notebooks/voxel_k-fold_crossvalidation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys \n",
    "sys.path.append('..')\n",
    "\n",
    "from model.patch2self import patch2self\n",
    "\n",
    "import numpy as np\n",
    "np.random.RandomState(seed=2014)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import dipy.data as dpd\n",
    "import dipy.reconst.cross_validation as xval\n",
    "import dipy.reconst.dti as dti\n",
    "import dipy.reconst.csdeconv as csd\n",
    "from dipy.reconst.shm import CsaOdfModel\n",
    "import scipy.stats as stats\n",
    "from dipy.io.image import load_nifti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### Load the dataset and denoise it with Patch2Self and Marchenko-Pastur\n",
    "- #### Select the voxel locations in the Corpus Callosum (CC) and Centrum Semiovale (CSO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(160, 1, 652536)\n",
      "Patch Extraction Done...\n",
      "Training with patch-radius:  [0 0 0]\n",
      " -> Trained to Denoise Volume:  0\n",
      "Denoising Volume  0  Complete...\n",
      " -> Trained to Denoise Volume:  1\n",
      "Denoising Volume  1  Complete...\n",
      " -> Trained to Denoise Volume:  2\n",
      "Denoising Volume  2  Complete...\n",
      " -> Trained to Denoise Volume:  3\n",
      "Denoising Volume  3  Complete...\n",
      " -> Trained to Denoise Volume:  4\n",
      "Denoising Volume  4  Complete...\n",
      " -> Trained to Denoise Volume:  5\n",
      "Denoising Volume  5  Complete...\n",
      " -> Trained to Denoise Volume:  6\n",
      "Denoising Volume  6  Complete...\n",
      " -> Trained to Denoise Volume:  7\n",
      "Denoising Volume  7  Complete...\n",
      " -> Trained to Denoise Volume:  8\n",
      "Denoising Volume  8  Complete...\n",
      " -> Trained to Denoise Volume:  9\n",
      "Denoising Volume  9  Complete...\n",
      " -> Trained to Denoise Volume:  10\n",
      "Denoising Volume  10  Complete...\n",
      " -> Trained to Denoise Volume:  11\n",
      "Denoising Volume  11  Complete...\n",
      " -> Trained to Denoise Volume:  12\n",
      "Denoising Volume  12  Complete...\n",
      " -> Trained to Denoise Volume:  13\n",
      "Denoising Volume  13  Complete...\n",
      " -> Trained to Denoise Volume:  14\n",
      "Denoising Volume  14  Complete...\n",
      " -> Trained to Denoise Volume:  15\n",
      "Denoising Volume  15  Complete...\n",
      " -> Trained to Denoise Volume:  16\n",
      "Denoising Volume  16  Complete...\n",
      " -> Trained to Denoise Volume:  17\n",
      "Denoising Volume  17  Complete...\n",
      " -> Trained to Denoise Volume:  18\n",
      "Denoising Volume  18  Complete...\n",
      " -> Trained to Denoise Volume:  19\n",
      "Denoising Volume  19  Complete...\n",
      " -> Trained to Denoise Volume:  20\n",
      "Denoising Volume  20  Complete...\n",
      " -> Trained to Denoise Volume:  21\n",
      "Denoising Volume  21  Complete...\n",
      " -> Trained to Denoise Volume:  22\n",
      "Denoising Volume  22  Complete...\n",
      " -> Trained to Denoise Volume:  23\n",
      "Denoising Volume  23  Complete...\n",
      " -> Trained to Denoise Volume:  24\n",
      "Denoising Volume  24  Complete...\n",
      " -> Trained to Denoise Volume:  25\n",
      "Denoising Volume  25  Complete...\n",
      " -> Trained to Denoise Volume:  26\n",
      "Denoising Volume  26  Complete...\n",
      " -> Trained to Denoise Volume:  27\n",
      "Denoising Volume  27  Complete...\n",
      " -> Trained to Denoise Volume:  28\n",
      "Denoising Volume  28  Complete...\n",
      " -> Trained to Denoise Volume:  29\n",
      "Denoising Volume  29  Complete...\n",
      " -> Trained to Denoise Volume:  30\n",
      "Denoising Volume  30  Complete...\n",
      " -> Trained to Denoise Volume:  31\n",
      "Denoising Volume  31  Complete...\n",
      " -> Trained to Denoise Volume:  32\n",
      "Denoising Volume  32  Complete...\n",
      " -> Trained to Denoise Volume:  33\n",
      "Denoising Volume  33  Complete...\n",
      " -> Trained to Denoise Volume:  34\n",
      "Denoising Volume  34  Complete...\n",
      " -> Trained to Denoise Volume:  35\n",
      "Denoising Volume  35  Complete...\n",
      " -> Trained to Denoise Volume:  36\n",
      "Denoising Volume  36  Complete...\n",
      " -> Trained to Denoise Volume:  37\n",
      "Denoising Volume  37  Complete...\n",
      " -> Trained to Denoise Volume:  38\n",
      "Denoising Volume  38  Complete...\n",
      " -> Trained to Denoise Volume:  39\n",
      "Denoising Volume  39  Complete...\n",
      " -> Trained to Denoise Volume:  40\n",
      "Denoising Volume  40  Complete...\n",
      " -> Trained to Denoise Volume:  41\n",
      "Denoising Volume  41  Complete...\n",
      " -> Trained to Denoise Volume:  42\n",
      "Denoising Volume  42  Complete...\n",
      " -> Trained to Denoise Volume:  43\n",
      "Denoising Volume  43  Complete...\n",
      " -> Trained to Denoise Volume:  44\n",
      "Denoising Volume  44  Complete...\n",
      " -> Trained to Denoise Volume:  45\n",
      "Denoising Volume  45  Complete...\n",
      " -> Trained to Denoise Volume:  46\n",
      "Denoising Volume  46  Complete...\n",
      " -> Trained to Denoise Volume:  47\n",
      "Denoising Volume  47  Complete...\n",
      " -> Trained to Denoise Volume:  48\n",
      "Denoising Volume  48  Complete...\n",
      " -> Trained to Denoise Volume:  49\n",
      "Denoising Volume  49  Complete...\n",
      " -> Trained to Denoise Volume:  50\n",
      "Denoising Volume  50  Complete...\n",
      " -> Trained to Denoise Volume:  51\n",
      "Denoising Volume  51  Complete...\n",
      " -> Trained to Denoise Volume:  52\n",
      "Denoising Volume  52  Complete...\n",
      " -> Trained to Denoise Volume:  53\n",
      "Denoising Volume  53  Complete...\n",
      " -> Trained to Denoise Volume:  54\n",
      "Denoising Volume  54  Complete...\n",
      " -> Trained to Denoise Volume:  55\n",
      "Denoising Volume  55  Complete...\n",
      " -> Trained to Denoise Volume:  56\n",
      "Denoising Volume  56  Complete...\n",
      " -> Trained to Denoise Volume:  57\n",
      "Denoising Volume  57  Complete...\n",
      " -> Trained to Denoise Volume:  58\n",
      "Denoising Volume  58  Complete...\n",
      " -> Trained to Denoise Volume:  59\n",
      "Denoising Volume  59  Complete...\n",
      " -> Trained to Denoise Volume:  60\n",
      "Denoising Volume  60  Complete...\n",
      " -> Trained to Denoise Volume:  61\n",
      "Denoising Volume  61  Complete...\n",
      " -> Trained to Denoise Volume:  62\n",
      "Denoising Volume  62  Complete...\n",
      " -> Trained to Denoise Volume:  63\n",
      "Denoising Volume  63  Complete...\n",
      " -> Trained to Denoise Volume:  64\n",
      "Denoising Volume  64  Complete...\n",
      " -> Trained to Denoise Volume:  65\n",
      "Denoising Volume  65  Complete...\n",
      " -> Trained to Denoise Volume:  66\n",
      "Denoising Volume  66  Complete...\n",
      " -> Trained to Denoise Volume:  67\n",
      "Denoising Volume  67  Complete...\n",
      " -> Trained to Denoise Volume:  68\n",
      "Denoising Volume  68  Complete...\n",
      " -> Trained to Denoise Volume:  69\n",
      "Denoising Volume  69  Complete...\n",
      " -> Trained to Denoise Volume:  70\n",
      "Denoising Volume  70  Complete...\n",
      " -> Trained to Denoise Volume:  71\n",
      "Denoising Volume  71  Complete...\n",
      " -> Trained to Denoise Volume:  72\n",
      "Denoising Volume  72  Complete...\n",
      " -> Trained to Denoise Volume:  73\n",
      "Denoising Volume  73  Complete...\n",
      " -> Trained to Denoise Volume:  74\n",
      "Denoising Volume  74  Complete...\n",
      " -> Trained to Denoise Volume:  75\n",
      "Denoising Volume  75  Complete...\n",
      " -> Trained to Denoise Volume:  76\n",
      "Denoising Volume  76  Complete...\n",
      " -> Trained to Denoise Volume:  77\n",
      "Denoising Volume  77  Complete...\n",
      " -> Trained to Denoise Volume:  78\n",
      "Denoising Volume  78  Complete...\n",
      " -> Trained to Denoise Volume:  79\n",
      "Denoising Volume  79  Complete...\n",
      " -> Trained to Denoise Volume:  80\n",
      "Denoising Volume  80  Complete...\n",
      " -> Trained to Denoise Volume:  81\n",
      "Denoising Volume  81  Complete...\n",
      " -> Trained to Denoise Volume:  82\n",
      "Denoising Volume  82  Complete...\n",
      " -> Trained to Denoise Volume:  83\n",
      "Denoising Volume  83  Complete...\n",
      " -> Trained to Denoise Volume:  84\n",
      "Denoising Volume  84  Complete...\n",
      " -> Trained to Denoise Volume:  85\n",
      "Denoising Volume  85  Complete...\n",
      " -> Trained to Denoise Volume:  86\n",
      "Denoising Volume  86  Complete...\n",
      " -> Trained to Denoise Volume:  87\n",
      "Denoising Volume  87  Complete...\n",
      " -> Trained to Denoise Volume:  88\n",
      "Denoising Volume  88  Complete...\n",
      " -> Trained to Denoise Volume:  89\n",
      "Denoising Volume  89  Complete...\n",
      " -> Trained to Denoise Volume:  90\n",
      "Denoising Volume  90  Complete...\n",
      " -> Trained to Denoise Volume:  91\n",
      "Denoising Volume  91  Complete...\n",
      " -> Trained to Denoise Volume:  92\n",
      "Denoising Volume  92  Complete...\n",
      " -> Trained to Denoise Volume:  93\n",
      "Denoising Volume  93  Complete...\n",
      " -> Trained to Denoise Volume:  94\n",
      "Denoising Volume  94  Complete...\n",
      " -> Trained to Denoise Volume:  95\n",
      "Denoising Volume  95  Complete...\n",
      " -> Trained to Denoise Volume:  96\n",
      "Denoising Volume  96  Complete...\n",
      " -> Trained to Denoise Volume:  97\n",
      "Denoising Volume  97  Complete...\n",
      " -> Trained to Denoise Volume:  98\n",
      "Denoising Volume  98  Complete...\n",
      " -> Trained to Denoise Volume:  99\n",
      "Denoising Volume  99  Complete...\n",
      " -> Trained to Denoise Volume:  100\n",
      "Denoising Volume  100  Complete...\n",
      " -> Trained to Denoise Volume:  101\n",
      "Denoising Volume  101  Complete...\n",
      " -> Trained to Denoise Volume:  102\n",
      "Denoising Volume  102  Complete...\n",
      " -> Trained to Denoise Volume:  103\n",
      "Denoising Volume  103  Complete...\n",
      " -> Trained to Denoise Volume:  104\n",
      "Denoising Volume  104  Complete...\n",
      " -> Trained to Denoise Volume:  105\n",
      "Denoising Volume  105  Complete...\n",
      " -> Trained to Denoise Volume:  106\n",
      "Denoising Volume  106  Complete...\n",
      " -> Trained to Denoise Volume:  107\n",
      "Denoising Volume  107  Complete...\n",
      " -> Trained to Denoise Volume:  108\n",
      "Denoising Volume  108  Complete...\n",
      " -> Trained to Denoise Volume:  109\n",
      "Denoising Volume  109  Complete...\n",
      " -> Trained to Denoise Volume:  110\n",
      "Denoising Volume  110  Complete...\n",
      " -> Trained to Denoise Volume:  111\n",
      "Denoising Volume  111  Complete...\n",
      " -> Trained to Denoise Volume:  112\n",
      "Denoising Volume  112  Complete...\n",
      " -> Trained to Denoise Volume:  113\n",
      "Denoising Volume  113  Complete...\n",
      " -> Trained to Denoise Volume:  114\n",
      "Denoising Volume  114  Complete...\n",
      " -> Trained to Denoise Volume:  115\n",
      "Denoising Volume  115  Complete...\n",
      " -> Trained to Denoise Volume:  116\n",
      "Denoising Volume  116  Complete...\n",
      " -> Trained to Denoise Volume:  117\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Denoising Volume  117  Complete...\n",
      " -> Trained to Denoise Volume:  118\n",
      "Denoising Volume  118  Complete...\n",
      " -> Trained to Denoise Volume:  119\n",
      "Denoising Volume  119  Complete...\n",
      " -> Trained to Denoise Volume:  120\n",
      "Denoising Volume  120  Complete...\n",
      " -> Trained to Denoise Volume:  121\n",
      "Denoising Volume  121  Complete...\n",
      " -> Trained to Denoise Volume:  122\n",
      "Denoising Volume  122  Complete...\n",
      " -> Trained to Denoise Volume:  123\n",
      "Denoising Volume  123  Complete...\n",
      " -> Trained to Denoise Volume:  124\n",
      "Denoising Volume  124  Complete...\n",
      " -> Trained to Denoise Volume:  125\n",
      "Denoising Volume  125  Complete...\n",
      " -> Trained to Denoise Volume:  126\n",
      "Denoising Volume  126  Complete...\n",
      " -> Trained to Denoise Volume:  127\n",
      "Denoising Volume  127  Complete...\n",
      " -> Trained to Denoise Volume:  128\n",
      "Denoising Volume  128  Complete...\n",
      " -> Trained to Denoise Volume:  129\n",
      "Denoising Volume  129  Complete...\n",
      " -> Trained to Denoise Volume:  130\n",
      "Denoising Volume  130  Complete...\n",
      " -> Trained to Denoise Volume:  131\n",
      "Denoising Volume  131  Complete...\n",
      " -> Trained to Denoise Volume:  132\n",
      "Denoising Volume  132  Complete...\n",
      " -> Trained to Denoise Volume:  133\n",
      "Denoising Volume  133  Complete...\n",
      " -> Trained to Denoise Volume:  134\n",
      "Denoising Volume  134  Complete...\n",
      " -> Trained to Denoise Volume:  135\n",
      "Denoising Volume  135  Complete...\n",
      " -> Trained to Denoise Volume:  136\n",
      "Denoising Volume  136  Complete...\n",
      " -> Trained to Denoise Volume:  137\n",
