main_framework.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries, global variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import re\n",
    "from Bio.Seq import Seq\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "alignment_root = './data/main_lancet_other_viruses_for_alignment_and_tree/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple sequence alignment (report Fig / paper Fig. 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## File preparation (global alignment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Individual file preparation : annotation removal\n",
    "for f in os.listdir(alignment_root):#Every Betacoronavirus sequence except SARS-CoV-2\n",
    "    if f != 'processed': \n",
    "        #Retrieve name\n",
    "        f_name = re.sub('.fasta','',f)\n",
    "        with open(alignment_root+f,'r') as file: #Original file \n",
    "            with open(alignment_root+'processed/'+f_name+'.fasta','w') as output: #Output file\n",
    "                output.write('>'+f_name+'\\n') #Headers\n",
    "                for line in file.readlines():\n",
    "                    if line[0] !='>': #If not a header line\n",
    "                        output.write(line)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#General MAFFT submission (without SARS-CoV-2)\n",
    "with open(alignment_root+'processed/mafft_submission.fasta','w') as output: #Output file\n",
    "    for f in os.listdir(alignment_root+'/processed/'):#Every virus sequence pre-processed\n",
    "        if f != 'mafft_submission.fasta':\n",
    "            with open(alignment_root+'/processed/'+f,'r') as file: #Orgiinal file\n",
    "                for line in file.readlines():\n",
    "                    output.write(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#General MAFFT submission (with COVID)\n",
    "with open(alignment_root+'processed/mafft_submission_with_covid.fasta','w') as output: #Output file\n",
    "    with open(alignment_root+'/processed/mafft_submission.fasta','r') as file: #Original file, Betacoronavirus\n",
    "        for line in file.readlines():\n",
    "            output.write(line)\n",
    "    with open('./data/main_lancet_10_ncov_seq_combined.fasta','r') as file: #Original file, SARS-CoV-2\n",
    "        for line in file.readlines():\n",
    "            output.write(line)     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## File preparation for Fig. 4 reproduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accession numbers used in Figure 4\n",
    "fig_4_nums =['AY508724','AY485277','AY278489','AY390556','KT444582','KY417146',\n",
    "            'KY417151','KY417152','MK211376','KY417145','KJ473816','KY770859',\n",
    "            'KY417142','MK211377','KY417148','JX993988','KJ473815','MK211374',\n",
    "            'KJ473814','JX993987','DQ648857','GQ153547','DQ084200','GQ153542',\n",
    "            'KF294457','WH-01','WH-03','WH-04','MG772934','MG772933']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Spike proteins MAFFT submission file (as for Figure 4)\n",
    "with open(alignment_root+'processed/spike_proteins.fasta','w') as output: #Output file\n",
    "    for f in fig_4_nums:\n",
    "        if f not in ['WH-01','WH-03','WH-04']:#All except SARS-CoV-2\n",
    "            with open(alignment_root+f+'.fasta','r') as file: #Original file\n",
    "                output.write('>'+f+'|Spike\\n') #headers\n",
    "                found_spike = False\n",
    "                prot_seq = \"\"\n",
    "                for line in file.readlines():\n",
    "                    if 'spike' in line or 'S protein' in line or 'protein=S' in line: #Names indicating that the protein is a spike protein\n",
    "                        found_spike = True\n",
    "                    elif found_spike == True:\n",
    "                        if line[0]==\">\":\n",
    "                            found_spike = False\n",
    "                        else:\n",
    "                            prot_seq = prot_seq + line\n",
    "                output.write(str(Seq(re.sub('\\n','',prot_seq)).translate(to_stop=True))+'\\n') #Remove escape character + translate \n",
    "        else: #SARS-CoV-2 seqs\n",
    "            with open('./data/spike_fig_4/'+f+'.fasta','r') as file: #orginal file\n",
    "                for line in file.readlines():\n",
    "                    output.write(line)\n",
    "                output.write('\\n')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7179 -1 -1\n",
      "-1 -1 7180\n",
      "-1 7182 -1\n"
     ]
    }
   ],
   "source": [
    "#As SARS-CoV-2 spike proteins were retrieved from NCBI, I further wanted to check that the sequences retrieved from NCBI match the ones I had before \n",
    "\n",
    "for ret_seq in ['WH-01','WH-03','WH-04']: #3 sequences to be check \n",
    "    seq = \"\"\n",
    "    cov_seq = \"\"\n",
    "    with open('./data/spike_fig_4/'+ret_seq+'.fasta','r') as seq_file: #retrieved seqs\n",
    "        for l in seq_file.readlines():\n",
    "            if l[0] != \">\":\n",
    "                seq = seq +l\n",
    "                b_seq = Seq(re.sub('\\n','',seq))\n",
    "        boo = False\n",
    "        with open('./data/main_lancet_10_ncov_seq_combined.fasta/','r') as cov_seqs_file:\n",
    "            for l in cov_seqs_file.readlines():\n",
    "                if boo:\n",
    "                    cov_seq = Seq(re.sub('\\n','',l))\n",
    "                    cov_seq_1 = cov_seq.translate(to_stop=False) #ORF1\n",
    "                    cov_seq_2 = cov_seq[1:].translate(to_stop=False) #ORF2\n",
    "                    cov_seq_3 = cov_seq[2:].translate(to_stop=False) #ORF3\n",
    "                    boo = False\n",
    "                if ret_seq in l: #Sequence is in one of the ORFs\n",
    "                    boo = True\n",
    "    print(cov_seq_1.find(b_seq),cov_seq_2.find(b_seq),cov_seq_3.find(b_seq)) #Displays -1 if not found, position otherwise\n",
    "                \n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each of the sequences retrieved from NCBI is present in the original file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identity measurement (report Fig /paper Fig 2b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I chose the seq WH19001 as reference (because = to the consensus sequence), together with the ones used in the paper AY390556|SARS-CoV_GZ02, MG772933|bat-SL-CoVZC45|Zhejiang, MG772934|bat-SL-CoVZXC21|Zhejiang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Retrieving sequences from the alignment file\n",
    "WHseq = \"\"\n",
    "MG772933seq = \"\"\n",
    "MG772934seq = \"\"\n",
    "AYseq = \"\"\n",
    "current_seq = 0\n",
    "with open('./outputs/Task2/fig2b.pir','r') as alignment:\n",
    "    count = 0\n",
    "    for l in alignment.readlines():\n",
    "        \n",
    "        if l[0] == \">\": #New sequence in the alignment file\n",
    "            \n",
    "            if current_seq == 2: \n",
    "                current_seq = 3\n",
    "            elif current_seq == 1:\n",
    "                current_seq = 2\n",
    "            elif current_seq == 0:\n",
    "                current_seq = 1\n",
    "            else:\n",
    "                current_seq = 0\n",
    "        else:\n",
    "            if current_seq == 0:\n",
    "                WHseq = WHseq+l\n",
    "            elif current_seq == 1:\n",
    "                MG772933seq = MG772933seq+l\n",
    "            elif current_seq == 2:\n",
    "                MG772934seq = MG772934seq+l\n",
    "            elif current_seq == 3:\n",
    "                AYseq = AYseq+l\n",
    "\n",
    "WHseq = re.sub('\\n','',WHseq)\n",
    "MG772933seq = re.sub('\\n','',MG772933seq)\n",
    "MG772934seq = re.sub('\\n','',MG772934seq)\n",
    "AYseq = re.sub('\\n','',AYseq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sliding window approach definition and ranges initialization \n",
    "window_size = 300\n",
    "sampling = np.arange(0,len(WHseq),window_size) #Window bounds\n",
    "plotting = np.arange(window_size/2,len(WHseq),window_size) #Window middle is chosen for plotting\n",
    "MG3_id = np.zeros(len(sampling)-1)\n",
    "MG4_id = np.zeros(len(sampling)-1)\n",
    "AY_id = np.zeros(len(sampling)-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Window identity computation\n",
    "include_gap_sim = False #If we want to count the gap as identity or not \n",
    "for i in range(len(sampling)-1):\n",
    "    MG3_id_loc = 0\n",
    "    MG4_id_loc = 0\n",
    "    AY_id_loc = 0\n",
    "    if not include_gap_sim:\n",
    "        MG3_gaps = 0\n",
    "        MG4_gaps = 0\n",
    "        AY_gaps = 0\n",
    "    for j in range(window_size):\n",
    "        if WHseq[window_size*i+j] == MG772933seq[window_size*i+j]:\n",
    "            MG3_id_loc +=1\n",
    "            if not include_gap_sim:\n",
    "                if WHseq[window_size*i+j] == '-':\n",
    "                    MG3_id_loc -=1\n",
    "                    MG3_gaps +=1\n",
    "        if WHseq[window_size*i+j] == MG772934seq[window_size*i+j]:\n",
    "            MG4_id_loc +=1\n",
    "            if not include_gap_sim:\n",
    "                if WHseq[window_size*i+j] == '-':\n",
    "                    MG4_id_loc -=1\n",
    "                    MG4_gaps +=1\n",
    "        if WHseq[window_size*i+j] == AYseq[window_size*i+j]:\n",
    "            AY_id_loc +=1\n",
    "            if not include_gap_sim:\n",
    "                if WHseq[window_size*i+j] == '-':\n",
    "                    AY_id_loc -=1\n",
    "                    AY_gaps +=1\n",
    "    MG3_id[i] = MG3_id_loc/window_size\n",
    "    MG4_id[i] = MG4_id_loc/window_size\n",
    "    AY_id[i] = AY_id_loc/window_size\n",
    "    if not include_gap_sim:\n",
    "        MG3_id[i] = MG3_id[i-1] #to ensure validity\n",
    "        MG4_id[i] = MG4_id[i-1] #to ensure validity\n",
    "        AY_id[i] = AY_id[i-1] #to ensure validity\n",
    "        if window_size-MG3_gaps !=0:\n",
    "            MG3_id[i] = MG3_id_loc/(window_size-MG3_gaps)\n",
    "        if window_size-MG4_gaps !=0:\n",
    "            MG4_id[i] = MG4_id_loc/(window_size-MG4_gaps)\n",
    "        if window_size-AY_gaps !=0:\n",
    "            AY_id[i] = AY_id_loc/(window_size -AY_gaps)\n",
    "\n",
    "            \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VUPklefeLMlOUrVWWsrXKUsatDPEH47FrZkf8kXgy4jKIPxwPQHpsOmnhaVhWtASg8uuVUWUU4b+FE70pGqfeTnnWZ+liScTvhl68Cl0rYOlqSXJQcqFjtW1kS3SA4ZKd5eqXuyNxBCjjUoaU6ymm5NH9bXfij8WTeCoRAK014UvDcejgkCNxBDC3Myc9Nh2L8hakx6ZjbvtPgpgSkkL48nBcX3WVxFEIIYQoQvuu7eOr6YZ/pBtValTC0YjbBd4MpNUxw0iyJzyeuC9tZL0Hsr/+RdluXu+xrDYKU/+9vl+L43ksrbI/ZwVVkAVzTgJNc9me17UfAVKy/Z4BWBh7+54AVgB9gT+zxdBSa93IeHPPNqw0t3rOGeM5DnyilBpv3J+e7fGYuhK11p8CrwJlgX1Kqcfu+mhLgdSwVNLC0/65H5KKRQULUsNS0amaauOqUfWDqlT9oCoOHRyIPxKf43gzSzOcejsRdyiOjMSMPOuzrmqNx3APPIZ7YFPfBtvGtiRfSSbxdKKpbOKZRFJvpGJdw5r4QEM7qeGppEenU6aSIcmzrmVNWkQaMXtjsG1sm+tjKt+qPPEH40m+/E9yqlP/6fWO2RGDslTYt76z17NcvXLEHzS0HX8wnnL1DPMp06PTCf0plErPVqJMxTJ3HCeEEEKIe9eiSguCRgQRNCKIJq5NSjoccZsmrk3Y+7+97P3fXlpUaXFf2sh6D2R//Yuy3bzeY1ltFKb+e32/FsfzWFplf84KqiDDVhWGXsO5Wus5xm3NgB5AK+NPR+AghmGrjwEjtNZPGst+Y9z3O1BOax2mlHIELmitHY3DVg9rracZyzcyzqmcCMRrracbt58AngRSgSitdbJSqi8wSGvdVym1BfhCa71RKTUDaGwctlpLax1krGM1sFBrbVrhxdPTU/v7+xf4CSsOR48eZezYscTExGBhYUGNGjWYNm0a8+fPJyUlhQ8//NBU9tSpUwwZMoRdu3bh5eXFpk2bcHIy9DZ+8MEHODs74+Pjk2t9WeWyO3/+POPHj+fy5ctYWFhQr149Jk+ejJ2dHSNHjuTYsWNYWFgwceJE2rRpYzruww8/ZN26dRw+fBgzs9zPSRw6dIhJkyZx8+ZNnJ2dcXR0ZPjw4TRq1IiqVavi5uaGre0/yae9vT0rV64kKiqKwYMHc/36ddzd3ZkzZw4VKlRg+PDh/PHHH3h4eABgbm5OaXsthRD3V9Z8aCHE/RUaGlrSIYi7uN+fhXm9/kXZbm5t3Gv99/p+fZi/Uwo6bDXf5NFYWWVgJoYev2TgMvAOMJjcF8zJLXnchGEupDWGXsvpWutFSiln4FugLoZhtDu11q/fJXmsA0wDMoE04A2t9UGlVFtgHhAK/I1h/qO3UuprDPMmMzDMuxxkHAoLlM7kUQghRME8zF/0QhQnSR5LN0ke86+rIB7m75QiTR7/yyR5FEKUlHfeeYfNmzfj7OzMjh077lr27bffpnPnzvTq1auYonswPMxf9EIUp/9S8jhz5kxWrlyJubk5ZmZmTJs2jSZNHuxhuQX5LExOTqZdu3akpKSQnp5O//79+eijjwpUf2hoKJUrV6Zu3bqmbX379mXy5Mm5lldKMXDgQH7++WcA0tPTcXNzo3nz5qxfv/6O8mfPnqVfv36m+1euXGHkyJGMGzfujrIzZsxg7ty5KKV4/PHHWbBgAdbWhhlrL7/8MuvXr8fR0dH0vRodHc2QIUO4du0aVapU4ccff8TBwYFp06ZhY2PDm2++aaq7OL5TlFIMHz6cL774AoDp06cTHx/PxIkT73vb+cRVoOSxIHMehRBC3AfPPPMMS5YsKekwhBDioXHw4EE2b97M5s2b2bZtG8uWLaNy5colHVaxsLKyYuvWrRw9epQjR47w559/sm/fvgIfb21tTUBAgOk2dOjQPMva2Nhw4sQJkpKSANi8eTPu7u55lq9Tp46pXn9/f8qWLZvrtcCvX7/OV199xcGDBzlx4gQZGRksXbrUtH/QoEGm64Fn+frrr2nbti1//fUXbdu25euvvy7wY74frKysWLlyZa6XnnsQSPIohBAlpGXLljg4OOTY9ssvv9C1a1c6duzIK6+8QmLiPwtY7dq1y3SdVhkxIYQQhRcaGoqjoyNWVlYAODk54erqWsJRFQ+llGltibS0NNLS0sh2tYMi1717d/744w8AlixZwrPPPlug43bt2kX16tWpUqVKrvvT09NJSkoiPT2dxMTEHMl/u3btcHR0zFF+06ZNpmuN+/n55UguT506Rb9+/WjZsiW//PJLoR7fvbKwsGDw4MHMmDGjWNorapI8CiFEKdKjRw82bdrE1q1bqV27Nr/++qtp37Vr11i1ahW//PILo0aNIjm58JfWEUKIh5m3tzchISG0atWKUaNGsXfv3pIOqVhlZGTQqFEjKlWqROfOnWnevHmBj01OTsbHx8d0W7169V3LDxgwgKVLl5KcnMyxY8cK3Nbq1avp27dvrvvc3d0ZMWKEaaFFe3t7unTpctf6wsPDTcNRXVxccvT4nTp1il9++YX169fz5ZdfEhISUqAY/6233nqLxYsXExMTUyztFSVJHoUQohQ5c+YMffr0wdvbmxUrVnDu3DnTvl69emFmZkbNmjWpVq0aFy5cKMFIhRDiwWNjY4O/v79p1fkhQ4bkGPb4X2dubs6RI0cIDg5m//79nDhxosDH3j5sNa8EL0vDhg25fPkyS5YsoUePHgVqIzU1FX9/f3r37p3r/ujoaNasWcOlS5cICQkhISHhX/UYdu3albJly+Lk5ESrVq3Yv3//PddVGOXLl+eFF17gq68Kd43F0kCSRyGEKEWGDRvG1KlT2b59O++9916O3sXbhxfdz+FGQgjxX2Vubk7r1q0ZOXIkU6dONQ2tfJg4ODjg7e19x/zAota7d29GjBhR4CGrW7du5fHHH6dixYq57t+yZQs1atSgYsWKWFpa8tRTT+Xbe1yxYkXTgk+hoaE4Ozub9pXk9+o777zDvHnzSEhIKLY2i4Ikj6VU5cqV8fHxoWPHjnTu3JkDBw7ctXxMTAwLFizIc/+FCxfw9fXFx8eHtm3bMmLECAD27NnDwIED840nKCiI5557jhYtWtC2bVtee+01wsPDcy2bkJBA3bp1iY2NzbF90KBBrFmzhmHDhuUY9uDl5UWDBg1M5ZYtW0b79u1p164dbdu2Zfbs2TnqmT17Nq6urkRGRpoeQ+3atU31Za1eJcSDKCEhgUqVKpGWlsbKlStz7Fu3bh2ZmZlcvnyZK1euUKtWrRKKUgghHkwXLlzg4sWLpvsnT540XS/6vy48PJxbt24BkJSUxJYtW3jsscfua5svv/wy48eP5/HHHy9Q+VWrVt21R7Nq1ars27ePxMREtNYEBATkWAE2N126dGHZsmWA4X/Mrl27mvZt2rSJ5ORkoqKi+Ouvv2jWrFmB4iwKjo6O+Pn5MW/evGJrsyhYlHQAIndZQwMAtm3bxpQpU+46tjwmJoaFCxfy0ksv5br/ww8/ZMiQIXTr1g2A06dPFziW5ORkBg4cyEcffWQaV757924iIiJyPTNkY2ND+/bt2bhxI8888wwAsbGx7N+/n9mzZ9OnTx9T2czMTJ566imefvppAAICApgzZw6//fYbrq6uJCcn8/vvv5vKX79+nZ07d96xYlfz5s2LbaKzEEXl9ddfZ+/evURFRdG4cWPef/99Ro4cSY8ePfDw8KBu3brEx8ebyj/yyCP4+voSHh7OZ599ZlqaXAghRMEkJCQwduxYYmJisLCwoEaNGkybNq2kwyoWN27c4MUXXyQjI4PMzEz8/Px48sknC3x81pzHLB06dMh35VIPDw+GDRtWoPoTExPZuXPnXV+P5s2b079/f5o0aYKFhQWNGzdm8ODBpv3PPvss27dvJyIiwvS9OnToUAYPHsyvv/6Ku7s7c+bMMZVv3LgxAwcO5Pr167z77rvFvvLue++9xzfffFOsbf5bkjw+AOLi4kwrMiYkJPDiiy8SExNDWloao0ePplu3bkyZMoUrV67g4+NDu3btmDBhQo46QkNDcXNzM93P7yxNditXrqRp06Y5JiS3adMGMHyQjBo1iqNHj2JhYcHEiRNp06YNvr6+LFy40JQ8btiwgQ4dOlCuXLkcdc+aNQtHR0eef/55wLCc8oQJE0wrn1lbW+foGR0/fjzjxo3jxRdfLHD8QpRW33//fa7bBw0adMe2B3FehBBClDaenp65XmfwYdCwYUMOHz58z8cXZjGZ7Cc+s3h7e+Pt7Z3nMeXKlStQ58ZHH32U5/Upsy5/dft1SbN3RGR5//33823rfsj+3Li4uORYVf1BIMljPsbdtOVEimWR1tnAKo1Jrnf+UWWXdXYnJSWF0NBQ05veysqKBQsWYGdnR2RkJD179qRr166MHTuWM2fOmHorbzd48GD69++Pl5cX3t7eDBgwAHt7+wLFe/bsWTw9PXPdlzVUdvv27Zw/f54BAwawZ88eOnTowPDhw4mKisLR0ZE1a9bw8ssv5zg2MDCQxYsXs3nzZtO2M2fO0LBhw1zb2rRpE25ubtSvX/+OfYcOHaJjx464uLgwYcKE+z4MQwghhBBCiIeNzHkspbKGre7evZslS5YwdOhQtNZorZk6dSodOnTAz8+Pmzdv5jn3MLtnn32WXbt20atXL/bu3UvPnj1JSUn513Hu37/fNOS0du3aeHh4cPHiRcqUKUPXrl1Zv349kZGRnDhxIsfZpoSEBIYOHcqMGTOoUKFCvu0kJiYyc+ZMRo4cece+hg0bcvDgQbZu3corr7yS59BdIYQQQgghxL2Tnsd85NdDWBy8vLyIiooiIiKCgIAAIiMj8ff3x9LSEi8vr1yv9fbJJ5+wZcsWAFNvpKurK8899xzPPfcc7du358yZM7m2FxgYaOrKHzlyJHXq1MlzJSutdZ5x+/r6MmPGDLTWdOvWDUvLf3pwP/jgA7p06ULbtm1zHFOnTh2OHTtmGhab5cqVK1y9epWOHTsChnH7Xbp0YePGjVSqVMlUrlOnTowePZrIyEicnJzyjE0IIYQQQghRONLz+AA4f/48mZmZODo6EhcXh7OzM5aWluzevZvg4GAAbG1tc4yhHjNmjOk6PGBY+jgtLQ2AsLAwoqOjc8yBzK5JkyamY7t27Yqvry8HDx7MMbx069atnD59mhYtWrBixQrAsCJrcHCwaQXI1q1bc+nSJRYsWJBj5ax169Zx6tQpxowZc0fbQ4cO5eOPPyYsLAyAlJQU5s6dS926dTl58iQHDx7k4MGDuLm54e/vT6VKlQgLCzMlsYGBgWitcXR0vLcnWwghhBBCCJEr6XkspbKvaKW1ZtasWZibm/PUU0/xwgsv0KVLFxo0aEDt2rUBw3K/TzzxBO3bt6djx453LJizY8cOxo0bh5WVFWBYeKZSpUqcP3+e3bt307hxY1PZOXPm4OXlZbpftmxZfv75Z8aPH8/48eOxsLCgXr16TJ48mUGDBjFy5Ei8vb2xsLBg1qxZpjbMzMzo2bMn69ato2XLlqb6Pv30U5KSkujevXuOGNevX0+nTp0IDw/n6aefRmuNUirfawOtW7eORYsWYWFhgbW1Nd9//71c/04IIYQQQogipu427PBh4Onpqf39/Us6DCGEEPfAxcWlpEMQ4qFw++qVonS535+Feb3+Rdlubm3ca/33+n59mL9TlFKHtNZe+ZUrdcNWlVLzlVJhSqkTeexXSqmvlFIXlFLHlFJNsu17USl13niTazkIIYQQQgghRBEpdckjsBDodpf93YHaxttg4DsApZQjMAFoDjwBTFBK5b+MpxBCCCGEEEKIfJW65FFrvROIukuRPsBP2mAf4KCUcgO6Apu11lFa62hgM3dPQoUQQgghhBBCFFCpSx4LwB24lu1+sHFbXtvvoJQarJQ6qJQ6GBV1tzxVCCGEEEIIIQQ8mMljbsto6rtsv3Oj1j9qrb201l5ySQchhBBCCCGEyN+DmDwGA1Wy3fcAQu6yXQghhBBCCCHEv/QgJo9rgReMq662AGK01jeATUAXpVQF40I5XYzbHkiVK1fGx8eHjh070rlzZw4cOHDX8jExMSxYsCDP/RcuXMDX1xcfHx/atm3T7nJLAAAgAElEQVTLiBEjANizZw8DBw7MN56goCCee+45WrRoQdu2bXnttdcIDw8v3IMChg0bho+Pj+nm5eVFgwYNABg7dixffvmlqezMmTMZPXq06f7s2bNp06aN6VqWy5YtA+DNN9+kdevWtG/fnnfeeYe0tDQAzp8/T8+ePalatSqzZ88udKxCCCGEEEKIf1iUdAC3U0otAbwBZ6VUMIYVVC0BtNbfAxuAHsAFIBF4ybgvSik1CcjKsj7WWj+wExqtra0JCAgAYNu2bUyZMoXVq1fnWT4mJoaFCxfy0ksv5br/ww8/ZMiQIXTrZlhD6PTp0wWOJTk5mYEDB/LRRx/RpUsXAHbv3k1ERAQVK1YscD0As2bNMv2emZnJU089xdNPPw3A6NGj8fHxoV+/fgAsXryYLVu2ALBo0SJ27tzJxo0bsbOzIzY2lo0bNwLw1FNP8e233wLwxhtvsHjxYgYNGoSDgwOTJ0/mzz//LFSMQgghhBBCiDuVup5HrfWzWms3rbWl1tpDaz1Pa/29MXHEuMrqW1rrWlrrx7XWB7MdO19r/Yjxlnc33AMmLi4OBwcHABISEujfvz+dO3fG29vblBhNmTKFK1eu4OPjw0cffXRHHaGhobi5uZnu161bt8Dtr1y5kqZNm5oSR4A2bdpQt25dkpOTGTZsGN7e3nTq1Indu3cD0L17d86cOWMq7+vry9GjR3PUO2vWLBwdHXn++ecBsLOzY8yYMabbyJEjsbe3B+Crr77i008/xc7ODoDy5cvzzDPPANCpUyeUUiilaNy4MTdu3ACgYsWKNG7cGAuLUneORAghhBBCiAeO/Fedjz3j9hBxIqJI63Ru4EzrSa3vWiY5ORkfHx9SUlIIDQ3l999/B8DKyooFCxZgZ2dHZGQkPXv2pGvXrowdO5YzZ86YeitvN3jwYPr374+Xlxfe3t4MGDDAlJjl5+zZs3h6eua6L2uo7Pbt2zl//jwDBgxgz5499O3bl7Vr1/LYY48RGhpKaGhojjoCAwNZvHgxmzdvzlGfr68vc+fOxdzc3NQjGR8fT3x8PNWrV79rnGlpafz+++9MmjSpQI9LCCGEEEIIUXClrudRGGQNW929ezdLlixh6NChaK3RWjN16lQ6dOiAn58fN2/eLNDcw2effZZdu3bRq1cv9u7dS8+ePUlJSfnXce7fv9+U5NWuXRsPDw8uXrxI7969WbduHQBr166lV69epmMSEhIYOnQoM2bMoEKFCjnqCwkJISwsjJs3b5KQkACA1hqlcltMN6fRo0fTokULWrRo8a8flxBCCCGEECIn6XnMR349hMXBy8uLqKgoIiIiCAgIIDIyEn9/fywtLfHy8iI5OfmOYz755BPTfMGs3khXV1eee+45nnvuOdq3b59jWGl2gYGBvP/++wCMHDmSOnXqsHfv3lzLap3r1VBwc3PD0dGRU6dOsWbNGqZNm2ba98EHH9ClSxfatm17x3Hjxo1jxIgRnD9/nunTpzNhwgTs7OwoV64cV65coVq1arm2N336dCIjI3O0I4QQQgghhCg60vP4ADh//jyZmZk4OjoSFxeHs7MzlpaW7N69m+DgYABsbW2Jj483HTNmzBgCAgJMiePWrVtNq5CGhYURHR2dYw5kdk2aNDEd27VrV3x9fTl48GCOIaZbt27l9OnTtGjRghUrVgCGFVmDg4OpVasWAH369OHbb78lNjbWNMdy3bp1nDp1ijFjxtzRbkBAABEREfj5+fHuu++yceNGzp49C8DQoUMZPXo0cXFxgGEe6M8//wwYFtbZvn073333HWZm8pYWQgghhBDiflB59Rw9LDw9PbW/v39Jh3GHypUrmxIurTVjxoyhc+fOREZG8sILL5CWlkaDBg04cOAAixcvpmrVqrzxxhucOnWKjh07MmHChBz1TZgwgS1btmBlZQUYLm/Rv39/9uzZw/PPP59j+OicOXPw8vLKcfz58+cZP348ly9fxsLCgnr16jF58mTs7OwYOXIkx44dw8LCgokTJ9KmTRsAwsPDadSoEe+++67p0iCtW7cmKSnpjuGq69evp1OnTsydO9f0uP/44w/mz5/PihUr0Frz7bffsmTJEiwsLLC0tOT111+nf//+uLu74+Hhga2tLQA9evTgvffeIywsjK5duxIXF4eZmRk2Njbs3LnTtOiOEOLB5+LiUtIhCPFQCA0NLekQxF3c78/CvF7/omw3tzbutf57fb8+zN8pSqlDWmuvfMtJ8lg6k0chhBD5e5i/6IUoTpI8lm6SPOZfV0E8zN8pBU0eZYyfEEIIIYQQQoh8SfIohBBCCCGEECJfstqqEKLIJGTCpVQLLqSaE5RiwcVUc5K1YqZbLHbmD/cQeSGEEEKIB50kj0KIe3IjPIWAjSFcvZpIsJsj5ypX4qxrRdKsLQFQaCpbZBKSboa7pQ0fu8TnU6MQQgghhCjNSl3yqJTqBswCzIG5WutPb9tfDZgPVASigIFa62DjvgzguLHoVa1172IL/AFy4+8bpMal5rovKsOMMG1OizaVsLctdW8PUYIyMzXHTsexd8M1bgVcxuFYMGaZGkfAEWhoLGfuZotdTQdcHrHH6RF7lletwTxqMcA+mXrW6SX4CIQQQghxLzIzNAu+OYNnXTsad3DD3NK8pEN6YB2LhwY2YKZKOpJ7U6qyA6WUOfAt0BkIBg4opdZqrU9lKzYd+ElrvUgp1RH4BPifcV+S1rpRsQb9gLm2/Rp/DPgj33LHHq/MsNXdKWNjWQxRiZKSpiE5M+9Pr4y0DP7eHc7xP6+Suf0i5YOjMQPMalci9dXmNO1ehSceL0/clRhigmK4FXSLW0G3iAmK4dKq85yNTcWdvXh++gxjytZkdbVo1AP6YVnaZWgwl+f2waA18ocghHiQ7Po7irRPdnAQCHSwpk6vGjzS5xEq9q2ImbksoVJQ5xKh0X7N1FqK0dVKOpp7U6qSR+AJ4ILW+iKAUmop0AfInjzWA941/r4NWF2sET7g9s0+RpyTLUs/fgaUQqFxtcjA3TITD8sMPCwzCT0VRflJ/iwetIUXFnfBvIycXfovupJqhu8VR0LSzLCNTsDpWiRO1yJwCo7E+VokTtciqRASjXlGJtaW5sR6VcPipca07+lO7eo2Oeqyqu+Mc33nHNu01iSFJ7H+mfX0+XI9n9R7i+UO1vg5JBfnw3wobI4rw+sh9pRRULNMOrXKZFDL9DOD6mXSKSvf7SUrNRWWLIEvv4SkJDh+HIzX3RVCiNLuwtFIAPaP6kHZA1dJX36W0z+fZpvrNur51aPBgAZ4tPBAyYmxu9p1IZ5Rvb9h1ftP8sbYBtiXtkysAEpbyO7AtWz3g4Hmt5U5CvTDMLTVF7BTSjlprSMBa6XUQSAd+FRrnWtiqZQaDAwG8PDwKNpHUIpFnY4icuc19r/SgVE+djQqm0Y1ywyszHL2Lqa3s+eN1O40/WQDm4Zuo/t3PqgHtW9d5Co+Q/FykA0dxy2n1pFLqPhsw5jLmEM1B6hTAbrVwrlhRXy6uFLBvnC90EopylUqR4dZHVjZfSXP/bCRj0f1oatdCvayeE6R2Z1gyZhdKQyatZDyiUmkakVqpuIWcMh4A7AwU9Qf+QR9e7uXYLQPoVu34Icf4KuvICQEatSAS5fgp5/gtddKOjohhCiQyBORlLGzZvbbVZgaUY9Prvei06HT9D9whkM/HGL/V/uxr2pP30V9qe5dvaTDLbVOBVymfEIK7Wb9yUzfR5jQ0LqkQyq00pY85pah3P5f5gjgG6XUIGAncB1DsghQVWsdopSqCWxVSh3XWgfdUaHWPwI/Anh6ej40/8Uem3OMTCsLrvdtzNP2iXmOmrJQ8M6QmkyM9MHnxwB2O1rRZmobOZv0H5Gh4Y0Qe2rM2MIju89Sd2BdHB9zxKGWA/a17LF1ty3SISgVG1ak8duN0TMCcWzTgM/KezDVNa7I6n+YHUy05IP1Mbww9jfKlzPHzeufixuna0jIVKZbxl9XObnqoiSPxeXyZZg5E+bNg/h46NTJ8HuXLtC8OXz2Gbz0EliUtq9hIYS4U/qZcNIfdcHaXPGxSzytylkxzOZx9rT35IcvU6i79wxbRm1hz+d77lvyGB6VwtwvDvHqe02p6PhgjtyI2n8NW0tzbGISOfzxNqKXdKfCAzZDrLR9awUDVbLd9wBCshfQWocATwEopWyBflrrmGz70FpfVEptBxoDdySP9yo9KZ2wI2G4tXB74BKppPAkzq84z8munjRzt8h3uk1963S8/q8Re2KSYMFerB2tafZ+s+IJVtxXn4TbEvxHEM+sPYjn6560nNjyvrfZ9J2mXN54Gb8Z6/iiwZs862DB47J4zr9yItmC8Ytv8sykldhXsaPP0p7YVbHLs/ynT/mjzocXY4QPqbNnYcIEWL4czMxgwAB47z1oZJiOH5EKNz/8hAZ9O8Pvvxv2CyFEKZaWnondhTBSnvY0betml8KWmpEMDavIs9eseKO5Jz36hXB0XiDpKelYWBV9ivHLnOOkTt3M1BXHGbrpeWpWsy3yNu6ntEywOhxMZtMqPNqwInruAb74sxGTe7mVdGiFUtpmwRwAaiulaiilygADgLXZCyilnJVSWXGPwbDyKkqpCkopq6wyQGtyzpX814LWBbHWdy27x+wmMyPznupIT0pH6+Lv7Dz500kyUjLY0a8lrcvlvtLq7d5xTuDyUG/O9vDk0BeHODHvxH2O8sGhNUTdSiuR1/LfWHbLml9OptF/+loqNa7EEx88USztmluZ4z3TG8vIBJ78zp/RN+3IfLCeulLlfIo5H399kScn/I5TfWf6re1z18QRwO4xR8pfiSQq+d4+u0QB9esHGzYYEsZLl+Dnn02J46kEaHwIvBx8CG7pDZ98YvgwEUKIUizwaCSWKem4NHDKsb2KZSY7G8GIKvBdCHxTsxbpSelc23Mtj5r+nfBzkWRYmFHuSiSzW8/n8PGo+9LO/XI0PI1KF25SqbkHT3/WkUyHsoSN2UB48oP1PVCqkketdTrwf8Am4DSwTGt9Uin1sVIq67Ib3sBZpdQ5wAWYYtxeFziolDqKYSGdT29bpfVfS401JF0nF55ky5AtpCcXrufk3IpzLKy/kB0jdhRr0pGenM7JBSehbXUiqjrT2qZgyaOVGXxROY7fhvci0fsRdo/dzfmV5+9ztKVPQiYEJlmwOLosH9y0o++VCvT6IZyl9ebzjddSdozdTfDOYDJSM0o61Ls6mGjJyGAbBk1ZjrXSdPq+U7EuhlSpUSUavdWI+huOELPjKktjHrxx/qXB5RTFpx8dp+MXG6jUvir9fn+Ssk5l8z2uSt0KWKRlEHg2sRiifEidPQsnT8LUqfD555BtTv3eGGhz2HDmORPF1DHfwbFjhkRTCCFKsSMHbgLwmKfjHfsszWBaLVj/OOyvVx1tYUaQf5EN+ssh6UIU8dWdaffHC1jEJ7O0/Xy27rpxX9q6H/btCcE8I5OG7apg7WBNy086434ymBkzj5R0aIVSqpJHAK31Bq31o1rrWlrrKcZt47XWa42//661rm0s86rWOsW4fa/W+nGttafx57yiji0rOfAa4cXF9RfZ8NwGUmJT8j0uPSmdHSN2sPWtrVhXsObM4jMc++FYUYeXpwurLpAUkcTJZ1pQwzIdd8uC9zw8US6Nl5xT+HKMH7bN3dn29jauBly9j9GWDldTzXj9uj2tg5x45Gwlelx24r2b5fktxhqHvy/Rc/IqUuu6cKlKJU78fIb1futZVH8R/q/5c275OZKjSteKosFpZgwKtqfn/K04nAjB+4v2lK9Wvtjj8HrPiwqPVqDfF2v5/KIl0RkP1vDvknYjGWYO+5tm83fi0u8xfH/qgmUBL6dTv6EDAGeOR9/PEB9uq1YZfvbpk2PzugjodBRqB93g829W8t7k35hnU5srjVsaeh+FEKIUu3b4JumW5jSsn/cIl55O0KdaGa43qMKF+5Q8ml2OhBpOdO3oQb/tL5NhZUFA14X8vvLifWmvqF0y9sh6tTOcWOz+midJTaqS8ekWrtxMKsnQCqXUJY+lWWaaIelqPLQxHb/tyM39N1nru5aE0ASi0hW/x1gzPKQ8b13/5/bu3nS+7rKG07+cJmxQc/atHIJdt0fY9/E+rmy5UugYrmy5wpHZRwrcc6m15tiPx6hQ15E/GzxS4F7H7D6oFI9LOTPmTXqWCnUd8X/Vn9BDoYWu50GhNbxzwx7/uDLUsUrnPecE5rvfYl+tCPYknqTN6N9xqu3AGyu60/OX7vywfgQrp/ph3q02N/++ydahW1nUYBFr+q7h9K+nC91DXdQSMhUvXnPAbV8Qj//6F3X/V5davWuVSCxZw1fLRsTzxLdb+CTswZqvUJJC4zP4+sVtPLY6ELc3mtD3m/aFukhztTr2ZJorwk8/WMN8HiirVoGXF1T5Z+r+vOua9+ee55X3FtFj0I9cXXkK6y1nqLf9FJM/nAN79sCuXSUYtBBC3F38sZvE1ayIVT6jlXo7wdmmtQg9fJOEsIQijSE1NROb69HY1DL0fjZr5MyQva+Q5O7AsWcW8+MPpX9qVeKBayRWd8LGuRxgWJXed3YPrOOS+GF4QAlHV3CSPBZCRoqh59GsjBm1n3qUhgt6EnExlh+6raXdNjP+L8SeP+OtOJRkyaEkS6LWnaPm8z+hwuLZPO0ZNrzaia0p5Rj99tOkPVqRLa9vIepswf+RO7noJBv/t5F9H+/jyLcF6+K+vvs6UaejcBjUiDhtTpsCznfMzsZMM90tltOWNlz66mmsHKw4OP1goespTudTzLmcem9DMn+PtWZvYhkmusQz3yOGERUT6FE+BYerkfz5/AbKOpWl55KeWDlY4WObysa6sVh1rMWoN/tzcuOb9PzDl8bDGpMclcyO4TtY3Gwxh2YcKpHeyEwNb4eU51pIEn6frsbxMUdaf9y62OPIzqWJC43e9KTJH4fZszmEw0klu25XTEIGPy28yLzvzuV5+2XRReITSu4kQMStNH58ehNVd5zF7cO29JnwRKEX7bKwtiClSgVSz0Xepygfctevw/794OsLQFpSOpM/D+Rw69k8O+ZXqt+IpNPnnXjv5ntUerwSvX7ZwUL7xwhq0MwwzFUIIUqhzEyN1ZmbqMcq5lu2iyNcbWY4OX0xoGh7A0+evYV5RiaVHv1n3mXtGnaM2vsSsQ2rcOP1FXz+0b4ibbMoJaVr7I4FY+VVJcf25s1diBv4BGWWHuLo3pA8ji5dJHkshIzUDFQZMyaG2dEqyIm+Hk2Y8+WLmCel8ubb81kWe4oTtcPZ7RHK5z+uov3E1Xg0qMCrW/ux4n8V2PdIJAcfCedp1wy++vh54qzKsP5/f5IUefeuaq01ez8/yK5RuzjXojZnvOvx95S/uex/Od+Yj31/jLLOZTnfuQEArWzS7umxd7BNxc8+ia+oiPXjLsSHxN9TPcUhTcPTVyvQ47IjN9MK9xaPzlBMDLWjadlUBjr887rE34hn/TPrUWaKnr/1xMbVxrTP3TKTldWiedMxgUWxNrxcoS6V3m2J3w4/nlz+JM4NnDnw2QF+afoLu8bsIuZyTJE91vxMC7dhw60yDJ/2OyopjU4/dMKibMkvsuw1wovyjzjQd/o6PgyyIqOE5oqna/hixAESR/uT9tHWPG/xo/yZ/dpOdAms8hOSBF+/uA3HY8G4zuhKn/+rf891WdVxxi4oXIYL3w+rDZcVTurYkx2TdzK56kwyRq2jvLU5vRb58s7FYbR+vzVlK5TF+yNvzC9F0ijgBB+Pmwd//gmHD5fwAyheyTHJ97zwnBCi+ARdiafcrUQc6zvlW9bGHBo84UqyfVmC/Is2eTx90nDis2a9nHG4VLRm8s6BxHSqS9LETYx7YwuZpXBFvn3Ho7CJSaRqqyp37Bv8mTfxFWxZ/vofD8TnoiSPhXA+HpItLFgQXY7qZTL4xCWWtU+W4cU/+mBva8G5Z1dxbskZVj25ilM/ncLzTU96r+yNrfs/Q/PKmcGXlWOZ4qlZNukZYm8msPylLXkutpKRnsnP7+zh2JcHOdytETe+8GXFqL5Y1KtEwBsBRN1lCFr0+WiuBlyl/qD67EkvR+0y6VSyuPc35UcucTiYZ3LA1oHEsNK76MafcVbcTDfnVobirRD7QiUmn4TZEp2h+Mw1DjPj/9fJ0cn8MeAPUmJS6PFrDxxqOtxxnKWC8S7x/OQRzbU0czpfcmR9nDUebT3ouaQnftv8qNWnFqd/Oc2SlkvY9MombhonoN8PqRrGh9oyI9KWt1Zug7+v0WZKGxzr3DnZvSRYWFvgM6sDdhFxuM3cxpDr9sSWQEIzdUscbquPYP58I3oEDsrzlvhOO8pvPcdPk4t3UvuFFHOmjArE7e+LeEzypu+zNf5VfS51HalwI5oj0vlY9Fatgjp1WDrmCNvHbSOophuJi/7HpFNDaPJCwxyLUz3W9zFcG7vS45cd/OpYlzN1mz40cx+11hyac4gvXL9gy6gtJR2OECIf+/cZFqSp5Zl/8gjQx8WMC41rcnZTUJEuDnnttOGL6/EGd8Zha2PBZxv7kzigKRbf7+HLSX8XWbtF5fAOw3zHJ7zvTB4fc7UmbUwXLI+HEPB96T+RKMljISQlZ5JuacHeWhEsqXqLlxyT8LDMxKGWA33X98W+hj3bh28nPjie7j91p+X4lnnOSXraPpmFPcuyf8yTJO6/zjfD/yb9tjMl52I0UwduJ/G3E5x5oRWvfNuGxTViqWRrxu7PnsbS1pKNL2wkKSL3nsvjc45jbmXOoy/W5+9ES9rcw3zH7CqYaz5xjeOyfXlSolNMw3hLm4XR5ahimcF0t1j2JJZhVoRN/gcBh5Is+flWWV5zTKSB8RqEaYlpbPzfRmIuxdBtYTcqNrz7sI0udqlsrhlJ7TLpvHbdgbZBTgy6Zs+3TlW5MbE79XYN4rH/a0LInhBW91rN8o7L2f/ZfkIDQ4usV+tqqhl9LjvyY5QNb1w6R8Xv9/CI7yPUebZOkdRfVFyauuD5ekO81gdyffkZulxy5Hhy8fWK/hxhRfqkrWQ62/DixCZUrWyd523oyLoE92pI0uy/2bO8eCbmByZZMObbKzy+bD+uL3nS5+VH/3WddR53QGk4efJWEUQoTKKiYPt2Yjv5cnX7ZbYNak+j5c/z2Qs1MTe786SIUooOH3fA7Fo0XpuP8fG4uYZrPp47VwLBF5/U+FRW/W8V6wevJ9ncjH3fHSQp+sFZJEKIh9HFQMOJ7saed544z82TThDkVZPkG3GEnyq6awtHXYgk2dYaD7dyue63tDDjk8U9iWr1CNGfbyM0pHR1coT8dY1kW2saNHTOdf/wtxpwpVF1do8NIDGidMV+u5Ifv/YAyUjNIMPSHHuzO//Jt3Gxofeq3pxceJLavrXzveYawKNWGXw3xI0ZV1vgvGAfI6q58MG7tbExy+Tri+bEvrmOKsevYjbGm+lvP4aFMiQ0vvbJfJ/uxNi53dj+9Br8X/XnyWVP5jiznRyVzLnl56j9VG3O2pQnMcKswNd3vJsn7VJY52r4w00MSyzQ4yxO51LM2ZNYhg+376GpUyb9WnsxPcKGljaptCyX95DddA0jb9jhapHJ+86GSd4ZaRlsfm0zYYFhdJ7TGfc27gWKoYplJqurR/NjVDkCkyy5kGLB1gQrUrUC7OGpKjj36Ey7LYepu+0UkbMOEzgjkHKVylG1c1Wqd6mOe1t3LMvlXEUzMyOT+OB4Yi7GcCvoFrcu3DIMH872dgxLN+NksgUNgGes01HHb2JZ1Y52n7cr9By54tBsZDMijkfQ57M1/BUXz5N+rfnYJY4XHJK4n+HuTbBk+bzz9Dh3gw7fdaKMXZm7lrc0UwyZ0ZIfr0ST8d5WqtWyxaNJpfsWX0B8GT5af4sBX2zAqX1Vek9qXiT1VqlXAYBrp25BB/siqVMA69dDRgaBZZsBx2niV58RVe9+SO2etXF/wp2ui3cyudP/8cGjjWnw+ecwd26xhFzcQo+Hsvzp5USej+LvVztwpFlthgz5kUM/BtJmVMnOwxZC5C36WCjKvQKODgVb2dvNCsp714LpEOQfRKX6RfNdmRIUharqiFkuJ+SymJkpnvqmG1uaf8fScQcYNq99kbRdFDICg8nw9MDMPPf4q5VV2E3uju7zA6ve38LzC3rnWq40kOSxEDJTDMljGZV7D5FVeSuavN2kUHXamMHYKZ7MuxrGozO2MMjNmbiaFek0YikeVyNo/m1nmjyVc2VM3/LJfBNpw95HquE9w5uANwLYNXoX7b9ob0oQTv18ivSkdBoObsiiRMM/xi2LIHlUCuxdjMljeOlLHhdGl6PK+RAsJgWwHRi+zI5A1wa8ed2eLTUicbLI/bWbH12OkymWzHG/ha25Rmdqtr+7nasBV2k3rR01e9YsVBxlFPyf0z9njjI0BKeZcyHVnKBUC4JSzDnn15TVT7bAJiaRZ46fpvG+swStCeLM4jOYW5vj3sadCo9WIPZKLLcu3CL2cmyO3t4y/8/eeYc1dbZ//HOygbCnMhVBcW9F3FvrrKPaWrWt3du21m7b2mGnb99a66i1te69997iwj0YCqgMmYGQhCTn98fBFhWVEYS+v3yuKxeQnDx5AuSc5/vc9/29nVU4BzojKAREEVLMctLNMtxlIsFKC6p8EWUdN6ImRz1QHFUVCo2Cvn/1ZcerO+CX7QRk5zLxmT4c1Kv4zi8Xrdz2dQtXTXJePatk9Owd+LarSfig0jnPBmoF2s3sRczApawas5mxWwajrVE6t9gtm66TLVPQq5svrg94T0uyNXx23MKznyzFJcSFATO7IVPYJknEJdgFi0ZJ/sUMINgmY9pBSln19+fg4TxuBnrydFTJO8vFEQSBzp91Zn7v+URuOsGkD2ez7Ok2MGnSbf0hqyU6ndSfcsgQUNx/GSGKIid/P8mGVzlOmUMAACAASURBVDYgOKtZ8sOT6FrX4mlfSGgWwr6fjhA5vm2ZnIPt2LHz8BDOpSDW9yvTc3o2dOV6kBfnNsUT+WakTeahvJKBtdUDduWATs08WTEuCo/pezi5J5ymHWvY5PUrQmamAde4NMT+9e973ITuPowb2gb53IMkjmtKUNSD329VYBePZcBqktJWVTaOiAgygTEzu7CoXw69J62g0EmNc14BfRf0JaDj3YuI+mozYSozK3M0jBkcRtalLI7/eByPeh40fq4xFpOFM3POENApAI8ID/ZfVdJAXXhP4VRWXPykhuT61OoVVs+3CizJUvPST+tw8NSgclax/5Wt/Lzei8E6f9644cqfAdl3RbSuF8qYku5EVycj/Zylvp1HvjrC5WWXaTWxFfWfvP+HvTTIBQhWWQhWWejGPyL+qknGrEwnFrg1Z3b7lnR+X8+TsZdx3RPL1a1XSd6VjEuIC26hbgR1DcKtjhuutV1xq+OGg5cDgiBwrVDG89dcOVqgYqy7nvd9dGj+RQnpcrWc7r92x8HbAX6L5sOcPL56/VFOGzyY5Z9DfY3tXE51FoEnk9yI/HU9mgITnb/uUKaIbJ8QBdHTBmF5ciFLntzCk2v63xUhLk7GlVzmTTiEZo+U6vpxxwjS3+5K5zpqemqNhKhuT/3+JcORKQkqXv1wNs6ClX7zeqN2VZfvzZaAIBOQ1fHAMTadTLOAh43OCbYi0SQjxXx/ERGmNuNeCZsK5Uavh82bKXj8GQx/XCXt8UiaOpfufyq0ZyiBUYF0W7CXL3q9xslajWj6/ffw44+VPOkK8s47MGMGjB0Lv/0GspJPOKZ8Exte2kDMnzE4tq/FV28+Ss0ALQcag4cS2g9vS633FnF++Xkajmj4cN+DHTt2Hkh6phGX5ExkI5qU6XkDveDtlrXx3nAcs8GMQlMxuZGbV4g2NQfCSld3+frk9vx35Sl2TdxHo92PVvnm1N5d1xBECI8qqne8dg1q1uTOBam/Guq915nsXef4q99CRq0dSVD76icg/0VLzKrHWmjBqpRXSjqd0knJoD9746IW8LIUMnjlgBKFI0j/a4+6GjhUoOJaoYxW77SiVt9aHJx0kMQdicStiUOfqqfxc40xWOFogapc/R3vhXuReMy6Ub3E4/IcDeHrY3A8c4PIjyPpMaMHhkwDqe9u5SPvXLbmqZmVdXeu/MepzlhEga/8dAgCnJ9/nhP/PUH90fVp/nrZIsllJVhlZbKfjuNhN/nAW8d5q5pngprw0TMDkG9+mjFXn2XEvhH0/qM3kZ9EEvFEBDUja+Lo7YggCGzLU9E9wZMLRgW/+mfztd+/SzjeQpAJRE2OovX7rZGvP8/Xn8/DkFdI3ysezM9ywBY19xYRXrzuivH4dRpuOEmT5xvjHu5e5nHebefA8U8HYDybxobXd5doCGAuMLPjm2Ms6rgYITqZzPGdqfFWW+oduUy74TPZPDWGqAvudIjz5PM0LYf1Sialavn8hhMvTVmK89UMes7qUaI5U0Vxj/DEJyGNU4bSpSA9LPKt0DXBkwFXPe57eyrZ9r+TCrF5MxQUcMirIzKzlQaD6pX6GiEIAl0+74KQoqP9hmN8/MFsmDkTbt6s3DlXhHPnpNTa8HCYOxdef52SPqDp59KZ3Xo2MfNicHizExMnjaJ+iJa9zSBAA45yiBwUTmaAB/u+P2hTYw07duzYhsOHpZ7etZuXLfJY3xH07ULBYCZxf2KF53H6XBYANeqWTjzW9lAie78LmvgMtvxytsKvX1HO70vCKhOI6ugvuWsHBsIbb5R47MQIFYv/M5Z0Zyfm9pjHhdUXH/JsH0y1XGYKgtBbEISLgiDECoIwsYTHgwVB2C4IwilBEHYJghBQ7LExgiBcLrqNseW8RJMF6wMapFYE50Bnhu0YxvDdwx9ozDLIReoZuCpXgyAT6PrfrnhEeLDt+W0c+/EY7mHuBHYJ5FiBEoMolKu/473w8dYgCpCeUn2MDkQRFlwR6TVrG35tahA2NAyvRl5EfhxJ4rZEWi89TG+tgc9TtZws1ldwe56KdToNb3jlE6yykLwnmT0T9hDYJZD2X7Z/aHWCbnKRV730HAm9ydQaOYjAGyluBF/yo8Z5n3veRiW546+wsKVWJoNcjA9lrpWFIAg0f605nX/oTP6BJCZM+J0oQxZvpbjwynUX8q0V+1tMTtOyI0fJ07+sw6mmEy3GtyjXOGoZfDrCl73PdeXG2liifzx+2+NXt15lbsclXPohmksd6hGw5Unen1CPge80ZdTexwjrGkCPmdv56Plp1D8Rx8wMRwZe9eDXTCfe/GszrnvjiJocRUCHykldrFXfDW1WPjHXbHdOsAW78tTkWWVM9s1lcWBWibex7noO6VUkmqrRpWvVKnB3Z/9JC7lezozsUbra6FvU6lKLkM4hdJ6/l00+DYkOrA8//VRJk7UB774LTk6wfz+MHw8//wwffHDbIZfWX2JWq1nob+ox//Ek7w7oTD8fGVubgHuxPYtx/gIHh7Ql9eh1kg4kPeQ3YseOnQdx/phkltOqTdnEoyBAi+4hWBQyLmyKq/A8LhW16QiNKL1j/AtD/EhoF0bc1Gh016q2vVzm4WSyavsQrDLAs8+CWi2d52fOvOtYXxWs6uPGwTlPkxTiy6JHF7N+2vESRq06qtEVWEIQBDkwDegD1AdGCoJwZ97gd8Cfoig2Bj4Dvip6rgfwCdAGaA18IghC2UML90A0WbAqKzfT18nXCQdPhwceV0tloZmmkFU5GkCKXPb+ozdytZycuBwaPdcIQSawX69Chkjb+5jFlBU/DeS7OZGTUn0ij9EFSgKm7UGdZ6DD1/+IvobPNCSkTwiHvzjMR+mx+CisvHDNFZ1FoMAK76c4U0dl5kWPfDIvZLLlmS24h7vTY2YPm9WZlQW1DEa4GdhZK5OFgVm86ZXHG17597x95qtjbUgmtVXV0/m2PNR7vB69fu9F7sVMHnnxDyYYrrEiV0PvBA/OG8q3ebMoW8P0TCde3noA8Xw67T5th9Kp/JG3EJWFYW82JKZHY45/E038unhyE3PZMGYTG5/cyA1Bxe6fHuf13zszJOyf13EOdKbXnF70nd8XF9FCs1cWMeOH+UyXJ/PT4QO4zD1C/TH1afhU5aXw+TeQLr4J56qX4+rGPDXucitj3QvopDWVeHvRQzKzWp2rqeLZFlFYCGvXUthnAKZdcWR2qUuYtuybHF0+7wI38+m4NpqPJ86A//5XqissD6IIb78t1STamp07JXOg998HLy/47jt47jmpzUhRq5GTc0+yaOAiPCO8uLT0eSYH1ubZGrC8ATjc8fFt5gwMaYLJRcOhH6tvc287dv6/knYyhQI3R+qElN3fYmCwisSGQZzeVHGH8msXJPHYuIQ2HffCRS5S55P2iBaR1R9VXesOq8WKMiYZsXmAdG6+fh127IDeveHll2HXrrue09oF9ndzpOma0VxtFcrRV9by4Zu7yTNXjwyN6ljz2BqIFUUxHkAQhEXAQOBcsWPqA28Wfb8TWFX0fS9gqyiKmUXP3Qr0BhbaZGYmC6LKdvVHFWWwq4GPU525bJQTprbgHOBMn3l9uLDoAuFDJVv//fkqGmnMuNiwRshHYUHn6Ux+Ner1uGhvNi3WHyfiucZ4RvxzchEEgc4/dGZZ92UcfnErP6/2ZGiWH++kuFBLaeZqoYJlQZlYMvRsGLUBhYOCPvP6VLnBjCBAF62JLtrqFR16WIT0DKH/0v5sfHIjXmMW8OfM/oz3CKPvFU++9MtlpJuhVONYRVivUzMhxYVuBZn4/bIX384B1O5XNgOkkhjsZmL/591IupbB1ld2IIgiJpmMnc91J3RcY34P0KORlSzqg7oFMTxqODG/xHD8p+MI2//Aarbi396fqMmV6zzpUbRzm3U+EwZXjxTQQhG26tT0cjaiuI/2Uh5NpntmHivVAbzqVQ3OP3v2QFYWe4P7IDdeoP6giHINE9Q+iNCeobBoP18+8hoH/CNoN38+vPBC2Qfbtw++/16qSTx+HMLCyjWnu7BapYVPUBBZL73GL1ch3yLAhOlQry+cPYP8icUoFlzA2r42K74ezpZCNR8Fw6chd5X2/M1TtVWseKQFqsUHyErIwr2WzfZ77ZSCpANJXNl9Bffa7njV9cIjzAOlo4oUE1zUw6UCMFrhlbIF1O38j2A+m0JhPb/7OpzeiygX+LJVbWrN2kFeah5a39IZzJVETmwGgqcWb4+yrcHHNFbz2pPtaPrbHq7urktwp4dvRpZwKh1VvhGfEBV8M1vK3oiMhEWLoG1byXgsOhpq374ukQvwcpiKG9tH8MMTa9FO3cUziXkMm9aHIb6ySnWkfxDVLvII+APF81eSi+4rTgwwpOj7wYCzIAiepXwugiA8JwjCUUEQjmZmZpZ+ZiYLYiWmrZaVAc4GBERWFtuF92nmQ8cpHVE4KNBb4XhBxfs73omvwkqepxZjNRGPaUZw/mIbVi8nIt9uedfjGncN3aZ3Q5esI++T7Uzw0rEqV8PUDCeGuhTQRtCzafQmDBkG+szrg3NA9XKQ/f+KXys/Bq4eiEwpI2H4Umbu3UNzjYk3b7jy2nUX8q33fq7BCvOzHOgU78mz19wIUVoY9dtmLEYz7b+wXTryZ0EFRH89lPSaHlxsF86sP15k1FsN+SZI/8DaU4VGQYvxLXhs92MEdAzAo54HPWb1qPTCfgcvB0R3B1Rx6WSYq0f7loN6FTlWGX2c7516ff3AddYNX0f795dzIV/GBWM1OBevXAkODuy9oKRAq2HkgPI72Hb+rDNk6umy5ggfvTYVfvmlxFrCB/LLL+DqCioVPPYYGG2Uzr5woSRGv/iCN5Md+DABvkuC75JlfNe0PzFZzVAsuMCZrg2Y8snjHLSqmRYGn9UqQThaLNJiSadjpA+cerQ1okzgyH+P2Gaudh5IXmoeS59cxZyoOex4fwfLRyxnRrMZfKX9ind8f2Riuz/59qn1zPnyEN+tTORC9bjc/89itYocPJJBhv4+F7aHjMFoQRuXhlOjsqWs3kIhg6Aekpt57NaECs2lMD4TU3Dpo463UAnQ741GZNZ0Z9PE/VhMDz9L69CuZAAaH14I9epJjtognafXrpXO8/37Q25uic+v4STnm5UDCX69HfVXHGXJiOX0iTYTX4WVY9VRPJa0mrnzCvo20EkQhBNAJ+AaYC7lcxFFcaYoii1FUWzp4VH6/GlMFkRFNViwFOGntBLlaGJVrqbENUa0XkUhgk36OxbHTSai99RiSc+36bjlZfHsy9S4dIMmH9+7JUWN1jVoNaEVcavj6L7pGJ2cjLjJRT721rHz1Z2knUyj2/RueDe5f62pnYeLR10PhmwZgn+UPzEf7OH5r5YyXpPB0hwNfRI8uXiHgMgwC/yQ7kTLWC/eSnFBI4j8UjOHv1JPc3X5JZq+1BS3UNtF2xxl8FMTM7/NeYHjXwxmeRsLg11LFxW9hUuwC73n9mbolqFo3Cs/HVMQBLR1PfGNrz6mORt0ahwEkU5OJQud3Ku5bBm3BYWTAtLyqXfg4t8p+1WG1QqrVmHp0Qvj9lhyO4UToC3/9SGgTQBhj4QRuegA+2s25phRKdUVloWUFFi+HJ5+Gn7/HU6ckJxRK0pBgZSq2rw5Jwc8zp+p8HYgmDqBvq2ZhTNX0npFNG0Cr7Fk52MYslaQ2wFeKr51m5sLS5fC6NHg6wutW8PAgbjJLPRu4MKFzg04Pvs4xtx/d+12dcMqQqweNmTAj0nw4jkroyYe4cs6P3N60Wn2PNGeb1a9w/TZL7Bz8lCuvtwFVbsQwkUTkbvO0GfaZp5+7XeuxVWvNPf/JbJyCpny1B5iBizl107LGbc4i+kZjsRV8QbZ0ZM3URRaCGxWPvEI0LO9H3oXBw5vqFjdo/pqBspaZVivF+MRLwtxb/dATMji6PRTFZpHeUg4kESeuxNRJ9dJ52VNsWtXnTqwbBlcugQjR0obayUgCAJjp/ag+/c9abD7HP4vzOe142Vba9iS6igek4HAYj8HANeLHyCK4nVRFB8VRbEZ8EHRfTmleW6FKLRANYo8Agx2MRJvUhBjuDsDeZ9ehQKRNjasdwRpF9nq5YQsQ4/VUrW7ZHnpBVim7ierRRBRQ2vd99hmrzbDv6M/Bz7az8/6eA6E3iR+ykHi18cTOSmSWr3v/3w7VYODpwN95/elzQdtSFgXT+AT8/gj/zKZFoHeCZ4szdGQYJIzMcWZlrHefHNTSxONmWVBmWyplclAx3wOvb8PbYCWZq81s/n86qktHKlzk821Mqmj/nfUnvo3cMcnIY0YfdVXLlhF2KRT00VrxLGEK5JJZ2Lj6I2IZgtjfdbhotTTfc1hVt5j0+yhcfQoXLvG9kbDUOcWUG9QvQoP2fnTzpBjIGr5If7s9yxMm1a2AWbPluowX3wRBgyQnFD/+19YvbpiE/vpJ0hMRPzuO96Ol+GugPeDwKgzsrDfQs4sOkP3Kd3pde4HhHaR8PjjUs1lQoL03B49pBrJ4cOl+/v2lUTtzp3w5ZeMqwF7h7bFpDNx/LfqZQzxb+eJ8xB2BB45DT+uTkI2aBZhUzZibeyPestLvPRjNw51cyRhrC+7PmjAnJ878uWqwXxwfBwfZE2g2dyhANy8XrWGI/+rHD6ezazuK3HdegHjE81xEyy0fH0x517aRJ/DKqLiPJmUquVAvpKHXe4Wc0Qyy2nWuvzisbe3jCstanN9e1y5HZVT0w04ZuXjFl72yCNIa9aXBvtyvn09jk89hi65nPXk5UR/8AqZtd3xeu4pKU31Trp2lc7TGzbAxLs8Qm8janwkg/8aTODpRPw+W185Ey4F1VE8RgNhgiDUEgRBBYwA1hQ/QBAEL0EQbs39PWBO0febgZ6CILgXGeX0LLrPJgim6iceH3ExoLwjdfUW+/OVNHMoxElm+zOOwscJwSpiyKy6nQ+AVZ9Go9SbqP95pwemIgoygW4/d0OlVbHjua0kzYnh5LSTNBjbgMbPNX5IM7ZTHgSZQLNXmzFg+QAK9YVcHbKE2Qf20lRj4tXrrkTGebEg24FBLgZ21b7J/KBsWhXmEbvqMpuf2kzmhUyiJkfdtydjRfBWWG3e/7UyqVHfA5WhkIvxVZ89cNKgIMUsLzFl1Wqxsu3FbWTHZjNUtQrf6zG0EKPxOJZIflw2J0rYNHtorFwJcjm7450pVCl4bGhohYes2aIm9QbVI2rZIZa1G0rhqtVSNLE0mM1SnWPPnv/UOU6ZAs2bw1NPQWI57fJv3oQvv4R+/djUuAvbs+HjEFBm5vFH5z9I2JnAoD8GETUhCkGrhfXroXFjKQ2rdm1JwCYnw5tvwt69kJoKf/4pze2JJ2DSJNrH7EHbtCZZzYI48tMRrObqk7r3bybJAIvTYKwynxmzV/PMq3OoU6Bn2NJhfLVvFB908WSINzTS3m1mBFK0wyfYFYCcjOrjrv6/gNUq8vuMWI4MWoZcb6LOX4N5/fvWPL9nKK3ebUWjQxcZP+Zn2i3cz9w0NY8metDwkjcLsx9exsW1EykUqhU0b1I+0QbgrAB1p1BkaXmknUkv1xinzkhmOQGlbNNREi0cCrG82xmzFXZ8dLDc45QV/ZU01NdyUPkJ8Omn9z7whRck85zvvpNaIN2Hxk80Rh8ViuPVDNtOtgxUO/EoiqIZeAVJ9J0HloiieFYQhM8EQRhQdFhn4KIgCJcAX+CLoudmAp8jCdBo4LNb5jm2QCi0IFQz8egmF+mqNbI6V4OlmEbUWQRiDEqbp6zewsFH6peoT7V9IURSoYzIWE+mpDlhvM8aIiU6hbxl5zj9WBseaeZUqrEdfRzpOq0rWZezOPDRAQK7BhI1OeqhteSwUzFqtK3BsK3DqBFZg5MTd/PSlGVMcsrgba88jta5ySeGRIy/H2fNo2uY22Au21/cTtqJNJq/0ZyQXiFVPf1qg0c9Kf0n5XxWFc8ENug0KBDpob1bPB7+4jCJ2xLprd5BLRLIWrGC2nNfQ4aFNisPsboqxe/KlVg6dcG0PZb8qFB83WxjstXx447I84yEbL3Ixubd4bffSvfEdeskkfbSS//cp1bD4sWSsHz8celrWfnsM8jPxzzlG96Jg1ANPGHJYU7UHG5euMnINSNpMrpYA3FXV6n35dNPww8/wOXLcP68JBbbtwd50TVUEGD6dKhdG+GJxxnnks/mwW3JvpLNhVUXyj5PO3cxPxUidp0lvN/PpC4+RdS7Ubx8/mXqD61f6muel5ckVnIz7eLRVmTrzEx5Zi/GT7ahaxbA8O1D6NnVByiqhX+zBSP2PkZIJ39Cf97Fly9O4+fE09RTm3n7hgt78st3rkk9lUrMnzGlPl5/JoX8Or4oK+g836avZARzcH35UlfjLkjL+LoNyi8eAd5pImffqA7c2BhP0q6H0xro7KvfAxDcpwE4PKCTwtSp0L275GD9gJIFmbMaeX7VpfhXO/EIIIriBlEUw0VRDBVF8ZYw/FgUxTVF3y8TRTGs6Jhxoigaiz13jiiKdYpuv9tyXtVRPAIMdjGQYpZzSP9PVOWQXokFgSgn26as3sLJt/LE46JsBxIKFfyYoaV7gifHCu6OFlnNVrZP3EeOtwvhr7e8r0PjnQR2CqTNB20I6BRAjxlV05LDTvlx8HbgkQWP0GpiK+JXx+H2+F9ETdvGji4LWNRuEQcnHcSQaaDpy00ZtG4Qo0+NpvXE1vYNgmK415UcLRWx6aRXoWmOKMJGnZp2Tibc7nCEvrDoAjG/xNBSfpzm3lfIWrcOc5MmKLtHEtq9Js03nWR9ioBlx46HP/Hz5+HiRTZGjUGblktdG6Ss3qJGsxoEdQ4hcuUR/hw5Xoomlkb0/fKL1Hj6kUduv79OHWmM/fvhk0/KNpnLlyWBN24cc90iOKuHrwItrB6xjPy0fEZvH01Y3xLcXL28YNYsKdpYp869x3d2lsRtejqjJ71AXFQ41kB3e9sOGyCKsCDOwKPfr8Uz1J0XTr1A96+7o9KWTXj4+EgLXn0VZxn9r3A0JpsZ3Vfiuvk8ppfb8c7yngT63S0qXIKkWvg+8/pAoZWbY1bw/FdLaZifzbPJrsSbyrYWzU/L56/ef7FqzCpST6c+8HirVcThQgrKBr5lep2SGNTYlfRgL05tLJ94TLmQgShAw4iKOTEHqyzUe6EJRgcVJzaWMxOjLOzYwcmjmVjkMloObv7g4xUKWLIEQkJg8GC4ePHeh2pVKOzi8d+BzGSuluKxh7MRR8HKqmKpq/v1KlSCSEuHyok8ehSJx2wb93q0irA0R0NHJyPzA7PQWwX6XXHn41Ttbe6aZ+eeRXf2Jltf7skTNcteZ9bslWb0W9yvylty2CkfgkygxRst6Le0H6ZcE6d/O422ppaoyVE8fuRxhu8aTpv32+DX0g+Z3H6auxOVVoUy0KXKTXMumeTEmRT0uSPqeOPIDfa+tYtaxNMtQhKOlpCQvx+v/1pb5AYLPsfT2PXeN9IF92GyciUAu5O9sMoFho6sa9Ph273ZFue0XGLTPMjMzpNSQe/HpUuwdSs8/7y0ALmTkSOlSOBXX8G2baWfyMSJoNGQ9/GnfHwFIl3A/b87ST6UTP9Z/QloawPb++bN4dtv8V3yF/1z49n3aBuSDiRx7ci1io/9/5ijOnBddBRFnpH+M/vjHVE+MzinIhOvAnvkscL88VscBwcsQ6kzUPvPQbz2UeMHRvWCewQzfPdwWr7dkuRNCYyeMB9NgYnRSW7kWEq38SdaRVaNXUVBZgEKBwWHpj54c+b85Rw0OgN+zWqU6jXuh78adO1CMR++itlQ9uwHXWwGeX5uaJ0qXqbwRk0jBhcH4rLKnxqffEPPtp0POD/l5cEzz3BdHsyNsBq08inlddbdXXJgLSyE+vVhxAjJmfoOVFo1qgITpirK8LevqsqArJpGHp1k0NvZyNpcDaaizfsDehUtHApxqKS/sFcN6YKSbmPxeKRASWKhguGuBrppTeyuncFY9wJmZjrRJd6Trcdy2fn6Tg5+epCrrWoT2q82vkp7fcz/V/yj/Hni6BM8deEp+i3uR6NxjXAJcqnqaf0r8KknmeZUpXjcqJPOI72L1TvqEnPZMmIVrpYMBrRNInflcqzety98/Vr54V7fkzaro1kw7GXpAvvzzw9v4itXYopsh2lHHAUtQ/DyfkA6UhkJ7xeOU20PWi09wqLBLzzYOGf6dFAq4Zln7n3MTz9JNvGjRkl1hw9i/35YsQImTOB7ky83TPDe1Tj2T9lP82eb0/CxhmV7U/fj1VdhwADGfTuefT2bInNW26OPFeSPK4VELjtEcM9QajQvvwCQK+WYHFWYsu3isSKsWJlMwQdb0TXyZ+i2YfTuXvqInkKjoOXbLen1ey/yLmQy8YelXDXIeP6aa6lMdA7+cJDYjbH0+qEXTcY04fT80+Sn3T/l/1iRWU79luU3yylOaI9Q5EYzJ3eUPeJnTcjAHFKxlNVbuMtFLI4qzLryB1ZmfH6Qvd1/Y9u6+HsfNHEilitJWG8K5DUNwLUsurduXTh9Gt56CzZulJypO3WSRKVVWu+qXdWoDIXk3K+2qxKxi8dSYjVbkVlFZNVQPIKUupptlbErT0WWReC0QVFp9Y4Afk4yCrQaslNse0FZmqPBUbDSx1lKkdHKRb70yeXP+OP0eWM+cY8s4PzqeKzDGrPkvUcZ62G/oP1/R66So3SqHi0n/k341PfAKymDU7lVl7a6UaemucZEjaINoEKdkS29/8CqN/JolxSMi+ciOt/dd1UQBBqNbYBvbCoHnJpgHPyoJEA+/LB8vRHLQlISHD3Kmp4v4nH1pk1TVm8hyAQ6vtmGgAvXWFt/iBRVvHSp5IPz8yX79yFDwO8+Cz0nJylCm5MDTz759yKkRERRWrjUrMmNV97im0QYIeRx8YWVeDfwpvfU3hV7g3ci/8Y9JgAAIABJREFUCDBnDj2TTuFjyCR5YFPOLj1LTmKObV/n/wkmK5ydewKnrHw6v9++wuOZXRwwZ9nTVivCpRWXyfdw4u2VvQiqWT7Tm6CuQUROiiRnSzyfLt3Mrnw1k1Lv35c6+XAy29/bTsSQCFq+2JK2b7TFYrQQPf3uaFZxEo6nIArQprVPueZ6J/36BmNRyNi9rmypq1ariENiJprQ8rXpKAmLkwoxv/zrY+NNPTKryI7Ry8lOLqE347FjMG0aqSPfQGY049oq8O5jHkRAAHzzjXS9+f57ybl6wAApGjlzJhpHSYtkZVfeOv9+2MVjKbEYpdTI6ioeO2lNuMutrMzVcEivQkSgvVPl/VP5KqzkeWrJs2HNY4EV1uRq6OdixEkG5gIz5+adY3GnxcQ/s5bQa+nkv9GB75e8waRnBxLgp6atQ+XUdNqx87+OZ4QnMouVpItVs0C/VigjxqD8x2VVFNnb5T/czJTTv7cOxfz/SIYv9yBsSBgyrYr6K46y8ZfF8Oyz8MUXMGyYZNhirKR6kFWrANidKgm1gY/bXjwCNB3bFFw0OG+M52JIffj115IPXLRIEoQvv/zgQRs2hP/8RxKjvXtLUciSbgMGwOHDMHkyn6Q5YjZb6TxpBUadkWFLhlWOa7GnJ/IF83l63WyW9G0NwJpn1nDk5yPEbYkj+2o2olWUhO3Zs/D119Kiys5drE+x0HTBAbStAwnuGFzh8SyuDljtkcdyU1BgxnFfAqbOdVApK7bsbvRsI+o9UQ/Tr0d4/dBRZmc5Mi+r5MwHQ7aB5SOW4+zvTP9Z/REEAa+6XoQ9Ekb0tOj7ppBmn0pFF+SJu4ttSnua+ahIaxxE2o6yicdrqUbU+UY8wmwTeQQQnVSgL//a0awzondxQCgo5JdHl2EpvKN06pTUS/JsaDcAwtuXQzzewsUFxo+HuDhYsEDaBHz+eRxXS6UaWTlVU/dY9U2+/iVYTLfEY/XU2yoB+jkbWJbjgEYAB0GkmabyhJWvworOwxlDmu3E42adGp1VxjAXPcd+PMbpWacxZBrwbuxNt1+6Ubt/beRKOd0LCvgyXc5Y9wLsHih27JSPW46rwuWbpPeogbfi4aa/bNRJwrBvkXjM/HUdl5JdiGpXgNfvH/KgD7fSSUm9x8Ip/PMcSy72YtCMGZJhzNdfw/LloNVKbSv695dMZLzLV/N1FytXom/UlMJdCZga1cSrktKkVVoVTZ5tjvXHg8x79yMmT38RJk8GR8d/DhJFKaW1USOIiirdwM8+CxcuwJo1EH+ftKsRIzg7ZDS/HYd31u0nZVcC/Wf3x7u+jX6PJdG+PU8dOsnnvm5YB9chefNV4rf9M0eFXMRDno2X6ToeZFCLBGp36gQtW1benP6FrJ17huDUHPrO7GsTozDBVYOQYxeP5WXX9hRUBSZq9g2p8FiCINDhqw7kxOUgn7SJAb968h4h1FaZebTYcaIosva5teQk5fDU3qdwcP9HYLZ9sy3zus/j9ILTNHu65N7H8nM3EJtVQPTcNW9w7Vwbxx92kHY9D5+a2lI97+JFKbIXVM924hEnFUJqCRHDUiLmGTH5u5M4OpJ67y1nzYTtDP6x5z8H5Epjn4/JIcfHhe51bXCNUCql2vURI2DXLpzeksRjdnbViMfqqYSqIbfEo6KaRh5BSl0tEAUW52ho7WhCXYl/XU+5lXxPLeZ024nHxTkO+CsshJ5LJnpKNN5NvBm4aiCPbn6UsEfDkCul330TBzOLg7JL7Atnx46d0uEa6oqglOFzJY2YKuiXuFGnIUxlJlRtAbOZM9/vRy0zUfe35x4oHG/RaEwD5IUWbsw/gc4iwEcfSX0J16+XeggePiz1OPT1hXbtJMOYe6V/loa0NNizhxVDx1PjwnXCB1ZO1PEWXd9oAzKBi5edsebkwMKFtx9w+DCcOCG15yitSBAEqYVGbOz9bwsXMuGKnLrnEnH8aScNRzS850LTloS8+SI9Eo4zZ1gH3vnCmbf6X2SM4xL6sYZWHMHNTSDVtzEHFJ1YwBMUfPpNpc/p30S6QcRx5j4sdX2o178EJ9xyIHNzQJ5rF4/l5fz6qxic1PQsQ53j/ZCr5PT8rSeOPo60e3cZ9bOzGHfNjfhif6JjM49xbuk5un7RlcDI20Vgra618G3sy6GphxBLSPO/kVaAc0oOHk1sM99btOsn9cJdv/o+m1Z3kBwrCbGIhrYTj4KTCrm+/Jl5Yp4JUavi09cacmxQK05NPcj5Fef/OaBIPGYeu0FSg0Calk4nlw5BgC5dcK4pOc/m5tjTVqs11iJLI7m6+orHto6F1FBYsCJUar0jgEwAi5cTws38Ek8+ZSW1UMbufBXDXAvIvij19On4TUdqtK1hb7Fgx04lIFfKca3jhm98GjEltMOpTDLNAof0SvoW1TYXTvuL83lBNOjpicq9dD1bAdzD3XHvEELT1UdZlVoUOXVwgL59pTTPpCSp/uSTT8Bkgvffl6J05RWQS5eCxcKubCkVsPfIyhWPLgEuuPavT9iGU2x/5Ckpylj8fPvLL1K7iyeesPlrb8uEnVcKGPHFctxC3Og3o9/DORfL5YzrEEqSTyDb561Fe2QHISMjabHqY3rmLGVk6o+8kvIhzxwchwUF59fFSq1T7ADw57wLeF+9SZt329vs76V006DMtdc8lgerxYqwM46cqFBcNLZbcjt4OtDnzz5Y8gsZ+/Ei5AWF9D8NuWZIPZ3K5jc2E9orlKh37s5IEASBtm+2Je10GgnbE+56/PBhyVCrTgvbmOXcok+HGhS4OnBuU+lTVzPjc7EoZNQPd7XZPGTaiolHIc+IoFUT7ggtvu5Jcj1/lo9dTcblDOmA3FxyNT4I13MxNwvAqRJkg3NR2WxeFaWt2sVjKTEX1TzKq3HkUSbAQBfpBF9Z/R2LI/dxQmY0Y8qtuFBdkavBisBQVwNZl7NQOCjQ+ttyu8aOHTt34hXhgX9CKqcqGHm8YJTz5nUXOsR5crYUY23JU2NBoI+zESEvj7NTDwIQ8Xn/Mr9211db4Zaaw6YVsXc/KAhSK4hPPkE8Es3+t1cy0zSWvGl/lPl1AFiwgKxW7TDvvoKltie+lZnCWcTACW1R602s8xkgRRmPHJEeuHlT6pE4erQkIG2IVYR34kRGfrsaIT2PoYuGona5d/2prRlQxx0vmYUnv1lHn803eP3d2fzSciDbjE4kGaT51WhRA4/arpyRNYUpUx7a3KozoiiSPHUf+f7u9Hmygc3GVbs7oNEVYLBUshnV/yDH9qfjkK3Hv1eIzcf2iPCg+/Tu5J5J5/3/LONivkjQdhNf9F9GjqOGCS8PxuOAgPs+/r5FHYesQmg4siFOvk4c/OHgXeNeOCo5rbZuY1vxqFIIFLavg2xPLIXm0pVJGBKyyQvweGBLk7Kg0KpQ6k3lDnzI9UZkWul8ODFMwbGvhqIXZCweupTCgkLQ6UhSSVFW37a2S/0tjquT9PvIz7WLx2qN8VbaajWOPAK87KnnM19dpdY73kLtI0UI9BU0zRFFWJKjobnGRB21haxLWbiHuSPI7BFHO3YqE88IT5xSczmXUfZ6R1GEfflKHk90o3O8F6tyNWRYZAxPdCfWeP/z5EadGn+FhSYaM4ofp3GiIILQDl44B5a9NiRiUF2svs7I/orm5j32sfQZehYOWMi272K4QU0O/n7h/m6jJXH1Khw4wNLHJxB88kqlp6zeIrStP8bmgcg3XEHn6SdFGwHmzJGiqS+9dNvxJitMiIO1N8v/mtOvg2reEYL2XaT7lO7UbFmzAu+g7KhlsKiRnG4+SlILBX67AS9fhh6nIOgQOO+FZkcFMh5pQoI1CN1fq6W/z/9zdq9PwO3cdTxfbofs1mJ79mwYM0Yyk1q2TDLzKChbCqqDuwOKQguZentbrLISvfYqZqWc7n0r5zMU3DOYth+2JXdDLL9u2s1LszfikXgTyw+DGVbPidG+/H173AeidTD8HKBU0OqlVsRujCX9fPptY96MSSHfU0utQNtv4NcbEI5jtp7NW5NLdbzsShZi8TYd165JztqG8kfClU4KZFaRQn3Ze04CKPKNKIp6hKtl8H0nN5a+P5j0U6lseHkD5OZy3iGCQrWCRq1sK8Bv4VEUztTbI4/VG6NBOmkqqqlhzi28FVae89DzMHSXk49UgK2voGnOWaOC80Ylw92kk0HWpSzcw90rPD87duzcH4+6RfbnlzNILSzdua1QhBU5GnomeDA00YNTBgXveuexU3aZmccOIlitDE90J9FU8nj5Vtidr6a3sxF5yg0uzYzGiIZG73Uq13uQK+VEPN2cOkdiWRSdedfjSQeTmNFsBvFb42nxQx+c2/tyVFeXgnXbyvZCixYBsDOzJjKrSOfHIso13/LQ4o1IXFOymd/nIynamJYmpeV27ixZtxdhtsKocyLr/jrHR9/H8NPaZAzZpV9kFWQV8N3qZH7//ii9f91KWL9w2r7RthLe0YPp5g4L68PxlqDrAMmRsKMJTA+D52qCoxymtWwECJwV68N331XJPKsTO77ch85Ty6iXm0p3pKZKC+3ly6U2NsOGQZMmkulScLBkKPXKK1I69n3QekrX+psZVVNf9W9FFEWMW+NIa1WLEPfKqytv8lITwoeFc+2b3aiXnaTj+x34fkxt/hPGbbdp4TAzHLZlwfg4aPliS+RqOYf/c/i28SxnUyiMqBzRM2hIHSwKGQdXXnzgsYVmK9rkTBzrFGvTsXSp1NN3585yz0GtlYSfrpxZc6p8IyrnfzIxurhDuwFh7HuyAyd/P8mJsyriLf5cr1uTVh6VE3Byd5HGNeRUTTp5tXRbFQShN/AfQA7MFkXx6zseDwL+ANyKjpkoiuIGQRBCgPPArf/KQ6IovmCLORmM/47I48PEzVe6oOSm5ONfgXGW5GhQIjLQxYBJZyL/ej7ude3i0Y6dysYjQroo+8SnEmMIpafy3hfTfKvAvCwHZmU6cs0sJ0xl5jvfHDpcTOD8lzFs2nwFRPjxMxOvdOrEsER3Vgdn4ae8PVqxM0+NQRTo42zA8f1vOFLYAr9Gbvg2L785Q9+XW3D+mz0cn3EUoiTXO1EUOfjDQbZP3I7K34Wzfz7Nx7418XEO4sV9M4j+ZAMdB/R8wMjFWLCAjK49YWc8op8z/q0eXjRu4Mi6HJzoRm6MUmpB8vjjUt+vYumaVhGejy5A9vYaHtt3AYAsYArg6O2IV10vPMI98KrrhWe4J6IoknEpg4yLGX9/1d+UNgL7A26h7gz6fWC1qDkXBPBXS7cuRZcGnRkC8r0wRPhxJr0TbWf/KBkm+dimL91DobBQiqSEhFR4qMRDycgPJpA9vgf+LkVLu6lTpf+XCxegZk3JCOniRanm99bXefOkWtq33pL6ysnu3vRx8dSQCmRnmiCk5LYQdu4m/lQmDik5iC+2qdTXEQSBTt91wpxlRpAJdJ7U+Z7Hjq0BZ/Lh+2Ro4ORE4ycbE/NnDF0nd8XRy5H8AjPOCemIPWxjtnQn3p4adC1DkG2+CPS477FxV/UoCi24hheLPJ45I33dtw/69CnXHNTOSsxAjs6CZ42yPTe/wIzcbEV2Rxr/d6EQ8Uxn6l9MZsNRC4XIudYjkCaVVH3l5KkFdBjt4lFCEAQ5MA3pvyoZiBYEYY0oiueKHfYhsEQUxemCINQHNgAhRY/FiaLY1NbzMhqlBZCqGtc8Pmy8ajhSAKTfKKC8e/B/RzGcjbjLRVIvZwHYI4927DwEtAFalFolvglpnDLUo6dzyeLxjEHBs8muJBQqaOdo4iuvbGrvvcip6TFsPJGGxkNDZONsUmMySPxc5PeOwTxpCWF4ojsrgzPxVPxTW7JRp8ZDbqX91VMkLj5BFiPo8WqrCr0PF39nZD0j8F59koSsLvhhZuWY1Vxee5GULvWYO34gjm4aJtSAb/FDbODC4ZN6ItOzUXq7PfgFzp6FU6dY/91n1HnvNMFPNX+oafUKhQynp9sgm7yZE+2fpNn2eeDnB4MGAVIK8VsrruH8yjICb+bS8/ue1OodxuRdGZw4dZOozAw8UzK4vP4yJ+ecvG1srZ8W93BPUrrU45CnJ62aePJ+Z088Q93/driujjgrYFwN2NepIZpfU8jEEY+pU+HLL6t6ag8mN1dKJ506VRKPp0/fFkEuD6s/20eBs4ZuL7eQ7sjJkVKchw6F8HDpvqZNpVtxLBZ4/XWpZ2ZqqpQOrbzdQMutKPKYnWl3OC8L+9YkYpUJtH+kcureiiNXyxm1ZRTAAzd8poTCeT28chlWPtUW8+wTHJ1xlI4fdOTIsXTkFiv+zSon8gjg368u+R9uJPrETVo187rncXGX8wCoVb+YeDx9Wvq6d2+5X9/BWYkOyM0te3lXRtFnQHOHePRWwTfhMl6bOISJo75F0IO8RWCldT2QuzljVuoxVVELnWonHoHWQKwoivEAgiAsAgYCxcWjCNwqjnEFrlf2pExF4lGpqvpd2OqCr5uCy2oFmSnl/+fdlafipkXOcFcdIKWsAriFlWJBZ8eOnQohCAIe9TwIvppKjOFux1VRhHnZDnyU6oyH3MoSzxTc15zh1MxTxCfqcKnlQoevO9DEdATPj6eSGdSQGYkBZI2cxR+732LUdU9GJrmzLCgLF7lIoQhb89T0cTbi9tZnbJC3x9nXkVp9a1X4vXR7tSXbN57jj7d3IN9ynsIUHVte7kXGqDb8ECgwxk9KddyfC/uf70P71xZzfPxftJn3yoMHX7gQZDJOr81DLZcx8INS9lS0ISNeaca8H3ayTtaJZsyD554DpRJRFPl40hGcv9yC4OPMM3ueIjAyAID/RnjxYUJdvkyEgZ5SGqigM5BxKQME8Az3xKLVMOwsbMyEz0Lgw+DSd/2oal71h2ZdGtL9122cafAYHadNg3ffBVfbOTPalKQk+OknmDlTEpAdOkByspRWWgHxmHY2jcyNF4kZ25FJwUWL2l9+kV7jvffu/2S5HP77X6hRQ0ptTU+XaiO1/4RMPIvEo66K2gL8W8ncHE9mo0Ba+j8cN+vSZgnIBelcEHkcxph8+Kp7KNHTool6J4rTRySznGY2NsspziPD67Lkw41sWXrxvuLxemwOAPVviUerVdrIEwTJOMxoBHXZjbwcXVToAF05xGNW0WfA0UV112NP+cHcECcWfjqcVouPEtQ5pMzjlxqtFrMmA7POXvN4C38gqdjPyUX3FWcSMEoQhGSkqOOrxR6rJQjCCUEQdguC0KGkFxAE4TlBEI4KgnA0M/PuGpmSMBalrarsaat/46u0ovN0RlcBw5wlOQ54yK100UofgKxLWcjVclwqqfG2HTt2bscjwgPP+DROFdx+bsuzCLx03YUJKS60czTx28mDxHWcy/4P9+Pk50SvOb0YsW8ETetk4fHphxi7d6fwwCY6dywg6YaGmkPeZZZvOucMCkYluZFvhQP5KnKtMgZcPUrGzgskWgJo+FyTf8w9KkBU7xB0tbwQ5hwisxCif3uadz9py4U2Ai/6S8IRoK8HbGtUDz+nDA4svf53D997IoqwcCEXGg9DuzuWnJc64hH08MVJhK+alMHNMe+/RvaXUpqhIcfA5EeWovhsE6b2dXj31PN/C0eQ1lhf1Iaf6sCaDOh5CgqcNPi39se/lT95Dhq6nYTNmTAjHD4K+fcIR5AyKHs0cCW5SRAx+jqIubn/GApVFKsVxo+HDRsqPtaJEzBqFNSuDT/+KLWSiY6GPXsgMhJWrqzQ8Lu/2o9JoyT4+TZo5IBeL71O797QrBS9OQUBPvgAZs2CrVuha1dJRBbh7S2Jx3x75LHU3EzIwSE2HVWP0IfiQVFWXBSwppH0/aL+bcm7kceZxWe4fjIFk4OSpg087j9ABYgIcyW7rh/pG+5f95gdl4NJoyQ0uGgjIyEB8vOlz4/RCEePluv1tVopbpanK7t4zM6SPgNOJbhPCwL8Gg6Xm9Thj29H07JGJTpUa7VY1HIsdvH4NyV9zO700x0JzBVFMQDoC8wTBEEG3ACCRFFsBowHFgiCcJcKEUVxpiiKLUVRbOnhUboPiKmoz6PSLh7/xldhJc9DS0F6+SKP2RaBLXlqHnUxcCugm3UpC7dQN5ssJu3YsfNgPOt5osgxoE8tIKXINOe8QUGvKx6sztXwnreO75LPcXT8TtzC3Bi8fjCD1gyiVt9aKBOv4jZuHJbQUHKmTweFglqL3yW4tsjO0370ePIJpnukcLRAydPJbqzK1eAoWOk36XUOOnZD6aSg3uO2cS0VBIGWU3qRO7Ilgw4/z8bR/gz2lnbZi9P31ib2Uy3JNao59d/d9x/4yBHM8Ymsud6E9CAvIt+ItMl8y0PLV9uACGtTwrgRm8+3jWdi3nyB9Le6M3nbCBw9S65HezUAFtWHI7nQ4QQkG+CqAdqfgJN5sLyBZELzb+TNADjZtRGZCTpSIwdLqaBldBMtkSVLJAH22GNw+XL5xjh1Crp3l9rFrF4tmdPExkqR7JYtidXDjGc/wRITA1eulOslshKyOLfoNMf6NWd0hKN055w5kvh7UNTxTsaNk4Ts6dMQFfX3nHy8paZyBfbIY6nZs1qKgTR/JLiKZ3JvQh2kz/7exqHoa3tz8IdDGE+nkB/uh/zOE6eNcetTF7dTSVxJzr/nMYVXsskP8kB2S33fqnd8ocjKpJypq1pnSTzqyyEec4vcTZ1dSxaG9R1F3ln6AwBtKzMGotViVQlY8yu/s0JJVMcVejJQPEE8gLvTUp8BlgCIongQ0ABeoigaRVHMKLr/GBAHhNtiUoV/Rx6r46+savBWWNF5ailMu/eH/36sydVgFAWGuf5zobc7rdqx83C53TRHyYJsDX2ueJBnFVgWlMVT+SlsHbcFl2AX+v7VF98WkrGNkJuL2+jRIJORPW8eYlGvQUEQaL9kFIJGzaZj/ox+vCdTnZLZna9mYY4D3bPiICaWc4Y61Hsiwqb9A0cMq8P3Cx6hU/C9TT0aO0FNFewcNRg/brD/631YLfdpQbBwIfvkHSlIM7HptT708qu6DcTHm7txsUMEcbOjmRn5G9l6M7G/j+XHb6JQPGCxN9wHNjaGRCO0OwHtjkNqIWxtAoMqv11lpRHpCppH6mOVyzgd0l9yop0zp2KDGgwwcaKUSqpWSwLSePsOvz5DT9LBJHKTc0seIzkZevWShNiUKVLK6o8/QkgIegt8nAANouGFkJ4s6TwcVq0q11QPfHcAqyBwY3Q7Il2QTHi+/RbatZPSYsvKgAGwbZvUR7RdOzh1Cic3NVa5gKkMzr3/30naGE9KmB9d6hZ1c7dYUB46hPzqVanOtJrQ2R2mhQtsG9yG1JMpuMYkom5QfvOy0tJpSF0EEdYsuXTPYxSJWRBSbD14q96xUyeIiCi3eHRxkdKICyoiHt3ucd0yGvnstw84fHouDSuzVblWCyoB9FWzoVMdlVA0ECYIQi1BEFTACGDNHcckAt0ABEGIQBKP6YIgeBcZ7iAIQm0gDIi3xaRuRR419sjj3ygFMHtpIb184nFpjoZwlZnGGqnXTqG+EF2Szi4e7dh5iHjUk8Sj75U03ktxZvwNV1o7mthWK5OW1nw2jt6IaBXp/Wdv1Ld2W81mXJ9/HnlCAtmzZmEJvn133TnAmbafd+CKWItTl7S8OLQ9XykSAHhyztcc8u2PiECjZxo91PcKUmpRX0/YYnImMiKDjJsiF1acL/lgi4Ws+RvYJ7bnWo8G+HapjefDKV8qETclaJ6PRDSYiW1ai3NLX+C3J4JQlvJK3tUddjeVekEC7G0KHf4HystfaeRIXMvaHN2XgxjZTnINLazAjvzPP8PVq1i+n0rap9M5f6KAfV0+YvVTq5kTNYdvvL7hW69vmdNuDj/V+Yldk3ZJzcFvodfDwIGQlwc7dsCECeAm/aLX3JRE4+dXYZg31HOEr57+FLEcqau513I58dsJTvZswtBGLlLK8YIFkJgI779f/hzkqChpYS6XQ4cOCLt3Y3R2oLCKesr928hP1aM6dR1j1zAciz6bjj//jMegQXi1aYNP7dp4duqE69NPo/3iCzSLFqGMjkbIyqqS+T5XE9qPaUy+qyOCCDWbldGCtBx0bOdHnq8rCWtLTl0tMFrQ3sjGIaTYCer0aahVC5ydoX172L+/7P16Adci8WjIK/s5Ir/oM+BWQs0jALm5yK1WWsvKty4uNVotgkJEZo88SoiiaAZeATYjtd1YIoriWUEQPhMEYUDRYW8BzwqCEAMsBMaKoigCHYFTRfcvA14QRbF0RY0PoNBgjzyWhMzbCXmeEXNB2ZqtJpjkRBeoGO5W8Pf1LScuB0S7WY4dOw8TjYcGR19Hwq6mcMMs4x2vPBYGZuMpmNn+0nayY7PpObsnbrX/+VxqP/8c9c6d6L7+msKoks1jIkZF4N/Rny2KvuTmCEwY1Iorv4/7P/bOOzyKcvvjn5lt2ZaeUEKVLiBgQaqCCgIqgqICKiKKcBURFcWChZ/S9Co27OXaQBH1itJF7EqRK70XIXRI2d5m5vfHbCCBlE2yu0lgPs/jE7I7M+9J3OzO9z3nfA/9v/2YNe5WNOrbiMSGVdPb3DcV8iXInXAraRzj1yeXoH6EFEVZvpyFxy5CNOr5bGTvEyWvVcmNvesx4/P72TVzKHO7W9Qet3LQwQ6bO8KmjsR2ZzyOXJ8OB65sS2BfPvsG3quKp9mzK3ax48dh8mR8va5h5j2beWPMJuYwmGV/WFk3bzN7QiL7erbif/f15otnb2LTJefy06SfeL7l66z9Zpt6Mzt8uNrnOHs2tG4NwC4vXLMert0AVhF+bA+fnAuPNYD1Wc2ZH0oq0mcYCb9M+QVJUvj5lu7cWgt17enT4bzz1L6wytC6Nfz+O2RlQd++hOwJyFrmMSL+/O4fBAVa9msEgHD0KNZXX8XfoweOF17Ac8cdhBo1Qr99O5Y33yRp3DiKNS6+AAAgAElEQVRSr7mGzFatsE+cCIH4Z5NeaG0g56YLAWjTMXZmOQWIooCuV3Osf+wkv5gM4NYdbkRZIb1Jof7yDRugbXjDsXt31VG4oJS1HCTbdMiiQKACmUdPeDZkckoJmUdHuBIhMcafbTYbok5C562azGN1dFtFUZQFqEY4hR97stC/NwGn3bEoivIl8GUsYgqGTRVMmngsgiFD7bHwHPGU60ZwTn4CAgrXJ578MMrZpup8LfOocTah270by9tvI7jdOGbMUHf740xqq1Ta7j3EwkY5tDerG0F/TF7BP0v/ofu07mR1O+lZljBrFta33sJz5514b7mlxGsWzB77oucXfN3sQW47+BwNP3qPP1oPx79R4ry7zov5z1USV6SAXoCFF/XmRv2zfLslnZ1LdtL0yqZFjts2/b9spzmpD/bAmZFI39h5SERM31T4pIedHslgreBLJaUKs6exQC9C35takD9Nz/Ltidx23nkwbZpqUlPM3MJSefZZcDj4Nn0wOct28MNDV7OjSR1y6qXht5owoNDUItDCAleZ4cB1LflyaQcueXEB/x0wm/cvzOKclNoMeOlNGl59NT4JntsHU/eqr7l/N4GxWZzIFg/OhCe2+pky5BGumjcP4Y47Igozf28+a95Zw/ar29OuZYo6fvHrb2DzZjX7GA3no/r1VTHavz+y3QgOLfMYCVsX7CE3K5URHWyAgu2FFxC8XpzPPovUtOh7DKEQun370O3YgWnpUizvvoth9Wry3n4buUGDuMWsF2H689346sJMenaLfeYR4IIBLdj0ySrmfbOLW29pUeS5XdtVB/4GzdR2CPx+dTZpeDzRiZLsX35RN0vKgVWEgNmI4C6/8PKGM49p1UA86sUQsla2Wr0JhUd1mLWy1SJYM1Xx6D4ceYpeVmBuvplLrAHqFBognrstF1EvktS4mtqsa2hEEcOqVSSNGEFaly6YP/4Y85w5WN5+u0piSWuZhndHDucZ1Q+irZ9vZe3ra2k9vDWth7c+GfMff5A4YQL+Hj1wPv10mddNbJBIpyc7kb06h99GvIZ7xB2sdLYmo30GteOwu11iXHrongQL3Qm0G3AOiaKTX6cU7Z8J5rpYuMxIRlKA3wZ1obYR2leDTJ0gQP909WfQOMnIZiZ2dm3Bzi83Ij80QRVR33xTvovs3AkzZ/LFnc+z4fMd/H3thbS+8wImXFWXrxvmsHPkeXieuJRN54f4uo06L+/jVrBmTGMGrRyNf0R77OsPc/SnWgz7uwXtfw3RehU8tQcGpMOWjjCulkT+tmNsnbeV357/jd/+70ceqifyR5su/LwqcmOen5/9GVkQ+G7wJdxbD9UVeMoUaNIEbrihfD93aWSoDbGC1YBQRQPJaxJ+hx9W7CPn0mbUMSrotm/H/PHHeIcNO104Auj1SI0bE+jVC+dzz5H33nvodu0irVcvTIsWxTX2tEQDI0e2PmlQE2OuvqoRfquJdf89vXT18E51TEfz5mHxuGWL2itakHls2FDNiv/6a7nXFQQIWkyEKlC26neWXbYKxF48Wq0YhSBGTwDp9KKZmKN9/ERIqEjmsYQaa0VBv3YtoXbtapbfeSVIqq0aU7jKMa5jhdfAvqCORzJcRR7P3ZZLUuMkdEZNoGucoUgSpkWLsLzxBsbVq5GTk3Hfdx/eESOwP/wwtunT8ffqVfxNRgxJaZmC5JPwvfsVXpfMzzNyqN/SSM+LjqELG3kIwSD2p55CatCA/LfeAn1kHx/n3nouu77dxW8zNiJNHEre+79w+aOdIp5JFiv6pcJDu+DgiFF0nvsYi3+2s+/3fdTvovq1/TLqE/KVJG55rBWPu3QMTKdaWu5rqCQboO6gNuiWb2S1/QI6NmmiiqkBAyL+PA499jgT75zCsZ+TyEySmPxqT1oXJN3r1YFnHoMhQ+Cpp2Dy5BPn6UXose9veswegqNNJ75sMBrDBz/i/X4te66/iEcDDiz/HOerbcfJ3ZWLcsrdXuegTGb3i5ja9DIudTrVnq5SyN2Vy98f/M3f11xAx5ZJXJcOLFumji4ox99mRKSrc/h0Fh3KAVcZB2tsWLwXMSRTr686u9b27LMoZjOuBx+M6Hz/VVeR06YNSSNHkjx8OO5Ro3BNnAiGM6xcADAn6PBf0gz9D9sIhmQMhVz2HbvyMCaZqZ0ezvAVmOUUiEdBULOPv/yibpyU8/MkaDVicJU/a+d3+hEsxpLdaOMlHo1GjGIQfAKOgExKnKsitcxjhEh+CUknUlri0fLWW6T16YP5o4/iF1gVk1pbzTweOxi5NfpKj/om2NtWtARGc1rVOGNxuzG//z5pXbuSfMcdiEeP4pgyhaN//YX7kUeQMzNxTp+OkpBA4v33x9WNT8jLo/4ytT/s0FOfsOD5vSSFjjNkyzOk3fMvkkePJnn0aJLuvRcUhbyPPkIpxxB2QRS49IVLUWSFXx75BWtdK+dcfU6sfpyIKehfXNj2Es7P3IfZGOLXqeou9vHtx/n9y4O0NW3l4B3XkRdSxaZG9WbU0Kb4rCbmf7hZHVOxejWMGgWhsnvy9//+Fz273sNi++XU35jNgH/3onXWKa69gwfDHXfA1KmqI2kBBw+qBjkZGSTOn83tX93ILUtuoZ5ZpNVLSzj8/moc+xzUblebbo90Y8BHA7jjzzt4OOdh2g1rx5/Tf+W+fVtZfGFv/vr+zzJj/fmZnwnpRH66uTuvNgvfN0+dCnXqwG23lfO3VgZh8WhIUDA6ojAC5Qznf9/txZlq4/KuaRh+/52ExYtxjx2LEv49RoLUsCE5336LZ8QIrG+9RcqAAYjZ2TGMuupo3r85llw3S3/YX+RxaXce3gaF3nQ3bFAFdPNCAxS6d4f9+ys05ka2GJErULYacgYIWUpxCHeq5bYxF49Agk59X8vJi3/pqpZ5jBApKCMZdJhK2GzQ/+9/2MI7keYPPsA7bNhZkX2slWFir07k+KHIP1S2+fVk6SXsupO7r5JfwrHHQZP+TWIRpoZGlSEcPUrqddeh376dwAUXkDdxIv6+fU/rbZRr1cL57LMkjRmD5d138YwaFdvAZJmEOXOwP/MMyTlO4FGWCH0wWvVc++aFuBveyKnF6HLt2idGcpSHxIaJdHqiE78++ittbm+DzlD11QWtLNDQBAvydIy85SYufuk3fvxOz+F1h1n64CL0cpDeg1OZkq9HB/TSxGO1p3myHnfvVoiLN5H/4YMk7dqlZh+PHFHNa8zFj3BZfFzhltxzUOpIjJv0PnW71uei4e2KX+SVV+CPP9R+yrVr1ZvEAQMgL091f6yljjlo0qsJd2+8G9dhF/Y6doQS0tZXvnQlO5fuJOHVVaT83zlMy03ki1J+xuPbjvP3R2tZcd3F/KuDnRYWYMUK1dX13/9WR4tEk6Qk0OkwGSRklw+/pGCK8QzAmkrIG8L/8x729W5LW1MQ+6RJSHXr4hk5svwXM5lwTplCoFMnEh94gLQrriD/lVcI9O4d/cCrkAHXN2PmGJHfv9xKv94np/SZ9uYS7FzIxXv9emjZsmgGtls39euvv6ourOVAthoRKpB5lFx+sJXyN1aQeazA52R5sehCOIDc/ADUSoj5eoXRMo8RIgUkQkY9RvH04mLB4SBp9Gg1e/DEExi2bMGwYkUVRBl/ahsV3ClWHEfKJx6bm4ruBOfvykeRFC3zqHFGIeTlkTJ4MLrsbHJnzSJ3/nz8V19doimO7/rr8ffujW3qVHS7ojJlqFj0GzeSMmAASePGEWrcGOfSBSSdk4wgCPR650oSr2iP1KzZaf9VRDgW0Pq21vT/qj/t/lXCTXmcKRjZ8X0u+IcNp6P8B0aTwBc3fMHO7/fQgx+w3XETC45DtyRI0rZaawSXDWuL0RPgP5/tUEtLX3kF5s2D3r3hlFEIIRkm7oK+66D24WxeenMWcp6PfjP7lSj2sFjg889Vp8dbb4U774SVK+GTT6Bd0de2qBdJzEos+VqAOcXM1W9dzbH1Rxjz1ly+bHQRW0rJJCx/+idCRj27h3fl8YJ766lTISUF7rorot9RuRAESEvDIvgRFDiWWzWjAWoCu3/KRucNktr7HMzzvsGwdi2uRx4pcdMiEvz9+5OzdClSvXqkDBuG7f/+r3JjaKoZtTIScFzQEM+Sk32Pec4Q1mNObIX9L9avP1myWkCbNuoInIrMe7QZKzQjUXH6USwl9DtC/MpWAYtRrVDKz4u/kZUmHiNE8ktIBh1G4RTxqCgkPvQQuuxs8t98E8/ttyMnJmL5z3+qJM54k6mXcKbZIzbMkRTYHjhdPGpOqxpnGoLLRfLNN6Pfvp28Dz4gcNllEZwk4HjuORSTicRx46Jevio4HNgnTiS1Vy/0O3aQP2MGufPmEWrThosfu5gr3ryC+j3rl32hiqwtCtTtUhdRX30+dvqlgkuCXxu0xdyuBRem7OD4tuPUsnvoWO8g+y/sylo31WJEh0ZkXHN1I3ypVtbP2oCsAPfeC599pgq87t0hO5u8IHx2GHquhcl7YcSvX/DNc/ezc3kuHe/tSO12ZZg5tWkDL78MS5eqzqaTJ590gawALa5pwXm3nIdh6SEabP6H51YfKfa4IxuPsPGz9fw54CKmd7Rh0aGW833zDYwdG7tsR3o6NlktxzuWUzXujjWBVd/txWc10a1rCrbJkwm2aYNv0KBKX1dq3Jic777DM2wY1tdfJ+W66xD37y/7xBpC1tUtSNpzjNVrjwOwZZv6WstsGhaPeXmwb9/p4lEUT84kLSeC1YiuAmWrijuAUlbmUaer1IZBpNgNmnis9sgBmZBBf1rZqvnTT0n45htcEyYQvOgisFjwDh6Maf58xCPFfwCcSdTSy7jSbASPRGaYsy+ow6cItDhFPOZuy0UQhSKz5DQ0aiw+H8m33Ybh77/Jf/NNAj16RHyqXLs2zmeewbhyJeb3349OPIpCwldfkda1K+b33sM7bBjHfv8d35AhJ0YZnHP1OWdd2XjPFDAJsOA4cNttdD70FU07pdHfMxtxyE0szFV/N1q/Y81BpxepdV1r6vy2je/2hN1Bb7yRHQt/ZEb7a7hs3j9k/KYwZDNs9cCHe5fwzpODWab0wZpppcekHpEtNHIkjBsH99+v9ldWkj4v98GcbuHWSbP4VM5kbzHGpoue+olAghHL6K5cW9BCN306WK2qSI4V6ekk+vMAyNPEY7HIIZnc73ezq3Mzen/9LrrsbFxPPVX+UTElkZCA87nnyH/jDfSbNpHWqxfGZcuic+0qpt+N6piOpXPU7OOe7Wr2rmHTcPZu40b1a5s2p5/crZvqxFrOGamizYi+AuJRdPkR7GWIx8TEuLStJZpU806XI/5/k5p4jBA5EEIy6NAXej3oNm/GPnEi/ksuwTNmzInHvbfdhhAMYv700yqINL4kiBBIs6EcjSzzuM2vluu1MBXNqORtz8PewI7erNWGadRwgkGSR47E8PvvOF5+GX8FhnX7brgB/xVXYJ8yBd3u3ZWLx+0mcexYku6+G7lePXIWLcI5bRpKsrZRY9XBpcmwIAcYMgSbzsfNh16grrQPhg5lQQ7UM0Fra1VHqlEerh3RFn1Q4qMPtzBhJ7RaCc3Ezjxw51SO2NMY/9XL/GZYz8Fz8xh2/xDWtBrKgW1uev+7NwlJEfYOCQLMmAEvvhiVG0Vzqlq+qj8aouunv/LC3qKu7ofWHmL3l5tYNehiXuxoUZf8/nv49FP4178gLYbp8fR0UtzqZnh+rjbrsTgOrjiILs+LoUcj0ma8gP/yywkUzCKMIr6BA8lZvBipdm1Sbr4Z25QpERlCVWdat0gmr1ktDi9QxePRnap4bNE8/MZ7qtNqYQp+x7/9Vq419VYjBq8fRSnfnAvR7UdXVuYxDiWrAIkmNXZXvpZ5rLbIARnZWEjYeDwkjxqFbLPheO21IrtLUpMm+C+9FPPHH9f4P+qIyLAi5nqQQyWMMCnEVr/6O2xmPD3zqJWsatR4JImkMWMwLV2Kc9q0ipcsCQKO559HMRhU91W57L+t4tBt2UJanz4kzJ2La/x4cr77Th0lpHGCfmmwxQO7k2rDlVeqzn0tWxJo247vc9Ws41ngfXZG0ahTFjRIwTx/AzOy1Q2Al5vCrothQ1cjUxfOpEuvi9ENHYInx8eyA+fS8JKGtL25mJvTONLy2pa07WLnkk9+4b8/H+JooYTC3Md+xGc10eWBzjQxA4cPq6Y9LVtCBDNXK0V6Omk5+wBwVoGzY03gj/c3EzTq6RJai+By4XziiZitJTVtSs78+XhuuQXrK6+QMmgQ4sGDMVsvHiT1aUHSun3sP+zDvTsPV6adZFvYHGf9erUku0GD00+88ELVJKqcpasGmx5RUgh5y9caYnD70dvL6HmMk3hMNqsfTB6HJh4BEAShjyAIWwVB2CEIwiPFPN9AEITlgiD8TxCEdYIg9Cv03KPh87YKgnBltGJSAhJyIYfAxIkT0W3fjmPmTOTMzNOO9w4fju7AAUxLl0YrhGqLIcOCoIDnaNmlq1sDeuroJRILOa3KIZm8nXmaeDzDENxubM88Q+rllyMePlzV4cQeWSZx/HgSvvkG5xNP4K2kZb5cpw7O//s/jH/+WaHy1YTPPyetb1+E3Fzy5szBPX58iUY9ZzMFJakLczg55mDoUH5zCDglrd+xJiIIAl1uaUPTNbv45J2veWnbRkYl+mlsBs45R81SnHsuLFrEsmaj8blCqklONdgl6PPJUBIEH32nf8sre9Qb239WHeD4gq1sHtKZCW3N6mbSsGGqac/nn6tlq7EkLY2MIzsBcGuZx9PYvWA3x+bv4NehXbn+tYl4hw5FatkytouazTj//W/yX3sNw9q1pF1xBSxZEts1Y0j3QS0QZYXv5+9H3pNL4NQxHW3aFL+LZzJBx46q42o5MIUFoNMZ+WaILCsY3X6MkZStxoE0q/r78GriEQRB0AEzgb7AucAQQRDOPeWwicAcRVE6AIOB18Pnnhv+vjXQB3g9fL1Ko4pH9deV8PXXmGfNwnPvvQQuvbTY4/29eiFlZWE+C4xzzJnqrEfP4bLFY3FOq449DuSgrInHMwVFwfTtt6R174515kz0mzdjff75qo4qtigK9iefxDx7Nq7778dzzz1RuazvppvwX3YZ9smT0UU6y8rjIXHcOJLuu49ghw7kLFsWk/KpM4VmFmhqDvc9DhwIzz0HY8awIAcMAlyuVffWSLo92Jl2t5zHnoXbmXvjXJ5Lf46Pe3/MildXkOcxwvLlZN87jTU7Euk0rhOZbU7fBK4KLI1rc+15/1BnxyFWPP8bjhB8NOFHPIlmRjzeCbMOeP55VSi89FLxpXzRJj2d2vmqQYtPyzwWwZfjY+nDv3CwaW2atvWQ4cnH/dBD8Vt/0CCOL16MnJ4OffqoRk41kJ7d6uDKTGT/kj2Y9+aibxh+41WU4p1WC9O9O6xZA+7I2qcATOGsZr4j8syjyxNClJVqIx7tSWoc/nII4GhR7cQj0BHYoSjKLkVRAsBnwLWnHKMABf93koAD4X9fC3ymKIpfUZTdwI7w9SpPQEIx6tHt3o39oYcIXHQRrtLeIPR6vLfeiumnn9Dt3BmVEKor9lqqeHSXIR5lBbb79cWa5YDmtHomoNuxg+TBg0keORI5JUUddHznnZhnzUK3eXNVhxczrM8/j+Xdd3HfdRfuhx+O3oULylf1ehLHjMG0eDG6HTtKtGrXbdtGWt++JHz+Oa777yd3zhzk8Nw5jZLplwo/5IFXNMBDD0FKCguOq/2QNq0Nu0ZiTjUz4MMBjD88nuE/D6fTuE449jlYNHYRLzd+mTe6fMJXC8zY69i59KniN4Grilaje1DXnk2n//zEM9PXIC/fTu7tXejfyKTOmHz8cbjhhtiM5iiO9HTMsp+gSU8grxgnn7OYbx/7g2Cuj93jL+X1CQPw3H133N9zpebNOb5wIfTtq5o3ldM8pjogigLiFc1J/G0XZoeXxLB5onjokDpipyzxGArBn39GvJ7ZropHhyPysSfHw1l3c1L1EI/6FDuSXiTo1DKPAFnAvkLfZ4cfK8zTwC2CIGQDC4ACm7FIzkUQhLsEQVgtCMLqnJyciIJSghIYdCSNGgU6Hfmvv150WGkxeIcORTEYMH/4YURr1FRSa6vi8fih0mc9ZgdFvIpAc2PRnZ7c7WHx2EwTjzUWtxvr1Kmk9eyJYc0aHM8+S87ixQQvugj3uHEodjv2Z56p6ihjgnDsGNYZM/Befz2uSZOi3iAnZ2XhnDIFw5o1JN92G+ndupHZqBFpXbqQfOut2J5+GvPHH2N+7z1S+/RBPHqUvNmzcU+YAHpN+URCvzTwyvCTaijJHi9s8mguq2cCol6kYfeG9HquF/dsvod7t99L7xd7Y0m34NjnoO+rfTGVlkmoCq69lpuds5AtBmwTv8WdbOGhxzuqN9GDB0P9+vDOO/Frxk1XrV0DtgRCVWDOUV1ZM/8fjv93K2uHdeXdH6egT7Tj+de/qiYYiwVeeAF8vhqbfTx/YAv0QfX+sFbYaVVfsOlcmnjs3Fn9WyhH6aolXLZaHvGYF866m6tJzyM2G8EEA8H8yOesR4vqeGdR3LvhqXZIQ4D/KIrygiAInYGPBUFoE+G5KIryNvA2QLt27SKyWhICEoqgYFi3jrx33kGuX/YsNDkzE/9VV2H+7DNcEybEvi+hiqhVy8Rh4Njh0l/ABWY5p5at5m7LxZZlw2AtXYxrVEMUBdOiRdgnTkS3fz/eQYNwPfUUckbGyUNSU3GPG4d90iSMP/9M4JJLqjDg6GP6/nsERcEzalTMbuZ8N9yAv1cv9Dt3ogv/p9+xA92uXVh++QXBp2YDAp06kf/GG8h16sQkjjOVS5PALKquq33Swv2PaP2OZyKpTVPpfH9nOt/fGUVRqkWf42nUqoWlawfaS/vZ+Gc69jHdaJZugOvvgAMH1J7NpKSyrxMtwuIxaDch52uZR4Dc4z5+fvhnHE0yuW98c7Iu/hrftdeiVOV9XsuWcN118Npr8PDD8RMxUeLqqxqx1mLE5AnQpHlYPG7Zoj5Z3JiOApKSoF27cpnmWO3q/ajbFbl4zA3PU7QlV4/MIzYbUoITScs8Amq2sLAyq8fJstQC7gDmACiK8geQAKRHeG7FCEgnpHagS5eIT/MMH47ocJDwzTdRCaM6Ussi4E6ykF9G5nFboGTxqJWs1kwsb7xB8u23I9vt5Hz9NY7XXisiHAvwjBiBVL8+tkmToj74vswYX3mFlAEDSiz1rCympUuR6tQhFOPeIyU5meAFF+C78Ubcjz5K/nvvkbN8OUd27eLoypXkzJ9P7ty5mnCsAAk6uDwF5h9XW2wW5MA5CdA89nOeNaqQaikcCxg4kEF/vsbA7wfx+KRO8Prr8PXXMHWqahAST8LiUbEaoArMOaobigKvP7waY66HDi9cRof//YTocuHv27eqQ1PLVvPz4Y03qjqScmM163F3aYykE2nRRK1o02/eDHXqlD2Kpnt3taQ7ws95e7hs1VOOzKMz/Nq3JZYgHkMh8HjiKh5lo4isiUcAVgHNBEFoLAiCEdUAZ94px+wFLgcQBKEVqng8Gj5usCAIJkEQGgPNgJXRCEoIhhB04Q+ahAjnQAHBiy8m2KoVlg8+UN9xzkBqG2RcaTZch0tvVt7m11NLL5Fc2GlVksndronHmkrCF18QuOgicpYuJdi5c8kHmkw4H38cw8aNJMydG7f4dJs3Y5s+XXUs/fzz6C/g82Fcvhx/r15VN89BFJEbNCB4wQVamWol6JcKu3yw3g3LctWsY3XWFhpnOAMGAHDeuiWI69bCAw+oPW0PPBD/WApu3C06xCookatuvPzlIdLmb4C7OnJNp2RMCxci22zVw5jsggugd291Bqm35v2/GvjURdR5rR8mo+p1qd+yJTJTqG7dVOH2998RrZOYqIpHbzkyj45w5jGxpJ5Hp7Pg4hFfs1LYbCgGwK0Z5qAoSggYAywGNqO6qm4UBOH/BEHoHz7sQWCkIAhrgdnAcEVlI2pGchOwCLhHUZSopDmEgHRCPCqmcvRHCALe4cMxrF+PYc2aaIRS7aill3Gm2gkcKd0wZ5tfT/NT5ju6sl1IPomUFpp4rGmI+/dj2LwZf79+Zfb/AvivvZZg+/bYpk9X3+RjjaKQ+OijKImJBNu0wTpjBviju0Nn/P13RI8Hf+/eUb2uRvzpG+5vnLBL7X/sq/U7alQlTZqoN82zZ8NNN6kC7sMPi8yUjhuJiaDXozMp6J1nd9nqvH0KgaeX4WuawV2PngeSRMKiRQQuv1wdG1EdePRRdQ7oBx9UdSTlplUzG9cNrKd+I0not22LTDwWCPcIS1eTwmWrXmfk4tEd7vdNTi6h57EKxKNgUBA8mngEQFGUBYqiNFcUpYmiKJPDjz2pKMq88L83KYrSVVGUdoqitFcUZUmhcyeHz2uhKMrCaMUkBiQEHSh6fbl3933XX49ss2GugX/IkWAVFXzpVqSjJWceFQW2BXQ0N51ilrNNM8upqZh++AEA/2WXRXaCIOB86il0Bw5geeedGEamkvDVVxj//BPXY4/hCvdkmmfNiuoapiVLUMxmAt26RfW6GvGnkRlaWWBRDiSI0EMb0aFR1QwcCKtWwfbt8OmnUExLQFwQBEhPx2CQMTrOXvG43qdn8RN/Yst1Mei1S9CbdBhWrUI8fhxfdShZLeDSS6FTJ3WkSyhU9vHVFN3u3Qh+f+n9jgXUqaNuuEQoHpPtehQBAq7IhZfHoR6bUlLPo8OhfrXbI75mpbDZEHUyOi3zWH0RgxKCTilf1jGMYrPhu+EGEubNQzh+PAbRVQPSrYjH3CgllObuD4m4ZbHYfkfQxGNNxLhsGVK9ekjNm0d8TrBzZ3x9+2J99VWEGNqJCw4HtqefJti+Pd6hQwlceimBiy/G+vLL0SvlURRMS5fi79GjXKXsGt3xXo4AACAASURBVNWXAnfVnslgicqEYA2NSnDDDapwe+IJ6NmzamNJT8ck+jH6gvh88e1brw4cCYlMnHOMtovW0uruDtRrrwp508KFKEajmnmsLggCPPYY7NkDn31W1dFUmIicVgvTrZvquBpBi5hVBwGzkWA5yla94Z7H1NQyxGMcM496MYROyzxWX8RgCJ2oVLgswTN8OEIggHn27ChHVj3QZVgRQzK+nOJ3JbeFnVaLm/FoqWXBVJp7lUb1w+/H+PPP+C+/vNyNYa7HH0fwerG98EKMglPnLorHjuGYNg10OhAEXA8/jO7QIcyffBKVNfQbN6Lbv1/td9Q4IyhwV9VGdGhUC9q0gd274emnqzoSSE/HoqjtBkdz4n+zWpVs9+sYssFMt+fmY2mWyiXjL1CfUBRMCxcS6NYNJV7Zpki56ir19TN1KshyVUdTIfSbN6MIApx7bmQndO8Ox47B1q1lHioIELCaCJUj8xhw+pFFgSRbCW06VSAeDWIQoycQd0sVTTxGgCzJiJKCKMgo5orZ70ktWhDo0gXzRx/F3W0yHiRkqs5YnhL6HgvE46k9jzXVaVU4fpyku+46uTNWAazTp6t9eDUQ44oVaq9fBXZbpaZN8Q4bhvnjj9Ft3x712PQbN2J57z28t95KqH37E48Hu3Yl0LUr1ldeiUrPpWnJEhRBIKCJxzOGnsnw2blwp2ZYq1FdaNiwejg3padj96uVQjlnkXj8Mj+BvtuTaD9tAfYcF31e6YHOFDZz2bQJ/d69at9/dUMU1d7HTZvg22+rOpoKod+6FalxY4j0vrucfY8hixHJHXnmMeDwE7AYEcUS/h6rQDwaCWDyBnAF46seNfEYAXJA3bURBblCZasFeIYPR793L8Zwr1hNxfjjjxhWrCjymL12WDweLv6mfKtfT7pOIlV/8gWuKEqNdVq1vPceCfPmkXjPPRAo/wepadEibDNmYJs+HfN//hP9AGOMcdkyFJOJQNeuFTrf9eCDKGYztsmToxuYLGN/9FGU5GRcjz56+roTJqA7elR1P64kpiVLCHboUOxoEo2aiSDATZnq6A4NDY1CpKWR5DkGQG7umS8evTKMP2hn3E4zw578nGY/bubixzqS2SHzxDGmBQtQBAH/lVdWYaSlcOONcM45MGVK9XH7X7YMFkZmR6LfvJlQq1aRX7tZM8jMVEtXI0CyGFHK0S8YcgUIWcqY8QhxFY8Jilrtl5Mf379JTTxGgBRQM4U6QaqUePT37YuUmRm1srmqwv7kk9imTi3yWHItdWcot4RZj8WZ5bgPugm6gjWv39Hnw/Lhh4QaNsSwaZPaR1cOhNxc7A8/TLB1a/yXX4798ccxRPhmV10wLVumzjut4EBkJT0d99ixJCxahOH336MWV8KcORhXrsQ5cSJKyumvq2DHjvh79sQ6cyaCy1XhdcTDhzH8/Xf1vWnQ0NDQiCbp6aTmq2Oz889w8bjTr+OqPal8+Q9MmPAfav25i+7Tu9NhTIcix5kWLiTYsWP13UDU6+Ghh2DlSli+vKqjUZk0Se3lPXSo9OM8HnS7dxNq2TLyawuC2vcYYeZRsZqgHGWrktOPZK1G4tFkwqKo99w5+fGd9aiJxwgoEI96QpWzYjYY8N10E6bvv0cs6w+nuqIo6PbtQ7d/f5GHM+uomccjxYhHRVHLVovrdwRqXOYx4auvEI8fx/HCC3gHDcL68svoN2yI+Hz7E08g5uTgePll8t98E6lJE5LvvBPd7t3lD6YCWc/KotuzB/2OHRUqWS2MZ+RIpLp1sU+aFJWeDCEvD/szzxC48EJ8gweXeJzroYcQc3Iwv/dehdcyLV0KQEAb0aGhoXE2kJ5Oukc1OXPlxn8oebz4b76J3ntSce13MfHBdzFtO0Lvd3vT+rbWRY7T/fMPhk2b8Fcnl9XiGD4catdWex+rAw4HuN1l9vHqt21DUJTyZR5BLV3dvRtOuUctFqsBoRyZR8XpR7GWMKYDTopHmy3ia1YKQcCiU++r8/O0zGO1Q/KHxaMSQqmkq6J3yBAESSJhzpxohBZ3hJwcBK8X8eDBIr2bdRJ1+C1G8g6dXrZ6KCTilMVi+x2hholHRcH61lsEW7cm2LUrzmeeQU5JIfG++yBYdu28cckSzHPn4h47llCbNih2O3kffwyiSPKwYQj5+RGFIbjdJN5zDxmtWqH755/K/lTlwrhsGUDl3eXMZlyPPIJh7VpM331X6bhs06Yh5ObinDat1FloofPPx9+rF9Y33kAoeLMvJ6YlS5Dq1SvfrqiGhoZGTSU9nUzvEQA8eWeeePTJ8PBBO6MPJNMx+yB3j3kP4aibqz67isb9Gp92vGnBAvW86i4eExLggQfg++/VsS9VjdOpZgjffVftxywB/ZYtAOX/jO3SRf16SmtVcQhWI2J5xly4Ayi2MjKPNptq0hcnbHr1vjM/zn+TmniMgOAJ8RistHiUzjlHNc6ZNatGOmAVZByFUAjx8OETj2fqJZxpdlzF9DxuLTDLKSbzmJCagDm9YiZEVYHxp5/Qb92K5667QBBQUlJwPv88ho0bVSOWUhDy8kh86CGCrVrhHjfuxONSw4bkvfsuut27SRo9usy5TPrNm0m98koSvv4aweeLy8zEwph++IHQOeeojeyVxHf99YSaNFF/d5XoydCvXYv5ww/x3n47oQhmQrkefhgxLw/L22+XfzGvF+Mvv+Dv3bt6GFloaGhoxJr0dGq51M983xmYeRyyL4WP8izct2crve7+CFFQuPaba6nbuW6xx5sWLiTYujVyw4ZxjrQCjB4NycnVI/vodMKgQWrLy4QJJR6m37wZJSGh/PcZtWqpXyPYiBdtRvTlGHMhuPyI9jLEY7xKVsMkhjOPTocmHqsdXr8q8gxysFI9jyeud/PN6PfsiWqvV7zQZWef/HehsoBaehlXqg1fMW6r2wIli8calXUELG+9hZSZiW/AgBOP+fv0wXvddVhnzEC/cWOJ59qffFIdH/Hyy2AsWvoQ7NIF57RpmJYvx/bMM8VfQFFImDWL1H79EBwOcufMwTdwIAmzZ1c4g1ZuvF6Mv/1W6ZLVE+h0uMeMwbBhA8aK9mSETXLk9HRcDz8c0Smhtm3xXXUVlrfeQsjNLddyxl9+QfB6tX5HDQ2Ns4e0NBKkAD5bAoE491fFmgNBkT88Rh5d9zfp//oCS4aFgd8OJK1VWrHHi0ePYli1qvqXrBZgt8O998LXX5ea7YsLTic0aqTOofzuuxJ7MfVbthBq3rz8WbyCBI+v+LFxRdawGTF4/CXOJz8VncePWFbmMc7iMcmoJrdcWuax+uEvEI+Sv3I9j2F8/fohJyWp2ccaRmHxKBYSj0migifVRuio+7Rztvl1pOpkMk51Wq1h4lG3dSum5cvx3n77aa8D57PPIicnkzhuXLHlq8alSzHPmaOWq553XrHX995yC+6RI7G+9RYJp7423G4Sx44l6YEHCFx4ITnLlhHs1g3PqFGIbjfmTz+N2s9ZGsbff0fw+QhcdlnUrum7/nqkunXLzNyWhHn2bIxr1uB68kmUpKSIz3OPH4/gcmF5881yrWdasgTZZiPQuXN5Q9XQ0NComaSnAxCwmQjF+Ub1WEhgjVcfs+uv9hrosOB/mO6fR9q5aVz7zbXY65c8t9G0aBGColTPER0lMXYsWCwwfXrVxRAKqaLOblfjadAAxo8vtgpPv2VLxdpCyiEeDTYDupBMyBfZ+Dy924/BXkbPY7zFo0n93XmcZ3nPoyAIfQRB2CoIwg5BEB4p5vkZgiD8Hf5vmyAIeYWekwo9Ny9aMfnDozqMki8qmUfMZnzXX0/C/PnlznpUNWJ2Nko4a6bbt+/E44IAcoYVoVjxqD8t6+g95sWf569R4tHyzjsoCQl4hg077TklNRXnc89hWL8e62uvFXlOyM9Xy1VbtixSrlocrqeewt+zJ4kTJmD44w8AdFu2kNanDwlz5+IaP568zz474e4WatuWQJcuWN59t8xy12hgWrYMxWyOrnAyGnHffTfGP//EsHJluU4VcnKwTZ5MoFMnfIMGlevcUKtW+Pv3x/LOOwjHj0d2kixjWrqUQI8ep2WPNTQ0NM5YwuIxZDOi5Jd9Yx5Nnj9oYchmK1KMpk38lS9w1UvzqdO5Dtd8cQ3mtNJbaUwLFhBq2LD8Zi5VSXo6jBoFn34KO3ZUTQxOp/rVbldnN06ZAmvWwCmb5UJODrrDhysmHgvu0SMQj0abQQ3LVbZfhSwrGD0BDNUs85hiVvWJ92wuWxUEQQfMBPoC5wJDBEE4t/AxiqLcryhKe0VR2gOvAl8Vetpb8JyiKP2jFZc/3PNoDPkq3fNYgPfmmxH8fhK+/DIq14sXuuxspIYNkVNSTnNc1WVY0XmDBApZHyuK2vN4mlnO1pplliMcP4557ly8gwahpBVfyuLv1w/fgAFYX3