Learning moment weights with contrastive loss - LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "670643e5-35f5-40e3-9256-1df4b21cfcad",
   "metadata": {},
   "source": [
    "## "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b509b759-7453-4b09-b02c-24e9196c68fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import numpy as np\n",
    "import torch\n",
    "from time import time\n",
    "from MomentLearn import utils\n",
    "from MomentLearn import model as model_utils\n",
    "from MomentLearn.model import LinearMoment, ContrastiveLearn, LSTMMoment, sample_double_proteins_with_sim_dist, loss_func\n",
    "import prody as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from caretta.score_functions import get_caretta_score\n",
    "from caretta.multiple_alignment import tm_score\n",
    "from caretta import superposition_functions, helper, multiple_alignment\n",
    "import umap\n",
    "from caretta import dynamic_time_warping as dtw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d835382b-a747-48e6-8cfb-56c5a90a703d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6549c8cb-eef0-4153-acba-f8c758093b0a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_names, mapk_pdb_id_to_class = utils.get_example_metadata()\n",
    "class_mapping = {\"JNK\": 0, \"Erk\": 1, \"p38\": 2}\n",
    "mapping = [class_mapping[mapk_pdb_id_to_class[k]] for k in X_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc55b8b8-ac20-481f-b4a3-4aba9c0457c4",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "text": [
      "50 proteins fetched in 1.52 seconds\n"
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      "@> 2913 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1a9u.pdb.gz).\n",
      "@> 2950 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (5etf.pdb.gz).\n",
      "@> 2947 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gcs.pdb.gz).\n",
      "@> 2881 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3nww.pdb.gz).\n",
      "@> 2984 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2no3.pdb.gz).\n",
      "@> 2894 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3v6s.pdb.gz).\n",
      "@> 2849 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (3ds6.pdb.gz).\n",
      "@> 2822 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (3nnw.pdb.gz).\n",
      "@> 3426 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4h3b.pdb.gz).\n",
      "@> 3032 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4h3b.pdb.gz).\n",
      "@> 3042 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (2lgc.pdb.gz).\n",
      "@> 3242 atoms and 10 coordinate set(s) were parsed in 0.06s.\n",
      "@> PDB file is found in working directory (3v6r.pdb.gz).\n",
      "@> 2957 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4yr8.pdb.gz).\n",
      "@> 2632 atoms and 1 coordinate set(s) were parsed in 0.05s.\n",
      "@> PDB file is found in working directory (4yr8.pdb.gz).\n",
      "@> 2457 atoms and 1 coordinate set(s) were parsed in 0.05s.\n",
      "@> PDB file is found in working directory (4yr8.pdb.gz).\n",
      "@> 2653 atoms and 1 coordinate set(s) were parsed in 0.05s.\n",
      "@> PDB file is found in working directory (4yr8.pdb.gz).\n",
      "@> 2620 atoms and 1 coordinate set(s) were parsed in 0.05s.\n",
      "@> PDB file is found in working directory (2r9s.pdb.gz).\n",
      "@> 2870 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2g01.pdb.gz).\n",
      "@> 2886 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4whz.pdb.gz).\n",
      "@> 3181 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3uvq.pdb.gz).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150 proteins fetched in 4.71 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "@> 2779 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4iz5.pdb.gz).\n",
      "@> 2875 atoms and 1 coordinate set(s) were parsed in 0.05s.\n",
      "@> PDB file is found in working directory (4iz5.pdb.gz).\n",
      "@> 2883 atoms and 1 coordinate set(s) were parsed in 0.05s.\n",
      "@> PDB file is found in working directory (4kin.pdb.gz).\n",
      "@> 2886 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (3zya.pdb.gz).\n",
      "@> 3129 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (3hv6.pdb.gz).\n",
      "@> 2915 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (5buj.pdb.gz).\n",
      "@> 3095 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gcq.pdb.gz).\n",
      "@> 2947 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2yix.pdb.gz).\n",
      "@> 3133 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1zzl.pdb.gz).\n",
      "@> 2981 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1kv1.pdb.gz).\n",
      "@> 2759 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4ux9.pdb.gz).\n",
      "@> 2914 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4ux9.pdb.gz).\n",
      "@> 2757 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4ux9.pdb.gz).\n",
      "@> 2804 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4ux9.pdb.gz).\n",
      "@> 2900 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4fmq.pdb.gz).\n",
      "@> 3075 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (2bal.pdb.gz).\n",
      "@> 2843 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1zyj.pdb.gz).\n",
      "@> 2865 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4iz7.pdb.gz).\n",
      "@> 3038 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1ove.pdb.gz).\n",
      "@> 3134 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3ocg.pdb.gz).\n",
      "@> 2875 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4kiq.pdb.gz).\n",
      "@> 2825 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4h36.pdb.gz).\n",
      "@> 2788 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3lhj.pdb.gz).\n",
      "@> 2783 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gcv.pdb.gz).\n",
      "@> 2943 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4zsj.pdb.gz).\n",
      "@> 3045 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4qp8.pdb.gz).\n",
      "@> 2838 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qp8.pdb.gz).\n",
      "@> 2831 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (3uvr.pdb.gz).\n",
      "@> 2854 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (6g9h.pdb.gz).\n",
      "@> 3342 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (5ml5.pdb.gz).\n",
      "@> 2821 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (5lcj.pdb.gz).\n",
      "@> 3447 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (5lck.pdb.gz).\n",
      "@> 3395 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (3cgo.pdb.gz).\n",
      "@> 2784 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3cgf.pdb.gz).\n",
      "@> 2831 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (5eko.pdb.gz).\n",
      "@> 3013 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (5ekn.pdb.gz).\n",
      "@> 2787 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (5k4i.pdb.gz).\n",
      "@> 3032 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4zxt.pdb.gz).\n",
      "@> 6019 atoms and 1 coordinate set(s) were parsed in 0.04s.\n",
      "@> PDB file is found in working directory (5byz.pdb.gz).\n",
      "@> 3210 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4zsg.pdb.gz).\n",
      "@> 3181 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4zsl.pdb.gz).\n",
      "@> 3068 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (5byy.pdb.gz).\n",
      "@> 2782 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (6g91.pdb.gz).\n",
      "@> 3298 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (5ax3.pdb.gz).\n",
      "@> 2725 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (5eti.pdb.gz).\n",
      "@> 2918 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (5etc.pdb.gz).\n",
      "@> 3062 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4x21.pdb.gz).\n",
      "@> 2834 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4x21.pdb.gz).\n",
      "@> 2761 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qp4.pdb.gz).\n",
      "@> 2919 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4xj0.pdb.gz).\n",
      "@> 2940 atoms and 1 coordinate set(s) were parsed in 0.03s.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 proteins fetched in 6.18 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "@> PDB file is found in working directory (3bv2.pdb.gz).\n",
      "@> 2862 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3qud.pdb.gz).\n",
      "@> 2937 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2i0h.pdb.gz).\n",
      "@> 3311 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4e5a.pdb.gz).\n",
      "@> 2892 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2xrw.pdb.gz).\n",
      "@> 3254 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2zb0.pdb.gz).\n",
      "@> 3131 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2gmx.pdb.gz).\n",
      "@> 2904 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4kiq.pdb.gz).\n",
      "@> 2861 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2waj.pdb.gz).\n",
      "@> 3010 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3tti.pdb.gz).\n",
      "@> 3032 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4o6e.pdb.gz).\n",
      "@> 2903 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4qta.pdb.gz).\n",
      "@> 3210 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4iza.pdb.gz).\n",
      "@> 2838 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4iza.pdb.gz).\n",
      "@> 3208 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4qp2.pdb.gz).\n",
      "@> 2834 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qp2.pdb.gz).\n",
      "@> 2944 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qp4.pdb.gz).\n",
      "@> 2910 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qpa.pdb.gz).\n",
      "@> 2858 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qpa.pdb.gz).\n",
      "@> 2736 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qp1.pdb.gz).\n",
      "@> 2928 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qp1.pdb.gz).\n",
      "@> 2922 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qp6.pdb.gz).\n",
      "@> 2751 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qp6.pdb.gz).\n",
      "@> 2805 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qp7.pdb.gz).\n",
      "@> 2832 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qp7.pdb.gz).\n",
      "@> 2832 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qp3.pdb.gz).\n",
      "@> 2906 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qp3.pdb.gz).\n",
      "@> 2797 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4qp9.pdb.gz).\n",
      "@> 2869 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (5bve.pdb.gz).\n",
      "@> 3016 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4xj0.pdb.gz).\n",
      "@> 2848 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (5bvf.pdb.gz).\n",
      "@> 2943 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (5bvd.pdb.gz).\n",
      "@> 3044 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1oz1.pdb.gz).\n",
      "@> 2999 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3hv5.pdb.gz).\n",
      "@> 2954 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1wbs.pdb.gz).\n",
      "@> 3268 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3hv5.pdb.gz).\n",
      "@> 2936 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3l8x.pdb.gz).\n",
      "@> 2652 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fkl.pdb.gz).\n",
      "@> 2968 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3mvl.pdb.gz).\n",
      "@> 2829 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2zaz.pdb.gz).\n",
      "@> 3189 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2zb1.pdb.gz).\n",
      "@> 3077 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4kin.pdb.gz).\n",
      "@> 3058 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (2no3.pdb.gz).\n",
      "@> 2903 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3lfe.pdb.gz).\n",
      "@> 2817 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fmj.pdb.gz).\n",
      "@> 3038 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2p33.pdb.gz).\n",
      "@> 2775 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3v6s.pdb.gz).\n",
      "@> 2913 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (2zdt.pdb.gz).\n",
      "@> 2974 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2zdu.pdb.gz).\n",
      "@> 2774 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fi2.pdb.gz).\n",
      "@> 2868 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gc9.pdb.gz).\n",
      "@> 2900 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3i60.pdb.gz).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250 proteins fetched in 7.65 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "@> 2910 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4fv1.pdb.gz).\n",
      "@> 3063 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4g6n.pdb.gz).\n",
      "@> 2978 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (6gdm.pdb.gz).\n",
      "@> 3311 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (3e7o.pdb.gz).\n",
      "@> 2916 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (6g93.pdb.gz).\n",
      "@> 3268 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4ic7.pdb.gz).\n",
      "@> 2842 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (6ge0.pdb.gz).\n",
      "@> 3319 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (6g97.pdb.gz).\n",
      "@> 3355 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (6g8x.pdb.gz).\n",
      "@> 3249 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2fsl.pdb.gz).\n",
      "@> 2897 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3ody.pdb.gz).\n",
      "@> 2636 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3rin.pdb.gz).\n",
      "@> 2939 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3mpt.pdb.gz).\n",
      "@> 3158 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3o8u.pdb.gz).\n",
      "@> 2926 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3heg.pdb.gz).\n",
      "@> 2828 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gc7.pdb.gz).\n",
      "@> 3296 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3hv7.pdb.gz).\n",
      "@> 2817 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4kin.pdb.gz).\n",
      "@> 2989 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3ha8.pdb.gz).\n",
      "@> 2823 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2gfs.pdb.gz).\n",
      "@> 3100 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3hv3.pdb.gz).\n",
      "@> 2881 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gi3.pdb.gz).\n",
      "@> 2892 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1wbn.pdb.gz).\n",
      "@> 3414 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3d7z.pdb.gz).\n",
      "@> 3220 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1kv2.pdb.gz).\n",
      "@> 2656 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4e5b.pdb.gz).\n",
      "@> 2704 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3ctq.pdb.gz).\n",
      "@> 2890 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3oef.pdb.gz).\n",
      "@> 3067 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2rg5.pdb.gz).\n",
      "@> 2768 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2onl.pdb.gz).\n",
      "@> 2709 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3obg.pdb.gz).\n",
      "@> 2727 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2fst.pdb.gz).\n",
      "@> 3116 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4aa0.pdb.gz).\n",
      "@> 2997 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3u8w.pdb.gz).\n",
      "@> 2915 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fmk.pdb.gz).\n",
      "@> 3195 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2baj.pdb.gz).\n",
      "@> 2851 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3flz.pdb.gz).\n",
      "@> 3034 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2qd9.pdb.gz).\n",
      "@> 2850 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3ds6.pdb.gz).\n",
      "@> 2870 atoms and 1 coordinate set(s) were parsed in 0.04s.\n",
      "@> PDB file is found in working directory (2npq.pdb.gz).\n",
      "@> 3014 atoms and 1 coordinate set(s) were parsed in 0.04s.\n",
      "@> PDB file is found in working directory (2baq.pdb.gz).\n",
      "@> 2701 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (3fmn.pdb.gz).\n",
      "@> 3151 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3kf7.pdb.gz).\n",
      "@> 2890 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3iw8.pdb.gz).\n",
      "@> 2915 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4geo.pdb.gz).\n",
      "@> 2948 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1wbw.pdb.gz).\n",
      "@> 3119 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4aa5.pdb.gz).\n",
      "@> 2803 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3mvm.pdb.gz).\n",
      "@> 2917 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fmm.pdb.gz).\n",
      "@> 3053 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3zsh.pdb.gz).\n",
      "@> 2935 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4kip.pdb.gz).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300 proteins fetched in 8.98 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "@> 2735 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gcu.pdb.gz).\n",
      "@> 2914 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3bv3.pdb.gz).\n",
      "@> 2869 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3hrb.pdb.gz).\n",
      "@> 2971 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gcp.pdb.gz).\n",
      "@> 2910 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3s3i.pdb.gz).\n",
      "@> 3056 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3flq.pdb.gz).\n",
      "@> 2961 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3o8t.pdb.gz).\n",
      "@> 2879 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4kiq.pdb.gz).\n",
      "@> 2896 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3iw6.pdb.gz).\n",
      "@> 2924 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3lfc.pdb.gz).\n",
      "@> 2757 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3itz.pdb.gz).\n",
      "@> 3006 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2okr.pdb.gz).\n",
      "@> 2813 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3iph.pdb.gz).\n",
      "@> 3213 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4kin.pdb.gz).\n",
      "@> 2972 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4kip.pdb.gz).\n",
      "@> 2719 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3flw.pdb.gz).\n",
      "@> 3091 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1zz2.pdb.gz).\n",
      "@> 2872 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fl4.pdb.gz).\n",
      "@> 3116 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3hec.pdb.gz).\n",
      "@> 2814 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3uvp.pdb.gz).\n",
      "@> 2799 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1yqj.pdb.gz).\n",
      "@> 3055 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3lff.pdb.gz).\n",
      "@> 2991 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gcu.pdb.gz).\n",
      "@> 2968 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3s4q.pdb.gz).\n",
      "@> 2651 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1ouk.pdb.gz).\n",
      "@> 2984 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3od6.pdb.gz).\n",
      "@> 2823 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1wbv.pdb.gz).\n",
      "@> 3450 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3mgy.pdb.gz).\n",
      "@> 2737 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1w82.pdb.gz).\n",
      "@> 3388 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3mh0.pdb.gz).\n",
