code/kaggle/kaggle_metadata.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registered S3 methods overwritten by 'ggplot2':\n",
      "  method         from \n",
      "  [.quosures     rlang\n",
      "  c.quosures     rlang\n",
      "  print.quosures rlang\n",
      "Registered S3 method overwritten by 'rvest':\n",
      "  method            from\n",
      "  read_xml.response xml2\n",
      "── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──\n",
      "✔ ggplot2 3.1.1       ✔ purrr   0.3.2  \n",
      "✔ tibble  2.1.1       ✔ dplyr   0.8.0.1\n",
      "✔ tidyr   0.8.3       ✔ stringr 1.4.0  \n",
      "✔ readr   1.3.1       ✔ forcats 0.4.0  \n",
      "── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "✖ dplyr::filter() masks stats::filter()\n",
      "✖ dplyr::lag()    masks stats::lag()\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  .default = col_character(),\n",
      "  `Patient ID` = col_double(),\n",
      "  `Patient's Birth Date` = col_double(),\n",
      "  `Patient's Address` = col_logical(),\n",
      "  `Patient's Telephone Numbers` = col_logical(),\n",
      "  `Slice Thickness` = col_double(),\n",
      "  `Repetition Time` = col_double(),\n",
      "  `Echo Time` = col_double(),\n",
      "  `Number of Averages` = col_double(),\n",
      "  `Imaging Frequency` = col_double(),\n",
      "  `Echo Number(s)` = col_double(),\n",
      "  `Magnetic Field Strength` = col_double(),\n",
      "  `Number of Phase Encoding Steps` = col_double(),\n",
      "  `Echo Train Length` = col_double(),\n",
      "  `Percent Sampling` = col_double(),\n",
      "  `Percent Phase Field of View` = col_double(),\n",
      "  `Pixel Bandwidth` = col_double(),\n",
      "  `Trigger Time` = col_double(),\n",
      "  `Nominal Interval` = col_double(),\n",
      "  `Cardiac Number of Images` = col_double(),\n",
      "  `Flip Angle` = col_double()\n",
      "  # ... with 30 more columns\n",
      ")\n",
      "See spec(...) for full column specifications.\n"
     ]
    }
   ],
   "source": [
    "metadata <- read_tsv(\"../../analysis/kaggle/combined_metadata.tsv.xz\", guess_max = 200000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  .default = col_double(),\n",
      "  PatientsAgeNum = col_character(),\n",
      "  PatientsAgeUnit = col_character(),\n",
      "  PatientsSex = col_character(),\n",
      "  pid = col_character(),\n",
      "  used = col_logical(),\n",
      "  set = col_character(),\n",
      "  PhaseEncodingDirection = col_character(),\n",
      "  ModelName = col_character(),\n",
      "  PixelSpacing = col_character(),\n",
      "  ScanningSequence = col_character(),\n",
      "  SequenceName = col_character(),\n",
      "  SequenceVariant = col_character(),\n",
      "  SoftwareVersion = col_character(),\n",
      "  inconsistentMetadata = col_logical()\n",
      ")\n",
      "See spec(...) for full column specifications.\n"
     ]
    }
   ],
   "source": [
    "patient_metadata <- read_tsv(\"../../analysis/kaggle/patient_metadata.tsv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the available metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Specific Character Set'</li>\n",
       "\t<li>'Image Type'</li>\n",
       "\t<li>'Instance Creation Time'</li>\n",
       "\t<li>'SOP Class UID'</li>\n",
       "\t<li>'SOP Instance UID'</li>\n",
       "\t<li>'Study Time'</li>\n",
       "\t<li>'Series Time'</li>\n",
       "\t<li>'Acquisition Time'</li>\n",
       "\t<li>'Modality'</li>\n",
       "\t<li>'Manufacturer'</li>\n",
       "\t<li>'Series Description'</li>\n",
       "\t<li>'Manufacturer\\'s Model Name'</li>\n",
       "\t<li>'Referenced Image Sequence'</li>\n",
       "\t<li>'Patient\\'s Name'</li>\n",
       "\t<li>'Patient ID'</li>\n",
       "\t<li>'Patient\\'s Birth Date'</li>\n",
       "\t<li>'Patient\\'s Sex'</li>\n",
       "\t<li>'Patient\\'s Age'</li>\n",
       "\t<li>'Patient\\'s Address'</li>\n",
       "\t<li>'Patient\\'s Telephone Numbers'</li>\n",
       "\t<li>'Body Part Examined'</li>\n",
       "\t<li>'Scanning Sequence'</li>\n",
       "\t<li>'Sequence Variant'</li>\n",
       "\t<li>'Scan Options'</li>\n",
       "\t<li>'MR Acquisition Type'</li>\n",
       "\t<li>'Sequence Name'</li>\n",
       "\t<li>'Angio Flag'</li>\n",
       "\t<li>'Slice Thickness'</li>\n",
       "\t<li>'Repetition Time'</li>\n",
       "\t<li>'Echo Time'</li>\n",
       "\t<li>'Number of Averages'</li>\n",
       "\t<li>'Imaging Frequency'</li>\n",
       "\t<li>'Imaged Nucleus'</li>\n",
       "\t<li>'Echo Number(s)'</li>\n",
       "\t<li>'Magnetic Field Strength'</li>\n",
       "\t<li>'Number of Phase Encoding Steps'</li>\n",
       "\t<li>'Echo Train Length'</li>\n",
       "\t<li>'Percent Sampling'</li>\n",
       "\t<li>'Percent Phase Field of View'</li>\n",
       "\t<li>'Pixel Bandwidth'</li>\n",
       "\t<li>'Software Versions'</li>\n",
       "\t<li>'Trigger Time'</li>\n",
       "\t<li>'Nominal Interval'</li>\n",
       "\t<li>'Cardiac Number of Images'</li>\n",
       "\t<li>'Transmit Coil Name'</li>\n",
       "\t<li>'Acquisition Matrix'</li>\n",
       "\t<li>'In-plane Phase Encoding Direction'</li>\n",
       "\t<li>'Flip Angle'</li>\n",
       "\t<li>'Variable Flip Angle Flag'</li>\n",
       "\t<li>'SAR'</li>\n",
       "\t<li>'dB/dt'</li>\n",
       "\t<li>'Patient Position'</li>\n",
       "\t<li>'Series Number'</li>\n",
       "\t<li>'Acquisition Number'</li>\n",
       "\t<li>'Instance Number'</li>\n",
       "\t<li>'Image Position (Patient)'</li>\n",
       "\t<li>'Image Orientation (Patient)'</li>\n",
       "\t<li>'Position Reference Indicator'</li>\n",
       "\t<li>'Slice Location'</li>\n",
       "\t<li>'Samples per Pixel'</li>\n",
       "\t<li>'Photometric Interpretation'</li>\n",
       "\t<li>'Rows'</li>\n",
       "\t<li>'Columns'</li>\n",
       "\t<li>'Pixel Spacing'</li>\n",
       "\t<li>'Bits Allocated'</li>\n",
       "\t<li>'Bits Stored'</li>\n",
       "\t<li>'High Bit'</li>\n",
       "\t<li>'Pixel Representation'</li>\n",
       "\t<li>'Smallest Image Pixel Value'</li>\n",
       "\t<li>'Largest Image Pixel Value'</li>\n",
       "\t<li>'Window Center'</li>\n",
       "\t<li>'Window Width'</li>\n",
       "\t<li>'Window Center &amp; Width Explanation'</li>\n",
       "\t<li>'Performed Procedure Step Start Time'</li>\n",
       "\t<li>'Performed Procedure Step ID'</li>\n",
       "\t<li>'Comments on the Performed Procedure Step'</li>\n",
       "\t<li>'file'</li>\n",
       "\t<li>'dir'</li>\n",
       "\t<li>'Id'</li>\n",
       "\t<li>'Referenced Study Sequence'</li>\n",
       "\t<li>'Referenced Patient Sequence'</li>\n",
       "\t<li>'Spacing Between Slices'</li>\n",
       "\t<li>'Number of Frames'</li>\n",
       "\t<li>'Contrast/Bolus Agent'</li>\n",
       "\t<li>'Contrast/Bolus Volume'</li>\n",
       "\t<li>'Contrast/Bolus Total Dose'</li>\n",
       "\t<li>'Contrast/Bolus Ingredient'</li>\n",
       "\t<li>'Contrast/Bolus Ingredient Concentration'</li>\n",
       "\t<li>'Source Image Sequence'</li>\n",
       "\t<li>'Contrast Flow Duration'</li>\n",
       "\t<li>'pid'</li>\n",
       "\t<li>'slice'</li>\n",
       "\t<li>'frame'</li>\n",
       "\t<li>'used'</li>\n",
       "\t<li>'set'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Specific Character Set'\n",
       "\\item 'Image Type'\n",
       "\\item 'Instance Creation Time'\n",
       "\\item 'SOP Class UID'\n",
       "\\item 'SOP Instance UID'\n",
       "\\item 'Study Time'\n",
       "\\item 'Series Time'\n",
       "\\item 'Acquisition Time'\n",
       "\\item 'Modality'\n",
       "\\item 'Manufacturer'\n",
       "\\item 'Series Description'\n",
       "\\item 'Manufacturer\\textbackslash{}'s Model Name'\n",
       "\\item 'Referenced Image Sequence'\n",
       "\\item 'Patient\\textbackslash{}'s Name'\n",
       "\\item 'Patient ID'\n",
       "\\item 'Patient\\textbackslash{}'s Birth Date'\n",
       "\\item 'Patient\\textbackslash{}'s Sex'\n",
       "\\item 'Patient\\textbackslash{}'s Age'\n",
       "\\item 'Patient\\textbackslash{}'s Address'\n",
       "\\item 'Patient\\textbackslash{}'s Telephone Numbers'\n",
       "\\item 'Body Part Examined'\n",
       "\\item 'Scanning Sequence'\n",
       "\\item 'Sequence Variant'\n",
       "\\item 'Scan Options'\n",
       "\\item 'MR Acquisition Type'\n",
       "\\item 'Sequence Name'\n",
       "\\item 'Angio Flag'\n",
       "\\item 'Slice Thickness'\n",
       "\\item 'Repetition Time'\n",
       "\\item 'Echo Time'\n",
       "\\item 'Number of Averages'\n",
       "\\item 'Imaging Frequency'\n",
       "\\item 'Imaged Nucleus'\n",
       "\\item 'Echo Number(s)'\n",
       "\\item 'Magnetic Field Strength'\n",
       "\\item 'Number of Phase Encoding Steps'\n",
       "\\item 'Echo Train Length'\n",
       "\\item 'Percent Sampling'\n",
       "\\item 'Percent Phase Field of View'\n",
       "\\item 'Pixel Bandwidth'\n",
       "\\item 'Software Versions'\n",
       "\\item 'Trigger Time'\n",
       "\\item 'Nominal Interval'\n",
       "\\item 'Cardiac Number of Images'\n",
       "\\item 'Transmit Coil Name'\n",
       "\\item 'Acquisition Matrix'\n",
       "\\item 'In-plane Phase Encoding Direction'\n",
       "\\item 'Flip Angle'\n",
       "\\item 'Variable Flip Angle Flag'\n",
       "\\item 'SAR'\n",
       "\\item 'dB/dt'\n",
       "\\item 'Patient Position'\n",
       "\\item 'Series Number'\n",
       "\\item 'Acquisition Number'\n",
       "\\item 'Instance Number'\n",
       "\\item 'Image Position (Patient)'\n",
       "\\item 'Image Orientation (Patient)'\n",
       "\\item 'Position Reference Indicator'\n",
       "\\item 'Slice Location'\n",
       "\\item 'Samples per Pixel'\n",
       "\\item 'Photometric Interpretation'\n",
       "\\item 'Rows'\n",
       "\\item 'Columns'\n",
       "\\item 'Pixel Spacing'\n",
       "\\item 'Bits Allocated'\n",
       "\\item 'Bits Stored'\n",
       "\\item 'High Bit'\n",
       "\\item 'Pixel Representation'\n",
       "\\item 'Smallest Image Pixel Value'\n",
       "\\item 'Largest Image Pixel Value'\n",
       "\\item 'Window Center'\n",
       "\\item 'Window Width'\n",
       "\\item 'Window Center \\& Width Explanation'\n",
       "\\item 'Performed Procedure Step Start Time'\n",
       "\\item 'Performed Procedure Step ID'\n",
       "\\item 'Comments on the Performed Procedure Step'\n",
       "\\item 'file'\n",
       "\\item 'dir'\n",
       "\\item 'Id'\n",
       "\\item 'Referenced Study Sequence'\n",
       "\\item 'Referenced Patient Sequence'\n",
       "\\item 'Spacing Between Slices'\n",
       "\\item 'Number of Frames'\n",
       "\\item 'Contrast/Bolus Agent'\n",
       "\\item 'Contrast/Bolus Volume'\n",
       "\\item 'Contrast/Bolus Total Dose'\n",
       "\\item 'Contrast/Bolus Ingredient'\n",
       "\\item 'Contrast/Bolus Ingredient Concentration'\n",
       "\\item 'Source Image Sequence'\n",
       "\\item 'Contrast Flow Duration'\n",
       "\\item 'pid'\n",
       "\\item 'slice'\n",
       "\\item 'frame'\n",
       "\\item 'used'\n",
       "\\item 'set'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Specific Character Set'\n",
       "2. 'Image Type'\n",
       "3. 'Instance Creation Time'\n",
       "4. 'SOP Class UID'\n",
       "5. 'SOP Instance UID'\n",
       "6. 'Study Time'\n",
       "7. 'Series Time'\n",
       "8. 'Acquisition Time'\n",
       "9. 'Modality'\n",
       "10. 'Manufacturer'\n",
       "11. 'Series Description'\n",
       "12. 'Manufacturer\\'s Model Name'\n",
       "13. 'Referenced Image Sequence'\n",
       "14. 'Patient\\'s Name'\n",
       "15. 'Patient ID'\n",
       "16. 'Patient\\'s Birth Date'\n",
       "17. 'Patient\\'s Sex'\n",
       "18. 'Patient\\'s Age'\n",
       "19. 'Patient\\'s Address'\n",
       "20. 'Patient\\'s Telephone Numbers'\n",
       "21. 'Body Part Examined'\n",
       "22. 'Scanning Sequence'\n",
       "23. 'Sequence Variant'\n",
       "24. 'Scan Options'\n",
       "25. 'MR Acquisition Type'\n",
       "26. 'Sequence Name'\n",
       "27. 'Angio Flag'\n",
       "28. 'Slice Thickness'\n",
       "29. 'Repetition Time'\n",
       "30. 'Echo Time'\n",
       "31. 'Number of Averages'\n",
       "32. 'Imaging Frequency'\n",
       "33. 'Imaged Nucleus'\n",
       "34. 'Echo Number(s)'\n",
       "35. 'Magnetic Field Strength'\n",
       "36. 'Number of Phase Encoding Steps'\n",
       "37. 'Echo Train Length'\n",
       "38. 'Percent Sampling'\n",
       "39. 'Percent Phase Field of View'\n",
       "40. 'Pixel Bandwidth'\n",
       "41. 'Software Versions'\n",
       "42. 'Trigger Time'\n",
       "43. 'Nominal Interval'\n",
       "44. 'Cardiac Number of Images'\n",
       "45. 'Transmit Coil Name'\n",
       "46. 'Acquisition Matrix'\n",
       "47. 'In-plane Phase Encoding Direction'\n",
       "48. 'Flip Angle'\n",
       "49. 'Variable Flip Angle Flag'\n",
       "50. 'SAR'\n",
       "51. 'dB/dt'\n",
       "52. 'Patient Position'\n",
       "53. 'Series Number'\n",
       "54. 'Acquisition Number'\n",
       "55. 'Instance Number'\n",
       "56. 'Image Position (Patient)'\n",
       "57. 'Image Orientation (Patient)'\n",
       "58. 'Position Reference Indicator'\n",
       "59. 'Slice Location'\n",
       "60. 'Samples per Pixel'\n",
       "61. 'Photometric Interpretation'\n",
       "62. 'Rows'\n",
       "63. 'Columns'\n",
       "64. 'Pixel Spacing'\n",
       "65. 'Bits Allocated'\n",
       "66. 'Bits Stored'\n",
       "67. 'High Bit'\n",
       "68. 'Pixel Representation'\n",
       "69. 'Smallest Image Pixel Value'\n",
       "70. 'Largest Image Pixel Value'\n",
       "71. 'Window Center'\n",
       "72. 'Window Width'\n",
       "73. 'Window Center &amp; Width Explanation'\n",
       "74. 'Performed Procedure Step Start Time'\n",
       "75. 'Performed Procedure Step ID'\n",
       "76. 'Comments on the Performed Procedure Step'\n",
       "77. 'file'\n",
       "78. 'dir'\n",
       "79. 'Id'\n",
       "80. 'Referenced Study Sequence'\n",
       "81. 'Referenced Patient Sequence'\n",
       "82. 'Spacing Between Slices'\n",
       "83. 'Number of Frames'\n",
       "84. 'Contrast/Bolus Agent'\n",
       "85. 'Contrast/Bolus Volume'\n",
       "86. 'Contrast/Bolus Total Dose'\n",
       "87. 'Contrast/Bolus Ingredient'\n",
       "88. 'Contrast/Bolus Ingredient Concentration'\n",
       "89. 'Source Image Sequence'\n",
       "90. 'Contrast Flow Duration'\n",
       "91. 'pid'\n",
       "92. 'slice'\n",
       "93. 'frame'\n",
       "94. 'used'\n",
       "95. 'set'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Specific Character Set\"                  \n",
       " [2] \"Image Type\"                              \n",
       " [3] \"Instance Creation Time\"                  \n",
       " [4] \"SOP Class UID\"                           \n",