wR/ebNJx63P/004tGjOF56qezMtV6vOrA2bEjyHXdgeeMN0vr2RcjNJW/OHNzjx59WxuEZNQrd/v2Y5s+v9M9ZKoqCcdkyAt26ndzdixLeoUORU1PLnX20TZ2KkJ+Pc+rUCvUfusaPR/D5sM6cGdHx+nXr0B0+rPY7amhoaJwt2GwoRiOKWQeO+IpH/1sruf32N9kfjE2P+Y71ueiDEm2Gt8ZgNZR6rOB0Yvz1V7Vktab1vD/8sLrpWVJrTKwpLB4BhgyB88+Hxx8H70mn/oL7pwqJ84J7LH/ZYsoUFo/5+WVvvOe7goiygimpmolHq3o/6D+bxSPQEdihKMouRVECwGfAtaUcPwSYHeug/L5w5jHojZp4DLVuTbBdO7XcsLoMb40AXXY2Ur16SFlZp4lHU6Y6rsNTqO/xSEgkXxaL7XeEmiMeLR99hODz4Rk5stTjHJMnoyQmnnBfNf7wA+bZs3GPGUOoffuI1lKSksj76CO1l2/SJIIdOpCzbBmB7t2LPd7fqxehxo0rZv5SDnQ7d6L/55/o9TsWxmLBM3Ikpu+/L7VvtDD6NWswf/IJnjvvrPAOsNSsGb7rrsPywQdFBH9JmJYsQRHF2PwONDQ0NKorgoCcmoqQICDGUTyGFDCv3kvy4Xx2Ho2svLA8+GVwr1NdZDPbZ5Z5vHHZMoRgsGaVrBZQuzbccw988gmE3UzjyqniURTh3/+GvXuh0MZxpcSjIKgCMoLMozlRrR7Kd5adeczJUcWZubSyVacz7uJRn2RD0okEzvKy1SxgX6Hvs8OPnYYgCA2BxsAPhR5OEARhtSAIfwqCMKC488Ln3hU+bnVOTk6ZQQXCPY+mgCcqPY8FeIcOxbB5M/q//47aNWONLjsbqX59pKysIj2PANZaYfFYyHG1wCynZTHi0ZhoxBI+J25IEoZVq7BNnoz1uecim5Po92N+/338PXsitWhR6qFKWhqO6dMxrFuHbdo0EsePJ9S8Oe4HHihfmOecQ+7s2TimTCF3zhzkAgex4hBFPHfeifGvvzCsXl2udcqDKTyiwx/FfsfCeG6/Hdlmw/rqq2UfLEkkPvIIcmammo2tBK5HH0VOTib5xhvR7dpV6rGmJUsIXnRRidlnDQ0NjTMVOTUVnVHGEEfxuMMnUmu7Ohd79y5n1K+/0a+n1paDiGlmbPXKns+XsGABUkYGwQsvjHosceHhh9WS0UmT4r+2y6V+tRfqJ+3ZE665Ri1hParOEdVv3YqcnFz6fU9pJCREJB4t4cyjy1F25jE3X71XtFSzzKNgtxE0Gwg6z+7MY3E1ACWl5QYDcxVFKbwV1UBRlAuBocBLgiA0Ke5ERVHeVhTlQkVRLkxNTS0zqEC4bDUh4I5Oz2MY38CBKGYz5tkxT55GB7cbMScHqV495GIyj8m1VSFYeNbjVr+aUm9uKrpjmLs9l5RmKQhxKPsQXC5M331H4tixZLRtS+o112B5/XVsL75I8q23IhS8oZVAwn//i+7oUTyjRkW0nv/qq/H174915kzEQ4fIj6RctRhCHTrgHTEC9GUbBfgGD0ZOSsLy1lvlXidSjMuWEWreHLlBg5hcX0lOxnvbbZjmzUO3e3epx5o/+QTDunU4J01CsZdsbBAJclYWuXPmIEgSKTfcgFjIFKow4v79GDZswN+rV6XW09DQ0KiJKCkpGMQgJqc3YofKyrJuh4cEt3pjfHhP6Z/VFWG110jWlv1ktM8s+37E58O4bBn+Pn1KnSVcrcnIUM1qPv8cNmyI79qnZh4LmD4d3O4T5bT6zZvVfseK3h9GmHm0JurDYZWdRMgLj6exliQeFUUVj5W8Hyk3NhuSSYfkOrvFYzZQv9D39YADJRw7mFNKVhVFORD+ugv4EegQjaACAVX4mCR/1MpWAZTERHzXXEPCV1+pfzjVnAKnVTlctirm5yM4T+4EptdWm8yPHDxZu77NrydZlMnQFXXTirXTqnD0KOb33yd5yBAyzj2X5DvvxLRkCf4ePch7802ObtpE/owZGH/9lZTrrkMI73idhqJgefttQi1aELj00ojXd0yZQqhRI9zjxxM6//wo/VQlo1iteG+5BdP8+Yh790b9+oLbjfGPP2JerukZNQoMBiyl9CAKx45hmzqVQNeu+K8trao9cqRmzcj9/HMEp1MVkEeOnHaMaelSAK3fUUND46xETk3FrHgQJQVvBCYj0WDn3yfNzPL3Rj/z+L9jMhn/HKV+h4wyjzX+8gui262Kx5rM+PHqMPunn47vuiWJx1atYORIeOMNtT1my5bKmRElJETU82i3q5lHr7PszGPBHEV7Ygni0eNRXWPjnHnEZkM2ichneeZxFdBMEITGgiAYUQXiaa6pgiC0AFKAPwo9liIIgin873SgKxCVgTbBgsyj5Itq2Sqoxjmiy0XCt99G9bqxoCDTKGVlIdWrB1AkS1M7zUDIoCP3UFHx2NwUKrKB5Mvx4T3qjal4TB45ksTHHkO3dy+eESPI+eorjm7YgOP11/EPGICSlIRvyBDyPvgA/fbtpPbvj+6ff067juG33zBs3Ih71Khy7YIp6ekc/+OPcperVgbPiBEgCFjeey/q1zb+8ova5xFj8ShnZuIdPBjznDmIhw4Ve4x98mQElwtHBU1ySiLUti15s2YhHj5M8o03IpxS0m5asoRQo0ZIzZpFbU0NDQ2NmoKcmoo1qM4UPnY8Pj1W+euPIutEQjYTgb1lD34vL3v/Po6gQEb7ssVjwsKF6pimbt2iHkdcSU2F+++HL7+EeLZNFYhHWzHlwU8/DQkJJN57L6LLVXnxGEHmMTEsHj0RbIQ4w7NNE5NL0AAFs7arQDwqBgHOZvGoKEoIGAMsBjYDcxRF2SgIwv8JglDYPXUI8JlStG6iFbBaEIS1wHJgmqIoURKPatYsIYpuqyeu3bEjoaZNa8TMx4LMo1SvHnJYPBYuXa1tUHCl2nCGex4VBbYG9LQ4td9xe+zNcnQ7duC96SaO//YbrqefJtilS7Hln4Hevcn94gvEvDxSrr4a/fr1RZ63vvUWcloavuuuK38QcXZik7Oy8F9zDeZZs8osxS0vxh9+QLbZCHbsGNXrFof77rtBkoqdv2hYtQrz7Nl4Ro1Cat486msHL7yQvI8+Qr97NylDhpzIrAtut+qwd+WVNc9hT0NDQyMKyKmp2H3qplpObuzFo6KAsPkIgabpKA2T0e3PJxTFatlDQRHTJnWTskyzHEnCtHgxgSuuiHoSoUq4/35IToYnn4zfmiVlHgFq1YIJEzCuWQNQsTEdBURYtpqUqIpHXwSGOe7wbNOkpBIMc6pQPAoGBdxnsXgEUBRlgaIozRVFaaIoyuTwY08qijKv0DFPK4ryyCnn/a4oSltFUdqFv0Yt/RIKFBKP0X7TEAS8Q4diXLkS3bZt0b12lNFlZ6Po9ci1ayNlqT5GhcVjpl7ClWrDG3ZbPSYJ5EqnO63uXqD2s6W2KrvftEJ4POiOHUNq3Diiw4MXXkjON9+A0UjKwIEYfv0VUN1FTUuX4rnttqiPpogV7rvuQnQ6SYhmH62iYFq2TC3bjcNsQ7lhQ3wDB2L+6KOi2b9QCPujjyLVrRvTjG6wWzfy3nkH/caNJN96K3g8GH/6CSEQ0PodNTQ0zlrktDSSAurmb15O7MXj/qBA+rZDWFpnYG6QSPKhPPYHdWWfGCF/eQ3U3XIAY5Ydc3rpsx0NK1ciHj+Or2/fqK1fpSQnq+Wr334Lq1bFZ83SxCPAAw8g1a4NVFI8Rli2mpyovpYKj5crCU+4LzI1pfplHgWdjM59drutVkuC4Z5Hc8gbffEIeG+4AUWvr/bZRzE7G7lOHdDpkDMzUQyGE9lIgDSdgivNRuiI2r+51a9m+pobT5rlHFp1iHVvr+PcYediq1u2s1lFOFFeW79+GUeeRGrenJxvv0XOyiJl6FBM33yD5Z13UIxGvLffHpM4Y0Ho/PMJdOyI5Z13QIqOrbluyxZ0+/cTiJHLanG4x4xB9HiwvP/+icfM//kPhg0bVJMcqzWm6wd69yb/tdcwrFhB8h13YJo/HzkxkeDFF8d0XQ0NDY3qipKaSmpYPDrikHlcu8ePNd9D1nnppDSwk3woj53e6FV+rPYayNq6n7pl9TsqCglffoliMhE4k8Y0jR0LaWnxyz46neqc6pI24y0WHDNm4B49GqUyIizCslWrTiCQYCAYQebRF+55TKuGZat6XQidRxOP1Q7Jf1I8xiIDpWRk4L/ySsxz5kQ2OqKKKJjxCIAoItWtW2RchyiAlG6DY6p43FYgHsOZx6AnyPL7lmPLstHpyU6xi3OfOu3lRKwRItetS8433xDs0IGk0aMxz5qF77rrkDPK7oWoTnjuugv93r2YFi2KyvViPaKjOKSWLfH16YPlvfcQ3G7Eo0exTZ+Ov0cP/FdfHZcY/AMG4HjhBUzLl2P+8ktVPBtKHyCtoaGhcaYip6SQ5jsGgCs39mVy2/+nmuW0Pj+VrEZ2dCGZXdnRGxOy7mCQlIN51Cql31G3dSspgwZh+eQTfP37oxTXr1dTsdvV0R2LFsHvv8d+PadTXbOU1o9Az564KmvkE6F4FAQIWE0EI+h59Dv8yKKAzVqC+31VikchhMEbiOvIeE08RkAoICOLAuZg9HseC/DefDNiTg6mxYtjcv1oUEQ8QrHjOoQMK7p8H5JfYltAT6IoU1uvlv2unLaS/F359HypJ0Zb7MofT/RmliPzWICSnEzuZ5+pvW2yHPF4juqEv29fpPr1sbz9dlSuZ1q2jGDr1mrWOY64x45FzM3F/PHH2CZNQvD7cU6eHNeeQ9/QoTjC9uG+/v3LOFpDQ0PjzEVOTSXTozpRe/JiLx6PrT+KLApktU4lq7Eq2g5EaVxHQIGctarLenH9joLbje2ZZ0i7/HL0GzbgmDYNx0svRWXtasU990BmZnyyjy5XfEZZRNjzCBCyGJEjKPkMOP0ErCZEsYT7jyoUjyb8mDx+vFL81KMmHiNA8kuEjHpMQX9MylYBApdeipSVVX1LV4NBxEOHiohHKSurSNkqgDFTLSf0HPUUcVo98McB1r+znta3tyarW1ZMQ9Xt26f2ZlZ0wKzZTP7773Psr78q5/hVVeh0eEaOxLhiBfr//a9SlxIcDgwrV1ZJqU7o/PMJdO2K9cUXMc+di/vuu5GaFDu6NaZ4R47k6Pr1+Pv1i/vaGhoaGtUFOTWVDOdRJL2ILw7iUd50FH+jVAxWA4kN1Zvy3H8cUbn2Rp+ejC0HQICMdoUyj4qCad480rp1wzpzJr5Bgzj22294hw9XSy7PNKxWeOQRWLYMfvoptmsVZB5jTYQ9jwCS1YQcQc9jyBUgZCnl/r+KxaOgQK4jPuNzQBOPESEFJCSDLqbiEZ0O7+DBGH/8ETFcdlmdEA8cQJDlItk8qV49dZxC6KQhjrWW2nTuOeJhq19Pc6NE0B3kx3E/ktggkU4TY1eueiLW7GykunUr90YvihUXn9UA75AhyDZbpbOPxp9+QpCkmI/oKAn3ffchOhxI9erhHju2SmIAalzpsoaGhka0kVNTMYf8eO1mAjEWj3mSQPK2Q5jOVbOCtiwbigDefdERj395DWRtPYCtaQpGu1oJpduxg+SbbiL5rruQU1PJ+fZbHC+9hJKeHpU1qy2jR0PduvDEE8S09tHpLH5MR7SJsGwVQLYaIYLMo+T0I9kiEI/xEMeFsdkwy+p4vJw4lJIXUELxrkZh5IBEyKDH5PMTjKHrpnfwYKwvvkjSuHGEWrQo8bhgmzb4hg6NWRzFUZBhlE8pWxUkCfHQoROPJ9WyAHBgv5fjTVWn1RWTV+D4x0H/r/tjsMa+Z0yXnY1cgZLVMwnFbsd7881Y3n0X/5VXErj8cpTyvKkpCrqtWzF/8glyUhLBCy6IXbClEOjeHdeECfgvvRQsliqJQUNDQ0MDsFhQEhII2ozo82N7o/r3Pj+Jx5xknKcKN51Rh1zbjpjtIKiAoTzdCx4P1pkzEXNzTzy0tuudNN6cTcO0I9gfewzB6SThv/9FSUjA8eyzaqaxmPFeZyRmMzz2GIwZA99/D7FyFY9n5jFC8YjVgHDQW/ZxLr8qNEvC4VDLZeM9xsVmwyq5OU4Gefl+ID7i9Sz5y6gcBZlHYzAQu8wjINevj2/wYEyLFqHfVMKIykAA8wcfEOzSBalRo5jFcionHEwLl60WmvVYIB7TapvxAhuz/dAU6q/ZzYb3N9B2ZFvqdq4bn1j37VPHSpzleEaOJGHePJJHj0YxGAh06YK/Vy/8vXsjN2hw+gmBAMYVKzAuWYJpyRL0//wDgOu++6ruQ1QQcN9/f9WsraGhoaFxEkFATklBshgQHdEzrimOLX+rQq9Vh5MjvYz1Ekk+mEt2UEdjY4Ru4n4/ySNGYPzpJ5Tk5BMPb7zgQVrneqjvW0XC1+tAEPANGIDrySfPzkqTO++E6dPV3scrroiNt4DTqfZXxppy9DwKVlNkYy5cAYSkUpJHDkf8S1YBzGZsWSGxuQAAIABJREFUkmpSmZ8fP8NNTTxGgBKQkfQ6BIiZYU4BjhkzYMaMEp8XDx0i/aKLsLz7Ls5nn41pLIU5YUJT96QALDzrsaDSunYtEztFgT0HfBg9fo499j1J5yTR8dHYD5cHwO9HPHy4QmY5ZxpyvXocW7UKw+rVmBYvxrR0KYkTJ8LEiQRbtiTQuzf+yy5Dl52NackSjMuXIzqdKAkJBLp1w3PPPfh79Yq7UY6GhoaGRvVETk1F1osI+bEVj4fXHaMW0KTdSfGY1CCR5J+y2RWIUDyGQiT961+YfvyR/BdfPFGxdSQkorynusaav3yFo+fX3BaVqGEywYMPwrhxsGMHNGsW/TWqYc+jaDNEJB5Ftx8lK6nkA5zOqhGPoohNUON3xqEPuQBNPEaAHJCQDWr/XCwzjxHFUrs2vmuvJWH2bFwPPYSSVMqLOYrosrORMjKKjCopEJJiIdOcWiZYn2Th2CEv/d5aijfbyZXzBmCwxGfEge7AAQRFKfeYjjMWvZ5gp04EO3XC9dRT6HbtwhTOLFpmzsT6yisASBkZ+K+5Bn/v3vi7d1eb6DU0NDQ0NAohp6Yi+kF3JLbiMbDpCN76KZgST95z1W5sJ+dLJ7ucCpeX1TonyySOG0fCggU4nn22SKvPX14DdbfuB71I2rlpMfoJaiCNG6tf8/Njc/1qWLaqtxkxeMoWXaLbD6VNCaiqzCOQpFfFo8txlotHQRD6AC8DOuBdRVGmnfL8DKBn+FsLkKkoSnL4uduAieHnnlUU5cPKxqMEJGRD2FsoxpnHSPCMGoV57lzMn36K5+6747KmmJ1dpN8RAKsVOTW1iONqLb2MM81O3T92knjMSbt/taP2RbXjEiNUbkzH2YB0zjl4Ro/GM3o0Ql4exl9/Rapbl1D79iBq/lkaGhoaGiWjpKaiOxLEEMOyVZ8Mti2H0bUrWuJYq6GNTcC+PW6oU8qNvKJgf+QRzHPn4nrkkf9n797j5Kzru/+/Ptc1p92Z2cPsBpCEhAQCAoEEjAiGemsrgncVihx+WI93f0q9b6mt9vbUVmlRW2vvVq2ltthy18NP8UCtQVGKt4qK4k1ATSAQEgIhmwOH7Pkwp+v6/P64rtlMNju7M7szuzO7n+fjsY/dnb2ua65Ngs57Pp/v58vE2952zI+3TURZ9dhBes7KEEk05cvgxVEaZjM21pjrL1R4jMfB84JhjrMsuYkmo0QKHsWcRyReechidCyHdswyMGeRwmNXJOj9G2vwOuRyTfdqUURc4Bbg1cDZwOtF5OzyY1T13aq6SVU3AZ8B/j08NwPcBLwEuBC4SUS653tPmvfQMDwuduURoHjuueQvvpj2f/3XYyadNtLUPR5LvFWrjtnrcUXEZ7QnRcfzI+ROzfDi9714Qe6vpDSp1iqPs9OuLnKveQ3FCy6w4GiMMWZWfiZDnByxsRx+0W/Iczz8TJHuw4P0nhusPYxs307yr/+arvg4AEf2jVQ+WZXURz5C+xe+wNiNNzL2h3943CEPjkVY9fhBTjx/AdbftZJSx9FoffbSPEY+H3wsVOURqmpdjaWCrrjhkcrbXHieEh/PE0vPEh4XetJqqDts4c6OLNyax2Z8xXghsEdV96pqHrgduHKG418PfCX8+jLgHlXtV9UB4B7g8nnfUd5DIw7qOE0zfWv8938f98AB4t/5TuOfzPdxDxyYPjyuXIlTFh4jAvkT0viOEP2ry4i0Leyfl7t/P+o4+CcvzHAeY4wxZrnwMxkSXhAuJhq0xuqRXwbDctZvClpK2//pn0h98pOc+vvXATC6d6DiuclPfYrkP/4j4//tvzH6p3963OCXgsLTe0aIjuZYsWkZDsaZSSMrj6VAuoDhUaoIj/Fwm5ah4cqFmMHhIJQlZguPi1R5zCSCN3EmFrBttRnD40qgfKPDvvCx44jIGmAt8INazhWRG0Rkm4hs6+/vn/2OSuExkWjMBKo5yF16KcW1a2n/539u+HM5zz+P5HLThkd/5cqgVbRsb6Cn3noxX/rEGznjxQv/P8zu/v3BgJfowqyxNMYYY5YLv7ublAZB4Pn+xlQ6Dm4PhtmcsyloHIvu2EH+4otx3vkW1AU9NErydVcT/973wD9a/Wy/9VZSf/3XTFx7LSMf+9i0r9cezUboefQgACdsssrjMRpZeRwJq8ULtc8j1YXHtvTslcfS/omJjuZc8xjraMd3hPzI8g6P06WzSruWXg98Q1VLY7eqOldVb1XVzaq6OZPJTHPKFIVisPqyCdY7TnJdxt/2NmIPPUR027aGPpUzwzpCb+VKnNFRZPjoxr2pUzrY+6J1nBFbmJbacm5fn613NMYYYxpAe3pIF4Mg0D/QmPA4/sizTJzUQXtvGzI2hrtnD/ktWxj7sz8lsrqLrsNDPF2I0PXWt9JzySW0ff7ztP3bv5H+8IfJ/vZvB1PrKyzF2DYRZeWug7htEbrPmPeqpqWlkZXHUnhcqDWPVBce20ttq0OVw+PAYPDvvL2zOSuPkk5RaItSWOZtq31A+av/VcDBCsdez9GW1VrPrV7eA1eaYr1juez11+N3dja8+uiG6wiPG5jDsdt1lJwc9Ug6PquijVkPMRN3/35b72iMMcY0gJ/J0FkYBGCwAZVHX6HtsWfQs4KqYOSRRxBVCuedB0BqTSfdhwd48It3MPjP/4x2dNDx/vfT8YEPkHvFKxj67GdnXF704ESUNbsOsOLcXpxIM74EXkQLUXlssjWPyY4gPI7NUHkcCgfRpCqFx1wu+Fik8EgqRbEtirfMK48PAOtFZK2IxAgC4tapB4nImUA38POyh+8GXiUi3eGgnFeFj82L5D0k0hzDcsppMsnEG99I/DvfwXn66YY9TykYVhqYA8du1/GHPWN8ftUgzkJ3+BaLOIcPW+XRGGOMaQA/kyGTC5b7DDeg8rj7iEdmfz/dZcNyIBgUCNC7Jk3XoUGe9GLkrryS/u9+l/5vfpPR97+fwX/9V4jN0FoIPDTicsLuw5xgw3KOl0gEFdtWrzzW0LaaSgdvNIyNVu6UGw7DY7rStNXS77aI4dGLOfijy7jyqKpF4EaC0Pco8DVVfUREbhaRK8oOfT1wu+rRxXaq2g98hCCAPgDcHD42L1Lwgtb5JguPAOO/93sgEkxebRC3rw+/owOd5j+MUjWyvPK4OuZzSbLyuziN4hw8iHjetBVSY4wxxsyPn8nQmw3WJI4O1L/Ssf3XwTCcdeGwnOjDD+P19uKfFGz5teLUNG2jWZ56PnyNIULh4osZe/e7ob19xms/VxTGd/fj5oo2LGc6IkH1sdXDYw1tq51h5XFihsrjSDgYqrOrQgYoLdtaxPCoMUFHl/k+j6p6F3DXlMc+POX7P69w7m3AbfW8H6fgIY4GA3OajL9yJbnXvpa2L3+Zsfe+F23AYmS3r2+yPfW45+/tRWOxY/Z6XCyTezxaeDTGGGPqzu/u5sSxZ4HGTFt9+tdHSAIbLwjWI0Z27AiqjuHwm841wQv0Z/aNwJm1vSZ7aCLGyl17ARuWU1EqtazaVjvCgTnZGcLj2HApPFaoajdBeJSIIgsYHpuu8tiMnLyH4/hNGR4Bxm64AWdkhMRXvjL7wXPg9PVVruY5Dt7JJx9TeVwspbWZ1rZqjDHGNEB7OxlvnFxbjFwDwuPIw88x0ZOk66QkZLNEdu2aXO8IkF4dhI/hp2fY67GCByeirHr0ALGuOB2nLtIL/WbXqMpjk27V0dXhApCboeVzPNyqI9PElUfH9XHGLDw2FadQxBG/6dY8lhQvuID8hRfS/rnPgefNfkKN3L6+Gat5/pS9HhfLZOXR9ng0xhhjGiKeaiebSpAfqv+L1eijz+CddSIAkcceQ4rFyfWOcDQ86v4hsjXO5Ns2EWXd4wc4YeMKpEm2XWs6S6jyWNWax6hDPjHzpNJcWHns6W7e8Oi6Hq6Fx+ahvuIWfVy8plzzWDJ+ww1Enn462PeojmR4GGd4eMbw6K1a1TSVR+/EE5trSxVjjDFmCfEzGQqpGN5gtq7XPTDs0b3vedIbgvWI0R07ACiUhcd4RxzpStB1aJB9BbfqaxcVHh5UOvc+a+sdZ9LINY+RyKwDjeqihjWPIlBoj1Ecq9y2mhvJ4UUc2tsq/HtrgvAYcQpEx5fxwJxm4+WDSp5DsWnbVgFyr3413imn0H7rrXW9bjXrCL2VK3EOH4bCwg/JKefMUiE1xhhjzPz4mQxem4sO17fS8avtQzi+smZjMCwnsmMHfmcn/urVxxzXdkqarsODPJWvfmzHY7kIHbufRTy19Y4zaWTlMZ2eXLvaUDWseQQotMfxZmhbLQznyCfjlavVTTBtNUae2ESenHfc1vYNYeFxFqXwGKHYtG2rALgu429/O7Ff/ILIL39Zt8uWtuCYaR2ht2oV4vs4hw7V7Xnnwu3rw7f1jsYYY0zD+JkMxAUZqm/lce+vjgCw8YIMANHt24Oq45QX7ZnVaboPDbA3X33lcdtElJWPBdt+2zYdM2hk5XEhWlahprZVAD8ZQ2eoPBZH8xTbZ3j93wSVxwRZHF8ZmGHwTz1ZeJyFnwua6l1t7sojwMTrX4+fStW1+lgaQjPT9hd+OIl1UVtXfR/3wAGrPBpjjDENpJkMEvVwR+obHgcffo5sRxsnrU5CoUDk0UePWe9Y0rMmqDw+ma3+JeyDE1HWPd5H+0ntJE9K1vO2l5ZGVx4XQq3hsT0GY5Urj95IDi85S3h0nFm3immYVIqEH/y3ONCAvVenY+FxFsWw8hj18k295hFA02km3vAGElu3Bm2kdeAeOIDG4/i9vRWP8abZ63GhOc88gxQKNmnVGGOMaSC/u5uo5IkOT9T1us7OZ8m98AREhMju3UguR7Fs0mpJx5oOIgWPAwerf/4HJ6Ks3nXQWlZnsxQqj6XX6lWGR1IxZIbwyGgOTc6wVnN4eOFacqeTStHujQMw0IAJyNOx8DiLbC5sW/Xzzd22Gpp405sQzyPx7/9el+tN7vHoVP6nUppuuph7PU5u02GVR2OMMaZh/EyGmJMnkvcoThTrcs2hCZ+uvc/SviEId5Ht24Fjh+WUdKwO2gMH91W3XcfzReFAf5G2ff02LGc2S6HyWMPAHABJxnBmCo9jeSQ9S+VxsVpWAVIpUl7wdzZU53XIlVh4nEUubFuNFrNN37YK4J1+OvkXvYjE174GOv+Fs1UNoWlrw+/pWdTtOtwq1mYaY4wxZn78TIY2gqrf+EB9WlcfengYt+iz8rxgWE50xw78ZBJv3brjji1t11HsG2aiiu06vjOS4OTHg5kMVnmcRTLYX7Pu276Nji5ceHQciMWqDo9OMkpkhm0unNEczmxtq4scHtOF4I2U4QELj01hIqw8xvxCS4RHgOy11xJ97DEiDz8872u5fX2Taxpn4q1cuaiVR6dUeaziXo0xxhgzN9rTQ8oPKh1H6rTGanc4LOfccFhOZMcOihs2TNv1lF6VBoGuwwM8VcXQnK8PJdi0J3iNsGKjVR5nlEoFn+vdujoycvTaCyEer7pt1U3FZtzmwh3L4XTM0ra6mOExmaQjPwTA2PAyXfMoIpeLyC4R2SMiH6hwzHUislNEHhGRL5c97onIr8KPrfW4n8nKoxaafs1jSfbKK9FYjMTXvz7PC2Vxn322qlbQxd7r0e3rw+/pCd41M8YYY0xD+JkMaS+odAz0zz7d8YejMXZmZ95W48j258m3xzjttDR4HpGHH552WA6AG3eJnZSi++AgT86yXccTOZdtEzHO272fjlM7SHS3RhFg0ZReQzUiPC5U5REgkai68hhNRYnki5MzTo77+XiOaDO3rbouXWEnwNjIMqw8iogL3AK8GjgbeL2InD3lmPXAB4EtqnoO8EdlP55Q1U3hxxX1uKd8GB5jLbLmEUC7u8ldeiltd9wxr70X3YPBWOtqw6PT11eXVtm5cPfvt/WOxhhjTIP53d105QcAGOyf+cXqiCe8cX8Xlz6Z4a+eTZKt0Gbq73yW8TNPxHEFd+9enPHxadc7lnSuTtN9eIC9hZkrj98YSuCgtO88bC2r1ShVB+u97rGJw2MsFVQVh6fZ5qJQ9IlNFIilmjg8At1uUHEcH1qG4RG4ENijqntVNQ/cDlw55Zi3A7eo6gCAqj7byBvKTbat5lomPAJkr7sO58gRYj/84ZyvMbmOsIpQ5q9ciTM+jgwOzvn55sPt67P1jsYYY0yD+d3dZHL9AIzM0rb6i4koHsKF7QU+fSTFK5/s4f+OR485Jlv06djzDLFzgnAX3bEDYNpJqyXda9JkDg3y5Axtq77C14baeOVEPxMHR21YTjUaUXnM5YJCxgKHx2rbVuPp4N/j8MjxlceBoeDfd6KzucNjJhoE39wybVtdCewv+74vfKzcGcAZInKfiNwvIpeX/SwhItvCx3+n0pOIyA3hcdv6+/tnvKFS5THu5dC2tlp+l0WV+83fDBa1f+1rc76GU0N49Oq416P095P8m7+pPoiqBuHRKo/GGGNMY7W10aNBZWp0lq0BfjYS5YZ3fI7fffu/cMvd/wf38DBX7uvmTw6nGfWCrQ22PzpKNFfkpHODLcEi27ej8TjF00+veN2O1R2knh/myRkKZD8fj3Kg6HLZtkcBWHmJzUSYVSMqjyPhVNyFDI/xeNWVx7ZUEB6Hho+vPPaHlfVEuonXPAJtyRi+I+QWqG115mbxhTfdJilT+yAjwHrg5cAq4CciskFVB4HVqnpQRNYBPxCRHar6xHEXVL0VuBVg48aNM/ZZ5vNhePRzLbPmEYBolOzrXkfbF76ADA6iXV01X8Ldvx91HPxwK46ZlIfH4oYNNT9XiTz/PN3XXkv00UfRzk7Gb7hh1nOc559HslkLj8YYY8wC6I0V8R1hYpbpjjsfPMLLdx0ktzLF6Md/yuv5KbkLVvH93ziXy37rhXz0bI/9vwyG5Zx1QTcQVB6LZ50F0WjF66ZXpxGFI0+PBq8Ip/H1oTbSjk/qnseQtR30nNMzt192OWlE5XExwmMNbavtHTFGgJFpwuNgWHlMVqo8el4QtBc5PEpHmkJblOJyXPNIUGks7z1cBRyc5phvqWpBVZ8EdhH+T4eqHgw/7wV+BJw/3xvKh22rca+12lYBJq69FsnnSWyd2+wg98AB/JNOmvF/wEtKwc2Zx8RV57nnyFx9NZGnnsLPZIjed19155Umra5ePefnNsYYY0x1uuIO2VSC3AyVx2FPcO59AnWEa+65hut/dj0vfv+LOWl0jN/+9Hd5w1Wf4jvXf4fdtz9GIR5hwws7QJXIjh0UZmhZBehYE7xYL/YNMzbNOsoxH+4ciXNlcYDDPzvIaa89DVmsTdxbyVKpPNbQtppMBXW0kWnWPA6FawhTHRVe/5f+nBY5PJJKUUxEKY4uz7bVB4D1IrJWRGLA9cDU5PMfwCsARKSXoI11r4h0i0i87PEtwM753lAhbFtNeNmWC4/F886jeOaZwZ6Pc1BLK6j29qLx+Jy363AOH6b7da/DffppBr70JbKvfjWxn/+8qr2G3DA8+lZ5NMYYYxou3pEkm0pQmGFAxy/Go6y/fzfJC04ikUnQta6LF737RVx373Vc+8Nr2fTOTaw99DwrH9rHyAtPIhp1cJ9+Gmd4uOKk1ZLSXo/dhwd5apqJq98bSTDmO7z8F4+gnnLaa0+b3y+8XDSi8lgKWE1aeUylg38/EyPF4342HL45kq5UeRweDj43QXj0Ei7+cqw8qmoRuBG4G3gU+JqqPiIiN4tIaXrq3cAREdkJ/BB4r6oeAc4CtonIr8PHP66q8w6PxbDymChmg3cyWokIE9deS2zbNty9e2s+3allHaFIsNfjHNY8OgcP0v261+EcPMjAV75CYcsWCpdcgjM8TCRcOD+TUni0tlVjjDGm8bS7m0Iyij+UrXjMz5/M8YLdh3nhq47tChIRes7q4aV/8hL+x7brufjOa7n2n14BBOsdgRknrQIkT0wiMZfugwPsnWZozteGEqyOFtHv7abj1A56NljLalUaWXlc4H0eqw2PHR1Bd934NJXHkfDNkc7uCuGx9Ls1QXjUqKDLtPKIqt6lqmeo6mmq+rHwsQ+r6tbwa1XV96jq2ap6rqreHj7+s/D7jeHnf63H/RTCfV/aihNoq4VHIHvNNajj1L7no+fhHjxYUzXPW7kSp8bw6OzfT/dVV+E89xyDX/0qhYsuAiD/0pcCEKuiddXt68Pv7EQX+z9eY4wxZhnQTAY/4aAzVB4PfH8fAKdftqbiMSLCxhf3sH5tUPGK7tiBRiIUX/jCGZ9fHCF9Spquw8dPXD1UcPjxWIxr/X4O3nfAWlZrsQzXPHaF4TE7TfAqbX3R2VlhYE4TVR6JAWMtXHkUkVUi8j9F5Fsi8oCI/FhE/lFEfltEmi6wzqQYtq22FSdarm0VwD/pJPIvexltX/86+BU2WJqG88wzSLE4OQinqueqsfLo7NtH5qqrcAYHGfjqVyls3nz0WieeSHH9+qrDo23TYYwxxiwMP5OBGMjwxLQ/H/KE5E+ewF/ZSfcZ3VVfN7JjB8Uzz6yq06tzdZoVhwd4ckrb6jeGEijCRT9/DPWUda9dV/XzL3vt7cHnZbTmsbMjePMhN03lcXwkCJSZSpXHJgqPjuvjtGp4FJH/DdwG5IG/Bl4P/A/g+8DlwE9F5GX1ft5GKZZXHlswPAJkr70Wt6+P6P33V31OLXs8lnirVuE88wzkZy+bu08+Seaqq5CxMQa+/nWKF1xw3DH5LVuCey4c/x/0Mdfav99aVo0xxpgF4mcyOFEPd3j6ttWfH4G1Dz7Jit9aU33VT5Xo9u2zrncsSa9O03Vo4JjKo2owZfXCtjxD391Nx5oOesMtQEwVHCcIkK1eeayhbTUVcynEIhSmqTzmhoNr9HQ1f3h0pYA71rptq3+rqq9S1b8PW0n3qOrDqvrvqvoHBFtsTJ2g2rSKOQ/fEdoKLbjmMZR99avxk8ma9nycU3hcuRJRxT10aMbjnH376L7qKiSbZeAb36i4EXB+yxac8XGiv/515YupBmszrfJojDHGLAg/kyHiFomNZFH/+B3PHvrRYaL5Ii96dfX/3+wcOoRz5Mis6x1LOtZ0EBvO0td/9A3m7dkIj+cjXOMPcOAnB1j32nXWslqrZHJJVB6rDY8iUGiPURw7vlCRG8lRjLq0JY5fVwscDY8L+btNJ5Ui4hSJtmrlUVUfnvqYiJwmIueGP8+r6p56P2+jeAUfL+oSL+Racs0jAO3t5F77WuJ33gnj41Wd4swhPPpVbtfR8Wd/hoyO0n/HHRTPOaficaV1jzNt2SEDAzhjYzZp1RhjjFkgfiZDXLKIQn7k+GrH4A+eotgeY81LZ98nuiQaDsir9IbyVB2rw+06Doww6gUB8WtDbcRF2fCzx2zK6lylUvWvPMZiwcdCqaFtFaCQjONNU3ksDOcoJGfoOmyiymNMCsTGcxSneTOn3hq+/lBE/gT4KPABEflio5+v3rychxcJw2OLtq0CTFx3Hc7YGInvfreq492+vmBNQ2nxdBVK6yNnWvcY+/GPid9zD2N/9Ed4Z5014/W0p4fC2WcT++lPZ7xPwCqPxhhjzALRTIZ2gvWOEwPHvkgfKELvT/fAS9fgxitUbKYR2b4dFaFw9tlVHT+5XcfBAZ4suOQVvjmc4LJUjkPfeYL06jS951nLas2SyfqHx4WuzNVQeQTw2mP4o8dXHoujeYrVhMcmqDzGyOL4ysA0W47UWyPWPP6BiJT/r8VGVX29qr4B2Fjv52s0P+9RjEWIFfMQjS727cxZ4aKL8Fatqnrqai17PJZ4J588ee70B3ikbrqJ4urVjL/97VVdM79lC7EHHqj4DpJt02GMMcYsLD+TIekHrY39A8dWbH68bYiO50dYe+nq6U6tKLpjB97pp1f9pnXHmqDa03U42K7jB6Nx+j2HqwlaVm3K6hylUvVtWx0dXfhwFY8jxWJVe4UDaCo27aRSfySHN1t4bG+HyPF7jS6oVIo2P1h/PDDY+NbVRlQeB4Dvichrw+//U0TuFZGfEOzR2FL8vIcXdXFjsaAxulU5TrDn449/jDPLmkSYW3gkkcBbsaLidh1tX/4y0UcfZfRDH4Iqq7iFLVuQbJboQw9VvE+wyqMxxhizUPzubtLFYC3bwJFjw+Ou/3waFdhyefXT2iGYtFqosmUVIN4ZJ9YZo/vQIE/mI3x9KEGv63HKTx7HL/o2ZXWuGlF5XMg9HuHojJIqq4/aHkOmWfPIaA5NztBuOzy8+C2rAKkU7X6wLG1wqPFDcxqx5vFLwGuBTSLyLWAb8GrgNar63no/X6P5eQ8/4rZ0y2pJ9tprEd8ncccdMx9YGkIzh2pepe06ZHiY1Mc/Tv6ii8i95jVVXy9/8cWo41RsXXX378dPJtGurprv1RhjjDFzEI/T6QRtq4NT2lYLP3qS4bNPpuvE9qovJ889h3voUNWTVks6Vndw0uF+HpqI8p8jca7uzPLknU+QPiXNio0rarqWCdW78rhIbatA9UNzkjGc8WlC11geSc1SeWyS8JgsBoF/sEUrjwCnAV8Ffh+4EfgU0Nag52oozXt4Uad1h+WU8datI795czB1VSsvqJWBAZzx8TkNofEqhMfkpz+Nc+QII3/+5zVVcLWzk+KGDRX3e5yctNrKVWFjjDGmxXRHg0rNyODRF90HD03Qs/Mg7b+5tqZrRR8OZi1WO2m1pGN1B5nDg/znaJwCwlUM2pTV+VoKax7Dgk+14dFJxYhM07bqjOZwWiQ8pgvB+svhoRYMjyLyb8AHgb8C3qOqbwc+C3xORD5U5TUuF5FdIrJHRD5Q4ZjrRGSniDwiIl8ue/wtIrI7/HjLfH8fzXtoxFkSlUeA7HXXEXn8ceLf/nbFY+ayTUeJt2pVcH5ZOHX37aP9c59j4tprKW7aVPM181u2EH3wwWknxbr799ukVWOMMWaB9bQF68nsce0dAAAgAElEQVTGyiqP9303ePN4w+XhUhJV0u97H+2f/Sz4fsVrRUqTVjdsqOke0qvTtB8aQnzl7HiBxA/34Bd8m7I6H8uw8uimokSnqTxGxnO46dZoW+0Iw+NoK4ZH4HxVfaOqXg1cCqCqv1TV1wLbZzs5HLZzC0Gr69nA60Xk7CnHrCcIqFtU9Rzgj8LHM8BNwEuAC4GbRKR7Pr+M5j38iFP1Gr1ml33d6yhs2EDX299O6iMfgcLxPd6T4XFlbesVIAiPMjGB9PdPPpb6yEfAdRn9kz+Z0z3nL7kEKRSIbds27b3asBxjjDFmYWXaoBCLkC1rk+v7/j6GV3Rw8aZOAKIPPED7F75A+i/+gq43vemY1wblotu3Uzz1VLSzs6Z7SK9O4+SKpPpHubYzy9479wYtq5usZXXOlkLlscY1j9FklGi2gFc49g2O6FiOaLo1Ko+d+SEAxoZbcM0jwbCce0Xk58CXy3+gqt+q4vwLgT2quldV88DtwJVTjnk7cIuqDoTXfTZ8/DLgHlXtD392D3D5PH4XtOCjEVkSbasAmkrR/+1vM/7mN5O85Ra6r74a5+DBY46ZT+XRn7JdR/T++0l8+9uM3Xgj/gteMKd7LrzkJajrHrffowwP4wwN2bAcY4wxZoF1tseY6GgjF4ZHL+cR+flTDP/GaSTcoGW07YtfxE+lGPnzPyf2k5/Q88pXEn3ggeOuFdmxo+b1jnB04uoFR57jCgbp+3Ef615jLavzUtrncYblTTVpgcpjLKwuDpdt15HP+0RzRWIdLRAek0m6s4MAjLdi5VFV308wMOdSVf2bOVxiJbC/7Pu+8LFyZwBniMh9InK/iFxew7mIyA0isk1EtvVXeBds8th8EVxZMm2rACQSjHziEwx99rNEHnmEnle+ktgPfjD5Y6evD21rQzOZmi99zF6Pvk/6wx/GO/lkxv77f5/z7WoqRWHTpuOG5swn5BpjjDFm7trS7WRTcQrhi9XHfnKQ6ESBE155KgAyOEjizjvJXn014+94B/133olGInRfdRXt//iPk+FEBgeJ7NtX06TVko7VwQv3jxQPMPr9J/ELNmV13pLJ4O9mYmL+11JtiTWP8VSwFd9Q2R6JR8I3RRIzVR5HRpojPMZi9HhB22p2pAXDo4i8ERhV1WkbpkXkNBG5ZKZLTPPY1Lc/IsB64OXA64F/EZGuKs9FVW9V1c2qujkzW0DKe+CyZCqP5bJXXUX/3XfjnXgi3b/7uyT/6q+gWDzaCjqHd+5K4dE5cIDEN75BdPv2oF21vfqpa9PJX3IJ0V/9Cinrw5/c49Eqj8YYY8zCymQotkfxB4OQ8cvv9VGIR3jJy08CIHHHHUg2y8Sb3gRAceNG+u+5h9xll5G++Wa63vIWZGCASDgsZy6Vx9SqYAuI4X3DPHHnE6RWpTjh/BPq8dstX6VtNeqx7jGXC/ZabPKtOtrCyuPQ0NHK40C4lreto8KaR9XmqTwCqbigAvmR1mxb7QF+KSK3icg7w8E2bxaRm0XkXuATwDMznN8HlKeBVcDBaY75lqoWVPVJYBdBmKzm3NrkveBPaSlVHst4p59O/113Mf6GN5D69KfpvuYaIo89Nudqnvb0oG1tRB5/nNRf/iWFTZvIvu51877PwpYtiOcR/cUvJh9zwvDoW3g0xhhjFpSfyeDHHXQwi6oy+IOneOpF6zi/W0GVti99icKmTccMwdHOTob+5V8Y/uhHif3wh/Rceiltt98OQKHGYTkAkUSE9pPaOfLIEfru7eO015xmLavzlUwGn+ux7nEk2Au02dtW29NB5XGkrPI4EO6XmOys8Po/m4VisWnCo3SkybfFyA+3YOVRVT8NXAB8BVgB/Fb4/QHgTap6tarunuESDwDrRWStiMSA64GtU475D+AVACLSS9DGuhe4G3iViHSHg3JeFT42Z1LwEJel1bY6VVsbI3/7twz9wz8Q/fWviezdO/dqngjeypW0ffnLuIcPM3LzzeDM/59ZfvNmNBY7pnXV7etDEwn83t55X98YY4wx1fO7uyGuOCNZBh4bIHJwCP/l64gJRB96iOijjzLxxjcef6IIE297G/3f+haI0PaNb+CtXInO8f/LO9Z08NT3ngpaVq+wltV5q2flsWXCYwSA0ZGjlcfhsG01WWnN43DQJtos4ZFUimJblOJo48NjpBEXVVWPYFjNPXM4tygiNxKEPhe4TVUfEZGbgW2qupWjIXEn4AHvVdUjACLyEYIACnCzqs68qHEWTt5DxF+SbatTZa+5hsJ555G+6SZyl1025+t4K1cS2bOH7BVXULjwwvrcXHs7hQsuOGa/R3f//jm31xpjjDFm7vxMBjfyLJHhHDvv3gfA2t8K3nhu++IX8ZNJsr/zOxXPL15wAUfuuSeYjbBu7qGv45QODv/iMKmV1rJaF0uh8lgq+FQZHtNh5XF8uCw8hhW8jq7WCY9e3MVfgLbVhoTH+VLVu4C7pjz24bKvFXhP+DH13NuA2+p1L1IoIo4u2bbVqbwzzmDwK1+Z3zXWrEHjcUb+7M/qdFeB/JYtJD/5SWRwEO3qCtZmWsuqMcYYs+D8TIaIe5BItsCu7zzFwfUv4Iq1CWT4CIlvfYuJa65BZ1nrpl1dDP/938/rPtKrg2BiU1brZBlWHjtK4bFs2upoWHnsnC08LvTvVkkqhSZc/LEWbFtdapy8h4O3LCqP9TL63vfS/53v4K9eXdfr5i+5BPF9YvffD5RVHo0xxhizoLSnh4RkASjseIYnX7qejW2FYFDOxMT0LasN0LW+C4DTrjxtQZ5vyVsKlccaw2NnVxAes2Vtq2PhFOGuzgoDc5qw8kgUGG3hyqOIuGH7aktzCx4u3tJe81hnumIFxRX136C3cMEFaCJB9L77yP3Gb+D091vl0RhjjFkEfnc3bTo++X3iFWuJEg7KOe88ihs3Lsh9rHvNOtKnpDnxghMX5PmWvKVQeayxbbUz7QaHl4XHieEghGW6W6dtVaKK0+KVxz0i8jcicnYDn6OhVBW34OFo0SqPzSAeJ//iFxO7777JPR59qzwaY4wxCy8aJekGL7BHMinOuyBD5Fe/IvrII0y84Q0Ldhtu1OWkzSct2PMteUup8pjNVnV4Ou5SjLoUxo5W7XLhfok9LbTm0XE83BYPj+cBjxPswXi/iNwgIk3yJ1wdv+AD4OItmzWPzS5/ySVEd+4k+utfA7bHozHGGLNY0rGgUvP4xevZki7Q9qUvoW1tddmiyyySRlQeF3qfx/A1u+Sra+EUgXx7nGJZ5TE3kqMQjxCLVYhKTRgeI1Ig0srhUVVHVPVzqvpS4H3ATcAhEfm8iJzeqOetJy8fdN26tuaxaRS2bAEg8fWvA9iaR2OMMWaRZDo8hnvTPH7ZuZxXGCDxzW+SveoqtFmGiJja1bPyWAqgC/3vwXXRaLTqNY8AxfYYXlnlsTCco9A+Q+GoCcNjTLPExvP4vjb0qRoWHkXEFZErROSbwKeBvwXWAXcyZZJqs/JyQXiMULQ1j02isHEjfns78Z/8BI1G8U+0NQ7GGGPMYuhJKX/39few8qKTSf/HN3HGxxlfoEE5pkEikaByV6/KYzwO0ej8r1UjjcWqXvMI4CVj+GXDZrzRPMXkLOExGm2ezsRUirjmcD2f4fHGjpxpZNvqbuBK4G9U9XxV/TtVfUZVvwF8r4HPWzd5C4/NJxqlcNFFQLCfJI4NDDbGGGMWQ1cs2Brjpcl8MCjn7LMpnn/+It+Vmbdksn5rHherCh2P11R59JMxKKs8+qM5/NQs4bGjo3n2Gk+lSGiwxvPIQGNbVxv5yvvNqvr/qurPSg+IyBYAVX1XA5+3biZywZrHqBYmF9+axZcPW1etZdUYY4xZPC9kjJu/8Be8uW8b0e3bg+05muXFtJm7VKp+lcdFCo+aSNQUHknGYLxsjeRIDk1W2KYDjobHZpFK0e4H048Hhlo3PE636+tnGvh8dZcrC49WeWwepfDo27AcY4wxZtFIJsOH/vefs+pfPhsMyrn66sW+JVMPS6DyqPF4TW2rJGO45XskjuWRmSqPIyNNFx6TXvB3NtTgymPd93kUkYuBlwIrROQ9ZT/qANx6P18jZbNhePTzNjCniRTPPZfCxo3kX/rSxb4VY4wxZtnS7m6AYFDO1VejnZ2LfEemLpZA5ZFYrKbKo5OK4ZZVHt2xHFpN22qzSKVIFYK/s+Hh6qbMzlUjKo8xIEUQTNNlH8PANbOdLCKXi8guEdkjIh+Y5udvFZHnRORX4cfbyn7mlT2+db6/SC6cthqzymNzcV36776b7LXXLvadGGOMMcuWn8kAIMVi0LJqloYlUnmsJTxGklGiU8JjJN1abasdhSEARhrctlr3yqOq3gvcKyL/pqr7ajlXRFzgFuBSoA94QES2qurOKYd+VVVvnOYSE6q6aU43Po1S22rMz9uaR2OMMcaYMqXwWDzzTAqbNy/y3Zi6SaXg4MH5X2dkBNasmf915kATiZraVqOpGLGJPL6nOK4QG8/hp2epPJ5xRh3utE5SKTpzQXgca7XwKCKfUtU/Av5BRI7baERVr5jh9AuBPaq6N7zW7QQTW6eGxwWRnwyPOas8GmOMMcaU8U8+GYDxN73JBuUsJclkfdpWR0cXr/JYY9tqNB1sJzI4nKe9LUIk7yGzhccmqzx25wYBmGhw22rdwyPwxfDz/5rDuSuB/WXf9wEvmea4q0XkZcDjwLtVtXROQkS2AUXg46r6H9M9iYjcANwAsGqGiZ25cKuOuJe38GiMMcYYU8Zbs4Yj3/sexfPOW+xbMfWUSrV82yrxODI8XP3hqRhF4Eh/jolk8Po/0TlLeFys3206qRSZbD8AE8MtVnlU1QfD9tO3q2qtDfDTvW01tXp5J/AVVc2JyDuAzwO/Gf5staoeFJF1wA9EZIeqPjHNPd4K3AqwcePG46qjJYXJ8JizgTnGGGOMMVMUN9VttZBpFvWoPKou+lYd5KuvwLWlo4wAg0M5suHMk7ZKax4LBZiYaLrKY6ltNTfSglt1qKpHMG11hpWm0+oDyvdfWAUc03StqkdUtfSn8jngRWU/Oxh+3gv8CJjXTrWFsG017mVtzaMxxhhjjFn6SgNztGJ9ZXYTE+D7LTMwpz0VtK0ODeUZHApCZ7JS5XFkJPjcTOExHsdxHfJtUQojrde2WvIUcF849XSy9q2qfzfDOQ8A60VkLXAAuB743fIDROQFqnoo/PYK4NHw8W5gPKxI9gJbgE/M5xfIh+88JLysta0aY4wxxpilL5WCYjGo3M319W8pYLXIVh3JjiASDQ/miIedh6lK4bHUDttM4VEE0mkKbgy/wZXHRobHg+GHQ7BVx6xUtSgiNwJ3E+wJeZuqPiIiNwPbVHUr8C4RuYJgXWM/8Nbw9LOAfxYRP3zOj08zpbUmpcpjomjh0RhjjDHGLAPJZPB5bKxlw2OtlcdUODBnZDg3uVVfRyuFR4BUCk8j+KMtWnlU1b+Y43l3AXdNeezDZV9/EPjgNOf9DDh3Ls9ZSbHU81ycmPt/PMYYY4wxxrSKVCr4PDoK4XYsNSuFx9K1FlitW3V0hOFxbCg3OfOko7PC6rsmDo9+wUVHW7TyKCIrgPcB5wCTCwZV9TcrntRkiqXKo+vbCGpjjDHGGLP0lVce52qx21ZrrDx2dgbhcWI4Pxkeu7tar/LImAMNDo8NGZgT+v+Ax4C1wF8QrIF8oIHPV3elymPC8Rf5TowxxhhjjFkA5ZXHuSqdu5j7PBYKwdCeKnSlXQCyIzkmwoEzPZnWC48SVWSssW2rjQyPPar6r0BBVe9V1d8DLmrg89WdhUdjjDHGGLOsLIHK4+Sskiqrj8mEixdxyA3nyIX7JHa3YNuqE/Fxx1q0bRUohJ8PichvEwzPWdXA56s7P+fhRV0SLjT2r8EYY4wxxpgmUI/K42KHx3CLPcnl0La2WY93HCHfHkdG8hTzHtIWJRqpUGNrxq06AFIpIo4H44XZj52HRobHj4pIJ/DHwGeADuDdDXy+uvPyPsVYhJjrWHg0xhhjjDFL3xKoPBILqoaSy1HtbpXF9hiM5nHyDrTPMChzeDiYhVL6c2oWqRRR8shYHtXGjWtp5LTVb4dfDgGvaNTzNJKXDyqPkai72LdijDHGGGNM49Wz8rhY01ZLbavZbNXneMk4MpLDj7lIcpbwmE6D08jVf3OQShH3x/GKDqMTRdLtjYl5db+qiHwGKod8VX1XvZ+zUfwwPNo2HcYYY4wxZlmoV+WxrQ0ijWxyrGyybTVf/fAYvz2KM5aDvIsmK6x3hCA8NlvLKkAqRaIwxhhp+gdyrRMegW0NuOai0LyHH7HwaIwxxhhjlol6VR4XqeoITL52r2W7Dk3FYSgLMTf4upImDo9tfn8QHgfzrFnZmLbauodHVf18va+5WPy8hx9xjpa+jTHGGGOMWcpiMXDd+VceF2u9I8FWHVBbeJRkFOfQMJp34ZSuygc2cXhMeqM8zwkMDTVuWkvdm3VF5FPh5ztFZOvUjyqvcbmI7BKRPSLygWl+/lYReU5EfhV+vK3sZ28Rkd3hx1vm87sElUepakqTMcYYY4wxLU8kqBrOd5/HxQyPNW7VAeAkY7hjOZyxHM5slcdF/N0qSqVIF4O/s+HBxoXHRrStfjH8/L/mcrKIuMAtwKVAH/CAiGxV1Z1TDv2qqt445dwMcBOwmWDd5YPhuQNzuRfyHuqKVR6NMcYYY8zykUy2dOWRsq06quWmYkTGcvhRF03PsuZx5cr53mH9pVKkC8GgopGh6td61qoRbasPhp/vneMlLgT2qOpeABG5HbgSmBoep3MZcI+q9ofn3gNcDnxlLjeiYXi0NY/GGGOMMWbZmG/lcWQEVqyo3/3UaC5tq9FUlNhEHj/n4LfomsfO3CAAo8Mt1LZaIiKvEZFfiki/iAyLyIiIDFdx6kpgf9n3feFjU10tIttF5Bsickot54rIDSKyTUS29ff3V76TggcuVnk0xhhjjDHLR4tXHueyVUc0FUUUXM8n3tma4bE7DI8TrRgegU8BbwF6VLVDVdOqWs2f9HRbWk7d+uNO4FRVPQ/4PlAa0lPNuajqraq6WVU3ZzKZyjeS98Cx8GiMMcYYY5aRelQem6FttYatOhJlraqJSm2rvh/8bs0aHrNBUWxipHFtq40Mj/uBh1W14p6PFfQBp5R9vwo4WH6Aqh5R1VKk/hzwomrPrUneA1dtYI4xxhhjjFk+lkjlsZa21UQqOvl1e6XK49gYqDZveJwIxrzkGjhttZE7d74PuEtE7gUmfwNV/btZznsAWC8ia4EDwPXA75YfICIvUNVD4bdXAI+GX98N/KWIdIffvwr44Fx/ASl4iDi25tEYY4wxxiwfqRQ8/fTczlUNqpaLuM9jac1jLdNW29NRRsKvkx0VXvsPhyvwmjQ8RvDJJ6LoaGuGx48Bo0ACmGFk0bFUtSgiNxIEQRe4TVUfEZGbgW2quhV4l4hcARSBfuCt4bn9IvIRggAKcHNpeM5cOHkPcWzaqjHGGGOMWUaSybm3rZaqc81QeaxhzWN7+mjlMdWK4TH88y60x9CR1gyPGVV91VxOVNW7gLumPPbhsq8/SIWKoqreBtw2l+edSgpFRFwLj8YYY4wxZvlIpebetloKnYu55rEUHmtY85hKH41FHd0VXvuPhLXJZgyPbW0ggtcWRVt0zeP3RWRO4bFZOHkPR/zJRbfGGGOMMcYsefOpPJYC1mKGx0gEjURqalvt7Dxaeex8eFtQPZ2qmSuPIpBK4Sdc/LHWDI/vBL4nIhM1btXRNNyChyseauHRGGOMMcYsF6lUELyKxdrPbYbwSLDusZa21Y6yttXum/4n/Nf/Crt3H3tQM4dHgFQKjbvQwLbVhoXHcGsOR1XbatyqoymoKm6hiCO+ta0aY4wxxpjlI5kMPs+ldbVJwiOJRE1tq11pd/Lrng/+Idx3H2zYAB/6EIyPBz9ogfAoMUHGWig8isgLw88XTPdR7+drFL/oBxuF4ll4NMYYY4wxy0dpUmoLh0eNx2vaqiPVHsF3gi3ju97zTti1C667Dj76UTjnHNi6tSXCoxtV3AaGx0YMzHkPcAPwt9P8TIHfbMBz1p2f9wGIULQ1j8YYY4wxZvkoVR7nsu6xFB4XcasOCLfrqCE8Oo6Qb4shqriuwAteAF/8IrztbfDOd8KVV0Jvb3DwYldVK0mlcIseMt64NY91D4+qekP4+RX1vvZCKmY9IKw8Wng0xhhjjDHLxRKoPBKP17TmEVWKbRFk6pyc//Jf4Je/hM98Bm66CTIZiEanvcSiS6WID+eR4QmyOY9E3J39nBo1om31xSJyUtn3bxaRb4nI34tIpt7P1yjZfBAeIxSsbdUYY4wxxiwf9ag8Lnbbao1rHslm8eIuxbZptqePRuE97wkG6Pz4x/W7yXpLpThRjuD4yo6dAw15ikYMzPlnIA8gIi8DPg58ARgCbm3A8zVENhe0rUa1aOHRGGOMMcYsH/OpPJYC52K3rcbjNbWtytgYGhW0fYbGzJNOCtY/NqtUivVjewB4dMfzDXmKRoRHV1X7w6//H+BWVb1DVT8EnN6A52uIiVxQeYz6BVvzaIwxxhhjlo/5Vh7b28Gtf8tkTWKxmgbmOGNjrE7vY9XLeht4Uw2WSrHx0IMA9O1sofAoIqXI/lvAD8p+1ogBPQ2Rm6w8WtuqMcYYY4xZRua75nGx1ztS+7RVGR3l3Ts+xR9fvsihdz5SKU58fh+jvWkGdrVOePwKcK+IfAuYAH4CICKnE7SuzkpELheRXSKyR0Q+MMNx14iIisjm8PtTRWRCRH4VfvzTXH+JUniMad4G5hhjjDHGmOVjvpXHZgiPiUTNbatAU9z7nKVSUCySX9eLt7sx4bER01Y/JiL/B3gB8J+qWppZ5AB/MNv5IuICtwCXAn3AAyKyVVV3TjkuDbwL+MWUSzyhqpvm+WuQDdtWY37eKo/GGGOMMWb5KIXHZVZ5BBZ9rea8hPceW9tN9NuPoKqISF2fohGVR1T1flX9pqqOlT32uKo+VMXpFwJ7VHWvquaB24ErpznuI8AngBpm8FYvnw0rj14OLDwaY4wxxpjloq0NROZeeWyGAFbjmsclU3kEMmtTxMdy7N8/h7+/WTQkPM7TSmB/2fd94WOTROR84BRV/fY0568VkV+KyL0i8hvTPYGI3CAi20RkW39//3SHkC+1rYoHTjP+MRljjDHGGNMAjhMMvWnxymNNbauloNwE9z5nYXg8dXWw5G77r+vfutqMqWi62urkdp0i4gCfBP54muMOAatV9XzgPcCXRaTjuIup3qqqm1V1cyYz/daT+XCfx5jj1fwLGGOMMcYY09JSqZZf8zinymMzVE3nKrz3c14QRKe9Dy+P8NgHnFL2/SrgYNn3aWAD8CMReQq4CNgqIptVNaeqRwBU9UHgCeCMudxEIR9UHuOuznKkMcYYY4wxS0wyOfd9HpsgPC7nttWzkuPk2mI899jyCI8PAOtFZK2IxIDrga2lH6rqkKr2quqpqnoqcD9whapuE5EV4cAdRGQdsB7YO5ebKIRtq21WeTTGGGOMMctNq1ce43Eknwffr+p4Z3QUIhGIxRp8Zw0Uhsfo2Chjp/Yy3oCJq00XHlW1CNwI3A08CnxNVR8RkZtF5IpZTn8ZsF1Efg18A3iHqk6/qHEWhXDaqlUejTHGGGPMsjOXyqPvN03lcXKrvXy+quNlbCy47zpPJ11QpZbb0VE4rRd3b/3DY9236qgHVb0LuGvKYx+ucOzLy76+A7ijHvdQDNc8Jtzq3q0wxhhjjDFmyUilgipiLZqp9TPcLUFyuar2bJcmCb3zUrr/0VHSZ6wm+u3tjA3nSXbUr5radJXHZlEMK4+JiFUejTHGGGPMMjOXymMpbDZBCNOw/bTadY8yNtbaw3LgmMrjyWevAGDHjvpWHy08VlAsrXmMLvKNGGOMMcYYs9DmsuaxFB6bIIRpaZ/2WsJjE4TeeWlvDz6PjnLGhl4Adll4XBheqW21KRt7jTHGGGOMaaAWrzwStqpWXXkcHW2K0DsvrhsEyNFRNp7Tje8IB3ZaeFwQXt6jGHWJx9zFvhVjjDHGGGMWVirV0uFRy9Y8VmNJVB5hsmK8IhVh+ORuhnZZeFwQXt7Hi7rEXfsjMsYYY4wxy0yp8ljlVhfA0TbXJghhpTWP1batOkthYA4c025cWNeL/4SFxwXhh5VHScQX+1aMMcYYY4xZWKUWzomJ6s9ppspjrW2rS2FgDhwTHhNn9NL2dD9+sX67R1h4rMDPe/gR9+hiW2OMMcYYY5aLZDL4XMvQnCYKjyzztlWAnjN7cQseT+0erNvlLTxWEIRHp6p9YYwxxhhjjFlSSlW4WtY9NlF4rGmrjkIhOG6JVR7XnhNMXN3x6+fqdnkLjxVoGB6xyqMxxhhjjFlu5lN5LJ27iCa7B7PZWY+VUkBugtA7b2Xh8dzzgvD41CP1W/fYlOFRRC4XkV0iskdEPjDDcdeIiIrI5rLHPhiet0tELpvrPWjOQyNibavGGGOMMWb5mWvlMZkEpwkiRmnNYz4/66FSCshLrPJ45gvaGO1O8nwdJ6423S6GIuICtwCXAn3AAyKyVVV3TjkuDbwL+EXZY2cD1wPnACcD3xeRM1TVq/U+NF/Ed8XaVo0xxhhjzPIz18pjk1TvatmqY6lWHiMOjK/thd1H6nb5Jnhb4DgXAntUda+q5oHbgSunOe4jwCeA8lr0lcDtqppT1SeBPeH1apf3wLXKozHGGGOMWYbmWnlskgA2uVXHcmtb7eiAoSFQBcA5rZfo3ufQ8Pv5asbwuBLYX/Z9X/jYJBE5HzhFVb9d67lVyxdRl8mStzHGGGOMMcvGXCqPzbRXYg1tq04pPC6FttUTToBiEQYGAOg4o5f4cJbhZ8brcvlmDI8yzWOTUVlEHOCTwB/XeqyxS9cAACAASURBVG7ZNW4QkW0isq2/v3/6myh44GCVR2OMMcYYs/y0euWxlrbVUkBuknuflxNPDD4/8wwAq8KJqw9vr8+6x2YMj33AKWXfrwIOln2fBjYAPxKRp4CLgK3h0JzZzgVAVW9V1c2qujmTyUx/F3kPcS08GmOMMcaYZajF1zwSiaCOA7WseVwKlccp4fHMDUF4fPzhpRseHwDWi8haEYkRDMDZWvqhqg6paq+qnqqqpwL3A1eo6rbwuOtFJC4ia4H1wP+dy004eQ8RtYE5xhhjjDFm+SmFxxatPAIQjyPVrHlcwpXHjWd0UohHOPRofcJj001bVdWiiNwI3A24wG2q+oiI3AxsU9WtM5z7iIh8DdgJFIF3zmXSKgRtq+Jgax6NMcYYY8zy47rB6+BaK49NVL3TRKK2rTqWYHjsiQuDq3vx6rRdR9OFRwBVvQu4a8pjH65w7MunfP8x4GPzvQen4CHiWNuqMcYYY4xZnlKplq48ajxedduqiiDt7QtwVw2WyQT7bIbhEcBb14vu6KvL5ZuxbbUpOAUPx/EtPBpjjDHGmOUpmay+8uh5MD7eXOExFqt6n0dNJkGmm73ZYlwXVqyAZ5+dfKhtfS+JQ4MUxgvzvryFxwqcoo+LZ+HRGGOMMcYsT7VUHpux9bPaNY+l8LhUnHjiMZXHFWf1IgpP7jwy70tbeKzAzRdxxIe2tsW+FWOMMcYYYxZeLZXHJgyP1a55dEZH0SZaqzlvU8LjunC7jkfqsF2Hhcdp+J6P46tVHo0xxhhjzPJVS+VxZCT43Ezhsdo1j6OjS7ryeO6GDCqwb+f8w2NTDsxZbF4uGNAaoWjh0RhjjDHGLE/JJPT3V3dsE4ZHal3zuFSUwqMqiLC+O8rgC7rxH7PKY0Pkcz5g4dEYY4wxxixjyWTLVx6rDo9LrW01m538O4k6MHFqL/k9Fh4bYqJUedSi7fNojDHGGGOWp1Sq+jWPpfDYRCFME4nqt+pYSpXHE04IPpe1rkZO7yH61BF8z5/XpS08TiNbqjyKF4y7NcYYY4wxZrlp8cpj1W2ro6P4TRR65+3EE4PPZdt1dJ7RSyRXpH/f0LwubeFxGtlsGB6d+SVzY4wxxhhjWlap8qg6+7FNGB51OW/VAcdUHk85ZwUAO+c5cdXC4zRy+aBtNWrh0RhjjDHGLFfJJPh+Va2fTRkeEwmYbasO38dZBuHxrPOC7Tp2P7wEw6OIXC4iu0Rkj4h8YJqfv0NEdojIr0TkpyJydvj4qSIyET7+KxH5p7k8fy5sW405VbzLYowxxhhjzFIUtnLK+Pjsx46OgkgQOJtENQNzSr/bkhqYsyKoMh6zXccp7Yx3tPHMPLfraLqtOkTEBW4BLgX6gAdEZKuq7iw77Muq+k/h8VcAfwdcHv7sCVXdNJ97KIXHqGvh0RhjjDHGLFNhEJSxMTSTmfnYkZEgbIoswI1VqYo1jxKu6VxSlcdoFHp6jgmPPVEYXNOLu3vpVR4vBPao6l5VzQO3A1eWH6Cqw2XfJoG6prxcOG015lrbqjHGGGOMWaZqqTyOjDRVyyqUVR5nWLMp4TTZJVV5hKN7PZbRdb3IE0svPK4E9pd93xc+dgwReaeIPAF8AnhX2Y/WisgvReReEfmN6Z5ARG4QkW0isq1/mo1PC2HlMW6VR2OMMcYYs1yVVR5n1YzhsbTl3gzrHpdk5RGC7TrKpq0CtJ/ZS2xgnPEjVbwZUEEzhsfpat3HpThVvUVVTwPeD/xZ+PAhYLWqng+8B/iyiHRMc+6tqrpZVTdnpinB5ycrjxYejTHGGGPMMlVr5bHZqnfxOMCMraulyuOS2qoDpq08nvjCYGjOU/MYmtOM4bEPOKXs+1XAwRmOvx34HQBVzanqkfDrB4EngDNqvYHJ8Bit9UxjjDHGGGOWiFLlsVXbVmMxoLrwuOQqj9OEx9M3BOHx0R1LKzw+AKwXkbUiEgOuB7aWHyAi68u+/W1gd/j4inDgDiKyDlgP7K31BibbVqNNtODXGGOMMcaYhbQE1jwCM241MjltdSmGx5ERmJiYfGjDC7soRl32z2PiatNNW1XVoojcCNwNuMBtqvqIiNwMbFPVrcCNIvJKoAAMAG8JT38ZcLOIFAEPeIeqHr+ocRaFfBAe26zyaIwxxhhjlqswUDktuuaRcM3jTJVHZykPzIGg+njqqQCcnnQ4ckoPzq4jc75s04VHAFW9C7hrymMfLvv6Dyucdwdwx3yfv5gP2lbjFh6NMcYYY8xyVQpU1e7z2GThsaq21aU6MGea8Bh1ILe2l8Luw3O+bDO2rS66Yti2mojbH48xxhhjjFmmqq08ZrNw5MjRzembxGTbajZb8Zglu+bxhBOCz1PWPUZP7yXWN0AxW5zTZS0dTcPLB3+YcQuPxhhjjDFmuYrFIBqdfc3jww+D58H55y/MfVWr1LY6y1Yd2vb/t3f3QXbd5WHHv8++SzI2krGMIxksB4dagLEdxaRDMJ2YYJl0LJJCIzeZcQIzniS4hTKZ1ikdk5rJFEybZjJ1GtzgCUkhsjHJVJmaOC7hJSmxLSGEZNkxlgW1VRmMkXHYlbRv9+kf56x09+69+3J3tfft+5m5s+f+zjn3/n469x7ts8/vZQ0MtGWHzObNZB5rlutY/9pXEJXke08teWQfYPBY11Q55nHtSH+LayJJkiS10Lp1C6/zuG9f8fPqq89+fZYgF7lUR65du1pVWj3V3VarXPozl3LP7/0KP9y0vqmX7bIQe2VMj0+T/X0MD/Yz2erKSJIkSa1yzjkLZx737YP16+HVr16dOi3SzJjHeWdbHRvrvjUeoci6nnvunODxrZeu4/svW8d5TTbZ4LGOyqlJcrCfgaEBg0dJkiT1rsVmHq+6CqK9lrk7nXmcb8zj2Fj3jXecUWetxx9bC7ctI8a322odlfEppgf7T/eTliRJknrSQpnHyUk4cKDtuqwCixvzODrafct0zKgTPC6XwWMdlVPTBo+SJEnSQpnHJ54ouoW2YfC4mDGPfT2WeVwug8c6KpNTVAb6zkzvK0mSJPWihTKPbTpZDlSNeVxgqY6uDR43bjR4XA05Pm3wKEmSJK1bt3DweM45cNllq1enxVrsUh3d3G31xRdhnvYvVVsGjxGxPSKejIjDEXFbnf2/GhEHI2J/RPxtRGyt2veb5XlPRsT1zbx/TkyT/WG3VUmSJPW2xWQer7wS+tovrFjUUh3d3m0V4HvfW7GXbLurHBH9wF3ADcBW4Kbq4LD0mcx8Q2ZeCdwJ/E557lZgJ/A6YDvw++XrLc3ENDkQZh4lSZLU2+Yb8zg9Dfv3t2WXVQAGBsiIxkt1ZBKjo925VAc0XOtxOdoueASuAQ5n5pHMnAB2ATuqD8jMf6h6ug7IcnsHsCszxzPzW8Dh8vWWJCeLzGOaeZQkSVIvmy/z+NRTMDbWvsFjFD0JGy7VMT5OTE93f+ZxBYPHdlzncRPwbNXzo8Cbag+KiPcBHwSGgJ+uOvfhmnM3LbkGE9PQh5lHSZIk9bZ164oxg5OTMDg4e18bT5YzI4eHG455jNHR4hiDx0Vrx8xjvdVFc05B5l2Z+aPAvwX+/VLOjYhbImJvROw9fvz43ApMVqAvHfMoSZKk3lZ26aybfdy3r/h9+fLLV7lSi5fDww27rc50x+3qCXOg64PHo8DFVc83A8fmOX4X8M6lnJuZd2fmtszctmHDhjkvGJPTRL+ZR0mSJPW4MitXd9zjvn1wxRUw0I6dGUtDQw27rfbNBI/dmnlctw7WrIHnn1+xl2zH4HEPcFlEbImIIYoJcHZXHxAR1XMB/yzwVLm9G9gZEcMRsQW4DHh0yTWYKjKPBo+SJEnqaY0yj5lF8NjGXVYBcmSk4Wyrp7utdmvmMaLIPnbzmMfMnIqIW4EHgX7gnsw8FBF3AHszczdwa0S8DZgEXgRuLs89FBH3AY8DU8D7MnN6qXXom5ymr6/PCXMkSZLU22YyjzXBY/8zz8BLL7V/8Dg83HCdw+j2zCN0f/AIkJkPAA/UlN1etf3+ec79beC3l/P+fZMVInDMoyRJknrbTOaxptvqwIEDxUa7B49DQ72beYQiePzWt1bs5dqx22rL9U1O0xcVu61KkiSptzXIPA4ePFiMdXz961tRq8UbHm445nEmIK6YeVw0g8c6ZoLHth78K0mSJJ1tDSbMGTh4sAgc2zzZkiMjjbutdvtSHVAEjy+8ANNLHslXl8FjHf2T0/T3zVnhQ5IkSeot9SbMyWTwwIG277IK5TqPvbpUBxTBY6VSBJArwOCxRlaS/umKwaMkSZJUJ/PY99xz9H3/+x0RPDLPmMe+sTFyYACGhla5Uqto48bi5wot12HwWGPyVJHS7e83eJQkSVKPq5N5HDx4sNjogOAx5xvzODpaZB0jVrlWq+jCC4ufKzTu0eCxxqmJIngcMPMoSZKkXjcyQkbMCh4HDhwg+/rgiitaWLHFmXfM49hYd493BIPHs+3UeAUw8yhJkiQRQa5bNyfzOP2a15zu0trW5hvzODpq8LhEBo81TpbB46DBoyRJkkSuXTtrzOPAwYNMdkDWERZY5/HECSrdPFkOwMtfXozpNHg8O8ZPFcGjq3RIkiRJzMo89n3ve/Q/9xxTb3hDi2u1OKfHPObcxFBPZB4jiklzDB7PjvHxYsyjmUdJkiRpduZxoJwsZ7JTgseRkWJjcnLOvtMT5nS7Cy/s3uAxIrZHxJMRcTgibquz/4MR8XhEHIiIL0TEq6v2TUfE/vKxu5n3nxnzOGTmUZIkSZqVeZwJHqde//pWVmnxymU46nVd7YkJc6DIPHbjUh0R0Q/cBdwAbAVuioitNYd9HdiWmVcA9wN3Vu07mZlXlo8bm6nDxEzm0eBRkiRJmpV5HDxwgKktW8hzz21xrRYnh4eLjTrLdfT1QrdV6OrM4zXA4cw8kpkTwC5gR/UBmfnFzJyZ7ulhYPNKVmB8ZsIcg0dJkiRpTuaxU8Y7wpluq1FnuY44caJ3uq0+/3zdcZ9L1W7B4ybg2arnR8uyRt4LfL7q+UhE7I2IhyPinY1OiohbyuP2Hj9+fNa+iYmy2+pQu/3TSJIkSasv16yhb2yM+MEPGHjmmY4Z7whAmXmc0211aoo4dap3Mo+Tk/Dii8t+qXbLr0WdsrohckT8ErANeGtV8asy81hEXAr8dUQczMyn57xg5t3A3QBvfOMbZ73+ZNltdXioXlUkSZKk3jKTeRx47DEApjpkmQ4oluqAucFjjI4W+3sl8whF19UNG5b1Uu2WXjsKXFz1fDNwrPagiHgb8CHgxsw8/UnIzGPlzyPAl4CrllqBybLb6vCgwaMkSZKUa9cSJ04weOAAAJOdMlkOjcc8zozh7JnMI6zIuMd2Cx73AJdFxJaIGAJ2ArNmTY2Iq4BPUASOz1eVr4+I4XL7FcCbgceXWoGJ8SkAhofb7Z9GkiRJWn25bh1x8iSD+/czvWkTef75ra7S4jUY8ziTeaz0UvC4AjOutlW31cyciohbgQeBfuCezDwUEXcAezNzN/Bx4BzgsxEB8Ew5s+rlwCciokIRFH80M5ccPE6dLNaAGRppq38aSZIkqSVy7VoABh99lMmrltyxr6WywZjH05nHXui2unFj8XMFMo9tFyFl5gPAAzVlt1dtv63BeV8Flj16dyZ4XDNi5lGSJEma6drZ/53vcLKTJsvhzJjH2m6rfb3UbfX886Gvryu7rbbc1KmZbqv9La6JJEmS1HozmUegs2ZahTPdVns589jfDxdcYPB4NkyfKjKPI2vaLikrSZIkrbrq7FwnrfEIVd1WG4x57InMIxTjHg0eV97UqWKpjpF1gy2uiSRJktR6M5nH6QsuoDIz+UqHON1ttZeX6gCDx7NlemKKSl+wZs1Qq6siSZIktdxMdm7qDW+A6Kzl7E5nHhss1dETs62CwePZUhmfZmpogJhZE0aSJEnqYTOZx8krrmhxTZowz1IdGQFV4zm72saNxVIdmct6GYPHGpWJaaYH+s8sKCpJkiT1sOnNm5netImJ665rdVWW7PTv9LXdVk+cKDKqHZZJbdqFF8LJk1B2122Ws8LUqExME4N9ZPlXCkmSJKmX5Xnn8cLXvtbqajRnsJjHZE631dHR3pksB4rgEYquqy97WdMvY+axRmWiQmWg73SKW5IkSVKHiiBHRuYs1dE3Oto7k+XA7OBxGcw81sjJabI/7LYqSZIkdYEcHp475nFsrHczj8vQlpnHiNgeEU9GxOGIuK3O/g9GxOMRcSAivhARr67ad3NEPFU+bl7qe+dEhRzoM3iUJEmSukAODdVdqsPgcenaLniMiH7gLuAGYCtwU0RsrTns68C2zLwCuB+4szx3A/Bh4E3ANcCHI2L9kiowWTHzKEmSJHWL4eG5Yx5PnOitbqsXXFD87LbgkSLoO5yZRzJzAtgF7Kg+IDO/mJknyqcPA5vL7euBhzLzeGa+CDwEbF/Km+dUhezHMY+SJElSF8iRkfpLdfRS5nFwEDZsKJbrWIZ2DB43Ac9WPT9aljXyXuDzTZ4712QF+nG2VUmSJKkL5PDw3G6rY2NUeinzCEXX1S6cMKfeYit1V7OMiF8CtgFvXcq5EXELcAvA5s2bZ++bqpB9wEA7/tNIkiRJWpKhoTmzrfZc5hFWJHhsx8zjUeDiquebgWO1B0XE24APATdm5vhSzs3MuzNzW2Zu27Bhw+zXnawQ/fTOgqGSJElSF8vaMY+VCn29NtsqdG3wuAe4LCK2RMQQsBPYXX1ARFwFfIIicKzuuPsg8PaIWF9OlPP2smzRYnK6CB4lSZIkdbwcGYGqMY9x8mRRbrfVJWu74DEzp4BbKYK+J4D7MvNQRNwRETeWh30cOAf4bETsj4jd5bnHgY9QBKB7gDvKskWLqYrBoyRJktQlcnh4VrfVGB0tynsx8/jDH0IZPDejLQf2ZeYDwAM1ZbdXbb9tnnPvAe5p9r37Jitkn9GjJEmS1BVqxjzG2BjQo5lHKLKPl1zS1Eu0Xeax1fqmpjF2lCRJkrpD7ZjHns08btxY/FzGch1tmXlspb7JCvTXndxVkiRJUoeZM+axDB57cqkOWNa4R4PHGv1T09DvTKuSJElSV2jUbbXXMo8GjyurUkkGJqdhwMyjJEmS1A3mTJhj8Nj0SzjmscrERAWA/gEzj5IkSVI3yJERGB+HLBJEfb06Yc7ICJx7rsHjSjk5XgaPTpgjSZIkdYfhYaJSgakpoIcnzIFlr/Vo8Fjl5KlpAAYGW1wRSZIkSSsih4YATndd7dluq1AEj8uYbdXgscp4mXkcsNuqJEmS1BVyeLjYKJfriNFRcs0aGOjB6V82bjTzuFJmMo+DgwaPkiRJUlcYGQEgyuU6YnSUXLu2lTVqHbutrpyZCXMGDB4lSZKkrjCTeTzdbfXEid5b43HGhRfC8eMwOdnU6W0XPEbE9oh4MiIOR8RtdfZfGxH7ImIqIt5Vs286IvaXj91Lfe/xcTOPkiRJUjeZGfM4q9tqL453hDPLdTQ57rGtOvpGRD9wF/AzwFFgT0TszszHqw57Bvhl4DfqvMTJzLyy2fcfP1FE4INDbRdTS5IkSWpCznRbrZowp+eW6Zhx0UVw/vnw0kuwadOST2+3KOka4HBmHsnMCWAXsKP6gMz8dmYeACor/eaTY8UHatjgUZIkSeoOM91WyzGPfb2cedyxA154AbZuber0douSNgHPVj0/WpYt1khE7I2IhyPinY0OiohbyuP2Hj9+/HT5+FjxgRoacaFHSZIkqRvMWaqjl4PHZWq34LHeYMNcwvmvysxtwL8AfjcifrTeQZl5d2Zuy8xtGzZsOF0+ebLotjo0bPAoSZIkdYM5S3WcONG73VaXqd2Cx6PAxVXPNwPHFntyZh4rfx4BvgRctZQ3nyzHPA6vaauhoJIkSZKaVW+pDjOPTWm34HEPcFlEbImIIWAnsKhZUyNifUQMl9uvAN4MPD7/WbNNnZoCYGjE4FGSJEnqBrOW6sgkxsZ6d6mOZWqr4DEzp4BbgQeBJ4D7MvNQRNwRETcCRMRPRMRR4N3AJyLiUHn65cDeiPgG8EXgozWztC5ocrwIHkcc8yhJkiR1hVlLdYyPE1NTZh6b1HYptsx8AHigpuz2qu09FN1Za8/7KvCG5bz36czj2qHlvIwkSZKkdlG1VEeMjQEYPDaprTKPrTY1Pg3AmnMMHiVJkqRukFVLdcToaFFmt9WmGDxWmS6Dx+F1wy2uiSRJkqSVcHq21fFx+sw8LovBY5XpiTLzuM7MoyRJktQVZtZ5PHXqTLdVM49NMXisMj1RodIXDK4baXVVJEmSJK2ECHJ4uBjzONNt1cxjU9puwpxWmp6sEIP9xNo1ZKsrI0mSJGlF5PAwTEyYeVwmM49VKhMVpgf6zvSLliRJktTxcmhoVuaxYuaxKWYeq1SmKsRAv8GjJEmS1E2Gh4sxj3ZbXRYzj1Xe/mPf442v/PrpQbWSJEmSOl+OjBRLdZw4UTy322pTDB6rvLn/WW565n9CRKurIkmSJGmF5PBwsVTH6Cg5MGCyqEkGj1VifNwuq5IkSVK3GRo6vVRHnnOOyaImtWXwGBHbI+LJiDgcEbfV2X9tROyLiKmIeFfNvpsj4qnycfOS3tjgUZIkSeo6OTJyesIcxzs2r+2Cx4joB+4CbgC2AjdFxNaaw54Bfhn4TM25G4APA28CrgE+HBHrF/veP7zzTl7Ys6f5ykuSJElqO9VLdRg8Nq/tgkeKoO9wZh7JzAlgF7Cj+oDM/HZmHgAqNedeDzyUmccz80XgIWD7ot85wv7PkiRJUreZWapjbIyKk+U0rR2Dx03As1XPj5ZlK3ZuRNwSEXsjYu/x48ebrqgkSZKk9pdVS3WYeWxeOwaP9Uav5kqem5l3Z+a2zNy2YcOGJVVOkiRJUmfJkREoM48u09G8dgwejwIXVz3fDBxbhXMlSZIkdaEcHi7WeTTzuCztGDzuAS6LiC0RMQTsBHYv8twHgbdHxPpyopy3l2WSJEmSelU55rHPCXOWpe2Cx8ycAm6lCPqeAO7LzEMRcUdE3AgQET8REUeBdwOfiIhD5bnHgY9QBKB7gDvKMkmSJEk96vSYR7utLstAqytQT2Y+ADxQU3Z71fYeii6p9c69B7jnrFZQkiRJUsfIkRE4dYrINPO4DG2XeZQkSZKkFTU8TGQxj6aZx+YZPEqSJEnqalm1lruZx+YZPEqSJEnqajk8fHq7YvDYNINHSZIkSd1tZOT0pt1Wm2fwKEmSJKmr2W11ZRg8SpIkSepq1d1WzTw2z+BRkiRJUner7rZq5rFpBo+SJEmSupqZx5Vh8ChJkiSpq1WPeXS21ea1ZfAYEdsj4smIOBwRt9XZPxwR95b7H4mIS8rySyLiZETsLx9/sNp1lyRJktResuy2mhGwdm2La9O5BlpdgVoR0Q/cBfwMcBTYExG7M/PxqsPeC7yYma+JiJ3Ax4BfKPc9nZlXrmqlJUmSJLWvsttqrlsHES2uTOdqx8zjNcDhzDySmRPALmBHzTE7gE+V2/cD10X4KZAkSZI0V1YHj2paOwaPm4Bnq54fLcvqHpOZU8BLwPnlvi0R8fWI+HJEvKXeG0TELRGxNyL2Hj9+fGVrL0mSJKmtzIx5dLKc5WnH4LFeBjEXecxzwKsy8yrgg8BnIuLcOQdm3p2Z2zJz24YNG5ZdYUmSJEltbGbMo5nHZWnH4PEocHHV883AsUbHRMQAcB5wPDPHM/P7AJn5NeBp4MfOeo0lSZIktS27ra6Mdgwe9wCXRcSWiBgCdgK7a47ZDdxcbr8L+OvMzIi4oJxwh4i4FLgMOLJK9ZYkSZLUhuy2ujLabrbVzJyKiFuBB4F+4J7MPBQRdwB7M3M38EngTyLiMHCcIsAEuBa4IyKmgGngVzPTQY2SJElSLzPzuCLaLngEyMwHgAdqym6v2j4FvLvOeZ8DPnfWKyhJkiSpc/T1kUNDVMw8Lks7dluVJEmSpBU1fdFFTF988cIHqqG2zDxKkiRJ0ko6/tBDZDnrqppj8ChJkiSp6+W5c1bw0xLZbVWSJEmStCCDR0mSJEnSggweJUmSJEkLMniUJEmSJC3I4FGSJEmStCCDR0mSJEnSggweJUmSJEkLasvgMSK2R8STEXE4Im6rs384Iu4t9z8SEZdU7fvNsvzJiLh+NestSZIkSd2q7YLHiOgH7gJuALYCN0XE1prD3gu8mJmvAf4L8LHy3K3ATuB1wHbg98vXkyRJkiQtQ9sFj8A1wOHMPJKZE8AuYEfNMTuAT5Xb9wPXRUSU5bsyczwzvwUcLl9PkiRJkrQMA62uQB2bgGernh8F3tTomMycioiXgPPL8odrzt1U+wYRcQtwS/l09JWvfOWTK1P1Bb0CeGGV3mu12bbO063tAtvWibq1XWDbOlG3tgtsWyfq1naBbWsnr17MQe0YPEadslzkMYs5l8y8G7h76VVbnojYm5nbVvt9V4Nt6zzd2i6wbZ2oW9sFtq0TdWu7wLZ1om5tF9i2TtSO3VaPAhdXPd8MHGt0TEQMAOcBxxd5riRJkiRpidoxeNwDXBYRWyJiiGICnN01x+wGbi633wX8dWZmWb6znI11C3AZ8Ogq1VuSJEmSulbbdVstxzDeCjwI9AP3ZOahiLgD2JuZu4FPAn8SEYcpMo47y3MPRcR9wOPAFPC+zJxuSUPqW/WusqvItnWebm0X2LZO1K3tAtvWibq1XWDbOlG3tgtsW8eJImEnSZIkSVJj7dhtVZIkSZLUZgweJUmSJEkLMnhcpoi4JyKej4jHqso2RMRDEfFU+XN9WR4R8XsRcTgiDkTE1VXn3Fwe/1RE3FzvvVZTRIxExKMR8Y2IOBQR/6Es3xIRj5T1vLec1IhykqJ7y7Y9EhGXVL3Wb5blT0bE9a1p0WwR8e2IOBgR+yNib1nWDdfttWWbZh7/EBEf6JK2DoH0AwAACT5JREFUvT8iHis/jx8oyzqyXQ3uG+8u21aJiG01x9f9DkXE9rLscETcVlVe93u6Ghq07SPlddgfEX8VET9Sli/5OkXEj5ff3cPlufWWaFqtdv1WRPy/qu/bO6r2dfo1u7eqXd+OiP1d1LY3RsTflZ+jv4iIczutbRFxcUR8MSKeKO8b7y/LO/4+Mk/bOvo+Mk+7Ov4+Mk/bOv4+Mk/bOv4+0rTM9LGMB3AtcDXwWFXZncBt5fZtwMfK7XcAn6dYj/IngUfK8g3AkfLn+nJ7fYvbFcA55fYg8EhZ5/uAnWX5HwC/Vm7/OvAH5fZO4N5yeyvwDWAY2AI8DfS3wXX7NvCKmrKOv2417ekHvkOx6GtHtw14PfAYsJZioq//TTGbcke2q8F943LgtcCXgG1V5XW/Q+XjaeBSYKg8Zmt5Tt3vaQvbdm7V9r+qulcs+TpRzKD9j8tzPg/c0MJ2/RbwG3WO7fhrVrP/PwO3d0vbKGZ1f2u5/R7gI53WNuAi4Opy+2XAN8v6d/x9ZJ62dfR9ZJ52/RYdfh9p1LaaYzryPjLPdev4+0izDzOPy5SZX6GY8bXaDuBT5fangHdWlf9xFh4GXh4RFwHXAw9l5vHMfBF4CNh+9mvfWFnH0fLpYPlI4KeB+8vy2rbNtPl+4LryL3k7gF2ZOZ6Z3wIOA9esQhOa0fHXrcZ1wNOZ+X/p/LZdDjycmScycwr4MvBzdGi76t03MvOJzHyyzuGNvkPXAIcz80hmTgC7gB3l967R9/Ssa9C2f6h6uo7iXgJLvE7lvnMz8++y+F/2j1mltjW41zfS8ddsRlm3fw78aVnUDW17LfCVcvsh4J+V2x3Ttsx8LjP3lds/BJ4ANnXDfWSetnX0faRRu+Y5peOv2cz+Tr6PzNO2jr+PNMvg8ey4MDOfg+JDB2wsyzcBz1Ydd7Qsa1TeUhHRX3YxeJ7ii/E08IPyl3eYXc/TbSj3vwScT5u2jeI/nb+KiK9FxC1lWVdctyo7OXOj7vS2PQZcGxHnR8Rair80X0znt2sxltqW82n8PW2ZiPjtiHgW+EXg9rJ4qW3bVG7XlrfSrWVXuXui7DZNl1yz0luA72bmU+XzbmjbY8CN5fa7Ke4l0KFti2KYyFUUPYQa6Yq2dct9pM4165r7SIPPY1fcR2ra1lX3kaUweFxd9frU5zzlLZWZ05l5JbCZ4i8ml9c7rPzZUW0D3pyZVwM3AO+LiGvnObbT2kbZX/5G4LMLHVqnrO3alplPAB+j+CPGX1J095ia55SOaNciLbUtbdnGzPxQZl4MfBq4tSzu9Lb9N+BHgSuB5yi6ZUHnt6vaTZz5IxR0R9veQ3Hf/xpFN7SJsrzj2hYR5wCfAz5Qk5mbc2idso5rWzfcR+q0q2vuI/N8Hjv+PlKnbV1zH1kqg8ez47tltwjKn8+X5Uc585cJKIKyY/OUt4XM/AHF+ImfpOgOMlDuqq7n6TaU+8+j6CrUlm3LzGPlz+eBP6cIjrvput0A7MvM75bPO75tmfnJzLw6M6+l+Gw9RRe0axGW2pYXaPw9bQef4Uz3nqW27Wi5XVveEpn53fKPbBXgv3OmS35XXLOyPj8P3FtV3PFty8y/z8y3Z+aPU/xC+3S5q6PaFhGDFL/Mfjoz/2yBw7utbR15H6nXrm65jzS6Zt1wH2lw3briPtKUbIOBl53+AC5h9mD8jzN7Eo87y+2fZfag7kfL8g3AtygGdK8vtze0uE0XAC8vt9cAfwP8U4pMVvWg3l8vt9/H7Alz7iu3X8fsgcNHaPGEORRjJV5Wtf1VinFvHX/dqtq4C/iVLvtMbix/vgr4+7JeHduu2vtGVfmXmD3RRd3vEMXEQUfKspnB968rz6n7PW1V24DLqrb/JXB/s9eJYpKCn+TMRBfvaGG7Lqra/tcU41y64pqVZduBL9eUdXzbOHMv6aMY7/aeTmtb+fn/Y+B3G+z/Eh16H2nUNjr8PjJPuzr+PjLf55EOv4/Mc906/j7S9L9JqyvQ6Q+KvzY8B0xS/FXhvRT9l79AkRn5QtXNKoC7KP46cZDZN/b3UAyqPUzVL/0tbNcVwNeBAxT9umdmyLqUYpayw+WHfbgsHymfHy73X1r1Wh8q2/wkqzQz4gJtu7T80n4DOAR8qCzv+OtW1mkt8H3gvKqyjm8bxR8wHi+v23Wd3K4G942fK7fHge8CD1YdX/c7RDH285vlvg/VfMbnfE9b2LbPlfeRA8BfUEx+0dR1AraVr/U08F+BaGG7/qSs9wFgN7N/Cezoa1aW/xHwq3WO7+i2Ae8v6/lN4KPVn6FOaRvwUxRd2w4A+8vHO+iC+8g8bevo+8g87er4+0ijtpX7/ogOvo/Mc906/j7S7CPKSkuSJEmS1JBjHiVJkiRJCzJ4lCRJkiQtyOBRkiRJkrQgg0dJkiRJ0oIMHiVJkiRJCzJ4lCSpCRExHRH7I+KxiPhsRKxt4jX+MCK2ltv/rmbfV1eqrpIkrQSX6pAkqQkRMZqZ55Tbnwa+lpm/sxKvJ0lSOzLzKEnS8v0N8BqAiPhgmY18LCI+UJati4j/FRHfKMt/oSz/UkRsi4iPAmvKTOany32j5c+IiI+X5x2sOveflOffHxF/HxGfjohoReMlSb1hoNUVkCSpk0XEAHAD8JcR8ePArwBvAgJ4JCK+DFwKHMvMny3POa/6NTLztoi4NTOvrPMWPw9cCbwReAWwJyK+Uu67CngdcAz4P8Cbgb9d4SZKkgSYeZQkqVlrImI/sBd4Bvgk8FPAn2fmWGaOAn8GvAU4CLwtIj4WEW/JzJeW8D4/BfxpZk5n5neBLwM/Ue57NDOPZmYF2A9csiItkySpDjOPkiQ152RtprBRt9HM/GaZlXwH8B8j4q8y845Fvs98XVHHq7an8f91SdJZZOZRkqSV8xXgnRGxNiLWAT8H/E1E/AhwIjP/B/CfgKvrnDsZEYMNXvMXIqI/Ii4ArgUePUv1lySpIf9CKUnSCsnMfRHxR5wJ7v4wM78eEdcDH4+ICjAJ/Fqd0+8GDkTEvsz8xaryPwf+MfANIIF/k5nfiYh/dNYaIklSHS7VIUmSJElakN1WJUmSJEkLMniUJEmSJC3I4FGSJEmStCCDR0mSJEnSggweJUmSJEkLMniUJEmSJC3I4FGSJEmStKD/D0NP/jBT5mBUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Displaying\n",
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "plt.plot(plotting,AY_id,'r',label='SARS-CoV GZ02')\n",
    "plt.plot(plotting,MG3_id,'deepskyblue',label='Bat-SL-CoVZC45')\n",
    "plt.plot(plotting,MG4_id,'purple',label='Bat-SL-CovZXC21')\n",
    "\n",
    "plt.xticks(np.arange(1000,30000,2000))\n",
    "plt.yticks(np.arange(0,1.02,0.05))\n",
    "plt.xlim(0,30620)\n",
    "plt.ylim(0,1.1)\n",
    "plt.ylabel('Similarity (%)')\n",
    "plt.xlabel('Position')\n",
    "\n",
    "#Annotations\n",
    "plt.hlines(1.09,0,13400,colors=\"forestgreen\",linewidth=3)\n",
    "plt.hlines(1.09,13400,13500,colors=\"forestgreen\",linewidth=1)\n",
