Assignment1_Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 1 by Lennart Tuijnder (VUB)\n",
    "\n",
    "All the exploratory work and fiddling around can be found back on: https://github.com/ltuijnder/BioInformatics_Assignment1 \n",
    "This notebook summarises all the important analysis and describes the code and my choices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# General imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1: GOR algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first start with the import of the data and along the way introduce concepts on how I do the analysis.\n",
    "\n",
    "## Importing the data: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a dataframe\n",
    "So we have been given two input data files: `dssp_info.txt`, `stride_info.txt` where each row is an amino acid entry of a given protein. \n",
    "\n",
    "The data file imported via the module `pandas` using the function `read_csv`, we now have hold of a `pandas.dataframe` object which is basically a fancy excel table. We can tell the dataframe that we want to group our data on the column on the proteins such that we can easily just loop over protein per protein. \n",
    "\n",
    "We should also group the data by `PDB_chain_code` since our data sets contains instance of proteins  that are present multiple times (4 times \"1n7s\", and 2 times \"1wmh\") but have a different `chain_code` attachted to them. One protein can fold in different ways and hence form different secundary strucutres (SS), so to our analysis with the GOR we treat them as different proteins. \n",
    "\n",
    "Now that we have grouped our dataframe we can loop over each individual protein and acces its specific information.\n",
    "\n",
    "### Converting residues to numbers\n",
    "\n",
    "Next, we turn our attention to the residues of the proteins. These will be converted to numbers since we will want to store information in matrixes whose row/column are in function of the residue. It is then easier to map every residue to a specific column/row number which makes working with the data Matrix easier. \n",
    "\n",
    "The same needs to be done for the SS where we also converge the SS \"Other\" to the same intiger value as \"Coil\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define conversion dictionaries\n",
    "aminoToNumber = {\"ALA\":0, \"ARG\":1, \"ASN\":2, \"ASP\":3, \"CYS\":4, \"GLN\":5, \"GLU\":6, \"GLY\":7, \"HIS\":8, \"ILE\":9, \"LEU\":10, \"LYS\":11, \"MET\":12, \"PHE\":13, \"PRO\":14, \"SER\":15, \"THR\":16, \"TRP\":17, \"TYR\":18, \"VAL\":19}\n",
    "shortAminoToNumber = {\"A\":0, \"R\":1, \"N\":2, \"D\":3, \"C\":4, \"Q\":5, \"E\":6, \"G\":7, \"H\":8, \"I\":9, \"L\":10, \"K\":11, \"M\":12, \"F\":13, \"P\":14, \"S\":15, \"T\":16, \"W\":17, \"Y\":18, \"V\":19}\n",
    "SStoNumber = {\"Helix\":0, \"Beta\":1, \"Coil\":2, \"Other\":2}\n",
    "shortSStoNumber = {\"H\":0, \"E\":1, \"C\":2}\n",
    "numberToStringSS = {0:\"H\", 1:\"E\", 2:\"C\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The converges happens within the `pandas` framework with the `map` function. The great thing is that entries that were not defined in the dictionary are automatically mapped to `NaN`. We can now easily flag residues that are not one of the standard 20 aminoacids to `-1`.  \n",
    "\n",
    "We let -1 represent \"invalid\" entry. We keep those -1's since our GOR algorithm needs to run over the sequence where the position between residues is very important! Hence, rather then removing entries we flag them with -1 such that we tell GOR to ignore these, this way we preserve the distance between valid residues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with the skips\n",
    "\n",
    "Last thing that we need to deal with in our input data is that sometimes full sections are missing in the proteins. For example, in protein \"1j3a\" the `PDB_seq_code` skips from 53 to 67. To really make sure that these skips correspond with real physical distance between the aminoacids of the protein I had a look at the PDB files. And indeed after looking around I found the protein \"1j3a\":\n",
    "\n",
    "> REMARK 465                                                                      \n",
    "REMARK 465 MISSING RESIDUES                                                     \n",
    "REMARK 465 THE FOLLOWING RESIDUES WERE NOT LOCATED IN THE                       \n",
    "REMARK 465 EXPERIMENT. (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN               \n",
    "REMARK 465 IDENTIFIER; SSSEQ=SEQUENCE NUMBER; I=INSERTION CODE.)                \n",
    "REMARK 465                                                                      \n",
    "REMARK 465   M RES C SSSEQI                                                     \n",
    "REMARK 465     GLY A    54                                                      \n",
    "REMARK 465     LEU A    55                                                      \n",
    "REMARK 465     ARG A    56                                                      \n",
    "REMARK 465     THR A    57                                                      \n",
    "REMARK 465     LEU A    58                                                      \n",
    "REMARK 465     THR A    59                                                      \n",
    "REMARK 465     ASN A    60                                                      \n",
    "REMARK 465     PRO A    61                                                      \n",
    "REMARK 465     ARG A    62                                                      \n",
    "REMARK 465     ARG A    63                                                      \n",
    "REMARK 465     GLY A    64                                                      \n",
    "REMARK 465     PRO A    65                                                      \n",
    "REMARK 465     PHE A    66 \n",
    "\n",
    "Thus the amino acids are know, but the location could not be determined hence on those location you can then also not infer the SS. And thus the information of the SS is simply missing over there. We can assume that this is the case for all skips that happen.\n",
    "\n",
    "But in the GOR III algorithm it is very important that relative distance between residues is correct. To solve this we are going to add additional dummy entries flagged with \"-1\" for both the residue sequence and the SS sequence. Flag them with \"-1\" such that the GOR algorithm know to ignore these dummy entries.\n",
    "\n",
    "It is important that we add these, since if for instance a skip of 1 happens well then the amino acid on the left of the skip needs to know that the amino acid on the right is not its neighbour (m=1) but its next to neirest neighbour (m=2).\n",
    "\n",
    "The number of dummy entries that we add is equal to the amount of skips that happend, except if the skips exceed 8 then just add 8. We only add maximum 8 because the window with which the GOR 3 algorithm looks around from a position $j$ is only maximumly 8 to one side. And since the GOR algorithm is going to skip the \"-1\" entries at its centre position $j$, we then do not have to fear overlap between two distant regions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The function \"createDataset\"\n",
    "\n",
    "All that what has been covered above is done by one function named `createDataset`. Which only takes as argument which input data file it should consider (either \"dssp\" or \"stride\"). \n",
    "\n",
    "It then returns the data as dictionary with the keys the protein name who are structured as follows: `\"PDB_code\"+\"_\"+\"PDB_chain_code\"`, such that each key always unique. \n",
    "\n",
    "At each key we store tuple that stores a numpy array of the residue sequence (in intigers) and a numpy array of the SS (also intigers).\n",
    "\n",
    "From this dictionary we will do the rest of our analysis.\n",
    "\n",
    "#### Small note:\n",
    "One last small note. I have only changed one minor detail in the input data. Namely the sequence numbers of the first three residues of protein \"1fsg\" in the stride dataset where all \"0\". This is ofcourse not possible and after a quick check on PDB to verify I changed it to [-2,-1,0] manually in the `stride_info.txt` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createDataset(dataSet = \"dssp\"):\n",
    "    # MODIFIED sequence 1fsg in stride data set!\n",
    "    df = pd.read_csv(\"inputData/\"+dataSet+\"_info.txt\", sep=\"\\t\", header=None, names=[\"PDB_code\", \"PDB_chain_code\", \"PDB_seq_code\", \"residue_name\", \"SS\"])\n",
    "    df['residue_name'] = pd.to_numeric( df['residue_name'].map(aminoToNumber).fillna(-1), downcast='signed')# Fill non matching (aka the not standard amino acids) = -1\n",
    "    df[\"SS\"] = pd.to_numeric( df[\"SS\"].map(SStoNumber), downcast=\"signed\")\n",
    "    \n",
    "    grouped = df.groupby([\"PDB_code\",\"PDB_chain_code\"], sort=False)\n",
    "\n",
    "    proteinDict = {}\n",
    "    for (proteinCode, chainCode), proteinData in grouped:\n",
    "        rPosition = proteinData[\"PDB_seq_code\"].values\n",
    "        skipped = np.diff(rPosition)-1\n",
    "        hasSkipped = np.sum(skipped)!=0\n",
    "        \n",
    "        sequence = proteinData[\"residue_name\"].values\n",
    "        SS = proteinData[\"SS\"].values\n",
    "        \n",
    "        if hasSkipped:\n",
    "            indexesSkipped = np.where(skipped!=0)[0]\n",
    "            SS = proteinData[\"SS\"].values\n",
    "        \n",
    "            # Add -1's on the places where it skipped.\n",
    "            # Add these seperately add these first seperatatly in a tuple, to not mess up the counting.\n",
    "            newSequence = () \n",
    "            newSS = ()\n",
    "            \n",
    "            previousIndex = 0\n",
    "            for index in indexesSkipped:\n",
    "                newSequence += (sequence[previousIndex:(index+1)],) # Add the section between the skipped area's\n",
    "                newSS += (SS[previousIndex:(index+1)],) # Add +1 since the coordinat where the skips happens is still part of the previous section before the skip.\n",
    "                numberSkipped = skipped[index]\n",
    "                \n",
    "                newSequence += (-np.ones(numberSkipped if numberSkipped<8 else 8) ,) # Add the -1's\n",
    "                newSS += (-np.ones(numberSkipped if numberSkipped<8 else 8) ,)\n",
    "                previousIndex = index+1\n",
    "                \n",
    "            newSequence += (sequence[previousIndex:],)\n",
    "            newSS += (SS[previousIndex:],)\n",
    "            \n",
    "            sequence = np.concatenate(newSequence)\n",
    "            SS = np.concatenate(newSS)\n",
    "            \n",
    "        proteinDict[proteinCode+\"_\"+chainCode] = (sequence, SS)\n",
    "    \n",
    "    return proteinDict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The GOR algorithm and the frequency table:\n",
    "\n",
    "I will now discuss on how I implemented the GOR algorithm and the frequency table\n",
    "\n",
    "The GOR algorithm is based on information theory and there for uses probability distributions. With GOR 3 we evaluate the probability that we would have a certain residue $r_{j+m}$ with \"$m\\in(-8,...,+8)$\" steps away from a position $j$ who has SS $s_j$ and residue $r_j$. \n",
    "\n",
    "These distribution will be approximated with freqencies or counts (in this text frequencies and counts are interchangeable). For the GOR 3 algorithm we need to be able to count the following frequency configuration: $$ F_{s_j,m,r_{jm},r}$$\n",
    "which represents the count that at position $j$ there was a SS $s_j$ with residue $r_j$ and at position $m$ a residue $r_{jm}$. All probabilities distributions that need to be approximated can be stored within this giant four dimensional matrix that has the following size: $$ (F_{s_j,m,r_{jm},r}).shape=(3,17,20,20)$$\n",
    "The first 3 is because the SS at position $j$, it can be Helix(H), Sheet(E) or Coil (C). There are 17 different m values, 8 to the left side of $j$ (the negative $m$ values), 8 to the right side (the positive $m$ values) and $m=0$ which results in 17 configurations. And lastly we have the 20 aminoacids, the one at axis=2 will represent the amino acid on position $j+m$ and the last axis = 3 will represent the residue at position $j$ \n",
    "\n",
    "And thus as explained above I will also store the central positions $m=0$ in this frequency Table/matrix. This means we are not super efficient with our memory since at $m=0$, $r_j=r_{j+m}$ and hence only the diagonal will be used. In the end I have decided to store everything in this one matrix and not bother with handeling a second one. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constructing the full frequency matrix of the data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To construct the full frequency matrix of the whole dataset we simply need to compute the frequency table of each protein seperatly and sum them up. For this I have constructed the function `freqTableOfProtein`, that returns this frequency table.\n",
    "\n",
    "To compute the frequency table. We construct a sliding window that loops over all positions on the sequences centered around position $j$ and then loop over all $m\\in(-8,...,8)$, and increment the frequency table at $s_j$,$m$,$r_{j+m}$,$r_j$. If $r_{jm}$ is -1, we skip it since it does not provide us any information. If at our position $r_j$ we skip that window position completly.\n",
    "\n",
    "The only problem residues at the sides, since we cannot centre a window at position 0. To solve this add eight \"-1\" to both sides of the sequence (for both the residues and SS sequence). Now we can simply add the window at position 0 and because on the left it is all -1 the window will ignore that side.\n",
    "\n",
    "It is ok to use windows where the information is only partially, Each position $m$ says something individually about $s_j$ and so it does not matter if there are some gaps.\n",
    "\n",
    "---\n",
    "Finally the function also returns the total count of each SS and the total count of each SS per residue. This information can directly be extracted from the diagonal of the frequency matrix at $m=0$. \n",
    "\n",
    "This is done for convenience since we later on need to compute for the self information:\n",
    "$$ \\log( \\frac{F_{n-S}}{F_{S}} ) $$\n",
    "and for the pair information:\n",
    "$$ \\log(\\frac{F_{n-S,R_j}}{F_{S,R_j}}) $$\n",
    "So it is handy to have those special frequencies already seperated from the big frequency matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def freqTableOfProtein(protSequence,SS):\n",
    "    \"\"\"\n",
    "    Input:\n",
    "        - protSequence : 1D-numpy array, Represents the proteins sequence, where the aminoacids are labeled as integers from 0 to 19\n",
    "        - SS : 1D-numpy array holding the know secundary structure of the \n",
    "    Output:\n",
    "    - freq : 4D-numpy array shape = (3,17,20,20) representing the frequency count of the protein sequence.\n",
    "            - axis0 = Secundary structure\n",
    "            - axis1 = relative position m from the window center positioned at j in the protein sequence\n",
    "            - axis2 = amino Acid at the position m. = R_{j+m}\n",
    "            - axis3 = amino Acid at the centre. = R_j\n",
    "    - countSS : returns the total counts of each type of secundary structure in the sequence\n",
    "    - coutSSperR: return the count of each type of secundary structure per amino acid.\n",
    "    \"\"\"\n",
    "    freqTable = np.zeros(shape=(3,17,20,20))\n",
    "    \n",
    "    # Pad the sequence with 8 -1's before and after such that we can easily perform move a window around. \n",
    "    paddedSequence = np.concatenate((-np.ones(8),protSequence,-np.ones(8))).astype(int)\n",
    "    paddedSS = np.concatenate((-np.ones(8),SS,-np.ones(8))).astype(int)\n",
    "    \n",
    "    seqLen = len(protSequence)\n",
    "    \n",
    "    \n",
    "    #Fill freqTable\n",
    "    for j, (r_j, S_j) in enumerate(zip(paddedSequence,paddedSS)):      \n",
    "        if (j-8) > seqLen: # We have reached the end.\n",
    "            break    \n",
    "        if r_j<0: # We skip over invalid entries. (These are the padded edges and invalid entries)\n",
    "            continue\n",
    "        for m in np.arange(-8,9):#  m=[-8,...,8]\n",
    "            r_jm = paddedSequence[j+m]\n",
    "            if r_jm<0:\n",
    "                continue\n",
    "            freqTable[S_j,m+8,r_jm,r_j]+=1\n",
    "            \n",
    "    middleTable = freqTable[:,8] # take all the freq counts on m=0. \n",
    "    countSSperR = np.copy(middleTable.diagonal(axis1=-1,axis2=-2)) # Extract the diagonal\n",
    "    countSS = np.copy(np.sum(countSSperR,axis=-1))\n",
    "    \n",
    "    return freqTable, countSS, countSSperR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting the SS via GOR 3\n",
    "\n",
    "Now it is time to implement the GOR alogirthm and start predicting the SS of sequences.\n",
    "\n",
    "In much the same fashion that we have constructed the frequency table, we again loop over the sequence with a running window centered at position $j$ and evaluate in a region $m\\in(-8,...,8)$ around it. Thus one again we add eight -1's at the beginning and the end of the sequence such that the running window has no difficulties with the edges. \n",
    "\n",
    "For the GOR III algorithm we have to compute now for each SS and position $j$ the following value: \n",
    "$$ I(\\Delta S_j, R_{j-8},...,r_{R+8}) $$ \n",
    "Which is a summation of all the $m\\in (-8,...,8)$ information terms that contribute to the total. Each $m$ term is then:\n",
    "$$ I(\\Delta S_j;R_{j+m}|R_j) = \\log(\\frac{F_{Sj,m,Rjm,Rj}}{F_{n-Sj,m,Rjm,Rj}}) + \\log(\\frac{F_{n-Sj,Rj}}{F_{Sj,Rj}})$$\n",
    "\n",
    "Special care should be taken in the case of the self-information term $m=0$. For the first term nothing changes since there always $R_{jm}=R_{j}$ and so we automatically take the correct elements. But for the second term, with the self-interaction, the $R_j$ element is ignored.\n",
    "\n",
    "This all is achieved by the function `predictSS`. This function takes as input the sequence, the big frequency table, and also directly the already logarithmic computed second terms of the information computation. \n",
    "\n",
    "The function then returns the predicted SS. But also the computed point wise mutual information $$ I(\\Delta S_j, R_{j-8},...,r_{R+8}) $$ for each $S_j$ such that we can later make a plot to see how the GOR algorithm got its result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predictSS(protSequence, freq, logDiff, logDiffRj):\n",
    "    \"\"\"\n",
    "    Input:\n",
    "        - protSequence : 1D-numpy array, Represents the proteins sequence, where the aminoacids are labeled as integers from 0 to 19\n",
    "        - freq : 4D-numpy array shape = (3,17,20,20) representing the frequency count of the data set.\n",
    "            - axis0 = Secundary structure\n",
    "            - axis1 = relative position m from the window center positioned at j in the protein sequence\n",
    "            - axis2 = amino Acid at the position m. = R_{j+m}\n",
    "            - axis3 = amino Acid at the centre. = R_j\n",
    "        - logDiff: 1D-numpy array of length 3. This is the logarithm of the relative SS structure count: log(f_s/f_{not_s})\n",
    "        - logDiffRj: 2D-numpy array of shape (3,20). This is the logarithm of the relative SS structure count per amino accid: log(f_{s,R}/f_{not_s,R})\n",
    "        - SStrue\n",
    "        - dealWithInvalidResidue\n",
    "    Output:\n",
    "        - SS_result: 1D-numpy array with the predicted SS of the protein sequence\n",
    "        - ISS: 2D-numpy array with the information per SS per amino accid in the protein sequence.\n",
    "        \n",
    "    \"\"\"\n",
    "    # Add 8 \"-1\"'s at the beginning and end of the protein sequence. \n",
    "    # This is done such that we can have running window of lenght 8 at each side at the edges of the protein sequences. \n",
    "    # Use -1 since we offcourse vieuw these as invalid entries.\n",
    "    paddedSequence = np.concatenate((-np.ones(8),protSequence,-np.ones(8))).astype(int)\n",
    "    SeqLen = len(protSequence)\n",
    "    otherSS = np.array([[1,2],[0,2],[0,1]]) # Have the index of the other Secundary Structure such that we can sum of the other SS\n",
    "    \n",
    "    # Construct the output value ISS\n",
    "    ISS = -np.ones(shape=(3,SeqLen)) # use -1 to later on detect invalid entries in the protein sequence.\n",
    "    \n",
    "    for j, r_j in enumerate(paddedSequence):\n",
    "        if j-8 > SeqLen: # We have reached the end.\n",
    "            break\n",
    "        if r_j<0: # We skip over invalid entries. (These are the padded edges and invalid entries)\n",
    "            continue\n",
    "        # r_j is valid so set the ISS level equalt to zero. Unvalid r_j are kept -1\n",
    "        ISS[:,j-8] = np.zeros(3)             \n",
    "        for SS in range(3):\n",
    "            for m, r_jm in enumerate(paddedSequence[j-8:j+9]): # m in [0,16],\n",
    "                if r_jm<0:\n",
    "                    continue\n",
    "                # Compute the logSum\n",
    "                ISS[SS,j-8] += np.log(freq[SS,m,r_jm,r_j]/np.sum(freq[otherSS[SS],m,r_jm,r_j]))\n",
    "                if m==8: \n",
    "                    ISS[SS,j-8] += logDiff[SS]\n",
    "                else:\n",
    "                    ISS[SS,j-8] += logDiffRj[SS,r_j]\n",
    "\n",
    "    SS_result = np.argmax(ISS,axis=0)\n",
    "    SS_result[protSequence<0] = -1 # Let the invalid protein sequences stay flagged as invalid.\n",
    "    return SS_result, ISS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing the GOR algorithm with crossvalidation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we make the analysis of the GOR algorithm with the leave-one-out cross validation. Which is done by the function `leaveOneOutAnalysis`. The analysis goes as follows:\n",
    "\n",
    "First, we construct the dictionary dataset with the function `createDataset`, which was explained earlier. With this dataset we construct the full frequency table by computing the one of each protein via the function `freqTableOfProtein` and adding them up. (We do the same for the SS counts and the SS per Rj). \n",
    "\n",
    "Next, we start our leave-one-out crossvalidation by looping over every protein which will represent our test protein. Since, the full frequency table also contains the frequency of the test protein, we construct a temporarily frequency table where we substruct that of the protein with the full one. Now we have a frequency table that does not contain any information of the test protein. (we do the same with the countSS and count SS per Rj). \n",
    "\n",
    "We then already directly compute $\\log(\\frac{F_{n-Sj,Rj}}{F_{Sj,Rj}}$ and $\\log(\\frac{F_{n-Sj}}{F_{Sj}}$ from the frequency counts of SS and the SS per Rj. These will then be given towards the function `predictSS`.\n",
    "\n",
    "Next, in the frequency table, we set all the frequencies equal that are = 0  equal to 0.00001 inorder to avoid problems with divide by zero. This change is allowed and here is why: The frequencies are in the first place an approximation of the probability density function (pdf) which equates to $P_i = F_i/N$ and frequency of zero is suppositly an approximation for zero probability. So by setting the Frequency = 0.00001 we have altered our probability approximation. But the value is so small that it does not affect the shape of the pdf distribution. And the frequency is probably only zero because there is not enough data. \n",
    "\n",
    "After all that we predict the Secundary structure of the sequence via the function `predictSS`. We then compute Q3 and MCC per SS for our prediction after first removing all the invalid entries that we do not care for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def proteinQ3(predictedSS, trueSS):\n",
    "    Q3 = np.zeros(3)\n",
    "    numberHelix = np.sum(trueSS == 0)\n",
    "    numberSheet = np.sum(trueSS == 1)\n",
    "    numberCoil = np.sum(trueSS == 2)\n",
    "    TPHelix = np.sum(np.logical_and(predictedSS==0, trueSS==0)) #Only look where trueSS = Helix, see if they are then equal.\n",
    "    TPSheet = np.sum(np.logical_and(predictedSS==1, trueSS==1))\n",
    "    TPCoil =  np.sum(np.logical_and(predictedSS==2, trueSS==2))\n",
    "    # Return nan such that we can later filter it out.\n",
    "    Q3[0] = np.nan if numberHelix==0 else TPHelix/numberHelix\n",
    "    Q3[1] = np.nan if numberSheet==0 else TPSheet/numberSheet\n",
    "    Q3[2] = np.nan if numberCoil==0 else TPCoil/numberCoil\n",
    "    return Q3\n",
    "    \n",
    "\n",
    "def computeMCC(TP,TN,FP,FN):\n",
    "    numerator = TP*TN-FP*FN\n",
    "    denominator = np.sqrt((TP+FP) * (TP+FN) * (TN+FP) * (TN+FN))\n",
    "    return numerator/denominator\n",
    "\n",
    "def proteinMCC(predictedSS, trueSS):\n",
    "    numberHelix = np.sum(trueSS == 0)\n",
    "    numberSheet = np.sum(trueSS == 1)\n",
    "    numberCoil = np.sum(trueSS == 2)\n",
    "    #Helix\n",
    "    TPHelix = np.sum(np.logical_and(predictedSS==0, trueSS==0))\n",
    "    TNHelix = np.sum(np.logical_and(predictedSS!=0, trueSS!=0))\n",
    "    FPHelix = np.sum(np.logical_and(predictedSS==0, trueSS!=0))\n",
    "    FNHelix = np.sum(np.logical_and(predictedSS!=0, trueSS==0))\n",
    "    MCCHelix = np.nan if numberHelix==0 else computeMCC(TPHelix,TNHelix,FPHelix,FNHelix)\n",
    "    #Sheet\n",
    "    TPSheet = np.sum(np.logical_and(predictedSS==1, trueSS==1))\n",
    "    TNSheet = np.sum(np.logical_and(predictedSS!=1, trueSS!=1))\n",
    "    FPSheet = np.sum(np.logical_and(predictedSS==1, trueSS!=1))\n",
    "    FNSheet = np.sum(np.logical_and(predictedSS!=1, trueSS==1))\n",
    "    MCCSheet = np.nan if numberSheet==0 else computeMCC(TPSheet,TNSheet,FPSheet,FNSheet)\n",
    "    #Coil\n",
    "    TPCoil = np.sum(np.logical_and(predictedSS==2, trueSS==2))\n",
    "    TNCoil = np.sum(np.logical_and(predictedSS!=2, trueSS!=2))\n",
    "    FPCoil = np.sum(np.logical_and(predictedSS==2, trueSS!=2))\n",
    "    FNCoil = np.sum(np.logical_and(predictedSS!=2, trueSS==2))\n",
    "    MCCCoil = np.nan if numberCoil==0 else computeMCC(TPCoil,TNCoil,FPCoil,FNCoil)\n",
    "    return np.array([MCCHelix, MCCSheet, MCCCoil])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def leaveOneOutAnalysis(dataSet = \"dssp\"):\n",
    "    \"\"\"\n",
    "        Estimate the Q3 and CVV of the GOR method for a specific dataset using a crossvalidation of leave-one-out\n",
    "    \"\"\"\n",
    "    totalFreqTable = np.zeros(shape=(3,17,20,20))\n",
    "    totalCountSS = np.zeros(3)\n",
    "    totalCountSSperR = np.zeros(shape=(3,20))\n",
    "    otherSS = np.array([[1,2],[0,2],[0,1]]) # Have the index of the other Secundary Structure such that we can sum of the other SS\n",
    "    \n",
    "    proteinDictionary = createDataset(dataSet = dataSet)\n",
    "    \n",
    "    #First compute the full freqTable\n",
    "    for key, (proteinSequence, trueSS) in proteinDictionary.items():\n",
    "        freqTableFromProt, countSSFromProt, countSSperRFromProt = freqTableOfProtein(proteinSequence, trueSS)\n",
    "        totalFreqTable += freqTableFromProt\n",
    "        totalCountSS += countSSFromProt\n",
    "        totalCountSSperR += countSSperRFromProt\n",
    "        \n",
    "    \n",
    "    numberProteins = len(proteinDictionary)\n",
    "    \n",
    "    Q3 = np.zeros(shape=(numberProteins,3))\n",
    "    MCC = np.zeros(shape=(numberProteins,3))\n",
    "    \n",
    "    # Now do the leave-one-out:\n",
    "    for i, (key,(proteinSequence, trueSS)) in enumerate(proteinDictionary.items()):\n",
    "        if i%50==0:\n",
    "            print(f\"i={i}/{numberProteins}\")\n",
    "        # Remove the frequency counts of this protein.\n",
    "        freqTableFromProt, countSSFromProt, countSSperRFromProt = freqTableOfProtein(proteinSequence, trueSS)\n",
    "        tempFreqTable = totalFreqTable - freqTableFromProt\n",
    "        tempCountSS = totalCountSS - countSSFromProt\n",
    "        tempCountSSperR = totalCountSSperR - countSSperRFromProt\n",
    "        \n",
    "        templogDiff = np.log(np.sum(tempCountSS[otherSS],axis=-1)/tempCountSS)\n",
    "        templogDiffRj = np.log(np.sum(tempCountSSperR[otherSS],axis=-2)/tempCountSSperR) # -2 since -1 are the amino acids\n",
    "        \n",
    "        # Set Frequency that are equal to zero = 0.00001 inorder to avoid infinities.\n",
    "        tempFreqTable[tempFreqTable == 0] = 0.00001\n",
    "        \n",
    "        # Perform the GOR3 prediction.\n",
    "        SS_result, _ = predictSS(proteinSequence, tempFreqTable, templogDiff, templogDiffRj)\n",
    "        \n",
    "        # Remove the dummy entrances, such that they not influence the Q3 and CVV calculations\n",
    "        # predictInvalidResidue = True, then invalid residue have also tried to be predicted. \n",
    "        # If we then now also remove them with the following code.\n",
    "        validEntries = SS_result>=0\n",
    "        ValidSS_result = SS_result[validEntries]\n",
    "        ValidtrueSS = trueSS[validEntries]\n",
    "            \n",
    "        Q3[i] = proteinQ3(ValidSS_result, ValidtrueSS)\n",
    "        MCC[i] = proteinMCC(ValidSS_result, ValidtrueSS)\n",
    "    \n",
    "    return Q3, MCC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Consideration per protein family\n",
    "\n",
    "Before we move on to vieuw the results we also have to vieuw the results per protein family. And so lets first also discuss how this is done.\n",
    "\n",
    "So from our function `leaveOneOutAnalysis`, we get back our $[Q3_{helix},Q3_{Sheet},Q3_{Coil}]$ and $[MCC_{helix},MCC_{Sheet},MCC_{Coil}]$ of each protein. Now we want to vieuw average result as a whole and from a specific protein family.\n",
    "\n",
    "The protein family information is given by the input file `catch_info.txt`. And after carefull examination it is indeed observed that the protein order is the same as that of the sequence input files. To vieuw $Q_3$ and $MCC$ results of a specific family we construct a boolian vector that is true for that specific SS. From this we can easily extract the wanted results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfProtFam = pd.read_csv(\"inputData/cath_info.txt\",sep=\"\\t\", header=None, names=[\"PDB_code\", \"PDB_chain_code\",\"protein_family\"])\n",
    "protFamily = dfProtFam[\"protein_family\"].values\n",
    "protFamilyName = [\"Alpha\",\"Beta\",\"Alpha/beta\"]\n",
    "Helix = protFamily == \"Alpha\" # Boolian vectors\n",
    "Sheet = protFamily == \"Beta\"\n",
    "Mixed = protFamily == \"Alpha/beta\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis GOR 3 algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i=0/498\n",
      "i=50/498\n",
      "i=100/498\n",
      "i=150/498\n",
      "i=200/498\n",
      "i=250/498\n",
      "i=300/498\n",
      "i=350/498\n",
      "i=400/498\n",
      "i=450/498\n"
     ]
    }
   ],
   "source": [
    "Q3dssp, MCCdssp= leaveOneOutAnalysis(dataSet = \"dssp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i=0/498\n",
      "i=50/498\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lennart/.local/lib/python3.6/site-packages/ipykernel_launcher.py:19: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i=100/498\n",
      "i=150/498\n",
      "i=200/498\n",
      "i=250/498\n",
      "i=300/498\n",
      "i=350/498\n",
      "i=400/498\n",
      "i=450/498\n"
     ]
    }
   ],
   "source": [
    "Q3stride, MCCstride= leaveOneOutAnalysis(dataSet = \"stride\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store all the summarising results in one multidimensional array. \n",
    "\n",
    "Construct an additional help function for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = np.zeros((4,4,3))\n",
    "\n",
    "def computeQ3MCCResults(Q3dssp, MCCdssp, Q3stride, MCCstride):\n",
    "    meanQ3dssp = np.nanmean(Q3dssp,axis=0)\n",
    "    meanMCCdssp = np.nanmean(MCCdssp,axis=0)\n",
    "    meanQ3stride = np.nanmean(Q3stride,axis=0)\n",
    "    meanMCCstride = np.nanmean(MCCstride,axis=0)\n",
    "    \n",
    "    return np.array([meanQ3dssp, meanQ3stride, meanMCCdssp, meanMCCstride])\n",
    "\n",
    "# Overall\n",
    "results[0] = computeQ3MCCResults(Q3dssp, MCCdssp, Q3stride, MCCstride)\n",
    "# Alpha protein Family\n",
    "results[1] = computeQ3MCCResults(Q3dssp[Helix], MCCdssp[Helix], Q3stride[Helix], MCCstride[Helix])\n",
    "# Beta protein Family\n",
    "results[2] = computeQ3MCCResults(Q3dssp[Sheet], MCCdssp[Sheet], Q3stride[Sheet], MCCstride[Sheet])\n",
    "# Alpha/beta protein family\n",
    "results[3] = computeQ3MCCResults(Q3dssp[Mixed], MCCdssp[Mixed], Q3stride[Mixed], MCCstride[Mixed])\n",
    "\n",
    "resultsQ3 = results[:,0:2]\n",
    "resultsMCC = results[:,2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, from this analysis when averaging over the results we remove `NaN` entries! NaN entries occur when a certain SS is simply not present in the true SS sequence. This would lead devision by zero in the Q3/MCC and hence the Q3/MCC score for that SS is simply undefined. There are different ways to go about dealing with these numbers.\n",
    "For instance completely removing them is maybe not the correct way. Since even though there maybe no Helix present, our GOR algorithm still predicts helix sections and thus we should still be able to say something about the helix section. \n",
    "\n",
    "Removing them, could artificially increase the result. Putting them them equal to zero would also not be correct since then it is equivalent to saying the result was the most wrong ever. Which is also certainly not the case since we did not classify all non helix to helix. \n",
    "\n",
    "So in my analysis, I simply remove the `NaN`, this could maybe artificially increase the performance. The impact is not that big since putting all `NaN`s to zero only lowers the result by a couple of percentages. And hence the correct answer lies somewhere inbetween.\n",
    "\n",
    "Plot the results of which the figure layout is explained below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.94, 'Result GOR III algorithm')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axesQ3, axesMCC) = plt.subplots(nrows=2, ncols=3, sharey=True , figsize=(15,10))\n",
