "# select a bunch of commonly-used descriptors\n",
    "# --------------------------------------------\n",
    "desc_names = ['MolWt', 'FractionCSP3', 'NumHAcceptors', 'NumHDonors', 'NOCount', 'NHOHCount',\n",
    "              'NumAliphaticRings', 'NumAliphaticHeterocycles', 'NumAromaticHeterocycles', 'NumAromaticRings', \n",
    "              'NumRotatableBonds', 'TPSA', 'qed', 'MolLogP']\n",
    "\n",
    "# -----------------------------\n",
    "# Dict use to store all results\n",
    "# -----------------------------\n",
    "RESULTS = {}\n",
    "RESULTS['TDC'] = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd13d23c",
   "metadata": {},
   "source": [
    "### TDC + Fingerprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c1ec0cb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found local copy...\n",
      "Loading...\n",
      "Done!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HIA_Hou_None (balance: 0.87)\n",
      "  -->  KNN LR RF SVC MLP "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found local copy...\n",
      "Loading...\n",
      "Done!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Pgp_Broccatelli_None (balance: 0.53)\n",
      "  -->  KNN LR RF SVC MLP "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found local copy...\n",
      "Loading...\n",
      "Done!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Bioavailability_Ma_None (balance: 0.77)\n",
      "  -->  KNN LR RF SVC MLP "
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/home/maldeghi/rogi-results/classification.ipynb Cell 5'\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000004vscode-remote?line=25'>26</a>\u001b[0m     model \u001b[39m=\u001b[39m models[model_name]\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000004vscode-remote?line=26'>27</a>\u001b[0m     cv \u001b[39m=\u001b[39m KFold(n_splits\u001b[39m=\u001b[39m\u001b[39m5\u001b[39m, shuffle\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, random_state\u001b[39m=\u001b[39m\u001b[39m42\u001b[39m)\n\u001b[0;32m---> <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000004vscode-remote?line=27'>28</a>\u001b[0m     cv_results \u001b[39m=\u001b[39m cross_validate(model, ri\u001b[39m.\u001b[39;49m_fps, ri\u001b[39m.\u001b[39;49m_Y, cv\u001b[39m=\u001b[39;49mcv, scoring\u001b[39m=\u001b[39;49m(\u001b[39m'\u001b[39;49m\u001b[39maccuracy\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mbalanced_accuracy\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mroc_auc\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mf1\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mneg_mean_squared_error\u001b[39;49m\u001b[39m'\u001b[39;49m), n_jobs\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m)\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000004vscode-remote?line=28'>29</a>\u001b[0m     RESULTS[\u001b[39m'\u001b[39m\u001b[39mTDC\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mFPS\u001b[39m\u001b[39m'\u001b[39m][tag][model_name] \u001b[39m=\u001b[39m cv_results\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000004vscode-remote?line=29'>30</a>\u001b[0m \u001b[39mprint\u001b[39m()\n",
      "File \u001b[0;32m~/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:266\u001b[0m, in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=262'>263</a>\u001b[0m \u001b[39m# We clone the estimator to make sure that all the folds are\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=263'>264</a>\u001b[0m \u001b[39m# independent, and that it is pickle-able.\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=264'>265</a>\u001b[0m parallel \u001b[39m=\u001b[39m Parallel(n_jobs\u001b[39m=\u001b[39mn_jobs, verbose\u001b[39m=\u001b[39mverbose, pre_dispatch\u001b[39m=\u001b[39mpre_dispatch)\n\u001b[0;32m--> <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=265'>266</a>\u001b[0m results \u001b[39m=\u001b[39m parallel(\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=266'>267</a>\u001b[0m     delayed(_fit_and_score)(\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=267'>268</a>\u001b[0m         clone(estimator),\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=268'>269</a>\u001b[0m         X,\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=269'>270</a>\u001b[0m         y,\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=270'>271</a>\u001b[0m         scorers,\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=271'>272</a>\u001b[0m         train,\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=272'>273</a>\u001b[0m         test,\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=273'>274</a>\u001b[0m         verbose,\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=274'>275</a>\u001b[0m         \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=275'>276</a>\u001b[0m         fit_params,\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=276'>277</a>\u001b[0m         return_train_score\u001b[39m=\u001b[39;49mreturn_train_score,\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=277'>278</a>\u001b[0m         return_times\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=278'>279</a>\u001b[0m         return_estimator\u001b[39m=\u001b[39;49mreturn_estimator,\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=279'>280</a>\u001b[0m         error_score\u001b[39m=\u001b[39;49merror_score,\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=280'>281</a>\u001b[0m     )\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=281'>282</a>\u001b[0m     \u001b[39mfor\u001b[39;49;00m train, test \u001b[39min\u001b[39;49;00m cv\u001b[39m.\u001b[39;49msplit(X, y, groups)\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=282'>283</a>\u001b[0m )\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=284'>285</a>\u001b[0m _warn_or_raise_about_fit_failures(results, error_score)\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=286'>287</a>\u001b[0m \u001b[39m# For callabe scoring, the return type is only know after calling. If the\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=287'>288</a>\u001b[0m \u001b[39m# return type is a dictionary, the error scores can now be inserted with\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/model_selection/_validation.py?line=288'>289</a>\u001b[0m \u001b[39m# the correct key.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/parallel.py:1056\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/parallel.py?line=1052'>1053</a>\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterating \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m   <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/parallel.py?line=1054'>1055</a>\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mretrieval_context():\n\u001b[0;32m-> <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/parallel.py?line=1055'>1056</a>\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mretrieve()\n\u001b[1;32m   <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/parallel.py?line=1056'>1057</a>\u001b[0m \u001b[39m# Make sure that we get a last message telling us we are done\u001b[39;00m\n\u001b[1;32m   <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/parallel.py?line=1057'>1058</a>\u001b[0m elapsed_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_start_time\n",
      "File \u001b[0;32m~/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/parallel.py:935\u001b[0m, in \u001b[0;36mParallel.retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/parallel.py?line=932'>933</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/parallel.py?line=933'>934</a>\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend, \u001b[39m'\u001b[39m\u001b[39msupports_timeout\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m--> <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/parallel.py?line=934'>935</a>\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39;49mget(timeout\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimeout))\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/parallel.py?line=935'>936</a>\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/parallel.py?line=936'>937</a>\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39mget())\n",
      "File \u001b[0;32m~/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/_parallel_backends.py:542\u001b[0m, in \u001b[0;36mLokyBackend.wrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/_parallel_backends.py?line=538'>539</a>\u001b[0m \u001b[39m\"\"\"Wrapper for Future.result to implement the same behaviour as\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/_parallel_backends.py?line=539'>540</a>\u001b[0m \u001b[39mAsyncResults.get from multiprocessing.\"\"\"\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/_parallel_backends.py?line=540'>541</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/_parallel_backends.py?line=541'>542</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m future\u001b[39m.\u001b[39;49mresult(timeout\u001b[39m=\u001b[39;49mtimeout)\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/_parallel_backends.py?line=542'>543</a>\u001b[0m \u001b[39mexcept\u001b[39;00m CfTimeoutError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/joblib/_parallel_backends.py?line=543'>544</a>\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTimeoutError\u001b[39;00m \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/rogi_py38/lib/python3.8/concurrent/futures/_base.py:439\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/concurrent/futures/_base.py?line=435'>436</a>\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39m==\u001b[39m FINISHED:\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/concurrent/futures/_base.py?line=436'>437</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__get_result()\n\u001b[0;32m--> <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/concurrent/futures/_base.py?line=438'>439</a>\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_condition\u001b[39m.\u001b[39;49mwait(timeout)\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/concurrent/futures/_base.py?line=440'>441</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/concurrent/futures/_base.py?line=441'>442</a>\u001b[0m     \u001b[39mraise\u001b[39;00m CancelledError()\n",
      "File \u001b[0;32m~/anaconda3/envs/rogi_py38/lib/python3.8/threading.py:302\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/threading.py?line=299'>300</a>\u001b[0m \u001b[39mtry\u001b[39;00m:    \u001b[39m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/threading.py?line=300'>301</a>\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/threading.py?line=301'>302</a>\u001b[0m         waiter\u001b[39m.\u001b[39;49macquire()\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/threading.py?line=302'>303</a>\u001b[0m         gotit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/threading.py?line=303'>304</a>\u001b[0m     \u001b[39melse\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "RESULTS['TDC']['FPS'] = {}\n",
    "\n",
    "for name, label_name in props:\n",
    "    tag = f'{name}_{label_name}'\n",
    "    RESULTS['TDC']['FPS'][tag] = {}\n",
    "    print(f'{tag}', end='')\n",
    "\n",
    "    # get dataset\n",
    "    data = get_tdc_dataset(name=name, label_name=label_name)\n",
    "    smiles = data.loc[:, 'Drug']\n",
    "    Y = data.loc[:, 'Y']\n",
    "    print(f' (balance: {sum(Y)/len(Y):.2f})')\n",
    "    RESULTS['TDC']['FPS'][tag]['size'] = len(data)\n",
    "\n",
    "    # compute roughness\n",
    "    ri = RoughnessIndex(smiles=smiles, Y=Y, verbose=False)    \n",
    "    RESULTS['TDC']['FPS'][tag]['rogi'] = ri.compute_index(min_dt=0.01)\n",
    "    \n",
    "    # compute MODI score\n",
    "    RESULTS['TDC']['FPS'][tag]['modi'] = MODI(Dx=squareform(ri._Dx), Y=Y)\n",
    "\n",
    "    # test multiple ML models\n",
    "    print('  --> ', end=' ')\n",
    "    for model_name in models.keys():\n",
    "        print(f'{model_name}', end=' ')\n",
    "        model = models[model_name]\n",
    "        cv = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "        cv_results = cross_validate(model, ri._fps, ri._Y, cv=cv, scoring=('accuracy', 'balanced_accuracy', 'roc_auc', 'f1', 'neg_mean_squared_error'), n_jobs=5)\n",
    "        RESULTS['TDC']['FPS'][tag][model_name] = cv_results\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34b24058",
   "metadata": {},
   "source": [
    "### TDC + Descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f6cb1e6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found local copy...\n",
      "Loading...\n",
      "Done!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HIA_Hou_None\n",
      "  -->  KNN LR RF SVC MLP "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "Found local copy...\n",
      "Loading...\n",
      "Done!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Pgp_Broccatelli_None\n",
      "  -->  KNN LR RF SVC MLP "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "Found local copy...\n",
      "Loading...\n",
      "Done!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Bioavailability_Ma_None\n",
      "  -->  KNN LR RF SVC MLP "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "Found local copy...\n",
      "Loading...\n",
      "Done!\n",
      "[18:44:52] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:52] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:52] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:52] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:52] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:52] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:52] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:52] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:52] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:52] WARNING: not removing hydrogen atom without neighbors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "BBB_Martins_None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[18:44:52] WARNING: not removing hydrogen atom without neighbors\n",
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      "[18:44:52] WARNING: not removing hydrogen atom without neighbors\n",
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      "[18:44:54] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:54] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:54] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:54] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:54] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:54] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:54] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:54] WARNING: not removing hydrogen atom without neighbors\n",
      "[18:44:54] WARNING: not removing hydrogen atom without neighbors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  -->  KNN LR RF SVC MLP "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "Found local copy...\n",
      "Loading...\n",
      "Done!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CYP2C19_Veith_None\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/home/maldeghi/rogi-results/classification.ipynb Cell 7'\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000010vscode-remote?line=12'>13</a>\u001b[0m RESULTS[\u001b[39m'\u001b[39m\u001b[39mTDC\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mDESC\u001b[39m\u001b[39m'\u001b[39m][tag][\u001b[39m'\u001b[39m\u001b[39msize\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(data)\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000010vscode-remote?line=14'>15</a>\u001b[0m \u001b[39m# descriptors\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000010vscode-remote?line=15'>16</a>\u001b[0m df_X \u001b[39m=\u001b[39m smiles_to_descriptors_df(smiles, descriptors\u001b[39m=\u001b[39;49mdesc_names)\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000010vscode-remote?line=16'>17</a>\u001b[0m \u001b[39m# normalize features (this involves some data leakage and slighly inflates performance, but it does not really matter here)\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000010vscode-remote?line=17'>18</a>\u001b[0m X \u001b[39m=\u001b[39m df_X\u001b[39m.\u001b[39mto_numpy()\n",
      "\u001b[1;32m/home/maldeghi/rogi-results/classification.ipynb Cell 2'\u001b[0m in \u001b[0;36msmiles_to_descriptors_df\u001b[0;34m(smiles, descriptors)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000001vscode-remote?line=13'>14</a>\u001b[0m     f \u001b[39m=\u001b[39m desc[\u001b[39m1\u001b[39m]\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000001vscode-remote?line=15'>16</a>\u001b[0m     \u001b[39mif\u001b[39;00m descriptors \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m name \u001b[39min\u001b[39;00m descriptors:\n\u001b[0;32m---> <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000001vscode-remote?line=16'>17</a>\u001b[0m         Xdict[name] \u001b[39m=\u001b[39m [f(m) \u001b[39mfor\u001b[39;00m m \u001b[39min\u001b[39;00m mols]\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000001vscode-remote?line=17'>18</a>\u001b[0m \u001b[39mreturn\u001b[39;00m pd\u001b[39m.\u001b[39mDataFrame(Xdict)\n",
      "\u001b[1;32m/home/maldeghi/rogi-results/classification.ipynb Cell 2'\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000001vscode-remote?line=13'>14</a>\u001b[0m     f \u001b[39m=\u001b[39m desc[\u001b[39m1\u001b[39m]\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000001vscode-remote?line=15'>16</a>\u001b[0m     \u001b[39mif\u001b[39;00m descriptors \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m name \u001b[39min\u001b[39;00m descriptors:\n\u001b[0;32m---> <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000001vscode-remote?line=16'>17</a>\u001b[0m         Xdict[name] \u001b[39m=\u001b[39m [f(m) \u001b[39mfor\u001b[39;00m m \u001b[39min\u001b[39;00m mols]\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bmolworkstation01/home/maldeghi/rogi-results/classification.ipynb#ch0000001vscode-remote?line=17'>18</a>\u001b[0m \u001b[39mreturn\u001b[39;00m pd\u001b[39m.\u001b[39mDataFrame(Xdict)\n",
      "File \u001b[0;32m~/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py:286\u001b[0m, in \u001b[0;36mqed\u001b[0;34m(mol, w, qedProperties)\u001b[0m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=271'>272</a>\u001b[0m \u001b[39m\"\"\" Calculate the weighted sum of ADS mapped properties\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=272'>273</a>\u001b[0m \n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=273'>274</a>\u001b[0m \u001b[39msome examples from the QED paper, reference values from Peter G's original implementation\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=282'>283</a>\u001b[0m \u001b[39m0.234...\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=283'>284</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=284'>285</a>\u001b[0m \u001b[39mif\u001b[39;00m qedProperties \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=285'>286</a>\u001b[0m     qedProperties \u001b[39m=\u001b[39m properties(mol)\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=286'>287</a>\u001b[0m d \u001b[39m=\u001b[39m [ads(pi, adsParameters[name]) \u001b[39mfor\u001b[39;00m name, pi \u001b[39min\u001b[39;00m qedProperties\u001b[39m.\u001b[39m_asdict()\u001b[39m.\u001b[39mitems()]\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=287'>288</a>\u001b[0m t \u001b[39m=\u001b[39m \u001b[39msum\u001b[39m(wi \u001b[39m*\u001b[39m math\u001b[39m.\u001b[39mlog(di) \u001b[39mfor\u001b[39;00m wi, di \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(w, d))\n",
      "File \u001b[0;32m~/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py:252\u001b[0m, in \u001b[0;36mproperties\u001b[0;34m(mol)\u001b[0m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=247'>248</a>\u001b[0m   \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39mYou need to provide a mol argument.\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=248'>249</a>\u001b[0m mol \u001b[39m=\u001b[39m Chem\u001b[39m.\u001b[39mRemoveHs(mol)\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=249'>250</a>\u001b[0m qedProperties \u001b[39m=\u001b[39m QEDproperties(\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=250'>251</a>\u001b[0m   MW\u001b[39m=\u001b[39mrdmd\u001b[39m.\u001b[39m_CalcMolWt(mol),\n\u001b[0;32m--> <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=251'>252</a>\u001b[0m   ALOGP\u001b[39m=\u001b[39mCrippen\u001b[39m.\u001b[39;49mMolLogP(mol),\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=252'>253</a>\u001b[0m   HBA\u001b[39m=\u001b[39m\u001b[39msum\u001b[39m(\u001b[39mlen\u001b[39m(mol\u001b[39m.\u001b[39mGetSubstructMatches(pattern)) \u001b[39mfor\u001b[39;00m pattern \u001b[39min\u001b[39;00m Acceptors\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=253'>254</a>\u001b[0m           \u001b[39mif\u001b[39;00m mol\u001b[39m.\u001b[39mHasSubstructMatch(pattern)),\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=254'>255</a>\u001b[0m   HBD\u001b[39m=\u001b[39mrdmd\u001b[39m.\u001b[39mCalcNumHBD(mol),\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=255'>256</a>\u001b[0m   PSA\u001b[39m=\u001b[39mMolSurf\u001b[39m.\u001b[39mTPSA(mol),\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=256'>257</a>\u001b[0m   ROTB\u001b[39m=\u001b[39mrdmd\u001b[39m.\u001b[39mCalcNumRotatableBonds(mol, rdmd\u001b[39m.\u001b[39mNumRotatableBondsOptions\u001b[39m.\u001b[39mStrict),\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=257'>258</a>\u001b[0m   AROM\u001b[39m=\u001b[39mChem\u001b[39m.\u001b[39mGetSSSR(Chem\u001b[39m.\u001b[39mDeleteSubstructs(Chem\u001b[39m.\u001b[39mMol(mol), AliphaticRings)),\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=258'>259</a>\u001b[0m   ALERTS\u001b[39m=\u001b[39m\u001b[39msum\u001b[39m(\u001b[39m1\u001b[39m \u001b[39mfor\u001b[39;00m alert \u001b[39min\u001b[39;00m StructuralAlerts \u001b[39mif\u001b[39;00m mol\u001b[39m.\u001b[39mHasSubstructMatch(alert)),\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=259'>260</a>\u001b[0m )\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=260'>261</a>\u001b[0m \u001b[39m# The replacement\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=261'>262</a>\u001b[0m \u001b[39m# AROM=Lipinski.NumAromaticRings(mol),\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=262'>263</a>\u001b[0m \u001b[39m# is not identical. The expression above tends to count more rings\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=263'>264</a>\u001b[0m \u001b[39m# N1C2=CC=CC=C2SC3=C1C=CC4=C3C=CC=C4\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=264'>265</a>\u001b[0m \u001b[39m# OC1=C(O)C=C2C(=C1)OC3=CC(=O)C(=CC3=C2C4=CC=CC=C4)O\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=265'>266</a>\u001b[0m \u001b[39m# CC(C)C1=CC2=C(C)C=CC2=C(C)C=C1  uses 2, should be 0 ?\u001b[39;00m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/QED.py?line=266'>267</a>\u001b[0m \u001b[39mreturn\u001b[39;00m qedProperties\n",
      "File \u001b[0;32m~/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/Crippen.py:170\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(*x, **y)\u001b[0m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/Crippen.py?line=164'>165</a>\u001b[0m   \u001b[39mreturn\u001b[39;00m numpy\u001b[39m.\u001b[39msum(atomContribs, \u001b[39m0\u001b[39m)[\u001b[39m1\u001b[39m]\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/Crippen.py?line=167'>168</a>\u001b[0m _pyMolMR\u001b[39m.\u001b[39mversion \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m1.1.0\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m--> <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/Crippen.py?line=169'>170</a>\u001b[0m MolLogP \u001b[39m=\u001b[39m \u001b[39mlambda\u001b[39;00m \u001b[39m*\u001b[39mx, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39my: rdMolDescriptors\u001b[39m.\u001b[39;49mCalcCrippenDescriptors(\u001b[39m*\u001b[39;49mx, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49my)[\u001b[39m0\u001b[39m]\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/Crippen.py?line=170'>171</a>\u001b[0m MolLogP\u001b[39m.\u001b[39mversion \u001b[39m=\u001b[39m rdMolDescriptors\u001b[39m.\u001b[39m_CalcCrippenDescriptors_version\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/Crippen.py?line=171'>172</a>\u001b[0m MolLogP\u001b[39m.\u001b[39m\u001b[39m__doc__\u001b[39m \u001b[39m=\u001b[39m \u001b[39m\"\"\"\u001b[39m\u001b[39m Wildman-Crippen LogP value\u001b[39m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/Crippen.py?line=172'>173</a>\u001b[0m \n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/Crippen.py?line=173'>174</a>\u001b[0m \u001b[39m  Uses an atom-based scheme based on the values in the paper:\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/Crippen.py?line=182'>183</a>\u001b[0m \n\u001b[1;32m    <a href='file:///home/maldeghi/anaconda3/envs/rogi_py38/lib/python3.8/site-packages/rdkit/Chem/Crippen.py?line=183'>184</a>\u001b[0m \u001b[39m\"\"\"\u001b[39m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "RESULTS['TDC']['DESC'] = {}\n",
    "\n",
    "\n",
    "for name, label_name in props:\n",
    "    tag = f'{name}_{label_name}'\n",
    "    RESULTS['TDC']['DESC'][tag] = {}\n",
    "    print(tag)\n",
    "\n",
    "    # data\n",
    "    data = get_tdc_dataset(name=name, label_name=label_name)\n",
    "    smiles = data.loc[:, 'Drug']\n",
    "    Y = data.loc[:, 'Y']\n",
    "    RESULTS['TDC']['DESC'][tag]['size'] = len(data)\n",
    "    \n",
    "    # descriptors\n",
    "    df_X = smiles_to_descriptors_df(smiles, descriptors=desc_names)\n",
    "    # normalize features (this involves some data leakage and slighly inflates performance, but it does not really matter here)\n",
    "    X = df_X.to_numpy()\n",
    "    _X = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))\n",
    "\n",
    "    # compute roughness\n",
    "    ri = RoughnessIndex(X=_X, Y=Y, metric='euclidean', verbose=False)        \n",
    "    RESULTS['TDC']['DESC'][tag]['rogi'] = ri.compute_index(min_dt=0.01)\n",
    "    \n",
    "    # compute MODI score\n",
    "    RESULTS['TDC']['DESC'][tag]['modi'] = MODI(Dx=squareform(ri._Dx), Y=Y)\n",
    "\n",
    "    # test multiple ML models        \n",
    "    print('  --> ', end=' ')\n",
    "    for model_name in models.keys():\n",
    "        print(f'{model_name}', end=' ')\n",
    "        model = models[model_name]\n",
    "        cv = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "        cv_results = cross_validate(model, _X, ri._Y, cv=cv, scoring=('accuracy', 'balanced_accuracy', 'roc_auc', 'f1', 'neg_mean_squared_error'), n_jobs=5)\n",
    "        RESULTS['TDC']['DESC'][tag][model_name] = cv_results\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c294bdf4-1292-439c-a3f1-808a224a8f19",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('classification_results.pkl', 'wb') as f:\n",
    "    pickle.dump(RESULTS, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "637c371d",
   "metadata": {},
   "source": [
    "### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9318233-6dd2-498f-a130-3e2225997f6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('classification_results.pkl', 'rb') as f:\n",
    "    RESULTS = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7db11883-5bdd-4680-9e42-ac3445828446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7ipLbP0/uonln6Bsz+LCo6QyBtRsJrFyv74c0DTkvF8njInm0ThdeZBPu2eeRu+gC5Pzcvu5ylmR9I0o0TiYQgj4UXtR0hljFfpRYHMe4kZg87j7ry+mgT4WX4uJiXnrpJXbu3ElDQwMlJSVMnDgR8SzciN2aNwCxvLiD4BLZtov44aPIeV7MZcWoyRTx/YdINTShptPkXDALJRLFt/xtfMvfZsA3v5ydsLtDiSeoe+hxUi0+MqEwSiCEKV/XWqmJJKb8XPzL3ya6o4LSr95yxiwxsYNHqPvTk1hKi5E8LsJb3yewegMDv3PXSbVop0J0zwH8K9ejxOK4pk3Ee+H5Z1woO9tRYjFqf/8YSjSOdchAbKOGkqxpoOXFZQgIeObNQC7MJ93qo+mv/8Y6ZBClt9/QYfHuiWRDM7G9B1DicdKBCOmGRgSTCeeksbhnTum1ltzg7McsCMSee5lUWxDLoHJc0ycTWLMJye3CXF6CEowSP3SU2IHDSE4H3osvQLLZSDY0UfunJ/EunEv+5Ref9D6+t9bQ+sqbaKpKqrkN0WHDUpyLGk8g2CwgitT98UmKPv9Z3NMmZa/TNI2211fgf3MN5qICHJPGddi8pn1+Qpu30fbqW+R9+iK8i+f3uXZVTWcIbtxKrOIA5qJ8ci6cjZx7+ubE/oJdlDCNHZEVXBLVdYQ2vocpx41crGcRjR88QiYYJhOOkv+ZSyGdIbp7P/7V71D2lZuwjxpG+N0dhDa9h2feTERzR0ugpmlEt+0ifqQa0WojcbQWOT8X0Wol1dyK5HbR8uIy/Ks2UH7PFxGtVtR0hrpHn8ExcUy3gsuJCJKEZXA5ydp6orv3GcLLWYaazlD3xydI+/zYx47ElJtD2hfA/9/VaIqKddhAPOdPR40nSBw+ytEHH6L861/qlauhlsnQ+vpK/G+uQYlEESxmJIcdyW7DOngAOReej2344I8055TdcROpFl+fWl3S/iA1v3kUBBDNZpr/+TLFt1yLa+qEPuvTR6XPpQSfz4fP5yMWi1FZWcmLL77I888/f0ptHDlyhOuuu45LLrmE6667jqqqqk7nPPTQQ8yePZsrr7ySK6+8kp/85CendA+PJCOd4A6TqKwiUVWLfcRQzIX5iFYLyZp6fVNQVkyqph41kcRSUoR79nmYvB7qHn66U4zPB2l85l+kWn0k6xpJN7cimGUyrT5EsxlzYT7RnXtwzZhMqsWH/823e2xL0zTUZAo1mTrpfT94XdOzL+IYPxrHhNFYB5bhOm8ickEera++2et2TkZk117q//wsgknCXJhHYNUGmp598bS1319pfOZ5QMB9/jQspUWgQWRHBfZRw7CNGExk+25EiwXbkIF4LphNsqGJlpeXn7RdTdOof/IfHPq//0PtHx6n/uFnaHnuJRI19QiyTHD9uxz54S+JHTh85h/S4GPh1ryBqIEwnvmzsA4sJbh2M5aSQqzlJchuF5LDiiBJCGYzgtlMZPtuJKcd+4gh5MybRWD1O4R37O7xHrHKI7T+ZzlKOEq6uRXJ40S0mEk1NGPKz0WNxlAiUdyzptL01+fJhCNA+zzz95cIrn+XnAVzcE6d0EnrLud6cZ03Ec/8WfhWrKPlhddPaS473WiaRv0jT+N/cw0IupLn6M8fIt3m77M+9RV745Hspi4TDBHa+B7WIQOwlBUj2W2kW3wAmAeXoSWSJA4cQc7z4pw8DueksdQ9+gxpf4C2ZStxjB/dSXABiFceIV5VAxqkm5tBEMj4Q4gWM7YRg0kcqcY2dgRqOt0+b+pzpWg2Yykp6rbvmqaRrG8i/N4ugpu2oSaSuKZNJBUI9mp8xQ4cou7hp6j+1cP4Vq5HU5Qez1cTSWKVR0jWN/bYfqqxmYan/knVA7+n+V+v6laGcxzff1eRCQRxz56mK041jeDaTVgGluGcNIZ0Y4u+97LbcIwfjX3McOoefuqk/xNN0zj0/f9H0zP/JhMKkwmFSbe0kThcTToUJhOOUP/oMzT/65WPNOeIVmufu4u1vrwck9uF5/zpuKZNwjVzKk1/ewE1ne7Tfn0U+lR4WbFiBRdffDF/+MMfuP/++/nb3/7G/fffz8svv3xK7dx///3ccMMNLF++nBtuuIH77ruvy/OWLl3Kyy+/zMsvv8z9999/SveoTcWzE7WmaUQr9mMpK0Iw6VYCJRxBS2eQ3A4kuxXBbCa85f3s9bZRw0j7A8QPVXV7j3Srj+iu/aSbWhFkCTk/Fznfi1yYT6q+CdFqQTCZSNY24Bg3Ev/qd9AyHdPRappG/EgNDU/+g4Nf+wGV37ifym/cR+U9P6LxmedJVNed9FmVYIiMP4C5tOPkbx0ykOjOvb39yk5K2+srcYwfhXXwAMzFhbhmTiW09f1zesJOt/qI7TmIY8Lo7HiLVx5BcjkxuZyYPG4Ei4VEVQ2guz44J40luG7zSbN7+Zatou3VtxBsFkSrBeuwgcglhSRr6glv34V99HAck8ZS98jTpFrbzvizGpx5imQLpna3wsTROgSzjCnnuLtAqr4JKceFuTAXJRwh3RYg7QsAevIF++jh+Ja/3eM9/CvXoWkaot2GYDJhynFjcjuRvG6S1XWYS4qylh25II/Qxvfar1tP9P09uM+fnvVf7w7JbsMzdwahTdsIrn/3o30pH4HEkWoS1XW4z5+GdVA5zoljMBfm4V+9oc/61FfE1OObw9i+Sky5OUh2vS6bllFIt7Ri8nqQTCYkt4vo7r26yxdgLszHXJinC73RGHJh56KjmqYRqzgAanucg8WMKceNpqkkjtYSP3AYNZUisOodLOUlRHbv0wP0V63DOqi8237HD1fT+p//Etr0HhmfHzUeJ9PqJ364msSeg/jfWtPjc0f3HNAVkYApL5fAqvU0/e2Fjn3PZFDi+nwcWLuJQ/c+QOMT/6Dm149S/Ys/kvYHO7Wb9gWo/tUjpP1BrANKiR+qouZXj5zTKee1TIbA2xuxjx2VXQ9TdY0IgoDs9SBIEnJBHrF9ldlrLGUlCJJE5CR7laa/v0TiUBXW0cPRUmnMZUVYBpUhOu2osQTJ6jrcF8wksm0XgbWbzuhznmniBw5hOcESJXs9CLKJVGNLH/bqo9GnbmO/+93v+PnPf86ll17K9OnT+c9//sMLL7xAZWXlyS9up62tjT179vDkk3rhsssvv5yf/vSn+Hw+cnNP3e8xFAoRCoU6HGtsbKQtc1xCVUJh1HQG0XHcvSbjDyLaLNkXTLTbSDUdT8UoCALm4iJdg/4Bv28lEiXd6iO0ZQeCJCI6dK3VsU2GYJIQbVbSbX5MOW6SR+uwDR0EgkCqsSUbIJ1u81P/57+RbvVhGVCKd9E8RIu+KVATSeJHqqn5//6MpayY0ttv7NbnUZBlNEUFVYUTXLi0VAqhl3E7mWCYVEsr5oK8bu+jhMJYBx9fZETZhGg2o0SimNyuXt2nP9Hd2DqR6J4DyIX5HVznEkfrMJ+wuMs5bpI19dhHDgX0jZ3kchI7eATnhDFd3luJxWh67iXMpUW6K2L7xG9y2tFicUSLheC6zeReuhBLaRGB1e9QeM0Vp+vRDc4w3Y2tw4kYs0R9TkrW1mPyHH+v9A1WHLPXjSAIiDYroJFqbEbOzQHAXFpE5P0KlFisW3fCWMVB1EQCyWlHtJwwB1otZHwBRNmEIIlkAiHMRQXEDhwmZ8EcfMtW4po1tdeur6JZxjl1Am2vvoXn/OlZd6WPk3SbH5Pb2cGNRPK4STW1fux9+bjozbyVqK7HNnRg9nMmHEGQ5axyT3LaSDXHyPgC2XgES3kpoS07sJQUdemWo4TCKMkUaiwGoohgMqGEo5BRwGJCTaSQcj1kWv0E1mxES6XxvbWWZE09jvGju3yW6O79RPcewDqgtFMMlbmkgMThapqefQnJ5cQzu2PioNiBQ7S89AbhrTt15U88iXnwAOT8XPwr1pK/9FJMHhdqMkXVz35Hxh8k/8pLaFu2Evf505CcDpINzSQqj1D7h8cYfN83Ozx3cMO7yAW5OEYPB0AuyCO4YQvRXXv7tXtPd/RmXKWaW0EUMbmPZ65MNrUgnRB7a8pxE921D03Tst+nXJhHbN9BXFM6luI4hhJPEFi1HrkwH6XVh+i0Z5OXiFYLotWMEo2RafXhmDAG//K3yZk3s9/GlspFBaR9/uz8ryaTKLFEJ0t3f6JPhZf6+nouvbRjzv6rrrqKOXPm8N3vfrdXbTQ0NFBUVITUvtmTJInCwkIaGho6CS+vv/4669evp6CggK997WtMmTKlU3tPP/00f/zjHzsdv8pz3GVMUxTd1eKEiUdT1I4DWxRA1Tq8UKJsQksdF4LiR6p17ePOPYgOOxlfgFRLG4IkoWUUNCB7B0EATffPVZIp/ZAooqGbM1PNrdT8+hHMZSXkXDS302IgWi04xozAPno48b2VHP1/f2Tgt7/apa+25LBjHzOc2N5K7ONGAhB5fw/hTe8h2u0E392BZ8bkTtcd+26a/vEyoc3bMDmdZCIR3NMmU3TD0k4xGY6JY4gfOopzyngEQSBZ36S7kBUVdtl2f6e7sXUiajqNmkyQqKpB0zQkmxUto3SMA5LETiZxUTah9aChC23erlvuRP3ajkKogMnjItXYTLrNj2VQOaF3t/d74UVTFBJVNQiyjGVAaZ/HSpxJuhtbi1wFxz+oKpzwDmqqPjcd+14kp13XxJ2Qql4QRQTJhJbp3gVDU1Ukq7V9/J3gXqGoetuiqM+Nqgrt7heRHbsRHfZTTqctez0gSUR378M5aewpXftBUq1txPYcxDllQq8TkdiGDCTd6kOJJ/R3U9NI1jaSc+Hsj9SXs5lejS3lA3OUqsKJ66EgIKCvk9lDZllfD7vZEGoZRR9/ooimqKiJJKJZRpMkRIuMlskg2W0oYhDbqGEkDlbhe2MlkssJmkaqpQ0trQdyy14PqaZWonsPYBs+uMsse4IgILl163bzP1/BUlaSjZuIH6mm7uGnsY8ZgZznRcpxE91XiZJM4Zoy7rjSzeNCiUbJ+ANILieBtZuwDhmA5HQQ3rSNZF0jgtVMev8hQu/uwDPz+B4kEwghtluujiHZrZ9YT4TerIdo+tanA6r6gYOd53VBENHU7l29ojv3Ysrx6HHFwTBy8QmxUekMoseN4LATP3QUz9wZqMkUqcZmLKX9sxRG/pLF1P7+MdRYAtFiJlFdh3fhnH6dBKVPhZe8vDxaW1vJz8+nrKyM7du34/V6z0idl+uvv5477rgDWZbZsGEDd955J8uWLcPr7bh5v+WWW7jqqqs6HGtsbGTZ144LU5LDgZZKZdPzAYg2K5loLHuOlkwj2mwdNkyZYAhHu2bc99Y62patwDpkADkXzUM0yyRrGwhu3obiD6JlMmQaW5FLCrJmc+vAUjKhKKZcD0o0hppMYi7IR00kqP39Y1gGlesWmR4QBAH72BFwQKL2D08w6Af3dDmRF3/+amr/8DjBNRvJhCKkGpp0K4wATU//ExQFz+zzOl3nW/42sT0HdKuPLKOmM4S3bKdt2Uryl1zS4dz8JYupe+hJAivXI1rMqKk0ZV+9uU80qh8H3Y2tG2+8EU1VCW7Ygu+N1XqGuXa3AjWVJuMLkFAVbMMGI5gklEgc0wkaE03TSAeCPQanRnfvxzZ0ENF9lWgnbFjVpD6OTW4XSjRGqrkV2/AhqPHkGfgGPj6StQ3U/vEJQN8Eybkeyu7+4ifSogfdj60n7/g/2c+mPC/Juqastk0wmUAQdSuybEKQTZDOIJ3wHWVCYQSThOTofpGzDiwjE4uRPFKNkkgi5bhB08j4Q8j5efrGM5lE8rhIHK3FPXsagbc3fujaHdaBpQTWbvzIwkvdH59EjSeI7t5H2Z239uoaOT+XvMsvpu21FcgFuWTCUcxFBeTMm/GR+nI205uxJbn1TfsxK7totehrZLvyTk2ndWXMCUJiutWPXJiPluh6rpGc9na3aAE1kUAQJZAkhHQGLaMg2mxoyRSizaon+REFLAPLib6/h5bnX0cwy7pApaioqRSaqiIX5vWcHlwD0W7FXFqE7621lN72OQB8y1ZiGz4E68Ay0q1+UvUNWAaXET9wCDnXoyvdSnSlm5zrpeima0hU1xBcs5n4oSrCm7ejJr93PggAACAASURBVFM4Jo1FslpQwlF8b6zqILw4J4yh6e8vYh08EFE26fNxU2vWEvNJo6f18BiSx0XaFySwZiOIop6xVZL09659vVOCIeQ8b4e9VrrVh2t61wpWACUSwVSQhxJP6GMDATQNNRpHTaWR872o8QRKJKZbpc1yj8rBsx3bkIEM/O7dBNZuQo3GKL7xMzg+4vzZ1/Sp8HLNNdfw3nvvcckll3Drrbdy8803I4oiX/jCF3rdRklJCU1NTSiKgiRJKIpCc3MzJSUda00UFBzXEs2ZM4eSkhIOHjzIjBkdFx23243b3dnNqcx8PP+7aDFjLi8h1eLDcmzCys8lVdeAlsmgCQJqLIZ71tTsNUokRrrFh3vmZILvbMH3xko8c2dkfYRBd9EQAHNZEcnaRpRoFLVWQZQkfdK1WslU1+GaPonY/ko8509HtJjxv/0Ogiz3KLhomkbGFyATCOqaUpuerSWybTfumZ0tUCaPi0E/uIfo3oPU/vZ/8V6ygPjBw4hWK/YRQ/D9d3WXwkvg7Y04p45HlGU9q1XFftRUirY3VncSXiS7nQHfuZNkTT1qIoF1yKBPdB2b7saWCYHY318mFYpgHzeSTFB36zq20U42tpA4Uo2S2I99xBAy/gDuEyxfyZp6zPl5PddX0VRMBXlYwhGiFQdIm4MIqoaaTGIdNhhBEnX9laahBIKnNaPcx42madQ//nesg8qxDh6gx6jt2kfz869T+sXr+7p7Z4TuxtaJ2IYNJrbnIGpRHqLZjCAKyIV5KIEQYl4OaiIJaNlNGED8wGFy5s/qUaHgXTiHpn+9im3UcMLv7iBV14QgCJhyc7AMLCXV0IxlQJk+B7b58cw+D/9ba7G2Z248VUxeD7E9Bz/UtR3acbtI+IJIJ/nePkju4vk4p4wnfqgKOS/3I2cjOtvpbmyd+MT20cOI7tyL5HbpFgyH7kKoxuJIDjtKMIy5KP94TIyikDxaQ8F1S2h6+nndqvKBuCfRYsFSXpqN09QUBS2VAjQETUVyO1ECIeSiQpRIFC2VIlFbj6apiA47thNcktPBMLGKA2jJFJLN2q0SQ02lMBfkYR08gMDKdWTCEUwuJ8m6RhyTxwHgmDCKjM9PovIoSjRGbM8Byv/P7QiirghIVtchWq1Ed+xpF9icyHleYvsPEd9XiW3EUN0qaTpuqcqEwqQDQSS3C9+ylcgFuSjBMPlLL8Vc/Mn0ROhpztIyGZpfeJ3gus0IJpFUYwtyUb4elxcIkWn1kbDIWEqLSbW0dXCrS7f5yYQjuM7r3tVOcjrR4gm8F82l5Z+vkK5rRJB093z76GGIsqxbwmxW1FRaj8s6C9ZELZPB99Y6RJtFrzN4CvOOpaSQouuWnMHefbz06U7xS1/6UjYt8tKlS5kxYwbxeJxhw4b1uo28vDzGjBnDa6+9xpVXXslrr73GmDFjOrmMNTU1UVSkB6Dv3buXuro6hgzpuuZAV3glGTUWh3ZLjWvyeHzLV5MSRb2glmzSBZraRlRV1etyjBsF6Ok+I+/tIv8zlyLIZlpeWIZrxuQOggvoLhrumVMJbtiCddhgUnUNKOEo5uGDkHO9xA8f1SfzQ1WAQN4VF6NpGv6V67GN6PpZNE0jebSWaHt+b8luRxAF/YWMRGl4+l84Jozq0p9dEPU8+KLNhnVQWXYxUGJxlPaMQR9EicYQ7XbSPj/BdZswFxciyibi+yqJHarC/oG6EYIgfGgN7CeFz+eVowZCeNp9aj3nTyOwZhNaqV4nw1yUjxIKk/YFCG+vwDV1fHvgqkayuo7YvkrKv357j/ewDRtCZOcePPNmkgmGUVMpZI8LU24OotmMpmko0Rgmbw7xw9X92g1GCYbI+AJZzZsgCNhGDCHYz4MuPyqSw45z0lgiu/djKS9GcjqwlBQS9QdJNbWgpjJIHrduMU2liO09iKaoeBdd0G2bmqrinDqB0LZdJI/W4V18AeFN25A8bkx5OaTqm1BiCSzlpYS37KDkC9cjOdo16h/WwiqKPWbJyYQjej0bScIxdmS3yQDK7voCyfpGrAOPb3I1TSO89X2S1XVYyktwTZ/cpZ+7uSAPc0HnIPNziXLz8fXLOqic+KGjJI/WYS4rRpRNWAeX665V4RhqIoln7kxAXz8iOyqwDh2Ma8oEYhUHiB88kq1jdSLOyWOJ7T2A5HKhJhK6pcVpR87PRQlFQBIRLTLxw9VomoZtUBkJDdLNrVgHlR3f3GUySC4HksNG/MAR7GNHdFp/NVVFCYaxzJmOaJaR3C5SDU2YXE7kogIybX5MLieiLONdNI9USxvB9e8y+KffAVWj9ZXlBNZsyrrDJWvqwWKGdFp3XzNJKNEY0Yp9mDwecubrc2xk1z4aHvs7cn4uQrsF1FxUQPG37+oQ63GuYEIg+ux/yCTT+pooywRWrkMNR5GLC/QERi4n8coqUvVN2EYOwzKoXHcTrq4jfuAwJbfd0GOhcOck3coF4D5/GtG9B7EMLEVqv0bTNDL+IO5Z5xGvrMIxcexZ4WIV3r4b/6r12ULpJ/O0+STTZ8KLoihMmTKFrVu3Ym4fMKWlHy6d3I9//GPuvfdeHn74YdxuN7/4xS8AuP3227nnnnuYMGECv/3tb6moqEAURWRZ5pe//GUHa8zJeCPYzOid+1Dz8xEtZiSnHe/i+YTf3U5sbyWSw4am6ROglkxiLh1IZPtulFgcgIJrr8Az6zxCW95HtFm7DWK3lJfgmTejPSjQipbJkDhSQ/Jove4uFonimjuD3MsuQrJZiR+qQkskuyzKdKwWTeJINeaSQiwDyzpI6koiQWzXfqp+8lsGff+eLvskOR1YSotIHDqKdZj+osT3H+rWZcM+ahjJo7V6TRqPGzk3h3SrD1Oul1jFgU7CiwEMsziyGaEAvRDpgvMJv7eTVEOz7oJBu5uPkiLV2EJ4yw7dVSw/j/Kv345tSM9abM+cafjefBvrsEF4zp+Gf+U6RKsVod2FIhMIgSSRbtXTm34wWLU/IVqtaKrWnmCiPWHFJzQJxMnIN3VcwO1jRiBaLUR27tUtJLKpPT5PRND05A/Bd7aiBEM4p4yn8JorOmzyjgnM/tXvENm+CzWRRJBN2EYMxTp0ILF9BzHlesmEQiRr67PJJCSXnQHf+HI2C5Ros+pa9B4qmquJJPFDVcSPVKMlUgiSiFyQh1xciGjt+jr/2+/Q+uIy5LxcNEWh8a/PU3r7jTjGjux0rmgxYxsysMOx1peXE9ywBUtJIaF3dxCvqv1EaStPJyOtDtRgGLxeBEnCu+B8wu/tIn7gkO4yLYoIkoQaiSHnekkcqiK2L4MSjuC5YBYFV16CIIrkL1nM0QcfIn74aKfNmCjLSA47yCa0aFQXDJJpUrWNiHYbos1CJhBEzveCIGLK8WARBGK79qGm0kjtsX2aqiIIujub5LLrCU9GdVSSZvy6xflYHJYgitlYr7xLLqTuf/+KaLchF+ShxuLEDx7Gu+B80k0t1D/6V+SCPFwzJmNyu4gdOIyWTmMuLSLV6iNV14RosaBJJuQ8L0U3fRbPjCmoyRQNTzyHa8akbPypfewIgms3k6xrwOQecab/jWcdn/WWooWjuC6Yld2veBfNI7bnILEDh/RYFk3FlJeLGomQbmwh+PZGlHgC2/DBlN9z20k39aLVSu4lFxJYsxHXjMnEj1STbmhBLCsCSSLZPr4y4QipxmZKbrnm43j0k2IuKURLpfU4rrPAEtSX9JnwIkkSgwcPxu/3Zy0iH5Zhw4bx73//u9Pxv/zlL9nfjwk0H5a1kTa+M3U8wXWbsY0ciqWsBJPLifeiefoAb2ol3dSCaDJResdNupYlHEVyOXW3gvaNaWTbrpMGfVlKizFfUUSmzU+quY14VTWDvncPoihgysvt4FqVamlD8ri6NB/G9h8iUVWDbfiQDibqY0hWK3JBHoJJpvahJxj0vXs6uYcIgkDJF6+n9k9PkaiuhYyCubSIgs9+ulN7aV8AQZbwr9yg+xqLAqqmoYYiyAW5eiClQSdqU3GmfuB7Nxfmk3fpQjKBEEo0pk9W+bmkmlqI7txL7qcXYSkq6NlV7ARMOR4KrrmclheW4Rgzgpz5swlt2kaqsVmvBxSNYfJ6kawWyr5680lT157NiFYLOfNmENq8HfvIoWjpDLH9lRRc3b8TEHwYhlkcnWoUWIcMxDJ4AJlACC2VyqY4DqxcR9Hnr8bkdmEZWNYpiF1NZ2h65l9EKw5gGVCKZ/5sXbDOZEjVNhI/eBiTx03B1Zcj2W3tcVkCotXcKc7APmYEybrGLhUmmqYR23OA6K59SB4X5vxcBLMMqkYmGCa0YQtyQV7WpecYiZp6Wv/zXzwXzNI3vOi+7/V//htDf/69Tpr2ru7rX7EW70Xz2lOJD8a3/G0KP3tZrwvAnks866vlf3btI5bOYB00ANEs4545BeeUcaQaW9ozRQnk3fgZnJPGkWpuRZBl7COGdJhfTDkeBnzrDmr/8DjpplYsg8sxFxciCAJKIqm7OYsStqGDsY04vpYiCEh2G6LTTtt/lmMfrQsjJrcLRIl0SytSua4QFWUTWkaPpRUddtINLajJlB5DgR5bmGpqxTNPtw5pmqZ7KrSPL/vo4RTfcg0tzy8jvGUHgslEzgWzcU4aQ+1DT+CcMr5DzKHJ7USJJUAUsRQVYM7LJXGkGoCiG67CM0N31c6mED8hcY7YnmAkvOV9HGPOPeFlit2DaezwDnsawWTCMXEM9vGjUOMJaM+OmGpsIVlVQ8kdN+mlBE5hj5F76ULUVBr/qvWYC/J0N/odFWiqpitd3E4E0BMbdaEc7gus5aUM+8UPQBR6tCydC/TpjHzFFVdwxx13cPPNN1Nc3HFDP3v22ee2Yls0l5zzJuF7cw3+t9bqZl5JRE2lyPiCuKZNJHfx/B59VDPRaK/S0wmCgJyfi+R2kqyuxVratYCnpdJdujVoikJ01z6sg8u7FFyO36g9xqamgWjFPpwTO1tUzMWFDPnJ/yVZ24Agy5iLCzoJS5lwlOpf/gk510vepy8iuq+S+P5DpOubsJSVYM7PwzOn/2rzzyTVqXi3fzPluDvU5TAXFxLbcxBzYX6vBZdj5Mydiez10vraW6TqGpFcTjKBEJaifFzTJuGZfR7mot5bI89mCq6+HLmogNCm9xBlmeKbrsHZ7rN+LhFU02T2VaLNnt5xM9BeJwF0C0fwna3kfXpRl3FsoGuuG554jlR9EzkL53TILCXIsl6ccHA5sb2VND/3HwZ8+w6SVbUk6xp1DbjLgXPi2GwKZu+F51P9q0ewjx7eaf6K7tpL/OARbCOHdipcKJtlPQV7YT41v36Ugd+9M+vyGtq8DUt5SVZwAT0W0ZTjIbprL+6ZUzkZepai9oQxanu2tE9wPMtHYXc8jPML1yLt2Etg5bqs25OmqKRbfFgGllLw5ZtwtruD9TRfmQvzGXzfNwhv24V/xTrCW95HMJl090BVwz1nWlag+SDxI9VITkcHAVNy2FAiesC7uSgfye1CUzLZ7GOizULGH8RcXICaTJE4XI1j3KhsDGu61Ydos3bos2vyeJyTxqHG9YxNiCJVP/kNjrEjOyVLkYsKEB1WkrUN2fVStFlJ1jV20JgLAtkMfMfQNI1MIKhbRc9BGjMJJnSjLBBEscP7bS4uIFaxHy2ZxlR6aspRQRAoWPopvBfOJrhpG+lWH4JJQs7LRc7PxVJW3GMSnL6iPysWTyd9+nY899xzADz00EMdjguCwMqVK/uiSyfFOWEMzgljSDU2kzhah5pOI9lt2EcP67YOwomIFsspFZ3SUhmEHiRssT2V7gdJ1jbofrs9uGWAnrpSNJuxDirDv3J9l8ILHI9/6Y7gus1ILmfWb9lcXIh71nn4/7ua4luvxTlhdMdUmgZZ0qdQvffYIthdzNHJcIwbiWPcSDKBIEo8gcnt6rAYfFIQRBHv/Nl45599SpCPk8dajnKx2Uxw3Wbd4lJWnBUW1GSKRFUNiaO1eBfOIffShd22E9lRQeJwNZ55M7p9jwVBwD5mOL7/rubgPfdhLsrHlOPRFTzJFC0vLsMxegT5SxZjKS/BUlZE4kgNtmHHXTzSbX5i+w5hG9F1Ott0iw9zQT6uaRMJb99Ny4tvUPz5z+rPE0/oFpp2NEUhcbSOVEsrsf2Hu41fObH/uZdfhP/NtciFeaRbfOReMt+Yt3pAKimkdPpUMsEwsf2V7f8DM9bBA7KCQG8RzWY8s87DM+s8PTtZKo1otVD960eOp93uAi2ZghMUdEowjMmbQ878WQRWbSBeWYUpNwdTQR6ZYBg5LwckESUeJ1HboLtITh6fdSPTNI34wSN4F3YuOSC0W3sAYpVHUKIxzF0IZYIg4F04l8j23cT3H0bTNCzlpbimTSL4zpZsjS7byKEoUT2RxTGhJrhuM/GDR5Dzcwlu3NqvXXg/DI2p3me6FAQBuTCf2P7Kk7pOd4cpx0PepxZ8qGvPBJqqkmovGyEXdVYUG+j0qfCyatWqvrz9R8JcXPihsoA4xo4ksG4T1gG9i+9J1jVg/0CqxFRTC/63NxLe+j5KKEyquQ0lnsA+Zriu/RIEkg3NHdJSdoWWUVBjcT3NoGyi7bUVHerSnAqxg4cx5Xa0KElWC3JhPuKxlJUGpwdN69ma1gtMOR5MOf23QJVB70hoKo6bP0tOWwj/ynX4d+9HdNhA1VDicVxTxus+4h+I/fgg/pXrsQ4d2ON7rKkqwXWbUaNxtHQaz/nTOmjD1XSGRFU11b9+hLKv3kLR56+m5lcP6xrudstybP8h5NycrgUXf5CMP4D3kgsB3ZUn+PZGCj5zGZLdpqeafe4/2IYNBlXF9+Ya1GQKJRgmvGM3SjSmu/T2ML/lXbIAa3mprjEvLeq22KFBR0weF+4ZnbNWflhEWYb2MZB70TxaXvov5rLirv93kggn1PRI+wI4JoxGctjJvewiknUNxPZVogTDKJEoajyBpiiI5iTOSWOxzZ2J5NQVOJqm6VnTnA48c6dn28wEgrQtf5vI9t0Ioohr+mSSTS1YBpR1O55Esxn3zKm4Z049njI6mSKwagPKtUt0lzezmfyln6LuT0+BpmHyuEi3+nQ3tJIiwu/uOOeEl5R2aqUyRNmEekKZiv5MqrmVuj89iRKNoykKltIiSu+4pdd1qM4l+lR4+f3vf9/t377+9a9/jD35+HDPnELrS8uyRc56Qi+A1kDZXbcCoKoqvlffxL9qg67FmT4JyWbFv2oDGZ+fwOp3kAvy8MydjpZOn1RgSLf5MZeXHDdDCoJeGOwUzNVqOkP0/QpCm7ehKQrytQWYjqXDVFWUcCTrKmLQNR6p99+3lsmQCYXPSnP2hyW8YzdNf3sR17RJFF1/ZV935xOHIEm4pk7ANXUCaX8QJRxBEEVMuZ5eWYvTvgDJ2nq8i+f3eF546/tkgiFsI4eQqKolWd/UwVoryibsI4ZiyvFQ9+gzDPruXZR97YvU/fFJMv4A1kHlJKvrsH0gkFpNp0k3+1DCEXIWzs36tUs2K6a8HCLbd+OZMx3HxDFY39lCcN27CCaRTDAMkoRz8jgcE8cQeHsj8YOHsY/sOptlqrGZ8I4KAJyTxmIp+WixmAanB8ekcbS+vpL4gcOdguxBr6sSiVbo9a6a2+CEDJaCpHsMWAeWoUSipNv8hN7dgRIK45g0BsfEMdnCvcmaehJH65DzvJTddUtWgM4Ewxz9xZ8w5XhwThkPqka0Yj+xvQfxLu4+E9+JZAtVW8x6TThfAMluQ1NV/Mvfxj3rPNBUEtV1yIKIls4QP3CY3HZB/VxCFk4tC6GayXQq7tlfaXjiH5jyvLjaXVz1mkWvUvKFT2aK/49CnwovjY2NHT63tLSwZcsWFi1a1Ec9OvNIdhs5C+cSfnc77vOndStgaJpG9P09mLw5BDdvo+6PT5JqbEaNJzCXFCE6bO2Tn4bJ6yFSVYMgiWR8AeKVRzB5PfrinpvTpWZITSZJNbWQs2ie/rk9dqa3Gv10m5/Auk0E127WC2YmkmjJFK0vvI5zynisA8uJVx7BNnTQactTnwmFyfgCyIV5vdp09RcGmXv/LImqWhyjR3yirCbBDVuQ3E4Cb79D4XVLetSMh7fvJrB6A5lQBPuY4eQuvrBXMWQGOrLXc8rfVyYYRrTbe3S5UqIxEoersY/RY1gEWdYDa7vAXJCHpbwU35trKb75agZ+7258y1YRWP2OXhguEEKRRFBUlGgUJRrHOrgcz9zpnVwcJaeDVHuGPEEUKf3KzUS276LpuZcwuZ24zpuYdb2Qcz0k65q6FF78K9fT+sqbmEsKAAHfG6vJvWwBeZecPe4k5yqibKL8ntuo+c2jROIJbKOGdVD8mXJzEC0W4pVVurvWogu6XFclpwPJ6UC0Wwm/uwMEEd/rKxFMEmo6g33UUIpuWIpj3OgOiWv8q9ZjcrtwThyTPeY6byLRiv0ka5swF55inKAotBff1N8bJRLNJgqwlJcQ2VFB/hWLkZx27F1kyfukUyz3PqZD0zTSLW3dloroTyjRGMnaenIvuyi7BtpGDSW47t0+7tnZySkJL8FgkGg0SmlpKa2trfz73//G7XZz7bXXIvdUtbYbHnzwwU7H1q5dy+uvv37KbfUn8pcsJuMPElz/LvaxI7OuXsfIhMJEKw6SbmpGsJgRLWYcE8eQamnDMXUCWjxBdNc+Qpu26+kfHTZMHjdqJo1c5tW1T6pGsqYBNZ3GNnRQh42HpqhEduzR0zqn9HoJiaO1OCeP69CPZG0Dieo6JJcDx5gRWReQ4ObtNP/9RcylRbhmTUVy2IntP4QaT5Koqib87g5C72zFM2c6JV+64SN/X2o6Q/NzLxHe+j6Sw04mGsN74Rzyl17S44aqv1Agm1HDkWwNoe5QkykSR6op/cpNH1PPPh7yPrWA5n+/RsHVn+5RcPG9tQbf8rexjxyG6HQQfncHwTWbGPKze5FzDQHmTCFIIqg9u3LEK6uQPO4TNo1aj++mbehAAqs3UHD1ZZjz8yi++RpyFs6h6se/QbCYdcux1YKttAjrkIGdAvezaCCIJyQikERc0ybphXGXrcoKLpqikG71Yx3Y2V033eqj9eXleObPysYzKCOH4lu2CtekcZ/YIoH9CdnrYdC9d9P6ypsE12zE5M3Junop0RhaOo2aSpP36UXZ412hZTLEKg5QcM0VeOfPRk2l9KxjVmu3BZKjO/diHd459a7kcpKsb8A1dXyvn0PTND3gv10Il+x2sJiJ7atEynGTOFyNpbS4y8LR5wolshVN6RzH2xXpljYku113Fe3nCGZZtwImU2QiUQRZRstkPpExqaeDUxJevvnNb1JZWcmaNWv46le/yq5duxAEgUOHDnHfffedlg7NnTuXb3zjG6elrbMVQRQpvuUaghu24H9rLdFd+3RtqCCgRKIo8QSiWcaUn4tn1lQEk4nItt3IXo+et95iRjDLRHftAw2sg8sR7TbilVVkWv1Ix+qCOGwooQjxQ0exDR8MgBqNkaxvwuTN0X1sXQ40VSVZU5fdFKupFA2PP6f7n7fntNfSGcruvIVUSxvNz/0H9+xpmDzH62YcS+nonDIOLZki7Q8S3bmHZH0j9uGdtSKaopKsb0QwmbrMXnYira8sJ3awipxF8/QCeokkoc3bMHk9eBecf/r+MX3EP311jHp/L2mXu1utuJpIEtr0Hu65M7p0nejPCCYT7umTMHlzUNOZLjcRaiJJ2+sr8cydgRpP4F+1HsnlIO0PcvSB3zHsVz/6RAiyZyNyQR5qPN5lFfRjJGvq9VobkE29LXm6r6sjmCQ0BI7+/CEESUIwmbCUlyCYTDgnjztpWuNjKKFwl5pv1/QpBDdsJbhuM6YcN+kWH47xo7F2Uf8hsmsf5uL8DveUbFbMJYVEdu4l1xBezgokp4OiG66i4DOXEt62m3SbH9CQ87w4p0yg7Y1VhDa+h2vaxC5rOimRGOH3duIYN5qcC2YBelzKyVLOCrIJLdU5yY65II9kfdMpPUO6qQVTbg5yvl6HKPjOVgRBILhhC6gqotVKxh+k6bmX8F58Aeb8c68QakU8xOh9h9Dm5PW4L1BTaaK79lFwzeWfiKB2UZbxXDgb/6r1ZHxBNE1DzvdSeO25l+K/N5yS8LJ//36mT5+Oz+dj165dLF26lG3btrFixYoPJbzU1NR0+ByPx3nttdcoKTm1FLD9EUEUyZk3E8/cGcQPVZFubEFTVUxeD0ooQuurb+KZfV5Wk5morsV8QrrkxOFqvY6CKBCrrNIDD0cMId3YQqKhCS0aRy4tJN3iQ/MFSByu1uNZJBHH+NHYRgzRNZKqSuS9XVgHD8hqL1pe+i+p5la8F1+Q3RAm6xqp+f1jaBkFz5yOgkuH5xIEBKsFS4me1rLhz88y9MHvdTDjR/ccoPHpf4GmoaUzSDluci+ej2NsZ3coTVUJrt2E54JZWR9k0WrBMW4UgdUbPhHCy454CPuViwkvW40p14t1yID2OCEBJRLRi5Q2NJG7+ELyPn3RKbWtptMokRiCJCI5HJ3q+HxUlHiC0JYdBNds1DcTgoC5qADvgvNxTp3QZeD1ibT8578E1mzEXJSPEo7S9vpKBvzfOzrFgyUbmhFtViSHnfC7OzAX5renEneROFTdKWuVwelDsllxTdMLuXVXd0JNpbIFT5VwFNFiRu6mAn388FHCW99HTaWQ87y6y4emkmpuQ43FaXttBXmXLzqpAKNEYmRCYZxTOqfAFmUTA775ZaK79pFqavn/2Tvv8LjqO91/zjnT+6h32ZKb3Ds2NmB6TAglgYRAEiCkE5K9uSnc5e4m+5BN2SV7s0tCWJIsgVRCKKFjwOCKjXG35CZbsnoZTe9zyv3jSGONNZIlY4qN3+fhwTNz5pwzmjO/8y3v93319W1aXd4gRxAF8s4Iq5pu4HkOHyiIFgvu80cOsRdfvxqjc+AemQAAIABJREFU18PAc68gDiafosmkz0z19CNHohRcuYqCKy6aULDrWr6IwNpNGEuLsu/TFAU1pRunjmd+dQjJ1g4KrrgITZbp+tWjpHv6sM+cjmfV+boQiyiixBMkjx6j7Uf3U/n12/MaLmqKSmzvfuLNLRg8blyD5phnAx4LdPEJSSK6fQ/2uQ15k0s5FCGyfQ+u5Ytwj0MC/UxB8XWrkYNhAi+vA6Do6svxrDzvfT6rDyYmTBvzer00NzcjCAJ33nknDz/8cF6DyPHg8ssv1wPoQalYq9VKQ0MDP/nJT05pf2ciBEHQOxOD3QlN0zh278+xTqvPCfjVTCY7SK/E4qipFEavSzfyisZRQmEMHjem8hKMZcX6vIzHDapGptdHZiCA9/ILMFdVZP/mqe5eEs2tmEqLqbjjZl0NJSMT3rwN90XLcirZ5soyIjv2IlrNeY3l8sFUVkyiuYXo3v045+ut9cxAgK6H/pA19VLSGQaefIGOn/8aU1U5td/9Wg5NQ5N1bX7xhJuDaLchn6Jc8AcRxlnTqLlgOeGtOwm+sZnIlh1omobB5cC9YikVX/7shIQPEi1t+n527EWQDKCpurHaRctxr1yKscCDHAoT2riN6L4DaMkUgsWMY/YM3CuXjOs7ju5uovvhv2DwurHUVusy2Zr+Hfuef42+x5+j8qu3jppUpPt8BF/fhOeSFYgmE5qmEdm2i+C6N0dIVxrcTl3FSlHQFAVhyFwumUY0GbMc8nN4d+C9ZAVt9/0Kc1V5XiM4wSBlv5t0Vy+OBbPyBojxQ0eI7mrCMrmGTG8/ptKibLfRWODFVFbMwNMvM/D8axR+9NJRExhN04em3RecN2rlXBgc1j8ZHPNm0v/kCzmml0o0Trqn/0PpDXSmQhAEvBefj3vlUmK7G4nsbtIpWlYzBasvwblozikZ+7lXnkd0dxOh9Vt13xdVJdWhq4AaCr1Et+0ac351CImjbWiyjHPRXHoe/RuZQBDXiiU5ppugz8XaZ8/AUFhA5y9/R+0/fiPHG0ZNp+m4/3902fCSQhIHj+B//lUq7rwtL8vhTENaU3HcdiPGTdsJvrYRY0nRoJm2ATWZIt3di5pIUvixy/GcZVL4gihSfvtNOBfMQbJbRxUXOYcJJi+FhYWsW7eO1tZW7HY71dXVhEIhXK7xBbMn4sCBA6f0vrMZqfYu5FAYx+K5Oc8LBoPu52I0ku4b0F2xhy126R5fTtdCkCTcyxchmkyk+/0EXn6d6M5GUq0dIIoosTjGQi9FH7sC19L52YVXTSZB05CsuUHDkHGWrSxXtvlkMNdUEly7KZu8hLfs0GWmB9WylFAYwWRCMpsQTUZijQdzkhfRZMJUWUa6syfHMCzV3nnW0ackmxXvxednu0mnIlutqSr9T75AaPM2LDVVeC+7IHvDlkMRYo0H8b+2AUtNJaljHfp3UVGKYDKhpdNE9zQx8PLruJbMp+Sm6/JSuDRVJfTmdvr++CT2+fpMwPDtzJVlmCvLSPf00XH/b6n65hfySvGmOroxFHiz5ycIAqaykqwT9XAYCzzYGqYQ3d2ItX4S4W27UMJR0n39WCbXYKkbW+r3HN4ZzFXllHzqWvoe+zuOBbP1YGLYtWkqKSbjC6Amk5hrKvLSszKBENGdjVinTEIwGlCi8ZygDMDoceNesYTQ5rcJvr45b6dRUxSiOxuRbFaKrrliwp9F0zTSPX3IoQiCIJAZ8CO5nPQ//hzGQi/GkiKUYJjiGz76oaTtnOkQjQaci+fhXDzvtO2v6q7PE93dRGTnPgRJpPSzN2CfpQ/TywMBQpvf1gPOPPM2mqIQP3SUTE8/1d/+CnIoTGxPUw6zIR/M5SXIvgECr2+i5Iars88H121BDkZwX3he9jeY6uyh55HHmfwv3z4r6LOCyUjZZ2+g6LrVhLdsJ9HcghqNItpsFH/8KhxzG3Jk2M8mCIKAc8H456jSvT7S3b0YS4sn7K10JmNC3/4111zDQw89RGdnJ7fccgsAO3fuZMaMc1r4pwvp3n4MHteIoNVcVY4cCCNZLWipdI6csWA2oQRC2cdKKIzB684GhabiAiyTayi54WoklwNNljG4XRhLikYcR7LbdN5tMJSTDKmZDGoyNWFXd1NJEcH1W7KP5XAYRIHE0WNoGRlNGOquZMioWt6gp+TGq+l84BGUaAxDgYdM3wDp7l6qv/2VCZ3LmYZT4fH6nn6ZyNt78Fy0fESV0eB2Yp/TQKqrh/CWHRRccSHmqtwBZlNpMbZZ04ju2EfnLx+m6uu3Z28SSjxOaMsOAq9uIL6/GYPLoXeIFAVzTSW2aXUYCr3Z8zaVlWBXVLp/8ycm3/vdETdVU3kJciCEJsvZY2R8/hxVn1jjQXoe+SuOebMou+1T9P7xSaK7mxAtZpRYDM9F51N++ydPSk87h3cO9/JFSDYrfY/9nXjTYZ2WY7WgZTIosTjpzm6cyxdhnzU9vxP6wSODylAmMj4/xpIiJMdI/wLr1MkgiQRe20jg9c1Y62t1ZUVFId3bT7qzB/vcmZR99oYJfe9qOk1k2278r21ACYb17m0gSKqrF2OBF/u8mcj+EJnefmr+z11YJ52a6d05nH0YLjd+Iso/fxP+F9fif3UDktOBuaIU0WzWRSIGAqQ7u7FOmUzN3V/HWOCh7/FndQbEOLzPLHW1hDZto+iaK7LreWTHXqx11bnFg4pS4k2HSPf6zqoA1uC0U3D5hXD5+CSpPyjQZBk5GEZTFCSn/V1RR1UzMj2/e4zYvoOINgupjm6MRQVU3XUH1g9BMW/CA/vLly8nk8mwcuVKMpkMP/nJTygtPTU9/B/+8IdcddVVLFx4nLO4Y8cOXnzxRe65555T2ucZD1UFRt74bdPq8L/0BqbSwhEvCwJZ6p2maWR8gZF0B02nWuXjz+bsSxQpWH0JAy+8hnPJPAxOPdmJ7zuAIEkTV96RJLSMTulJdfUQP9RCdHcjgtmMIIBktaKmM2ipFOVf/EzeCr1tWj013/4qgbUbSff0Ya6touy2T2IahVP/YUW6z0dw3WY8F68YlR4R3dOEJstYp9UR2b4XU2X5iEBTNBpxLp1PeMsOfM+8QvHHV2cV5oxFBUgOO6ay4uwCqWZk5IEAgdc3YSz04l55XlYdylRRSuJwC/EDzdhPkP00l5fiXDiH0IatGMtKUCJRtHRG538PIrRlO4LFTHDDVko+fR0VX7hZlxeNxTEWFpz2GZ5zGBuOeTOxz5lB/EAzkZ37UKMxRKeDkhuvJrh+Kwj5k241kyF5rAPrtMmo6TTpvoHs0HQ+WOtqUWMJXVY2lSbZ70c0G7FNq6fijpsxFhVM6LzlYIiO//otWkbBUleDcfE8UFX6n3wRx7yZqKk0qbZODA475ppKIm/vHpG8qJkM0V1NyP4A5uoKbDOmnBVV7tMJNZ3W7zXmidOzzlQIokjhRy/De/mFRHbsJbqzkYzPj2gyYptWR/kdN+V08OKHjub4H40FyW7T2RO9vqyxtWiQ0JSRQ1qaqr5j8+JzmDiUeJx402HkSAwlGiPZ0kbi6DF9bRBF1GQK24x6vBevxDZz6mkTF/C/8CrJ9i5cyxfiX7MeQ6GXzECAlh/cR/1P7znrfaomlLw8/fTT1NfXM2eOXn0QRZHa2lp8Pt8pHfy5557ju9/9bs5zs2fP5s477/zQJi+S24WaSIx43uB2YZlURbKlHcFsRo3HYcgMMiMjGo36HEtnj+5YXX28oq4pim4WOc4bvmfVcjRZxv/iWhiU7rPOqNcVylQVxlExyh47lUK0WkgcaaXjFw9jKi/Vsy05gzDY4RFtNoxlJQw8twbbtLq8lSNzVTlln7tx3Mf9MCK4fgvmyrJRAwdNlkkcPoq1fhKCyUimt59Mrw9T2Ui1JkEQcMxtILj+TUSXA//zr+JavgiD24X/pdcxFh6fvxGNumKcsbSIVEc3gVfW4738QkSTEUEQMFeVE9r01ojkBaD0Mx8n1niI+MFmjEUFuJYuyJlzKLhiFf2PP4d31fnZQFGy287JR76PEEQR+8xpI75P28xptP3bA8T27sc2c1pOZVmJxnXVpoysy7LPmZH3uhsOY0khmX4/Vd+44x2drxKP0/4fD2HwurFOr88GD3IkhiAKSDbdM8vgcZFq7ybV0T1CGjrtG6DjPx4CgwHJbtMHuIu8VN11x6gKbEo8zsALa4ls34MgSbjOW0DBlatOae7iTEHkrV20/dsvsU6ZjG3wGrHPmjbuOclTgZpKE9m+h+CGrWQG/AiSpFNmLz5fF2l4FxJMTVXpe/xZMn0DlH7mExgHmQ7uZYtwL1s09ntleUL3UCQxZ6bPtXyx7klUVnxc0OdoG6aSonMFvTGgaRqpYx2kevpAVTF43Nim14+rA5YPqY5uAq9vIvL2bgweN2oyRaKlTZc8FgSs9ZOwTa9HcthItnXR/bu/YJs+hfLbPnlaKG/BjW/hXDyfVGc3ksOGpbIMtbiA6J79hN/aRfG1V454j5qRdYpZkfeM98qb0F/w7rvv5tZbb80mLwC//e1v+f3vf8/+/fsnfPDhw/pDUBQF9SSeAmczbFMno2Vk5FB4xILvXDKf8NYdpFo7UaIxRKcdUZJQYgkku133WrBZ8aw6P+cHmWzrxDpl0rjN6QRBoODyC/GsOh/ZH0C02TA47bT/v4dItXdhmQCdItXWiW3mVDofeATH3AbUdAZjcSGS3YocCCGYTRiLvHgvv5BUWyed9/+WSf/y7XM0oFNAZOtOHEtG53kn2zp1P4PB5MbgcZFsbR81iJTsNgRJov/PT1PwkYtzfBWMJSNvkkOJSqqjm/CmbXgGZ3cMbiepzp4R24MeCDvmzMAxJz/11FJdQfW3vjT6hz6HDwwMTgc1372T3kcfJ7BmHabKMoxFhQiSSLq3HzkUBlXDuWjuSTvAgF44OQ1CDL5nX0Ewm7HNyJ3XEy1mnbqqKLpcsyBgri4n1ngQJZ5bQOr945MYiwuz+xgSl/CveYOia/IHCe0/ewgEvVulqRqRt/eQOHKMqm9+4ayQds2HVHcvmqISP9BM/EAzvidfAMBUWYZj9gwqv3HHaf3s8cMtdD34KJLDhrmmShcH0TTSPf26W7nXReXXbh9VHfNUkerqIbRpGwaHg/DWHSMERsaCwe3Wi4mj3I/lYAglEgNAtNtRY/EcJTHXsoXEDx0l8OpGjMUFupWBqlH9zXeW5J+t0DSN8NYdBNasQw5FdJVWQUCJxSEj47n4/JzZ0PEguH4L/U+8gLm2Uo+TQhGCb2zGPnMqkt2Gms6QGQjgf/kNnIvnYq2fhKW2kvDWnfT8/gnKbvtkzu8gfvAI/lfWk+nzYa6tovDKVSel6KvJNKLZpCfng744gsEAsoKQxxdLzci03/cAGX8INI2a7915Rie740petm3blv13T09P9rGqquzbtw/xFCsbixcv5uc//znf+c53EEURVVW5//77Wbx4pAzihwWCJOFZdT6RHXtGDBwKoohr2SIydZMIvLqOdHu3TstK6ZKjtoapulfCsO9DTWdIHjlG+ec/PeFzEY0GTKXHA1vvJSvp/cvfx528aKpKsr0LZ0UZhgIPprIS0j19IMuYSoowl5WQbOvENGgkZ6mtItXVS3TnPlxLP7wmXacKJRYfU1420+/PGSgVTKa8Xb7hkMMRRKc9dxBVEEDLv/1QAhPf35ydm9I0Dc7Raz4UMDjtVN55Gxmfn+DGraSOdaCmdbVAg8tF4TVXjGoGeCKUiD7j9k6gJlOE39yedTAfDtFixlRZRqqjW183JQk1mQJVGwwIVV26NpEkcaiFgtXHA1RBELBOrSP81q68yUts737UZFJXkxoMUgznLSC4diOJI61nhSpUPpTc+DEKr7qU+IFmYo0HiTUdJn7wCOnOHuJm03GpYU2j/Wf/jWVSFfZZ07FOmTThglXi6DE6f/kwjvmzcu5TANYpdiz1tSQONNP+swepufvO01ppNpUWY6mpItPvwz57+oTe616hd06GU8eGugKxpsMo0RiSzaIrN4bCiAaJZEsbhgKPbkUgipTf9klSnT0kWtowOB3YZ007awfY3wk0VaX3z08R3b0fe8MUHItzPeXkYJjw1h1E9x6g6ht3jEvyOrhhK76nX8K9cgmSw54tZJgrS7OMANFkxFxegtHrIrJ9LwgC1rpaXEvnE3x9M8nWDqyT9TgqvH0PvX94AtvUOmwNU0n3+Wi771dUf+vLY9IL7bOnkWxpw1JXQ/zgEZJH25DjCUSrFc/KpSO2T3V0IQfCeC4+n9iuRqK7Gym47MyaJRqOcV3tn/3sZ/UfjSCwZs0a1qxZk/N6Tc2pDQfdc889fPnLX2blypVUVFTQ3d1NcXExDz744Cnt72yB5+LlhN/aQfxA84hqoSAImEqLKLp2NQMvvIaaTOFcsQT7jJEqYGoqTXjLDpxL52Obmd+fYSKwz56BaH6R+OEWbFPHvvlqmkZs7wGsdbXEdjdinzsTAGNpMcaSYhKHjurdIYMB24zjqmGWmkoCazedS15OBZKUrSLngyYruRQKVYUxbnhyJKorQg0qww3B4Hbpnb9R6GmCIGDwuIkfPIrrvAVkBgKYK8sm/nnO4YyFsaiA4utW5zx37Ke/IPLWTj2RVXWDXEtd7aiO6KnObko/fd24j6mpKtFdjYQ2bUNNJLA1TNW7xm7XqEm9a9lCwlt2Et/frMs9a+BcOp90Rw/xQ0ePr6uaiv/1zWR6fUh2C65lupIjWv4sPnmsM0e8AvTfhbHQS7ozv3Hv2QLJbsO5aC7ORbpipibLJI4eQ40ns9uku3sJrt2YfSwY9fkQ2yDNzD57xpiFGE3T6Hnkceyzpo9IXLL7FARsDVOJ7NjLwAuvU3LDR0/TJ9TnAmtOUTDGMX82fX/5O+k+H6aSIj343bqTVGc3ptJizNXlWQ829XAay6Qaeh97hviho5R8+rrsNTWk6ngOo8P/wlpiew/iXrk0b9HE4HHhPG8h0V2NdD/0h5N2BlPdffQ/8TzuFUuyYiOyz4+aTCK5RyYaosWCZXI1kW27MRYXYnA6dAXWNzZhnXyTrg76t+dxLpyTpfUbPC4EScL39EtjUmaLr7+Ktn//lb7WzZxG6lg7kqJQ/d0788rZG4sK0RSF2O4m0v0DWKrHN3f1QcW4kpeKCn1+oqurC7vdjtuttzslSaKsrIy77rrrlA5eVlbGU089xZ49e+ju7qa8vJy5c+eecifn/UBs3wEy/iAGrxuD143R68mRMT4VSDYbVf/wJTr+8zeEt+zAUl+Lsaggu081ndZ5joNc7UxPPwmTEVNZiW6aFU/q3M7uXrwXr6Do2itPS6tekESq7vo8bf/2S1CUUfnEmiwT23cQLZOh5Iu30Pr9f8+2yAVBwL1yCfJAAE1WMBZ5cypGxuJCorubJnxuajJJ31+fJXG0DXNlGSU3XZv3B3w2wzq5hnR3H5baqryvixYzmXg8+1iJxrLGpPmQPNqGaDGPqEbZptURfnv3CJnb4TAWeUkcOopj8VxS7V05Up/vNjL+AKm2LgxeN+aayrOWonOmQJNl+p98gfjhFtRoDHN1JYIokOyKEGs6hKmsWE8Ghs2OpPsHQFGxzxxfVVvTNHr/8CSxfQf0gXxnEdG9+4nuatLNf4u8eRMG0WjEc8FS1GQKNZVCctj1Dkw0phsIzphCormVtM+PmkghOe1k+v34Hn8OJBHRaqX5Wz/AtXwRnguXYyrVE31jcQHRPY0jjidHYhgKRv/dnI0QDIYRfhUGl5Pq//1lYo2HiDUdItXWqXdqGg/S/zjU//s/Z6WIEy3tepes7HjVPNHcihpPYBpH8G6bVk9o01sUXXP5B2LeSDQaqPjiLXQ++CiO+bPJ9PaT7u7VJcQHC0+arJA81oGxtBjHwtloskJ48zYMXjeFqy95nz/BmQE1mcL/yrpBg+vRQ11BEHDMn0Vw7SaSre15RYOGEFy3GXNVRY5KYrKtE8k9UiF2CJLVgsHjInG4BefCOVgmVRN4ZT1lt30KJRxBjcdHzCObq8oJvr5pzM9nLC6k+ltfIrxtD5nePrwXrxzh0SaHwvj+/jLRXY0IBgP2eQ0YCwsoGpzHOZMxruRl7dq1AFxyySXccMMNfO1rXzttJyCKIvPnz2f+/PmnbZ/vJQaef43gujdznhNMRgweN475s6j+hy8C+tD8wItrMXo9g4mOB6PXhWjJ36Y0et3U3v11Qlu2E3htI7HdTYg2CygqSjSGY/5sqr/9VSy1lcQPNBNYu4nwlh1omQySw47rvAVUfOVz455zGS+MhV5q7r6Lnof/QuCV9ZirKvSbisGAlk6T6uol3dWDbdZ0yj57Azq/KPdHLQjC6OIBunTahM+r++HHyPj8WKfVkWrvovMXD1Nz99c/VIGr95IV9D72zKjJi7m6nGTLMbSyYjRZQYnExvRHkSNRtEwGc3VFdjZNEAQ9YNi+h4w/OKpx5pDaWHz/YcxlJSPa3/GDRwi9+TZqIolj7kycSxeMm040GjRVpe+vzxLatA1joRclGsNYXEDl12770CWyY0GTZbp+8yesdbVY6mqw1Fa9azNmmqLQ+eCjpPsGKLzqUsJbdqDGYphrKjGKIlp5KemePvwvv6EPs1vMuh/M9r1UfOmWcavJJY+2Ed3diGfV8mwxxFRaTOSt3SCJJFvax+x2iBZz7uC9KKHJCv1Pv0Row9Zs0KtE4/qaIgiINiuixYKhpJBkawfHNv4XBVdcSMFVl+JcNJeBZ9YQP9yiz/doKvGDRxGNxrzCFR82SA473ksvwHvpBYBOT43vbybWdJD4wSNYp07Kbtv9P38iun0vBq8b+6zpepW5vQtTecm41nfJoQt8JA4dxT77g2HrYJsxhcqv3krXQ38g2dqOeVI1aiKJpqjI4QhKKIKlvhbnork668VowLlkPv6X38B78YpRRSLO4TjCb+/G4PWM2cEbgj7vVkFg7Sasd+S/J6rJFOEtO0ZQUNVk8qT3LmORl0RzK465DTpjQVPR0hk9/tM01FQ6h8mgRKJIrtHntOKHW+j781Ok+/2gaTjmz8Jz8fIcaqSaTNH277/C4HTgWrEELSMTP9CMEoxQeNVI/6whaKqq+xL1+TBVlOoCPx/AOGpC0cLLL7/MM888ww9/+ENisVj2eUEQ+NGPfjThg58NUsm2GVPQZJlMIIQcDCEHQqiJJJk+H0o4kt1ODoXpeuCREe8XrRYMXjeVX789a+QYO9BMsrUdo9eNubyUyq/fri9sqTSCwYCpNNcfIZ/yz7sJo9dN9be+TKq7l+C6N0kcbtF/fBYL9tnTqfjiLdmqvKaqWUO6E+khcjhC4nArydY21GQKQZL0IXGzaUz6Uz7EGg/iveIiRKMRg8eF/4XXUBOJM15RYyKwz2lAfGp0Wp+xuBDBbEIOR5AHAjrPfIxKpBwKo2VknYYzOMAsWq1Yp07Gdd5CQhu36qotJ9BjQK+Eq6k0mZ5+au/5Zs5rAy+9TmDNOsyTqhDNZgZeXEto89tU/cMXT3oTGMu4M/zmdqK79uG9/IKs+l583wF6H/0blXfeNuZ+P0xItnUy8Mww6q8kYampxFpfi6WuFs+Fy0ZNSieKwGsbSXf34zp/EYIo4rlgKaGNb5E43IKx0IuhwIO5soxUZw/BDVsxlRaR7uqj7NYbsc8a/yxBdNc+3Wz1BBqkZXINiSOtI6XjTwI1lSLZ2k78YDPuC84jtGEriZY2JJcD0NCSaRwLZoOikDh4FPfyRVinTCKw7k0QRQpXX0L1t75E75+e0lUbBbA3TKPiC58+J++dBwaXE9d5C3CdN5IubHC5kFwO5ECI0Ma3CG18S39BFJGjcVxjiJQMQTSb9QHtDxBs0+spuPoyBp5dg2gwIAfDIEmYK8uxXrR8RNA9pIgXfnt33pmGc8hFbO+BkyoaDoe5qpzw5rdHfT1+8AgGl3NkMiQZ0FKZMfctms2IJiPp/gFMZSVoqoZgMCBIok5b292Ic+EcBIMBNZki1niQgivzi0Ck+3x0/vJh7LNn4Fg8Dy0jE2s8SNd//5Hq//XF7HZDKof2Yb5pzqXzCb62kdSxjryzy3IkRsd//lqfN3Q7yQSCmMpKqbrztpyEOdZ0SJ9XLinCsWD2+5LcTCh5ueeee3j22WcBclTCTjV5ORukkouuvZKiEyTp1GSSTCBM7lSzQMHqi5EDeoKTCRxPdNKJZE6gHtr0Fr4nXhhxLMmhc8Trf/KP2ed8f38Z0WbF4HFjLNCpawa365Tl/yYCc3kppTfpnHRN05D9QQSDlNO2FEQR98ql2arD0LaBVzeQONyCYJB0JbSiQr06eegooqrSeu/PqfrG5zGOk2JhcLnI+PyYy0uRg2EESUI0n3z47mxCltZ334O6vPW0umwHBAY54DOmEFq/FVN1BY4FIw3XhhDb30zqWCcGp103XRtcsNVEklR7F/GmQ1gbppBu7yLd79elQgfpZUosgRwMoikKNd/5ak4gLIfC+F94Dfeq87N0NHN1BeHNbxPZtgv3+fnFOqK7GvE987JuKFhUQMFVl+Bevjhn0Qxu2Ipten22izDEefevWacPweYxRPwwwuByUnb7p0gcPUbyyDFSnT0kW9pItrQBG3T+9eB3NvDS68j+IJa6Gqx1tXoCPM4blaaoBF7dgH3+rCy9VDAYcF+0nHRXL/GDR4g3HkIwGtFUBTkYxrloDhVfuGVMSmLeY2kjO7wA9jkzSLa2TWh/ajpDpn8AJRDGsWSeruhjNOhSz4MFJGvDVNRYHE1WMFWUEt3TRMFVl+JatoiBF9fiOm8BprISqr/1ZdRkEhDOVctPETXf+ao+0N7RTbzpELHGg4S27NA9hoZVq5Mt7YS2bMdcUYq5vBRTRWn2N68pCsIHgDJ2IhIHjmCbPmXcvi+m0mLi+w+fS17GATWVQppAx10wGnSfolGgxGIIeeY8TSVFxPYdgDzkAHfEAAAgAElEQVQKnCfuX0ulSXf1YqmpzBYxSm68mp7fP4H/lfVIdhtKLI734hV4LsrvgxVcvxVzZXl23kkwGXHMn0XglfWkunuz/i7J9q6s4ImaTJE40kryyDEyAwFaf3w/zgWz8a7KlRPv++szg2v0sqwacPTt3fief5WST+gzY4G1Gxl4/jWMpUVkfAESzS2UfPKaMT/7u4EJJS+vvvoqJpOJ1atX43Q633G2dbqkkltaWrj77rsJBoN4PB5++tOfMmnSpLzbHj16lOuvv56bb76Z733ve6d66mNCtFgwl+cGzsYCD1V35Q5faZquapMJBHNMrGxT6/BefmG2kzOU6CjRWLYCDnpXo+vXfxzhSYAgILmcVHzh09m2fOLIMaK7G/XkxuPGOEhdk5z2d6yDr2YydP7ydyRb29EUFfeKJZR86prs9eG5cBmh9VuRaysxuF1Ed+4j2dyKubocNI1UezemkiLkiD7QWbD6YpKtHbTf9yA1/+eucVF+ym7/FF0PPKJ3geIJym7/1IeywmksKqD27jvp+9vzBF/bgLGkSE88NA05GEaNxXGvXEKypYNUeyfm6lynZ01RCG/fQ3x3E/aZ08gEgjmeKpLNilRj1RfDg0ewz5+Nyesm3tyKEonqw/oFbizVFaiZ9AhT0/ihoxgKC3LmaIYUyqJ7mvImL9G9B+j+3WM45s3EsWgusj+I78kXQVXxrDzewlcTiZEBiiQhiBJqOo3EueQF9Guk5MaPZR+rySSJlnaSR49lZ8aGEHhlPfH9h7OPJacDa10Nljqd0pLPcXwI8YP6EPyJ1FVBELLDxmo6jZpMI4gi8UNHMBZ4J5y4ADjmzyL85nZs0+tyrmc5EMRYXEi6q/ek0qNDSLa2YyovRY3GMHrdqKkU6e4+nPNn6WIDgkC6t590Zy+IIpLdqtN9BgIYiwowV5QR2qi7ogOjUoPPYfwQBAFLdQWW6goKrlyFe9tu+v/2HLbpddltUl09yD4/ss9PbI9u3SDarJjKS1HC4TEpsu8XtHQKcTDByvj8qKl0jn/LiRCMBtTk6AH2ORyHZLehplLj3l5NppCsY1DMhPzxhKWmgsjbu0dQv0ZgMNRNtnVSdPVl2adFk4mKOz6NHAqT8YcwlRaOyRjJ9A8MdoCHnZogYHA5kAcC2eTFVFpEdNc+onuakAeCGLwuzNUVKKkUzgWz0TIyXf/zZ4xeN5V33o5ktxPdsRfvFRdmYzdBELDOmEJky45s8jLw/Gs4ly7A4HKgpjMEBuXi3+vizISSl+LiYhYuXMiPf/zj03Lw0yWV/P3vf5+bb76Za6+9lr///e/88z//M48++uiI7RRF4fvf/z6XXXZZnr289xAEIa/hnufCZSPcpzVVRYnEdCnPoedkmeLrVw8mN0G9qxMMIYciKKEwiMcXwOje/XT/5k8jT0KSMBZ4mPHw/8smMb5nX8maOBkGuzlGjxvRbsubsAZe24gcCOK9/EI0RSW0YQuOOTOytA9TcSElt1xP35+ewja9nviBwwiD1UxNVtBUlUwgRLqrF89FyxBNJmzT6ogmEgw8+wqlN19/0r+lbepkJv/rd0n3DmAs8k54xkFTVeL7mwm+sYlUZw+CwYBjwWydQnMKwdT7CYPHTcUXbkYOR4hs34scDCFIEqbyUhzzZyEaDcQPHWHgxbUEXlmvV9MNBjRZJt3TT8bnx3PxCix1NfieegklnhjRKhctZqyTa4jt2IvlY5fjXrYw5/XQpm0UXfuREecmWixoeapbaiqdDXL17+IwwTc2k+rpJ3GkFcluQw6GMRR4MBYV4Fgwm4HnXsW9Ymn2mnTMnUloyw4YdJq2TJlMuqtbd0bfdxDHrOln3Hf5XkC0WLA3TMXeMFKRsOj61cRnTiV5pI3E0VaUcJTo7iaiu5vQFCWbvKQ6e+j/23NY6mp1+tnkGmR/cFQlseyxTaYsddHgdpLpHzilz2Ctn4R99gxCG97CWl+LYDGT7uxBDgQpv/VG+h5/DkPByfnvcjBEsqUN+9yZyMHQ4GfrRXLYcihpciCE5HYgms2ke/owlZeSGUxeLLWVhLdszyYv53D64Zg/i76/PI2aTCGazdnAX/K4QBDQUmnURBI1niB5pBXBZsU4jBHQ/+QLWKdMwjat/n3tiIkOB2oiMSgrfQjBYECyWvFefmHe4psaT44IXM8hPxzzZ+P7+0tjDuAPR6qtC8fC2aO+bnA5corHQxAMBmwNU0keacFSVztqMVjLZHSPFUXRKadDzysKsb0HSLS0IzntGAvGnlO21NUQeWtXTrdOzWTIBEI5BRrr1DriP/8NCGAoKkCJxEh19SINKp4JgoClvpb4/mba73uQ6u98JSsPn/P5BlVMs+erqgjiYByYjQcnPqf8TjGh5OVzn/sc999/P2vWrKGhoQFpWHVgSJFsIjhRKrmrq4uSkpIJSSUPDAzQ1NTEww8/DMDVV1/Nvffei9/vp6Agdyj8oYceYtWqVcTjceLx/PzXcDhMOBzOea6nJ7/B3nsJQRR1o61hZluiyUT5HSP9WzRFQQ6Fcyp+1roaiq77CJlAEDkQziY7SjSGJss5F2z/357LG0QIRiMln7omm0ykOroJvLGZ+MEjoKhkevt1aVKXk8xAIOe97qULkKxWOv7rN2iyimizkmrvBk1DsllRQhE8l6zIMU2yTqsjtG4LxR9fPa7qpWSzYZ088RkXTVHpfvgvxA82Y5lUg33+LH247eARguvepPyOm0c1UZwIJnptyZGYHjSdYofT4HLiHTSKPBG2afXYptWT7vOROHQUNaUHAenBqqV1yiR9u+n1JI4ewzK5esR5iBYzBpdDnymYc5xXm+7zoSZTeecMbDOm6Gp5XT2YK/QKv5JIkDrWoftDHDxCzyN/RdM0zNUV2GdNI3msHWNxQbZ7aJ02Bcf8mSjhCFomk+22mGuqSP7574hWC2oySazxEKLVoqsNrd+C72/PY506maLrPoKleuLr1QcZ79a65Vm5NEtR0TSNzIB/MJE5lpPsxA8043/5jeNvHJTLRhTQFBXHvJkIJxMF0LScgstEIAgCZbfeSOTt3YQ2bUPx+bE3TMV7yQoMbhdqMo3vuVdyJElzD62R7u4jtmc/ZbfeSKK5NTuzqKZSI87dUOAh3dGNKsYwej0I6NK2AKLNhhyJjTjGmYoP4j1RNBoouv4j+J56CVNVObHdTRhLCnHMbTjetdB008zUsU5Em5Xwtl24lswn3ec7XsiTJD3xnTk1KwZwukVuxoJr0Vx6H3uGTL8Py6RqRIuZxKGjescwz3Wa6uqh9OJPvGfn927i3b6uhhLcfIbfJ0KTZVIdXZTeMnqh1DqtHjWRRI5ERxRH7bOno4QjJI4cw1xZNqJIIkdjyJEoUihEzf/+SpbaLEdidPz816iJBIaiArRkkoFn11B++02jzul5Vi4l+MZmonv2Y6mtQk2liO9vxn3+En3NHUSypQ0MEganAyUU0RMQVcN78Yqczop95pCc+Frss6aRaG7NsehIHG7BMazDXnDlKgKvbsBUUUqmfwDXeYvel+7yhJKXe++9F0EQ+OY3cwdwBUGgqWni8ranQyq5u7ub0tLSbCIlSRIlJSV0d3fnJC8HDhxg48aNPProozzwwAOj7u+RRx7hF7/4xYQ/y3sFJR4ntvcAos2Kfdb0vFm+IEkjZkUcc2fiGPRaGQ41k8m6+Q6h8OrLyPT7kQPBEUIEw9uiiZY2+v70VPZxdNe+7L8jO/bimNuQ/TENvLgWORBC0zSsUyahZTJoxYVITjvWyTUYhklBD0GyWjF43cT2HRxh2Hk6MfDCqyRb2vFctDynXW8s8JCpLqf7t3+m9p5vvOPjTPTaOvLtf0EOhbHWT8I6ZZL+/6mTdUns0zQgZyopwjTMx+XoP/0bgtFAYO0mNFlGtOjiCcn2LizVFSOOayj06ovbYPKS8QeJ7thH5dduzTt8LxoNFK6+lK7f/gkUFdFmQTQaKf7ER1GTSbp/+2cc82blDFoOBYeW2krUTIbUsTZk3wCSy5kTVCYOH8XWMIXE4RZEqwXBZMS5ZD72QUlITVFItrTT/rMHqfzK50Z4KJ3JeC/WLUEQMBUVYioqHDFYbWuYQsWXP0viSCuJo22k2jqQA0EAwlt34hw2XxXa+BaIIqaiAt37YLBanvEHccyb2GB9zvmJIq6lC/J6RHkvXYnkdtL/xPOgaZirypEcDp1OGY6Qau9Cstuo/Kp+XWR8fuLNLfp+JTGbmAzBXFqsUzsUGcntItnaka3ga4qcM2t2puODek/0rDyPxJE2fH9/CXNVOUavO7t+K4kkGV8AJRqj8NorEM0mev/wBAaXE4PHTeHHLteHjlvaSBw6QuLQEXxPvwRA/X3/nBXAUaKxURkHpwP2OQ3wp6cQjPrAvmSzoslKdn5wONJ9PhCErIT0mY53+7rSE9zV+J56EdeKxaNSwjRFIfzWbhzzZ2cpV6Ptz33hecQPHhkRSwmCgOv8xdk5PkRBT2AEAS2TId3nwzplMjXfzp0B7f/bswhGCdei4wyCTCBE98OPUfev38s7pynZbdR+704GXlhLbE8TotVCwVWXjJiDSra06zPF0+pAFEge68BWNykvtc02YwqhDVup/vZX6fzF/yCHIhhcDjKBEIIg5HSRC65cham0mGRLG+5li3CdwLp4rzBhbdITZ1RGe2688Pv9+P1+4vE4zc3NNDc3A3DDDTec8j5PRCaT4Z/+6Z/48Y9/nNMtyodbb72V66/Pzb57enq45ZZbTtv5nCqUeJxjP74fQTKgJhLY5zRQ9pl3VoURjUbEE5SFhvPhh0MfPD0Oc1UFJTdfT8YfIHGohXRPH5osoym6DODwH15gzTq9QwNkunpzP1ckmp3NUWJxotv36DKkNhtKLEH8SCuWupp3RYhAzWQIrt2sqyHl2bexwIu5olSXw172zuS8J3JtqRkZORzVaTo79xHdeTwxFO02Kr5wMwVXrtK3TacRJMM7mvHRNA3/868R23cAU3kJBo9bT1zSGVA1Ml29qNEYlprKnJu5aLWgJpIoqRSp1g6Sre2U33HTqBrygXVv4nt2De5lC1FlBdmvJ8eCyUj3b/+Mc8m8EYm3fW4DoY3bBme5HBiLi4gfOIJn1bLcoEISdWPXhilINivxA3pQEtu5D+u0yTgWzME6ZRKS20nnQ39g0j3fPGtoZO/3umWuKMN87fFZGTUjkzzWQed//QbBYoaha1PTiO07gJaRs9sKBgOGAg9qKpXXrf50wbV4Hs6Fc4g3HSL05nYy/T5AwFhcQOFHP5sjCepcPBffs6+gzZExFhXoNLkTlO4MTn19U9MZ1HgiS9lId/ZiPYmJ75mE9/vaGgvpzm7cK5YgB0PEDzSDJA2q8wvYptVhnTo5WwW3z5pO/1MvUnv316n86q0AKPEE8QPNxJoOEW86RKK5VZe2HkTbfQ8SP3AYW8NU7DOnY585Deu0yadNWlyQREpv+Thdv/5DljHhOn/xCCq5HAwR3bmP8jtufsczqh8UvBfXlWflUtRYnIGXXsdaV4O5tjpbVNNUlXRXL4lmPb4o+8zHT76/C5YRfONNMlXlI+5TgiBgnzEF27Q60t29yIEwmqqgyQqIIrXf/VpOZ0TNyER27MV72QU564rR68ZY6CWyq3FUYQaDx31SOn38QDP2GVNJHGgGUcRY5M0xBB8OyWbF4HaR6e1n8g/+N5G395Du7ce9YgmOhXNyrndBEHAumI1zwegUu/cCE0peDhw4cFoP/uqrr/Kd73yH2tpampubmTJlCocPH2bhwoXjTl7Ky8vp7e1FURQkSUJRFPr6+igvP8796+/vp62tjS996UuA3q7UNI1oNMq9996bsz+Xy4XLNXaL8f1C/MARBEHEdd4CNFlm4IW1lHzq2nfsjzFenNgatE6uxjp5pNyepmn68PQwjnjBRy/FPm8m/hdfR/K40JIplHgCNZ7IketVwpERJpXxxoP0PvI4CAJT/+verLGi/9X1upGXxz3MO2diQgTJo22IVvOYSlTmmgqiO/ZieofJy0SuLdFoYOaffkmmf4BEc6te0R78v+wP5qio+F98ne7f/RVrXc3xDs2USbqiieHk14amafQ//iyRHXt1RbvqSgTDsA5UUQFyKEKiuYXksQ4EgxHJZUeQDGiqgppIEnxtI84Fc6j59ldHHYxWEkl8T76Ae+XSnL93xuen55HHsTVMzasuZ64ow71iMdHdTSSPdSA5HTiXLSDZ0o6akY/fjGQZyW4j1dEDqorktKPJCtbpdSQOt2Ktm4TB48JUXIiptJjgujcp/vhVJ//7qCqxvQf0LtjUyWNW594vfNDWLdFowDZlEmWfv4neR/+mzyZYzKBpeC+7kHSfj3SfD9kfQI0lyPT5AEj39mf3Ed62i+Abm3U/mvparHW1GMbwPhgPBFHEPnvGSf0+jAVe7DOnEj94FPusaUh2O0oonBN8DCHd069TfgbluVPtXXnpvGcqPmjX1hCSbZ36fN5g8KctXaDPhIoiosU8Yv03VZYR23+YVEd3do2SbFacC+dkZ7dO5PxnevtRwlEiW3cS2boT0OnT1qmTKfzoZaPScicCx7yZlN36SXr/8ASmynK9EzkIORwh2dJOuluXDz8d9OUPCiZ6XampNILJOOEuWMGVq7DU1xJ4bQPBV9fr905BQInGMJWXUHzD1TgXzRk1XlDicWKNh1HjcQSTieIbr6b/8edwLp6Xt/gliOKgGlg5mf4BIjv2UvmVz41YO7RMGlQtL51WMBvzztdMBHI4jPeKVTgWzEZT9G7eWH870WpBDkcQLRbcZ4Ca3Ukjm66uLhwOBy6Xi66urlG3O5WZl5///Of86Ec/YvXq1SxZsoSnn36aJ554Itt9GQ8KCwtpaGjgueee49prr+W5556joaEhhzJWUVHB1q1bs4/vv/9+4vH4u6Y29m5BcthQ4nHUVHrwIjNPuNquxOJE9+5HCUcRDBLmqgqsUyef1ra4IAgj1DIKLrsQADkUQTQZj2uMa1rOMJjkcOBeuVSX243FSXfoTulKNI4SjuQYN4W37CS8eVvec7DPmUH9T/8voHcmOn/5O114wOvJESKQQ2E4SYAvGI2o6bF13N8NCIKQpXUNV+HK+AM5nNp0bz9aKkV8/+EcZSjBaMQxt4HJ9x6XIx8e7A8hfvAI4a07cV+4jMCadSjxBIYThkINbie26fUk27pwLp2P7AvoSlHpNMbiQup+9I/ZSvRoSDS3YHC7RiSKktuFEo5gLBzdW2ToZjAcoWCY6K5GXEvmoabSRLbtpuCjl4IsgySR6R8guqsRNRoHTctJ5Kx1NYQ2vkXhx64YM/nXNI2eRx4nfuAwBreL/idfoOIrn8N+FlHO3k04588m3dNPYM06rA1TEQwSqc5uXRRDEhGMJkS7gKmsBNeyhVk5dYDoniaCr28m+Prm7HPG4kIsdTXYpk+h9KZr39VzL/30dRz76S/1CubcBsKb30a0WrO0C03VSHf3ZgdwNU0j3ngQY6FXp2qcw7uK6O5GjGXFx/n7BgOSYwwndVHEVFZMdE/TqAWWEwPYqQ/8mExvP7GmQ8SaDhNvPEiyrZN406Gcqnis8SCBVzdgm6V7rpnKSyd0T3UtmY+1rpbgxq2E1m/NqmSJZjOei5ZT8aVbxm0bcLai69d/JPDaBkxlJZjLSzCVDf5XXoK5qnzMopJtymRsUyYjhyNk+gfQFFUvZA2jTJ8I3Z1+DZG3d2MocCOazajpDKm2DgSDgYEXXsPo9WCbMwPrCcbQGX+QZEsb8kCAii9/Nq8Yimi1YiwqINPry6FJa6pKpt+Ptb52xHsmAkEygKqOrYA2HKr6nlhsnC6cNHm59NJLufXWW7n77ru55JJL8v4gT3Xmpauri9WrV+c8d/3117NixYoJJRY/+MEPuPvuu3nggQdwuVz89Kc/BeCLX/wi3/jGN5gzZ3Q5zzMJ1ql1uM9fSmDNOkSrhYovfWbcLeRMIMTAMy8T2b4XY5EX0WzWB3BffB3JasH7kVUjfDPeDXgvWUnvn57EXFuVdaoeHlRKTntWiSPV0U2mvJja730d0LmpDPu8BVeuwja9blBOOpj10JEDoZwBPTkQJvDK+lHPyVReiqaoCJJIormVdJ8vN1nw+TGXl4z6/vcaJ97EKr70GUo+da3enRnWoUl39eZQOtVkisabvoK5qjxnjibw6nosdTWIJiPWaXUkDh0ZkbyA7jMkWcxo6TSOBfpsQnTHXjwXLjtp4gKD0uh5ZNAzvgG9WjqWTGUemMpLie7eh2vJPNL9PkSzCcOwxEhy2FHCEdL9fpzLFuYoX0kOu25yN+AfIeecc269/UR3N+G9bCWCJJHq6Mb39EvY7/76hM71w4zCj1yMscBL14OPIofCSC4HksuhU0tNJl08QRCIvL0b54LZWSGHgssvwlxZTnJwjiZxtI1M/wCZ/gHkgWA2edFUldbv34e5qjyrdmaurnzHHWmDx03Nd75G16//QGp/F8biQmIHmjEOmrLKoTDGwgI8q5ajplIk9jSBplH1v7407nU02dGFf816UBQ8Fy3DNi0/reMcRkKJxiasFCZazBMSUxAEIRskey9ZmT1ubH8zltrjak+Rt/fgf/mNrGiFwevG1jAN+yz9v/F8r8ZCL8XXfoSij12BmtIVGUWz6ayhib1TyMEwWipN6lgHqWMdOa/Z5zZQ/xPdG1BNpel84HfHE5uyEkzlpUguhz7zNI7ubWYgQNt9v8LgceO5+HwEk5FY40GSx9qzHVZUFU1WCL6ynpDJhLmqTJdnTiRBEPBcvAL3+YtHVT4VBIHi61fT/bvHsKszdHn2WJxY4yFdFnwYfVHTNFLHOki0tqOlM4hmM9ZpY7MALLVVpHv6sJyQWOWDLsYSwHwGCdmcdHXXNC0nADqdMy+FhYX4fD6KioqorKxk586deL3eCfu81NfX8/jjj494/te//nXe7e+6665TOt/3G4IgUPzx1RRdd6UuUaeqRHc1kuzqQUtnMLidOObNHBHcpnv7af/Zf2MsLsBzyfmI5uMLvqZpZPoH8D31EqljHZTcdN27msDYZ01DstlIHDwy5sC0Ek8QazxExReO0y9OrAq4lswb1V15eJAs2a1UfuOO48lNMDfZMVeVkWxtw1o/iVRnD+meXmBxdj/JljZKPnkNoXfwud9tGNzOHPoD6F02JXr8Rp3q7EHLyCSPtpE82paT0EkeF5LNhmVSNdFd+1Ci8bwyt4YCj84Lr5+EHIroid6KJeM6R+vUOpRobIT6S7rXhyBJOXSJ8UC0mPSuCqBlchNb0H8vjvmj83IFSdL5yOgBiZpKY/C6c4IFVZZ1Ct2QyaLZhJZ577twZzI0RSG0eRuWSdWYJ1ejRmJomoZkt2IsKTo+qOrz0/XQH6j82m3YptVhqanMkQPVFJVUdw/JI205Hed0bz+R7XuIbN+TfU4wSJhrqrDW1VD88avyukmPB8YCDzXfvZNUWyfB9Vt0l+yuXtRMBkt1BaLdRmTbbtRkCvcF51Fw5aocD6OxkO7z0f6z/8YyqQpBMtD5wCNUffML45Z2/bBDNJtBVk6+4TBospJTiU73+Qht2ka6pw8AY3ER7hVLxixWSQ77iPuO56JlSA6b3qFpPIQcCBHevI3w5m2YKsqY8Zv7sttGdzXqszj2/MqYgiiO+xr6MGHS//0mSixOuqePVHcf6e4+0j29pLv7c/x+0r39eYuVotWCqbyUqm98PptMpnv70VQV06BdQLrXR3jHHgaeehHRYdOLeJJIaMNbyIEg1sk12YRZjsRItXdRdOPVpI51kjjSinvFUhzzZmKprRpX0umYP4vyO27WC8vbdiNaLXguWEbh1ZdmfRAjb+3C/8o6ZH8QY3GBrsioyPQ/9QLmynIKrrworxiT95IV9D72zLiSl3RPPwa365TXyfcDJ01ehs+5nO6ZlxtvvJHt27dz5ZVXctttt/G5z30OURS5/fbbT+txzjpo4F/zBsHXNiKYjHrQJ4qoyRT9T76IfUY9Rdet1k3gkik6/vM3mCdV5b0pDlGTDBd4CG96C2NxEQWXXfCunbogilTddTttP3uQ6M59WKfX51CgNFUl3dlDrOkwRddccVJu+ljHGYLksFP4kYtH3TbZ0UX7fzwEgoh16qRswKQkksT2NGGuLMcxbxah7tFpkx9EnOghZK2vZdbffq0r7Ax2aGIHmkm3d6EEw0hWC6LJiPv8JQy8uBZNUzG6XEhOB5LTjsHpQLSY9cp3KEx4605Kbr5+3J46otlE6S0fp/ePT2KprcLgcZEZCJJqbcdcWTbhpFmTj7tmD2nw59Opz/teRUWJJ0AU6fzVI7rHgiQhOeyU3nx9Vm3IXF6KsbiQ6Nu7MXg9JFvbKLp+9Un2fg7DEX5zO5k+H64VS/TvJo8ELOizVY55M+n+n79Q96O78/gNiFiqKrBU5VYHjV43k/7lO7rJ5mCXJt3VQ/LoMZJHj1H40eO+Xv1PvkCs6bA+H1Y/CUt9DcbCkUqHOccVBCy1VZR99vgcZqq7j1RHF1pGxuByYJ0+ZcKdnljjQYzFhdlASk2niby9+1zyMk5YJtfkJKzjQWYgQMFlugF09yOPkzw6KG3rcSMAyZZjhDZuxVxdQfmtn8wrV5z3XCZVY5lUTfEnPqrLbnf2EB30bjENm4vI+AMc/ccfgyBgmVSNfeZUbINCAMaSwned+XCmQ7LbdNbA4NxrPhjcTiq/fvtgcjOU6PSiJpIkjx7LmbHte+wZ/C+9DoKAaDEBAqLVgpJIYkQguqeJzBubESQD9jnTEYexRAxOO7LLQaq1Hce8WZirygi+sRn38kUT6pY55szAMWeGXnAVhOw1oKkqvX94gti+g1hn1ONYOCfn+tBUlVRnDz2P/BX3BcsouvbKnNftcxoQn3qR+OEWbGMIiKjJFPHGg5Te8vEz6p2OW5sAACAASURBVPob92oryzKrV69m5cqVfP/73z8tBx8aoAe47rrrWLp0KYlEgvr6c63z0aDJMp3//XvSXX04Fs4ZUa3WZJlESxtt9/2KyjtvI93Tj2AynfSGKBoNOBbNxf/iWjwXLR/1RqxmZJRQGA0Ng8uZsxCMFwaPm9rvfR3f868S2rBV34/VkuV6mitKqfjizVmTy3cblqoKar71ZXr/9BSJw0cxeD2ke3qRQxHcK5ZQdP1V70jJ64MEyWrBPnNaNjjP+Py0/ut/4pg3K2t+Zq4qR7RYUIKhLE0nC4OuahZ+czslt3wc15KJiRi4lszXh+XfeJN0vw9zdTmFH7uMrl/+TvcbGofAwBDkAT+O/8/eecfJVdd7/33K9L69Z3fT26ZXQhIITapUFRXk8qA0C3r1Qb0PXq/32q4KlotIUcqVIiIgIEVIIY0UQnrfJNv7Tt3pc87zx9md7GTbTLIbkrDv14sXyeTsmTOzp/y+7fOZVQVoLRf6onwiDc0YCvNQFRVBJw94M47UN2KqKKXluVdQY1GyLlmmzcm0tNP46P9S9n/vxVCYhyBJlH79djrfXUe800P+F2742FVWziZUVaXzvbWYxlem9UDXF+QRPFBN156DyeFkJRrFv20X4SO1KLEYst2Gbe6MpFePaDT2qcImgiHCx+oIVdekZBP923YR2LYrZVZOstswVY7BvnB22saShsK8U24lFQ16lFA4qWKmBMMZt06eCySCQRLBELosV2aLvqrJtDz7t7R8PEBrnSaeQF+cT81Pf4c+L6fbDPJ4Rd9QWoR5iuZ1UfOz/6Hs23cNOhfRH4IgEK5roPMf76FEY+jmzUjOGsa9AcyTxxM6dITw0VrCR2vpeOM9QAvey3/47X5FcEZJH9lhJ/vyFSmvqapKwhfQDGWLj6siiiYjktNOwuNDCWlzRkpIU1VVAl1Ypk8i1u4GUcW3cRuyy45kMiGaTEgmA7LNQvDgUSzTJ2vKpCVFuFeuI/9zQxtrn8iJ537bX18neKAax/nz+30uCqKIsbQIfV423vVbkCxmsi5eevzfJZGSr/4Ltb98BDUSwTS+MqXqqKoqsdYOunbtw3XBeSldG2cDaa8UZFlGURTCJ8jlDicnM/T/SaPlhb8TbWnXpH3783iRZczjK5FtNhoffgrBaMQ8IT3ZTtlmRTQZCWzf3WdRGm1tx7NmI971W5I3ezUWxzZ/Jq7liwccgBwIyWoh/zPXkHvNZXTtPUjC34Wgk7TWkqKCoXcwzBhKCin7zt3JLI2g02myuud4+V7u9kYQjfpebrlQcPO1RFra6Np/iGhtI4lQWBuEj8eRc/Ko/PF3kSxmgoeOcuzff4FpXEWK0pkud+AsorGsmIJbUtUETeMrCdc2YqpML+usRGNEm9pw3KO19ynhMPq8HDrfWqWpt4iiljGvLMM8cWzK4kZNJAgdqSXrkqV0/mNlUq0IQF+Qi6GjCO/aD8i76WpAWxznXHlR34MYZUgidY0kfAF0+ekvAA2lxXjXbsIyZQIdb7yLe9V6ZJsVOceFIMnEWjvwvP8B+rwc8j776X4Xe5LZlBKk91D0lS8SOnSEUHVNd6WmRlM43L5ba8noJtrSTs1PfotpbFm32lk5pvKSYTVjs82dgef9TXjXbkbsVvdzLls0bPs/G/Cs20zrC39HkCX0+bmUfP3/pH3PFSSJrIuX4V69AcfiuYMOG2su5vtwXbyUpkf/F31hPuYBRBUEUcQ8oRJBlmj4nycp/8E3MwqqIo3NtL7wd03y2GzCt3k77ndWk33FRZgqShn3yx+gRKKEDh2ha89BTaZ530FinW70va6Tul/9gViHG8uUCZinTsA8cdw5/zwaKQRBQHbYNKPvXmRfeTFdew5gvWQZgl5HwhvAs2YjksWEZLMS7ehENBoQLWYSXh/xtk7i/ew/fFRrOzdWluJ+ew36wgKMY0rQF+Se1O8s2tKGd/0WnBcuGTKhJxoM2BfOpuO1f+I4QWZbl5PFmPvvpe3lN/GsXI+c49La3hSFWIcbyWwm9zNX4+jHG+tMJ6M69y233MJDDz3E/PnzmTVrFvpeWffRwGPkiXu8+DZt03TBh7iZ6gtyiTa10LX3II4l6c0kgGZaGNx/OCV4CezcS9Mfn8dQXKjJ3HZfHEo4QuiIVuXJveHKATXJe47d+8E2TekjFke0mLFMHodl6qR+I35VVTWFqNM8rDgcGdWzCUGScC5fRGDnvtSKgiRiKMrHUHR8IFBNKHS+uZLC2z+bPAdC1ceIu734t2zHv2X78R+3WzGNLafs/q+mNczvWrGEpiee06o+aRj8hQ5oClCyw0akqZX63zyOaDCgy89FicUwlZeixuPE2j10vrUaS9VkLJPHo8bj+DbvwDy+HH1BHpLV0tcc1WYh1uEe8hhGGZp4pwfJbs2oHUF22ghV19D42J+J1DXiWDyvz/yVeeoEInWN1P/6sYzU34ylRRhLi5LD1z0zf6HqmhRn9VD10aR5YW8kmxXThApKvnEH+uz0WooGQtTrKf3WnQT3H4KEgnnS2I/FqfrjIhEM0vrC33EuXYBoMRPYugP3u++Tc1V61S8A10XnE65twLdhK9a5Vf0aESaCIfwf7sQ8vhJjeQmdb6/COcgsXA+myjFE6poI7juckTFktKkVXZYzORhuKCkkXNuQso1o0KdIdquKQrS5NdlCraoq/q07iHt8xz2+RBFT5RjMU8bjPH/hOWNW+XHi/ucaDGXF6PM1tS+dy4m01YyxshRRryd46Cii0YBk0KMvKSLW3oEux4UaS6CEw1rlNBpD6L5uJZOJRFeQup//T/I9ZJcjKfpgnjQ25fw+0TuqB8/7H2AoKUrb7FYym9DlZuHd+GFK27+qKCihMK7li3EuW0SksRklENRmAseUpHhbnW5iHW6iza0IsqxJzaeritZNRsHLT37yEwRB4Hvf+17K6yerNjZKZnjWbcZQmJe2QZa+pBD/1h2QSAwpB9yDoJM1rfxuQtU1NP3xeWzzZqY4w4Km3GKZMh5DaSFtf30d2WrBOjPVITtc20DHm+/Rtecg+oJcZJsNJJG4V5O5JfE3nBech2vFeSiRKN4NW/Gu3US804OqKIgGA5ZpE3FesPhjvdDOZXqMtyINTUk54rjXT+hIDQl/ANAWbUo4gnliJdZebulZlyzDWjU51Yvm8DESvgDB/YeRLMcXE0e+/1PURCJF6cxQXIggiZgnj8c2fya+Dz7EvnD2gO2IqqoSOlBN3Oun6K5biXu81D/4KIaKUi1gSSTwbfyQ4P5qTRo7y4HstNO1Yy/R5jbUaAzrzCkUfOF6EsEQcbcXJRpNeb9YSzv2RR+Pa/AoGrGWNtRIbOAKsyBohqkmI41/eIaKH3yzXw+WoegtR95DIhgkeOgo+hLt3ERRNcNYr4+EP0Dgw10c+e6PKfryLdjnVlH3y0eIe/2Yxo5Jqp3pC/LSSryIOhnr9MlDbncuogTDCJKYNL2VHDbivkBG+xBEkcJ/+Sztr/0Tz8p1yC4n+sI8RL2uuzrbStzjxXnhEnKuvEhLwpUVp/0cMZQW4V61PqNAQV+YR6zTQ9znRzKbiNQ34RjChVwQxZSOA0EQGP+7/0rKM3ftPUiouobQ4aOEDh9Fn5udPKbwsTq69h7EMnUihtKiUXWyNEkEg/i37sR5glePZDVrLZx6PSQUkEUQBSSLETVmQzQYMI7X5mKVWIzQ/moMve4fksWMPi+HRKCLaHNbUhgouO8QcZ8vGbwkgkH2fv5e9Pm5Kapourwc3Ks24FyeWRXWWF6K9/0PyLrofJRwGM/azbhXrkcJhVCjMdR4HF1BHq4VS3AsnjtsRquZEm1po/WFvxM6fAzZZUeNJ0h0BXEuW0TOVRen3TqesZbkcKqNfRJRIlG69hwAUcAydVJGQ56BHXszkrKTnXZUBKKtnRiK0qsmKOEI+oLjLTZtr7yJeeLYPoFLyvvYrFhnTqP1r29gmTEl+WDwb9tF89MvYqocg+uipX0+q3nSOGJuL94NW+h47R0QBK2cP3mc1uYjiqiRKOG6Rhr/8AySxUzh7Tcne91HGR5kh42Sr91O/a8fJ9rYQrTdTcLn11rKDHrUSIRIbSNKPIZsn0msvRN9bjZw/KFrKCrAuXQhcDybHW1pTz5I1Xicrt0HUGMxunbuS763YDAkFaHybrwKQZLwrNqIoawoRdmlxw05XFOPoJMp/de7kG1WWl74O3K2C1P3XIMgSTiWzCfm9hI6WE24pl5zcxcEok0tVPzoO8ltZbsN5wXn4Vu/FdPESkSjkUhNPUo0mraC2iiDI+e4SHj9A2YY+yPW4Sbu8eFcft7QFebcbPR52XjWbcm4tS/S2EzTE88RbWnHNHaMZiypKNQ9+BiCQU/WZRektHz4P9xJ+FgdgtmM4vXR9OgzxFouxb9tl1Z93Lojua1oMmKsKCP78guTVZ5RUpGznBgK8wls3YFktxE+Wkf2py7MeD+CKJJ7zaVkX7oM7+btdO3eT6wziGgy4LrofOxzZyTvI5HaBsz9eG4MhD4vJ+X3mg6GogLyPnM1rc+9ihKLYZ9bheuS5RntAzRJfOeS+cmOhkQoTOhANV17D2Kbe3y+y7t+Cy1//hugtWObJ4/XWs2mTMA8oTLjjPYnhdDhY8hOex+5bdOEsXTt3ofstCPIMmpCaxRTEwpIIrG2TnS52Zp4TXsnxsoxCL3WNrLTQdGdt2CqKEVNKMQ6tQpDtKk1OVcKWmuqGokSqW0gUtuA/4Tji3dOTorhhI7UEGt3J1vfZEf3cfe6p0oOG7FuNdW6Bx8DUcA8eRz+Lds143BVJRHowvPuWnwbtlLytdsHVLwbKaItbdT+/GEMY4pxXXJ83iwRCOL/aDfRphaK7rwlrX1lFLwMh9rYr3/967S2+/rXv37K73WmoYQj1P737zUDKkXF7VxDyX1fSTuAUULhtMuIAKJOh2QxEW1qTit4UVWVaFMrWd3KXJGmViK1jbh6DYENhC4vm669BwgdPIJ54li6du+n+akXsS+cPagErmy3ooTC2tCly4l1xuSUyFswGjCP1+YpInWN1P3yEYrvvQ3zuPTmeEZJD2NZMQW3fZa6/34YJRpNVlpUfwBEEduc6RjHjiF8rJ7an/0Ppd+6c8D2uv6y2YIsM+lPv0pWZnqqNLG2DoL7DqFEIgiiSN4NVwLQ/spb+DdvR7KYEc1GUMFQVkzutZ/CUjUFsbtC6Nu4Fcf5C/ocg87lQLdgNvT6J9/GD4k2tyaDF4Ccay9DX5SHZ80HKMEQlumTybp0WR+T1VFODkNxIbLLQbS5La12TFVVCe47jD4/t1+p7v4wVpThWbOB7E9d0GfuQYlENRnlrTtIdAURjQasM6dimzuT+t/+CUNJIZYZUwjuO0zj48+S8AWQHLZ+TeWUcERrAcnLIawq2nDuqvXkXH+F9lp1DaEj2n/xDjfBvQdTnKp9mz+i+cm/JKszpsoxGCvL0lbrO9cQRJGSbjGMhM9P9uUrBpxDSQfRaMS1dCGu7iRKf5zoFTb0ToUUE+V0cSyai33hHM1BfZjEXiSTEevMqX26G0zjynEsW0hwz0Fi7Z0pLbwnyjTH/YFP7Pl2IkoogtDPespYVkRg2y5tTZLlIHy0FiUcIdEVBEFAjcUJ7j2knUeKgq1XJ0Lc4wNFwVimJVgFSdQSLLnZcEKF1VRRxtS/Pkq0qY1oUwuR7gAn0tBE195DKXOa4eoagvtTzdsFnYzksGMsLsCxdCGg2WfU/vxh5CwnlqkTifsDJAJBzJPHgaLQtecgzhVLCO7aT8PDT1H6ra+c1kpd6wt/xzCmGPP41Otcspqxz5+Fd+0mrSMnb2hD1pN28YrFYuhOouzU3Nyc/HMkEuGdd95h2rRpFBcX09jYyK5du7jkkvR7Xs8mArv2JbO6qqriXbuZ4P7DSVWdoRANBi2LnAGyy0G0rSOtzGesvRNBJ2OeqKm9hQ4dQZ+XndbNVxAE9LnZBA9UY6wso+lPL2CbWzWkd4dv83YSXUHMVZOJ1NRr/cX9fB/JNhG9nsaHn6Ls/nszVoEZZWCUcISWZ17Evngu+sJ84m5P0hNBznImz52eTF79b56g4j/+NaPSsy7LhW6+C3uv4cC410+o+hjGXmp4sdYO4p0eABK+7nyUKAIqvk0fYZujKYyFjtUhWc0pUtugLVDiXq1lo3dWTV+YR2D7HhwL5yRfEwQBx8I5Ka+NMnwIgoBrxfm0v/YO+vycIR+U0cYW1GgU/cT0FSdlh9Z6EPf6UjyuvJs/ovW5V5DtVvRFBeiddtRoDO/azbT99Q0SwaCm5CMImCdU0v7q25gqywb0nzJPHIt75XqttUlRtNbHyjF4Vm+g8r/uT5n5i3u8hI7UJtswAUKHjhI+Vkf4WB2eleuSr+tyszGNr2DMd792zqgapsvpFsOQnXYSgUC/Jrz9kQh09RnyThdBEEAa+TZn+4LZ2BdobWnR1nZNAKBbCKC3AErc62Pv5+7GUFKIuVvMwjJlAvqTkKk/FxAMOtR43/WUIMs4ly/CvXId6PTdSQ+TVjVRVJRQGMlpI97uRjSb8G36COeF5yGZjISP1OBcvjhtp3rJbNYSGWN7GVIqCoe+/gBCr4qZaVw5otlEwucn5vVp1exojHh7J7Hu6knCH0BVVW3xD/i37ECyW0n4/FqwJYlIVguiKGKpmox3zQcE9x/uI2oyUsQ63IQOH8N1Sf/JcEESMVaW4Vm9AeGmK4bcX0bBSzQa5cEHH+TVV1/F7Xbjcrm45ppr+MY3voHBkJ7T7U9+8pPkn++77z5++ctfcumllyZfe+edd3jrrbcyOayzFu1+kX7LnWXqBIKHjqatPZ8IhREAfX4eXTv3YqmaMuBNKhHoIvDRbgpvvel4q08sBmlehACCTocSiRDYthvRYh7yOONev1bGnzQOQRTR5+cSPFiNeeqEARc5+oJcYh2ddL6zhoIvXJ/2sY0yOP4PdyIajUmPm8ECQ+OYEiINzQS27ca+4NRUSnrMNXtTcOuNOJbMS6nQROqbCB+pTTFYVYJhgvurSXSF0Odmo8vLRnLY8a7eiBKJoCYSOJcvTn4WwaAn4T2xOH96UBMKwYPVmsO8xYJ5Uua+IGcr9gWz8X+0C/+WHdjmVg34YI+2tNG1e792n8pwES9IkmZU2o13w1ZaX3wN+4JZfWR0DcUFxP0B2l74O771W7Vz7Vg9SjiMaULlgPdIXU4W2VddRMLXhexyJKvg+rwcfBs/1OS2u5Gdjj7nde71l2ObU6VVZ3rUzo7VEWvrQNDrUj7zgbvuR3ZoEs7aLE05xtLCjKTER+mLY/E8Ot9Zk7aiZaS2AfsALaSqqhI+Vk+o+ihqLI5ks2KdMeVjrWz0VLxdy7U5jt5mzZH6JgSDnkh9E5H6JtzvrAFActixTBlP0Ze/kBxc/yRgqigj1ulBicX6JOF0OVmYJ47Dt2kbyDJKNKJV4WJxUFUS/i5M4yqQsxxEm9rwvLcO06RxxP1dOJb27QTIBEEUsS+YRfhoHZYpWgXYWFGWkuBDVVHCEeI+f7J1LFJTD4qCaDKihMIk/IHkzGqP/LPrUq2rRrvHqdT94hEcSxegL8jDUJiPviAPXU7WiCRRoi1tWiveIGtKXW42voNHSEe2JKM74Q9/+EP+9re/JWdcOjs7efLJJ/F6vfz4xz/OZFcAvP/++/ziF79IeW3FihV897vfzXhfZwPW6ZPpfGs13g1bQVGQHHbMk9Lvv3UsXYBn9UbUqRPSiuwjR+uwLZhN7qcvo/43T+Db+CGm8RXaydl9wivRmJYNPFpL7g1XpJSkRYsZNRpN+/iUcBjJZsX93lqMY4qH3F7zVHEkLxTJbEKQJKINzYPO9pjGluNZvYHc6y7vk3UfJXNUVdV+Z+VDO/H2YBxTgvu99085eOkPyWLGWjUl6RqshMOEahqIe7wpv++4348SDBE6UE3oQC9VKFlCdjrQuRwE9xxMBi9KOJJ2K9Jw4t20jfaX/oEgS4gmU1KlJuvyFbhWnPvzEIIkUvTlL9D8pxfwrNqAoawYY3kJol6fnI8K19ST8Popvuc2gvsO9WmRGAw1kUj53ca9PlpfeBX7efMGXEjKNiuuS5fT+dYqom3tSGYThtKiIf1CJJOpj6KVsaIU96r1Q6pAikYj5knjUio7akIh0tCUXGT0HH+kpp4IpM6HyTLG8hIKvvSZZGCUySzRKGCbU0Xri68R9/iG7AqI+wPE2jr6Hbbv2nOAtr/9g7jHhy43C0GSNJPov/wdS9UU8m68Mi3vmZGm9/lomTqRaS8+SuhIDV17DhLcq1Vn4m4vvg+2UfKN4757Lc++jBKNYpk6EfOk8WkpRp5tyA47lqkTiRyrx3SCiaMajxM8UI111jRQFEJH6zR/O1XFNLYcfUFu8rqTsxwED1Sj7NpHxQ+/PSzBq2v5Ymp/+QjmiZX9r/UEzUxT3z2Tp0SiRJpaUBWFgu7ZvYQvQNzrJ+7zazLPXj+GwuPBaSIUJtrcSttfXkvdtSxhnTWNih9+W/suVBXfpm3o8/NOWvq5Z7/9Vbp6k4nXW0bByzvvvENWVhb/8R//QWVlJdXV1TzwwAP885//PKngZcyYMfz5z3/mlluOD+g8++yzlJWdmw7DotFA2Xfu1gb2BQHLtMwG9vU52ViqJhP4aA/WOdMHfWjFOt2E6xrJ//y1SBYzpd/6Ct71W3GvXEfXzn1IZhOqopDw+bFUTaH0G3ekmLkBWKdNouXPL6NEokMO/amJBNGmViw3T6DjtXewnNCX29/2oSM1fZxyJZuVSGPLoMGLaDQgZ7nwb92RHBIf5eRRI1GizW1Yew2BDoW+KB//1h0ZG0tmSqSxmfqHHgNJQgmGyL3ueDnZNm8WxvJS9AV53QuNdmJtnRBPEG/vRDKbkgOS/q076Np7CNvcKnxbtmMaWz6oCMVw4Vm7ifaX38Q6pyrl/eL+AJ1vr0YJBmH24NfKuYCo01F4x+e1lqnVG3C/8z4I2uJdn5uN86Lzsc+biWQyIpmMuFeuH7QC25tIXSOm8eXJ4VPvui3o83OHXEQYS4twnDcP2eVEslmI1jed1GfTZblQgiFN5CJDxTNBEpPVzh4ku43Jz/xWqzoeqSVcfYzQkRqiTa2EDh9L+U40h/CVyHYb5snjsVZNwTZn+idKcjkTRKOB/M9fR8uzL2NfOGfA9rFEIIj/g23k3nRVn/k378attL7wdyzTJ2GdU5XyHFaiUUIHj1Lz099R9u27UtoYzwQEWcY8YSzmCWPh2k9pc67NrYSP1CYDFFVV6XhzJfEON228BoLAhP/5cZ/1wblA1iXLqf/1Y+gKUu8X4dpGTSK5e6FunTaRYLeKZrzTgxqJavMv8ThKJKpdw/F4ik/PqWAoKcQ6fbJWrZ4/M+WaVxMKcbcHRBHZ5YBEAt/mj3AsmY937ebudkUJ2eXQ/n0AbLOriHu8ZF96AdHmVs3jrrlVa9nu9X4Jn5+a/3gw+XfZaUdfmI++IBd9QR6uC5dgKB66kmksLyURCpMIdCFZ+w+GI7WNWGdOIZbGd5TRqsPpdDJ//nxWrNDcSysqKli1ahVbt27NZDdJ/vM//5N7772Xxx9/nPz8fFpaWpBlmd/+9rcntb+zAdGgPyUn04Iv3kD9rx/Hv2U7lmmT+vb7KwqR+iaC+w5RePvN6Au0IVlRr8d1wWKcyxcRqdcyfYIsoy/MG/AhL1kt2GZPJ3TwSL9zKL0JHanFWF6KZDUj6PVDZgM1OWahT1AkyBJKGtUe2Wkn0tQy5HajDI0SjQ7qRt8fgiAgyLI23D+CwUvLsy9jKCvGNLacRCBI20uvY509TTMtNBtxXbKUrj0HcV14HgBqPIF3/RbCx+q04ezuYcrQoWMkvD48763D8542byBnOTGNq8C+cDbZ3SIVw4kSDtP20hvYF8/tc43JNiuOxXPpfHctxspzb1HQH4IgYKoow1RRRsGtN6FEogiy3CeBYygpxFCUR6S2YcgFk6oohI/Vkd+rhdS7cWvailKmyjG4V67DNneGZm56kgh6PYlQBHkY4mFBENBlu9Blp86HJYJBwkdqMXYnfBLBEO531hBr7SDW2oFoNhFtbaf1xdfIufZTuD5hhpfpYp83EzWeoPX5V9AX5mOsKEsGMYlAF6EjtUQbm8m57nKcS1JbgMJ1jbQ+/3fs5/W9pkF7zlqmTSR48AgNv3+aMd/7Wr/31Wh7B961mwl1D4OLBgPGMcU4z184bAvgwPY9+DZtQ7SYyb78wn4DKUEQMBTmYyg87ueFqlJy721JA81IfWNGKqdnE6aKUvJuuorWv7yGdeY0dHmauXL4SE1y4a/GE0SaWhEliezPXI0SiWozoQkF0aDXfl+ShHf1RsI19Zgqhif5nv/FG2h89Bl8G7ZinjIBXZYTJRrD8+5aEqEwSiKBbLdqz7mpk8i9/gq8azehRCIp7dUDIZqMGAryyf/8dSmvK+GIZkqd/HsU27wZRJvbNOlnj4+4x0dw3yEArFVTksFLy3Ov4FmzsbsNTfO20ff8Pz8X1wWL8W3ZgX3B7D6taXGvj0h9I/mfv462RBprwCG36MWXvvQlfv/737Nq1apk5WXNmjV8/etfp7GxMblduoaVU6ZM4e2332b79u20tbWRm5vLzJkzT0oI4JOCaNBTct8dtL/8Jt61m5CdDuQszSU90RUi2tSCPi+Hkq/+S5+qBnQPvmdwI8q97lPU/PR3BA8dwTSuot8bcbi2gUhNPWXfvkvLwvfqsx0INR7vP6uqqgji0C1xgiyhhNNvaRtlYESTETUWR00k0h40VOPa9qJhZDO8cY8Py7SJAMnAOBEIJhcOWRctxb91J8H9hzFNHIsgSziXLYRlxytyca8PyWrGecEi1HhC80qoriHe6cG/+aOU2axIYzMNDz913ItmnGZmeTKtOf4Pd2nX5wDJAdFolDyfpwAAIABJREFUwFBUQLTHhO4ThCCKg7Yf5H3209T96g+IZtOA81dqQsH/4Q4MZcVYpk5Mvh73+pHSbHMRjQZt7FAQT0pVKnks8TiiYWSfW5LZfNzUMKHQ8Ls/Yqwsw7FsIbEON6byUkSTkUSgi45X39au5fJzc9F5qjgWzcE8sRLvui141mgzcqDNbTrPX0Dhv3w2KQffm5722qGqeqbxFXhXbSB0+BjmXi1Jkfom2v72D0KHj6EvLtAkd3UyajxO+GgdNWs3YRxTQu71V2Ack34b74n4P9pN89MvYp5QSbSpldqfP0z5A/elpaKozVwcFwHI5LlwNqKZ4Fpo/ctrBPceRF9cQKzTjex0EPYHSPgC6EsKsc+fiajXayI2/VTsRLNpWGcqRZ1M8Z234l65Fve7a0EUUcIRYj4fOpcT/AFiTa1kXXkx+V+8HkEQsM2dQfho3YCiI70JH6vD3k9LpGg0pArd5OccbyFTuqWfm1qT/xl6VY7DtfUDSj8bK8sY/+sfEWlsxbv2A9R4HH1BPoayIiJ1jUTqm8j/4g2aKmV9/ZDHn1Hw8qMf/QhBELj77rtTXn/ggQeSf87UsFKn0zFv3qinQiaIOh15N11NztWX4t+6g3BtA0osjqG4kPzPfXpYfVBkh52yf72L+t/9Ee+aDzCUFWuLPQHibi+RmgZUVaX0vi+jL8hLLgASwdCg8yiCTtfvYiERjmBMo9SuRmNI5tHWiOFA1OkwjasgUt+U9gMzXNuIZcqEEVdHslZNJrBrH+ZJ44i2tCMaDSnBhmS1UPrNr2gzXeu2YBhTrAUbkkjcFyByrI5oSxv5t96Eo9d8To+jdejwsZTFcfDgEQLbdhHYtiv5mmjpUYQpJ/9znx6w5H0i0Za2IVWNZKedcFtnul/HJwZjWTHFd3+JxkeeJprj0rLj3S1ZaiJBpLaRcE0dhrISiu64OSURovVWJyCNJJiqqpq0aXEBnmO1J3WsiWAIVBXJdnKqVCeD5mXixbF0QVKavAfJasE2bybtr7yF4d5bT9sxnW3oslzkXH0J2VddjBqJoKpacnCgVsVEMIR/2660fHsEQUBfWoRn9YZk8BI8UE3DI09rvme9PC560BfkYZ48jnBtA3W/epTC//O5kzYw9a7fgnnSuGRLom/jh4QOH0vOEaZLoitI+FgdqqJiLC08KSPYswFr1RQs0yYROngE3+btydd7qv5pzXmoKoI8vEGeIIlkXbwM14Xn07X3IK1/fQ0URatmzJhKpLEJ05iSZHLNtXwx9b9+HGNl2YBGz6Cdy9HGZgpv/2xmxyOK6HOy0ef0lX4GKPnq7eTdcFV3G1qLVq1paiHa3IqhqABBkij68ufxrt9C7U9+S6i6BnPHOKwzp1LwheuTnULpMCwmlZn8+4msX7+eN954g87OTh555BF27dpFIBBg0aLRkvdQiEYDjiXzGenbiS7bRfn/u4/g/sN4Vm+ga+deUFV0eTnk3nQl1umTkzdiQZKwL5qjKWUM4kosmowIOplEVzDZq64qCgmPD+OSodU6Yu2dOEfbIoYN14oltDz/Slru06qqEqlroOCLN474ceVceznIEsHdB9HluCi878t92ox0WU7K/983COzch2flOi1LlUggOWw4lyzAcd7cPg/d3uaavbHNmsaYf/tGUuUsdPgocbeXrp37CO49SMGXbkpu2/DI0wDJKo2xtCjVo8igR4kN3r2rRKMpkpijHMc8oZLyf/+Wlh1fvUH7riQJJRLBPGEsBV+8AfOUvnMx5nHlRJtaMFWOGWDPx4m1tKEvyMO+aDZtr76ltfAY01PO7CF8pBb7ojmnVT3Os24TxjEDX6uy045o0BM/UnPajulsRRAEhDRmhOKdHiSTMW3TR122i3C19v2H6xpp+P1TWGdNG1TJUZAkTBVlyA4bTU88R8lXb0+R0U0XrQrgA7Tnak9rWrokgiHa/vo6/q07kZ02EERibg+WqRPI/8w152QQI4hiUlBDjceJ+wNp+w6piQRxjxddwciotQmSiHX6JARRpPHxP6PLyUaJRIi1dWKeePwYjeWl2JfMx7fhQ+wLZ/d7L0t0BfF9sI3sKy7SgpBhRDKb+kg/99ATGwii9llyb7wSyWwm7zNXn9R7nXaTyt4888wzPP3009x44428/fbbABiNRv7rv/5rNHg5wxBEMakLPxTOZYuo/dn/aBLIA2TmBUHAPHEs4Zr6ZPAS7/Sgy8sZUhEq7vWjhCNYq04uKzVKXyzTJiHbrQT3HtQWhAMsilRVpWvXfnQup2Z8NcKIOpm8666A6wbXfRckCdusadhmTTul95MddhyL5+JYPDf5WqzT3W2o2ZmU1FRVFc/KdSQCwePHoNNhrCjFNLYc5/LF2GZMxf3uWtRpyoDZ3GhjC7pLl/X7b6OAbLeRffmFZF26nITfjxKLI1n6evv0xnnBeTQ/9ReMFWVDBuLho3VkXbYcyWzGPncmoSM1GfkeqImE1qd9mmXb451ejBWDzwNJFjMJf9dpOqJR+qV7wdb63CuYJlSm7U2my3JhmTKBlv99iTEP3Jdx22rOFSuo/e/fa8/KUEi7L52gqDUQSjhC/YOPogrgvPC8ZLCmxOKEDh6h9ucPU3b/vRkdz9mGc9kiGh5+EtP4/lvlTyRS14ixomzYg4ETsUydQP7N1+FZtQ5Blim+57Y+1Yrc6y5H1Otw/3OtNlRfXIBo0KOEIkTqG4m1dZLz6UtxXnDeiB7rifT+HmWng8LbMqv6nEjawUs8HudTn/oUS5Ys4Qc/+MEpvWkPTz31FE8++SQlJSU89thjAFRWVnL06NFh2f8oHw+GwnzME8cS2LEH66xpA178prHldO3ajxKJgqoSbWnHeeHQF1To0BGcyxaNeh4MI4IkUnzPbdQ9+BiBD3dinjSuT3tU3B8gtP8wCCLF37jjtDrzfpz0mGumoKiU3f/V4xWa6mNEG1sIHTxC6OARTJVlWKdPwlhWog1RRmPo83MxFOah6+6lDx48gmg0Io89N9UVhxNBEtPO9ponjUOX7aJr134s0ycNeP8JHjqKqqrY5mgqe1mXLafmp79Dl52V1tC0qqr4t+7AOnOq1qd9GpFsFpRgCLIHbrFVwhGEk5Q1HaUvcpZDG5SORgdtyekh3ulGX5RHpLGZSGMLrovPz+j99CWFBA8eIXy0Nq0qYsrP5udS/sB9hA4fQzQakl5q6eBesxElGsO2YFbKtSPqZCxTJxDYsZeO19+Fpeduu7+xsgzZ5SB8pHbIypcSjRI6fIzC2z5zWo7NPm8G9nkDK4MKgkDOVZfgXLoI7/rN+LfvQQlFkCwm7Avn4Fg8N5kwPptJe/UnyzKKohAOh4feOE26urooLNQciHsukng8Pjqwfw5QeNtnqXvwD3R9tBvLzKn93jhFowHLjCl07diLiop93sx+hyR7EzxQjRpP4Lro3PfHON3Idhtl37mbzjdX4l23GcliRrRaNFOuQBdKMITj/AVkf+rCjFtrzjUEScQ2e3qKcmCiK6gZEB46inWGJn9cdMfNHLrv34k2NBHafxjjuDFYq6YSqW1AVRRK7/syLaHR7PhwIogixXd/SVNl3PQRpvEVyFnO488Yr4/Q4WMooTCl9305mVnW5+VQfOctNDz8JOYpEzCUFA4Y+CjRGIFtu5BdDvK/cMNp+2w9OBbNpf31fw6oApXoChL3+TGOKz+9B3YOI5nN2GZOI3ysfsh2IlVVCdc1UnT7zXjWbMRQWphxskcQBAylRbhXbcg4eAHtfp6psqmqqnhWb8AybeCg3zShAu/7mzAunJnxMX1cRBqaiba0oSYSyDYrpnHlgyY/BUGg6MtfoPbnD4MgYKwo7ff7UMIRfB9sw75oTlJM40xBdtjIvnwF2Zev+LgPZUTIKHV9yy238NBDDzF//nxmzZqFvlf2IV2Fsd7MmzePRx99lLvuuiv52tNPP82CBafmUDrKx49oNFB631do+uNzuP/5PsbSIgwVZcnBN1VVibW0E+/uy5WMRiSrdUDTNSUcIbj/MIlgiNL77kiqpkSbW4k0tiS9aAwlhWmX5kfpi2Qyknvd5WRfdTFdu/ZrevKAnOXK2Jfok4ZkMWOdPjllyFayWhhz/z10vLWKwEe7QdGELrIuW45tzgxt4Vw/GrwMN5LVQum/3oXn/Q/wrFqPEo0hGg2o0RiqquBcthjX8kV9qovmCZWUfvMrND7+Z0KHj2IsLcZQWoSg14GqEvf6iRyrJdrchm3BbPI/c/XHUgG2zp5GxxvvEtx/uI+ykBKN4t+6g6xLlhHSjyYChxPXRedT96s/YCjKH1S4I3T4GLLVimlCJa3Pv4pxXObBB4C+MI+u7XtO9nAzRo3FSHh9g3pgSSaTJpPvGz5lrZFAVRT8H+7E/e5aoq3tyE4HgiighMIo0RjO5YtwLV884O9Rn5dD2bfvpuHhJ4nU1GMoLUJfkKupfgVDhGsaiLa0kX3ZcrLO0QDhTCaju+5PfvITBEHge9/7XsrrmSqM9fBv//Zv3Hnnnbz44ot0dXVx6aWXYrVaeeSRRzLe1yhnHqLRQPHdXyJS34R7zUa8qzeAKCFIIkokii43i+yLl2GbO4PArv10vP5Puvbsx1BShOywIYhi0jk23uHGNm8mudd+CtFowL9tF+731hFpaNJutJKmMBTvdGMcU4rrwvOwTJ884mpY5yqiTndSfkSJYAglHNYMvtKQ5fwkYBpbTsk9t33ch/GJQzToybp4Ka4VS4g2tpAIhjR56uKCQaVfjWXFVPzw24QOHsG9ej3u99ahdgsvyC4HzvMXauaWjtOnLnYiok5HyTfuoOF3f9R8FYryEQ0G4h4f0cZmHOcvIOuyC2joZWEwyqljLCsm9/oraHvpH1hnTEGXn5NqUhmLETp0lFhrO2XfvhtBELT2vZPsJhF1um5PtNODIEmo6uDyyKqqjrg58amiJhI0P/UXgvurMU2oxFI1OaXyFff6COzYg2/DVkq+cceACU99fg7lP/gmwYNH8Kxaj3/bLtRYHMlmwb5gtnYfGEI2e5SRYVjUxjJVGOshLy+Pl156iV27dtHQ0EBhYSFVVVWIGZZXjx49yv3334/H48HpdPKzn/2M8vLylG1eeuklnnzySURRRFEUbrzxRm655ZaTOu5RMsNQUkjB568j76arUbq6tKFbkxHRYk7e+O3zZmCbW0Wo+hjedZuJtXWQCIUR9XqsVVNwLJmPPieLRDBI3YOPEutwYywvxTVl6Qnuswki9U20PP8K+tUbKPrKF9OTORzlpFFicQLbd+N+dy2RhiZNBjsWw1CYj3PFEmxzqpKD7iOBGo8TOlKjDSerKpLVgnFs+WiVaARREwkQxZPyvzndCKKIoaQws5/pFhQxTxwLnJmfV5flZMy/fYPgvkP4P9xJIhjCPGnsgB4lZwuqMrDAxZmA8/wFyHYbbX/7B117DqDLzUKQZJRwmGhLO5ZpEyj8l6+i6zY5FPQ6Tbr7JFDjCa3qd5oQJAnL5PGEaxsGNFuM9VQxPsbgfShann+F0NFaTUq8nyBMdtixza4iVF1D/UOPUfbdrw4YhAiiiGXSOCxpeKeMcvr4WNXG/vM//5PLL7+c2bNnU1VVBcC2bdt48803+f73v5/2fn7wgx9w8803c8011/Dqq6/ywAMP8PTTT6dsc+mll3LdddchCAKBQICrrrqK+fPnM2nSmdWneC4j6mTEQYZuBUHAWFZMbNI43A3NmrqTyYB33Wbc767FPHEskfomZJcDx/kL+l1ICJKEcUwJhtIiAjv20PCbJyj55pdHdPF8JhL3+ol7fQiiiJzlGLEqSNzro/43T6BEohjLS7HMmIIgCKiqSrSplY433qPzzVWUfP32fh2eT+m9PV486zbjWb0RQafTfH9UzdleiURxLluIY8n8YX/fTyKqqhI+Wot71QYC2/dolQhBwFhWhOvC87HOnn5OB4tnqkmfIIpYpk5MMek821ATCl179uNeuY7Q4RrUWAzRoMcyfTLOCxZjGlt+RgWNANYZU7BUTU4KdqjRGJLNim3W1D7CEobiAuId7mQwkwmxjs6MvC+Gg6xLltHwh2fQ5+f2UfVTojG69hwk9/rL8Z9hv5MeIg3N+LfsxHnheUNet6axY4h7fbhXriP3mstO0xGOMhxk/LTxeDy8/fbb1NbWcuedd7J//34qKirIycl8zuD111/nO9/5Tspr06ZN45577kk7eOno6GDv3r386U9/AuDKK6/kRz/6EZ2dnWRlHTezs1qPR9XhcJhYLHZG3BBVVe12lT9zM02ni+CBahof/V8kixlDeUmKUpkSi+P+5/vE2tqRu7owja8Y3ARTFLHOnIZ/y3baXvoH+Z+95nR9jI8NVVHo2tW9CDhSi2Qxgaq1clmrhn8hkAiGqHvwMWSbFeucqpT9CoKAoSgfQ1E+wQPV1P3qUcbcf2/aBo9DEdi1n6YnnkWfn4NtbhWyw57y73FfgK69h3CvXE/BLTeeVAvcKBqJUJimx/6XcG0jhtIiXCvOQ9DrQVGINrfR/sa7tL30BsV334qxfHD53jMdJRbHv/kj4v4AlqkTh9Xwd5S+RFvbafjtH1HiCS0QvmQpgiyjRqKE6xpp/MMz6PNyKLrzVmTb8Nw7hgtBEDCPq8A8bnAJYueyRTQ98Ryi2URw30FA0AQh0lCoi9Q2kHfTyflgnCzmSePIufJi2l97B8OYEq3NUhCJNLUQqanHvmgu9gWz8Tc0nNbjShf36g0YSwvTTqaYxmkCBNmXXzSsCRglGiW49xBxnx9BljGUFCZNQ4eb4IFqAjv2IpoMSFYrvo1bibV3osTiiCYDlikTcC1fjKGXoeXZTka/qUOHDnHrrbfidrsBuPPOO7njjju45ppr+OEPf5jxm/dkaHuTSCRQFCXtfTQ1NZGfn4/UHWFLkkReXh5NTU0pwQvAe++9x69+9Stqa2v51re+xcSJfbNVPp8Pn8+X8lpzc3Pax5MOSjiMd/N2PKvWE2tpR1UUJKsF+8LZOJcu/EQOnHft3k/j488ObOKVSJDw+bFUTSbe6cX91ipcly4fVPJPEAQs0yfjXbORnKsvGTTYOR2M5LmlhCM0Pv5nIvVNGMtLybp0WTLrpERjhGvqaXz4KWwLZpF341XDEiy7V65DEAXMU8YPup154lj8XSE6315N7vUD+7WoqooaiYIkDfoQ6QlcbPNmDFhVke1WrDOmECsrpvnpvwKc0wHMSJ1bSjRK/a8fR1UULZPZ+8EnSRiKCzAUFxBpaKbuoccp/eaXR+wBPdKoikLD7/5IrNONZLXQ+dYqiu+6Ndk69kllpM6taHsHtf/9MMby0j5qWoLRgHl8BaZx5QR3H6Dul49Q9p27P/Z7+MlgGl+BmkjgWbU+qQ7nff8DXBefP2hVOOb2osYTWPpxMh9pXCuWYBpfgWfV+m6hERVjZRlFX/li2saNQzES55Wqqvg3bcOxPH2fQNluRTQaCB04PCyKYXF/F51vr8K7bjOyzYpoMmoCRe2d6HOzyLrswiGfRYmuIL7NHxGpa0RVVfT5uTgWzu5XLj6wYy9Nf3oeQRIJ1zYi6vU4LliMefJ4EEXUaJRIfRP1v3kCOctJwZduwlhy9idlMgpefvrTn+Lz+SgtLaWurg6bzca8efPYuHHjSb353Llzeeihh/j2t7+dnEX57W9/y9y5c4f+4ZNgxYoVrFixgsbGRu655x6WLl1KZWXqhfjUU0/xu9/9bkTeHyCwax9NTzyH7HJgHFOKbU4VCAKJriDhI7XUrN2EffFc8m646hMzbB5taafx8WexzZ2BbgDfglD1MSS7FVGnQ5+fQxTwrFxP1hUrBl2ISyYjcpYL3+aPcC1fPEKfID1G6txSEwqNj/2ZuNurtdOd8H2Ieh3m8RUYx5Tg27QNQRDJu+mqU3vPeBzP6o3Y5laltb15QgXedVvIvvqSPi180eZW3Gs+wLdhC2o8gaoo6PNzcV10Pva5M1JkmeMeL01/fG7QwKU3OpcD+4KZND/9oqYWcxbPAgzGSJ1bHf94DyUSwTZvZv8qgNFo0mjWMmU8jX94hooffeesrCRH6hqJNDQng7Sw3Ubn26uHDF5iHW48azfRtfsASiiEIMvo8rJxLl2IZcrEs/4+PlLnVvMTz2MoLRpUBlgQBMzTJhL4aA9tf32dgltuHPbjGGl6nNvD9U1IVjOiXo8SDBFtah3wHqaEIwS27iD3xis/tvPHWFZMwa03jdj+R+K8UqMx1HgcyZRZkCuaTcR9gVN+/1iHm7pf/QHJYsZx3vwUw22tlbqFlj//jfCxenKuvazPPVWJxWl76XV8Gz5El5uF7HKAIOBvaKbjH+9hrZpC/s3XpgTx7lXrUFVI+LqwVE0mUlOPZNAff27qdZgnjcM0cSyRmnrqfvkHiu/50pAVwzOdjIKXnTt3ctFFF5GXl8czzzwDaBLJW7ZsOak3//73v89XvvIVlixZQlFREU1NTeTm5makNlZYWEhLSwuJRAJJkkgkErS2tib9Y/qjqKiI6dOns3r16j7By6233sq1116b8lpzczOf//znM/tw/RDYsZemPz6PbX7fhZdssyJPn4Rp0lj8mz6iORSh4NYbz5kS32C4V67FUFI4YOACmhO2Luf4v+vysgkdPka0oXlAr4MejGVF+D7Y9rEHLyN1bvm37iDa2Ix9yfxBF42iXod9wWw8qzdgXzj7lDLkocPHEPW6Pu1aAyFZLYgWE8H9h1OkhN2rNtD+ypsYSopwLF2IZDZpWarWdtzvrqXjjfco/frtyb5vz7rN6POyM5pjkZ0ODEX5eNZ8QN4NA1d+zmZG4txSYjG872/CvmjOgPchz8r1KLEYSjiKfW4VaiJBcN9hLFPTd6k/Y8hQeCbW4abluVcIHaxGX1yAsawIwWBATSRIeHy0PPsKKAmyr7oY55KzV/5/JM6tcG0DkeZWXBcvHXJbQRCwTB2PZ+UGcq+/4qw02LNMmYB/2y6Ch45hLC9BCUcQDf17ZcW9PvxbduC6cAmORSOTyD0TGJHnoSiiKuqAlgsDoqinPNemxGLU/+YJdHk5/VantFbqAnQ5WXjXbUHOcqSsSdR4nMaHnyTW6cF54eI+54cSixPcc4C6XzxC6b/emQxgIvVNxDvdWKZpnUSqokI/6wBBEDCWlyIajTQ+/BRl//ce9Pm5p/SZP04yCl5MJhPBYDDltQMHDuB0DqwJPhgFBQW8/PLL7Nixg+bm5pNSG8vOzmby5Mm8/vrrXHPNNbz++utMnjy5T8tYdXU1Y8dqGbTOzk42bdrEJZdc0md/drsduz29BVkmJLqCNP3peWzzZw6qoS7qdNgXzsazdjP+rTsHdVI9F1DCYXybPsJx/uAPdyUcTlFdEQQBXZaT4IHqIYMX0Wwm4T/1rMqpMlLnlvu9tRjHlqeV7Rb1OgylRXhWbzilLGbcH0DMUMVNNBlJ9MpueTd+SMdr7+BYsiAlQyUIAvr8XPT5uYSO1lL34KOM+d7XkCzmjKo9vTFWjMG7fjM5V1+cljv22cZInFuB7XsQrRZEi5lwTT3xTg9yTlbSvFFVVWId2kMz1tZBtL0TQ2kx7tXrz8rgxVBWjKG4EN/GrUhWC9GmVorvurXfbSMNzdQ/9Bj6onxcFy/tIxurczkwVpQSc3tpf+Vtos3t5F5/+VmZjBqJc8uzdhOGkqK0vw/RYECXm6VV0C84b1iP5XRgmz8L35btBPcdIrhrP6LZhKCTUaIxBJ2MGo8Ta+0gXFOP0hUk57pPndUBbzqMxHkl6mRkl5O42zvoOqs3qqoS9/rQ5Z1aVT6wbTeqogzZVifq9djmTKfj9XdxLpmfvHd0vr2aaHvngMkiUSdjmTGFwPY9tL7wKoW3fZa410/CH0QQRSJ1TSiRCPr8HK1iMwD6glxiHi8d/3iPwts+e0qf+eMko+Blzpw5vP322xw8eBCAG264gT179nDFFSefzRRFkVmzZp30zwP8+7//O/fffz8PP/wwdrudn/3sZwDccccdfO1rX2P69Om88MILrF+/HlmWUVWVL3zhCyxZcvpc2r0bP0SX7UrrghJkGfOECtzvvn/OBy+BHXuRnY6T6mWWXQ6Cew+R6Aqeldm44SDS1Eq0rQPLzKlp/4ypogzPqvXkfe7TJ63CJsiyluHJBFVN3qiVWIy2F1/DNn9mSuDS37EmfH4631mDtWpKRtWe3khWM5LVQvBAdUrlZ5SBiTS2IDtsBHcfIHiwGsluI1R9DCUwCfPkcQiCgGl8BcH91SAIWGdNR9DrTqup3nAiiCLF996Gf9M24oEuLJ+7tt/qZNzjpf7Xj2McVz5k9VLXrYzY0/+edemykTr8s4pIbQP64oJBt4l7fUQbW9Dl56DLciG7nETqzk7fGlEnU/K124nUN6EmEsQ6PHhWrcO95yBqNIqg12EoKiDnqouxzpr2iVPHHE6cyxbi/3Bn2sFLrKVdk9c/RbER93vvYywvSWtb2WFHNOgJ7NyHbfZ01Hgc98r12Ob3357bg1aFnIDnvXXEbwjgXb8Z45hijOPKibW0Ixr06LuTS4Nhqizr3kfXGSeEkS4ZBS/f+c532L17N3V1dQDs3r2b0tJSvvnNb57Um0ejUV5++WX27dvXp6Lz85//PO39jB07lhdffLHP64899ljyzycaa55uPKvWawNUaaIvzKdrz0Ei9U0ZexScTcQ6PYhpBC6i0YgajUGvrLkgiohGA0ooPGjwogSDSOeokVS8041st2aU0RWNBpAlEv4A4knKCBtKCol3ugc1M+uNqijE2t3Jcznw0W5EqyWtQMQ0rgLvus0YSgoRjSfv2SMaDWdEBe6sIR5HEAS6Dh3BUFaMZDKSsFsJHjqCebLmeWCbNxPz5PGIBj2iXk+iK4gSj3/MB37yiDoZx5L5g27T8eZK5GxX2m2Xol6rpne88U/si+eetYuFTFHjcQJVTH/XAAAgAElEQVQ792nVhGgM2WHDNmsa+vxczeRwkFmORChE59trkGwWArv2kXXpBZq5cezsPbcEUUyeM6aKMuzdFeQz3dfmbMNx3jw631xJ3Osf0khWVRSCh46Qc/mKU6qKKuEwkYYWLDOnpf0z+oJcunbvxzZ7Ol17DyIaDcj2ob1zRL0eXV4O/i3b8azeiHXWVG3sIIM1Ts8+fB98SFYarZtnIhkFL4WFhbz22musXr2ahoYGiouLWbZsGaYMh6N6uP/++9m/fz8XXHDBSUktny0osTixTg9ympkA0G50uiwnkabWczp4UeNxBHHom4axolQbDD5RalcQUBODq9OF6xpxnr/wVA7z3EPt/u8k0edmY6woJVLXmFbGKtrYgr4wD0N3tjWwYy/6gvT6bSWLGcliJtrcDt2nSiIYIuEPINmsmVXtTuEzn6skgiFC1TUo4TCi0YCpcoz2nTtsWm++XocaiYDJmPx7D4IgpDw0E13Bc9pxOtnmujSzlh7JbOpeLGwl6+Jzu/qiKgqd76zB/e5aRKMeOculKSEdraXzzVUYy4oRRBGlKwQDJE8S/i4EWcJQUkj4aB1xr49EMITxHBTcGA1chhfZZiXv5utoff4V7AtmDxjAqIkE/q07MRQXYD/F2SIlHEHQyRkFQIJejxIKAxDr8GRkIyBZLUSaWkkEQ/0qkKWD7HISaWg6qZ89E8hY1NpoNHLZZcNj5rN27Vree++9EZkDOJPoyTJlHNkLImri7M00gRa4CbI04GeXzGbUNLJpprHldO3arzkOy8cz/Wo8PqgDsRKOEO9wY19waq2JHzc9SiU9D3V9fi6S1YLscpDwd2U0oKhEopoiyylmgLMuWU7jY39Gl5+LNMj8ixKJENx/OGXGRgmF06q49SDodAiyhBIKE6lvwrt+i1Z1C0dwLJmfDIoGQw1Hhs1n5lwg2tJG5ztr8G/dgeywIeh0qLE4cY8P6+xp2OfNpKO5DeucKrxrNxNt7UBNKLguHHjmIFLbgH3hnNP4KU4vvq07kV2OjNWMQEvAuFduwLXi/HN2waoqCk2PP0v4WB22uTP6LBzVaZMI19YT2neQaId7wHlFXbYLyWwmuPeQFvjl5xI6eITc6y4/HR9jlLMcx4JZgPr/2XvPADmqM1//qarOcbp7ch7NKKGAslAClBACDOICxphgksGZte+yxtf3Oqz3by+79i62cVjjBDZOmGCbjCSUUAJJIKE8mtHkPJ1zV9X/Q4lBw6RuxRmpn0+angqnRqdPnfec9/396PzD8xgKcjFVlfdN8pVEglh9E/GmViyTqim8+9bTVnQTTSbUZDKjXTQlHkfv0ha0M77/B/6AulMXGdDep/FTPv98k1Hw8r3vfW/Qzw0GA5WVlaxataqfGeRIFBUVkUgkMmnCmEQ0GjTn70Qio2JhJRZDso7NyZYSi9H6i6cJHzyKaDJSdPet2C69ZMBxlkk1dP/jjRG/9KLJiLG8hHirpi4mCAJyJAow9MqKqhLedxDHZbPGpD8AaLUhge278a7drKV5Wc0gK8ihMLYZU8lZtgidO4dEeyfGooJBr6EqCvGGFlRFwVRZSqy+Efvs6adduG69ZAKeq5fS+9oGbLOnDaoAlvT6Ce3eR86VC/r9/4tWc0YDpxJPYK6uxLt+C/4tOzFVlSFZLcihMIFtu8gbQUVMjkRJBUMXvWfHB0QOH6Pl509hLC0i58qF/eSolXiCWH0jrU88jc7jQglHyV2zCjkURrLZ+u28nIwcjZHs6sW54MINXmJ1jRkp3Z2M3u1CDoVGTHMdy3T//XViDc04Fs4ZNJ1UkETMVeVIZhM9/3hDyywYxKxRkCRcV12OEokims0k2jvR5TjGvAlqlnOHc/4srJNq8L/1Nr4N25DDERAFUFXss6eTe8M9mCrLzoiIhmgyYqosS0v99AMSbV19amPGkkKSvd60FyGTvT7sc2cQ2L4rc2W1E6jJ1JhOp88oeHnyySeH/SP9+Mc/5ne/+x1lZekNMGvWrOFzn/scd911Fx5P/+3gBQvSNxka7QiiiH32dGL1TWlPnuRQGCUcxTJpbE62up5/hZQ/gOe6FZo3x2/+RNW3Hx4QaBhLCjEUF6T1pbfPvRTv65u0FKTiAlI9XiwTqgcNelRVJfTufgSDYVhjxNFMKhim5fFfIUdimKsr0ed7+r5/SiJB7HgzzY89gXXqJGK1DRgK8wf9fobe3U/seDOCKGi7N5EopWtWn5E2ulddieS00/38KyCKGArzNB+DRJJkRxdqMkXumlUDlHPss6fT9cyLmKuH9njo+zsEQqiJBObxVTgvv4zOp5/rUzoTzSaURGLEATxW34hjwZx+k/SLlXhrOy0/e3JIQ1jRaMAyqQZ9nofA1nf6Jo76XPcgV9NQEkmCO/bgXnXFBb27pcRiCKfhwi3q9Six+AUZvCixGL4338J5xWUj1sEZCvMxVpXj37Sd3BtWDfq9FEQRyWYlFQgS3neI4s/cOSbV2rKcWVRVJXrsOP4tO0m0d4Gqos/zkLNkPuYJ4/r1EZ3Tgeea5bhXL0ONJ1BlGdFkOiveOa7lS+j660tpFcwne30gy1inaKaYpnEVSBYLyc4eDAXDl1DIoTBKKIJ93gx6X32TVI932LF5yDb09GKdPnbFazIaha+55hpeffVVJkyYQHV1NXV1dRw+fJhly5ZRV1dHXV0djz32GD/4wQ/Sut7vf/97AP7rv/6r3+eCILBu3bpMmjbqyVm6kJbHf4O5piqtL0702HEci+eOWdWReHObtkMiiujdLiSLmWR376C7JO4VS+j8098wFBcM+9IT9XpcK5bg27CVyOFjKNEYjo/knquyQryljVh9E4aCXIofvHNMSuMqyRQtj/8aRGlQ6UTRYMAyYRzG4kL8b+1EslgI7d6Hbda0Accmu3ow5HtAEIjW1pN7/SrMVWduBdN52Wwcc2cSPnCY0N6DKJEoeocd1xULsE6bNOj/qW3aZDqefo5kd++IA2/0aB3Oy+cj6nW4Lr+Mrmf+QbS2AUNBLsleb99O3FCkAiHizW0U3nHTaT/rhUD3P97AVFU+aOByMnqPC8uUCSS7vQR37cVUUYqpqlzbST6BqigkWjuIHD6Gfe4M3NcsP9vNP6+IJiOpQPCUz1eSSSLHjhP+22vIfs1dXOd04Jg3A8slE8Z0Opl/57vo3Dlpp9Q5L5tNz0tr8W3chuWSCRhLCvs9v5pKEWtoJnr0OPm334g1A8GbLBcm8dZ22n75B1KBIMayEgzFWraB7AvQ+qs/IJqMFN/3SUwV/VW/BEFAOMsLV7bpk+l9dT2R/UewTJkw5DtJjsYI7dpL3sc/NCIXBAH3tcvp+vPf0eXM7zfGnowqywT3vI9rxWIkgwHX8sX4t+zMOHiRo1FSvT6c82Zk9pCjiIyCF51Ox8KFC/nlL3/Z99n999+P1WrlpZde4u6772b37t1pX2/9+vWZ3H5MY6oswzx+HKHde7HNnj7sSyrW2EKq1497eWZSzomOLrpfXEv4vf2oqvZl8ly3Ysh0orOJqaqc8L5DmuZ6rw85GhtSR902YwrB3fsI7HgXx/wZwwcwRgOOBXPwrduMvrSIwJa3NUlESYITEpSm8hLyP3E9tmmXjFl36+Cu95BDYRyL5g47MZdsFuzzZmp1C7ku/Jt3YKoq7zcR0NS6dqAkkthnTyf/1hvOeHsFScQ2bXLaMsSCJFF4+020/fbPWlFlzsC6N1VViR4+hhJP4F6hKaLoPS5KH7qf1p/8lpQ/gLGkCNswq0epQIjA9l3k33oDksNG4G3t7wqgc9iwXDJh2HqdC42UP0Bk/2FyVixJ63hTRSnRw3UUPXiHpm6z/i10nhxEgwFVVkh292Aoyqfgkzdimzn1gl8ZN5aVENuyI+PzVFUltHsfibZOup57BVNJIdKJhRw5HKH96ecAcK+8nJwrF47JICZ68OiIAfHJSDYLhsJ8XMsWEXrvAN4DR9DnujUZ9mSKRFc31kk1lP7T/Zirys9iy7OMBWLNrTT/1y8wj6/CNufS/mNNngdTTSWJ5jaa/vsXlH7pPszjRt7VP5MIOh0lX7yP5v9+guDb72IePw79SX4rqiwTa2wherQez+plOD9SG+icN5Nkeye+jduxTJnQL5NCVVVS3b2EDxzBMrEG9+plADjmz6T7hVeRQ5FhLQc+SvRoPY75s05LvfN8k1Hw8sYbbzB9en+DOFmWef3113n00UeZN28ee/bsyagB3d3d7N27F69Xy/f7gJtvvjmj64wGEp3d+Le+rW1lAob8XBwL5mAs0jph0b230vqzpwhsfQfLpBp0Hle/L6AciRKrPU6yp5fSL92fkYpEvK2Tpu//TMthX7oIBIFYQzON//kzyr/y4DlXLMu9/ipSvgDeNzahc9goefDOIVWIBFGk8FMfp/3Jv+DfshPLxGr0BXkDJkJqKkWssZXo0Tryb7sR19KFxNs6SLR2aCpIZpOWhjaGXWNBG6i8azdjqq5IazKodzmRzCZylsxHkCS867bg3X8YyWpBVVXkcATHgjk4F83FPnv6qJlg2mZMofDOm2l/6hkM+bkYK8u0HFxVIdHWSayhBclspOwrD/ZLs3HOn4Wo09H+u78i6nUosQSSrf9QJkeixOoaibe04VpxOdGj9XT+8QV0bmefc7ESi5N66q/Y583AtWzReQnyzzXh/UfQ53nS3tEVJAl9YS7Jzh6K7r4V+ebrCB+sRYlGEfR6TGXFF7Qa4kdxzJ9B9/OvaONNmiu5qizj3/I2sYZmrFMn4pg/a8Ax5ppKkr1eet/YRLSugaK7bx1gfDnaUeJxREtm6XCCXsJUUYr7qiuIt3UQq2vUakNNJiwTx51yfVGWCws1laLlJ7/FPKkG0xDp5YIgaKnnOh0tP32Scf/21XOeJqyz2yj/l8/ifXMrvje3giQimU2oikKq14+5ppLiB+/EOqlm0PNzr1+FsbSYnpfXEXn/MDpXDgjaIpxo0OO+ZrlmbHlicUOyWMhdczU9L63DuXhu37ttOGLHm0j1+vA8eCegzTdQ1TG3YJLR6FhaWsrOnTu56qqrqKqqoqGhgYaGBsaP17Zz6+rqBtSuDMfatWt5+OGHqaiooLa2lpqaGo4ePcqsWbPGVPCS7PHS/ru/EqtvxFhSiHTCuyJ6rAHfxu2YKkoouOMmDPm5lHzhHnwbt+FdtwU1ldJ8LgRBm0j5/DgWzKbo07enbbD0Ad1/exVjeUk/d1fLhHEIokjX869Q+sV7z+gzj4RoMFB8/yfTP16vo+jeTxDYsQfvG5sIv38YQ2E+osmAqqgo4QiJ9k4sE6sp+cI9WGqqADAWFVxwk85Uj5dkVy+2WdPSPsdYUkRw115KPnMX9lnTSPZ4SQWCmpStxzVq5Wvts6djHl+Ff+s7+LfsJOXzI0gSpooyCj5xA9apkwbdPbPPno6xrBjfpu3433obyWbpW0VS4wlSgSCOBbMxTxiH941NGCtKyFm6cMDLTI7GiNc30fjoT8i/9YYLutgc6As6MkHU61FOiGNINusFb5w7HJLFgn3OdKLHjmOdMnHE41VVJbBtF0mvD9GgHzbHXO924Vw0l+COPbQ//RyFd90yahYa0kG0WlBimakXqfFEX/3ahTiWZzkzhPYe1Hxy0iiGNxblE29oJrhrL85Fcwc9JhUMk/L6MJYUnfHsDNFkwrN6Ge6rriRa14AcDGmy38WFaaV32WdNwzZzKvGmVhJtnaiqgj7XjXlcxaABhmvpIuRgCN/G7dhmTBmwKP4BSjJF9Gg9yY5Oiu6/ncDOd/Ft2k6q14eqKIhGI5ZLxuNaugjz+KpRP/ZkFLx885vf5LOf/SyNjY00NjYC4HA4+Na3vkUoFCKVSnHbbbelfb3HHnuM7373u6xevZq5c+fywgsv8Oyzz1JbW5vZU5xHEp3dNH3/ZxiKCnCtvHxAypNl8niidcdpfPQnlP3zZzEW5eNatpicKxcSPVpPoqMLVVbQOWxYp0w8pZUCVVYI7T2I++qlA35nqiqj9+X1GSudnQ8EUcS5YDaOy2YRO95MeN9BUsEQgiShczlxzLkUvefCX4mTwxEkiykz40mLmWRnd9/Peo9rzPytdA47nquX4hmk/w6HIT+X/JuvI/f6q4gcqkUOaulgktWCZVINgZ176H7hVZyL5w5ZRC6ZTVguGY+htIjOv/wdBAZs519ICHo9KMP7In0UVVaGlSO/2HBfs4zG7z2Ozp0z4mQ70d5JvK0TQRKxTKoZsR5EkCTs82bg27CN6NH6fotRox37zGl0Pfdy2uk6Sa8fEDCWXDw7d1lODe+bb2FM0xQWwFRZinfd5kGDl3hLO00/+DmIIqaKUkq+cM9ZmagLkohlfNWpnSsImMpL0jbCzb1+FfqCfHr+/hpqKoWxtBid24kgSiiJBInWduJt2uKv5ZIJtDz+a/T5uVoGUI4DRBE1niDW1ErrE08jWcwU3Xdb2vc/H2QUvMyZM4f169fz5ptv0tHRQUFBAUuXLsVu13J3f/jDH2Z089bWVlav7q96dOONN7Jo0SK++tWvZnSt84EqK7Q8/huMFaVDDthaBx6HaDDQ8uNfUfWvDyPodAiiqHWkMyLdqsJQUsMffCmVsePOJwgC5qqyM1pUPpYQdDpUWc7oHFWWL9oJpmgwYJveX4Y73tZJ13Mv41zUP3DR/HI6SXb3oiaSCHoJyWHHVFaM47JZdP7xb5hrKjHkXnhmeADmcRV0Pf9K2n4EqqqS7OrBPC5bc/ABhlwPJV+4h5Yf/xolGtPkVof4W4b3HUJNJDCNq8A6iFT8YAg6HabKMrxvvjWmghfbdE2EI+UPaBkFIxCrayBn2aIxW5eY5dwRb2ol54r0FWj1+bn4t+yk6Se/RRQFPKuX9clsh/YeQJ/vwTr9EnpfWY8cDKXlbD/acc6fiWPupUQOH8O3aTuxuibUZBLRYsY6ZRJF999B559fIHasgZzliwcsZgsmI5bxVZhrKok3tdL0X/9DyefuHrVjUEbBSyqV4vXXX+fgwYOEw2Hq6urYtm0bgiDw3e9+N+Obezweuru7yc3NpaSkhD179uByuVAyXBk8X4QPHEZJpdJaaTJVlBJvbiO09yD2IdKBwgeOkGjvxD57elqD/wcIkoR5fBXx5tYBKhuJlnaM5SVnPPcz0d5JoqtX+3KYjJjKSy5oidRziT7PjZJIajswaUqqJrt6sF4y4Sy3bOzg2/AWxtLivj6pprQt8/ChWlBBspkRJAlVUVAaWrQCy+pK9Lku/Bu3j1l57ZEwlhZhKMgj0dqRVq1KsqMbyWHDdI6LX0c75qpyyh7+LB1PPYP3jU2YykowlBQiGg1afrvXT7S2ntjxZhyXzdJU9zJY3TVWlOBbu5mUz3/KDtrnGkGnI/fGq+l+/lWci+cN+86JHm9CDkXIWTzvHLYwy1hFlRXIoCYjFQqT7PGCqqKmFJp++Esq/+8/ofe4MFdX0PvaBpToHvQe95j10hsMQRSxTh4/qDJf++/+SqKzB8dls/ottiixOIn2TpREEkEQ+mqHRZORlp8/RcVXvzCifPP5IKPg5Wtf+xovvvhiv88+8Fc4leDllltuYdeuXaxatYq7776bu+66C1EUueeeezK+1vnAu/6ttHIwP8BUXoJ33eZBg5fQvoO0/eZP6F05eNe/RdW3H85oRSr3hlW0PP4bzX29WHMbT7R1Et5/mJLPfirt6wyHkkwRfm8/veu2aN4PTjuCKKIkU8j+ALaZU8m5cmFWGeY0EQ0GnIvmEqtvwjo1jbz6VIp4Sxs6l5OjD30DnctB0T2fGBDIXiwosRiBHXtwnpDRlqOa/4QqyxiLCwYNCJV4gmR3Lymfn0RbJ+6PrSTZ3qXlA6dSiBYzpsqyMWt2ejKea5fT/ps/o/O4hlVaU+IJwu8fIv8Ta0Z9/vP5wFhUQPlXv0C8uQ3vxq2E9x5Ajmo+MHqPG+vUSYgm47BqeEMh6vXoXDnEGlqwjZHgBSBn8XxkfxDvui2YJ1YPqCmQwxGitcdJef2U/tOnswteWdJC57AhhyOIhvS+C4nmNgSDAcsJH7FUr49YfaMm/z6hmrIvP0CivfOElP+Fv/OX6Owm+M57uFYs6Qtckj1eIodriTe2ItksCDoJVQU1kURJJLHUVKLPddP72psU3nXLeX6CgWQUvKxbtw6DwcDq1aux2+2n/UJ74IEH+v69Zs0a5s2bRzQapbp6bBgzxuobybky/a1MQ1E+ofcODH6txhYM+blYp06i95U3UWKZuTBbaqoo+cxddP71RcJ7DwKgz3VT/OCdWIZQtsiElM9P849/jRJLYKosxTp1Yv/oPZEgVt9Ey49/jX3eDPJvuf6iGBTOFjlXLKDhrR9hKCnsJ7f4UVRVJbTvEJLDrm2tX7mARGc3zY//mup///qIZnEXIpFDx9A57EhmM0oyiW/dFgSDflg/GNFo0FabzCYih2o59s//iqjXIzlsWoCeSCIHgtjnzcR1xYIxrbBlmzYZ16or8L62USvwzHUP9Abq9RLas5+cKy67qAv008FYWkTh7QM9hPxbdhJvbD7l6wo6HUosdjpNOy94rl2BsbyU3tc34F27Sau9E0WUWBw5GMK5aC7uq67IKLsgy8WNY8EcwvsODPsuPJmUP4BkNqHE4lrqq9+P7qQaUFNF6UW1uOfbtF1bSNDpUFWVyP7DhA8cRe/JwTKpeoCyoRKLE+/oIuXzEzveRO6N1xCrbyDe0t6ntGgsLcZ6yYTzNs/LKHjJy8tj1qxZfO973zsrjSkuTn8XYzSgJlOZTQ4lCTUlD5pv7pgzA9+GrXjf2IR9zvRTcmC2TKqh4usPIQeCoKpITscZWTFNBUM0/ufP0Oe6NY+aQa4pGgxYJlZjGleuqeVE4xTe/fGM7q/EYsiRKKLBgGi1XNSrvYb8XE0++rd/wXrpJRgKB0pHK4kkkf2HQFExlRWBSl8KX2T/EeRQZFBT0AsdORxBOGHyFXrvAAgChqL8EfuTHI0Ra2wBtJW6vNvWIJ70PZWjMWL1jTR+/2e4V16O+5rlY7aPelYtRefKoedvr6G+fxh9YR6iQY+aSJLo7AZBIPfG1eQModaTZXBUVUWNx1HiSRAF1NOoNVQVJWNluNGCbdokbNMmEW/rJN7cippMItmsWCbWDGnAlyXLUOQsnov39Q3IE2Mj+nIpiQSyP4h71ZV4124GwHP9yowzQkL7DtH7+gYkq4W8m68ds3WQaiqF/623cS7UxvLI+4eIHKnDXFOJOESdrGgyYiotImW3En7/MEe/+HWMxYVIOQ5EnYSSkvFt3gGyTM7SReRcPh8pQ5n00yWj4OWuu+7ixz/+Ma+//jqTJ09GOmninm7gsW3btrSOW7Ag/R2N84VktSBHY2nL0CrRGKLFNGhxp6Egl3H/9ghyKIwuQ5nkkxEE4YyvaLX98g/oXM60dnBEvR7HZbPwb9mJb/MOXJdfNuzxaiqlGZSt20KsoRnBoEdNyegcNnKWLcJ52exTCuQuBOwzpyKajHQ8/TyRQ0e1wcNmQZUVUj29JNq7sM2cSsFtawjsfJeeF9ci2awkunuRnHYk+0WaknEioFCSKWLHGjCPrxwxyFBicSIHjiLZrdr3U1FI9Xgx5H34wpLMJqyXTMA8rgLfxu0oiSR5N64e5qqjG+e8mTjmaAWe4f2HUSJRRLMJ91VXjHm393NNyufHt+VtfBu3okRjCJIOORxGDoQ0QYjKMkR9+q9bVVWR/YEhjX3HCsaifIxF+ee7GWMWJZ4guHsfscaWvhVvU0UJ9lnTRr166JlEl+PEffVSvG9uxblwzpD1VEoiSWD7bpxLLiP/5mv7DJkz3R2It3XS9qs/YJ0ykVQwRPMPT4gtjcHFKjkUPlHnaSHR2U34YC3m8ZUjen0psTix+maQdMihMPb5Mwb0uaTXT3Dnu/i3vk3ZQ58+pwqnGQUv3/nOdxAEgYceeqjf54IgcODA4OlQH+XrX//6iMcIgsC6desyadp5wT5vBrH6RnRTJ6V1fPx4E/Y5Q6dgiEbDqFuVije3EWtowbUyPUdu0NIdrFMn4X19Yz9DpY+S7PXR/KNfoSaTmlHZlGUIoqi5yfZ4CWx9h96X1lH8mTuxTBgbqYRnGuvk8VR952GiR+oI7NijebfoJGwzp+NcOKdvZ8W5eB5yJErw7XfRuXIoe+j+i3byqXPYUCJR4g3NSBYzakom4e/RFPkkTV3s5BUnVVWJHK5DslmRbBaUcATJlUPk8LF+wcsHiCYjjoVz8G/egbm6YoDS2VhiuALPLCOjygpdz72Eb/MOjEX5/cRWFEWh57mXCb+3n9CefTjmzsCU5upvoq0TvceFqXRsZSNkOTOkfH56X9+Ef+vb6JwOdC4ngl5Hyusjsv8wnX/+u5Z+t/KKi2Z33b16GWoqhffNrVraV1VZ32RaSSSJNTQTb2jGPm8Gef9LW1Q61ZSmRFsHOpdTEzhRVXpffAM1nkA4x6aXZwIlnkTQaRsN4f1HEE0G4g3NKMkUgiQi2Wzo89z9ghklkSR8qBbJbEK0GJFDEWJ1jQMWsPUuJ/q5lxI5WkfTD35O+de+eM485TK28FXVgdvgg302FOvXr8/0lqOWnCWX0bB5B+aJ1SNGsWoqRayprW8lYKzg3bgNU1lRxhNhnceFmkgSOVI3qJtsKhii6Qc/R5/nGSAXLQgC+lw3+lw3iY5uWn76JKVfvA9z9cWpeCQIwoiy2oIonpJXysmoskzKF0By2NJ2YB+NmCfWoERjhPYdRD6hOiMaDSBoaTxqfRO6HAeGE8X7sj+IqshIdgtKIomqgqE4n9jR40PeQzQaME+spve1jWM6eMly6qiKQusvnybe2Ipr+ZIBKRiiKGK7dArhQ0cxFuYTeOc9lHgSy6ThF2JUVSV2vAnPtcvPZvMvClRVJdHagRyNYiwaXKxjtBFvbqP5R79C59PCG+YAACAASURBVM7BuXjeoG2WQxGiR+tpeGcvpQ/df1HsbgmCQO71q7BOm4zvza14123RHOUFLavFNm0yxZ+5C3PNyDvtI2GqKCXlCxA5fAw5HMFYVtKXijzWEM1GbQfvvf1ED9Ui2q2aj5xBD4q2UJxoaUfnycFUUYogScSONyEaDUh2K6lACMlmJXL4GOaJ1YP+bS3jxxGOJ+n4w3OUPHjXOXmujIKXQ4cOnfEGdHd3s3fvXrxeb78g6Oabbz7j9zrTGApycSyYTXDHHk1+Tjf4n1OVZQI738U+a9qYK/QN7tyDc4TUr8EQBAFDSSHBHbsHDV66nn0JyWEf0efGUJCLddok2n71B6r+7asX7W7C2UYLJv+HlD+AIEmUfum+UW1QNRyCTkJyOojUHkfvzkHK6S8uoioKcihC5GAtpuoKTQ7YqqmIKaEwhoJcRL0eNZXqU1McDGNJId4DR4i3dWSdwS9Cul94lXhjC44Fc4Zc4TVWlhE+cAQ5FMY8roLQ3v1IdivGksIhrxs9Wq+ZVc6efraaflEQa2yh/cm/kPL6Ec0mUsEQzoVzybvp2oxS+M4lie4emh57AvP4qmHHX8lmwTZjCrHjTTT/9y8of+Tz6N1jw5T4dDFXlWOuKic/GiPlCwAqOqf9jNZc6D0uyr78AL4NWzGWFuG5eumYTBkDEK0W5HCE8Lva2DMgFdVqRpUVZF9AeyeOK0cOBNEX5gGgxhLoSwtJdvUi+4OaqeUgmCdW41276ZzJu4/4DW5tbcVms+FwOGhtbR3yuFMptl+7di0PP/wwFRUV1NbWUlNTw9GjR5k1a9aYCF4A8m+5nvZYAv/mHZiqK/tJQ6qKQqKlnWjtccwTqij45Jrz3NrMUJIplEQScYQCuaGQbJYTg0t/5FCY0O595CxbnNZ1jMWFRI/WEzl4FOuUkaWDs2SO941NiAY97quuIFrXSNdfX6LsKw+MfOIoxHvCHwNJBEkY8NIRRFFLLTMZiNYe1ySUS4tQYnGUeAJ9ngfS2E0WRBFjUQHBd/Zi/NjKs/U4WUYhciiM982tuEYwWRT1OlzLF9P7+kaQFQxFBYTeOzBo8KIqCtHDx0h09VL+8GfG9O7n+SbZ66P5sScwT6zuE5lR4nGCu99HTaUovGOgOtxooON3z2IsK0574chUWYYcjdHx9PN4rluJb8NbhA8eRYnEEHQS+lwXzssX4Jw3A9F0au/x0YpkNo1YvH86mMpLRqVEcKYEtr2DIIkIen1f+thHESQRye1E9vqJHDqGYDadsMFIosoyencOsj+IEo8PeR9Rr8NYlI9vy9vkXrfibD1OHyMGL8uXL+dTn/oUjzzyCMuWLRs0+syk5uVkHnvsMb773e+yevVq5s6dywsvvMCzzz5LbW1txtc6XwiSSOGnbiH03n5867bgfWMjksMOAsiBEMaSQvI/cQO2GVPG3q6BqiAIAyd/6SMMmlIYePtd9HmejOp7jGUl+DZuywYvZwklmepTNhINelLhyHlu0amhxGL0vPgGziXzQVGINbUhGo2D9jXRYNAC7B4vaiJBqteHeXwVokGvqd5ZzCP2fdFiJuUfGKBnSZ9ofSO+N7eS6OhEn+shZ+lCLDVV57tZw+LftgtDvict81/JZsV99VL8m3eSbOtEicaIt7ZjOLFbp8RixOubiTW1YCwrpuKRL1w0dQxnC9/GbegLcvsFAaLRiH3OdHxvbCb3uhWjzvwz0dFN7HgTrpWXD/hdKhgifrwZORwBVUU0GTUZ/TwP+jwPPX9/ncjReszjynHMnamlOCkyKW8A35tv0f3sS7iWL8Fz3YqxNw/JcsqoikLvy+txzJ9F7+sbERl6ziUIApLLSfJYIwa7tuuiBMLo8z0f9pkR3ofG0mJCu/eNjuBFVdV+E9DTrXk5mdbWVlav7q/Wc+ONN7Jo0SK++tWvpn2d+vp6HnnkEXw+Hzk5OTz66KNUVlb2O+YnP/kJL7/8MpIkodPp+PKXv8ySJekXoQ+HIAjYZ0zFPmMqie4eUt1eAHTuHAz5o8+ZNF0Evb5Pnz+dl/RHUSJRdI6BL+FERxdShi9nvSeHyIGjGbchS3q4li6k8T9/RmqLHzkcofjBOwccoyoKKZ8f0WQ857KI6eLf+S46dw6S1YJtxlSSvT5SPT5NDMNuGaCWop6QL0/2eLFMqO6bNKZ6vFiqK9O7qaKc4ae4ePBu3Eb3C69iqirDWFpMyuun5Se/xbN6Ge6rrjjfzRsS38ZtI6a8noxkMeNedQXJXh+BrW/je3Mr0onCVtGg17yxbr1+zKUVn2/UVIrgrn0oiQT2WdP66kMih49hGKQORNTr0XlGp/mnb9M2jKXF/ewXEu2dhPcfIdndg+R0aOOXAKlQiOix44CAHIkiOe0YCvM+0id1GArzMBTmIUei+LfvItHZTdG9n8gGMKdJyh/Eu24zwd37ALBdegmu5UvQn4ZS7NkgcqgWVZYxFBdgrq4gcqgWncsxpMWHIAgIooAciaHKCqqiYCzKR1VVlHhCqzEaBtFkRI6cm4XPEYOXk+tcznTNi8fjobu7m9zcXEpKStizZw8ulwslw8nAN7/5TT75yU9yww038Le//Y1vfOMbPPXUU/2OmT59Ovfeey9ms5lDhw5xxx13sGXLFkxneCvVkOsZs3rgH0UQBOyzpxFraM7oRf0B8dYOCm4bKFCgysop7OYIqNlJ4lnDkJ9L1bf/N/HmNvR5uf3MwFRVxbdpO70vrUNJJFBTKSwTa8i/9fpRF5wHt+/CWKalsOoLcpGsFnRlTpRonER7Fwj0DdxqStZOkkT0eZ6+wEWVZVL+IM4rRpZrl6MxjGO0Nuh8k/L56X72ZZyXz++bdOpz3RhKi+h5cS22GVNGXf8C7fuQ7PGic2U+UdG7c7DNvhTZF6Dki/cCjNr6i9GOqqq0/M/vSLR2IBj0eF/fSMXXH0I0GRFNJpR4YvDzEslTWow724T3HcJ8Un1o5OBRQvsOYijIwzJ5/ICAQ85xEt53SBMikWXizW3YZ04d9NqSxYxz4RwCW9+h67mXyb/5urP6LBcyyV4fjf/xE3ROhyYpLwhE6xoJ7PwR5Q9/blSNWZHDx9Dn5yIIAo55M4nWHifZ0aMFMCbjoPMwVRBQgiEE1YZlcg2CTkfKH0SymJEcwyuJqYp6zoyx0wq/Dx06RGdn5xm/+S233MKuXbsAuPvuu7nrrru44YYbuO2229K+Rk9PDwcOHOC667Qv43XXXceBAwfo7e3td9ySJUswm7Wi3IkTJ2oTMp/vDD3JhUvOlQuJN7VmvLuW9PpBlrFOGSgjrXc5te3vDJBD4SELxbKcGSSLBcuE6gEuxt7XN9Lz4lqsM6bgXnUl7quXoiqKtlMzylKmUoFQ366QIAhYp00i2dGNId+DbeYULOOrMJYVYywrxjKhCtvMKUg2K0o4CmgTonhTK6aKUiSLedh7qapKoq1jyAlDlg9RUylSwVC/cSS4a19fgHkyktmEoTif4DvvnetmpoeiaDVR4qml0wqSlksu6nXZwOU0kENhIodqcSycg2PeTK1mqK4BAOeC2cSPNw1Y8Ep296IkU5hrKs9Di4dHjkT70lujR+sIv38Yc00leo9r0J2SRGsHkt2KoTgfNZ4g1d077HtakCTs82fh27Rdez9nOSW6//4aeo8L24wp6F1OdDkObNMnYywpouu5l8938/qhmX6fSAc3GbFMGIfO5UQOR0l2dJMKhFCiMZRYHDkSJdnjBVkGQcAyZUJff0z1erFMqhnZ6DkYOmfpmGmNnDfeeGNf3ct3v/tdfv/7359SjctHeeCBDwuC16xZw7x584hGo1RXp7/K39bWRkFBQZ9hpiRJ5Ofn09bWhtvtHvScF154gfLycgoLBxZNBgIBAoH+E7L29va023OhYaosQ+9xEa2txzJ+XFrnqIpCZP9hcpYvHrSY1T7nUnpf24A6dVLaUXq8sQX3NcsyavtoYyz2LSUWp+eV9f0kOwVJwjJhHEokim/jNnKvX3Xm7pdMETlUixwMgqIiWi1YJlaPGEh8gKCT+k1YzOMqSPkCRI81YhpXpnm5fOQcndNByutHVRTija0IBgP2eTNGvFeitQNDnmdUqLKN5r4VO95E8+O/QYknMJUVUfql+xBNJuRwZMg0BNFkQg6d2fQDTa6+FSUcAUlCl+PEUJiX8S6wIEkIeh1qMqXJjWbajkQy7f48GhitfUs0GREkiWR3L5LFjByO9KUp2+dMJ7BzD4Gt72CqrkSymEi0dxE73kTRfbeds9XhTBB0Eiiq5te1a59mJDiEEaWaSpHy+tAX5J6oVXCQbO0k3tiCqaJ0yHuIBj3GogL8W3aQ+7GrztajpMVo7VfDoSoKwXfew7ViYMmBaVwF3tc2oCQSo8ZAVLKYSbR/uPFgmTwe79rNmCdUoSZTJDt7tCJ8Vdsx0eflIjnsJNs6+xZWUr4ASiKJcZh+9QHncp6WVvCSTt3L6dLT08P777+fUeByKuzcuZMf/vCH/PrXvx70908++SSPP/74WW3DWEIQBIofvJOGf38cQafHXFU27PGqLBPctRdDcQHuFQMLD0FLUTKNKydW35TWCljKHyQVCGKfNe1UHmHUMBb7VqyhGclqGdRrwFhaROjdA2ckeEn2evFt2oF/03ZEs6mvWF6JJ0j5/NjnzsB15cIRawKMJUUku3v77R7ZZk5FNOgJv38YyenAkOfqp7yjqiqqLBPaexBTWQnORXNGnNwoyRSRw8fI//j1p/fgZ4jR3Lc6/vgC5ppKjOUlBHfuwbtxO55VV2KqKMW/bdeg5yR7vDgXzT0j90/2+vBv2Ylv4zYEnQ7RZEBVNVlsnTsH1/Il2Gdn5lhumVBNvLkN87j0TCdPJtHeiWPBnIzPO1+M1r4l6vUUP3AH7U8+g5JIkPuxq/rGB0GSKPnsXfi378a/ZSexUBhzdQVl//szmMpGp/Gn3uMm5Q+Q6OwCUSR69DhKMomAgGA2YijIQ+d0IIgCiW4vgtH44TiVlJEsFiKHaocNXgBMVeX4NmzDc+35Ld4frf1qWFQt3VgYRAlQ0EkgaIElZyh4SXb34t/6Dom2Dq3mxGrGMqEax9xL01KPs0yoJrB9N+pkTfJf73FhnzWV4Lv7NcnpQbzzYs1tIIkoiQRKRBMXcS1fMuIusRyKkAqGztk87bzsWXd0dPCd73yH2tpaZs6cyb333ssdd9yBKIoEg0EeffRRrr322rSuVVRUREdHB7IsI0kSsizT2dlJUdHASc6ePXt4+OGH+elPf8q4cYPvInzqU5/ixhtv7PdZe3s7t99+e+YPeoGg97go/+fP0PzDX5Ls7sFcVY7O4+rvnSHLxJtaiR1vwjSuQisKHEZCNP+m62j8/s+1be+CvCGPkyNRgjv3kPfxj42a1YxTZUz2LUlElQevNVJPONafLuH3D9H6yz9iKMrDftmsAQ69SixOtL6Rxu//jNzrryJn6aIhV8tzrlxI6y9+38+oTBAErFMnYaoqJ1p7nMiRur7aFzUpk/L5yLliIdGjx7TzhvBr6mtPIklwx25sM6ZgnzM6vDhGc99SYvG+YFQ0GlETWi2CdeokRMPLhA8e7UtJUFWVaG09yDK2GaefjufftovOP72AoTAf+9wZ/VS8VFUl0d5J76vr6f77a5R+6b60/XpcyxbT9tQz6HLsyAEtHU4ym7T88mEmhEo8TrKrF+eC2af9bOeK0dy3rJdMoPrRrw/6O0GnI2fxPHIWzzvHrTo1ci6fT9tTz5BobkO0WtA5bEgOK6oKaiJBrKEZVBVzdSXKSelAoInjGMqKtFSgEXw2dE47ajKJHI6is1vPxaMNymjuV0MhSCKmyjISrR0DFtKSnd3ocz2I5tPfVY0db6L7H28QPVqHoaQQXY4T0WJGiSfwrttC119fxHHZLDzXrRzW0d4yuQZEkVSvD71H8wEyjx8HgkBw1z50OQ70HldfDZiqKMj+AKaKMsLvH0Y0m3AtW9x37lCoskJozz5cyxefs3la2sFLY2Mj69ato7GxEYD169f324FZvjx9N+BvfvObuN1uvva1r/HKK69w33338W//9m+sXLmStWvX8sMf/jDt4MXj8TB58mRefPFFbrjhBl588UUmT548IGVs7969fPnLX+ZHP/oRU6ZMGfJ6DocDhyNbW/FRDAV5VPzff8K/fRe+9W+hJpNIDjuCKGpqTd09mKrKKPjk/8I6bdKIKzrG0iJKPn83rT97kmRRD6aq8n6r+0oySex4M7H6RnKvW0nOwjOzCns+GYt9y1xZhppMkvT6B9TCxI434Vx4eivI4QNHaH3iD9jnXjrkACmajFgnj8dUXkLPy+tRZRX3ysGVAs01lUhWC/GGZkyV/XcJJasF26WXYJ06CTkcRkkkiR4+hmvZQgrvvJlYQzPNj/+aZEcXxqryAc+rJFPEG5qJHW/S1KFu+dioMS4bzX3Lc90KOp5+TvPWiSVwnvguC5JI6UP30/bLP+B7YxOS06GZo+W6Kf3KA6ddD+LbvIPu51/BsXAuukEKTQVBwFhUgLGogFhDM03f/zll//zZEd3KVUUh6fMTb2gm0dqGzuEAQUBJJEBWME8Yh2Vi9aDy3JHDddhnTxsTTu8fMJr71oVEoruXRGsHGAwYCj4s+hYA9DokqwUlGiN6pA