    "plt.axvspan(0, 13500, facecolor='lightgray',alpha=0.5)\n",
    "plt.text(6500,1.11,\"1a\")\n",
    "\n",
    "plt.hlines(1.09,13500,21400,colors=\"forestgreen\",linewidth=3)\n",
    "plt.hlines(1.09,21400,21500,colors=\"forestgreen\",linewidth=1)\n",
    "plt.text(17000,1.11,\"1b\")\n",
    "\n",
    "plt.hlines(1.06,0,21400,colors=\"forestgreen\",linewidth=3)\n",
    "plt.hlines(1.06,21400,21500,colors=\"forestgreen\",linewidth=1)\n",
    "plt.text(10500,1.03,\"1ab\")\n",
    "\n",
    "plt.hlines(1.06,21500,25200,colors=\"forestgreen\",linewidth=3)\n",
    "plt.hlines(1.06,25200,25300,colors=\"forestgreen\",linewidth=1)\n",
    "plt.axvspan(21500, 25300, facecolor='lightgray',alpha=0.5)\n",
    "plt.text(23300,1.03,\"S\")\n",
    "\n",
    "plt.hlines(1.06,25300,26100,colors=\"forestgreen\",linewidth=3)\n",
    "plt.hlines(1.06,26100,26200,colors=\"forestgreen\",linewidth=1)\n",
    "plt.text(25600,1.03,\"3\")\n",
    "\n",
    "plt.hlines(1.06,26200,26300,colors=\"forestgreen\",linewidth=3)\n",
    "plt.hlines(1.06,26300,26400,colors=\"forestgreen\",linewidth=1)\n",
    "plt.axvspan(26200, 26400, facecolor='lightgray',alpha=0.5)\n",
    "plt.text(26200,1.03,\"E\")\n",
    "\n",
    "plt.hlines(1.06,26400,27000,colors=\"forestgreen\",linewidth=3)\n",
    "plt.hlines(1.06,27000,27100,colors=\"forestgreen\",linewidth=1)\n",
    "plt.text(26600,1.03,\"M\")\n",
    "\n",
    "plt.hlines(1.06,27100,27200,colors=\"forestgreen\",linewidth=3)\n",
    "plt.hlines(1.06,27200,27300,colors=\"forestgreen\",linewidth=1)\n",
    "plt.axvspan(27100, 27300, facecolor='lightgray',alpha=0.5)\n",
    "plt.text(27050,1.03,\"7\")\n",
    "\n",
    "plt.hlines(1.06,27300,27700,colors=\"forestgreen\",linewidth=3)\n",
    "plt.hlines(1.06,27700,27800,colors=\"forestgreen\",linewidth=1)\n",
    "plt.text(27400,1.03,\"8\")\n",
    "\n",
    "plt.hlines(1.06,27800,28200,colors=\"forestgreen\",linewidth=3)\n",
    "plt.hlines(1.06,28200,28300,colors=\"forestgreen\",linewidth=1)\n",
    "plt.axvspan(27800, 28300, facecolor='lightgray',alpha=0.5)\n",
    "plt.text(27700,1.03,\"10b\")\n",
    "\n",
    "plt.hlines(1.06,28300,30500,colors=\"forestgreen\",linewidth=3)\n",
    "plt.hlines(1.06,30500,30600,colors=\"forestgreen\",linewidth=1)\n",
    "plt.text(29200,1.03,\"N\")\n",
    "\n",
    "plt.hlines(1.09,28300,28500,colors=\"forestgreen\",linewidth=3)\n",
    "plt.hlines(1.09,28500,28600,colors=\"forestgreen\",linewidth=1)\n",
    "plt.text(28300,1.11,\"13\")\n",
    "\n",
    "plt.hlines(1.09,28700,28800,colors=\"forestgreen\",linewidth=3)\n",
    "plt.hlines(1.09,28800,28900,colors=\"forestgreen\",linewidth=1)\n",
    "plt.text(28700,1.11,\"14\")\n",
    "\n",
    "plt.legend(framealpha=0.0)\n",
    "plt.text(-100,1.11,\"Gene\",horizontalalignment='right')\n",
    "plt.text(-100,1.08,\"2019-nCov\",horizontalalignment='right')\n",
    "plt.text(-100,1.05,\"Consensus\",horizontalalignment='right')\n",
    "plt.savefig(str(window_size)+str(include_gap_sim)+\".png\",dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ORF level phylogeny (report Fig / paper Fig )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the non-annotated genes, I took the positions from Pubmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Accession numbers to be included \n",
    "fig_s_nums = ['GQ153547','DQ084200','KF294457','GQ153542','KJ473815','DQ648857',\n",
    "             'MK211377','KY417142','KY417148','JX993988','KJ473814','JX993987',\n",
    "              'MK211374','KJ473816','KY770859','KY417145','WH-03','WH-04',\n",
    "             'WH19005','YS8011','WH-01','WH19001','WH19004','WH19008',\n",
    "             'MG772934','MG772933','AY485277','AY508724','AY390556','AY278489',\n",
    "             'KY417146','KT444582','KY417152','KY417151','MK211376','GU190215',\n",
    "             'KY352407']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, as we retrieved annotated sequences from NCBI, check the differences between NCBI retrieved and original seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29858 29868\n"
     ]
    }
   ],
   "source": [
    "test = 'WH-03'\n",
    "found = False\n",
    "original_seq = \"\"\n",
    "retrieved_seq = \"\"\n",
    "with open('./data/main_lancet_10_ncov_seq_combined.fasta','r') as file:\n",
    "    for l in file.readlines():\n",
    "        if found : \n",
    "            original_seq = Seq(re.sub('\\n','',l))\n",
    "            found = False\n",
    "            \n",
    "        elif (test in l):\n",
    "            found = True\n",
    "with open('./data/retrieved_wh_seq/'+test+'.fasta','r') as file:\n",
    "    for l in file.readlines():\n",
    "        if l[0] != \">\":\n",
    "            retrieved_seq = retrieved_seq + l\n",
    "retrieved_seq = Seq(re.sub('\\n','',retrieved_seq))\n",
    "print(len(retrieved_seq),len(original_seq))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_seq in retrieved_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len('---------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Method :\n",
    "Download seq from NCBI\n",
    "\n",
    "Align original and retrieved seq with MAFFT\n",
    "\n",
    "Usually, differences at the ends => from MAFFT, infer the shift\n",
    "\n",
    "From NCBI, find the location of gene of interest. I also downloaded the protein sequence from NCBI, in order to check\n",
    "\n",
    "Annotations were retrievable only for WH-01 , 03 , 04 => for the rest, I inferred the spike sequence from the alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spikes_wh = {'WH-03':[21526,25347], #Take care, here numeration begins with one !\n",
    "             'WH-04':[21563,25384],\n",
    "             'WH19005',\n",
    "             'YS8011',\n",
    "             'WH-01':[21563,25384],\n",
    "             'WH19001',\n",
    "             'WH19004',\n",
    "             'WH19008'}\n",
    "shifts_wh{'WH-03':-17, #retrieved shorter than original\n",
    "          'WH-04':15,\n",
    "          'WH19005',\n",
    "          'YS8011',\n",
    "          'WH-01':25,\n",
    "          'WH19001',\n",
    "          'WH19004',\n",
    "          'WH19008'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "aligned_covids = dict()\n",
    "with open('./outputs/Task4/all_covid.fasta','r') as alignments:\n",
    "    key = \"\"\n",
    "    for l in alignments.readlines():\n",
    "        print(key,len(l))\n",
    "        if l[0] ==\">\" :\n",
    "            key = l[1:-1]\n",
    "        else:\n",
    "            if key not in aligned_covids:\n",
    "                aligned_covids[key] = re.sub('\\n','',l)\n",
    "            else:\n",
    "                aligned_covids[key] = aligned_covids[key]+re.sub('\\n','',l)\n",
    "            \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\eloi0\\Anaconda3\\lib\\site-packages\\Bio\\Seq.py:2859: BiopythonWarning: Partial codon, len(sequence) not a multiple of three. Explicitly trim the sequence or add trailing N before translation. This may become an error in future.\n",
      "  BiopythonWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Seq('MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDL...HYT', ExtendedIUPACProtein())"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Seq(re.sub('-','',aligned_covids['WH-03'])[21525+17:25345+17]).translate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_seq_in_align(seq,align):\n",
    "    if seq in align:\n",
    "        start = align.find(seq)\n",
    "        return([(start,start+len(seq))])\n",
    "    else:\n",
    "        i=-1\n",
    "        while seq[:i] not in align:\n",
    "            i-=1\n",
    "        following = find_seq_in_align(seq[i:],align)\n",
    "        start = align.find(seq[:i])\n",
    "        return [(start,start+len(seq[:i]))]+following"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(21877, 23591), (23592, 25698)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_seq_in_align(re.sub('-','',aligned_covids['WH-03'])[21525+17:25345+17],aligned_covids['WH-03'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDL...HYT', ExtendedIUPACProtein())"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Seq(re.sub('-','',aligned_covids['WH-01'])[21562-25:25382-25]).translate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(21877, 23591), (23592, 25698)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_seq_in_align(re.sub('-','',aligned_covids['WH-01'])[21562-25:25382-25],aligned_covids['WH-01'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(21877, 23591), (23592, 25698)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_seq_in_align(re.sub('-','',aligned_covids['WH-04'])[21562-15:25382-15],aligned_covids['WH-04'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All sequence seem to be between these positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "spikes_seq = dict()\n",
    "for key in aligned_covids.keys():\n",
    "    spikes_seq[key] = re.sub('-','',aligned_covids[key][21877:25698])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3159\n",
      "3820\n",
      "3820\n",
      "3820\n",
      "3820\n",
      "3820\n",
      "3820\n",
      "3820\n",
      "3820\n",
      "3820\n"
     ]
    }
   ],
   "source": [
    "for key in aligned_covids.keys():\n",
    "    print(len(spikes_seq[key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's ok, except for the first one but we don't care as it's not part of the SARS-CoV-2 strains that we will use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3159"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spikes_seq['WH19002'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WH19002 26973\n",
      "WH19008 29891\n",
      "WH19001 29891\n",
      "WH19005 29891\n",
      "WH19004 29896\n",
      "YS8011 29890\n",
      "WH-01 29866\n",
      "WH-03 29868\n",
      "WH-04 29872\n",
      "WH-02 29388\n"
     ]
    }
   ],
   "source": [
    "for key in aligned_covids.keys():\n",
    "    print(key,len(re.sub('-','',aligned_covids[key])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And actually, WH19002 is really shorter than others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WH19002 False\n",
      "WH19008 False\n",
      "WH19001 True\n",
      "WH19005 True\n",
      "WH19004 True\n",
      "YS8011 True\n",
      "WH-01 True\n",
      "WH-03 True\n",
      "WH-04 True\n",
      "WH-02 False\n"
     ]
    }
   ],
   "source": [
    "for k in aligned_covids.keys():\n",
    "    print(k,spikes_seq[k] == spikes_seq['WH-01'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually, all the sequence we want are the same except 19008 (what was expected from the paper) ! (they excluded the 2 not matching)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2762 A G\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(spikes_seq['WH-01'])):\n",
    "    if spikes_seq['WH-01'][i] != spikes_seq['WH19008'][i]:\n",
    "        print(i,spikes_seq['WH-01'][i],spikes_seq['WH19008'][i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Genome level\n",
    "alignment_root = './data/main_lancet_other_viruses_for_alignment_and_tree/'\n",
    "with open(alignment_root+'processed/spike_proteins_dna.fasta','w') as output: #output file\n",
    "    for f in fig_s_nums:\n",
    "        if f not in ['WH-03','WH-04','WH19005','YS8011','WH-01','WH19001','WH19004','WH19008',]:#I had to download this from the web directly\n",
    "            with open(alignment_root+f+'.fasta','r') as file: #orginal file\n",
    "                output.write('>'+f+'|Spike\\n') #headers\n",
    "                found_spike = False\n",
    "                prot_seq = \"\"\n",
    "                for line in file.readlines():\n",
    "                    if 'spike' in line or 'S protein' in line or 'protein=S' in line:\n",
    "                        found_spike = True\n",
    "                    elif found_spike == True:\n",
    "                        if line[0]==\">\":\n",
    "                            found_spike = False\n",
    "                        else:\n",
    "                            prot_seq = prot_seq + line\n",
    "                output.write(re.sub('\\n','',prot_seq)+'\\n')\n",
    "                #output.write(str(Seq(re.sub('\\n','',prot_seq)).translate(to_stop=True))+'\\n')\n",
    "        else: #COVID seqs\n",
    "            output.write(\">\"+f+\"|Spike\"+\"\\n\")\n",
    "            output.write(spikes_seq[f])\n",
    "            output.write('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\eloi0\\Anaconda3\\lib\\site-packages\\Bio\\Seq.py:2859: BiopythonWarning: Partial codon, len(sequence) not a multiple of three. Explicitly trim the sequence or add trailing N before translation. This may become an error in future.\n",
      "  BiopythonWarning,\n"
     ]
    }
   ],
   "source": [
    "#Protein level\n",
    "alignment_root = './data/main_lancet_other_viruses_for_alignment_and_tree/'\n",
    "with open(alignment_root+'processed/spike_proteins_prot.fasta','w') as output: #output file\n",
    "    for f in fig_s_nums:\n",
    "        if f not in ['WH-03','WH-04','WH19005','YS8011','WH-01','WH19001','WH19004','WH19008',]:#I had to download this from the web directly\n",
    "            with open(alignment_root+f+'.fasta','r') as file: #orginal file\n",
    "                output.write('>'+f+'|Spike\\n') #headers\n",
    "                found_spike = False\n",
    "                prot_seq = \"\"\n",
    "                for line in file.readlines():\n",
    "                    if 'spike' in line or 'S protein' in line or 'protein=S' in line:\n",
    "                        found_spike = True\n",
    "                    elif found_spike == True:\n",
    "                        if line[0]==\">\":\n",
    "                            found_spike = False\n",
    "                        else:\n",
    "                            prot_seq = prot_seq + line\n",
    "                #output.write(re.sub('\\n','',prot_seq)+'\\n')\n",
    "                output.write(str(Seq(re.sub('\\n','',prot_seq)).translate(to_stop=True))+'\\n')\n",
    "        else: #COVID seqs\n",
    "            output.write(\">\"+f+\"|Spike\"+\"\\n\")\n",
    "            output.write(str(Seq(spikes_seq[f]).translate(to_stop=True)))\n",
    "            output.write('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1b protein"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Difficult to retreive this annotation => looked on NCBI at join (..., ...) and took the second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "oneb_wh = {'WH-03':[13468,21555], #Take care, here numeration begins with one !\n",
    "             'WH-04':[13468,21555],\n",
    "             'WH19005',\n",
    "             'YS8011',\n",
    "             'WH-01':[13468,21555],\n",
    "             'WH19001',\n",
    "             'WH19004',\n",
    "             'WH19008'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('RVCGVSAARLTPCGTGTSTDVVYRAFDIYNDKVAGFAKFLKTNCCRFQEKDEDD...VNN', ExtendedIUPACProtein())"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Seq(re.sub('-','',aligned_covids['WH-01'])[13467-25:21554-25]).translate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('AAQALVLMSYTGLLTSTMIK*LVLLNS*KLIVVASKKRTKMTI*LILTL*LRDT...LLP', HasStopCodon(ExtendedIUPACProtein(), '*'))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Seq(re.sub('-','',aligned_covids['WH-03'])[13467+17:21554+17]).translate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('RVCGVSAARLTPCGTGTSTDVVYRAFDIYNDKVAGFAKFLKTNCCRFQEKDEDD...VNN', ExtendedIUPACProtein())"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Seq(re.sub('-','',aligned_covids['WH-04'])[13467-15:21554-15]).translate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(13760, 16085),\n",
       " (16087, 16873),\n",
       " (16884, 20200),\n",
       " (20201, 20438),\n",
       " (20440, 20894),\n",
       " (20895, 20968),\n",
       " (20973, 21869)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_seq_in_align(re.sub('-','',aligned_covids['WH-01'])[13467-25:21554-25],aligned_covids['WH-01'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(13760, 16085),\n",
       " (16087, 16873),\n",
       " (16884, 20200),\n",
       " (20201, 20438),\n",
       " (20440, 20894),\n",
       " (20895, 20968),\n",
       " (20973, 21869)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_seq_in_align(re.sub('-','',aligned_covids['WH-04'])[13467-15:21554-15],aligned_covids['WH-01'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "orf1b_seq = dict()\n",
    "for key in aligned_covids.keys():\n",
    "    orf1b_seq[key] = re.sub('-','',aligned_covids[key][13760:21869])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WH19002 7479\n",
      "WH19008 8087\n",
      "WH19001 8087\n",
      "WH19005 8087\n",
      "WH19004 8087\n",
      "YS8011 8087\n",
      "WH-01 8087\n",
      "WH-03 8087\n",
      "WH-04 8087\n",
      "WH-02 8100\n"
     ]
    }
   ],
   "source": [
    "for key in aligned_covids.keys():\n",
    "    print(key,len(orf1b_seq[key]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WH19002 False\n",
      "WH19008 True\n",
      "WH19001 True\n",
      "WH19005 False\n",
      "WH19004 True\n",
      "YS8011 True\n",
      "WH-01 True\n",
      "WH-03 True\n",
      "WH-04 True\n",
      "WH-02 False\n"
     ]
    }
   ],
   "source": [
    "for k in aligned_covids.keys():\n",
    "    print(k,orf1b_seq[k] == orf1b_seq['WH-01'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Except from the two strains not included in the fig, only WH19005 sequence is different : we see what is in the paper !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7202 G A\n",
      "7211 G A\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(orf1b_seq['WH-01'])):\n",
    "    if orf1b_seq['WH-01'][i] != orf1b_seq['WH19005'][i]:\n",
    "        print(i,orf1b_seq['WH-01'][i],orf1b_seq['WH19005'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Genome level\n",
    "alignment_root = './data/main_lancet_other_viruses_for_alignment_and_tree/'\n",
    "with open(alignment_root+'processed/orf1b_dna.fasta','w') as output: #output file\n",
    "    for f in fig_s_nums:\n",
    "        if f not in ['WH-03','WH-04','WH19005','YS8011','WH-01','WH19001','WH19004','WH19008',]:#I had to download this from the web directly\n",
    "            with open(alignment_root+f+'.fasta','r') as file: #orginal file\n",
    "                output.write('>'+f+'|orf1b\\n') #headers\n",
    "                found_spike = False\n",
    "                is_ab = False\n",
    "                is_ab_seq = \"\"\n",
    "                prot_seq = \"\"\n",
    "                for line in file.readlines():\n",
    "                    if '1b' in line or '1B' in line: \n",
    "                        found_spike = True\n",
    "                    elif ('1ab' in line or 'protein_id=ABG47068.1' in line or 'nonstructural polyprotein' in line):\n",
    "                        found_spike = True\n",
    "                        is_ab = True  #orf 1ab instead of 1b\n",
    "                        is_ab_seq = line\n",
    "                    elif found_spike == True:\n",
    "                        if line[0]==\">\":\n",
    "                            found_spike = False\n",
    "                        else:\n",
    "                            prot_seq = prot_seq + line\n",
    "                if not is_ab : #if it was orf1b, it's the full seq\n",
    "                    output.write(re.sub('\\n','',prot_seq)+'\\n')\n",
    "                else: #otherwise, we have to disentangle the 1b part\n",
    "                    start1a = int(re.sub('\\..*','',re.sub('.*join\\(','',is_ab_seq)))\n",
    "                    end1a = int(re.sub('.*\\.\\.','',re.sub(',.*','',re.sub('.*join\\(','',is_ab_seq))))\n",
    "                    start1b = int(re.sub('\\..*','',re.sub('.*join\\(.*,','',is_ab_seq)))\n",
    "                    end1b = int(re.sub('\\).*','',re.sub('.*\\.\\.','',re.sub('.*join\\(.*,','',is_ab_seq))))\n",
    "                    clean_seq = str(re.sub('\\n','',prot_seq))\n",
    "                    output.write(clean_seq[start1b-start1a+1:]+\"\\n\")\n",
    "                    \n",
    "                #output.write(str(Seq(re.sub('\\n','',prot_seq)).translate(to_stop=True))+'\\n')\n",
    "        else: #COVID seqs\n",
    "            output.write(\">\"+f+\"|orf1b\"+\"\\n\")\n",
    "            output.write(spikes_seq[f])\n",
    "            output.write('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Protein level\n",
    "alignment_root = './data/main_lancet_other_viruses_for_alignment_and_tree/'\n",
    "with open(alignment_root+'processed/orf1b_prot.fasta','w') as output: #output file\n",
    "    for f in fig_s_nums:\n",
    "        if f not in ['WH-03','WH-04','WH19005','YS8011','WH-01','WH19001','WH19004','WH19008',]:#I had to download this from the web directly\n",
    "            with open(alignment_root+f+'.fasta','r') as file: #orginal file\n",
    "                output.write('>'+f+'|orf1b\\n') #headers\n",
    "                found_spike = False\n",
    "                is_ab = False\n",
    "                is_ab_seq = \"\"\n",
    "                prot_seq = \"\"\n",
    "                for line in file.readlines():\n",
    "                    if '1b' in line or '1B' in line: \n",
    "                        found_spike = True\n",