    "labels = [\"All\",\"Alpha\",\"Beta\",\"Alp/bet\"]\n",
    "locationLabels = np.arange(len(labels))\n",
    "widthBars = 0.35\n",
    "\n",
    "for i, (SS, axQ3) in enumerate(zip((\"Helix\",\"Sheet\",\"Coil\"),axesQ3)):\n",
    "    if i==0:\n",
    "        axQ3.set_ylabel(\"Q3 score\",fontsize=15)\n",
    "    axQ3.set_title(f\"Q3 score of {SS}\")\n",
    "    \n",
    "    axQ3.grid()\n",
    "    axQ3.bar(locationLabels - widthBars/2, resultsQ3[:,0,i], widthBars, label=\"dssp\")\n",
    "    axQ3.bar(locationLabels + widthBars/2, resultsQ3[:,1,i], widthBars, label=\"stride\")\n",
    "    axQ3.set_xticks(locationLabels)\n",
    "    axQ3.set_xticklabels(labels)\n",
    "    if i==2:\n",
    "        axQ3.legend()\n",
    "        \n",
    "for i, (SS, axMCC) in enumerate(zip((\"Helix\",\"Sheet\",\"Coil\"),axesMCC)):\n",
    "    if i==0:\n",
    "        axMCC.set_ylabel(\"MCC score\",fontsize=15)\n",
    "    axMCC.set_title(f\"MCC score of {SS}\")\n",
    "    \n",
    "    axMCC.grid()\n",
    "    axMCC.bar(locationLabels - widthBars/2, resultsMCC[:,0,i], widthBars, label=\"dssp\")\n",
    "    axMCC.bar(locationLabels + widthBars/2, resultsMCC[:,1,i], widthBars, label=\"stride\")\n",
    "    axMCC.set_xticks(locationLabels)\n",
    "    axMCC.set_xticklabels(labels)\n",
    "    if i==2:\n",
    "        axMCC.legend()\n",
    "        \n",
    "fig.text(0.5,0.07, \"Protein Family\", ha=\"center\", va=\"center\",fontsize=15)\n",
    "fig.suptitle(f\"Result GOR III algorithm\", fontsize=18,x = 0.5, y = 0.94)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explanation figure:\n",
    "The figure above pictures the analyis of the leave-one-out cross validation. \n",
    "\n",
    "We see six pannels with bar graphs plotted in them;\n",
    "\n",
    "- The top row describes the Results of the Q3 score for the different bar graphs \n",
    "    - With the first figure describing the Q3-helix score\n",
    "    - Second the Q3-Sheet\n",
    "    - Third the Q3-Coil\n",
    "- The bottom row describes the Results of the MCC score for the different bar graphs \n",
    "    - With the first figure describing the MCC-helix score\n",
    "    - Second the MCC-Sheet\n",
    "    - Third the MCC-Coil\n",
    "Next we see that two colors of bar graphs, the blue one describes the results from the dssp data set and the orange those of the stride data set.\n",
    "\n",
    "Lastly, for each score and data set, we consider 4 bars represeting the score for only a specific protein family. The \"Family\" : \"all\" is just the averaged result of the three families and represents the whole Q3_perSS score of the full data set.\n",
    "\n",
    "### Discussion results figure:\n",
    "\n",
    "**First thing to note,** is that there is no significant difference between the two data sets. For all score we see that blue is equal to red. The maximum distance is observed the Q3_sheet score in the alpha protein families there stride is like 2% better. But overall one data set has no advantage over the other. \n",
    "\n",
    "**Second thing to note,** is that the Q3 is much bigger then the MCC score for each respectif SS. Which is expected since Q3 only measures true positives and says nothing about False positives. The MCC score is much more robust and we see that it is not so naive as the Q3, and gives a score that represents more accuractly how the prediction went keeping in mind everything from the confusion matrix.\n",
    "\n",
    "**Third thing to note,** is that we see extremely large differences between the Q3 score for the different SS. The Q3_helix performs extremely well (with over 70% accuracy) while the others are much lower. However looking at the MCC score, this difference between the SS vanishes (when just looking at \"All\"). From this we could infer that indeed the because of the high Q3, many helix regions also get assigned helix, however because of the low MCC it also seems that we miss classify alot of non-helix regions as Helix. And hence we could infer from this that the GOR 3 algorithm likes to easily classify Helix regions (this will be comfirmed later on when we go into detail of the GOR analysis on one protein).\n",
    "\n",
    "**Fourth thing to note,** as was already said above the MCC score for \"all\" is the same for all SS. From this we can infer in general the GOR algorithm when considering everything of the confusion matrix performs equally well for all the different MCC. (but as explained above, it does seems to like helix a lot).\n",
    "\n",
    "**Fifth thing to note,** the only big difference between the protein families for the scores happens at only two points. The MCC_helix score is significantly worse with the \"Beta\" families and MCC_beta is significantly worse in the \"Alpha\" family compared to the others. This is easy to understand since the \"Alpha\" family will predominantly be helixes and \"Beta\" predominantly sheets. Hence, the scoring for the in those families can be difficult because of the potential large False positives. And since Q3 does not measure FP, it is not reflected in the Q3 score. \n",
    "\n",
    "**Sixth thing to note,** There are here and there small difference between families. For example the Q3_helix is bigger when for the \"Alpha\" Family which is logical since there are more Helixes present. And there are other like the \"stairs\" at Q3_sheet. But the difference are not to large to call them out of this world. \n",
    "\n",
    "\n",
    "**Overall Average**: The overall average scores for the Q3 and MCC are as follows:\n",
    "\n",
    "- Q3 = 58.36 %\n",
    "- MCC = 35.92 %\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investigation one specific protein:\n",
    "\n",
    "To convice that the GOR3 algorithm is indeed working. We will focus for a moment on 1 specific protein and vieuw its results of the GOR algorithm. \n",
    "\n",
    "1. First we will do a GOR algorithm with a frequency table that is constructed with just that protein. We then indeed see that the prediction becomes 100% correct and that the GOR algorithm works correctly.\n",
    "2. We vieuw the analysis of the protein the proper way, with a full Freq table with the test protein left out. We then evaluate the mutual information level of each SS and then indeed see that the information rises (somewhat) where it needs to be. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.) GOR 3 algorithm with itself:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The protein that we will be considering the is called \"1gpp\". This is a challeging one since it contains 3 different skips and changes often from structure.\n",
    "\n",
    "First we again create the dictionary database and extract the \"1gpp\" protein, which has a chaincode of A."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsspDictionary = createDataset()\n",
    "residue_1GPP, SS_1GPP = dsspDictionary[\"1gpp_A\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we compute its specific frequency table. \n",
    "\n",
    "And as with the analysis where it is zero we put it equal to zero. We also now need to do this with the SS per RJ count since the protein 1gpp is not long enough to cover every SS with every residue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "table_1GPP, countSS_1GPP, countSSperR_1GPP = freqTableOfProtein(residue_1GPP, SS_1GPP)\n",
    "table_1GPP[table_1GPP ==0 ] = 0.00001\n",
    "countSSperR_1GPP[countSSperR_1GPP==0] = 0.00001"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the logDiff terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "otherSS = np.array([[1,2],[0,2],[0,1]])\n",
    "logDiff_1GPP = np.log(np.sum(countSS_1GPP[otherSS],axis=-1)/countSS_1GPP)\n",
    "logDiffRj_1GPP = np.log(np.sum(countSSperR_1GPP[otherSS],axis=-2)/countSSperR_1GPP) # -2 since -1 are the amino acids     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the SS prediction of the protein 1gpp on its own frequency table. \n",
    "\n",
    "We now also store the mutual information of each SS at each residue in `ISS_1GPP`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "SS_result_1GPP, ISS_1GPP = predictSS(residue_1GPP,table_1GPP,logDiff_1GPP,logDiffRj_1GPP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the information for each SS toghether with the true underlying SS of the 1gpp protein."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gor 3 analysis of the protein 1gpp on itself')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (15,10))\n",
    "plt.bar(np.where(SS_1GPP==0)[0],height=300,width=1,alpha=0.3,bottom=-150)\n",
    "plt.bar(np.where(SS_1GPP==1)[0],height=300,width=1,alpha=0.3,bottom=-150)\n",
    "plt.bar(np.where(SS_1GPP==2)[0],height=300,width=1,alpha=0.3,bottom=-150)\n",
    "plt.plot(ISS_1GPP[0],label=\"Helix\")\n",
    "plt.plot(ISS_1GPP[1],label=\"Sheet\")\n",
    "plt.plot(ISS_1GPP[2],label=\"Coil\")\n",
    "plt.legend()\n",
    "\n",
    "plt.xlabel(\"Residue position\",fontsize = 15)\n",
    "plt.ylabel(\"mutual information\",fontsize = 15)\n",
    "plt.title(\"Gor 3 analysis of the protein 1gpp on itself\",fontsize = 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation figure:\n",
    "\n",
    "This figure shows us the mutual information $I(\\Delta S_j)$ for each SS with the x-axis going overall the different residual positions. The computed mutual information for helix is shown with a blue line, while Sheet has an orange line, and Coil a green line.\n",
    "\n",
    "Then we have the background color which represents the actually true SS at that residue position in the protein. The background color follows the some color code as the mutual information lines.\n",
    "\n",
    "### Discussion figure:\n",
    "\n",
    "Immediatly we note that the highest mutual information curve corresponds directly with the true SS, meaning our prediction would be 100 correct (as is shown in the code below). This is ofcourse expected since we only used the frequency table of this protein. But it is indeed a nice illustration that the GOR algorithm works as intended.\n",
    "\n",
    "We also note that in some regions, there are no predictions. These are the skipped regions and here the SS is not know, and so the GOR 3 algorithm also makes no prediction here. \n",
    "\n",
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To verify that indeed the prediction is perfect. Compute the Q3 and MCC score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q3=[1. 1. 1.], MCC=[1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "validEntries = SS_result_1GPP>=0\n",
    "ValidSS_result_1GPP = SS_result_1GPP[validEntries]\n",
    "ValidSS_1GPP = SS_1GPP[validEntries]\n",
    "            \n",
    "Q3 = proteinQ3(ValidSS_result_1GPP, ValidSS_1GPP)\n",
    "MCC = proteinMCC(ValidSS_result_1GPP, ValidSS_1GPP)\n",
    "print(f\"Q3={Q3}, MCC={MCC}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And hence we see that the prediction per SS is perfect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.) Analysis protein proper way"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now analyse the same protein 1gpp but now the properway by computing the full frequency table of the whole data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "totalFreqTable = np.zeros(shape=(3,17,20,20))\n",
    "totalCountSS = np.zeros(3)\n",
    "totalCountSSperR = np.zeros(shape=(3,20))\n",
    "    \n",
    "#First compute the full freqTable\n",
    "for key, (proteinSequence, trueSS) in dsspDictionary.items():\n",
    "    freqTableFromProt, countSSFromProt, countSSperRFromProt = freqTableOfProtein(proteinSequence, trueSS)\n",
    "    totalFreqTable += freqTableFromProt\n",
    "    totalCountSS += countSSFromProt\n",
    "    totalCountSSperR += countSSperRFromProt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reload the sequence and freqTable of 1gpp since we previously changed it the zero elements to 0.00001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "testProteinSeq, testTrueSS = dsspDictionary[\"1gpp_A\"]\n",
    "(table, count, countPerR) = freqTableOfProtein(testProteinSeq, testTrueSS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "subTable = totalFreqTable - table\n",
    "subCount = totalCountSS - count\n",
    "subCountPerR = totalCountSSperR - countPerR\n",
    "\n",
    "subLog = np.log(np.sum(subCount[otherSS],axis=-1)/subCount)\n",
    "subLogRj = np.log(np.sum(subCountPerR[otherSS],axis=-2)/subCountPerR) # -2 since -1 are the amino acids\n",
    "\n",
    "subTable[subTable==0] = 0.00001"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict the SS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "SS_result, ISS = predictSS(testProteinSeq,subTable,subLog,subLogRj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Proper Gor 3 analysis on protein 1gpp')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (15,10))\n",
    "plt.bar(np.where(testTrueSS==0)[0],height=10,width=1,alpha=0.3,bottom=-5)\n",
    "plt.bar(np.where(testTrueSS==1)[0],height=10,width=1,alpha=0.3,bottom=-5)\n",
    "plt.bar(np.where(testTrueSS==2)[0],height=10,width=1,alpha=0.3,bottom=-5)\n",
    "plt.plot(ISS[0],label=\"Helix\")\n",
    "plt.plot(ISS[1],label=\"Sheet\")\n",
    "plt.plot(ISS[2],label=\"Coil\")\n",
    "plt.legend()\n",
    "\n",
    "plt.xlabel(\"Residue position\",fontsize=15)\n",
    "plt.ylabel(\"mutual information\",fontsize=15)\n",
    "plt.title(\"Proper Gor 3 analysis on protein 1gpp\",fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion figure:\n",
    "\n",
    "The figure above show the correct analysis of the $I(\\Delta S_j)$. And immediatly we see that the predictions are less nice, but the lines still follow the correct trend.\n",
    "\n",
    "Most often when orange -> Orange goes up, when Green -> Green goes up. This is most notable with the giant green spike in the middle. \n",
    "\n",
    "We also observe what we thought from the leave-one-out analysis, namely that the GOR algorithm like to predict Helix regions. This is most notable in the region after the last gap. For some reason the GOR algorithm thinks it should be Helix, but it is not. We should also be carefull here, this is a prediction made at the edge and so no sequence information is given to the left side. T\n",
    "\n",
    "This highlights that information from both sides around the residue is need inorder to correctly predict the structure.\n",
    "\n",
    "\n",
    "But overall, the GOR algorithm does what it needs to do, just not perfect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q3=[0.88888889 0.44545455 0.35955056], MCC=[0.27036806 0.2887275  0.22380015]\n"
     ]
    }
   ],
   "source": [
    "validEntries = SS_result>=0\n",
    "ValidSS_result = SS_result[validEntries]\n",
    "ValidtestTrueSS = testTrueSS[validEntries]\n",
    "            \n",
    "Q3 = proteinQ3(ValidSS_result, ValidtestTrueSS)\n",
    "MCC = proteinMCC(ValidSS_result, ValidtestTrueSS)\n",
    "print(f\"Q3={Q3}, MCC={MCC}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace the \"1gpp_A\" and one can explore other proteins. This \"1gpp_A\" but with others it can do it a bit better. For example the protein \"2gpi_A\" produces much better see here under:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q3=[0.96774194 0.58333333 0.29411765], MCC=[0.49863239 0.49713862 0.40736645]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3cAAAJcCAYAAABaL11XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeZhbd3U//ve52jXS7JvtGXvsxLvjrE4oZHFCSFgCZQkEKJSWUsjvW2joj0Kh/aXQ9suepyyhFPKkNOxLQwgQlgZITEI2EmdPvMXx2B579hktM9qlz++PqytrxqMZSSPpXmner+fxM/bMle5HGs34Hp3zOUeUUiAiIiIiIqL6ppm9ACIiIiIiIlo+BndEREREREQNgMEdERERERFRA2BwR0RERERE1AAY3BERERERETUABndEREREREQNgMEdERERABG5TUT+7zLv41ci8q5KraneiMjXRORGs9cxn4g8JyK7zV4HEVG1MbgjIqoxERkUkaiIzIjIaDao8Jm9roWIiFNE/llEDojIrIicyAYwV1XwHJ8TkeMiEhKRoyLyj5W671pTSr1KKfVNs9dRKSKyR0TeU+zxSqnrlVL/Vua5bhKRQyISFpH9IvLn5dxPgXVtV0rtqdT9ERFZFYM7IiJzvFYp5QNwHoALAPx/8w8QEXutFrPIuW4H8KcA/hxAG4D1AL4E4DVlnse2wKf/C8AWpVQzgJcC+DMReWM590/FE52VrgNmAbwWQAuAdwH4koi81NwlERHVFyv9UiciWnGUUicA/ArADgAQESUifyMihwAcyn7ur0XkBRGZEpGfichq4/bZ4/9WRF4UkQkR+Xz+BbuIvFtE9onItIj8r4ism3fbOefKJyJXAngFgD9VSj2ilEpk//xaKXVD3nFbsxmeQLb87XV5X7tNRP5TRH4pIrMALl/gOTiglJrN+1QGwJkLPV8i0iYid4nIePYx3SUifXlf3yMi/yYiD2QzQHeLSGfe1/9HREZEJCgi94nI9gLneVZEXpv3b0f2+T1XRNwi8h0Rmcw+5kdFpCfv/O/J/v1MEfl99lwTIvLDhc6VPfZ12ecukL2PrXlfGxSRvxeRp7P39UMRcRe4n7/IPvavZI/dLyIvn/f8fFJEHgAQAbBBRF6afQzB7MeXZo/9JIBLAHwlm2X+SvbzW0TkN9nX4wEReUve/edKW0Vkt4gMiciHRGRMRIZF5C8LPQdKqY8rpfYrpTJKqUcA3A/gT/Lu+89Fz+xOisiN2eflyuzXPiEit2efm7CIPC4iZ897Dq8sdG4iokbB4I6IyEQi0g/g1QCeyPv06wFcBGCbiFwB4NMA3gJgFYCjAH4w727eAD37dx70LNu7s/f9pwD+EcAbAXRBv1j+/rzb5s61wPKuBPCIUmpokfU7APwcwN0AugF8AMB3RWRz3mFvB/BJAH4AfyhwPx8VkRkAQwCaAHyvwCk1AP8NYB2AtQCiAL4y75i3A/jL7HqcAP4+72u/ArAx+7XHAXy3wHm+BeAdef9+NYBhpdQT0LNKLQD6AXQAuD67jvn+Dfrz0gagD8DNC51IRDZB/758EPr36ZcAfi4izrzD3gLgldAzpzsB/EWBdQP69/MwgE4AHwdwh4i05339nQDeC/37EQbwCwBfzj6WfwfwCxHpUEr9E/TXzPuVUj6l1PtFpAnAb6B/f7oBvBXAV0VkodcPAPRCf67WAPgrAP8hIm2LrN14TjwAdgF4LvvvbQC+CuDPoP8cGPeZ708B/A+A9uz67sy+PomIVgwGd0RE5rhTRALQg53fA/hU3tc+rZSaUkpFoV/MfkMp9bhSKg7gYwD+REQG8o7/bPb4YwC+COBt2c9fn72vfUqpVPYc5+Rn7+ada75OACPGP0SkPZtZCopILPvplwDwAfhMNqt3D4C78tYAAD9VSj2QzcjEsACl1GegBxvnAfg2gGCB4yaVUj9WSkWUUmHoQeNl8w77b6XUwexj+hGAc/Ju/w2lVDj7XH4CwNki0rLAqb4D4NUi0pz99zuz6wKAJPRA6EylVFoptVcpFVrgPpLQg9DVSqmYUmrBwBbAdQB+oZT6jVIqCeAmAB7oJaqGLyulTiqlpqAH0+cscD+GMQBfVEollVI/BHAAc8tob1NKPZd9TVwF4JBS6ttKqZRS6vsA9kMvj1zINQAGlVL/nT3+CQA/BvDmAscnAfxrdi2/BDADYHOBY/N9DcBTAP43++9rAfxcKfUHpVQCwD8DUPNus1cpdXv2Ofx3AG7or08iohWDwR0RkTler5RqVUqtU0r9n3nB1fG8v6+Gnq0DACilZgBMYm7WIv/4o9nbAHpg8aVsQBYAMAVAFrntfJPQsyTGuaeUUq0AzgfgylvfcaVUZt4aij1HjtI9AT0L9i8LHSMiXhH5erY8LwTgPgCtMncv30je3yPQg0+IiE1EPiMih7O3Hcwe04l5lFInATwA4E0i0grgVTiV5fs29KDjByJyUvSGMAtliD4C/fn+Y7bk8t0FHvr873EG+nOW/xwu+JgKOKGUyg988l8TwCKvr7zj52fFDOsAXGS8prKvqz+DnqFbyGQ2iCx27RCRz0MvU35L3uNYnb9upVQE+uszX/7XM9CzwKtBRLSCMLgjIrKe/Avzk9AvqAEA2bK4DgAn8o7pz/v72uxtAP1i933ZINL441FKPVjgXPP9DsAuydvTtoCTAPplbmOOtfPWt9g5FmIHcEaBr30IeubnomwDlkuzn5ci7vft0Ev3roRe1jewxG2/Cb00880AHsruj0Q2C/UvSqlt0LNr10BvODOHUmpEKfXXSqnVAN4HvXxxob2E87/HAv17emKBY4uxJnsfhvzXBLDI6yvv+BMLHAvor6nfz3tN+ZRS/0+Za51DRP4FeiB91bxs6DD00lbjOA/0n4N8/Xlf17LHnwQR0QrC4I6IyNq+D+AvReQcEXFBL618RCk1mHfMh0VvNNIP4AYARuOOrwH4mGSbhohIi4gUKp87jVLqbgD3Qi8hvUj0sQgOzC11ewR6NuYjojcd2Q29pG/+vsAFiYgmIu/Lrl9E5EIAfwM9sFyIH3pmL5DdR/bxYh9P9rZx6BkfL+aWwi7kTuhlojdA34NnrPlyETkrmy0MQS89zMy/sYi8OS8wnoYeKJ12HPTS0deIyMuzz++Hsut8cIFji9EN4G+z3483A9gKfR/fQn4JYJOIvF1E7CJyHfT9l3dlvz4KYEPe8Xdlj39n9v4dIrJL8hrAlEtEPgY9AL9SKTU/K3c7gNeK3vzFCb2kdn5Qfr6IvFH0zq8fhP4cPrzcdRER1RMGd0REFqaU+i2AG6HvaxqGntF667zDfgpgL4AnoTfH+K/sbX8C4LPQywdDAJ6FnhUpxRugX9B/B0AAwBHoZXhXZ8+RgB7MvQrABPSmF3+ulNpf4jkOQ2/u8R3ojUcWbD4CfU+hJ3uuhwH8uoTzfAt6yeEJAM9jiQv/bKnsj6E3Mbkj70u90IONEIB90PdMfvu0O9AbgjySbRTzMwA3KKVeXOA8B6BnCG/OPq7XQh+VkSjhseV7BHrTmAnoexKvXSBYMs49CT3z+CHoQe9HAFyjlJrIHvIlANeK3pn0y9l9jldBfw2ehF4u+lmcKtNdjk9Bzxq+IHp3zhnJzjxUSj0HvVnPD6D/HMxA31sYz7v9T6HvX5yGvkfyjdn9d0REK4bMLcsnIqJ6IiIKwEal1Atmr6URicg/A9iklHrHkgdbgIj8BYD3KKUuNnst1SQiPuhvNmxUSh0RkU9Ab3BTF98nIqJqYeaOiIhoAdmyz78CcIvZayFARF6bbajTBL2j6DM41RSHiIjA4I6IiOg0IvLX0JuH/EopdZ/Z6yEAejOck9k/GwG8VbH8iIhoDpZlEhERERERNQBm7oiIiIiIiBqA3ewFlKKzs1MNDAyYvQwiIiIiIiJT7N27d0Ip1bXQ1+oquBsYGMBjjz1m9jKIiIiIiIhMISJHC32NZZlEREREREQNgMEdERERERFRA2BwR0RERERE1ADqas8dERERERGtDMlkEkNDQ4jFYmYvxRRutxt9fX1wOBxF34bBHRERERERWc7Q0BD8fj8GBgYgImYvp6aUUpicnMTQ0BDWr19f9O1YlklERERERJYTi8XQ0dGx4gI7ABARdHR0lJy1ZHBHRERERESWtBIDO0M5j53BHRERERERUQNgcEdERERERLQAn88359+33XYb3v/+9y96m/xjvva1r+Fb3/pW1dY3HxuqEBERERERVcH1119f0/Mxc0dERERERFSi8fFxvOlNb8KuXbuwa9cuPPDAA6cd84lPfAI33XQTUqkUdu3ahT179gAAPvaxj+Gf/umfKr4mZu6IiIiIiMjS/uXnz+H5k6GK3ue21c34+Gu3L3pMNBrFOeeck/v31NQUXve61wEAbrjhBvzd3/0dLr74Yhw7dgxXX3019u3bt+D92O123Hbbbbj22mtx880349e//jUeeeSRyj0Y4zwVv0ciIiIiIqIG4PF48OSTT+b+fdttt+Gxxx4DAPz2t7/F888/n/taKBTCzMxMwfvavn073vnOd+Kaa67BQw89BKfTWfH1MrgjIiIiIiJLWyrDZoZMJoOHH34Ybre76Ns888wzaG1txdjYWFXWxD13REREREREJbrqqqtw88035/6dn+FbyB133IGpqSncd999+MAHPoBAIFDxNTG4IyIiIiIiKtGXv/xlPPbYY9i5cye2bduGr33tawWPnZiYwEc/+lHceuut2LRpE97//vfjhhtuqPiaRClV8TutlgsuuEAZNa5ERERERNS49u3bh61bt5q9DFMt9ByIyF6l1AULHc/MHRERERERUQNgcEdERERERNQAGNwRERERERE1AAZ3REREREREDYDBHRERERERUQNgcEdERERERNQA7GYvoBHsOb7H7CUUZXckWvVz7PF6qn6OpayUxwnU5rHWi+V+T/hcnvLb9HlmL8Ey7P59Zi+hIlLhld1KvNKWel3w+a4/jfKzXgu1vM5qSbUgnAhX/XyF+J1+AMAnP/lJfO9734PNZoOmafj617+O6667Do899hg6OzuXdY7BwUE8+OCDePvb316JJTO4IyIiIiIiWshDDz2Eu+66C48//jhcLhcmJiaQSCQqdv+Dg4P43ve+V7HgjmWZRERERERECxgeHkZnZydcLhcAoLOzE6tXrwYA3HzzzTjvvPNw1llnYf/+/QCA2dlZvPvd78aFF16Ic889Fz/96U8BAOl0Gh/+8Iexa9cu7Ny5E1//+tcBAB/96Edx//3345xzzsEXvvCFZa+XmTsiIiIiIrI0128+Dm30uYreZ6ZnO+Kv+JdFj7nqqqvwr//6r9i0aROuvPJKXHfddbjssssA6IHe448/jq9+9au46aabcOutt+KTn/wkrrjiCnzjG99AIBDAhRdeiCuvvBLf/e530dLSgkcffRTxeBwve9nLcNVVV+Ezn/kMbrrpJtx1110VeUwM7oiIiIiIiBbg8/mwd+9e3H///bj33ntx3XXX4TOf+QwA4I1vfCMA4Pzzz8cdd9wBALj77rvxs5/9DDfddBMAIBaL4dixY7j77rvx9NNP4/bbbwcABINBHDp0CE6ns6LrZXBHRERERESWtlSGrZpsNht2796N3bt346yzzsI3v/lNAMiVatpsNqRSKQCAUgo//vGPsXnz5jn3oZTCzTffjKuvvnrO5/fs2VPRtXLPHRERERER0QIOHDiAQ4cO5f795JNPYt26dQWPv/rqq3HzzTdDKQUAeOKJJ3Kf/8///E8kk0kAwMGDBzE7Owu/349wuHIdQRncEREREZElfHXPC7hn/5jZyyDKmZmZwbve9S5s27YNO3fuxPPPP49PfOITBY+/8cYbkUwmsXPnTmzfvh033ngjAOA973kPtm3bhvPOOw87duzA+973PqRSKezcuRM2mw1nn302G6oQERERUWNQSuGpoSASqQyu2NJt9nKIAOj76R588MHTPj84OJj7+wUXXJArr/R4PLlOmPk0TcOnPvUpfOpTnzrta/fcc0/F1svMHRERERGZbiaeQjqjMBqKm70UorrF4I6IiIiITBeM6nuRJmbjSKYzJq+GqD4xuCMiIiIi0wUienCnFDAeZvaOqBwM7oiIiIjIdIFs5g4ARkMxE1dCVL8Y3BERERGR6QKRRO7v3HdHVB52yyQiIiIi0wWjSXidNtg0YeaOqEzM3BERERGR6QLRJFq9DvQ2uzEaZnBH1jEyMoK3vvWtOOOMM3D++efj1a9+NQ4ePLjgsSdPnsS1114LANizZw+uueaaWi6VwR0RERERmS8YSaLF40BPs5tlmWQZSim84Q1vwO7du3H48GHs3bsXn/70pzE6Orrg8atXr8btt99e41WewuCOiIiIiEwXiCTR6nGip9mFYDSJaCJt9pKIcO+998LhcOD666/Pfe7ss8/GxRdfjA9/+MPYsWMHzjrrLPzwhz8EoA8337Fjh1nL5Z47IiIiopUumc7g2RNBnNPfChGp+fkzSiEY08sye5rdAIDRcAwDHU01XwtZ0xf3fhGHAocqep8bWzfig+d/cNFjnn32WZx//vmnff6OO+7Ak08+iaeeegoTExPYtWsXLr300oqurxzM3BERERGtcHuPTuM/9hzG8amoKeefjaeQzqhcWSbAcQhkbX/4wx/wtre9DTabDT09Pbjsssvw6KOPmr0sZu6IiIiIVrqpWX0MwbGpCNZ2eGt+fmOAeavXgW6/CwKOQ6C5lsqwVcv27dtN3UNXKmbuiIiIiFY4I7gaCkRMOX8wO8C81eOEw6ahw+dk5o4s4YorrkA8Hsctt9yS+9zTTz+N1tZW/PCHP0Q6ncb4+Djuu+8+XHjhhSauVMfMHREREdEKNx3VM3dD0+aUZeZn7gCgx+9mcEeWICL4yU9+gg9+8IP47Gc/C7fbjYGBAXzxi1/EzMwMzj77bIgIPve5z6G3txeDg4OmrpfBHREREdEKF8wGV8enIlBK1bypSiAbXLZ49OCuu9mFh1+cNWUtRPOtXr0aP/rRj077/Oc//3l8/vOfn/O5gYEBPPvsswCA3bt3Y/fu3bVYYg7LMomIrGrwAeCZ/zF7FUS0AkxHEtAEmE2kEciWSNZSIJJEk9MGh02/NO1pdiOaTCMUS9V8LUT1jMEdEZFVHXsQOPJ7s1dBRA0uoxSC0STO6PIBMKc0MxhNotXrzP27N9sxc4ylmUQlYXBHRGRVsQCQrv076ES0soRjKWQUsH11MwC9NLPWAtEkWrMlmQDyxiGwY+ZKp5QyewmmKeexM7gjIrKqWIjBHRFVXSCi73db0+pBe5PTlMxdIJJAi/dUcNfR5IRNE4wwc7eipW1pBKeDKzLAU0phcnISbre7pNuxoQoRkRUpBcSC+kcioioy9ti1eB3ob/PUfBxCRimEoqk5mTtNE3T7XRgNM7hbyWabZjE6NYqJ8QlTzu+2lxZYVfz8bjf6+vpKug2DOyIiK0rFgLT+bjqUAtgtjoiqJDeGwONEX5sXz5wIIpnO5JqbVNtMLIW0UnP23AF6aSbHIaxsSlOY8c+Ydv5z+8817dzlYlkmEZEVxYKn/p5haSYRVU8gkoBAH0PQ1+ZBRgHDgdoFVbnMYV7mDgB6ml0YC8WRybCCgahYDO6IiKwoP7jjvjsiqqJAJIlmjwM2TdDf5gUAHK9haaax56/VOz+4cyOVUZjKfp2IlsbgjojIiuYEd7ywIaLqmY4mcoFVt98Fh00wNFW7pipG5q51XuauN9cxk6WZRMVicEdEZEXx0Km/M3NHRFUUjJwaQ6BpgjWtHgxN1y5zF8wGd82nlWVyHAJRqRjcERFZEcsyiahGpiNzB4j3tXlxfDpas/bzgUgSPpf9tAYuzW473A6N4xCISsDgjojIiliWSUQ1kExnMBNPzdnv1tfmwUw8lcuoVVswkjytmQoAiAg7ZhKViMEdEZEVsVsmEdVAaIH9bn1tHgCo2TDzQN6ev/l6/AzuiErB4I6IyIpiQUCyv6JZlklEVTKdnXHXNq8sE6hdcBeMJk9rpmLoaXZhciaBZDpTk7UQ1TsGd0REVhQLAt52/e8syySiKglE9d8vLXmZM5/LjjavA8dr0FQlk1F6cDdvgLmht9kNBWA8zKYqRMVgcEdEZEWxEODt1P/OzB0RVUkgsvAYgv42b00yd+F4Chl1+gBzQw/HIRCVhMEdEZHVpJNAchZoYnBHRNUViCRh1wQ+l33O5/vaPBgJxpCqcjlk0AguC+y56252AQA7ZhIVicEdEZHVGM1Umrr0j2yoQkRVYjQzEZE5n+9r8yKtFIarHFQZZaGF9tx5nXY0u+2cdUdUJAZ3RERWYwww93boH7nnjoiqJBBJotVz+n63XMfMqeqWZubKQgvsuQPAcQhEJWBwR0RkNfMzdyzLJKIqCUSSC5ZE9jS7YdcEQ1VuqhLIjmJodtsLHsPgjqh4DO6IiKwmF9xxzx0RVVehGXM2TbC61VP1pirBaBJ+tx12W+FL0p5mF0KxFCKJVFXXQtQIGNwREVmNEdx5smWZ3HNHRFUQS6YRS2YWLMsEgP42T9XHIQQiiYL77Qy92Y6ZY9x3R7Qk04M7EbGJyBMicpfZayEisoRYELC7AIcbsDm4546IqiKwRKfKvjYvQrEUgtHqvcEUiCYLjkEwcBwCUfFMD+4A3ABgn9mLICKyjFgQcLfof9ecLMskoqqYjmQ7VRYM7vSmKieqWJqp7/kr3EwFALr8Lgg4DoGoGKYGdyLSB+A1AG41cx1ERJYSCwGubHBnczC4I6KqMJqZFAqujOCuWqWZmYxCKJZcsizTYdPQ4XNyHAJREczO3H0RwEcAFJyQKSLvFZHHROSx8fHx2q2MiMgs+Zk7BndEVCWByOIz5vxuB1o9jqo1VQnFklAKaCmQOczX0+zGaJiZO6KlmBbcicg1AMaUUnsXO04pdYtS6gKl1AVdXV01Wh0RkYnmB3cZ7rkjosoLRJLwOGxwO2wFj+lr81RtHEIuc7hE5g44NQ5BKVWVtRA1CjMzdy8D8DoRGQTwAwBXiMh3TFwPEZH5MhkgEQLczfq/ueeOiKokEE0umTXra/NiOBhDKlOwyKpsRqOWYjJ3vc1uxJIZhGIch0C0GNOCO6XUx5RSfUqpAQBvBXCPUuodZq2HiMgSEmFAKZZlElHVBSIJtC2RNetv8yCVURgNVn6/W65bZ4FRDPl6ml0A2DGTaClm77kjIqJ8xow7d6v+kcEdEVVJMZ0q+9q8AFCV0sxAJAEB0OyxL3msMQ6BHTOJFmeJ4E4ptUcpdY3Z6yAiMl0uuMuWZXLPHRFVgVKquBlzLS7YNcHxKjRVCUaT8LntsGtLX462e52wa8LMHdESLBHcERFRVi64M8oyueeOiCpvJp5COqPQtsR+N7umYVWLuzqZu+jSYxAMmibobnZxHALREhjcERFZyfzgTmNZJhFVXm6/2xJlmYBemlmNcQjFlIXmMzpmLtfUbAKTMwwSqTEtXeRMRES1EwsBmg1wNOn/tjmBNMsyiaiypo0Zd0V0quxv9+ChFycRjiXhdxeXaStGMJrEunZv0cf3+N14eiiITEZB06Ts8958zyEMTUdx1poWXL6lG9tXN5d9X0RWw+COiMhKYkHA1QJI9sKFDVWIqApKmTHX12o0VYli66rKBHfpjEKoiFEM+Xqb3UhnFCZnE+jyu8o6bzKdwVAginUdXgxOzuJLvzuEbr8LV+xUuGSLHU3u8oNGIitgcEdEZCXxvAHmAIM7IqqKYLYsc6mGKoA+yBwwgrvKZLnCsSQUigsuDfnjEMoN7k4GolAKeOX2XpzT34q9x6Zx7/5x/OCBGdzxSAIv2WTHy3fYsa6r8GB3IitjcEdEZCWx4KlOmQC7ZRJRVUxHEvC77bDblm6/0OxxoMXjwPEKNlUpZc+fIX8cwo41LUscvbChgL53sK/NC7tNw0XrO3DR+g6ciD2L3z2bwsMHU7jv+RS292v40Gvd0ISZPKovbKhCRGQlsfmZOyeQSQOZjHlrIqKGU0qnSgDoa/VUtKlKKWWhBr/bDo/DtqymKkPTUThsgu55mb91XTa8+3IXvvAuLy7eYsdzxzOY4dQFqkMM7oiIrEKp04M7LXvhk2FpJhFVTiCSRFsJWbO+Ng9OBqJIZ1SFzq9XJJSy505E0LPMcQhD0xGsafUUbMjS5BZs69NLMiPxyjxWolpicEdEZBXJKJBJ6Q1VDLbshQ/33RFRBQUiiaI6ZRr62r1IZVTFhogHo0kIgOYSu28udxzC0HQUfW2Ld+j0ZpN6DO6oHjG4IyKyilhA/zi/oQrAcQhEVDGpTAbhWKqoZiqG/KYqlRCIJNHsccBW4kiD1a0eTM4mEEmkSj5nMJpEOJbKPZZCvE59TRH+2qU6xOCOiMgq5g8wB/Q9dwDLMomoYkLRFBRQUlnmqmY3bJpUrKlKIJosKbg0DHToWbejk6WvYyi79iWDO1c2uGPmjuoQgzsiIquIh/SPC2buGNwRUWUEShhgbrDbNKxucVcsuAuW2NDFMNDRBAA4MjFb8m2NrOOa1qWCO/0jgzuqRwzuiIisYqHMncbgjogqK9epsoTMHaCPDxiaqlRZZml7/gxNLjt6/K6yg7tWjwP+Jfb55coyGdxRHWJwR0RkFbEgAAFc/lOf4547Iqqw3Iy5EjNn/e0eBKJJhKLLe7OpnD1/+QY6mzA4WU5wF1myJBMAXA5AE+65o/rE4I6IyCpiQcDlAzTbqc8Ze+6YuSOiCglEErBpAp/bXtLt+rNdJpdbmmns+Ss1c2hY39mE6UgS0yVEX6lMBsPB2JKdMgF95ILXxcwd1ScGd0REVjF/xh1wKnPHhipEFReOJfGF3xzEnU+eqFiL/3owHdGbmWhSWqdKI7hbbsfMYDbzV27mbn2nvu9usITSzNFQHKmMKipzB+ilmQzuqB6V9pYNERFVz0LBHffcEVXNgdEwnhsO4bnhEO56ehgbOpvwkg0d2DXQtuS+rHoWiCbKambic9vR5nXg2NTyMnflNHTJ19/mhU0ERyZnce7atqJuU2ynTIPXJSzLpLrE4I6IyCpiIaB9w9zPcc8dUdWMheIAgP/7+h146vVdJ/UAACAASURBVHgAD784ie/98Rh++Ohx7FjTjJds6MDZfa1w2hur0CkYSWJVS3FBznz97d5lZ+5yDV3KzNw57RrWtHlKaqoyNB2FTRP0NruLOp5lmVSvGNwREVnFgmWZ3HNHVC1j4ThaPA70NrvRu70XV2/vxdB0BA+9OIlHXpzCU0NBeBw2vP2itfiTDR1mL7dipiNJbFnVXNZt+9u8ePZEEMl0Bg5beUFvMJKECNC8jOzo+s4m/PHIFDJKFVVeOjQdxaoWN+xFrtnrEgRmM2Wvj8gsjfVWFBFRvUolgFQUcM+74OKeO6KqGQvH0O13zflcX5sXbz6/H5970078v1dugtuh4dHBKZNWWHnxZBrRZLrsrFl/uwcZBZwMlJ+9C0STaHY7oGml7fnLt76jCdFkGmPheFHHF9sp0+B1siyT6hODOyIiK4gvMOMO4BDzrF8/O4JfPjNs9jKowYyG4uiaF9wZNE2wbXUzBjqbMFFkAFEPjJLItjI7VeY6Zi5j3l0gWt6Mu3xGU5ViSjNn4ilMR5Loa126U6aBZZlUrxjcERFZwUIDzAFAM8oyV/ZbyPcdGsfdz48io3ixRZURT6YRjCbRs8QerC6fC+MzcagGee3lZtyVGVx1+V1w2TUcW8Y4hGAkWXbm0LCqxQ2XXSsquDuR3SNYUubOJUikgFS6Mb7vtHIwuCMisoKCwZ0NgKzozF0yncH4TBwz8RROLKMUjCjf2IyejZtfljlfp8+FZFrl2vfXu0B0eZ0qNRH0tXly3SfLW0Oy7DEIuXVognUd3qLGIZTaKRPQyzIBDjKn+sPgjojICgoFdyJ6aeYKDu5GQjEYSZMDI2FzF0MNw+iU2eNfInOXDf4mZhrjKj+XufOUV5YJ6KWZx6eiZWUzU5kMwrFU2QPM863vaMKxqQhS6cUbnwxNR+Fz2UsKKL2ubHDH0kyqMwzuiIiswAjuXC2nf83mWNENVYYD+nBpuyYM7qhixsL666rQnjtDl0//+vhMY+y7C0SScNk1uB3lXwL2t3sRTaYxOVt6wBuKpgCUPwYh30BnE1IZhaElMvpDgSj62jyQEoa2e7MvCwZ3VG8Y3BERWUEsCNg9gH2Bd7NtzhW95244GIUIcP66NhwYDXPfHVXEaCgOv9sOj9O26HEdPicEaJimKtMRvZlJKYHOfP3Z8sbjZQwzX+4A83xGU5XFSjMzGYUT2eCuFCzLpHrF4I6IyApiodNLMg3ayi7LHA7G0OlzYcfqFkQS6WUPUCYC9MzdUiWZAOCwaWj1OhomcxeMJpdVkgkAa9o8EAGOl/GzaOxdXO6eOwDoaHLC77Yv2lRlfCaORCqDvrbiO2UCLMuk+sXgjojIChYaYG6wOVZ05u5kMIrVLW5s7vUDAPaPhExeETWC8XAc3c2Ll2QauvwujDdI5i4QSS47a+ay29Djd5eZuTO6dS5/z52IYKCjCUcmCwd3Q2V0ygRYlkn1i8EdEZEVxIKnDzA32JwrNnOXziiMhuJY1eJBe5MT3X4X993RssVTaUxHkkt2yjR0+lyYaIDMnVIqV5a5XP3tHhwvo2NmIJqEJoDfZV/2GgC9NHM4EEMsmV7w60PTEYgAq1vKLMtkcEd1hsEdEZEVxJfK3K3M4G58Jo50RmFVq14+t7nHj4OjM8hkeMFF5TOycN1FlGUCelOV6UgSySW6MlrdbCKNVEYtuywT0DtmTswkEEmkSrpdIJJAi8cBTSt/z1++9Z1NUACOTi4caA5NR9HT7IbTXtolr8sBaMI9d1R/GNwREZktkwbiM4sHd5mVeYUxnO2Ct6pFvwjf0utHNJle1gBlotHsGIRiyzI7c+MQ6jt7ZzQzaatI5k7fw1bqHthgBWbc5Rvo0NdRaN/d0HQUfa2lZe0AveTT6wKiCb6RRPWFwR0RkdniIQBqkeBu5ZZlDgf1dvWrmvWLM2PfHUszaTmMMQjFlmUa4xDqfdadsd+tpRLBXZkdMwMVaOiSz+92oNPnXHDfXSyZxvhMvOT9dgavU1iWSXWHwR0Rkdli2QYh7JZ5mpPBKNq8jly7+lavEz3N3HdHyzMe1scgeJ3F7fsyZuHVe1OVQLZTZVsFmpm0eBzwu+0ld8wMRJIVCS7zre9sWnAcwomA0UyltE6ZBq9LEKnvbzmtQAzuiIjMFgvoH7nn7jTDwRhWzWuEsKW3GQfHwkhz3x2VaTQULzprBwDNbjucNq3uxyEYZZmVKIsUEfS3eUtqqpJKZzATT1WkoUu+9Z1NmJxN5MYsGIyS0f5yM3cuIMKyTKozDO6IiMwWC+ofXYW6Za7MPXcZpTASjGF169ymF5t7/IglMzg6Vbj9OdFixsKxopupAHog0+l3WnqQeTCahFKLByKBSBI+lx0OW2Uu//rbPDgxHUUqU1yjmUrOuMs30JEdZj6vNHNoOgKPw4b2pvIylXrmjsEd1RcGd0REZsuVZbYu/PUVuuduejaBeCpzWuaO++7MNzWbwC+eGUZmiWDCihKpjD4GochmKoYun8uymbtYMo1/+PHTuO3BwUUDvErMuMvX3+5FKqMwGizueZnKZg5bKxzcrWv3QgSnlWYOTUexptUDkfI6c+p77iqxQqLaYXBHRGS2WFDfV+coUDq0Qvfc5ZqptMzNsLR4HFjV4mZwZ6J79o/hJ0+cwLEyhlibzdg311NC5g7QZ92Nh+NLZsfMMDWbQCqj8MDhSfzq2ZGCxwWiiYoGVv3ZvWzFlmbuPToNmyZYl820VYrLYcOaVs+cjplKKb1TZpklmUC2LJOZO6ozDO6IiMxmDDAv9O7yCt1zdzI4dwxCvi29fhwamym6HAzQ9xtxn15l7BvRs80vjM2YvJLSldop09DldyGe0veMWU0opv9+WN3ixh1PnMBjR6cWPE7P3FWuU2VPiwt2TYrqmBlLpvGHFyZwwbq2ipdlAnpp5pGJ2VzwPTWbQDSZXmZwJ4ingFSavzeofjC4IyIy22IDzAE9uFNpfR7eCjIciMHnssPvPv1CcHOPH/FUpuDg4vnGw3F87CfP4CdPnKj0MlecmXgKx7LP+6E6DO5KnXFn6PRZt2OmsZftPZdswBldTfivPxw5be5bOqMQjFW2LNOuaVjd6imqY+aDhycRS2bw8i3dFTt/vvWdTZhNpHPjKoaW2SkT0MsyASC68rY8Ux1jcEdEZLbYUsFd9p32zMrK3g0v0EzFUOq+uzufPIFkWuGe/WOnddSj0hwYCUNBz3y9MDZjyTLFxYyF9TcNih2DYOjyW3fWnfGa7vQ58Te7z0SLx4Gv3PsCJvP2CIZjSShV+f1ua9v1jpmLvQ4ySuGeA2NY39mEDV2+ip7fsL5TL/U0glqjU+aaMgaYG7wuPbhjaSbVEwZ3RERmiwUB1xKZO2BFlWYqpXAyGD2tmYrB73ZgTasH+4sI7o5NRvDIkSlcONCOVCaDXz07XOnlrijPD4fgsmt4+ZZuBKNJyzYZKWQsXNoYBEOnT3+TxYqPNxRNwa4JPA4bmj0OfOCKjUikMrj53hcQS+oZf2OAeSXLMgGgr82DcCy16Jsm+4ZDGAnGcEWVsnYAsLrVDYdN8oK7CDp9ztyMzHJ4sy8TjkOgesLgjojITErp3TIXy9xpKy+4C8VSiCTSC+63M2zu8eOF8Rmk0ovvu7v98SE0OW14x0vW4qVndGLPgXFMR6yXfakX+4ZD2Nzrx5ZefXRHvZVmjoXiJZdkAoDLbkOLx2HZsswWjyPXFXJNqwfvu3QDTgaiuOW+F5HJqNxrvtIz5ta2G01VCpdm/m7/GPxuOy5Y11bRc+ezaxrWtnvnZO6WU5IJnCrLZMdMqicM7oiIzJSY1ffTLbXnDgDSKycgGV6kmYphc68fiVQGRyYLz7t77mQQzw+H8Jqdq+B12vGas1ZBKeCXzzB7V47JmTjGwnFs7W3GqlY3vE4bXhitn+AukcpgKpIouVOmodPnxIQlM3fJ05qU7FjTgrdduBZPnwjiR3uPI5DNrLVVIXMHoGBTlbFwDM8MBXHZpq6KzdcrZH1nE45NRRBLpjESii2rmQrAskyqTwzuiIjMZAwwdxcYYA6syD13wwFjDELhi7PNPYvvu8sohR8/fgIdTU5cvlkvB+vyu3Dxxk7cf2hizn4kKs6+Yf253raqGZoIzuzy4dB4/QR3RkllOWWZwKlxCFYTiiXRvEDjocs3d+PKrd347b4x/G7fGDQB/K7S9houxeu0o9PnLDgO4d7949BEsHtTV0XPu5D1nU1IpDN47Og0lEIFgjv9I8syqZ4wuCMiMlPcCO645y7fcDAGt0ND2yIlZD63HX1thffdPTo4hWNTEbz+3DVzMgav3tELAPgFs3cl2zcSQrPbnmt0c2a3DyPBGMKx+nhtjoWyYxCay8vcdfldmIokLNcaPxhNotmzcND2lvP7sbOvBSOhGFo8DmhaeQO9F9PX5sXxqdPLMo3xB+eva6v4Xr+FDGTn591/aDy3ruVgWSbVIwZ3RERmymXuWgsfswL33BnNVKTQ7L+sLb1+HB6fQXLevrtkOoOfPHEC/W0eXLS+fc7XOnwuXLKxEw+8MGnJLIxVKaXw/HAIW1c1574vG7v1zof1Mu9uLLy8zF2XzwWlgMmwdYK7TEYhHE8VnB2naYL3XrIB/W0e9C5S5rwc/W0ejIZiiCfnjmt5+MVJRJPpqjZSydftd8HrtOHw+CycNg3dvvK+zwaXA9CEZZlUXxjcERGZqaiyzJW45y626H47w+YeP5JpddpMr98fHMfETAJvOq8P2gIB4mvOWgUR4K6nT1ZszY3uRCCKcCyFratOvVYHOptg16Sugrsmpw1NZZYmGuMQxi0U3IXjKSgFNC8y4sDtsOEfX70VH7h8Y1XWsLbdCwX9NWJQSh89sq7DizO6mqpy3vlEJDcSYXWre9lZShGB18WyTKovDO6IiMwUCwIigNNf+Bhjz90KydxFEnpb9WKCu009fggwpzQzmkjjrqeHsaXXj+2rFw6aW71O7N7chYdenMRotlSPFmfst9vae+q16rBpGOhoqpuOmWOhGHrKLMkE8gaZBxfv0FpLxgiCQpk7g8OmwWmvzmWfUf6Y3zFz/0gYJ7PjD5bKwFfS+mxp5nJLMg1epzBzR3WFwR0RkZliQcDVDGiL/Do2MncrpKHKcHDpZiqGJpcd/e3eOU1V/ve5EczEU7j2vL5FLypftWMV7JqGnzN7V5R9wyH0+F3omFfqdma3D0enIkikrBPwFDIWjueyb+Vo9Tpg1wTjIetc7IeKDO6qqdPnhMdhm9Mx83f7x+Bz2XHhQPsit6y8gU4juFteMxWD1yXcc0d1hcEdEZGZYqHFB5gDK66hitEp02jasZTNefvuApEE7t43il0DbbmLvEJaPA5cvrkLjxyZwslA4RldBKQyGRwYDc8pyTRs7PEhnVEYXGQkhRUk0xlMzSaWlbnTRNDpc2EsZJ1A1sjcLdQts1ZEBH1tnlzHzPFwHE8dD+DSTZ1VH38w35ZePy4caMe5/YvsYy4ByzKp3jC4IyIyUyy4eKdMIK+hysrYc3cyGIVdE3Q2FZdh2dzjRyqjcHh8Bj9/ehjptMIbzl1T1G1fuaMXTpuGu55m58zFHJmYRTyVWTC4O6NLb6pi9dLM8XAcCuU3UzF0+pyWytzlgrsC3TJrpb/di6HpKDJKYc+BMYgAuzfVppFKPrfDhvdeuuG0DHO59Myddb7fREthcEdEZKZigrsVtuduOBhDb0vxzRA29fggojdRuf/QOC7b1IXuIodU+90OvHxLNx4dnMKJaWbvCtk3HIZAz5LO53PpoxEOjS08ksIqltsp09Dld2HcQpm7UCwJj8MGl91m6jr62zyIpzI4MR3F/S9M4Ly1bWhvqv74g2rT99yZvQqi4jG4IyIyUzy4eKdMYMV1yxwORrG6iP12Bq/TjnXtXjw6OA2HTcM1O1eVdL6rtvXC5dDws6e4966QfcMhrOvwwlegy+TGbj8Oj80ik7FuhmMsvLwZd4ZOnwuRODAbs8ZjXWzGXS31t+sNTP5n7xAiidqNP6g2r4ujEKi+MLgjIjJLKgak4kVk7lbOnrt4Ko3JmURRnTLzbe7RM0qv3N67aEv4hfjcdrxiaw/2HpvGsbyGEKSLJdN4cXwWW3oLvwlxZrcP0WQaJ4LWzX6OheLwOm0FA9Ri5cYhWCR7F4wmTW2mYljT6oEmwPPDIfS3eXIzEOud1yWIp2C5wfVEhTC4IyIyS27G3VJ77uyAaCuiW+ZoUN8XtarIZiqGl53ZiZdsaMcrtvWUdd5XbOuBx2HDz5m9O83B0TDSSmHbAvvtDLlh5qPW3Xc3Gl7eGARDlzEOwSL77kLRlKnNVAwOm5Ybkv7yLT01HX9QTV6n/jiiK6NwghoAgzsiIrPEQvrHpYI7QM/erYCyzJPZzE8xYxDyrW714D0Xb4DbUd6+I6/Tjks3duLpoSAiiVRZ99Go9o2EYdcEZy6SielocqLN67B0U5XxcHzZ++2AvFl3zNydZkOnD363HReur+34g2ryuvTgjqWZVC8Y3BERmaXYzB0AaM4VUZY5HIxBE6CnAhfhpTpvXRvSSuGZE8Gan9vK9g2HcGa3b9EB2CJ68PeCRYO7ZDqDydlERYI7j9MGvxsYs0DmLpHKIJpMWya4e8sFfbjxNduqNizdDJ5sTxiOQ6B60Tg/fURE9SYW0D8Wnblr/ODuZDCKbr8b9hrPxgKA9Z1NaHbb8cSxQM3PbVWhaBJD09FFSzING7v9mIokMDljvdaCEzNxKLX8ZiqGzmbNEpm7UMwYg2CN4M7rtDdEh8x8pzJ3Ji+EqEgM7oiIzGKUZbqWvnBeKcHdcDBWcjOVStFEcE5/K545EUQybf6FuxXsH9HHGyw0324+o2zTitm7So1BMHQ1C8aD5mdyjBl3VsncNSKWZVK9YXBHRGSWWBBwNJ3qhrkYmwPINPaeu1Q6g/FQvORmKpV0Tn8r4qkMDoxYe2ZbrTw/HILXacO6bJv7xfS1euB2aJbcdzcayo5BqFBw192sYXJGIW3y6IeQMcDcbf4ohEblZVkm1RkGd0REZilmgLnB1vh77sbCcaSVKrmZSiVtXdUMl13DE8dZmqmUwr7hEDb3+osaKK9pgjM6fZYM7sbDlRmDYOhqEaQzwNSMuRf8zNxVH8syqd4wuCMiMksxA8wNWuOXZQ4H9eyKWWWZgN7OfceaFjx5PICMWtnv1I/PxDE5m8C2RebbzXdmjw8nA1HMxq3VcXQspHfKrFR7/q5m/fLJ7HEIoVgKAn1WI1WH2wGIsCyT6geDOyIis5SUuWv84C43BqFCTS/KdW5/K4LRJAYnZk1dh9n2DRe/386wsdsHBeDwuLWyd6PhGLr9lXtddTXrQaLZTVWC0SR8bjvsGi/nqkVE4HWyLJPqB38bEBGZpdTgrsGHmA8HYuhocsJV5qy6SjlrTQtsIiu+NHPfcAhtXgd6movfp7a+owk2EUs1VUkZYxBKeBxLafcJbJoFMnfRpCUGmDc6r0uYuaO6weCOiMgM6QSQmC0xc9fYDVWGg1FTm6kYmlx2bOrxlRXcPXgghZNT9d9pM6MU9o+EsXVVc0mljC6HDWs7vJbadzcxk9DHIFRwdqJNE7T7xBKZO+63qz49uDN7FUTFYXBHRGSGyKT+0dtZ3PENPsQ8k1EYCcVMbaaS79y1bRgJxjA8XfzF+9HxNG75bRy/frL+v09DU1HMxFMllWQazuz24cjErGXGSYyF9b2cPRUu9+22wDiEUIzBXS2wLJPqCYM7IiIzzE7oH5uKDO4afM/d5GwCybQytZlKvnP6WwEATxwpvjHInY/q35/RoDWCmuV4flifwbi111/ybTd2+5DKKBydjFR6WWWp9Iw7Q5fJg8yVUghGk2j2sJlKtbEsk+oJgzsiIjMwuJvDaKay2iKZu/YmJ9a2e/H4kXRRxw+Op/HEkTTsNvP3YVXC/tEQVre40WoM+SrBmV36MPNDY9aYFTgaisHjqNwYBENXiyAcA6ImZXSiyTSSacXMXQ2wLJPqCYM7IiIzRCYACODpKO74Bh9iPhwwfwzCfOeubcXhkQwCs0tnZ+78YxJeF3DlWXZMzSgkUvUd4E3MJLCqtbxAu9mjN2GxSlOVsXAc3c2VG4NgODUOwZzsXSiqZ5Vb2FCl6rzO0kchPH00hcdftNZIEFoZGNwREZlhdgLwtAG2IrMJDT7E/GQwihaPA00Vzq4sx7n9rVAAnhxcPHs3OJbGk4NpvPIcB9Z26p0+6z17F44m0byM2Wkbu/14YWzGErMCjRl3ldadG4dgzmM0Bpg3M3NXdV6XIJ4CUuniv9d3PprEz/c27u9ssi4Gd0REZohMAE1FZu0AfYi5ygCZ4soE681IMGaprB0ArGn1oKtZlizN/MmjSTS5gFfsdKCnRb/gH6vjfXepTAazifSyWuyf2e3DbCKNkexgerOk0hlMzMbRU8EZdwazB5kbwR3LMqvP69J/rqMlFE9MhZVpJbu0sjG4IyIyw+wE4O0q/nhb9gKuAbN3SikMWzC4ExGct96G54+nC16kvTiaxlODaVx9jgMep6C7Rf9vdczkLorLMRPTS8n8y8rc6fvuHjkyVZE1lWtyVh+D0FXBGXeGJrfA4zSxLDPGzF2teLMvn2JLM5NphUBEcZ8emYLBHRFRramMPgqhlMydEdw14L676UgS0WTaMmMQ8p23wY5UBnj22MLZu5/mZe0AwOcGPE5gzOT5Z8th7OVaTtDQ7Xdh10AbfvHMMH63b7RSSyvZaCiWW081dDdrpo1DCEaTsGmCJqfNlPOvJF6nnrkrdhzC9Ix+XCxZv2/yUP1icEdEVGuxIJBJAU2lZO6yXQsbMHM3ODkLAFjX4TV5Jac7s1eD3w08vsBIhBdH03jqqL7XzpO9+BPRs3f1nLnLZYSWUZYpIviri9fj3LWt+P6jx3HvgbFKLa8kxhiEapRlAnrHTLMC+WA0iRa3o+KNYuh0RllmsZm4ybD+858ocZ8eUSUwuCMiqjVjDIK3xD13QGMGdxOzsIlgbbv1gjubJjh7wI6njqZPu0i789EkfG7gyp1zg6DuZqnrPXdGcLecskwAsGsa3nfJBpzd14LvPnIM9x0cr8TyljQ5E8dDhydx24OD+NWzI/A4bMt+LIV0NWuYCClTGseEOOOuZk4Fd8V9nydnTv38xxrvVzZZHH8rEBHVWsSYccc9dwBwZGIWa9o8cNis+X7jeett+MP+FA6ezGBbv14Cd3gkjaePpnHtS05l7Qw9LRoeP5JGOqNg0+ovq5JrsV+BvVx2m4brLzsDX91zGN96+Cg0EVy8scjZjkWamInjwEgYB0bDODgaxsSMXrrsddqwqduPl2xor1p2q6tZkMoAgVmFdl9tv9fBaBJtTaXPIaTSGeMeiy3LNDJ3gD4H0eeuv98DVL8Y3BGRpSTSCoE40O1t4P8Mc5m7Ei5yc8FdY+25yyiFwckILlzfbvZSCtreb4PTDuw9ksoFd0bW7uVnnR4AdbUI0hn9Aq+7pf5ex6FYEk6bBpe9MsG2w6bh/+w+A1+55wV886FBaBrw0jOWH+BNzsTxlXtfwPHpKADA57JjY48PV27tweYeP9a0eaBVuWQxfxxCu6+qpzpNKJbCQEdTbU+6QpVblqnfhmWZVFsM7ojIUj79mMJPDwOPvQ1VvzAzzewE4PACzhLKEHN77horuBsLxRFNprG+07oXqS6HYHu/DU8cSeMdlygcHs3gmWNpvPlPTs/aAXrmDgDGQgrdLbVe7fKFYyn43faKZrscNg1/c/mZuPmeQ/jvBwdhE8FFG0ooS55nOBjFv//mIGLJDN66qx9bev1Y3Vr9YG6+/EHmm1fXrrFJJqMQiiU5BqFG3A5AhGWZVB+sWQNDRCvSTFLhRweBqTgwHjV7NVUUmSgtawc0bFnmkQm9mcp6i2cgzltvw9SMwtGJDO58NAm/G3j5joUvrLvrfNadvper8kGD067h/VeciU3dftz6wBH8scwxCYMTs/jsrw8gnVH4yNWbceXWHvS1eU15M6jDLxCg5h0zw/EUlOIYhFoREXidpZVldvpL26dHVCkM7ojIMu48DMxmmxIeD5u7lqqanQCaSg3uspm7TOMFdy67ZrkZd/OdM2CHCHD7w0k8eyyNV53rgHuBrB0AtDYJ7LY6Du5iyao1IHHZbfjbK87EmV0+3PqHF/HQ4UmoEpqR7B8J4fN3H4DboeEfXrkF/SY34bHbBO2+2nfMDHGAec15XVJUoKaUwtSMQl+HfoldyuBzokpgcEdElqCUwnf2K3Rmr/GPMbibq1Ezd5OzWNfhhWbxxiN+j2DTKg3PHkvrWbsF9toZNJFsx8z6fMc+FEstawzCUlwOG254+UZs6PThvx44gk//aj+eOxlcMsh78ngAX/ztIXQ0OfEPr9yCnmZrvCHQ1SIYD9X2e31qgDl319SKHtwtfVw4po9AMII7zrqjWmNwR0SW8PgYsH8a+MDZeplTwwZ3yQiQnC29LFNrvIYqqXQGx6cili/JNJy3Xr+QftW5Drgciwej+qy7+svcZZRCOFb9Fvtuhw1/f/Um/PmfrEMgmsQXfnsIn/vfA9g/Elrw+AcPT+Cre15Af7sXH7l6C9q81ukS2dWs1Ty4CzJzV3PFlmVOhfWf+752/RKbZZlUawzuiMgSvr1fwecArt0IrGoCjoUb9D/E2Un9Y7llmQ2UuRuajiKVUZZuppLvkq12vPEix2lz7RbS0yIYC6mSSg6tIBJPI6OWN8C8WHZNw6Ubu/DJ1+/An120FhMzcdx090HcdPcBHBo99e7Ob/eN4hsPDGJzjx8fesUm+KpUMlquVa2CYEQhHK3d99oI7mrxfSJdsWWZkzP6Mb2tApsGxBrn/TiqE9b6DUlEK9JkTOGXg8BbNwFNDkG/X+H4jNmrqpLcjLsyyzIbaM9drplKnQR3XpfgdRcURzh07QAAIABJREFUlzHqatGQSAHBiEJrU2klp8PTGfS2StVmsy0mV+5Xw6DBYdNw+eZuXHxmJ35/cBy/fGYYn/3fA9i+qhm9LW78bv8Yzl3bivdessGSsxA39NgAJPHiaBpnD9TmsioUTcFl1+B21K5D50pXbFnmRPaNyQ6/Bk8JTViIKsV6vyWJaMX5n0NAIgO8Y4t+Mdvva+CyzNlx/SO7ZeLI5Cz8bjvaG3AQszH/bLTEfXdHxtL42PeieO54uhrLWpKZe7kcNg1Xbu3Bp994Ft58fh+OTkXwu/1jeNkZHbj+0jMsGdgBwECXBhHg8GjtynCDUY5BqDWvs7gSy6lwBk474HMDHqcgyuCOaoyZOyIyVUYpfO+AwoU9wKY2/YJ4rV8wGlGIpRTcdms32ijZ7CSg2QBPa2m3a8A9d4MTs1jf0WRKhqracrPugqXNPzs4rAcIB4Yz2LG2KktbVCiqt6v1m1ju57LbcPX2Xly2qQtHJmaxpddv6deI2ynoa9dweLR2ATln3NWe1yWIp4BUWsFuK/x6nJxR+ogMEXicwrJMqjlrvg1GRCvGfSf0LJ2RtQOAtX7941AjlmZGxgFvByAl/vrVbIDYGiZzF02kMRyM1U1JZqk6/AJNUHLHzMExPUA4UsMsUL5TZZnmv/frdtiwdVWzpQM7wxk9Gl4czSBToz2WwSrNIqTCvC79dbjUaIPJsEKHT//9zrJMMgODOyIylTH+4JXrTn2uPxvcNeSsu9kyBpgbbI6GCe6OTs1CoX7225XKbhN0+Euff3ZkLJP9mDalGUsoloQmQJPL/OCunpzRqyGaAEamaxfctbCZSk15XfrHpUozJ8MZtGcHmDNzR2YwLbgTkX4RuVdEnheR50TkBrPWUkm/P/573PL0LWYvg8g0pbxzfWJG4Z4h4LpNgNN2euauIffdRcqYcWewORqmoYrRTGWgTsYglKO7pbRZd9GEwkhAoatZMBsvPetXCeFoCn63A1odZMus5IwevfS2FqWZyXQGkUSaM+5qzOvUfyYWy8QlUgqhKNCZC+6YuaPaMzNzlwLwIaXUNgAvAfA3IrLNxPVUxFRsCgemD5i9DCJTHAoonP1dhdsPFfef2fcPKCgFvG3z3AvJTjfgsQPHZsr7TzEUDyGSjJR126rKpIDo9DIzd43xNvDgRARdPpfl2tpXUndzabPuBsf1Y3dv15+TF8dqX5oZiiUtUZJZb3rbBB5nbZqqhIwxCCzLrCmjLHOxjplT2f+zOnz5mTsGd1RbpgV3SqlhpdTj2b+HAewDsMas9VSK2+5GIp1AOmNOpzMiMx2cBsJJ4MN/UPjBwcX/Q0ukFX5wCLiiH+jzzQ3uRGRZHTNvffZWfGHvF5CwWiAUmQKUKj9zpzkbpizzyMRsw5ZkGrpbNMzGgZlYcRd3g9lg7mWb7XDagRdr2KDDEI6lTG2mUq80EWzo0XB4pPrBXTDGAeZmOBXcFf55nsqOQWj3G3vuZMk9ekSVZok9dyIyAOBcAI8s8LX3ishjIvLY+Ph4rZdWMrfdDQCIp4sYhkLUYCai+sfzuoGPPqDw7f2F/xO8+5h+/Ds2L1z+tdZf/p67YDyIkcgI7jh0R3l3UC3GjLsVvucuGE1iKpLAQKfX7KVUVXeL/touNnt3ZCyNDr+gtUnDQJeW239XS6FYkuV+ZTqjx4ahqQziyepmaoyOptxzV1ve7MSWxcosJ8L6z+ypzB2QyujlmkS1YnpwJyI+AD8G8EGlVGj+15VStyilLlBKXdDV1VX7BZbIY/MAAKKpqMkrIaq9iZiCJsB3rhZc2Q/c+JDCN55b+D+1b+9X6PMBlxbI1/dng7tymkrEUjE4bU48cPIBPDn2ZMm3r5rZMgeYGxpkz129DS8vV24cQqj4zN36bv0267s1HB3PIJWu3UWhUgqhaKqmA8wbyYYeDUqh6kF5kGWZpiimLHNyRkEAtOeVZQJgUxWqKVODOxFxQA/svquUsthb7OUxMnexdMzklRDV3kQUaHcBHrvgq5cLrl4L/OsfFW55Zu4F6qGAwiMjwJ9tFti0Qpk7wWwKmCoxCa6UQiwdwyVrLsFa/1p8f//3MR2bLvchVdZsJTJ39X+VMDgxC02Ate2Nnbnrai4+czcbUxgLKQx0ZYO7HhuSaeDEVO2yd/FUBol0Bn7uuStLrZqq5Pbc8ftUU24HILJ0WWZLk+Tm4HmKaMJCVGlmdssUAP8FYJ9S6t/NWkelGcEdM3e0Eo1HgU49eQ2nTfCVywWvGQA+9ZjCfzx16j+37+5XcGrAWzYVvq9yO2YmMglkVAZNjib8xfa/QEql8K3nv4WMMmdu2ByRCcDVAtid5d2+QfbcHZmYxZpWD1z24od71yOXQ9DqLa5jptFMZX23/pxsyGbwXqzhvLvcjDtmhMri9wi6m6Xq37NgNAmfyw67zfTiqxVFROBdovvl5EwmV5IJ6GWZANhUhWrKzN8MLwPwTgBXiMiT2T+vNnE9FZHL3KWYuaOVZzJ2KrgDAIcm+NJlgtdvAD7/uMIXnsggklT48QvAqwaADnfhduvlBnfGz57H7kGXtwtv2fQWvBB4Ab85+psSH00VzC5jDALQEHvu/n/23jRKkuu+7vy9iNyX2reu6h3dQKMbBECCWEhwgUgth5JIbSPJsmVJtsdjSyPZRx6Pz9jjGZ8Z+chje2SPvNCWbGuxZckSbZkiJVKiKK4AAYIEATSARmPpvau6uiqzllwjt3jz4UVkZVXlEpkZkVlL3HP6ZHdXZnZ0VUbEu+/e/71SSq6lD36Yig1Vh9B5sX/NKi+3lbvpEUEi4r3FrxH+LFf/uGdO4+1l09OOwk1/LnJoiIVFW+UunZVMJhvJnbPicx8+3MTQrg5SyqeBA1ekY8/c+eTOx2FEqrhFymwENMEvvR8CmuSXX4KvLkqyFfjxc+1P/6MJ9Xi7S3Jnq+YRXW20PDb3GK+vvc5nrn2Ge8fv5dToqe7e0E3kUzB2tPfXHwByt5ItUSjXDnS/XSNmRjVevdXZpnd91WRmRBC3NjyEEJya0bm6MrjEzKyl3Pm2zN5xz6zOs2/WWMuXmUyEPfk3Mn6B+dCgyF3zr5lSks5J3nV6SzeJOghh8eHDbfiavsvwZ+58HGakdih3NnRN8E/eJ/ixe+Fbq3BuHN490/69ogHBdBRuZru7Kdrnnn0uCiH40ft+lLHwGL/x2m8MzzItpbJlxvoIhtKDYO7vLeDrhyRMxcbMqGAjLzsmKF5bMTk5s/2WfHpGY3Gt82vdQsZQyp1vy+wdp2fVz9AODfICm8WK/zMaEtrZMrNFqNbYYcu0A1V8cuc1xm+/yNjSxWEfxp6AT+5cRjTgK3c+DifyFUmxClMtrJaaEPziewX/1+PqUY3dtsfxZO+2TJvcgTovf+rCT7FR2uB33/hdTy1TLVHKqjCU+GTv76Hv/5m7a+k8IV1jfqzJLsABxIyVmLnaJjEzU5Sks7I+b2fjlJW+aM/jeQ175i4Z9pW7XnF8SiOgwxWPyJ2UkoxR9TvuhoR2tsy0XYPQxJZZ2N97cnse0cwdHvj8L3L6G7857EPZE/DJncsIakE0ofmBKj4OHeyOu2bKnQ0hBD95XvCuGWeO7ONJuJnr7jgaZ+4acWr0FB85+RFeuPsCzy8/392bugG74y7eh3Kn7X9b5rVUnhOTsZYpqQcNM1Zi5t02c3fX7Xm7XcqdInuDClXJFCvEQrof1NEHArpQHYWr3pC7UtWkXDV9cjcktLNlpi2XyXZypx6LvnLnHaTJfV/9V+i1MtHs3WEfzZ6AfwV3GUIIInrEt2X6OHRIWR/5duSuWxxLwp08lLvo+rI3Vuz510Z858nv5MzYGT7x5idYLay6dpyO0G8NAuz7mbuqaXJzrXBo5u2goeuuTWKmrczZYSo2RmKCyaSoh614jYxR9e1+LuCeWY0ba3mqNfdJud9xN1zEQq2rENI5i9wlts7jgC4I6n7PnZeYv/wnjC2/Sm7iJMFSFr1cGPYhDR0+ufMAkUDEV+58HDrUlbtI++d1g2MJgSlhqYtN8HqgSmD3gWhC4yfO/wS60PnNS785WHtmvwXmsDVzNwxbqQtYWjeo1OShmbcDiEcE8TCsZlov9K+tmMyNibqFqxGnZ7SBKXdZo+J3p7mAe2Z1KjXJ7XX31wE2ufMDVYaDWFhQqkK1yYZjOmsSCUJsR45OtEN9go/eEc6tcvobv8Ha/EPcfPCHAIjkVoZ8VMOHT+48QCQQ8WfufBw6OLFldgs7efNWF3N3OwNVdmI8Ms63n/h2bmRukKt06fnsB4VV0MMQSvT+HnpIETs5uARFN3EtfbjCVGzMjGodbJkmp2aa345Pz+qkspJM0fvFYaZYZcQnDX3DDlW56sHcnV1g7tsyh4NYuHW1wVpOMpHYPU8eDQk/UMULSMl9T/9rhIQ33/c/U0zOAj65A5/ceYKoHj3Y5K5Whrc+B3/yv8PyK8M+Gh97BLYtc9JF5a6XrjujahDSQ2ii9eVtNDRaf+7AkE8r1c5BkExLaNaCrrY/PT7XUnkS4QBTiR5L3PcpVNdd88XdRt5kPS85OdO80N0mfdfuek/oM0bFJ3cuYDIpGI0GuZpyf/Noy5bpK6zDgK3KNbNmprKSqeTu+040JPxAFQ8w+/YXmVh8kauP/gRGchbDJndZn9z55M4DRAIHdOauUoBLn4JP/Q345q/B2pW+yN3Smsk/+N0i2QHsSPvwHqmiZDQEId29oIzZGIS07uoQjKrRdN6uEdGg+vpA7dOFPgvMQdkyYd/O3V1L5Tk5GXOUlHqQMDOikc7KplYue97u1HTz2/HJaQ0h4KrHZebVmkmhXCPpk4a+IYTg9FScqx6EqmwaFTQBcT/RdCiI1UvJd5/La1mTieTua1s05FchuI1QYY0zz/17NmbPs3j/RwCoREap6SEifqiKT+68QDQQPVgzd6UMXPw9+IOfg5d/G8aOwYf+D7VQLa71/LZX7ta4kTK5MoAdaR/wlUXJw79t8s++ZVLwoDcr3aLjrh9oQnA0Cbe62AAvVostLZk27CTNQnWAg9f5VH9hKqBsmbAvyV2pUmNps3joLJmglDtTbqXpNeLaiokQKkK/GSIhwcK44JrH5K7ececrd67g1FSclWyJnPV9dQu2dVY7ZBskewW2LXNnYmapIska2zvubERDoqmN00ePkJKzX/u3aLUKb7z/Z8F26QiBkZzxbZmAv/XjAQ7MzF0hDZf/EN7+AtRKcPQxOP99MHmP+np0AgrrPb+9rdgtb/g7Wl5DSskvfUtSqsG/eBk+8Zbkf3sUPnYK11SUVNFdS6aN44kubZk1Y1cNwk4MvI+yWlKbJK4pd/tvpXBjrYCUh2/eDra67lYyJrNj20nc9RWT+XFBpEmYio1TszovXasipfRM9cxaHXd+oIo7OD2tPudXUzkePDrm2vv6BebDhU3u8juUuHpSZktb5mBCkQ4Dpq89w/SNr3Pl0Z+kOLqw7WtGYtYnd/jKnSeI6AeA3K2+AZ/+G/Dmn8Cxx+G7/194/9/aInagyF0fyl3O+hYtr/sXPa/x3DK8nIK/96jg9z4imIjA3/yy5Ic/I3kl5Q65ThVh2oNe6mNdFpkbVYOI7ky5G5jCXnChBgH2tXJ3zQqXOEw1CDZmR+2uu+3nmpSSaytmy3k7G6dmNLKGmunxCnXlzicOruDkZBwhtj73bmGzWPHDVIaImHUJ3jlzt9akwNyGb8t0D0Ejw9lnf5XM1BluP/B9u75uJGd8WyY+ufMEkUCEqqxSMfffAqyOpW+BBL73/4P3/AyMHt39nNi4Inc9xrJnDUu5a5Mi58MdfPyiZCoKP3wGHpsTfOqjgn/8pOB6Bj72acnfedpkpdDfzWfVA1smwPGkIFOGzRbdQjthVI29Z8t0owYBtpS7fXhtuZbKMxkPHUryMBoThAKwuuNat55XKZit5u1s1NMXPaxEsFMYfVumO4gEdRbGoq7P3WV8cjdUtLJlbnXctbZlDrR654DizHP/jkA5zxvv/zmktntTzEjMECznCZQGmIS9B+GTOw8wcMuXF8gsQXIOEjOtnxOdVHazSm/qR862Za77Fzwv8WpK8tUl+MvnBZGAuvHomuBH7xV84YcEf/UB+O9X4EO/L/mVV2RXheE2jKokW4apiPuWsWNd1iE4mbkL62EEYnDKnVvkTtu/gSrX0/lDackEZX2eGRG7lDt7ju5kixoEG0cnNAI6npaZZyxbZtK3ZbqG01NxrqbymC4t6k0pVaKpH3ozNESCKvB4Z29dOisRAsbizZU7U0LZ3fHLQ4fJm88ze+Ur3Hjoh8lPnGz6HMOvQwB8cucJbEvYviZ3m4swstD+ObEJ9dijNdNW7jYKsmnylA938G9ekSSD8OPndn9tJCT4e49q/Mn3Cx6fg3/0TcmvX+r+31izPureKHfq8abDjTgnM3ea0AYbfFRIqRVBdKK/99mnM3dZo0IqVz605A7U3N3KDuXu+oqJ1iZMxUZAF5yc9rbMPGNUCQU0IsH2FlEfznF6KkGxUuNuxp21QL5UxZR+gfkwIYQgFtpty0xnJeNxQaBJWnS0TcKmD2fQqiXufebfkBs/wc2Hfqjl8wxLkDjsdQg+ufMAdeVuv9Yh1KqQW4bRDuQuOq4eC+me/plsURKyNiCXN3xrphe4tin57A34C+cUkWuF06OC//DtGgtxeKMHJTXlIbk7ZnV+O5m7M6VJqVbqqNyBlWrbo+rcNfIpReya2Ei6wj6duTvM83Y2ZkYFqxm5TcW5tmqyMKERCnRWvE/NaFxfNamZ3iwQM8WKH6biMuqhKi5ZMzf9AvM9gVhY7CZ3ObPpvB1skTu/6653jC9dJFxY4+pjP4XUW3/+/SJzBZ/ceQB7Yblv6xByyyDNLpS73hIzc4bkHmuW5I5vzfQEv/qqJKApS6YTHInDUg/rkJT1UfciLTMZEoyHnXXd2Wp5p0AVGHBliRsdd7BvZ+7eWM6iC8GJydiwD2VomBnVqNRgI68+x1JKrq/U6iXlnXBqRqdchaU1b66VWaPqz9u5jLnRCNGgzlWXQlW2Csz9n9MwEQvvLiVPZ2XTeTtQtkzwQ1X6wcStF6gFIqwfeUfb51XDSarByKEPVfHJnQewyd2+tWVmFtVjJ3JXV+66t2XWTEm+BPfM6gjhK3de4G5B8t/eViEqMzFn5G4+AXf6IHdepGWCsmY6mbmzz7lOtkz7OQOdues3KRP2ZYm5aUq+fm2NBxZGDrXlr16HYM3dpbKSnNF53s5GPVTFo7m7jFHxyZ3L0ITg5FSMq6vuhDtkimpoy1fuhotYCIoNyp0pJWs5yUSTGgTwlbu+ISUTt19gff7BtqodoLru/DoEn9x5AVs12LfK3aZN7o60f54eglCyp5m7vMV7xxKC6aTwyZ0H+LXXJFUJ/9MDzkNOjsQUues2AGDVQ1smqFAVJ+SuWFPnnGNb5iDOUdNUGyBuKHfa/pu5u7ycZaNY4T2nJ4d9KEPFzIhdh6CuddetMBWnyt3sqCAWhmsezd1lin5Qhxe4ZyrB7Y0ipUr/pNy3Ze4N7LRlbhYkNbN5UiZANOzP3PWD2OZtorkV0scecfR8vw7BJ3eeoK7c7deZu8yiUhkcLJBVHUL3tkw7TCUZEcyNa74t02VsliS/dRm+5yScGHFO7ubjgrIJ6S4/uumiJB6AqIPZoV5wPAm3c3ScN9qTyp2xDrIG8en+32sIM3elSo3f+Np11vK9Ecpnr6aJBnUeOuZekfN+xGRSoGuwail311ZNdA2OTjq7DQshODWjcXXFfXJnSkm2VCXpK3eu49R0HCnherr/2pVNo0IooBEO+Eu3YWKnLTOdtQvMW5A767TybZm9YeLWtwBYO+qQ3CVmlHJ3iKsn/CuEBzgQtsxOlkwb0YmebJnZ4ha5OzImuLtpuhYX7QN+6zLkq/DTD3ZHto5YeRfdWjNTHnXc2TieFFQl3OmwPrLJmqOZu+CAyJ1dgxBzQbkagi3zairP02+n+ONXl7t+balS41s313n3iXGC+uG+3eiaYCopWMnYyl2No5MawSbpeq1wekbndtqkXHX3WpkvVZESP1DFA5y2EmKvpvq3ZmaKFUYjQYTwZhPNhzPsTMtcq5M735bpBSZvf5P82HFKCWcbpEZilkClSKB8eLvuDvfd1iMEtSABLbA/yZ00Vcfd6Lyz58cmerJl2spdIgqzYxrlKqznfHLnBopVya9dkjy1AOcnulsEzFvJlEtdXhNTRW/CVGwcd5iYWQ9UcWjLNGoGNdO77jCgoePOBeVOG3ygSjqnViTPXElR6LKo6cVbG5SqJu+553BbMm3MjGrc3ZRIKbm2YnYsL9+J07MapoQbq+6qdxlD/Vx95c59JCNBppNhV0JVfOvs3kAsLDAqW06SdgXm4Aeq9AO9UmR0+ZJjSyYoWyZwqK2ZPrnzCFF9gGENbiKfUvM83Sh3RkbVJ3SB3DblTn0Mlzf8C58b+MRbylbZrWoHMG8pd90mZqaK3ip3TovMbSu0E1tmLBDb9hrPULCVOzdm7jRVpzDAmbt0vgRAqWrytSvd1Z48ezXNZDzEmZmEF4e27zAzKljZNLm7KSmWnYep2LDn89y2ZmbqKYw+cfACp6fiXF3NI/t0p2waFX/ebg8gVp+hU39OZU2ioa2/3wlNE4QDu7vxfHTG2NJFNLPK2tF3OX6NkbDqEA5x151P7jxCJBDZnzN3mSX16JTcxSYAqeaKusCWcieYG1cXxDt+qErfqJiSX3lV8sgMPDbb/evHwxDW4U6+u5uQ1+TuSBx0Abc61CF0o9wNrLIkn4JQAoIuSZtaaKC2zHS+zHgsyD3Tcf7s8opj+/RGocylOxmeOD2J5tvIAJgZ0SiW4dWbSi12GqZiYyyuMZEQXLvrrtqcMSxy5yt3nuD0VJzNYoX1Qn/nbabo11XsBcTC6tEma2tZ2XLezkY0LCjun5DjPYPJWy9QDUbZnL3f8Wvqyt0hTsz0yZ1HiAQi+9OWadcgdCowt9FjHULOkESCENQFYzFBJAjL6z656xd/dA0Wc/DT7xA9zWUIIVjosuuuakrWS96Su4AmWEjAzQ520WK1iCY0Qlqo43va6p7n5K7gUg2CDT04UHKXypWYjIf58LlZVrMlXl3cdPS656+vISWHPiWzETOj6px8/u0qAR0WJrq/BZ+a0bjmsnKXtWyZPnHwBqesubvr6d6tmdWaSa5U9ZW7PYCYPUNX2rJlTiban8vRkG/L7Bp2BcLCw50rEBpQDSeohuJEfVumD7cR0SP705aZWYRwEsIjzp4f7a3IPFuUJKPqAimEYG5M822ZfcKUkn9zUXLvGHzoWO/v022R+ZoBEpiOeKvOHE86mLmrGUT0iCNia9syB6LcuVGDYGPA5G4tX2YyEeJdJ8YYjwX5/OvOdkOfvZLm5GSMuVEPhzH3GeyuuzfvmByf1Ah0EaZi4/SsmtvLGe5dLzPFCroQxMKHt4fQS4zH1WaTTaJ7gT0X6ReYDx+2/bKgHOuks2Zn5S64u/jcR3vE128Syae6smTaqCdmHlL45M4j2GEN+w7dJGWCZcuka+Uua0CigQzMjQlvbJnShOtPq66xA44v3oY3NuCvv0P0ZYM7Eu8uLTNlfcwnPVTuAI4lOs/cFatFR5ZMGJByJ6U35M4czCqhZkrW8xUmEyECmsZT981w6U6GpY3237PF9SK31ou+arcD0yMC+8zsdt7OxqkZRcCuuVhmnjGqJCMB3z7rEWIh9TPLl/ogd37H3Z6BTe7yZYlRluRLMNEiTMVGNOwrd91i4vYLAKQdViA0onjIu+58cucR9q0tc3OpO3IXSqgEvy4TM3NFSbKB3B0Z11jLStcjvrn7Gjz7r2DlNXffdw/iV16RLMTho6f7e5/5BKwU1fyeE6Ssdf6UxwLN8aQgbUC+0vq4jKrRPbmreEjuKgWoFt21ZQ5w5m6jUKYmJZNxNWTygbNTBDTBFy633xF99moaXQgeOzUxiMPcNwgFBOPWIrDbeTsbJ6c1BLhqzcwYFZJ+DYJnCOkauiYolHsn5JuGH3qzVxCzXP+FkqwnZU61qEGwEQ0JP1ClS0zcfoHcxEnK8e43CQ97151P7jzCviR3RgbK2e7InRBWkXm3yp0kEd0id7NjGhK467Y1M2ft3Hi5gN8DeCUlef4u/KULgqDW3+77fFxgSrjrsHO3Tu68Vu4cJGYaVYOo7uxAbHJXqPZfLtwS9RqE/WnLTFvF5ZOWrSwZCfL4qQm+djXdshbBNCVfv5bmwsKIH63fBNMj6vw8OdObBTIWFhyb0nj+7apr3aAqYt//WXkFIQSxkN51lUgjNm3lzj+nho5GW2Y6qzZZOtoyQ6o+4dDirc/Bra87f36lwOjy6z2pdgBGcha9WiJoZHp6/X6HT+48QlRXtsx+o48Him7DVGxEJ6DQ3cydUu62/nxkTF0Yl922Ztrkbr8R7S7x65ck8QD8yNn+36vbIvO09a31mtwdt8hdu7m7bmyZkUAEgfDWllk4IOQusRVQ8+Fzs5SrJk+/nWr6mjfuZlkvVHxLZgscGdeIBGF+vPdNmO9+Z5Dbacnzb7tjzcwafgqj11DkrvefV86auUv4CuvQEQmqfe1CWbJmKXcdbZmhQ1yFICVc/F34+r9VIoITLL+KJmusddFv1wgjYSdmHk5rpk/uPEIkEMGUJuUBzca4ApvcdaPcQddF5uWqpFSlHqgCMGd13bk+d2cP1B5gcrdSkHz6GvzwWRgJ9T8z023XXaooCemQ9HhtWCd3bRIzjZrhqOMOQBMaYT3sLbnLr6pHt9MyB3RdSedUYoBtywQ4Phnj7EyCL1xewWxi3X32appoUOeho2MDOcb9hu9/NMjf+b4Ieh8K+2NndY5OCD75fLlepNwrpJRkjAojPmnwFLFQgGIf5K5QrhHQBCHdX7YNG0qJVWQtlZVoAsYSjnqOAAAgAElEQVTinQNVjAquqe37CsV1KOeVg+rV/+rsNUsvUg3Fycyc6+mfNJKHu+vOv0p4BFs92FfWzM1F0MNbISlOEZ1QgSoOL1pZq8C8MVAlHBRMJIT7iZl1cldy930bkC9J/uVnDZbWhhPa8p8uS6om/NR5d8IQbOVuqUPtgI3Vopq366V6oRuMhiAZat91183MHShrprfkLqVmUiMO02edQB/czF06VyYZCRAKbL9VfPv9s6RyZS7uqEUoVWu8cGOdR06M73qND4WxuMbp2f5SKTUh+IHHQyxvSL72Ru9WPwCjYlKpSd+W6TFiIZ18H7bMQrlKLKR7fp314QyxsJqhS2dNxhOi42ZN1LJylg6jNXPjpnocPwVvf35LSGgFKeHOS6wtPIzUertWbil3Prnz4SIieu8FyV+89UVeWnnJ7UPqjMwijMyD6PJjEZ0AswJlZ2zAjvBuVO5AJWa63nU3AOXu8xcrvHC1xmdeHPxV26hK/vNl+PAxODnizk0/ERSMhJwXmacM7y2ZoMjj8UR7W6ZRNernnhNEgx6Tu0IK4pPdn1PtoAWhNiDlLl+uz9s14uFjY0zEQnz+9e2Wl5dublCqmr4lcwB41ymdUzMan/xGhWqt900xu8DcD1TxFrGQ3pdyly/XiIX9n9FeQSysqg3WcpLJDpZMULZMOKTWTJvcvffnlIDw0m93fn5xvacKBBu1UIxKOHloEzN9cucRbGtYL8rdF25+gU+8+QmqZn87sl2j2xoEG13WIWStb0kispPcadzZMN2bUyznoGJ5Cz0id8Wy5HMvVxBCFRPnB3zh/oOrsFaCv3LB3d3c+S667tJF75MybRxr03VXqVWoympXyl0sEPNeuXPTkgkDnrkrMZUI7/p7XRN827lpLi9nWVzf+v49ezXNRDzE2dnEQI7vMEMIwQ8+HiSdlXz5Uj/9aVYKoz9z5ylioQD5fmyZpSrxkN9DuFcQC0GxJElnZccwFVCBKgDGPprUcQ2bN5UIMDIP578PFl9QSeatcEeJG/2QOzjcXXc+ufMIdVtml113UkpylRyZcmaw6l3FgEK6+zAVgOi4enQ4d5crNlfujoxpFMuQcWut3XhSe2TL/MKrFfIl+IsfCFGuwrNvDo6QSyn5tdck90/AE3Puvnc35G5Qyh2oubvbueZzC/a55nTmzn6ut8pd2t0wFRgYuTOlJJ1rrtwBvP/MNEFd8GeX1c7oRqHMa3cyPHF6wu9LGxAeOKZz7xGNT3+z0nONTKbol2MPArZy1+vmZb5cIxbylbu9glhYkDMk63nJZKLzUrqu3B3GrruNWzB2XP3+vu9WG54v/pbqIW6GpRdh/BTlbkeEdsBIzhD1lTsfbqLXguRyrVxX7L50+0tuH1ZrZJfUYz/KXdFZYmbWtmXuVO7GXU7MbCR3FfeVu1JF8scvVXjgmM6HHghyclrjS69VBpaQeum2yRsb8JfPC9fnMJwWmZtSKuVuQOTuWFJQqqk5v52wz7WubJlekrtaRZ0Trit3g5m5yxpVqqZksolyByq174lTkzx3dY1cqcrz19eQEt+SOUAIIfihJ0JsFCR/9kpvG0vZunLnEwcvEQvp1KSkXO3t/mbP3PnYG4iFBXc3JTWzcw0CbCl3xcNG7swqZG7D2DH150AIHvpzsH4Nrj+9+/nlPKTehPmH+/6njcQs4dzqoey688mdR7AXmN3aMnMVNbd2LHmMG5kbXNu85vqxNcVmj0mZABFLuXNoy8wVJQKI71gz1hMz3Zq7s8ldfBpq7it3X75UJVuEj71b7Xg/dSHA7bTk6t3BBKt87uUKUxH46Cn333s+LlgvQbGDGrBZgqqEychglJp2dQj2ubZnlLtCWj16odwNIC2znpSZaK7cAXz4/hnKNZOn30rx7JU0JydjHBkdENP3AcB98zoXjml85lvlnhaOGT9ifyCwVbderZmFco24r9ztGcRCULNu9b4tsw2yd8Cswejxrb878V6YOA0X/8tuV9XyK0rRO9I/uSsmZ9BrZULFjb7fa7/BJ3ceoVdbZt6aEfvQsQ8RDUT50q0vuX1ozZFZBKGDFR/bFfQAhEcd2zKzhiQeAW1HutRkQhDQcS8xM78C4SRExlxX7io1yWderHDfvMa982o39fGzAcIB+FIf8y9Osbxh8vKNGj9+ThAJuE+s5q2RqU7WzNSAOu5sHLeOqxm5qyt3XaZlGlUDs5U9pB/YBeZeKXce70buLDBvhqPjMc7NJfnMq3e4tV7kCV+1Gwp+6PEQWQP+9GL3im7WqBAP6QQ0fzngJWzVrZdQFVNKiuUasbCv3O0V2EXmgG/LbIeNW+pxrIHcCQ3e+ReVIPDGZ7Y/f+lFCMVhsv/SXiNh1yEcPmumfzX3CPYCs1tVwCZ3E9EJnjjyBC+tvsRGaQC7DplFRey0HncGYxPObZlFuStMBRTZmxsVLtoy70JiBgIR6JJkd8LTr1fZyEs+9u6thW80JHji3gBff6vqeSLWn16sENDgL/RWAdMRTovMU9bHe3pAgSoLCRDAzSZ1CPZGSrfkTiIpeaDsXr15A4CN0Iy7b6xZs1EeBy6lmnTcNcOHz81QKNfQBDx2sr8ZCR+94fSszjtP6nz2xQp5Q3J57TK/9uqvOdq0yBSr/rzdAGCTu17qEIrlGrLhPXwMH43kbqIrW6Znh7Q3sXFTCQc7XWEz98PRR+HSH4CtrEkT7rwMcw+BC5tNRvLw1iH45M4jaEIjpId6tmUmggk+cPQDSCn56u2venGI29FrUqaN2MSWDa0DcoZkJNr8Yjg3rrk7cxefhWAEKu4t3ium5I++VeH0rMb5o9tPoacuBChX4bm3vFt45w3JV1+v8sS9AaZbfB/7hdMic5vcDUq5C+mCI3G45aItE6BYcd+aGbr9LJfME1ypTLv7xrq1EPd47m4tXyYW0ol2WFA+dHSMmWSYh46O+SRhiPjBx0MUy/DZlyq8mnqVF1deZKXQeVGjCsz9n5vXsG2ZhR6UO/s1vi1z7yBm7XnFw1vErR3CQbUxeehm7jZuwsgR5fDaiYd+TN3H7GLz9RtgbLgybweHu+vOJ3ceIqpHuyZ3tnIXD8aZik7xjql38MzSM5S97LUyq5C92x+5i45DwXmgSjPlDtTc3WpG9tXbBCiPdz6llDs97GoVwh9cgVRW8rF3B3cFmZyc1jgxpfHFV6ueBat8+VKFchW+40HvbvSzMfXYUbkbsC0TVB3CrSaVir0GqgAUqgVXjq2O7DJHS2/zydp7uePyW2+RO2+3gNslZTZC0wR/77vv56+8z4PhTx+OcWxK47EzOn/6coWVvLoWO5nZzhgVv+NuALBVt0IPyp2t9vnK3d5BzCJ0Ew4smQCaEERC+5TcmTW0XhPHN29un7drxMg8nP0OuPJnsHmrXoHAkYd6+7d2wAxGKEdGfVumD3cRCUS6nrnLVXIIRH3R+cFjHyRfyfPC3Re8OESF7F2QtT7J3QSUs2jVzgvOXBESLRSnI2OCmgmrmT4vgIW0+j8lZpRy51IVQs2UfPyi5PiUxkMndt9ohRB88EKAW2mTayvuz3HVTMnnX6lybkHjxLR3N/qwLpiOwlKu/c8hVZToAsbaO/dcxfEWXXf2Rkq3tkzo3j7dCbXrzwDw6dp7WXad3FmEy/RWuUvlSy2TMnciEQ4QCfoLz2HjBx4LUa7BzQ01/3x182rH1/i2zMFgi9z1oNyVLOXOLzHfM7BtmU7CVGxEQ2JfBqocv/j7PPbffrZ1dUErlAtqk32sBbkDeOAHIRBVxeZLL8LEPRAZ7e+AG2AkZ4hkfeXOh4voJYkvX8kTD8bRhPrRnB07y3xini/d/pJ3EfsZKymzl447G1YdQqhDYqaUkqwhd9Ug2LATM/u2ZtoyfGIWAu2Vu68sSp5fdva9/aPrcDUDH22i2tl4z70BQgH40mvuWzNfuFpjLSf5roe8X4w56bpLFWEiwkB7zY4nBHcLYOxI8jRqBkEtSKCLudFYUEmUrpI7Kalee4avm+e4wyR38y6ft5r3tkzZoePOx97EkXGNJ+8LkKuoGZZOyl2lZlKs1PwahAHAtmX2EqhS8JW7PYfeyN3+DFSJb9wiklshvn6zuxdu2mEqx1o/JzwCF35AETuXKhAaoYrMfeXOh4uI6JGebJnxYLz+ZyEETx19iqXcEm9vvO32ISrY5C453/t7WEXm4Q5zd8Wyig9uZ8sEF+oQ8ja5swNVSi13nf7WVyQ/8lnJL37DpNzGDmpKyb9+WXJmFB453fomGw0Jnjgb4Lm3qq5bMD73coWZEdFUNXQbTrru0gMsMLdh1yHc3mHNNKpGV6odeKTcrV8nnF/ik7UnCet4aMv0jtzlyzVKVbNtDYKPvYnveUQgAjl0otwt3K1b/Zsha9Ug+DN33kPXBOGA1lOgil2f4JeY7x3ErEvjZKJb5W7/kbugFXgyuvxady/csMhgO+UO4N7vUpVV4EoFQiOMxCyR3Gr3quM+h0/uPEQk0D25y5Vz28gdwCOzjxAPxr2rRdhchNiksi/2Cku5C+fbk7ucXWDeghDEI4Jk1IU6hNxdldAUnVTkDqCJZbRYlaQMRRh+9VX4gT+UvN3i3/7Tm/DGBvzsQ6KjUmUHqzz7pnvq3dW7Nd5eNvmOB4O7aiS8gK3ctVOMV4swNaCkTBtHW3TdFavFrubtwCNyd+MZquh8VX+Mh6bg7j6cuUs7TMr0sfcQCG0CUNo8D7RX7zJFq8Dct2UOBLGQ3mOgirqPxH3lbs9gIiH4kfcEec99zgl3NCQo7ENbZshQ15Sxbsnd5i0IRjvXAekhePSvwvH3KFumizCSM2hmlZDDTIiDAp/ceYheZu7ylTyJYGLb34X0EE/OP8krqVdIFVNuHqJCv0mZoGbu6GzLzBYtctem9PrImAuJmblVVR6tacqWCU2tmbbt8OffKfjVDwmW8vC9n5L8zhtyG6mRUvKvXpacSML3OsiNODWjcXxK40uvuRes8rmXK0RD8L77B7N7Ox8XFKqQaXMzShkwPWDlbsb699I7fpxGzegqKRO2wldcI3emCTe+xgv6Q5yYTHIkDssd1M+uMYCZu3rHna/c7TvY1TnlzAMItPbkzrDInW/LHAhioUBP5C5fqqFrglDAX7LtFQgh+O53hRiPO/+ZRPdpoEqortxd6q5fdcMKU3EytnHkQXjyb7pSgdAIw+pujh6yUBX/SuEhepm5y1V2K3cA7z/6foQQfOX2V9w6PAVpQmapf3IXjIEe7qjcZevKXeuTfc4Ncpe3Ou6gQbnbTe4WLWvffBy+84Tgj79f8MgM/N2vSf76FyTr1vF+eRFeScPPPCgIOFDNhBA8dSHAzZTJ9dX+7QDFsuQbV2q871zAUeyyGzjSoQ5BSkmqCJMDVu7s8JbNHRk5xWqxa1umrumE9bB75G71dSiu8Z+NJ3loGuZiSrlzdV62W1vmn/4D1SXUBdK5zgXmPvYm1g21Q300OYOozLcNVclYtsykb8scCJRy172bo1CuEgvpLee8fewP7MtAFWkSNDKUI6OEi+tEs8sOXycVuWs3bzcAHNY6BJ/ceYiIHqFUKzkqkgW1ACxUCk3J3Vh4jIenH+a5O89Rcin5EYDCmppH6ydMBdTOTGyi48ydrdy1mrkDmBsTZIqQ76cIPLeiwlSggdzt/r7ZxGXBEktnY4L/9F2Cv/tuwRduw0f+QPK1O5J/+bJkPg4/0IVj4Imz7gWr3Fk3qZlwbmFwtpx563vSitzlKlCqwZRHXXutkAiCLmBjx+ejl5k76G0TpiVuPENND/O52rt4cEowFxeUTVhzsyO9m0AV04T0m3Dr6139E+l8iXBAI+Gn8+07rJcUuXv/vZMY2RNc37xJzWyuFtVtmb5yNxDEQ4EeA1VqfsfdAUC3gSpSSv7718tcX+3+M+MWgqUcQpqsnnwP0MXcXSENlULneTuPYSTULN9hS8z0yZ2HsBeapZqzlV2pVqIqqyRCiaZff+rYUxSrRb6+3N1CrS3sMJV+lTuA6ISDmTv12E65OzJuJWb2GqpSKUApC3FbuWtty1zMSTSx1esGKvnxr71D8N+/RxALwJ//Y8kLK/DX3yEI6c6JTCwsePxsgOfe7D9Y5Y6lZB4ZG9wp26nIPD2EjjtQquhYGDZ27IAaVaPrmTtQ5M6VnrtaBW5+nWsjj2IQ5qEppdyBy9ZM25bpZOaunFM7qOvXoeLcIm4nZfpKwf7DRmmDeDDOk/fGEKUTVGWZxdxi0+dmjSrhgEbYr7EYCKIhvR6O0g3ylnLnY38jGhKUq6rSyAkKZfiDb1b41De8rb1pBztMZXPuAuXIiPO5u3pS5nDJnRkIU4qOH7rETJ/ceYhuwxpyFeURbKbcAZwcOcnx5HG+fPvLjtXAjth0kdzFxgl3mrkzJLoG7VxAfdch5FbVo0Nb5lwMgk2slg9MCf7wY4IfuxcenoYfOdv9oTx1PkCpCl9/qz/1bnlDkdCZ0cEttqciEBBwp0WUf8r6WA+a3AGMhmFjx55JLzN3YCl3FReUuzsvQyXPn2lPMhNVn6s561R2NVSlG1umNQiPNFXMtEOk82Um/Hm7fYl1Y53x8DjRkOAds6cBeHOtuTUzY1T8pMwBIh7We1buYmGf3O132CMVRYfWzDWrZ/bizdrQZvXsebtydIzN2fNq7s4J7KTM0eHaMuFwdt05IndCiAUhxHuFEB+wf3l9YAcBtnLnNDHTjqzeGahiQwjBU8eeYqWwwuW1y+4cZGYRQkmIjPT/XtEJFajShnjmipJkVLRVBKZHBJqAO70mZto7NHVbpq3c7VZQF/NbClUzxIKCf/Skxie/VyMS6J5YnZ7VODap9W3NXF43mR4RBLpQDvuFrglm47CUa/71Orkb8MwdwFho+8ydKU2Mam/kLhaIuWPLvPE0hEf4r9kLPDilzte6cucFuXMSqGJsbP1+1fk1I50rMeUnZe5LrJfWGYuMAfCh+6cwK6O8eKcFuStWGIn6dr9BIRrUKVZqmA6VGxuFkm/LPAiIWvtlTonaWlatpao1ePHacKyZ28jd3AWi2WXCeQfBfhs3VQp7qM0Ca0BQdQi+crcNQoh/DDwD/H3gf7V+/W2Pj+tAwLaIOSV3nZQ7gHfOvJOR0Ih7tQiZRRjto9+uEbEJNLNK0Mi0fEq7AnMbAV0wPSL6UO7sjjurN6WNcreU25q38wJCCD54IcD1VZPrK71fnJc3ZF3RHCQW2hSZrw7JlgkqVKVRuSvUykhkTzN3kUCkf3JXKcDiC5QWnuCtTICHptRnfDoKmoBlN4vMu6lCsJW7YNwxuctVJPlyzU/K3KfYMDYYD6ve0fvmNQKVEyzmmydmZoyqH6YyQNg9dYVKd/cC35Z5MGArd05DVWzlLhKE5992r1apGwSte0glOsbGnKpXcaTebdwauiXThpGcIZxLIVrMHh9EOFktfj9wn5Tyu6WUH7V+fczrAzsIsBeaxZqzhWOhorb325G7gBbgyfkneX3tdTKl1iTKMdyoQbARtbvuWlszc4Yk4WD9PTeu9T5zl19Ri1l7drEFuauZkjsdlDs38J57VbDKly/1dnE2peTupsnc2ODnn9oVmaescJyJYSh3O8hdzqoc6XXmrm9yd/ubUKvwxsiTADxk7ytogqmIy8qdZs/cdWHLPPoIpN+CWufPoJ0g63fc7T+UaiUK1QJjYaXcaUJwdvw0NW2DK2u3dz1f2TJ9RWhQsK2V3SRmmlJS9ANVDgRscuc0VGXNygR4//0BXr1Z6y9krkeEihtIoVEJJ8hPnKIajHYmd7WqJRzsEXKXmEWTtY5VXQcJTsjdVcDf2usBtkWsW+WulS3TxoPTDwJwKe3Q+9wKpYwKHnGN3Knd4naJmVnLltkJR8YEy5uya/sKoGyZ9rwdtLRlrhahKmEh4S1piocF9y/ovLXc267RWlZSrjIU5e5IXBETs0mUf9qA8XDzeUWvMbojUKVO7npQ7mxbZl91Bdefgfg0T5fVYOaDDZ2t9vfQNejWIs8pudN0WHhEPX+9dSy+jTq585W7fQe7BmE8Ml7/uw/coyJ+v3D9+W3PNU1JrlT1C8wHiFjQJnfO7wVGpYYEf+buAKBrW2ZOMhYXPHE2QNWEF68NXr0LFjcpR0ZBaEhNZ3P2/s6hKtklkLWh1yDYMJJWHcIh6rpzslosAC8JIX5FCPEv7F9eH9hBQNczd+U8mtA6LlAXEguMhcd4Le0wtagV3AxTAYjZRebtyV27GgQbc2Ma1Rqkc72Qu9Xt5E5vnpZpL2IXBmAJX5jUuLMuqda6///Y9tS58WHYMgUVc2u+rhGp4nAsmQBjYUG2DFWL/OesRNqeAlWCUSSSgtljX0FxA+5ehBNP8nIKTiTV8dmYjbmclik00ALOyV14FKbPqT+vvN7xJbfryp1P7vYb7AJz25YJcGHmGEIGubz+yrbNslypipT4gSoDRNyqFmkWqnLq0sc5+tZv7fr7fEk9N+Yrd/se3QeqmEwkBKdnNSYTguffHrytMGRsUomO1v+8OXue+MZNJQy0gh2msldsmYew687JavFTwC8AXwNeaPjlowOievdpmfFAHE20/7EIITg/eZ7La5db9hc5gl2D0G/HnY3IGFJoLesQaqakUKLjzB001CF0O3cnTWXLbCR3mqYI3k5yt6PjzkscndComXB3sxdyp15zZEi2TGg+d5cqDidMBVSgCkDGukn2o9zZhDDrcBNmF249p+oGTr6Pi6ktS6aNuZjLyh2ouTvT4cxdZFT9Ss47mrtbzEkCmvAVnX0IW7mzA1UAdE1nOnKcSvAary9vWfkzht9xN2hErbm5fBNb5sKV32bu1h/u+nvbwhn3Z+72PXpR7iYSKoDu0TMBXrtVI28M1poZKm5QbriebM5dUL9ZfaP1izZuKcdI0qU8hz5hJKaRCKK+crcFKeVvAr/DFqn7bevvfHRASA8hEBg152mZcYfJQucnz2PUDK5sXun9ADOLivTEJnt/j0ZoOuXoWEtyly+BpH3HnY16HcJ6dxeycGENzOpWUqaNQHiXLdNW7uYHQO4WJtT/eTHd/RzhnQ2TSBBGY8Mjd83m7lJFmByacqce7bk7m9zZGyrdwCZ3GYezsbtw/WkYO8FKcIE7BXhwavvPaS4uyJShWHXxpqyFnCt3EWvXdeYcrL7ZNs0WlHI3EQ+h+R13+w7rpXUEoj5zZ+MdM6fRIkt89e2l+t9lDUUa/ECVwaGVLVOv5IgUVwgZu1MI7ef6yt3+x1agSud7gZSSdE4ykVBrocfP6tRMeOHqYK2ZwR3KXWb6LKYebO8C2bwJyYWtEYIhQ+pByrEJX7lrhBDiKeAt4F8DHwfe9KsQnEEIQSQQ6aoKIR5wRu7OjZ9DF3p/c3eZJRiZVzYvl1CKTbacuctZARwJB+RuJKp2ue50qdxFMtbOTHxm+xcCkV3K3VJeMhqCRND7ReyRcQ0hYHGte3K3vGFarx/8YttWNRebkTtjeMrd6E5yVx2ScpddhvTbdUsmwENT25/iTZF50FlaZqmB3E2fg0pe7aq2wWLen7fbr9gwNkiGkgS07YuqM+OnEcLk4uqr5EtqcZgpWsqdX4UwMNi2zEJpO7mLZVWaaTNyZ6t8/szd/kcooNKTCw4u3dmiqkCYtDIBTk5rTI8Inr8yWGtmqLhBObq1WST1IJnpe2G1DbnbuLln5u1sFJOzh6rrzsmq/peA75RSflBK+QHgu4B/7u1hHRxEdOcx67lKzrFyFw6EOTN2htdSfczdZRYVuXMRpfhEy7TMrGUncGLLFEIwN6Z1bcuMZpfVbxI7yV1z5W4Qqh1AKCCYHRXc7oXcrcuhJGUCjIYgGthdZG5UJbkKTDsg6l7AVu7srru8G7bMXpS7G19Tjyfey8WURBdwYWL7U2xyd8ftrrtOyp2UYGS2kzvoaM28nfOTMvcr1kvru1Q7gJMjJ9VvIjd4/pq6Pm/WbZm+cjcohAOaWtxXtqsv8awKOgpUC+iV7btANhH00zL3P4QQREPObJlrObVWmEiK+msfPRPg0q0auQFZM7WKgV4tqUCVBmzMXYD161Bpcs8s56GQ3jPzdjaMxIwfqLIDQSll3VwrpXwTPz3TMbpV7jolZTbiwtQFlgvLpIoOCiV3ompAPuVemIqFcmyyZaBK1lbuHK6/58ZEfd7MKSLZZRAC4jvkk6bK3WDCVGwsTGhdK3elirJmDCMpE9QNpVkdQmqIHXfQYMu0dkCzNQOBIKx3T0p6Vu6kVMXl0/dDfIqXU3B2TBXfN2LW+ozdHTS5q+SVRdm+McenVehRG3JnVKWy2/rK3b7EhrGxLSnTRiKUYCZyjPjIbZ6+ou4XWaOKrgm/P22AEEIQCwV2KXfxzFaKbai0/f5ZV+78n9OBQDQkHAWq2GFykw1p3o+f0THl4KyZdoF5Jbp9w2hz7oKy96fe3P2iPRamYsNIzhAupBHmcPoCBw0nK8ZvCiH+vRDiKevXvwO+6fWBHRREA1FHM3dSSqXctem424kLk2qwtSdrZuaOenQrTMVCKT5JsJxHq+5OHrR3m5zM3IGyMq7lJKVKc4K3kTf5wqsVnn1z62SNZu+qGcIdtqRm5G7R4wLznTg6oXF3U1LuYvbq7qaVlDkkcgeqB9CeT7Rhp2cOndzVlbsSYT3cMYyoGXpW7tavK2vzySeRUnIxBQ9P736ad7bMDuTO7rizyZ0QSr1bvayIaRPYwTlTvnK37yClZL20vi0psxEnE+cRkevcSOe5tVYgU1Qdd8Owex9mREP6rpm7WLaB3Bmr275WKNfQhSAcGN49wId7UOTOiXJn9cgmtn7ux6c0ZkcFz7/lnKCksya/8aWS4xCXRoSse0g5ul25y8zcp8Z5mm0Ublq2/9G9Zcs0krMIabbMhDhocHK1+GngEvA3rF+XrL/z4QBOlTujZmBKsyvlbiY2w3R0urdKhIzLNf790HsAACAASURBVAgWSjG7yHz3CbSl3DlbTNRDVRqsmemsyZ+8XOEXf7/Iz/9Gkf/45TK//sVSvYctkl2G+OzuN9thy8yUJdmKivofFBYmNaSEO12Us9uBMsOyZYIid7uUO4sHTQ5p5i4ZBAGsW6Wu2ZrRUw0C9KHc3XhGJYIde5ybWUU0d4apAMSDgmQIlgsuWmn0UOe0zJ3kDhS5K66rRNkmuO133O1bFKtFSrXStqTMRpxKPECFHMFwmmeupMgYVT9MZQiIh/TdtszMVYoxdS/eOXdXKNeIhXWfhB8QREPOAlXWcpKADsmG25oQgsfOBLi0aJIpdn4P05T8yp+W+NJrVS7d7n5WL2grdztsmbVgFMZPwUoTcrdxE4Ix94L6XEK9DuGQWDOdpGWWpJT/TEr5g9avfy6l7LEQ6vDB6cxdrqxWVd0od6BSM99af4uyk3CFRmQW1c5LYq6713VAKa5O6GahKjlDEgmq+TMnOGKRu9du1fjMt8r8358o8r/8xyK/83SZYhm+/7Eg3/OuIOUqpLM2uVvZPW8Hu5S7QSZl2jg6of4/3czdLe8B5e5IXBW+lxs6+oZty9Q1wUhoa+YuVzV6mrcDCGgBQlqoe+Uu9RZM3gvhZD1M5cGp5k+dc7vrzklaZlNyd796bJF0tuh33O1bNOu4a8SpxHkATi6s8tzVNdbyZb8GYQiIhvRttkxhVonlrrM+8xgA4R3kLl+q+pbMA4RoSDgKVFnLmUzExS5S/9iZAFLCC1c6q3d/9K0Kb95Ra4ibq93P+4eKtnLXZMNo5pwKE9u59ty4pSyZe2wzwkiqTf/DkpjZcsUohPg96/EVIcTFnb8Gd4j7G05tmfmqWvl1S+4uTF6gYlZ4a/2t7g5sc1ERO5ejasvWbk1T5c5wrtoBzI4JBPB7z1b4vWcrSOB/eCLI//MXovzCn4vyfY+GePCEuundWTfRqiXCxfUW5C4Mla09iaUBdtzZmBkVBDRYXHOu4NxZVyWm4QEkerbCfFwg2T4zlrZtmUNS7kBZMxurEHold6DO06zD4KM6immwNjNeTknCOtzXfF3tftddL7ZMUDbsULzl3N1iToXCjMV8crffYHfcNZu5A5iOHCOmJxkdXyRXqrK4UfS7DIeAWChAobJF7iL5RTSzwsbUI0ihNVXu/DCVg4NulDs7TKURRycFc2OC599uT+6u3q3xyW9UeOyMzpFxwc1Ua3L33NJz3MruTlEOGs2VO0BtFJoVSDfUcUmpahD22LwdKOFBCu3QKHftrhh/03r83kEcyEGFU1tmvqzYRje2TIAz42cIaSFeS7/GhakLzl+4fhUmznT1bzmBbcsMtbBlOp23A6Xw/fn3hTAlPHJaZ2pk917EvFV2vrQueWLEOml3dtyBUu5qu5W7QQaqBHTBkXGtq6675Q05lPLyRsw3FJkfS6rfrxYlySBEHKqwXmAXuQsne36vaCBK1mEfJaCGyYvrEFWf94splZIZ1Jp/P+bi8NZGz4e3G07JnRAQavi+CG1r7q4JFvPqWPUW/w8fexfrJavAvElaJoAmNE4k7met9BbjsY+wXqj4SZlDQDykUyhtLcztpMz8yFnKofEm5K5KIuyTu4MCpdw5IHdZyX0LuxVbIQSPnwnwqRcqbORNxuK710VGWdkxR2OCn/xgmP/0lRJv3Wm+7qiaVf7LG/+Fd868k5+88JPbvhYqblANxTEDTTb7pu9Tj6uXYcZyhBRSKkFzj83bAUgtQCk2eWjqEFoqd1JKK3GDn5FS3mj8BfzMYA5v/yOiR6iYFaodEnpyld5smUEtyH0T9/Fa+jVki5CEXcin1C/75HQRtVCMajDW0pbZjXIH8B0PBfmuh4NNiR2ocJZkBJbWzdY1CADBiJq5swqcF3OSkDZ4W+HChPM6BCklyxsms0O0ZELzIvOUMTxLpo3RcGOgSu8zd6DIXaYb5a6UBbMGsQmqpuSVVGtLJijlbqUIVdOluTs96GzmLjwC2o7Pz/Q51c9X3M02b+cGu+Hhwz1sGBtoQmM03GSX3cKpxAXuGjd49LQ6V5K+LXPg2BmoErOSMvPJU5QjU7ttmeWaX2B+gOAkLdM0Jet5yUSi+Xqpbs282nyO7neeKbOyKfmr3x4mHhEcn9JI52TTCoXl/DI1WWO1uLrra6Hi5q4ahDrCSUXiGufu9mhSpg0jOevbMhvwHU3+7iNuH8hBhb3g7KTe5a1um0Soe5/ghckLrBlrLBeWnb3A3rW3e69cRik+Qbiwu+suW5SOaxC6wZFxjTvr5laBeTNyZ0fkW/7wxbwiLdqAfeELExrprHSUXLVZkBTLW7OHw0KjcmcjVRxemIqNsfDWzF22X1tmMEquG+XO/nxHJ3hrA4waPDzd+rM0GxOYciuIpm/oDmfumtppWvfdLebg6ACtyj7cw3ppndHQaNvE2JMJ5e44fmSVUEBjYWzIOzSHEPFQgKopqdTUJl88e4VSeJJqeIxyZHp3WmapStwvMD8wiIZUOXml1noNsFmQmJKW5G5hUmN+vLk184WrVb58qcpH3hXkfkv5Oz6lrgm3mlgzb+duA7Ba2E3ugsZm83k7G9PnIPWG2uiEBnK395Q7UHUI0UNiy2w3c/fTQohXgPt2zNtdA/yZO4ewF5yd5u5ylRya0Ijo3S9Qz0+qQXnHhearlyEYhbETXf9bTlCKTTaducsZkhEPSq/nxzWW1kwi2WWqwch2G5qNoPV9raifw9KAaxBsHJ1Up5yTvju7429ufLgWuVhQMBaGpYYi81Rx+MrdWGir5y5fK/Wt3HUVqFK0yF1sgosdwlRgS/10be5Oc2jLbEbuxk+pzY4d5K5iSpYLPrnbr1g31lsmZdo4Hr8PDY212pv88o8+zAMLrVU+H94gaoWj5C1rZjx7lcLIaQDKkclttkxTSgoVX7k7SIiG1P28nXqXrtcgtL73P342wJtLJuv5rbXEet7k179Y4sS0xg8+tmW5PjalPnPN5u4Wcyo5vVAt1EUGG6HiBpVom2vEzP0qqG7jhvrzxi3VMRyMtX7NEGEkZggV1ppWdR00tJMEfhv4KPAp69H+9YiU8scHcGwHAja565SYWagUiAfjPcUdj0fGmU/MO++7W70MU/futmu5hHJ8d5F5uSoxKt0FqjjFkQmNfAmCG8sYybnmKU22cmcpqIv5LUVqkFiY6IbcDT8p08bOIvP0HrBl2spdqVbFMCs9bYzYUIEqXSh3xXXrhRO8tCpJhuDkSOunz7rdded05q4ZudMDMHVmF7lbzoMpYaHNgsLH3sVGaaNlUqaNsB5lPnYP13OXCOrDv64cRtjhKLY1M5a5Sj6pyF0pMq3InTViYVRqSOkXmB8kRK2lSLtQlbV6gXnrc/TRewJI4JtX1OfIlJL/8GdlylX4a98RJqBvXcdHY4KxWPNQlcXsIiq6brd6FzTa2DJhywVipy9v3oTRvWnJBFhKnkEgefCz/yfh3G6l8iCh3czdppTyupTyx6w5uyIggYQQYu/+9PYY7AVnJ1tmrpLrOkylERcmL3Bl80rn2oVSDjZve2bJBBWqEi6sb0n1dF9g3g3mLWUrlLlLMdkkTAW2lLtqiXJNslIYjnI3NSIIBXaTu9j6Tc5+7VfqM4GgyF1Qh8kmiVmDxnx8y5ZZMSXrJZjygKh3g9GwleJpqHOr77TMWtH53GphTW0iREa5mIKHptpbfOtF5m4pd3pQJZW1Ol4prZm7Fjfm6XNqt7W8dUB2x90wzgsf/UFKqchdi6TMRpxMnOdG/nVqsvveKx/9wyZqhXKNYGmNUHmDQtJW7qbQzTKBShaAokUA/bTMg4OolXzdLlRlzYFyNz+hcXRyy5r5+YtVXr1V4889GaoHzTXi+JTGjdT2c15KyWJukXvG7gHYNncnzBpBI0ulnS0zNqHGYFYvq83GzJ09O28H8Kn8w/xc+WeJr13n3Z/8eSZufXPYh+QZOm7dCSE+KoR4C7gGfBm4DnzW4+M6MOhm5q7bMJVGXJi8gClNLq81T8GrI/WGevSU3E0ipEnIjmKn+wLzbqAuZJJE4a5S7prBXvhXDZYLapdikAXmNjQhWJjYnZg5e+XLLLz+mW2zinc2JLNjYuBzgc1wJK6srABrQ+64szFm7YDeKfZP7mKBGFVpUjQ7qGE2CmsQGcMwNd5Yb2/JBJiIQEhzschct9LLWh1v1VDzpa12XafPKQJoXw/YSpD1bZn7D7lKjopZaZmU2YhTiQuUTYM7hasDODIfOxGtk7vqVpjKyJZyB9Tn7vK2uufP3B0YOLFlruVMwgGIhdu/12NnArx1x+TijSq/92yZh0/qfNuF5hsBx6c17qxLytWte9B6aZ1CtcA7pt6BQLBS2AobCRoZBLK9cgeqEmHlsupOlrU9Te5SGZNPm+/ldx79p5Tikzz4uV/g9Dd+E9Eh8HA/wokv4x8CTwBvSilPAR8GnvP0qA4Qupm560e5Ozlyklgg1nnubuUyaAGYvKfnf6sT6kXmDXN3Oeu/74VyN5EQLAQ2CZnl1spdYMuWuThkheLohMbtHV138XXlWQ8Y2frfLa+be8KSCarrbrMMhYpkdQ903MEWubtrKP98PzN39nnqeO6uuAbRCV5bg6qEB6faf641IZiJbe8K7AuaNU/RyprZrOOuEVNnQejbrJm2cnfET8vcd6gXmDtS7lSoyvWcQxu/D1fRaMus1yAkt2buAEJG2nqOWnT6tsyDA6e2zInk7gLznXjsjPos/fJnSsRC8Je/LdzyNcenNGqmSha3Yc/bnRw9yXhkfJtyF7TSlNsGqoCauytn4eaz6s97NEwFIJVV3/O3zSN866P/hKX7vovjF3+fhz/z9wnn1azr6+nX652h+xlOVo4VKWUa0IQQmpTyi8C7PT6uAwOnM3f5cn/Kna7pnJs4x6W1S5iyzTzX6mWYuGdr598DNCN3WduW6YFyJ4Tg4RF1Ueqs3JXq5G4YM3egkq42C7KuZgLE11XKVLCkyF21JlnNyD1E7tTjUl7N28EeUO6sj/BKyVLu+py5A5wXmRfXtoWpPNxBuQNlzbzj5swd9E7uAhGYOLmN3C3mJTNRCOvDV4p9dId6gXmHmTuA8dAMo8FJruUcBnD5cBXRBltmPHOVmh7GiM0DULaUu7Ct3JV8W+ZBgyNbZrZ1DUIj5sa0Omn7Hz8cZiTW+jV2YubN1QZyl1Xkbj4+z0x0ZtvMne28ahuoAlsusCtfBE2H5JGOxz0spDLq/57OSsxAmDff9zNceupvEV+7xiOf/HniN5/n4y9/nOeXnx/ykfYPJyvHDSFEAvgK8J+FEL8MuLVEOfCI6p1tmVJK8tX+yB0oa2a2nOV29nbzJ1RLsHbVU0smQDlm7T42hKrkbFumB8odwPmouii1Vu62bJn27NiwFIqFCfU9sOfu9EqxHs9rk7uVjIpCHnaBuY0jDeTOjvMfNrkbtXZAU2V1bvWj3MUCaijOcR1CYR2i41xMKUI058DiO+umclcndy28PZ3IHajrQPrKVj3IkBJkffSPeoF5h7RMUJthJxMXfOVuSIg12jKzVykkTqpFMWrmDqgnZvrK3cFDNOzElimZaBOm0ogfe1+Iv/RtIR480X4DYGZUEA5sT8xczC0yHZ0mEogwHZtmtbhanzt3rNwlZiE6DqUMjBxVzrA9CCllXblLZ7e+Byv3fJAXPvZLlKPjzHzxHwMwHmqTjrZP4OTT832oMJWfB/4YuIJKzfThAAEtgC70trbMYrWIKc2+yd39k/cjELyWbrEjm35beaJnPCZ30VFMoW+bH8saEgHEO3jIe8U9gRVMKdgITzd/Qt2WWWIxJ5mKQiQwHOJ01ErMtMvMYxu36l8LGhkA7u6hpEzYUu7u7CFyZ9sy18vuBKoAzorMqyWo5CE6wcurneftbByJq0AVx6Et7dBp5s6wCsrbkrv7wawqgoffcbefsWFsEBABx9b+k4nzrJWX2SynOj/Zh6sI6hohXavbMu15O4BKaBRTBBrInaXchffmgtlH94hal+5iqfl9oFqTbBacKXcA9y/ofPB8sOPzNCE4NqVtI3e3c7dZSCwAMBWdolgt1usQQkVLues0cyfElmCwhy2Z+RIY1u3SJnk2imNH+dbH/imvnXocgHM3Xhj04bmOjitHKWVeSlkDYsCngd9C5VH4cAAhBNFAtK0ts15g3sfMHUAylOTEyInW5G71MiBUDYKXEBrl2Ph2W2ZREguDrnlDqI6KFe4yzmKmxU1wh3K30MCja9Jsb2V1GWNxQSwMSxa5sy2ZAEFr5u6O5Yufa5J6NQzMxUGguu5SRUlEh/iQ1xu2crdRcY/cZZ0od1bHXSE0wdVM+/LyRszFBMUqZNrs2DpGR1um2iQg0mYHcvo+9bh6GVNKdV745G5fYr20zlh4rG2BeSNOWXN3N3Kve3lYPlogFtIpl4pE87frSZmAundGJglb5C5frqIJCAf2xn3AR/8I6oKABsUWl+71vETSPimzVxy3yJ0pJUbVIFVM1cndTGwG2ErMDBkbmFqAasiB6GCTuz1cg7BqWTLj4d3kDsAMhHn17FMAVO/7yCAPzRM4Scv8a0KIZVRx+TeBF6xHHw4R0SNtbZm5ihoCizs5iTrgwuQFbmZuki1nd39x5bJKMnLh3+mEUmyScKMt05CehKnYmKmscFPO1EnRLujbA1UaF7E/8drH+aWbf+TZse2EEEKFqliJmfG169T0ELVAhEBJLcqXNyQjUYiH94YtM6gJpqOWLdOAySg9dTK6fUyJIGSsc8u2QPeCaLCLmTtLkb5aVvNNTpW7WTfrEJwEqoSS7S0y4SSMHoXVy6wUoGL6HXf7FU4KzBsxE1E77KnSkleH5KMNYiGd0eIthDTrYSo2ypHpelpmoaQKzId9rfXhLqJhFahSrpV5JfXKtq9tddx5Q+6MCqQykqWcOvcXkorcTUeV68lOzAwWNyhHR5v3Bu/EkYfUBvrsBdeP2S2kMur7et+8zmZeUq3tJnh2MFV89OhAj80LONkO+tvAA1LKk1LK01LKU1LK0x1f5aOOSKA9uXNLuQM4P3keidxVaC7MGqTf9HzezkY5PkEo32DLLHpL7kaKd7ktZ1habyEqaxroIWS1xGKDcmdKk0v5RT6XfsUdu5xDLExoLK6ZSCmJr98kP36cSmSkPnO3vLF3kjJtzCe2bJnDTsq0MRpWc3IBoRPow+tvE0Nnyp2ab3q5MAE4J3dz1mfOFXLnZOauk50G1PVg9U0WrRkE35a5P+GkwLwR0UCCsBZjw7dlDgXRkM6UoRKSCyM7yd0koZKdllkj7s/bHThEg4JCSfLSykv86sVfZTm/XP/aVsed+/f/E9PqPW+kTG7nVDbD/8/emwdHkufXfZ9fZlZWZt0ACndf08fs7By7M7szw13Sa1IkRdGWKVq0VzQtmQrSIYnW6UNyiJRM2UHJIYVERVgRlkjRClkbkkjTpGyJpKXlkktyqeVyZ3ZnZnfOnm70ibNx1H3m8fMfv8xCAagCCkACqEbXi6jo7kJVIdGFrPy933vf9y6kFJGZsCcQiG3lrlHCGXTDKD0Dn/0/TzSF/bgIlbtn5nUk2//P3Si2iiSMBHH9hOaHThGD/PYsAFHFADyRsAxr35m7kNwdd+YO4EL6Ahkzs4fcpTbvqFmhE563C7FXuTuZjjsAzW1j1TfZik/tiPndA8Oi2WzS8mAu2BVbdyq40mO1XeRec73/cyPG/IRGrQXFekjuLuPE09u2zGEkd8lt5W7yjOftQuRMqPst0rp1rN3tmB7DFMahlLvXSmNcTkNuQHU1LDJfiyKOKpi589wWv7n1zt6NicOQO7dB5ZFaaI7I3eMHX/oUW8VDKXcAOTNPyTm9z7wRtpE0DWYcNWtdT13Z8bVWl3JXa7skRvN25w52XNBwoBKU1e8kd2oNcxK2zPlxDU2oUJWl6hIJI9HpxjQ0gwlropOYaTaLB4epBHB9yf/xjqTmDO/E1kZFkozDxQm1rgqVvG4UW8WBukIfBwyyevxx4PeEED8rhPgH4e2kD+w84aCZu44tMwJypwmNZyee5f2t9/F8r3N/di0ge/mPHPt7DIJWcgLDaaC31b5ApSlPpAYBwKoqG0E9NdPflglgxGm2VCdaqNyttLb7TL5SunUix9cL80GoyuZKkXijsE3uWhVqTUmlATNDkpQZYjbZpdwNC7mLQ8Nvkoxgpy1tWJQH6blrbEHM5v2KxdODiyUdW+ZKhMrd79Qf8N9++DleLy/s/HqztP+8XYhAydc3VSXC/BklyI5wdFTaFXzpH0q5A8iakyPl7oxgmzrz7kOaiVm8Xdf9tpVXPXfSp972SMRGyt15g22qQJWGo643a/W1zte2qiqfwDKjv/6bhmB2THTI3XxqfsemaD6R7yh3sUbp4BqEAF9dhb/5uuRX70Z+yJFhoyLJpzUm0iL499614lE2yYYVg5C7nwW+iCou/3rXbYQBcdDMXc2poQv9WD1d3Xhu4jkaboO7pe0zLbv6HqSmIDEeyfc4CK3g+8Trm0ipOt1OqgbBCmoEvLEZHpUk7R5eagAMi1bQiTYXKBTLAbmLC+NMyJ23osJUlC0zjdGqsBIkZc4OSZhKiNmkCgRZHyJbZi4OLdkkHcG5k9Ztqvucpx3Ut5D2OA8qcCk9+OubumDCgtVaFGmZitzdCn5/b9ZXdn59UOUumYdknrHyB4zFIREbrg2FEQ5Gp+NugALzbuTMPMX2SLk7CyRNg4v+4p55O4B2PI8mPWKtIvWWSyI+InfnDXZM0GhD3VU7fWu1LnI3YMfdUaFCVVyWq8udebsQk/Yk6/V1pO9jNku0ByQ6t4Jw5rfWh1i5K/vkM4LxlEAI1XW3G8Xmk6XcxaSU/72U8p9KKf9ZeDvxIztHsAxrf+WuXSUZS0Y2NP30mErDvF28re6Qkuza+6c2bwfQSqpBpHhtk6YDnn8yBeYAVkVZGvTJGXwJ98p9HmjEcYPY/Asdcqc+lb574gVeLy/gdKmdJ4mMLcjYAmtT2eFqY5dwA1vmsNUghOhWdSZOcH7yMMjGwaFJMgJylzHsgdMynfgYTQ8uHfIiPBNV110QqLLgqN/f2/VtWw9uG9zGYOQOYPIZ5hsfjiyZjyk6HXeHXJTkzCkqzhae757EYY2wDxIxjadYptqD3LXsoOuutUGtrQJVRjhfsOOCRltuk7tdyt1JzNuFuJTXKbXXcXynM28XYioxRdNr0qivo3mOClQZALdLiii9OaR7RWHHXT4tMHTBWFLsScx0fIeKU3miyN2/FUL8aSHErBBiPLyd+JGdI4Qzd/0CO2pOLZIwlRCJWILpxDT3K4o4JEpLmM3SqZK7djIsMt+iEhaYn5DaY1fW8HST7LTaub5d6vNAw8J3WiQMyAZdMyutAjkjwXeOPUfNa/Fu7WGfJ0ePCxOCsfIDHDNFOzGulLt2jdWCir+ezAwHgQrRXfo+TMqdR4tUJMqdNdjMXWOLsq4+Ai8dsut0JhmVLVP9At911czG7cb24mCgjrtupGYYk0UuJk9nY2OEaHEc5U4iKTubBz94hEgxoxdIiSalxJU9X2tbKrXQbKxTb7ujQJVzCNtEkTtHXQwe1R911odbVf/ElTvNUk6PsAYhRJiYuVVSa8cDO+4ChMrdh0WGcu6u0oC2C/mMojwTabHHlllqqYXjk0Tufohg7o5tS+aoCuEQsHQLX/o4fQqHa04tknm7blzOXOZ+6T5SSmXJhNNV7kJbZm2TalOd7CeVlmlVVmmmp5kdUxfB28U+DzQshNdkPrUd47/cLjAbH+NbstcRiFO3Zs63VVImQuDE0wgk1a0Kkxm1wzRMmOsmd8Myc2cK0JpYWgTkzrCpHDRz5/vQKLIR7G8dxpYJau4umkCVGB5w11OLg1v11e2uxmawuzHghVnG1Q9x3e5RnzLC0KPYKmJqJgkjcajn5WJqIVcYWTNPHRe9JQA2rMt7vta2lHKn1R7hS0bK3TmEbQqazrYts+k1KbfLtF1JpXkyYSohLuY1tPgKAp2Z5MyOr00mAnJXUUmagwaq3C7CbAJ8Cd8cwjHeMCkz3DCfSIk9tsywBuGJIHdCCA34q0H9QfdtVIVwCIQFyf3m7k6K3FWcCoVmgezau7StLKRnI/0e+8E34jhminh9s6PcnZQt066s0UjPEI8JJtKiYxHYAyOO7rV2kJSVVpE5c4yskeDZ5PypkrsL44IbLLKRUsWfTrDIbhbLQ1Ne3o28DVmtySTF4UnLjIPQWsSIIFBFtygfZMtslUD6LPljCA6fLjmTEGy1oNVvLnRQ6DGWDYMWPh9LXaLhtzsWY1phgflgF6mqrkjg1fiI3D2OCDvuDmvrz5lqIVcakbtTx6yjZq3X4ntLn1txRe5ETb0vo5m78wfbVKMqdafRcW2t1ddOtOMuRMYWWMll4nJqT33QhDWBJjTWg667QQJVNpuSzSb8wHX