      "Denoising Volume  137  Complete...\n",
      " -> Trained to Denoise Volume:  138\n",
      "Denoising Volume  138  Complete...\n",
      " -> Trained to Denoise Volume:  139\n",
      "Denoising Volume  139  Complete...\n",
      " -> Trained to Denoise Volume:  140\n",
      "Denoising Volume  140  Complete...\n",
      " -> Trained to Denoise Volume:  141\n",
      "Denoising Volume  141  Complete...\n",
      " -> Trained to Denoise Volume:  142\n",
      "Denoising Volume  142  Complete...\n",
      " -> Trained to Denoise Volume:  143\n",
      "Denoising Volume  143  Complete...\n",
      " -> Trained to Denoise Volume:  144\n",
      "Denoising Volume  144  Complete...\n",
      " -> Trained to Denoise Volume:  145\n",
      "Denoising Volume  145  Complete...\n",
      " -> Trained to Denoise Volume:  146\n",
      "Denoising Volume  146  Complete...\n",
      " -> Trained to Denoise Volume:  147\n",
      "Denoising Volume  147  Complete...\n",
      " -> Trained to Denoise Volume:  148\n",
      "Denoising Volume  148  Complete...\n",
      " -> Trained to Denoise Volume:  149\n",
      "Denoising Volume  149  Complete...\n",
      " -> Trained to Denoise Volume:  150\n",
      "Denoising Volume  150  Complete...\n",
      " -> Trained to Denoise Volume:  151\n",
      "Denoising Volume  151  Complete...\n",
      " -> Trained to Denoise Volume:  152\n",
      "Denoising Volume  152  Complete...\n",
      " -> Trained to Denoise Volume:  153\n",
      "Denoising Volume  153  Complete...\n",
      " -> Trained to Denoise Volume:  154\n",
      "Denoising Volume  154  Complete...\n",
      " -> Trained to Denoise Volume:  155\n",
      "Denoising Volume  155  Complete...\n",
      " -> Trained to Denoise Volume:  156\n",
      "Denoising Volume  156  Complete...\n",
      " -> Trained to Denoise Volume:  157\n",
      "Denoising Volume  157  Complete...\n",
      " -> Trained to Denoise Volume:  158\n",
      "Denoising Volume  158  Complete...\n",
      " -> Trained to Denoise Volume:  159\n",
      "Denoising Volume  159  Complete...\n"
     ]
    }
   ],
   "source": [
    "dpd.fetch_stanford_hardi()\n",
    "hardi_fname, hardi_bval_fname, hardi_bvec_fname = dpd.get_fnames('stanford_hardi')\n",
    "img, gtab = dpd.read_stanford_hardi()\n",
    "data, affine = load_nifti(hardi_fname)\n",
    "data_p2s = patch2self(data) \n",
    "data_mppca, _ = load_nifti('dwi_mppca.nii') \n",
    "\n",
    "cc_vox = data[40, 70, 38]\n",
    "cso_vox = data[30, 76, 38]\n",
    "\n",
    "cc_vox_p2s = data_p2s[40, 70, 38]\n",
    "cso_vox_p2s = data_p2s[30, 76, 38]\n",
    "\n",
    "cc_vox_mp = data_mppca[40, 70, 38]\n",
    "cso_vox_mp = data_mppca[30, 76, 38]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initialize the DTI and CSD models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dti_model = dti.TensorModel(gtab)\n",
    "response, ratio = csd.auto_response(gtab, data, \n",
    "                                    roi_radius=10, fa_thr=0.7)\n",
    "csd_model = csd.ConstrainedSphericalDeconvModel(gtab, response)\n",
    "\n",
    "dti_model = dti.TensorModel(gtab)\n",
    "response_p2s, ratio = csd.auto_response(gtab, data_p2s, \n",
    "                                        roi_radius=10, fa_thr=0.7)\n",
    "csd_model = csd.ConstrainedSphericalDeconvModel(gtab, response)\n",
    "\n",
    "dti_model = dti.TensorModel(gtab)\n",
    "response_mp, ratio = csd.auto_response(gtab, data_mppca, \n",
    "                                       roi_radius=10, fa_thr=0.7)\n",
    "csd_model = csd.ConstrainedSphericalDeconvModel(gtab, response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run 3-Fold Crossvalidation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dti_cc = xval.kfold_xval(dti_model, cc_vox, 3)\n",
    "csd_cc = xval.kfold_xval(csd_model, cc_vox, 3, response)\n",
    "dti_cso = xval.kfold_xval(dti_model, cso_vox, 3)\n",
    "csd_cso = xval.kfold_xval(csd_model, cso_vox, 3, response)\n",
    "\n",
    "dti_cc_P2S = xval.kfold_xval(dti_model, cc_vox_p2s, 3)\n",
    "csd_cc_P2S = xval.kfold_xval(csd_model, cc_vox_p2s, 3, response_p2s)\n",
    "dti_cso_P2S = xval.kfold_xval(dti_model, cso_vox_p2s, 3)\n",
    "csd_cso_P2S = xval.kfold_xval(csd_model, cso_vox_p2s, 3, response_p2s)\n",
    "\n",
    "dti_cc_MP = xval.kfold_xval(dti_model, cc_vox_mp, 3)\n",
    "csd_cc_MP = xval.kfold_xval(csd_model, cc_vox_mp, 3, response_mp)\n",
    "dti_cso_MP = xval.kfold_xval(dti_model, cso_vox_mp, 3)\n",
    "csd_cso_MP = xval.kfold_xval(csd_model, cso_vox_mp, 3, response_mp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3QAAAGkCAYAAAB5MWe9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdeXxTVfr48U/3EAqUQoGyKgotghYsyiAgVnRAZXcrCAgM1G1QQHEUGFBEv8Og4s8FtIAMUBRHEIqoA1qHxYoVSqeiUgVZy162kpYuSe7vj5BLkiZp0iZt0jzv16sv0pube2+ivU+ec55zTpCiKApCCCGEEEIIIfxOcG1fgBBCCCGEEEKIqpGETgghhBBCCCH8lCR0QgghhBBCCOGnJKETQgghhBBCCD8lCZ0QQgghhBBC+KnQ2r4AZ0pKSvj555+JiYkhJCSkti9HCCGEFxkMBs6cOUOXLl3QaDS1fTk+T2KkEEIEhsrio08ndD///DOPPPJIbV+GEEKIGrRq1Sq6d+9e25fh8yRGCiFEYHEUH306oYuJiQFMF9+iRYtavhohhBDedPLkSR555BH13i+ckxgphBCBobL46NMJnbmEpEWLFrRu3bqWr0YIIURNkPJB10iMFEKIwOIoPsqkKEIIIYQQQgjhpyShE0IIIYQQQgg/JQmdEEIIIYQQQvgpSeiEEEIIIYQQwk9JQieEEEIIIYQQfkoSOiGEEEIIIYTwU5LQCSGEEEIIIYSfkoROCCGEEEIIIfyUJHRCCCGEEEII4adCa/sChP/KycnhH//4B2FhYTRr1ox58+YRFhZW25clhBBC1CqJj0KImiQ9dKLKWrZsyfLly0lLS6NNmzZkZGTU9iUJIYQQtU7ioxCiJklCJ6qsefPmaDQaAEJCQggOlv+dhKhxihGOZcKepaZ/FWNtX5EQAU/io/B1hSs2UJr7W4XHwj/JHUY4dPHiReLi4ujWrRsJCQkkJSXx6aefVtjv6NGjbNu2jTvuuKPa57xw4QJPPfUUXbt2JSkpic8//9zp/t26dbP66dSpE6+88goAf/zxB2PGjCExMZG7776br7/+2uXXCuEXFCNsfQ7++wzkLjL9u/U5SeqE8DJ/iI9mhw4d4sYbb+S5556z2v7FF19wzz330LVrV+666y527dqlPldZ/BT+q3DFBs6+mkrhig2cmfG2+vji8vTavjRRDZLQednHH3/M4MGDuffee7nvvvuYNm0ax48ft7vvnj17ePrpp50ez5V9nMnKymLgwIEu7bt3714aN25MTk4Oubm5TJ06ldmzZ3Pu3Dl1H51OxwsvvMA///lPwsPDq3xdZnPmzCEsLIzMzEzmz5/PSy+9xL59+xzun5OTo/5kZmai0WgYMGAAer2eJ598kqSkJH788UfmzJnDtGnTOHjwYKWvFcJvHN8B+dust+VvM20XwsdJfHSPu/HR8nU33nij1bbMzExef/11/u///o/du3ezatUq2rRpA+BS/BT+qXDFBgpXbKD898OgiaDsp98oXLEBAE3X+Fq+OlEdAZnQ1VQ387x589i8eTMffPABX375JZ9//jm9evXi4Ycf5uTJkxX2v/HGG3n77bedHtOVfTwlLy+Pzp07q7/feuutGAwGCgsLAdNNf+rUqfz1r3+lffv21T5fcXExmzdv5plnnqF+/fp0796dO++8k/R011qNNm3aRHR0NN27d+fAgQOcPn2asWPHEhISQs+ePbn55psdHsvytUJ4nafKJM/lOdgupTOi6moiRkp8dE9V4+MXX3xBgwYN6Nmzp9X2d955hyeffJKuXbsSHBxM8+bNad68OYDb8VP4j4iEOMJv6oj+2CkMx04BEKzVqM/VKhk+UC0BN8uluXUi/KaOaLrGq4+bvfm8R89z8uRJVq9ezZYtW2jUqBEAwcHBDB06lJ9//pkPPviAAQMG8Oqrr6LVaikqKuL5559n3rx5bNy4kdTUVNasWaPeuDMyMvj222/JysrilVdeYePGjWRlZbFgwQLatGnDvn370Ov1vPzyyyQmJmI0GnnttdfIzc2lqKgIRVGYO3duheucOHEiycnJ9OvXr8Jzv/76qxqwCgsLeeONN+jcuTPt2rUDYOPGjeTm5rJw4UIWLlzIiBEjuPfee62O8dhjj5GdnW33M0pMTOSDDz5Qfz906BDBwcFce+216rb4+Hh27tzp0me+bt06hg4dSlBQEIqiVHheURSHrZmWrxXCq8xlkpY9a61vh76vQ5CbbWzRDlpUo2s5MAu/VRMxUuKjibfjo06n4+233+Zf//oXa9asUbcbDAZ+/vln7rzzTu6++25KS0u56667eP7559FoNG7HT+E/IhLi0HSN5/J/f8Rw9gIAQfVMCd3F5enV+ztXjKbqkHN5ptjUsqfrMc2TcTFABVxCZ26dKPvpd8p++h3wTjdzbm4u7du3V4OVpdtuu4233nqLAQMGsG/fPr755htatWpFVlYWANu3b+ezzz5jzZo1NGjQgBkzZjg8z08//cTs2bPp1KkTH374IQsWLCAtLY3c3FxOnz7NJ598QnBwMKmpqSxevJhx48ZZvX7x4sUOj7137142b95MWloaRUVF9O7dmyVLlqhJz9ChQxk6dKjTz8EyIFWmuLiYBg0aWG1r0KABRUVFlb72+PHj7Ny5k1dffRWA9u3bEx0dzZIlSxg7dixZWVns3LmTHj16VPpaIbzKWZlkq17uHatlT1PQsw2CLXs6fo0QTtREjJT4aOLt+PjWW29x//33Exsba7W9oKCA8vJy/vOf/7Bq1SpCQ0N58sknWbRoEVOmTHErfgr/Ym6wCdJqCCrWEKzVEN6hHcFNGlXv77y6CZkn42KACsiETtM1Xg1U5m3eoNfr7W4vKytTb/qxsbG0atXK6vmtW7cyYMAAGjZsCMAjjzzCDz/8YPdYLVu2pFOnTgDccMMNrFu3DjBN+NGoUSNWr17N0aNHycrKon79+i5fe1lZGQcOHOCrr76ibdu2bNq0iRkzZnh1HR2tVotOp7PaptPpXLru9evXk5iYqI4BCAsL47333mPu3LksWbKELl26MGDAALvjGGxfK4RXOSuTdDdwBQWbAubxHabXR8e51yoqhI2aipESH93jbnzcu3cvO3bsUN+zJfPsm6NHj6ZZs2YAjBs3Tk3o3Imfwke42DtmbrDRdI0nIiGOi8vT0XSNp+GYwdU7f3UTMk/GxQAVcAmduXXCUrW7me3o2rUrhw8f5syZM8TExFg9l5WVRbdu3QDTTdpWaGioVclDSEiIw/OYb8yAVanhli1bePXVVxk3bhz9+vWjffv2bNiwwdFhKvj9998JDw9Xk5z+/fvz7rvvsmnTJh544AGXjzNhwgSnJSVLlixRf7/mmmswGAwcOnSIa665BjCNU7j++usrPU96ejoTJ0602hYfH09aWpr6e3Jyst0WU3uvFcJrPF0mGRRsCngS9IQH1ESMlPho4s34mJWVxbFjx0hKSgJMPXwGg4Fhw4axbt06WrRo4XSIgavxU9SOwhUbiEiIIyIhjsLl69Fq1xNa+vPVHRz0jkUkxFn9LXvs77q6CZkMH6i2gGvGNbdONBwzmJg3pqktFZ7WvHlzRo8ezdSpUzl16pS6fe3atWzevNlpAtG3b182b97MpUuXAKxq312VmZlJUlISI0eOpEuXLnzzzTcYDAaXX7937146dOhgdcPv27cv3377rVvXsWTJEqvZJC1/LIMVmIL33Xffzdtvv01xcTHZ2dlkZGQwZMgQp+fYvXs3p06dqjBDZV5eHqWlpVy+fJmlS5dy+vRphg8f7tJrhZ/yh0HV5jJJS1ImKXxETcRIiY8m3oyPDz/8MF9//TXr169n/fr1JCcnc8cdd7B06VIAhg8fzsqVKzl79iwXL15k+fLlVksruBI/Re0wN7pcXJ5O4YoNlHy+FGX/N9Y71fRsx9VNyCQuVpvHe+jKy8uZPn06x44do6ysjCeeeIKWLVvyyiuvEBISQnh4OPPmzaNp06bMnTuX3bt3qyUDCxcurFAj7mlea52w49lnn+XTTz/liSeeoKysjLKyMm688UZWr15Nq1atyM/Pt/u6nj178tBDD/Hwww+j0Wjo0KED9erVc+vcycnJPPvsswwaNAi9Xk+vXr3YvHkzRqP1F1xHg7737t1LXJz1H2KfPn1IS0ujtLSUiIgIt67HVbNnz2b69OncdtttREVF8dJLL9GhQwf1+QkTJtC9e3cef/xxddv69eu5++67iYyMtDpWeno6a9asQa/Xk5iYyLJlyyqUjDh6rfBD/jKoWsokhQ+rqRgp8dF97sTHevXqWX0uWq2W8PBwoqOjAXjyySc5f/48/fv3JyIignvuuYcnnnhC3d+V+CmsWfWaWTz2NNtxrtrWF9SZKq3UZLlidcdzS1ystiDF3nRG1bB27Vry8vKYMWMG58+fZ9iwYbRu3ZoZM2bQqVMnVq9ezcGDB3nxxRcZMWIE7733nnqDsZWfn0+/fv3IyMigdevWnrxMn7Znzx5ycnIYM2YMAMuWLSM3N5e33nqrlq9MCB92LNO0sLatpP9XI0GtpoJ5XRao9/yqCsTPS+Kj8EU1NYO67fkAwqNO0fTPBysmdTUU+1TqOL7fSD1dHxpdR4osBeUxld3vPd5DN2DAAPr376/+HhISwptvvqkOvDUYDERERGA0Gjl8+DCzZs2ioKCABx54wK3a87rs2muvZfHixfz73/8mKCiI2NhYXnnlldq+LCF8Wy0Oqq7pYO4rJIkVNU3io/BFNTWDOlQc51p2oRklJy6gve7y1Z1qo1zxynju1JMaUv/IBnZDUBApiYk1ex0ByuMJnbl8UqfT8fTTTzN58mQ1mdu9ezdpaWmsWrWK4uJiRo0axbhx4zAYDIwZM4YuXboQHy8r1UdGRtbY4qhC1Bm1OKi6JoO5rwjUJFbULomPwhfV5Azq9maq1F8zCJJiar1cMTU7m1SLiX7MjyWp8z6vzHJ54sQJnnrqKUaOHMmgQYMA+PLLL1m0aBGpqalER0erSZy5xvtPf/oTeXl5ktAJIaqmhtdks+yRKs39jZAmUVbP1/WeqkBMYoUQwp6amkEdKhnnWouzHdsmc5bbQZI6b/N4QldQUMD48eOZNWsWPXuavkilp6fzySefsHLlSqKiTF96Dh06xJQpU1i3bh1Go5Hdu3czbNgwT1+OECJQ1OCgatveqfMLVmAsLiGsTQt1H28Fc19Rky3SQgjhyxyt71bXWCZnkqj5Fo8ndO+//z6FhYUsXLiQhQsXYjAY2LdvHy1btmTSpEkA3HLLLTz99NMMGjSIhx56iLCwMIYMGWI1W5MQQrithtZks+2dCtJqCGvVnIYP9a/TwdxSTbZICyGEL6vJGdRri2UP3LbDh8krKFCfS0lMVBM72146y+eE93g8oZs5cyYzZ850ad+JEyfKgs5CCL9j2zsVXE9DzKtPqz1UtsG8Lk4eEigt0kIIEegsk7kzxcXszcujqVZLjFZr1VNnm9RJMldzvDKGTggh6jJ3eqfq6uQhgdAiLYQQgc42mSsoLgZQ/3WU1Jl/r4q62AjqbZLQCSHq5M2zuu/J2fgAd3qnZPIQIYQQgaQ6vXJ1tRHU2yShEyLA1cWbZ3Xfk+1sXbbByZ3eKZk8RAghhL+yLKOM0WoBU++cueTSdp/qllhKI2jVSEInRICrizfP6rynKq2joxivzK6ZZ1oPz2J2TZk8RAghRI1xEo+qyjap69O2rTopir3JUKqT1EkjaNVIQidEgKuLN8+qvieH6+j8sBWOfUdKYveKwVExwtbnKq5/1/d1CAr2nclDvBDkhRBC+JBK4lF12I6Ns9fwafm4OuPnpBHUfZLQCeGmnJwc/vGPfxAWFkazZs2YN28eYWFhtX1ZVVYXb54efU+XjkK5DvIPQ+HKisHx+A7r4Amm34/vgFa9fGPyEC8GeSGEMKtr8dHvVBKPqsLZhCfeWEzcZxpB/YwkdEK4qWXLlixfvhyNRsOCBQvIyMhgwIABtX1ZVVYXb55VfU8VSkfKdVCuI6XhYVIaHjFtsw2O5/LsH+zcb15fD89lXgjyQghhq67FR7/jJB4VZpx1e6KwysaTe4NPNIL6IUnohHBT8+bN1cchISEEB/t3D0ddvHlW5z1ZJXX6EutkzswyWYt2kChG+1DZqj8knUIIv1fX4qPfcRCPin48Q+HH37s1UZgr48llMXHfIX9pQti4ePEicXFxdOvWjYSEBJKSkvj0008r7Hf06FG2bdvGHXfcUe1zXrhwgaeeeoquXbuSlJTE559/7nT//Px8Jk6cyC233EKvXr2YM2cOer3epWP98ccfjBkzhsTERO6++26+/vrral9/XWMORildrquYzIF1stayp6l80VLr203bfYU/JJ1CCJ/nD/Fx9OjR3HjjjXTr1o1u3brRv39/l4/lLLb6BQfxKDRxsDpRmHk4grOqFWellJUlb5LM1Q5J6LwkPz+fuLg4Ro0aVeG5F154gbi4OM6dO1ft82RlZTFw4MBqH8fb58nPz6dTp04MGTJE/Rk8eDBr1qyp1nWNHz/eI5+jpb1799K4cWNycnLIzc1l6tSpzJ492+o8Op2OF154gX/+85+Eh4dX+5xz5swhLCyMzMxM5s+fz0svvcS+ffsc7v/yyy/TpEkTvvvuO9avX8/OnTv56KOPKj2WXq/nySefJCkpiR9//JE5c+Ywbdo0Dh48WO33UJeoLZF3PlgxOLbqYxqTtmcpHMs0bev7OiT9P0h40vRvNcemFa7YQGnubxUeV5k/JJ0iYEh8tCbx0Tl34yPArFmzyMnJIScnh02bNrl8LGex1S8EBduNRxFdO11J4BTCo06hbf0bmnZFpljmAWojqCRztUZKLr0oIiKCgwcPcuzYMVq1agVAcXExu3fvruUrqx0ajYb09HT191OnTjFw4EC6dOlCfHzVxmxlZmZ66vJUeXl5dO7cWf391ltvxWAwUFhYSHR0NHq9nqlTp/LXv/6V9u3bV/t8xcXFbN68mc8//5z69evTvXt37rzzTtLT03nuuefsviY/P59Ro0YRERFBTEwMvXv3Zv/+/ZUe68CBA5w+fZqxY8cSFBREz549ufnmm0lPT2fy5MnVfi91QYUxA31fvzI75G/QuAPs+wy2TLn6AvPkIq16eaR80SvrApqDvPl9RMfJLJeiVkl8tCbx0b6qxMfqHMtRbPUrQcEV4pEprqTTKD6LiOhTpt2+nwbKTrsNkFUppZRErnYFZjRXjKaWdXMLu4daKGyFhIRwzz33WHXpb968mX79+pkuQ1GYO3cuDz74IPfeey/33HMP2Vf+eLKyshg8eDDJyckMGjSIsrIy1qxZw3333cegQYMYM2YMJ06cAEw3qSlTpjBkyBAGDBjArl271PN9++23PPjggwwdOpTk5GRycnLU4ycnJzNt2jSGDh3KwIED1XM7Oo/Zrl27uOOOO9TA+8knnzBw4EAGDx7M+PHjXe7tad68Oe3atePQoUMYjUaHn0VRURFPP/00Q4YMYdiwYcycOROj0ciLL74IwKOPPsr69eutWkgtW0ztfZYTJ04kIyPD7nX9+uuvasAqLCzkjTfeoHPnzrRr1w6AjRs3kpuby8KFCxk9ejRffvllhWM89thjdO/e3e7PY489ZrXvoUOHCA4O5tprr1W3xcfHOw0iY8aM4YsvvuDy5cucOnWK7du306dPn0qPpShKhWMpilJpa2egsDdmIHV3jikw3jjeFPSObbd+kXlyEQ+xXEPPldIYl5mD/I3jTf9KMiccqYEYKfHROYmPJlWJjwBvvPEGPXr0IDk5maysLJeP5Si2+hN7FR4RCXHU7x6BtkMJoa2bE1QvgmCtxmn8klJKP6P4sKNHjyodO3ZUjh496rmDGg2K8t8pirIy8erPf6eYtnvQ0aNHla5duyp79uxRBgwYoG5/9NFHld9++03p2LGjsnPnTmXSpEmKwWA69wcffKA89thjiqIoyg8//KDEx8cr+fn5iqIoyt69e5UePXoox48fVxRFUZYtW6b8/e9/V3744QelU6dOyv/+9z91+5gxYxRFUZSDBw8qAwcOVM6dO6coiqL8/vvvSq9evZSioiL1db/++quiKIqydOlS5ZFHHnF6nvvuu0/ZsWOHctdddyl79+5VFEVRvv/+e+Wuu+5Szp49qyiKoqxdu1a55557FKPRaPfzsLR7927llltuUY4fP67s3r3b4Wexbt06Zfz48YqiKIper1dmzJihHDp0SFEURenYsaNy9uxZ9frMLH+3/SwrM3DgQCUhIUHp1q2b0rFjR2X8+PHq+/OGnTt3KrfddpvVtk8++UQZNWqUw9fs379fGTZsmNKpUyelY8eOyt/+9jfFaDRWeqyysjLlzjvvVFJTU5WysjJl+/btSufOndXPt04yGhQl/ztF+WmJ6V8Hf+sf7NqlJH7wQYWftgsWKI+sXWva6acl1vcO889PSyu9jIvL05WS/+VVeOxo36P9/qL+ONu3LvHKPd/HlJWVKc8995wyYsQI5f7771e++eYb5ddff1VGjBihjBo1Shk/frxy5swZRVFMf7vDhg1THnzwQeXbb7+tcCx/jZESHyU+uqoq8fF///ufcunSJaW0tFT57LPPlK5duyqHDx926ViOYqu/MMeOU1PmWT1WFKXK8euDXbuUD3btqoGrF85Udr8PvJLLGp6+u0uXLoSEhPDzzz/TpEkTioqK6NixIwDt27dn8uTJrF69mqNHj5KVlUX9+vXV18bGxqqlKDt27KB3797ExsYCMHbsWMDUutamTRsSEhIAU2vT2rVrAVO5hbm8ziwoKIgjR0yTPLRs2ZJOnToBcMMNN7Bu3Tqn5zl58iSPP/44I0aMUEtAtm/fzr333kt0dDQAw4cP59VXXyU/P582bdpYfRYlJSUMGTIEAIPBQOPGjZk/fz6xsbHExsbSqFEju59FYmIiCxYsYPTo0dx22208+uijamugqyw/S2fKyso4cOAAX331FW3btmXTpk3MmDHDq+voaLVadDqd1TadTmf1/4Ilo9HIX/7yFx5++GFWr15NUVER06dPZ/78+QwcONDpscLCwnjvvfeYO3