    "                    elif ('1ab' in line or 'protein_id=ABG47068.1' in line or 'nonstructural polyprotein' in line):\n",
    "                        found_spike = True\n",
    "                        is_ab = True  #orf 1ab instead of 1b\n",
    "                        is_ab_seq = line\n",
    "                    elif found_spike == True:\n",
    "                        if line[0]==\">\":\n",
    "                            found_spike = False\n",
    "                        else:\n",
    "                            prot_seq = prot_seq + line\n",
    "                if not is_ab : #if it was orf1b, it's the full seq\n",
    "                    output.write(str(Seq(re.sub('\\n','',prot_seq)).translate(to_stop=True))+'\\n')\n",
    "                else: #otherwise, we have to disentangle the 1b part\n",
    "                    start1a = int(re.sub('\\..*','',re.sub('.*join\\(','',is_ab_seq)))\n",
    "                    end1a = int(re.sub('.*\\.\\.','',re.sub(',.*','',re.sub('.*join\\(','',is_ab_seq))))\n",
    "                    start1b = int(re.sub('\\..*','',re.sub('.*join\\(.*,','',is_ab_seq)))\n",
    "                    end1b = int(re.sub('\\).*','',re.sub('.*\\.\\.','',re.sub('.*join\\(.*,','',is_ab_seq))))\n",
    "                    clean_seq = str(re.sub('\\n','',prot_seq))\n",
    "                    output.write(str(Seq(clean_seq[start1b-start1a+1:]).translate(to_stop=True))+\"\\n\")\n",
    "                    \n",
    "                #output.write(str(Seq(re.sub('\\n','',prot_seq)).translate(to_stop=True))+'\\n')\n",
    "        else: #COVID seqs\n",
    "            output.write(\">\"+f+\"|orf1b\"+\"\\n\")\n",
    "            output.write(str(Seq(spikes_seq[f]).translate(to_stop=True)))\n",
    "            output.write('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA clustering "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspired from https://towardsdatascience.com/machine-learning-for-biology-how-will-covid-19-mutate-next-4df93cfaf544?gi=61a92d5d5a3a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kaggle dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_data = pd.read_csv('data/kaggle_data_MN997409.1-4NY0T82X016-Alignment-HitTable.csv',names=['query acc.ver', 'subject acc.ver', '% identity', 'alignment length', 'mismatches', 'gap opens', 'q. start', 'q. end', 's. start', 's. end', 'evalue', 'bit score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>query acc.ver</th>\n",
       "      <th>subject acc.ver</th>\n",
       "      <th>% identity</th>\n",
       "      <th>alignment length</th>\n",
       "      <th>mismatches</th>\n",
       "      <th>gap opens</th>\n",
       "      <th>q. start</th>\n",
       "      <th>q. end</th>\n",
       "      <th>s. start</th>\n",
       "      <th>s. end</th>\n",
       "      <th>evalue</th>\n",
       "      <th>bit score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>MN997409.1</td>\n",
       "      <td>AY283796.1</td>\n",
       "      <td>79.325</td>\n",
       "      <td>1925</td>\n",
       "      <td>357</td>\n",
       "      <td>35</td>\n",
       "      <td>19</td>\n",
       "      <td>1923</td>\n",
       "      <td>3</td>\n",
       "      <td>1906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>MN997409.1</td>\n",
       "      <td>AY282752.2</td>\n",
       "      <td>82.304</td>\n",
       "      <td>17716</td>\n",
       "      <td>2948</td>\n",
       "      <td>169</td>\n",
       "      <td>3956</td>\n",
       "      <td>21577</td>\n",
       "      <td>3868</td>\n",
       "      <td>21490</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>MN997409.1</td>\n",
       "      <td>AY282752.2</td>\n",
       "      <td>80.063</td>\n",
       "      <td>5417</td>\n",
       "      <td>988</td>\n",
       "      <td>68</td>\n",
       "      <td>22539</td>\n",
       "      <td>27910</td>\n",
       "      <td>22414</td>\n",
       "      <td>27783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>MN997409.1</td>\n",
       "      <td>AY282752.2</td>\n",
       "      <td>90.189</td>\n",
       "      <td>1641</td>\n",
       "      <td>142</td>\n",
       "      <td>12</td>\n",
       "      <td>28257</td>\n",
       "      <td>29882</td>\n",
       "      <td>28088</td>\n",
       "      <td>29724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>MN997409.1</td>\n",
       "      <td>AY282752.2</td>\n",
       "      <td>79.305</td>\n",
       "      <td>1928</td>\n",
       "      <td>358</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>1923</td>\n",
       "      <td>1</td>\n",
       "      <td>1907</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    query acc.ver subject acc.ver  % identity  alignment length  mismatches  \\\n",
       "258    MN997409.1      AY283796.1      79.325              1925         357   \n",
       "259    MN997409.1      AY282752.2      82.304             17716        2948   \n",
       "260    MN997409.1      AY282752.2      80.063              5417         988   \n",
       "261    MN997409.1      AY282752.2      90.189              1641         142   \n",
       "262    MN997409.1      AY282752.2      79.305              1928         358   \n",
       "\n",
       "     gap opens  q. start  q. end  s. start  s. end  evalue  bit score  \n",
       "258         35        19    1923         3    1906     0.0       1312  \n",
       "259        169      3956   21577      3868   21490     0.0      15175  \n",
       "260         68     22539   27910     22414   27783     0.0       3936  \n",
       "261         12     28257   29882     28088   29724     0.0       2121  \n",
       "262         35        16    1923         1    1907     0.0       1312  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% identity</th>\n",
       "      <th>alignment length</th>\n",
       "      <th>mismatches</th>\n",
       "      <th>gap opens</th>\n",
       "      <th>q. start</th>\n",
       "      <th>q. end</th>\n",
       "      <th>s. start</th>\n",
       "      <th>s. end</th>\n",
       "      <th>evalue</th>\n",
       "      <th>bit score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.0</td>\n",
       "      <td>263.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>86.064958</td>\n",
       "      <td>10711.114068</td>\n",
       "      <td>919.235741</td>\n",
       "      <td>57.821293</td>\n",
       "      <td>11295.684411</td>\n",
       "      <td>21970.517110</td>\n",
       "      <td>11212.646388</td>\n",
       "      <td>21888.836502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14240.34981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.609654</td>\n",
       "      <td>10530.955700</td>\n",
       "      <td>1085.784789</td>\n",
       "      <td>60.654780</td>\n",
       "      <td>12022.056513</td>\n",
       "      <td>10652.170843</td>\n",
       "      <td>11963.759492</td>\n",
       "      <td>10619.703758</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19226.72192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>77.559000</td>\n",
       "      <td>1603.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1923.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1672.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1011.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>80.048000</td>\n",
       "      <td>1925.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>21577.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21489.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2101.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>82.304000</td>\n",
       "      <td>5417.000000</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>3956.000000</td>\n",
       "      <td>27910.000000</td>\n",
       "      <td>3875.000000</td>\n",
       "      <td>27783.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3936.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90.189000</td>\n",
       "      <td>17716.000000</td>\n",
       "      <td>989.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>22539.000000</td>\n",
       "      <td>29875.500000</td>\n",
       "      <td>22429.000000</td>\n",
       "      <td>29729.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15175.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>29882.000000</td>\n",
       "      <td>2952.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>28257.000000</td>\n",
       "      <td>29882.000000</td>\n",
       "      <td>28137.000000</td>\n",
       "      <td>30256.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55182.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       % identity  alignment length   mismatches   gap opens      q. start  \\\n",
       "count  263.000000        263.000000   263.000000  263.000000    263.000000   \n",
       "mean    86.064958      10711.114068   919.235741   57.821293  11295.684411   \n",
       "std      7.609654      10530.955700  1085.784789   60.654780  12022.056513   \n",
       "min     77.559000       1603.000000     0.000000    0.000000      1.000000   \n",
       "25%     80.048000       1925.000000   142.000000   12.000000     16.000000   \n",
       "50%     82.304000       5417.000000   359.000000   35.000000   3956.000000   \n",
       "75%     90.189000      17716.000000   989.000000   68.000000  22539.000000   \n",
       "max    100.000000      29882.000000  2952.000000  172.000000  28257.000000   \n",
       "\n",
       "             q. end      s. start        s. end  evalue    bit score  \n",
       "count    263.000000    263.000000    263.000000   263.0    263.00000  \n",
       "mean   21970.517110  11212.646388  21888.836502     0.0  14240.34981  \n",
       "std    10652.170843  11963.759492  10619.703758     0.0  19226.72192  \n",
       "min     1923.000000      1.000000   1672.000000     0.0   1011.00000  \n",
       "25%    21577.000000      1.000000  21489.000000     0.0   2101.00000  \n",
       "50%    27910.000000   3875.000000  27783.000000     0.0   3936.00000  \n",
       "75%    29875.500000  22429.000000  29729.000000     0.0  15175.00000  \n",
       "max    29882.000000  28137.000000  30256.000000     0.0  55182.00000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(kaggle_data.corr(),cmap='bwr',center=0,vmin=-1,vmax=1,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_data_preprocessed = StandardScaler().fit_transform(kaggle_data.drop(['query acc.ver','subject acc.ver','evalue'],axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263, 9)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_data_preprocessed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2 The average silhouette_score is : 0.5115761580920425\n",
      "For n_clusters = 3 The average silhouette_score is : 0.6851660836686123\n",
      "For n_clusters = 4 The average silhouette_score is : 0.8772925303415421\n",
      "For n_clusters = 5 The average silhouette_score is : 0.9514666476057506\n",
      "For n_clusters = 6 The average silhouette_score is : 0.9561026609374067\n",
      "For n_clusters = 7 The average silhouette_score is : 0.9597718694007071\n",
      "For n_clusters = 8 The average silhouette_score is : 0.9563147528185567\n",
      "For n_clusters = 9 The average silhouette_score is : 0.9559933859076365\n",
      "For n_clusters = 10 The average silhouette_score is : 0.9610376064338941\n"
     ]
    }
   ],
   "source": [
    "range_n_clusters = [2, 3, 4, 5, 6,7,8,9,10]\n",
    "for n_clusters in range_n_clusters:\n",
    "    clusterer = KMeans(n_clusters=n_clusters, random_state=42)\n",
    "    kaggle_cluster_labels = clusterer.fit_predict(kaggle_data_preprocessed)\n",
    "    silhouette_avg = silhouette_score(kaggle_data_preprocessed, kaggle_cluster_labels)\n",
    "    print(\"For n_clusters =\", n_clusters,\n",
    "          \"The average silhouette_score is :\", silhouette_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_clusterer = KMeans(n_clusters=5, random_state=42)\n",
    "kaggle_cluster_labels = kaggle_clusterer.fit_predict(kaggle_data_preprocessed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aa7x07HGsmz4dGhqgro4377iD5e8/gWhoKHv9eZLGkIj4DbA+Iu6JiE8AB5Q5LrMuae3Kl6lbu6bImmD50w93eDzW+az5y/XEm29CxMaFdXXUv/gia++7r+z150ka69PfpZLeJ2lvYIf2qFzSbyUtkzS3mfWS9HNJ8yU90lLPu2bVrmf/gZv8Xy/Ud7BHUbaW1T31FPHGG5stj/p66hYsKHv9eZLGdyQNAr5INmLfpcAX2qn+y4AjSqw/EhifXpPxk+jWyfXo1Yex7ziO2l59Nlle27sve5xwRoWiss6k555vRf36bbZcNTX03G23stffYtKIiKkRsTIi5qaeb/eJiJvao/KImAa8UqLIccDvI/MAsLWkEe1Rt1ml7PPxb7DjQUdR07MXPXr3o0effux5ytmMPrDU7yezTN/jjkODBkFt7caFvXrRY8J4eu23X9nrLzUI0y/IOigsKiLOLktEm9oeeL5gflFatrSwkKTJZGcijB49ugPCMmu92h692P+M7zLxtHN5c+Vy+g0dSW1Pd7pg+dT07cvwW25m5QXf4c3b/w49aul3wgkMPPdrSGp5A21U6pbbmenvQcDuwNVp/gNk44Z3hGJHYLNEFhFTgCkAkyZNajbRmVWTnv0G0LPfgEqHYZ1Q7bbbss1Fv6hI3aXGCL8cQNLpwGERsT7N/wq4vUOiy84sRhXM70D2dLqZmVVAngvhI9m0+5ABaVlHuAk4Ld1FdQCwMiKWtvQmMzMrjzzPnl8IzJZ0V5p/B3Bee1Qu6UrgUGCopEXAt4CeABHxK+BW4Ciy4WXXAB9vj3rNzKx18vQ99TtJfwX2T4u+FhEvtEflEXFqC+sDOLM96jIzs7bL0zwFUAu8BKwAJkg6pHwhmZlZtcrTYeEPgFOAx4DGjk2CbNhXMzPrRvJc03g/sEtErC13MGZmVt3yNE8tIF2cNjOz7i3PmcYa4GFJdwIbzjY66IlwMzOrInmSxk3pZWZm3VyeW24v74hAzMys+uW5e2o88H2y/qcKh3sdV8a4zMysCuW5EP47snEs6oDDgN8DfyhnUGZmVp3yJI2+EXEnoIj4d0ScBxxe3rDMzKwa5bkQ/qakGmCepLOAxYDHpTQz64byJI1zgH7A2cAFZE1Up5UzKLOuoKFuPY/f/Bvm33YldWvfYMTbDmKvD3+J/sO2r3RoZq2Wp3lqTESsjohFEfHxiDgR8PB4Zi3410Vf4fG/XMIbK5axfs1rPPfA7dx27kmsXbWi0qGZtVqepHFuzmVmlrz2wnMsmXkX9eve3LgwGqh78w3m33F18280q3Klxgg/kmwsi+0l/bxg1UCyO6msi3nknut47OJvMuTt7+M9Z/240uF0aq8+9xTq0RPWb9plW/36tbz09OwKRWXWdqWuaSwhGyf8WDYdE/w14AvlDMo63pWn7LZh+uV7b+HKe2+BYTtz6kU3VzCqzmvAtqOJhvrNltf06Mmg7XeqQERm7aPUGOFzgDmS/lQwPvhgYFREuFG2CylMGJt4aT7LnnyI4btO7NiAuoDBO+7C1qN3YcWzj9FQt37D8poePZlwxIcrGJlZ2+S5pvF3SQMlbQPMAX4n6SdljsuqxJ3f8hdcax369Slsv+87qenRE9X2YNCo8Rz2jd/47inr1PLccjsoIlZJ+hTwu4j4lqRHyh2YWWfXq99WHHzO/1K/fh0N69fRs9+ASodk1mZ5zjR6SBoBnAxMLXM8Zl1Obc9eThjWZeRJGucDtwHzI2KGpHHAvPKGZdXifT+7rdIhmFkVaTFpRMS1EbFnRHwuzS9ID/hZF3Hq1U8UXT7pzB8ycDs/x2lmG5V6TuObwMUR8Uoz6w8H+kWEm6y6gMbE0dBQj1SDpApHZGbVqNSF8EeBmyW9CTwEvEQ2nsZ4YC/gDuB7ZY/QOlRNTW2lQzCzKlbqOY0bgRvTIEwHASOAVcAfgckR8UbHhGhmZtUiz3Cv8/CFbzMzI9/dU2ZmZkCFk4akIyQ9JWm+pK8VWX+6pJckPZxen6pEnGZmlsnzRHhZSKoF/g94N7AImCHppoh4vEnRqyPirA4P0MzMNtNi0pA0DPg0MKawfER8oo1170f2wOCCVM9VwHFA06RhZmZVIs+Zxo3AvWS32G7e13PrbQ88XzC/CNi/SLkTJR0CPA18ISKeL1LGzMw6QJ6k0S8ivlqGuos9PRZN5m8GroyItZLOAC4HDt9sQ9JkYDLA6NF+gtnMrFzyXAifKumoMtS9CBhVML8D2cBPG0TEyxHROPTZr4F9im0oIqZExKSImDRs2LAyhGpmZlC6G5HXyH75C/i6pLXA+jQfETGwjXXPAMZLGgssBj4IfKhJDCMiYmmaPRYo3kmSmZl1iFJPhG9Vzoojok7SWWQ96NYCv42IxySdD8yMiJuAsyUdSzYm+SvA6eWMyczMSlNE08sITQpId0bEO1taVi0mTZoUM2fOrHQYZmadiqRZETGppXKlmqf6AP2BoWls8MYL1wOBke0SpZmZdSql7p76DHAOWYJ4qGD5KrKH8szMrJspdU3jZ8DPJP1nRPyiA2MyM7Mqlec5jcWSTmiybCXwaEQsK0NMZmZWpfIkjU8CBwJ3pflDgQeACZLOj4g/lCk2MzOrMnmSRgOwW0S8CCBpW+CXZF1+TAOcNMzMuok8T4SPaUwYyTJgQho7fH15wjIzs2qU50zjXklTgWvT/InANEn9gVfLFpmZmVWdPEnjTLJEcRDZsxq/B66L7KnAw8oYm5mZVZk8Y4QH8Of0MjOzbqzFaxqSTpA0T9JKSaskvSZpVUcEZ2Zm1SVP89QPgWMiwj3Mmpl1c3nunnrRCcPMzCDfmcZMSVcDNwCNAyIREX8pW1RmZlaV8iSNgcAa4D0FywJw0jAz62by3D318Y4IxMzMql+eu6cmSLpT0tw0v6ekb5Y/NDMzqzZ5LoT/GjiX1GVIRDxCNp63mZl1M3mSRr+IeLDJsrpyBGNmZtUtT9JYLmknsovfSDoJWFrWqMzMrCrl7XtqCrCrpMXAs8BHyhqVmZlVpTx3Ty0A3pV6ta2JiNfKH5aZmVWjZpOGpP9qZjkAEfGTMsVkZmZVqtSZxlYdFoWZmXUKzSaNiPh2RwZiZmbVL8/dU2ZmZoCThpmZbYE83YiMzbPMzMy6vjxnGtcVWdYuQ79KOkLSU5LmS/pakfW9JV2d1k+XNKY96jUzs9YpdcvtrsAewCBJJxSsGgj0aWvFkmqB/wPeDSwCZki6KSIeLyj2SWBFROws6YPAD4BT2lq3mZm1TqlbbncBjga2Bo4pWP4a8Ol2qHs/YH56eBBJVwHHAYVJ4zjgvDT9Z+AiSYqIaIf6zcxsC5W65fZG4EZJB0bE/WWoe3vg+YL5RcD+zZWJiDpJK4EhwPLCQpImA5MBRo8eXYZQzcwM8vU9NV/S14ExheUj4hNtrFtFljU9g8hThoiYQtY/FpMmTfJZiJlZmeRJGjcC9wJ3APXtWPciYFTB/A7AkmbKLJLUAxgEvNKOMZiZ2RbIkzT6RcRXy1D3DGB8un13MdnATh9qUuYm4GPA/cBJwD98PcPMrHLy3HI7VdJR7V1xRNQBZwG3AU8A10TEY5LOl3RsKvYbYIik+cB/AZvdlmtmZh1HLf1wl/Qa0B9Yl14CIiIGlj+8LTdp0qSYOXNmpcMwM+tUJM2KiEktlcsznoZ7uzUzMyBfNyKS9BFJ/53mR0nar/yhmZlZtclzTeNi4EA2XqReTfYkt5mZdTN57p7aPyImSpoNEBErJPUqc1xmZlaF8pxprE/9RAWApGFAQ1mjMjOzqpQnafwcuB4YLum7wH3A98oalZmZVaU8d09dIWkW8E6y223fHxFPlD0yMzOrOnmuaQC8SNaVSA+gr6SJEfFQ+cIyM7Nq1GLSkHQBcDrwDBs7Cwzg8PKFZWZm1SjPmcbJwE4Rsa7cwZiZWXXLcyF8LtlATGZm1s3lOdP4PjBb0lxgbePCiDi2+bdYpb1Z9yZLVi9mSN+hDOo9qNLhmFkXkSdpXE42Nvej+PmMqhcRXPPUVVw771pqVUNdQx37jziAz0/8Ar1re1c6PDPr5PIkjeUR8fOyR2Lt4u7n7+LP865lXf2Gk0IeXDqdX835JZ+feE4FIzOzriDPNY1Zkr4v6UBJExtfZY/MWuXP865lbUHCAFjXsI5pi+5mbd2bFYrKzLqKPGcae6e/BxQs8y23VWrl2lebXbembg29e/TpwGjMrKvJ80T4YR0RiLWP3YfswfSlDxBsOrjWgJ4DGNTbN8GZWdvkebivN3AiMKawfEScX76wrLVO2/1jzHnpYdbWraUh3bfQu7Y3n9nzs9QoT2ukldvy5cv597//zapVq9hmm20YN24cW23lsc6sc8jTPHUjsBKYRcEtt1addthqFD897Bdc89RVPPHyE4wYMIIPTDiZ3YfsUenQur2IYNq0aTz99NMUDrM8Y8YM9tlnH/bee+8S7zarDnmSxg4RcUTZI7F2M6L/CD4/8QuVDsOaeO6555g/f/4mCQOgoaGBhx56iFGjRjF06NAKRWeWT572in9JemvZIzHr4p5++mnq6+uLrquvr2fevHkdHJHZlstzpnEwcLqkZ8mapwREROxZ1sjMupimZxhNNTT42VmrfnmSxpFlj8KsG5gwYQLPP/980bONHj16MG7cuApEZbZl8jRPvVbktaScQZl1RTvuuCNjx45F0ibLa2tr2Xnnndluu+0qFJlZfnnONB4CRgEryJqmtgaWSloGfDoiZpUxPrMuQxKHHXYYb3nLW5g3bx4rV65k4MCBjB8/nm233bbS4Znlkidp/A24PiJuA5D0HuAI4BrgYmD/8oVn1rVIYvjw4QwfPrzSoZi1Sp7mqUmNCQMgIm4HDomIBwB3m2pm1o3kOdN4RdJXgavS/CnACkm1tLKrdEnbAFeTPWW+EDg5IlYUKVdP1iU7wHNdeQyPJasXc+5dX2VF/WaHgW+89b/Zf6cDirzLzKxj5TnT+BCwA3AD2dPho9OyWrKhYFvja8CdETEeuDPNF/NGROyVXl02YTzx8uOcccfkogkD4LuPXtDBEZmZFaeW7h0vS6XSU8ChEbFU0gjg7ojYpUi51RExYEu2PWnSpJg5c2Z7hVoWj7/8OHc+93fW1a/nkB0O4bK5v+P51c+VfM+43uP46ZG/6KAIzay7kTQrIia1VK7Z5ilJP42IcyTdDGyWWdr4y3/biFiatrNUUnNXBftImgnUARdGxA3NxDoZmAwwevToNoRVfn964o9cP/8vrKtfRxA8sPR+1ta3PM7FgrULOiA6M7PSSl3T+EP6++PWbFjSHUCxG8+/sQWbGR0RSySNA/4h6dGIeKZpoYiYAkyB7EyjNfF2hBdff5Hr5v2Z9Q3rNyzLkzAAhmpYucIyM8ut2aTR+PxFRNzTmg1HxLuaWyfpRUkjCpqnljWzjSXp7wJJd5MNCLVZ0ugsHn5pdqu7J//tcZe1bzBmZq1QqnnqUYo0S9E+fU/dBHwMuDD9vbFI/YOBNRGxVtJQ4CDgh22os+L61Pahpsi9B7WqZWjfYby45oWi7zty26PKHZqZWS6lmqeOLmO9FwLXSPok8BzwAQBJk4AzIuJTwG7AJZIayO7yujAiHi9jTGW373b7bTaiHmRJ47z/OJ8a1fDsqwu4/enbeGjlLN4+8hA+u9eZDOi1RfcCmJmVzRbfPSXpIOBDEXFmeUJqm2q/e2rOS3P43vTvpLmgvqGez7zts7x7x/dUNC4z697afPdUk43tRfZsxsnAs8Bf2hZe9/W2YW/j90f+kTnLHqauoY49h73NZxJm1ioNDQ3Mnj2bp556ioaGBsaNG8fEiRPp06dP2eosdU1jAvBB4FTgZbInuBURh5Utmm6id21v9hvhLrvMrPXq6+u5+uqrWb169YZlc+fOZeHChZx88sn06JHrnGCLlbqV50ngncAxEXFwRPwCKD7smJmZdahp06ZtkjAavf7668yfP79s9ZZKGicCLwB3Sfq1pHeS3TllZmYV1NDQwDPPFH/6ICJYsqR8Qx41mzQi4vqIOAXYFbgb+AKwraRfpu7RzcysAurr60sOHzxw4MCy1d3ik2YR8XpEXBERR5N1XPgwzXcwaGZmZdajRw8GDGjUNFF7AAAQV0lEQVT+BprddtutbHVv0ePJEfFKRFwSEYeXKyAz6wQaGmDOH+F374BL/wNm/Arq1lU6qm5DEgcffDC1tbWbrXvHO95B//79y1Z3eS6vm1nXdv1p8OQNsP71bP7FOfDY1XDanVDTuq5ybMuMGjWKY445htmzZ7NixQoGDx7Mfvvtx+DBg8tar5OGmW2ZF+bAk9fD+jUbl61fA0tmwjO3wfgjKxdbNzN8+HDe+973dmid/klgZltm4T0QRQbtXLcaFtzZ8fFYh3LSMLMt038Y1BRppKjtA/237fh4rEM5aVint3r1aqZPn86tt97KzJkzWbNmTctvstbb5bjiSaOmBt72kY6PxzqUr2lYp7Z8+XJuvvlm6uvraWhoYOnSpcydO5fjjz+eQYMGVTq8rqlXv+yC91XHwZuvAoIeveGkq2CrEZWOzsrMSaPR/Nvg9i/DS09Aj17QUAc1PWG3E+C9P4H+QysdoRUxbdo01q/fOBJifX099fX13H///RxxxBEVjKyLGzkRvvBcdlG8YT2MmAg1m9/+aV2PkwbAgn/AVcdD3RvZ/Pq67G/9Oph7FSy6H858HGp7Vi5G20x9fT0vv/xy0XXl7EbBEglG7FXpKKyDOWkA3Pn1jQmjqYb1sPoFeHoq7HZ8vu1FwMK74aXH4em/wfypxcuN2AdOuhKGjG9V2N2dJCQV7U6hXD18mnV3/p8FsPyJ0uvXvQ7LHsuXNN5YAZcdCisWZLcglrJ0FkzZFz73KAwalTtcy9TU1LDzzjszf/58Gho23gJaW1tb1m4UzLozJw2AwTvBC7ObX19TC4PH5tvWrf8JLz0JDTm7VFi7Ev71YzjyZ/nK2yYOOuggVq9ezbJly5BEQ0MDo0aNYuLEiZUOrd00NDQwffp0Hn300aLrjz76aEaOHNnBUVl35aQBcPh34NoPbPqEa6GGOnjm77Dnh0tvJwIevzZ/wmj03H1bVt426NmzJ0cffTSvvPIKq1atYvDgwV3qrqm6ujquueaaouMmNJo6dSqTJ0/uwKisO/NzGgATjoLjfw9bj2m+zNyrYF2O+/8bWjFO1ZBdt/w9toltttmGMWPGdKmEAdlIbKUSRqMpU6Z0QDRmThob7X4inPMs9BtWfL1q0j3pJUgw7l1Z2S1x0Je3rLx1G+Ucgc2sNZw0mhp9EEUHKOw1AAbk6CLh6F9C3yHQs1+OymrgI3/zbYvWrGJdX5tVkq9pNHX4d2HBHbD+DYjU1NSzHxzx03wPLw0eC2fPh0evyB586tEPFs2AZQ/D+tTMsPMJcMIU6LtNdnZi1ozdd9+dadOmlRylDeCYY47poIisu3PSaGr47jB5FtxzPjz/Lxg8Dt7+dRi3BeNO9RkI+362fDFatzFhwgQWL15cspmqpqaGESPcfYd1DLX0C6azmTRpUsycObPSYZi1qxUrVrBw4UIee+yxTTpk9F1T1l4kzYqISS2V85mGWScwePBgBg8ezN57713pUKyb84VwMzPLrSJJQ9IHJD0mqUFSs6dDko6Q9JSk+ZK+1pExmpnZ5ip1pjEXOAGY1lwBSbXA/wFHArsDp0ravWPCMzOzYipyTSMinoCsl9IS9gPmR8SCVPYq4Djg8bIHaGZmRVXzNY3tgecL5helZWZmViFlO9OQdAewXZFV34iIG/NsosiyovcHS5oMTAYYPXp07hjNzGzLlC1pRMS72riJRUDhIBM7AEWHY4uIKcAUAEkvSfp3G+veUkOB5R1cZ7l4X6pTV9oX6Fr701X2Zcc8har5OY0ZwHhJY4HFwAeBD7X0pohopsfB8pE0M89DMZ2B96U6daV9ga61P11pX/Ko1C23x0taBBwI3CLptrR8pKRbASKiDjgLuA14ArgmIh6rRLxmZpap1N1T1wPXF1m+BDiqYP5W4NYODM3MzEqo5runOpOuNAKO96U6daV9ga61P11pX1rU5TosNDOz8vGZhpmZ5eakYWZmuTlptEJX6nBR0jaS/i5pXvo7uJly9ZIeTq+bOjrOUlo6zpJ6S7o6rZ8uaUzHR5lPjn05PT2L1PhZfKoSceYh6beSlkma28x6Sfp52tdHJE3s6BjzyrEvh0paWfC5/E9Hx9hhIsKvLXwBuwG7AHcDk5opUws8A4wDegFzgN0rHXuROH8IfC1Nfw34QTPlVlc61tYeZ+BzwK/S9AeBqysddxv25XTgokrHmnN/DgEmAnObWX8U8Fey3h8OAKZXOuY27MuhwNRKx9kRL59ptEJEPBERT7VQbEOHixGxDmjscLHaHAdcnqYvB95fwVhaI89xLtzHPwPvVAu9ZVZIZ/k3k0tETANeKVHkOOD3kXkA2FpSVY5bm2Nfug0njfLpLB0ubhsRSwHS3+HNlOsjaaakByRVU2LJc5w3lInsodGVwJAOiW7L5P03c2JqzvmzpFFF1ncWneX/SF4HSpoj6a+S9qh0MOVSzd2IVFRHdrhYbqX2ZQs2MzoilkgaB/xD0qMR8Uz7RNgmeY5z1XwWLcgT583AlRGxVtIZZGdQh5c9svLoLJ9LHg8BO0bEaklHATcA4yscU1k4aTQjOrDDxXIrtS+SXpQ0IiKWpqaBZc1sY0n6u0DS3cDeZO3vlZbnODeWWSSpBzCI6mxqaHFfIuLlgtlfAz/ogLjKpWr+j7RVRKwqmL5V0sWShkZEV+jIcBNuniqfDR0uSupFdgG2qu46Sm4CPpamPwZsdhYlabCk3ml6KHAQ1TMYVp7jXLiPJwH/iHT1ssq0uC9N2vyPJeuXrbO6CTgt3UV1ALCysam0s5G0XeN1Mkn7kX23vlz6XZ1Upa/Ed8YXcDzZr6S1wIvAbWn5SODWgnJHAU+T/SL/RqXjbmZfhgB3AvPS323S8knApWn6P4BHye7meRT4ZKXjbrIPmx1n4Hzg2DTdB7gWmA88CIyrdMxt2JfvA4+lz+IuYNdKx1xiX64ElgLr0/+XTwJnAGek9SIb0vmZ9O+q6J2I1fDKsS9nFXwuDwD/UemYy/VyNyJmZpabm6fMzCw3Jw0zM8vNScPMzHJz0jAzs9ycNMzMLDcnjW6soOfauZKuldSvQnGcU6m6U/0/Sr0W/6jJ8mOb651Y0uo21He6pJEF85dK2j1Nf70V2+sr6R5JtSXKbPF2W6iz1fuf3n+6pIvS9BmSTmuHmBZKGiqpl6Rp6UFOa2e+5bYbk7Q6Igak6SuAWRHxk5zvrY2I+naKYyHZPfoVeXpW0ipgWESs3YL3bDh2rajvbuBLETGzPbYr6UygR0T8rESZVsfb1u2lh94UEQ0Fy04n+8zPaseYFqZtLpf0LbLOH69or+1bxmca1uheYGcASR+R9GA6C7mk8RespNWSzpc0naxztn0l/St10vagpK0k1aZf7jNSp3qfSe89VNLdqZO9JyVdkZ4EPpvsoci7JN2Vyv4ydY74mKRvNwYo6aj03vuUjcMwNS3vr2y8gxmSZkvarGfYVNeP0lnVo5JOSctvAvoD0xuXFbyn8NfwWEn3pzouaFLuywX7++20bIykJyT9Ou3H7emM4CSyByevSMe3bzoukyRdCPRNy6+QdIGkzxfU8910vJr6MOlJfkkj0q/sxjPItzfdbip3g6RZKbbJBXWsTvXMUdY55bal9l/SAEl3SnooHdfjmuz/xWT9Mo2S9HFJT0u6h6xXgcZtnCfpS5JGauN4FA8rOxPeUdIwSdelumdIOii9b0g6rrMlXcKmfVndkI6LtbdKP13oV+VepDEyyPoguxH4LNlYITcDPdO6i4HT0nQAJ6fpXsACYN80PzBtZzLwzbSsNzATGEs23sBKsv6FaoD7gYNTuYXA0IK4Gp9KryUbs2RPsqe6nwfGpnVXksYvAL4HfCRNb032RHX/Jvt6IvD3tM1tgeeAEYXHocjxOZ00dgWpy4s0fWbBsXsPMIXsC6sGmEo29sIYoA7YK5W7piDGuyl4+rlwvjCWtI2H0nQN2ZPTQ5rE2At4oWD+i2x8krwW2KrYPhYc477A3Mbtps/4mDT9w4LPsrn97wEMTNNDyZ66V4q9ATggrRuRjvmwFPM/C47teWRnXoXxnQlck6b/xMZ/K6OBJ9L0z4H/SdPvS7EPLdj3lyr9f6wrvtzm1731lfRwmr4X+A3Zl/4+wAxlXen0ZWMnhvXAdWl6F2BpRMyAjR22SXoPsGf6RQ1Z54DjgXXAgxGxKJV7mOyL5b4icZ2cfv32IPuy2Z3sS3NBRDybylyZYoXsi/tYSV9K831IXy4F2zyYrHfYeuDF9Gt3X/L3B3YQWeIB+AMbOwp8T3rNTvMD0v4+BzwbEY3Hd1ba39wiYqGklyXtTZboZsemHRZC9kX9asH8DOC3knoCNxTU39TZko5P06NSzC+TfU5TC2J+d5pubv8FfE/SIWRJYvsUK8C/IxsnA2B/4O6IeAlA0tXAhGKBpTOJTwFvT4veBeyujUOgDJS0FVlyPgEgIm6RtKKxQETUS1onaauIeK2ZY2Ct4KTRvb0REXsVLlD2P/PyiDi3SPk3Y+N1DFG8G2sB/xkRtzXZ7qFkfXU1qqfIvz9JY4EvkZ3BrJB0GVkSKDVokoATo/TAWO0x6FJz+/v9iLhkk4XZkLJN97dvK+q8lOyMZzvgt0XWv0F2fLIAI6alL/D3AX+Q9KOI+H2T2A4l+yI+MCLWKLvG0riN9ZF+qrP5Z1Rs/z9MdvawT0SsV3ZdoXFbrzcp2+IFVGUdMv6GrK+txovtNSnWN5qUbWmbvYE3W6rTtoyvaVhTdwInSRoOG8YQ37FIuSeBkZL2TeW2Una3ym3AZ9MvXSRNkNS/hTpfA7ZK0wPJvmxWpvb0IwvqG6eN43sXXn+4DfjPlPBIv8ybmgacouyayzCyX6kPthBXoX+S9ToLm7aV3wZ8QlLjDQXbNx67Egr3t6n1jccuuR44guys6LamhSNiBVArqU+qf0dgWUT8muzLt3Hc7cLtDgJWpISxK9lQqy1pbv8HpfrWSzoMKPZvBWA6cGi6DtET+EDTAmn5NcBXI+LpglW3k3UI2Fiu8YfOtMZYJB0JDC4oM4SseWp9jn2zLeCkYZuIiMeBbwK3S3qE7DrAZkNwRjYc6SnALyTNSeX6kP0yfhx4SNJc4BJaPqOdAvxV0l0RMYesqecxsl/W/0z1vUE21vffJN1H1rvwyvT+C4CewCOpzgvY3PXAI2S9kP4D+EpEvNDyEdng88CZkmaQfVE2Hofbydrc75f0KNlwss0lhEaXAb9KF3ubnn1MSftxRdr+OrLebK+J5u9Wu52s+Q2ya0cPS5pN1pzUeEdV4Xb/BvRIn+8FZL2ytqTo/gNXAJMkzST7An+y2Jsj6/L8PLJrWXeQXRxv6j/IkuO3Cy6GjwTOTnU8Iulxst5lAb4NHCLpIbImwucKtnUYcGuO/bIt5FturdOQNCCykdEau9SeFxH/W+m4yklSDdkX7AciYl4zZfYG/isiPtqhwVUxSX8Bzm2hydJawWca1pl8Ol1Af4zs1+4lLZTv1JQ98DcfuLO5hAEQEbPJbllu9uG+7kTZAFY3OGGUh880zMwsN59pmJlZbk4aZmaWm5OGmZnl5qRhZma5OWmYmVlu/x/1C39m8D5H4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(kaggle_data_preprocessed[:,0],kaggle_data_preprocessed[:,1],c=kaggle_cluster_labels,cmap='Set1')\n",
