4_Feature_Analyses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import scipy.stats\n",
    "import pickle, os, time\n",
    "import itertools\n",
    "from datetime import datetime, timedelta\n",
    "from collections import Counter, defaultdict, namedtuple\n",
    "from PIL import Image\n",
    "import yaml\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn import preprocessing, model_selection, metrics, utils\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from tqdm import tqdm\n",
    "from joblib import Parallel, delayed\n",
    "from sklearn.base import clone\n",
    "\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "data_dir = '/data/GVHD/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi'] = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(Xtr, ytr):\n",
    "    np.random.seed(42)\n",
    "    random.seed(42)\n",
    "    \n",
    "    # Specify hyperparameters and cv parameters\n",
    "    base_estimator = LogisticRegression(\n",
    "        penalty='l2', \n",
    "        class_weight='balanced', \n",
    "        solver='liblinear'\n",
    "    )\n",
    "    param_grid = {\n",
    "        'C': [10. ** n for n in range(-6, 7)],\n",
    "        'penalty': ['l2'],\n",
    "    }\n",
    "    \n",
    "    cv_splits, cv_repeat = 5, 20\n",
    "    cv = model_selection.RepeatedStratifiedKFold(cv_splits, cv_repeat, random_state=0)\n",
    "    clf = model_selection.GridSearchCV(\n",
    "        clone(base_estimator), param_grid, \n",
    "        cv=cv, scoring='roc_auc', iid=False, n_jobs=5,\n",
    "    )\n",
    "    clf.fit(Xtr, ytr)\n",
    "    return clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def boostrap_func_all(i, y_true, y_prob, threshold):\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_true, y_prob)\n",
    "    y_true_b, y_prob_b = utils.resample(y_true, y_prob, replace=True, random_state=i)\n",
    "    y_pred_b = (y_prob_b > threshold)\n",
    "    tpr_cutoff = metrics.recall_score(y_true_b, y_pred_b)\n",
    "    idx = (np.abs(tpr - tpr_cutoff)).argmin()\n",
    "    \n",
    "    return (\n",
    "        metrics.roc_auc_score(y_true_b, y_prob_b), # AUC\n",
    "        tpr[idx], # sensitivity\n",
    "        1-fpr[idx], # specificity\n",
    "        metrics.precision_score(y_true_b, y_pred_b), # positive predictive value\n",
    "    )\n",
    "\n",
    "def boostrap_func_confusion(i, y_true, y_prob, threshold):\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_true, y_prob)\n",
    "    y_true_b, y_prob_b = utils.resample(y_true, y_prob, replace=True, random_state=i)\n",
    "    y_pred_b = (y_prob_b > threshold)\n",
    "    tpr_cutoff = metrics.recall_score(y_true_b, y_pred_b)\n",
    "    idx = (np.abs(tpr - tpr_cutoff)).argmin()\n",
    "    \n",
    "    return metrics.confusion_matrix(y_true_b, y_pred_b).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(clf, Xte, yte, Xtr=None, threshold_p=55, verbose=True):\n",
    "    y_true = yte\n",
    "    y_score = clf.decision_function(Xte)\n",
    "    y_prob = clf.predict_proba(Xte)[:,1]\n",
    "    \n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_true, y_score)\n",
    "    test_auc = metrics.roc_auc_score(y_true, y_score)\n",
    "    \n",
    "    # Picking a risk threshold based on training set if possible\n",
    "    if Xtr is not None:\n",
    "        if verbose: print('Risk threshold based on train set')\n",
    "        threshold = np.percentile(clf.predict_proba(Xtr)[:,1], threshold_p)\n",
    "    else:\n",
    "        if verbose: print('Risk threshold based on test set')\n",
    "        threshold = np.percentile(y_score, 55)\n",
    "    if verbose: print('p_Threshold', threshold)\n",
    "    \n",
    "    if verbose: print()\n",
    "    if verbose: print('Confusion matrix (95%CI lower, upper)')\n",
    "    y_pred = (y_prob > threshold)\n",
    "    conf_mat = metrics.confusion_matrix(y_true, y_pred)\n",
    "    if verbose: print(conf_mat) # Rows: actual 0, actual 1; Cols: predicted 0, predicted 1\n",
    "    \n",
    "    confmats = [boostrap_func_confusion(i, y_true, y_prob, threshold) for i in range(1000)]\n",
    "    confmats_ = np.asarray(confmats)\n",
    "    if verbose: print(np.percentile(confmats_, 2.5, axis=0).reshape(2,2))\n",
    "    if verbose: print(np.percentile(confmats_, 97.5, axis=0).reshape(2,2))\n",
    "\n",
    "    if verbose: print()\n",
    "    if verbose: print('scores')\n",
    "    tpr_ = metrics.recall_score(y_true, y_pred)\n",
    "    idx = (np.abs(tpr - tpr_)).argmin()\n",
    "    if verbose: print('AUROC={:.3f}'.format(test_auc))\n",
    "    if verbose: print('TPR={:.3f}, FPR={:.3f} PPV={:.3f}'.format(\n",
    "        tpr[idx], fpr[idx],\n",
    "        metrics.precision_score(y_true, y_pred)))\n",
    "\n",
    "    if verbose: print()\n",
    "    if verbose: print('scores (95%CI lower, upper)')\n",
    "    auc_scores, sensitivities, specificities, ppvs = zip(*[boostrap_func_all(i, y_true, y_prob, threshold) for i in range(1000)])\n",
    "    if verbose: print('Test AUC {:.3f} ({:.3f}, {:.3f})'.format(np.median(auc_scores), np.percentile(auc_scores, 2.5), np.percentile(auc_scores, 97.5)))\n",
    "    if verbose: print('Test AUC {:.3f} ± {:.3f}'.format(np.mean(auc_scores), np.std(auc_scores)))\n",
    "    if verbose: print('sens.\\t {:.1%} ({:.1%}, {:.1%})'.format(np.mean(sensitivities), np.percentile(sensitivities, 2.5), np.percentile(sensitivities, 97.5)))\n",
    "    if verbose: print('spec.\\t {:.1%} ({:.1%}, {:.1%})'.format(np.mean(specificities), np.percentile(specificities, 2.5), np.percentile(specificities, 97.5)))\n",
    "    if verbose: print('prec.\\t {:.1%} ({:.1%}, {:.1%})'.format(np.mean(ppvs), np.percentile(ppvs, 2.5), np.percentile(ppvs, 97.5)))\n",
    "    \n",
    "    return test_auc, auc_scores, fpr, tpr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop = pd.read_csv(data_dir + 'population/d10_with_vitals.csv').set_index('BMT_ID')\n",
    "extracted_features = pd.read_csv('data/ts_features.csv', index_col='id')\n",
    "df_label = pop.join(pd.read_csv(data_dir + 'prep/label.csv', index_col='BMT_ID'), how='left')\n",
    "df_features = pd.read_csv('data/df_features.csv', index_col='id')\n",
    "feature_names = df_features.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with np.load('data/Xy.npz') as f:\n",
    "    X = f['X']\n",
    "    y = f['y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temporal split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_date = 201701001\n",
    "split_idx = -85\n",
    "\n",
    "assert (pop[:split_idx].index < split_date).all()\n",
    "assert (pop[split_idx:].index >= split_date).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtr_all, Xte_all = X[:split_idx], X[split_idx:]\n",
    "ytr, yte = y[:split_idx], y[split_idx:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtr, Xte = Xtr_all, Xte_all\n",
    "clf = train_model(Xtr, ytr)\n",
    "auc_main, auc_scores_main, fpr_main, tpr_main = evaluate_model(clf, Xte, yte, Xtr, 55, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.01, 'penalty': 'l2'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sensitivity Analyses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Omitting specific subsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_corr = df_features.corr().abs()\n",
    "np.fill_diagonal(df_corr.values, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_groups = []\n",
    "feature_groups_desc = []\n",
    "for variable, stat in itertools.product(\n",
    "    ['Temp', 'HR', 'RR', 'SysBP', 'DiaBP', 'SpO2'],\n",
    "    ['__mean_', 'slope', 'sample_entropy', 'abs', 'angle']\n",
    "):\n",
    "    to_drop = [i for i, name in enumerate(feature_names) if name.startswith(variable) and stat in name]\n",
    "    feature_groups.append(to_drop)\n",
    "    feature_groups_desc.append((variable, stat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_corr_group = []\n",
    "for (i1, g1), (i2, g2) in itertools.product(enumerate(feature_groups), enumerate(feature_groups)):\n",
    "#     print(i1,i2)\n",
    "#     if i1 < i2:\n",
    "    if i1 != i2:\n",
    "        co = df_corr.iloc[g1, g2].max().max()\n",
    "        df_corr_group.append((i1,i2,co))\n",
    "\n",
    "df_corr_group = pd.DataFrame(df_corr_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(df_corr_group)[2].hist()\n",
    "plt.xlabel('correlation between features')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_groups = defaultdict(list)\n",
    "for it, row in df_corr_group[df_corr_group[2] > 0.6].iterrows():\n",
    "    grouped_groups[int(row[0])].append(int(row[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_groups_ = []\n",
    "for i, g in grouped_groups.items():\n",
    "    found = False\n",
    "    for gg in grouped_groups_:\n",
    "        if i in gg:\n",
    "            found = True\n",
    "            gg = gg | set(g)\n",
    "    if not found:\n",
    "        grouped_groups_.append(set(g + [i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{1, 3, 4}, {6, 8}, {10, 12}, {15, 20}, {16, 19}, {21, 24}, {25, 26, 27, 28}]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_groups_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temp __mean_ 632\n",
      "Test AUC: 0.662 (0.539, 0.784)\n",
      "Test AUC: 0.662 ± 0.064\n",
      "Temp slope 592\n",
      "Test AUC: 0.626 (0.500, 0.752)\n",
      "Test AUC: 0.627 ± 0.066\n",
      "Temp sample_entropy 632\n",
      "Test AUC: 0.662 (0.538, 0.788)\n",
      "Test AUC: 0.662 ± 0.063\n",
      "Temp abs 592\n",
      "Test AUC: 0.626 (0.500, 0.752)\n",
      "Test AUC: 0.627 ± 0.066\n",
      "Temp angle 592\n",
      "Test AUC: 0.626 (0.500, 0.752)\n",
      "Test AUC: 0.627 ± 0.066\n",
      "HR __mean_ 632\n",
      "Test AUC: 0.651 (0.534, 0.774)\n",
      "Test AUC: 0.651 ± 0.063\n",
      "HR slope 612\n",
      "Test AUC: 0.666 (0.545, 0.784)\n",
      "Test AUC: 0.665 ± 0.062\n",
      "HR sample_entropy 632\n",
      "Test AUC: 0.655 (0.531, 0.779)\n",
      "Test AUC: 0.654 ± 0.064\n",
      "HR abs 612\n",
      "Test AUC: 0.666 (0.545, 0.784)\n",
      "Test AUC: 0.665 ± 0.062\n",
      "HR angle 632\n",
      "Test AUC: 0.657 (0.529, 0.779)\n",
      "Test AUC: 0.657 ± 0.064\n",
      "RR __mean_ 612\n",
      "Test AUC: 0.634 (0.508, 0.758)\n",
      "Test AUC: 0.633 ± 0.065\n",
      "RR slope 632\n",
      "Test AUC: 0.656 (0.533, 0.783)\n",
      "Test AUC: 0.657 ± 0.064\n",
      "RR sample_entropy 612\n",
      "Test AUC: 0.634 (0.508, 0.758)\n",
      "Test AUC: 0.633 ± 0.065\n",
      "RR abs 632\n",
      "Test AUC: 0.664 (0.542, 0.788)\n",
      "Test AUC: 0.663 ± 0.063\n",
      "RR angle 632\n",
      "Test AUC: 0.648 (0.524, 0.771)\n",
      "Test AUC: 0.649 ± 0.063\n",
      "SysBP __mean_ 612\n",
      "Test AUC: 0.671 (0.555, 0.787)\n",
      "Test AUC: 0.672 ± 0.062\n",
      "SysBP slope 612\n",
      "Test AUC: 0.645 (0.522, 0.766)\n",
      "Test AUC: 0.644 ± 0.063\n",
      "SysBP sample_entropy 632\n",
      "Test AUC: 0.629 (0.503, 0.751)\n",
      "Test AUC: 0.630 ± 0.064\n",
      "SysBP abs 632\n",
      "Test AUC: 0.623 (0.497, 0.754)\n",
      "Test AUC: 0.623 ± 0.066\n",
      "SysBP angle 612\n",
      "Test AUC: 0.645 (0.522, 0.766)\n",
      "Test AUC: 0.644 ± 0.063\n",
      "DiaBP __mean_ 612\n",
      "Test AUC: 0.671 (0.555, 0.787)\n",
      "Test AUC: 0.672 ± 0.062\n",
      "DiaBP slope 612\n",
      "Test AUC: 0.648 (0.522, 0.776)\n",
      "Test AUC: 0.647 ± 0.065\n",
      "DiaBP sample_entropy 632\n",
      "Test AUC: 0.662 (0.536, 0.781)\n",
      "Test AUC: 0.661 ± 0.063\n",
      "DiaBP abs 632\n",
      "Test AUC: 0.655 (0.534, 0.780)\n",
      "Test AUC: 0.656 ± 0.064\n",
      "DiaBP angle 612\n",
      "Test AUC: 0.648 (0.522, 0.776)\n",
      "Test AUC: 0.647 ± 0.065\n",
      "SpO2 __mean_ 572\n",
      "Test AUC: 0.669 (0.545, 0.779)\n",
      "Test AUC: 0.667 ± 0.062\n",
      "SpO2 slope 572\n",
      "Test AUC: 0.669 (0.545, 0.779)\n",
      "Test AUC: 0.667 ± 0.062\n",
      "SpO2 sample_entropy 572\n",
      "Test AUC: 0.669 (0.545, 0.779)\n",
      "Test AUC: 0.667 ± 0.062\n",
      "SpO2 abs 572\n",
      "Test AUC: 0.669 (0.545, 0.779)\n",
      "Test AUC: 0.667 ± 0.062\n",
      "SpO2 angle 632\n",
      "Test AUC: 0.643 (0.518, 0.764)\n",
      "Test AUC: 0.642 ± 0.064\n"
     ]
    }
   ],
   "source": [
    "def boostrap_func_auc(i, y_true, y_score):\n",
    "    yte_true_b, yte_pred_b = utils.resample(y_true, y_score, replace=True, random_state=i)\n",
    "    return metrics.roc_curve(yte_true_b, yte_pred_b), metrics.roc_auc_score(yte_true_b, yte_pred_b)\n",
    "\n",
    "results_2 = {}\n",
    "boot_auc_scores_2 = {}\n",
    "for gi, (group, (variable, stat)) in enumerate(zip(feature_groups, feature_groups_desc)):\n",
    "    grouped = False\n",
    "    for gg in grouped_groups_:\n",
    "        if gi in gg:\n",
    "            grouped = True\n",
    "            to_drop = [i for i, name in enumerate(feature_names) if name in feature_names[\n",
    "                sum([feature_groups[i] for i in gg], [])]]\n",
    "            break\n",
    "    \n",
    "    if not grouped:\n",
    "        to_drop = [i for i, name in enumerate(feature_names) if name in feature_names[group]]\n",
    "    \n",
    "    to_keep = [i for i in range(652) if i not in to_drop]\n",
    "    Xtr, Xte = Xtr_all[:, to_keep], Xte_all[:, to_keep]\n",
    "    print(variable, stat, len(to_keep))\n",
    "    \n",
    "    clf = train_model(Xtr, ytr)\n",
    "\n",
    "    y_true = yte\n",
    "    y_score = clf.decision_function(Xte)\n",
    "    roc_curves, auc_scores = zip(*Parallel(n_jobs=4)(delayed(boostrap_func_auc)(i, y_true, y_score) for i in range(1000)))\n",
    "    print('Test AUC: {:.3f} ({:.3f}, {:.3f})'.format(np.median(auc_scores), np.percentile(auc_scores, 2.5), np.percentile(auc_scores, 97.5)))\n",
    "    print('Test AUC: {:.3f} ± {:.3f}'.format(np.mean(auc_scores), np.std(auc_scores)))\n",
    "\n",
    "    results_2[variable, stat] = (np.mean(auc_scores), np.std(auc_scores))\n",
    "    boot_auc_scores_2[variable, stat] = auc_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results_2 = pd.DataFrame(results_2).T\n",
    "df_results_2.columns = ['AUC_mean', 'AUC_std']\n",
    "df_results_2.index = [n_a + ' ' + n_b for n_a, n_b in df_results_2.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results_2['AUC_mean_drop'] = -(df_results_2['AUC_mean'] - 0.659)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC_mean</th>\n",
       "      <th>AUC_std</th>\n",
       "      <th>AUC_mean_drop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DiaBP __mean_</th>\n",
       "      <td>0.671861</td>\n",
       "      <td>0.061826</td>\n",
       "      <td>-0.012861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SysBP __mean_</th>\n",
       "      <td>0.671861</td>\n",
       "      <td>0.061826</td>\n",
       "      <td>-0.012861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpO2 sample_entropy</th>\n",
       "      <td>0.666833</td>\n",
       "      <td>0.061733</td>\n",
       "      <td>-0.007833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpO2 abs</th>\n",
       "      <td>0.666833</td>\n",
       "      <td>0.061733</td>\n",
       "      <td>-0.007833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpO2 slope</th>\n",
       "      <td>0.666833</td>\n",
       "      <td>0.061733</td>\n",
       "      <td>-0.007833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     AUC_mean   AUC_std  AUC_mean_drop\n",
       "DiaBP __mean_        0.671861  0.061826      -0.012861\n",
       "SysBP __mean_        0.671861  0.061826      -0.012861\n",
       "SpO2 sample_entropy  0.666833  0.061733      -0.007833\n",
       "SpO2 abs             0.666833  0.061733      -0.007833\n",
       "SpO2 slope           0.666833  0.061733      -0.007833"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results_2.sort_values(by='AUC_mean_drop').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results_3 = pd.DataFrame(results_2).T\n",
    "df_results_3.columns = ['AUC_mean', 'AUC_std']\n",
    "df_results_3['AUC_mean_drop'] = -(df_results_3['AUC_mean'] - 0.659)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_drop_grid = pd.DataFrame()\n",
    "for i, j in df_results_3.index:\n",
    "    df_drop_grid.loc[i,j] = df_results_3.loc[(i,j), 'AUC_mean_drop']\n",
    "\n",
    "df_drop_grid = df_drop_grid.rename(\n",
    "    columns={\n",
    "        '__mean_': 'average', 'abs': 'abs($A_1$)', 'angle': 'angle($A_1$)',\n",
    "        'sample_entropy': 'entropy'\n",
    "    },\n",
    "    index={\n",
    "        'Temp': 'Temp.', 'SysBP': 'SBP', 'DiaBP': 'DBP',\n",
    "    }\n",
    ")\n",
    "df_drop_grid.index.name = 'Vital'\n",
    "df_drop_grid.columns.name = 'Trend'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Trend</th>\n",
       "      <th>average</th>\n",
       "      <th>slope</th>\n",
       "      <th>entropy</th>\n",
       "      <th>abs($A_1$)</th>\n",
       "      <th>angle($A_1$)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vital</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Temp.</th>\n",
       "      <td>-0.002578</td>\n",
       "      <td>0.032435</td>\n",
       "      <td>-0.003259</td>\n",
       "      <td>0.032435</td>\n",
       "      <td>0.032435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HR</th>\n",
       "      <td>0.007749</td>\n",
       "      <td>-0.006428</td>\n",
       "      <td>0.005009</td>\n",
       "      <td>-0.006428</td>\n",
       "      <td>0.001755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR</th>\n",
       "      <td>0.025586</td>\n",
       "      <td>0.002294</td>\n",
       "      <td>0.025586</td>\n",
       "      <td>-0.004335</td>\n",
       "      <td>0.010127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SBP</th>\n",
       "      <td>-0.012861</td>\n",
       "      <td>0.014802</td>\n",
       "      <td>0.029064</td>\n",
       "      <td>0.036455</td>\n",
       "      <td>0.014802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBP</th>\n",
       "      <td>-0.012861</td>\n",
       "      <td>0.012080</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>0.002723</td>\n",
       "      <td>0.012080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpO2</th>\n",
       "      <td>-0.007833</td>\n",
       "      <td>-0.007833</td>\n",
       "      <td>-0.007833</td>\n",
       "      <td>-0.007833</td>\n",
       "      <td>0.017107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Trend   average     slope   entropy  abs($A_1$)  angle($A_1$)\n",
       "Vital                                                        \n",