cuS5YsoUuXLgwYMMAj4xx8UjXXXztTXExBcTFf7d/PqM8+I62H+5OLpGZnc3lHLvevd22Gsbq4LqC4asOGDURFRTF//nzOnz/PsGHDaN26NX//+9/p1KkTq1evZvHixUyYMIGVK1eydu1aSktLGTlyJL169fL+32oNxkiJj1dJfLTP3fgIqP+9AYYNG8bGjRvZunUriYmJTo/lLLY+/7x/3H8tKzzKfvodsKjwqOLkWNIr5x8Cr+bG2fTdXjJ48GA2bNhAenq6esMG2Lp1q1pe0K9fP0aMGGH1Oq1Wqz4OCQkhKChI/b2kpIQ//vgDwOpmGhQUpJbVGY1GevbsSXp6uvrz73//mw4dOgCmmn3b1zk7T0hICB9++CHr1q0jNzdXPYctRVHszgplHiOQnp7Oxo0bWblyJX379gVgy5YtDj+LNm3a8PXXX5OSkoJOp2PcuHF8++23Vse2fN8A5eXlDj9LZ37//XfCw8PVYNu/f39iY2OtBlW7YsKECeoMW7Y/EyZMsNr3mmuuwWAwcOjQIXVbXl4e119/vd1jX7hwgRMnTjBq1CjCw8Np3Lgx999/P9u2bXPpWPHx8aSlpZGVlcXSpUvJz8/npptucuv9+Q1nX05t2JaTmJM5TWgoJXo96/LyGJV1yq3JRcwlnCsMOlYntnepjNIckBuOGUzMG9PUJFDUDQMGDOCZZ55Rfw8JCeHNN99UkweDwUBERAQ//fQT3bp1Izw8nAYNGtC2bVvy8hzEL0+q4Rgp8RH1fBIfqx8f7TG//8qO5Sy2+ouIhLgK8UJda85HJ8fy+CRgASrwErpamL57yJAh/Oc//+HLL7+0qmPfs2cPSUlJjBw5ki5duvDNN99gMBjsHqNHjx7s2LGD06dPA7B69Wrmz5/v9Lw9e/YkMzNTDThbt25l8ODBlJSUOHyNs/PExMRw880387e//Y3nn3+ey5cv06dPH7788kt1hqu1a9cSFRXldgthZmamw8/io48+4sUXX6R3795MmzaN3r178+uvvwKmIKrX64mOjub48eOcPXsWRVH44osv3Dq/2d69e+nQoYNV0O7bt2+FAFmZJUuWqDNs2f4sWbLEal+tVsvdd9/N22+/TXFxMdnZ2WRkZFh9ubEUHR1N69at+fjjj9Hr9RQWFrJu3Tri4uJcOlZeXh6lpaVcvnyZpUuXcvr0aYYPH+7W+/MbzhZZtRNEzEmdbTJnti4vj1Hners0o6XleLygehpWRYWyqsXVVmVHC7qa19BrOGaw1WNRN9SvX5/IyEh0Oh1PP/00kydPplmzZgDs3r2btLQ0xo4di06no0GDBlavs+1d8IoajpESHysn8dH1+FhYWMj27dspLS1Fr9ezYcMGdu3aRe/evSs9lrPYWiOqMXbVHG8cVXgADmfArM3x1Obrvbg83eqxcF/glVyaWyhsS7C82ELRvHlzrrvuOho0aEBUVJS6/d5772Xu3LkMGjQIvV5Pr1692Lx5s91Wvbi4OKZNm6a2XsXExPDaa69ZtTTZuv7665kzZw5Tp05FURRCQ0NZtGiR01IFV84zbNgwNm3axD/+8Q9efvllxo4dy6OPPorRaCQ6OpoPPvjA7cVEk5OTefbZZ+1+FkOHDuXHH3/k3nvvpV69esTGxjJ69GjA1No9evRo3nnnHZKTk7n//vuJiYnhjjvuYM+ePQ7PN3HiRJKTk9UB+GZ79+6tcPPu06cPaWlplJaWEhER4db7ctXs2bOZPn06t912G1FRUbz00ktqSzGYWjS7d+/O448/DsC7777La6+9xuLFiwkODqZHjx5Mnz7dpWOlp6ezZs0a9Ho9iYmJLFu2rO6WXFayyOrqxPaEbW+hlkM2e/N5UhIT2Xb4MF/t32+VzJltP3KE1HbtSEkc7/C0tpOrGM5ewHD2IqtaRALwyMkiKaMMYCdOnOCpp55i5MiRDBo0CIAvv/ySRYsWkZqaSnR0NJGRkRQVFamvKSoqskrwvKaGY6TEx8pJfIosPkgAACAASURBVHQ9Pur1et566y0OHDhASEgI7du357333lNn3KzsWM5iq1dVY3iAZbwpb96KB69UdUQkxHFxebp1j52dGTBrk9MSUeGWIEWxM+2dj8jPz6dfv35kZGTQunVrzx1YMcr03UIEAgdBsjRqIu98vI4VBtMX5kdO6njiz/2sesJGffYZ62xK3JpqtcRotZXO9mWb0CmXSzAUXCBIq2HCDZ15cOsvaLrGS8+bDa/d831IQUEBo0ePZtasWfTsaUqS0tPT+eSTT1i4cKGa1Jw5c4bx48ezZs0aysrKePDBB0lPT7f60iwxUog64lgm/PeZituT/p/T5MuVhb59nW2vYswb0xxWsASyyu73gddDBz7XQiGE8JIrJSap334KRadMQa5lT5bvzmFVVCicNe22qkUkkY1CeMripWnDh1slda4mc1BxDZ+gehpC27S4+toh93n8rQr/8P7771NYWMjChQtZuHAhBoOBffv20bJlSyZNmgTALbfcwtNPP83o0aMZOXIkiqIwZcoUr/WAVCAxUoia5WzsqoO/Q3vJnHk7+MdkJjIJmOcEZkInhAgYqbtzSP3jIqCBkxo4mcOizRkYzl602m/xrmzCWrewCoLmpG77kSMuJ3Nmtkmdv7WaCu+YOXMmM2fOdGnfhx56iIceesjLVySEqHU1OHa1cMUGIhLiiEiIs3pcG8wllw5LRIXLJKETQtRZti2Yr27fTovISIK1GpTiEoK0GoK1GrUc0p604cOr3OJpuyirEEIIUUEVxq7aNhpabncUb8w9Yq4soVMTzBN/mUnPXNVJQieEqJNskznzzJUFxcV0adaMoHpXEzirckg7qpOMSSInhBDCKfMMlG6OXXW3EkQmIam7JKETQtR55mTO7KROR5+2bckrKAC8VA6pTiyRZyqnkYklhBBCOFLFsau2lSDOKkrM69SZkznzNuH/JKETQtRJ5mD26vbtVsmceXKTvIIC4ps25fZ27byTzFVxCmohhBDCHZY9dZaVKbaxzXYSklUt6hPy0VqeS6iBpRmEV8k3CyFEnZWSmEiftm3V3zWh1m1YXknmwNQzZ5nMgen34zs8fy4hhBABzzaZszcLprnksuGYwaRPGsbH1zZhVVSo3dkyhX+RhE4IUaelDR/OsPh4NKGhlOj1FBQXc6a42LuzTjqbgroyitG0JtGepaZ/lYoLKQshhBBmzpYwsNxunoRkdedWLDt1jNA2LQhpEuXw9TVG4l61ScmlEKLOu71dO7YfOUKJXl8zJ6zqFNRSqimEEMKLfG79Ool7HiEJnRDC93hwQhFz8IrRatVtMVqt3amePaYKU1CjGCH3A9j3GYRoIKw+BAVdLdWURZ6FEEI4EN+0qTrRl5lfrH/qbIiCxD2XSUInak1OTg7/+Mc/CAsLo1mzZsybN4+wsLDavixR27zYWmeZ1AFsO3zYKgB6LPC5OwW1+T3v+wyKz5i2hUdCZGtTUnfuNwlsQgQQiY91l6cX9rbscbNM6hwlc1VZv86rnA1RkLjnMunLFLWmZcuWLF++nLS0NNq0aUNGRkZtX5LwBR6eUMRRkLJtzfTUGILCFRsozf0NgoIpzDhLqfHKNNTmZM7eWAHzew6xWNy8TAflRabHlZVqCiHqFImPdZN5lsmLy9OtHleVbdwyz95cWXJm+3yt9uRVdYiCsCI9dKLWNG/eXH0cEhJCcLC0Lwi80lpn2yJprzTF8vmqBjZzgA6/qSOarvHq42ZvPm/awV7vY9T1EBJhSuDC6pt65sp0pucMJdD6XuelmkKIOkfiY93kyYW9HTVC5hUUcHu7dpW+3jahqzVVGaIgKpA7hPCaixcvEhcXR7du3UhISCApKYlPP/20wn5Hjx5l27Zt3HHHHR4574ULF3jqqafo2rUrSUlJfP755w73zc/PZ+LEidxyyy306tWLOXPmoLeYOCMtLY3hw4fTpUsXXnjhhWqdS7jIS6115hbIlMREl4JdVVgGa/NaP1bB2rb38dJR+ONzOPpf02NdvqnMskEb0MbALX+TgeFC1EH+EB9Hjx7NjTfeSLdu3ejWrRv9+/dXnysrK2P69OkkJSXRrVs3hg4dytatW61e/8cffzBmzBgSExO5++67+frrrz3yHvyZeWFv2221xSfG2JmHKCT9P0h40vSvxD23Beyn5e0pWvPz8+nUqRNDhgxRfwYPHsyaNWsqfe348eM5d+6c032ysrIYOHCg3efS09MZPHgwQ4YMITk5mT179qjP/e9//2P06NEMGjSIgQMHMmHCBPbt21fpNVmeb/bs2dx5550sWLDA6Wv27t1L48aNycnJITc3l6lTpzJ79myr96bT6XjhhRf45z//SXh4eKXX4Yo5c+YQFhZGZmYm8+fP56WXXnL4Hl9++WWaNGnCd999x/r169m5cycfffSR+nyzZs148sknuf/++6t9LuEic2udJTdb6xz9fVsmdY7GFlQnuFUarC17H8t0V3viCLraM1deZHrcYTgkPOYXQU0tM7V5LPyXN2OkxEf/iI8As2bNIicnh5ycHDZt2qRu1+v1xMbGsnLlSrKzs3nmmWeYPHky+fn56vNPPvkkSUlJ/Pjjj8yZM4dp06Zx8OBBj7wPf2W7sDdQ5ZJLb8WxWhEUbKrAuXG89RAF4bKALLm0DVTe+h9fo9GQnn71D/XUqVMMHDiQLl26EB/vuIs9MzOzyuc8cOAA8+fP57PPPqNZs2Zs3bqVSZMmsWXLFsrKynjsscf48MMP6dy5M2AKbhMnTiQjI4OQkBCXzvHJJ5+wZcsWWrRo4XS/vLw89TwAt956KwaDgcLCQqKjo9Hr9UydOpW//vWvtG/fvsrv2VJxcTGbN2/m888/p379+nTv3p0777yT9PR0nnvuuQr75+fnM2rUKCIiIoiJiaF3797s379fff7Pf/4zAHv27OHUqVPVOpdwkbsTithIzc7m1e3b1d8d/X3blmF6Igg6CtZqyaVl76Oh5Orj0HpQr6kpmWvZE26cUK2ZPWtSpWWmwu/URIyU+Oj78dEZrVbLpEmT1N+TkpJo3bo1v/zyC61bt+bAgQOcPn2asWPHEhQURM+ePbn55ptJT09n8uTJHnk//shcxaHpGk9EQhwXl6dXueQSvBPHAo2nJ6mpLQGX0NkGqppcd6N58+a0a9eOQ4cO0bFjR1577TVyc3MpKipCURTmzp2rtlA++uijpKamEhsby5o1a1i2bBnBwcE0btyYefPmAaab85QpUzhw4AClpaXMnTuXFi1aMHfuXJo1awZAly5dKCgooKysjMuXL3Pp0iWKi4vVaxo8eDCRkZEYDAZCQkL49ttvWbRoEeXl5Wg0Gv72t7/RrVs3df+RI0eiKAoTJ05k9uzZdO/enYkTJ5KcnEy/fv2s3u+vv/6qBqzCwkLeeOMNOnfuTLsr5W4bN24kNzeXhQsXsnDhQkaMGMG9995rdYzHHnuMbAetxImJiXzwwQdW2w4dOkRwcDDXXnutui0+Pp6dO3faPcaYMWP44osvuPXWWyksLGT79u0888wzDv4LWnP3XMIN5tY6N8fMmZO5guJiFL2euRnfApD8yzG7N+kqjyFwsKxCpcHacqyAeQKU8MirSxSER1J0LJ7Q2KZEtAr2fHDx4HIQZp4cEyJqX23FSImPvhcfAd544w1ef/11rr32WqZMmUKPHj3s7ldQUMChQ4e4/vrrAVAUpcI+iqIEfAWLeWFvM080fHliLFytrUFXy+pSg2RAJXS1vZhiTk4OR44cISEhgdzcXE6fPs0nn3xCcHAwqampLF68mPfff5/PPvuM5cuXEx0dTV5eHq+//jrr1q0jNjaWf/3rXyxatIj77ruPkydPsmDBAhISEvjXv/7FO++8w/Lly2ndujVgunn+3//9H3feeSfh4eGEh4czbdo0JkyYQNOmTbn55pvp0aMH9913H+Hh4Rw6dIgFCxawYsUKGjduzL59+xg3bhybN29W38NHH31EXFycen0Aixcvtvt+9+7dy+bNm0lLS6OoqIjevXuzZMkSgoKCABg6dChDhw51+pnZBqTKFBcX06BBA6ttDRo0oKioyO7+t956K59++imJiYkYDAaGDRvGXXfd5ZVzCe+yTebQGygoLmbuV5s4n3eKcf/LU2/SlomSOdlzmZNlFSoN1la9j3vh8Ndwfr8pmQNKC1tx/rPfCf8l3fPBxUvLQZjLTM3JnHmb8D+1GSMlPvpefHzuuee47rrrCA8P54svvuDxxx8nPT2dtm3bWu1XXl7Oc889x7Bhw7juuusAaN++PdHR0SxZsoSxY8eSlZXFzp07HSaEwjWO/har87dZU1VrvqguNUh6JaErLy9n+vTpHDt2jLKyMp544gmuv/56XnjhBYKCgujQoQOzZ88mODiYd999ly1bthAaGsr06dO56aabvHFJtaKkpIQhQ4YAYDAYaNy4MfPnzyc2NpbY2FgaNWrE6tWrOXr0KFlZWdSvX7/CMXbs2EHv3r2JjY0FYOzYsYCpZr9NmzYkJCQApla2tWvXqq8rLi7mhRde4OTJkyxZskTdPm7cOB588EF27tzJzp07Wbx4MYsXL2bNmjVkZmaqJRJmQUFBHDlyxO33XlZWxoEDB/jqq69o27YtmzZtYsaMGV5fR0er1aLT6ay26XQ6u5+t0WjkL3/5Cw8//DCrV6+mqKiI6dOnM3/+fJ5/vvIv0O6cS3jftsOHKbjSuh4UHIwSrIDRSEFZKVkNI3jiyk262i1y1V0E1bL3sct469LSM9GE7//cO8HFS4u3VlpmKoQdEh99Oz4C6ucHMGzYMDZu3MjWrVsZPXq0ut1oNPL8888TFhbG3//+d3V7WFgY7733HnPnzmXJkiV06dKFAQMGeGwsYCDyRuJVm1VrvqAuNUh6JaHbsGEDUVFRzJ8/n/PnzzNs2DDi4+OZPHkyPXr0YNasWWRkZNCyZUt+/PFHPv30U06cOMGkSZOsbrqeVtOLKdqOEbC0ZcsWXn31VcaNG0e/fv1o3749GzZsqLBfSEiI2mIHpiB47NgxAKubf1BQkFricPz4cR5//HGuu+46VqxYgUZjKu3Kzs4mJyeHCRMmkJSURFJSElOnTmXgwIFkZmZiNBrp2bMnb731lnrcEydO0KxZM3bt2uXWe//9998JDw+nTZs2APTv3593332XTZs28cADD7h8nAkTJjgtKbEMxgDXXHMNBoOBQ4cOcc011wCmsQrmMhBLFy5c4MSJE4waNUptob3//vt56623XEro3DmX8L7b27Vj+5EjpqQuOBiCjWCE6HIjACsbhfAUHmiRs7OsguHsBYy5Wwlr1cu9Mkmb0tKIVqD5aR+X//sjQfU0BGs1lB89SWnub9UPMl5avNXTY0JE7anJGCnx0bfjoz2WnyOYejlnzJhBQUEBixcvrpCQxsfHk5aWpv6enJxcaa+jsM8biVdtV635grrUIOmVEfcDBgywGocUEhLCL7/8wq233grA7bffzvfff092dja9e/cmKCiIli1bYjAYKp29qrp8ZTHFzMxMkpKSGDlyJF26dOGbb77BYDAAps/LPHV+jx492LFjB6dPnwZg9erVzJ8/3+FxdTodo0eP5s9//jMLFixQgxVAdHQ0ixYtsgo+Z86cQafT0bFjR3r27ElmZiZ//PEHAFu3bmXw4MGUlJRUOE9l9u7dS4cOHayCbd++ffn222/dOs6SJUvUGbZsf2yDFZhaIO+++27efvttiouLyc7OJiMjQ20JthQdHU3r1q35+OOP0ev1FBYWsm7dOuLirn5x1uv1lJaWYjQaMRgMlJaWqv9t3DlXXeYrMxymJCYyo08fNKGhlJebSi6jy43cerGU/dowFu8yBS9zi9yqFvVZ1cLUMu1WsmSzrILh7AUMZy+i25Zf7YViC1ds4PyCFVeOafqp7sKzjq776vbqJYrmMtOGYwZbPRb+yRdipMRH13gzPprHlJtj3oYNG9i1axe9e/dW95k9ezZ//PEH77//vtVnaZaXl0dpaSmXL19m6dKlnD59muHDh7v1HoXzxMubs7UHAnODZMMxg4l5Y5raOOmPvNJDZ+6+1+l0PP3000yePJl58+apN6/69etz6dIldDodUVFRVq+7dOmSWnvuLb6wmGJycjLPPvssgwYNQq/X06tXLzZv3ozRaGTAgAGMHj2ad955h7i4OLWuHyAmJobXXnuNQ4cO2T3uqlWrOH78OF9//bXVmi//+te/uPbaa3nvvfdYsGABJ0+eJCIiggYNGvDaa6+ps2jNmTOHqVOnoigKoaGhLFq0qNISQnuDvvfu3WuVGAH06dOHtLQ0SktLiYiIqMrH5pLZs2czffp0brvtNqKionjppZfo0KEDYGrR7N69O48//jgA7777Lq+99hqLFy8mODiYHj16MH36dPVYixYt4t1331V/37BhA3/961/V2b2cnSsQ+OKA4sjwcHQlJUQEBdOrSRP2t4DgggsEaTWkZmdzeUcuJT/ksqpFpPqace60yNksghpUT4M+JJaiXaWwy87ac24wBxfjuYsYzl3EUHCBYK3GMwFGFm8VLqrtGCnxsfbjo16v56233uLAgQOEhITQvn173nvvPfWzOHbsGJ988gnh4eFWSd7LL7/M4MGmBp309HTWrFmDXq8nMTGRZcuWScmlD3GlR76uzADpiDcmqaktQYq9qYg84MSJEzz11FOMHDmSBx54gNtvv51t20xfJL755hu+//57rrnmGkpLS5k4cSJgGgT84Ycfqgldfn4+/fr1IyMjQx3ILITwHaW5v3FxebpV/XnDMYNd66Hx8IyLlq2VZ4qL0ZWVERkeToxWa7Xf6QsXUXTFNK1Xj2CtBkPBBcY1b80zfx3v+snUazeNfSv85gyFKzeqT8e8Ma1aQc+2DKS6x1PZXLevLY0g93z3yOclRGCw1xvnqd5zy2PbJnO+1mAbyCq733slkhcUFDB+/HimTZum1oPfcMMNZGVlAbBt2za6d+/OzTffzHfffYfRaOT48eMYjUav984JITyn0oW0HTHPuPjfZyB3kenfrc+ZtleBbbCL0WqJDA+noLiYgxcucObKZClnios5qy8nOLoRIU2iCKqnIbRNC1aG690rXbFYBLUw46xVMgdVXygWPLvwbAWyeKsQQvgWxQjHMmHPUtO/duKgN0uhzceyPableHNzTPLXcsRA4JWSy/fff5/CwkJ1/RSAGTNmMHfuXN58803at29P//79CQkJoXv37jz88MMYjUZmzZrljcsRQnhJlQcUe2nGRUsxWi26sjJ0ZWWUXBlzA9BUq63Qa1cdnp4URCYZEUKIAOHGcjLmZGvb4cMevwx7yWFdmgEyEHgloZs5cyYzZ86ssN1ytiOzSZMmqeORhBD+pcrJh4dnXLQ3FuBMcTGR4eFqT11BcTHD4uO5vV07j5aueLoGvy7V9AshhHCiCo2beQUF5BUUAN4d41qXZoAMBAG1sLgQwrOqnHx4YcZFy6TOXGJp1vRKj1xeQQG3t2tHSmKi3TEDVebh8YBCCCHqKMt4cX4fKApYzHgK2G3crOk146RaxL9IQieEqHlemnHRsiRFXZOOimWWHp3Fz42SGSGEEAHMNl6U6aD0PES2tk7qbBo3a2PNOKkW8S+S0Akhal5QsCnh8cKMi+bAtv3IEXWbObGb0aeP+rzHAmANjAcUQgjhh2yrNxSjdbwIjzQldOVFpscgy8mIKpGETohA5AslguYZFz2c9JhbMs09cgU25Zce5+HxgEIIIfxf6q5d8PunpBgzrm7U2JnJvUEbaHc3RHVw2LjpyppxIrBJQidEoAmgEkHLMktPzmxpxQvjAYUQQviv1OxsUrO2waUyaNiWlIZXKkYKr/xr7o0zaz+w0gZA26TOYTLnoMHW22PuRO2ShE6IQFPHSwRtg545kfNaS6aXxgMKIYTwP+p4N32J6ffCdgCmpC6sPtRrAiXnrr7AjXhR6fhviwbb1MK2wBek3HANqZEjSd292/lrMc1sGZEQR0RCnEuPhe+QhE6IQBMAJYIut2R6ghfHAwohhPAfVpOXhGqubrdM6v70d1N8qGK8cBrLrjTYpha2Vc+5LbuQPM02CItUr9HecczLFJhntqzssUyS4lskoRMi0ARIiaBHZ7KsjJfGAwohhPBTYZGmn3Ld1W2t+pj+9db49XN5VsncGUM4e8tiaWooJibqapmnvaTOvExB2U+/q4uJm5cssLdd+BZJ6IQINAFUIihjBYQQQtSUCpOXNGgD5TpS2oSScvNw2PcZbJly9QUeHr+eejpSTeYsFZQrUFzsdCx5REIcmq7xatJm3haREIcmoSMc2U5o5AX0uigiburgkesVniMJnRCBRkoEhRBCCK+oUPL/p76mbccy4dh26509PX690XUQ9rPaKxgTUgYhERQYQypco6OSS0sXl6ejSehI0M6ZRN1wSt2uT0+BhK3yvcGHSEInRCCSEkEhhBDCK+yW/NfA+PWU7t0BTDNs6ksgVENMWCR9mjYlr6BAvR571Svm0kpzmeXF5elousajaVdE0PELBGsbEVRPg+HsBSK0J+rMRGp1hSR0QojaoxhNrZYHvgQUaH+fKUBIq58QQgg/ViFpqqHx6yndu0NQUIVJwSpbtiAiIc5qohP18Z6l0KaFuj1Ye+VxHZpIrS6QhE4IUTsUI2x5FvI+grIrg8Z/WQbxI+GON6ySutTsbLYdPszt7dq5Pd2yEEIIUetqYvz6lTXoUsLz4LpG0Og6NWZWeUx5gEyk5i019d1EEjohhNs8coM6vgMOfnU1mQPT44NfQccH1Ja/1OxsXt2+nYLiYrYfMS3K6up0yzKtshBCCJ/gwvj1ai3+bbEGHUAKQMjtoNxcvaqXAJpIzdNq8ruJJHRCCLd47AZ1Lg8MJRW3G0rUUg7LZA7gpE7HixkZgGvTLQshhBA+w8n4das17KhCUndlDTornph0RSZSq7Ka/G4i/zWEEG6xvEGZZ8Ry9QZlFbCi4zEU6VGMRlAMKPoyFIMegiMgOq5CMqc3GtEbjVwoKeHFjAyrwGeebtn2OoUQQghfZ5vM2f7uEmeTrlSXORG9cbyMc3dDTX43kf8iQgi3VLxBKWjaFZkGTh/LNJV92GEOUOafwm/OULw/CkVvQDGUgaIHox7C6kNsD6vXmpM5M11ZGdsOH1Z/dzTdshBCCOHLHCVvbid1MtbNPeZJ2Sr57lIdNfndREouhRBusb5BKTSKzyLo+01XZ8Gys1CqvdbH8uatGBnZB0PxUYJCywEI0jYmJDgYTmSRkmgqEXkxI4MLJVdLM0ODg2kRGUleQQGp2dmkJCY6nG5ZCCGE8HcujVuXsW6usxlvCHh8kXdwvBSEN0hCJ4Rwi+UNStOuiKDvNxGs1VzdwaZm31Er47JTx1Ca1yflfDiKIRyA0JjGpievjKFLSUxk2+HDfPrrr+iNRjWZi9FqK1yT3emWhRBCCB9muxC55faUxETXx63LWDfXeWu8oY2a/G4iCZ0Qwi1WNyib9WlULqxPYzh7gZIDZyHWeluwtoVViUja8OEAfLV/P5Hh4Woy52hx1Cq5MtUz5/JMZSsSBIUQQtQQ26TOMr65NbGGk0lXApptjD/7q/39/HhtPUnohBBV50LNvqPWxwk3dObBM0bKld/QNCvAqCsGo4I+oguhNiUiacOHW/X0eTyZq4HSCyGEEH6glhr4LGOa7SzOmq7xajJn3mZPtZY9qKvsxfio6+zv68fjDSWhE0JUnYs1+45aHwsvGji/Yh/1u0eguTaUS18fhLPxNHuwYvB0FOyqrYZKL4QQQvi4Wm7gsxfbHE2sYVu+V+1lD+oqezH+/H5o3AEu7L+6zc/HG0pCJ4SoOjdq9u0lZKZSkjiKdv1O0a4yoDkNB9/g8HS2AcojrZHOpnqWhE4IIeoElyYW8cEGPlcm1rA38Rj4SVLn7R5RezE+KAja3gXdJtWZ8YaS0AkhqseNmn3b4OJOKYml1Oxsth0+TF5BQYVjux3IZKpnIYTwXy4kBC5PLOKDDXyVTazhbNkD8PGkriZ6RB3F+Cad6tR4Q0nohBC1xtVSEkuWC4431WqJ0WrttkyCi4FMpnoWQgj/5GJC4PLEItLAV7Nqokc0QGK8JHRCCBNXyh48XBrh7hotlskcoP4bo9Xy6vbt6mPzvuBCUidTPQshhH9yMSFwuRrED7/8V7bsgctqYzKYmugRDZAYLwmdEMKlVs7C5evRatcTWvozhrMXCKqnIbjjn6tVGuHOGi2OykoKiovRlZVRotfT1GZ9OreSujpUeiGEEAHBxYTA5WoQH/zy70occ7bsgUtqazKYmuoRDYAYLwmdEH7GpYHd7qqklbNwxQZKPl9KvcQcDFoNhrMXCapXQrCXBos7C2DmHjhz75yZufxSCCFEgHAxIXCrGsSHvvy7M3NltWaCrq3JYPywR9RXSUInhB9xeWC3uypp5YxIiIPfI1Aul2K4XApAsFZjtY8778FZQuoogFm2QFomdU21Wmb06aM+Z2l0WSiPhkZWOK8QQog6wMWEwJ1qEF9RlZkrqzw5WG1NBuODPaL+ShI6IfyIywO73VVJK2dEQhz6XbfA4a3qU0H1NFb7uKKyhLSyAGab1PVp25bb27WzClrm14wuC+X+9d9z8UCBZ5NfIYQQvsELCYEvzA5ZnZkrq7QeXW1OBuNDPaKe4JUqKhdIQieEH6nqNP+VqqSVs3DFBgo//p1G8c2JiD4FgOHsBYI7PuRWaYSzhNTVAOasrMTy90dDI7l4oMDzya8QQgjf4cGEoLYX5zYnAHAlxmo1VxtPXVDl9eik9NEjvFZF5QJJ6ITwI1WZ5t8llbRymhcAVxIGwTWXKfn6M0KadSes7zS3WkLNCemy08cAeORkUZUSUlfHEXgl+RWiGsrLy5k+fTrHjh2jrKyMJ554guuvv54XXniBoKAgOnTowOzZOHCAswAAIABJREFUswkODubdd99ly5YthIaGMn36dG666abavnwh6qzaXpzbMhlI7hrP+V+O8fG1TQht00Ldx9lkJw4bRX/YCse+IyWxu+PeSyl99AivVVG5QBI6IfyIu9P8u8VJK6ft+IPwviNcPqxlycGpJ15h2dkTfNS2sfr8uCsJqcemXrY4r1eSXyGqYcOGDURFRTF//nzOnz/PsGHDiI+PZ/LkyfTo0YNZs2aRkZFBy5Yt+fHHH/n00085ceIEkyZNYu3atbV9+ULUSZ5enLsqr7NNBh4BNH9KYCV69Vhux8JLR6FcB/mHoXCl85kr61jpY23wWhWVCyShE8KP+MPAbstAZlt+sOzsCT5q3YiQJo0I1mr4uF4EmuZNeebKa51NvexugPRq8itEFQ0YMID+/furv4eEhPDLL79w6623AnD77beTmZnJtddeS+/evQkKCqJly5YYDAbOnTtHdHR0bV26EH6ltsYyVbVs014y8HifXtTT61w6ToVG0XIdlOtIaXiYlIZHTNtqYubKAFabDcmS0AkhPMY2kD1q0eK47PQxPmrbmJAmjQhpEgVAaJsWrERPvexsp2PkqhIgq5L81tYXABE46tevD4BOp+Ppp59m8uTJzJs3j6CgIPX5S5cuodPpiIqKsnrdpUuXJKETwgXujmXyVIVIdco2HSUDKW4kA1bvQ19incyZeXvmygBWmw3JktAJITzCbiBLTCTZpsVRXe7ACdtZK2tiXENtDmYWgeXEiRM89dRTjBw5kkGDBjF//nz1uaKiIho2bEhkZCRFRUVW2xs0aFAblyuE36nKWKbqLs5d3bJNTyUD6nku7CflxGcVd6iJmSsDVG1WUXktocvNzeX1119n5cqVTJkyhYKCAgCOHTtGQkICCxYs4PHHH+fChQuEhYURERHBkiVLvHU5QggvsJdoWVq0OYPzvxzjEUwToAB8XC+i+oO8vZDU1eZgZhE4CgoKGD9+PLNmzaJnT9MMcjfccANZWVn06NGDbdu28ac//Ym2bdsyf/58/vKXv3Dy5EmMRqP0zgn/oBivTK6RZ5oOvxYm13A6lsnJ9TmaRbkmJkjxZDKQkpgISjfYmiUzVwYIryR0ixcvZsOGDdSrVw+ABQsWAHDx4kXGjBnDiy++CMCRI0f44osv1FITIaqsBgOIlOWZWCZb8U2b2t0nWKshpFVzGv65CxEJcYxbno6medPqDfL2ktoczCwCx/vvv09hYSELFy5k4cKFAMyYMYO5c+fy5ptv0r59e/r3709ISAjdu3fn4Ycfxmg0MmvWrFq+ciFcoBhh63MVkwhHE3F4icOxTG88V+n12cYkV0v+PT2xV6Uq+94jM1cGFK8kdG3btuWdd97h+eetWxfeeecdRo0aRbNmzSgoKKCwsJDHH3+cwsJCUlJSSEpK8sbliLquBgOIr5Xl+cqg77yCAuKbNiXvSk+82WO9e10NZIqRZs/24plzedQ73QgaXef+IG+L7VUOkA6CoMyKKWrCzJkzmTlzZoXtaWlpFbZNmjSJSZMm1cRlCeEZx3dYx2KolYk4HJYvunJ9FjEi9XQkqX9cVHetrKeuumWbLnP1e4/MXBkwvJLQ9e/fn/z8fKttZ8+eZceOHWrvXHl5OePHj2fMmDFcvHiRESNGcNNNN9GkSRNvXJKoy2owgPhSWZ7XkstKWv0clUHaJnVWgcwm+KQAhNwOys2VJt0eDZBOgqDMiimEENV0Ls/B9pqdiMNh+eKepfZfYL4+ixiRWtiW1MJ2EBYJDdqou7qa1Dnbp9p8JHEWvqPGJkX5z3/+w8CBAwkJCQGgadOmJCcnExoaSpMmTejUqRMHDx6UhE64rwYDiC+V5VUpuaysRKOKvZ1niosBSGnXjtvbtTM9tgxk1Qw+HguQTq4jIqGXqRzn+A449x3Nnu3lE2MNpMRXCOE3oh3EIF+ZiKOy67OMEfpSMOqh9CJoGpsSOxd5fSiBjyTOAcsHxonaqrGEbseOHTzxxBPq799//z2rVq0iNTWVoqIi9u3bR/v27WvqckRdUoMBxJfK8txOLl1J1ipJvCxbJs2PzxQXU1BcTFOtVn2uAsvgoyhQXgSGEvhjo8s3Qo8ESGdBsGVPnxj7YcnXSnyFEMKplj1N901fnYijsuszx4hLR0lR9kLoeVLLEkB3Ahp3AHxk7LevJ851mY+ME7VVYwndwYMHadPmapd13759+e6773jooYcIDg5m6tSpMoOXqJoaDCC+VJbndnLpSi+Zk4Qn9aSmQtnjq9u3q8lcjFbruBTFHHwUBXT5UGZaKJX968BYWnM3QmdB0AdLWHypxFcIISrl6xNxVHZ90fGm+HQlRqVE5AKQqu9uWqT7T309l8zZ9PKknoiAoCDXju/riXNd5oPfFcCLCV3r1q3597//rf7+xRdfVNhnxowZ3jq9CCQ1GEBqc40Re9fiVnJZSbIGkNLC/utTT9cn9Q/rJQrMM1uakznL58AmqTMHnwNfXk3mwiNNPx68EVZanugsCP68zP5Ba7GExZdKfIUQwiW+PhGHs+tr2RMatoVLRwFILU2A0AhS6udDm+s9m8xZ9PKkFrYl9XJndaxepefx9cS5LvPRcldZWFzUDb4eQLzA7eTSQe+UVbJ2882k2CQ8qcH9rGb5Mtt+5AgAMVqtOobOMrEDm+Su7+sQHAG/fQwhGlMyZ+aBG6FL5YnOgqAPlrD4UomvEEL4M5fGIwcFw5/+DpsnmpIsfTwYQkjRHPZsmaVFL486+Qo6KNe5vuZdAH7vcYXXx5374HcFkIROiMBhp3fKNllL3b0bbh5JStL9VxOeExGwe3eFw8VotcQ3bcr2I0couJLQAfRp29Z0LHtr91w3EI58XfHaPHAjdLk80VEQ9MESFl8q8RVCCH/l1njkVr1I1Qwh9XQZXOnwSr3cGU5EkNLKQxd0pZfnajJ3hb4EwiJrZCHzuqhGxp374HcFkIROiICRujsHIq8ma6aeuYo9b6m7d0NiIimJ4wFMASwoyO5acHC1pw5AV1bG9iNHrHrvwKKn7mbv3QirXZ7ogyUsvlTiK4QQ/srUOHY9HNmO/tznhEdFoUkYaHff1N05pOqugwY6U4IVqjElWbt3uz7GrTKOenlCNdU/dgCrkXHnPvhdASShEyIgWPWWmZO17Gyg4npy9l4L1jNbWq4NZ07adGVllOj16MrKAAgNNt3cKiR1HroR2pZVlB89yeX//mi1j9vliVLCIoQQdU7ETR1odP0OgiJ/ULcFa5uAMthx/AmLVJcqMJy9QLDWlGxVt4yvcMUGIm7qQETr2/lL7gaMmnKWlFxvdT6fmEnTD9XYuHMf/K4goyeFwHSDLc39rcLj2uSpa7ItfTT/7ihgWG4372s7u6Xt63RlZWoiZ6mguFgdX6cy3whvHH/1Zngs07Tg67FM02DxSpjLKi4uT1cfl/1+iPCbOtJwzGBi3pimllz4Al/8/0sIIQJF0YfzCTr5vdU2Zf83psZFG7YxznD2AoazFxlxXEfyL8fU2FMVauxa8TmFh2/n7PZOjCzQ8Gi9pighMQCMLgvl0VDX17wTVzkadx4IpIdOBDxfXOurutdkm8A5ez6+aVPyCgoA+8mc5Wssnzf/++r27ejKytAbTYlYi0hTICqwSeTsJpBVXM/FXllF/X5/ouGYweo+vlKe6Iv/fwkhRCAJj9XD6QiCtRqC6mmu9rg5mJDLMg4GazU8ckFPcvYBCrMPAK6X8dlWtVSMXc1RzrZj2A8HKU1sT1ibSO5f/z0XDxRIjKiCQB53LgmdCHi+uNZXda7JMhEzLy0AVJiJctvhw2oiF9+0Kbe3a+cwmbM8NlQcqG0urzSznO0yRqt1XD5SxfVc/Gk6f1/8/0sIIQJJWMIdcG6d+nuwtoXpgYMJuQpXbODRhDhITOTyjlxGNdRwmQPq867EG3sTg9mLXdo7b+VyvQiSs38HNxNGYS2Qx51LyaUIeOYbrO22qvJEeZ31NSmER52iXtM9lZYk2gaQvIIC4ps25UxxMWcKCynQ6ThTXMx1hSXsPZpvtZ+7zOeK0WppqtXSIjKSFpGRapnljD59mNGnj/OxAM7Wc3HCn8oqPP3/lxBC1DjF6HZp/P9n793jo6rv/P/nmftMJiGEBAKBoMhNUSiGil1EV+2WbruitUUR1Fats2p/tHVrb9ra7VK23VZcW93aDlrLTVlcdUXbftstbYVar8EGRYOKmnALzYUkTC6TzMz5/XEyk5nJnLnPZCZ5Px8PHsycnDnzmYjnPa/35/V+vwuKYGfCcHQacoVb+lcfOMoVD/865XijV+oQK3b1vdAgMULIGNmhE8Y92Zz1lS173fCaVCbMfwlrxQloeE3LMOpYEvV21fY2N3Oq2wM+P6pB5VS3h794e6k0+TFOnhIqwo6ukws/FkRPnEXPn1teW5tcQXea81yKyVYhs+QEQShq0rTGFxQpdCaMdlUodhuW6VMoXbUibrxJptRhcEoNq6Jil9rnlRghZIwIOmHck5E4UANDAaIRKuZjXTgnK/a64JpKFqjY/S/hb7eGOmwlY0kM0trbS1tvL5U2KyWeAKp3EIOph3azBYNhkKpThzVBVzpjxGujRV20mNMTfcFduaRIc55LuK2ie8suJnz28twNEc2QYhKfgiAII0jTGl9wJNmZMNoWaXDYqPzeF0NxJZbQ0it1iMY8vZrJ934y9HzyvV/D23AwFBckRgjpIoJOGPek7bmOkbW0Tr8Q26ILM67tCq3p9YehwTbs9w8So5A7WmD52ztRjQqVDgcVfQOoRiOKrxODqQ8FByhG7YWDHhj04Dr/ohFCLJaAi/eeKbdazmCeS/ioAsVuxXb+Ivr++HLBNRwZz55+QRDGAPGs8cUk6JIkVVeFXqlD9DxWvfgoMULIBiLoBCFdYmQt/Q276N/7DjAldCwp60TUTl9I1KRoSXTV1YGq8rPf/RrV08XtChimnsVDxxsxlfRjsPlQlADfNO4BxcxD6lJQwDXDpCvEEgk0V10de5qakjo3JmnMcwkGXGPNZBS7FW/DQQbebsI8o1oym4IgCNkkTWt8sZKKqyJeqUNwNiuk6FwRhDQQQScI6RIja6nYbdjm27DNXRk7EMQSbqBfn5CqJVEN4PI8SsDxDmqgj5sGGuj/azn9J89m59lTQTHweeOruIyvoZiMGCwTCfQH+NyE89L+Nbjr60NNVYKjDXQZ+vzu+lehpBrXJavSqsEIr3Hwt3cChCyphWS3FARBKHrStMaPFnrdmJMl0x2zUKmDw4HTYtEOqqrWTCY6aSsIWUIEnSCkS4yspcFho+zWL4d2myICgV5h+exPxa9PSMWSOLRreEsl+JVOft6xAHVqgH/u34/T3wYBhZsNrwMqaiDA5/2v0NM8mZ+0v0eZM4EYi0GsTl6gE0iHPr/7zQ9wd88EPoAT+3Bd8/2UA5t14RxKFqgYm1+j/5SCnwkoQ4JOiskFQRCySAbW+HwTa1RALokuOwgXc+HNwtzPPQ32A7jKNBtm0TWVEQoeEXSCkC4xspbe7hporcBaw8gGHXqF5QZL7Ot3HKR7d/vQNZYNPa7EWhMnAAztGvrbO/l5x3QeVhaAEQbKHNzqfR7FaMTns6EYTSimAJ6mObgH69hRY8YYJsaSyXCGB87gkFbFbuPB3+1m8EgLX7j8k5EvOPZCmJgbukbzAPzhcVyXXq3/maJRA3gf/BTmlr9gmh/AOduPt62KnsHzME6aWBSWy/B/G4XYyEUQBCGCNKzx+SalBGMWiRZ10WIuWKeOPexFxdhURihoRNAJQrpEZS17Xm7l5JNvY3n3GWz73xk5skCvsFxRYh7uebmV7sf+ktoIhKFdw03qmTxsnIliMKCqKr+0nYXSE+BW54sAqH4D/S1TcQ/Wsb26FPPQ7pa7vj5i4DhEBcNwy2inNu7A396Jv70LtbcfxWHD395F318aIErQuetfjRBzoeNvHILyFHYHj72A2XQIv92KcUhEGiZ0YpxcTsmNd2jre/3hgrW1ZGu0hSAIgqChV8uWb1EX/r6tvb0AVCn9uMqahnfngozRpjLC6CCCThAyISxraQocxHLgaf2RBUGL5oAH/P1gtIHFCad/AvzeEVZMU/lKLAeeSW0EwrSP4DZcykP9AyjBjT/FjlJi4xHHMgaPTuFzLfsYaLWyddb5PFpbCoC/rRPTjGpae3t5q7ExlGEMD4buV19lzytPcqHaiKusGReA81J+3luDau8n0OeFPi9rWzy4PrZ05NpKqoEPRh432eJ/pmg6GjE4Ijt/GhzVmBdNKopZSdEzjiC90RaCIAhC4RAu6vY0NfFWo5bEXT5zEi61eeQLxmhTGWF0EEEnCFkienZN8FiIqUu1wuhTh4ePBXeRapaNqE+wKgZs+99JfgTC0O7Znr5JtBq8VFmNYLKhmJ0ogU4C7V0EBivo616M/1QnSokd84wp+Ns6URy2kPc/SDC7GNy12/v+27T1GNlrnAOgibrAbjjnqzz4cn9IzK1t6Ym5Ttclq+DEPs1mGcTsxLX0wtSyp3od1/yDRTErKeG/E0EQBCGCRDZ1vbmoKY/SSZcw94r7b04a27qoHLJdNvYouJ2XavEySJpNZcSuL+ghgk4QskTC2TXHX9L+Lp0xvEMXPB6sTQgTHinNwglrONLYPRP8FloDVqoqtAGnBoeNtZ0+XB9bGuq+eeuH5jNxQc1wABwScMEgFBR3noEB3u/spH9AE2JtfgsbTg6LuuvefZ6TBzTb6NqWHv11KgatAcofHtdsliabJuaWLEn4u41Ar+OaQed2VmC2llRnHAmCIIxnkrWpZzwXNV3CGp65u2u10gKzk6rSGaFT3J4z4IxzcU3uSbupjNj1hXiIoBOELJFwdk1Ho1YvZ3ECzrDjsQVHKrNwohuOVBkHaPVD66mTVJVO5J8vWBYR2IIBwDX03F1fT5XDwfLaWvY2N4fEnC8QoKu/H6PBgCms1i9c1Pkmnomx5iSuBWcnXqdi0BqglGdQ16DXce3YC7HPLzBbS0r/XQVBEMY5qdjUw2NK3ua+xWp4FmyEYg6L9eWz4Zz01yR2fSEeIugEIUsknF0TbRUM1tL5vVqGLypbl8osnFgNRzRRN8j8ysq4gS36Z3ubh73+TosFz8AAvkAADAbACGoAk6ICsEeZT+MpI5xRSdmCGlxRa072PVMmVse1IpmVlOmMI0EQhPFEqjb1vA/wDmt45iprZk9fBXv7J1Hl6w8JumzsFopdX4hH4XQKEISxTlBwgFZHd+ow+Prg9Yc0u4YaSP/aJdUxD1eVOLhw5sjOkt1bduFtOBh6/FmTMxRsqhwOKof+nF5eTrXTiclgwB8I4MOADyM2k4Xlp82i0Xha6Jp6XcbyRnDn7uIfw6LbtL8LrCGKIAiCkBp6NvWCISxZ6+6upXFQazbW6tNiT7asnwX/exBGFfmmIwj5Iig4zr4JTHatls45XbNhBpt3JCBaiAUfuy5Zhas2ap6dTsORYFDo2vx0xGMYDjxVYXN0qhwOPlRdjdGg3S5MRiOYrOxt7RuxvoIQdTXL4Jwbh22Yrz8MR5/PTDALgiAIo0LQalh2/UqqNn41ZFnXI5M4pBdjAS2GHH1+ZEwZStaG6ueAKpsFjLaEDplUSPX3IIwvxHIpCPlEMYDRAvbKkT9L0LwjbkF0Cg1HEvnwowvL51dW0tjWRrXTiWdgAKdFE47BOruIAaqFQliReogCHGEgCIIgxCe18oNIMZeKmIobY2PEFLfhUpi7CteSJbida3Cre8Der43iMTupAhrb2nDXpzBnNQ5i1xfiIYJOEPKNXtv9eM071AC2mT1wfif9jXvp3n8QUCKzc0k2HLEumsdjsyrp/9vRUFfKaB++69zF0Pku9LRAySCNbVDRN0Dl0CBvf3snmMwjrp23rmKJiFWknsURBtI6WhAEobCIFnOpDhWPm+yMiinabtwAnNyjuWwURauXC2+CUsDka+C6kD9E0AlCvkm1ecdQZtByZA/Gqk5shi68HVPoalwae95bghv0jx/4BQ9/cAiqnbxUZmVpt5cbwtvmD72f6/jQ+rph8NRCft5ejdprQ3HY8Ld3sc5YQtlVl+S/RXQyhBWpRx7PfISBtI4WBEEoLPRslrGEi15CLm7TkbCYEm6txNcf2oFz1dWN3hy8FMhkF1MoXMR7JAj5JtXmHUOZQX97Jz/vmM5DygKsFSewlP8t5YJod309W/weDHYrneUl/HZaGQ/MquCxWWEW0Bi7W7eWvswNVi+BPi/+9i7WtnhwLTg7FKwKLmilswuaJOFZ3GCButQxCIIgFD7xasjjNh0ZiikRYg40eyWxhWN0XIxbn5cnYu1ijmrdu5A1ZIdOEJIg6xa7WG331cDQbLVGLXgEB48OZQbdvbN42DAHxWhEMVpYM11F0RESsYJL8Mat2G2cnFRGe28vYKDDZuBeXzf2oM8/xu6WwWHjltpBBvb6AG2AePDzF5SQC5LDEQbSOloQBKGwiK79Dj8eHqPi2SrjzggNxpQ3Pxi+eAyLpd4cvEJwdqSyiykUHyLoBCEBebkRx2viUTGfnx2r4iHvLDBow70f8s6hv8TG7devHCEE3cetuPftC10m/Cbd2tuLZ2CAfp9vxBJCN/XqkSLR397Jqb3vs7ZzSnDB9D72Y6yGukjxWSjoDR/Pwhr1srhiuRQEQRg9okVdLOdIvIRc3KYjQzHFNecFqK/HfdgXIebC3yuWMJKh4EKuEUEnCAnIy404ThMP93Erm9TFYOiGgIoa8KP6TGxxTqHk1VdxeR4NvdbdXYu7b4E2EoHYmTfPwAAApqExBJVhIwoAun/fisNxNibvG/jbO1HsNtRpF0DtfMpWnoV14Rz8T7uwlh+Hhpe1F8XpIDlqDURi7YJmgbhZXEEQBGHU0NshC5JRQm4oprhqlmmiLoX68UJwdiS7iykUJyLoBCEBebkRx2vioSxCmXgagbYWAp4u8BsJ+E2Yq2zQdQiOh4m57pmABwY9oeyhu76ePU1NNLa1hYRbi8eDLxCg2ukMHXPV1bH6wFG6tz6Df4kdR+1peBvfwT/hI0z8/k+ZvGpIrB19Hs7oA8qH16nTQbIQbCbZRlpHC4IgFC7xxEm2EnKJhGM0heLsSGYXUyhORNAJQgLCb8T+9k4Uhy0UBLK22xSniYerpo6+Fxp4uMML2EI/uuaYB9clJXA8xut8/SFB19rbS2tzM1UOB61Ds+OCM+WCBG/qXqODiuVvY/I3QgvYqkCtjnqDFDpIis1EEARBKBSymZBLRQgVkrMjVTEqFAci6AQhAcEbsdrnJdDbr9ke+7zZ3W1K0MTjluXLGDzcwvZyEwaHTRNzC86GikmAJjQ/b++HMnB3TEf1GVCGLlPtdNLi8dDa2xsaBl7pcHB6eTnzKyu5cObM0E19c/t+AnYnN3qGl2E2HYrcfUuhg6R14RxKFqiYOg7i85Qz0DlZGogIgiAI44pCc3aIkBt7iKAThAQEb8TehoN0bX6agf1vM/hOE5DF3aYETTysi+Zxx6I7KYuuiVMDeLtrUNqbUOz93OTYT3/nIL/wVmIqhfmVlTS2teEZGMAzMBCqm2vr7WV5bS3brrwytAR3fT0/e/F1lMFZBJQ+Pq8eADSxaAjffUu2g6QawPvgpzC3/AVzrXbI2zGFrs1PMfneb2Tn9yYIgiAIgjDOEUEnCEmS81q6JJp4jMiqKQb4ux/Qs+N+1OY38HkmcGXnZNQrpvNKZTmNbW209vaGulr6AgFMBgOVDgeNbW2hgaihkQY2J/g7+YVhIQZnKTf1vYbBYYvcfUu2g+SxFzCbDuG3WzE4bCh2G4q9E8NkBUEQBEEQBCE7FFCfcUEobOIOHc3T+8caSmr90JkYz/o4vUfm8ohtFturnZhmTKGxrU1rjjLgAdUf2p2zmUwRXS3DZ9MopRUoJRPBZOSh/tk8MuE8jItWjtx9C4rPc27U/o41DqCjEYPDhnlGNcZJ5RjsVsxTnJRMa9Qaq6iBHPyWBEEQBEHIJoUwFF2IT8Idup/97Gc89NBD2GzDzRj+/Oc/J7xwQ0MD99xzD1u3buXAgQPccsstnHbaaQBcc801fOITn+CBBx7gT3/6EyaTiTvvvJOFCxem/0kEIQOSaa0/mkXN8bpFBn+2vbqE7dVaI5S5L+2DSQMw6KEKAAttfjsmowWnxQIMN0IZMWi0dIYmBH39MO98uGhVevPbwmvtVBU8RzRxCdD+etxRB4JQLKQbIwVBKHCiZrwW3LzVPDEWu1WPRRIKut/85jfs3bsXu92e9EU3bdrErl27Qq958803ueGGG7jxxhtD5xw4cICXX36Zxx9/nOPHj7Nu3TqeeOKJND6CIGRGsjer0Sxq1usW6a6vZ3CCkb660/nvCX7MZpWAZ5D3rCpncZxGSgGoMg51tLTaqHI4Yg5BjRB2Zieu8y/KrHA6vNZusEcTcxan9gd0Rx0IQjGRTowUBKHAUQPw3B0ja8XHYRJSulUXBwn/VdbU1ERkHpOhtraW+++/P/T8jTfe4E9/+hNr167lzjvvxOPxUF9fzwUXXICiKEybNg2/309HR0fqn0AQMiT8ZhW0VBbazSpYvxfO1glG3PX1/PD9d7h/oheD4SSKvxOjvQeD8RSNg6XMN58KnX/XxHe4a44j5tyZ6GOxzknZchGstbv4xzD1I9rO39DAcwY80NcG7z0r1kuhqEknRgqCUOAceyFSzMFwEnKcEev7x4j+AWpAK6V4/WEpqRglEu7QDQ4OctlllzF37lwAFEVh48aNcV+zYsUKjhw5Enq+cOFCVq1axdlnn82DDz7If/3Xf1FaWkp5+fBg4pKSEk6dOkVFRUW6n0UQ0iIvg8MhI/tGdP3e9uoSHnu1HtOMavB7afGCx2DndHOfdoLfC8CF9g4utGuJEldZM9TVQU3sXbd4s2nStlwEa+1As1kCnDo8bL3OVQNSAAAgAElEQVR85yltreMw6ymMDdKJkYIg5BZ3dEfoVElh3upYJ+FQdNnNLAgSCrqbb7454zf5h3/4B8rKykKP169fz6WXXkpPT0/onJ6eHkpLSzN+L0FIlYQ3q2yQ4Q0vvH5v6wQjj71aj+IY2hXwDwLQGbDw/iCaqFOMuCa14XI0R75fdHOTKPSCX8aWi6D98r1fD4s5ixPMJWK9FIqabMRIQRCyR3ijL0hT1KUwbzVIMrX4xUjC/gHxdjMlrueNhN8kzzrrLP74xz/y0EMP8fvf/z6UhUyFm266if379wPwwgsvsGDBAs4991z+/Oc/EwgEOHbsGIFAQHbnhFEheLMqu34lVRu/GrpxZZUM7RvB+r2y61dinl6NaahzZGtvL22DKiZFxUQAT8BEq9+Ca0ITrk+4NLvjotu0vzPIliVluYhD99Zn8ZbfDLOvIBAoIWCeAs7poAyNMOiQjllCcZKNGCkIQnaIFnPRz5MmmIQMJyopGn7tYGK4a/PTEY/zSa46UYZ//wh/HCLebqaQNxLu0N155518+MMfZuXKlbz88st84xvf4Gc/+1lKb/Kv//qvrF+/HrPZTGVlJevXr8fpdLJkyRKuvvpqAoEAd999d9ofQhAyIS/NTrJo3whmGzfs3Utbby8oRlCMVBv7hk8qnzM8TiALGbJMdjHD7ZolCyZiPqmi2AcwlIXNo4uT9RSEQiYbMVIQhMzRE29p2S8TzFsNvldrby97mpp4eNGCUW0cMqqdKNPYzRSyT0JBd/LkSa677joAzjzzTH77298mdeHp06ezc+dOABYsWMCOHTtGnLNu3TrWrVuXynoFoTjJ8g3PVVfHnqYmnmrUhGJlaTlV5jJt1IDJBnMvxL3vtdC5mRLPcpHIZhJp11SZMH8Kjjn9wxdPwgoqCIVKujFSEITCJEIA1iwbkRQNF3Ntvb2hOPzTfNTi6zCqnSjDO1oHkbiedxIKOq/XS2trK1VVVbS1tREISOcaQUiWkMBZ+BG83TWYTYcwBGvfMrzhbbvySq598kn2NjcPDwo3O2OOIchU1OntYiaTFYxsOqPQ1bgU66qPYqzoG5H1FIRiQ2KkIMQnX7VlMUfwELtrsx4j6u/OXRzRzMx93Ip7376QmAvy5Btv0Nvi4Sdh18p6LX4c8tbcLRYJdjOF/JBQ0H3pS19i9erVOJ1Oenp6WL9+fT7WJQhFzwix82QlJUtqmLh2SdZueNuuvDIiAMUKaBl3+4pDMlnBkXZNhc6nD8tQUmFMIDFSEPTJtxUwOgZmIubc9fVwcCeuwO7hkwyX0to7NULMASgGA69UOnnirPncsnzZyMYhOSYvzd3iESzvkCYoo0ZCQbds2TJ2795NR0eHNC0RhBQYKXYUjGd9HM5ZmfC18YgWaNHBKms1BEmQTFYwYYcsQShiJEaOMzIYPzMeGQ0rYLwRPHrErL8b9OBuHoCyWm3sD+AK7GZP1c081RQp6CqdTqocDux1i0Y4WoLkcqdS4qygK+j+7d/+jbvvvpurr74aRVEifharHk4QhEhyYYHQa8esZzXJNclkBfPSdEYQ8ozEyHGIzNtKmdGyAmYleenrj3l420Iz19rnD9ewOxxUORxxdwNzvVMpcVbQFXS33XYbAP/xH/+B2WwOHe/q6sr9qgRhDJC2BUInAxzTDkLsbGTovEEPeLtwTfOz9rV2vEYH1g+dmbXsoGQFhfGKxMhxiMzbSplRtwImScykqMmGq6wptDsXomIe265cFlHDnsjaOapNS4Rxga6gU1WV999/n69//ev88Ic/RFXV0HiB//mf/8nnGgWhKElL7OhkgN3ONbj37Rtxuq6oU1Xczz0N/R24zK9x89F9BLwK3qd/RXfDHXRvfTYr2cGcZgXF2iQUMBIjxyFZHD8zXiimpN+I+rulF+LyHIEjYYIurJlZsIY9/LV6jGrTEmFcoCvoGhoa2Lx5M++//z533303qqpiMBi44IIL8rk+QSha0hI7ehngqUtTem/XVC+Y94H/JC5rA2pAQTH7MQ++geeZh4EpBRtUAbE2CQWPxMhxyDict5Vp3VfOrYBZTvyNTI6eG7d7Y7LWzmLZqRSKF11B99GPfpSPfvSjPPfcc5x33nnY7XZOnDjBlClT8rk+QRiT6Gb1ojLA7u5a7bxFPVBeF5p9A8S3eXQ04rIfALUV0DpwYVBRDH5Mzi4GOqcUdnZQrE1CgZNpjGxoaOCee+5h69atvPXWW3znO9/BaDRy2mmnsWHDBgwGAzt37mTHjh2YTCZuvfVWLr744hx/KiEu42ze1qgOq06GHCX+ImJqGt0bY8X3YtqpFIqThF0uX3/9dV544QW+8Y1vsGHDBs4++2xcLlc+1iYIRU+s7OZmn0d/RlxYBtjdXYu7e6b25G8luC7Vhom/NVSIvby2Vj87WDEfjLbQU9XnR/X5ABMmRxeW8hN0bX6Kyfd+I2ufNauItUkoEtKJkZs2bWLXrl3Y7XYAHnjgAb7whS9w0UUX8ZWvfIU//elPnHPOOWzdupUnnngCr9fLmjVrWLZsGRaLJR8fS4jFOJu3VfB1XwWY+NNrXCZNS4Rck/Au9Ic//IFvfEP70veTn/yEP/zhDzlflCCMBYLZza7NT4ce3//oEyMam0QUYQ9lgCPEnNmJ+1AX1z75JI1tbVQ6HFQ6HDS2tel3tZz2ETj9H8Hi1J4rgMGAocSE80N+Jta9xoTZL2gZzkJkHFqbhOIknRhZW1vL/fffH3p+5pln0tnZiaqq9PT0YDKZ2L9/P4sXL8ZisVBaWkptbS2NjTqJDiF/BHdszrlR+3uMijkYrvuKPlYwxEv8jQKxGpflu/O0MH5JeCdSFIWBgQEABgcHUVU154sShLFAeHaze8sutleXsL185KZ4xE1fMWgNUNQLwF4FpTOgdAatvb081dhIa28vVUMtkqNfG30d/n4jXPY/sPiLKPNXYZg0C8Pk2RgcNswzqrGWHdUymYVI0NoUzhi2NgnFSzoxcsWKFZhMw/eCoM3yH//xH2lvb2fp0qV4PB5KS0tD55SUlODxeLL/AQRBB726r4IhC4m/bIkuveuIqBPyRULL5erVq7nsssuYO3cu7733Hp///OfzsS5BKHpidbUyOGxxXjGEooDZqf1Jkpg2D8UA05drf15/GDreGvnCQrUwjjNrk1C8ZCNGbtiwge3btzNnzhy2b9/OD37wAy644AJ6enpC5/T09EQIPEHINQVf9zXtI7gNl0LnO8OjBVJI/OnZI5PtXCkIhURCQbdq1SouvfRSDh8+zIwZM6ioqMjHugSh6InObq5t6cF4zMOOMyJFXXRjk1jzcKocDpbX1rK3uTm0S6