7RbEIwaN8PJZlETcnoXU6USIxEe9eIdQeCTndiEeH8BS9jtV/lfmwlrU88jWg29b2zkl4/4b0HKbjjptN+JwR376P9qWcw11ThuuqKAQuE5qpy5EiU6NE6Gr/3Y0r/6dNDigQIoohn9VK6/6HZB3wwppprqjAU5hM9Uk/kWL32uSQhB0LaeQY9pqpyEIQRa42VZIrg2+9iLC/Fc036ccDpkvbbYcOGDWzYsKHv589//vN9/87U52XPnj1s3rwZg8HAvHnzmDNnDitWaLrQK1asyEgmGeBb3/oWjzzyCD/96U9xOBw8+uijAHz605/mS1/6EtOmTePb3/42sViMb3zjG33n/cd//AcTJ2Z9Q9JFsphxL1uM68qFmmpFV88JE0sj5nEVGatsWMZXUfH1h/Cu34J/y04kmwVB/4E+vR/r1EmUfuHeQbc2s5wbBJ2OvFuvp/MPz2OZPB5jcQFKLE7kSD2o4Fw8/5SvLYfCtD7xNPY500dc2QEt+HAsmE3PS29grqkcNIVREASKH7idpu//HERx0HoUQRKR7DZi+w4hGgzk36KJfZgqSqn8xlfwv/U2vjffAlHS+qQooiaTJLp7sU6ZQPGDd2AeP27UBC6jHcfcGZjKikl292Kq1OqOPkDvzqH8Xz5HvLmNZHcvOo8LY2nRaf9tY8eb6Hr2JZyL5va731B8kGrT8uNfUfnth4cMnJRkirYnfk+sqRX73EsJ7T2IPs/dF4zI0RjxphZi9Y24li/ud+/osQZkf4Dcz951Ws+W5cIjcqQO39rNWCbVaPK2ioowiCCEaDahEwQSbV3o3A5Nvr7Xj6G4AEEUNTGIRHLYe6mqqglGjELFtbGAdcpECu68ma6//P2EmbEAqkreLdeddspU+FAt7U8+g+OymcMGoJLFjO3SKUSPNdD82BOUf+2LQ+7AOJfMJ97aTuCtt7HNntZ3nGSzYps1Feulk0m0dRI5UodktVJ0721YJo4DRaHjD8/jXbdFM06tKu9nCipHo8TqmzSVu9nTKbhtzTlNQ0wreCkuPrM5oslkEsOJrSWz2YzVau2fgpRhTU11dTXPPPPMgM+feOKJvn8/++yzp9jaLB9FEEUsE8bBhPQK+IdD73GRf8vHyL1+FdG6BpRoTCsqLCtG5xx7qzIXIs55M9HZrPS8sp7eVzcgmow4FszGs3rZaTkc+7ftQu/OQZ87uLDGYEgWM6aqcrzrt2C+b3BVQmNxIWVfeZDmx3+tKYeVl2AoLoATQUisoYV4UyuGwjxKv/Bgv9xhnd2G5+qluFdeQeTIMVJeH2pKRjSbsIyvGnXGdmMFQ2E+hsKhdzSMpUVn1OOk941NmMZVpBW4fICpopR4cxvhvQewzx6YDqiqKu1P/plEZzfORfMQJBGd04F/y070eW70Hpfm+l1RSqKzG++6LbhXL0VNyUSP1CEHQ5T+06cH9b7KcnHjXbsZU3UFot1GtK5Rq9vzuAZNvRZNRgSjgVQgjBJLojvh8QL0FV4PR6rXh2SzIZ5Fd/oLHcfs6dhnTiXe3AaAsaTwtFOoVVmh/Td/wjZzatrvGXN1BXIoTPcLr1J4582DHiMIAvm33oDe46L3lTcRrRaMxQWIJqOWNdPVTaK9C9usaRR8Ys2HQa0oUnjXLeQsW4Rv4zb8G7chmkyaKE5KRonFcMyfRcEn1mAsGSh+dbZJK3hZv379Gb2pLMts3769L0hJpVL9fs7U5yXL2Ec0GrBOHn++m5FlCKyXTMB6yYQzdj1VUfCufwvrlMyvaaosxbduC6lgaMjVJmNpEVXf+Srh9/bTu24LwT3vay92nYRt+iUUP3BHv7qYjyJIYrY/jlFSgSDhfQfJWZ65CbGpvITedVsGDV5ix5uIHKwlZ+nCvkmlsaQQ18rLCe87SORgLZLTjmQxI+j1KPEEva9tQDQYtGD/muXD5qdnGZsoyRShd/efWHyLIpqMGIoKcMy9NC0z36TXT/jQUdwrLz9hBGhActhItnchWExINmu/nUAlkdR2WIIhpDw3pqryvnFMiSdGVLKL1TfiWrYoa1R5mghD7OyfKuH3D4Ek9UsZTAfLxGp8G7aSd9O1Q/7fC4KAe+UV5Fy5kNCe9wnu3key149oNGCbNR3nwjlDLqqYSospvP0m8m+6lmS3FzkWQzKZ0Oe6z+vu3Xkp2Pd4PPyf//N/+n7Oycnp9/NQEsdZsmS5MIg3t6GmUuhOwZFYNBjQ5boJv3942MJnUa/DPudS7HMu1RZGZHnEYvwsY5/Qnv3o83P7FTSni6GkkPD+wyR7vejd/VMZvevfwlheMmCFVe/OIeeKBciRKLFjDaT8AdRkCr3HjRwMMe7/+xqSdexII2dJDzkSofeNTfg37dDqH3LdCHo9qUCQyJE6up99GfvcS3GvunLYlOp4cyt6dw6CToek02HI8yDodRgL80l0dpPs7AFUEIS+NCVdvgc1nkCQdB8GLokESjSGYZgdTDkc0QQjTrNWMcuZx7tuyykFQ6LJiM7jIrBjN66li4Y/Vq/HMW8mjnkzT+E+pjO6O366nJc3+ZneycmSJcvYQg6FkcymU65tEI0G5FA47eMFQYBs4HJRkPIHTjklRhBFRIsZORDqF7yoqRShPe/jGkIoArSURuu0/qa8vo3bibe0Yplwdi0Aspxbkj1emh57AtFowD5v5qCCEEosTvTYcRr//XGKP/cpLDVVg15LTST77YJYJlYTeOc9dO4cTGXFGEuKUFNJVFlBkEQEnR5BFFD8QZRolGS3F32ui2S3F3N1xdD1WvEEgR278dywakwJRlwMfKCy6F699JTONxTkETlUO2LwciGR3TfMkiXLeeH0/KKyxfJZBkeVFW2V+hT5QLL5ZORoTAtsMpQBlU4EQlkuHFLBME3//Qv0uW7ss6cPGrjACZXEKROxTptMy09+21cfMeA4i1lTqjuBoaQQUa8/seMCgiggGgxIZhOiwYAgCqiKgqoouJYuItnr1epkvH7MEwcGyaqqan4hm3fgXDQP17KLZ4I7VlBTKYBTzgwQDQbk8Jk19R3tZIOXLFmynHMkhw0lGjvlAEaNxZHOo0dBltGLzmFFjcVP6VxVVVGi8QG1CoIonlJfVVUVRqGbe5ZTp/u5l5BsVk20Jg0MhXlYJlbT9qs/DtqHtKLrCHJIm3wKokjOskXIgSDx9q5Bz0n5AujzPCdMB6cj+wOIej2x2noS7V0kvX6S3b1Ea4/j37CNyKFa8j7+MfLWXJ1VSRyFCJKkBaSn+j6U5THvg5cp2eAlS5Ys5xxjcSGiyUiyuzfjc5V4nGSPF9u0yWehZVnGOtapk0l0dKHKcsbnJrt6kOxW9Pmefp+LZhOiQU8qg10UVVWRff6MJeSzjF7kcITgrn1YJmWWBmgsLyEVDBGraxzwO9FgwLl4HrH6hr7PJIsZ16orIZkkeriORGd3X39WVZVkjxe9x4X/rbeJHjtO5b8+TM33/x+2S6eS7OomeqSOWEMLotFA4T23UvWdf8E5f9ZpPXuWs4cgiujdOaS8vlM6P+ULoB/G7PtCJJsEniVLlnOOIIq4li/Gt3kHhjzPyCecRKy+acwZ/WU5dxgKcjFVlBFvbuvzb0mX2PEmclYsGbA6LYgiOVcsIHzgCLZLL0nrWonWDvT5nvMiI5rl7ODftgt9nhvRmJnKkiAIGMuL8b751qC+Za6li2jYtgt9XmefpLhkMeO6einJzh4ih2uJHDgKOgklGkdNJqGqnNwbVmGbMbWvzsVz7XI81547o8AsQ6OqKon2LpRYHFNZ0YgpYc4rFhB8+70BQiEj3kdRiDe3UXDbmtNp7pgjG7xkyZLlvOCYP5OeF9eSaO/60KdgBORQmNjxZvJuvu4sty7LWMa18nLaf/tnDEUFaauOJbt6SPkCOObOGPT3zsXz8K7djFw9sn+MKstEj9aTd9M1Gbc9y+glvO8ghqKCUzrXWFqMf/OOQX+n97go+dynaP7JbzEFQpjHlSPoNCUxQ0EuhoJcUoEQkYNHUKIxSh+6H2NxNigeraT8QVp//hSJ9k4EgwFVUSi+/5NYBqlJ+gDnwjn0vLi2Ty47XeIt7RiK8i+6RZJs2thFhhyJ4N/6Dt43t5Lo7D7fzclyESNZLBR/9i5C7+0n0TFyX0wFQwS27SLvpmswlZ1Z49wsFxbWqRNxzJ9FYPuufsXQQ5Hs8RLctY/iB+4Y0nhV784h75brCGzbNazSnZpKEdj5LuaaCuxzLj3lZ8gy+pDD0YwmlicjGg3D1vmZqyspf/iziDqJ3tc3EdzzvNWunAAAIABJREFUPpHDx4gcqiWwcw+BrW9jHj+Oym98JRu4jHLan/wLCAI5Ky8n58oFWKdMpOXnTyFHY0Oeo7PbyLniMoLvvKeJjqSBHAoTOXCE3BtWnammjxmyOy8XEdH6Jloe/zWSw4ag19H9wivkrlmNa+nC8920LBcplpoqSj53N60/f4q424Wpqgz9R7xf5FCEWH0D8ZZ28j5+PTmL5p6n1mYZKwiCQN7N14Io4N+0A1NVOaaKkgGpG3I4Qqy+iXhLG0X3f3JEY9KcJfMB6HrmRQxFeZiqytE5HcAJadzjTcSbWrBNv4TCO27KGgFeYAh6HeopmmirstK3mzIUxqICSj5/D8leL8Hd+0j1+hEkEUNBHrbZ04cMrLOMHuRIhMiRY7ivXtr3f20oyEXnsBPZf3jYBY28G68h2d1LYPsu7HNnDLtrnPT6Ce7cQ95N12KdVHPGn2O0kw1eLhJUVaX9yb9gnljdt2othyN0P/8K9plT0OU4z3MLMyd8qJaef7yOZLeRf/N16HMHmptGjx0n9O5+El09FN5zK9JJucrB3fvofX0jhoJc8m66dkiH2Swjo6ZS+LbsJPjOXkSTgZwll6VdG2CZMI7Kb/8z/q3v4HvzLVA5YeonoCTiyOEozkXzKLz71nNS/KwoCv6N2whsfQdzTRW5a64edLU18PZ7RA7XIhqN5N/yYRqbHInS9dzLxBtbsM+eTs6KJYhZxalzjiCK5N98Hdapk/Ct20zv65swFOQi6vVaMX0kihwI4lw4l8JP3ZJ238pZMh/r1En439qJb8M2lFgMBBFQccydQf5N1w5pNqfE4nT/7VWitcdxLJqL8/LLED8S4CixOP5tu4jVN2KZVI1z4YfBerylne6/vUbKH8B6yQTc165A1GX71rnCkOch5Q+c0jgk+wPo3em9Z/VuF+4Vl2d0fW3c2q7t0NRUkrtm9YBxK/jOe/i3voMcjmj955pliHptghxraKbruZcRRJG8m64dVYaEFwojiYkJkkjxp++g69kX8a7bjLEwH9O4DxdIVFUl0dZBrKEFORii8K5bsM+adtbbHWtqpevZlwDIu+naQTMf4s1tBHfvI9HWQe5N12DI1WpZEx1ddD3/CsnuXvQeF56PrcRUevqZE6MmeLn99tt5+umnz3czLljkQJBUrxf7vA/zuSWrBX2ui8jhOhzzM3dcPZ8kOrtp/PfHqfjaF4k3t1H/rR9Q86PvDJgIhA8cBUHA+8YmCu+6ue/z6LHjtP78Kcof+QKhvQdp/M+fMu47Xz3Xj3HB0PqrPxJvbME0rhw1maL9qWdwX7Mc9/LFaZ2vs9vwrLoS98rLidY1aN4YioK+KB9jUcE5XcEOvPU2va++Scnn76H3lfW0/eZPlHzmrgHHhd7bj6jX49u4rV/w0vRf/4OpsozCe26l/bd/QU2l8Fy74py1P0t/rJNqsE6qIdnrJXKwFjkcQZAkLJeMx1CYf0rSsXqXk9zrVuK5dgVKLA6KgmgyIUjD99O2X/0RQa+j4M6baXn8N4h6Pc6P7CSmgiGix46T6vURfj/VF7woikL9/3uUwns+gbmmitafPQni/8/efcdZUR76H//MzOnnbGcrHaQjSAdpQUGiIiqx98QSExOvMTdXk1ivSYwmv5jEEq8meG3RG40hilhRQQSUJkVAYNll2d5P2VNn5vn9McuBdRfZBZYD+LxfL18vOeU5z875npl55imj0uO8OV2uv3R4MqZNoPLpv+M+pf/X5sZRkIe79aTTCEcwY3Gqn3+FjOmTu61ugZVraXzrA3r+6Ls0vv0hVQtfoucPrm3zmkhJGRkzJmHLSKf6uVfQgyEKrvoORiRKyT2/o9dtNyJ0nZJ7Hmbw/zycbNhInad5PLgHDSC8fRee4YNRFIV4XYN1wWHE4EO+X9FU8i6ZT/ZZM/F/spbmZSutYaqaDc3rofC7l+C5/hSEEMdk2WszFqfk7ofp9ePvgaJQcvfDDPmfh9s1jCPFezAjEQKfbiBr9nRobbwIU5A+fjT2/FwiO3az++cPMvSZR1CP8KbRx03jZf369amuwklNdToRAkQigXLAeuBGNIbmO/JVm2r/8TqKzUbg0/X4Ro8g/4oLj7jMr9O8bBWZ35qCZ8hAPEMGUv/6O0R372l3F+N9B/baf7ze5vGm9z8mZ94c3AP64uzXm/p/vZVcflLqmmh5JZEvi8k8Y1ry5M2ek0XD4vfInDH5oHd87oiiqu2+w7KHHsczZCBNH35CwdUXdfs8gqZ3l5F3yXxcfXqSf9V32P692ym66ap2DaiiG64guqecwJrPk4+ZhkFw3Sb63vkjFJuN3O+cQ/mjC2Xj5QgkGppoePN9wjtLcOTmkP3tbx3WHevt2VltGgqJxmYqHl2I4rDTsmkbAx76Jba0jm84eDCKonR6KI8wTJqXrWLo//4RzeMm9+LzaHjz/XaNF0duDkU3XEH9G+8Rr6rZ/1mmie4PkjFlHIrNhm/MSBJy3uIx5R40wFriva7ha3tfPEMGUvH4M3gGDaDpw0/Iv+oibOnpZH5rSrfVrem9ZeRdcl7rfmsB2797O0Xfv7rNfivv4vOS/9/j3NnULXobgMDqdXhHDiVtzEgAGt/5iODaTWRMGddt9T2ZFV57MRVPPEvz+x+jOB2IeIKim69B87g7XYYtM4Occ88k+5wzEIkEIp5Adbto/mgloc+30rJlO/a8HHr9+Ppu/EsgsOZzPEMGkjZuFACeoacQ+GxDcgjtPpkzrYa5f1Xbc3lnYR7OQmsFPWdRPpVPv2jdh8t3kjRepO6lupykTxhNaP0WvKOHozrsRHaWoGgqnqMwXtK/4jM8QwbS/1d3oKj7hzGU3Pv7Du/lkXfZ+e3C3xXx6lo8Q/ePT3cWFZCoroOvnPgeTKy6NvljVFUVR2E+8Zo62Xg5DPGqWmxZGW2uOms+L4qiWDdP62A4X2cZLWGaP15N2vhRnPLI/QjT6ndPNPkpueuhDt/T7+7bksuNHo5YdW1y5RbN60Fx2DGCLdgyDj2sUNU0VJeLRJMfR24OiYZm4tV1h12Xbzo92ELZw49j75GDd/hg9GY/FU88S8+brzni/VZkZwn+lWvo/6s7KLrxKkTrRczAms+p/t9/tHu9luZj4G9/cdifp/sDqG5X8gTG2dPa53SWYrNReOOVlP73Izh7FhAtq6DXT2487PpIXacoCjnnzaHu/17HNn1ih0smW/OqBM3LVuE7bSSnPHI/RiSK3uzHDEfY9Yt7Oyy77123JU/yDke8ug7Hvv2Wx4PqdGAEQ8khRwcyTZPGpR8nV9aLV9W2Wa3KWVTQpWxKbdkyM+jz8x8Tr6jGjMVw9unVpYt4B1IUxbrg3HrROfDpBsxEgj4//xGqc/+Fk/JHFxLevqvd+7NmTyf3wrMP7w+ho2zkd/mYFiuvov7f7xApLSP/igsPuVpjZ8jGyzdI3uUXUPfPJTR/uBKh63iHDaL3f9yIcoTj8c2ETry2gYG/v6ddN3O/+37apbJqXv43/mWr2j3uGzOSopuuSv5b6AaKekCXqap2eoUOAAwTDrgipajqYd3UTmptODY0IQwjmaV9N/PTOjhwdkVkdxm+00aSdeZ0oHVqAWDLTGfQY7/uUlmlDzxCvLKm3eO5l5xH1qyp+x8wzP0fRNezUXDtxZTe+3u8o4YRKS6Fw5zgK0Fg9Vq0NB/ekUMA63tHVWl48/0jb7wUl5J74dnJnr59e5O08aO71LunB1vY/V8PdPhcn1/emhzfLUyj/T5H1zv9OcIw8S9fjffUYbj69iJSvIfIl7uTY8ulYyNj4hgSVTU0r/iM9Mnj2l1N19J9RHeX4Rs1gvTJYyn99Z+wZ2fS8+ZrQVW6vt/61R+JV1S3ezz3onlkHTAsVxhG295hVUXoHe+36l7+Nxgm2a09wsI02y5moSogj4dHRFGUbpk3FNm9hwEP/gLN03bETM8ffbdL5TQt/Zi6V99s97izVyF9f/kfyX8Lo+1+C1UFs2vZsGWmkzFjEo7CPBrf+Yjss2aiurp2r6R2ZR7Ru6UTimq3k3/Z+eRdch7CMA/7SsBXxcrK8QwegOpoP6m59N7fk6hraPd43hUXdtjzknfpfPIunX/Iz7Tn5hCv29+jk6hr6HDC/kHf3yO7Tb3idfXY5UnAYXH2LMB36lD8K9fi7tcbM6ETLS4l5/y5R5yxSHFphxP/9eYAJb94sMP39Lv39g57XvredVunPtPeI5tEfQOO/B6Y8ThGS7jDq5cHk/PtWaRPPI14bQOZ3zqdyief6/R7pbbi1XXttr09O4tAcekRlx3ZVUruJee1ezy45nOqn/m/do9r6T4GPnRXu8dVr5tBT3ScxQOXxbVnZmAEQ5gJHdVuI17X0KUbtEb37EX3B5M3o1PdLupeWdxu2JnUeeFdJTS+9SHx2gbcA/uSc+6ZnfpOcubPRfV4qX/9HeumqP37JFdJtGWkEdlejO+04fhXfIa7X2/yL78ARVPR/QF23/mbDsvse8/tHfa8HHgi+XXsPbKJ1zbgyM/FTCQwQi1WY/8ral95g/DO3fT9xX8kF3uw98gmsrMk+ZpEXQPu/n069bnSsWOEWlBUtcOMVjy20LqR6VdkzZlB7oL295vKPGMamWccek6qIzeb0KZtyX8n6hvxnTq0S/XWfF58pw7Dd+owAp+uJ7yzpMtlfJVsvHwDKap6VCdAR3aV4h7Yr8PnDtbzcrBFNzo7AS1j6kTKfvsoPebNJlZdS6K+EfeQAQDUvrqY7DkzsWWkYbSEMSNRME0SDc0Iw8SW5iNj2kRqXnyNzG+dTssXX6K5Xdjzu38lq5NVwXWXEVi9juDajSgup7Xs7PBDT048lMiuUrLOmNrucS0jjUGPd3wScMTZmj6JhjeX4hl6Co3vLCNtwmjrxCMYovGtD8i7xGpc681+9CY/QtdJ1Deiej1obhfxmnps2Rk4VZWyh/9Cj/O/3anPldpzD+hLw9sf4hrYN/n9xSqrOrxLeVdFS8s6PEHzjR/NoC70vHx1kZADHZg5xWYjbeypNL37Edlzv0XDkqVkTJsIQHhnCfHKGjJnTsY0TYzG5uS+K1HfiC0zHXtONomGJuumdEX5hNZvxlF0eDdMlKyGS8Vjz+AePADv8EHEKqope/gJ+t112yGHiCqKQvac6aRPHpNcJdGMRFHsdvIvv4BIcSmZs6biGtAH+wEreappvu7bb02bSOOSpXiHD6LxnY9IGz8aRdPQgy00vrWUvEvmU/faEvyfrKHPHT/C8AcwbRq2zAwyJo+l9qVFxOsbQDdo2bydnj+8rlOfKx07kd17cB1k39fzlq71vHQ2V2kTx1D97CvWvdgUCG3YQuH1lwPW3CjviCE4exViRqMYoTBC19GbA+jNfmyZGUT3lGPLzEBL8xLZVUqsovqo3FDzuGm8HOzGTdLxT+gGvjEjOnzuYI2kI10jw92/N3mXzGf3XQ+hedz0ufOW5OoV0d1lmAnrxnT+FZ/RvHw17kEDqHzyObwjh5J/+QV4TxtB5t5Kin/239iyMun9sx9+7UmI9PUUTSVj6oSjfhVYc7twn9Kv3eNfe8J4hJ+Z/e1vkahvZOetd+Pq24uiG63hiiKhE9ldlnxdzcv/Jra3EntOFnsfeYqc8+aQMXkcoc1bqV/0NoqmkXPubDJmdt8KQye7tAmjaV6+isDq9TgL89D9QRJ1DfT+6fePqFwzGsN76rAOl8Duzv1A0c1XU/X032lY8gHpE08j66yZgLUaZLzaGtIodIO9jzyVfM/eR56i163X48jPpfdPbqT8z3/FjCVw9e2ZPImQuq5xyQd4Bg/E1b83YA1tMeNx/Cs+I+fcMztVxoGrJBrBEGY0hi0ni8Dq9bhP6ddu+f1u3W/NPWC/1adncpi1SCSIFO8BILa3Es3roeKxhYA15LfnLddhy8yg5y3XUfbgY6Aq9PrJTWjeI1/IRzq6jHCE9IljO3yuu1bktKX56HXr9ZT97gkQgl4//l5yYZNoeSWuftbvJ7R5O/WL3saWkUbda0uS2YpX11L+579hhCM4emTT52c/bHcvt8OhiOOk1XD11Vfz/PPPp7oaHSovL+fMM89k6dKl9OrVK9XVkVKkO3IgsyV1VwZOlmyZsTj+1euI7NiNPS+HzOmTsGfLhTU6Q2br4Hb/8rd4Tx3WppclUlKG5nJS+D3ZKDwUeTyUuktncnDc9Lwcrw0XSZIkKXVUp4OsmVPImtl9y8xK3zyuAX2JVdUkGy9CCBI19XhnnZ7imkmSdChynIwkSZIkSd8oPebNIV5eRWjjVqKlewl+ugHFppExWd7bRJKOdydF46WkpIRLL72UuXPncumll1JaWtruNStWrGDBggWMHDmShx7q+P4QkiRJkiSd/Bz5Peh39214hw9GsdnI/NYU+vzsh0e8hKskSd3vuGu8GIbBY4891qX33HvvvVxxxRW88847XHHFFdxzzz3tXtO7d29+9atfcf313Xs3UkmSJEmSjn+2zAx6zD+LohuvJGvWVNlwkaQTxHEz52UfwzB4/PHH+dGPftSp1zc0NLB161aeeeYZAObNm8cDDzxAY2Mj2dn77/vRt6+1vNzSpUuJx+MHLS8QCBAIBNo8VlFRAUB1dfsbRUnfHPu+f+Mwb94lsyV15EhzBTJbUsdktqTuIo+HUnfpTLZS0nj5+c9/ftDnuvpDqKqqIj8/H631zt6appGXl0dVVVWbxktnPfvsswft+bnyyiu7XJ508qmrq0s2hrtCZkv6OoebK5DZkr6ezJbUXeTxUOouX5etlDReFi9ezEUXXURGRka750zTTEGN9rv22mu58MIL2zwWj8fZu3cv/fr1SzaSTiTV1dVceeWVvPjiixQUHPnNgU5mX7etDMOgrq6OkSNHHlbZJ1u2ZK46rztzBTJb32QyW10js9V58njYNTJbnXek2UpJ42Xw4MFMmzaNM89sfyOoWCzGU0891cG7OlZYWEhNTQ2GYaBpGoZhUFtbS2Fh4WHVLT09nfT09HaPDxgw4LDKO54UFBTItdM76WDb6nCvXsLJmy2Zq87rjlyBzJYks9VVMludJ4+HXSOz1XmHm62UTNhfsGABB7s3ps1m6/R8F4CcnByGDRvG4sWLAatXZ9iwYYc1ZEySJEmSJEmSpONXShovV155JbNnz+7wOU3TutR4Abjvvvt44YUXmDt3Li+88AL3338/ADfeeCObN28GYO3atcyYMYNnnnmGl19+mRkzZvDxxx8f2R8iSZIkSZIkSdIxc9ytNnY4Bg4cyCuvvNLu8aeffjr5/+PHj2f58uXHslqSJEmSJEmSJB1F2n333XdfqishdT+n08mkSZNwOuU69ocit1XnyW3VeXJbdY3cXp0nt1XXyO3VeXJbdY3cXp13JNtKEQebfCJJkiRJkiRJknQcScmcF0mSJEmSJEmSpK6SjRdJkiRJkiRJkk4IsvFyEikpKeHSSy9l7ty5XHrppZSWlrZ7zYoVK1iwYAEjR47koYceOvaVPE50Zls9/vjjnHvuucyfP58FCxZ8Y1enk7nqPJmrrpHZ6jyZra6R2eo8ma2ukdnqvG7LlpBOGldffbVYtGiREEKIRYsWiauvvrrda0pLS8UXX3wh/vCHP4jf/va3x7qKx43ObKvly5eLcDgshBBi27ZtYty4cSISiRzTeh4PZK46T+aqa2S2Ok9mq2tktjpPZqtrZLY6r7uyJXteThINDQ1s3bqVefPmATBv3jy2bt1KY2Njm9f17duX4cOHY7OdFKtkH5bObqvp06fjdrsBGDJkCEIImpubj3l9U0nmqvNkrrpGZqvzZLa6Rmar82S2ukZmq/O6M1uy8XKSqKqqIj8/H03TAOtmn3l5eVRVVaW4Zsefw9lWixYtok+fPhQUFByrah4XZK46T+aqa2S2Ok9mq2tktjpPZqtrZLY6rzuz9c1tEkpSJ3322Wf86U9/YuHChamuinQSkbmSuovMltRdZLak7tKVbMmel5NEYWEhNTU1GIYBgGEY1NbWUlhYmOKaHX+6sq02bNjAz372Mx5//HEGDBhwrKuacjJXnSdz1TUyW50ns9U1MludJ7PVNTJbnded2ZKNl5NETk4Ow4YNY/HixQAsXryYYcOGkZ2dneKaHX86u602bdrET37yE/785z8zYsSIVFQ15WSuOk/mqmtktjpPZqtrZLY6T2ara2S2Oq9bs3VUlxWQUmrXrl3ioosuEmeddZa46KKLRHFxsRBCiBtuuEFs2rRJCCHEmjVrxPTp08WYMWPEaaedJqZPny6WL1+eymqnRGe21YIFC8SkSZPE/Pnzk/9t3749ldVOCZmrzpO56hqZrc6T2eoama3Ok9nqGpmtzuuubClCCHG0W1uSJEmSJEmSJElHmxw2JkmSJEmSJEnSCUE2XiRJkiRJkiRJOiHIxoskSZIkSZIkSScE2XiRJEmSJEmSJOmEIBsvkiRJkiRJkiSdEGTj5SR255138sgjj6S6Gh16/fXX+d73vndY7y0vL2fIkCHoun6UayV1lsyW1B1krqTuIrMldReZrWPPluoKfBOcccYZ1NfXo2kaHo+H6dOnc/fdd+P1elNdtZSZP38+8+fPT3U1TngyW+3JbB05mav2ZK6ODpmt9mS2jg6ZrfZO1mzJnpdj5Mknn2TDhg0sWrSIrVu38tRTT6W6StJJQmZL6g4yV1J3kdmSuovM1jeDbLwcY7m5uUybNo1t27YlH1u6dCnnnnsu48eP5+qrr6a4uDj53JAhQ9izZ0/y31/tnnz66aeZNm0a06ZN45VXXmn3+kAgwE033cSYMWO4+OKLKSsra1P2Sy+9xFlnncWECRO4//77OfCepa+++ipnn302EyZM4Prrr6eiogIAIQS/+c1vmDJlCuPGjeO8885jx44dACxbtoxzzjmHMWPGMH36dP72t791uB1ee+01Lr/88k7VxTAMHnroISZNmsSZZ57JsmXL2pQVDAb5xS9+wbRp05g+fTqPPPIIhmEQj8c5//zzef7555PlXHbZZTz22GOH+ppOSDJbFpmto0vmyiJzdfTJbFlkto4+mS3LSZstIXW7WbNmiU8++UQIIURVVZWYN2+eeOCBB4QQQuzevVuMHj1arFixQsTjcfHUU0+J2bNni1gsJoQQYvDgwaK0tDRZ1h133CH+8Ic/CCGEWLZsmTj99NPFjh07RDgcFv/5n//Z5vV33HGHmDBhgti4caNIJBLi9ttvF7fddluyrMGDB4ubbrpJ+P1+UVFRISZNmiSWLVsmhBDivffeE7Nnzxa7du0SiURCPP744+LSSy8VQgixfPlyceGFFwq/3y9M0xS7du0SNTU1Qgghpk6dKtasWSOEEKK5uVls2bKlw23yz3/+U1x22WWdqsvf//53MXfuXFFZWSmamprEVVddJQYPHiwSiYQQQogf/OAH4u677xYtLS2ivr5efOc73xEvvfSSEEKIL7/8UowfP17s2rVLPPHEE+Liiy8Wuq4f3hd5HJLZak9m68jJXLUnc3V0yGy1J7N1dMhstXeyZkv2vBwjt9xyC2PGjGHmzJlkZ2dz6623ArBkyRJmzpzJ1KlTsdvtXH/99USjUTZs2HDIMt966y0WLFjAoEGDcLvd/OhHP2r3mjlz5jBq1ChsNhvz589vcxUC4MYbbyQ9PZ2ioiImTZrE9u3bAXj55Ze56aabGDhwIDabjZtvvplt27ZRUVGBzWajpaWF3bt3I4Rg4MCB5OXlAWCz2di1axehUIiMjAxGjBjR6W10sLq89dZbXHvttRQWFpKZmcn3v//95Hvq6+tZvnw5v/jFL/B4POTk5HDdddfx5ptvAjB48GB+8IMfcMstt7Bw4UIefvhhNE3rdJ1OBDJbhyaz1XUyV4cmc3V4ZLYOTWbr8MhsHdrJkC3ZeDlGHn/8cTZs2MDzzz/P7t27aWpqAqC2tpaioqLk61RVpbCwkJqamkOWWVtbS0FBQfLfhYWF7V7To0eP5P+7XC7C4XCb53Nzc5P/73a7aWlpAaCyspLf/OY3jB8/nvHjxzNx4kSEENTU1DBlyhSuvPJK/vu//5vTTz+du+++m1AoBMCf//xnli1bxqxZs7jqqqs6tWM4VF1qa2vb/G0Hbq/Kykp0XWfatGnJut5zzz00NjYmX3PBBRdQUVHBjBkz6NevX6frc6KQ2To0ma2uk7k6NJmrwyOzdWgyW4dHZuvQToZsydXGjrGJEyeyYMECHnroIZ544gny8vKSYxjBGuNYVVVFfn4+YAUrEokkn6+rq0s+l5eX1+aHV1VVddTqWVhYyM0333zQVSquueYarrnmGhoaGrjtttv461//ym233caoUaP4y1/+QiKR4MUXX+S2225rN26yq3Jzc9v8bQf+f0FBAQ6Hg9WrV2OzdRzn+++/n1mzZrFixQrWrl3L+PHjj6g+xyuZra6T2To0mauuk7nqHJmtrpPZ6hyZra47kbIle15S4Nprr2XlypVs27aNs88+m2XLlrFq1SoSiQQLFy7E4XAwZswYAIYOHcrixYsxDIPly5ezZs2aZDnf/va3ee211yguLiYSifD4448ftTpedtllPPXUU+zcuROwJmq99dZbAGzatImNGzeSSCRwu904HA40TSMej/P6668TDAax2+14vd6j0m149tln8/zzz1NdXY3f72+zekheXh5Tp07lt7/9LaFQCNM0KSsr47PPPgNg0aJFfPHFFzz44IPcdddd3HnnncmrDCcjma2ukdnqHJmrrpG56jyZra6R2eo8ma2uOZGyJRsvKZCdnc3555/PE088wYABA/jd737HAw88wOTJk/nwww958skncTgcAPzyl7/kww8/ZPz48bzxxhvMnj07Wc7MmTO5+uqrueaaa5gzZw6nnXYaQPK9R2LOnDnccMMN3H777YwdO5Z58+axfPlyAFpaWrjrrruYOHEis2bNIjO2u7eVAAAgAElEQVQzM3kTpH//+9+cccYZjB07lpdffpmHH374iOtyySWXMG3aNM4//3wuvPBCzjrrrDbPP/zwwyQSCc455xwmTJjArbfeSl1dHZWVlTz44IM89NBDeL1ezjvvPEaOHMmDDz54xHU6XslsdY3MVufIXHWNzFXnyWx1jcxW58lsdc2JlC1FiAPWa5NOaMXFxcybN4/NmzcftFtPkg6HzJbUHWSupO4isyV1F5mt1JM9Lye49957j3g8jt/v53e/+x2zZs2SPybpqJDZkrqDzJXUXWS2pO4is3V8kY2XE9zLL7/MlClTmDNnDpqmcd9996W6StJJQmZL6g4yV1J3kdmSuovM1vFFDhuTJEmSJEmSJOmEIHteJEmSJEmSJEk6IcjGiyRJkiRJkiRJJwTZeJEkSZIkSZIk6YQgGy+SJEmSJEmSJJ0QZONFkiRJkiRJkqQTgmy8SJIkSZIkSZJ0QpCNF0mSJEmSJEmSTgiy8SJJkiRJkiRJ0glBNl4kSZIkSZIkSTohyMaLJEmSJEmSJEknBNl4kSRJkiRJkiTphCAbL5IkSZIkSZIknRBk40WSJEmSJEmSpBNCShsvDz74INu2bUtlFSRJkiRJkiRJOkHYUvnhuq5z/fXXk52dzfz585k/fz4FBQWprFKHotEoW7ZsITc3F03TUl0dKUUMw6Curo6RI0ficrmOSpkyW1J35ApktiSZLan7yOOh1F06ky1FCCGOcb3aMAyD5cuX88Ybb/Dhhx8yevRoLrjgAubMmYPX601l1ZLWrl3LlVdemepqSMeJF198kfHjxx+VsmS2pH2OZq5AZkvaT2ZL6i7yeCh1l6/LVkp7XgA0TWPWrFnMmjWLnTt38tOf/pQ777yT+++/n3POOYdbb72V/Pz8lNYxNzcXsDbk8dgzJB0b1dXVXHnllck8HA0yW1J35ApktiSZLan7yOOh1F06k62UN15CoRBvvfUWr7/+Ojt27OCss87i3nvvpaioiIULF3LDDTfwxhtvpLSO+7ovCwoK6NWrV0rrIqXe0ezOltmS9jnawyRktqR9ZLak7iKPh1J3+bpspbTxcuutt/Lxxx8zYcIELr/8cmbPno3D4Ug+//Of/5xx48alsIaSJEmSJEmSJB0vUtp4GT16NHffffdBu4ZUVWXlypXHuFaSJEmSdHTEqmoIb91BxtSJqC5nqqsjSZJ0wktp4+X6668/5GvcbvcxqIkkSZIkHX21Ly2iZcuXKHY7mTMmp7o63SrR2EzzRyvJPmsmmu/4WHBHkqSTT0rv8/KrX/2K9evXt3ls/fr1/PrXv05RjSRJkiTp6Mk8YyppE0bjHTEk1VXpdg1LllL117+z7ZpbqXj8f4mWVaS6SpIknYRS2nhZvHgxI0eObPPYyJEjWbx4cYpqJEnHj/rF7xH4dAPCMFJdFUmSDlPaaSPp+YNrsedkpboq3c47fDC+00ZgRmM0vPk+O26+g913PUTgsw0I00x19SRJOkmkdNiYoih89TYzhmFgyp2c9A1nRKJUL3wZMxrDlpVB1pnTyZozA1fvolRXTZIkqUNpY08lbeypRPeUU//6uzR9sILQ+s2E1m8me+636PUfN6S6ipIknQRS2vMyfvx4/vjHPyYbK6Zp8uijjx7Vm2lJ0glJCPIuvxBnr0L0Jj91ry5mx/f/i10/vZ+Gtz/ECEdSXUNJkqQOufr2otePv8ew5x6l8IYrsOfnkjF9UvL5aFkF0fLKFNZQkqQTWUp7Xn75y1/y/e9/n2nTplFUVERlZSV5eXk8+eSTqayWJKWc5nGTd/E8ci86l/C2nTS9t5zm5asJb9tJeNtOvMMHo/XpeUzrlGhoonn5alq27sCMRFHsNhy5PcicMQnP8MEoakqvhUiSdJyxpXnJXXAOPc7/NqhK8vHq514hsHItaeNGkTN/LmnjTu2W/YcQgsiO3TR/soZEXQMioaP5PHhPHUbGlLFoHs9R/8xUEEIQ3V2Gf8WnxGvqMOM6mteNZ9ggMqZOwJbmS3UVpROIGY1StfD/cPbpSY95s1NdnQ6ltPFSUFDAv/71LzZt2kRVVRWFhYWMGjUKVZ4ESRLx2nqC6zejN/txFOXT88ffxYjEiJXsxXVAw2XPr/+Eq38fsmZPx5HXo0ufkahvJNHsR29sRg+EUO02sNsgoROvrSdSvIdEYzN6YxNGOIKzdxGeYYPQ3C5M3SBWupfyRxcigPTJY8lsvbqqOhw4CvOt8o4RIxxB9wcABXtWxhEtS2tGo0RKyzHDERRNw5aVgbN3EYqiHPrNkvQNJXSd0KZtREvKMGNxtDQvvlHDcfXdf8NBIQS2zAwUh53guk0E123CUVRAj/lnkTV72iEbFGZCJ15TixmJEa+tA1VFtVn7mUSTn3hlDfHqGoxAC7HqGhTNhmfYQBwFuSiqih4M0fDGu9Q8/yruAX3IOmsGqtNFrKIKxW7HmZ+Le8hANLera3+7aRLesZvmDz8hUlKGiCdQVBVXv174xo3CN3Iotoz0du8zIlFELIbqch3WPiv0+RfUvf4Ohj+Is09P7Pm5KJqGGYsTXLeJxiVL8Y4aTt5F52LLzOhy+d8keiBIvLoOYZrYMtJxFOSCEIS37yK6pxwzFseW5sU7chiO/PbH2mhZBeEvizEjUVS3C8/QU07Iod6xqlpCGzYT2VUiGy8H09jYSGNjI+FwmF27drFr1y4ALrroohTXTDoeGeEwzcs/RXO7yJg6AcWW8ggfddGyCupeW0K0pAxHYR6qxw2moOWLHejNftInj8OMRlFdLiIlZfg/WYP/kzXUvPgavjEjyZ4zg/Qp41APuOHrgYRpEvr8C5qWriCyczdGOIIZiSIUBUXTIJHA1HUURUH1uDHCERQUtAwfekMT/uWrseXmoNfUYcYSYLchYnHqX1tC/WtvoXncKE4nigK+MSPJu+x8nAV5XdoGZiJBaP1m/KvWY/gDoKk48nuQOX0y7sED2lyljZSU0bR0BaGNX6C6XSBAxGKkjR9N5hlTcfXq/MEjVlVD80crCaxej5bmRXU4EKbACIbQ0n1knzmdtPGj5f06jjIhBNHSvcQrazBjMVSnE2fvojaNdOn4JQyTxnc+pGnpClS3C3t2Jtg0zPJKmj9ciT0nix4XnI13xGAURaHXj75LwTUX0fjOMhoWv0e8sprKJ5+j+tl/0OeOW0ifOKbdZ+jNfpqXr6bpo5UY/hB6IAhCgKKAoiDicUABTQVVRURjqA47arqP0Mat2HKyMfxB9PpGUBWEgMSazwmsXg+qiuJ0oKgqqsuJ6nKSOXMKOd+ehb1H9iH//lh5FRX/8zwiFsfRqxBHXi6R4lKMljDx2nqal3+KYtNIP308OWefiatfb0Kfb6Hp/Y+JlVeh2G2IRAJn3944exWSqK5FDwQxI1HMeAJhmqiahj0ni4wZk0mfMBrV5aLx3WU0vPUBvlHDsI/PbXdxxVmUjxlPENmxmz0PPkbvn9yIo4v74m+CSPEeGt9fRnjLDrSMNFAVjEAITIGp69jSvNhyslE01TrWvf4uzr698I4YQryimuievUTLKhC6gbNXEbacTNB1GpYsxZGXQ868s/CdOjTVf2anufr1pvCmqzuV/VRJ6Znf+++/z89+9jP69u3Lrl27OOWUU9i5cydjx4497hsviYYmghs2E9lVCqaJq38ffKeNxFnY9R1DrKKawNqNREvKMFrCOHsW4Bl6CmljTz3oCejXlldVS2jjF+j+IIqiYMtMxzdmJI7cnC6XdbypeOI5jJYwIhYjXltP3sXnpbpKR1XLtp2U//lvaD4Pit2OHmzB4Xbj6tsLdYgTIxwhvG0nZb97kt6334SrTy/6P/BfNL67jMCqdcnJsZrPQ+bM08mcNZVoWTmx8koQ4OxVSHj7LmJVtagOG0Y8gbN3EarXQ2T7LkQ8gZFIoLhcaG4nepMfW04WtnQfRqDF2vaGSaKmHsVhR/V5MQNBayUhUwAmZjwOuo69RzbBtRvxL19N1txvUfi9y1Dt9q/9+4UQNH/wCQ2L30dN8+LqVYg9NxuEQG/yU7XwJRSHk4Krv4NnyEAa3vqAxneX4erfm6zZM1AdVvlmNEakdC97f/8kuQvOIXPGZIxwmJYtX2K0hFEdDtyD+id7qoQQNL67jMYlS3H27knGjMloHnebeiVq6ml8fzn1b75Pr1tvOKzfeiqZ0RjB9ZuI7q1E0TS8I4bgGTLwsIbrmPE4gXWbCH72ObGyclA1HAW5ZEwZR9q40ajOzu23zHic4Gef07j0Y4xACHtOpnXiaZjoDU3Yc7LIPHOaVeYx7MWTOk/oOhVP/C8tX+5G83kwozGMaAxnYR72U/rDyKHEK2uofPoFci86l8xpVu+sLT3NGhq74GwCq9dT//o7tGzdibNnIYFP11vzYsoqrP2T00nzRyuxZ2ditkRRnQ68I4egB0PE9lSAaSB0HS0rAzMcBV3H0bcnimGgNwUQQHT7LtA07IW5iHgCvaG5dZ8FKKDYbdgy0rFlZZCorcf/8WeE1m+m163X4+rX+6B/f7Ssgr1/fBrP4AE4ivLxL/8UIxjCnpuDq39va2EiUxCrrCa0bjPhL3ejaCq2tDRc/XvjHT0cRVGI7q3Ev2wVoXUbUVuHeYmEjmq3YcbiuPoUYS/Io+mDFdT/6y3SpowjuGodGdMnorXeD8+MJ0CINr8/1WHHO3IIkZK97P3TX+l754+xZaR1Txi6QaKxieD6LfiXr0YPhsicNpH008fjLMzvcll6s5/g518QK6/CCIRw9CywHtuwBc/AvmTOmYFqtyGEwL9yLbG9lVaDxevBO+wUFJv1nLJ1B6ENWwh8sgZ7fi5GIGitJmi3kahrQG9owjN8EBlnTCXyxQ72/uF/8I0eQcaUcfhGD0N1da1X71hTFIX0CaNTXY2vldKjwR//+Ed+85vfcPbZZzNhwgQWLVrEP//5z2Tvy/HIjMepefE1mleswQyFrCv/ikLTR6vQfItJO204hd+7vFM36DKjMaoWvkRoyw7r6rICis1Gy6ZtBFato/b/Xqfwu5fiGzW8U3ULbdxK43vLiJVX4SjIQ3U7QUCkuJSGN9/HNaAv2WfNxDts0JFuhpSJ7a0gY8Zk9CY/0dLyVFfnqAp/WUzJvb8DUxCvrrWyZZqEt+1Ec7vwDBuEb8xIfGNPpWXzdiqefI7et3+ftHGjSBs3Cj0YovmjlTS9t5zIrlIa3v6Q0Kat6IGQdXUSYc1VURRs2VmY8QSeIf1R7HbCW3ei2G1Wj4vLBYaBGY2jOJ0YgRA2nxctM41EqR/iCZTMNEQkhtnSgmK3QzwOdjvCNBEJHXuBtUP3jhxCwh+k6b3lxMqr6PfL/zhor4UQgrp/LiHw6XrSpoxtN07bnpOFa2Bf4tV1VPzlWXxjTqVl8zYypk9qN8RDcTrwDBmIq1chta8sxr9qHfGKamzZmaguJ0LXqf3H67j696bH+WfT8sWXNC9bRcbMyckTgTblKQqOglwcBblE95Sz9/d/oc9/3dLh0IHjUXD9Zqqfe8U6OcvORBgGwc8+R3W76PnD67r0d4Q2b6Pqby8Rb2hCGAY2nxcRT5CobSBRW0/D4vfp9ZObDnmxRPcHKP/zQkQ8jmtAX+z5PdpcORZCEK+qpeGN9/EvX03PH14nb3x4nBFCsOc3jxL8fAuKohAXAkXTEMWltNjt2LIyyJgxCWfPAmwZadS9+iaOHjl4hp6SLEPRNDKmTiBj6gTqXn+XPQ89jhFqwQi1YIYjyd4VLc2L3uzHlpmBozAPwx8kvrcKW3YGidpGVI8bEYkhdN2qQyCEltPaA9TYjOJ2odht6PVNCMMEBatXW1GsnueEjhFsQXU68QwZSLRkL0KB8kcX0vcXt3a41LUZjVHx2DN4hw/CUVSAf9lqzEgU18C+bXtBhIk9OxOh6+h1DaCquE/ph7NnAdB6AXPlWtyn9AOHnZb1W1DsdnxjRlj7btMkuqccYZaQMX0S8epaav/+L7LmzEzur4yWMI1LliJMQdacGVbv1wHc/XtjBII0LHmf/MsvPPphOMrMRKL1XOszzGALRjgMhknky2KqX3wN3+gR9L79pk7N5zETOrX/92/8K9dghFqsIX02G0Y4CqaBLScLLS0teYGkZeMX6LUNeIcPAkUhtqcc/ydrSZ86nsAna9Abm3EP7EvCHyRWvAfPyCHYs6wheSKvB0aohcDqdZgfrUJx2FE0jeblq4iW7KHmJYX0iWPIOW+OnIt0BFI6uaSyspKzzz67zWMXXnghixYt6lI5JSUlXHrppcydO5dLL72U0tLSdq959NFHmTJlCueffz7nn38+999/f5frK4Sg8qkXCH3xJRi6dTJ52gh8o4dbXYJCEC2rZO8fn8ZMJA5ZVsWTzxEuKSNRW4sZj6HYbTh75uM7bTjCFGg+L1V/e5nwzhKEYWIm9I7LMk1qX3mD6udfQUvzkTVnBr7Rw/EMHohnyEB8p40ga451Vbrq6RdpePP9dktUnyiyZs/Av3w1LZu3kTV7eqqrc9SYLWH2PPSYNXzBYcfZuxBnrwKcfYqw5+VgJnQiu/cQXLMRRVHwnjqUeHUtkeLSZBm2NB89zjuLAb/9JZ5hg7Dn5RCvrUcYBprXjREIIXQdUwgSjU2IeAxFVTH8Qcx4HMXthHgCzetG9bgwW8KoHpfVA+QPojcHIaGD3YZitp6kxBPWe202VKfDapgoYARCKA4H0T3lJCprMA2Dlo1b2fXT+9GDoQ63gX/lWgKr15ExbWK7nbowDHR/gERDE2Y8jpaVSd1rS3ANbTs23UzoBNZupO4fr1P793/hX7UW3e+nZeMXZMycTPqkMfhGDydt3Ciy5sxAcTgo++2jNCx+j4zTx3fYcPkqV99euAb2pfyxhdZJ0HEuUbyH6udeIW3iGNInj8UzeADeYYPI+NYU7Hk57P3Dkwf9Tr4qvKuEyqdeIFHXgBkMQUJHJBK4+vfGUZhLor4RW04Wex95isCaz2n++FMCn64nXlvfphyjJUzZ759E87pJmzLOmo/wlSEviqLgLMonfep4hMDar0ZjR227SEcu+sFKghs2o9hs2LIycfYpSu63cNgwIlGa3l+B7g+i+bx4hg2i/o33Oiyr+eNPaXp/OXqTH73Zj2LTrBN3IcA0MfxBK3etQ1pj5VVomWmYLRFUpwPN60HoBoqqWg2dYMjqBfIHwWEHXQeHHRGNgWmiOqxhYoqioDgd1jAzj4t4dS3x6jr0UAvRnaUkGpqofbXje88F121C9bhx9iwkUddAor4BZ5+eySwLIYjurSS0fgstW7YTL6/CCIdx9Cki9PlWhGH1GDUvXwVAeGcJLZ9vteppGATXbSb42ee0fLEDbDbCO0uoefE1mpauQLHbCK7ZQKyyBrDm/AkhUDQV4yC/Z/eg/gRWr8eMRo/0q+9W+861WrZsh4SOZ8hAvCOHYmttQIpojOCn6/ny+p/S+N7yg54b7VP97D8IrttMoq4RMxxBdbus78luQ3E4UB12mj9amTy+hLcX4+zXM5kPZ5+e1vC/D1eiB4K4BvZF9bjR6xrQstJJHLB/M8IRwlu+xGiJWCMSFAXV60Z1OonuqUB1u4mUlFH24KMkGpqOeFslGpvxf7KG0OdfdLgdgus2sefXf2LX7fex909PE9m9p83zRjhMw5IPqFz4EsF1m06Y+zGltOclJyeH+vp6evToQc+ePdmwYQNZWVldvs/LvffeyxVXXMH555/Pv//9b+655x6ee+65dq+74IILuOOOOw67vkZpOYnScsyWCM5eRWhea2KhME1QFRw9C4iWV6G6nATXbiJjyriDlhUpLiVSXEq8ph5sdhy5WZixBJFde3D264Wrfy/rB9SzgNL7/4Dm81g/ApeTjKkTyJwxOTnkpW7R2wTXbSZj+uTksJmvUjQNV7/e2PNzafpoJYrdTvZZMw97W6RKj/PmkD5lLKrd3uHkxxNVbOVa9GAIBQV7Xo4196SV5nZBuoEwrXkBnuGDsKX5cPbuSfOHK/Gc0r9NWf5P1mDG4+hNfrQ0H7YMH4mmAOz7XRlxrAFeEC2vRsRiaF43ZjBsXSVSFIRuZRrdQHU50P0hME0Um83qoYm0Hvz2jbponXukKAqKw4EZDqOme4lX1uLomY8zO4N4VR3x2nrK//AUfe/5Sdur7KZJ45Kl+EYPb5NhoyVMZGdJsgFvNPsRiUTypCbw0SpcV1yYLMv/8WrMcAT3oP6gqoQ2fAGmgZqeRnjbTmw9sonsLCFavAdhmtgz0xEKmDEdMxZDDwTR0nyHnKzr7t+HWHkVLVu/xHfqsMP4xo+d6Hsfk3HglUFh9ezFK6oxYwl0f5CaF/5J4Q1XHnJoVv2idzACIYxwFEfvQmticChMePsufKOGoQdDhHfuRq9vpObvr+HI7WENM6xvwNWvNznnnIlnyECqX/gnms+DpxO9wPsa66H1m6l9dTEFV33nqGwX6cg4FZXoex+jut2oDhta2v6J9oqqYu+RTaK6HlsPn9VDOm0izl6FVi9sVU2bYT9mIkHdq2+iNwcwWlpwFOZaQ7qEwGgJW/NYYgkwBbHiPSTqGsE0sad7MWob0NJ9CCH2n3gpCqrdRry+yRpGZbdb+41I1CpLmHDAcElV0zA060IOWEPBHHk5GJpm7Zve/ojs2TNwD+ybfI8QgsalHycXIwh/WYwtOwvlgFXVYuVVJGrrMXQdVVWtGw4LiBbvwd4jm9jeSvRgi9W7nZOFLSudWHkNxOKAQLHbcfQqQK+uJ1ZsnXQakSjYbCgeJ4naehrf/pCsM6bi6NMT39hTrXkXB5kkrnnc2LIyCazZmFxc5XhklFWQKCnDDEdx9ipE9boxG2MoNg3N58WMxayeslAL5Y88BUD2nBkdlhUtqyC4YROJ+mYrl3k9kvOAUMBRkEuitgFn756EPt+Co2ehNd/xgCHOiqqiOh3E9laSNn5UcuEHYZjYsjPQq+vRQy0YwRYiu0pAUVGz0lFVFaN1IRwtP4d4VS3RPXsRsQSK00Hxz3/DwIfvwt6JhRSEEMQqqkjUN0Lrxe1EYxO1f1+EPTcbMxZD+eeb9P7PHySHBTav+JT6f72NZ8QQPMMHE6uqofzPf6PXf9yIu39v9GCI4v/6FYm6BhS7jaa3PyJz1un0/MG1R+Fb7F4pbbxcfPHFrFu3jrlz53LddddxzTXXoKoq3/3udztdRkNDA1u3buWZZ54BYN68eTzwwAM0NjaSnX10JxvF123GkZVBtKkZLd2HEYmSqKkjUd/aelawVkCx22h4833SJ4896OpEgZVrMWNxawecl4Nis6HZbCiaSry8CsVhxwiGiFVWAyaZZ05D83owWyJEd5ex5+NPyT77DFwD+xJY8RkZMw7ecDmQ5naRPnkcDW8uxXvq0OQBROzrljYMXH17H/H4cjMeJ1qyF83nTXaNHy2OHif+3J2viq/agGq3o9ptbRou+6heD4mqWmx5PYju3oNv9AhcfXrS9P7H7V7b9NFK9MZma1x1doY15Ck3m2gwBJqGiMVBCEQ0RqykDFQVV/8+6E0BVI910r5v6IUZi2NzOqy2gmlYQ8pUtXWs+L7eu69cMdc062JpIISiadaOX9PQXE4Up4PI7jJiZRVtViAKf1mM0I3klTWAeE09zctXoaX5cPUpIrxtF0II1Jys1qv+CfTGZoIbtpA+9lQSjc0kGprwDD0FRVHQmwMoNg1hKhgNTbREYoDACATBZgdN3X/ly2Gn/vV3k1dvs+bMSJ7sH4yrdxFNH6w4rhsvuTYHZqMfx+kTAIjs3kPLxq3Wik/pPqsx2nrVMbKrlKzZ08me+60O58EkmvxEdhQnJ7DuW+FJS/NixhNEK2pI1DdZPTED+qDaHfjGjASsnrPY3kr2/umvuPr1omXjVlyn9CO8Yzeufr0Pue9SFAXviCE0f7iS3AVnnzRL3J7ITnWnIxK61bOb0/63Yi340boqYXkVZiKBarfjyM8lvHVHm8ZLy+btmPE4IqFjy8pIzvVUvR6MUBhnUT6xskpQWxcUCbVgz8lCxKxeX0VRrYaLENZFF8NEcTgQTX6wWfsgWvdnoFiNl6/SNKtHQlH2r/ylaegNzWg+D7WvvEHfO3+UfLkRaiFR10ja+NHWiWV5Fd5h+4fDCcMgUVOPECaa04Fis1lzUlQFEYmiupxEyyqI7a1E9XmteYXhiDXHJZEA01oIQURiVgMOrH2vEJBIYPNmkWixFlsJrPkcZ2U1Ga2/869jz80hsqvkuG68xNduxpGdSaRxD0IIQhu+QLFpqF43dp8XFEjUNCAMw+qNPeAQ1PTBChyF+cnjgP+TzxBxHUwTW1528lzL8Ies71vTrKwAel0jZjzR4YVRs7XHbt++UW8OWBkxTExdp2XTNoRuWBcJbQrCH8R0Oq1zuUAIW2629XvRrTyobidGKEzpff+PgQ/fdfAFdnSdwJqNNC39GL2xGS0jHUUBPRQmWlyKd9RwvCOHorqchDZ+kRwWKEyThn+/S9r4UclV5tz9+4Bp0rjkfXre8l0a3niPREMjvtNGoDodRCtq8K/4jJx5c9B8Xhrf/gDV6SLnnFnH3TydlDZebrjhhuSyyBdccAETJ04kEokwcODATpdRVVVFfn4+WusJn6Zp5OXlUVVV1a7x8uabb7JixQpyc3P58Y9/zJgx7Vc0CQQCBAKBNo9VV1cDYDYHUDxuFKfDWo6xqgbV42ltfFifb40D1wlv20XN86+Sf+WCDk9G9SY/5r5uXtv+5xWnw5ps/OVuNI8be3am1f0Yi6P4vK1r1A/FObAP9a+/i9ANNJ+byO49OHsWYks/9BhKzePG2buQ5mWryL/sAiIlZVQ+9YJ1QqoqiHiCgusuPezVMcJfFlP5P8+jupwY0SiuPr3oefM13/gVmg6WLQUwW1pQFQUOsnqaolrPqTYVI2QdyBSHHaHrCMNE0fafbOr1TYwJJGEAACAASURBVK3DzxxtezcUBVVT0TIz0EMh6/0tEUCg2NRkF7dI6Nawwn1DNtpURLHKVJTWtov4atsl+byI66Cp+6+Gaiqay0ki7CfR2Nym8RLa+AWOwrxkfRONzTQvW2mt3JLuI1ZdhxAmqtuJZtMQigouFyISJbjmc9JGDbNWBHO7k2XEa+qsXqOGZhSH3RpK0tBkbWPDQMvIwAi2QCKBlubBaPDjOXUoenUd8YqqQzZenL2LaHz7I4yWcLIXNlUOlq001YbicaEoCqGNW4ns2I2zdyGq15PcTjbDwGgOkDZxDP6