172G0ZoYWzHxaravyGY1CTeL529fhYuB4eSICVaSUPvBXTulYzi0sQ6kK/eoQqk6VlBntwMuVzBUA7pXvkV19j9LMs4OVUUaIVnKCeG2Lykkqd1J2lDuAuTFtX+XO9JudAnMpJcutArPBTs2nszf4ZuXBYKEaEeBGokBG1Lmvq0JhN0g3dKsVZocsKRNAE4KfjP88v2D+1NAodxlTgt5El8dX7lK6TcVt7N93GNQg3HPGmU1C/JDqath19+i41kwtxkJMzd39oYmPAXC7EczdHVK5W/PUpsKFWL9h1RGGGYftuAsRkruiM4Rb7eccM84DKtJmk73vmxdL4elx9IZaYCdHyt25gx0kYdademettlZbY6ty+I67htvoWAoHhWat4jX3bvbrms64Nc6jprrODaLcheutV6YFT2WGM1QlVO7yQVJmPi3wfCh2hZs9UcpdgN8QQvxlIcTF0czd0RAqd71CVXzpn4hyN5eaw9AMFjc/ySTlJgAAIABJREFUwK4+ojj9XKSvPwjaiXHM+iaV4Mc+iZ67WLOM4TRppJW9YG5McKcEfo8FuqPFsWkyn1THUXLrNPw2c8HC6NPZG3j4e2PlTwg3UNaHD3xF7kLlLuNXhi5MJcRHtCWuaStktNZZHwoACdNBCB8RAbnLGBaO9GjJfQImggLzm60xLh7SkgnbXXcrx7VmCsGduFIrv2c8IHf13eRusIHAZVc9blobkbvHEYVm4dDzdgCWnsTUrFFi5hlgonmfBTlL3elR3SMEbWuSWBBJnxjN3J07KHInabh1ZpIzxPU4a/U1No/QcfdLH/4SP/21n95RfbUfKu0KnihTLc/SdveukybtSVYddS1wBriGhPN2N3Lw4qRS7vbdID0DbFQkaWu7XmK7DmEnubN0q7Nef9wxyAryB4E/B3yJ0czdkRBWHPSyZTbcBhIZaaAKqELKC6kLPCwom2Fp5qORvv4gcKwMsVaFalOia8qKEDXCpMxmRil3s2MaTQ+WqnsfW/EtbNrMJ9UH6HJbLdRDcvdi+gq2Fjs1a+Z4VVlz3myooeaQ3I2J6tDVIISYRe3ya7XhWBCaMXVOSS+aKgRgf+W2rsInvlkb5/IxyN1qBKEqC6bJFAYz8RyzZo4Pu8ldLNEJXTkI99rqAp4fkbvHDp7vUW6Xj7TbLIQgZ06OyN0ZYKx+jwU5R73de0Hejk8Qb6nPmpFyd/5gm4Bo4+OTjCWZSkztsGWOHYLc3Svfo9Aq8GHhw4Eev1RV855ec5alrb2bC1OJKVa8Bu14Gqkd/Lt3uyhJGmom/8W8YL0By1HMlUeIjYrshKnAtj1zcxe5Oy+WTBiA3PWYtxvN3B0SoS2zl3JXc9RZELVyB8qaebe1STNmURt/KvLXPwhOPIXRqnUKzKOqeuiGHXTcbSt36le6lzWz6MfRhOSC3QbUvB3QsWWamsEn0ldPjdwlCw/Y1Ma4WVTvvWMptpCjynR2+GyZSJ9xP0jWq67u/9hTgq4rIuZ50ZSYA/sXmTe2kELnZjPDpfTh36PQlhlFHcKdWIyrKGvmjcRMly2zOLBqB3CrnsSTAtsdkbvHDaVWCYk8knIHypo5mrk7XehOlURzjQV/jnq7t0ugZeVJBORuNHN3/mCbAqGr60yYbv6o/oitqiRjq7LxQdDyWqzX1fn72uprAz1nMQhL8VqzPNjYS+7ydp4GPquJQWsQ4HpObRa9NKXue2vIPlI2yn5HrQN6FpkXW+en4w4GIHdCiJgQ4i8KIX4puP15IUQsim8uhPheIcRNIcRtIcRfjeI1hxH7zdydJLm7nLlMC8k3pq8jtdO/QLhmEsOpU6t7pE5I6Q4LzDszd0E5eK86hC1XvQ/zlnofwgLzOXN7YfTp3A3uNddZbfUb3IsOycJ91uxLrBR8PF/ixRK46EwaFbKJISR3zRJCBjvNleEgd1pgD3UjVO72rUOoF3DjOSTakWyZ6RgkDFg5ZpG5lJI7hsYVafD33/C5Ys1wt7GuehoHLTAP8F5Bo6qloTUKVHncEHbcHWXmDgiUu9HM3WkiUbkHwJ39lDtrkmRQUTGyZZ4/2CYIXe3wJQxF7raaW2xUW4eat1uuLiORTFgTfGP9G/v2KYdYqi6Ri+ewtGRPchcmZt6zB3OT3SrCteBy88wYmPpwzd35QcfdZJdyZxqCjL3LlnmOCsxhMFvmPwI+CfzD4PbJ4L5jQQihA/878B8BzwI/JIR49rivO4zYLy2z6ij/YNS2TIBrQV/OG9mpyF97ELhmEiF9vGbjxJIy0xu3aCUm8A2l3KQswYSlrAK7sd5Wj5mMKUKw3CpgaybZrgjxT2dvAJy8eud7JAoPKWcv4fqwVpIgBGWRYs6snYjKeWzUuhaBQ0LuWqhzqu1EodwF5G6/xMzGFrWYGjk+ii1TCBFJkflau0RdE0y3NP7BN8BtTuNKj/vN9UORO19KPtiCppGB5ki5e9wQdtwd1U6UjeUpO1t4ctRxeFpIVu4AsGxcoN7qR+4mSHglDOFhxUbk7ryhW7mzDZvppNqc3mw+OtS8XWix/L5r34fjO7z16K2BnnMhdYFLea0nuZuy1XrxXvzga2qpJVmrw40gAM7UBc+PD1diZrkucb3tMJUQE2mNzUC5O469fVgxCLl7RUr5J6WUXwxuPwK8EsH3fhW4LaW8I6VsA78AfH8Erzt02G/mrtY+OeXuWmmNnOfxzhldHNx4QFgb1RMJUzHrW+QfvM6jq5/Zcf+1bG/lbs1R74PhKXK30ioyFx/bQaRu2DNMxFInTu7syhq618abUlHYS5vqQ6YgU0wZPQYGhwH1gNzF7KGxZdaC97LZjiZQBQ5Q7hpbFDSlkhy24y7EdOL4M3cLQWn55aA6s15TtuTb9dWA3A12kXpYgZoLxDOq5mGExwphwttxlDuJT7k9KjI/LSTKd5BCYz02T93pZ8ucREMyH6uhDeNG3wjHQi9bJkDZOTy5sw2bT0x9gkl78kBrpuM5rNXXmE/NczGv8XDD3xM+N26NY0jJA2OAebvgknGj63Lz0hS8vQmOPxzq3Xo5rEHY+f+aT2933ZXbZSTyiSN3nhDiWvgPIcRVIIptvnngYde/F4P7dkAI8aeFEF8TQnxtfX2ItgMOAV3TiWmxnpL5SSp3ubX3eb7tcNs5m0WbayrCqrdqJ1KDMPPhbyCkz/Iz37Pj/us5NXO3O7FpqRnsRAWEQBWY7zyZhRB8KnuDr5ZubZdCnwCShfsA6PNPIQQsbvm0HMmGn2ZCG1JyFyp3U89CYIc9a1QDla3ePr5ylwo2YfYtMq8XWJXjpGMwdsRvOZuMktyp3/HlYh4djVu1FWjXBlbu3leJ15jJDDRHtszHDYVWAduwO9b/w6LTdTeqQzg1JCt3aSQvEovb+9gyVdfdBXN0Tp5HWDFAC8idkSBv5xEIXH39UORusbLIhdQFhBC8OvMqt4u32Wj0P5dXaiv40mc+Pc+lvEbTgfVdvcCG9Jl3XRbFweTsdldSZogX84KWBze3Bv4xThSh9bLblglKuduoSHwpt2sQnqRAFVTP3W8JIX5bCPE7wBeB/+FkD2sbUsp/LKV8WUr58uTk5Gl928hhG3bfmTtDMzAHTLY7DLJr7/K0nmalvta3QP0kEZK7WLsWvXLne8zd/AKF2RdoZHfuCVzPCkpt2Nj1Iy+1gtW4o76w0irsmLcL8ensDbbc2nb64AkgWXiARNCeuMhURrC05bNa9CnKFBmG9IJe31Sq3fhV9XevfdZH1CF3tVZ0M3flfueK0wC3wQNX1SAc1To7nYC1Wu+6jkFxp/GIMQk5T+38f7AZ45KV51ZNDcsPSu7eK0g0AelUZrv8fITHBoVm4Vi7zZ2uu1GoyqkhUblDLX2VhKnvY8tU5G5eH52T5xGGLojFtpU7UzfJmONo5jrj6cFm7nzps1xdZj6t1j+vzrwKwNdW+4fZhzbO+dQ8lyfV97m/y5ppNktcdFyW5MHX99tFSVyHC13axIvBMv3NIdkvCjvuJnood66nbJvHdUAMI/r+FgkhPhv89Q5wA/iLwF8APiKl/K0IvvcScLHr3xeC+84lLMPqm5aZNJKRz1hpbov0xgKXs1eQSB5UHkT6+p4v+anXfN7d7L9AdYNi9gzRK3fjS29hVR+x/Mz37vna9WCt052Y6fmSxUZAANwmda9N0a0z2+Nk/lRn7m6waOGjIFm4TzM9jR+zuDChBeROUpApUm5vcrdeX8f19+lgO2nU1iExCalpQEL10dkdS4CQ3FWaxyd3lhbDEHrnNfegobYib7fHj2zJBJhJCFwJm8fYb7nTeMRVaSB85ctcqsEVa5rbgaJ3GOXuSgYMOwPtKpzl79cIh0axVTxyUiZA1lQkYkTuTgm+R6Jyl1omIHd9bJkhuZvRh3Sjb4RjwzCbgCCuq03njDGFFl9nYkDlbr2+TttvM59S5G7cHudG7gavrb7Wt2duqbqEqZvk7TxzYxq6xp65u1ijxGXHZcWrH9hXF4ap6Nr2MV9IQd6GNx8Nhy1zoyLJ2IJ4bBe5y6h/b1bk9uzyE2LL/PHgz1+WUraklN8MblG1F78O3BBCPCWEMIH/Avg3Eb320MHSrb6BKkkz+nm79PotNN9lZuYlAO6X70f6+p9/AP/kXfh7b+xD7uLq58qIeuSBKnMffJ62lWXj8rfs+dr1YF3bPXf3qAFlGSh3bouVMCmzx8k8bWa5Zk+f6NxdsvCA2thlAObHNdaKkoebPkXSWE4Fdn2olltl/tZX/xZfWf7KiR3TgahtQnICgtqJYQhVqXpNdGlSah+/F1AIQUa3+s/c1RW5e695tI67EJ06hCN2AUkpWaivcU3GOuQOICVnWHTK1IU4FLn76Bjb1QmtIbUEj9AThWbhWLvNCT09KjI/Rdj1JXS/TT19lYRp9FXuWgG5m9JGc7DnFYbRQJc2mlDXrjiTaOY6ueRgpGixqlwaF1IXOve9Ovsq64117pbv9nzOUnWJ+eQ8mtAwDcHsmNhD7sxGkUuOQ0u6lNv7K8e3Stub6SGEELyYh7eGRLnbLPsdIteNia6uu2KriKmZ56bAHPYnd5tCiF8HnhJC/Jvdt+N+YymlC/x54PPA+8AvSinfPe7rDissw+pryzyRebvV95AI/LmXyNt57pXvRfbaUkp+5m31AfTbi3Cv3PvDKLRlZqmRilC5M2ubTDx8ndWnvwup723lmE1C0oCFrsTMpSrU5LZy16lB6LMw+nT2Bm+U79LqWjxHBeE52KVlamMqTOXCuIYv4c27Li0zhea76Ls2AhZKC3jSY7m2HPnxDIz6BiTykBoicuc2MYVFMaItp7Rh95+5C8jdojfGxSN03IUIi8xXjjh3t+lUKXsNroo4Wtfvp9eaQaL67wYhd+W2ZLEKz44LFagCKoxlhMcCjudQdarH2m0WQpA18yNyd0pIBEmZtcxVbFOn7vQmd76RoIrNpDj5Sp4Rzga60UDIbTKhuVMIzUEYg30GL1WX0IXOTHKmc9+Lky8S02K8trI3WEVKqchdenuM5VJe36vcNUtcdpWiHHbo9ULNkSxV4UaPTt6XJgV3SipN86yxXpZ7wlRgO2Blo+J3CsyHMqX8iNiP3P1h4CeBDeCne9yODSnl/yelfFpKeU1K+beieM1hhW3Y/ZW7E0jKzK69S23sEm48xZXMFR6Uo7NlfmUVvrkBf+lF0AX88w/6kbsEEhEod5F9e2Y//IIKUvnI9/T8uhCCa7mdyt1SDRp0KXdtRe562TJBkbuWdHkj6CSKEonSEpr0OuRufkKdhktbEhJKEjJ2hVssFBcA2Kif0XaY01S2vWQe4ikwU0ORmFnxmsRFnJYHbff4F5K0bvefuQtsmavymLbM4HQ/aqhKGKZyVbPQfQdTU4SxUFYR1rfMwcjdB8HA+0fH6VLuRjM+jws6cyLHsGUC5GKTI3J3SkiWFbmrp6+SNHXaro/r7Q3uklKy4WcZlyNyd14h9Ab42+TOa6lhtY3mYOfiUnWJmeQMhradamkZFh+f/DhvPHoDx9u5Mb3V3KLhNjo2ToBLeY1iTVKub187Q+UOYL3R/1gWeiRlhngpmLs7a/XO9yWbVbknTAVUYmkirmyb563jDvYhd1LKtpTy94FvlVL+zu7bKR7juUA/W+ZJKHfC98g8+oDSjKoNvJy5TLFVZK0dza78z7wtyVvw37wg+N4r8Iu3oO70WFgLjZZukyHCQBXfY/bmF9iae5FmZrbvw65nd87cLVWhTrdyV8QQOpOx3qv0lzNXMYTOV4rRWzPDpMzQljmdFejBmain1SI71upD7vZJwjpRhKQyoexCpKeHIjGz5rVIaOp9rTUjIHeG1V+5axRo6UlamMeyZeYttSmydsQi8zsNNet4TUugyzZxHZ6bgDubE8QR3I5bMEB64vvd5C4ekMFRkfljg7DA/LiLkpw5SWlYi8ylT2brm2d9FJEhUblDOz6GEx8jYapFea/EzJbr84gsY37htA9xhNOC1gBvm9w1auraulYb7Lq6WFncQdRCfMvst9BwG7yz+c7Ox/ewcV7Kq4XHg83tDQazUWJK6mhC25fcheur3bZMgBfyIIC3znjPqFiXeP7eGoQQ+bTWsWU+MeSuC2NCiH8shPh1IcQXw9uJH9k5g23YewJVfOlTd+qRK3eJ4kMMp0lp6hlAkTuAd6oP93vaQHhvS/KlJfiRZwWWIfiTHxWU2/Cv7/R+fF1LKuUuIlvmxOIbWLUNlp/5Q/s+7lpWsFqHSjuIiq9JMnEN9FjHljljZjt+991I6HE+nrrE75dPgtw9wNcM6tk5QCVnzQYloPHsXnLXcBtqEFoz2Wxunk2oSliDkAzIXWpmKJS7qtckGVQYVCOwZqZ0i0o/5a6+RVkfRxcwd4z9GF0TTNnHU+7SusWkbmH4DnEDnhuHOyWNq9LgljWYTP5+QZKLBzZRK2CrI1vmY4MwBODYyp2Zp+xsDmWR+eTyb/Lqb/znJEu3z/pQIkGyrJIyARKm6p/tZc2stz3WZZasNyJ35xVSNPC6yF2xkkSTFmv1g8lduV2m3C73JHdPjz1N1szusWYuVZcQCGZT25viHXK3vv07GGsU8e0ceTu/ry3zVlES0+ByZu/X0qbgRg7eWj9bW2an467HzB2oBM31ikupXXoiyd3/DbwJ/HVULUJ4G+EQsAyLltfa0Z3WcBtIZOTkLrWphmmrE+oiciF1AV3ofLN6fGvmz74tSRrwJxRv5OUptfP/z96Xe5KV3tl4h19OJRgTNUwjGnI3+8HnadljbF5+dd/HXQvO09A6sFSFuSRK0QgCVfrN24X4VPYGH9SWKThHTL7og2ThPvXs/I55wdCamRgLyF1z2x53p3QHieTFqReRSLaaZ1Ag01HuJtSf6Rl1nxf9TOJhUPWapPXolLvMfjN3jS02xBhzSYhpx/t9nj5G192dxhpX7WmEHseQDnEdnp0Q+BKuOHDb0Ad6nTBMRQihbLZCjGyZjxE63UzHXJRkzUl8fCrO8BGJZFk5FhLVe2d7IBEhWblDfTe5a+3drKu1XTZklpQ7fO/JCNHAE3U8R5E7KSWFKiS0qYHI3VJFBctfSF/Y8zVNaLwy8wrvbb1HpV3Z8ZzJxGQnnRMgZQkmUjtDVcxmEcfOMmlP8qjRPxH7VlElLfe7Fr40qZS7gxI3TxIbFfVz9bJlglL0tuoVfOk/keTOlVL+Iynla1LKr4e3Ez+ycwbLsJBIWt62vFBtn0yBeWrrHp5udvrfYnqM+dT8sZW7hxXJr96FH/oIZOPqhBZCqXcfFOD1XZ9JX1r8Ej+XkWT0aMhRvLrOxOLXVZBKl8+8FzqJmYF1YLkK8ykCctdkuV3sO28X4tPZG0gkX404NVMlZV7acd+FcXUqZifVgRtdyt1CcQFNaLw8/TJwRtbM+iYIDexx9e/UjEr0rJ1tHULVbZKJqYtVLYLh7bRu7VOFUGDRP968XYiZBKwe8bRYaDziqj0FegwDF1vzeS54W64026xrUDxgQ8LzJTcLgSUT1HtrZka2zMcIhWaBVCx17I7U7a67s6822Q07uGZZ9aMHSc3e+3/46Ot/LapDOjJirQJma4taJiR3O22ZvvTxA/W03vJYlzlsr4wYgj7REaKFlBKPBq5j40tJrQVtF3LmFI/qB5+H3X11vfDqzKv40udra1/b8Zxej7+U13aQu1ijRNvOMZmYZKO+0Zec3S72nrcL8eKkoNCC+2d4SdkIlLt+xfD5tEYbpQCcpwJzGIzc/YoQ4s8KIWaFEOPh7cSP7JzBCtSF7rm7WrAAi1q5S27dpTZ2Calt7+BfzlzmnepDPLl3eHtQ/JN3JQL4r5/beaJ8/1XImPC5XcEqNadGQ4PNeB8l5JCYvfnrICUrfYJUunE5A4aA2yWlKC7VYD4JGHEct8l6u8ycuf/J/FzqAmndirQSQXca2JW1PeTuDzwf489+T5z0xF5b5kJxgUvpS50P5v188CeG2jokxkELPjKGpA6h6jUZiwW2zEhm7myavkN7t/XV96BZ5K4THblbO4JyV3RqbDlVrgXkDiClu1xIqXPwRkN9pnT67vrgbhmaHnx0vOtcttIjW+ZjhELreAXmIbbJ3fDN3Vk1NScUr68c+TUmF3+dqaVfj+qQjoxERTlq9tgyA3L3T2//Df6ve38/uM9lHfXems3he19GOB7aXhuJj/RsWg5shgqTPU2xVeyZz9CNpeoSY/GxvmvH2dQsF9MXO9bMulNns7m5Y94uxKW8xkpR0gpyE8xmEcdSyl3bb/esQ2i7kgfV3vN2IcIy86jm7hzf4XPvfY7V2uBrjo2yJJcQfZ1jE2mBFgvI3ROo3P1JlA3z94CvB7ev7fuMEfbAMvaSu6oTKHdmhMqdlKS27lIbv7Lj7suZy9T9Nnf3kdn3w1ZT8gsfwvdfg9nkzhPFNgQ/eAP+3T0o1LbJY91Rq9db1vGHoYTvMfvhb7B14RM009MHPj6mCa5k1O5SuQ1VB+ZTAgyLVa+ORB6o3BlC59XMdb5SuhWZtSBRVDvRYZhKiJQlePWGgdQM3FiCWJCW6XgOD8oPuJa7RtpMY+rm2SRm1ja3w1RABarAmZI7T/rU/TbjZoSBKrqyyVR2q3fNIkjJXWeMS8eoQQgxkxRUHKj2CiLaB2GYylV7ukPu0nobIQTPjfk8F9gqb9X3f192hKmEiGdHtszHCMXm8QrMQ+SCIvPSECZm2rXjK3d2fRndPaIHOkJ0kjIz14Bucqc2khbrt7lVfhOAWttjQ6qNvnhrRO7OG+rB76P0bZptyVZVXQfm0yrx+CD1brG62NOS2Y1XZ15lsbrIcnWZ5ao6f3oqd5MaUsLSlg/SJ9YoK+XOnux7LKtFH1/2rkEI8XQOEga8GdHc3WJlkddXX+f3V35/4OdsVHp33IXIp0WneuKJI3dSyqd63K6exsGdJ4TliN1ddx3lzohOuTPrW5jNMtXxp3bcH4aqvH1Ea+bn3lc7/X/m+d4nyp94RuBJ+O13txWPmqt+vnfiPZ9yKEw8eJ14fevAIJVuXM+pmbulwKE2Fyh3K756Dw6auQNlzVxpF7kf0e5psqDmHncrd91wrHRHubtfuY8rXa5lryGEUEPOZ6HchR13Icw0xJJQPbvEzFpgcR6PWcQ0qEUQqJIONmH2FJnXo6lBCBF23R3WmhnWIFxLTENgx0tpau7xlVyFWc8lLQxuH0TuChJD7LLVWGlojmyZjwuiUu4SeoaYMIeuDkH4Llag2FnHUO6s+jKa7yBOoLP0MEhU7uBrMRoJtcDutmX60qfsbFFoP6LuVpRyJ0fK3XlFh9x5NvU2HXL3VE45Yvabu2t7bdZqa30tmSE+Of1JNKHx2uprHRtnL0LYCVXZ8DFaVTTp0baULRN6O4WWC+p497Nl6prgY/nolLuQoN4qDO6k6tdxF2IiraHFSmjoJ9I3fZboS+6EEN8Z/PkDvW6nd4jnA6Fy152Y2SF3ZnTkLrV1D2APuZtKTJHWLd4+QqhKw5X8s/cl33URnh7rfaJczgi+44Iid64n8XxP/awS3rJi+O7x5gZmb36eVmKCrYsvD/yc61nl974XiBHhzN2SVBf5gchd7gbAHmtmy2vxYeFD/t3df8c/fOsf8pNf/knul+8f+HrJrXt4Rnxf9dGJpzszd2EFwtWc2k+ZtCdPf+bO9xW5SXaROyGCOoSzU+7C2bi0YZONRxSo0lHudpG7oONuTY5FQu6mA3J3WGvmncYjbC3GjJkFLbRlqt/nj6VKCOCSluV242Dl7loW4nrX+RzPQGtky3wc0HJbNNxGJMqdEIKcOXxdd/H6Cpr08LXYkZU73akSCyqA9N0bNqeMZOUO9dQVCMYlYrrA0AT1tkfNLXXm7Zbrd6i3PDZCcncWm3kjnChCV5P0bBqBcqdrcGU8jya0fcndSm0FiTyQ3KXNNM9NPMfrq6/zoPKAVCxFxtwbbZlPq76326s+ZmDLd+ws49Y4hjB6JmYub/loAp46oE71xUl4bwuaEXTQLtfUZ8DDysM9yfO94Pnq/7VfmApAygLdLBHjfBWYA+yXSvHtwBeB7+vxNQn8qxM5onMKO1g07rZlxrQYpna8gfhupLYCX//4TtufJjSeS108knL3ix9CoQU/9sL+v/w//FHBb31B8rU7Hs9dViffRCPLZqLEcuEWFyafO/T3BrAqa4wvvsn9l/7YjjnCg3A9J/Cl5MvL6oMlTMtccVwEwQL5AFyMTzAfH+O3Cu8yHkvxVuUeb1bu8X59uZN8OpucpdAqcHPrZkch7Ydk4QG13CUVYNEHbjzdsWUuFBeYTc52vPV5O8+7G+/iS79vjUPkaBZAejvJHahQla2ziygPyV1Sj5MzoRpBoEpKD5W7XbbMiJW72SMWmd9pPOIpe0q99+HMXaDcfcRWF+Yx8nyjfh8pZd8L1vtb8KmZXXdaWWjX4CyqNkY4FMKOu7EBNqgGQdacpOgMl0JkB/N2pYkXya1/DeG1kYcMj+kmhbpbx+2xuD0tJMp3qOY+0vm3EIKEqVNvuzt6BpfqC9TaOWqmem/jI+VuKOFLn1/68Jf49gvfznTy4FGRbnTbMhW58xlLCkzdZMKa2NeWuZ8KtxuvzrzK2xtv8+ajN7mavdrzeiCE4OOXdd6866J/RH2utO0cmtCYsCd6JmYuF3wup3dtDvbAS5MCx5e8uwWfnDrwcPfFcnWZmBbD8R0Wigs8n39+38cXqhJf9q9BAPWzm/Eymn/wWvBxw34l5n8j+PNHetx+9PQO8Xygn3KXjCUj3TFIbt2jkZrCje+VmF9IXeRWfZXGIdK3XF/yc+9KPjGpag/2w7fPw1RG8JtvOx1Vcryqutxub90c/IfYhdmbvw5CsPL0HzzU88LEzC8tgalD3gaMOMvCZzKWJnZA4iaok/9T2Rt8pXSLv3zrn/NLj76KrZl896Xv5sc+9mP8nc/8HX4WBgI5AAAgAElEQVTiW36CXDzH6gFWOOidlLkboS3Tlz53S3e5lrvW+dqkPYkrXYrN4j6vEDFqu2oQQqRnVNCKdzZkoBoQsJRukYtIuUsH9ulyD+XOxcAz052k2OPgOLbMa3awkAgWuomA3M3pityJ9iwVr8lau7cKV2hKVuu7wlQA4gFrHSVmDj3CjruoEt5yZn7oZu7CebvC5KsIJPEDQoJ6waotdf5+lnN3wmtj1x52ahBCJEyDetuj5Gx27ltuLFBve5imjWNmMUczd0OJYrPI7y797p6y8EHQcNT1RSl3ypYZJjpOJ6b3LTJfrCxi6Rbj1sG5hs/lnyNhJHB8Z18y+Op1g1oLHi2rzxXHUounSXuy54z/8pa/b5hKiJciClWRUrJcXebFyRcxhMHt4sGbyuuVoOMuvf8muDBKeO2z2/Q5KRy8uh0hEnQCVbydyl3kNQibd6ntsmSG+FjqEh4+79eW+ESm92N249fuwWIVfvJVcSAJ1YTgu14w+Pkvt7mzrhaIopXnatvhw9IdvuMwP0gA4bvMfPibbF74JK3U5KGeezUgdw+rcCWtjg/DYkXjwDCVbvzY/Hfz0cQ8z6bmeSYxR0wz+O2EveMxM4mZfT+QQXXXxRuFPWEqu+HEFblbqi7R9Jpcy26Tu7yt1LP1xjrj9imF1taDhcdu5S7dVYeQmTudY+lCqNyF5O5RBK6rsDOvuke5K1DQxrjcJ1L5sLAMQdaUrNYlMNhrVl1F2K52yJ1S7pKa2qzRgzCUjdolyKjEzJkeM1m7w1Q86dPyHRLx4ALXLEN0ZoIRTgBhx11Uyl3OnKTU3sCXHpoY3B1xkrBri/jCoJj/BKDm7pqpi4d8jS5y16+/8hSQqD5Ak16nBqFzv6krchcod9PWZZbqCxhtl0Rcp6XnRzN3Q4pwLdfdJTcotmfuEh1b5rVpRUKmk9N8UPigrzsnrDQYxLkT02J8YvoT/Pulf7+vjfP5Szq2CY+W1OdK21bXjcnEJDcLN3cci+tJ1kqS/3SAU3EqIZhLyqDM/OjXznK7TN2tczlzmUKrMNDc3UY57Ljr/3196eNpJZzmE6TcjRAt4nocgdhThZCIJSL7HprbIlFepjpxpefXn0+qs/Ht2mDWTCklP/u25FoWvnt/samD/+AZA9OAr95RH3gtL8srzSa3ast4vjfYi3Rh4v5rxBsFVg4RpBIiERNqzg46f2JYLOsac4cIIpiJ5/jBmU/zQupSX7VvOjnNan11R0n9nuMZIEwFwIlnMNo17gQfYDuUu2DI+VTn7jrK3W5bZkAyzihUpUPuDCu6mTuj/8zdqhzjYoR7MbOHLDLfTsoMJPSA3CVEEBTRLOFi8MGm+v261SeE4r2gFzkkdz99/9f4I9/4e3gd5W6UmDnsKDQLCERkCW+5ISwyt6sPaSZmaQTXraPM3e22ZZ4VkhU1O11LX9txf2jLLDubCATPZF9mtXGfWqtNwtRpW5PEm8OlqI6gELqwjkTunDoCAb5JvRUod4HCNJWYwvVdtppbe57nS1+Ru/T+83bd+Mz8Z5hNznI9d73vY2K64BNXDeqbW0ih4QTOr0l7Esd3KHXNYq+VJJ4PN3KDkbWXJuHNY/4Kh2Eqc6k5rueuDzR3t1FR1V39Ou5ArcElHq1mhkb77MrWTwIjcndK0IRGXI/vIXdRKnfJwgOE9PeEqYTIm2nmzDHergwWqvK7y2oY9s88L5TqNcgxWIJvfdrg5qr6wKt7Y7zSaNKUDg8rh5/3m/vg8zSTeTYvfOLQz4Vta+Zc8N/s6yarhs5sLFoZfiY5Q9trd3bUeyFZUIErB5E711KL7DtbHzJuje8ITcjFc2rI+TSH7OsbYCYhtlOt3O66O3qS3XGwrdzFlS0zgpk7WzPR0fbM3Mn6FvfdaObtQkwfsusuJHfXQnKnheQusFm3SrRiWcrNFONGmtt9hvLf35JM2jBpCzadKr+49hXW2iVuyuB1miNyN+wotAqkzTT6IWaQ90N2CLvu7NoijdRFWrb6nDlKYqZV77Zlnp1ylywpG9le5W7blpkyclxMPo0nHWr+MknToB2fwGxu9nrJEc4Y4VruqMqdSlDXWCv5eD47bJnQOzFzs7FJy2v17Kvrh7nUHD/xLT9xYPjSq9d0sl6ZupHuhP70SsxcLqgN7BsDil0vTgoWq7DROPr1OZwznEvNcWPsBhLZCZvrh42yZCwlMPaZCwzXa9LNsll5Qshdv5TMUVrm0WEZ1k5bZrsaaYF5GKZS3dVx143nDxGq8rNvS6YTqtvuMPiuF2J4Qq1aa+4YLzdVRv2HhQ8P90JSklt9h/Wnvq3zYXNYhL7w+aCbb10XuEIwF2H9BChbJrBvwWay8ADHTNFO7G+ndOJpJHC7fG+HJRPoDDmfunK3W7UDla4Ys6FyVsqd+r1K6TY5U9B0lGXkOBBCkDasnTN3UiIbBVbkOJf3sXgcFjMJWDnEzN2dxhqmMJgPZi1kQO6sLuWOYFYir81wq09i5vtb26rdv1z9Mu0gpe/1cOZqpNwNPaLquAuRi6nzu9g+Wg/qScCuPaSRvIBvWLTiEzuI2uCvsUwrrmaFz1q5ayTm8Y2dTp1uW2bWzDOXUJ/3DfGwo9yNbJnDiXAtF/YVHwZ1t04ilsCKBf1ywMRuctdjzCMkOQclZR4Fz13UmdZLbLLN2sKuu+7EzOXgeK8OTO7Un8eZu1uuLZM1syRjSZ7KPIWhGQdaMzcq/r41CEAnu8B3smxU+ruuHkfsp9x93z63/+TkD+38wTKsjpQcVgVEqtxt3cONWfvG7H8sdZHldoGNA3abvrkh+fIK/Oiz4sBEpN24mNfIZxtIqVHxx5jwfS7pyYGGYLuhO3U036WVnDj4wX1wPSjZDJW7FREkXOp2v6ccCTPJg8ldqnBfqXYHqKCOleGBYVB2azssmSEm7cnTV+52z9uB+jlSM1A9mzqEqttER8PWYuSCLsUouu5SurWz586poXktVuUYlyK0ZU4nYaMBjj8YIV1orHHFnsQIZqLaIiR3QaBNs4SVyqIJiHnT3Kmv4e2yCTu+5HYRPjoGda/FL6z+Ht859hxXrDyv1xdViuuI3A01fOmzVl+LbN4OIGcqNXhYlDvdqWG2tjqWzGZi9ojK3TK1jLKj6d4Zkrvy7T2qHXTZMtubZGITTFkXMUQMx1giYRq0rDyGW0MbghL2EXbiWMqdUydhJLBNweKm+owOlbuUmSJhJHomZi5WF9GExmxy9hhH3huGLrhkVVhqp3GCTdIxa2yPU2i5oEhTIjbYuvD5CTDE8crMV6orzKXUXH9Mj3Elc4Vbxf3J3XpZkt+nBgG2U4efKOWuT0rmKC3zGLB1u/OBUHfrSGS0HXdhmMo+g7bPp5Ql8J195u58Kfkbvy8Zj8MPfaTvw/bFbL6J9BKIuIUvdF7QkiyUFg41dxcL7GFO/OgWypcmQRfwbKBULEu1EJ7TrCO/Zi+kzBSpWKp/P42UKilzfP8wFVDK3RuWYiu9yF3ezrNR30DKU/owqm32Vu4g6Lo7u5m7pB5HCEE2JHcRJWZWuhR26uoCsCbHuRihLXM2IZDA+oDrtjuNR9vzdkArSD2xQltms4RuZ7magUZthpZ0ebjL0rVQhLYPz44LfvnRa5S9Bj8y9+28krnG1yv3cM30yJY55Hhn4x0KrQIfn/x4ZK+ZNDIYIkbJGY75LiuoQWgmlf2slZg79Myd5rWIN9epZQNyd1a2TN8jUbnbIZndSJgGvoSSs0nWnEAXOtPWFYS5TDKu07bU5+6oDmH40B2octhrccNtBOQOGsHH93hqe902nZzuuZZYqiwxnZgmFsxbR40prcSan+WdB2qdpgmNvJ3fpdxJ5sYHn+iyDcEz40dX7jzfY7W+2iF3ADdyN1isLHb6AnfD9SSF2v4F5qBsmZrQ0P3Uk0PuuiGE+MNCiP9RCPGT4e2kD+w8olu5C6sCIlPupCS1dW9fSybAR5Pz6Gj7WjN//qYagP3rrwoy5tFsaAmrgSYTpCwNN57kJRmn7bV5MOC8H3SRO+vo5O6ZccE3/rjg+Qn1cywHc0VzIvo4wOnEdF/lLl7fxGjXDkzKBEXuvm7FSWtmx6LRjXwiT9tvU26fwiLcqYNT663cAaRnVVrmGXSjVb1mp5duW7mLpsh8h3IXFJivM6a6EiNCWIewMgC5a3htllqFXeQuUO5wQPqKlFlZnpuA1aJSkneXmb8f5GXcGPP43Mrv8sn0U3w8fZlXMteoek0+SGZGyt0QQ0rJF+5/gQlrgpemXorsdbeLzIeDRCSCzcdGKlTu5pRyd4hFdEgGO8rdGalfdn0J3WtRy+zdqEuYOuBSdYtkA2vsZPwpNGsFO7ZN7kbWzOFDuFHvSW+gUu1u1N06dszGDtZXMV0VaoeYTvQmd4vVxROxZIZIOiXKWpbXbm9fzycT204hz5esFH3mxg4X1/HiJHxjQz3/sFhvrOP67g61Mpy7u1O60/M5W1WJlPsnZYIid1kzy0Raf6JsmQAIIX4G+EHgL6CyTD8LHLxCHWEPumfuQnIX1cydVX2E4dT7hqmESOgm1xPTvF3tTbIe1SV/++uSb52FP3rIWbtu1N0a0+kkf/RVE9dM8qKrTurDzN2FRd7HIXcAqS77wIrfJOt5JPzoT+SZ5AyrtdWeu3jJAZMyQQWqvGHFedbI9KyfCH3wpzJ3VwuUn90ddyFS04pY1E5/8VH1WqSMneSuGolyZ3XCWoBOgblIjKNr0c3cTQen/iChKvea60jkdscd0JKK3MVxVPm49BS5GxesFacQCG7t6l58f0ti6nDTfYvVdpEfnfsOAF4JFp6v2fGRcjfEWCgucK98j++89J2RhamEyJr5oZm5C5W7RqDcNROzGG4do093Y+/XCMmd+t0+K3KXLAdJmb2Uu7iOMNTMVtZUn7FjxhU0o4ZmlGl1yN1wKKojbKOb0B3WmtltywRlyey+1k8npqm0KzuUqZpTo9gqDlRefhRobgvDbWJNjPHmXY92sGabtCfZaGzgS5/1ssT1YH53R+oBeGlSUHVgYfDTt4Nec4ZXMlf2nbvbKA/WcVdsFslZOfJpwcYTqNx9q5Tyh4GClPJ/AT4NPH2yh3U+YelWZ7cnHMKNitylNg8OUwnxQuoS71QXe8b2/9RrkpYLf/PTB/fa7YeaW2MqleJTTxu4Zoq802Q2OcvtwuBzd2ZTfRIcl9x1Y9lrMOd6sLvHLALMJGeou/WeA9aDJmUCbHktHsZivEBv62hnyPk05u7CAtO+yl2YmHn6c3dVr0FKV6wuSuUurduUu8ldoNwlMtHNOAHMHqLIfCEocO4md43AlmniqDAVACvHsxOANMkb4z3IHdzI+Xxu5Xe4bs/wmdwzgErSvWpP8XpMjJS7IcYXHnyBVCzFp2Y/Fflr58ypoVHu7OpDXCOJY6pzrplUlqzDWDPDAJZG8gKebp+ZLXOb3PVQ7mIGwlDnWyamyF1GU9eIhlgc2TKHGN3J5xVncHInpewEqtiBgWhil30wdOx0z90tVtSGx0kpd2ZDhYtMzI7RdODtwJo5mVB1CMVWkZUgKfMoyh0czZq5Ul1BExrTye1r30Fzd+uBCpcfQLnLxXNMpLUn0pYZfiLWhRBzgANEP835BMA2tmfuorZlJrfuIRED2f5eSF2k4jW4v+uC8duLkl+5C3/u44Kr2eMpFHWn3iGurpnEaNV4euxp7pTu4A5o4Yu1wpm76AadVrwqs64LzsmQO+gdqpIsPKCVGMcd4GdZCKwGH/d6n57j1jia0Nion4Zy16fjLkQqIHdnEKpS9VodW2Y2uEjWInhb08auQJVGgS2ZZi4T7ZxDLq6sEIUBCOmdxiN0NC5Z2wpqy9PxpVDKXUju4hmeC+ZL03JmTx3C+1uQn7jJ7cYaPzr37Ts2cF7JXOMN4eCcALnLbH6DZ7721+EIXZcjKCxVl3hv8z2+4+J3YOrR28pzsTwlZ2Pfrs7TQliDEIZPNRNqyXGYUBWrtowUGi17Gs+wz6zEPFm+TcvK45p74wUTpo4WU+duqNzZUikzZXkfJz6ORIxsmUOIptdUXXWo5PNB0fba+NLfpdztvNaHRKbbmnmSSZkAseAaMjGbI2XB64E1szsxM0z2nD0kuXsqAxkT/sVNyXtbhyNRy7VlpuwpYtrO6++Nsf5zdxsViSb277iTUnaRO0GpLjtq5XnAIO/QrwohcsDfBd4A7gE/f5IHdV5hGRZtv43ne50Pg8iUu627NLKz+LGDg0JeCENVuubuGq7kf/qK5GoWfuyF4x9PzamRCGKf3XiSWLvK9dx12n6bB+XB5u5izTK+ZuDt7lc7IqSULDsV5lwXvAhiFXch3G3rSe627g2k2gEslBawJTzjtHt+Xdd0xuPjp6Pc1TZUDYXdpyzZyoJhnY1y527P3KVNtQ6sRqTcNfw2TkBEnOoWK3KcS/tcKI4CIQS2Ac0B9jru1B9xycoT04zOfU1f0CJGjPY2ubOzjFmCuSR4rWkeNDdo+aoq4VFdstGENfNLzJo5vnfixR3f45XMNer4vCccRPCcqDB7///lwp1fZHLltyJ93ScJv3H/N4jrcT4z/5kTef2cOYkvvaEoMg9rEEI0E4dX7uz6Ei17GqnF8IwEunuI3pEIkSwv9LRkQmjLVJsp4cyd61r47TEKzn2kZtCOj4/I3RCi6TYZD2ppDjP/Xg/swbZhd5S73SRkwppAE9oOcrdYXSRrZkmbEaZ6dSFU7rzEGC9fNTrWzO6uu+WCZCwpSMQPdy3UhODHXxbcKcF//K8lf+o3fd7eGOxavVxdZja1V0+6kQv67kp7++42yj5jKbHvGEXdreP4Drl4rhO8slV9gsidlPKnpJRFKeUvo2btnpFS/k8nf2jnD6q0Uu341N06pmZGtgOb2rp74LxdiKv2FAnN5Jtd5O4fvCV5WIX/9VsPX32wG22vjeM7XcpdCqNd48bYDYADI2xDxJplZck8hj20GyW3TsN3mHW9E1HucvEclm6xussKJzyHZOEBlYnBhhgXigs85+tYzf67gflE/nRm7uobYE/0T2Dt1CGcfmJmd6CKJgTJeERpmcFrhnN3bm2LVTnOpWh77wGwDWgMIGYtNNa4lpjacV/LU6EqMdlty1TqwLPjUChP4+FzN9gEeL8Amn2fJe8uPzz7HxLbNbP1ShDV/rplEWtFu8BPFT8A4MLtfxHp6z4p2Gxs8sajN/i2uW8jEUsc/IQjIGsqclFqn/F8l5RKuQtqEADa8XE8zTyccldfphGQwjOzZUpJsrJALb23BgFCW2YFgU7CUB8wtbaL15plo61GLdpWfmTLHEI0vSYT9gQCcSjlLiR3ypa5PXPXDV3TmbQndyp3lSXm0ycXphJrqGtI28ry6g2DlgvfuO+Ri+eIaTHW6+ssb/nMHXLeLsQPfUTw7z8r+O9eEnx1Fb7vVyQ/8gWfNx71v2Y33SabzU3mknN7vrbf3N1GRQ4UpgKq7iGsTDhPc3eDBKr8cHhDBat8f/D3EQ4JK1g0Nt1mpAXmeruOXVkbmNzpQuOZ5BwfBhfKmwXJz70Dn70Bn5o5PpEKP7x22DLbNZKxJHOpuYFDVWLNSrTzdm21YD0p5U4IwXRyb2JmsnAfzXep5g8md3WnznJ1mee1BLFWfx9/3s6f3szdQT2D6ekzmrlrdgJVAJJxEc3MXbAJUwlsXFpzizU5xqUT2DC1dGgcoNw5vqo0uGrvTE5thuQutGUKDUxl835uAlYKyjIbzt29vwXmxO+Q0RP8wNSre77PeCzFdSPDa3Ycs7UVwU8XQErSpZt4us3E2pdJlHsnnI3QH198+EUEgj9w6Q+c2PfodN05Z0skzOYGutfcodwhtCAx8xAzd7Vlmgm1GFbK3emTu3hjDcOp9lXubFNHM8qY5NCCDbR620M2Z9loLdH2mrSt/Ei5G0I03SbJWJJkLHmombvQRpgwElz2Fvm52E8zbe3dbJ5OTPOopmbuHN9htb7KhdTJhKnAtnLn2Fk+MqeRsZU1M6xDeNR4xHLh8EmZ3cjGBX/pRcGXPyv4K58UvLUOP/Brkv/q8z6vre69dq/U1Bq1lxU1psd4KvNUz/7k9bIcKEwF1KZ8WCC/eY4SMwd5l17pun0G+J+BP3KCx3RuYQUL0YbboObUIrRk3gMYmNwBTJs51ttlfCn5id+TpE348ZejUcjCecJwh9mNJ9E8B81t8XTuae6W7uIMYPuKtcrH6rjbjZVgp2bW50SUOwgijGs7Vaz0hrINDKLc3S3dRSL5aCy3L7mbtCdpuA1KJ50AV9uAwJbRF6kZqD461Xmqlu/gSK+j3AEkLRHNzF3wmhW3AZ5D3Ckr5e4kyN0Atsz7zQ08/B01CKCe15IxjFC5s7IdhfW5cYHfzqOjd+oQvlZYI5Z+jx+a+TSJPo6BVxPzvBWPozWjU2Kt+hKGU+XeM38KX4txYeFfRvbaTwIq7QpfWf4Kr8y8Qi7exx4dAXKBclc8Y+XODpMyUxd33H+YInPhu8QbqzSTIbmzz6TEPFnpH6YCoGsC3SwTY/t9rbc9dG8eic9K496I3A0pGm4DS7dImalDpWV2K3evlL7EH9S/zrONd/Y8bioxxXpjXfW81VbxpX9iSZmgAuzcmI1vxNE1wcvXDN6659FyJHk7z1p1g7YL84fouOuHtCn4cx9TSt5PvCx4fwv+2L9VSp7bVZewXFWbOb1smQDXx67vmbtzPElxwI47UORuLCXQxHbK5nnAILbMv9B1+1PAJ4CIytmeLITkruk1qTrVCMNUlH2jNnFl4OdMmRketUv8/E3J1x/BX3tFMG5FS+66lTsAo13j+th1HN/hfvn+ga+jbJnRraiXA6vZHLETScsEmE3OUmqXdsQkpzcXcMwkzfTezrrdWCgtoAuda9YkRrPSt9cpHHJ+cJIXfd9TSZFdyp0vfbzdgQvpGRXDfxoBLwGqgfLaTe5SESt3Za8JDfU7U42N7ajUiAqWrhS4/bDQULu313Ypd8qWaWLIYOYuvh3Y8OwEgM64NtlR7r7pfglNxvgvZ76t7/d6JXOVhqbxoHrzSD9PL6QDS+bW9LexduF7mbv3r9Cds5l/ehzxpcUv4fou33Xpu070+ySNLLqIDQG5CzrukjsXsocpMo831tCkt23LNBJnUoWQLO1P7gA0o4Lmb5+79baL7Stiu9xYoGVNqiqEQxZlj3CyaHpNLMMiHUsfitw1HLU2sA2bZx312Xit9I09j5tOTuNJj83mJkuVkw1TAWXLbHfN1r9y3aAdWDNnkjNsNtdBtA8dprIfkjHBn35B8LufFfylF+G3FuHX7m1/fbm2TFyPd2Ybd+Pp3NNq7q64PXcXpl4OYssUCDJmBl0TjKXEueq6O8q7VAMGl4hG6KBD7tymUu7MPsqd7x3qgzy1dQ8nnqbVr4usB6bMDC3p8rffrPOpGfjPertGjoRwF6UnuctdRyAGqkTozNxFhOVWAVuLkdNNcKO3ZcJ2ylW3NTO1sUB14tpAs4MLxQUupi9i2Dk06aE7va1E+SC98kFzM4Kj7oNGQf0ediVl/t37v8oPvv2/7ezyO4M6hGpAnsMqBICkFVHPnW4H32Ob3Em798XluBgkUOVOYw2B4Iq9U0ENbZm636XcBZhPqgTRmDvD7foqD+pF6vab3NBfZnyfTaWXx55BSMnNenTkLlW8iURQzT7N4vU/juFUmXnwK5G9/nlGy23xpcUv8cLkC5003pOCJjRyZv7MZ+5C5a6Z2EnumolZ4o1HA4X9hCSwo9yd0cxdsrKAE8vQtvZxPxhlhNtF7loeCX0KS0+wVF+gbU2g+230HhU7I5wNfOnT9tqK3JnpnvVH/RAqdykRI7eh5sXGl3uQu8R2YuZidRFTN8nbfVKrI4DZKOJ0XUM+MquRsQWv3Xa5lruGj4duP4hEudsN21B2zetZ+Jm3ZWd9sVxdZvb/Z++9w+S47zPPT1VXVec0OWKAGQwyQTCAQZQoiooUZSoHirJlWfI9Z/ue3du1fb5b+c722t61L+zus3crPfdYOss2JVvJsmSZQcwUKVEiQRIgiDzAAJNz51jh/qiunp7pVD3TPYCgeZ9nHjyYruqunpmuqvf3fYO3tyhZXo+hwBCyKK/JcViMWTUIdWSZ2UiB2Jne8w6/cF3VIdjx3P2zIAg/KHz9EDgL/FPrD+36g7tw05hRq0/uBF3jlh/8Hnt+8mXbz+tbumT22zUQPNIlm6QpR4w/f8vmOu3WoyjLLKZlmu9TyppS1H5ff33fna4hZRNNJXcz2Qi9zjCC5GrZ5K7Hs7YOQdDy+JbHidvw2+W0HJdjlxkJjRTrH6pJM9sLkfgtndyt67jL6yrfXzjG2dRMsXcNMIvMYWvJXYXJXbM8d4HCIkxcSxc77py+5nbcWXBL9T13Y6k5BpxtuNZFQVuBKg6jnNwJgsDBdkinupnJRfhP408COh9su7vma4U8nezJ5zmVn9zoWyqDL3qGtG8Hmuwl2n4TsdABBi88vD2JsIEXp18kpaZ49453b8nrhZTOq951505MkHV1oUtrk58znj4EDJyp+ucZV3KquA9cPc+dmZRZfWEvq6VBTKOrq9e5ZE7Fp0j0uoeZTo0ViaFzu8j8moFVaeVymOSuUVmmgEBHZApRV4n0HMITm8YZn1+zXZHcJeeYSkzR7+2vSnKaATkTJedevYaIosDR3Q5OXNbod+8CRDzBi/iapPBaD1Ewp3inl+H56UK6eWKaPl95mErxmK2+u5JQFSsUpa4ss1BgbqHdL/5yBaoA/yfwfxW+/iNwt2EYf9DSo7pOYU3ukvkkaTVdMfWsa+x5/EsX6Tn3FHLB4FoTuoZ35bKt8nKAjGowlTA4vWBeTH5lT5yRTXbarcd6WWa+OLkzV7dGw6OMx8bJa9VXYOVcEgGjqZ676dwKvUoYJGfLyF27ux1JlIopV97IBCWsgMoAACAASURBVKKu2iJ3l2OX0QxtLbnLVI5YVhwKIWeIyUwTgy/WI7mW3P00er4YMvLsyunV7dxhcDi3tOvOSrJc77lLZUHXN3eCtiZ3cTWNljR/vv5gayZ3Tkf9tMyLmfkyvx2seu5EPVdG7gAOtsF81LxBeCr2c9TYYe7qrPM+BJGjOYMz+jKqXrmKo1H4I2eIF8rSEQQmdz+EL3qO0OIrTXn+6xWqrvLMxDOMhkbZGdy5Ja8ZlDuuuizTlZwkXSE4IuO133XntiZ3peTuanjuYhdqSjJjeVN5oeZW7QepnIbHKdHvGWE6dZG00/zMbvvurh1kCtcft+TGr/hJq2lbOQJgKpvckpu2uVMAjN/8KQDC66Z3HtmDX/YzlyqQuxYmZUJhcreu8ui2gjTzzJSCpPaj+FobhvWhYejxwJdPGMznY6TUVE1yB+b95FRiqqgYW4wZOEQIe+0VmFto9wusJA1U7fogeHbI3fsNw3iu8PWiYRiTgiD8ZcuP7DqEVYWwVJDSrZ/cCbrKztf+gbSvC1FX6T33ZMXnMQyD5YTO1JLO4sVJHFqOk9oQL57N89QbeR55Ncd3X8rxtWez/NdHMvzZd9P8wcMpDj2ss+/vDO76tsF/PWZeTG7vjzb9fSbzSWRRLtY8lMoywewnqee7s0hNcyd3K/Q5Q2YvW4tkmaIg0uXpKk7ufIUwlYSNMBVLNz4cHC6+b6lOqEpLJ3fFAnNzSvjo0nH8Djejnh6eWzm1up0gFBIzt64OoUjupLWeO4DUJjmJx6EgIBDTMiSiy2QMmc5ga2zGbgmyNSZ3qqExnl4oS8qE1cmdmI2Bnod1n5WD7QLZ9KqUT4jezZANC+tRQyGHweXkGdvvoxoc+STuxBUSwb3F783u+AB5JcjAhYc3/fzXM16Ze4VINsK7ht61Za8ZUjqvepH5+o47C4103bmSU2Sd7cXpnya5Tc/dFk6L5ewySna5JrmL5sx7gVx29fySyml4FQf9nhGyeprpQjWRkt0md9cKipM7yVW8j7Nbh5BW03gkD8G506SC/UR6DpH1tNE29XrZtt3ebs4snyGtplualImuIWdi5NYtEI72ioQ8Aj8/r5KLD5OXJshpzVn0qwTFIfCFgwIvzcLjM+YiTqUahDXHaPXdFe6fFuI6bT4BsUbHHZSTuw6/gGHASvKXh9xV0oPc1+wD+WWALMqIgljsJ1ufltl94Vnc8Vku3PmbrPQepu/0YxUTCJ86qfJv/ybNF/8hzbNPmOPo//t4H3/1ZI6/ez7Ht36a54ev5nn1osp8VEd2wK4ukY+Pwu/fLPCXdwl86W3mh3hFsy8nsIuUmlrz3kplmQAjoREEBM5Fqkszm03uUlqOiJqizxkukLvWTO7AlGZa5M6/OIYqe0gH6vtlxqJj9Hp78cpe1DqyTDDrEK5kW+i5Sy2C4gfJRUbP8/TySd7ddoh3hQ9xPHGF5VKfga/nmpjcweZ9d6Ig4nO4iKtpsoUC86E65uyNwlVHljmZWSZvaGUddwAZzSAvKAiWfHb95K4djHwIp+DCmx9lr6e/ZqmrhVtEP4IB52PlNxuNwhs7j4BBwprcAbrkZnrnR+mafAIlvfX9iL8I0A2dJy8/Sb+vn/1t+7fsdUNKJ5qhklSbv+hnB4KWw5WeXdNxZyHjtj+5c6WmyJTcFGoON4Khm1PuLYI3Vj9MJVqonchkVsldMqviUST6POZ+lwzzd+Hcil7TbdjCelkmYFuamVJTuGU3gbnTRLoPgCCw0ncjoZkTsG5RpcvTVUx1bGnHXTaOgLEmUAVMqeTR3Q5eH9fIxIcx0LgUvdSy4wD41F4IKPCtywVyV2dyNxRc67tbjNVPykyraTJaZh25M+nQ9eK7k6o9IAjCbwG/DYwIgnCi5CE/8GKrD+x6hCAIuBwultLlkztByzP02jeJdYyyNHgUQdc49NRf0D7xCktDt695nrFZjYAbPvM2J++4PIV2xcGDHxlBcSm4FAGXDIpEmY/unlSp50Dhzxc8zOcqy/42g2Q+uUZyqq6TZXpkDwP+ATNUpUo0T5HcNUmWOVNIyux1WrLM1kzuAHq8Pbw2/xo5LYd/aYx4x3D1EvACNN08aR7tOQpQIsusTe5+mk+Q1DJ4Ha6q220YqaWiJPOFyBlSeo73dRwh4HDz5akneSFylgc6bzG39XfD9Kug6yC2zhdgobLnzvy3GUXmAclFXMtgpFaYo83WxGsjcNdJy7S8jeuTMsGUZaqCDJa8eR252xUAp0PkbcYXeGoqyP7y++WKCLqC7NESXIi/Dmyu0tRKyoyXkDuAyd2fZse5v2Zg7JtcPPSvNvUa1yNOLp5kLjXHZw98tql+6HoIKaa/K5JbwC+3xmdaC67UDIKhVyR3uuQi52yzObmbJhFaJcWaZF6DHGoKvSSEqZVYJXfV08pihcldJuND0w003UDVDTyKgx73TgREJnJz6IJkJmZu45pAumBPcEkuXBQ82ja77lL5FAFE5FySaM8BAFb6j9Bz4RkzP6HEwmH57gSEuhOszWC14668auW23RJPnFAhPYSAyPnIefa27S3brlnwyQKf3W/wV/OzhL3BupVhsiizK7ir6LtbjBvcOOSouU9pDYKF9gIhNBMza+//i4Bad2HfAH4F+H7hX+vrFsMwPrMFx3ZdwiW5WMyUT+56zj+FOzHP+C2fBkFgacdtZLzt9J96pOw5plcMdnQ4uG1UYli7TDo0wGCPi+6QSNAj4JTtBaSYdQitIXdeafW9GQ4ZzaEUZZkAu0O7uRS7VNV3J2cL5M7dHHJXrEHYismdtwcDg4XEDN7lcVuSzKnEFFkty0jQ3Naadtbsuiv0z03Y9d3pKsy9aW9bgORCkdw9unicNsnL0cAw+719dMp+ni2VZvp7zeffojqEhGpN7krTMs2/+abUITjcxNU0SnaJecJ0l9tjm4J6k7tLhRqEXe7ytL2sViB3xSdbe2GWRIF9YXhtcoBY2s+BNpskwRngtnSG8cRpcvrmFkF8kTOosq9YJm0h7dvBUu/d9F/8JkILZT6/iDAMgycuP0G7q52bum7a0tcOXuWuu2INgq/ySoStInNDx5WaJl06uStYIsQtDFXxxi6iOdxFOWklRPNLOHCC7iSd00jlzJOB1ymhiE66XANMpy+Sc7Xh3PbcXTMolWVakzu7ssyUmiKYN8950W5zAWKl7zAA4XXSTIvcdXm6ijaXVkDOmNPhnKuc3I30iKZ/TXfR7x1cE17SKnx2v4DDNYuQt5cQPBoyfXeRdIJoyqCjXg2CVWDuKid318vkriq5MwwjahjGOPAHgFHy5RMEYcfWHN71B7fkLmqWfYp5Ay9oeYZe/xbRrr0s95sXc0N0ML3vfbRNv447OlXcXzcMZlZ0esPmH6Jv+VJD5eWl6JLNrrtmI5VPla22qE5vUZYJsCe8B1VXGY+NV3yO1cldc0YmM7nC5E4JtTRQBVYTM5cXT+HQcrbCVCxJwUjI3NYQHeQVb13PHTSQmHn6h/D0n8KCjZh7wygUmLeT0rI8HznNu9sPIwkOREHk7vB+fhI9R14vMBMrMTOxNTK7pJbBKUjI4qr4wFeUZW7++f2FyZ1PjZCW2xBbND1xSebkzqjiBToWv0SvEqo4mc1qoK0hd8GybQ62w3jhT2i/3UwYV4A7kjE0I8/lxOn629eAP3qGeHBvxbTAid0P4cws0DX1xKZe4xcNYj6DM1n9M3s5dpnx2Dj37ri3GNO9VQgrpvz36pG7QoF5Bc8d2CsyVzJLOPTcGlJlkTtpC7vuzDCV2qqNaG4RtxgGBFJ5lWTOHON7FPP33u8ZKdQhdG577q4hrJFlyo3JMtP5NG2ZOFlPmEyhRijnaSMRHioLVekqyPFb2W8HpZO78muIKAi8bb9EV0Bgb9tuLscut9R3BxBy6UjOeZaXu1lJ1Pf/7g7vxsDg9VlzWm5JLKuh0uROdgiEPMJ1k5hpRz/1L8APC/8+BVwEHm3lQV3PcJXKyArTrb6zP8KVXOLSLQ+tuQma2fsedFGi//Tqj3spbpBTob9NRM7EcKaWGyovL0WXEmwJuUvmk+XkTvEhl6xsFX13VSoR5EwcTXKhS82R0ExnI0iCSKcSWA1UaZG5vtPTiSiILC2bXX5xG5O7k4sn6fP1EXatSqFUp7+uLBNgwk7XnZqDs4W/o4mf1d8+nzIJsKeDZ1dOkdHz3Nd+pPjwPeEDJLUsL8cK6VnFrrv6fhhmjtvbrgbiWmZNmAqYVQjQxMldPolMHs3VOnmauxCWkK0gzXwzMcELkbN8tOv28gcL+2jFegQBKiyEHCyZ1u2zS+6cAW7KZM0+yvgmfHeGji9ydo3frhRLPXeT8g4ycOHrG3+NX0AMH3uYW77/u1XPP6/MvYIsykWJ9lbCLDKXrh65S0ygizJZV7nHFKzJ3UzNc/f6jjsw0zKBLU3M9MbHSPprn/uj+SV8khlYlcpqpArpSha56/OMsJKbZ8kV3k7LvIZgpWW6JBdOyYkiKrbInWEYJNUk7ckVogW/nYWVvhsJzZ1CLLGMtLvb6fH2cKD9QPPfRAmUdGFyV4HcAXzoNpn/8Gk3o+FRNKP1vrvL6UV0QUPP9PD48TpdQaz23Z1dNu8n7RSYAwSda99vu19g6TopMq9L7gzDuMEwjMOFf0eB24Cftv7Qrk9YiZmKQ0F2yIhqlh3Hv02k5yCR3sNrts27Qyzsegs9554qllnPrJh/eL1hEe+y+QHb6OSuUwmwlE+gGnWy2BuAdfJaX/OgKt41sky35GbQP8iFSOUyczkTbXqBebcSwiGIJrnDgBatPkmiRIe7g9nkLKrsIh2srZVP5BKMRcY43LHu9+8KFOWpleCSXLTJPibshKpcehayUfB2wuTP6xPbkhqER5eO06UEuck/VHz49sBunILEc5HCZMcdBodSPzFz8Rw89xfw+t/XP+YaSGiZNX47AE8TPXd+yU28sMoveVtTgwDm5A4qSzO/NPkEQcnDQz13Vdw3Uzq5c/qhwpTnoHnfyA6/6WWwd1AB/IbBkHNwU6Eq7uQkkpokHqxM7hBEJnd/mvDiK3gjzStNv9YRnDuFko7gqvBZ0Q2dV+df5WD7weK1YishCuJVrUNwJyfNqV2ViWXG04ukJpHyNc6LqbUdd1BC7rZIlunIpXClZmqGqYBZheC3yF1OK07uvIp5YugvhKqcdnm2PXfXEDJqBgEBZ8EW4FN8tjx3OS2Hbui0ZRNFSaaFlf4jiFqe4NyqWkIURL54+xe5vbfyAl+zIKcj6IIDVamcCi0KApJDYDg0bC76VblvaxbOF6bz+7v6ePbNfN0FW8t3N5G0Jnf1yZ1f9iOv645t9wssxn55JndrYBjGq0Br/9KuY1hdd1aYSt+Zx3CmVrh086crSpem9r8fKZ+ia+w58//L5h9eX1jEtzQObJzcdSsBdAyW8va04naQ0TLohl5hcreW3EGh7y46XnHEL2di5F3NS7Eo1iCAKcuE1oaqeHqYUuMk2uqHqZxcOomBweHOdeTO6a/puQPY4WyvL8vUNVOS2T4Khz5qErflOn01Be9czOXnhchZ3tt2eE2BqtuhcEdwlOdWTpmSQkE0pZm1EjPVDLz0JZNYLpzZ1OS0ErlziAJupVmTOxcxq8vI3zpy5y6Qu/WhKifiV3g+coZf7727bEJpIauBYfkwKkgyAfaFwSHA/kaGj4UQowPKIFeSZ8hpG9O5+qImYas2uQOY3vlRNIeTwV+S6Z2g5fEumxUwvqXyz+D5lfPEc3Fu7r55qw+tiKDSUUxx3GpUq0GwYE3javnu3IUC83SJz1NzWJ67rZnceQpWilrkzjAMorlFgopF7tSi587jXJ3cAZx3CCiZ5bI0xW1cHWTUDE6Hs3hNtFtknir8/QV0nWj3wTWPRXoOootSme+uHrzLlwlNn6i/YQ0omagpyaxjP7AW5VvtuzuXnsWByAePDJDJwzMn63cIjoZGiarTSFKKoJ2Ouwr+wo6AyHLCQN/CypRWoS65EwTh35Z8/Z4gCN8A6sdVbaMiLHLnlb2I+Qw7jn+Xlb7DRHsPVdw+1rWPeNsuM1il4Lfzu8HvFvAtXyLrCVfUSdtBl2Lut9DEUJVigbm03nPnW+O5AzjYfhDVUHn6ytNlzyNnY00uMI+YBeZQmNzRUt9dt6eLSUFnpX247rYnFk4QdobLemzyrtqyTIBBVztX6skyr7wEyXk48EHov9UkYhM/r71PYXL3dH4J1dC4r+PGsk3eHt7PVHaFC1acvb+n9uTu9W9AfBZ2vhWysU1JMxNqObkD03e32SoEMMldylBRgVC4hZO7woBi/eTuS5NPEJI8PNjzlqr7ZlTQrZXHKuTOJQn83s0Cnz3QgGewMDG/QexAM1QuJRoI4SmBL3IGA4FEcLTqNqozxOyOX6H38veRWhDudK3BuzKBWPCp+iuQu2Nzx3A5XBxsP1j22FYhrHRetcmdKzlZMSnTQsZTvw7BlZwiLwfQlNXFQWtyt1WeO0/EDIapRe5SWhzVyNPmNL3TqZxGqui5M1d9AnIbPinEmJhDNFTkXKTFR74NO8homeK9HIBf9tsKVLHInVeQSLQNrXlMl13EuvaV+e5qwtA58Mz/wcGn/nJTxF9OR8tqEKphNDzK5dhl0i303Z1PzTLk7mC4y8kNOxz86HienFr7ur47tBswCLaP1/XIr2RW1vjtLHT4BVQdoqlfAnKHWX1gfTkxvXcfbOVBXc+wPHc+2Uf/6UdQMlFzalcNgsD0gffjW7lMcO4U0ys6fWHz17aZMBWATtm8iZtrou8ulS+cvGxO7m7uupnHxh9jMj655jE5E2+aLDOvqyzkYmZSJmwJuRsSFFRB4EKwsnfEQk7LcWb5DDd03lCWcJp3+pFqyDLBJHdzuSgZvcrKlmHA6e9DoB/6bwanD7oOwuTPak/OUosgSjwWO0e/s41DFW643h42ZSXPrRRkJL5uM1Cl0kVm9g04/yPYex8c/LD5PTvBLlWQ0LIVyZ3XKZBsSqCKudIfFxx0t9m76G0ElWSZr8cv82L0LJ/ru6dmxUVWA4rkrvpn5bcOC7yltwFyV1hUOWS4ERG5EG/gZqME/sgZUv6d6FLtqNHJ3Q/h0NL0jn9vQ6/ziwTfkikbyivessmdqqu8vvA6N3Te0NJkvHoIKp1EcotVQ35aBUcujpKLkKk1ubOKzJPV15ddqek1HXewGqiyVbJMb8T0DqZ91bPnrBqEdpfpnU7lNJKW505elaX2e0a4ZJiLfNu+u2sDGXUdubM7uSvcH0mBwYrS4+X+G/EvXURO27sna7/yMt7IBHIuUZwWbwRKOkK+ygLheoyGRlENlROJKxt+vXo4n5plT2Eh5/03y8TS8MKZ6t67C7Maf/90N4YuE2yr7wdcX2Buod1XqEO4DqSZdjx3f1Ly9eeGYXzdMIzW3RVf5yjKMh1Odpz4R5YGbibWXbukdm7k7eQVL32nHmF6Rac3LCJoeTyRSZJtOzd8LN2tnNxVI3frbhg+sfcTeGQPD59+GFVf/fCasszmkLvZXBQDo4TctV6WuTtjnsQvuGt3tJxdPktez3NDxw1lj6nOAFI+g1ClLgJgR+HGYKpaHcLM6xC5AvsfWJWHDt5mTtCiE9UPLLnEsreDl6IXeF/7jRWrNbqUIAe8/TxnVSL4e0DPQ2rdseSS8LMvQ6APDj8I/j6zHH1T5C6Dt0JfldcFiSYFqgBcFgMMBqvWgW4alWSZX5r8EW2Sl091V5/aWfsYdSZ3G4LiRRccBHNxBr17ubBB350vepZEsH4fUjx8kEj7TQyMfeO6l535F8dQZQ9LO46WkbvTy6dJq2lu6brlKh2dCbPIPE9C3dopUb0aBICcsw1NVGrLMlPTZdUbWx2o4olMkvINrX4+KyCat8hdJ6JgyTI13LIDUVw93/Z5RphUl8izTe6uFWTUzJpwPMtzp9c5f2XT5rVRaKs80V3pN0PLbE3vDIMdx79LvuCTK/XqNQolHbE9ubN8dy8Xehzt4nJ6gUUbBDipZZjKLjNaSB3f1ycy3C3y6Gt5NH3ttX0+qvOlxzP82XczLMYc9Lp3k5SOEatxT5tVs6TVdOXJXeD6KTK3I8u8VRCE7wmC8KogCCesr604uOsRlkm+OzaPnI0zXmtqV4AuOZkdfSed4z/Fm12hPyziiUwi6uqmJndh2YsDsalddxa5Wx+oknf6EAy9GAxjwSt7eXDvg0wlpnh8/HHA9KVI+VQLCswtz13rJ3d7Y+ZF+LJRWyt+YvEEbsnNaKhcumZ5Dmv57gadpl+jqu/u1A/A0w5DJaEcA0cBobY0M7XAkwE/Gjr3tZdLMi3cEz7A8cQVlvMJ8FmJmet8d8e+BukI3PE7ICmmrr9zLyyeqf76dZDUMvgreNHMyV0zAlXM5552BHFLrSuRtmSZmcK6xquxS/w0ep7f6LsHT53pTUYFJIvcNXG6KIjknWHk7BKjgSNcSZ0lqzU28XDkE3gSV2r67UoxufshvPFLtM1f31ld/qULJNqHSbSP4EyvoKRWio8dmzuGV/ayr83ez6xVWC0y31oiUSR3NSZ3CCJZT291cmcYuJJTpNd1y2kO83q0VT13nshE3TCVaOHnG3J24FGkoizT61w70en3jKCicUmWt7vurhGktXTZ5E43dNJ1/r6Mgtdd76q86BVvHyGveG2Ru+DsmwQXznLplk+TcwUIbJDcSdkEzuQi6UCvre0t310xKdsGUlqOz7z53/jsqS9VVxkVcD5lWjtG3eb9hCAI3H+zzELM4JUxcxU0mTH4hxez/LtvpHl9XOODt8r85UNuPn/kI+T0HN88+82qygMrKTNcIQW7Y02R+S827Mgyvw78NfBR1paZb2MDsFZ7BhcusLjjKPHO6n6UUkzvvw/R0HjQ8Qy9bSI+KymzfePkziGIdCh+5mskjzWKpFp9cgeUSTMBbui8gdt6buNHl3/EldiVIplpVqDKdOHDXC7LbN3krmNpnB5dYK5QQl0JuqFzcvEkB9sPVuyzsjr+avnudrhMclcxMXPhLCychn33g6Nk+uQOmeSqFrlLLvGYIrDT1VmUR1TC3aH9GBj8OHJmtQ6hNFRl4ucw/mNTillaCdG51ySB6canA7qhk9CyFSWLPpfQlECVQGFytyg3L9SnEtZP7r40+QTtso9PdN9Zd19Tllk7UGWjyDvbULLL7PYfQTc0LibeaGh/X9SMpI7bJHfz/e/GEERCC8caPtZfFAi6im95nHjHCPGCF9ea3mW1LG8svMGRziNb3m23HiHZVANEt9h3t9pxV31yB7W77qR8DElNlskydYcTA2FLPHeimsMdnyMZ2F1zu1hhcheQ2/EojkJaplr021mwQlXOKvJ2YuY1goyaWZNma7frTo+aUka9u4qnVnQQ6TtshqrUkUXvOPFdcq4gs3veRaxrH8H5jS2WBmffRMAg0lM596ESRsOjvJG4Ytt39/2FV4ioKa5klvjyZO1eUyspc7TkvuOmXQ56QgKPvJrnieN5/qeHUzz+usqdeyT+8iE3H75dwaUI9Hh7eP+u93Ni4QSvzr9a8fkrddxZcMoCv/0eJ7eOtE6ts1WwQ+4WDMP4gWEYlwzDuGx9tfzIrlNYJ4T2XIbxm+pP7Sykg32cCx7h09JT9Ac1fMvjaA6FdKB2zH49NLvIvDi5k8qrEKAyuQP46OhH8ct+Hj79cFHW1yxZplVg3qOsT8ts0eRO1/AvXWRA8jKbrJ4eeTF6kUQ+UVGSCavktlaReVDy4He4K4eqnP4BKD4Yubf8scHbIHqlcqiJrjKfi/IKGe6rIsm0cMDbT5ccMH13njbTA2ZN7jJRePkrEN616rOz0Fm46V9sXJqZ1nMYGPiree6ybDrtyvLcxZTafrHNojRQ5eXYGD+LXeDzfe/AbcNzldFAkFogy8SUvynZZXb6DuAQJC7EGvPd+SLmjUbVGoR10CU3Kd9QkRRej/BEJhG1PPH24eKinEXuTi6eJKfnuKX76koywfTcwdYXmbsTE+TlAKpS+7xvdt1Vnty5klYNwrrSZ0FAkzxb4rlzx6YRDL3+5C6/hMfhRxaVArlTSWW1YsedhS7XIJIgc8bpRrHTabqNlmO9LNOv2CN3+cQcggFKDVXSSt+NuJKLuGPVpce+xYu0T77K5KEH0CUn0a59eKLTtr16pQjNnERzKLYHDWD67vKGZst3pxk6fzvzPId9O/hw51H+Zvp5TiUnq25/PjWLR1RW080xqxjef5PM5UWdr7+QY6hT5I8/4eLz73QS9q2lMfcO3stQYIhvn/t2RXlmLXIHcNuoRE+o4SKBaw523sEfCYLwFUEQHhQE4SPWV8uP7DqFnC+Me8MjJDrqJymW4jHPe+gRVhhdfBnf0iWS4R0Ym1zl7VKCTfXcpfIp3JK7bPVZdRbIXbZyopRH9vDg/geZSc7wz5PPAs0jd9PZFTplP4pYWI1psSzTE5vBoWbo8fQwl5qrqsN/Y+ENHIKD/e2VPZeqs74sUxAEdrjay4vMoxMwdQz2vG/1/ZZi4Dbz30rTu9QyT3jdGMD7KqRkrn/9u8P7eTFylpyhl4SqGPDzv4J8Gu78bRDXrYSFd5m9eAuNrzbGC7+3ioEqLgHDgPQmg7wU3Tw1ppyt7RqzJnfpvMF/m/gRnbKfj3ffYWvfrAZR1wD4eyE0VH+HBpBztiFnlnE63Ozw7m04VMUfPUteDpCtMfVdj0RwD97rmNz5F02PSqJjN5riJe3vKZK7Y3PHCCpBRkK1CcFWwC+HEAXH1pO75GRNv52FjKcXZ3oeoYK8y2113HnLFz01yYO4BZ47O0mZwJoahFVZplrsuLPgEBz0uHdxxuXBuT25uyZQlpZZIHeJGrVSopojk47gFRxraoXWY9ny3dWoRNhx4ruospvpfe8DKOY2BDYwvQvNvkmsaw+6ZD/EaTg0jGjTd/f0OrqeMgAAIABJREFU8ptMZpf5XN/b+d2h+wnLXv5o7Dvk9cr9yudTs4x6est+Rm/ZK3HfTTL/4/1Ofv8BF0Odle99HaKDz+z/DFktW1GeWa3A/HqDHXL3OeAI8D5WJZkfaOVBXc/YdfYkb02lueL/cP2N1+Hx7BFmhQ4GTj+Cb/nSpsJULHQqAeaa7LlbP7UDiuWY1SZ3YFYj3Nl7J/+yfILjTqWJnrsIvc4SfXWLZZm+RbPgsyO8i7yeZ7lC2IlhGJxYPMGe8J6qZcV2ZJlgJmaWkbtTPwCHE/a8t/JO3g5oG4GJn5U/llrkUa+XPXKYYXd3zdcGuCe8n5Se45XYRfB3m5O7S8/D1Ctw+JMQrHDD5pCgffeGQlViBf+Xr1KgSpOKzBPTy/h0nQue+v06m4GVlnk2O8ax+CU+338vrhohDBYMwyCrQdrdDR/4z+bUtInIOdtRsubf7W7/ESaT58ho1T+76+GLnCER2lu3N6kUieAePInLW+aL2mr4lsZQZRepoEk84u3D+JcuksqnOL10mpu7b65507dVEAWHWWS+xV139TruLGQ8fQgYONPltStWimbaU/48muTeksmdNzKJgUDKV9syEcsvESxIYFdlmeWTO4B+zzDnZBF5m9xddeiGTlbLNjy58y+eJy6Cp8r13kLG30Pa10XbVOUFNXdshs7xnzC97z5Up3lfFe8YQRclgnONkTspm8C3dJFIT2X1UDW4JTcHvAN1fXeGYfC1mecYdLbzjvBBgpKHL+76EGdS0/zNzPMVtz+XmimGqaw5VofAJ9+icGSnVFNNBNDj7eH+XfdXlGdGshG8sveqJhJvBexcSY4ahnGrYRifNQzjc4Wv32j5kV2HEHSV2yee5NPT3byROtDw/lMRgWdC7yU0exI5G99UmIqFbiVAXEs3rbMkmU+W+e2gvizTwodHP0y7w8kfdrSTVJozNZnOrqz67aDlskz/4hiaQyHcYa6mVZJmziZnWUwvlhWXl8KaXMo26hCmsyurK2HJBbj8Iuy+F5w1PGODt5ll5sm1NwzTsSscdzm5L1w7xdXC7cFRXKLMc5HTZqhKfBZe/Zopvdz7/uo7du6FlXHIN/Z7sMJjBgt+w1L4XOZJf7O+u8T4Se6PJznGNJG8fVLTKExZpsHz6SfoUoJ8rOs2W/tlNWv/1oS95J1tyPkYgpZjNHATOjo/W3jc3s6Gji961rYk00IyuAcBA2+DKWy/KPAvXiDRNlxMrU20D+OOz3Jy9mVUQ72qxeXrEVI6ttZzZ+hmEEodvx2U1CFU8N25UlNoDhd5Z3lYguZwb4nnzhOZIOPvRq+kmChBNLdEoDi5cxRLzD3OcnLX5xkhIhrEstuBKlcbWc1cFC6d3HllLwJCTXIXnDtNTBRx1Vu0FgRW+o8QmnkDocJ0a/DE9zBEB5OHVqMvdMlJvH2k4cldcO40AgbR3sZ7NY8Ghuv67l6Lj3MicYVf630bjsJ5711tN/CutkN8efIJxtNrzzHz+RgxLV2R3DWKe3fcy87AzjJ5ZiRTuQbheoMdcvcTQRAaZyLbKEPnpRcJ5Zf5qnYfY3OVR9LVkMwaRFIGZwfuRXeYK/vNIHedBX/DQpNCVVJqqjK5K8gy5SqyTAtuyc2/cu9mXJH53vSPN308uqEzW1pgDma/jCg3TCrswr90kUTbLrp95k3IXKp8hfnEohk4W81vB+YJW3MoNT13ADucHWjoRW8hZ/7FnJjsvb/2gQ7ebv67Tpr5eNycPL7XpjzQJcrcEdzNcyunMHzdZh2CocMdvwVijVNM5z5zu6Xztl7HwoXCz3OXu7xD0OsskLtNTu7CkTe4PRUmZ2h8f7F1IR8uCRzeC8zo4/xm3ztw2pjawSq5q3Af2BTknOYkUM6tMOI/zIHg7fxg8v/lvI1aBHdyEklNmZO7BpAI7gG4Pn13uoZv6RKJjlWpXqIQqvLa9Et0uDsY8jdXWrsZhLa4yNyZnseh52xO7qwi83JPkjs5bZK/Civ7muRB3BJyN0kyVJuk6oZGPL9MUF6VZSayKnnNKJNlgpmYCTCub5eYX21YiZil5E4URLyytza5mz1FRHHhVuqHdK30H0HKp/AvrL02Kqlles4/xezoveTWqTVi3fsJLJ6vWZ20HqGZk+gOmVhnY+dqgFsDw3V9d1+beZ6Q5OGDnbeu+f6/2/khnKLMH1/8zhrbyvmUuRDeDHInCiIP7X+oTJ5ZrePueoMdcncH8LogCGcLNQhvbFchbACGwcDJH3BZ7OM5/Uamlw1SDUwXZlbMD0CwM8T88NswBJFk2+ZvBroKXXfNqkNI5pNlNQgAauF79SZ3AEc1kY8nMjwz+RwXIhc2dTyL+Th5Q1tjzjUPxAVaC2SZho5vaYxExzBe2Ytf8Vec3J1YOMFQYKiu7lt1+m3JMgEzVCUTg7GnYehtpvSyFvw9ENoBk2vJ3aPZOQ7lVAa99k+wbw8dYCq7wgVP4cJ106+Z/rta6Bg1b8Ia9N1dTM/R7wzjqdhzZ97UJTbxq12KJhjVxgh6b+RG3xDfmXupZYXOkgCuzifwEOQjNqd2sJqu6WoVuXOZNw5KdhlREPnVkX9Hp3OAr134ExYz1Y3+AL6IGclttwbBQsq7A83hwhfdeP/htQpPdAqHliumZIJJ7pZEkVPJSW7puqWu1GgrYZG7rSoyd1lJmTY9d+Y+5X+HrtQ0aW9/2ffBJHeOBis9GoWga3iiU6RCtUlqPB9BRyeorMoyrQqvSrLMPrf5dzMmZBD06mXO22g9MgXFz3o7hV/xE89XuVbrGsH5M0QkZ8X7o/VY6b0BA4Hw9NrFtIGTP0AwdK7cUG7riXbvQ9TyRW+vHQRnTxLrbMxvZ+Fm/66avrtL6XmeXTnFJ7vvLAsI61QC/P7QBzgWv8R35letIVZSZq2E7kZQKs88Nmcu0m6Tu1W8DxgF3sOq3267CqFBBOdOE1i8wFfz76OvzYEBXJq336UxvWxu2xcWuXD7b3D8fX9c1FtvBt2FyV2zEjOryTIRHaiyxxa5k7Mxficj0uZq4+unv16UQWwE1pSnb71MR3a2ZHLnjs0g5dPEO8wY7B5PTxm5i2ajXIlfqTm1s5B3+WsGqkBJHUJmCc49Bloe9tv8iA7eDgvnipUEl9MLnCbL+7TGTvZvL0g4nyMNH/gvsPud9XeSPWYQSIO+u7H0fFUvYDM8d+fPnUISdNp2HeYT3XcwnllsuLDVLl6MnkN0X2Ef964G/thAqyd3+cLkTin4RV0OL18Y/VMAvnL+D2v67/yRMxiCSCKwp7EXFR0kA7uvy8mddcNlnRcAcp4wj4Q70OGakmQCBOVOVCNPUm2eH7sWPIWOu4yNyZ0uuck5w1VlmRlP5QTprfDcueKziLpKqs7krrQGAdYSuvVVCABuyUen6OOcIm8XmV9lZDTzvsG1LtDLr/hJ5Cork7yRCaRckrgoVMwkWA/VFSDeMUK4xHcnZRP0nXmU+V13kanQSRfrMhfT7EozHbkU/qWLRHrtVyCUwie5avru/nbmx8iCgwd77qr4+Ic6b+X2wG7+05VHmC2EnJxPzdKlBAna+BnZRak8czm9TCKf2CZ3AKX1B9tVCBvHwMnvk1V8fCv/Nt66z5ReNSLNnF7RkRzQGRBQnX4ifbVTDO2iUy7IMpswuVMNjbSaxitVIHeYvrtqaZmlkDMxZFeAh/Y/xGJ6kccuPbbhY/rbmR/TJnm5dX1ymcMFWvPJXfEmrtDp1uM1EzNLV8DfWDQ7w2r57SzknfXJXYfsxy3KXEnNwvnHYeBWCFZevS7D4G2AUZzePbZkDuXfK5X72WqhUwlw0DvAs5HTq313tnbcZ8oyba5Gq4bGpfQ8u6uRuyZ47tTpEyQNF8PDu3lP+2ECDjffnq8QPNMEfHnyCVBD9GiNReBbpeeuFtXxFGWZ2dUwoA5XH7+++49YyE7xd2P/Ad2ofP7yRc+S8u2s6zmqhERwz7VN7gwDd6xyx1ot+JdMH25q3efyEZ+PYQ36fJurtGk2QoWJUiRXvaezmXAnJjAQSK+vMKiCjKe/TJYpqimU7Ep1cufw4FBb558FU5IJkKwzubMKzEtlmRbWl5hbGFT6OKsouAqJoNu4OrAmd6515zef7KsqywzOvokBJIx81QC19Vjpu5HA/FkcOVNK3Hf6UaR8honDlcPqc54waX8PQZtl5sG5UwiG3lC/3XpYvrv1xeRL+QQ/WDjGA5230C5XHkIIgsAfDX8UzdD58/HvrYapuDcvySyFJc/M6Tm+evKrAIRc2+RuG02AKzZLx+Wfcbz3PWRwMtor0hcWGJtrYHK3YtAbEhHF5kp3fA4XblFpSmKmFVFfTXagOn32JneZGDlXkNHwKEe7j/Lc5HPE6oSKVMKbiUlejJ7lV3vfhmd9MpLshHzzZZm+pTF0h0wqbK7cdnu7SavpNYbeEwsn6HR30mNDV553+pHqyDIFQWDQ1cHE8jnIJWH/A/YPOFCI0p/4OYZh8NjS69yczdHjLfez1cPbw/s5kbjCUo046DJ07jVTS1fsrRdNZpbJG1rVyZ3sEHBKG5/c6YbBUPINLrkOIDpkXKLMA5238OTyycbelw2ktRwnEldwJW8lpzXG0lrvuTNvOpXs2qTX0cARPrLjdzgV/Rk/nPxqxX19kTPEG/TbWUgE9+DMLKwhldcS2qZe4/Zv//cE5hubNvsWx8xuu5KKmOXMMidFlftjUcQWJfduFCGr626LEjNdyUmynh4Mmwl2lYrM3QWZZqaWLLPFkztvoQah3uQuWpjclcoyLbgryDIB+ryjjMsSqdg1vPjxS4AiuVs3uQsogerkbu40UU87mqHbkmWC6bsTDY3Q7ElENcvAm//M0sAtRa9uJUS7C2XmNuTUoZk30UWJWNfGztVQ4ruLr71+/8PsT8gZKr/W+7aa+w+62vkfBt/Lsyun+eHia1xMz7OnCX679bDkmVfipj9we3K3jaZg4NQPMUQHj/nMWPq+sMhIt4OLs5ptT8PMik5vuPmeDEEQ6FKaU2QeKayKVpRlUpjc2SR3VoH3fbvuQzM0nrj8RMPH85Xpp/E7XHyy+87yBx2ulqRl+hfHSIR3YhQkdhaBs6SZaTXNuZVz3NBxgy2PjWpDlgkw6GxnIrsM7aOml80uBMGc3s2f4vXl01xIz3F/PAGeTvvPUcDbw/sxMHgh0oCHrqNwYbHpuxsrxJ+PeKqTT69L2LDn7tzEHIPCHFrP6lT14923oxoa3194ZWNPWgXRQriD0wgUJ3F20WrPnaoE0AUHSra8NPmurge4q+sBnpn9Fi8v/mjNY458Ak9ykkSDSZkWVkNVGgvZ2SpYkqfOSy/a38nQ8S9dXCPJBHh1zozovi+RxLtSvwx4K2GRu61KzLRbg2ChWGRecv20JlrVPXfulnvuPJEJsp52NKX2DXwsv4SAiE827QKlCZmVAlUAjnT/Ci7D4K9W/rlqd+o2Wo+iLHP95E7xkdEy5NcHmhgGodlTTHWb12U7skyAaPd+M3V76jg9555EyUS5cvijdfdR0hFc8XKf/3qEZt8o+O3Kvet2cZN/Z8F3tyrNTGs5/mHuJ9wTPlAx9Gw9PtPzVg55B/n3l75L3tCaEqZSCZY8E7bJ3TaaAEcuSc+5J5jf9VZOx0O0+QQ8ToGRHpF4BhZi9cldNm+wGDPoC7fm19WlBJqSlmndrDaH3Jly0U5PJ7f13MYL0y+wklmxfSwXUrM8uXySB3vuwl9JBiG7mt9zZxiFm7hVCWhPIZRktpACdWrpFJqh2ZJkQokss84iwA4lwIRooDXYVwOYvjtD5+tXHsMvKnwgkawfxlIB+z39dClBnluxJwsBzH42X5dt351F7oZrXDS8TmHDk7uZi6Zkdsfu1Z/jsLubm/27+M7cz5p6U7VSWAxxCh7SDZK7YhVCi2SZCCJ5Z7jqBO3Dg7/Nbv8Rvjn+nxlPnCp+31cg9vEGw1QsrJK7azNUxSod7xj/qa3VcQBPdBqHmiHRvlYafmzuGLu8fQyqavF5rxX45TAiIpHc1kzu3MlJWzUIFjKeXiQ1iVRy3bICVjJVpJ2a5DGrEFpIjLyRibqSTDBlmX45jEMwSV2pLLNSoApAu3c3/yaW47g+z4/nvtecA95Gw6iUlgklXXfrQlVciXmcqSWm2810c7uTO8MhE+05SNvUq+x445+Idu0l2lM7uN7y3dWTZjpyKfyLY0R7Gq9AKIVfcrPf27/Gk/79hVeIqCk+1/t2W8/hEET+ePhjqAWZ/2iTwlTWQxREfv3gr3PfrvvoqrE4fL1gm9y1GL1nn0TKZ5g89ADTywZ9beaPfKTbPIFfmK1/oZmN6BhQ3LfZ6JSbU2Rej9zlnfU9d6KaxaHl1hSYv3fne9ENvaHp3Vemn8Etynym562VN5CcTZ/cueKzSLnkmpu4gBLALbmLk7s3Ft/AJ/vYFbRXY5F3BhAMvS4pHsznyQsC8207Gj/w8C5m/Z08mZnlI56deAwDPI157sCcAr89tI8XI2fJNZLo1rHPJHc2bpbHUnP0KiG8jup+Lp9r454798IJ5oUOwp1rfTuf6L6DiewSL0U3l95aikjh8+IWPMVJnF1Yk75WyTKhUGSeKZ/cAThEiV/f/b8SVjr5/87/EStZ05vlL5CyRpMyi6/p6iSnhPBeo74739JFNMmFOzGPb/mSzX2sMJXV88Jcco7JxCQ39d6OqnivOXInCg4CSvuWeO5ELYsrPdfw5A7W1iG4UlPogkTWVVl1oDnchddrTQUOho4nMlVXkgmmLNPy2wF45NqBKhbuE7q4S3Xzw8mvMJ26tv5mfllgyTKd69Ka/bJJ7taHqlhEay5kLjrYndwBrPTfiCc6jSsxz5UbP1ax4qMUyfAOVNljSjNrIDh/xvTbbTBMpRRHA8OcKPjuNEPnb2d+zGHfDm7y77T9HHu9vfzWwLtpk301F243i3Z3O+/f9X5E4fqnPtf/O7yKEHSNgVM/JNJzkFjbMNMrOv0FaWV/m+kNshOqMr1i3qi2anLXrQRZyMU2HXtt3axWO3nZmdzJGZNkWpM7gA53B3f23slPpn/Ccrq+F2cis8Sji6/z8e47CFchmq2oQlhNxFu9iRMEgR5PD3PJOVRd5dTSKQ51HLJ9crHkqfW67gbj5lRzwlO7WqEiBIFvde1Ax+BTRuHn5W1clgmmNDOl53gtPm5/p869kI1Cor6UZCw9V9VvZ8Hr2tjkLppWOZB/k1n/obKL6LvbbiAkedbENm8WVjm6R/RufHLXQnKXd4ZRstWn5V4pyOdH/5ScnuWrF/43sloaX+QMeSVIdqOmeEEgEdx7TYaqyJkYruQSU/vfjyGI5vTOBvyLhTCVkpv+Y/PHEBC4ufsW4u278F9j5A6g3dnLTNoegd0MXMmCnNJGDYKF1a67Vd+dOzVNxtOzxtdYCq1wXWqV786ZXMKhZurWIADEcksElRJyV5jWuWQRRw1ffdYzwB9Gs7gdPh6++B/J69ULpLfRGmS0DC6Hq+waXpzcrfPdBedOoSpelt3m43YndwDLfUcASIZ2sDR4a52tAUEk1rWXwFwdcjdzEl1wEO3a2CJcKY4GRoq+u2dW3mQiu8Rne+9uuNrlv+t/J0/d/MWGUqO3UR3b5K6F6Lj8Eq7EPJMHH2AhZpDXVqdvoiiwq1vkoo1QlellHUGA7lBrepA6lQA5Qy1O3jaKSF1Zpg8pn0bQqxPaSuQOzOmdgMDjlx+vexxfnX4GSXDw2d67q28kNb8Kwb80hi5KJMNrp2fd3m5mU7NciFwgraY53GFPkglmzx1Qvw6h4Nm5sgF5bVbP8x3i3JNKM3DxRXDI4KxftFoJN/nNieTxRAOBup2FC0yd1UbN0LmUXmC3pw65cwokNvCrPXXhIgEhhWew/PejiBIf7LyVZ1beZLFGUW0jKH5eBM+GPXetntzVCzbpcQ/xayNfZDo1xiNTf40/coZ4cF/dFeZaMBMzz7dUPrcRWNO15YEjRLsP0HHZHtH3L46RbBvCKJAOwzA4NneM3aHdBJ1BEm3DeJfHa54XrwZuCN3FVGqMuXRrw7HdhRqEDU3uSrruXMnpqpJMKCV3rSkyt8JUkqH66olofqlYgwAgOUQUSaw5tQPTT9ibnOFTO3+XmfQl/qVKqNE2WoeMmimTZILpuYMK5G72FNGufaS0yv14tZBsG2J29z1cuOPzYHNBONq9H+/KlZoqqdDMSeKdo+hy44nG62H57n4eG+Ovp59jwNnGO9s2NhGUhBZe0H7JsE3uWoiBkz8g7e9hccdRpgsl5P0l0sqRbgdXFnVyau0pw/SKTndQQHa0htwVi8w36buLqilEQax68lKdJulz1JjeVSN3YVeYt/S/hZdmXmIxXd0HspJZ4fsLx/hw19Hi+6oIqRCo0sSSXt/iGMnwEIZDXvP9Hm8P8Vycl2ZeQhZl9rbZT6eyfg41i8zVLN2Ll5ARzCLzBvHo4nFW9AwPZYHUIng6NnxzHpDcDLu7OB5v4IYw0AeKv67vbiq7TM5QGbEzucsaDU+iExMn0A2BnaOVL0wf67od1dD53sLLDT1vNVgBRH5p47LMlnnuMOsQKgWqrMeB0O3sDd7Khdjr+KLnNizJtJAMjiKpybKo+6sNi9wl2naxMHQHvpXLuKN1jtHQ8S1dJN6+GqYylZhiPjXPLd1m/UWifRiHlsMdvbYi7m9qfwcCIseWnmrp67itAvMGPHc5Vzu6KK+Z3LlSk2S81SsltMJ1qVXkzlNMyqxNUlU9R1KNFpMyLXgVB94qfjsLGU8fDi3LEdcIb+36IM/NfZez0WObO/BtNISMmilLyoTKnjspE8MbmSDavZ9UvrayqSIEkTNv/zes9B+xvUu0ez8CBoGFyuoHRz6Nf/FCUySZsOq7+878zziRuMKv9d6N45dA9nitY/s30CL4588SnD/D5MEPgOhgarkSuRPRdLi8UHuF2kzKbN2vqqtYZL5JcpdP4ZE8VcfxqmKSu1rSTLlQeZCvMDl6z9B7cAgOHr30aNX9n77yNIZh1DfzSi7AMAu/mwHDwL84tkaSacFKzHx17lX2t+1HsRn3Das/B4v0VsTiORyGxoDkZyLbWACCYRh8Y/YFdru7ua3zJvObnsbDVEpxo2+IE4kr9smVIJjSzMXakzurkL4eufM5QdMh28Cv1jAMOqNvcEXeheQOVNxmp7uT2wIjfHf+Z2hNmCpF1BR+hwuP5NiwLLOVk7u8sw05H0fQ6ku/+tzDzKUvo2tpEsGNR2sDxf2vNWmmb+kSGW8HqivA4s47AFOdUQvu2CxSPrXmvPDa/GuIgsiRLvOGzYo2v9akmQG5jb2Bmzm29HRL0xndiQk0h5NcFa9cRQhioQ7BJNeClsOZXqjacQcl5K5FiZmelQnyTj95d21pfCxvTsNLPXdgViDUm9xZJe+u1DS/MvCbdLl28PeX/neS6ubTrrdhD9Umd06HE8WhrJncWd63aM8BUmoKAaHivs1EvHMUQxAJVAlVCcydQTS0TfXbrcfRwDBL+QRBycMHO23IR7fRcmyTuxZh8OQPUGUPs6PvBGBqWafNJ+BWVonPcLf546/Vd6dqBnPR1iVlAnQ1qcg8oqZq6sltkbsqkzuAoDPIW/vfysuzLzOfKjf6x3NxXpx+kfs7bqLf1Vb7YK343yaFqrgS88i5RMUOmm6vSUYMDG7obCzN0vLc1ZRlzp8GQWC3r5+XYxcbkg2+Fh/ndGqaB3vuQhi83fzmBpIyS3GjfwcRNcWVTANEs3MvxGchHam6yUUbSZmwsSLz+bkkh4zzJOtIZj/efQdT2RV+Etk88VjJJwlJXlwOGp/cbYHnLucybz7lXP2U2j7PMBoaF2V5w0mZFhJBMzLcF7m2EjN9yxeLn++sr5N4xwgd47XJnRWmUhqydDl2mQHfQFG+ngoNoDmUay5UBeCW9nexnJtlPPFmy17DTMocaFgtYNYhmJM7V3oWAaOmtFOTCsqRVskyo5Mk7YSp5MxpeEBZS+7esaeLt47WPvemC+TVnZxEcbj41eH/hYQa5Vvj/2XTnvlt2ENGq0zuwAxVWUPuZk+hixLxjlFS+RRuyd3yMA9NdpNo20Wwiu8uNHsSQxCJdW/eb2fhaMA8v32y+87yTuFtXBVsk7sWwJlYoHP8J8zsfU+x72Zq2VgztQMIeUXa/QJjs9Xv7OajBpoOfW2tkWSC6bkDmNtk111UTVX124FZYg4g19CCy5kYhiAWieB6vHvo3ciiXHF698zEM6i6yhf631H/YK2Tc5PIna8YprK77LE2VxuyKCMgcKi9sdUyVfFiCGLtQJWF0xDexe/seD9pLcefXPqO7Qv912dfxO9w84GOm6FrP7SNmP9uAod9QwAcTzTQ3WX57har39CPpefpVoL46qx8ep3mZyXRQKhK5vRxJEGne1dt8v3O8EHaZB/fbkKwSlRNEZI8uCUa9txlNfO9tdZzZ3ZwVUvMLEWfx7y4n3M6SQYb6FmsAE32kfYOXFOTO1HN4olOr1m8WRi6k+DCWZRk9Z+Pf9Hy4Zo3/YZhMJWYot+36g0zRAfJtqFrktzdEL4LRXTxSgulme7kRHEi1QhKJ3dWKEvNyZ3DkmW2YHJnGHhWJmwmZZqLXkF5LZF7x74u7hyunVJsFbRb73vAO8p9/Z/jxMqPeXnpR7V23UaTUE2WCaY0szQtMzh7injHbnRJIa2mG5NkbgLR7n0EFs5V9PGGZk4S6xhFk+17/+rhLaE9/MHQA7brD7bRemyTuxag/9S/ADB54H4AdN1gZkWnvwJBG6kTqmJ59Vo5uVNEibDk3fTkLqqm8Eo1yJ2tyV2cvNNXNfHMr/i5e+Bujs0dYyax6rdI5VP8ePLHHOk6Yqs4c5XcNScx0780hi44SIaHyh4TBZEB/wCj4dGi6do2CkS3qudOy8HiBejaz4inm3+94308u3Kaf7JRuD3Qo8QyAAAgAElEQVSbjfDU8kk+2nXUXG0THfDeP4ddNYJobGDE3YXP4eJEI+QuvMsMcqlRZj6WmmV3HUkmlE7u7L982/TrpHHSMbin5nayKPGhzlt5fuX0phdDVtQkIdmLSxLI6aDpDZBRFRQHDSeSNYK805x+10rMtNDlGkQ2BE76OtAdGy/FtWCGqlw75M63PI5g6CTaVitMVqWZ1Yl+MUyl4MON5qIk8gn6/WuDPxLtwya5u8amL06HmxtCd3F8+TnUViQzGkahwNy+385CxtOHMzOPoOeLZCdTpcAcWuu5kzNR5FyiocldUGm8bkaTfeSVYJHMAryj52OM+A/zj5f/HxYz15ZP9XpEzcmd4i967sR8Zo23LaWmcDeRUNVCrGsfDjWDd3l8zfetY4r2bq7fbj0kwcFnet9ad+F1G1uHbXLXZDjyafrO/oiFnXeS9ZskY95KyqxA0EZ6HCwlDFaSlQmeRe56Q639VXUpAebzm7tZbYosMxtb03FXCe/c8U4Uh8Kj46vTu+cnnyejZXjP0HvsHWyTZZn+xQukwjvQpcqShC/c8AV+49BvbOi58y5/0YtYhqUx0PPQaU7bPtPzVo4GhvmL8e8zmamddPituZcwMPhU91s2dFzVIAoiN/gGGwtVcUjQvrtqqIpm6FxMz9etQQDwFSZ3dusQsnmDQ+kTTHn2mwSzDj7WdTsaOv84/3Nbz18N0XxhcldYx2hEmpnVWivJBDMtE6ibmAngEBwMqzpnnM1ZmU4ERvHEL9ny+20FrKlavGRylwoNkgr203m5SiWCYeBbGlsbphI3b8oHfGsnVfH2YeRcElei9b1yjeKWjneS0uKcjjYnSKgUUi6KlE80lJRpIePpRTB0nOl53MkpDAQyNSo4WpmW6bUZpgIQyy/hEGQ8jtrXuWpIe/pxp1bJnSg4eGjXHyAIIl+/+BdoxrWVunq9oZrnDsAn+4qyzOC85W0ziVSqkEmwFYh2m/cD68vMg/NnEHW1qX67bVyb2CZ3TUbP+aeRckkmD32w+L1KYSoWRgq+u2rTu+llnXafgEtp3Qo9mNLMTQeqqElbssxaEb1yJlbRb1cKn+LjnsF7eG3+NSbjk2TVLM9MPMPB9oMM+G3eJDRTlmkYZiJehTAVCwElUPNnUwt5p7+6527+NCBAoa9GFET+bOSTiILIH459s2rwR0bP8+35l7gnfKC+P3EDOOzbwbnUDKlGugQ798HKeMWKiunsCllDZcRTfyrrLfxq7Xrupi7MsEuYRey1V1Ex6GrnzuAo/zj/8qaCVVbUgueukKHQSKhKVmutJBPMtEzAVmKmlIuxL5PkoqM5AUWJ0F5EQ8UTb33Pmh34li6RV3xkfWtDPxZ23klo5mRF2bQrPoecS645L0wmzGTIPt9a+aAl97wWpZl7Arfgk0K8svRk05+7mJTZQMedBav2wJWaxpWaIuvuxKjh9ymSuxYEqnhW7JO7aG6RoNy+4al7xtu3ZnIHEHZ28/Ghf8148hTHWvB72oYJ3dCLPXeV4Ff8JPIJdEMnNFPwthWuzak6i9/NRNbXScbbTmBdvVBw9k0MQSySv21cv9gmd82GobM0cAuxrtXEuOkCueurQO6GOkUkEcZmK98kzqwY9FbYr9noVoKbIndZPU9az9ckMJrkMv1jdQJVrBCRWrh38F7ckptHLj3CC9MvkFJTvHfne+0fcBNlma7EPEomtmZVv5nIOwNI1WSZ86chNAglcs8+Z5j/eecDHItf4uGZH1fc7dHF14moKR7quasVh8yNviF0DN4s3MzaQuc+s9ts6XzZQ2NpKymzfjl2o547x4XXAOjfbT/s5uNddzCbi/BCpHbCZzWYn5ccIdlTJHfX2uROVQLogoRiY3Lni55lby5PxMgSz9eXcdZDIrjn/2fvveMkOegz7291dXVOMz15Znd2ZjZpV9JqJaFIEsLASQYbMMHG2IfPxvadX/t1OmPg7PvYYB++1/aL03HYxiYZTPABEkEEIxAgUNqg3ZV2d2Z3Z3Zy6OmcKt0f1dWTOlT3dPfMQH8/Hz7G06nY6e6pXz3P73kKz7s7rJm+lUKYyqYT8uXhuxB0jfDUVlXLXwxTWftemEnO0OXu2lIXk+oYRhdsu3K4EwWRW8Mv43z0+6SVxnQ8mqx13NUz3K0VmVfruIPm7ty543OoooOchTCqmLxSlyXTJOsZMmyomyy8Jzvvw2lzM50ar/u521QmV7hYWa7uye/wo+kaGSVDcP48ifBYMXchI7du5w4g3nPDFuUuNHduwzG1+eGlPdw1mJnjr+bZV/7Bxp8V1Dd3CfVNEgX2d9mYWNh6ZqfpOnNRjYGO5qp2AN1SgBU5iVKnpSNWpcAcAEFAcXi3bcsE8Ege7tt3H88uP8sj1x7hcMdhRoIjVR9XpIG2zM7pZwCIDlgvJ68FxVVauRM0BZYvQc+xLbe9pus2XtZxnPdf/wqX0/MbbjPqD77LQXdfMeWq0dzsN4p8ayoz7zpknDyX2LubSFtLygRw2MEuWt+5G1o+w7KtE2eHdWvYSzuO0SX5+bfF+qxqZoG5mZYJtYWqZFug3CHYkJ0dlgJVfNHnOZw3LJQz6Yltv3TaP4Jmk/DFrCdm2vNx+q9+tuF7a4Km4l2dJBne+v2S6DpI1humu0Rqpq8QppLsPFD82XRieoslE0CzO0mF9u26OgST28L3o+oyZ1a/3dDnXeu4q8+WCWvKXaV9OwBddKAJdkSl/N+fenGmlsn5rPWDxjcVmNdKxjuIXUlvSbEVBIFu1xBL2et1P3ebymQL5wuVdu4AkullAkuXiBZ223RdN3buaigw3y6x3qO4Uss4k0uAEQplHFPbkvmjQHu4awGlkjLXM9Zn4+qitiVQYSWhk1eaG6Zi0uMIoKPXFKO/nqiV4Q6QnT7suTJ/XHXdCFSpYss0eem+l+Kxe8goGeu7diYNtGWGp54k4+8jHaz9BMUK5WyZ/uUJUHMl0y0FQeAPRl+P3+7mneOfRNbWJodnEtd4Pj3Lz/Td07RAjqDdwwFXd22JmZIHgsMl9+4mMgv0OIIELPxxFAQBn1OwtHO3FJW5TT3HZOeJmqLYJZvISf8BrmWXLD9mPVHZHO6MtEyozZaZVZpbYG6Sd3Za2rnzR59nBONq8Fx6+wOKbpNI+UdrUu6Gn/8gx5/8fTzJa9t+/fW4YzOIar5kzQmCwPLwXXTMnMK2yU7sX54g1bG/GKaSVbIsZ5Y3JGWuJxkeKVYn7Db2eQ7T49rX8EJzd/I6eWcHqlRj0BSg2d3knR24UtO40vPFmoBKqHZ3U5S7p7Lz/E7QbakPMJZf3lJgXgtmUftmayZAt2uIxVwNbok2NVEc7srYMn2F97G2ZOy2xQq7bXktj6qrLbNlwtrenWnNDCxebO/b/QjRHu6ajKYV1LdKw12vSF6B6ZWNfxiKds4WDXdQf5F5rHCyWs12UEm5E+U0Nk2xPNy57W5ed+h13DtwL4c7KqccbqFBtkxRztAxe4bl/S+ouafJKrLTj6jksCkbwyWCc+eM/9Jduq8mLPn4w5HX83x6lv81s7aH8S/z3yUgunmw69amHK/JCf8wZxOTtfUvdR8xbJnaxklnIrPAmJUU1AJ+t8ClOZVomaAik5XnLhMSUuRGT1o/xgKdko8Vufz+aCWiBfWgw+4tDne12jKbrtxhDHdW0jIDq+ewB44QlLqYyTRGfTISM7dadEuia/RNPQyAK9XYk1t/iTCV9SwfuBtRzRcVfON4dPwrExsGwtnULDp62b3gZHgUZ3oVqULX404hCAK3he9nInGWSG6hIc9pz8fpnv06idBW54FVsp4BgitnsOlKVVsmGHt3zdi5+3cyPCbKPBcpXRxtklXT5LTM9pS7wv9Od3prMmaPa4jV3EJzkk3bkFUrK3eBwnmUsnwZHaE4YKUtnh81klTnCKrdWbRmmv12sb72vt2PAu3hrsksxnUUlZI1CCZjfaXLzGdXjZPiSoNho+hxBAFYkusc7lRryp0x3JU+Ia5UYF6OO/vv5M1H31y7AtUgW2bHzGlsmsLK/ju29TyVMHcQN4c2hObPQ2AQXMGyj31Z53F+svt2/nHmm5xJTK6rP7ij6WWjJ3z7iSgppi0oP0V6jhoD9+qanVPTNa5mFhmzkJRp8oa7JSJJnT/6TJbrK+UHPP+UsW+njZ2wfowFwpKPmJJGLtElVA1T6Q5KnqItsyblrgU7d2DUIVQLVAkun8IffY6lgfsZ8Iwy2wBbJkAqeAh3egbRwgAdWn66eLLrLqFobAffyhVU0UEmWHp4iPUeQ3b66Z5cs2Y6k0tIucSG3svphDF0llfujEFwN1szAZ5pkHp38Nk/x5GLMH7z79b9HFlPf1HdNRWtSqh2T8PTMgVV5nLhs/i92e9VvG+8UIMQ2pZyVwiSKXERo9s1ZDhwcnNbbmuzfarZMs2qo1zMsHGbIXKZglrcSuVOt4nEuw8TLCh3obnzJMMjqGU6hNv8cNEe7ppMpaRMky6/QMBdarjTCLjB52rNzh3UX2Ru2syqDncVbJn1DHd1YxPBJm17uAtPPYni8BLrq//qczUUpzHcbbBmairBhQuWCsd/b/g19DlCvGviX/nnuW+ho/Om3rubdbhF1srMa9m7KwQRrbNmzuaiZDS5puHu5mE773ydC02H9342w7NTWycnWdU5lDjLpHMExROyfowFwgULzqpSu3oXldeUu2KgSo1pmbvFljl88e/JO0LMjL6BAfcoC9kpFG37qZnJoPFesGLN7Jt8CFV0o9mkYkhHo/CtXDUCT8p0b+o2keXhOwlffwpBNf53m2EqiU1hKh67h5Cz9HvN7NDbjaEqAGFnPyO+G3lq5eu1qfElCKycZnDik1w/+HMkOran3Anohf9uQbkT3Y2vQkgscE2y4xbsnFs+RyxX/m+oWWC+HeVOkQIokg9Xaqty1+0yVOHFHdy7++bcp/jn8T/asddvJuaQ5hZLrwd4JS8CAun0crECAdaUu1bu3IFhzfStXMWejRNYuki0z3poWJu9TXu4azIzFqyVgiAw1isyMb9RAZhb1ehvgSUToFPyYhdsdReZW925q2TLNIu6WzLcgaHebceWqamErz/FytCt6LbmnWnL5nCXXfvd+CJXscuZYr9dJXx2F+85+Eamsit8fP673NdxvCn1B5s56OnFKzo5k6hh787TCd6eDaEqV8ykTI/14Q7gQLfIH/yUi+6Ajb98OMc3z20cOK5dT3GSyyz21q7agWHLBIjItQc0rAWqrFPuahAAs0prbJmyK4wkJ8r2zXniE3TPfIPpg29Bs3sY8Iyh6SoL2Rp+52WwmpgpqHl6p7/M4uDLyXq2xsRvi0LNScl9u3UsD9+FPZ8iNPcsYOzb6YKN1OYwFf9QWZeB4vSR8ffu2uEO4Pbwy1nITjGTrj+RUdAUbnj6D8i5e5i48de3dTxmqApAxpJy1/idu8XIJTRB4CfDt6DpGj+YK19qXyww38ZwhyBs6boz6SkMd0vZndm703SNby18lrOrj5Gp46LXbqeaLdMm2PCLLlYFvbhvB0YNArTWlglGmbmgawxd+CI2VS4GvLT54ac93DWZ2YhG2F+9p26018Z8VC+GQOi6zuyq1pJ9OzC+lLqk+rvu4koah2BHslUuga443BWKuq2kZTYEu2tbyl1g6TKObKyplkxYG3bXK3ehufPGf7Gg3AG8IDDGW/tfCNC0+oPNiIKNG737alPuwNi7m3kaHv5N+Oq7GD/3CQBGL3wZTn0czv8fuPLolr28UnT6bLzzdS5u3C/y4W/l+eR382gF1SF/8VkkQUU/XPu+HUBYMobuFbn2EKKoksIrOpFs9rWdu1qVuxbt3AFl6xCGL/4jmujg+sG3AjDoMYagRlgzs54BFMlXNTGza/7bSPkY88OvIeMdbKgt05WeRconSyZlrmd14ASK5CqmZvpWjDAVrWD/VjWVudRcWUumSTI8agQl7VJOdL4YUbBvK1hl3+UP448+z8WT764rSGU92UKISt4RQrNw4mzs3DVWuZuJXwPgZO9tHAwd5PHZx8sGq8RkY7gLbKMKAQxrZqmLGC7Ri1/q3LHh7mryPDF5BR2dyVTl/cO9SDVbJkAHAiuiSHSdm6c43LXQlgkQ7zmCjsDg+YcLO4DNcxi12V20h7smUy0p02SszzhTu7JoXL6PpXXSudbs25n0OAIs1rlzF1XShOyeqrtvitOHqOa3hINAi22ZsO3hLnz9SXTBRmSoucEkpnK3fucuNH+ejL/PULos8lv7H+RzN/82Lwg2p/6gFDf793MpNUemjPJTkhteA2P3QccBcPiY0PN0azrB2TNw+RE4+6/wgw/A+c9Zejq3Q+A3HnBy/012vnJa5m+/kiMn6/TMnyKLg+xgfX/wOu2GSh2pI1TF/LwAdQ13LalCAPLODoCS1kxHZoH+yc8ze+D1yAUluMs1hF2QmE03oHxcEEgGDldV7vomHyLv7CTSey9ZzyCuEopGvfijFwCqKnea3UFk6Da6pn4Amop/eWKDJXMps4SsySVrENaTCI/iTswjVqiL2Um89gDHgnfydOTf0eqozXGmZhk9/9cs9d/H0mCNCcclMIe7rMUqBWPnrrHK3VRqnoCq4g2NcO/AvSxnl7m8WjoIKJZfxmnz4BK3d5Kf9QyUVO7AUO8Wd2i4Ox15FElwIGDjavLCjhxDMzGHO6foLHufLjnPktODsu48ZicCVcA430p17C9eoDJ3ANv88NMe7pqIqunMrWqWhruRHhsCa2XmxTCVFil3YBaZ17lzp6QJWrgqpRSWeUupd1I2jmazo0ot8qVv05bZNfkE0b5jTf/CLO7cmUXmukZw4ULNfTWiYKvZ2rhdbvENo6JxvpYEw9A+eMEvwr2/AS/9fa50DDIaOgSv/QC88SPwxo/C4G1w8csgW7sKL9oEfvZFDn7mhQ6euaLyns9muTV/lmv+Y2j2+oJl1pS72oe7VTlFqDAc1mXLbNlwZygMpZS7/Zc+gqCrTB35heLPREGkzz3CbKYx6lMyeAhv7HLZ7joxn6Br9hss7HsA3WYn4x3CmV3G1oCKEwD/6nPogm1DV105lofvwpGJ0n3teziyMZIlwlTKJWWamEOkb+Va3cfcbG4L309CjnA5frrmxx459ccIus7FW/+gIenCpi0za6EGAZqzczeZj3JU1tCcXk50n8Bj95QNVolvs8DcJOMdxC4nsZdw2nQ7B3dEudN0lTOrj3FD6A4GPKNcS55v+TE0m6yaxSk6sQmlz8sETaE7k2RF2vg3JaNkEBAqKn7NIt5jpGm3KxB+tGgPd01kMaajaJWTMk3cDoHBsFAMVZldNXf1mh+mYtLtCNS9cxdT0gQtXI2sNtzJLn/TKgW2sA3lzpVYwBudarolEwxVQLU7i7ZM7+oUUi6xYWF7t3Kzr1BmnqjRmllA0zUmMgscXD+UihIcfx3IKbj0iOXnEgSBV5yQ+PUHnNhii4zZ5kgO31LXcQF4RScOwV6Xchdbp9zVG6jSkp27MrZMMZ9g6MonWBh6FZnC79hk0DPKbPrKtkM3wNi7c+SjOLKLJW/vmfkqopZnbvg1wFoZtivdmJNbf/Q50sGBor2yEiv7bkez2Rl5xrARbw5TsQt2eqtcXFkb7nbv3t2x0F24RC9PrXy9+p3X0T3zdXpmv8GV479WtXDcKnlXF4rkI+WvbJs1aXRapqqpXNMyjGGsI0iixB19d3Bm6QxJeWulRUxeISjVn5RpYobHlEvMTCpR0kp9nbX1ciVxjoQc4ZbOlzLiO85k8rm61N1G4UzNlr0oVC9ZJVu24w7AtzxBl5JnlY22XLPAvNxQ2ExivYXhrl1e/iNFe7hrIlaSMtcz1ityZUE19u0iGm4HhLytG+56pAAJNUu6FgtdgZiSJmRFuSuoXPbc1hNiKRdv3b4dGMqdXJ9yF556AqAlwx1sLDIPzRtXRGN7YLgLSV6GXV2craXMfB3z+RgZLc/o5qTM8BgMnITnvwRybQP6yRE7f3byLADaofr27cAYFsN1dt2tKilChfAhmyDgECGjWDsR0XSdfKvSMl2GyrDZljl05ZPY5SSTR39py2P63aMklSgJpXo/XjWqJWb2TX6BtG8/8U4jFMccGhq1d+ePXiDZWdmSaaI6PKwOnMATmzHUvnXD3XRymj5vH2KZxE2TvKeTvDuEP7J7hzvJ5uCWjhfz7Op3yFnsjBPlFEdO/THJ4GGmDv/Hxh2MYOOJ+z/FtRt+2dLdG91zt5BeIC/AqN1f/Nk9A/eg6ipPrnxty/3j+ZVt79uBodxBua67fQAsZRtbCVKN05FHkWxOjgXv5IDvGDktw1zmWkuPwcSTuMoLv3gf4flvNfR5M0qmYuJlaP48YVUjqyvk151HpeV0yy2ZJksjL+TS3W8nsu+2HXn9NjtDe7hrIuZw1x+y9s882msjlYOFmF5Myqy5v20bmEXmS3VYM6NKiqCFL6/Kyl2idft2YCh3an3KXdfUE6SCQ2QC/dXv3ABkpx97wZYZnDtH1ttN1t9ai2W9nPANcyZZY5l5gYl0ISmzVA3C8ddBPgHjW0+iKmHPxjl5+VPEeo6S7hiu+ZjWE5b8dQ1365U7ALdovcQ8X7ifU2z+d4MiBdAEO47sWtedoObZf+nDrPTeQ6Jj6wWGRoaqFBMzo1uHO0dmgc7F7zO//9VFtT9T7ADb/omtlFvFlZ6rGqaynqUDRsVIOjhUVPt0XS8mZVohER7b1codwG3hl5PTMpyPPm7p/qPn/xpXeo7nbvsj9CqhW7WSDhy0HMyi2t2Iag7q6KYshWm3HXZ3F3/W7+tnJDjC95e+tOE7T9f1gnK3/eFuTbnb+j7fiToEtWDJPBa8E6foZsRnfC/slDXTv3oeAZ1A5FxDnzerZitaK0Nz5/AWzqOS6/p800oad6vWTTah2R3MHnuwqYnebXYf7eGuicxENLosJGWaHOw1rupOzKvMruot3beDtSLzWhMzdV0vnKxWL8esbsts4XAnuerbucunCc6db5lqB6C4/EaaqK4Tmj+/pyKNT/j3syInmcnVruRMmDUI7p6tN3Ydgr6b4bmHavo9jj35YaRckkv3/uq2LcCdkpdIjWmZsqaQVLMbPi9uu3VbpjkEtiItE0FAdnZssGX2T34eZ3aRySNbVTuA/sJwN5Pe/oCiOENk3T0lEzP7ph5GQGe+YMkEyLu6UW2Ohih3vqiR9lctTGU9K/vvQBdsJLrWHhPPx0nKyapJmSbJ8Aje1amSoVO7hVH/TYQc3Zasmb7oc+y7/GGmR99IrKu54VPVUAurA41S72bik7g0jS7fxp2/ewbuYTF7nSvJteEipcRRdXlbHXcmsrMDVXSXfJ+Hnf0I2Fq6d3clcZakEuVk50sB6HD0EpDCOxaq4o0ZVR3eeP2VHaWoaMvUVIILz+EMGTb1+Lpwuoyc2THlrs2PJu3hronMWgxTMenvFHA74OyUSiytt3TfDoydO6DmxMy0lkPRNYIWCjorDnettmWKzvp27ubPYNNVVva/oPHHVAbZGUDKJvDEZnBkY3vCkmlilpmfrbUSAWO4C0u+ooVxCze+DnJxGLe2/xOcP0//pa9z/caf2NBBVi9hyc+KUluyYayQ1rdeuXOJ1gNVcuZw16ILsRuKzHWN4Yv/QDx0jEjvPSXv77UHCEndzDVguANIBQ7ji29NH+ybfIhYx42k1+9bCTYjJr4BO3f+1dqHO9kd5MJ9v8PkiTcUfzadLISpVEnKNEmGRxF0De9qfXuqrcAm2Lit834uxp6qbL3TNY4+/YcojiDjN/3Olpsfmfko/zb5N8070E2ohb9Rjdq7m41f41BeRvF1b/j5yZ6TuEQPjy99sfizeKEGIejY/s4dgkDGWzoZ1m6TCDv7WjrcnYo8isPm4obgHYXDEzjgO7Zjyp0vbg53ja0VqaTcGd2zaexdhpV8s3LX6hqENj/atIe7JqFqOvOr1moQTGyCwGiPjaevGGdvraxBAOg1h7sabZlR2SxkrkG527xzp6lIuaQRqNIqpEKgSq12wemnkZ1+Yj1HmnNcJZBdxs5dcN64EryXkq8Oenpx2xycqWPv7kpmsbQl06T7KPQch+cewlZFvRNUmcPf/QBZbzfXTr655mMpRafkIyIna7KcrhaGwfUDq6sW5a5wv1YEqoAx3JnKXffsN/AmrjJ59Bcrqp4DntEGJmYeNq7Ar7PSeWPjBKIXNqh2JlnPIO5a0lnL4I9eIOvuq9lNsDRyL5nQ2iA3kzBOwAf91pS7VMjYmXLH52p63Vbzkr7X47EH+MjEe8hrpT97g1c+RWjlNJdOvAPFGdpw26nIo3xl9sM8tvg5LsdPteKQUQsXVBpRh6DrOlOpeY7m8+S8Gwc2p+jktvDLORv5djHYJJZfBrZZYL6OrGegrP242zXEUq41w52qq5xdfYzjobtxrFO1RnzHWcnNEZdLd2Q2E1Ox8ySuNsyCCwXlrsxwV+yeLfxtTuTXHB07uXPX5keT9nDXJBZqSMpcz2ifiFL4Lmq1LdMruvDYHDXbMqOFq6BWdu40uwNVdGxR7uz5FIKutX7nTtdBla0/RlNh7hQr+26HKuEIjUR2+rHnknTMPkvO09GyXb9GYBdEbvLtqzkxU9d1JjILlYc7MNS7bJT+S5XVu6FzX8AbneLyPW9HkxoTSR2WfCi6SrwGm1fUHO7WK3d2yFgc7nKttGViJGY6chHQdYaf/3vS3iEWh15V8TEDnjEWstdRtO1bC5Ohw4hqDs865bdv6gvogo2FfQ9uuX+jisz90edIdGy/9HcmOUPYFa4YxLAec1Bwppa3/drNxC918JbRdzCfucbnpv7XltsFNc/BZ/+cSM9dzA//xIbblrIz/OvVv2DYewMhRzcPXf/7ssXflcipGf7ywq/xg6UvW7p/UblrQJF5JBshreU5mpfJbVLuAHcc/vkAACAASURBVO7ufgBZzxetq7GicteY4S7jHcJVIlAFCsNddqauf9NaGY+fJqXEi5ZMkwM+47NzrcXWTEHN405OkXP1IGr5sn2A9VBxuJs/R8bfhyto2DLN4U7X9WJaZps2raI93DWJWpMyTcZ6jftLInT5W2vLBGPvrtbhLlbDcAeGerd5uGt5gTkYaZlQW6jK8iXIp1pqyQTDlimg0zn9DLHe462ri2gQN/v2czE9S1azPkgv5GOk1Fz14a7nGHQfZf+ZzyKUGdRdiQUOnPokS8N3NnRXsrMQ5FBLqIqpdHes37kT14a2amSLgSqWX3Jb5F1hHLkVQstPEVo5zdThX6i6nD/gHkHTVRay9aWkrmdLYqau0zf1MJGee8i7t55UZ72DOHIRbNuw3tmUDN7EFRKhG+p+DpPp5LTlfTswUjcVhxdXcmnbr91sjgZv52V9b+LxpYc5HdmYTOjIrSDlY8zve3DD95Wi5fnIxHsQBZGfH3s3/2HwbVxPX+LM6rdrfv2vzn6MqdTznIuW7pXbzJpyt/3hzrTb3pDLk/NsHdgGPQfZ5zlcDFYxbZkBqXPbrw2GcufIRxFLfPf0uIbIa9niazaTU5FHcdo8HA1u/Js45DmIXZBabs30JKew6QqLQz8GNG7vTtM1cmqu9M6drhGcN7pnHaIDp+gkUdjFzmt5VF1t2zLbtJT2cNckZiMaAtBfo/o2WghV6QvZsNl2YrgLsFTjzp053IWsDndO7xZbpsMc7lpahVD4kq4lSn/mKbCJRAbrj9CvB6VgV7XL6T3ZV3PCP4yia1xIWrcKjZthKtWK1wUBjr8OZ3qltHqn6xz63v8Gwcb4XaVDQOolXBzurIeqmMrd+osh7hqUO9OW2aqdO9nZiV1OMnLh78g7O5gdeX3Vxwx4xoDGhKqk/GPogq0YqhJceQZ3apr54VeXvL/ZdedOlVY1rOCLXULQtW0Pdzk1x1J6ybIl0yTr7dr1yp3JA4NvY9h7lH+99hdEcvPFnzsKAUqys2PD/b9w/YNMpy/z0yO/S4ezl9vD99PvHuFL0x9CqeHiz1z6Ko8ufAYBG9dTW3cyS6GK5s7d9m2Z04lpbMCwzY1md5S8z909DzKXucpU6nmi+WW89iB2W+n71opZ+1FKvTMTM5u9d6dqCs+ufocbO+5B2vS/y25zsM97uOWhKuYwtzj0ysL/3xh7eE7NoaOXVO68q1NI+WSxe9Yv+Ys7d+nCxby2LbNNK2kPd01iJqLRFRBwSrUNaAG3wL6wjdHenfnVGMpdjTt3e125qyUxc+YZ6DmO6mjtF7XsXNtF3Avl5Zu5ySwzryFUZS0p00LlQ99NxLqPlFTvuia/T3j6aa7e+jMl7VPbwVTuIrL1UBXz87J5565WW2Yrd+4Awgvf5frBt6JZsBd1uwaRBEdD6hA0u4u0bxhfzDiB75t8CFV0sTj4YyXv34gic38hKXO7tszZ5Cw6uuUwFZPcHhruRJudt469C9D5yMR7UTXjjWyG8JjvH4Czkcd4bPFzvLj3ddzYYQTy2ASRHx/6RZZzs3x/6UuWXlPTNT49+X7copf7+99ETF4mIVdP4220crdfFxG85QNSTnbeh8Pm4vGlLxJvUA2CSaZCp6PZdbfY5OHuUuIZ0mqCWzpfUvL2A77jXE9daog92yre+Dg6ArHOE+RcPXUPd4Km4kitKZ/ZQvhaKeUuNLdxF97v8BdtmZnChYS2ctemlbSHuyYxE9HqDkR5x2tdvOVFjbm6VyvdUoDFfLymgIg1W6Y1T7ni8G0d7nI7MdwVvqStJmbGZyExB4Otj/M2g2byrgDpQuDCXiIs+djnDNdUZn4lvUCn3UtHuaTM9QgCkyffhCu1RO/4o8Ufi/k0hx7/e5KdI8wc//E6jrwyYcn4vdSq3LltEq51fV+uGnruWlqFwNrJuSq6mT74FkuPsQkife4DzGYak5iZDB7GG7uEoObpvf4llgbuL9ttZnaAuWtQiTfjX72ALAWKz1UvM0njxLvW4S7r694TtkyTsLOfNx34bSZTz/HlmX8GKIbwyIX3z0pujk9c+//Y7z3Cq4c2Kug3BO9gzH8zj8x+lKyFfbgnlx/havIcrx56O0cCRjnzdLq6elcc7hpQhTCdmOZIXt0SprIel+jh1s77OBV5lMXs9cYkZRao1HUXkMI4bK6md92djnwLl+jlaKB0QfaI7ziqLjOdbmwlQSW88XEy3iE0u5tUYLRuW+bAc1/mrn/9JTpmTgNGUiZQUrkLzZ8j6+sh5zcqe3wOX3G4M5W79s5dm1bSHu6agKLqzEdrS8pcj9cp4LDvzE5VryOArKtFdcEKMSWNV3QiWSzJVJxepE22zJ1R7moc7maeNv7vYOk/ZM3EVO5ifXtv387khH+YMwnrZebjmYXqlsx1RIZuJd51kOEzn0YoqAcHnvkXHOkIF+/9VfQmBOCE7B5sCERq3LnbnCxbS1rmTlQhAMyMvmGLxa4SA54xZtNX6iqv30wyeBhPcpLu2a/jyEdLpmQWj9fVhSo6t8TEX4w9zd9d/F2eXvlG1dfzRy8YlsxtftamE9O47W46XNb/3cBQ7qRcomoC7G7ils6XcFf3A3xj/pNcjD29QblTNJmPTLwXgJ8bezf2TUXmgiDw6qG3k1SiPDr/6Yqvk5RjfOH6Bxn13cQLul7BoOcgANOp6ifxxUAVeXvKXVKOEc1FOZZJkfVVHtju6n6QvJZlKTvdkI47k7wrjGpzlKxDsAk2upyDTbVlKprM2dXvcFPo3rJWUzNU5WoL9+688QmWA6N8ZOK9XPUPGspdHd9BwYXnsOkqx//9z/BEp9eUu83Dna4b+3brHDV+h7+4c5dW2rbMNq2nPdw1gcWYjlpHUuZuYK3I3Lo1M6qkLe/bQTlbZgLV7kQzrZKtoFZb5swzENoP3sZa+6yQd3egSB4jpXOPcrNvP0tygrl8tOp9dV3nSmaRUSuWTBNBYPKWN+JOLNAz8W18y1cYuvBFZo++kkSTaitEwUZI8tYWqKKktnxe3PYalLsWVyHEO2/m2pFf5OoNv1rT4wY8o6SUWENCHZLBwwjojJ17P3lHiJW+F5a/syCQ9QwU7WrzmUk+eOmdfODS73ElcY5/ufI+zke/X/7hmoIvdpFEx/bDVGaSMwz5hhBqHBKLiZl7SL0DeO2+/0yva5iPX/kfpLJzaIKI4gjwxel/ZCr1PG8+8NuEnaWTfod9RznR8WK+Of/pivH5D01/kKyW5qcO/AY2wYbb7qPLOWBNuSuWmG9vuJspKFHHM8mKyh3Afu8RBtzGDmqjkjKBYqdjud3SnkJiZrO4GH+arJoqa8kEI1G1yznQslAVQVPwJq7whK+DU5Fv8n2XA7uSwlmw+NeCb+UKsZ4jaDY7N331j5HTxntyswLniV7HkY1t2IU3d+40XVsb7tq2zDYtpD3cNYF6kzJ3A8Ui8xoSM6NKikA9w926q2ktLzCH2pS7XAKWL+6IagegSS4e/+kPMX/o/h15/UZwwm/s3Z21UImwKMdJqllr+3brWNl/B8nOEYZPf4rD3/07ZKefq7e/ta7jtUqn3Vebcqekt5Syu0Vj586KytXqKgRddDB+4r8iu2o7MR1wG+XfjbBmmomZ3sRVFvY9gL5J+dlMxjtEJj3FZyb/iv957pcKFr5f4r+f+CSDnoN8ePyPuJJ4tuRjPYlriGqORGh7+3aarjGTnKk5TAUMWyaAa4/s3Zk4RBc/P/ZusmqK9+eeIucIcS72Ax5d+Awv7PkJTnS+uOLjHxz6BRQtz1dnP1by9onEWZ5YfoT7et9Av/tA8eeDnoOWhjvN3phAFdNmeDQvVx3uBEHg7u4HAAhKjbNlQqHrrsxuabdriEhurqaQmlo4HXkUj+jncKDymsIB3zGuJi80RMGvhjt1HZsm87zD+H6YlYz/W+venZhP44nPsrLvds792LtwplfoPv1JYOvOXWjeGFw3K3c6Oik51Q5UabMj7L3pYw8wYyZlhvbeP2+xyFy2rtzFlUxtyp3Th6BriPLaH1gpG2+tJRPWKXcWhrvZ06BrOzbcAaiSe89aMgEOe/px2SRLZeYTaeNK68EahzsEgWsn34QnPkdg+TLjd/0nFGfp3axGEZZ821buXHYBVQfZQi1Vq6sQ6mXAUxjuGpCYmfHuQy2cVFWyZALIWp6PeuDN/gSPLz7MPT2v5l03fYSX9b8JnxTk7Yf/hJCzh3+4/N9KHps/aqT7lUrKnIxP8qmLnyqesFViKb2ErMk11SCY7JWuu1L0e0b4yf3/mWeI8RcdAT5x5c8Y9BzkNft+uepju11D3N39II8vfXGLpVDRZD597f10Ovp4xcDPbrhtn/cwkdx8sTC8HLrNjmpzbHu4m02PE7Z7CWpacRCvxO1dL+fWzvuqDkK1UqnTsds1hIbGSm6uoa8JxmfsXPR73NRx7xaL7WYO+I6RkCNE8vMV79cIzP26ccH4m74gaIWf1zbc+VauApAMjxHvOcLzL/p19Ljx7+wSN7qLQnPnyHk6yfr71h7vMP7mJPNJMkoGAaFsP16bNs1g700fe4CZiEZ3HUmZu4GuQkBErcpdrbZMYIM10xju/OUe0hyKyp0FW+bs0+AKQedoc4/phxi7IHLcO2SpzNxMyhytYefOZHn4TmI9R1jefweLo5WVgkbQKdWm3K2W2bkDa9bMVqdl1ovH7qfD0dOQxExsIsngIdLeIWLh0jUkuq5zauWb/Omzb+ND+iS3Z7O84+hf8frh/wefFCrezyeF+JXD78Nhc/K/L/3+hvh+MMJUVJuDdGDts67pGl+b/Bp/8fRf8NjMY3zpavVUR7MHrdYwFYCcN4yOgDO594Y7gLu7H+SlsoNPulVUXeXnx/7blqj8crxi8K3YBYkvTn9ow88fXfgMC9lJXjf8azg2qSdDnkPAml2yEqrdg6hYT7ctxXR6glG7cQJfTbkDcIle3jr2LrpcA9t63c1kPWan49ZhtaeJdQgXY0+RVdMVLZkmB3yGotWKMnNvzPj9T8mLAKyocWQpgDdR23eQf8W4f6LLsNMujr2Yqf3Ghd2Dz39t7Y66TnD+vJGSue7Cq99hnMsk5ARpJY3L7sImtE+327SO9rutCWwnKXOnkWx2OiVfbcOdnCZot5BoWKD8cLdLlTtVgbkzMHAS2l/Q2+KEf5jn0rPkqliFJjILdNi9xR65mhBsnH7wTzj38ne2ROk0lDtraZmKrpJQtyrdpsXSSqhKVjXsTbt9uAPod48ym7nakOd67vb3cvbevy37O302+l0+cuW9uEUf7wy9nr9ZWOKAVvrz2uns5ZePvA9Fy/OBi7+3IUbfF32OZPBw0foZzUX529N/yxcmvsCJ7hPc0XcHj808xmyyco/eTHIGURDp8/ZVvF8pdFEi7w7hSu2tnTsTQRB4V1zhTs3Hz46+g26XdfUyIHXy0r43cGb120wmnweMpM2vzn6MmzteyPHQXVseY4aqXE9dqvr8mt29rbTMnJphKXudQ7qELtjIexpTSl4Pa113W9W7ZnbdnYo8itce4JC/et9rv/sATpu7JaEq3vgES94BlvKGWhnNL5IKHKw5MdO3MkHO04nsXrsoNNtrKPnHn/kEXVe/B4A7Poszs0q0f2M9UXG4yydIy+m2JbNNy9mRM1VBEP6nIAjPC4JwVhCE/yMIQqj6o/YGiqqzEKs/KXM30CMFWLI43Km6RkLNWq5BAJALNjl7bt1wl0u0fufOZjf+U025W3oO5MyOWjJ/WDjhG0bRVZ4rYyUymUgvMOruqft1dJu9ZRbWsOQjreXJqNW7nGKFK+xbdu5qUe4UY7CrNaRjJxj0jLKYmUJuQM9VMnSUZIVS8eupS9iw8ZvH/oaxTqNDrZxlDYyTzl889B6i8jIfvPQuI4Jf1/FHnyv2251d/Q5/+oM/5Vr8Gm85+hbedvxtvPbga3GKTj57+bMV94imE9P0efuwW0wR3kzO27VnlTuAzmyUP7TfUuyzq4X7+t6Azx7ioekPous6n538a2zYeO3+/1Ly/j4pSIejx1LkvmL3bMuWOZe5io7OEVkl7+5oSgqvVda67rZeaPDY/fjsoYbXIeS1HOejj3NTxwsRLby3bYLIsO+GloSqeOPjnAsa/yb97gOs5pdIBEZqtmX6l6+QCI9t+FlWzeIUnSR7jnDDt/4S/9Ll4r5drO/GjY+X1g13SrodptKm5ezUBPI14EZd128GLgG/v0PH0XAWikmZe3i4cwQsp2UmlAw6+habWSXWlDvDyiZoCvZ8qvXKHRjWzGrK3fUnQJSg76bWHNMPMTcXysz/PXIeWSs9yei6zkRmseYwlZ3C7LqzYs2MFexgpdIywVqReU7dG6odGHUIGhrzGevl9fWykpulw9mL3SatFZmnKqsWI/7j/PzYf2M2Pc6Hxv8QW3IKRz7KSvAQn7r2//NP4/+dsDvM773g97hr4C4EQcDn8PHgyINcWr3E2eWzZZ97JjlT176dSc63d4rMNyNoClI+VlN1xnpcoodXDPwsE4mzfHry/TwXe4JXDf48IUf5/bYhzyGLiZnubdkyzdc4lk6RtWDJbCaVuu4Aul2DLOXWbvNHniW09OS2XvP52JPktAy3dFS3ZJoc8B1nNn3VUodh3Wgq3sQE591G4vfx0D2ousysz7CumtUc1bApOTyxaZJdG1cwskoWl+ji3MvfiewOcePX30v3le+Qd4dIBzd+zj2SB5tgM3bu5Ey7465Ny9mRCUTX9a/qum6exnwfqH0pYZeylpS5+6+ql6PbEWBRtqbcRYsF5vXv3ElZw9K2M8Ods/Jwpyow9X0YfMGajbNN3XQ5/Jz0H+Cf5r7Fy575Y/74yr/xVPwKmr6WJLIkx0momZo67naSzoIKF1GqD3erhSCOLTt3hWHNynCXVVuXlLldzMTMuQaVmVdiKTtLl9PYaZKdnaiiq6JyZ3I8dBdvHvkdLsdP8fFrf8YFh8Tvph7h8aWHeVnfm/it236LHs9GFfmFgy+k39vP5y5/DlndajGO5+LE83GG/PX/act6uwxbZhNSBu35GC/+3B10LP6g4c9tPr+AXuxIrIe7ux+kyznA40sPM+Ae40W9r614/yHvIZay02TVyoObtk3lbiY9gUf0sy8VIVel467Z5FzdaDappC0TDGvmomnL1HVu/MHvcOTUe7b1mqcj38JnD3EwcIvlx4z4jqGjMZV6fluvXQl3egZRzXFJEvDZQwz7DJX/esE2a1W980WuIehaSeXOZXchu0M8+2PvRpSzdM6eMVIyN7kobIINn+Qjno+TVtJ4JesXv9u0aQS7QV76BeDL5W4UBOHtgiA8JQjCU0tLu3//YCaiIQjQ37Eb/mnro9cRJCKnyior64kVhzvrV6aUTbbMHSkwN7G7Ktsy505DPgEHKvRqtamJf7jh7fzV4Z/n7uAhHlp+mrdd+ACvOPWn/Pnkw1xITTNeSMrca8qdlcTMqKncSZvTMo3/azVQpVUF5tulyzWAZHMy04hQlSqs5NaGOwShkCRobd/oBV2v4Cf2/QpPZC/ypsF+UrrCrxx+H6/e90slbZWiTeR1h17HcnaZb17/5pbbZ5LGyXY9YSomOV83opIrOhwaiTM9jyMfJbByuuHPDeAoqCTyNoY7u03iNft+GY/o540H/l9EofIVDXPvbqZKOqti92xr524mNc6g5yCu1LKlMJWmYhPJuvvKXsToce0jIUfIqin80Qt4E1ctK1il0HS1YMm8t+rvYz3D3hsQEJoaquIpDG9X9CQDnjE6Hcbfj1mHcW5iebhbNu6XDJdQ7gohbKnOYS7c9zvogo3IYOm9Q5/DR1JOGrbM9s5dmxbTtFMEQRC+DpTaJH+XruufL9znXYACfLzc8+i6/kHggwC3335784tStonbIXDjPhGHfQ8rd1IAHZ0VOUGfs/I6pDnc1WLLVAsntkXlLlcY7pwtTsuE6rbMa98Bpx/6b27dMf2Q47DZua/zOPd1Hiet5vjm6gW+vHyaj81/h3+e+za+QhLeXhnuOguhL1ZsmeZw17Ed5U7ZO7ZMmyDS7x5pSB1CJVJKnIyaJLwujTDrGSyraJTipX0/RffVTzOrRLj7rr/HJwUr3v9o51Fu7rqZRyYf4c7+Owk61+5vJmVuy5ZZLDJfRmnwd6O9oDK70o2PyYe14W47yh3ATR33cix0l6VBwkzMnE5dZsxf3kKv2t2ISn32QFVTmMtc5SXhVyGqebLe6jUIzSbrHcSVLh3uY4aqLGanuWnyIQCkfLTu11rJzZPXsgx7y+++lsJt99HnHm5qqIovPoEMzMgLvKjzXjochtq+iIwquvHGrX0H+VcmyLsCWwZ305ZpEtl3O9/76X8qe1HaL/lJ5BNtW2abHaFpw52u6y+vdLsgCP8R+HHgfr0V7ZYt4lW3SLzqlsq9L7udnkLX3UI+VnW4M22Zm5WISug2EUVyF69I76xy5yyv3OXTMPM0jL3MCF5p03A8opMHu07yYNdJonKKr0We5csFNaGupMwdwBzuLCl3sql0lw5UyVncudsrtkyAAfcIz0a/i67rTQuBWckaJ7dF5Q6jyDwQKb8TtwVd51cXr7Hcfx8Xqgx2Jj958Cf5kx/8CZ+f+Dw/d+znij+fSc7Q4ezYVpCCuc/lSi2RCo/U/TylsBfeq+4aht9akBo03AGWFaKgI4xf6qy6d6eJnrqHu4XsFIouMyoau4Q7bcsE4yJGeP6xkrcVEzMz1+m9/kUARDWHTckUC91rYaGwO9vnHq75sQd8xzkVeRRN15pSC+CNj3PR14uiKwx5DuISvThtHlblZVKBUTwW6xB8K1cM1W7Td1VWzW64gANsSNPcjN/hZ251DkVX2oEqbVrOTqVlvgr4r8BrdF1v4oZtm3rodRhfYEsW9u5MJaKWnTsAxeFDyu8WW2YZ5e76D0CT25bMFhGSvLyh9y4+dOxX+NCxX9kTaZAALpuEV3RaVu6cgh33puLfonJnwZaZ3UOBKmCEqqSUODF5pWmvsVwoag47+4s/y3qHcOSjiBY7CN3JSRy5VaJlevRK0e3p5r799/Hk/JNcja1VPswkZra1bweGLRNoSmKmOdw1S7mTcka1RL2BKvWyz3OImSrDnaHc1WfLNHv0DmpGZ9+O2zIxEjOd2UWEEmm9Xc4BBAQSkSdwZRaI9Bg1Eubvp1bms8Zw1+veX/NjD/iOk1VTLGSn6nrtanjj45wNGGaxQc8YgiDQ4exhNbdIyj+Gz0IdgqDKeFentuzbAWSUTE1F5H6Hn3ghdbxty2zTanZqMexvAD/wNUEQTguC8IEdOo42JeguKnfVh7uYksGGgF+0/qUHoDi9W3fudpst89p3wN8H4YOtPaY2ew6rXXdRJU1Q8m4ZXGtJy8wqe2fnDozhDmhMmXkZVnKGcrd+uDNj4sslCW4mtHIKgFiX9eEO4JXDryTgCPCZS59B0zXyap6F9MK2LJkAeXcIzWZvStedWBzuKnf11cvazl1rh7sh7yHmM1Pk1fJW+807dwl5lX+8/AeW+uBm0hNINifDecPtsRuGu6zXUKtL/S4lm4MOZy/R2BlU0c3syOsBcNRpzVzITBGUunCJtQeEjPiMepGmVCLoOt74BBdcHiSbs6hYdjh6WM0vkgqM4UrPIcqVw3a8q1PYNIVk19bhbrMtsxq+dc6TtnLXptXsVFrmQV3X9+m6fkvhP7+yE8fRpjQddi92QbTUdRdT0gTs7pptForDuy4tM44iedDFHbCzlrNlppZh8QIceFHL+tLa7F067T4iVU4cwFDuOkpcxS323P2QVSGAYcsEmG1iYuZybpaAFMYprlnNssUOMGuhKsHlUyiSj1Sgtos5TruTnxj7CaYSUzw5/ySzqVl09G0rdwg2ct5wU+oQzJ07u5zEbrHTtBYcuQiyFCgWwbeKIc8hdLSK7zXN7sGmyUWl64nlr3Iu+j0+OvEnqFrlD+BMepwB9wjezCKazU7ebc2+20wyHuN9VjZUxTnIQn6RxcGXk/UYg6CUr0+5W8hM0luHJROgyzmI1x5sSqiKMzOPXUkxLmoMuEewFay8IUdPocjcGNY8icrfQf4V4wJUYlOYiqZr5NRcTcpdwLHmRGrv3LVpNXs30rFN0xAEgR7JWtddVE7VbMkEw5ZZ3LnLxXfGkgnllbvJ7wE6DN/b8kNqs/cIS35ryp2cLhk+5KzRlrmXdu7cdh8djt6mhqosZ2c3qHZQ/aR3M8GVU8Q6b4E69oFu77udA4EDfGHiC0xEjRPE7Sp30Lwic/s6q2oz1DspF2nIvl2tDBUSMyvt3amFCwCmencq8k289iDX05f4yuxHyj5O13Vm0hNGUmZmjpw3XNd7pdGYFzHKhQftUwWm7ALz+x5Edhg7YlKuduVO0zUWslN17duBcV4x4jvWlFAVb3wcHbiqrTLgWbs40+HoIalEWfXtA6hqzfQtX0FxeMn6N2YB5tU8OnpNw53PsU65a9sy27SYnf9marMrMYrMLSh3aqa+4W6TLVN27YAlE9Z67jZn+lz7DnQdMmyZbdpUoVPyWt6521xgDsZwJwA5pXq21F5T7gAGPaNNt2WuD1MBwxKoim5c6erKnSgn8cUuEQtb7+5aj02w8frDryeej/OlK1/CJboIu8J1Pdd6ct4uXM1Q7tapzM3Yu3PkItuqQaiXkKMHrz3AdKr8SbxaUFFEJcNSdpqZ9Dgv7/9p7ux6Fd+Y+wQTidIhPJH8PBk1yaDnIM703K6wZALk3L1oglj2IsbhxCxpm42r4WNFm2w9iZmx/BJ5LUuvq/Z9O5MDvuMsZadJytUvHNeCLzbOrF0krWUZ9KxZKs3EzDmHC02wF+sSyuFfmSARHikZpgLUZMv0O9bOadq2zDatpj3ctSlJt9XhTi59slqNjbbMBLJzB5U7XTeCU0xWJyE2ZVgy27SxQFjys6qkUfTK0ltUd1t6lAAAIABJREFUSRMqUWgrCAIuu/US87023A14xljMTpPbRr9YOfJajpi8Qpdr43BndN0NWVLugpEzCOhEu26t+zgOBA5wZ9+d5LU8g77BhgQCZX3dOFIrYKFztBZEJYlqM0JBmqPcrbZ83w6Mz9Gg51BF5U4pKOeikuZU5FEAbul8Ca/d/18IOwf42JU/JaNsvVAzUxgYhzwHcaXnd81wp9vs5Nx9JZU7m5LmxmXDBrmYn0cuhKXVE6iyFqZSn3IHxnAHcC3VWGumNz7OOa9xMWFovXLnNIa7iBwh7R+u2HUnaAreyDWSJcJUsgV3T02BKtK64a6t3LVpMe3hrk1Jeh1BFi1cXYsqpW1m1ZAdPuxyGjTVUO52andBKnxZr7dmXnsMBBH237Uzx9RmzxGWfOjoxaqDUqi6RkzJlL0Y4hat2TL3Uom5yajvJnQ0riSfbfhzr5RIyjTJeActBaoEl0+hIxDrPLGtY3nN2Gtw292MBBtTXZDzdmHTVRyZ+rvJSmGXk2S9Q2g2CVeq8cOdI7dKfgeGOzBO7ucz11C0remRQLECQFQznIo8yqjvJkKObpyim58d/X3i+RU+Pfl+Njc0zWQmsGGjz7UfZ2ahWFWxG8h6B0q+z7tn/53RgkNmMTuNbpNQJF9dyt1Cxki57KsjKdNkn/cwNkHkWqKx1kxvfIJzvi4EbPS71z57pnJn7N0drDjceRJXENX8ln07MJIyobbhzrRlCgg1Pa5Nm0bQHu7alKTbESCl5ool5eUwA1VqRXEYA6FdThs7dzul3InOwgEVQlU0DSa/CwO3wE4dU5s9h5Wuu4SSQUcvezHEbbcYqLKHSsxNRvw3YhckLsVONfy5S3XcmWS9g5YCVYIrp0gFD6E6tmcPDzgDvPuud/PAyAPbeh4TsyS70dZMu5xEdgTIevobr9zpOlJ+dUd27sBIzFR1hblCJ9tmTFvmfPoq85lrnOx8afG2Yd9RXjn4c5yKfJOnV76x4XHTqXF63PvxyQlsulKsqtgNZDyDuEv8HvsmHyIkdSEJDpayxvCXd4Tq2rlbyEzis4fw2uu/EOuwORnyHGqscqfreOPjPO9w0uMawrHOOhmUuhAQjMRM/xie1FTJygiAwKoxcJZLygRwi9bPdRyiA5fowmV3NaXXr02bSrTfcW1K8oKAcfXqY3PfKXsfWVNIa/m6lDvFaTzGmYogKrmd27nbrNwtnofMatuS2aYmwgULTqSEnctktdAJGSqzf+G0YMvUdJ28Bi5xbyW4OmxORnw3cin+TMOfe7lQg7DFlokRqiLJ8cqJkLpGcOUM0Tr37TYTcASQGpT8a5ZkNzoxU1SSqHYfWc9Aw3fu7HICmybvyM4dwD7PYYCyfXeqaHz+nkw8jYCNE50v3nD7y/t/mhHfjXxm8q+KqrDxfONGmEp6HtgdNQgmWe8gzswCwrr1Aim3Snj+MRb3/zhdrkGWcsZFDtkRqistcz47VVe/3WYO+I4xlXy+ajKpVRzZJSQ5zriQ27BvByDa7ASkcLEOQdA1PMlrJZ/Hv3oB1e4kHShxkUit3ZYJhnrXtmS22Qnaw12bktzk289/CJ/gn2YfZbaMPz9aUPXq3bkDcCWMP5Q7lpZpKndyYbi79h2Q3DBQ/+5Nmx89Ogt7dJUSM2PFz0sZ5U40LJeVMG/fa8odwKHASWYzEyTk+mLYy7Gcm8UlevGIW79DqiUJAnjjV5DkOLHw7vvMmwOEM9nYrju7nESRzOHOWpqoVcx9rp1S7sLOflyil+lU6eFOsXvQgR+kz3EwcAK/tNE+ahNEfnb0HQgCfPzK/0DVVZJylJi8XNi3Mwa+XWXL9Awi6BrOwuAJ0DP9CDZdYWH/j9PtGmIxex0wgoZqtWXqus5CZpI+V/37diYHfMeQ9XzDqlG88XGiNhvLempDUqbJ+q478/6l8K+eJ9k5AratX67Fnbsa+3z9Dn87TKXNjtAe7tqU5Tf3P4AgCPzl1JdK3m4Od8G6bJmGjc0TN66675gts6jc5Yz/XH8C9t0JdsfOHE+bPUlRuatgy1wtJBSW6rkDLAWqmLbNvbZzB3A4YAxP4/HTDX3elayRlFkqwCTjrV6HEDTLyxuk3DUSxeFFkVxNsWUaw10/zsziBsVnu+xUgbmJIAgMeQ5yvYxyp9ndPO+QmFdXN1gy19Pp7OOnhn+Dq8nzfGPuE0ynjYFgcN1wZ6qqu4FMocjcvW5Q75t6mJR/hEToGD2uIVZyc6iaguzoqNmWmVBWyajJbYWpmJhpm8vZxlxU8MXHuegwlPKhUsOds4fV3CJp/yg6Qum9O13DH32ORAlLJtSv3P3Y8I/xiuFX1PSYNm0aQXu4a1OWfmcHb+t/CV9ZOcMz8atbbjeViGCJ9L9qmLZMd9z4Q7mjPXdg2DJnngYl07ZktqkZv+hCEsSKO3drF0PK79xVG+72snK3z3sIl+htuDVzOTdXMkwF1il3VYa7vCNE2t+YEJSGIgiFrrsGK3frbJkCOs7MQsOeWyoMdzul3IFRZj6bnihp/VPtHr7i9SAicHPHC8s+x23h+7m182U8MvMRHl/6IgCDnjFc6TlU0V28QLkbyBYuYpjhOM70PKGlJ5nf/2oQBLpdQ2i6SiQ/j+wM4ajRlrmQMZMyt2/L7HT2AhDJL277uQA88StccBvnDwOerWEoZpG5IjrIeAdLDnee5CR2JUWyRJgK1JeWCXBT103c0rP7Lhq1+eGnPdy1qcjbBl5KryPI+ya/gKZrG26LNcCWufPD3bpAlWvfAU8n9NywM8fSZs8iCAKdkq+ichc1d+7KKXeiUXNQCfP2vVRibmITRA75T3Ix/syWJMJ6UQsnrKXCVMDYL1Ls3oqhKsGVU4Zq14DqgmaQ83Y1dudO1xDlFIrkJVtQfBqZmLmm3O3gcOc9hKLLLGSnttym2Fw84vVyQuyrGg7yU8O/TtDRxdnVx+hw9OKx+3Fm5sh6+nbV+yXr7kNHKL7Pe69/EQGd+f0/DkCPyyjxXsxOIztC2OVkTWrtvDncNcCWaVio/azm5qvf2QK++DjnPCGCUniLxRYMW6aiy6SUWNnETH8hTCVRogYBjLRMh+hoB6O02TO036ltKuIWHfzm/ge4kJrh80tPb7jNPFmtq8TcHO5iOz3cFa7EpRZh7gwMvxDaX+Bt6qBT8lVV7uyCiNfc89yEy0Ja5l5W7gAOB06yml/YEFSxHaL5RTRdpcs1WPoOgkDWO4irzHBnz0XxxSeIhU825HiaQdbb3VBbpqikEdBRCsodNLbrbncod4Y9z7RTrudqbpIZyc6LbL1Vn8dt9/GW0d9HwMaQ5xBglL5nPaWV4p1CFx3k3D3F32Pf1MPEOm4k4z8AQHfh87FUGO4ApLz1IvGF7BRu0UdAaszvtMPZSyTfGLXYGx/nksPGYAlLJqzVIazmFkgFxvAkrm7pjfSvXkCzSaQ79pV8jqySrXnfrk2bnaR9FtumKg+Eb+GEb5j3X/8yyXV9cLFC90s9yp0qudEFG87UMjoCsqN2a2dDMJW7K98CXW1bMtvUTbiacienCNk9Zcut3XYLyt0e3rmDtb27Rlkzl4s1COVPto2Y+NK2zGDkDACxrt073OV8XTgyUQS1MXtx9sJ7VJW8ZN3Gv1sjEzMduVVU0V3sk9sJul1DOGyukqEqp1a/jaTr3KOHLD3XmP8m/tOhP+KBobcBGAXm7t013IERquJKzeCJXyGwep6F/a8u3ua1B/GIfkO5