d3bsxglE0LYzJdxbLReSyNYnRByDfZiJETJkzA6dQSOJMnT2bfvn0sXLiQ++67D6/Xy8DAAIcOHWLu3LnZXr4g6GJdOIfJX1k2dB+fxOSNdxRUUs297zXcnjNAnQJlJi3mJRlr9Oa6Rj9OVtTFnGPHyPguCLlCV9D99Kc/5bbbbuNf/uVfUBQl4mcbN27M+cIEodiJld10LZhP2YKaCNEV62avlync29xM21CXy7uWL484J5wRoi5b3dmS6SomIweEcUA2Y+T3vvc9br/9dkwmE2azmfXr11NVVcV1113HmjVrUFWV22+/HavVms2PIAj65OA+nukIhHAiBJnZibsFaLHhqkldzAXZsHcvQERJA6Qv6kZLzGXz9ywUD7qC7pJLLgE0O4kgCKmj19UqvP9dvJt99M/c9fWhQJMy2bIwJtNVrAA7jwlCtsk0Rk6fPp2dO3cCsGTJEnbs2DHinKuuuoqrrroq/UUKQrpk+T6ezREI8erVID2rZGtvL229vVRGxdh0RV2668iUgh81IeQMXUHX2NgoHbUEIUekEhyig1d49jCYAUzK5pENC2My4wRk5IAwDpAYKYxpsnwfL6QRCHr2yMqwhmMjSKGMYDQtloX0exbyi66gO3ToEKANP7XZbCxevJjXX38dn8/HFVdckbcFCoIQn7zaPJKpxRtDIwfEuiLoITFSyAUFc8+pmA+qCoM94O/X5pqaS9K+j8dqEpbu58pGvVr0NfRKGFx1dbjOXVw0ZQTZ/D0LxYWuoPvKV74CwE033YTb7Q4dv/HGG3O/KkEQQiQTvPJm80imFi9b9XqjjFhXhHhIjBSyTUHdc6Yu1f4+dXj4WMX84eMpojcCId3Plo1Epl7cHHHNo88XTRlBtn/PQvGQsMtlR0cH3d3dlJWVcfLkSTo7O/OxLkEQwkgmeLnq6ujesguvyZm77G4ytXhjZOSAWFeEZJAYKWSLgrrnHH9JG6FTOmN4h05RtOMZWC6zOQIhG4nMWHF0xOMiKiMo+FETQs5IKOhuueUWPv3pT+N0OvF4PPz7v/97PtYlCAJE+PZd1fPh3HNBUWIGr0TZ3UwKxiNsQFufHXocJ5CNgZEDYl0RkkFipJAtCuqeExQxFicQNhM1gxq6WE3CMiUXjpQR1yyiMoJc/Z6FwiehoFuxYgUrVqygvb2dsrIyzGZzPtYlCKNGwdQwxGgb/dnuGvi7H4xYJ2oA28weOL+T/sa9dO8/CCihzJy7vp4Hf7c7NNh89YGjSX+ugrIB5RGxrgjJIDFSyBYFc89RA+AfgL42bWfOEiboRlnEZDr0O63XF2IZQTZmvQpjioSC7pVXXuG73/0ufr+fj3/840ybNo1Vq1blY22CkHcKSrxEtY32t3eitDfRs+N+vPs/Pry2jXfAc3dgObIHY1UnNkMX3o4pdDUuxbpoXkjM+du7UHv7efB3uzl54Cg3/LUxqc9VUDagPCLWFSEZJEaODXKeyEviC3hB3HOCicTDz4GvD3pbNUFXOmPURUx0x+dURV3ary+0MgKZ9SrEIKGgu++++9i2bRvr1q3jlltu4ZprrpFgJYxZCkq8RPn2FbsNxd6P2vwG3a8OAENrGxJ+/vZO/O1dAFgrTmAp/xv3P/oEO86oxOCwofb2E+jzQp+X7dVObKdV8qUkllFQNqA8ItYVIRkkRhY/OU/kJfkFvCDuOcFEoqKAc/pwl8uzb4JF/zxqgiFajKW605bp6wuqjEBmvQoxSPh/psFgoLy8HEVRsFqtlJSU5GNdgjAqBMVL9LFcoDccNUSUb9/gsGFw2PB5JkSubUj4aYLPinHSBEzTp2Cbb8M8o3r4Z0N2yyCmGVOSWqeeDUgQBImRY4HwRF7wXpfVRF68L+AZ0L1lF96GgyMeZ0R4IlFRtN05eyUYrRmJuYTxLo3XJnvNmOcNenD/+be4d/+3JriLiXhNWoRxS8L/O2tra9m4cSOdnZ243W6mTZuWj3UJwqiQL/ESDDBxA1LQtz+Ev72T3ndsDHROBmB7dQn3P/pESPgZHJqAM1ZMwGDyUfax2XzpU/NwnXtuxO4dwNoWD6ueO5DUWoNfdsquX0nVxq+GstiCIEiMHAvkPJGXgy/gwVjVtfnpiMcZk4MGIEnFu3xy6rD2p6+VQP0v8f3PjaAGsieKc00RNWkR8kdCQfed73yHadOmUVdXh91uZ/369flYlyCMCvkQL7GsHzGDXNC3f/GPYdFt+D+0nj7LtZRdfzlPr/sUj50+ie3lJtzHrcPCT1XBcwS8J6H5/+CPX8LleZTPn3kWGBQMpSX88/K/Y22nD+Ok8rjrDAa34JedYE3J5Hu/Rtn1K7P5KxGEokViZPGT80ReDr6A52xXMSqRCGRUO5d0vIuD3oy5ZGfPRZw36NH+AJ83NHKj51XUd3/P/T/5Pg/+bndxuE+y/N9IGBskNbbgF7/4RT7WIhQ5BdMdMgNyXcMQzzoCMfz8Yb59CzD5Iu3cR+rrMQ3ZKd379sG5a3Bd/Gk49Cy8+1RkV7Ije/icdwqeY6cw1ji4rstPd78Xf7v+vKyCag4jCAWMxMjiJ+fNSHLQJTFntc1ZbACScryLQ6aDxEOv//NvtedlTXzedBi/38qm/tk8fKqDQAq15aNKoTVpEQqChIKutLSU3bt3c9ppp2EwaP9YTj/99JwvTCguRADkB90AuW8f1NXhmjg7UswNYZnq54bJNQzUv013/XtA/GxuQTWHEYQCRmJk8ZPzZiQ5+AKe0xEHhdQAJIxMB4m76uqg8104+CqusmbAxs9PzONh5oHfCD4fm3s6sNfX52S+XVYp0P9GwuiRUNB1dHTwy1/+MvRcURS2bNmSyzUJRYgIgOSIzjKGH89KANGx9pgXXYStrT3pbO547WwpCKkiMVJIiix/AS+IEQcJyEW8yzROui5ZBcaX4EgzD74/gYcG5xLwGQl4FVSfH9+J9oxn3Y0JZM5d0ZFQ0G3dupWOjg6am5s57bTTKC+PX3cjjE9EACRPJtaRhAFSDcS09nT/vpXurc9GvCZeNrdgBtwKQoEjMVIYDQpixEESZGqVzDphu6X+k7vwHQ6gDhjAYACDOqIb9LhE5twVJQkF3aOPPsrmzZuZPXs27777LrfddhuXX355PtYmFBEiACJJlOFLxjqid424AVLH2mPd/w6WhreTzuYWQ/ZXEAoBiZGCEJ9MrZJZZ2i39P9btwz/A7/g4Q8OaYctBkxVE0dfdOaRmL0PKttkzl0RklDQ7dy5k127dmG1Wunr6+Paa6+VYCWMQATAMNF1bsmIulSvETdAxrD2pJrNLZbsryCMNhIjBSExqQqkoLjY7PPQ90IDtyxflnXXT/eWXXz6f/9Cf3UJ26u12vNrjnnGlZiL2fvghkmxX9BxUARdAZNQ0E2aNAmj0QiAzWYTO4kQExEAGrFaNINOMNPxqCd7jfESdAShkJEYKQjZJSg0dtTNYnu5CX97F4OHW7hj0Z1ZfZ9gIvrWD83HOcFI318acC04O6vvUcjo9j6o0BF0MueuoEko6FRV5YorrmDx4sW8+eab+Hw+vvKVrwCwcePGnC9QEAoNPYGVUotmHY+627lG61iZ5HsKgjC6SIwU8sFYGAuULNZF89hRN4st/h5oVzEYfTxa1kvZ7v/WmppkqY4rPBH9BYDLP5mV6xYLur0Pps3J+pgNIfckNYcuyGWXXZbTxQhCoZOsnTIhx16I7VGfujSD1QmCkG8kRgq5ZryNBdrs87C93ATtKiZbD4rJh6J4cb/yIpzYh+ua78cUdZL4TI24vQ9kzl3RkVDQnXfeeflYhyAUJOEBIpEVMqUWzR2NMd/PNbkHyutyN9ZAEISsIjFSyDXjbSxQ3wsN+Nu7MBh9tJtMgIlKnxfFYoDOd2I258hasnUcEbf3gcy5KzoSCrp0aWho4J577mHr1q289dZbrF+/HqPRiMVi4T/+4z+orKzke9/7Hvv27aOkpASAn/70p5SWluZqSYKQEuEBYk9TE41tbTDoAV8/mGxgdiYUdbpCTGdeHBXzcNUUWJtnQRAEYdRIdSxQPu2ZudgVu2X5MgYPt/ATZx/tKqCqgIG7yg5pA8GjmnOkVLsuhJDeB2OLnAi6TZs2sWvXLux2OwAbNmzg29/+NmeeeSY7duxg06ZNfPOb3+TAgQM89NBDVFRU5GIZgpA20QFib3Mz9HeA3wtAlbEVzE4onTHitUm1aJ72kbge9YJr8ywIgiCMConGAoWLthO3rsfbcBDb+Qtzbs/M1a6YddE8ynwrUP70e+jxgKKAMczuF9acI6XadUEYw+gKun/5l39BUZSYP0tU6F1bW8v999/P176m3UDuvfdeJk+eDIDf78dqtRIIBGhqauLuu++mra2Nz3zmM3zmM59J93MIQtaIFSCqzAHeP+XHE7BiUlTtGB4+29fKZ01au+PwoJowiOjMiwv3qEsgEoTCJZMYKQipEM8aF11f591/EFQ15/bMXO6KBa9dVToRBnvA76XKOIC7eyaUz8ElzTmywnhqtDMe0BV0q1evTvuiK1as4MiRI6HnQTG3b98+tm3bxvbt2+nt7eXaa6/lhhtuwO/3c/3113P22Wczf/7Y9YULeUBnFECmtPb00q8acBp89KtG2vwWLlBaWPtaPV1N5vQyoVEedckoCkLxkEmMFIRUiGeNi66vM9htmOfMZPCdpohzskm6u2LpCIiqiumRpQ5zL4yZ+JS689QYb412xgO6gi5Y6O3xeNi0aROtra38/d//PfPmpXdj+PWvf82DDz6I2+2moqIiJOKCtszzzz+fxsZGEXRCJKkINJ1RAFx0T0qiLjpAtPb20jaoUmkcoMo4QKvfAsBBytlc82FWvZR5JlQKugWhuMh2jBSEdIiur/O3dxLo7cfgsIXOCbdnjhapCIgRIs3sBLNTV6QlXbsuhBhvjXbGAwm/5d55553MmDGDDz74gMrKSu66666U3+Tpp59m27ZtbN26lRkztJqjDz74gDVr1uD3+xkcHGTfvn0sWLAg9U8gjF2CAu2PX4KGB7W/n7tDOx4LvVEAx15I6W27t+zisyYtePjbO1EHBqksKaXKpgm5qiFhh7UMw9SzIl6bTiY0lnUlVvZTEITCIxsxUhDSJbq+TnHYQFUpu34lVRu/GhJQ2SSesAodVwNw9Hl4/WE4+jzWhXNCAiK43njrCr9Wa28v8ysr44q04Pki5pIjmAiIPiYULwmbonR2dvKZz3yGXbt2ce6556Kqakpv4Pf72bBhA1OnTmXdunUAfPjDH+aLX/wil112GVdddRVms5nLL7+cOXPmpPcphLFJPIEWq5WuziiA6I5Y8RjOIs5m7QKFnmOvQVkNJZ+6Rhv4HWb9uE6dwKf/N1IsBjOhQUG2+sDRuBYTKegWhOIm0xgpCJmgV19Xdv1KIMyemeVyhLi7YjHcMtbpF2JbdGHSnTqD19zT1ERrczONbW246+sTijohORI12hGKj6S6XB46dAiAlpYWDIbkbgDTp09n586dALz88ssxz7n55pu5+eabk7qeMA5JVaDFGQWQLFpwnI19YBvmphN8YSIYJ03A6FHh3DWaqBuyfnzW5KTrvfYRgTQo0vztnZw8cJQb/tqYU4961sVfjuoQBWGskk6MFIRskFTr+SyUI8Sqf4vuxhw6p7INf8MuFLstZP30N+yif+87wBQAtleXYHz0Ce5YdKfue7rr62lsa6PK4Qg9D76XkBlxZ9AJRUlCQXfXXXdx5513cujQIb74xS/yne98Jx/rEoTUBVqCUQDJYF00j5IFCuamE6Fjit0GR/bguvjTEDVvLjqQhu+4GRw2Hjt9Erx/lLVbYnvUMy3oznrtXZbqEAVhvCAxUih4UnW7RBGv/i0Yc8LPKZ13GGN7F4q9H4OjGtDiqG2+DdvclWydYOSxV+tRHCbKdHbdxL2SI4YStlZDI5O/siyUsJWdueInoaCbN28e//3f/52PtQhCJKkKtCRGASSie8sufHufxVw7fMzf3qkFpY6DuOpu1H1tdABS7DYUh43t1dpYg7UtPTEtJukWdOekbXSGgV8QxhsSI4WckS23RIblCFsnGOmrm8Xqev0GGuFNNk41n6D8LCIasxgcNspu/TLuFhuP1NdjmqEJPRFoeUQStmMaXUF3ySWXRMzYMZlM+Hw+LBYLv/nNb/KyOGH8ErJuXHQPPb/4EZapfsyLLkoc0KJGAaSKddE8/G+eg3HScRS7bUjMDQWlFKyboAlBf3tXxDE9j3qqg8Rzlr3MQh2iIIwHJEYKOSWbX74r5oOqDs106wejDcwlScU0d309j5w4ir/chL+6hLUtPcDI+rfwbpsDnZPxdkyhZHrYCdMvxH3cintf8nFrfmUljW1tI44JaSIJ2zGNrqD7f//v/6GqKt/97ndZvXo1Cxcu5M033+TRRx/N5/qEccgIe8dj72j2jk/k/oZjXTQP68IHQoE0aBdJxroZvdNmcNhQe/u5Ycp0brluWUKPekFkKLNQhygI4wGJkUJOyeaX76lLtb9PHR4+VjF/+LgO4fXg/vauCLdJdHIyssmGQlfjUnxOKxPXLhl2y+x7DdC6VgKh2rgI1ADuPzyO+41DYLIxf+ppIVEXFHjB5wURM4sJSdiOaXQFncWitWg/fPgwCxcuBOCss87i/fffz8/KhHFL3uaj6NlZMrBuhos6xW7jtis+GbPeLlNyNkw1C3WIgjAekBgp5JRsfvk+/hIoCpTOGN6hUxTtuM61ouvB1d5+FIeNnTOmYDzmwbUgtuUyvMmG8az5cM7K0DnBrpVvNQ5/tiqHYzhuqQHcj30Td/NA6OeN/SeZf/pi7XHYbp1YNdNAErZjmoQ1dKWlpdx3330sXLiQ1157jZqamnysSygSYnW+ynSWSfSg1OCxrJLIzpKBdTNV+2S65GSYahbqEAVhPCExUsgJ2fzyHRSHFifgDDuenDhU7LZQzRtA2VXLKIuKNcl02wx2rax0OGjr7aWtt5fltbXDsewPj0eIOQAGPex9/20w2kbs6ImoSxFJ2I5pEgq6e+65h6eeeoq9e/cya9YsvvzlL+djXUIREK/zVTauG07W56OkY2dJoUA92QCTaUDKiXjMsA5REMYTEiOFnDB1KZTPhuMvajtqFmf6X77TEIfZdoGE7/iFC7OI+XI9LbFf7PeBMfn3Ca4zJcbDuB5J2I5pEgo6q9UKgKqqBAIB/H4/RmOS/2cJY5pcWSPzMh9Fx87irq+HFtvIYBBvRw8ts0hPC666JUnfIGOOHEgjqEh2UhBGD4mRQtZRA7Dna3DyHU3M+fuhfBFc+MP0vnzr7My4j1uhRX9Yd05cIEPEqp9z1S2BI8/h7p4ZcfyuujlQPjuhuEx7jM946v4oCdsxS0JB9+1vf5uysjIuuOACXn75Zb71rW/xwx/+MB9rEwqcXFkjkxqUmikxMpbu7lrcXT78B3YzeKSFL1z+Sbq37GLrBCNmpwfX8Rg7ekefx73n2WGbyJHncJ11WkQgiGVL3ezzjBw5oKq4PI+Oj6AiCGMEiZFC1gk6SBRl2CbZeShuzVtcYuzMaB0n94VOiRY/sXa6MhFzSe34TfuIFj/f/CAk6ly1FlyXrBqa+GAAACAASURBVArFwGhxGWt0j976dZHuj8IYIKGga2pqYvv27QB89KMfZfXq1TlflFAc5MUamSuiMpbu7lrcfQvwD/jwt3ex6dV6fIdP0P9iA4+dPglTpQnMtbjKmiMu4/7zbyM8/+7umfDmB7jmaIEgli11R90sdpwxsvWy+6U9oHyAqyzsoAQVQShoJEYKWScX3QjDdmbc9fUR4wOixU/ETlfnu7gme4ZGHwQySi4m3PEbEp6uOS9AfT2UTIkQc9HiMrhOva6ZSYs66f4ojAESCjqv10tfXx92u53+/n78fn8+1iUUAXmxRuaKsIylu74ed5cPSp0Y+vpRe/sJ9Hn50dH3YUYZVQ4bmEy4u2aCz4vLfgCMNtx9Z+H2jrRWubtnQn09rpplMW2p5hnVgG/kmnz9YI6xVgkqglCwSIwUsk4OuxEmmmEa8fjUYdyvvAVlTbjKHsyKYyThjt+Q8HTpxLyYohNo6+3FMzCA02KJPQ4hHtL9URgDJBR0119/PZdffjlz5szh3XffZd26dflYl1AE5MUamUuCGcsWG7RogUGx21AcNjp8g3SYDVQMBrTB4matTsbdXQv9J3FZG8B0FljKYKB75LVLpgCxbam3LF+GPcpyCeA6+wxcx58ceS0JKoJQsEiMFLLOKHUj3NPUNDwaYNCj/YFh+2OWHCOZ1uFFi7kqhwPPwACd/f14BgZCx5Ku+ZPuj8IYIKGgW7lyJRdeeCGHDx9m+vTpTJw4MR/rEoS8EZ7xCw5QZUjMVQwG8Ld1Yqp2ggIYzFpNQ+kMXJZmmLIK919bYbAHUMBgHPb8o29LdQ2J3wjrybmL4bmXJKgIQhEhMVLIOjnsRhivlg3CZr35+mNfoAAdI629vfT7fJgM2u8nehzCCGI1H5Puj0KRoyvovvnNb+q+6Pvf/35OFiMIo0Xwxv/zPz+P2tvPV6dMxzRjCpterUdx2ELBzVV+BFdZB8FZPq6uR8DhxN01HVBxTenFtfpHoUAQz5Ya03oiQUUQigKJkUJOSbYbYQadkfVq2dz19WCyDZ9f1jRcP54lx0gmI3vC1986NM8OoNo5PGMvYhxCOEPDy+l8Z/gzBa2k0v1RKGJ0Bd0bb7xBf38/K1euZPHixaiqms91CcVKAcxySXfYeSyBZZ5erQWNUydZbj4V2RRlwAMDHlwThnbvAJe5OaITWSJb6ohgIy2FBaEokBgpjDoZtNvXq2WLEHtmJy77gUjhkwXHSNrjBcIIvmbD3r0AVDocSdXODQ8vH7KRljVHNh8rgO8wgpAOuoLumWee4e2332bXrl243W4+/OEPs3LlSmbOnKn3EmG8UwCzXGJ1lVTsViq/98WkBF5456xg1nJPUxOtvb00+qfi7u4YDm5ltdDXrr0uXOgVoCVFEITsIjFSGHUybLefaP4c6rm4pn48q46RaDGXjZ26iNq/sJ/FGsXgfuPQ8PPuMFHXcVD7fONlHp0w5ohbQzd37lzuuOMOAF555RU2btxIS0sLO3fuzMvihCKjAGa5RHeV3OpUgAFuGrI6BsWeXgOXWB3AgoGi1TgJt3oBlJmGh4D/6faRF0nTkpLuzqIgCKODxEhhVMlhu/0IMZSl+J2ow2a6oi48ERt+LOZ7h1lJIUzUVcwriO8wgpAuCZuieDwe/u///o9nn32Wvr4+Vq5cmY91CcVIAcxyCe8qub26hMcmO1BsFgzNR1m7Res0qTdaITrYBK0cQMijj8PBhvcC7PGdYNunrshaZ6yRO4tPU7LEitVQJ7YPQShgJEYKo4a02w+R9AB0s1P7M9TBE4DyOVqMfeOR2K8pUteNJInHF7qC7je/+Q2/+tWvOHbsGB/72Mf47ne/y/Tp0/O5NqHYKIDgEhRG26tL2F49VCDt84cer23piXlD08sctni0m36we1bw+d7mZtz7XsOVpSYmkTuLB5kw/yUcU/uh4WXtBLF9CEJBITFSSAW9L9cZfekusnb78TpsZjrKIPz6Cd+7dIYm6Hz92rig4PDyAvgOky1ilZ/EcycJxY+uoLv99tuZNWsW8+fP5+233+Y///M/Qz/buHFjXhYnFBlZDi7pBDrronnsqJvFjnITZocN39G/oQ4MgskUEnU3bH46rZuaLxDAFwiExN2wvSPzJibhO4uW8r9hrTiBYp8yfILYPgShoJAYKSSL3pdrvcdJx6dkxxsUUKOPRB028/beZieu8y+KfO8iE8jxiC4/AX13kjA20BV0W7Zsyec6hLFAFmfnpJtdsi6aR5lvBcahYGGqmYy/rRPFYcPgsGE0lmBbMPKmppc5DLZBbvF4QmIuKOjCz880IIXPqzM5OwHwt3dicFQPn1Sktg9BGItIjBSSRe/LdVa+dCfqjFwAzcqiiVXftvrA0bzYA+NaM3M4/y/fhCeJw48JYxddQXfeeeflcx3CWCHVtvs6mcNMAl24OFPsNkwzqkPHk7ZkAHctX07fCw1s8XvwWCyc6uvHRPLtkVMhfF6dbWYPyl++isERWbxdjLYPQRirSIwUkkXvy3VevnTrNPro+cWPMC25YtTqq8IbmfjbOzl54Cg3/LUxL/bAuAnYPIwOykdtW3iSOEhXmu4koThI2BRFEHJGnMxhpoEuXVtH+DmrDxyl+3//wmDdLLaXm2jtH0QxmyPEXLbsIhHz6tQAqK+MCduHIAjCeEfvy3VQvEQfz+qX7hjNyvztnXhf+xU9BxRdAZVr0RFet25w2Hjs9EnwfuLmZcVOvmrbwpPE1kXzQv/ehLGLCDoh/wR35Q49A+/9GizO4Z8N1Yp1725POtDp2R6T7noVRfBcr8mJZeFcVte/jb+6BADb+YvYii90nt51M7JijiHbhyAIwpgnQY2a3pfrvHzpjtHoQ7HbUKrPZuDV2A6YXIuO6CZkit2G4rAlbF42FshXbVtEkhhkZ24cIIJOyC/hu3J9bdDbqgm60hnD53QcxLpoWVKBLjow6Iq6GAHXve+1mK8JEr5LuLalB4Cq5cuw+zxxX5doTeHoZkHzYPsQBEEQMiSJGrV4X65z/qU7RqMPw9yPYbReCK8+GzoWLqDy3VDD396Jv70r4lgu7IGF0MZfatuEXCGCTsgv4X5+41CN2IBH+xPcqauYh7UmdgAM3/mKFk66u2IxAq7bcCluzxmh57FEl55NxhUnyCS9JqStsCAIQtFT6MOoYzg+un/fSvfWZyNOCxdQqYqOVB0p0SURBocNtbefG6ZM55brluVkp3I04224eDxx63q8+w9isA/XyGdNvBZQN1Mh/4igE/JLuJ/fXKKJuAEP+PsBZ9xasXCxtKepica2tpjnQFRgiQq47u5a3N0DUOoBs1M3GKVqh9GbZZfo+tJWWBDGHw0NDdxzzz1s3bqV9vZ2vvWtb9Hd3Y3f7+eHP/whtbW17Ny5kx07dmAymbj11lu5+OKLR3vZQjQxatS04wXUlTjK8WH90EEsDW/rxrZUGmqk4kgJJ7p52W1XfDJ0LBcia7TibbSQ9DYcBFWl7PqV2bXZFmA3UyG/iKAT8ku4n19RwDkdBntgzqdg1j/pZpSig8be5maA2N0mO9+F1/86nKEKC7iamJupPfH1g9kZuj5EBqOgTSYY2IKPvQ0Hs2KREOuFIIxPNm3axK5du7Db7QD86Ec/4rLLLuMTn/gEL774Iu+99x52u52tW7fyxBNP4PV6WbNmDcuWLcNisYzy6oUIsjyMOh+2wET1VckmM1NxpMQi3Tr3dBiteBstJA0OG2XXr6Ts+pVAFsVroe8UCzlHZLuQX4J+/iCKArM+ARds0G46SYg5GBZyrb29EcddzkO4jv8IGh6EP35Jy1hNnBt7LSZb7ONhdG/ZxYO/2839jz4RyrR1bX465rl6TVL0jutlQQVBGNvU1tZy//33h57v27ePEydO8LnPfY5nnnmG8847j/3797N48WIsFgulpaXU1tbS2KizGyTkjGASL/pxiOiYBml3JQ6PMYniTS6xLpzD5K8so2xxK9bKNiZvvCMkQILEc6TEOq5HvgaLj1a8DQrJ6GNZJ95OsTAukB06Ib9ksYNjlcPB/MrKkPXSdcYEXMd3R5zjfvMD8B3FNVQU7irTdvbcfQtCu3OgH1S2TjDy2OmTCPT14P/dbtYS36aRyrgEaSssCOOTFStWcOTIkdDzo0ePUlZWxi9/+UseeOABNm3axGmnnUZpaWnonJKSEjwez2gsd9ySVN1VFmNaQdjwx6h1b7Tibd7mwWV5p1goPkTQCfknxQ6O0SIp/HiwOQqAy/JXOD7885C98sB7sGwNros/DR0HcVXMg+NW3Pv2RVwnGnd9PQ+9eQBMJsDL9kob6sAgtyfIriVrI5G2woIgAJSXl3PJJZcAcMkll/Cf//mfnH322fT09ITO6enpiRB4Qu5JWmBlqStxQdjwk7TuJYrLqZJrq+loxdu8CckY3Uxlfu34QgSdkFWSvimn2I0p3s5XKHgc7Q+dH1ErZ7Jp4q2uDlfdjdpratDsnoDr3MVw9PkRIw0e/N1u/CfaUQMqoEJAZXu1E+U+N1/+situsMmphUQ6WQnCmKKuro7nnnuOK664gldeeYXZs2ezcOFC7rvvPrxeLwMDAxw6dIi5c3Xs40JOyLfAyttuTjxSaPKSiiMlHmO543PehKTMrx33iKATskbSN+U0LR0Jd76GMlTuNz8YFnNmp27jE1ddnf5anGswOGwEHDbUU72gqqAoKEYDgZMn8x9kg4xRO4wgjGe+/vWv861vfYsdO3bgdDrZuHEjEyZM4LrrrmPNmjWoqsrtt9+O1Wod7aWOK/ItsArChp+idS8bjU0Kwmo6FpD5teMaEXRC1kj6pnz0eXjv19qoAqNNG1+QZDemuAEjmKHyPw5vHNKanoTVyUUQ3OU69Iy2FkvYeUf2aPbMC5bx4O92o/YPoPr8EAiw5mg313rU0Qs20slKEMYE06dPZ+fOnQDU1NTwyCOPjDjnqquu4qqrrsr30oQh8i2wRssWGJHsTMO6l4kjJfjeq0fbaioIRY4IOiFrWBfNQ+3zEujrx2C34W/vRLFHZZTVALy4Hk4dHj5mcWrjC7Ixt0cx4Lr0aigf2WkrZAcJ3+Xqa4PeVm0NpTOGT+44yOoDlZw8cFSzWRoCXP1WC1c3d8KM6tELNsUw80gQBGEMMB7qnGPOkcuTdS/43v72Tk4eOMrasJ+NmgtGEIqUnHm0GhoauO666wBoamrimmuuYc2aNXznO98hEAgA8MADD/CZz3yG1atXs3///lwtRcgTJ25dT8+vnsPf1om/Xftz8t4tkScdewG6myOPDXi0WXRZ7sY0v7Iy9DjC2x/c5VJVTdwFfODtAu+p4RdXaLV/N0yu4fqAlTXHTrGmuRODQxt1MGrjBaSTlSAIgpAKakBzxrz+sPa3qn0HGzFH7sXncO/6sRYjp30EzrlRd5xQpoS/t8Fh47HTJ/HEFX9H1cavhnZFBUFInpzs0EUPTf3+97/Pl7/8ZZYuXcrdd9/N7t27mTZtGi+//DKPP/44x48fZ926dTzxxBO5WI6QJxyXnIf3r42o/V4Uuw3FbsMafVPuaNR2wyxOTcgFKavNWjem8EAxv7KSC2fOjLSEdDRqYs5zZGgNAfAHoPe4tq4ZF8G0j2CtMTD53q+xruGgZrX5p4+P/ngB6WQlCIIgJItO3bXbuSbU6RnQXDODHtxvAkeeg/I5MHcVriVLknuPFBp1RQtJxW7DNKOarfiw+zy4ZGdOEFImJ4IuODT1a1/T/qc8cOAA5513HgAXXnghzz//PKeffjoXXHABiqIwbdo0/H4/HR0dVFRU5GJJQh4oXbUCtc8bKiI3OKopvyWq/iO4w1Q6QxNTwTq687+dlSxgdKBobGvjwpkzR65hsGdYUBosoPjBVALnfB4W/XPEWrJlu8lKW2bpZCUIglBQ5Lrlfkbo1V1PXTr8fNCj/RliT18Fjd0DcHIPKIpujZy7vh5UFZfnUWnUJQijTE7+b1uxYgUm07BWVFUVZahFfElJCadOncLj8eB0DjeiCB4