       "Temp. -0.002578  0.032435 -0.003259    0.032435      0.032435\n",
       "HR     0.007749 -0.006428  0.005009   -0.006428      0.001755\n",
       "RR     0.025586  0.002294  0.025586   -0.004335      0.010127\n",
       "SBP   -0.012861  0.014802  0.029064    0.036455      0.014802\n",
       "DBP   -0.012861  0.012080 -0.001982    0.002723      0.012080\n",
       "SpO2  -0.007833 -0.007833 -0.007833   -0.007833      0.017107"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w/zOL9xTpydsfVX2tk7U3dsZ4HfWtaeqVlaay4lJt+Hytvvxguepr6/Xi3c8ovV9vDRk3LKgxuovq/DzlP/qwbH2dImJjlXHamUoaOVpN9XUqXbFcBxd/qrr9+7TzgXs1/JZfKzKuc8fQvtdfaQkuxQ0cpIxTTldMRqbqigpV9P47qsnP08HFnykyKVl9z/12wPVFxMUpbuAg1e3bp4YyrkS3NSotUX8+brTioyJVWd+gx9YXaPm+EsVGRuj0wZm6YEQ/DUlJ0H1zxumyt1epqqFzJ8H/N3lIS3Dp6wMVmr+hQAXl1cpOjte8sdkak56kb+f008Gaet23eqfXdRi5zxkq6hu08UCFBqckKDO+c5mXL2zerec3+85yre7kz9qd5KQk6rajRykuMlJVDY16emuBviouVWxkhE7qn6FzBvXVoKR4/WnaWF23aLWqGzv3O7lm1OCW4NLm0go9l7tLu6pqNCAhTpcMH6CRqUk6e1BfldTV65FN3i8seJ4xVtY3aEtZpQYmxqt3EJm3Ga5lC2tq9cmeYq05UKZ91bWKNNLYtBRdPLS/MuNjdXp2lqKM0e9Xbe7Uz9sdjM1I1L2njFF8dKQq6hr0r6/ytXRXieKiInV2TqYuGdtPw3ol6JGzxuu8BV+psr5z+/7Hxw5pCS6tKyzXv1cVaGdptQanxuv7k7M1PjNZF4/tp+Kaet39xQ6v6/Dc9+V1DVpfWKFhveKVldjxjNmfL9yshGj/QdyctATdd6rzmeLzXSVHLLiEbmWM6+vWAOVnG728pqM8P9hu9LmU93ECBpiMMZFymnwfJafPU3Nw6YPuFlySCDD1aMaYeXKnCg611u44jGPFSFovKUfSRdbaBYdrrM4wxvxT0g2SnrDWfi/c29NRd9xyhuLjY1Rf36jLrn1CK1cXtDy3eNl2bc87oF//+FQNH5qh6+bN1N8e+DjoMYYN7q0ffG+mJGnVul264HuPqabW+fu8et1uvbdwk16af5Umjx+gH8ybpede/sprttSc8/6pRh8lGx9+ukWPPbVUbzx3rYYMTNd135upfz2+WAdLW2dd7N5bpp/99nW9+Noq1bX54LR63W699MYaPfPQFTp2ymAdO2WwLjhnoha8vjron7kr+O+DL6u+tl4RkRG6+o8/0OCxQ1uey5k8Ur0HZOrtR15XUUGhPn1poU654oygxygs2K9PFyyUJGWPHKjr/nqjomOdD/MDRw3SmOnj9fBP71PB5nx9uuAjTT3tWK/ZUis/WKYd67dJkqafc5zO+78LW54bOHqwRk0do/v+727VVtXo9Qde1o8eGaXIyMh265l5zvFK69tbA0cNUq+sNEnSLafdHNTPtOilj1uCS7MvOklnXjO31fPjZk5Q/xHZ+u8DL8s2WX383Aea97trva2q29vz4rOy9XVSRKSG3PgjJQwb3vJc0qgxisnK0r5XFqhu/z4VffCu+px9rp+1eVe7b6+KPnCyyOMHDdHQH/1METHOMZQwZKhSJk7S9r/dpeq8HSp6/12lzZjlNVsqeex4JY4YpfjBQxTbt59MRIS2/f0vBJi8+NnU4YqPilR9U5Ou/2id1hS5M+WX7ytVXnm1/t/RwzQkJUFXjhmgf631fvLvz6DkeF05xpkleX1xuf7n/TWqdf39Xn+gQp8UFOvRUyZqXO9kXTk2W6/m7vOaLbVozwGt2F+i9cUV2lZaJSvpkZMndDrAdKCmXrmlvjMTvwluHDtUcZGRamhq0k++WK8NJe79/1VxqQoqa3T9mCEalBSvi4f11/wt+UGPkZ0Yp4uH9ZckbSwp101L1qnOVXK3qbRCi/cd0D0zxmt0r2RdMmyA3s7f7zVb6ovCEq06UKaNJeXaWVEtK+kf08cHFWDKq6jWvzft1Kd7itX2E8SGkgq9X7Bf982cqEFJ8Tp5QKZez9urNa7S6p7m17NyFB8dqfrGJs17Y62+2ufe90t2lWhHabVumTFMw3ol6OpJ2bp3hffArz9DUuN1zaRsSdKa/eW65NXVLe/9tYUV+mBHsZ49d5ImZiXr+5OyteDrvV6zpT7JO6gvdpdqzf5ybT3ovPefnjsxqABTQQey0M8b6f5/8sqmfR1eN7oWY0x2R5az1hYEXqrVeuMkZbge+n2ttfagMaZSUqKkgcGMI8lz+wNto+cfZb/jGGP8XSX/UlK3OWf1RJNvHCo/lBNcWifppWBeaIzpb4ypMMZY1+2Gw7B9f5ZUJ+kKY4zvOrAuZPL4AZo+dYgk6blXvmwVXGr20PzPtTl3vyTp6u9OV1RU8G/pa66YruhoJxjw6z++1RJcalZTU69f//EtSVJ0dKS+f6X3EihfwaVmB0ur9cyClS3rOXpS+/81P/r1q3p6wcp2wSXPbbn1d2+0PD7rlI71Dupq8jfu1I51TsBm2mnTWwWXmh1/wYnKcl1dXvzqp2rsxJX7xa98oibXfpl7wwUtwaVmMXExmnvDBZKcLKVFr3zidT2fuYJUCckJ7QI6kpQxIFNzLjlZklS8u0jrF3svYZl90UmacPykluBSZ+z8eockp6zzpEtP9brMzLnHKyE5QZKU51q+p6nasU1VW51eZGkzj2sVXGqW8a1TFdvXuUpdvPBD2Ua/fSW9Kv7oA6nJOfb6XXxpS3CpWURMrPpdfKnzoKnRWd6L1KnHKG3GLMX1H+CzjA7S+N5JmpLllAu9mruvVXCp2RNf72oJwlw2qr+iTPCVB5eP7q9o1364c3luywlms5rGJt253KkGiI6I0OWuYFRb7+0s0uvb9ivXFVxCaEanJrWUi72Vv79VcKnZC9t2aUe5s/8vGNpfkZ3Y/xcO7a8o1/6/d/22luBSs9qmJt3ruqgQFRGhC4f297qehXuK9E7Bfu1wBZc649YVX+tjL8GlZqX1DXrwa3cCwAk9tLfexKxkHdPf2fcvbtzbKrjU7JFVBdpyoFKSNG9if0VFBL/vr5o4QNGRzr6/fdHW9u/9hibd7uq5GR0Zoau8fFaTpDdzC/XSpn3acvDwvfeNpLkjsiRJFXUNend70WEaqYcypuvcnKBLR27BSva4X9GB5StdX4PtexDMOJUe9zvTX6FK0vWSZlprd3fi9WHHpzyEzBiTLOnnroe/70Qzs9/LiSY3m3hINsyDtTZP0n/k/L/63aFe/+Fw+rdGt9x//pWvvC5jrW3J4OmVGq+Zx7QPVARy6hxnnC3bCvXlGu9B+S/XFGjrtkJJ0mlzRgU9RrMKj9TmWB+9RQLZtHW/il0fsAYPTO/0toTT+s/dAZgppx3jdZmIiIiW/kE1FdXKXR1cY3NrrTYsWSdJyhyYpUFjhnhdbtCYIcrMdj7AbViytl3JYWHBfu3Pc64aTpg92WfT8SmnuH+O9YvXBLWtwWisd4IkCSkJikv0XvJljFF6P+ckpKGHltSUrV7Vcj9txiyvy5iICPU61gkIN1VXqWLTpqDGsNaqbI0zTmyfvkoY2j6IJUkJQ4crpo9TalW2ZlW3LVvtCuZku0+eX8v1frXeSnpjm/NcSmy0pvUJ3L+mrRNd42wrrdLaYu/Nk9cWl2u7K5B1YnbPPKnvao7r6/6f9naB7/3/3i7nwlJydJSO6kD/orZm9XHG2VlRpQ0l3s+VNpRUKM/V5+64PuH9X/tVsTvTsb+Pv/vd3SlD3e+xBRt97/tXNjv7PjU2WtP7B9cgW5JOHuKMs/VglVZ5CWJJ0qp95co9WNVq+XCYmd1L/ZKcjKh3thUdsubm6FE8/yB0pH6y1vU1/jCOU+txP9A4E1y3yZJOk3Sna/1/lXRnJ2e7CzsCTDgUrpfUW1KepBeDeaExZpLc6X/NEd9DHmByudv19YzukMU07ahBkqTKqlqt8TPz2tIVO9q9pqMGZaepX5+UduvxPo6Tit2vb6oGBjnrh+Sc9J9z2riWx1tDuBLVnHHVeIhm0TnSmsvNYuJiNGCE7+zZoRPcJ/Q71wfVI1AH9harzPWhfNiEHL/LDp3ojFNWVNquCXfztkrSsIm+15OcnqKMbKe8bueG4LY1GBmuYFhVWZVqKn2n1x/YUyxJLcGznqYq1wk4RsTGKn7QYJ/LJY5wB4SrtvmfCbKt+uIiNZQ6s0gljPAfWE4cMVKS1FByUPXFXGXurMmu7KWq+kZ9fcD3rFkr97tPuCdnpgQ1xoCkOGUlxLZbj79x+iTEqn8QpS/onAlpzr6sbmjU5lLfF8lXFbtLxManJftczpt+8bHKjHP25epi/6VmzeNkxseqb3z49n+0R9ZjUw+NX091TZJSWd+odYW+3/vLdrtn9pvSL7j3/sDkOPV1BWw81+N9HOe93y8pVtk+Jpk53M6nPK4nGdjBW7A8Pwh2pDa3+Q+Z95lRDs04nn8s/Y5jrV3nuq221r5nrb1Vznnwfkk3S3rT1Z+pWyHAhJC4Dvr/cz181lob7Bn/X+Uchx9I+q/re+Nd00EeUtbaTXLqWSWp4/Ohh8mIYc7J+o68A37LzzwDNSOGZvhczpuRw939drZu839S2GqcYR2b1SwiwqhvVrJOPmGkXnjse5oxbYgk6dMludqcWxjUtjYbN7pvy4x6gba5qyrMdz4o9e6f4bVXUbNMj9l5mrOIOmr/TvfymQP9B1n8jRPUerKd9ZQWlqiuptbvsp01/SwnW8daq4XPve91mSWvf6YqVwnJsWfNPCzbEW61e52gc0xmloyfYyi2r3uWn+bXdFTNHndmtud6vI7Tp/PjwG1oilPamV9erUY/J9Lby9yfWYemJgQ1xjCP5XcE6He0o8z9/LAgx+mMUwZl6KWzp2jJxTO1+Dsz9Po5U/W7GSM1tRNZWt3R4CTnd7yr0v/+z/OYQbP5NR01JNm9fJ6fmTglKa/SfZwFO86hNCndHUjZGWCbu6vhaU6iw85S//s+96B7nwzvFdw+yUl3L59b4v/8OrfE/XvOSQs22SN0CVEROnWY85m2oKxGS3fTry9oEV3nZq0t6MitEz+lZzS2I+VozRUzHSmn6+w4nlU5wY4ja22+pP91PTxF0tXBriPcaPKNUJ0id8T56WBeaIw5U9LJcrJ+fyqpeQqrZElD1IGu+53wtKSjJV1kjLnRWuv7MlEYxcZEqXe68/dpzz7/VxhLy2pUWVWrxIRY9e8b3Ifw5uyljoyze69HinqAcXatu93nc2vW79bNv3ilg1vY3k3fn91y/7/vrOv0esKlvq5elaVOsl5qhv9MsITkBMXExaiupk6lRQeDGqe0yH11MjXT/zi9PJ4vLWw9Tqv1BNje5vVYa1VaWNIqcHWojJgySnMuPUULn31fn7zwoYp2Feqok6Y4s8gdKNOGz9dq5fvLJElHnzJNU0899pBvQ7g11derscL5zBLdy38/q8iEREXExqqptlb1B9s35/en4aD7WAg0TnSau4Sm/mBwxyocMRFG6XFONvy+av8B2vK6BlXVNyohOlJ9EoLLLOmT4L74ui/AzKN7Pcqagx2nM4b3Smz1ODE6SoNS4nXOsD76KL9Iv1myWRWdnDmrq4uJMOoV6+z/whr/+6WioVHVDY2Kj4pUVpAN1ZuzlzoyTqHHcRjsOIeKkXTZcHcfoI93d88LS/7ERBr1dv1+91b4f++X1TWosr5RidGRLeVjHdXXIwsx0Dh7PJ7vl3TkM5hOH56pRFe2+mtb9h/x8dE9WGtrjDHFcipp/DYSN8akyR38Cbbfk2fwK1DDcs9MrM70lZKk9+RkP8VLulDSw51cT1gQYGrDGDNe0nmSjpc0TlKmpHpJeyR9LulBa+1SP6//raTbJMlaa1zd7W+UdKmkEa7Fvpb0hKR/+ZtO0RjTW05vo3MlDZJUJmmNpAesta8cqlnijDFzJM2T8zP3ldQgaaekdyT9PUCDse+4vm6x1nrv7Ot9zEhJd7kePm2tXWWMGeKxyEQdngDTS3JK5RLk/F6fOgxjhCwx0f1BrjLACYAkVVXXKzEhVgkJwX0ATPT4sBFonKpq9/OJQY4jSVVVdbrjr+/q+Ve+8tnEO5AzTx6js11ldqvX79JbH3zdqfWEU22V+0NbTAdKDpoDTLXVwU3NW+txYhAT538cz75KbcdptZ4A29tqPQFOXEJx2ryzNGxijhY+94HWL17TrudT/5xszbnkFE04ftJh2zFwfBcAACAASURBVIZwaqpxZ2pHxAY+hkxMjFRbq6ba4LLKGms9x/F/guG5HU21gWcGQnuJ0e6PZFX1gZOBqxucAFNCdHDZ84lR7uWrAvQoq250P58Qdfiy9KsbGvVxQbGW7S3R9rJqVTc0Ki02WlP6pOrCnH5Ki4vWSQMzlBITpR98uE4NPbDPV7zH79fz9+5LdaMTYIr3k8EY6jiezwc7zqFy0dD+GusqA/xkT5E2l1UGeEX3k9Tqvd+Bfe8KMCUG+d5PivF47wcYp9rjb0NC9JEveDl/pDtj+mXK4zrn0BeDdFUb5JzD5hhjovycW4/2uB/sCcQGH+s51ONIkqy1jcaYg3ICTL77IHRRBJg8GGNOlLTQy1MxcmZIy5F0pTHmTleNZKD19ZETpJnc5qlprtupxpjzvJWVGWMmSHpfkmcKQJycjJ+TjTEPS1oS8Ifyv31xcgJUl3h5erzrdr0x5lJr7X+9LCNJc1xffQbdfLhG0lg5Na2/dH1vvcfzEyW9FuQ6A7LW7jTG7JUTSDtDXTTAFBfrfmvWd+DDRl2d87c0Li64t3RcTMfHqatzP++5fd6cdN4/JUmRkUYZvZM085ihuuKiqfr1T07V8CEZ+v3f3lNDkM0ac4Zl6G+/P0+SVF1dp5tufTmo13cVDXX1LfcjO3DCFun64NlQWx9gST/jBPgQGunx4ba+rvU4wWyv53qC3d5glBWXauV7y3zOELd3226tfH+Zsgb1UZ/B/ku7uqOmevfv1kQGfs9HREWrUZKtDy7oZz3HCbDvTZR7Ozy3Dx0XE+k+GajvQH+55pm/YiODO/mL8Vg+0Dh1HuXZsZ2YpbSjTn35C5V7+R+0dG+Jnt20W/+cM15j0pM0tU8vXTSyn57d1C0n1vErJsJzvwQOoDUvE/T+j/A8zvyP4/l8sOMcCpPSU3TtaOfc6kBtnf6+bluAV3RPsZHB7fvm92VckO/JYMap86jTizvCwcW+iTE61tXA/Mu9ZdpRGmy7HHzDLJITYEqUNEXSFz6WO8Hj/uIgx9guabek/m3W401zqcUuSTuCHEeSZIyJkdTc9yToMrtwI8DUWpScRtNvSvpI0kY5WUNZcrKZbpITRbzFGLPZWvu4rxW5vCwniHKvnP5CBySNkvRrSWMknSPp+5Ie8nyRMaaXnMBUc3DpSUnPSCqUE+T6oaRrJXX68ryrx9ECSWe5vvVfSS9I2iapSdIxkn4sJ3NqgTFmlrV2RZt1ZMspZZOk5UGMnSypuYbqPtcMb3KNXSMnkDYhyB8pGMskzVXgPxBhU1PrDr5Hd+AKVYwrUFRTE9w05DV1HR8nxuPKl+f2ebNpq2c68z59+nmunnhumRY8fpW+f+UMjczJ1OU/eEpNHezW2SczWU8+cLmSk+LU1NSkH/3mtW7bfykqxj0hRGMHZjhrnjUtKja4iSRajRMgeNg8hiRFx7Qep+32RsT4/kDruZ5gt7ej9uft1SO3PqiyolIlpCTq7GvP1Zjp45WUlqyq8iptWblR7/3nLW38Yr22r83V926/xm9z8u4oItr9u7WNgd/zTQ1OwMdEB5d5aDzHCXCs2gb3dnhuHzrO84TOs6mxL80BibbTjAcex718oHE8g1G1h3EGJ2/BpWYHaur1k8++1qtnT1F0ZIQuHdm/RwaY6po890vgzIPmZYLe/02ex5n/cTyfD3acUA1JitfvpoxWVESEahsb9dsvN6mkrmcGr2sbg9v3ze/LYGdVC2Ycz4B3TQcy6g6lc0f2UaRr+8heQge8Kqk58eMqeQkwGWMiJF3pelgi7wklPlmnLOk1ORNbjTbGTPdW0WSMmS53BtNrnZhZvdm5cjcT73CFUFdBk+/WVknKttZebK19yFr7ibX2K2vtu9bav0kaKSerSJJu60BX92mSTrPW/tBa+4G19ktr7bOSjpPU/BfzBi+vu01OhFSSbrbWXmmtfcdau9Ja+7ycKO1rkkJpLnKNnOBSvaQzrLVzrbVPWWs/t9YutdbeKyfzar2cA/wfXtbh2T33qyDGvkVO8OyApD82f9Na2ygnqCcdvpnkJGml6+sAV5ZZl1NZGVw5WkK8c0JX1YFyutbjuEtmAo2TEB9c2V5bu/eW6Zd/eFOSdMLMHF367aM79LpeKfF65uErNCjb6QHzqz++rdff7n69l5rFevQxqQvQZ0WS6lylZrFB9r+I9ShnC9Rwu86jnK3tOK3WE2B7W60n7vD063jhrqdVVlSq6NgY/eDuGzX9nOOUmtlLkVGRSk5L1tEnT9MN/7hZSWnJqq2q0XN3PqmGuuACr11dRJy7XK0jZW+2ztkvHSmn8xQZ6zmO/7I3z+0IVE4H7yo9ArQdKUlpLnXqSElNq3EaOl725lkWFaic7nDaVVGjpXudfnCDUuKVGaZ+QIeTZ0lSR8rRmpfpSDldZ8fxfD7YcULRNz5Wdx07Tikx0Wpssrrjq81ac8B/n8jurKLVe78D+961TGWQ7/2KOs+ytwD73rOUtgMlu4fSea7yuNqGJr25tXMTwkBOiVxXuR1G1tplkj5zPbzaGDPDy2I/lpPcIUn3WGtbRauNMScaY6zrNt/HUP+Q1Pwmus8Y06r7vevxfa6HDfJy7myMOdkY4/eqpzGmOTml2RP+lu+KCDB5sNYWWWt9zttpra2T04xacjKZ2pa+tXWftfZjL+s5IHfvpAnGmJaOycaYWDn9kCRpubX2Hi+vb5R0nVpPmdhhruyln7se3mutfcfbctbag3L/vLOMMSPaLOLZ5KxDHfhcWU//z/XwD15+382Rg5y2b9xDyHNbhx2mMUJSW9egAwedPgOejbi9SU2JU6IraOHZiLsjPBt7BxrHs7F3sOM0++TzXFW7evycderYgMsnJsToqYcu1+gRThzwL/d+qP88t6xTY3cV0THRSkhxegx6NtD2pqq8qiVok5rhv8lyW54NuUsL/Y9TUujZELz1OK3WE2B7m9djjAnYWLwzdufuUsFmp1/iUSdNUdYg7+VvKb1TNXPu8ZKccrpNK7pfry5/IqKjFZnoTGJSX+K/oXZjVWVL8MezEXdHRKW5j4VA43g2EI9OC+5YhaOuyepgjfOZt0+AfmfJMVEtJ4j7qoLrreXZ2LtPgAsLfRM9G4IfnpkhO2qbx4x34Wo4fTjVNVmVujJ0MgME6JOiIlsCAPuD7M9X6HHBIdA4mR7HYbDjdFbv2Bjdfew4ZcbFqsla/XnNFi3eF9wEBd1NXaPVgWpn3/cN0Lg7JSaqpffSngCNutva63FRMdA4ng3E91Qcub56EzKTNNI1yc1HO4tV1sMuEOGw+aGcpthRkt4zxtxqjJlujJljjHlI0l9cy22W04s3aNbazXL3D54qabEx5mJjzFRjzMVyyu6mup6/y1q7xctqjpO00RjzrjHmR66A01HGmGnGmAtdLXBWymnlIkmPWWs/6sz2hhMBJj+MMbHGmEHGmLHGmPGuBuCeYdhAJWr+ZlVrzqIxkoZ6fH+qpOYzM5/9gay1+yS9G2B8X8ZKGu66vyDAsp963G8bEfacq76j0wb9UU7Dsh2S7vfyfHMfpkg523k4eH5S6bINWjbnOldthgxKV6Sf3gc5QzNa7m/ZHlzZWPMYktPjyJ9W42zr3BWlpiarkjLng0p2P/8BiLjYKM3/52U6aoITx3zgsUW65+FP/b6mu8ga5ATMincXqdHPVeHCfHdqePNrOjzGYPfyhfn+47/+xglqPQXOelIzewVsLN4ZntvZP8f/JB4DRrgn8fB8XU8R26+fJKmucL+sn2Oodu9e92v69gtqjLh+/Vvue67H6zj7Oj8O3LaVOUGUgcnxivRz0Xdoivv6y/bS4KZt9wzUDEn1P835kBT389uCHOdQs+p5jb3b2lHu/I4HJPrf/4OS3PtlZ0Vw+6V5jLbr8TpOovs4C3aczkiNjtJfjx2nAa5x712/Te/t+mZksGw96Px+B6f63/fD09z7JLckuH2y9YB7+eG9/F/DHd7LfWxsPXjkeiCdP9L9mYPyOHSUtfYrSRfLaW2TJOd8c4mcljfXuhbbLOmsEGcQ/6Wkx1z3j5L0nJw2Mc+5HkvSo5J+5WcdkZJOlRPoel/Sl3Lat7wop3VOnJxMqbs8tr1bIcDUhjEm0RX1XC2nH9NOOUGPta6bZymY/zNyd7mXN55BjmSP++M97q+UfysCPO/LVI/7SzxSAtvd1LqxWNtgjOfl8IABJmPM0ZIudz38pSsjrK22jb69redyY8xDxpgVxpha17bOCzS+j21N9LlUmC3/ymlNlZgQq4ljfZ+wTZ86pN1rOiqv4GBLFpPnerw5dqrTaHPP3lLl7/KfyeJLdFSk0tOcDy3+yuyioiL08N8v1sxpTuz1ieeX6w9/e9/n8t3NkHFO4lxdTZ12bfE9g+n2tbkt9wePG+pzOW/S+/ZWSm8n62zb2q1+l20eJyUjVWl9Wme5NG+rJG1b43s95QfKVFTgnAgMHhvctnZUhEegtSlAPxDPwF1EmGY/OpwShjsJpU21tarO2+lzucotm9yvGRZcL6ro3hmKSnUCwVUe6/GmastmSVJUrzRF9w70rxG+rNrvZIcmREdqTHqyz+WmZLkzSlcVBlc6tKuiRvtd2Uie6/HmaNfz+6pqtbsyvBlMwzyCYYVHKJvmSFt70NmX8VGRGpma5HO5yb3dGcfrDgZ3rrSnurYli2lSb/+Zy83PF1bXam8HSrpDkRgVqb8cO05Dk539/NDXO/TqTv+B7Z5khSszPDE6UuMzfb/3j+nvvji3ck9w7/388hrtdWU9ea7Hm2n9nff+nopaFZQfmQymqAijs0c4166Lq+v0SV7Pzlw77CK60O0IcE1INVHS3+UEk6rk9FtaIady5yhrrf8PxIHHaLLWXi2nzcxrchp/17m+vibpTGvtNd4m8HL5u5yZ5R+SM0HWTjmZV7WS9kr6WNIdkkZZa3/mqlrqdggweTDGDJETRPqjnAM00FmJ3/C/tdbfpQXPA89zHM/agkCXbTp7WScr8CJetb3U5fkfpyPlbHfLydhaKelZH8sEDDBJ+r2ciO5gSXs6MG5bntvaZTtGvvOhOz558flHeV3GGKML5zqJdCWl1fp82fagx3lvoTPOiGGZOnqi96yQoydma8Qw55/+uwv9n2j6c9pJoxTraki+cYv3K1MREUb//POF+tbskZKkBa+v0q2/e6PTY3ZF42a6e9ivfNd7yV9TU5O+/MDpnR+XFK/hk9pWqPpnjNHYGU68ujB/v88Z1/K+3tGSmTR2xgSZNrXymdlZLVlNaz9d1arPkqeV77t/jnGzDk8LtbS+vVvu71iX62dJafsa9/PpfYMrDesOUia5K7QPLvE+GYptalLJF85koxHxCUoaNSqoMYwxSpnojFO7b6+qtnv/nVdtz23JYEqZOLndMYSOW1hQ3HL/3OHesxaNpLOHOc+V1dZr+b7gS5Y/do0zLDVBE3p7P5md0Du5Jajzscd2hUP/xFhN7+t8PMorrz5i5VpH2qK97hPqM7J97/9TBzgf48rrG/RVcfD7v7nkbHBSgsb28h7IGtsrSYNdGU6LDnOJWmxEhO6cNlajXEG1J7fk69ltuw7rmF3N+9vd77ELR/ve9+e7+hOV1tZr6e7gL/Z9sMMZJyctQZP7eH/vT+6TrBzXxcDm5Y+EEwalqber/PX1LfvV2POTFnGIWWt3Wmt/ZK0dZa1NtNamWWunWWv/4u+83Fr7sbXWuG7zOjDOW9ba86y1A6y1sa6v51lr3w7wulJr7XPW2h9Ya2dYa4dYaxOstXHW2n7W2jnW2tustf4/5HZxBJhae1JOuZqVk/52qqSBclLVIqy1Rq2DQd31U7Tnz3COnBnbOnJ7oM16PANcfs/gjDFzJZ3oejhFUpOPjCnPN5Svs9RrJA2x1mZK+pe/cX3w3NbOpeIcAavW7dLSFTskSZecf7SmTGof/Llu3kyNHO582Hj06aVqaDOjyIxpQ7Rr3e3ate52/f3353kd55Enl6rB1fTzd784U3GxrSeXjIuN0u9+caYkqb6+UY88uaTdOo6fPkxDBvo/iR8xLFN33Hpmy+MFr6/2utxdt8/V2aeNkyS9+d56/b9fvep3vd3RwNGDNWS8kxm0/N2l2rmhfWDws5c+1v48Jwg367zZimzTjDd39RbdctrNuuW0m/XCX71X4846/wRFuGaJev2Bl1Rf2/qkrL62Tq8/8JIkJzvouPO9T6x4/IVzJDk9od5+5PV2zxfvLtLC5z6QJPXun6Fxsw7PJJD9hw9QSoZzVXXdojXa+tVmr8vt2pKvL950gi7RsTEaPjm44Fx3kDBkmBJynJ/r4OeLVLWt/WeRog/fU+1eJwbfe863ZCJbv7crNm/Uuhuu0bobrlHBE4+1e70k9T7pZMl1DO15/lk11bU+hprq6rTnedf1gohIZ3l02rriCq10ZTGdN7yPJma0PwG8cswADXcFfp7ZtFsNbSapmZqVqlXfPV6rvnu87pg+0us4T23crQbXbGK3TBvebgr62MgI3TLNqaSvb2rS0xsP38n+7AHpfkuC0uOidffssS0zZ72wuTPXlbqHjaUVWu0KGJ05MEtje7Xf/98ZNkBDXFk+L23frcY2+39yeoo+PmuWPj5rlm7xMYPmgu271eja/zeNG9YyI2GzmIgI3eTKXm1oatKC7Ydv1r4oY/S7qaM1IT2lZdse3RxcNnZPsGZ/uZbtdvb9RaP76igvwZ9rJmdrhKs/0fw17vdws2P7pyr3+tnKvX62/jLH+3v/8TW7Wl5323E5Xt/7tx3nHDf1jU2av6YgtB8sCJ7lca9s6lBrVwBdUFTgRb4ZjDGj5TTekqQ/Wmt91U4e7kvhnuVbmXJS/HzJ9POcP56XI0qstZ2dksszwJQmJ82vHWNMlNzN1YLh9SzVWvtBJ9blyTNLrEt/ivnNnW/rtSevVnx8jJ55+Erd9+/P9Pmy7YqLjdK5Z0zQ5d9xqh1ztxfpofmfd2qMbTuL9eD8z3XjNcdr8vgBevXJq/XAY4u0I/+ghgxM0w3/c5wmjHV6sfxr/mJt95KyPO3oQXrqX5dr0Rfb9cnirfp68z4dKKlSVFSEsvv10uyZw3XBOZMUH+fMdvfsy19qsZdsq9/85DRdcr4zu9zXm/fp3n9/1pI55cumrd3zQ8g5139b//rRPaqvrdejv/iX5lxysoZPGqH62nqt/uRLLXvLCeRlZGdq9gVzOjVGZnaWZl80Rx8//6EKNufrwR/dqxMuOkm9+2eoeHeRPnnxI+3e6nx4nH3hScoY4P13PeXkY7Ti3S+0c/12LfnvIpUfLNcxZ0xXfFKC8jfl6aNn3lVtVY1MhNHcG76tSB8labtzC7Q71/uJasWBcq14r/XMshOOn9xqFruIiAidftXZeuGup9XU1KTHf/WQjjlzpsZMH6ekXkmqKq/SlpWb9Plrn7UE00685GTFB+gz0l31u+hSbfvrnbL1ddpx39+UedpZShw1Sk119SpduUwHFzk9y2Ky+ijj5NM6NUZsn77KOPk0Fb33tqrzdmjb3Xcq85TTFZOZpbrC/Sp8/x3V5Dt/RjNOOU2xWd6vvNeXlqpiQ+t/NQ1l7vKOtllYCcNzfK6rp/vLilzNP3WS4qMi9eBJ4/Xo+nwt31uq2KgInT44UxeOcEqmd5RV6YmvOxf4ySuv1n++LtDV4wZqXO9kzT91kh7fkK+C8hplJ8fpqrEDNSbdySZ5YkOB8nyUyPSOi9as/mltvuduHD13WOuE6a/2lym/TcPgW6YOV1REjj7IK9KaonLtrqxRbWOTesVGa2qfVF2Q00/prv8dX+4v1fObD1+woyu4b8N23T9zguIiI/XXY8fpqa35WlVcqpjISJ3UL0NzBzvdCvIqqvX8ts79Lgoqa/Tctl36bk62RvdK1v0zJ+jZ3F3aXVWj/glxunT4gJYSvee27dKuKu/7Pz02Wse0mRgiPTa65f7p2a33/9oDZe3W9eujRrasY2VRid7M36ehfv5m19smFVQeuabTR9LvFm/VC+dNVnx0pOafPUEPfpmvpbtLFBcZobNzsnTpOOe9v62kSo+u7lzgZ0dptf69Kl/XHz1IE7OS9cL5k/TwVwXKK6vWoJR4XXtUdkuJ3r9XF2hHqfffdUZ8tE4Y1PqUKNNj0oALRrX++71iT6l2lvnebykxUTppsJOlvKm4UuuLKnwui46xZBMjTAgwuY3zuP+8n+Wm+nnuUPAsEZsipyP9od4Wzz5SsyQt6uR61nrcHylplY/lrpM0Sk5m2P8qcL+meZJOk5RpjOlrrT3URfjNl3VqJYVUi3u4rd+4V9f/5EXde+cFSkmO0603t88OyN1epCtveNpvT6NA/nzPh8pIT9Sl3z5aE8b214N//U67ZZ55aaX+fK/viQyioiJ14qwcnTjLd5+XhoZGPfzEEv3pH95jhGeeMqbl/piRffTuiz8IuO0Dxt8WcJmuaEBOti79xff0/J+fUm1Vjd59/M12y2RkZ2reHdcqNqHz076fOu8sVZRUaMW7X2j31gI9+6f2s51OPX26Tp13ppdXOyIiI3TlbVfr8V89rILNeVq3aLXWLWqdgRYVHaW5/3uBRk3z3Zt//edr9eFT3ucmKCzYrwV3t66cHTYxp1WASZKOPnmaKg6W6935b6qxoVFLXv9MS17/TG0ZYzTr/Nk66dJTfG5Pdxc/cJAGXn2tCuY/qqaaau17/eV2y8Rk9dHgG25SZFznj6E+c89XQ3m5SpYsUk1+nvIfe7jdMmkzj1Ofc7xnSUpS7b492vXk4z6fb/vcgCuu+sYGmDYdrNTPF23UH2aOUnJMlG6a3L6n2Y6yKt24cL2qGjrfnuH+VTuUHhut83P6akx6kv5y3Jh2y7y8da/uX+27x9fQlATdMcN36WXb536zZFO7AJMkZSXE6rLRA3SZn+19P69IdyzdrPqmnl03s7WsUrd/uUm/nDxSSdFRunb0kHbL5FVU69blG1Ttp8F/II9s2qlesdE6a2AfjUxN0m1Ht9+Pb+bt06ObfF+HG5QYr1v8lG+3fe7O1VvaBZhO6Ofu2TYlo5cen+29JUCzvVU1umRhoBal3dOGokrd9P7X+tu3Ris5Nko/nd7+vb+tpErXvLlOlfWd3/d3f7FDveNj9J0xfTU+M1n3ntr+vf/813v0ty92+FzH8LQE/eUk3+/9ts/97KNNfgNMZ+VkKjbKyaZ6ZTPNvYHujACTm+fvwl/j58BnvKFZIalUUqqchtj3elvIGNNHThCmM76UVCApW9K1xph7rLWduRy0Qk4fpjhJ0yS94GU7UyX91vXwVWvtg4FWaozJlvtnmyin6dmhNM319StrbZftwdTs/U826+RvP6BrLp+ub80eqX59UlRX36gd+Qf0xrvr9fizy1RTE9qPYa3VT37zmt56f4O+e+EUTRo/QOlpCTpwsEqr1+3SUy+u0MJFvmNx/35iibZuL9LMaUM0dmRfZWUmKSM9USbCqLSsRrnbi7R05U4teH2VduZ3dMLBnm/s9PG6+V8/0+JXP9HGZRtUWliqyOhIZfTP0ITjJ2vG3OMVE2Aa6UAiIiJ04Y8u1fjjJmnZW5+rYHO+KssqlJiSpOyRA3XsWTP9BoWaJaYm6fp//FDL31qiVQu/1P78faqrqVNK7xTlTB6pWefNVp8hR2b2sNkXnaQx08fpi7eWaPvaXBXvLlRddZ2i46LVKytdQ8YN1bQzZijbYya5nipl4mTl/PK3Kl74gcrXrVF9yUGZyCjFZmYp5egp6n3iSYqICW1GPxMRoewr5in1qKN1YNGnqt65Q42VFYpMTFL84CFKP/4EJY87PGWR31Sf7jqg77z1pS4b1V/HD0hXn4RY1Tc2Kb+iRu/nFem5TbtVE6DRfSBW0u1fbNGH+UW6IKevxvVOVq/YaJXU1mt9cbkWbN2rxbsP/9/rXy/ZrClZqZqYmazspDj1io1WYnSkqusbtbeqTquLyvTfbfu0piiUiX+6lyX7D+rqz1bpgiH9ND0rXZlxMWqwVrsqq/XxnmK9smOPaptC3/93rdmqT/cU65xBfTSqV5JSo6NVWl+vTSUVej1vr5YVdtkuAj3WRzsP6MwXVmrexAGaMzhdfRNjVd/UpJ2lNXo7t1BPrNutmobQ9/2tH2/Wu9sKdcnYfpqQmay0+GgdrK7X2sJyPbthjz7JO7Kf1Zp7SzU0Wb22uXtmpnc5JDAhTIy1PftKUEe5ZjhrviTykLW2XSDJGHO9Wvchut1a+9s2y/xW0m2S5OrZ5Gu8EyUtdD2cY6392OO5eyXd6Hp4s7X2njavjZD0sqRzPb491Fq7o81y8yQ97u35Nj/Li5KusNZ6nSLEGJMi6Upr7f1envtY0gmSPrXWtmvgYoz5s6Sfyfl/Ntlau8bbGG1eM1dOJ35J+om19m4/y94i6U+SrrLWzu/AumMllUuKlnSHtTao9JcB42/jDfMNdf8b08O9CQijp3J9z+iEni33mzORFdro1YtWpd9k+XndcgInHCK518/u1iGaEbMf6jLnLFs+va5b/y4RHDKY3L6StE7SeEnXGWPS5DT93iMn0+dySRfKKVmbdZi35beSLpLUV9I/jDFTJD0tp+dRjqQfSpopaZmkY1yvCfaPyL8knSLpfNdYRxtjHnKts1RSiqTRchpzz5WTqdQuwCQnEHSCpGOMMcnW2pZLjMaYwZJucj18qSPBJRfPvlOHejqq2XKCS5L0yiFeNwAAAAAA30gEmFystdYYc4Wkj+Q0gf6O6+ZprZxgzGHtMGmtPWCMOV3S+3IaeV/hunmaL+kzuQNMQZW4uX7eiyXdI6fsb7j8N+L2la/6hJwMojg5wSrPBi/N32+Su0yuI3IlNcqZ7e5QB5iaWzyst9b66hkFAAAAAN1TBElDCA9yfz24Ag6T5WT37JRUL+mAnKyen0g6xlp7RObHtdauljRW0t2Stshpp1lDSQAAIABJREFUSF0kp6zuMmvtVXKyjJqVdmKMemvtDZImSbpPTgCtVE5wp1RO0+5H5WRute8A6KyjWE65nuQO3sgYc4ykS1wPX7TWrm/7Wn/bJal5irExrlnoQmaMiZP0bdfDB/wtCwAAAAAAOo4MpjastXmSrg+wjM+QsKsn0287MM7HCtB+zVpbJCew9RMfi4x3fS3w1qTb1ZNofge2Za3cpWydcY+kSyWdbIwZbK3daa1dphACmNZa39OSdN6FcoJyxWqdaQUAAAAAAEJABlM3ZYyJl7vJ99Jwbou19gs5WUyRkm4N57b44mqM/gvXw7ustRXh3B4AAAAAOCyM6To3fKOQwdRFGWOGS9pmvUzzZ4yJlPSgpAzXt/5zJLfNh1/IaQZ+lTHm99bagsM1kDHmGknHuR42z419jWtmPklaZK19pM3LLpJT5pcn6d7DtW0AAAAAAHwTEWDqun4tZ2a25yR9IafJdrycptffl3S0a7kPJL0Zli30YK3dZIz5HznNwgdJOmwBJjnBpe+1+d4stZ7dr22AKVLS7ZI+stZWH8ZtAwAAAADgG4cAU9c2Rk5QxJfFki7xluUUDtbaJ4/QOPMkzQvyNc8clo0BAAAAgK6EyjSECQGmrutPkjZLOlnSEEmZkqLlNKheIel5Sc9Za5vCtYEAAAAAAAASAaYuy1q7SdIfXTcAAAAAAIAuiwATAAAAAAA9RQQ1cgiPiHBvAAAAAAAAALo3MpgAAAAAAOgpDBlMCA8ymAAAAAAAABASAkwAAAAAAAAICSVyAAAAAAD0FFTIIUzIYAIAAAAAAEBICDABAAAAAAAgJJTIAQAAAADQU0RQI4fwIIMJAAAAAAAAISHABAAAAAAAgJBQIgcAAAAAQE9BhRzChAwmAAAAAAAAhIQAEwAAAAAAAEJCiRwAAAAAAD2ENdTIITzIYAIAAAAAAEBIyGACAAAAAKCniCCDCeFBBhMAAAAAAABCQoAJAAAAAAAAIaFEDgAAAACAnoIKOYQJGUwAAAAAAAAICQEmAAAAAAAAhIQSOQAAAAAAegpDjRzCgwwmAAAAAAAAhIQAEwAAAAAAAEJCiRwAAAAAAD1FBCVyCA8ymAAAAAAAABASMpgAAAAAAOgpSGBCmJDBBAAAAAAAgJAQYAIAAAAAAEBIKJEDAAAAAKCnMNTIITzIYAIAAAAAAEBICDABAAAAAAAgJJTIAQAAAADQU1AihzAhgwkAAAAAAAAhIcAEAAAAAACAkFAiBwAAAABAT0EaCcKEQw8AAAAAAAAhIcAEAAAAAACAkFAiBwAAAABAT8EscggTMpgAAAAAAAAQEjKYAAAAAADoKUhgQpiQwQQAAAAAAICQEGACAAAAAABASCiRAwAAAACgh7AR1MghPAgwAUAHzO5bH+5NQBgd1bsk3JuAMBkz7plwbwLC5OGP5oV7ExBGc08J9xYAQPdDiRwAAAD+P3t3HR7F1TZw+DdxI0GSIAkhuBPcHQq0SNHCC5TSUoO21KAuVN+6QykvBUqLtDgUWlyLOwSHKBIlCXGb74/ZZDdZT6AJfM99XVxsMjPnTHbO7sw8c85zhBBCCCFKRXowCSGEEEIIIYQQ9wpFhsiJsiE9mIQQQgghhBBCCCFEqUiASQghhBBCCCGEEEKUigyRE0IIIYQQQggh7hUyQk6UEenBJIQQQgghhBBCCCFKRXowCSGEEEIIIYQQ9woH6cIkyob0YBJCCCGEEEIIIYQQpSIBJiGEEEIIIYQQQghRKjJETgghhBBCCCGEuFcoMkROlA3pwSSEEEIIIYQQQgghSkUCTEIIIYQQQgghhBCiVGSInBBCCCGEEEIIca+QEXKijEgPJiGEEEIIIYQQQghRKhJgEkIIIYQQQgghhBClIkPkhBBCCCGEEEKIe4WDjJETZUN6MAkhhBBCCCGEEEKIUpEAkxBCCCGEEEIIIYQoFRkiJ4QQQgghhBBC3CtkiJwoI9KDSQghhBBCCCGEEEKUivRgEkIIIYQQQggh7hGqdGASZUR6MAkhhBBCCCGEEEKIUpEAkxBCCCGEEEIIIYQoFRkiJ4QQQgghhBBC3CskybcoI9KDSQghhBBCCCGEEEKUigSYhBBCCCGEEEIIIUSpyBA5IYQQQgghhBDiXqHIEDlRNqQHkxBCCCGEEEIIIYQoFQkwCSGEEEIIIYQQQohSkSFyQgghhBBCCCHEvUJmkRNlRHowCSGEEEIIIYQQQohSkR5MQgghhBBCCCHEvUK6kYgyIk1PCCGEEEIIIYQQQpSKBJiEEEIIIYQQQgghRKnIEDkhhBBCCCGEEOJeoUiSb1E2pAeTEEIIIYQQQgghhCgVCTAJIYQQQgghhBBCiFKRIXJCCCGEEEIIIcS9wkGGyImyIT2YhBBCCCGEEEIIIUSpSIBJCCGEEEIIIYQQQpSKDJETQgghhBBCCCHuEarMIifKiPRgEkIIIYQQQgghhBClIgEmIYQQQgghhBBCCFEqMkROCCGEEEIIIYS4V0g3ElFGpOkJIYQQQgghhBBCiFKRHkxCCCGEEEIIIcS9wkGSfIuyIT2YhBBCCCGEEEIIIUSpSIBJCCGEEEIIIYQQQpSKDJETQgghhBBCCCHuFYoMkRNlQ3owCSGEEEIIIYQQQohSkR5MQtwFAqr7MGlcR/p0r0+Naj5k5eQSEXWTdRtDWbDkIJmZObelnl5d6zFuVFtaNq1B5cqeJCamcTz0GouWHWb7nksWt+3YthZtWwbRsnkAdWpVoXIlD3y83cjMzOXq9WQOHYtk8YojnDpz3WI59ev40adHAzq1rUXDev74+Xqh5qvEJaRx7FQ0y9eeYNvui7fl7y1vrl+7ybLFe9i7+yyxN5JwdnEioGYV+vQLYcTozri5u9y2uvbtOcea5fs5GxpN0s1UKlbyonHTQB4c2ZFOXRvZVEZubh7rVh5k04ZjRITHkp6eha+fN+061GfU2K7UqVfN5v1JupnGn6sPsntHKFejEriVkoFPRU/8q/rQsk0devRpRvOQYJvLy8/P56lHZhJ6MrLwd3tPfG7z9v+2mOuJrF66h4N7zhIXox37GoFV6N43hMEPdcHN7fYd+0P/nGXDqgNcOBNF8s1UfCp50aBJTR4Y1oF2XRrbVEZebh5/rT7Atr+PER0eS0ZGFlV8fWjVvj4PjulKcF3bj72hdcv38sMnKwt/fvnd0fQb3M7kujHXEzmy/wLnQ6O4cuEaSYm3SL6ZhoqKd0VP6jUIoFvfEHr2b4mTk2OJ9qcsBQX4MuXRAQzo3ZLAGlXIys4lLCKGFX/uZ/Yvm8jIzL4t9fTrGcKksX1oE1IH38rexCemcOTEFX5evJVNO05Y3LZrh8Z0bNuAdiF1qVenOlUqVaCSjycZmdlEXYtn76HzLFi6nWOnwmzenyqVKvDI6J4M6teG2kFVqeTjSWJSKtHXEthz4Cxr/j7EgaN39zkgKTaRQ2t3culQKClxSTg6O1Gpui9NurWi7cBuON+mz/ulw2c49vderl2IID05FQ8fL2o0qEWrAZ2p17aJTWXk5+VxbOM+Tu84TEJUDNmZ2XhV9qZ2y4a0H9IDv1rVLW4fF3mdS4fOEHHqEnER10m9mYLioOBZsQIBDYNp0bsd9do1tVjGhwOn2vw3F3hr/Xd2b1MWrl9L5PdFO/lnVygxMUm4ODsRUNOXvv1bMWpMt9t63t+7+wyrlu/l7OkIbt5MpVIlLxo3q8WwkZ3p3M229pCbm8ealfv4e/1hIsJiyEjPxtfPm/YdG/LQuB7UrWe+Pbz35m+sX3vQrn1+54NxDBraweSyxIRbrPxjD/v+OUtEWCxp6Zm4u7sSGFiFth0bMnJ0V2oEVLGrPiFE6Smqqpb1Pghx1who9u6//oG5r0cDvvtkBN4V3EwuvxwWz4QpiwiPSixxHYqi8NmMwYwd0cbsOouWH+HV99Zh7jvj8JaXqF7Nx2I9+fn5zF9ykHc/+dtkOd98NIxRD7a0ur/b91xkyvTlpNzKtLru7XLysPn35nbYs+MM7725hLRU039TUC0/vvjhMQKDfEtVT35+Pp++v4J1q8xf5A0e3p5X3x6Bg4P5Tq5JN9N4+ZmfORsaZXK5i4sTL70+lCHDTV8YGtq26QSff7SS5KR0s+t069WUT7+ZaLWsAsuX/MNXn6wu8rvSBJhu5dy5rub7d4Xy6dtLSE8zfewDg/x4/9tJBNQs/bH/9qPl/L3G/LEfMLQDz79h+dgnJ6Xx1tS5XDhj+tg7uzjxzCvDuN/MTYE5CXHJPD7y8yLvg6UA04JZf7Fk3lar5dauV50ZX06kWglvNBo3XVyi7Urjgb6tmffNM/h4e5hcfuHyNYZN/IwrETElrkNRFGZ+8jiP/qe32XXmLdnGs6/NNfu9f+nADwRUt/y+5ufnM/uXTUybsdBsOQWGD+zAtx9NwrdyBbPrrNt4iIee+MpiObfLnG0Tb3uZFw6cYs0Xv5KVbvrzXjnAnzEznqJyDb8S16Hm57P++6Uc37Tf7Dot+3di4LOjUSx81tOTU1k6YzbXLkSaXO7o7MSAySNp1b+zyeVrvvqNU1utBxTqtG7E8Fcn4uZlur3bG2CqHODPlDlv2bWNKUOCSl2ERbt3nOKd1381f94P9ufrmU9RM6jkbQG0z+DH7y1l7Urz7eHBEZ14/Z3RVs77qbwwZTZnTptuDy4uTkx7YyRDR5huDyUJMM399UVatKxt9Pv9e8/y1vRfSEkxf93g6ubM6++M5oHB7e2qs4CPS/+7eoxZ7Vf+LDc3+WGfDbqr30thH+nBdJdTFGUG8C6Aqqpl+uFVFMUFCAXqAaNUVV1elvtTnKIoM4EpwEJVVR8p6/2xRdNG1fjxi1G4u7uQmpbFD3N3s/dgGG6uzgy5vxnjR7Wlbm1fFs4ax/2jfyItvWRPtF99vk9hcOnUmWvMmv8PEVGJ1KpZmSmPdqF5kxqMG9mGxJtpfPKt6Ru69Iwctu+5yJET0YRFJBAbd4tbaVn4+3rRsnkg40e1wd+3ApPGdSQjI4f/frPFqIxq/tpNxc2kdNZvOsPeQ2FEX0siNy+fZo2q8+SETtSr40evrvVZ8MNYRkycb/WG5W5w/uxV3n71N7Iyc/DwcOXhSb1o3a4uWZm5bNl4nLUrDhAZEce0Z+fx85KpeHqaDjba4qfv/y4MLjVoFMC4iT0JqFmFq1EJLFqwgwvnrrJu5UEqVfLi6an3mywjLy+f11/8pTC41LNPM4aM6IC3twehpyJZ8L+t3ExM5bMPVuDn72OxR9Rf6w7z0Tt/kJ+v4uvnzbBRHWneMhhvHw/SUjO5fPEGu3eE2tUDJS4mmdnf/4WiKPhU9CDpZpod79C/69K5q3z8+m9kZeXg7uHK6Im9CWlbl+ysHHZsOs5fqw4QHRnHO8//zPe/Po9HKY79gll/FwaX6jUMYNSEnlQPrML16ASWLdzBpfNX+Xv1AXwqefLYMw+YLCMvL5/3pi0oDC516dWc+4d1oIK3B+dPR7J43haSElP57uPlWm82G3tEAcz8bBXpaZlUrOxFUmKq1fUVB4U6DWrQNCSYug0DqOxbgUqVK5CensX16AQ2rT3EmZPhhF26zmtT5jB76Uu4ubvavD9lJaRpML/OnIqHuyu3UjP4fOYadu07g5ubC6OGdGLS2D40qFuDVQteocugN0k1E5i05r1XRhcGl46dCuPr2eu4EhFDnVpVefHpwbRqXpvH/tOb+IQU3v3sd5NlpKVnsWnHCQ4cvcjlsOtcj03iVmoGVf0q0rZlXSaN7UM1/4pMeXQA6RlZvP3JUrP7M3ZEN+Z88TSOjg5cu5HI/37bwv4jF0i8mYp3BQ+aNarJoH5tycnNK9HfWx7cuBzFyk8XkJuVg4u7K51H3Udwi/rkZOdwZudRjm3cS+LVWJbO+IlJ30zD1aNkn/ftC/8sDC5VqxtIpxF9qFTdl5vX49m3Yis3LkdzfOM+PHy86P3IYJNl5Ofls+yjuYXBpUadQ2jVvxNuFTy5dj6cPb9vIi3pFht++J0KVSqa7BF1Kz4JAPcKHjTq0pJazetTsWplHBwduHE5mgOrt5MQHcuVo+f4/f05TPhkqsmA15MzX7P6N5/cepD9K7cB0KJPyQIK/6bzZ6N4Y/qCwvP+I4/fR5t29cnKymHzX0dZvWIvkeGxvPjMT/yydFqpzvs/fvdnYXCpYeNAHn60D4E1fYmOiufX+Vs5fzaaNSv2UamSF1OeN90e8vLymf7C3MLgUq++IQwd0QlvH09Onwxn/pxNJCbe4pP3f8ffv6LJHlGTpw5i/ETzAW2AlJQMJj/2Hfn5KkHB/iaDS1ej4pn+/FyydL33u/dqzgOD21GteiXiYpPZue0U69ceJCszh/ffWkRAoC8hrerY9Z4JIUpOejDd5cpZgGk68BlwGmih2ti4FEWpAVwAPHW/ekZV1Vl3YP+CgIuAM9BOVdUj9pbxb/dgWrHgUTq2DSYnJ48RE+dx5ER0keVPP9qFt1/uB8CXs7bz1awddtdRp1YVtq1+BmdnR46fvsqIR+aRmZVbuNzNzZkVCx6lZbMAcnLy6DnkB5O9pRwdHcjLyzdbTyUfd/5c+iTBNSuTk5NHq56fczM5o8g6X30wlCMnoli25jjZOcY3EW5uziz+6WE6tKkFwPNvrGT5WstDOG6XO9mDafKjszhxNAxHJwdmzZtsNBRs0YIdzPx6PQCPPX0fj0/uV6J6IsPjGDfiC/Jy82nUNJAf503B1c25cHlmRjZTJv3IudBoHJ0cWLJquskeU3+uOsjHM5YBMHx0J6a9MbzI8ujIeB79z7ekpWYSGOTL4lXTTAaIwq/EMHH0N2Rn59KuY33++/UjeHiYDgDk5OTi7GzbM5HXXljAru2hDBrajqvRCRw7fAUonz2YXn5iJqePheHo6MAX/5tCkxbBRZYvW7idud9px378E/fx8FP9S1RPdEQcTz70OXl5+TRoEsgXc54peuwzs5n+5CwunInG0dGB/y1/xWSPqY1rDvLVB38AMHhUZ559teixvxoVz7PjvyE9LZMaNX2Zu2w6jjYEB/fuOM170xbgU8mT0Y/0Zs436wDLPZjycvOslv3jl2tYvWQ3AJOnPcjQMd2s7ktx/3YPps3L3qFrh8bk5ORy36j3jYaDvfjUID5+cxwAH369nI++XmF3HfVqV+Pols9xdnbiyInL9B35HplZ+qHW7m4ubF72Dm1C6pKTk0vL3tNM9pay9r1fuaIXu9d9SJ1aVcnJySW4zWQSk4yDhw3r1WD/hv/i5ubCll0nGfPkV6SlZ5ks09nZkRwT54c74Xb3YPrllW+JCr2Mg6MDEz59nsDGRW+e963YytZ5awDoNnYAPcaZDvRaknA1lp8mf0x+Xj7V6wcx4dOpOLvqh1nlZGaz8LXvuH4xEgdHB56e/abJ3lLHN+3jz2+XANBmYFfun/JQkeWJ1+L4+fnPyUrPpFINPybPfgMHx6Kfx7VfLyKwUTAt+rbHydmZ4nIys1n8ziyiQrXv6CEvjS9xcGjei19y7UIEKArPzXsXH//KJSrH0J3swfTkI99y/OhlHJ0c+Gn+80aBlF/nb+X7r7S28PjkATw5xf62ABARHsuYYR+Tl5tP46ZB/LRgapEh15kZ2Tz16HecDY3E0cmBP9a8abLH1NpV+/jwHa09jBzdlVfeKtoeoiLjmDD6c9JSM6kZ5Mfva94o0dDk5b/v5rMPteuLp54dyCQT57zPP1rGsqXa9/r4R/sw9aUHjdb5fdFOvvxE+27s0r0pX898yu59kR5Mt4/0YPr/RZJ8i9tCUZQKwKu6Hz+0NbhUsD764BJAi9u2YwZUVY0EfgEU4IM7Ucft1LJZAB3bBgOwdNVRo+ASwE8L9nLhciwAk8Z1xMnJ/o/04w93xNlZuwh4++MNRYJLAJmZObz98QZAu7B/YkInk+VYuskAuJmcweLlRwrLaR0SaLTOS2+vZtHyIyaDSwX78voHfxb+PPA+23IGlGdnTkVy4qiWn2Tw0PYm8wz9Z0J3guv4A7Bs0R5yS3hz9fui3eTlasfppdeGFgkwALi5u/DSa0MByMvNZ+lvu0yWs3jhTgC8fTx49sVBRssDg3x5+LFegBZs2rXttMlyvvpkNdnZufj6efPxlxPMBpcAm4NLO7acYtf2UCpW8uSZFwfatE1ZOXc6ktPHtGPf/8H2RsElgBHjexBUWzv2q5fuIbeEvTdWLdlV+BmdMn2Y8bF3c2HK9GGA9lletdj0sV/+2w4AKvh48Pjzxsc+oKYvYx7VnlBfi4rnnx2mj72h9LRMZn6+CoAnnh9MBR/Tw2SKsyVwNfqRXoWvC97r8qxtSF26dtB6fS34fYfJXEPfzFnP2Yva+eCZxwaU6Cbu2UkPFH6mXnpnQZHgEkBGZjYvvbMA0D57zz1uvkebJYlJqcxfsq2wnPat65tc76v3J+Lm5sK1G4mMffobs8El4F8LLt1uV89HEBV6GYCW/ToZBZcAOg7rhW/NqgAcWrOTvBJ83g+u3kG+7rj0f3pEkeASgLObC/2fHgFovZQOrN5uspyC3kDuFTzo+9hQo+WVa/jRedR9ANy8Fse5vSeN1hny4jha39/FZHCpYF8MA1dn9xy39ueZlBAdowWXgFrN692W4NKdFHoqguNHtbYwZFgnk710xj3Si9p1tLbw+287S3zeX/rbjsLz/rTXRxjl83Nzd2Ha61p7yMvNZ8lC0+1h0QKtPXj7eDD1ZeP2UDPIj4mTtPYQFRnHjq3G7cEWG9YeArQhvA+Yebhw8kRY4TqPPWn6gdtDY7vjrTuXnDpR/r/77wilHP0T/69IgEncLpOBKkAksMzWjRRFCQEKhqsVjGO5IwEmnS91/9+vKMqdTapTSgP66IcV/b7qmMl1VFUt7MFT0cedzu2NL1Ks6ddLq+filTiOnjQOYgEcPRnNpStxAPTv1dDuOgqkpumH8Lm6lGyE7vlLsSQkak2lVs3yfRFpi13bQwtfD3zQ9MWUg4MDAwZpzfXWrQyOHLKccN0UVVXZraurVm1/mrWoZXK9Zi1qERSsPb3cvf2M0RDEyPA4wq9oQc0+/VqYTUA68MG2ha93mggwhYfFcviA9neM/E8XPL1K3v2/QFpqZmHepWdeHIhPRU8rW5StvTv170v/IeaPfd+B2nuZeiuDE4dLduz37dSOfc1gfxo3N33sGzevRWAt7djv2xlqdOyjI+KIDNOOffe+IWYTj983SH/s/9luPcA074cNxMck06JN3SLb3g4envqgZXb27ZkM4U4a3F//9//6xw6T66iqyuIV2tP7Sj5e9Ohkf6B9cD/t++TcxascPGa6TR08donzl64WWb8kbqXqe6q6uRoHGhrUrUHvrs0B+PGXTUXWv5ec36+/4Q7pazo/meLgQPPeWg+ezLQMwk9esKsOVVW5cOAUAFUCqxLYyPQ1QWCj2lQJ1ALXF/afMvqsJ1yNJT5K67HWuFsrs0nHDf+O8/tKFlDwD66Bh7f2XX3zRnyJyji59VDh67theNzObfr3arCZXHUODg6FuYNu3crg8CH72gJo7WHXdq09BNeuSvMQ0+2heUhtagVr7WHXduP2EBEeS9gVrT307d/K/Hnf4G8pSYApMiKW0yfDAWjdti7Va5i+xisIMvtU9MDLy93kOoqiEBCo9cItaXBOCFEyEmASpaYoiiPwrO7HJaqqWn6kWdQXaO1wC7BO97tmiqLckXi3qqrngaO6H5+7E3XcLu1aaX2z09KzOGlh5rX9h8ONtrFVUGAlqlf1NirHdD3a08Hq1XyoGVDRrnpAO9kP7q+fKeZSWMkuJIHCHld5+fY0tfLphK5Xhbu7Cw2bBJhdr1Vbff6AU8fD7a7n2tVE4uNStLLaWM5FULA8LjaZ61dvFll20qAXSMs2dc2WUcXXmyBdsOKkif3dvkk/tLFrD/0NclpqJlERcdy0IQdPcbO+3UB8XAot29QxG6wrT0KPa++lm7sL9RsZ9+gr0Ly1/niFngi3u54bVxNJ0B17w7JMadFaO6bxscnEXCs6FLZgf7X1zJdT2debQN0QizNWnhyfPRXB+hX7cHZ25DndU/TbacdGfY+ImrobqPKsczstgJ+alslRCzOv7d5/tvB1p3b2Bf2Dg/ypUU27cdt94KzFdQuWB1SvQq2a9icaVhSFEYP0vV7PX75mtM7wgfqb0vWb9SPXK3i5Uze4msWE33eTgmFgzm4uVK9f0+x6tZrXK3wdfca+nhdJNxK4lZBsVI4pQc205bcSkkmKKfpZL+hpBVCrmflyvCp7UzlA+1xFnbli174aKuipZSnBtDmqqnJquxZgcnZzoXEX6xOFlLXjR7X3yt3dhUZNzLeFVm317/3JEvTAvBadQFxsslFZprTWLY+NTeba1aLt4cSxy0brmeLr602Q7nv25HH720NB7yXAYmLugmBYclI6qRYC0lejtevMoLvgu1+Ie4kk+Ra3w31AwRlyka0bKYryANAXUIHpQMF4iwpAMHCn+rQuAloDoxRFeU5V1Vt3qJ5SqV9Hu5gPj0y0OAzBMFBTv7Z9s0w1qKu/Ybh0xXLAp0g9dfyIuppktXwHBwV/Xy+aNa7OUxM706ldMAC79l3mwuU4u/a1QNNG1Qpn1LO2z3eDCF2PkMCgKhaHutSqrb9ACtdtY4/wy/r8KYZlWa8rhhqB+qeIBU8xtfUs33AG1fYjMiKO2BvJZKRn4+6hf+p5+pSWKNTJyZFatf3Z/8955v20mdMnIgrXqVqtIv0HtebhR3tZ7eF06kQ4q5ftx8nJkelvDre4bnkRpTuONWr6WhzuZRgYiQyzf9awCINtrAVZitYVW2TmNXvKCQz2JzoyjriYZDIzskwm187NzePbj5aTn68yemLP23YTcCslnZhrN9m64Qhr//gH0ILSg8zMbFSeNKynBZkvh9+w+L1vGKhpVM98YNqUxvX1618wEfCN7WsQAAAgAElEQVQxdOFS0Xoioqx/bzs4KFTzq0hIs2BeeHIQ3TpqQ/627j7F2QvGvWTbt9KGzWVn53L+0lX6dm/Bmy+MoGPbBoXrRF2NZ/HK3Xz547q7todTQtQNACpX9zPKVWSoSmDVwtfxum1sZbh+QQ8lc3wN6kmIukGlavrPenyk/rNuuD+my/En8WosKfFJZGdm4eJmXyL9G5ejCmfUKxgeaI+IkxdJidMehDTqHILLXZDIPzxMO06BQX4Wz/vBtfXvR9gV+9oCwBWDbYKtnvf1dYVfuUFAoL49hBlcPxjukynBwf5EhscScyOJjPQs3C0MfTekqip/rdcCTG7uLvTuZz5QOHxUF7ZvOYGqqiz432aefXGI0TrLluwiJVmbYW74Q11s2od7jeogY9NE2ZAAUxlTFKUZMBToBjQF/IAc4DqwF/hRVVXz84oal1cReBEYCdQCsoETwBxVVZdY2K4GMBXoB9QFPIBEIBYtafdGYKWqqikmNi8YQH9RVdVTNu6nI1CQcXeRqqrHFUUJNlilBXcuwLQCbaicB/Ag8NsdqqfEXF2cqFJZ6zJ+PcbUW66XnJJJWnoWnh6u1KjmY1c9Bb2XbKnn2o3kwtfW6rl6+j2zy06GXuOFN1bZuIfGpj7RvfD1ur+tD78pz7KycgpnOPPzt9wrzNvbA3d3FzIysom5YT24V1xsjP74+VW1fPz8q+n3JbZYXYbl+Fe1vM9VdctVVSU2NqnwqSPoA15eFdz4Y9Fuvv/yT6PtY24ksXDuNnZsOcU3s5+gWvVKJuvJzcnjk/eWo6oqYx/pQe269t+k/Nuys3JITtKOva+/5eNRwdsDN3cXMjOyiTd4/20VH6M/hn5W6jJsG3ExRY+9Yd2+Vo59QTmqqhIXk2wyILVs4Q7CLl2nekAVxk7qa7E8a76YsZTNfx42uczVzZlX3h9LdYMbpvLI1dUZvyrad/LVG8YTKRhKSk4jNS0TL083AqvbN1Q4oLr+fbh6PcHiutEGywNrWH7/MiLNXmJw9NQVnnjpR5PLCgJeSSlpPPvY/Xz6zsNG69QM8OXV54Yx9P72DB7/X6KuWd7v8iY3O4f0FO3zXsHX8mfHvYIHzm4u5GRmkxJv33e94freVurx9tMvTy5WT0qCPeXovpdVlVvxSVYDUsX98/vmwteNu7Wya1uAk9v0vV4KhheWZ4bnfWvnUG+f0p739dtYPV8bnPeL12VfOVp7UFWV2JikIoErS44dvsR1Xc+pnr1bWJw1r0PnRjz6RD/m/28TC+dtISoyjgED21K1eiUS4pLZse0U69ccAGDgkPYMHtrRpn0QQtweEmAqQ4qi9ARMZdNzAerp/k1QFOUTVVVft6G82sBmtABRAU+gJ9BTUZShwDhVVXOLbdcN+BPwpih/3b9mwBggXrdecQVZVG0OhAGPA02ATOBN3e9CDZa3ANbYUZ7NVFWNUBTlBlANuJ9yGGDy9NT39EhLz7awpiY9IwdPD1c8PEyPizdfj/7JkrV60jP0yz3trAcgPT2b97/YyO+rjplN4m3NA30bM0g3zO5E6FU2bLE8vKO8S0/TJ7J1t+E9ddNdaGbY0CaM6jJImmutnbgb5FdIL1aXYTnW9tkwT0PxfU5J0XohpKVm8sNX6/H0cuPp5wbQ674QvCq4ceXSDebO2sTe3WeJDI/jzZd/Zc6vz+LoaDyE4tf52wm7HEONgMo8+mTpAhX/liLvow1P3AsCTBkWkh+bY7iNm5WnyUWOWUbRujJ0vQygaBuxVk5mhnF7vRoVz+KftRvLZ14dhouJ3Dy3Q89+LZk0dVCRoGl5VcHghiotLdPCmrp10rPw8nSze/pyw3pS0yy3J8Nk214e9udJS0vP5LUPF7Hwjx1kZ+eaXKdSRS8AfCp48N+3xpGcks67ny1l5foDJN9Kp2nDmrz10kge6NOahvUCWDz7BXoMfYf8/HIzQZJVWRn642lLDxsXN1dyMrPJzrDv8264vrWeRM4Gy3OK1ZOdbvv+GiYRt3d/z/5znLP/aMNYq9erSaPOIXZtn5OZzTnd9hV8K1I7xHQS+fIk3eCzbWliiwLu7q6687793/2G1xjW6jI8DxX/7jfcZ2s9ktyKXD/Yvs/r1+kDhQOHWA8UTp46iDbt6jN/7ia2bznB9i1FZxRu2DiQR5/oR+/7yv+QyTtGejCJMiIBprLlhJbYej2wDTgHpKAFdZqi9SiqBbymKMoFVVXnWynvd6A2MBtYDiSjBWpeBRqg9TS6htbDCQBFUVyBpWjBpVvAj2hBr1i0QFdtoDMwzFSFiqIEog1nAzhkah0T21QACrq4fK+b3Q3gClrAyQ1obktZpXAQGAL0uMP1lIibq/6jacuMOQUX7m5u9n2k3Vxsryc7W7/ccP9M6T10JgCOjgq+Vbzo3L42D49qy9vT+lE32JcPv9pEbq59+ZPq1fHlqw+1mUsyMrKZ+vpKu7YvjwxvuArySlniojteWVn2JyvONtjGycny8TOcsa14XdlZtu+zi0H7ysosWk5B0CEnJw8HB4XPvp1Iq7b62HijJoF89t1Epj83n317znE2NIrtm0/Sd0DRi8WoiDh+mbsVgJdeN54Zr7wyfB+dbDj2BcekRMfejnbmbHDMsjOLBgQMy7G2z4blmNrn7z5eTnZWLt36tqBd50ZGy+01ccr9jByvfZ2npWURdvEaf60+wI5Nx4mLTebldx4iwMTU2+WJYQLs7BzTwRhDBUnL3c0kYDbH1Y56sgyOubmk7gXa9J0OgKOjA/6+PvTo3JTHx/fhv2+Oo0Gd6rzx8WKTsyB66m5YXV2dycvLZ+SkL9hjkBvq2KkwRj72BSvnT2dA71a0bVmP4QM7sHydPc+0ylauwftoy+yHjrrPe66diekN13ewMvOmo8HynGL15ObYvr+WyrEkPvIG677Wsio4uTrz4MsPY2/6zfP7TxYGtZr3aotSghxO/zZ7vkdB/11a6vO+lfZg+J2dWex8nWXHOcTFyne/KVlZOWzbrAUK/f19aNexgZUttByR61bv57SZvISXLlzjzzUHCK5TlTp1q9u0H0KI20MCTGXrOBCoqqqpfq8bFUX5Aa3H0H3Au4qiLFRV1VIUoB0wtthQuMOKoiwDdgMhwFRFUX5WVbVgbFEXoIbu9VhVVYv3UNoPLFEU5UW0IWXFGSa1MD3VmbHXgKpoQ/A+Lvilqqp5iqKcA1pyZ2eSAziCFmAKUBSlqqqq9ic2uYMy7biJB/0JPTPT+k1JkXrsumjQLzfcP1POXzLMERTDrr2XWbj0IMvnP8oTEzrRoJ4f45/+zeYn0FX9KvDrrPFU8HIjPz+fl95ZU+7zL8XFJBf20inO29sdv6o+RS7E7Akkupagt4dhD5HcXMvHL8fg5qJ4XS7Fgp+uruYv6A0vpIsHflxcnMjQBZk6d29cJLhUwMHBgWdeHMi+PecA2LrxhFGA6dMPVpCdlUvPvs3p3K2xxb/r3xIfm0xqSrrJZV7eHvj6+xR5H22Z4abgmJTo2NvRznIMjplLsYC1YTm5OXm4WDj2huUU3+dN6w5x/NAlPDzdmPzyg5Z33ka+/j5Fhho2DQnm/mEd+eHTlWxYuZ+pE7/js9mTqdughoVSylamwc2Yi5WbQQAXF+19zci0r0djlh31uBa56bRcz5li+ZW27j7FnIWb2LTsHZ57/AEaNwjkwQmfGH3vZ2bl4KULYmzYerRIcKmAqqq88fFiBvTWhlCNHNTprgowORm8j3kmgmzF5ek+704u9n3eDdfPtxI8zDNY7lysHsNgRF5uHk4u5j/rlsox51ZCMktmzNaCQ4rC4OfH4htUzaZtDZ3cerDwdYs+pmdj+zfFxiRxy8x3fwVvD/yrVjT6HrUm53ad9620h5wiweSidbm6lPC8b+M+79x2krRUrZfUgEHtrCZ7D7tyg+eemElsbDI+FT15fvpQuvVoRpUq3iQnp3Fg3zlmf7+ePTtDOXbkMl9+9wSt25X/3m1C3CskwFSGVFW1eIesqmq2oijT0QJRtdACL0csbPKnqTxLqqreUhTlSeAA2oxtT6Of9c3wjL7Lwr7kovWuKs5w6iOrmYd1PZ4KelB9ZCK4dhrt76ynKIq7qqp3Kpun4b7WAcpVgCktzb7haB7u2km8+HAm6/Xouy9bq8fD3b5he8Vdu5HCmx+t57fZD9Ojcz3+M7w1i5Zbas6ait7uLJ7zMEGB2rj+tz7+i7V/lf/cSz/98Bcb1pr++x4Y0oa3PhhTZAp1W4a9FfT6sWU4XXGG3eOttZMMgyFNxYfTGZaTkZ5t8QLScGhU8X328HQtrKdDJ/NPK+vUq4afvw9xscmcDY0qsuzPVQc5eugyHp6uvPjK7QlU3A4LZv1lNh/QfYPaMm3GmKLvow3DSvTH3v4EtobbZFoZslDkmBUbGuNuMEQqIyPb4rA2w3IMh0wk3Uzlf99oE4Y+Mrk/VfzsyxtnD0dHB6ZMH8qhf84RF5PE95+s4Jt55Xfy0FsGw1BsGfZW0PPHluF05urx8rTcnjwN2k5qun31AERfT+SFt+azZuFr9O3egoljejFv8bZi+5OBl+7v3brL/NTmZy9Ec/V6AgHVq9AmxPwMluWRq7v+eNoyjCw7U1vH3oTVhusXlGFOjsFy52L1uHgU3V9Lga6cLP1n3Zb9zbiVxuK3Z5Gsm7luwNMjaNqjjdXtiruVmEzY8QsA1GgQVKIE4bfbj9/9yfq1B00uGzikPe9+NB4Pg8+2LUPICs4PJfnuN7zGsFaX4Xmo+He/4T5npGfZfN63ZQggFJs9boj1GWBnvPEbsbHJuLm7MOeX56ldR38rU8XXmwcGt6d9x4aMH/UZiQm3ePu1haz6653CoPz/G3dmQu5yTVGUWmgjgAaiTUCVBVwG/gBmqqpqOgJsfz33A0+ide7wA+LQRvLMUVX1LyvbVgUGA73R7nmD0EYNJaDlTV4J/HoH74HvOAkwlSO64WpVAS+0QBCA4bdDCJYDTGaH0KmqelBRlFC0oXeGSUquG7x+FPjWnn1G+1AVuGl2Lb2PAXcgHPjBxPKCPEyOaDmarEcgSsYwg6r9j83usKzsXBJvplG5kmeRRNym+Hi7Fd4EGCbitoVhYm9r9Rgm9ra3ngI7914mIyMbd3cXBvZrYjXA5Onhwm8/jadRfe3C8bPvtvLLUtMXb3cjV1dnfCp6kJyUTlys5QSeKSnphQGZqiXIJ+NfJHmz5eNnmNi7eO4aw3JiY5KoWMnTbDkxusSgiqLg71+8nIokxN8yWYfRvlfTAkwFiVEL/DZ/BwCt2tTh+FHTcwLcTEwtfL35L60Lvru7C117NrFY553m4uqMt48HKcnpxMdaPh63UtILL9p9rSRoN8UwIXeclbriiiSDL3pcDOuOj0nCp6L5Y19QjqIoRRKH/736ACnJ6XhVcMfbx5MdG407vp47HVnkdcET/5bt6lHRzinrnZ2daNu5IX+tOsDZkxHExyZbTapeVrKycohPvIVv5QoEVLOcuLuij2dhUCb6uuWE4MUZJvY2TPhtSqDB8ugSJtbesusk6RlZeLi7MuyBDkYBpqvXEqjurz1AsPa3RF/TAky+draDsubk4oy7tycZKWncspK4O+NWOjm63mLWEmwXZ7i+tQThKXH65T7F6vGuUrQcDx8vC+XoLv0UxWoC86z0TJa88yNxEdqlZ4+HB9J2UHeL25hzevth1HxtqP3dkNy7gHbe9yQ5Ka1I8mxTUpJLe943mLDDSl2Gib2L11W8nIqVzLeHmBtae1AUxWpCcICE+BQO7NN6KTdqUtPqcLYL569yNlQ7Rwx4oG2R4JIhXz8fHhrbndnfrycuNpl9e87So/edHhwhypKiKIPR8uoa3tB4AG11/x5XFGWgqqqXSlGHAzAHmFRsUYDu31BFUeYCT6mqapQLRFGUJ9DS0ZgaNlJN968/ME1RlJGqqpp/6lKOSYCpjCmK4okWaR2DFvyxNE7J2hz01nIgHdTV0UBRFBdVVbOBPWi5j+oA3yiKMg5Yhdab6ZBuHUsMr4ItBpgURWkNjNf9+KaZsosn+jaKQCiKMh5t1r02aLmaXIBHVVVdYGVfDRnuq/m7pDJ04XIcHdt6EhxUGUdHB7NTVterrW8WF8PsGzZ24bJ+yul6dSw3ryL1XLE+VbUp+fkqSSmZuLu7EFjd8oWHm6sTC2aOpVVzrZPcrHl7+HaO2U525c5bH4zhrQ/GWF0vuE5VThwNIzoygdzcPLNTFkeE6TvdWZtu2GQ9BjOrGZZlva6iT4Vr1zEsJ44GjcxPkR4ZprUT/2o+Rj2YatetWtgjKd/CdOzacm1ITfEE3wX5Y/7ZdZZ/dllP+P7ua1q+j2o1Kt3RANO0GWOYNsP6sQ+qU5XTx8K4FhVPXm6e2VwnUeH64xFk44w8hgxn8TEsy3pdRdtZ8XLqNjR/7KN15fhV9cHN4Gl4wTCM1FsZfPr2Yqv7vn7FPtav2AfAZ7OftjvABOBTUX8zFHvjZrkNMAGcuxhN1w6NqRtczeL3fsO6+qF+5y5dtauOsxf16zeoa3nIYIN6Ja+nQH6+SlJyGh7urgQFGJ9nzlyIpm3LegA4WklKW/AdkGvlO6M88q1ZjajQyyRejyM/Lw8HR9Of94TomCLb2FuHvhzLn/V4g3qqFKvHN0j/WU+IjqFa3UDMidfV4+1b0WJi8ZysbH5/fw7XLmjBgU4j+tBtTH+L+2jJqe3aJa+jk2OJekDdCe9+NJ53Pxpvdb3adapx/OhloiPjLJ73w8NiimxjrzoG24RbPe/r6wouVpfhzKzhYTE0aGS+PYTrvvurVqtoU6+rv9cfLvyesyW5d/iVG4WvGzYxvx8AjZvU1G8XFlM+k66K20JRlFZouYjdgVTgv2g5hd3R7rGfQMtHvF5RlLaqqt4qYVUfoQ8uHQM+Q+shVRd4BWiFNpFVHPCGie2rot3rZ6OlwdkEnEXLg1xXt5/9gPrAFkVRWquqGm2inHKt/GfDu4cpihIMnELr1dMCy8El0D4kllgbolZw9lCASgCqquagddMruDtrp9ufPUCSoih/K4oyVlEUc/tm2Gfe2v59qav7CGBuPuPiASZTPkTrlliLoj2w7GG4r/ZnTvwXHDqmXYR5erjSoon5Jzod2wYbbWOryOibhb2YDMsxpUPbWgBcv5FM1FX7p8sFcHZypHIlLZWXpWF2Tk4OzPl6NJ3b1QZg4e+H+OirzWbXv5uFtNL+xoyMbM6fMX8Dd+zwlcLXzVsG211PjYDK+PppD3WOHblicd2C3kB+/j5UD6hUZFkL3f4CHD9y2WwZCfEpREZoAaYWJva3ZZs6ha+vRlvutXDtakLh/txLmrbU3svMjGwunjN//XDqqP54NQ0JtrueagGVqaI79oZlmazrmLbc19+HqjWK9qIp2F+AkxbKSYxPITpSO/ZNQmqbXe/fkhCn75VVkmEm/6a9h84D4OXpRuvm5t+7bh31+cb26baxVXhkLNduaJ+5bh0s5y3r2l5bfvV6AhFRJXuw4OzsSJVKWmDQ1DC7PQfOFb6uHWQ5gBqsW16w/3eTmk2177yczGyuX4wyu17EKf3D9cAm9n1+KlarQoUqPkblmBJ5Wvv+rlDFh4pVi37WazbVD0GMOG2+nNTEFBKvapeeNZvUMbteXm4eyz+eR6Run1o/0IU+j5V8WPONy9HEhl0DoF67pnh4l8vnhGa1bK29VxkZ2Zw7Y74tHDusf+8Nz722qhFYpfC8aViWybp053N/fx9qBBRtDyGt9O3hqIVy4uNTiNQFmFq0NN8eDG3QzR7n5ORI/wesBwoNHzTlWZksxnBSAXNBvHuaQzn6d+d9i3Zvlwv0U1X1Y1VV96mquk1V1SfRgj+gBZleLkkFiqI0AKbpfjwMdFFVdamqqodUVV0KdNX9HmC6oij1TBSTBnyKloN5hKqqP6mquktV1WOqqi5XVbU/8JVuXT/g/ZLsa1mTAFPZ+hVtljYVmIcWsayJNouag6qqCkWDTtYG05Zozl5VVc+g9QQaptuPgrOHO1o3vUXAAUVRTHWbMLziNNunX1GUIUBP3Y9tgHxFUdTi/9CiwAXMBZgeB4JVVfVDmzGvJAz3tWTRkjvs7636i+7Rw1qZXEdRFEYO0ab1TUrOYO9B08OELNm0Xaunfh0/Wrcw/TSodYtA6tfRRkNu3G7fzYyh/r0bFiaLPHfRdNorBweFmZ+OpE93LS/P8rXHef2D4rnn7x3dezUtfL1+jelOiPn5+fz9p9aZr0IFd9q0M3XOskxRFLrp6ooIi+X0yQiT650+GVHYg6lbryZGs/oEBfsRXEf7Kti66aTJKei1v0Wfg6hH72ZGy7v1bFJ4wbdrm/mcWscOXyY5SRsyH9K66AX2yr/eYO+Jzy3+a9VWf5Fb8LuVf5l6qPTv69xD/75sXGv+2G9Zr72XXhXcCWlbsmPfqYd27KPCYzl7yvSxP3sqorAHU6ceTY2OfWAtv8JeTbu2nDCb9Nkw/1SXXkWP/cNP9Wfj4S8s/nv53dGF67/87ujC35fkb8/MyOLQXu07ztXVmRqB1joCl611G/Xv3cMP9TS5jqIojB3RDYCbyans3HfG/no2ad8njeoH0L6V6fe1fat6NKofUGT9khjcr21hzpbQc8Y30+s3HylMDDxkQFuz5XTt0LhwaNw/B8+ZXa+8athRf0lzYssBk+uo+fmc2qYNA3fzdCe4hfXZtAwpikKDDtokvAnRMUSfM31NEH0urLCnVIOOzY0+61UC/AtzGp3dfaxwyF5xhn9Hw06mL9ny8/JZ/dkvXD6stdPmvdtx/5SH7PirjBW8RwAt+tw9w+MKGA7VWrfadFvIz89nwzrt76xQwZ227exrC6C1h+69tPYQHhbDqROm28OpE2GFvaW69zJuD7WC/Qt7L2/ZeMz8ed/gb+nZx/pwtEsXrnHxvPZgrXO3JhaH3hWoEaAftnv8qPmHXABHD+uXG24n7i2KorRHG9kC8LOqqvtMrPYl+s4UzyuKUpKEXC+gH/31XPEcSbr8TgWJHp0wmLXdYJ2vVVV9TVVVS09sXkffgWK4bljeXeWu2+F7haIojdAinQAfq6o6SVXVzaqqRquqmqWqakGwyHIihqKsjZ0oWK5SbDibqqp5qqqu1u1HfbSZ5R5DP0StDfCTiTINPyCVTCxHURQntC6E9mpu6peqqm5RVdX0HZLtDPfVvm4//5Ljp6+y/3A4AGOGtaZNiHHw56mJnWlQV7vh+3nRfnKLPc3p1C6Yq6ff4+rp9/j6w6Em65n76/7CpzwfvPEAbq5FR866uTrxwRsPANrsIXN/Nf7e7taxDsE1LTfV+nX8eP/1Bwp/Xr72hMn1Pn9vCIP6azfD6zeF8uJbqy2We7dr0jyoMHCybvVBTpmYcnfJwl2EX9Fu/EeN62pyauOjhy7TOWQ6nUOm8+HbS03WNXpct8Knf199spqs4lMRZ+bw1Sfa++3o5MDocd2MygAYO0HraJ6SnM7Mr9cbLY+OiufXedsBCAzypbuJAJNPRU8GD9duDE4eDzcZXEtPz+Kbz9YW/jx0VEeT+3O3atQsiGa6p9Ib1xzkzMlwo3VW/LaTSF3Ab+iYriafwp44fIn+bafRv+00vphh+tgP+093HHTHftbnq0we+1mfrwK0J8TD/mP62I8c3xOAW8npzP3WOPB7LTqepfO1HDs1avrSpafxsb8dkpPS2L3VcmqC7Kwcvnz/D5J0ebi69mlhNJtheXP4xOXCWdQmju5Jh9bGMx+98ORAGtfXzgcz5/1d5Ck9aL2bMiKXkBG5hDlfPm2ynh9+3lC43VfvT8StWNJeN1dnvnp/IqDNYPjDz8Y5S3t1bUadWpYvOxrVD+CL9x4p/HnRit1G6yQmpTJ/qfZ90bldI8aPNM7J4+nhyufvTij8ee6irRbrLY8CGtYq7Bl0fNM+os8a3+zvX7Wd+CjtRr/dgz2Mhs2Gn7zIhwOn8uHAqaz96jeT9bQf2hNFNwvXxtkriiThBm2o2sbZKwBwcHSgw4M9TZbTcXhvQMsJtXX+GqPlidfj2LtM61lcqYYfjTobBxRUVWX990s4+4+W/65RlxAGvzDOKIBhj/y8fE7v0C5N3b09qdeuqZUtyp+mzWvRsrXWFtau2sfJ48ZtYdEv2wm7orWF0eN7mDzvHzl0kfbNp9K++VTee9N0exgzvmfhef+L/64wejCQmZnNF//V2oOjkwNjHu5pspxxE7X2kJKczvdfGbeH6Kg4FvystYeaQX42BZgME6LbMjwOoGHjQPx1vbK2bznBwf2mH3qeOxPFyj/2ANpEE23byyxy9zDDGxyT+Yh1+ZAW6n6sCPSypwJF+9Iq6HZ5TlVVk9OY6n5f0CgfVErwZadLIfOP7kcf4K6LjkoOprJjeEb83cJ65h/nGWsHmO9rqy0HuGgtt5KqqteB+Yqi/AbsB1oDg0zM7HbK4HUDtBnvinsKaIgW2HoG68nAJ6L1nPJTFKWaqqo3rKxfEgWPgrLQ99gqd9755C/W/DoJd3cXFs+ZwPf/283eg2G4uTrx4P3NGf+Q1jwuh8Xz04K9JarjSkQCPy7Yy3OPd6NlswBW/zqJWfP2EB51k+CalZjyWFeaN9HycMxe8A9hkcZDE9q1DuK32ePZcyCMnf9c4uyFGBKT0nFyciCwekW6d67LiMEhuOtu7pasPMo/JnpbvTOtP2OGtQbg7IUYvvvf7sKeU+acv2R18sJy74VXHuTpiTPJyszhxafnMuHx3rRuV5eszBy2/H2cNSu0p4JBtfz4z4SSJUQFrffR2Ed68Ou87ZwLjeapR2Yy/tGeBNSswtWoBH6bv4ML57SnieMe6UnNWqbf+/uHtOXP1Yc4eTycFb/vJSHhFkOGd8Db250zp6OYP2cLaamZODgovPjqg2a7pj8+uR97d58l5noSn7y3nDOno+jVtwVeFdy4ciDoi4sAACAASURBVOkGv83fXhhYG/ZQJxo3rWmynLvZ5JeH8tKkH8jKyuGNZ//HmEd7E9K2HllZOezceJwNq7RrmMAgP0aML3kGicBafox6uCe/L9jGhTPRvDTpB0Y90osagVW4Fp3Asl+2c0n3JHnkhJ4EBJk+9n0HtWXj2oOEnghn3bK93Ey4xf3DOuJVwZ3zoZEs/nkL6WnasZ8y7UGzeaVKKyM9iw9fXUiNmr507d2chk2D8PX3xtnZieSkNM6HRrFxzUGu64ZX+vr7MGnqwDuyL7fbtBkL2bZyBh7urqz77XU+m7mGXXtDcXNzYdSQTjw+Tpun48Lla3w7xzjAa4tLYTf4+qc/mf7Mg7QJqcu2le/x1ey1XImIpU4tf156egitdEP0vv7pTy6HG5+GO7dryNqFr7H9n9Ns2XmSU+ciSbyZipOjA0GBvvTp3oKxw7vh7qblX1uwdDs794YalQPw4VfLuL93S4IC/fjxsydp27IeqzYcIOVWOk0a1uTlyYMLg2o/LdzM0ZOWh3qWV/2fGs6C6d+Qm5XD4rdn0eWh+6jVoj65WTmE7jrKsb+183jlAH86DutdojqqBPjTaURv9i7bwvWLkfwy/Rs6jexLpeq+3Lwez77lW7hxWRuS23FEHyoHmM7p16JPB45vPkD0mSsc/nM3qTdTaNW/M25eHly7EMGepRvJSs9EcVDo/9QIkzmltvy8mhObtfOXX63qdHmoH/FRli/p/IMt5wW7cvQsaUla+pSm3dvcse+YO+3l14bz+IRvyMrMYepTs5j4xH20aVefrKwcNv91lFXLtbYQFOzPuEdK1hZA6300fmJvfvl5C2dDI3liwjdMeKwvATV9uRoVz8J5Wzh/VmsPD0/sQ1At0+1h4JAOrFt1gBPHrrBs6W4SElJ4cERnvL09CD0VwbyfNhae919+bYTVIWl5efls3KD12PT28aBrD9sChQ4ODkx5YTAz3viNvLx8Xpg8m2GjutCtZ1MqV65AcnI6B/ae448luwp7Wj0y6T4qeHvY+pbdO/7/zCJX0GEjDcuTQ+00eN0FLf+RrWqjdb4oXo65ehqiJf0OBuwfXgKG4/nzzK5VTkmAqewYvveWBo+bfvxo2iNoUxsaURSlHVDwKHmLrQWqqpqjKMpOtACTE1rU1zDAdBgtD5MbWgDrj2L1+gAzdD+uVlX1R2t1KooSiBZgAm2Y3J0IMBUE247p8lCVS6HnbjB52jK++2QE3hXceP2FvkbrXA6LZ8KURRZzGlnz6bdb8a3syX+Gt6Z5kxr8+IVx9/XFK47w6XfbTGytcXJypGeXevTsYn4YS25uHnMW7uO/35hugg/cp88H0rhBVTYus978A5q9a3Wd8q5h4wA++HQ87725hLTUTGZ/Z9xbIKiWH1/88JhN05db8tRzA7iZmMqfqw9x4dxV3nl1kdE6g4e158lnzSdfdXR04JNvJvLyMz9zNjSKHVtOsWPLqSLruLg48dLrQ+nUtZHZcipV9uLrWY8zfep8rkYlsOqPfaz6w7iH3KCh7XjxlZLn6yjP6jUK4I3/jufTt5eQnpbJ/JnGxz4wyI/3v51UZKrokpg4ZQBJialsXHuQS+ev8t83jJ94D3iwPRMnDzBbhqOjA+9++ShvTZ3LhTNR7Nl2ij3bih57ZxcnnnllGO26WM7vcztci4rnj1+2W1yncYtavPr+WKr4Wp4ps7w4ERrOw898x7xvnsHH24MPXjVOGH/h8jWGTfyM1DTjnEa2evez3/Gr4s3EMb1o1bw2v8583mid+Uu2MePzP0xsrXFycuS+HiHc1yPE7Dq5uXl8N3cDb39iLvUixCfeYsiET1gxbzp1g6vx1IT7eGrCfUbrLVi6nWkzfrHyl5Vf1erWZPirE1nzxa9kpWey/RfjXoCVA/wZM+MpXD1K/nnvNWEQaUmpnNi8nxuXo1n16QKjdVr260ivh80HXR0cHXjorcdZOmM21y5Ecu6fE5z7p2jPY0dnJwZMHkm9tqYnTTBcPy7iOj8//7nVfX9r/XcWl5/cpu/t2qKP9Snty6uGjWvy8ecTeef1X0lLzWSWiR6hQcH+fD3zqVKf9ydPHURiYirrVu3n/Nlo3py+wGidIcM78vRz5tuDo6MDn3/7OC9Mmc2Z05Fs23yCbZuLtgcXFyemvTGSzt2sT6JxcN854uO0HKD9BrTG2dn2W9IHBrcnMeEWs779k9zcPJYt2cWyJcaTwCiKwpjxPXjsyX42ly3uDN29nVUlTGhdcLFxSVXVXAvrGY6ttvcCxbBRWxujXbweuwJMuuF7nXQ/xqiqetclHZQAU9m5aPB6IlovoSIURZmMvjueLYYoivKQqqrFgzxe6Ie35Ru8RlGUbsB1c1M2KoriAoUTL6RSdEgcqqpmK4pyQLeOqf6tb6DNfqeiDzRZc8HgdXPsizBbpSiKK/r8Tre17Dth884L9B0+i8fHd6RP9wZUr+pNdk4e4VGJ/LkxlPlLDpKZWboYmaqqTHtnDRs2n2HcyDaENAugciUPEm+mc+L0VX5bdpjte8x39Prfwn1cCounc7tgmjSohr+fF76VPVEcFJJTMrkcFs/+IxEsX3uciChrHdj+f+raswkLl73EH4t2s2/3OWJjknB2diIgqAq972vByDFdcHN3sV6QFQ4ODrzx3kP07NucNSsOcPZ0FMlJafhU9KRxs5oMHdnRYlCoQMVKnvy08BnWrjzA5g3HCQ+LISMjG18/b9p2qM9DY7tSp571WW+C61Rl4R8vsWrZXrZvPkVUZDwZ6VlUquxF85bBDB3ZkTbt7c+9czfp2L0ps5e+xOolezi45yxxsdqxr1GzCt36hDBkdBfc3G7PsX/pnYfo2rs5G1bt58KZKFKS0vCu6EmDJjUZOLyjTUEhn4qefDPvWTasPsD2v48RFRZDZmY2VXx9aNm+HkPHdCO4rv0zHtnDv1pFvpz7DEf2n+d8aBSx129yM/EWGWlZuHu44letIg0a16Rb3xa07dSwVENyysKGLUdp3/9VnnlsAAN6tyKgemWyc3K5Eh7DyvUH+HHBRjLM5MWxlaqqTH5lDqv/Osiksb1pE1KXKpUqkHDzFkdOXGbuoq1s2mF6KDPA93M3cOHSNbp1akKLxkFU86+En683DorCzZQ0Lly6xp6D51i0YhdhEdZ7mp6/dI32/V/jyYf7MuyBDtStXQ0vDzfiElLYd/g8cxdtZVcJ8k2VNw06NOeJma9xaM0OLh46w634JBydHKlUw4/GXVvSblB3nEv5eVccHBj8wlgadwnh6N97uX4xkvTkVDx8vKheP4jW93cxGxQy5OHjxcQvXuTY33s5vfMI8VEx5GRm41XZm9ohDWn/YA/8almeVv52ykrP4MIBLaDtW7MqNRrU+tfqvhO69WzO4hWvsfS3Hfyz+4x23ndyJDDIjz79WvLQf7rftvP+2++PpXffEFYt38vZ0EiSbqZSsZIXjZsGMXxUF5uCQhUreTH31xdZvWIvGzccIfxKDJm68367Dg0ZPb4HdevZ1h4KknsDPGDj8DhD4yf2oVuPZqxavpejhy8RHaVdO7i5OVOtemVCWtfhweGdaNw0yO6y7xlWZuX8l1kaYWPIrp1WFMUN/SzrFoNTqqreVBQlDa1jh71d4g0DZNaCYIZ/a0m63j+J/m9aVoLty5yiT/Uj/k26MZkn0fcq+gMt6fd1tEY8HhiJNgazi26d91RVnVGsnBlAQReOw2jTI/4ELAdS0AIpr6J11QP4XlXVqcW2fxvYDazX7VMcWoLvBmg9qAq++b9VVfUFE3/Li2gZ7zMB/4KpHxVFqYUWxXUDlquqOsrG96YR+kRsC1VVfcTCuq+hTUX5qKqqC2ws/z70gaVWqqqaGtZnUkCzd+UD8//UycPlYxpkUTZu5ZSrCzXxL2rcdHFZ74IoI3O2TSzrXRBlaMj/49iEAB+X/nf1iT/4vY3l5p4lYob5ntGGdBNc2UxRFD/0s6j/rqqqcZffouvHAP7AaVVVTeb6NbPddPT5hO9XVfVvC+veD2zQ/ThNVdUv7ainDnAULfdSKtBQVdVrtm5fXkgPpjKiqqqqKMrDwDa0hNMP6f4ZOgWMAmxtWA8BW4Epun/FrQBeMvF7B7QeSJYSfKxBy2pvykK0II8b2kx0BUnUCn6Xj+29l0CbSS4PbQY961kC7TdW93+oPcElIYQQQgghhBB2uVNJNA3Hj9rSrTdL97/7Hawny+C1zfUoiuKBlurGR/er5+7G4BJIgKlMqap6XFGUlmiBm/vRkofdQks6/QcwU1XVTFu79quqGqYoShtgGlqgpxaQA5wA5qiqapxsBb5A67XUF633Uw20yC5ouY8OovUiMptJVFXVBEVRVgL/QQveLNRNGVkQRV6mqqrpzJ6my8tR/o+9+w6Potr/OP6e9ISQUJJQAiH03rsI0mwgIBbELl4U2/XavXaxXn/Wa9erYkfFCiIqCAgiRaRK7yGhJCQQSG/z+2M22U22ZZOQkPh5PU8eEubMOZOdzc6Z73zPOYaxB2gHdDYMI8DLmNpys6VSXmD78fWqqFNEREREROSUcQoNkavg3Erl4TgRYXnGkxZPnp3tsVTl2nGcoLtc7dhWXJ8FFE9o+EZ5R+acihRgqmGmaSYAN3op4/YTwjZk7lGHn48CD9i+ytN+Bla01OXk4D74L1aAabRhGK1M01yFlRlVIaZpnqz1RC8CIoBU7JlWIiIiIiIiUnuccPg+vBzlixfWyjiJ7Tgu3uW1Hdu0Oe8DY2z/9QVwiy8Hd6qpcABAxJFpmiuxglT+uB9KV6MMw/DDmnQc4FlbcE1ERERERERqEdM0c7CSBqD0RNxODMNoiD34U95Jx4s5ZmB5WxHPcThgedp5Dbjc9v084ArTNIt8OLZTjjKYpCrdD4wHphiG8cTJSoc0DGMqcLrtx+IJ2qYahjHc9v1vpmm+42LXi7GWi0wAPK+DKyIiIiIiUguZtWz11ErYDAwF2nmZVsVxieQtbsp4asNVPZVqxzCMZ7CPZFoCXGiaZuWWBj8FKMAkVcY0zW2GYVwLtAXi8L6MY0WdDpRdWW4I9tX2AFwFmPyB6cBC0zR9HXsrIiIiIiIip47fsAJM9YC+wEo35RwXs1rmYxt7sBbdao7nRbEAhtn+TQL2uitkGMaDwD22H/8Azqsr96cKMEmVMk3zo2po4xrgmgrsp7WmRURERERE6oZvsU/PMgUXASbbNClX2X48BizypQHb6u/fYWUbdTIMY5BpmitctDMIewbTd6Zpmq7qMwzjX8Djth83AueYpnnCVdnaSHMwiYiIiIiIiNQVfqfQ10lkW1hqqe3HfxiGMdhFsTuxpkkB+G/ZYWiGYQw3DMO0fb3vpqmXgELb968YhhFapo5Q4BXbjwW28k4Mw5gCvGj7cTtwpmmaaW7arJWUwSQiIiIiIiIitdG/sIa9hQI/G4bxFFaWUigwGbjeVm478HxFGjBNc7thGM8C/wb6Actscyjtwpoe5l6gt634s6Zp7ihbh2EY5wP/AwzguO24ow3DiPbQ9B7TNDMrcsw1RQEmEREREREREal1TNNcaxjGJcDHQATwlIti24GxlRyK9gAQA1yLFUz6zEWZd4EH3ex/PtacwNiOc1452hwBLPbpKGuYAkwiIiIiIiIidcXfZxU5AEzTnGMYRg+srKCxQAsgD9gJzAJeNU0zq5JtFGENw/sKKyuqPxAFHMGaqPst0zTLEzSq0xRgEhEREREREZFayzTNfcAdti9f9luMNWytvOV/AH7w6eCo+EJVtY0CTCIiIiIiIiJ1hd/fK4NJTh1aRU5ERERERERERCpFASYREREREREREakUDZETERERERERqSs0RE5qiDKYRERERERERESkUhRgEhERERERERGRStEQOREREREREZG6QiPkpIYog0lERERERERERCpFASYREREREREREakUDZETERERERERqSNMrSInNUQZTCIiIiIiIiIiUinKYBIRERERERGpKwxlMEnNUAaTiIiIiIiIiIhUigJMIiIiIiIiIiJSKRoiJyIiIiIiIlJXaJJvqSHKYBIRERERERERkUpRgElERERERERERCpFQ+RERERERERE6gqNkJMaogwmERERERERERGpFAWYRERERERERESkUjRETkRERERERKSO8FMaidQQvfVERERERERERKRSFGASEREREREREZFK0RA5ERERERERkTrC0CpyUkOUwSQiIiIiIiIiIpWiDCYRERERERGROkIZTFJTlMEkIiIiIiIiIiKVogCTiIiIiIiIiIhUiobIiYiIiIiIiNQRhsbISQ1RBpOIiIiIiIiIiFSKAkwiIiIiIiIiIlIpGiInIiIiIiIiUkdohJzUFGUwiYiIiIiIiIhIpSjAJCIiIiIiIiIilaIhciIiIiIiIiJ1hIbISU1RBpOIiIiIiIiIiFSKAkwiIiIiIiIiIlIpGiInIiIiIiIiUkcYSiORGqK3noiIiIiIiIiIVIoymERERERERETqCE3yLTVFGUwiIiIiIiIiIlIpCjCJiIiIiIiIiEilaIiciA8WLetf04cgNeR/24Jr+hCkBr3zxOGaPgSpIS1aDa/pQ5Aa0qNRfk0fgtSowJo+AJEK89MQOakhymASEREREREREZFKUYBJREREREREREQqRUPkREREREREROoIrSInNUUZTCIiIiIiIiIiUikKMImIiIiIiIiISKVoiJyIiIiIiIhIHaEhclJTlMEkIiIiIiIiIiKVogwmERERERERkTrCUAqT1BBlMImIiIiIiIiISKUowCQiIiIiIiIiIpWiIXIiIiIiIiIidYShNBKpIXrriYiIiIiIiIhIpSjAJCIiIiIiIiIilaIhciIiIiIiIiJ1hBaRk5qiDCYREREREREREakUBZhERERERERERKRSNEROREREREREpI7QEDmpKcpgEhERERERERGRSlGASUREREREREREKqVKh8gZhpFWlfW5YZqm2bga2hERERERERGpVTRETmpKVc/B1AAwgZP5ljZPYt0iIiIiIiIiIuKjkzHJd3mCS45BIk/ly1tORERERERE5G/PT3fOUkOqOsDUsBxlrgSeA4KAv4CPgOVAApAJ1ANaAoOBK4AeQC5wF/BxFR+viIiIiIiIiIhUUpUGmEzTTPe03TCMfwEvAIXALaZpvu6iWCpWsGkZ8JxhGDcAL9u+Ak3TfKkqj1lERERERERERCrnZAyRc8kwjG7As7YfbzVN883y7Gea5puGYZjAG8AzhmEsNE1zw8k6ThEREREREZHaSpN8S03xq8a2bsAKaCWUN7hUzDTNt4B9tv2nnYRjExERERERERGRCqrOANMorEm7l1Vw/2VYE32PrLIjEhERERERERGRSqu2IXJArO3f7Arun1OmHhERERERERFxoCFyUlOqM4Op0PZv5wru38n2b1EVHIuIiIiIiIiIiFSR6gww7cEa4jbIMIwevuxoKz8Ya4jdnpNwbCIiIiIiIiIiUkHVOURuNtALK8j0hWEYo0zTTPK2k2EYscAXtv1M4NuTepQiIiIiIiIitZThpzFyUjOqM4PpZeCI7fsOwEbDMO41DKO5q8KGYTQzDOMeYAPQ3vbfqcCrJ/1IRURERERERESk3Kotg8k0zTTDMK7AykAKBhoATwFPGYaRCCQAWUAYEAe0sO1aHH7NBa40TTO1uo5ZREREREREpDbRJN9SU6pziBymaf5sGMa5wAdYQaTit34L7AGlYo5/FvuBq03TXHzSD1JERERERERERHxSnUPkADBN81egK3AvsN3234aLL2zb7wG6KrgkIiIiIiIiInJqqtYMpmKmaWYCzwLPGobRDOgNRAPhQAaQAqw1TfNgTRyfiIiIiIiISG2kIXJSU2okwOTIFkRSIElEREREREREpJaq9iFyIiIiIiIiIiJSt9R4BpOIiIiIiIiIVA0NkZOaogwmERERERERERGplBrJYDIMoxFwITAAiAcigMBy7m6aptn3JB2aiIiIiIiIiIj4qFoDTIZh+AOPAXdS/oBSqSoAs0oPSkRERERERKSO8NMQOakh1Z3BNBMrc0lveRERERERERGROqLaAkyGYVwIXIQ9A2kr8CGwFkgF8qvrWEREREREREREpOpUZwbTFIfv3wJuMU2zsBrbFxEREREREanTtIqc1JTqXEWun+3fA8A/FVwSEREREREREakbqjODqQHW8LhfTdMsqMZ2RURERERERP4WjOpMIxFxUJ1vvWTbv5nV2KaIiIiIiIiIiJxk1Rlg+gtr9bj4amxTREREREREREROsuoMMH1g+/d0wzCiqrFdERERERERkb8Fwzh1vuTvpTrnYPoCmAqMAl4FJldj2yK1QvLBNOZ8/ht/LNvMkcPpBAb50zQ2iqGjezLm4iGEhARVWVurf9/CT9+sYMeW/aQfzSCyYTjtO7fk7ImD6Hda53LVUVhQyM/frWTxj2tI3JdMTnYujaIi6TmgPeMmDaVV26Zu983PK2D1si1s35zAjs37STl0lOPpmWRn5hJWL4TYVtH0HNCBc84fRFSTBm7rOZZ2gj9/38qOLfvZtTWRtCMnOJGeSX5+AfUjwohv14xBZ3Rn5Ni+hIQG+/w61bSMlDQ2z1tM4ppNZKYexS8ggIimUcQP7kPns4cREFw174nEtZvYtmAZR3YlkHM8g5CIcKLaxtFx9BBa9O5arjqKCgvZ/svv7P5tNceSDlOQk0tYo0iade9Il3OH07BlM4/7z7r5YTJS0ry2Ex7diItfe6xcx1TbNY+qx9VjOjK8TyzNGtcjr6CQhEMZ/LB8Hx//uI2cvKpZL2NYr+ZMPrMdPdo2plFECGnHc9iwK5XP5u9kyboDHvdtHBnCqL4tGNS9CV3iG9Isqh6BAX4cO5HH1n1H+WllAt8u2UOuD8faLCqMSSPbMbxPLLHR9agXEkja8RwSUzJZsekQP/y+jx370yv7a5/SmjcJ56qLujF8cBzNYsLJyy8kIek48xbt5uOvN5GTWzXTWQ4b2JJLxnemR6doGjUIJe1YNhu2pvD57C0sWbnf476NG4YyckgrBvdpTuf2UTSLCScw0I9jx3PYujONn3/dw7c/bS/XuQ8K8ueiMR05e3gbOrVtRHi9II6m57BlRyrf/rSdub/sqpLftzZKOZjGvFm/sWbZZlKT0wkI9KdpiygGj+zJ2RcNIbgK+wZrl29hwbcr2LVlP8ePZRDRIJy2nVsy+vxB9B5c/r7BL7NX8tvPa0jal0xOVi6NoiPp3q89504aSss27vsGAIl7DrFm2RY2r93F/t2HOJZ2AsMwiGxUn/Zd4xh2bj/6lLOfcqo7eCCNzz/5lWVLNnH48DGCAgOIbRnF6LN7c/HkoYSEVt25/X3pZr758ne2/LWPo0czaNgwnM7dWjHxotM4bWiXctVRUFDId18v58e5q9m35zDZWXlERUcwYFBHJl1+Bm3bub/OT3/gY+bOXuXTMT/8+OWcd/5Al9vSUk/w9Re/sXzZFvbtSSYzK4fQ0GBatGhMv0EdueiS02ke29in9kSk8gzTNKuvMStzaQ4wAJgL/Ms0zT3VdgAilbQ9/fuT9gezaukmnn/4U7Iyc1xuj42L5uEXp9K8ZeUSAIuKinj1qVnM93CRP2vCQG6+7yL8/NwnOaYfy2D6be+wY7PrG5DAoACm3TWRs88f5HL7gf1HmHbh016PNyQ0iBvuvoBR5/V3uf2nb1fw6lOzvNYT06wh9/3natp1bum1rCtf763+4FTC6o0seeUD8rNdvycimsVw5n03EtE0usJtmEVFLHt7JjsWLndbpsPI0zjt+skYHt4POcczmP/0GxzZtc/ldv/AAAZdO4kOo05zW8epHGB654nD1doewMi+sTx/6xDqh7m+wdh94DjXPb2QfYcyKtyGYcCT0wYxaVQ7t2U+X7CDB99eiavuwiWj2jH9ugEE+HtOiN5z4Di3PL+EbQnHvB7Tled05K7Le1EvJNBtmRlzt/Dk+396rasqGIeqf+rIkae14rmHRlA/3PXnzu6EY1x3zzwSko5XuA3DgCfuGcak89zfqH8+ZwsPPbvE5bmfNK4T0+8YSkCAl3O//xj/fGg+23a5/9tu3TKSN54+m7atGrots3TVfm558GeysqtvnZgvv4uttrbcWb10E69M/5RsN32DZnHR3PfcVJpWQd/g7f/MYuEc932DkeMHcv29nvsGx49l8PQd77Bri/u+wbV3TmTUeNd9g1cfm8mSeau9Hm/PgR257fErqVc/1GvZiooPd/8ZVBWWLt7Iw/d9RGaG63MbFx/Di69No2Vcxa/xYJ3bp6Z/xuyvV7gtM+HCwdz38CUez+2xoxncdtObbP4rweX2oKAA7rr/Is6/0PV1viIBpnc+up0evVo7/f+K37fw4N0fcPx4ltt9g0MCue/hSxgzboBPbRaLDDq7VufeDJ39W/Xd5HuxdPzptfq1FN9UWwaTYRgv2L5dC/QCxgJjDMNYC2wE0rFWmfPKNM07TspBitSQXdsSeeb+j8jLzSc0LJiLrh5Jj77tyM3NZ+n8dfz07QqSElJ47PZ3eOGD2wirF1Lhtj56Y15JcKlNx1guvHIETVs05lBiKl99tIjd25L4+buVRDYM56qbxriso7CwiKfufr8kuDR4RHfOPn8Q9SPC2PbXPr6YsYBjaRm8/p8vaRwT6TYjqkGjcLr3bUf7Li2JadqQhlERBAT4k5qczh/LNvPrT2vIyc7jv49/TmTDcPoNca7HMAyat4yie992tOkQS+OYCBpGRZCfW0DyoaMsnvcna1ZsI/ngUR7651u8OvNuGkdHVvj1qy6pe/az+KX3KMzLJyAkmB7nn0Wzbu0pyMtnz7I/2f7L7xw/mMz8p99g/H/uITC0Yu+JPz+bUxJcatS6Bd3Hj6Z+k2hOHE5h4+wFpO1JZPvC3wmJC19XmQAAIABJREFUCKfvZeNd1lFUVMTC5/5XElxqNaAnHUYPITg8jJQde1n/9U/kpJ/g97dnEtYo0mtGVFy/HvSZfJ7b7X4B/hX6XWuTLvEN+e/tQwkNDiAjO5+3vvmLFZsOExzkz3lD4pk8uj1tmkfwv/tGMvHeH8jMqdhN952X9ioJLm3ancbb320i4XAGcU3CuX5CV7q2acQlo9uTdjyX52euc9q/cWQIAf5+5OUXsvDPJH5bf5BdSelkZucT17Q+l4xqx9BezWndPIIPHx7NhHt+4FCa+xuCmy7oxh2X9gKsANrnC3awcVcqJ7LyaRAeTJfWDTlrQEuXAY+6okv7xrw0fRShIYFkZOXx1sfrWLHmACHB/owd1Y7J4zvTJq4B//u/c7lg6tdkZudXqJ07rh9QElzatD2F/326noSk48TFRnDdZT3p2iGaS8Z1Ju1YDi+87XxTGNUwlIAAP/LyCln0+z5++yORXfuOkZmVR1xsBJPGdWbogJa0btmAD148j/P/8RWHUpyDdY0ahPD+i2Np3qQ+AD8s3MU3P24n+UgmMVH1mHhOB8aMbMvQAS156dHRXH/vjxX6fWujPdsSeekhq28QEhbM+VeOpFvfduTl5rNswTp++W4FBxNSePqud/jPe7cRWom+wWdvzisJLrXuEMv4K0bQJLYxh5NSmf3xIvZsT2Lh7JVENAjnshtd9w2KCot47t/vlwSXBgzvzujxgwiPCGPH5n18PWMB6UczePuZL2kUHekyI+poipWZGB4RxqARPejSpy0xzRrh5+/H3u1JfD/zVw4kpLB+5TaeuftdHn39Jo9BkVPVti37uf/u98nNyScsLJirp55J3/7tyc3NZ/68NXz71e8k7E3m9pvf4oPP7qJeJc7tGy9/XxJc6ti5BVdOGUWLllEk7j/CRzN+YduWRL77ajkNG4Zz07/GuayjsLCIu297pyS4NGJ0T86/cDARkfX4a8NeZrz9M2lpJ/jPY58TE9PAZUbUjbeexxXXjPR4rMePZ3PjtS9TVGQSFx/jMriUtP8Id//rHXJzrM++YSO6M2Zcf5o2a0hKcjq/LtzI3NmryM3J57EHPyG2RRQ9e7fx6TWrCwyNTZMaUm0ZTIZhFOEcQDJc/J9XpmnW/bsLOSWdrAymf1//GpvW7cbf34//vHUznXrEl9r+9UeLmPHK9wBcOvUsLrv+7Aq1k7QvhZsn/x+FhUW069yS/7x1M8EOWQI5OXncN+11dm7Zj7+/H69/ca/LjKn5s1fy8hNfADDmotO48Z4LS20/sP8It1/1IlmZOTRrGcUbn9+Df5mgQFFREYZheLwAbt+UwL3XvUpBQSFtOsby34+cY8uFBYVOdZf13cwlvPPidwBMuHQYU2+f4LG8K9WdwfTDIy9yeMsuDH8/xky/jZgOpTtHG2cvYPXH3wLQ66Jz6T1prM9tpB84zDd3PolZWERU2zjOnX4bAUH2bJmC3DzmPfoSR3YlYPj7ccGLD7nMltq+cDnL3vwEgE5nDWXw1EtKbT9+KIXZ9z5DfnYO9ZtGc8GLD+Ln73zOijOY2p0xkKE3X+nz73MyVXcG06fTz2RAlybkFxRx2SM/s3b7kVLbp47vwr+v7APAy19s4OVZG3xuI75Zfea9MI7AAD827Ezl0kd+LjWUKSTIn0+nn0WPdo3JLyjinNtnO2VLTRnbiagGIbw7Zwtpx3NdtnPfVX34xzjrZmPWwp3c94brp+iDuzXlo0dGA/D14l3c/+YKCgpdf+QGBviRX1Dk8+9cEdWdwfTpK+Pp36sZ+QWFXHbLHNZtKv3em3ppT+69ycr+ePm91bwyw/dMrviWkfzw4cUEBvizYUsyl90yu/S5Dw7gk1fG0aNzDPkFhZxzxRdO2VLXTOpOVMNQ3vt8A2nHXGdg/PvmQfxjck8Avpy7lfv+86tTmYdvG8KVF3bz+Pvcem0//jmlLwD/fOhnflxcPcnvNZ3B9MiNr7HF1jeY/sbNdOgeX2r77I8X8fFrVt/gon+cxaSpFesbHEhI4c7LrL5B284tmf76zQQ59A1yc/J49KbX2WXrG7w4816XGVML56zkzaesvsFZF5zG1LtL9w0O7T/CvVNeJDszh6YtonhxpnPf4PUnPqNDt1acMaY/gUHOz8Fzc/J48ra32breeg/c/NClnDGmX4V+b29OZgbT9Vf/l3VrduEf4MdbM/7lFEj5aMYvvPKC1W+ZeuM5XO/mgZ83+/YmM3niUxQWFNG5axxvvX9rqekWcrLzmDblZbZsSsA/wI8vvnvAZcbU7G+W88TDMwG46JLTuefBSaW2709I4apLniUzI4eWcdF8/t39BFTgYdCXny/l/56wstKn3TKWf0xzfk8/++QsZn22FIArpozi1juc+3Sff/Irz//nKwCGDOvKi69N8/lYansG07A5y06ZRzFLxg2p1a+l+Ka6Q/5GmS9X/+ftSyrIMIzFhmGYhmEsruljEbvtmxLYtG43AGeOH+gUXAI4//IzaNm6CQCzP19KQUHF5l357rMlFBZaN2XT7ppYKrgEEBISxLS7JgLW06rvZjrfDAB884n1//Ujwphyq/PTruYto7jI9pTq4P4jLF/8l1MZPz8/r09XOnSNo0c/K7ti97YksrOcb2C9BZcAzrt4CKFhVoBo07pTf1Ruys69HN5izTfSYcRgp+ASQLfzRhIZa81jsXneYooq8J7Y/MNiTNv7YeCUi0sFlwACgoMYOOViAMzCIjZ9v9BlPX/N+QWA4PAw+l850Wl7RNNoekw8C4ATh1LYt2q9z8f6d9KjXWMGdLH+3mct3OkUXAJ4d85mdiZaw82uHtORAH/fL49TxnYi0Da86bH3/nCaJycnr5DH3vsDsAI6U8Y6ZxvMmLuVZz9Z5za4BPDcp+s4bMtaOntgnMsJPw0DHrvOGsaweW8a973hPrgEVFtwqbr16BxN/17WHCZffr/NKbgE8O5n69m59ygAV1/c3evwRFeuubg7gbbPzsdfWuZ87nMLePylZQAEBvgzZVIPpzre/2Ijz721ym1wCeD5t1Zx+IgVoDtrWGunc+/nZzDhrPYAJB48wWsfrHFZz6vv/0nSoRMAXH9573L8hrXfzk0JbLH1DUaMG+gUXAI477IziI23PivmfVHxvsEPn9v7BlPumFgquAQQHBLElDvsfYPvP3PdN5jzqfX/4RFhXPlP575B05ZRTLzK6hscSjzCql+d+wY3PTiZ0ecPdhlcKj4Wx8DVikW173qyaeM+1q2xrvHjJw52maVz+dUjaN3GOreff/wrBfkVO7effbyYQtvn5V33Xeg0l2dIaBB33We9noUFRcz8cJHLej5537r+R0SGceud5zttbxkXzTX/OBOwgk2Lf/H9oQfAD7Ota45hGIwZ53pqhA224KJhGFx7/Vkuy0y6bBgRkWEAbFx/6vf7ROqS6gww3V5FXxoeJ3XKCocO1mg3F1M/Pz9GjLGe3maeyGbD6p0+t2OaJiuXWG21iI+hU/dWLst16t6K2FbW06uVSzZRNssxaV8K+/dYNz2nj+7pduLx0WPtv8vyxRt9Pt5ixYEhsCYGrwj/AP+Szmp+XsWGk1SnhFX2jln7EYNdljH8/Gh3hnVDnpeZzcFN231qwzRNEv6w2omMbUJMB+cOLkBMh9ZENrc6uQmrNzq9H9IPHCY96RAA8YP7uJ10vN1w+3wbCQoweXRmf/s8YV8tcj2xsWnCN79anebI8GAGdfU8aa4ro2zt7ExMZ90O5yAWwLodR9iVZA1ZGd2/hc9tgBUMWrMtBYCIekE0rO+cDTi0ZzNaN48A4O1vN1NYdMo8eK1Wo4fa/w6/mrfNZRnThG9+tP7eI+sHM6hPc5/bGXV6PAC79h5l3eZkl2XWbU5m1z4rkDX6dNfXC2/yC4pYs9H6fIioH0zDyNLDfOJbRBJhez8sW51IkZvzXlRksmx1IgDdO0XToln9Ch1PbbJqib1vMMLNHIR+fn6cca69b7Dpz4r1Df6wtRXbKoYO3Vyf6w7dWtHcltmyeqlz3+BAQgpJe62+weBRPd1OPD7coW+w6teK9Q3i2jajfoN6ABxOSq1QHTXp14X2a/w4NxNY+/n5lcwddOJENqv/8O0aD9a5XbLIeo3jWzehe0/X1/nuPVvTKj4GgCWLnK/z+/Yms2e3dW5Hn93b7cTjYx1+l4oEmBL2JfPXhr0A9OnXlmbNG7ksl28LtkU2CCM83PUcXIZhENvCyrKraHCutqvpleO0itzfV7UFmEzT/G9VfVXXMYtUh822jJqQ0CDadXJ/A9e9d9uS77ds8P1pzOEDaaSlWEMcujnU5Uq3Ptb21OR0Dh8oPTHr5vW7ncq50jAqglhbZ3SLrcPgq/SjGaxfvQOAiAb1iLB1KH21ftV2jh+znqK3aBVToTqq0+FtVlAhIDiIxm3cT0retIt9YubkbbvdlnMlIzmVrKNW4KBpZ/cTPAM0sbWTlXaMjJTSnfnDW+3tOh5PWWENIohoZr32h3081r+bvp2sv5vMnHz+2u1+YuRVm+3ZLcX7lFfLmHCaNgpzqsd1O1YAomnjerSIqdjfYFCgPdPQVfDo3MHWTW1RkcmiPxNL/j8yPIhWTesTGV51Kymdyvp1twKFmVn5/GULyrmyat3Bku/7dPctuNiyWX2aRtdzqseVP2zbm8aEVzioU+rcl8lKaxBpDzampmV7rOeIw/Z+PXwPqNY222zX+eDQINp0dN836OJwPd9Wgb5B8oE0jh6x+gadvfQNittKS0kn5WDpz6atDn2DLh7qadA4gma2vsG2jXt9Pt5ixUGD2jj/0ro11msVGhpEpy7ur/G9+9mvqRvW+n5uDySmkpKc7lSXK31s25OT0zmQVPrcrl+7y6mcK1FREcTZAlUb1vl+nS/OXgI8TsxdHAxLP5ZFRob7z42kROvBSfExiUj1qLZJvkXEtf22J37NWkR5HO7VwuECuX+P6yfOniTstt9EtvBysXUMwiTuTaapwzKvCXt8qCc+hqSEFI4cPkZOdi4hod7nMcrPKyA1JZ31f+zgqw8XknHc6jyMnzzM676OsjJzOHL4GL8tWM+3DkP9xk0e6lM9NeFYovUaRzSNdjlXUbHI5vabrGOJh3xsw35jWTzUzp0Gtgym4mOrHxPl8HP564mMbcLxg8lkph4jPyeXwBDX74dDW3by3d1Pc/zwEcyiIkIjI4hq14o2Q/oS179HnZ+4sl0LaxL6fYdOeMzk2eUwJ07bWN8mri9uA2C3l5XIdtsymIrbSUz2bU6iAH+D3h2s90zKsWzSM/KcyvRqb21PTMkgM6eAcafHc8PErnSMs68qVjzp90fztpFXR4fItY1vAEBCUrpTMMbRbltmEUDbVg18aqNda4fX1Muqfrsctrdt1YDEgyd8aivA34/e3azPj5TULNJPlB5KmZVlzygN9xJErF/Pvr1dvPvV5uqKRFvfoKmXvkHzMtdrn9txuKbHenkAU7atmOb2voEv9cS2iuFgQgqpPvQNHO3Zlliyql5sLQwe7N1jXa9bxEV7nKcovrX92rtnt2/XeIDdDvvEt/b8OrVyaGvv7kPEtrCf2z277OfW8ZhciY+PIWFvMocPHSM7K7dUFronpmkyb64VYAoJDWLkWb3clr3g4iEsWrAe0zR5/3/zueV25wVIZs1cwvF0a2j2BZOGlOsY6po63lWSU5gCTNXMMIwRwDXAUKApUADsA34EXjRN84CLfR4FHgEwTdMwDCME+CdwKdDeVmwL8CHwpmmaBWX2fx+42uG/zjAMo2zPdZ9pmvEO+xRvn26a5qOGYYwEbgQG2Y47ybG8bZ8gYCpwMdANiATSgDXAp8Cnpmm6vCtwOMZ9pmnGG4YRC9wJnAe0ADKBVcArpmk6LSFjGMatQHF222DTNN2vxWqV/wq4ADgKNDdN0/0kEidRXm5+SWZN4xjPN4jhEWGEhAaRk53HkcPel/ouKzXZvk+Ul7aim9hvWFLKtJWabL/Z9HbMUbZ6TNPkSHK62+yhjX/u5P4b33Bbz8gx/bjgyuEe2wL49O2fmPnOzy63+fn7cf0dE+ja69ReSaQgL5/cE9ZEymGNPd84BoeHERAcREFuHpmpRz2WLSsz1X5e