PPyW6t5KiG65o14AxgiHQrIYoX3lO0VTildXYcjIxmgLWKm3x/UNndX+A8I7dGMEQgVXrrAsx4QiR+kZCG7bg6tcb39iRX7ugg+pyYs/Nxr96PdlnTAOsq5FCN1Bs2hEvYx3avJ2GN94l0eTHO3wQuQvOPaEmNneXg2UrU7Oz7wLGwVZ9VFqXXVc0DTMaQ7XbUez2djfZ1Zv8CF235qEceOGsdRiUGY2jaBqq04makYapW/uWfRPuE3UNKE7H/v3TvkbMvv2YVZsD/t0+K6qiWsVpanK/p2gqGAZquq/1QuJ+ZiSKuq+n2jDAFG22Q7KhEtWtuTW60boftyPiCUQigRnTMCJRa6EKAMNsneMi9lUKIxrb//fQWj/dwIzErHMLu4JnyEBaNm8nfcr4Q/4OVLs1nC/Vvv5cy4/icgOCaHFpcq7igVS3C1uGDz0QovGtD8EUZEyfRMUTz1q976f0J2f+HOK19ZjRGMI0rfmZrZR9+TCtczCR0FGdDsxoFOUr84XMWBwj1GId14RoHZ1gIAydeFVtsj5mSxhs9tZVKs3kPlBHoHrdVk9NuhdbdjqJqjqc/XoRr6i2Rs50MFLHCEeo+Muz6I3NuAb2xTf21P3HyIam5FDFhiVLyTpjKs7eRUR2lFjvbQlbFx+/0qvjyM8juHYjAJGSvWhud3KBB3uGD72+gXh1LU3vLQdVxYxGMYJBCq69pPNf7jGQssaLYRiMGTOGtWvX4mhtcRYVdd962Jdddhk333wzdrudTz75hB/+8IcsWbKErKy2k/CeffZZHnvssQ7LUDzWRGa9OQAC7Pk9wKTNzkIRYPP5UJ0uwjt3U/3cqxRcd0m7HYqWkYZit6NEY9bBt3WHLRI6ZkLHnp2BiCWsK+XxBKrT+uEKIQh/WUx4205EIkGirgFX395ES/fSsmkbtpws0kYPx36QybJGS5jonnJ0f5DQxq04crOpX7wU74ghyR6SREMTVX99kX733N7hJMWvYyZ0Kp96Ae/o4TjyeiCEsFYTencZPeaf1aWyTjZfmy2HwxoWYRgdPi+EAMNAGAesJGNY47sPvPkbgObzogeD1lW71h0tWIdrRdMQrf+2Z2dYwyLiVmNFUVWrK7659YCiqgfs7IVVQrIts79RoygdTZ3bf0BIfr4pQNUQ8Vi73jhrouz+K0+hDVuw5+YkG+NGMITqsg4OovVArgBCVcAwiJaWY8vJ2j/uW1HQW8LWa0zTujq27yRAN5K/KwyjdTKwD6OhmVhJGSJh4M05dK+tomnW1bNgKOWNl4NlK8/mRESihHfvIbJjN65T+qHabET27MWRn4fmdv5/9t47TK7yPP//nDa9by9arXrvBTWQEAhhRLFNcQf3hmMndhInceI4v2/ikuLYcZw4tmM77jbYYBAGhBBICCEJFVRAbVfb++60nX7mnPP7450dabWrimQtWPd1wQW7M2fOzpx5z/s8z10wszkkhx3V58G3YjHxl/bS/+jTlL19uB5R8bjAQmwczxDWGwlh3KDY7ZiqgpnOoobETTPX3Ut0605sFaW4Z09jcP9hgCLt1dR1cp29RDZtI7ju+nM6LNprqxjc/QqhtauIv/wK/b97Gn0ggur1EFq/hsDalZdUxKQamuj631/gnj0N55SJpE+20P7N7zP+bz9zwW5sRiJJz68fJ/nKYWSng+C61QRvWvWGzwY627W1whMqNjis0zrSw2CYoMjFwgXAMvLF+9kQZLdT3AMtMbEoQqKw6VSxhppriI0iQ82WfL44SQbRpLH0vDjesAZMYQ0r/vdwmJYQ8WOeVvCYlnC/y+XQKsuGn3NBlyL+RwaGvw+ypgpNnFJYB+XC+jk09VFVZFUVk5acjuLm1FouC5E+poViUzGGzkWRT1F1bZqgwRkWmZZ2tJLgBV1rlmGMeP+vBs55P3S7MFNZ8gNRbDUVo7oXWqYhajqHA9+KxQw8uQVbdSUlG24m/PRzpBuaaP/6d8FmE4wGEOt9ocAsXhZDkzpVxtDzyE7HCC2jkUgi2eyg5069x5ZFPjqI6vNg5nRRIFkgKwUKtSwL7ZKug2GKyAFZQlIkwUZwOjCzOay8QfJYw4jixdTzdPz3/2FmcvhWLhm5h3S7wDSxVVVgDCaIbN6Oc+oEbNXCBVNxOkARzU7Fc+r+pIcj2MrF/tA9ayqJA6+S6xtA8bjJdvWCLGOvrUYfiOBdMl9k3/QNnPfz/EPjqhUviqJQX19PJBKhouLiLe+GUFVVRU9PD4ZhoCgKhmHQ29tLVVXVsMeVlZ1aeFauXElVVRUnTpxg6dKlwx73wAMP8La3DXfi6O7u5j3veQ+2eTPJbXoBM5lCqypHUhRyvb0Y8YTgyXvdWIWL0Tm5HtfUiUS37SJ5+NiIKYZ/+WLiu/ZjJFMY0ThyWUg4kfSHxXjUYScfSyDJMmrAh+JxCfu+QlKwvbpCiBEHk6AqOGqrsEyTfDhKZMuL+FYsHhaOZCRSDO55hVx3H4rfW9ggWPT86nHykRhaaUiIZgte8raqCuI791Gy4aaL+jxyHV1IqlrcmEiShGNiHYmDR/7oi5dzXVvavJnoL76Mmc9jBXwjFioznUGy2zCSKRz18wHIdvaMdLUB/CsWEX72BXLdfUKc6HIKqhiIYkRRxCY0q6OVlWDEE5jpLLLTIdxXThPJWtksetg4NZUzjdOaltbwjuDQTw0TkJBcTsxIVHQOVUUUFliowcCIME3JZhMWy0B+MIHePzBMDyGpKpKSR1KFxkKSKFJAZLud1JEThG6/GVtZCZnmNiRNwxxMFk7IEsLYQl6CKGDymMm06NRqasE1UMZWVYlzQt1FuG9JI4ZTVwPnurYkn4fEngM46mqQNRV9IEq2qY1sUxtaealwZitw+SVFwbNoLtGtOwjdumaYXbQWCuKcNJ70yVZyXT3kE05km4YxKNx7tOoQRiKFGgqKPKKl84QD3rZd2OtqUL0FpzALYcmdSqO4nMiahr2ummxHD7FtuwicY8MvO50YiSSD+w7R+4tHcc+fha80RD4aJ/z081imQWjdxWv5os+/hHPS+GJR7Z4zndjzL5FpasU5qf6CjtHxnZ9g5nJ4lswX1Iynn0e228Y0PedCcLZr6xP3P1AQuwv6y5lTf8uyMFNp1FAQxesRLn+Whd4fwTGhbthji9RLCWHx73KKOiIjvuNSgboqaSpWJou9upxMSydKSFyfStAvpjkFSqwxWHBYVGQoNHEswxABvDldHPPMa6zQmZcKhYNlmBipDJLdTj4ap+ST7x/2cMUn7qN6JIYW9GMrLyUfjRddviRVRQsFyMfigg5ms4n1MyNMTzAMbFXlWLJE+lgjitOBbLcLythQw0eSwOkEuwaZ3KnmVoE+ZhkGnnmz0EqDOMaf3c75dOQjMTwL5lzQY68kzrnXmjuDzMO/F5OvUZYCyzCxMoW91qTxKC4njvpxxHfto/pD76Li3W8juvUl+h/bROZkS/Heog9EsVWUCsZCvvA5WBZmJivoZG4nWnUlek/fsOvZMgwwdJEBM/SznC4+H1XBSqULxTTDzleSJOHCWaCLWYZRnCBJdlvRTW8017H4zr3kIzF8K0afpskOO67pk0g3NKMGA1imSfLgESZ97W/F8VWV0E3XE92+G8/C2UIj0x8mdbSB6g+/B4DQLTcQ37mPbFuHoGRrGiUbbsJeVU7pW2+l91ePIakK1R+//8I+1D8gript7I477uDjH/84999/P5WVwzuxy5cvv6BjlJSUMGPGDDZu3Mhdd93Fxo0bmTFjxgjKWE9PT7FIOnLkCB0dHUyYMGHE8Xw+Hz7f6EJwddoksr95UgT3xRKik1SgxBiDiSKtgoEI/lVLkVQV58S6Ai9+ePHinDoR5+R6JE0l29IhNqea4J/LDgf5cAwlGEDvHSCwWrwXif2HyfeHcU4aL1yiEilkh11MX2qriiJJ2eUkvmMPyrrr0UJB8vEEkWe2ovi8uGZMLtLYzHQGJeAT59DeRT4SJbBmhegoO+zkExfmQHQ6FK9H2E4aRvF1jMEkWvDNI66/VJzr2nLcuBxj94FiAaqGAqcca/Q8+Ugc1e9F9XmLupBsawdl99w+4liBNSuIbt+NrbKMbHu3cAGzadjrx5Hr7AZJFjSOVBrb5AkYLifZjm4UvwczmUIpDWGks1hDn/9pDiaWJf4lOxyiIBjFtcbMi2RpSZGxVZYjKTJ6dz9IoAYCowor7eOqiL/4MkwcT669C8XnHdbJtZWFSB1tRC0NYWWzmOlsYWODsGVOpjESSfzXL2XgyefINreLwsSuQd7AkmW0ylKsvCG483nRmZU1FcluF+dnmVh5/YILF8s0MTOZYV2tq4VzXVu22dPIHG/CGuoIOx3YKsvIdfedcsmxLLSSELbqChSnAzUUJL57P8E1K4Ydq/TO9bR/+0fYx9eSbWlHtttRg37MrOCDG6m0MAcZX4sa8DO45wBqwHeqcGHI1EHwwIeKI0mSsNdUkDraKPJdzhqOZoEkM/DEZtxzphftmLWgH8/iuYSfep7g2lWjUnXPBTObFZ3sYeeoFgrk8yPX2y+EuIZJ+kQTGAaOiXVEn9/xhi9eznZtxY082sLZ6C/tw8oLys1Qh9yyLIyYKCCMRJLA4rmAoHcpToewBD4NittFcO0qYi/uIdvWQa61E0lT0YIB5IK9MTab0L9UVaCGgtDWhWRZYjNoWWgBH/lIVJiNFPUvQpxv5nQwTTFhzUSKdNahNcY0TEElLQ1iDqZQ/T7Rhc7lkBwOPAtm41syf9g5S7JMYM0KEq8cQgvOwTl9Mom9B1GD/uLa7RhfS6qhqUAxMoqW9c7J9eQHIjgm1mGvrSJbYEOQN5AskBw2hmhu+e4+VLcbuVS4R0pIoCkYg0lUvw/f0gXDaMPngqnr5Lp78a84u5nQHwrnWrOUqRMwY4PIPi/5cAytLFT8TlumSX4giuxxYWWzxeaCo75WONsNJlG9bkK3rCa47gaSh4/R+i//RT6RwEymyLZ2ikLF6yU/OIjeF0bx+9B7B/AtX4QWDDBwrBGrquLU+ypJmDkdZ2H6ZhmmcGhUFOFSV8jikYp06lMo0qjzomg29TySQ2hGjXQGU9fxr1g87DmWZRHZ/ILYI55jmuaeNwutrJRseydK0E+uo2vY/Sh021pQZCKbX8DMZFCDASreezfuWVPF++xyMeH//SWJV14lH47gnFyPY6JoZAWuv05c84oyJjO2ruoZ/eIXvwCEjfHpkCSJZ5999oKP86UvfYm/+qu/4r/+67/w+Xx87WtfA+AjH/kIn/70p5kzZw5f//rXefXVV5FlGU3T+Od//udh05gLgiRGxXg9GINJch3dSE47it+LkUyLrohhYgwm6PnZbwnctArX1IlENm1DD0eH+a5LkkTNJx6g87s/xcob6AVhoZESx1Hc4ovpK9iImtks6eMncZ4eKidLYqR9xg1WcTnRykIkDx3Fv+o6olu2o5UER9kQWGgBP7muHpxTJ5Jt7WRw70G8S+aT6+krFk0XA60kiHveTOIv7cUxcbzgtTc2U/OpkSYMuZ5+8pEotprKC/Rq1+n7zRPYa6sJrFp63se/kaCUBKn68Ltp+9b/IhsGufZupCExYDaL4nSgeD34b7hOBJo1twHgmTtSLK6VBKn99Ifo+PaPsNVWkQ9HkAp8XCuri027w45kE+YAaihApq1TUBvsNox0WozRnUIrYeV0LEkSupisLh7nEnoTClQzSxGaA9M0Qc8j+zxYqQz2mVOQZLk4+k4dOkLghpGbOf91Cxl49GnMbLbYBRv2/njc2KoryXV2ITnsokOqKliGKcS8ze1Y2RzZSEx8b5YtIB8bJNPagWlkkKVCYrIlNu9WwcpcDQoXNBQZ2e3GXn3h5hK5jm4cdTVj3qvfyuZwTplAtqmNvNuJ7HaJzZ8kYQwmMdIZcu1d9LV34ZhQR8ntN2OvriB54LURxYtr2iSq3n8f3T9+CEf9OPT+sEgCT2XQc3kUtxPnlHrcc2cKe+/GFpxT6ocdQyp0O8+05JQkCS3oJ3W8Ef9ZihcjkUL1e8g0t+GeN2vY71SvR9AeU+mL/kx8SxfQ+9BGtLISZIddOLFlxPt2IbAMk3w0jhr045oxGUvPkzoiAhffzHDfu4F0Uzu5nn707j6hs9MUzIwOWMhOB96l87FVlGHqeZKHj1H2treMuiEre9ttQgeq6+SjMSxJFq6IWR1jMCHoObqE7HWLYreqgmxHN7LXLYqbQmGLUqCVWVZxCmgmkmC3Fcw6FJBkzFzuFB07lxUTad1AC/gEvbKtYDJgt1F1Fr6/f9USwk8/Rz4aw15TSfLAq+g9/dgKm1xJVXBPnyz0B+kM+XiSbFc3en8Y19TJggFhE5lU2e5eHLXVmPk8qUNHkVQFz4JZxbVQD4tQzeAtN5BPJAk/vlloIC6wcAGEW+WsaWPeqVPS86ihAEgS+Vzu1P1QkrAyWSRNQ5btBNauKk4yZJsN1edB7+0rNkskScIzZzpTv/1lOv7jf8l09wqnOlVF7y5YTBfskL1L5xfZKraaSrIdXUXXtnw8IQwoCjlTZi6HpMgobid6fxitsgwrHMOSBC1SUk7R3CxLMBSsrI5j4niMWBy9q1dYfisK7hlTRgRuZprbMJIptPJzN9KGQshluw3F5yEpSUR37MFRW0Vky4vChClvoPg8BG9YRmD1crTA8M9e1tSzBlKOZZ3yVS1etmzZclmOM2nSJB566KERP//e975X/O+hgub1wEpnkBSV0rvWkuvsIXW8UTiNWZZwt5hULzQpR06QaW4r0rAUr5vB/Yfxzp81TEOiuJyM+9OPkGntILZzL5nmNlJHTiC7XLimTRQuPAWecPpka8G+79RHJmta0fP+TGglQVJHGgo5INKIwsUqjEq1yjLs0RjZk62oIT/pYycxMzpaaWjUjfGFoOqBe4ls3UnilcOoPi+1n/mwcLkowEhn6Pr+z0k3NqN43RixQfyrl4ub2jn45ZmWdiKbt6P6PG+64gUguHYllmHQ88tHkW0aZkpQxRz143BNnSg0VkC6oZlMcxvj/vwTZ+0wOyfUMfGf/orBvQeI736FbHsXSOCaNplcT68oAAyTdGMz9ppKnFMnkj7agKRqWAlhmSx7XBgDUbTSIErAh5FIke8bEBOHgnWygImZtkSWQkbkFVmGiWNiHWY6Q667D8+C2WSb2/Bff92oN07F7cK7aA6po43CwGIUNyB7dTlayI9eEPdaWKglIdINLVg5Yf+ceu04WkUZosNlYCbTaJVl2CpKyYejgISjrhrZ5SR56CiOCeOLfOWSDTePCHY7FzKtHZS9/bYLfvzVgpXOYK+pwrd8MZmmVvS+MEY6jaN+HPbxtShuF8lDR0gceK14s5TsNvK9/cJ17oxC0rtwDu6ZU4nvOUC6oYlcdx/Jw0dF6nVtVXHN0sNRJLtthIbFVlZCprVj1GtXDfpJn2w969+SbesktH41ZjqD3h/GXlVe/F0+nkDS1GFUtwuFd/E8sp09RJ/dLjRQPg81D77/nAYCw/6myjKxsRqi9RR0F87TwhjfjJBUlUn//Lc0/39fRy/kSFk5Ha1cpMu7JtUL45ZkShhrLJyN7ywRApIiU3n/vQTXriKyfRfpo43kIzHsVRXILgeZpjbcE+pIHjkB+XxR45aPRJFUBSOeRK0owYgOYuk6trpqJAraFJs2vMknCb2MOWQcYFlChypJ2KoryDQLO1vZ5aTiPW8fZi5yOlSfl8oH7qP7xw/jXTqfwI0riTzzApm2TmwVZUUXPcXtEqLvZBJ7bTVGLC7yWApmH97F8zFf2EXiwGtYumgWqZqDbFcvisddCOzM4F+1hGx7F9nWDrzXLRDr8eT6C5o06v1hMidbqfvzT1zUZ3w1YOUNZJtG8JbV5Lp6SR1rRB8QFsFqeSnOKRNw1NWM/LsV0Uw7E6rXQ91f/wmpIw0iE+XVY5jJpLgmTJN8JEby4BFkhwPHuGp8yxaKz/FkCyBst9XSkMg+83oKzRcR+qz6/ZiJNJLTgZXNjtCaDp2PbNew11Qg1VZiZLJkGltQSwKU3rV+xPnqfQOo/pH08dNhZrLEduxB7+tHLtCuFa+X3oYm7BXlOOqqCdywDGQZI5Ek9eoxolu2U3b3BvyrlpI61khs+y5yvQNiap7JYqUzKB43vuWLCd58/ZicuAzhqp7ZN7/5zbP+7jOf+cwf8EwuEIIzgyTL2GursNeO3lVzThovnEhOy4ro/eWjtP/7d/EtnU9o/Y14F88rdkwcdTU46moACG/aSnz3K7imTBx2zGxrxwjXCMXnBV0X9rVnYKhoSh46ilY+XLxvpDNk2zrIxxMk9h1Cdruwj6shFxbjdFtlKTWffOCiqRfF11ZVQjetInTTqlF/3/vLR8nH4yIkUBYdsPiOvdgqyopFSbq5ja7v/hQ9Fse7eB5V77sH54TxlN93B/bqS9dIjXWE1t2A4vfS9+vH0UJB7LVVKB43lmmSOtpAtr0TW3kZdZ9/cIRu5EzIdhv+FUtG2Geauk7kmW1Ent+BbLOROt4kKFOWhZlIiuIjL4T7it9LPhonHxtE1jSUYAAznREWk4WOEiCmjulTfGzV50Hv7kMNBUSgZlcPrikTKXv7hrOeb+nbN9D6tf9EUhXy8SS2KmvE4i077NgqSsk0t2OvqsB//XVkmtuJvbCL2Iu7yfeHUYIBch3d2GoqCd60isH9h7GyuWHhcQBaaUjw61UVxa2iFLp1+XgCIxZH8XnO2qHMdnRj6folF/h/SEh2G9ZgSmzwUmmyHSKLKpfLkWluQ3Y48C6ZP4wHb+l59L4Bjn7oc5TdvYHQLauHdeFkh53AqqUEVi0VKev/798FZ/+0zb6V10ddQ5RQAKuhaVS6naQqIkxw6BiWVdi8NJDr6sUYTJAfTGCvrSSx7xDSknloZSXko3ES+w8TunXNJa1bkiRRdtd6StavwUilUAP+CxbqDz2/9Lab6Ht8E8noa1imhWS3UfbWkRuTNxsUj5v6v/8c3T/6NanjjdimVGKrKEVSFPT+MNmObvLRGKFbbxQ23OcRldtrq6h851tH/DzV0MTAE8+iOh3oA1EyLR1INq0QOWAh2TTyPQPivqsq5Nq6kAoTVSXgFzqrRFJcX6evXVB0R7QUhdTRRmSHHceEOsrfcSfu8xSg3oXie9P9fw+hlYXwXreAbGsH6RNNBfG3gZlKYWZ17OOqKdlwM575M4lueZHY9t0oLieSTSPb219wD3UL455MFjORItfRjeJxo/i9JPYdwrtkPuX33oG9ppKuH/yS2I49eBfPO2s2lWVZ5Dq7SR4+RvVH34utprKw7ilX3WjkbJCcdrHpN03sNZUXFLcgNFYZ5LPQeCVZxj1rapEyBZA62kD/45uIbt1JrquXgd89jex24Zk7A8XtJNsZR5IknDX1mHqebHsXVlkIPRwVTaHJ9YKC29tPtq1TmEYUHLpQFNHgM0xklwPF4y5e+2Za3D8VlwvPwpH6I2vIde4cf2vkuReRZBnXDMFuMFJpBvceQvW6R7Ab5FBAONcmkvT++nF6fv0YkmWhBgLYqstJ7H+1YM9toeo60Rd2kWluHdN5L1e1eBmyxRtCX18fL7/8MjfffPNVOqNzQ3I6RLWdyZ53nDZUuFimoJHZq8rJdfcR37mP+M59aKUhgresJnTL6mGbUN/yRQxsfEaMtE/rWJo5HfWMKliSJWSbfRhXexgUBSM+iHOy4DCa2Rzpky2YheRXrayk0BHKkunuRdJU3LOnjdiEXE6YmSyD+w4RvPmGU64sNhuu6ZOJbX2JwKql6OEoJ//6y6IAc7sIP7EZvS/M+L/85BsyWPNi4V+6AN/COSQOHiH20l4ybZ3IqoKtporye24vFrqXClnTKLntJkLr14iOVjhyStzvcWPpOsZgknwkRq67t6AnSWAVbiTuOdORbRrpk22kjzag94excqe6mpZhoPi84tpKZzASSUrvvAXfskXn3BCqXjfjPvdx2r/5fcxEklx3r9DMDHWwDAM9HCU/EEWrLMO/fJGgqiUSlN29AXtNJf2PPIl77gwUt6v4HVVLQwzu2kfqSANqweUP00APR2AgQs2nP0SmoZn4zn3YqitI7D2I4nRipNJ4l8zDOXH8sPPMdvWQPHyUcZ/92FktYscS1Ppx5J59kVx/mPxABOeU+uLaIrQJcaHNWLMcW4Wgu+jdvRipNHrfAJ3f+TE9P3+E0jtvoeSOdSMoWZIkEbzpegaeeg6touyUu5yijuqeZ8QHUYMB9L4wsss5bDNrneYGZKTTRJ/bgZnJiuwPlwPntEm4p00m09KOmc0S27YLVAUt4KPktrUEzqC5XSxkh/2SqRJl994BqkJ8935Un4eKd78dLXjhk7w3MhS3i5oH30+ub4DYC7tINzYXHLRchG6+Hu/iea+bguKaPAHXZz5MrrefdEMzejhCpr2raPVqptLkE0lynT3oA2GMVBozm0O2abjnzhTNC0Umc7KVwZdfIR+JFafHjsn1OGoqQVFxTKzDt3DOiGbHueBdOAfnlAnEduwh+tyLwqgg4CMfE25UvsXz8C1biHv29OIaWH7vHZTeub54Lff89Le4blyBrawEI5ki+vwOqj/5AK5JE4STlWGMCE+s+uA76X/8GaJbRYmAXwAAIABJREFUtqOWBHHUjxOFtyJj5XSy7V1k2jqQHXZqP/NhJEWh5cv/Qa67DywL19SJVLzvnvPawv+hIakq7llTybR0DAsGPRfyAxFku+2iqL+u6ZOpmz6Zqg+9m/BTz9H/+CackycQXHcDqs+LY+J4Mq3tZBpaSB1vJHOyhfxgEu+iuaQbW4Q2TpKwV5RhKyslfbKFXE+fOLhpFqysNSRNPbW2hqMi2qK6knGf+fCoey3F4xYF0Fmg9w0IO+ipE09FA3T1iu9BTh91Yg4FvVA0Rj42iK2yjHw8TvpkM0gS7nkzkSSJbEePsLZPpci0drzu/caVwlW9837lK18Z8bNt27bxxBNPXIWzOT8kVXAD081t5+3GDCHX2YOtporxn38QPRIjsnkb4aefJ9fZQ+/PH6H3F48y4R8/j7cQ5qZ6PXgXzyd54AiexXOLF6awXRy+EdDDUWSHHdM0xSJ5RiaBZRhYBR6wmc2RfO24CN/yuTGTaRzjqgsZM17hOhWLkzxwBNfM86deXyqE64dUtIYegux0FIO4Bn7/LJgW7gUzkCQJJRRgcM8rI3RDb2ZIqop34ZxiV++KvIai4J459fwPLCDT2kH0+R0kD7xWMJjQcE6ZQPVH3o17wWwyx08y8NRzqH4f7hlTROdJloi9+LKYYFzARkAL+hn/hU/T/dPfEnnqOSHMtIsAMSubw1ZZjn/VkuImWe8Pk+3qpeqD7yLb1Yvscoyw99aCfkK33ogeiZFt7Sieu1oSQvV78S+Zj2/RXCRVofeXv8NWU4nidpFPJIi+sBvHhLpi6GWmqRV9IELtZz48Zhf1M6FOnUD60U0YgwlcBQ3SEARn2g9IxHfvp+T2dVh6nlxPH5P+5e9IvXqM3l9vJH28kZ6f/oa+hzcSestayt72lmFUVO+SeUS2vEjqyAnRCZQk1JIAZjaHmdOL9BkR7DdA4KaVJPYeItveib22urjO5SNxbJXlmNkckU3bkF1OHDWV5No6xdo4fzaSIjqozqkTGdy9H+eEOio+8I5iZtjVgqTIlN9zO+WjmGhkWjvINLXhmjXlTRmyOwRbWckVp1LaykvPO3UeQn4wSXznXmIv7CR9rFEYCzgdBNasILBmOWZOJ/zYJiofuBdbpaAgxl58mdSxRtE4GcWi92xQvR5K1q8htO4Gkc2Wy4lcqnMEqsp2G66pgmURn1yP3t2LVhoSa5Sq4ppYP8wI4UxIskzZXesJrbuB+O79RJ9/CX0gLDJLHHZcMyZT/aF345w6ESORpPnv/xXn1IkiPNY0SR9rpP2b36f+i392UZPGPwQCN66k+0e/xjGx7oKKyHRTK4EbV17S36GFAlS8+22U3XuHELYXGjSR53bQ9q//jXfxPErvvIXyd95F+9e/i5nJ4poxmdj2l1GDPuGGKUvC+cztJNPWJYxidB3JYcdMZ8mHo8KcKZMhsHo5lR94B6pv9Bwp1zTxeZ1pczwEIz44rPFj5fPo4YgwyrHZMNKZUXV/sR17Ck0FpwgQr64QmuvefozYIFrQj72mQtwnszmy7Z1j9j435tqGq1at4s/+7M+u9mmcFYE1K2j/9+/inDT+vNMJyzRJN7ZQdregymhBP+X33kHZ3RtIHjzCwNPPkTx4ZNgGMrZjD/41y8j9spfEngO458wQ4WwVZcK+z+vBMk30/jD6QJTQuusx9TzR514UAsWCGMsyLcxkGtnpFGL/hmZkpwNJUTDiCeE6dno4ZmETowbjpI41Fh07LjcUrwctFCDX3TtMpJZt7cA1axoAua4ewV3N6cInPZ1F9XrInGz5oylexiIcdTVU3n/vWX/vnj0d9+zhrnpdP/oV0c0vEN38ArbqSkLrbiB406pzuEmJyVDV++9D9bqJbt+Fa9IElIAP1eMudm8twyTT2k7qeCPVH30vWmlIdJUisRGhnUPQgv5hHcbEgdeKAk1Jlim753bCTz2P7HSQOtoAksh3SOw7JIw09DyBNSvwr1qK6vdiGSapI8fRB6LYx1UVi5yxBmG37icfjY0WbwGI3Klcdx963wDZlna8i+ej+X34VyzBt3wxyYNH6H3ocRL7Donp1qypw0T1ss1G7ac/RPs3v8/grv04J9eLTvCEcej9EbTSAHp/hHw0jv/6ZdjKSgnctIrYtl2kjzehlQREeGo4in/NchIHXyvYY8tkTjSjlZfgX7lEUHDSGRFKabfhu24B0W07Sb92fMS1N1aQaWmn7d+/i1Yaov+xp6n/0ufGvMnDmwWq101o3Q1Fh8PTtQhD8JxmyW5ZFt0/fohsWyddP/gFoVtvpGTDzUVXuwuBJMuFe+fF3T/L77uTjm//gPDvn0XSNKo/8p4LnlYpLifBNSuKBhuj/Z3xnfvEd3JIv6MouGZOJbp1J6njJy+4IfuHgmv6ZGxVgtLkWTDrnGtruqEZKzfStetiIWsqsnbqu6n39SOpKoMvv8Lgy68IKvLaVaQbmwWV3+chdaIJ58Q6JJutoG+RihQ+M6djplKigJVlAjeuoPTtt2E7T36ebLPhX7WUdGPLqNRkucBqKJ5nJIYkK8LtVpZGvW70SEyY9ygyit8rNIPVFSJAVVXR+wbQCk55WnkpyYOvCWnCGMVVLV7a2tqG/X86nWbjxo0jMlrGEhx1NXiXLmBw13681y08q6DJMk0S+w5hryrHu3D2sN9Jsoxn/iw882cJN5NCEWSkM7T923cw0xncc2eg+nxEtmzHVlYqOOq9/ViWqLq1UIDQ+jVFL/LgTdcTfX5HYTrhxzKFAFFxOkgfO4mRTGKpCoqm4Zo5ZVR+rGVZmNkc9ppK4jv3XFJewvkgSRIV73orHd/5MflIDNXnRe/rJx9LUPXhdwNgr6sh19NH+kQTkiIj2e0oXk/RIvga3jgYuoYiz24n19lN9//9mu6fPIR34VxCb1k7aqowFDQId2/AVlnGwBNboKNLhLCpKmY2S66rD0ddDeP+9CM46kW+ga28FHtdDdmOrvN2i6x8nlxnN9WFa27oNf2rlpA+2Ypr1jTSR05gq60iuGYFajCAa/qkop4iH43R9u/fw9R1VK8HfSCCY3wt1R9/3xWjXL4eSJqCraqczMkW7HU1Izq5kiQhO2wk9h/GXlNF+bvuGvY7z7yZeObNJNXQRPTZ7fiWnfrcBp7YjHPKBFxTJ1H3l58k9tIeIs9uFzdXWSbb2o7e149r2mR8yxcVN+6yphFYu5Jcdy+pow0kXjmMrGmkTjSRPnIC2enA7q3Ct3IxZk4n8ux28gORIpdcdgoamWNcDZEtL17x4sXM5Yhu301i/2Fkm4Z/5VI8C2aft2BNN7WilZXgXTiH2Au7yHX1XiterhLO21wwLcrvu4P+3z1NuqGZvl8/Tt/DT+BfsZjSu9bjmjn1ijUoVL+Xur/+NGY6jWx3XJSL2JkY7Rz1cGTULr7idRfMTMYWJFmm+mPvo+M/f8Dgrn04p00eQW8zEinSJ05iJJLUfvajl2TWcS6U33cnofVrCD/1PANPbCbX0U3PTx5GctgJ3boWSVOJPb+DwT0Hi1MPW1W5mHRF4ygeF5XvuwdHfd1Ff57BG1cSf2lvkT59OkQEgUKudwCtTEzqjGwWLejHUVc76j0o09wmAqxjgyKfqxBGrZUEyba0Y6RFwPOQgYGk2bDyo4dmjwVcVPESi8VIJpNUV1fT39/PQw89hM/n47777kO7hBv2unXrhBVrQTjndDqZMWMGX/3qVy/6WH9IlN93Jz2GQWzbThz143CMrynyCy3TJNvRLTYJ1ZVUf/S95xSQnn6Rmek0/hWLib6wi+TBI+L3Xg9qMIDqDggnIMMkeOsatDOExFpJkNK33kq2rZPkq8fItomwSMsyhYhMVVA0kRybbe/CMb5mhAuQERtE0lRcs6cT2bJD6FKuwELtmj6Zus9/iujWl9B7+3FOm0zw5uvJNrfT89PfoIcj6ANRMd3yecj3i0TYoU3qNbxxYK+ppOr976DyffcwuO8QkWe2Ed+5l8E9B4T1c6F4Ga1TKEkSgVXX4V+xRDjnnWzGzOZQfV48c2eMWNABStavoesHv8RWCF/EskbQCCzLInHoKK5ZU0dMgCreew/hp7aQaWrDt3geJbetHTXxvednv0XxevAVxJ+WaTK4ez+RTdsuOtj1DwHZbsdeV0O+d4DkkRMoLgeK31e0mzbiCXI9/XiXLqD2T0fnYUNBdzD5lH1wrm+Azv/5CVbewDNvJmX33iEoOauXC4F9MkWmpZ3+x59BqyhF8QzvRkuSJGzcQ0EkRaHs7g2kj5/ESqVFOJssE3nuRaxMFrUkiGvWNCRZ3DOMRIpMQ5PI+9EU8vHBs9IwXi+sfJ72b36ffDyJo74WK5+n55ePkm5spvzeO875XPfMqQw8/gzRF3YhK8qYpWFcg6D+BW+6nsDaVaSOnKD/sU3Etu8u/jP+7/7srA2Xy/L6knROmtnrgbO+juSho1hTTukkLMMQOXFj9JpUXE5q/+yjRJ7dTnTLi6AqYqolyULjFB/Ev2qpaOReoYaA6vdR/o47KbtnA/GX9tL/+CaSh45iry6n9PZ1VH/gHeQGBkgePk6msUXco/xefNctwDFxZIj0hUIrCVLzyffT/u0f4kykcEwYV9xLSpJEYO1KsYc63Ed+MIGEhK2iHO+i0anmZiqN4vWQj8YxM1lhK44wSVF9XpAkkoePAWCvqsA1fZKwGB+juKji5bOf/SwNDQ1s3bqVT3ziExw6dAhJkmhsbOSLX/ziRb/40aNHL/o5YwGSIlPxnrfjXTSX6JbthJ/ZhuJ2FYIjhRVi+X134pk366KqbS0UZNznPk71x95H5LkdhJ96jkxTK4k9BwCY8u1/ovfnj5I92YIya3qRR37qvBTUgB8zm0Pxe/EunodWGmLg8U1YpoVWVgISGPEkyVeP454xpTheNJIpsh3dQrRbEmRwcBAznb5iC6mYSM2h8zs/Jt3QTGTzNmRNwzVzKo4JdVimRaatE/es6QRvul6Efo5BSs41XBgkRcG3ZD6+JfPJxwaJPr8D17RJxd/HX3yZ3l8/RnDdDQTWrBh2IxrNJeZscM+eTvCW1fT99vfCmAJwTqzDt2xh0ZEl9dpxJFmm8v6R2Q2yplJ6xy3nfA0zkyV55ASh9WuGnaNz6iRiO/eMyeLFu2gO8V378S6ai3PGFLIt7WTbu0TQraZiq60Cy6L2UxduDwyC3lD61rcw8MRmEgdeI3HgNZyT6ym79w78K5YgKTKuqRNxTBhH768eI320AVt1JYrbiWVa5CMx9F5hpV376Q+iuFzkw1G08hIkTSOyaSuSpmI/I6xNkiRUrxvV60aPxEgfP0m6saWoHbzcSLzyKvlIHN+qJcXzsFWWE312O4EbV5xTx2IrL6X+i58l190rmkZjODvhGgQkScI9cyrumVPJ9Q8QfmIL8Z178RbCNgHiu/bhnFR/TgrsWIJn4RzCm7aS2HsQx8TxWPk86eNNuGdNO6tz6liArGmU3HojoXWrSR09IQxiTAvV78U9e9qozaUrAUlR8K9aKuhcTa3DmmcDv3uG2PbdlNx+M6FbVo9o0lwqnJPGU/e5j9P7q98ReWabcB8tBDgbyRSy04nscOBbvojk0Ub8159jn6QoYFk4J44ndbQByaaR6+4lH0+glgQJrF6OZeRBkpE1VUyT3ixWyceOHWPJkiWEw2EOHTrEW9/6Vvbt28fmzZsvqXh5I0OSJNwzpuCeMYV8bFAESFkmqt83QjB8sVA8bkrvWEfJ7TeTPn6S8FPPkevuxTlhPOM+9zF6fvEofQ9vFBbL9eNAUUSeRmc3+fggmBald9xSHBErbjeSpqL39CO7nSheF5Iikzp+Esek8RjhKPl4Av+qpUVHDElRrujI0DJNOv/nJ7jnzEAN+en9xaPYykuLfFxbeSmDLx/ANWMywRtfn4PQNYwtqH7vCG/76Au7SDc0k25oput7P8e3fBGhdTfgWXBxIWwA3gWz6Xv4CWSXQ4hST7ZgJJKoPg/52CC+5Yspu2v9JW8ii+GKZ0x0JEUGY2Q+zViAb9liwk89T7arB3tVBc7J9cWUczE1egX/9Usvulmh+r1UffCdlN93BwNPPEv/754i3dBM61e+ha2mkqnf+ichHJ48gfovfIZMawfx3fvJR2LIqopr6gR8yxYNL1YVGStvkNh/GCuXw1Zbec7GhRb0k7HbCD/13BUrXpLHGtEqS4edh2zT0MpCZBpbzyvCV/3eEYYq1/DGgK20hMoH7qXi/ntOGUsMJmn56rex8nn8q5ZQeud6XNMnj+kGm6ypjPvcRwlv2iZsxjWVwNoVI4JoxyqEUce0q30aAMNy6yzLInHgVXLdvXR9/+d0/+Q3BNeupPTOW86aD3QxsNdUMu6zHyPX00dsxx5y3b2Yeh6tNETJbWtxFmI1mv/+X9H7Bs5qZmGrKCV58AjaxBIUnwd7bbUwjJgxFVt1hXBOK4RrWoaB3j+Ao75u1GONBVw0bSwYDNLQ0IAkSTz44IP88Ic/HDUg8kLwj//4j9x2220sXLiw+LN9+/bx5JNP8oUvfOGSjnk1cKVuTJIk4Zo2Cde0SUVqnexwEFizkr6HNpIYiJBuasNeVS66nXdvIL73oBgPnsZtlZ121KAfW0UpuZ5+ct39IIGVyZI92Yp79nT8k8ejOAVf1NR1ERLlvLz80dNhZrKY2SxaRSl6T78I4koPtwa0VZWTPtYIp3W4r+HNiXGf+xj+FUsIb95KYt9hkdvywi600hBl991B6e3rLvhYuZ4+7DWVeK9bgN43IHIbNJWS29fhKRhgvB4oLieO+joyTW1FG0/Lskg3NONZOPc8z746UL1uaj71ATr+84fkunqxj6tGtmno4SjZlnbsdTWU3T3SJetCoXjclL/jTkrfeiuRzdvo+80TOMbXnjJYKITinp5pNRpSxxqJvbSXwb2HwDKRNBv5V17DVl6CVlE6apfV1HUkVSXb2U2uf+CKuHmpfh/Zts6Rr53OFPOBruHNjdMLEzOdxrdkHrEde4ht3Uls606cUyZQeud6/Ddcd1V0b8IGNz7CTvl0KC4XZW+9lbK33voHPrs3LyRJYvLX/4H4y/sZeGwTiVdeJfzkFsJPbsE9dyZV778P12UwQ7BVlFH2trec9feh22+m71ePoV6/FNk+8h7nqKth8OUDZArOjb5C1MBoyLZ14hg/DlvFhbn6XQ1cVPFSUlLC1q1baW5uxu12M27cOGKxGD7f6EFu58PGjRv5y7/8y2E/mz17Ng8++OAbqnj5Q+D0i0xx2inZcBORLS9iRGOkorGCV3yO1JEThG5bO+y5jgl1ZE624Bhfi3NineBsGwZ6fwTJbsM9Z7jQNdvcjnvujAtOV7VMsxAUZiI7LkxoKDsd2CrKSL12AsUvOuLO02hEIGxVtZI3xkj+Gl4fZJuNwOplBFYvI9c3QOTZ7UQ2bSXX3TssGdtIJEFRzhrIBmCvriQfi6P3DYgQNssisHYlviXzL9v5Vrzn7bR9/X/QByJC8DogbMtLbrvxsr3G5YZzQh31X/ocsRdfZnDPAcxMFltlORXvvQf3rKmXxSpVttso2XAzoVtvxEikij9PHjxC8z9+g9Lbb6b0rvUjAncty6L/kaeIbd+FvX4citctrLGryzH1PHp8kFzvAM6pE0Zw2/X+CM4J45AddgZ3v0LJbZeftudftpDI5m3kqsqxlZdiWRaZxhaQJFzTxpZL0zVcedjKSxn/N58m1zfAwBObCT/1HOkTTbT923fo+sEvmPrfX71i+qvRYGZztH/ze2Q7upFsGuM+85ExTQV7s0FSZPzLFuFftohMawcDj28i8ux2kgdfwzojdPdKTef8Sxegd/cR3foS7tnT0SqGT4r1gYhg4HT3UXr3hrOeRz4WJ3W0kZo/+cAVOc/LhYsqXu68806++93v0tHRwXve8x4A9u/fz/Tpl+bycrpYfwiGYWCaY5N6MVZgqyyn5sEPUPWhdxHdtovw08+TOnKCyKatIEsjLkrnxPEim6MQfCnJsrBz9HnQ+waGPdYyDDKtHVR/9L3nPQ+9P0y00CE3czmR42FZeBfNJbBmBY76cWf9gkiSRM2nPkjPz35LtqUDW1kJisuFZRgimTkcIdPSXrSZvoY/HtjKSqh4512U33cHyVePDevU9z36FP2PPIn/+usIrVstckvOuMa00hBVH3o3vb98FDOdwbdyCYEbll3Wc7RXlTPhH/6cwT0HhNPYDctwz5t1wQX/1YLq9VBy642U3HpliyxJUYZNowf3HMBMpuj91WP0PfIkoXWrKbv7tiJvPLJ5G7GX9uC/YRmy3VZ0ILMsS9iXBv2Y6Qzp4ydxzZx6yoo0I/ITfEvmofdH0MOxK/L3aKUhqj/yXrp//GtSrx7D0g3UkiC1n/7w63KFuoY3NmxlJVS9/x1UvOttRLe+RP/vnkbxuIYVLummNpwTrqzZzOD+Q+QHkwTXryF9oon+jc9Q8/H7r+hrXsPocNTVUPPgB6h84D5iL+0tRkAAtPzTf6B4XJTeecuI8OPLgdI7b8FeXUH/E5tJHj6KVhoESSbb2U22rRM1FES224lv24V77nTstVVFEwAzlyPT1EamuY2K9949zJhlLOKiBfvLly9H13VWrVqFrut89atfpaKi4vxPHgWLFy/mG9/4Bn/xF3+BLMuYpsm3vvUtFi9+fV7dfyyQHQ5Ct6wmdMtqMs1t9D68keShIyJBHOGQE9u+G9f0ybjnzCB15ASOSXVnjJRPT7c2GXz5AM4pE3BOOfuFa1kWA48/Q2TzNmzVlfiWLyoK1Ia+AB3/+UMcE+uo/tC7z0rT0YJ+aj8lqvv8YILuH/5KCHTtNiQkKt53z5h1QbmGKw9JlvHMGe5xn23vwkxniGzaSmTTVmw1Q9kx1w/TmnnmTMcz56+u6PkpLudlL4rerKj60LvwLV9E38Mbie/cx8ATmxl4cguBG5ZRctd6Bp54Fv+qpUULZ/u4ajInW8n3hVFLAkiKgux0IOd0cp09OCeNx0gkybR24l08FzXgJ9c7gKReuULCPWsqE7/8N2Q7u5E1tRiUeg3XINtthG5ZTXDdDcMmjqnjJ2n40y/inDqJ0rtuwb/quivS4DBzOnpfP8nXjmPl9MsmGL+GS4ficRfzhUA0e+M794BpEdm0FfesaZTceQv+5YuKbrWXA97F8/AsmkumqZVsWxdmTid9/CRl996OFgyQ7egmefQEZipD5JltKG43YGEkkngWzGbc5z5ezD8by7jgdyyfz/OWt7yFlStX8qUvfQkAWZZZunTpJb/4F77wBT72sY+xatUqqqur6erqoqysjO985zuXfMw/Vjjqx1HzyQdo/PyXixOWdGMLyUNHSR46Cg47isNOYt9htLIS7OOqMZIplELYXraji8zJVhz146j64DvPeVPu+80TDL58gMCNK0cUJrLNhmvaJJxTJpDYf5j2b/0vtX/60fMu2KrXQ+2nP0Q+GsNIpbFVlJ3TYvoa/jgx/q8+ReY9byeyeRuRzS+Q6+im+0e/pvvHD1H5wDsov/fSdRvXcGXhnjkV9xc/S6alnb6HNxJ5bgfR53eIrJyAX1D8ClB9XlGsuF3oPf1INhuy046kyOi9/UUzEd+yhcUGRz4avyDBvmVZZJrbSB45gZlIITvs2Ouq8cyZcd41R1LkYTd2y7JINwqjCdXrwbNg9mXPmriGNw6GXPCGkOvuRfG4SR9vpO1f/puu7/+ckttuInTbTSMySy4V+cEEA79/Fks3GHxpL5KqUP7lv74sx76GywetNMS07/wz/RufIfLMNpKvHiP56jG0kiChDTdTevvNl63olCQJ58TxOCeOx8zl6H/k96gFeYfi8yArKuM++1ERSxGOIikKtvLSYWvwWMcFFy+qqmKaJtls9rK9eGVlJY888ggHDx6kq6uLqqoq5s6di3wZuNd/jFBcLrwLZwsh8dSJwgbV7cJMpyGTxciIzy7b0UW2vQtJVbDX1RB5Ziv2uhrK33Ennvmzzsl9Tx4+SvylvUKUeA6LQkmW8Sycw+Du/Qw8/gxlbxdCMyufJx8bxMobKF7XMHcjM5cjebSB6Nad6ANhJFlkIgRuXIFr2qTLwsm/hjc+HOOqqfrAO6m8/14G9xwk/My2gm3pqTF8uqkNsIa5wlzD2IBjfC3jPvdxKt57D/2PPomZyxU7j+nGZiRZxlE/DsXjRvV5cNRUkusPYwwmwLSQbBqOyeNFsVFYE4x0hvxAGO/ieed87fjLBwg/+Sz52GDRAMDKG8R27qXzf36CVlqKa8Zk/NctxDGxTlCbTZNsawdGIomkacKu1C0orp3f+xnphma0shLMTJa+3/6emk994Np1dw0ABG5Yhm/pAiLP72Dgd0+TaWmn52e/pfdXjxFav4aaB9//ul9jcM8BVLeL4OrlWJZF8sCrpI424JpU/7qPfQ2XF/baKmo+fj+V999L5NntDGx8hmxbJ70/f4SSt1wZKq9ss+GeM4PBl/djqywn09KOr5BVpIVExtYbERc1q7r//vv5xje+wdKlS1mwYAG20zav1dWXNmaSZZn58+czf/7lE9P+MSN40/W0/ut30Hv7yfUN4J4zHdluR+8Pk+voJh+NQd4AVUVSFNyzpokk8wu0dw5v3oZzcv0FeatLkoR71nSiL+zEt2Ixg7v3Ed26EywLZBkzm8U9fTKBG1ciaRqd//MTFJcTe12N2IhaFrmefrp/+CtUv5eaB98/Quh7DX+8kBQF33UL8F23gHwsjuI5JeTu+enDxF/ai3PyhNOyY65RKcYSbBWlVH/sfbT/x/8KqqthEnthN8ZgAjUUwFFXS667D+cUN/bKMqgUNu6Z5ja0YKBYuFiWReq143ivW3jWqYdlWfQ98iTxF1/GPXsansWnKF+mnie7ZTtmVifT0ka2s4vB3fvxrViMGvATe34Hlp5HdjmxDIN8bBDvojloJSGwbvksAAAgAElEQVQyrR0E1qwo6l6y7V10/eCXTPiHP7/WbLkGAGHkceuNhNavIXnwCP2PPU185z44TStl6kLUfUmUMtMSelOGjH0ksM79lGu4ulBczmIcRmL/YaFHKQSPW4ZBy1e+hX+lyJS5HDTDqg+/i8imrWQ7ewitv5HA6jc+3fmi3pWvfOUrSJLE3/zN3wz7uSRJvPbaaxf94m8Wq+SxBEddDa6ZU4g+sw3ntInIDjuSJGErL8FWXkI+kSLT2AyWRWjDzST2HSK+Yw/J145Rcuta3HNnnJUyluvtJ9PURvA0HueZGKJkpI81YqTSaCVBrJxO09/9M85J4/EuXYDqE5tMK58n09JBx3/9H/lojMBNq7CfkZru9LhxTKwjfayR1n/7DuM//6lrfN5rGIGhhR/ENWirKEPxuEg3NJFuaKLr+z/Ht2IRoXWrLzo89hquLFS/Fz0Wx8LCM28mif2HyYejJMJRJJuGkU7jnjm1qIcxc/miFahlmiQPvoYkSZSfw9wjsvmFs06MU0eETsA1XbgdZls7sJDo/83vsY2rwjt/NmoocKrYyebINLUSfuo5PPNmDruWbDWVpI42kOvsueb2dA3DIEkSnnkz8cybSa67t6hNBYg+9yLdP36Ikg03U/KWGy+qSeddNIfw08+T2H8YJAm9P4x/2cLzP/EarjokScK7cA7ehXOKP4vv3Ed8xx7iO/bQ9f2fEXrLWkpuW/u6JiSyplGy4eZLfr5lmJjpNLLbNWZ0fhd9B7csa8Q/l+oOtnHjRmbPHs5Rnj17Nhs3bryk412DuMiyJ1vxr1qKEUuQPtZIpr2LbEc3mZZ2ss1t2OtqKX37bWhBP+65M+j77RPEtu7k5F9/mWMf+XN6H3ocPTLStSfT0o5WEjwnLzyx7xCJfYdRvG4c42swUmkyLe1INhXPvJnFwgVAUlUcE+sKDmMyufauUY8pSRKu6ZNRnE76n9j8+t+ka3hTQ5Ikqj/6Xmb89D+p+/yDeBbOEeYVW3fS9Ldfo/+xp897jExrB6ljjVjGlQtqvQYB75L55Dq6QZbxLJhNxQP3EbhpFWrAj5XTMaJx4i/tJdXQTK5vAEvXQZFJvnqMyKatqD4ftZ/92FmNQcxMhoEnNuO7bsGoE+PMydairagkSdhqq8m1dYCmono9Ys07PZzSbsM1fTJaWQmJQ0fRByIjX3SM3OCvYWzCVlk+zGBkcO9B8uEoPT95mCP3f4a2r/8PqRNNF3QsNeBn/N/8CZ4Fs/HMm8n4v/4TtNJrEQNvVHgXzaHmTz6IY3wt+UiM3p8/wpEH/pTWr32b5JETYt9tmmQ7ukk1NJFp7ShO7q4Ecn0DnPzbr3L8T/6Wpr//F8zM5ZOOvB5c1OTl6NGjl/XFr1klX34kDx8FVcE1fTLOaZPI94fJ9YfBMJDsNuy11cMyMrTSEKrfh2feLBL7DpHr7KH7h7+i+8cP41+2kNCGm/DOFwWmlc0hqWcvXIxEkvSJJpE0XHicmUyheFzkB6Kjepzr/WGsXA7ntMmkjzXinDZpRI7DEJzTJhF7YRdld92K7LCTj8XpfWgjjroagutuGDMdgWsYGxDZMcsJrF5OrrefyOYXiGx5kcD11xUfE3luB5gG/pVLkB3iezHw9HOEn3oe2WYTugi7nXw4QvVH34utouxq/TlvWoj1QhXhmdUVIkl75lTc06eQ/v/Ze+/oOK7zfv+Zme190QsBEGDvvVOsIiXK6s2RbIlyt2XLjuOvYjlOpCRy7Oh3FEeWbEWRLavFkmz1zt4p9t4JEiABogPb++7M/P5YcsklABKgQLDtcw7PAQazM3eXd+/c9973/XyqjuPfths5FMHYLyn5LprNhCurMQ8dQP7X7kJfmHfO63s370TjtHdajKomEmkLMkogCIKAgIAa7zx4NQ3ql1wl3bwDjd1GrKEJORROpr9t3oFzxuS0CWqGDJ1R+tiPCCyYTdtHS/Bt2pEcq5atxTR0IPn335G2Mt8RGoed7AVzznlOhisD0WAge8Ecsm6cTXDPAVo/Wopv41Y8qzcQPFBJ3v134F31BUosjqhP1uyp8Tj26RNxzJrS4zUsrs+Wo8TiCKJIaH8l7tUbyL4MjMO7nUxXW1vLO++8Q319PcXFxdx1112UlFyYhnlGKrnnCR0+iu7kqosgCGhzs9Hmdu44LQgC+uICjAPK6fPIt/Bv24Vr0Sp8m3fgXb8F0aBPBS+iQY9yjod5rLEFyWo5HbhEoskiV7MRxR9CCYXbTSCix08g2W2IWg0ap51IZTWWTgZqyWREY7MSOngEy+hheNZsJFJdQ2DHHqwTRveYekuGqw9dXg75999B3n23p4JcVVFoev0dYo3N1D3/Ko4Zk3HOn4l7yRrsU8cjmk24PluBmkigzXLg37H3ovujXIsIokjBN75K3e9fRtBIKRPIhNeHaDRgnzYBVRRJtLRhHjqQgm/eR+Ujv0Sb5UCVz7/i6Fu3GUNpn07/rivKJ+72pFJWY03NiFYLssuNtgOHaTkQIlJbl1p4ixw9jiY7C9GgR9TrsU0cTfjQUbwrvyDrpjlk3TDrSy+sqKqaTNvQd80EOMOVhSAIWEcPxzp6ONGGZto+XoJryWpC+w+382LLcG0gCAKWkUOxjBxKrLmV5nc+Jbj7AL4NWzENHYig0xHaewDL6GFAUpbbu24zRQ8v7FmPFkFE0GiSY60gXDZeZt1qxe7du1m4cCGRSCQ1cL/66qu88sorjBp1bpWXjjhbKrm+vp68vLxuSyVXV1fz2GOP4fF4cDgcPPXUU/Tt27fDc6uqqrjjjju4//77+fnPf97tNl/uKJFoWi5tVxA0GtRIFEESsU0cg23iGOJtblxL12AdezqtL+72ENxzAG2WPWmwdHZBqiSiqqd3zRJeH4JBjxI5uWPTQQGrHIki6pLt1TjtRGrrU8GLHAoT2LmXaG1DUqK0ogw0GuRgUkffPHQQ7uXrMVT0RbKakUOhNPWyDBnOJm0SqSjk3nsL7qVrCB2oxLV4Fa7FqxCNBtREAtOQASAKmEcOQfYHsI4feekafpVj6l9O8cMLqXvhddR4HCUUQUkkEHValGgMJRBEslkp/OZXCR44fNrrZ2ky0My952ZMA/t1eO24x4txYEWn9zYPH4xr8WoEBDRZdhJuXzIFTZKSJm9nIAeCtC1aiWgykmhuQ47H0eTnos/PQd+nEH1JUcpHSx5YgXv5WhCEL7VSmfB4qfvDK0QbmhA0Ggq/dT+WERdmDJ3h8kdfmEfRd79O/gN341mxHsesqam/Nbz8VxIeLzm33pCmrpjh6kaymIg1NGEa0h/TybHMt3E7/q278W/bg7FfGZaRQ9Hl51D3h1coffQH6IsKeuTe2V+ZS7iyioRGi3XiIOxnZC5cSroVvDz99NOEw2Guv/56KioqqKqqYtmyZfz2t7/l1Vdf7fbNe0oq+YknnuD+++/ntttu48MPP+Txxx/ntddea3eeLMs88cQTXH/9hRcuXe5IZhOx5tZuvUaJxRDP2hHRZjvJ/7vb0o55125GCYVxfb4SyWzCNHQg5qEDkU7WseiLCvBv3okSiSIa9EkvBkWFWAzdWelqpxA0GpRYLPmzVoMSiyfbFE/gWrIayWjAOLAcZIVYfWPSAOzk6rexXxkDnvk3AOpeeI3Azn3k3rGArMtgSzPD5Y+g0aTc5iM1dbiXrsG9Yh0Jt5dwZTUIIoXf+LsueYdk+PIYykowlBUTOlyFoNUgSiKCJGEoyMU4oAIlFqPtsxUYyvow8Pnf0PrhYtzL1uJdvwXv+i1YRg8j955b2v1/CZx710Njs5J1w0yCew4SPnocJRZDV5iPoNEinmUeFz56HI3FnAxe2txIGgOiALrCvJRjdtztJXz4KAlfAI3dRtuHi7FNHHPBO8NNf3kfQacl66a5JFweGl56g4pfP5ZZqLnKkYwGsr8yN/V7sv8vRwmGcC9dg3n4YHJunY9tyriMJ9pVjmfNJkStNhW4ABj69iHh8RI+ciz1T5OThS4/l5b3PqPPj77ZI/fWZjsp/ZefEtpfiRIJ49+yC8lqxjSgIq3OUFUUwpXVRI6fQGO3Yhk1rNM6xJ6g2zUv8+fP59lnn00d+/GPf8zGjRsvuAEulwuXy0UoFOLIkSMcOXIEgLvvvrtLr29ra2P//v28/PLLANx88808+eSTuFwusrLSi9ZefPFFZs2aRSgUIhQKdXS5Kx7z8MF4N2zDNGRAl1IVVFUl3tSKeejA855b8g/fpemtD3F9vgI5GMK/ZSf+LTvRlxZjGTMCQ2kR1gmj8G/ZhSbLjhwOI3v9iGYjtk7UT/RF+QS274W8HNSEjHgy5Sx6rBZBEtEXn1w90IKuTyH+LbuS6WvRGP5tu/Bt3E7C6ye4/xCiwYB71RfYZ0zuMFDKkKEzDKXFFH7rPgoW3oN/226867fQ5yffTk0K6v7wMoJWi3PeTIzlF5Ymm6FzVFWl4aU3kL1+sm+a26nMsL4gH/+WnbiWrqH4kW+S/7U7af1gEW2fLSewcx+CVtMueNFkO0l4/ec0YNPYrNinTQCg+W8fo81xIvv8CPqzHr4nVWhjjS1IFhNKKIKqKCkhgFhjM57VG9FmO5FMxpMmcG7aFq2g4L47LuiziTY0YR4+KJkGnO1E1GlJuLyZ4OUaQ9TpGPC7f6ft46W4lqwmuPcgwb0H0eZmk33z9WTdOLvTetHOUGUF74atxBqbMVaUYRkzPFM7epmhKgqelesxjxyadlxXkEfWjXnIgSDBvYcI7j1IotVFotVF9PgJCr5+15e2loi73HjWbMK7ZiOiyYBoMIKQFEGR/UFsU8bjnDUFTZaT+udfIXKiAW1OFkooRMu7n9Hnp99BX5j/pdrQGd0KXnQ6HW53urKK2+1O83vpDsuWLePRRx+lrKyMI0eO0L9/fyorKxk7dmyXg5eGhgby8/ORTk4yJEkiLy+PhoaGtODl4MGDrFu3jtdee43nn3++0+v5fD58Pl/ascbGxgt4d5cG44ByJKOBeEsburz2+dpnE6tvQpvjTLlUnwuNw07x9x/E0K8vre9+CqJEtOYE0Zq65OtLizBWlKFx2AlVVhFvbgOthpy7voKmk4mDvrgQ36YdyKEwCY8f3clgJe72tJtsJFweNA47nuVrafzzm2jsVvQlRegK89BkOYgcPwFA1WP/gW3KeHLvXNAlP5re4krvW9cCgkaDbdJYbJNOB9sJfwDX4tWoiQStHyzCOKCcrPkzsc+Yctl4x1zpfStcWUW4qibpmXKOnXdBErFOGIVnxXoix05gLC+h8Fv3kXvvrbg+W5b2gA8driJcdRz7tPG4l65BX9S1h6ihpIhITT360qJ2+d3Gir6EDh0l4fYgWkyggqAo6ArzUFUV/9Zd6IsL0DiS0t0auxUSMu6la1PBS9ObHxDcc4CChfdiGtRxqltae8pLCR89jnnEEOLNraiy0i6d7WJypfetqwl9UQFF33uA/Afuxr18La0fLSFW10jjy39Nqnl2M3hpfO3tZJ1sXg6+jduJNbf2Wl1fpl91jeiJBlBUtFmODv8uWczYJo/FOmEU4cpqArv2o83NIrDnII7rJhE+egzZH8QwsAL/lp0Ed+5DjkSRTEas40dhHTeiw3lSYNd+Gl5+C11hHtbJY1N9S5WVZOq+JBKtruX4b57DOKgfsVY3jtlTU8FvuOo4ja/8jbJfPHJRPpduBS9jx45l6dKlzJ8/n/Lyco4dO0ZNTQ3z58+/oJs/88wz/PrXv2bBggVMmDCBDz74gHfffTe1+9JTxONx/uVf/oXf/OY3qSCnM1599VV+//vf9+j9exNBEMi+7Qaa/+9dpOsmIhk7Nm2DZOFpcO9Bir7ztW7dI3veDCSjnpa3P8EyejgoMoaKMuRwhOiJevzb9iD7/FjHjACthkSbu9PgRZBELGOHE9i2B1WRsU1MmpVKdlsy+DlJwh8g1tyKGokRqWvAPn1iWnCjcdhTW6pyOEJo70Fqf/sifX78rXbGdQl/ACUaRZvl7FUjuSu9b12rSBYz/Z7+F1xL1uBZvYFwZTV1ldXUv/gX7FPHk//1u9AXFxDcexD36g3JdLQFc7q0INBTXOl9y71yPYay4i4VowuShL60GM/qLzCWfxUAjdVM3lfT01yb3nwf/6YdSE47okaDYUBFl8x4Df37Eth7EPv0ie3+JllMOGZOwb14FbrCAtRYDI3dhqjTocRiJPxB9GXp4gCabCex/YeBpLKZZ9UGtHnZeDds7VLwkn/fbTS8/Ffci1ehyXJQ/MOHUsp4vcGV3reuRpImh/PJPunV5t+5L63mq/5Pb2AeMgDb5LGdppTJoRD+bbvIunE2giShKy7AvXRNrwUvmX7VNeRAEKELmSSClFSZNQ3uT2DPAZRQGIDG/3sP/6btCDot+qICzCMGo3PYUCJRXJ+voPlvH5F33+3YJ45JXSuw5wANf34L68TR7YImz8r1xFvaMA7qh3XsCPRlxbS882nS8+qMXTtDeSnuxauIu70XRUypW8HLz3/+c/bt20dNTQ01NTUAFBcX8+ijj17Qzevr61mwYEHasTvuuINp06Z1uZi+sLCQpqYmZFlGkiRkWaa5uZnCwtMGYS0tLdTU1PDd734XSEb8qqoSCAR48skn0663cOFC7rgjfXu/sbGRr32texP8S4lt3EgSLg9tny3HPGwQuqL8tE6lKgqxukaC+w+Te+dNmIcN6vY9HNMnYR03Ct/mHbiXraHt02XIgSCiVosciUBCxrdxG2g0SEYD9tlTMZZ1rPhj7FtKaH8lss9PrLkV0WTAWF5CaN8hIrUNqKqC7AuAoiLotDhmTztnXZRkNGAZP4rgrn3Uv/BaWvpP60dLcC1djaDRoC/Io/jH3+q1FLOroW9diwiCgGlgP0wD+1H0na/h/WIr7qWrCezch2fNRgoe+iqhQ0ep/+MbGAeUo8Zi1P73i5T904/RnUPprye5kvuWEk8Q3H0QZzdq1QzlJbiXrKHgwXs6XYBwzppKvLmNSHUNMtDy1oeYRwzBNn4koqnjRR1VVogcPY6xbwnho9XocpwIZ9W9SFZLUhRFEpEDIezTkwWsgiQl5f8TMsIZOzZJEZXk74JGQ84dNxLccxDn3Ou69F4lk4k+P/xGl869GFzJfetqRxDF5Or5+NOCSeHqGlrf+4xWOJlSNo+sG2e135URRFCTfV6QJFAUEHsvZSzTr7qGqNdBops+LrKCoNMmhbUEQCOhxuJEjtUSrW/CPHQA5pFDMJQWk/D4aH7zA5AV7FPGkfD6aHjpTawTR3W425Pw+BBNBhKe5K5Z0gvLQWh/JcZ+ZSnDaFWWkYMhfBu3JWv+elg2vlvBS3FxMZ988gmrVq2irq6O4uJiZs2ahfEcq/vnIjs7m9bWVnJyciguLmbHjh04nc5u+bxkZ2czZMgQPvnkE2677TY++eQThgwZkpYyVlRUxKZNm1K/P/fcc4RCoQ4DJJvNhs1ma3f8SiNr3gx0Bbm0fbKM4L5D6PJyEDQSakIm1tSKvjifou89gHnIgAu+h2Q0YOrXl7ZPlmIeNghjv77JPPBolNChKgJ7DiK73Mj+AK6PliA5bNinT8RYXgok89xj9U2EjxzDMnwQzvmz8K7diHvZOkSdFtGgJ97aljSP0+tAVcm+/cYuCToIgoB51DC8azfh27wTfXEBcY8Xz6ovcM69DkGnJbBtN22fLiPv7psv+DPoDldL37qWEfU6nLOn4pw9lVhTC4HdB9Dl5+BevhZDeQm+DVsRTSYkk4Hg7gPo5k7vlXZdyX1LCYcRNFK3JDhFnS5ZfxKLIXSyC+GYOQX7jMn4t+6m5W8fEdx3iOCufQT3HMA5fyamAaflRFVVJd7SRujQUQwlRRT84hGa3/oAz+qNGMr6oC/rg6jVoKoqsj+AGk8Qa2gi+8bZSJbkDrAgSRjKS4nVNaAvLUYQRZR4gtiJeswn5UyBlEjElcKV3LeuRXT5ORR97wFaP15y0rftLZreeA/n7Glk3zI/VbMnGQ3YZ0zGu34z2mwn8aZWcm6/sdfamelXXUNfXIAcDCGHw+fMpDnFqbHMWFFGrK6RhMtDwYP3EKtrJLBrP7HGZgI79xHYuQ/nvBmYBvfHNmUczW99gHlIf7zrt6LLy+nUL8YxawrRugaM/U6Pn8b+5QR27CV48Aj2SWNRZYW2j5aghML4t+3BvXQNJf/wPfR9Cju85oXQreDlgw8+oF+/fmm7JU1NTVRVVTFs2LBzvLJj7rnnHrZt28YNN9zAQw89xIMPPogoinzjG91bZfrXf/1XHnvsMZ5//nlsNhtPPfUUAN/5znf48Y9/zIgR5zZ4ulqxjBiCZcQQIjV1hCurUCIxRIMe05D+PSKjl/D6OfHsnzD2L09LkRH1eiwjh2AZMZhYcyv+bbuJHD2O7PER2L6XaG09gighBwLo8nPJvWMB1gmjEEQRU/++5N8XJeHzI0gSks2KIInUPf8KCEJql0QOR4i3tCHqdWjzcjosMhQEAUEUOfHMHzH0LSHe6gJVBVFMFr/m5WQ09DNcMLr8XLLmJU0rRbOReKuLhD8Abi8AJ37/ZwL7DmIa2I9w1XEiVTVJ6eUhA3DOnoahb6bwH0guqsid+0d1hKqqqLIC0rkfYYIgYJswCtuEUQT3Habh5TcJHThC+NBRFH8AtBrUWIKE14tkNJJ942zs0ycmvWcW3kv4cBXuletxL16FoNWgyjIauw37jElEa+qR7OmTL+u4EXg3bCN0oBJRr0eJRBC0GgoeuKfbn0uGDBeCZDKRc9sNZN8yD/+23bR+tJjAtj24Fq3EvfILhr7xh9RzNO/eWzANqiDe3IahrE+X0hgz9C6iwYBt0lgiVbWYh51fWCnW0Iwm24m+tJimN95DX1KMZDZhHFiBcWAFseZWgrv2Ez56DH2fIiC5eyKZzbiWr8O/cXunXnuQNDbX5qSLYZkG9SNaW09o9wFEnS75LPT4yLljAVqnndCho7QtXkXRt+77ch/GGXQreHnsscdYuHBhWjDw0ksv8frrr3PgwIFu3/xUGhfA7bffzsSJEwmHw/Tr170vUL9+/Xj77bfbHf/jH//Y4fmPPHJxCoguVwylxRcl/969aj0ap6PzawsCuvxcsm+aS+hwFbGGJsr++e+Jt7hQ5QSNL/812cm9vrQVVNGgR3eGxF7c5SF8uArnvBlA0j/GtXRN0l02GkNXVJCUizwrgInU1BFtaEbQ67CMHoaqKrS+vwjPyvXYJo8lUl2TkVXO8KVRIlGsY0bg27AN0+ABJNweZH8Q2efHvWgV7kWrEM0mshbMQWO1EK1roPZ3fyL7puvJmte11KGrGdFoRLKYibs8nRalnk281YUuN7tbuzXmYQPp//QTxF0e1HiC0KEjyMEwja+/g74on9x7b8Ey/LR/iiAImAb1wzSoH0oshhIKI2i1iCYjaixOzVN/ILS/EtPQ08qOgkaD47pJyKEwsj9A6HAV5sEDMHRRLCBDhp5CEEVsE0ZjmzCaSG09bR8vRdBpU4GLEk/g+mw5jjnTU0bUZxJ3eQjs2EPCF0TjtGEdN7LbggAZegbn3Okc/8/foyvIPWf61al634KF94Kq4tu4A8fsqWnn6PJy0M2bgWPWlNOegIpKqLKawK59aLIcmNWuZz9BctfZOX8mnuXr0OVkYSwvJWg0pGpdBJ0WNR7v3ps+D10a+bds2ZL6ubGxMfW7oijs3bu3274snVFUVNQj18lw8VHiCbxrNmGdNOb8J5P0ZIkcPY4aiWIsL0EORwgfOZZ8wB88Qv3/vo5j9jSybpzVzh02WteIxulI5Z4Hdu5H63Sgy89BVRRCh6pIuDztvtShQ0fRFeQi+/zE3R6M5aU4583AvWQ1fq0Gx/SJOGZO6ZkPJMM1haoohA5U4l6xntDBI8lAWlGQ/QEMZX3Iuf1GQkeP0/r+58iBIKqiJFM3JRHToH7oS4pwfb4cfUkhWK5tWW9BEHDMnoZ/y64uBy+RY7U45ky7oPuduocuPyfp6xIOE9p/mKp//BWmoQPJu+cWrBNHpy2GiDpdmiKPoNfR5yff5sSzL+HbsA1DeQm6gryTNS8JYo0tRI7VYB48gPz7b7+gdmbI0FMYSooofnhh2jHvuk3U/+/rNLzyN5xzp5Nz63wMpcUo8QTNb32Ab/NOdAV5iAYdSjhC63uf4ZiTPK83hW4yJHf5i759P/V//AvGgRUYSovThBiSlhctBHcfIOvG2VhGD0MOR0BVOq3pPdPMXI5EkGwWlGCQRJub5v97L2mBMXIo+pIi5GAQQattJ36Udj1BQJuThWlwf6wTRhPcd4jAjr2IJiOR6lqKvv9Az30gdDF4eeCBB5IpOILAkiVLWLJkSdrfS0tLe7RRGS5/ojV1CDptl1diBElCm5dNcP9hHCd9WIa8+gyetZtxLVqZdDj/bDmuz5Zj7N+X4h9/KxXEKJFo2hdVicVSX0hBFJO56B1E9Uo4gsZuRZFE1Hiy4M1QUoS+pIiyn/+oxwvIMvQO3i+24Fq6BmN5KXn33dGt1feeINbUSt0fXkaJxdCXFJN146xUYK3KMpGaOhrfeJ/osRPYr5uIsayEuNeXpqQlmYwY+vfFvWQ13HlDr7b/csQ+dTyuz1cSb3W1S0k4m1hzK7LHh21i1xZOzoWxXxlDXnmG1o+X0vbREkL7D3Ps3/4LQ1kfcu+5GcfMKZ2qNWnsVkof+yH+rbtxL1tLYPseBE1yhdE0ZACFD/1d2q4MJA0s4y1tqKigqIh6HbrcbCTL5SG5neHaQZebjWXMcAI79qaevZbRwxFNBpRoDOe8GWljqxKJ4tu4HYDcXqyNyZDEPGwQJX//HVre/xz30jXo8nMRdDpUOUG8xYVks5D/4D0pnytBo0GVFVRVPa93j2QyknPz9bR+tBhUlbjLQ7SmjmhNHWgktM7kgo9p2CAsIwZ3fiFVTdJVav0AACAASURBVF7PaKD05z/EvXwdSjhM9k1z0ww2e4IuPfVP7YjU19djNpux25NbQZIkUVBQcM2lYWVImhSJOu35TzwDUatFiURP/24wkDVvBlnzZhA5Votr8Srcy9cRrqpJW4FVYjFU5XROvLFfGf5tu1EVBSUcRQU02e0nPLr8XBJuH6qspiaX8VYXol6H5iJI92W4+CT8AZrf+hDL+FGEDh3Fv3lHylywN4g2NFH79AsY+pWlhCfORJAkjOWl6HKzaampw7d1Nxqno8MdBUNpMW2fLsekzOuNpl/WaKwWir6TXFm0jB6OLr9jj6poQxPB3Qco/sHCc64CdhVVlokcP4EuL4f8B+4ifPQ4vq27iBw/QeOrb2O/bjLCOdT1Ra0W+5Rx2CaPRQmHUaJxJKOhQ2fpprc+pO3jJSjhSFLtsKIUURSRA0HMI4cm66AqSjMmgRl6BfPwwVT8x2NEaupo/WgJ7uXrCOzcC4CutLjdopBo0GObPAbPinU4e0mIJEM6hr4llPz0u8SaWgnuPYgcCiHq9Rj798VQnj52iFoN+sJ84k0t6AryznvtaH0Txv7lyP4gObfdSGj/YXxbdqLKCqahA1BlmdCBw2iznZ16ZsmRaGohRmO1XNQgt0vBy4oVKwCYM2cOd999Nw8//DDxeByttnuT1wxXD4JehxLrnnyfKicQz3asPomhbwlF33uAgm98ldDBIymlC1VRqP/f15Nu1zod5iEDMFaUgSAQrqwGQcVx3aQOV9/NwwbhWrQSORhEVSoIH6kmfLSG/Afuymx7X6GcGpzVRAJVUaAX/x/lcIQTz76EcUA5hk5kv0+hygqS0Yg2LxvPyvVk33x9eyOwU22Xu5dffLViHjaI4h8spOHPbyZVv0qLUgXxCY+XaG09AMU/+ka71NLuospy0rdnxToErRaN3YoqgBIMo8t2oh3cH+v4UalxJeH141q0gqybru/QmFQQBCSTCakDOytVlqn/4xu4lqxKmurm5xBvcyMA9usmocRiRI6doO5/XsXYv5zCb371sjLXzXB1Yygtps+PvkHBwnupffp/COw5gKni9MKMHAyBoiBZLYh6Pdq8HPxbd8OATMbNpUKXn4Mu//wBpPP66bgWrzpv8KKqKtHaOvK/dhdNr7+DEolgGTuCeJsbJBFBFE9muWhpff9zdIX5WEYNwVhelpLXlkNhZK8P89ALV7DtDt3Kt/j888955plnmDp1Km63m6ysLG699Vb+/u//Hn0nk9Kz+d3vftel837yk590p2kZehlDSTFKOIwcDKWZRXaGKivEmloxDTr31qGo02E5wyU71tiMGk+gxuL41m3Gt2Ebxv7l6AtySLi9iCYDnpXrybpxTkqy9BSSxYRl3AiCew6iBENos7Po88g3MfYru7A3neGSI1nM5C+8F/fSNVjHDE+ZmvYGvk3bEQ2GTgMXVVGI1TcRd3tQonHkYBCdNh9RryNSVYNpcP+08xMuT7KgUXNu49xrCdOgflT8+p8I7j2IZ+1GwoerANDlZZN//52Yhw380gsPSjxB/QuvEWtuwTJuRMqX4BRyKEzo4FF8G7Zhnzoejc1K68dLaH7jfZr/9jFZN80h9/YFXUo7VRWF+j++QbiyCm22E8NJqVCNxUzclVSlE3U6TAMrMPYrI7B9D3W/f4XiR77Z6+mQGa5tNFYz+uICdAV56EtO1x8Hdu7DNLg/p0YpyWQk4fYAmeDlcsc6bhRtn68kVFmdJg1/JqqqEtp7CI3DjnnYQByzphDYuRfLmBHoivIJHTqKZDGjhCPJQFYQiNU14KprQLKYMY8cgnnoQCLVtdgmj+01A91ujY5PPvkk7777btL4Bmhra+OVV17B6/Xy61//ukvXaGxsTP0cjUZZsmQJw4cPp7i4mPr6evbs2cP8+fO706wMlwBRr8M+bQKRqhrM58qBPEn0RD2G0j5d2r48E31RAUNee5bmtz6g7eOlyIEg4UNHCB86gqDXYe1XRqy5lWhdQzuZR1VViTU0U/DgPdinjOvWfTNcvtjGjcQ2bmSv3lNVVdzL12Hs4AGgKgqh/ZWEDh1BkCREkwEEEUGjIbjnIBqrhcDuAxgH9Tu9c6SqhA9X4Zg9jVAmTSgNQRKxjBqKZdTQ8598ATT/9UPirS5sk8d1GAhJJiPWscMJ7j9M3R9epvTnP8I6ehihA5UEduyl9d3PaPtwCc6508m96yvn9C5wLVpJtLYO2/SJuD5dRqy5DclsJNrQnNxBPgNBkrCMH4V/0w5a3v2U/L+7rcffe4YM50Kymok1tqTV52mc9rRAXTkjNSjD5Y2o11Hyk29T+98v4vf5Mfbvm7ZYE3e5CVdWI0ga+vz9txFEEfu0ibiWrCbe6sI0dCBqQiZyvBZRrydrwRwkizk5Fu7ej+z14/tiK76N25GsZvLve6LX3lu3gpfFixeTlZXFv//7v1NRUcHRo0d5/PHHWbp0aZeDl9/85jepn3/605/yX//1X9xww+mC1SVLlrBo0aLuNCvDJcI5ZzrHf/0s2tyscwYlCV+A0IEjFP9wYYd/l0OhpOpPJIZkMmIc0DctbULUasi//85koa4vgKrIBHYfQE0kUGJxlFA4KbXa5k7WFpycDIZPqkBZx/fuRDfD1UfkWC1qJNqumFyVZTyrNyL7A+hLitLqMLQ5TkJ7DyPH4ygeL8G9B7GMGIISiRLcfxjRqMcxYzKhlubefjvXLAmPF9/mHTivv+68OzimIQPwrt5A6NDRVH1AqLKalrc/xrt+C67Fq3AtWU3+A3d3GGgo8QTuZWuxTh6LZNDjvP46/Ft3EWtuw1he2uGijyAIWEYPw7PyC3JunYdkOv+udoYMPYVt4ljqnn8lbaHFPPS0t8gpJT3ruJGEwsFL1cwM3UCbk0XZLx7BveoLPKs2IEgiglaLEo0iaDQ450zDcd3kVJ2exm49qWz2BtYJozpcSLKMHoZl1FAix0/g37abWH0T2uws9MVJ/0BVVUFROhU76Qm6Fbw4HA4mTpzI3LlzASgvL2flypVs3br1gm6+Zs0ann766bRjc+fO5Re/+MUFXS9D76LNdlL88EOc+MPL6D0+jBWlaUHHKeWl8KGj5N1/B6aB6Tsj8TY3bZ8tx791Fxq7DUGjQYnFkANB7NMnknXD7FR+uSCJFP/gQWp/+yKCoKXg63fj37Yb2evDNHQgGqedxpffQrJaMA3qn6wnSCQo+dn3ETO1WRm+JIk2N5LV0q6Y2rdxO3Iw1GGhtajVYho2kOjxE8RCEXwbthFvakVNJLBOHEPeXTch6jO1Db2Jd/1W9IX5XRoTBEFAX1KMZ8U6zEOSedymAeWU/dOPiZ5ooOW9z3AvW5u24yuHI4gGPYIgENi5F9FsSikyauw2nHPTfX1OZTGkFdoa9Ghzs/Bu3E7WnExhdIbew1BRir64kMDOfVhGD0vrl6os49+6G8vYEcmdmBOZ4OVKQbKYybl5Htk3ziZa14gSiSKajOiLCzpcxDEPH5wUUHnpTXR5ORjKS9HYrWnnyMGkl5VkNlP0w4cwDx2U+lto/2Fq/vP3ZH3lerJvnIXG0fMCSd0KXh566CH+53/+h5UrV6Z2XlavXs1PfvIT6uvrU+d11a+lrKyMv/zlLzz44IOpY2+88UZGevkKwtivjLKf/5C2z5bjXrYObY4TUadDTSTl+4z9+1L8yDfbFdhGTtRz4pk/oSvIxTFrapo6jxwIET5cRc2OvZT87PsppSbJZKL0//2Apjffx7t2I7qCXPTDByGZTcSaWhANemSvH//mHQBYJ44hXFmFZcyItG3wzojU1hPafxglHkdfVIBl5JCUSlmGaxslkUgVJp4i4fUTPdGAaUj/ThWiRJ02mWqm0SDqtEgOG6U/+15mRf0SEdx/GF1BbpfP1/cpwLPyiw6OF9Lnx98i/+t3pSkXnvjvF4k1tZB3zy34tuzEUNL5szDh8eJeugbBYCDrhpnpu80WM81vfkD4cBWWUcPSxAMyZLhYCIJA0fcfoP6F1/GsWIe+uBDRZET2B4nWNWAeMoD8r915qZuZ4QIRNJrzis2cwjx8MOVP/AOedZvxrNqAqNMiGg0gCCiRKHIwhH3aRAoe+iq6vHR1SO+6zcTb3DS99jbNb7yPY+Zksm+Z36Nyyd2ueREEgYcffjjt+OOPP576WRAE9u/f36Xr/epXv+JHP/oRf/rTn8jPz6epqQmNRsNzzz3XnWZluMTo8nMp/MbfkXt3kNDByqQUqE6LsX9fdDnZ7c6XQyHqnv1z0mypg4e7ZDFhGTOc0KGjnHjuJfr+809TwYdo0FP4jb8jcacf7/ot+DZtQ/YFUVUF0+AB6PJziTW1Eti5F//mHfg370BXkMvA5/+zQ/lSSE5C6//4F6J1DUntdEnEt3E7TX95j4KF96QJCGS4NpFMxpRX0CnClVVonPYuFZALgHFgBeGDR1DC0UzwcolQYrFuLUgIGi1KrHNn6DMlsOVQmOC+QyTcXo7/+lkEvS5V9NpR+kSsuTW52xwOk/D60eUmx8rggUqCu/Yn76uqtH2yFN/GbfR55JuZxZQMFx3JZKTPT79DpOo43o3bkX0BDKVF5H311nMG4xmuPjQOe2rHJnzkGAl/AFQVyWzGOLC80x3swu9+HeuE0bR+vAT/5p24l6/DvXwdpkH9yL3nZuxTv7y9QbdHwlPb3Bf69zMZOnQoixcvZufOnbS0tJCbm8vo0aMzEsxXKBqrGduE86s/eTcki7vONxCaBvXDs3ZTslbgrJxLjd1K9k1zyL5pToevjbe5cS9bQ9uiVcnVo5OBi6qqBLbvSW6JSxJKJErtb/8XyWbFOW9GuqFcq4uGl96i6AcPYj5LKSrDtYWhvIy4y4MSjaVSvSLH6zCUnf9hriZk5GAIXUEesjtZ++KYOeViNzlDB0hmU5rX1PlQIpFOHarbXdtkZPCf/xvX0tW0vPsZ8aYW/Bu3EdxzAOuY4ZiGDUrzxjL0LSHe4kI06lO1VEokSnDXfvTlpUSPn8DQtwR9WR986zbj37G3S+NrhgzB/YdpeuN9rONHknv7gm6/XhAEjP36YuzXt+cbl+GKQ9Bo2qllnvN8QcA6dgTWsSOINjTT9uky3EtWETp0lNDBo70fvBw8ePBL3/BstFotEyb0nslchkuLqqp4Vq5vpwzWGYbSYtwr13VbeUib7STvq7eRe88tyP7TubnBvQep/pf/D022k6x5M1I+Eh1pk2tzsjCPGETLu59i/mVGuvtaRmM1Yx07nMix2lTfVeNxhC4stMTb3OiKC5GMBgSdNik3meGSYBs/CteytedUCDuTyPE6LONGdPn6ol6XXKlcMIejjz5JtL4J2ZfcJTb26wtnBC+iTtfOYDXe0oZoNiEIpIJkQRDQFRUQ3HMwE7xcoaiqSnDXfhI+P9YJo7scEF8o7pXrEXRaXItWkXPrDRlfswtAleVkFoled9nXzaqKAoJwWRrc6gvzKPr2/RR8/U7cK7/AeoZSqGvJauzTJnTJbuNszhu81NfXY7FYsNlsaXUtZ9PVOpezWb9+PZ9++ikul4sXXniBPXv2EAgEmDIlszJ5NaJGYyTcHjTn8UhIeHwEdu5FDoVRwlFUVb2gL6YgimmFZkokiq6ogFh9I81vfQiAtiAXbU5W0jH9rNoYXVEBof2VROsbyXBt45g1lbpnX8JQXpKsT5BE1PMoqqiJBHGXG8esqcnfFRVBd3k/CK9mrONH0fzOJ8iBUDtfqLNRZZnoiXryv3prt+8jSBJZC2bj27gDbY6TeJsbyWY5eWEV35admAf3R7KlF8EKOi1qPEHC7cVQUpw6rkSiaHPbp+BmuDLwrt1E60dLkMxGfJt3Uvr/vn9R75d1/Qya3vyA7JvnZQKXbqAqCqEDR3CvXE9w/+Hk2C7L6Irycc69DsuYYSTcvmTBu16LNjfnktei+bbvpunlvyHodRT/8KHkPOYyRDQYyF4wBzkUwr1yPf4dewlXVhNracM5c0qXF5ROcd5Pfe7cuSxcuJDHHnuMOXPmdDiB7E6dy5m8/vrrvPbaa9xzzz0sXrwYAIPBwH/8x39kgperFFWRkx4Y5whElEgU17I1aLOcSBYz0bpGfJt3Yp805kvf3zZhNNbxowjuPYhr0Uo8qzYQb2zB9fkKNNlZ5N93W0pqGU46Z1vMJDw+sGXqFK5ljOWl2K6bhG/jdmyTx6HNciL7AoidBOKqLBOprsVY0ReN3Uq0uZVobR3WsSNI+AMpFaoMvYdo0JNzy3zaPl+BfdqETuvgVFnGv3knlpFDu/1QPYVt8njaPl6KecRgDH1LUsfD1TX4N+3Av3knxoEVWMeNTPloaPNyQIBYYwu2qeOBpNR89EQ9eRcQRGW4PAhXHUdfUpTMJFi+9qLfzzSoH+X/+rOLfp+rCTkUpv6F14k1NqMv60P2gtkIGk3Sfb6mjsbX3yH+3y+iy89BsphREzIoCvYZk3BcNzmt/q03aX3nM6wTRpHwBWj7dBl9fvTNS9KOrhA+epy6519BY7ehK8rHNmkMscYWap5+Afv0CeTe9ZUuL1KfN3hRVTWtjqWjmpbu1Lmcyauvvsorr7xCnz59+OMf/whARUUF1dXVF3S9DJc/osGAIInIoXCaJ8aZxNtciDodurxsEv4gGruNwPY9PRK8wEkvhRFDsIwYQrzNjaDVEqmuwdC3T1rgAsm+rYQjiMaO25rh2iL39htR43G8azehyXYSqa5p57Suqiqy10+ssRlNdhbRllb8r+5OPezq/vd1Gv78JuZRQ8m9fQGqueMJdIaLg2PONORwGM+K9RgqyjCUFacK4VVFIVbfRPjIMQwVpeR//a4Lvo/GasYydiSh/YexjBl++rjTjnFQf8KHjxI+lPyn71uCbdxIdEX56EuKiVRWEdi6C0GnQw4EM8XSVzj26ROp+/3LRI4eayeXfSlQZZmEL3ByB0GHxm69qJ4clztKPE7dc39GlWXss6akTaATbi/+rbsQDQb0RfnI/iCOGZOTi5onPew8KzdQ9O37MA8/v2F3T6PNzSJSU4cSjWEeNuj8L7hExN1e6v7wMuYRg9N8AXX5uRj798X3xRa0TgfOuV2Thz9v8HJmnUtP17wEg0EKC5OrWqc6SyKRyBTsd5NYUwuhg0eSHgN6PcZ+ZRhKi8//wkuAIIrYpo4nUl3T6RdN1OtRotHkAOtyo3HY0uRIexLHdZPx79pH/v13JPNGzyLR5kbQajCUFcM50iYzJHEtXYt76WoEnZa8r96GpQMjvouNqiiEDh4hUlOHZDRiGTu8x3Y5BFEk795bMQ2ooG3RChIuD6HKKrQOBwigxOIkvD4kswlVEAgdPJKSSFbDEXQFuWizs0h4vIT2HKR62x6kAX17pG1XEqqsEDpYSbzVhaqoaJw2zMMG9UpuuSAI5Nw8D9OACtzL1uBaugaN1QoiyP4guoI8cu/+CtZxI790yk3evTdT89TzhA5UYhyclNTWOh1kzZ+BPHkM/h37CO4/RPRYLS3HapP9Iz+X/r/7dxKeZHqKsby00x2iDFcGpv7lVPz6MZRwtN1iR28Sd3nwrtuMZ/WGZMqrRoOaSCSd1WdMwjF90iVt36XCs2oDiUAI25Sx6YGLz497+brk9/LkHCTW2IJ/6y4cs6aisVmwjBpKvI+b+j+9QfHDC9v52V1sCr99P+6laxD1epzzLn1g3BnetZvQ5mZ3aGgu6rRYRg/HtWgljlldy7rqcrJeIpFgwYIFTJ8+nSeeeKLrLT4HEyZM4MUXX+QHP/hB6thrr73GpEmTeuT6VzuhQ0dp+3QZkeMn0OXngEYDCZnWjxajy80i68Y5WMd2vdi0t3DOnMLx3zyHoawPksXc7u+abCfGijKCew+hhCOYhgwg+ytzL+heSjxBYMcePKs3Em9zocYTiCYj5mEDccycin36RNwr1xM6eJSE30+ssQU1FkeQJESTASUSJf+BuzN5w10guO8wrkUrsE4YhRKN0fDSG/R9/Kdos3rvYZjw+jnx7EvIgSCabAdqNEbLu5+Qe+8tOKb3zLhyppKKb8tO6v7wCnIkisZuRWe3YRk3Eu+ajSRa3eiK8hD1OhJtHjRWC7rC/GQBdn4u2rwcEh4/oUNHeqRdVwJKLIZ72To8q74AjZQKKuVQmKZX38E+fQLO+bNS5rQXE9OgfpgG9SPh8RJrdYGsoHHa23kWfBkkk4mSf/guJ579M76N2zH274s2JyuZjmqz4pg5GdvE0Xg3bid0oBIkiZKffQ+t04FkMiFopGt6RfxqQjKZLplEuqootH28BNfydegL87FOGJ1WC5rwn9xBWL4O+8wp5N6x4Jp55qmKgnvFeszDBrZLWfJv2YU225EKXCC50xE6cAQ5GEoVmmuznZhHDqXx1bcpf/Ife/Wz01gt5N55U6/d70Lxbd6BaejATv+ucdhAkghX14Dh/ItYXQ5eNBoNiqIQiUS6+pLz8s///M98//vf5+233yYYDHLDDTdgsVh44YUXeuweVwuqLBPYfYDAjr3IgSDxVheRugasY4YnJX7PeMCpqkqsvommv7xHpKaOnNtuuKxUKHQFeeTe9RVaP1iEZfyotIHhFPriQmINTeQsvDdpYtnNojhVVZOrqp+vRLSYMZQWJfPOJRE1GiV6ooGap36PNtuJoNPgXb8Z0WhITiwsJuRgiFhTK5LNQtsHi0BRoPzy3M26XIjWN6LNzUZzUsFNslqINbf1avDS8Oc3EY0GLONGpPq8HAjS8s6n6IsLe7yY0TZhNPr/eIy6F15FjSeQCpIpjrGmFrQFuSjhKLLLizY3C31Zn7TvYXIV3pbcedl78fPgLzVyKMSJ372EEolgHj2s3fdeDgQJHjyKf/seSn763ZR88MVG47BfFAfo1PXtNkof/QHeDdtwL19HcO9BtFlOBElEVVQSbg+CJFL0va9jnzo+9X1p/eBz2hatIveum8iaNzOlPpbh6kUOhlAiUTQOe5eMlbuCqqo0/eU9gvsO45wzDVHffhdPY03uIChD+uPbtAPZF6Bg4T3XRAATrjoOioLmrJqVU/Ms05B0iWBBkpDsViLHatOyR3QFuYQPHSF06CjmIe3VS691lHAE0XDuMUzU61Aj0Z4NXgAefPBBnnnmGSZOnMiYMWPQneEIfCFqY3l5ebz77rvs2bOHuro6CgsLGTlyJOI18IXpDt4vttDy/ufJOpDCPORgiFBlNRqbBf/W3SR8ASyjhqYGGkEQ0BcXoM3JwrtuMxqbFeecaZf4XaTjmDEZwaCn5e2Pk++rIA9Bp0WJRIjVNyFIGoof+dYF+auoikLT/71HcN9BrJPHtk8ZMhqSE5acLNyLVyEIAlm3zEcNh4nU1oOsYCjrg3FOXySziYTHS+uHi1GHZrxezoWhrA9tny0n1tKGGokiB4Lou+Fm/mWJNjQTqanDef11aUGCZDFjKC/Fs+oLjOWlhI5U41m9IakINX8m+qKCL3VffXEB5f/2KKEDR3AtW0P4cBWiQY8aDKPJcqIbUH7Oiad4kWVTLwdUWaHu+VdBELBOGtvhYopkScpRhyqrqf3dnyj7xSOd1sVdaYgGPc7ZU3HMmkL4cBXREw3I4TCS0YCuIB/TkP7tJor+rbuJN7VQ//yrNP3lfXJvv4Hsr1zf4W51hiubWHMrzW99SOhwFYJWg6jVknXjbByzp37phcdTAbN9+sTzmpyKOh32qePxrtuCa8lqsm+c/aXufSWQcPuQLKZ2n3O4qgbJbu0wgBP1unaS94IgoO9ThHfdpkzw0gHa3OzkZ91J/bCqKCS8fjTZWSDHznu9bgUvv/nNbxAEgX/6p39KO36hamO/+tWvuOmmmxg7diwjRya1n7dv387nn3/OL3/5yy5fp7q6msceewyPx4PD4eCpp56ib9++aee8++67vPLKK4iiiKIo3HPPPTz44IPdbnNv07ZoJe6la7CMG4nWaUdVVdo+XoqxfxkamxUlFidaU4fsC2CfMSntCyjqdVgnjqbt46XYr5t42WmV2yeOwTZ2BIHdBwjuPYgcCqPNziJr3kxMg9s/zLtK28dLCB44fM7BOuEP4F2zKem9oCj41m4i68bZaapAp9A47Nivm0jL8nUX1J5rBdPACvLuvRX3kmTNS/HDD13UFe2zibe0orHbOuw32iwHkZo6Isdqqfv9yxj79UWVZWqffoGyX/7kS+d5C6KIedhAAnsPIJlNWMePPP+LriGCew8Sb3W3G6M6wjSgHJ/bg/eLrWRdf/nmcF8IgiCk0tXOR8V//hLfhq00v/0x4cpqGl99m+a/fUzWTXPIvWNBaodGDoVIuH2IBj2aLMdltcue4fwkvH5qn/4fdMWFZN04C0GSSHi8uBatRIlEyL7pwlKmISnV7vp8BdaJo88buJxCkCSs40bgXrwa59zpl928oacRJBE6EJ2S/QGkzmrNVNqJ+0Ay9SlSe+3WxsZa2/B9sY1YSytqLIFkNmEeOgDzqGE4Zk6h7ZOl6ArzOhyjoica0OXnoC/MgxMnznuvbgtU96Ta2CeffMI//uM/ph0bPnw4P/zhD7sVvDzxxBPcf//93HbbbXz44Yc8/vjjvPbaa2nn3HDDDdx5550IgkAgEOCWW25h4sSJDB7c+wXFXSW4/zCuxauwXzcpZWoVb25DjcWRTu4miDothvISIlU1BPcealcgrbFaEK1m/Nt2Y588rtffw/kQNJpU/UBPkPB4cS9fh2POtHMO1oFd+9E67am8XzkUIbjvELZOFM1EnQ7tqCGwuEeaedVinzIO+5RL0880TgeyP9ChJ1DC60Obk4V/+x70JUUY+/cFTqYq7T+M47qeqYdxL12LJrd30p2uBFRVJXr8BE1vfYho0BFrbEGXn3PehQljRRmeFetwzpl2TaSudIQgidinT8Q2bQKBXfto+dsnBHbupfXdz7CMGILaV6b1oyUEdu1HMhpQojG0OU6ymHqZWwAAIABJREFUv3L9ZVnreDkTrq4l3tyKoW8fdPm9t1sM4Fm9AY3DjmlgReqYxmHHOmkMriWrk6lehgvbnQ3sPoBoNKA5y0/ofEgWM6LVTGD73k6fiVcL2rxsEh5ft7zklEjkgiXUr0bCR4/R9ulywkeOoSvKT86rRJGE10vLB4toevMD7NMnIJqMBHftwzx88GmFR1Ul1thM6EAlfR7pusxzt4KXnlYbEwShXeAjyzJKB6pPndHW1sb+/ft5+eWXAbj55pt58skncblcZGWdnkRYLKdThyKRCPF4/LJfoXItWYVpYEWaG2+k5gQahzU9d14U0fcpIHTwCOahA9oVeOqLC/Bv2XlZBi89jXf9VnSF+R3m9Z5CiUSJnWjAdEZKmjY3i3BlNZYxwxE7MREUroH0nisZfZ9CNFkOIkePp4ITSP5/R6prKfzO14hU16CEwsAZMtg9qOSU8PkxFpT32PWuVFRFwbdpB+6lq4m7vURr69EV5BKtbwJFwTiwH6bB/TutZdNkO1HiCaInGi5L5UQ5FMK/dTex5lYEUURXlI91zIiLUpciCALW0cOxjk6m1HlWfYG+TxE1T/0BXXEBuoJc9H0K0eZmE2tsoekv7xFraSX7hqs/5acncK/6gtYPF6PNchBvc1P8/QfTng0Xm8DuA+hL26fdSyYjksVMuLr2gtOQPGs2or9AiW1DaTGeNRuv+uDF0KcITbaTWEMz+qL81HHJYibW0NTufCWeQA4EMZS3z9JIeH3oeqlW73LBu2EbzW99iHFQBc75M9rNP439y0n4/AR3H0BVk2O7a8ma5CKWJJLw+BC0WoofXpjMhOki3Qpe9u7dS3NzMzNnzkSSJGRZZvXq1eTl5TF8+PDzX+Asxo8fzzPPPMOjjz6aSud67rnnGD9+fJev0dDQQH5+PtLJD0ySJPLy8mhoaEgLXgCWL1/Ob3/7W2pqavjZz37GoEHtpXp9Ph8+ny/tWGNj77urx5pbiVTX4pw3I+24Eokg6No/IEWDAVGnJVpb3y71STIZibS5L2p7LwdURcGz6gss48696hg+ehzJZkHQnP6SCRoJRAHXktVozCaQRCSLGWNFaY+lPl0ufetqRRAEir7zNWr/+0XirW1oc7JQwlGidY04512HeXB/DKVF+DbtwLt2M6oso83LwTqm+2NXpyjKJdkpuJz6liorNL76V0KHqjAN7o9hYD9knx9DWR8gqSwWPVZLtLYO55zpHQaPgiAgGQ3IgWBvN/+cKLEYLe98im/jdjQ5zmQ9naoS2LWP5rc+xDF7Gjk3z+uxYuuzMQ0oxzSgPKnWWFGKLjuLxtffAVXFOmkMtolj0DhsuD5bgWXksB655+XUty4GrR8uxjZlHBqrhUhtPW2fLuvV4EXQSCB3vGCrysqX6kvxVhfmLsjVK5EoSjQKoohkMiJIEhq7jXBlNUokCoKAoNP26ILv5dSvsq6fQct7n52cUCfnBYaKUkIHK9EV5afGdFVVk0FOWQniWfMwVVWJ1tZTsPDeXm//pSKwcx/Nf/0Q27Tx57Qj0NisWCaMJrTvMAmv7/9n77zD46jO/f+Zme1Fq1XvsuSOu3EBF2xsgykJtkMoAYKBBJLATYGEXCD5UVJIQhJuSEIK5Iaa5BLgYi6m28bGFXfLxlVu6n13tasts1N+f6y9WEi2JNuyZHs+z8PzSGdnznnHHM2e95z3/b4UP/gfRA9WosXjWHOzsQ8u6fH3Zo+clx/+8Id4PB5mzZoFJByF5557Dp/Px+LFi3s0MMCPfvQjvvGNbzBt2jTy8vKora0lMzOz19TGZs+ezezZs6mpqeGee+7hkksuobS0tN01L7zwAn/84x97ZfyeEKuqTShhfc6LFUQTerzzZCbJaSfe7OvgvOiq1mO1rrMRtS2MFpOTalfHI97iS0oc6qqGXFuPXN+ErqqJLxGPG3SdeFMLkfKDmDwpiS8A+6nt0PeXuXUuY8nKoOTRHxDcvJ3I/kNIBQ6yb/lSMilfcjgofvDbRMoPIUhi4qV5GqVoBbMJLRaHnkVpnDL9aW41vr6YyP4KPNMnIUgSaiiMrn12wi457IgDCpBr6vEtX0Pa5TM6/eJK1KHoPzLBWkym6qln0aIyqZdO6eB0qW1hghu2ItfWk3fXLb3mxEYra5AbmvCOuQA9EsU1dgRtO3YnnUPJbsNamIf/47Uw9dRP2/vT3DoXcU8YQ2DVeiyfEzeJ+wLosoytpPik+z5aw6XTzzSNWFUt4d3lKC1+BLMJNB1d07DkZKJGY8QOV1H+/UdBTzhZ7knjSJ15MbaCUy+Y2p/mlXviGNo+3UPr2k24J41DtJgxuV2YM9KJN/mwZKUnVVzR1E7DMuP1jQiS6Yw6vn2JFpOpfeHfCdntbtRREwQBx4ghhDZso3XjNlKnTkKNRBBNJnRZRuhhaGSPVrRVVVUdTkVKSkooKyvr0aBHycnJ4Y033mDbtm3U1dWdlNpYbm4u9fX1qKqaPA1qaGhIFr/sjLy8PEaNGsXy5cs7OC8LFy5kwYIF7drq6uq4+eabe/Zwp4gej0NnicfpqUT2H+pcRlQUEwvwzxFvasZaVNAbZvYrtFgs8QLuAj0eR7RY0BWF8J4D6KqaqAkixxFEoZ2EqyUnC8UfILBqA0Jpx2PintBf5ta5jmi1nDD3RrRacI44vt78qWAbUIhc14Al48wWeusvc0vxB/CvWo939vSkUygecfq1WCwZzikIApa8bCLlh5Br6jvEj2txBaU1eFprrpwqja+/jRaVcU0Y3blamtNBykUX0rp243GVmtp27aPpzfcR7Tayb5yfqM/VQ2IV1claMYLDjmfapOSC6yiW7Ewi+w9jOg3OS3+ZW71Fxry5R8LGvMSbW8j/5pkV8kmdNpHA6g0EN5VhH1KKaLMh19YT3rWPrK8sOKWNR8lhR4vJHRTqtFgM//K1aOEIpnQvjguGIIgCWjRG2579hPceAF1HdDpJ/8JlAKiRKNGDlVT+9q/YCvPQrz61sMT+NK8EUSRn4fU0vLYY39KVWHOzsRbl4RgxFP9Ha1CDoUROWVpqonTD50LL4y0+Qtt2kv+thedNjl5wUxmmFBfmz0lMH0WLxYgcqCBWVYseVxDMJszpXlRNpf6l1/AtW41ks6IrKpos4x4/itQZU7AN6N5atUd/FRkZGWzYsAFZlrFYLMiyzIYNG0hPT+9JN+0QRZFx404+pjI9PZ3hw4ezePFi5s2bx+LFixk+fHiHkLH9+/czcGBC4aWlpYVPPvmEyy+/vEN/KSkppKSceOf+TCA5HWiRjjV1bCVFhLZ+ihZXOrzUdFnpILuqqxqxylqyb762V+3tD4hWa8Lp6wLBbEZTFGJ7DqCjY0pPKPSosRhI7Y+CBVHAnJaK5HIQ2nPglOzrL3PLoPfI/PLVVDz+h07/PnuT/jK3/KvWY83NavflLkgS9kEDiDc2Y83/TJZaEBJ/W227yzs4L7HDVTiHDz6janUnQg2HaV23uUvpWkEScY4cin/pKtI+V39LbQtT89eXcI4cihoKU/PXFxnw8H2d9qOrKqHtu2j7dC9aWxjBZMKclUHKReMSKkda+1zRzy+mdE07bSE+/WVu9RbemVOwFRf2WcK+aLNRdP83aXn3IwJrN6FFothKi8i76xacR4r66YqC76M1yLX12EqL8UyZ0K1FsnPUcMJ7D7RTU9TiCr6lqxEkEdugAZ/Vw4pECe/ah+R0YEpPRWlsQQuFaHzzfcweN6LdhrUoH/vQUiJ79hN65p+n9Nz9bV4Jkkj2DdeQdtl0AqsSzqQaiWDJTCPe2IylIBfniKHt8tqUYIjowUrk2npyv3ZTt1QEzwV0XU84ecUdN3R1VSO4uYzo/sNIbhcmjxvBLSE3+Qhu2AYkIhQcQ0qTdde0mHxECfTv2AYUol81s0sbevTtOnHiRN58803mzJnDkCFD2Lt3L42NjcybN68n3SSRZZk33niDXbt2EQ6318x+4oknut3Po48+ygMPPMCf/vQnUlJS+NWvfgXAnXfeyXe+8x1GjRrFK6+8wurVqzGZTOi6zi233MK0adNOyu4zgX3oQLRwFDXU1m7XRLRasBUXINfWYy3MS754dE1HaQ0mvtyOIbL3ALYBBQn5uSOE9x6g/n8WoUWieGdOwXv5jH4vXtAdJKcD0WYj7gt0WvjyKOZ0byJ5TFUxZXiTz67F4li8ne+YixYLUkEOfNIrphucI7jHjUJKcRE9VIF9UMk58XfVE0Kbt2PrpBCofXAJkX0HMad724Vbmbwe2nbsSYS3HFGf0WIy0YMV5H3jq2fM7q4IbizDnOHtlriDKdUDZhNtO/e1U39UgyEQBSx5OWjRGIHlazrcq8kyvg9X4l++BsFsxpydgWi1JOpf7diN74MVWPJziFXV4Bo3AgQBua4RLRxB1zREixlzdiZyTT3OEUM5fSWlz23sJYXYO0nAPlNIDgeZ115N5rVXd/p53YuvEjlQgSU7k+a3PiDe2Ezmgiu77Dd1+mT8H61BHzEk6UiHd+8DVcVSmPvZ+kFRiOzZj+h0gqAjV9ejR2NIqSkojc2YUlyooTCtazchCCKO4YMQT/AdezZjTvOScc3lZFzz2ea2EmwjsGYD/o9WoysqosWMrijoiopn+mTyvn7TKcvtn01o0RhyXSOuC9uXBNA1Df+KtaihEI5hA5PvdLm2AaXZh6UgB0ESkesaaV27EVthHoLJhGi14Bg6EPvgEkLbdhJ57t9d2tAj5+W+++5j69atHD58mIaGBgCKi4u59957e9JNkgceeIDdu3dz6aWXkpFx8uEBAwcO5NVXX+3Q/uyzzyZ//nxtmv6OaDaTMm0ikb0HcI1tn3jpmjAa3wcriFXXY83PRhAEFJ8fk8ed3KnUdZ3IvoPIDU0UP3BP8l4l2Eb1n1/AOWIokstBy4cfY87OwD32NCYt9xGCKJI6ayqhzWWYvZ0n7eu6juR2EW/2IbqdKP7WhJa7yYQuxzGnd34ECiAYFa4NukAQRQr+4w4O//KPRCtqsBXldenAKDUNZ8i63kcNd67eZnK7cF84iuCWHdhLCpPSr4IoIkgSWlxBMpnQojFa127CM31yn+xiKsEQck09tuKCds8hNzQl5em7g8ntIt7U0q7NnJWBrSA/IRYhy6TObF84WA21UfXH59AiUVzjR2FK7bgrrY8cSrSiGsXfSuOi9xAARBHRakUQQFdUlNUbQNPwzp1pOC/nAJosE9xYRtpVsxAkCUtuFoGVn3TLeTFnpGEfUkJ47wGcwwejaxqRvQfabXwCxBtbEmHnsRhqOJKYUy4Hlsw0lGY/elzBmpeNOSsdLRwhvGsfquP8Ud80uZ2kz51J2mWXINc3JlQqLRbM2ZnnRT7x59EiEQRrRwGH0LadqKEQtpKi5GeKv5VYdR3mrPRkDqOU4kINthFYsxH3hDGIVguCJCGIIq6xI4is2dClDT36V8/OzubNN99kxYoVVFVVUVBQwIwZM7CdpAb5ypUrWbp0ab86OuxPpF12CYc3bCNysCJ5vAYJx8Z72SUEPv6E8O5yJLsdNRgiddZU1EgUubqOaGU1Jreboh/e3S70It7UjGizJsM3LDmZxCpqzgnnBcAzZQIt7yxFjUTbSUwDRA5WJIphBttAFNBjMrqmEw+2oalqYrFwgqN4Xe46JM3AwH3haPK+cQu1z/yDcFsblvwcTCnt5c11XUdtCxOrqEZrDfahtacX0WJOOCKdfGYfVAKiSHDDNiSXIyFIYrehqypaW5jInv3EausTi4RTKMx3ssSbfRx+/PcIVgui2UzxQ99JhoiczAmaIAqf+10k/9t3ENlTjmCztpMF1eJxqp5+HgD35HHHHU+QpMRJvMVMvL4Jk9eDfWBxIlxYVYk3+xHawlhLi2h4+XX0UR0VNQ3OLgRJAkFI5K447GjRGMJx5Pw7I/umBVT88mmiDntiJ/yIothRdF0nVt+IoCfyFLCaIRZP5mOJLgfxhiaseYmNUsnpwDawmGD5wdP+rP0dQRSx5mZ3feERQtt3U//Sq6iRGKmXTCbz2qv7XU6MFlcIbtiCFoniHDEUS05Wl/cIZnMHhTwtrhDZewD74AHt3l+x6jokjxvBJCXKE7RFUJv9aIpK+NO9xKrrEAQBW3EB9qGlmNO8mIZ1vXHVI+clEAjQ1tbG5ZdfTlNTE6+99hpNTU1cf/31mE+iCmtubi6y3LlylkFCXq7w3jupeupvKC1+bKXFyXAo0WLBffGFhHfsJlJ+CMntIrhxG6LFgn1IKXlfuwn7kNIOX4KWzHT0qEyssgbR6UCubSDt8hl98Xi9ginFTdrcmQl9+qmTkrsioa2fEtl3EEt+DoJkAgHUUBiOSCQLUQ09GiNWUY21KL/Dv5uuKKhV5448qEHvkjbnEixZGdT+97+I7D2IYJKQUtwIkoiuaqitIXQ5hrWoAPvt18Odq/ra5NOC44LBxKrqjhu2aS8txpqfS/TAYcJ7D6D4AuiKQmj7LlKnTybvm7eeMOSzNwnvO4DkcZMycSz+j1Yj1zUkFbws+TmEynZ1qx9d11F8gU5zJ0SzCefIjtK1vqWr0MJh3JPHn9BRijf78H+0GntxAQwQiOw9QKyiJrHoFASsRfl4L78Ec5o3IevcSWiawdmFIElkLLiS5reXYE73Em9qIefW67p9vznNS8G9d1H11N/QYnKH4s1qMASKiirLCcGbWBxLTkZS/EawmNFicrsijoIoIuV1fxF/PqIEQ9T+7R+4J4xBcjlpXbcZW1E+KZPH97VpSXRFoep3z6K0BpEcdpreWkLhvXcm33vHQ3I4AAE1HEk6wrHDlYgOezsJaTUcQYtEMae60SJR5IZmBFFEFwRMaR4ETceU7sWcmY7S7MO3ZFVCAKAbmy49DhsrLy9nxYoV3H333ezYsQNIJMM//PDDPekKgPnz53P33Xdz6623dkj6v/jii3vc37mIJTuT4oe+g3/VevzLVoOuI9is6IqCFomScvEE8u++rduqPJLLSf5/3E7D/yxCragm7arZuEZf0MtPcWZJu2o2SjBEYNUnuMeNQm5sJlJ+MLEjYDIRb/EhWsyIWenItQ2JxOG8bESziXhDM4LF0i5HSA1HiFZUIxXmwro+fDCDswrX6AsY9LufEDlYQfOi94gcqkKXY0hWK87JY8n80lXYCvOpqqrqa1NPG6kzLqbiV0/jGD7ouDuMotWCY/hgHMMHE1i7ibS5M0mdOvEMW9oRe2kxqq81EddvMrVzPtzjRtHwP2+itoWTMuvHQ2n2IZhN2IeUnvC6o+iahv+j1TjHjDih46KrKv7la7Hm5SQqWJMo+OYcNghbaXFiE+aY+0WLBfOYC+Cdbplh0I9JmzMd+8ABxBsasRbktRO+6A7W3CyKH/w21X95kdDmMiK6huR0IIgScZ8fNRJBQEB02xIJ1t2QKO9vJwj9DaXFj2i3JZVhzVnpxGr7JkRYVxSilTUAyTwTSNS8ize14JlxEYIgEN53gJYPV5D39RMrvgmSSMqUCUQPVOAcmXA0YrUNmFLah9bGm3yIDjtaJEq8sSXh6Jgk1EAQk9uJJsdRW4NY87Kx5GRizs5Arq4ntnpjl8/UI+dlz549TJw4kZaWFrZv3878+fPZvHkzS5YsOSnn5eWXXwbgySefbNcuCAJLly7tcX/nKpLLSfoVl5J22Qxi1bVokSiCxYw1N/ukqoPbBxZT/KPv9oKl/QNBEMi6YR6WnCyaFn9I7HA11oJctLiCoKjocQUlFEHQNMxeD7qmozb70R12pBQnsapapBQXejRGvMUPmoZr7AhiJ8iHMTDoDEEQcJQW47jvG31tyhnBmpuNY0gpoW07cY098WI8eqgSPR4nZcKYM2jh8bFkZVD84+8Sq6zBPmhAu3eraLWQOnMKwU3bSLnowuMu3LS4Qtv23aR/YU63F3dtn+4FSeryxClWWYNgNrXLhTGne2nbva+datSx9CS8yKB/c6qiAiaPm/S5M1Fbg1jyclBa/GiyfES+XMBSlIvYSQRNQub29BaoPB+wZGeApiVOdF0uYlW1pF8564zbEVizgcbX30k6LLqqknntVXgungC6DseEtwqCCPrxemqPd8ZFHP7FHxMbVZLUTnTlKHosBiRyqiRnImxRi8aQ7DYEkwlBUdHUz8LxBUHAWpCDfLCiy/F7HDbm9XopLy9HEATuuecennvuuU6T5bvDsmXLTuq+8xVBErEV5fe1GWcFgiDgnTkFU4qL2uf/jSBJyLUN6KqaqOciSThHDEkuUNRIlHh9E0prED0eJ3qwAktWJikTxiRCzUSRmM/Xx09lYND/yb3jRir/6xlCG8twjBjSLr4ejsRG7zuIXFdP4X3fbCc92teY073HVQ3K+OLlyLX1tK7ZiHPUsHbFcHVdR2n20bZ9N67xI/FMn9ztMcO792HJ7LrcQNuufZjT2js4gtlEvKkloU4GCBYLlpwsbMX5HRYSBgaOEUPQXoxiyUrHXprIow1uKiNWW9+p4wKJ8GpzN+anQXtEm42Ce++i8bW3iTc2k3XjvKT09ZkiuGUHja+9jXvimGTuc9wXoPHVxUh2O44RQzGluAh+sgXRaUeuqafg21/rVt+WnCyco4YR2rwD14TRidp5nytVoWs6ajiMaLEgmEyJmi4xOVmQVdc0BKnjJo+Y1fV869HbLT09nRUrVnDo0CGcTieFhYUEAoFTSrhvamqirKwMn8+Hrn/m8n35y18+6T4NDI4S2roTx5CB2Ad+VqVYk2Wa3ngXjqnBINltSEeKI8WbfaBD6kwjdNEgwdG6G/4V64jXNaLF44h2G45hA/HOmNKhTsn5jGizUXjfN2lc9B6BlZ9gSvUkwpwEAbUtjFzfiHPEEIof+PZZJS8qSCJ5d32Vlg9X4Fu6MiGf7naBrqP4WxNhW1YL/lXraft0L7m334C9tOvq6GoojGA5sQOnRiKogWByninBNuTqWtRQG7qioqsaojWxeGj7dE+i4OHAAegFRl6CwWeIZjOeaZNo27UvWSVedDlBVdvltBxFV1X0SBTL4JK+MPesx5qbTcG37+iTsXVdp+mtDxIbLceINpm9Hhwjh9L01gcMGDuCwnu/QWDtJtRwmOyvLMBWmNftMXJu/TJVv3uW0MYyLLlZtO3Y3a6AuiAJaJEYJm8KelxBbQtjzkhLblhp4SjmTlIeunPK1yPn5ZprruGZZ56huro6WQV1y5YtDBvWMQGxOyxZsoT777+f4uJiysvLGTRoEPv27WP8+PGG83KWE/cFaHl3GaLdRvqVs04qvO1k0GIywU1lRA4cRgtHCJXtwpTmwVqQk6zuLVos2IoKiDe1tMttOYpgNiUUyT6HrnXzPNXgnMK/6hOa3/wgEapZmIdz3IjEMbkcJ1ZTT8Vv/4I1J5Psm681nJgjiFYL2TdcQ+a8uQQ3lx059dQwp6fivnB0vyk+2VMESTwSwnsJbTv3JeSQhcQuZOv6LcQOVZJ22Qzijc1UP/08pT//z6