Xihe9jmARBHeYQNsNs1fCrE9kpf4rWszpHp/2EW1HMBzrBCitAaM1J0Eo+Lvp2vx0xOO0CHayyqEdRhAEQUhMVu/tuUCn7to1uWdYqPmGR/7MN5+icbA0dDxRXHW/tAf3mx9E/jDYqEsHvfEGrro69jQ1ce2TT8b/TIIgjCAvTVEMhuEvnD09PZSVleF0OmVo6hgjqY5gKewwRY8Z0D0etHscfRUG/fqdLcPXcP634Xc3D+8QBrtc5qgWTdoyC4IgjD2ydW/Xi3e6JGtzjFN37aoZmiP34nNAlJgDrVN0jLVFiDxfP+4+zQXjKgurj4/RqCt893L1gaMMTqnhkRNHQ9fe09TEU42aAL32ySfZduWVyf0uBEHIj6A766yzeOmll1i6dCl79uzh/PPPp7a2lh/96EfcdNNNtLS0EAgExG5Z5CRtTUxiVkrMzluxjp+7OFQf4AJQanGfWkCrcRIAVQ5H7GxgzTJtZzBPtWj5HlArCIIg5J5s3Nv14p0uqcwjTVB3rXV+VuHtx6GzY1jQhc1wjbdWTDZa/RY2nJyjXS8o6qKSo7Hm1K5aOBfz2ksBIsQcwFONjSLqBCEF8iLovv71r/Ptb3+be++9l1mzZrFixQqMRiNLlizh6quvJhAIcPfdd+djKUIRMKLzls7jDXv3sqfxVbapw4HKVdbMnr4K3uqxgGJkeW1t7OCY51q0fA+oFQRBEHJPpvd2vXgXV9SlMo80iVjnWrIE6s7Vzqmvx33YFyHm9CySAK2DBtoCdlD97Omr0ARdjOSotpM5G5r34ut4Bkt5ObZF/4Srro5rn3wyQswFEVEnCMmTM0EXPjT19NNPZ9u2bSPOWbduHevWrcvVEoQiRM+vv2HvXkDbcQNo7e2ltbubp051ca3jQ2w2/QnFbuMh31waB0upNCtgcdDY1oa7vl5f1CXYKcwW+R5QKwiCIOSeTO7t8erTII6oS3UeaTKxbugcV80yTdSFrSF8HeFumdbeXlo8HlBMVDvsNJpm4Z76D7guWjUiOWpdOIcJs19Acb4YOmZwTAJ1pf6a0iXFrpuCMBaQweJC0REUc/j8qAbY2zORn/uno5hNPGzSvPxVJQ4wa+Iv5dqEHDAeBtQKgiCMN0bl3p7jeaSxBFz0z/c0NfH6m2/iCwQwGQxaQzG7A/ehLih/bcTren7xI8wtf4k4pr77ezj2AtuuvDLmLt2n5s9PfXcuFTuqIIwh5F+3UFDoWTvuWr6cu5Yvp7W3l7beXhSDAQwGKrx+Jg0M8LCygD8barSTdbz/+aB7yy68DQdHPBYEQRCEcOJ1e4ybgAzvFh0kyzXgCdcwhMlgwGQw0NbbS2tvr+55lqk+FLsV46QJmKZPQbFbMThs2q4isO3KK/nU/GGhmpaYg/h2VEEYw8gOnVBwhFs6gs+Dx0KF0wYDk0wGJnoG8A2WYDD6uOC0qVw0Y46WIYy6Xj5252IVfVsWzpXdOEEQBCEm8eKdLvmoAY9jW3TX17O3uRmnxUK/zxd6SVtvr27dunnR30PHU6HnBke19iBsVzG4Uxd8nBap2lEFYYwggk4oSPQsH8Eb/nNvNjKxMzj2QuGao14+PWhh8ueuhnJ9/38uycVogoIeWCsIgiBkTCKLY0zi1MWlUmYQ89wEtsU9TU20De3G2UymkKirdDi4cObM2G80dSmUnwHHX8LfM4hiL8cwbwXdv2/F+qGDobiWcQOUHNtRBaFQEUEnFCx6wWjblVfyX8ZfsenVehSHjc+ftYBVzx0Iiae0gmMWyPZoAtnxEwRBGB9kK1alMgJB99w4tsVrXzrB3uZmKh0O2np76ff5sJlMOC0W7lq+PPb7qQHY8zXoPIS/x4d6qpvBU5UMWC+ge9uzWBrezl5cSzCmQRDGKiLohKLkC5d/EvN0zbLhqquDyz8Z8fPRaICS7dEEMoxcEARBSJZEIxDCXR4/fuAXbPF7UOwxhofHsC26u2t5+Ik9vOFRAW03LlzU/ePs2fpxN0wgKo5yAn1g6DtG/7OPAFOyG9fyPJJIEAoFEXRC8THk7XdZhrz9aqAgbtbZHk0gw8gFQShmxDKePxKNQFh94GjI5fHYrEoe/uAQBrsV04zqEee6qiPjlru7lg0n59ASGAzF2haPh2qnk8qhUUK6VkuIEIgGuxXVrqL6+rFNbmagc3L2/03kcSSRIBQKIuiErJPTIB7P2w/ZnT2T4iybbLevlmHkgiAUK2IZLyzCHR/9fzsK1U4Uhy32yWG2xaCYawvYMRlNeH0+AqqKoighUadrtQwSrGtTVQKth1AGe1AsYJ/ShGLw07X5KSbf+43sf2hBGEeIoBOySjJBPKO5cEefh/d+Df5+MNrA4tTE3dHn4d2nsjd7pgBm2cgwckEQihWxjOeX6G6Z4ceDPws6Pta2aA3Fds6YonsuF92D+w+P424/BDYjeP34AgHUoXNVVcUXCFDtdCaO5UGB+N6vURQvqkEBowPjxOnYLJ0YJiuZfXhBEETQjVdytYuWKIinUrA9AjUAL66HU4eHj1mcUDpDE3l6s2fSsV3Em2WTJxuHDCMXBKFYGfeW8SiHh9bN8cyc2k+jRd38ysrQz6IdH2tbejAe87DjDFvotRHxWDFA+Wywd1EFePyddPb3az9SFBRggs2GPxDAXV8fP5YH69oMVpSDj6EYbWAugcEeDJUmzHM7C6Z0QhCKFRF045CsW2HCApe1cj6PzZpE/99KQlnAYNBKVLCdkGMvQHdz5LEBj/ZHj3Rnz8gsG0EQhLQZ15bxKIeHv70T5R0bXQ3XYvvQWanH3BTs/+EzWxvb2mhsawPgszEcH64F8ylbUBPxuljXctfX47RY8AwM4AsEMBkMVDudVA3VzyWFYoAz/gma/097furwcOx+5ynwe/PqgBGEsYYIunFIVq0wUYHrZ8eq2Nw9F191CaBlAbs2P83/rr00bsF2UqKuo1HL6lmckSKurBZmfWI4UIST7uwZmWUjCIKQNmPRMp60syXK4aHYbdhqOumrf57u/e8CKcTcoRjrb9iFYrdhcNjwdtfA3/0A64fO1H1ZUMjBUJytq8MVw/HhSvD20bt+noEBnBZLSMylNOs1zHoZiuEWpxbX8+yACSKNe4Sxggi6cUhWrTBhgcvdXctDvhkoFj8mh42dM6oxHvPgWpClIF4xHxQFnNPxt7egmMFgL6Wn82JMbZVYszl7RmbZCP8/e28f3lZ93v+/zpEsHcmyYxI7cYjt8JTEJDSGOCvQkPDr2FW2boM1lJYSurYQVPpd90z728p6bbu67Nd2wNay77gqCixPTQolkJS2GzRjJIVQWgWSEnBIArETkwc/RFZsPR/p98eRZEk+kiVZdvxwv66Ly/Lx0XmQyOdz3p/7vt+3IAhlM91SxkvKbMnJ8FCdGomghtU1QMRn1K3Z25YUF3n7YB/6gV3ofQMojhCJoIbS18nQ9kewX/0fI049mttlObXrue9JHeuzESufs7oAihND6dRLGxzebtTBV1Ub8zpMeAZMMd+pCD5hqiCCbgZS0VSY5MTl8bfg8S8EFRSbiuKwgkNj++UatcsWpCeEDXv3ApS8uufxeiFhx920xpjc+iMoDjtRZQnndhzF9vaPmftQBXvPSC8bQRAEIUlJmS05GR56nw+9b4DY4HBEbWDjs8y95ezoxlv9HSgOLSnmwujBsHHuSyf28c3d3p4lCoP7DnDbc68y8F7vCDFUUAQpKlz+h9D185EnmeAMmNG+U3FqFaYSIuhmIBVNhcmXmmjNY4cM9AYCAAWtjvM2QF1xJ5+/+rcIvbiDkDdExFcPKMb1V7r3jPSyEQRBECgxsyUnw0NxaMQs89H+8B5mXX0lAxufpWZJJxz5WXaUyiztcHZrOsKXEnMAllbzbJFi3C7LITPy525v577Vqxh4rzdLDG27rB7+/Qlue+7VwiJokmTAjPadToRTq0QAhUohgm4GUtFUmOTA7E6nXS6EKpfxH8OTSGoyKKaIOrUqtr39Mn71oot3Tp9JN0D17N/Pnvp6fqvx97jttVez7kkQBEEQxoOSMltyMjzU2UtwpjI8EnEjMnfkZxDoMfa3ucDVZIi63LTDi68n7F+A0teZ3hTun0dwZydzbzS/1lxRlynmykm9NDU0a2/njgwxtLWxmqd0oy4u2n4Zd3gLiKBSM2BK7AlbLKN9p+Pt1CoRQKGSiKATxkbGwOzuPwxnq/EcGwBGirkUKVGXb2Kxty1he/tl/JsapN8fZnaVSkOyAWpPIMDbb73F/wajhBqHnTRnjHuaIAiCMP7kiAj78kWlZbbky/BI1Z1bMrJYIoMQHTKEXW7aoaLCR77J0PZH0C61Ymm9nuDOTrSrlxa8/Mx5NVPMldo2KF9N3qMv7ObcoW7WYYi5rY0u1F4f1uZGttaF0BtHOl2PuK9iMmDGoSdsZiRMcdjRrmvD9Qc3jvhOx9upVXo1CpVEBJ0wdjIGZjdAXbZQC+47QCIWQqmKEff1o2gulJrZeQ+3MTbI1jor+I3f+6tUVIuCEgjQGwigqCpKlZXtyxbgun0Zt798aEoNgpJiIQiCMIkxERH2pjVGnXZSRJT9UJ8yTLG5sh2b9RA0fdw07dB+9ZVZBij5InO5ZAq2MbcNykF1algWzOOZ6xp5Sh9E7fWhOLV0veDWxmosDj93R48S2PYd7Mv/vbAAyxeFq3BP2NyoWCIYRu/zjchcgvF3ap3xvRqFiiKCTqg4mROEf9MubnvuFSyrh/hPy1yUmA66DxjCfeOtpu5Zj76wG71vgJTk669SOR0IoEQsWFWV+oz+N0+e6aZq3U1jqg2YSCTFQhAEYZJTYRGRRWbdeU2zIej0EPzW/wttX0yLnrEKrkxKcb7MXXD8XNsSyDBESfHFG1alM3AUrxdrcyMAiWCIRCCERRvE1dRLTfB9VOcpQyDnfBqX1wAAIABJREFUi6oVisJVuCdsKVGx8XZqndG9GoWKI4JOGFfsbUuoXmnHre9CjS/jcWUZvThYzSnc88Om71GdGolACMWpMc+pofSd40zS1rje6SytmekkQ1IsBEEQJjkVFhFZ5BqC2FxGZC5HzJWaGlkOPYEAezo708fPt+CY6l9nVpOXW5unODSsjS7cypu4a08BhtArKIgLCegK94SdTFGx6dirUbhwiKATxhV72xLir1mgG9YnDvEL5tOrVtMRrcHj9eLOGdzNHLrmNc3nxvp6ILtZamr/qRKdg8k1mQiCIAgmjEVEjGbgMYohiFlq5J7OTtYsXFj2XGc2r/Yk3aY7enuNubi9veCCo1lNXr7ju5stuP1dIy8knyAuJKCv+nxFHTEnU1RsuvVqFC4sIuiEccW/aRehF49TtxS+ryzjsDKb+ngAqMJzIgbJiSQTd3s7ezo72dvVRYPTOcKhy+P1ovf5WL90Ge729ilVhzaZJhNBEIQZSyHhVa6t/igGHh6vl+C+A9y3ehX2tlX4d/dhb6vHvsBczIEhvN7p6GBvlyGQKiHqUmLOzKCs0IJjoXNnCb7GELy0eeRO+QRxIQFd4Z6wEhUTpisi6IRxxd62hNCbq3hsVgNPhmvojVYBCg1VGlS5THP4PV5vOhLXWl8/YmUwuO8AoUPd3N4D/gF9StWhyWQiCIJwgRnNObFcEVEgddBzWkvXh0dPnMZ94KpR566epBFYirHW1aUWS3uSi6WZpMTmbc+9mrU9d8GxkKlX+roS8dIE8WgCuoI9YSUqJkxXRNAJ44oxeP4NVb/+NT0v76Y3HjZ67VhqaDDZP3OFssHpzEoHSWHW0HSqiCKZTARBmBGMU++wilCM6Uk5IiJP6qDH68VzWkvXh/9bLMirr/2C75I9d6UWLDfpg/Qm4vT4/cRQqNHso7b7KZY1CxeOKF1IYW2eV3DBsWhTr1IFcYWjcIIwExFBJxTFmK32FQUsdlB1YvE4pwcNq+YHVq/O2yMnRe4ENlodmrQFEARBuICMQ++wijJepicmqYMefwuegRhUGYYhfXYr/YrOf9U7+DNm89iJ04QPHMbetoQzX/oGnzhwmOjqpXy3BmJ6HBSFUCxGTyBQEUMws3q61HZ3ezvc+vvpbfls/ItaTC1VEFcwCicIM5FJMLIKk53UqtzAxp1Zr4slJdQanE40q5VYPE4sHmcwEoFEArpfgd88Dr6jJV1PJqnrGeu1CoIgCGOkQATMv2kX4QOHAbJeTygVdk5Mk0odzKRuEVS5ADhztpe+yLC78+s1VXzvlX3p+Sp88DAkEujdZ5hzbpBqPY5VNRyeewMBegIB05ryUsk8ht7n4wvzFqTr0Qt9H6nF1NxtM45EfPi5pfsV43dBuMBIhE4YlUpZ7fcEAoRiMayqsY4QisXY86sduGteMHbwt9CqtNJhuSTrfblOlpl1aJtnWQi+egD3staKXqsgCIJQJnkiYMHnf4j/6cCF78NZrunJaJikDrovvh72v8GGvXvpJw6qCqpCvebgotggP7xqAZYPulm36V1Uh8b25S08Yo/SX6UyOxqnVnPQG4uOOJVZa4NSslPKqUcfV1OvyZyim8lkjz4LMxYRdMJIcgZW+/Lrx2S1nyrEfqfDmORTgq7ebqFjIIgn0QKAx78QCNLanKBjyFiVdK9YYThm/ebx9CCfqkPzeL086fXC5fXULluAG2kLIAiCcMHJEwGztF6PbfmJC7/gNp41Wyapg6k58NmODhSbOtxPtW4Wep8PPjgPwGaXwjaCDFmqiCnQX6UyZzBA/exZRkZLErPWBilDk1LEcqn16ONm6jWVRFKh+suLr58aolSYloigE7IxGVjD/gX4d9QDSnpbqatyaxYuZG9XV9qxq97ppEEJQBT2BGfTEa1J79vR20drcytrWlpwD/4AXho5yHv2vzFiQgO441A3/k270Pt8KA6jCL3nge/SsOHPRNgJgiBMBHkiYLY1n0brfH5yLLhNcM3WlrVruWvHjnQ7HjDSHe841M2600Nsbaxm21wn/VUWwjYroBCLx+lTFRyRCKFYDJfNxp7OTlNTk036INH2y7jDW5w4S8+ZxSyAJhd57WoHc/96VVqoVCyyWoxJTQUZU5193vrLd+DIM1NDlArTEhF0QjYmA2uV9RjVKxdgWfq7Za/KpVImN+zdCyT730TjtFadzxJzAFg1Onp7WaMeh54dYNHAZtQgcHIPnv95Gs+xgRHn8Hi9ROct4FbNDoBt0UJifT707jPS600QBKFUyk2DyxMB829+fkb34dyydm1WdG390mXc3gPax1pxzbJwzvtr+q0KitVKFRCLK0TicfRIhEaXMQf+7OhRXDbbCIMUxaHxqwbQG6tZd3oIyC+WM/u5njvUzbqMv6W/j9R33/c2dO3OrnFPChX/5ucrY0A2XiY1JuQ6dZ77103Yli9m/sZ/Lu4e8tVf6tEJFaWCkIsIOiEbk4FVdWpctG4lfOgWoHyr/dzGpqtbLmFN6AwdXcOpJFS5oKoazp+AwC9B6TG221xQ02y8HjoDaKbnqGpqpGHDnzGwcWd61dEyp07q6ARBEEphrGlwJhEw6cPJiL6qKVfJKq8X9f1ZKBl951K4bDbAMEYhoUMsBHqIhprZ6X0u94d45/QZ3mk0hN+600OGOHvo/ixR7jllx7N/P2DM7dsunYN2XZuRfpn6PjK/+8igMR9nzsEn9zD0xL/g33akMvWQ42VSY0JmnX3wpdfR+3zE+weyhF7Be8hXf6nmeZweB1EqCGaIoBOyqfDAmsrtv2/1KuxtS7jjUDcvWWz0EDCicCtuxz3vGJ63joHVaDZOdBC34xBueyec1w0nzPAA2C8Cm8uYBE9r+W2XQeroBEEQxsI4pMFJH06DfD3kUlG3VGmCy2YjFIsNb9MjkNAJAVeofejnh6Cmmdb6et4JnkR12FGcGk81z8PywSDupYuzRLnH38L3zl0B1U0oTo14IISlvo6HY35+dewQW1LfR/crw9+9HjJ+RgaN/5LZMrb5euUMyMbLpMaEzDp7xaGhODT0pKCDIu4hX/3lB/vM9x8HUSoIZoigE7Kp4MDq8Xp59IXd6H0DRE+cxn3gKh59YTfvXDqHhuZGY5/9+w0hdsMVw/3mmi24Bzrh/DmI68OWwEMfwKX3wMXX416gps8B2WJuXJ24xspUcfISBGFmM4FpcIJ5f7gGpxOLqvLW2bNGZC6hA1BviaCj0sopqG6io7cXxaFhTc6rANsv16htCuPOmMvjg0GIDhE/dxaCteh9A/QGQ/RrVvZ2deHxeo3ryPzuLRnZMHoIMARdVduNaL19lVk4ncDG4pnPB6pTIxHU0Ht9qMnPrqh7MKu/nEBRKghmiKATsqnQwJrK0VedGolAiE3BIV597RccbXRhcSYniOggxEJ4XnsZ97Vr+GzEirV5Hu72OvSnv42aOI+iVgFxI0pnr4NFa9PXMiJ1JcmkTeuZSk5egiDMbCYwDU4wyBV1mbXnvYN+wBBzDRajTGGNox9qwnQMmZcgMHQ6+/h1J0hEonwv0EZ8KEF/lUq/1RCODU7n8Hkbh797T3Ap6PNxW14fFndNa/D/vAf/5ueTeyWw1Z0l/PQD2Ov/sjwxNkEmNZnPB9ETp/Fv2oXqHP78yl78nUBRKghmiKATRkweYx1YM4u+FYeG4tQgGOb1WYZZyTyHZuTkRwfT74m+/B/c9jMF2/Il+H2LsZ0G2xwdxVYFWMDuAkcDvPdTOPduOrpllroyadN6JtjJSxAEoWwk4pDFmJwRSyDfQuWG/30RQkNpMeeu7cRd2wUmJQhGjXoL7vZ58NLm9HbVqeGuOc5QZzP/Vt9Ef5VKg+bIMljxeL2wYgXupjV43j6O5/xCSCyExsW4L78IUOCyj2Ovq8d24F20tsU4nc+ROPqGIYxe+vNJvVCZ+XwQPnAYvc9XucXfCXZOFYRMRNDNcMyakxZNEemDep8Pvc9wpJwdjXNFIMr7trNYHEPJY4C7+l3Wq28yeN2H8b92mMjBd7HVXcOcxjfBaTVWBauqYfAkHH122PFyEk8apkgKkyAIUwWJOKTJdUbMZ55RKdGXOw+729shkcDz8k6IRobFXFJgZ5Yg9CTr7zp6e/GcasGdIcr1Ph+BIxp3vK/yapWxyHrR+SDUzcq+AEXB47oTT2IPOEJg1fD0n4PgIeO8XS9ib1rD3IeS/3+89BZkpHtOlYXKSbv4KwhlMGGCbseOHTz77LMAhMNh3nnnHR566CG+/e1vM3/+fAD+9E//lA9/+MMTdUkzHrPmpFCkqMvTr46PfBN3ezvBfQfYpA+mUy4Vp2ZYNL98iKfqzvKfYQwxZ38Td+J10ME151WU1gYGOq4l4ptHYsHvQPgt4+CRZDQvJeZgykwaaSSFSRCEqYREHIBsZ8TIwXfR+3xY5tQBwyIufOBwjujbSfVKO3a1vSL10u6VK40XA8dwz/3QCIGdal7ek9HnzrN/P6y4E/dHb4P+w+j9DoKdndT+8VK2tS3hkR88w1Zn9mNgOu1z//6k67RhVEZ0EE90obFPbdfw/NvfYZRERIeMGrvUAqwsVArChDJhgm7t2rWsXbsWgH/8x3/ktttu49ChQ3zlK1/h5ptvnqjLEJLkirnM7VCEqMtJH9T7fCh9nQxtf4Twwd/ltudeJdp+Gdsvr8fa3DhsWnLr7/Pl7lew7XwIYmFDzAGJmI4+GMY++wy2urNEfPPo39fK3L++15gYzh2BrhdHXsdUmjQkhUkQBGHKkemMaGSd+Ii8ezwrcjfrc7dmiL7DzGr9JVtnN6PuPY279tGKZJQYom6l6d88Xi8dvb0j+tN59u+H9nbc7XdjA+beOPy3+9u+Rm3Gs0Bqnh7xbBALmV9Q/2G4aLGRPRMZLqHA5oKLFo3cX0zBBGHcmPCUy9/85jccPXqUv//7v2f9+vW88847bNy4keXLl3P//fdjtUoW6JQgJ33QsP8Nkeh6C/+vkzn+y66idtkC43WmQLz4etxLL4EjOyDVcsfmApuGxalx0d3Xc+7nViOXPbU63P2KuaCbStEtSWESBGEKUsn6sYmqRaskmc6IilNDCWiEXjuI3n0WIF2DlRJ9trqzbJ5TzxOxxeBPRtCSES3/7r5Jdf9mNXsjHDethmlIOtUzxewlwy7UoyGmYIIwrky4evre977Hn/zJnwCwatUqfud3foempib+/u//nu3bt3PXXXdN9CXNSMwsklPbi0q5zEkfTNn/xgaHc/HtbUtwm01UKWFTtwh+9S2waDwWWgqzjFQOS9uNzP14TtRtukS3JIVJEIQpRLH1YxN9rIkk1zm59+++yyZLBNXlZN3pobQ4S4m+rfOr2aI0QyyGYlXx+JogAZ99/of4nw6My/2XNKfnRMrcK0YuLGYdr8qFu8WGO54h5lLz71tPGg3HI4PDKZc2l5FV07R6eH8xBROEcWVCBZ3f7+e9997juuuuA+C2226jtrYWgJtuuon//u//nsjLmfGYWSQXbYqSI7BSxdYR39z0LgXtfxUV2r4IviOGk5bfyM2nbhFuM5Em0S1BEIQJJ7d+DMpvIF3JY00U6fkxOZf5N+1iyywr2xrr0vt8YePOdMrltsvq2RI+izLUCUoc4kYEy+ObT+zSVj65fHBc77+1vp5Dh4+iOjW+eMMq7jjUTdjqMqKAibiR7fLaN8DfNarBWMqMhYFjuBuugPglYKmC2VcOz7+pxV2bi1SPOmBk9oyYggnCuDKhgu5Xv/oVH/nIRwBIJBLccsstbN++ncbGRvbt28eyZcsm8nIE8lskj0qOwMosti7a/ldRRzppDbpg/xvm1yLRLUEQhAklM5Uwc9uFPtZYMUv93Bgz6sAyFztzXaA3z7Kw7dI5WJwaqlNjm8OONq+eP086Jjq8XpTXXoaQOiId0Tp3NtrVTcXffwk1Z6lr1ft8XHqil4/U1HHHoe7hKOBD9xspj+/91GgbBIYIq2nOHylLxHEP/gBO7YFTyW1Na/Dvr8d+9RHjs/t5D07nVViTBmYef4v5wqyYggnCuDKhgu7999+nqakJAEVR+Kd/+ie+/OUvo2kal19+OZ/61Kcm8nKEJCUJuUwyBFZusXUxKSQerzfbSStj+5iua6KRQm9BEKYpmamEKcptvlzJY40Fs9TP7UkTr0xyXaD3dHbS0duLNcOi39rcyGZiOLze4SyX7l/gOW+DuA4k6Ik7WK3189ljr9L3dCDrHAMbd/LcupuAnDmvQM2Zf/PzWWJ08ywL337/CAD1To335ri49v0z+L3vAckoYCrlUc8wOIkMGv/ZXOaRMpM0Sf3ALvQ3f0n4yHxiv/4t/NveJbTcMDDzeL14BmJgtjA7XcomBGGSMqGCbv369Vm/33DDDdxwww0TeQnCJCXVOyfXoWvSI4XegiBMY3Lrx8bSfLmSxxoLuamfWxur2V5nxZL8+4a9ewFjPsqcm/Z2daVfF8LdvhJOvozHv5Ae3QYKdERreLJ2MZ9cfj7r/rddVs/mnCigx+sF31Hcp0bWnA098S/4tx1Jf46PvrCbf79sDv2alVg8zqDNRotTY2ujsUiaqvHzeP8L/C24tcHsY+ohwGUeKTNJk1RVHzWtfcSHDqOc/Dn1K6vRr7gCzyk7ntMaVBn7jViYzcjqCT7/Qyyt1/Of1YsI/t//5L7Vqya9MY4gTHbEUnIaMOUiWklS17th7156A8Orlg+sXj117qWChd5T0f1NEITpTSWbL0+WRs6ZqZ9bG6vZ2uiiyqll7dMbCDAYiRCKxdLbGpxOWuvr6ejtzdp3RP150sl5j9dPjz6HBksEqlw8cd6Cdd1N6X2fW3dTlpjLjAIS7IWqlmxXScA2X88So79cPJs+K+jxOLF4nHPBIPGoTnPGex75wTNsb9Ho8S1ijzabLc4fkwgMgKqgWDSjh2zPbOwLcj6ojDRJvc+HYoujxoOga6j2QVSrjkUbwuP/X77/csBI38zATNT5d/fhfzrA9vb32Vp3Ar1vgOiJ09zf9rW835cgCKMjIYSJJFWQ/JvHjZ/F2v0WIJU3n6+v3Hieu1T8m3YRPnB4xOvJdsySKFToXQKpFKCBjTuzXguCIAiVxSz1U+/1GS+igzQoATQ1wWAkkv57byBAa309W9auHVF7PmIBMlkf3qEtpcE1yxA6SbFTaM7uCQR4tqPDiApaNTz+hUZNWgZVF19EzZIT2OrOsLXRyVFnFZq1iljSfEUBBqwWbHPq+Iuv/B+2t1/G1jorPVGV3riDZ4fms+7czcQGNSKBJobin6NnRz0Dm3488oNKpkkavfcGSPjPoYcU4kNBVMuw0CUYTDcfHw172xK2t1/GJn0IvW8AgK111sLPL4IgjIpE6CaKcUjNy50Q8kbqRjl3bmRo8ywLsRNn0mkQlYgWFapZyExfaXA6i444Zh4zEQwTPnAY7brl6XSWCYluVajQeyq6vwmCIExFMlM//6JtCZYfPMNWp9UwC4kO0qPbcAEuq53euBG5q3c66ejtxZOslUuRd55SlBH14YXoCQSyMlVS7005QLtruyCRQP/fB7H0+6lbCo4A9FddTSgSwWq1GKJOUaiyqPQ4qrjn2CE6Lq+nP3Vsiw0SOs/pl7Mudh8P7+0BjgGK6Xzj2f8GuO7k81f/FqEXd6B3fIBzwTuoVSGwqChWC4mYzr3V76Ha6vFEG7Lu10zsbowNsrXOCn3D21SnNmUzjQRhsiCCbqKocA+WfBE500HR5Nyet4+D/jR3dDuyhNajL+xm68U1EE8QPXEa94Grkn+/grl/vbps4w8zwVLV3AgYq3zl1M6ljhl86XUjHcShkQiGJ7a3UYUKvSeT+5sgCOPPgQMHePDBB9m8eXN6249//GO2bNnCD3/4QwCeeuoptm/fjtVq5Utf+hIf/ehHL9TlXlAqnY6em/p5f9vXqN39Qzy/eg2A1VofHdEaIAJ2J1g0ZgcjJJJznn/TLj43yjUU2xcu9+/1TufwfFjTbES9mi6FZgvs/y6KNYTqBEWr5ctN7/OLE8t4Xq3Cqg7Px40uV/oYI4SiYgGLhdfsVWxtDLLu9FD6M8kk6xmjvZ07Fn+Wodd2Yq0eQGs4iWJNoKgqiqsWahpx0wXzP43n2ABEB3E3W3A3howF5YxnheC+A+nIXAq915dlNCMIQumIoJsoclLzUgJE7T+Mf3ff+EaUcs7t8bcYxdreI7w0p4nvJIXWk2e72droQq12QizGpuAQ+gu7WUeCWVfsg5d+NHyQ0aKLOc6P9uXXjxAs961ehSM2WHZz85QICr12MCnmQoQPHkZ1aBMX3apQf7zJ4v4mCML489hjj7Fr1y4cDkd62zvvvMOPfvQjEokEAD09PWzevJlnnnmGcDjMnXfeyapVq7DZbBfqsi8IE9WM3D13EGo7jde1Xel5ssEaRw9E0PsGWOeLZbcCGOUaRuv1mvn3BqeT1S0tI+vzrrsR94pr4JnfhcGTqMD31TaI23E7/Dz1qcXcdaSOZzs6sKpqWhCmznXXjh0825H9DDAnDnW+oaxtmfNN7oLxoy/s5tyhbtahMNBxLcHTlzDrmnexz1ezetm519wGZx4A3xHc/i54afOIZ4X7Vq8ieuI0W+usqE4NvdeH4tRK64M7DZEaemGsiKCbKHKKi/W+ARRHiODrPfi3vVryBFXs6l/uudNiTrfRqyfYdeYDjjRU0bJ4Nkedhj2