63lpp16U/WaubDtZaQ71NPJST2NbPaZJVtqxkqF3ZWUkl86SykhJJSMllb3L1xDTsQ3Db7/Wa1u1VVCgH40irGFEh1Ldr7YGcDwzj8ycfOqFBNIsKsyndpo2tpf3tKobwEGH42jW2PcMpsmj25f8TvOW73PabhjQJtYaHnf0RC4PTunHNWM6OZVr0zyC+67qy1kDWjL16UWcyDr1h7v6IijIn0YNrOEerlZbc3Q8I4/MrHzqhQXSLCbcp3aKs5esdjI8lIRDyfbtvrYDcMn4ziW/07zFzhkN+5KOk5dfSFCgP/17NvNYV/HcVADNm/h+LLVJXm4+J4r7Bl5WPQ2PCCM4NIjc7LxS1/nyctynvNd0gNQyfYO0FHvfoJGXehrH2PsGacnppQJX5fHNB7+UfD94lPtAxKkoNzefY0etcxvTxPN1LCIyjNDQILKz8zh8yPdzm+xwjry11aSpfXvZtnyrx7rOm6ZJ8uFjpQJXnqxdvZODtsyp4SN7eFw1b+BpnZhy3VnM+N/PfPjeAvYnpHDO2H40adaQ1JR0Fi/cyNzvVgIwdvwAxp0/yG1dIlL1FGCqJrag0AxgsovN3WxfNxqGcalpmnM81NMEKxhV9ora3/Z1lmEY57sL5FSEYRhPAvd7KRMPzAPK3hU0Ac61fU0zDGOCaZrux3xYdfUD5gKOPY5QYAwwxjCMF0zTvLPMbh8D/wcEYwXw3AaYDMOIAopnn/y0poJLQKlJq8vzlKc4wJST7X5C3fK0FeKlreBQxxVGSrdV6pi9PHUstVKJiwm6vYlp1ohb7r+I3gM7+ryvo5792zPt7om0jC9fR6cmFeTY347uMnwcBYQEU5CbR36Ob6+vY/kAL+04zqtUUKad/OyK1ePqeP0C/GnZrzuxPTrRMK45gWGh5GVmkbJ9D1t//o3M1KMkb9vNT4+/ynlP3klQmOu5F2qz8FD75LpZOd7nHMvOKaBeSCBhIb5dzuuVasdzoMbxOOr52E7LmHDuuNS6XGVk5/PmN5ucytQPC8LfNsylY1wDeraL4nBaFs98tIbFaw+Qm19Ij7aNueeK3vTuEE3fTjH856bB3PzcEp+O5VQXHmY/J5nZ3oNn2TlWgCks1MdzH2b/O8zM8vwey8q2bw8L9W1FrZbN6nPHddZ8OxlZebz10VqnMtk5BaxYc4BhA1vSuV1jzhvVlu9/cZ537LxRbenU1p5R4fg71EU5PlyvwbrW5mbnVeg6W6otL9f08vYNKlOPNysWbWDFImt+nzadWjBweHef9q9pWZn2a3xYOc5taGgw2dl5Lhc58d6WfR9vbTn257LLnBPHY/bWV3WcnynLh2OeO8c+PG7sePfD44rdeOt59O3fnhnv/MyiBetZtKD03I4dO7dgynVnMfLM2hWAFKkLaizAZBhGA+B84HSgJdAQ8DdNs2+ZckFA8XiMDNM0Pefyn4IMazzHl0DxOuJzgC+A3UARMAArWycO+NIwjCGmaa52U93XQBfgZVs9aUBH4CGgM1bg5DrgLYd9HgCewwpw9QNWA1PK1Os8ZsFyAdAd2Ai8CPyFFewp+cQ2DCMc+AUoTg35FngPOAC0Bm4BzsA613MMwxhmmqa7GffCgFlY2U//AX4AcoGBwH1AM+AOwzASHOfjMk0zzTCMr7GyuiYbhnG7aZruBmZfDhT3lN9zU6Za5DlMOF2e5VwDAq0/2bxc35/c5zlMkO2trcBA+0dDXpmbT8e2AwI91xPgsApMrodjbt+lJa/OvAuwhsilHDrGyqWbWDTvT16a/hmXTzuHsya4ngjT0ZiLTmPIKGu1o5zsPPbvPcyiuX+y/o8d/N/9H3HL/RfT0c0EpqeKAofz5Bfg/SPa31am0MfJyx3L+3l5P/g5vB8KyrRTmG//2duKfv4O9bg63vOeupvges6ZOM26dqDzOWew8IV3OLB+K+lJh1g36wcGXH2hU9naLtjhb6o8K6UVDxULcbPiUnna8TbcLM9hZargoPIvOx0S5M9rdw8jwja06bH3/iD5qPPHclhwgMM+AWTlFHDF9AXsOWC/3P+xJZkrpi9g1pNn0yW+EWcPjKNnu8as31n7Jvh1J8jhtc3PL8e5t81BExLs47l3bMfLqmN5DpPjhgT7cO6DA3jtybNKJvB+/KVlJLvJyHvlvdUM7tucwAB/nnlgBHGxEXzz4w5SUrOIbhzGxHPac/M1fcnLKyx5jYJ9OJbayOe+QVAV9Q28XNNL9Q3KtJXvQ9/AUz2eJO49zBtPfAZAUHAgtzxyWa0bMu3L6w2ULFDiqQ/ltq1S58Tz54Tjqn05Zfp9uQ7HHOjlmIPK2e8rVX9uPgvnrwMgJiaS/oM6eN0nJTmdOd+u4K/1e11u37n9AN9/t5L4Nk1o09ZzdmRdVcv+NKQOqfYAk2EY/sB04FbAMdfeAFxNOBCKNfwrHFiLFSCpbaZiBZfygfEuhnitMAzjI2Ap0BV4CSsY40p/4CzTNBc7/N8awzB+AjZjZQzdhEOAyTTNJCDJMIzinPtM0zSd14Z1rTtW8GisaZqOjyIcHx0/gj249IRpmg85bPvTNhztI6zAzmnA9YC78VDRQANgtGmajm2sstWzEmvI3JOGYXxqmqbjLKjvYAWYIoGJWMPyXCkOrq03TdP1mshVIDU5nYwTrjvU4fXDaBwTSVCQ/YlweZYXLsi3LvJBwb49SYbSF31vbeXn2zsTZZcrdmy7IL+QoGD3E2w6BkuCPRxzSGgwrRw6AO06t2TwiO6MOKcP0+94l1ee/ILUlHQunep6OdpiDRrVp0Ej+0S0Hbu1YvR5A/j8vQV8/OY87r/xdR549lr6DKpcRtTJ5BiUKyrwnsFSaCvjH+Tbe8KxfJGX90ORw/shoEw7/oH2nwsLCgkIcv9+KHSox9XxugouFQsMDWHE7f/gy1seITcji20LltH38gklAba6Itfhhj4wwPvktUG2Mjk+rrDo2E6Ql3aCHG5wyy5n746/n8Erdw6jS7y1CtAnP23jaxdDpMoeC8AXC3eWCi45tv3CzPW8c98IAMYOia9TAaY8h9c2MLAc5952o5eT6+O5d2zHS/DCcYLunNxynnt/g1ceH01n27xan3yzia/nuV8Ba93mZB5+dimP3T2UoEB/br9uALdfVzqDITsnn2deX8mjd1hdo8xaPDwyzUvfoFFF+gZ5VdQ38LLaVqm+QZm2An3oG3iqx520lHSevuMdsrNyMQyDGx+4hBanWFZy8uFjnDju+tzWjwgjpkkDn15vsK+e66kP5U7p/prnzwnHVXpDyvT7gh2OOT+/kGAP5zavnP0+R78u3EBmhpUldc55/b1O3L5n9yH+ed1rJCenE9mgHv+6+3yGntGNxo0jSE/PZOXyrbz5ylx++3UTa//cxfMvX0ef/u091ikiVadae+aGYYQCPwFDsAJKXpmmmW4YxnvAv4DehmF0Nk1zy0k8zCply1661/bjy67mDwIwTfOoYRh3Y2XsDDEMo71pmjtcFH2lTHCpeP80wzBmAP8GuhuGEWmaZrrT3r4rAqaWCS6VMAwjGCuABrAJeNTFsZmGYdwEnAM0xspocj/hDrxVJrhUXM8BwzDuBD7HCk5ejZWZVWwRsAtoixVEcgowGYbRB+hp+/GkZi99+MYPLJzrOhFt5Nh+3P7IpaVSjcuT/pyTbSWa+TohJpROa/aWRp+bbU9oK9tWqWPOzvXYOczJcainnBM9Ouo5oAPjLxnKVx8t4rN3fub00T0rNMztkmtHs2rpJrZvSuDVp2bxv6/v85ptU1MCQuzzDpRn2FvxkLXyDKdz5Fi+7LA3pzZy7eex7DC4wNDS9ZQNQLmrx9fjBQgKC6X1aX3Z+vNSCnLzOLIrgSYdT+05tXyV4TA0qjzD3kJtZcoznM5RZql2PN8EOB5HZjnb+b+bT2NEn1gA5v6+l0ff/cNt2bLDwX5b735ls983HiS/oIjAAD+6OwyZqgsyHIIm9coxHC3Udt4ch7GVR2aW/e+wXpjn95jj8LuscgzbA3jm/hEMt60KOPeXXUx/8Tev+3z5wzY270zl5qv6MKR/C+rZhgvmFxTy6/L9PPvmSuo7TAJ+/ITvw4VOFTPf+oFff3DdNzhjTD9ufujSUtfL8gx7K77WVuQ6W6otL8PVyts3yPHSN/BUjysZ6Vk8edvbJSvXTbnjfIac2dvrftXtjZe/Z+7sVS63jR0/gEeevIIwh7mFyjOErHi4Wnkny3YUVs++j7e2HIfFlZ3+wPGYs7NyPQaOchzObXmGAEKZ1ePG9/da/tH7PyY5OZ2Q0CDe/uBftG5jX2CkcVQEY8YNYMCgjlxx8f+RlnqCh/79Id/Me7hU4FZETp7qXtvzPazMHANIB54BzsYaEuWJY6Dg3JNzaCdNF6yAB1jD5DxxDKoMdlPmEw/7/2n718AamlYVlpmmudfD9r5YGUcA77sb+mYb2viF7ccuhmF4yled4WHbN0DxbIOjy7RhYg8ajTQMI87F/sXZS3l4fi2rRVBwIPUjrawNx8mzXck4nlVy4Y7yMsmiK8WTagIc8dKW48Te0WXacpwE1NsxF09GbhiG14nF3Rl4RlfAWr58+aKNFaoDYOAwq56UQ0fZvjmhwvWcbAFBgQTXt5I7s1I9T+qZm5FVErQpmUC7nBwn9s700k7mEfvE3mXbCXOYbDszzUs9xROEG0ap/XzRoIW9I5nlpb3aKC+/iLTj1pNcx4m4XYmoF0Q9W5Dh4BHPE3WX5TiBeNNGnttp5nAcB1O9ryA3fWp/JgyzLkGL1yRx58vLMN0viEZeQRGp6fY5Pg4ecd9GXn4RR23BhUYRvt9wncry8go5QRVJAAAgAElEQVRJO2YNIXSciNuViPCgkiDMwWTPE3WX5TiBeNNoz5NlN3WY2Ls87Tx6x+lMOMvKFFi8PIG7Hl/o8dw72rz9CDc/+DN9x8xg6IUfM+qSmfQ6ewY33v8TuxOO0cph5cMde3xb1KC2KdU3SPHeNygO2Dhe58vLcZ/yXtMBGpfpGzRymIw8zUs9xROLG4bhdULw7Mwcnrz9bfbbVkS75PpzOOcid0n+p77g4EAiG1h/38leFmw5np5Ftu3cOk7CXV6OE3J7a8txYu+ybflWj/W3aRiG1wnBAVKPHGfl8q0AdOrS0utwtu3bktiyyerDnTOmX6ngkqOo6EgmXWatPpySnM7y32pNbkKV8TNOnS/5e6m2DCbDME4DLsEaBrcbawjUPts25/UlHZim+YdhGGlY8zSdDrxwkg+3KjkO6Vvuw1hxd+t9b/Wwj+Pk2fXdlvLNBi/buzl8v9JL2ZVYK9EV7+fqMXUesN7F/wNgmma+YRhrgRFYw/fKeh94DPDHynB6vHiDLdvqMtuP35mmeVLHVtz+yKXc/silXsvFtW7KpnW7OZh4hMKCQreZNY7LD7f0stysy3ba2DN/vC1lnLjPvr1FmSWA41qXrqdNh1j39djaiWrSoEJZVwCRDew3OMmHKn5TEdnQoZ6DR+nco6pisFWvQYumHN6yi+OHUigqLMTP3/V7Iv3AoVL7+NaGvROXnuR5+eNjB+xLFDdoUTqDrGw9jeNbuK0nPcmqp17jBhXKYAL+FpMK7ExMZ0CXEFo1rY+/n0Fhkes79La2ldcAdiX5lrC6M9Fevo1DPa60ibXfAHpr5+7Le3P52dYQ1FWbD3Pz80soKPQeYdix/xiNI633sL+X3mjx9sJy1Fvb7Np7jEa9QomLjcTf33D7O7ZpZQ/07trnW6B1p0Nwpk2c5xvAtg7bvbVz9w0DuXyiFchfte4Atzz4MwWFvq83UlhocijZOcjYrWNUyfcbtni+hp3Kbn7oUm5+yHvfoEXrpmxZt5tDXvoGBzxcr8ujhcM1PWmf59fVU1tl64n30Dcobqexl75BXk4+z9z9Hru27Adg/OXDuXDKmR6PsSY98uQVPPLkFV7LtW7TlHVrdpGYkEJBQaHbebb27jlcah9ftXHYZ+8ez+d2n0Nb8WXaat3Wfm737jlMh07ur/N7bf2+Jk0blCvr6se5qym0fU6UZ3Lvvbvt/ZWOXdwfB0DnLi3t++05zBleaxeRqlCdGUxXO3x/eXFwyQfrsDJzOlfdIVUL36/2FpePlE3T9PSY2rEnV1Xjf7zd0Tdy+N5bj8/xLraRmzJpHiYAL1Z8FXSqwzTNA1jDDAGuMUpH9CY47FOjk3s76tLLCnTkZOexc2ui23Ib19pX1qlIcKRJ80Y0irZuJP9a67xKj6NNa625UhrHRNKkeemXuUtP+5Ckv9a4r+fokeMkJaTYjjfe5+Mt5vj01tuqdR7rcXiiWpFU8+rUpKOV9FiQm0fq7v1uyx3avLPk+xgfh4qFxzQmrKEVODi0ZafHsodt28MaNSA8uvSwpCad7O06Hk9ZWceOc/ygreNZiWFtxxLtHyPFx1/X/LnV+rupFxJItzbuPiphQBd7p794n/Lan5zBobQsp3pc6d/ZuowdSs0k0cWNf7GbLujGtPOtAMP6nUe47ulF5Z6z6Q+HgEFLD0vQh4cG0tA2cfThNN+ytmqD1Rut93e9sEC6dYx2W25AL3tgd81GzwHisvYfPFGSxeRYjyv9e1rbDyVnkHjwhNtyN13Vm+svt9b+WL85mevv+bHc5748/PwMzrJlxR04fII1fx32skft19F2nc/NzmP3Nvd9g80O1/OOFegbxDRvRMMoq2+wxUvfYMs6q2/QKDqS6GalP5s6OfQNNnuo51jqcQ7a+gYdu8e7LVdQUMjz979fUteZEwdzxS3j3JavTXr1sV6r7Ow8tm52f41fu9p+Te3R2/dz27xFY6JtGWKOdbls60/rdY6JiaR5bOlz27N325Lv13io58iR4yTYAkw9epXvOv+DbfW4gAB/zh7T10tp8Pe337oWelmgwnH+svJMli8iVaM6A0zDsLKX1pmm6XqAsmcHbP/6HsKvWY6faOOwsm7K8/V69R6mW770EKvicXJV1PGO7d82WO+7YsXD4xKBn6ugnSox6Ax7EtiCOa7nKSkqKmLRD9YIyHr1Q+nRr53P7RiGwcBhVluJe5PZutF1jHfrxn0lmUcDh3V1WqEltlU0LW1PKn9bsL7UPEuOFsy1/y6DK7GM8G+/2BPaWrWr2J9/UVERvy+yJ+O1OsVXFIkb0KPk+x2LlrssYxYVsfNX66M0qF4ozbp6X3XFkWEYxPW32klPOkzy9j0uyyVv31OSeRTXr7vT+yGyeRMiY63zsnf5mlLzLDnauXhFyfdxA3q6LONNXlY2e363/g4CgoOIautqFGztN/8P+w3HhSPauixjGDDxDOuGIz0jlxWbfAsyAPxia6ddi0h6tY9yWaZX+yja2YYmLfjD/U3u1WM6cselVoBh676jXPvkwnLP1wTw4wr7sNWzBrg/r2cOaImfLYPpj1qcxeLOgqX2v8MLz3W9GIFhwMRzrL/39BO5rFhzwGU5T375bS8AbeMb0quL6+dgvbrE0DbeypRa8Jv7Z4JXX9StZFLurbtS+cddPzjNq1VZF4/tRGxTKzH7s++2UOQmq68uGTDM3jdY9L37vsGv8+x9g659K9Y36G9rK2lfMtv/cn2ut/+1ryTzqN9Q575B87hoYm1zJC7/ZT25bvoGix36BgPOcN03KCos4uWHP2atbejUsHP6MvXuurNq6Bkj7df4Od+6Tv4vKirihznWNb5+/VD69fftGg/WuR02wnqN9+45zMb1rq/zG9fvKcmWGjbC+TrfKj6G1rYs+AU/rS01z5KjuQ6/y/BRPVyWcbRz+wF2bEsC4LShXWjQ0POQXYDmsfaHXOs8POQEWLPavt1xv7+Lmh4WpyFyf1/VGWBqbvvX7fAnL4ofm3qemODU4zgM65hpmn+V86u29Jwdh+V5m33ZMTqQ5qZMY9tKg54Ut+OujrnYh99NATAMIxYoXoLsA9M0fc/bP0k6dI2jq+1Jz/zZK9m6Ya9TmW8/+ZX9tov/+EuGunwSs/HPnYwbcCfjBtzJi9NnumxrwuRh+Nme/rz13Dfkll2KNieft577BrCeEk2YPMypDoCJl1uJxieOZ/H+K987bT+YeIQv318IQLOWUQwe3s2pzK8/rSEzw3nJckdL56/jx2+swES98BAGDnWu56dvV5SkV7tSVFTEuy/NYd8u6wa8a682TllZp5rodvE06WwFFrYvWk7ydufVt/76fmHJ0LYu5w7Hr8x74uCm7cyYdAszJt3C0tc+ctlOlzHDMWyrtaycMYuCvNKdxoK8PFbOsKbIM/z96DJ2hMt6uo0bBVhzQv3x8bdO248fSmHDN1ZMt37TaFq5CDAlrtvs1L6j/JxcFr34LrknrEtB+xGDS61gV5ds2JnKqs3W3/vFI9vRu4Nz8Ocf47rQroU1fOmDH7Y5DUMb2KUJO2ddwc5ZV/DMza6n9Jsxd2vJEKaHr+1favl6sJazf/haa8LV/IIiZsx1PUL7wuFteOBqazT47gPHufrxX0jPcH8uXdmWcIzFa6wbjfNOb8Xgbs7B5KgGIdwx2Xrv5OUX8tVizzcXtdGGLSn8sc66fF10Xkd6dXW+rP5jck/a2QI/H8za6DQMbUCvZuxYOo0dS6fxzP3DXbbz/qyNFNie/j902xCX5/6h24YA1kTb789yPVr+wjEduf+fpwGwO+EY19w+l/QKTMDdJMr9PGCD+jTngVsHl7Tx7ufeRu7XDe26xtHZ1jdYNGcl2zfudSrz/ae/krTX+qw4d5LrvsGmNTuZNPhOJg2+k9ced903GHOJvW8w44VvyCvTN8jLyWfGC/a+wVg3fYNxl1l9g4zjWXz8qnPf4FDiEb750OobNG0RxYAznK/ppmny5tNfsML2UGjgiB7c9OBkp6BHbda1eyt69bGu8bO/Wc6Gdc6Bn08+WMSe3da5veSKMwgIdD63f/6xgwHdb2VA91uZ/sDHLtuafMXwkqyf557+yumhYE5OHs89/RUA/gF+TL5yuMt6Lr9mJGDNC/XKC985bU/cn8L7784HoGVcdLkCTI4TopdneBxAx84tiLFlZS1asJ5VK7a5LLd1836+/sJaYCAkNIh+A7SKnEh1qc5V5IrvBCr6WKt4TiH3OdqnprUO3w8BvC+ncvKcjEd+fzl8PxBY6qGs49XjLzdlgrBWeVvjaqNhGAFAL091mKZZaBjG+8B9wEWGYdyCNUTTD+s18DSJeI247s4J3DP1VfJy83n41re5+JpRdO/bjrzcfJbMX8tPtiBLbFw0519e8VHksa2iueCK4Xz5wUJ2btnPPde9wkVXjaBpbBSHko7w5YeL2G17mnTBlSNoHud6iMbIsf2ZP2cVW9bvZe6sZRxNPcHZEwYSHhHG9k0JfP7efLIyc/DzM7j+zvNdzh3x4zcreO3pLxl4Rje69W5DbFw09cJDycnJJWlfCssWbmD1MmtSRsMwuO6O80smPXX06lOzmPnOzwwZ2YOO3VoR07QhwSFBZJzIYte2JH75/g/27rRu2MLqhXDDPRdU+PWrTgOvuYi5D71AYV4+Pz3xGj0nnkXTrh0ozMtn9+9/sn3BMgAimsWUBHh8Fdm8Cd3Gj2Ljt/M5siuBuQ+9QPcJZxLRJIrjh4+w8bv5pO2xsla6jxtNZDPXmQ7thg9kx6LlJG/bzdaflpB97DgdR51GUHgYKTv3sf6rH8nPzsEwDAZNucjlnFIbv/2ZJS+/T6sBvYjp1IaIptEEhASTn5Vt1Tv/t5LJxiObN6H3pDEV+p1riydmrObzJ84mNDiAGQ+O4s2v/2LFpsOEBPkzdkg8l55pdZZ3HzjOu3M2V6iNvQdP8M7szdwwsRs92jXmi8fP5q3vNpFw6ARxTeszbUJXutqG6L0zezP7Djlffkf3b8GTNwzCz8/gRFYeT8z4g0YRwR4n4E5MziDbxZL3T7y/mt4doogMD+Z/9w3n/blbWbwmiZy8Qnq2i+KGiV1pFmU9Y3rxs/UcTvMcoK6tnnh5GZ+9PoHQkEBmvDCGNz9ay8o1BwgODuC8UW2ZPKELYAVb3vusYsGWvfvTeeez9dxwRW96dI7h8zcm8PYn60lISicuNpLrL+9J1w7W5/87MzewL/G4Ux2jh8bzxN3DrHOfkcsT//2dRg1CaNQgxKlsscSDJ8h2kdk298NJrFp3gMXLE9ix5yh5eYU0bxLOmcNaM/7Mdvj7+3E0PYd/PTyfvCocenequ+a2CTw0zeobPHHb20y8ahRdbX2D3+evZcF3Vt+gWVw04y6teN+geVw04y8bzrcfLWTXlv08NO0VJlw5giaxURxOOsJ3Hy1iz3arbzDu8hE0a+m6bzB8TH8Wfb+KbRv28tNXyziWdoJR4wcSXj+MnZsT+GrGfLIzczD8DKbc4bpv8NErc0qynFq2acrEq0eRuNfzkMi4Uzwr2ZU7/30BU696idycfG6d9jrXXHcmffu3Jzc3n/nz1vDNl78DEBcfw+VXj6xwO63iY7jimpF88O4CtmxK4LqrXuKqa0cT2zKKpP1H+PC9BWzbYl3nr7xmFHGtXF/nx44fyJxvVrJ+7W5mfbaU1NTjTLjwNCIiwti0cR/vvfUTmRlWv+/Of1/odUhaYWERP9lWU4yIDON024Iu3vj5+XHTbeN49P6PKSws4rYb32TixUMYOrwrjRrVJz09i5W/b+WLmUtKMq2u/seZ1I/wvJiFiFSd6gwwpQAtsGcy+ar4k6e2ZPYUW4M1JKsFcL1hGP81TTPHyz4nS3G7VTkBzZ9Yq7o1AK42DOMFV9lBhmHUBybZftxsmqb7daitYJDLABMwEWuyd4AFHup4F/g3VsbbJcA1tv9fYprmKffYu23HFtz71JU8//CnZGXm8OHrPziViY2L5uEXp5ZaLrYirrzxXNLTMpg/ZxW7tyXxfy6eep05fgBX3HCO2zr8/f144NkpTL/tHXZs3s/vCzfw+8LSNzqBQQFMu2si/U5zP21adlYui+f9yeJ5f7otEx4RyrS7LmD4OX3clklNTmf2Z0vxFN9s2boJd0y/jPh2taMj2rh1S4bfdi1LXvmA/Owc/pw5x6lMRLMYzrzvRgJDK/6e6Dt5HDnpGexYtJy0PYn8+pJz/LX9yMH0mXye2zr8/PwYdff1zH/6DY7s2se+levYt3JdqTL+gQEMunYSLXq770TmZmSxfeHvbF/4u9syTbu0Y9it1xAcXtuSWX2zee9R/vXiUp6/dQj1w4K463LnJbl3HzjOdU/7NhStrOdnrqNxZAgXj2xH1zaNePn2oU5lvvhlJy98ts7F3taQtQDb0/H6YUG894D3YOflj8xn5WbnG8a9B09w/TOLefXOYUQ3COWGid24YWLpDIeiIpPXv/6L/82uWFCtNti8I5XbHvmF5x4aQf3wYO6aNtCpzO6EY1x3z7xKDUV74e1VNG4QysXndaJrh2j+O320U5kvvt/Ci/9zPavBmUPjCQiwnfvwYN573nvQ9/J/zmbVOufLf4C/H2cObc2ZQ13PM7N9dxp3PvYLW3e5S1yum1p3bMFtj1/JK9M/JTszh5lvOvcNmsVFc99zUwmtZN9g8g3nkn40g0Xfr2LP9iReesi5bzBy3AAmT3PfN/Dz9+PuZ6b8P3v3HR5Vlf9x/H0nPSEJhCS0EEKT3kEBQcCCClZQsaBi2bXsquuqv7Wsuq6u69p7L1hQUZRiQbEAgoD03iEJSYAklCSkt/v7406SSTI1ARLC5/U882Qm995zzsyd5Nz5zvecw3///i67tqTwx/z1/DG/9rXBjfdcyoBhzq8NHPdP2b2f+6e84LHtXyx9zuM+jU23Hu158pkpPPLAx+TlFvL6S7UzvuITYnnhtVsIq+e5ve3OCzh0KJdvZi5j25ZUHrpvaq19LpowlFvvGO+yDD8/G8+8dDN/u/1NNm/cw68/rePXn6oPSgkM9OfeBy9j+MieHtu0fOlWDmRageux5w0kIMD7j6TjLjyVQweP8PpL31JaWsaXn/3Gl5/9Vms/wzC4cvIobvzzWCelNH02o+kPJZbG6XgGmDYB7YFhhmEEmKbp9VWRYRidgL5Y2SeeViprVEzTLDcM40msOZU6AR8ZhnGtaZpOc8gNw4gArjNN89Vj0JyKq7pOhmEYpunt4sGumaZZZBjGu8C9WCvDPQw85riPfaLtV4GKcR6enttthmF8aZpmtWwvwzBaA8/aH+YDH7pp1y7DMBZgrTb3BFXD8xrN5N41nTqyF698eg9zPl/Eyt+3cCAjG/8AP9rERTPirL6Mv2IEwcGB9a7HZrNx58OTGH5mX36YuZQdW1LIycojonkYXXu057wJw9wGhSpENm/GM+/ewY+z/mDhj6tJSUqnqLCYqOhI+g3pyoWTRtKhs+s5k+7+11WsWLyZzWsTSU3OIOvQEXIO5+Ef4EdE8zA6dG7DoGHdGHXuQJq5+ebphY/uZtWSrWzbmEz63kNkHTpC3pECgoIDiYqJoHO3dgwb3YfTRvU+4SZ5jB/ch0uefZDN388nZfUm8g9lYfP3I6J1DAlDB9DjvFH4B9XvPWHYbIy47Ro6nNaf7T//zoFdyRQeySM4PIzozh3ods7pboNCFYIjmjH+ib+z/Zcl7F68kqy0/ZQWFhMaFUmb3qfQc9wYWrR3Hdwbcu2l7N2wncztiWTvS6cwJ4/i/Hz8AwMJjYokpksCnU4fRNt+PZrUUAl3fl2Vxvh7vmPK+O6MHtiO1lGhlJSWkbw/l7lLk/n4h20U1jObwzThgTeW8cOyPVx5dlf6dGlJVHgQh44UsWHnQT77aQe/rfV9jp+6WrU1k/Pv/obrzu/O2afG0T62GQH+NjIOF/DHpnQ+nruNzUlNe4l6gF+XJHPBlBlcf3kfRg+Lp3VMGCWl5SSnZvPDgt18/NUmCovqHlgE69w/+L+F/LhwN5Mu6kGf7rFERQZzKLuQDVsz+Hz2Fn77w/UExEfTQ08vZMSQOPr2iCWmZShhIQEcyipg665D/LBgN7N/3FGnFemagsEje/Hsx/fw/ReLWL1kC4fs1wat46IZemZfzrtsBEFH6drgtocmcdqYvvw8eym7NqdwJDuP8MgwOvdszzmXDHMZFHIU0bwZT7x9B7/M+YPF81aTlpROYUExUTGR9B7clXFXjKR9HVZEa4pGju7Dp1/dz+efLOD3RZvJSM8iwN+PuPgYzhrbnyuuOoPgkKNzbh/+99WceXY/Zs5YwpZNe8g6nEvzFs3o0SueCZef7lVQqHmLZrz78d3M+moJP36/iqTd1rmNjolgyGndmDR5FJ29/BLve4c5R8d5OTzO0eQpZzFyVG9mzljC6pU7SU05QEF+EcHBAbRuE0W/gZ24eMIwevRqmnM1ijRmxlGIMXhXkWHcAbyEFSS6wzTN1x22vQL8BTBN06z1CdAwjC+Ay+zHXm2a5vTj0uijxB5g+Qor+wZgF/AWsBzIBiKA7sBo4CKg0DTNaIfj/wU8CmCapstPVoZhjAbm2x+OMU1zQY3tNwPv2B++CHxirx+gxHFlP8OoDHs/Zprmvzw8v3CsVf4qloz4CmsY2j6gI/BX+3MDWAqMrLlSnH1I2/VYmW75WPMsvYC1IlwR1vC6B6nKgLvHNM3nPbTramCaw69ygDYeVuJza3v2t/o64CT1dVLjXnlOjq13n2j6q1aJc8Z+1yvnSdM2Y3a7hm6CNKCEZk1znj/xTmTguSf0t1nnz1vcaD6zzB074oR+LcU3xzODaSrwCNYy8f8zDGNzzQBITfbAzHNUBZeSgS+PbTOPPtM0TcMwJmEF2G4FOgNPuznkWA0D/BxrXqJOwN/stwrJQEJdCjVN84hhGGcBc7ECZRPtt5p+By6qGVyqIR/rfM+1t/UBJ/u87Cm4ZPc1cJiqIXXT6xNcEhERERERERHnjtsqcqZpHgHuAwwgFPjJMIx37YGJiIr9DMOINwzjNMMw/g5sAe6qKAL4S2Na/csXpmmWmKZ5O9YE1q8AG7Cyh8rsP9dizRt0GeA5B7lubcgFhmMFurZgBXOOVtlJWM/tr8BCrNXzSoB04AfgWuAM0zQ9TqBgmuZKYCDwMla2V6G9vB+AcaZp3uXmcMdyCqkekGy0w+NERERERERETmTHM4MJ0zSnGoaRgJXJZMNaQv6Gis1Ywaeaa3Ua9m3/Z5rm3OPT0mPHNM0NwJ0+HvMv4F9e7LcA6/Vyt0861TOXXO3ncyqjaZrFwGv2W72YppmCFVz0KpjkRsUMsZtN01xWz7JEREREREQateOWRSJSw3F/79mDJZcB+7GCIRU3qAoyOd72ARO9HBIlUskwjG5YGVug7CURERERERGRY6ZBgpumaX6NNd/PFOALIAVrqJgBlGPNQfQNcAvQyTTNWQ3RTjnh/cP+sxBrDjAREREREREROQaO6xA5R6ZplgAf2W8AGIYRChSYx2tpO2lSDMMIAdphzfF1CVYAE+Bt0zQPNlS7REREREREjheboY/T0jAaLMDkjFb4kno6DZhf43cpeDF/lYiIiIiIiIjU3XEbImcYRpn9lmEYRsDxqldOSiawF/gEGGGa5uEGbo+IiIiIiIgcI4ZhdDAM4znDMLYahpFnGMYhwzBWGIZxn32k1NGq53zDMGYahpFqGEaR/edMwzDO9+LYIMMwhhqGcYdhGB8bhrHNMIxywzBMw2gaaWfHM4OpHCugtdA+PE6kkmmaU6ga0lbXMhbgYRU9ERERERGRpsx2kn0iMgzjQqzkggiHX4cCg+23mw3DGG+a5s561GED3gZuqrGpnf12iWEY7wK3mKZZ7qKYN6nnZ97G7nhO8p1h/6m5cERERERERESkXgzDGABMxwou5QIPYa0kfhbwjn23U4DvDMMIr0dV/6EquLQGuAo41f5zjf33NwNPuGuuw/0jwEJgfz3a1OgczwymJKA10Oo41ikiIiIiIiIiTdNLQAhQCow1TXOpw7ZfDcPYATyNFWS6hzrMz2sYxinAvfaHK4EzTNMssD9eYRjGHKxg0WDgPsMw3neRLTUXWACsALaYplluGMYCrDhJk3A8M5hmYkXszjAMI/A41isiIiIiIiJyUrA1otuxZBjGqcBI+8P3agSXKjwHbLHfv6uO80H/jarknDscgktA5WJld9gf+gN3OyvENM3ppmlONU1zk5thdCe04xlg+gDIBJoDjx7HekVERERERESkabnE4f4HznawB3I+sj9sDozxpQLDMAzgYvvDraZpLnNRzzJgm/3hxfbjTjrHLcBkmuZBYDJQCNxvGMZThmEEHa/6RURERERERJo6m9F4bsfYCPvPPGCVm/0WOtw/3cc6OgJtnZTjrp52QIKP9TQJx20OJsMwLrLffQp4GLgP+JN9vOIqrOymAheHV2Oa5pxj0kgRERERERERORH0sP/caZpmqZv9tjo5xls9XZTjTT2JPtZ1wjuek3zPAkyHxwbQArjOfvOWyfFtt4iIiIiIiIj4yDCMOG/2M00z1cdyg4Fo+0O3x5qmedgwjDwgDGjvSz2AY/s9tTHF4b6v9TQJxztQ4yxJ7qQcmygiIiIiIiJytBmG6Xmn4yfF8y6A73GBcIf7uV7sXxFganYM68lzuO9rPU3C8QwwzaF6BpOIiIiIiIiIiK+CHe4Xe7F/kf1nyDGsp8jhvq/1NAnHLcBkmuYlnvcSERERERERkSbiWA0VK3S4H+jF/hULjHk173Md63FcxMzXepqEox5gMgzjEfvd5aZp/nC0yxcRERERERER547D6m1e83VuJR8ccbjvzXC0MPtPb4bT1bWeMIf7vtbTJNiOQZn/Ah4Fxh2DskVERERERETkJGaaZiFw0P7Q7UTihmG0oOAuiuIAACAASURBVCr44+2cUBUcA2SeJix3zNbytZ4m4VgEmEREREREREREjqXN9p9dDMNwNzqru8P9LXWso2Y5R7ueJkEBJhEREREREZEmwtaIbsfYYvvPMGCQm/1GOdz/3cc6EoG9Tspx5gz7zzQgycd6mgQFmERERERERETkRDPL4f4NznYwDMMGXGd/mAXM96UC0zRNYLb9YXfDMIa6qGcoVRlMs+3HnXQUYBIRERERERFpImyG2Whux5JpmsuBRfaHNxmGMczJbvcAPez3XzJNs8Rxo2EYow3DMO23qS6qehEos99/xTCMkBplhACv2B+W2vc/KR31VeRERERERERERI6Du7CGvYUA8wzDeBIrSykEuBL4s32/7cBzdanANM3thmE8A9wPDAZ+Nwzjf8AuoDPwD2CAffdnTNPc4awcwzBaA+fV+HVrh+1TamxbbJrmzrq0uaEowCQiIiIiIiIiJxzTNNcYhjEJ+ASIAJ50stt2YLxpmkfqUdVDQCxwI1Yw6XMn+7wH/NNNGd2BD9xsr7ntBkABJhERERERERE5/mxGQ7fg+DJN8xvDMPpiZTONB+KAYqzgzJfAq6Zp5tezjnKsYXhfYWVFDQGigQPACuAt0zTn1qeOpuBYBpguNQyj9zEo1zRN86xjUK6IiIiIiIiInGBM00wG/m6/+XLcAsDrkJxpmt8D3/vUuDrWdSI6lgGmtvbb0WQAJ+Vs7CIiIiIiIiIijdWxDDA16ciciIiIiIiISGOjpeKloRzLANNy4KQfgygiIiIiIiIi0tQd0wCTaZqPHcPyRURERERERESkEdAqciIiIiIiIiJNxMm2ipw0HhqeKSIiIiIiIiIi9aIAk4iIiIiIiIiI1IuGyImIiIiIiIg0ETbDbOgmyElKGUwiIiIiIiIiIlIvymASERERERERaSI0ybc0lGOVwaS3tIiIiIiIiIjISeJYZDB1tP/MOQZli4iIiIiIiIhII3PUA0ymaSYf7TJFRERERERExDNNtCwNRe89ERERERERERGpFwWYRERERERERESkXrSKnIiIiIiIiEgTYTPMhm6CnKSUwSQiIiIiIiIiIvWiAJOIiIiIiIiIiNSLhsiJiIiIiIiINBE2o6FbICcrZTCJiIiIiIiIiEi9KINJREREREREpIlQBpM0FGUwiYiIiIiIiIhIvSjAJCIiIiIiIiIi9aIhciIiIiIiIiJNhLJIpKHovSciIiIiIiIiIvWiAJOIiIiIiIiIiNSLhsiJ+GDM6SsaugnSQNavHNTQTZAGlPBUdEM3QRrIU8s6NHQTpIHctVTfw57MZp+T29BNEKkzm2E2dBPkJKWeU0RERERERERE6kUBJhERERERERERqRcNkRMRERERERFpImxGQ7dATlbKYBIRERERERERkXpRgElEREREREREROpFQ+REREREREREmghlkUhD0XtPRERERERERETqRRlMIiIiIiIiIk2EJvmWhqIMJhERERERERERqRcFmEREREREREREpF40RE5ERERERESkiTAMs6GbICcpZTCJiIiIiIiIiEi9KMAkIiIiIiIiIiL1oiFyIiIiIiIiIk2EVpGThqIMJhERERERERERqRcFmEREREREREREpF40RE5ERERERESkiVAWiTQUvfdERERERERERKReFGASEREREREREZF60RA5ERERERERkSbCZpgN3QQ5SSmDSURERERERERE6kUZTCIiIiIiIiJNhM1o6BbIyUoZTCIiIiIiIiIiUi8KMImIiIiIiIiISL1oiJyIiIiIiIhIE6EhctJQlMEkIiIiIiIiIiL1ogCTiIiIiIiIiIjUi4bIiYiIiIiIiDQRfg3dADlpKYNJRERERERERETqRQEmERERERERERGpFw2RExEREREREWkibIbZ0E2Qk5QymEREREREREREpF6UwSQiIiIiIiLSRNiMhm6BnKyUwSQiIiIiIiIiIvWiAJOIiIiIiIiIiNSLhsiJiIiIiIiINBEaIicNRRlMIiIiIiIiIiJSLwowiYiIiIiIiIhIvWiInIiIiIiIiEgT4achctJAlMEkIiIiIiIiIiL1ogCTiIiIiIiIiIjUi4bIiYiIiIiIiDQRWkVOGooymEREREREREREpF4UYBIRERERERERkXrREDkRERERERGRJsJmmA3dBDlJKYNJRERERERERETqRRlMIiIiIiIiIk2EJvmWhqIMJhERERERERERqRcFmEREREREREREpF40RE5ERERERESkifBr6AbISUsBJpETQLs2kdx0zVDOOqMrbVtHUlRSSnLKYb75cRNTP1tOYWHJUalnzIguXHP5YPr3aktUVBiHDuWxdtNepn25kvmLd7o9dujgDgzuH0//Pu3o1KElUS1CiYwIprCwlLR92axYs4dPv1rFhs373JbTtVMMZ406hWGDO9CtSywx0c0wy00yD+axZkMqM+as49dFO47K821s9u09zJefLmbJoi1k7M8iINCfdu1bctbYfkycNJzgkMCjVtfSxVuZPWMZWzalknU4l+YtmtGjVxwXXzaUYSO6e1VGaWkZ33y9nHnfryE5KYP8/CKiYyIYclpXLr96BJ26tPa6PVmH8/h21nIWLdhEWspBjuQUENk8jNhWkfQf1IlRZ/WmT78Er8srLy/nlutfY9P6PZW/W7LuGa+PPx6y0g+xbM5CdqzYTHZmFv4B/rRo05JeIwdw6gUjCQw+Oud7x4rNrPxhCXu37yEvO5ewyGa0PSWewecNp+uQnl6VUVZWxuoflrJ+wSoOpKRTXFhMeFQEnQZ0Y+hFZxDboY3b4zP27Gf78k0kb9hJRvJ+cg/nYNgMwpqHE9etA/3OGsIpQ3q5LSMr/RA712wlbdse0hPTyD18hPycXEwTQiPCaNM5jl4j+tN71ED8/E/8S+s2YUFc1a0tI9tG0TosiOKyclJzC5mXnMn07fsoLCs/KvWc3rYFE7u0plfLcFoEBXC4qIRNB4/w1c79/L73sNtjA2wG3Vs0o1fLcHpHN6N3y3Diw0OwGdbkH/2nLfKqDWuvGenVfivTs7j55w1e7XuiaxUSxISENgyLbUFMcBAl5eXszS9k/r6DzEraR1H50Tn/p8Y058L41nSPbEZkYADZxSVszc7lmz37WZ6Z5fbYAJtB14gwujUPp0dkM7o3DycuLLjy/I/+7nev2tAtshmnxbSgT1Q4Cc1CiQwMoMw0OVBYzMbDOXyfks6Gw0fq/Vwbo317DzF92kJ+/20T6elZBAb40659NGefO4DLrxx5VPv9JYs2M3PGErZsTObw4VxatGhGj94duPSy4Qwf6V1fUFpaxuyvl/LDdytJTkynIL+Y6JgITh3ajSuuGUXnLq77gsce+oTv5iz3qc2PPH4NF1xymtNthw4e4esvFrP09y0kJ2aQl19ISEgQcXEtGTy0G5dNGkHbdi19qk9E6s8wTS1hKOKtdr0fPe5/MOeMOoWXn5pIRHiw0+27Eg9w3e3TSEo5VOc6DMPg6X9dyNUTB7ncZ9qMVfzjsW9w9T9j5c9/p03rSLf1lJeX88Fny3n0qR+clvPify7l8ov7e2zv/MU7uP2+GeQcKfS479GyfqXr1+ZoWLxgM4899Bl5uc6fU3yHGJ599Ubi4qPrVU95eTn/+/dXfDPT9UXehRNO5R8PT8Rmcz2KOutwHvf85T22bEpxuj0w0J+/P3AJF01wfmHo6Nd563jmP1+TnZXvcp+RY3rxvxeneCyrwozPfuf5p2ZV+119Akw/pR29i3yAbX9s5KtnPqYo3/n5btkulmse+zMt28bUuY7y8nK+eXk6q+ctc7nPwHOHceEdV7g913nZuUx79C3Stu9xut0/wJ9xt13GoPOGOd3+9XOfsO6XFR7b22VQdy77x/WENAt1uv2XD7/jt+nzPJbTKqENVz3yJ1q0PjofLJ5aFnZUyvHFGe2i+M/wboQHOv8eMCknnzvmbyLFxf8LbxjAw6d1ZYKbQPDXO/fx+B87cdXxPTa0Kxd3dn38iR5gat68YWaSGBbbgof6n0KzAOfnf09uAQ+s2Eyai/8f3jCAe/t0YXx8K5f7fLtnP89t2OXy/P+jbxfOb+/6eG8CTC8N7U2/lu6vHQB+SM3g2fU7KT2On1tmn5N7TMtftGADjzzwset+PyGWF167hfbxde8HwOoLnnzsc+Z87bovuHjiMB54ZJKHfj+Xv93+Jps3Ou8LAgP9uffBy7hk4nCn2+sSYHr347vp279jrd8vW7KFf973ITk5rq8bgoIDeOCRSYy78FSf6qwQGXjuCT1N9uub5zWaD/m39xx7Qr+W4htlMIlHhmEkAR2AD03TnNKwrTm59OremjeevZyQkEBy84p49d1FLFmeSHBQABed35vJlw+mc8doPnr9Gs6f9BZ5+cV1qucfd51VGVzasHkvr3/wO8kph+jQPorbbzidPj3bcs1lgzh0OI+nXvrFaRn5BSXMX7yDVetSSUw+SEbmEY7kFREb3Yz+feKYfPkgYqPDuemaoRQUlPDfF3+uVUbr2HAADmfl8928zSxZkUjq3ixKy8rp3b0Nf75uGF06xTBmRFemvno1E6d84DLgdSLZtiWNh//xCUWFJYSGBnHtTWMYOKQzRYWl/PzjWuZ89Qd7kjO596/v895ndxIW5jzY6I23XvmhMrh0Svd2XDNlNO3atyQt5SDTpi5g+9Y0vvl6OS1aNOPWO893WkZZWTkP3P1hZXBp9Fm9uWjiaUREhLJpwx6mvvMLhw/l8vTjXxETG+k2I2ruNyv5zyNfUF5uEh0TwaWXD6VP/wQiIkPJyy1k1479LFqwCX8fMlIy07N585W5GIZBZPNQsg7n+fAKHXv7dqXy5VNTKSkqITAkiJFXnE3Hvl0pKSph42+rWfXDUg6mZTDt0be55aV7CAqt2/n+5cPvKoNLbTrHcfplZxHVpiWH9h3k9xm/sG9XKqt/XEpYZBhnT7nQaRnlZeV8/sR7lcGlHsP7Mui84YSEh5K6LZnfPv+RvKxcvnl1OhEtI51mRB05mA1ASHgoPUf0p2OfLjRvFYXNz8a+XWksnTmfA6kZ7Fy1lU8fe4cb/neH0w85hs2gdad2xPfsROtO7QiPiiCseTjFBUUc2neANT/9QcqWRNKT9vHhg69x++v/IDA4qE6vXUPq1iKM/43oToi/H3klpby/KZUV6VkE+dk4r0MME7u2ISEilFfG9OLquWvJLy2rUz1/7Z9QGVzaciiXqZtTST1SQFx4CFN6xtEjqhkTurThcGEJr6xLdlqGQdVnhtySUrYeyqVDRCgxdcy6+GL7XqZvd53lWlDH53oi6RIRxqMDuxHs50d+aRnTdqay5mA2QX42zmwbzYXxrYlvFsJ/h/TklsXrKCir22tyc7cOlcGl7dm5fL4rjbT8QtqFBnNl53acEtmMC+Jbk1VcwrvbnAcUHD8x5pWUsiMnj/ZhIbT0Ifsy2r5vZmERC/cdZP2hHNILivAzoGeLCCZ1bEtMSBDnxcXibxg8sXZ7nZ5vY7NtSwoP3je1st+//uZzGDSkK0VFJfw0dzWzvlrCnqQM7v7LW3z4+b316vffePnbyuBStx5xXHvDWcS1jyY15QAff/AL27akMvurpbRo0Yzb73LeF5SVlXPf396tDC6NObsfl0wcRkRkGBvXJ/HB2/M4dOgIT/17OrGxzZ1mRN125wVMnnKm27bm5BRw240vU15uEp8Q6zS4lJZygPvuepcie/b+GWP6MO7CIbRu04LMjGwW/rqB7+Ysp6iwhH//cxrt4qLpN6CTT69ZU6BV5KShKINJPFKAqcrxzmD6auoNDB2cQElJGROnvM+qdanVtt96w+k8fM9YAJ57fT7Pv77A5zo6dWjJr7P+QkCAH2s3pjHx+vcpLCqt3B4cHMBXU2+gf+92lJSUMfqiV51mS/n52ShzM2SjRWQI337+ZxLaR1FSUsaA0c9wOLug2j7PP34Jq9al8OXstRSX1L5oDg4O4NO3ruW0QR0AuOvBr5kxZ53Pz7kujmUG0203vM661Yn4+dt4/f3bag0FmzZ1Aa+98B0AN956DjffNrZO9exJyuSaic9SVlpO915xvPH+7QQFB1RuLywo5vab3mDrplT8/G18NvM+pxlT385czpP/+hKACZOGce+DE6ptT91zgBuueom83ELi4qP5dOa9TgNESbvTmTLpRYqLSxkytCv/feF6QkOdBwRKSkoJcPFtfk33/20qv83fxAWXDCEt9SBrVu4GGk8G0/v3vUzypl3Y/Gzc+PSdtO9R/QJ68Yxf+On9OQCMvvo8xkx2Huhz50BqBq/d9l/Ky8pp27U9Nz59JwFBVc+huLCYD/7xMnt3pGDzs/HXtx50mi21et4yZr/4GQBDxo/ggr9cXm37wb2ZvHXnsxTlFxLVNpq/vvUgfn7Vz/XM56fRvkdH+p99Kv5OzmFxYTEfP/wGezZZ5+nSe66h/1m1v3EuKyurVXZNc9/6mmWzFwJw/i0TGHrxKLf7e+N4ZzC9d05fBsVGUlJezk0/rWf9gepDg67v0Y67B1oflt5cn8ybG5x/+HcnPjyEry4YSIDNxqaDR7jxp/UUOfz/Dvaz8d45fenVMpyS8nImfLPKabbU2A7RBPvZ2HQwl93Z+ZjAu2f3YXCr5oDvGUx1fT7HSkNkMFVk9JSWl3Pn0o1szqp+/id1asdtPRIAmLp9D1N3OM8idScuLJipZwzA32Zja9YR7ly6kWKHIXdBNhsvDetN9+bhlJaXc/3CNU6zpca0iSbIzyojObcAE3hxaG/62zOSvMlg+u/gHvyYlsFv+w7i7AoiMsCfV4b3Jb5ZCAB3Lt3A+kM5Pj/nujiWGUx/vv4l1q7ehZ+/jbc+uKtWIOXjD37hlednA3Dzbefx59vH1ame5KQMrrz0ScpKy+nRK563pt5JsEMAsLCgmFtueJktm/bg52/ji9kPOc2YmjNzKU88YvUFl00awf/984pq21P2ZHLdpGfIyy2kfXwM02c/6NMXQxVmTF/E009Y1xe3/HU8N91ybq19nvnPl3z5ufW/ZfINZ3Hn3y+utc/0aQt57qmvADj9jF688NotPrflRM9genNL48lgurWHMphOJlpFTqSR6t+7HUMHJwDw+czVtYJLAG9NXcL2XRkA3HTNUPz9ff+TvvnaoQQEWBcBDz/5fbXgEkBhYQkPP/k9AAEBfvzpOufDYNwFlwAOZxfw6YxVleUM7BdXa5+/PzyLaTNWOQ0uVbTlgce/rXw8/hzv5gxozDZv2MO61YkAXHjJqU7nGbrqujNI6BQLwJfTFlPq4vXxZPq0RZSVWufp7/dfUi24BBAcEsjf778EgLLScj7/5Den5Xz6kfUBPiIylL/efUGt7XHx0Vx74xjACjb99utGp+U8/9QsiotLiY6J4MnnrnMZXAK8Di4t+HkDv83fRPMWYfzl7vFeHXM8pW5LJnnTLgAGjh1aK7gEMHzCGGLsw06WzVlIWR2yNpbNXkC5/W9y3G2XVQsuAQQGBzLutssAK0tp6cwFTstZ8tWvgJV9NPam2hfxLdvGMPKKswE4tPcAW5esr7XPpX+/hsHnD3caXKpoi2PgavNi50FjT8ElgBGXn115P9kesDqR9G7ZjEGx1ofzWbvSawWXAD7aksaubGtYyNXd2uJv+H7dPrl7WwLsWWJPrdhVLbgEUFhWzlMrrPdpgM3G5B7tnJYzL/kAc3ZnsMseXJL66R7ZrHK42PcpGbWCSwBf7E4j6Yh1/id2bItfHc7/ZR3b4m8//y9v2l0tuARQVF7Oy/a/H3+bjcs6tnVazvx9B/ghNYMke3CpLh5YuYUFLoJLANklpbyxJbHy8aijNPS1IW3akMza1dbf10WXDnOapXPN9WPo2MnqB6Z/srDO/f7nnyyo7PfvfWBiteASWP3+vQ9MBKx+/7OP5jstZ9pUqy+IiAzlznsuqbW9fXwMU246B7CCTQt+qd0XeOP7OdZwasMwGHfhEKf7rF+XWLnPjX92/oXbFVefQUSkNdx6w7pEp/uIyLGhAJNII3XeWVXDiqbPXON0H9M0KzN4mkeGMPzU2hcpnowdY9WzY3cmq9fXDmIBrF6fys7dmQCcO6abz3VUyM2rGsIX5GJuEU+27czg4CFryFOH9lF1bktj8dv8TZX3x1/s/GLKZrNx3gVWBtWRIwWsWuF+wnVnTNNkkb2uDh1j6d23g9P9evftQHyC9e3lovmbaw1B3JOUSdJuK6h51ti+LicgHX/x4Mr7C50EmJISM1j5h/U8LrvqdMKa1T39v0JebmHlvEt/uXs8kc2P/9w5nmxdWnXRPeAc5/NT2Ww2+p1lvRcKcwtIXO/bpPamabJ1mfWaR7dvRfvuCU73a989geg4K3C5bdmGWuf6QGoGmSnpAPQaOcDlpOP9z656HlucBJi80SqhLaER1vk6tO9AncoACHIIUpYWH53FD46nMXFVH55n70p3uo8JfLvb2hYRFMCQVp7nr6lptL2e3dn5bDjofPLkDQePkGgPZI2OO/E/1J8IRrSu6tPmpro+//PSrP/B4QH+DPBi/qKaTm9l1ZOcm8/mLOdZOpuzctmTa53/Ea0atq9dYx9mC9C2HkPFGouFv1b9n7zQxQTWNputcu6gI0cKWLnC96GBpmny23xrzrKEjq3o08/5NWKffh3pkGD1Bb/Nr90XJCdlkGj/n3P2uQNc9/sOz6UuAaY9yRlsXJ8EwMDBnWnT1vn7rsQebItsHkoze2ZbTYZh0C7OysCua3DuRGczzEZzk5OLAkwijdSQAfEA5OUXsd7NymvLVibVOsZb8XEtaNMqolY5zuux5uBo0zqS9u2a+1QPWJ39hedWrRK1M7HuHyIrMq7KjtIqOg1p3Rrrm7WQkEC69XSeJQAwYHDV/AEb1ib5XM/etEMcyLSGFQwY5H4ugortmRnZ7EurvorU+jVV3wT2H9TZZRktoyOI72AFqtY7ae/8eVVZKiNGVWWi5eUWkpKcyeFDvg9NeP2l7zmQmUP/QZ1cBusa2p5N1usXGBxIm67tXe6X0KdL1TGbfcvEObz/YOW8Rwm9XZ8jgA72enIOZpOVXn3oq2O9ju2pKTwqgpbtYu3H1P2b4opMLVs9Jo7YsHB15f1oN5MPN1b97dlL+SVlbDnketWsVRlVH7j7x0T4VEe7ZsHE2gNxjuW4q6dVaBBtw068+axONH1aWOeyoLSM7dmu/weuPVg1RKx3i3Cf6mgTEkSMfW6ydQfdDzWrqCcmJIjWIQ13/gMc5mQrbwKfVdeutv63hoQE0r2n635gwOCq/7uOfa+39qYeJNP+N+xYljMD7dszMrLZm1a9L1i3Zlet/ZyJjo4g3h6oWr/W9wzSiuwlwO3E3BXBsOysfHJzC1zul5ZqXWdWtElEjg9N8i3SSHXtZH04T9pzyO3wM8dATdeOvq0wdkrnqnH2O3e7D/hUq6dTDClp7pcvBuuDYmx0M3r3aMMtU4YzbEgCAL8t3cX2XZk+tbVCr+6tK1fU89TmE0FyovVNdFx8S7fzFXToWHWBlGQ/xhdJDtkQjmV5riudtnFV3yJWfItp7ed+ZZv4jjHsSc4kY382BfnFhIRWfeu50T7Pir+/Hx06xrLs9228/9ZPbHSYTLhV6+ace8FArr1hjMcMpw3rkpj15TL8/f2476EJbvdtSJkp+wGIahvjdshXdFxVcOTAHueZDC7r2LO/qhwPQZaKDKaKtjmuvFatnDj375no9rEcTMsg50AWxYVFPk+uvW9XauWKetHtXa9K5kzBkXyyMg6x7pcVLP/WmpfDz9+PIeNO96mcxqBjhDWkI+VIAWVuPkgn5lR9qOoY6XzVPVc6OeyflO16BSawVqtzPG5vXpFPdfnqnPhozukQQ9uwIMpNk4MFJaw7kMPs3emsTHcfDGsKOthXUEzLc3/+KzKLHI/xVkJ41f6O5TitJ6/qfdahWSj7C47t+XelX1RVEDXZQ5tPBEmJ1v/WuPgYt/1+Qseq/9+Ju/e73M+V3Q7HJHjs96vqStq9n3YOWYuJDtcPjm1yJiEhlj1JGaTvz6Igv4gQN0PfHZmmydzvrABTcEggZ451vaLwhMtPZ/7P6zBNk6nv/MRf776o1j5ffvYbOfb/bxOuOPH6gqPBT7MeSQNRgOkkYxhGW+BOYCzQGQgFDgEZwEbgR+Br0zR9nkHRMIwLgeuBoUAMkAtsB2YDr5qm6fTrOMMwpgAf2B92BPYBdwBX29toAFuAD4G3TNN0m+tqGIYfMBm4HBgItLS3ZQvwNfCGaZquv/JoBIIC/WkZZQ0X2Zfu/lRk5xSSl19EWGgQbVv7lipfkb3kTT179zukqHuoJ23jYy63rd+0l789ONPLFtZ255/OqLz/zQ/O5/Y5URQVlVSucBYT6z4rLCIilJCQQAoKiknf7zm4V1OGw4ezGA9DamJbV7Ulo0ZdjuXEtnLf5lb27aZpkpGRVfmtI1QFvJqFB/PFtEW88ty3tY5P35/FR+/+yoKfN/Dim3+idZsWTuspLSnjqcdmYJomV18/io6dG2fmSklxCfk51vmO8DCsJSQ8lMDgQIoLi8k+4Nv5znHYPyLa/TmKjKl6TbMzq9fjUznRVjmmaZJzIKtagMwbv03/qfJ+75GuP1hUmPn8NNb+7Hy564CgQCbcO5moNr4F3BtaoM0gyj4vWrqHD/JHikvJLykjNMCPVl5+gKvQyiHQm+5h5dH9DsOafa2nLjrXGNYaFuBPfEQIF3Zqxa8pB3hk6XZym+hwl0CbQfMg6/xnFro/L7mlZRSUlhHi70esjyv2xTgEfz3Vk+nwPvS1nqPFAK7uXDVn44K9J/YXS479vqc+NCKyvv1+1TEe+2uHfr9mXb6VU9UXZKRnVQtcubNm5U722TOnRp/Z1+2qeacN784NfxrLB+/M46P3fyZlTybnjR9MqzYtVoNQLgAAIABJREFUOJiZzYJfN/Dd7D8AGH/RqVx4yVCv2iAiR4cCTCcRwzBGAt8CNfPpY+233sCVwAH7ft6WGwx8ClxaY1MUVrBpKHCHYRjjTdNc66G4FsAMoOaSXafab5Ps5bgKVsUDc4B+Ttpyuv12m72MRrvWbVhY1YVcnocPAAD5BSWEhQYRGurbBWCYw5AHT/XkF1RtD/OxHoD8/GL+/eyPTJ+5xuUk3p6MO7sHF9iH2a3blMb3P2+pUzmNRb5DNkCIF69psP1Cs8CL90StuvKr6vL0Pglx+CCRX6Mux3I8tdlxnoaabc6xZ2Dk5Rby6vPfEdYsmFvvOI8x5/SjWXgwu3fu593X57Fk0Rb2JGXy0D0f8/bHf8XPr/bI7o8/mE/irnTatovihj+fXWt7Y1Hs8GEt0IvhJgH2AFOxj1kDRY71eMgkCqy2slz1eop8aG+Aw/xMxQW+vT83L17L5sVW19C2S3t6nF7z37f3eo8ayNgbL6oWODtRhDlMgp5f4nn4b0GpFWAKDfBtpaYwh4yJfA8TyBeUVW0PrcOKUN4qKC1jQepBlu/PIjGngILSMloEBTCoVSSXdWlDi+AAzmwfTUSgP7f+spHSJrgCcojD6+v4urtSUGYFmEK8mPy+rvU4bve1nqPl8o5t6WkfBrhw3wG224P0J6r8vKrV+NwtbFEhJCTI3u/7nj3meI3hqa4Qh//xBTX6HMc2e8pICq52/eB9m7/7pmp43PiLXA+Pq3DbnRcwaEhXPnh3HvN/Xsf8n6svDtGtRxw3/GksZ57j+QsLETm6FGA6SRiGEQR8jhVcOgK8AczHylwKxMocGk7tIJE3PnQ4bh3wHFa2UBRWwGoK0Bb4xTCMvqZpprkp6y2s4NJ0e7kZwCnA3cAQ4AzgY2ftNAyjJbAYaA8UAe8AC4EkoBlW1tZdQBdgrmEYA03TbJQ598FBVX+aJV4EY4qLrZXfgoN9+5MODvS+nuLiqu2O7XPmzEteA8DPzyC6ZTOGn9qRay8fzMP3jqVzQjRPPD+P0lLf5k/q0ima55+wVi4pKCjmzge+9un4xqjivEHVvFLuBNrPV1GR75MXFzsc4+/v/vw5rthWs67iIu/bHOjw/ioqrF5OoT0IUVJShs1m8PRLUxgwuGq+oO4943j65Sncd8cHLF28lS2bUpj/03rOPq/6xWJKciYfvvsLAH9/oPbKeI2J46TTfh7OAVC56pqvk1WXOryv/DwEBfwcznVpjXNdvb3uy3FcIa6k2PsAU+ae/cx64VMAAoICmHDvZAwvVsU667rxDJ9grVRYlF9EemIaq35cysaFq8k5kMUlf7uqcl6oE0Wgw3iGEi/ml6tY+SvISdDVfT1V+3uqp9hheHZQHVYp9dbYr//giJM+aNn+LD7btpfXxvSmR1QzBrdqzuWntOGzbXuPWVsaSqDN8bx4DqBV7OPz+bc5vs/c1+O43dd6joZ+URH8ubu1IMWhomJe2HjirQxZk2O/7+9Fvx9wtPp9DyuxBjj014U1+usiH65VqvX7Xra5qKiEX3+yvmSIjY1kyNBTPB6TmZHNN7OWsXFdktPtO7fv5dvZf5DQqRWdOrfxqh1NTT2mMxSpFwWYTh6nYwV5AK42TbNmhtIy4DPDMO7GGjbnFcMwxgNX2B/+AowzTdPx08U8wzCWAm9jBZyeBya5KXII8KBpmv91+N0qwzC+xMqqOhe4xDCMcaZpfl/j2JexgkvJwBjTNGvOiLjAXs4ioBPwf8BDXj7V46rQhw/xUNWhFxaWetizRj0+XTRUbXdsnzPbdjrOEZTOb0t28dHny5nxwQ386bphnNIlhsm3fkK5l7N1tooJ5+PXJxPeLJjy8nL+/sjsRj//UmZ6dmWWTk0RESHEtIqsdiHmSyAxKMj3IEqgwzGlpe7PX0lJ1faadQXWCH4GBbn+0OF4IV0z8BMY6E+BPcg0/Iwe1YJLFWw2G3+5ezxLF28F4Jcf19UKMP3v8a8oLipl9Nl9GD6yh9vn1dD8A6tegzIP5wCg1H4eHI/zrp6qc1TmIUOlzOFc+9c419XbW4Yt0PW5LnUoJyDQuwzHnIPZfPLoWxQVFGEYBhf/7Wpi4r2bfykiunm1YXvxPTsy6LzhfPf6l6ycu4R37n6BKU/9ldadXE+c39gUO0y64zipsSsVAYkiN3P0Oa+nan9P9TgGo4p8/FLAF86CSxUOFZZw76ItzLpgEAF+Nq46pW2TDDAVlzueF8+fDCv28fn8lzu+z9zX47jd13rqK6FZCI8P6o6/zUZRWRn/Wr2NrEa+MmRGehZHcpzPERUeEUpsq+bV+n1vVjcrOVr9fomHft+hvw6u0V8HBdax3/eyzQt/XU9erpUldd4FQ7B5+L+UuHs/d/zpNTIysolsHsZd913CyFG9adkyguzsPP5YupU3X/mOxQs3sWbVLp57+U8MHNLVq7aISP0pwHTycLxq/83VTqZplgK+zL/0F/vPEuCGGsGlijLfMQzjCuBsYIJhGG1M03S1LNp64Cln7TIM42ZgNxAA3A5UBpgMw0igKnD1VyfBpYpy1hiG8RpWcGkKjTTAlJfn23C00BCrE685nMlzPVXpy57qCQ3xbdheTXv35/DQf77jkzevZdTwLlw1YSDTZqzyeFzziBA+ffta4uOsIS//fHIuc+Y2/rmX3np1Lt/Pcf78xl00iH8+fiWhDkMUvRn2VpH1481wupoc0+M9vU8KHIY41RxO51hOQX6x2wvIQodyarY5NCyosp7Thrn+trJTl9bExEaSmZHNlk0p1bZ9O3M5q1fsIjQsiLv/72I3z6hxcBxm5s2wtxL7/CjeDKdzFORYT6H7eoqLqs5RzeF0QTXaG+Am0FXiMJdLoBdzteQfyePjf75euXLd+bdOpM+ogR6Pc8fmZ+P8WyeyY+VmsjOz+Pa1L7j5ubvrVebxlOfwATA0wHOAqWKoU76PQ47zSr0f9uY4LMrTcLpjKS23kGX7sxjZLor4iBBiQgLJ9HEoZmNXUOrbcLSKfbwZTlfXehy3+1pPfbQOCeKZ03oRERhAWbnJv9dsZ/0hn6cGPe7eePlbvpvjfG648RedyqP/mUyow9xC3gwhqxiu5u1k2Y4crzE81eU4LC6kRp/j2OaC/CKv+31vhgBCjdXjLvK8Auy/HvyEjIxsgkMCefvDu+jYqeojTsvoCMZdeCqnDu3G5Muf5tDBIzx8/0fMnPsIgT5+WSMidXP8812loTgGdG44GgUahuEPjLI/nGeaZoqb3d+x//QHRrvZ70PTdD65gmmaqcA8+8PR9sm8K4wH/IB8YK6HplcE2Nra52xqdIqKSzlknwTScSJuZyIjggmzd+KOE3F7w3Fib0/1OE7s7Ws9FRYu2VUZVBg/tqeHva2g1ydvTaZ7V2uSyKdf/oUPP3d+8XYiCgoKILK5lTCYmeF+As+cnPzK185xMk5vxTpM7J3pYTUmx4m9Y2vU5ViO48SfzqTbtxuGQWxszXKqHteso6ZY+3uvYmLUCp98sACAAYM6sXZ1Ij/NXVvrdvhQ1XRtFb9bvGCz2/qOlYDAAEIjrEmMcw66PwcFR/IptgdtIj1MsF2TY2ZPjocJwrMzD1fej4ypXo9P5RywyjEMw+OE4EX5hXzy8JtkJFsrHJ157ThOu3Ck22O85R/gT5dBViZbypYkj+1uTIrLTQ7bh6a08hBUDA/0r5x7Kd3HuVkcJ/Zu5SFY3TrMcULwhllBrMJuhxXvGmrC6WOpuNwk256hExPs/vk18/erDDBm+Bhoy3QIOnuqJ8bhfehrPXXVMiiQ507rRUywtZLg/9bv4Hd7ILopsPp9qx/w1IfmZNe333dYsMNTf+3Q79esy7dyqvoCTxOCAxw8kMMfS60s5e4923sczrZ9WxpbNlmr0J43bnC14JKj6JhIrrjaWhQmMyObpYtP7Dk768JmNJ6bnFyUwXTyWIyV/dMJeNEwjGuAmVjBlhXOMo+80Imq4XR/eNjXcXtvN/utcLMNYDlWMCnMXv8O++8H23+GAqXezOFh1xrY4+3Ox9P2XZkMHRxGQnwUfn42ylykp3fpWLVS0o5E34aNbd+VWVVOJ/crLlWrZ3emmz1dKy83ycopJCQkkLg27i88goP8mfra1QzoY60e8/r7i3npbZfJd43OPx+/kn8+fqXH/RI6tWLd6kRS9xyktLTM5ZLFyYlVww49LTfstB6HldUcy/JcV/UVYDp2ciwnk1O6ux6CtCfRep/Eto6slcHUsXOryoykcg9DL8rtQ4dqTvBdbM/4+P23Lfz+m+eLx0fvnwZA67YtGDHac4DzWIhp35rkTbs4tDeTsrIy/FxkEBxIrVoWOjretxXZHIeZHUhJd7MnHEitOtcx7atfqFcrJzWDNg4rOdUqJ8UqJyK6uduJxUuKivn0sXdI22792z194pmMuupct230VWhks8r72ZmHPQa8GpPdOfkMCo6kfXgIfgYul6rvGBFSeT8x27dl2x0DNQmR7kfEJ0RUbd/tYz1Hm0nTm9i7pqQj+fRrGUm7MPfnP75Z1XlJzvXtvCQdqdrfsRyn9YRVvc98racuIgP8efa0XrSz1/vypt3MS6vb9UZDePQ/k3n0P5M97texU2vWrt5F6p5Mt/1+UmJ6tWN81cnhmCSP/X5VXQk16nJcmTUpMZ1TurvuC5KSrHpatW7uVdbVD9+trLy+9WZy76Td+yvvd+vpuh0APXq2rzouMb3yG3ERObaUwXSSME2zBLgQa/JtsOY6ehIr8JRlGMYPhmFcXSMryJMoh/vuey7Y73A/yuVenstx/LTkWE5dZ3P1er6p423FGusDWFhoEH17uv5GZ+jghFrHeGtP6uHKLCbHcpw5bbA10ea+/dmkpNUtKyDA34+oFtZL7m6Ynb+/jbdfmMTwIR0B+Gj6Cv7z/E8u9z+R9RtgPceCgmK2bXY9//2alVWTm/bpn+BzPW3bRREdY2WprVnlfqLUtautEaYxsZG0aVd9Na6+9vYCrF21y2UZBw/ksCfZ+mDQ10l7+w/qVHk/LdX9t9N70w5WtudEF9/Lev2KC4vZt8N10mfShp1Vx/Ts5HI/Z1q0bkl4S+u1Stro+hwBJNu3R7SMpHmr6v+aHet1bE9NRw7lcDAtw35MR5f7lZWWMf0/71eWNXjc6Yy96egPbTzikB3maRW9xmZthtX20AA/ekSFu9xvkMPfwtpM34YOpeUWkmHPRhrk4W9qoH17en4Re/MaNoOpk0MwrKkNj6uw4bB1LkP8/TjFIVBaU/+WVRnHGw8f8amOfQVFlVlM/Vq6z1yu2J5ZUMR+H1ez9FWYvx9Pn9aLjuHWeX5rSxKzkvd7OOrE1H+g9b+1oKCYrZtd9wNrVlb933Xse73VNq5lZb/pWJbTuuz9eWxsJG3bVe8L+g2omiNxtZtyDhzIYY89wNS3v3f91vf21eP8/f04d1zNBaRrc/yiqczDvHClDsNBXQXxROToU4DpJGKa5magD9YKbO8DFb1ECNbk2dOAPwzDqEuw5mh9tVjXcip6jgNYz9Hbm6eMqQbzwy9bK+9PunSA030Mw+Cyi6wlvbOyC1iy3OnUU27Nm2/V07VTDAP7Ov82aGDfOLp2igHgx/nbfK6jwrlndqucLHLrDueZFTabwWv/u4yzzrDm5ZkxZy0PPF5zTvqm44wxvSrvfzfb+duxvLycH7615nMKDw9h0JAuPtdjGAYj7XUlJ2awcX2y0/02rk+uzGAaOaZnrRW94hNiSOhk/Yv4Zd76avMtOPpu9srK+6POrJ20OHJ0z8oLvt9+dT2n1pqVu8jOsr457zew+gX213MfZMm6Z9zeBgyuusit+N3Xcx90Wd+x1n1Y38r7a35ynvhZXl7Oul+s90JwsxA69vVtclLDMOg+1HrND6Skk7I1yel+KVuTKjOcug3tU+tcR8fFEtPe+uZ606I1lUP2alr7c9Xz6DG8r9N9ysvKmfH0R+xYaX3H0e/MIVzwl8u9f1JeKi4sYsdKawhkQFAAUW3dZ2Y2NvNTD1bev7iz88w1A7jAnkmYU1TCCg9DXp1ZYK+nU2QofVo6D2T1aRleGdRZ4NCuhtA2LIihra1g954jBcdtuNbxtnh/VbD9/DjX53+sfYXEIyWlrPEw3NaZiiFnHZqF0rO580BWz+bN6GDPcFp8jIeoBdlsPDWkJ93sQbWPd6Tw2W53Cw6f2EadWfV/8ptZrvuB77+xpgQIDw9h8BDPK6vVZBgGZ4zpA1gZPBvWOb9G3LAusTJb6owxtfuCDgmxldnLP/+4xnW/7/BcRp/lvC9wtHP7XnZss87z8JE9ad7CdVC1Qtt2LSvvr13t/guU1Surtjsed7Jo6GFxGiJ38lKA6SRjmmaZaZqzTNO8yTTNrlgry90IVMxGPAh4y8viHK84PI3hcMy3dXel4qkcx+2O5VRc/YYDW0zT3OjlrfqkLo3I2o1pLFuZBMCVlw5kUL/awZ9bpgznlM7WheZ705ZRWuPbnGFDEkjb+BhpGx/jhScucVrPux8vq/yW5/EHxxEcVH3kbHCQP48/OA6wVg959+OltcoYObQTCe3dJaZZAax/PzCu8vGMOeuc7vfMYxdxwblWIOS7eZu4+5+z3JZ7ouvZJ74ycPLNrOVscLLk7mcf/UbSbivoc/k1I5wubbx6xS6G97uP4f3u44mHP3da16RrRlZ++/f8U7MoqrkUcWEJzz9lvd5+/jYmXeN8Xpyrr7MSzXOy83nthe9qbU9NOcDH788HIC4+mjOcBJgim4dx4QQrHX792iSnwbX8/CJefHpO5eNLLh/qtD0nkrhuHejQy/o2ePW8ZaRsqX3Bv+Tr+WTaAz9DLxqFX41vXhPX7+DRcXfx6Li7mPn8NKf1DL14dOVKPN+/MYOSouofCEqKivn+jRmANTn2sEucDx4YPvFMwJoT6qf359TafmjfARZ98TMAUW2j6e4kwGSaJnNe/pzNi61lqHue3o9L7r661ocYd/KycyuPd6WkuIRZL3xGXlauvZ7+BASdWHP1bDyYyyp7FtMlnVvRN7p28Oe6Hu3obA/8fLptL6U1pi0cHBvJ2mtGsvaakfzbxXLfn2zdS6l9NbH7h3SutQR9kJ+N+4dY79OS8nKmbT12H/bPaBeFn5u3QlRwAM+d0bNyRbsvtrtaI+TEtzU7l3X2gNG49rH0bF77/F/RqR0J9iyfrxL3Ulbj/PePimDB+NNZMP507u/r/MuIGYl7KbOf/zt7dapckbBCoM3Gnb2s4HxpeTkzEo/dqn3+hsHjg7vTJyqism3vbW+UMxccNb36dKD/QOvva87MpaxfW7sfmPbhfBJ3W/3ApMmjnPb7q1bs4NQ+d3Jqnzt57KFPnNZ15eTRlf3+s//9isIaXxQUFhbz7H+/Aqx+/8prRzst55opVl+Qk53PK8/PrrU9NSWTqe9Zmebt42O8CjA5TojuzfA4gG494oi1Z2XN/3kdy5c5/9Jz6+YUvv5iMQDBIYEMPlWryIkcL5qD6SRnX83tA8MwPgGWAQOBCwzDCDFN0/ka61V2Y02qHQqc5mFfx57D3RJgQ4BFHrZjr9dxnM8a4GogCGs+Jk9zQp0QHnlqLrM/vomQkEA+ffs6XnlnEUuWJxIc5M/F5/dh8hXW1FO7Eg/w1tQldapjd/JB3pi6hDtuHkn/3u2Y9fFNvP7+YpJSDpPQvgW33ziCPj3bAvDm1N9J3FM7PjhkYDyfvDmZxX8ksvD3nWzZns6hrHz8/W3EtWnOGcM7M/HCfoTYl7797OvV/O4k2+qRe8/lykutlaS2bE/n5XcWVWZOubJtp6dRlY3f3/7vYm6d8hpFhSXcfeu7XHfzmQwc0pmiwhJ+/mEts7+y3s7xHWK46roz6lxPfEIMV18/io/fn8/WTanccv1rTL5hNO3atyQt5SCffLCA7fYPktdcP5r2HZy/9udfNJhvZ61g/dokvpq+hIMHj3DRhNOIiAhh88YUPnj7Z/JyC7HZDO7+x8UuU9Nvvm0sSxZtIX1fFk89NoPNG1MYc3ZfmoUHs3vnfj75YH5lYO3SK4bRo1d7p+WcaM6/dQLv3fsiJUUlfPTPNxh5xTl07NeV0qISNvy2mlVzrb/llu1iGT5hTJ3qiI6LZfjEM1n85c/s3ZHCe/e+xIjLzqJFm2gO7zvA4hm/sG9XKmDNg9SynfPE1f5nncqaecvYszmR5d8uIvdwDoPOG0Zws1DStiez8LMfKcovxLAZjLtlotM5pX58d1ZltlZshzaMnHQOmSnuh760Smhb7XFxYRHTn/yAqLbR9Dy9H+1O6UBEy0j8AvzJz8kjbXsyq39cxuH91ncNES0jOefGC31+3RqDp1fuYurYfoT4+/HGmb15b1MKK/ZnE+Rv47wOMVzW1RoynZSTz0db6hb42XOkgA+3pHJTr/b0ahnO1LH9+GBzCqlHCokLD+aGnu3pEWVlE3y0OZU9RwqdltMyOIDT27ao8buqoN5Fnaq/r9Zk5JCSW72s+wd3xt/WhZ/3HGD9gSPszSukqKyc5kEBDG4VycQubYiy9x2rM7KZvv3YBTsag1c2J/Lq8D4E+/nx7Gm9+GRnCmsPZhPo58eZbaK5qIP1fd2e3AKm767ba5GaV8jnu9O4pksc3ZuH8+rwPny2K429+YW0DQ3mqs7tKofofb47jbR85+c/KiiAU2Na1PpdhfPiqp//DYdyapX18IBTKstYdSCL71LS6ehmbqgSs5zUPOftOZHcc/8Ebr7uRYoKS7jzlteZ8qdzGDSkK0VFJfw0dzUzZ1j9QHxCLNdcf2ad6+mQEMvkKWfy4Xs/s2XTHv503Ytcd+PZtGsfTVrKAT56/2e2bbH6gmunnEV8B+d9wfiLTuObmX+wbs1uvvx8EQcP5nDxxOFERISyaUMy77/1Y2W/f8/9Ez0OSSsrK+fH761M54jIUEaM6uV2/wo2m43b/3Yh/3rwE8rKyvnbbW9y6eWnM3J0L6Kiwsn+f/buOzyqYv/j+HvSCyQQIPQQeu9VkF5UEEHsomLB3usV67Vevbaf5V4bKrargoKIYAGpSpfeeycJEEhIb+f3x9kkm2RLNgED8fN6njzscubMzOYk2dnv+c5MUhrLFm9h8lcLCzKtxt00lKoRZ+yKGCKVjgJMAthrNBljFmAHmAKAaoDHAJNlWTmOcy4AhhpjGjh2enNlvOPfHGC+h2qvNca84WonOWNMfWCY4+l8y7Kc98ydAfwbO3v8PuAqT30/W2zcEsftD03hrZcuIaJqCBPuG1KizM7dR7nuji89rmnkzctv/kbNqHCuGtOF9m3q8e6rl5co87/v/uTlt+a6rSMgwJ8BfZoxoI/76Vs5Obl88NkS/vV/c1weHz60dcHj1i1q88uU27z2vX67p72WOdO1bF2f516+hmce/4rUlAzee6vkRogxjWrx6js3Eu60XXBZ3Hr3+RxPTOHH71ewbctBnvpHyQyYkRf34Ja73C+87O/vx0v/dz0P3vkRmzfuZ/6c9cyfs75ImaCgAB6YMJpzzm3ltp7qUVV447/jefieTzi4/xjTJi9h2uSSGXIXju7O/Y+c+rV6Kkrdpg247NHr+e6Vz8lMy+C3T0tOAa1RP5qxz9xCcFjZr/fgcSNITTrJ6l+XcXjnAaa8/GmJMl2G9WLQdSPc1uHn78eVT47ny6ff5+C2fWz6Yy2b/iiafRgQGMDw2y+leXfXC6dv/mNdweOEvYd5/55Xvfb9mVlvuvz/xENH+X3Kbx7Pbdg6ljEPXUvVqLNzza6tx1P5x+9beKF3S6oGBXBPp5Jrr+xJTuPueRtJyyn71vHvrNlDVHAgFzerQ+uoKvz73NYlykzdEcc7a11PpwVoHBHGs+e0dHu8+LGnlmwtEWACiA4L5upW9bnaQ39n7zvKs0u3kZ1XuRf73pGcyjOrtvJ4pxZUCQzgllaxJcrsS0lnwopNpOeW/fpP3LqXasGBjGhYmxaRVXi6S8nrOHNfPB9tdZ9NFBMeyqMd3WeGFD/20trtJQJM/esWTmPtWrMan/RzvSRAvri0DK6c96fHMmeDlq0b8uIr1/PUhM9JTcngv2+WfB+IiY3mjf/cWu73/dvvuZDExBRmTFvK1s0HePzhSSXKXDSmF7fd7f69wN/fj1feHM99d7zHpg37mDt7LXNnF30vCAoK4KHHLqV3X++baCxfsoWjjvXjhp3fhcDA0n8kHT6yB4nHTvLfN38kJyeXKV8tZMpXJTeBMcZw5TX9ufGWYS5qqfz8TeX+WylnLgWY/iaMMX2Bw5ZluVydzxgTBAUbLKQApd224z/YAaYg4CNjzIWOBcWd676RwsDQVEfWlDudgIexg0XOdQQAHzraAXjX+bhlWVuNMVOAy4ErjTErLMt63V0jxpjGQC/Lsr7y9gIr2uwF2xgy5r+Mv6YXg/u1oG7tCLKyc9mzP5Eff9nIJ18tJ6PYVCdfWZbFQ09NZ9bsTYy9tCsd29UnqnoYicfTWLvhIF9MWcm8390v7PjhZ0vYsfsovbvH0qZFHaJrVaFmVDjGz5CUnMHO3UdZ+udevv1hDXv3H3dbz9/ZuQPa8NmUB5j85SKWLNpCQvwJAgMDqB9Tg0FDO3DplX0IOQVbc/v5+fHYM5czYEh7pn+3jM0b9pN0IpXIauG0bteQ0Zf28hgUyletejjvf3YnP0xdxuxZa9izO5709Cxq1oqgW8/mXH71uTRp5n3Xm9gmtfls8gNMm7KYebPXs3/fUdLTMqkeVYX2nWIZfWkvuvbwfc2pM13Lnu244z//YOn0BWxbsYnkoyfwD/Qnqm5N2p7bmR4j+xLkZQtxb/z8/Bh939W06dORP39awsHt+0hLSiEssgr1m8fQ7YLeboNCzsIjq3DTa/fx589LWD//T47ujycrI4uqURE06dSCXqP6E93I89bS5RVZqzo3vnIPO1dt5eC2vZxIOE7q8ZNkpmd7iR+fAAAgAElEQVQQFBpMZK3q1GvekLbndqZZ11Y+Tb87Ey08mMjls1Zxdct69K0fRe2wYLJz89ifksHsfUf5eushMrzsvuiNBTyzbDu/7T/KJc3q0LZGVaoFB3IiM5uNx07y7Y44/jh0+v9eP7lkG12jI+lQqyoNqoRQLTiQ8EB/0rNziUvLYu3RZGbsimfdUd8Wsz6bLUk4zk2L1nBJbF16RUdRKySIHMviYGo68w8fY9qew2Tmlf/6v7JuBwsPH2NkTG1aVqtCZGAgSdnZbD2Rwg/74lh+pGwbekjp9B3Qnv999yhffzGfPxZtst/3A/xpEFOLwcM6cflV/U7Z+/6Tz17NoCEdmfbtYjZv3MeJ4ylUq16F1m1jGHNZn1IFhapVr8LEz+/n++8W88usP9mzK54Mx/t+954tueKa/jRtVrr3gvzFvQGGl3J6nLNrrh9M3/7tmPbtYlat3MGB/fbYISQkkDp1o+jYpQmjxpxD67YxPtctIuVjXCSKSCVkjPkn8CT29LOZwDrsIFIo0AK4jcJpbG9alnWf07l7gEbAp5ZlXe+i7slA/mqtq4DXgS1AdeBK7DWeDPaaSR0syzpY7PzrgU8cT1diT3H7CvgMe1e55sADTv2bYVnWRS76EYW9aHf+qr4LHXVsBDKBGkBH4HxgEDDNsqxLXXy73Krf7mn9wvxNrVvpfXcTqbxmHzy71vKRU+elpeEV3QWpINWqaanSv7PpQ1MqugtSgSKDzjur71R8vfPnM+Yzy5VNzz+rv5fiG2Uw/b34YWcpuV7N1TYdmOBjvddh/yxdjD3FztVKg4eAEcWDSy7cAnyEPcXN1TS3P4Cxrk60LCvRGNMHmAz0Bfo5vtzxbW9nEREREREREXFJAaa/j1exs5aGAJ2xd4/LX8kvDlgOfGZZVsktobywLCsDGGOMGQlcD/QCagKpwDbge+Ady7JKcyvoONAbex2lK4Cm2NlPm7Gzkd4ttvZS8b7EAf2MMSOwA1TnYO9gFwicALYDS4AfLMsqOWFbRERERERERHymANPfhCO4M9Xx5eu5saUsNwN7se1ycQSsXnJ8lbWOmdhTAUVERERERP42/DQpTSqIJpeLiIiIiIiIiEi5KMAkIiIiIiIiIiLloilyIiIiIiIiIpWEpshJRVEGk4iIiIiIiIiIlIsCTCIiIiIiIiIiUi6aIicVzrKsScCkCu6GiIiIiIjIWc/fWBXdBfmbUgaTiIiIiIiIiIiUizKYRERERERERCoJLfItFUUZTCIiIiIiIiIiUi4KMImIiIiIiIiISLloipyIiIiIiIhIJaEpclJRlMEkIiIiIiIiIiLlogCTiIiIiIiIiIiUi6bIiYiIiIiIiFQSmiInFUUZTCIiIiIiIiIiUi4KMImIiIiIiIiISLloipyIiIiIiIhIJeGvKXJSQZTBJCIiIiIiIiIi5aIAk4iIiIiIiIiIlIumyImIiIiIiIhUEn7GquguyN+UMphERERERERE5KxljGlkjHnNGLPFGJNqjEk0xqwwxjxsjAk7he1cYIyZZow5YIzJdPw7zRhzgQ91BBhjbjPGLDLGHDHGpBtjdhpj3jfGtD1Vfa0IymASERERERERqST+blkkxpiRwBdAhNN/hwHdHF/jjTEjLMvaUY42/IAPgJuKHarv+BptjJkI3GpZVp6HemoCs4DuxQ41AW4Bxhlj7rIsa2JZ+1qR/m4/eyIiIiIiIiJSCRhjOgPfYAeXUoDHgd7AYOBDR7EWwExjTNVyNPUChcGl1cBVQA/Hv6sd/z8eeN5DX/2BaRQGl6YCFwA9gXuABCAYeN+XjKgziTKYRERERERERORs9CYQCuQAwyzLWuJ0bK4xZjvwb+wg04PAP31twBjTAnjI8XQl0M+yrHTH8xXGmB+ABdjZUg8bYz52ky01DjjX8fi/lmXd6XRsuTHmJ+BP7GDZW8aY1pZl5fja34qkDCYRERERERGRSsLPnDlfp5MxpgfQ1/H0o2LBpXyvAZsdj+81xgSWoan7KEzOudspuASAZVlpwN2OpwHA/W7qyQ9SJQIPFz/oCEr9y/G0GXBxGfpaoRRgEhEREREREZGzzWinx5+4KuBYD+kzx9NqwEBfGjDGGGCU4+kWy7KWumlnKbDV8XSU4zzneloArR1PJzuCUq5McnqsAJOIiIiIiIiIyGmWP90sFXtqmTsLnB738bGNxkA9F/V4aqc+EFvs2LkuypVgWVYcsM3x1Ne+VjitwSQiIiIiIiJSSfif5qlpZ5D8jKAdXtYq2uLinNJq46ae0rSzuxz1tAAaGmPCLctK9drLM4QCTCIiIiIiIiJyyhljGpSmnGVZB3ysNwSo6Xjq8VzLso4bY1KBcKChL+0Azv331sf9To+Lt1OWeozjvK0eyp5RFGASERERERERkdNhv/cigB1M8UVVp8cppSifH2Cqchrbcc40Kt7OqarnjKYAk4iIiIiIiEgl4Wesiu7CXyHE6XFWKcpnOv4NPY3tZDo9Lt7OqarnjKYAk4iIiIiIiIicDr5OSSutDKfHQaUoH+z4N/00thPs9Lh4O8XrycA9T/Wc0RRgEhEREREREZFTzte1lXxw0ulxaaaRhTv+Lc10urK2E+70uHg7xevxFGDyVM8ZTQEmERERERERkUrC72+wi5xlWRnGmGNADYouoF2CMaY6hUGb0q4Jlc85QOZtwXLnbK3i7RSv52gp6rHwviD4GcWvojsgIiIiIiIiIuKjTY5/mxljPCXPtHJ6vLmMbRSvx9d2ylLPfsuyUj2WPMMowCQiIiIiIiJSSfiZM+frNPvd8W840NVDuf5Oj//wsY3dwCEX9bjSz/HvQWBPsWO/Oz12W48xpg7QwvHU175WOAWYRERERERERORs873T4xtcFTDG+AHXOZ6eAOb50oBlWRYw3fG0lTGml5t2elGYeTTdcZ5zPdsozGq63BgT5qbJ650eT/Olr2cCBZhERERERERE5KxiWdZyYJHj6U3GmHNcFHsQaO14/KZlWdnOB40xA4wxluNrkpum/g/IdTx+2xgTWqyOUOBtx9McR3lXXnX8GwX8u/hBY0xTYILj6Q4UYBIRERERERGRiuJ3Bn39Be4F0rE3MPvVGDPBGNPLGDPQGPM+hYGcbcBrZWnAkX30iuNpN+APY8wVxphuxpgrsKeydXMcf8WyrO1uqvqUwmlvdxpjvjXGnGeM6WGMuQtYDEQAecA9lmXllKW/FUm7yImIiIiIiIjIWceyrNWOIM8X2MGZF10U2waMsCzrZDmaehyIBm4EOgNfuyjzEfCEh77mGmNGA7OA7sAlji9nmcBdlmX9VI6+VhhlMImIiIiIiIjIWcmyrBlAB+AN7GBSGvZ6SyuBfwCdLcvaUc428izLugkYgb0m0yEgy/HvdGC4ZVnjLcvK81LPUaA3cAf2wt/HgAxgF/Ah0NWyrInl6WtFUgaTiIiIiIiISCVhTv/ubWccy7L2Ag84vnw5bz5Q6u+YZVmzsDOQyswx9e1dx1elogwmEREREREREREpFwWYRERERERERESkXDRFTkRERERERKSS+BvOkJMzhDKYRERERERERESkXJTBJCIiIiIiIlJJ/B0X+ZYzgzKYRERERERERESkXBRgEhERERERERGRctEUOREREREREZFKQlkkUlH0syciIiIiIiIiIuWiDCYRH/y6qHtFd0EqyNOrwyu6C1KBfn79YEV3QSrIjilNKroLIlIBkrJSKroLIiJnHQWYRERERERERCoJY6yK7oL8TWmKnIiIiIiIiIiIlIsCTCIiIiIiIiIiUi6aIiciIiIiIiJSSZiK7oD8bSmDSUREREREREREykUBJhERERERERERKRdNkRMRERERERGpJIzmyEkFUQaTiIiIiIiIiIiUizKYRERERERERCoJJTBJRVEGk4iIiIiIiIiIlIsCTCIiIiIiIiIiUi6aIiciIiIiIiJSSfhpjpxUEGUwiYiIiIiIiIhIuSjAJCIiIiIiIiIi5aIpciIiIiIiIiKVhGbISUVRBpOIiIiIiIiIiJSLAkwiIiIiIiIiIlIumiInIiIiIiIiUkkYzZGTCqIMJhERERERERERKRdlMImIiIiIiIhUEkpgkoqiDCYRERERERERESkXBZhERERERERERKRcNEVOREREREREpJLQFDmpKMpgEhERERERERGRclGASUREREREREREykVT5EREREREREQqCT/NkZMKogwmEREREREREREpFwWYRERERERERESkXDRFTkRERERERKSS0Aw5qSjKYBIRERERERERkXJRgElERERERERERMpFU+REREREREREKgljrIrugvxNKYNJRERERERERETKRRlMIiIiIiIiIpWEFvmWiqIMJhERERERERERKRcFmEREREREREREpFw0RU5ERERERESkkjCaIycVRBlMIiIiIiIiIiJSLgowiYiIiIiIiIhIuWiKnIiIiIiIiEgloSwSqSj62RMRERERERERkXJRgElERERERERERMpFU+REREREREREKgntIicVRRlMIiIiIiIiIiJSLgowiYiIiIiIiIhIuWiKnIiIiIiIiEgloRlyUlGUwSQiIiIiIiIiIuWiDCYRERERERGRSkKLfEtFUQaTiIiIiIiIiIiUiwJMIiIiIiIiIiJSLpoiJ3KGSTicyMzJv7Nq8SaOxicRGORP7fo16TO4Ixdc2ofgkKBT1taqxZv5dfpSdmzaT/KJFCKqVaFZm4YMG9WLLr1bl6qO3Jxc5vywjIW/rOLg3gQy0jOpXjOSDt2bM+LyvsQ0qePx/P274/jzj81sXL2TfTvjOJF4EmMM1aKq0rxtDAOGd6NrKfuSb/vGfcyduYINf+4g8UgSlmURGVWV+o2i6dCtOf0v6Epk9So+1Xm6pB89xr7Z8zi6dj0ZicfxCwwgLLoWtbt3peHgAfgHn5rrfXTdBg7MX0Ty7r1knUwhqGoVIho3osGAvtTs0K5UdeTl5nJwwR/ELV1O6uE4cjMyCa4WSVTb1sQMHUiV+vXcnrvhw0kc/mOpT31ue9N11Ovbu8j/zb7+Np/qABg66T2fzzlT1KsZzrjhLRnQpT51a4STlZPLvrgUZi3Zyxc/byUjK/eUtNOvUz2uHNqMDk1rEBURQmJyBut2HuPr2TtYuOaQx3NrRIYwuGsDerWvTZvY6tStGU5ggB8nTmaxZe9xflm2j+8X7ibTh77WrRnG5YOaMaBLferXCic8JJDE5AwOHEll6cY4Zi3ey/b9SeV92WeVgwcT+PzzGcyfv4K4uKMEBQXSsGEdLrigL2PHDic0NOSUtbVgwUomT/6F9eu3k5iYRFRUJO3bN+fyy8+jf/9uHs/Nyspm06adrF+/nXXrtrFu3Xb27j2EZVkAbN06w2v7KSlpLFiwkvXrt7Nhw3bi4xNJTEwiMzOLqlXDadasIf36dePSS4dSvXrEKXnNZzJd+8p77Q8fSuSbLxfwx8KNxMefICgwgPoNazLkvM5cdmVfQkJP3Zhv8aJNTPt2MZs37OX48RSqV69C63aNuPjS3vTu26ZUdeTk5DJ96hJ+nrmSvbvjSU/LomatCHr0asnlY/vTtFldt+c+8/gXzPxhuU99fuq5sVw4uqfLY4nHTjJ18u8s+WMze3cnkJqWQWhoMA0a1KBbr5ZcesW51Ktfw6f2KhPNkJOKYvL/6IuIdxuP/3haf2FWLNrIm//8H2mpGS6P14upxeOvjaduw5rlaicvL493/zWF32a4f6MfclFPbnv0Uvz83Cc6Jp9I4fkHJrJj036XxwODAhj/4MUMHdXL5fG3nv2K+bNWeu1vp14tefC5awmvGuqxXHZWDh++OpXfZizH09+2f7x8PT37t/farrN3t4T7VL40jqxex4YPPiYn3fX1DqtTm87330lY7egyt2Hl5bFp0pccWviH2zL1+/Wh9fVjMR6uddbJFFa//jbJu/e6PO4XEEDLa6+kQf9zXR4vS4Cp+xOPUK1ZkyL/52uAKaxObfq89IxP57jy8+sHy12HrwZ1rc9r9/ShapjrDxi7DiVz87/msjcupcxtGAMv3NqLywc3c1vmmznbeeKDZbj6lbpicDOeubkHAf6eE6J3H0rmrtcWsnXfCa99uvb8ljw0thPhIYFuy3wyczMvTPrTa12nwo4pPf6SdjyZO3c5Dz/8GikpaS6Px8bW54MPnqJRI/dB3tLIy8vjySff4dtvZ7stc9llw3j22TvdvjdMmPB/TJ36m9vzSxNkWLx4DTfc8KTXctWrR/DKKw/St28Xr2XPVrr2rv0V1z4pa/dpqxtg0fz1PDXhc1JTXI8BYmKjeeM/t9Iwpla52snLy+PFZ77mh6nu34NHXXIOE566wuOY78TxFO674z02bdjn8nhQUAAPPXYpoy/p7fJ4WQJMEz+/nw6dGpf4/6WLN/PEw5+SnOz69wIgOCSQCU9dwfCRZfsbHhl03lkdozmQOuOM+ZDfIHzkWf29FN8og0nkDLFr6wFee+JzsjKzCQkLZsx1g2jftRlZmdn8PnsNs6cv5dC+I7zw4ERe+eQ+QsPLfsfyf+/9VBBcatyiPhdfO5Da9WsQf/AY0z6fx+5tB5nzwzIiqlfhmtuHu6wjNzePl/8xqSC41GtAe4aM6kXViDC2bdzLt5/MIel4Cu+//C01akW6zIhKTLAzEKpEhHHOoA6069KU6LpR+Pn7sXvbQWZ8tYCDe4+wZulWXnzoI5579w63g5/s7Bxe/scnrFqyBYD23ZrR77wu1G8UTWBQIMePJrNl/R6WzF1b5u/bqZS8dx/r3v2QvKxs/EOCaTzifKq3bkleVhZxy1ZycMHvpMXFs/qN/9Dz6QkElPEO9Y7vphcEl6o2akjsBcMIja5FesIR9vz0Kyf37ufgwj8IjKhK80tHu6zDystj7dvvFQSXort2pn7/cwmsEkbSzj3snjGLrOSTbJ70JSHVq7nMiGp26ShiLxjqsa/ZqWmsfOl1sCzC6tQuEVwCOOd57x88Dv2+lL0/2x+S6vVxHdw807WJrc6b9/clNDiAlPRs3p+2gaUb4wkO8ufCPrFcOaQ5TepF8OGEQVz8j1mkZuSUqZ0Hr+pUEFzauCuRD6ZvZF98CjG1q3DLqLa0bRLFFUOak5icyWtfrSlxfo3IEAL8/cjKzmXunwf5fe1hdh5MIjU9m5g6VblicDP6dqpH43oRfPbUEEY9Mou4RPcfCO4Y044HruoE2AG0b+ZsZ/3OY5xMy6ZalWDaNK7OsB4NXQa7KqtNm3Zy//0vk5GRRVhYKLfeeik9e3YgIyOTWbMWMXnyL+zZc5BbbnmW7757nSpVwsrc1htvfF4QYGjTpgnjx19Cw4Z12L8/jokTv2PTpl1MmfIrUVGRPPDAdS7rcA7uh4eH0rZtU3bvPsiRI8d96kvdujXp2bMDbds2pW7dWtSqVZ28PIu4uKP88ssfzJ69hOPHk7n99uf49tvXadWq5IfQs52ufeW99ls37+exhyeRmZFNWFgw48YPpWv35mRmZjP7p1V8/91i9u1J4P473+fTrx8ivBxjvnff+rEguNSydQOuvWEwDRrW5MD+o3z+yW9s3XyA6d8toXr1Ktxx70iXdeTm5vHwfRMLgksDh3Rk9CXnEBEZzoZ1e/jkg19JTDzJS89+Q3R0NZcZUbffcyHXXD/IY1+Tk9O5/ca3yMuziImNdhlcOrj/KA/fO5HMjGwA+g1sz/CR3alTtzpHEpJYMHc9M39YTmZGNs8+8SX1G9SkY+eS4wkROT2UwSTig9OZwfTEbf9h05pd+Pv78fx7d9KyfWyR499/MY/P3vkRgMtvGsaVN59XpnYO7TvCvVf9m9zcPJq2bsjz795JsFOmQGZGFk/c/l92bt6Pv78fb339D5cZU7/NWMZ/XpgMwPmX9OaWhy8pcvzw/qM8fP0bpKVmULdBTd76+hH8A/yLlHn7ua9p2b4RA4d3JzCoZLw7MyOLZ+/9gM1r7buIdz91FQOHu07R/+qDn5ny8WyMMdzy8BjOG+P6DhrYKd4BxfrizanOYFrx4quc2LYD4+9HtwkPlQim7Jn1K9snTwWgyagRNL3Y9aDPk9S4eJY8/gxWbh4RjRvRbcKD+AcVZsPkZmax8qXXSN69F+PvR+8X/+kyW+rgwj/Y9PHnADQY1J/W111V5HhafALL/vkiOekZhNauRe8X/4mfv2/fX4D9cxew5bOvAGg65iKaXOQ6uOnNsmdfInnXHjCGc199gdAaUWWqx9lfncH0v2eG0qNNbbJz8rj66V9Zve1okePjL2rDo9fad+7fmryOt6as87mN2LpV+en1kQQG+LFuxzGuevrXItPYQoL8+d8zw+jQrAbZOXmcf/8PJbKlbhjRiprVQvhoxmYSkzNdtjPhui7cNNL+sDFl7g4mvOv6Lvo57erw+dNDAJg6fyePvbeUnFzXf3IDA/zIzsnz+TWXRUVnMI0d+ygrV24kIMCfL754ic6dWxU5PnHiVF555RMA7rrrKu6+++oytbN790EuvPBOcnJyadeuGV9++RIhIcEFx9PTM7jmmgls2LCDgAB/Zs36r8usmVmzFpGRkUn79s1p2rQhfn5+XHvtBJYv3wCULoslNzcXfy9/Q+bMWcKdd74IwNCh5/DOO4/58nLPCrr2rv1V1/50ZjDdMu5N1qzaiX+AH+9/cm+JQMrnn/zG269PB2D87edzyx1lez/cuyeBKy9+kdycPFq3jeH9SfcQ4rTUQkZ6Frfe8BabN+7DP8CPydMfd5kx9cO0JTz/lP3+fOkV5/LIE5cXOb5/3xGuu+IVUlMyaBhTi2+mP+bzOAvg228W8e/npwBw610juOnWkmPdV16YwpSvFwFwzQ2DueeBUSXKfPPlAl576TsA+vRryxv/udXnvpztGUyH0s6cDKZ6Ycpg+jvRIt8iZ4DtG/exac0uAAZf1LNEcAngoqv70yC2NgAzJy8iJ6dsa6/M+Hohubn2B7PxD15cJLgEEBwSxPgHLwbsO1Yzvl7gsp7pX9r/XyUijHF3lwx+1G1YkzHj7DtVhw8cZdmCDSXK3P3klQwbfY7L4FJ+X255pDBw5S77KO7gMaZ9Nhewg12egktAmQY9p1LSrt2c2LYDgHp9+7jM1Gl0/hDC69nrV+2bPY+8Mlzvfb/+huW41i3HXlEkuATgHxxEy7FXAGDl5rH3F9dTG/KzgQLDw2lxxSUljofVjiZ2xPkApMcf4cifJTNdSqNgCp0x1O3tes0Fb1IPx9nBJaB6qxanJLj0V+vQrAY92ti/61Pm7igRXAL4aMYmdhywp5uNG96SAH/fx243jGhFYIA9DHj24xUl1kjKyMrl2Y9XAHZA54YRJbMQP5m5hVe+XOM2uATw6v/WEO/IWjqvZ4zLrZONgWdvtgM5m/YkMuFd98El4C8LLlW0deu2sXLlRgAuuWRoiQADwI03jqZp04YAfPbZD2Rnly2b7dNPpxe8rzz55K1FAgwAoaEhPPmk/SEtJyeXSZOmu6xn+PC+jBkzhObNG3mcbuOJtwADwJAh59C4cX2Agu9RZaJr797Zfu03rt/LmlU7Abjo4nNcZumMHTeQxk3s94FvvlhATnbZxnxffzGfXMffy4cmXFIkuAQQEhrEQxPs9/XcnDy++myey3q+nGSPsSIiw7jnwZLZzg1janH9TXaW8v59R5j/m+83PQBm/WC/5xhjGD6yu8sy6xw3HY0x3HjLMJdlLr+6HxGRdkbf+rWnd6qjiBSlAJPIGWDZwsLgy6ARrt9Q/fz8GDC8KwCpJ9PZ8OcOn9uxLIsVi+y26jeKpmW7Ri7LtWzXiPqN7DtYyxduLLGe0aF9RziwJx6APoM7ul14fKDTa1m2YL3P/QVo1LQuEdXs7KH4g8dclpn9/RJycnLx8zOMuW5wmdr5KyWsKgyU1e/rOhhm/Pyo29ue3pWTlsbxLVt9asOyLI442gmvW8dlEAugWrMmhNWxB7FHVq8tca1T4+JJPRQHQO0eXd0uOl7v3HMKHies8j3AlBoXT9JOexBYvWVzQmuWbWFO53WeztbpcUO7Nyx4/N28nS7LWBZMW2B/vyKrBNOrrefF9F0Z7Ghnx4Ek1mwvGcQCWLP9KDsP2lNZh3Rv4HMbYAeDVm09AkBEeBDVqwaXKNO3Y10a17MX7P3g+03k5p0xN14r1Jw5hT/Pl1wyxGUZPz8/Ro8eCEBycirLlvn+wc6yLH77bRkATZo0oFOnksEMgE6dWhV8sP/tt2Ue17r7K4SH2+vyZWZmV2g/Tgdde8/O5mu/YG7hdRrpZgFrPz+/grWDTp5MZ+WKbT63Y1kWC+fZY6/YxrVp39H1VML2HRvTKNbOXl44b32Ja7t3TwK7d9ljviHndXa78PgIp9dSlgDTvr0JbFi3B4Au3ZpSt57rG0TZjmBbZLUwqlRxvTanMYb6Dezs+7IG50SkbBRgEjkDbHHcXQkJDaJpK/cf4tp2blp4zjrf78jEH0ok8UiyXVeXph7LtnG0lXgkiYTDiUWObV67q7BPHuqpXiOCeo5U682OQUNZ5A8O3N0RXewYrDVu2YAa0ZGAPbBKPJpM3MFjZKS7z66oCPnZS/7BwVSNjXFbrnqrFoXnbHcdaHAn/chRMk/YgYFqLZt7LJvfTubxE2QcLRrEy+8r2IEfd4KrRRYEqnztK8DhP5YVPK5bxsCQZVkcXmKvLeYfHEx0t85lqqeidW1l/86kZmSzYVei23LLN8WXOKe0GkZXoU5UWIl6XLeTAECdGuE0iC7bVNGgwMKsBFfBowvOsYPdeXkW8/48UPD/kVWCaFSnKpFVTt1OSmeTP//cBEBYWAht27pfiL1798JNC1at2uxzOwcOxJOQkOioy/Oukj162Mfj449x4IDnn53TadeuA2zZYr8PNmlSv8L6cbro2rt3tl/7NavsMVRoaBCt2jR0W65zt3bFNiQAACAASURBVMLrvm6172O+QweOccSx1qVzXa50cRxPSEji0MGi7ztrV+8sUc6VmjUjiHEEqtat2eW2nDv52UuAx4W584NhSSfSSElJd1vu4AH7xkl+n/5uzBn0JX8vWuRb5AyQnw1Up0HNEusUOavfqPBN8sCeBN/b2V04IHSuy5UGxdqqXa8wo2S/D/XUbxTNoX1HOBZ/goz0TEJCS2YveLJr64GCXfXquxgkJB1PKchsatS0DtnZOUz99Dd+mbaEE8dOAuDnZ2jeNoZRVw+g18AOPrV/OqQetjOCQqNreVyrKLxu7cJzDh32rQ2n8uF1PWe3OLeTcugwobUK19zytZ60uHgyEo+Tm5mJf3DprrUdGLIDTH5BQdTuVrZdgY5v2UbGMXtgHN21EwEhp27r7r9SswZ2kHRv3EmPmTw7DyYXPG5aP7JMbQDscqrHlV2ODKb8dg4kpPrUVoC/oXML+2fqyIl0klKySpTp1Nw+fuBICqkZOYw8N5bbLm5Ly5jqhf1wLPr9+U9byfqbTJHbudPeRCEmpq7Hqb1NmhTemMg/xxc7dhTuCuVcl7e2du06QMOGvmfPlVV6egbx8YnMm7eciRO/K5jWNW5cyTVYzna69kVVpmu/Z7c9BmgQU8vjtY1tXPjevHtXnM/t7HI6J7ax57FaI6e29uyKo36DwjHf7p2FYz7nPrkSGxvNvj0JxMedID0tk9Cw0o8DfpppB5hCQoMYNKyT27JjLuvDvDl2xvWkD2dz1/0XlSgz5auFJCfZU7PHXN6nVH0QkVNDAabTwBjTDhgN9AXaArWAbOAwsBh417KspcXOCQPigSrA/yzLGuuljXMcdQHcaVnWf12UqQPcDZwHNAHCgQRgCfCBZVlz3NQdC+TfKrnBsqxJxpgxwHigExAN/G5Z1gCnc3oBFwLnAq2AKCADOAAsAN62LGuTp9fkqCcGmACcD9QFEoHlwJuWZc0zxvwTeBrAsiy3QXFjTCRwh6NPLYBI4BiwEvgU+M6q6Pxuh6zMbJJP2B/Y8rNv3KkSEUZIaBAZ6Vkcjfe+3XdxxxIKz/HWVo3a1QoeF2/rWELhB05v9dR01GNZFscSkrwGpIr77tPCdYH6DC454HAOmgWHBPHk7f9l24a9Rcrk5VlsXb+Xf0/4lPPG9ObWR0quI/RXyc3KJvukvVBySFR1j2UDw8PxDw4mNzOTjETfduHJSCy8ZiFR1TyULNqPzGLtONcT7KW/BfVYFhmJx70GpPId37q9IHMqumunMu+Y5zw9rqxZUBUtKNCPqAj79ccdc7/bGkByahapGdmEhwRSt6Zvu0fVqVFY3tOubgCHnfpRt4bvGUxXDmle8Jp+WrK3xHFjoEl9e3rc8ZOZPHFDN64fXnKaTpN6EUy4rivDejRk/L/mcTLt7Jsa44vMzCyOH7eDf3XqeJ4yGhlZhbCwENLSMoiLcz3d0ZO4uMLMxTp1Sm7q4KxOncJsucOHj/jclq+mTp3DhAlvuj1+yy2XMnJk/9Pej7+Srr2tMl77zMxsThy3x3zRtT2/N0dEhhEaGkR6ehbxcb6P+RKcxm7e2qpdp/B48bZ8q8ceB1iWRUL8iSKBK09Wr9zBYUfm1IBBHTzumtezdytuuHkYn3z4K599PIf9+45w/ohu1K5bnWNHkpg/dz0zp9s3rUZc1IORo8/O8UB5GXNGfMyRvyEFmE4xY8wAwNUKeUFAM8fXdcaYlyzLmpB/0LKsNGPM98A1wChjTLhlWZ5uE+cHoHKAyS76MRZ4Hzuo5KwBcBlwmTHmI+A2y7I8rQppjDGfAdd6KHA98ImLQ4FAa8fXzcaYe1wFwpzqGQRMxw6y5asLjAIuMsY84aGfzvUMBr4Bio/K6mAHnC4EZhljrrAsK6X4+X+19LTC6Vulye4JDrEDTGWZ9uVLW0V2GUkr2pYv9QQX2a3Etz4vmbuOJY7pb01bNaDXwPYlypxMLvzw+9uMZWRl5tC8TQzX3DmCFm0bkZOdw6olm/n07RkkHknml6mLadAomhFX9PWpL6dKbkZGwePSZPj4BweRm5lJbqZv37ui7XgO2Dj3IyejaDu+9Nf5uC/9PRXrJuVmZhG/cjVgB8KiWrcsUz0VrUpo4aL7aRneF+xNz8ghPCSQsBDf3s7Di7TjOVDj3I9wH9tpGF2FB66yA8Mp6dm8N63kgrxVw4Lwd0x/bRlTjY7NahKfmMbLn69i/upDZGbn0qFpDR65pjOdW9Sia6toXrrjHO58daFPfTnbpKYWTv0IC3O9zoiz0FA7yJCWluG1rOe2PP+9CHX6m1+Wtk6V1q2b8Oyzd9KhQwvvhc8yuvaenc3XPj8jGyCsFNk9oaHBpKdnFRl3lb6twnO8teV8bdOLjdWc++wtI8l5faY0H/o8c0bh9LgRF3nfufP2ey6ka/fmfDLxV+bNWcu8OUU3gWnZugE33DyMQUPdZ0KJyOmhANOpFwCkAjOBucAWIBk766ctcA/QCHjUGLPNsiznwMyX2AGmcOygyv9cNWCMCcAOEgH8YlnW0WLHLwc+x572ugt4B9gEHAFigZuA4Y5/k4EHPLye+4AOwCLgXWAbUM1Rj/NrPo4dHFoIbHd8D+oBXRyvuSbwjjFmi2VZc128pibAD47XnuNo63tH/9oBDwMvAMuKn1usnj7AT9jBrXjgbWAtcMjRnyuwv8fDsTOZKi6VxSE7q/DDXUCg991T8ndcyyrDwpZZWYUfFAO9tOW8s1vxtnzpc5F6vHyQdXZgdzzvPP81AEHBgdz79NUYF9tPZaYXTrfJyswhpmkdnv3v7QWBreCQQPoO60LTVg15aNzrZKRn8c1HvzJkVE+3i5OfTnnZhd8Dv1LsZucXYH//crN8u96+tJPfRvHzfK3HONVT2v7mZmWTsHIVAMHVqxHVxvUCs94krFpTEAyre04PTBl3MKpowU6/T6XZKS1/qliIm50YS9OOt+lmWU47GAYHlX4HxpAgf/7zcD8iwu3fs2c/XkHC8ZLrZYQFBzidE0BaRg7XPDOH3YcKp+6t2JzANc/MYcoL59EmNorzesbQsVkN1u5wvfB/ZZCZWfi3LTDQ+/UNcvwMZGSUnIJ4KtsKCioMTpalLV8NGdKLdu2aO9rLZP/+OH766Xdmz17Cgw++ymOPjWfgQO8fSM8muva2ynjtncdhvoz5yrKYufPYLcDLtXUeq2UUG6tl+jB2DHKqp7R9zszMZu5se3OQ6OhIuvfyHjg8kpDEjO+XsmHtHpfHd2w7xI/TlxHbpDZNmtYtVT9E5NRQgOnUWwM0sCzLVS7rL8aYd4AfgaHA08aYzyzLyh+9z8GewhYNXI2bABMwxFEG7KBUAWNMTeAD7ODSx8CtxTKUVgFTjTEvAI8B9xpj3rcsy90WVR2Az4DrPUwp+wl7Wl/xeRargZnGmLewA08dgGewA2/FvUZhttVllmV973RspTFmMnZmmNu9y40xgcAX2MGln4FLivVpFfCjMWYh9vdojDFmqGVZs93VWV7HEpJIOel6+kmVqmHUiI4k0GnAVpqdLrIdb/RBwYFeSpbk/Maf7aWtbKcBRfG2ivc5KNj9h/ki9YSUrs+JR5J4/oGJpKdlYozhrieuoIGbNOvA4KJ/xq68+XyXgaN6MbU4b0xvpn85n5TkNNYu30aPfp4XND0d/AILvwd5Od6vd16O/f3zD/LtevvSTn4bxc9zVY9/kPtrbTnVU9r+Hlm1hpz0/MBQzzIHhopmQZ3joeSZLdPp9zIwwPv3IshRJiPLe7aTu3aCvLQT5BRYzMwq3W48/n6Gtx/sR5tYexegL3/ZytT5rhd9zSz2t2jy3B1FgkvObb/+1VomTrB3zRrRJ/asDTDFxx8jKcl1Am1kZBVq165BsNOOjaXZfj7/g2vxbchLw5e2spyCx2Vpy1cREVWIiChMbO7QoQUjRvTj++/n8uij/8cdd7zACy/czZgxrndaO9Po2pfe2XbtE+JPFMmqdlY1Iozo2tWKjMN8GfMFl2XMF+w8VvN8bZ3HaiHFxmrBxcaOwR7GfM4BtNL2ecHcdaSm2OOA8y/s7nZDl3y7d8Vx983/ISEhichq4dz78Gj69m9HjRoRJCWlsmzJFt57eya/L9jI6j938tpbN9Olu+fNTiojLa4tFUUBplOseDaRi+NZxpiHsQNRjbDXNPrTcSzHGPMN9rpJw4wxNSzLcjV6zp8el4KdNeTsduz1hg4Cd3iY/vY0MA6oD1wHPO6m3AngLk/rFVmWddDdMcfxJGPMU9gZSecWf13GmHrASMfTb4sFl/LrSDPG3IL9fXPnSuzMqgzgOhcBr/y6PjTGjAd6ANcDpy3A9L/3ZjFv1kqXxwYO78bdT11VJN24NFPIMh13DX1dLBvwqS3nu5MhxVKii9fjKdiV6VxPKfp8MimNZ+79oGDnuvEPjubcoe53A3PuizGGjh4GEZ16tmT6l/MB2LF5f4UEmPydFp4uzTSyXMfd5dIumO26Hc9TGZz7ERBStJ3i/fUUOHKup7T9PbTYed0kt/FjjzJPJJG4aQsAEY0bEV7vr1t49lRLSS/8AFeaaW+hjjKlmU7nLLVIO54/BDj3I7WU7fz7zt4M7GLv7jRz8R7++dEKt2Wd+wLw+1r3C9ovXn+Y7Jw8AgP8aN/U89o0Z7I33viMadNc3WuBiy8exEsv3V+wDTtAWpr7nZLypTsCtd6mOblStC3Pfy+cp8+Upa1TZfToQcyfv4Kffvqd5557n0GDelKtWtUK609p6dqX35l67d9960dm/rDc5bERF/Xg6ReuIcxpbaHSTCHL/56XdrFsZ2HhzlMaPbflfG1Di43VnPucnpbpMXCU4ZRVXpopgFBs97iLunst/8/HviAhIYmQ0CA++PReGjcpfM+vUTOC4SN70KNXS6657N8kHjvJk49+xrSfniqSgScip8/ZOYfgLGKMCTbGxBhj2hhj2jkWAHcOKncsdkp+RlIgcLmL+kKxFxAH+N5FECV/K4UfLcty+27iCDwtcTz1dLt/hmVZJz0cL8EYE26MiTXGtHV6zc6fIIq/5oFA/i3yzz30eS32dDd38l/7AsuyvK0+mb94R4WnOgQFB1I10l5w13nxbFdSktMK3rxrello0ZUa0YXneGvrmNOijsXbcl7Y21s9+QuEG2O8LgienprBc/d/wH7HzidX3XI+F1x6rsdznPsWXjWEUA8LQzqXzV9Y/a/mHxRIYBU7Wc/bwt3ZqakFQRtvC4IX57ywt/NC3a4496P4Qt7O9RRfANxtPcaUqr+ZSckkbrC31a4aG0OV+vW8nuPK4SXLsPLsaV5n6+Le+bKy80hMtj/kOS/E7UpEeBDhjuDQ4aOeF+ouznkB8TpRntup69SPw8e8/948M747o/o1BmD+qoM8+NYfeNpSISsnj2NJhR9sDx9130ZWdh7HT9q/E1ERvn/gOpsEBwcVfGh2XojZlaSklILggLeFml1xXkja20LRcXGFb69169byUPL0GzzYDkqnpWWwaNGfFdqXU0nX3ruz9doHBwcSWc0eAyR42awlOSmNdMeYz3kR7tJyXpDbW1vOC3sXb8u3euxxgDHG64LgAMeOJrNsiX2DqFWbhl6ns23bepDNG+2dD88f3q1IcMlZzVqRXH51P8CeTrfk981e+yIip4YymE4DY0w49rpDV2Kvu+RpwnKR0YBlWcuMMTuBptiZSu8WK38RhYtgF58e54+dEQVwqzHm1lJ22dPt/nWlqcAxNe8B7DWNmuM5M7P4CMg5jcTbKGElJQNU+bo5/j3PlH7rhNOa6nD3U1dx91NXeS3XsHEdNq3ZRdyBo+Tm5OLvZq2bg3sTCh43iPVtNzagyDQz57pcOeChrYbF6mncor7bevLbqVG7mscMpsyMbF586GN2bLK3WR49dgCX3TjUYx8B6ja0t/nNycklL9fzZc/LK1xrxt+/4uLr4fXqcmLbDtITjpCXm4ufv+vrnXo4vsg5vrZRWI/n7Y2d26lSrJ3i9VRt1NBrPSFR1UuVwRS3ZHlBYKg809oOL7aXZjMBAdTp5f3u55lux4EkerQJoVGdqvj7GXLzXP9cN3XsvAaw86DnQK+rNvI1carHlSb1CwPD3tp5eGxnxp5nL7C+fFM8d762kBwvv5cA2/efoEak/efY389zYn/+8dxS1Humeuml+3nppfu9lmvWLIaVKzeyb99hcnJy3W5pvmvXgYLHTZu6/x311I6rury15W1b+9MtKqrwZ/PQodO/q9mpoGt/apyJ1/7pF67h6Reu8VqucZM6rFm1kwP7jni8tnucdsl1F0jxpInTOXt2ex7z7XVqK7ZYW42bFo759uyOp0Ur99d+zx67ndp1qpUq6+rnmSvJzbXHAaVZ3HvPrsLxTMs2nn8GW7cp/H3Yszues2u/wfJzsWypyF9CGUynmDEmFlgPvIi95pC3FfxcbQ+SHzjq7ajPWf70uATsNZucRVG2oKGn29de90Y3xnTFXsx8AtAC79N+i79m51QHb6MET8d9j7i4/v7/5Vp1tO/2Z6RnsXOL+wHextU7C8/p0NjndmrXiyKqlv1hcuOqnR7Lblptr5cSVSuS6LpRRY617tiksE8e6jl+LJlD++xL1rpDrNtyOTm5vDJhUsHrO+/ic7ju7pFuyzsLCPCnRftGgL3TSfIJ9xsDxh0ovBMcVctzNtXpVK1FM8CeUnZyzz635Y5v2VZ4TvOmPrURWqsmwdXs13hi63aPZfOPB1evRkjNotOO8vsKcNxDPZknkkiLi/epr4cc6yYZf/8yB4ZO7t1Pyn57lm7NDm0JqlLFyxlnvj+32L8z4SGBtGsS5bZcjzaFg/78c0prf0IKcYlpJepxpXtr+09r3LFUDiS4zy66Y0w7bh3dFoC1O45y87/mlXrNphWbCz/8NKzt/hpWCQ2kelX7Q0t8om9ZW2ejrl3bAHaWxsaNO9yWW7FifcHjLl1a+9xOgwa1iY6OctS1wWPZFSvsnQBr165Bgwal24L8dImPL/ybXpFTtk4HXXvPzuZr36mLPYZKT89ii+OmmiurVxZe9w6dfR/z1WtQg1qOzHHnuly29ac9/oqOjqRe/aLvOx07F76nr/JQz9GjyexzBJg6dGritpyzWY7d4wIC/DlveFev5Z1vDuZ62aAix2n9SXdBPBE59RRgOvU+BxoDFvYi28OAhkAI4GdZlqFo0MlVMOZLp2MF6S/GmCjgPMfTb1ysr+Rc70SgfSm/hnl4PR4/HRhjgoDJQA3saXCvA/2BukCIZVnG8ZqdP3Gerph6/uv/idK/9pL73leAnk5rAc2d6Xqtkry8PObPshO8wquG0q5rM5flPDHG0L2v3dbBvQls3bDXZbmtG/YWZB716Ne2xO5t9WJq0SDWHlz+8dvaIussOZvn9Fp69nf9rc7NzeONp75glSNFuv8FXbnlEd829ztnQIeCx8sWuB8gL51fOBBv09H3wdqpEt2lMAnv4KLFLstYeXkcdqxPFBAWRvVWLX1qwxhDLUc7qYfjOLHD9QLLJ3bsKshwqtW5Y4lrHV6ndsGaRvHL/yxYE6q4Q78vKXgc3cX7tsAn9x8kZb8dTK3ZoR1BVcsWGHJew+lsXtzb2ewVhR84LhnoOlhnDFzc3/4ZTkrJZOlGz1lqrvzmaKdZg0g6NXc9taZT85o0a2B/QJmzwn3we9zwljxwlX3dt+w9zo0vzC31ek0APy8tDLQO6xHjttzQHg3xc2QwOQelKqshQwqnfH73XfF7Sra8vDy+/34eABER4fTs2cFlOU+MMQVTjnbtOsCaNVtclluzZktBFsvgwT1d7uz5V/r55z8KHrdoEVtxHTkNdO09O5uvff9BhddpxveuN0fOy8tj1gx7PaeqVUPp1t37zmrFGWPoN9Aee+3ZHc/6