    "plt.xlabel('Percentage of identity (standardized)')\n",
    "plt.ylabel('Alignment length (standardized)')\n",
    "plt.title('Sequence clustering before PCA')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_pca = PCA()\n",
    "kaggle_pca.fit(kaggle_data_preprocessed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(1,len(kaggle_pca.explained_variance_ratio_.cumsum())+1),kaggle_pca.explained_variance_ratio_.cumsum())\n",
    "plt.xlabel('Nb of PCA components')\n",
    "plt.ylabel('Cumulative explained variance (%)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_pca = PCA(n_components=2)\n",
    "kaggle_pca.fit(kaggle_data_preprocessed)\n",
    "kaggle_scores_pca = kaggle_pca.transform(kaggle_data_preprocessed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2Lf27dcsfIo9u3W6mWzfn5/zDDzP46quxnHfe5axZs4QTTqhSrCS2d+9OKleuRlJSEp99NoJOna4JuF/+36ezznJai/Xqhfb7dO21uckLoHFj2LEjb1ddrNQ1mvVt3/5m2rd36vvzzzOYO3csp59+OZs2LaF8+SpUqZK3vocP7+fIkf1UqVKb7OwsVq+eSePGbQH4/fcN1KzZGIBVq6ZRq1bTooMfj/BddNwE+P+BNwA2+++gqv4jD94AnvN7b6d8751R0gMKlrDnAgdUtcCAORH5uSSB161zfun+9Cenf+2LL3K39ekT/Ft/2TK49FLo29cZ1uf//sK0bNmR5ctn8sQTXSlXriJ9+jxzbNvTT/fgb3+bwK5dW/nii+HUqdOUZ565GoBOnW7hggt606LFhaxc+R1PPXUZSUnJ9Or1MJUrF30doUxyMgNvu43+zz9PtirXdOhAeoMGDPv4Y1o2aUKXM8/k+fff58ChQwx41Wlp1a1Rg+H33w/ATU8/zbotWzhw6BAdBgxgcL9+XBhCH3eZ5GQG3ngj/V9+meycHK45/3zS69Vj2IQJtGzUiC6tW/P8Rx9x4PBhBrz+uhPXHb63dutWnhwzBklKQnNy+FP37nlGlwTTpk1HFi+eyYABXSlfviJ33pn7c37kkR4899wEAN5993m+++5zjhw5yN13d6Bz59707n0vK1fO5/33/wkIp57alttvfzJozHXrnCFuGRm5w0R9+vYNbcjaqacWtzskOnWF6NX35JM7snr1TP75T+dvqFev3Pq++moP7rlnAkePHmTs2LvIyjqCag5Nm7bn7LNvAODrr1/k99/XIyJUq1afHj3+XrwDCEX4EvYCIF1EmuCMArkBuMl/BxGpq6pb3JdXAb6f6FfAM34XGi8BHivpAUngPqnw8e8SiZS2bSMd0dH5hHnRCXzoUFTC/pjSMSpxv/468jH9uvcjKhp1BacxFQ29exd6iSd0zz0Xes555JEi44nIZcDLOMP63lLVwSIyCFioqhNF5FmcRJ0F7ATuUtVV7ntvB/7qftRgVR1V7LrkE6wP+yQgTVW/y1d+IbBZVdeW9ACMMSaswjgOW1UnA5PzlQ30e/4YhbScVfUt4K2wHQzBR4m8DATq4TrobjPGmNhSjFEi8SbYETdW1aX5C1V1oYg09uSIjDGmJErxrelF3WJXMZwHYowxYZHACTtYzRaIyJ/yF4pIP2CRN4dkjDElEL47HWNOsBb2fcCnInIzuQm6LVAOuNrLAzPGmOMSh4k4VEUmbFXNBM4Tkc6Ab6KJSapavKnjjDEmUkprwhaRCsCdwEnAMuBNVc2KxIEZY8xxicPRH6EKVrPRwFHgW5wpBk/F6SYxxpjYVFpb2EALVW0FICJvAvO9PyRjjCmBUpywj82iq6pZnkw2bowx4VSKE/YZIuKb8FmAiu5rAVRVwzO1nDHGhEtpTdiqmhypAzHGmLAoxRcdjTEmvpTWFrYxxsQdS9jGGBMnLGEbY0ycsIRtjDFxwhK2McbEiQQeJZK4X0XGmNIpjNOrikh3EflZRNaIyKMBtj8gIitFZKmITBWRRn7bskVksfuYGI6qJe5XkTGmdApTl4iIJAP/BroCm3DWB5ioqiv9dvsRaKuqB0TkLuB54Hp320FVbR2Wg3FZC9sYk1jC18JuB6xR1XWqegR4H+jhv4OqTlfVA+7LuUCDsNfHjyVsY0xiKUbCFpEMEVno98jw+6T6wEa/15vcssL0A77we13B/cy5ItIzHFWzLhFjTGIpxkVHVR0BjChkc6DZ7jTgjiK34KzG1dGv+ERV3SwiTYFpIrJMVdeGfHABWMI2xiSW8A3r2wQ09HvdANicfycRuRh4HOioqod95aq62f13nYjMANoAJUrY1iVijEks4evDXgCki0gTESkH3ADkGe0hIm2A14GrVHWbX3l1ESnvPq8JnA/4X6w8LtbCNsYkljC1sN01AO4BvgKSgbdUdYWIDAIWqupEYChQGRjvrhfwP1W9Cmd1rtdFJAenYTwk3+iS42IJ2xiTWMJ4p6OqTgYm5ysb6Pf84kLeNwdoFbYDcVnCNsYkFrs13Rhj4kQC35qeuDUzxpRO1sI2xpg4YQnbGGPihCVsY4yJE5awjTEmTljCNsaYOGGjRIwxJk5YC9sYY+KEJWxjjIkTlrCNMSZOWMI2xpg4YRcdjTEmTlgL2xhj4oQlbGOMiROWsI0xJk5YwjbGmDiRwAk7cWtmjCmdypQJ/RGEiHQXkZ9FZI2IPBpge3kR+cDdPk9EGvtte8wt/1lEuoWjapawjTGJJUyrpotIMvBv4FKgBXCjiLTIt1s/YJeqngS8BDznvrcFzirrpwHdgf+4n1eyqpX0A4wxJqaEKWED7YA1qrpOVY8A7wM98u3TAxjtPv8I6CLO8uk9gPdV9bCqrgfWuJ9XIp73YT900SKvQxQ0bVrkYwLcdFN04pYrF5Wwn/wrKmHp3z/yMUeOjHxMiE5dARpNfCU6gbm35B9RjD5sEckAMvyKRqjqCPd5fWCj37ZNwDn5PuLYPqqaJSJ7gBpu+dx8760f8oEVwi46GmMSiiKh7+sk5xGFbA70QRriPqG8t9gsYRtjEkpOTuj7Jhfdq7wJaOj3ugGwuZB9NolIGaAqsDPE9xab9WEbYxJKVlbojyAWAOki0kREyuFcRJyYb5+JQB/3+bXANFVVt/wGdxRJEyAdmF/SulkL2xiTUIrTwi6K2yd9D/AVkAy8paorRGQQsFBVJwJvAmNEZA1Oy/oG970rRORDYCWQBfxZVbNLekyWsI0xCSVcCRtAVScDk/OVDfR7fgjoXch7BwODw3c0lrCNMQkmnAk71ljCNsYkFEvYxhgTJ0K4mBi3LGEbYxKKtbCNMSZOWMI2xpg4YQnbGGPihCVsY4yJE5awjTEmTtgoEWOMiRPWwjbGmDhhCdsYY+KEJWxjjIkTlrCNMSZOWMI2xpg4YaNEjDEmTlgL2xhj4oQlbGOMiROJnLBtEV5jTELJyQn9URIikioiU0TkF/ff6gH2aS0i34vIChFZKiLX+217W0TWi8hi99E6WExL2MaYhBLGVdODeRSYqqrpwFT3dX4HgNtU9TSgO/CyiFTz2/6QqrZ2H4uDBbSEbYxJKJFqYQM9gNHu89FAz/w7qOpqVf3Ffb4Z2AbUOt6AlrCNMQklggk7TVW3ALj/1i5qZxFpB5QD1voVD3a7Sl4SkfLBAtpFR2NMQilOIhaRDCDDr2iEqo7w2/4NUCfAWx8vzjGJSF1gDNBHVX1H+BiwFSeJjwAeAQYV9TmWsI0xCaU4CdtNziOK2H5xYdtEJFNE6qrqFjchbytkvxRgEvA3VZ3r99lb3KeHRWQU8GCw47UuEWNMQolgl8hEoI/7vA8wIf8OIlIO+BR4R1XH59tW1/1XcPq/lwcLaC1sY0xCieCt6UOAD0WkH/A/oDeAiLQF7lTV/sB1QAeghoj0dd/X1x0R8q6I1AIEWAzcGSygJWxjTEKJ1I0zqroD6BKgfCHQ330+FhhbyPsvKm5MS9jGmISSyHc6WsI2xiQUS9jGGBMnLGEbY0ycsIRtjDFxwhYwMMaYOGEtbGOMiROWsI0xJk5YwjbGmDhhCdsYY+KEXXQ0xpg4YS1sY4yJE5awjTEmTljCNsaYOGEJ2xhj4oQlbGOMiRM2SsQYY+KEtbA9NmvJEga/8w45OTn07tyZjKuuyrN91KRJjJ8xg+SkJFJTUngmI4P6tWoVP87GjQyeO5ccVXqfcgoZZ5wRcL8v169nwNSpfNSjB63cOKt27ODJ777jjyNHSBLhox49KF+m+D++WfPnM/jVV526XnYZGTfdlGf7giVLeObf/+bndev45xNP0L1jx2LHOBZr7lwGDxvmxLriCjJuvTXP9iNHjvDw00+z4uefqZaSwkuDBtGgbl2OZmXxtyFDWLl6NVnZ2fTs3p078r03mMsug5NPhqNH4ZNPYMuWgvvcfjtUqeLsAzB6NOzfD40aOe9PS4Px42HFiuDxVJX//GcwCxbMpHz5Cjz44BDS008rsN+oUS8xZcpn/PHHXiZO/PFY+UcfjeLLL8eTnJxM1aqp/OUvz5CWVj8m6xrN+s769VcGf/ut8zfUogUZZ50VcL8v16xhwJdf8lHv3rRKS2NpZiZPTJ9+7NjvbdeOrs2ahVbZYkrkhB31RXizc3IYNGoUIx9+mElDh/L5nDms2bQpzz6nNm7Mx08/zX+fe45u7doxdNy444szZw4ju3Vj0jXX8PnatazZtavAfn8cOcKYFSs4w+8LISsnh4dmzODv55/PpGuv5Z3LL6dMUvF/dNnZ2QwaNoyRQ4YwadQoPp82jTUbNuTZp25aGs8+8ghXdCmw8lDxY/3zn4x84QUmjR3L5998w5r16/PsM/7zz0mpUoUpH3xA3+uv54XXXgPgy2nTOHL0KP995x0+efNNPpgwgU2BslAh0tOhRg14+WWYMAGuvLLwfcePh//8x3ns3++U7dnjJL5ly0Kv74IFs/jttw2MGvU19933D/71r6cC7te+fWdeeWV8gfKTTjqVV1/9mNdf/y8XXtiNkSOHhhQ3GnWF6NQ3OyeHQTNnMvLKK5l00018vno1a3buLLDfH0eOMGbpUs5ISztWlp6aysfXXceEG25g5FVXMXDGDLI8yqyRWoRXRFJFZIqI/OL+W72Q/bJFZLH7mOhX3kRE5rnv/8BdsLdIUU/YS9esoVFaGg3T0ihXpgyXn3suUxctyrNP+9NOo2L58gC0Tk9na4BfkqBxtm+nUUoKDVNSKJeczOVNmzL1118L7Dds0SL6n3465ZOTj5V999tvnJKaSvMaNQCoXqECyceRsJeuWkWj+vVpWK8e5cqW5fKLLmLqnDl59mlQpw7NmzUj6Tg+P0+sn36iUYMGNKxf34l18cVMnT07zz7TZs/m6ksvBaBbp058v2gRqoqIcPDgQbKysjh0+DBly5ShcqVKIcc+9VRYvNh5vmkTVKwIlSuHfuy7d0NmZvH+oObMmUrXrj0REU49tTX79+9lx45tAY6tNTVq1C5Q3rp1eypUqHhsn+3bt4YUNxp1hejUd2lmJo2qVqVh1arO31B6OlPXrSuw37B58+jfpk2ev6GKZcsea+Qczs5GQq5p8UVw1fRHgamqmg5MdV8HclBVW7sP/+6D54CX3PfvAvoFCxj1hJ25axd13EQIkJaaSmYRCfmj6dPpUEhXRpFxDhygjl/SSatUicwDB/Lss/L339m6fz+dTzwxT/n6PXsQEfp98QVXf/opbyxZUuz4AJm//06d2rl/PGk1a5K5fftxfVbQWNu3541Vq1aBWJnbt1PX3adMmTJUqVSJXXv20K1zZypWrMgFPXvS+ZpruP3GG6mWkhJy7JQUp+Xos2ePUxZIr15w993QqVPIHx/Qjh2Z1KpV59jrmjXrsGNH5nF91pdffsTZZ3cIad9o1BWiU9/M/fupU6XKsddplSuT6TtVcK3cvp2t+/bRuUmTAu9fsnUrl7/3HleNG8ffO3U6rrPUUGRlhf4ooR7AaPf5aKBnqG8UEQEuAj4qzvuDdsKKSApQS1XX5is/XVWXhnqAhVHVQDED7jth9myWr1/P2CeeCE8cv+c5qjw7dy7PBugzzs7JYdHWrXzUsycVy5Sh7+TJtKxZk3Prh9bHWeQxFFLXkgolVmH7LF25kqSkJL797DP27tvHTXffzXlt29KwmPUNZvx42LcPypWDG2+E1q1zW6vFFaguHEc77ptvJrB69XJeeCHgQtfHLZx1hejUN1jEHFWe/fZbnr344oDvP6NOHSbddBNrd+7kkW++oUOjRsd1HSiYCPZhp6nqFgBV3SIiBU9lHBVEZCGQBQxR1c+AGsBuVfV9bWwCgv6BFfnTEpHrgJeBbSJSFuirqgvczW8DZxbyvgwgA+D1v/6VjF69Co1RJzWVrTt2HHuduXMntasX7Aqas2wZwz/7jLFPPEG5smWLOuzAcSpVYqtfayBz/35qn3DCsdf7jx5l9a5d3DZpEgDbDx7krilTeK1rV+pUqkS7unVJrVABgA4NG7Jix45iJ+w6tWqxdVvuaWvm779Tu2bNYtclpFi1a+eNtX17gVh1atdmy7Zt1Kldm6xT8HK5AAAd5UlEQVSsLPbt30+1lBQ+nzKFC885h7JlylCjenXObNWKZatWFZmw27WDtm2d57/9BlWr5m6rWhX27i34nn37nH+PHIGlS6F+/eIlsYkT32Xy5A8BOOWUVnlO63//fWvAroCi/PDDHMaNG84LL4ylXLnCuxOjUVeIXn196lSqxFZfRYDMP/6gtt9Z6/4jR1i9cye3ffopANsPHOCuSZN47fLLaeXXn90sNZWKZcuyeseOPOXhUpyE7Z+rXCNUdYTf9m+AOgXeCI8X45BOVNXNItIUmCYiy4AAvyUBvxPzCPb19lfgLPfbox0wRkT+qqqfUMTXuVthp9KLFhV5EK2aNWPD1q1s3LaNtNRUJn3/PS/ec0+efVZu2MDAN99k5COPUMP/r6MYWtWqxYa9e9m4bx9pJ5zApHXreLFz52Pbq5Qrxzy/kRC3fv45D59zDq1q1eLElBRGLl3KwawsyiYlsWDLFvq2bFn8Y2jenA2//cbGLVtIq1mTSdOm8eLjxfl/L2asjRvZuHkzabVqMembb3jxySfz7HPR+efz6Rdf0KZlS76aMYP2Z56JiFA3LY15P/xAj27dOHjoEEtWrqTPddcVGW/+fOcBzoiJc85xLqQ1aACHDsEff+TdPykJKlSAAwec56ecAmvXFvzcolx11c1cddXNAMybN4MJE8bSqdPlrFq1hEqVqhQrga1Zs5JhwwbyzDMjqV69RpH7RqOuEL36+rRKS2PDnj1s3LuXtEqVmPTLL7x4ySXHtlcpX555/fsfe33rJ5/w8Pnn0yotjY1791K3cmXKJCXx2969rN+1i/rF6GYrjuIk7Dy5KvD2wKcLgIhkikhdNz/WBQpeRHA+Y7P77zoRmQG0AT4GqolIGbeV3QDYHOx4gyXsZL8m/3wR6Qx8LiINCOHbIBRlkpMZ2Lcv/YcMITsnh2s6dSK9QQOGjR9Py6ZN6XLWWTz/7rscOHSIAf/6FwB1a9Rg+IMPFi9OUhIDzzuP/l98QbYq15x8MunVqzNs0SJa1qxJl0aNCn1v1fLl6duyJdd+9hkiQocGDeiUr5875Lreey/9H3mE7Oxsrrn0UtKbNGHYqFG0PPlkupx/PktXreKegQPZ+8cfTP/+e155+20mjRpV/FhlyjDwgQfo/8ADzs/18stJb9qUYSNH0rJ5c7pccAHXXnEFD/3jH3S9/nqqpqTw0lNPAXBzr1489swzXHHrrSjQ67LLaH7SSSHHXr3aSWT335871M3n7rudURLJyXDbbc6/SUlOAlu40Nmnfn2n26BiRWjeHC66CF55peiY7dp1ZP78mfTt25Xy5Svy4IPPHNt25509GD58AgBvvPE806d/zuHDB7nppg50796b2267lzfeeJ6DBw/wj38MAKB27boMGjQ8JusarfqWSUpiYIcO9J8wwfkbatGC9Bo1GDZvHi1r16ZLgH5rn0WbN/PGDz9QJimJJBGe6tSJ1IoVg1f0OESwS2Qi0AcY4v47If8O7siRA6p6WERqAucDz6uqish04Frg/cLeX+DzAveFHQs2B7jVv/9aRKoAnwEXqGr5oFUK0sL2xLRpEQ8JQL4x1RETwumsF574V/HHwoeDXyMuYkaOjHxMiE5dARpNDOFbwwv33lviizqPPRZ6Y/LZZ49/wIqI1AA+BE4E/gf0VtWdItIWuFNV+4vIecDrQA7OII+XVfVN9/1NcZJ1KvAjcIuqHi4qZrAW9l3k6/pQ1X0i0h0o+hzZGGOiIFK3pqvqDqDADROquhDo7z6fA7Qq5P3rgHbFiRksYe8H0oA1+crbA3OLE8gYYyKhNN/p+DKwL0D5QXebMcbElAjeOBNxwVrYjQONtVbVhSLS2JMjMsaYEojHRByqYAm7QhHbvLnEa4wxJZDICTtYl8gCEflT/kIR6QcsCrC/McZEVWnuErkP+FREbiY3QbcFygFXe3lgxhhzPErtAgaqmgmc594w47u1b5KqRmmgszHGFC0eW86hCjaXSAXgTuAkYBnwpt9kJcYYE3NKbcLGmfLvKPAtcClwKk43iTHGxKTSnLBbqGorABF5E5jv/SEZY8zxK80J+6jviapmeTV3szHGhEupvegInCEivnlbBajovhZAVdWb+RGNMeY4ldoWtqomF7XdGGNiTalN2MYYE28sYRtjTJywhG2MMXHCErYxxsSJ0jxKxBhj4koit7CDzdZnjDFxJVKz9YlIqohMEZFf3H+rB9ins4gs9nscEpGe7ra3RWS937bWwWJawjbGJJQITq/6KDBVVdOBqe7rPFR1uqq2VtXWwEXAAeBrv10e8m1X1cXBAlrCNsYklAgm7B448y3h/tszyP7XAl+o6oHjDWgJ2xiTUCKYsNNUdQuA+2/tIPvfAIzLVzZYRJaKyEsiUj5YQLvoaIxJKMUZJSIiGUCGX9EIVR3ht/0boE6Atz5enGMSkbpAK+Arv+LHgK04C8KMAB4BBhX1OZawjTEJpTgtZzc5jyhi+8WFbRORTBGpq6pb3IS8rYhQ1wGfqqr/hHpb3KeHRWQU8GCw47UuEWNMQolgl8hEoI/7vA8woYh9byRfd4ib5BFnGtSewPJgAS1hG2MSSgQT9hCgq4j8AnR1XyMibUVkpG8nEWkMNARm5nv/uyKyDGc1r5rA08ECWpeIMSahROrGGVXdAXQJUL4Q6O/3egNQP8B+FxU3piVsY0xCsVvTjTEmTiTyremWsI0xCcUStjHGxAlL2MYYEycsYRtjTJywhG2MMXHCRokYY0ycsBa2McbECUvYxhgTJyxhG2NMnLCEbYwxccIStjHGxAkbJWKMMXHCWtjGGBMnLGEbY0ycsIRtjDFxwhK2McbECbvoaIwxcSKRW9i2CK8xJqFEahFeEektIitEJEdE2haxX3cR+VlE1ojIo37lTURknoj8IiIfiEi5YDEtYRtjEkoEV01fDvQCZhW2g4gkA/8GLgVaADeKSAt383PAS6qaDuwC+gULaAnbGJNQIpWwVfUnVf05yG7tgDWquk5VjwDvAz1ERICLgI/c/UYDPUMJGrMPIMPiJl5Mi5u4MaMZ93iPFVjo9yj2sQMzgLaFbLsWGOn3+lbgVaCmm8h95Q2B5cFixXoLO8PiJmRMi5u4MaMZt9hUdYSqtvV7jPDfLiLfiMjyAI8eIYaQQGGLKC+SjRIxxphCqOrFJfyITTitZ58GwGbgd6CaiJRR1Sy/8iLFegvbGGPi2QIg3R0RUg64AZioTj/IdJwuE4A+wIRgHxbrCXtE8F0sbhzGtLiJGzOacSNKRK4WkU3AucAkEfnKLa8nIpMB3NbzPcBXwE/Ah6q6wv2IR4AHRGQNUAN4M2hMt8PbGGNMjIv1FrYxxhiXJWxjjIkTMZWwReQKEYmpYzLGmFgRU33YIjIWpwP/Y2CUqv4UobjnA08BjXCGOgqgqto0EvHdY0gCKqvqXo/jVAIOqmqOiJwMNAe+UNWjHscdo6q3BisLY7z/UsS4VlW9you4fvGfU9VHgpWFKdYrFF3X/wt3zHzxBbgZaKqqg0TkRKCOqs73Mm5pFFOtWVW9BWgDrAVGicj3IpIhIlU8Dv0m8E/gAuBsoK37r6dE5D0RSXGT6ErgZxF5yOOws4AKIlIfmAr8P+Btj2MCnOb/wp1j4SwP470AvAisBw4Cb7iPP3DmgPBa1wBll3oUayGwCKgAnAn84j5aA9kexfT3H5yG1o3u630482eYcIv2raGF3M5ZE7gP2AB8gfPLd6+H8eZFqZ6L3X9vxvnCKAss9TjmD+6/9wIPu89/9DDeYzh/wFnAXvexD9gBPBuBn/GsUMrCGO8uYBlwAFjq91gPjPW4rtOBsn6vywLTI/Az9v1O/ehXtsTruKXxEVN3OorIVTgtvmbAGKCdqm4TkRNwxjC+EuZ4Z7pPp4vIUOAT4LBvu6r+EM54AZQVkbI4k768qqpHRcTrPioRkXNxviR8s4N59nugqs+KyHM48ync7lWcItQSkaaqug6cKS2BWh7Gew+nkfEs8Khf+T5V3elhXIB6QBXAF6eyW+a1o+4ZkwKISC0ggWeljp6YStg4d/28pKp5pitU1QMi4sUf+4v5XvvPaas4s2l56XWcs4glwCwRaYTTAvXSAJxW76equkJEmuK0zDyjTn/5GV7GKML9wAwRWee+bgzc4VUwVd0jIvuAVqr6q1dxCjEE+FFEfP+fHXGuzXjtX8CnQG0RGYzzd/y3CMQtdWLtomPELtTki3GsBVZUWST4zS2QUETk38DbqrogCrHL41xcBVilqoeL2j9MMd8FHlPV/3kdK1/cOsA57st5qro1QnGbA11wLthP1QgNGChtYi1h/6CqZ+YrW6qqp0ch7iJV9fKiGCKSBjwD1FPVS92Jzc9V1aC3qJYg5snAgzgtzWNnWKrq6dmEiKwETgZ+BfaTOxLH0/9bN/Z5FKzvOx7HnIZz4Xo+Tn19cb0enVKf3NFOvpiFTrAfhnhJONddWnoVw+SKiS4REbkLuBtoJiJL/TZVAb7zMG5znNELVUWkl9+mFJwr7l57GxgFPO6+Xg18QAhzCpTAeGA4MJLIjCDw8WqERJFEZAzONZHF5NZXAU8TNvB3jz+/APdawfXACnL7kJUiVkQpKbe7a4mInBjps4nSKCYSNtG7UHMKcAVQDbjSPy7wJw/j+tRU1Q9F5DFwJooREa+TaJaqvuZxjAJ8/bkiUpvIfBn6tAVaaIRPJVV1ZiTjuXoCp0SiyyefusAKEYno2URpFCsJW1V1g4j8Of8GEUn1Kmmr6gRggoicq6rfexEjiP0iUoPcq+vtgT0ex/yviNyNc5HIf0SMpyMY3BFAL+KMWtiGc9r+E/nGZ3tgOVAH2OJxnDzc/8tXgFOBckAysF9VUzwMuw5nKF+kE3bEzyZKq5jowxaRz1X1ChFZT8HVGFQ9vuOwkDvF9gAL3aTuVdwzcf6oW+IkllrAtaq6tMg3lizm+gDFkfgZL8EZdfONqrYRkc7Ajarq6eok7oiJ1jh9yf5fUF73JS/Emft4PE4r/zYgXVX/6mHMj4EzcG6I8q+rp3c6urHTyL3ZbL6qbvM6ZmkUEwk72kRkBM4ogvFu0TU4/YANgXWqep+HscvgdM0I8LN6fIt4tIjIQlVt6ybuNm7f53xVbedx3I6Byr3usvCr77GL5iIyR1XP8zBmn0Dlqjraq5hu3OuAoThrGwpwIfCQqn5U1PtM8cVKlwgAIjJVVbsEK/PAScBFvuF0IvIa8DXO7cXLPI7djtwRDGeKiKcjGNybkB4ATlTVDBFJx+n3/NyrmK7dIlIZ5wLYuyKyDefuR0+p6kx3fHu6qn7j1j/Z67jAAXeFkcUi8jxOl0wlLwOq6mgRqYjzfxtsNe9wehw429eqdm+c+YbcFcFNmMTEXCIiUkFEUoGaIlJdRFLdR2Mic6dWffL+MVXCGWqXjYf9ge4IhhfIncPEN4+Jl0YBRwBfS28T8LTHMQF64NyufT/wJc58MVd4HVRE/oSTOF53i+oDn3kdF2d17CSc1Ub245yt9SryHSUkIlfijIb50n3dWkQmehnTlZSvC2QHMZJbEk2stLDvwJk7pB7OJDa+Puy9RGYSmedxWkIz3NgdgGfcSZm+8TBuNEYwNFPV60XkRgBVPejOtua1ge4NUDnAaDg2DM3Tm6KAP+OcxcwDUNVf3JEqXuupqsOAQ7gX5URkADDMw5hP4dR1BoCqLnZvxffal+IsjzXOfX09zqgvE2Yx8S2oqsNUtQnwoKo2VdUm7uMMVX01AvHfxGlxfuY+LlDVkaq6X1W9nD3PN4Ihko64p82+kSnNiMyogkjOXufvsKoe8b1wrxlE4gsyUH9yX49jZqlq/lFGntfV/Rt5HTgd56LnCFV92Ou4pVGstLABUNVXonFXmisJ2O7GPUlETvLqDjHJnau5CrDSHb8aqREMT+KcMjd0b58+Hw8TSbRuivIzU0T+ClQUka7usfzXq2DumctNQJN83REpOF0FXlouIjcBye61if8D5ngc0zeh1mRV/cR9XVFEGqvqBq9jlzYxNUqksLvSvB6WVNgdYl4lzsJGLvhEYARDDaA9TvfPXFX93cNYVYHqRGf2Ot+t0/2AS3Dq+xXOzIGe/OK7FzibEKC+OLdwe3ah1b2g+jhOXcGp69OqesirmG7chcB5vjMZ92Lrd6rq+ZzypU2sJeyfiMJdaSLyM3B6pO8Qc1smW3x/UG5XRZrXLRP3NvwLcFr5s1X1Uy/juTGbAZtU9bCIdMI5fX5HVXd7HTsaJEor+0SDiCxW1db5ypaoarRmaExYMdGH7ScafbqQe4dYpI0n77zB2eSOBfeEiPwHuBNnuOJy4A5xZtLz2sdAtoichDNXShOcKQkSVbRW9omG7e6drACISA/As7O20iym+rBxVpqJdJ8uOMPNFotIpO8QK+N/QUxVj7ink17qCLT0ncWIyGi8H2sOkOPOldILeNm9XvFjBOJGi6gzj3s/4BVVfT6B63snztj6V3G6nTbi3NlpwizWEvZTUYo70X1E2nYRuUpVJ0LEWiY/AyfiTHMKzvhgz26F93PUvSB3G7kTbUXjrCZSRCK4sk80qepaoL17Y5So6r5oH1Oiiqk+bDh20SbPXWmR+AWIxh1ibr/uu+TeHLQJuNX9A/Aq5kxy52nGff49zlmGZ2cz4sz1fSfwvaqOc/vvr1fVIV7EC3IsGao6wuMYHXDmHf9OVZ8TZ2Wf+yIxr0e+47jC67tY3fHlo3AurL6BsxDwo6r6tZdxS6OYStjuXWkZQKqqNnOHJg33+tZ09w6xF4ByqtpERFoDgyIwQVATVV3v3zLxlXkYM6ojVGKBiNyhqq8H3zP+icjfVfVJj2MsUdUzRKQbzo1KTwCjNN+iIKbkYi1hL8a9K01V27hly1S1lcdxF+HMJDcjwnGjudKNzaxmwkLcCa5EZBjO39CnIvKj72/JhE+s9akddi+8ARG9Ky1LncVT/cs8iytRXOlGCs6s9oqIJOzMaiLyQFHbVfWfkToWr4lIb+BL90ztbzhdE/9QVa8vdi4Ska9xRv48JiJVsFXTPRFrCTuid6X5ifQdYtFc6aa0zazWFudswndR+UqcIXcbo3ZE3nlCVceLyAVAN5xuvuHkLsrrlX44c46vc0fG1MAZxmjCLNa6RCJ6V5pfXP87xHxx/xGBO8QivtJN/q4e92e+xOvun0KOJRIX/74GrvFduHZbf+NVtbuXcQs5Fk8vAPq6IUTkWWCZqr5nXROJJaZa2Kqag3OV+Y0Ixz2Ak7AfD7ZvmP0ozrJop+HXFaKqt3sYM9DMapM9jFeUSMwSeCLOdLI+R3DmqomGswEvR2z8JiKvAxcDz4lIeWLv5jhTAjHRwhaRZRTRZ6zuih0exPVNwlRYXK9HiYwHVuFMFjQIZ8zuT6o6wOO4vlvTBZgViVvTo0VEHgeuw1nDUoGrgQ9U9dmoHpgH3DPF7jit619EpC7QyobXJY5YSdiN3Ke+RXjHuP/eDBxQ1UEexY32JEy+U1jfVfaywFeqepGXcaMhmhf/xFk780L35awIXISL5gXAiBORMap6a7AyU3Ix0SWiqr8CiMj5qnq+36ZHReQ7nNanF3GjPebYNxHQbhFpCWwleqfrXovaxT9V/QH4wes4+UTrAmA0nOb/QkSSAU+HppZWMZGw/VQSkQtUdTaAOHNje7oOXpSNEJHqODcaTAQqAwOje0ieqQmc6Xfx7ymci3/9o3pU3vFND3w58JqqTnDrnDBE5DHAN6prr68Y5zqBpxeTS6uY6BLxEZGzgLeAqm7RbuB2t4VkwsSdYKo5Tp/uz/4TUHkYcxVwhm8KW/eC2BJVbe517GgQkc+B33AuAJ4FHMS5SSnhphwVkWdV9bFoH0dpEFMJ20dEUnCOLf9yR17F662q44OVeRC3Gs5kSI3Ju8KOZ/NNiMjlOKfma3FaQ02AO1TV0zX4StPFPygdFwBFpLmqrnKvERRgDa3wi4mELSK3qOrYwi5MeX03WiG3iBco8yDuHGAuzvSmx+4MU9XRHsZcBVyhqmvc182ASZFo6Ubj4p/xjoiMUNUMEZkeYLMm4sXzaIuVPmxfP3WVSAYVkUuBy4D6IvIvv00pgGdLOfmpoKpFjp7wwDZfsnatAyIyl0iULv4Zj6hqhvtv52gfS2kREy3saBGRM3BuqR1E3ot9+4DpqrrL4/j3A3/g3Ezhv3CCZ2sdishrQCPgQ5yuid44c2R/58b+xKvYJjGJSAWcaSR8y859izPLpqd3CpdGpTph+4hIWY3CWnvuXY6DcS6u+v4jVFWbehhzVBGb1eO7LE0CEpEPcRo5Y92iG4Hqqto7ekeVmCxh44z/xlntphFON5HgceJ0464FzlEPVy03xmsSYMHdQGWm5GKlDzva3gTuBxaRO342ElbgrvQSKe7paz8iO3+JSWw/ikh7VZ0LICLn4HaxmfCKyYQtIu2BZ4DywFBV/czjkHu8HtZWiGycxX+nE7nFf8fgzF/SDb/5SzyMZxKU3xxAZYHbROR/7utGwMpoHluiiokuERGpo6pb/V5/CNyO0zUxJwIrvwwBkoFPyJs4PR3RICJ9ApV7PKyv1MxfYrzlNwdQQL4pJ0z4xEoLe7g4y3QNda8s78aZwS4H2FvkO8PDN79DW78yxVk2zDP+idm9Rb2hqnq9gnlpmr/EeMgScuTFRAsbji2EOwAYDXyMk7BPAMap6vZoHptXRGQGcBXOF+diYDsw08ux2SLSH+fnezrOSteVgYGqOtyrmMaY8IiZhA3HZvm6G2fCnMGq+m2E4qbh9JnXU9VLRaQFcK6qvulxXF/3RH+c1vWTvq4KL+MaY+JTTKxGISJXichsYBqwHLgBuFpExrm3TnvtbZxlweq5r1cD90Ugbhl3jonr8HYlkmNE5IEAj34i0joS8Y0xxy8mEjbwNM6ohWuA51R1t9stMBDnxhKv1VTVD3Hn81DVLCIzvG8QzhfFGlVdICJNgV88jtkWuBOo7z4ygE7AGyLysMexjTElECsXHffgtKor4jevhar+4pZ7bb+70rPCsWGFns8U6M4GON7v9TqcLy0v1cCZl/oPABF5EmfF9A4449Cf9zi+MeY4xUrCvhrndtajOBcbI+0BnAUEmrkr3NQCrvUqmIg8rKrPi8grBFhT0uNx2PkXpT0KNFLVgyJyuJD3GGNiQEwkbPfW7FeiGP8Hd33HU3DGfv/s8dwivhtVFlLEIsAeeQ+YKyIT3NdXAuNEpBJ2s4MxMS2mRolEizs65XIKLiTg9TzcZ+MsseQfV70eJeKu7ONbNX22qi70Mp4xJjxiooUdA/4LHCLfQgIRMBZ4KNJxVXURTn+1MSaOWMJ2NIjS2Oftqjox+G7GGGNdIgCIyHPA1EivtyciXXAutk4l7xwmtoiAMaYAa2E75gKfikgSzqgJ33zYKR7H/X84q5eXJbdLRHEmoTLGmDyshQ2IyDqgJ84K1xH7gYjIMq9nIjTGJI5YudMx2n4BlkcyWbvmuvOWGGNMUNbCBkTkbaAp8AV5+5K9Htb3E9AMWO/G9XXF2ORPxpgCrA/bsd59lHMfkdI9grGMMXHOWtjGGBMnrIUNiMjJwIMUvNPRls0yxsQMa2EDIrIEGE6+VdPdOwKNMSYmWMIGRGSRqp4V7eMwxpiiWMIGROQpnHm4PyXvKJGd0TomY4zJzxI2ICLrAxSrqjaN+MEYY0whLGEbY0ycsFEigIj0ClC8B+dW9W0BthljTMRZCxsQkUnAucB0t6gTzoRQJwODVHVMlA7NGGOOsRa2Iwc4VVUzAUQkDXgNOAeYBVjCNsZEnU3+5GjsS9aubcDJ7igRL9d2NMaYkFkL2/GtiHwOjHdfXwPMchem3R29wzLGmFzWhw2IiOAk6fNxF6YFPo7CdKvGGFMoS9jGGBMnSnWXiIjMVtULRGQfztJcxzYRmSXCjDEmZNbCNsaYOFHaW9ipRW23uUSMMbGkVLew3TlEFKcLJD+bS8QYE1NKdcI2xph4Uqq7RPyJSHUgHajgK1PVWdE7ImOMycsSNiAi/YEBQANgMdAe+B6wJcKMMTHDbk13DADOBn5V1c5AG2B7dA/JGGPysoTtOKSqhwBEpLyqrgJOifIxGWNMHtYl4tgkItWAz4ApIrIL2BzlYzLGmDxslEg+ItIRqAp8qapHon08xhjjYwnbGGPihPVhG2NMnLCEbYwxccIStjHGxAlL2MYYEycsYRtjTJz4/yc/Ve3YxIZiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(kaggle_pca.components_,\n",
    "           yticklabels=['PC1','PC2'],\n",
    "           xticklabels=['% identity', 'alignment length', 'mismatches', 'gap opens', 'q. start', 'q. end', 's. start', 's. end', 'bit score'],\n",
    "           cmap='bwr',center=0,vmin=-1,vmax=1,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2 The average silhouette_score is : 0.5897637058174753\n",
      "For n_clusters = 3 The average silhouette_score is : 0.8212980035539653\n",
      "For n_clusters = 4 The average silhouette_score is : 0.8987306652182682\n",
      "For n_clusters = 5 The average silhouette_score is : 0.9559376803218907\n",
      "For n_clusters = 6 The average silhouette_score is : 0.9588512950522278\n",
      "For n_clusters = 7 The average silhouette_score is : 0.9687563415410297\n",
      "For n_clusters = 8 The average silhouette_score is : 0.9693368275112374\n",
      "For n_clusters = 9 The average silhouette_score is : 0.9692661677667841\n",
      "For n_clusters = 10 The average silhouette_score is : 0.9673070645530749\n"
     ]
    }
   ],
   "source": [
    "range_n_clusters = [2, 3, 4, 5, 6,7,8,9,10]\n",
    "for n_clusters in range_n_clusters:\n",
    "    clusterer = KMeans(n_clusters=n_clusters, random_state=42)\n",
    "    kaggle_cluster_labels = clusterer.fit_predict(kaggle_scores_pca)\n",
    "    silhouette_avg = silhouette_score(kaggle_scores_pca, kaggle_cluster_labels)\n",
    "    print(\"For n_clusters =\", n_clusters,\n",
    "          \"The average silhouette_score is :\", silhouette_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_clusterer_PCA = KMeans(n_clusters=5, random_state=42)\n",
    "kaggle_cluster_labels_PCA = kaggle_clusterer.fit_predict(kaggle_scores_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(kaggle_scores_pca[:,0],kaggle_scores_pca[:,1],c=kaggle_cluster_labels_PCA,cmap='Set1')\n",
    "plt.xlabel('PC 1')\n",
    "plt.ylabel('PC 2')\n",
    "plt.title('Clustering after PCA')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}