      "@> 2787 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3mh3.pdb.gz).\n",
      "@> 2766 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3ds6.pdb.gz).\n",
      "@> 2870 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3dt1.pdb.gz).\n",
      "@> 2946 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3pg3.pdb.gz).\n",
      "@> 2868 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fls.pdb.gz).\n",
      "@> 2836 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3zsg.pdb.gz).\n",
      "@> 3086 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2okr.pdb.gz).\n",
      "@> 2807 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fc1.pdb.gz).\n",
      "@> 2837 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fly.pdb.gz).\n",
      "@> 3113 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3que.pdb.gz).\n",
      "@> 2849 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3lfd.pdb.gz).\n",
      "@> 2716 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2fso.pdb.gz).\n",
      "@> 3109 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3hv4.pdb.gz).\n",
      "@> 2898 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fsf.pdb.gz).\n",
      "@> 2941 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3huc.pdb.gz).\n",
      "@> 2988 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3nnu.pdb.gz).\n",
      "@> 3013 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fln.pdb.gz).\n",
      "@> 3153 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3mvl.pdb.gz).\n",
      "@> 2838 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2bak.pdb.gz).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350 proteins fetched in 10.19 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "@> 2819 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1w84.pdb.gz).\n",
      "@> 3427 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3iw5.pdb.gz).\n",
      "@> 2851 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3ds6.pdb.gz).\n",
      "@> 2822 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4a9y.pdb.gz).\n",
      "@> 2819 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1wbt.pdb.gz).\n",
      "@> 3121 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3kq7.pdb.gz).\n",
      "@> 3257 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fko.pdb.gz).\n",
      "@> 3080 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3mw1.pdb.gz).\n",
      "@> 2794 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2fsm.pdb.gz).\n",
      "@> 2856 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3o8p.pdb.gz).\n",
      "@> 3030 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4aac.pdb.gz).\n",
      "@> 2884 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4aa4.pdb.gz).\n",
      "@> 2861 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3hv4.pdb.gz).\n",
      "@> 2839 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2y8o.pdb.gz).\n",
      "@> 3027 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fi4.pdb.gz).\n",
      "@> 2941 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3l8s.pdb.gz).\n",
      "@> 2770 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fkn.pdb.gz).\n",
      "@> 3103 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3zsi.pdb.gz).\n",
      "@> 2812 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3odz.pdb.gz).\n",
      "@> 2772 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3nnv.pdb.gz).\n",
      "@> 3107 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3iw7.pdb.gz).\n",
      "@> 2900 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3hub.pdb.gz).\n",
      "@> 2846 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2onl.pdb.gz).\n",
      "@> 2714 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3nnx.pdb.gz).\n",
      "@> 2915 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3bx5.pdb.gz).\n",
      "@> 2789 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3d83.pdb.gz).\n",
      "@> 3291 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3mpa.pdb.gz).\n",
      "@> 2867 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4l8m.pdb.gz).\n",
      "@> 2850 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1w7h.pdb.gz).\n",
      "@> 3317 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fmh.pdb.gz).\n",
      "@> 3076 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3oc1.pdb.gz).\n",
      "@> 2736 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3lfa.pdb.gz).\n",
      "@> 2874 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3mh1.pdb.gz).\n",
      "@> 2794 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fml.pdb.gz).\n",
      "@> 2960 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fsk.pdb.gz).\n",
      "@> 2915 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4kiq.pdb.gz).\n",
      "@> 2871 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (1wbo.pdb.gz).\n",
      "@> 3171 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3lfb.pdb.gz).\n",
      "@> 2752 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3obj.pdb.gz).\n",
      "@> 2832 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gfe.pdb.gz).\n",
      "@> 2992 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3zs5.pdb.gz).\n",
      "@> 3106 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (3mh2.pdb.gz).\n",
      "@> 2733 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3mvm.pdb.gz).\n",
      "@> 2912 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1w83.pdb.gz).\n",
      "@> 3234 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (6g9j.pdb.gz).\n",
      "@> 3322 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (6g9d.pdb.gz).\n",
      "@> 3358 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (3pze.pdb.gz).\n",
      "@> 3031 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4izy.pdb.gz).\n",
      "@> 2708 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3vud.pdb.gz).\n",
      "@> 2863 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3o17.pdb.gz).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400 proteins fetched in 11.45 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "@> 2895 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4qtd.pdb.gz).\n",
      "@> 3399 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (3v3v.pdb.gz).\n",
      "@> 2859 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3o17.pdb.gz).\n",
      "@> 2895 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2g01.pdb.gz).\n",
      "@> 2886 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3elj.pdb.gz).\n",
      "@> 3142 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2h96.pdb.gz).\n",
      "@> 2926 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3vum.pdb.gz).\n",
      "@> 2897 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4awi.pdb.gz).\n",
      "@> 3158 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4l7f.pdb.gz).\n",
      "@> 3380 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3vuh.pdb.gz).\n",
      "@> 2924 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4hys.pdb.gz).\n",
      "@> 2656 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2h96.pdb.gz).\n",
      "@> 2927 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3vui.pdb.gz).\n",
      "@> 2900 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3vug.pdb.gz).\n",
      "@> 2906 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2gmx.pdb.gz).\n",
      "@> 2905 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3vuk.pdb.gz).\n",
      "@> 2887 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3vul.pdb.gz).\n",
      "@> 2811 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2xs0.pdb.gz).\n",
      "@> 2846 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4g1w.pdb.gz).\n",
      "@> 2680 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1uki.pdb.gz).\n",
      "@> 2679 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4hyu.pdb.gz).\n",
      "@> 2709 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1ukh.pdb.gz).\n",
      "@> 2679 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (6gdq.pdb.gz).\n",
      "@> 3085 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (6g9m.pdb.gz).\n",
      "@> 3347 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (6g9k.pdb.gz).\n",
      "@> 3298 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3g90.pdb.gz).\n",
      "@> 2887 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2ok1.pdb.gz).\n",
      "@> 2832 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3rtp.pdb.gz).\n",
      "@> 2735 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2exc.pdb.gz).\n",
      "@> 2840 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3ptg.pdb.gz).\n",
      "@> 2810 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4h39.pdb.gz).\n",
      "@> 2846 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3ttj.pdb.gz).\n",
      "@> 2893 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3oy1.pdb.gz).\n",
      "@> 2918 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2o2u.pdb.gz).\n",
      "@> 2680 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2b1p.pdb.gz).\n",
      "@> 2919 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4u79.pdb.gz).\n",
      "@> 2811 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fv8.pdb.gz).\n",
      "@> 2877 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3da6.pdb.gz).\n",
      "@> 2757 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4w4y.pdb.gz).\n",
      "@> 3027 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4kke.pdb.gz).\n",
      "@> 2994 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2o0u.pdb.gz).\n",
      "@> 2899 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4kkg.pdb.gz).\n",
      "@> 2897 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3fi3.pdb.gz).\n",
      "@> 2922 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3kvx.pdb.gz).\n",
      "@> 2595 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3v6r.pdb.gz).\n",
      "@> 2906 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4w4w.pdb.gz).\n",
      "@> 3144 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (3g9l.pdb.gz).\n",
      "@> 2876 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4y46.pdb.gz).\n",
      "@> 5666 atoms and 1 coordinate set(s) were parsed in 0.04s.\n",
      "@> PDB file is found in working directory (3g9n.pdb.gz).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "450 proteins fetched in 12.72 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "@> 2841 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4y5h.pdb.gz).\n",
      "@> 5727 atoms and 1 coordinate set(s) were parsed in 0.04s.\n",
      "@> PDB file is found in working directory (3oxi.pdb.gz).\n",
      "@> 2724 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4kkh.pdb.gz).\n",
      "@> 3042 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4w4v.pdb.gz).\n",
      "@> 3130 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4w4x.pdb.gz).\n",
      "@> 2975 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2r9s.pdb.gz).\n",
      "@> 2799 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4eym.pdb.gz).\n",
      "@> 2906 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4myg.pdb.gz).\n",
      "@> 2848 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4yno.pdb.gz).\n",
      "@> 3092 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3coi.pdb.gz).\n",
      "@> 2920 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4eyj.pdb.gz).\n",
      "@> 3052 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4myg.pdb.gz).\n",
      "@> 2810 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gc9.pdb.gz).\n",
      "@> 2905 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gp0.pdb.gz).\n",
      "@> 2898 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gc8.pdb.gz).\n",
      "@> 2880 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3gc8.pdb.gz).\n",
      "@> 2874 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4fv7.pdb.gz).\n",
      "@> 3095 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4zzm.pdb.gz).\n",
      "@> 2865 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4zzo.pdb.gz).\n",
      "@> 2970 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (2y9q.pdb.gz).\n",
      "@> 3396 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (1wzy.pdb.gz).\n",
      "@> 3116 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4fv8.pdb.gz).\n",
      "@> 3011 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4fv4.pdb.gz).\n",
      "@> 2732 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4fuy.pdb.gz).\n",
      "@> 2942 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4h3p.pdb.gz).\n",
      "@> 2857 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4fv3.pdb.gz).\n",
      "@> 3038 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4iz7.pdb.gz).\n",
      "@> 3069 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4nif.pdb.gz).\n",
      "@> 3176 atoms and 1 coordinate set(s) were parsed in 0.04s.\n",
      "@> PDB file is found in working directory (4h3p.pdb.gz).\n",
      "@> 2830 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4zzn.pdb.gz).\n",
      "@> 3143 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1tvo.pdb.gz).\n",
      "@> 3020 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4nif.pdb.gz).\n",
      "@> 3198 atoms and 1 coordinate set(s) were parsed in 0.05s.\n",
      "@> PDB file is found in working directory (4h3q.pdb.gz).\n",
      "@> 2929 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4fv0.pdb.gz).\n",
      "@> 3026 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4fv5.pdb.gz).\n",
      "@> 2883 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2ojj.pdb.gz).\n",
      "@> 2910 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4fv2.pdb.gz).\n",
      "@> 3067 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3i5z.pdb.gz).\n",
      "@> 2910 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4n0s.pdb.gz).\n",
      "@> 6165 atoms and 1 coordinate set(s) were parsed in 0.04s.\n",
      "@> PDB file is found in working directory (4iz5.pdb.gz).\n",
      "@> 2879 atoms and 1 coordinate set(s) were parsed in 0.05s.\n",
      "@> PDB file is found in working directory (3tei.pdb.gz).\n",
      "@> 2868 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4qte.pdb.gz).\n",
      "@> 3455 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4fv9.pdb.gz).\n",
      "@> 2912 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (5bue.pdb.gz).\n",
      "@> 2846 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (1pme.pdb.gz).\n",
      "@> 2973 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2oji.pdb.gz).\n",
      "@> 2941 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3sa0.pdb.gz).\n",
      "@> 6115 atoms and 1 coordinate set(s) were parsed in 0.04s.\n",
      "@> PDB file is found in working directory (3w55.pdb.gz).\n",
      "@> 2842 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2ojg.pdb.gz).\n",
      "@> 2970 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4g6o.pdb.gz).\n",
      "@> 3000 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4fux.pdb.gz).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500 proteins fetched in 14.24 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "@> 2985 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4iz5.pdb.gz).\n",
      "@> 2883 atoms and 1 coordinate set(s) were parsed in 0.05s.\n",
      "@> PDB file is found in working directory (4fv6.pdb.gz).\n",
      "@> 2820 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (5bui.pdb.gz).\n",
      "@> 3062 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4qtb.pdb.gz).\n",
      "@> 3452 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (2zoq.pdb.gz).\n",
      "@> 3052 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4qtb.pdb.gz).\n",
      "@> 3544 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (2zoq.pdb.gz).\n",
      "@> 3050 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (2i6l.pdb.gz).\n",
      "@> 1984 atoms and 1 coordinate set(s) were parsed in 0.01s.\n",
      "@> PDB file is found in working directory (2i6l.pdb.gz).\n",
      "@> 2180 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3npc.pdb.gz).\n",
      "@> 3064 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3npc.pdb.gz).\n",
      "@> 3066 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (3e7o.pdb.gz).\n",
      "@> 2775 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4b99.pdb.gz).\n",
      "@> 2659 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4ic8.pdb.gz).\n",
      "@> 2375 atoms and 1 coordinate set(s) were parsed in 0.02s.\n",
      "@> PDB file is found in working directory (4ic7.pdb.gz).\n",
      "@> 2738 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (4ic8.pdb.gz).\n",
      "@> 2432 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (5eta.pdb.gz).\n",
      "@> 2713 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (5eta.pdb.gz).\n",
      "@> 2718 atoms and 1 coordinate set(s) were parsed in 0.03s.\n",
      "@> PDB file is found in working directory (5nhv.pdb).\n",
      "@> 2975 atoms and 1 coordinate set(s) were parsed in 0.02s.\n"
     ]
    }
   ],
   "source": [
    "start_time = time()\n",
    "pdbs = []\n",
    "for i, (pdb_id, chain) in enumerate(X_names):\n",
    "    if i > 0 and i % 50 == 0:\n",
    "        print(f\"{i} proteins fetched in {(time() - start_time):.2f} seconds\")\n",
    "    pdbs.append(pd.parsePDB(pdb_id, chain=chain))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a8bf1940-70e4-4574-b7ed-419572daa567",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50 proteins in 9.14 seconds\n",
      "100 proteins in 12.14 seconds\n",
      "150 proteins in 15.28 seconds\n",
      "200 proteins in 18.39 seconds\n",
      "250 proteins in 21.36 seconds\n",
      "300 proteins in 24.53 seconds\n",
      "350 proteins in 27.55 seconds\n",
      "400 proteins in 30.53 seconds\n",
      "450 proteins in 33.73 seconds\n",
      "500 proteins in 36.69 seconds\n"
     ]
    }
   ],
   "source": [
    "from geometricus import MomentInvariants, SplitType, MomentType\n",
    "\n",
    "invariants_kmer = []\n",
    "invariants_kmer2 = []\n",
    "invariants_radius = []\n",
    "\n",
    "start_time = time()\n",
    "for i, key in enumerate(X_names):\n",
    "    if i > 0 and i % 50 == 0:\n",
    "        print(f\"{i} proteins in {(time() - start_time):.2f} seconds\")\n",
    "    invariants_kmer.append(MomentInvariants.from_prody_atomgroup(key, pdbs[i], split_type=SplitType.KMER, split_size=6, moment_types=list(MomentType)))\n",
    "    invariants_kmer2.append(MomentInvariants.from_prody_atomgroup(key, pdbs[i], split_type=SplitType.KMER, split_size=12, moment_types=list(MomentType)))\n",
    "    invariants_radius.append(MomentInvariants.from_prody_atomgroup(key, pdbs[i], split_type=SplitType.RADIUS, split_size=10, moment_types=list(MomentType)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "115a0ee5-00e2-4daa-80a3-f908743b9974",
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_moments_kmer = [((np.sign(x.moments) * np.log1p(np.abs(x.moments))) / 6).astype(\"float32\") for x in invariants_kmer]\n",
    "ind_moments_kmer2 = [((np.sign(x.moments) * np.log1p(np.abs(x.moments))) / 12).astype(\"float32\") for x in invariants_kmer2]\n",
    "ind_moments_radius = [((np.sign(x.moments) * np.log1p(np.abs(x.moments))) / 10).astype(\"float32\") for x in invariants_radius]\n",
    "ind_moments = [np.hstack((ind_moments_kmer[i], ind_moments_kmer2[i], ind_moments_radius[i])) for i in range(len(ind_moments_radius))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7eed7c84-cc81-4c33-9acd-c6d21ec87a42",
   "metadata": {},
   "outputs": [],
   "source": [
    "def unpack_and_desort(x, sizes, desort_idx):\n",
    "    x, sizes = torch.nn.utils.rnn.pad_packed_sequence(x, batch_first=True)\n",
    "    x = x[np.arange(x.shape[0]), sizes-1]\n",