Qs9HH7NJtBC53wGi0qI1gSoTvxxhaih6uQUpyYc7OI1zZi9nqSCwJzRhpaTEaub0A+1HX4hcH5RdrlMwhu3k547wEcQ0oxuZyJws3hSLt8Ll1ViTf6sORm96vTUYPuocdk4nWNuDsJy7XkZBHaWJYIG7RZ8V465aTGEC0WCr53J3UvvkaobCeKL4DSGkrmvuiCAEcUxnRNw5yVnjyJT0TByFgy0k40xHHpccL+xRdfTDweZ9q0acTjcX75y1+SnX1yuzu/+93vePzxx7nyyiuZOHEiixYt4vXXX6e8vPyk+jPoP9Q++zK6qqFFY2gxmewbT66QaXdRgqFEheI1GzCluDFnpiGYzeiqSqyqNpH3UpSHc+RwTCkuHBcMoeX95ZjcznZFQAHQ9I6xm7qOsnt/rz6DQf9C13Wa3niPwJoNuMaP6piT4LBjSk3BMWwg0cNVVPzmL+TfvRBHNxO1zwdEmxXPlL5Pxj/dCJLUrgAlQOO/38I2sBjRYsaan0Nk737iTb4uHVpLdjqxisoTjycAOii+ANHDVZgyvYhH3m+gJ5WhjiJaLdiK8olXVJ/M4xmcw0guJ4X33kXlU8/Suj6Q2Pk+smEnOuxHFpvhRLhYVgaW/M7Xd4m5Z9BfEUwJiW09rnTIfdPjcRCEZOHSU0G0WMj7+k3I9U3U/O0fhDZvR3TaEUQJNdQGuo7odiaEII4WRdV1lObE3OuOOESn4/b0hosvvphLLrkEURQxm81MmjSJ4uKuj8Y7o6amhiuvbF9oacGCBSxatOik+jPoPyj+Viy52ZjSPCg+f6+OJdc3UvGLPxDZfwjPtEmkXHwh9kElCd3wQQOQHA4cwwaitkVoee8j5IYmTB43numTiB6uRgm0trc9GMKc+dlugK5qtG3ZQbcz2QzOCfzLVhNYuwHP9EknTKYWRBF7SRGusSOo/suLfaYoY9C3WAtyiR2uRtd15MZmNFnBlNb1CVPKpHHIR0IRj4dos6HHZcL7D2NKT03mJ6ihMKb0tOOKA0gZZ09YnsGZw5zupfjB75B6yUXI1XUImoYejxOrqEGubQRRxD64NKFM1UkIj66qRumAfo5gMuEaN5LIvo71eCL7DuK+cPRpcV6OYsnOoPjBb5P+hcuw5mSReukUHCOHJpxj5TNHV1cUlMYWRIcN63FC1LROIl8+T4+dF7/fzyuvvMITTzxBMBhkw4YNNDU19bQbIJFDc/Te/Px8tmzZQkVFBZqmdXGnQX8n68Z5hMp2Eq9vIuOLl/faOIo/QOWTz2ApyMU1dkQHGVN7aXFCx56EXKS1IAf/8jXEW/xYc7NJnTUVub6J8L6DxJt9aPE4aiCIfVAJaluYth178H24AlO6F9ft1/facxj0L7RolKb/e5+USePb6dafCEt2BraifJr+7/1etu78RA2HCe89gNzU3NemdErW9V9EsFhofutD2rbtJO/0leBDAAAgAElEQVSum4/UQzgxJk8KjhFDiR46vmS2aLMiWK2Angzh0XUdLRxJFhM0MOgJkt2G99KplPzkfrJvvR5LXi7W/FxMKa6EHPfnZG/hyI55MESk/DBi5smF+xicOTIXXEm8sZnQ1k+Jt/iJt/gIbdlBvKmFjPlzT/t4giiSs/A6vLOn01a2i3htAwgJZ1eua0JubCHe2IIpw4t9cEmnjrEaakOtb+xyrB6Fje3bt4+FCxfiO6K69K1vfYs777yTefPm8dhjj/WkKwCuu+46Nm3axNy5c7ntttu49dZbEUWR22+/vcd9GfQvXKMvYPCTj/b6OA2vvY05Iw17SVGnn4s2K9biAmLV9ViL8jCluNGzFVrXbCTt6tlYMtPJmH8FcnUd4T37CW3egWA24/9oNYIk4Zk6kZyvXoslJ+ucqsdhcGL86zYjeT1Irq4Xn8diKy3Cv2QlSqC1nRqVwakh1zVQ8du/IlrMqKEwWTd8sd+Fo0kuJ0U/+GZCkt0k9UhaNv3KWVQ++VcsmWnHzwc6RtBG13VUXwDJ7eyg5mZg0BMEUSTtsksIbtiClOIGXSe8ax8IAiaPG/FIyJEWk1H8rQgmE84xw2k7IoNr0H8xp3spfug7+JavJrR5OwDuC0eTOnMKphR3r4wpiGKidtesaTS/u4ym1xZjzcshVlufUMwtKUFyuzq8H7V4nHiTD8XnxzJhNKz/8ITj9Mh5+eUvf0lrayuFhYVUVlbidruZOHEia9eu7fkTAnfddVfy5/nz5zNp0iQikQgDBw48qf4Mzi+U1iBtZTtJnT39hNe5J4zB9+EKYlW1WPNzMKWlEm9sRmn2Yc5IhFxY8nOI+1uRXE7y7r4NU4oL0WoxCnGdh/g/XkfNH/6OYLFgSnHjHD642/eKZjOWnCwCqzeQftXsXrTy/KLprQ+x5ufgGDoQJRii4V9vnrDeSl8imnsuUWwryifn1uuoe+HfuCeMaafYA4nQVTUcQTCZUEJhkGVAwD5wwAn71eNKj20xOP8QzSYKvvN1Kp54GktWBunzryBe20D0cBVaNAqCgOR04hwxDFNWOrFDVWiHjXyqswGTx03mvCvInHfFGR1XNJvI+OJlhDZuwzawmNSZFxPed5DI7nLk2kYktzOR76JpaFEZta0NW3EBKZPGEdS6fm/16C1bVlbGnDlzyMrK4qWXXgIgLy+PDRs2nNzTfY68vO5LtBmc3WjRGIFPNhPasgNBEHCNH0XKpHE9UjUJrN6IJSczuTN0PESzCe+cS2hds4HwrnJMXg+iw0Fo+27c40cSq65LFKYrKaLgntu6rKBtcO6iBII0vLoYwWrBccEQ2sp2Yi3IxeTuGEJxPEzpXqKHjVO604kWjSUVC0WbFU1R2p1EnAu4x49CtFqp/fu/EB12bMUFWPKyEQQBPR5HEEWkFDdyVQ1SihvniMEnjFnXZBm1svYMPoFBf0PxB/Cv2kBo6w7UtjDCkWKonmmTcY0f2a62i9nrofiBb1PzzEsElq7CWpiH+8JRSZVOLa4QO1xJ2869SE4Hzq/fCB+81lePZtBP0OIKWjSaqOViaV/MVBAEUudMx/f+csxTJ+IcNgjH0IHIdY3EGxrRYjKCZEJKcWIrLkiGaGuVXX9/9sh5sdvthMPhdm179uwhNbX7lce7e0pz8cVGjY1zFSUYovI3fwHAWpgLOvg+/Bj/R6sp/P43u+08tH26G0tu95TuRIuZ1JlTUAKthPcdJFZRjVxTh2gx4xg+mOwb5hkytwZo0RiCmFBhES3mhCTyCRKpO0Mwm1ADwV6y8PzEO2saNc+8jBJoRWnxk3rJ5NOabNpfcI4YQukvHyK0ZQe+pasIbd6OYLUkaiQEQ9gHl+AcPZzQxm1EK2swp3uRXM52CwZNlo+EXwSQBhbDuj58IIM+QWkNUv+vRbR9ugdrbha2onwEmxV0HbU1RNPbS2h45U28c6aTdsWlyRNMk8dN0f13E62oxv/RanxLViU6FABVwzlmOHl33oR9cCnV1cbJy/mKFo0R3FRGy9KVyLUNiGYTuqIgpbhJnTkFz9SJyQ2/1KkTCW3eTmjLDlzjRiIIQiL/uJMyFZAQIYlv29WlDT1yXi688ELef/999u7dCyQKSX766adcffXV3e7jRz/6UZfXCILA0qVLe2KawVlE0xvvItptuMZckGyz5OcQ2vIpTW99QPaN87vVjxoKY8roWeVfkyeFlAlj0MeOoOW95ZT+7D97dL/BuY05Mw37oAFEl60mUn4IyZPS45okelzpsraHQc9wjhhCwffuJLJ3P+Z0L64jBfbORUSzmZRJ40iZNA4tGkONRNF1jQMP/gLBbEKPRHGNG4kWk4nsPYBcXXfkVEpAV1W0aAzbwGJSJo8jqBlytucb8aYWKp/8KyZvKt45l3QIYzSluLEW5KKG2vCvWk+supbcO25qV+ncVpRPzsLryf7qlxNhYzqIdlu/DNM0OLME1m6i4ZU3MaV6sBUX4J4wJrl5EvcFCG0qo+WdpaTOmkbGNZcjSBL53/wq1X9+kda1G3EMG4Q5raMKoq6qxCprCO/Zj232VFjyvye0o0fOyw9/+EN27NhBZWVCk37Hjh0UFhZy3333dbuPZcuW9WRIg3MMXVFo3bAN76yp7doFQcAxbCCBFWvJuv6aLl+S4fKDhHftI7J3P9bCPDzTL2r38u3SDk3rUMvFwEAQRfLvvo3IoUosWZk4Rwzp8Re20tyCa+y5u7juK+wlhdhLCvvajF5DVxTUSBQ0DdHhSOxm6jqBlZ/gX7E2kUi9uxxTqgc9JqPFZOyDBiROnxUl8U6zWBIVrI8uWI+I6xicH6jhMJVP/Q1zThaOwSUnvFZyOfFMmUDrus00vPIm2Tct6HCNIIrdUswzOD9o+fBjWt5dRspFF2LydEz4N3s9mL2j0GIxWtdvQfEFyFl4HaLNRsG378D30Rp8y1aDrmHJzkS0WdE1DTXYhlxbj620iPxvLaTF2XVR8x6t3tLS0njggQcIBoM0NzeTnp7O3LlzsdtPXu2kqamJsrIyfD4f+jExzF/+8pdPuk+D/osmx0HXkvHrxyLabWhxBV3Vulww1j3/KuacTASzCcXfSvRQRZfJq8ei+Foxp3c/3NHg/EGQJDLnX4lv6aoehyZpchy5thHPPf1LCcvg1NF1ndDWT/F/tBq5sRlLZgbeWVNxjrmgR8pin+8zeqgS//I1BDdtT2zACAK6HMdWWoxc14CU4sI9aSyCJNH81odYc7MQJAktGkOuayBWWYN3znQjV88A//J1iGZTl47LUQRJwj1pHP5lq0i9dOpxQ3kMzm90XSe05VOa31mKZ/okpC7W/KLVimfKBAKrNtDyzjLSvzAHwWQi7bJL8M6eRnhXOaEdu1BDbYgmE7biQnLvuBFLVkL2vaUbyq7ddl7ef/99HnnkEQKBQLLN4/HgcDi4/PKTq+OxZMkS7r//foqLiykvL2fQoEHs27eP8ePHG87LOYpot2FKcRNv8XU4Oow3NmPJTO+WWo8WjeIYXEpo6w4Em7XHqjrRw5WkXjq16wsNzktSJo6l8fW3UUNtSC5nt++LHjiMc/TwTnelDM5umt54j8DaDdgHl+IuzCPe4qfu5ddJragi45qe10xQAq3U/OUl5IYmrEX5eC+b/lnCqizT9OYH6NEoYqsL+6ASTClubCWFRA9XYyspRLRZsRXlI9c34lu2mrQrLu3w7tRCXRd7Mzg30FUN//I1OMeO6NF9otmEtSAP/8dryb5hXi9ZZ3C2Eq2opvpPLxDZdwBrYV5SwKErBEnCPXEMLR+uwDtnWjKUWhBFnCOG4Bwx5JTs6lY8RFlZGd///vfx+/3oup78z+/3c99997F9+/aTGvx3v/sdjz/+OIsWLcJut7No0SJ+8pOfMHLkyJPqz6D/IwgCaVdcSmjrTrToZzrxajhC247dpF05q1v9pF85i0j5QdRQG1oojLW4oNs2qJEISouflIlje2y/wfmBaLOSMf8KWj/ZgtbNegZHd8Ezrum9oqwGfUOstgH/irV4pk3CVpiH5HJiK8rHM20SvqWrkBt6VqhZ8Qeo+NXTCGYTqbOn4RhS2q4Yqlxdj2gx4Rw7AnN6Kr4PPybe7MM9YSyS00H0YCW6mshnsWRnAjrRgxXtxog3+4hv3XnKz27Q/9FiMhVPPE3kYAVtZTsTEQ49wFZSSOvaTWgxuZcsNDhbqXv+FUzeFCSPGzUcIXqostv3Sg47Jm8qgfVbT7td3XJe/v73v6MoCldffTXvvPMO27Zt45133uGqq65CURSef/75kxq8pqaGK6+8sl3bggULWLRoUY/6OXjwIDfccANz587lhhtu4NChQx2uefrpp7n66qu55ppr+NKXvsTKlStPymaDU8czfTKpMy7C/9EaWj/ZTOu6zQRWrMM7ezopF43vVh/e2dMovO8bZCy4EpPHjSB2L2xDV1WCG8tInTUNyW4kVRscH+/MKaReMpnAyvXEW/zHvU7XNCL7DxPavpv8e247spg0OJcIbirDkpfdzsEAEK0WLLlZBDd3fwNPVxSqfv/fmLMycAwf3GnIWdvufZjTvQiCgDktFWteNv6PVqPJMqkzp2DJziC8q5xoZS1aNIo5I43w7nI0VSVWXUtg9QZCW3bguPaqU352g/5P87vLiFZUY8nJRA2FCW3Z0aP7JYcd0WIm3tzSSxYanK0owRDxFj/mNG9Cpr6HDq5tQCGB5WtOu13dChsrKyujpKSEX//614hHchFKS0v59a9/zc6dO9m69eS8qvT0dJqamsjIyCA/P58tW7bg9XrRNK1H/TzyyCPcdNNNzJs3jzfffJOHH36YF198sd01o0eP5o477sBut7N7925uueUWVq1ahc1QBTrjCIJAxhcuw3vpVMJ79oMg4Bha2uPEQFtRPraifCS7Hf/H60iZPP6EFdE1OU5w/RbsAwrJ+OJlp/oYBucBGdfMxZyZTtOi90CSsBXmYfJ6ECQTWjyOXFNPrKoGa34uRT/4Jta8nL422aAX0GMxhOOEswomc7dP5wBCZbvQojKuCaWdfq62hVFbg+2k202pKSjBENF9B3COvoCUyeNxjhxGpPwQ4X0H0GIyWluY5jfewz5oABnz5uIaO5Ka+rqePajBWUm8oQnRbkcQQLRZUIOhHvchmEztoiEMDAC8c6ZT99wrSG4naDq2wvwe3W/yeghuKjvtdnXLefH5fFx11VVJx+UokiQxfvx43n333ZMa/LrrrmPTpk3MnTuX2267jVtvvRVRFLn99tu73UdzczM7d+7kueeeA+ALX/gCP/3pT2lpaSEt7bMqxdOnf1aFfejQocmwt5wcY7HRV0hOB+7TIHmacc3lSC4HTYvex5yZhm1AIaYju5aQKDwYPVhBrLae1OmTybz2akPy0aDbeC6eQMqk8bTt3It/xVraPt2LLscR7TYcwwaSffMCrN2sN2RwdmIfVELrxjL0YXq7kxJd14k3tZB+5aXd7su3dBXWAQXHTfLXYjKC2dzhc3NGGuG9B3CMGIYgiUhOB64xF+AcPRxdUWlds5GchdfhHD745B7S4KwlZfJ4Ams3gg6IAu4LR/e4D11Ru53PYHD+kD73UvzL1mBK82AfVILk6JlAlyCJ6ErPcpK7Q7ecl2g0SktLS6e1V1paWoj1YNfpWO66667kz/Pnz2fSpElEIhEGDhzY7T5qa2vJzs5GOqIKJEkSWVlZ1NbWtnNejmXRokUUFRV16ri0trbS2trarq2uzti96u94Z01LvMDXbcK/bDVKIIhoMaPFlUSByhkXk/eNr2JO6zuFMWNunb0Ikohr1DBco4b1tSmdYsyt3sU5chiS4wPCO3YnQr1MiaJsbZ/uxZTixNFNh0FubCZWVYv3guNfL0giHKO8eRTJbkMwmZBr69udygiCgGA2IYhCpyqOp4oxt/o/rjEXkHnt1TS/9SHuyeN6vJmiRqJoMRlzesf6G72FMa/OHsxZGZi8nh47LpCIeOmN91K3nBdd11m+fDnLly8/7QYcpbm5mR07dvTIcTkZ1q9fz1NPPcXf//73Tj9/4YUX+OMf/9irNhj0DpLTQdrs6XhnTUOLRNAiMQSLBclp7xcnLcbcMugtjLnVuwiSSMH3vk79i6/h+/BjJJcTJdSG84Ih5NxybbffL/GmFgS77YRy8KLDga6oiS99i7m9HVYralu4wz2aLKOG2rBk9qxob3cw5tbZQfqVs/CvWHtSC8zYwQpSJo/rlUXm8TDm1dmDc/ggQlt3nJSUtlxdi2PY6V/Xd8t5ycvLO62D1tfX89Of/pTy8nLGjRvHHXfcwS233IIoigSDQX71q19x9dVXd6uv3Nxc6uvrUVUVSZJQVZWGhgZyc3M7XLtlyxbuv/9+/vSnP1Fa2nm88cKFC1mwoH2xprq6Om6++eaeP6hBnyAIApLD0e+Kaxlzy6C3MOZW72Nyu8i/5zYUf+BIAmsqplRPt+/XFYWGV98iWn6QeF09nikTsRZ2/G4VzSZsAwqJN/mw5rVfLAiCkFQZO5booSqcY0b0SNa7uxhz6+xAkCRSZ0whtHVHj8LGdEUhWllL1o3ze9G6jhjz6uzBM3UiLe9+hDYyjmg2d33DEXRdJ1pRQ96dN512m7rlvCxbtuy0DvrII4+QlpbGgw8+yLvvvsvXvvY1fvazn3HZZZexZMkSnnrqqW47L+np6QwfPpzFixczb948Fi9ezPDhwzuEjJWVlXHvvffy+9//nhEjjq+DnpKSQkpKyik9n4FBZxhzy6C3MObWmcOU6umR03KU4JZPUVr8mLypWPOzCXyymaxOnBcAx9CBtLy/HHN6KqL1M4UzXVM7LB60aIzowUoKvvu1HtvUHYy5dfbgvXQKrWs3EjlwGHtpcZfX65pG64ZtuMePwpp/ZvN/jXl19mDypOAcNYzI3gM4Rwzt9n1yTR2i3Yp9cOeHBadCn8TSbNmyhUcffZQZM2bwyCOP0NzczJw5cwCYM2cONTU1Perv0Ucf5eWXX2bu3Lm8/PLLPPbYYwDceeedyRo0jz32GNFolIcffph58+Yxb9489uzZc3ofzMDAwMDAoBN0RUFyO9HjcXRNA6XjCcpRTKkpOMdcQPRARVLJTNc01GAb5iNVqCFRHyuwZgNpl03HXlLU689g0L+RnA4Kvvt1YhU1hHfu7fSU7ihqOELrmo1YczLJvunMnroYnH1kfvkLxBuau13nJd7UQtuOPeTedsNxxUlOhW6dvJxu4vE4liN6+Xa7HafT2UHBpScMHDiQV199tUP7s88+m/z59ddfP0lrDQwMDAwMTg3X2BG0fLACQRSJ7DmAu4uaVs5hgxAkkdDm7UguF4LZlEiadTmJ+wLEDlUi1zWQ/sXL8c6ZfsK+DM4fLFkZFD/4H9S99Dq+Dz7GkpedqIxus4KmowRDxCqqUPytpM6cQsYXL0+IRBgYnABzWioF995J1e+eRQ2GsA8u7TRHSosrRA9VED1QQd6dt3TrBPBk6BPnRVVV1q1bl3RSFEVp93tP67wYGBgYGBj0ZyS7jeIH/4Pgxm3UvfQ6tuKCLu9xDC7FVphPuPwgwQ1bkZxOmt9egik1hdTpk/FMvQ2Txwi9MWiPKdVDwbfvQG5qJrByPaEtO1DDYQRJwuRNJe2yGbgvHH1GE/QNzn6sudkUP/htmhYvwb98DaY0L5bMdASLCV1RUXx+YrUNOEcMoegH32qnini66RPnJT09nYceeij5e2pqarvfjydxbGBgYGBgcLYiWix4pkxE8QXwr1hHypQJ7XJaOkMwm9BCYdIun0HO7TciiGKvhGEYnHtYMtLJXHAlmQuu7GtTDM4RTKkecm65lswvXUXr+i1E9h9Gi0QRrRZcY0aQ/62FJ5UT2GM7en2ETjjdAgAGBgYGBgZnC2lXzUaLxQms/ATH8MFY8rI7OCS6rqM0+2jbtQ9bUQE5C29APFLPzMDAwKAvkRx2vDOn4J05pU/G7xPnxcDAwMDA4HxFEAQyv3QltpJCWt5bRtvOPVjzcpCcDhBFtHAEua4BwWwm7fIZpF5yUb+oVWVgYGDQHzCcFwMDAwMDgz7APW4k7nEjiVZU07p+K4rPD7qOJTeL9KtmYx9cYjgtBgYGBp/DcF4MDAwMDAz6EFtRPrai/L42w8DAwOCswNjSMTAwMDAwMDAwMDA4KzCcFwMDAwMDAwMDAwODswIjbMyg36JrGv7lawisWo8mx0mZNJa0uZd2KS1qYNAddF1HDbQiWCxIDntfm2NgYGBgYGDQDQznxaDf0vi/7xDcvB3HsEEIJhPBzTuIHqqk4Dtf72vTDM5yopU11D3/CvHGZnRdxzV2JDk3f8ko2mZgYGBgcN4Rq20guGErmizjHjcS+8ABfW3SCTGcF4N+iRqO4P94Hd5ZUxGtiQWladJY/EtWEq2swVaYh//jdTS9+T6Sy0HOwuuxlxZ36Kf+n28Q3rM/+Xv+t27FkpMFQPO7y2h5fwWWzDRybr8Ba17OmXk4gz5Fi8lUPfU37AOLcV04Gl1Radu6g/r/WUTubTegKQqN/36L1nWbsJUWk3v7jZg87g79HH7892gxGQDRbqP4gf9I9l/38mu0bduJ44Ih5Cy8HsluO6PPaNA/0RSVxlffonXtJmwlheTefkOnBd0qfvU0ajgCgGi1UPzQdwBoXPQerWs2osViuEZfQNYtX0KyGg63AUT2H6LuhVdRwxEy5l9B6rRJx702WlFN7X//i5zbbsBeUghAy/vL8S1ZiWC1kP2VBThHDDlTphv0McGtO6h7/t+JNZAk0bpmI95Z00j/whwA/KvW07ToPSSHnZyF153QsfF/vA7f0lWUPPYDADRZpv4f/0tb2S4sednkLLweS1bGKdvcb3Jebr755r42weA0oOs6uq6fcj9KIIhosSQdF0jURpBS3GjRKNGKaupeeo0BD99L1o3zEwtJRe3QT3jfAdLmziT/7oXk370QKc0LQGjbTlreX07p4w/gnTOdiif+dMo2G/Quuq6jadop99O2YzeS04FtQCGCICCaTTjHjCC4qQxNUfC9v4LooUoG/uZhbEX51Dzz8nH7OTqvcu+4Mdne8Opb6HKcgb99BNFspv6f/3vKNhv0LqdrbnWFf+lKIvsPMfA3/w97aTHVf3mp0+tCZTs/m1tfuynZLjkdFP3wbkp/8RByQxNNr73d6zYbnBqn4/uwKzRF4fDPf0/2TQsY8OPvUvf8v4lW1XRuj6pR88zLyPWNqKEQAKHtu2h66wOKH76P/Htup/K3f046zwb9k9O11tJVlYZ/vIF7whico4bhvGAwnumTaf5gOUprkGhVIkphwI+/S/ZNCzj889+jKUqnfcWbfbR8sILgprJkW+Nrb6P4Wxn4m4fxTJ9M5ZN/PWWboR85L5s3b+5rEwxOgeq/vEj108+z/wc/oeGfb5xyf+aMNNA0FH9rsk2TZeLNPqwFeQRWfoL30qmYPCk4hw/G7E0lsnd/p31psRhaJIo53YtkMQOJ3YG0Ky5FcthJmTweNRRGrms4ZbsNTj/l9z1KzTMvU/7dhwkd81I8WbSYjGBqX6lcMEmg6wiCgH/lOtK/eDmixUL61XNo/WQzutrJwlYQUIIh0HVMGWnJ5sDKT8i4Zi6i2UzG/CsIfPzJKdts0DuU/+Anibn1vYcJfrKl18fzf7yO9C/MQbRYSLtqFsFNZejHWQiooTZ0TceclZ5sS7vsEswZaYn31qSxxGrqet1mg57jX7GOil//iQMPPs6hx548bf2Gtu/m8K+eZv9//pyav/0TuaEJgMjucsyZaTiGDcKU6sF76RQCK9d32kfTm+/hmTIBc1pqsq3t072kTB6Pye3EmpuFbUAhwQ1bT5vdBqePmmdepvrp59j/g59Q/49T3xiLN/vQVRVzujfZJtqsmNO9qG1hAivXkzpzCqZUD45hgzBnpRPZXd6hH03TqPnLi+R+7SsgCMn2th17SL10CoIk4bnoQiL7DxNv8Z2y3UbYmMFpIbRtJ95Lp5B390KEYybuwUd+TbyxpcP1WV+ZT+r0ycftTzSbGPTko+3a6l95E+/saUh2G3JjM86RQ5OfmbPSOx3HkpVBcGMZ/o/WINc3UvLz/8SSkU68sRnP1InHXJeO3NicDCkz6B8ogSDhfQfJuf1G8u66Jdke9wU4+KNfdnrPgIfvPeH/R8+UCXimTEj+risK9a8uxlZciCBJxBuasRxZMIrWRDK/Emht92UPYC3Mo/n/PiB6qApzhpeih76DaDIhNzQnF5ymtFSUQCu6qiFI/WavyABQgm2E9+wn55Zr280tpTXIgQce7/Se4h99F2v+yYeXxhubkyETotmMye0k7gtgyUxvd52tuJCmN98nWlGNyeOm+MffQzSbk59rskzT/31Azq3XnbQtBr1H2669qG0RBvzk/nb/3+r/+QaBVR2dCteFo8g75oTteLhGDcM1ahgAoR27qfj1nyn5yf2J765jQnHMmemEO1lgxqrrCG7ZTslj97ezw5KZRmBtYgNZV1WilTXIDc3df2CDM0Zo20480ycz8O7b2q+1Hv0t8SPO7LFk3TCP1BkXHbc/S1ZGh7VWsGwXanAVkstJvLEZx7BBn12fmY7c2ILzc/0EPl6HtSAPe0lRu3ZzVjqxw9W4x45EbmhCC0eINzRjTvNyKhjOi8Epo0VjqMEQGV+6ut0fE0DJY/f3qK/6fy3Cv2Jth3b3uJFkfWU+ktsFkKg6feyJqQ50sjjM/9bCz/r+x//S+Nrb5H/zVhBFOObIVdd0BFHqcL9B3xLZf4iUyeOSX9hHMXs9DPnTL3rU16GfPEmspr5De9b1X8TkcpB2dCH4ubmBrnfqeAz85Y+AxI7ToR/9kuC6zXimTUIQhc/u1/XELpTQ4XaDPiZ64DDuC0fjGjuiXbspxd3zufWz3xGrqu3Qnnnt1aRddslnDZ97b+la53Or9KAKbdMAABfqSURBVPEHgCNz6+Ff07pmI6kzLk7coyhU/OppPFMm4r5wdI/sNDgzRMoPkX/3wnaOC0D2TQvIvmlBt/sJbt7eadiqZLdR+sSPMXncRMoPIohiu1dWZ+8sTdOo/vML5H3zq4nvz2PwXHIx/pXrKb//J6BqSHYbotlYHvY3NFlG8QfI/HIna61Hv9+jvhpeeRPfR2s6tLvGXEDqpVOxZGcguZwd10o6HeaPEmilefGS5HvrWLJvnMehn/2O4KYy1LYw5ow0hM/9XZwMxuw0OGUihypxDB2IaOq4+D/4/55Abup4IpL9lfmkXtJxNyD7K/PJ/sr8Lse0ZGci1362EI3V1HeZBGYtyid6ZLfJkpNJrKYe94WJl7pc14A5O/2E9xuceSL7D+EcOaxDe9wX4MBDnS8wBzx8H9bcjicvAx6+r93vuqahKyq6qiKaTQimxOvQkpNJrLYBS04WajiCFpOTTnNniKKItTCfuD9w5P4s5NoGbMUFxBuaMKeldnjZG/Q9ibk1tEO70hpk/3/+vNN7Bvz4e52evAz48feSP+uKghqNgaqiRWKEduzGdWQOW7IT7x1rfg5aNIoWjmDydEzYP4ooitgK81B8ibmlKQqVT/wJ24BCsm64pkfPa3Bm0FWNeH0jts/tQENiA83fycmL+8LR5H2948mLe/wohv7lV0BiXgpmc3LRGq2oRnK7kOsbsRXktf8+rK3HnJXZvjNVRa6tp+LxPwAg1zVQ9V/PUPj9b+IcMZSSR7+P2hZGMJk49NiTndpv0LdED1ViH1KKaOq4dD/4yG+SYYTHkn3jvOTGx7Fk3TCPrBvmAUfyZ2IyWjyOHldo27WX/HtuRxCExDvrmLkl19ZjyW6/1opV1qAEWin/3iOJBlVl790PMui/HsOSk8XgP/wctTWEYDax584fYMnLPpV/BsBwXgxOA5Hyg8dVnyj56Q97ZczUmRez/4HH8UyfTKyqFl2WsQ1K2FD99PNkfWUe5jQvvqUrcVwwBCUQpOGV/yNzwRUAeGdNpfLJZ3CNG0Hbtp1Y87KxZBjOS38jUn6I9C9c1qHd7PUw9M+dh411F0EUESwi0H4XyDt7Oo2vLsaSk0XLO0tJnXExgiii+APUvfQ6Bd++g1htA7GqWmyFuUQOVRJYvZ7Sa3505P5pCeWyhddT/8838M6efkp2GvQOkfJDpF0+o0O7KcV90nNLrm/k8C/+gGP4YKz5OcSbfYS2fkruHV/BNWoY3tnTaHz9bawFubS8/1HipE4SUQJB6p5/hYLvfh25voloRRW2wjyih6vxf7yO0l88BED1U39DDbXhGjuC0PbdSC5nUi3KoH8Qq6nDkp/TYWccIPvmL5F985dOqt/6f72BFpVxDCkFEgvOeH0jpqkTsQ0uQQ1HCKxajyU3G//ytQx84scANL+9BEtuNu7xoxj235/l3xx46BdkfWU+zhFD0eJx2rbvxpKTSeu6zWiyjGNUx00jg74lUn7o+GutI+peJ4MgCAg2a7JUQOrUz5TqUmdcxP4f/ozU6Rch19ajhiPYBpcAUP3nF8i67os4Rw5j2LO/Sd5T9sWFydNrub4JJdCKaLXQ8O+3+P/t3X1Uk/X/x/GX20ABnQQyGJm3v0DyptABqRBfVEQNJTWSMrSvhmn65aDfCrrx3lT0d/Kkov60PKevR7NjN6SolXY6kJap/ThqQt6bBgMUFARMcHx+f1DL/cbNBru2XfB6/DW2a1fvbU+ha7uuaw9FP2WTs286zcaLPc7IQdJQqbuY7BNpD66+PnhkwSso2fUFlOrO6LX431D8+e62srM70KH+8t2r13E75yco3d2giY+F+s/jbNwD+kL7zwQUbf8Err7d8Mjrr9p1frJMp9494NbX/BTYUuoa+SRETQ0Kt/wHbo/2hnbqn7t5dFDUt4X6A/wrjp1E6b4yqLw80WvRAnTq7g8A8J4wGsg6jILN/4HHwH7wnhBj1/nJMp16dYfbn3+EbeX29z+hY3ctOvV4GED9/uTuQY/i1qFsdB7YD+rwUNTdq0Hh/+yAW58e0Cb9eZZNhaJ+Fw0AHZQKVBz7X5TuOwSVlyd6LpxvXJ/KyxOKTh1RnnMMANDxkYe58eJkRG0tvMZE2Xy93SaMwfX/3gxDVTVUXTxQU3QDKu+H0OWJ/uigVKL3kn+j5JO9MFQeQ4/XZv99bFWnjlC4mu+mow4NNjnuoPzoifrjGwL7oveSv/+ekvNQqrvA7b9s+zurOa6abujx2myUfPIllJ096ttQ1u9lo/RwBxTmG+neY0cYL4v793Hzi4Mw3P0DXYfp4Dki3CZzOc3GS0hISPMLkVNqaPcve3jw4MUH+U1/zni5qYMg1WHBUIcFSzIb2YZvQpzd/5sKhQJeMf+AV8w/TK5Xde0C7T/rT4ns6uON7v+a2fD9VSr4PDMGPs+MkXpUagXNc7bf7er+rXIo3N1MrlN29kDNn8fDKBQKeEU/ZXocDABVF4/6s/Sg/kyL3f81o8H1/9UfOS+3Pj0b/M6x1uqo1aDXovkoP3oStaVlUIcGo/PgQX/v7urrg+7J5r+TGvvkt9sDv58ULi6NNkfOo6nv7pGSx4B+De6+3dgJQx5+9e9jjTs+7IceqXNtPpPTbFrv2NHw+e4tceXKFUyZMgUxMTGYMmUKrl69arbMkSNHMGnSJAwYMADp6emtmJSIiMicR/8A1BQUmexJcO/qdbj3Nz+2hshaqq5qeI8bAb/EZ6EOG8yD6qndcpqNl9ZYvHgxXnjhBXz99dd44YUXsGjRIrNlHnnkEaxYsQIzZzb8bikREVFrdAkJhouPN8pzjqHql19R/sNJ1P1xD97jRjR/ZyIisojsN15KS0uRl5eH2NhYAEBsbCzy8vJQVmZ6hquePXviscceg6qBszQQERG1lsJFhe7JM+E7dTLcgx5Ft9ho9FyYAlUTZ6sjIiLrON3/yRsMBmzevBnz5s2zaHm9Xg9fX18o/zqASKmERqOBXq+Hl5dXM/c2V1FRgYqKCpPrior4LcbUemyLpMK2nEcHhaLR4/HkiG2RFNgVtYZTbrxkZGRYvPFiax999BE2btzY4G38h9W+/fX6GwyGFt2fbVFDWtsVwLaoYWyLpMK/hyQVS9rqIBxwjuI333yz0dsMBgP27duH/Px8i9ZVWlqKmJgY/PTTT1AqlTAYDAgLC8M333zT4CcvGzZsQHV1NVJTUxtcX0PvBpw5cwYpKSkNLk/tz86dO6HT6ay+H9uiprS0K4BtUdPYFkmFfw9JKk215ZBPXrKysvDss8+iawPfLFxXV2fVury9vREUFISsrCzExcUhKysLQUFBLdplDADUajXUarXJdd26dcPOnTvh4+Nj3D3NloqKijB16lTs3LkTfn7m397sbOQ0ry1nNRgMuHHjBgYMGNCi+7OtpslpVsB287a2K8D+bbXX18pe2Fb7e63sgX8P5fNaAfKa195tOWTjJSAgAOHh4Rg5cqTZbffu3cPWrVutWt+SJUuQlpaGTZs2Qa1WG0+FnJSUhOTkZAwcOBAnT57EggULUFlZCSEE9u/fj3fffRcREc1/+3WnTp1a/K6VNfz8/NC9e3fJ/zu2Iqd5bTVrz562PX8/2zInp1kB28xr664A+7TVHl8re2Jb7eu1shf+PZTPawXIa157teWQjZdJkyahsb3VVCqV1ce79O3bF3v27DG7ftu2bcbLOp0OOTk51g1KREREREROwyEbL1OnTm30NqVS6bCD9YmIiIiIyHnJ/nteiIiIiIiofVAuWbJkiaOHIKBjx44ICwtDx44dHT2KReQ0r5xmlYKcHr+cZgXkN68tye2xc175kNtjl9O8cppVCnJ7/HKa156zOuRUyURERERERNbibmNERERERCQL3HghIiIiIiJZcMjZxtqLK1euIC0tDbdv34anpyfS09PRq1cvk2UyMjJw4MABKJVKqFQqzJ8/3/jdMxs2bMCuXbug0WgAAIMHD8bixYsdOm9TMxkMBqxYsQLff/89OnTogFmzZiE+Pt6h877xxhs4d+6c8edz584hIyMDI0eOtPvza0tsS7q22nNXANtiW9KRU1ty6srSedtqW3LqytJ52VYzBEkmMTFRZGZmCiGEyMzMFImJiWbL5OTkiOrqaiGEEPn5+WLIkCHi7t27Qggh1q9fL1avXu1U8zY10xdffCFmzJghDAaDKC0tFREREeL69esOnfdB+fn5IjQ0VNy7d08IYf/n15bYlnRtteeuhGBbbEs6cmpLTl1ZOu+D2lJbcurK0nnZVtO425hESktLkZeXh9jYWABAbGws8vLyUFZWZrJcREQE3NzcAACBgYEQQuD27dtOO29TDhw4gPj4eCgUCnh5eWHUqFH46quvnGbeTz/9FOPHj4erq6skM9kL25KurfbcFcC22JZ05NSWnLpq6bxtpS05dWXNvE1hWzzmRTJ6vR6+vr5QKpUA6r98U6PRQK/XN3qfzMxM9OjRA35+fsbr9u/fj/Hjx2PGjBnIzc11inkbm0mv18Pf39/4s1arRVFRkcPnBYCamhrs27cPkydPNrneXs+vLbEt6dpqz10BbAtgW1KRU1ty6sraeYG21ZacurJ2XrbVOB7z4iSOHz+O999/H9u3bzdel5CQgNmzZ8PFxQVHjx7Fq6++igMHDuChhx5y2JzOOJMlDh8+DH9/fwQFBRmvk+tjsRbbkk577gpgW1JiW87flrPNY6n23JYcunLWmSxhr7b4yYtEtFotiouLYTAYANQfYFVSUgKtVmu2bG5uLl5//XVkZGSgT58+xut9fHzg4uICABg+fDi0Wi0uXLjg0Hmbmkmr1aKwsNC4rF6vN3lnwxHz/uWzzz4zeyfAns+vLbEt6dpqz10BbAtgW1KRU1ty6sqaef/SltqSU1fWzMu2msaNF4l4e3sjKCgIWVlZAICsrCwEBQXBy8vLZLnTp09j/vz5WL9+Pfr3729yW3FxsfFyfn4+CgoK0Lt3b4fO29RMY8aMwZ49e1BXV4eysjIcPnwYMTExDp0XAIqKivDzzz8b99m05LE4M7YlXVvtuSuAbbEt6cipLTl1Zc28QNtrS05dWTMv22paByGEaNUaqFGXLl1CWloaKioqoFarkZ6ejj59+iApKQnJyckYOHAgJk+ejIKCAvj6+hrvt2bNGgQGBiI1NRVnz56FQqGAi4sLkpOTERkZ6dB5m5rJYDBg2bJlOHr0KAAgKSkJU6ZMcei8ALB582acP38e69atM7m/vZ9fW2Jb0rXVnrsC2Bbbko6c2pJTV5bOC7TNtuTUlaXzsq2mceOFiIiIiIhkgbuNERERERGRLHDjhYiIiIiIZIEbL0REREREJAvceCEiIiIiIlngxgsREREREckCN17asLS0NLNT1jmLvXv3YsaMGS267++//47AwEDcv3/fxlORpdgWSYFdkVTYFkmFbdmfytEDtAcjRozAzZs3oVQq4e7ujoiICCxcuBAeHh6OHs1hJkyYgAkTJjh6DNljW+bYVuuxK3PsyjbYljm2ZRtsy1xbbYufvNjJli1bkJubi8zMTOTl5WHr1q2OHonaCLZFUmBXJBW2RVJhW+0DN17szMfHB+Hh4cjPzzde9+233+Lpp5+GTqdDYmIiLl26ZLwtMDAQv/32m/Hn///x5LZt2xAeHo7w8HDs2bPHbPmKigrMmjULwcHBiI+Px7Vr10zW/fHHH2P06NEICQnB0qVL8eB3ln766acYO3YsQkJCMHPmTBQUFAAAhBBYuXIlhg4diiFDhmD8+PE4f/48ACA7Oxvjxo1DcHAwIiIi8OGHHzb4PHz++ed4/vnnLZrFYDAgPT0dYWFhGDlyJLKzs03WdefOHbz11lsIDw9HREQE1q1bB4PBgJqaGsTFxWHHjh3G9SQkJGDjxo3NvUyyxLbqsS3bYlf12JXtsa16bMv22Fa9NtuWIMlFRUWJo0ePCiGE0Ov1IjY2VixfvlwIIcTly5fF448/Lo4cOSJqamrE1q1bxahRo8S9e/eEEEIEBASIq1evGteVmpoq3nvvPSGEENnZ2WLYsGHi/Pnzorq6Wrz22msmy6empoqQkBBx6tQpUVtbKxYsWCBSUlKM6woICBCzZs0S5eXloqCgQISFhYns7GwhhBCHDh0So0aNEhcvXhS1tbUiIyNDTJkyRQghRE5Ojpg4caIoLy8XdXV14uLFi6K4uFgIIcTw4cPFiRMnhBBC3L59W/zyyy8NPiefffaZSEhIsGiWXbt2iZiYGFFYWChu3bolXnzxRREQECBqa2uFEELMmTNHLFy4UFRVVYmbN2+KyZMni48//lgIIcS5c+eETqcTFy9eFJs2bRLx8fHi/v37LXshnRDbMse2Wo9dmWNXtsG2zLEt22Bb5tpqW/zkxU7mzp2L4OBgREZGwsvLC8nJyQCAAwcOIDIyEsOHD4eLiwtmzpyJP/74A7m5uc2u8+DBg5g0aRIeffRRuLm5Yd68eWbLREdHY9CgQVCpVJgwYYLJuxAAkJSUBLVaDX9/f4SFheHXX38FAOzevRuzZs1C3759oVKpMHv2bOTn56OgoAAqlQpVVVW4fPkyhBDo27cvNBoNAEClUuHixYuorKxE165d0b9/f4ufo8ZmOXjwIKZPnw6tVgtPT0+88sorxvvcvHkTOTk5eOutt+Du7g5vb2+89NJL2L9/PwAgICAAc+bMwdy5c7F9+3asWbMGSqXS4pnkgG01j21Zj101j121DNtqHttqGbbVvLbQFjde7CQjIwO5ubnYsWMHLl++jFu3bgEASkpK4O/vb1xOoVBAq9WiuLi42XWWlJTAz8/P+LNWqzVbplu3bsbLnTp1QnV1tcntPj4+xstubm6oqqoCABQWFmLlypXQ6XTQ6XQIDQ2FEALFxcUYOnQopk6dimXLlmHYsGFYuHAhKisrAQDr169HdnY2oqKi8OKLL1r0i6G5WUpKSkwe24PPV2FhIe7fv4/w8HDjrIsWLUJZWZlxmWeeeQYFBQV46qmn0KtXL4vnkQu21Ty2ZT121Tx21TJsq3lsq2XYVvPaQls825idhYaGYtKkSUhPT8emTZug0WiM+zAC9fs46vV6+Pr6AqgP6+7du8bbb9y4YbxNo9GY/MPT6/U2m1Or1WL27NmNnqVi2rRpmDZtGkpLS5GSkoIPPvgAKSkpGDRoEDZv3oza2lrs3LkTKSkpZvtNWsvHx8fksT142c/PD66urjh27BhUqoZzXrp0KaKionDkyBGcPHkSOp2uVfM4K7ZlPbbVPHZlPXZlGbZlPbZlGbZlPTm1xU9eHGD69On44YcfkJ+fj7FjxyI7Oxs//vgjamtrsX37dri6uiI4OBgA0K9fP2RlZcFgMCAnJwcnTpwwrmfMmDH4/PPPcenSJdy9excZGRk2mzEhIQFbt27FhQsXANQfqHXw4EEAwOnTp3Hq1CnU1tbCzc0Nrq6uUCqVqKmpwd69e3Hnzh24uLjAw8PDJh8bjh07Fjt27EBRURHKy8tNzh6i0WgwfPhwrF69GpWVlairq8O1a9dw/PhxAEBmZibOnj2LVatW4Z133kFaWprxXYa2iG1Zh21Zhl1Zh11Zjm1Zh21Zjm1ZR05tcePFAby8vBAXF4dNmzahT58+WLt2LZYvX44nn3wS3333HbZs2QJXV1cAwNtvv43vvvsOOp0O+/btw6hRo4zriYyMRGJiIqZNm4bo6Gg88cQTAGC8b2tER0fj5ZdfxoIFCzB48GDExsYiJycHAFBVVYV33nkHoaGhiIqKgqenp/FLkL788kuMGDECgwcPxu7du7FmzZpWz/Lcc88hPDwccXFxmDhxIkaPHm1y+5o1a1BbW4tx48YhJCQEycnJuHHjBgoLC7Fq1Sqkp6fDw8MD48ePx4ABA7Bq1apWz+Ss2JZ12JZl2JV12JXl2JZ12Jbl2JZ15NRWByEeOF8bydqlS5cQGxuLM2fONPqxHlFLsC2SArsiqbAtkgrbcjx+8iJzhw4dQk1NDcrLy7F27VpERUXxHxPZBNsiKbArkgrbIqmwLefCjReZ2717N4YOHYro6GgolUosWbLE0SNRG8G2SArsiqTCtkgqbMu5cLcxIiIiIiKSBX7yQkREREREssCNFyIiIiIikgVuvBARERERkSxw44WIiIiIiGSBGy9ERERERCQL3HghIiIiIiJZ+D/LaqCzkeFkHwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 828x396 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(11.5, 5.5), sharey='row', sharex='row')\n",
    "colors = ['#DF9216','#d1495b','#00798c']\n",
    "\n",
    "#metric = 'test_accuracy'\n",
    "metric = 'test_balanced_accuracy'\n",
    "\n",
    "x_text = 0.95\n",
    "y_text = 0.05\n",
    "\n",
    "tdc_names = RESULTS['TDC']['FPS'].keys()\n",
    "\n",
    "# FINGERPRINTS\n",
    "scores = [RESULTS['TDC']['FPS'][p]['rogi'] for p in tdc_names]\n",
    "sizes = [RESULTS['TDC']['FPS'][p]['size'] for p in tdc_names]\n",
    "for i, m in enumerate(models.keys()):\n",
    "    accuracy = 1. - np.array([np.mean(RESULTS['TDC']['FPS'][p][m][metric]) for p in tdc_names])\n",
    "    plot_scatter(axes[0,i], scores, accuracy, title=f'$\\\\bf{m}$', marker='o', s=[50 * s/2000 for s in sizes], # size 50 = 2000 instances\n",
    "                 fit=True, fit_outline=False, fit_zorder=0, fit_lw=2,\n",
    "                 color=colors[1], edgecolor=colors[1], linewidth=1, alpha_face=0.4, alpha_edge=0.8,\n",
    "                 annotate=True, text_color=colors[1], x_text=x_text, y_text=y_text, fontsize=10,\n",
    "                 zorder_scatter=5, label='')\n",
    "\n",
    "# DESCRIPTORS\n",
    "scores = [RESULTS['TDC']['DESC'][p]['rogi'] for p in tdc_names]\n",
    "sizes = [RESULTS['TDC']['DESC'][p]['size'] for p in tdc_names]\n",
    "for i, m in enumerate(models.keys()):\n",
    "    accuracy = 1. - np.array([np.mean(RESULTS['TDC']['DESC'][p][m][metric]) for p in tdc_names])\n",
    "    plot_scatter(axes[1,i], scores, accuracy, title='', marker='o', s=[50 * s/2000 for s in sizes], # size 50 = 2000 instances\n",
    "                 fit=True, fit_outline=False, fit_zorder=0, fit_lw=2,\n",
    "                 color=colors[1], edgecolor=colors[1], linewidth=1, alpha_face=0.4, alpha_edge=0.8,\n",
    "                 annotate=True, text_color=colors[1], x_text=x_text, y_text=y_text, fontsize=10,\n",
    "                 zorder_scatter=5, label='')\n",
    "\n",
    "# Fix labels, ticks, etc.\n",
    "if 'balanced' in metric:\n",
    "    axes[0,0].set_ylabel('$\\\\bf{Fingerprints}$\\n$1 -$ Balanced accuracy')\n",
    "    axes[1,0].set_ylabel('$\\\\bf{Descriptors}$\\n$1 -$ Balanced accuracy')\n",
    "else:\n",
    "    axes[0,0].set_ylabel('$\\\\bf{Fingerprints}$\\n$1 -$ Accuracy')\n",
    "    axes[1,0].set_ylabel('$\\\\bf{Descriptors}$\\n$1 -$ Accuracy')\n",
    "\n",
    "for j in range(5):\n",
    "    axes[0,j].set_xlabel('Roughness index')\n",
    "    axes[1,j].set_xlabel('Roughness index')\n",
    "    \n",
    "plt.tight_layout()\n",
    "fig.savefig(f'plots/classification-rogi-{metric}.png', dpi=600, bbox_inches='tight')\n",
    "fig.savefig(f'plots/classification-rogi-{metric}.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "26c11e35-e830-42ee-aec2-b084268bce01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 828x396 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(11.5, 5.5), sharey='row', sharex='row')\n",
    "colors = ['#DF9216','#d1495b','#00798c']\n",
    "\n",
    "metric = 'test_accuracy'\n",
    "#metric = 'test_balanced_accuracy'\n",
    "\n",
    "#x_text = 0.35\n",
    "#y_text = 0.9\n",
    "x_text = 0.95\n",
    "y_text = 0.05\n",
    "\n",
    "tdc_names = RESULTS['TDC']['FPS'].keys()\n",
    "\n",
    "# FINGERPRINTS\n",
    "scores = [RESULTS['TDC']['FPS'][p]['modi'] for p in tdc_names]\n",
    "sizes = [RESULTS['TDC']['FPS'][p]['size'] for p in tdc_names]\n",
    "for i, m in enumerate(models.keys()):\n",
    "    accuracy = np.array([np.mean(RESULTS['TDC']['FPS'][p][m][metric]) for p in tdc_names])\n",
    "    plot_scatter(axes[0,i], scores, accuracy, title=f'$\\\\bf{m}$', marker='o', s=[50 * s/2000 for s in sizes], # size 50 = 2000 instances\n",
    "                 fit=True, fit_outline=False, fit_zorder=0, fit_lw=2,\n",
    "                 color=colors[1], edgecolor=colors[1], linewidth=1, alpha_face=0.4, alpha_edge=0.8,\n",
    "                 annotate=True, text_color=colors[1], x_text=x_text, y_text=y_text, fontsize=10,\n",
    "                 zorder_scatter=5, label='')\n",
    "\n",
    "# DESCRIPTORS\n",
    "scores = [RESULTS['TDC']['DESC'][p]['modi'] for p in tdc_names]\n",
    "sizes = [RESULTS['TDC']['DESC'][p]['size'] for p in tdc_names]\n",
    "for i, m in enumerate(models.keys()):\n",
    "    accuracy = np.array([np.mean(RESULTS['TDC']['DESC'][p][m][metric]) for p in tdc_names])\n",
    "    plot_scatter(axes[1,i], scores, accuracy, title='', marker='o', s=[50 * s/2000 for s in sizes], # size 50 = 2000 instances\n",
    "                 fit=True, fit_outline=False, fit_zorder=0, fit_lw=2,\n",
    "                 color=colors[1], edgecolor=colors[1], linewidth=1, alpha_face=0.4, alpha_edge=0.8,\n",
    "                 annotate=True, text_color=colors[1], x_text=x_text, y_text=y_text, fontsize=10,\n",
    "                 zorder_scatter=5, label='')\n",
    "\n",
    "# Fix labels, ticks, etc.\n",
    "if 'balanced' in metric:\n",
    "    axes[0,0].set_ylabel('$\\\\bf{Fingerprints}$\\nBalanced accuracy')\n",
    "    axes[1,0].set_ylabel('$\\\\bf{Descriptors}$\\nBalanced accuracy')\n",
    "else:\n",
    "    axes[0,0].set_ylabel('$\\\\bf{Fingerprints}$\\nAccuracy')\n",
    "    axes[1,0].set_ylabel('$\\\\bf{Descriptors}$\\nAccuracy')\n",
    "\n",
    "for j in range(5):\n",
    "    axes[0,j].set_xlabel('MODI')\n",
    "    axes[1,j].set_xlabel('MODI')\n",
    "    \n",
    "plt.tight_layout()\n",
    "fig.savefig(f'plots/classification-modi-{metric}.png', dpi=600, bbox_inches='tight')\n",
    "fig.savefig(f'plots/classification-modi-{metric}.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7d9c113-79ff-4d05-b62f-ddf9abd50296",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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