Vpc5FPBCCYJitjS4sDj9/ZT0GZBSNF4oumqR4hv0L8O+ox8jwNxjYuDPdrLWc5uapiV51aiSCGnowhN7rQ21unNiBsAL98SaL+5sgCONPS0sLjzzyCF/9qjH+nTt3jgcffJCvfe1rfP3rXwfg4MGDXHPNNdhsNmw2Gy0tLXR0dLB8+fILeekTjjE2XgFde4n1/xhbXR1a2x9U/kSzW3HXPpr+NWVG4kk0ozqthpjzvpfdCqAIRkvPzP17ppgaboPwitEIHPCE2/BE2iACnOvCPXsJW9au4q4dO9jb1ZUl5gC2rF2bJerqnU7qFZXPnAf3x64dMd+YZf+oTo1tl85Bu66NuwZiRN/tJNy0nljsMLbZOlVtNxoLmR/swx3fDbUZb855VrC3LTEiosnzWJsbRcxN0KKFML0RQTdRZKTmKQ4NxREi1F3HwO53yZe/niLfys1oq3+55/a8fXxYzMUdYLEQjcbYn4jw9hwHs2NxZkfjRLvPQjyRfrtFC6L3+VCdOSkRZn1rwDTFs8p6jOqVC7As/d0RE0jmNd9xqJuw1VXUKlVKBCWCYeLBEErAaPQKUy+6NVnc3wRBGH9uvvlmTp48CYCu6zzwwAN87Wtfw263p/cZHBykpqYm/Xt1dTWDg6ObTkw37MsXMeuKfSiu19LbVOccSNxS2dYwJunz7qWXgGsNKIoRmUuKOTBJiS/Qj3Q0sZJP9KVf93eAzYVH/zCeSMYCbXApnLLjXmAIt3x1aClRZyb4YPT5RnFoRkpkBIIvvY5t+WJQVM4l2yfM/fiq4es0w+RZoag6xGLJ99lPkR6xUkMvVAIRdBNFRmqe2n+Y4Os9aTEH+eulRjP+KK44O3lu/Wl6vEfojemgKMT0GDoJQCFmVQk6NM6FosyxWCEWQ3FqrF+6jDv3nUsLpUw8Z6shp0AcMO9d49S4aN1K+NAtwMgJxN3eXvIqVUoEhQ8cTgtEiW4JgjCVOHToEJ2dnfzDP/wD4XCYo0ePsmHDBq677jqGhobT4oaGhrIE3kxh6Il/oer0q1nbEkd/Xnb9eV7ypM+7k8ZhBVPiK2x6ZpZl4/G34IksBNVoVg4KuObj2b8fFGXUKFchwWd2brPsn89ZXQy815tfeJRoElaRqFy+z37Nt2HPV6dEj1ipoRcqgQi6iSSZmuff3Yd/26vkph+aCZdijD+KGhQVFfdv386ed/+OZ3ssxBIKsWTXiiqrFauq4nI6wOngHGSv4t3yeyMGTI96k1H8jMkEUabzY7mrVBLdEgRhqrJ8+XJ+8pOfAHDy5En+6q/+igceeICenh7+7d/+jXA4TCQS4dixYyxevPgCX+3EY5sfg7N2VKeG4tCS2SJa/gyRsZAnfX7UlPgyTM9Kqpm6+HqoWwT+CKjJ9uclOGimKFZAFcr+KSg8SjAJK7lmLF+0Ld9nf/CxihrRjSdSQy9UAhF0F4BS6qXKNv4wG/w+2MeWmhe4K3A1Tw9eDICVOFYlkeWs1Vpfz5qFC7OagWauXHrOVifFnMGIVb8ynR9llUoQBMGgoaGBz372s9x5550kEgn+8i//Mislc6ZQ1fb/QP+z6d/Tqf8ltoYZC6MuGpaQagj5a6aeW3cTezo7s+dfMBZkP/P/wf88jeetY2DV0mKutb5+zPdnhln2z6jCo0iTsJJrxgpFQPN99qdfN98+HgsBY0Rq6IVKIILuAlBKRKks4498g9/sKwHYMu9NAH4WmEcooVJfpaTFXN60jeTKpee0hufYKO0SynR+lFUqQRBmCk1NTTz11FMFt33qU5/iU5/61ERf2qTC//MenM6rsIbfGnaHXvyxklvDjCeesy7wt6SNVACIDMK5dw1Dk5z5zywbZdtl9Ty8dy+9gQB7u4zjjBB1N30a6oZNS1rr6+no7U07Y5rN3WPp75b7HnvbEhSHHe26Nlx/cCO9f/ddLHPqst9UhElYydk4hSKg+TKCGj8Mvb8ZuX0CFwKKRbKMhEoggm6SU5bxR76+cxc34U7+vmXem3j8LewJzqZDWwqU5jA5KmU4P8oqlSAIwgyjgHGFf9Mu/JufJ7S8leplVxJ+4ycojVdx0b2Tpw7K4/UaGSvBZUDSHfP8CeOPnS9C189H1G7lZqNsbazmW/4zDMV1rKpKbyDAhr17jePlzMmp3/d0dma1OPB4vZBI4J4fTn+WnlN2o8Yu573lEj5wmEQwjN7ny3o9GmbplSVl4xSKgF71efOMoOX3gu/ImHvECsJUQQT0l/J8AAAgAElEQVTdWJgAB6VyjD883l9nrRamWhUQDUHDTYatMMbE4156CZ6ki1cxg31J7RJKRFapBEEQpiemNVPLFxU0E8mO5AAspva3f3dyibnUXFjTjCd6EVj7cdt/BFXV6P0DRkTx5B6GnvgXrCv/KH3/qWyUrY3VfPOSWQxEIiiqUVc/mqgDRvSrA/C8vBMch4b76AWXGc3JKS1Sl2/fcurczdIrFYedRDCctV/BbJxCdfmFMoIq0CNWEKYKIujKpcKuVqNRrNjxeL14TuhwfuHwNn/ytVXDM1gPl6/APXcoy8WrFIpul2DGFLERFgRBECpD3pqpv15V0LhiMtdVm/Vro8qF50wIqpZyz/mDSQMXBYYU9CM/YOhQgrkP/01WNsqb0X6GTnSmPdJi8ThgiLrR6AkEAMPEjOggBAeIW6KGmOtvAtUP2mC63q4YUZd7X5n7lvN9mIrA69rQ+3zFZ+OMVpefLyOoAj1iBWGqIIKuXMpwtRpv0gNx0v1qw7lFADRYIlmOWJ5jA1DXjntB+RG1snrIFBDB/s3Pl+Z4JQiCIEwJ8kZ2RjETmZJ11VajJELR7FhcIZREGCLgmBfB3rAPEvH0Aq3H6+WI9zTz62ZxenAwLeZi8Tgum40HVq/O24x8T2cn73QMf35zhvpZHz3IPcGjPB75EImYDqqCEgsV7YaZK+ZyBaB/0y7O/esmI+roNBxH+x/eyPyN/5z3mGYi0PUHN2bN76N+nxJtE4RRkX8N5VJoIroAjFgtrGkGbTa9iWp6qual0y5S7OnsHLm6WCIlp1nmEcFDT/wL/k27GNi4Mz2BD2zcOaZrEwRBECYHqYf63G2jtbhJCcHaP76Fhoe+ko5qjSuJuGFk8pvHjZ+JuOlu+eY/97VrcC+9BLVKR7XEjEPqVhK6lSrrMWMezKHB6aTR5UpH5ayqyu9dcUXe+dXj9dLR20t90sysNxCgdfZs7tWOkgiGuXvw19yTOISiqmmBWeiaU8c0eybI3B49cZp4IIRlziwcH/0w8UCIeP/AiPdkkk+Ul0wq2vahu42fIuYEIQuJ0JVLmb3WJpKGmovAEhixvRhnrHEhjwi2zdfL6j8nCIIgTH7yRtoeur9gKt1E1FVn1fZtfA6n8zms4bcAo/6cukVGywATAZG3/CCxgvDbXVgC3STiGgndCihGCmaGbX7m+1NO04ORCL93xRVsWbvW9HozBVbme14Jq3hsH+Pe4I8BWJ84hDL3QzyOK/va8hxzT2fnqJ+V6w9uRO/zETn4LsGXXqequZGa228u+B4xOxOEiUEEXbkU0Wut5MaZJqQnihXXFKw9y2dW8sDq1VnbU2JuxPEnQtTlEcFVbTei9fZNyjoJQRAEYWzkfaivUCpduXNtbm1f6MeP42h/A5obh83E/BH4n6eNlgEmmJYfKCrKsttIvPrayIboOYu+uaIuN83Sv2kX9uWLsDf0E3x+O7HapfQEDEORlKALxWIEQyF2n57D7Wevw+oaIDY4i9sGF2BZ1z7iOjPJFIi5zwep96XeW24NnZidCcL4I4KuXEaZiEpunGlCVgrE4afS7pR6n4/ExTdg/eQT+DftQrskiG12AHdjK6xYkbYpzl2Ry7Q5ziymnjBRl0cE+3/eg3/z81m7Tvo6CUEQBKEoCj7Uj9G4YixzbW5tn7PJEF1pMQcQ0/ne/sNQ5+WOQ92mYtFs7rSt+TQkfpWe71RnY17b/Hw16ca97WT26nfR605h6RtA6WkD60fojWsMRiKEYkZaZ73m4L05sG3pJ7hv9aq0aC7FAKWjtzdL1OU+Q0zJmkZBmCGIoBsLBSaicux9M8kaaKODeLoiUNvCPdGD6H0DKIGfM/T4t1De3MrjJxqxzK7FXfso7qY1sOLOdBuCXLHW0dtLTyBAb2A4FTO1yjfu5BHB9oNHsB14V1IyBEEQhJIYy1ybG3GKDdbxWKKO72eIuURMJx6N8OgLuzl3qJsvvNlRnIApMfpoJrzsbUuoXmnHqneg98H3lWU8wVIaOM+gascXCmFVVRpdLmMer5vFZmI4YoO4c64x91kgX81cStStWbgwb9uC1Fyd2VhczMwE4cIigq4IyknnGIvd8oiBNhYytvsXEtei3ON4w2jouX87WxrqeYKl4LcA4D65B/dHb4MFq0zth3OdsXoDAVa3tExcHV2GCDY+yyPpzyr1ucpqnyAIglAMo861ozUuz4g4RXxziQ0MgR1IAEoCFAuJYIT4kNFAu6TFxjFGH+1tS4i/ZoFuQ8w9rixL96pzqQkGi2htABnPFNFB6P4F7vaVkLDn3d9MzKWuJzU/+zftSjcWz4ySyvwtCBcGEXSjUG46R0VTEzJcqlRbFY9HPkRcCRJ1zGKT0pQe4FMpIu7+w3hOayPsh1Mpl/VOZzpCV+900tHbi8frnThRR2VSUgVBEISZTcG5dpR+sWa1fX98yWJsC0LG/JnQUSxgcQxx92AXa0/PzRKLlaiTH+3eQi8ep27p8LZETEexqTRUO8GmZmXbQE6aZCKO53+exvPWMYj4QQ/jeRs4+TKfQyd68b08efaDdH2f4tCKdq8eaxaSIAiVZUYKulIG4XIHrbE4O40wOEn2kHM7DhEfDPJY4DJ6EtUk7FbmMpAe4MEQdXsORukYGplKsberC8hOsZywdMscZDIQBEEQxkrBuXaUfrH5avvc3a/Au0/h6W8iEdO5J3GI9c5D+OquS4vFsSz2lvL8EXpzFYlGlfusx1DOHuZxlg73lY0GuGruXPRk/7oRYm7b3xrlGnEd4lEjYmix4elvIhY9yJ1dPyUYaWQTQRLVDr4wr4nPWYvrWTeZm74Lwkxkxgm6UgfhcgetsTo7ZYm66CDuK+ZA4gYeO3KeeCIKNif9tjiKHmeumrFCV+UCRwMM9Y44ZoPTmS54zhRyJfeTqwAyGQiCIAhjJTcNcNbnbsXetoTv/PsT2FzdfMnsKSejdYAp/R24a7uIR6IkBoO4a06iOOahxTVY3Jo+b6mLkuU8f8x9+G/SaaNf6j+M5Ww1nmMDaWMzPR43r3n7YB/4jgALMfJHMY4TTzYcr7KiH3qdta/XEbmqGVvrpdz23KsMvNdrej3+jc+lDdiGXu8ldHoWwZd+nbWPGKQIwoVjxgm6UgfhUlMnK5mC4V5xDRx+yhiUu43om1LlwrKghbmAEgjQG1BR7HU0WONg1YympitXmhY8p4Rb5t8uhJgDccsSBEEQKkemWNp2WT2PHz+GpVrBMq8Fd21X9s6j9YtNtti5r/4U1AMYxh+1X/qLtBAs18K/rMyUjFo8N7BnYAc9XV3phdlU9k3mXO7xGmLLXduJx9eUcbAE7rqT3BM9Tk/4MhSHjTveOoHVFwWnZno9/o3Pofzq71A+8KE7Nar6BiByGfryT6BdvXTKmJmNd4qsIFxIZqSgK2UQLiV1suJ1YR/sM1oV1CYbnIJR1BwdhCpXdrqkw5klzu441E103gKePNON3udj/dJl6b/ls0guRKVbG0izUUEQBKFSpOaUJ892s1UdMDZqtXiCywCGRV2e1gFZmLXYWbAazzsnwPsd3O0ry2q3U4nMFI/Xm5Vlk3Ktfrajg7t27GDL2rXGou0JHc4vxF3biXvWSTy++ZCI43a9xz3RwwSOaMSCzajOAfRgKFlH12h6PdolQZQPfIYJStDogafN76P6k6vTAndSLcaaGOH4Nz8vdfvCtGbmCLrkP/Dg89sJvXgcjBgXUHgQLiV1suJ1Yf3DbpSpycjjX2i4XlYZee6pxuEwLLZSwvL25YuJXVZP6FA3t/cAt/7+iH2Lwcwtc6xIs1FBEARhLGRGWcIHDrPlIhtb1eEaMNWpgaMOT/QiqLUac9cojcvTi5eZLQcuWoRnz/N4un5p7HTyZe5uqCW0vLWkCFWxmSnFLqDmtiDa29XFXTt2GH3kknV2Hn9S1NWdAmcD7pWfINJfTbCzk6pFUeKBwygODdWpsbWxGssPnuH+tq9lncc2O4Du1NJiDkBxaCNSVyesp20h8hjh2JffK3X7wrRmZgi6jH/gdiWEtd1HfFY7fOSbDGz6ccX+URez+lbSgDc7+7rSoi7RnD5GPmvh1MB1W2rg+lhrQfvmfOSKuUkxYAuCIAgzmtyMmHP/uonQ/BpYenF6H73Xh7W50RA3C9qN/wowcvHSSHP0/Hw7nuNBkr0Mko7Snbj/urQIVTGZKaMtoKZ+37B3b5aY06xWBiMRnu3ooN7pNCJ4Nc1GH9toA+6rLsf927eDomID5t4I4QOH09eweZaFbb/2ojit1Oa4Xg+93mukWWag9/lQM1JXx2PhtyzyGOHYF91GIhgmHgihOjX0Ph+KI3/rBkGYaswMQZfxD1x1aqjORqAbGvorGhkabfWt5AHPJO3DvfQScK1JNw43w1RYLl9U0L45EzMBZ/b3rPOXIRYFQRAEoRxyM2IUh8bnLE6qL7mcTfogeq8PxWm0/CmmVjzf4iWJhPE6Hh3eOaEbos7rxV1Cj7nRMlMKLaDm1r3v6ezk2WRPWc1qJRSLEUu6XWaRcsSsu2LEnJy6Ho/Xy5NeryF+GTnHW9tvIXbyeex1p1AchhiKz2pPp65OqoXfjMymTAa+8yBDPzmP4tBIBI17OPfwJmpuv3mCL1AQxoeZIejy/AMf1emqRAqtvpU14CkqHtedMP9a3HOHjELui6/HPYpQMhOW//H096lecBx3LRAZBD0E7/0UrvgENA2nbWZeZ2t9fXE3PkqvH0EQBEGoJLkLl6pTo/6f/ow/b1uCo0TjLzMTsdT21uoE6OHsP6TcIp1zofuViixkFrqGVA/ZFO72drasXctdO3bws6NHCcViAFhVFc068rEu8zPIffYodN7Ufvarr4S2l9MpqGryWQRFLer9E8ps84yrquWrse71kgiFUZwaSkDDLimXwjRiZgi6PP/AR3W6ykeeaFS+1bdyBzyP14tn/37jl7p23KOki6RICctEMIzztz/Mf/z0BbYrKhZfM3F/D1+0H0JRk5POa9+A2/7LdGDu6O012hycOm7U7Vk1ox9e7gQ5Sq8fQRAEQagkhTJiyjH+yseamhBrMkxF0set78dt+SW89ODwzuOwkNkTCGQ5WmY+N6xZuJC9XV1pQZdKtewJBOgJBGhwOkeIubLTIjOcNic1ZoY2TWtw3vgX1MWfT/8/ozY3Unffpy7QRQpC5ZkZgi7PP/BRna7MmKBoVCkRPTMr3pSL03+EfGydZUUJRlGronxfWQqJCPc530q+uQs+2IfntGYqOjvef4NWTtERrTHO32LDvWJ91j7B57djVzLz0o0C60pHQAVBEAQBRq9HK0Ws5EavMre7G0Pg32z8faAJSOB2vYd75e/Cm//XyHaxaFBVPaaFTLNrSPWaa3A6ky6UGopD49EXdhM9eZqqpsYst+vU61TP2czedKM9U+Rrc1TOdZfy/oqjqMbzWMrQJhlNTLlcZiKtkoTpxMwQdHn+gZclwMqIRpU64JUS0cvXKmHW525le/tlbI2eJxHTScRUErqCYoXvx65BDVtw1xwzJqH+w0DbyAtPtkhYU9vPGke/ce54V9a9+jftIvTicaztvmRe+gCKI2TUKZYbARUEQRCEAlTaKdksJdHd3m4s4jatwc2elDE27isXw7FdcP7E8AFsLnA1jWkhM/caVre00NHbi97nQ+8bIBEIoTg1Er5eYr9+jj9Z+XlYsWI4kyfjONk96Yp4pkgk8PxyD8RChoHKimuKvu5CbZLSTGSdvUk0UVolCdOdmSHooHLpAmXW4+WdLMZIvlYJ9rYlVO1thONDKGqcRBz0kAOlOoBiUUG7yJh8FAVmL0mnc2YN+rGQYXec25Q1417tbUsIvbmKUPf72GefAZJW0eVGQAVBEAThAmCaqpmxIOxOLQgn4vD+z7LfHBmE6NCYFzJzr8Hj9fK9X7xCIhAiHgxhTfRxr3aAL84+CS+9gbtpDay4My3qynq2SMRxD/4AlONQBe5TO+DlXxaVeVRMm6TJUGcvrZKE6c7MEXSVYgz1eMXm9RcV0UuudtnVDqqXJYgcNOyUgXTq5W3PvUrApfCDplkAxEMqCVsV6y2HcM/2GftnCK8RovOqy42BvcC9GumdSxnYdC22urNYXQO4bvoSljWfnnSGKGapqaU2dRUEQRCmL6Zzc+6C8G8eNyJyNpch5FLUtlRkIdPsWeHRF3ajRgZZbznA+sRbKFW1EOyF936K+2OfgOR+Ztc/6jNF9ytwco9hmpaiyPTRvG2SMpE6e0EYd0TQlcoY6/GKXTkrGNHLWO3S+3xU9Q0wq3UeAx3XAgoDG3cy63O3Ylu+GPecOqynutjiBKXayZ2BBH986cXQ1mCaepo1kay4xlilK3Cvw4XpChHfPCK+ecR2nmDujZNPzJmlpsoqnSAIglASqYXdmuZh12iLBtd9fVwWMu841M25Q93YLjrDevtbWLQhGPKDrcrY4bVv4E6am+XD9JlixTWGmDv4mHEfNlf2m/oPG/P9B/ug722Ix0C1wpylWWZwo/XfnSincUGYyUyooPujP/ojamoMc42mpiY+/elPs2HDBiwWCzfccANf/vKXJ/JyyqOS9XijkDei98E+OPEyRIdQLEFUJzhnB7Hf/jv4dp5Ip1ymxMpXgFklWAhn7TPKvU6VvPR8qamCIAiCUBKZC7s2F+CCpjV4TjvgjLci5RSZ2NuW8IU3O6hedjHW8Osw5B92qoa0udlo4mjkgm0yDTIySOLccbC5UC66ZNjc7KKM/rXnTwyLPlcTNN8INz5YnNlIKZlNxdbaSe9bQchiwgRdOGz0cdm8eXN626233sojjzxCc3MzbrebQ4cOsWzZsom6pPIppx6vzMHHdGLoexsGT0JkEBVQHYBNxXLR0MiIU/K8blvyvIl4aYNe5r2a3MNUyUsvahVREARBKJkZl85usrDrOWXPMicp1I6o0N/z7v/wV405+Jnd0H1meIdU6mdGtKvQ95E+bzLNEkD3RyGsoMT9xHtPo/eHiVnm44S04EunlqZqBZMpk0Ut6hab2VRsrd0kqMkThMnGhAm6jo4OgsEgd999N7FYjD/90z8lEonQ0tICwA033MC+ffsuuKAbl4kpOfh43j4OgLv20bENPvFYdt4+QGQQz3t+iHpNUzPTFHNeM/EJU3oALdSzSBAEQSiPaZ/Onm8xNmOx0+gZO3qboVL7wJnuf93X4YV7h9M8U2mSyWhX0d9HRhqk4nSgB2dDYIiwz0ngxDVof3gPnEsugOqh7PfqIcAQkfa2VSMWdf2bdhE+cDj7OaqYzKZia+2kJk8QRjBhgk7TNO655x5uv/12jh8/zr333ktt7XAFbnV1NSdOnChwhPFn3CamD/bhefs4Hv/C9Cb3WAYf1YpH/zDEwrjtBwDw6B/G0xmCsxkTSTmDXj4ReMUnpvQAOlVSQwVBEKYS0zqdvYhF0WLbDGXu1xMIsGHv3qy/m73ftG/cilVw2cfzRruK/j4y0iBVp0Yi6EAPRgicWMKT2mW46qr4k9Q+Fi37vanfTVImCz5HjZbZVGyt3Sj7zbiIsSAwgYLu0ksvZeHChSiKwqWXXkpNTQ0+ny/996GhoSyBdyEodWIqNnXC4/11lphLvXaXWRDs6anFE7ka4rqxOqdW4Ykshhot+7psZRQi5xOBqr30Y00ipkpqqCAIwlRiqqazF/XQX6FIUK6Y6002DM8n6kYViQWiXUV/HxlpkKk+d+H+eTypXcrWRhfqr71ULfi40RYhVSuYqqGrqs5rBjcmgV9srV2B/aZ9xFgQ8jBhuXI/+tGP+OY3vwnAmTNnCAaDOJ1Ourq6SCQS/OIXv2DlypUTdTmmpAbC3G1mpAbcfANv1n4n9JHb/QvxnK0ufEGJuJHn/pvHjZ+JuHG8YwNQ5QLVwoaha9hw/irj96phhyqP14vnrMv8uIVaLORb+cpHCT13UmkYua8FQRCEqUm+dPbJTOqaBzbuzHo9gkKRoCT5+r6Zbc8UcwC9gQB7OjtLv4FUuueH7jZ+ZqQuFv19pOoAP/od4ovuZihyO0+0/iVP/dYVqA47ilPDs38/Hted8NHvwA0bjP1XbYDf/m7ecotSnqMy8W/aRbhnNjStQe/zEQ8k0zzNhGNKjGaS3C9TUKY+h2kTMRaEAkxYhO6Tn/wkf/u3f8tnPvMZFEXhn//5n1FVlfvvvx9d17nhhhtoa2ubqMsxpdg6q7t27GBvVxcNTidQRKQuJbaig9nbZl1u+n6P1wuJhNHoM2N10KPehGfQeA81zcbxhgL0RhNgqaEh97yzLgdLiS0W8q18XfZxiIfLbtcgq2aCIAjTj6mYzl5OWmL29myBUrDNUPL3PZ2dvNORLRDrnU46envxeL3pJuJmx8s8zmgZQSV9H0lhWLVgFU/P87LZ60VBw9rcmN7Fs38/tLfjbl9f8LwpyqlXz3o+aFtDaO8RtFaN2i/9hXmtXQG38akaMRaEsTJhgs5ms/HQQw+N2P7UU09N1CWMSjED4V07dvBsxqA8mqgbHpgxBFgsBFYN97VrcK9caRrh83i9xr7K8exGn74jkJg3HImrctFQ54KMFb/M87rb2yGxorQWC/ncqFK572W2a5jWdRaCIAgzlKmYzl5OWmKaPAuZpm2GMgxV1sxysdfpTEfo6p3O9PMD5DdMyScSC93bWL4Pvc+H6tRQHFr69ahk3Ke20Elo+RVoVy8tWuCPfD6Yh7b4lsJprXncxsUATZipSGPxDEYbCD1eL3u7utK/pwbmzEHZjKyBucqVHphzB/BUPn2D0wmxEJ5gstautmv4Z60Vz+ns4z+wevXw8ckZ+EttsTBan71S2zUkkVUzQRCEScYM7eVV9EN/iX1nswRXjqGKG2DuTWw4Ox8Yfm4wi8aZLRCPtbfdaJlE7vZ2gvsO8HjfAIlACMWpofcNsM4XK3zunPu0AXNvWQM3fhUUtSghVcnng6kYMRaESiCCrkhS4is1CKfEXOrnA6tXFxz0cgdmj9ebJeByc+sbqpIGJ/4cUdfeDqc1Hn1hN6pT44s3rOKOQ93G4Jc8x5ibmhYQgeW6R8mqmSAIwiRiCvbyqpR7YVEP/bli96rPl/a5mBiquOO74UNfMergyZ9ambkt73xeghgvtl3CfatXET1xmk3BIQiGWXd6EPfHri35Pks1jqnk88FUjBgLQiUQQVcGuaJudUtLUSIqc589nZ1ZAm4EZnV3yVSPO3Y/z7lD3VgWzOOOQ93J3PMrcP/1amNw7w6Ny0rrWOrgZNVMEARhEjHFenlVsg571If+ItsVQAHBlcdQxT13COqGF18LmarlpdD1QZbQy214Xui67W1LcB+4Cv2F3QCsOz00umAuttVAAeT5QBDGzowXdMWu+OWupKVE3eqWFrasXVt0C4PUMTp6e6lP5tP3BgLUO53UJ4+ZTuGsacZ9+Szccz80ouj3C292EPEeJnRsH84mH65LO+B/ngZFMd47DiutY6mDk1UzQRCESUQFHsQnkgmtwx5F7BYV8SpgqOJeMDKVsiQDlMzrSyQgOgRHdhhGaL6j0G1k/3j8LXiCywwTtQzyPa+kRPO6jG2jRsqKbTVQAHk+EISxM6MF3agrfjkpDe4VRiF0ajBMpVkWM7ib5cdnRvp6AwE+0drKmoULRy2CtrctQWtbjCOyCfvsMyiWKNZYGAZrwdWE3j+AEngBddE+/Lv7KtZUU+rgBEEQpgkVeBCfSCZ0/ikgdj2ntVHr3YCyDFWKNkBJXV8iAYMnjf5wAK99A0hkC7jooPFf1cg2RrnXXlakrIT7FARh/JjRgq7gil+elAZ3KqUBTMWc2eCeuU9rfX3WNWQaqqxZuLCoImj/pl2Efvw4dUvPAKCoOomYjhIZRO87jd4fQXGECD//Q/xPByrSHsC/aRfRE6cJvvQ6ep8PxamhOjSpgxMEQZiKTLEH8Qmtw84jdj1nq/EcK7LebQyGKqNm+qSuLzo0LOZSRAbTDcBTtfeeaEOWoDOLCrrb28uLlJV4n4IgjA8zXtDlXfErkHLhbjfSUfI1Fc8c3HP36ejtpbW+no7e3vS2Bqczy/ky9d5C1827dixzZqE4NOL9Z1HUKABKFSgOO4lgmIFnDhIPNqWjj+VG6lITqWXBXBSHHYCqK1qwzKlDa1tsND2fYS5pgiAIU5oL/CBeqsHJhNZZ5RO7rsuB/XnfNoISXaaLNjRLXd+RHcPbbC6w10HkPOghwBBw7toumP/pgkYspZSMmFKqm7YgCBVnRgu6git+FagvyCf4ckVdvjYG+QZXe9sS7PV/CS/9OQCqswXOn4DIIKqjhkQsxtAxB4EOC4rDl46sjaWAPBXJBLDMqaP6puuo/ewfGFHMl6aOS5ogCIKQ5AI9iJdjcDKhdVZ5xK5bUUFRymr4PS7XV7cIfvUtsGiGoEskjJ+WjN5xTWtw33g71L2R3lSWq6YgCJOaGS3oCq74FVlfkBttA0ZE28xYs3AhaxYuTO9fTOpmFrkriDXNUHcFwZMNnN97nNCpWhTHAIlgkPB//yfOyyzYly0yUklLFFt5I5lTzCVNEARBuPBMqMFJueQRu+U2/K44igptXwTfkeF5WFGg9U5YtBbOHcmKuhZqkSAIwtRnxgu6vCt+o9QX5NbF5UbbUq9T+2aSOwHki+Q9+sJuoidP8ye3/v7IlJQ8K4jqwSPw7k7qbmkleuIDrO9+G23BORSrFat+Dl4+M3oELccMxv/zHvybn8/aZWDjTuZ+YY75+yepS5ogCIJw4ZnqBluVbPg9JnKfAy5aZGw/927eEoiyXDUFQZj0zGhBlyJvLn+e+oJ8dXGZpia5EbZSV/P0Ph963wDBVw/gH9DNU1JMVhBTItW/aRf6Gz+hZul5Ul+z3udDHS2CZmIG43ReRWh5K9rVS7MjmbPzCLpJ6pImCIIgXHgm1OBknJg04if1HHDx9UU3ii/nuaRSTd0FQRgfZkByR1sAABPVSURBVLygK5jLbyKYCtXFpVIoC9XCmQ2aZitmqi3O5x1n+cw7p/F7jwFKSSkpucYpep8P1ZnMqy8UQUumUep9PhSHhurUUD74BXW33oztxlsAsts6TCGXNEEQBOHCI42kx4EiSyBSYszd3k5w3wGszfOKEnOVauo+HojYFAQRdBXL5df7fERPnsaDl0df2G0IIYeWHalLxPM6QmaJuvMn+KLjEPdYD6IvHSDcP4+BjmtLGqBGGqc0Dv+xUAStvyMdHVQcIRJBDb1vgNCLO7Dd+JnsfaeIXbEM9oIgCJMHaSQ9DhRh5JYrzG577lVsyxfDrb9f8NCTueZxsotNQZgoRNDl5PIbkSnDmt/s4d8smqb3+bjjUDdBX4zv1x1C7xsgEQhhbW4c3jeRwD34g4LpEO72dvAdhcP/xT3