cxaGuxr27hYyk/S69pf97qqVTkcvkdz2hzspG0HLrzJFdktSpolZZL6aXyTtH0PU8luUfP/qeRLBI+i20l+sWTXb7qprs6doD3dtqiIIAu848BpW5CR/P/vvxZ9H5TSSIOK21RE+IggoDq9xBdnpK5lQ1RLMq3HJeQgNQ6j0lbs2barRaa+u3FWqDXGJ1nfu9qItE4yT7pDU3bjhrlCDEC5jywQjVKXczl1w+RSaIBLvuKkhx9MMcoWuu0ZZM8VCXYci+dDsLnLOcEMTM6VcZMcKzE1sgsiAZ4zpTaEqmq5xOvIt7smqBMr0nZXieOgu+tzDCGoeR3bJsGXuMjJe4yJG39TD6AjM79/YtdjtGiood8bvppY6hPnMZEPCVEw6nL2s5he2bc/2JiaYkCQ09JJJmWDs3IEx3CWDB4uPK6LrBKIXSHQcL/s6WSWLewcvVrRpUyvt4a6NJW707eM1Xbfx0bnHuJ5dASCmVFYiqmFaM3fMkgkg2sG8Wneg/HJ9mzbVCEt+InKy7AlLtPB5KYeVQJXsHrdlCoLA4eCtXI6f2rLDWw8ruVnsgkTQUT65MOsdRJITJbvugiunSAaPoNYRCtUqsoVURleDQlXshdRWRTICQYwi80Yqd5Ed3bcz2ec5xEx6fMP77GryPDF5mZfnbYhquubndGYXEdB3nS0TCl136Tn6ph5itedO8u6NttMe1z6WsteLKaZW6xBSSoykEqWvgcNdp6OXvJYlrVbon7SANzbO8xWSMgE8oh+HzVUsMjcfZ+JKzyDlY5WHO7Vty2yzt2gPd20s8xv7X4VdEPnzSSM5LKqk69q3MzETM3d0uIOCNVOA4Xt39jja7GnCkpe8rpBUsyVvjyppOioMEa6CLbPS1eyiLXOPDncAh/wnSasJZtIlIslrZDk7R6ezv2LQQaaQmLklVEVTCUbO7mpLJqzrumuQcmfaMhW7MdzlPAMN37nbaeUOjFCVvJbdELl/OvIoks3JPaobsbBWUAvFjjtPeaV4p8h6BrDpKp7kZDFIZT3driFi8grxgr1QKtNfu5mFjBFKY1YYNIIOMzFzm9ZMb3ycCy4vTpuHTmdpNVUQBDoKiZmqw0/O1bNBuSuGqXQcK/s6WaVty2yzt2gPd20s0+MI8kuDL+Mbq+f4QWyc2HaHO1O526mOOxOHF3pvNJIy27Spk84qXXdROVXx8+ISDQW8UpF5MVBlj+7cgVFmDnC5AdbMldxsxX07WB8Tv3G488UvYVdSuzpMBRo/3Jm2TLWg3GW8A7hTs9CgBFNHbnVXKHdmAIppzVR1ldORb3EseCdO0VPXcGeWhO9OW6bxPtdsEotDr9xye7fLuH1ON5Rbqzt3xaTMBit3YIScbAdvfJzn3F4GPWMVL/B0OIyuO4BUYHRDYmZg9QKaIJIMHin7+HagSpu9Rnu4a1MTP9f/IgadHbxv8gtE5FTFHaJqyIWeINnlb9Th1cc9vwF3/vLOHkObPU+4cLJcau9O0zViSoaOCp8Xd2FgqxSqsperEEyCjjB97gPb3rvTdZ3l3Gz5pMwC5YrMg8tGeXl0lw93mt1J3hXAlWyUcvd/27vzKDfzu873758e6ZEe1V5lu8p22233lk466XR3OiSZbE0WYEKGMEyAYcIycwlzuMDAvSdchstw5sLcC+deYLY7Z5YbwjAZzhz2ZSCdCSQQyAIJIel0upN0utvd3sp22eWqskul5Xn06Hf/eJ5HripLpaVka/u8zunTZUkl/VyWZX313XaXZR7BCUttl+m1kvHX+zopM7GYO07aZDgXD1U5tfkkheoGD88/Rpj2cKqdl2XWF5gP5ECVKJu4uvQmqu7NKx4OxcHdSrBKmHLb/vNeKZ/FTeWYdQ/25JwT157nG//iBwH2PTEzf/0Uzzm26TCVxGz2EOt+EtzF6xDiDzOm1r/M1vQ91JpMMQYNVJHho+BOOpJNZXjf8XfyXPESL5YvM7OPJuOB6LkDWLgbJpr37Ii0Iwnu1qo3B3ebYZmQGjOZvTJ30f/36rsb9lUIifumH+GFwtMEtfaHWuxWqG7g18pNF5gnqplpqpnJm4aGzF59gkruQD2zN8gqEwdvWVlm0j/Wi767VLVMulqs93X1k5NKcyR/Vz1z98Tan5NNebx09jWE6XxXPXe50kWCzPRA9miWJo+xevjNnH3J/9Tw+uRDkGRiZtvBXekMh3LHe7bj7ciLv81S4Rwezr7KMtP+NVaqa5RNrWVwN+ceil8vKmxN3026ukW2tALWRpMy9+i3s9aqLFOGjoI76djb5l/Oo1N3ATCzj8zdwPTcifTAfJK5828O7q7FWYJ2Mnd7BXfDPlAlce/0wwS1CqcLX+n6Pq7EvVTNFpjXGROtQ9idubv6RFSSOUDLqJupTBzo3UCV6o1VCBCN0Ad6MjFzEHbcbXc0fy/ni89RrQV8ae2TPDD7OtxUNgruuuy5G8RhKgA2leGLb/xlNg6+uuH1birLnHuoPjGzk567Ja9H/Xa2xuK5D2OAI0GVdb/7ReYT10/xTDaa1N1sx10i2XV3zb/C1vTd9e93y5fJVq7u2W/nhz4Wq2mZMlQU3EnHjDH80xPfgkOKpezN5R/tqiZlmf3uuRPpgWRYSqPM3XpcCrfXtMxcG2WZ9VUIQ9xzB3DP1IOkSO2r7+5qJdlx13q4RXnijh09d5nyVfKFswPfb5coTx7oXc9dUCB0cthUNGWwvsh8a/+ZOzcO7gah5w6iCYrlcIvPXPkwxXCThxe+HoDQ6X6gSmUA++3adTB3jAulFwiyc21l7srhFhvBlZ71282sfoFcaYXVpTdxNChzrXi66/uauB5NykzhtJzkObdtHcLW9D31759ejz5cut5iUiagnjsZKgrupCv3TxzhDx/6cd596DVd38fAlGWK9EDaOMym8w177pLMXas9d9CiLLNqMYA75K/cOWeC45Mv3Vff3WrlIgbDfHax5W1LE0ejaZlxn83s1aTf7qGuH/92qkwcIB0Ucfytfd9XOijU++0AguwcoZPrycRMd8Ayd3dMRENV/vjCr5FzJrh/+lUAceau859ltnixvvh9GN0//Soull7krJvHbSNzt1I6B/RuUubSuccJnRxfefXPczi0rPvdZ6Mnrj/PM9kcS96dpFvs2q3vuqtcxs8uELgzTFw/xdT6l7EYNmdf2vR7S/GHACrLlGEy5G8RpJ+O5w7gprpPIagsU0bNQmaKq8HmTZevx28kZ/fouUvKMisteu6yDl3vlhwk900/wtmtZyk1yHS242r5ArPuwZZv7CAaqpKubpH2rwFRSWYtlWFz7uVdPfbtlkzM7MVQlXS1UO+3A8CYeNfd/oO7ZDH2oAR3R7yTpIxDobrBg3NvqD9XwrSHE5Y6mhCaqpZw/Y2BLctsxyvmol2un8xU28rcrZSjSZm92HFnalUOnfsIq4cfw/cOMZ0/QYEq5eBaV/cXlWXmWpZkAsy6BzCYaKiKMWxN3c3E5ql4mMpd1PaoqChXlbmT4aPgTvpm/ejDnHvg77C5cFe/jyLSE/OZiYarEDY6ydy1mJY57P12ifumH8FS4/nNJ7v6/tXKBQ5k956UmSgnu+7ivrKZ1SfYnH0ptSH5NL48GU0q7EVpphMU6msQ6vffo113bjkpy+z/QBWAdMrlsHcCgIfnH6tfHqbzGFsj1cFAn1wp6g8b5rLMA7kjHPZO8ilzLfqgo0Vwe6l0hrTJMN9iaFE7Zq/8NdnKVVaOfzMAuYXXAlC9+NGu7q9ceJ6rqdaTMiF6Hkxl5tmoT8y8OyrL3PgKm7PNSzJhW1nmkLxWiICCO+mjIDfNqde+F+tk+n0UkZ6IMncNgrtgC4cUU3t8+ltfhbDXQJXq8PfbJe6cuB83leu6NPNq5SILufbedJbyNxaZm1rA9PpTXFt4pKvH7Ycbu+72P1Rld1kmEGfu9t9zl6msUTNpqpnBqcY4OflypjML3Dt1o78ydKJMTSfrELL1BebDm7mDKHv3VbvOurGkG1QZbLdSOsvB3B04Zv+fKC2dfZxqeoLVpTcDkFl8DIDw0sc6vi8nKHA6jLLE7QR3AHPuwW3rEO7GrayTK17cc5gK3MjcaaCKDBMFdyIiPTKfmWyauZvJ5Pcsp2xnWmZlhDJ36VSGu6ce5NnrT3T8veVwi0J1o61hKrBzkfnUxldxwgobB4aj3w7Az89jTaqHZZk7M8jl/FGy5Sukwsq+7tutrEVZuwEqG37nHe/lxx/4TzjbWgjC+I16J8Fdrr7AvL3n3KB6cO71WODP817L0syV8pnelGSGPoeW/4QrR95az5bPescAKG482fHzbmLzBZ5x25uUmZh1D+0I7hJ7rUEAlWXKcFJwJyLSIwuZSTbDMpVasOPyjerWnpMy4UbQ1qosc5gXmO923/QjXC6fZaPDwQqr5aiEcKHN4K7qThNkpvC2lplZ/SLAUGXubMqhkp/vSeauYVnmRJSNyha7H00PUeZuUPrtElnHYyqzs0z0RnDX/sTMJLNZ9oa3LBPgiHc3B5wZPjaR33Mdgl+rsFa5xGJu/8Hd/OW/JONfq5dkAkymZ8mYNCupkIVLn+jo/pJJmQuZBbz0ZOtvAObcRTYql7HW7gzu9himAlAKNVBFho+COxGRHpmP32gkqw8SG0Fxz3476Gygyqi4bzoKsDrN3q1WojfaBzroBYrWISwzc/ULlL2loeudqkz0Zh1C47LMeB3CPvvuXH99YPrt9hLGfxc7WWSeLV2kkl3AOq0H+AwyYwyPTL6Sz3o5quXmwfyV8jkstidrEJbOPk6Qmebq4ut3nGPOXeK8m2fx7Ic6ur/5lb/kmWyWIxMvaft75rKHCKzPVvUa5fwRQsejOHmcqrt3CXGSucs62Y7OKNJPCu5ERHpkIVlkvquXpZ3MXa6dJeYj1HMHsOSdYDI92/G+u/qOu1z7JXKliaN4xWVmrn6RjQPDsd9uu/Lkwf2XZVpLurq1c1omvQvuMpV1/KEI7rrL3FWGvN8u8eDM6wiM4ek9hhldKkWTMhf3ucA8FVY4uPwxLt/xDTcFxvPZJc56sxy88HGcoL3VFNNXn2Tm7B9xNpPmaP7ets+xfdcdJsX6wUdZ2xZsNlOulnFTLk5qhD5Vk5Gn4E5EpEcWMlMANw1V2agW60vOm8mkDGkD5bD5BLvKiJVlpkyKe6cf5tnrT2A7GEu/WrnAZHqWnLP3z3S7cv4O8tdfwCteGJrl5dvVM3e21vV9pGo+qVpwc+bOW8Ji9p+5q6wNzALzvYRONz13FykPWba3mWMzjzAfhny+/LWmt1kpnSFFioNtTqRtZuHiX5CubrFy7Jtvum4uu8jFlMUJyxy88Ket78zWeMkT/xdPTx7C0v4wFdi26y7uu/viGz/AM4/8TMvvK4dllWTK0FFwJyLSI/P1zN2N4M5ay0Z1i5kWmTuIsnJ7Zu5GrCwTotLM68FVVspn2/6eq+WLLHQ4nr00cZSUjX64wxrcpWpVMuXrXd+HEz8vd/fcWcfFzx3c18RMUwvI+NcGrueukTDd+bTM3JAvMN+ulp3j64slvhCcJ2iyDmKlfJYDuTtIp/Y3zXrx3OP42XnWD73mpuvm3UUKtS3W84fbKs08fOYPmFl7kk+feDvQ/jAV2Ja5q0TBHca0NfinXFVwJ8NHwZ2ISI8kwd32iZlbYYWqrTHXoucOWgd3o9ZzB9v77tovzVytLLc9KTOR7LoLnWzLIQqDKNl1lyt0P1QlHT8vd0/LBPa9yDxTiSYvDkXmrh7ctVeW6fibpKtbI1OWiUnx5kqKMmHTv3eXSmdZzO2vJNMJtjh44eOs3PFN2NTN9eRz2UUAnr7jzSxc+tSeA16coMA9X/olNhYe4iu5CfLOFLPuwbbPMpGeJpPK1nfdtascljUpU4aOgjsRkR7JOy5eyt2RuVuvRr0k7WTuPCcK4JoZxczdfHaRA9kjbffdVWs+G/4VFjrotwMoxesQrs+9fCiHYtzYddd931262jhzB1CaOEJuax/BnR+9MR+unrv2Mne5UrLjbjTKMgEeYpK8TfHU+qduuq5aC1gtn2dpn/12By5+HCcs75iSud2cGwV3XzvwIClb5dD5P256Xye/8u9xy1d59uGf5kLpBY7m79lztcxu0QCXG+sQ2lWqlpS5k6Gj4E5EpIcWMpOsVW8Ed9fiN5Cteu6gjczdiA1USdw7/QjPXX+S0O4R2cbWKitYbEeTMiEK7ixmqFYgbFeJM3fZfQxVqWfuGgR35fyRKHPXQe/jdm5lDRiSzF2yxLzNaZm5EVlgvp1x53ldmOXpjb+66e/dauUCNWr7npS5ePZxyt4hNg68quH189koWF7OuGxNnWTx7OMNb5e/forjz36QCyf/HutzD3Cx+GJH/XaJ2S6CO5VlyjBScCci0kPzmcmdmbt4ClyraZkQrUMo7xHfjGJZJkSlmZVakbOFZ1redjWZlNnhoIcwM8kX3/jLnLn/vV2dsd+C7BSh45Lbx647p16WeXNwV8kfxqn5ZOIgrVPJ9w1Dz13NyWIxbZdl3thxNzrBXZCd5evLIVvVa7y4+fSO6+qTMvdRlpn2r3Pg0ie4fMc7wDR+qzmdmccxadb8FS4dfydzV/765l2L1nLfF3+eWjrH8694H1fK5wmsz5EugrtuMnflqsoyZfgouBMR6aGFzNSOnruNOHPXas8dRJMwWw1UGaVpmYl7px8ibTL89epHWt52tYs1CImrh980FHvYGjJm37vukrLMRpm7Uj4Klr3iclf37ZaHJ3OHMYTpfNvBXbZ4CWtS+N6hW3yw2ydwZ3njVoG0yfDUxqd3XLdSOoPBcCh3rOv7P3jhY6RqAZeOv6PpbVImVQ+4Vo6/E4Nl8dz/2HGbAxc/zoFLn+TUAz9KkFtgufg80NmkzMSce4jNYI1qkyEyjZTDMl5cxisyLBTciYj00HxmYseeu41q+5m7vcoyw5olqEHWab/PZFhMpKd57cF38LmrH2W9srLnba9WLuKmckymZ2/T6QZHZfJATwaqNOq5S0oOu52YWS/LdIfjzyUK7trbrZYrXaSSO9RwKMiwCtxZZiob3Df9Kp5a/9SOVSQr5bPMZ5dw95GxWjz7OKX8Ua7Pv3LP2825i6xVLlGcOsn1uQdYPHdjamYqrHDfEz9HYfpuzt/zHgCWi6dwTKarrOJcNgrON/z2PiCx1qosU4aSgjsRkR5ayEzme6EZAAAgAElEQVSxHmxRi/eRbVSLpDBMtfHp714DVZLLR7HnDuAtS98BwJ9d+q09b7dajiZldjJMYVSU95m526sss77IvMuhKhl/ncCdGZoAKEx7OGH7ZZmj1G8HUVmmE5Z55cxrWPcvcz7OiEGUudtPSWamssb8yl+ycvwdLdcNzGcX66WSl469k5m1p/A2o7LQ48/+Kvmtczz70E9j45UMy8VTHPZO4HTxPJvbteuuFb/mY7Eqy5Sho+BORKSH5jOT1LD1csyNYIvptIfTpO9ku70yd/XgbgTLMiEai/7qhbfzmSsf5nrQvO9rtXKRhQ7XIIyKyuRB3OI6prZH7e4e0kGBmklTc7I3XVd1Z6imJ7peh5CprA1Fv12i07LMUZqUCeC7UXnyw979GFI8tf5JAEIbcrl8bl/DVA6d/xNSNmy4uHy3OXeR68FVqjU/CgaBpXOPky1e4uRX/iOXj76dtaXXA1Em7ULxVFclmXDzIvNWytUygDJ3MnQU3ImI9NDCrl13G9ViW/12sPdAleTyURyoknjr4e8itCF/fum3G15fszXWKhc5kButLEq7KhMHMFiyW1e7+v50dSvqt2uUTTFmX7vu3Mr6UPUzhk6eVBurEEytird1ntJE9/1ngyjIRuWzc2HA3VOv4EvrUd/dWuUiVRuwtI/gbvHch9maOtnWPslk1926f5lK/jDrBx5l8eyHuPdLvwA25NlX/mT9tteDqxSqG10NUwHqe/Ha3XVXioN/Ze5k2Ci4ExHpoYW45O1qPbjbaqvfDvYeqFKOLx/VskyIhqQ8svAWPn35jygE1266/pq/StUGHU/KHBXlfe66c4JCw367+v3nD++r5264Mnce6TaCu1zxAilbpTR54tYf6jYK4sxdxt/gFXNvYKV8hsvl86yUzgLdT8p0S5eZu/xZVo61LskEmI933a3FvbaXjr+TyevPs3T2Q5y5/wcoT94IqpeLpwA4mr+nq7NlUi5T6bmWfb2Jchhl7jRQRYaNgjsRkR5acBtk7trYcQdR4FZuUZY5ypk7gLcf/i6CWoVPrPzuTdddjSdlLnS4425U3Nh1191QlXS1QHWPLHI5f5Rcl9Myh68ss72eO68Q9X8Vp/a3823QJJm7jL/BK2b/FgBPrX+KS+V4DUKXC8wXz38Eg22rJBOinjuAdT8KuC7f8U3UjEM5f5jT9//jHbdNgrsj3smuzgZRplBlmTLqFNyJiPTQ/O7MXdB+5s5LG/xaNBlzt/KI99wlFr07eXDujXzy8h9Q2rYMHqJ+O+huDcIoqMSZu1yXmbt0UGi4BiFRzh/GrayTarMXrc5aMpX1ejZoGITp9soy85unAShOjlhwF081zVQ2mMsuckf+Pp5a/xQrpTPMZg6Sc9r7QGq32SufozhxjK2Z9rJrM+5BUqTqmbsgN89XH/05nnrtv6G2K2O2XHye+ewSXoOBQO2KVi+09+GIgjsZVgruRER6aDrtkTYp1oIC1kaDVeba7LlLArdGfXfjkrkDePuR91AOi3zy8h/suHy1vEzKOPXBCOMmzHgE7uStK8uciCdmdliamQ6uk7LhcOy4i4VOewNV8oUzVNMT+LmDt+FUt08S3Ln+BgAPzr2BM1vP8Pzmk11n7SD6eW1Nt98T5xiHGfdAPXMHcPHkt3HtwMM33fZC6QWOet2VZCbm3ENs+Jd3rH5oph7cqedOhoyCOxGRHkqZFHPpaNddqebj2yozbWfuov83Cu7GJXMH0YLiB2Zfx19c+l3K4Y3symrlAvPuEo4Zgx9CE5XJA2QLXWbuqlsN1yAk6usQOhyqkuy483NDFNy12XOX3zxNafJ4W/1jw8Q6LtX0BJlKEtxFEyk3/CvdT8q0Fq9wllKHWc5o193efXCVsMRqebnrSZmJWfcQfq1MMbze8rZJz50ydzJsFNyJiPTYQmaKtWqhvg5hrt2euzhmaTRUpRJflh3hgSrbvf3wP6AYbvKXl/+oftnVysWxLclMVCYO3MKyzO6Cu0wS3A1T5i6dj3ru4n2UzXiFsxRHbJhKIsjOkfGjP7tF704O5aLhJV0PUymvkq4WKU529v3Rrru9g7uLpRex2H0Hd8ki8/VK6747Ze5kWCm4ExHpsfnMJFf9AuvBFkDbmbtkEmajoSrjlLkDuHPypdw3/So+fum38WsVrLWsVi5wYEx33CXKEwe7H6jSIrireIewJtVxWaZbWQcYrrLM+O9kKs7ONJKsQeg0WBkWgTtbz9wBvGI2yt51uwYhGT7TTeZuw18l3GN/43K8ZH3fwV0Hu+7KYRk35eKkxuRFV0aGgjsRkR5byEyyVi1wLcncdbDnDhoHd+PUc5f4hiPfTaG6wWeufJhieJ1yuDW2kzITlckDZPwCTtDZ0BNTq+KEJcI9yjJtKkPFWyS31dnEzHrmbpgGqjjRsI69+u5yW8ukbJXi1InbdKrbK3Bnyfg3grvXH/oW3nDoXRyfeElX95cvRGsUOh0+M59dwlLjWtA8I71cPEXemdp3v21HwV21rJJMGUoK7kREemw+M8nVoMB6NcrczWba33MHUNqj526cgru7p17B3VMP8vGLv1Xfv6XMXXe77pz4ubhX5g6623WX9NwNY+YuHf9cGskXTgOjNykzEWR3Bndz2UP8vTv/CemU29X95QtnojUGE539HU0CrrXKpaa3WS6e4kj+bsw+ex8n0jNkjNvWIvNStaTgToaSgjsRkR5byExSrgVcjMvVZnuRuRuDJeaNvP3we9gIrvA/lj8IwMLY99wlu+46C+7S8WqO1sHdka567qrpPLUheiMcxmP291r7kE/KDEc5c7etLHO/vMIZyvkj2FSmo++bzy4BsNYk4AptyMXiC/suyQQwxjCbPdRez11YVr+dDCUFdyIiPTYfv4E+VYreQEzv2tfUzJ4DVcYwcwdw3/QjHJ+4n+c3vwiM7wLzRGWyy8xdHNzttQoBoJQ/Qq50qeWgke3cytpQZe3gRuZur0Xm3ma8BiG7cLuOdVv52TkywXXMHr1unch3MSkTYM6NPrBYbzIxc7V8nsD6PQnuosc7pLJMGWkK7kREemwhMwXAqdIK045Hus3R/UlWrnFZpsUA7pi9ahtjePuR9wAwkzmAm8r2+UT9VcnPYzHktjobqpKOF8JXW2SRy/kjpGoBbrn9+89U1vGzw9NvB9uCuz3WIeQLp6OSzBFbg5BIdt2l/Wv7vzNr8Qpnuho+k065TGcWWPMbl2UuF08BcDS/vx13iWTXXSsK7mRYjdnbBBGRW28+Xn3wYuly2/12cKMss9Ikc5d12HfPyTB6YOa13JG/jyP5u/p9lL6zTgY/P3sLyzKjzGgnfXduZZ1giIapwI2BKnvtussXzlCcGs1+O4hWIQBk/PV931fG3yATbHaVuQOYdxeblkouF0/hmEx9VcN+zbqHuB6sUa0Fe95OZZkyrBTciYj0WJK5K9eCtvvtYFvmrtEqhOr49dsljDH80Et+ge+9+5/1+ygDwffmcUud9Uolwd1e0zIByhNHAfA6mJiZqawN1Y47aN1zZ2oBua1lSiO64w5uZO560XfnJZMyuwyG57KLe2TunmfJu5N0h718TR/LPYTFsuHvnZ0uV8t4bZbUiwwSBXciIj02vy2gm21zxx2AF1dvlhuUZVbC8dlx14iXniTntB8ojzLfm+04uHM6GKgCnWbuhrHnLnouNSvLjNYghKOduYuDO9fff3CXDJ/pdifgvLvIhn+F2q5eT2sty8VTPeu3gxuLzPcqzbTWKnMnQ0vBnYhIj2VSaabjsq+eZe7C8RumIo0F3gyZcoeZu2p7wV2YmSTITLU9MTNVLeKE5aHN3DUbqDLqaxAgWoUAvcrcncFiKE90Vzo5l10ktFWuB2s7Lr8erFGobvSs3w7a23Xn13xqtqaeOxlKCu5ERG6BpDSzk567lDG4DpSq9qbrxj1zJzf4uVnc0jWwNz9PmrlRltn6+RitQ2gvc+fG6z6GLnPXoucuv5lkok7criPddkmfZC967vKFM5Tzh6k53Q08ms8uAjfvurtQH6bSu8zdTDKdc4/grlwtAyi4k6Gk4E5E5BZIhqrMdZC5gyiAqzSallmF7Jj23MlOvjdDqlYl7TdfwL1bOtiKJmWa1v/sR8Fdez13mXiB+bBNy7SOS82kSTUL7gqnqWYmhy5o7USYzlNLZXrWc1fqsiQTorJMgHV/5zqE5VIU3B3xejdMyU1lmUzPcrl8vult6sGdyjJlCCm4ExG5BZLM3UwHPXcQTcxsVpapzJ0ABF5cTlduf4S9Uy20LMlMlPOHO8jcRcHdMAZBYTqP02Sgilc4G2XtRnk6rTEE7lzPMnf7KWGdjUsl13bturtQPMV8dgmvxSCgTt019XI+f/Vj/Kev/VMuFF+46fpyqMydDC8FdyIit0CyyHwu01nmznOaD1RRz50A+LkZgI6GqqSDToK7I2T8a/UhLHtx65m7YQzuvOY9d5unR7rfLhFkZ8nsc6BK2r+OW1nveg0CQNbxmEzP3py5K57iqNe7frvE99z1U7zr2A9ybutZfunLP8hvvvgvuV65Xr9eZZkyzBTciYjcAgvxG+lOM3e5PTJ3Cu4EommZAJlS+5m7dFBouQYhUZ5of2JmZqiDuzxO9ebSVhP6eMXl8Qju3Nl9l2XW1yDsoywTokEn23vuKmGJK+XzPe23S6RTLo8tvZufevCDvHHxW/nc1Y/ys5/5WT7y4kfwQ59SnNFVWaYMI3VwiIjcAknmrpNpmdA8uFPmThKB13nmrrOyzCS4u8DWzL173jZTWaeWyhC2ed+DJHS8hmWZ3tZ5jK1RGuE1CInAnSW/eXNZYidurEHY389rPrvExdKL9V9fLL2IxXIk37t+u90m0tP83eM/xBsOvYvHL/0rHn/xcT594dOcnDkJKHMnw0nBnYjILfC2+ZdzNdjkbu9QR9/XbKBKZYyXmMtOQVKW2UHPXTooUPEW27ptJ7vu3MpaNHVxCHvTmvXceYXRn5SZ8LNzzKzur+cuydyVJrtbg5CYcxf5ysZnsNZijGG5Pimz92WZux3MHeW9r3gvz288z+8/9/s8cfkJAC0xl6GktwoiIrfAfGaS//mOt3f8fV4aVhu0AWkVgiRsyiHITpHppOeuutV2WWYld5CaSZPbaj0x062sD92kzESY9hr2m9UzUWOSucv48VqNLgP0fOEMZe8QtQ5L0Hebyx4isD6F6gZTmTmWi6fwnMn6Xrrb4Z7Ze3jfo+/jCytfYKW4Qn6fvyeRflBwJyIyQLx044Eq6rmT7XxvpqPMndPBQBVSDpX8Uts9d8PYbwdR5q7Rsvb85mmCzFR9D9woC7KzpGwVJygQulNd3YdXOLOvYSqJ+ewSEE3MnMrMcaH4PEfzd2Nuc1Y4ZVI8uvTobX1MkV7SQBURkQGSc5oMVFFZpmzje7Pt99xZ29G0TICtqZPMrn4eag0+adjGrawN5RoEaN5zVx/rP4Slpp1KAlh3H+sQ8oWzPRk+k+y6W/MvUbMhF0ovcuQWDFMRGXUK7kREBkiuQeauWrNULWSd0X+zKe0JcjNtT8t0qmUMlmoHu8Iu3PUdeMVlDl78sz1vl6msD3XmzmmwxDxfOENp6sTtP1AfBNl48mqX6xCcYIts+cq+FpgnkvLL9coKV8rLBLXKbem3Exk1Cu5ERAZIzomydNslA1bUcycJ35ttuyzT8aMAJuxg5+KVI2+llD/Csed+reltTC0gE1wnGNqeu5uDOxP65IoXxmINAkQ9d0DX6xC8rd6sQQDw0pN4ziTr/uVtw1SUuRPplII7EZEB4jVYhZAEd+q5k0SQmyXtb5Gq+i1vmw6i0sNOyjJtKs35e97D/OXPMLnxTMPbZCpRKd/wZu48nJq/o/TU2zqHsTWKY5O5iwLzTJdlmfn6pMzeBMPz2UXWKpdYLj6PY9Is5vYfNIqMGwV3IiIDxEsbQgtBzdYvSzJ56rmTRH2ReRvZOyeIslOdBHcAyye/ndDJNc3eufEC8+HtuYsmITrhjb67Xu1sGxb7ztxtxj+vid4EYXPuIuv+CheKp1jy7iSdyvTkfkXGiYI7EZEBkpRebs/eKXMnu/kdLDJPx2WZnfTcAVSzs1y8810snf1DMnEgt11y2fBm7uLgbltpZn7zNNC7TNSgCzLTWEzXPXf5whn87HzXkzZ3m3MXWaussFw8pX47kS4puBMRGSBJdm57311ZPXeyS9BR5i7KTHXSc5c4d+/34IQVjrzw2zdd58ZlmUObuYsXVG8P7rzCWQJ3Zmj7CDuWcgjcmXqJbae8Hk3KTMxnF6nUSmxW1zniqd9OpBsK7kREBkgSwG2fmKmBKrKbn7v1mTuArZn7uHrodRx7/r9hasGO625k7oYzEGoU3OULp8emJDMRLTLvPnPXi0mZiWQdAmiYiki3FNyJiAwQL87clRpk7lSWKYl6z10b6xCSnruww567xLl7v5dc6RIHlz+243K3sobF1Pu2hk29LHN7z93mmfEL7rLdBXepaplc6VJPf15z8SJzUHAn0i0FdyIiA8RrVJYZf53VQBWJ1TI5wnS2zcxdPC0z3XlZJsDq4ccoThzj+HMf3HF5prJO4M5Aajg/dbjRcxf9fFJhhVzxwtj02yUCd65eYtsJb+sc0NvhM/PZaNfdvLuE10WmWUQU3ImIDJT6QJUGZZnK3Ml27e66c4IioZPFOm53D5RyOH/PdzO7+gWm1p6uX+xW1oa23w4gdJKyzC0AvMI5DHZs1iAkus3cefU1CL0ry8w702RTnrJ2Ivug4E5EZIBooIq0K8jNkGkncxcUu+q322755LuppvMce+6/1i/LVNaHdlIm3Jy588ZsDUIicGe7WoVwK9ZGGGP4tjt/hLcc/s6e3afIuFFwJyIyQBoFd8rcSSNtZ+78Utf9donQneLiib/L0rnHccurQJK5G85hKnBzcJcvnAYYv8ydO4sTlkiFlY6+L5ksWs32tufy6w58IycmX9bT+xQZJwruREQGiNeoLFNLzKUBPzfT1kCVdFDseIF5I+fu+R5StYCjp34DiKZljkTmLowGzuQ3z+C7s1TdmX4e67ZLAvRO1yHkC2co9rAkU0R6Q8GdiMgAaTRQRasQpJEgydzZ2p63c/z9l2UCFKfvYnXpjdxx6tdJhRUy/saI9NzFwV3h9NgNUwHq00477bvzCmfG8uclMugU3ImIDJCcViFIm3xvBmNrZCqFPW+XDvZflpk4d+/3kS1f4egLv0nKhkOduSPlEKbcbT13ZylOjV+w0k3mzoQ+XvHC2PUnigwDBXciIgOk0RLzctWSMpDRK7ZsE8SLzFsNVXGCItVMd2sQdru69Aa2pk5y4qv/H8BwB3dEpZlOtRivQbhIcfJEv4902/ldZO684jLG1no6KVNEekNvFUREBkjWAUMU0CUqYXy5Mf07mAycZJF5q1136R6VZQJgUpy753vIlq8ADPVAFYiDu7CEVzg7lmsQIFqFAHQ0MXNcJ4uKDAMFdyIiA8QYQy59c1mm+u1kt7aCO2txglJPBqokLp741vr9DXPPHUCY9nCqxfqkzHHMRAVuXJbZQeYuvxntuFNwJzJ4FNyJiAwYz9lZlplk7kS2C+LgLrPHOoRUGJCqVXvWcwcQZiZZPvluACq5Az27334InTi420wyUSf6e6A+sI5LNZ0n47ffc+cVzlBNTwx9cC8yijRYW0RkwChzJ+0IspNYk9ozc+cE0STInpVlxl544MdYP/RafO9QT+/3dgvTEzjVEl7hDH52jqo73e8j9UWni8yjNQh3gkrFRQaOMnciIgMml96VuasqcycNmFTLXXdOEE2C7GVZJkCYmWD1yFt6ep/9EKY9nLAUBysn+n2cvgncWdyOMndnx7KEVWQYKLgTERkwOWfnnrtyqAXm0ljgzeCWm2dc0n4U3PWyLHOU1HvuNk+PdbASZOfaztyZWhVv67wWmIsMKAV3IiIDxttVlqmeO2nGz83i7pm5i8syFdw1VHPyZCrr5EqXxj5z1+5AlVzxAilb1QJzkQHV1+DOGPM+Y4w1xgx3R7aISA/ldg1UKSu4kyYCb2bPgSrpW9RzNyqq6TxuHNSM4wLzRJBtP7jzCpqUKTLI+hbcGWOOAd8AnO3XGUREBtHuzF25qrJMacz3ZvceqOIrc7eXMO3Vvx73zF3av46pVVveNh/vuBvnMlaRQdbPzN2/Bn4CsK1uKCIyTnYPVPGVuZMm/NwMTrVCKig3vD4dD1QJ0xO381hDo5bO178e50xU4M5hsKT96y1v6xXOEDo5KkM+KVVkVPUluDPGvAtYttY+2cZt/7Ex5m+MMX9z5cqV23A6EZH+ajhQRcGdNBC0WGSuzN3eQifK3PnZeUJ3qs+n6Z8gG+9MbGNiZj6ZlGk0tkFkEN2yQh9jzMeApQZX/TPgp4hKMluy1r4feD/Ao48+qiyfiIw8DVSRdvneDABu+Rrl6Zv/yU0HRaxJUXNyt/toQ6EaZ+7GuSQTwM/OAbTVd+cVzlCcOnmrjyQiXbplwZ219m2NLjfGvAI4CTxpouWXdwBfMMZ8nbX20q06j4jIsLhpoIp67qQJP87cZZpl7oIS1Uxey6abCJPgboyHqUDUcwe0XodQC8kXzrJ6+LFbfygR6cptf7tgrX0KqBdqG2NOA49aa1dv91lERAZRkrmz1mKMUeZOmgpyceauyTqEtF8kdPMNrxOoxQNVxj1zVw/uWmTusqUVUrVAaxBEBpgKpkVEBkwuHWVZKiFUa5aqhZyjzIvczG/VcxcUqWa8htfJjWmZY5+5S8oyW2TukkmZWmAuMrj6XuhjrT3R7zOIiAySZHhKOYSM3XmZyHbWyVB1J8iUG78pT/slZe72cG3+lSyffDdri6/v91H6KkxPUDNpXH9tz9vdWIMw3sGwyCDre3AnIiI7efErc6kKYRzUqSxTmvFzM3tm7pLSTblZmJnkq6/++X4fo/+MiRaZt8jceYVz1FIZyl6jeXkiMghUlikiMmCS4Snl6o2VCBqoIs343iyZcpOeu6BIqLJMaUPgzrXsucsXzlCaOAYpfdokMqgU3ImIDBgvft9UCqO+O1DmTpoLvJmmA1Ucv0RVZZnShiA72zK48wpnxnrZu8gwUHAnIjJgdmTu4uBOPXfSjJ+bbVqWGWXuFNxJa4HbvCzTCba486vvZ2LzhbEfPiMy6FToIyIyYLYHd8l6MmXupJnAmyFT2cTUQuy2cjlTC3GqFaquyjKltcC9OXPnBAWOPfdrHH/2V3H9DVaX3sjZ+/5Rn04oIu1QcCciMmC2l2XWgzu9WksT9UXm5Wv4+fn65U5QAlDmTtoSDVRZB2txggLHn/uvHH/uv5Dxr7F6+M288LIf4frCK/t9TBFpQW8XREQGzPbMnaPMnbTgx9MwM6VdwZ1fBFDPnbQlcOdI2Sp3P/UvuePUb5AJrnPlyFt48WU/xPX5B/t9PBFpk4I7EZEBs30VghN3RqvnTpoJti0y39p2eTqIgjtNy5R2+PEi85PPvJ/LR97Kiw/8CJtzD/T5VCLSKQV3IiIDZscSc61CkBZ8L8rcubvWISRlmVWVZUobVo+8hdP3/wCXjn0zhbmX9fs4ItIlvV0QERkw28sy3Thzp7JMaabec7drYmY6LssMVZYpbQiyczz/4P/W72OIyD4puBMRGTBJWWY5BFerEKSFMJOnlkrftOvOicsyqyrLFBEZG9pzJyIyYDIpg2OgVLVaYi6tGYPvzeKWd2XukmmZytyJiIwNBXciIgPIS0cDVcrquZM2BN4Mmd2ZO189dyIi40bBnYjIAMqlo7LMSmhJGUibfp9IBpmfa5S5S6Zl5vpxJBER6QMFdyIiAyjnRFm7chh9bYyiO2nO92ZuGqji+EWqmRwY/VMvIjIu9IovIjKAvHrmTsNUpLXAm40GqlhbvywdFAlVkikiMlYU3ImIDKCcE/fchRqmIq35uVlStSpp/8Yacycoqd9ORGTMKLgTERlA2weqaJiKtBLEi8wz2xaZp/0ioas1CCIi40TBnYjIAMptK8tU5k5aSRaZu9v67pygqMydiMiYUXAnIjKAtg9UUXAnrfi5OHNX2p65K2nHnYjImFFwJyIygLx0FNwpcyftCJpm7lSWKSIyThTciYgMoJwDpVA9d9KeIDeNxeBu77kLSpqWKSIyZhTciYgMoGSgijJ30g6bcghyUzd23Vkb7blTWaaIyFhRcCciMoCyjvbcSWeC3Ew9c+dUyxgsocoyRUTGioI7EZEB5KUNfghFlWVKm3xvtj5QxfGLAMrciYiMGQV3IiIDyIsDumsVlWVKe4LcTH2gSjooAajnTkRkzCi4ExEZQEkpZlllmdIm35u9UZYZxJk7BXciImNFwZ2IyADytpViKnMn7fC9GdL+FqmqTzouywxd9dyJiIwTBXciIgMouy24yzmmfweRoZHsusuUr+HEZZnK3ImIjBcFdyIiA8jblq1T5k7a4edmgGiR+Y3MnYI7EZFxouBORGQAbS/L1LRMaYe/I3OX9NypLFNEZJwouBMRGUA59dxJh5KyzChzl0zLVHAnIjJOFNyJiAyg7WWZmpYp7UjKMjOlKHMXOi7WyfT5VCIicjup2EdEZADtyNzplVraUMvkCNM53NIGTlhR1k5EZAzpLYOIyABSWaZ0w/dm4l13VsGdiMgYUnAnIjKAdgxUUXAnbfJzs2RK16g5GaqalCkiMnbUcyciMoByWoUgXQi8GdzyBumgSKgddyIiY0fBnYjIANoe3GkVgrTL96LMneMXqboqyxQRGTcK7kREBpCTMrjxK7Qyd9KuIBf13KX9LWXuRETGkII7EZEBlWTs1HMn7fK9WYytkdtapargTkRk7Ci4ExEZUMlQFZVlSrt8L9p1Z2yNUGWZIiJjR8GdiMiASjJ2KsuUdgXebP1rZe5ERMaPgjsRkQHlqSxTOuTnZupfh1qFIMH2+iwAAAnhSURBVCIydlTs0wOPHXus30cYGI/1+wC3yWP9PoDc5LF+H+AWyE5+GufaNdIvfcdtfdy33dZHG3SL/T5AZxbWgH8CwL2Lj3Cv/n26RYbseSFt0J/pIHms3wcYYsrciYgMKC+TIpfWy7R0IDcLJqnnnervWURE5LbTuwYRkQHlZRyyGdVkSgdSKZg4GH2t4E5EZOwouBMRGVC5jKPMnXROwZ2IyNhSz52IyIB61Z1zeMrcSacmD8IKkJ3u90lEROQ2U3AnIjKg3vvGu/p9BBlGytyJiIwt1fuIiIiMEgV3IiJjS5k7ERGRUXLfN8LWFch4/T6JiIjcZgruRERERsnJN0X/iYjI2FFZpoiIiIiIyAhQcCciIiIiIjICFNyJiIiIiIiMAAV3IiIiIiIiI0DBnYiIiIiIyAhQcCciIiIiIjICFNyJiIiIiIiMAAV3IiIiIiIiI0DBnYiIiIiIyAhQcCciIiIiIjICFNyJiIiIiIiMAAV3IiIiIiIiI0DBnYiIiIiIyAhQcCciIiIiIjICFNyJiIiIiIiMAAV3IiIiIiIiI0DBnYiIiIiIyAhQcCciIiIiIjICFNyJiIiIiIiMAAV3IiIiIiIiI0DBnYiIiIiIyAhQcCciIiIiIjICFNyJiIiIiIiMAGOt7fcZ2maMuQKc6fc52nAAWO33IUQa0HNTBpmenzKo9NyUQaXn5ni601p7sNEVQxXcDQtjzN9Yax/t9zlEdtNzUwaZnp8yqPTclEGl56bsprJMERERERGREaDgTkREREREZAQouLs13t/vA4g0oeemDDI9P2VQ6bkpg0rPTdlBPXciIiIiIiIjQJk7ERERERGREaDgTkREREREZAQouOsxY8w3GWO+Zox53hjzk/0+j4wvY8wxY8zHjTFfMcZ82RjzY/Hl88aYjxpjnov/P9fvs8p4MsY4xpgnjDEfin990hjz2fj18zeNMW6/zyjjxxgza4z5HWPMM8aYrxpjXqfXTRkExpj/Nf73/GljzK8bY3J63ZTdFNz1kDHGAf498LeBlwHfZYx5WX9PJWOsCrzPWvsy4LXAD8fPx58E/tRaey/wp/GvRfrhx4Cvbvv1/wP8a2vtPcA68P19OZWMu38LfMRaez/wSqLnqF43pa+MMUeBHwUetda+HHCAv49eN2UXBXe99XXA89baF6y1PvAbwLv6fCYZU9bai9baL8RfbxK9QTlK9Jz8YHyzDwLf2p8TyjgzxtwBfDPwgfjXBngL8DvxTfTclNvOGDMDvAn4FQBrrW+t3UCvmzIY0oBnjEkDeeAiet2UXRTc9dZR4Ny2X5+PLxPpK2PMCeBh4LPAorX2YnzVJWCxT8eS8fZvgJ8AavGvF4ANa201/rVeP6UfTgJXgF+NS4Y/YIyZQK+b0mfW2mXgl4CzREHdNeDz6HVTdlFwJzLijDGTwO8C/4u19vr262y0C0X7UOS2Msa8E7hsrf18v88isksaeAT4j9bah4EtdpVg6nVT+iHu83wX0QcQR4AJ4Jv6eigZSAruemsZOLbt13fEl4n0hTEmQxTY/Tdr7e/FF68YYw7H1x8GLvfrfDK2Xg98izHmNFH5+luI+pxm43Ij0Oun9Md54Ly19rPxr3+HKNjT66b029uAF621V6y1AfB7RK+let2UHRTc9dbngHvjyUUuUaPrH/b5TDKm4h6mXwG+aq39V9uu+kPg++Kvvw/477f7bDLerLX/u7X2DmvtCaLXyT+z1r4H+Djw7vhmem7KbWetvQScM8a8JL7orcBX0Oum9N9Z4LXGmHz873vy3NTrpuxgouoC6RVjzDuIekkc4D9ba3+uz0eSMWWMeQPwSeApbvQ1/RRR391vAceBM8B3WGvX+nJIGXvGmMeAH7fWvtMYcxdRJm8eeAL4bmttpZ/nk/FjjHmIaNCPC7wA/COiD8P1uil9ZYz5WeA7iaZhPwG8l6jHTq+bUqfgTkREREREZASoLFNERERERGQEKLgTEREREREZAQruRERERERERoCCOxERERERkRGg4E5ERERERGQEKLgTEZGBYYwJjTFfNMY8bYz5I2PMbBf38agx5v9tct1pY8yB/Z+0N4wxHzbGzMb//dC2y48YY36nn2cTEZHho1UIIiIyMIwxBWvtZPz1B4Fne7kv1BhzGnjUWrvaq/vsBWPMCeBD1tqX9/koIiIyxJS5ExGRQfVXRAt6McbcbYz5iDHm88aYTxpj7o8v//Y4y/ekMeYT8WWPGWM+FH+9YIz5E2PMl40xHwBMfPkJY8zTyQMZY37cGPMzez3WdsaYnzHG/Jox5q+MMc8ZY34gvtwYY34xPtNTxpjvjC8/bIz5xLas5Bvjy5NM4v8N3B1f/4vbz2eMyRljfjW+vyeMMV8fX/4PjTG/F5/1OWPML9yCPwMRERki6X4fQEREZDdjjAO8FfiV+KL3Az9orX3OGPMa4D8AbwH+OfCN1trlJiWc/wfwKWvtvzDGfDPw/W08fLPH2u1B4LXABPCEMeZx4HXAQ8ArgQPA5+Kg8x8Af2yt/bn495bfdV8/CbzcWvtQ/Ps/se26HwastfYVcaD5J8aY++LrHgIeBirA14wx/85ae66N36OIiIwgBXciIjJIPGPMF4kydl8FPmqMmQT+FvDbxpjkdtn4/58G/osx5reA32twf28Cvg3AWvu4MWZ9rwdv8Vi7/XdrbQkoGWM+Dnwd8Abg1621IbBijPkL4NXA54D/bIzJAH9grf3iXufY5Q3Av4t/D88YY84ASXD3p9baa/HZvwLcCSi4ExEZUyrLFBGRQVKKs1d3EpVQ/jDRv1Ub1tqHtv33UgBr7Q8CPw0cAz5vjFlo83Gq7Pw3MBf/v+ljNbC7ab1pE7u19hNEgeYyUTD6vW2es5XKtq9D9KGtiMhYU3AnIiIDx1pbBH4UeB9QBF40xnw71PvaXhl/fbe19rPW2n8OXCEK8rZLSiIxxvxtYC6+fAU4FPfkZYF3xo97vdljNfCuuB9uAXiMKDv3SeA7jTGOMeYgUUD318aYO4EVa+0vAx8AHtl1X5vAVJPH+STwnvg89wHHga81ua2IiIwxBXciIjKQrLVPAF8CvosouPl+Y8yTwJeBd8U3+8V40MjTwF8CT+66m58F3mSM+TJReebZ+L4D4F8Afw18FHhm2/c0e6zdvgR8HPgM8H9aay8Avx9f/iTwZ8BPWGsvEQV/TxpjngC+E/i3u36vV4FPx8NWfnHX4/wHIGWMeQr4TeAfWmsriIiI7KJVCCIiIh2KJ2sWrLW/1O+ziIiIJJS5ExERERERGQHK3ImIiIiIiIwAZe5ERERERERGgII7ERERERGREaDgTkREREREZAQouBMRERERERkBCu5ERERERERGwP8PelsE9nMo4SsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "testProteinSeq, testTrueSS = dsspDictionary[\"2gpi_A\"]\n",
    "(table, count, countPerR) = freqTableOfProtein(testProteinSeq, testTrueSS)\n",
    "\n",
    "subTable = totalFreqTable - table\n",
    "subCount = totalCountSS - count\n",
    "subCountPerR = totalCountSSperR - countPerR\n",
    "\n",
    "subLog = np.log(np.sum(subCount[otherSS],axis=-1)/subCount)\n",
    "subLogRj = np.log(np.sum(subCountPerR[otherSS],axis=-2)/subCountPerR) # -2 since -1 are the amino acids\n",
    "\n",
    "subTable[subTable==0] = 0.00001\n",
    "\n",
    "SS_result, ISS = predictSS(testProteinSeq,subTable,subLog,subLogRj)\n",
    "\n",
    "fig = plt.figure(figsize = (15,10))\n",
    "plt.bar(np.where(testTrueSS==0)[0],height=10,width=1,alpha=0.3,bottom=-5)\n",
    "plt.bar(np.where(testTrueSS==1)[0],height=10,width=1,alpha=0.3,bottom=-5)\n",
    "plt.bar(np.where(testTrueSS==2)[0],height=10,width=1,alpha=0.3,bottom=-5)\n",
    "plt.plot(ISS[0],label=\"Helix\")\n",
    "plt.plot(ISS[1],label=\"Sheet\")\n",
    "plt.plot(ISS[2],label=\"Coil\")\n",
    "plt.legend()\n",
    "\n",
    "plt.xlabel(\"Residue position\")\n",
    "plt.ylabel(\"mutual information\")\n",
    "plt.title(\"Proper Gor 3 analysis on protein 2gpi\")\n",
    "\n",
    "\n",
    "validEntries = SS_result>=0\n",
    "ValidSS_result = SS_result[validEntries]\n",
    "ValidtestTrueSS = testTrueSS[validEntries]\n",
    "            \n",
    "Q3 = proteinQ3(ValidSS_result, ValidtestTrueSS)\n",
    "MCC = proteinMCC(ValidSS_result, ValidtestTrueSS)\n",
    "print(f\"Q3={Q3}, MCC={MCC}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is another nice figure where, we see the GOR algorithm at action. We can clearly see that at regions wher it needs to be high you see the curve trying to go up. Sometimes not perfect. But the general picture is there. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: Prediction protein family"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will discuss the protein family prediction based on the SS sequence.\n",
    "\n",
    "So given is a SS sequence and we want to classify it in a protein family. For which the solution is given in the text file: `catch_info.txt`. \n",
    "\n",
    "For the classification I have decided to work with the python module `sci-kit learn` [Homepage](https://scikit-learn.org/stable/index.html) and do the classification via a descion tree. \n",
    "\n",
    "To train the tree a set of feature vectors is which is determined from the set of the SS sequences and a target vectors that contains that says to which protein family the SS sequence belongs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the target.\n",
    "\n",
    "Let's first creat the target variable, which will just be an numerical vector where:\n",
    "\n",
    "- 0 = Alpha\n",
    "- 1 = Beta\n",
    "- 2 = Alpha/beta\n",
    "\n",
    "We do not consider the \"None\" category since it is way to small. \n",
    "\n",
    "We should then also remove the \"None\" entries from the SS sequence data set. This is being done in the following lines of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the data sets:\n",
    "dataDssp = createDataset()\n",
    "dfProtFam = pd.read_csv(\"inputData/cath_info.txt\",sep=\"\\t\", header=None, names=[\"PDB_code\", \"PDB_chain_code\",\"protein_family\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove entries of \"None\"\n",
    "# Remove the invalid proteins from the dictionary\n",
    "dfProtFam_grouped= dfProtFam.groupby([\"PDB_code\",\"PDB_chain_code\"], sort=False)\n",
    "for (PDB_code, Chain_code), dataFrame in dfProtFam_grouped:\n",
    "    ProtFam = dataFrame[\"protein_family\"].values[0]\n",
    "    if ProtFam ==\"None\":\n",
    "        key = PDB_code+\"_\"+Chain_code\n",
    "        del dataDssp[key]\n",
    "# Remove the invalid proteins from the protFam dataframe\n",
    "dfProtFam = dfProtFam[dfProtFam.protein_family != \"None\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the \"None\" entries have been removed from both the sequence dictionary and proteinFamily datframe, we create the numerical target vector that tells to which category the SS belongs.\n",
    "\n",
    "This is done in the code below, with some additional summary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total = 492\n",
      "Number Alpha = 132 = 26.83%\n",
      "Number Beta = 100 = 20.33%\n",
      "Number Alpha/beta = 260 = 52.85%\n"
     ]
    }
   ],
   "source": [
    "# Create a target vector that the classifier needs to assign:\n",
    "classDictionary = {\"Alpha\":0, \"Beta\":1, \"Alpha/beta\":2}\n",
    "target = pd.to_numeric( dfProtFam[\"protein_family\"].map(classDictionary), downcast=\"signed\").values.astype(int)\n",
    "N = len(target)\n",
    "_, counts = np.unique(target,return_counts=True)\n",
    "print(f\"Total = {N}\")\n",
    "print(f\"Number Alpha = {counts[0]} = {np.round(counts[0]/N*100,2)}%\")\n",
    "print(f\"Number Beta = {counts[1]} = {np.round(counts[1]/N*100,2)}%\")\n",
    "print(f\"Number Alpha/beta = {counts[2]} = {np.round(counts[2]/N*100,2)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there is a class imbalance present but not extremely severe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting the features\n",
    "\n",
    "We now want to extract some specific features from a given SS sequence which we will give our Tree as input:\n",
    "\n",
    "The following 9 features are considerd:\n",
    "\n",
    "1. 3 Features on percentage of a SS in the sequence:\n",
    "    1. percentage of Helix in SS sequence\n",
    "    2. percentage of Sheet in SS sequence\n",
    "    3. percentage of Coil in SS sequence\n",
    "2. 3 Features on average length of a particular SS in the sequence:\n",
    "    1. Average lenght of the Helix regions\n",
    "    2. Average length of the Sheet regions\n",
    "    3. Average length of the Coil regions\n",
    "3. 3 Features on the longest length of a particular SS region in the sequence:\n",
    "    1. number of residues in the longest Helix region.\n",
    "    2. number of residues in the longest Sheet region.\n",
    "    3. number of residues in the longest Coil region.\n",
    "    \n",
    "If for a particular SS sequence, some SS does not exist we then say that the region lenght =0\n",
    "\n",
    "The function `featVect`, extracts the feature of from a given SS sequence. The function `lengthSSRegion` helps with the determination of the features. Lastly, the function `featVectFromDict` returns the sets of feature vectors from a dictionary set of SS sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def featVect(SS):\n",
    "    \"\"\" Return the feature vector from a given SS\"\"\"\n",
    "    numberFeatures = 9\n",
    "    featVect = -np.ones(9)\n",
    "    # features is simply the percentage of each SS.\n",
    "    featVect[0:3] = np.sum((SS==0,SS==1,SS==2),axis=1)/len(SS)\n",
    "    # Average length of each SS. \n",
    "    lengthsRegionSS = (lengthSSRegion(SS,0),lengthSSRegion(SS,1),lengthSSRegion(SS,2))\n",
    "    for i, lengthSS in enumerate(lengthsRegionSS):\n",
    "        featVect[i+3] = np.mean(lengthSS)\n",
    "    # the longest length of each SS.\n",
    "    for i, lengthSS in enumerate(lengthsRegionSS):\n",
    "        featVect[i+6] = np.max(lengthSS)\n",
    "    return featVect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lengthSSRegion(SSarray,SS):\n",
    "    \"\"\"Returns the length of the SS regions\"\"\"\n",
    "    lengths = []\n",
    "    isInRegion = False\n",
    "    count = 0\n",
    "    for ss in SSarray:\n",
    "        if ss==SS:# We are still in the region or we just started with one.\n",
    "            count+=1\n",
    "            isInRegion=True\n",
    "        elif ss!=SS and isInRegion==True:# Aka it stopped\n",
    "            isInRegion= False\n",
    "            lengths.append(count)\n",
    "            count=0\n",
    "    if isInRegion: # If the for loop ended with still a region store it\n",
    "        lengths.append(count)\n",
    "    if len(lengths)==0: # Return a 0 in the case the \n",
    "        lengths.append(0)\n",
    "    return lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract features from the SS array:\n",
    "def featVectFromDict(dictionary):\n",
    "    \"\"\" Returns the feature Vector from the dictionary dataset\"\"\"\n",
    "    numbProteins = len(dictionary)\n",
    "    for i, (protName, (seq, SS)) in enumerate(dictionary.items()):\n",
    "        if i==0:\n",
    "            featV = featVect(SS)\n",
    "            numbFeatures = len(featV)\n",
    "            featureVectors = np.zeros((numbProteins,numbFeatures))\n",
    "        featureVectors[i] = featVect(SS)\n",
    "    return featureVectors\n",
    "featureVectors = featVectFromDict(dataDssp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which dataset to use?\n",
    "\n",
    "Now comes the question on which data set do we train our Tree? In my exploration, I first started on the true SS sequence of the proteins. The prediction results of the three were very strong ($\\pm$ 84%). However, if you then feed the Tree, that was trained on true SS sequences, on SS sequences predicted by the GOR algorithm the accuracy went way down. \n",
    "\n",
    "So to get better results with predictions we will train the descion Tree directly on the SS sequences that where predicted by the GOR data. \n",
    "\n",
    "Create a new dictionary that contains the GOR prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the total frequency table.\n",
    "totalFreqTable = np.zeros(shape=(3,17,20,20))\n",
    "totalCountSS = np.zeros(3)\n",
    "totalCountSSperR = np.zeros(shape=(3,20))\n",
    "otherSS = np.array([[1,2],[0,2],[0,1]])    \n",
    "\n",
    "proteinDictionary = createDataset(dataSet = \"dssp\")\n",
    "    \n",
    "#First compute the full freqTable\n",
    "for key, (proteinSequence, trueSS) in proteinDictionary.items():\n",
    "    freqTableFromProt, countSSFromProt, countSSperRFromProt = freqTableOfProtein(proteinSequence, trueSS)\n",
    "    totalFreqTable += freqTableFromProt\n",
    "    totalCountSS += countSSFromProt\n",
    "    totalCountSSperR += countSSperRFromProt\n",
    "\n",
    "totalLog = np.log(np.sum(totalCountSS[otherSS],axis=-1)/totalCountSS)\n",
    "totalLogRj = np.log(np.sum(totalCountSSperR[otherSS],axis=-2)/totalCountSSperR)\n",
    "totalFreqTable[totalFreqTable==0] = 0.00001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the data set based on GOR algorihtm\n",
    "dataDsspGOR = {}\n",
    "for key, (protSeq, trueSS) in dataDssp.items():\n",
    "    SSGor, _ = predictSS(protSeq, totalFreqTable, totalLog, totalLogRj)\n",
    "    dataDsspGOR[key] = (protSeq, SSGor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code above we did not predict the SS fully correctly, because the freqTable that was used to predict the SS for a test protein also contained the frequency count of that test protein.\n",
    "\n",
    "Lastly, create the feature vector using the GOR SS data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "featVectGOR = featVectFromDict(dataDsspGOR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction SS structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training and prediction will follow the methodology of the sci-kit module. What we do is first create a pipeline. This pipeline contains preprocessing of the data and the estimator (in our case the tree). \n",
    "\n",
    "This pipeline is then trained on trainings data and can then predict unseen data. To split the data we use the function `train_test_split` where we can tell how much percentage should be trainings data. I chose 75% trainings data.\n",
    "\n",
    "One can also do cross validation which is also shown below with 5-folds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe =  make_pipeline(\n",
    "    preprocessing.StandardScaler(), # Z-normalise the input features according to the traings set\n",
    "    DecisionTreeClassifier(\n",
    "        max_depth = 5, # Let the descion tree only grow maximum 5 layers. (prevent overfitting)\n",
    "        random_state=0 # Seed used by the Tree\n",
    "    )\n",
    ")\n",
    "\n",
    "# Split the data in to test and train.\n",
    "X_train, X_test, y_train, y_test = train_test_split(featVectGOR, target, train_size=0.75, random_state=0)\n",
    "\n",
    "# Fit the classifier: on the train data.\n",
    "pipe.fit(X_train, y_train)\n",
    "\n",
    "# Do crossValidation \n",
    "CVresults = cross_validate(pipe, featVectGOR ,target,\n",
    "                        cv= 5,\n",
    "                        scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It has been chosen to limit the Tree to maximum depth of 5 (meaning only maximum 5 decesion layers). \n",
    "\n",
    "This is to prevent overfitting, because the fully grown tree is namely 19 layers deep and achieves 100% accuracy on the training data. However, one achieves higher test accuracy with a smaller tree.\n",
    "\n",
    "During the exploration a maximum depth of 3 produced an even higher accuracy as that of 5. But the class accuracy for the \"Beta\" was significantly worse. So to balance things out 5 was eventually chosen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results training.\n",