       " [5] \"SOP Instance UID\"                        \n",
       " [6] \"Study Time\"                              \n",
       " [7] \"Series Time\"                             \n",
       " [8] \"Acquisition Time\"                        \n",
       " [9] \"Modality\"                                \n",
       "[10] \"Manufacturer\"                            \n",
       "[11] \"Series Description\"                      \n",
       "[12] \"Manufacturer's Model Name\"               \n",
       "[13] \"Referenced Image Sequence\"               \n",
       "[14] \"Patient's Name\"                          \n",
       "[15] \"Patient ID\"                              \n",
       "[16] \"Patient's Birth Date\"                    \n",
       "[17] \"Patient's Sex\"                           \n",
       "[18] \"Patient's Age\"                           \n",
       "[19] \"Patient's Address\"                       \n",
       "[20] \"Patient's Telephone Numbers\"             \n",
       "[21] \"Body Part Examined\"                      \n",
       "[22] \"Scanning Sequence\"                       \n",
       "[23] \"Sequence Variant\"                        \n",
       "[24] \"Scan Options\"                            \n",
       "[25] \"MR Acquisition Type\"                     \n",
       "[26] \"Sequence Name\"                           \n",
       "[27] \"Angio Flag\"                              \n",
       "[28] \"Slice Thickness\"                         \n",
       "[29] \"Repetition Time\"                         \n",
       "[30] \"Echo Time\"                               \n",
       "[31] \"Number of Averages\"                      \n",
       "[32] \"Imaging Frequency\"                       \n",
       "[33] \"Imaged Nucleus\"                          \n",
       "[34] \"Echo Number(s)\"                          \n",
       "[35] \"Magnetic Field Strength\"                 \n",
       "[36] \"Number of Phase Encoding Steps\"          \n",
       "[37] \"Echo Train Length\"                       \n",
       "[38] \"Percent Sampling\"                        \n",
       "[39] \"Percent Phase Field of View\"             \n",
       "[40] \"Pixel Bandwidth\"                         \n",
       "[41] \"Software Versions\"                       \n",
       "[42] \"Trigger Time\"                            \n",
       "[43] \"Nominal Interval\"                        \n",
       "[44] \"Cardiac Number of Images\"                \n",
       "[45] \"Transmit Coil Name\"                      \n",
       "[46] \"Acquisition Matrix\"                      \n",
       "[47] \"In-plane Phase Encoding Direction\"       \n",
       "[48] \"Flip Angle\"                              \n",
       "[49] \"Variable Flip Angle Flag\"                \n",
       "[50] \"SAR\"                                     \n",
       "[51] \"dB/dt\"                                   \n",
       "[52] \"Patient Position\"                        \n",
       "[53] \"Series Number\"                           \n",
       "[54] \"Acquisition Number\"                      \n",
       "[55] \"Instance Number\"                         \n",
       "[56] \"Image Position (Patient)\"                \n",
       "[57] \"Image Orientation (Patient)\"             \n",
       "[58] \"Position Reference Indicator\"            \n",
       "[59] \"Slice Location\"                          \n",
       "[60] \"Samples per Pixel\"                       \n",
       "[61] \"Photometric Interpretation\"              \n",
       "[62] \"Rows\"                                    \n",
       "[63] \"Columns\"                                 \n",
       "[64] \"Pixel Spacing\"                           \n",
       "[65] \"Bits Allocated\"                          \n",
       "[66] \"Bits Stored\"                             \n",
       "[67] \"High Bit\"                                \n",
       "[68] \"Pixel Representation\"                    \n",
       "[69] \"Smallest Image Pixel Value\"              \n",
       "[70] \"Largest Image Pixel Value\"               \n",
       "[71] \"Window Center\"                           \n",
       "[72] \"Window Width\"                            \n",
       "[73] \"Window Center & Width Explanation\"       \n",
       "[74] \"Performed Procedure Step Start Time\"     \n",
       "[75] \"Performed Procedure Step ID\"             \n",
       "[76] \"Comments on the Performed Procedure Step\"\n",
       "[77] \"file\"                                    \n",
       "[78] \"dir\"                                     \n",
       "[79] \"Id\"                                      \n",
       "[80] \"Referenced Study Sequence\"               \n",
       "[81] \"Referenced Patient Sequence\"             \n",
       "[82] \"Spacing Between Slices\"                  \n",
       "[83] \"Number of Frames\"                        \n",
       "[84] \"Contrast/Bolus Agent\"                    \n",
       "[85] \"Contrast/Bolus Volume\"                   \n",
       "[86] \"Contrast/Bolus Total Dose\"               \n",
       "[87] \"Contrast/Bolus Ingredient\"               \n",
       "[88] \"Contrast/Bolus Ingredient Concentration\" \n",
       "[89] \"Source Image Sequence\"                   \n",
       "[90] \"Contrast Flow Duration\"                  \n",
       "[91] \"pid\"                                     \n",
       "[92] \"slice\"                                   \n",
       "[93] \"frame\"                                   \n",
       "[94] \"used\"                                    \n",
       "[95] \"set\"                                     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metadata %>% colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Manufacturer</th><th scope=col>Manufacturer's Model Name</th><th scope=col>Magnetic Field Strength</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>SIEMENS</td><td>Aera   </td><td>1.5    </td><td>333810 </td></tr>\n",
       "\t<tr><td>SIEMENS</td><td>Skyra  </td><td>3.0    </td><td> 39453 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " Manufacturer & Manufacturer's Model Name & Magnetic Field Strength & n\\\\\n",
       "\\hline\n",
       "\t SIEMENS & Aera    & 1.5     & 333810 \\\\\n",
       "\t SIEMENS & Skyra   & 3.0     &  39453 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Manufacturer | Manufacturer's Model Name | Magnetic Field Strength | n |\n",
       "|---|---|---|---|\n",
       "| SIEMENS | Aera    | 1.5     | 333810  |\n",
       "| SIEMENS | Skyra   | 3.0     |  39453  |\n",
       "\n"
      ],
      "text/plain": [
       "  Manufacturer Manufacturer's Model Name Magnetic Field Strength n     \n",
       "1 SIEMENS      Aera                      1.5                     333810\n",
       "2 SIEMENS      Skyra                     3.0                      39453"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metadata %>% count(`Manufacturer`,`Manufacturer's Model Name`, `Magnetic Field Strength`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>used</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>FALSE </td><td> 15503</td></tr>\n",
       "\t<tr><td> TRUE </td><td>357760</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " used & n\\\\\n",
       "\\hline\n",
       "\t FALSE  &  15503\\\\\n",
       "\t  TRUE  & 357760\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| used | n |\n",
       "|---|---|\n",
       "| FALSE  |  15503 |\n",
       "|  TRUE  | 357760 |\n",
       "\n"
      ],
      "text/plain": [
       "  used  n     \n",
       "1 FALSE  15503\n",
       "2  TRUE 357760"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metadata %>% count(used)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Different kind of inconsistencies mostly on sequence name, dimension, flip angel, etc. Some of these are fixed in the conversion process others are ignored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>inconsistentMetadata</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>FALSE</td><td>1116 </td></tr>\n",
       "\t<tr><td> TRUE</td><td>  33 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " inconsistentMetadata & n\\\\\n",
       "\\hline\n",
       "\t FALSE & 1116 \\\\\n",
       "\t  TRUE &   33 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| inconsistentMetadata | n |\n",
       "|---|---|\n",
       "| FALSE | 1116  |\n",
       "|  TRUE |   33  |\n",
       "\n"
      ],
      "text/plain": [
       "  inconsistentMetadata n   \n",
       "1 FALSE                1116\n",
       "2  TRUE                  33"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patient_metadata %>% count(inconsistentMetadata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect slice spacings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These inconsistencies have mostly been fixed in the conversion from dicom to nifti but still show up in the metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/a/8189441\n",
    "Mode <- function(x) {\n",
    "  ux <- unique(x)\n",
    "  ux[which.max(tabulate(match(x, ux)))]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "spacing <- metadata %>%\n",
    "    filter(used) %>%\n",
    "    select(PatientID=pid, SliceLocation=`Slice Location`) %>%\n",
    "    unique %>%\n",
    "    group_by(PatientID) %>%\n",
    "    arrange(SliceLocation) %>%\n",
    "    mutate(\n",
    "        dist = SliceLocation-lag(SliceLocation),\n",
    "        meddist = Mode(round(dist,digits = 1)),\n",
    "        dev=abs(dist-meddist)>1,\n",
    "        any_dev=any(dev,na.rm = T)\n",
    "    ) %>%\n",
    "    arrange(PatientID)\n",
    "inconsistent_spacing <- spacing %>%\n",
    "    filter(any_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IVLE+1WkHa9Y9X29Ha2SRxJjUhHAHwgmIUdEGwdqgGKuUB6bNdj0hEA+EMwC7un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\nsQMASIRiBwCQiP8DxNhlOvyDi7cAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inserted slices are not shown properly as they inherit the original slicelocation from the dicom metadata (but they are fixed in the nifti)\n",
    "inconsistent_spacing %>%\n",
    "    ggplot(aes(y=SliceLocation,yend=SliceLocation,color=dev)) +\n",
    "    geom_segment(x=0,xend=10,lwd=.8) +\n",
    "    facet_wrap(. ~ PatientID, scales=\"free\") +\n",
    "    theme_minimal() +\n",
    "    scale_color_brewer(type=\"qual\",na.value=\"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+aHkP0ydqxWASDCUdmoJef0D+y8RBoU2BFCDoO6/ylj+OAHP7j33x/72MeCnzDq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DxKuUEjuk7PzvBxKJZG1chQ8AyQVDAp8kMazsBvyycZUAACAASURBVPBubM8uiu0P\naX21rU2psGOLS7FC2LV0A4tKsdTzttQqrkKP3adt7UfPjbuN+J398ElX/e7D5Wp5R44CYG9W\n2JUYRh2QG47S6z4sBwAOjAlhx3f3AShHL5c62pGjRIzcDZ5eXFYwNy2uqGRwtomYOA44Z5Ub\nNzkBQPiU1HHq1GKcWUbEGFw5PyGRbADyOpTMWzuBLGEXxCSIz792QlAv7NgdRV0A26US5qwU\nU1WuacQ/7bE7T17GZfJnH5kAfvuL7r//FUdT+L/8uBwvY9odz0xOCMQfpX8VHDva7z0qhQDu\n6bvjtye7+wDIksLueV8FcBA85caFtbsRwqs1Ojj7zpBOTtxMYadqXNeJ69TVCgBdDzAalpdI\nJK8WeR1K0kG/TKtjyAKfhc3JdOLVR2IB1FQh7Io6dvuGhfweOwAwLeK6VcXUiLrhjl3Xox8d\nafe3otdaUcVg37jv2z798Qu9+BGObApgZ6bHLi3FXgXHjva6j4RjN1OKxfKOnRB29/ynF7oE\ngtfrxB+SIN1rkmad3MhSLABuVYnniG9oiu7hipx4Esm1R16HktFapKx9YiLrZGokVhhCNWOl\nxU01aqKgsIt6APb0hkp5bo7daJUn4agr1oYLu58c6gC+fDsU//vVewGAHz9fQtidOIpKeWNG\nW9dKCa6IY0d6nUclIewmHDtuVXjZVE6Olzras75KgLvh08zJ7nXBanUA1B7FX9/UtRMCblnE\n9+vEBEA0G9Kxk0g2A3kdStIEh0xh12MOgKlS7OojsRj12PULxJ2cxH0AO1qjpPE5jh0vm2AM\n/nBLrW348MSHRzqAz+2mwu5OI26a7MMjvWA1lgNtT9musNkhZlPnKuVXYniC9rqPSqFBtF2t\nOXUX294h9oCEYcFDceDFQG1S10o8dpHCjtcaAEg/bbOjgz4AXm9c3DNuMqK5sM5UAIk6wFWZ\n2pFIrjvyOpSApmuRMtdOOBj1w52SCrsVS7GKhWKbJ07iHiV0S6mVNe7HuWesqIhR19lSa04y\nDFm0ysu7OBJGPjnRmmZyOtNKgHf2gyAmnxxrRY4wGNIoIWKB2BQEqJXYZQ5PMIY/ed/6b/+0\n+cc/Ws64or3OQyu4Z+yehtidHXN7F5yTwqZdz6NeSO6SNgBc2PAEgKRWB0AGqWNHO20AbKt1\ncc+4yYjmwkakAAjVLuS6WIlkM5DXoQQYeshZOyGE3Uw6sYLRAOa5SQOKC5Rij6Jeg1ZUopQ0\n7sckL1FBjOkR1xWDscJr3ECe9NQwJp/bndCdn9+LAHxcTNiJ3RKtLGEHoF5mXkjPkY13Pv71\n49Kff1xuu8pffVr6TuHZXpLER1HXpsnrxu3Ze5PWNgClXVTYHdoqgFvJMQBmGAX/1Tng9TpG\nRh0A2mlzw7i5PXbCsfM5gEBt44r0AEgk1x55HUpGYfpZn0+9JKMUu5bhCZOWNKL2iw1P7OlN\nAGWNM4YwbzA2TTxxxPazjc0oftpTARw0JwZaH2xFCsWnJ8WEnaMA2J6ZnBDUS4xfVlEsTMi/\n+NDUKP9Pf7Vf1vg//8CKi50UxB58VB4CeNO4O3sv294FQE+OCr6MlwMFwO3oELhYxy4txQph\nlyR00GPNG2rXYSTsqgED4Cs9lfKeJz9QJJJXj7wOJele88yp2G4ywOyiWJ9SioqxkrADUKXm\nQsfOZb7H/G21AaCkMgDDvOUT1qljV8MGJ54866kA7k4KO13ldxvxi4Ga99ONIxy7zFIsLjfx\n5IfPDNunv/y6/1or+uq9wAvJh4eFRkCIPfjY9AFkOnYjYbecY3d7+JQrKtcKiePzwSpVUEq7\nHQC030OS3Ng6LABWrQKoegkAJxnWy0x4+RKJ5NUihZ0EcF2uqlzP+EjOLMX2hrReYnTlWl9N\nWSzsRNbJjloHUNY48hNPRjuOHBFlt7GDsU96qqbwXWs6gu71VsQ4Pmsv1iWpsMtx7KolBsC5\nlPnE91/qAL52EAD4yp0AwLvPClVCiW1/YvoA3sgSdrxc5qZVvBT7cqCoFPvec1xk1gkAKErS\n2lFOjkgS024bAL/xjl3NiwHYidcoMzckUXI19hRLJNcYKewkoEMvsw4LQJRKG2Ol2ITBCeiK\nI7GCulpZGFAssk52hGMnhF3O/MT48AQ2datYEJNjR7ldj+nMD/H6dgQUE3aOoqs8709QMTgA\n27/wz9eYkY+OtaaZ7FdjAAdbUbOcvP9SL9LeR+3Bx2YA4I3SncwHsNY2cewig7GM48hWtq1Y\n8ewLDbFLn+72HSQJefnihk9O4FTYOSEAhw1F+E5PRtlJJK8aeRHeeDgnQy9zcgJAP5meih34\nlHPUSyuF2AlqihXx2GP+nMcIYSdKscKxG4bzeuzIaF3sZjp2z/sq57jbmLbrANxrRAR43FVn\n7xqHcZy4dNvKNUYsnQFwwwu/tH96ooUxeWcv1V4EeGs3ihLytLe4Hkcd+5OybxJjX9vKfABr\n7YBzWsC0a7tKxMh+JSJxxMsZk93rJbl1F4D68jk9fJm+zpsKVzVeKtcGPgA78epS2Ekkm4G8\nCG86xB+CsbzJvtmA4r4YiV1tckIgWvdEtTePo7gLQAxPGPN77EolrqpwbOHY9Tayx+5FXwFw\nu54hi02d71STZz2Vzf3V9oZKwrKzTgRC2DmXIuwAvL1/Nt57fysC8Kiz2HRkdv9T039DvzWb\ndZI+YKsFgLRPFh7qWKzNNYYAWFZkz3pJbt0GoDx9pH76Ea9Uk+2bK+wA8Gq11vcAOGwovuxd\niT3FEsn1Rl6ENx2xKDavhjVgnkJohZ7d21vHSKxACLs+m9dmJ9KJtwv02AHgpkVdN52K3UjH\n7shRAezO7HgVHDTjMCEv7XmmndAxs8vEThFDLc7Fl2IfdVQC3GucCbsHrRjAo+5iYfcweBFQ\n/mb5Xt4Dkq1tAEqnvfBQ6S9EsZF/Gq8R1trhhqF+/CEJgujtL2A2JPomwSvVcphoUMTwBEZf\n/CQSyStECrubzmjtRK5jV1MsSs7OE/GNfHaZ1TkQKSrCFMzjKOoC2FXTuBPk99gBQKVKht4W\ntbCpjt2hrQA4jSaeQoik+dXY+ZMTAColjosvxTKGZz11p5qY+lmw4LaVVA32WXtBNRnAT/gR\ngLfL93OP39oGUKQUmzp26AFAzmm8TgiJfv4XwTmA+PNfvPCn22yYVQVgUcNhnnhPkI6dRPLK\nWfwWLLnepFknZnaPXS92mtr694kJROve/FLsaCr2bHhiTiAIMy3K+VaoYFMdu0NbqZeYUKiz\nHGzFAB531V98kHuEE5siP+sEgEa5rvKLFnYvbTVMyLhdJzjYit97oXeHSrOc34WZJO+rXQBv\nl3KFHa/WuK7TYqVYAuyyDgCWcxqvl+CX/y4rm0r7JNnPGOm9UbBKBUAVhp3I4QmJZFOQF+GN\nx3ORs3aCgw+Ym51OvI7hibTHbq61dhR1Cci21gBQ1heVYitVACXPN2lpAx07NyROQPPsOgD7\n1Vil/GlvXinz2BWl2HnC2tLZRU/FClvx/tZ0Tfl2LQbwsj+vHkdc54PKEMDb5YP8BxG2tU37\nXSQLzrRjR6mVmTHsA7iE4QlB9HO/4P/G37+c59pkRGJzhWtO4pV1pim8N5SlWInkFSOF3U1n\nztoJJxnGPGlMCTtfIWQ9jp2QjL25uSTHca+uWjpRsSigGACv1gAQe7ClVjtzK7yvhGPRYFfN\nbrADoFDcaSRHtpK3XQPAsa2YOjf1eb//qsGHEWV5y9fWwZOuCuBec/pn2aslAF7ac4WdPXjP\n8spcva/vz3kYb22DMREXl4cfESegu5UkbRW9+OEJyTi8VgdQjRWPBYyzpsk6cvmERPKqkRfh\nTYeKT8SsGpYwvRrqhObrDWnVYMo6Tpx6ganY47gnGuywKMcOQFKpAiD2oKlUNzDH7nCgANjL\nd+wA3G3EjONZP7tHIkxIb6jkzV6cYhmMcXgXWY19aauUZvwse9UYo1UQeSTu4GMreAsthcx7\nhUXmJ45H29XmnMaSi4PV6gBqMeXgHvOb5SSMycITT/vgR+b/+k+Ux59dymuUSG4cUtjddIRj\