    "    x = x[np.argsort(desort_idx)]\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a1c8b49-9750-4cf2-a74b-ced825cdc945",
   "metadata": {},
   "outputs": [],
   "source": [
    "from MomentLearn.model import LinearMoment, ContrastiveLearn, RNNMoment, sample_double_proteins_with_sim_dist, loss_func\n",
    "\n",
    "batch_size = 150 \n",
    "rnn_model = LSTMMoment(2, len(ind_moments[0][0]), 5, 2, batch_size=batch_size)\n",
    "optimizer = torch.optim.Adam(rnn_model.parameters(), lr=0.0005)\n",
    "epoch = 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "08cf4438-7746-47c1-916a-cb9dd077ef79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03670612350106239\n",
      "0.02961456388235092\n",
      "0.02957928329706192\n",
      "0.03121219575405121\n",
      "0.028507611826062203\n",
      "0.027740571722388266\n",
      "0.02678492747247219\n",
      "0.025875803921371697\n"
     ]
    }
   ],
   "source": [
    "\n",
    "current_losses = []\n",
    "rnn_model.batch_size = batch_size\n",
    "for e in range(epoch):\n",
    "    (x1, sizes1, sort_idx1), (x2, sizes2, sort_idx2), which = sample_double_proteins_with_sim_dist(ind_moments, np.array(mapping), batch=batch_size)\n",
    "    hidden_batch11, cell_batch12 = rnn_model.init_hidden()\n",
    "    hidden_batch21, cell_batch22 = rnn_model.init_hidden()\n",
    "    (x1, (h1, c1)), (x2, (h2, c2)) = rnn_model(x1, x2, hidden_batch11, hidden_batch21, cell_batch12, cell_batch22, sizes1, sizes2)\n",
    "#     break\n",
    "    x1, x2 = h1[-1][np.argsort(sort_idx1)], h2[-1][np.argsort(sort_idx2)]\n",
    "#     x1 = unpack_and_desort(x1, sizes1, sort_idx1)\n",
    "#     x2 = unpack_and_desort(x2, sizes2, sort_idx2)\n",
    "    loss = loss_func(x1, x2, which)\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    current_losses.append(loss.item())\n",
    "    optimizer.step()\n",
    "\n",
    "    if e % 50 == 0:\n",
    "        print(np.mean(current_losses))\n",
    "        current_losses = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "df9ba8a2-d942-4b54-b789-6074beda3298",
   "metadata": {},
   "outputs": [],
   "source": [
    "sizes = np.array([len(x) for x in ind_moments])\n",
    "sort_idx = np.argsort(sizes)[::-1]\n",
    "sizes = sizes[sort_idx]\n",
    "all_moments = torch.tensor(np.concatenate(np.array(ind_moments)[sort_idx]).astype(\"float32\"))\n",
    "rnn_model.batch_size = len(ind_moments)\n",
    "hidden1, hidden2 = rnn_model.init_hidden()\n",
    "res = rnn_model.forward_single(all_moments, hidden1, hidden2, sizes)\n",
    "res = res[1][0][-1].detach().cpu().numpy()[np.argsort(sort_idx)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c86a0b65-5f32-45e3-b1a7-d220c4391129",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "reducer = umap.UMAP(metric=\"cosine\", n_components=2, n_neighbors=20)\n",
    "reduced = reducer.fit_transform(res)\n",
    "# reduced = res\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(3):\n",
    "    indices = np.where(np.array(mapping) == i)[0]\n",
    "    plt.scatter(reduced[indices, 0],\n",
    "                reduced[indices, 1],\n",
    "                edgecolor=\"black\",\n",
    "                linewidth=0.1,\n",
    "                alpha=0.8)\n",
    "plt.axis(\"off\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c768c709-df29-4346-afa0-89b1073cadd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "proteins = [rnn_model.linear_moment(torch.tensor(x.astype(\"float32\"))).cpu().detach().numpy() for x in ind_moments]\n",
    "residue_coords = [x.coordinates for x in invariants_kmer]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5a96552a-ffbd-44e1-9109-66351ea2d5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "i1 = 3\n",
    "i2 = -1\n",
    "protein_a = proteins[i1]\n",
    "protein_b = proteins[i2]\n",
    "coords_a = residue_coords[i1]\n",
    "coords_b = residue_coords[i2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "fb8daccc-96ad-48a4-8040-37bb9c331a32",
   "metadata": {},
   "outputs": [],
   "source": [
    "def svd_superimpose(coords_1, coords_2):\n",
    "    centroid_1, centroid_2 = (\n",
    "        helper.nb_mean_axis_0(coords_1),\n",
    "        helper.nb_mean_axis_0(coords_2),\n",
    "    )\n",
    "    coords_1_c, coords_2_c = coords_1 - centroid_1, coords_2 - centroid_2\n",
    "    correlation_matrix = np.dot(coords_2_c.T, coords_1_c)\n",
    "    u, s, v = np.linalg.svd(correlation_matrix)\n",
    "    reflect = np.linalg.det(u) * np.linalg.det(v) < 0\n",
    "    if reflect:\n",
    "        s[-1] = -s[-1]\n",
    "        u[:, -1] = -u[:, -1]\n",
    "    rotation_matrix = np.dot(u, v)\n",
    "    translation_matrix = centroid_1 - np.dot(centroid_2, rotation_matrix)\n",
    "    return rotation_matrix.astype(np.float64), translation_matrix.astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "f84516fc-f003-4631-8eda-2d3bc5babc0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "score_matrix = np.array([[np.abs(x - y).sum() for y in protein_b] for x in protein_a])\n",
    "score_matrix = score_matrix.max() - score_matrix\n",
    "p1_idx, p2_idx, _ = dtw.dtw_align(score_matrix, gap_open_penalty=3.5, gap_extend_penalty=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "7ee9c72f-6758-4235-935c-d5b03a07cfe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8231203696234228, 0.036702942631759244)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def score(coords1, coords2, gamma=2):\n",
    "    rot, trans = svd_superimpose(np.array(coords1).astype(float), np.array(coords2).astype(float))\n",
    "    coords2_rotated = np.dot(coords2 - np.mean(coords2, axis=0), rot)\n",
    "    coords1_translated = coords1 - np.mean(coords1, axis=0)\n",
    "    return get_caretta_score(coords1_translated, coords2_rotated).mean(), (coords1_translated, coords2_rotated)\n",
    "\n",
    "core_p1_idx, core_p2_idx = helper.get_common_positions(p1_idx, p2_idx)\n",
    "aligned_score, (p1_rotated_common, p2_rotated_common) = score(coords_a[core_p1_idx], coords_b[core_p2_idx])\n",
    "unaligned_score, _ = score(coords_a[:min(coords_a.shape[0], coords_b.shape[0])], coords_b[:min(coords_a.shape[0], coords_b.shape[0])])\n",
    "aligned_score, unaligned_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "70fc335a-ddfb-41d0-8ada-ac12e68367a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_gaps_for_plotting(alignment_idx):\n",
    "    while np.where(alignment_idx == -1)[0].shape[0] != 0:\n",
    "        current = np.where(alignment_idx == -1)[0][0]\n",
    "        if current == 0:\n",
    "            alignment_idx[current] = alignment_idx[np.where(alignment_idx != -1)[0][0]]\n",
    "        else:\n",
    "            alignment_idx[current] = alignment_idx[current - 1]\n",
    "    return alignment_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "a199f7e8-c981-4d83-8c3f-11bc3688d559",
   "metadata": {},
   "outputs": [],
   "source": [
    "p1_idx_filled, p2_idx_filled = fill_gaps_for_plotting(p1_idx), fill_gaps_for_plotting(p2_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "f69b0b5a-24d9-438a-a29f-1ede7db7d2a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KAFJQUOgUiAbQBhQBpKCgoKCgoKCgoKCg0DlQBJCCgkKnwNz+BQDl5Z1/HAoKCgoKCgoKCgoKCn9HKAJIQUGhU6AUQAoKCgoKCgoKCgoKCl0HRQApKCh0CqwUQIoAUlBQUFBQUFBQUFBQ6BwoAkhBQaFToFrAFBQUFBQUFBQUFBQUug6KAFJQUOgUqBYwBQUFBQUFBQUFBQWFrkOHEECMsQ8YY0WMsfSOeD4FBYWjD6oFTEFBQUFBQUHhMELTgF27gIqKrj4SBQWFboqOUgB9BOCUDnouBQWFoxBWBJCKTxQUFBQUFBQUOghz5wL33QfcfLMKshQUFCzRIQSQpmnLAZR1xHMpKCgcfaivB+x2um0TVp2KCipWKSgoKCgoKCgoHAI0DfjpJ7pttwPr1nXt8QDIygLWr1exnoJCd0KneQAxxm5kjG1gjG0oLi7urJdVUFDoBhDVP7GxgL8/3W5sBOrquuaYFBQUFBQOM+x2YPXqbtHv63QCn30GfPkl3VZQOOqwf79suLhjR9cdC4j8ue8+4MkngTlzuvRQFBQUBHQaAaRp2mxN08ZpmjYuNja2s15WQUGhG0CMR2JigMhI/nM3yAsUFBQUFA4H/u//gGefBe6/H2hq6tJD+e47In8++wz4448uPRQFhcOD9evln3fu7Jrj0PHjj1ToA+j6U/V/BYXuATUFTEFB4bBDJIB69JAJINWirqCgoHAUorAQ+Osvul1QAOTlddmhaBqwdCn/efv2LjsUBYXDh40b5Z/z87usylZfD6xYwX92OoEvvuiSQ1FQUDBBEUCdhKIiYOFCoEw5JSn8DSG2gPXoAURE8J8VAaSgoKBwFGLVKvnnnJwuOQwA2L2b+KhucCgKCocHNTXWLV9dpAJauZJIIBFLlxInpaCg0LXoqDHwXwBYDWAQYyyPMXZdRzzv0YKqKuqBffdd4IknuvpoFP7OyMgA/v1v6sX26oGwbBlw993Azz932OuKBJBqAVNQUFD4G+DPP+Wfu5B1Mbd87d+vTGkVjjJs3gy4XJ73d5EP0JIl/Lbh+6hpygtIQaE7oKOmgF2iaVqCpml+mqb11jTt/Y543qMF774LlJfT7awsZT6o0DWorASefhrYuhX46ivgoYcsFGkVFcAbbwB79wLvvNNhkjXVAqZwpGDtWvIJqak5hCfRNLrQMjM77LgUFI4oFBV5nv/Z2V1yKE1NwPLl8n0NDcCBA11yOAoKhwcbNvDbycn8dkZGpx9Kfj7nnXx8gAce4L9buZJCTAWF7ga7nfaK3NyuPpLDD9UCdpixapXcAwuQIkhBoTVwOq0LOm1CTQ207+bh7ccOSOdeRgYJfRYupKR3zx7A+cNP3LHP5eowp0yzAkhsAVMKIIXOgqGQd7lAF5cpA/zjdw1P312Cz/6zH7NfrW3fi2gaMHs2Se3uvbdLgm8FhS6HWf0DdJkCaOtW631m377OPxYFhcMCTZMJoMsv57czM3lc10kQ/bbGj6d/xx7L71u8uFMPR0HBK6qrgUWLgEceocvmhRfk8/dohW9XH8DRjKoq4O23re+Pju7841E4snDwIA1OcTiAZ56RCzotobKSJp2EhQEn7/oAO38rwKqs3sDwSCAwEIxRvFBeTgo1AIDLhZSsSDzX3xcBPrpM7bffgHPOaf7F6uupnFNRQS8cFARMmgQEBrofYlYARUXxn5UCSKG1aGwEFiygCuLYsbRZM9a6v62rIz6moAA4YeA+3F3yMJ1806YB//gHCot98eZD+UBWAQBg9ftFcF7bF7694tp2kJ9/TqNPALrIfvsNSEtr23MoKHQm0tMBmw0YMqTjntOKACotpWg7LKzjXqcV+P13ftvHhw8jy8kBjjmmUw9FQeHwYM8eznJGRADjxgEJCWR85XTS7ztpH2pqAn79lf980kn0/8knc1uwLhIDKihIsNuB227jXToG1qwBrr669fHlkQhFAB1G/O9/1lUnpQBSaA1++ol3YH3wAbVvtQaaBvznPxTTo6EBc7dOhB9rol+UleKUaxMxeTJN562uFv6wtBSZZT3wBpuFe5Pn0cKXk0M7df/+/MnFFbG8nFgqs5b+1FOBW28FQLGHcR0wRuRPiybQDgclJL5qiVKg027dOuD997mR6969QG0tcOON8inpdFKQuXs3MGMGMGAA3f/NN0DBfiewfz/W/ZULjKmgXyxfjka7A89vuRz1ew+6n6e+1oUdt72FEe/eCvTsifp6Oi3Dw5s50AULqH9MxObNh/r2FRQOH9au5ZvLJZcAl17a+r/VNNLK9+gBhITw+0tKgF276LbNBvTsyS/cnBxg+PAOOfTWwOGQvahPPpmqvYBSACkcBcjMpDYD8SQfN442xSFD+HWXkdFqAsgc5rUFhgDWSKijoqhYAwD9+vHHGR5c3THB/uorEr9ffDHVh45KNDYCfn5dfRRdjrVrPcmfAQOoMNDUdHSnIEfxW+taVFbK3TMxMVwFoVpeFFqDbdv47S1bqIAzcGDLf7dli07+AEBxMTSNwaHRpR5Xuw/XXpuIoCCy+lm6lLibooMatn5OJM4fpcMwKLoYs6JW0nP8+itR4W++SUHGjBnAdbrP+zPPWBsprFwJ3HILwJhkIxQVRRXYZlvAcnJ4w/j11wMnnNA9owSFw468PFpH//hDnuBj4McfyU/qwguJp/zzT5KWG+fU4sXAC/+nIbQwE/NftAHFFUCTC9UIgtNlg6+N+is/nheOvQcOejz/hqxojHjwQRTd9Aj++XZ/VFYC//oXMKl3LkWvffvyB2/aBLz3nudBHjhAB5+QQIzuxx8DiYnABReo81qh6yHKY774gtQ5s2a1/HcVFcCrr9LY6ZgYum0s7GIyOmIE/b6VBJDTSfZBCQnWl4fTSS+1aRPlME1NQGwscOWVcouJgTVr+CSixETaTgwCqKsnga1fTx/frFl0bAoKrYHTSbyq7dclFMiZ3czHj6f/09K4FGfHDuDcc1t87k8/BebPB848E7jqqrYf21df8esLoOfx8aHb0dHEE9vtVLwpLaWloTuhvJzU85oGvPYacWjd7RgPGa++SufF8OHARRfRGv03jUU2buS3TziBVOVH3fftBYoAOkxYu5avyUOGEPP900/0syKAFFpCXR0RPiK+/ZaSz+YgTVjQXIiuzEYZAty/vzPuCwSV9AH69EGPjJW46NdPKIPu1QtvhgZicd0YwMeG99l16F+1H8PC91OCUFbGzawWLaKDi4nhVV7GgAkTyGyhrg6orkb2qkIs39ML27fz4+vRg/5vtgVs4UKKEADagTduBG6/Xa4wKxzVcDqJS1m40PN3oaG0nhrn1Zw5wLx5/JQRUV+v4amLt6N/5SY4ygdJv6uYeDJiegei+MtfseDARPf945OKsd53EpC1FxsqU3Bt8VJ8c8PPKHOdDISF4783ZGNsv2fh79ME/POfwNSp9IfffceffMgQKh1t3Uo/b95MGe0771BGCgB9+lCrpIJCV0HT5EoDQOV7ABg2jNp56+tp/S8vp4pxVBRJR99+mwczJSWkfrvySnpOkVSaMkWeBd0M6+J0kqA0M5P+7L77ePJoYP58T2u6/HzgueeoTnHuuTyXcbkoITUwfTpxtkYLdEEBKYSMCUVtwiHKFyorSanrcJBa8eWX2/1UCocb9fVUWRsypNPbF83Yuxd4+GEgyNaAl+yfI0okf/z9qUBn7CtiS+fOnS2esxkZwNdfA2hy4ttvfXHeebTfthaLFxN5YmDaNOC880Axod0OFhODvn25Ld7+/Xqy7XIBv/xCjxs/Hujd2/0cHa0Samyk8LV/f8mlwI19+3ju5nAQIfaPf3Tc63c5Sko4KbhtG/0bPJgU+4bS/2+Cpibgr7/4z2ed9fchfwBFAB02iAWwY4+VJ8ooAkihJbiNagX8+ScXEnjDxo2ck/GtKsNLKe/C7gzA+ooUpIYWYER4Dj3RzJlUBWhooCfNyMBN/XyQXdcTu0PGoCmiB17aeTH+F/oifCsrPZ3Md++mfwZuuIHKmE8/DaxdixJHOO653wdOk9dVfDz9HxbGg/Dqagr83VJLs2nuypX0pv79b97Po3DUorKSkjmROASA4GCq0FxyCQVuTz7Ju6vM5E+PHnRffWE5inJqUQSB/AkKAnonovySCYgZqGFfYSKwhX6VEluO++dNwqW3R6PRxwe5mQzZtT3xW/EIwFUCFJegFMDi4NGY1XM9RYdTpgDFxZQgAHRi33cfsHo1J4A2bSJZ/tq1/Dh+/VURQApe8fPPpNCcNQs47rjD9CJ5edYBiUECtQULF1K2t2ULn/7l60taerHXqhkCKD2d/+nKlXTN3347TwBLS2VCx4yPPiJS55Zb6KVXrqQkE6A147TT6P/4eNr2XC76CCz99Qz/rp9+osToxht5y8TixcCHH5Ik95Zb2iXfycigBBOg93zwIHXKKXRDPPkkJcp9+lDc1C7GsGOwYAHlEzXZ+VgSMBgX9loJ9OpFMdiIEfKx9e5NDE5NDV3nBQVez1VNI9sK7NsHHDwILTYGe/cmY+TI1h1Xbq7seTpqFBEnrKKcTFaqq4F77kHfvjMkAmjMGBCj+9ZbdOcHH1CF56STkD1sFh5/0oawMIoJOoJ7e/552oYHDgReeomUVOb3IWLZMlIxHTWhpxi3G9i5kzwh3n7bk21zuahoVVwMnHjiUVWI3b2b5+bR0UBSUpceTqdDTQE7DLDbeS4AUIwv+kb83TyADnmK1d8Q7hYuAZpGSgdv0DS5+nJK8ArE+FehX3Axzp9ygMgfgKLiOXOI/BHgZ2vCgwO/QfjAOIAxlIT2w6pyU8/4iBGeO+YppwBnnEG3hw4FAGyv7gtnhZyVx8UBZ59Nt202L9eE3c4jdhHFxVQaXrnS+wegcMSjcMUe3D1xNbYv2OMuw40eDTz4IHEtN95IQaCfH/DQQ0BqKv/bqCgS4zz4IHkF3X+PE0yM5iIjqCI6fBgQFU1934yhdNzJFN3Fx6PvRccisFc0dahERADDhuKlmpvQ4JJ75b8umIqGJl/KItevJzLHKBuOHk09KaNH8z/YupUSSbFau2HD328zUGgVKitJLLZrFyUpIm/YoRA3mhEj2h4BR0XRwg5QT8f8+cTCGDjjDLqORPOPffu8BgXr1sk///ILdaUZ+PBDLibq149+9/HHJFYS/+b554lc+fxzfv+ZZ+p7TmEh+vXgFTlLPqqoCHj8cUr2d+0iNu7DD+l3O3ZQsmoEenfeCfzwg2cbTgswiC73e399DREN5l8odC0cDq6Sy82V+5u6ADt3ArDXAMUl2FurV9RuvpkKDGZiyvABMiAmJib8/juQubMJKNJboYtLkLmpxuvjzZg7l1/Wycm0P/v6gmI2w2xy3jypa9od6plViPv2Ae+9h+9fy0JZGf3YER97SQlfS/fs0T9LE/Ly5J81jTipNl7e3Rfi+pKYyCWWeXkyUe900mJ6883Evr33XvsKA21AQwOFUkYR+3BDbP8aO/bv1wWnCKDDgA0b6NoBKK+Ii5M9T/4uMb+mUSB29tnUvnREwOEgWYHkjtz5EPdD0Y5h6VJPwzIDq1fztjH/plpcoH1NP9hsRJ4YwcG+ffKMwyuvpBcZOxYxD1yPWRcG0f0xMVhYNI4/7rjjSOHz1FOcvRkzBrjpJr5y6sFGQX20+zOcNo32jvfekxN2Sx8gQ6YMUInmoYd4xcHhoBPq88+Pot1YQcQnT+agpNwHKCsDq67CVVcBTzxBKkpzbBsURC0UTz5Jk+w+/phO82OPpZhmfPkvuDJGjxp9feCTkowhE0IB0LlqXEdlZSDJUN++6JFEJUbDtBKBQdgXNx4YPAgIDQEL8AMSe6EiKgmLjGtjwQL5ejJGnvTuzXse7XZP9rapyVNZp6AAytOMZErTgBdf9CQqNI321aeesubMWwWRAJowgdb3M84ABg0C+vWDFhtHqocRI2j9nzyZ1vg+feg8f+MN2TT6yy+5109oKJlzAbTYG32/DQ2WvnGG0bsZX3xBJNgPP8itXzfdRC8RHU1rwIwZ/Hdr1gB33EGtYQApic4+G/TB3ngjkn6Z7S79ehBAGzeSYkHsDQDoAJYupS9D3H8cDkqMXnqpTfuSVIivrcW6uTlEJn/wQaufwwqaprbHDsVBkzfc119b9xu3F7t2UVzz2GOer2VCVRVQWKi5E/U99gRg4kS52GDGqFH89qZNlg+pr9d5W7sdEM6dPWtLW/UWDhyQr81bb6X9GYCs6M7ORt8Q/pzudUu8CIUsPHt7nfu2+XJsDzZskH8WOzUMmBVAANVv1q8/9NfvFhAXniuvpDXdgDG5saGBfDjfeEM2X1yxQm5n6WDMmUOc+wMPWHs+tgdNTbSkz55NXNb551PIBsgE0Lhx1n9/NEMRQIcB5vYvQFY7/F1awBYuJPJf06g6cEQEJe+8AzzyCFX1Ghu75BDq6jhJzxi1vKSk0M+NjaTUNKTjBnJzgddf5z+fGrMB0f76Qj1xIjH94gpnfBnjxpEZ7Y03UsVzxgzMnKkXBYKCsSNgDHJq44jFuftuOqARI0hi8dJL9DeiTf6AAYC/PwobomgTaXRgxAiStpvZ9chIftt9TYjBQloayedeeokkzga++EJvVFc4mqBpQPreIPfPD5y+Deef33xVxs+PYt/ERNPj6uqAzz/HeQmrcFGvFUgYEo1b7/QzBGoAOAFUKsS40XrLokcwEB6BsIlDce3ro4HE3kCvXvjmwBTUN/lRdFhURI8LC6PrDaADEgNzoyogYtky72+um2D7dgqcj4j1+yiBeXBcfT0RPWLssHIlJW3r1lGBts3fj6bJBNCwYUTU3HQT8OKLyLrnTVyrvY97At+G/aFnqK3xgQcoWX37bdojIyKIGDJUQCIuvlju2RDVRRaym9xcnv8GBcmXzu+/y8XnKVNkH2k/P2o3ET1uCwr47bPP1g9l+XIAQL/Ag26lgzQJLCuLWGVDZsSY3DLz2mukRgWIfRLf0x9/eBr3eYGmmYQ+lZVIr+oHuzOgXc7URUWU1DzxBHFuV1/NyS+FQ4SZlKmuppGSh4rdu8nM57776GL+6y9Zwm2BXbsAlJYBNURAFTmiUH3R9c2/zpgx/PbWrZQRm/Dtt3ohxG6Hv43vU5nb6j0ea4Vvv+WE9ciRxB+7YWrp71vI5Yz79wNak0u+CG+7DQDg0hhyC3iKunPnofNuZhJn1SrPdVMkgNyFIFAOc8TDvPCkpFgTQEuXWstwGhsPqwrfeHmnk1slmlFVRWtddnbLz6dpdIk9/jjx9/n5lJa8/768XPv4oNWtjkcTFAHUwWhokFlFw+Lh79YCVlxMFXkDdrt35Uq3gdPpDhBRUtJlI0JE/5+kJApcL7uM/z49HXjhBb6PV1TQAmdsjtFRGi5omMP/4NRT6f8pU+QXstmAa6/1eP3oaGGaSnIyFp7xDpndikRPYCDJeczZua8vMGgQCup15UN1jcTdiBAJIPe5YSaAAArAX3pJzgjmzOHflcJRgdLsKlTYqdUqyMeBSVEW+uzW4rvvgMpKMAZcPiodsxcmYuZM2XzcigAyBDu9enl6bZ18Mokj4uIA+AegKiwRn+VPlx80fbo8WtWqMnvaaVx2vWuXnKl2M2RkUM7/xBOyr+/fFvn5JDN7+unDtpFrmkwAGR23RUXAs89SDF5fLwtF8vLkuKNVKCyEe0RjSIiHAegnn9A2mJlJnQBe4eurO70KiI+n81yESJZYRO+i+mfMGBJ/WgXlAQF8CKUIxoj4MERHBkJDqf0LgHtP7xdUBFRUAprGt/nSUpISGeRPbCxttC++aE1w3XEHOTeLbLG3rMWEggJTMltVBafmg01VA4CaGuzeZMc775Dt3ZVX0jZt9kQzkJFBPknvvUdEreHZbQwd6Y6orKQ4xoKL6H6wmnL6/fd8rG97kJNDvcrmlqwNG5r1TNi5E/LrJsRjT01886/Vqxd3tq2t9fCAcTopOQYA2GtwQ9/F8NNJoKJCZ4sF69JSWQArXX8lJR6fU1T6CrexdF0dUJpRxCuakZHufs7C+ig4angR1uXy0sHmcBApITG51g8z/31xsczZ1tTwoSR+fsRxG9izx7qGc0QhL48+dIA+65gYYrkC9EExubn0Oc6fz//m9NOBK67gP//222E5tOJizq0Dnl2BAO2NTz5Ja92//iXHblbIzvZup/HSS/zntLSjytqo1VAEUAdj0yZurdK7NymlgRbGXh9l0DQqENabigftlql3FrKzZWlNC3LcwwVxwTKqnGPHAtdcw+9fs4aSgU8/JeWwIUAIDAQevWgXImp1/WRkJI+ix4+X+2hOOYWfoCacfjq//fsq/7ZVXoYMoRYwAKiu9mpa7XFNNDXJwYlBAAEUxT/6qJwRvPKKp2F0d4XDQdXj//znqF4ADkUlsntlkft2SkgBWFE7r7/GRq7xBWiup37eWxFARg4McAIIkPM6xiif9fUlYQMAIL4n5h84BivLBI8Fo/3LgDmDZYwUd+KTd2MVkEgqmOXzfzs4HLToZmSQkcRTT3lKMTsABw7wQDgwkAg4g2ffsYP21rlzPfNPMWZvFcSNZsgQydutqkomoVokl048kcvnAGJidCJU0+gac/YVnJYtiisiATRhAr33p54ijuWss+jatdnIc9nbpBbG6HJ3X6Og2yEhoAxSTxJ7BZbBz1UP1FSjrAyoLq6nzMLIKEJCiPUcNIj2nvvvl8eRnXIKVUn8/IgZNmDVU2IBKQd3udzt0usqUrC+IgX33UeeJ1u30jpVXGxtv+F0Am++yXM6ETt2tOJA9u2j9/bmm50m8aurI5HHgw8emqWIw0Edh19/fZiJJKs40Gww1VbMm8fXDpuNx2XV1dYmvTp2bXPIxHNcXMuiM7MS1dQGtmMHJyNjnAcwM3YT+gfr79leiz07ml/j5s3jxEhamqzMs4rP2I7t6NuT+0/uWyd8vklJJBf38UFOXU/6jFz8y7Vch+bMIVXiPffIm7kJ27Z52F4CkC9ZUf2TmEhLWmws/ex0evoDHXEQz62UFDo3AgLkeOS11zjpGRpKa/nJJ/P1LyOj4/qzBJjXq+3bPa/r7dsNYZKGukpHs56ogEz4DRpERQUj7xCXO1Hp9XeCIoA6GKtX89tuFQU8FUBHs5x+xQrrZMGqt7ZbwbxZWVV+OgEi8y2aW557LnDOOfzndeso+MnKop8Zo1huQM6v/EGTJ/PA3hiDAtCuJno3mDBkCPftrK9vG+lfkzQM1U5q5fGvrZCSahGiAqisDJQUGKxhTIxnlO/rS1GjQVo5nVSJPxIIle+/pzLZn38CP/7Y1UfT4aitJSX7TTe1n+jdvYEHtqkh+e0nYHfs4BlRz56SOUhLCiAxhxUHdE2ezAPBE08kLhWhoUBICF7LnoXcuhjyrDKPUY2IkEcMTZhA57VoWPL77912QxDjvEMKfmtqKFMw9zYdSfj8c/nk3rmTSOgO/u7Ej2j4cDoPr76a37d0qXU7wpYtzcviNY1IhS+/1G0czO1fAv78Uw6+t2+3Jhnc8PdH3V0PYFnw6cg76Rop+Pnf/0BeXgtGwqXpTJaJAKqs5IasjPF8hDHKU66/Hvj4Qxfmf1yJE05o5jj0v7nsMmqLe/RRQf1z4IA7A/RhLvQJLAHKKwAA+978gW+kNhuxbmJxZNAgUvwEBpKnynXX8a999GheQc/La1WgI3ZhTOhf7FZ9rKtIxQt7z4VW75mpZmXxQzSwaJE85ezGG/nv9u71LMJ54IMPKO5ZvNirP0xHIz2db9k//dQ+AaSmEWf12WdUBGtuMlyrYLd7v47FfUgk+Jcta59NQGWlrF5+5hl5zJ8Xpt3lAnatr+THGRwM+Ae0rutQbAMzfc9u4rXRgWOCtsLGNAwM1hd+TcOeld7j4OJiWWl24YUmUbhVgU7T0LeJL1T70wVCq18/ivMSEsh6AADq+En8118WX5PB4Dgc1nIPHSLBLF7aYhuYuMcZjxG379a0HWVmUn2gW6rbRAJINOQU28DExckYmxgRIbMkh6FoZVY41taa1rusLPzwn+1A5m46Eban46eftGbDf/FUP/lk2kv/8Q/Px/0d/X8ARQB1KJxOeVqHmED4+XFTNJerYz3kDjsOHqSmyVZozA3HfAOiyuOII4C6QAFUXy+vv6a4HNdcA68B8I03AuNHO3kjLUBjkURcfTWVVF97Tf5yTGBMVgG1RU5eGD7I8NlFQlMeWF2t5eNEZdD27bBu/zIjJIQkT8axV1V1/1YwY5ywgS5qLTyc+O03qswUFrZDiaBjdwaPmFJCCuj6a09yLbpFjhsnKRtEAqisjOJ3I4BgTCYlhw8nM8szz6T/DTAG3HsvkJBA/iD1Ln88k3kh7KddYH0806fzPzTG4I0fzzXHBw50W3mNmQBqN9fxySe0MTz++GGpHnY4NI3kF2vXEluSkUFthWYYRjzeYAQFbWBFxaql4d96zjnyum98D2YLB1H4Zsb335PF3WefAXfcoSF9uVAtN2005iXV6Wx2gBAA4M1f0/By7c24b9W5KK+gDaCkhPPdm7Mi8Gel/joHDkjrvegxNXiwXDADADgcYHfeAXbF5a0m0IcNo8vMnZCa1t1+wUVARTngcOCvHwUW4uabZeNcAyecQMzbU09h2epAXHIJCQ8abQFyciRWAb1AzMNOifsL0f6kALI7A1HX5A80NCA2lvwrDEsxgCbkGKislEUoF19MsxyMKUsuV7NiEmL0tm7lP5vZpcOEvXv5bU1rn53f0qVyDrpgwSHM7fjxRzJavP9+6x4fMQ48+WRqbwTosS20HVli8WL+OoMG0YkqZqBe3Ib37wfqD9JmZWMu92bWKgJo5Eh+Ieza5U5ANE3oWrTbMTGSfF9SQ7mBVOYG6wxb08gj2BAyJSdbKCnEMVsjRrhv9i3bzN/XboGlNAoovXuTAggA6jnzXFJiyiOqquTvx0uVQtPkLfa664jXAIiANL5G8bkNAkis6bREAGVlUSHs6adpve1w5OSQUvHLL9u3GYvJhUgAWU2Q8/WVJ9Acfzy//dtvHV74sFIsupenL79E8c2PYPXSGiLtnU1AoxOOqnqvn7PDIZNKxpI+diwPwwCqx4lDKv9OUARQByI9nRM7sbHkhyviiG0De+klyuqeeabFA6+o4BX1wEBSBBjo1gSQpnmuQF1AAFn5/4hgjPqS//EPkrpffjm15z7+uD6JfetWHgn16CGPAAVIxpmS4vnEFpgxQ24Nbm3Le2FFEBAUDABICCjzOtNx9Giem+/eDZRtFHZXbwQQQKoOUb3UTZNnN/bskU/+LlKWHU6IuUNrqmRmuFxA5j7unZMaWkAloPZMnBAJIFNEKip8KipkX7KoKLnLAyD7rBtu8LxcQkJIThzQMxIYMgT5yVMwv+hYWOLMM6lh/dlneaLt78+JIYBkEl1kOt8cRK7G4ZB79NsEI5Jramq2StttsGYNGbA8/TStNY8+ygPeUaPkwPi777zPrX33XXqO226j59iypdnA2eWSc3IjaGWMnsK8LN50k6wK/eMP6y6I4mLqlDBQku/AQ2tm4ePc41HvFyYFK6Wl1n4zzS2zdjsvxNvtwJIldFsSt9ls+EY7j//83nvuX4o574QJFi+wYQMn0ebNa1/yYSKAJkbuBuobgKwsLCwcQwbMgwZRe1czcDrpcrXbif/78EPIcu8W2sCcTnm9TC3+ExMiZabG31nrJn/OOIPf//vvnDv4+GMeb/bqxZVO4pbfbIf0pk0y4SEkz5WVRBS22VeqFRAJIICInLaEWvv302Ulora2/YUHfP89nU87d3quTZom79c9e5LS04CZfWnJJMbplOeZG1/uqFF888nKsryId25vcsff4yL2wDcmEgC1/7dIfoWF8eM2yG2YjNcdVRgWRkzIwF6cdNmzy1rKsnQpV1gwRryppP6pr5e/bEHG2O/AWndrl8SNG5l4797YV6dLbuvqJR9J6ZyUnNThlQDav5/bJAQHEx8mcm7GJdsSAdQSR/rbbzx+F8UAHYYPP6TF8rPP2j4WrbFRDs6MyTKAp+s+QISPWBETi1YHD7ayx7R1qK625lLd3RBLlmBR0ThouoI03LcW8PMFHA78+KO1oCIjg5OTiYlcwQ2Qr9q0aZSTX3fd32/8uwFFAHUgzNO/zCfVETkKXqzUNTa2WG4QE6rYWJlk7tYEUEmJ56bbBQSQuMGYuRsDNhutzRddRP8uvFDIc8Wx0lOmHNLKFhhI1VgD3kwozSgogDtjTggo87pRhIbKhed1fwoJcHMEEKD34OjYurUVWvcuhFkue+BAt235aS/E3Gr//mZ9LC2Rn+tCfTUFz9H+1ejhpy+Qbb0Gy8r4wfj6mgwJ6Jw2Kn8Oh7wmeWtV9IakJAp6ERIKhIZh6VIvX6uPD12LZjnfJZfwgKqwsHn5RhegutqTf2vXGt7UJCdRR4ICTpx0oml8fQkJAe66i3qSxIDZWzAu9h1s2kSSjuee83qBZGXxZC4qSm5V8PMjTqp3b/r5nHOIrxg0iK/TTidN3zTjv/81LZEV5dA0hm8KJ+Oa3Q/isy993K+7YgU/j0XFXHOT4DZulHPfxYvpLZpbh7OCh2GTXQ8Kdu9G3ZKV+Ogj2TtZVLy4IfbFFRW1r29IPO8CAjApaicSA0uBqirUNgVgYdF46h1rYc/cskVOuH/4AVjpmMCHJOzdy7NNC+zbx7neuOhGROzfhgmRciL7j5G/uVtPRozg3dBVVZT/bdwoG+/eeCP3nhe3zmYJIDOjp2fiTU0ksv3yS/JgksKizEwaN3oIM7HNSbTL1foJS/X1ZKNnJHaicev337dDBeRwyGuTmQCqqSF2CaBqWESEXNk1CA5NIznY+ec3L2las4ZXSCMj+WCO4GBIIyotmLddfxwg5QOAYb0r0H9osMdhNAsLHyCRpBgbugu+NlqXel8wCQE2OklLi5tQVipf+CUlxN8aOOssi5AtM5Ovc337EuGgG8H39SskMsvVhP0lwbSuMOaWr9XH9cWBBlp8bA11vI0TpqXWTAB5GX0nnq5jxtClah5+pWny/mass2YFkLc1UNNk8V9WVgeHeQZJaWDx4rb9fXY2X6QTEjyrWuIHAsiVBYCKVmJHQQdOhRDflhiHbd8OOO0NaDhQjsXFY6izICkJt78zFImnjwbCI1Bba73niVuGWdDp50dzbebM8ZyN83eCIoA6CC6XfPGL7V8GjshJYGI7EdCilN1w0AcoeIyL48rCyspu/L6tIqWiok5P1MVYRJw+2yo4nfJJaG7/agfEnLVtBBCNeegVWEZBj5fP0R3sOxxYu0+X/AYEeHqpmBEby6fKtKY/oasgTpYzUF/fjS+EtkPwVgVAMXXhxoI2yeN3rylzB4spIQU8B2srASRGh0OGcLZHgJjUiny2qA5qLaZP53FUSYms3mgRERHyeL8vvzy0yTIdDKtOrXb5ABUVyYYI3Z0AMo9HF3HzzZSN22yyj5MYwRqorLQefbl6tZy9CzAHrVZFpNdfJwWKOBRAlLQvWSKTMWvXyonev26rwUg73ydqgmLx5Zc0aerzz2W++tJLeZJdWur9kjZ3PRUVUVLvQRgGBOCb0KugacDvJcNw8602fDu3yX16JCfzxEuC2TuqPX414nl32mmwMQ0X9uJE3/y6magfPKrFp/njD8/7Xv9fEAqThQSqmTYwyYYjtBBwuTA6fC9GxeTBlzXhmj5LMSWAZ6xGwcfA55+TmNDYUsePl4WOYiK+c6f+uLIykgm//DK1fmmaJ4mj93h+/z0nFJqahBw7I4M8+JYsIRZGDPZElJXRh2Sxx1VXW3Njv/5KnOvGjXSOeQu7Pv2Un1P+/nQYBklaX48WTWE9YO5rNV/34v4TF0cXpJUCaM8eegNNTXSQ3s5PsX3xlFPkyarjxsHh8sVbOafhPy/5eny8u9bxOwZNjcOAgXxxaFUbWLMEkIYJGl8kfI6bguRo/fWcTdi7km8Ghv+SwYv16kVKdA9YtfQfcwwAIMLXjrDqQqCuDvVNfih2RNAT6cnCftbPrfZI9CmUSOH0dIHMNvc4eulTFnl4o3Yodj3t308hpHFuMsbj7/h4HkZUVXmfZrxvn3xu19V1cA35wAH+oQP0ppoxvfaAefy7GRMn8iBoxgzrhVhULa9b12H5kZhbTJnC1Tr19cDeNcX4o1T3FQ0IRM/hcZh4RiwuuJCf//PnewqoxUvQqqNXQRFAHYZdu/h+GBFhLWAQCaAjpgXMTAC1UAIWN63ISFpIxSpmt1UBWRFATmfbFtgOgJh0eZue5RUbN3ItZFycLL9qJ0QVUms7NwoLQReBzUYEUE6OVxWQe2OvqcGWqv6ob/Kjkra5F8cKreib73Js2mR9sR9FbWCFhaZhSJUV2Hfv6zSbuJU9BLsFn4HUEKG6fygEkGh8KcAbAdRWBRBA8bsYE4keHa3CaadxIrOhQTZQ62JYnaLtIoDMf9RcGbUFNDVRsH5YeXnzePQ5c0h688IL8pctyiN37fI8KFFu36ePXGr89FO3q/L+/aTQ+b//k/NDqxHoAFUv4+NlcuiYY3jsXl7OxR319XK7zMyZwJQds/FU/w9wR/8fkRBZB8TFuh/7xRdcoeHrS4csXkZWbWCNjdb3f/YZvz1iBF/StzUNxd17bsdLWeegrNoPOEDXeFoa8QseApyiIk82sq0EUH09fw6bjRgzmw3TotMRF1ABAKiOHYCfFzev/nE4ZG7HUHXX1QEv5JzPT4GlS72qgCQbDhcRh742F566sxhfj30e5yas9iBNRf+nnBy+3sbFUWugiPh43rlht+uk3bvv0lq8bBn5ce3Z40ng1NXhYEaZ9L0BesyWlUVT0YwxSg6HnFUbcDrJQPvFF8mrxHRNiJfEwIE8vnA6SUDz+OO0bVjZPGVkCOPKQVxsUpLcDf7DD22srZgD0l27vE+CNbx/RAVQTo51Aeq11zzlk+npPNP18aH+YhHjxuHLgqn4uWgs/twajq+/4N9/TbWG3CzKcH2YCwNPH9RsJ5olBg/mTMaBAyjPOODmT2yOBowL0PttwsKA+HikDOLXQuaKA3T+fPstMteVubd1xoC77+ZWARKsCKBp09x/1682wx0b7a+LlYxY9jXy4LefKwcx0S73r51Oan/UXJqnAsjh8Cii5OfzQ2GMk6WBgbL39uzZ/HSNj+eKOsZa1wYmqhhbemy7YJZ5NTW1LeDwZgBtIDiYztsnnyTDeyukpfGKV1lZi/3+9fXkO/f66xSaeTPGFtODoUNl4faCbxrxQe6J9ENgIE4/nZbw447jysjqajk/qaqSPf1NQnAFHYoA6iCI7V+TJkm+o25YeQBt3EgLT3sUzYcdRUWeC2wLCiCzpwZwBBJA4pfXyW1gYtJlxBstwukEvvmGMggDh9j+ZWDQIF6kys1tXXBVUADAxxeI6UEEEODVuLNnTz3/raxEo8sXf1UO8N77ZobYBrZ+ffdsq/I2Pu0oIoCkGMDlAnL2IadG35lbKVPenc6D7tRoIYBry/XncsmJYScQQABNBTPw559yka5F+PjIRmkrVrTNcf0wwmpPahcBZH6i6mrvZdRmoGmUIN52G+WYzU6lOhSIYxiHDqWN+5hjZMIHoAXa2NTtds8PR1ScpKVRpmScZBUVwDffQNPIWu/HH+mrFyfSeSOArODjI5+Hv/xC/8/9womStXuBjB0Ir8zF1amrgGXLwBgwM3YT3vk0FP/8l4/bOFjE2LHUpivy7FZEj9iBK8Y44nJ8wQUCd+bjg6wIrkaIKs3EPbfU4fnnvex5VurOrVtb9lsRIUqXjPnOw4bB1+bCBQl/UlITHobvvjOR2SasX8/fa2IinY/G/phpT0RBg/795uRQm+Dzz3u0pUiTmMsEadaECfDroVcJm5qkk6FXL89tMTqazh3zusWYyQfozzI5O1240LLnStOAd15v9BiVnb+9gvyrzEYbVhlvejon2nbt8lBoiDlscjJ1wVrhyy/l78HhoCTSOKfGjuXn++TJ8sRS49xvFczxrNMpH7MVARQWRoGL8fh9+zwJydJSecZ9QwO9AQNTp3pITkuDemNBuc5INDVhw68V7t/tXpYPNNAHkhxRCv/RQ9tOAJlaojd8t9/9eQ6JKUKYr76gpqYCjGHgGH4x7/kxg072jz7C0gd5TDN9updufXO7krF29unjXtj6BhYBBXSu7K8TFN0AsguD3AxMUmAhcPCgwR0BoPXyjf/Y4Sq3KK6Z1mExBJkwQV6jxNYyK/8fA60xgrYS/bXHD9ErrPr8fvml9XGvNwNoEZGRpBQz2C8zbDZZSdaC/+b8+WR5tWQJtZVecw0VBsSl2+GQD23IEMkvHCvW+sPuJOIyItoHM2fS/b6+svWayEeLVnupqXKrqAKHIoA6AJrmSQBZwdwCVl1NUt4ffqACY4uJg6YRnXrzzfzfo48268xZWtquWJtgZWaYm9vsgmNWAAHyYtreEdGHFfX1fKVmTNYLdiIB5HTyr5IxHmM0i8pKcoT++GMeMQUG0rSKDkBAgKx4bsn3zW7nJJFfYhz3clm1ymt7y8QJGk1jAbC2YpDFKAkvGDSoTdWITofdbmqyF97X0UoA6aOW3QaO6ektBigOB5C9j29FKVOEE78t119mJq+6RkVJAaUIkQAST8n2EkD9+/OXcjg8RZMtYtgwuZ3onXdaNUnocMOqBaxdBL6VL0M7pueUl/NOoB07qFBpTlY7BCIB1FzpkDGZFDK3gYkEUFISLaZXXsnvmz8fBelllkTbhAm8utlaiATQhg1U05j/XgkFAdU1uMbxX4S9+Rx/0LRp8JkwFtOm0TSfu++WX9PYQsRlKyOD2j++/ppXWMVT9fjj5eAdoDx3xAjgvPOEO2Nj4RPsj3PiV+PdIW9gRt0i7/UKc/sXQHu2N+NtK5i/C4DUdwBOSNiB6GG9ADCUl1MtxRvE9q9p02hvdLdJ+/khb7ig6tA0agt6+GF36bu+nsdAzOnAwGqdOPD1pcxHZMBMe4T4/UZEEPnjrUgkEkA75pvUaWazEj1QW1E2FBs9rKw05P/wF69YGqNsAfpezN575pjR1Ooo5rADBlCodeedFDOPH8+386oquRXxq694Xh8YSCSwcb4wJtuViPZdLcJqQROlBGYDaAPmoMgqMFq2jMh8TSPFn7GghoRIhsgGPv+CwRHGN6HCXVXul0//nktJBg3zA3x93RPTAdomzf5HhYUW5rhC8v7nb3zxnBAhXEt6e1DqcVyFk1HdG/VNfnC4fLF8V5z7ezeScQ9s28b34vBwSC7OuvF1v6Aid9v3LnuirADaByCIkv6koCIgLw/nniuLKJcscuClrLM9wwuBAGpslE9Bs797UpJ1e5CZAGppFHxxsbXap0MVQFYs34EDrbM/qK7mn4vNJr+htkIsvLZAAJndD8rLidwVr9Hduzkh1Ls3rW3StqtPgQv3rcVTtxRIZI44MEDsSFPtX61DhxBAjLFTGGO7GGN7GGMPdMRzHknIzuZq35AQzwDIgFkBtHcvz9lLSiiHbxbr1hGdmp/P/23aRPpxC2zcSA7n117bzoXIKpOprZXLlCaYPYAASNXFbqkA2r2bm9X16yfT/Z1IAImWQz16eE5ltMScOXJwm5xMrKK44R4iRG/ClnyAxKQxPjkEbIS+krtcXpUNE+OygUbaAdbbh6BpQCtb13x8ZJWHUQLoLkqgdet4Y3Jysuxu2k1HYTc1td36yn36ORxAIWWzObV6sFxd3SIxl50NNNkpoEwMLEXIFKHC1BaizKz+8ZJRigSQiPZ4AAH0MmJyZmXvUl3dvLIAt97K+/I1jdqNzEnFvHk06/n999t3oG2E1SlaWdkOo1UrAqgdPkBmgU16OiXBHTo8TdNaTwABREIbMBNA4nlv7CkzZvAWEocD217nWW5aGtVzXnyRCkISXC4KGH75BXj7bTI/MRlOJSRw1ZChlnIcJAXmwJBCnBAjJAohIaRQ0WGzUZvRf/8L3HMPcRZGnB8RwU9Nl4sq6p9+SoTR3Lkyxz1pkmdny4wZ9Px9+pBHrq8vMHYcw5vPVOHavksR7NNADr5WX6SmyQmOWLluSxuYFQE0eTLw9tvwm/0WLrqRB2dff219etrtcr5jtI+IVhm5ky6kD17MOkpK3OfUnj18be0bVIxAH/09Dx5MBGEzBNAJJ9DwufHjaYu39ErS4VZlNDmxY0MzUjkfH3dW/P3BiW5ZnbuIWVWFvEK9d8/Pj9rAjM+vsVFuuTUbYQKUAQosrRiHGpfBSSfRRMVHHyWlmIEFC+iz2rUL+PZbfv8118gTfQAS6Ike3K3eXq0qkiIBJMZ/3gigH37gGWyfPjKZ//bb5DYrzqq+/nqPakNurj45T9yEikuwaXk1tKpqrPyNbx4jTiVzGl9fOVQVL5NPPyVj8FtvNXUi6gTQkuJR2Lgz2H0yTtQENZd+jfU6pi9iQmlfrnEG4fuDE7G6fDCpMSorkJAgx4ZuZGXRCeo+4BHyXjx+PBAbi2Hh/LPfVpUErV8SADqknBwAgUQ29tMJIF9f+ijde22NHctLh2Fj5UDZ60/YKFav5vtVbKy1KFj0TzPQnALIKpcSxXBxcc0/tl3QNJk9FUmY1qisd+zgC8/AgV569loJMbbaudNrQJCby/M9Pz95oJi4xYphjkFcx8UJl1tdPSL97Hh28CfoP16uigwdSp1rAJ3nhn+YWDMwDzdT4DhkAogx5gPgLQCnAhgC4BLGWCt7OI5cfPkl8NZbVDl75x1+//jxsq+bCPMUMHOQsWhRCz4r3uQX69Z5yOwdDjqupibam9ps2F5SwgNaI3oz0AyLI6qNrBRA3ZIAEtu/Bg+WN/pOVGpI5Elr2r80TY5IL7+cTB6tDN4OAW0hgMTTsFcvyDNsFy+2zIIHHliJaH/aRKpDE7B2fRuWJbEEsGQJcP/9wLnnUhDelhaBwwHxej32WPlL7YIJcy2hqIgqq9ddJ4+MbgnuPDc3F2giIrWwIQoNTfpCKO72Ftid7nAnCalhBXLPSVvYKNFvyEv7F+CdAGqvAgggKbzhcbJjh3wtr19PXs9XX92Mx3NgIGmkDeOvxkYaH278wZ49NALWbidddSd4k4nvQRwY0uY2MCsCqB1qPavX3bSJDJFRUUHreFvHz5lh9v9pyYzemwLI6ZSTSyNpZkwiXrb9WeUmPiZPpvhh0CChC7m8nOQP111HjMsbbxCR/uefxBKZxnuJFfna8nqgmqrw1/dbApY2mB/DTTdZXgj+/pS/midxnXmmJ5+qaWQnI3ofDhpEybgY7Iv58FVXUTL/+OM0acid8BrGwWbk5HD1SViYLPVoLwEkfqd9+gBxcTjlFE6aNDWRFYbZr2LNGpnPN0xiRSImv0A3GXnqKXnv00vekg+rTcgODaKxmdjDZqOk/tFHYdmyJyI5WS8gFRejqDYUs/edjOeKr8cHMfeTz56BoUOBwYNR7QzCbnsvoK4ejJEPT2AggMIDqHYGoaoxiJiatDS3ka/7QzGQkeHpK1Rb6yaF6uv5NcyYtUBz5kyey+fmksXJs8/y72LoUE+CEaBLVVSqtUoF1NhozRRlZPDYwaoFDJAJIPE5Ro2ia0uc4CH6g40ZIxs66fjkE/0h4WHwj9A/AE3DX1/swt7//YZCO7UPBIX5YuwVPL0SlV5vv02nzNKlfBBZWRmdy+4tNDER2QGD8e6+U2mimN2OE46xo1eBEEfqBBDz88Ulj6bQnpSSgm+CrsCCA/rCUFGBE0+0qLHk5cntguHhwBVXyI/x8QFOOw19AosR4UePq3aFILsu3nhqUpEHBSLQx4GeARXuE8dmI8XY9Olwv8aq8jR5gpWwUfz8M7975kxra44xYzzJVPPP/frx91pQ4Cl8Ey+Ds8/mHVQlJe0omJhQXg7MfrkGv2Tp51xIiKwkXbOmZW+GthQ1WkJ4OC98aJrXCZiiGHD8eCLvDIjroNn/xwARcxp64gCeG/wx+gUXe3wxvr6eNeDly3knRXBwh1ihHrXoCAXQBAB7NE3L0jTNAeBLAGd1wPN2ayxfTovLL78AO1cUA39tBDJ2YJL/Rq+lXnMLmFWV6Y03mqkUixLAm2/m1KameYwQ/vFHee9qVY+wCPHqHTlS3mma6eOyUgDFx3NSrLS0jR4ZnQGRABoyRA7COjFR9xZreEVhIU8Qg4OpvNoa8+Q2Ii2Nb3579zY/cV2MhXr1AmUSRl9BZaVlZMbWr8PESL3vPjIS//kPkZce8mUrjB7ND66oiAdvGzdaexR0JsSEMC2t2epuV6OkhKqwRq4uFjWbg92ub7b2GviWFVGwBkCL7kF9/UCLBFDGmkpAD1BT+jookzQYh8bG1vWw1tTwlhDGmi37dLQCCKBDFnkr0fpp7lxaoqurW/CniIigviajUlBdDbz6KmU/77wjE2GtmvvbApxOUhM9+qhHS1ZdHV/LfX1lP5q8PNAi8OGH1NDvzdkR+uOsFKPtaAETCSB3fmW349d3M+G6+loif996q83PK0GswAwZYp0xiEhJ4Y/JzeWLVkEBv4BiYshMx8CwYUBqKg0bq+7r/htx4iIAcmS+5hpiY70xhz/+SBmR/uFMmiSQdfrnPjk6A0NnxJGq7P33yZtEZGVagenTaezzI49Qfmtl0zZxIn0Uvr6kIkpLI/Wx0NkBQPhI/fxI0mLgu+88yV6xlDtypDwaLTOzddmVpsmEowXzYCSWRuK2Z488UaqkhIp+BkQvcDEnkUhKcQrn6tVAU5Psw1q7mf9gJGUdtEf4+gKDUlxug+0fDk7AKhyLeQePxYM5N6HMoZ+PEyYAffpgS1V/mrpUX4fUVL1rJ7TSTb7lN8RwqYRIAK1bx89zMWYUW8WWLAFA8a7x9fbubS1CCAmRSczXXuN8bGgocNdd3q0NxfagFSu8fDAiCgo4YdyzJ4/7HA4u1fKmABKNoEWMGkVv4uWX3UbjbgQFkbmu6Q1kZYmhCsNd93KCbst6B37/kp8Hk06JgH8A//tzz+X7VnU1LeXmJXDrVm7BaK9leK7gKjhcFJD38yvAzb1/5J/D8OFSpfqEyxPQ59g+QFQU6kJikGknZTmrrsLxk0zKsvp6OgCDsA0JISLUSo1+0klg/n4YGZ7t/my2ptNn5d4aAoPQL6iYPi7hwmIMOP00zb1urqtIgWvadP7cehCTn89DD5uN+EsrMEZj7EWYCaCAAL7naJq8fZkNiEVPKuDQnAkcDvpIf5jnxBvZZ2BXTSKde0lJnNkw4l0R5nVUjME8Npp2oCVjOMgNJJMnE2dqnPr79lHNz+XyTL8MnHEG8MnLpXh3yBvoHVRKi5J5dD3kYsXq1fLwgTPO8C7IUOgYAigRgKjryNPvO6rB82wNyMsFnE2Ic+Rh7E9PU6nX3PwIzylg4iIissuW1XdNk1mcCRMo4TewdKk7GKqspKKhCFF63CIcDpIjGZg8udUyHisPIB8feQ9okwpI00hj/q9/SYFuh0JcoVNTu4wAavMEMDE4Hj78sJA/AAVexobmcllPPDYgKoASEkDHdPrp/E6zFO3AAWDfPpwdvwbBfo1ARDg0jU6/229vxVTssDAvOmSL1+pMuMevgKKP1FTSIBsXemlpCz1BnYfSUiJ/xFO9rs7T/90KbhK7qBh9g4oxIKQQiI4CEhOxr07XQqene1VmOJ3AxnWcQBg+Qv982noNbt4sS5wtAgUDVgSQv/+hGwWKo5qXL6fDKSmRAxyxXcYS8fHyKKQtWygCNI+77QgC6KOPSE20aRMRT4Krstn6QlQc5OWBVCjffUdZsYWhrBvigiB+8Pv3t1mhJwqJrroK6FG0A9i+HY6D5civ0ZOW5cubJ6QsUF5O53FTE9peKQ0M5ISCpvGLxpvixMCgQShsiEaZIwyw2z3FRiUlNO9bfC/h4bQXX3WV3AJQWEhEIYjAoPNQA0pK3WPF3YxFXFwbpgvIiIujsOOMM6j1TrfQcUPkBUaPppkEomDHEqeeKks+zMmE2NcyahRtRkbio2kebXCWKC3lxFxIiFdzpd695YlSc+ZQW35eHomtjGvC31+eHiSKPaQp1GlpPDuvqgK2beNraqMDKfX6uebnxyvqHVgkGBMiTLTy8yWJo48P9sROwr2ZNyMnZhz108TG4i+7/vqNToxJowpd7xL+2eclT+NBSXIy/wztdt5eIhJAN93E17CtW4GiIqkdpjkLklmzPEkem42M35uLiyZM4ARedjZ3SXjhBfoePZYbMRDt21dOjNPTiXky/igsTCa1wsLkXh+AYh1jzQgOJtXeG2/QgSUmUgxrce6JCohJk4CpNwxGbAztl3WNvliYq3tKBAZi2lXyWhIdTXu3keQWFvJDFgm2jz4iVdCddwKFjD7EQB8HHkz6AoHLhNZ8k7zKx4c4aAB04uv9NqPD9iIm16TAW7eOSy8CA0nm5+2LjogApk3DiLAc+jk42H0pu7e2oEAkBel7vynuHxRWgEgbqV4qEYFd/sPlKnNdndQZNX588wrfGTP4V+PNNNibEfS6dTy8SU2l76Q1U8Nagw8+0LeSWlq//qocwMlHccEVp+D++CMtZEbRqKZG9jht7ZCV5iDuPxs3esR3hYX8Jf386OHBwZxYc7noc8nJ4YKA6GhP39Oomlz42vTn9tLzOnYs51n37OF5VGgoEaQK3tERBJAVH+9BNTDGbmSMbWCMbShuxrT4SMGFF1J/7W0Xl+HO3vNwb/I8vDTkAwT4OImIefFFD3mp2AJWUSELaUzekJ5xTUEBv1IiImi1Gj6crzQOh9tj5fPPPVU2dXVtmDT25Zc82g4KovYVMQPwogBqapKViCLh1WofoCVLgFdeIV3/l18C995LrRA7dtCK0lyy0R7Y7bzE5OtLAZjYYF5S0mmtRM22gDkcFJF+8QVPCsTguC0jY9oBkWNprk3RQwEEyNHy9u0y8aH79vQKLMMbl67GuAmcxDJXXb3i2mspgB43TjZX3Lix3fpbow995Uo6hvfes+5k8Yrdu3km0L8/BUS+vvzc0jSvY4I7Ey4X5f5WSvjWdFjk5OhPUlaGpOAi9AsqppM3MBA5Nj34s9u9RkHbtgG15dT+1TOgAv1G6wlTW9swRRlyCybiVgRQjx6HPjRv3DieIxQUUCBrtlHLymoFqTl0qEzuWyW5h0oArVghq0aLiqQR9GYyWvI5yTW9/vz5niOPDYgXTUoKj7Kdzjb7YIk5QO/AEgyo4N/5HrueGYqG/q1Afj5www1UmL/0Ug1PzumPn4vGwOmytb5SatUGZuH/43AQAVhaCiAlBduqdFbdbvcUG4kjTeLjST//0UeUBZ9/Pklx7r6bJz27drlJpzPOAMJQA9TX49LEP9Azot6zp+sQ4esL3HILJZQJCUQ6iUXhViMkRHZm/e47fruxUd5sDF8d0V+npUXK4ZALAWIvhwXOOYd3UDc1kSH0Lbfw2MnXl/J4US3YowfnsGpqhBiIMak1pXLJOjeX7VdbxZPbQYO44V8HEkBn+f+EC3qtxIyYrbjs7FpccpmNzrHwcJQMnYZ/1T6GA9Uh0MCwycE3+NGx+UBxMXoX8HMwL0VQjDHm2Qa2Zw9f2EJDac8XezN+/dXDANob4uPlpwfoGm0pxAkOlpf+zz6jy2b5cvoeP/nE9AdiHNunjycB1NJIVrENDKDvUSSJAAp8H3kEePddr/uSuB8MGAAwG8OYkzm55NQoJgobGIeRoz1TtkGDqHVbRHg4qafE4QSffqqHHOHhAAPuTPoBiYUbuEIzIsJygs24cQIXrld1T4zZLJMOgKxCOfdcz6mJZlx0EUb0KgF8bEBsLNLTaVtwTzb398fASP3DqaqSkguWuRsTDNV4SCjWbvCR2EEtL19SgVm1DYoICCBbtdtuI/8zK3gjgMQ93phKJfJe7SWA1qyhgX0AADsldBk1ffjFI5Iwxox1u52UuTU1VEXdudPT/8cwzTkU9O8vS89MBSrxMxk9ml8WojvF7t2ypcSQIV5aCg14IYDCwqw5rQsuUNO/WkJHEEB5AETLrN4APKgGTdNma5o2TtO0cbFmB7cjEFOn0qJySvxmnBS7GdNj0hHZN5xfFJomG7+BLgIjXnM4eB4cFUUTMoz9UtOIA5FialH9Y2jpzOMPfvwRuVmNkteuSDq1qg1s715Px72wME8CyEJOVFnJ7w4Pl6V3rRIQ/fUXjcr87Tf67D77zFOG0KxJUjtgXmB8fCgYE7/HTiIsxXjDo9L1yy8k6/r8cyLBXC7P6uhhhDk28gYPBRBApIdRJnU45JKXIImIO34YHn2UkgoDv//eilawlBQiXB97jC4kURrb5pFM9JU/9hglhc8/T6fhggXERbb69BNlH+Kc1G7WBrZ3Lw9QfH1lM2Np+M78+VRKFUctQA+EKsqBpiYkBR1EUh+n3u7CsC9YCAC9tIGtWQP3F3xM1C6wZD3KaosCSNNaNf7dQESEZ6BxKP4/Bvz95fh5+XLr00/M7b3i0ks9syQxcDuUsuL+/fJIYgM//+wO4s3Xsbh+5+VBPnftdrlfRoRIACUmyu03bSBqGho4X2qzAfE1e5AcrB9DYCD2Rgnsg3jtGTBkhe+8IyUS69Zxj9rasgasL+iNt3JOx1M5V6AuwbN63dhI7ZFSi4kVAWRhOvzKK1TLuOsuoLRHKtKrOQE0fJhpPxWlYmedRSOnxNG8jJGXiHjC6e7j8fHA68fPx6tD/4cLev1JWcmhmH42g5NOoo6yf/zjEAjUs87i6tX0dJ5MrFjBv5z4eL4miHudtwXZaG+88kp5uoaXyYAGfHxIgCd6e4u/e+AB2XYOoPfttQ1M6EvK/JXHTcn+ebyyLW6u0dH8e66ubn7zq6z0TqJqGvwytuLK3stwT/ICXHxXT1x6Ke1rlIwx1NZScSMvDygBEbMhvvVI9dkLzJuHxAA9+Q4LQ16jqTQvMjQ//URyLx0Lgi/Bnff44o8eQvn9t9+QtZef480RQAAlb0b8eOqpsoi4OYhddytWyPWfefNM63FzCqAdO2SCyGokq5kAaqfbrBheGvz46HP7A8ECmRQQgGPPjPXaznLiiTwV8PcnVVBiIl2XZmF4RA8/3D7pL0ztYfIUPekky34ZxgQVUGQkwnzrMDFqN6n1jFhA02QCqDVscEIC4r9+HTEnjgJCQ1FXRyGu8bUEBTFMHVHJHy/uJTt34pgoveU7JITiCOEiLNxSJLkjtCY87tmTuGhvLeIiqWNwKna7HHoYl3tLU8NaQkkJEXhu6AqgnTW94eqvXzz9+slKvIwMOunFAusPP3R8+xdAJ4VIaL76KvVf6eeDKAYUR7WLfjy7d3v3/3GjFQQQ4LkmR0fLFmwK1ugIAmg9gBTGWH/GmD+AiwF838LfHD0Qg80TT6QV18CSJRKLw5isijGQlES/u+su3rlQUkK9vO5cS3IPFGjUqVM5WVFejt9n73b/zciRcnGtRQLI6aQLWewHNp4gOponIHa7pS+HVfuXgVYRQGL1T4SfH9+YiorapJzQNNr0vbYtmatABsQNvxOUGprWQsFJPM8MiZgRHEZHNz8SpAMgMuzbt3u2HAOkOjPav/38TJM6xODIYBZqauQAfsIE90Qlg29saGhHJ5do0GBlLNoCtm+3Lizb7VTMaxWn1E0IoM2bqQ3/55+tW0DNPtXXXMMTuV279FNs715KppYvpye7+27a4TWN8twSqiD2Dz6IfienwRCF5mhJ/MktCCBNA9ascrmDm2Mid/IIoS0EUG4uL6OGhLTo+ufj47kOH4r/j4hp0/jtX3+15iJabAMDaL27915pFKD9xn9gWfko7KpJhHawqH3qtoYG1D/5f9heHIfNlf2x3ncSMgecws+N118Hamo8yOhevfh5ceAA0FhokjF9/721EaXZFV5MwNswCUx8mp49Ab/9ezEwRE9+IyOxN0QYvWk1LGHFCiJ/Fi2iOeY6pPxZ+Dz/co7Avx7ykeyLNI145tmzKd91rxEiAWQYvpo8Z/LyuP1ZZSUw+8de2FarB/KNjRiWKOyntbWy6qs59Y7I2C5bRnt4UxNi/voFA0L0L7GNfj+djpgY+cIxvIDEeezi+0xN5fFAfj7fdEQsXUr7pEig+PnJalQviI0lrvu++3h+ZbORxZS3r8IrASS0gWUWRQDVdI2kOoXSt9hqyFjr1r6CAlK+3ngjZYpmY76iIq7sCApyZ6RjxlBHqYFly/Ruf71EPzI8Gz6rVgALF6J3oH6NJyR4ql+HDuXBqtPp3st21STivd1TkZ0NvLNiGFxBVIJ3FhQhJ4PL0lvyVk9JIcL0ySdJgdVacnHChOanp772msAjmGO/nj150FJXJykiW6UAamcRTlQAGS8/chQD69Ob91f06Y1pM5pv8b/2WuCll2iZM5Lp5GSyC/Xxoee+8Ubayk8+x6QCYQw4+WSvz52SQvnJmKkh+NfIn+Fvc1LAbyQVe/fy6zAiwvOz8QLm54uRYzjpJFpXzJwJhPQX2uyMCXNFRcDSpRgZno0AWyMQGkrtfsE8L9qyll8Pw4Z1jDuCKNTLyaHQde1a3iAwcCC/dMVtLjeXPp5HHyUCuTXzG774gqeOMWENiNAqAAB1CMS+Rl1az5inF4/ut+XGqlXyZL5DNYAWIRYf8vOBZ59F0733Y8PSCne66uMjr5li6pqZaTEBzG6niqsRZLeTALrkklZOUf6b45AJIE3TnABuB7AYQAaArzVN2978Xx1FEM/gtDRiXYyrv6FBtqGHrMgxYDw8OlpWP6xcKeSv+hXlcPmiKkEoT/n6SkaKGct5OeGUU4QLrqEBez5aSSWghQutjTnF+af+/rJhHWMtsjhWE8AMiH+akWFhJJydzRUtjNHkgPPOo/9nz5aZ65ZGUQl4/32Sdv7znx5fhef7EA+ykyc2VVTwYmdIiOwZCkA+TrtdLg+MHHno/SstIDparq69+aZncVKUeMfHmw7JSra/di0nG1NS3IEyY7K/xKJFbZzsPnUqf/H0dGsFV1mZ1ycVDXyTksjL0agKOeudeP6Ranz6iebdDNvl4obEgJwgdiIB5HJRQLhuHZHJTz/tyRmI/NuwYUSMGNUrl0v/vTmhzsoCnnsO2ltvI2e3A6isAEAEUMLZE90bb4UtGpWNwfyFTN4smZlAWV4t4NIQ5luHtBQnXyDbQgCJ7V8jR7Yq2jNX+TpCAWS8vJEXiXyIKKDculWy2/GOPn0oYkxNRe2FV+Of3x6Dlwsuxn07rsXVW+7Gu89Xerb17t9PiW9JCbKziZcRlaRVc77HLYvPxgMZV+GRzCvxZPXduGfnjZhXpU+mKSsDvvjCowXM359bXmhNTSg4YAod6utl5agBs3OzmPm1gQDyiAOzsjjBERyMrLp4fjmLkneAInTRWG/dOneyIr7Pm8aux7kJeukyNBTZ2cTBGZfyDz/IlU03ERwfzxnFmhoKxI391c8P6NVLUuUCwKrVDKV+dI4H+TiQ7BAqFH/9xbOK5GTPmdciRo3iLEVVFb23BQt4NSYqisYwd3eISuZVq+gc5lIAWQLi7y9nElYsq+jB2LMnKerefbfV/heMEVf07rukpnjrLbmSbYbZB0h6Ir0NbLe9F11fjY1IadBjGF9fz1aZ1uwRy5bxSv/SpUTKi6Sj2cxcWBOHDePtVC6XbhAcRD1so8OzaIFyudArsIwWs8gIHDhg6oT39SWGTMh0NQ14/+AZQDit4fY6G/JTpgMA9tfFwllcAYDWkWYs2txISpJnPLQGgYFyThwTQ2Se8ZHW1dFUMUetU2aV+/ShFxJNoMQN3psCyPhcQ0LaPX1VJICMSzk0FEidEEVMztAhiEzu0SrxRmqqpzXRKacQl/rBB5QqBATAU600ZkyL3mAnngg88STDyJOEFzDawMwTONvwpYnLk7Fs22w0eVDK7OfPp6Dhww8BhwP+NifG9C93r71rynleZBhKo7ERI4a3JXj0jtBQ2edv3jx5pok4iCw4mCvgm5rIFH/TJkpdjAlt3uByyUWiO0/fg6Fh+91PnLFL2HvFNrClSz29ApuaePzUUf4/BsaNIz+6oCA4XTZ8sP9EXPnV6XjiHk7Ijxwp5zL9+8t+VQYZFhysLyVvv02yxP/8h/bRVhJAiYlckJSaKtcLFLyjIxRA0DRtkaZpqZqmDdA07ZmOeM4jAjU1PEgxjF4Z49MSAI9xOlYKINEx/phj5CkIc+eCVoS9e1HRGIIbtt6Oy18YIUvQjz8eYAxNmg27c/zcgUFamk7Eu5qAzN3YsxfQNv5FUc3VVxMZZLD227fLhitXXunZh9SCD5DVBDDxT409tKZG6PM1IPpRTJ5MJktXX03/x8S0vgdJwN69chfe229bCEJaowDqBALIrP6R9k+Xy9P8WowaDnP7l4GbbuLnb0mJXCADZN9wUfQCQDapzsqik0X8zk295zNmcF+F/futY3yviIyUPxOzIft//0sb16OPepBADQ3ypn777eTl+MILQGIceVJo2zPw9Uv7ccst9FgPHmn/fp7hR0fLCZx+TdmdAaje14pSUCuxfj31r4s+B9nZ8jW5bh0RzEZ1RtNkLtW4xMTYcNMmeG03KlywDg1bdwEaEOlnR8TIJNh6xfNlIjAA+/z1oLiuzuN5qP2L2IkJkbvhM0Qgts0EbH09yZmsJoK1cvy7CLPip6MIIF9feRqNgVmzOPfhdLZhgvX48dBefAkv7zuPtpoQItTKHGFY+LMPHnpIUHw7HORW+/77qHzoeTz4oIb//Y8SWKcTQEkJ5r5djBKHfhH37QsEBQN+flgYfgk/j//4AwX53NTR2Abcy2NDA/LqBWmEgR9/lL8fTZOTrMREebNrAwEkKhAMAqiHXxXCfWuBkGDUIgQHbHpltKxMVm0uXSozPU1NblmheHijtM24ps+vuDt5AXxCSRFRWkoc3Pvve6537hZJxuT96dln+e1+/VDf6OO53wFug4Ihofvhky3Ic8XovyXvHptNHiv9ySfkFWTg5JNbnmTWHdC/vzzVVPywTzvNsyIibjBmgrq8nMcIjJFc65JLPDPjViAggLamlgS2XhVAADBlCvmD23sBxSVAXh5SQ/QTWpQVGGhN8cn8nvPzZbZSjJEseisuvth0h64AGhPBqziBQQwxY/sBYGhqsuCixowhk+PZs4Frr8Xqwdcgo+d06XzbHUtZ8c6a3u614XCPZb76auJixo6lfXvwYGrrMz7m/fuBxV9VyFP6DJMSsZdKhBUBFBYGXH899bPdcUe7ZCYul1yLFT2ix40DEBwChIRi6tRDu4w9OrsGD5bbQlsyyREhkg7Ll3tOoWrBg88MK3568mT9cp02jT9A08h9XgjQJl4/3B0sr83v7X7Ylt2BdC1s2oSRi57zOoSirRBTu/Xr5dqTed8Xax3iy4udc1bIzJTFVKN80jEkVM8zg0PkWHjECN4yKiohrSTNAwZ0rCkOY+RH9957WJFyLeYdmIQqZ7C7IAh4isr8/KzVf2lpgK3OLldYZs+W/VmtrkEBDzxAS/2zz6rJX63FERAZdGOIVf4BA/iCOm0aZ0DKyiTDgOYUQAauv17erAo3FgANDfileDTKEA3N1x+ffiosItHRwPDhyKmNQ0OTH1BWhpgYSmqiozREHtwF1Nahvskf+fVCpvPXX+RomJVF2nbjCYcNk8ezGmhBAVRRzlc1swLIZpNHLS5YICyKZWUyMyOusgbaqADSNCJ8xIVW02g6p9SCYe4DN9BWE9pDRLMTwA4cIPMJbzjMBtAGIiJIjm3gl194IltcLK/dHv23wcGyscJHH/GKZUCA3KuoP1xU64vkkhUOHjSpKsQ2MLGHrLGRS8E2b5Z3cJBa1niexES5I+n/jluIoQF6olZSgpJiDc8/72H15dn+JbJ5PXtirz0el2+6F1d+dnKrJm0dPEjqnQ8+MA0OW78euO46VD3/Dl54QcP+/UQYG89p5R1cUkLP1dhIp76hCAoP50mMyJ1t3gxZ2vXgg24p2Pbqvu4Pq3/wQXd5jOf4DO+Wno95hcfQ2GFT3+CaNXDLUyZF7ZTPD5E0Ky6mqP6RR+gEFImDhgZ5PWglAXS4FECA3M0C0No3aZKcy3trA9M0iqkfeIBEfunpJIt3Pz44GCG+emW61o7SUvBpJzt3uhm/JRujYc+ngDA7m0SfJW9/jYUFo9zPkzYt1r3PFCHOTdY5yu0ozaETQ+xIcSe59Q2Yd2AS6pr8KQA1JGMOB0q+W45HHqFOssbSKi4TDAykDz0xkUdnxcXAN9/AkVfkERBv3Ur+W8b7Fpfp3lF2oKQEjAEDwoqAwCCAMeztIVSLjQTZ4SA9vRm//YbGRi4OZExDzxI6j06I2YonnuCxstNJxWfzcLGDB4Wt4bLL3H/gdLiwqTIZRQ0RQFIS/viDfww9ewpFoBAiNYaH7+MXrdMpm6ua3XCtIBJA+fl800tLAy66qOW/7y447zzP+/z8PGc0A3Il20yGCF4UGDq04/o7m0GzBFBaGorjhqKyMQTQNISU55K6BrBuyWhJAeR0yrGncRE3NpLTL+Ap7TRh2DDT3QEBSAwuQ1yAkERedhl6p/JWIa9DEBIS4Jx1Dj6qOteDqNvNBgG+vthp702tjQ0NLXoDHyoSEijOe/xxTqgkJ8uDVr6d60KjSydsxLgPoD5os9zLW/J5xhlkmyDKP9qAigq+roSHy1zgrFlEAo0adRguYz8/HmcPG9Y2B/fRo3l1Lj+fSGfDY4GxNnshxcR4Top3pwCMURXO+GBEtuy44zD+/H7u8GpnQTjKHKHIqeuJ6hobUFmJCD87+hWsbqUBastITJT5L+O7GzDAU0DlbQDawYPND+QRPQLHj9PA1q9DmkEAhQTLy11goDWDdvvtnhPnOsr/x4zwcGzvf4aboYxqKsXpU6vwwgvWqkkrodyQIaB1W5QZiklRYmKLDGhgIG15h8nu7qiEIoAOBeb2LwN+fnIG/O67FBH/9RciwuVI19xZBVAxQkzC1iyiYGFF2RB3kFlYKHsA47jjqMoCAKWl7sNhP/+ElGpebs4cdg49ubFq5ueTfNhQlISFUSVJuNj+/JP2xHfWCcmVmQD6+muUPz8byKfox0wAAeQxZwTVhYVCUrNoEb/wBw+2dmBMSeFMd36+tRJAwOLFXA3p68sl2i4XqQvT00GqAtFZVNyFulgBJEFUKYlGoACdPB2ZvbaAKVPkWOfll2lP/vFHTuiNGOGlx18MDMSS+MyZlppwsQ3szz+trR4AEq5dfz11C375pd5KN2mS3LBt7Lg5OfIm88MP0nO5D6uuFsf32gnWoCfbTifCf52H5wZ/jDv7/4AIVuUetWd4crshEkAW8v7vD06EU/OBs64RPy1qWZ787rt0rcybBzzxhM65aBpNyisqwtefO1F3kPd2GQGEuD7MmMGvvbIyej5zkdhYEtLS+EeXn+tCcWYFf+CoUcC990I7djK+O8BVWyOj9rtLYGKFN1frgw9yT8KNW29H5nfb3IlZfr6+hNTUwN/mxKjwLPm6NxuxGxm03U4fglEZSk/n5GifPs23ywgwE0AdmSMOHSrHXiNGEHkqqtnXr/ckFCorifR44QXitJYuJb7ts8/4Y86apeGz0S/h8t7L3JNBvv1W/wh0nyWXxvBT8VhpUfns3WrM/jwUjS4iX1Kn9sTz/8f4MTGGdT2oCnywIRJaKX2+sbGAb101UFPDg7mGBuyqScTjuy9BfUxvibD/7/s+2LyZrAi+nyNcsImJdIL5+roTrsrGYLz0aCUuGJeFB07cgLISuoi2bCFx6sqVtFZXVJi8pF187xnQz+k+cfcGCkoHY29euJCfK5GR/MTOykLRxlw3TxAbVg+/Or1nLzQUI4+LxGuveQaroaGyvYVbBdS3L/DYYyhwxeO+jGvx6K7LcOPW2zE3/1iJvJ41ixSFANwX5LAwnQDSNDpu41yPiWnZLAWgjNdMJMTEyPOhjwSIZKKBk06ydmUV4629e3n/NOC9L+MwQvTIOnjQRNQzht3n/MudmQwMLuQ1gZYIIKuJq+L7jYujPl/jCbdsoZNSnFnvpTXpkkvEY7RhdF9BkTpwIHDWWZ7T/7zgp5+s/agz9/kDo0bx2LS8/LATQN5w6qn8VCo92IhfS/TCmTkAZ4x6doxzLDm5+fnzhwArA2gDISG0Dj71lHXh+JBx+eXUGvvss21TL4WE0N8amDePE66pqdZtDi1A5DCGDDGpxBISZPYOoGz/mmsQHs4FbpqPL76rPAFbqviaOSIshy6NffvafEzeYCUQs1L9TpnCl98TTpDDXysPTQMiATQhcCuwezeSgw/A36cJCI9AUZHJR8hM3vXoQSoss3O6sNa4XMTZ/fQTeUa98ILnvB0Dmkbx/euve59gmpnlA4RTDH/fgHm4ecQqr9e5lQJwyBA079d5mD1O/65QBNChQHQWNve8nHoqpyJraykifuwxhC//UXpYYqK1WZU0ZXNVE3LrYpBT21OS8Em+Ascei4xavfRut2NwXCklov/7HzfLjI3F3mFn0Y7ywAN8dRLLr3ffLe1EmkYjsEtKgEV/xSO3Tv+dGJg0NgJffIEKux+QXwA4GixjtsBAWWk6bx4o6xEjZCv1D0AfkpggNqMCqqyUB3+cfz7tccYe7nSSCmLfWpPTqRgsHyYCqLHR2ru12Qlg4md90kmylL2T2r9E3HILD0gqKkiV61YhwLpgC8C6MmSzef3Ok5P5V+5toNfGjTxBbmig2zffDKzZEiQrowxSxlwJ2rjRnV2W5NRgy8+FQHo6WPo2HP/nU6SQM/rCysrAGHBS7Gb8d/hbiNaoGlVZaepKbGZdaAoOw/oa/T6XC1uXFlGW//jjlrtrZaUsUtq6lYQw1Rl5QGEhihoisPDgOKnXyzAmFC+Ryy5y4uzEdXRn+jYsvudnbP+/hUA5RRJil4C/v/BzXR02l+vrSkICSbN8fLB68n3IC6BMONinAafM8nOvTccfT4STry8o4vb1QYPLD3/sind/UGvXAmh0AA0OjInYi4Agm6cUUjTWEFFSQu6gOTnuyUcAWq3+ATwJ6o7kUBmTfXcNMdrAgfLkVPF73bGDWviaMxgfPhy45p4o+Ng0nBO/BpFNpYCrCaWlOnGpf7akPokkT5jaWkBzoW53LlaX6xFZVBSuvDcWjJlIqSa6Pgvqo4GyckBzoZd/CTmHXnop0rQduOkmuJPPHdV98cQfx8ExeiLg54dqZxA2ZPUA6kkV9v1CX15lF75L7cKL8GvlONyy7Vb8XjocLs2GHeku/POCHPz5J60nBkdrjAWWWsBquc/BgCG83LfXJZAlO3bQNTF3Lr/vkkukjbXgR/4FJAQIEbU+laFnTyLkDDGsvz/lhaK4UCRZlx1Iw12O/8PeWiokNGk2fLJusLvz0d+fEoEZM/TKaIA/UqOKaX+2201VEZBkrLVeGiedxG/7+1MroFUVpjuDMVkFZLNZq4IASjKNxN3p5NmLuf2rOeOeDoTkkaV5kiG7i6NoLwgMRGqofjL7+loXuwYM4N/79u2eT2YenZOUJMsL33iD3x482CsJOHy4LKQaNy2Yv5k77wR8fKQl2JsCyOmUzXsvvJDfzs4GSodMRWE9LXy+laVelRGHDZWVQGMj/P2F5L2uDnMLJ8PpsnkqgAA+F/z55+nfIXgsNjWRyN4YLpGVxUNuKwPoToXVSMzW4IwzrEe5tbH9y8DMmXSa+vhQIc8Ds2bJxbSLLnJv2mK8uah6Kv4o1ZUuPj4YEZ5Dt9szissKX32FYZ8/hAFRcvu+FQGUmEitw+++S2mV6HJgEp67UVTEBc6+NhdGr3nHfTt1dLA7WZTawERJEkCbjM1G/VdGcunj477YNY1iyH/+k7okFi0i1fHjj1vPcViyhFwTliyhriwzGhr0Yw6PAGMaBgQXmkbIyjATQL6+QGpchbyZmhlPRQAdFigCqL1oavJu9AqQouHuuz36zsMzN0oKBG9TSSdM4Otyxh5//HhQv8gFAmjNGkERGRqKnYE84R28awElSU4nBgYXUOLWrx9neY89ljxQAgJQ1RiE+iY/WmRNdup5ecIm5e+P/U16RFBVxSUZhYWA04mKRv3YSsu8xp6zZvF4JGNrI3bd/ApnROLimpe8i1lqMz5Ac+dy49P4eBopGh1NvJdBTNntwGPPBSCjujfWlqfix9rj5T0iJoZXRSoqWjm6p3mUlJAy89JLiZASN38xxvNQAImlt/79yb8G8Mw0OwkRETQRxRCJZWbyonVCgud+5MbAgZ49yFOmNOvNIBpPm5X+paWkQDKjpISSyC2BQlBskDJWZY4ffgCKivD7tZ9A258L1NZiRFgOYvyrKFp7803qAREQ4tuAY0N5j5W76Fxezr9MPz+PinZGBlDtwze3g+v24eDGPCKinnjCrSoy8Oefnu3ru3YB/7zLgQ0VA/F5/nFwaj4SAZSdTX9neFjGBVai5/P34IRtr4HV1gC1ddi8LxobM8Oomlxf56EOdnN1tXZsrNQlD3rAp2nAN/N96ftMiMdpU6sRctvV7r8NDKREec4c4KJLbEA0BWpZtT3dUyrS0wHU0EkzIXI3PZc5Ubn4YoqiRo0CHn6YyqHGSbd3L/kuiNX+NhBAZsVPR3eJXHQR5a5XXcWNI8356P/+R0qBigpaD0SF20knkQLOuFwSEoiL9AkOAPr0gb/NiXPjV3EV2ldNcO4gYmRREa8Iprm20zlsLIiMYcTJCW5udNw4YepbSQ9UhvXGz8VjaI+qqESf9J/obzUNWLgQZ5wBXD+MTxVJPxiH+b8EA2PGYFXZYDoXy0idWVbchGWlemlXyCTnHTwWr4Y+jOq+Q91VQwAoyijBf/5d7WGQPX8+P5dDQ4HwAwIBNDbSfXtvZQw0pp8f+/dTRGvsLT17UpYhuHgWrtzrzsQSmEDyC6obPz/ivz76iILf8eNlXnnLFnqKBQtoLaoPjAJSU2i/jYsDwnhLzHHH0fEzRvWXV19leOa8v+DD9At89Wp5vnxr2r8MTJ3KjTP+9a9WT+Hpdpg8mS8+Lfn2WLWBdUH7l4Hm2sAyM0HJWNpgpAzQj2/mTOt+hZgYvpZpmuf0CpHZNz4DUXEu+l810/bBGIm9J02i9WrMo2cQcfjaa+5rQHxP3gig9HS+dkVH07JtxDBOJ7C4YqJ7mtWApt3wq/Ui5T0cWLaM2oevvx6orMSpp+piY7sdRQ2RWF42zJoAAmivGTKEtzu1Aw4HbVN33UXX/KOP0m0jnLAygD4i4ONDwayZPGonAZSSQt7OH33k5ZS12ahQNn06VXSFouHEiZxHbezdH3sixgADkoHk/hgZrgf1HaEAysgA5swBS9+Gcxq4X6pV+5eB6Gi+9YkfzbZtJpWgjg0b+O0RgbsQWKpfdGFhSDub55gSAdSzJ48zbTbugBwWRidbSgpVRfX2zPR0a3uAqioq9osoLSUSy8DmzZ7xaFaWfl9EBBIDSxHi20AboxffpcRE+ZJKSQH8162U1+1rrpH/SBFAhwWKAGovsrO5DDc21nr1njKFrqgXX3S3F4X71UljWURPTBEREfrerrmg1daSpB8AgkPcfnUuF5/6V14OHAygjczf5kTy1gXu1xmYYAdSBgI2G/buFa7L0aOx/OyXcfnex3Fb+VMoPdN00cFsVsqkUYtuZYoe7ZQ36sFuaamlAgigBXHaNFD1f+dOLNisfwCMATfc0LwUtRU+QPX18iRE0U+pZ0/KCYzPr/SgE/dnXIOnMy/Cf7cdi3vvFfgBm01WbzzzDI1SaNM4Kg6nkwpKRifS6tWkpJk3j37XLAEkKoD69qUP8I03iJZv5+SJQ8WIETR21IxZs5opKPn4ePoVeavw6hBjfHHTc7nosjIqFtHRlKiJhYNX/pyAGmeg/MdWveC//grtgQfxa1aS+64TegoE4++/cx8c4fyc4vjNfT6sWgU05eTKM3YHDvRo2VuzBkCAHFBurdJfNyeHXOyE3iDRvkgUe+XvrccTuy/hMvb6esSF8LFsbgVccRFGZnwFZGcjNqAKY8K5n4/dGQi4NATlZaJ/knxeuwkgey1Wlw9CYX2UmwDaskW/Tmw2+A/oi7P+e5plm0ZIiG5or5c2s2rjoa38E7Db6ePU16fBoXnWlfARI6h89tRTFOWNGyebUImIimpTj7t4uGFhnp2Vh4qAAMo7zj9fvh4uukhug/32W5JWGwlUeDitUXfeSW/1k0+IWHj9deHc1r+HU+M2IsxFF0BRlh2/HhiK4oZwrK/hgeJdPm9iiiaQZL164cpbOSkRFsaXOU1jmMMux4YKWlNY1l6cGvAb/1t9cTwrcjm1oOlvdP58oH7icfi9VJeYl5XSBVpeju8KJ8GlMXcUrGk05Rs+PkBMDOKmDsZVx2bSOF8NdJ05nQgJ4UGiaGSemAiwbG4mHj82EcG6cKHK7oPSRKGXwLhmGQNuvZUIxtGj3aRAYVmA+4NPcAgJgkXbVY8eXCXWrx//LqqriWwVFae90iLx0uJhuPShJNhs/MsXFfmM0dcYmCa81kcf8dbmsLC2eTb4+nK3avNM3CMJPj50Acyda+FUbIKVEXQXtH8Z8EYA2e3CtuPnj9R376G48OabvT+ZeLIsWcKzRaNN0IBRFBsxwjpJsjCAFhEXR52Cl19OI7lxzDHS8zTrbaRDVC1OnUprqRiW/LQ8xJ18Dg7No1YPc//r4UBZGfW3OJ10e9kyBAYCZ59YA9RT7P71wWlw9LFQsnQQNm2ybp1bpi+fzbWAdXsMHKiP6tIRHn5I8WhkZAuixehoYiyvukoqFjEmdIj5+wO9+wA9YhDbJxDxAfp6mpPTfOxusPj33SfL2UUI42Gn1i/BKSc6kZxMw1Fag7g4fj05HO6ObQnu9i+nE+MLBXuCSy5B2ihOFnsMRfnHP6hqdP/90OITcOCAnudNm0YBhOCxKb691FT5K1y2jKuTNI2mH4o1ybo6TzGVe/BYUCBSYivodk2NV98lm00+TYYOhTyoZdo0KmyL+3AX5TlHOxQB1F6YjV69gTFKbnRZRISvXeoB8qYAAvQCYF0d4NKgaQwI8EdAqB9uuIE/ZvFi2kt37gStnj42DAwpgK9NZ3mCghD93D8RnUDRdEMDr+Q0NQEfLO0LbfAQFMUMxZffemZB5mk1eX7CARs7mx4ZuBVAtbWIrPESLQA4+9QGYOcuoK4Oq8sHo6YpiBZeU8XT5TKNix88mCfgOTmWvVS//84XrF69PGPh5GQKeHx8ILsGBwWisZHUvu7x5rffzlvBNI2i/BdfbOUMZxkffCALxgB6bx98QGt3ZYULcDrh62sKBMwTwAzZe1LSYetLby3OPFMWIIWEyJ0IlhBVGqNGeXfK09G/Py+SFhVxxdv338tK//vuI/Lp9de5nVCpMxxv7zud9v39+ym5MipBjPHPr74emdm+yKuLAWwMgal9Men7Bz1HGABUXdG/oCF+mYj2pQS8cm8x0q97RTYoNv29pulCMlPVd6uPqTl89mxA01BUxJcZm43e4733AoGsQThJCWMj9uD8VF7WKS4GXeD79mN4mH5M/v6YeXUvSixTU91V2SFN6bD9IleZk5L0vvzaWrg0G74smOYmHsSumpNOaj5oi40FQnsGA8HBsDsDUVwThLKFq6mHvaYGgT4OJAaWeiooveGUU0gjbrNRFj52LPUcPP+8dS+tFyQm8qWkNTYrHYXISNnO4IsvZM/f++6TK4WGhYdUhNa/h0CfRpzdX5dNV1fjzZwzcF/GtdCie5CPTXg2EoPKcF3fJYjwswM9e2LGZb08uDZxjfy5gBO00yK3ok+QSaZYWQkcPIjz4lchLqACCAxAdTXw2Z6J2G5PAgCwuloE5e0G6uuRX98Da2pHuNnL3bvlCSdvvc1w/v9OxrNjvqGJXg4H/PbvxcMPyybwBnrHO/l6yBhY/yRpCdkbZWFmesMNfN2x2dw9XIX1UUApvb+EamF8bnObMr2sRMa+8gq3oUpOJk/Y1FQSsPzf/5Ei/x//sO6YsAxsfX2pcnsk+fd0JGy21qkuxOrAzp10fnZB+5cBb2qZL77gcUxCAtAjhlFc0VzrzdixXP1UXc2VYXl51s79jHlOXvDWYtYG9OjBt6vqasolxY74piYqZhkwODfxtK6ogJtxHxyaR9LH886j+KqZVpFDxvvvy7Ga/lqnJ252G+nn+/TFY0/7mbfTDoMoVBddBXJziZfq8hawQ8Vll/Fz7OKLD6lV7lAwYoSnG8LIiUFgQfo6UlUlVxJEaBrJj957j4L0N9+kc0ckjEweBDaXE7edmoXXXms+/YPdLhn2iHu72Qeovl7ogiosIP8fgBKZU0+VQqS9e6n++9NP+nKQlERVo8mT8frrtOXdfrsn+VhdLQ9rue02eqw4uOKtt0jV8+OPcmxiwNx8wUX1DKkjuWl8c9e20Q7HGHDsoFI50J08mf5/+GGKt2+/3bsdgMIhQRFA7UVrCSADerAS4VcL1LSOAJo4Ee5KBQAgOBgTJlDSbVQgS0pIVbBzJwCbDxAZRZssQAHAww8DAwdKinCD4V27VjbVX7JE3twbGz1Z6nyXQDoYypT8fDhdNlQ3kbSGMQ0Rm373+r76b56HFBtVZ53wwYrjH/MYnWO3E7N+2WWC8CYwUJa2m3qC9C4FN047zXo/GjWKWpiiXaWIC6jAqIgsBEXRRnHwIJEImgZadF5+Wa7ELl9OrX1ZWUQmzJ1LahxxUpIJK1bIXsOnnSarjnMyG4HNW4BNm9DTViSb3YuOklFRlmbJXQVjQMPEifTV3HBDK+J2wwBj0CDvag4Bvr5yz7Bx2YlVjIsv5v520dEkuQYA2Hywon4cfisdQV/o4sVc/paYCJx7rvs5fi0ZSW8oJQWTz4tHYHQwnYDmBO2ss9yOhYwBk3vuoYw2Kxsri1JQ4gjHo3uuxK3aW8hIOF760/37da+nHj3AbIyeoHcitg44B9oFgnHCokXA4sVSUWT0aLrmp08HZl/2B06L2wAbcwGMwZc14ao+v2F8zTL5WMvLAZeL+uDj44E338SE+6cjIiGYmAidABsatp8CoLw8Kei5/JImN5u6rHQ48gIG4OefuXzYx0f6CC3BGJCczIBYIs2yauOxZ36629g5OfgAbExrW6Jy4YWkof/0U1ILXHFFm8nQqCi6jGfMgESodwZOOYWfVmKMedZZrRygIjAeZwT/RntBNRGRZY4wICwciI/HaXGkJ4/xr8Jrd2bhiQ/74q67PRdEiSQPDQEC/MGYhkt6/eH52uvWAQ4HfG0unJf0F+BDJMX8Rf7QdCZwZHg2TmfcoO7biGuhhdOGJUrcx43T14uYGKQ+chFeHzYbN/RdjNcS/oNhzs2WZHJvv4P8Q9O15CKxssfPtBefdppnYqyz1gUN0UB5BdDYiIQaPYplzHtLiAAx2TCWZ8Zo7TEUpgCd1nffLXWeyTCvL35+1IbT0vh3BVrTxJ7uO+/ssvYvwFots3+/vPebvWy9wmaTDRMNn0Rz+5cY4Bx/vHzypaQc8kgcxuT9d9kyEi59/TU/HLH9y0hSPXjNqGiAgcemTU0UPz3/vHdn2UPB5s2yqgCgYLaxESF7tuBSY20LC0d6OhUFvQ2ZOBSIX9dNN3FOz+k02SvgCFQAAXS+/ec/dEJYTQ7uRJivrREjmdxiYdUGpmlE9sybJ98/fz4Vew27jo0bPQvOLY1wLSykFoTrrnOzLqJfs5kA2rxZfzlXE/rVZvCJfLriKSyM13+bmoigefttuh6NzoJ167gtYm4uteKLCr3ffuPFipQUHkrceCNPLYqKqP4g+v2I56bZimG3UDtJmSbEYWb1gIDTTqNt7vnngZQ8IW41Al2ALpa77rIuxCp0CBQB1F60lQDSpbhRfjXkfeFyISjI+3RJgHKapMgKfod/AKZOpaRYDI5ff12owiQkIC3qAEXW//ynO1kVE4u5c6kw8qPsR42mJtnMb+dOebgGAOTX9+BJi0Ev5+ejyhlMKiUA4b618Fm+zFpyWVICfPMNjo/Rs8h+/fDbQU+Z8qpVlCw7HCS8eeklPdAWJc1ffcVXs/x87LztDeQsyQScTrfhpgdcLkDTcOz4Rnw8+Dm8P/INPDX4c9x1H1cPrFolfDbh4dSCIo4pLyighen226lH45dfqPVH1Eo2NQHV1aiulj0ZJ41pwM2n7sNrr2q46ipdtFBaSu9D0xBfLhgIA3L7l3laRTeAvz9xjF9/7eXztvqDBx+U2iJbglnpn5fHA2x/f88uskmThOsjNAyvZp2FO9JvxNx3irG6fBA2VgzA9rBj4JhMTGqjywfLy4fTjhgRyd+Hnx9FhkYiMWMGfQfCyIqpTb8D+3IAACvLhuDefXdgU+LpyGV98dTTTDL3Fkd5T7ppBEKnjgZ6JaKsnCF/xuUyCTp7Npb/wCNS8VdR6StwS9JPeHvYO7j+4ho8O/gT9A8+iJisdRjYr5E/sKwUiYGl6OFfTZtoQgJ8fYVktBcl0MPC9tGCcMstJFt44AEgPR1pYXkYE0ZBjuYXgGfeCMdbb/GnnzGjeYsOA8nJAHrE0Khuezz27HS6G8dTQgopumirCzNjh1xtnD6dAqQWBB8dDpuNPmrx8JOS2pAcCgRQcN5uPH/NTkz020SEIACEhyEmJRITZoTQi8yahR7/vBZjxjLLDtvevcW2UwZER+P4HluRGFRGAb6ozBSSqhNHFsudf/p1Mr3HNpzZcy38bE4gNBS7G/u7q4ZiRVEino45Bj1Om4gz49eR6uijj5Caonm0SPd2Ceuh/jmIBNDGsv6oDtPXlbFjKbI1nydJSXD2TiKjbJcLKCxEvL9epe3Vq1VJs7mTFSCeqc3WO9HR/A0EBJDPVVtGMv+dwZisAjJkNozJTsSdBJEA2rePErrZs3nNYfjwNnalnXQSV4Ht3k0Jp1X7l4GgIHkTthoP3Q7cfbfs6+d0Eve+apWcXB57LL/UBg40XXYBAegxui9ixiXJa31NDRXZvPiFtAuNjdQ6bIaDbAeQno4z49fhmj5L3VlvVhZt9Va+LO1FXZ3chZqWJqtNs7PlFrAjUgEE0DkqEo9dhJQUHtuEhOjLqLi5i+psA3PmUOuXAXFDW76cyC1Nk3vxDZgJIPM5vGgRnd9OJ52P9fUYMoRvLwUF3PqhspK6AQAAZWWYEKIzh/Hxknv0WWd5bmdVVXSYVVWep319Pf3ugw90Py6hcDpzJr8dEUFclRXi4igkNLB9O0/tqqv5e/D1BfqfIhTydu702i3h40NhRdoAh8yQi6afCocdigBqDxwOPVmMIKKlNRlERASQmIgw3zqcEbcOgc4aXHZZyznMMb14wBsUanNLCM88kxOltbWCh0xwMAZ/8Thd8YIEeuZMvsFUVpLPn6HuEY/h11/5c1kRuHaEosqpy/z276eVIC+Pt3/ZbIj0tRONvHOn5xN88gnQ0IBp0enwDQ0AYuOwc6enwaB5+ukff9AiVD3+eNl9ePZsWtgfeAALfw8h1UNWFqYfpxlt5xzr15MJx913E/1urGJxcZg8w18qFH/wgaCG8vUlreS993qXuFRVUV8SQKvu/fcDl16KTc/97F4De4bacdeuW8HuuB2+P87H+edTi/qESE6hjwowNfeKH4Q3w6hugMOp/DX7AImS83HjrPO1G27Qk1r9JMip7YlPdk7Es5kX4vHdl+KBZSfjvn8HoP7fT2H98GtRkzIaiIxEXJzJeiMmhsoszz1HFWZAGqc5+MDv6OGiMl4NQlGWNBYIouujupp82A1ecM0a/rSTpgdg+Gje4rFlK6Pn19eS3KoIZP+eAzQ1wd9fyMHtdveFmxhUhrMeHo60kTp52dSEiXG6P4qzEais4iaIAoPk3vhtNvinJvEpgcbzb99OrsQbNuCyxN/p/pBgqRMxJcV7wGBGcjLoGoqKQnZdT+yxJ7ilhwOCC1vf/nUUISWFT6QJDqalpdUdbCEhXIKiaUj876N4eMAX+HjUq7h25EacdpY/Hn/CBt+nHydm1ooEEWCeBmaL74mLUjfRd3b77XLWKrhH+ifGyiNxIyPh5+vCpKhdiPK344TYbe6JWt9+S0p4IyHy9bUYYHj55fxD2LsX7M+VHiqgRLtQbtQJIFFtkJnjh5sbX8dvZ74K7dHHvHrKFY88EU0a7SPR5XsR6KMTp63sB4yNlVXp0dHyZOQ24d//povptdesmSUF7zAX32JiaO1qlZSuYxERwWMyh4Mm7RgtHTZbi5eh9ROKCdFjj8kzosWN0cCllxLxOWqUbO5xCIiLo/rWf/4jq4HefFMmgMRlIjDQs141aFo8eSl+9JE8WWvbNt0YrAVUV9PjbrmFZL933EEb7Ny5sqfQwoU8oAwOlscv/f67u3p0bp/1uPNfQe7D2L9fbo85VOzcyTmBpCQKRcTlJTOTW34x1umCtaMSd9xBBUm3FYAYM5sJoOJiuZ/92GOpDey00/h9a9fSfeJ1Z8AggOrrqXf74ou5rEfT5ICvvBz49lv4+4u8rIYX7i/Gnoc+wBPnbUVhHp3D/iWFOKGHvnCY2hhOPplyk4cfpv3G4Iezs+m9G4RieLjsJTpvntwSFhjo2WI9YwZ5FiYk0LWbkkLE77//Tde9oRCqrOSXl2jzk5QE+MdF8nzY6aTK/c8/e04yNLBkCW+Ri4pSBFAnQxFA7YG/P5ULPv2UGjGbMy4WoW/YN/X7GV9d94v3cdkCpsXscE8JmTGxzh0fR0XRKHMzydGzJxAZH+jRKuTvL49XNG/cxqLkcgFf6gb3Ygunew0K8Edeky7zq6igzdRuJwNoHxvQowci/fSGajNrvnu32/0u3K8O407jffCGKZ4BK+O8zEzg49/7kaRSR+UPy7Hl+jewJDMJf5aluY/r9BDTawssPLKyKKE3oEv+r72WF9edTuDzz/lD7HbgN9d0lD72On9QUJAchM2bR0HK7NluXeSW7/fRAudy4cSKuQix6xM6fvwR0DTExWp4JOE9/F/ah3g45SucGfiLXEkQP4huqADqDAwezM+/rCz5tPI2KCcoiPKAKScEwN/m9HxASAiys4H//tIfv4ac6b6Qjj/eIkgPCSFWyNht4+LcuytjwJRovSrbuw/g54eQEG4qnJtLMe+nn/J4wWYj4kos0G7dCiAgANmX/huzC87A/RlXU/tn1l5MSKsmo1ujEmUEuykplPAI5dmJjfqFXVYOaBq1fw0eLEl1evemWCU6GrjmrnD43XMH9aqIVbzqauCTT5AaWoDxkZlAMJ/eNnAgieLMA928wS1YSUzE3qb+RAAZzxVSeMg+FUcqrr6aPGLeeqsdKqTbb4fb/VhnmCP97DjnzCbccosQ97Zygo2oMJs5KwAJX7xMbYHTpslZn7g2xcfzqToAYPPBhBENCPYh2ei510WBhdAxbtwox9pDhvDDd6NHDzlp/fRTzJjqdF92gYFAQqlgQKArZxIT5RarqvoAvPL9AHz1tfdsu7A/L4708hf6MNrwRYix6k03Wbyf1iI2lkq7yueg7Zg8mZ/jxxxDmV9bzLM7EMYcC6tL7vTT26k0nDWLF7yqq7n3W0CAtX9eWBi1xT71FGWBHYihQ4lvMdpBqqtlPy8zH2VuA5N4/iFDqBhnYM4c2STRMEi87z6aavfgg7RgGq3Kdjsl9OvXU1HxJ95yij+E1tXLLnN7fgHg/TEAkJqKk07zw6WX8ru8+f9+/z114rzxhof9nleIXinGKSkSQBs28BpkZOTf1/KrI+HrS92z7nCnOQXQTz/xLyAtjTom/P2pp0pMzr7/nkvDEhN5gJibS+fpsmV07tbVEVmkaSQBFOXfAJGXpaWYMa2JOiHStyPzl2z84/1hyNxWD+zdA2avxj97fkLqWz8/PtFLQEwMvceLLiJS2YBgNYTrriNvOlG5JxbZp03zFG0xRuf47NlU83z5ZSJ+k5M9xZZGa6MognKHCSL5vnYtBTg33kgJq/gHjY3k72HgvPPa5OOocOhQBNChoK20vXAF2TKsp1iZ0acpB88O/gS3JS3ENZc3Sr9LSqLJ0WLA0Vwxffp06wLn6afTPmlg2TLajw1210hYCQz5oULCppdMKpwhQGAQ0KMHtbkBtMAuWEALYmEhpP6RSZNw/KWcov7tN7ljTBS+iMqcZcuA6umzgOOOw47qPrh68914eNtFeD17Fo0gBvWZJy96U36SlSvl8ahiIqMTK8bIX/G19u0jnuvOO2lBfezdBGivvErSnc8/JyLJ0H7X1lK2L4wh21qVRPR8djZG1AlVhKIi+kwOHgTKy5EWloeJUbthczrkxnCxb7kV3hRHI0JC+Ft3ufjX6uPT/MCb2FjgX0+GYM7xH+De5Hk4PmYrjonaheHh+wA9MV26VG5L8erVYYagAjq+xxaw0BAgLhYJCVT0MMRCAJE7hmcCQMFgaKjnOOnHHwfufDYePwScjxqnvjuXV+D4Px6j8s7VV8saX4P9Enb5pKzf0CuuESgthb/NSQbQJn8tgK73jz/Wr60TTqCWvK++ooMwAhz9Grk8cRl8w+l4kpMpCWgt+QNQzOTnByAoCCWDJqM8eQwQHY1AXycS+/nKTuJ/IxhtAe3yfujZkyZbmdHO5HfQIAr2brxRXwMDA7m7d0KCZ6UBAOLjERjIu20YA05/eBRVUs88Ewm3nC2pAsSWY6/X7XnnSWPSwv/4AXfdqWFQigu3pi6Fb5agANI3M8bIZPnhh+XP8ttvTUMEBBQ6Y92vkxBYzn/Rhiz9oovoOn/qqU73G1YwEBNDSdcbb1BRros98o47jmqCYidWZCQkkqFNSEmhEry513bw4C5hDEJCSEBtxrHHQvYuhMwbAxax6cUXc/K/qYku4I0bKeB68EEqqO3aRW1v6enN92cZFcSSEh64+vrSFzFihMeeBsC9Vs6cyY89Pd1TjV5XR7xTWRl1+99xBx3mokXUQnzeeaTg/O47OecXu/WM0F/k7ERRxBHp/3MkQFzPc3P59+9wyGzfuefy64kxqgZbqQhPPpkHo5pGklbRayovjxRtovrHgMMBvPgipnx2C65pnA3fOpOnUEUlbnG9jWOidCJ02rQW17NTTvEM70aMoJAqNJRUiNde67lUtMdWR+w4NQggyf/HIHxPO806L167li6Yxx6jSu6SJTzXiYyUbTYUOgWKc+5MiFdQRgYtIC1pgouLMSSsBEPCcoFenmNDU1PpevrPf4h/aM6HzVjXHnmE35eURIfFGCmHjc4o0Qto0CB6HSNRzvNPBgxRhd6PU+4IpaQhPAyRfnopVNMoODNcqg1DNV9f4JprMD6W1rfqapIubttGi1d9PZcy+vgQm71jB60ZDgew9FeGs2+7Hf/9dI2b9CG34BQgJwfnxK+mB77wAmXjfn6UDXiDQKwMHUpkl1Gd+egjOj6DO9q3DyivYIgWG/4vu4wkzYDk+lfkiMSBhigATQgoP4DU/qbIYvNm697p/HwK+MwTwP6mBBBAibLZw2/EiFYQEYwhaNgATK9ahekxVJLT+vTFywMucCuJxCJQq72ER4xwE33JoUV4/JUI5GoMJ5xAG29iIhFVourBgLHP9e5N+15FBVUV3aaAPXoANdXoWbEbZ/Zci3ERmUCO6UlsNmDKFLqdmkol2MpKsKpK/Is9iR8C+2JSfAbC/ev541qCsQicfbZkipgcchDPPmTDvkpKcNra7u/rS4qUPXv014iIBCIiMWDIANieO8Yzc1BoHY47jk4aUT55COoHsVoowXCCNaYHGNAN7M4+m87lkBAgLS0CmPyg+yHnnSdP5jbg1eYmNJQYpQ8/pJ8/+ADTI7/D9KAgIFPImPr04f02OiZOJFL17rtpCa2vp61HFAAYKCwEXWd2OxIChNJpG0bCmb34FLoIYu9VN0B0NJ2DZ55JMc3kydb8aasxYQIlowsXUmDW0IBWyccPE0aOpJcXrVOsvI1EBZCvr8UUPB8fUvjcfTdtgPX1VF2IjJTlDCKSk6lqMWYMBWUPPURx5e7d9LNYzRk2jAcIKSlytgq4izjR0bT2GR59v/wCXHMNf9iWLTx0BSg2ffxx+al276Z/H35I4eC558qCJiP079mT9k+zNcoR6//T3REWRl9wWRnlBIWFFJytWEG2DQB9+ObNz2YjG4d77uFMHWPEtuzbx4PRtWtlp2+ACt+GKzNAAd/P+pTV9HQwAOcmFGJMxF68vO9cZCMZqK7Ghb1W4tQwYbMUW9G8wBjEkpVFqYK/P9WFjLSSMWo1HzaM0qHCQlpK2jNV3awA0jT5knITvvHxZKy9Zw/luVu2yI7Xf/1F/iKid8O55x6yYb1C26EIoM5EfLyc8e3b13zF0eWSN0IvRqnDhtH1BrR8DY0aRQuA4e8zaxZfLO6+mwQsZuue0aNldXo+hB90U4cKZygQFAiAIfLyM4D0jXwHNM8NvPpqMqQFrafG5K5lyyivFrueevWi4OH007mZ8qJFQHR0ILJ6HgPUZMHf5sSkC/ugx8AwDOkRh4kfZwEOkOTzmWdoITXknwEBxIC98ALplxnzKE1dcQWfViNOrTFQVmYiuCdPpu9RlJjGxGDbMf8GtlArzpCwXPjaXBSQGBrizZutA9eCAvrQi4p41Ssiosurm12JIUP4Hmqg1VX3wYOl5n6WmoJbb6LTU6zCtcrE2sDEicQWFRYCl16KMWf2xhjTQ664gi7Z/fvp64uMpCDY2CgZo/NdLCAxRqfTqU/3w/DyfWDLNWCTL49Aw8Ioe545k5toM0ZR59tvAwCSKzfjrv6b6XcjRsrGhq3B5ZfTJm0EORERSDsmAmmH4POUnCz3iwPAwBQG2A6jedTfATffTB9sbi4lRYerlGxFAAkmA94InYEDKWF0j7cFXTbNdjudcQYZQxrVwYoKeYTvoEFUcrdAYCBdx598Qj//+qs1AVRQAFrEc/dzBVBwsMrEFDoMycnWXVrtgp8fMa2zZhEB1O5+w47BlVeSsjU7m8h9K945OZlqVvv3Uw3CsrsjPp6KZ088QcyKGPMyRtW/gQNp/4uIoBczAtYePSigNYK0lStl40rR+2fUKDlb9fGR4r6TT+YE0K+/0t5tqCasYsDm8Nln9BUZ80kSE/kWzBiFiuIMGUApgA4r+vXj51RODsVNohz1tNOsbTwMCc0//0kx++TJdM6lptJJAritHCSsWsWVRnqxG6WlMjkZEoKky07CyydPw5r0UAR8/SnGZQuFnIEDW83SBAXRJfTrrxRPWu2tKSkUHu7bJ19CbcGAAZQ+NTRQarJlC/ewCgyUTfDh60vX1+DBxEAZE5OXL6fPS9O4PDciQp54qNBpUARQZ8JopDSS0R07mieAKir4QhIe3mx/ZFvI03vvpUpFbKxcwYyMpI6mTz6RpyKOHStLCPMbPYPk8sYQIJSkAVF9w4BLnyWXQLE6PWiQx2jtGTM4AbR5M60LVrY3xx1HLeF2O8ls33wTQEAgMGQIzjyfelcJPQH/G3i72V9/ydnHzJmUkbz8MiUZqakeK2ZyMhFT5imi7vdabrqDMeDyy2F//AXsr4vFoLAC2O69F1t+GQj0PQDs248RYdn0ARv97ABFUFZSSUODfIQYQHcGzF6fjLVhUrL5jwcORFAQfRX33UexZWBg64UyAGjXff11ukZFtz3TMZ5+evNPc+qp5MflctG1cOGFxunIABwHTD+OTvqdO4k8TE21VsyceiolCW++KRtimp3+WgN/f1ok7ruPCEhxvEs7YZUItXlikoIngoOBV1+l88Pcc9GRMD83Y60mS84/X16Cm2vbBEDn35NPkj/Axo3caCQggDLPM85oVjU2YwZ5bmkavW5JiWeCVViov05oGHoFkCG5YVitoNBt4ePT5eQPQJfOCy9QzDZ4sHX+7ONDncVZWS3YvPXrRw988knuEu/vTxt0S4vFlCmcoVm6VFZeiH87apTchz1woOSdMHYs5falpbTcrFtH256myQTQ/ffTy/z1F4kFZ84k8nv7dopjDYsT0drE7I3Uv78igDoVSUmcGMzJoQ/bqEb5+cnjsMzo04dyiV27qMACyIGLwfIZz9XYKLcZjhxJ1+tNN9GmY7cT23j22UBICHwBTJkKYOLFwL+38er7GWe0aS8KD4c8kMECliq8NsDgdIy9XOwkGTiwBSF3v34UT150EbVViKba557baq9ChY6FIoA6G2YCqDmZn+gF04E7RESEdR83QBf5tdeSZHXBAqrspKbKXgoHasPh9LHBh7nQpNH/FY2h7os4MhK0gf/jH/REGzbQbjp9useiZuzD9fX0douLrQmggABapw1iyjie0FBKMCSccgqxNIaLs5EQ22y08ALUYiWYSZtx2WVUUDLWcn9/LsbRBxhJqB8xAffVPI68LAfGTw/BI0MHYdtLIM1vkwsjJocBdz1OmbARadjt1o6CRhAjKor+pgbQBnr2pCqaQb4NHtwGYcuAAXRiGyoanYAcMIDaJxcvJq+9tvjaAKAT1wv501oMG0ZkrI9PM56dISFwj/9rDieeSOf1s8/SeRUSIldB24L+/YlM2ru3Q8ZSWxFA7ZEhK1hAHi1yeGAmgGJjW+1BMnIkXWtGbtciAQTQevePf1AGlplJWeTYsa0inWJi6OPYsoX7pot7hMsleHXE9EB8YAXd/puakSsotAcBAS0XYYKCPKfVWyI6mnwM5syhRPnii1u3QRxzDN/bxcDRGJBgYPBgLl8AJA8/gELDk07iA1AWL6aQdd8+Hu+FhtJ9U6dS/CnmrPHx1EV0xx2e3WtmdZTVXqiEh4cRYpF92za5I+G441o2S+/RQ5ab9+8vx5MABXDXXksGYCKM+KtnT/IN9QZ/fwpGv/2WEqhWm1F2LoYOlYs5Blq1pwO0rz/yCH0PCxZ4Dn5Q6FQoAqizIe6GmzfThmTIdzSNfjZ2lsNEALUGEyfKm3tgIB1CSQnQ5BuIg02xWFI4DN8WHosY/yrUufxlAgggsufkk5t1HPPxIYGGQdBv3y7v46LtzamnAvPny4rLCy7wkrhffDGx8aIJy3HHeZopekGvXqTcnDOH1v7oaG4j5KEAArBwEUNe5DBgDLC+ipJ6+voYggf2woBXbgeMKtmoUVxCaiA4mM8LNwgg0UXwb54tG+I5Y3pdm3gNPz+qFP7+O/WfCGWQUaMsxlF3MtraodUsRoygcdLLl1Mb4aGYTyQktMEUqXn070/foXHtBgXxDjaFIwAREbR2GmZouv9Pa8AYCcref5+WMVPu1fIfp6a2Wd10wgk8UP3tN/IiMmoPJSU8do8YEIvgmbNoUT/vvDa9hoKCQgciMBC4/vq2/U1ICCkzzGO6zcyUnx9t9Eafl8Wmf9JJZLGkaRSP7t0rd5SNGcOVTlaChfBw4qxFZQTgSQBZ2YwpBdBhhEgAmf16xAkzrYWfHylajIoGQCfHyScTg2goVtskUwfFaryVoVti3Dh5OnJaGmkY2iw0Hz68jYGAwuGAIoA6G/37y1rT778nFqO2lpzldu0iueBpp8lSk26wQyQm6qQGY1jWNA3fFlIPdYkjHAjwd2sA25rQDhsmE0Bi55MofElIoCKwIcmNiWlm/WaMGrldLmJugoPlsaOtwNlnk9khY3LLsLnCU1fn6TEtttANHWqSSI8e7UkATZlC7oMAlacdDtmMyawjPpxwOslRrlevbjWW8YILqI0/JqYdAwPuvJMivOTko3/eas+e9GF1IwQG0ulkdDcOGKC6bY44DBrECaA2Kt/69PE0Tj2cmDSJK0tzc0nxn5JCS9uiRfxxvRJZtw+6FRQUmsGUKZ4EkDEhU8R111GMmpRkqZiMiyMlw9q1RAK9847c1tIaIeyoUdSKY8R/MTGe6h7Dg0UsZHaD8P7oRe/e9EWKrVkAKU/a2xOVkiITQFOnEjF00km8/2/wYKEafnQgJYUcLPLz6RI7GhsTnn2W3tfpp7dtyPeRCDV+pbPh40PqFAPffktjpt56ixqDDcICkBVAXgygOxOiVc7cfJMEI5D8fwIDW1ZUmiFyG5s3c3k+Y56GZhdfTJyEzQbccEML/ARjZDj99ts0PrtZ51HvTwHIC4GZAPr+e/oKvUEc9w3AWnIyahSPFDSNDOOM9rCIiENuNWo1NI2k2HfcQcaMZoO7LsSAAaQi+L//a/skKvj5UdB3SONYFA4FYuWzO/v/2O0U4HSjU797QByLezj9hjoAgYHyZKJHHyXbt3vukcn6v7m1WufA5SIWTvQm6yJoGrV2W01pVjhCMXEi7e8G4uOtJ6YmJNDUsEsv9Vp9EEdm79rFvXoY4xYwLeGKK2jtCQmh8NP8UgEBsvrVZjv6E80uhb+/nGQMG0Yx7g03tP85RUW+vz9X+px5JrF5vr7drgjXUTj2WHprRyP5s2cPDbb++mvgxht5U8bRiqO8FN5NcdJJ1MuUn0/ZxsMPk8eBgaIioKamS1vArCC6vLsCgwGUw8ZcuLP/D9jR7zRkJQzyaqjfHFJTeUvtwYP8/vh4T4Jn0CCqzDQ2toHP6YCVStygxRawmhpZ7XPqqTStSkwePYpNERGUDWdn8/vS0ugNFRfTz0uX8t8NHdp5colt27hMeutWkma1NvLpBCjVyJGLyZP5SHCrscGdAYeDLrFevazPpdxcqnBVVlIAMGtWO1+ospK00iEhNFXtaBh1f8IJtOA1NZHfVDfHiSdyoWVNjTyPAKAYvo2i0KMXRtvx4ejLfPll4I8/aI97/vkuXcQ//ZR3hT/ySBu8KxS6L4KDKUYx4pZjjmn3OdarF/mFGV5ABlJSrAe2WsHPD3jgAYoBvR1GcjJXw0ZHHx3bQ7fGP/9Ja9DAgUQAHeoaNGoUT1qmTuXG7FFRwHvvdYtJfQptx/ff89uTJh39X6FadroCPj5UJjAgkj/ifd2wBcwNXYJxUsxmnBCzFXdcWYlXXpGnirUW/v7WFjfeeJu4uHaJeQ4JIgEkfi3z53OhTq9e1L0nepqFhXkZ9CaqgGJj6fsVg++NG/lt8xSrQ0BuLvnU/fyzPMDADXMvmzGiTUHhEDF5Mqm3XntNmsDbZqxbR14Ln37qqepuDg4HTXG5+WZSkplRs7sAT129F5V/pgObN2P+fw+0TwVUX0+GjosWUbZpbvc8UmGzUX/D+ecfEW2Uw4ZRd5d56/T3p0r/Cy90i22167F1K21ct9zS8efqhg2UeAEkp8jL69Cn1zSalSB2S3vD9u3ydCaDL1A4CnDhhST7i4hon6+LgAsu8LSKbM8chOY4BlENqwygOwHR0bR3DR/eMQS0MWzjllto7RTRTSb1KbQNZWXAihX857+DN/UhRXGMsQsAPA4gDcAETdM2NP8XCm4ceyyxHsbcSDOys7tdC5ioAEJQEPxtTlySuNzil23H0KGeozG7k8RQ9DWqqKDE0+WiSfIGLr2Uc3tZWTRs4KKLvOw3xxzDpUOGwkYkgMTM81D9fwoLgblzsbJpEl79c7x7EMbcuXTMM2boFaisLJpvKmL9elKkNWeerWlkhBQUpCQ6Cl7B2KFzmZmZpN5ubCS5bmkp2Tu1poL6/fe8bf/333W/UacTWLUKTd8twPOLxqOwko9oKdqwH/vn7Ea/K6a1/gCbmojlEv0B1qxpHzOucMg4/3zyds7KooS/poZUXR3kbX504Kef6H+Xiyb/9ezp6VzbHjidVA0XsXNnh2zs1dVk7r10KR+WefHFNL3TCrW1JEQSt1VjErTCUYDUVOCzz2gjOERy2t+fcvqnnuL3dcAgTAni5XUoo7kVuhBpaR1anO0SVFdTzF9VRV5aHTqR5MjCTz/x4RBpaX+PuTuHWsZLB3AugP+29EAFEwx/mn//m37296fRfz//TD/v3StLTboBARQbK4xDDwjErIQN6OGvm98coiRn6FC5Ogd0LwLIz4/UPNXVFCdXVdFto0c0OpqUoAD1eD/zjDzQzQNDhgB33UWSnHPPpfusPkN/f+u5oS2gvJxkzE1NQPzKP1G6wwc/HtSAITVuH5yiIuDVV0lJMWUKMHXXb0g1y5Y1jVbGK68kudO6dZRBGWMxa2tJ7bBzJ30IqakU3Zx8cjNvXkGh7aispKKbqFwzBAstkUAVFdTX7X6u8iY4v/kBvgsXACUl+Hj/idhcabrONGD9q3+iX0SFZzmoooKiBVFComnA7NlEmorYupUeayQmLhddZIos7RQwRkmWSrQs4HKR8Z4Bp5Mushdf9N4OVlJCEyorK2mTa2wkRas5GfrhB97nYmD37hbJ0Pp6Gr89YIB1Ll9URB0dZi++r76iUdxW9lSzZ3P/cgP79lEs043mHCgcCjrwi5wwgQZN/Pwzndod7VmXlgbcdhsJ4o5SqxiF7oy//qKAaMcOzoqvXk1rvxU0jZKJI0D52x44HPJwiLPO6rpj6Uwc0repaVoGADAVyLYPI0YQCbR2LUlFgoI4AbR5M6cjQ0O7RTLNGPmArl0LREUznHdKBLAYRAYcIkGVluY5GcHKx68rER3NzZ7LyrhdD0DHKiagjLXiKzP7aFgRQIZBUhugaSRCSE8HAA34qz/g1D/MoiL0HBCKujoisQDiGRd83YAFW0fg2Cg//GvAt7BdchFvhF+8mKLn5braa/t2MlOcOBF46SWuvy8rI7XDmjWkYLv77jYdt4KCNzgcdE4bokhxrfj1VzqXL7+cuNK8POC77+g0PfZYUgZ89hmJ1NDUREZjBw6gfPZcxAZUoaghAvMP6r4RkZFIGR6IzI1VgN2OdRWpOP9//yNSZ9YsYoG/+44WQZuNDIMMA8jff5ejCOMg6+sp0BoxgmaTP/ccSVCee65brOsKf2Ps2UOyKBHV1dQrmZREsYcxdcHlIgLHTOoAlEy88grvd66o8DRSAVrs1XI4aNvIzycPwCuuoMKKEWJqGrWQmskf8Xevvip7Aq9fL3e2GUWspiZSD3VzP3OFLsKtt1JYHhV1eLj6Nk8y7SSUlgKrVpHqSSklj0I0NJCMuq5Ovn/bNgqwzH3RmZm0sBYWUu/9lCmdd6ydhD/+4PlQbKz1EMGjEUcnnXck4bzz6B8gW46LTsPdyKjgvvsooBo8GAiLvQq44uy2j/2yQEgI9UWLdkiH2FXW4YiKoqohQAGoGAd3iCdRXBz1kInTUtrR/rV2rUH+AKhvAJz8+cZp63Dfsz3BQkOwYAGNt686UEtKJA1YVZaG74ZcgvMvuYg09kVFlBAY5A9AkfYLL9AqaR6/amDduuZdEBUUWkB2NvDhh/R/RQW/nzEycF29GliyhO5bv57+JSXRNWqQQ998Q8bTBw9qFNzk5gKNRKyXNYYhNqAK+3ySoSX2BmLjkDLED489BlxxqRPart3YWdMbVY1BcK7bg8fmHESpow4PDSzAsHC9Ivbee8DYsXRQX3zBD3LaNPIBMAj9TZvIf2D2bDIN27OHXIlPPfWwf44KCl4htvwOGkQXm8NB6p4tW1r/PE4nMGcODbQAgI8/5vFMQgKN9tQ0ujjr670Sn6tW8X31wAHaZubNI0P2tDSypNu6lX7PGPl5paWRIqihAdi/n7gooxXM4aBLzsBxx9FhGNvZnj2KAFKwBmOdFHo3NZHKuqyMela70D/mwAHg3nspGV64kAaudHQIt3o1pTcnnSQTtQqdhKwsTv4wRi0L9fX088aNpN4HaKGcP5/WciMn+fhjMnM8THF9bi7wwQeUC15xReekD5ommz/PmtX2QUZHKlp0TmCMLWWMpVv8a5NIijF2I2NsA2NsQ7EonVDgCA62pty7QfuXgcBAqsi5jesiIjrsKh06lN+OiWnHqO/DDPFrOCwEkI+P57j3NhJATiclzQbG9zmAs+LXYnJ0Bq7tswSPJH+OkI3LERwMXHJMNj7p/RCebHwQx9g4kfNZ+anI2W+zTk6NL8XhkEmhc84h/whjzHp1NZ8sI0DTyID3hResq7gKCgDxjg8/TLyJSP4A5Fs1fjxwxx3AaafJv8vJ8RzffiC7Ftr2DCAr203+AEBZaF/g1ltRcssjQK9EwM8PSUm0pA0e6gsMGgQtOgYbq1Lw7r5TkVPbE9XOILySfRYcLr12cuAAlY9WrqQKGUBs9m23ycYRGzdShW3/fn7fBmWZp9DF2LSJ3z7jDMr+WlKc+vkBI0fSxSeOyVu7lhRCGzbIUyxvugno149ua5p330OQ2NSMPXtIkPTyy8BHH/H7zzuPDqF/fzL7NjB3Lo3wBih/OXCAboeG0qGI3g4t+gC5XERaGdJfBYWORGMjSVv/+186cb/6qssOpaYGeOIJroTIz+9wz3YsW0ZdRu+8Q9emQhdAXPSmT5eN04zhMy4XqYQ++EAuSB840DrH/Xbi009p+5g7l8Ilb2jXcA4vyM7mPnKBgX8vu8YWFUCapnXIvFdN02YDmA0A48aN68Cv7yhDcjJPJAx0IwVQW5GfTxW7Y49teYzmsGHcVNlyclYXQ/RHKyuT+Y0Om0rWqxdnlhhzj0uqrOQTdO+4w5MnMvDTT/y4QkKAu8euQHjxL/KDfvmFGtv//W/4VFdjdAQwIjwH/9xxDTKDRsAZEoGXXwZefnwmfL/4Qjd9AvkATZ5M5VYjSgBIAXH11dQmMHgwT2x37vT4YFav5ht/eLjnAAUFhfp64Omn5VOMMSJgTzmFBr4Y991yCzBzJil9/vyTBwbjxxN3OveTWtRmZADOJjCmYVBIPnY6koHERJTf+C/gVB+UfMpfx1hqJ0wAMjJ8gAED8Fn4wzhYWwL4FgNNTSgKG4jv4m/FxUWv04O/+kpOms88k8j8ESO4oi87mxQSIrZsUSYkCl2H2lo5mB89mjbpjz+mNsmaGiI+DNMtTSOV6uDB8jlbUcHHp7z/PmdcANr4x46lhd+IsnftIjWcCbm5XLlqsxEf9fPPfPtZtow/tm9fIoINnHEGHUJGBl1uDz9MqiHR9+uKK6iDU/Rz8cpFlZURifXzz9TrHRVFvkjGMITff6f3OmQI9Qq1dka4goKBhgZiQ0QV3l9/Addc0+mH4nRSR7KZ8Nm9u+O8OKuqgP/97//bu/Lwpqr0/d4kTTdKS1sKZWspS1laKLLJKkUUERU3QFFx3x101FHHGVGZcXQcnVHRn9uI6IwiCqKjgiICIqDsFcqOUNpCSze6N03T3t8fX27OOTc3adKmC3De5/GxLWl6k5x7zve93/u9H/u+BXkECW/gCaC+fWkIjTYWddcuWgxbtpAcU4PL/BXUGdBC5teHDrGvd++mEEqPFSuoEDBmDPD4483XH/BnwIgRrIZ9LkCOgW9vMDL8PUMJoLo6CsT+7//IKqYxjB5N8WL37tR73d7Aj4I/fboFFED6J0pIIBYHwMcfEyO+ezcwf767KgKgzhK+C2X2bKDjif3uDzxyhLxLtKqm2QzzhLF4+N2BsA7oDUDBsWPAkm860g47Zgx5/sycSQTV/PksAejenQghzQCJn+9tcMJrlVn91xISAPPxOHaMvrdYaBrLihWkbDOaqtenDy3Tt96iXOz112mJXjutCv8X/RTGdcxEmLkWc3v9iLSLOgNDUoHOnVFcSjpf3mtf22pHjmQ/O1UeSlHwsGHA8POApCR8VpSOArNTrZmXR9krQCUkTRURGioGSvoxh7W1XK+mhEQrY/duqvQCFHdoJEbHjiSTGTaMWhkvvJD+mzKFInI9YTlnDrsp9+1j0s7ISLohAWov0+Bh4+fVP6NHA3feSfe03o/BbAYeflhsH1EUmqngPC5hswGvvQbXxMvERNbZkJTELjc7m+U1LqxeDdx+O5WjNbX66dOsl+zkSWDhQjqEN2+miozWlyYh4Qvq6oBnnnGfunr8uGgF0VQcOUJmWr//PcltfvjBq4pt0SLjJexFrOc33n9fvAS9KbtEK4H/UPv2pRheI7Y1v8Ivv2SPmTyZ1qqGn34y2DSbj8pKMRbbt8/9MZotQEMDFfy8qYR8hRZrAufeoIhmEUCKolylKEougDEAvlEUxUDAK+EXevd2/9kZSgCdPMlMW/fubVy2ZzYTL/HWWyKP0F7AE0C5ucymyWLhWuKaC54AdFZJHQ5WYAUo33z6afc4YfFidsDGxQGXTVdFtp+fParttFYrSYsefxw9LkzG3Lksu/78c6AseRSRP2PGsN9NTiY9/l13US+XFnUDYsJrQABpRWCAYh0t/5CQAEjBtnEj+/7ee0ms5ktPdrdu1LWYkABaWP/4B2KKD+GJvsvwyfmv4NqPrkLMlRMAEz2Zdv9qexTA2jx79WIxkYaOkQoSk+h37Q0WvB96n/tFXHYZyQw0DB/u/aJlG5hEW4Fv/zrvvKY/T48elCTo8bvfMVJJTwDpggG7nQrLGjSD3M6daVDqU09Rd7zFQseOUaDevTt10xidxXffzfaQsDBWZ2loEBMArFxJ5I42gIPHli3kbbdwoZgAnT5NlS7eSKKZcDgoAVq2jNSQf/mLKKzSo6GB2XhInAFYuVIk/4OD6f+qGpjK2MKFNEn4yBH6W6+8QmSs3vgXFDOuWsW+HzaMfR0oAigzU+wKBcQhKhKtBJuNybwUhbHhfLv60qWsWGWxUH9tSgprO6iq8uz92QzwuQFAtwG/DWtDVvmjY/16359fVcmy8dZbqWtfA+87a5R+n81oFgGkquoKVVV7qKoarKpqF1VVpwbqws5ZnEUKoFOn2Nd2+5kfoPAEEM9txMd7H0HtFyZMoCbU88939brs2OFevDl6lALDqir6/vvvmd8sQB1ZQYUn2YEfGQlcf7373/v974Xg/IormEeCwyEG5QISEthUJB79+7PyqkE1i9/k7XbvQa3EuQc+SJw+ndq7moT//If1swNQfv8Q0K+fWxsnIBJA2larKKIKCADuuIMIKQ0by1LwQcElKKtzmnZarcCVV4q/ZJRY8zNGt28PbEO7hISv4NUHfNbXFFx/vdgGOWUKm44HkIJOM7c9fVq86UAdYnzxQn85o0ZR8P/JJ+6+Xzx69aJOLT6MmjBBrH0AHtrAvvqK1BL8kz36KKmfNPz972KfmjYAQzO340vYTYTNBsybR6rGDz5gvNNrrxk//uRJytFmziTrsTfekNtKu0ZtLTF7Gq6/XpwIq1eK+ovffhOzWg2lpYayis2bWaLdrx8teQ1Hjxpzof6gro7WpB5VVSx+lWglHDvGNoYePZgZP08A8VKwiRMp8VEUkeT3mBg0HdqAHQ12u7iMN21yF0xv2uS7GOnYMRI2FRUREaSq9B9fAJAEkETbIjravZ+8HZlA+wN9cm/UtnQmgSeA+E0nYO1fAAXR8+ZR2TMqCoDofcAbWO7ZQ0XWr7+mNjsNEyc6JzXqe31TU0kmoeGGG9xGOiqKGGB/952fgWRICNtFVVVo6q2ocDd+1rP+EucubDaKXQFahzfd1MQnqqyknjENs2ZRFgjxHi4poSVq1AIGiLfGkCHklzhoEE0SAgCYLVhWNwO3/fogPsiZjIap09z37t69XfcxAGKL585lgVdenqFZuoREiyIvjx3QwcHN93To0oWZ8vTpQ2wpD0URDy9O5aCqogJh6lTPvg6aUMIboqOJp7nqKlIE3n+/+2N4AujIEdBAA35cWHIyyYkuuAC47TZW6OAP/quvJqWFZpLS0BCQVrC1a1lHKY89e8S2cw3vv89iq+xsKgQ9+yyRamc7cnON35N2jVWr2AcWE0OTv7y1CvuL1Zzn43nnieynwZvFqyEmTSJOUxN7OBw6hVwTL0cTnYSGil6asg2slcFLuvj9ODXV2PyfL1alp7Ovd+wIeEJnlAtofGVtLbMp4lFdTdNffQFvaVRaSrdCfj4TJkRGimvzXIAkgFoJWVnARx/54KqvKO405FmgAALOEAKopoaCQYOTydPmEFACSAe92vL3vxcnnhQW0gAJrUqTmEikkKLAfbNXFCrvDBtGSagHo6Xx41mx9sQJat/zCx58gPQMP9D84ELi7MGBA6wlkLO/8h87d7LJFb17Azfe6PonPQFUXc0CgJAQcQJvSgrZKMycSV2QWlJ6xx2cwqBbN9jje2EZrsXnHW9xvxa9vPqyy0gpNHQo+5lsA5NobWzaxL4eMiQw85hnzqQg56WXhJv3++9Jdv9BwTRWTOAIoBUr2BljMoliiKYiJIR4m/vuM95HBALosCoatA8cCCxYwH6xY0eS1PLo3p2UG9HRlDVraCYBpCfDRowQu+d4lS9AFfJffjF+rhbo0mhX2LWLFJn33gtkZLT11fgIvfpn1iw6D3gC6ODBpvfG2+0io3PttSSf06AjgIqLmapCUVjRo39/9pjmtIHZ7TTRScOcOeKAl/ZKAO3fT+o7/TyeMx76orCGkBB3Y/7UVFFK2bUrm0rc0CCuswDAKD/QuNDly5loNDKSOhU08AVyb9AT4pmZosKI94Y7VyAJoFaAzUYt4p98Ajz3nA+/wN90YWHtbx66j9ATQJrnRrvGG2+Qr80jj7hpC61W42CyJQmgjRvZEJa+fanYeO21JA/Xd1+Fh1OiqokLXHIK7ZcBIoIWLKBg3cNuFxIixrRGo3m9gieAuGqWEcNvtOl7hcMhte1nKXh1uhZnNAk8oTJ2rLDOo6LYt2Vl4h4VE+N+S1x4IXGl/H0fFQX86190D/ZOMgHdewA9e+Kb7yzGcfsNN1AQPm0ak9fxpJAkgCRaE1VVZPCmQe+y3Bx07ChUkm02KlAUFQHL9g/GjjLnOeQkgH75RRztfsklIknbUujThzOC/vU0bLnO7CI8nAz2eCYYoLZs7VzTRnFqZth84uQnAbR/Pw1u0JSxhw6xczI4mOo1fOf2Dz+IYcnHH7OvR48m8bAGfqLO2QhN5Kmq4vvQrrFyJR08ABV2tZnTnTuzhV9TQ1KupmDTJtZXFR9PVQw+QNURQD/9xMKpIUPYJfDikOaso++/ZwrbTp3o+OO99VqLAMrOJm9R3vbME2pqSEG3bBl5Hze3Ba5dwRMBBLj7FfLqHw18G1gAJYaqapwL7N1L0+N4QffcuWQPoMHIIkOPEyfcb6k9e8QCtJH7ytkOSQC1AjZsYHt+bi51KHgFvxLP0PYv4AxUAKkqS8ZKSw3lWkbBKd9VFWjwJmc8KTN+PE070vLIoCAaxhXvHEyEhgbvm30j0CamABRTNLbBCuAJIM7w02iD96sFLDOTkul772U3lMRZA54AGjy4iU/S0CASKjzRAjKC5bu0+OqmP0JLk4nuwZdfZkRsUZH7UBfXEz/1FMkRtOSYv67MTCr7yzUt0YJQVfLY+vzJ7agrc3qzdelibOAcIOzcySZxITwci3IuQr1qAo4cwW+H6vHSSywBHTyYJn+1BkJCyAIDUKGezMOx6i70D9OnG1d5FIWIoZtuIvM9foPq14/1phUU+JzVlpbStMKPP6azW2/GO3EiXcp557GkuaKC5V1Hj5I/kIYbb6ROV43Yys019PxtHk6epJFRbTy9sKREVP3s338GTBW12UjKoGHmTKa8U5TAtIHx7V8XX0zP64UA4kUcrtZmBIYAqqsT1T/XXEOcKU8AtYYRtKoCL7wAfPMNmanr7MfccPAg49BOngy40KXtoDeA1neajBrFNo/4eHcTREAsFuzfHzATp8JCZhfaoQPbgsvKyJZNU2n36kUK0W7dmErN4RAFrUYw4qr27j23DaABSQC1OFSVSH8eemLEDampLKDQy/LOEKjqGegBVFkpbmgGkiUjAqilFEAFBaLfJH9Aa9cyfz6ZQ771lo7AP3GC7ZqdOvldVk1KYpxRXZ3vMksAJBXVsuyqKpehgRHZk5fnhzn4xx/TKXHihDimUuKMh8MhBvBNVgAdPMjYyuhow3FB/K3QVAJIQ1CQ2LLis1ouNpZp4R0OMvGaO5emtcjReGcPtm6lXqTXXms75WJuLrB7N7ZscuDVl+rw/mfhWJHvnOp4ww3G3g8BghCYBwUhp74bvi9Mw9HSaDz9R7uLHOraldSrLXgphNJS+kPz56Nvp2LaK6qq8Ft1PN3Ml1/u+Xc7dKCWnSFDxJ9bLOKG5eNs4s2b2dlXUAA8/7w47VObhKYoYkFGawPjVS/jxtF2EhLinIIIWm68CLjZcDhIErFiBUkkGq1kthx+/NH9dvriiza5FN/xySfG6h8NzSWATp4UA0aN2O3ShU0pKSpyLboTJ1iN0GIRB73yCrmmEom8+icqigSwgEgANZoLBQBHjzJPrbo6kWQ1gv6tX7qUdZSf0Th6lN00PXtyrQJOxMeThHD8eOCJJ4wn20RGspgqQJ5ngFgcTkwUbwV+Kuz117PL4gvijeUnRm2yxcXi5UsFkETAcfiw+yHc6OSj6GgyIJw3j5rnz0BUVLgn9u2eANKbsRpcsJ5HCQtz930NBLSRhRqGDRO9ZDVoRL5+ZLWb+qcJza180MkXlhqForj5AKmqSADx75lPbWAlJWLVcfXqs0ybe27j2DG2X8TFNcP2jHcEHD7ccN3zokq+utlUsSU/qWzrVnejc4+YOVOcb9/QQD0enkw9JM48vP8+lTe//75tJAo5OeSC/Kc/4ceHVtC5UN+ATSUDKdLWVxUCiLo6A4POkBB8mDsZfzxwM8oKif0JD6dChjZQq0Xx+edE0OzahcQ1/waOZQEAcm0x1O9pdMj6Ap4U8jEp4k1JAbosrb0rKUlUYUyZwhKfzEzg9ttF9Q/fJmY44SwQWLOGmaLYbG1qvGM0hGjTpvbrKYPsbJGhuvFGd98tD63zPoNXF40cyYJVi4W5OgOuOJdXtowYQfymhuYSiXr1z7XXso7J1lYA8QQCQEUab5Oj+Am/AC15f8aNt1t4MoDmMWUK9bZ7Y0P4yaaGkmf/wecGegJIQ0ICEd3Yvx/46CNMHFrm2hP37fMcdxUXs6PXZBI91bQChNXasp0c7RWSAGph6NU/gI+sd1ISVQj0LO0ZAiOS64wjgHxQAHXv3jLGYV99JcoWvRUmDcFntn62f2m44AJ2aB8/7mdwxQcze/eioIAl+B07ivGyT21gGzeKJb+ysnNjzMk5goD5//AZp5GEGaKZO08+NpV06tGDDVppaBBH2XvFxInUTnHXXWLAJdVtZwf0bcR+u+kHAFu2AA0NcDSYsONkvEsdd7S6K0pm3Gpc5Q0QMjKYcsBF6oYEo8IRiur6YMBmc1nuaIO0WhycOqdH0CnXoZRri6WxYU2F3geoEbVXebl3odC0aWJcER1NHj8a+LN4/HiWrANibhcwAshuJykEjzbyLjt2jMUMVisLNVSV4qYWgz8KPpuNSJ/6evq9N99kMpLBg43bLvv0YaRQfr5/ppnffitW6fjqHSDK1E+ehM1GnLQGIx64OW1gX35ej6KtR4HMPYiqOoFLLmISGkMPoOJiapGeM4c8OH/6ifUENQOq6t4eVFbmTgrxjzfi6c8KFRDP4hkoo32GngAKgLKVzwESEoxjwOuvd+6Jn30GfPIJIn9/G1IiWADniYvi62lDhhhb3iUkiLW4cwWSAGoGGtufKypESa+GRhVAZwGMSK4zjgAyuGD9JLCWYI0PHqS8UMPll7v7s3nF4cPiuBB+pIMfCA0VJ4j6VfDjf3HzZmTtZxrixERxEoRPBNCGDe4/M2JXJc5IBIQAKipii8liAdLSDB/Gk7h8t1Vzhi3yKqDVq/2IiaKj6QafP59FIPv2Bbh0L9Em4Bc10Pzxzk2BUwmaWZGAmnor+3lEB+zCsBb903ziNX48dTgihA206KSU4YUXmj+B3mdUVwtJUI/QYtfXuREDm3eY9+nDinVFRY1W+Zy8HACqSPNbVUiIcUJ++eUiKRQSQh73et+kFiGAvvvO3TwlQMmfv+DVP+efLw4z/e67gPAG7vj0U5Kx8LJsTzh9mtT7999PI1ufe46pl81m8jA0qhpaLOKHp5eieEJmJnkAaBg/3s37TiCAcnPxySfiVCWjWkljk8C04SR6FBcDS98opD9QXYNZle8h+JEHXMWZ6Gh21JWVOVUYn35KasWKCor1XnyRxm02OjbZO44dM57k9fXXxks3J4e5QHTsyFRReXlngRdQMzxBBQwYwAYTFRS4501NgL4FrH9/sR04IYH2OuTksCJfXR2GX8Bkazt2uD+vngAcM8bYVeVc9P8BJAHUZBw8SAfvihWez0B+YgO/mANGAH3/PW2cPpuotB7OSAJIf1IYXLC+TSTQ/j/V1cDf/86qDf36kYWEz6ioIMc7fi68h0TYF/C/6ssEBReSk8mxDQBsNhz/hrVvJSSIG26jBNCpU6wsYzaLWnituVvijIWqiuKIJhNAvPonJcXj9ERPdljN8dsfN44Fi6dOAb/+6ucTxMSQg6uGFi1lS7QK9Ea5+/e3fsLsDPq3lCaTx0OHcOq56t0bO3e13Mxbh0NsURo7ljwbUgcT6xEfUoIXx34hFAJaHPz7n5CALjMnwqLUAyYTiqP6Ns8w2WIRjaEb8QHik5Jx48gEWjsT58wx3rpSU4knvuUW8gtasgT44x/d97PevVm8mZ/v5wAHI9TWiv08Gk6fFsfoaCgpoSS+BTyC6uvFRHzyZCqOkaE3Kc4C3q6jqiQBsdtJneltNrjDQeSF9piyMvFGmDFDlGvpwR9+vrQSFhQAf/sbCxiTkoCHHnInmLhANXtvhdCNduutzHKUh56L0m6dhgYaJHvtteJ0Jg3vL1JhO07SnoTQAlwat4OInAULgB9+gMkkFlsKTzW490MCtHCbWeTjlT7DhzOB1eHDxqomnqMfNEgchPXJJ2ew60BlpWgA3RzDG4tFlPAbMS9+wOEQeb6EBFL28a1aLvUPP71y9GgMv5gFbbt2iSotLRXit+LRo4mr16/3c9H/BwBa2nLvrERhIQ2CqK0lpUZODhvwYrPR4f7DD+LCu/RS4H//o68DQgBlZJCxJEBU/A03BOBJAwcjAqjdj4FvggIo0ATQpk2sLzo8nLzYfDbGVFUaS6TpasPCmu2sOYwrEv/6K/0Jn1reFIVKlm+8AQDIWp8FdBkBKIqhAsjr8/IyuvPOo9ejtX99+23rjY6RaBHk5TFvzPBwxhv6DZ4AGjXK48M8EUDNUQBZrVS1/+Yb+n7PnibwrldcwbKXn36iTK81ZmJ7gcNBRe/qairItopPy9kCfctXeTmdMS01NUCPigrg1CmoKrC1LBlI6Q6YWK/Vrl2UzLVEF1hmJsv/Y2OpoqsowDNPqziStRh9w/NgLevk/UkCDf7zGDIE5rtuR/wvNcjJCwIsFuTmerbG8AlDhrBkaPdud5NfJ6qqRIJ43Di6r/75TyIweC8WPUaMcBd36GGxEAmkqTaOHBHPcb+xciUL3qKjKTPTzt/t28XsqbaWPETy8ynT+uc/A7rAMjLYpURF0R6rKDS87e236ec//UTxdsBw+rRoGrNpE7EfRvjwQ88T0mJjRbMmIwwdSvPHAXoht9/uFr/Z7aRgCQ8HLs5YDEVj+KKiaNqkEZvj3HNUFXjzuyTUO4feeepGAygRDwujvb+oiPztRo8mUkU7apcsAS67jBEre/cCP3552lWUvrv/OpjDQ5gsa+lSYPJkxMUprhzh1M9H0UOLtSMjiSnW2qB/+YXiuyb4LKiqSABNm0Zv0Q8/0PdffSWSDIAouho4kDrpvvyS9rK8PGrv1szZzyhkZjIGr29f4zXiD4YPZ8Tmzp0UuzQRublE3ISH1+G663KRlUVrZ84cet+DgmhP3J9ZTze8Flh16gRU78djjzE15d699Hi7nY7bsWOdyiHQS9aGND7yiHhLd+rUNgLdQCIkJAQ9evRAkN5bzAskAdQEaIZR2p71/fe0OcTFiZMdNERE0PAIjQAqLAxA4MVHELt3tzsCyIjkstkoPmju3tNi8EEBpM/HAt0CxnNQl11mYO7sDd98I7LxDz/MzYVvGhIS6EwuK6N84uhRP9qH09MpIKqoQFZhOBBcCnTqhIQEioXCwykYrqwk2bArAXc46KaqqyONN1/ymzCBTnEtAP3hB+otaI+LqrIS+NOf6MWNH08RiLfqXztAWRklDCkpzXhLVZWCgro6ihgbCd6E9q8uxVAefJbep4cf9j3wczjEiqmXLMmIUwkKYuPcmwpeVd0kkr9fP4o69++n17NyJZmFtiG++46RWl27UlDWJJSUUGLTpw+Z7Z4NqK2lQ71zZ2L/+LVaVWWsjti3r/UIIGe70/GaOBSY4wGTCeHhFL+cPk37+eHD7klQIKCX3WtvjbVnFwyKPEEBUGEhReFWq/GTBBp8Yu5sUe7RNxQ5zoJLswkgvrcgI4PWgMFI+a1bmZKgb192xlsszd+DNPTrxwigw4ebSQDxZjGzZ5M8STt/d+6k4FbDmjVs8/vtN8rAJ05sxh8Xwbd/TZrEWonGjQPeeYepSUtKAsid61vffvrJmADatEmUxMyZQ/v5+vUUS86d27in55AhdOElJXQY79rl1p/1zTfkLQ+7HRHVxRgb5fyHJ57wXMVw7jnfFw1D5okooIsKk0nx2I0G0HqcNo15S3/2GdVVPvmEPaamhood551Ht/Tbb6lAHn3+46P3IfWGIcCV88nnrrqaYuxDhxAXxzadwk2cFGfsWCp8rFlD909hIe2jTZBo8O1foaF0D8TGMgJo40ZKm/gQmScBBgyg2/eaa4APPqCfLVlChFlrbVkBAx8bDR3a/OfjfYA09/omvila+9dVV+Vi0KAI9OuXCMVoURYVsaA0NNQl+zt1isgegG6dyEh6zrAw9quRkfTZazl3SQmbUAdQWNKCdngtDlVVUVxcjNzcXPT2o5/tDH7JbYfISFIA8cx5ZiYdTjz5oyiUhzz9NP2ONvnI4RAXX5PA93MeO9Z2I2Y9wFMLfLttA6uocJcstwEBxMcaXbr4+cu89vnqq0XXyCZCUZrRBhYcDEydiroGM07YooFTFBgkJNDz8lyI0Aa2ejWNxX73XaqA8Y6P559PF6Sd2lVVbWZG2Sh+/pkYs7IyitoeeIAIoXYqhcvPJ3uCZ55h1dQm4cMP6Umee45FW17AKyUHnVpH+9n69f71UR05wkY6dOnilfg0SgxiY5tv5s4PWmmyypPXnK9c6X1cSSuA715oVrflO+9QyfWVV86ets2XX6a1/vLL7s7fntq99L5AXmC3UyDb5KPd1f7VHwinaHj4cDF292s/9xHV1aJoU6vAAqCskj/YAuAf4RPsdtHIxNmupbUOAc22G6EkVZPvaOPmNWkjB54cE96bAKI5Br4C7HaxdWTyZHEB7d/PTFPq68UWDYAUHwGKTaurRUNXPv7u1Inxb6pq3FHUZOhHVR096r5ua2uB119n348cCVx3HcUqDz1E56EvJIbJRIUzDTzjVVQElJSwY/HUKWwtcVYdUlPFFkQd1KhO+LQwHa9nTQcc9UCdo9FuNICEHZoA6eBBigv027fGBW7dChzbUwlUViLYVIfbe68lFXhkpHN8kxPr16NzZ9eVoWAnd+ONH09/kDe95A8hP8DfZ6NGUfjYpw9bJ/X1jNgBKAU4cYK+tlhYQefyy1nMUFJC6qszDjwBxLdvNRVdurDkx27361zTQwvvu3SxITo6xpj8qa8X91JuWiPPsVdXU26tKYLMZrrMuDiR4OFbbIOCzmzyBwAURUFMTAxsftrBnOEvu+0QFET7+ty57v/Wqxf5tixeTOSPVmELSIIAuM9lrKlpV87SWnFPAx9ktVsCyCgQLS93s/4PDmYu8mPGiCxzIMATQOyQ9AE1NSzAVRTPEuUmgK8e+j35dfp0HKrugQbVBJRXIL5DhasIxreBCcVyt9nBTowcSTu3ooh+KY34LbQZjMam7d7dLiMIhwN46SXmF6EfuuYLysuBgve+YhJ2oNGRWA4HBY4ahjRksG/8SQ4NqvueEBXlTvY0x/9HQ0D29/PPZzd+RYXvRqAtgNpa8W1tctGirk4kaY2UMWcafv5ZnEL473+LmzffbsRn4z7qzB0O4oofeIB8X5rEAzpjhK2l/YEwipJHjRJzq2baNxhi1SrGCcTHG3h66SYStQoOHmSym549XdW4gBJAZjMVLDQcPUomPdyNY7eLpFtrEEDNMoLOyWEHQXw8KVgiI9kfaGhgQcHGje5nXnZ2wKZ1bt7M7oPERHfj1vHj2deepjw1CXoFkNEfOHqUFRCjo/1Tr+rBE0BbttDN9NNP1IN7++04tqUAaKgHCguwp8LJ4PCFAx1qa4F/vKTgPwVToap0Td07VjTajaa9lClT2PeaGpTHli20RFatgktyc3mXrYidxo2inzSJ/cKGDegS64yrKypRUOJkmDp2ZCQWP6qJZ/38AP9r/Nq4+Wb29aZNbEvmj9qkJCZoCQ4WjcaXLWP72xmB0lIms7FYAue6zx8kK1c2mejVtAyKAgQHe7hnyssZq2O1CqyPlhIAJMDQ1EAA8VQGIkyEhBBXbzK1eZd9wGBInDUCSQA1A4oCzJxJbbdpadSy889/UiHgqqvcF1bACKDCQndnv6NHm/GEgUVxMYu1IiPFgt8ZRQCpqmEF78knSaDyxz8G/jJ44swvT5J9+9gGmZgYOC05RMXovn3+JSMHi2Px3On7XN8n1bDEiC+IufKihgaxmqBtaooiNvbzcvv2SgCVlLCvefMorczUjvDxx+L4U3855bw84JZL8nH7k12wrZTLPvbtE09kHX79lcXNnWMa0K+Gq1TpK6/e4AcBZDYzNaaG5vj/aIiJYV4I5eVNnEZjNosV9jYkgHbvFie9NJkAOnCAqbOa9UTtBFVV4tQdgD7s119nQTBPAM2YwcroJ04Ynil6fPst++gzMkhkxE+sczho21u0iKYmG9qOHD6MPFsnHKrsDoSHw2SimF3zTQHonm+2STAHux2CwezMmQbVVV4221oEEP95cEqJgBJAAGXL8+axNzgnh5SQznVRUMDOz7i4lusG7NmTdRuVlIhHkV/gpbl8xUbPIqoq6xUCxOB3yZKAqIDWrWNfG/nWjB3L3vZ9+wK4zRidQ/rxvjypPXSodyOnxpCQwPrs6+qokvzKK0B9PcpqrCjZdZwSekc9CmqjUNCxr/EYL9BaW7DAebkh1D6TEnEcL16/y9OMBDdcfbU7lxUSwkLM06epS3/nZhtQWgpFUTGty076RQ2pqeyQLS9HXLEz4DtdgoJa52E8dizr6Rs+3LVnnthbiuKDBiScF5SXE/cI0FPyRczkZLF+uGgRLU+9/w+Piy9m+VtFhbjHtXvw8XH//o23IfqKMWPY1z//TAOJ/ER9vfi+e7w0/pDSVfDMZuPfCw83Jn8A+vX4eMpBeF9Ds9mMtLQ0pKSkYObMmaj2I4jLyMjASh9My7dv34558+b5/Lwadu3aBUVR8N133/n9u54gCaAAYNQo4C9/Ae6+mwojnoi4gBFAvPpHQzsigPj2r65dBbVee+1+8RyIGjBWikIBVnNbRvRQVTFo8SspDbTEk0NsLAuU6+rcvU09YedOqmJXRFKUG2K2Y2Y42yB5YsmVbGZlscw5Ohr4z39oRMoLL4iva8AAllRlZ/uUVLU6+Kibr4AZKYPaELt3i6IdDf4INTZ/dgJ1RyniWl/METCq6rVFT5iGk1IGBVyi4CsBVF8vkoZepPAa9OR8IAggRRHJ7ibv8QMGsK/bkADSq0OKi5uYx+n7jM50AuiDD9i93aEDOwh27GCjP3nZRVqaaBDVyGdaVUWELI/Nm4E336Qk+MUXyRrqySfJcmTDBipG/POf3HFVUYGfD0Th4X230/WFhmLwYLrciAg24llVm6Dq5LB7Nw3A+Mc/aAtes4ZdQ2ysKGZwgWc9WosM90AQ68VIOsFv03DRRXRmacns4cOu+IIXk7j5++XliTKXZsBkEr363n6byD6/719+PjPfL8QTQOvXUwCsHRjBwUR6aX4dWVmizLMJKCxkIY6ikOWWHpGRLETQxj//8ANwzz2kxPcYIpw+TXHGzp2e/7geWVkiY8jH34GYKc37pH37rWtNHKvuQi+ukC2kPcnXGvawOBw0Lc4VGoaE4tK47fhL8n/RsTTb89/+9lt605yTKOPjxQ4ugEy3eZHOm2/CFdwPjzyCuHH9xJtL96F13vcjABUoOY0CexT9kP8jYWFAaio2lwzAPXvux1231vmV4vDhgJHn8dy5LHw8cIA8lXjFEH8EA/RY3maVj1vaPVoqN0hJIZMoDf/9r99vTFYWs00xmTzMq1FVcU80IFf1nRiK4ltMp8/hQkNDkZGRgczMTFitVrylK/TUezkgfCWARowYgde0AU5+YMmSJRg/fjyWLFni9+96giSAAoHDhylB/fBDz+Y3EAkg7WFLlwIPPui568UQvP+PhnYkq+ffgi5dRPFDu1UAeRrt2YoXfPo0U05FRPhJ1PMsP6+OCRD8bQMrKaEYsLYWQIcO6Gitwd+SP0Sf8l0uNr9rV2bVUlvrVAHxgfrgwRTVTZzo3kcQEiLq3NujCohPdPmIwh9lSwvD4aDiopYY8HGkP1tK/tp90LibvNA+ojGohx5+h0Mn007UleB9fZ+OHYNrhnNMjLjReoCeAApECxgQIJKfXytNytoCA30+pG/D9xl6AsiopeJMgKqSP9mqVexn999PJhEa3nqLpnNqG3mPHrSH8SXlRvwSli5lBU8+IP72WyJ5fvrJuAVh3TrqQLr7buDBu2342+FZqHSEAmGhUMwmzJzJHsvn719+2fQl9u67JHLZsIGEL0uXsn+76ioPAT2vAGoNAsjhEFvvOII4PJztBQ5HALn5CRNEI3rnoelR4VtWRqNpnn/eXV3WRPDbyObNwKOPEi/l13R2ngDiFUD9+zMZZV2dGMBefDGte16xq/MGKimh/YUXBnoDb2+Ylua5bYNv9fngAzrbTpygv7VggcHfU1V6zz/9lFgio9Hr/IfGHxR8G1gzCaATJyj0can8Jk5kBKKGDh1wzNxX/JnZhD0h7gMP6utJNcjXXubOKMO9iatgMTV4vu9qa+mmPnGC/u8kunlHgeBgurd5Aqi6Gq5Y+dK4HaI6RANXBOu8dz2U344AdjtK6jrAER7pHreefz7WFhNhYS8qw2efGV+yETwI/lzo2pU6NjSsWMEUQ4A7AQSIg0Xz8gJEFrcGAm0ArUFRyNybJ5X++U9xz+BQXU1E4YcfilO7NAQFeSiq2+3sgAoKcr8v4K70iYxsvlH3hAkTcOTIEaxfvx7p6emYM2cOUlNTYbPZcOuttyI1NRXDhg3DunXrYLfbMX/+fCxduhRpaWlYunQpqqqqcNttt2HkyJEYNmwYvnROtlu/fj0ucy6+Z555BrfddhsmTZqEpKQkj8SQqqpYtmwZFi9ejNWrV/vt9eMJcgpYc1FWRgeHFrEtW0YS/uuvdxuvoU8OsrKINAXII9ODitMd7VwBxCc+XbuKEru2IIAcDtozvCp2eAVQTAxL3lvxgvm8yC9FQlWV2EjrgwLCX6SluQpC2LkTuPVW74/fuZOR9rFxZvx12np010i2Q4dcGciwYYx727ULGHKSOxEaaeVBaioL7PfsEaO/9gBeAdSvH7ErDQ20plpz+o0XHDnC4tuICGrZWLSIvvd5SykvR/7+0wAoIcjvMRyYlMQkwdpi0L3e3bvZthkbC/Q36/Y1XwkgfbTngzSvJRRAgOg93WQCqHt3qnJVVpKWPS8v8G7zjeDkSWNOvKhIVHQ2irIy9/PqTCSAjh6lxJwnEkaNoqr1iBGUAOflUQLFTy3U9rBBg9iEIC8+QPn5bJ8FgN//nvjTDRvcH9u5M8UMZWWs8Gq3O4+yPNa7FxtnxqPPi8fClCl0ezocxDFqI579QV6e2CHEb3cdOxIPYIjW9gA6coQdRl26uN3sPXqwa8/NbfbgTIahQxn5nZEBTJ/uWeG7Zg3bDANkzHTZZXQs8ibQBw/SUK+rrvLxSfgPmFcAmUzEKL3zjugKbDIBV14JVQWUGTPYmj9yhJI4RUFODhFRVVX0Htx6K/Fl+m17xw7i9jt1Eq3kPI0tB4h3ePNNOmb1QqpDh0g99+STXA558KB4P77yCrBwoZhR8ufQFVc4R3CBmNjrriMmgE96/SSAcnOJR25oIP/QuXOBUaMioQwfzpRTJhPw+OM4tqwXcOgYGTkDQGxn7DnoPq5z2TKRn5o1C5g5NhjQCCFPBND+/eyN0+aoX3EF+vQhcvnHH+m5IiMpJgwOdpJqDgdQWYHOwWUYHnnEOJnRjJuOHYPFYUN0RTaKEQFVVVCUmo6uusReHTkKByudhb3yCmz+yYGi2y0+ndV7fQgjZ80ihZi+BTYpyTgeCAtjA9o0sjhge0VLoaiI7bFWa+BHPlosNH3u0Ufp79jtdIA98IDbQ994g51j0dG0P/G1ECE85IsqDgdjbj30e1kB9KgB6htoHwkLBeApDOQPWA9wOBxYtWoVLrnkEgDA1q1bkZmZid69e+Pll18GAOzZswcHDhzAxRdfjEOHDmHBggXYvn07XneawT/55JOYPHkyFi1ahNLSUowaNQpTeEMtJw4cOIB169ahoqICycnJuPfee91GuW/atAm9e/dGnz59MGnSJKxcuRJX8y2WTYRUADUX77wj7iCqSifX00+7zYPXE0B8G3F+vlerDPH5jRRAxcXtpg1GrwDiE4bWJoAyM0ku/7vfMZGAIfhsh1ebtOIFN8v/R2PI+UkkAURqKqvmZmU1rg7hyYNLLgG6jzCetcnbnezcoTZeuuHBVx7amwLI4WD3o6LQicdXD9tJEswLrkaNEj8PnwmgtWuRVxNFX3cIR0VDB1RE9mDRkc1m+PnwAeq4cYCSp0sGi4t9K7Pxz90YaehESymAAtICpihioNYGbWCeclC/u7d+/dVdXnImtYCpKs0//v3vxUSxa1fqfVIUCkiffNLY0EU7S3gF0OHDHtt8PviAiYc0r4qHHqL/BwXR08ydSznqe+/R1L4nniB1Q8+e3BNVURvtuOj9WPh4rttW2rkztXFo4KuyGk6dIlujjz4yVgh58/adMcOLgjU2lkX7ZWUt76jKf24G+0PAfYA08OMz9+wBGhqEM9415EFTlmkoKfFNprN3L0l7tAWjQ2wsGfu//LK7XYcLqkrxg0653tAAHNxZicoCZzu21eqe7aalUVb30kvU9pacjGOzHsdf3o7D9dcDKzbEMCLFbgfKymCzkeBG+8iLiqh98IknRPXVli00OEtT8Wg5bEiIqDzRg28D08B/v3UrhewuOKvyLhQWkqG7hro6FgMqCjB1Klu72dl00VriC7A51H5g505272Vn04Thxx8HiiZdS39TU1qkpeFYWTTQrz8xL2FhQLduKChwV67xiqnLL6cYWCgi5Ocbn636yZsc83zllcC//sXWktXKKQnLygAVuKTzTpiS+3muEnA9oXHWUvoiLAwFY690e2ihGotSq7NPUlXRcLpMEF96gs3Gag6K4tnzOCKCOhcvu4zEaldcQW1ef/qT5+dusb2ipcDHRgMHMpPCQCIigs5CDQYK1wMHxCLG2rVs69Hg8dL4g8nDuC4FdEsEW4HQkKbbc9TU1CAtLQ0jRoxAr169cLvT1H/UqFGu8eobN27ETTfdBAAYMGAAEhIScMhg1OLq1avxwgsvIC0tDZMmTYLNZkM2LzNzYvr06QgODkZsbCzi4uJwyqCLaMmSJbjuuusAANddd13A2sAkAdQcbNsmrmq++lxV5ebMGB3NkuiyMnHCI+BjwnX6NEss9W0w7aQNjE982poA+uIL+iiOH3f37XOBHwEfHCxWcNpIAeTXBLAW9P/REBoqTixpzIeM5yiTkuDR1yQ1lVXjju63oazIWbmOiKBymDfwPkA5OQElQN96i9QwWgHTb/BmV5060YvkTR/aiQ+Q3jqne3d2EBcV+WAQq6pwfLsGhXanzK8zvcb8U4ooKdB5QLi1f42HuxqgoaFx91J9FNFEAihQCiCe5PfUVeoT2tgHiCeAeP8Ev7kboznjJSXubEN7RHk58OyzIjtisdDGsHChyBomJtJkgEcfZUxMZCSrhEdGMoJIc3DW4dgxkRS9804KJ4KCgMceoy6aF1+kP5+YKAa5w4ZRLv6f/5AC4sXBH+DtIW/gib7L0CHVWJEwcyYjabKzReHSxo3U0vXdd8Ann1D7mR48kXDjjYzriosTySU3KErrGkHzcZFmfsShxZK6Hj3YRuNU6fJnvGv57N3r/h7oZ23rcfAgsSbPP08TpzwEj4pCL/n++9l6OXCAC2uWLiW24cEHhQ3rvfeARx9uwP2Z98BWH0Rr2ij5cpLVRXPm4aWuL+HBpWOxdSu93A8/BGwxHCl66pThGHGAtvBnn6VCv83muQtu3LjGW+Ovu44eEx0N/PnP1Ip+zTXs31eudL5dhYXG8+LXrGHKLf0HFh4unjG7donrqwntX0auEfv3A39dPhD2V/6P9prp01FX53zvIiKAoUMx4NoU12HNbydlZWwdWyzALbc4P/vQULHf0ahCoW+BO3jQayXDRcaVnoZZacCU2AyxV0qP6dPpsE9KQucRCfRepgxGQb17BebQIYhkWmUVb4fE0NBAuZbzcDpwgG3XCQne66F9+lDL7L330n573XUG3lwcfNkrKiu9OoG0LnhCr4VyAwAUr2iBfE6OQGCrqsipAlQD2b6dhcnh4YadXQQfCCDtn5o70l3zAMrIyMDChQthdZK94ZwiUPWxX1pVVSxfvtz1fNnZ2RhowEYGcwGW2WyGQ0fo19fXY/ny5ViwYAESExPxu9/9DqtWrUJFAKY3SAKoqaipoYBPQ3o6+QDx4xh1Jgomk7i56EUARsIeN/AP6tNHdPprJ21g7UkBxL9dHt8ePviKj28z0yLD6qAv4A/tFvD/0TB1Kvt6/XrP/fuqKsZEffpALMMcPOja1MPCuFy3vAK7ypyjwQYNapzGDw4WA/oAqYBKSmjcqc1Gam8D0t63J9GgBV38h9oOfID0A9cGD6aAkVf6N8opHziAwt/K0aCaALMJiKHXmpcHkQDSZsU6sWeP2P6VnAzjRLCx9+n4cfZEkZFihOYFPAFkNvvZ1uQFfJG8WUEgf7/4ODo8ULDbxVuJ76z0iwBSVWMCSGuDbM84fZqSYp4JGziQkrG5c42zUJOJjE7feINkC2+9JWYffFuEwWjjTz5hX48d679aX1FoHfeIqsTA2gx0Cylxv6E5REZSVV/Dhx+S4uLFF4G//12cYvf552IcXlLCeEmTibxAX3iBLCBefdXz9BUXmuIDtGwZSZ38LXTxj+d9bJxoMQJIUUTPjV9/NT7jefWPhsYIIN7c5dgxIoE++cSjmVNkJBPUqqrTsqesDC5jlaoq13Xk5QFffw2gugYl9ggcqOxh+L5pUFWahPvjj+KfdziAX+sZWfLDKrvQynXnndSKpiV+2dlkO7N0KYuNIyLIDmfgQLonGms9B+h1fvwxDc/SjqCbbxYFWYcOgQ55bVEPGUJ/SMMbb9BGaNSXzxsi6gkgfrSpj+DPiZQU9n789hvw1tc9XPdvTg4T7XTtKh6v/H7N1wv69tW11vB7gT4grqoSzes1GPWfOjF6NBAd1QCUleGizrsQba307mVhtRLh+Oqr6DJ9hNO9VzE8Kw8cANCB20iqKlFeblDIXbKEXPDvvx8oLRVE5HrryOaCF3ka7RX5+cAdd9B//NS6VoXm7P/009TjpqElCaDgYHHtcyNlN24UJ8xqePdd9vWAAbpw/6uv2H/vvEMu9m+/TYo9/t+a8l8zMXHiRHz00UcAgEOHDiE7OxvJycmIiIgQSJmpU6di4cKFLsJol1Es5APWrFmDoUOHIicnB1lZWTh+/DiuueYafBGAUXTNIoAURfmHoigHFEXZrSjKCkVRopp9RWcK/vMfdjh07Eh3PCC6KxpMFfDmT2pk7eP1QX36iBWHdkAA1dWx3FdzYm8rAqi0VExYfCKAunVrs7FlTfIAqqhgAUgL+f9oSE1lCW5VlWfD/5MnWfdjVJSTT4uNZVm3zSb0zLvajioqsKvcSWj6qORoiXHw/HJQVeaJ4xf4hae97namADp+nEnxo6LYZ8tvKY3mWt99h7xa5+uLiQFMFL3m5YGidi0BLi4WbkDeM3TcOECpsxu3xTVGADXB/wcQCaDo6MBN9ONbwAoKmmEU2b8/u6jjxxvpXw0s9uxhVdaePcUg2q/Oxdxcdh+Eh4tJZDtpgfSIFSvEa7z2WlJb+EIwKgqdzfrSM9+7oiNEs7JEMYJT7d008NLihASvsv+rrmLjnIuKiGMxUsrm54vXx/u6p6RQCKQoJEj2qQPZXwLot9+Indq2TdfD0wgcDjFTMyDDmkIAVVQQScJb5BiCZx0yMtw9gDwdpI0RQPpgsb6eevU8SVZtNoyuWQ/k5wGqSuqtFStEOcX69YCqYulSJy9SQwxgdk1njyQiQFs0/77xwrjtFVSgKa8LxVtLWWEtPZ1ak267jfJ2Dd99J06Tv/128gt68UXK8X3trtIbyupDo5PH60RZ2xVX0OQrLfY7fZrIECPGju+TzsgQq4zNVADddhsRYxq+/57xg3qhkafQxysBwheM9WsoM5PtSbwcg5cG6hAWBrx23wH8re/7uLvXt7SofXwP+GKJUZHt0CG4mOSBHXKorVVV8fXX3NbJt09WVQHbtvnlIuAv+L3CaNvSOg4A+NSu1iJ47z1iZPn8s1MncRJlS8BAtWy3ExGrgeeIPLluCHA4WIurJodtY9x3332or69HamoqZs+ejcWLFyM4OBjp6enYt2+fywT6qaeeQl1dHYYMGYKUlBQ89dRTTfp7S5YswVU607ZrrrkGH+tHhTYBzVUAfQ8gRVXVIQAOAfhjs6/oTMDx484SiRN33cWcjgcNYpT7iRNuZeCAE0D8HdUOCCC+CycqigqQHTsyWV5VVUCmnPoE/ft57JiHAhm/E+kJoPbuAcT7//Tt6z4PMYBQFNHY06gtAHBfoooC92ZsTtVABTWVCKCyJHo5vp7cLUAA6Vt3duzwPCHWI1pLAdSMCVF6o0QtYPZ5S6mqAn76Cfm2KPqee315eaAgkq8GcmoKPuBLTQVlmEavpbH3ycN458bQuzcja/jWxuYiOJh93PX1zeA5QkNZ0qWqopNrC4P3M01NFRM6vxRAfMVr6FBx/bd3Aoi/9nnzSELgUaPuIwYOZBns6dNCWZRv6T///GZMkt6zR2Ss+YKUAcLCaF6FEcaPp7xYw7Jl7Bbl27+8ebJ4hL9G0HqJg6/jo06cYAlE586G0qTYWNbmWF7umxfjq69SQfqhh9xtUwRwCqDqPb+hupIUJ1ark3j78UcWEPG9C40RQDwbwJs/ffaZsafS669j9N5FQHYOkJWFjO11sP1PpzwqKkLe2v1MueAknXNssV4JIJ7/SE2lLkgN2/N7QlWBdcVDYKukz6FbN2afBZAhOS++0dbYoEHeDZ/9Bb/kTmw8xt6n+Hg6pyIixPvl0CHjqlzPnmxTrKoSF4CfCiBVdbdMuPRSwSoHb75JMZWeAOrThwkReR8gXtXrlljzJIC+5YBXkuu9jrwwnZH7f0Fqx+M0XWzkSJ+rKfxbpY/VHQ7nz4KsgNWK3/X+GkGwAzU1OHKE6yjPyhJiLUfmAUFt0pIEkJ4sttlE7v3gQVFF2SpoaBADc0WhAOe55zyMZAwgDOL7L79k67JjRxJwGqlDPX5OfLIYHBy4Sp0HVBp4r02aNAlfc/l+SEgIFi9ejD179mDXrl1Id96s0dHR2LZtGzIyMjB79myEhobi7bffxp49e5CZmel6Dv75nnnmGTzKbZiZmZlI1KktFy9ejHvuuUf42RVXXIFVAWAYm0UAqaq6WlVVrWHtFwC+6e/PdPTqRaaQ2oHBn15Wq5iU6jJHPQHUoQOLK/PyfPBD1GfXvBlAbq7vgVELgSeAtFhXUcTKTWt5VesPFZvNQzuzXgHEt4C1kQLIWx+ygFbw/+ExZQpbr/v3G8ep+iXqggdfkz59gIigWsBux+m6Djje0NP3QKoFfICM8pH33vNTzcETQFqwGGgC6OefyXzj739vEhHkqVLoMwG0ciVgtyO/thONXeBOdtd7yO+F3JPpOVePKgBv71NdnRh8+xHtWSzUzfPSS1RlDiQCYgQNtJkPEH/N3bo1w7ucJ+fS0kRWuz0bQZeUsITHYiEH5kDAZBL9MZwsil7944mQaRQFBdSHpW1UffsCs2c3+muXXUYeQzfcQN1tt9xC5qiPPUaTcrQ88Lff6LipqhKPHaOJz41CyMZ9UADxbZDa6DJf4GmMOQdF8e9ynEIDAPRWP/+8F74mJsaVNRZVhwGVFa4fKwrE9i/nxBkA3vuOy8vZjWi1UruhJqeorHRvc9i9G/jxR8SHnEZi2CmgsBB1uw9g5yl30/LP3ihwdkWpQLWTAKrp7LUFjD/v+/alPFA7CopqI5BV0wXfFZ4H2Ck2veYasYNSUYgQ4mNjk4l8WQKZ7wmc4x5u/7n8cka+8X2Xhw4ZK4AURWwD085eI6PsRlBRwdTSISGUUigKqaK0t9zhIPNlvgbQuzdtTfyRt2ULhf/85+FmOcITQL/9JsYN/Fk6erTI7H76KRVwjh8Xf6euTvT38+b/o0NCAgvdTp0S/QaPHWO5f1wXoGdoESbFZLoSpH/9y3mf6qYVHPmlyPV7XbsGbriDhs6dRf96/pp//FEU6jY0tMFsEm0SJUCMy9tvk3ROmBDQQuDjlYMHUXiqAUuXsh/dcAPlgfphvRaLaGUrgCeA2sHU3LMNgfQAug1AW4neWheKQhT9m29SdVB/SvFVBN0GxScHAJGz3tpyBZSViQd/jx50amhyalUVA55AQZNfZ2Q0+lBeMMMLadpCVGOkqDJUWfHZaHy8yFaVl7eKYanDwbgmbWiUT2gl/x8NUVFi77mRGTS/hgUeh49GuITWZALSYlgEvaPDRN+r7XofIJ3ZcFNgZN6bnU1ybJ9hRADxrF4gCKBPPqH1uXGj3wSB6mXgGh/r5+RQjOeGykqXVj+vNhro0hX83E3Xe2jQomq3s5evKM7An2fd+KjN2/u0eTOLwDp39ls2ERpK8X6gi0p8DtDujaAPHiSfAC7Q4q9ZH0QXF/vBNfIl0r59myElamXw59ygQY27zvoD/TgmZ8uNhtGjm2AjUltLxrV//jOTr0RF0TgbH4JmRSGO67rryBj6mmuIr9MKN/zk2sWLydpI45j69WuigTrfApaT471Urqru699g0owhGvH/0cDnR55amzVkZIghQVUVTazyWCtyqoCK7B2BMvp8OncG3WhaQBIUBMyZw869wkLPbZ/8AZuQQJ8x3zPI96E4HIKj8ugoJ4tQU4NfSp1kh9O38lRtFNZuCWcz1J0fco5D54uog54AMpu5LqngYHx04gLk1MQCtbUICTHmU8PDiXDUbrVZs7x+XE2Ca8mpDcgvMKFedaY/fAGXjyU8EUCASABp6N3bbwdavkGga1d2FgUHU96uKdOOHxc5UO2o42OxlSvpkjXBW8+erDHBhbg41qPJuxWXlrLcwWymfY9/X376iRb5Aw8QQ/zuu9RCeNddjDG1Wv0qQlos4mfMryOe7BowkN6Um3v8gFhQ/lNVRaKW6l9E0+q9v4W4ApaWcEPQ+9drL11V6f3Xw6s6sCXA73d9+7bunPrYWHbG22x47+VSFxeVmMg8RCdNEn+tXz8vxxQvaOCnUUgEBI3uVoqirFEUJdPgvxncY/4EwAHgIy/Pc5eiKNsVRdle2A4MUAOCyEjjTJ3vEf71V2FMp14BNGGC97ZcAfw/9u7NggUuYqzadxxPPUWxX7Od6HNyqLz10EOk/3722UYTV14+zR8+7YUAMvQ00Z/CFgszRlBV3zThzURJCUusOnXyUa1ZXs4q1WZzi/r/8NCbQfMJoaqK77uQzCQlsR7evDxBrTO8nDnm/WIzCK68ga9UeRz15jv4BJjvYPr4Yz/aF408gPQKoGa0b6G+XqwS+9n+mZ8vTmDgA7GwMBY31Nd7qG5//rkryci39ABixVJbaakzf+nViwXFeXlATQ1OnWIvvXNn55LgCSDeONXbfsNLYC+5pMXlwb6C3+ObtQfrKmrNWi9GyM6mSUKvvEKGkc7Frd8OO3RgAZrN5qOsvaHBXebFMwXtuQWMb/8ySvSag6FDWZablwf70VzBD9qj98+hQ0T48u+bNqL+5pupJ0l7vy0WGksfoNF2V13Fbq0jR8QR001S/wAUHGisi93ufaxkYaH7NEDdhFWP8EEBBIikxKpV3ocP6mp6AEh89fzzHm5Rpw9Qkb2jK5aIjYVIYg0ZQvEkn116MiQyqrBccAGTuFRVsfHmX37JNvDQUJw/ml3gttJ+cMT3JOOZbt2wPG8s6h0qUHoaKfElCDZRIl1uiUZZufHeqqruc0kAYMQI5w+Crdhy2kk01doxYbyK0FDjl9WvH3kv/+MfxIUFGtpkMFRXw1GvoKA2kjY4vuCnEWoAfah8MMOf3xpDyqMJfZv69i8e3brRra1HWBirJU2aBNf7mZtLQh0Nhr4qiuKuAgJEqUpyMr1Zw4cb7yElJcD//ketpvx+NGGC3yoNo0sBRIFf//MoFo8Mqsafkpe5/kROVj0WfDkUqwuHIbO8F97MmoaPT1zgmkDVUuGwURvYoUPGIZgPNXP/oPWZeYr3mjmRrlngbB4yynpj04+scnjvvSxdHTxYXFZejbqlAqhF0SgBpKrqFFVVUwz++xIAFEW5GcBlAG5QvcxHU1X1HVVVR6iqOqKzXyOOzkB068Z2aJtNqF5168aIzOhoEm34TADxmRgfzHBZ9jdfq8jIIGHI3/7moXrvC1avJh0qr013OBq1tm8vCiBPoxjd9k27nRERZjNL1lv5gnlPYJ9vDz4I7tcvsJVqL0hLYzFTWZlYreFHh4eH6wjPoCDxxNfui8OHMap8DUxKA6AoOGDv3ej0bwF89P7rr81qA1NVkYu47z62JE6f9ux75AYjAkjTeAOi5KspyM8XiGV/J+Pw6p+BA90Ll16NoE+fpgAQTg+D2BRAoSfgY+n8fNChzZfXs7Lc278A/wmg7Gz2IsxmUabQxgjYKPhu3RiLXlHRuDTBX3z7LVtDmZnASy+hvq5B2Iu0qjQv3ikpAbF7775LylCj3siiIvbckZG0GZwJCiD95LJAE0BWK5cdAwdX7BMq9oY+nYWFROh89BHwyCPsvfviCxrdxfeNW63Unm4wbrap6NpVFANoCA4m3qFJUBRxBNkXX4j7GQ8j9dv+/Z4fz8NHBdCoUey9t9up3mUEVRW7+mfPZjzA/v0erFJSUwFFQWFtR6C6CnA43AkgLQvq1Yv9zFMbmBEBZDaL7OGKFaQI441Cb7gBfV6+D7ExFKZXOkKROfJWwGSCfcKF2FDizJgLi3B9/x3oEepM7sNCPba4FRez4zY0lO3nw4c73xezBbA4M7+GBlw8psLweTTExRlMBAogunWDiyA4aYt2H7VnsYgBOR/78Vlrx47uN2szDaD1BBBA7Zl6azve9SE0VPRJ0osXDcG/Po2945Xk2vlrsQB//SvJsdLT6eda/MIjKsrdzdtHGF0KIBJAyRewA7Vv2Q48cLczqSkvw97ynlh47DL88cDNWFkwAvYGC1BZAZNJDCMCCSMjaL4WNXEiq3Pm5AS41rF4MfW/PfQQkbv6lLstCSAAGDAAjgYT3s6+xHWfpaeLa1FRyOdKg8cigqpKBVALo7lTwC4B8DiAK1RVbW27q/YLRfHYBhYSQhM7x4+n4qvZ7JkFdwNfEeKTKq6H7LcjTJt89CiNsfYbqkqzOI34vLVrvVaijTyAgNYfrMXHSPxUEjcCiM92YmNZJtzKBFCTJoC1cvuXBpNJVMbwXVf8+9u7t0Egx6saNCfRr75ChKUGKRHZNEY8yCpMmWkUsbHshGloaFaiXFbGevK1nHXmTPbvy5b5qAIyMoEGAucDpE8O/CSAeO7QqFLmdbjgZ5+5DubSboNgi6AFGx4u7mWGbWDHjglcj0uhzP9wwABW7amqMjZG45m488/3o2ey5cETQM3yAFIU4MIL2feLFgXO483hcJ/u8vPPKH7pfdTX0/7eqROLudzEO99+SyTgsmXiQAQNel81wydphzh2jB1iHTs2aaxzo+AUi3vXsQzQY8X6o4/Y515SQk6av/wiHu7x8ZSEvf++MVvTTNx3Hw1Cu/RSUmfcdx95aPnsVWeE9HTWWlRSIkqLeBgRQHZ741MzqqrYOrNYRHWNDopCHhUavv3WeIkePy4Otrv+etFE3rAzLTwc6NePFEAqgIpyuhX4nh6NsOPjOn8UQAB97lp2arNRMUQ7rBITgcsugxIehjF3pwLxXYGEXvj+NJGROyIno8pBBaSutiykbn4bvUKd51NomEcCSK/+0c77yEjO18NKm0jP0CIkd2wOI958dO8OV2J6whYjxiMa+DYwDVarez+Vnhxu5gh4IwJIUchlgs999Xk9n0zz8EgA6Y2g9awm38bVvTtw002UtPz1rzT9+C9/oWkgI0fSyLL33iOZYBMmNBkVvysq2PFhsQBJKWHs3q2vR3rCUYrJ+GQjjMnKkkzUBdGsvckLeAIoJ4eulxeez5ghvvde28CKi6n38amnGjeAVVUWM6sq8O9/k8cPX4BpBwTQ90XDkFsTC1RUIjSUOgb1uOYamu733HPuHKwLdXUs17RYmj+EQcINzfUAeh1ABIDvFUXJUBTlrcZ+4ZwB3wamM4IeOxZ4/HF25ut9nLUE1A18QMA72nFVo+MnxE34q68gSMx9QnY2I0bCwshkVlOXnDhB4zE9wBMBxLeQt4YCiI8Nzz+f5ZMlJTqBCE8A8SdGKzNWTSKAeNluKxhA8+B7z3myxkgOLoAPmn74gaoYztNzTKcDriiIF575BH2/ehOht4PSJp9p4oXTp30Y71lbyw5zbQyehkD5AOkj8qwsn72q8vLESqFR4ulxQkdRkfAG5E+5EZr3T9euYo7l4gB0rtJuCiCbjRFmZjOtAW8To/TjNqZNc38BbQgjAqiujvK9Tz8ln15+opJXzJ7N1k9hoWvMc3U18ZxNFrvt2MFaWzmWNv/bDNeeyL8ON/EO31rDfxYaeCdd7axqspkQQ04O1Saa3d5sgPp6MsH9b+4kOBpMxm0egcCIEa4e372HrUAdJemGCdvx4+7v79GjFDlr79/AgdQ7c9VVBqYfgUFYGLWj3HsvkR7TpgXAXiIoSBwz9vnnxmuCJ4D4NcTLGI3Ay3F69my0r3r4cJaM1NURz60H3/41bJh757XHS0pLQ1Gd87MpK0dsaBXbw81mRjrwBJCRAqi2lsWBiiKqmkwm4NZb3des1UreLc4E6qIrwoCevYAuXbH5ZwUVFcCPe2OBCKqUXRCTCUUBeoQ4ma4wzwSQx4EP4IpETvZiauedUAoL0JYgAojO5pO1BgogwPhnsbHu7ysfy+g/Cx/hrQVMQ3y8OKhA77Pcq5e7Sig62vPzubEu27axODg83EtGDlpDaWnA734HzJ9P928zWnMSE92H4OjNrq1WiC7Bhw5h7k0qXur3Dm7u8QPGRu9Hz/M6Y3Sng3huwH/wSq9/YsSQlhs1rG8B++YbxrP26UOX6uz6BNAIAfTppxQYZGSQj5s3FBe796Z+8w3w4ou0b1ZUsFgpKEjMEVsJalIfrCxyLtDaWsy+rMqwNmcyUcjulrbYbPSm5uWJDtuy/atF0NwpYH1VVe2pqmqa8797Gv+tcwRDhrCd7ehRcTHrEBzMzn1V9VLM5wkgfheKiwOCg2FvsOBkRQe3vq9XXvGz4KqNuACIyBo0CBg3jv3MSxuYfgy80detMQWMJyL69fPS0uILAdQKjJWh16DdDrzzDpVa9aOpyspYgGixBFTy7wvS0ljB5/hxlpDxxUlDAigtTSyZ/vvfLin/+aMagHAKQvfscRXqfMO4cSxA27u3yQoDI3WK1equAvIqxOAP6U6dxMCRJzYKmhEM65MDu73Rccp2O3UE3H8/q2JbrcZtJ/zPDh/mui1WrmTfDBhA7V9OxMd7MEBuTAHE/6BLF9o3vSmlfvqJEWzdurU6+dkYoqJYxbaqit7zm26iQt9//kPEzQcf+OhXHh5Ov6zhs89QdbwIDz9MRNITTzSxzfeHH9jX11zjmjucX9sJKKD3myeA3MQ7/P119Kg7IWmkAAoNJSYBoIv2ciYaISeHCtH//S8VUBqdmOknliwBPvxfFJaenIDP88eKRZxAwplk1asmHKjs4dro9EkcAGrx0kgRo5J2dDQtgiZU39sFpk1jJiY5Oe43RW2teKjMmMG+bswHyEf/Hw16FdDq1UQ28gU5vpanibz1BJAhrzl0KCmAAKC8HLGlXICSlMQ2DL4FzIh14acwdevm3vY9ahTw2mvUMrhgASWH778vJPW9e7N82uGgHHLrVtB7FBmJCwYWAOHh6NWhhBS5HcJ9UgDpzxHmAxQMi1KP9JjdLcPc+oFuERWuw/uEPc5YJWGkADLqyx8wgO1tvLeXH2hMAaRh2jTymHrxReNtafp08ftBg7xw1127sjFtFRXUVqRh6tRW3UuCgsQhOL/9JnbgugRaPAF0+DCQk4Pkukxc220z/jh0Ff7v0874c/pmDOmYBaXeIbJIAQbPq+TnM7stgLYnRREJoIwMD3uCqgLbt7PvDftHOfBEOE9mb95MATOf2CQktIli5sARC7KURABAsKkOl/RshKTXo6SE2ssrK8U4upXav8xmM9LS0pCSkoKZM2ei2ifDQ0JGRgZWGjmB67B9+3bMmzfP72vbtWsXFEXBd9788vxEIKeASfAICxMz4EamuDTqA1RVxYiIoCAxGFQUoFcv5NTEQlUVoKYGsbHszKqqojjSZ/BBmFbG4RuNf/zRY/89T+40yQRaVSk7PXKENseMDD+cdxn0lSmPLS38CdwEBVBFBe2/zU1GDAmgDRtIwrV6NVVcli9nck9e+ZxXkQAAWYVJREFU/dO/f6v3x4aEiD3W2pLxaACtQdM0G5SPY6+b4jrn6+tFHrJRREWxC1JVmozVBOgVQBouuogVn0tLG1EBeWr/AgKnADKqDntpA1NVCiCXLGGEgaJQNd8o3uMriHa7856pqxPHFl9zDfLyWZTZtav4nrmqm/zNl5WFkyeYUqlbN4hkgRZdeSLKVFVsOWpH5s8aXJPNnFiyxHh/0HdgecTFF7veQ7XWjldvzcCJLNoTc3P98KUCfZZ/erQW17yejo0lTsnJlCnAPfcAFgvya6NIXmS3CwkJv4yLi+Hu4bNhg/i9EQEENHmmfFUVdSBoyXhxsZi3NBf5+cDnn9W7SKlfTieLUXygMWgQjlZ1ga3eClRUonNngxxz3z62sSoKebpcfTX7d83suR21P/qN8HBRwffZZ2K2dPgwO/N69hSlp/v2eVeR+ej/wyMtjdVSHA4iG+++m+6xigqxxUtLxBMTGYdVUmLM66vJA1DkiKJvbDbE7uPuF1761a0b28/y891jn0YrLM4LGjOGFCoDBxqqwvhBDq4zITQMSdOS0fPjvwOffIKeH70A9OkLQDEkgPQG0HoCqE8fugwl2IqZ3TahY1BN84oeAUD3GnbBJ60JxqqwuDhRvg4Yy7ItFjpUH3uMSFg/0dAghgD6ATF6pKR4rvOdf76osvdqrKso4trRPlyTiUyHWhn8pWRkiH7wLpEVT8odPEispYa0NCI7+DeHb68MMEJC2HKor2eFyvh4JkRPSmJ2SadPUxH+kUdojo6r7pGXJ94PnlhWDbwx0pVXit6Xmza1ffsXnHGx03NjYsxehGf7+Tl4an9pJQVQaGgoMjIykJmZCavVire4CYoAUG/kd+iErwTQiBEj8Nprr/l9bUuWLMH48eOxZMkSv3/XEyQB1JLgN6RGxpZ6MkNzgVf/dOvm7trasyeO1ziTy5oa9OtH1VIN69cDWUccjRsnVlQwsor3MkpNZbteRYXxKAz4ZgLt0f/Tbqdd8pZbyMjy2WepN3bOHHK03rjRp7aBmhqWf5hMtBfqulAY+A2Yz3h86FlTVTr3n3+euuSaA8MWML6Nwm6njOeRR+jBvP9PGykg9G1gp06JyhJepCYgPNy9ah0TA5x/viAO0tqdfQbfBqZPSH2EoUEx6PXMmsW+X7LEyzo2GgGvoXNn1DWYsSh7Cv69qltTuE2KHI38IbwQQAcOiMWmfv2Al18mXw9P4IPIfftATKfG8MbGAiNHChJ2PQHk4gC4aYl1tnoU5lAFVlGct5wRWeBJAbRrF7uBrVbRI6cdwSiYj40VY7Zt23zktk0mGrcL4H+nRuPnvR2BX3dTwFjvwKefemkb1mH9emD3htOw15vxf1mXwtZnMJFuYWFASgrybc59r7TUowKouBju5M2PP4p7s1ELmOETNQ5VpbWqF7h9+63vw6Aaw6JFQF1hqes1HFGTUBkc4/2XmoNBg5BZ4Sx9V1QY+//wDNekSXSQ3XILBf+JibSHemvXOFMwYwZLxA8eFD3c+MLZwIG0wWjBRFWVqPLRg/83XmbgBYpCcRMvxCkpoQ67O+9k4VPv3ox3M5nEUM+oDazaYYUtjH4h2FSHDj9/z/6R96GxWtkmqqru+7zeZK+JmDiRCVb4zuFJk9jXXeMV18dSXOw+/a+khIVGISHuHSeKQvzkZy9nY053J9vdxgRQ16JMKArd44Wmrsb7r6K4q4A8TeaIjqZNXVPU+IGSEraeOnZs3gwPi4V5gIeE+DCdz4g8HDvWjwkkgQNPHK5Ywc6yhARORZaUxHKekyfFmetajuIWsLQcjLqrZs5kohuTSQzL164lUdL27RQ7AhClTgAV9bzlNzwBNGCAaP60aZP7lOhWRnm5s+7qvBcujdvunzdlfT0j+/minqLA4+jAFsSECRNw5MgRrF+/Hunp6ZgzZw5SU1Nhs9lw6623IjU1FcOGDcO6detgt9sxf/58LF26FGlpaVi6dCmqqqpw2223YeTIkRg2bBi+dErF1q9fj8ucROszzzyD2267DZMmTUJSUpJHYkhVVSxbtgyLFy/G6tWrYfM14GsEkgBqSfAbUiOMNL8fb9xoQAJ58v/R0KsXsqoZAZSQQBUDTcCjVlTiP1d8RlGMN2fSHTvYJpSczCohiiKqgIx8H+DZA0jzUwFo/zZU1v3yi7G/UG0tsQF//7vI/HvAsWPsJfTsSTGVTwQQf/j5IFk6fZoJMXbt8j0JMwKfT7kuw6hX7rffgD/+UczmW9EAmgdvBJ2ZSVydhj59GlGgJiWR6kDDzJmAxSIELjt2+Ol5e/75LJE4fLhJI5g8KYAAUgFpP6uuBt5808N57Y0AiovD16dGYkX+GHz5ayKWL/f7EolpM+r78XLY8vHSxInASy+JqmojuG1f/L03bRpgNru9X126sPu8qIgjOJw34KnaKKhVdPN37uws7PA3ZGMEEG/MMXVqi3meNBf8fhMSQv68775Lxofa9m2zibexV6SkYP/wG/F+jnPaWUMDlPyTwKFDKC1VDX2Y9VBV5zooIuKlwhGK76O43saRI6kFDHAjgAThTn6d+2aXl8f2bodDVFbyN1ITJoEtWSKqAXmblNdfb5JAVEBGBvDzD1VAFrt/1MgogWPn0dDQJPsiEQMGYG+lk2WorsLgfroXkZPDYgaLhfUmKQoZgixcKDLwZzKio4HLL2ffv/8++1B5AkgbD8X3ynliAFVVbKnwIyHq2pW6qObNE8VVvIqPn/EBiG1gRrlnYSFce1WstRwKuAWkl2vwC1yvCvBFAeQDQkPdvcIVRSSozWaxCKK/FL3a15MQM7gHt5e3cQuY5cgBdLGWAgDU8A6eQwRfCaBmwBf/H38wbRptC//+tw8+kkZ933x7ZSuCvxS+Pj1zJremgoNFVlZDz57MnkIfsPjoidgU6IubnTu7uqhd4ONjHq4zX+cNi5oawzPRbge++qIeu3Zwr6d/f3q92gZVViYS521AAP3wgzMsDQtDv/CT6Bue5x8BxMe0995Lk84eeIA24quvpjMiEP/5AIfDgVWrViHVmVtt3boVzz33HPbt24c33ngDALBnzx4sWbIEN998MxoaGrBgwQLMnj0bGRkZmD17Np577jlMnjwZ27Ztw7p16/CHP/wBVQZS8AMHDuC7777D1q1b8eyzz6LOILbftGkTevfujT59+mDSpEk+KY18gSSAWhJ8ZUcw0jB+qHbY1taS0b5QZOUrqkbSil69kF3jPKScBBAAzJ3r3ERzc7C1uA/2HwuhqSKewEfa+h2M3+G2bnVLAmw2lrBbLCJpGxLC9iRV9dCiy/8wNpaknXwwpL8+DzAyJkxIEI22XcSCJwWQDwSQ5qGqwVsx0htsNiYLtVi4P80TQKNGMXIjP58lxRaL8SSLVkBMDISWLS2YMptJtNUoLr6YSL2nn3ZVM7p3Zx+53e5RaGaMiAjRmNFPM2j9CHg9ARQURGeRhi1bPPwJoxHwGjp3xqbTzkDFbsf33zchmeTbv/iE2sNhW1YmdsRdfbW7gNAIQlV7SyXUfVxCetFFAETCrGtX+qfORvG+8+Y/aYt2sb/x8aBNjnep19ayEQG0fz9L+MxmMr1tp7j8cupOu+IK4K236FItFtqDxo9nj/N1YJ2qAq/lz0J9vwFAWBj6dziBO3t9B1RUAmVlWL688TbUI0eA3zJr2ANNCr44OoQNEeEJoPJydI1mpIQg3MnzwLhoPW2nTrHgOzZWbE/1cxJYZaXI+V1zDZ2NmpXQiRPko9lUOBzAO69WAwcPAfUNCDbVESvZtatglK7hwAHa2+6+u3ledmp4B+xrcN5gKjA4RNf3zS+M0aMDkyG2Z/Bm5wUFNGHObjeelMWzLXwrNI/CQkqoADoXeEWvDzCbaYt7+20yv9bzzC5lghN87mmkACoqAtCRKmKxVi5wiItzPyM8EUANDQFt8+DbwAB6W/XEgbehZN7avwTwbc8FBQFgT/1AXh61Tr76Kt2whw6hu2Zu3aGDZ9u8FiKAsrOpltnQ4Lv/j6/QfKj13WuG0H9gycltFkcmJrrHI127iuckANFzICICuOMO+ly1wyA+nr34qirgT39qMcWZvgZ/7bXu3YTp6STWHD6c1OPaMZiXB+TlOGBYYdCxrKpKXn/vvGbD/MxZ2F7al+6nTp3oTeO9WflKSIAJIJvNez1VVTlbhOAQTOvmdL4uKXFPlDyBz49NJvrPam1VL6OamhqkpaVhxIgR6NWrF253uq+PGjUKvZ3v6caNG3GT05dxwIABSEhIwCGDhHb16tV44YUXkJaWhkmTJsFmsyHbwLph+vTpCA4ORmxsLOLi4nDKgCRfsmQJrnNK/K677rqAtYFJAqgloTfS8DK21GKhc0pTkpaUUAeUSynjyQBaQ0ICsmqcf6umBokJdMgmJgIXnFcOlBPD8EHuhVB/3GBciXE4xIxbP26gRw+28zkcbpp8/j6PinKvCPHni6Egir+J7r2XIv3/+z+xv0obW+kFvIhIq8SHhDCCzWW0XVfH1BqKIibTPrSA6RMAPydxu8DnQTEx3PvG/4GrryY9tf6U4UdmtwH0S8RiIYGSz/YZgwZRNM0tFr4NzAfBl4hmTAOrrGS5cUiIyAFqSEkRLSveftvgfPNCAJ1uiMTBauf966hHYX69R6WBR/CHyOjRrJWuuNjQWPf779nZmpzse+G4Vy+2H5X9Voi8WudrGTsW6NQJNhtbohYLSx68TQLLq2UEULduID8fjYEYNIjdsEYEEM8EpKe3iVzdV0REkNn2nXe6i8D4wHbrVt8ULHv3ArknFCAqCqEjB+PxO09jWucd6Bp8GjhVgMpKIkK8bY0rV0JUp0VFoeB0kItrqOoYj4og2veCYEd07m7+oa4gvbzYAXuDcx/i+xY2bKDMxpP/D+C3B9CmTWLbzdy59BS33soe89VXjXc2e8LWH2uQs/YIUFeHELMdvxu4BkjuDwRZ3QggVSVP/qoqCoa//97wKT0iIwN49FFqNztyBKhwtph1tFSjR7FuVAzP2LplQmchwsNFB+alS6ntUdtcIyJY7MEngp5konr/nyb6hIWEUHL33nvUfZeUROSuXrTTvz87mnNz3WODoiJQomoxiwSQkVkLr3Tg9/qTJ9lmER1tfED5gX79RGukCy5wf4y3oWQ+E0Dh4ewgsdtbZwqIhsWLaQzTmjW0Idts6BZSQjGT1SrUVQXo5bE+j2b1jOPHqb3wb3+juIEPwRvz/wk44uMZcQKI0/haGVaru7jn2msN8v7Zs6mSctNNJKedMUO0EdBGtmrIzCQFia9VFj/Ad5RGR5ONnh4mE4k1n3mGLplvCdv5lYdxzzoCaP16Z83baTT0RtZ01CRxBLjR2RAX16SWRE8oLyex/l130VlrhN27GUEU3kHBhPO4apSviRGvfPGlQtkC0DyAMjIysHDhQliduVU4936qPhLYqqpi+fLlrufLzs7GQAMTr2CuQGY2m+HQBTP19fVYvnw5FixYgMTERPzud7/DqlWrUOHnEA0jSAKopeFHG1jPnmKOn5VF02IANEoAVYZ2RnE9sd9BDTbEh7FDdk7nNTArVJHdW9ELe8p6ucYJCzhwgGXAsbHGffP8SaUzsfXU/qXBa4uuwyESZHwFZuBAtqGVlzdqnsu3yvIWCfxTbt4Md+aFJ1f4F1BWZignDZQCiN8fhZyW/wORkaTI+vOfxUOvjScgnX8++9piIVuK5nYmTJ3K9v/du3Ute75ckEaIZWUZmyV7gF794ylnuOUWFg+Wl9NkJwFeTKC3bVcAK6eIsNcKA5l8Ah8k9O4t3qfHjmHvXpoS/dlnVATnDav100K8QVGcRffaWqCoGPsrnfuOU62ll7BrnxmvnHJ9dkYKoOha0cGYV/TovWJ27WLqP0Xxbl7UzpGQILaB6ZXgJ07Q28J3sPDe2+npCuJungaLqYG8NcpKgVobPv8c+Mu8QhS9/okbOV9Z6bTFcq7NkVGHgWgiIJYvJ3Lj1Cm4ksouwaVQtjO1pcnELeU6O0rsZPSIUaMYWV5aSjesrwSQDy1g69ezr6dMYWts6lQmLKiubnwiuCcc+XCzKxCf1jUDY1++BsFRlBjl5YlJ2p49YnHBSCHkCapKxeqDB+noffJJAB3IJXRQRA6U/dyBmJvLDhOr1V1ucrZi6lTGONhs4vrgpzz27MkeV1tr3EfJK4UDUA0PCSH12auvUiJkNGmd5wz0oV5REeiXOnZEZysXKBm5+vLXy48V4wMbwwkL/kFRiKCOiqIwgu/w1+BJjFRfL9brGi0q8BKX1moDczjEzdUZoHYPKXYZ1XpUAEVEsM+mU6eAMDQff8y4ym+/FfePVhf4KQoLBgYMEKtubQCeQIyJMV6LiIigXupZszwTHHPmkBmSdlDU1AD//Kf7+PRmIiWFjr7ISJrP4ksNlm8b3fFDKfuG/2XuJispoYIDAKCKCKAie0d8eIJ7cwYOdFcQBlj9s3o124o9DZzYvJl9nZ4OhPTlctTGppsBtMfxxMf//kdsU0v810xMnDgRHzm7aA4dOoTs7GwkJycjIiJCIGWmTp2KhQsXugijXXrPJx+xZs0aDB06FDk5OcjKysLx48dxzTXX4Isvvmj2a5EEUEvDT2f6IUOA++5j32/ZAqG/xtFggi3G3QPoeLYChFDPVc+QIphPOBNfVUX8zm9wYSyrMP5a3pvKl/pKDH8XjxxpnAF7GWPNC2WMCCD+rTh4UMepHD/OqltxcWJ1Sz+1wNAlm1Bezg51i0X8Nb7CtW4d4DjhYQIYQCSLM0hAQwOMSkX6t8+XfU6PtWvpfNIgSEuNGLXhw8lsJzaWiEDeCK4NkJhIZ3JaGnVyBcKWonNnsbBhxFV6REiIKEvyQwXkzf+HR1iYeI/++KNOxeHFA2jLFgDB3IFfa8emTX5OkeOZxp49hQM/65d8PP00dVV9+CEpJbTbNCJCVAz7gkHdy4AD1E+/tyKB2ItBg8TgBGJ8zFeVly51vub4eCA4GCdro6nSU2dHt5wt7IXHx4ufm9XK9oCGBmD+fPZvY8ca+6CdIVAU8XPYsIFy/h9+IPXcPfeQ6ewjj1A8WFUlFjEvvhj0fg0fjgtiMpEcfoI+5PIybPssC/f9rQf+ftkGfPR2JX78kUi4b78F7GU1QE0NksLy8VDyKlg7055y9CgVyfPz4XrP44NPkzyJq3a5lrLdzkZax8WJqrvvv/dOAPlhAl1YyDr+TCbxzyiKuFz008N9wuHDyN7Cbvqkm8YhaFiKYDHDJ2nLlom/vm+f7/5D+/eL9QabDa4xMYMjsqn4oh2I/Dk8YkTz3GHPJJjNVDLnERVFamDeLw4QbyD9xEebTRyx10oKKm9tYK7PvmMkYqxc5daIAEpIYHtfeTmLd3hSy9A13H8MGULnxHPPGSexnqbSb9nCwpPISC8DHzTw8dXJk2RU88gj/jO3ZWUko3nggcaJpH37DFUWRAARgeBRAQTQdK/bbiMlejNHox89Kt7WDQ1iOtDqCiCA5JQffEAKe6NpaK0InhyZNasZb7fml/bii+yssdsDNy3g9GkgMxMmRcVTT9GUQF/5eW1qIADs3q2grsEpceLZLmfBUlXJY1KbMBZqY5OIvznQh60dfU854IoH7XYiHR94gPgUT1BVz11aqkriOQ1aPKJ/zJYt7Ptx4yCSUL4ogE6eZLGG2dzm69Eb7rvvPtTX1yM1NRWzZ8/G4sWLERwcjPT0dOzbt89lAv3UU0+hrq4OQ4YMQUpKCp7iTVL9wJIlS3CVzu7gmmuuwccff9zs1yIJoJaGfhKYD/KxyZNZv2hxMVC8vwBwOFDhCMXdhx7GzJvD8NhjRGZquebx43CZ7iSEFbAkcc8eoKAAQzpmARYzEBaGo9VdaHfgd4Xdu8Xxyp4czDyZs6JxBVDnzoysrqnRiTP4cpK+/xoQSwReCCC+SNanj3iQDBvG/n5pKbDrF046blSC4SM6A9MtIwLI1/Z2VSWvy3/9ixHfERGcCKKujvX/KQojo7QXsmgRnRA+NXy3LK66imKkQE5N5ve7DRv8mhgtOllu2ODzh+JpApgRRoxgJFFNDTfQQVU9KoBqa50JpVMBFGOtAHJzYT+eh03/880Q120CWK9ersO2yhGMv70fL3RE8If1xRf72S1YUICBK18GainL3V/VC7jrLmT8qmDePNF+gxeipaezhKC+nnrYd+wyAYmJpAACgJxcdNvO7T9XXuku+9WTshpmzjT++RkEPn/96SfKcV95RYxT7XZSHKxdy4iGpCSO1J4+HSZFxV+S/4vpltXAYWqPram3YuOJJHzyjxy89A8VDz7oVJI61+WlcdvRcVwqLp7GNPbLlzsJoA4dAIuZWsuKiuiMWLMG2LcPMdGq68JK6pwzbmNixElsP/8s7uXeCCDdTV1XRwGmth/yOXxamnvHi54Act3mn39OhKHRQAENDQ3Am28ip8Z5PVGR6HVFGgCxw0gjgI4edR/aUlfn+7RhQx462ApYgzC4w3HaRLQKAs/2+cvYnukYPpyS7kGDWKvHpZe694Pw78v27WIb2MaNLPHv0aPVvE14TkbPa7hCJV4BFB5urLJWFDFb3LGDbgp+Aep7r5sBb91x/FT6U6fY28oX0i+5xIeODT6+evdd4MsvaZ94+WX/+jdXrKD7+vhxym69gVeGDR/uil27hZ52xU1eCaDYWApEfJwg5w2NXWqbWXxFR7dZuw2PceNIPf7442KLfZORnOzyKQTQdIkoj6Iikvv88Y9EYPqJ+HhnzFhXh9qKWuyr7InqhhD8p3YWPjs5Dg2q4poEtnkzZ43ocOBPPT7A8MgjgKJADQ3HwoXcbWNAAO3dS36VS5bQrfLvfxvzpWVlNJjihhuIB9R30x44IN4jqirmWACdjVotp0MHZ8rrLwHEn9UhIU1u2W0uKjXGjcOkSZPwNZcbh4SEYPHixdizZw927dqFdKc3bnR0NLZt2+YygQ4NDcXbb7+NPXv2IDMz0/Uc/PM988wzePTRR13PnZmZiUS+ggpg8eLFuEdXALniiiuwipf2NxFtf+ef7UhIYHLF0lKf5K9msygnPrSF2N+figehAHRS7N9PFfg77yT29fhxAGFOAii0kJVrnPRtUlg+BevduuFotbPc8M03dAgXFRFjrkXQAwa4j7nQ0AwCSFG8+ADxu0ozCCD90BAeJpPoY71mA5cNG/mJ8I7xa9a4Ud961ryy0rfBNqpK1kaff85+1qsXKYFcwga+v7NjR/cNsY02yNZC375suFlDg/cKhvYYVwI4YgRzID950uceMr2hsTfoJ6a4ps7X1LAoOThY6LPPyHAm8iHB6BlahKu6/kwkX04O1szf4F7JNkJBAWMDoqKINezdm0ZlH70Kefm0LkJCRBsrRaFA3WfU1gJ//jP61+6BRakHTApye56Pj/YMwfz57F5XFFJdX3kl+9XQUKooawSZw0G+BzuCRqPQThuDUlyILpXOls+ICONx7nwAFxtLSdEf/tCs6TftBb17e1aZaf6HAG2L/DRw3uIAw4cDXbog1GzHPfFf4oX+i5jBKUB7yAmOLCwpQajZjgtiMoFx43DllWwbychwVqgVExAZiS7BpfQP//43sVCPP46YUue+a6/DZ3njsKssCWp0DL0YbX+uqxP3Zz0B1KEDYyFralD24Hx8cuG7eHL6r7juOhX33Ucxdl4eqTQ1TJrk/j6lpDBxTH6+kxfNziZmfdcuIsk94dtvUXfwKPJqOwEmBUpCArr3oDeD95H/9VfaV/hpffzWa9QGpqr0e5ryrqFB5HTuvVdTySlI6lWPPuHOXsp9++iFa/tVUNC50/7F46qrKBuZNcuz+knfR8l7F/L9khdf3GpnJV/r++03MR5yxQUhIYi9fAwFSLfc4jn55uOvnTtpbWgFobg49+EYLQSrVfROXL6ceErei9+nhJ0n8/nYprDQd5WuqoqP3bzZ+5hQfk1Mn07VtptvRuyCeQjqSOdyWZmf6tsm4LffRIWE/qNTlHZtZ9cq0FSx48cH8HZtbDSfP1BVGrGm3dS8nMuX33Vi+HBQ4qACv5wegL8U34dP18Xiw7wp+DxvDFBZiYbTZfjwQ/brlww5iaEds3Bf4kqEdLQCJhNycjh/zAED2AJSFKw6PghPPOFO3OjbtyoqyFFCS702bqTv+dvTyOdOX/Tg1bcjRji5ep7EyM5unOTlCSB+aIREi0ISQC0Nr6yHZwgE0K80zWJ3RW+3gMhuJ+Lg11/hSnoTQ0/RTVdd7Yo8u4cUwxofC3TqhBJTZ5TVhdHJ98gjwIMPinreJ57wHJjoJzpwaIwAArx0xPmrAPKg6vDk/6OBzzO37o9AhcNJFBiVYIYOZRpom81tNzSSTTbmA6SqNBWI34xHjQL+8Q8d6cC/me101HVLg1cBffut5ykEGzeSP8PcueTHU1RuFc2JXOwM6AM4cYIyN13k520CmBH0Zr61tXBX/3DRjCsIjO2M0SlVmBSzx+XNtb+8O0789X13mYEevGxOW5uJiViWNw7bSvsRAaU24MEHiSC+/npSjdx5p58y840bgbw8WE0O9IkoAPr1ByKj8Mkn7Nbr1An461/pb+i3i+hoIoG07cJuB/6yYzpUCyX/sdZyWE3OoODSS40P/UsuoYmFS5dSUv/ss+6zi89QKApw441seWhDD+fOpZfqHPgAgPF9VqvOqNVkEkydBkdk443RH+KlOw7goaQvcW38JoyuWY/u5nyYamug2Koxt8dahISZgBEj0KWLSGK69s7oaFIA6ZBYvNN1QVnVXTD/4A14dkl/uj4nWZdbE4OH9t6Be/fci5ePXoX/bYsXjwnObD+7pjMe+nwiPsocij3ba2EvpA01Nxf4/e/ZUg8OBsaMcX8Pg4JEocSWLRAPlWPHjM+Jigrgww9xwhYDVVWAbt0Q1yvEtQQTEtj5pQXJfN45ezb7+lfWWQ2A+K+//IV+53e/ozpMZiZ1DgDE2V5yCam9/vUv4B+P5MOkOK8xI0PU2593nmjUKsHgaZxeTg5bA2azBzORlkGHDizmaGhgH6WqirWy2Ptn00HljZFPS2Obw4EDJAPUMGpUqxaALruMff3ZZ2Lr75gx7ib3hvAmcVm2zDeV7pEjYsxps3nu/SwqYhuIxULVpMhI4NprYRo9Ujjfv/yy8T/tCTt20H3sTWzIq3/GjSMvah56+0mJACE5mQUmx4+zfqqmYM0a0U+qqKjx5zt9mj7sm25yFUXOOw9AOcX2X58aicy6/gDIvmNZ/jhUOkLw0xfFrlg03FKLW8JozGVccBlumMKMF5cscZI1ikIj01NSUDf3diz6PMr1GH5drV7NfJYrK+mM0ttWHDhANba8PLq9jLhZvsgOiOSmywKiQwem9nU4GpHawV0BJNEqkARQa4AngHxkonny4tDBBqgqsKc8AQilm+O669j9VV3tvL9cLWCFdPgtW+bKHky9E9A7JZw2i4ReOGrjKrMak2EyEfnj7UT3kQDyNKCCJ4BcG0l1NWtrMZmMK/xduzIlVUWFoRF0Q4PIIxkRQD17Mn7JUVOHDcXOKoFRu4miiNMRvvpKMC4yGmbhTe2oqqR+5rvJLriAplW6xfm+sGlnOUaMYK1ENTVUOX/jDbFzpKqKfuZwkMDu00+pg+DhHXPw0m9X4aPcC5C1dAtlYw8/TFXle+4hH6WHHxZIII8tYHV1lB1nZQmG4ImJXBG6pBo7nvuWJO0auPuooYGLVYOCMPqlmYj8+E2MvLSz68Bbf2ogsSY6ja3dTgTYihXAj6uqkVneC44Gk6uUWFjTAZ8UO9UyqoqrJhRj/Hh62jlzSMDBi9mgqpRsfv65Z8kal4gOuqi72xocOJCe15sHeefORBBpb0N9cBiRqsnJ6DY4mj7ckSOJvfOEjh3P2iR44kRKqD79lEifv/yFutuio+n/+s6DsWPFTlAA5IysBUwWC8xPPYnkv96ECy8y4+aea/Hnfp/irfo7sTzpD1g2/AVc1mUbvefO3zF86zt1QtebLqI/yLUCTzb/iKuvBoIcNa6f7TgcQeqYCy4ArFb8O/ti/FYVj9yaWKyvGI5337fgnntE+xL064fd5Yl4bP8tzEsIAPLzXHktz82ef77nmNDNB4hXH1VVGU7Fw7ZtQFUVcm2xQEgw0DVe8DpRFLGddfdulp+mpUFQTh05wv5EXR3w/PPstVZXAy+9JCqZxo2jI85sppqGdSh3IG7ZIs60P9fav/yF0Tg9vkgzenSrn528GmblStr3N25kJG5EhHM7a4zA6diRVQFVVSSAPLXntxCmT2fd8PX1YusvTw55hZ4AuvBCdlNnZxsbeethlI3yNxcP/vlSU902EL4+tGRJ4wpjI5SXk7J17VpKpo2O0qwsdu4rChVLBg8W20zbxP/nXEBICMslVNX3fl09ioqMW74aq/Z++imt7bIy176emqLCUlHKHtPRuT+FhqLKEYLleWOxdLmFAtojR3BF9usI3856oS+7JsRFXlZVAZ984vyHIUOA559HZp8ZLgF6bCxNm9PEQeXlxJPX1tJkMk1oqijUGaFtSSdOUPvYK68wMTvvM83b1RUVsdk9FouonhXawLwZpNbXiyp9qQBqNUgCqDXgdfyVMXgRzOHjwThW3QXljjAgJASRkZTYuQl1rFaEW+sQE1ROUSk/Nvmii5DUx3mHR0bht+v/TBkIH4jceisEB0wj8D3DpaWCC6YvnEVSEvPlyctzGkfzip6EBONoX1FEFZBBySU7m4gC7TI9yWpdKiB7LX4ocp7EnvxGJk1ymXWioECgu40IIMMzISsL+OEHbF1XJfTOT5xIlW5DsZV+Atg5CEUhlby2ROvriQi5/36W533+uXshRlWBw2Vx+LEsDZ+cnIg/7L4RRYdKaM3wppAnTwKvvw6oKoqLgfLCWqCgANaGGsbdZGQQo/Too0Qi3XgjRXEbNrA2MJsN2LsXP32WLyagXGR36JAosuvfH0B0NNLv6EMEcbAVP5UMhmpzns4cy7V4MZFcixYBL33aC388cDPu3XMf8jvQ/fDhh4A9hBLpPuF5uHlohvEbardTVnLffUSAvf8+vS69Bj4vT3DfHXyVqMhLTyeeim8x84T4eHqsixA2UYtR/GXDycNq/nzWrncOIjjY+OVbLCTM5Ldnof1LQ0QEZR8XXkhsW2oq/dIjjwh7muXEcaa44oiFpCQj7y4FXW6+hLwOnnjCdRGW/Fzcem0F3hn0CtJjdwMKAEsQ1q0D1LBwlKVdgF3l3HQi5z5eV+dsAdxBt8qnXX6Hp2v/iKruyUDfPggx12Fe76/wQe9n8ff7s13brQaj9i8NI0aIQomyfbpKo9GYH2exIbumM01CM5ncTGyN3muLhW7/8HB2PqsqJcQOB3UtCUQXKK7lRT284goAschurJ7zjwXQ5+WshL4N7G9/EwkgwxumZTFhghgubNtGRrEaLrnED/EOL2/T4qPg4MZjtABDUWgv0vvH9e5tPMXeEAkJxHooClUjHnxQVEDpHdb1UFXjFumdO42l2Hr/Hx2uu05MVt99V7xPfcHmzSz8ra6mM1ovZOJtOsaMYaT+jTeyuK+VP85zC03IvQSoKsWIWvslD28EUEWFuBft2gXU1SGkNB8pwc4qtdkMhIdTUdxZ2F+ePxY52Srw2xGEluXjirhf2HN07w7LmJG49Vb2o2++EcU1/PkzbhyFAPxt9s03VKPka4xabfTxx1luZrOJbctXXslIIJuNiev4v5eSohvO5qsPUG4ua+W0WKQcrhUhCaDWQP/+bFFnZ/s0Lio2liVYNZUOrCp0HmIhoa4YPzmZclEGBb262t0DjLQ04NJLhcmhR8tiSOu3cCGNVH7wQWDGDMNrqamhvSw3F7Rp8QohLVFtaEDpcaaO8MRZBAWJPM6BA2i8/UtDIz5A+vYvT4HWxImAxdwA2O04XNUNy06OhRoTa/zg4GBxB+VKRUYEkPDRqirpi+fNg+Ofr+H9uzaTWYXagLFjadN1eVvm5VGFQosgpAIIABVx//53MUiqriZSIStLlG/PmMEpUhQTKUwUBbZ6Kz47yVXT+Za6jRuB77/H7sU7KZPLysKAQ19B+fsLwHvvEUHBj7fTLuC99wBVpYSuuBhQVWwt7Q9bvfMETU4W5BX8dY4cyYK/ESNAfd3JyTjh6IJj1V2I0XJGjmLnoeoKdvNrO+HZr85DRoZzVLbz5L2j52qYjxl4ZNntNHv6zTdFE+miIqdDMAd+Lv3w4Rie3hFDhlDl+uabibT0Z0pH9+7ETfCJPa+4kDBGv37A3XfTRztlipdEYehQkoDzngeRkdQbzJe6AdrPdL4yehVQVBTHwVutTIanqsDOnYi1luOBxG/Ick5RkJdHe++GyMvRoNLC7hOeh7suOuYq/DscdM/eeSfwn89C4OjcDejSBdF9Y/D3mzJxUecMRFsrMXD3UqEdtmdP7+bykZFMYKs2NGD7r7rg0QsBlFMT63qh+vU4ZAj5tP3xj7QFLFhASaKmKuUr+Bs30gREXgpvNNwpJsYgYTabqdzaty+xpXFx9H4/8IDnUccSBP04vR07WDUgNlZXjm4dWK2iddmrr7IlGB4OXH21H09m5MOYluank39g0K0bdbLwuPxyP8gsRSGJ45IlwF130fczZrC4eN8+7wqNQ4eY6js8nKmj6uvdiSGHQ+zNNPDRCgqi45C/T994w03U7hV6QdK2baJxfU2NKNzi1VIDBtBwhIcfFltKJQIMb6P5fMGmTcxLSlHEMbfe8rhvvhH9qZxFQmRkYEK0k4iKiMAFkxS88AKQ1IfOTVVVKJ4sK8flXbaig8VGRZ3HHiMiKiwM55/Pjvr6euYRqKpiR6QmFLz4YnabHThAcxo03HUX26/GjaNYW18M0Tppjew7+DPPrV7hiwLI4RBb62T7V6tCEkCtgZAQMRD3wb1bUZxciMMB1DmwpjCNssZgq8scF6COFn6PS+qtK0EMHUrVfotFJIA0xV1CAmV1U6YYnuaqSoH7a69xYgGjUfB//SvK1myjTEBVPbaAAQYbSWN9WxoaIYD43lRvT9OhA3DB8CrA+VZ9UHAJ/v2h1XMb+qWXsow9MxPYvRuqatxdkJvr9Duz26k5/N//BlQV3xWehxMVkUB2NsL278D9Fx9m5M8vv1B/02OPsWxfegC5MHAgFXcXLGA5UVERiRy0MzYxkSYIP/ccKWKefx646ZE4YPh5wNChWN3tZhQ8/jKdlv/9r0jqvf469ry/3UVeDul4jA7+L75ghFxEBJ2M2klaUgLs3YtevYBedbQW7Q0WbBt1Pz3/Sy+5TtKjR8UYlW8TsFqdMUVIKJCYiJ9KnCf7pk2AquLnnzkZrqkU4zr8iiCTA7BYkFsVxaajh4fj/E4HkdIx29gk/b33RIaUTyJWrWKKn4YGkQCaMgUWC72vS5YQV9wU+4mEBCKBBg6kHGbKFP+f41zE9Okk89argXxCZCRlOb//PWuju+QStyBr6FAIZ4NbSwIfyDkr61aTA+N6M3Jl3Trgx+OJNNkKwNTOu3D5JXX429+YEKmuTuRSExPpNkm6kzNm27gR3YMKsHAhES8vvNB4QdAVeNbU4H8nRiDPxknTjIzDeALIWXk18tTt2ZN1wQ0bJg4w40mpn36iNjEN115LgTQv4AC8GJyOGUNnxTvv0H365pvGpugS7rjoIuOWgRkz2my6Ea/y4WOEq682Fnt5RL9+7r/QhqqwK65gBZZu3Zpgx6YoIqkZGytO5eCnYujBH6BjxojeTuvXi4/dtYsdml27ehzpGRJCxK3mV+twiEefN5w+LbbCaXjnHbbH/fijOIxOT+APHEgvXwoeWhB8UeTwYaFjQYDdTh/eHXeQ07mqUqHv3XfZYy69VAzePJEadrs4UVnD9u3Ar7/iothduD/xG9x/fTEeeoi2qZtu5RZBfT1CzHZc2fUXYln/9jeSFjoXiqJQrKvhl1+IP83NZTOGQkPZS4+KonNMjxkzdNYAoC3n1VfFNucLLhALLQDlbTabeO7x3BgA7wqgggJSus+cKQ5rkO1frQpJALUWOLNOrF1rLCnUoX9/ADbqaXKoZvIrgCL4bphMJOQZPJjut6tncRtJaiqRP85kLyGB3dQnT4rdMJ7wyy+smFJV5ey84tulCguBsjKoW7ehzBFGEU9BgVfOgieAdu4E1ANcYsq7X+vRiBE0n982Nvn1zouPIyXCKeG0BuN//yNppCEJFBsrBhyLF6OyQnX1wYaFsbfE4QBy1x8hdsLZn17lCMbHeSzQmdVpDTq+8CTtokePUhZUX0//qM1+5GXN5zgBBNC6HTaMSEhtDfNn+dy57OedOlGwNXMmMCjFDAQHw2ENx9Jd/akMryh00GtZn6pid3kifR1kwZCILPGPDx1K5cE33xTbCn76CcjJwYQgZxnEZMJHJy5AhUlUbPEtAKNHu4vcXG0hUVH4qWwIrcETJ4Djx8UKYuwWPNF3GX7f+0unwbTJtV7NHcNwa0+nhv3YMXHqwsaNovHUrFl0UfyJvXAhvaG7dzNVX0SEkHA0N59KSqJhg3/5yznd9dW6UBTauxYtIkUQHzlyD5k5k33Pb7MAxECOm6wzeQhrU1y7Fjh4SAHiu8GsNGB87AFgxAjExREZy9cMYmJI4PKvfzl/3rcvyywbGoAPPkDIqeMYOczh09bnWsZV1Tha3RX3Z96DT05MIJ8svQKooQHIy0O9asIJWwwRr3CvejaGAQOMhRg33cT2ogcfFLfus8S/vH2ha1eaqvD440R03n8/xTwe1Mytgfh4d/IvMlK0E/QJZrO7/K0Np8KZTOTDv2AB7eMBydV4SdTWrcZGOvr2rwkT6D/tQNq/n8xOPv2UPvsFC9hjhw/3ypyHh4sKnDVrfPOjdtZnAFCxUYv/KioojrTbxTrvtGln/eDW9onISNYm6nBQArNhA0k733uP4qzCQto/vvqKGJTFi6k9/uOP2VCPTp1oc+eN+Y4fN14sa9eyAi7P7m3dCuzeTRNZ43biklu7uf55+EXRGBjJzqrL4rYhIi6UegUN0K+f2Br94YeiGue888Q/rZ/UN3o0ORsYwWqlMOHVV0mhdu+99HO9f+tnnzFT6cREAxeN+Hh2SJaUsPdEkwPv2OE+HayN/R7NZjPS0tKQkpKCmTNnotqHPF1DRkYGVvJxtgds374d8+bN8+u6EhMTkZqairS0NKSmpuLL5jjXc5AEUGth8GBxopQn8zoORABxMsKQUHTqxI0KdyI2liqlr70GxF09nrx8br6ZSqjcKR0cLKr5s7Jo73vxRdpA9ISQw0H7II9jx+BuBH3kCGwNVtQ10I4TnJeFkNoyeoJ33qHdhOuHTUlh+0LWITsyjzt7Q0JCvPeGdOnCKmKVlYzuBlBZ3oCc3aeB8jKXwaYhiouB6mqEV57Cs8kfY1z0flfV+scfge++8/B7N9zA+l4OH0b5WtZjHhnpPBfq7MDRozg+/z2hOrAs5i6Up44DevVCXHgVLu+yld7s+fMpouKlovlOl3/ZAmaIESPcz8SBA43jYkWhj03DmjWcICA4mBRXVisKaiNxqjYK6ByL4JFD0e/dx+h07d6d7qMFC1g/Jm/isXEjsGEDJsfuJlVOVCRO5FuEj/TgQdYnrU1+0uO885yFUZMJp0ITcKSKXP6KVm1zka8KGpBeuoIuIWYfbrzZLDzH9BlB6NbLeeLX1bEm7bw82hg0jBtHFxEaSie7duCePEkJFD/iZdIkWZ48W6C1TXjIQsaNo3azK64QJ5ABEAkgTtIweDAjdlxnR1xnDJ/dFxGLXnWN0ouLoymH11xDS+6dd4CpU3VLi+9D27CBGKJZs4h09VS1daJnT7pNlWrysqprsOCjE5Pw1vFp7gqgU6cAhwP5tig4gkIAsxkxMf53WwUFiRX9kBAy8581i73F0dFU5BwxgoJtb93NEs1AbCzJqyZPJvlNK0/JMgJf7wO8T7T3Cr4NrE8f0Y21DaAZvQYsJOnRQyR/jeZO//qrWJQYMoQugGfZvv6aJqtlZIi/62a65Y7Ro0XfJiNljx58+9fkyeSjoiEjg4qymsream3VYXQSevAtEu+8Q4dRZiYpvO+5hw4lvWp6xQqxb/+OO+iQiIlhOUh1tTiMxuEgMpJXss2Zw278vDx2fkZFCbmOYjbh9xN3YECHXIzudBAzu22iv+mFELnhBmYhsXcvXbIGvU/84MHsdhk4kAqpjRX0evcmhZp2+Xr/Vn5egaFPn8kkEmaaCmjJEtH0OS6ObsKOHdu8BSw0NBQZGRnIzMyE1WrFW2+9Jfx7vVaoN4CvBNCIESPwGh+T+4h169YhIyMDy5Yt85tA8gRJALUWFEWMCr75ptFSQ79+EMmB4GCX/49HmExUVbn2WsObiR+wdeQISdV/+onY3AcfFL2VV61yj5+PH4dYzi0sBA4fRmkdi6AjlTKqys2fT6x6QQH1rzqfvEMH7kCsrMSKfOeM3759hV1p3z4iZTSW2c0IOjMTDQ1AzsFqfHP75/T8Bw6it/WEcXXqu++IHLvxRmD5clhNDjzWZzkuPo9VnRYtEqdMuRAbKzRxly1ZCagkAeporkLvYz8Av5J6IqvaSZBZrSiaMw9fVl9EO3XXrrj5xcGwdnK+VzabODYcoPdKVaUJtBfMnMmsH8xm+kg93RNDhojx5dtv09mjqqCyxV//ij39rgb69QV6J2FQqhmWfr1JwfXWW3Qf8Sfl4MEsCC8vB1asQFxwGR5J+gKK8+cHD5LqYe1acXjEhAlMbs4jKIjrEI2OdrWBrV9xGqpzjxjSOR+xdmeFKDYWsx7u4epii493Ju38vaGNZnjlFeaM3rUrRaramxUTA8FRMDubxihrkH1a5wwUhba3O+802G54Aoj/ndgYg8BPwaRrY92m/sTEkKH7pZd6sDAZNsxd/VlXR8q1Rx81buXicO21wMsjlqBvOHvc6qJhOHGkRjxnnY6ZOTY2fc+o/csXXHcdxay9e1NOobdbAuglPf00cNVVTfsbEmcmhg9nbZU9enif+O4V48fTEwUH6w0fzx7wb8533wmTVlFaSlJBDWPGMOb4yiuNn09R6ANYsEBsAfKAoCBqc9FgxEHxKCpifsKaDVVaGuX6Gvj8dsIEP1v/JAILfg3wH4wGLccym41Z+iFDGJGoKGIQd/w4rdeFC+lAeOwxdlaFh9OhyhvGadAM0TnED4jEPwa9jz/3+xRhwweKUw4N0LUrFVI0aCmDorgXRBWFZkUsXEhigabwLHr/Vg3Dh3u+FYX36v/+j6a48MOJ7riDlFh//nObkz96TJgwAUeOHMH69euRnp6OOXPmIDU1FTabDbfeeitSU1MxbNgwrFu3Dna7HfPnz8fSpUuRlpaGpUuXoqqqCrfddhtGjhyJYcOGuVQ769evx2XOXPKZZ57BbbfdhkmTJiEpKcknYqi8vBydfJnA4gNkebc1MWkSSWpsNkq0MjMhGProEB4O9AgpgsuyNSTY69hlX5CUxFqmP/2Uepk1nDxJlYuLLiKiaMkS998/dgzABB0BVFmJMgdHAFmq3Y35GhpIG/vqq0BwMK68kvYCVFRgW2k/5NbEoAe3UWdn0wAaVaVK65//7KzS9u3rqvJkvfAJnp/fBSdPqICNsenJJ9cBjuvEEnNlJbE7qkqJhTPRNSkq7p51GntXUm5QU0P71FNPsf3ZZqOqUE7w9RitbEN3NRflJyqA/ANAfQM6Ht6NxOhfgQaqYmfVxFHT7e23Y+lncS4Cq18/YMI1ccDo58mbQ2sWN5noP4eDDqOyMqkA8gJFoXU6dizlmd78ngAKzLRe5R076L/oaOIBL7poIH7tMxBwCmYavb+00V9adcgZPIzrcgR3zumAdz4Q/44Gk0lUI+kxYYLTfyAyCj/lDsGt6hqsPdQD6FMDhIVhspUrOV5wARSTgvvvZ9MZQkNB98bmzfSYw4dpwWmRqsVCMme91GHqVHqMXpGYnCwaw0icu+jUifYgvet9TAzSR4jxXGhoE21KtAj122+JvDx6lDHxx46ROm3ePGMzAwBwONCvdBteHvQznj40BxnlfaCqwPKjwzCvvJztoU4CKLum+QTQwIGsvVO2d0jwMJmo1XXPHjpTmuzbHBJCJH59/dmrxhwzhu0vRUV0cI4cyUbraUWyiAixX2voUKroHDlCv1dSQsqKSZNEwy4fcNFFzLZl82YShnhSBfLTkYYOZVvL9dfTPvDRR+Lj9e03Eq0Mo1F12siqrVspJ4iKooQjOZksGbQP2WIhhRC/wSckMM/ErCzKLVavdv8bM2bQgThypNifBRiTQpdcQo8LC6NJrT4cKrNnk7KdF8n272+cMgQFGRcg/cHAgaJXe2IicV4uP1M9Jk1ijGpeHlkpaEhN9dgXq/cmCiT4Scye4HA4sGrVKlziJKe3bt2KzMxM9O7dGy+//DIAYM+ePThw4AAuvvhiHDp0CAsWLMD27dvx+uuvAwCefPJJTJ48GYsWLUJpaSlGjRqFKQZF1QMHDmDdunWoqKhAcnIy7r33XgQZTFlJT0+Hqqo4evQoPuXlV83AWXqitFOEhZH0RZOJffONVwIIAJJDspALktIjODAEkAae/FEU2ge1Uds8YmJYa3ZODlAfEwfX/V5QANjtKKtzHrhhYYgK0o2VDgoi0uXECZo4dNdd6N6dVH9bMom2/iL/fDzAaep//ZUVbjMziS9ZsACIHD8e+OILVNiC8JdDs1BQW+P2GseYtwBr40S/li+/9Oi7ZO3eGfPmMcJp2zbqPFBVer0HD2qtqqH4svxxLIp4EGV1YUAFTRyJDKtEYpjTDDs8HLt7XIoTczvBrFM0u3xqevYkY7dnn6U39r77KPrQqhOnTkkT6EZgNvvuqTF4sHOtcWdwSYmzZTJOlHw3cjsSJk4U5cEAcN55uPzaYJyuFhNiDVOmePSiBEDxQEQEUFFhQlFYT8zNeJhUdadLEBIZjLEF3N/jXrjQDqr3yOJJnTFjjMs3ikKN3nfeSYn3kSPEeDa5ZC1x1kFRSOaib6+IiUHPnnSmaFvX2LHN8AaJjmZldFUlNcDbb9PmW1VFsrrJk2l0iT47y8kB6upgUoDZg/ciIyMFqK7GuqJUzNlXgNgxzoiYN4AOJ/+f5kykk8SPhCd07CgOKWsyFOXsJX8Aem1TppD5LkDS82HDSD6rJdpa1UdvNNKtm/eD1UckJdEWd+wYJdM//WR8BB48SGG7Bn2H2XXOuqM2WLNfP9n62ebo2pUIQa2gMH48xTxBQfSzQ4co8NP6AP/wBzqLfvmFWD29QRzPomRlibPQY2MpmBs+nN38Rv4ERqMtU1KojdGPUejR0USWaLcO0LI+8amprMMtOprUrV5te4YMoffzjTfE/Cs0lCaXtrMDtKamBmnOz2bChAm4/fbbsXnzZowaNQq9nUrojRs34nfOns8BAwYgISEBh/hBRk6sXr0a//vf//DSSy8BAGw2G7I1awYO06dPR3BwMIKDgxEXF4dTp06hh4Ep4bp16xAbG4vffvsNF154ISZNmoQOzZQWnsWnSjvFpZcyAmjzZmJFnV4JRuiPw/jBSQDFdrO6T2jxE0ZF/bg4upFff914Eqem0isqIh7nZF1nuIqmp04BqooyRwJgNgH9+qHjKeeGaLHQTV5bS9pDgOjXUaOAtDRceVEVtrxOBM7a4qG4KX4AIrmn5XH0KDHN8+b1wcBXXsPLN/+GglraeawmB4Z0ykHSoBCcV7QagyOySb6Unk6bfEWFmLBfdRVt+nv30nufmopBVurQ06pAnga1lXToiYKIPijLYwlIx6Bq9BjVDYmxvZFVFQs7FLz8Mp0bWstoSoqO9O/Zk1qMHA6q8m3bxrIovlcYkARQAPD44zT+cutWMh7X3t4XX2TS2dBQL95RPPr1I+kRv0idh/1NN1EMwBuSx8aKJrtGsFjoKb79FkB0NEqLnVXPomKM7fUbQuqdF9yzp8eWHOHijx0TDTUbMyGIiKCgxNvMbYlzF0YEkLPKftVVJO40m929T5oMRaEMrE8fIn40r4W1a6k68MQTotM/5+GQMiIEg0oqse+ACQ7VjBXL6pHembw9C74ZiZtCs5FjiwViSAHkrwG0hIREgDF1Kstit2+noJM/v266iUihFsRFFzELvDVrRAKoqIj+jR+hbTZTXUWPa6+lPWXPHhI4tLMc99yDopCn3CefEDEzaxZr64+NdVeLmc1UZLjrLuPn431tdu4kBZD2e//6F9xGIMfEiFWS+HjRRoNHE9qgrrmG8hWNX9H7/wQSw4fT+s7JoYK2T0K7iRNJWfXyyyzBvOsuA9fotofmAaRHOFdwUn1xiXc+bvny5UjWtSic0iW3wVzFzGw2w6E3xtahT58+6NKlC/bt24dRzWT7mkUAKYryFwAzADQAKABwi6qqJ73/1jmOhARKsjIyqMq5bJnoIMfDZsMISwaCTGNRpwZh4pTgZh8mEREiGQ6Q+WevXtQbumsXCQGys0ncM2wYJaY//MB+Jys/BD1JruCS6ZTVhQFh4UBwMKJmTwU6O0j+168fPWbLFsq+ATpJ33gDg7EX/cJO4nBVN9SFdcQ3P4S4CsCaFzKPkycp7u/WrSdORvQEkkuBk3l4dOx6jFkwjZj+Oz4DykAX++23RI+vWMF8UHr2JDMKk4lkIB06uPTZc+fSJWqT7XlYLE4VkMmM3FufQvlX2cD27kBICCJvT4FyYxgePkaFBW3YAO+ndMMNBoEAz/Tznhn8hLPw8LO7+tdKCAqic2jiRFK+3X8/LV/eaiklxYuUlYei0BNpUh+LxVV2URTieC+91P9rvP566grM2BkJ228moKEBZnsNppd9DGhE/8SJniPKiAg6VAsKaBFq0vnIyBYPniXOchiRjk7Pq0mTKOEJCwtIMV6ENpf2nXdY73JxMfDXv5JCQAuYeRPPvn0xy1GEZw5QgLnyx3B8ddi5pRYG4cW6a6Aoqut3m6MAkpCQCADi48W4mCd/xoyhrLOFccEF5BLgcFAB5/hxCtcbGsjOkrfHM5moTUwTjehx/vnGnmASbYThw0VD9eaAJ4A08gegdaonfzSMGsUIoAAX2SIiyL/1ww/pEjzVBwMBRaGBC36jSxcq5GzeTDmNfkyiDr60abUVJk6ciI8++giTJ0/GoUOHkJ2djeTkZBw+fBgVXOF+6tSpWLhwIRYuXAhFUbBr1y4MC0AcXlBQgGPHjiGBX4dNRHNNoP+hquoQVVXTAHwNYH6zr+hcAN/HvHatB9dhAPn5iAsuw0sDF+EPI9fjhpsC49nNG0GPHMkkgyYTI8gffZTUEVpfs171qGewyxzhLll+5MBu1FKimXoqCpFcWrDu7KtS9mbiyq7OsecREdjOBmsJ4opZs5j7PMBN9o2MwrVPDcSYD++lCwwJEaUWS5ZQLxe/m8yZw9j/6GihOT80lNrMLrmEeKM77iBbig8/pOqQhhPVnVDeeygxaR06IDKelEi9e1OhSo+0NHFijCF4AoiXE0r1T8DRqRN13enhU/uXhvR0to7OP9//MUIGiI6mSUJLPzPj1Vt24aGkL/H3gYvRv4NzwYeHN27MrDfSBShD94nZkpDwAH1UGRkpbMp9+7YA+aMhIoJM2f/4R+amWlYm9tdqpufOizlvbAiSwqiK4KiuJfKnvt41UUCFCQgORmSk3GIlJNoF9GY5UVEUr/3hD60io+nYUSRtNCuEbdtE8mfCBPKJlF3S5yjCwozVK96qfldcQSqYxMQWITPHjqWGgiaRM60Fs5lunkbIn/aO++67D/X19UhNTcXs2bOxePFiBAcHIz09Hfv27XOZQD/11FOoq6vDkCFDkJKSgqeeeqpZfzc9PR1paWlIT0/HCy+8gC66QRtNQbOkBaqqcvVzhAPwTRt1rmPwYOam5XBQU6WR3NApg0kKP4Wk87oDTTUS1OHSS0np07EjVTF8AU8AuUbBc476pXXhQJSTADLyLI6KoptfC9q//x7IykJqBJlyIiICublM+MIrgK68kvLepUvJ1kQbEjF0qMFY7WnTaLxjURFJPPixfAkJjTbld+9O6hCjn2s4ccKzRc+VV5KCmfeV8Wb+6wJ/M/PJjDSAbhGMH09y7g0b2M/88tfq2ZNmPB8+HPBI0GQCkh6fiaTod4GaYKD3OFq7vOOkJ/TtK7pUAnIGrUTz0aMHJ4MEydpbG2PHknxPG836+ecsadRGzAJA375QcnMxq9sHeOHItYCNjNrPSypFw5GjyChLomKBojTZAFpCQiLAGDOGiqPZ2XRAjx3b6urnadPY/JK1aymh1mwBAGq3ueWWVr0kifaIxESxVaBHD+9V3ogIMpeWaNeo5BVdTkyaNAmTuHGnISEhWLx4sdvjoqOjsY33gwLw9ttve32+Z555Rvi3TM3zTIesrCyv191UNHt3VRTlOQBzQY036c2+onMBikIHnfbhf/cdfa9P7vjRt801/+Fw3nk0qSAoyHdhgH7yIc4XGfAyRxhTAHnKUS+6iBFAGzYAdjuighoQbrGhqkMEamoovjebyYcWILK9QwfaPx96iN6mb74homjOHIPrt1pptPU//uH+92+8scmVJD0BxPuZ8QSQyUQDa37/eyKJJkwQrSo8gv98eUt/SQC1GO65hzwmS0ro8/VbOjtsWMu1VkVFUeXTX+hNjBIT5TQviebDYiHSUyNa2oIAAugMWbKETQxav576NbQ9MzaW9syGBozttB/Xxm/CQXtvXPrYIIyr242GrCX4d/bF+NpOAwJkZ6SERDuBohhU9FoXqakUC2gx3scfM+szrb1bQgKJiczSAiDmUJo9SZxhaJQAUhRlDQAj9uFPqqp+qarqnwD8SVGUPwJ4AMDTHp7nLgB3AUAv2XRPLEyfPqT2sNtJ3qJXAfEymAASQID/XmPdu7MCcEEBUBXRFXlV8VhZMAJdg0/jVH1nIITMrDxyFgMGEFOem+tieBQF6BFXh4POSk9urjhFpmtXcV+Nj6fWLK+YOJES4awsMg8tKaHvm9GUrSeATFw3nv71du5MntdHjxpPezSEJ0M02Z/QYoiIoAm7mzdT66MpMB2WbQu+vxOQ6h+JwCEpiRFAfo5ZDhisVhqv++GH9P077zB/N4CNlY2KghIagpt7rqXvh6QDX52AWWnA3QnfYszonigc08/nSYISEhJnPxSF/KgXLaLvV6xg/zZ6dLv0rZVoC/D+K1arjLMkzkg0mvKoqjpFVdUUg/90c5DxMYBrvDzPO6qqjlBVdURnTw7o5xIUhcxtNHz1FfD++6wHChAJIC+TwloDWgFYw69liXjq4A34vjAN/8lNR565OwBiajz5oEFRRDMdJ3r0C3V9nZsr+v80uc2xWzeSEM+YQYog/bxOPxEXx9TIJSWkVNJgRHh16kSkgs8K5uBg4zdOKoBaFJ060eSiAPOrbYeICCZlCgoiZ0sJiUCAZ7PbcrbxpZey2bM8+TNxIvXgAnTW8KZEeXnE3DsxZEw4LrxQ9JaTkJCQ8LQvXHZZ61+LRDvF0KGsij5tGvOmk5A4g9CsmreiKLzj6BUADjTvcs4xjBkjOsJ//jlJEjSfhRZUADUFfBvY618notLBiBveBNeraGXyZDepRffUTq6vc3Pb3csGQK1mPAenjXe3WJo0udEYRmyXJIAk/MXDD5Mv0Z/+5JrUJCHRbEyaBNx7LylVuZ74Vkd4uPu8+X79aBQKLxflCaCTJ+lw0SBnv0tISBigY0eyIOLRo4efHoESZzciI4E33iAbD2kKJXGGorlNDy8oipKpKMpuABcDeDAA13TuQFGAp54ibamGtWtJf9rQIJqMtQMmhCeAKuoY6zEy6jDCY4gMmjhRGKzljqgoGj3Gocco1l/VXgkgQGwD0xAZGcDWXyMCSLaASfiLxERyMg/U2FMJCYCZYFx+eaubs7phxgymAoqOpnGN+oOHZ+xPnODGR8J4M5eQkJCA+0Cyyy6TFi8SOsTF+Snzl5BoX2juFDCPLV8SPsJqpfG2b75JZtAAsHo1VfA1JVBUVABlJk0HTwAhyAKYTZgYtRt/6LMC9f+eitNmH71BL74Y2LKFvu7eHT0GMZIjN5epa4BmtIC1ADwRQAGDEdslFUASEhISIiIjgb/9Ddixg1SlRko3XgH02WesvToyUkr2JSQkPGLAADaoNyZGWrxISEicfTgbbE/PfJjNVLHXzLFra4EvOYulNvb/0SAQQFAQntAZdySsAS66COYusYiN9bFKMmIEtRB06gTMnYv4eNYVVljonDLmRHtXAAVUoCMVQBISEhK+oU8f8tHzZEjNj/XjvfWk+kdCQsILFAWYP58mz/7970BoaKO/IiEhcYbDbDYjLS0NKSkpmDlzJqr5cc+NICMjAytXrmz0cdu3b8e8efP8uq7ExESkpqYiLS0Nqamp+PJLvQVz0yAJoPYCRYEwkmTtWvZ1O2FBOnUSBSm3PJ2ATl8uBvxczDCZgEceoUkuY8fCYhFfYlkZ/V9RaKJWe4GRbUSLE0BSASQhISHhP/r0AebMcR8laTCIQEJCQoJHhw5kCN2eVOgSEhIth9DQUGRkZCAzMxNWqxVvvfWW8O/1fHuKDr4SQCNGjMBrr73m97WtW7cOGRkZWLZsmd8EkidIAqg9gSeAtPYvoN0QQIpC8XRICAl4pk5FI4Y/vsOIXImJCdjTBwR8R4GGgPIzkgCSkJCQCByuvx54911g2TJg4ULgvfeAKVPa+qokJCQkJCQk2ikmTJiAI0eOYP369UhPT8ecOXOQmpoKm82GW2+9FampqRg2bBjWrVsHu92O+fPnY+nSpUhLS8PSpUtRVVWF2267DSNHjsSwYcNcqp3169fjMudIwWeeeQa33XYbJk2ahKSkJJ+IofLycnTq1KnRx/kC6V7VnhAfT9NMDh92/3k7waWXkkFeoA3xevQAtm4Vf9ZOeC8XOnakqlBlJftZQPkZrYdOa1cIDqb/JCQkJCSaDqtV38MsISEhISEh0c5w+eUt99xffdX4YxwOB1atWoVLLrkEALB161ZkZmaid+/eePnllwEAe/bswYEDB3DxxRfj0KFDWLBgAbZv347XX38dAPDkk09i8uTJWLRoEUpLSzFq1ChMMSg+HThwAOvWrUNFRQWSk5Nx7733IigoyO1x6enpUFUVR48exaefftqMd4BBKoDaGyZMcP9ZO2NCWmIagpECqL1JbxXF3T4ioC1gFovY8ybVPxISEhISEhISEhISEi2GmpoapKWlYcSIEejVqxduv/12AMCoUaPQ2+kpuHHjRtx0000AgAEDBiAhIQGHDh1ye67Vq1fjhRdeQFpaGiZNmgSbzYbs7Gy3x02fPh3BwcGIjY1FXFwcTp06ZXht69atQ2ZmJvbs2YMHHngAlbwSoYmQCqD2hgkTaAw8j3ZGALUEjAig9viyu3cHDh5k3weco+naFSgooK+lAbSEhISEhISEhISEhESLQfMA0iM8PNz1tcoPlPACVVWxfPlyJCcnCz/XEzzBXJeH2WyGg7d/MUCfPn3QpUsX7Nu3D6NGjfLpWjxBEkDtDbGxwKBBwL599H1wMI2BP8txJiiAgBZWAAHii5YKIAkJCQkJCQkJCQmJcwC+tGm1FSZOnIiPPvoIkydPxqFDh5CdnY3k5GQcPnwYFRUVrsdNnToVCxcuxMKFC6EoCnbt2oVhw4Y1++8XFBTg2LFjSEhIaPZzyRaw9ogLLmBf6yeYnKWIiHAnU9qjAkhPVAWco+EJIKkAkpCQkJCQkJCQkJCQaFPcd999qK+vR2pqKmbPno3FixcjODgY6enp2Ldvn8sE+qmnnkJdXR2GDBmClJQUPPXUU836u+np6UhLS0N6ejpeeOEFdAmAQkLxVc4USIwYMULdvn17q//dMwYVFTRavagIuOkmYNastr6iVsFjjwH797PvP/gAiI5uu+sxQlYW8Lvfse//+98Ak0CHDwMPP0xfz5snRxZLSEhISEhISEhISJyV2L9/PwYOHNjWl3FGw+g9VBRlh6qqI4weL1vA2iMiIoDXXwcKC8+pySU9ejACyGoFAjTpLqDo1o28mh0OusaIiAD/gX79gH/8g0jA4cMD/OQSEhISEhISEhISEhIS5yokAdReER5O/51D6NmTfd2lS/vsfLNagblzgS++AK66CjC1RBPlgAEt8KQSEhISEhISEhISEhIS5zIkASTRbuCcsgcA6NWr7a6jMVx1Ff0nISEhISEhISEhISEhIXGmQBJAEu0GQ4cCl15KPjvXXdfWVyMhISEhISEhISEhISHRklBVFUp7bP04A9AUP2dJAEm0GygKcO+9bX0VEhISEhISEhISEhISEi2NkJAQFBcXIyYmRpJAfkJVVRQXFyMkJMSv35MEkISEhISEhISEhISEhISERKuiR48eyM3NRWFhYVtfyhmJkJAQ9OjRw6/fkQSQhISEhISEhISEhISEhIREqyIoKAi9eSNYiRZHS8wwkpCQkJCQkJCQkJCQkJCQkJBoR5AEkISEhISEhISEhISEhISEhMRZDkkASUhISEhISEhISEhISEhISJzlUJoyOqzZf1RRCgEcb/U/3DKIBVDU1hchcVZAriWJQEKuJ4lAQa4liUBCrieJQEGuJYlAQa4liUCiPaynBFVVOxv9Q5sQQGcTFEXZrqrqiLa+DokzH3ItSQQScj1JBApyLUkEEnI9SQQKci1JBApyLUkEEu19PckWMAkJCQkJCQkJCQkJCQkJCYmzHJIAkpCQkJCQkJCQkJCQkJCQkDjLIQmg5uOdtr4AibMGci1JBBJyPUkECnItSQQScj1JBApyLUkECnItSQQS7Xo9SQ8gCQkJCQkJCQkJCQkJCQkJibMcUgEkISEhISEhISEhISEhISEhcZZDEkBNhKIolyiKclBRlCOKojzR1tcjceZBUZQsRVH2KIqSoSjKdufPohVF+V5RlMPO/3dq6+uUaH9QFGWRoigFiqJkcj/zuHYURfmjc686qCjK1La5aon2Cg/r6RlFUU4496cMRVEu5f5NricJQyiK0lNRlHWKouxXFGWvoigPOn8u9ycJv+BlLcm9ScJvKIoSoijKVkVRfnWup2edP5d7k4Rf8LKWzpi9SbaANQGKopgBHAJwEYBcANsAXK+q6r42vTCJMwqKomQBGKGqahH3sxcBlKiq+oKTWOykqurjbXWNEu0TiqJMBFAJ4ENVVVOcPzNcO4qiDAKwBMAoAN0ArAHQX1XV+ja6fIl2Bg/r6RkAlaqqvqR7rFxPEh6hKEo8gHhVVXcqihIBYAeAKwHcArk/SfgBL2tpFuTeJOEnFEVRAISrqlqpKEoQgI0AHgRwNeTeJOEHvKylS3CG7E1SAdQ0jAJwRFXVo6qq2gF8AmBGG1+TxNmBGQA+cH79ASjYkZAQoKrqBgAluh97WjszAHyiqmqtqqrHABwB7WESEgA8ridPkOtJwiNUVc1TVXWn8+sKAPsBdIfcnyT8hJe15AlyLUl4hEqodH4b5PxPhdybJPyEl7XkCe1uLUkCqGnoDiCH+z4X3g8lCQkjqABWK4qyQ1GUu5w/66Kqah5AwQ+AuDa7OokzDZ7WjtyvJJqKBxRF2e1sEdNk8XI9SfgERVESAQwDsAVyf5JoBnRrCZB7k0QToCiKWVGUDAAFAL5XVVXuTRJNgoe1BJwhe5MkgJoGxeBnspdOwl+MU1X1PADTANzvbMOQkAg05H4l0RS8CaAPgDQAeQBedv5crieJRqEoSgcAywE8pKpqubeHGvxMricJFwzWktybJJoEVVXrVVVNA9ADwChFUVK8PFyuJwmP8LCWzpi9SRJATUMugJ7c9z0AnGyja5E4Q6Gq6knn/wsArADJAU85+961/veCtrtCiTMMntaO3K8k/IaqqqecAU4DgHfB5MpyPUl4hdMTYTmAj1RV/dz5Y7k/SfgNo7Uk9yaJ5kJV1VIA60GeLXJvkmgy+LV0Ju1NkgBqGrYB6KcoSm9FUawArgPwvza+JokzCIqihDtNDaEoSjiAiwFkgtbRzc6H3Qzgy7a5QokzEJ7Wzv8AXKcoSrCiKL0B9AOwtQ2uT+IMghYQO3EVaH8C5HqS8AKnOeZ7AParqvpP7p/k/iThFzytJbk3STQFiqJ0VhQlyvl1KIApAA5A7k0SfsLTWjqT9iZLW/7xMxWqqjoURXkAwHcAzAAWqaq6t40vS+LMQhcAKyi+gQXAx6qqfqsoyjYAnyqKcjuAbAAz2/AaJdopFEVZAmASgFhFUXIBPA3gBRisHVVV9yqK8imAfQAcAO6XUywkeHhYT5MURUkDyZSzANwNyPUk0SjGAbgJwB6nPwIAPAm5P0n4D09r6Xq5N0k0AfEAPnBOcjYB+FRV1a8VRfkZcm+S8A+e1tJ/zpS9SY6Bl5CQkJCQkJCQkJCQkJCQkDjLIVvAJCQkJCQkJCQkJCQkJCQkJM5ySAJIQkJCQkJCQkJCQkJCQkJC4iyHJIAkJCQkJCQkJCQkJCQkJCQkznJIAkhCQkJCQkJCQkJCQkJCQkLiLIckgCQkJCQkJCQkJCQkJCQkJCTOckgCSEJCQkJCQkJCQkJCQkJCQuIshySAJCQkJCQkJCQkJCQkJCQkJM5ySAJIQkJCQkJCQkJCQkJCQkJC4izH/wOdCP7ykoHhzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,3))\n",
    "plt.plot(protein_a, lw=3, alpha=.7, c=\"r\", label=\"Protein A\")\n",
    "plt.plot(protein_b, lw=3, alpha=.7, c=\"b\", label=\"Protein B\")\n",
    "plt.title(f\"Without alignment Caretta-score: {unaligned_score:.4f}\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.figure(figsize=(20,3))\n",
    "plt.plot(protein_a[p1_idx_filled], lw=3, alpha=.7, c=\"r\", label=\"Protein A\")\n",
    "plt.plot(protein_b[p2_idx_filled], lw=3, alpha=.7, c=\"b\", label=\"Protein B\")\n",
    "plt.title(f\"With alignment Caretta-score: {aligned_score:.4f}\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e94e0918-7304-448b-b0e1-2725357fafad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}