Rg+h9hs2wffYZbHVnS09JmX8t1F0Op35puF5VVUPzjYUjaLNbURxaUsyF0YNhACyXrjTff5LbFctgLwiCMEPJqQefjAuOFaMII7dyhZlpzePyRZOiZdFkFpuCMJHMeEGXmctvRKZ8nHt4E4lgOO/Df66oW790Gbf3wJPd3ei60ftFcWpZ78F3FLp+avSHSYkrk3QI99xBONVFPKCB4kd1OVBn1WD9YIjEnLribywRhz1fBd8x43x6COZeDWu+XXjQvfh61MUfI3FgV1rMhfvnYVt7S/HnnkTIYC8IgjADMakHn9ZtdYpoFF+uGc3ImscE+k43XB7MPtcF+GynusGOIFSKGS/oMnP5FYfdEHOhcHrwyvfwn1sX5x/QWbfJGFC2NrqG69UA94oVuI9/zegVl8LmAlfTyHq25CpbIhiCRIJETCcRDBHzVUOfr/gby8ynt7kAlyEqT/2ycDRNUfF3riG09whW1wCxwVlEfHOxbfoxc6++svjzTxJksBcEQZiBTIa2OhMZISyiBKJcM5rcmsfAtu9grzsFZCwyX6CWRdPBYEcQKoEIupxc/lRkLvPv+UiJuswBZd3pIQC2OexYmxsN96jGEBzsyn5zZBCiQyPr2ZKrbErghXTaY6C7johvLrV/uLj4FIcxNEa3X30loQOrsV7dSvUUL1iXwV4QBGEGMoY5sCJciAjhKCUQ5ZrR5D4n2dV2OPD6yB0vQMsiMdgRBIMZL+gyqdTq1Rc27kSbV4+jvc0Qfb953IiS2VyGkEtR2zKyni25yqYu2kf4+R9y/sX3ifjmAuB0PgcvvTW8b6HJocjG6PnuZ7oUrMtgLwiCMAMZwxxYEXIihHqfDyXwAuqiffh39+U15xpPE6+Kze1FfrYTYUg2nZ5XBGEsiKDLIOvhf/kiAtsfQbv0hBEVKxANMxtQ/jxzh9TgV9NsCLpUHd11Xzc/pqLi392H/+kAMA8AW90ZEkffgOYMt8pCKQ5F5NPPBGSwFwRBmIFc6DkwI0KY6Rwdfv6H+J8OmNbnTxkTryI+2ylzL4IwTZjRgs5s9Wjuw19Np0rYr0qmFLy0zzwaVmx+fObgl6pna1pTMDUhN7IUfvqBrLq8NPlSHKZISwFBEARBqDgXeg7MiGJlOkeff/F9YJ5ppsiUMfEq4rOdMvciCNOEaS/o8oX8C64eFVNMXUp+fBkTy4ic9YyeclkUSh+Z5C0FBEEQBGHcuJBzYMZCrurUSAS1dD08mNfnTykTryLq9abMvQjCNGBah2tSom1g486s15C9ejTC0bJQMXWKQqLPjNTg96G7jZ+lrhKmJodMZmAKpSAIgiBMelILuR/9DsGhj9C390oGOq4FFID0s0gm+er4pyLT6V4EYSowrSN0hUL+BVePiin4TYo+oxG5hurU0Pt8xA+8TNV4rAZe6PQRQRAEoWIcOHCABx98kM2bN/POO+/wjW98A4vFgs1m41vf+hb19fU89dRTbN++HavVype+9CU++tGPXujLnnGMydgjuZCrXlcPh3dSe0thc67pZOI1ne5FEKYC017Q5RNtBR0tiymmnt2aVeicCGrofQMMnTrJRR8fpxuSFEpBEIQpz2OPPcauXbtwOBwAbNiwga9//etceeWVbN++nccee4z169ezefNmnnnmGcLhMHfeeSerVq3CZrNd4KufOVTK2KNYc67pZOI1ne5FEKYC0zq8Uyjkn1o9qv3jW2h46CvpARvISpWg7f8YP3Nr4y6+nsTFN6A47CSCYfS+AcL987Bc+bGJuj1BEARhCtLS0sIjjzyS/v3hhx/myiuvBEDXdex2OwcPHuSaa67BZrNRU1NDS0sLHR15ygGEcaFgaYYgCMIkYtpH6PKF/EddPRotGqaoWD/5BENP/AvhvT8hNjiLiG8uDfdeOV63IwiCIEwDbr75Zk6ePJn+fe5cwyhj//79bNmyha1bt7J3715qamrS+1RXVzM4ODjiWML4IcYegiBMFaa9oBvPkL9/8/P4tx35/9u7+5gq6/+P48/DERE5OIdk6kTFlI10qEybFemmY5rjpsy4UWGFrXAKoStvKNTGTZqpbSoak39Swoxq9oeztpZzJpjTbkSkWZJteIs3I1BEz/n8/nBSIGXfX3EuLs7r8ZcX5+BefHif633eXNd1LiCi7Wv/5EbkIiIif7Zv3z62bdtGSUkJISEhuFwumpub2x5vbm5uN+BJ1/vbSzNERLqRHj3QdTVd9CsiIv/W3r17+eijj9i5cyf9+/cHICoqivfee49bt27R2trKL7/8QkRExAP+J/kvqceLiF1ooPsXdNGviIj8G263m8LCQgYPHkxWVhYAkyZNIjs7m7S0NObOnYsxhiVLlhAQEGBxWt+iHi8idqGBTkRExMuGDh3Knj17APj22287fU5SUhJJSUnejCUiIjbUoz/lUkREREREpCfTQCciIiIiImJTGuhERERERERsSgOdiIiIiIiITWmgExERERERsSkNdCIiIiIiIjbltdsW3L59m9zcXOrr62ltbWXhwoUMGjSIzMxMRowYAUBqaiqzZs3yViQRERERERFb89pA9/nnn9O/f3/Wr1/PtWvXePbZZ1m0aBEvvvgiGRkZ3oohIiIiIiLSY3htoJs5cyYzZsxo23Y6nVRXV1NXV8dXX33F8OHDyc3NxeVyeSuSiIiIiIiIrXntGrqgoCBcLhdNTU1kZ2eTk5NDVFQUy5Yto6ysjLCwMLZu3eqtOCIiIiIiIrbn1Q9FOX/+POnp6SQmJhIfH09sbCxjx44FIDY2lpqaGm/GERERERERsTWvnXLZ0NBARkYGq1at4vHHHwdgwYIF5OXlERUVRWVlJWPGjGn3PW63G4ALFy54K6aIiFjk3r7+3r5f/p56pIiIb3hQf/TaQLd9+3YaGxspLi6muLgYgBUrVlBUVIS/vz+hoaHk5+e3+57Lly8DMG/ePG/FFBERi12+fJnhw4dbHaPbU48UEfEtf9UfHcYYY0Gef6SlpYXq6moeeughnE6n1XFERKQLud1uLl++zNixY+nTp4/Vcbo99UgREd/woP7YrQc6ERERERER+Wte/VAUERERERER+e947Rq6f8vj8bBmzRp++uknevfuTUFBgU9eY/HMM88QHBwMwNChQ0lOTqawsBCn00lMTAyLFy+2OKF3/PDDD7z77rvs3LmTs2fPsmLFChwOB6NHj2b16tX4+fmxZcsWDhw4QK9evcjNzSUqKsrq2F3iz2tx8uRJMjMzGTFiBACpqanMmjXLJ9bi9u3b5ObmUl9fT2trKwsXLmTUqFE+WxudrcegQYN8tj56qk8//ZTPPvsMgFu3bnHq1Ck2bNjAO++8w+DBgwHIysriscceszLmfey8D/9z9lOnTpGfn4/T6aR3796sW7eO0NBQCgoKOH78OEFBQQAUFxe39W4r2blf/Dn7kiVLaGhoAKC+vp5x48axadMmMjMzuX79Ov7+/gQEBLBjxw6LU9u7N3WWfciQIbaoeZ/rgcYmvvjiC7N8+XJjjDHfffedyczMtDiR97W0tJjExMR2X0tISDBnz541Ho/HvPTSS6a6utqidN5TUlJi4uLizPPPP2+MMeaVV14xVVVVxhhj8vLyzJdffmmqq6tNWlqa8Xg8pr6+3syePdvKyF2m41rs2bPHlJaWtnuOr6xFRUWFKSgoMMYYc/XqVTN16lSfro3O1sOX68MXrFmzxuzevdts3LjR7N+/3+o4f8nO+/CO2efNm2dqamqMMcaUl5eboqIiY4wxKSkp5sqVK5bl7Iyd+0XH7Pdcv37dJCQkmIsXLxpjjHn66aeNx+OxIuJfsnNv6iy7XWre13qgbU65PHbsGE899RQA48ePp7q62uJE3ldbW8vNmzfJyMggPT2do0eP0trayrBhw3A4HMTExFBZWWl1zC43bNgwNm/e3LZ98uTJtr8+T5kyhcOHD3Ps2DFiYmJwOBwMGTIEt9vN1atXrYrcZTquRXV1NQcOHGDevHnk5ubS1NTkM2sxc+ZMXn311bZtp9Pp07XR2Xr4cn30dCdOnODnn38mOTmZkydP8sknnzB37lzWrl3LnTt3rI7Xjp334R2zb9y4kcjISODuhxYEBATg8Xg4e/Ysq1atIiUlhYqKCqvitmPnftEx+z2bN29m/vz5DBw4kIaGBhobG8nMzCQ1NZWvv/7agqT3s3Nv6iy7XWre13qgbQa6pqYmXC5X27bT6ex2Taqr9enThwULFlBaWspbb73FypUrCQwMbHs8KCiI33//3cKE3jFjxgx69frjbGFjDA6HA/hjDTrWS09dm45rERUVxbJlyygrKyMsLIytW7f6zFoEBQXhcrloamoiOzubnJwcn66NztbDl+ujp3v//fdZtGgRAE8++SR5eXmUlZVx48YNdu/ebXG69uy8D++YfeDAgQAcP36cXbt28cILL3Djxg3mz5/P+vXr2bFjBx9++CG1tbVWRW5j537RMTvAlStXqKysZPbs2cDdU+wyMjLYunUrW7Zs4e233+bKlStWxG3Hzr2ps+x2qXlf64G2GehcLhfNzc1t2x6P574Xd08XHh5OQkICDoeD8PBwgoODuX79etvjzc3N9OvXz8KE1vDz+6OM761Bx3ppbm7uFtcvdLXY2FjGjh3b9u+amhqfWovz58+Tnp5OYmIi8fHxPl8bHdfD1+ujp2psbOTMmTNMnjwZgOeee46wsDAcDgfTp0+npqbG4oR/z+6v03379rF69WpKSkoICQkhMDCQ9PR0AgMDcblcTJ482fI3t52x+/5g//79xMXFtd2yIzQ0lJSUFHr16sWAAQOIjIykrq7O4pR32bk3dcwO9ql5X+qBthnooqOjOXjwIADff/89ERERFifyvoqKCtauXQvAxYsXuXnzJn379uW3337DGMOhQ4eYOHGixSm979FHH+XIkSMAHDx4kIkTJxIdHc2hQ4fweDycO3cOj8dDSEiIxUm73oIFC/jxxx8BqKysZMyYMT6zFg0NDWRkZPD6668zZ84cwLdro7P18OX66MmOHj3KE088Adw92pWQkMCFCxeAP37P3ZmdX6d79+5l165d7Ny5k7CwMAB+/fVX5s6di9vt5vbt2xw/frxb/g7svj+orKxkypQpbduHDx8mJycHuPum/PTp04wcOdKqeG3s3Js6y26Xmve1HmibQ1yxsbF88803pKSkYIyhqKjI6kheN2fOHFauXElqaioOh4OioiL8/Px47bXXcLvdxMTEMG7cOKtjet3y5cvJy8tj48aNjBw5khkzZuB0Opk4cSLJycl4PB5WrVpldUyvWLNmDfn5+fj7+xMaGkp+fj4ul8sn1mL79u00NjZSXFxMcXExAG+88QYFBQU+WRudrceKFSsoKiryyfroyerq6hg6dCgADoeDgoICFi9eTJ8+fXjkkUdISkqyOOHfs+s+3O12U1hYyODBg8nKygJg0qRJZGdnEx8fT1JSEv7+/iQmJjJ69GiL097P7v2irq6ubaAAmDp1KocOHSIpKQk/Pz+WLl3aLd6Y27k3dczudrs5ffo0Q4YM6fY172s9UDcWFxERERERsSnbnHIpIiIiIiIi7WmgExERERERsSkNdCIiIiIiIjalgU5ERERERMSmNNCJiIiIiIjYlG1uWyDSUxw5coScnBxGjRqFMYY7d+6Qnp7OrFmzOn3+uXPnqK2tZdq0aV5OKiIi4j3qjyL/PxroRCwwefJkNm3aBNy9AWpaWhrh4eFERkbe99yqqirOnDmjhiUiIj2e+qPI/04DnYjFgoKCSE5OZt++fezatYsLFy5w7do1pkyZQlZWFiUlJbS0tDBhwgSCg4PZsmULAC0tLaxbt47w8HCLfwIREZH/nvqjyD+ja+hEuoEBAwZQU1PD+PHjKS0tpby8nPLycpxOJy+//DJxcXFMnz6d06dPs379ej744AOmTZvG/v37rY4uIiLSZdQfRR5MR+hEuoFz584xYcIETpw4QVVVFS6Xi9bW1vue9/DDD1NYWEjfvn25ePEi0dHRFqQVERHxDvVHkQfTEToRizU1NfHxxx8THBxMcHAwGzZsICMjg5aWFowx+Pn54fF4AHjzzTcpKipi7dq1DBw4EGOMxelFRES6hvqjyD+jI3QiFqiqqiItLQ0/Pz/cbjdZWVmEh4ezdOlSjh07RmBgIMOHD+fSpUtERESwbds2xowZQ2JiIklJSfTr14/Q0FAuXbpk9Y8iIiLyn1F/FPnfOYz+hCEiIiIiImJLOuVSRERERETEpjTQiYiIiIiI2JQGOhEREREREZvSQCciIiIiImJTGuhERERERERsSgOdiIiIiIiITWmgExERERERsSkNdCIiIiIiIjb1f8c50yM2KNoKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('seaborn-white')\n",
    "\n",
    "fig, ax = plt.subplots(1, 2)\n",
    "fig.set_size_inches([15, 7])\n",
    "\n",
    "# the R2 values in plots may vary due to random seed\n",
    "ax[0].plot(cc_vox[~gtab.b0s_mask], dti_cc[~gtab.b0s_mask], \n",
    "           'X', color='crimson', label='Original: $R^{2}=0.797$', alpha=0.8, markeredgewidth=0.0, fillstyle='full')\n",
    "ax[0].plot(cc_vox_mp[~gtab.b0s_mask], dti_cc_MP[~gtab.b0s_mask], \n",
    "           'o', color='darkorange', label='Marchencko Pastur: $R^{2}=0.809$', alpha=0.8, markeredgewidth=0.0, fillstyle='full')\n",
    "ax[0].plot(cc_vox_p2s[~gtab.b0s_mask], dti_cc_P2S[~gtab.b0s_mask], \n",
    "           'D', color='teal', label='Patch2Self: $R^{2}=0.810$', alpha=0.8, markeredgewidth=0.0, fillstyle='full')\n",
    "\n",
    "ax[1].plot(cso_vox[~gtab.b0s_mask], dti_cso[~gtab.b0s_mask], \n",
    "           'X', color='crimson', label='Original: $R^{2}=0.469$', alpha=0.8, markeredgewidth=0.0, fillstyle='full')\n",
    "ax[1].plot(cso_vox_mp[~gtab.b0s_mask], dti_cso_MP[~gtab.b0s_mask], \n",
    "           'o', color='darkorange', label='Marchencko Pastur: $R^{2}=0.508$', alpha=0.8, markeredgewidth=0.0, fillstyle='full')\n",
    "ax[1].plot(cso_vox_p2s[~gtab.b0s_mask], dti_cso_P2S[~gtab.b0s_mask], \n",
    "           'D', color='teal', label='Patch2Self: $R^{2}=0.529$', alpha=0.8, markeredgewidth=0.0, fillstyle='full')\n",
    "\n",
    "for this_ax in ax:\n",
    "    this_ax.set_xlabel('Data')\n",
    "    this_ax.set_ylabel('Model prediction')\n",
    "    this_ax.legend(loc='upper left', fontsize='large')\n",
    "fig.savefig(\"model_predictions_DTI.png.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute $\\mathbf{R}^2$ for each voxel  with the DTI model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corpus callosum\n",
      "DTI Raw R2 : 0.799386613530265\n",
      "DTI MP-PCA R2 : 0.8023934243796694\n",
      "DTI P2S R2 : 0.8112701578510251\n",
      "\n",
      "Centrum Semiovale\n",
      "DTI Raw R2 : 0.5038433583150319\n",
      "DTI MP-PCA R2 : 0.501516492686054\n",
      "DTI P2S R2 : 0.5279911876857333\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cc_dti_r2 = stats.pearsonr(cc_vox[~gtab.b0s_mask], \n",
    "                           dti_cc[~gtab.b0s_mask])[0]**2\n",
    "cc_dti_r2_mp = stats.pearsonr(cc_vox[~gtab.b0s_mask], \n",
    "                              dti_cc_MP[~gtab.b0s_mask])[0]**2\n",
    "cc_dti_r2_p2s = stats.pearsonr(cc_vox[~gtab.b0s_mask], \n",
    "                               dti_cc_P2S[~gtab.b0s_mask])[0]**2\n",
    "\n",
    "cso_dti_r2 = stats.pearsonr(cso_vox[~gtab.b0s_mask], \n",
    "                            dti_cso[~gtab.b0s_mask])[0]**2\n",
    "cso_dti_r2_mp = stats.pearsonr(cso_vox[~gtab.b0s_mask], \n",
    "                               dti_cso_MP[~gtab.b0s_mask])[0]**2\n",
    "cso_dti_r2_p2s = stats.pearsonr(cso_vox[~gtab.b0s_mask], \n",
    "                                dti_cso_P2S[~gtab.b0s_mask])[0]**2\n",
    "\n",
    "print(\"Corpus callosum\\n\"\n",
    "      \"DTI Raw R2 : %s\\n\"\n",
    "      \"DTI MP-PCA R2 : %s\\n\"\n",
    "      \"DTI P2S R2 : %s\\n\"\n",
    "      \"\\n\"\n",
    "      \"Centrum Semiovale\\n\"\n",
    "      \"DTI Raw R2 : %s\\n\"\n",
    "      \"DTI MP-PCA R2 : %s\\n\"\n",
    "      \"DTI P2S R2 : %s\\n\" % (cc_dti_r2, \n",
    "                             cc_dti_r2_mp, \n",
    "                             cc_dti_r2_p2s, \n",
    "                             cso_dti_r2, \n",
    "                             cso_dti_r2_mp, \n",
    "                             cso_dti_r2_p2s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.style.use('seaborn-white')\n",
    "\n",
    "fig, ax = plt.subplots(1, 2)\n",
    "fig.set_size_inches([15, 7])\n",
    "ax[0].plot(cc_vox[~gtab.b0s_mask], csd_cc[~gtab.b0s_mask], \n",
    "           'X', color='crimson', label='Original: $R^{2}=0.794$', alpha=0.8)\n",
    "ax[0].plot(cc_vox_mp[~gtab.b0s_mask], csd_cc_MP[~gtab.b0s_mask], \n",
    "           'o', color='darkorange', label='Marchencko Pastur: $R^{2}=0.869$', alpha=0.8)\n",
    "ax[0].plot(cc_vox_p2s[~gtab.b0s_mask], csd_cc_P2S[~gtab.b0s_mask], \n",
    "           'D', color='teal', label='Patch2Self: $R^{2}=0.978$', alpha=0.8)\n",
    "\n",
    "ax[1].plot(cso_vox[~gtab.b0s_mask], csd_cso[~gtab.b0s_mask], \n",
    "           'X', color='crimson', label='Original: $R^{2}=0.591$', alpha=0.8)\n",
    "ax[1].plot(cso_vox_mp[~gtab.b0s_mask], csd_cso_MP[~gtab.b0s_mask], \n",
    "           'o', color='darkorange', label='Marchencko Pastur: $R^{2}=0.715$', alpha=0.8)\n",
    "ax[1].plot(cso_vox_p2s[~gtab.b0s_mask], csd_cso_P2S[~gtab.b0s_mask], \n",
    "           'D', color='teal', label='Patch2Self: $R^{2}=0.958$', alpha=0.8)\n",
    "\n",
    "for this_ax in ax:\n",
    "    this_ax.set_xlabel('Data')\n",
    "    this_ax.set_ylabel('Model prediction')\n",
    "    this_ax.legend(loc='upper left', fontsize='large')\n",
    "fig.savefig(\"model_predictions.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute $\\mathbf{R}^2$ for each voxel  with the CSD model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corpus callosum\n",
      "Original R-Squared: 0.7836926295772647\n",
      "Marchenko-Pastur R-Squared: 0.8597981631711089\n",
      "Patch2Self R-Squared: 0.9766926169507287\n",
      "\n",
      "Centrum Semiovale\n",
      "Original R-Squared: 0.6214990361088389\n",
      "Marchenko-Pastur R-Squared: 0.7288195635860747\n",
      "Patch2Self R-Squared: 0.9560768859316392\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cc_csd_r2 = stats.pearsonr(cc_vox[~gtab.b0s_mask], \n",
    "                           csd_cc[~gtab.b0s_mask])[0]**2\n",
    "cc_csd_r2_mp = stats.pearsonr(cc_vox_mp[~gtab.b0s_mask], \n",
    "                              csd_cc_MP[~gtab.b0s_mask])[0]**2\n",
    "cc_csd_r2_p2s = stats.pearsonr(cc_vox_p2s[~gtab.b0s_mask], \n",
    "                               csd_cc_P2S[~gtab.b0s_mask])[0]**2\n",
    "\n",
    "cso_csd_r2 = stats.pearsonr(cso_vox[~gtab.b0s_mask], \n",
    "                            csd_cso[~gtab.b0s_mask])[0]**2\n",
    "cso_csd_r2_mp = stats.pearsonr(cso_vox_mp[~gtab.b0s_mask], \n",
    "                               csd_cso_MP[~gtab.b0s_mask])[0]**2\n",
    "cso_csd_r2_p2s = stats.pearsonr(cso_vox_p2s[~gtab.b0s_mask], \n",
    "                                csd_cso_P2S[~gtab.b0s_mask])[0]**2\n",
    "\n",
    "print(\"Corpus callosum\\n\"\n",
    "      \"Original R-Squared: %s\\n\"\n",
    "      \"Marchenko-Pastur R-Squared: %s\\n\"\n",
    "      \"Patch2Self R-Squared: %s\\n\"\n",
    "      \"\\n\"\n",
    "      \"Centrum Semiovale\\n\"\n",
    "      \"Original R-Squared: %s\\n\"\n",
    "      \"Marchenko-Pastur R-Squared: %s\\n\"\n",
    "      \"Patch2Self R-Squared: %s\\n\" % (cc_csd_r2, \n",
    "                             cc_csd_r2_mp,\n",
    "                             cc_csd_r2_p2s, \n",
    "                             cso_csd_r2, \n",
    "                             cso_csd_r2_mp, \n",
    "                             cso_csd_r2_p2s))"
   ]
  }
 ],
 "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": 2
}