nx8wMq0OshRgPsWMMtk/XMhKLM8cuV9j5PHSSoRiJBWConJLFjh117KZaDVnkzk3Iu3xO3AUD\nrQDuNiIAT3rZwujIVvjIFZtDRSSeBBdVjeUchwNl20pUOu0KbluJStPlrXl86j4KCfu8sqBB\njW2J+Yl5bXZnv9J0LZ507C4V4djVQgLATrwtiwGYb9rpf/PXpX/2R8rTx+b/+b+on31yOa9T\nIrlRSGF30yGuA0J4lrAT86rjPXaDgDKOxpy++GVY2GMn0ol3tdSxI0BJ4/N67Go1ANQeiIzi\n+fO2l0970UArgHuNGMDTnMHYlwU8P4yWT1zc/ETHU8KE7GfVlBWKbSs+spU5heAfBg8BfNHI\nnZwQsO1tAKQzT9h1XAXAlpkQz4WicKPQQjPJuuCaxsvlSsCRLp9IAHS9XFlPAl//zl/yUsn/\n+98EY/r/++3Le60SyY1BCrubDvE8bhhQMt6LheRqqmPpxOnkxHocu4XCTmSdiJFYgaGyeY5d\nJe2xS4NUNqwae+IqmpK7CkywU0lKKs9z7I7nxuCdYhkXu3zixUABcCvHONyvJzEjQsVm8iP2\nDMCXrDfnPwurN6Eo8x27tkcBbFmMDl1umjc8Ve6VwGqNms8AOMmwtcix0/+/vyH+MPzqN6Iv\nfiW+/7ry4pny4tnlvVaJ5GYghd1NhwzdvALWaFHsWY+dSB9dS4gdgIpSVonSy/fVxNqJU8cO\nQFnj84SdpvFymQw20bHjQNulLWvBIlVCcK8Zt10lU5aJGLyFjl26fOLCBmMPByqAvVq2vtyr\nJBh1v2XyA3JEOL5Qf3vB0ygKazRp50SExmWSOnalmHgek3XYVwGv16sRAeCwoci46eYPxqrv\n/YAravS1bwCIvvoLALQffv+yXqlEclOQwu5GQ5KEBEFey/nsotg1rp0AQEDqijVHfo2vnRCY\nOk8YCXMXpYJVqsQZNJUKNsyxGwxplMxbBXbKvWYE4HEnw7Q7tJWSxmuLfv+WzgF40atx7FqV\nBKPut1k4+A/19v3AqFcWL+NirW0SRWTQz3tAd6hUDGYkQyRJZjuB5KJh1Xo1VgDYidc0GYCO\nm33iKSdHtNtJXnuDl0oA4tfe5EZJffTpZb5aieQmIIXdzcZzwXneolgxrzoed9Jfq7AD0FCq\nc1aKzZZiTY0B8MJ58xMkjrd4GRvm2Amh0yog7A6aaUzx1O1BTDqesl/Nj0IZIcJQ3Pzf0ooc\nOYpKedPM/lmEeM0rxT4Jj3o0+lm3Brr4zScdjM2pxsYM/SFtWYw4NkYphpJLhtfq1UQB4LCh\nZcyLslM/+gBA9DMjp5bS5O4B6fdor3tZL1YiuRFIYXejGa2dyLY6Bmx6Kra3vnRiQUOt9BOX\n8WyleBT3AOyNlWLFAqthvhclBmO3QooNc+zaSwq7JzMxxc/6Kue401jQYIdTx+5ieuw4R9tR\ntiuM5uhG8TOe5JRif+h9AuBnw61Cz7W9g/zFYr2hwjiaZkJsGwCrSmH3CmD1RiWmAOzEI0DL\nYh13ZlgaAKB88hEUJXnjrdNbkvuvAZC5JxLJepHC7kYjYl0zs05w2mM3tlJs4Ct0TenEgoZS\nYZwNEi/z3sOoA2BPOxMBQti5+UEevFoH8P+z9+ZBjmT5fd/3vZdIIDNxo66uPqd7Znru2Z3Z\n2Z1ZLrnLXQbFXYtD2aZkHn9YdthhibZsywpSDukPW+Hw8Y9JMRSigkE7LMkWaZImw157zRW5\norhr7g5nj5mde6an766uuwpnJhLIzPf8xwPQKAAJJKpQXSjgff7oaCQSiQdUAvnF7/j+Ci4w\nZRG76MIuGecFK7i7r/GDb7NslT0fQdgZo+KaR6FUpx4nC1boMoyYsHS+Wxv83fJG5QMAn8Qw\na+IOfGEJAN0dLOw6LbHUrkFF7E4Ins23InZBHUDBCpoBqbp9f/26w7Y3g5VVYRidbS1hd0cJ\nO4VikihhN9/IK2L4oFi0e1dbW5yJuRNLWo2xfHBj7JZXpIQeqLGLyeET4R7FmQyAQtXHlI2L\n3bPljNdImvhS3nN9snbQpnitpAE4F0nYCUqOq8ZOdkUspYa9kIUkL7ssGNQo8kbtIyLwIj0f\n5bl4vgBK2X6IsHMogILFqUzFptIDd1McKyKXTwcMgC3qaCfi+yssY/fuQAj/wiPdG4PCokgY\nqjFWoZgsStjNNdSpAYPniQEoBzUCkm6nYjlHtTExd2JJXksD2PMGV8dvensFlo6RB/qmFbEb\nUmOXzQMoVFwAUzVVrFhnlCIdLYv96KIH4MbOgWzsvZIW18Rwf2MJITBi/Jgidq1hD+EROwAL\nVsBFS8t2Ewj+w+atK/VE1hzdOQFAMI1nsmRnZ2BjrKzlyhoBqVaghN0JITTNjKcA2IGLdky6\nv8KS3b2NdojuAYTwpRVaKZN6/aEsVqGYC5Swm2vk9ykP7Yq1U8zszH0qu5QLZCbkTiyR0TjZ\n/dqDgNj2it0FdoiQZOTZHIDCfp0SOlU1dvs2zSSiBjuvLHgAbuzpnS1Ok+7b7FzWj+jUZsXF\nMdXY7doaRs3PKISEbT5q3LVF46WKFX32F19YIl5TSrceig4FkDNbzRNcpWJPCNPMA3B8G+EV\nluzOLaFpfPVcz/ZgeQVC0J3Nh7JShWIuUMJurqH2sOaJcmB3d05UJmpiJ5HCbm9QzrTk15rC\n726JBWDFRzVPmJaI6VqpnGHWuKnYP62+8ZVrv/zPd78+1qOi0AyI3aS5yJo4a/AFK7i9p/nt\nbOaHWzHRFnxRMGPc9Qif5N+qhYzYRRF2+30TCN60PwbwqbIVVtbZT1CQjbEDsrGlOiVA1uCk\nWgFjwlCDYk8GI1UAYLsVtFOxOwcrLIlj0+IeXz0v+ozQ+fIKALalhJ1CMTGUsJtriCNr7EJS\nsX4tfWwmdpKClgGw4w/wO+jvnEA7YjfcyINnsqRczLP0WBG7H9gf/fyNf/A9+8P/fuN/a4rR\ndWxjIZ29wvxBBvLEcrMZkA+3WkG7D7d1AFeXmhEfbupCHI+V3U6VmjqXOfEwwvzMvmu/D+DT\n5WR0zzmxuAyAbm/131WqMyvONSpotSKSKTV24qQw04sAnGYNQNbgGhU9wVpt7S6E8M8NKKwM\nls4AoErYKRSTQwm7+cauiZAJm03hu6LZ73WSm6iwW4xlERKxk14nK/oBYWeNsjsBIPJ5EgR5\nGOXADkKMVPr5g+I3ueBXExd2/fL/Xfp2xEdFRE7PzEfrnJB88lwDwBv3dABc4ONtPRnnUTon\nJMfUGOtzlF02sgVESth+P7Pv2h+YQvtEzUT0iJ0UdrvbPdu5QNmlOZMjCEjd4SF1ooqHgJFd\nBmD7NgBCpOMJ6xb+9P49AMHqAGHHc3kRi7HtjYezVIViHlDCbq6htg0rOTDUUQqq6JknVqdA\n1PL/iBRk88QgYTcwYpeIoFd4JgcgL+Jc8CGDaLsREH9U/guLJv7Jpb9DQH57708iv4JIyP7N\n6KlYAOdzfsEKPtzSnSZ9f1O3m+SJ5Wb0mJTMWU88Yld0GBejTVvScc6o6JkZuudXbrjrn2ou\nxDgJK+vsh+fygmn9EbuqSzlH1uDErkEIVWB3gmiFZV1Qx281QCwmg2ZA5NeFhN2/B0r52UGt\n0JTywiIt7iOY5BeLQjHPKGE3xwhB6k6oiZ1vA0jTB/dOdlCsZEHLAtjxBqZiiwCWtAPNExpF\nXBvRFiD7J/K+jsiOJz90rq81d34i/alnjcuX46tvONfCPJMPh5yeOVbEDsDLl1yfkz94y/r6\nBxYh+NErbvTHSl+YIYZ/h2O/FXoccQ0mBDmD99TYvV57X0C8UsuCkDHq4SjlC4usuEcOXvhl\nODCTCKhsiU1nBj9ccfwEuUIyoDXREnZLqQDAdrXVzE48j21vBovLQtcHPlwUFsA5Le49nNUq\nFDOPEnbzC3Hr4Dy8c2LAoFhKkZqcOzGANDN1GtsLBqZiizg4dkJijjLy4PkCgAWXIPLwie/U\n3gHwk5mXADxnXqkF9ev1SXprtWrsxmwo/tHL9XNZ/531+HaVvXjePZMeo/LvmMbFStvhKG58\neYu7Hun+S33X/gDAZ4uWiCfQV0Q/BL64jCCg+wcGixXrFEDW5KS4j7aaV5wMjFlcs4UHzgEs\nJn0A2+3GWLq+hiAIzl0Ie3RrcNy+EnYKxWRQwm5+IbUaAGENHTvRNSi2VKfpeOggqUOuASTP\nUgPtTtabuwBWYws9201dDK+xk+MKCjUOYG/Qkft5x7kJ4BPmYwCeNS4D+GHtWpQHRqTsMkrH\n7juhFL/4qepnL7s/9aTzl58Onag7kFYqdtI1dnvtYQ8j95QqtrvM7tu1dzTCXtmOR/c6kfCl\nZQDkYH19uV3xSUv7AEQu0owyxTFh0oRDAzn1dTkVANiqtv70bO0uAB4u7Hh+AQANmQisUCjG\nRQm7+YXYNQBhVedS2HUidgFHbdLuxJIFLbPrlfq3r3u7lNCVWO8F29SFz9Hwwz2KTUsYxkLZ\nBVD0I9XYvVO/adLElfhZAM+ZVwC8OVFhV3RoJnEYTVywgr/ybO2LjzvDu1D7aTdPTPgDLrOr\nUQaj5SyOdnEhgHJgv1O/8ZxxOe140VtiJcGSdMQ4UF9fcmSPdkBbETsl7E4SiyZqGme72wCW\nUgEBtiotYaet3QEh/hBhV1gAQPYHCLtr7r1f2/q9X9/6/eNZtUIxm2ijd1HMKMSxgdCxEyW/\nCqBjd1JxqRDITLRzQrIQy75bv1UJ7G5rFQDr3t6ilu0eOyExdQ7A8WhcC10MLywWauuIVmPn\ncPdG4/4L5uPSirkVsat+PP5LGYzPid2glwpRLegmgtlKxU4+YhdjIko6Pm8EaLcDA3it9m4g\n+I/qTwIb0U3sJHzlDAhhm+vdGysNBiBjcFoqglKuxk6cKJae9P3Nxs4GffxJnYmsyXdqGgAE\nAV2/z3OFIWqeZ3NgrD9ixwX/uRv/9b3mNoDHE+e/nHn5OF+BQjE7qIjd/EKdYe7EFe6ga1Ds\ncXROSAZ6FAeCb3n7Z/XePCyAZFwAqLlDy+wWlgpNDdFq7N6r3w4El4E6AHktfU5ffNu+EfkV\njKBUp+J43rohmMdgdyKAfZsWrEEjYPuQVnbFdsTuz6tvA/gx7TKA6F4nreeN6TxfoNtb6DJc\nLtcpJUjFOS0VeTo7VtGeYuIY8RSAxt6avLmU9O0mqTUo21wnvjekwA4AGOPZHN3f7Rkc9+3a\nO/ea25+ynqCE/rfr/2t06yKFYs5Rwm5+kRG70K7Yg80Tx+FOLCmwNPqmim15+74I+gvsACTj\nHEC1MezUDRYWC56GaBG7t53rAJ41rnS2XI6v7nqlij9eWVsYUtxkzYcr7CIY/o1LzaUeJ1EK\n7ABkjQDt3wMAvll7Syfay/4ZhI+wGwJfWSW+x7qCOmWXJuOcuXXi1lXnxIlj6SkA9e27Upwt\npwMAmxXGbt8EEFy6PPzhPL9APE8Wh3T4veK/BvB3V37hZ3Nf+Mi9+63aW8e0eIVixlDCbn6R\nX6OhXbG+DSDT9rGTterHEXaSHsXbBx1P7nuDOyfQFnb2cMeThcVCkwHYH9Rv28NH7j0ATxmX\nOlsu6MsAbtXXwx4yFq1Z9ceQxR5CjAmNislG7FpeJ9GEXSrBGW39Hlhr7nxYv/NK8hnT8QEc\nYvZXsLIKgLazsVyg2qAZ1TkxNVg0AcBu2jKjKju4Nypa7PYNUOpfeGT4w6U0l3/N1hbBv1Z6\n7Uys8KOp538293kAf1z+7vGtXzFLkLkfQqOE3fxCbBsAD+mKlda+GXogYjdZd2LJ2dgigHXv\nQIXN/eYOgHP6Yv/+SX10xI4vrhQCnYDseqO7Yq837gO4HF/tbLkQl8JuMm74pfas+okcLTrG\nqPbhcZGdEBHd+ChBOsFltPIble8D+FL6RVp3gNCyziH4K2cBsPV78matQTlHJsHJ3i4AroTd\nSWNKYcc4u38PwGomALBRBN1c5ytnhGEMf3hb2D34dXeruVENnJeTTzNCfyT1XJIZXy+/fowv\nQKGYIZSwm1+IXQNjSAz+zq3xOoAUawVXKsdWY3dWXwRwv3lA2G14ewDOxAr9+7dSsUNr7EQi\nwTKFtE8HzrTo4Ubj/pKW63bsm3TE7gRSsQAs/SQjdgAyicBuUJ+TP628AeBL6ReJ4wDg40fs\n+PKKiMXYvTvyZsWlANIGl62ywfLKuAdUTJZWxI4F2v17AJaSPqNiY0+Ac+/ilVGPbgk7aUko\neb9+G+04uk60z6c+sdbc+aB+51hWr5hiSBdhd/Vs7Nz18FY5ZShhN79QuyZC5okBKAc1RmiS\ntWRfqV2rPvFlyA6J+95O90YZsTs7KGKXTggA9tCIHQB+5uxiM7Y3aKZFNw53N5p7VxKr3Rtn\nIBULwIjxhk/8yf3FWjbLkRVqzuQC2LL9b1Z/eFFfeTxxXpZ1HiIVC8b42fO0uE9qVXSm28UD\ntrUBQvjSmbEPqJgoFksAqBqM3boOzhnFcirYcg2fsODqUyMfLnIFHIzYvSeFXeKSvPml9IsA\nVJndvEEIEV10a7Xuu7q3CyHkv0KMZxE1SyhhN69wTuqOSIYmxcq+nWYWQesDU67TVILTYzhf\nVrQ8JVQquQ73W+7EAyJ2VoTmCQDBytnFplbmdU8MG9hwo7EuIKSDXYeLMhXrTkbYlV2a0EQi\n9rC/ZYyYAOBOLhsrB6PlIkfsZHz3X+9dd7j707nPot2vE+aJPZzg3EUA2r07aEfsMvGAbm/y\nXCFsVpXioWExA0Dl3ApxbO3OLQCrVsMH21h6MlhcGvlwnkqDsYPC7haAp9uVry9bTwN4vfb+\nMaxdcfqQqq5zs0fbKZSwm1NI3QHn3AwXdkGtMyiWc9Qa9JgMO3QaW2CZde/AQKHbzQ2daCuD\nhJ3OhK6J4c0TAPyV1QUvJiCGN8Zed9cAPJo4IOwWfCd0ywAAIABJREFUtazJEhOssXv4eVg8\n8Cie2Pfdnk0tXSS0qApVuh6+tr8G4NXs5yDPOkJESPZ/OP6FSwDY3dvoCDuvRDwvWFbhupPH\nJAkA1bPLALT33gJwvnIDwO2LL0V6PKU8lekRdjkt1YnZP5o4W9DSf1F7b9ILV0w7/dE4xUiU\nsJtTqD1snhiASmCn2wV2pTrh4lg6JyTn4ovS30TeFBC3GhuPxFelY3A/SZ1XhtbYAeDLK4ue\nDmDgINoONxrrAHoidgTkYnzldn1D4KjfI06TNANyHMbOI5nsuFjOUXZZ9HAd2mWFt2r2eX3p\nE+ajAEjdEYkEDhX4DVZWRcLQblyDELJsMV9Zh7QvVpw0smajmjJ5Jhv76P3Ym9975Nq3ANxJ\nPxrxCDyXI26d1OsAqoGz1tx5MnGxcy8B+bT11I5fut3cDD+GYtboZFqHFNjNeTndQJSwm1Pa\n88RSA+91RbMp/C6vEwIgE2HewOFY1RZ8EWx5rdLpTW/f5m53m2oPqYSoe5QPXw5jhUQewF5l\n2JXgRuM++oQdgIuJFTuoR7HBG05nQMIRj3MIWlZ2E4rYlRuM8zE6J9BOxca8/L+d+7zM6ZN6\nXSTGL7CTMBY8+jixa+z+XZmIz93/AMAI81vFQ8GkcQC2cN0v/wwISXzjjy7YdyjEWi1qdFZk\n8wBouQjgbnOrv0Di09aTUNnYuWSgvBN9nOAKpw0l7OYUMjRiVwlsAJ2IXUvYHZs6kf0TnWzs\nzcY6DvqP9JCKcyFGWNkBKKRXARS3rw/Z53ZjkxJ6Md7bVnk2vghga1TvxUjaI01PLhU7oali\n43ZOAMgYAQDLX/7FhZ8EgCAgDVeM707cwXv8SQDaR+9XXKpRkbrxnkhnVCp2GrCoAcDhjeD8\nRfcrf8V/5nn+lS8vpfl6iQXRThmezgCg5RIAOUasx+3o08knAXzf/nDSa1dML9HzsCpo140S\ndnMKrUlhN7jGTroTd2rsis7xRuzajiet/gkp7Ho6VbuJ2D+Ry58HsF9cG7LP7cbGaqyg902k\nXdHzADa9/UEPGoPSsRk7j8SY6PAJOfV1rFTsDf+6T9xFXLikrwAgbh1CHKYltk1w6YpIGLH3\n3qm4NM0axGt6jz8Z1tateJi07E54HYD3xNP1L/+M/9Rz57K+x8lWNdJEcpHJAiDlIoC1QU3x\nz5lXYkR7w7k28cUrTh0DO2RPcD3ThhJ284pdRXgqtmeemIzYpY9NnZzXlwDcaraaFW64oyN2\niCLsChcB7FdCeyBs7u765Yv6ABe0M/oCgM2DLR2HoCzL/E+ixm6y42Kl1XB+nIjdP9z6fYft\nJINWU6R0Jz5KxE4w1nzpFd706k2ScfcAeFefPvTRFBOkZVDM3e6NZzMegLVSJGHHM1kAtFxG\n2/yoJ2KXIPqTxsUP6ndc0ZzQqhXTTicDK+nvhO3f3n3XQ1/vtKCE3Zwix06EpWKrvA4g3RkU\ne8wRO2lD+p5zS94cmYqVAbCSM+LsXYjnAezyane3XTd3mptom5v0cCZewClPxU52XOz+mBG7\nD+t3vlZ+jehVz4t5AQEA6XUSMsIuIt6nPlPJrAiQjLPnPf8iX+0tjlScCNLHzg4OCLsLOR/A\nnf0xhB0pl9CO2PUPnnnBfNwT/tvOjUksWXE6GFJFN6S6bs6r7pSwm1NorQpCkAyJ2Pk19A2K\nPb6I3SV9xaKJ99yWsPvIvZti5kosdE6UHDAvs5xDKLA0gB3dZ7cHXwluuRsALsUHFGmd0QuY\nRMROTuw4QWE3qYjdvkPJOBG7v3//t7jgV1JptP9StF7H4dyJuxBabPunfh5AMqe7X/qpoxxK\nMUFkxM45GLE7k/FjVNwrxaIcQRimiOm0UgJwv7lDCe0fFf2idRXAG7bKxioUw1DCbk4hti0S\nCcHYwHvLgY2uVGypTixdxOhx/QCihD5pXLrV2Kjzxo5futXY+IT5WMcbuZ9WxK4+ePEdFmJZ\nADu6r92+OXCHVsRuUCp2dVLNE3VqxMbwfpsgCW2SqdhSnVlxHmORXsgfFr/1repbP5Z6/ulM\nAe0fBjJid4h5Yr0riWUBWI9eQMjZq3j4yK7YHmGnUZzN+lsVFvEkFNksKZcgxFpzZ1nLxfoq\nX18wHwegyuwUiuEoYTenELsqQsJ1aHfFpmi7K9YhqWOuEnvauBQI/kH9zuv2+wLiM9aTQ3aW\nvZkjI3Y60dLM2kkE7O5tDDJHudPcAnBpkLBbjGUZoUdvnijX6YmE6wAYMUHJZFKxAUe5TiO2\nxN5v7vzdtX+SIPr/cO5vZOIB2oWGclAsjlBjJ6k1KIDksRUGKA5BjGg6jTm80bP9Yt4X0cvs\n0lni+55d3vKL/XlYAI/Gz2aY9QP7owmsWKGYXSJ93k4juq7rxzBoiFJqHWog0lRBGi7xPJLJ\nhr2WOmsCWLYKlmU1Aq0ZIG+RY33hn8xc/We7X78u1j9s3AHw+cILw5/Oiouyq41c0ko8v+Vv\nk4ZrlUs4d77n3nveNoCn8pctrfc4uqYv6fktf/8or7rukWZAchZO6pxJxEQjYNGfnRDC2ID9\nd2uECyymRp8DnvD/5o1fLfm1X73yn36icPXtOgNQDxKWpRGvCSCRLxxupFgHl2sAFjO6ZU0m\nYqdpGoB4PC7/Mz9QSs0j6+wOBtEb8HrOkMfPsG9ex3rN/OQjwyb7SUhhATeuFesbXPBL5urA\nk+2l9FPfKH7P1ptLsdwhFikL6jVt9FfHjBGLxRhj83aGzy0z+2cWQvARDraHPGwQnECH42Qh\npZIGcCsZ9lpKXg1AippBEOzbrSqxY33hnzAfA/BHe6+tN/c0wj5lPTH86XKm2KxQ3w+Gdz4t\na/lr5J5DeeLmx8GZ3m6MW+5GiplZMvh9WI0vvFO74Qf+kKTwcPaqsiX2eN+6IZi6qDVI9Gen\nlA48w3cqrc6JkYf6+3d+87XKu6/mP/cfLP3lIAhScQHoRRtBEDC7RgA/nsDR3o2yowGwYn4Q\n0SFtFJRSAJyf2J/ppJBfkpOqMbeYYQf1nvfwQo4D+s0dGuW9pekMA9b27wBYjS0MfMiL1tVv\nFL/3evm9r+ReOcQi5d96Nr7Gx0LTtDk8w+eWmRV2nud5njfZYyaTSSGE67qjd51u2O6OBniG\n2Qx5LfuNMoBEoLmuu1thAJIx71hf+FPahefMK1/bf01AvGQ9oXnE9YY9XSahrxX13XIzNTQl\nt8AyADYT3sWbH7svfqb7rkDwu82tx+PnBr4uXdfPxAs/qHy4XtspaOlDvSZsl3UAKf1437oh\nGLHEvqPV3VHz19owxmKxWP9qt8oJAGm9OfyF/Mvyd//x+h9ejq/+w3N/q+E2ABiMAuZeTbiu\na1arABqaJo72bhRtHUCCua47GWFHCInH457nNRq9mcTZxjAM13UnJewMEi8G1Z4zJE6QMxO3\nd6lTd+moszBmJRmwWV0HQ45YA0+25+OXAbxWfOeLxicPsUjGmGmaQRDMwNf4WDDGfN8/1jM8\nmQydPK54yKgau3mkNXZiWI2dg7ZBcdWlAIbrpwksCeTvLP+cgLBo4n+88J+M3D+bCAAUR5XZ\nLcdyANYXTbp2lxz8tbru7Ta5d0kPnVsgrey2/MOX2clZ9emTqwYzYoJzNPyj9k+0TOysYS+k\n5Nf+9t1/FCPsty79Sqo9sySpc4223gfiOkKLCS1Sj+QQqg1CgKSuauymC5PG7aDev/2Rgu/6\nZLMyOoggh0/suLsAFrXswH1etK4SkB+o/gmFIhwl7OYRalcBiBB3YgDloEZA5OW55U58/Ork\ny9nP/PLKz//TR/5e9/DvMLJmpMbYZS0PYGM5TXyfbh1wKr7T2ARwKTGgc6L12HgewHbz8I2x\nshv0pJon0J4qdvT+CTl2QrrMhPHfbPyzHb/0n6/8tefMK52NhCCV4PJ9ILaNSRQ2VV1q6Jyp\nr64pw6SJhvAC0Xu2X8p7AG5HcLOTwm7XKyFc2OVZ+pH4mR/YH/lCZRUVisGob8d5hFSrAHh4\nxK7KHZPGNcLwEIUdAfmVM7/whXSkDEvB4gB27VHCTkbscjoAtn5gtphsiR3odSJZjGUB7Prl\nKOsZSGvsxMkJu5ZH8ZEdT4oOJUMHxd5srP+LvT++Ej/7ny3/1Z670omg1qRBIIhbP7rXCYBa\ng6YT82s9OrUMdDwBcKkgbYpHR2qFYQpN2+ZlAIvhvRGftp50uPte/daRlqtQzC5K2M0jpFYF\nIMJLIkp+rcud+HjHThyOxaQPYKsyQtjJ1rmNpABADwq7240NABf1AWMnWo/VcziisKu3micO\nfYQjYkzIyq5YZ1ac6+Emdr+29XuB4L9y5hf6p+5mElwIVMoeOD/KPDFJ0yfNgCTj03U2KgCY\nLAGgLnqruJaTfiImbu9FqOcmRKQy26IGYCk2OGIH4NPJJwF81/7gKKtVKGaYmW2eUAxBjp0Q\nVqiwq3LnnN4a8Vl2KaWwpuxSumAFjGK7OjIVmwOwyVxhGNrG/e67WiZ2g8ZOSKSw2wsOL+xK\ndapRYZxcNZgxialinKPs0rOZULuKTW///9j/syvxsz+T/Vz/vTLWW6t4AHC0eWJoDwhOTdnZ\nqMCD4RO9wo4QXMh517b1sktH/sgRmewOa1BC8yy0Y+nTVkvY/YeLP33kVSummHqdr92d8DEJ\noY8+PuFjTh9K2M0jxK4Jwwwz7g8EtwM3RQ15s+yQdEKM7Gh7yDCKghVs15gQGOJ4shzLA9j2\nisGZq9rN66RaFalWAvpOc5MROtAHVbKo5wDseKVDL7LqsozBT/Cda00V8460hLLLOEcuvMDu\nd/f/lS+Cf2/xK4wMUJAyE12pckxi7IQUdipiN4WEpWIBXMz717b1u/uxZ1dHdGXydHo7HuSI\nJetABvJ44nxeS6uI3czDN9e9/+kfT/igjMX/u1+b8DGnDyXs5g8hSK3GC4Ww+6uBIyDSzAIg\nBCoNcj43jdfRpaS/XY2X6mzIZPqsltSJtuUXg5Wz2s3rbGvdT12Vd91ubJ6NLfaPLXpwfBmx\n8yuHW57PidMky6mTfOtk88QRU7H7DkV4gZ2A+J29f6XT2F/Nf2HgDjJIU7YFAGEYR1kJgKpL\noITdVCIjdnYwQNjJ/ok7+9pIYSfS2e2Yd56E5mEBEJBPW09+vfz67ebmwLExilmCr54Tly5P\n5FDs7TdRdyZyqClHCbu5g7h1EvgiGZrpqHIH7UGxtSblHBljGmvVl9PBuxvYqg4TdgRkOZbf\n8vb54hIAtrPtP3oVQNGv7vuV59OPDjn+QixLCT10jV25TsWJdk6gHbFzj5aKlV4nYRG7N+2P\nbzTu/3T2R8JyZ/IdkH0k4sipWLspU7HTeELOOe1U7ABhdyHnU4I7xdH9E9VUwvaCpWDED4DP\nJp/5evn112rvXsorYTfjiIuPBF/8SxM5FLl5nc6HsFPNE3MHrZQBiHQmbIdyYAOQEbtKq69z\nGq+jy6kAwNaoMrszscKeX6kv5gGQnS258UbjPoAr8d5ZFN0wQnMsdehU7Imb2AEwYhNIxRbr\nDOEmdl8vvw7g1eyPhD1cvgPlhoZJCDuVip1aDKoDqPfV2AGIa2Ip5a+VWDCqMGErRQAseSOm\nQb6SfBrAd2rvHnKtCsVMo4Td3NFuiR3iTtwl7OrTK+xkOf+dUf5YF+LLAuJOwhMxnbWF3c3G\nOoDLQ4UdgAUts+sfUtiduIkdJpWKtSnCTez+uPK9GNG+mH4h7OGyyrDs6ZhEjV1NCbtpxWIG\nBjVPSC7k/ICT9fKIn2HbBgAsNUbs9qxxJcXMb1ffOcxCFYpZJ5KwI4SQ4SM5FacHUq0A4KnQ\nVKwUdilqAqg0ZNhpGoXdQjJIxvnt/djwxZ2PLQG45++IxSVaKhLfA3CjsQ7gSuLsiKfQMjZ3\nXdE8xPJaJnYnGrGbSCpWukDnB9XY3Wtuv1e/9XLyafkzYCAaFaYuyjwBTKIr1iVQXbFTiUlC\nU7EALuR8AHdLI7Kx27EmgGVnxBnLCP2M9dS95va95vZh1qpQzDSRvvGFEEIIJe9mg5awCzex\na0fsTLTniU1nxI4Al/JerUF3a8N+31+MrwC429gKFpfAOd3dAXDDvY8IETvpUXy4bKyM2KUT\nJ+mPrzOh0QlE7Cyd69qAc+DPqm8C+Mn0S8OPkEoEFW5iEs0TtQYlmDrzHQWGdsUCOJ/zAdwr\njoiv7/EagEV79KfmR5LPAlBBO4WinzF+ynfLO6XwTi9MpmJToTV2Fdk8oSXRKRSbSmEH4PKC\nD+D2UFN7aWhyr7kdLCwBkMLuZmNdpzEZzBtCQUvjsB7FpbrMYJ6wBDFi/CjCjnOUGyyswE5e\nVj+Xem74QTIJ3iSaHUuJeOLQK5FUG9TQhaZKSKYPQwq7YHAqdjnl65q4O6pwYt+vAFioBuAj\nPjifTT4D4Ns1JewUil7G/oKU8g4qP3tqkRE7MSQV6z9IxdamOBWLto3CxzvDhJ2M2N1pbvJc\nAQAp7gmIm431S/rKQN+1bha0LIC9Qwm7aoMRcvLVYKYujmJQXG4MM7H7Tu3drJZ8atRsX1lo\nWEyHekFHx27QE39LFQOxpN0Jrw+8lxKczfh7NqsPbeUpBTUAOY/KUuAhPGdeSTFTCTuFop+x\nv/E7ek7lZ08ppFoRiYTQQ/vOWnYnWqsrlpDpdZc4m/FzJn9vQ3f90JNwVSswQu81t0W+AIAW\n99eaOzZ3H0ucG3n8hZicSn4YYVdxaTJ+8rPqjRhv+CQ4rBaSnRMDTexuNTY2vL1XrGfoKH0s\nG2OLxoj46Eg8TlyfJE9ukodiCK2IXUjzBICzGV8A6+VhQbuiXwWQ9zRaHeEfqRH2svW0KrNT\nTJZD65mpEkJjXHakhhNt5MaOvDue5SkmD6lWh5jYASj7B+xOUnFBT1qdhEEIXjjvegF56348\nbB+dxlZihbuNLZ7OCMZoce8d5yaAp41HRh4/x1IAisGI4EE/QqASYYDSQ8DUhTjCVDHZOTEw\nYifNJmRGbDhZ1gBQSuQPt4YOqiV2mpE+dgPtTiSrGR8jhV3QFnaV0b+m5Ln3mjI9USgOMkZX\nbLeeU5xSiFsnXpOHe52gqytWANUGmdoCO8lLFxoE+PaNxJCg1AV9adcv10VTZHO0uP9u/SaA\nZyIIO1ljtx+MPXyi1qQBP2GvE0lrqthhy+xkxG5gjd337Q8BfCb51MiDZFADUI4dWdjJsRPT\nWhgw51hsWFcsgLPZAJEjdiSCsHs5+RSA12rvjbtUhWIgMkTVHagiXfTs2b2x/4EnS9Tf8QMl\nXedlKMF3WiBV2TkxWthlmOU0ScDJ1BbYSfJm8Mnzjc2q9s3roR2Xj8bPCYgP3bs8VyCe917t\nYwDPGKPH1OS1NIA9b2xh1zKxm4KInbSyO3TETroTDzSx+4HzUZzEnk5cGnmQDK8BKIWPdY+I\nHDuhUrHTSXvyRGjEbinpa1TcHxWxM0k8zsnIVCyA581HTZr4C1sJO8VkkEqmo2e6q87QJXi6\nU5edfTBNQmhac2yK44FWR4ydAFDmNiU0yQzpdZKaAnUynFefsS2d//GH1g/uDW66fN58FMAP\nnY95Lg/gHedmhlmyW3Y4BS2DQ0Xs2t3EJ//WHVPErhbUr7n3njcf1enoOVFZvwSgTIf9nIiC\nSsVOM9LupC5ChR2jWEkH2zXmhc+f2PerBZZCu8drODGivWhdve7e3/L2D7VkhWIE3VVn3dun\nPKo1Qth1go1kENP5khRDkAkOPlTYVQMnRQ0CIsc3ZaY7YgfA1Plf/0xF18TvvZH8+gdW/3Kf\nN68AeMu5znP5UixY48VnjMsEo7VOK2Lnjy3spNdJOn6SJnaS1vCJw04VK9ZZMs511vumvulc\nCwR/wXo8ykGSjaImgpI46tiJaoNACbtpJUF0AmIHg7tiJasZn3Nsh48BLPqVTCwlGIuSigXw\nsvUUgO/ZH467WoUiCgNTsdNvDDJC2HWCkGIQD2WFiknSdicenop1ugfFpqa7xk5yMe//zc+V\ns0bwp9eMr73bO97gKeMRncbecq7zbP7/y1YExIvW1SiH1YmWZIas+xmLSrR5YsR149/8hvGH\n/zupD7scHgUZsTtcKjbgKNfpwJbYH9jXALxoRnobad3J+OVycFQTO7sVsTsFJ+QcQglNUH1I\nKhbASjoAsFkZLOwc7jaFn9fSIpmKkooFID/Ibzofj79ehWI0YZqnPz87VUSdPHHc61A8HJg0\nsUtnh+xTCexuE7vpj9hJzqT9v/X58mIy+NYN4821A02yOtGeTFz8yL3npsyvLZYA/ET6UxEP\nm2fpQ/jYVRoMEWrsjD/8Hf2739FuXDP+z99FcCzhPfMI42LLLuNicEvsW/XrACJG7KjjZIJy\nLYgd2nVFUmuqVOxUY9L4kOYJAMspH8BmZXCZ3b5fBZBlSZFKE7dOfH/kM37SeoyAvOFcO9R6\nFYojMbXSSNXYzRekIoVdaBm7J/w6b6Q1C4BMxaZOibADkIzzv/6ZCqPia+9ZjYPOdp80H/OE\n/y16548K5Vygv2Q9EfGYBS1dCmpcjCcmyhEiduz2DXb/XnDpsn/lcbZ2N3btg7GeIiJHidgN\naYl9y7me01Ln9UjWdMSp5byStIA5xDI6tGrsVPPEtGLSxBC7EwBnhkbspDtxXkvzdAZCRCmz\ny7P0RX35Lef6uJ9QhSIK3Q0TPT2w08yw79nhBXbTnGBWhEEqJWEYQgsteK8GDg4Oip1yu5Me\nFpPBjz3qVlz6nVsHEn9/Lf9FAH977Te24t5fKuU1Mmy8bDd5Le2LoBzYYy2j7NKEJuKD5qt2\niL/+HQDuj3yh+bkvANA+PJbmPiN2+OYJ2RKbN3sjdvt+5V5z+9lodYoAiG1n/DKAihv1bR9I\nrUEYFYnYaToh5wqTJoanYpNxbul8qxoWsaugHbFDtP4JAC9Yj1cD55p7b/z1KhS9dI9d6Kml\n626k6O80mKp5DcOE3fACO1Vmd/oQgtSqPHxKLNpjJ2SN3ZTPEwvjx644jIrv3z2w7pesJ76U\nfnHD27O49jduF6LnPQ/XGFuu0+HhOuLY7N5tvnqOr54LllZ4Ls9u3SCNYRfFw2EcIRUbNnbi\nnfpNAM+ZVyIehzh2ltjAUSN2VZeeurNxrjBofHjEDsBKOijX6cDBYg8idlLYReufeMG6CuCH\nzvWxl6tQDKJ/BEO/2hmy8eEtNByVip0jiGOTIBjudVIJHgi7aoPG2Iiw0xRi6eLJ5eZOjd3d\nPxCY/Adn//1/I/vKv9z58stFk9q1iEfLsSTa1T8RcX3S8El6aIEdu3kdQvhXWjVq3tWnSOBr\ntyZ/cTJighI4h0rFFkPGTrzt3ADwnBFZ2NVqGdYAUD6CsBOA41FL5WGnGJPGPeE3uTdkn5WU\nL4DtQUE7GbHLaSkZsWOjxsVKpNP4B+6dw6xYoZhFon7P9iSYpyfkqIiOnNIzStjJeWIyFUtO\naaH6SxcaAH54cM7Y1cSFf/rI33su8QgAUi5FPFQhlkH7khMR2RKbTgwLCmo3PwbgX35M3gwu\nXQZA708+nUQIjJg4dMSODKqxe7t+A8Cz0YQdaTaJ18zEfbRLDw+H0yQBhxJ204zRsrJrDtln\nMRUA2KkNSMrLiF2OpeR3VMSI3ZPGRShhp1B0EXWk2MBeX6XtThfSQUCmOcLozBPjHI53WjNf\njy15MSo+3hlQSig7giNeMwDk2dhTxWRcalgqlnPtzk2RTAWLreYDvrwKQrStjejPEh0jxt3D\nNU84LJXgGu09B95xbpo0cTl+JspBiF1Fu1LzKBE7+7S18swhBolj6LhYAIvJcGHn1wBktSQf\np8Yuz9JLWu6D+u3x16tQzCZjf88qX+LTiwxTRUnFpphZa1IhkDqdETuNiksFf7vK+pUET6cB\nsMjCrhBLY9yInTvC64Tt7hDXDS4+go59ua7zwiLZ3DgO0xMrLhyP8DE/tT4nVZf2d0443L3d\n3HjauERJtJ+FtRqAdJKQo0XslNfJ9DNyXCyGCrtyUAOQppZIGIJpEYUdgKfNRza9/bE+pArF\nDKNq7OYIUikBCIaPneAOgAxLVk7JPLEwriw0AVzvC9q1InbVqMIux1IAin7UmjwAJYcCyIZH\n7OjGGgD/zNnujcHKGRL4bHc7+hNFxIhxITCwXH0I+zYVQKEvD/t+/XYgeJRJuxJZzqglTVMX\nR2mekD3aSthNM61U7NCIXcbgOhO79kBhZwPIakkQIlJpWosq1J5IXADwoXt37BUrFLPIeN+z\nKlx3qqHSxC4z1J3YtwGkmHHaB64/tugBuLGr92xvZ3miNkPIqWJjBQNGpmLZ+n0AvEfYnTkL\ngG6uR3+iiFitcbHjfdhl50S2L2L3fv02gKeMSxGPQ2pVAMJKphNBpd6X1o1MrUEAVWM31ZhU\nRuyGCTsCLCSDnRrtDyG3C3wtACKVJvU68Yf1YXSQZXbvu7cPs2iFYuYY7CfUQ385XY+ti+JU\nQColwZgweyduddOxOzntA9fPZn2dibvF3jNcJBKCMTJGV2wKY9bYlUa5E7PN+4JpweJy98Zg\naQUA29mOdCkbB1OXjicUGCPPu2dTAAt9Ebv33NsAnjYeiXgcYtsAeDKZMfhGRXOaRArNcZGp\n2FNaGzAnmDQOYPi4WAALVrBe1ooOK1gHTshyYGuEyYN0rOxErjDyea/GLwC47q4deuWKqYW9\n/Sa5dWMihyK7OxM5zvQTSdihT8MpSXcaoZWySGcwtOWlY3eyWSdopWJPZb6eEqxm/DvFmOuT\nRLdjCyFIpmg0JwUcLmJXpxoVZkhsiTRcurcbrJ4DO5CNEvkFALS4F/2JIiKFlD1mY2zRYQBy\nfRG7d52blNBxInYVACKZkkWHFZdZ+uhRUf3U1KDYqafdFTvCyk42xu7a/cKulqaWdL2WtbCk\nWkEEYfdI/AyAm+7ko92KE4QUFrR/6+cmfdC56PiMKuwUpx3SaBDX5csjOhk7XbEfyybE03wd\nPZf1b+/H7pe0KwsHomAimaLra+AcdLRm1YkqThJcAAAgAElEQVRm0YQ0YohIxWUZg4d9f9Dt\nLQgRrKz2bBeJhDAtuj95YScl5rhTxWTErsfETkB84N65pK9YNBHyuF5kjZ0wk2mDAyjX6Zlh\nbdmh2Kc8hDwPREnFAliwAgC7NXb14ES6sm9ntFY+gSdTaI9AHElOS+W01K3GsTSVK06K+3rj\nXyxPJlbXgYL+Ml6Z7DGnkKjCLszZRIXuTgu0WgbAh3ZOACj7NQAZZnU1IR5pDNQJcj7nA1jr\nE3aBlaRCELsmhjq/dMhpqT0vasTO48RukuVUeB52ZxsAX1ruv4vnC+z+PeI1Ray3NPAoyHGx\ndmPM5gmHabQ3oXynuVUNnC+kPhn9OKRWFTFdxOOym+TQjifVBiGqxm66kcJu5PAJ2ZEjfzl0\nEBAVbl9krc9Fx6M4YnT3kfiZt5zrTe7pNHReouJ0cau+/l9+/BuTPWaMaL986Rcne8wpJJKw\nUz0TM0Db62RY5wSAKq9TQi2aqDVOfUnTuWxL2PVsF8kkAFqrBhGFHUt96N0VEFFGo7ZaYvvG\ncHWgO1sA+GKIsFu7S4vFYJDsOzSmnCo2ZsSu6LCsEdCDr/g95xbG6ZwAQOyafMPlKI5DO55U\nXWronJ3KuoB5waA6ogg7MwCw7xz4xWgHri+CjJaUN8caFwvgEf3MG/a1NW/ncrw3Fq441Xwl\n98q/s/CliRzqv7r7P99vzkWZnUrFzgvSkne41wmASmAnqUEJrbkkRoV+2uaJdbNgBQlN3B8g\n7FIAovdPFGIZr+7bgZtkxsidpaPHUBO7bVDKCwv9d3FZZre/O1lhZ8XHrrGzm6TukfO5vpbY\nVufEpYjHIb5PGg2+sAQgkwhwBGFnN0eM31WcODJiZwfDfOwAJBM8xsTeQceTtomdKW+O5VGM\nTpldY10JuxnjMePcv1n4sYkc6tfWf3dOhJ36/TsvtOaJZUYLOzlPrNakyVNrYichBMvpYN9h\n3sGCN26l0LbhiELLyi6ItH+rJTZsnpgQZGebZ3NCG5Aw4vkCgImX2ckau7FSsfs2w6CW2Ped\n2xirJbZahhAyTpw5QirW46Thk1MdP54HIjZPEKBgcWmU2KHSdtCUN4VhCqZF95uUwk6V2SkU\nUMJufoiaig2cFDW5gNOkM9CBuJzyucBO9WD/aTKJcYRdXksB2PMjXWNkRCodEluilTLxmsHC\n0sB7ea4AgEy6MVZ2xY7VPLEf0hL7gXsnyYzz+uD190PKMk6cBmDEhM6EHMsxLjVXmdidAqRT\nyfDJE5K8GXicVLtUfreJHQAQIpJJGjliJwN1qjFWoUBEYafGws4ArFoGpbLXLAxfBA5vpJnl\nNCkXs9CBuJIOAGxWeoRdCkB0x5P28IlI+5ddhvCxE7LATgwqsIOMpxJCy6WIC4uIRoXOxFgR\nO1nY3jNPzOHurebGk4mLUWoNJfLC3GlSyRi8dKhUbE0Nij0NWMxAhK5YtE+t7jK7sm8D6HTF\nQnoUuy4JIrVPXIqfAXDX2xpzyQrFDBJt2iMh8t9+jnl5iolBymWRTPV4p/VQDRwBkW63xM5A\n5msp6QPYrh0os2vV2I1pZVeM5nhSdKQkChF2e7sABhbYARBME6ZFI8+xjY6pC3v8iF3PPLH3\n67f5OMPE0K7s7MSJswZ3PdLwx/7eqCqvk9OATMW6vDlyz/7GWFnqkKEPhB1PZSBExCExBS2d\nIPr95u64a1YoZo+oEbswjnt9iolAfJ849kivEzl2IsUMmfmagVTs4IhdwjjM8IloHsWlOtWo\nMEKShrJ+LkzYARCZDKlVEYwxIiIKps6dcZonpLDLH/SPHXeYGNqVndJsFkdojD3tc1DmBIPo\niJaKldbE+139EzIV2+mKxZiNsQTkjF64781FabxCMRxVYzcXkMqDGvYhdMZOdJnYnW7SCW7q\nokfYAYBpUceJeJB8bIzhExWXZcPdiVlxD4TwbD7s4UE6CyGip4kjYuki4GOEyvZq1NR54mBP\n9AfuHYzTOYG2e6JItX5RtBpjx++fmAHznXmgZVA8qisWQNYIABS7JH5ZCjvWnYpNYZzG2LP6\nYsmv2RFkpUIxQaYwdTnGN2x3+nUKX4liCLJzIkiPsG3r1C/L6+hs1KovJv1iva8x1rSIYyNa\nyDl6jV3TJ06TDDOx29/j6YzQQm2GpPgmky6zs+IcgN2M9Hn3OcouW0z2TYmt3yIgTyQuRH9e\nUimLhCHicXlTNsYeon+i2gohz8IJOcPIVKwzqisWQM7kAEr1B2dCtf2rsrNFFgRH7584py8C\nmBM/C8UxMRvaJqqwkx7Fndyraqc4XbQCJ5nRLbEAUsycpczXghUIgb3aQZt70wLncEdMK5fI\nrtj9YPQFpjjU64TUHdQd6WkShsxaTrzMTmp0KY9GUnQYF72dEwA+cO+c15dSzIz6rEKQaoV3\nuUDLnhLp4TwWVRWxOw1ohOlEi5KKjWvC1EXR6Y7YHfCxA8CTabRnDUdhNbYAJewUD50prEmL\n2jzRv3Sl7U4RMgLEU6NN7ACkqCEVwGxcRxdTHMBO7WCZnWUBoLYd5QhtH7vRNXnlOkP42IlW\ngV0+tMAOgMjkcAwRu2TLozjS573dOXFA2K01d0p+baw8LKk7xPdFV2VnthWnOaSwS6qu2KnH\nZImRkyckmURQqj+wsqv0RexkjV30soSzsQUA697kpy0r5oSehGR322hnS3/n6MD9T1YdqRq7\nuYBVKogQsWvVL7Ok3aqxm4Xr6FKyNXG8e6MwkwBIPZKwSzFTp7EoNXZSsmRDjJ3p/i7aLsRh\ntCN2kxZ2+hip2N0aRZ+we9u5AeCZsQrsSkUAvOusk7HMQwg7u0kTmojRWTghZxuDxKPYnQDI\nmTzgpNYuuBzQPGFZoDRiVyyAsyoVqzgaMoDVHcbqzlVKrdZzs4f+/U8EJezmAlIpgRAxuiu2\nDiDJjFqDEtKaWHDakQJl1x4UsYvWP0FAMtSKIuxkKjYXFrErFtF2IQ5Diu/Jp2LjHG2b35Hs\nDRo70R4mNo6w6xOypi50TXRXVkWk4tLUKZ+DMidYkSN28mPS6Z+ococRKi2OW1AqrDE8imUq\ndk0JO8Xk6BZ5PdVow/c/2fzs4Q2KB+Zn55C31+MfbuknvYoR0GpFGOaQmn1Jp3651iBGjNOZ\nyLQvJANCelOx3DQBkGipWAB5LR1lpJisHkuH1diV9gGIXGhLLAAR00XCiB6liIgMvtbGScX2\neJ28W78J4FnzgIndcMuYgann7PgexQEnDY/MRivPzGOQuB1EKl2VjbEdlV8NnBQze7yvRSpN\nHBs80p9eNk+se8rKTnFcnBYT36jfsFLbdaeQlaoDsFlhv/391P/yF+k/+TByRfnDh3NSrfBR\neVh0dcXaTZqaiTwsgBgVWYP31tiZFgDYkYdPaKlaUG9yb/hu8ioVHrHbF4wNH/4BQKRS0QvG\nI5Icpyt2z2a6JnpaZ95xbmaY1RkmRhzb+IPfTv7Grxr/7/9FvMGGtG1hdyBCmTV4wyd1b4yv\nxYpLBKAidqcCkyYawgvE6D9WK2LndFKxTor2fosGyRQ4j+glnmKmRROb3v6YS1YoItGdip1y\n/TPGT2dlTdzP//NekgvEY+Ib18zqoaabPwRItQLOu5sTw5DCziLJhk9moyVWspgM7CY94NBr\nJRE5FYt2Y+zIoF3RoZYu4trgTwctF0UmCzriPBHJFPE80pikHZcMd8lm5xHPLrBn07wZdCuv\ncmDfa24/Y1zuBFQSf/I17eZ1EU9o770V/9M/Hngour8rYjFxUMi2rezGyMaqeWKnCJMlAETJ\nxrYidm1hV/Zr3Z0TkpZHceT+iaVYbstXwk5xXJwW5TOlWuRUUKyza9uxS3nvJx53hMA761Oa\nkI3odQKgGtQBEC+JWTGxk0jnjr2uMjtuWgCIEzUV2x4+MewCI4R0Jw71OiGNxhBr4gdrs6Qv\n6ySzsYmY0ChqEcbFFh3ic9JjYvde/ZaA6BTY0Z1t7eOPguUz9n/8X/B8IfbuD9l234xOzmmp\nyHMFHMxZtBpjx3E8aQm7GTohZxizZWUXxaOYoy3xPeG7otnvpCN/FbDIwm45li/7dkOMiKwr\nFIejpwF2ahn29TpwOKyaFdvh7r4G4IkV79mzTQK8sxEf+ZATgZQPzOscQjmoERDiW5gtawnZ\nP7HXNXFcGCYIoZGFXUHLYFTEruJSn4fmYYnMS2ZzI5+LJ5MYJ0oRBQJYOrcjROy2KgCwcLDA\n7ofOxwA+YT4qb+rffw1CeC9/TjCt8eM/Cc71177ZcxxaLiEI+luAW1Z245TZqUGxpwjpURwl\nYpeMc0pbY0jkMMN0v7Abc/jEkpYVENtecaw1KxQduqvO+u/CwVK0qZVAw75eexKv/anY0xKW\nPCbulTQA5zJezgjO5fxbe7GxxnE+NNjBeZ1DqHLHpPF6U8NsRez6B1OCUpEwMNFxsUVpYhcS\nsaPlIkZ1TkjGTT9FxIpzuznaL2SnSgAsJA+8irec6wCel8KOc+3GNWFa3mNPAPAvPxYsLGnX\nr/VcfdnOFgCxsNhz/J6S+SjIIgdVY3cqaE0ViyDsKEE63uqk6Texk/AxPwvLsTyALVVmpzgC\nQxROvyLq3q1n/xMUSMqg+PDc3dcIcD7nA7iy4HGBtVLspBc1AFKJmoqtBE5n7MRsuBNLChYH\nsGcfHD5hWaQevcYujVERO1kGnreGep1ESMXK9NPEx8Um48LnaIzqWtiuEPRF7N6u37Bo4kr8\nLAC2cZ/U6/7lRzs5Vu+TnwLnsbfe6H4IXV8D4K+c7Tl+T8l8FCoN2Ws8OyfkDCNTsREbYzMG\nrzVowFHxW21bPTsIOXwiclnCUiwHYNubsA2kQnG6UDV2h4QLrJe1xVRgxASAc1kfwFp5hJ/I\niSCFXcTmiTSzqg2CGYvY9dXYARAJgzSbJBgcYOshpyUB7HmHj9ixshR2o+X1sQk7jgiOJ61U\nbFfEzubuzcbGM8ZlRigAdvNjAMHlxzo7+E89J3Rdf/fNblsKdv8eCOGrfcLO4GRcYecqYXdq\niB6xA5BOBEKg2qDDUrGEyCrhKCzHcgBU/4RizlHC7pBsV1kzIFLPoS3s7pemUdjRWkW6ow3f\njQtuczfNLGeGxk5IpHlHd40dOo4n0Rpjx4jYhdXYlYogJEqlo/RDIZGtWCLSEnajyuy2qyR+\n0OvkbecGF/z5doGddusGGPMvPTC0E7ruX32aVKva7RtyCwkCurURFBZFPNFzfEZFMsGLY6Zi\nCWkNz1BMOQaJWmMHIJPgAMp12h5m2CfsmCYSiTG6YrUcgB0VsVPMN8qg+JBIX7SlZEvY5czA\n1MXaVAo7Ui6LzIiZEwBs7nLBU9SQbmezFLEDULCCikt9/uA0FoYJgEbLxkaqsXMohtTYVUrC\ntEZ6REMqznEmKUVERrwqQ015uMBulRSsA14nbzrXADxvXgFAPI/tbAVLKz2KrfnsJwDE3nlT\n3iSb6yQI+NlzA58lb/KqS4PI51e1QS2dj3KJUUwFFksAsHkks56MIc9JJmvs+rtiAfBkmlSr\niHatadXY+ap5QjHXjG1Q3GGeVR0eDEpvXZ0IcC7rFx0axSrsYULcOvGaPBnVxC7NrKpLMHNN\niAsWlw5tnS3cMAAgWmNslIjdvs0SmjD1AZ8LEvikVoviEQ3ISUrWxFOx6fhoYVd0qM97C+x+\nYH8E4EXrKgC6tQ7OgzOrPQ/kZ8/LFgppShy7cQ2Af+7iwGfJGgEXUa3sBFB1qcrDnhZkKna8\niJ3bSsX2R+wgh08EPnEjKcWlWBaA6opVzDnKoPiQtMYumQ8ugasZH8BGZew5mMeKnLQ4ckos\ngEqrzMWym5RRGIMEyulF/qUONMaaFsaJ2FFCh0TsuECpznrGcHUglTKEiOJ10jqamSR2LWKU\nIiJpY7Swk3HoxYMtsd93PsppqcvxVQBsfQ0AXx0QivNe/hw4j7/+5yTwY2+/IRKJ4LEnBj7L\nWP0T9Sb1+Uy18sw20u7EGSdiV65T2TyR0XqbJ9B2PKHRZrEUWIYRqrpiFXPONKYOTwUy9tN9\nIV9KBgB2quyxxSmyx2y1xEbwOulE7GoNaup8xhqec1LYdZV2ccMEQOqR2vcYoRlm7YULu3Kd\n+vyA0O+GlksAokbsZP/E1gapO61CwEkgtdHw+ShbVQZgKfXgVWx6++vN3S+lX5QzJ9jGOoDg\nzCBhd/Up/dvf1N57O2HXSL3e/NTLIja4STybCBBZ2KmW2NOFRRMA7CCSsOuUBwyL2HUaYxeX\nRx6QEVpgmW1f1djNDr++/vu/vv77kzpajMyF5pmLF3kcyLyb1RXWWkz6AHbs6XpL2y2xESJ2\nQetH826DFJKRekVPEQtJDmCv1iUmTBMA6lE9ivMsvRt+wehJzfdASkVEc5yRCMsCQG07mJyw\ny0SosduuUhyM2H3f+RDAp6xW7I1trAnDHKxQKXVf/Vnjd/+5duuGMC3vhU+HPYt0hInYP1Gp\nEygTu9NDy6BYREvFtt2qZY1dRkv278OlR3Hk6cmLsezH7r2IOyummRfST7z/2d+Z7DE7QxFn\nm+lSIacFmXdbSvndG+XlsGfY/IlDq1VEcyeW362mSHucpGaoJVbSSsX2DJ8YZ1xsQUvfbKx7\nwh/4m68/Nd8NLZcxTsSOW0kAqFWxuBTxISPRNRHXxIhUbJWRHmFX+wjAi+bjAIhjk2rVv3QF\nIQaWwdKy83P/Llu/5z/1rIiFTtiT/SX7drSInfQ6UanYU4I5TipWo8LURcWl1cABkKQDOvfH\ntbIraOn3hF8L6kk2wgdAMeVUqok/e+PSZI9JCZ748ckechpRwu4wyLxbT3jG1EUyzrer0yXs\niBwUGzliZ/IcAGvmrqOpBNeo2D/QPCEjdtGFXUZAFIOqtFToYd8e5k5M5NiJTNQaO2ElAZDI\ngzEikk7w4cJuq0ozpohrD2T9a/a7jNBW58T2JgC+tDLkCHxxiY8So3mLk4Miewhq7MTpwmRj\nNE8ASMV50aFlvwYg02dQjDFr7NCe/rcfVJSwO+1UXXzv1oSPySj+owkfchpRwu4whIVnFpPB\n7b1YMyA6m5aIF62UQYiI5k4MIBZkMHNeJwAIkLd4d42dTMVGbJ4AkNdSAPa8ymBhNzxiVymD\n0ige0S2sJAB6DMJup8Y8TmKDRovVPVJr0CfPPLjL4e47zs2njUfkSAC2sw0gOHIQUWfCivNi\nNGEnlajM2Smmn7EMigGkE3yrymxfEBBrUMSOW3JcbOSIHUsD2PXKF/TRNXmK6eezl72feLI5\nkUP91p8bstpk5lHC7jDIuu9cn2PZYjK4tRfbqbGzGX/Q404AUq2IhBHFPq0a1AHoQQaz5U7c\nIW8G21VWa1Dp5CK0mNBi0aeKLWhZAHv+YBP8fYcREupOTMslkUwhshVb26N48sIOQKVOC4O6\nd2WweTnz4E//PftDT/ivWE/Lm2x7CwBfmsD1smDxu0Ut4ISNml4rXVGUsDstjJWKRTsW22wm\nLJaQo016EIYhNC16jV0h1orYRdxfMeUkYiKsdnlctNGzsmeEuVCvE0emh9J9F5ulaSuzE4LU\nahEDRbIxDb6FWYzYYeDEWMsikWvsZMRuP8TKbs+mWYMPlCnE90nd4REcZzocUypWXkSrIVaL\n2zUNwJn0g5fweu19AC8nW8KO7GwKxniucPSV5M1ACOxHaIwtu5RSZXdyajDJeBE7+Zf1m8bA\nlliJSKZYLepnoaC1InYR91coZg8l7A6DtGDov9hI95OIxUMPAeK6JPDl7NGRyFTsDAu7/v4J\nnjCIW49oF5dnaYRE7FyP1Bp0YaiJXZRhYh3aXbETFnayMbZUH/yp36wwAKu5B+/Gt6pvEZBX\nks8AQBCw/T1eWACbwOkt3WeiZGNLdZpJcDoXrWyzgOyKtYNILkJoR5F50xo4dkIiUmm4deJH\nSoN0auwiLkChmD2UsDsMYVPJZSYuYrvfQ0BWHMvq45GUfRsAbyYApBIzGLLODWyMDQLSiBRd\nKMSksBtwwZAx2oUQjxhSLgEIIjQmP1iYFhPx+MQjdi1n4JDzc6uqAVjNtv70taD+hnPtGeMR\nGQKh+3sIAhHBSywKA6Kng/A5ag2aScya+c4MwwjViTZG80SCAyB+Kj2oc0LCk2kIEXFirDxd\nh1hOKhQzz7RIkNNFxaUESPepH1m6FLEq/CEgK45FhHliaEfsPC+OGY3YSTHRLbul4wlxI0UX\nCiwDYG9QimfXZkDvJK4OVHpER26Jba3NShI7qsdeRFpxshADuY0KSyd4Mt66+ee1tz3h/3j6\nBXmT7e0ACAqLE1lJf/R0IFWXCaEK7E4ZFjPGap4AYAT5oRG7MazsWs0TyqNYMccoYXcYag1q\n6qK/oCquCUsXu1MTsSPVCtoOnyOpBLZJE06TYlaF3YCIXQJAxP4JGbEbmOIZLuxIpYRoVoLd\nCCtJGm7E9FNEZLvPQDllN0nVpSvpBy/hm9UfAvh86hPyJtnbAcAXJinsRkbsyrIlVnmdnCpM\nmhijeSLOARj+wkATO4ksJqHRGmNbzRP+hEctKxSniOPtin311Vc7///qV7867g6vvvpqz8aR\nB3w4hPUVAsiZwXpFCzjYFKg7KeyieJ0AqPF6ipm1xgwOipXomrB0fkDWtCJ2kS5CssZuYPPE\n7tBUbDtiN0aNHQBuJRlAHDvKnN+ImLpIxMTAWV4yD3smHQCt9+dPyt83aPwzyafkTborhd1k\nDJOzBteo2LNHROxKDgWQDek1VkwnJo3vRA6YSdVu8kIq3HZOfoNFTMXKsc5h3esKxQQhhAgh\nuv8TtsND5hiFXY8sG6jSwnboFnDRD/hwqHvE4yRtDP5r5c1graSVXRZmafYwkTV2PHLzxFIs\n53gzOCi2Q8HiayXN59AoAPCEAUT1KE4xUyfarjfgirVrM0pbic5+aLkEQsbVZ60psbUqJifs\nAOSMYLvGhOgdHrFRZgBWMlwKuw/qd+40N7+ceTlOWvNe2e6O0GJj9fYOgRAsJPmezQSGjfgp\nhxSzKqYZkyaip2J1TWhaYAQLQ7piA9kkXo2UitUIGz7WWaGYHo5J+U1BWGkQX/3qV08wIDec\nlhV+iP9CxKrwh4P8jRulK1ZAVHk9zayaS2YyDyvJW4EcB9e6bYznUbwQyw68YOzZLGcEWsjf\nnFYrwrQEG/NHlPQodiZcZleweMBJpc/x5H5ZA9DxX/x65XUAP5X5TOvuIKDlIi8shA0TOwQL\nVtAMSCWkRVdSrstU7Mn/RlJEx6TxJvc8EbWKIB5rmEFhRFds5IgdgIKWVhE7xcPkRMJyQ5gK\n/XG6CGuJlUT3cXgIkGpVaDGRGD1apxbUueBZ5GZyUGyHVs1+OwPYithFa54AsKBldv2ywIH3\np9qgTpMshuRhwTmpVQ8R6OLHY2Un57T2n59rJY1Rsdyusft6+XVG6E9mXpI3Wy2xE8rDSmQx\nw+7QbKyU4FnVPHGqGHf4BNPrOk8mSejvT2ElQQiLLOzyLF0JnOjKUqGQkL4frp0tpIvhDwzb\nrf8I8j8DHzvwWaIzO5Mn3nrrrd/8zd/s3PylX/qlq1evTvxZGGMBSwJYzOqZzIB37/wiAeBw\nM5OJ99/7kOFODelMJjNaVVQbDQAFdhZA1mLdD6GUAojFYse2zIfHuQWKa3C4lckYALC0xIFE\nEBh9b5GmaQDSBzseziQW3nZucJPmYw+2b2wQAOcXtMHvc7nEOWf5QpS/Qjfi/2fvzaMkS8/y\nzuf77hL3xr7lnrV3VXW3em91S63WglpIaiyBMULG7PKBMXBmxoBhjmeYGThzBtvgM/aMPTMG\njw+MjcEswiDEAGbT1kKtXtSLuru6uvaqzMo1MmOPuOv3zR9fZFZWViz3xpKVmfH9/qqKvHHj\nZmZkxBPP+77POzHBAdPzoiHv2J3ZHMUVWDyeSt1SS46PtZp6NMvTybiqqsVI49X6xQ+kHz6V\nO9o6YuEaA7S5eX14F3N0guIS6v5tV7KLsq2qCo5MJYZnFLZBPMOj0ahhGCN8mP0HpTQZcqYn\nCKlIAoAa11N6oGeLGlkHSEaf6fI3wuMJWqsG/COaMnK8zv0oyd9xAeL9UtO0sH+PBx1FUXRd\nH7dn+LAQT5vtdrou9dNdR3Y/A+d856mCP0pPDo+w29zcfPHFF7f/+5nPfGYUWoQQUrMVALmE\nomltzIapNACUGlTT7rYb6rl2o0GnZoL8HGq2BSBJJn0gFW1z8cowYmnvOtNpANhstH53PJly\nAGI1O/2Idt0+beQAbPLqlHZr+8JaDQCOZNv/xlm1wgAll1dDPht5JusAtFEPe8fuzKQBYL1+\n27P3RhGM4XieiO/39wtf4uDfN/Px7W/fK6wDUGfn6PAuZjbb5kp2UaghH4eu78WHCkVRDseT\nPBSjeJFMaFEANnEDnpzodQBR5Lsc7yRTWFnSVDVIM0A+kgZQQWNeax+7SCmlgff7HSbG8Bke\nijuVlvj3ToEljul+kjuPDHiGtvftg8Mj7D784Q+//PLL2/8tl8uFQmG4D5HP5z3PWy26gMmd\ncqHg3nkMZ4Qgt1x0C4W73ORBy6UYYEeMcoCfw0J9CYDajPuAwhuFwq22M8MwKKWNwKu39jOa\nR4HszQ2nUKgAII4TB9xy6c4fUTKZ1HV9Y2Nj5x9k0jcBXFy/NtGMb994ZSUOGFFSKhTalH7U\nhRsm0FA1J+SzkThuHLA3N0pDfRrHCAWyl5dbPwHBW9cMIJ43asWiG4/Hf3PxzzSiflh7aPsv\nyFi4rgElTWfDuxjxu1hYv+1KdlJ3SNPJHUl3PGBYmKYZi8Wq1aodLKr60JDJZEql0tDbg1SX\nAFgsLGejHdvmdmJhE4BdUbu8YptmVPX9jRvXxba97pieBuBqYWGyubu8qyhKJpOxbbsaLDzl\n0BCLxTzPG+kzPJ/Pj+7kd50Ba6MBzzD4owjG8VPLgNRtAiCqta8faZTHIqwUYAnmqBHtWUFe\nBwGU/RqACEvhkIbYCZIm0yhfr7V+Oy1IX7wAACAASURBVFzXoSgIrFnzagrAriiH1YpCgMlE\nt6wTlghd9+HRGAgJ3jAekLTJYjpfKt/2ie56UQNwJOMBeK128Zx17UOJR0S8i0DZLHBFZWG2\novUkYbCIyrv02IlGwKzMOjlobPXYBY2yc5QSAOJ2e6US8xM0YOKJmgBQlFF2kiGxXSQVjO4M\n/Hb6vuC7rz8OHHWHAoh3njBIm6xiK+xuTyC0RmIDCjuvDkD30wDih3fhOgGyMbZZV7Z/Odww\nqR30HWhCTeMOYbdSVVImM9T2v2/xVsT76GRSFBgmGfZULIDZlFdqUpFELbhc0KI6n054AH5t\n6Y8BfH/uo7fuwBjZ3GDZHIZavRJquFBX/A7pOpstYSdHYg8YLWHHg5pDTboJgLvd7D0x2h8w\n8SStxAGU/CEPHknGge0a6K4Wt8GN7b2cnL0Lwq5tRt0Bou4QApgdHDsAadP3WWt49i4ihF3A\nELsqawBQ/DiA2GFMJ94mF/Mdn9S2fjvcjCKweGo5djui7Go2bTh0MtFx/k4simUh04kFLBYb\nfCr2urPy2c0vPld9ffuWmZQH4Ga5ZZWtVZWKRU/mXELQYNbvrP3VhJr+eOrJ7eNpqUh8f1g7\nJ3YylfAYw1qtvWknlr9lY4f2Y8ZhJUYNAHU/6LB5Q9kA4Lndhvf9MMIuqyYhHTvJUNk1ytrH\nkUHOsGsIt79LxUh77D7/+c93XxTR84ABjx8RDYeaOu+yWEKsWi81lbsb07BViu24WnsnwrEj\nXgyHuhSLrcVfhboito9zw6AFl/g+D9BWPKllcLuwW64oaC1saA+plrmqBkmcuRMejdPCOnEd\nrul93B3ABWvhY+/8ozqzAPzIxCf+2fyPEZDZpAdgqayennABXNnQAJzKuwB+u/BXZa/2k1Of\n1sitVwa6sQ6A54bfQDOV8AGsVpWZZBtlLB27A0q0JeyCGuFVspYEXCcCdNSCLccuWClWOHZS\n2En6Y9cIxfYt2+XULiOrdx7Z/Qzbx2z/u+28RVhGOzzRVnvtvLG7OGurBYdyYYNQd2inBjuB\nEHbFBj2e3atragdtCbtAjl2NNQFwzwQQO7ylWADZWGtf6omcC4CL/u5mAwGsTeHYFXZkn4pm\ntdlUR8eOVis8keov15fFYgpA6nWe7kfYOcz9kWu/XGfWfzP1XX9RfunX1v9kSsv+9NTfPZr1\nAFwuaB+6pwngwpoO4NSEyzj71bU/0oj6IxOf2HkeZaMAwM8N37ETq2lXKwrm2nx1s0GBjvs8\nJPuWKI0gTI9dma7OAZajdRN2ySSAgFF2rR67dtv/JJIg3Cmq2pZl7/zHEP89CLLHLhyMo+mS\nWNcI360M2Lteiq0h2NoJABW/DsB3DUoR1Q5zKVY4drfmJwwTAA2WUZxTU5TQwo4eu+7CjjgO\nsax+GuzEtQ2WUfyXlZfPN69/OvvhX5j9+394+p9Ma9lfWv6tF+rn8jF/MuFfWtebLqk79Pyq\nPpnwpxPeH5e/dtm6+enJZ2Z2hLkAIIU1AGximOnEgsm4h601tXeyXlWiOj/cjQGHkhg1AdSD\nCzuUPdoQMVKdaC2fCNNjJx07ydgihV04ahY471GsFBXYW3ur7hKkVuWKwoMlUoqpWNvRoxob\naRjsXScfZwAKW31d3BSOXSBhpxIlTeNrO0qxN8uqpvBOaydIpYS+RmJb1xaNYQBh93vFLwD4\n0fwnAUyqmV89/rOc85+68a9t7j40a3sM51b0VxYiHsMTRy2P+7+0/JsKof/42A/sOg9dX+OK\nylKZ/i6jC+koM1S+Wm3zl+L4pNRUhPKTHCzCOnZVv+Eo5doda+52wjWdRwwSLKNE9NjJ4QnJ\n2CKFXTjqDgEQ7Srstnrs7vLPltZriCcCFgErfgOA5ahdpn0PB2nTVyhf3xZ2hgGABF4XO6ln\nth071yeFmjKT9GmHnzGtVgGwfh27QbaKbfqVv6p847Qx/1jsjLjl6fiDP5x/9pJ1858v/6eH\nZm0AX7gQ/eJFU6F4/Ij9Hzf+/JJ18+/mnrkvevz2i2C0uMHyE8MdiRUQYCrpb9QVx9v9E1yr\nKnyrCU9ysIgpJsKsFKv6DV+tNB3ide0B4YkkqQYKB41RQ6eaLMVKxhYp7MJRs4Bec6NRnWmU\n32VhxxhpNgJOTgCo+HWNR12fHO7JCQCUYCLOCnVFNDNwIwqAWEHdhSk1U2dW1W8AWCorjHdr\nsBOVI1FF6gch7PpKPPly5TWHuX8n88GdN/787Gfm9Yl/s/aHN5VzT59orteUuk0/+a5ala7/\n4tJvxBXz52Z/cNd56OYG8X0+gpFYwXzaYxw3y7urscLGm+o8lSLZt8QUA0CdBXLBGWc11uRq\njW8lSXWCJxLEdUmwcKI0jctSrGRskcIuHNWWsOumfgiQMlnFupulWNJsgHMWDRRiB6DCGhlM\n4VCH2G0zEfddnwjlLXrsiBXUsZvRcwBW3E0AC6Vbub5tEQYD77sUG4tjawgmLC/W3wbwvvgD\nO29MKNF/efS/9Tn70Wu//IF3LX/Xw7Xvfbz67hPVH7n6SxW//j/O/NCcvlvAKRvrGM3khGAu\n5QJYLO0Wdms1FVtNeJKDxVZAcSDHrs4sxhlRGwDqXauxLEziSUZNSMdOMrYcnpVie0PNJgC6\nD08ASEdZYV2xPRLpkFs7ami9jsBZJwAqfj3HzwCIH3bHDkA+LuYnlEyUcdMEQJpBHbtpNQtg\n2d04bcxf21ABHM+22SwnoJUKAJYINL9yJywmeuz6cey+XntLp9pj0TO7bv9w4tGfmvr0/776\ne99x4X/4V0f/YYQa33Pp375UP//J1Pt2DcMKyPoqRjM5IZhPe2gn7FYrCmQp9mAieuwCOnZi\nbEvVLQDd2+xYvLV8guV7PxvTSvwd64bLvZ3BPZKDSNUiC8XhuCSOf6j7x3cgn/ThqNlAgKQ3\nMT9RbtJOm6ZGjWjMYsHWTgCo+PXjmEQAzXoImIh5ANZryplJN6xjN621hB2A65taTOf5DpMT\nAOiAjp0ZBaW0Edqxq/j1t63rj0bPmDRy51f/+5nvL7P6r6//ybMXflbc8q3Jd//KiZ8haPOq\np6yuAPAnpsNeQ0AmE76u8jbCrqoYKk/e1SRISX+IqdiAjp3oatA1F0DV7rpbPSEcuxCJJyW/\nJrbFSA4uL13XXrquDetsXQJoDxNS2IUjSI8dgJThAyjdPWEntimIscqeeNyv+1aC5TE2pVgA\nhboKoOXYBYs7ATCj5QGsuBvFBi1b9L5pp8t7EalWeMTgkTbqKhCUcsPsw7H7RuOCz9mTsfva\nn5XQX57/8Q8nHv3D4ld8zr41+e7vyT3TVtUBUNZXuBnl/ZqOPaEEcynv2qbWdIm5lbNTd8hm\nXTmRc8fl8/XhwiQ6AQm4eaLKmgDMiOv0KsW21sUGLMUqrXWxUtgdXNJRfOjeIZ+z06DbIUMK\nu3A0AkzF4rbEk451upHSaswK5thV/QYHj/lC2I2BYxf3AayJlA0zCoAEizvBjh6765sagGOZ\nbr9fUqmw9EApISwWo5ubYe/1RuMygMdjZ7sc82zqPc+m3tP9PKTZINWqf+JU2AsIxYmcd3VD\nu1zQHphxxC0LRY0Dx3Kywe5AQgk1qB5w84RYZmjqvtN7eCKFrb7VnqTVOICyP/xVy5I9YzKJ\nH376bl/EwWQ8fMnhUbcBwOwV4ZuOtkqxe3BJbRGjlCzaba/2NhVWB2CyDA77PjFBVOdJg4lt\nYFxRuKYFzLEDIMJ7l92Ni+u3NnG1p9kgntv/SKwgniC+F3xoV3DeugHgXuPoQA8N0NVlAP7E\n1IDn6c49eQfApcKt7RqiebG7aJbsZ6I0EjDHruLVASQNoFcpNtTwhMgoLsn5CclYIoVdOLaE\nXQ/1I0qxxbsn7LaGJwI5diLELuJnMB6lWAAzSa9m01azthkNuHkCwISaVghdcTcvruumxo+k\nO7pKisg6SfbZYCdoZRSHTDw537yuU+1kZHaQh8ZWgx2bmhnwPN05nvM0yi+u3WqjuV7UCHA8\nKx27g0qUGgF77MTwRCZC0Gt4gpsm13QarMcupcQAlDyZUSwZR6SwC0fDga7yng2YW47d3Us8\nET12ZiDHruzVACh+HOPh2AGYSfkAhGnHDCO4Y6cQOqGma/V4qUnvmXC6pPYOGmIHoK+MYp+z\ni9biKX1WJYM+/ejaCgB/crSOnUr5iby3XlPEFj6fkYWimo/7PRseJPuWuGI2eIhSbDqiUtqj\nFIswGcVpNQGgzGQpVjKOSGEXjoZDgqxSNVRuaHczo5g0alxReCTQPjHh2FEvrlCY47Gacybp\nAViuqABgRInvES9o4W9Gy0Wq9wE4O9m1wa4qsk4GEnZ9RNldc5Yt7txnHhvkcQXq8k1umCyT\n633oYNw3ZQP4xoIB4K0V3fHJ2Uln1A8qGR3CsePo/WJS9ZsAEooZ1Vh3xw4ATyaJbRO7txfY\nKsVKx04ylkhhF46G3bsOK0ib7C4KO1qvIxYPvE+sDoB70ajOxmNmCNNJH8BKRYVw7AA0QmQU\nH6s/QwjuneomPlohdv3uExPw8I7d+eYNAPcOLOxIrUrKJX9mLuCzaBAeP2LrKn/hmsEYXr4R\nAfDuo0EXUkn2ITFqMM4s1ludi7iTBI3GdVazejzTRJRdENMuqcSwtQJbIhk3pLALgevD8REN\n5mmlDN/1Sb1bGsbI4Jw0GyxY1gm2hid81xiTOiyAibinUiyVbw3G0mCrigDM+/flnfsms8Wk\n0XUByWAhdgIWjwMgtRDvTxfsBQBnI4NOTqhLiwDY7PyA5wmCofHH5u2yRX/31cSFNX025XVZ\n1CbZ/4gou3qA+QnxqTKhRBMGdxmx79gavBPxMSlIm5107CTjjBR2IWgEG4kV3MU2O2JZYIwH\nG4kFUPJqGo8ypiTGIOtEoFJMJbzVquoyIqLs0Azq2CXL7wVg5C50P4y2euwGSoDjsQQAWg8x\n3HfFXgJwIjLoxANdWgTgzcwNeJ6APHOmkTH9VxcjhPCP3xv0dyHZn5hUB9AMMD8heuySSjQW\nYQiaeNJ7MFbGnUjGGSnsQlB3gAAjsYKtKLu78BMm9SoAHnhRbJU1DD8DIDYeI7GCYznPZ1gs\nqmL5BA0WKeL4pLJ6r0vqlcQr3Y8klTLMKFcHykzvoxR73V4hIMcig048qEuLoJTN7pGwS5vs\nv/5g+clj1k+8v3zftGywO9gIx64WIKO4VYpVoqJcUO1ajRWjSEFKsSkag4w7kYwrUtiFoJV1\nEqwUmzZ9AKXG3RB2zQYCr50AUPJqLWE3NqVYAEfTLoAbRTWUY/fCNcN19beT//m6d73bcZyT\nWtUfeGEDj0S4poUqxV6zVya1jFjE3jfEtujyTX9iKuD8zVBIGuy7H6kdzcgi7IEnphgIti62\n4jdUokSpIaLRuzt2Yu0yrfQWdjrVTBqRpVjJeCKFXQhEKTbIVCy218Vad6EUSxtC2AUtxVZY\nXaQTj08pFsCxrAfg+qbWWhcbIPGk7pAvXDA1yt9JfPa6s9rlSNKoE9/nySGsM+KxOKkFNR5s\n7q56xWP6oHadcuMaGPOPj3bnhOSwEg/j2CWUKLY+VfYoxSbT2BpL6klaicu4E8l4IoVdCEQp\n1tj3pVhhPrFgIXYAyl59DEuxuZgfj7DrRVWk/QVZF/snb8XrDv3I2WbaIFftpS5H0mGE2Al4\nPEFsi/iBfKzr9grj7PjADXbqjWsAvGMnBjyPZDxplWIDOXb1BI1iKxq91nX5BNd1mFFSLga5\nhrQal46dZDyRwi4EoRy7lMkIULwbpVjh2CFwKbbs12I8ByA+TqVYAMezbtWiyyyNAI7d168Z\nL9+ITCe9D97TOB6Z2fAqoj2oLURknQxcioXIKOac1AN5D9edFQBDcOyuXuaqyuaPDHgeyXgS\nV4JOxW47dkFKsQD8ZIrWquC9X4RTSrzBLIfJxXSSsUMKuxC0hieC9diplMci7K6UYrd67AKX\nYv1Gik1ibPaJbXPvlAvgXDWNXo7dKwuRz70Rj+r8h56sqhTH9WkA15yVTsezWvlLmcpGfKDJ\nCQGPJwAg2IpMUSA+Opiwo+trtLjhHzvJFXWQ80jGFlGK7fLJR+Byz+JOcmcptmdGcSoF3w/S\nnCASTypMTlhLxg4p7ELQCLYodptMlJUtyvZeLNVrCFOKrbJGnOex9aF5fDg75RDg7Y0YCCEd\nhic48IUL0d99NaFT/pn3VPIxH8AZ4wiAc81rbe/yRvPKI/xfPfv4O4+4/+IPil8Z8CJDLZ+4\nZi0DOB6ZHuQR1QvnALhn7x/kJJJxZsux6+GCi503IkxYCLvupVgALJHCVqtDd0TiiazGSsYQ\nKexCUA+TYwcgbfqMoWzt9Q+ZiqpisFIsB6/4ddPPYvwcu5TBZlLe9ZJeN9KkXUCx45Hfeinx\nX96OJiLsHzxdPp5tlXUeNE8CeKNx+c67bPqVH77yTxZp7VOrGUb4T93415ftm4NcZKh1sYvu\nOgZ27LQLb0NR/FNnBjmJZJwJODwh0omFsDN1HmhdbCoNgAZos5OJJ5KxRQq7EIhSbMDNEwAy\nrfmJPa/GNupcVbkWqA5Y85s+Z7qX1iiPqOPl2AF4aM5hDF9LPUXuWCm2UcP/9ZXkN5cix7Le\nP/xQaT59a3zhwegpAvJG88qdJ/yFxV9fcNZ+tnDmt948/b/N/UST2f/dwq8McoViAoMGG4xd\ncgoqUaa0bP8Pt3CdFta946e4sXdBJ5JDRkwJIexSSgwAAWIa6ynshGNHyr0TT9JqAjKjWDKW\nSGEXAtcDAE0JLOyid2cwljbqiAVNJxYvfKqfiBtjp+oAPHnUUih/Lv4e2PbOjuzrG+QXP4/l\nivrEUevHni7t2h6WUmJH9ak3m1d3rTk/Z137veIXTxvzP391nkdjn8o/83T8weeqr3+znbcX\nkJawC7BGCcCSuzGlZRUywFPuha8BcB95d/9nkIw98WArxcSLj5iKBRDTWc9SLE+lECzKLtVa\nFyuFnWTskMJuhKRMH0Bxjx07zoltMcMMeHiF1Qko9eLjVocVxCPs4TlnnWaej7+bOK2FB+dW\n9H/5F1rFwicfaHz60Zra7q/kQfNkxa/vSrP7Z0u/yTj7n2d+SKvWWCIJ4CcmvxPAv13/fN9X\nGDxt3+Xeulea1XJ9PxYaDf7W6zyV9o6f7P8kkrFH9Nj1jDvZWYoFEDe46xPH76bt/KRw7Eo9\nryHZEnayx04ydkhhN0KywrHb28QTYlvw/eAjsWWvHvGTABmrtRM7+ejZhg73sxOfuraGpkv+\n9FzsP7yYZBw//mF86J6O70wPxU4BeK1+cfuWc9a1Py+/+Ej09N9SHgBjPJkE8NHku09EZj5X\n/EqlX+eAaxo3TBLAsVtxNxlns1q+vwcCoL38PFzXefQJUPnKIOmfGDUA1HpNxW4NT9xy7NAz\n8cSM8ogRqMdOCDtPOnaSsUO+fI+QlmO3x8KuGTrEzmAZjN/kxDa5mP/pyEsWjfzfr8z9wp/m\nvnTRTBnspz/qPtE1nff98YcA/Hnlxe1b/sXy73Dwn5r6tHDXWDwJgBL63Zlvcbj3p6Wv932F\nPJEIsvh8yS0AmNX7FHak2VS/8QKJxWQdVjIgMWoSkJ6l2FaPndrqGxFT+bWu62IBsFSaVsro\nFTeQasWdSGEnGTuksBshMZ3rKt/jUqwYAgixdsJvrZ0Yq31iu3givvTTi//nmXjlVN599v7G\nP3qmeHKix0/j8eiZKS37F+WXRALqm82rf1z62v3G8b+Vfq8i1k4kU+LI78x8AMAflb7a9+Wx\neIJ4bs8I5SWnAKBvx05/4avEtvH0hwKO3UgknVAINWkk1FQsAjp2AEtn4Ps9E09Squyxk4wp\nUtiNlmyU7XUptlFHmHTiKmuIRbFjtU9sN6Z5pnHxJ+Ze/bGny8+cbhgBpoMpoZ9IP1Xx68/V\nvsnB/5el/5eD/9zsDxIQcvs+sTPGkfuN41+uvtZ3pFbAwdhBHDtaKWuvvMgTCfLUB/q4u0Sy\nizg1A/bYJWirIThglB3PZAGQ4mb3w5JUlGJlj51k7JDCbrSkTd/xSa1XnPoQERsUeGDHruTV\nDBFiN649dgBasyYdMoo78bfT7wfwCzd//Z8u/8cvVV59Ov7gx1NPAiCVMgC25dgB+Lb0e13u\nfbH6Sn+X15qfqPWwKJacDQD9DU/oX/4r4vvOB56Bpvdxd4lkFzHF6BlQXG7FndxWig3k2AWI\nshM9dnLzhGQMkcJutGRjDHvbZtfaJxZ4Krbs16J+DkDCGF9hBzOK7WDnwLwv/sAP5599x7rx\nf6x8dkJN/+rxnxW3iyIR23LsAHxr8nEAf1X5Rn9XJ9r1erbZtRy78KVYenNBe+ecPznlP/ho\nf1cokewiTs2epdgqu314IhK4FAvQUg9hF1dMhVA5FSsZQ+QuyNGSNX0AGw3lSMbrefBQIE0L\nYUqx245dYoxLsf05dgD+6fyPGUSPKeZ3Z75leisWmFTKoJTviBJ8NHomqyT/uvIy44yGD5nb\nSjzpIeyW3Q2F0EktE/Ls3PjSX4Jz+8MfB+lRBZNIAhJXTJu7Lvc00vFdRkzFbg9PBN0qlgok\n7AhIUonJqVjJGCKF3WjJRH0Am/W9dOzqCOPYVVjdZFkAibEMKG4RjWKrih0Knai/OP9f7bqR\nVso8kdyZGKIQ+pHU45/d/OLrzcuPRk+HfRSWSABQeoWyrribOSWlknDDOurF88rSonfytH/0\n+J7vSJEcWmJbW8UyaqLTMWW/plPNIK3qf8BSLE8kuaL07LEDkKQx6dhJxhBZih0tuVYpdu/e\nMcXsZPAeu6JXNVmWEkS18XXshA6+c6tYHxDfI83Gzjqs4EOJRwB8ufJaP5eXyoAQ0tWi4OBr\nbnFaD7lMjLHIV78ISu0PfqSPC5NIOiGi7LonnpS9epLeeqUyNUYJ6j07kinlmSwtbu5cFdOW\ntBqXPXaSMUQKu9GSFY7dngq7BggJ0WPn1aN+Nqazca7C8UgEitKHY3cnpFIB5zsnJwQfSjxC\nQJ6rvd7P5WkaN6Pd1yhtelWXe1NqOGGnnnuDbhS8+x5kE5N9XJhE0gmxfKL7/ESF1bezTgBQ\nAkPrvVUMgJ/NE8/tuVgspcQc5jaZHeySJZJDghR2o8XQeFTnG3tZirWaXI9ACSoly6xu+Jmx\nTSduQQiPGCIpZtAzVcsAeGK3sJvWsmeMIy/Uzlnc6eO0PJ0htSp8v9MBK+4GgKlQDXaMRZ7/\nChTFft8H+7gkiaQL8a1SbJdjqn4jpdwWpR6P8J6lWAAsNwGAFta6HyYST0qyGisZM6SwGznZ\nqF+ylF4x6UODNOrcDGrXAWi6hHJ9rBvsAAA8Gh2KY0crFQAsubsUC+CDiYdt7n69+lYfp/WT\nKTDWJZR1zSsBCDU5ob31TVoquu96SIwZSiRDJK5E0XVdbINZHvcTym1NIzGd2R7xWa8ou1we\nAN0odD8sqUYho+wk44cUdiMnG/UZQ9nak2os58S2g9dhPe77jokx3ie2DTdM4jiksyUWENpy\n7NoIuw8kHgLwN/U3+jit0F6k1LFhfNXdBDClZsCYsnhDvfQO8bsOYvu+/vXnoCjOe2UisWT4\n9HTsRIidMNVu3UtsFes5GJufBEA31rsfllYSkFF2kvFDTsWOnHzMB7Beo2JCdqQQ2wJjwR27\nil8XayfGOetE0AqIaTYQ7zjEFwSRTsyT6Tu/9FTsAUro31TfwEz4y0ulAdByudNzaMXZBDDN\n49Hf/Q1l8QYAlkxZn/wuf+5I2+O1N1+jpaL70GMs1eZSJZIBEVZctbOoEkaaMNW22d4qljK7\nvSKxbBaU9nTsRJ1XDsZKxg3p2I2ciQQDUKjthWPXKiaasV4Htij5W2snpLAzTAB04GpsqxTb\nzrFLq/H7jWOvNS41ei1HvxOWFMKu1OmAVW8TwPHnX1UWb3gn7nEffpzWqubv/5aysnTnwcRz\nI89/hSuq7K6TjAgRO1z1Owo70fqWVW/7Swm6VUxRWSpNN9a7D8YmxbpYGWUnGTOksBs5uZgP\noFDfk1JsowGAhXLspLADAHChhsNnFO+CVEo8YnDDaPvVp+MPutx7oXYu9OWJUmznNUqrbhHA\nzELBO36q+anvtT72iea3/W3iuuYf/A6p7l4yqz3/HKlW3ceeaFsylkgGJ9lyyzqKql37xAQB\no+wA+JPTxHFo1zS7FJWOnWQckcJu5EzERSl2L4Rdy3AKLOxKXs1olWLHfnjCNBF+q9gdZ+Gk\nUm5r1wmeTjwI4Pla6PkJlkxxRelSe1p1Nwkw6Zv2xz4hFkh49z9of+AZUq+Zn/td4rrbRyqF\nNf2lr/NY3HlK2nWSUSGEXU/HbtdU7HYptuf52fQsALpys+1XiW3pf/Plya++ANljJxk/pLAb\nOTGdRXW+p45diLUTjaifx5gvigWwJewGdOxIs0E8j3fuWntP7H5K6PP18IOxlLJMjm5uoMN8\n9VpjNetoyrtu65lznnyfe/+DysqS8UefFdqONJvG5z5LfM/+yLM8Egl9GRJJMBLUBFDp7NgJ\nYZe+3bET62IDRdnNzAFQltt0GoBz83OfjXzty9nVDQDVwmKoK5dIDjpyeGIvyMX8myXVY1BH\nLKS3HLswayf8E5DDE1u7OgZ07GhrcmJ3iN02WTV51jjySv0dm7sRooU6OctPKIU1Wi6xTJsU\n4jW/fNLRnXe/57ZbCbGf/Q5aq6lXL0V/499595zRzr9FKmXn8fe4Z+8P9egSSSiEY9dF2BW9\nKoCMctusUvBSLJuaAaXqytKd6cP6y19Xblz1jp+KPDKP2vna8mXcz+UeZMn4IB27vSAf8xnf\nk8ViVhNhHLuSX436OUK4qICMM60lbINlFAth1zbEbpun4g843Hu1fiHsybkIZW0X8dBYvtKg\n/oSSYundmo8rSvO7v8996FFa3NBf/BqpVuynPmB/y0fDPrpEEopWKbZzjp1ofUurtzt2ohTb\nc6sYwHWd5fJkdWV3anezoT//cyUIzwAAIABJREFUFW6a1ie+M3bsfgAVr6peDv3nJpEcXKSw\n2wsm4z6AterIhR21LCBEj13Vbxp+NqI78tOsEHZkwFJs56yTbZ6KvQtAH9VYlhdp+22EXfH8\niwDyiem2d+SKYn3822s/9pPNT31v7cd/ynn/h0HlH75ktMQUQyG0i2PXdngiqjMC1J1Ar0f+\n7DzxPWXptkpr5KWvE9t23v0Uj8bEyUuar731zX6+B4nkYCJf3/eCqaQPYHX0wk60iPHApdiS\nXzVZNhbpmmQ7HrSEXWMwYVcuAfC7jpq+J34/gOerb4Y9uZ/NA1DunJ/w/c2FtwDkMke73J0n\nkt7J03ywlD6JJCAEJE7NnqXYXT12KkVE47UAjh0A7+RpAOqld27d1Gxor7zIzaj7+JMAYoqh\nEqWkQ7l2ucs6PonkkCGF3V4wGfcArFZH3tFIbQsAj7TP2riTssUpV+VILAAeiUBRBlwX2+qx\n6xr5O6PljupTLzfe8Xm48jfLZKEopLC663b1ysUVXgUwpeVCXq9EMkISSrTSdSqWEppUdodu\nxnQWpMcOgH/8JFdV9dKtMqv+jReI6zhPPMU1HQABSSqxkkmI44jUbolkHJDCbi/Ix3yF8j1x\n7JpQFK7rAQ+vOQqAjDH2hVgAhHAzSgcsxZaLXFF5LN79sPfG31X1G+esa+HOrij+xJRSWCeO\ns/Nm9dwba7oLYEKTOyQk+4gEjXadiq0mlahCdr8HxSO86ZAgy7W5qvnHTtLSprK+CoA06vor\nL3HTdB99YvuYJI2WVQ+AeuVif9+FRHLgkMJuL6AUE3F/taoEebUaBNJs8IgRfP7LsjUAGVMK\nOwBgZhSDlWJpucRTqZ4//ydj9wF4oR46ptifOwLG6NLC9i3EaqqXL66mNACTWibsCSWS0ZFU\nYk1mu7x9p0fRq6Zom49AMZ1xoOEGq8Y+8DAA/bkvAoh84S+IbTnv/cDOT7YpNV6GDUWRjp1k\nfJDCbo+YTvo+IxsjHowltsUDj8QCcB0TMutkm1iM+B6x78xPCEazQRwnyOrVlrALv3+CzR8F\noNy81S2uvv0m8b3VyTiAvNoxZkUi2XtSqhiMbf9hqcqau0ZiBQG3ignc0/eyuSPq5QvR3/73\n2ttv+JNTzmNP3nYNSszlXmUyq6yvEk82E0vGAins9ojppA9gpTJKYccYse1Oy6za38M1ASQM\n2WMHCMdue99ueJRyCQBP9bbNzhpH0mr8pfr5sA/hzR0BoN685T1o33wFlC4nVQCTqizFSvYR\nW1F2bYRdnVkOc3dNTgiCR9kBACHWR57liYSyeMOfmLS+83t2TXwnaQxAcXYCvk/XVsJ/ExLJ\nwUMKuz1iOuEBWKqMcH6C2DY4Dz4S63GfuAkAybFfOyFo/ejqfc5PbIXY9bbNKKHvjt5701m/\n6bTJLukCj8VZJkdvLgpbkd5cUNZWveOn1lCjhOakYyfZTyRoFB22irUdiRUE3yom8Kdmav/g\nJxvf9/ebP/Cjd/rlwjXcnEgC2BWMIpEcVqSw2yPm0x6AxeIohZ3VAIAQ+8TqJstBlmK3icYA\n0Gafwk5knfB0oEa3J2L3AujHtLv/AeK56luvAzC+9hUAzmNPrrnFrJJQyZ6srZNIgtFl+UTb\ndGJB8K1it6DUnzvC1TavriLKrphPAqBS2EnGAyns9oikwRIGWyyNMvGktSg2aCm25NXEotik\nKYUdALDoQBnFtFxCMMcOW212L9bfDvsozkOPgVL9tZe1N19Trl3254/6J04VvLIciZXsNxKK\nCaDstRF2W4ti26QqhivF9iKpRAGUowo3DGWl3WJZieTQIYXd3nEk7dUdWmqO6mcuQuyCr50o\neTXTy0OxNCp77IBbGcV9O3ZFACyYY/dY7IxKlJfDO3Y8nnDP3k83CsaffZ4rqv3hj9X8ZpPZ\nk6ociZXsL7o4dkV/aKXY7qTUOIAya7D8JK2Ud0UFSSSHEins9o45UY0dnWnXDLkoltWibELV\nB4rkPUzwaAwDLJ+gpRKPRAL2OEapcb95/C3rms3dsA9kP/vtzhNPsYnJ5t/7IX96dt0rQY7E\nSvYfW+ti2/xBFdwygLzW5knbmoq1hpPBlKRRAGWvxianwTlZ353vLZEcPqSw2zuOjFjYUTHO\nGVjYrVsNjZm63m+6x6GDR6MA+swo5pyUiyzASOw2j0fPOsz9ZuNy6IdSNftbPlr/zI/7s/MA\n1rwiZIidZP8hDDlRdd1FwSujw6eR4ZZiRY9dmdX9/CQAORgrGQeksNs7jmRcAlzb0Eb1AFYT\nAA9cit1scgBmRNYmWnAzBgD1Nu9DPSGVMvH9gHVYweOxswD6qMbuomV+SMdOss/IqkkAm17l\nzi+JGzPteuw0hesKH56wa5WD/Qkh7KRjJzn8SGG3d8R0PpnwbxRVj41k0wO1LADBA4qLTQI5\nObEDbhhQFNJX3AktFwHwTDb4XR6PngXwjfo7PY/sjnTsJPuTrJpAB2EnHLtOEz8xnYWbiu1M\nq8fOq/OJSRBCZSlWMgZIYbennMi5HiOjqsYKxy6wsKvZCoBUiDzjww4hPBanfTl2tCQmJ0II\nu1PGbEZNvNK40PvQrnSpakkkd5GskgRQbFeKFcMTbR07AHGDNxzKhjHT1eqx82tc01kqQ9bX\nwOWsmOSQI4XdnnIi5wK4UhiJsCNNIeyCKjXL0QFMmPI5cAsWjZFmo4+XfloMMRIrICCPmPcs\nOGvCcusbKewk+5O4YupU2/DKd36p4JZ0oiaU9pNGMZ0xDivYutjupNUEgLJfB8DyE8S2eKXN\n9Ugkhwn5pr6nnMy5AC4X9J5H9gG1LRDC9UjA4y07AmDKHMnFHFB4LA7G+oiyo6VNhBR2AB6J\nngbwWv1S2IfbyZpbBCDjTiT7DQKSpnGxZGIXBa+c75y8GGpdbHdi1NCI2hJ2uQkAXM5PSA47\nUtj1BefqtcvE98PeL2Wy6aR/dUOzvRG02VkWj0R2rUrsAnOiAGbiIxvmOIDwWAwADd9mR0qb\nXFF5IhnqXi1h17gY9uF2su6WCEiXt0mJ5G6R05IbHYYnsh3qsBj2YGyCRsWiCz+bA8BXpbCT\nHHKksOsH7c3Xzc/+VuQLf97Hfc9OOh7DpfXhyyliNXkkTMecmwB41pDPgVuwqBiMbeMxdINz\nWtzkmQxIOL3+SPQeAK81BnLs1r1SSo3pZJRLTSSSvsgoiQazdoU1Vv2Gw70uH0Xira1iw8oo\njontFzwvHDs5PyE55Mg39X7QXnkBgPb6N5RroUPI7p1yALyzNvwCKLGt4JMTADQ366oVRa6d\n2AGPxQGEHYwl1QpxXZbJhX24WT0/pWUHdey80oQq7TrJfkTMT+wajN3wKwBySkd7Wzh2QxuM\nVeIVVufgLJcHIbIUKzn0SGEXGrq0qKytskwOnGtvfTPs3Y9nXUPj51b04c5mEdeB7yPw5AQD\n1/2sq5aGeRGHgFgcAA25VYxubgBg2dDCDsDD0VPrXumms97HfQHY3K36DTk5IdmfZLV2ws4t\nYyvlri1D3iqmxDzuN5jNNZ0nklLYSQ49UtiFRr16GYDzwWe4pimry2HvrlC8a9qpWPTa5lCr\nsSH3ia02bIVr0OSA2G2wlmMXLvGEFvsXdg+apwC80bzSx32xNTkhHTvJ/qRtlJ3ouuvyaWTo\nwg5AyasCYPkJXq+j333QEsmB4ND25RBCSMiGp4AIe8afmeOT03RpkXou18LVVR+ac76xEHlj\nKXIy7w3tqmwLAAwz4He93HAAKJFGz+PFASP6Ye474nHsGJ4I+F0rLWGX7+On9FD0FIA3m1e/\nLf3esPfFVlVrQksP6xc0uj+cfc54fuOj/pa3o+x2PtCmKMVqqU6PHjdawxNDuTyxVazCGvOE\n8NwErlzC+hrJTQx+5gPEeL2Mjz2HVtgZhhGNBlrHHha1tAFdT84fYUeO8ZsLiXqNHDsR6gxP\nxPE7r+DNFeMH368O6w+Nb64zQE+ljVSgqlx5tQ4gZrqpXsdTSgHo+nikokQiPqA5lqZpAJLJ\nQFOurFLmQPz4CSJmL8LwPv1hXMF570bPX0Rb6q4D4Eh8ur+734miKMM61UFBPMNN0zQCdzIc\nDiilAZ/hfTPXmALQ1G97namUmwCOJ+c6PdPEbr+mpw3lqTgZy2IDvoFUKsXn5tlLUDY3Uifv\nGfzMBwhKqaZpZuCFk5IDzaEVds1m03Xd3seFIw8ApaKfzVXLZS2TNYDm5UtOmNXvgvunEt9Y\niHzjUv2eieFcpFoomIAFOKVAbXM3CnUAht4s9TreMAxKaaMROtrtgBLXNFYuwXV1XS+XyzxA\nL2RsdYWYZtlx4YTuWUzBSKvxV8rv9PxFtOVqeRFA0jf7u/suFEWJx+Pl8ngV6E3TjMVijUbD\ntu27fS17SiaTCfgM7xvDUQHcrKyWoreen1cqiwBSrtHlSauruXKDDeVZbXgagJvl1RJKWixh\nAP7KUnUYZz5AxGIxz/NG+gzP5/OjO7kkFLLHLjy+z3J5AGxqBgBdXerjHI/OWwBeXQwaJtwT\nYodbFFuyKICkETqK79DD40lSbZO81QniubRaYdk+X9QIyAPmyUVnvW2Oa0/k2gnJfkb02O2K\nsltxNwFMad3278V1VrWGU87Y3ioGQLx0Y31tKGeWSPYnUtj1A89NAGDZPAihxc2dX1pyCj9w\n5X/9d+t/7LBuVtw9E24iwt5cjvhsSK1RzSaA4FOxVUsFkJXG/B3wRJJYFpygH23pRgGcs3z/\nLTsPmCc4+FvNq33cd90tQQ5PSPYrQr2ture9SK64mwqh3XelxCO84Q5nXezOrWKIxkgshoIU\ndpLDjBR2/dBKMFcUHo3R2zcP/uPFX/3z8os/t/j//PzSr3U5AyV4aM5uuuTt1SHNxlpNACxw\nC4XlRABMRpXhPPohorU9IvBCSbq+BoDlp/p+xHcZJwC8bV3v477rXgnAhFw7IdmXTKkZSuiS\nW9h547K7MalmFNLt3SceYZyjMYzBWDEV2xJ2AJmYQrlEXGfwM0sk+xMp7PphO9iCp9KkXsPW\nbrEvVF/5L+UXnojdO6vnf3vjryt+t6H6R+ZsAN+8OZxqLBXNE4E3T7h21KG1nD6S+ZIDjZ9I\nAEAlaDWWbqwD8AfoL7nfPA7gXPNaH/dtCTvp2En2JTrV8kpqZYdj53O25hanu9Zhsb0udhjC\nLtkSdq0MIzI5Dc5FuIFEciiRwq4ftleC+okkGKNbsWefL34VwP80+8Pfn/1og1m/v/mlLic5\nmvUyUXZuRXf8YVRjmw0A3Awk1DjAnERdWRUdMJKd8HgCAC8H7a3ecuwm+37Es+ZRhdA+HTu3\nFKNGlI7XOKfkADGj51bcTcaZ+O+6V/K4P6P3CH1MGBzAUNrsWo6dt+XYTU0DIBuFbveRSA4y\nUtiFhqva9kpWnkoDIOWi+O9Xqq8nlOgTsXt/IPcxSuh/Ln65y3kI8NCs7fjkwtoQqrGt4Qk9\nkP/XcChhWk1dyUhhdwdCtZPApVhlY52bUR4+6GSbCNFORebebl7ffvMLjtwnJtnnzGp5l3vC\nWgaw4m4AmNF6CDuxLrY+jHWxLWHHthy7qWkASqHPXS8Syf5HCrvQiHWiAiZEQLkM4Iq9tOCs\nPR1/UCPqrJ4/GznyWuOixbt1crT2xq4OISKO2hYI4ZFAwq7YoAAa6mqKxnsePG6E6rEjtkWq\nFTbRv10nuM841mDWdSfcenKXe0WvOqmFTtuRSPaMWT0HYNltlT6XnEDCboil2IyaBFDytkux\nM9jqoJBIDiVS2IWBcwA8fksMCcdOqZYBfLn6GoAPJh4WX3oq8YDDvVfr3fa7n8i6psbPrepD\nmP2yLB4xECzvuNSkADyt2L1/eTwRYj1gKZaur4Fzf4A6rKC/NrsNr8LB5eSEZD8zreawpeew\n5dhNB3PsavYQSrE6UQ2i3xJ2qRRMk8rBWMnhRb6vh8H3cLtjx5MpbJXtXmlcAPB04kHxpSdj\n9wF4oX6uy/koxekJp2rR5fKgSdGk2Qxo1wEoNxUAVAu3EXVM4GaUK0pAx04sC/anZgZ80HuN\nYwDesW6Eupcob8kQO8l+Rjh224Oxy54Qdj2GJ+IRDqA2jFIsgIyaKLMdo2z5SVoukeEn2Esk\n+wIp7ELAvd3CjiVSAGilAuB884ZG1Hv0OfGl98TuB/D12lvdz3l2ygVwuTBomx1x7ODpxMUG\nARAxmgM+6OGEEB5PkEogx05ZWwHABxd25lEA5/sSdrLHTrKfmdXy2AolBnDJugngZGS2+71a\nPXbDKMUCSKvx0s4A8CkxGCvnJySHEynswnCHsOOGwXWdVMqMs3esG6eMOZ22JNq8PjGr57uX\nYgGczLkArm4MJOyI68L3g2edrDU4gGhkvBYoBYen0rxeR4D1O2R1mSuqn+1RV+rJMX3aIPr5\nZrjB2DW3CED22En2MzO3O3YXrIUoNea0HvFAMY0RMjTHLqXE68y6FRo/OQ2AyP0TkkOKFHZh\n8HwALH7bJCmPxUmjdt1ZbTL7XuPozi+9yzix6Vd2pa7vIhfzEwa7sqEO1GYn0okDr53YbIKD\nJU25T6w9LJUBwHt9oCe+p2xusIlJKIPmPCuEnjbmL9s3PR7ilyKeWt0T/CWSu8ucPkEJvWov\nA3C5d9VePm3M017dvZTC1Ia2VSytxAHcqsbOzGLLbpdIDh9S2IWh1WN3W7AFj8aIbZ+vXwWw\nS9jdZx4D8HYvG+ZE1m04dL3avzgglgWE2CdWaWoNpZDT5Ehse3g6A4Bv9Igwpetr8H02NT2U\nBz1rHnW4d80O8Waz6hYBTEnHTrKPMYh+KjL7VvOqz9kVe8nl3hnjSJA7Jg0+LMdOCLvt+QlM\nzYIQ0SArkRw+pLALgxB2tzt2LBYH529XL2KrBX4bofN6Nk6dGLgaSxwbAA9WivUYLEerq2sZ\nRYbYtYelAzl2dGkRgD/do1soIGdbz5YQ1djVAMvUJZK7zgPmySazL9s3L9qLAAIKu3iEOT5x\nvCGYdmk1DqC0tXwChsHSGbq+KoIOJJJDhhR2YRA9dubtUbSxOIALzevYaoHfJmCGxbGsB2Bx\ngMFYYjUBINjwRLmpgJO6sizTiTvBUmkAvFc2vbJ0EwCbnR/Kgwb8GLATuShWciB40DwJ4M3m\n1QvNBQCnI4H+ZFqJJ8OYn2iVYv1bOQD+5DSxbVoqdrsbY9qFt9U3XyeenJ+VHCQGTdkYLzhA\nsKuhSlRmrzorKlGO6rdtgj9tHFGJ0tODmUn6CsXN0gC/C8sCwMKkE9fUlZwmYzLaw9JZALzX\nNkllaYFHDD83MZQHPW3MA7hgLQS/y5pbSikxgwwh4FoiGR0PmCcAvNG8fM1awR0fgDsR1xmA\nqkWyA2+0Tu0qxQJsagbvnFNWl1mmveFNLMv8nX+vrK8B4M99of6DP7qrViOR7FukYzcoLBoH\ncN3fnNFyGrlNnOlEPRWZO2/d6L4qSqF8Mu6tVFQv9EKpFtQWPXaBHLtiUwFQU1ZkKbYT3DRh\nmN1LsaReo+USm5kLGArdk2P6tE7UcMLOK8qRWMn+56HoPQC+Wn3jLysvHdene2adCIRjVx1G\nm12rx47tcOymZwHQ5Zvt78C58f/9gbK+5p25z33oMVKrmn/yOVm3lRwUpLAbFB6LVRV/A/Vj\nkTZN9KeN+Sazt9fpdGIu7XkM67U+TTsxPMGDDU+0HDttOasm+3u4sSCT5cVNsI5aW7m5CMCb\nnRvWA6pEOWXMXbZuBtwY22BW1W/IkVjJ/ienJu8zj73WuGhz9/vyHyUI9FkoYQwto1i0ndzm\n2M3OgVJlsX3ng/r2m+rVS/7RE81v/5T1sU94x08pN66ql94Z/Eokkj1ACrtBYdHYddMBcFRv\ns1dKfDa9Yi91P8lc2gcGqMaKuJNgwxOlLccuKx27LuQn4Pu082IxZfE6AH8uUFEpIKcj8xZ3\nFtxA8VprXglyJFZyQPg3x37GpBGF0L+X/UjAuyRaW8WG8CaVUmIAdmYUc033p2aUtRVyR1wl\n8T3jq1+Eolgf/wQoBSHOhz4CQH/lxcGvRCLZA6SwG5hY/JppA9jVYCc4EZkBcMXuMVc/l/IA\n3Cz1mXhC7BBxJ9s9drIU2wUyOQ2Arq92OkC5cZUrCpsPNN8XEDEtGLAaK9OJJQeIB8wTv33q\nF37l2M/M9NoSu018eMIurSSwM8cOAODPHwVjytLuPzft9VdJueQ8/LjotQXgT07780eVG9eo\nzDSWHASksBsUHotdNW0Ax/Q2pVjh2F3tJeymEh6AtUFLsYF67Dbr1FWqpkq2l2RI2jA9g87C\njjQbSmGdzc5zdZg/w9PGEQAXrcUgB7eyTlSZdSI5GDwdf/DvZD4Y/Hgh7CrDyChuxZ14t23H\n9o8cA6As3DbcRnxff/FvuKo6733/ztudhx8HoL7Tbfe3RLJPkMJuULiqXY95AI5G2jh2AUux\npsbjEbbab0ZxK+4kQCmWcVRspS7tup5MzQCghfW2X1RvXAPn/tETw31M4di9EyzxRDh2MutE\nclhJRDgZaim2uHNdLODPH+WKqr7z9s4b1W++QqoV96HHdq6OBOCfvAeKol6WbXaSA4AUdkPg\nqhB27UqxU1omSo2ewg7AVMKvWNRy+/p4altc1XiAxVblJvUZyspNOTnRg3QGeqSTY6dcvQTA\nOzZkYXePMUcJDejYia3qwQtbEsnBQqHc1Hl9GDl2GlHjirmrFMsjhnfqNC1tKluzscTzIs8/\nx1XVec/Tu87ADdOfO6qsrZJKefDrkUhGihR2Q+CGaRuMTCltYuEIyInIzDV7ueeo41TCB7BW\n68e0o7YdsMFOTE5U1SU5OdEDQuj0DC0V22STcq5evshN058Z2kiswCD6vDZxye4QwXA7K54U\ndpJDTjzCqtZw3qQySmLTrey60b/vAQDquTfEf/WXvkbqNffRJ9pG1nn3nAGgXrk4lOuRSEaH\nFHZD4KZqzVo6FQtb7+BkZNbm7k23xxqDiYQP9Jt4YjUDZp1s1CmAmrIk04l7QmbmwBhZ223a\nKUuLpFH3Tp0BHf6fz2ljvuhVC15vV2DZ2YAUdpJDTVxnlkdcfwhtdhk1sTPHTuCdOs2jMe31\nV+hmQVlf1Z9/jkdj9h12XevgYydwR0+eRLIPkcJuUCzubCr2nK2TRqPtAcciUwBuOB3nKwWT\ncQ9AP212jBHXDbgotthQANTUlbwqhV0PyLETANSFa7tuVy+eB+CdOjuKBxX7J4JUY5fcQkKJ\nxpVAEzMSyUEkaQwtozirJh3m1vzmzhu5otofeZb4XvT3/5P52/8Bvm999BMw22+6YLkJmFG1\nQ/SdRLJ/kMJuUESr05yttSYY7uCINokgwk6UYvsQdrYFzoMKu1YpdjmryB67HtBTpwEoN27/\ngM6Ydv5Nrun+iVOjeFCxRjNI4smyuyHtOsnhprV8YhjVWDEutuntrsa6977LfexJ1Krwfevj\nn/TO3NvxFIR4c0dIrdol3lIi2Q/IXbGDIipis90cu2kA1+0ewi5pMF3hG/XQwi7UPrFWKVZd\nzmlS2PWApDMsnVFuLsD3txcEq9eukGrVffARro0kLGYr8aSHsKv5zZrfnIlKYSc5zCSG6di1\nF3YArI88Sz7wDHyfmz1eRf35o+qld+jCdZaS0+iS/Yt07AZlxd0AMGPrnRw7MS3b07EjQDbG\nNuo07D7CkPvEFKo1PdKUjl0Q2NETxHWUlVtDzdobrwJwH3x0RI8YMKNY+MTTmgyxkxxmxPKJ\nSnMYPXbCsfOrbb/Kdb2nqgPgzR0FoN4MsdBZItl7pLAbFLEHdtbWSbPe9oCjkSkCct1e6Xmq\nbNT3GAldd7AsACwS6Xmgz1CxKPQSgJyMOwmAd+o0AO3N18R/6fqaevG8n5/0Z+dH9Ig5NZlV\nkj0HY5fdAoBpWYqVHGqSBgdQHUbiiQh42hVlFxY+NQ1Foas9AuclkruLFHaDsuQUAMzbOmm2\nd+wiRJvUMgtO7100uZgPIGw1NngpttRUGIerrQPIq7KU0Bvv1BmeTKnn3hR2bOSrXwTn7tMf\nAhmChdCJ08b8orNeZ+2HrAXi48SMdOwkh5pWKXaUPXah4Iri5/JKYQ2+P/glSSQjQgq7QWk5\ndpZGmu177AAc1SdX3E2b35GIdju5qA9gsxGyzc62AAQZnthsUAB1bRVbHSeSHlDqPvw48dzI\nF/8i8vxz6qV3/Jk593Tn9uphcMY4wsEvW91Muy1hlx/plUgkdxdRih1Oj52WxMDCDgCbmoXv\nKwW5NFayf5HCblBW3E1K6JStdnLsAByLTHPwBbvHa0EuzgAU6uF+Ka0euwClWCEZK8qiQmha\nifc8XgLAefTd/sSU9ubr+le/yKMx6zu+e6R2HYItFpM9dpJxIKYzhQ7HsUvTOAYuxQLwp6YB\nyGqsZD8jp2IHZdndyClJHYrfzbGbArDgrt1jdNtVkBWOXchSLBGOXYBSrAixKyjX00qcEqnp\nA8EjRvN7fijy13/GY3H3ocdYcuT5f2fM3vMTYhZnPjIx6ouRSO4ihCCmD2f5xLAcO39qFoCy\nutKj/iKR3D2ksBsIDr7qbp4xjvCI0cWxO6JPAliwV9G1/pmNMkpCl2KFYxdkpdhmnQJYJpdz\nMp04DNw0rU9+1549nIiyu2h3yyhesNcMok/IRknJYSdpsOWKyvmgRvlQeuwA8MkpUKp02CIt\nkewHpG0zECWv5nBvUs1w0yRWR8duXpsAsOisdz+bQnnSYJshS7Gwmgjm2G02FEqwRq7Jkdj9\nzLw+EaVGd8du0V2fj0wQjLYoLJHcdeIR5jM03EHfqpJKVCVK0d+9VSwsXFVZKk0Ka+Bhk6kk\nkj1CCruBWPdKACb1DMwosSww1vaweX0SwKLbQ9gBSJt+zaY+C/GGHbzHbqNO44bLiCcdu/0M\nATlrHLlmrzisfbWn5NWqfuOIPrXHFyaR7D0pkwGoDFyNJSBpJT64YweA5SeJbZPqEE4lkYwC\nKewGQgi7CTXNzCg4J7aSdA3bAAAgAElEQVTd9jBhrgRJPElHGQdKzRC/F+rYIITrPYSd45G6\nQ6NmEzLEbt9z1jjqcu+ys9T2q+ITgrCBJZLDTTIyHGEHIKsmhyLs/PwEAGWj9wd1ieSuIIXd\nQKy5RQCTakbEyHWqxhpEz6nJm71KsQAyJkNIYYdmk0eMnh0om00FgBqpApC9Wfuce82jAM43\nr7f96g17FcCRyOSeXpNEcjcQUXZDEXY5NVn1GxZzBjwPz08CoAUp7CT7FCnsBmJNlGK1DBOz\nC13nJ5bdDY/3iLVMGT6AYiPE74XYVqA6bI0C4HoRgCzF7nPuNY4BON8h8WTRXQNwRJPCTnL4\nSQ5R2CkpAOtOccDzsPwEACqj7CT7FSnsBmLdFaXYlHDsWksg2jGnTXjcF/FjXUhHGYByM8xg\nrG0FSScuNhUArl4AkJfCbn9zr3EUwHmrvWMnavrSsZOMA8nhLZ+Y0NIA1p3SgOfxMzkoinTs\nJPsWKewGYt0rAsir6dZQamfH7mhkCsBirza7TDRcKZZ4LvH9IPvENuoUQENdgeyx2/fM6vmk\nEnu7QylWCDvZYycZB1LDc+zEB1rRPzMQisJSabpZkIOxkv2JFHYDse6WsaMUKzaKtmVen8DW\nW3IXWqXY4D12TQsACxBiJ9KJi+oCgLwme+z2NWIw9oaz2mRtxnEu20sG0We03N5fmESyx8Qj\njNKh9dgBWBu4FAuAZXLEcUh90PAUiWQUSGE3EKvepkqUjBJvlWKtbqVYADfdQvcTRnUeUXkp\ncCmW2E0ACLQoVtEoX8dNyOGJg8CD0VM+Z281r+663efsqrV0ypiTu0Mk4wAhiOtsKMJuQssA\nWHN69MMEgWVzAGhxY/BTSSRDR743DMS6W5pQ05TQlmfWy7HrWYoFkDZZiFKsbQPgwdZOZGJs\nwytTuSj2IPCgeRLANxuXd91+w1m1uXvamL8bFyWR3AWSBqvadPCyp3DsBu+xw7aw25TCTrIf\nkcKufzj4hl8RDbk9HbstYde73zZlMscjlhsoo7hV/O3VY1ezqeOTbNRfd0tpGldJuK1lkr1H\nCLs3mld23X7JXgRwT6Tb0mGJ5DCRNJjPUHcGfbcSPXZS2EkOPVLY9U/JqznMzSspANw0gW6O\nXVZNRqkRRNglDR9AOWDpwbIAsF5xJ5sNCiAXY5t+Ja/JkdgDwH3mcY2obYSddRPAPdKxk4wN\nw0o8ER/Ch9Njl81DCjvJfkUKu/4peGWIfWKAiAjuMjwBYF6fCFKKFVNg5WDVWCpKsb167Dbr\nCoCE6VT9hsw6ORDoRD1jHHnbur5rsdgl+yaA0xEp7CTjQqiXxC6IYsVQeux4NMYNQwo7yf5E\nCrv+2fQrALJKEgAo5bpOOufYAZjXJurMKnrV7qcN9fG09Yi9euy21k7suGDJvufh6CmHubti\nii9aCwTklCFLsZJxQayLDVrE6AwlNKMkhuLYAWDpLK2U4PfInJdI9h4p7Pqn4JaxMxPOMEnn\nHDsA85FJbC367EJr6bUdrA3OagLgvXrsNusUAI8UsVWPkOx/nojdB+CF+rntWxhnbzavHtEn\nY7T3uIxEcjhodacM7NgBmNDSIlV+cFg6A8ZotTyUs0kkQ0QKu/7Z8MoA8lvRISxi9CzFIsBg\nbMuxC/YqRiwLAUqxIsTO1tYh04kPDu+J3Q/g+dpb27dcsBaqfuPx2Nm7d1ESyV6T6mODdgfy\nWrrhWzW/2wt1QFg6A4CWhuP/SSRDRAq7/tnwK9ipk0wTvk9ct9Pxc1oeAaLsQiWtE8dCgLiT\njTqN6nyTrUOG2B0c7jHm8mrqhdotx+6VxkUAUthJxoq0KGJYQ5jln1TTGMryCYCnswBIcQgd\nexLJcJHCrn92lWJZpGeU3SSAxf+/vTsPj+S87wP/fevq+8QNDOYecobk8CbFm6Luk7KsyHFi\nx5LXju1NrNiK8yTOnpGSWLv77G58PNHaj4/Y8SnLlkRKsizJlCiS4iGS4qUhZzicEzO40ffd\nVfXuH4UBQQBdXd1ooNGN7+cPiaiurn6B7un+9nv83mqToVin0rrHCSWi3HyOnS2RraiJgLV6\ntQftfALijvC182b6bHXaOfLD0ikANwev6mq7iLaVT5N+TW5+jh2AMWMAwHS1ybdrL9hjRzsW\ng137lodiV6asBZqUspt0gl2zoVghEPHZXieUVMtSVaWquZySLSuWjUTQWjAzAJz6LNQT7o4c\nB/Dt3HPOj88VTxmKfn3wUFcbRbTdYgHPb4muRnUn2DUvO9UUgx3tWAx27Vseir2yyHR5BUPj\nHrtRPakKpelQLICY3y5UFdtu3gZRrTbdTyxdVgEMhGwn2HHxRA/5YOxORShfTj8G4FJt4bXK\nhZsCR3xC73a7iLZVLGBXPZdtd+H02M3UOlCmRIYjUtOULIMd7TgMdu1bqGcMoUXUoPOjs4LB\nZf2ELrQRPemllF3Ub9sSeQ+V1kWlslwbuTFn5UQiYDkzS4Y1DsX2jDF94I7QNc8XT52rznwh\n9Ygt7Y8l7+92o4i2m7MwdvPrJ8acHrtaB4ZiIYSMxUU6jc1vdkbUUQx27UuZuYFVxX6dFQxN\nS9nNm5maNN2v7CyMzTedUyKlqDXvsVsqKgAGwvaimfUJfSWJUk/4WOJ+AP95+r//5dIjPqF/\nNH5ft1tEtN2Wi0BteprdqJ5Eh3rsANjxpDDrolTsyNWIOoXBrn1LZvYtuzj4A7hSf6SRPcaQ\nLe3pZqOxEZ+ndzFRrUDKpvuJrfTYLdQzHIftOT858K4bgocfyjxxoTb7Y4l741q42y0i2m5x\nZ/OJTS+MHdUHBMTlTsyxA2DH4gCUbGcK4xF1CoNdm3JWsSbNwVU5aTlgNQl2wwCmqnPuF484\nPXbVpsGuCi/ViUuKAGIBc8nKstZJzzGE9kcHfv364KFPj/zE/zv5y91uDlEXxIM2gExpsx9Y\nPkUf0GMznVgVC0A66yc4zY52GLfVlORiyXxrETss99gprkOx4/oAgMvNZng4PXZNh2KX5/M1\nC3aZshry2XnkLGkP65xg13v2GiOPXP2b3W4FUdfE/BaAdCcWxo77Bk+XpjZ/HQB2JAZAyXLz\nCdpZ2GPXJqfWyep9V5uuigUwaYzAw65iHnvsUC4DsF2L2Fk2smUlGbSdjXS47QQR9ZxE0AaQ\nLXegRvGEf6hsV9337H6uePL/m//Kq5Xz7pdyeuwEh2Jph2GPXZtSZh5vzUnOUKz7HLtJn6dS\ndhFviyeWewdde+yyFdWWbxaxY48dEfUcnyYDuuzIrmLjvkEAs/VUQotseMI/5J77J2c+A+DA\n4tgTxz5viIafknYsBkDJMdjRzsIeuzalrByA5OoOMJ8fQrhvF+vsKnap1qzHzrCF8NBjV2m+\nn1iqqABIBq8UseMcOyLqQTG/lS4pm68sMukfATDV4Nu1Ka3/cPm/KUK5P3LjuerM5+e/7HIp\n6fNLv19kGOxoZ2Gwa1Nq/Rw7RZGG4V7uJKqGYmqo0XvKqishZNjNV8VWyrhSP68Rpzpx/EoR\nOwY7IupF8aBt2qLkobqnu4OBcQAXarMb3vpQ5olTlYs/kXzg9/f/24ga/JPFb7hfzY7GlXwW\nXqrJE20XBrs2OUOxCfWtnfn+gPtQLIA9xvCl2rwtm7wRhH12riLcv546IdJ9VWy6pABIhux5\nMw0OxRJRb3Km2aU3vTD2YGACwLnqzIa3/m3qewD+xdBHE1rknvD1l2oLKzs1b0jG47AsUXCb\nsUe0zRjs2uQsnlhdoBjONLtmwW7SGK5J0xkYdRH125YtyjW3LXSWQ6TrUOxKEbu5ehpX6nMS\nEfWWWIc2n3DpscuYhe8VXjzm33cssA/AvZHrATyef9nlanY0DkDJcWEs7SAMdm1KW3kAa6ff\n+gPCMoXptrHEpDEM4GKz0dioX6LpwthKGYDtOhTrFLFLcI4dEfWyeKAzC2NHfQMhNXBho2Ki\nX81+f3/+fXed+ZN//9XBh18J3R28AcBj+RddriZjcQCC6ydoJ2Gwa5NTxy751qHY5XUMrtPs\nnGDXkYWxy6tiXfeKzZSUkM/WVTlXTwUUX1QNuT8uEdEO1KmhWAFxwD92oTorsXaqy+PnlbuX\n/icNWtCwnzgbePqV60a05JOFH7lczY7GAKi53CZbRdRBDHZtWjKzUTVkKPrqg846BlF2LWXn\nG0bjNVkrnBrFBfceu3IZqio1vdHttkS2qjrfdOfq6RGNE+yIqCfFAxaA1KaDHYAD/vGKrM3W\nU6sPZmtWYOYDlqh86v7cv31n+uBA/eSccWfhFxbNrPM1fkMyGgdL2dEOw2DXppSZW7ty4so6\nBveFsXv0IXioeBL2Na9RLCplafggGs7Dy5YV20Y8YNXsetrMc+UEEfWoWMBWFGQ6UaP4QGAM\nwIXqW6bZ/eWrRZ8V08eeHYlYPk3+1G35sM9OLH4wYo6/Xmm4U4UdjQIQnGNHOwmDXTtsaWet\nYnJ9fUsn2HnYfOJirdl2sR567ES1Kl3HYZ09s50idhKSwY6IepQiEPNZmx+KBXA4sAfA6eql\nlSO2xNnp4boovvNIzTkS8dkfuKYEW7s18y9PVS42upT0B6TPx8UTtKNs7c4TDz744Mp/P/zw\nw95P2PD46oMu19wGWatoSiu5bnsuZ/MJ9zl2A1o0qPg99tjlKo1XxUopalWZdFvl6rwJxoP2\nvJkBMMJgR0Q9KxG0zy7pNUsY6qYKFd8YPgLgpdIb/2zgvc6R0wsG6uFL4W/cF7tx5bRbJivf\nPgPkHjiV+mMMNryaHYkpmRSkdBk8IdpOWxjsHnzwwTVZbU0Oa3SCyx27leTWcLadGHxrrRNg\nufKIUqm6332vb+R8ZUZCCjR8I2jaYyeqFdi27XMtYnelOvGlegrACGudEFHPSgRtLCFTUoYj\n1mauc33osC60F0tvrBz57nkTQGLknE/ctnJQCLznaOELPwgXpt6GqxpeTUZjYnFeVMoyENxM\nq4g6hUOx7ViuTrxuKFb6mg/FApjUhyuytmi69d4HDKkpbnPsRLWKZtWJMyUFQCJoO0Xshrl4\ngoh6ViJo4cr31c3wK8bV/snXKhdq0gRgS5xfCJXUxQfGB9acecuYlvKfCBSuOZ9quEbNqXii\ncP0E7RgMdu1w9hNrbygWwD7fCNZN3V1DACGf7dZj58THJtWJFQCJgOUUsRvWWcSOiHpVpyqe\nALgxeKRm10+UzwG4lNFs0zfjf/Zd0VvWn1ka+SaAv3/N1+hSzvoJTrOjnWNr59h1nMukvfn5\n+ZdffrNE+LFjx6LRtcFr0yoAfD5fvlYGMORL+Hxv+dcuYjEAWr0GX8N3AQAHgxMAZuyUz/W0\nqF9ezqqG4dtw5oZTBlkJhVwukq1ohiYTEWNxMQtgMjjq/ogb0jRNCNHGHXuaoigAfD6flJvf\ndrxnKIqiKMpue641TQOg6w27ZPqVEMIwjG63ogVDUQVArmb4fG3+q3T+XSuKcmv02J8tfevl\n6pk7Etc9M1cCEEvM7wnds/4uo0O52bkfYvHm8xn76pENhoCV5CAAvVRUdvA/HFXtwGpi6hW9\nFOzcp+idOHHi13/911d+/PznPz8xMdHpJmQBhEKhYqkKYCIyEom8tUAxZA3QTDMQWbdgdpWr\ny/txAbPIRFxPS4QxlYYwIpGNeuVsyDrgiyfUxhdJlzAYRiQSmbczAK5K7o8Ybo/oYrd92DvC\n4XC3m9AF7i/LfuX3+/2u/d99qbee670SAPJVIxLZVB7Vdf1DE/d++txv/236e7925KdfmM0B\n8qNH92/41zgc2fv38c9/ePYPvvpK8OZDUNZ9zbbHxuuAUS5pPfXHpD7WS8HO3aFDhz71qU+t\n/Dg0NFQsFrfigYql0mxpEUDIMtY+hC01wCrka64PPYoEgDcKU+4tDOk+QJtLlZWYvf5WJZtR\ngKqqyQYXKdZE1QzGA1axWJkqzxmKHqzrxXrLfxNd14UQtVqt1Tv2NL/fr6pqqVTabT12hmFU\nmu133Gd0XTcMo1qtmq6bAfafQCBQqVR66BXuE1BEaC5rF4tN5jE3oihKIBAwTXPUjt8fu/HR\n7AtfuPidau6eonHhw8njG74hjynJBeNEbOT81Nz+b7xYe/tV9bVnGD4NMJcWqlvzidMRhmHY\ntr2lr/BQiNsa7RT9E+z27t37iU98YuXHbDZbdt0Bom3VSmW+kgYQsnzrHyKsaXap5P7Qw3YM\nwNnSZffTApoCaAvZWtxY91YC+HI5A6ipqtngIrNZDQhGfPVyuTxdWRzREpVyOx/YUkpFUbbo\nj7lj6bquqmq5XO6hj73NU1VV07Td9lwDMAyjVqtVq03Ws/cZv9/fc6/wWCCwWEDbL1FVVQOB\ngGVZ5XL5Z5LvfTT7wq+88pcfkO+YTNRQtcvY4LIjiAOojn4jmP6lh1/SD8SLa9fkqlpEVe1U\naif/w1EUxTTNLX2FM9jtHL20eGJ9HbtuSdVzANbvPAEAPr/SbPFEXAvH1NDFjXahXq1JxZNK\nGa6rYjNlBUA8YNeluWhlR1nrhIh6XCJgFapKzepAxbj3x+74xaEH95g3AXj3+Fij05Z395Zn\nfux4oW6JP3w6mluzhbei2KEwF0/QztGFYLdz8lnb0lYeQFLfYHGGDATQrNwJgElj+FJ9wZIb\njLGucN9VzNmR1iXYLVcnDlhz9bQt7TF97Up+IqLe4lQ8SRU78MmlCfU/7fnnn/D/HIC98YZj\nlGP6gCbUqdr8jXuq7zhSSpfU33w0/vyUr2a+GS5lLC7KJWFuMLRCtP22cCj24Ycfdt95otEJ\nrR7ffikrF1T8frHBBF7p8yv1Rdg2FLe3nv2+sR+Vz83Ul/YYQ43OCbv22DnlTly2FHP2E4sH\n7Jn6EoBxo3HpdCKiXjAQsgGkSupodFM1ildcTGt+TY40rnisCXVUT07V5gG895qST5PfOhX6\nwg8jfw2E/XY8YO9N1N8TmRjGBZHLySS/P1P3be0cuw2z1+qDjcJZq8e32VI9t8FGsQAA6fND\nSlQrcK1Cvs8YBXC+OuMS7Jyh2HyDXcWWg52v4Tq+lerEr9aWAHAoloh63XKN4lJnineUaiJV\nVA8O1t03A9tjDD9TeLVsVwOK74GryteN135wwX