tbtdllu/dndBtlS/ge1LXNtGsdE0bmKP+eb8srrIOkvOZjq9lgGDvf8s7th2iO1b7RtEvfu2oVp17+OAevULb4Kt8XKjdNXKwuPO54nI6aUA0ylkjGkF5C8Y86JlWTdZljXbsqwDlmVlOi2U7f6WNGBZ1jbsqWBg7yaX71Igf9uOItPjHBKdu2NZ1oZSfm1zUVdpDQLyb1PcYVnWg5ZlLbQsK67YGlCeXrNzlpS3Sf2ejuffzgry4bV7vl33F2neNoY2jrs9v/2wjK3r95Qo88P/FnBgjz0AGHF5X5d3Yzb8uYMxvR5kTK8HefvZr1y2NfLKfvg57gBNfG0amcW3o83IZuJr0wD7TtGFV/RzWc+osXaycUpyGp+982OJ43EHjjL1U3sh07oNatKzf8kFtS3L4t1/TWbJXHsmZq+BHbjriSt9HrgOGtmDSMfA5JuJv5B4pOQ0no2rdrLgZztAF9O0TkHWWEWIbNK4IDPo0KI/XAZ/9v48h9RDdtAgZuhA/Ipd78TNW5l9/W3Mvv42Nnw4yWU7McMGF+zKtvXLb8jNKjoozM3KYuuX3wBg/P2IGTbIZT2NzrenKmanprJ98ncljqclHGHPzJ8BCK1di1pdvQeYDv9RGJAq67pJVl4ecUvsIGZglXBqdjwj4sXltm7HMZZvsn/XLxvUjM4tSgZ/bhrZhmYN7PUtPp21tcQ0tJ5tarNjyjXsmHINL9/pOvD2ycwt5DimJjx1Y3eCg4r+jAUH+fPUjXZmWXZOHp/MdP3B85IBTXh8nD1DedehZMY99xtJKb5tYb513wnmr7I/aFx4biPOaVdyOkjNaiE8cKUdNM3KzuW7+Z4/XFQGHTq0oFs3Oyvsu+9ms3p1yWvw8cffs3OnHSy87rqLXG41v2zZelq2HEnLliN59NE3XLY1btyoguyA5557n4yMoss4ZmRk8txz7wN2NsC4caPK/sK8mDp1Dplugtn5Jk36ngUL7KFSgwa16datzWnrT0XQtXfvbL/2bds3olMX++bBD9OWsG5NycDPl5/OY/cu+33gimv6ExBYcsz354rt9Gh/Dz3a38Mzj3/hsq0rrxlQcG1f/dd3RTZwAXtDl1f/Zb+v+wf4ceW1A1zWM/Z6e3yQnJTG268X318IDuw/wqSP7D1zGsbUKlWAyXlB9NJMjwNo2boB0Y6srHlz1rJ8qetNsLds2s/Uyb8DEBIaRLcef89d5M6UL/l70RpMp1Zbp8ffeCjXzcOxfF86yrUzxnSwLGsdhdPjdlqWVeKWh2OHuo2OfvQpZZ/L61S85o1Oj7ti74BXlnpWA/WAbsaYIMuyfPuEU8FuvH8Uj93yDlmZ2Txz7wdcMm4w7bo2Iyszm99nr2b293amRr2YWoy6uuwzyevF1GL02AFM/WwuOzfv57Fb3ubiawdSp0FN4g4cZdrn89i9zb4Eo64ZSL0Y1zG9AcO789uM5WxZt4efvv2D48dOMnRUT6pUDWP7pn1M+Xg2aakZ+PkZbnpgtMt1pSa9NYO5P9oBgpimdbhk3OCCIJo7jVwsABkaFsxND4zmjae+JPFIMo/c+H+MuW4wzdvGkJOVw+qlW/nhqwXk5ebh7+/HrY9cWuF3X1uOvZwVL7xCXlY2q159k8YXXkD1Vi3Iy84mbtlKDs5fBEBYndoFAR5fhdepTaMLhrJn5i8k797LihdeIXb4eYRG1yI94Qh7Zv3Cyb32h5NGFwwjvI7rKQ/1zj2HQ4sWc2L7Tvb/toDMpGTq9z+XwPAwknbtYfcPs8hJzwBjaDX2CrdrSuWz8vI4vNQRGAoPp1ansgWGjq7fSFayvZ19nZ7dSwThzmbPf7KSb54/j9DgAD55YjDvTd3A0o3xhAT5M6JPLFcNtQfLuw4l89GMTWVqY8/hk0z8YRO3XdyODs1qMPm583h/+kb2xZ0kpk5Vbh3VlraOKXoTf9jE3riS+z0M6d6AF27rhZ+f4WRaFs9/soKoiGCPC3AfSEghPbNkcuzzk1bSuUVNIqsE8+GEAUyauYX5qw6SkZVLx2Y1ue3ittStaS+O+8bXa4lP9L67VmXw+OM3c9VVj5CRkcWNNz7FbbddRs+e7cnIyGLWrIV8880vAMTG1ueGG0Z7qc29xo3rc9NNY/jgg2/ZsGEHV131CDfffCkNG9Zh//44PvzwWzZtsoPhN900hthY14vyHzlyvMSiy0eOFN5Hmjq1aDZO165taNSoaF3vvPMVL7/8McOG9aZr1zY0bFiH8PBQUlLS2LZtLzNmzGfVKnvR3sDAAJ577i78vfzdORvp2lfea//go2MYf93/kZmRzT23/pfrbx5K1+7NyczMZvZPq5j2rZ3dHBMbzdhxrm/+lEaj2GiuuX4Qn340h80b93Hzdf/HdTcOoX7Dmhzcf5TPPp7D1s12Ztq11w8mppHr1SZGXNSTGdOWsXb1LqZ8vYhjx5IZdUlvIiLC2Lh+Lx+//wupKfaY78FHL/E6JS03N49fHLssR0SGcW7/th7L5/Pz8+OO+0byz8e+IDc3j/tuf4+LL+tD3wFtiYqqSlJSGssWb2HyVwsLMq3G3TSUqhGeN7MQkVNHAaZTy/n7Ge6h3G2lqOtr4FXsaV9jjTGJQF/HMVfZS/l+wA76tDLGnGdZ1i+laKs8ir/mEp9AjDF+wM0e6pgP5GFn1F2L/RpKMMZ0xP0C3zjOGwFEAjcA73soe8Zp0rIBDz5/LW/+83+kpWbw5buzSpSpF1OLx18b73GntNK4+rYLSDqewm8zlrN720Fef7Lkna/BI3tw9a3nu63D39+PR/99A88/MJEdm/azdN46ls4ruiZ8YFAA4x+8mC69Xa8L4Vx+3844Hr7e9d1VZ1OXvuby/88d2pmTSal88uYPJB5JLsjCchYSFsx9/7ya1hWYvZQvolEMHW6/mQ0ffExOegY7vv2+RJmwOrXpfP+dBISW/Xo3u2QUWcknObRoMSf37mf9uxNLlKnXrw/Nxlzk4myb8fOj4z23s/r1t0nevZeElatJWLm6SBm/gABaXnslNTuUzFQr7tiGzWSdsLPM8mtc7wAAIABJREFU/r+9+46TrKoSOP47wzBDzkmSiEgGAUkKOAQBkaAoiAEU2UVRF5QFXMG87mIEVFizyxCUIEEJIrKLREGSKEiSRZCcBxhgGMLZP+5r+k1PV+iu7qnqnt93PvXpV1X33Xdrqqq76rxzz112s42ZMHF4f4oevLI/zj7WV48b6Ja7n+RTx1zOUQdtwcILTOLQD244W5u7Hnia/b82tKloAx11yo0sueh87Lntaqyz6hJ87+CtZmtz+v/eydGn3jjo/ttvuhITq7PjCy8wif/+3HYtj/nBL13EH2+ZPZB894PP8NFvXMJxh7yVpRebnwN2X5cDdp/19fTKK8n3z7qZn5wzvKDaWLT22q/nmGP+jcMOO4rp05/j6KNPnK3NKquswI9//EUWWqizL1EHH7wPjz/+FGeeeRG33HIXBx/8zdna7LHH9nz6040LGd91130cfvh3G94/8L6vfe1TswUZAKZNe4bTT7+Q009v/DFmueWW4sgjD+Itb2mdNTkW+dyP3+d+jbVW4shv7csXDz+JZ6fP4PvfnT0TfOVVluGY//oYC3b4me/jB+3CE09M59yzr+b2W+/jc4dNna3Nbu/enAMO3LlhH/PMM4Fvffef+fQnfsgtN/+Diy/6MxdfNOvCzpMmTeTQI/bgLVu1zii75qrbeOzRcoJoh7dvNGj2XSPv2HVTnnj8Gb7/3fN46aWX+eUpl/HLUy6brV1E8L69p7DfR5tVApE00gwwjax6Bdx9gasHNoiIjwMtc4sz86GIuBjYnlKH6XH6swybBZi+CxxIWWnu+IjYPjP/2qhxROwM3FtlSA3HwMf89UHafA3YqFEHmXlfRJwP7ArsERHvysxZvm1HxPzAj1uM5QTg85SaV9+OiFszc/a/OP19bgnMk5mXtuh3jtlkq3U4+uRDOP+0y7n+D7fy+CNPMXHeeVhuxaV4y7br8449t2TyfJNad9TChAkT+OTn9mLzbdbnol9dxZ233svT055lkcUWZLW1VmKHd725YVCobpHFFuJrPz6Qi379Ry7/3Q3cd/fDvDBjJosvtSjrb/wGdt5rK1Yexsonw7XTHluyzkarccEZV/KXa+7g8UefYsI8E1h2+SXYcPM12eV9b2WJpZqvmDUnLb3h+mz+1S/wj4su5rE/38yMJ55kwsSJLLDs0iy7yUastN02zDO5s+c7JkxgnX/6EMtsvCH3X3IFT//9bmZOf5ZJCy3IIq9bhRW32aqtoNCkhRdik89/hvsvvYKHrr6WZx94kJdfmMnkxRZlibXXZOUdtmWhFQY/qz3Qg7PUTRpeYOil55/n0RvLh9sFl1+ORVddZVj99LKLr7+fnQ85n313XpOtN1qB5ZZYgBdfepl7HprOBVfdw0m/vZ0ZbRbRbiQTDv/B1fz26n/wvre9gfVWW5IlFp7ME8+8wE13Ps4pF/2Ny258YIQeUWvX3/YoOx18Lh/aaU3etumKrLTMQsw7cQKPPPk8f/zrw5x0we3ccnfLtSfGnW233ZRzzjmWE088h0suuY6HH36MeeedyMorv4a3v31L9t57Z+bvIBDdZ8KECRx55EHsuONbOO2033LTTX/jySefZvHFF2G99d7AXnu9nSlT2knC7sxPf/oVLr30Om644RbuuedBHn98GtOmPcPkyZNYcsnFWGut17H11puw005bjsjj7mU+9+P3ud9q6/X4xZmf5dSTL+HKy2/hkYenMe/EeVhx5aXZbocNeO/738p884/MZ74v/PsH2PZtb+TsM/7ArX/9B9OenM5iiy/EWuuszLv33KKtoNBiiy/ET086mF+d+Qcu/M313H3Xw8x4fiZLLb0Im2y2BnvtPYXXr9beird9xb0B3tHm9Li6vffdjq2mrMvZZ/yBG667k/vufYznn3uB+eabl+VeswRv3GhV3vnuN7PWOo1r+o131sFRt0R/WSB1Ksqcm78Afd/WTqcU/X4QWBHYm1JH6Ur6p7B9JTO/3KC/DwNTq6vTgMWA6zKz6XJLEfFu4AxKQGpG1ccFwH3AvNVYNq3Gsiqwa2aeV9t/FaBvQvhHMrNvDIMda0HgLsoKbi9TioufDTwGrEbJXNpuwGOerc+IWA34M2VFu5eAH1T9PE35//wMsDZwLbAJQFU8fOB4NqdkRE2uxnMq8Kvq8UygFB9/E7A7pcD3gZl5XKPHN9BfnzzPN8xc6ge3NUtK1Hj326ObzdzVeHbnL4f+5UfS2PfUzMGLYmvusOikHcd0+aDHZ5zTM99ZlpxvtzH9f6mhMYNpBGVmRsQ+wMXA4sB7q0vdTcCeQDunhM+iBFrmpwSXoHn2Ut84zoqId1ICS0tQpuQ1mpb3CtB43enWx3o2Ij5ECeLMB3ysutRdAvwL0LCgdmbeGRHvogSVFqRkYR04oNlXKEGiTSiBs8H6uToitqYE91ai1K364GBtK083uU+SJEmSxpQulxrVXMzsuRGWmTcCGwA/BO4BXqSs7nYNcCiwaWY+2GZfzwDn1m7qy8hpZ99zgddVx7wYeLgay/OUbJ7zgH8FVsnM37fTZ5NjXUgpvn0yJXD2IvAocCnwUUoGU8sgVmZeRMlW+hHl/25mNe7zgbdXmV5985tmXyasv5+rgTdQgmrnV2OaSQlK3Qv8DvgcsGZmzl7QQJIkSZIkDYkZTKMgM/8BfLxFm7biypm5F7DXMMfxNHBUdRnKfnczxFUlqzpP+zRp0laf1bGbFUHvm374tyZtyMwXKIGqMVXoW5IkSZKkscgAk8aMiFgeeGt1dbYC6pIkSZIk58ipO5wip55RFfpudN/8lJpS81Y3ObVNkiRJkqQeYQaTeslPq1XpTgeup9SuWphS3+kTlFXpAH6WmTd1Z4iSJEmSJGkgA0zqNRtXl0bOZvbV5SRJkiRJQDhFTl1igEm95F+B3YFtgRWBpSkTiB+h1Fw6ITN/073hSZIkSZKkwRhgUs/IzBuAG4AvdHsskiRJkiSpfQaYJEmSJEkaJyJcy0vd4StPkiRJkiRJHTGDSZIkSZKkccMi3+oOM5gkSZIkSZLUEQNMkiRJkiRJ6ohT5CRJkiRJGifCKXLqEjOYJEmSJEmS1BEDTJIkSZIkSeqIU+QkSZIkSRo3nCKn7jCDSZIkSZIkSR0xwCRJkiRJkqSOOEVOkiRJkqRxIsI8EnWHrzxJkiRJkiR1xACTJEmSJEmSOuIUOUmSJEmSxg1XkVN3mMEkSZIkSZKkjpjBJEmSJEnSOBFmMKlLzGCSJEmSJElSRwwwSZIkSZIkqSNOkZMkSZIkaZxwipy6xQwmSZIkSZIkdcQAkyRJkiRJkjriFDlJkiRJksYN80jUHb7yJEmSJEmS1BEDTJIkSZIkSeqIU+QkSZIkSRonIlxFTt1hBpMkSZIkSZI6YgaTJEmSJEnjhhlM6g4zmCRJkiRJktQRA0ySJEmSJEnqiFPkJEmSJEkaJ8IpcuoSM5gkSZIkSZLUEQNMkiRJkiRJA0TEAhHxmYi4NiKeiIhnI+K2iDgqIl47wsdaNyJ+FBH/FxHPR8SjEXF5RBwQEU1nn0XEhIhYOyL2jYjvV+N9ISKyumw9kmNtxClykiRJkiSNG+aRjISIWA34DfCGAXetUV3+OSI+mJnnjcCx9geOAybVbp4P2LK6fCQids7Mxxp0sQ8wtdNxdMpXniRJkiRJUiUiFgbOpz+49BNgO+AtwOeA6cAiwGkRsUGHx3oH8ENKcOlh4CBgM2An4Kyq2abA2RExT6NuatsvAjcAN3UyruEwwCRJkiRJktTvMGD1avszmfnRzLw4M6/KzCOBHYGXgAWA7wz3IBExL3AsJTbzNLBFZh6bmddk5m8z8z3A96vmW1IylQZzCyUw9WZgkcx8E/3BqTnGAJMkSZIkSeNE9NC/sagK+hxUXb0VOGpgm8z8A/Cz6uqUiNhkmIfbHVi12v5aZv7fIG0OA56sbc+mCkgdm5lXZ+aMYY6lYwaYJEmSJEmSim2ARavtEzLzlQbtpta2dx/msd7VoL9XZeZzwOnV1bUjYvXB2vUCA0ySJEmSJEnFlrXtS5u0uw54rtreosNj3Z6ZDzVpVx/HcI816lxFTpIkSZKkcSKid6amRcSK7bTLzPtGeyxDsHZt+7ZGjTLzpYi4E1gfWGuoB4mIhYCVWh1nkPuHfKw5xQCTJEmSJEkaDfe22a53omLQFxR7NjOntWh7LyXAtHRETM7MF4ZxHIBWAbb6/+NKDVt1mQEmSZIkSZLGjV6K1YxJC1c/p7fR9tna9kLAUAJMC9e2Wx1r4HF6kgEmSZIkSZI0Gno226aJ+aqfM9toWw8ozT/M47RzrE6OM8cYYJIkSZIkSSNuNGsrRUSOQDcfycypA26bUf2c1Mb+k2vbzw/x2DNq262O1clx5hgDTJIkSZIkjRPhYvGdeqb62c5UtAVr2+1MqRvsOO0cq5PjzDEGmKQhWGfxXZzQPJc67s3dHoG66pfdHoAkaU5adNLq3R6CpNZGYjW1Bwe57T5gM2DBiFisRaHvvimAjw6xwDfA/bXtVqvt1acatls4fY4zwCRJkiRJksaUzLxtlLq+BXhPtb0mcPVgjSJiIvD66uqtQz1IZj4TEfdSgkdrtmhev3/Ix5pTzJ2TJEmSJGnciB66jElX1LanNGm3Mf1T167s8FhrRMRyTdrVxzHcY406A0ySJEmSJEnFJcBT1faHI6JRpGzf2vbZwzzWrxr096qIWAB4b3X1lsy8Y5jHGnUGmCRJkiRJkoDMnAl8r7q6FnDowDYR8Wbgn6qrl2bmtYP1FRFZXe5ucLizgbuq7cMj4vWDtPkWsHhtu2dZg0mSJEmSpHGiccKNhuBbwF7A6sA3I2I14FTgeWAb4AhKPOV54NPDPUhmvhgRBwLnAosAV0bEfwDXUIJK+9NfD+oK4KRGfUXEvgNu2qC2/faIWKV2/c7MvIIRFpk50n1KkiRJkqQumPnKdT3zJX/ShI3HbLSrCir9BnhDgyZPAx/MzPOa9NH3XNyTmas0abc/cBwwqUGTa4CdM/OxNo7VjhMyc98htG+LU+QkSZIkSZJqMvNOYEPg34DrgGnAc8DtwDHA+s2CS0M81k+ANwE/oUyZmwE8Tsla+jiwRbPgUq8wg0mSJEmSpHFi5ivX98yX/EkT3jRmM5g0dGYwSZIkSZIkqSMW+ZYkSZIkaZwI80jUJb7ypC6KiFVqS1cO+9Ltx6HeFhFb114vX25zn6m1fVYZcN++TV6P0yPirog4IyL2jAj/znTZgOd/4OW5iLg3Is6LiP0iYnKLvhr1MzMiHo6ISyPicxGxzJx6fCoiYsGIOCAifhMR90fEjIh4ISIejYhrI+K/I2L/iFhpkH2nNnluX4qIJyLi6oj4akSs2GIcX27QzysR8XRE3BoRx0fEW0fvf2P8a/K+7nu+/h4Rl0XEMRHxnohoVDS23ufdTV4HL0TEgxFxUUR8MiIWaNHXJQ36ebka37UR8a0oBXQ1iE7e06M0nnmqv+snR8QdEfFURDxfvW4uiIh/iYjF2uxr44j4YkT8LiLuqx7X9Krf4yNiy9F+PJJGhzWYpC6K8sX97532k5nObVZDEbE18Pvq6lcy88tt7DMV+HB19XWZeXftvn2B49s8/BXAbpn5ZJvtNcIGPP+t/BXYpf58D+ir3Q8NTwDvz8zftdleHYiIN1OWTl65jeYPZ+ZyA/afSv/7vZXpwIcz86wGY/ky8KU2+5oK/HNmvtxme1WG+L4GeBT4HvD1zHypQZ93A69ts7+7gHdk5u0N+roEmNJGPzOBgzPz+20ed67Q6Xt6FMazJfAjYO0WTR8HDq+KFTfq6zJgqzYOeyKwf2bObHugetWLr/ypZ77kzzthQ7+nzEWcIid11/3Aek3uv6n6eR3wkdEfjjRknwd+Xbu+PGUFjMOAxYEtgZOBnef80DSIHwD1L3LLAOtSnq8VgXWAcyJiwxZf+gf+TloYWA34BLA5sARwVkSsl5kdB9HVWESsDlxIeQ4AzgHOAO6gfHlfCngjsD2wTRtd7gg8ULs+L+W1sQewD7AQcGpEvDEzb23R137AtX1DBZYEtgM+XfWzLyXw8Zk2xqXGBr6vF6L8/l2f8v/9NmBp4KvArhGxS2Y+2qS/Byivg7r5gTWA/YG3AqsC50fEOpn5Qovx1T/nzEN5Pe1JCWpOAo6LiHsy8/wW/cwVRuE93el49qD8He/LcL0E+DlwG/ACJSi5G/B+ynv8xxGxRmYe2qDL5aufDwC/BC4H/kF5bbwZOARYAfgQ5ffPB0b2Ec0tjOmoOwwwSV2UmS8CNze6P+LVPw7PZmbDdlIX3T/gtXkz8LuIOKnaXhR4R0RsnJnXdWWEqntkkN8lF0fE8cBfgFUoXwZ3p3yhaWSw30lXRcTJwOmUYMSClC8K/zISA1dD/0n/F9GPZObUQdpcBHw7IpYG3tuivzsGyWD7E3BuleHyRcqXvk8DH2vR198HeZ1cEhG/Bq6kBBc+FRHfHAtLL/ewwd7XABcA34iItSkBgg2BTYGzI2LbJpkhLzbo79qI+AXwv8DWwOuBdwOnNBvcIH39mRKcup6SVRXAvwMGmIqRfk8PW0RsSAkmTaIEtz6Smb8Y0Oxa4IyIOBo4lxJAPCQi/i8zfzBIt7cBRwBnDnIi4+rq88OVwOrA+yPih5l52cg9KkmjydoYkqQRl5n3AcfWbnpbt8ai1jLzGeA/ajcN6/nKMu/+s532o/ZExDz0Zwde1+CL6Ksy89HM/K8ODvnt2vYmw+2kCjafVl2dxBzIwpibZeYtwBaUQCHV9ieH2dcrwFG1m4b9OgD+C7in2t4orN3Wjfd0s7FMAE6ivEcB9hskuFQfy42UjLlnq5uOiojZpvhl5i6ZeXqjLNkq2HxI7aY9hjN+Sd1hgEkaZ6J4X0ScXSuc2Feg9bMRsVCTfX9VFeG8sbq+ckR8LyLujP5iwCdHxBsG7Ld6RPyoavd8RDxQFZ9sWAw2Ir5THWtadX3piPh6RNxWHeuxqvjj7iP1f6M57qba9hwpQqqOjNTzdRf9XzB83kfX0pSpSwB3jvbBqkBkX6ZR04Lwbbimtt1u3R8NU2Y+T5ni2FeX5dCImHeY3dWnvQ77dVAFq+qZre3UGxrvRuQ9Hf0F26dW1zeJiFOqz3Ezqp/HR8SaTbrZlTJtGuCCzPx5q+Nm5h2UqZhQHsenhvkQ6vXFXj/MPuZq0UP/NHcxwCSNIxGxLPAHSrr6uyhz2CdRajFsBnwNuCUi1m+jrzdTznYeSPnjPj8l7fmDwDURsVHVbteq3UerdvMBr6HUZ7k2IlZt41hrAzcA/0ap8TA/ZR7/9pQ6Lj+N2nxBjRn16Rcvdm0UateIPF9VFlNfEWGf99FVf87WGu2DRcSClN/NUGqmdKL+2pinw77Uhsz8K2VqFZQ6OMPNPqoHBH0djKwRf09HxH6Uz4bvo3yOm1z93Be4MSL2bLBrvfD/d4ZwyB8BM/r6GObnt3rg0kUApDHEAJM0TlSZSZdSCuy+CPwE2ItSb2EK8BXgKUpGwW+rYFQjiwNnVv0cSim6+Bbg65Q/9IsBUyNiHUq9lYeAj1OCWFMoHy4AlmPWwqODmUgp8rgc5QPMNtWYD6A/df6fKHU/NLbUPxzf3a1BqG0j8nxFxGsotbc66ketZeYT9P+efGNE/Fs1rWW0HEp/5dhfN2vYhnrh5wcattJI+5/adjsrec2iChb8a3U1KTV3OuHroGYU3tMbAD8EHqGcMOz7nPYNSoHuycDPI2LjQfbte308R6m71ZbMnAb01UxaktYrzw2mvgJhq8UEJPUQi3xL48c3KNk/DwPbDLK6z2URcQrlLNZrKAGbRjUYVgbuAzataun0uSoiplNqtaxHSWG+HZiSmU8NONYEymozO0bEqpl5V4NjLUj5YrvrgBVkro2IM4CrgDcAR0TECY2WT1fblomIddtot1gnB4mIhekv7vwy8KtO+tPoqup+HFa7qVmB71YOH6F+1J5j6a+N9HXggIg4h/K7/pphrOK3+oCp1BMp2Q67UzIeoHx5PH64A46IFSjZsFCCFBbwnXNuqG2v3qDNvIP8nZivav/P9NfM+mZV32lYImJn+qdg3ZWZ9w63r3FmJN/Tb6QErDbPzIdqt18WERcCv6MU7f8+5eQe8Op7dKnq6l9arCo6mBuAHWpj+Gu7O1afH+u1/E4f4rHFLAsFSXOUGUzSOFCtIrJfdfXQRktHZ+bt9H9o2af6UtnIYQOCS31+QH8Nh6WBAwYEl+rt+mzZ5DgAPx9seeLMfJz+IMUkygdbdebjlFo7rS7vHE7nEbF8NW3yKvqXIj7GwGBvqmqfbUvJftywuvmMzLxiiP0sHBEbVavR9b1n/0Yp4qvRdQzw37XrqwAHAacCd0XEQxFxakTs2uZUlQuZ9XfBnyhZKvtRslU/BezQxtL0s6jqAy4ZEe8FrqA/iH1aZt7TZFeNrMdr24s3aLM8s/9NuJaymtg2lPpZ787MzzbYv6GImFDVdzyEWQMH3xxqX+PYSL+nDxkQXAIgM39PyXYH2GRAFtNSte3Z9m3Dw7XtJRu2GtzB9Ae7zsrM64dxfEldYoBJGh92oJxdfAU4u0XbvjPFC9N/5nCgmY36qdK3+wJP92Tm1Q36uKkaD0CrOkzNzoRfVDueK1L1nuOrQqIZEQncD5xDeW09CRyRmYc17UFz0pcGPF+PUKY+bEGZBnE08IE2+pkyoJ+ngevpz3D5NbB1Zj454o9As8jMVzLznyh/B35Lf/2rPstSpkufQ6mf10nB3OUpmam7tNn+97XXyCuUAuGnUb4wQwlaHNDBeDR002vbCw+zj42BT0TEei1bAgN+V7xMyaj5NrBA1eTHmfmjhh3MZUb4Pf0kzaez1gNZ9c9Y9ddG/TXTrvo+i7S7U0RMoWRtQfn79PFhHFtA+ZrfKxfNTXzGpfGh76zTBGB6/cPcwAvlzHGf5Rr0d0+Ls9PTqp93NGqQmS/Rv5JUqw+x1zbpJ2v3t/VhVk19JTOj1QU4YQSOdTn99bjU+24EvpeZnRbmfgD4TmbO9fVU5qTMvCgzd6JkC7wD+BIl86ieYboxcHlVJ6uR1w34XTCh6nMnSqbbusAvI2K4q0O9TJk+8ylgiwYZsBo99b/HTzdoc88gfxPmpUyV/BClsPfbgCsiolWGciPPUU4g7ZaZHxtmH+PaCL2n/1R9HmvkRvoLi9c/Yz1T2264+nAT9X0avc5mUdX1PJsyLXcGsGdmPjKMY0vqImswSePDMsPcb4EGtz/XYr++zKR22zWbijejWvq6mb5U6wUiYvJQp2ZoVH2e/rOjkymrC+1DWcVwN+B3EbFlZs5osL/mrB/QX3i/r7bOHpTn7C3AJRGxaWY+2qKf6ygrRUIp+rwcpSDsQZTVK38bEdtn5uUjPH61kJlPAxdUFyJiMiUr7SjKlKjXUJYRb2vKcRXkf4LynF5Eqb23FXBURFyYmbc12X0/+k8QJOWkw8OZ+fxQH5dGTH3q0xPt7lQFKe4HToqI/wFuBpYATo6I1VoEMeqBi5cpwYsHh1HXZ67U4Xu6aYAmM1+KiCcov8OXqN31WG270cnIZuoLyTzesFUlIl5HqQe1OOU18r7MtDabNAYZYJLGh74Azgv0ZzO1oxfqXmTrJuph92fmzbXr1wNnRcSXgC8Db6IUoB9utoNG1iMDnq8bgfMi4vfAVMrUpZ/SugbXswP6uQm4KCJOpxSiXZiyMtG61ZcjdUkVkD8+Ih6gTLcBeHdEfDQzX2my62B9vRwRR1MCTPNQslmOaLLL3we8TtR9G9a2bx9OB5n5YEScRPm9/lpgW0pwoFF7XwMjaIjv6eF+xnqAEmRaClg/IuYZYkBwo9r2n5s1jIjlKasbLk8Z736Z2ekqlXO9wCLf6g6nyEnjQ9/ZocnA3Zl5c5uXVplDc8L81YpjzfSdCXvO7KUx46v0Zy58IiIarVakHpCZJwBnVld3qwp/D6efm+kPOKzErCvTqYsy80Kgb5WuxRl64d0+9Ywlpy2PPdvXtodUzH8AXwdd1uZ7etlBbntVREykP3Pp1Yy2KnOx7/WxALBdu+OKiEUpQWgon08brjQYEUtRpkr21eo8MDNPbPdYknqPASZpfPhTbXuLro1i+DZpdEe1QkpfVpZnQceI6ixq33L1E4F/7+Jw1J4jKFMTAI7soJ8fAX3LaB9cfYFQb6jXxRpuZsPEBtvqcRGxLv2BgnspU12Hy9dBb2j1nt6gCiI18kbKKr0w+2esqbXtg4Ywpo8C81fbJ1TBqtlUgagLgbWrmz6bma48Ko1xBpik8eECoK8w71icivThJve9jZIJASWFWmNEZv4vcFV1dc+IWKOb41FzmXkH/cuGbxYR2zdr36SfF+lfBWhBypLT6rKIWID+L3JP00ZdlAbq07DvbdhKPSUi5gdOhFfnzXy7Rd2kVnwddFmb7+klgF2bdLNfbXvgZ6xzgVur7Z0j4n1tjGk14IvV1eeB7zZotwBwPv1T6f4zM7/Rqn8NRfTQRXMTA0zSOJCZ9wPHV1d3ioivNGsfEStERLOgzpy2d0S8feCNEbE4cGx19UVKbZiBbT5dWyXv06M8Tg3df1Y/J9C8Vot6w5H0nwX/fAf9TKUUBAb4ZHWmWiMsIhaKiD9GxC4R0fAzXXXfsfSvIHZOo6yCFsdbnP7MRChfENXjImJtynSnvvpLl1IK/g+3v42AvmDDTDz5M2JG6T19dETMNlUuIqZQso0Ars/MWVb0rTKR96H/BObUiNiryZjWB/6X/hXkDsnMfwzSbhJltbi+jPvvZmYnf28k9RBTWqXx4xDKKlDrAl+MiB0pQaebKGeRFqfUSdiRUoPhUkZmKfpOPUtZ8viciDgWOKe6bSPKF5lVqnZfz8y/D9qDelZmnh8RNwIbAB+IiC/7PPauzLw5Is6hFPl+a7UC4JDrtGTmzIj4NnAMsChlesVXR3Zqo+jCAAAJt0lEQVS0qmxKyTS4PyJ+RckavIeyUtdilKDCfvTXyXkK+EKT/laPiPoS41H1sxlwILBydfvllN/X6r5lqulvfRak/M1fnzIlbnv60wiuBvaoMg0bmXdAf1CKui9LySr+JKXmI8C3XEp+xI3ke/rPlCyn6yPia8A1lOfuHZTs0onAS5TndDaZeX1E7EPJfpsMnBoRHwN+TqnDNZPyO2E3ysp2fd8tj8rMRkHMU4Adqu2LgZ8N8nqrm1ll2EoaAwwwSeNEZk6vzkb9ghJE2qy6NNIrKzu9RFkm/SLgX6vLQFMpK5JpbDqSMvVqIvBZ4GPdHY5a+E/6V5H7AuX3yXD8GPgcZRWiT0XEMZk5fQTGp34vAQ9RlhFfgfIlcdAvipW/Ae/PzLubtLmwjeP+nhKkcBXQ3vDx6tLMo8B3gG+2MTVuecrJqWYS+B7Ng5UaupF+T98IHEfJWDtukPtnAh/OzD82OkBmnhYRDwI/BNYCtqkug3kCODwzf9xkzO+ubW8L/KVJWyjBtVVatNEApYSpNOcZYJLGkcx8Anh7RGwH7A1sSfmQMh8wDfg/ytnL8yhnjXpCZt5SpdwfSjkLthIwA7gB+H5mntXN8aljZ1LOdK4J7BsRX83M+7o8JjWQmddGxEWUrIcdImKTgVMn2uznuYg4hhKwWpLyBfhbIzvauVtmzoiIFYDNKZklmwNrUDJN5qNkgz5AyWL4NXBmZs4cxqGmAw9SVoY8BTjf4FLPeoWS6fIU5Yv59ZRss/OG+dwP7Pcu4ErgZ5l5Y4dj1QCj8Z7OzJ9GxM2UjKUtKUH/RynT2b6RmQ1Xeav1cVlErEc5IfhOyuIsywLzVn3dQpkye1JmThvq45Y0foSfDyR1Q0R8h1KQ/KnMXKzb45EkSRoPIuJu4LWUVdz27e5o1A3JrT3zJT9Yy3SquYgZTJIkSZIkjRuu5aXu8JUnSZIkSZKkjpjBJEmSJEnSOBE4K03dYQaTJEmSJEmSOmKASZIkSZIkSR1xFTlJkiRJkiR1xAwmSZIkSZIkdcQAkyRJkiRJkjpigEmSJEmSJEkdMcAkSZIkSZKkjhhgkiRJkiRJUkcMMEmSJEmSJKkjBpgkSZIkSZLUEQNMkiRJkiRJ6ogBJkmSJEmSJHXEAJMkSZIkSZI6YoBJkiRJkiRJHTHAJEmS5hoRkX2Xbo+lmYjYtzbWqd0ejyRJUisGmCRJUk+IiG/Xgip3R0QMs58lIuKFWl/7jvBQJUmSNIABJkmS1CtOqG2/FpgyzH7eB0yqtp8FzhjKzhFxSS04tfUwxyBJkjRXMcAkSZJ6QmbeBPypdtOHhtlVfb8zM3P68EclSZKkdhhgkiRJvaSexfSeiJh/KDtHxOrAZg36IzOj79LBGCVJkjSAASZJktRLfgG8WG0vArxriPvXs5f+Afx+JAYlSZKk5gwwSZKknpGZjwIX1G5qe5pcVRR879pNJ2VmT68WJ0mSNF4YYJIkSb2mPq1t+4hYrs39plCKg/c5cWCDWvHu2QJPtdvrxcV/X9+n2cp0EbFMRHwkIk6IiD9FxBMR8WJETIuI2yLi+IjYsc3HIkmSNKZM7PYAJEmSBjgPeAJYApgH+ABwdBv71bOdrsrMO0ZhbIOKiIMoY5xnkLsXrS5rAPtGxMXAezPz8Tk1PkmSpNFmgEmSJPWUzJwZEacAn6xu+hAtAkxVMfA9ajed0KhtE/9V/dwdWL7a/hVw/yBtbx1wfXn6g0t3Vfc/CswAFgPWA9ap7t8W+J+I2DwzXxjGOCVJknqOASZJktSLTqA/wPTGiFgvM29q0n53YOFq+wXgtKEeMDP/BSAi1qU/wPTdzLykjd3vAA4Ezs7MwQJSRMT6wM+AjYENgMOA/xjqOCVJknqRNZgkSVLPycxrmTVLqFWx7/r9v87MaSM/qsYy878z87hGwaWqzV+AtwEPVTd9IiIGm1InSZI05hhgkiRJvao+ze0DjYIxEfEaSuBmsP16SmY+BZxdXX0NsHYXhyNJkjRinCInSZJ61cnAkZQTYstTgkgXDtLug/TXP3qoQZs5JiKWATYH1gIWBxYEotZk49r2BkCzqX+SJEljggEmSZLUkzLz/oj4H2CH6qZ9GDx4VJ8e9/PMfHnUBzeIiFgb+AawE4OvJjeYpUZvRJIkSXOOU+QkSVIvq0932z0iFqrfGREbUlZoG6z9HBMROwI3ALvQfnAJ+guTS5IkjWkGmCRJUi87G3i62l4A2GPA/fXspT+1WGluVETE0pRV6yZXN90DHA5sSZnatwAwITMjMwP4Sm13P4tJkqRxwQ81kiSpZ2Xm88Avazft07cREROB99fu61Zx7/2BRavtPwPrZ+bXM/PKzHwwM5/PzKy1N2tJkiSNOwaYJElSr6sHjraOiJWq7R2BZavtF4FfzNFR9duutv0fmfl0w5bFa0dzMJIkSd1ggEmSJPW6K4C7qu0JwN7Vdn163AWZ+egIHS9bN5nF8rXtplP0ImIeYIshj0iSJKnHGWCSJEk9rZpedmLtpn0iYlFgt9ptIzk9bkZte9422r9S216gRdt3AcsNeUSSJEk9zgCTJEkaC06kP7NoLeCbwHzV9SeA80bwWI/Xtldoo/1dte3dGjWqioEfM9xBSZIk9TIDTJIkqedl5t+By2s3fbS2fUpmzhzBw91c294jIqJF+3Nr24dHxN4DG0TERsClwErAs50PUZIkqbdM7PYAJEmS2nQC8NYGt4+ks4AjgQB2Bv4SEX8Anqm1OTUzr6sd/xBgdWAycFJEHEFZUW4GsC6wcdX2z8CFwGdGeMySJEldZYBJkiSNFb8EjmXWOke3Zua1I3mQzLwjIr4OHF7dtG51qbsZuK5q/0JE7ApcAKxa3b9Wdam7EtgL2H8kxytJktQLnCInSZLGhMx8Bjh7wM0jnb3Ud6wjKNlLZwB/B55r0f4OYEPgCErg6RngBeAeSn2oDwBTMvP+0RivJElSt0VZmEWSJEmSJEkaHjOYJEmSJEmS1BEDTJIkSZIkSeqIASZJkiRJkiR1xACTJEmSJEmSOmKASZIkSZIkSR0xwCRJkiRJkqSOGGCSJEmSJElSRwwwSZIkSZIkqSMGmCRJkiRJktQRA0ySJEmSJEnqiAEmSZIkSZIkdcQAkyRJkiRJkjpigEmSJEmSJEkdMcAkSZIkSZKkjhhgkiRJkiRJUkcMMEmSJEmSJKkjBpgkSZIkSZLUEQNMkiRJkiRJ6ogBJkmSJEmSJHXEAJMkSZIkSZI6YoBJkiRJkiRJHTHAJEmSJEmSpI4YYJIkSZIkSVJHDDBJkiRJkiSpIwaYJEmSJEmS1JH/B7FVjj0SzHxUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(df_drop_grid.T, cmap=\"YlGnBu\", annot=True, linewidths=0.)\n",
    "g.set_xticklabels(g.get_xticklabels())#, rotation = 45, ha='right')\n",
    "g.set_yticklabels(g.get_yticklabels(), rotation = 0)\n",
    "plt.gca().tick_params(length=0)\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.xlabel('Vital', fontsize=12)\n",
    "plt.ylabel('Trend', fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig/drop_imp_5x6.pdf')\n",
    "plt.savefig('fig/drop_imp_5x6.svg')\n",
    "plt.gca().set_ylim(0,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_drop_row = df_drop_grid.mean(axis=1)\n",
    "df_drop_col = df_drop_grid.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 768x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(3.2*1.2, 2*1.2))\n",
    "df_drop_row.sort_values().plot.barh(color='silver', edgecolor='k')\n",
    "plt.axvline(0, c='k')\n",
    "plt.xlabel('Feature importance')\n",
    "plt.xlim(-0.005, 0.02)\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig/drop_imp_vital.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 840x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(3.5*1.2,2*1.2))\n",
    "df_drop_col.sort_values().plot.barh(color='silver', edgecolor='k')\n",
    "plt.axvline(0, c='k')\n",
    "plt.xlabel('Feature importance')\n",
    "plt.xlim(-0.005, 0.02)\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig/drop_imp_trend.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}