    "\n",
    "#### Cross validation results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy CV = [0.68686869 0.71717172 0.64285714 0.78571429 0.69387755]\n",
      "mean accuracy CV = 0.7052978767264481\n"
     ]
    }
   ],
   "source": [
    "print(f'accuracy CV = {CVresults[\"test_score\"]}')\n",
    "print(f'mean accuracy CV = {np.mean(CVresults[\"test_score\"])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that we get a 70% accuracy when we predict the protein family. (Not bad)\n",
    "\n",
    "Problem with the cross validation is that you cannot go much more in detail. With the manually split train and test set we can explore this further\n",
    "\n",
    "#### Train test set results:\n",
    "\n",
    "Here we explore the accuracy of the test set per protein family and vieuw the confusion matrix of the predicted test set which will give us more insight on how the protein family is predicted.\n",
    "\n",
    "Lastly, we also vieuw the F1 score per protein family. F1 score can be a better measure then accuarcy in the case of low number of predictions and class imbalance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.7073170731707317\n",
      "Accuracy per class = [0.81578947 0.51724138 0.73214286]\n",
      "Confusion matrix = \n",
      "[[31  1  6]\n",
      " [ 3 15 11]\n",
      " [ 7  8 41]]\n",
      "F1 Score per class = [0.78481013 0.56603774 0.71929825]\n"
     ]
    }
   ],
   "source": [
    "y_predicted = pipe.predict(X_test)\n",
    "\n",
    "accuracy = metrics.accuracy_score(y_predicted, y_test)\n",
    "cm = metrics.confusion_matrix(y_test, y_predicted)\n",
    "normalisedCM = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] #https://stackoverflow.com/questions/39770376/scikit-learn-get-accuracy-scores-for-each-class\n",
    "f1Score =metrics.f1_score(y_predicted, y_test, average=None)\n",
    "print(f\"accuracy = {accuracy}\")\n",
    "print(f\"Accuracy per class = {normalisedCM.diagonal()}\")\n",
    "print(f\"Confusion matrix = \\n{cm}\")\n",
    "print(f\"F1 Score per class = {f1Score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "discussion:\n",
    "\n",
    "First, we see that the accuracy on our test set is the same as that we got from the cross validation. But next we see that there are large differences in the accuracies per class. With the helix performing really well but the sheet not so much. \n",
    "\n",
    "This result is best understood with the confusion matrix. Each row represents the real protein family that is being predicted, while the columns represent the protein family in which it got predicted. \n",
    "\n",
    "We see that 11 of true \"beta\" families got predicted as Alpha/Beta. This can be explained when remembering the analysis of the GOR algorithm with one protein. There we saw that the GOR algorithm quickly liked to predict helix, while the sheet did not so quickly came up. And so \"Beta\" family probably has only beta sheets, but the GOR algorithm quickly introduce somes alpha helix in the SS sequence -> Making it look more like \"Alpha/beta\" family. \n",
    "\n",
    "This is not the case with the \"Alpha\" family, because again it seems the GOR algorithm more easily assigns stuff helix then sheet. Hence the mistake that \"Alpha\" gets mixed with \"Alpha/beta\" does not happen that much. \n",
    "\n",
    "What is certain is that the confusion of an \"Alpha\" to be \"Beta\" or reverse does almost does not happen at all. Which is expected.\n",
    "\n",
    "Lastly, we see that F1 score per family is very simular to that of the accuracy. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The important variable:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The nice thing about descion Trees is that they are not black boxes. And bases on the splitting that three has done. It is possible to asses an importance to the input features. \n",
    "\n",
    "Which in our case is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.21933894, 0.51211807, 0.        , 0.05757946, 0.09933744,\n",
       "       0.00188193, 0.02939098, 0.04120913, 0.03914406])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.steps[1][1].feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that 2 features already represent 70% of the importance in predicting the data:\n",
    "\n",
    "- 51% importance to the feature: percentage of residues that has Sheet as SS\n",
    "- 21% importance to the feature: percentage of residues that has Helix as SS\n",
    "\n",
    "Next, it the average lenght of an Helix/Sheet region seems to be the most important. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part2.5 Saving the GOR and evolution predictions\n",
    "\n",
    "Since this seemed to be aks from us. I also provide the code that stores the predicionts in a seperate text file.\n",
    "\n",
    "This is mainly the same loop as that in function `leaveOneOutAnalysis` only that we now also store the results in and predicted Protein family in the seperate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i=0/498\n",
      "i=50/498\n",
      "i=100/498\n",
      "i=150/498\n",
      "i=200/498\n",
      "i=250/498\n",
      "i=300/498\n",
      "i=350/498\n",
      "i=400/498\n",
      "i=450/498\n"
     ]
    }
   ],
   "source": [
    "def convertSStoString(SS): # Small function that [0,1,2] -> \"HEC\"\n",
    "    return \"\".join(pd.Series(SS).map(numberToStringSS).values)\n",
    "\n",
    "def createOutputFile(dataSet = \"dssp\"):\n",
    "    \"\"\"\n",
    "        Estimates the SS sequence via GOR and predicts the protein family and stores it in the output file. \n",
    "    \"\"\"\n",
    "    totalFreqTable = np.zeros(shape=(3,17,20,20))\n",
    "    totalCountSS = np.zeros(3)\n",
    "    totalCountSSperR = np.zeros(shape=(3,20))\n",
    "    otherSS = np.array([[1,2],[0,2],[0,1]]) # Have the index of the other Secundary Structure such that we can sum of the other SS\n",
    "    \n",
    "    proteinDictionary = createDataset(dataSet = dataSet)\n",
    "    \n",
    "    #First compute the full freqTable\n",
    "    for key, (proteinSequence, trueSS) in proteinDictionary.items():\n",
    "        freqTableFromProt, countSSFromProt, countSSperRFromProt = freqTableOfProtein(proteinSequence, trueSS)\n",
    "        totalFreqTable += freqTableFromProt\n",
    "        totalCountSS += countSSFromProt\n",
    "        totalCountSSperR += countSSperRFromProt\n",
    "        \n",
    "    \n",
    "    numberProteins = len(proteinDictionary)\n",
    "    with open('outputFile.txt', 'w') as file:\n",
    "    \n",
    "        for i, (key,(proteinSequence, trueSS)) in enumerate(proteinDictionary.items()):\n",
    "            if i%50==0:\n",
    "                print(f\"i={i}/{numberProteins}\")\n",
    "            # Remove the frequency counts of this protein.\n",
    "            freqTableFromProt, countSSFromProt, countSSperRFromProt = freqTableOfProtein(proteinSequence, trueSS)\n",
    "            tempFreqTable = totalFreqTable - freqTableFromProt\n",
    "            tempCountSS = totalCountSS - countSSFromProt\n",
    "            tempCountSSperR = totalCountSSperR - countSSperRFromProt\n",
    "        \n",
    "            templogDiff = np.log(np.sum(tempCountSS[otherSS],axis=-1)/tempCountSS)\n",
    "            templogDiffRj = np.log(np.sum(tempCountSSperR[otherSS],axis=-2)/tempCountSSperR) # -2 since -1 are the amino acids\n",
    "        \n",
    "            # Set Frequency that are equal to zero = 0.00001 inorder to avoid infinities.\n",
    "            tempFreqTable[tempFreqTable == 0] = 0.00001\n",
    "        \n",
    "            # Perform the GOR3 prediction.\n",
    "            SS_result, _ = predictSS(proteinSequence, tempFreqTable, templogDiff, templogDiffRj)\n",
    "            \n",
    "            # Remove the dummy entrances, such that they not influence the Q3 and CVV calculations\n",
    "            # predictInvalidResidue = True, then invalid residue have also tried to be predicted. \n",
    "            # If we then now also remove them with the following code.\n",
    "            validEntries = SS_result>=0\n",
    "            ValidSS_result = SS_result[validEntries]\n",
    "            ValidtrueSS = trueSS[validEntries]\n",
    "            \n",
    "            Q3 = proteinQ3(ValidSS_result, ValidtrueSS)\n",
    "            MCC = proteinMCC(ValidSS_result, ValidtrueSS)\n",
    "            \n",
    "            # Compute everything needed for the output file and convert it to a string\n",
    "            ValidSS_string = convertSStoString(ValidSS_result)\n",
    "            Q3avg = str(np.round(np.nanmean(Q3),2)) \n",
    "            MCCavg = str(np.round(np.nanmean(MCC),2))\n",
    "            \n",
    "            # Predict the proteinFamily\n",
    "            featureVector = np.array(featVect(SS_result)).reshape(1,9)\n",
    "            prediction = pipe.predict(featureVector)\n",
    "            family = protFamilyName[prediction[0]]\n",
    "            \n",
    "            # Now create one string that represents one line to be written to the output file\n",
    "            # line = nameproton +  predicted SS + Q3 value + MCC + predicted ProtFam\n",
    "            line = key+\",\"+ValidSS_string+\",\"+Q3avg+\",\"+MCCavg+\",\"+family+\"\\n\"\n",
    "            \n",
    "            file.write(line)\n",
    "            \n",
    "createOutputFile()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3. Using evolution information\n",
    "\n",
    "Next, we need to evaluate 6 given proteins sequences with also the SS given. On the website uniprot.org we find simular sequences using the BLAST. These sequences are then homologically related to each other, and since SS is better preserved through evolution these other protein sequences should also have the same SS. \n",
    "\n",
    "After finding these simular sequences we should allign them with our original one sequences such that we ofcourse predict the SS of each residue at the same place in the protein. This MAS is also done on the website uniprot.org and can easily be done by just highlighting the wanted sequences from the BLAST result. \n",
    "\n",
    "We then download the MAS results in a FASTA format and copy that a .txt file which we then loop through."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the FASTA file\n",
    "\n",
    "So we have saved the MAS output in a FASTA format and saved it in a .txt file. We now want to read out these alligned protein sequences such that we can predict the SS with the GOR algorithm. This is all done by the function `readFASTA` which reads out the sequences as follows:\n",
    "\n",
    "The FASTA file is structured such that each protein sequences is given one at the time. First, the protein name is announced on a line, with a \">\" in front of it, and then the sequences of the protein follows in a couple of lines. \n",
    "\n",
    "The first protein will always be our given input sequences. We use this knowledge in order to know what the valid region is that we want to do be looking at, because we only want to predict the SS of the region where our original sequence alligned. This is important to do because the MAS has introduced gaps both before and after our sequence since the other protein sequences are of greater lenght. However, we do not care for thoses regions in the other sequences before or after our sequence start. \n",
    "\n",
    "The `readFASTA` function deals with this as follows: First it reads in our original given protein sequences from the MAS. In doing so it also store the number of text lines that the sequence used, in order to read more effectif the other proteins. Then, after storing our original sequence in a long string we convert it to integers with the function `protStrToInt` (whose functionality is explained in comments there) and in doing so converts all gaps \"-\" into -1's. Then with boolian vectors we extract the first and last valid index -> These are the indexes where inbetween we want to predict the SS. Note that the distance between the first and last index maybe larger then the original sequences length because of introduced gaps from the MAS, this is all taken into account by doing it this way. \n",
    "\n",
    "Next, we extend this valid region of the residues that we want to read out by 8 to each side compared to the first and last index. We do this because we need to predict the SS from the first index to the last index. But to predict SS at the first index the GOR 3 algorithm looks at a region of lenght 8 around it. Those other sequences often have a longer sequences and hence we can use these extended regions to help predict the SS of the first index. \n",
    "\n",
    "After extending the regions, we now read in all the other sequences and after converting them intigers and we only store the residues in the valid region in a long list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def protStrToInt(inputSeq):\n",
    "    \"\"\"\n",
    "    Converts the string representing a protein sequence to numpy array with with invalid entries = -1\n",
    "    \"\"\"\n",
    "    # This function is just created such that the code below is easier readable since the conversion is just one line \n",
    "    # What happnens is the following:\n",
    "    # First we create a list of each charachter from the input string by simply: list(inputSeq)\n",
    "    # Next convert it to pandas series, since the pandas series have an easy method to covert the values\n",
    "    # with a dictionary namely: .map(dictionary). \n",
    "    # Handy of this function that if the value is not in the dictionary it is automatically Nan\n",
    "    # With a pandas series we can explicitly tell to replace NaN with -1 via. .fillnan(-1)\n",
    "    # Next extract the numpy array from series via .values\n",
    "    # Lastly convert it to an interger type since the acquired numpy array is floats.    \n",
    "    return pd.Series(list(inputSeq)).map(shortAminoToNumber).fillna(-1).values.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readFasta(proteinName = \"1arl\"):\n",
    "    listOfSequences = []\n",
    "    with open(\"Fastafiles/Fasta\"+proteinName+\".txt\",\"r\") as file:\n",
    "        # Extract relevant information on the first protein read through (which is our given sequence).\n",
    "        linesPerProt= 0\n",
    "        hasReachedNextProt = False\n",
    "        stringInputSequence = \"\"\n",
    "        file.readline(-1) # This is the protein name.\n",
    "        while not hasReachedNextProt:\n",
    "            output = file.readline(-1)\n",
    "            hasReachedNextProt= output[0]==\">\"\n",
    "            if not hasReachedNextProt:\n",
    "                stringInputSequence += output[:-1] # Remove last character since this is a always a \"\\n\" (on my linux architecture atleast)\n",
    "                linesPerProt+=1\n",
    "        inputSequence = protStrToInt(stringInputSequence)\n",
    "        firstIndex = np.where(inputSequence>=0)[0][0]\n",
    "        lastIndex =np.where(inputSequence>=0)[0][-1]\n",
    "        # The valid range is now 8 before and after the start end end of the sequence. \n",
    "        # Keeping in mind the edges.\n",
    "        validRangeLeft = 0 if firstIndex<8 else firstIndex-8\n",
    "        validRangeRight = lastIndex+8 # numpy can handle overshooting. And so no if else needed\n",
    "        inputSequence = inputSequence[validRangeLeft:validRangeRight]\n",
    "        listOfSequences.append(inputSequence)\n",
    "        # Read other proteins\n",
    "        reachedEnd = False\n",
    "        while not reachedEnd:\n",
    "            stringProteinSeq = \"\"\n",
    "            for i in range(linesPerProt):\n",
    "                stringProteinSeq += file.readline(-1)[:-1] # Remove \"\\n\"\n",
    "            listOfSequences.append(protStrToInt(stringProteinSeq)[validRangeLeft:validRangeRight])\n",
    "            nextLine = file.readline(-1)\n",
    "            if nextLine==\"\":\n",
    "                reachedEnd = True\n",
    "            \n",
    "    return inputSequence, listOfSequences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After reading in the FASTA and storing our sequences in a big list. We now also should update our trueSS for potential gaps that were introduced in our original sequence by the MAS. \n",
    "\n",
    "This is done by the following function `constructTrueSS`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def constructTrueSS(inputSeq, cleanSS): # This way we take into account potential gaps that were introduced in the MAS\n",
    "    trueSS = []\n",
    "    indexCleanSS = 0\n",
    "    for r in inputSeq:\n",
    "        if r<0:\n",
    "            trueSS.append(-1)\n",
    "        else:\n",
    "            trueSS.append(cleanSS[indexCleanSS])\n",
    "            indexCleanSS+=1\n",
    "    return np.array(trueSS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Storing the original 6 protein sequences:\n",
    "\n",
    "I have decided the way to store the 6 protein sequences is with a dictionary. This is done because 1) the sequences have different lengths and the dictionary can handle this well. 2) I can loop through each entry of the dict. and get the key name which is just a string equal to the protein name from which I can then use to dynamically acces the correct FASTA file.\n",
    "\n",
    "And ofcourse, I store the sequences in there numberical form using again the dictionaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in all the proteins:\n",
    "stringSeq_1arl = \"ARSTNTFNYATYHTLDEIYDFMDLLVAEHPQLVSKLQIGRSYEGRPIYVLKFSTGGSNRPAIWIDLGIHSREWITQATGVWFAKKFTEDYGQDPSFTAILDSMDIFLEIVTNPDGFAFTHSQNRLWRKTRSVTSSSLCVGVDANRNWDAGFGKAGASSSPCSETYHGKYANSEVEVKSIVDFVKDHGNFKAFLSIHSYSQLLLYPYGYTTQSIPDKTELNQVAKSAVAALKSLYGTSYKYGSIITTIYQASGGSIDWSYNQGIKYSFTFELRDTGRYGFLLPASQIIPTAQETWLGVLTIMEHTVNN\"\n",
    "stringSeq_1ava = \"ADPPPVHDTDGHELRADANYYVLSANRAHGGGLTMAPGHGRHCPLFVSQDPNGQHDGFPVRITPYGVAPSDKIIRLSTDVRISFRAYTTCLQSTEWHIDSELAAGRRHVITGPVKDPSPSGRENAFRIEKYSGAEVHEYKLMSCGDWCQDLGVFRDLKGGAWFLGATEPYHVVVFKKAPPA\"\n",
    "stringSeq_1avm = \"AVYTLPELPYDYSALEPYISGEIMELHHDKHHKAYVDGANTALDKLAEARDKADFGAINKLEKDLAFNLAGHVNHSVFWKNMAPKGSAPERPTDELGAAIDEFFGSFDNMKAQFTAAATGIQGSGWASLVWDPLGKRINTLQFYDHQNNLPAGSIPLLQLDMWEHAFYLQYKNVKGDYVKSWWNVVNWDDVALRFSEARVA\"\n",
    "stringSeq_1hge = \"GLFGAIAGFIENGWEGMIDGWYGFRHQNSEGTGQAADLKSTQAAIDQINGKLNRVIEKTNEKFHQIEKEFSEVEGRIQDLEKYVEDTKIDLWSYNAELLVALENQHTIDLTDSEMNKLFEKTRRQLRENAEEMGNGCFKIYHKCDNACIESIRNGTYDHDVYRDEALNNRFQIKG\"\n",
    "stringSeq_1hmo = \"GFPIPDPYCWDISFRTFYTIIDDEHKTLFNGILLLSQADNADHLNELRRCTGKHFLNEQQLMQSSQYAGYAEHKKAHDDFIHKLDTWDGDVTYAKNWLVNHIKTIDFKYRGKI\"\n",
    "stringSeq_1jsu = \"HPKPSACRNLFGPVDHEELTRDLEKHCRDMEEASQRKWNFDFQNHKPLEGKYEWQEVEKGSLPEFYYRPPRPPKGACKVPAQES\"\n",
    "dictSeq = {}\n",
    "dictSeq[\"1arl\"] = protStrToInt(stringSeq_1arl)\n",
    "dictSeq[\"1ava\"] = protStrToInt(stringSeq_1ava)\n",
    "dictSeq[\"1avm\"] = protStrToInt(stringSeq_1avm)\n",
    "dictSeq[\"1hge\"] = protStrToInt(stringSeq_1hge)\n",
    "dictSeq[\"1hmo\"] = protStrToInt(stringSeq_1hmo)\n",
    "dictSeq[\"1jsu\"] = protStrToInt(stringSeq_1jsu)\n",
    "stringSS_1arl = \"CCCCCCCCCCCCCCHHHHHHHHHHHHHHCCCCEEEEEEEECCCCCEEEEEEECCCCCCCCEEEEEECCCCCCHHHHHHHHHHHHHHHHHCCCCHHHHHHHHHCEEEEECCCCHHHHHHHHHCCCCCCCCCCCCCCCCCCCCCHHHCCCCCCCCCCCECCCCCCCECCCCCCCCHHHHHHHHHHHHHCCEEEEEEEEECCCEEEECCCCCCCCCCCHHHHHHHHHHHHHHHHHHHCCCCEEEEHHHHCCCCCCCHHHHHHHCCCCEEEEEEECCCCCCHHHCCHHHHHHHHHHHHHHHHHHHHHHHHC\"\n",
    "stringSS_1ava = \"CCCCECECCCCCECECCCEEEEEECCHHHCCCEEEEEECCEEEEEEEEECCCCCCCCCCEEEEECCCCCCCCECECCCCEEEEECCCCCCCCCCECEECCCCECCECEEECCCCCCCCCCCHHHCEEEEECECCCCCCEEEEEECCCEEECEEECCCCCCCCEEECCCCECCEEEEEECCC\"\n",
    "stringSS_1avm = \"CCCCCCCCCCCCCCCCCCCCHHHHHHHHHCHHHHHHHHHHHHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHHHHHHHHHHCECCCCCCCCCCCHHHHHHHHHHHCCHHHHHHHHHHHHHCCCCCEEEEEEEECCCCEEEEEEEECCCECCCCCCEEEEEEECCHHHCHHHHCCCHHHHHHHHHHHECHHHHHHHHHHHCCC\"\n",
    "stringSS_1hge = \"CCCCCECCCECCCECCCCCCCEEEEEEECCEEEEEEEHHHHHHHHHHHHHHHHHHHCCCCEECCCCCCCCCCCCCHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHEEECCCCEEEECCCCCHHHHHHHHCCCCCCHHHHHHHHHHHCCCCC\"\n",
    "stringSS_1hmo = \"CCCCCCCCCCCHHHCCCCHHHHHHHHHHHHHHHHHHHCCCHHHHHHHHHHHHHHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHCHHHHCCCC\"\n",
    "stringSS_1jsu = \"CCCCCCCCCCCCCCCHHHHHHHHHHHHCCCCHHHHHHHCEECCCCEECCCCCCCEEEECCCCCHHHHCCCCCCCCCCCCCCCCC\"\n",
    "dictSS = {}\n",
    "dictSS[\"1arl\"] = np.vectorize(shortSStoNumber.get)(list(stringSS_1arl)) # Another way to convert string to numbers. Given a dictionary:\n",
    "dictSS[\"1ava\"] = np.vectorize(shortSStoNumber.get)(list(stringSS_1ava))\n",
    "dictSS[\"1avm\"] = np.vectorize(shortSStoNumber.get)(list(stringSS_1avm))\n",
    "dictSS[\"1hge\"] = np.vectorize(shortSStoNumber.get)(list(stringSS_1hge))\n",
    "dictSS[\"1hmo\"] = np.vectorize(shortSStoNumber.get)(list(stringSS_1hmo))\n",
    "dictSS[\"1jsu\"] = np.vectorize(shortSStoNumber.get)(list(stringSS_1jsu))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "The analysis of a specific protein is done with the function `analyseProteinViaEvolution`, which extracts first the necessary information from the dictionary and calls the `readFasta` function to get a list of all the proteins that need to be evaluated. \n",
    "\n",
    "We then loop through the list of all the proteins and use the function `predictSS` to predict the SS. When doing so we use the full frequency table, that was defined above, which was contructed from the whole dssp dataset. \n",
    "\n",
    "After, getting the SS of all the protein we only keep the result off those residues on which the original input sequence is defined. Important is that we use the function `constructTrueSS` which allows us to deal with potential gaps that where introduced in the original sequence by the MAS.\n",
    "\n",
    "The function then returns the Q3, MCC and resulting SS sequence.\n",
    "\n",
    "\n",
    "One now simply needs to loop through all the protein sequence of the dictionary and apply `analysisProteinViaEvolution` on each of them. We then store all the results in a giant numpy array from which we can then plot our results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyseProteinViaEvolution(protName=\"1arl\"):\n",
    "    cleanSS = dictSS[protName] # Get clean SS out of the dictionary. \n",
    "    inputSeq, listOfProtein = readFasta(protName)\n",
    "    trueSS = constructTrueSS(inputSeq,cleanSS)\n",
    "    \n",
    "    predictionsSS = -np.ones((len(listOfProtein),len(inputSeq)))\n",
    "    for i, proteinSeq in enumerate(listOfProtein):\n",
    "        predictionsSS[i] , _ = predictSS(proteinSeq, totalFreqTable, totalLog, totalLogRj)\n",
    "    \n",
    "    # Only evaluate relevant Regions:\n",
    "    relevantRegion = predictionsSS[:,trueSS>=0]\n",
    "    SS_result = np.max(relevantRegion,axis=0)\n",
    "    Q3 = proteinQ3(SS_result,cleanSS)\n",
    "    MCC = proteinMCC(SS_result,cleanSS)\n",
    "    return Q3, MCC, SS_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The choisen sequences for MAS and GOR analysis\n",
    "\n",
    "The analysis result depend heavenly on the chosen sequences that are being MAS and GOR predicted. \n",
    "The max number of choisen sequences was 25, but the ones that were chosen was also largely based on the identity result from the BLAST. \n",
    "\n",
    "For some proteins BLAST results, there were sequences with 100% identity, meaning it was the same sequence. When doing the analysis I left in our original input sequence (since for some proteins there was no 100% match). But I also included at least one 100% identity sequence since these contain side information that our original sequences does not have. \n",
    "\n",
    "In the list below I give details on the sequences choisen for the MAS for each protein and also included the Blosum matrix that was used in the BLAST:\n",
    "\n",
    "- 1arl: Blosum62, Many High identity: Took first 25 (including 2 100%) overall all are at: identity >82% \n",
    "- 1ava: Blosum62, Not so many high: Took first 15 who are identity >60% \n",
    "- 1avm: Blosum62, Many in 70% identity: Took first 25. \n",
    "- 1hge: Blosum62 , Many high identity>95%, Took first 25\n",
    "- 1hmo: Blosum62, Not many high identity, Took first 5 who are identity>70%, The next best was at identity 49%\n",
    "- 1jsu: Blosum80, MANY high identity>95%, Only took one 100% (there were 4), and then spreaded out took 24 between >96%\n",
    "\n",
    "When I say \"took first\". I took the first $n$ results from the BLAST sequence orderd by \"Score\". The score closely relates to identity and so it can be represented as taking the first $n$ in identity.\n",
    "\n",
    "It is important to take ones that are closely related to the original one. Since if the sequence has to low identity you run the risk that the SS of the homologeous sequence is different. That is why I stick to higher identity.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the resulting Q3, MCC score and save them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evolution results.\n",
    "Q3EvoResults = np.zeros((6,3,2))\n",
    "MCCEvoResults = np.zeros((6,3,2))\n",
    "for i, (key, cleanSS) in enumerate(dictSS.items()):\n",
    "    Q3EvoResults[i,:,0], MCCEvoResults[i,:,0], _ = analyseProteinViaEvolution(protName=key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now also compute the Q3 and MCC score of the protein sequence if we did not use any evolutionary information at all in order to compare:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Its own results.\n",
    "for i, (key, sequence) in enumerate(dictSeq.items()):\n",
    "    SSPredictAlone, _ = predictSS(sequence, totalFreqTable, totalLog, totalLogRj)\n",
    "    Q3EvoResults[i,:,1] = proteinQ3(SSPredictAlone, dictSS[key])\n",
    "    MCCEvoResults[i,:,1] = proteinMCC(SSPredictAlone, dictSS[key])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Put potential nan results equal to zero:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q3EvoResults[np.isnan(Q3EvoResults)] = 0\n",
    "MCCEvoResults[np.isnan(MCCEvoResults)] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the results in a figure where we can compare evolution versus not evolution for the different MCC and Q3 por SS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.94, 'Result evolution GOR III algorithm')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA30AAAKICAYAAADEopOnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeZglZXn38e+PYRlERIXRKCiDsgiCoo4IcUMFM4qCGzqIC0bC64KvcYlgTAjyuoAajVGMIpohSgbUKAIScQOViDijggQQRBydwW1AREAWB+73j6oeD2d6OdPTffrM6e/nus7Vp6qeqrqr+vTT567nqadSVUiSJEmShtNGMx2AJEmSJGn6mPRJkiRJ0hAz6ZMkSZKkIWbSJ0mSJElDzKRPkiRJkoaYSZ8kSZIkDTGTPkmaZkkWJxmK5+MkqSSLp2nbx7bbnz8d29f0SnJY+/vbd0OLZZBil6TpYNInaYOTZN/2C1rn6+YkP0jyhiQbz3SME0nynCTHznQc/bahHHeSuUlek+QbSVYl+VOS3ydZmuSEJA8bY737JXlPksuS3JLkpiQ/THJMknuNUn7KPsvtumd3zTs/yc1d80aS6wW9bnsYJNmzPfb5Mx2LJPWbSZ+kDdkS4KXAy4C3A5sA7wc+MpNB9eg5wD/NdBAzYLzjfgewOfDz/oWztiQPAX4AnEjzf/IDwBHAPwI/Av4auCzJtl3r7QNcDvwt8D3gjcBRwNXAscAlSXYeY7cb8md5EH2K5rP0rY55e9J89ubPRECSNJMG/mq4JI3jB1X16ZGJJB8BfgwcnuRtVbVq5kLTuqqq1cDqmYwhyebAl4CHAs+rqi+MUmYu8AagOub9BfBFmv+rj6+qpR2rfCTJM4EzgDOTPKqqbu3arJ/lKZBky6q6qaruBO6c6XgkaVDY0idpaFTVLcB3gdB8aV8jyQOS/FuSXyS5I8kvk5yU5H5d5e6b5ANJfprktiTXJ/l+kr/rKDPSJe+w7hh6uX8vyfnAy9v3nd361treKOsuSPKFJNcluT3JlUne1tkNMMnp7TFuPcr6u7T7+peOeRsnOSrJ5R3H/IUke/QQz/x2e8eOsuxu9+hNdNzd5bv28akkv2mP+adJ3pXkHmPsb5d2+cq2/CVt0tWLw4GHAe8dLeEDqKrbqurdVfXLjtl/B8wD/r4r4RtZ5xzgX4BdgFdOFMR4n+V+SvKwJB9pu6velOSP7d/D4euwjflJ/ivJH9rXF5PskGR5+5noLn9427311iQ3JvlKkieMUq7av7enJbmg7cZ6VrvsbvfotZ/Pf29XPa/js7e4a7MbJXlz+xm7PclVSV4+zr6fmuTC9rysTHJUu/w+ST6R5LftsrOTPLDXcyZJU82WPknDZuQL8u9GZiR5MHAhsCnwCeCnwI7Aq4GnJFlQVTe2xT8LPAn4KE1Xvs2BXYF9gfdOUYzvpLno9kSaLn0jvjPeSkkOAD5P013wn2mOcR/gOJquawe3RU8BXggcAny4azMv6ygz4tS2/FeBfwP+AngtcGGSJ1bVD9fh2MazzsedZHuarpJb0XR1/AnN7+KtwOOTPK1tIex0CvAn4H00v/O/Bc5IsnNVLZ8gxhe0P0/u4Xg6PR+4A1g8TpmP0ySHz2ft38to1vosz4B9af4ezgZ+BmxB8zn7eJJ5VfXu8VZuLzx8G7g/zd/UFTS///PabXWXPwF4C83v/O+BLWm61p6X5KA2ee60gOZ8fpy7f6a7fR54QLutd7VxQFMXdHoXzd/8x4DbaeqIxUmurqr/6Sr7KODZwEnAf9D8DR2f5DaaixvLabr17gj837bMfuPEKEnTp6p8+fLla4N60XwRLeAYYBuaFpY9aO7BKuCirvJfBH4LbNc1fwFNd8Jj2+mt2vU/0uP+Dxtl2eKmal33eRPscy7wa5p7lDbuWjbS1XDfdnoO8Cvge13lQnO/3I865u3frns6kI75j2zPzbe7tlHA4o7p+e28Y0eJ+dh22fxejnuM8qe2857ZVfa97fxXjrL+2V3H8th2/rt7OM/XAzeOMn9O+1nrfG3eLtuy3f6Petj+H4DrJvtZnmDbBZzdNe984OYxzvOCHra5xSjzNmq3eyOwScf8wzo/h+2897TzDu3axsj88zvm7QLcBVwAbNox/4HA72mSqDldx1vAfqPEOFosa80bZdkPu/a9LU3yt2SUc30X8LiOeZvS/N3dBfxrV/n3t+vs0uvv05cvX76m8mX3TkkbsrcDq2gSuh8Br6G5on/QSIEkWwHPAs4EbkuyzciL5kvk1cDT2+K30nzBe1x3F8MBsD9Na8m/A/fuOo6R1o+nA1RzP9OpwGNz91Em9wUezN1bRJ7b/nxnVa3pllpVl9B0lXtCknnTcDwTSrIRcCDww1q7hefdNF+un7vWivDBrmNZCtwM7NTDbu9Fk5h125Xms9b5em3HOtAkQRP5A83FhW4TfpZnQjXdTIE1I5puDdwX+ArNcY86immHZ9MkQku65r9vlLIH0VyYeE9V3dERwy9pPvfb07Sudbqkqr7Ww6H06iNd+74WuIrRPzsXVtVFHWXvoGmhDPCvXWW/3f7s5TMoSVPOpE/ShuwkmmTomTSjJP4O2A64raPMLjR13StZ+0v7qnb5/WHNl7a/BXYHftbex/ShJE/ry9GMb9f25ydZ+xh+3C67f0f5kcTuZR3zXkYzuMWpHfN2oEmermBtl3WUmQnzgHt2xLFGVf2OJpl4yCjrXTPKvOuBte5xHMUf+HMS1+lnNJ+1/YE3j7IOjJ7MdbsXoyeHvXyW+y7JPZO8L8kvaC6KXEfzmXtnW+Q+E2xiB+Dqqrqrc2ZV/Zam9a67LIzy++6Y1/37vmqC/a+rdfnsjFb2hvbnz8aY38tnUJKmnPf0SdqQ/aTjKv9/J7mApmvYR4FF7fy0Pz/N2Pf8rBlJsao+muSLwAHAk2nu8ToyyelVNbLN8QZqma56deQ4/g64eIwyawYWqapLk1wMHJrkbTT3KT0f+EpV/XoK45qJczGRsUZtzBjzO/0v8KQkO1TVmi/ubYvX1wCS3O0ewqq6qU2Kdklyj6r646g7T3ak6Qp6/iiLe/ksz4T/pGkpP4mma/H1NOf3mTTdimf64vGo53o9rMtnZ8zRQdvW9l63I0nTzqRP0tCoqu8k+RTwsiT/WlXfoem+WTT36fTUDayqfkUzkMfJSebQPPPrkCT/3HYVHBlY476jrD5ay9Oou+mx3IiftD9vWYfubKfQPGPuKTSDWGzJ2onvNTRf3Hel6VbYabf2Z3erRad1PRfrctyrgJuAh3cvSHIfmmMaKwGerM/RDFxyOPC2dVjv8zStxC+jSdRGc3hH2XGN8VnuqyT3pkn4PlVVr+pa1uuAJMuBHZNs1Nnal2bU3Ht3lR1pOXs4aw+wsltXmclY1785SRoaM32FTpKm2v+juQJ/HEBVXU9zz9vzkuzdXTiNee37e3Q/BqC9Yj+SDI0kNj+jGeTkbl98k/wlsNY+xnBzu85oydJozqW53+vo0dZJsnmSLbtm/2cb58va1400g9p0OqP9+dYka1ohkuxOcz/dBTXOM+Kq6iaaAWae2rX+Q2gexN6t5+Nuk4SzgEclWdi1+Gia/2GjPlZhPZxM013275KMdr8gjN5a816aVrB3J3n0Wiskf0XzsParaEaQ7cXdPsszYKS16m7Hm+QB/DmBnchZNMn5IV3zu7vIQnPfbdGc+0269vcKmkGI1mck2Zvbn73+zUnS0LClT9JQqaqrk5xG063xiVX1bZph1y8AvpXkP2i+OG5E0xJ1EM1Q6scCOwPfTPIFmm5+N9C0gL2aJtH7druPm9vnex2eZAlNd72daL6Y/ohm5MuJfBc4kubB3V+iecTARZ1dCruO65YkL6NJ0q5M8kmaVsx70wym8TyaQU3O71jnt0n+m6aL6lzgE1V1W9d2v5rkMzRdCO+T5Gz+/MiG22iGmp/Ih4F30HRLPINmtMVX0ZzDx67PcdMM278/zSMXPtIe85OAF9F0NxxvmP51VlW3to/GOBv4fJrnyH2FJrEdGbjkRTQJ0YqO9X6Z5Dk0SfWFSU5tj3UOzQA6LwB+ARw4VvfPUWIZ7bPcN2231a8AL0lyK7CUZjCV/0Pz99DL/WknAC8G/j3JXjQJ9ROBv6S5P7BzwJ0rk7yX5pEN30pyOn9+ZMM9aUYAXZ8Hri+luX/1bW1L8S3AzzoHY5GkYWXSJ2kYvZOmZeE44ClVtSLJY2gGyDgIeAlNQrOCpiXiM+16K2gGSnkKTSvVZsC1NM8AO6Hry/obaFpAnttu8/s0IxUeQW9J3xKakQgX0Tz3bCOapHHMrpRVdW6Sx9K0cr2EZqCTG2i6wr2ftbtnQpMUPbt9/x9jbPpQ4Ac0w9b/M82X4W8C/1hVl/ZwLCfQDGLyUpoE53KagXMew9pJ3zodd1X9PMnjaH6XL6FJclfSjN75jlr7GX3rraquaT8vf02TrL2J5vhuoUk6T6ZJoK/sWu+CJA+nacU6oD3Gu9p1jgP+pf78PMhe3e2zPOmDmryXAMfTfIZeTtPN+G00yfq/j7MeAFV1XZoHq/8zzfksmmf0PYUmCbu1q/xRSa6mGb30eJpnH14EvHh9k96q+kWSv6apB/4N2ITm78OkT9LQS8eo1pIkSdOuffTDdcDHuu8XlCRNPe/pkyRJ0ybJ5qPMPrr9+dV+xiJJs5UtfZIkadokOY9mEJYf0FxsfhrNqKDfAZ60nvfpSZJ6YNInSZKmTZI30YweO5/meZEraR5b8fZ29FdJ0jQz6ZMkSZKkIeY9fZIkSZI0xEz6JEmSJGmImfRJkiRJ0hAz6ZMkSZKkIWbSJ0mSJElDzKRPkiRJkoaYSZ8kSZIkDTGTPkmSJEkaYiZ9kiRJkjTETPo0KyTZPMlZSW5M8tlp2P78JJVk43b6v5O8fKr3I2k4rG+d1NY3O05HbJI0mj58l7osyb7t+2OTfHqq9zGbmfTNIkkOS3Jpkj8m+XWSjyTZqmP5oiRXtn/Mv01ySpJ7zWTMU+gFwP2Bravq4O6FY1Uuk/1iVVXPqKpTJheqNDtYJ41bJ907ySfb83JTkquSHD3dQSVZnOQd070faUNlvTV2vQWQZOckn01yXXsOfpTkjUnmTLTxqnp4VZ0/xTGrZdI3SyR5E3AC8HfAVsDewHzgK0k2aYv9D/D4qtoKeAiwMTAj//xHWsym0PbAVVW1eoq3K2kSrJMmrJM+ANwT2JXm/BwIXD3FMUhaB9Zb49dbSR4KXASsAPZoz8HBwAJgyymORevIpG8WaK8wvR14XVV9uar+VFXLgRfSVEgvBqiqFVV1XceqdwKjtnKl8YH2KtYf2qteu7fLNk/yz0l+3l7luSDJ5u2yA9vm+98nOT/Jrh3bXJ7kqCQ/Am5JsnGSvZN8py1/yUiz/xgx7dpu8/ftPg5s578dOAZ4UZKbk7xykudxoyRHJ/lpkuuTfCbJfccoe36Sw9v3/5bkvzqWnZDk60kymTikDZ11Uk910mOB/6yqG6rqrqr6cVV9rqvMfkl+0m7/xM46JclfJ7kiyQ1Jzk2yfceyhyX5apLftS0SL2znHwEcCryljeussY5Nmm2st3qqt94OfKeq3lhVv2rPx5VV9eKq+n2Pse833u9B66GqfA35C1gIrAY2HmXZKcCpHdNPAG4ECrgFePoY2/wr4PvAvYHQXI1+QLvsROB8YFtgDvCXwGbAzu029wc2Ad5Cc+V603a95cDFwIOAzdv1rweeSXOBYv92et4o8WzSbuvvgU2BpwI3Abu0y48FPj3OORp1eXsedmzfvx74LrBdezwfA5a0y+a3ZTdup88HDm/f3wO4CjgMeCJwHbDdTH8ufPmaqZd1Uk910snAZcArgJ1GWV7A2e3xPhhYBSxslx3U7ntXmlaGf6D5IgawBc1V+Fe0yx7V1km7tcsXA++Y6c+IL1+D9rLe6qne+jXwinGW9xL7fr3sy9e6v2zpmx22Aa6r0ZvjfwXMG5moqguqaY7fDngvzR/gaP5E01T/MCBVdUVV/SrJRsBfA6+vqmur6s6q+k5V3Q68CPhSVX21qv4EvI+mQvrLju3+azVXyW4FXgKcU1XnVHOl+6vAMpqKq9veNF2hjq+qO6rqGzRfiA7p5QS1XtheeVrz6lr+KuBtVbWyPZ5jgRdkgu4TVfVH4KXA+4FP01wlXLkOcUnDxjppYq8DTgWOBC5PcnWSZ3SVOb6qfl9VvwDOA/Zs578KeHd7DlYD7wL2bFv7ngUsr6p/r6rVVfVD4L9oumBJGpv11sS2bs/FWHqJXdPEpG92uA7YZozk5AHt8rupqmuBLwOnjbbBtiL4MM2VqN8mOant+rANMBf46SirPRD4ecc27qK54rxtR5kVHe+3Bw7uSsKe0MY82rZXtNsc8fOubU/kM1V1785X1/LtgS90xHIFTbeN+0+04aq6CLiG5kreZ9YhJmkYWSdNoKpurap3VdVjaL5IfQb4bO7epfzXHe//SPNlbSTOD3bE+Duaumfbdtnjuo7hUOAveolLmsWstyZ2/RjbXZfYNU1M+maHC4Hbged1zkxyT+AZNN0HRrMx8NCxNlpV/9p+IdmNpsn+72gqvdvGWO+XNJXPyP5D0/3g2s7NdrxfAXyqKxHboqqOH2PbD2qvjo14cNe219cK4Bld8cxtK/VxJXktTbeMX9J0Z5BmM+ukdVBVf6BprdsC2KGHVVYA/6crzs2r6jvtsm92LbtnVb16lOOV9GfWWxP7GvD8cZb3ErumiUnfLFBVN9LcXPuhJAuTbJJkPs2V4+touhCR5NAkD27fbw+8E/j6aNtM8tgkj0szWtUtNJXTXe1Vm08C70/ywCRzkuyTZLN2fwckeVq73ptoKtDvjBH6p4FnJ/mrdjtzk+ybZLtRyl5Ec6X7Le3x7Qs8mzGurk3SR4F3tueGJPOSHDTRSkl2phm56yU03TzfkmTP8deShpd10sSS/GN7TJsmmUtzT/HvgSt7WP2jwFuTPLzd1lZJRrpvng3snOSlbVybtPsZGUzhNzSDUkjqYL3Vk38C/jLJe5P8RXuMOyb5dJJ7TyJ2TSGTvlmiqt5Dc2Pu+2huyv0ZzQAj+1XVLW2x3YDvJLmFZsjhK4G/GWOT9wI+DtxA01R/PU2/dYA3A5cCS2m6FZ0AbFRVV9IkPh+iqSCfDTy7qu4YI+YVNAMS/D3NIAUraK6ArfW5bbfxbJqrbdcBHwFeVlU/nuDUrIsPAmfSDM18E82gLo8bb4W2G8ingROq6pKq+gnN8XyqrbylWck6aUIF/Hu77i9pBj44oKpunnDFqi+0x3hakj8A/9vGQVXdBDwdWNRu99dt2ZH66BPAbm03sDN6jFWaFay3xldVPwX2oRnc7rIkN9LcM7wMuGldY9fUSpU9OWajJK8AjqN5lswvZjoeSbObdZKkDY31ljYkJn2zWJKXAn+qqqnsAilJk2KdJGlDY72lDYVJnyRJkiQNMe/pkyRJkqQhZtInSZIkSUNstAdMbnC22Wabmj9//kyHIWkKff/737+uqubNdBzrw7pJGk7WT5IG0Xh101AkffPnz2fZsmUzHYakKZTk5zMdw/qybpKGk/WTpEE0Xt1k905JkiRJGmImfZIkSZI0xEz6JEmSJGmIDcU9fZIkSZIG35/+9CdWrlzJbbfdNtOhbLDmzp3LdtttxyabbNLzOiZ9kiRJkvpi5cqVbLnllsyfP58kMx3OBqequP7661m5ciU77LBDz+vZvVOSJElSX9x2221svfXWJnyTlIStt956nVtKTfokSZIk9Y0J3/qZzPkz6ZMkSZI0q5xxxhkk4cc//jEAy5cvZ/fdd5/hqKaP9/RJkiRJmhHzj/7SlG5v+fEH9FRuyZIlPOEJT2DJkiW8/e1vn9IYBpEtfZIkSZJmjZtvvpkLLriAT3ziE5x22mlrLb/tttt4xStewR577MGjHvUozjvvPAAWL17M8573PBYuXMhOO+3EW97yljXrfOUrX2Gfffbh0Y9+NAcffDA333xz346nF7b0SZIkSYPi2K0mud6NUxvHEPviF7/IwoUL2Xnnndl66635/ve/z9Zbb71m+YknnkgSLr30Un784x/z9Kc/nauuugqAiy++mB/+8Idsttlm7LLLLrzuda9j88035x3veAdf+9rX2GKLLTjhhBN4//vfzzHHHDNTh7gWkz5JkiRJs8aSJUt4/etfD8CiRYtYsmQJRx555JrlF1xwAa973esAeNjDHsb222+/Jul72tOexlZbNYn5brvtxs9//nN+//vfc/nll/P4xz8egDvuuIN99tmnn4c0IZM+SZIkSbPC7373O77xjW9w6aWXkoQ777yTJLz2ta/taf3NNttszfs5c+awevVqqor999+fJUuWTFfY682kT5rlJnMDda83SUuSJA2Sz33uc7z0pS/lYx/72Jp5T37yk1mxYsWa6Sc+8YmceuqpPPWpT+Wqq67iF7/4Bbvssgs/+MEPRt3m3nvvzWtf+1quvvpqdtxxR2655RauvfZadt5552k/nl45kIskSZKkWWHJkiU897nPvdu85z//+bz73e9eM/2a17yGu+66iz322IMXvehFLF68+G4tfN3mzZvH4sWLOeSQQ3jEIx7BPvvss+ZREIMiVTXTMay3BQsW1LJly2Y6DGmDNKgtfUm+X1ULpn1H08i6SRpO1k+aVkM+kMsVV1zBrrvuOtNhbPBGO4/j1U229EmSJEnSEOt70pdkYZIrk1yd5OhRlj84yXlJfpjkR0me2e8YJUmSJGlY9DXpSzIHOBF4BrAbcEiS3bqK/QPwmap6FLAI+Eg/Y5QkSZKkYdLvlr69gKur6pqqugM4DTioq0wB92rfbwX8so/xSZIkSdJQ6fcjG7YFVnRMrwQe11XmWOArSV4HbAHs15/QJEmSJGn4DOJALocAi6tqO+CZwKeSrBVnkiOSLEuybNWqVX0PUpJGY90kaVBZP0mzV79b+q4FHtQxvV07r9MrgYUAVXVhkrnANsBvOwtV1UnASdAMOzxdAWvDMJnHDoAPGdfUs26SNKisn6TGnDlz2GOPPdZML1q0iKOPXmt8yQnNnz+fZcuWsc0224xZZvHixTz96U/ngQ98IACHH344b3zjG9ltt+5hTaZXv5O+pcBOSXagSfYWAS/uKvML4GnA4iS7AnMBL0dJkiRJw2ayzyUcc3sTP69w88035+KLL57a/Y5h8eLF7L777muSvpNPPrkv++3W1+6dVbUaOBI4F7iCZpTOy5Icl+TAttibgL9JcgmwBDishuEJ8pIkSZIG0pe//GUOPvjgNdPnn38+z3rWswBYsmQJe+yxB7vvvjtHHXXUWusuX76c3Xfffc30+973Po499lg+97nPsWzZMg499FD23HNPbr31Vvbdd1+WLVs27nbvec978ra3vY1HPvKR7L333vzmN79Z7+Pr+z19VXVOVe1cVQ+tqne2846pqjPb95dX1eOr6pFVtWdVfaXfMUqSJEkaTrfeeit77rnnmtfpp5/Ofvvtx0UXXcQtt9wCwOmnn86iRYv45S9/yVFHHcU3vvENLr74YpYuXcoZZ5zR035e8IIXsGDBAk499VQuvvhiNt988zXLxtvuLbfcwt57780ll1zCk570JD7+8Y+v9zEP4kAukiRJkjQtRrp3jrxe9KIXsfHGG7Nw4ULOOussVq9ezZe+9CUOOuggli5dyr777su8efPYeOONOfTQQ/nWt7613jGMt91NN910TSvjYx7zGJYvX77e++v3PX2SJEmSNHAWLVrEhz/8Ye573/uyYMECttxyy57W23jjjbnrrrvWTN92223rFccmm2xCEqAZdGb16tXrtT2wpU+SJEmSePKTn8wPfvADPv7xj7No0SIA9tprL775zW9y3XXXceedd7JkyRKe/OQn3229+9///vz2t7/l+uuv5/bbb+fss89es2zLLbfkpptuWmtfvWx3KtnSJ0mSJGnWGLmnb8TChQs5/vjjmTNnDs961rNYvHgxp5xyCgAPeMADOP7443nKU55CVXHAAQdw0EEH3W17m2yyCccccwx77bUX2267LQ972MPWLDvssMN41atexeabb86FF164Zn4v251KGYaBMRcsWFAjo+BodvI5fZM3mXPXj/OW5PtVtWDadzSNrJuk4WT9pGk12UcY9PCogkFwxRVXsOuuu850GBu80c7jeHWT3TslSZIkaYiZ9EmSJEnSEDPpkyRJkqQhZtInSZIkqW+GYUyRmTSZ82fSJ0mSJKkv5s6dy/XXX2/iN0lVxfXXX8/cuXPXaT0f2SBJkqSpN8ijUA5ybENuu+22Y+XKlaxatWqmQ9lgzZ07l+22226d1jHpkyRJktQXm2yyCTvssMNMhzHrmPRJWndeIZUkSdpgeE+fJEmSJA0xkz5JkiRJGmJ275QkSZK04fP2kzHZ0idJkiRJQ8ykT5IkSZKGmEmfJEmSJA2xvt/Tl2Qh8EFgDnByVR3ftfwDwFPayXsA96uqe/c3SknaQHj/giRJmkBfk74kc4ATgf2BlcDSJGdW1eUjZarqDR3lXwc8qp8xSpIkSdIw6Xf3zr2Aq6vqmqq6AzgNOGic8ocAS/oSmSRJkiQNoX4nfdsCKzqmV7bz1pJke2AH4Bt9iEuSJEmShtIgD+SyCPhcVd052sIkRyRZlmTZqlWr+hyaJI3OuknSoLJ+kmavfid91wIP6pjerp03mkWM07Wzqk6qqgVVtWDevHlTGKIkTZ51k6RBZf0kzV79TvqWAjsl2SHJpjSJ3ZndhZI8DLgPcGGf45MkSZKkodLXpK+qVgNHAucCVwCfqarLkhyX5MCOoouA06qq+hmfJEmSJA2bvj+nr6rOAc7pmndM1/Sx/YxJs5jPOJMkSdKQ63vSpw3X/KO/NKn1lh9/wBRHIkmSJKlXgzx6pyRJkiRpPZn0SZIkSdIQM+mTJEmSpCHmPX0DZjL3zXnPnCRJkqSx2NInSZIkSUPMlj5JkiRJmk4z/JgwW/okSZIkaYiZ9EmSJEnSEDPpkyRJkqQhZtInSZIkSUPMpE+SJEmShphJnyRJkiQNMR/ZIEmSJGlgzD/6S5Nab/ncKQ5kiNjSJ+IBU7oAACAASURBVEmSJElDzKRPkiRJkoaYSZ8kSZIkDTGTPkmSJEkaYiZ9kiRJkjTE+j56Z5KFwAeBOcDJVXX8KGVeCBwLFHBJVb24r0FKU2jSI1Adf8AURyJJkqTZqK9JX5I5wInA/sBKYGmSM6vq8o4yOwFvBR5fVTckuV8/Y5QkSZKkYdLv7p17AVdX1TVVdQdwGnBQV5m/AU6sqhsAquq3fY5RkiRJkoZGv5O+bYEVHdMr23mddgZ2TvI/Sb7bdgeVJEmSJE3CIA7ksjGwE7AvcAjw8ST37i6U5Igky5IsW7VqVZ9DlKTRWTdJGlTWT9Ls1e+BXK4FHtQxvV07r9NK4KKq+hPwsyRX0SSBSzsLVdVJwEkACxYsqGmLWJLWwfrUTZMZ9Gf53HVeRdIs5Xcnafbqd0vfUmCnJDsk2RRYBJzZVeYMmlY+kmxD093zmn4GKUmSJEnDoq9JX1WtBo4EzgWuAD5TVZclOS7JgW2xc4Hrk1wOnAf8XVVd3884JUmSJGlY9P05fVV1DnBO17xjOt4X8Mb2JUmSJElaD4M4kIskSZIkaYqY9EmSJEnSEDPpkyRJkqQhZtInSZIkSUPMpE+SJEmShphJnyRJkiQNsb4/skGSJEmaCvOP/tKk1ls+d4oDkQacLX2SJEmSNMR6TvqS3C/JCUm+nuSqJA9v578+yT7TF6IkSZIkabJ6SvqS7AX8BHg+sBx4KLBZu/gBwJumIzhJkiRJ0vrptaXvA8B5wM7A/wHSsex7wF5THJckSZIkaQr0OpDLo4GDququJOladj1wv6kNS5IkSZI0FXpt6bsRmDfGsocAv5macCRJkiRJU6nXlr4zgbcnuRD4eTuvkmwDvBn4/HQEJ0nawB271STWuXHq45AkaRbrtaXvKOAPwOXAt9p5HwWuBG4Fjpn60CRJkiRJ66unlr6quiHJ3sBLgacBtwC/A04G/qOqbp++ECVJkqQNz2QeHu+D4zUdJkz6ksyl6d75rqr6BPCJaY9KkiRJkjQlJuzeWVW3AY8F5kx/OJIkSZKkqdTrPX1nAs+ZzkAkSZIkSVOv19E7zwXem+QBwDk0j2iozgJVdU4vG0qyEPggTcvhyVV1fNfyw4D3Ate2sz5cVSf3GKckSZIkqUOvSd+n25/Pa1/dih66fyaZA5wI7A+sBJYmObOqLu8qenpVHdljbJIkSZI07SYzOA/M/AA9vSZ9O0zR/vYCrq6qawCSnAYcRPMoCEmSJEnSFOv1kQ0/n7hUT7YFVnRMrwQeN0q55yd5EnAV8IaqWjFKGUmSJEnSBHodyIUkGyd5UZIPJTm1/fnCJL22FvbqLGB+VT0C+CpwyhjxHJFkWZJlq1atmuIQJGlyrJskDSrrJ2n26inpS3I/YBmwBDgAeEj78zSa+/Lm9bi/a4EHdUxvx58HbAGgqq7veNj7ycBjRttQVZ1UVQuqasG8eb3uXpKml3WTpEFl/STNXr229L0f2BrYu6oeUlX7VNVDaLpmbt0u78VSYKckOyTZFFhE8ziINdoRQkccCFzR47YlSZIkSV167Zr5TODIqvpe58yqWprkrcCHetlIVa1OciTNIyDmAJ+sqsuSHAcsq6ozgf+b5EBgNfA74LAeY5QkSZIkdek16dsMuGmMZTcBm/a6w/Z5fud0zTum4/1bgbf2uj1JkiRJ0th67d75XeCoJFt0zmynj2qXS5IkSZIGTK8tfW8CzgNWJPkK8BvgfsBfAQH2nZboJEmSJEnrpaeWvqq6GNgJOAmYB+xPk/R9FNipqi6ZtgglSZIkSZPW8zP2quo64OhpjEWSJEmSNMV6SvqSPBLYth2EpXvZM4GVVfWjqQ5uOsw/+kuTWm/58QdMcSSStOGYdN05d4oDkSRJ66zXgVw+QPNMvtE8tl0uSZIkSRowvXbvfDRw/BjLLgRePzXhSJLUJ8duNcn1bpzaOCRJmma9tvTNAbYYY9kWrMNz+iRJkiRJ/dNr0rcUOGKMZUcAy6YmHEmSJEnSVOq1e+exwNeSXAScAvwaeADwMuCRNI9wkCRJ0pBxICdpw9dT0ldV30rydODdwIdoHsh+F3ARsH9VfXv6QpQkSZIkTda6PKfvfGCfJPcA7gPcUFV/nK7AJEmSJEnrr9d7+taoqj9W1bXAZkn2TLLZNMQlSZIkSZoCPSV9Sd6e5PiO6acCvwC+D1yT5OHTFJ8kSZIkaT302tJ3KPDjjul/Bi4AHt/Of/cUxyVJkiRJmgK9Jn0PBK4BSPIgmhE7/6mqvgu8H9h7esKTJEmSJK2PXgdyuQnYqn3/VJpBXL7XTt8G3GOqA5MkqRcOJy9J0vh6Tfq+CRyd5C7gzcAXO5btDKyY6sAkSZIkSeuv1+6dbwBuB04Dfg+8rWPZy4BvTXFckiRJkqQp0FPSV1XXVtVTq2rLqnpiVf2qY/FfAa/rdYdJFia5MsnVSY4ep9zzk1SSBb1uW5IkSZJ0d+v8nL5uVfWHqrqjl7JJ5gAnAs8AdgMOSbLbKOW2BF4PXLS+8UmSJEnSbLbeSd862gu4uqquaRPF04CDRin3/4ATaAaJkSRJkiRNUr+Tvm25+6AvK9t5ayR5NPCgqprccGySJEmSpDV6Hb2zL5JsRPPcv8N6KHsEcATAgx/84OkNTJoJx241cZlR17txauPQOrFukjSorJ+k2avfLX3XAg/qmN6unTdiS2B34Pwky2ke+n7maIO5VNVJVbWgqhbMmzdvGkOWpN5ZN0kaVNZP0uw1qaQvydwkD0myro+2XQrslGSHJJsCi4AzRxZW1Y1VtU1Vza+q+cB3gQOratlk4pQkSZKk2W7cpC/JG5NcnOTyJK9p570ZuB74CfC7JO/sdWdVtRo4EjgXuAL4TFVdluS4JAdO+igkSZIkSaMa856+JK8G3gssoUny3pHk/sBbgHcBPwCeALw5yf9W1ZJedlhV5wDndM07Zoyy+/ayTUmSJEnS6MYbyOXVwLur6h8AknwZOBt4e1X9v7bMl9ounkfSJIeSJEmSpAEyXvfOhwJf75j+FhDg/K5yXwF2mtqwJEmSJElTYbyk73bgHh3TIw9Kv7mr3J+6ykmSJEmSBsR4Sd9PgUeMTFTVncADgEu6yu1C85B1SZIkSdKAGS/p+yRNd841quo3bfLX6eXAN6c6MEmSJEnS+htzIJeq+rdeNlBVe01dOJIkSZKkqTSph7NLkiRJkjYMEyZ9SXZP8tEkVyS5KckfklyZ5JNJntiPICVJkiRJkzNu0pfk9cAPgRcBVwAnA58ALgOeA5yf5F/ashsl+dD0hitJkiRJWhdj3tOX5JnAB4D3AO+qqj90Ld8SeCtwVJJrgScDTwVeN33hSpIkSZLWxZhJH/Bm4JSqOnq0hVV1E/D3SR4AHA/8GnjK1IcoSdIsc+xWk1zvxqmNQ5I0FMbr3vkY4LQetjFSZkFVXbT+IUmSJEmSpsp4Sd9GwOoetrEa+GNV/WpqQpIkSZIkTZXxundeBjwL+PoE23gW8L9TFtGgsquNJEmSpA3QeC19HwVem+RvkmS0AkkOB14D9PQgd0mSJElSf43Z0ldVi5M8DvgY8OYkZwE/bxdvDxwA7Ax8rKr+Y9ojlSRJkiSts/G6d1JVr05yLvB64LXAZu2i24HvAEdX1RenN0RJkiRJ0mSNm/QBVNUZwBlJ5gDbtLOvq6o7pzUySZIkSdJ6mzDpG9Emeb+ZxlgkSZIkSVOs56RvqiRZCHwQmAOcXFXHdy1/FU1X0juBm4EjquryfscpSZIkqctkRrR3NPsZN97onVOu7SJ6IvAMYDfgkCS7dRX7z6rao6r2BN4DvL+fMUqSJEnSMOlr0gfsBVxdVddU1R3AacBBnQWq6g8dk1sA1cf4JEmSJGmo9Lt757bAio7plcDjugsleS3wRmBT4Kn9CU2SJEmShk/f7+nrRVWdCJyY5MXAPwAv7y6T5AjgCIAHP/jB/Q1Q68a+35pFrJskDSrrJ2n26nf3zmuBB3VMb9fOG8tpwHNGW1BVJ1XVgqpaMG/evCkMUZImz7pJ0qCyfpJmr34nfUuBnZLskGRTYBFwZmeBJDt1TB4A/KSP8UmSJEnSUOlr986qWp3kSOBcmkc2fLKqLktyHLCsqs4EjkyyH/An4AZG6dopSZIkSepN3+/pq6pzgHO65h3T8f71/Y5JkiRJkobVQA7kIkmSJGn6zD/6S5Nab/ncKQ5EfdHve/okSZIkSX1k0idJkiRJQ8ykT5IkSZKGmPf0SZI0TbxnRpI0CGzpkyRJkqQhZtInSZIkSUPMpE+SJEmShphJnyRJkiQNMQdykSRJA2fyg+C8eN1XOvbGSe1LkjYUtvRJkiRJ0hAz6ZMkSZKkIWbSJ0mSJElDzKRPkiRJkoaYSZ8kSZIkDTGTPkmSJEkaYiZ9kiRJkjTETPokSZIkaYiZ9EmSJEnSEOt70pdkYZIrk1yd5OhRlr8xyeVJfpTk60m273eMkiRJkjQs+pr0JZkDnAg8A9gNOCTJbl3FfggsqKpHAJ8D3tPPGCVJkiRpmPS7pW8v4Oqquqaq7gBOAw7qLFBV51XVH9vJ7wLb9TlGSZIkSRoa/U76tgVWdEyvbOeN5ZXAf09rRJIkSZI0xAZ2IJckLwEWAO8dY/kRSZYlWbZq1ar+BidJY7BukjSorJ+k2avfSd+1wIM6prdr591Nkv2AtwEHVtXto22oqk6qqgVVtWDevHnTEqwkrSvrJkmDyvpJmr36nfQtBXZKskOSTYFFwJmdBZI8CvgYTcL32z7HJ0mSJElDpa9JX1WtBo4EzgWuAD5TVZclOS7JgW2x9wL3BD6b5OIkZ46xOUmSJEnSBDbu9w6r6hzgnK55x3S836/fMUmSJEnSsBrYgVwkSZIkSevPpE+SJEmShphJnyRJkiQNMZM+SZIkSRpifR/IRdPg2K0mud6NUxuHJEmSpIFjS58kSZIkDTGTPkmSJEkaYiZ9kiRJkjTETPokSZIkaYiZ9EmSJEnSEDPpkyRJkqQhZtInSZIkSUPMpE+SJEmShphJnyRJkiQNMZM+SZIkSRpiJn2SJEmSNMRM+iRJkiRpiJn0SZIkSdIQM+mTJEmSpCHW96QvycIkVya5OsnRoyx/UpIfJFmd5AX9jk+SJEmShklfk74kc4ATgWcAuwGHJNmtq9gvgMOA/+xnbJIkSZI0jDbu8/72Aq6uqmsAkpwGHARcPlKgqpa3y+7qc2ySJEmSNHT63b1zW2BFx/TKdp4kSZIkaRpssAO5JDkiybIky1atWjXT4UgSYN0kaXBZP0mzV7+TvmuBB3VMb9fOW2dVdVJVLaiqBfPmzZuS4CRpfVk3SRpU1k/S7NXvpG8psFOSHZJsCiwCzuxzDJIkSZI0a/Q16auq1cCRwLnAFcBnquqyJMclORAgyWOTrAQOBj6W5LJ+xihJkiRJw6Tfo3dSVecA53TNO6bj/VKabp+SJEmSpPW0wQ7kIkmSJEmamEmfJEmSJA0xkz5JkiRJGmImfZIkSZI0xEz6JEmSJGmImfRJkiRJ0hAz6ZMkSZKkIWbSJ0mSJElDzKRPkiRJkoaYSZ8kSZIkDTGTPkmSJEkaYiZ9kiRJkjTETPokSZIkaYiZ9EmSJEnSEDPpkyRJkqQhZtInSZIkSUPMpE+SJEmShphJnyRJkiQNMZM+SZIkSRpifU/6kixMcmWSq5McPcryzZKc3i6/KMn8fscoSZIkScOir0lfkjnAicAzgN2AQ5Ls1lXslcANVbUj8AHghH7GKEmSJEnDpN8tfXsBV1fVNVV1B3AacFBXmYOAU9r3nwOeliR9jFGSJEmShka/k75tgRUd0yvbeaOWqarVwI3A1n2JTpIkSZKGTKqqfztLXgAsrKrD2+mXAo+rqiM7yvxvW2ZlO/3Ttsx1Xds6AjiindwFuHKaw98GuG7CUjNjkGODwY7P2CanH7FtX1XzpnkfU866aS2DHJ+xTc4gxwbWT2OyfrobY5u8QY5vtsc2Zt3U76RvH+DYqvqrdvqtAFX17o4y57ZlLkyyMfBrYF71M9BRJFlWVQtmMoaxDHJsMNjxGdvkDHJss82g/y4GOT5jm5xBjg0GP77ZZJB/F8Y2eYMcn7GNrd/dO5cCOyXZIcmmwCLgzK4yZwIvb9+/APjGTCd8kiRJkrSh2rifO6uq1UmOBM4F5gCfrKrLkhwHLKuqM4FPAJ9KcjXwO5rEUJIkSZI0CX1N+gCq6hzgnK55x3S8vw04uN9x9eCkmQ5gHIMcGwx2fMY2OYMc22wz6L+LQY7P2CZnkGODwY9vNhnk34WxTd4gx2dsY+jrPX2SJEmSpP7q9z19kiRJkqQ+MukDknwyyW/bx0Ws67qHJfnwIMU0nQY1rhGjxZfk/CQzOpLToMY1mjFiPS7JfjMZ12xl/dSbQYyp2yDWA4MY01ismwaLdVNvBjGmboNaDwxqXKPZEOonk77GYmDhuq7UPlJiuixmEjH1wWIGM64RixnM+BYzmHGNZjFdsVbVMVX1tZkJZ9ZbjPVTLxYzeDF1W8zgxbiYwYtpLIuxbhoki7Fu6sViBi+mbosZzBgXM5hxjWYxA14/mfQBVfUtmpFC10jyN0mWJrkkyX8luUc7f3GSjya5CHjPTMeUZKskP0+yUVtmiyQrkmwy1jH0I652/uIk/5bku0muSbJveyXkiiSL2zKvSvLejm2t19W/0eJrHZzke0muSvLEdl/3SPKZJJcn+UKSi0auHiV5epILk/wgyWeT3HOyMU0irsOSnJHkq0mWJzkyyRuT/LA9l/dty+3ZTv+ojf8+6xPjeLG2v8sXtO+Pb8/Zj5K8r3t5O33zVMQi66epjqmd3/e6aawYWzNWP1k3abKsm6Y2pna+ddPk47J+moBJ39g+X1WPrapHAlcAr+xYth3wl1X1xpmOqapuBC4GntyWeRZwblX9abTy/YqrY9l9gH2AN9A8g/EDwMOBPZLsCfwX8NyO8i8CTpuGGDeuqr2AvwX+qZ33GuCGqtoN+EfgMQBJtgH+Adivqh4NLAOm63c9WlwAuwPPAx4LvBP4Y1U9CrgQeFlb5j+Ao6rqEcClXetPiyRb0/y+Ht7u9x3TvU+NyvppkjF1LBuUugkGs36ybtJkWDdNMqaOZdZNk4sLrJ/GZdI3tt2TfDvJpcChNH9wIz5bVXcOUEyn0/zRQ/Ncw9MnKN+vuADOqmaI2EuB31TVpVV1F3AZML+qVgHXJNm7/aN4GPA/0xDj59uf3wfmt++fQFtRVtX/Aj9q5+8N7Ab8T5KLgZcD209DTGPFBXBeVd3Unp8bgbPa+ZcC85NsBdy7qr7Zzj8FeNI0xdjpRuA24BNJngf8sQ/71Nqsn9YvJhicugkGs36ybtJkWDetX0xg3TTZuMD6aVx9f07fBmQx8JyquiTJYcC+HctumYmAGDumM4F3tU3XjwG+MUH5fsUFcHv7866O9yPTI5+/04AXAj8GvlDT8xyRkX3fycSf+wBfrapDpiGObmPF1X2uOs/jjP3dVtXqJHsBTwNeABwJPBVYTXsRqe0us+lMxThLLMb6aX1igsGpmzpjGaT6ybpJk7EY66b1iQmsm3ph/TQJtvSNbUvgV0k2obkKMwhGjamqbgaWAh8Ezu64ktavY1jf/XwBOAg4hOnrojCa/6GpNEmyG7BHO/+7wOOT7Ngu2yLJzn2Ma0Jt15QbRvqyAy8FvjnOKlOi7Z+/VVWdQ9P15JHtouW0XTyAA4FNpjuWWc76aT1iWgczVTfBBlo/WTfNetZN6xHTOrBumoTZXj/Z0gckWUJzlWWbJCtp+vf+I3ARsKr9ueWAx3Q68FnufrVoyo9hOs5VVd2Q5Apgt6r63jTEN5aPAKckuZzmatllwI1Vtaq98rYkyWZt2X8ArupTXL16OfDRNDeAXwO8Ygq2OV6sRfO7/WKSuTRX9Ub663+8nX8J8GVm7oru0LF+mraYJjSVddM4MY6lL/WTdZMmy7pp2mKa0GyomyYRV69mbf2U6WsRlgZbkjnAJlV1W5KHAl8DdqmqO2Y4tIGT5Czg/VV13kzHIs0G1k+9sW6S+su6qXeDVj/Z0qfZ7B7AeW33igCvsdJaW5JP0pyrC2Y6FmkWsX6agHWTNCOsm3owiPWTLX2SJEmSNMQcyEWSJEmShphJnyRJkiQNMZM+SZIkSRpiJn2SJEmSNMRM+iRJkiRpiJn0SZIkSdIQM+mTJEmSpCFm0idJkiRJQ8ykT5IkSZKGmEmfJEmSJA0xkz5JkiRJGmImfZIkSZI0xEz6JEmSJGmImfRJkiRJ0hAz6ZMkSZKkIWbSJ0mSJElDzKRPs1aSzZOcleTGJJ+dhu3PT1JJNm6n/zvJy6d6P5KGw/rWSW19s+N0xCZJo+nDd6nLkuzbvj82yaeneh+zhUnfEEqyPMkdSbbpmv/D9kvB/I55eyU5J8nvk/wuyfeSvKJj+b2S/EuSXyS5OclP2+m7bXsD9QLg/sDWVXVw98KxKpfJfrGqqmdU1SmTC1XacFkn9WyiOuneST6Z5NdJbkpyVZKjpzuoJIuTvGO69yMNEuutno1bbwEk2TnJZ5Nc1yaHP0ryxiRzJtp4VT28qs6f4phnJZO+4fUz4JCRiSR7APfoLJBkH+AbwDeBHYGtgVcDz2iXbwp8HXg4sBC4F7APcD2w17QfQZeRFrMptD1wVVWtnuLtSlqbddLEJqqTPgDcE9gV2Ao4ELh6imOQ9GfWWxMbt95K8lDgImAFsEdVbQUcDCz4/+3dfbxVZZnw8d8VoKCSFTI9KipUmDJamifSXsxSR0yTqXTEfEp9Mh4n6XHsxWwqI2uKXsY+NVKOmnOcMkCtDJPHt1FzLDPQKALUIR8UNBVRyTdU9Hr+WAtmczznsNlnn3026/y+n8/+nL3Wfa+1rn3O4eJc677XWsDIJsei3mSmr4q9gOXA54H5Neu+BXwOSGBsue4WYGYv+zkZeAjYrs7jBsUfJQ8DfwEWAXuVbSOAfwbuBdaUxx5Rth0FLAYeB24C9uzyWT4D/AF4FhgK7A/8uuz/e+CgXmLas9zn4+UxjirXfwl4DngeeBL4SDfbTgd+1M36BF5Xvn8ZcCbwJ4oEfinwqrJtbNl3aLl8E3By+f77wE9q9vl1iv8UYqB/f3z5avbLnLRRTH3JSX8E/raXfSdwCvBf5f5n1uYU4H8BS4HHgGuA3Wra9gCuAx4F7gL+rlw/tYzpuTKuKwf698mXr1a8zFsbxdSXvPUj4KpNfOZNxX5I+X463fxd5qvO3+mBDsBXP/xQy38g5X/cewJDgJUUZ2OSohjZBngBeFcv+5kNXLwZxz0MuB14RZm09gR2LNtmlv+Qdy7jeSuwNbA78BRwKDAMOIPizPVWNZ9lIbBLmex2piiu3kNRcB1aLo/uJp5h5b7+EdgKeDfwBPD6sr3X5NFTOxsXfacBvwHGlJ/nX4FZZdtYei76tgHuBk4E3gE8AowZ6N8dX77642VO2hBPX3PShRR/GJ0EjO+mPYFflJ93V2AVMKlsm1wee0+KP/g+D/y6bNuW4iz8SWXbvmVOmlC2dwJfGejfI1++Wvkyb22Ip69560HgpF7a64ndoq8JL6d3VtsPgQ9T/ENaCtxf0/ZKin/of+5l+1GbaO/qeYqh+j0ozi4vzcw/R8TLKM4wn5aZ92fmC5n568x8FjiW4gzQdZn5PMVZtBEUiWy972bmisx8BvifwLzMnJeZL2bmdcACisTV1f4UU6FmZOZzmXkDxR9Ex3XTtyd/V87R3/Dq0n4K8LnMXFl+nunA0ZuaPpGZTwMfAs6hOAv28cxcuRlxSVsic1LfctLHgUuAacCSiFgWEYd36TMjMx/PzPuAG4F9yvWnAF8rvwfrgK8C+0TEbsCRwPLM/LfMXJeZvwN+QjEFSxrszFt9y1ub+vz1xK4msOirth8CH6QYTfr3Lm2PAS8CO/ay/epNtG+kTATnUpyJejgizo+IlwM7AMMppkB2tRPFNIX1+3iR4ozzzjV9VtS83w04pksR9vYe4twJWFHuc717u+x7Uy7NzFfUvrq07wb8rCaWpRRn/V69qR1n5m3APRRn8i7djJikLZU5qQ85KTOfycyvZuZ+FH9IXQpcFhGvqun2YM37pyn+WFsf53dqYnyUIvfsXLa9pctnOB74H/XEJVWceatvf0tt6vPXE7uawKKvwjLzXoqLkN8D/LRL29PArcAHetnF9cBhEbHtZhzzu+UfJBMohuw/TTFNaC3w2m42eYAi+QAQEUEx/aD2TFrWvF8B/LBLIbZtZs7oYd+7lGfH1tu1y777agVweJd4hmfmJo8REadSTMt4gGI6g1Rp5qTm5aTM/AvFaN22wLg6NlkB/O8ucY7IzF+Xbb/s0rZdZv59N59XGlTMW33OW9fT+/enntjVBBZ91fcR4N2Z+VQ3bWcAJ0bEpyNiFEBEvDEiZpftP6RIDD+JiD0i4mURMSoi/jEiXjIFICLeHBFviYhhFPOz1wIvlmdtLgLOiYidImJIRBwQEVtTnKk+IiIOLrf7JMVFxr/u4fP8CHhvRBxW7md4RBwUEWO66XsbxZnuMyJiWBTPeXkvxfz6ZjkP+KdyihQRMToiJm9qo4jYHfgKxRSLD5Ux7tP7VlIlmJMazEkR8YXyM20VEcMpril+nOKao005D/hsRPx1ua/tI2L99M1fALtHxIfKuIaVx9mzbH8IeE09MUoVZd5q/G+pLwJvjYhvRsT/KD/j6yLiRxHxigZiV4Ms+iouM/+UmQt6aPs1xQW57wbuiYhHgfOBeWX7sxQXMd9JcVe3vwC/pZhicFs3u3w5cAHFdId7KYb0v1m2fYriDlTzKaYVfR14WWbeRVH4/AvFWaz3Au/NzOd6iHkFxQ0J/pHiJgUrKM6AveR3udzHeylum/wI8D3gw5l5Z3f7btB3gLnAtRHxBMVNXd7S2wZRXO/3I+Drmfn7zPwvis/zwzJ5S5Vlo/WL2gAAIABJREFUTupTTkrg38ptH6C4xuiIzHxykxtm/qz8jLMj4i8UdwI9vGx7AvgbYEq53wfLvuvz0Q+ACeU0sCvqjFWqDPNW43krM/9E8YiKscDiiFhDcc3wAuCJzY1djYtMZ21IkiRJUlU50idJkiRJFWbRJ0mSJEkVZtEnSZIkSRVm0SdJkiRJFWbRJ0mSJEkVNnSgA2iGHXbYIceOHTvQYUhqottvv/2RzBw90HH0hblJqibzk6R21FtuqkTRN3bsWBYs6PbxKZK2UBFx70DH0FfmJqmazE+S2lFvucnpnZIkSZJUYRZ9kiRJklRhFn2SJEmSVGGVuKZPkiRJUvt7/vnnWblyJWvXrh3oULZYw4cPZ8yYMQwbNqzubSz6JEmSJLXEypUrGTlyJGPHjiUiBjqcLU5msnr1alauXMm4cePq3s7pnZIkSZJaYu3atYwaNcqCr0ERwahRozZ7pNSiT5IkSVLLWPD1TSPfP4s+SZIkSYPKFVdcQURw5513ArB8+XL22muvAY6q/3hNnyRJkqQBMfbMq5q6v+Uzjqir36xZs3j729/OrFmz+NKXvtTUGNqRI32SJEmSBo0nn3ySW265hR/84AfMnj37Je1r167lpJNOYu+992bfffflxhtvBKCzs5P3v//9TJo0ifHjx3PGGWds2Obaa6/lgAMO4E1vehPHHHMMTz75ZMs+Tz0s+iRJkiQNGj//+c+ZNGkSu+++O6NGjeL222/fqH3mzJlEBIsWLWLWrFmccMIJG26csnDhQubMmcOiRYuYM2cOK1as4JFHHuErX/kK119/PXfccQcdHR2cc845A/HReuT0TkmSJEmDxqxZszjttNMAmDJlCrNmzWLatGkb2m+55RY+/vGPA7DHHnuw2267cffddwNw8MEHs/322wMwYcIE7r33Xh5//HGWLFnC2972NgCee+45DjjggFZ+pE2y6JMkSZI0KDz66KPccMMNLFq0iIjghRdeICI49dRT69p+66233vB+yJAhrFu3jszk0EMPZdasWf0Vdp85vVOSJEnSoHD55ZfzoQ99iHvvvZfly5ezYsUKxo0bx4oVKzb0ecc73sEll1wCwN133819993H61//+h73uf/++/OrX/2KZcuWAfDUU09tGBlsFxZ9kiRJkgaFWbNm8b73vW+jdR/4wAf42te+tmH5Yx/7GC+++CJ77703xx57LJ2dnRuN8HU1evRoOjs7Oe6443jDG97AAQccsOFREO0iMnOgY+izjo6OXLBgwUCHIamJIuL2zOwY6Dj6wtwkVZP5SWrc0qVL2XPPPQc6jC1ed9/H3nJTy0f6ImJSRNwVEcsi4sxu2neNiBsj4ncR8YeIeE+rY5QkSZKkqmhp0RcRQ4CZwOHABOC4iJjQpdvngUszc19gCvC9VsYoSZIkSVXS6pG+icCyzLwnM58DZgOTu/RJ4OXl++2BB1oYnyRJkiRVSqsf2bAzsKJmeSXwli59pgPXRsTHgW2BQ1oTmiRJkiRVTzvevfM4oDMzxwDvAX4YES+JMyKmRsSCiFiwatWqlgcpSd0xN0lqV+YnafBqddF3P7BLzfKYcl2tjwCXAmTmrcBwYIeuO8rM8zOzIzM7Ro8e3U/hStLmMTdJalfmJ2nwanXRNx8YHxHjImIrihu1zO3S5z7gYICI2JOi6PN0lCRJkqQ+GzJkCPvss8+G14wZMxraz9ixY3nkkUd67dPZ2ckDD/z3LUpOPvlklixZ0tDx+qKl1/Rl5rqImAZcAwwBLsrMxRFxNrAgM+cCnwQuiIjTKW7qcmJW4WGCkiRJkjY2ffsm72/NJruMGDGChQsXNve4Pejs7GSvvfZip512AuDCCy9syXG7avk1fZk5LzN3z8zXZuY/levOKgs+MnNJZr4tM9+Ymftk5rWtjlGSJEnS4HH11VdzzDHHbFi+6aabOPLIIwGYNWsWe++9N3vttRef+cxnXrLt8uXL2WuvvTYsf+tb32L69OlcfvnlLFiwgOOPP5599tmHZ555hoMOOogFCxb0ut/tttuOz33uc7zxjW9k//3356GHHurz52vHG7lIkiRJUr945plnNpreOWfOHA455BBuu+02nnrqKQDmzJnDlClTeOCBB/jMZz7DDTfcwMKFC5k/fz5XXHFFXcc5+uij6ejo4JJLLmHhwoWMGDFiQ1tv+33qqafYf//9+f3vf8+BBx7IBRdc0OfPbNEnSZIkadBYP71z/evYY49l6NChTJo0iSuvvJJ169Zx1VVXMXnyZObPn89BBx3E6NGjGTp0KMcffzw333xzn2Pobb9bbbXVhlHG/fbbj+XLl/f5eK1+Tp8kSZIktZ0pU6Zw7rnn8qpXvYqOjg5GjhxZ13ZDhw7lxRdf3LC8du3aPsUxbNgwIgIobjqzbt26Pu0PHOmTJEmSJN75zndyxx13cMEFFzBlyhQAJk6cyC9/+UseeeQRXnjhBWbNmsU73/nOjbZ79atfzcMPP8zq1at59tln+cUvfrGhbeTIkTzxxBMvOVY9+20mR/okSZIkDRrrr+lbb9KkScyYMYMhQ4Zw5JFH0tnZycUXXwzAjjvuyIwZM3jXu95FZnLEEUcwefLkjfY3bNgwzjrrLCZOnMjOO+/MHnvssaHtxBNP5JRTTmHEiBHceuutG9bXs99miio8DaGjoyPX3wVHUjVExO2Z2THQcfSFuUmqJvOT1LilS5ey5557DnQYW7zuvo+95Sand0qSJElShVn0SZIkSVKFWfRJkiRJUoVZ9EmSJElqmSrcU2QgNfL9s+iTJEmS1BLDhw9n9erVFn4NykxWr17N8OHDN2s7H9kgSZIkqSXGjBnDypUrWbVq1UCHssUaPnw4Y8aM2axtLPokSZIktcSwYcMYN27cQIcx6Di9U5IkSZIqzKJPkiRJkirMok+SJEmSKsyiT5IkSZIqzKJPkiRJkirMok+SJEmSKqzlRV9ETIqIuyJiWUSc2U37tyNiYfm6OyIeb3WMkiRJklQVLX1OX0QMAWYChwIrgfkRMTczl6zvk5mn1/T/OLBvK2OUJEmSpCpp9UjfRGBZZt6Tmc8Bs4HJvfQ/DpjVksgkSZIkqYJaXfTtDKyoWV5ZrnuJiNgNGAfc0IK4JEmSJKmS2vlGLlOAyzPzhe4aI2JqRCyIiAWrVq1qcWiS1D1zk6R2ZX6SBq9WF333A7vULI8p13VnCr1M7czM8zOzIzM7Ro8e3cQQJalx5iZJ7cr8JA1erS765gPjI2JcRGxFUdjN7dopIvYAXgnc2uL4JEmSJKlSWlr0ZeY6YBpwDbAUuDQzF0fE2RFxVE3XKcDszMxWxidJkiRJVdPSRzYAZOY8YF6XdWd1WZ7eypgkSZIkqara+UYukiRJkqQ+suiTJEmSpAqz6JMkSZKkCrPokyRJkqQKs+iTJEmSpAqz6JMkSZKkCrPokyRJkqQKs+iTJEmSpAqz6JMkSZKkCrPokyRJkqQKs+iTJEmSpAqz6JMkSZKkCrPokyRJkqQKs+iTJEmSpAqz6JMkSZKkCrPokyRJkqQKs+iTJEmSpAqz6JMkSZKkCrPokyRJkqQKs+iTJEmSpApredEXEZMi4q6IWBYRZ/bQ5+8iYklELI6IH7c6RkmSJEmqiqGtPFhEDAFmAocCK4H5ETE3M5fU9BkPfBZ4W2Y+FhF/1coYJUmSJKlKWj3SNxFYlpn3ZOZzwGxgcpc+HwVmZuZjAJn5cItjlCRJkqTKaHXRtzOwomZ5Zbmu1u7A7hHxq4j4TURM6m5HETE1IhZExIJVq1b1U7iStHnMTZLalflJGrxaOr2zTkOB8cBBwBjg5ojYOzMfr+2UmecD5wN0dHRkq4OUpO6YmyS1K/OTthRjz7yqoe2WzziiyZFUR6tH+u4HdqlZHlOuq7USmJuZz2fm/wPupigCJUmSJEmbqdVF33xgfESMi4itgCnA3C59rqAY5SMidqCY7nlPK4OUJEmSpKpoadGXmeuAacA1wFLg0sxcHBFnR8RRZbdrgNURsQS4Efh0Zq5uZZySJEmSVBUtv6YvM+cB87qsO6vmfQKfKF+SJEmSpD5o+cPZJUmSJEmts1kjfRExAdiP4mYsF2XmgxHxOuChzHyiPwKUJEmSJDWurqIvIrYDLgI+AKwrt7saeBD4KnAf8Kl+ilGSJEmS1KB6p3eeA7wVOAQYCURN2zyg2weoS5IkSZIGVr3TO98PnJaZN0bEkC5t9wK7NTcsSZIkSVIz1DvSNwLo6bEJI4EXmhOOJEmSJKmZ6i365gMf7qHtaODXzQlHkiRJktRM9U7v/AJwXURcD1wGJPCeiDidoug7sJ/ikyRJkiT1QV0jfZn5n8DBwNbAuRQ3cvkS8BrgkMyc328RSpIkSZIatsmRvojYmmI077eZ+Y6IGAG8Eng8M5/u7wAlSZIkSY3b5EhfZj4LXAjsVC4/k5kPWPBJkiRJUvur90Yui4Dd+zMQSZIkSVLz1Xsjl9OBzoj4M3B1Zq7rx5gkSZIkSU1Sb9F3BbAN8HMgI+Ixijt4bpCZf9Xk2CRJkiRJfVRv0TeTLkWeJEmSJKn91VX0Zeb0fo5DkiRJktQP6h3pAyAitgL2Bl4FPAosyszn+iMwSZIkSVLf1Xv3TiLiDOAh4LfANcB84KGI+HQ/xSZJkiRJ6qO6Rvoi4h+ArwHnAXMoir9XA8cCX4uIZzPzu/0WpSRJkiSpIfWO9J0KzMjMUzPz5sy8q/x6KvB14P/Ue8CImBQRd0XEsog4s5v2EyNiVUQsLF8n17tvSZIkSdLG6i36dgFu7KHtJmBMPTuJiCEUdwI9HJgAHBcRE7rpOicz9ylfF9YZoyRJkiSpi3qLvvuAv+mh7dCyvR4TgWWZeU95A5jZwOQ6t5UkSZIkbaZ67975XeC7EfEq4HKKa/r+CjgGOBE4rc797AysqFleCbylm34fiIgDgbuB0zNzRdcOETEVmAqw66671nl4Sepf5iZJ7cr8JA1edY30Zea5wP8GJgHzgAXA/y2XT8nMmU2M6UpgbGa+AbgOuLiHmM7PzI7M7Bg9enQTDy9JjTM3SWpX5idp8Kr7OX2ZeUFEXEhx/d6OwJ+BlZmZm3G8+ymuD1xvTLmu9jiraxYvBL6xGfuXJEmSJNXYrIezlwXeCjaeork55gPjI2IcRbE3BfhgbYeI2DEz/1wuHgUsbfBYkiRJkjTo1fucvouAbTJzSjdts4AnM/Ojm9pPZq6LiGkUD3cfAlyUmYsj4mxgQWbOBf5PRBwFrAMepbhmUJIkSZLUgHpH+g4FPtFD20+Ac+o9YGbOo7gusHbdWTXvPwt8tt79SZIkqQ1N377B7dY0Nw5JdT+yYTTFqFt3HqO4k6ckSZIkqc3UW/TdCxzYQ9uBFI9ekCRJkiS1mXqnd3YCX4yIh4GLM/PJiNgO+DBwBvClfopPUjtyyo4kSdIWo96i7+vAa4F/oXhI+1PAtkAA55ftkiRJkqQ2U1fRl5kvAidHxDeBdwGjgNXADZl5dz/GJ0mSJEnqg819Tt9dwF39FIskSZIkqcnqupFLRLwjIibXLI+KiB9HxMKI+OeIGNZ/IUqSJEmSGlXv3Tu/AexVs/xd4GDgNxQPT/dGLpIkSZLUhuqd3vl6ysIuIrYB3gf8r8ycHRHzgX8sX5IkSZIa5R2y1Q/qHenbClhbvn8bRbF4Vbl8N7Bjk+OSJEmSJDVBvUXfncCk8v3xwK2Z+US5vBPwaLMDkyRJkiT1Xb3TO88GLouIjwDbA5Nr2iYBv2t2YJIkSZKkvqv3OX1zI2JPYF9gUZdn890K/KE/gpMkSZIk9U3dz+nLzHuAe7pZf35TI5IkSZIkNc1mPZxdkiRJktqSdz7tkUWfKmHsmVdtulM3ls84osmRSJIkSe2l3rt3SpIkSZK2QBZ9kiRJklRhvU7vjIi9gccyc2UP7WOAV2bmov4ITu3FKZTV1MjPdfnwfghEkiRJ/aLHkb6I+ADwW+AVvWz/CuC2iJjcS5+u+50UEXdFxLKIOLO340dERkRHvfuWJEmSJG2st+mdU4GLMvOPPXUo234AnFLPwSJiCDATOByYABwXERO66TcSOA24rZ79SpIkSZK611vR92ZgXh37uBqYWOfxJgLLMvOezHwOmA10N0r4ZeDrwNo69ytJkiRJ6kZvRd82wF/q2Mdfyr712BlYUbO8sly3QUS8CdglM3u90CgipkbEgohYsGrVqjoPL0n9y9wkqV2Zn6TBq7eibyWwZx37mADc34xgIuJlwDnAJzfVNzPPz8yOzOwYPXp0Mw4vSX1mbpLUrsxP0uDVW9H3C+CTEbFtTx0iYjvgdODKOo93P7BLzfIYNi4YRwJ7ATdFxHJgf2CuN3ORJEmSpMb09siGrwLHAL+OiM8C/5GZzwJExFbAwWWf7YCv1Xm8+cD4iBhHUexNAT64vjEz1wA7rF+OiJuAT2Xmgno/kCRJktQOfCyS2kWPRV9mPhwR7wYuoRj1WxcRq4AERgPDgNuBd2fmw/UcLDPXRcQ04BpgCMXdQRdHxNnAgsyc27ePI0mSJEltZvr2DW63pimH7/Xh7Jl5F9AREQcCB/LfN125H7gpM2/Z3ANm5jy63BU0M8/qoe9Bm7t/SZIkSdJ/67XoWy8zbwZu7udYJEmSJKltNTJlFwZ+2m6PRV9E7AicC5yfmdf00Ocwioe4/329UzwlSYNII9NZmjSVRZIkFXq7e+engNcA1/bS51pgHHU8YkGSJEmS1Hq9FX1HAudlZvbUoWz7V2ByswOTJEmSJPVdb0XfbsCSOvaxFBjblGgkSZIkSU3VW9H3DPDyOvaxXdlXkiRJktRmeiv67gCOqmMfk8u+kiRJkqQ201vR9z3gIxFxQk8dIuLDwEkUd/mUJEmSJLWZHh/ZkJk/iYjvAP8WEdOAq4H7gAR2BQ4DOoBvZ+bPWhGsJEmSJGnz9Ppw9sz8ZETcBPwDxSMcti6bngV+BUzOzF/0a4RSf2rkGWLgc8QkSZK0xei16APIzCuBKyNiKDCqXL06M9f1a2SSJEmSpD7bZNG3XlnkPdSPsUiSNpej1ZIkaRN6LPoi4qzN2E9m5pebEI8kSZIkqYl6G+mbTvH8vaeA2MR+Eqh20efZdEmSJElboN6Kvj8BuwG3A7OBn2bmEy2JSpIkSZLUFD0+py8zxwNvBRZTjOI9FBE/jYhjImJEqwKUJEmSJDWut4ezk5kLMvNTmbkrMAl4kOJB7A9HxCURcWArgpQkSZIkNabXoq9WZt6cmR8DdgHOA46leH6fJEmSJKlN1f3Ihoh4GzAFOBoYCVwOfL+f4pIkSZIkNUGvRV9EvImi0DsWeDVwNXA6MDczn27kgBExCfgOMAS4MDNndGk/BTgVeAF4EpiamUsaOZYkSZL6ZuyZVzW03fLhTQ5EUsN6e07fXcA44AbgixR37/xLXw4WEUOAmcChwEpgfkTM7VLU/Tgzzyv7HwWcQ3E9obRFavg/yxlHNDkSSZIkDUa9jfSNB9YC+wFvAr4R0fPj+jLzr+o43kRgWWbeAxARs4HJwIair0thuS3FMwAlSZIkSQ3orej7Uj8cb2dgRc3ySuAtXTtFxKnAJ4CtgHd3t6OImApMBdh1112bHqgkNcLcJKldmZ+kwavHoi8z+6Poq0tmzgRmRsQHgc8DJ3TT53zgfICOjg5HAyW1BXOTpHZlfpIGr7rv3tkk91M88mG9MeW6nszGO4RKkiSpmaZv3+B2a5obh9QidT+nr0nmA+MjYlxEbEVxZ9C5tR0iYnzN4hHAf7UwPkmSJEmqlJaO9GXmuoiYBlxD8ciGizJzcUScDSzIzLnAtIg4BHgeeIxupnZKkiRJkurT6umdZOY8YF6XdWfVvD+t1TFJkiRJUlW1enqnJEmSJKmFWj7SJ0na8ow986qGtls+vMmBSJKkzeZInyRJkiRVmEWfJEmSJFWY0zslSZK0RXLquVQfR/okSZIkqcIG3UifZ4QkSZIkDSaO9EmSJElShQ26kT4NgOnbN7DNmubHIUmSJA1CjvRJkiRJUoVZ9EmSJElShTm9U2pXjUyLBafGSpIkaSOO9EmSJElShVn0SZIkSVKFWfRJkiRJUoVZ9EmSJElShVn0SZIkSVKFWfRJkiRJUoX5yAZJkiRpkBl75lUNbbd8xhFNjkSt0PKRvoiYFBF3RcSyiDizm/ZPRMSSiPhDRPxHROzW6hglSZIkqSpaWvRFxBBgJnA4MAE4LiImdOn2O6AjM98AXA58o5UxSpIkSVKVtHqkbyKwLDPvyczngNnA5NoOmXljZj5dLv4GGNPiGCVJkiSpMlpd9O0MrKhZXlmu68lHgP/brxFJkiRJUoW17d07I+J/Ah3AN3tonxoRCyJiwapVq1obnCT1wNwkqV2Zn6TBq9VF3/3ALjXLY8p1G4mIQ4DPAUdl5rPd7Sgzz8/MjszsGD16dL8EK0mby9wkqV2Zn6TBq9WPbJgPjI+IcRTF3hTgg7UdImJf4F+BSZn5cIvjk6QB08jts5cP74dAJElSpbR0pC8z1wHTgGuApcClmbk4Is6OiKPKbt8EtgMui4iFETG3lTFKkiRJUpW0/OHsmTkPmNdl3Vk17w9pdUySJEmSVFUtL/rUu4amd804oh8ikSRJklQFFn2SpC1aIyfLwBNmktSQ6ds3sM2a5sehzdK2j2yQJEmSJPWdRZ8kSZIkVZhFnyRJkiRVmNf0SZIGp0auSwGvTZEkbXEc6ZMkSZKkCrPokyRJkqQKs+iTJEmSpAqz6JMkSZKkCrPokyRJkqQKs+iTJEmSpAqz6JMkSZKkCrPokyRJkqQK8+HskiSpOqZv38A2a5ofhyS1EUf6JEmSJKnCLPokSZIkqcIs+iRJkiSpwiz6JEmSJKnCWl70RcSkiLgrIpZFxJndtB8YEXdExLqIOLrV8UmSJElSlbS06IuIIcBM4HBgAnBcREzo0u0+4ETgx62MTZIkSZKqqNWPbJgILMvMewAiYjYwGViyvkNmLi/bXmxxbJIkSZJUOa2e3rkzsKJmeWW5TpIkSZLUD7bYh7NHxFRgKsCuu+46wNFIUsHcJDXH2DOvami75cObHEiFmJ+kwavVI333A7vULI8p1222zDw/Mzsys2P06NFNCU6S+srcJKldmZ+kwavVRd98YHxEjIuIrYApwNwWxyBJkiRJg0ZLi77MXAdMA64BlgKXZubiiDg7Io4CiIg3R8RK4BjgXyNicStjlCRJkqQqafk1fZk5D5jXZd1ZNe/nU0z7lCRJkiT10RZ7IxfVmL59g9utaW4ckiRJktpOq6/pkyRJkiS1kCN9kiS1G2dwSJKayJE+SZIkSaowiz5JkiRJqjCLPkmSJEmqMIs+SZIkSaowiz5JkiRJqjCLPkmSJEmqMIs+SZIkSaowiz5JkiRJqjCLPkmSJEmqMIs+SZIkSaowiz5JkiRJqjCLPkmSJEmqsKEDHYAkSVU19syrGtpu+fAmByJJGtQc6ZMkSZKkCrPokyRJkqQKs+iTJEmSpApredEXEZMi4q6IWBYRZ3bTvnVEzCnbb4uIsa2OUZIkSZKqoqVFX0QMAWYChwMTgOMiYkKXbh8BHsvM1wHfBr7eyhglSZIkqUpaPdI3EViWmfdk5nPAbGBylz6TgYvL95cDB0dEtDBGSZIkSaqMVhd9OwMrapZXluu67ZOZ64A1wKiWRCdJkiRJFROZ2bqDRRwNTMrMk8vlDwFvycxpNX3+WPZZWS7/qezzSJd9TQWmlouvB+7q5/B3AB7ZZK+B0c6xQXvHZ2yNaUVsu2Xm6H4+RtOZm16ineMztsa0c2xgfuqR+Wkjxta4do5vsMfWY25qddF3ADA9Mw8rlz8LkJlfq+lzTdnn1ogYCjwIjM5WBtqNiFiQmR0DGUNP2jk2aO/4jK0x7RzbYNPuP4t2js/YGtPOsUH7xzeYtPPPwtga187xGVvPWj29cz4wPiLGRcRWwBRgbpc+c4ETyvdHAzcMdMEnSZIkSVuqoa08WGaui4hpwDXAEOCizFwcEWcDCzJzLvAD4IcRsQx4lKIwlCRJkiQ1oKVFH0BmzgPmdVl3Vs37tcAxrY6rDucPdAC9aOfYoL3jM7bGtHNsg027/yzaOT5ja0w7xwbtH99g0s4/C2NrXDvHZ2w9aOk1fZIkSZKk1mr1NX2SJEmSpBay6AMi4qKIeLh8XMTmbntiRJzbTjH1p3aNa73u4ouImyJiQO/k1K5xdaeHWM+OiEMGMq7ByvxUn3aMqat2zAPtGFNPzE3txdxUn3aMqat2zQPtGld3toT8ZNFX6AQmbe5G5SMl+ksnDcTUAp20Z1zrddKe8XXSnnF1p5MusWbmWZl5/cCEM+h1Yn6qRyftF1NXnbRfjJ20X0w96cTc1E46MTfVo5P2i6mrTtozxk7aM67udNLm+cmiD8jMmynuFLpBRHw0IuZHxO8j4icRsU25vjMizouI24BvDHRMEbF9RNwbES8r+2wbESsiYlhPn6EVcZXrOyPi+xHxm4i4JyIOKs+ELI2IzrLPKRHxzZp99ensX3fxlY6JiN9GxN0R8Y7yWNtExKURsSQifhYRt60/exQRfxMRt0bEHRFxWURs12hMDcR1YkRcERHXRcTyiJgWEZ+IiN+V38tXlf32KZf/UMb/yr7E2Fus5c/y6PL9jPJ79oeI+FbX9nL5yWbEIvNTs2Mq17c8N/UUY2nA8pO5SY0yNzU3pnK9uanxuMxPm2DR17OfZuabM/ONwFLgIzVtY4C3ZuYnBjqmzFwDLATeWfY5ErgmM5/vrn+r4qppeyVwAHA6xTMYvw38NbB3ROwD/AR4X03/Y4HZ/RDj0MycCPwD8MVy3ceAxzJzAvAFYD+AiNgB+DxwSGa+CVgA9NfPuru4APYC3g+8Gfgn4OnM3Be4Ffhw2effgc9k5huARV227xcRMYri5/XX5XG/0t/HVLfMTw3GVNPWLrni+9zZAAAGuUlEQVQJ2jM/mZvUCHNTgzHVtJmbGosLzE+9sujr2V4R8Z8RsQg4nuIf3HqXZeYLbRTTHIp/9FA813DOJvq3Ki6AK7O4Rewi4KHMXJSZLwKLgbGZuQq4JyL2L/9R7AH8qh9i/Gn59XZgbPn+7ZSJMjP/CPyhXL8/MAH4VUQsBE4AduuHmHqKC+DGzHyi/P6sAa4s1y8CxkbE9sArMvOX5fqLgQP7KcZaa4C1wA8i4v3A0y04pl7K/NS3mKB9chO0Z34yN6kR5qa+xQTmpkbjAvNTr1r+nL4tSCfwt5n5+4g4ETiopu2pgQiInmOaC3y1HLreD7hhE/1bFRfAs+XXF2ver19e//s3G/g74E7gZ9k/zxFZf+wX2PTvfQDXZeZx/RBHVz3F1fV7Vft9HLB/t5m5LiImAgcDRwPTgHcD6yhPIpXTZbYaqBgHiU7MT32JCdonN9XG0k75ydykRnRibupLTGBuqof5qQGO9PVsJPDniBhGcRamHXQbU2Y+CcwHvgP8ouZMWqs+Q1+P8zNgMnAc/TdFoTu/okiaRMQEYO9y/W+At0XE68q2bSNi9xbGtUnl1JTH1s9lBz4E/LKXTZqinJ+/fWbOo5h68sayaTnlFA/gKGBYf8cyyJmf+hDTZhio3ARbaH4yNw165qY+xLQZzE0NGOz5yZE+ICJmUZxl2SEiVlLM7/0CcBuwqvw6ss1jmgNcxsZni5r+Gfrje5WZj0XEUmBCZv62H+LryfeAiyNiCcXZssXAmsxcVZ55mxURW5d9Pw/c3aK46nUCcF4UF4DfA5zUhH32FmtS/Gx/HhHDKc7qrZ+vf0G5/vfA1QzcGd3KMT/1W0yb1Mzc1EuMPWlJfjI3qVHmpn6LaZMGQ25qIK56Ddr8FP03Iiy1t4gYAgzLzLUR8VrgeuD1mfncAIfWdiLiSuCczLxxoGORBgPzU33MTVJrmZvq1275yZE+DWbbADeW0ysC+JhJ66Ui4iKK79UtAx2LNIiYnzbB3CQNCHNTHdoxPznSJ0mSJEkV5o1cJEmSJKnCLPokSZIkqcIs+iRJkiSpwiz6JEmSJKnCLPokSZIkqcIs+iRJkiSpwiz6JEmSJKnCLPokSZIkqcIs+iRJkiSpwiz6JEmSJKnCLPokSZIkqcIs+iRJkiSpwiz6JEmSJKnCLPokSZIkqcIs+iRJW4yImB4RWfN6ICJ+EhGvbcK+p0bE3za4bWdELOhrDJIk9YehAx2AJEmbaQ0wqXz/GuDLwH9ExF9n5lN92O9U4I/AFQ1s+2VgRB+OLUlSv7HokyRtadZl5m/K97+JiPuA/wTeA1xW2zEiRmTmM/0dUGb+qb+PIUlSo5zeKUna0t1efh0bEcsj4p8j4gsRsRL4C0BEbBMR342IByNibUTMj4i/Wb+DiLgJ2A84oWbq6Ik17SdHxOKIeDYi7o2IM2oD6Dq9MyJOLPexd0RcFxFPRcSdEfH+/vs2SJLUPYs+SdKWbmz59cHy6weBdwIfA44t110AnAT8E/A+YAVwVUS8vWz/GHAnMA84oHxdBRARnwa+TzHt88jy/ZcjYlodsf0YmFse87+A2RExppEPKUlSo5zeKUna4kTE+v+/XgN8D3gCuJ7i2jqAIzNzbdl3T+A44KTMvLhcdw3wB+ALwGGZuSQingJW1UwdJSJeDnwR+EpmfqlcfV1EbAN8PiK+n5kv9BLqtzPzonJftwMPURSO5/XtOyBJUv0c6ZMkbWlGAc+Xr7soCr9jM/PPZft/rC/4Sm8Ggprr/TLzxXL57fTuAGBb4LKIGLr+BdwAvBrY1KjdtTXHXA08XMc2kiQ1lSN9kqQtzRrgECAppnQ+kJlZ0/5Ql/47Ak9m5tNd1j8EbBMRW2fmsz0ca4fy6+Ie2ncB7u0l1se7LD8HDO+lvyRJTWfRJ0na0qzLzN6eiZddlv8MbBcR23Qp/F4NPN1LwQfwaPn1SF5aTEIx0ihJUluz6JMkVd18ikLwaODfASIiyuVbavp1Nwp3K/AMsFNmXtX/oUqS1HwWfZKkSsvMpRExCzg3IkYCfwI+CuwB/H1N1zuBwyLiMGA18P8yc3VETAe+ExG7ATdTXA+/O/CuzHxfCz+KJEkNseiTJA0GHwW+DpwFvAJYRHGHz9qRvq8AuwKXAi+neMRDZ2Z+IyIeAE4HPgmsBe4G5rQufEmSGhcbX/suSZIkSaoSH9kgSZIkSRVm0SdJkiRJFWbRJ0mSJEkVZtEnSZIkSRVm0SdJkiRJFWbRJ0mSJEkVZtEnSZIkSRVm0SdJkiRJFWbRJ0mSJEkV9v8BJlSO6Jxc2gwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting results:\n",
    "fig, (axesQ3, axesMCC) = plt.subplots(nrows=2, ncols=3, sharey=True , figsize=(15,10))\n",
    "labels = [\"1arl\",\"1ava\",\"1amv\",\"1hge\",\"1hmo\",\"1jsu\"]\n",
    "locationLabels = np.arange(len(labels))\n",
    "widthBars = 0.35\n",
    "\n",
    "for i, (SS, axQ3) in enumerate(zip((\"Helix\",\"Sheet\",\"Coil\"),axesQ3)):\n",
    "    if i==0:\n",
    "        axQ3.set_ylabel(\"Q3 score\",fontsize=15)\n",
    "    axQ3.set_title(f\"Q3 score of {SS}\")\n",
    "    \n",
    "    axQ3.bar(locationLabels - widthBars/2, Q3EvoResults[:,i,1], widthBars, label=\"Alone\")\n",
    "    axQ3.bar(locationLabels + widthBars/2, Q3EvoResults[:,i,0], widthBars, label=\"Evolution\")\n",
    "    axQ3.set_xticks(locationLabels)\n",
    "    axQ3.set_xticklabels(labels)\n",
    "    if i==2:\n",
    "        axQ3.legend()\n",
    "        \n",
    "for i, (SS, axMCC) in enumerate(zip((\"Helix\",\"Sheet\",\"Coil\"),axesMCC)):\n",
    "    if i==0:\n",
    "        axMCC.set_ylabel(\"MCC score\",fontsize=15)\n",
    "    axMCC.set_title(f\"MCC score of {SS}\")\n",
    "    \n",
    "    axMCC.bar(locationLabels - widthBars/2, MCCEvoResults[:,i,1], widthBars, label=\"Alone\")\n",
    "    axMCC.bar(locationLabels + widthBars/2, MCCEvoResults[:,i,0], widthBars, label=\"Evolution\")\n",
    "    axMCC.set_xticks(locationLabels)\n",
    "    axMCC.set_xticklabels(labels)\n",
    "    if i==2:\n",
    "        axMCC.legend()\n",
    "        \n",
    "fig.text(0.5,0.07, \"Protein\", ha=\"center\", va=\"center\",fontsize=15)\n",
    "fig.suptitle(f\"Result evolution GOR III algorithm\", fontsize=18,x = 0.5, y = 0.94)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Discussion results:\n",
    "\n",
    "We see that the results are mixed. \n",
    "\n",
    "- **Sheets**: The results improved for the Q3 and MCC (but not for 1hmo since it does not contain any sheets and hence Q3/MCC is undefined). And thus the evolutionary information helped us with the sheets. \n",
    "\n",
    "- **Helix**: For the helix the result is much worse. We see that for all proteins the Q3 value drops. But to so much for the proteins: \"1arl\", \"1hge\" and \"1jsu\". Notable these are also the proteins that had many other high sequenes identiy sequences, while the others not so much. The result for \"1arl\" and \"1jsu\" even improved in the MCC case and hence can conclude that there it improved. Further below I go into detail for the protein \"1arl\". \n",
    "\n",
    "- **Coil**: The Q3 coil score improved for all, but the MCC score not necessary (except 1arl). This to me says that what we had before with helix (that the GOR algorithm over estimates it), is now the problem with Coil. After evolution allignment the likes to over estimate Coils, giving high Q3 score. Looking at the plot it is as if the Helix and Coil switch places after the analysis.\n",
    "\n",
    "The evolution information did not improve the results by that much. But the results are highly depended on which sequences you take. And some of the proteins simply did not have enough matches with high identity and were thus forced to take allignment with sequences that would maybe have a different structure. (especially the protein 1hmo was a problem)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------\n",
    "## Small exploration number of sequences needed\n",
    "\n",
    "In the following lines of code I show you how the result improves by taking more and more sequences in the MAS.\n",
    "\n",
    "Here I take give a look at protein \"1arl\" and I take the $n$ sequence from the BLAST result who are ordered by \"score\". This scores is highly linked to identity and so you could imagine it as taking the first $n$ sequences with orderd by identity.\n",
    "\n",
    "the figure below shows you the evolution of the Q3, MCC per SS for different $n$. The last entry in the figure is called \"Diff\". This is mode where I took only 15 sequences but focused my self on taking lower identity (between regions 80-85%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First compute the GOR 3 result on the sequence 1arl alone. \n",
    "seq1arl = dictSeq[\"1arl\"]\n",
    "SSPredictAlone, _ = predictSS(seq1arl, totalFreqTable, totalLog, totalLogRj)\n",
    "Q3Alone = proteinQ3(SSPredictAlone, dictSS[\"1arl\"])\n",
    "MCCAlone = proteinMCC(SSPredictAlone, dictSS[\"1arl\"])\n",
    "\n",
    "# Compute now for the different amount of sequences:\n",
    "dictSS[\"1arl_25\"] = dictSS[\"1arl\"]\n",
    "dictSS[\"1arl_15\"] = dictSS[\"1arl\"]\n",
    "dictSS[\"1arl_10\"] = dictSS[\"1arl\"]\n",
    "dictSS[\"1arl_5\"] = dictSS[\"1arl\"]\n",
    "dictSS[\"1arl_Different\"] = dictSS[\"1arl\"]\n",
    "\n",
    "Q3 = np.zeros((6,3))\n",
    "MCC = np.zeros((6,3))\n",
    "Q3[0], MCC[0] = (Q3Alone,MCCAlone)\n",
    "Q3[1], MCC[1], _ = analyseProteinViaEvolution(\"1arl_5\") \n",
    "Q3[2], MCC[2], _ = analyseProteinViaEvolution(\"1arl_10\")\n",
    "Q3[3], MCC[3], _ = analyseProteinViaEvolution(\"1arl_15\")\n",
    "Q3[4], MCC[4], _ = analyseProteinViaEvolution(\"1arl_25\")\n",
    "Q3[5], MCC[5], _ = analyseProteinViaEvolution(\"1arl_Different\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Evolutionary GOR 3: protein \"1arl\"')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the figure\n",
    "fig, (axQ3,axMCC) = plt.subplots(nrows=1, ncols=2 , figsize=(10,5))\n",
    "SS = [\"Helix\", \"Sheet\",\"Coil\"]\n",
    "labels = [\"1\",\"5\",\"10\",\"15\",\"25\",\"Diff\"]\n",
    "x = np.arange(len(Q3))\n",
    "\n",
    "#for i, ax in enumerate(axesQ3):\n",
    "axQ3.set_ylabel(\"Q3 score\",fontsize=15)\n",
    "axQ3.set_xlabel(\"Number of Seq. in MAS\",fontsize=15)\n",
    "axQ3.set_title(f\"Q3 score\")\n",
    "axQ3.plot(x,Q3[:,0],label=\"Helix\")\n",
    "axQ3.plot(x,Q3[:,1],label=\"Sheet\")\n",
    "axQ3.plot(x,Q3[:,2],label=\"Coil\")\n",
    "axQ3.set_xticks(x)\n",
    "axQ3.set_xticklabels(labels)\n",
    "axQ3.set_ylim(bottom=0,top=1)\n",
    "axQ3.legend()\n",
    "axQ3.grid()\n",
    "#for i, axMCC in enumerate(axesMCC):\n",
    "\n",
    "axMCC.set_ylabel(\"MCC score\",fontsize=15)\n",
    "axMCC.set_xlabel(\"Type of MAS allignment\",fontsize=15)\n",
    "axMCC.set_title(f\"MCC score\")\n",
    "axMCC.plot(x,MCC[:,0],label=\"Helix\")\n",
    "axMCC.plot(x,MCC[:,1],label=\"Sheet\")\n",
    "axMCC.plot(x,MCC[:,2],label=\"Coil\")\n",
    "axMCC.set_xticks(x)\n",
    "axMCC.set_xticklabels(labels)\n",
    "axMCC.set_ylim(bottom=0,top=1)\n",
    "axMCC.legend()\n",
    "axMCC.grid()\n",
    "    \n",
    "fig.suptitle(f'Evolutionary GOR 3: protein \"1arl\"', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Discussion results:\n",
    "\n",
    "We see that for a higher number of sequences. The Q3 value for the Helix drastically goes down, but the MCC actually increases! \n",
    "\n",
    "This once again can be explained by the some what mysterious role that Helix takes on with the GOR analysis. As we saw in analysis above, our GOR algorithm likes to classify stuff quickly as Helix. This is good for the Helix regions since these are then often classified correctly -> Resulting in high Q3 score for helix. But there are many other regions wrongly classified as Helix -> resulting in bad MCC score. \n",
    "\n",
    "But, it is clear that hear more evolution information actually imporoves this problem. And hence MCC score of helix goes up. Even though the Q3 score goes down. More evolutionary information clearly also benefits the Coil and Sheet regions.\n",
    "\n",
    "Lastly, we have the \"Diff\" entry which uses 15 entries but not ranked to highest identity. We see that it compares better then the taking the 15 first entries.\n",
    "\n",
    "Still the first 25 perform better then the \"Diff\". "
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}