8hpaXL6uWsNpXWnsXHfyGUPpDNgMGOdoD+\nWTyxnTJW/mp978a3+QMAlGrFdg12+32jAC7U5jaom3RFqEmPXUUaBhpXJ0qXFSEQ9VuzxRSA\nUQ7FElGPu9Jj15lJRNNZTQITsSZrRfcaI0/jxKXawhH/HgBDYeuD1y4vgK3b4odTvodeCv7h\n2Cd/ZemF5IGOtItoU3pp8cQOUbDKNWmu33bCIf1+AKJZwYh9TrBz3XwiaEhVcR2Kdd9PrKxG\nfLamYLq+CGBc51AsEfW2zvbYzeQ0AGPNgt24PgDAmdOyhq7It+2rfGzifFkJPLxwsCOtItok\nBruWZewiNtoo1mE7Y6PN1k/sd4Zia012FQsbjXcVK5fsxsHOspGvKPGADWC2ngIwonOjWCLq\nbVGfrSlysROLJwBMZzUA47Em0/WceSyzGwU7x237qvvLF16tjU6lOQhG3cdg17K0mQcw0KDH\nztm8VWlWLmiPMaQK5YJrsAMQ9tmFmrK+zJSo14VlofHKiVxFsSXiAQvAVHVeQHDxBBH1OiGQ\nDNmpDvXYXc6qqiKHw0167EYb99g5ZCz2odQ3ADx+xm0UhWh7MNi1LG3n0aiI3ZWhWDQbitWF\nNq4Pnq80D3aWjXJt3czecgmAS49dpqwCiAdtAJfrC4NabMM1vEREvSUZtGqmaDiU4Zlli4WC\nNha11GZXcmpFzdXTjU6QunHMPj9gZU7MGh2psUe0GQx2LUtbbkOx0nDm2DUvZXfAN5aycjnL\nbReaRqXslnsEGy+JzV6pTmxKa7aecll7S0TUQwaCFoClTY/GzhdUy8ZotPkuW85Q7EytYY8d\nABmJ3pT/Yd0SJ+f4FZq6jMGuZSkrByCpNlg8EfADEM02nwBwwDcG4Lzr+omoX2LDhbHlEgAZ\nbLjwdmXbidl6ypTWHmO4aXuIiHa+gbANYKm42dHY2ZwKYMxDPbxhPaEKZc5MuZwjo7Fbcz8E\n8NJlBjvqMga7lmXMAhr32MEfhLceu4O+cQBnq9Mu5zSqUaw0K2LnVCeO+a3L9QUA7LEjov6Q\ndDaf2PQ0u7m8BmAk0rzHThPqgBpzVqE1IqOxfZWLMb1+ZtHoqd13qQ8x2LUsY+YBNCp3Yvt8\nQPM5drjSY3euNuNyTsPNJ5ptO7G8n1jQnqrOg8GOiPpFp4Kd02M34m0Hi1EjOVtP2Y03gbQj\nUQCHA+lSTThVVIi6hcGuZSmrAKDRzhPOSlXFy1CsMQZvPXbrN58QpRIA2bgGcrasaArChn2p\ntgBgQmewI6J+MBCyBbBY2OyH11xeDegy5nfbsHvFmD5Ql2baKjQ6QUZjAA4rMwDOLOqbbBvR\nZjDYtSxjFeCyKlY3oKooexiK9U8oQjlbcQt2kQaLJ65sFOs2xy7it4SAMxQ7yTl2RNQXdFVG\n/JuteFK3RKqkehmHdSyvn2hc8cSOxgBcVb8ABjvqNga7lmXsgiqUsNJwGFT6/F7m2BlCm9AH\n3XvsIn6JDYOds3iiQbmTui1KteXqxMs9dhyKJaJ+MRCy8hWlvonCInN5VUqv47C4UsrOpUax\nHYkBGC5cigXsCykGO+omBruWpa1iXA0rouGfTvr9XlbFAjjgG1s0sy4VT4K6veGuYstDscHQ\nhvfKlBQJJII2gKnafEjxNyynTETUawZDlgSWNtFpN5dXAYxEvAa7IS0O91J24TBUVeSyk3Gz\nWBPpcmdKKBO1gcGuZRkz13BJrMMfENUq7OZTN5oujBUCIcPOVzZaFSuEdBZqrG+hU+vEb9nS\nPl+d2e8ba9oSIqJe4VQ82cw0u/mCBqDpnhMrhvU4gAUz0/AMIexwRMllJuImgMsZrp+grmGw\na1nerjQqYuew/X5IKZrtKgbgkG8CwBn30VhnV7E16+dLJfgDUDZ++rJXtp2Yri9VZd1ZpUFE\n1B8GQ06N4vZ7xebzKoDhFnvs5hv32AGQ0bgol/eEKwAupdljR13DYNcyCdmo1snyCf4AvNUo\nPuyfAPBG5ZLLORG/bdko1d8ym0RUSnbjWicr1YnPVacBHPSPN20JEVGvGOhEsDM0GQt4WhIL\nYFhPwL3H7srC2EllCcClLHvsqGsY7NrRaEnsMifYeVg/cdi/B8Ab1csu51ypeLLqmbJtUa02\nWjmBK/uJxQL2ueoMgP2+0aYtISLqFU6P3WK7wc60sVRSh8MtLL5wgp17j50VjQKIVlIxv82h\nWOoiBrt2uPfY2c6GEB6C3aQ+7BP6adceu+i6hbGiUoaUjVZOAEg7wc5vna/N4krBPCKi/uDT\nZNhnt91jt5AXtt3COCwAvzDCasBLj53IZcdiZrGm5NbNjSbaHnzltSOuht1u9vsBKB7m2KlC\nOeAfP1O57FLQfP3mE8tLYt167FRDk0FDnqs4PXYMdkTUVwZDVqasmHY7FU9mswKtTLBzDGsJ\nj8HOWWzrLLwl2n4Mdu1ouO0EgJXIVS55udRh30RF1qYbl0daX6NYVJxaJ27ViZ0idudqM4bQ\nxvUBLy0hIuoVAyFLyjYXxi4HO89LYh1DWjxrFmuy4b2cGsVqLjcatQDM5zkaS93BYNcO93In\n0u8HIDxsFwvgsG8CwOnKVKMTNuixK5dXHmW9Uk1UTREPWLa0z1annf0tvLSEiKhXDEVstDvN\nbjbnBLvWeuyGtLiEXHApZReNQQiRyziRkT121C38yG9Hp1bF4sr6iderDafZRdYtnljedqLB\nHLtsRYWzJLY2U7arx/z7vDSDiKiHDDnrJwrthKf5vKIoSIZaC3YjRpOFsVLTpT8gspnhiCUY\n7Kh7GOza4T7HTvqcHrvmiycAHPXvBXCyfKHRCRH/uqFYp8fOt/Ecu5Ulsa9VLgA4FmCwI6J+\nMxi2ACy0FezmsiIZsLQWP/2ulLJzm2Znx+JKIe9TrHjQ5lAsdQuDXTsGtJjLrTLglDvx1GN3\nlX9SEcqpysVGJwR0qaty9QIrUSoCkKGNe+zSJQVAImCfKJ0DcI1/v5dmEBH1kIGQJdoais2U\nUK63vHICV4Kd+/oJOxqDbYt8bjhsFmti/W6QRNuAL7t2xDXXVbGBIK4MmDYVUHx79eFTjefY\nAYj637KrmOK6UawzFBsLWCcrF8EeOyLqR4YqowG7jaHYmQwADLU4wQ7AUNNdxQAZiwNQ8lkn\nOLZdaY9oMxjsWhZQDL8wXE6Qqio1HWVPQ7EArg7szVnF6dpioxMiPrtUE9aVhf2iXMyr1v9d\n/Mb/eOH/+W7uh2tOXu6xC9qvls9H1OCkMeyxGUREPWQwZOWrSsVsreLJbBZovdYJgEEtDmDR\ndShWRuMARCYzsIkpgESbxGDXsibbTjiCQaXqNdgd9e8D4DIaG/XbEshVlt+/zFL+n95w5nML\nf/U3qUd/8uxn/nDha6tPzpQVAWhG8Xxt9ph/n0A7dZ6IiHY4p9et1fA0k33zvq09nB4HsGhm\nXc6xYzEAaj67vDdGW9VYiDaJL7uWxdWGWz6ssH1+lDz32PknAbiMxjrrJ1am2f1O7OS3k9m3\nR2/6wuHPJNTw/zb9Rz8qn1s5OVNWQz77+fIJW9q3ho56bAMRUW9pb/3ElaHY1orYARhUY2gW\n7JZ77LLZoXD71ViINonBrgWWkAASioceu0BAWKYw614ue01gP4ATlXONToiuqlFctWu/PXox\nbKu/t+/fvCNy83/Z+6maXf+Vi7/l7F1hS2QrSiJoP1F4GcDd4eNeGkBE1HOcQnTzLQa72SzC\nPhkyZKsPF1YDfmEsuK+KjcYAKLlMLGBpCtre9IxoMxjsWlAXNpptO+Gwlzef8NRpd8S3x1D0\nH5XONjphdY/dX899c96ofzJ32Kml9/7YHQ/G7365dOZv0o8CyJYV20bMb30//4oqlDvC13hp\nABFRz3HmyS20UrxtXsUAACAASURBVC6uZolUAaOxllOdY1CPNemx8/mcUnaKQCJosceOuoLB\nrgV12Gi6UawjEACgeKt4Yij6Ed+e1ytT9Qab1UT9b9Yo/ov0IwrEv6jdtHLr/zrxSUPRf2Pm\nzyqy5iyJDQWqL5fPXB84FPUwakxE1IsSAUtTZEs9dvN5RQKj0XaDnRZPWTmXrb0B2NGYks/B\ntgfDVtVkxRPqAr7mWuD02LnvJ+aQ/iAAVDxVPAFwXeBATZqnG+w/EQ1IALmKcqm28Hz17B2Z\n8B7fm2td9xujPzf4wcu1hf8696VMSQFwXr5uSfsd0Vs8PjoRUc8RAoMha7GgSs85bT6vAhhp\nP9jFTGllrILLOTKegGWJQn4gyIon1B0Mdi3w3mPnbOSqeK54cm3gAIBGo7Erc+y+mvm+hPzY\nXEIGg6tP+Ncj/zipRn977m/fyOcBPFp9VBfaJwbf5/HRiYh60VDEMm2RKXsNT073XvtDsVrz\n9RMr0+wGwjaApSI/ZGm78TXXguU5dl6CXSAAAN52FcOVYHeifH7DWwOGrSoyV1G+lnlSgfjx\n+aQMvqUNcS38n/b8fMmuPDT3IwCn7RMfjt81pg94fHQiol7krJ/wvivrfE7BJnrsvFU8WS5l\nlwxYAFIl9tjRdmOwa0HdWRXrJdj5AriyqasX1wcOCYgXSq9veKsAYn47XRbPl07dZI+MVfX1\n2058PPnAzw9+yK5FAcSD5r8f/2ceH5qIqEc56ye8B7u5vKqrGAhvYY+djCcAKNlMImgBSLHH\njrYdX3MtuDIU62GOXbCF7WIBxLXwQd/4S6UzVoNpubGAXampsPX3VfYCWDMU6/jc5C8eVa4X\nau3vrvnMfmPU40MTEfWokYiFKzPnmrJtLBbVsTiUdqu2O7uEL9TTbo8SjQNQc5lkyAbgfZiY\nqFMY7FqwvHjCS7Bb7rHzungCwM2hq0p25VR14/0n4gFbAkFz8L25IQD2RhvFSqBSCYwElaQa\n9f64REQ9ajhiKQLzBc3LyYtF1bQwHm//4bwMxcp4HEKITNpQZciwORRL24/BrgUmnKFYD8Eu\nEAAgPO8qBuDm0FUAfljceDQ24jcBTCqHb07pEEKGNhgOLlSVuiWcr4lERH1PU2QiaM3lVC9j\nq86I7dgmgp2z+cSSmXM5R2q6DASVXBZAMmRny4rFt2TaXgx2LbCFBKALD380J9iVWuixuzFw\nGI2DXVaZAXCLfqtSLMIfgLrBt0BnMkci0PIeiEREPWokYlVMkS03f1t2OvY202M3uDwU67b5\nBACZSIp8TlhWImDZErkKO+1oWzHYbQmpalI3vM+xA3B94JBfGM8UX93w1tft1wAcUa4RxYK1\nUXcdgHRZBZAI8ushEe0Wo1Fnml3z0VhnKt6mgp0eB7BkuQ3FArCicUgpcpmBECueUBfwBbdV\nZDAoSkXv5xuKfnv42OuVqdl6av2tz9WeATBUHxemicjGY8HpkgKAQ7FEtHuMREwAs7nmvWJz\neVVTMLyJGciG0CJq0H2OHQDbWRibTjsLY9NcP0Hbi8Fuq8hAUFTK8F4THbg3cgOAJ/Ivrzk+\nVZs/Yb0CoFRSAWw4wQ5AuqQCiHMoloh2jbGYBWAm16THzrYxn1eHIpa6uQ+9QS3mPscOK8Eu\nk3bGT5yv3ETbhi+4rSIDQdh2S6Oxd4ePA3iisDbYfTXz/ZK6CGFnKwoAO7Rxj91SUQUwEGKw\nI6LdYjhkqoqcadZjN19QTVuMxzb79jioxdJm3pRu15GJJACRSTlfsxnsaJvxBbdlgkEAwvN2\nsQBuCh6JqqHv5l+QeEs/39czTwEy4rMzFR2ADG/cY7dUVEKGDOht1t4kIuo5ioLhsDWXV90X\nn87lNQBj0c0Hu7iETFlunXZv9tgFWMqOuoDBbqvYgSBa2XwCgCbU98Zun64tPls8uXJwurb4\nXOnULaGrB4KyYBk1YWw4FGvbyFRUdtcR0W4zHrMsWywU3UZjZ7IqrozbbsaAFkXTiifBkNR1\nJZMyNBk0ZMbDil2iDuILbss4wa6V9RMAPhK/B8CXU4+tHPmzpW/Z0v5HifsHQrYEUlrCDm8w\nFJsuq7bNcVgi2nXGoiauRLdGZvIagA4MxeoeKp4IIeMJkclAynjAypbVVuZaE20Wg91WaaPH\nDsADkZtiauihzBMVWQNgSutPl74ZUvwfTz7gLLBaMgbWbxSLKxPskgx2RLTLjEZNANNZtx67\n2awaNGTUv9miAYNaHMBS84WxSWGZopBPBG3TRr7Kj1raPny1bZXlzSfKrfXYGYr+TwbetWBm\n/mD+awD+dOmbs/XUx5Jvj6qhZNAGsKgPyI167BaLCoCBIIMdEe0ue+KmAKbSDYNdsSYyZXU8\nZm7+sQbUKJrtKoY3p9lx/QR1AV9tW0UG2+mxA/Dp0Z+IqMHfmv/if1/8+89O/3FEDf7rkZ8A\n4PTYLfqHpa6vv5fTYzcYZhE7ItpdgoYcCFmXsprdYMTzckaXwGS8E8FO8xbsEgMAlHQqHrAB\nZLn5BG0jBrutIv3OHLsWVsU6kmr0P4z/bM4q/drUfy1Y5c9O/NyEMQQg6QzFBoY3vNdyrRP2\n2BHR7jOZMGumcPaWWO9SVgMw0YlgN6Qn4GUoNpEEoKRTLGVH26/5NizUnis9di0HOwA/M/i+\nQ/6Jr6Wf/GjyvttDx5yDcTuvytiSPrDhXebzql+T4U3PICEi6jmTCfOFS75LGX10o4Iml9Ia\ngD3d6bGzAGQY7GgbMdhtFekPQAil0vJQrOPu8HGnXvEKrZBNmOElPQbk15xs2SJVUifipmiz\nsUREPcwZZr2Y1m7du8Gtl7NayJCJoAVsdkh0QI0KiMV6k2Anw2Gp60p6iUOxtP34NWLLKAr8\nARRbWzzhQuSyg/WlInyl2tr8tlBQbImRCMdhiWg32hM3NUWeW9pg/nG+oqRLykS83pHvvYai\nx7RQ0x47CGEnkiKdiuimqoCl7Gg78dW2hexAsNVVsS6UfG6kPgdgobD2y998QQMwFO7AQAMR\nUc9RFbkvac7n1fWFRc4s6gAODHTs7XFAjS2arnXsACxXPLHUYj7qtxnsaDvx1baFZCgkqlVh\nduYNReRzY5VZAAuFtQPozpRh9tgR0a51cLAugbOLazvtnGB3aLDeqQca1GJZq1izm1zQTg4A\nEEuLMb9VrCqmzZkytE0Y7LaQ7VQSLhY6cjWRzYzW5gDMrVv55QS7YQY7ItqtDg/WcSXGrXZm\nUddV2ZFaJ44hPQ5gyXW7WFxZP6GmU/GgLYEsO+1ou/CltpVCYQBKi7uKNaLkc2PmPID5dUOx\nc3lVVWQywGBHRLvU3oRpqPLUvLG6mF22rCwW1QMDpqp0bFevQS0GLwtjkwMAlNSis36Co7G0\nbfhS20LO3l+tbhfbiJLLRgIyoMs1tZpMW8wXtLGopfDJJKLdSlXk0ZFauqSs3oLiRzM+AEeG\nah18II/BTiYHAShLizG/BQY72kZ8qW0hu3PBTtRrolS044nhiJUqqfVV0zUuZ1TL7kyJJiKi\n3nXDRA3Ay9O+lSMvXPIJ4PrxagcfZUCLAViop91Pk36/DIaU9HKPHYdiadvwpbaFZCgEQOlE\nxRMlk4aUdjwxHDalxFzuzU67y5mOFVUnIupdR0dqhiZfuuSzbAFgqahOpbUDA3Vn+4dOcXrs\nlswmc+wA2AODolCIqxWwlB1tIwa7LdTBxRNKOgXAjif2Jk0AF9JvThCeymjo0DaIRES9S1fl\nbXsr2Yry1Hk/gG+fCkrglr2d7K5DS8EuOQgpE9VFcCiWthFfalspFEanhmLTKQAyMbAvUQdw\nIfXmJJLLGU1T5EiEwY6Idrt3XlU2NPkPp4LfPBl8Yco3GjFv2VPp7EM4wW7BbDIUC8AeGAQQ\nyc9qiuSuYrRt+FLbQs7iiY6silUyaQB2PDkSsQK6vJBa7rEr1ZT5grYnbqp8Jolo1wv77A9f\nW6yY4pFTQUWRP35jseOryob0BDwsnoDTYweoS4uxgJ3brqHYqimKNX4e7GrcK3YLSV2XutGR\nHjslszwUKwT2Juqn5o1cRYn67ZNzui1x9XDHam8SEfW0t+2vjEat0wv6LZOVzs6ucyTUsCZU\nL8HOGlgOdvEh+0xRrVtCVztWdWVDF1L6n/wgUqgq+5Lmz9+Z9Wlb+3C0MzHXby0ZDHZq8YQM\nhaVhADgwaAI4NW8AODlnADg62snF/EREPW1fsv6uq0tbkeoAKEJJqJGFevNdxWQkKg1DWVzY\nnlJ2uYry+09FSzVlPGZeSGl//UJ4Sx+OdiwGu60lQ2FUyrA2VTpY1Osin7PjCefHmyaqQuCZ\n837LFq8vGFG/PR7jBDsiom0yqMXmm5U7AQAh5OCQkk3HfCa2Ptj9w6lgzRQfuq74y/dl9iXN\nV6Z9pxfW7sNBuwGD3daywxFIKTa3MFYszENKe2jE+TERtI4M1S+mtT9/LlKqievHq9yDkIho\n2wzriaqs561S0zOt5BBsO2HnAGzpNLtMWXn2oj8ZtO7YX9YUfOCaIoAnzga27hFpx2Kw21oy\nHAEgCpsKduriPABraHjlyF0HygB+NGNE/Pa7jzZ/cyEiok4Z1hMAFszmo7HOwthEZcsrnrxw\nyWfZuPdQWVMA4MBAfSJmnpwzloqsn7frMNhtLRmJAFAK+c1cRFmcB2APvhnsrhmt/eLd2WvH\naj95cz6gc3osEdH2GdLiALyMxjrv24nCDLY42L14yacoy3tvOO44UJESL08bW/egtDMx2G0t\ne7nHrnkpSxfqwhyEWB3sABwarH/i9tyRIa6HJSLaVk6w89JjZw0OARjIXQKwdaXs5vLqTE47\nMlgL+95cL3LtaE0IvDrrc7kj9SUGu63lDMUqm5tjpyzMy0hU+v0dahQREbVvSPfaY+e8dUcW\nLuqq3LrtYp3yCNdPvKU8Qthn703UL6a1fJUf9LtL39ax8/v9/q1JQqFwOBKKeD17ZAyAUS4b\nEc93WSOfQ7mEyWORtq+wOaqqrvzv7qFpGoBweHfVCxBCqKrarVdatzivbb/fbxi7a9BKUZRd\n+AoHoGnaJl/k+8xxADm14uk6QyPi0sXkQZmrbvZxGzmb0gHcuN+IhN6yDPbmfeJCCuez0b2j\nQtO03fYK37X6NtjVajVrc0VGGqlWKmXF699N6IYfsHOZarnc3sOpZ94wgPrQiNnuFTbJ5/MJ\nISqVDm/Ls8MpiqIoSqVSkXIXTWFUFCUYDJa79ErrFp/Pp2lavV6v1XZXPUhN03bhK9wwDMuy\nNvkij9lBANPlBS/XMQaH1KkLMaU8Vwul8xV/p4sG1y28MWcMR6yAUlrTnANJFYi8dtm+65A0\nTbNe38KpOz4fx3x3ir4NdrZtm+aWVHczTbOFKyuKzzCQz7XdGHXqPID62IS1Nb9OU5qmKYqy\nRX/MHcv5tDNNc1d97KmqKqXcbc+1rusALMvabb84duUrHMDmX+RJEQYwV015uY6SHFSBmJ0D\nQkt5eyTS4R6H0/N63RZHBmvrGzMcMv16+MyiZtvVrftMpJ2GQ+9bToajIt/+qlht+hKEsMcn\nOtgkIiJq24AWU4TiZfEErqyfSNaXAGTLLc9pWTJz38o+e7J8odEJZ5d0AIc3WkinCOxP1tMl\nJVVktdNdhMFuy8lwRFQroq0+cGGZYnbaGhiSPq6cICLaETShJtSwp80nAKe2fKI4h9YrnqTM\n3PtO/dpPnf3sfSc/9Y3s0xuecyGlC2D/wMa9cQcG6gBOz/Ozfhfhk73lnFJ2It9OxRNldkZY\nlr1nb6cbRURE7RvWEx577GQgKEPhgcwltB7sfvnCb56vzX4ofpchtH954b9M1ebXnGDbmEpr\ng2ErZGy8Me7BARPA2QV+1u8ifLK3nBWJAlBynt4C1lDPngZg7TvQ4TYREdEmjOjJsl3NWUUv\nJ9tDI8n8NFoMdifLF76de/bW0NE/3P/vPjvxc3mr9Pm5L685Zzqn1SyxL9lwRGgibmoKzi/x\ns34X4ZO95WQsAUBks23cV3vjdaiqyWBHRLSTjGgJAHPeRmOtoeGkmUKLc+z+ZOnvAfzC0IcV\nofz0wHuGtcRfpR9ZEyUvpHUA+5INV0VoihyNmtNZpWZxmt1uwWC35exoDG312IlcVl2ctyb3\ncYIdEdGOMqInAczWl7ycbA0O++2qXzG999jV7PoXU98d1GIfjN0JwFD0Tw69v2CV/yb16OrT\nLqU1AHsTbstdJxOmbeNSmh/3uwWf6S0n43EAIuPpi91q+umTAMyDRzrfJiIi2oQRvYUeOzk0\nDCAh896D3bOlk1mr+KH4XYayXHP4J5PvFBBfzT65+rRLGU1X5UjYLdg5se9iancVmd/NGOy2\nnB2JQQg11/JQrP7KC1CU+tFrt6JVRETUtlF9AMCcmfJysj04BEVJ1lN1S5Rqnj52v5P7IYB3\nRG9eOTJpDF8fPPRU4UdL5vJSvJopFgrqeMxSXC+5J14HcCHFj/vdgs/01lNVGY6IbGtDscr0\nJWVh3jx0lQztrj1/iIh2vtZ67FTNTiSTpTkAaW+ddt/JPa8L7d7wDasPfjB+pyXtb2Z/4Px4\nOavZEntiTWppDYctn47LGfbY7RYMdtvBjsZEqShaqfpt/PBZAPXjN25Zo4iIqE3OHLu5uqce\nOwDW0EiingKQLjX/2F0wMyfK528PHQurgdXH3x99G4Bv5551fryU0QBMxJt8sgiBPXF7Pq9U\nTa6f2BUY7LaDjCUgpfA8Gqtk0vqpE/bAICfYERHtQM6q2FnPwU4OjQzUUwAyHhbGPlN8VULe\nHTm+5vjRwL5xY/Dx/EuWtAFczmoA9iSa71G2J2FLiZlc324iSqsx2G0HKxYHoGS9rp/wPfUY\nbLt6xz0Q/IJFRLTjBBRfVA15HIqFU/GkngaQ8dBj92zhJIDbgkfX33Rf+IasVXyh9DqA6aym\nK3I41HwsaE/izSBIfY/BbjvYyQEAytKil5OVhXnt1Vfs5KB59LotbhcREbVpRE94LHcCwBoa\nSZppeJtj92zxNUUoN4euWn/T26M3Afhe/kXTFvMFdbTZygnH3qQEOM1ut2Cw2w5yYBCeg53v\nsUdg29X73gEv/16JiKgbRvWBol0p2hUvJ8toLKZWFdhNh2Jr0nyp9MbVvsmoGlp/6/2RGwXE\nY/mXZnOqbWM04mnq9ljM1lRMs8dud2B02A52chBCKKnmwU6dOq+dPW2N7zGPbNAJT0REO8SY\nPgBgpua10w6DA3Ez23TxxEulN2rSvD18bONraLGjgb3PFU9eyADARMxTsFMVjEbt+bxmbryj\nLPUVBrvtIHVdRqLK0kKz86Tve48AqL793dvRLCIiate4MQBguu5pKAaANTySrKeKVcW03SZP\nv1g6DeCW0NWNTrgrfF1Nmq8sFQCMxZqvnFhubcwybSwU2GnX/xjstomVHBTlsiiXXc7RTp9S\nZy6bh6+2Jia3rWFERNSGcX0QrQQ7e2g0aaZks4onL5XeAHBD8HCjE+4OHwcwlVUEMOatxw7A\nRNwGMJ3lNLv+x2C3TezlaXaNO+2k9D35KISo3fvAtrWKiIjaM6EPAbhcazYUc4U9PDJYc0rZ\nuaWrl8tnfEI/4tvT6IS7wscFlGoxnghafk16fHSnb48VT3YDBrtt0jTY6a+/pizM149eaw0O\nb2O7iIioHRPGIIDLNc89doNDSSsNINW4x65sV1+vTF0XPKiLhglsQIveoN+s2MGRaJM9J1Zz\nKp7McP3ELsBgt03s0XEA6sz0xjdLqT/1OISove2ebW0WERG1xRmK9d5jJzU94avDdSj2lfJZ\nS9o3Nh6HddyEewDYgVmvbQVChoz57Zkch2L7H4PdNrEGh6WmqTOXN7xVO/O6ujBXv+qYPcTu\nOiKiHpDQIkHFP216XhULJOIagEym4YqHl0tnAFwfaBLs9lnXA5jVX/X+0ABGo2ahquSr/Nzv\nc3yCt4uq2iNjytKCqG5Q9Mh45vsA6m+7e9ubRUREbRo3Bi7V5r2fHx8KKdJOFxpOjFsOdsFD\n7tfxVScBvCyf8v7QAMbjFoAZrp/odwx228cam4CUyuzMmuPq1Hl1+pJ54LA1MtaVhhERURsm\n9KGCVc5ZRa93GBmOmdlUxWh0+4/KZw2hXdV45YQjnQ9YSvnp+vdrdgvT7Jxqxlw/0fcY7LaP\nPb4HgDZ9ac1x31OPA6jdwdl1RES9ZLniief1E9bQyIC5lJf+urVBKbuaXT9VnToW2G8oustF\n6pZYKqpKYKlkl18on/be2rGoCWCWwa7fMdhtH3PvfiiK+sap1QeVy1PqhXPWnr3Wnr3dahgR\nEbVhjzEEYKrudTRWRiIDdrZRKbuTlYs1u35d4ID7RWZzqi0xGq0D+H7+Fe+tHQpbqiJZyq7v\nMdhtHxkIWhN71bkZkc2sHPQ//l0A1Tvv6167iIioHXuNEQAXanPe7zLgqwNIpTdYP/Fy2Vk5\n0WSCnTOWel0yBOD7hRaCnapgJGItFDSLG4v1NQa7bVW/6iik1E+fdH7UzryuTp239h+09h/s\nbsOIiKhVB/xjAM5X1s6cdpGMSABLCxvsQuSsnDjebOWEE+yuTvqO+Pf8oPBaTXrdfAJAxGeb\nNmobDQRT32Cw21bmVcegqsbzz4h6XZTL/m9/HYpSvf9d3W4XERG1bJ8xCuB8rYV6csmkDiCd\n2SCNvVw+owql6VDsdFYVwGjUvDt8vCJrzxVPttJk6n8MdttKhiO1W94mcln/330l8MU/E/l8\n9Y57reHRbreLiIhaNqInAorvYktDseNRAEvFtSsY6tL8UensVb7JgOJzubsEZnOas5nYfZEb\nADyWf6n1hlM/Y7DbbrW77rPjSe3119S5mfo1x2t33tvtFhERUTsExF5j5Hx1VsLrnq2BkZhP\nVhet4JrjpypTVVm/odmeE5mSUq6L8ZgF4J7w9YpQHsu/2EbLqY9x2fN2k7pR+h9+STtzWmqa\nefBIt5tDRETt2+8bPVW5OF9Pj+hJT3dQ1QE7O68kbXNJ0d5coPpS6Q0ATYPd5awGYCxmAkho\nkeOBgy+UTuetUkRdmxRp12KPXRdIVatfdYypjoio1znT7FpaGDuolU2hFWYyqw86S2KbBrsZ\nJ9hFl6fo3R+50ZTWE4WXW2oz9TcGOyIiojY5C2PPVVtYGDsYtgCkZwurDz5fPKULrenKCWdJ\n7ER8uVrKA5GbAHw390IrTaY+x2BHRETUpiO+PQBer0x5v8tAQgWwuPTmbmBlu3qifO66wAH3\nlRMAprOqX5fxwHKwuz10LKT4H8k933K7qX8x2BEREbXpWGAfgJOVC97vMjgWArBQeHOC3Qul\n06a0bgle7X7HiinSJXU8aq6UoTMU/Z7I9Rdrc2er0y02nPoWgx0REVGbhrVEUou+Vm4l2CUU\nAPP1MOTyWlqnFt2t4aPud5zJavLKyokVzmgsO+1oBYMdERFR+476916qLRSsDTaT2FDEZwdQ\nndMHlUzKOfJ86RSAW0NNeuwuZTQAe+JvCXbvid0O4Fu5Z1ttNvUrBjsiIqL2HQ3sk5CnKhe9\n32VILy1pA3J2HoAt7acLr47pA84CWxdOrZOJt/bYTRrDxwL7nsy/4j1ZUn9jsCMiImrfUf9e\nACdbCXbDIcsWSmq2AOBE5XzKzN0Tub7pvS5nNF2Vw2FrzfF3R2+rSfPRPNfGEsBgR0REtBnX\nBPbjSiE6jwaHdAALSyYAZ+uIe5sFu7olFgrqeMxS1n1uvzd2O4Bv5n7gvQHUxxjsiIiI2ndD\n8LCh6M8UXvV+l6G4ADBf1CHlY7mXANwTbhLsprOaLdeOwzpuDV49rCW+lX3WlGs782gXYrAj\nIiJqn18YxwMHT1Yu5K2Sx7uMRi0AM+pQNT33VPHEQd/4pDHsfpepjFOaeINgpwjl3bFbU2bu\nmWIL4ZL6FYMdERHRptweOmZJ+/niKY/nD4QsA9ZlY/w7s98r29X3x+5oepeLKQ3AZLy+4a3O\nFb6Redpzk6lvMdgRERFtyu3hYwB+UHzN4/mKwHCgMmcMP1z4IYAPxe9sepeLac2nyeHIxoOt\nb4/eFFYDX88+JSE9t5r6E4MdERHRptwRulYRyndbWZc6moAl1KekOWEM3dKsgl2xJtIldTJh\nKmLjE3xCf3f0tku1hRdLb3hvA/UlBjsiIqJNGdRitwavfr54arq26PEuowkA8NX3fzxxv0CD\nvHbFVFqXwGRigwl2Kz4cvwvA1zJPemwA9SsGOyIios36cPxuCfn17FMezx+O1gEM1Y78guJh\ngl1aA7C3wQQ7xzujtwQU30PpJzgau8sx2BEREW3WB+N3Coi/Tn3X4/mP1P8OwLHideNzxaYn\nn1vSBbB/wK3HLqj43xW99UJtlqOxuxyDHRER0WZNGsPvjt36Yun0t7LNt219pvjq/7HweyVt\nzm9ercxcdj/ZsnExrQ1FrJBhu5/5kcQ9AB5KP+G92dR/tG43gIiIqB/8u9Gf+nb2uc9O//Hb\nwtfE1JBz8I3K5ScLr1yszdel6VeMkOI/VZn6SuZxC9beuLW4GEjNl0Kul72U0eqWODDgNg7r\neE/0tqDifzjzxP8+8cmm8/aoXzHYERERdcD1wUOfHHz/f1v8uw+//u8+nnxgwcw8knv+9crU\n+jPHjcHf3PuvxGzs7xZxsRI9Vi4hEGx02XNLOoD9yebBLqD43hu7/cvpx54rnrotdHTNraJc\nFgUBDLT4a1GPYbAjIiLqjM/t+cWyrP7V0iOfnf5jAIbQ3hO77YHIzdcE9vuFkbOKtpBxJXxj\n8LAilDNVE8AFY/L45SnzcMOKJ28s6gAOeuixA/DRxL1fTj/25fRja4Kdcvpk+OG/VZOfQGhA\nmHXo/PTvW3xqiYiIOkMVyu/s/dVfHfn4ifL5qBK8PXwsqPgbnbwnbipCng0cUC8/3SjYmbY4\nt6gPha1EsMkEO8c7o7fG1NBD6Sf+48TPq2J5Gr1443Xti38uhYCqAoDl6VLUo7h4goiIqJMO\n+SYejN/9wrhqMAAACzBJREFU9uhNLqkOgE+TE9H6Rd9k9dJco3POLmp1W1w1VPP40IbQPhS/\na95MP5Z/0TmiZDPii38GRSl//Kekz+f9t6AexWBHRETUHUdGLFsoZwshUd84up1eMAAcGfY0\nDuv4WOJ+AF/KPAYAUvr//mFUq+Z7P2xN7t98g2nnY7AjIiLqjkODNQAn/UfUqYsbnvDanKEq\n8tBgC8Hu7vDxcWPwa5kny3ZVO/GyevG8PHy1fdOtnWkx7XgMdkRERN1xYMDUhP1a8Kh64ez6\nW2fz2nxevXq47tNa2ExCEcrHEvcXrPLXlx7zP/4dqaryAx/pXJNpp2OwIyIi6g5NkVcN1eaM\n4dlLhfW3vnTJAHD9eLXVy/7j5DsAfOHCQ6KQr992J5IscbKLMNgRERF1zQ2TdQAvmgdEPrf6\nuAReuuxTFXnNqNeVEyuu9u+9OXD4MfXiVFyt3X5Xx9pKvYDBjoiIqGuuHa0ZsJ6L3KSeOb36\n+OvzxmJRPT5W8+stjMOu+On0pA35Rzdr0ue2Mpf6D4MdERFR1xiavG64tKAPvnrWXH388TMB\nAHcfLLdxTVEq/tPniiFb/RPfa5Zk1brdhcGOiIiom+6/1hLAt83jK0VPTi/or8/r+5LmvqTp\nft8NGc98P16RP65cN11feiT3fEcbSzsdgx0REVE3jUXNa4zpC769Tz5bAFCui6+8HBYCH7p2\ngxUVTYl8Xn/xORmJ/MyRnwbw+4tf7XBzaWfjlmJERERd9mPXF88/U/zqwqG5l+tnF7WFgnr3\nwUp73XW+px8Xplm5494bw0dvDx17NPfCj0pnjxp7O95m2pnYY0dERNRlsYnEz5e/HLBKT57z\nz+a12/ZWPnxdO911Sialv/KCHYvXj98E4BeHPwLgdy7/TYebSzsYgx0REVH3Hbg6/h/Pfeaf\nx5/+N+9If/ymgiLauYjx2HdgWdV7HoCqAvhg7M7D/om/mv+HC9XZDjeXdioGOyIiou6rX3eD\nocqbXvvqcLDlwnUOdeqC/vpr1siYeey65SNC+ZWRj9el+X9d/vPOtZR2NAY7IiKi7pP+gHnt\nDSKf106eaOf+luX7h28AqL7zfRBvdvf9o8TbrwpO/sXCt0+Uz3WqqbSTMdgRERHtCLVb74Cq\n+r7/KCyr1fsaTz+hLs6b115vTUyuPq4J9Tf2/5Il7f/50u8D7dQ6pt7CYEdERLQj2Ilk/fhN\nSjZjvPBsS3dUL0/5nnlChiOVt79n/a0fSN757vht3y+8ckkrdailtHNtbbmTBx98cOW/H374\nYe8ntHqciIioD1Tvuk87ecJ4/LvmoSN2YsDLXUQuG3joi5Cy/L4HZSCw4Tm/feBX3/byL7yu\n50db7gqkHrOFwe7BBx9ck8nWRLFGJ7R6nIiIqD/IULjyzvcGvv6VwENfLP3kJ6W/yU6vIp8L\n/vWfimKhev87rQOHGp024Rv6rX3/6i9nJQBuMtbfOBRLRES0g5jXXF+/4WZlYT74t38hSkWX\nM9VLF0N/+gdKOlW79Y7a7Xe7X/ZDsbuSlgGgKCudbC7tMNx5goiIaGepvOsDqFT0U6+G/vj3\nqve90zx2nVTV1Seoi/PGs09pJ14GUL33HbU77vFyWYG2iuNRT2GwIyIi2mEUpfLhj8mhEf2p\nx/zfeEh+91vW+B47noCiiEJenZtR0ikAdnKg8p4PWZP7ut1c2kH6J9g9/fTTn/vc51Z+/Mxn\nPnP8+PFOP8g0gP/lO36g3ukr72S76pddsdTtBnRFHdiFYzR1INftNnTFfLcbsP3qwEy329CS\nO3DVHW/+VAYACGAUGL1y8CXgpabv0hnn/3T7GIBoNJpIJDrbUNo5OMeuBZPhubI6wzpARETU\ni+pKumK8PByNd7shtIX6p8fujjvueOihh1Z+zGaz6XS6sw/xf3703aZpZjKZzl52h/P7/Yqi\nlEq7q/pRNBo1DGNpaUnKXZTjVVUNh8PZbLbbDdlWgUAgFArl8/lqtdrttmyrRCKRyWR22ys8\nkUhUq9V8Pt/ttmyrUChkmuaVV/h4Ltf5LurBwcGOX5Pawx47IiIioj7RhWC3usgwEREREXXK\nFg7FPvzww+4bRTQ6odXjRERERARA9OsEi2w2W693eDnn4OAg59jtEpxjt3twjl23G7J9OMdu\n6x6Cc+x2Ds6xIyIiIuoTDHZEREREfYLBjoiIiKhPMNgRERER9QkGOyIiIqI+wWBHRERE1CcY\n7IiIiIj6BIMdERERUZ9gsCMiIiLqEwx2RERERH2CwY6IiIioTzDYEREREfUJBjsiIiKiPsFg\nR0RERNQnGOyIiIiI+gSDHREREVGfYLAjIiIi6hMMdkRERER9gsGOiIiIqE8w2BERERH1CQY7\nIiIioj7BYEdERETUJxjsiIiIiPoEgx0RERFRn2CwIyIiIuoTDHZEREREfYLBjoiIiKhPMNgR\nERER9QkGOyIiIqI+IaSU3W5Dz/iN3/iNsbGxn/3Zn+12Q2jLfeELXzhz5synP/3pQCDQ7bbQ\n1nruuee+9a1vfeQjH7n22mu73RbaWqlU6nd/93evu+66Bx98sNttIdoq7LFrwVe+8pVHH320\n262g7fDkk09+6Utfqtfr3W4IbbkzZ8586UtfunjxYrcbQluuUCh86Utfeu6557rdEKItxGBH\nRERE1CcY7IiIiIj6BIMdERERUZ/g4gkiIiKiPsEeOyIiIqI+wWBHRERE1CcY7IiIiIj6hNbt\nBvSM1QUtH3744S62hLbChs/v+iqmfOp7nctzyn/j/afR081/2tTHGOw8efDBB1f/s1/zI/U6\nl+eXT3T/2fA55b/xvrTmSWR2p92AQ7FERNT/GNZpl2CwI+J3dyIi6hMciiV6i/VDciv/zfzX\nH/ic7kLru+v4MqB+xWBH9KY17/7rPwn4AdDr+JwS+DKgvsahWKJlfHMn6kv8p027CoMdEcC3\nfiIi6gsMdkQNU936YlfU6/ic7jYb/uvmy4D6GIMdERERUZ8QUsput6E3cAlVH+NuBLtKo+eU\nz3Vf8tIfz6eb+gmDHREREVGf4FAsERERUZ9gsCMiIiLqEwx2RERERH2CwY6IiIioTzDYERER\nEfUJBjsiIiKiPsFgR0RERNQntG43gIg6TAix5kh75SqF2NY6l06zO/WIjRq/zb8UEdE2Y48d\nUR+Sb7U+6jXi/cztv1pLNvytmeqIqO8x2BH1v5ay3ep7bUVjNuRErvbaSUREKzgUS7TrrAlP\nKwHOOb4yJLqmf2vlXmsOrkljja625nFbTY2rH91Lw9afuWF3XaP7uvyJVn7fzf9SREQdx2BH\n1P/c883KkfWZycsVGv3onqtaGhVdcx3vv1pLl/XyJ3I/k0O9RNR1HIol6kPirVanjTaSx/or\ndLabavX114/GurTfvWGrj7hntTX3df+NGN2IaCdjjx1RH2qpM2xLW7LmsVYHuO183K27e7d+\nKSKiDTHYEe06a/rbvOSezee/1TPS1s+Qc++l20zD3MeXvVzW5U/U6JciIuoWBjui3aW9eWCd\nSi0bJiGXNQ1b2rBG9231T8R4R0Q7B+fYEVHL2ujA257Vo5vpWdyxvxQRkXcMdkS7mpc0s2Ys\nsoNrPxtdas1ShjWP3pGGeb8vS+sRUQ/hUCzR7rJ+TevqlbNrirRteK82wtP64sOtjnU2ynnt\nNcz9vu5/okZXaKMBREQdx6pLRNRjWC6OiKgRDsUS0Y7GkVAiIu8Y7Iiol7C7jojIBd8iiWin\n4zw2IiKPGOyIiIiI+gSHYomIiIj6BIMdERERUZ9gsCMiIiLqEwx2RERERH2CwY6IiIioTzDY\nEREREfUJBjsiIiKiPvH/Aw8UENctsNU+AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patient_metadata %>% ggplot(aes(PatientsAgeYears,color=set)) + geom_density(adjust=.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kaggle Metadata Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count patients with split datasets (_a and _b) only once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "uniq_pat_meta <- patient_metadata %>%\n",
    "    filter(!str_detect(pid,\"_b\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>PatientsSex</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>F  </td><td>470</td></tr>\n",
       "\t<tr><td>M  </td><td>670</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " PatientsSex & n\\\\\n",
       "\\hline\n",
       "\t F   & 470\\\\\n",
       "\t M   & 670\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| PatientsSex | n |\n",
       "|---|---|\n",
       "| F   | 470 |\n",
       "| M   | 670 |\n",
       "\n"
      ],
      "text/plain": [
       "  PatientsSex n  \n",
       "1 F           470\n",
       "2 M           670"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uniq_pat_meta %>% count(PatientsSex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>AgeBin</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>(-Inf,17]</td><td>202      </td></tr>\n",
       "\t<tr><td>(17,30]  </td><td>173      </td></tr>\n",
       "\t<tr><td>(30,50]  </td><td>298      </td></tr>\n",
       "\t<tr><td>(50, Inf]</td><td>467      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " AgeBin & n\\\\\n",
       "\\hline\n",
       "\t (-Inf,17{]} & 202        \\\\\n",
       "\t (17,30{]}   & 173        \\\\\n",
       "\t (30,50{]}   & 298        \\\\\n",
       "\t (50, Inf{]} & 467        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| AgeBin | n |\n",
       "|---|---|\n",
       "| (-Inf,17] | 202       |\n",
       "| (17,30]   | 173       |\n",
       "| (30,50]   | 298       |\n",
       "| (50, Inf] | 467       |\n",
       "\n"
      ],
      "text/plain": [
       "  AgeBin    n  \n",
       "1 (-Inf,17] 202\n",
       "2 (17,30]   173\n",
       "3 (30,50]   298\n",
       "4 (50, Inf] 467"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uniq_pat_meta %>%\n",
    "    mutate(AgeBin=cut(PatientsAgeYears, breaks=c(-Inf,17,30,50,Inf))) %>%\n",
    "    select(PatientID,contains(\"Age\")) %>%\n",
    "    count(AgeBin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>minAge</th><th scope=col>maxAge</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.03846154</td><td>88        </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " minAge & maxAge\\\\\n",
       "\\hline\n",
       "\t 0.03846154 & 88        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| minAge | maxAge |\n",
       "|---|---|\n",
       "| 0.03846154 | 88         |\n",
       "\n"
      ],
      "text/plain": [
       "  minAge     maxAge\n",
       "1 0.03846154 88    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uniq_pat_meta %>% summarize(\n",
    "    minAge = min(PatientsAgeYears),\n",
    "    maxAge = max(PatientsAgeYears),\n",
    "    #minHeight = max(PatientsHeight),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>MagneticFieldStrength</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1.5 </td><td>1025</td></tr>\n",
       "\t<tr><td>3.0 </td><td> 115</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " MagneticFieldStrength & n\\\\\n",
       "\\hline\n",
       "\t 1.5  & 1025\\\\\n",
       "\t 3.0  &  115\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| MagneticFieldStrength | n |\n",
       "|---|---|\n",
       "| 1.5  | 1025 |\n",
       "| 3.0  |  115 |\n",
       "\n"
      ],
      "text/plain": [
       "  MagneticFieldStrength n   \n",
       "1 1.5                   1025\n",
       "2 3.0                    115"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uniq_pat_meta %>% count(MagneticFieldStrength)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>min(EchoTime)</th><td>1.04                                </td></tr>\n",
       "\t<tr><th scope=row>max(EchoTime)</th><td>1.54                                </td></tr>\n",
       "\t<tr><th scope=row>min(RepetitionTime)</th><td>14                                  </td></tr>\n",
       "\t<tr><th scope=row>max(RepetitionTime)</th><td>54.72                               </td></tr>\n",
       "\t<tr><th scope=row>min(PixelBandwidth)</th><td>915                                 </td></tr>\n",
       "\t<tr><th scope=row>max(PixelBandwidth)</th><td>1235                                </td></tr>\n",
       "\t<tr><th scope=row>min(SliceThickness)</th><td>5                                   </td></tr>\n",
       "\t<tr><th scope=row>max(SliceThickness)</th><td>8                                   </td></tr>\n",
       "\t<tr><th scope=row>min(PixelSpacing)</th><td>[0.58928573131561, 0.58928573131561]</td></tr>\n",
       "\t<tr><th scope=row>max(PixelSpacing)</th><td>[1.953125, 1.953125]                </td></tr>\n",
       "\t<tr><th scope=row>min(NumberOfPhaseEncodingSteps)</th><td>112                                 </td></tr>\n",
       "\t<tr><th scope=row>max(NumberOfPhaseEncodingSteps)</th><td>416                                 </td></tr>\n",
       "\t<tr><th scope=row>min(smallerDim)</th><td>120                                 </td></tr>\n",
       "\t<tr><th scope=row>max(smallerDim)</th><td>608                                 </td></tr>\n",
       "\t<tr><th scope=row>min(largerDim)</th><td>160                                 </td></tr>\n",
       "\t<tr><th scope=row>max(largerDim)</th><td>736                                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "\tmin(EchoTime) & 1.04                                \\\\\n",
       "\tmax(EchoTime) & 1.54                                \\\\\n",
       "\tmin(RepetitionTime) & 14                                  \\\\\n",
       "\tmax(RepetitionTime) & 54.72                               \\\\\n",
       "\tmin(PixelBandwidth) & 915                                 \\\\\n",
       "\tmax(PixelBandwidth) & 1235                                \\\\\n",
       "\tmin(SliceThickness) & 5                                   \\\\\n",
       "\tmax(SliceThickness) & 8                                   \\\\\n",
       "\tmin(PixelSpacing) & {[}0.58928573131561, 0.58928573131561{]}\\\\\n",
       "\tmax(PixelSpacing) & {[}1.953125, 1.953125{]}                \\\\\n",
       "\tmin(NumberOfPhaseEncodingSteps) & 112                                 \\\\\n",
       "\tmax(NumberOfPhaseEncodingSteps) & 416                                 \\\\\n",
       "\tmin(smallerDim) & 120                                 \\\\\n",
       "\tmax(smallerDim) & 608                                 \\\\\n",
       "\tmin(largerDim) & 160                                 \\\\\n",
       "\tmax(largerDim) & 736                                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| min(EchoTime) | 1.04                                 |\n",
       "| max(EchoTime) | 1.54                                 |\n",
       "| min(RepetitionTime) | 14                                   |\n",
       "| max(RepetitionTime) | 54.72                                |\n",
       "| min(PixelBandwidth) | 915                                  |\n",
       "| max(PixelBandwidth) | 1235                                 |\n",
       "| min(SliceThickness) | 5                                    |\n",
       "| max(SliceThickness) | 8                                    |\n",
       "| min(PixelSpacing) | [0.58928573131561, 0.58928573131561] |\n",
       "| max(PixelSpacing) | [1.953125, 1.953125]                 |\n",
       "| min(NumberOfPhaseEncodingSteps) | 112                                  |\n",
       "| max(NumberOfPhaseEncodingSteps) | 416                                  |\n",
       "| min(smallerDim) | 120                                  |\n",
       "| max(smallerDim) | 608                                  |\n",
       "| min(largerDim) | 160                                  |\n",
       "| max(largerDim) | 736                                  |\n",
       "\n"
      ],
      "text/plain": [
       "                                [,1]                                \n",
       "min(EchoTime)                   1.04                                \n",
       "max(EchoTime)                   1.54                                \n",
       "min(RepetitionTime)             14                                  \n",
       "max(RepetitionTime)             54.72                               \n",
       "min(PixelBandwidth)             915                                 \n",
       "max(PixelBandwidth)             1235                                \n",
       "min(SliceThickness)             5                                   \n",
       "max(SliceThickness)             8                                   \n",
       "min(PixelSpacing)               [0.58928573131561, 0.58928573131561]\n",
       "max(PixelSpacing)               [1.953125, 1.953125]                \n",
       "min(NumberOfPhaseEncodingSteps) 112                                 \n",
       "max(NumberOfPhaseEncodingSteps) 416                                 \n",
       "min(smallerDim)                 120                                 \n",
       "max(smallerDim)                 608                                 \n",
       "min(largerDim)                  160                                 \n",
       "max(largerDim)                  736                                 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uniq_pat_meta %>%\n",
    "    mutate(\n",
    "        smallerDim = pmin(Columns, Rows),\n",
    "        largerDim = pmax(Columns, Rows),\n",
    "    ) %>%\n",
    "    summarize(\n",
    "        min(EchoTime),\n",
    "        max(EchoTime),\n",
    "        min(RepetitionTime),\n",
    "        max(RepetitionTime),\n",
    "        min(PixelBandwidth),\n",
    "        max(PixelBandwidth),\n",
    "        min(SliceThickness),\n",
    "        max(SliceThickness),\n",
    "        min(PixelSpacing),\n",
    "        max(PixelSpacing),\n",
    "        min(NumberOfPhaseEncodingSteps),\n",
    "        max(NumberOfPhaseEncodingSteps),\n",
    "        min(smallerDim),\n",
    "        max(smallerDim),\n",
    "        min(largerDim),\n",
    "        max(largerDim),\n",
    "    ) %>% t"
   ]
  }
 ],
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